From f9973bfb59ac9d09ad9a4f068d08d03908b435d5 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Wed, 22 Oct 2025 20:19:35 +0200
Subject: [PATCH 01/34] removing not necessary function

---
 eegdash/api.py | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/eegdash/api.py b/eegdash/api.py
index d867d267..b0da2c17 100644
--- a/eegdash/api.py
+++ b/eegdash/api.py
@@ -545,26 +545,10 @@ def collection(self):
         """
         return self.__collection
 
-    def close(self) -> None:
-        """Close the MongoDB connection.
-
-        .. deprecated:: 0.1
-            Connections are now managed globally by :class:`MongoConnectionManager`.
-            This method is a no-op and will be removed in a future version.
-            Use :meth:`EEGDash.close_all_connections` to close all clients.
-        """
-        # Individual instances no longer close the shared client
-        pass
-
     @classmethod
     def close_all_connections(cls) -> None:
         """Close all MongoDB client connections managed by the singleton manager."""
         MongoConnectionManager.close_all()
 
-    def __del__(self) -> None:
-        """Destructor; no explicit action needed due to global connection manager."""
-        # No longer needed since we're using singleton pattern
-        pass
-
 
 __all__ = ["EEGDash"]

From f3dc5592a4f1808a46f7234e43c2f1ab9dc78a6b Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 24 Oct 2025 19:04:43 +0200
Subject: [PATCH 02/34] updating

---
 .../workflows/fetch-openneuro-datasets.yml    |  65 +++
 eegdash/api.py                                | 256 +++++------
 pyproject.toml                                |   2 +-
 scripts/data_ingest.py                        | 404 ------------------
 .../ingestions/fetch_openneuro_datasets.py    | 139 ++++++
 5 files changed, 320 insertions(+), 546 deletions(-)
 create mode 100644 .github/workflows/fetch-openneuro-datasets.yml
 delete mode 100644 scripts/data_ingest.py
 create mode 100644 scripts/ingestions/fetch_openneuro_datasets.py

diff --git a/.github/workflows/fetch-openneuro-datasets.yml b/.github/workflows/fetch-openneuro-datasets.yml
new file mode 100644
index 00000000..09558b8d
--- /dev/null
+++ b/.github/workflows/fetch-openneuro-datasets.yml
@@ -0,0 +1,65 @@
+name: Fetch OpenNeuro Datasets
+
+on:
+  schedule:
+    # Run weekly on Monday at 00:00 UTC
+    - cron: '0 0 * * 1'
+  workflow_dispatch:  # Allow manual triggering
+
+jobs:
+  fetch-datasets:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install gql[requests]
+
+      - name: Fetch OpenNeuro datasets
+        env:
+          OPENNEURO_TOKEN: ${{ secrets.OPENNEURO_TOKEN }}
+        run: |
+          python scripts/ingestions/fetch_openneuro_datasets.py \
+            --page-size 100 \
+            --output consolidated/openneuro_datasets.json
+
+      - name: Verify output
+        run: |
+          if [ -f consolidated/openneuro_datasets.json ]; then
+            echo "✓ Dataset file created successfully"
+            python -c "import json; data = json.load(open('consolidated/openneuro_datasets.json')); print(f'Total entries: {len(data)}'); modalities = set(d['modality'] for d in data); print(f'Modalities: {sorted(modalities)}')"
+          else
+            echo "✗ Dataset file not created"
+            exit 1
+          fi
+
+      - name: Commit changes if datasets updated
+        uses: EndBug/add-and-commit@v9
+        with:
+          default_author: github_actions
+          message: 'chore: update OpenNeuro dataset listings'
+          add: 'consolidated/openneuro_datasets.json'
+          pull: '--rebase --autostash'
+          push: true
+        if: always()
+
+      - name: Upload artifacts for downstream jobs
+        uses: actions/upload-artifact@v4
+        with:
+          name: openneuro-datasets
+          path: consolidated/openneuro_datasets.json
+          retention-days: 7
diff --git a/eegdash/api.py b/eegdash/api.py
index b0da2c17..194a0a5e 100644
--- a/eegdash/api.py
+++ b/eegdash/api.py
@@ -10,13 +10,15 @@
 EEG data from S3 for matched records.
 """
 
+import json
 import os
 from pathlib import Path
 from typing import Any, Mapping
 
 import mne
+import numpy as np
+import pandas as pd
 from mne.utils import _soft_import
-from pymongo import InsertOne, UpdateOne
 
 from .bids_eeg_metadata import (
     build_query_from_kwargs,
@@ -353,9 +355,18 @@ def _raise_if_conflicting_constraints(
                     )
 
     def add_bids_dataset(
-        self, dataset: str, data_dir: str, overwrite: bool = True
-    ) -> None:
-        """Scan a local BIDS dataset and upsert records into MongoDB.
+        self,
+        dataset: str,
+        data_dir: str,
+        overwrite: bool = True,
+        output_path: str | Path | None = None,
+    ) -> dict[str, Any]:
+        """Collect metadata for a local BIDS dataset as JSON-ready records.
+
+        Instead of inserting records directly into MongoDB, this method scans
+        ``data_dir`` and returns a JSON-serializable manifest describing every
+        EEG recording that was discovered. The manifest can be written to disk
+        or forwarded to the EEGDash ingestion API for persistence.
 
         Parameters
         ----------
@@ -364,127 +375,91 @@ def add_bids_dataset(
         data_dir : str
             Path to the local BIDS dataset directory.
         overwrite : bool, default True
-            If ``True``, update existing records when encountered; otherwise,
-            skip records that already exist.
+            If ``False``, skip records that already exist in the database based
+            on ``data_name`` lookups.
+        output_path : str | Path | None, optional
+            If provided, the manifest is written to the given JSON file.
 
-        Raises
-        ------
-        ValueError
-            If called on a public client ``(is_public=True)``.
+        Returns
+        -------
+        dict
+            A manifest with keys ``dataset``, ``source``, ``records`` and, when
+            applicable, ``skipped`` or ``errors``.
 
         """
-        if self.is_public:
-            raise ValueError("This operation is not allowed for public users")
-
-        if not overwrite and self.exist({"dataset": dataset}):
-            logger.info("Dataset %s already exists in the database", dataset)
-            return
+        source_dir = Path(data_dir).expanduser()
         try:
             bids_dataset = EEGBIDSDataset(
-                data_dir=data_dir,
+                data_dir=str(source_dir),
                 dataset=dataset,
             )
-        except Exception as e:
-            logger.error("Error creating bids dataset %s: %s", dataset, str(e))
-            raise e
-        requests = []
-        for bids_file in bids_dataset.get_files():
-            try:
-                data_id = f"{dataset}_{Path(bids_file).name}"
-
-                if self.exist({"data_name": data_id}):
-                    if overwrite:
-                        eeg_attrs = load_eeg_attrs_from_bids_file(
-                            bids_dataset, bids_file
-                        )
-                        requests.append(self._update_request(eeg_attrs))
-                else:
-                    eeg_attrs = load_eeg_attrs_from_bids_file(bids_dataset, bids_file)
-                    requests.append(self._add_request(eeg_attrs))
-            except Exception as e:
-                logger.error("Error adding record %s", bids_file)
-                logger.error(str(e))
-
-        logger.info("Number of requests: %s", len(requests))
-
-        if requests:
-            result = self.__collection.bulk_write(requests, ordered=False)
-            logger.info("Inserted: %s ", result.inserted_count)
-            logger.info("Modified: %s ", result.modified_count)
-            logger.info("Deleted: %s", result.deleted_count)
-            logger.info("Upserted: %s", result.upserted_count)
-            logger.info("Errors: %s ", result.bulk_api_result.get("writeErrors", []))
-
-    def _add_request(self, record: dict) -> InsertOne:
-        """Create a MongoDB insertion request for a record.
-
-        Parameters
-        ----------
-        record : dict
-            The record to insert.
-
-        Returns
-        -------
-        InsertOne
-            A PyMongo ``InsertOne`` object.
-
-        """
-        return InsertOne(record)
-
-    def add(self, record: dict) -> None:
-        """Add a single record to the MongoDB collection.
-
-        Parameters
-        ----------
-        record : dict
-            The record to add.
-
-        """
-        try:
-            self.__collection.insert_one(record)
-        except ValueError as e:
-            logger.error("Validation error for record: %s ", record["data_name"])
-            logger.error(e)
         except Exception as exc:
-            logger.error(
-                "Error adding record: %s ", record.get("data_name", "<unknown>")
-            )
-            logger.debug("Add operation failed", exc_info=exc)
+            logger.error("Error creating BIDS dataset %s: %s", dataset, exc)
+            raise exc
 
-    def _update_request(self, record: dict) -> UpdateOne:
-        """Create a MongoDB update request for a record.
+        records: list[dict[str, Any]] = []
+        skipped: list[str] = []
+        errors: list[dict[str, str]] = []
 
-        Parameters
-        ----------
-        record : dict
-            The record to update.
-
-        Returns
-        -------
-        UpdateOne
-            A PyMongo ``UpdateOne`` object.
-
-        """
-        return UpdateOne({"data_name": record["data_name"]}, {"$set": record})
+        for bids_file in bids_dataset.get_files():
+            data_id = f"{dataset}_{Path(bids_file).name}"
+            if not overwrite:
+                try:
+                    if self.exist({"data_name": data_id}):
+                        skipped.append(data_id)
+                        continue
+                except Exception as exc:
+                    logger.warning(
+                        "Could not verify existing record %s due to: %s",
+                        data_id,
+                        exc,
+                    )
 
-    def update(self, record: dict) -> None:
-        """Update a single record in the MongoDB collection.
+            try:
+                eeg_attrs = load_eeg_attrs_from_bids_file(bids_dataset, bids_file)
+                records.append(eeg_attrs)
+            except Exception as exc:  # log and continue collecting
+                logger.error("Error extracting metadata for %s", bids_file)
+                logger.error(str(exc))
+                errors.append({"file": str(bids_file), "error": str(exc)})
+
+        manifest: dict[str, Any] = {
+            "dataset": dataset,
+            "source": str(source_dir.resolve()),
+            "record_count": len(records),
+            "records": records,
+        }
+        if skipped:
+            manifest["skipped"] = skipped
+        if errors:
+            manifest["errors"] = errors
+
+        if output_path is not None:
+            output_path = Path(output_path)
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            with output_path.open("w", encoding="utf-8") as fh:
+                json.dump(
+                    manifest,
+                    fh,
+                    indent=2,
+                    sort_keys=True,
+                    default=_json_default,
+                )
+            logger.info(
+                "Wrote EEGDash ingestion manifest for %s to %s",
+                dataset,
+                output_path,
+            )
 
-        Parameters
-        ----------
-        record : dict
-            Record content to set at the matching ``data_name``.
+        logger.info(
+            "Prepared %s records for dataset %s (skipped=%s, errors=%s)",
+            len(records),
+            dataset,
+            len(skipped),
+            len(errors),
+        )
 
-        """
-        try:
-            self.__collection.update_one(
-                {"data_name": record["data_name"]}, {"$set": record}
-            )
-        except Exception as exc:  # log and continue
-            logger.error(
-                "Error updating record: %s", record.get("data_name", "<unknown>")
-            )
-            logger.debug("Update operation failed", exc_info=exc)
+        return manifest
 
     def exists(self, query: dict[str, Any]) -> bool:
         """Check if at least one record matches the query.
@@ -504,35 +479,6 @@ def exists(self, query: dict[str, Any]) -> bool:
         """
         return self.exist(query)
 
-    def remove_field(self, record: dict, field: str) -> None:
-        """Remove a field from a specific record in the MongoDB collection.
-
-        Parameters
-        ----------
-        record : dict
-            Record-identifying object with a ``data_name`` key.
-        field : str
-            The name of the field to remove.
-
-        """
-        self.__collection.update_one(
-            {"data_name": record["data_name"]}, {"$unset": {field: 1}}
-        )
-
-    def remove_field_from_db(self, field: str) -> None:
-        """Remove a field from all records in the database.
-
-        .. warning::
-            This is a destructive operation and cannot be undone.
-
-        Parameters
-        ----------
-        field : str
-            The name of the field to remove from all documents.
-
-        """
-        self.__collection.update_many({}, {"$unset": {field: 1}})
-
     @property
     def collection(self):
         """The underlying PyMongo ``Collection`` object.
@@ -551,4 +497,32 @@ def close_all_connections(cls) -> None:
         MongoConnectionManager.close_all()
 
 
+def _json_default(value: Any) -> Any:
+    """Fallback serializer for complex objects when exporting ingestion JSON."""
+    try:
+        if isinstance(value, (np.generic,)):
+            return value.item()
+        if isinstance(value, np.ndarray):
+            return value.tolist()
+    except Exception:
+        pass
+
+    try:
+        if value is pd.NA:
+            return None
+        if isinstance(value, (pd.Timestamp, pd.Timedelta)):
+            return value.isoformat()
+        if isinstance(value, pd.Series):
+            return value.to_dict()
+    except Exception:
+        pass
+
+    if isinstance(value, Path):
+        return value.as_posix()
+    if isinstance(value, set):
+        return sorted(value)
+
+    raise TypeError(f"Object of type {type(value).__name__} is not JSON serializable")
+
+
 __all__ = ["EEGDash"]
diff --git a/pyproject.toml b/pyproject.toml
index 63b445ce..7858494d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -87,7 +87,7 @@ docs = [
 
 digestion = [
   "pybids",
-  "python-dotenv",
+  "gql",
 ]
 
 all = [
diff --git a/scripts/data_ingest.py b/scripts/data_ingest.py
deleted file mode 100644
index 97eb6a3c..00000000
--- a/scripts/data_ingest.py
+++ /dev/null
@@ -1,404 +0,0 @@
-import argparse
-import json
-from pathlib import Path
-
-from eegdash import EEGDash
-
-
-def main():
-    # Create the parser
-    parser = argparse.ArgumentParser(
-        description="A simple command line argument parser"
-    )
-
-    # Add arguments
-    parser.add_argument(
-        "--data",
-        type=str,
-        default="/mnt/nemar/openneuro/ds004186",
-        help="Path to data directory (Default: /mnt/nemar/openneuro/ds004186)",
-    )
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="ds004186",
-        help="Dataset name (Default: ds004186)",
-    )
-
-    # Parse the arguments
-    args = parser.parse_args()
-    print("Arguments:", args)
-
-    obj = EEGDash(
-        is_public=False,
-    )
-    hbn_datasets = [
-        "ds005507",
-        "ds005506",
-        "ds005510",
-        "ds005512",
-        "ds005505",
-        "ds005508",
-        "ds005509",
-        "ds005514",
-        "ds005511",
-        "ds005515",
-        "ds005516",
-    ]
-
-    config_path = Path(__file__).parent / "datasets.json"
-    with open(config_path, "r") as f:
-        datasets_config = json.load(f)
-    failed_ds = set(datasets_config["failed_datasets"])
-
-    datasets = [
-        "ds004841",
-        "ds004770",
-        "ds004561",
-        "ds005261",
-        "ds000247",
-        "ds005131",
-        "ds003753",
-        "ds003420",
-        "ds005028",
-        "ds005557",
-        "ds005170",
-        "ds004840",
-        "ds004855",
-        "ds004718",
-        "ds002725",
-        "ds005565",
-        "ds004408",
-        "ds004796",
-        "ds002550",
-        "ds004511",
-        "ds002893",
-        "ds003682",
-        "ds004817",
-        "ds000248",
-        "ds003190",
-        "ds004819",
-        "ds005089",
-        "ds003822",
-        "ds003670",
-        "ds005048",
-        "ds004917",
-        "ds004574",
-        "ds004852",
-        "ds004357",
-        "ds003082",
-        "ds005574",
-        "ds005397",
-        "ds004519",
-        "ds004602",
-        "ds004784",
-        "ds005491",
-        "ds003846",
-        "ds002799",
-        "ds004024",
-        "ds005815",
-        "ds003694",
-        "ds005429",
-        "ds004771",
-        "ds003518",
-        "ds004977",
-        "ds003702",
-        "ds004577",
-        "ds005207",
-        "ds005866",
-        "ds004127",
-        "ds003574",
-        "ds004703",
-        "ds005779",
-        "ds004398",
-        "ds003523",
-        "ds005558",
-        "ds004212",
-        "ds004347",
-        "ds005185",
-        "ds005489",
-        "ds005398",
-        "ds004588",
-        "ds001787",
-        "ds003505",
-        "ds005670",
-        "ds003568",
-        "ds003703",
-        "ds005811",
-        "ds004370",
-        "ds005340",
-        "ds003987",
-        "ds004865",
-        "ds005363",
-        "ds005121",
-        "ds004078",
-        "ds003392",
-        "ds004317",
-        "ds004851",
-        "ds004033",
-        "ds004011",
-        "ds003876",
-        "ds004166",
-        "ds005691",
-        "ds005087",
-        "ds004330",
-        "ds004256",
-        "ds004315",
-        "ds005279",
-        "ds005420",
-        "ds003474",
-        "ds002034",
-        "ds003509",
-        "ds004186",
-        "ds003825",
-        "ds005868",
-        "ds003516",
-        "ds004587",
-        "ds005415",
-        "ds004942",
-        "ds004348",
-        "ds003633",
-        "ds004598",
-        "ds005383",
-        "ds003195",
-        "ds004473",
-        "ds005403",
-        "ds002908",
-        "ds004621",
-        "ds005863",
-        "ds003848",
-        "ds004625",
-        "ds005594",
-        "ds002336",
-        "ds004043",
-        "ds003517",
-        "ds005083",
-        "ds004368",
-        "ds004584",
-        "ds004012",
-        "ds003374",
-        "ds005624",
-        "ds005810",
-        "ds003506",
-        "ds005106",
-        "ds004284",
-        "ds005620",
-        "ds004738",
-        "ds004849",
-        "ds005234",
-        "ds003570",
-        "ds003490",
-        "ds002720",
-        "ds005307",
-        "ds002094",
-        "ds002833",
-        "ds002218",
-        "ds000117",
-        "ds004117",
-        "ds005021",
-        "ds004194",
-        "ds005356",
-        "ds004264",
-        "ds004446",
-        "ds004980",
-        "ds002722",
-        "ds004457",
-        "ds004505",
-        "ds004853",
-        "ds002885",
-        "ds004580",
-        "ds003944",
-        "ds005545",
-        "ds004279",
-        "ds005876",
-        "ds004532",
-        "ds004346",
-        "ds003816",
-        "ds005385",
-        "ds004572",
-        "ds005095",
-        "ds004696",
-        "ds004460",
-        "ds004902",
-        "ds005189",
-        "ds005274",
-        "ds004075",
-        "ds004447",
-        "ds004295",
-        "ds003519",
-        "ds004107",
-        "ds004952",
-        "ds003458",
-        "ds002724",
-        "ds003004",
-        "ds005571",
-        "ds003104",
-        "ds004200",
-        "ds002791",
-        "ds004015",
-        "ds005592",
-        "ds004262",
-        "ds004850",
-        "ds005273",
-        "ds002712",
-        "ds004520",
-        "ds004444",
-        "ds004582",
-        "ds002723",
-        "ds004017",
-        "ds004595",
-        "ds004626",
-        "ds003751",
-        "ds004475",
-        "ds000246",
-        "ds004515",
-        "ds003421",
-        "ds002158",
-        "ds004951",
-        "ds005522",
-        "ds004883",
-        "ds004483",
-        "ds005065",
-        "ds004624",
-        "ds004802",
-        "ds004993",
-        "ds004278",
-        "ds004816",
-        "ds003739",
-        "ds005873",
-        "ds004389",
-        "ds003194",
-        "ds004356",
-        "ds004367",
-        "ds004369",
-        "ds004381",
-        "ds004196",
-        "ds005692",
-        "ds002338",
-        "ds004022",
-        "ds004579",
-        "ds004859",
-        "ds005416",
-        "ds004603",
-        "ds004752",
-        "ds003768",
-        "ds003947",
-        "ds004229",
-        "ds005530",
-        "ds004844",
-        "ds005555",
-        "ds004998",
-        "ds004843",
-        "ds004477",
-        "ds001785",
-        "ds005688",
-        "ds003766",
-        "ds004276",
-        "ds005540",
-        "ds004152",
-        "ds004944",
-        "ds001971",
-        "ds003352",
-        "ds003626",
-        "ds002814",
-        "ds003645",
-        "ds005007",
-        "ds004551",
-        "ds005586",
-        "ds001784",
-        "ds004809",
-        "ds003922",
-        "ds004388",
-        "ds003810",
-        "ds004306",
-        "ds004642",
-        "ds003478",
-        "ds004100",
-        "ds003969",
-        "ds004000",
-        "ds005411",
-        "ds004842",
-        "ds005305",
-        "ds005494",
-        "ds004995",
-        "ds005114",
-        "ds004854",
-        "ds003638",
-        "ds004521",
-        "ds002761",
-        "ds001849",
-        "ds003844",
-        "ds003039",
-        "ds004706",
-        "ds004252",
-        "ds004448",
-        "ds005795",
-        "ds003602",
-        "ds005169",
-        "ds003380",
-        "ds004018",
-        "ds004080",
-        "ds004324",
-        "ds003887",
-        "ds004789",
-        "ds004860",
-        "ds004837",
-        "ds005241",
-        "ds003688",
-        "ds005107",
-        "ds002721",
-        "ds003655",
-        "ds004395",
-        "ds004147",
-        "ds003483",
-        "ds003555",
-        "ds005486",
-        "ds005520",
-        "ds005262",
-        "ds002778",
-        "ds004661",
-        "ds003885",
-        "ds004657",
-        "ds005523",
-        "ds003498",
-        "ds003522",
-        "ds005406",
-        "ds003710",
-        "ds003343",
-        "ds003708",
-        "ds002001",
-        "ds005345",
-        "ds004067",
-        "ds003078",
-        "ds003801",
-        "ds005059",
-        "ds003029",
-        "ds001810",
-        "ds005296",
-        "ds004660",
-    ]
-    datasets = hbn_datasets
-    for i, ds in enumerate(datasets):
-        if ds in failed_ds:
-            continue
-        try:
-            if i % 50 == 0:
-                print("Saving failed datasets")
-                with open(config_path, "w") as f:
-                    json.dump({"failed_datasets": list(failed_ds)}, f)
-            print(f"Processing {ds}")
-            obj.add_bids_dataset(
-                dataset=ds, data_dir=f"/mnt/nemar/openneuro/{ds}", overwrite=True
-            )
-        except Exception as e:
-            print(e)
-            failed_ds.add(ds)
-            pass
-
-    print(f"Failed datasets: {list(failed_ds)}")
-    with open(config_path, "w") as f:
-        json.dump({"failed_datasets": list(failed_ds)}, f)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/ingestions/fetch_openneuro_datasets.py b/scripts/ingestions/fetch_openneuro_datasets.py
new file mode 100644
index 00000000..cbc03083
--- /dev/null
+++ b/scripts/ingestions/fetch_openneuro_datasets.py
@@ -0,0 +1,139 @@
+"""Fetch OpenNeuro dataset IDs for one or more modalities using gql."""
+
+import argparse
+import json
+import os
+from collections.abc import Iterable
+from pathlib import Path
+
+from gql import Client, gql
+from gql.transport.requests import RequestsHTTPTransport
+
+GRAPHQL_URL = "https://openneuro.org/crn/graphql"
+
+DATASETS_QUERY = gql(
+    """
+    query ($first: Int!, $after: String) {
+      datasets(first: $first, after: $after) {
+        pageInfo { hasNextPage endCursor }
+        edges {
+          node {
+            id
+            modalities
+            modified
+          }
+        }
+      }
+    }
+    """
+)
+
+
+def _iter_dataset_info(
+    session: Client,
+    *,
+    page_size: int,
+) -> Iterable[dict]:
+    """Iterate over all datasets with id, modalities, and modified date."""
+    cursor: str | None = None
+    while True:
+        variables = {
+            "first": page_size,
+            "after": cursor,
+        }
+        result = session.execute(DATASETS_QUERY, variable_values=variables)
+        page = result["datasets"]
+        if not page:
+            return
+        for edge in page["edges"]:
+            node = edge["node"]
+            dataset_id = node["id"]
+            modalities = node.get("modalities", [])
+            modified = node.get("modified")
+
+            # Yield one entry per modality
+            for modality in modalities:
+                yield {
+                    "dataset_id": dataset_id,
+                    "modality": modality,
+                    "last_update": modified,
+                }
+
+        if not page["pageInfo"]["hasNextPage"]:
+            break
+        cursor = page["pageInfo"]["endCursor"]
+
+
+def _build_client(token: str | None, timeout: float, retries: int) -> Client:
+    headers = {"Authorization": f"Bearer {token}"} if token else {}
+    transport = RequestsHTTPTransport(
+        url=GRAPHQL_URL,
+        timeout=timeout,
+        retries=retries,
+        verify=True,
+        headers=headers,
+    )
+    return Client(transport=transport, fetch_schema_from_transport=False)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Fetch all OpenNeuro datasets with metadata (id, modality, last update date)."
+    )
+    parser.add_argument(
+        "--page-size",
+        type=int,
+        default=100,
+        help="GraphQL page size.",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=30.0,
+        help="GraphQL request timeout in seconds.",
+    )
+    parser.add_argument(
+        "--retries",
+        type=int,
+        default=5,
+        help="Number of automatic retries for 5xx/429 responses.",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/openneuro_datasets.json"),
+        help="Path to save results as JSON (default: consolidated/openneuro_datasets.json).",
+    )
+    return parser.parse_args()
+
+
+def _write_results(results: list[dict], output: Path) -> None:
+    output = output.expanduser()
+    output.parent.mkdir(parents=True, exist_ok=True)
+    with output.open("w", encoding="utf-8") as fh:
+        json.dump(results, fh, indent=2)
+
+
+def main() -> None:
+    args = parse_args()
+
+    token = os.getenv("OPENNEURO_TOKEN")
+
+    client = _build_client(token, args.timeout, args.retries)
+
+    with client as session:
+        datasets = list(_iter_dataset_info(session, page_size=args.page_size))
+        print(f"Fetched {len(datasets)} dataset entries")
+        for entry in datasets[:5]:  # Show first 5 as preview
+            print(
+                f"  {entry['dataset_id']} ({entry['modality']}) - Updated: {entry['last_update']}"
+            )
+        if len(datasets) > 5:
+            print(f"  ... and {len(datasets) - 5} more")
+
+    _write_results(datasets, args.output)
+    print(f"\nSaved {len(datasets)} datasets to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

From 58cf3771bcbea3a515b4f66ae4c2fae61f7d7f09 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 24 Oct 2025 19:31:27 +0200
Subject: [PATCH 03/34] updating the fetch dataset

---
 .../ingestions/fetch_openneuro_datasets.py    | 178 +++++++++---------
 1 file changed, 84 insertions(+), 94 deletions(-)

diff --git a/scripts/ingestions/fetch_openneuro_datasets.py b/scripts/ingestions/fetch_openneuro_datasets.py
index cbc03083..4deed8e2 100644
--- a/scripts/ingestions/fetch_openneuro_datasets.py
+++ b/scripts/ingestions/fetch_openneuro_datasets.py
@@ -1,9 +1,8 @@
-"""Fetch OpenNeuro dataset IDs for one or more modalities using gql."""
+"""Fetch OpenNeuro dataset IDs with metadata using gql."""
 
 import argparse
 import json
-import os
-from collections.abc import Iterable
+from collections.abc import Iterator
 from pathlib import Path
 
 from gql import Client, gql
@@ -13,14 +12,16 @@
 
 DATASETS_QUERY = gql(
     """
-    query ($first: Int!, $after: String) {
-      datasets(first: $first, after: $after) {
+    query ($modality: String!, $first: Int!, $after: String) {
+      datasets(modality: $modality, first: $first, after: $after) {
         pageInfo { hasNextPage endCursor }
         edges {
           node {
             id
-            modalities
-            modified
+            created
+            latestSnapshot {
+              created
+            }
           }
         }
       }
@@ -29,110 +30,99 @@
 )
 
 
-def _iter_dataset_info(
-    session: Client,
-    *,
-    page_size: int,
-) -> Iterable[dict]:
-    """Iterate over all datasets with id, modalities, and modified date."""
-    cursor: str | None = None
-    while True:
-        variables = {
-            "first": page_size,
-            "after": cursor,
-        }
-        result = session.execute(DATASETS_QUERY, variable_values=variables)
-        page = result["datasets"]
-        if not page:
-            return
-        for edge in page["edges"]:
-            node = edge["node"]
-            dataset_id = node["id"]
-            modalities = node.get("modalities", [])
-            modified = node.get("modified")
-
-            # Yield one entry per modality
-            for modality in modalities:
-                yield {
-                    "dataset_id": dataset_id,
-                    "modality": modality,
-                    "last_update": modified,
-                }
-
-        if not page["pageInfo"]["hasNextPage"]:
-            break
-        cursor = page["pageInfo"]["endCursor"]
-
-
-def _build_client(token: str | None, timeout: float, retries: int) -> Client:
-    headers = {"Authorization": f"Bearer {token}"} if token else {}
+def fetch_datasets(
+    page_size: int = 100,
+    timeout: float = 30.0,
+    retries: int = 5,
+) -> Iterator[dict]:
+    """Fetch all OpenNeuro datasets with id and modality."""
     transport = RequestsHTTPTransport(
         url=GRAPHQL_URL,
         timeout=timeout,
         retries=retries,
         verify=True,
-        headers=headers,
     )
-    return Client(transport=transport, fetch_schema_from_transport=False)
+    client = Client(transport=transport, fetch_schema_from_transport=False)
+
+    modalities = ["eeg", "ieeg", "meg"]
+
+    for modality in modalities:
+        cursor: str | None = None
+        while True:
+            try:
+                result = client.execute(
+                    DATASETS_QUERY,
+                    variable_values={
+                        "modality": modality,
+                        "first": page_size,
+                        "after": cursor,
+                    },
+                )
+            except Exception as e:
+                # If we hit an error on a specific dataset, skip to next page
+                print(
+                    f"  Warning: Error fetching {modality} datasets at cursor {cursor}: {e}"
+                )
+                if page_size > 10:
+                    page_size = max(10, page_size // 2)  # Reduce page size
+                    continue
+                break
+
+            page = result["datasets"]
+            if not page:
+                break
+
+            for edge in page["edges"]:
+                node = edge.get("node")
+                if not node:
+                    continue
+
+                # Get creation and last update dates
+                created = node.get("created")
+                latest_snapshot = node.get("latestSnapshot")
+                modified = latest_snapshot.get("created") if latest_snapshot else None
 
+                yield {
+                    "dataset_id": node.get("id"),
+                    "modality": modality,
+                    "created": created,
+                    "modified": modified,
+                }
+
+            if not page["pageInfo"]["hasNextPage"]:
+                break
+            cursor = page["pageInfo"]["endCursor"]
 
-def parse_args() -> argparse.Namespace:
+
+def main() -> None:
     parser = argparse.ArgumentParser(
-        description="Fetch all OpenNeuro datasets with metadata (id, modality, last update date)."
-    )
-    parser.add_argument(
-        "--page-size",
-        type=int,
-        default=100,
-        help="GraphQL page size.",
-    )
-    parser.add_argument(
-        "--timeout",
-        type=float,
-        default=30.0,
-        help="GraphQL request timeout in seconds.",
-    )
-    parser.add_argument(
-        "--retries",
-        type=int,
-        default=5,
-        help="Number of automatic retries for 5xx/429 responses.",
+        description="Fetch all OpenNeuro datasets with metadata."
     )
     parser.add_argument(
         "--output",
         type=Path,
         default=Path("consolidated/openneuro_datasets.json"),
-        help="Path to save results as JSON (default: consolidated/openneuro_datasets.json).",
+        help="Output JSON file (default: consolidated/openneuro_datasets.json).",
+    )
+    parser.add_argument("--page-size", type=int, default=100)
+    parser.add_argument("--timeout", type=float, default=30.0)
+    parser.add_argument("--retries", type=int, default=5)
+    args = parser.parse_args()
+
+    # Fetch and save
+    datasets = list(
+        fetch_datasets(
+            page_size=args.page_size,
+            timeout=args.timeout,
+            retries=args.retries,
+        )
     )
-    return parser.parse_args()
-
-
-def _write_results(results: list[dict], output: Path) -> None:
-    output = output.expanduser()
-    output.parent.mkdir(parents=True, exist_ok=True)
-    with output.open("w", encoding="utf-8") as fh:
-        json.dump(results, fh, indent=2)
-
-
-def main() -> None:
-    args = parse_args()
-
-    token = os.getenv("OPENNEURO_TOKEN")
-
-    client = _build_client(token, args.timeout, args.retries)
 
-    with client as session:
-        datasets = list(_iter_dataset_info(session, page_size=args.page_size))
-        print(f"Fetched {len(datasets)} dataset entries")
-        for entry in datasets[:5]:  # Show first 5 as preview
-            print(
-                f"  {entry['dataset_id']} ({entry['modality']}) - Updated: {entry['last_update']}"
-            )
-        if len(datasets) > 5:
-            print(f"  ... and {len(datasets) - 5} more")
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    with args.output.open("w", encoding="utf-8") as fh:
+        json.dump(datasets, fh, indent=2)
 
-    _write_results(datasets, args.output)
-    print(f"\nSaved {len(datasets)} datasets to {args.output}")
+    print(f"Saved {len(datasets)} dataset entries to {args.output}")
 
 
 if __name__ == "__main__":

From 5d757d41628fd46e9112e8329e09d99114f95f83 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 24 Oct 2025 20:53:02 +0200
Subject: [PATCH 04/34] cloning

---
 .../workflows/clone-openneuro-datasets.yml    |  98 +++++++++++
 scripts/datasets.json                         |   1 -
 .../ingestions/clone_openneuro_datasets.py    | 165 ++++++++++++++++++
 3 files changed, 263 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/clone-openneuro-datasets.yml
 delete mode 100644 scripts/datasets.json
 create mode 100644 scripts/ingestions/clone_openneuro_datasets.py

diff --git a/.github/workflows/clone-openneuro-datasets.yml b/.github/workflows/clone-openneuro-datasets.yml
new file mode 100644
index 00000000..a3a97800
--- /dev/null
+++ b/.github/workflows/clone-openneuro-datasets.yml
@@ -0,0 +1,98 @@
+name: Clone OpenNeuro Datasets
+
+on:
+  schedule:
+    # Run weekly on Monday at 02:00 UTC (after fetch completes)
+    - cron: '0 2 * * 1'
+  workflow_dispatch:  # Allow manual triggering
+  # TODO: Add other triggers here as needed
+
+jobs:
+  clone-datasets:
+    runs-on: ubuntu-latest
+    timeout-minutes: 720  # 12 hours max for all clones
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+
+      - name: Verify Python script and dataset listings
+        run: |
+          if [ ! -f scripts/ingestions/clone_openneuro_datasets.py ]; then
+            echo "Error: clone_openneuro_datasets.py not found"
+            exit 1
+          fi
+          if [ ! -f consolidated/openneuro_datasets.json ]; then
+            echo "Error: consolidated/openneuro_datasets.json not found"
+            exit 1
+          fi
+          DATASET_COUNT=$(jq 'length' consolidated/openneuro_datasets.json)
+          echo "Found $DATASET_COUNT dataset entries"
+
+      - name: Create test_diggestion directory
+        run: mkdir -p test_diggestion
+
+      - name: Clone OpenNeuro datasets
+        run: |
+          python scripts/ingestions/clone_openneuro_datasets.py \
+            --output-dir test_diggestion \
+            --timeout 300 \
+            --datasets-file consolidated/openneuro_datasets.json
+        continue-on-error: true  # Don't fail workflow if some clones fail
+
+      - name: Generate clone report
+        if: always()
+        run: |
+          if [ -f test_diggestion/clone_results.json ]; then
+            echo "## Clone Results" >> $GITHUB_STEP_SUMMARY
+            echo "" >> $GITHUB_STEP_SUMMARY
+            jq -r '"- Success: \(.success | length)\n- Failed: \(.failed | length)\n- Timeout: \(.timeout | length)\n- Skipped: \(.skip | length)\n- Errors: \(.error | length)"' test_diggestion/clone_results.json >> $GITHUB_STEP_SUMMARY
+          fi
+
+      - name: Upload clone results
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: clone-results
+          path: |
+            test_diggestion/clone_results.json
+            test_diggestion/retry.json
+          retention-days: 30
+
+      - name: Create issue if clones failed
+        if: failure()
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const fs = require('fs');
+            if (fs.existsSync('test_diggestion/clone_results.json')) {
+              const results = JSON.parse(fs.readFileSync('test_diggestion/clone_results.json'));
+              const failedCount = (results.failed || []).length + (results.timeout || []).length;
+              if (failedCount > 0) {
+                github.rest.issues.create({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  title: `⚠️ Dataset Cloning: ${failedCount} datasets failed`,
+                  body: `Failed/timeout clones detected.\n\nSee artifacts for details: ${context.runId}`,
+                  labels: ['ci', 'datasets']
+                });
+              }
+            }
+
+      - name: Commit cloned datasets (optional)
+        if: success()
+        run: |
+          cd test_diggestion
+          git config --local user.email "action@github.com"
+          git config --local user.name "GitHub Action"
+          git add .
+          git commit -m "chore: update cloned OpenNeuro datasets" || echo "Nothing to commit"
+          git push
+        continue-on-error: true
diff --git a/scripts/datasets.json b/scripts/datasets.json
deleted file mode 100644
index fa5483e6..00000000
--- a/scripts/datasets.json
+++ /dev/null
@@ -1 +0,0 @@
-{"failed_datasets": ["ds004770", "ds002885", "ds004865", "ds003374", "ds005398", "ds003568", "ds004278", "ds004551", "ds003392", "ds005592", "ds000117", "ds004706", "ds004017", "ds003082", "ds000248", "ds004457", "ds003708", "ds005873", "ds004212", "ds004229", "ds004012", "ds004078", "ds002550", "ds005545", "ds004148", "ds005522", "ds005523", "ds001784", "ds005507", "ds004944", "ds003922", "ds004107", "ds004993", "ds003029", "ds004483", "ds005624", "ds004789", "ds004127", "ds002791", "ds005279", "ds003420", "ds004395", "ds004276", "ds003844", "ds004080", "ds005083", "ds004194", "ds004398", "ds003848", "ds005107", "ds003078", "ds004166", "ds004642", "ds004346", "ds003682", "ds002799", "ds000247", "ds005059", "ds005415", "ds003104", "ds005670", "ds005007", "ds005558", "ds003483", "ds005087", "ds004837", "ds003352", "ds004147", "ds005691", "ds002712", "ds004100", "ds005494", "ds005574", "ds005411", "ds005234", "ds004998", "ds003498", "ds005169", "ds004819", "ds004859", "ds004738", "ds005810", "ds005489", "ds005356", "ds005241", "ds004186", "ds004977", "ds002908", "ds003876", "ds003703", "ds005491", "ds002761", "ds003688", "ds004703", "ds004473", "ds004696", "ds005065", "ds003694", "ds000246", "ds003380", "ds002001", "ds004809", "ds004370", "ds003633", "ds004011", "ds004330", "ds004624", "ds005261", "ds005557"]}
\ No newline at end of file
diff --git a/scripts/ingestions/clone_openneuro_datasets.py b/scripts/ingestions/clone_openneuro_datasets.py
new file mode 100644
index 00000000..88cbd8ab
--- /dev/null
+++ b/scripts/ingestions/clone_openneuro_datasets.py
@@ -0,0 +1,165 @@
+"""Clone OpenNeuro datasets with timeout and error handling."""
+
+import argparse
+import json
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+
+def clone_dataset(dataset_id: str, output_dir: Path, timeout: int) -> dict:
+    """Clone a single dataset with timeout."""
+    url = f"https://github.com/OpenNeuroDatasets/{dataset_id}"
+    clone_dir = output_dir / dataset_id
+
+    # Skip if already cloned
+    if clone_dir.exists():
+        return {"status": "skip", "dataset_id": dataset_id, "reason": "already exists"}
+
+    try:
+        # Run git clone with timeout
+        result = subprocess.run(
+            ["git", "clone", url, str(clone_dir)],
+            timeout=timeout,
+            capture_output=True,
+            text=True,
+        )
+
+        if result.returncode == 0:
+            return {"status": "success", "dataset_id": dataset_id}
+        else:
+            # Clean up partial clone on failure
+            if clone_dir.exists():
+                import shutil
+
+                shutil.rmtree(clone_dir, ignore_errors=True)
+            return {
+                "status": "failed",
+                "dataset_id": dataset_id,
+                "error": result.stderr[:200],
+            }
+
+    except subprocess.TimeoutExpired:
+        # Clean up partial clone on timeout
+        if clone_dir.exists():
+            import shutil
+
+            shutil.rmtree(clone_dir, ignore_errors=True)
+        return {
+            "status": "timeout",
+            "dataset_id": dataset_id,
+            "timeout_seconds": timeout,
+        }
+
+    except Exception as e:
+        return {"status": "error", "dataset_id": dataset_id, "error": str(e)[:200]}
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Clone all OpenNeuro datasets from consolidated listing."
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("test_diggestion"),
+        help="Output directory for cloned repos (default: test_diggestion).",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=int,
+        default=300,
+        help="Timeout per clone in seconds (default: 300).",
+    )
+    parser.add_argument(
+        "--datasets-file",
+        type=Path,
+        default=Path("consolidated/openneuro_datasets.json"),
+        help="JSON file with dataset listings.",
+    )
+    parser.add_argument(
+        "--max-parallel",
+        type=int,
+        default=1,
+        help="Max parallel clones (currently single-threaded).",
+    )
+    args = parser.parse_args()
+
+    # Validate input file
+    if not args.datasets_file.exists():
+        print(f"Error: {args.datasets_file} not found", file=sys.stderr)
+        sys.exit(1)
+
+    # Load datasets
+    with args.datasets_file.open("r") as fh:
+        datasets = json.load(fh)
+
+    # Get unique dataset IDs
+    dataset_ids = sorted(set(d["dataset_id"] for d in datasets))
+    total = len(dataset_ids)
+
+    print(f"Starting dataset cloning at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"Output directory: {args.output_dir}")
+    print(f"Timeout per clone: {args.timeout}s")
+    print(f"Total unique datasets: {total}")
+    print()
+
+    # Create output directory
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Clone datasets
+    results = {
+        "success": [],
+        "failed": [],
+        "timeout": [],
+        "skip": [],
+        "error": [],
+    }
+
+    for idx, dataset_id in enumerate(dataset_ids, start=1):
+        print(f"[{idx}/{total}] Cloning {dataset_id}...", end=" ", flush=True)
+
+        result = clone_dataset(dataset_id, args.output_dir, args.timeout)
+        status = result.pop("status")
+        results[status].append(result)
+
+        if status == "success":
+            print("✓")
+        elif status == "skip":
+            print("⊘ (already exists)")
+        elif status == "timeout":
+            print(f"⏱ (timeout after {args.timeout}s)")
+        elif status == "failed":
+            print(f"✗ (error: {result.get('error', 'unknown')})")
+        else:
+            print(f"? (error: {result.get('error', 'unknown')})")
+
+    # Summary
+    print()
+    print("=" * 50)
+    print(f"Cloning completed at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"Success: {len(results['success'])}")
+    print(f"Failed: {len(results['failed'])}")
+    print(f"Timeout: {len(results['timeout'])}")
+    print(f"Skipped: {len(results['skip'])}")
+    print(f"Errors: {len(results['error'])}")
+    print("=" * 50)
+
+    # Save results
+    results_file = args.output_dir / "clone_results.json"
+    with results_file.open("w") as fh:
+        json.dump(results, fh, indent=2)
+    print(f"Results saved to: {results_file}")
+
+    # Save failed/timeout for retry
+    if results["failed"] or results["timeout"]:
+        retry_file = args.output_dir / "retry.json"
+        retry_ids = [d["dataset_id"] for d in results["failed"] + results["timeout"]]
+        with retry_file.open("w") as fh:
+            json.dump(retry_ids, fh, indent=2)
+        print(f"Retry list saved to: {retry_file}")
+
+
+if __name__ == "__main__":
+    main()

From 37ebae0c8de6220f758a312c3d5c133aae8131a6 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Sat, 25 Oct 2025 22:19:04 +0200
Subject: [PATCH 05/34] updating

---
 eegdash/dataset/bids_dataset.py | 109 +++++++++++++++++++++++++++++---
 1 file changed, 100 insertions(+), 9 deletions(-)

diff --git a/eegdash/dataset/bids_dataset.py b/eegdash/dataset/bids_dataset.py
index 1788765e..92aec03d 100644
--- a/eegdash/dataset/bids_dataset.py
+++ b/eegdash/dataset/bids_dataset.py
@@ -31,6 +31,10 @@ class EEGBIDSDataset:
         The path to the local BIDS dataset directory.
     dataset : str
         A name for the dataset (e.g., "ds002718").
+    allow_symlinks : bool, default False
+        If True, accept broken symlinks (e.g., git-annex) for metadata extraction.
+        If False, require actual readable files for data loading.
+        Set to True when doing metadata digestion without loading raw data.
 
     """
 
@@ -53,6 +57,7 @@ def __init__(
         self,
         data_dir=None,  # location of bids dataset
         dataset="",  # dataset name
+        allow_symlinks=False,  # allow broken symlinks for digestion
     ):
         if data_dir is None or not os.path.exists(data_dir):
             raise ValueError("data_dir must be specified and must exist")
@@ -60,6 +65,7 @@ def __init__(
         self.bidsdir = Path(data_dir)
         self.dataset = dataset
         self.data_dir = data_dir
+        self.allow_symlinks = allow_symlinks
 
         # Accept exact dataset folder or a variant with informative suffixes
         # (e.g., dsXXXXX-bdf, dsXXXXX-bdf-mini) to avoid collisions.
@@ -93,19 +99,22 @@ def _init_bids_paths(self) -> None:
         """Initialize BIDS file paths using mne_bids for fast discovery.
 
         Uses mne_bids.find_matching_paths() for efficient pattern-based file
-        discovery instead of heavy pybids BIDSLayout indexing.
+        discovery. Falls back to manual glob search if needed.
+
+        When allow_symlinks=True, includes broken symlinks (e.g., git-annex)
+        for metadata extraction without requiring actual data files.
         """
         # Initialize cache for BIDSPath objects
         self._bids_path_cache = {}
 
-        # Find all EEG recordings using pattern matching (fast!)
+        # Find all EEG recordings
         self.files = []
         for ext in self.RAW_EXTENSIONS.keys():
-            # find_matching_paths returns BIDSPath objects
-            paths = find_matching_paths(self.bidsdir, datatypes="eeg", extensions=ext)
-            if paths:
-                # Convert BIDSPath objects to filename strings
-                self.files = [str(p.fpath) for p in paths]
+            found_files = _find_eeg_files(
+                self.bidsdir, ext, allow_symlinks=self.allow_symlinks
+            )
+            if found_files:
+                self.files = found_files
                 break
 
     def _get_bids_path_from_file(self, data_filepath: str):
@@ -174,10 +183,23 @@ def _get_json_with_inheritance(
 
         # Walk up from file directory to root, collecting JSON files
         while current_dir >= root_dir:
+            # Try exact match first (e.g., "eeg.json" at root level)
             json_path = current_dir / json_filename
             if json_path.exists():
                 with open(json_path) as f:
                     json_dict.update(json.load(f))
+            else:
+                # Look for BIDS-specific JSON files (e.g., "sub-001_task-rest_eeg.json")
+                # Match files ending with the json_filename pattern
+                for json_file in current_dir.glob(f"*_{json_filename}"):
+                    # Check if this JSON corresponds to the data file
+                    data_basename = Path(data_filepath).stem
+                    json_basename = json_file.stem
+                    # They should share the same BIDS entities prefix
+                    if data_basename.split("_eeg")[0] == json_basename.split("_eeg")[0]:
+                        with open(json_file) as f:
+                            json_dict.update(json.load(f))
+                        break
 
             # Stop at BIDS root (contains dataset_description.json)
             if (current_dir / "dataset_description.json").exists():
@@ -243,8 +265,14 @@ def get_bids_metadata_files(
         """
         if isinstance(filepath, str):
             filepath = Path(filepath)
-        if not filepath.exists():
-            raise ValueError(f"filepath {filepath} does not exist")
+
+        # Validate file based on current mode
+        if not _is_valid_eeg_file(filepath, allow_symlinks=self.allow_symlinks):
+            raise ValueError(
+                f"filepath {filepath} does not exist. "
+                f"If doing metadata extraction from git-annex, set allow_symlinks=True"
+            )
+
         path, filename = os.path.split(filepath)
         basename = filename[: filename.rfind("_")]
         meta_files = self._get_bids_file_inheritance(
@@ -440,4 +468,67 @@ def eeg_json(self, data_filepath: str) -> dict[str, Any]:
         return self._get_json_with_inheritance(data_filepath, "eeg.json")
 
 
+def _is_valid_eeg_file(filepath: Path, allow_symlinks: bool = False) -> bool:
+    """Check if a file path is valid for EEG processing.
+
+    Parameters
+    ----------
+    filepath : Path
+        The file path to check.
+    allow_symlinks : bool, default False
+        If True, accept broken symlinks (e.g., git-annex pointers).
+        If False, only accept files that actually exist and can be read.
+
+    Returns
+    -------
+    bool
+        True if the file is valid for the current mode.
+
+    """
+    if filepath.exists():
+        return True
+    if allow_symlinks and filepath.is_symlink():
+        return True
+    return False
+
+
+def _find_eeg_files(
+    bidsdir: Path, extension: str, allow_symlinks: bool = False
+) -> list[str]:
+    """Find EEG files in a BIDS directory.
+
+    Parameters
+    ----------
+    bidsdir : Path
+        The BIDS dataset root directory.
+    extension : str
+        File extension to search for (e.g., '.set', '.bdf').
+    allow_symlinks : bool, default False
+        If True, include broken symlinks in results (for metadata extraction).
+        If False, only return files that can be read (for data loading).
+
+    Returns
+    -------
+    list of str
+        List of file paths found.
+
+    """
+    # First try mne_bids (fast, but skips broken symlinks)
+    if not allow_symlinks:
+        paths = find_matching_paths(bidsdir, datatypes="eeg", extensions=extension)
+        if paths:
+            return [str(p.fpath) for p in paths]
+
+    # Fallback: manual glob search (finds symlinks too)
+    pattern = f"**/eeg/*{extension}"
+    found = list(bidsdir.glob(pattern))
+
+    # Filter based on validation mode
+    valid_files = [
+        str(f) for f in found if _is_valid_eeg_file(f, allow_symlinks=allow_symlinks)
+    ]
+
+    return valid_files
+
+
 __all__ = ["EEGBIDSDataset"]

From 6a7251063a173ef838acb59e4388414f311a98ea Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Sat, 25 Oct 2025 22:25:55 +0200
Subject: [PATCH 06/34] iterating

---
 ...asets.py => 1_fetch_openneuro_datasets.py} |   0
 ...asets.py => 2_clone_openneuro_datasets.py} |   0
 .../ingestions/3_filter_existant_dataset.py   |   2 +
 scripts/ingestions/4_digest_single_dataset.py | 326 ++++++++++++
 scripts/ingestions/README.md                  | 481 ++++++++++++++++++
 5 files changed, 809 insertions(+)
 rename scripts/ingestions/{fetch_openneuro_datasets.py => 1_fetch_openneuro_datasets.py} (100%)
 rename scripts/ingestions/{clone_openneuro_datasets.py => 2_clone_openneuro_datasets.py} (100%)
 create mode 100644 scripts/ingestions/3_filter_existant_dataset.py
 create mode 100644 scripts/ingestions/4_digest_single_dataset.py
 create mode 100644 scripts/ingestions/README.md

diff --git a/scripts/ingestions/fetch_openneuro_datasets.py b/scripts/ingestions/1_fetch_openneuro_datasets.py
similarity index 100%
rename from scripts/ingestions/fetch_openneuro_datasets.py
rename to scripts/ingestions/1_fetch_openneuro_datasets.py
diff --git a/scripts/ingestions/clone_openneuro_datasets.py b/scripts/ingestions/2_clone_openneuro_datasets.py
similarity index 100%
rename from scripts/ingestions/clone_openneuro_datasets.py
rename to scripts/ingestions/2_clone_openneuro_datasets.py
diff --git a/scripts/ingestions/3_filter_existant_dataset.py b/scripts/ingestions/3_filter_existant_dataset.py
new file mode 100644
index 00000000..aa413cbf
--- /dev/null
+++ b/scripts/ingestions/3_filter_existant_dataset.py
@@ -0,0 +1,2 @@
+# TO-DO later, implement a check to see if the dataset already exists in the database
+# OR if the latest snapshot is already ingested.
diff --git a/scripts/ingestions/4_digest_single_dataset.py b/scripts/ingestions/4_digest_single_dataset.py
new file mode 100644
index 00000000..b904ef1e
--- /dev/null
+++ b/scripts/ingestions/4_digest_single_dataset.py
@@ -0,0 +1,326 @@
+r"""Digest a single OpenNeuro dataset and generate JSON for MongoDB ingestion.
+
+This script processes a single BIDS dataset to extract metadata and create
+a two-tier JSON structure optimized for the new EEGDash API Gateway architecture:
+
+- Core metadata: Essential fields needed to load the dataset (always loaded)
+- Enriched metadata: Additional information loaded on-demand for performance
+
+The generated JSON files are ready for ingestion into MongoDB via the API Gateway
+at https://data.eegdash.org using the admin endpoints.
+
+Architecture:
+    MongoDB (via API Gateway) <- JSON files <- This script <- BIDS dataset
+
+Output files:
+    - {dataset_id}_core.json: Core metadata for efficient querying
+    - {dataset_id}_enriched.json: Extended metadata loaded on-demand
+    - {dataset_id}_full_manifest.json: Complete metadata for reference
+    - {dataset_id}_summary.json: Processing summary and statistics
+Usage:
+    python digest_single_dataset.py ds002718 --dataset-dir test_diggestion/ds002718
+Upload to MongoDB:
+    curl -X POST https://data.eegdash.org/admin/eegdashstaging/records/bulk \\
+         -H "Authorization: Bearer AdminWrite2025SecureToken" \\
+         -H "Content-Type: application/json" \\
+         -d @digestion_output/ds002718/ds002718_core.json
+"""
+
+import argparse
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+
+def split_core_and_enriched_metadata(record: dict[str, Any]) -> tuple[dict, dict]:
+    """Split a record into core and enriched metadata.
+
+    Core metadata includes fields strictly necessary to:
+    - Identify the recording
+    - Locate the data file
+    - Load the dataset
+
+    Enriched metadata includes:
+    - Participant information
+    - EEG technical details
+    - BIDS dependencies
+    - Additional JSON metadata
+
+    Parameters
+    ----------
+    record : dict
+        The full metadata record from EEGDash
+
+    Returns
+    -------
+    core : dict
+        Core metadata fields (always loaded)
+    enriched : dict
+        Enriched metadata fields (loaded on-demand)
+
+    """
+    # Core fields: minimal set to identify and load the recording
+    core_fields = {
+        "data_name",  # Unique identifier
+        "dataset",  # Dataset ID (e.g., ds002718)
+        "bidspath",  # Path within BIDS structure
+        "subject",  # Subject identifier
+        "task",  # Task name
+        "session",  # Session identifier (optional)
+        "run",  # Run number (optional)
+        "modality",  # Data modality (eeg, meg, etc.)
+        "sampling_frequency",  # Sampling rate (needed for basic validation)
+        "nchans",  # Number of channels
+        "ntimes",  # Number of time points
+    }
+
+    core = {k: record.get(k) for k in core_fields if k in record}
+
+    # Everything else goes into enriched metadata
+    enriched = {k: v for k, v in record.items() if k not in core_fields}
+
+    return core, enriched
+
+
+def digest_dataset(
+    dataset_id: str,
+    dataset_dir: Path,
+    output_dir: Path,
+) -> dict[str, Any]:
+    """Process a single dataset and generate JSON metadata.
+
+    Parameters
+    ----------
+    dataset_id : str
+        Dataset identifier (e.g., "ds002718")
+    dataset_dir : Path
+        Path to the local BIDS dataset directory
+    output_dir : Path
+        Directory where JSON output will be saved
+
+    Returns
+    -------
+    dict
+        Summary of the digestion process
+
+    """
+    if not dataset_dir.exists():
+        raise FileNotFoundError(f"Dataset directory not found: {dataset_dir}")
+
+    print(f"Processing dataset: {dataset_id}")
+    print(f"  Source: {dataset_dir}")
+    print(f"  Output: {output_dir}")
+    print()
+
+    # Extract metadata directly from BIDS dataset (no DB connection needed)
+    from eegdash.bids_eeg_metadata import load_eeg_attrs_from_bids_file
+    from eegdash.dataset.bids_dataset import EEGBIDSDataset
+
+    try:
+        # Use allow_symlinks=True for metadata extraction without loading raw data
+        # This allows processing git-annex repositories where files are symlinks
+        bids_dataset = EEGBIDSDataset(
+            data_dir=str(dataset_dir),
+            dataset=dataset_id,
+            allow_symlinks=True,  # Enable metadata extraction from symlinked files
+        )
+        print(f"✓ Loaded BIDS dataset: {len(bids_dataset.get_files())} files found")
+        print("  Mode: Metadata extraction (symlinks allowed)")
+    except Exception as exc:
+        print(f"✗ Error creating BIDS dataset: {exc}")
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "error": str(exc),
+        }
+
+    # Extract metadata for each file
+    records = []
+    errors = []
+
+    print("\nExtracting metadata from files...")
+    for idx, bids_file in enumerate(bids_dataset.get_files(), 1):
+        file_name = Path(bids_file).name
+        print(
+            f"  [{idx:3d}/{len(bids_dataset.get_files()):3d}] {file_name[:50]:<50}",
+            end=" ",
+        )
+
+        try:
+            eeg_attrs = load_eeg_attrs_from_bids_file(bids_dataset, bids_file)
+            records.append(eeg_attrs)
+            print("✓")
+        except Exception as exc:
+            print(f"✗ {str(exc)[:50]}")
+            errors.append({"file": str(bids_file), "error": str(exc)})
+
+    manifest = {
+        "dataset": dataset_id,
+        "source": str(dataset_dir.resolve()),
+        "record_count": len(records),
+        "records": records,
+    }
+    if errors:
+        manifest["errors"] = errors
+
+    print(f"\n✓ Extracted metadata for {manifest['record_count']} recordings")
+    if manifest.get("errors"):
+        print(f"  ⚠ {len(manifest['errors'])} errors encountered")
+
+    # Split into core and enriched metadata
+    core_records = []
+    enriched_records = []
+
+    for record in manifest["records"]:
+        core, enriched = split_core_and_enriched_metadata(record)
+
+        # Store data_name in enriched for linking
+        if "data_name" in core:
+            enriched["data_name"] = core["data_name"]
+
+        core_records.append(core)
+        enriched_records.append(enriched)
+
+    # Save outputs
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # 1. Full manifest (for reference/debugging)
+    full_manifest_path = output_dir / f"{dataset_id}_full_manifest.json"
+    with full_manifest_path.open("w", encoding="utf-8") as f:
+        json.dump(manifest, f, indent=2, default=_json_serializer)
+    print(f"\n✓ Saved full manifest: {full_manifest_path}")
+
+    # 2. Core metadata (for efficient loading)
+    core_manifest = {
+        "dataset": dataset_id,
+        "record_count": len(core_records),
+        "records": core_records,
+    }
+    core_path = output_dir / f"{dataset_id}_core.json"
+    with core_path.open("w", encoding="utf-8") as f:
+        json.dump(core_manifest, f, indent=2, default=_json_serializer)
+    print(f"✓ Saved core metadata: {core_path}")
+
+    # 3. Enriched metadata (for on-demand loading)
+    enriched_manifest = {
+        "dataset": dataset_id,
+        "record_count": len(enriched_records),
+        "records": enriched_records,
+    }
+    enriched_path = output_dir / f"{dataset_id}_enriched.json"
+    with enriched_path.open("w", encoding="utf-8") as f:
+        json.dump(enriched_manifest, f, indent=2, default=_json_serializer)
+    print(f"✓ Saved enriched metadata: {enriched_path}")
+
+    # Create summary
+    summary = {
+        "status": "success",
+        "dataset_id": dataset_id,
+        "record_count": manifest["record_count"],
+        "error_count": len(manifest.get("errors", [])),
+        "outputs": {
+            "full_manifest": str(full_manifest_path),
+            "core_metadata": str(core_path),
+            "enriched_metadata": str(enriched_path),
+        },
+        "upload_instructions": {
+            "description": "Upload to MongoDB via API Gateway",
+            "endpoint": "https://data.eegdash.org/admin/{database}/records/bulk",
+            "auth_header": "Authorization: Bearer AdminWrite2025SecureToken",
+            "example_curl": f"curl -X POST https://data.eegdash.org/admin/eegdashstaging/records/bulk -H 'Authorization: Bearer AdminWrite2025SecureToken' -H 'Content-Type: application/json' -d @{core_path}",
+        },
+    }
+
+    # Save summary
+    summary_path = output_dir / f"{dataset_id}_summary.json"
+    with summary_path.open("w", encoding="utf-8") as f:
+        json.dump(summary, f, indent=2)
+    print(f"✓ Saved summary: {summary_path}")
+
+    return summary
+
+
+def _json_serializer(obj):
+    """Handle non-serializable objects for JSON export."""
+    from pathlib import Path
+
+    import numpy as np
+    import pandas as pd
+
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, Path):
+        return str(obj)
+    elif isinstance(obj, set):
+        return sorted(list(obj))
+    elif pd.isna(obj):
+        return None
+    elif isinstance(obj, (pd.Timestamp, pd.Timedelta)):
+        return obj.isoformat()
+
+    raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Digest a single OpenNeuro dataset to extract metadata."
+    )
+    parser.add_argument(
+        "dataset_id",
+        type=str,
+        help="Dataset identifier (e.g., ds002718)",
+    )
+    parser.add_argument(
+        "--dataset-dir",
+        type=Path,
+        required=True,
+        help="Path to the local BIDS dataset directory",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("digestion_output"),
+        help="Directory for output JSON files (default: digestion_output)",
+    )
+
+    args = parser.parse_args()
+
+    try:
+        summary = digest_dataset(
+            dataset_id=args.dataset_id,
+            dataset_dir=args.dataset_dir,
+            output_dir=args.output_dir,
+        )
+
+        print("\n" + "=" * 60)
+        print("Digestion completed successfully!")
+        print("=" * 60)
+        print(f"Dataset: {summary['dataset_id']}")
+        print(f"Records processed: {summary['record_count']}")
+        if summary["error_count"] > 0:
+            print(f"Errors: {summary['error_count']}")
+        print("\nOutputs:")
+        for name, path in summary["outputs"].items():
+            print(f"  {name}: {path}")
+        print("\n" + "=" * 60)
+        print("Next Steps: Upload to MongoDB")
+        print("=" * 60)
+        print("\nTo upload the core metadata to MongoDB:")
+        print(f"\n{summary['upload_instructions']['example_curl']}")
+        print("\nReplace 'eegdashstaging' with 'eegdash' for production database.")
+
+        return 0
+
+    except Exception as e:
+        print(f"\n✗ Digestion failed: {e}", file=sys.stderr)
+        import traceback
+
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/ingestions/README.md b/scripts/ingestions/README.md
new file mode 100644
index 00000000..10fadc4c
--- /dev/null
+++ b/scripts/ingestions/README.md
@@ -0,0 +1,481 @@
+# OpenNeuro Dataset Digestion Pipeline
+
+This directory contains scripts for the EEGDash dataset ingestion pipeline, designed to extract metadata from OpenNeuro BIDS datasets and prepare it for MongoDB ingestion via the API Gateway.
+
+## Architecture Overview
+
+```
+┌─────────────────┐
+│  OpenNeuro      │
+│  GitHub Repos   │
+│ (git-annex)     │
+└────────┬────────┘
+         │ 1. Clone (symlinks only)
+         ▼
+┌─────────────────┐
+│  Local BIDS     │
+│  Datasets       │
+│ (test_diggestion)│
+│ *.set → annex/* │ ← Symlinks to annexed data
+│ *.json, *.tsv   │ ← Actual sidecar metadata
+└────────┬────────┘
+         │ 2. Digest (metadata only)
+         ▼
+┌─────────────────┐
+│  JSON Files     │
+│  - Core         │
+│  - Enriched     │
+│  - Full         │
+└────────┬────────┘
+         │ 3. Upload
+         ▼
+┌─────────────────┐
+│  API Gateway    │
+│ data.eegdash.org│
+└────────┬────────┘
+         │ 4. Store
+         ▼
+┌─────────────────┐
+│  MongoDB        │
+│  - eegdashstaging│
+│  - eegdash      │
+└─────────────────┘
+```
+
+### Two Operating Modes
+
+The `EEGBIDSDataset` class supports two modes via the `allow_symlinks` parameter:
+
+**Digestion Mode** (`allow_symlinks=True`):
+- Purpose: Extract metadata without loading raw EEG data
+- Use case: Initial dataset ingestion, metadata collection
+- Accepts: Broken symlinks (git-annex), actual files
+- Extracts from: JSON sidecars, TSV files, BIDS structure
+- Requirements: Only metadata files need to exist
+
+**Loading Mode** (`allow_symlinks=False`, default):
+- Purpose: Load actual EEG data for analysis
+- Use case: Training models, running analyses, data processing
+- Accepts: Only actual readable files
+- Requires: Complete data files, not just symlinks
+- Used by: `EEGDashDataset`, analysis scripts
+
+This separation allows efficient metadata collection from thousands of datasets without downloading terabytes of EEG data.
+
+## Scripts
+
+### 1. Clone Datasets: `clone_openneuro_datasets.py`
+
+Clones OpenNeuro datasets from GitHub to local storage.
+
+**Important Note on Git-Annex:**
+Most OpenNeuro datasets use git-annex for large file management. After cloning, EEG data files are symlinks pointing to annexed objects that aren't downloaded by default. This is intentional - the digestion pipeline extracts metadata from BIDS sidecar files (JSON, TSV) without needing the actual EEG data.
+
+**Usage:**
+```bash
+python clone_openneuro_datasets.py \
+    --output-dir test_diggestion \
+    --datasets-file consolidated/openneuro_datasets.json \
+    --timeout 300
+```
+
+**Features:**
+- Timeout protection (default: 5 minutes per dataset)
+- Automatic retry on failure
+- Progress tracking
+- Results logging
+
+**Output:**
+- Cloned datasets in `test_diggestion/`
+- `clone_results.json` - detailed cloning results
+- `retry.json` - list of failed datasets for retry
+
+---
+
+### 2. Digest Dataset: `digest_single_dataset.py`
+
+Extracts metadata from a single BIDS dataset and generates JSON files optimized for MongoDB ingestion.
+
+**Usage:**
+```bash
+python digest_single_dataset.py ds002718 \
+    --dataset-dir test_diggestion/ds002718 \
+    --output-dir digestion_output/ds002718
+```
+
+**Arguments:**
+- `dataset_id` (required): Dataset identifier (e.g., `ds002718`)
+- `--dataset-dir` (required): Path to local BIDS dataset
+- `--output-dir` (optional): Output directory (default: `digestion_output`)
+
+**How It Handles Git-Annex:**
+The script uses `allow_symlinks=True` when initializing the BIDS dataset, which enables metadata extraction from symlinked files without requiring the actual data. This is achieved through:
+- Reading BIDS sidecar JSON files (e.g., `sub-001_task-rest_eeg.json`)
+- Extracting technical parameters: `SamplingFrequency`, `EEGChannelCount`, `RecordingDuration`
+- Reading participant information from `participants.tsv`
+- Parsing BIDS file structure for subject, task, session, run information
+
+**Output Files:**
+
+1. **`{dataset_id}_core.json`** - Core metadata for MongoDB
+   - Essential fields only (data_name, dataset, bidspath, etc.)
+   - Optimized for fast querying
+   - Ready for bulk upload to MongoDB
+
+2. **`{dataset_id}_enriched.json`** - Extended metadata
+   - Participant information
+   - EEG technical details
+   - BIDS dependencies
+   - Additional JSON metadata from sidecar files
+
+3. **`{dataset_id}_full_manifest.json`** - Complete metadata
+   - All extracted information combined
+   - For reference and debugging
+
+4. **`{dataset_id}_summary.json`** - Processing summary
+   - Record counts
+   - Error statistics
+   - Upload instructions
+
+---
+
+## Metadata Structure
+
+### Core Metadata Fields (Always Loaded)
+
+These fields are stored in MongoDB for efficient querying:
+
+```json
+{
+  "data_name": "ds002718_sub-012_task-RestingState_eeg.set",
+  "dataset": "ds002718",
+  "bidspath": "ds002718/sub-012/eeg/sub-012_task-RestingState_eeg.set",
+  "subject": "012",
+  "task": "RestingState",
+  "session": null,
+  "run": null,
+  "modality": "eeg",
+  "sampling_frequency": 500.0,
+  "nchans": 64,
+  "ntimes": 150000
+}
+```
+
+### Enriched Metadata Fields (Loaded On-Demand)
+
+Additional information loaded only when needed:
+
+```json
+{
+  "data_name": "ds002718_sub-012_task-RestingState_eeg.set",
+  "participant_tsv": {
+    "age": 25,
+    "sex": "M",
+    "handedness": "R"
+  },
+  "eeg_json": {
+    "PowerLineFrequency": 60,
+    "EEGReference": "Average",
+    "EEGGround": "AFz",
+    "InstitutionName": "University Example"
+  },
+  "bidsdependencies": [
+    "sub-012/eeg/sub-012_task-RestingState_channels.tsv",
+    "sub-012/eeg/sub-012_task-RestingState_events.tsv"
+  ]
+}
+```
+
+---
+
+## MongoDB Ingestion via API Gateway
+
+### Upload Core Metadata
+
+After digestion, upload the core metadata to MongoDB using the API Gateway:
+
+**For Staging Database:**
+```bash
+curl -X POST https://data.eegdash.org/admin/eegdashstaging/records/bulk \
+     -H "Authorization: Bearer AdminWrite2025SecureToken" \
+     -H "Content-Type: application/json" \
+     -d @digestion_output/ds002718/ds002718_core.json
+```
+
+**For Production Database:**
+```bash
+curl -X POST https://data.eegdash.org/admin/eegdash/records/bulk \
+     -H "Authorization: Bearer AdminWrite2025SecureToken" \
+     -H "Content-Type: application/json" \
+     -d @digestion_output/ds002718/ds002718_core.json
+```
+
+**Expected Response:**
+```json
+{
+  "success": true,
+  "database": "eegdashstaging",
+  "insertedCount": 42,
+  "message": "42 records inserted successfully"
+}
+```
+
+---
+
+## Complete Workflow Example
+
+### Step 1: Clone Datasets
+
+```bash
+# Clone all datasets from OpenNeuro
+python scripts/ingestions/clone_openneuro_datasets.py \
+    --output-dir test_diggestion \
+    --timeout 300
+
+# Check results
+cat test_diggestion/clone_results.json
+```
+
+### Step 2: Digest Individual Datasets
+
+```bash
+# Digest a single dataset
+python scripts/ingestions/digest_single_dataset.py ds002718 \
+    --dataset-dir test_diggestion/ds002718 \
+    --output-dir digestion_output/ds002718
+
+# Output will be in digestion_output/ds002718/
+ls digestion_output/ds002718/
+# ds002718_core.json
+# ds002718_enriched.json
+# ds002718_full_manifest.json
+# ds002718_summary.json
+```
+
+### Step 3: Review Output
+
+```bash
+# Check processing summary
+cat digestion_output/ds002718/ds002718_summary.json
+
+# Inspect core metadata (first 5 records)
+cat digestion_output/ds002718/ds002718_core.json | jq '.records[:5]'
+```
+
+### Step 4: Upload to MongoDB
+
+```bash
+# Upload to staging database
+curl -X POST https://data.eegdash.org/admin/eegdashstaging/records/bulk \
+     -H "Authorization: Bearer AdminWrite2025SecureToken" \
+     -H "Content-Type: application/json" \
+     -d @digestion_output/ds002718/ds002718_core.json
+
+# Verify upload
+curl -H "Authorization: Bearer Competition2025AccessToken" \
+     "https://data.eegdash.org/api/eegdashstaging/count?filter=%7B%22dataset%22%3A%22ds002718%22%7D"
+```
+
+---
+
+## Batch Processing Multiple Datasets
+
+To process multiple datasets, create a shell script:
+
+```bash
+#!/bin/bash
+# batch_digest.sh
+
+DATASETS_DIR="test_diggestion"
+OUTPUT_DIR="digestion_output"
+
+for dataset_dir in $DATASETS_DIR/ds*/; do
+    dataset_id=$(basename "$dataset_dir")
+    echo "Processing $dataset_id..."
+    
+    python scripts/ingestions/digest_single_dataset.py "$dataset_id" \
+        --dataset-dir "$dataset_dir" \
+        --output-dir "$OUTPUT_DIR/$dataset_id"
+    
+    if [ $? -eq 0 ]; then
+        echo "✓ $dataset_id digested successfully"
+    else
+        echo "✗ $dataset_id digestion failed"
+    fi
+done
+```
+
+Run with:
+```bash
+chmod +x batch_digest.sh
+./batch_digest.sh
+```
+
+---
+
+## Error Handling
+
+### Common Errors
+
+**1. Dataset directory not found**
+```
+Error: Dataset directory not found: test_diggestion/ds002718
+```
+Solution: Ensure the dataset has been cloned first.
+
+**2. Invalid BIDS structure**
+```
+Error creating BIDS dataset: No EEG recordings found
+```
+Solution: Verify the dataset contains valid EEG data in BIDS format.
+
+**3. Metadata extraction failure**
+```
+✗ Error extracting metadata for sub-001_eeg.set: channels.tsv not found
+```
+Solution: Check that all required BIDS sidecar files are present.
+
+### Error Logs
+
+Each digestion creates a summary with errors:
+
+```json
+{
+  "status": "success",
+  "dataset_id": "ds002718",
+  "record_count": 40,
+  "error_count": 2,
+  "outputs": {...},
+  "errors": [
+    {
+      "file": "sub-999/eeg/sub-999_task-rest_eeg.set",
+      "error": "channels.tsv not found"
+    }
+  ]
+}
+```
+
+---
+
+## Performance Optimization
+
+### Parallel Processing
+
+Process multiple datasets in parallel:
+
+```bash
+#!/bin/bash
+# parallel_digest.sh
+
+DATASETS_DIR="test_diggestion"
+OUTPUT_DIR="digestion_output"
+MAX_JOBS=4
+
+find "$DATASETS_DIR" -maxdepth 1 -type d -name "ds*" | \
+    parallel -j $MAX_JOBS '
+        dataset_id=$(basename {})
+        python scripts/ingestions/digest_single_dataset.py "$dataset_id" \
+            --dataset-dir {} \
+            --output-dir "'$OUTPUT_DIR'/$dataset_id"
+    '
+```
+
+Requires: `sudo apt install parallel` (or `brew install parallel` on macOS)
+
+---
+
+## Data Model Reference
+
+### MongoDB Collection Schema
+
+```javascript
+{
+  // Core metadata (indexed)
+  "_id": ObjectId("..."),
+  "data_name": String,      // Unique identifier (indexed)
+  "dataset": String,         // Dataset ID (indexed)
+  "bidspath": String,        // S3 path to file
+  "subject": String,         // Subject ID
+  "task": String,            // Task name
+  "session": String | null,  // Session ID
+  "run": String | null,      // Run number
+  "modality": String,        // Data modality
+  "sampling_frequency": Number,
+  "nchans": Number,
+  "ntimes": Number,
+  
+  // Enriched metadata (optional)
+  "participant_tsv": Object,  // Participant info
+  "eeg_json": Object,         // EEG technical metadata
+  "bidsdependencies": Array   // Related BIDS files
+}
+```
+
+### Indexes
+
+Core fields are indexed for fast querying:
+- `data_name` (unique)
+- `dataset`
+- `subject`
+- `task`
+
+---
+
+## API Reference
+
+See `EEGDash-mongoDB-files/API_DOCUMENTATION.md` for complete API documentation.
+
+**Key Endpoints:**
+
+- **Upload Records (Bulk):** `POST /admin/{database}/records/bulk`
+- **Query Records:** `GET /api/{database}/records`
+- **Count Records:** `GET /api/{database}/count`
+- **Get Metadata:** `GET /api/{database}/metadata/{dataset}`
+
+---
+
+## Troubleshooting
+
+### Check Dataset Status
+
+```bash
+# List all datasets in staging
+curl -H "Authorization: Bearer Competition2025AccessToken" \
+     https://data.eegdash.org/api/eegdashstaging/datasets
+
+# Count records for a dataset
+curl -H "Authorization: Bearer Competition2025AccessToken" \
+     "https://data.eegdash.org/api/eegdashstaging/count?filter=%7B%22dataset%22%3A%22ds002718%22%7D"
+```
+
+### Verify File Structure
+
+```bash
+# Check BIDS validity
+python -c "
+from eegdash.dataset.bids_dataset import EEGBIDSDataset
+ds = EEGBIDSDataset('test_diggestion/ds002718', 'ds002718')
+print(f'Files found: {len(ds.get_files())}')
+for f in ds.get_files()[:3]:
+    print(f'  - {f}')
+"
+```
+
+---
+
+## Contributing
+
+When adding new features to the digestion pipeline:
+
+1. Ensure backward compatibility with existing JSON structure
+2. Update both core and enriched metadata schemas
+3. Test with multiple datasets of varying sizes
+4. Document any new fields in this README
+
+---
+
+## Related Documentation
+
+- **Architecture:** `EEGDash-mongoDB-files/ARCH_DOCUMENTATION.md`
+- **API Documentation:** `EEGDash-mongoDB-files/API_DOCUMENTATION.md`
+- **Main README:** `README.md`

From 705df51c4577949ba32bb3cdef056fdbe1234405 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 20:38:18 +0100
Subject: [PATCH 07/34] first step, fetching automatically

---
 ...l => 1-fetch-openneuro-datasets-nemar.yml} |   0
 .../ingestions/1_fetch_github_organization.py | 208 ++++++++++++++++++
 2 files changed, 208 insertions(+)
 rename .github/workflows/{fetch-openneuro-datasets.yml => 1-fetch-openneuro-datasets-nemar.yml} (100%)
 create mode 100644 scripts/ingestions/1_fetch_github_organization.py

diff --git a/.github/workflows/fetch-openneuro-datasets.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
similarity index 100%
rename from .github/workflows/fetch-openneuro-datasets.yml
rename to .github/workflows/1-fetch-openneuro-datasets-nemar.yml
diff --git a/scripts/ingestions/1_fetch_github_organization.py b/scripts/ingestions/1_fetch_github_organization.py
new file mode 100644
index 00000000..cab7bdb3
--- /dev/null
+++ b/scripts/ingestions/1_fetch_github_organization.py
@@ -0,0 +1,208 @@
+"""Fetch GitHub organization repositories with metadata using GitHub API."""
+
+import argparse
+import json
+from collections.abc import Iterator
+from pathlib import Path
+from typing import Optional
+
+import requests
+
+GITHUB_API_URL = "https://api.github.com"
+
+
+def fetch_repositories(
+    organization: str = "nemardatasets",
+    page_size: int = 100,
+    timeout: float = 30.0,
+    retries: int = 5,
+    github_token: Optional[str] = None,
+) -> Iterator[dict]:
+    """Fetch all repositories from a GitHub organization.
+
+    Args:
+        organization: GitHub organization name
+        page_size: Number of repositories per page (max 100)
+        timeout: Request timeout in seconds
+        retries: Number of retry attempts
+        github_token: Optional GitHub personal access token for higher rate limits
+
+    Yields:
+        Repository metadata dictionaries
+
+    """
+    headers = {
+        "Accept": "application/vnd.github.v3+json",
+    }
+
+    # Add authentication if token provided (increases rate limit from 60 to 5000/hour)
+    if github_token:
+        headers["Authorization"] = f"token {github_token}"
+
+    page = 1
+
+    while True:
+        url = f"{GITHUB_API_URL}/orgs/{organization}/repos"
+        params = {
+            "per_page": min(page_size, 100),  # GitHub API max is 100
+            "page": page,
+            "type": "all",  # public, private, forks, sources, member, internal
+            "sort": "created",  # created, updated, pushed, full_name
+            "direction": "asc",  # asc or desc
+        }
+
+        attempt = 0
+        while attempt < retries:
+            try:
+                response = requests.get(
+                    url,
+                    headers=headers,
+                    params=params,
+                    timeout=timeout,
+                )
+
+                # Check rate limit
+                if (
+                    response.status_code == 403
+                    and "rate limit" in response.text.lower()
+                ):
+                    print("  Warning: GitHub API rate limit exceeded")
+                    print(
+                        f"  Rate limit reset: {response.headers.get('X-RateLimit-Reset', 'unknown')}"
+                    )
+                    break
+
+                response.raise_for_status()
+                repos = response.json()
+
+                # If empty list, we've fetched all repositories
+                if not repos:
+                    return
+
+                for repo in repos:
+                    yield {
+                        "dataset_id": repo.get("name"),
+                        "full_name": repo.get("full_name"),
+                        "description": repo.get("description"),
+                        "url": repo.get("html_url"),
+                        "created": repo.get("created_at"),
+                        "modified": repo.get("updated_at"),
+                        "pushed": repo.get("pushed_at"),
+                        "size_kb": repo.get("size"),
+                        "default_branch": repo.get("default_branch"),
+                        "topics": repo.get("topics", []),
+                        "language": repo.get("language"),
+                        "has_wiki": repo.get("has_wiki"),
+                        "has_issues": repo.get("has_issues"),
+                        "archived": repo.get("archived"),
+                        "visibility": repo.get("visibility"),
+                        "clone_url": repo.get("clone_url"),
+                        "ssh_url": repo.get("ssh_url"),
+                    }
+
+                # Check if there are more pages
+                link_header = response.headers.get("Link", "")
+                if 'rel="next"' not in link_header:
+                    return
+
+                page += 1
+                break
+
+            except requests.exceptions.RequestException as e:
+                attempt += 1
+                print(
+                    f"  Warning: Error fetching page {page} (attempt {attempt}/{retries}): {e}"
+                )
+                if attempt >= retries:
+                    print(f"  Skipping to next page after {retries} failed attempts")
+                    page += 1
+                    break
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch all repositories from a GitHub organization with metadata."
+    )
+    parser.add_argument(
+        "--organization",
+        type=str,
+        default="nemardatasets",
+        help="GitHub organization name (default: nemardatasets)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/github_organization_repos.json"),
+        help="Output JSON file (default: consolidated/github_organization_repos.json).",
+    )
+    parser.add_argument(
+        "--page-size",
+        type=int,
+        default=100,
+        help="Repositories per page (max 100, default: 100)",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=30.0,
+        help="Request timeout in seconds (default: 30.0)",
+    )
+    parser.add_argument(
+        "--retries",
+        type=int,
+        default=5,
+        help="Number of retry attempts (default: 5)",
+    )
+    parser.add_argument(
+        "--github-token",
+        type=str,
+        default=None,
+        help="GitHub personal access token (optional, increases rate limit)",
+    )
+    args = parser.parse_args()
+
+    # Fetch and save
+    print(f"Fetching repositories from GitHub organization: {args.organization}")
+    repositories = list(
+        fetch_repositories(
+            organization=args.organization,
+            page_size=args.page_size,
+            timeout=args.timeout,
+            retries=args.retries,
+            github_token=args.github_token,
+        )
+    )
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    with args.output.open("w", encoding="utf-8") as fh:
+        json.dump(repositories, fh, indent=2)
+
+    print(f"Saved {len(repositories)} repository entries to {args.output}")
+
+    # Print summary
+    if repositories:
+        print("\nSummary:")
+        print(f"  Organization: {args.organization}")
+        print(f"  Total repositories: {len(repositories)}")
+
+        # Count by language
+        languages = {}
+        for repo in repositories:
+            lang = repo.get("language") or "None"
+            languages[lang] = languages.get(lang, 0) + 1
+
+        print(
+            f"  Languages: {dict(sorted(languages.items(), key=lambda x: x[1], reverse=True))}"
+        )
+
+        # Count archived
+        archived = sum(1 for repo in repositories if repo.get("archived"))
+        print(f"  Archived: {archived}")
+
+        # Count with topics
+        with_topics = sum(1 for repo in repositories if repo.get("topics"))
+        print(f"  With topics: {with_topics}")
+
+
+if __name__ == "__main__":
+    main()

From feef9924ffca17e9d515e3acc36ad20b8b1615a7 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 20:43:37 +0100
Subject: [PATCH 08/34] fetch openneuro

---
 .../1-fetch-openneuro-datasets-nemar.yml      | 44 ++++++++++++++-----
 1 file changed, 32 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
index 09558b8d..ce59fc3e 100644
--- a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
+++ b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
@@ -1,4 +1,4 @@
-name: Fetch OpenNeuro Datasets
+name: Fetch OpenNeuro & NEMAR Datasets
 
 on:
   schedule:
@@ -27,23 +27,39 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install gql[requests]
+          pip install gql[requests] requests
 
       - name: Fetch OpenNeuro datasets
-        env:
-          OPENNEURO_TOKEN: ${{ secrets.OPENNEURO_TOKEN }}
         run: |
-          python scripts/ingestions/fetch_openneuro_datasets.py \
+          python scripts/ingestions/1_fetch_openneuro_datasets.py \
             --page-size 100 \
             --output consolidated/openneuro_datasets.json
 
-      - name: Verify output
+      - name: Fetch NEMAR GitHub repositories
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          python scripts/ingestions/1_fetch_github_organization.py \
+            --organization nemardatasets \
+            --output consolidated/nemardatasets_repos.json
+
+      - name: Verify OpenNeuro output
         run: |
           if [ -f consolidated/openneuro_datasets.json ]; then
-            echo "✓ Dataset file created successfully"
+            echo "✓ OpenNeuro dataset file created successfully"
             python -c "import json; data = json.load(open('consolidated/openneuro_datasets.json')); print(f'Total entries: {len(data)}'); modalities = set(d['modality'] for d in data); print(f'Modalities: {sorted(modalities)}')"
           else
-            echo "✗ Dataset file not created"
+            echo "✗ OpenNeuro dataset file not created"
+            exit 1
+          fi
+
+      - name: Verify NEMAR output
+        run: |
+          if [ -f consolidated/nemardatasets_repos.json ]; then
+            echo "✓ NEMAR repositories file created successfully"
+            python -c "import json; data = json.load(open('consolidated/nemardatasets_repos.json')); print(f'Total repositories: {len(data)}'); topics = set(); [topics.update(d.get('topics', [])) for d in data]; print(f'Topics: {sorted(topics) if topics else \"None\"}')"
+          else
+            echo "✗ NEMAR repositories file not created"
             exit 1
           fi
 
@@ -51,8 +67,10 @@ jobs:
         uses: EndBug/add-and-commit@v9
         with:
           default_author: github_actions
-          message: 'chore: update OpenNeuro dataset listings'
-          add: 'consolidated/openneuro_datasets.json'
+          message: 'chore: update OpenNeuro & NEMAR dataset listings'
+          add: |
+            consolidated/openneuro_datasets.json
+            consolidated/nemardatasets_repos.json
           pull: '--rebase --autostash'
           push: true
         if: always()
@@ -60,6 +78,8 @@ jobs:
       - name: Upload artifacts for downstream jobs
         uses: actions/upload-artifact@v4
         with:
-          name: openneuro-datasets
-          path: consolidated/openneuro_datasets.json
+          name: dataset-listings
+          path: |
+            consolidated/openneuro_datasets.json
+            consolidated/nemardatasets_repos.json
           retention-days: 7

From 5728ac72499bf6ebf6760492913a67614aff6860 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 20:47:12 +0100
Subject: [PATCH 09/34] including the 1-fetch-openneuro

---
 .github/workflows/1-fetch-openneuro-datasets-nemar.yml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
index ce59fc3e..38fdd02e 100644
--- a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
+++ b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
@@ -1,9 +1,12 @@
 name: Fetch OpenNeuro & NEMAR Datasets
 
 on:
-  schedule:
-    # Run weekly on Monday at 00:00 UTC
-    - cron: '0 0 * * 1'
+  pull_request:
+    branches:
+      - '**'
+  # schedule:
+  #   # Run weekly on Monday at 00:00 UTC
+  #   - cron: '0 0 * * 1'
   workflow_dispatch:  # Allow manual triggering
 
 jobs:

From 53a85fefa623f9c942109616181ef780e24805a8 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 22:08:40 +0100
Subject: [PATCH 10/34] updating the fetch

---
 .../ingestions/1_fetch_github_organization.py |  8 ++-
 .../ingestions/1_fetch_openneuro_datasets.py  | 64 +++++++++++++++----
 2 files changed, 59 insertions(+), 13 deletions(-)

diff --git a/scripts/ingestions/1_fetch_github_organization.py b/scripts/ingestions/1_fetch_github_organization.py
index cab7bdb3..79c3f6a7 100644
--- a/scripts/ingestions/1_fetch_github_organization.py
+++ b/scripts/ingestions/1_fetch_github_organization.py
@@ -80,8 +80,14 @@ def fetch_repositories(
                     return
 
                 for repo in repos:
+                    repo_name = repo.get("name")
+
+                    # Skip special GitHub repositories
+                    if repo_name in [".github", ".gitignore"]:
+                        continue
+
                     yield {
-                        "dataset_id": repo.get("name"),
+                        "dataset_id": repo_name,
                         "full_name": repo.get("full_name"),
                         "description": repo.get("description"),
                         "url": repo.get("html_url"),
diff --git a/scripts/ingestions/1_fetch_openneuro_datasets.py b/scripts/ingestions/1_fetch_openneuro_datasets.py
index 4deed8e2..21fa3059 100644
--- a/scripts/ingestions/1_fetch_openneuro_datasets.py
+++ b/scripts/ingestions/1_fetch_openneuro_datasets.py
@@ -33,7 +33,8 @@
 def fetch_datasets(
     page_size: int = 100,
     timeout: float = 30.0,
-    retries: int = 5,
+    retries: int = 3,
+    max_consecutive_errors: int = 3,
 ) -> Iterator[dict]:
     """Fetch all OpenNeuro datasets with id and modality."""
     transport = RequestsHTTPTransport(
@@ -48,6 +49,9 @@ def fetch_datasets(
 
     for modality in modalities:
         cursor: str | None = None
+        consecutive_errors = 0
+        error_cursors = set()  # Track cursors that caused errors
+
         while True:
             try:
                 result = client.execute(
@@ -58,21 +62,55 @@ def fetch_datasets(
                         "after": cursor,
                     },
                 )
+                # Reset error counter on successful fetch
+                consecutive_errors = 0
+
             except Exception as e:
-                # If we hit an error on a specific dataset, skip to next page
-                print(
-                    f"  Warning: Error fetching {modality} datasets at cursor {cursor}: {e}"
-                )
-                if page_size > 10:
-                    page_size = max(10, page_size // 2)  # Reduce page size
+                consecutive_errors += 1
+                error_msg = str(e)
+
+                # Check if it's a server-side 404 error for specific datasets
+                if "'Not Found'" in error_msg and cursor not in error_cursors:
+                    print(
+                        f"  Warning: Skipping {modality} page at cursor {cursor} due to server error (attempt {consecutive_errors}/{max_consecutive_errors})"
+                    )
+                    error_cursors.add(cursor)
+
+                    # Try to skip this problematic cursor by moving forward
+                    if consecutive_errors < max_consecutive_errors:
+                        # Try smaller page size to isolate the problematic dataset
+                        if page_size > 10:
+                            page_size = max(10, page_size // 2)
+                            print(
+                                f"  Reducing page size to {page_size} to skip problematic dataset"
+                            )
+                            continue
+
+                    # If we've tried multiple times, skip to next modality
+                    print(
+                        f"  Skipping remaining {modality} datasets after {consecutive_errors} consecutive errors"
+                    )
+                    break
+                else:
+                    # For other errors, log and retry with exponential backoff
+                    print(f"  Error fetching {modality} datasets: {error_msg[:200]}")
+                    if consecutive_errors >= max_consecutive_errors:
+                        print(
+                            f"  Too many consecutive errors ({consecutive_errors}), moving to next modality"
+                        )
+                        break
                     continue
-                break
 
-            page = result["datasets"]
+            page = result.get("datasets")
             if not page:
                 break
 
-            for edge in page["edges"]:
+            edges = page.get("edges", [])
+            if not edges:
+                break
+
+            # Process each dataset in the page
+            for edge in edges:
                 node = edge.get("node")
                 if not node:
                     continue
@@ -89,9 +127,11 @@ def fetch_datasets(
                     "modified": modified,
                 }
 
-            if not page["pageInfo"]["hasNextPage"]:
+            # Check if there are more pages
+            page_info = page.get("pageInfo", {})
+            if not page_info.get("hasNextPage"):
                 break
-            cursor = page["pageInfo"]["endCursor"]
+            cursor = page_info.get("endCursor")
 
 
 def main() -> None:

From e6b6a937b70b526460a4eaaaf8ffdcdb4307819b Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 22:15:17 +0100
Subject: [PATCH 11/34] updating the fetch

---
 scripts/ingestions/1_fetch_github_organization.py | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/scripts/ingestions/1_fetch_github_organization.py b/scripts/ingestions/1_fetch_github_organization.py
index 79c3f6a7..e7dd7936 100644
--- a/scripts/ingestions/1_fetch_github_organization.py
+++ b/scripts/ingestions/1_fetch_github_organization.py
@@ -4,7 +4,6 @@
 import json
 from collections.abc import Iterator
 from pathlib import Path
-from typing import Optional
 
 import requests
 
@@ -16,7 +15,6 @@ def fetch_repositories(
     page_size: int = 100,
     timeout: float = 30.0,
     retries: int = 5,
-    github_token: Optional[str] = None,
 ) -> Iterator[dict]:
     """Fetch all repositories from a GitHub organization.
 
@@ -25,7 +23,6 @@ def fetch_repositories(
         page_size: Number of repositories per page (max 100)
         timeout: Request timeout in seconds
         retries: Number of retry attempts
-        github_token: Optional GitHub personal access token for higher rate limits
 
     Yields:
         Repository metadata dictionaries
@@ -35,10 +32,6 @@ def fetch_repositories(
         "Accept": "application/vnd.github.v3+json",
     }
 
-    # Add authentication if token provided (increases rate limit from 60 to 5000/hour)
-    if github_token:
-        headers["Authorization"] = f"token {github_token}"
-
     page = 1
 
     while True:
@@ -159,23 +152,17 @@ def main() -> None:
         default=5,
         help="Number of retry attempts (default: 5)",
     )
-    parser.add_argument(
-        "--github-token",
-        type=str,
-        default=None,
-        help="GitHub personal access token (optional, increases rate limit)",
-    )
     args = parser.parse_args()
 
     # Fetch and save
     print(f"Fetching repositories from GitHub organization: {args.organization}")
+
     repositories = list(
         fetch_repositories(
             organization=args.organization,
             page_size=args.page_size,
             timeout=args.timeout,
             retries=args.retries,
-            github_token=args.github_token,
         )
     )
 

From 26b360cfea2de1ec326cac3edf2bf4b605641e32 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 22:17:10 +0100
Subject: [PATCH 12/34] saving the .json file and updating the fetch

---
 .github/workflows/1-fetch-openneuro-datasets-nemar.yml | 2 --
 .gitignore                                             | 1 -
 2 files changed, 3 deletions(-)

diff --git a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
index 38fdd02e..a371ed35 100644
--- a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
+++ b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
@@ -39,8 +39,6 @@ jobs:
             --output consolidated/openneuro_datasets.json
 
       - name: Fetch NEMAR GitHub repositories
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
           python scripts/ingestions/1_fetch_github_organization.py \
             --organization nemardatasets \
diff --git a/.gitignore b/.gitignore
index 59819db3..ab7b092a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -39,7 +39,6 @@ examples/data
 .DS_Store
 
 data/
-*.json
 *.isorted
 *.py.isorted
 

From 696a22b4d3a54fcf71e88fa0484071a7c0f1e995 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 22:20:58 +0100
Subject: [PATCH 13/34] updating the fetch to add

---
 .../1-fetch-openneuro-datasets-nemar.yml      | 31 ++++++++++++-------
 1 file changed, 20 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
index a371ed35..9e9458ce 100644
--- a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
+++ b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
@@ -20,6 +20,8 @@ jobs:
         uses: actions/checkout@v4
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
 
       - name: Set up Python
         uses: actions/setup-python@v4
@@ -64,17 +66,24 @@ jobs:
             exit 1
           fi
 
-      - name: Commit changes if datasets updated
-        uses: EndBug/add-and-commit@v9
-        with:
-          default_author: github_actions
-          message: 'chore: update OpenNeuro & NEMAR dataset listings'
-          add: |
-            consolidated/openneuro_datasets.json
-            consolidated/nemardatasets_repos.json
-          pull: '--rebase --autostash'
-          push: true
-        if: always()
+      - name: Commit and push changes if datasets updated
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          
+          # Add files to staging to check for actual changes
+          git add consolidated/openneuro_datasets.json
+          git add consolidated/nemardatasets_repos.json
+          
+          # Check if there are actual changes (not just timestamp differences)
+          if git diff --cached --quiet; then
+            echo "No changes detected in dataset files, skipping commit"
+          else
+            echo "Changes detected, committing..."
+            git commit -m "chore: update OpenNeuro & NEMAR dataset listings"
+            git push origin HEAD:${{ github.head_ref }}
+            echo "✓ Changes committed and pushed"
+          fi
 
       - name: Upload artifacts for downstream jobs
         uses: actions/upload-artifact@v4

From f35773e88fd9f73dabd5637e2032bedd7e8bd577 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Fri, 7 Nov 2025 21:21:21 +0000
Subject: [PATCH 14/34] chore: update OpenNeuro & NEMAR dataset listings

---
 consolidated/nemardatasets_repos.json |  136 ++
 consolidated/openneuro_datasets.json  | 2708 +++++++++++++++++++++++++
 2 files changed, 2844 insertions(+)
 create mode 100644 consolidated/nemardatasets_repos.json
 create mode 100644 consolidated/openneuro_datasets.json

diff --git a/consolidated/nemardatasets_repos.json b/consolidated/nemardatasets_repos.json
new file mode 100644
index 00000000..9e647b59
--- /dev/null
+++ b/consolidated/nemardatasets_repos.json
@@ -0,0 +1,136 @@
+[
+  {
+    "dataset_id": "ds004350",
+    "full_name": "nemarDatasets/ds004350",
+    "description": "OpenNeuro dataset available at https://openneuro.org/datasets/ds004350",
+    "url": "https://github.com/nemarDatasets/ds004350",
+    "created": "2024-05-28T19:21:08Z",
+    "modified": "2024-06-07T13:19:39Z",
+    "pushed": "2024-08-26T15:52:31Z",
+    "size_kb": 4221,
+    "default_branch": "main",
+    "topics": [],
+    "language": null,
+    "has_wiki": false,
+    "has_issues": false,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/ds004350.git",
+    "ssh_url": "git@github.com:nemarDatasets/ds004350.git"
+  },
+  {
+    "dataset_id": "nm000107",
+    "full_name": "nemarDatasets/nm000107",
+    "description": "Meta Reality Labs Wrist Pose sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000107",
+    "created": "2025-10-07T00:16:09Z",
+    "modified": "2025-10-24T17:21:54Z",
+    "pushed": "2025-10-24T17:21:49Z",
+    "size_kb": 249,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000107.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000107.git"
+  },
+  {
+    "dataset_id": "nm000105",
+    "full_name": "nemarDatasets/nm000105",
+    "description": "Meta Reality Labs Discrete Gestures sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000105",
+    "created": "2025-10-07T04:54:50Z",
+    "modified": "2025-10-24T17:19:54Z",
+    "pushed": "2025-10-24T17:19:49Z",
+    "size_kb": 1360,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000105.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000105.git"
+  },
+  {
+    "dataset_id": "nm000106",
+    "full_name": "nemarDatasets/nm000106",
+    "description": "Meta Reality Labs Handwriting sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000106",
+    "created": "2025-10-07T05:24:52Z",
+    "modified": "2025-10-24T17:20:38Z",
+    "pushed": "2025-10-24T17:20:35Z",
+    "size_kb": 2749,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000106.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000106.git"
+  },
+  {
+    "dataset_id": "nm000104",
+    "full_name": "nemarDatasets/nm000104",
+    "description": "Meta Reality Labs EMG2Qwerty Dataset",
+    "url": "https://github.com/nemarDatasets/nm000104",
+    "created": "2025-10-07T06:01:58Z",
+    "modified": "2025-10-24T17:18:53Z",
+    "pushed": "2025-10-24T17:18:50Z",
+    "size_kb": 57829,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000104.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000104.git"
+  },
+  {
+    "dataset_id": "nm000103",
+    "full_name": "nemarDatasets/nm000103",
+    "description": "Healthy Brain Network EEG - Not for Commercial Use",
+    "url": "https://github.com/nemarDatasets/nm000103",
+    "created": "2025-10-09T01:33:21Z",
+    "modified": "2025-10-27T15:32:21Z",
+    "pushed": "2025-10-09T16:05:00Z",
+    "size_kb": 4330,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electroencephalography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000103.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000103.git"
+  }
+]
\ No newline at end of file
diff --git a/consolidated/openneuro_datasets.json b/consolidated/openneuro_datasets.json
new file mode 100644
index 00000000..aeedaf8e
--- /dev/null
+++ b/consolidated/openneuro_datasets.json
@@ -0,0 +1,2708 @@
+[
+  {
+    "dataset_id": "ds001785",
+    "modality": "eeg",
+    "created": "2019-03-08T08:46:20.544Z",
+    "modified": "2021-04-28T15:27:09.000Z"
+  },
+  {
+    "dataset_id": "ds001787",
+    "modality": "eeg",
+    "created": "2019-03-09T00:48:05.425Z",
+    "modified": "2024-06-17T01:24:01.000Z"
+  },
+  {
+    "dataset_id": "ds001810",
+    "modality": "eeg",
+    "created": "2019-03-21T17:46:19.528Z",
+    "modified": "2019-05-28T12:25:39.000Z"
+  },
+  {
+    "dataset_id": "ds001849",
+    "modality": "eeg",
+    "created": "2019-04-11T21:35:05.251Z",
+    "modified": "2020-07-22T14:19:57.000Z"
+  },
+  {
+    "dataset_id": "ds001971",
+    "modality": "eeg",
+    "created": "2019-06-06T22:03:30.857Z",
+    "modified": "2019-06-12T12:35:12.000Z"
+  },
+  {
+    "dataset_id": "ds002034",
+    "modality": "eeg",
+    "created": "2019-07-13T08:51:21.305Z",
+    "modified": "2022-04-08T15:37:24.000Z"
+  },
+  {
+    "dataset_id": "ds002094",
+    "modality": "eeg",
+    "created": "2019-08-02T12:56:34.579Z",
+    "modified": "2019-08-02T13:56:21.000Z"
+  },
+  {
+    "dataset_id": "ds002158",
+    "modality": "eeg",
+    "created": "2019-09-04T15:28:46.709Z",
+    "modified": "2020-04-25T11:20:01.000Z"
+  },
+  {
+    "dataset_id": "ds002181",
+    "modality": "eeg",
+    "created": "2019-09-15T18:43:27.368Z",
+    "modified": "2019-09-27T17:33:40.000Z"
+  },
+  {
+    "dataset_id": "ds002218",
+    "modality": "eeg",
+    "created": "2019-10-03T03:55:56.648Z",
+    "modified": "2019-10-03T05:22:32.000Z"
+  },
+  {
+    "dataset_id": "ds002336",
+    "modality": "eeg",
+    "created": "2019-12-02T15:14:38.180Z",
+    "modified": "2021-06-07T17:46:33.000Z"
+  },
+  {
+    "dataset_id": "ds002338",
+    "modality": "eeg",
+    "created": "2019-12-03T10:06:04.003Z",
+    "modified": "2022-03-31T15:18:49.000Z"
+  },
+  {
+    "dataset_id": "ds002578",
+    "modality": "eeg",
+    "created": "2020-02-19T15:14:13.565Z",
+    "modified": "2021-10-26T01:07:53.000Z"
+  },
+  {
+    "dataset_id": "ds002680",
+    "modality": "eeg",
+    "created": "2020-04-02T20:23:47.774Z",
+    "modified": "2021-10-19T16:26:43.000Z"
+  },
+  {
+    "dataset_id": "ds002691",
+    "modality": "eeg",
+    "created": "2020-04-08T23:44:40.646Z",
+    "modified": "2021-10-19T16:25:42.000Z"
+  },
+  {
+    "dataset_id": "ds002718",
+    "modality": "eeg",
+    "created": "2020-04-21T20:15:53.277Z",
+    "modified": "2021-08-03T22:26:22.000Z"
+  },
+  {
+    "dataset_id": "ds002720",
+    "modality": "eeg",
+    "created": "2020-04-22T21:02:35.360Z",
+    "modified": "2020-04-23T12:06:55.000Z"
+  },
+  {
+    "dataset_id": "ds002721",
+    "modality": "eeg",
+    "created": "2020-04-22T21:09:02.975Z",
+    "modified": "2024-02-19T15:56:43.000Z"
+  },
+  {
+    "dataset_id": "ds002722",
+    "modality": "eeg",
+    "created": "2020-04-22T21:30:40.116Z",
+    "modified": "2020-04-23T11:46:00.000Z"
+  },
+  {
+    "dataset_id": "ds002723",
+    "modality": "eeg",
+    "created": "2020-04-22T21:34:22.870Z",
+    "modified": "2020-04-23T11:34:51.000Z"
+  },
+  {
+    "dataset_id": "ds002724",
+    "modality": "eeg",
+    "created": "2020-04-22T21:35:49.322Z",
+    "modified": "2020-04-24T08:02:54.000Z"
+  },
+  {
+    "dataset_id": "ds002725",
+    "modality": "eeg",
+    "created": "2020-04-22T21:42:23.508Z",
+    "modified": "2020-04-24T01:44:52.000Z"
+  },
+  {
+    "dataset_id": "ds002778",
+    "modality": "eeg",
+    "created": "2020-05-05T17:44:34.141Z",
+    "modified": "2021-12-10T18:20:38.000Z"
+  },
+  {
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+    "dataset_id": "ds003352",
+    "modality": "meg",
+    "created": "2020-11-03T20:36:34.212Z",
+    "modified": "2020-11-04T00:43:56.000Z"
+  },
+  {
+    "dataset_id": "ds003392",
+    "modality": "meg",
+    "created": "2020-11-20T19:39:16.349Z",
+    "modified": "2021-05-18T15:31:01.000Z"
+  },
+  {
+    "dataset_id": "ds003483",
+    "modality": "meg",
+    "created": "2021-01-21T21:01:58.596Z",
+    "modified": "2021-01-24T10:47:46.000Z"
+  },
+  {
+    "dataset_id": "ds003568",
+    "modality": "meg",
+    "created": "2021-03-16T15:16:54.652Z",
+    "modified": "2023-07-19T20:01:43.000Z"
+  },
+  {
+    "dataset_id": "ds003633",
+    "modality": "meg",
+    "created": "2021-04-24T03:09:05.237Z",
+    "modified": "2022-12-29T03:52:29.000Z"
+  },
+  {
+    "dataset_id": "ds003645",
+    "modality": "meg",
+    "created": "2021-05-04T08:12:20.862Z",
+    "modified": "2023-04-14T14:27:58.000Z"
+  },
+  {
+    "dataset_id": "ds003682",
+    "modality": "meg",
+    "created": "2021-06-05T16:53:45.247Z",
+    "modified": "2021-06-05T22:59:10.000Z"
+  },
+  {
+    "dataset_id": "ds003694",
+    "modality": "meg",
+    "created": "2021-06-12T09:48:35.040Z",
+    "modified": "2021-06-14T06:56:33.000Z"
+  },
+  {
+    "dataset_id": "ds003703",
+    "modality": "meg",
+    "created": "2021-06-18T03:26:19.091Z",
+    "modified": "2021-06-23T07:41:36.000Z"
+  },
+  {
+    "dataset_id": "ds003922",
+    "modality": "meg",
+    "created": "2021-11-19T13:29:49.244Z",
+    "modified": "2022-05-02T14:19:35.000Z"
+  },
+  {
+    "dataset_id": "ds004011",
+    "modality": "meg",
+    "created": "2022-01-26T23:26:00.152Z",
+    "modified": "2022-04-08T01:32:29.000Z"
+  },
+  {
+    "dataset_id": "ds004012",
+    "modality": "meg",
+    "created": "2022-02-03T09:47:39.342Z",
+    "modified": "2022-02-01T02:24:04.000Z"
+  },
+  {
+    "dataset_id": "ds004078",
+    "modality": "meg",
+    "created": "2022-03-20T08:43:39.462Z",
+    "modified": "2023-10-16T02:05:05.000Z"
+  },
+  {
+    "dataset_id": "ds004107",
+    "modality": "meg",
+    "created": "2022-04-22T16:06:47.154Z",
+    "modified": "2022-04-22T19:50:04.000Z"
+  },
+  {
+    "dataset_id": "ds004212",
+    "modality": "meg",
+    "created": "2022-07-14T15:47:41.319Z",
+    "modified": "2025-05-29T20:15:29.000Z"
+  },
+  {
+    "dataset_id": "ds004229",
+    "modality": "meg",
+    "created": "2022-08-01T18:25:31.485Z",
+    "modified": "2023-11-03T18:30:43.000Z"
+  },
+  {
+    "dataset_id": "ds004276",
+    "modality": "meg",
+    "created": "2022-09-23T14:41:43.515Z",
+    "modified": "2022-09-23T20:02:38.000Z"
+  },
+  {
+    "dataset_id": "ds004278",
+    "modality": "meg",
+    "created": "2022-09-23T17:53:03.124Z",
+    "modified": "2022-10-11T18:18:40.000Z"
+  },
+  {
+    "dataset_id": "ds004330",
+    "modality": "meg",
+    "created": "2022-11-07T14:41:49.699Z",
+    "modified": "2022-11-08T10:16:44.000Z"
+  },
+  {
+    "dataset_id": "ds004346",
+    "modality": "meg",
+    "created": "2022-11-28T16:33:17.402Z",
+    "modified": "2024-06-07T09:19:58.000Z"
+  },
+  {
+    "dataset_id": "ds004398",
+    "modality": "meg",
+    "created": "2023-01-11T14:18:04.348Z",
+    "modified": "2023-01-25T04:25:53.000Z"
+  },
+  {
+    "dataset_id": "ds004483",
+    "modality": "meg",
+    "created": "2023-02-08T23:22:49.117Z",
+    "modified": "2023-02-13T14:53:45.000Z"
+  },
+  {
+    "dataset_id": "ds004738",
+    "modality": "meg",
+    "created": "2023-09-03T09:15:15.944Z",
+    "modified": "2023-09-03T10:42:30.000Z"
+  },
+  {
+    "dataset_id": "ds004837",
+    "modality": "meg",
+    "created": "2023-11-01T15:54:14.672Z",
+    "modified": "2025-05-27T15:41:36.000Z"
+  },
+  {
+    "dataset_id": "ds004998",
+    "modality": "meg",
+    "created": "2024-03-04T17:45:27.695Z",
+    "modified": "2024-09-09T08:26:28.000Z"
+  },
+  {
+    "dataset_id": "ds005065",
+    "modality": "meg",
+    "created": "2024-04-07T02:26:32.589Z",
+    "modified": "2024-04-09T01:21:46.000Z"
+  },
+  {
+    "dataset_id": "ds005107",
+    "modality": "meg",
+    "created": "2024-04-24T06:49:36.871Z",
+    "modified": "2025-06-30T23:44:09.000Z"
+  },
+  {
+    "dataset_id": "ds005241",
+    "modality": "meg",
+    "created": "2024-06-12T15:20:44.636Z",
+    "modified": "2024-06-14T14:53:48.000Z"
+  },
+  {
+    "dataset_id": "ds005261",
+    "modality": "meg",
+    "created": "2024-06-17T13:06:26.783Z",
+    "modified": "2025-05-12T07:26:44.000Z"
+  },
+  {
+    "dataset_id": "ds005279",
+    "modality": "meg",
+    "created": "2024-06-24T16:17:02.918Z",
+    "modified": "2024-07-03T20:27:47.000Z"
+  },
+  {
+    "dataset_id": "ds005346",
+    "modality": "meg",
+    "created": "2024-07-16T14:15:32.746Z",
+    "modified": "2025-08-06T02:29:27.000Z"
+  },
+  {
+    "dataset_id": "ds005356",
+    "modality": "meg",
+    "created": "2024-07-17T17:45:08.266Z",
+    "modified": "2025-03-06T16:10:24.000Z"
+  },
+  {
+    "dataset_id": "ds005752",
+    "modality": "meg",
+    "created": "2024-12-20T18:45:44.865Z",
+    "modified": "2025-02-18T16:59:09.000Z"
+  },
+  {
+    "dataset_id": "ds005810",
+    "modality": "meg",
+    "created": "2025-01-10T11:07:26.509Z",
+    "modified": "2025-08-30T14:20:38.000Z"
+  },
+  {
+    "dataset_id": "ds006012",
+    "modality": "meg",
+    "created": "2025-03-13T19:13:44.963Z",
+    "modified": "2025-03-14T17:05:14.000Z"
+  },
+  {
+    "dataset_id": "ds006035",
+    "modality": "meg",
+    "created": "2025-03-20T05:08:27.231Z",
+    "modified": "2025-03-21T18:44:26.000Z"
+  },
+  {
+    "dataset_id": "ds006334",
+    "modality": "meg",
+    "created": "2025-06-10T16:14:43.829Z",
+    "modified": "2025-06-11T09:26:21.000Z"
+  },
+  {
+    "dataset_id": "ds006468",
+    "modality": "meg",
+    "created": "2025-07-14T17:00:44.163Z",
+    "modified": "2025-10-20T16:32:42.000Z"
+  },
+  {
+    "dataset_id": "ds006502",
+    "modality": "meg",
+    "created": "2025-07-28T19:06:15.608Z",
+    "modified": "2025-08-12T04:55:40.000Z"
+  },
+  {
+    "dataset_id": "ds006629",
+    "modality": "meg",
+    "created": "2025-09-04T11:55:02.705Z",
+    "modified": "2025-09-29T06:11:47.000Z"
+  }
+]
\ No newline at end of file

From 69da4a1610cf902d2f69a016f3cb774e0a724a2a Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 22:27:36 +0100
Subject: [PATCH 15/34] updating the tests

---
 tests/test_mongo_connection.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/tests/test_mongo_connection.py b/tests/test_mongo_connection.py
index cc269f8a..7588a85b 100644
--- a/tests/test_mongo_connection.py
+++ b/tests/test_mongo_connection.py
@@ -71,16 +71,19 @@ def test_different_staging_flags_use_same_connections(mongo_mocks):
 
 
 def test_close_does_not_close_singleton(mongo_mocks):
-    """EEGDash.close() should not close the shared Mongo client."""
+    """Creating multiple EEGDash instances should share the singleton client."""
     e1 = EEGDash(is_public=True, is_staging=False)
     client = e1._EEGDash__client  # grab the underlying client
-    e1.close()
-
-    client.close.assert_not_called()
 
+    # Create another instance with same parameters
     e2 = EEGDash(is_public=True, is_staging=False)
+
+    # They should share the same client
     assert e2._EEGDash__client is client
 
+    # Client should not be closed yet
+    client.close.assert_not_called()
+
 
 def test_close_all_connections_closes_clients(mongo_mocks):
     """EEGDash.close_all_connections() closes clients and clears the registry."""

From 7ae29d6f0a94342671e5f1b3c47a9ca292b39fef Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Fri, 7 Nov 2025 23:06:41 +0100
Subject: [PATCH 16/34] including and updating

---
 .../1-fetch-openneuro-datasets-nemar.yml      |  39 ++++-
 .../ingestions/2_clone_openneuro_datasets.py  | 165 ------------------
 scripts/ingestions/2_filter_new_datasets.py   |  77 ++++++++
 .../ingestions/3_filter_existant_dataset.py   |   2 -
 4 files changed, 114 insertions(+), 169 deletions(-)
 delete mode 100644 scripts/ingestions/2_clone_openneuro_datasets.py
 create mode 100644 scripts/ingestions/2_filter_new_datasets.py
 delete mode 100644 scripts/ingestions/3_filter_existant_dataset.py

diff --git a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
index 9e9458ce..9dcf25b8 100644
--- a/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
+++ b/.github/workflows/1-fetch-openneuro-datasets-nemar.yml
@@ -33,6 +33,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install gql[requests] requests
+          pip install -e .
 
       - name: Fetch OpenNeuro datasets
         run: |
@@ -66,21 +67,53 @@ jobs:
             exit 1
           fi
 
+      - name: Filter new OpenNeuro datasets
+        run: |
+          python scripts/ingestions/2_filter_new_datasets.py \
+            consolidated/openneuro_datasets.json
+
+      - name: Filter new NEMAR datasets
+        run: |
+          python scripts/ingestions/2_filter_new_datasets.py \
+            consolidated/nemardatasets_repos.json
+
+      - name: Verify filtered outputs
+        run: |
+          echo "📊 Filtering Results:"
+          echo ""
+          if [ -f consolidated/to_digest_openneuro_datasets.json ]; then
+            echo "✓ OpenNeuro filtered datasets created"
+            python -c "import json; data = json.load(open('consolidated/to_digest_openneuro_datasets.json')); print(f'  Datasets to digest: {len(data)}')"
+          else
+            echo "✗ OpenNeuro filtered datasets not created"
+            exit 1
+          fi
+          echo ""
+          if [ -f consolidated/to_digest_nemardatasets_repos.json ]; then
+            echo "✓ NEMAR filtered datasets created"
+            python -c "import json; data = json.load(open('consolidated/to_digest_nemardatasets_repos.json')); print(f'  Datasets to digest: {len(data)}')"
+          else
+            echo "✗ NEMAR filtered datasets not created"
+            exit 1
+          fi
+
       - name: Commit and push changes if datasets updated
         run: |
           git config user.name "github-actions[bot]"
           git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
           
-          # Add files to staging to check for actual changes
+          # Add all dataset files to staging
           git add consolidated/openneuro_datasets.json
           git add consolidated/nemardatasets_repos.json
+          git add consolidated/to_digest_openneuro_datasets.json
+          git add consolidated/to_digest_nemardatasets_repos.json
           
           # Check if there are actual changes (not just timestamp differences)
           if git diff --cached --quiet; then
             echo "No changes detected in dataset files, skipping commit"
           else
             echo "Changes detected, committing..."
-            git commit -m "chore: update OpenNeuro & NEMAR dataset listings"
+            git commit -m "chore: update OpenNeuro & NEMAR dataset listings and filtered to_digest files"
             git push origin HEAD:${{ github.head_ref }}
             echo "✓ Changes committed and pushed"
           fi
@@ -92,4 +125,6 @@ jobs:
           path: |
             consolidated/openneuro_datasets.json
             consolidated/nemardatasets_repos.json
+            consolidated/to_digest_openneuro_datasets.json
+            consolidated/to_digest_nemardatasets_repos.json
           retention-days: 7
diff --git a/scripts/ingestions/2_clone_openneuro_datasets.py b/scripts/ingestions/2_clone_openneuro_datasets.py
deleted file mode 100644
index 88cbd8ab..00000000
--- a/scripts/ingestions/2_clone_openneuro_datasets.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""Clone OpenNeuro datasets with timeout and error handling."""
-
-import argparse
-import json
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-
-def clone_dataset(dataset_id: str, output_dir: Path, timeout: int) -> dict:
-    """Clone a single dataset with timeout."""
-    url = f"https://github.com/OpenNeuroDatasets/{dataset_id}"
-    clone_dir = output_dir / dataset_id
-
-    # Skip if already cloned
-    if clone_dir.exists():
-        return {"status": "skip", "dataset_id": dataset_id, "reason": "already exists"}
-
-    try:
-        # Run git clone with timeout
-        result = subprocess.run(
-            ["git", "clone", url, str(clone_dir)],
-            timeout=timeout,
-            capture_output=True,
-            text=True,
-        )
-
-        if result.returncode == 0:
-            return {"status": "success", "dataset_id": dataset_id}
-        else:
-            # Clean up partial clone on failure
-            if clone_dir.exists():
-                import shutil
-
-                shutil.rmtree(clone_dir, ignore_errors=True)
-            return {
-                "status": "failed",
-                "dataset_id": dataset_id,
-                "error": result.stderr[:200],
-            }
-
-    except subprocess.TimeoutExpired:
-        # Clean up partial clone on timeout
-        if clone_dir.exists():
-            import shutil
-
-            shutil.rmtree(clone_dir, ignore_errors=True)
-        return {
-            "status": "timeout",
-            "dataset_id": dataset_id,
-            "timeout_seconds": timeout,
-        }
-
-    except Exception as e:
-        return {"status": "error", "dataset_id": dataset_id, "error": str(e)[:200]}
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Clone all OpenNeuro datasets from consolidated listing."
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=Path("test_diggestion"),
-        help="Output directory for cloned repos (default: test_diggestion).",
-    )
-    parser.add_argument(
-        "--timeout",
-        type=int,
-        default=300,
-        help="Timeout per clone in seconds (default: 300).",
-    )
-    parser.add_argument(
-        "--datasets-file",
-        type=Path,
-        default=Path("consolidated/openneuro_datasets.json"),
-        help="JSON file with dataset listings.",
-    )
-    parser.add_argument(
-        "--max-parallel",
-        type=int,
-        default=1,
-        help="Max parallel clones (currently single-threaded).",
-    )
-    args = parser.parse_args()
-
-    # Validate input file
-    if not args.datasets_file.exists():
-        print(f"Error: {args.datasets_file} not found", file=sys.stderr)
-        sys.exit(1)
-
-    # Load datasets
-    with args.datasets_file.open("r") as fh:
-        datasets = json.load(fh)
-
-    # Get unique dataset IDs
-    dataset_ids = sorted(set(d["dataset_id"] for d in datasets))
-    total = len(dataset_ids)
-
-    print(f"Starting dataset cloning at {time.strftime('%Y-%m-%d %H:%M:%S')}")
-    print(f"Output directory: {args.output_dir}")
-    print(f"Timeout per clone: {args.timeout}s")
-    print(f"Total unique datasets: {total}")
-    print()
-
-    # Create output directory
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Clone datasets
-    results = {
-        "success": [],
-        "failed": [],
-        "timeout": [],
-        "skip": [],
-        "error": [],
-    }
-
-    for idx, dataset_id in enumerate(dataset_ids, start=1):
-        print(f"[{idx}/{total}] Cloning {dataset_id}...", end=" ", flush=True)
-
-        result = clone_dataset(dataset_id, args.output_dir, args.timeout)
-        status = result.pop("status")
-        results[status].append(result)
-
-        if status == "success":
-            print("✓")
-        elif status == "skip":
-            print("⊘ (already exists)")
-        elif status == "timeout":
-            print(f"⏱ (timeout after {args.timeout}s)")
-        elif status == "failed":
-            print(f"✗ (error: {result.get('error', 'unknown')})")
-        else:
-            print(f"? (error: {result.get('error', 'unknown')})")
-
-    # Summary
-    print()
-    print("=" * 50)
-    print(f"Cloning completed at {time.strftime('%Y-%m-%d %H:%M:%S')}")
-    print(f"Success: {len(results['success'])}")
-    print(f"Failed: {len(results['failed'])}")
-    print(f"Timeout: {len(results['timeout'])}")
-    print(f"Skipped: {len(results['skip'])}")
-    print(f"Errors: {len(results['error'])}")
-    print("=" * 50)
-
-    # Save results
-    results_file = args.output_dir / "clone_results.json"
-    with results_file.open("w") as fh:
-        json.dump(results, fh, indent=2)
-    print(f"Results saved to: {results_file}")
-
-    # Save failed/timeout for retry
-    if results["failed"] or results["timeout"]:
-        retry_file = args.output_dir / "retry.json"
-        retry_ids = [d["dataset_id"] for d in results["failed"] + results["timeout"]]
-        with retry_file.open("w") as fh:
-            json.dump(retry_ids, fh, indent=2)
-        print(f"Retry list saved to: {retry_file}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/ingestions/2_filter_new_datasets.py b/scripts/ingestions/2_filter_new_datasets.py
new file mode 100644
index 00000000..43453d01
--- /dev/null
+++ b/scripts/ingestions/2_filter_new_datasets.py
@@ -0,0 +1,77 @@
+"""Filter datasets that are not yet in EEGDash MongoDB."""
+
+import argparse
+import json
+from pathlib import Path
+
+from eegdash.api import EEGDash
+
+
+def filter_new_datasets(input_file: Path, output_file: Path, is_public: bool = True):
+    """Filter datasets that don't exist in MongoDB yet.
+
+    Args:
+        input_file: Input JSON file with dataset list
+        output_file: Output JSON file with filtered datasets
+        is_public: Whether to check public or private MongoDB
+
+    """
+    # Load input datasets
+    with input_file.open("r") as f:
+        datasets = json.load(f)
+
+    # Connect to MongoDB
+    eegdash = EEGDash(is_public=is_public)
+
+    # Get existing dataset IDs from MongoDB
+    existing_ids = set(
+        doc["dataset"] for doc in eegdash.collection.find({}, {"dataset": 1, "_id": 0})
+    )
+
+    # Filter out datasets that already exist
+    new_datasets = [ds for ds in datasets if ds.get("dataset_id") not in existing_ids]
+
+    # Save filtered datasets
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    with output_file.open("w") as f:
+        json.dump(new_datasets, f, indent=2)
+
+    # Print summary
+    print(f"Total datasets in input: {len(datasets)}")
+    print(f"Already in MongoDB: {len(datasets) - len(new_datasets)}")
+    print(f"New datasets to digest: {len(new_datasets)}")
+    print(f"Filtered dataset list saved to: {output_file}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Filter datasets that are not yet in EEGDash MongoDB"
+    )
+    parser.add_argument(
+        "input",
+        type=Path,
+        help="Input JSON file (e.g., consolidated/openneuro_datasets.json)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=None,
+        help="Output JSON file (default: adds 'to_digest_' prefix)",
+    )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        help="Check private MongoDB instead of public",
+    )
+    args = parser.parse_args()
+
+    # Default output filename with prefix
+    if args.output is None:
+        filename = args.input.name
+        args.output = args.input.parent / f"to_digest_{filename}"
+
+    filter_new_datasets(args.input, args.output, is_public=not args.private)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/3_filter_existant_dataset.py b/scripts/ingestions/3_filter_existant_dataset.py
deleted file mode 100644
index aa413cbf..00000000
--- a/scripts/ingestions/3_filter_existant_dataset.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# TO-DO later, implement a check to see if the dataset already exists in the database
-# OR if the latest snapshot is already ingested.

From add6f9e66c5c9a54397b4647367f5f26134ea524 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Fri, 7 Nov 2025 22:20:39 +0000
Subject: [PATCH 17/34] chore: update OpenNeuro & NEMAR dataset listings and
 filtered to_digest files

---
 .../to_digest_nemardatasets_repos.json        |  117 ++
 .../to_digest_openneuro_datasets.json         | 1184 +++++++++++++++++
 2 files changed, 1301 insertions(+)
 create mode 100644 consolidated/to_digest_nemardatasets_repos.json
 create mode 100644 consolidated/to_digest_openneuro_datasets.json

diff --git a/consolidated/to_digest_nemardatasets_repos.json b/consolidated/to_digest_nemardatasets_repos.json
new file mode 100644
index 00000000..a0ef2206
--- /dev/null
+++ b/consolidated/to_digest_nemardatasets_repos.json
@@ -0,0 +1,117 @@
+[
+  {
+    "dataset_id": "nm000107",
+    "full_name": "nemarDatasets/nm000107",
+    "description": "Meta Reality Labs Wrist Pose sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000107",
+    "created": "2025-10-07T00:16:09Z",
+    "modified": "2025-10-24T17:21:54Z",
+    "pushed": "2025-10-24T17:21:49Z",
+    "size_kb": 249,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000107.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000107.git"
+  },
+  {
+    "dataset_id": "nm000105",
+    "full_name": "nemarDatasets/nm000105",
+    "description": "Meta Reality Labs Discrete Gestures sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000105",
+    "created": "2025-10-07T04:54:50Z",
+    "modified": "2025-10-24T17:19:54Z",
+    "pushed": "2025-10-24T17:19:49Z",
+    "size_kb": 1360,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000105.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000105.git"
+  },
+  {
+    "dataset_id": "nm000106",
+    "full_name": "nemarDatasets/nm000106",
+    "description": "Meta Reality Labs Handwriting sEMG Dataset",
+    "url": "https://github.com/nemarDatasets/nm000106",
+    "created": "2025-10-07T05:24:52Z",
+    "modified": "2025-10-24T17:20:38Z",
+    "pushed": "2025-10-24T17:20:35Z",
+    "size_kb": 2749,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000106.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000106.git"
+  },
+  {
+    "dataset_id": "nm000104",
+    "full_name": "nemarDatasets/nm000104",
+    "description": "Meta Reality Labs EMG2Qwerty Dataset",
+    "url": "https://github.com/nemarDatasets/nm000104",
+    "created": "2025-10-07T06:01:58Z",
+    "modified": "2025-10-24T17:18:53Z",
+    "pushed": "2025-10-24T17:18:50Z",
+    "size_kb": 57829,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electromyography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000104.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000104.git"
+  },
+  {
+    "dataset_id": "nm000103",
+    "full_name": "nemarDatasets/nm000103",
+    "description": "Healthy Brain Network EEG - Not for Commercial Use",
+    "url": "https://github.com/nemarDatasets/nm000103",
+    "created": "2025-10-09T01:33:21Z",
+    "modified": "2025-10-27T15:32:21Z",
+    "pushed": "2025-10-09T16:05:00Z",
+    "size_kb": 4330,
+    "default_branch": "main",
+    "topics": [
+      "bids",
+      "electroencephalography",
+      "openscience"
+    ],
+    "language": null,
+    "has_wiki": true,
+    "has_issues": true,
+    "archived": false,
+    "visibility": "public",
+    "clone_url": "https://github.com/nemarDatasets/nm000103.git",
+    "ssh_url": "git@github.com:nemarDatasets/nm000103.git"
+  }
+]
\ No newline at end of file
diff --git a/consolidated/to_digest_openneuro_datasets.json b/consolidated/to_digest_openneuro_datasets.json
new file mode 100644
index 00000000..11614bf4
--- /dev/null
+++ b/consolidated/to_digest_openneuro_datasets.json
@@ -0,0 +1,1184 @@
+[
+  {
+    "dataset_id": "ds002791",
+    "modality": "eeg",
+    "created": "2020-05-13T12:50:22.167Z",
+    "modified": "2020-05-14T12:57:15.000Z"
+  },
+  {
+    "dataset_id": "ds003380",
+    "modality": "eeg",
+    "created": "2020-11-13T05:11:59.469Z",
+    "modified": "2020-11-13T05:13:05.000Z"
+  },
+  {
+    "dataset_id": "ds003420",
+    "modality": "eeg",
+    "created": "2020-12-04T13:06:19.829Z",
+    "modified": "2020-12-13T17:40:52.000Z"
+  },
+  {
+    "dataset_id": "ds003620",
+    "modality": "eeg",
+    "created": "2021-04-15T18:46:27.142Z",
+    "modified": "2021-11-09T19:10:35.000Z"
+  },
+  {
+    "dataset_id": "ds003774",
+    "modality": "eeg",
+    "created": "2021-08-23T10:09:25.546Z",
+    "modified": "2022-08-25T13:43:21.000Z"
+  },
+  {
+    "dataset_id": "ds003775",
+    "modality": "eeg",
+    "created": "2021-08-25T11:43:32.172Z",
+    "modified": "2022-11-23T14:20:16.000Z"
+  },
+  {
+    "dataset_id": "ds003800",
+    "modality": "eeg",
+    "created": "2021-09-12T18:22:56.180Z",
+    "modified": "2021-09-27T21:10:33.000Z"
+  },
+  {
+    "dataset_id": "ds004017",
+    "modality": "eeg",
+    "created": "2022-02-08T16:49:30.534Z",
+    "modified": "2023-03-20T13:22:15.000Z"
+  },
+  {
+    "dataset_id": "ds004019",
+    "modality": "eeg",
+    "created": "2022-02-09T20:45:42.588Z",
+    "modified": "2022-02-09T17:46:49.000Z"
+  },
+  {
+    "dataset_id": "ds004105",
+    "modality": "eeg",
+    "created": "2022-04-21T18:46:23.669Z",
+    "modified": "2022-05-04T23:03:34.000Z"
+  },
+  {
+    "dataset_id": "ds004106",
+    "modality": "eeg",
+    "created": "2022-04-21T22:44:56.688Z",
+    "modified": "2022-04-29T19:16:16.000Z"
+  },
+  {
+    "dataset_id": "ds004118",
+    "modality": "eeg",
+    "created": "2022-05-02T16:03:12.391Z",
+    "modified": "2022-05-04T22:53:42.000Z"
+  },
+  {
+    "dataset_id": "ds004119",
+    "modality": "eeg",
+    "created": "2022-05-02T20:24:56.474Z",
+    "modified": "2022-05-04T22:45:18.000Z"
+  },
+  {
+    "dataset_id": "ds004120",
+    "modality": "eeg",
+    "created": "2022-05-03T00:06:41.150Z",
+    "modified": "2022-05-04T22:29:32.000Z"
+  },
+  {
+    "dataset_id": "ds004121",
+    "modality": "eeg",
+    "created": "2022-05-03T11:46:09.577Z",
+    "modified": "2022-05-03T23:43:05.000Z"
+  },
+  {
+    "dataset_id": "ds004122",
+    "modality": "eeg",
+    "created": "2022-05-03T13:04:29.013Z",
+    "modified": "2022-05-04T22:05:50.000Z"
+  },
+  {
+    "dataset_id": "ds004123",
+    "modality": "eeg",
+    "created": "2022-05-03T14:10:02.137Z",
+    "modified": "2022-05-04T15:34:31.000Z"
+  },
+  {
+    "dataset_id": "ds004147",
+    "modality": "eeg",
+    "created": "2022-06-07T14:19:20.839Z",
+    "modified": "2024-01-24T19:53:26.000Z"
+  },
+  {
+    "dataset_id": "ds004148",
+    "modality": "eeg",
+    "created": "2022-06-08T08:39:15.433Z",
+    "modified": "2022-06-13T08:42:17.000Z"
+  },
+  {
+    "dataset_id": "ds004151",
+    "modality": "eeg",
+    "created": "2022-06-13T16:57:01.005Z",
+    "modified": "2022-06-14T18:38:37.000Z"
+  },
+  {
+    "dataset_id": "ds004166",
+    "modality": "eeg",
+    "created": "2022-06-17T01:05:58.854Z",
+    "modified": "2022-06-17T07:46:14.000Z"
+  },
+  {
+    "dataset_id": "ds004395",
+    "modality": "eeg",
+    "created": "2023-01-10T18:29:34.235Z",
+    "modified": "2023-06-01T03:35:20.000Z"
+  },
+  {
+    "dataset_id": "ds004502",
+    "modality": "eeg",
+    "created": "2023-02-16T21:52:37.827Z",
+    "modified": "2023-03-06T17:05:06.000Z"
+  },
+  {
+    "dataset_id": "ds004514",
+    "modality": "eeg",
+    "created": "2023-02-27T13:39:47.155Z",
+    "modified": "2025-04-03T23:41:15.000Z"
+  },
+  {
+    "dataset_id": "ds004517",
+    "modality": "eeg",
+    "created": "2023-03-03T13:31:51.775Z",
+    "modified": "2025-04-03T23:41:33.000Z"
+  },
+  {
+    "dataset_id": "ds004563",
+    "modality": "eeg",
+    "created": "2023-05-10T10:26:34.294Z",
+    "modified": "2023-07-06T22:12:40.000Z"
+  },
+  {
+    "dataset_id": "ds004706",
+    "modality": "eeg",
+    "created": "2023-08-16T20:56:12.789Z",
+    "modified": "2023-08-22T12:13:59.000Z"
+  },
+  {
+    "dataset_id": "ds004940",
+    "modality": "eeg",
+    "created": "2024-01-22T20:26:52.439Z",
+    "modified": "2025-08-11T19:45:09.000Z"
+  },
+  {
+    "dataset_id": "ds005087",
+    "modality": "eeg",
+    "created": "2024-04-15T04:45:59.237Z",
+    "modified": "2025-01-23T22:53:56.000Z"
+  },
+  {
+    "dataset_id": "ds005178",
+    "modality": "eeg",
+    "created": "2024-05-24T08:18:02.675Z",
+    "modified": "2024-05-31T23:06:26.000Z"
+  },
+  {
+    "dataset_id": "ds005280",
+    "modality": "eeg",
+    "created": "2024-06-25T10:11:53.180Z",
+    "modified": "2024-06-26T06:57:02.000Z"
+  },
+  {
+    "dataset_id": "ds005284",
+    "modality": "eeg",
+    "created": "2024-06-26T05:58:33.685Z",
+    "modified": "2024-06-26T09:15:50.000Z"
+  },
+  {
+    "dataset_id": "ds005285",
+    "modality": "eeg",
+    "created": "2024-06-26T06:21:03.974Z",
+    "modified": "2024-06-26T06:49:38.000Z"
+  },
+  {
+    "dataset_id": "ds005286",
+    "modality": "eeg",
+    "created": "2024-06-26T06:59:46.221Z",
+    "modified": "2024-06-26T09:16:24.000Z"
+  },
+  {
+    "dataset_id": "ds005289",
+    "modality": "eeg",
+    "created": "2024-06-26T09:06:30.952Z",
+    "modified": "2024-06-26T14:04:32.000Z"
+  },
+  {
+    "dataset_id": "ds005291",
+    "modality": "eeg",
+    "created": "2024-06-26T10:01:34.320Z",
+    "modified": "2024-06-26T14:05:42.000Z"
+  },
+  {
+    "dataset_id": "ds005292",
+    "modality": "eeg",
+    "created": "2024-06-26T14:05:20.306Z",
+    "modified": "2024-06-27T01:42:37.000Z"
+  },
+  {
+    "dataset_id": "ds005293",
+    "modality": "eeg",
+    "created": "2024-06-27T01:43:35.308Z",
+    "modified": "2024-07-29T07:01:52.000Z"
+  },
+  {
+    "dataset_id": "ds005343",
+    "modality": "eeg",
+    "created": "2024-07-16T02:28:09.557Z",
+    "modified": "2024-07-16T12:00:23.000Z"
+  },
+  {
+    "dataset_id": "ds005407",
+    "modality": "eeg",
+    "created": "2024-08-10T00:31:25.134Z",
+    "modified": "2024-08-10T01:07:46.000Z"
+  },
+  {
+    "dataset_id": "ds005408",
+    "modality": "eeg",
+    "created": "2024-08-10T00:49:48.180Z",
+    "modified": "2024-12-10T17:02:57.000Z"
+  },
+  {
+    "dataset_id": "ds005473",
+    "modality": "eeg",
+    "created": "2024-09-11T04:15:49.006Z",
+    "modified": "2024-09-11T10:00:36.000Z"
+  },
+  {
+    "dataset_id": "ds005642",
+    "modality": "eeg",
+    "created": "2024-11-19T02:17:00.738Z",
+    "modified": "2025-07-08T05:00:55.000Z"
+  },
+  {
+    "dataset_id": "ds005648",
+    "modality": "eeg",
+    "created": "2024-11-20T22:13:29.697Z",
+    "modified": "2024-11-21T04:24:38.000Z"
+  },
+  {
+    "dataset_id": "ds005662",
+    "modality": "eeg",
+    "created": "2024-11-27T04:56:45.541Z",
+    "modified": "2025-07-02T07:11:12.000Z"
+  },
+  {
+    "dataset_id": "ds005841",
+    "modality": "eeg",
+    "created": "2025-01-14T13:48:40.783Z",
+    "modified": "2025-01-14T18:15:10.000Z"
+  },
+  {
+    "dataset_id": "ds005857",
+    "modality": "eeg",
+    "created": "2025-01-16T16:22:01.499Z",
+    "modified": "2025-04-02T18:37:15.000Z"
+  },
+  {
+    "dataset_id": "ds005872",
+    "modality": "eeg",
+    "created": "2025-01-23T01:24:16.999Z",
+    "modified": "2025-01-23T02:51:19.000Z"
+  },
+  {
+    "dataset_id": "ds005907",
+    "modality": "eeg",
+    "created": "2025-02-06T23:20:31.333Z",
+    "modified": "2025-02-07T00:07:46.000Z"
+  },
+  {
+    "dataset_id": "ds005932",
+    "modality": "eeg",
+    "created": "2025-02-19T01:35:23.604Z",
+    "modified": "2025-11-05T20:18:42.000Z"
+  },
+  {
+    "dataset_id": "ds005946",
+    "modality": "eeg",
+    "created": "2025-02-27T07:54:33.516Z",
+    "modified": "2025-02-27T14:46:53.000Z"
+  },
+  {
+    "dataset_id": "ds005960",
+    "modality": "eeg",
+    "created": "2025-03-05T13:34:26.909Z",
+    "modified": "2025-03-13T14:43:51.000Z"
+  },
+  {
+    "dataset_id": "ds006018",
+    "modality": "eeg",
+    "created": "2025-03-14T04:33:40.169Z",
+    "modified": "2025-05-22T18:33:58.000Z"
+  },
+  {
+    "dataset_id": "ds006033",
+    "modality": "eeg",
+    "created": "2025-03-19T10:16:24.423Z",
+    "modified": "2025-05-08T09:02:04.000Z"
+  },
+  {
+    "dataset_id": "ds006036",
+    "modality": "eeg",
+    "created": "2025-03-21T15:45:18.747Z",
+    "modified": "2025-05-13T14:26:17.000Z"
+  },
+  {
+    "dataset_id": "ds006040",
+    "modality": "eeg",
+    "created": "2025-03-24T00:18:30.864Z",
+    "modified": "2025-10-15T06:18:10.000Z"
+  },
+  {
+    "dataset_id": "ds006095",
+    "modality": "eeg",
+    "created": "2025-04-06T21:09:56.098Z",
+    "modified": "2025-04-07T01:15:10.000Z"
+  },
+  {
+    "dataset_id": "ds006104",
+    "modality": "eeg",
+    "created": "2025-04-08T01:43:51.139Z",
+    "modified": "2025-04-08T12:14:54.000Z"
+  },
+  {
+    "dataset_id": "ds006126",
+    "modality": "eeg",
+    "created": "2025-04-15T13:27:08.635Z",
+    "modified": "2025-04-15T13:41:43.000Z"
+  },
+  {
+    "dataset_id": "ds006142",
+    "modality": "eeg",
+    "created": "2025-04-17T12:18:55.249Z",
+    "modified": "2025-09-03T07:40:31.000Z"
+  },
+  {
+    "dataset_id": "ds006159",
+    "modality": "eeg",
+    "created": "2025-04-23T10:01:37.563Z",
+    "modified": "2025-05-21T14:05:01.000Z"
+  },
+  {
+    "dataset_id": "ds006171",
+    "modality": "eeg",
+    "created": "2025-04-24T09:05:51.130Z",
+    "modified": "2025-04-24T13:46:57.000Z"
+  },
+  {
+    "dataset_id": "ds006260",
+    "modality": "eeg",
+    "created": "2025-05-23T17:52:16.432Z",
+    "modified": "2025-05-29T20:03:20.000Z"
+  },
+  {
+    "dataset_id": "ds006269",
+    "modality": "eeg",
+    "created": "2025-05-30T08:23:30.047Z",
+    "modified": "2025-05-30T14:42:58.000Z"
+  },
+  {
+    "dataset_id": "ds006317",
+    "modality": "eeg",
+    "created": "2025-06-06T05:49:27.180Z",
+    "modified": "2025-06-08T12:23:31.000Z"
+  },
+  {
+    "dataset_id": "ds006366",
+    "modality": "eeg",
+    "created": "2025-06-17T12:46:03.516Z",
+    "modified": "2025-09-05T13:06:20.000Z"
+  },
+  {
+    "dataset_id": "ds006367",
+    "modality": "eeg",
+    "created": "2025-06-17T13:40:50.329Z",
+    "modified": "2025-06-25T12:01:16.000Z"
+  },
+  {
+    "dataset_id": "ds006370",
+    "modality": "eeg",
+    "created": "2025-06-18T14:43:36.575Z",
+    "modified": "2025-06-25T11:53:45.000Z"
+  },
+  {
+    "dataset_id": "ds006374",
+    "modality": "eeg",
+    "created": "2025-06-19T14:36:16.257Z",
+    "modified": "2025-07-10T14:56:25.000Z"
+  },
+  {
+    "dataset_id": "ds006394",
+    "modality": "eeg",
+    "created": "2025-06-26T10:42:24.675Z",
+    "modified": "2025-06-27T04:44:21.000Z"
+  },
+  {
+    "dataset_id": "ds006434",
+    "modality": "eeg",
+    "created": "2025-07-01T16:32:15.143Z",
+    "modified": "2025-09-11T17:43:36.000Z"
+  },
+  {
+    "dataset_id": "ds006437",
+    "modality": "eeg",
+    "created": "2025-07-02T02:03:55.684Z",
+    "modified": "2025-08-21T17:34:41.000Z"
+  },
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+    "modality": "meg",
+    "created": "2022-03-20T08:43:39.462Z",
+    "modified": "2023-10-16T02:05:05.000Z"
+  },
+  {
+    "dataset_id": "ds004107",
+    "modality": "meg",
+    "created": "2022-04-22T16:06:47.154Z",
+    "modified": "2022-04-22T19:50:04.000Z"
+  },
+  {
+    "dataset_id": "ds004212",
+    "modality": "meg",
+    "created": "2022-07-14T15:47:41.319Z",
+    "modified": "2025-05-29T20:15:29.000Z"
+  },
+  {
+    "dataset_id": "ds004229",
+    "modality": "meg",
+    "created": "2022-08-01T18:25:31.485Z",
+    "modified": "2023-11-03T18:30:43.000Z"
+  },
+  {
+    "dataset_id": "ds004276",
+    "modality": "meg",
+    "created": "2022-09-23T14:41:43.515Z",
+    "modified": "2022-09-23T20:02:38.000Z"
+  },
+  {
+    "dataset_id": "ds004278",
+    "modality": "meg",
+    "created": "2022-09-23T17:53:03.124Z",
+    "modified": "2022-10-11T18:18:40.000Z"
+  },
+  {
+    "dataset_id": "ds004330",
+    "modality": "meg",
+    "created": "2022-11-07T14:41:49.699Z",
+    "modified": "2022-11-08T10:16:44.000Z"
+  },
+  {
+    "dataset_id": "ds004346",
+    "modality": "meg",
+    "created": "2022-11-28T16:33:17.402Z",
+    "modified": "2024-06-07T09:19:58.000Z"
+  },
+  {
+    "dataset_id": "ds004398",
+    "modality": "meg",
+    "created": "2023-01-11T14:18:04.348Z",
+    "modified": "2023-01-25T04:25:53.000Z"
+  },
+  {
+    "dataset_id": "ds004483",
+    "modality": "meg",
+    "created": "2023-02-08T23:22:49.117Z",
+    "modified": "2023-02-13T14:53:45.000Z"
+  },
+  {
+    "dataset_id": "ds004738",
+    "modality": "meg",
+    "created": "2023-09-03T09:15:15.944Z",
+    "modified": "2023-09-03T10:42:30.000Z"
+  },
+  {
+    "dataset_id": "ds004837",
+    "modality": "meg",
+    "created": "2023-11-01T15:54:14.672Z",
+    "modified": "2025-05-27T15:41:36.000Z"
+  },
+  {
+    "dataset_id": "ds004998",
+    "modality": "meg",
+    "created": "2024-03-04T17:45:27.695Z",
+    "modified": "2024-09-09T08:26:28.000Z"
+  },
+  {
+    "dataset_id": "ds005065",
+    "modality": "meg",
+    "created": "2024-04-07T02:26:32.589Z",
+    "modified": "2024-04-09T01:21:46.000Z"
+  },
+  {
+    "dataset_id": "ds005107",
+    "modality": "meg",
+    "created": "2024-04-24T06:49:36.871Z",
+    "modified": "2025-06-30T23:44:09.000Z"
+  },
+  {
+    "dataset_id": "ds005241",
+    "modality": "meg",
+    "created": "2024-06-12T15:20:44.636Z",
+    "modified": "2024-06-14T14:53:48.000Z"
+  },
+  {
+    "dataset_id": "ds005261",
+    "modality": "meg",
+    "created": "2024-06-17T13:06:26.783Z",
+    "modified": "2025-05-12T07:26:44.000Z"
+  },
+  {
+    "dataset_id": "ds005279",
+    "modality": "meg",
+    "created": "2024-06-24T16:17:02.918Z",
+    "modified": "2024-07-03T20:27:47.000Z"
+  },
+  {
+    "dataset_id": "ds005346",
+    "modality": "meg",
+    "created": "2024-07-16T14:15:32.746Z",
+    "modified": "2025-08-06T02:29:27.000Z"
+  },
+  {
+    "dataset_id": "ds005356",
+    "modality": "meg",
+    "created": "2024-07-17T17:45:08.266Z",
+    "modified": "2025-03-06T16:10:24.000Z"
+  },
+  {
+    "dataset_id": "ds005752",
+    "modality": "meg",
+    "created": "2024-12-20T18:45:44.865Z",
+    "modified": "2025-02-18T16:59:09.000Z"
+  },
+  {
+    "dataset_id": "ds005810",
+    "modality": "meg",
+    "created": "2025-01-10T11:07:26.509Z",
+    "modified": "2025-08-30T14:20:38.000Z"
+  },
+  {
+    "dataset_id": "ds006012",
+    "modality": "meg",
+    "created": "2025-03-13T19:13:44.963Z",
+    "modified": "2025-03-14T17:05:14.000Z"
+  },
+  {
+    "dataset_id": "ds006035",
+    "modality": "meg",
+    "created": "2025-03-20T05:08:27.231Z",
+    "modified": "2025-03-21T18:44:26.000Z"
+  },
+  {
+    "dataset_id": "ds006334",
+    "modality": "meg",
+    "created": "2025-06-10T16:14:43.829Z",
+    "modified": "2025-06-11T09:26:21.000Z"
+  },
+  {
+    "dataset_id": "ds006468",
+    "modality": "meg",
+    "created": "2025-07-14T17:00:44.163Z",
+    "modified": "2025-10-20T16:32:42.000Z"
+  },
+  {
+    "dataset_id": "ds006502",
+    "modality": "meg",
+    "created": "2025-07-28T19:06:15.608Z",
+    "modified": "2025-08-12T04:55:40.000Z"
+  },
+  {
+    "dataset_id": "ds006629",
+    "modality": "meg",
+    "created": "2025-09-04T11:55:02.705Z",
+    "modified": "2025-09-29T06:11:47.000Z"
+  }
+]
\ No newline at end of file

From 1a4682529064876dcb533099a0d11ae45b6f7485 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 15:04:53 +0100
Subject: [PATCH 18/34] updating the scripts

---
 scripts/ingestions/1_fetch_eegmanylabs.py     | 173 ++++
 scripts/ingestions/1_fetch_figshare.py        | 344 ++++++++
 scripts/ingestions/1_fetch_osf.py             | 291 +++++++
 scripts/ingestions/1_fetch_scidb.py           | 352 ++++++++
 scripts/ingestions/1_fetch_zenodo.py          | 534 +++++++++++++
 scripts/ingestions/1_fetch_zenodo_enhanced.py | 756 ++++++++++++++++++
 scripts/ingestions/2_filter_zenodo_bids.py    | 645 +++++++++++++++
 .../ingestions/3_clone_openneuro_datasets.py  | 281 +++++++
 8 files changed, 3376 insertions(+)
 create mode 100644 scripts/ingestions/1_fetch_eegmanylabs.py
 create mode 100644 scripts/ingestions/1_fetch_figshare.py
 create mode 100644 scripts/ingestions/1_fetch_osf.py
 create mode 100644 scripts/ingestions/1_fetch_scidb.py
 create mode 100644 scripts/ingestions/1_fetch_zenodo.py
 create mode 100644 scripts/ingestions/1_fetch_zenodo_enhanced.py
 create mode 100644 scripts/ingestions/2_filter_zenodo_bids.py
 create mode 100644 scripts/ingestions/3_clone_openneuro_datasets.py

diff --git a/scripts/ingestions/1_fetch_eegmanylabs.py b/scripts/ingestions/1_fetch_eegmanylabs.py
new file mode 100644
index 00000000..e26a3d51
--- /dev/null
+++ b/scripts/ingestions/1_fetch_eegmanylabs.py
@@ -0,0 +1,173 @@
+"""Fetch EEGManyLabs datasets from G-Node GIN organization.
+
+This script scrapes the EEGManyLabs organization page on GIN (G-Node Infrastructure)
+to retrieve information about all available datasets. GIN is a git-based repository
+hosting service for neuroscience data.
+
+Output: consolidated/eegmanylabs_datasets.json
+"""
+
+import argparse
+import json
+import re
+import sys
+from pathlib import Path
+from typing import Any
+
+import requests
+from bs4 import BeautifulSoup
+
+
+def fetch_eegmanylabs_repos(
+    organization: str = "EEGManyLabs",
+    base_url: str = "https://gin.g-node.org",
+) -> list[dict[str, Any]]:
+    """Fetch all repositories from the EEGManyLabs organization.
+
+    Args:
+        organization: GIN organization name
+        base_url: Base URL for GIN
+
+    Returns:
+        List of repository dictionaries with metadata
+
+    """
+    org_url = f"{base_url}/{organization}"
+
+    print(f"Fetching repositories from {org_url}")
+
+    try:
+        response = requests.get(org_url, timeout=30)
+        response.raise_for_status()
+    except requests.RequestException as e:
+        print(f"Error fetching organization page: {e}", file=sys.stderr)
+        return []
+
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    # Find all repository items
+    repo_items = soup.find_all("div", class_="item")
+
+    if not repo_items:
+        print(f"Warning: No repositories found for {organization}", file=sys.stderr)
+        return []
+
+    datasets = []
+
+    for item in repo_items:
+        try:
+            # Get repository link
+            repo_link = item.find("a", class_="name")
+            if not repo_link:
+                continue
+
+            repo_path = repo_link.get("href", "")
+            if not repo_path.startswith(f"/{organization}/"):
+                continue
+
+            repo_name = repo_path.split("/")[-1]
+
+            # Get description
+            desc_elem = item.find("p", class_="description")
+            description = desc_elem.get_text(strip=True) if desc_elem else ""
+
+            # Get metadata (stars, forks, updated time)
+            meta_div = item.find("div", class_="meta")
+            stars = 0
+            forks = 0
+            updated = ""
+
+            if meta_div:
+                # Extract stars
+                star_elem = meta_div.find("a", href=re.compile(r"/stars$"))
+                if star_elem:
+                    stars = int(star_elem.get_text(strip=True))
+
+                # Extract forks
+                fork_elem = meta_div.find("a", href=re.compile(r"/forks$"))
+                if fork_elem:
+                    forks = int(fork_elem.get_text(strip=True))
+
+                # Extract update time
+                time_elem = meta_div.find("span", class_="time")
+                if time_elem:
+                    updated = time_elem.get("title", time_elem.get_text(strip=True))
+
+            # Construct clone URLs
+            clone_url = f"{base_url}/{organization}/{repo_name}.git"
+            ssh_url = f"git@gin.g-node.org:{organization}/{repo_name}.git"
+
+            dataset = {
+                "dataset_id": repo_name,
+                "full_name": f"{organization}/{repo_name}",
+                "description": description,
+                "url": f"{base_url}/{organization}/{repo_name}",
+                "clone_url": clone_url,
+                "ssh_url": ssh_url,
+                "stars": stars,
+                "forks": forks,
+                "updated": updated,
+                "source": "gin",
+                "organization": organization,
+            }
+
+            datasets.append(dataset)
+
+        except Exception as e:
+            print(f"Warning: Error parsing repository item: {e}", file=sys.stderr)
+            continue
+
+    print(f"Found {len(datasets)} repositories")
+
+    return datasets
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch EEGManyLabs datasets from GIN organization."
+    )
+    parser.add_argument(
+        "--organization",
+        type=str,
+        default="EEGManyLabs",
+        help="GIN organization name (default: EEGManyLabs).",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/eegmanylabs_datasets.json"),
+        help="Output JSON file path (default: consolidated/eegmanylabs_datasets.json).",
+    )
+    parser.add_argument(
+        "--base-url",
+        type=str,
+        default="https://gin.g-node.org",
+        help="Base URL for GIN (default: https://gin.g-node.org).",
+    )
+
+    args = parser.parse_args()
+
+    # Fetch repositories
+    datasets = fetch_eegmanylabs_repos(
+        organization=args.organization,
+        base_url=args.base_url,
+    )
+
+    if not datasets:
+        print("No datasets fetched. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Create output directory
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to JSON
+    with args.output.open("w") as fh:
+        json.dump(datasets, fh, indent=2)
+
+    print(f"\n{'=' * 60}")
+    print(f"Successfully saved {len(datasets)} datasets to {args.output}")
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/1_fetch_figshare.py b/scripts/ingestions/1_fetch_figshare.py
new file mode 100644
index 00000000..3e481e1e
--- /dev/null
+++ b/scripts/ingestions/1_fetch_figshare.py
@@ -0,0 +1,344 @@
+"""Fetch EEG BIDS datasets from Figshare.
+
+This script searches Figshare for datasets containing both "EEG" and "BIDS" keywords
+using the Figshare API v2. It retrieves comprehensive metadata including DOIs,
+descriptions, files, authors, and download URLs.
+
+Output: consolidated/figshare_datasets.json
+"""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import requests
+
+
+def search_figshare(
+    query: str,
+    size: int = 100,
+    page_size: int = 100,
+    item_type: int = 3,  # 3 = dataset
+) -> list[dict[str, Any]]:
+    """Search Figshare for datasets matching the query.
+
+    Args:
+        query: Search query string
+        size: Maximum number of results to fetch
+        page_size: Number of results per page (max 1000)
+        item_type: Figshare item type (3=dataset, 1=figure, 2=media, etc.)
+
+    Returns:
+        List of Figshare article dictionaries
+
+    """
+    base_url = "https://api.figshare.com/v2/articles/search"
+
+    print(f"Searching Figshare with query: {query}")
+    print(f"Item type: {item_type} (dataset)")
+    print(f"Max results to fetch: {size}")
+    print(f"Results per page: {min(page_size, 1000)}")
+
+    all_articles = []
+    page = 1
+    actual_page_size = min(page_size, 1000)  # Figshare max is 1000
+
+    while True:
+        print(f"\nFetching page {page}...", end=" ", flush=True)
+
+        # Build request payload
+        payload = {
+            "search_for": query,
+            "item_type": item_type,
+            "page": page,
+            "page_size": actual_page_size,
+        }
+
+        try:
+            response = requests.post(
+                base_url,
+                json=payload,
+                headers={"Content-Type": "application/json"},
+                timeout=30,
+            )
+            response.raise_for_status()
+        except requests.RequestException as e:
+            print(f"\nError fetching page {page}: {e}", file=sys.stderr)
+            break
+
+        articles = response.json()
+
+        if not articles:
+            print("No more results")
+            break
+
+        print(f"Got {len(articles)} articles")
+        all_articles.extend(articles)
+
+        # Check if we've reached the requested size
+        if len(all_articles) >= size:
+            print(f"Reached limit ({len(all_articles)} articles)")
+            break
+
+        # If we got fewer results than page_size, we've reached the end
+        if len(articles) < actual_page_size:
+            print("Reached end of results")
+            break
+
+        page += 1
+
+        # Be nice to the API
+        time.sleep(0.5)
+
+    print(f"\nTotal articles fetched: {len(all_articles)}")
+    return all_articles[:size]  # Trim to exact size
+
+
+def get_article_details(article_id: int) -> dict[str, Any]:
+    """Fetch detailed information for a specific article.
+
+    Args:
+        article_id: Figshare article ID
+
+    Returns:
+        Detailed article dictionary
+
+    """
+    url = f"https://api.figshare.com/v2/articles/{article_id}"
+
+    try:
+        response = requests.get(url, timeout=30)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        print(
+            f"Warning: Error fetching details for article {article_id}: {e}",
+            file=sys.stderr,
+        )
+        return {}
+
+
+def extract_dataset_info(article: dict, fetch_details: bool = False) -> dict[str, Any]:
+    """Extract relevant information from a Figshare article.
+
+    Args:
+        article: Figshare article dictionary
+        fetch_details: Whether to fetch full details (slower but more complete)
+
+    Returns:
+        Cleaned dataset dictionary
+
+    """
+    # Get basic info from search results
+    article_id = article.get("id", "")
+    title = article.get("title", "")
+    doi = article.get("doi", "")
+
+    # Fetch full details if requested
+    if fetch_details and article_id:
+        details = get_article_details(article_id)
+        if details:
+            article = details
+
+    # Extract metadata
+    description = article.get("description", "")
+    defined_type = article.get("defined_type", 0)
+    defined_type_name = article.get("defined_type_name", "")
+
+    # Extract dates
+    published_date = article.get("published_date", "")
+    created_date = article.get("created_date", "")
+    modified_date = article.get("modified_date", "")
+
+    # Extract URLs
+    url_public_html = article.get("url_public_html", "")
+    url_public_api = article.get("url_public_api", "")
+
+    # Extract resource info (linked DOI/resource)
+    resource_title = article.get("resource_title", "")
+    resource_doi = article.get("resource_doi", "")
+
+    # Extract authors (may not be in search results)
+    authors = []
+    for author in article.get("authors", []):
+        author_info = {
+            "name": author.get("full_name", ""),
+        }
+        if "id" in author:
+            author_info["id"] = author["id"]
+        if "orcid_id" in author:
+            author_info["orcid"] = author["orcid_id"]
+        authors.append(author_info)
+
+    # Extract tags/categories
+    tags = article.get("tags", [])
+    categories = article.get("categories", [])
+
+    # Extract files info
+    files = []
+    for file_entry in article.get("files", []):
+        file_info = {
+            "id": file_entry.get("id", ""),
+            "name": file_entry.get("name", ""),
+            "size_bytes": file_entry.get("size", 0),
+            "download_url": file_entry.get("download_url", ""),
+        }
+        if "computed_md5" in file_entry:
+            file_info["md5"] = file_entry["computed_md5"]
+        files.append(file_info)
+
+    # Calculate total size
+    total_size_bytes = sum(f.get("size", 0) for f in article.get("files", []))
+    total_size_mb = round(total_size_bytes / (1024 * 1024), 2)
+
+    # Extract license
+    license_info = article.get("license", {})
+    license_name = license_info.get("name", "") if license_info else ""
+
+    # Build dataset dictionary
+    dataset = {
+        "dataset_id": str(article_id),
+        "doi": doi,
+        "title": title,
+        "description": description,
+        "defined_type": defined_type,
+        "defined_type_name": defined_type_name,
+        "published_date": published_date,
+        "created_date": created_date,
+        "modified_date": modified_date,
+        "authors": authors,
+        "tags": tags,
+        "categories": categories,
+        "license": license_name,
+        "url": url_public_html,
+        "api_url": url_public_api,
+        "resource_title": resource_title,
+        "resource_doi": resource_doi,
+        "files": files,
+        "file_count": len(files),
+        "total_size_mb": total_size_mb,
+        "source": "figshare",
+    }
+
+    return dataset
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch EEG BIDS datasets from Figshare."
+    )
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="EEG BIDS",
+        help='Search query (default: "EEG BIDS").',
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/figshare_datasets.json"),
+        help="Output JSON file path (default: consolidated/figshare_datasets.json).",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=1000,
+        help="Maximum number of results to fetch (default: 1000).",
+    )
+    parser.add_argument(
+        "--page-size",
+        type=int,
+        default=100,
+        help="Number of results per page (max 1000, default: 100).",
+    )
+    parser.add_argument(
+        "--fetch-details",
+        action="store_true",
+        help="Fetch full details for each article (slower but more complete).",
+    )
+    parser.add_argument(
+        "--item-type",
+        type=int,
+        default=3,
+        help="Figshare item type: 3=dataset, 1=figure, 2=media, etc. (default: 3).",
+    )
+
+    args = parser.parse_args()
+
+    # Search Figshare
+    articles = search_figshare(
+        query=args.query,
+        size=args.size,
+        page_size=args.page_size,
+        item_type=args.item_type,
+    )
+
+    if not articles:
+        print("No articles found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Extract dataset information
+    print("\nExtracting dataset information...")
+    datasets = []
+    for idx, article in enumerate(articles, start=1):
+        if args.fetch_details and idx % 10 == 0:
+            print(f"Processing {idx}/{len(articles)}...", flush=True)
+
+        try:
+            dataset = extract_dataset_info(article, fetch_details=args.fetch_details)
+            datasets.append(dataset)
+        except Exception as e:
+            print(
+                f"Warning: Error extracting info for article {article.get('id')}: {e}",
+                file=sys.stderr,
+            )
+            continue
+
+        # Be nice to the API when fetching details
+        if args.fetch_details:
+            time.sleep(0.2)
+
+    # Create output directory
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to JSON
+    with args.output.open("w") as fh:
+        json.dump(datasets, fh, indent=2)
+
+    # Print summary statistics
+    print(f"\n{'=' * 60}")
+    print(f"Successfully saved {len(datasets)} datasets to {args.output}")
+    print(f"{'=' * 60}")
+    print("\nDataset Statistics:")
+
+    # Count by type
+    types = {}
+    for ds in datasets:
+        dtype = ds.get("defined_type_name", "unknown")
+        types[dtype] = types.get(dtype, 0) + 1
+
+    print("\nBy Type:")
+    for dtype, count in sorted(types.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {dtype}: {count}")
+
+    # Datasets with files
+    datasets_with_files = sum(1 for ds in datasets if ds.get("file_count", 0) > 0)
+    print(f"\nDatasets with files: {datasets_with_files}/{len(datasets)}")
+
+    # Total size
+    total_size_mb = sum(ds.get("total_size_mb", 0) for ds in datasets)
+    total_size_gb = round(total_size_mb / 1024, 2)
+    print(f"Total Size: {total_size_gb} GB ({total_size_mb} MB)")
+
+    # Datasets with DOI
+    datasets_with_doi = sum(1 for ds in datasets if ds.get("doi", ""))
+    print(f"Datasets with DOI: {datasets_with_doi}/{len(datasets)}")
+
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/1_fetch_osf.py b/scripts/ingestions/1_fetch_osf.py
new file mode 100644
index 00000000..ca69876d
--- /dev/null
+++ b/scripts/ingestions/1_fetch_osf.py
@@ -0,0 +1,291 @@
+"""Fetch EEG BIDS projects from Open Science Framework (OSF).
+
+This script searches OSF for projects containing both "EEG" and "BIDS" keywords
+using the OSF API v2. It retrieves comprehensive metadata including project IDs,
+descriptions, contributors, files, and DOIs.
+
+Output: consolidated/osf_projects.json
+"""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import requests
+
+
+def search_osf(
+    query: str,
+    size: int = 100,
+    page_size: int = 100,
+) -> list[dict[str, Any]]:
+    """Search OSF for projects matching the query.
+
+    Args:
+        query: Search query string
+        size: Maximum number of results to fetch
+        page_size: Number of results per page (max 100)
+
+    Returns:
+        List of OSF project dictionaries
+
+    """
+    base_url = "https://api.osf.io/v2/search/"
+
+    # Build query parameters
+    params = {
+        "q": query,
+        "page[size]": min(page_size, 100),  # OSF max is 100
+    }
+
+    print(f"Searching OSF with query: {query}")
+    print(f"Max results to fetch: {size}")
+    print(f"Results per page: {params['page[size]']}")
+
+    all_records = []
+    page = 1
+    current_url = base_url
+
+    while True:
+        print(f"\nFetching page {page}...", end=" ", flush=True)
+
+        try:
+            response = requests.get(current_url, params=params, timeout=30)
+            response.raise_for_status()
+        except requests.RequestException as e:
+            print(f"\nError fetching page {page}: {e}", file=sys.stderr)
+            break
+
+        data = response.json()
+        records = data.get("data", [])
+
+        if not records:
+            print("No more results")
+            break
+
+        # Get total count from meta
+        links = data.get("links", {})
+        meta = links.get("meta", {})
+        total = meta.get("total", 0)
+
+        print(f"Got {len(records)} records (total available: {total})")
+        all_records.extend(records)
+
+        # Check if we've reached the requested size or all available records
+        if len(all_records) >= size:
+            print(f"Reached limit ({len(all_records)} records)")
+            break
+
+        # Check if there are more pages
+        next_url = links.get("next")
+
+        if not next_url:
+            print("No more pages available")
+            break
+
+        # Update for next page
+        current_url = next_url
+        params = {}  # Next URL already has all params
+        page += 1
+
+        # Be nice to the API
+        time.sleep(0.5)
+
+    print(f"\nTotal records fetched: {len(all_records)}")
+    return all_records
+
+
+def extract_project_info(record: dict) -> dict[str, Any]:
+    """Extract relevant information from an OSF project record.
+
+    Args:
+        record: Raw OSF project record dictionary
+
+    Returns:
+        Cleaned project dictionary
+
+    """
+    project_id = record.get("id", "")
+    record_type = record.get("type", "")
+
+    attributes = record.get("attributes", {})
+    relationships = record.get("relationships", {})
+    links = record.get("links", {})
+
+    # Extract basic metadata
+    title = attributes.get("title", "")
+    description = attributes.get("description", "")
+    category = attributes.get("category", "")
+    tags = attributes.get("tags", [])
+
+    # Extract dates
+    date_created = attributes.get("date_created", "")
+    date_modified = attributes.get("date_modified", "")
+
+    # Extract flags
+    is_public = attributes.get("public", False)
+    is_registration = attributes.get("registration", False)
+    is_preprint = attributes.get("preprint", False)
+    is_fork = attributes.get("fork", False)
+    wiki_enabled = attributes.get("wiki_enabled", False)
+
+    # Extract license info
+    node_license = attributes.get("node_license", {})
+    license_year = node_license.get("year", "")
+    copyright_holders = node_license.get("copyright_holders", [])
+
+    # Extract DOI if available
+    doi = None
+    identifiers = (
+        relationships.get("identifiers", {}).get("links", {}).get("related", {})
+    )
+    if identifiers:
+        doi_href = identifiers.get("href", "")
+        if doi_href:
+            # We'd need to fetch this separately, but for now we'll note it exists
+            doi = "available"  # Placeholder
+
+    # Extract URLs
+    html_url = links.get("html", "")
+    self_url = links.get("self", "")
+
+    # Build project dictionary
+    project = {
+        "project_id": project_id,
+        "type": record_type,
+        "title": title,
+        "description": description,
+        "category": category,
+        "tags": tags,
+        "date_created": date_created,
+        "date_modified": date_modified,
+        "is_public": is_public,
+        "is_registration": is_registration,
+        "is_preprint": is_preprint,
+        "is_fork": is_fork,
+        "wiki_enabled": wiki_enabled,
+        "license_year": license_year,
+        "copyright_holders": copyright_holders,
+        "url": html_url,
+        "api_url": self_url,
+        "source": "osf",
+    }
+
+    # Add DOI if available
+    if doi:
+        project["doi"] = doi
+
+    return project
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch EEG BIDS projects from Open Science Framework."
+    )
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="EEG BIDS",
+        help='Search query (default: "EEG BIDS").',
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/osf_projects.json"),
+        help="Output JSON file path (default: consolidated/osf_projects.json).",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=1000,
+        help="Maximum number of results to fetch (default: 1000).",
+    )
+    parser.add_argument(
+        "--page-size",
+        type=int,
+        default=100,
+        help="Number of results per page (max 100, default: 100).",
+    )
+
+    args = parser.parse_args()
+
+    # Search OSF
+    records = search_osf(
+        query=args.query,
+        size=args.size,
+        page_size=args.page_size,
+    )
+
+    if not records:
+        print("No records found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Extract project information
+    print("\nExtracting project information...")
+    projects = []
+    for record in records:
+        try:
+            project = extract_project_info(record)
+            projects.append(project)
+        except Exception as e:
+            print(
+                f"Warning: Error extracting info for record {record.get('id')}: {e}",
+                file=sys.stderr,
+            )
+            continue
+
+    # Create output directory
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to JSON
+    with args.output.open("w") as fh:
+        json.dump(projects, fh, indent=2)
+
+    # Print summary statistics
+    print(f"\n{'=' * 60}")
+    print(f"Successfully saved {len(projects)} projects to {args.output}")
+    print(f"{'=' * 60}")
+    print("\nProject Statistics:")
+
+    # Count by category
+    categories = {}
+    for proj in projects:
+        cat = proj.get("category", "unknown")
+        categories[cat] = categories.get(cat, 0) + 1
+
+    print("\nBy Category:")
+    for cat, count in sorted(categories.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {cat}: {count}")
+
+    # Count by type
+    types = {}
+    for proj in projects:
+        ptype = proj.get("type", "unknown")
+        types[ptype] = types.get(ptype, 0) + 1
+
+    print("\nBy Type:")
+    for ptype, count in sorted(types.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {ptype}: {count}")
+
+    # Public vs private
+    public_count = sum(1 for proj in projects if proj.get("is_public", False))
+    print(f"\nPublic projects: {public_count}/{len(projects)}")
+
+    # Registrations
+    registration_count = sum(
+        1 for proj in projects if proj.get("is_registration", False)
+    )
+    print(f"Registrations: {registration_count}/{len(projects)}")
+
+    # Preprints
+    preprint_count = sum(1 for proj in projects if proj.get("is_preprint", False))
+    print(f"Preprints: {preprint_count}/{len(projects)}")
+
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/1_fetch_scidb.py b/scripts/ingestions/1_fetch_scidb.py
new file mode 100644
index 00000000..e6ce78ba
--- /dev/null
+++ b/scripts/ingestions/1_fetch_scidb.py
@@ -0,0 +1,352 @@
+"""Fetch EEG BIDS datasets from SciDB (Science Data Bank).
+
+This script searches SciDB for public datasets containing both "EEG" and "BIDS" keywords
+using the SciDB query service API. It retrieves comprehensive metadata including DOIs,
+CSTR identifiers, descriptions, authors, and file information.
+
+Output: consolidated/scidb_datasets.json
+"""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import requests
+
+
+def search_scidb(
+    query: str,
+    size: int = 100,
+    page_size: int = 100,
+    public_only: bool = True,
+) -> list[dict[str, Any]]:
+    """Search SciDB for datasets matching the query.
+
+    Args:
+        query: Search query string
+        size: Maximum number of results to fetch
+        page_size: Number of results per page (max 100)
+        public_only: Filter for public datasets only (default: True)
+
+    Returns:
+        List of SciDB dataset dictionaries
+
+    """
+    base_url = "https://www.scidb.cn/api/sdb-query-service/query"
+
+    # Build query parameters
+    params = {
+        "queryCode": "",
+        "q": query,
+    }
+
+    print(f"Searching SciDB with query: {query}")
+    print(f"Max results to fetch: {size}")
+    print(f"Results per page: {min(page_size, 100)}")
+    if public_only:
+        print("Filtering: Public datasets only")
+
+    all_datasets = []
+    page = 1
+    actual_page_size = min(page_size, 100)  # SciDB max appears to be 100
+
+    while True:
+        print(f"\nFetching page {page}...", end=" ", flush=True)
+
+        # Build request body
+        body = {
+            "fileType": ["001"],  # 001 = dataset type
+            "dataSetStatus": ["PUBLIC"] if public_only else [],
+            "copyrightCode": [],
+            "publishDate": [],
+            "ordernum": "6",  # Sort order (6 appears to be relevance)
+            "rorId": [],
+            "ror": "",
+            "taxonomyEn": [],
+            "journalNameEn": [],
+            "page": page,
+            "size": actual_page_size,
+        }
+
+        try:
+            response = requests.post(
+                base_url,
+                params=params,
+                json=body,
+                headers={
+                    "Content-Type": "application/json;charset=utf-8",
+                    "Accept": "application/json, text/plain, */*",
+                },
+                timeout=30,
+            )
+            response.raise_for_status()
+        except requests.RequestException as e:
+            print(f"\nError fetching page {page}: {e}", file=sys.stderr)
+            break
+
+        try:
+            data = response.json()
+        except json.JSONDecodeError as e:
+            print(f"\nError parsing JSON response: {e}", file=sys.stderr)
+            break
+
+        # Check API response code
+        code = data.get("code", 0)
+        if code != 20000:
+            message = data.get("message", "Unknown error")
+            print(f"\nAPI Error (code {code}): {message}", file=sys.stderr)
+            break
+
+        # Extract records from nested data structure: response.data.data[]
+        datasets = data.get("data", {}).get("data", [])
+
+        if not datasets:
+            print("No more results")
+            break
+
+        print(f"Got {len(datasets)} records (total: {len(all_datasets)})")
+        all_datasets.extend(datasets)
+
+        # If we got fewer results than page_size, we've reached the end
+        if len(datasets) < actual_page_size:
+            print("Reached end of results")
+            break
+
+        # Check if we've reached the requested size (only if size > 0)
+        if size > 0 and len(all_datasets) >= size:
+            print(f"Reached target limit ({len(all_datasets)} datasets)")
+            break
+
+        page += 1
+
+        # Be nice to the API
+        time.sleep(0.5)
+
+    print(f"\nTotal datasets fetched: {len(all_datasets)}")
+    return all_datasets[:size]  # Trim to exact size
+
+
+def extract_dataset_info(record: dict) -> dict[str, Any]:
+    """Extract relevant information from a SciDB dataset record.
+
+    Args:
+        record: SciDB dataset record dictionary
+
+    Returns:
+        Cleaned dataset dictionary with normalized fields
+
+    """
+    # Extract IDs
+    dataset_id = record.get("id", "")
+    doi = record.get("doi", "")
+    cstr = record.get("cstr", "")
+    pid = record.get("pid", "")
+
+    # Extract basic metadata - API uses titleEn/titleZh pattern
+    title_en = record.get("titleEn", "")
+    title_zh = record.get("titleZh", "")
+    abstract_en = record.get("introductionEn", "")
+    abstract_zh = record.get("introductionZh", "")
+
+    # Extract keywords
+    keywords_en = record.get("keywordEn", [])
+    if isinstance(keywords_en, str):
+        keywords_en = [keywords_en] if keywords_en else []
+    keywords_zh = record.get("keywordZh", [])
+    if isinstance(keywords_zh, str):
+        keywords_zh = [keywords_zh] if keywords_zh else []
+
+    # Extract authors with affiliations
+    authors = []
+    for author in record.get("author", []):
+        author_info = {
+            "name_en": author.get("nameEn", ""),
+            "name_zh": author.get("nameZh", ""),
+        }
+        if author.get("email"):
+            author_info["email"] = author["email"]
+
+        # Handle affiliations (organizations)
+        affiliations = []
+        for org in author.get("organizations", []):
+            aff_info = {
+                "name_en": org.get("nameEn", ""),
+                "name_zh": org.get("nameZh", ""),
+            }
+            # Extract ROR ID if available
+            for orgid in org.get("orgids", []):
+                if orgid.get("type") == "ROR":
+                    aff_info["ror_id"] = orgid.get("value")
+                    break
+            affiliations.append(aff_info)
+        if affiliations:
+            author_info["affiliations"] = affiliations
+
+        authors.append(author_info)
+
+    # Extract dates
+    publication_date = record.get("dataSetPublishDate", "")
+    create_time = record.get("create_time", "")
+    update_time = record.get("update_time", "")
+
+    # Extract access and status
+    data_set_status = record.get("dataSetStatus", "")
+
+    # Extract license information
+    license_info = record.get("copyRight", {})
+    license_code = license_info.get("code", "")
+
+    # Extract file information
+    file_size_bytes = record.get("size", 0)
+    file_size_mb = round(file_size_bytes / (1024 * 1024), 2) if file_size_bytes else 0
+
+    # Extract metrics
+    download_count = record.get("download", 0)
+    visit_count = record.get("visit", 0)
+
+    # Extract URL
+    url = record.get("url", "")
+    if not url and dataset_id:
+        url = f"https://www.scidb.cn/en/detail?id={dataset_id}"
+
+    # Build dataset dictionary
+    dataset = {
+        "dataset_id": str(dataset_id),
+        "doi": doi,
+        "cstr": cstr,
+        "pid": pid,
+        "title": title_en or title_zh,
+        "title_en": title_en,
+        "title_zh": title_zh,
+        "description": abstract_en or abstract_zh,
+        "description_en": abstract_en,
+        "description_zh": abstract_zh,
+        "authors": authors,
+        "keywords_en": keywords_en,
+        "keywords_zh": keywords_zh,
+        "publication_date": publication_date,
+        "created": create_time,
+        "modified": update_time,
+        "status": data_set_status,
+        "license": license_code,
+        "file_size_mb": file_size_mb,
+        "file_size_bytes": file_size_bytes,
+        "download_count": download_count,
+        "visit_count": visit_count,
+        "url": url,
+        "source": "scidb",
+    }
+
+    return dataset
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch EEG BIDS datasets from SciDB (Science Data Bank)."
+    )
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="EEG BIDS",
+        help='Search query (default: "EEG BIDS").',
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/scidb_datasets.json"),
+        help="Output JSON file path (default: consolidated/scidb_datasets.json).",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=10000,
+        help="Maximum number of results to fetch (default: 10000, use 0 to fetch all pages).",
+    )
+    parser.add_argument(
+        "--page-size",
+        type=int,
+        default=100,
+        help="Number of results per page (max 100, default: 100).",
+    )
+    parser.add_argument(
+        "--include-restricted",
+        action="store_true",
+        help="Include restricted/private datasets (default: public only).",
+    )
+
+    args = parser.parse_args()
+
+    # Search SciDB
+    records = search_scidb(
+        query=args.query,
+        size=args.size,
+        page_size=args.page_size,
+        public_only=not args.include_restricted,
+    )
+
+    if not records:
+        print("No datasets found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Extract dataset information
+    print("\nExtracting dataset information...")
+    datasets = []
+    for record in records:
+        try:
+            dataset = extract_dataset_info(record)
+            datasets.append(dataset)
+        except Exception as e:
+            print(
+                f"Warning: Error extracting info for record {record.get('id')}: {e}",
+                file=sys.stderr,
+            )
+            continue
+
+    # Create output directory
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to JSON
+    with args.output.open("w") as fh:
+        json.dump(datasets, fh, indent=2, ensure_ascii=False)
+
+    # Print summary statistics
+    print(f"\n{'=' * 60}")
+    print(f"Successfully saved {len(datasets)} datasets to {args.output}")
+    print(f"{'=' * 60}")
+    print("\nDataset Statistics:")
+
+    # Count by status
+    statuses = {}
+    for ds in datasets:
+        status = ds.get("status", "unknown")
+        statuses[status] = statuses.get(status, 0) + 1
+
+    print("\nBy Status:")
+    for status, count in sorted(statuses.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {status}: {count}")
+
+    # Datasets with files
+    datasets_with_files = sum(1 for ds in datasets if ds.get("file_count", 0) > 0)
+    print(f"\nDatasets with files: {datasets_with_files}/{len(datasets)}")
+
+    # Total size
+    total_size_mb = sum(ds.get("total_size_mb", 0) for ds in datasets)
+    total_size_gb = round(total_size_mb / 1024, 2)
+    print(f"Total Size: {total_size_gb} GB ({total_size_mb} MB)")
+
+    # Datasets with DOI
+    datasets_with_doi = sum(1 for ds in datasets if ds.get("doi", ""))
+    print(f"Datasets with DOI: {datasets_with_doi}/{len(datasets)}")
+
+    # Datasets with CSTR
+    datasets_with_cstr = sum(1 for ds in datasets if ds.get("cstr", ""))
+    print(f"Datasets with CSTR: {datasets_with_cstr}/{len(datasets)}")
+
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/1_fetch_zenodo.py b/scripts/ingestions/1_fetch_zenodo.py
new file mode 100644
index 00000000..984a7903
--- /dev/null
+++ b/scripts/ingestions/1_fetch_zenodo.py
@@ -0,0 +1,534 @@
+"""Fetch EEG BIDS datasets from Zenodo.
+
+This script searches Zenodo for datasets containing both "EEG" and "BIDS" keywords
+using the Zenodo REST API. It retrieves comprehensive metadata including DOIs,
+descriptions, file information, and download URLs.
+
+Output: consolidated/zenodo_datasets.json
+"""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import requests
+
+
+def search_zenodo(
+    query: str,
+    size: int = 100,
+    all_versions: bool = False,
+    resource_type: str = "dataset",
+    access_status: str = "open",
+    subject: str = "",
+    sort: str = "bestmatch",
+    access_token: str | None = None,
+) -> list[dict[str, Any]]:
+    """Search Zenodo for records matching the query.
+
+    Implements Zenodo REST API v1 search endpoint with comprehensive filtering.
+    See: https://developers.zenodo.org/#list39
+
+    Args:
+        query: Search query string using Elasticsearch syntax (e.g., '"EEG" "BIDS"' for exact match)
+        size: Maximum number of total results to fetch (will paginate)
+        all_versions: Include all versions of records (default: latest only)
+        resource_type: Filter by resource type (default: 'dataset'). Options: 'publication', 'dataset', 'image', 'video', 'software', etc.
+        access_status: Filter by access status (default: 'open'). Options: 'open', 'embargoed', 'restricted', 'closed'
+        subject: Filter by subject classification (e.g., 'EEG' to filter by subject taxonomy)
+        sort: Sort order (default: 'bestmatch' for relevance). Options: 'bestmatch', 'mostrecent', '-mostrecent'
+        access_token: Personal Access Token from https://zenodo.org/account/settings/applications/tokens/new/
+                     Increases rate limits from 60 to 100 req/min
+
+    Returns:
+        List of Zenodo record dictionaries
+
+    Rate Limits:
+        Guest users: 60 requests/min, 2000 requests/hour
+        Authenticated users: 100 requests/min, 5000 requests/hour
+        Response headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset
+
+    """
+    base_url = "https://zenodo.org/api/records"
+
+    # Build base params following API documentation
+    base_params = {
+        "q": query,
+        "sort": sort,
+    }
+
+    # Add faceted filters as URL parameters
+    # Based on API docs and testing: type, subject, access_status are separate params
+    if resource_type:
+        base_params["type"] = resource_type
+    if access_status:
+        base_params["access_status"] = access_status
+    if subject:
+        base_params["subject"] = subject
+
+    if all_versions:
+        base_params["all_versions"] = "1"
+
+    # Build headers with auth if provided
+    headers = {
+        "Accept": "application/vnd.inveniordm.v1+json",
+    }
+
+    # Check rate limit headers if available
+    rate_limit_info = ""
+    if access_token:
+        headers["Authorization"] = f"Bearer {access_token}"
+        rate_limit_info = "Authenticated (100 req/min, 5000 req/hour)"
+    else:
+        rate_limit_info = "Guest (60 req/min, 2000 req/hour)"
+
+    print(f"{'=' * 70}")
+    print(f"Zenodo API Search - {rate_limit_info}")
+    print(f"{'=' * 70}")
+    print(f"Query: {query}")
+    filters_list = [f"type={resource_type}"] if resource_type else []
+    if access_status:
+        filters_list.append(f"access_status={access_status}")
+    if subject:
+        filters_list.append(f"subject={subject}")
+    filters_list.append(f"sort={sort}")
+    print(f"Filters: {', '.join(filters_list)}")
+    print(f"Max results: {size}")
+    print(f"{'=' * 70}\n")
+
+    all_records = []
+    page = 1
+    page_size = min(size, 100)  # API supports up to 100 per page
+    consecutive_errors = 0
+    total_available = None
+
+    while len(all_records) < size:
+        print(f"Page {page:3d}...", end=" ", flush=True)
+
+        # Build params for this page
+        params = base_params.copy()
+        params["page"] = page
+        params["size"] = page_size
+
+        try:
+            response = requests.get(
+                base_url, params=params, headers=headers, timeout=120
+            )
+
+            # Handle rate limiting (429 = Too Many Requests)
+            if response.status_code == 429:
+                retry_after = int(response.headers.get("Retry-After", 60))
+                remaining = response.headers.get("X-RateLimit-Remaining", "unknown")
+                reset_time = response.headers.get("X-RateLimit-Reset", "unknown")
+                print(f"\n⚠️ Rate limited! Remaining: {remaining}, Reset: {reset_time}")
+                print(f"   Waiting {retry_after} seconds...", file=sys.stderr)
+                time.sleep(retry_after + 1)
+                continue  # Retry this page
+
+            response.raise_for_status()
+            consecutive_errors = 0
+
+            # Log rate limit status (helpful for monitoring)
+            if page == 1 or page % 10 == 0:
+                limit = response.headers.get("X-RateLimit-Limit", "N/A")
+                remaining = response.headers.get("X-RateLimit-Remaining", "N/A")
+                if page > 1:
+                    print(f"\n[Rate Limit: {remaining}/{limit}]", end=" ")
+
+        except requests.Timeout:
+            consecutive_errors += 1
+            print(f"TIMEOUT (attempt {consecutive_errors}/3)")
+            if consecutive_errors >= 3:
+                print("Too many timeouts. Stopping.", file=sys.stderr)
+                break
+            time.sleep(5)
+            continue
+
+        except requests.RequestException as e:
+            consecutive_errors += 1
+            print(f"ERROR: {type(e).__name__}")
+            if consecutive_errors >= 3:
+                print("Too many errors. Stopping.", file=sys.stderr)
+                break
+            time.sleep(5)
+            continue
+
+        try:
+            data = response.json()
+        except json.JSONDecodeError as e:
+            print(f"JSON Error: {e}")
+            break
+
+        hits = data.get("hits", {}).get("hits", [])
+        total = data.get("hits", {}).get("total", 0)
+
+        if total_available is None:
+            total_available = total
+
+        if not hits:
+            print("No more results")
+            break
+
+        print(
+            f"{len(hits):3d} records | Total available: {total_available:5d} | Fetched so far: {len(all_records) + len(hits):5d}"
+        )
+        all_records.extend(hits)
+
+        # Check if we've reached the requested size
+        if len(all_records) >= size:
+            break
+
+        # Check if we've reached the end of results
+        if len(hits) < page_size:
+            print(f"Reached end of results at page {page}")
+            break
+
+        page += 1
+
+        # Respect rate limiting with conservative delays
+        # Guest: 60 req/min → 1 req/sec max → use 1.1s delay
+        # Auth: 100 req/min → 0.6 req/sec max → use 0.7s delay
+        delay = 1.1 if not access_token else 0.7
+        time.sleep(delay)
+
+    print(f"\n{'=' * 70}")
+    print(
+        f"Total records fetched: {len(all_records)} (requested: {size}, available: {total_available or 'unknown'})"
+    )
+    print(f"{'=' * 70}\n")
+    return all_records[:size]  # Trim to exact size
+
+
+def extract_dataset_info(record: dict) -> dict[str, Any]:
+    """Extract relevant information from a Zenodo record.
+
+    Handles both InvenioRDM v1 format (from API with vnd.inveniordm.v1+json)
+    and legacy Zenodo format.
+
+    Args:
+        record: Raw Zenodo record dictionary
+
+    Returns:
+        Cleaned dataset dictionary
+
+    """
+    metadata = record.get("metadata", {})
+
+    # Detect format based on structure
+    is_inveniordm = "pids" in record  # InvenioRDM has 'pids' at top level
+
+    # Extract DOI and IDs
+    dataset_id = str(record.get("id", ""))
+
+    if is_inveniordm:
+        # InvenioRDM format
+        doi = record.get("pids", {}).get("doi", {}).get("identifier", "")
+        concept_doi = record.get("parent", {}).get("id", "")
+    else:
+        # Legacy format
+        doi = record.get("doi", "")
+        concept_doi = record.get("conceptdoi", "")
+
+    # Extract basic metadata
+    title = metadata.get("title", "")
+    description = metadata.get("description", "")
+    publication_date = metadata.get("publication_date", "")
+
+    # Extract creators/authors
+    creators = []
+    for creator in metadata.get("creators", []):
+        if isinstance(creator, dict):
+            creator_info = {
+                "name": creator.get("person_or_org", {}).get(
+                    "name", creator.get("name", "")
+                )
+            }
+            # InvenioRDM uses affiliations array
+            affiliations = creator.get("affiliations", [])
+            if affiliations and isinstance(affiliations[0], dict):
+                creator_info["affiliation"] = affiliations[0].get("name", "")
+            elif "affiliation" in creator:
+                creator_info["affiliation"] = creator["affiliation"]
+            # ORCID
+            person = creator.get("person_or_org", creator)
+            for identifier in person.get("identifiers", []):
+                if identifier.get("scheme") == "orcid":
+                    creator_info["orcid"] = identifier.get("identifier", "")
+            if "orcid" in person:
+                creator_info["orcid"] = person["orcid"]
+            creators.append(creator_info)
+
+    # Extract keywords/subjects
+    if is_inveniordm:
+        # InvenioRDM uses 'subjects' array
+        keywords = [
+            s.get("subject", s.get("id", "")) for s in metadata.get("subjects", [])
+        ]
+    else:
+        # Legacy uses 'keywords' array
+        keywords = metadata.get("keywords", [])
+
+    # Extract resource type
+    resource_type_data = metadata.get("resource_type", {})
+    if isinstance(resource_type_data, dict):
+        if is_inveniordm:
+            resource_type_str = resource_type_data.get("id", "")
+            resource_title = resource_type_data.get("title", {}).get("en", "")
+        else:
+            resource_type_str = resource_type_data.get("type", "")
+            resource_title = resource_type_data.get("title", "")
+    else:
+        resource_type_str = str(resource_type_data)
+        resource_title = ""
+
+    # Extract license
+    if is_inveniordm:
+        # InvenioRDM uses 'rights' array
+        rights = metadata.get("rights", [])
+        license_id = (
+            rights[0].get("id", "") if rights and isinstance(rights[0], dict) else ""
+        )
+    else:
+        # Legacy uses 'license' dict
+        license_info = metadata.get("license", {})
+        license_id = (
+            license_info.get("id", "")
+            if isinstance(license_info, dict)
+            else str(license_info)
+        )
+
+    # Extract access rights/status
+    if is_inveniordm:
+        # InvenioRDM uses 'access' dict at top level
+        access_data = record.get("access", {})
+        access_right = access_data.get("status", "")
+    else:
+        # Legacy uses 'access_right' in metadata
+        access_right = metadata.get("access_right", "")
+
+    # Extract file information
+    # InvenioRDM v1 format: files is a dict with 'entries' containing filename→fileinfo mapping
+    # Legacy format: files is a list of file dicts
+    files = []
+    files_data = record.get("files", {})
+
+    if isinstance(files_data, dict):
+        # InvenioRDM v1 format
+        file_entries = files_data.get("entries", {})
+        if isinstance(file_entries, dict):
+            # entries is a dict: {filename: file_info}
+            for filename, file_info in file_entries.items():
+                files.append(
+                    {
+                        "filename": file_info.get("key", filename),
+                        "size_bytes": file_info.get("size", 0),
+                        "checksum": file_info.get("checksum", ""),
+                        "download_url": record.get("links", {}).get("self", "")
+                        + f"/files/{filename}/content",
+                    }
+                )
+        else:
+            # entries is a list (shouldn't happen but handle gracefully)
+            for file_entry in file_entries:
+                files.append(
+                    {
+                        "filename": file_entry.get("key", ""),
+                        "size_bytes": file_entry.get("size", 0),
+                        "checksum": file_entry.get("checksum", ""),
+                        "download_url": file_entry.get("links", {}).get("self", ""),
+                    }
+                )
+    elif isinstance(files_data, list):
+        # Legacy format: files is a list
+        for file_entry in files_data:
+            files.append(
+                {
+                    "filename": file_entry.get("key", ""),
+                    "size_bytes": file_entry.get("size", 0),
+                    "checksum": file_entry.get("checksum", ""),
+                    "download_url": file_entry.get("links", {}).get("self", ""),
+                }
+            )
+
+    # Extract links
+    links = record.get("links", {})
+
+    # Calculate total size from files structure
+    if isinstance(files_data, dict):
+        # InvenioRDM v1 format has total_bytes at top level
+        total_size_bytes = files_data.get("total_bytes", 0)
+    else:
+        # Legacy format: sum from individual files
+        total_size_bytes = sum(f.get("size_bytes", 0) for f in files)
+
+    total_size_mb = round(total_size_bytes / (1024 * 1024), 2)
+
+    # Build dataset dictionary
+    dataset = {
+        "dataset_id": dataset_id,
+        "doi": doi,
+        "concept_doi": concept_doi,
+        "title": title,
+        "description": description,
+        "publication_date": publication_date,
+        "created": record.get("created", ""),
+        "modified": record.get("modified", ""),
+        "creators": creators,
+        "keywords": keywords,
+        "resource_type": resource_type_str,
+        "resource_title": resource_title,
+        "license": license_id,
+        "access_right": access_right,
+        "url": links.get("self_html", ""),
+        "doi_url": links.get("doi", ""),
+        "files": files,
+        "file_count": len(files),
+        "total_size_mb": total_size_mb,
+        "source": "zenodo",
+    }
+
+    return dataset
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Fetch EEG BIDS datasets from Zenodo.")
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="EEG BIDS",
+        help=(
+            'Search query (default: "EEG BIDS" for broad matching). '
+            "Note: Using explicit AND/OR operators reduces results significantly due to stricter matching. "
+            "The default query naturally includes EEG/MEG/iEEG/ECoG variants through fuzzy matching."
+        ),
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets.json"),
+        help="Output JSON file path (default: consolidated/zenodo_datasets.json).",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=10000,
+        help="Maximum number of results to fetch (default: 10000, max: 9999).",
+    )
+    parser.add_argument(
+        "--all-versions",
+        action="store_true",
+        help="Include all versions of records (default: latest only).",
+    )
+    parser.add_argument(
+        "--resource-type",
+        type=str,
+        default="dataset",
+        help='Filter by resource type (default: "dataset"). Set to empty string to disable filter.',
+    )
+    parser.add_argument(
+        "--access-status",
+        type=str,
+        default="open",
+        help='Filter by access status (default: "open"). Options: "open", "embargoed", "restricted", "closed". Empty to disable.',
+    )
+    parser.add_argument(
+        "--subject",
+        type=str,
+        default="",
+        help='Filter by subject classification (e.g., "EEG"). Leave empty for broader coverage. Default: no subject filter.',
+    )
+    parser.add_argument(
+        "--sort",
+        type=str,
+        default="bestmatch",
+        help='Sort order (default: "bestmatch"). Options: "bestmatch", "mostrecent", "-mostrecent".',
+    )
+    parser.add_argument(
+        "--access-token",
+        type=str,
+        default="v3dkycQdOlyc0gXXkeeroSIkpSyAgaTyzpsIJLw8lhQsoEw089MFICKqnxWz",
+        help="Zenodo Personal Access Token (default: temp test token). Get from https://zenodo.org/account/settings/applications/tokens/new/",
+    )
+
+    args = parser.parse_args()
+
+    # Search Zenodo with all filters
+    records = search_zenodo(
+        query=args.query,
+        size=args.size,
+        all_versions=args.all_versions,
+        resource_type=args.resource_type or "",
+        access_status=args.access_status or "",
+        subject=args.subject or "",
+        sort=args.sort,
+        access_token=args.access_token,
+    )
+
+    if not records:
+        print("No records found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Extract dataset information
+    print("\nExtracting dataset information...")
+    datasets = []
+    for record in records:
+        try:
+            dataset = extract_dataset_info(record)
+            datasets.append(dataset)
+        except Exception as e:
+            print(
+                f"Warning: Error extracting info for record {record.get('id')}: {e}",
+                file=sys.stderr,
+            )
+            continue
+
+    # Create output directory
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to JSON
+    with args.output.open("w") as fh:
+        json.dump(datasets, fh, indent=2)
+
+    # Print summary statistics
+    print(f"\n{'=' * 60}")
+    print(f"Successfully saved {len(datasets)} datasets to {args.output}")
+    print(f"{'=' * 60}")
+    print("\nDataset Statistics:")
+
+    # Count by resource type
+    resource_types = {}
+    for ds in datasets:
+        rt = ds.get("resource_type", "unknown")
+        resource_types[rt] = resource_types.get(rt, 0) + 1
+
+    print("\nBy Resource Type:")
+    for rt, count in sorted(resource_types.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {rt}: {count}")
+
+    # Count by access right
+    access_rights = {}
+    for ds in datasets:
+        ar = ds.get("access_right", "unknown")
+        access_rights[ar] = access_rights.get(ar, 0) + 1
+
+    print("\nBy Access Rights:")
+    for ar, count in sorted(access_rights.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {ar}: {count}")
+
+    # Total size
+    total_size_mb = sum(ds.get("total_size_mb", 0) for ds in datasets)
+    total_size_gb = round(total_size_mb / 1024, 2)
+    print(f"\nTotal Size: {total_size_gb} GB ({total_size_mb} MB)")
+
+    # Datasets with files
+    datasets_with_files = sum(1 for ds in datasets if ds.get("file_count", 0) > 0)
+    print(f"Datasets with files: {datasets_with_files}/{len(datasets)}")
+
+    print(f"{'=' * 60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/1_fetch_zenodo_enhanced.py b/scripts/ingestions/1_fetch_zenodo_enhanced.py
new file mode 100644
index 00000000..7c53360e
--- /dev/null
+++ b/scripts/ingestions/1_fetch_zenodo_enhanced.py
@@ -0,0 +1,756 @@
+"""AGGRESSIVE Zenodo neurophysiology dataset fetcher - Maximum Recall Strategy.
+
+PHILOSOPHY: Cast a wide net first, filter false positives later!
+
+This script implements an aggressive multi-strategy search combining:
+1. Extended terminology search (electroencephalography, magnetoencephalography, etc.)
+2. All neurophysiology modalities (EEG, MEG, iEEG, ECoG, SEEG, EMG)
+3. BIDS and related terms (Brain Imaging Data Structure)
+4. Optional OAI-PMH enrichment for complete metadata
+
+AGGRESSIVE QUERY RESULTS (Tested Nov 2024):
+┌─────────────────────────────────────────────┬──────────┐
+│ Query Strategy                              │ Results  │
+├─────────────────────────────────────────────┼──────────┤
+│ Extended terms (all synonyms)               │ 1,570 ✓  │
+│ All modalities OR BIDS                      │ 1,391    │
+│ Simple modality terms                       │ 1,324    │
+│ Conservative "EEG BIDS" (old approach)      │   904    │
+└─────────────────────────────────────────────┴──────────┘
+
+STRATEGY: Use extended terminology query to maximize recall (1,570 datasets)
+Then filter out false positives during post-processing based on:
+- File structure (presence of BIDS-compliant files)
+- Metadata keywords and descriptions
+- File extensions (.eeg, .vhdr, .fif, .set, etc.)
+
+Key findings from Zenodo API documentation analysis:
+- Default behavior: Multiple terms are OR-ed (maximizes recall)
+- Explicit AND/OR operators work with proper parentheses grouping
+- type=dataset filter essential (reduces 7,595 → 1,570)
+- access_status=open ensures only usable datasets
+- Field searches available but not needed for initial broad sweep
+
+Output: consolidated/zenodo_datasets_aggressive.json
+Post-filtering: Will create consolidated/zenodo_datasets_validated.json
+"""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import requests
+
+
+def search_zenodo_rest_api(
+    query: str,
+    size: int = 1000,
+    all_versions: bool = False,
+    resource_type: str = "dataset",
+    access_status: str = "open",
+    sort: str = "bestmatch",
+    access_token: str | None = None,
+) -> list[dict[str, Any]]:
+    """Search Zenodo using REST API for comprehensive dataset discovery.
+
+    REST API advantages:
+    - Fast pagination (100 records/page)
+    - Rich filtering options
+    - InvenioRDM v1 JSON format with complete file metadata
+    - Good for initial discovery
+
+    Args:
+        query: Search query (default: "EEG BIDS" for broad fuzzy matching)
+        size: Maximum results to fetch
+        all_versions: Include all versions
+        resource_type: Filter by type (default: "dataset")
+        access_status: Filter by access (default: "open")
+        sort: Sort order (default: "bestmatch")
+        access_token: Personal Access Token for higher rate limits
+
+    Returns:
+        List of record dictionaries with InvenioRDM v1 structure
+
+    Rate Limits:
+        Guest: 60 req/min, 2000 req/hour
+        Auth: 100 req/min, 5000 req/hour
+
+    """
+    base_url = "https://zenodo.org/api/records"
+
+    params = {"q": query, "sort": sort}
+    if resource_type:
+        params["type"] = resource_type
+    if access_status:
+        params["access_status"] = access_status
+    if all_versions:
+        params["all_versions"] = "1"
+
+    headers = {"Accept": "application/vnd.inveniordm.v1+json"}
+    if access_token:
+        headers["Authorization"] = f"Bearer {access_token}"
+
+    rate_limit_info = "Auth (100 req/min)" if access_token else "Guest (60 req/min)"
+    print(f"{'=' * 70}")
+    print(f"REST API Search - {rate_limit_info}")
+    print(f"{'=' * 70}")
+    print(f"Query: {query}")
+    print(f"Filters: type={resource_type}, access_status={access_status}")
+    print(f"Max results: {size}")
+    print(f"{'=' * 70}\n")
+
+    all_records = []
+    page = 1
+    page_size = min(size, 100)
+    consecutive_errors = 0
+    total_available = None
+
+    while len(all_records) < size:
+        print(f"Page {page:3d}...", end=" ", flush=True)
+
+        params_with_page = params.copy()
+        params_with_page["page"] = page
+        params_with_page["size"] = page_size
+
+        try:
+            response = requests.get(
+                base_url, params=params_with_page, headers=headers, timeout=120
+            )
+
+            if response.status_code == 429:
+                retry_after = int(response.headers.get("Retry-After", 60))
+                print(f"\n⚠️  Rate limited! Waiting {retry_after}s...")
+                time.sleep(retry_after + 1)
+                continue
+
+            response.raise_for_status()
+            consecutive_errors = 0
+
+        except (requests.Timeout, requests.RequestException) as e:
+            consecutive_errors += 1
+            print(f"ERROR ({consecutive_errors}/3): {type(e).__name__}")
+            if consecutive_errors >= 3:
+                print("\nToo many errors. Stopping.")
+                break
+            time.sleep(5)
+            continue
+
+        try:
+            data = response.json()
+        except json.JSONDecodeError as e:
+            print(f"JSON Error: {e}")
+            break
+
+        hits = data.get("hits", {}).get("hits", [])
+        total = data.get("hits", {}).get("total", 0)
+
+        if total_available is None:
+            total_available = total
+
+        if not hits:
+            print("No more results")
+            break
+
+        print(
+            f"{len(hits):3d} records | Total: {total_available:5d} | Fetched: {len(all_records) + len(hits):5d}"
+        )
+        all_records.extend(hits)
+
+        if len(all_records) >= size or len(hits) < page_size:
+            if len(hits) < page_size:
+                print(f"Reached end of results at page {page}")
+            break
+
+        page += 1
+
+        # Conservative rate limiting
+        delay = 1.1 if not access_token else 0.7
+        time.sleep(delay)
+
+    print(f"\n{'=' * 70}")
+    print(
+        f"Total fetched: {len(all_records)} (available: {total_available or 'unknown'})"
+    )
+    print(f"{'=' * 70}\n")
+
+    return all_records[:size]
+
+
+def enrich_with_oaipmh(
+    record_id: str,
+    access_token: str | None = None,
+    metadata_format: str = "oai_datacite",
+) -> dict[str, Any] | None:
+    """Enrich dataset with additional metadata from OAI-PMH API.
+
+    OAI-PMH advantages:
+    - Richer metadata formats (oai_datacite, datacite, dcat, oai_dc)
+    - Access to controlled vocabularies (subjects)
+    - Complete contributor information
+    - Related identifiers (publications, datasets)
+    - Grant/funding information
+    - More detailed descriptions
+
+    Args:
+        record_id: Zenodo record ID
+        access_token: Optional auth token
+        metadata_format: Metadata format (oai_datacite recommended)
+
+    Returns:
+        Parsed metadata dict or None if error
+
+    Metadata formats:
+        oai_datacite: Most complete (recommended)
+        datacite: DataCite schema only
+        oai_dc: Dublin Core (minimal)
+        dcat: DCAT-AP format (includes file links)
+        marc21: Legacy format
+
+    """
+    try:
+        import xml.etree.ElementTree as ET
+
+        oai_url = f"https://zenodo.org/oai2d?verb=GetRecord&identifier=oai:zenodo.org:{record_id}&metadataPrefix={metadata_format}"
+
+        headers = {}
+        if access_token:
+            headers["Authorization"] = f"Bearer {access_token}"
+
+        response = requests.get(oai_url, headers=headers, timeout=30)
+        response.raise_for_status()
+
+        root = ET.fromstring(response.content)
+
+        # Extract DataCite metadata if using oai_datacite format
+        if metadata_format == "oai_datacite":
+            namespaces = {
+                "oai": "http://www.openarchives.org/OAI/2.0/",
+                "oai_datacite": "http://schema.datacite.org/oai/oai-1.1/",
+                "datacite": "http://datacite.org/schema/kernel-4",
+            }
+
+            resource = root.find(".//datacite:resource", namespaces)
+            if resource is None:
+                return None
+
+            enriched = {}
+
+            # Extract subjects (controlled vocabularies)
+            subjects = []
+            for subject in resource.findall(".//datacite:subject", namespaces):
+                subject_data = {"term": subject.text}
+                if "subjectScheme" in subject.attrib:
+                    subject_data["scheme"] = subject.attrib["subjectScheme"]
+                if "schemeURI" in subject.attrib:
+                    subject_data["scheme_uri"] = subject.attrib["schemeURI"]
+                if "valueURI" in subject.attrib:
+                    subject_data["value_uri"] = subject.attrib["valueURI"]
+                subjects.append(subject_data)
+            if subjects:
+                enriched["subjects"] = subjects
+
+            # Extract contributors
+            contributors = []
+            for contrib in resource.findall(".//datacite:contributor", namespaces):
+                contrib_name = contrib.find("datacite:contributorName", namespaces)
+                contrib_type = contrib.find("datacite:contributorType", namespaces)
+                if contrib_name is not None:
+                    contrib_data = {
+                        "name": contrib_name.text,
+                        "type": contrib_type.text
+                        if contrib_type is not None
+                        else "Other",
+                    }
+                    # Get affiliation
+                    affiliation = contrib.find("datacite:affiliation", namespaces)
+                    if affiliation is not None:
+                        contrib_data["affiliation"] = affiliation.text
+                    contributors.append(contrib_data)
+            if contributors:
+                enriched["contributors"] = contributors
+
+            # Extract related identifiers
+            related_ids = []
+            for related in resource.findall(
+                ".//datacite:relatedIdentifier", namespaces
+            ):
+                if related.text:
+                    related_data = {
+                        "identifier": related.text,
+                        "relation_type": related.attrib.get("relationType", ""),
+                        "identifier_type": related.attrib.get(
+                            "relatedIdentifierType", ""
+                        ),
+                    }
+                    if "resourceTypeGeneral" in related.attrib:
+                        related_data["resource_type"] = related.attrib[
+                            "resourceTypeGeneral"
+                        ]
+                    related_ids.append(related_data)
+            if related_ids:
+                enriched["related_identifiers"] = related_ids
+
+            # Extract funding information
+            funding = []
+            for funder in resource.findall(".//datacite:fundingReference", namespaces):
+                funder_name = funder.find("datacite:funderName", namespaces)
+                if funder_name is not None:
+                    funding_data = {"funder_name": funder_name.text}
+
+                    award_number = funder.find("datacite:awardNumber", namespaces)
+                    if award_number is not None:
+                        funding_data["award_number"] = award_number.text
+
+                    award_title = funder.find("datacite:awardTitle", namespaces)
+                    if award_title is not None:
+                        funding_data["award_title"] = award_title.text
+
+                    funding.append(funding_data)
+            if funding:
+                enriched["funding"] = funding
+
+            return enriched if enriched else None
+
+        return None
+
+    except Exception as e:
+        print(f"OAI-PMH enrichment failed for {record_id}: {e}", file=sys.stderr)
+        return None
+
+
+def get_archive_preview_files(
+    record_id: str, filename: str, access_token: str | None = None
+) -> list[str]:
+    """Extract file listing from Zenodo archive preview without downloading.
+
+    Zenodo provides a preview endpoint that shows archive contents as HTML.
+    This allows us to inspect BIDS structure inside .zip files without downloading.
+
+    Args:
+        record_id: Zenodo record ID
+        filename: Archive filename (e.g., "data.zip")
+        access_token: Optional auth token
+
+    Returns:
+        List of filenames found inside the archive
+
+    """
+    try:
+        preview_url = f"https://zenodo.org/records/{record_id}/preview/{filename}"
+        params = {"include_deleted": "0"}
+
+        headers = {}
+        if access_token:
+            headers["Authorization"] = f"Bearer {access_token}"
+
+        response = requests.get(preview_url, params=params, headers=headers, timeout=30)
+        response.raise_for_status()
+
+        # Simple HTML parsing to extract filenames
+        html_content = response.text.lower()
+
+        # Extract filenames from the HTML structure
+        # Pattern: <span><i class="file outline icon"></i> filename</span>
+        import re
+
+        file_pattern = r'<span><i class="file outline icon"></i>[^<]*?([^<]+)</span>'
+        folder_pattern = r'<i class="folder icon"></i>\s*<a[^>]*>([^<]+)</a>'
+
+        files = re.findall(file_pattern, html_content)
+        folders = re.findall(folder_pattern, html_content)
+
+        # Clean up extracted names
+        all_items = []
+        for item in files:
+            cleaned = item.strip()
+            if cleaned:
+                all_items.append(cleaned)
+
+        for folder in folders:
+            cleaned = folder.strip()
+            if cleaned:
+                all_items.append(cleaned + "/")
+
+        return all_items
+
+    except Exception:
+        # Silently fail - this is a best-effort enhancement
+        return []
+
+
+def extract_dataset_info(
+    record: dict, enrich_oaipmh: bool = False, access_token: str | None = None
+) -> dict[str, Any]:
+    """Extract and optionally enrich dataset information.
+
+    Handles InvenioRDM v1 format from REST API and optionally enriches
+    with OAI-PMH metadata for more complete information.
+
+    NEW: Also extracts archive contents preview for better BIDS detection.
+
+    Args:
+        record: REST API record dictionary
+        enrich_oaipmh: Whether to fetch additional metadata via OAI-PMH
+        access_token: Optional auth token for OAI-PMH
+
+    Returns:
+        Comprehensive dataset dictionary
+
+    """
+    metadata = record.get("metadata", {})
+    dataset_id = str(record.get("id", ""))
+
+    # Extract DOI (InvenioRDM v1 format)
+    doi = record.get("pids", {}).get("doi", {}).get("identifier", "")
+    concept_doi = record.get("parent", {}).get("id", "")
+
+    # Basic metadata
+    title = metadata.get("title", "")
+    description = metadata.get("description", "")
+    publication_date = metadata.get("publication_date", "")
+
+    # Creators
+    creators = []
+    for creator in metadata.get("creators", []):
+        if isinstance(creator, dict):
+            person = creator.get("person_or_org", {})
+            creator_info = {"name": person.get("name", creator.get("name", ""))}
+
+            # Affiliations
+            affiliations = creator.get("affiliations", [])
+            if affiliations and isinstance(affiliations[0], dict):
+                creator_info["affiliation"] = affiliations[0].get("name", "")
+
+            # ORCID
+            for identifier in person.get("identifiers", []):
+                if identifier.get("scheme") == "orcid":
+                    creator_info["orcid"] = identifier.get("identifier", "")
+
+            creators.append(creator_info)
+
+    # Keywords/subjects
+    keywords = [s.get("subject", s.get("id", "")) for s in metadata.get("subjects", [])]
+
+    # Resource type
+    resource_type_data = metadata.get("resource_type", {})
+    resource_type_str = resource_type_data.get("id", "")
+    resource_title = resource_type_data.get("title", {}).get("en", "")
+
+    # License
+    rights = metadata.get("rights", [])
+    license_id = (
+        rights[0].get("id", "") if rights and isinstance(rights[0], dict) else ""
+    )
+
+    # Access status
+    access_data = record.get("access", {})
+    access_right = access_data.get("status", "")
+
+    # Files (InvenioRDM v1 format)
+    files = []
+    archive_contents = {}  # Store archive preview contents
+    files_data = record.get("files", {})
+
+    if isinstance(files_data, dict):
+        file_entries = files_data.get("entries", {})
+        for filename, file_info in file_entries.items():
+            file_dict = {
+                "filename": file_info.get("key", filename),
+                "size_bytes": file_info.get("size", 0),
+                "checksum": file_info.get("checksum", ""),
+                "mimetype": file_info.get("mimetype", ""),
+                "download_url": record.get("links", {}).get("self", "")
+                + f"/files/{filename}/content",
+            }
+            files.append(file_dict)
+
+            # Check if file is an archive - if so, try to get preview
+            archived_extensions = [
+                ".zip",
+                ".tar.gz",
+                ".tgz",
+                ".tar",
+                ".rar",
+                ".7z",
+                ".gz",
+            ]
+            fn_lower = filename.lower()
+            if any(fn_lower.endswith(ext) for ext in archived_extensions):
+                # Try to get archive contents preview
+                preview_files = get_archive_preview_files(
+                    dataset_id, filename, access_token
+                )
+                if preview_files:
+                    archive_contents[filename] = preview_files
+
+    total_size_bytes = (
+        files_data.get("total_bytes", 0) if isinstance(files_data, dict) else 0
+    )
+    total_size_mb = round(total_size_bytes / (1024 * 1024), 2)
+
+    # Build dataset dictionary
+    dataset = {
+        "dataset_id": dataset_id,
+        "doi": doi,
+        "concept_doi": concept_doi,
+        "title": title,
+        "description": description,
+        "publication_date": publication_date,
+        "created": record.get("created", ""),
+        "modified": record.get("updated", ""),
+        "creators": creators,
+        "keywords": keywords,
+        "resource_type": resource_type_str,
+        "resource_title": resource_title,
+        "license": license_id,
+        "access_right": access_right,
+        "url": record.get("links", {}).get("self_html", ""),
+        "doi_url": f"https://doi.org/{doi}" if doi else "",
+        "files": files,
+        "file_count": len(files),
+        "total_size_mb": total_size_mb,
+        "archive_contents": archive_contents,  # NEW: Archive preview contents
+        "has_archive_preview": len(archive_contents) > 0,  # NEW: Flag for filtering
+        "source": "zenodo",
+    }
+
+    # Enrich with OAI-PMH if requested
+    if enrich_oaipmh and dataset_id:
+        enriched = enrich_with_oaipmh(dataset_id, access_token)
+        if enriched:
+            dataset.update(
+                {
+                    "enriched_subjects": enriched.get("subjects", []),
+                    "contributors": enriched.get("contributors", []),
+                    "related_identifiers": enriched.get("related_identifiers", []),
+                    "funding": enriched.get("funding", []),
+                    "oaipmh_enriched": True,
+                }
+            )
+
+    return dataset
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Fetch EEG BIDS datasets from Zenodo with optional OAI-PMH enrichment.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+AGGRESSIVE SEARCH STRATEGY - Maximum Recall:
+
+Query Performance (Tested with type=dataset filter):
+  Extended terminology    → 1,570 results ✓ DEFAULT (Maximum recall)
+  All modalities OR BIDS  → 1,391 results
+  Simple "EEG BIDS"       →   904 results (old conservative approach)
+
+The aggressive query captures ALL neurophysiology datasets by using:
+  • Full terminology (electroencephalography, magnetoencephalography, etc.)
+  • All modalities (EEG, MEG, iEEG, ECoG, SEEG, EMG)
+  • BIDS variants (BIDS, "Brain Imaging Data Structure")
+  • Boolean OR logic for maximum coverage
+
+Expected Results Distribution:
+  - True BIDS datasets: ~60-70% (need validation)
+  - Neurophysiology non-BIDS: ~20-30% (can be converted)
+  - False positives: ~5-10% (will filter out)
+
+Filtering Strategy (Post-Processing):
+  1. Check for BIDS-compliant file structure
+  2. Validate metadata keywords (BIDS, EEG, MEG, etc.)
+  3. Verify file extensions (.eeg, .vhdr, .fif, .set, .bdf, .edf)
+  4. Analyze dataset descriptions for BIDS mentions
+  5. Flag potential BIDS vs non-BIDS datasets
+
+OAI-PMH Enrichment (Optional):
+  Adds: controlled vocabularies, contributors, related identifiers, funding
+  Trade-off: ~2x slower (requires extra API call per dataset)
+  Recommended: Use --enrich-oaipmh for complete metadata extraction
+
+Examples:
+  # Maximum recall (default - gets ~1,570 datasets)
+  python scripts/ingestions/1_fetch_zenodo_enhanced.py
+
+  # With OAI-PMH enrichment (slower but complete)
+  python scripts/ingestions/1_fetch_zenodo_enhanced.py --enrich-oaipmh
+
+  # Conservative search (old approach)
+  python scripts/ingestions/1_fetch_zenodo_enhanced.py --query "EEG BIDS" --size 1000
+
+  # Custom modality search
+  python scripts/ingestions/1_fetch_zenodo_enhanced.py --query "MEG OR magnetoencephalography"
+""",
+    )
+
+    parser.add_argument(
+        "--query",
+        type=str,
+        default='EEG OR electroencephalography OR MEG OR magnetoencephalography OR iEEG OR intracranial OR ECoG OR electrocorticography OR SEEG OR EMG OR electromyography OR BIDS OR "Brain Imaging Data Structure"',
+        help="Search query (default: AGGRESSIVE - ALL neurophysiology OR BIDS for maximum recall)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets_aggressive.json"),
+        help="Output JSON file path (default: aggressive unfiltered results).",
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=2000,
+        help="Maximum number of results to fetch (default: 2000 to capture all ~1,570 aggressive results).",
+    )
+    parser.add_argument(
+        "--resource-type",
+        type=str,
+        default="dataset",
+        help='Filter by resource type (default: "dataset").',
+    )
+    parser.add_argument(
+        "--access-status",
+        type=str,
+        default="open",
+        help='Filter by access status (default: "open").',
+    )
+    parser.add_argument(
+        "--sort",
+        type=str,
+        default="bestmatch",
+        help='Sort order (default: "bestmatch").',
+    )
+    parser.add_argument(
+        "--access-token",
+        type=str,
+        default="v3dkycQdOlyc0gXXkeeroSIkpSyAgaTyzpsIJLw8lhQsoEw089MFICKqnxWz",
+        help="Zenodo Personal Access Token (default: temp test token).",
+    )
+    parser.add_argument(
+        "--enrich-oaipmh",
+        action="store_true",
+        help="Enrich datasets with OAI-PMH metadata (slower but more complete).",
+    )
+    parser.add_argument(
+        "--oaipmh-sample",
+        type=int,
+        default=0,
+        help="Only enrich first N datasets with OAI-PMH (0 = all if --enrich-oaipmh).",
+    )
+
+    args = parser.parse_args()
+
+    # Search via REST API
+    records = search_zenodo_rest_api(
+        query=args.query,
+        size=args.size,
+        resource_type=args.resource_type or "",
+        access_status=args.access_status or "",
+        sort=args.sort,
+        access_token=args.access_token,
+    )
+
+    if not records:
+        print("No records found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Extract dataset information
+    print("\nExtracting dataset information...")
+    if args.enrich_oaipmh:
+        oaipmh_limit = args.oaipmh_sample if args.oaipmh_sample > 0 else len(records)
+        print(f"OAI-PMH enrichment enabled for first {oaipmh_limit} datasets")
+        print("(This will be slower but provides richer metadata)\n")
+
+    datasets = []
+    for i, record in enumerate(records, 1):
+        try:
+            enrich = args.enrich_oaipmh and (
+                args.oaipmh_sample == 0 or i <= args.oaipmh_sample
+            )
+            dataset = extract_dataset_info(
+                record, enrich_oaipmh=enrich, access_token=args.access_token
+            )
+            datasets.append(dataset)
+
+            if enrich and i % 10 == 0:
+                print(f"  Enriched {i}/{len(records)} datasets...")
+        except Exception as e:
+            print(
+                f"Warning: Error extracting record {record.get('id')}: {e}",
+                file=sys.stderr,
+            )
+            continue
+
+    # Save to JSON
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    with args.output.open("w") as fh:
+        json.dump(datasets, fh, indent=2)
+
+    # Print statistics
+    print(f"\n{'=' * 70}")
+    print(f"Successfully saved {len(datasets)} datasets to {args.output}")
+    print(f"{'=' * 70}")
+
+    # Modality analysis
+    modalities = {
+        "EEG": sum(
+            1
+            for d in datasets
+            if "EEG" in (d.get("title", "") + " ".join(d.get("keywords", []))).upper()
+            and "IEEG"
+            not in (d.get("title", "") + " ".join(d.get("keywords", []))).upper()
+        ),
+        "MEG": sum(
+            1
+            for d in datasets
+            if "MEG" in (d.get("title", "") + " ".join(d.get("keywords", []))).upper()
+        ),
+        "iEEG/ECoG": sum(
+            1
+            for d in datasets
+            if any(
+                x in (d.get("title", "") + " ".join(d.get("keywords", []))).upper()
+                for x in ["IEEG", "ECOG", "INTRACRANIAL"]
+            )
+        ),
+        "EMG": sum(
+            1
+            for d in datasets
+            if "EMG" in (d.get("title", "") + " ".join(d.get("keywords", []))).upper()
+        ),
+    }
+
+    print("\nModality Detection:")
+    for mod, count in modalities.items():
+        print(f"  {mod:15s}: {count:4d} datasets ({count / len(datasets) * 100:.1f}%)")
+
+    # Metadata coverage
+    print("\nMetadata Coverage:")
+    print(
+        f"  With DOI:       {sum(1 for d in datasets if d.get('doi')):4d}/{len(datasets)} ({sum(1 for d in datasets if d.get('doi')) / len(datasets) * 100:.1f}%)"
+    )
+    print(
+        f"  With license:   {sum(1 for d in datasets if d.get('license')):4d}/{len(datasets)} ({sum(1 for d in datasets if d.get('license')) / len(datasets) * 100:.1f}%)"
+    )
+    print(
+        f"  With keywords:  {sum(1 for d in datasets if d.get('keywords')):4d}/{len(datasets)} ({sum(1 for d in datasets if d.get('keywords')) / len(datasets) * 100:.1f}%)"
+    )
+    print(
+        f"  With files:     {sum(1 for d in datasets if d.get('file_count', 0) > 0):4d}/{len(datasets)} ({sum(1 for d in datasets if d.get('file_count', 0) > 0) / len(datasets) * 100:.1f}%)"
+    )
+
+    if args.enrich_oaipmh:
+        enriched_count = sum(1 for d in datasets if d.get("oaipmh_enriched"))
+        print(
+            f"  OAI-PMH enriched: {enriched_count:4d}/{len(datasets)} ({enriched_count / len(datasets) * 100:.1f}%)"
+        )
+
+    # Total size
+    total_size_mb = sum(d.get("total_size_mb", 0) for d in datasets)
+    print(f"\nTotal Size: {total_size_mb / 1024:.2f} GB ({total_size_mb:.2f} MB)")
+
+    print(f"{'=' * 70}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/2_filter_zenodo_bids.py b/scripts/ingestions/2_filter_zenodo_bids.py
new file mode 100644
index 00000000..3ad58180
--- /dev/null
+++ b/scripts/ingestions/2_filter_zenodo_bids.py
@@ -0,0 +1,645 @@
+"""Filter Zenodo aggressive results to identify genuine BIDS datasets.
+
+CRITICAL INSIGHT: A dataset can be BIDS-compliant without mentioning "BIDS"!
+
+Many neurophysiology datasets follow BIDS structure perfectly but never use
+the term "BIDS" in their title, description, or keywords. Therefore, we must
+prioritize FILE STRUCTURE over keyword mentions when classifying datasets.
+
+NEW FEATURE: Archive Preview Detection
+-------------------------------------
+Zenodo provides a preview endpoint that shows archive contents WITHOUT downloading!
+We now inspect .zip files to detect BIDS structure inside archived datasets.
+
+This dramatically improves BIDS detection accuracy:
+- OLD approach: 89% rejection (couldn't see inside archives)
+- NEW approach: Expected 30-50% BIDS detection (can inspect archive contents)
+
+Example: https://zenodo.org/records/13790279/preview/database.zip
+Returns HTML with complete file tree showing:
+  - dataset_description.json
+  - participants.tsv
+  - sub-01/sub-01_eeg.json
+  - sub-01/sub-01_channels.tsv
+
+This script post-processes the aggressive Zenodo fetch results to separate:
+1. Genuine BIDS datasets (detected by structure OR keywords)
+2. Neurophysiology datasets (convertible to BIDS)
+3. False positives (not relevant)
+
+Filtering criteria based on BIDS specification analysis:
+
+PRIMARY INDICATORS (File Structure - Most Reliable):
+- BIDS naming patterns: sub-XX, ses-XX, task-XX, run-XX
+- Core files: dataset_description.json, participants.tsv
+- Sidecars: *_eeg.json, *_channels.tsv, *_events.tsv
+- Directory structure: code/, derivatives/, sourcedata/
+- NOW: Archive preview contents inspection!
+
+SECONDARY INDICATORS (Keywords - Less Reliable):
+- Explicit mentions: "BIDS", "Brain Imaging Data Structure"
+- Related terms: "BIDS-compliant", "BIDS format", "OpenNeuro"
+
+CLASSIFICATION PRIORITY:
+1. Strong file structure (≥3 BIDS indicators) → BIDS strict
+2. Moderate structure (≥2 indicators) → BIDS moderate
+3. Weak structure (≥1 indicator) + neuro files → BIDS probable
+4. No structure + neuro files → Neurophysiology (convertible)
+5. No indicators → Rejected
+
+Input: consolidated/zenodo_datasets_aggressive.json (~1,570 records)
+Outputs:
+  - consolidated/zenodo_datasets_bids.json (validated BIDS)
+  - consolidated/zenodo_datasets_neurophysiology.json (convertible)
+  - consolidated/zenodo_datasets_rejected.json (false positives)
+"""
+
+import argparse
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+# BIDS-specific indicators
+BIDS_KEYWORDS = {
+    "strict": [
+        "bids",
+        "brain imaging data structure",
+        "bids format",
+        "bids-compliant",
+        "bids specification",
+        "bids validator",
+        "openneuro",
+    ],
+    "moderate": [
+        "bids-like",
+        "bids standard",
+        "neuroimaging data structure",
+        "standardized format",
+    ],
+}
+
+BIDS_FILE_INDICATORS = [
+    # Core BIDS files (definitive indicators)
+    "dataset_description.json",
+    "participants.tsv",
+    "participants.json",
+    # Modality-specific sidecars (strong indicators)
+    "_eeg.json",
+    "_meg.json",
+    "_ieeg.json",
+    "_channels.tsv",
+    "_electrodes.tsv",
+    "_events.tsv",
+    "_coordsystem.json",
+    # Task and run indicators
+    "_task-",
+    "_run-",
+    "_ses-",
+    "_sub-",
+    # BIDS metadata files
+    "README",
+    "CHANGES",
+    "code/",
+    "derivatives/",
+    "sourcedata/",
+    # Specific BIDS naming patterns
+    "_bold.nii",
+    "_T1w.nii",
+    "_dwi.nii",
+    "_eeg.eeg",
+    "_eeg.vhdr",
+    "_eeg.set",
+    "_meg.fif",
+    "_meg.ds",
+]
+
+NEUROPHYSIOLOGY_EXTENSIONS = {
+    "eeg": [".eeg", ".vhdr", ".vmrk", ".edf", ".bdf", ".set", ".fdt", ".cnt", ".mff"],
+    "meg": [".fif", ".sqd", ".con", ".raw", ".ave", ".mrk", ".elp", ".hsp", ".ds"],
+    "ieeg": [".edf", ".eeg", ".vhdr", ".trc", ".bin"],
+    "emg": [".edf", ".cnt", ".poly5"],
+}
+
+NEUROPHYSIOLOGY_TERMS = [
+    "eeg",
+    "electroencephalography",
+    "electroencephalogram",
+    "meg",
+    "magnetoencephalography",
+    "magnetoencephalogram",
+    "ieeg",
+    "intracranial",
+    "ecog",
+    "electrocorticography",
+    "seeg",
+    "emg",
+    "electromyography",
+    "electromyogram",
+    "evoked potential",
+    "event-related potential",
+    "erp",
+    "resting state",
+    "task-based",
+    "paradigm",
+]
+
+
+def check_bids_keywords(dataset: dict[str, Any]) -> tuple[bool, str]:
+    """Check if dataset mentions BIDS in title, description, or keywords.
+
+    Returns:
+        (is_bids, confidence_level) where confidence is 'strict' or 'moderate'
+
+    """
+    text = " ".join(
+        [
+            dataset.get("title", ""),
+            dataset.get("description", ""),
+            " ".join(dataset.get("keywords", [])),
+        ]
+    ).lower()
+
+    # Check strict BIDS keywords
+    for keyword in BIDS_KEYWORDS["strict"]:
+        if keyword in text:
+            return True, "strict"
+
+    # Check moderate BIDS keywords
+    for keyword in BIDS_KEYWORDS["moderate"]:
+        if keyword in text:
+            return True, "moderate"
+
+    return False, "none"
+
+
+def check_description_for_bids_evidence(dataset: dict[str, Any]) -> int:
+    """Analyze description for BIDS structure evidence.
+
+    Many archived datasets describe their BIDS structure in text without
+    mentioning "BIDS" explicitly. Look for telltale phrases.
+
+    Returns:
+        Evidence score (0-3): 0=none, 1=weak, 2=moderate, 3=strong
+
+    """
+    description = dataset.get("description", "").lower()
+    title = dataset.get("title", "").lower()
+    combined = description + " " + title
+
+    score = 0
+
+    # Strong evidence phrases
+    strong_evidence = [
+        "participants.tsv",
+        "dataset_description.json",
+        "subject-level",
+        "session-level",
+        "organized according to",
+        "follows the structure",
+        "standardized format",
+        "sub-<participant_label>",
+        "sub-XX",
+        "task-<task_name>",
+    ]
+
+    for phrase in strong_evidence:
+        if phrase in combined:
+            score = max(score, 3)
+            break
+
+    # Moderate evidence phrases
+    moderate_evidence = [
+        "organized dataset",
+        "structured data",
+        "standardized data",
+        "metadata files",
+        "sidecar files",
+        "json metadata",
+        "channel locations",
+        "event markers",
+    ]
+
+    if score < 3:
+        for phrase in moderate_evidence:
+            if phrase in combined:
+                score = max(score, 2)
+                break
+
+    # Weak evidence
+    weak_evidence = [
+        "well-organized",
+        "structured",
+        "metadata",
+        "annotations",
+        "documented",
+    ]
+
+    if score < 2:
+        for phrase in weak_evidence:
+            if phrase in combined:
+                score = max(score, 1)
+                break
+
+    return score
+
+
+def check_bids_files(dataset: dict[str, Any]) -> tuple[int, list[str]]:
+    """Check how many BIDS-specific files are present.
+
+    IMPORTANT: Checks both exact file matches AND BIDS naming patterns.
+    Many BIDS datasets don't have "bids" in metadata but follow BIDS structure.
+
+    NEW FEATURE: If dataset has archive preview contents, we can inspect
+    the internal structure without downloading! This dramatically improves
+    BIDS detection accuracy for archived datasets.
+
+    Returns:
+        (count, matched_files)
+
+    """
+    files = dataset.get("files", [])
+    filenames = [f.get("filename", "").lower() for f in files]
+
+    matched = []
+
+    # NEW: Check archive contents if available
+    archive_contents = dataset.get("archive_contents", {})
+    all_filenames = filenames.copy()
+
+    if archive_contents:
+        # Add all files found inside archives
+        for archive_name, contents in archive_contents.items():
+            all_filenames.extend([f.lower() for f in contents])
+
+    # Check if dataset is mostly archived (heuristic approach needed)
+    archived_extensions = [".zip", ".tar.gz", ".tgz", ".tar", ".rar", ".7z", ".gz"]
+    is_archived = any(fn.endswith(tuple(archived_extensions)) for fn in filenames)
+    has_preview = len(archive_contents) > 0
+
+    # Check for specific BIDS files (now including archive contents!)
+    for indicator in BIDS_FILE_INDICATORS:
+        if any(indicator.lower() in fn for fn in all_filenames):
+            matched.append(indicator)
+
+    # Check for BIDS naming patterns (sub-XX/ses-XX/task-XX)
+    bids_patterns = [
+        r"sub-[a-zA-Z0-9]+",  # Subject ID
+        r"ses-[a-zA-Z0-9]+",  # Session ID
+        r"task-[a-zA-Z0-9]+",  # Task name
+        r"run-[0-9]+",  # Run number
+        r"acq-[a-zA-Z0-9]+",  # Acquisition
+    ]
+
+    pattern_matches = set()
+    for fn in all_filenames:
+        for pattern in bids_patterns:
+            if re.search(pattern, fn):
+                pattern_matches.add(f"pattern:{pattern}")
+
+    matched.extend(list(pattern_matches))
+
+    # If archived WITHOUT preview, check archive name for BIDS indicators
+    if is_archived and not has_preview:
+        for fn in filenames:
+            # Check if archive name suggests BIDS content
+            if any(
+                term in fn for term in ["bids", "openneuro", "ds0", "sub-", "dataset"]
+            ):
+                matched.append("archive:bids_indicator")
+                break
+
+    # If we have archive preview, note this as additional evidence
+    if has_preview and any(
+        "sub-" in fn or "dataset_description" in fn for fn in all_filenames
+    ):
+        matched.append("archive_preview:confirmed")
+
+    # Deduplicate
+    matched = list(set(matched))
+
+    return len(matched), matched
+
+
+def check_neurophysiology_files(dataset: dict[str, Any]) -> tuple[str | None, int]:
+    """Check if dataset contains neurophysiology data files.
+
+    NEW: Also checks archive preview contents if available.
+
+    Returns:
+        (modality, extension_count) where modality is 'eeg', 'meg', 'ieeg', 'emg', or None
+
+    """
+    files = dataset.get("files", [])
+    filenames = [f.get("filename", "").lower() for f in files]
+
+    # NEW: Add archive contents if available
+    archive_contents = dataset.get("archive_contents", {})
+    if archive_contents:
+        for archive_name, contents in archive_contents.items():
+            filenames.extend([f.lower() for f in contents])
+
+    modality_counts = {}
+    for modality, extensions in NEUROPHYSIOLOGY_EXTENSIONS.items():
+        count = sum(
+            1 for fn in filenames if any(fn.endswith(ext) for ext in extensions)
+        )
+        if count > 0:
+            modality_counts[modality] = count
+
+    if not modality_counts:
+        return None, 0
+
+    # Return modality with most files
+    primary_modality = max(modality_counts, key=modality_counts.get)
+    return primary_modality, modality_counts[primary_modality]
+
+
+def check_neurophysiology_terms(dataset: dict[str, Any]) -> bool:
+    """Check if dataset mentions neurophysiology-related terms."""
+    text = " ".join(
+        [
+            dataset.get("title", ""),
+            dataset.get("description", ""),
+            " ".join(dataset.get("keywords", [])),
+        ]
+    ).lower()
+
+    return any(term in text for term in NEUROPHYSIOLOGY_TERMS)
+
+
+def classify_dataset(dataset: dict[str, Any]) -> tuple[str, dict[str, Any]]:
+    """Classify dataset as 'bids', 'neurophysiology', or 'rejected'.
+
+    CRITICAL: A dataset can be BIDS-compliant without mentioning "BIDS"!
+    We prioritize file structure indicators over keyword mentions.
+
+    NEW: For archived datasets with preview contents, we can now accurately
+    detect BIDS structure by inspecting the archive contents directly!
+
+    For archived datasets without preview, we use description analysis to find BIDS evidence.
+
+    Returns:
+        (category, metadata) where metadata contains classification details
+
+    """
+    # Check BIDS indicators
+    has_bids_keywords, keyword_confidence = check_bids_keywords(dataset)
+    bids_file_count, bids_files = check_bids_files(dataset)
+    description_score = check_description_for_bids_evidence(dataset)
+
+    # Check neurophysiology indicators
+    modality, neuro_file_count = check_neurophysiology_files(dataset)
+    has_neuro_terms = check_neurophysiology_terms(dataset)
+
+    # NEW: Check if we have archive preview
+    has_archive_preview = dataset.get("has_archive_preview", False)
+    archive_file_count = sum(
+        len(contents) for contents in dataset.get("archive_contents", {}).values()
+    )
+
+    metadata = {
+        "has_bids_keywords": has_bids_keywords,
+        "bids_keyword_confidence": keyword_confidence,
+        "bids_file_count": bids_file_count,
+        "bids_files_found": bids_files,
+        "description_bids_score": description_score,
+        "primary_modality": modality,
+        "neurophysiology_file_count": neuro_file_count,
+        "has_neurophysiology_terms": has_neuro_terms,
+        "has_archive_preview": has_archive_preview,  # NEW
+        "archive_file_count": archive_file_count,  # NEW
+    }
+
+    # Classification logic - MULTIPLE PATHWAYS TO BIDS CLASSIFICATION
+
+    # PATH 1: STRICT BIDS - Strong file structure evidence
+    # Many BIDS datasets don't mention "BIDS" but have perfect structure
+    # NOW WITH ARCHIVE PREVIEW: This is much more accurate!
+    if bids_file_count >= 3:
+        return "bids_strict", metadata
+
+    # PATH 2: STRICT BIDS - Explicit BIDS keywords + some evidence
+    if (
+        has_bids_keywords
+        and keyword_confidence == "strict"
+        and (bids_file_count >= 1 or description_score >= 2)
+    ):
+        return "bids_strict", metadata
+
+    # PATH 3: MODERATE BIDS - Good file evidence OR strong description evidence
+    if bids_file_count >= 2 or (description_score >= 3 and has_neuro_terms):
+        return "bids_moderate", metadata
+
+    # PATH 4: MODERATE BIDS - BIDS keywords + moderate evidence
+    if has_bids_keywords and (bids_file_count >= 1 or description_score >= 2):
+        return "bids_moderate", metadata
+
+    # PATH 5: PROBABLE BIDS - Weak BIDS evidence + neurophysiology data
+    if (bids_file_count >= 1 or description_score >= 2) and modality:
+        return "bids_probable", metadata
+
+    # NEUROPHYSIOLOGY: Has neuro files and terms but no BIDS structure
+    if (
+        modality
+        and neuro_file_count > 0
+        and has_neuro_terms
+        and bids_file_count == 0
+        and description_score == 0
+    ):
+        return "neurophysiology", metadata
+
+    # REJECTED: No clear indicators
+    return "rejected", metadata
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Filter aggressive Zenodo results to identify genuine BIDS datasets.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--input",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets_aggressive.json"),
+        help="Input JSON file from aggressive fetch.",
+    )
+    parser.add_argument(
+        "--output-bids",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets_bids.json"),
+        help="Output file for validated BIDS datasets.",
+    )
+    parser.add_argument(
+        "--output-neurophysiology",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets_neurophysiology.json"),
+        help="Output file for neurophysiology datasets (convertible to BIDS).",
+    )
+    parser.add_argument(
+        "--output-rejected",
+        type=Path,
+        default=Path("consolidated/zenodo_datasets_rejected.json"),
+        help="Output file for rejected datasets (false positives).",
+    )
+    parser.add_argument(
+        "--output-stats",
+        type=Path,
+        default=Path("consolidated/zenodo_filtering_stats.json"),
+        help="Output file for filtering statistics.",
+    )
+
+    args = parser.parse_args()
+
+    # Load aggressive results
+    if not args.input.exists():
+        print(f"Error: Input file {args.input} not found!")
+        print("Run 1_fetch_zenodo_enhanced.py first.")
+        return
+
+    with args.input.open() as f:
+        datasets = json.load(f)
+
+    print(f"{'=' * 70}")
+    print(f"Filtering {len(datasets)} aggressive Zenodo results")
+    print(f"{'=' * 70}\n")
+
+    # Classify datasets
+    categories = {
+        "bids_strict": [],
+        "bids_moderate": [],
+        "bids_probable": [],
+        "neurophysiology": [],
+        "rejected": [],
+    }
+
+    for i, dataset in enumerate(datasets, 1):
+        category, metadata = classify_dataset(dataset)
+
+        # Add classification metadata to dataset
+        dataset["classification"] = {"category": category, **metadata}
+
+        categories[category].append(dataset)
+
+        if i % 100 == 0:
+            print(f"  Processed {i}/{len(datasets)} datasets...")
+
+    # Combine BIDS categories
+    bids_datasets = (
+        categories["bids_strict"]
+        + categories["bids_moderate"]
+        + categories["bids_probable"]
+    )
+
+    # Save results
+    args.output_bids.parent.mkdir(parents=True, exist_ok=True)
+
+    with args.output_bids.open("w") as f:
+        json.dump(bids_datasets, f, indent=2)
+
+    with args.output_neurophysiology.open("w") as f:
+        json.dump(categories["neurophysiology"], f, indent=2)
+
+    with args.output_rejected.open("w") as f:
+        json.dump(categories["rejected"], f, indent=2)
+
+    # Calculate statistics
+    archive_preview_count = sum(
+        1 for d in datasets if d.get("has_archive_preview", False)
+    )
+    archive_preview_in_bids = sum(
+        1 for d in bids_datasets if d.get("has_archive_preview", False)
+    )
+
+    stats = {
+        "total_input": len(datasets),
+        "bids_total": len(bids_datasets),
+        "bids_strict": len(categories["bids_strict"]),
+        "bids_moderate": len(categories["bids_moderate"]),
+        "bids_probable": len(categories["bids_probable"]),
+        "neurophysiology": len(categories["neurophysiology"]),
+        "rejected": len(categories["rejected"]),
+        "bids_percentage": round(len(bids_datasets) / len(datasets) * 100, 1),
+        "neurophysiology_percentage": round(
+            len(categories["neurophysiology"]) / len(datasets) * 100, 1
+        ),
+        "rejected_percentage": round(
+            len(categories["rejected"]) / len(datasets) * 100, 1
+        ),
+        "datasets_with_archive_preview": archive_preview_count,
+        "archive_preview_percentage": round(
+            archive_preview_count / len(datasets) * 100, 1
+        )
+        if datasets
+        else 0,
+        "bids_with_archive_preview": archive_preview_in_bids,
+        "bids_archive_preview_percentage": round(
+            archive_preview_in_bids / len(bids_datasets) * 100, 1
+        )
+        if bids_datasets
+        else 0,
+    }
+
+    with args.output_stats.open("w") as f:
+        json.dump(stats, f, indent=2)
+
+    # Print summary
+    print(f"\n{'=' * 70}")
+    print("FILTERING RESULTS")
+    print(f"{'=' * 70}")
+    print(f"Total input:           {stats['total_input']:5d}")
+    print("")
+    print("Archive Preview Stats:")
+    print(
+        f"  Datasets with preview: {stats['datasets_with_archive_preview']:4d} ({stats['archive_preview_percentage']:5.1f}%)"
+    )
+    print(
+        f"  BIDS with preview:     {stats['bids_with_archive_preview']:4d} ({stats['bids_archive_preview_percentage']:5.1f}%)"
+    )
+    print("")
+    print(
+        f"BIDS Datasets:         {stats['bids_total']:5d} ({stats['bids_percentage']:5.1f}%)"
+    )
+    print(f"  - Strict (confident): {stats['bids_strict']:4d}")
+    print(f"  - Moderate:           {stats['bids_moderate']:4d}")
+    print(f"  - Probable:           {stats['bids_probable']:4d}")
+    print("")
+    print(
+        f"Neurophysiology:       {stats['neurophysiology']:5d} ({stats['neurophysiology_percentage']:5.1f}%)"
+    )
+    print("  (Convertible to BIDS)")
+    print("")
+    print(
+        f"Rejected:              {stats['rejected']:5d} ({stats['rejected_percentage']:5.1f}%)"
+    )
+    print("  (False positives)")
+    print(f"{'=' * 70}")
+    print("")
+    print("Output files:")
+    print(f"  BIDS:           {args.output_bids}")
+    print(f"  Neurophysiology: {args.output_neurophysiology}")
+    print(f"  Rejected:       {args.output_rejected}")
+    print(f"  Statistics:     {args.output_stats}")
+    print(f"{'=' * 70}")
+
+    # Sample analysis
+    if bids_datasets:
+        print("\nSample BIDS dataset (strict confidence):")
+        strict_samples = [d for d in categories["bids_strict"]]
+        if strict_samples:
+            sample = strict_samples[0]
+            print(f"  Title: {sample.get('title', 'N/A')}")
+            print(f"  DOI: {sample.get('doi', 'N/A')}")
+            print(f"  Keywords: {', '.join(sample.get('keywords', [])[:5])}")
+            print(f"  BIDS files found: {sample['classification']['bids_files_found']}")
+            print(f"  Modality: {sample['classification']['primary_modality']}")
+            if sample.get("has_archive_preview"):
+                archive_count = sample["classification"].get("archive_file_count", 0)
+                print(f"  Archive preview: ✓ ({archive_count} files inspected)")
+            else:
+                print("  Archive preview: ✗ (classified by keywords/description)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/3_clone_openneuro_datasets.py b/scripts/ingestions/3_clone_openneuro_datasets.py
new file mode 100644
index 00000000..5f90ebb9
--- /dev/null
+++ b/scripts/ingestions/3_clone_openneuro_datasets.py
@@ -0,0 +1,281 @@
+"""Clone OpenNeuro, NEMAR, and GIN datasets with timeout and error handling."""
+
+import argparse
+import json
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+
+def detect_source_type(dataset: dict) -> str:
+    """Detect dataset source based on fields.
+
+    Args:
+        dataset: Dataset dictionary
+
+    Returns:
+        'openneuro', 'nemar', 'gin', or 'unknown'
+
+    """
+    # Check for explicit source field
+    if "source" in dataset:
+        return dataset["source"]
+
+    # GIN datasets have clone_url with gin.g-node.org
+    if "clone_url" in dataset and "gin.g-node.org" in dataset["clone_url"]:
+        return "gin"
+
+    # NEMAR datasets have clone_url with github.com/nemarDatasets
+    if "clone_url" in dataset and "nemarDatasets" in dataset["clone_url"]:
+        return "nemar"
+
+    # Generic dataset with clone_url (could be NEMAR or GIN)
+    if "clone_url" in dataset or "ssh_url" in dataset:
+        # Default to nemar for backward compatibility
+        return "nemar"
+
+    # OpenNeuro has modality field
+    if "modality" in dataset:
+        return "openneuro"
+
+    return "unknown"
+
+
+def get_clone_url(dataset: dict, source_type: str) -> str:
+    """Get the appropriate clone URL for the dataset.
+
+    Args:
+        dataset: Dataset dictionary
+        source_type: 'openneuro', 'nemar', 'gin', or 'unknown'
+
+    Returns:
+        Git clone URL
+
+    """
+    if source_type in ("nemar", "gin"):
+        # NEMAR and GIN datasets have clone_url in the JSON
+        return dataset.get("clone_url", dataset.get("ssh_url"))
+    elif source_type == "openneuro":
+        # OpenNeuro datasets need URL construction
+        dataset_id = dataset["dataset_id"]
+        return f"https://github.com/OpenNeuroDatasets/{dataset_id}"
+    else:
+        raise ValueError(f"Unknown source type: {source_type}")
+
+
+def clone_dataset(dataset: dict, output_dir: Path, timeout: int) -> dict:
+    """Clone a single dataset with timeout.
+
+    Args:
+        dataset: Dataset dictionary with fields depending on source
+        output_dir: Directory to clone into
+        timeout: Timeout in seconds
+
+    Returns:
+        Result dictionary with status
+
+    """
+    dataset_id = dataset["dataset_id"]
+    source_type = detect_source_type(dataset)
+
+    try:
+        url = get_clone_url(dataset, source_type)
+    except (KeyError, ValueError) as e:
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "error": str(e),
+        }
+
+    clone_dir = output_dir / dataset_id
+
+    # Skip if already cloned
+    if clone_dir.exists():
+        return {
+            "status": "skip",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "reason": "already exists",
+        }
+
+    try:
+        # Run git clone with timeout
+        result = subprocess.run(
+            ["git", "clone", url, str(clone_dir)],
+            timeout=timeout,
+            capture_output=True,
+            text=True,
+        )
+
+        if result.returncode == 0:
+            return {
+                "status": "success",
+                "dataset_id": dataset_id,
+                "source": source_type,
+            }
+        else:
+            # Clean up partial clone on failure
+            if clone_dir.exists():
+                import shutil
+
+                shutil.rmtree(clone_dir, ignore_errors=True)
+            return {
+                "status": "failed",
+                "dataset_id": dataset_id,
+                "source": source_type,
+                "error": result.stderr[:200],
+            }
+
+    except subprocess.TimeoutExpired:
+        # Clean up partial clone on timeout
+        if clone_dir.exists():
+            import shutil
+
+            shutil.rmtree(clone_dir, ignore_errors=True)
+        return {
+            "status": "timeout",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "timeout_seconds": timeout,
+        }
+
+    except Exception as e:
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "error": str(e)[:200],
+        }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Clone OpenNeuro and NEMAR datasets from consolidated listing."
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("test_diggestion"),
+        help="Output directory for cloned repos (default: test_diggestion).",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=int,
+        default=300,
+        help="Timeout per clone in seconds (default: 300).",
+    )
+    parser.add_argument(
+        "--datasets-file",
+        type=Path,
+        default=Path("consolidated/to_digest_openneuro_datasets.json"),
+        help="JSON file with dataset listings (supports both OpenNeuro and NEMAR formats).",
+    )
+    parser.add_argument(
+        "--max-parallel",
+        type=int,
+        default=1,
+        help="Max parallel clones (currently single-threaded).",
+    )
+    args = parser.parse_args()
+
+    # Validate input file
+    if not args.datasets_file.exists():
+        print(f"Error: {args.datasets_file} not found", file=sys.stderr)
+        sys.exit(1)
+
+    # Load datasets
+    with args.datasets_file.open("r") as fh:
+        datasets = json.load(fh)
+
+    total = len(datasets)
+
+    print(f"Starting dataset cloning at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"Output directory: {args.output_dir}")
+    print(f"Timeout per clone: {args.timeout}s")
+    print(f"Total datasets: {total}")
+    print()
+
+    # Create output directory
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Clone datasets
+    results = {
+        "success": [],
+        "failed": [],
+        "timeout": [],
+        "skip": [],
+        "error": [],
+    }
+
+    # Track by source
+    source_counts = {"openneuro": 0, "nemar": 0, "gin": 0, "unknown": 0}
+
+    for idx, dataset in enumerate(datasets, start=1):
+        dataset_id = dataset["dataset_id"]
+        source_type = detect_source_type(dataset)
+        source_counts[source_type] += 1
+
+        print(
+            f"[{idx}/{total}] Cloning {dataset_id} ({source_type})...",
+            end=" ",
+            flush=True,
+        )
+
+        result = clone_dataset(dataset, args.output_dir, args.timeout)
+        status = result.pop("status")
+        results[status].append(result)
+
+        if status == "success":
+            print("✓")
+        elif status == "skip":
+            print("⊘ (already exists)")
+        elif status == "timeout":
+            print(f"⏱ (timeout after {args.timeout}s)")
+        elif status == "failed":
+            print(f"✗ (error: {result.get('error', 'unknown')[:50]}...)")
+        else:
+            print(f"? (error: {result.get('error', 'unknown')[:50]}...)")
+
+    # Summary
+    print()
+    print("=" * 60)
+    print(f"Cloning completed at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print()
+    print("By Source:")
+    print(f"  OpenNeuro: {source_counts['openneuro']}")
+    print(f"  NEMAR: {source_counts['nemar']}")
+    print(f"  GIN: {source_counts['gin']}")
+    if source_counts["unknown"]:
+        print(f"  Unknown: {source_counts['unknown']}")
+    print()
+    print("By Status:")
+    print(f"  Success: {len(results['success'])}")
+    print(f"  Failed: {len(results['failed'])}")
+    print(f"  Timeout: {len(results['timeout'])}")
+    print(f"  Skipped: {len(results['skip'])}")
+    print(f"  Errors: {len(results['error'])}")
+    print("=" * 60)
+
+    # Save results
+    results_file = args.output_dir / "clone_results.json"
+    with results_file.open("w") as fh:
+        json.dump(results, fh, indent=2)
+    print()
+    print(f"Results saved to: {results_file}")
+
+    # Save retry list (failed/timeout/error datasets with full metadata)
+    retry_datasets = []
+    for status_list in [results["failed"], results["timeout"], results["error"]]:
+        retry_datasets.extend(status_list)
+
+    if retry_datasets:
+        retry_file = args.output_dir / "retry.json"
+        with retry_file.open("w") as fh:
+            json.dump(retry_datasets, fh, indent=2)
+        print(f"Retry list saved to: {retry_file} ({len(retry_datasets)} datasets)")
+
+
+if __name__ == "__main__":
+    main()

From a989519faf2477cca6b07254db10b927756bb450 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 11 Nov 2025 14:09:34 +0000
Subject: [PATCH 19/34] chore: update OpenNeuro & NEMAR dataset listings and
 filtered to_digest files

---
 consolidated/nemardatasets_repos.json         | 26 +++++++++----------
 consolidated/openneuro_datasets.json          | 10 +++++--
 .../to_digest_nemardatasets_repos.json        | 26 +++++++++----------
 .../to_digest_openneuro_datasets.json         |  6 +++++
 4 files changed, 40 insertions(+), 28 deletions(-)

diff --git a/consolidated/nemardatasets_repos.json b/consolidated/nemardatasets_repos.json
index 9e647b59..7898d84d 100644
--- a/consolidated/nemardatasets_repos.json
+++ b/consolidated/nemardatasets_repos.json
@@ -24,9 +24,9 @@
     "description": "Meta Reality Labs Wrist Pose sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000107",
     "created": "2025-10-07T00:16:09Z",
-    "modified": "2025-10-24T17:21:54Z",
-    "pushed": "2025-10-24T17:21:49Z",
-    "size_kb": 249,
+    "modified": "2025-11-08T15:17:18Z",
+    "pushed": "2025-11-08T15:02:45Z",
+    "size_kb": 255,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -47,9 +47,9 @@
     "description": "Meta Reality Labs Discrete Gestures sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000105",
     "created": "2025-10-07T04:54:50Z",
-    "modified": "2025-10-24T17:19:54Z",
-    "pushed": "2025-10-24T17:19:49Z",
-    "size_kb": 1360,
+    "modified": "2025-11-08T15:17:38Z",
+    "pushed": "2025-11-08T15:04:10Z",
+    "size_kb": 1366,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -70,9 +70,9 @@
     "description": "Meta Reality Labs Handwriting sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000106",
     "created": "2025-10-07T05:24:52Z",
-    "modified": "2025-10-24T17:20:38Z",
-    "pushed": "2025-10-24T17:20:35Z",
-    "size_kb": 2749,
+    "modified": "2025-11-08T15:17:29Z",
+    "pushed": "2025-11-08T15:05:41Z",
+    "size_kb": 2756,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -93,9 +93,9 @@
     "description": "Meta Reality Labs EMG2Qwerty Dataset",
     "url": "https://github.com/nemarDatasets/nm000104",
     "created": "2025-10-07T06:01:58Z",
-    "modified": "2025-10-24T17:18:53Z",
-    "pushed": "2025-10-24T17:18:50Z",
-    "size_kb": 57829,
+    "modified": "2025-11-08T15:17:48Z",
+    "pushed": "2025-11-08T15:03:19Z",
+    "size_kb": 57834,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -116,7 +116,7 @@
     "description": "Healthy Brain Network EEG - Not for Commercial Use",
     "url": "https://github.com/nemarDatasets/nm000103",
     "created": "2025-10-09T01:33:21Z",
-    "modified": "2025-10-27T15:32:21Z",
+    "modified": "2025-11-08T15:18:20Z",
     "pushed": "2025-10-09T16:05:00Z",
     "size_kb": 4330,
     "default_branch": "main",
diff --git a/consolidated/openneuro_datasets.json b/consolidated/openneuro_datasets.json
index aeedaf8e..ad980e6a 100644
--- a/consolidated/openneuro_datasets.json
+++ b/consolidated/openneuro_datasets.json
@@ -93,7 +93,7 @@
     "dataset_id": "ds002718",
     "modality": "eeg",
     "created": "2020-04-21T20:15:53.277Z",
-    "modified": "2021-08-03T22:26:22.000Z"
+    "modified": "2025-11-08T05:07:40.000Z"
   },
   {
     "dataset_id": "ds002720",
@@ -1503,7 +1503,7 @@
     "dataset_id": "ds005397",
     "modality": "eeg",
     "created": "2024-08-01T12:27:38.523Z",
-    "modified": "2024-12-02T08:31:42.000Z"
+    "modified": "2025-11-10T12:51:56.000Z"
   },
   {
     "dataset_id": "ds005403",
@@ -2087,6 +2087,12 @@
     "created": "2025-10-29T19:00:26.774Z",
     "modified": "2025-10-29T23:48:25.000Z"
   },
+  {
+    "dataset_id": "ds006923",
+    "modality": "eeg",
+    "created": "2025-11-11T00:27:28.823Z",
+    "modified": "2025-11-11T04:35:58.000Z"
+  },
   {
     "dataset_id": "ds002799",
     "modality": "ieeg",
diff --git a/consolidated/to_digest_nemardatasets_repos.json b/consolidated/to_digest_nemardatasets_repos.json
index a0ef2206..207804aa 100644
--- a/consolidated/to_digest_nemardatasets_repos.json
+++ b/consolidated/to_digest_nemardatasets_repos.json
@@ -5,9 +5,9 @@
     "description": "Meta Reality Labs Wrist Pose sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000107",
     "created": "2025-10-07T00:16:09Z",
-    "modified": "2025-10-24T17:21:54Z",
-    "pushed": "2025-10-24T17:21:49Z",
-    "size_kb": 249,
+    "modified": "2025-11-08T15:17:18Z",
+    "pushed": "2025-11-08T15:02:45Z",
+    "size_kb": 255,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -28,9 +28,9 @@
     "description": "Meta Reality Labs Discrete Gestures sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000105",
     "created": "2025-10-07T04:54:50Z",
-    "modified": "2025-10-24T17:19:54Z",
-    "pushed": "2025-10-24T17:19:49Z",
-    "size_kb": 1360,
+    "modified": "2025-11-08T15:17:38Z",
+    "pushed": "2025-11-08T15:04:10Z",
+    "size_kb": 1366,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -51,9 +51,9 @@
     "description": "Meta Reality Labs Handwriting sEMG Dataset",
     "url": "https://github.com/nemarDatasets/nm000106",
     "created": "2025-10-07T05:24:52Z",
-    "modified": "2025-10-24T17:20:38Z",
-    "pushed": "2025-10-24T17:20:35Z",
-    "size_kb": 2749,
+    "modified": "2025-11-08T15:17:29Z",
+    "pushed": "2025-11-08T15:05:41Z",
+    "size_kb": 2756,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -74,9 +74,9 @@
     "description": "Meta Reality Labs EMG2Qwerty Dataset",
     "url": "https://github.com/nemarDatasets/nm000104",
     "created": "2025-10-07T06:01:58Z",
-    "modified": "2025-10-24T17:18:53Z",
-    "pushed": "2025-10-24T17:18:50Z",
-    "size_kb": 57829,
+    "modified": "2025-11-08T15:17:48Z",
+    "pushed": "2025-11-08T15:03:19Z",
+    "size_kb": 57834,
     "default_branch": "main",
     "topics": [
       "bids",
@@ -97,7 +97,7 @@
     "description": "Healthy Brain Network EEG - Not for Commercial Use",
     "url": "https://github.com/nemarDatasets/nm000103",
     "created": "2025-10-09T01:33:21Z",
-    "modified": "2025-10-27T15:32:21Z",
+    "modified": "2025-11-08T15:18:20Z",
     "pushed": "2025-10-09T16:05:00Z",
     "size_kb": 4330,
     "default_branch": "main",
diff --git a/consolidated/to_digest_openneuro_datasets.json b/consolidated/to_digest_openneuro_datasets.json
index 11614bf4..2c10fb12 100644
--- a/consolidated/to_digest_openneuro_datasets.json
+++ b/consolidated/to_digest_openneuro_datasets.json
@@ -575,6 +575,12 @@
     "created": "2025-10-29T19:00:26.774Z",
     "modified": "2025-10-29T23:48:25.000Z"
   },
+  {
+    "dataset_id": "ds006923",
+    "modality": "eeg",
+    "created": "2025-11-11T00:27:28.823Z",
+    "modified": "2025-11-11T04:35:58.000Z"
+  },
   {
     "dataset_id": "ds002799",
     "modality": "ieeg",

From a67911e9d9f9a5584174b3a998f30e5ac15318ce Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 16:09:04 +0100
Subject: [PATCH 20/34] renaming for the correct entities

---
 .../{1_fetch_github_organization.py => 1_fetch_nemardatasets.py}  | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename scripts/ingestions/{1_fetch_github_organization.py => 1_fetch_nemardatasets.py} (100%)

diff --git a/scripts/ingestions/1_fetch_github_organization.py b/scripts/ingestions/1_fetch_nemardatasets.py
similarity index 100%
rename from scripts/ingestions/1_fetch_github_organization.py
rename to scripts/ingestions/1_fetch_nemardatasets.py

From e56da2cdb20e6b5b845786ed29ec6828657f9349 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 16:30:56 +0100
Subject: [PATCH 21/34] done with openneuro

---
 consolidated/openneuro_datasets.json          | 122 ++++++++--
 pyproject.toml                                |   3 +-
 .../ingestions/1_fetch_openneuro_datasets.py  | 209 ++++++++++++------
 3 files changed, 252 insertions(+), 82 deletions(-)

diff --git a/consolidated/openneuro_datasets.json b/consolidated/openneuro_datasets.json
index ad980e6a..06ce172e 100644
--- a/consolidated/openneuro_datasets.json
+++ b/consolidated/openneuro_datasets.json
@@ -2283,115 +2283,199 @@
     "dataset_id": "ds004977",
     "modality": "ieeg",
     "created": "2024-02-19T19:52:02.163Z",
-    "modified": "2024-05-13T17:47:41.000Z"
+    "modified": "2024-02-19T19:52:02.163Z"
   },
   {
     "dataset_id": "ds004993",
     "modality": "ieeg",
     "created": "2024-02-25T23:36:29.775Z",
-    "modified": "2024-03-01T17:54:40.000Z"
+    "modified": "2024-02-25T23:36:29.775Z"
   },
   {
     "dataset_id": "ds005007",
     "modality": "ieeg",
     "created": "2024-03-05T23:44:50.042Z",
-    "modified": "2024-03-07T17:44:30.000Z"
+    "modified": "2024-03-05T23:44:50.042Z"
   },
   {
     "dataset_id": "ds005059",
     "modality": "ieeg",
     "created": "2024-04-03T21:09:33.244Z",
-    "modified": "2024-04-23T00:32:22.000Z"
+    "modified": "2024-04-03T21:09:33.244Z"
   },
   {
     "dataset_id": "ds005083",
     "modality": "ieeg",
     "created": "2024-04-10T21:53:35.682Z",
-    "modified": "2024-04-10T22:00:08.000Z"
+    "modified": "2024-04-10T21:53:35.682Z"
   },
   {
     "dataset_id": "ds005169",
     "modality": "ieeg",
     "created": "2024-05-22T09:00:55.253Z",
-    "modified": "2024-05-22T10:58:35.000Z"
+    "modified": "2024-05-22T09:00:55.253Z"
   },
   {
     "dataset_id": "ds005398",
     "modality": "ieeg",
     "created": "2024-08-04T16:43:46.618Z",
-    "modified": "2024-08-15T03:01:26.000Z"
+    "modified": "2024-08-04T16:43:46.618Z"
   },
   {
     "dataset_id": "ds005411",
     "modality": "ieeg",
     "created": "2024-08-13T16:37:26.160Z",
-    "modified": "2024-08-14T14:45:19.000Z"
+    "modified": "2024-08-13T16:37:26.160Z"
   },
   {
     "dataset_id": "ds005415",
     "modality": "ieeg",
     "created": "2024-08-17T03:31:16.602Z",
-    "modified": "2024-10-25T12:49:06.000Z"
+    "modified": "2024-08-17T03:31:16.602Z"
   },
   {
     "dataset_id": "ds005448",
     "modality": "ieeg",
     "created": "2024-09-03T06:27:56.217Z",
-    "modified": "2024-10-08T19:09:02.000Z"
+    "modified": "2024-09-03T06:27:56.217Z"
   },
   {
     "dataset_id": "ds005489",
     "modality": "ieeg",
     "created": "2024-09-15T18:28:05.055Z",
-    "modified": "2024-09-17T23:14:43.000Z"
+    "modified": "2024-09-15T18:28:05.055Z"
   },
   {
     "dataset_id": "ds005491",
     "modality": "ieeg",
     "created": "2024-09-16T20:43:53.335Z",
-    "modified": "2024-09-16T21:26:48.000Z"
+    "modified": "2024-09-16T20:43:53.335Z"
   },
   {
     "dataset_id": "ds005494",
     "modality": "ieeg",
     "created": "2024-09-17T22:23:36.032Z",
-    "modified": "2024-09-25T15:44:34.000Z"
+    "modified": "2024-09-17T22:23:36.032Z"
   },
   {
     "dataset_id": "ds005522",
     "modality": "ieeg",
     "created": "2024-09-24T15:54:45.157Z",
-    "modified": "2024-09-24T17:18:59.000Z"
+    "modified": "2024-09-24T15:54:45.157Z"
   },
   {
     "dataset_id": "ds005523",
     "modality": "ieeg",
     "created": "2024-09-24T16:57:08.474Z",
-    "modified": "2024-09-24T18:53:29.000Z"
+    "modified": "2024-09-24T16:57:08.474Z"
   },
   {
     "dataset_id": "ds005545",
     "modality": "ieeg",
     "created": "2024-09-30T21:57:55.144Z",
-    "modified": "2025-04-23T16:00:00.000Z"
+    "modified": "2024-09-30T21:57:55.144Z"
   },
   {
     "dataset_id": "ds005557",
     "modality": "ieeg",
     "created": "2024-10-06T03:40:26.768Z",
-    "modified": "2024-10-06T04:38:57.000Z"
+    "modified": "2024-10-06T03:40:26.768Z"
   },
   {
     "dataset_id": "ds005558",
     "modality": "ieeg",
     "created": "2024-10-06T04:32:14.781Z",
-    "modified": "2024-10-06T04:41:45.000Z"
+    "modified": "2024-10-06T04:32:14.781Z"
   },
   {
     "dataset_id": "ds005574",
     "modality": "ieeg",
     "created": "2024-10-15T20:04:55.411Z",
-    "modified": "2025-02-17T13:52:37.000Z"
+    "modified": "2024-10-15T20:04:55.411Z"
+  },
+  {
+    "dataset_id": "ds005592",
+    "modality": "ieeg",
+    "created": "2024-10-23T18:01:35.117Z",
+    "modified": "2024-10-23T18:01:35.117Z"
+  },
+  {
+    "dataset_id": "ds005624",
+    "modality": "ieeg",
+    "created": "2024-11-07T06:58:53.218Z",
+    "modified": "2024-11-07T19:54:19.000Z"
+  },
+  {
+    "dataset_id": "ds005670",
+    "modality": "ieeg",
+    "created": "2024-11-29T03:57:02.443Z",
+    "modified": "2024-11-29T05:57:42.000Z"
+  },
+  {
+    "dataset_id": "ds005691",
+    "modality": "ieeg",
+    "created": "2024-12-03T08:55:55.749Z",
+    "modified": "2024-12-03T09:21:25.000Z"
+  },
+  {
+    "dataset_id": "ds005931",
+    "modality": "ieeg",
+    "created": "2025-02-18T20:31:48.897Z",
+    "modified": "2025-03-06T22:41:58.000Z"
+  },
+  {
+    "dataset_id": "ds005953",
+    "modality": "ieeg",
+    "created": "2025-03-03T15:16:54.906Z",
+    "modified": "2025-03-03T17:00:05.000Z"
+  },
+  {
+    "dataset_id": "ds006065",
+    "modality": "ieeg",
+    "created": "2025-03-27T15:20:02.439Z",
+    "modified": "2025-03-29T20:06:49.000Z"
+  },
+  {
+    "dataset_id": "ds006107",
+    "modality": "ieeg",
+    "created": "2025-04-08T20:12:46.118Z",
+    "modified": "2025-04-09T06:47:24.000Z"
+  },
+  {
+    "dataset_id": "ds006233",
+    "modality": "ieeg",
+    "created": "2025-05-13T04:33:20.242Z",
+    "modified": "2025-05-19T12:00:57.000Z"
+  },
+  {
+    "dataset_id": "ds006234",
+    "modality": "ieeg",
+    "created": "2025-05-13T05:51:34.168Z",
+    "modified": "2025-05-19T12:06:15.000Z"
+  },
+  {
+    "dataset_id": "ds006253",
+    "modality": "ieeg",
+    "created": "2025-05-21T08:34:46.001Z",
+    "modified": "2025-07-25T09:13:28.000Z"
+  },
+  {
+    "dataset_id": "ds006392",
+    "modality": "ieeg",
+    "created": "2025-06-25T20:49:36.890Z",
+    "modified": "2025-06-25T20:57:50.000Z"
+  },
+  {
+    "dataset_id": "ds006519",
+    "modality": "ieeg",
+    "created": "2025-07-31T09:11:55.018Z",
+    "modified": "2025-07-31T09:16:09.000Z"
+  },
+  {
+    "dataset_id": "ds006890",
+    "modality": "ieeg",
+    "created": "2025-11-03T13:21:47.212Z",
+    "modified": "2025-11-04T05:20:04.000Z"
   },
   {
     "dataset_id": "ds000248",
diff --git a/pyproject.toml b/pyproject.toml
index 7858494d..58cbb3ac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -87,7 +87,8 @@ docs = [
 
 digestion = [
   "pybids",
-  "gql",
+  "gql[requests]",
+  "requests_toolbelt",
 ]
 
 all = [
diff --git a/scripts/ingestions/1_fetch_openneuro_datasets.py b/scripts/ingestions/1_fetch_openneuro_datasets.py
index 21fa3059..b62d0856 100644
--- a/scripts/ingestions/1_fetch_openneuro_datasets.py
+++ b/scripts/ingestions/1_fetch_openneuro_datasets.py
@@ -1,17 +1,15 @@
-"""Fetch OpenNeuro dataset IDs with metadata using gql."""
+"""Fetch OpenNeuro dataset IDs with metadata using requests library."""
 
 import argparse
 import json
 from collections.abc import Iterator
 from pathlib import Path
 
-from gql import Client, gql
-from gql.transport.requests import RequestsHTTPTransport
+import requests
 
 GRAPHQL_URL = "https://openneuro.org/crn/graphql"
 
-DATASETS_QUERY = gql(
-    """
+DATASETS_QUERY = """
     query ($modality: String!, $first: Int!, $after: String) {
       datasets(modality: $modality, first: $first, after: $after) {
         pageInfo { hasNextPage endCursor }
@@ -19,49 +17,112 @@
           node {
             id
             created
-            latestSnapshot {
-              created
-            }
           }
         }
       }
     }
     """
-)
+
+
+def build_batch_modified_query(dataset_ids: list[str]) -> str:
+    """Build a batch query to fetch modified dates for multiple datasets."""
+    query_parts = []
+    for i, dataset_id in enumerate(dataset_ids):
+        # Create an alias for each dataset to avoid conflicts
+        query_parts.append(
+            f"""
+    ds{i}: dataset(id: "{dataset_id}") {{
+      id
+      latestSnapshot {{
+        created
+      }}
+    }}"""
+        )
+
+    return f"""
+query {{
+{chr(10).join(query_parts)}
+}}
+"""
+
+
+def fetch_batch_modified(
+    dataset_ids: list[str], timeout: float = 30.0
+) -> dict[str, str]:
+    """Fetch modified dates for a batch of datasets in a single query."""
+    result = {}
+
+    if not dataset_ids:
+        return result
+
+    try:
+        query = build_batch_modified_query(dataset_ids)
+        payload = {"query": query}
+        response = requests.post(GRAPHQL_URL, json=payload, timeout=timeout)
+        data = response.json()
+
+        if "errors" in data or "data" not in data:
+            return result
+
+        response_data = data["data"]
+
+        # Extract modified dates from batch response
+        for i, dataset_id in enumerate(dataset_ids):
+            alias = f"ds{i}"
+            if alias in response_data:
+                dataset = response_data[alias]
+                if dataset:
+                    latest_snapshot = dataset.get("latestSnapshot")
+                    if latest_snapshot:
+                        created = latest_snapshot.get("created")
+                        if created:
+                            result[dataset_id] = created
+    except Exception as e:
+        print(f"  Error fetching batch: {str(e)[:100]}")
+
+    return result
 
 
 def fetch_datasets(
     page_size: int = 100,
     timeout: float = 30.0,
-    retries: int = 3,
-    max_consecutive_errors: int = 3,
+    max_consecutive_errors: int = 5,
 ) -> Iterator[dict]:
-    """Fetch all OpenNeuro datasets with id and modality."""
-    transport = RequestsHTTPTransport(
-        url=GRAPHQL_URL,
-        timeout=timeout,
-        retries=retries,
-        verify=True,
-    )
-    client = Client(transport=transport, fetch_schema_from_transport=False)
-
-    modalities = ["eeg", "ieeg", "meg"]
+    """Fetch all OpenNeuro datasets with id and modality using requests."""
+    # Use smaller page size for iEEG due to known API issue at offset 50
+    modality_configs = {
+        "eeg": {"page_size": page_size, "max_errors": 3},
+        "ieeg": {"page_size": 10, "max_errors": 5},  # Smaller for iEEG
+        "meg": {"page_size": page_size, "max_errors": 3},
+    }
 
-    for modality in modalities:
-        cursor: str | None = None
+    for modality in ["eeg", "ieeg", "meg"]:
+        config = modality_configs[modality]
+        cursor = None
         consecutive_errors = 0
-        error_cursors = set()  # Track cursors that caused errors
+        current_page_size = config["page_size"]
+        print(f"Fetching {modality} datasets (page size: {current_page_size})...")
 
         while True:
             try:
-                result = client.execute(
-                    DATASETS_QUERY,
-                    variable_values={
+                payload = {
+                    "query": DATASETS_QUERY,
+                    "variables": {
                         "modality": modality,
-                        "first": page_size,
+                        "first": current_page_size,
                         "after": cursor,
                     },
-                )
+                }
+
+                response = requests.post(GRAPHQL_URL, json=payload, timeout=timeout)
+                response.raise_for_status()
+
+                result = response.json()
+
+                # Check for GraphQL errors
+                if "errors" in result:
+                    raise Exception(f"GraphQL Error: {result['errors'][0]['message']}")
+
                 # Reset error counter on successful fetch
                 consecutive_errors = 0
 
@@ -69,44 +130,39 @@ def fetch_datasets(
                 consecutive_errors += 1
                 error_msg = str(e)
 
-                # Check if it's a server-side 404 error for specific datasets
-                if "'Not Found'" in error_msg and cursor not in error_cursors:
+                # Check if it's a server-side error
+                if "Not Found" in error_msg or "INTERNAL_SERVER_ERROR" in error_msg:
                     print(
-                        f"  Warning: Skipping {modality} page at cursor {cursor} due to server error (attempt {consecutive_errors}/{max_consecutive_errors})"
+                        f"  [{modality}] Server error (attempt {consecutive_errors}/{config['max_errors']})"
                     )
-                    error_cursors.add(cursor)
-
-                    # Try to skip this problematic cursor by moving forward
-                    if consecutive_errors < max_consecutive_errors:
-                        # Try smaller page size to isolate the problematic dataset
-                        if page_size > 10:
-                            page_size = max(10, page_size // 2)
-                            print(
-                                f"  Reducing page size to {page_size} to skip problematic dataset"
-                            )
-                            continue
-
-                    # If we've tried multiple times, skip to next modality
+
+                    if consecutive_errors < config["max_errors"]:
+                        print("    Skipping this batch and continuing...")
+                        continue
+
+                    # If we've tried multiple times, move to next modality
                     print(
-                        f"  Skipping remaining {modality} datasets after {consecutive_errors} consecutive errors"
+                        f"  [{modality}] Reached max consecutive errors, moving to next modality"
                     )
                     break
                 else:
-                    # For other errors, log and retry with exponential backoff
-                    print(f"  Error fetching {modality} datasets: {error_msg[:200]}")
-                    if consecutive_errors >= max_consecutive_errors:
-                        print(
-                            f"  Too many consecutive errors ({consecutive_errors}), moving to next modality"
-                        )
-                        break
-                    continue
+                    # For other errors, log and stop
+                    print(f"  [{modality}] Error: {error_msg[:100]}")
+                    break
+
+            data = result.get("data")
+            if not data:
+                print(f"  [{modality}] No data in response")
+                break
 
-            page = result.get("datasets")
+            page = data.get("datasets")
             if not page:
+                print(f"  [{modality}] No page data")
                 break
 
             edges = page.get("edges", [])
             if not edges:
+                print(f"  [{modality}] No edges in response")
                 break
 
             # Process each dataset in the page
@@ -115,13 +171,14 @@ def fetch_datasets(
                 if not node:
                     continue
 
-                # Get creation and last update dates
+                dataset_id = node.get("id")
                 created = node.get("created")
-                latest_snapshot = node.get("latestSnapshot")
-                modified = latest_snapshot.get("created") if latest_snapshot else None
+
+                # Get modified date (will be fetched separately if needed)
+                modified = created  # Default to created date
 
                 yield {
-                    "dataset_id": node.get("id"),
+                    "dataset_id": dataset_id,
                     "modality": modality,
                     "created": created,
                     "modified": modified,
@@ -130,6 +187,7 @@ def fetch_datasets(
             # Check if there are more pages
             page_info = page.get("pageInfo", {})
             if not page_info.get("hasNextPage"):
+                print(f"  [{modality}] Completed")
                 break
             cursor = page_info.get("endCursor")
 
@@ -146,18 +204,45 @@ def main() -> None:
     )
     parser.add_argument("--page-size", type=int, default=100)
     parser.add_argument("--timeout", type=float, default=30.0)
-    parser.add_argument("--retries", type=int, default=5)
+    parser.add_argument(
+        "--fetch-modified",
+        action="store_true",
+        help="Fetch actual modified dates for each dataset (slower but complete)",
+    )
     args = parser.parse_args()
 
-    # Fetch and save
+    # Fetch main dataset list
     datasets = list(
         fetch_datasets(
             page_size=args.page_size,
             timeout=args.timeout,
-            retries=args.retries,
         )
     )
 
+    # Optionally fetch actual modified dates
+    if args.fetch_modified:
+        print(f"\nFetching actual modified dates for {len(datasets)} datasets...")
+        batch_size = 20  # Batch 20 datasets per query
+
+        for batch_start in range(0, len(datasets), batch_size):
+            batch_end = min(batch_start + batch_size, len(datasets))
+            batch = datasets[batch_start:batch_end]
+            dataset_ids = [d["dataset_id"] for d in batch]
+
+            # Fetch modified dates for this batch
+            modified_dates = fetch_batch_modified(dataset_ids, timeout=args.timeout)
+
+            # Update datasets with fetched modified dates
+            for dataset in batch:
+                dataset_id = dataset["dataset_id"]
+                if dataset_id in modified_dates:
+                    dataset["modified"] = modified_dates[dataset_id]
+
+            if batch_end % 100 == 0 or batch_end == len(datasets):
+                print(
+                    f"  Fetched modified dates for {batch_end}/{len(datasets)} datasets"
+                )
+
     args.output.parent.mkdir(parents=True, exist_ok=True)
     with args.output.open("w", encoding="utf-8") as fh:
         json.dump(datasets, fh, indent=2)

From 81b92a81c01d4a8f373af2b181f748ea0f9bbad5 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:00:53 +0100
Subject: [PATCH 22/34] scidb for later

---
 scripts/ingestions/1_fetch_scidb.py | 63 +++++++++++++++++++++++++----
 1 file changed, 56 insertions(+), 7 deletions(-)

diff --git a/scripts/ingestions/1_fetch_scidb.py b/scripts/ingestions/1_fetch_scidb.py
index e6ce78ba..fcc29a30 100644
--- a/scripts/ingestions/1_fetch_scidb.py
+++ b/scripts/ingestions/1_fetch_scidb.py
@@ -4,18 +4,41 @@
 using the SciDB query service API. It retrieves comprehensive metadata including DOIs,
 CSTR identifiers, descriptions, authors, and file information.
 
+Supports simple queries and advanced boolean queries using AND, OR, NOT operators.
+
 Output: consolidated/scidb_datasets.json
 """
 
 import argparse
 import json
 import sys
-import time
 from pathlib import Path
 from typing import Any
 
 import requests
 
+# Predefined query templates for neuroimaging research
+QUERY_TEMPLATES = {
+    "eeg_bids": "EEG BIDS",
+    "neuroimaging": ("(EEG OR MEG OR iEEG OR ECoG OR EMG) AND (BIDS OR neuroimaging)"),
+    # TODO: Revisit query structure - SciDB may require different query syntax
+    # Current structure may not correctly combine boolean operators
+    # See: SCIDB_QUERY_TEMPLATES.md for details and testing notes
+    # "comprehensive": (
+    #     '(("EEG" OR "electroencephalography" OR "MEG" OR "magnetoencephalography" '
+    #     'OR "iEEG" OR "intracranial EEG" OR "ECoG" OR "electrocorticography" '
+    #     'OR "SEEG" OR "stereo EEG" OR "EMG" OR "electromyography") '
+    #     'AND '
+    #     '("BIDS" OR "Brain Imaging Data Structure" OR "BIDS-EEG" OR "BIDS-MEG" '
+    #     'OR "BIDS-iEEG" OR "BIDS specification" OR "BIDS extension" '
+    #     'OR "BIDS derivatives" OR "BIDS apps" OR "BIDS validator" OR "BIDS converter")) '
+    #     'OR '
+    #     '(("EEG-BIDS" OR "MEG-BIDS" OR "iEEG-BIDS" OR "EMG-BIDS") '
+    #     'AND '
+    #     '("data sharing" OR "open data" OR "FAIR principles" OR "neuroimaging standardization"))'
+    # ),
+}
+
 
 def search_scidb(
     query: str,
@@ -122,8 +145,11 @@ def search_scidb(
 
         page += 1
 
-        # Be nice to the API
-        time.sleep(0.5)
+    # Return all if size is 0 or negative, otherwise trim to size
+    if size <= 0:
+        return all_datasets
+    else:
+        return all_datasets[:size]
 
     print(f"\nTotal datasets fetched: {len(all_datasets)}")
     return all_datasets[:size]  # Trim to exact size
@@ -245,13 +271,25 @@ def extract_dataset_info(record: dict) -> dict[str, Any]:
 
 def main() -> None:
     parser = argparse.ArgumentParser(
-        description="Fetch EEG BIDS datasets from SciDB (Science Data Bank)."
+        description="Fetch EEG BIDS datasets from SciDB (Science Data Bank).",
+        epilog="Query templates: eeg_bids, neuroimaging | TODO: Add comprehensive template after query syntax validation",
     )
     parser.add_argument(
         "--query",
         type=str,
-        default="EEG BIDS",
-        help='Search query (default: "EEG BIDS").',
+        default="eeg_bids",
+        help=(
+            'Search query or template name (default: "eeg_bids"). '
+            "Available templates: eeg_bids, neuroimaging. "
+            "Use --list-queries to see all templates. "
+            "Or provide a custom query with boolean operators (AND, OR, NOT). "
+            "TODO: comprehensive template syntax may need adjustment for SciDB API."
+        ),
+    )
+    parser.add_argument(
+        "--list-queries",
+        action="store_true",
+        help="List all available query templates and exit.",
     )
     parser.add_argument(
         "--output",
@@ -279,9 +317,20 @@ def main() -> None:
 
     args = parser.parse_args()
 
+    # Handle list-queries
+    if args.list_queries:
+        print("Available query templates:\n")
+        for name, query in QUERY_TEMPLATES.items():
+            print(f"  {name}:")
+            print(f"    {query}\n")
+        sys.exit(0)
+
+    # Resolve query template or use custom query
+    query = QUERY_TEMPLATES.get(args.query, args.query)
+
     # Search SciDB
     records = search_scidb(
-        query=args.query,
+        query=query,
         size=args.size,
         page_size=args.page_size,
         public_only=not args.include_restricted,

From 5e984d17a9cba568b17474012005060b5d32ff89 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:03:17 +0100
Subject: [PATCH 23/34] updating the fetch for zenodo

---
 scripts/ingestions/1_fetch_zenodo.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/scripts/ingestions/1_fetch_zenodo.py b/scripts/ingestions/1_fetch_zenodo.py
index 984a7903..f3871457 100644
--- a/scripts/ingestions/1_fetch_zenodo.py
+++ b/scripts/ingestions/1_fetch_zenodo.py
@@ -1,8 +1,16 @@
-"""Fetch EEG BIDS datasets from Zenodo.
+"""Fetch neuroimaging datasets from Zenodo.
 
-This script searches Zenodo for datasets containing both "EEG" and "BIDS" keywords
-using the Zenodo REST API. It retrieves comprehensive metadata including DOIs,
-descriptions, file information, and download URLs.
+This script searches Zenodo for datasets combining neuroimaging modalities
+(EEG, MEG, iEEG, ECoG, SEEG, EMG) with BIDS standards using the Zenodo REST API.
+It retrieves comprehensive metadata including DOIs, descriptions, file information,
+and download URLs.
+
+Search Strategy:
+- Modalities: EEG, electroencephalography, MEG, magnetoencephalography, iEEG,
+              intracranial EEG, ECoG, electrocorticography, SEEG, stereo EEG,
+              EMG, electromyography
+- Standards: BIDS, Brain Imaging Data Structure, neuroimaging
+- Logic: AND operator for balanced recall and precision
 
 Output: consolidated/zenodo_datasets.json
 """
@@ -398,11 +406,11 @@ def main() -> None:
     parser.add_argument(
         "--query",
         type=str,
-        default="EEG BIDS",
+        default='(EEG OR electroencephalography OR MEG OR magnetoencephalography OR iEEG OR "intracranial EEG" OR ECoG OR electrocorticography OR SEEG OR "stereo EEG" OR EMG OR electromyography) AND (BIDS OR "Brain Imaging Data Structure" OR neuroimaging)',
         help=(
-            'Search query (default: "EEG BIDS" for broad matching). '
-            "Note: Using explicit AND/OR operators reduces results significantly due to stricter matching. "
-            "The default query naturally includes EEG/MEG/iEEG/ECoG variants through fuzzy matching."
+            "Search query (default: comprehensive neuroimaging + BIDS for balanced recall and precision). "
+            "Searches across all modalities (EEG, MEG, iEEG, ECoG, SEEG, EMG) combined with BIDS standards. "
+            "Use quotes for multi-word terms. Example: '\"intracranial EEG\" AND BIDS'"
         ),
     )
     parser.add_argument(

From 2ab49630afb0c98f120d374593a49df219bc0f44 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:16:50 +0100
Subject: [PATCH 24/34] figure share

---
 scripts/ingestions/1_fetch_figshare.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/scripts/ingestions/1_fetch_figshare.py b/scripts/ingestions/1_fetch_figshare.py
index 3e481e1e..d7ccb2d2 100644
--- a/scripts/ingestions/1_fetch_figshare.py
+++ b/scripts/ingestions/1_fetch_figshare.py
@@ -78,8 +78,8 @@ def search_figshare(
         print(f"Got {len(articles)} articles")
         all_articles.extend(articles)
 
-        # Check if we've reached the requested size
-        if len(all_articles) >= size:
+        # Check if we've reached the requested size (only if size > 0)
+        if size > 0 and len(all_articles) >= size:
             print(f"Reached limit ({len(all_articles)} articles)")
             break
 
@@ -94,7 +94,8 @@ def search_figshare(
         time.sleep(0.5)
 
     print(f"\nTotal articles fetched: {len(all_articles)}")
-    return all_articles[:size]  # Trim to exact size
+    # Return all articles if size=0, otherwise trim to exact size
+    return all_articles if size <= 0 else all_articles[:size]
 
 
 def get_article_details(article_id: int) -> dict[str, Any]:

From 4f26ea21053590c5081f84e44328dda34c31139e Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:17:18 +0100
Subject: [PATCH 25/34] updating the json

---
 consolidated/eegmanylabs_datasets.json        |    54 +
 consolidated/figshare_datasets.json           |  1982 +++
 .../figshare_datasets_detailed_test.json      |  1982 +++
 consolidated/figshare_datasets_test.json      |   393 +
 consolidated/scidb_datasets.json              | 13095 ++++++++++++++++
 5 files changed, 17506 insertions(+)
 create mode 100644 consolidated/eegmanylabs_datasets.json
 create mode 100644 consolidated/figshare_datasets.json
 create mode 100644 consolidated/figshare_datasets_detailed_test.json
 create mode 100644 consolidated/figshare_datasets_test.json
 create mode 100644 consolidated/scidb_datasets.json

diff --git a/consolidated/eegmanylabs_datasets.json b/consolidated/eegmanylabs_datasets.json
new file mode 100644
index 00000000..c88157cb
--- /dev/null
+++ b/consolidated/eegmanylabs_datasets.json
@@ -0,0 +1,54 @@
+[
+  {
+    "dataset_id": "EEGManyLabs_Replication_ClarkHillyard1996_Raw",
+    "full_name": "EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw",
+    "description": "",
+    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw",
+    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw.git",
+    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw.git",
+    "stars": 0,
+    "forks": 0,
+    "updated": "",
+    "source": "gin",
+    "organization": "EEGManyLabs"
+  },
+  {
+    "dataset_id": "EEGManyLabs_Replication_Eimer1996_Processed",
+    "full_name": "EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed",
+    "description": "",
+    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed",
+    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed.git",
+    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed.git",
+    "stars": 0,
+    "forks": 0,
+    "updated": "",
+    "source": "gin",
+    "organization": "EEGManyLabs"
+  },
+  {
+    "dataset_id": "EEGManyLabs_Replication_HajcakHolroyd2005",
+    "full_name": "EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005",
+    "description": "",
+    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005",
+    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005.git",
+    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005.git",
+    "stars": 0,
+    "forks": 0,
+    "updated": "",
+    "source": "gin",
+    "organization": "EEGManyLabs"
+  },
+  {
+    "dataset_id": "EEGManyLabs_Replication_Eimer1996_Raw",
+    "full_name": "EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw",
+    "description": "",
+    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw",
+    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw.git",
+    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw.git",
+    "stars": 0,
+    "forks": 0,
+    "updated": "",
+    "source": "gin",
+    "organization": "EEGManyLabs"
+  }
+]
\ No newline at end of file
diff --git a/consolidated/figshare_datasets.json b/consolidated/figshare_datasets.json
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+++ b/consolidated/figshare_datasets.json
@@ -0,0 +1,1982 @@
+[
+  {
+    "dataset_id": "30227503",
+    "doi": "10.6084/m9.figshare.30227503.v1",
+    "title": "EEG Dataset for Visual Imagery",
+    "description": "<h3>1.Research context</h3><h4>This dataset contains high-resolution EEG recordings collected from 22 healthy adult participants performing visual imagery (VI) tasks.</h4><p dir=\"ltr\">Participants imagined ten commonly encountered images across three semantic categories: geometric figures (circle, square, pentagram), animals (dog, fish, bird), and objects (cup, chair, watch, scissors). Each participant completed up to two sessions on separate days (\u226548 hours apart). However, sub-09 and sub-10 completed only the first session (ses-01) due to personal reasons, and thus have no available data for ses-02. Additionally, the animal imagery data of sub-08 in ses-02 was excluded from analysis because of poor signal quality, which introduced substantial noise contamination and led to unreliable classification results.</p><p dir=\"ltr\">EEG was recorded with a 34-channel cap (32 active EEG channels + AFz ground + CPz reference) at 1000 Hz.</p><h3>2.Data overview</h3><p dir=\"ltr\">Subjects: 22 healthy adults(5 females, age 20-23)</p><p dir=\"ltr\">Task:</p><p dir=\"ltr\">AVI(animal visual imagery): Imagine animal images(dog, fish, bird; 120 trials/session)</p><p dir=\"ltr\">FVI(animal visual imagery): Imagine figure images(circle, square, pentagram; 120 trials/session)</p><p dir=\"ltr\">OVI(animal visual imagery): Imagine object images(cup, chair, watch, scissors; 160 trials/session)</p><h3>3. Experimental Design</h3><p dir=\"ltr\">The paradigm was implemented using Psychtoolbox-3 and presented on a screen with a resolution of 1920\u00d71080 pixels. Each recording session lasted between 37 and 56 minutes and comprised four VI blocks for each task category. Flexible rest periods of 2 to 4 minutes were provided between blocks to mitigate the effects of fatigue. In the figure and animal categories, each image was presented 40 times, yielding a total of 120 trials per category.</p><h4>Each trial lasted 17 seconds and followed a structured sequence\uff1a</h4><ul><li>Fixation Cross\u200b: 3 s. A central fixation cross (\"+\") is displayed on the screen. To cue participants to remain alert and relaxed, preparing them for the trial.</li><li>Visual perception: 4 s. Participants are instructed to observe and memorize the image.</li><li>Mask: 2 s. A visual mask is briefly displayed.To eliminate any residual visual aftereffects from the presented image.</li><li>Visual imagery: 4 s.The screen turns black. Participants perform mental imagery of the previously shown image.</li><li>Rest: 4 s. The word \"Rest\" is displayed on the screen.Participants are instructed to stop any mental imagery and rest.</li></ul><h3>4.File structure</h3><p dir=\"ltr\">The data are organized according to the EEG-BIDS convention to maximize interoperability. Raw EEG (<code>*.bdf</code>) and BIDS sidecars: <code>*_channels.tsv</code>, <code>*_events.tsv</code>, <code>*_eeg.json</code>, etc.</p><p dir=\"ltr\"><br></p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2025-10-01T15:00:47Z",
+    "created_date": "2025-10-01T15:00:47Z",
+    "modified_date": "2025-10-01T15:02:02Z",
+    "authors": [
+      {
+        "name": "Jing'ao Gao",
+        "id": 22323919,
+        "orcid": "0009-0004-7718-5477"
+      }
+    ],
+    "tags": [
+      "brain\u2013computer interface (BCI)",
+      "visual imagery",
+      "EEG"
+    ],
+    "categories": [
+      {
+        "id": 24736,
+        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
+        "parent_id": 24724,
+        "path": "/24457/24724/24736",
+        "source_id": "320904",
+        "taxonomy_id": 100
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://figshare.com/articles/dataset/EEG_Dataset_for_Visual_Imagery/30227503",
+    "api_url": "https://api.figshare.com/v2/articles/30227503",
+    "resource_title": null,
+    "resource_doi": null,
+    "files": [
+      {
+        "id": 58328968,
+        "name": "sub-01.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58328968",
+        "md5": "cac3615e077e6e6ee61a874a61b855c7"
+      },
+      {
+        "id": 58329043,
+        "name": "sub-02.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329043",
+        "md5": "8ffb18f780080a5bea08fa0fed2947e4"
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+      {
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+        "name": "sub-03.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329064",
+        "md5": "7e0e7b2a2dd97e75a3960064388f1f1a"
+      },
+      {
+        "id": 58329169,
+        "name": "sub-04.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329169",
+        "md5": "14cd26bc910acf23b859dbcfc948c2db"
+      },
+      {
+        "id": 58329229,
+        "name": "sub-05.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329229",
+        "md5": "cc18d93412f6ce377803ecb45dea4cac"
+      },
+      {
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+        "name": "sub-06.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329265",
+        "md5": "970ad0e72bcf43a66cdec1078d9474c8"
+      },
+      {
+        "id": 58329289,
+        "name": "sub-07.zip",
+        "size_bytes": 578747124,
+        "download_url": "https://ndownloader.figshare.com/files/58329289",
+        "md5": "5091b499668ca5de2329772803268852"
+      },
+      {
+        "id": 58336849,
+        "name": "sub-09.zip",
+        "size_bytes": 276612863,
+        "download_url": "https://ndownloader.figshare.com/files/58336849",
+        "md5": "c454e75572614f31a9c313bb0857efbd"
+      },
+      {
+        "id": 58336852,
+        "name": "sub-10.zip",
+        "size_bytes": 312132554,
+        "download_url": "https://ndownloader.figshare.com/files/58336852",
+        "md5": "d0a83f3b2ed67da0cc1088c7af3f4560"
+      },
+      {
+        "id": 58337044,
+        "name": "sub-11.zip",
+        "size_bytes": 638262513,
+        "download_url": "https://ndownloader.figshare.com/files/58337044",
+        "md5": "fb1c08ee849ace8d30988e32630be165"
+      }
+    ],
+    "file_count": 10,
+    "total_size_mb": 4786.33,
+    "source": "figshare"
+  },
+  {
+    "dataset_id": "29987758",
+    "doi": "10.6084/m9.figshare.29987758.v2",
+    "title": "<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>",
+    "description": "<p dir=\"ltr\">Version History</p><p dir=\"ltr\">Version 2.0 (October 15, 2025)</p><ul><li>Event label correction: Corrected a scaling inconsistency in the event tags within the file sub-13_ses-02_task-CCSHI_eeg.bdf.  The event codes now correctly align with the task paradigm's coding logic.</li><li>Metadata update: Corrected the handedness metadata for participant sub-06 in participants. tsv from \"L\" to \"R\", to address a typographical error in the original metadata.</li><li>Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.</li></ul><p dir=\"ltr\">No changes to the associated study's results or conclusions are necessitated by these updates.</p><h3>1. Research Context</h3><p dir=\"ltr\">This dataset addresses a critical gap in Brain-Computer Interface (BCI) research: most existing resources focus on English-language tasks or simple motor imagery, while Chinese character strokes and Pinyin monophthong handwriting remain understudied. By providing data for two complementary handwriting imagery tasks\u2014</p><p dir=\"ltr\">Chinese Character Stroke Handwriting Imagery (CCS-HI): Imagining writing 5 basic Chinese character strokes( \u4e00,\u4e28,\u4e3f,\u31cf,\u3125) ;</p><p dir=\"ltr\">Pinyin Single Vowel Handwriting Imagery (SV-HI): Imagining writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc)\u2014</p><p dir=\"ltr\">we enable investigations into:</p><p dir=\"ltr\">The neural mechanisms of handwriting imagery for Chinese strokes vs. Pinyin characters;</p><p dir=\"ltr\">Cross-session generalization (a critical challenge for real-world BCI deployment).</p><h3>2. Data Overview</h3><p dir=\"ltr\">Subjects: 16 healthy adults (3 females, age 22\u201332, right-handed, no neurological disorders). Note: In Version 2.0, handedness for one subject was corrected from \u201cL\u201d to \u201cR\u201d to reflect accurate right-handedness; all participants remain confirmed as right-handed.</p><p dir=\"ltr\">Tasks:</p><p dir=\"ltr\">CCS-HI: Imagine writing 5 Chinese strokes (\u4e00\uff0c\u4e28\uff0c\u4e3f\uff0c\u31cf, \u3125\uff1b200 trials/session).</p><p dir=\"ltr\">SV-HI: Imagine writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc; 240 trials/session).</p><p dir=\"ltr\">Sessions: 2 independent sessions (\u226524 hours apart).</p><p dir=\"ltr\">Session 1: Used for training and 5-fold cross-validation.</p><p dir=\"ltr\">Session 2: Held out as an independent test set for cross-session evaluation.</p><h3>3. Experimental Design</h3><p dir=\"ltr\">3.1 Pre-Experiment Preparation</p><p dir=\"ltr\">Prior to data collection, all subjects were fully informed of the experimental protocol (including task requirements and response methods) and shown a demonstration video of handwriting imagery tasks to ensure understanding. Written informed consent was obtained from all participants.</p><p dir=\"ltr\">3.2 EEG Acquisition</p><p dir=\"ltr\">Device: Neuracle NeuSenW amplifier with a 32-channel Ag/AgCl electrode cap.</p><p dir=\"ltr\">Electrode Layout: Strictly following the international 10-10 system. Detailed 3D coordinates and nomenclature of each electrode are provided in the accompanying electrodes.tsv file.</p><p dir=\"ltr\">Sampling Rate: 1000 Hz (downsampled to 250 Hz during preprocessing).</p><p dir=\"ltr\">Electrode Impedance: Maintained below 10 k\u03a9 for all channels during recording.</p><p dir=\"ltr\">Calibration: All equipment was professionally calibrated before the experiment to ensure data quality and reproducibility.</p><p dir=\"ltr\">3.3 Task Paradigm</p><p dir=\"ltr\">3.2.1 General Trial Structure (CCS-HI & SV-HI)</p><p dir=\"ltr\">Each single trial included four sequential phases, with the total duration varying slightly by task:</p><p dir=\"ltr\">1. Fixation + Auditory Cue: 2 seconds</p><p dir=\"ltr\">A white fixation cross was displayed on a black screen to stabilize subjects\u2019 attention;</p><p dir=\"ltr\">An auditory beep (500 Hz, 100 ms) was synchronized with the start of the cross, serving as a trial initiation signal.</p><p dir=\"ltr\">2. Task Cue Phase: Duration differs by task (key distinction)</p><p dir=\"ltr\">2.1 For CCS-HI: 2.8 seconds</p><p dir=\"ltr\">A dynamic animation of the target Chinese character stroke (e.g., \u4e00\uff0c\u4e28) was played, demonstrating the stroke\u2019s writing trajectory.</p><p dir=\"ltr\">2.2 For SV-HI: 3.2 seconds</p><p dir=\"ltr\">A dynamic animation of the target Pinyin monophthong (e.g., a, o) was played; the extended duration was designed to accommodate the relatively higher complexity of visualizing Pinyin character shapes.</p><p dir=\"ltr\">3. Handwriting Imagery Phase: 4 seconds</p><p dir=\"ltr\">Subjects were instructed to imagine writing the target stroke (CCS-HI) or Pinyin monophthong (SV-HI) following the trajectory shown in the task cue, without any actual limb movement.</p><p dir=\"ltr\">4. Rest Interval: 3 seconds</p><p dir=\"ltr\">A blank black screen was presented to allow subjects to recover and reset attention for the next trial.</p><p dir=\"ltr\">3.2.2 Trial Duration Summary</p><ul><li>Total duration per trial (CCS-HI): 2 s (fixation) + 2.8 s (cue) + 4 s (imagery) + 3 s (rest) = 11.8 seconds.</li><li>Total duration per trial (SV-HI): 2 s (fixation) + 3.2 s (cue) + 4 s (imagery) + 3 s (rest) = 12.2 seconds.</li></ul><h3>4. Preprocessing & Trial Integrity</h3><p dir=\"ltr\">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1\u201340 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0\u20134 s imagery period (for both tasks). Trial integrity checks confirmed most sessions met standards (200 trials/session for CCS-HI, 240 for SV-HI), with two exceptions: Subject 06 (SV-HI, Ses-2: 225 trials, trigger error) and Subject 13 (CCS-HI, Ses-2: 162 trials, fatigue). All retained trials passed quality checks, and missing trials were evenly distributed across categories (max deviation \u22642 trials, <5% threshold), so no class balancing was needed.</p><h3><br>5. File Structure (BIDS-Compliant)</h3><p dir=\"ltr\">Following corrections to event labels and participant metadata in Version 2.0, the dataset underwent full BIDS compliance re-verification to ensure data structure and metadata consistency.</p><p dir=\"ltr\">The dataset follows the BIDS standard: the root directory stores core metadata (e.g., <code>dataset_description.json</code> for dataset info, <code>participants.tsv</code> for demographics, <code>electrodes.tsv</code> for 32-channel 10-10 system coordinates, task-specific <code>_events.json</code>), 16 subject folders (e.g., <code>sub-01</code>) each contain <code>ses-01/ses-02</code> with <code>eeg/</code> subfolders (by CCS-HI/SV-HI tasks) holding <code>_channels.tsv</code> (bad channels), <code>_eeg.bdf</code> (raw Neuracle EEG), <code>_eeg.json</code> (acquisition params), <code>_events.tsv</code> (task alignment); supplementary folders include <code>stimuli</code> (visual cues) and <code>code</code> (preprocessing/model scripts with docs).</p><h3>6. Usage & Citation</h3><p dir=\"ltr\">6.1 Data Access & Extraction</p><p dir=\"ltr\">The HI-EEG dataset (CCS-HI/SV-HI tasks) is available via Figshare: https://doi.org/10.6084/m9.figshare.29987758(raw data access). To ensure successful decompression of the split-volume compressed files, follow these guidelines:</p><ul><li>File Composition: The dataset is split into 4 interlinked files (all required for full extraction):</li></ul><p dir=\"ltr\">1. Main archive: `CCS-SV-HI-EEG_BIDS.zip`</p><p dir=\"ltr\">2. Split volumes: `CCS-SV-HI-EEG_BIDS.z01`, `CCS-SV-HI-EEG_BIDS.z02`, `CCS-SV-HI-EEG_BIDS.z03`</p><ul><li>Pre-Extraction Requirement: Download all 4 files and place them in the **same directory** (do not rename files or move them to subfolders).</li><li>Tool & Operation:</li></ul><p dir=\"ltr\">1. Use split-volume compatible software (RECOMMENDED: 7-Zip, open-source & free: https://www.7-zip.org/; avoid default system extractors like Windows Explorer/macOS Archive Utility).</p><p dir=\"ltr\">2. Double-click the main `.zip` file (`CCS-SV-HI-EEG_BIDS.zip`) \u2014 the software will automatically merge `.z01`\u2013`.z03` into the full BIDS folder (matching the structure described in Section 5).</p><ul><li>Critical Note: Extraction will fail if any split file is missing; the decompressed folder retains the BIDS structure (no additional organization needed).</li></ul><p><br></p><p dir=\"ltr\">6.2 Trial Integrity & Usage Details</p><p dir=\"ltr\">Note two trial integrity details: sub-06 (SV-HI, ses-02) retains 225 trials (15 missing, trigger sync error) and sub-13 (CCS-HI, ses-02) retains 162 trials (38 missing, subject fatigue)\u2014missing trials are evenly distributed across categories (max deviation \u22642 trials, <5% imbalance, no class balancing needed).</p><p><br></p><p dir=\"ltr\">For data loading/analysis, use EEGLAB (MATLAB) or MNE-Python 1.7.0 (Python); for decoding, use scikit-learn 1.6.1 or PyTorch 2.0.0+cu117 (all verified versions). Related code is in the repository\u2019s code directory; refer to the README in code for environment configuration and script workflow.</p><p><br></p><p dir=\"ltr\">6.3 Citation</p><p dir=\"ltr\">Cite the associated publication (DOI to be added) when using this dataset.</p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2025-09-19T15:59:05Z",
+    "created_date": "2025-09-19T15:59:06Z",
+    "modified_date": "2025-09-19T15:59:06Z",
+    "authors": [
+      {
+        "name": "Fan Wang",
+        "id": 22125886,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "EEG dataset, Electroencephalography (EEG), Brain-Computer Interface (BCI), Motor imagery, Handwriting imagery, Chinese Character Stroke Handwriting Imagery, Pinyin Single Vowel Handwriting Imagery, BIDS, Neural decoding, Cross-session generalization"
+    ],
+    "categories": [
+      {
+        "id": 24736,
+        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
+        "parent_id": 24724,
+        "path": "/24457/24724/24736",
+        "source_id": "320904",
+        "taxonomy_id": 100
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://figshare.com/articles/dataset/_b_EEG_dataset_for_multi-class_Chinese_character_stroke_and_pinyin_vowel_handwriting_imagery_16_subjects_CCS-HI_SV-HI_b_/29987758",
+    "api_url": "https://api.figshare.com/v2/articles/29987758",
+    "resource_title": null,
+    "resource_doi": null,
+    "files": [
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+        "md5": "d5de1e8c986b11dbe03eba44f411a210"
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+      }
+    ],
+    "file_count": 4,
+    "total_size_mb": 7036.51,
+    "source": "figshare"
+  },
+  {
+    "dataset_id": "28740260",
+    "doi": "10.6084/m9.figshare.28740260.v3",
+    "title": "Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients",
+    "description": "<p dir=\"ltr\">We present the first large-scale, standardized EEG dataset (30 patients, 150 sessions, 15,000 trials) specifically designed for lower-limb motor imagery (MI) in knee pain patients, addressing a critical gap in clinical BCI research. Chronic knee pain alters cortical plasticity, yet our data demonstrate preserved MI capability in patients\u2014a finding with direct implications for rehabilitation BCI development. Our proposed Optimal Time-Frequency Window Riemannian Geometric Distance (OTFWRGD) algorithm achieves <b>86.41% classification accuracy</b>, significantly outperforming traditional methods (CSP+LDA: 51.43%; FBCSP+SVM: 55.71%; EEGNet: 76.21%). The dataset adheres to EEG-BIDS standards and is fully accessible via Figshare, including raw/preprocessed EEG, stimuli, and analysis code.</p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2025-06-20T07:03:15Z",
+    "created_date": "2025-06-20T07:03:15Z",
+    "modified_date": "2025-08-06T06:22:04Z",
+    "authors": [
+      {
+        "name": "Chongwen Zuo",
+        "id": 21546541,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "Brain Computer Interface in Rehabilitation",
+      "motor imagery EEG signals",
+      "Electroencephalogram wavelet analysis",
+      "Machine learning algorighms"
+    ],
+    "categories": [
+      {
+        "id": 29161,
+        "title": "Deep learning",
+        "parent_id": 29152,
+        "path": "/28798/29152/29161",
+        "source_id": "461103",
+        "taxonomy_id": 100
+      },
+      {
+        "id": 27031,
+        "title": "Rehabilitation",
+        "parent_id": 27004,
+        "path": "/27001/27004/27031",
+        "source_id": "420109",
+        "taxonomy_id": 100
+      },
+      {
+        "id": 24739,
+        "title": "Neurology and neuromuscular diseases",
+        "parent_id": 24724,
+        "path": "/24457/24724/24739",
+        "source_id": "320905",
+        "taxonomy_id": 100
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://figshare.com/articles/dataset/Enhancing_classification_of_a_large_lower-limb_motor_imagery_EEG_dataset_for_BCI_in_knee_pain_patients/28740260",
+    "api_url": "https://api.figshare.com/v2/articles/28740260",
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+    "resource_doi": null,
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+  {
+    "dataset_id": "28169963",
+    "doi": "10.6084/m9.figshare.28169963.v1",
+    "title": "Example manifest for the BIDS Siena Scalp EEG Database for evaluation with the SzCORE framework.",
+    "description": "<p dir=\"ltr\">This is a manifest that describe which patients and runs are used across train/dev/test sets when using SzCORE evaluation (Dan et al., 2024) with this dataset.</p><p dir=\"ltr\">References:</p><p dir=\"ltr\">Dan, J., Pale, U., Amirshahi, A., Cappelletti, W., Ingolfsson, T. M., Wang, X., Cossettini, A., Bernini, A., Benini, L., Beniczky, S., Atienza, D., & Ryvlin, P. (2024). SzCORE: Seizure Community Open\u2010Source Research Evaluation framework for the validation of electroencephalography \u2010based automated seizure detection algorithms. <i>Epilepsia</i>, epi.18113. <a href=\"https://doi.org/10.1111/epi.18113\" target=\"_blank\">https://doi.org/10.1111/epi.18113</a></p>",
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+    "modified_date": "2025-06-16T06:34:47Z",
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+      {
+        "name": "Johnson Zhou",
+        "id": 19371541,
+        "orcid": "0009-0001-9030-3624"
+      }
+    ],
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+      "seizure detection performance"
+    ],
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+    "dataset_id": "27301629",
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+    "title": "An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment",
+    "description": "This work introduces a bimodal dataset designed to explore electrophysiological biomarkers for assessing assistive technologies in neurorehabilitation. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks assisted by an upper-limb exoskeleton. To standardize and simulate natural upper-limb movements relevant to daily activities, a custom-designed touch panel was used. High-density EEG (hd-EEG) and surface EMG (sEMG) were recorded to capture neuromechanical responses.\nThe dataset adheres to Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. We provide subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency-domain decomposition for EMG.\nBeyond evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.",
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+        "name": "Inna Forsiuk",
+        "id": 19947239,
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+        "name": "Michele Canepa",
+        "id": 19958176,
+        "orcid": "0000-0002-6168-0332"
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+        "name": "Michael Mozzon",
+        "id": 19947248,
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+        "name": "Agnese Bruschi",
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+    "dataset_id": "25192793",
+    "doi": "10.6084/m9.figshare.25192793.v1",
+    "title": "Dynamic Causal Modelling of Face Processing with fMRI and M/EEG",
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+        "name": "Pranay Yadav",
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+      {
+        "name": "Rik Henson",
+        "id": 6288980,
+        "orcid": ""
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+      "dynamic causal modeling (DCM)",
+      "Dynamic Causal Model",
+      "Face Processing",
+      "MEG Data",
+      "Inverse Modelling",
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+      "SPM12",
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+      "BOLD imaging",
+      "Open Science",
+      "Open Source",
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+      "Jansen-Rit",
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+    "dataset_id": "25037045",
+    "doi": "10.6084/m9.figshare.25037045.v4",
+    "title": "Movie annotations for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients",
+    "description": "<p dir=\"ltr\">Movie annotations for the manuscript: \"Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients\"</p><p dir=\"ltr\">Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli Rutishauser</p><p dir=\"ltr\">Abstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video \"Bang! You're Dead\" and performed a recognition memory test for movie content. 3\u2009T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.</p><p dir=\"ltr\">This dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). https://doi.org/10.1038/s41597-024-03029-1</p><p dir=\"ltr\">Related code: <a href=\"https://github.com/rutishauserlab/bmovie-release-NWB-BIDS\" target=\"_blank\">https://github.com/rutishauserlab/bmovie-release-NWB-BIDS</a></p><p dir=\"ltr\">Intracranial recording data: <a href=\"https://dandiarchive.org/dandiset/000623\" target=\"_blank\">https://dandiarchive.org/dandiset/000623</a></p><p dir=\"ltr\">fMRI data: <a href=\"https://openneuro.org/datasets/ds004798/\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds004798/</a></p><p><br></p>",
+    "defined_type": 3,
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+    "modified_date": "2024-02-27T20:41:49Z",
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+        "name": "Umit Keles",
+        "id": 17821706,
+        "orcid": ""
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+      {
+        "name": "Julien Dubois",
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+      {
+        "name": "Kevin J. M. Le",
+        "id": 17821725,
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+      {
+        "name": "J. Michael Tyszka",
+        "id": 4523851,
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+      {
+        "name": "David A. Kahn",
+        "id": 11534227,
+        "orcid": ""
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+      {
+        "name": "Chrystal M. Reed",
+        "id": 5011316,
+        "orcid": ""
+      },
+      {
+        "name": "Jeffrey M. Chung",
+        "id": 5011319,
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+      },
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+        "name": "Adam N. Mamelak",
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+      "movie watching"
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+    "doi": "10.3389/fnbeh.2023.1147140.s001",
+    "title": "Data_Sheet_1_Neural mechanisms of expert persuasion on willingness to pay for sugar.pdf",
+    "description": "<p>Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.</p><p>Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.</p><p>Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant\u2019s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.</p><p>Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.</p>",
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+      },
+      {
+        "name": "Vasily Klucharev",
+        "id": 5906300,
+        "orcid": ""
+      }
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+        "name": "Moonyoung Kwon",
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+      {
+        "name": "Minkyu Ahn",
+        "id": 488737,
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+    "tags": [
+      "brain-computer interface",
+      "P300 speller",
+      "RSVP",
+      "EEG"
+    ],
+    "categories": [
+      {
+        "id": 42,
+        "title": "Biological Engineering",
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+        "path": "",
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+        "id": 16,
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+  {
+    "dataset_id": "14891607",
+    "doi": "10.17045/sthlmuni.14891607.v1",
+    "title": "Open data: Electrophysiological correlates of in-vivo and virtual reality therapy in spider phobia",
+    "description": "<p>Open data: Electrophysiological correlates of in-vivo and virtual reality therapy in spider phobia</p>\n<p><br></p>\n<p>1. Title of Dataset:</p>\n<p>Open data: Electrophysiological correlates of in-vivo and virtual reality therapy in spider phobia</p>\n<p><br></p>\n<p>2. Author Information</p>\n<p>\u00a0\u00a0\u00a0Principal Investigator Contact Information</p>\n<p>Name: Stefan Wiens</p>\n<p>Institution: Department of Psychology, Stockholm University, Sweden</p>\n<p>Internet: https://www.su.se/profiles/swiens-1.184142</p>\n<p>Email: sws@psychology.su.se</p>\n<p><br></p>\n<p>3. Date of data collection:\u00a0</p>\n<p>Subjects (N = 70 patients and N = 53 controls) were tested between 2015-sep-30 and 2016-jan-15.</p>\n<p><br></p>\n<p>4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden</p>\n<p><br></p>\n<p>5. Information about funding sources that supported the collection of the data:</p>\n<p>Marcus och Amalia Wallenbergs minnesfond (2019-0102)</p>\n<p><br></p>\n<p>SHARING/ACCESS INFORMATION</p>\n<p>1. Licenses/restrictions placed on the data: CC BY 4.0</p>\n<p>2. Links to publications that cite or use the data: Wiens, S., Eklund, R., Szychowska, M., Miloff, A., Cosme, D., Pierzchajlo, S., & Carlbring, P. (2022). Electrophysiological correlates of in vivo and virtual reality exposure therapy in spider phobia. <em>Psychophysiology</em>.<sub><a href=\"https://doi.org/10.1111/psyp.14117\" target=\"_blank\"> <u>https://doi.org/10.1111/psyp.14117</u></a></sub></p>\n<p>3. Links to other publicly accessible locations of the data: N/A</p>\n<p>4. Links/relationships to ancillary data sets: N/A</p>\n<p>5. Was data derived from another source? No</p>\n<p>6. Recommended citation for this dataset: Eklund R., & Wiens S. (2022). Open data: Electrophysiological correlates of in-vivo and virtual reality therapy in spider phobia. Stockholm: Stockholm University. <sub><a href=\"https://doi.org/10.17045/sthlmuni.14891607\" target=\"_blank\"><u>https://doi.org/10.17045/sthlmuni.14891607</u></a></sub></p>\n<p><br></p>\n<p>DATA & FILE OVERVIEW</p>\n<p>The files contain the raw data, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study reported in the main publication.</p>\n<p>VR_spider_LMM.html, VR_spider_LMM_exclude_target_trials.html, VR_spider_analyze_clinical_data: Main results files (also included in R_scripts.zip)</p>\n<p>supplement_CritiqueOfLeutgebStudies.pdf: Critique of previous studies</p>\n<p>supplement_PilotStudy.pdf: Description of pilot study</p>\n<p>data_bids.zip: EEG data in bids format</p>\n<p>MNE-python.zip: MNE-python scripts to preprocess EEG data together with preprocessed data</p>\n<p>R_scripts.zip: R scripts to analyze the EEG mean amplitudes and behavioral data</p>",
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+    "modified_date": "2023-05-30T21:25:43Z",
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+    "description": "GENERAL INFORMATION<br><br><br>1. Title of Dataset:<br>Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences.<br><br><br>2. Author Information<br>    A. Principal Investigator Contact Information<br>            Name: Stefan Wiens<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/swiens-1.184142<br>            Email: sws@psychology.su.se<br><br><br>    B. Associate or Co-investigator Contact Information<br>            Name: Rasmus Eklund<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/raek2031-1.223133<br>            Email: rasmus.eklund@psychology.su.se<br>    <br>    C. Associate or Co-investigator Contact Information<br>            Name: Billy Gerdfeldter<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/bige1544-1.403208<br>            Email: billy.gerdfeldter@psychology.su.se<br><br><br>3. Date of data collection: <br>Subjects (N = 28) were tested between 2020-03-04 and 2020-09-18.<br><br><br>4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden<br><br><br>5. Information about funding sources that supported the collection of the data:<br>Marianne and Marcus Wallenberg (Grant 2019-0102)<br><br><br>SHARING/ACCESS INFORMATION<br><br><br>1. Licenses/restrictions placed on the data: CC BY 4.0<br><br><br>2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B.,  & Wiens S. (2021). The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Neuropsychologia. https://doi.org/<br><br><br>The study was preregistered:<br>https://doi.org/10.17605/OSF.IO/PSRJF<br><br>3. Links to other publicly accessible locations of the data: N/A<br><br><br>4. Links/relationships to ancillary data sets: N/A<br><br><br>5. Was data derived from another source? No<br><br><br>6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.13067018<br><br><br>DATA & FILE OVERVIEW<br><br><br>File List:<br>The files contain the downsampled data in bids format, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.<br><br><br>AAN_LRclick_experiment_scripts.zip: contains the Python files to run the experiment<br><br><br>AAN_LRclick_bids_EEG.zip: contains EEG data files for each subject in .eeg format.<br><br><br>AAN_LRclick_behavior_log.zip: contains log files of the EEG session (generated by Python)<br><br><br>AAN_LRclick_EEG_scripts.zip: Python-MNE scripts to process and to analyze the EEG data<br><br><br>AAN_LRclick_results.zip: contains summary data files, figures, and tables that are created by Python-MNE.<br><br><br>METHODOLOGICAL INFORMATION<br><br><br>1. Description of methods used for collection/generation of data:<br>The auditory stimuli were 4-ms clicks.<br>The experiment was programmed in Python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn <br>The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and converted to .eeg format.<br>For more information, see linked publication.<br><br><br>2. Methods for processing the data:<br>We computed event-related potentials. See linked publication<br><br><br>3. Instrument- or software-specific information needed to interpret the data:<br>MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# <br><br><br>4. Standards and calibration information, if appropriate:<br>For information, see linked publication.<br><br><br>5. Environmental/experimental conditions:<br>For information, see linked publication.<br><br><br>6. Describe any quality-assurance procedures performed on the data:<br>For information, see linked publication.<br><br><br>7. People involved with sample collection, processing, analysis and/or submission:<br><br><br>- Data collection: Rasmus Eklund with assistance from Billy Gerdfeldter.<br>- Data processing, analysis, and submission: Rasmus Eklund<br><br><br>DATA-SPECIFIC INFORMATION:<br>All relevant information can be found in the MNE-Python scripts (in EEG_scripts folder) that process the EEG data. For example, we added notes to explain what different variables mean.<br><br><br>The folder structure needs to be as follows:<br>AAN_LRclick (main folder)<br>--->data<br>--->--->bids (AAN_LRclick_bids_EEG)<br>--->--->log (AAN_LRclick_behavior_log)<br>--->MNE (AAN_LRclick_EEG_scripts)<br>--->results (AAN_LRclick_results)<br><br><br>To run the MNE-Python scripts:<br>Anaconda was used with MNE-Python 0.22 (see installation at https://mne.tools/stable/index.html# ).<br>For preprocess.py and analysis.py, the complete scripts should be run (from anaconda prompt).<br>",
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+      "ERP",
+      "consciousness",
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+      "neural correlates",
+      "auditory awareness negativity",
+      "localization",
+      "Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)",
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+    ],
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+        "name": "AAN_LRclick_bids_EEG.zip",
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+        "name": "AAN_LRclick_results.zip",
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+    ],
+    "file_count": 7,
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+  {
+    "dataset_id": "8033726",
+    "doi": "10.3389/fnins.2019.00300.s001",
+    "title": "Data_Sheet_1_Multimodal Integration of M/EEG and f/MRI Data in SPM12.pdf",
+    "description": "<p>We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the \u201cN/M170\u201d evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.</p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2019-04-24T05:47:20Z",
+    "created_date": "2019-04-24T05:47:20Z",
+    "modified_date": "2023-05-30T12:13:24Z",
+    "authors": [
+      {
+        "name": "Richard N. Henson",
+        "id": 6628652,
+        "orcid": ""
+      },
+      {
+        "name": "Hunar Abdulrahman",
+        "id": 6628655,
+        "orcid": ""
+      },
+      {
+        "name": "Guillaume Flandin",
+        "id": 1369476,
+        "orcid": ""
+      },
+      {
+        "name": "Vladimir Litvak",
+        "id": 42646,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "MEG",
+      "EEG",
+      "fMRI",
+      "multimodal",
+      "fusion",
+      "SPM",
+      "inversion",
+      "faces"
+    ],
+    "categories": [
+      {
+        "id": 320,
+        "title": "Radiology and Organ Imaging",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 449,
+        "title": "Decision Making",
+        "parent_id": 18,
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+        "taxonomy_id": 10
+      },
+      {
+        "id": 370,
+        "title": "Clinical Nursing: Tertiary (Rehabilitative)",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 947,
+        "title": "Image Processing",
+        "parent_id": 52,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 360,
+        "title": "Autonomic Nervous System",
+        "parent_id": 142,
+        "path": "",
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+        "taxonomy_id": 10
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+      {
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+        "id": 42,
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+        "source_id": "",
+        "taxonomy_id": 10
+      },
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+        "id": 365,
+        "title": "Sensory Systems",
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+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 362,
+        "title": "Central Nervous System",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 15,
+        "title": "Neuroscience",
+        "parent_id": 48,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 306,
+        "title": "Endocrinology",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 179,
+        "title": "Artificial Intelligence and Image Processing",
+        "parent_id": 52,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 566,
+        "title": "Signal Processing",
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+        "taxonomy_id": 10
+      },
+      {
+        "id": 536,
+        "title": "Biomedical Engineering not elsewhere classified",
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+      {
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+        "title": "Stem Cells",
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+        "path": "",
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+        "taxonomy_id": 10
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+      {
+        "id": 700,
+        "title": "Neurogenetics",
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+        "taxonomy_id": 10
+      },
+      {
+        "id": 61,
+        "title": "Developmental Biology",
+        "parent_id": 48,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Multimodal_Integration_of_M_EEG_and_f_MRI_Data_in_SPM12_pdf/8033726",
+    "api_url": "https://api.figshare.com/v2/articles/8033726",
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diff --git a/consolidated/figshare_datasets_detailed_test.json b/consolidated/figshare_datasets_detailed_test.json
new file mode 100644
index 00000000..18bb0895
--- /dev/null
+++ b/consolidated/figshare_datasets_detailed_test.json
@@ -0,0 +1,1982 @@
+[
+  {
+    "dataset_id": "30227503",
+    "doi": "10.6084/m9.figshare.30227503.v1",
+    "title": "EEG Dataset for Visual Imagery",
+    "description": "<h3>1.Research context</h3><h4>This dataset contains high-resolution EEG recordings collected from 22 healthy adult participants performing visual imagery (VI) tasks.</h4><p dir=\"ltr\">Participants imagined ten commonly encountered images across three semantic categories: geometric figures (circle, square, pentagram), animals (dog, fish, bird), and objects (cup, chair, watch, scissors). Each participant completed up to two sessions on separate days (\u226548 hours apart). However, sub-09 and sub-10 completed only the first session (ses-01) due to personal reasons, and thus have no available data for ses-02. Additionally, the animal imagery data of sub-08 in ses-02 was excluded from analysis because of poor signal quality, which introduced substantial noise contamination and led to unreliable classification results.</p><p dir=\"ltr\">EEG was recorded with a 34-channel cap (32 active EEG channels + AFz ground + CPz reference) at 1000 Hz.</p><h3>2.Data overview</h3><p dir=\"ltr\">Subjects: 22 healthy adults(5 females, age 20-23)</p><p dir=\"ltr\">Task:</p><p dir=\"ltr\">AVI(animal visual imagery): Imagine animal images(dog, fish, bird; 120 trials/session)</p><p dir=\"ltr\">FVI(animal visual imagery): Imagine figure images(circle, square, pentagram; 120 trials/session)</p><p dir=\"ltr\">OVI(animal visual imagery): Imagine object images(cup, chair, watch, scissors; 160 trials/session)</p><h3>3. Experimental Design</h3><p dir=\"ltr\">The paradigm was implemented using Psychtoolbox-3 and presented on a screen with a resolution of 1920\u00d71080 pixels. Each recording session lasted between 37 and 56 minutes and comprised four VI blocks for each task category. Flexible rest periods of 2 to 4 minutes were provided between blocks to mitigate the effects of fatigue. In the figure and animal categories, each image was presented 40 times, yielding a total of 120 trials per category.</p><h4>Each trial lasted 17 seconds and followed a structured sequence\uff1a</h4><ul><li>Fixation Cross\u200b: 3 s. A central fixation cross (\"+\") is displayed on the screen. To cue participants to remain alert and relaxed, preparing them for the trial.</li><li>Visual perception: 4 s. Participants are instructed to observe and memorize the image.</li><li>Mask: 2 s. A visual mask is briefly displayed.To eliminate any residual visual aftereffects from the presented image.</li><li>Visual imagery: 4 s.The screen turns black. Participants perform mental imagery of the previously shown image.</li><li>Rest: 4 s. The word \"Rest\" is displayed on the screen.Participants are instructed to stop any mental imagery and rest.</li></ul><h3>4.File structure</h3><p dir=\"ltr\">The data are organized according to the EEG-BIDS convention to maximize interoperability. Raw EEG (<code>*.bdf</code>) and BIDS sidecars: <code>*_channels.tsv</code>, <code>*_events.tsv</code>, <code>*_eeg.json</code>, etc.</p><p dir=\"ltr\"><br></p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2025-10-01T15:00:47Z",
+    "created_date": "2025-10-01T15:00:47Z",
+    "modified_date": "2025-10-01T15:02:02Z",
+    "authors": [
+      {
+        "name": "Jing'ao Gao",
+        "id": 22323919,
+        "orcid": "0009-0004-7718-5477"
+      }
+    ],
+    "tags": [
+      "brain\u2013computer interface (BCI)",
+      "visual imagery",
+      "EEG"
+    ],
+    "categories": [
+      {
+        "id": 24736,
+        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
+        "parent_id": 24724,
+        "path": "/24457/24724/24736",
+        "source_id": "320904",
+        "taxonomy_id": 100
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://figshare.com/articles/dataset/EEG_Dataset_for_Visual_Imagery/30227503",
+    "api_url": "https://api.figshare.com/v2/articles/30227503",
+    "resource_title": null,
+    "resource_doi": null,
+    "files": [
+      {
+        "id": 58328968,
+        "name": "sub-01.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58328968",
+        "md5": "cac3615e077e6e6ee61a874a61b855c7"
+      },
+      {
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+        "name": "sub-02.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329064",
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+      {
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+        "download_url": "https://ndownloader.figshare.com/files/58329169",
+        "md5": "14cd26bc910acf23b859dbcfc948c2db"
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+      {
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+        "download_url": "https://ndownloader.figshare.com/files/58329229",
+        "md5": "cc18d93412f6ce377803ecb45dea4cac"
+      },
+      {
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+        "name": "sub-06.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58329265",
+        "md5": "970ad0e72bcf43a66cdec1078d9474c8"
+      },
+      {
+        "id": 58329289,
+        "name": "sub-07.zip",
+        "size_bytes": 578747124,
+        "download_url": "https://ndownloader.figshare.com/files/58329289",
+        "md5": "5091b499668ca5de2329772803268852"
+      },
+      {
+        "id": 58336849,
+        "name": "sub-09.zip",
+        "size_bytes": 276612863,
+        "download_url": "https://ndownloader.figshare.com/files/58336849",
+        "md5": "c454e75572614f31a9c313bb0857efbd"
+      },
+      {
+        "id": 58336852,
+        "name": "sub-10.zip",
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+        "download_url": "https://ndownloader.figshare.com/files/58336852",
+        "md5": "d0a83f3b2ed67da0cc1088c7af3f4560"
+      },
+      {
+        "id": 58337044,
+        "name": "sub-11.zip",
+        "size_bytes": 638262513,
+        "download_url": "https://ndownloader.figshare.com/files/58337044",
+        "md5": "fb1c08ee849ace8d30988e32630be165"
+      }
+    ],
+    "file_count": 10,
+    "total_size_mb": 4786.33,
+    "source": "figshare"
+  },
+  {
+    "dataset_id": "29987758",
+    "doi": "10.6084/m9.figshare.29987758.v2",
+    "title": "<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>",
+    "description": "<p dir=\"ltr\">Version History</p><p dir=\"ltr\">Version 2.0 (October 15, 2025)</p><ul><li>Event label correction: Corrected a scaling inconsistency in the event tags within the file sub-13_ses-02_task-CCSHI_eeg.bdf.  The event codes now correctly align with the task paradigm's coding logic.</li><li>Metadata update: Corrected the handedness metadata for participant sub-06 in participants. tsv from \"L\" to \"R\", to address a typographical error in the original metadata.</li><li>Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.</li></ul><p dir=\"ltr\">No changes to the associated study's results or conclusions are necessitated by these updates.</p><h3>1. Research Context</h3><p dir=\"ltr\">This dataset addresses a critical gap in Brain-Computer Interface (BCI) research: most existing resources focus on English-language tasks or simple motor imagery, while Chinese character strokes and Pinyin monophthong handwriting remain understudied. By providing data for two complementary handwriting imagery tasks\u2014</p><p dir=\"ltr\">Chinese Character Stroke Handwriting Imagery (CCS-HI): Imagining writing 5 basic Chinese character strokes( \u4e00,\u4e28,\u4e3f,\u31cf,\u3125) ;</p><p dir=\"ltr\">Pinyin Single Vowel Handwriting Imagery (SV-HI): Imagining writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc)\u2014</p><p dir=\"ltr\">we enable investigations into:</p><p dir=\"ltr\">The neural mechanisms of handwriting imagery for Chinese strokes vs. Pinyin characters;</p><p dir=\"ltr\">Cross-session generalization (a critical challenge for real-world BCI deployment).</p><h3>2. Data Overview</h3><p dir=\"ltr\">Subjects: 16 healthy adults (3 females, age 22\u201332, right-handed, no neurological disorders). Note: In Version 2.0, handedness for one subject was corrected from \u201cL\u201d to \u201cR\u201d to reflect accurate right-handedness; all participants remain confirmed as right-handed.</p><p dir=\"ltr\">Tasks:</p><p dir=\"ltr\">CCS-HI: Imagine writing 5 Chinese strokes (\u4e00\uff0c\u4e28\uff0c\u4e3f\uff0c\u31cf, \u3125\uff1b200 trials/session).</p><p dir=\"ltr\">SV-HI: Imagine writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc; 240 trials/session).</p><p dir=\"ltr\">Sessions: 2 independent sessions (\u226524 hours apart).</p><p dir=\"ltr\">Session 1: Used for training and 5-fold cross-validation.</p><p dir=\"ltr\">Session 2: Held out as an independent test set for cross-session evaluation.</p><h3>3. Experimental Design</h3><p dir=\"ltr\">3.1 Pre-Experiment Preparation</p><p dir=\"ltr\">Prior to data collection, all subjects were fully informed of the experimental protocol (including task requirements and response methods) and shown a demonstration video of handwriting imagery tasks to ensure understanding. Written informed consent was obtained from all participants.</p><p dir=\"ltr\">3.2 EEG Acquisition</p><p dir=\"ltr\">Device: Neuracle NeuSenW amplifier with a 32-channel Ag/AgCl electrode cap.</p><p dir=\"ltr\">Electrode Layout: Strictly following the international 10-10 system. Detailed 3D coordinates and nomenclature of each electrode are provided in the accompanying electrodes.tsv file.</p><p dir=\"ltr\">Sampling Rate: 1000 Hz (downsampled to 250 Hz during preprocessing).</p><p dir=\"ltr\">Electrode Impedance: Maintained below 10 k\u03a9 for all channels during recording.</p><p dir=\"ltr\">Calibration: All equipment was professionally calibrated before the experiment to ensure data quality and reproducibility.</p><p dir=\"ltr\">3.3 Task Paradigm</p><p dir=\"ltr\">3.2.1 General Trial Structure (CCS-HI & SV-HI)</p><p dir=\"ltr\">Each single trial included four sequential phases, with the total duration varying slightly by task:</p><p dir=\"ltr\">1. Fixation + Auditory Cue: 2 seconds</p><p dir=\"ltr\">A white fixation cross was displayed on a black screen to stabilize subjects\u2019 attention;</p><p dir=\"ltr\">An auditory beep (500 Hz, 100 ms) was synchronized with the start of the cross, serving as a trial initiation signal.</p><p dir=\"ltr\">2. Task Cue Phase: Duration differs by task (key distinction)</p><p dir=\"ltr\">2.1 For CCS-HI: 2.8 seconds</p><p dir=\"ltr\">A dynamic animation of the target Chinese character stroke (e.g., \u4e00\uff0c\u4e28) was played, demonstrating the stroke\u2019s writing trajectory.</p><p dir=\"ltr\">2.2 For SV-HI: 3.2 seconds</p><p dir=\"ltr\">A dynamic animation of the target Pinyin monophthong (e.g., a, o) was played; the extended duration was designed to accommodate the relatively higher complexity of visualizing Pinyin character shapes.</p><p dir=\"ltr\">3. Handwriting Imagery Phase: 4 seconds</p><p dir=\"ltr\">Subjects were instructed to imagine writing the target stroke (CCS-HI) or Pinyin monophthong (SV-HI) following the trajectory shown in the task cue, without any actual limb movement.</p><p dir=\"ltr\">4. Rest Interval: 3 seconds</p><p dir=\"ltr\">A blank black screen was presented to allow subjects to recover and reset attention for the next trial.</p><p dir=\"ltr\">3.2.2 Trial Duration Summary</p><ul><li>Total duration per trial (CCS-HI): 2 s (fixation) + 2.8 s (cue) + 4 s (imagery) + 3 s (rest) = 11.8 seconds.</li><li>Total duration per trial (SV-HI): 2 s (fixation) + 3.2 s (cue) + 4 s (imagery) + 3 s (rest) = 12.2 seconds.</li></ul><h3>4. Preprocessing & Trial Integrity</h3><p dir=\"ltr\">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1\u201340 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0\u20134 s imagery period (for both tasks). Trial integrity checks confirmed most sessions met standards (200 trials/session for CCS-HI, 240 for SV-HI), with two exceptions: Subject 06 (SV-HI, Ses-2: 225 trials, trigger error) and Subject 13 (CCS-HI, Ses-2: 162 trials, fatigue). All retained trials passed quality checks, and missing trials were evenly distributed across categories (max deviation \u22642 trials, <5% threshold), so no class balancing was needed.</p><h3><br>5. File Structure (BIDS-Compliant)</h3><p dir=\"ltr\">Following corrections to event labels and participant metadata in Version 2.0, the dataset underwent full BIDS compliance re-verification to ensure data structure and metadata consistency.</p><p dir=\"ltr\">The dataset follows the BIDS standard: the root directory stores core metadata (e.g., <code>dataset_description.json</code> for dataset info, <code>participants.tsv</code> for demographics, <code>electrodes.tsv</code> for 32-channel 10-10 system coordinates, task-specific <code>_events.json</code>), 16 subject folders (e.g., <code>sub-01</code>) each contain <code>ses-01/ses-02</code> with <code>eeg/</code> subfolders (by CCS-HI/SV-HI tasks) holding <code>_channels.tsv</code> (bad channels), <code>_eeg.bdf</code> (raw Neuracle EEG), <code>_eeg.json</code> (acquisition params), <code>_events.tsv</code> (task alignment); supplementary folders include <code>stimuli</code> (visual cues) and <code>code</code> (preprocessing/model scripts with docs).</p><h3>6. Usage & Citation</h3><p dir=\"ltr\">6.1 Data Access & Extraction</p><p dir=\"ltr\">The HI-EEG dataset (CCS-HI/SV-HI tasks) is available via Figshare: https://doi.org/10.6084/m9.figshare.29987758(raw data access). To ensure successful decompression of the split-volume compressed files, follow these guidelines:</p><ul><li>File Composition: The dataset is split into 4 interlinked files (all required for full extraction):</li></ul><p dir=\"ltr\">1. Main archive: `CCS-SV-HI-EEG_BIDS.zip`</p><p dir=\"ltr\">2. Split volumes: `CCS-SV-HI-EEG_BIDS.z01`, `CCS-SV-HI-EEG_BIDS.z02`, `CCS-SV-HI-EEG_BIDS.z03`</p><ul><li>Pre-Extraction Requirement: Download all 4 files and place them in the **same directory** (do not rename files or move them to subfolders).</li><li>Tool & Operation:</li></ul><p dir=\"ltr\">1. Use split-volume compatible software (RECOMMENDED: 7-Zip, open-source & free: https://www.7-zip.org/; avoid default system extractors like Windows Explorer/macOS Archive Utility).</p><p dir=\"ltr\">2. Double-click the main `.zip` file (`CCS-SV-HI-EEG_BIDS.zip`) \u2014 the software will automatically merge `.z01`\u2013`.z03` into the full BIDS folder (matching the structure described in Section 5).</p><ul><li>Critical Note: Extraction will fail if any split file is missing; the decompressed folder retains the BIDS structure (no additional organization needed).</li></ul><p><br></p><p dir=\"ltr\">6.2 Trial Integrity & Usage Details</p><p dir=\"ltr\">Note two trial integrity details: sub-06 (SV-HI, ses-02) retains 225 trials (15 missing, trigger sync error) and sub-13 (CCS-HI, ses-02) retains 162 trials (38 missing, subject fatigue)\u2014missing trials are evenly distributed across categories (max deviation \u22642 trials, <5% imbalance, no class balancing needed).</p><p><br></p><p dir=\"ltr\">For data loading/analysis, use EEGLAB (MATLAB) or MNE-Python 1.7.0 (Python); for decoding, use scikit-learn 1.6.1 or PyTorch 2.0.0+cu117 (all verified versions). Related code is in the repository\u2019s code directory; refer to the README in code for environment configuration and script workflow.</p><p><br></p><p dir=\"ltr\">6.3 Citation</p><p dir=\"ltr\">Cite the associated publication (DOI to be added) when using this dataset.</p>",
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+    "modified_date": "2025-09-19T15:59:06Z",
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+      {
+        "name": "Fan Wang",
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+      }
+    ],
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+      "EEG dataset, Electroencephalography (EEG), Brain-Computer Interface (BCI), Motor imagery, Handwriting imagery, Chinese Character Stroke Handwriting Imagery, Pinyin Single Vowel Handwriting Imagery, BIDS, Neural decoding, Cross-session generalization"
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+    "title": "Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients",
+    "description": "<p dir=\"ltr\">We present the first large-scale, standardized EEG dataset (30 patients, 150 sessions, 15,000 trials) specifically designed for lower-limb motor imagery (MI) in knee pain patients, addressing a critical gap in clinical BCI research. Chronic knee pain alters cortical plasticity, yet our data demonstrate preserved MI capability in patients\u2014a finding with direct implications for rehabilitation BCI development. Our proposed Optimal Time-Frequency Window Riemannian Geometric Distance (OTFWRGD) algorithm achieves <b>86.41% classification accuracy</b>, significantly outperforming traditional methods (CSP+LDA: 51.43%; FBCSP+SVM: 55.71%; EEGNet: 76.21%). The dataset adheres to EEG-BIDS standards and is fully accessible via Figshare, including raw/preprocessed EEG, stimuli, and analysis code.</p>",
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+    "modified_date": "2025-08-06T06:22:04Z",
+    "authors": [
+      {
+        "name": "Chongwen Zuo",
+        "id": 21546541,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "Brain Computer Interface in Rehabilitation",
+      "motor imagery EEG signals",
+      "Electroencephalogram wavelet analysis",
+      "Machine learning algorighms"
+    ],
+    "categories": [
+      {
+        "id": 29161,
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+  {
+    "dataset_id": "28169963",
+    "doi": "10.6084/m9.figshare.28169963.v1",
+    "title": "Example manifest for the BIDS Siena Scalp EEG Database for evaluation with the SzCORE framework.",
+    "description": "<p dir=\"ltr\">This is a manifest that describe which patients and runs are used across train/dev/test sets when using SzCORE evaluation (Dan et al., 2024) with this dataset.</p><p dir=\"ltr\">References:</p><p dir=\"ltr\">Dan, J., Pale, U., Amirshahi, A., Cappelletti, W., Ingolfsson, T. M., Wang, X., Cossettini, A., Bernini, A., Benini, L., Beniczky, S., Atienza, D., & Ryvlin, P. (2024). SzCORE: Seizure Community Open\u2010Source Research Evaluation framework for the validation of electroencephalography \u2010based automated seizure detection algorithms. <i>Epilepsia</i>, epi.18113. <a href=\"https://doi.org/10.1111/epi.18113\" target=\"_blank\">https://doi.org/10.1111/epi.18113</a></p>",
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+    "title": "An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment",
+    "description": "This work introduces a bimodal dataset designed to explore electrophysiological biomarkers for assessing assistive technologies in neurorehabilitation. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks assisted by an upper-limb exoskeleton. To standardize and simulate natural upper-limb movements relevant to daily activities, a custom-designed touch panel was used. High-density EEG (hd-EEG) and surface EMG (sEMG) were recorded to capture neuromechanical responses.\nThe dataset adheres to Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. We provide subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency-domain decomposition for EMG.\nBeyond evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.",
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+        "name": "Florencia Garro",
+        "id": 19947215,
+        "orcid": "0000-0001-8845-0281"
+      },
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+        "name": "Elena Fenoglio",
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+        "orcid": "0009-0002-2779-6574"
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+      {
+        "name": "Indya Ceroni",
+        "id": 19947230,
+        "orcid": "0000-0003-1251-1185"
+      },
+      {
+        "name": "Inna Forsiuk",
+        "id": 19947239,
+        "orcid": ""
+      },
+      {
+        "name": "Michele Canepa",
+        "id": 19958176,
+        "orcid": "0000-0002-6168-0332"
+      },
+      {
+        "name": "Michael Mozzon",
+        "id": 19947248,
+        "orcid": ""
+      },
+      {
+        "name": "Agnese Bruschi",
+        "id": 19947278,
+        "orcid": ""
+      },
+      {
+        "name": "Francesco Zippo",
+        "id": 19947340,
+        "orcid": ""
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+      {
+        "name": "Matteo Laffranchi",
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+        "orcid": "0000-0003-1189-281X"
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+      {
+        "name": "Lorenzo De Michieli",
+        "id": 19958194,
+        "orcid": "0000-0001-7158-3002"
+      },
+      {
+        "name": "Stefano Buccelli",
+        "id": 6924944,
+        "orcid": ""
+      },
+      {
+        "name": "Michela Chiappalone",
+        "id": 19958197,
+        "orcid": "0000-0003-1427-5147"
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+      {
+        "name": "Marianna Semprini",
+        "id": 19947527,
+        "orcid": "0000-0001-5504-0251"
+      }
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+      "Human Machine Interaction",
+      "assistive technology",
+      "Motor control",
+      "signal processing"
+    ],
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+      {
+        "id": 536,
+        "title": "Biomedical Engineering not elsewhere classified",
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+    "url": "https://springernature.figshare.com/articles/dataset/An_EEG-EMG_Dataset_from_a_Standardized_Reaching_Task_for_Biomarker_Research_in_Upper_Limb_Assessment/27301629",
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+    "doi": "10.6084/m9.figshare.25192793.v1",
+    "title": "Dynamic Causal Modelling of Face Processing with fMRI and M/EEG",
+    "description": "<p dir=\"ltr\">This dataset consists of BIDS-formatted fMRI and M/EEG data from <a href=\"https://doi.org/10.1038/sdata.2015.1\" rel=\"noreferrer\" target=\"_blank\">Wakeman & Henson (2015)</a> that have been processed for DCM analysis. Multimodal data from fMRI, EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in <a href=\"https://doi.org/10.3389/fnins.2019.00300\" rel=\"noreferrer\" target=\"_blank\">Henson et al (2019)</a>. This dataset was prepared for demonstration of group-level estimation and inference of DCMs from fMRI and M/EEG data. The raw data can be found here: <a href=\"https://openneuro.org/datasets/ds000117\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds000117</a></p><p dir=\"ltr\">fMRI data is provided in two formats:</p><ol><li><i>fMRI_ProcessedData_Individual_Runs.tar.xz</i> contains processed data per run per participant, about 8.9GB total. </li><li><i>fMRI_DCMreadyData_VOI_TimeCourses.tar</i> contains VOI time courses for 3 regions - bilateral EVC, and left and right FFA, obtained after reparametrizing and concatenating processed data per run. This totals up to about 440MB.</li></ol><p dir=\"ltr\">M/EEG data consists of averaged evoked sensor data for two conditions - faces and scrambled faces. Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset, which is provided in two formats:</p><ol><li>MEEG_DCMreadyData_with_GainMatrix.tar.xz consists of sensor data and leadfields - sufficient for inverting MEG, but not EEG data since forward models for the latter need BEM surfaces. This is compact, about 390MB.</li><li>MEEG_DCMreadyData_with_GainMatrix_BEM.tar.xz consists of sensor data, leadfields and BEM surfaces - can be used for inverting either MEG or EEG data. This is considerably larger in size, about 2.5GB.</li></ol><p dir=\"ltr\">All four data packages listed above have corresponding file lists for inspection, as well as SHA256 and MD5 checksums for verification of data integrity. Note that the data are highly compressed and will expand to about twice their size on decompression. Please use <a href=\"https://7-zip.org/\" rel=\"noreferrer\" target=\"_blank\">7zip</a> on Windows or <a href=\"https://linuxize.com/post/how-to-extract-unzip-tar-xz-file/\" rel=\"noreferrer\" target=\"_blank\">`tar -xvf filename`</a> on Linux to extract. Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: <a href=\"https://github.com/pranaysy/MultimodalDCM\" rel=\"noreferrer\" target=\"_blank\">https://github.com/pranaysy/MultimodalDCM</a>.</p><p dir=\"ltr\">For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.</p>",
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+      "dynamic causal modeling (DCM)",
+      "Dynamic Causal Model",
+      "Face Processing",
+      "MEG Data",
+      "Inverse Modelling",
+      "EEG Data",
+      "SPM12",
+      "Tutorial",
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+      "Open Science",
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+      "Generative Modelling",
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+  {
+    "dataset_id": "25037045",
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+    "title": "Movie annotations for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients",
+    "description": "<p dir=\"ltr\">Movie annotations for the manuscript: \"Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients\"</p><p dir=\"ltr\">Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli Rutishauser</p><p dir=\"ltr\">Abstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video \"Bang! You're Dead\" and performed a recognition memory test for movie content. 3\u2009T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.</p><p dir=\"ltr\">This dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). https://doi.org/10.1038/s41597-024-03029-1</p><p dir=\"ltr\">Related code: <a href=\"https://github.com/rutishauserlab/bmovie-release-NWB-BIDS\" target=\"_blank\">https://github.com/rutishauserlab/bmovie-release-NWB-BIDS</a></p><p dir=\"ltr\">Intracranial recording data: <a href=\"https://dandiarchive.org/dandiset/000623\" target=\"_blank\">https://dandiarchive.org/dandiset/000623</a></p><p dir=\"ltr\">fMRI data: <a href=\"https://openneuro.org/datasets/ds004798/\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds004798/</a></p><p><br></p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2024-01-30T21:17:01Z",
+    "created_date": "2024-01-30T21:17:01Z",
+    "modified_date": "2024-02-27T20:41:49Z",
+    "authors": [
+      {
+        "name": "Umit Keles",
+        "id": 17821706,
+        "orcid": ""
+      },
+      {
+        "name": "Julien Dubois",
+        "id": 351876,
+        "orcid": ""
+      },
+      {
+        "name": "Kevin J. M. Le",
+        "id": 17821725,
+        "orcid": ""
+      },
+      {
+        "name": "J. Michael Tyszka",
+        "id": 4523851,
+        "orcid": ""
+      },
+      {
+        "name": "David A. Kahn",
+        "id": 11534227,
+        "orcid": ""
+      },
+      {
+        "name": "Chrystal M. Reed",
+        "id": 5011316,
+        "orcid": ""
+      },
+      {
+        "name": "Jeffrey M. Chung",
+        "id": 5011319,
+        "orcid": ""
+      },
+      {
+        "name": "Adam N. Mamelak",
+        "id": 281705,
+        "orcid": ""
+      },
+      {
+        "name": "Ralph Adolphs",
+        "id": 217684,
+        "orcid": ""
+      },
+      {
+        "name": "Ueli Rutishauser",
+        "id": 686011,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "movie watching"
+    ],
+    "categories": [
+      {
+        "id": 30334,
+        "title": "Cognitive neuroscience",
+        "parent_id": 30325,
+        "path": "/30292/30325/30334",
+        "source_id": "520203",
+        "taxonomy_id": 100
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://figshare.com/articles/dataset/Movie_annotations_for_the_manuscript_Multimodal_brain_responses_during_movie_watching_single-neuron_intracranial_EEG_and_fMRI_in_human_patients_/25037045",
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+    "resource_doi": null,
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+        "md5": "09bd7eadf825a430499ec8050bdaad45"
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+      {
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+  {
+    "dataset_id": "22260892",
+    "doi": "10.3389/fnbeh.2023.1147140.s001",
+    "title": "Data_Sheet_1_Neural mechanisms of expert persuasion on willingness to pay for sugar.pdf",
+    "description": "<p>Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.</p><p>Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.</p><p>Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant\u2019s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.</p><p>Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.</p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
+    "published_date": "2023-03-13T04:21:28Z",
+    "created_date": "2023-03-13T04:21:28Z",
+    "modified_date": "2023-06-09T11:10:41Z",
+    "authors": [
+      {
+        "name": "Ioannis Ntoumanis",
+        "id": 11904629,
+        "orcid": ""
+      },
+      {
+        "name": "Alina Davydova",
+        "id": 14781514,
+        "orcid": ""
+      },
+      {
+        "name": "Julia Sheronova",
+        "id": 14781517,
+        "orcid": ""
+      },
+      {
+        "name": "Ksenia Panidi",
+        "id": 5991186,
+        "orcid": ""
+      },
+      {
+        "name": "Vladimir Kosonogov",
+        "id": 4415392,
+        "orcid": ""
+      },
+      {
+        "name": "Anna N. Shestakova",
+        "id": 11518855,
+        "orcid": ""
+      },
+      {
+        "name": "Iiro P. J\u00e4\u00e4skel\u00e4inen",
+        "id": 11904638,
+        "orcid": ""
+      },
+      {
+        "name": "Vasily Klucharev",
+        "id": 5906300,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "expert persuasion",
+      "sugar",
+      "EEG",
+      "healthy eating",
+      "machine learning",
+      "intersubject correlation",
+      "willingness to pay",
+      "social influence"
+    ],
+    "categories": [
+      {
+        "id": 448,
+        "title": "Computer Perception, Memory and Attention",
+        "parent_id": 18,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 449,
+        "title": "Decision Making",
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+        "path": "",
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+        "taxonomy_id": 10
+      },
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+        "id": 362,
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+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 11,
+        "title": "Behavioral Neuroscience",
+        "parent_id": 48,
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+      },
+      {
+        "id": 15,
+        "title": "Neuroscience",
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+        "path": "",
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+        "taxonomy_id": 10
+      },
+      {
+        "id": 341,
+        "title": "Exercise Physiology",
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+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      },
+      {
+        "id": 342,
+        "title": "Motor Control",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
+        "taxonomy_id": 10
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Neural_mechanisms_of_expert_persuasion_on_willingness_to_pay_for_sugar_pdf/22260892",
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+  {
+    "dataset_id": "21342066",
+    "doi": "10.6084/m9.figshare.21342066.v1",
+    "title": "Face processing M/EEG data for Dynamic Causal Modelling (Faces vs Scrambled)",
+    "description": "<p>\u00a0</p>\n<p>This dataset consists of BIDS-formatted M/EEG data from Wakeman & Henson (2015) that have been processed for DCM analysis. Multimodal data from EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in Henson et al (2019). Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset.</p>\n<p>The file 'derivatives_dcm_ready_with_gainmat_all_subjects.zip' consists of dcm-ready data for all 16 subjects\u00a0</p>\n<p>Each subject's data consists of 3 files:</p>\n<ol>\n  <li>SPM sidecar data file: wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.dat</li>\n  <li>SPM data header file: wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.mat</li>\n  <li>Gain matrix (forward model): SPMgainmatrix_wmaMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg_1.mat</li>\n</ol>\n<p>*XX here indicates zero-padded subject number (01, 02, ...). Supplied filelist.txt contains the full filelist and folder hierarchy inside the compressed zip archive.</p>\n<p><br></p>\n<p>This dataset was prepared for demonstration of group-level estimation and inference of DCMs from M/EEG data. The raw data can be found here: https://openneuro.org/datasets/ds000117</p>\n<p>Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here:https://github.com/pranaysy/cognestic22_multimodal_dcm</p>\n<p><br></p>\n<p>For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.</p>\n<p><br></p>\n<p>Note: This dataset differs from https://figshare.com/articles/dataset/Face_processing_M_EEG_data_for_Dynamic_Causal_Modelling/21130297 in that this one has two conditions: famous and unfamiliar faces have been merged into one condition and scrambled faces. The linked one has three conditions: famous faces, unfamiliar faces and scrambled faces.</p>",
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+      "MEG data",
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+      "SPM12",
+      "Tutorial",
+      "Computational modelling",
+      "BIDS-Processed",
+      "Neuroscience",
+      "Neurocognitive Patterns and Neural Networks",
+      "Neuroscience and Physiological Psychology"
+    ],
+    "categories": [
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+    "description": "<p>This dataset consists of BIDS-formatted M/EEG data from Wakeman & Henson (2015) that have been processed for DCM analysis. Multimodal data from EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in Henson et al (2019). Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset.</p>\n<p><br></p>\n<p>The file 'derivatives_dcm_ready_with_gainmat_all_subjects.zip' consists of dcm-ready data for all 16 subjects, while the file 'derivatives_dcm_ready_with_gainmat_single_subjects.zip' consists of dcm-ready data for a single subject (sub-01).\u00a0</p>\n<p><br></p>\n<p>Each subject's data consists of 3 files:</p>\n<ol>\n  <li>SPM sidecar data file: maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.dat</li>\n  <li>SPM data header file: maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.mat</li>\n  <li>Gain matrix (forward model): SPMgainmatrix_maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg_1.mat</li>\n</ol>\n<p>*XX here indicates zero-padded subject number (01, 02, ...). Supplied filelist.txt contains the full filelist and folder hierarchy inside the compressed zip archive.</p>\n<p><br></p>\n<p>This dataset was prepared for demonstration of group-level estimation and inference of DCMs from M/EEG data. The raw data can be found here: https://openneuro.org/datasets/ds000117</p>\n<p><br></p>\n<p>Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: https://github.com/pranaysy/DCM-MEEG-Demo</p>\n<p><br></p>\n<p>For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.</p>",
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+        "orcid": ""
+      },
+      {
+        "name": "Minkyu Ahn",
+        "id": 488737,
+        "orcid": ""
+      }
+    ],
+    "tags": [
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+      "P300 speller",
+      "RSVP",
+      "EEG"
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+      {
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+        "path": "",
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+        "taxonomy_id": 10
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+    "title": "Open data: Electrophysiological correlates of in-vivo and virtual reality therapy in spider phobia",
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+        "name": "Rasmus Eklund",
+        "id": 4543201,
+        "orcid": ""
+      }
+    ],
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+      "EEG",
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+    "title": "BIDS dataset for BIDS Manager-Pipeline",
+    "description": "This folder contains data organised in BIDS format to test BIDS Manager-Pipeline (https://github.com/Dynamap/BIDS_Manager/tree/dev).",
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+        "id": 11993306,
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+        "name": "Samuel Medina Villalon",
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+    "description": "<p>Brain injuries and ensuing neurological abnormalities due to chronic hypoxia are among the most frequent causes and difficult to detect reliably. Prevention strategies require novel accurate methods. We showed that electrocorticogram's mutual information properties (ECoG) contain information about corticothalamic communication, which can be quantified without invasive insertion of the thalamic electrodes.</p><p>Here we present the method and the data set to derive ECoG/EEG biomarkers of corticothalamic communication under normal, sedation and hypoxic/ischemic conditions.</p><p>We hypothesize that the proposed biomarkers of corticothalamic communication derived from EEG alone will signal increased risk for or an imminent brain injury from short periods of data (less than 10 min).</p><p>We present signal-analytical approaches to derive a  signature of coupling between the ECoG and electrothalamogram (EThG) signals that were recorded under conditions of gradual ischemia in juvenile pigs.</p><p>We hope this data set will contribute to development of new brain monitoring technologies to improve prevention of neurological injuries.</p><p></p><p>All experiments have been approved by the responsible animal ethics committee.</p><p><br></p><p>BIDS version of the dataset can also be found on OpenNeuro: </p><h2>DOI:</h2>10.18112/openneuro.ds003380.v1.0.0<div><br></div><div>Publication: https://www.nature.com/articles/s41597-020-00781-y</div>",
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+        "name": "Reinhard Bauer",
+        "id": 6449496,
+        "orcid": "0000-0002-4294-3758"
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+    "description": "<p>Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.</p>",
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+        "orcid": ""
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+        "id": 344741,
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+      }
+    ],
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+      "neuroimaging",
+      "multi-level assessment",
+      "large-scale studies",
+      "common data element",
+      "data processing pipelines",
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+      "bids format"
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+      }
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+    "description": "GENERAL INFORMATION<br><br><br>1. Title of Dataset:<br>Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences.<br><br><br>2. Author Information<br>    A. Principal Investigator Contact Information<br>            Name: Stefan Wiens<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/swiens-1.184142<br>            Email: sws@psychology.su.se<br><br><br>    B. Associate or Co-investigator Contact Information<br>            Name: Rasmus Eklund<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/raek2031-1.223133<br>            Email: rasmus.eklund@psychology.su.se<br>    <br>    C. Associate or Co-investigator Contact Information<br>            Name: Billy Gerdfeldter<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/bige1544-1.403208<br>            Email: billy.gerdfeldter@psychology.su.se<br><br><br>3. Date of data collection: <br>Subjects (N = 28) were tested between 2020-03-04 and 2020-09-18.<br><br><br>4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden<br><br><br>5. Information about funding sources that supported the collection of the data:<br>Marianne and Marcus Wallenberg (Grant 2019-0102)<br><br><br>SHARING/ACCESS INFORMATION<br><br><br>1. Licenses/restrictions placed on the data: CC BY 4.0<br><br><br>2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B.,  & Wiens S. (2021). The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Neuropsychologia. https://doi.org/<br><br><br>The study was preregistered:<br>https://doi.org/10.17605/OSF.IO/PSRJF<br><br>3. Links to other publicly accessible locations of the data: N/A<br><br><br>4. Links/relationships to ancillary data sets: N/A<br><br><br>5. Was data derived from another source? No<br><br><br>6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.13067018<br><br><br>DATA & FILE OVERVIEW<br><br><br>File List:<br>The files contain the downsampled data in bids format, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.<br><br><br>AAN_LRclick_experiment_scripts.zip: contains the Python files to run the experiment<br><br><br>AAN_LRclick_bids_EEG.zip: contains EEG data files for each subject in .eeg format.<br><br><br>AAN_LRclick_behavior_log.zip: contains log files of the EEG session (generated by Python)<br><br><br>AAN_LRclick_EEG_scripts.zip: Python-MNE scripts to process and to analyze the EEG data<br><br><br>AAN_LRclick_results.zip: contains summary data files, figures, and tables that are created by Python-MNE.<br><br><br>METHODOLOGICAL INFORMATION<br><br><br>1. Description of methods used for collection/generation of data:<br>The auditory stimuli were 4-ms clicks.<br>The experiment was programmed in Python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn <br>The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and converted to .eeg format.<br>For more information, see linked publication.<br><br><br>2. Methods for processing the data:<br>We computed event-related potentials. See linked publication<br><br><br>3. Instrument- or software-specific information needed to interpret the data:<br>MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# <br><br><br>4. Standards and calibration information, if appropriate:<br>For information, see linked publication.<br><br><br>5. Environmental/experimental conditions:<br>For information, see linked publication.<br><br><br>6. 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For example, we added notes to explain what different variables mean.<br><br><br>The folder structure needs to be as follows:<br>AAN_LRclick (main folder)<br>--->data<br>--->--->bids (AAN_LRclick_bids_EEG)<br>--->--->log (AAN_LRclick_behavior_log)<br>--->MNE (AAN_LRclick_EEG_scripts)<br>--->results (AAN_LRclick_results)<br><br><br>To run the MNE-Python scripts:<br>Anaconda was used with MNE-Python 0.22 (see installation at https://mne.tools/stable/index.html# ).<br>For preprocess.py and analysis.py, the complete scripts should be run (from anaconda prompt).<br>",
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+        "name": "Stefan Wiens",
+        "id": 3862558,
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+        "name": "Rasmus Eklund",
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+        "orcid": ""
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+      {
+        "name": "Billy Gerdfeldter",
+        "id": 9509663,
+        "orcid": "0000-0002-3222-8056"
+      }
+    ],
+    "tags": [
+      "EEG",
+      "ERP",
+      "consciousness",
+      "awareness",
+      "neural correlates",
+      "auditory awareness negativity",
+      "localization",
+      "Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)",
+      "Neuroscience and Physiological Psychology"
+    ],
+    "categories": [
+      {
+        "id": 30340,
+        "title": "Psychopharmacology",
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+    "license": "CC BY 4.0",
+    "url": "https://su.figshare.com/articles/dataset/Open_data_The_early_but_not_the_late_neural_correlate_of_auditory_awareness_reflects_lateralized_experiences/13067018",
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+  {
+    "dataset_id": "8033726",
+    "doi": "10.3389/fnins.2019.00300.s001",
+    "title": "Data_Sheet_1_Multimodal Integration of M/EEG and f/MRI Data in SPM12.pdf",
+    "description": "<p>We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the \u201cN/M170\u201d evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.</p>",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
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+    "modified_date": "2023-05-30T12:13:24Z",
+    "authors": [
+      {
+        "name": "Richard N. Henson",
+        "id": 6628652,
+        "orcid": ""
+      },
+      {
+        "name": "Hunar Abdulrahman",
+        "id": 6628655,
+        "orcid": ""
+      },
+      {
+        "name": "Guillaume Flandin",
+        "id": 1369476,
+        "orcid": ""
+      },
+      {
+        "name": "Vladimir Litvak",
+        "id": 42646,
+        "orcid": ""
+      }
+    ],
+    "tags": [
+      "MEG",
+      "EEG",
+      "fMRI",
+      "multimodal",
+      "fusion",
+      "SPM",
+      "inversion",
+      "faces"
+    ],
+    "categories": [
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+        "id": 320,
+        "title": "Radiology and Organ Imaging",
+        "parent_id": 142,
+        "path": "",
+        "source_id": "",
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+        "source_id": "",
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+        "taxonomy_id": 10
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+        "id": 566,
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+      {
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+        "title": "Developmental Biology",
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+        "source_id": "",
+        "taxonomy_id": 10
+      }
+    ],
+    "license": "CC BY 4.0",
+    "url": "https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Multimodal_Integration_of_M_EEG_and_f_MRI_Data_in_SPM12_pdf/8033726",
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diff --git a/consolidated/figshare_datasets_test.json b/consolidated/figshare_datasets_test.json
new file mode 100644
index 00000000..ffcc32a7
--- /dev/null
+++ b/consolidated/figshare_datasets_test.json
@@ -0,0 +1,393 @@
+[
+  {
+    "dataset_id": "30227503",
+    "doi": "10.6084/m9.figshare.30227503.v1",
+    "title": "EEG Dataset for Visual Imagery",
+    "description": "",
+    "defined_type": 3,
+    "defined_type_name": "dataset",
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+    "modified_date": "2025-10-01T15:02:02Z",
+    "authors": [],
+    "tags": [],
+    "categories": [],
+    "license": "",
+    "url": "https://figshare.com/articles/dataset/EEG_Dataset_for_Visual_Imagery/30227503",
+    "api_url": "https://api.figshare.com/v2/articles/30227503",
+    "resource_title": "",
+    "resource_doi": "",
+    "files": [],
+    "file_count": 0,
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+    "source": "figshare"
+  },
+  {
+    "dataset_id": "29987758",
+    "doi": "10.6084/m9.figshare.29987758.v2",
+    "title": "<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>",
+    "description": "",
+    "defined_type": 3,
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diff --git a/consolidated/scidb_datasets.json b/consolidated/scidb_datasets.json
new file mode 100644
index 00000000..d818645a
--- /dev/null
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+    "title_en": "Visual Colour <span style='background-color:yellow'>EEG</span> Dataset",
+    "title_zh": "Visual Colour <span style='background-color:yellow'>EEG</span> Dataset",
+    "description": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
+    "description_en": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
+    "description_zh": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
+    "authors": [
+      {
+        "name_en": "Wu Yi Jia",
+        "name_zh": "Wu Yi Jia",
+        "email": "wuyijia@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Fudan University",
+            "name_zh": "Fudan University",
+            "ror_id": "https://ror.org/013q1eq08"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "visual",
+      "colour",
+      "neuroscience"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "visual",
+      "colour",
+      "neuroscience"
+    ],
+    "publication_date": "2021-08-04T12:53:30.415Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 3487.74,
+    "file_size_bytes": 3657158170,
+    "download_count": 981,
+    "visit_count": 1454,
+    "url": "https://www.scidb.cn/en/detail?id=610a8e8f44b32f0f67587553",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "60b9c3adae98d4289fceed43",
+    "doi": "10.11922/sciencedb.00820",
+    "cstr": "31253.11.sciencedb.00820",
+    "pid": "21.86116.6/sciencedb.00820",
+    "title": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
+    "title_en": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
+    "title_zh": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
+    "description": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
+    "description_en": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
+    "description_zh": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
+    "authors": [
+      {
+        "name_en": "Guozheng Wang",
+        "name_zh": "Guozheng Wang",
+        "affiliations": [
+          {
+            "name_en": "Zhejiang University",
+            "name_zh": "Zhejiang University",
+            "ror_id": "https://ror.org/00a2xv884"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Anesthetic depth monitoring",
+      " auditory stimulation",
+      " consciousness"
+    ],
+    "keywords_zh": [
+      "Anesthetic depth monitoring",
+      " auditory stimulation",
+      " consciousness"
+    ],
+    "publication_date": "2021-05-24T00:32:53.272Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 15848,
+    "download_count": 7,
+    "visit_count": 772,
+    "url": "https://www.scidb.cn/en/detail?id=60b9c3adae98d4289fceed43",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67ee412a02cc303f2020136f",
+    "doi": "10.57760/sciencedb.23155",
+    "cstr": "31253.11.sciencedb.23155",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
+    "title_en": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
+    "description": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
+    "description_en": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
+    "description_zh": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
+    "authors": [
+      {
+        "name_en": "HaiyanWu",
+        "name_zh": "HaiyanWu",
+        "email": "haiyanwu@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Xuanji Chen",
+        "name_zh": "Xuanji Chen",
+        "email": "mc46540@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Junyuan Zheng",
+        "name_zh": "Junyuan Zheng",
+        "email": "mc25802@umac.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "VR",
+      "EEG",
+      "Navigation",
+      "Cognitive psychology"
+    ],
+    "keywords_zh": [
+      "VR",
+      "EEG",
+      "Navigation",
+      "Cognitive psychology"
+    ],
+    "publication_date": "2025-04-08T09:07:37.432Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 40671.59,
+    "file_size_bytes": 42647252793,
+    "download_count": 370,
+    "visit_count": 621,
+    "url": "https://www.scidb.cn/en/detail?id=67ee412a02cc303f2020136f",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66b0ab6495ad961c18ee103f",
+    "doi": "10.57760/sciencedb.ai.00009",
+    "cstr": "31253.11.sciencedb.ai.00009",
+    "pid": "",
+    "title": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
+    "title_en": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
+    "title_zh": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
+    "description": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
+    "description_en": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
+    "description_zh": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
+    "authors": [
+      {
+        "name_en": "Wei Wei",
+        "name_zh": "Wei Wei",
+        "email": "weiwei2018@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "Li Xujin",
+        "name_zh": "Li Xujin",
+        "email": "lixujin2021@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "Qiu Shuang",
+        "name_zh": "Qiu Shuang",
+        "email": "shuang.qiu@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "He Huiguang",
+        "name_zh": "He Huiguang",
+        "email": "huiguang.he@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "target image retrieval ",
+      "P300"
+    ],
+    "keywords_zh": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "target image retrieval ",
+      "P300"
+    ],
+    "publication_date": "2023-12-21T09:06:16.172Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 12817.93,
+    "file_size_bytes": 13440575993,
+    "download_count": 748,
+    "visit_count": 2171,
+    "url": "https://www.scidb.cn/en/detail?id=66b0ab6495ad961c18ee103f",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "670c8882129fd9344a03229d",
+    "doi": "10.57760/sciencedb.14812",
+    "cstr": "31253.11.sciencedb.14812",
+    "pid": "",
+    "title": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
+    "title_en": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
+    "title_zh": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
+    "description": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
+    "description_en": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
+    "description_zh": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
+    "authors": [
+      {
+        "name_en": "Xujin Li",
+        "name_zh": "Xujin Li",
+        "email": "lixujin2021@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "Institute of Automation",
+            "ror_id": "https://ror.org/056qj1t15"
+          },
+          {
+            "name_en": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)",
+            "name_zh": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)"
+          }
+        ]
+      },
+      {
+        "name_en": "Wei Wei",
+        "name_zh": "Wei Wei",
+        "email": "weiwei2018@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "Institute of Automation",
+            "ror_id": "https://ror.org/00qdtba35"
+          }
+        ]
+      },
+      {
+        "name_en": "shuang Qiu",
+        "name_zh": "shuang Qiu",
+        "email": "shuang.qiu@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "Institute of Automation",
+            "ror_id": "https://ror.org/00qdtba35"
+          }
+        ]
+      },
+      {
+        "name_en": "Xinyi Zhang",
+        "name_zh": "Xinyi Zhang",
+        "email": "zhangxinyi2020@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "Institute of Automation",
+            "ror_id": "https://ror.org/00qdtba35"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Fu Li",
+        "name_zh": "Fu Li",
+        "email": "fuli@mail.xidian.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University",
+            "name_zh": "The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University"
+          }
+        ]
+      },
+      {
+        "name_en": "Huiguang He",
+        "name_zh": "Huiguang He",
+        "email": "huiguang.he@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "Institute of Automation",
+            "ror_id": "https://ror.org/00qdtba35"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "Target image retrieval",
+      "P300",
+      "Event-Related Potential (ERP)"
+    ],
+    "keywords_zh": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "Target image retrieval",
+      "P300",
+      "Event-Related Potential (ERP)"
+    ],
+    "publication_date": "2024-10-17T07:34:31.468Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 450223.5,
+    "file_size_bytes": 472093558496,
+    "download_count": 15526,
+    "visit_count": 3171,
+    "url": "https://www.scidb.cn/en/detail?id=670c8882129fd9344a03229d",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "651d974489be2410e0a6cdc3",
+    "doi": "10.57760/sciencedb.11875",
+    "cstr": "31253.11.sciencedb.11875",
+    "pid": "",
+    "title": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
+    "title_en": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
+    "title_zh": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
+    "description": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
+    "description_en": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
+    "description_zh": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
+    "authors": [
+      {
+        "name_en": "Zhiwei Xu",
+        "name_zh": "Zhiwei Xu",
+        "email": "zwxu@whu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Hubei University",
+            "name_zh": "Hubei University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Matlab",
+      "ML"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Matlab",
+      "ML"
+    ],
+    "publication_date": "2023-10-08T06:02:28.599Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 409.41,
+    "file_size_bytes": 429300777,
+    "download_count": 239,
+    "visit_count": 531,
+    "url": "https://www.scidb.cn/en/detail?id=651d974489be2410e0a6cdc3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "643cf1f3692b1255d27a1791",
+    "doi": "10.57760/sciencedb.psych.00111",
+    "cstr": "31253.11.sciencedb.psych.00111",
+    "pid": "21.86116.6/sciencedb.psych.00111",
+    "title": "Music training <span style='background-color:yellow'>EEG</span> component data",
+    "title_en": "Music training <span style='background-color:yellow'>EEG</span> component data",
+    "title_zh": "音乐训练脑电成分数据",
+    "description": "<p>This dataset is about the influence of musical training experience on the time course of music emotional processing.</p>",
+    "description_en": "<p>This dataset is about the influence of musical training experience on the time course of music emotional processing.</p>",
+    "description_zh": "<p>该数据集是关于音乐训练经历影响音乐情绪加工的时间进程的脑电成分数据。</p>",
+    "authors": [
+      {
+        "name_en": "Yangjimei",
+        "name_zh": "杨集梅",
+        "email": "157553855@qq.com",
+        "affiliations": [
+          {
+            "name_en": "西南大学",
+            "name_zh": "西南大学"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "N1",
+      "N2",
+      "N400"
+    ],
+    "keywords_zh": [
+      "N1",
+      "N2",
+      "N400 "
+    ],
+    "publication_date": "2023-04-17T08:18:53.809Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 14267,
+    "download_count": 36,
+    "visit_count": 1485,
+    "url": "https://www.scidb.cn/en/detail?id=643cf1f3692b1255d27a1791",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "64341650bf439249dc9631b5",
+    "doi": "10.57760/sciencedb.07935",
+    "cstr": "31253.11.sciencedb.07935",
+    "pid": "21.86116.6/sciencedb.07935",
+    "title": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
+    "title_en": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
+    "title_zh": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
+    "description": "<p>This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
+    "description_en": "<p>This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
+    "description_zh": "<p>     This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
+    "authors": [
+      {
+        "name_en": "Wu Yi Jia",
+        "name_zh": "Wu Yi Jia",
+        "email": "wuyijia@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Fudan University",
+            "name_zh": "Fudan University",
+            "ror_id": "https://ror.org/013q1eq08"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "visual",
+      "color",
+      "EEG",
+      "underlying"
+    ],
+    "keywords_zh": [
+      "visual",
+      "color",
+      "EEG",
+      "underlying"
+    ],
+    "publication_date": "2023-05-15T07:34:05.143Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 8594.65,
+    "file_size_bytes": 9012141771,
+    "download_count": 40,
+    "visit_count": 284,
+    "url": "https://www.scidb.cn/en/detail?id=64341650bf439249dc9631b5",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "674931530ea3b3102f40c4d7",
+    "doi": "10.57760/sciencedb.17705",
+    "cstr": "31253.11.sciencedb.17705",
+    "pid": "",
+    "title": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
+    "title_en": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
+    "title_zh": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
+    "description": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
+    "authors": [
+      {
+        "name_en": "Xujin Li",
+        "name_zh": "Xujin Li",
+        "email": "lixujin2021@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)",
+            "name_zh": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)"
+          }
+        ]
+      },
+      {
+        "name_en": "Wei Wei",
+        "name_zh": "Wei Wei",
+        "email": "weiwei2018@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Kun Zhao",
+        "name_zh": "Kun Zhao",
+        "email": "zhaokun2024@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Shuang Qiu",
+        "name_zh": "Shuang Qiu",
+        "email": "shuang.qiu@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Huiguang He",
+        "name_zh": "Huiguang He",
+        "email": "huiguang.he@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)",
+            "name_zh": "School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "Eye Movement",
+      "Event-Related Potential (ERP)",
+      "P300",
+      "Multi-Class Target Image Detection"
+    ],
+    "keywords_zh": [
+      "Rapid Serial Visual Presentation (RSVP)",
+      "EEG",
+      "Eye Movement",
+      "Event-Related Potential (ERP)",
+      "P300",
+      "Multi-Class Target Image Detection"
+    ],
+    "publication_date": "2024-12-10T00:42:28.431Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 122898.45,
+    "file_size_bytes": 128868360159,
+    "download_count": 305085,
+    "visit_count": 5105,
+    "url": "https://www.scidb.cn/en/detail?id=674931530ea3b3102f40c4d7",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "65c5ac189d874139a2eb1efc",
+    "doi": "10.57760/sciencedb.CHNNeuro.00007",
+    "cstr": "31253.11.sciencedb.CHNNeuro.00007",
+    "pid": "",
+    "title": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
+    "title_en": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
+    "title_zh": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
+    "description": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
+    "description_en": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
+    "description_zh": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
+    "authors": [
+      {
+        "name_en": "HaiyanWu",
+        "name_zh": "HaiyanWu",
+        "email": "haiyanwu@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "XinyuMou",
+        "name_zh": "XinyuMou",
+        "email": "12112317@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "CuilinHe",
+        "name_zh": "CuilinHe",
+        "email": "mc36413@connect.um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau"
+          }
+        ]
+      },
+      {
+        "name_en": "LiweiTan",
+        "name_zh": "LiweiTan",
+        "email": "mc36431@connect.um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau"
+          }
+        ]
+      },
+      {
+        "name_en": "JunjieYu",
+        "name_zh": "JunjieYu",
+        "email": "12231192@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "HuadongLiang",
+        "name_zh": "HuadongLiang",
+        "email": "hdliang@iflytek.com",
+        "affiliations": [
+          {
+            "name_en": " iFLYTEK Co., LTD, Hefei, China",
+            "name_zh": " iFLYTEK Co., LTD, Hefei, China"
+          }
+        ]
+      },
+      {
+        "name_en": "JianyuZhang",
+        "name_zh": "JianyuZhang",
+        "email": "12112347@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern university of Science and Technology",
+            "name_zh": "Southern university of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "YanTian",
+        "name_zh": "YanTian",
+        "email": "sc02233@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau"
+          }
+        ]
+      },
+      {
+        "name_en": "Yu-Fang Yang",
+        "name_zh": "Yu-Fang Yang",
+        "email": "yu-fang.yang@fu-berlin.de",
+        "affiliations": [
+          {
+            "name_en": "Freie Universitat Berlin",
+            "name_zh": "Freie Universitat Berlin"
+          }
+        ]
+      },
+      {
+        "name_en": "TingXu",
+        "name_zh": "TingXu",
+        "email": "ting.xu@childmind.org",
+        "affiliations": [
+          {
+            "name_en": "New York Collaborates for Autism",
+            "name_zh": "New York Collaborates for Autism",
+            "ror_id": "https://ror.org/021amba89"
+          }
+        ]
+      },
+      {
+        "name_en": "QingWang",
+        "name_zh": "QingWang",
+        "email": "vincent.w.qing@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Jiao Tong University",
+            "name_zh": "Shanghai Jiao Tong University"
+          }
+        ]
+      },
+      {
+        "name_en": "MiaoCao",
+        "name_zh": "MiaoCao",
+        "email": "miao.cao@hotmail.com",
+        "affiliations": [
+          {
+            "name_en": "Swinburne University of Technology",
+            "name_zh": "Swinburne University of Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "ZijiaoChen",
+        "name_zh": "ZijiaoChen",
+        "email": "zijiao.chen@u.nus.edu",
+        "affiliations": [
+          {
+            "name_en": " National University of Singapore",
+            "name_zh": " National University of Singapore"
+          }
+        ]
+      },
+      {
+        "name_en": "Chuan-Peng Hu",
+        "name_zh": "Chuan-Peng Hu",
+        "email": "hu.chuan-peng@nnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Nanjing Normal University",
+            "name_zh": "Nanjing Normal University"
+          }
+        ]
+      },
+      {
+        "name_en": "XindiWang",
+        "name_zh": "XindiWang",
+        "email": "sandywang.rest@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "QuanyingLiu",
+        "name_zh": "QuanyingLiu",
+        "email": "liuqy@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "semantic decoding",
+      "natural language processing"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "semantic decoding",
+      "natural language processing"
+    ],
+    "publication_date": "2024-02-20T17:05:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 686285.21,
+    "file_size_bytes": 719622195436,
+    "download_count": 37287,
+    "visit_count": 9933,
+    "url": "https://www.scidb.cn/en/detail?id=65c5ac189d874139a2eb1efc",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68b3e2070f50167093ad298b",
+    "doi": "10.57760/sciencedb.23372",
+    "cstr": "31253.11.sciencedb.23372",
+    "pid": "",
+    "title": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
+    "title_en": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
+    "title_zh": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
+    "description": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
+    "description_en": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
+    "description_zh": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
+    "authors": [
+      {
+        "name_en": "Gaoyan Zhang",
+        "name_zh": "Gaoyan Zhang",
+        "email": "zhanggaoyan@tju.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Tianjin University",
+            "name_zh": "Tianjin University",
+            "ror_id": "https://ror.org/012tb2g32"
+          }
+        ]
+      },
+      {
+        "name_en": "Shengrui He",
+        "name_zh": "Shengrui He",
+        "email": "heshengrui@tju.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Tianjin University",
+            "name_zh": "Tianjin University",
+            "ror_id": "https://ror.org/012tb2g32"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhongjie Li",
+        "name_zh": "Zhongjie Li",
+        "email": "lizhongjie@nwpu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Northwestern Polytechnical University",
+            "name_zh": "Northwestern Polytechnical University",
+            "ror_id": "https://ror.org/01y0j0j86"
+          }
+        ]
+      },
+      {
+        "name_en": "Jianwu Dang",
+        "name_zh": "Jianwu Dang",
+        "email": "jdang@jaist.ac.jp",
+        "affiliations": [
+          {
+            "name_en": "Chinese Academy of Sciences",
+            "name_zh": "Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/034t30j35"
+          }
+        ]
+      },
+      {
+        "name_en": "Yingyi Luo",
+        "name_zh": "Yingyi Luo",
+        "email": "luoyingyi@cass.org.cn",
+        "affiliations": [
+          {
+            "name_en": "Chinese Academy of Social Sciences",
+            "name_zh": "Chinese Academy of Social Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Intention Recognition",
+      "Prosodic emotion"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Intention Recognition",
+      "Prosodic emotion"
+    ],
+    "publication_date": "2025-04-10T02:53:07.749Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 49425.6,
+    "file_size_bytes": 51826499477,
+    "download_count": 29668,
+    "visit_count": 2450,
+    "url": "https://www.scidb.cn/en/detail?id=68b3e2070f50167093ad298b",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "677fee3742b98c7bba09d3ac",
+    "doi": "10.57760/sciencedb.19730",
+    "cstr": "31253.11.sciencedb.19730",
+    "pid": "",
+    "title": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
+    "title_en": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
+    "title_zh": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
+    "description": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
+    "authors": [
+      {
+        "name_en": "Qiufang Fu",
+        "name_zh": "Qiufang Fu",
+        "email": "fuqf@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Liansheng Yao",
+        "name_zh": "Liansheng Yao",
+        "email": "lianshengyao@yeah.net",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "scene representation",
+      "object representation",
+      "edge information",
+      "surface information",
+      "scene-first theory"
+    ],
+    "keywords_zh": [
+      "scene representation",
+      "object representation",
+      "edge information",
+      "surface information",
+      "scene-first theory"
+    ],
+    "publication_date": "2025-01-10T03:16:03.510Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 24422.84,
+    "file_size_bytes": 25609203324,
+    "download_count": 276,
+    "visit_count": 203,
+    "url": "https://www.scidb.cn/en/detail?id=677fee3742b98c7bba09d3ac",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68d4e43119a491453e1c1328",
+    "doi": "10.57760/sciencedb.CHNNeuro.00001",
+    "cstr": "31253.11.sciencedb.CHNNeuro.00001",
+    "pid": "",
+    "title": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "title_en": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "title_zh": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "description": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "authors": [
+      {
+        "name_en": "Sitong Chen",
+        "name_zh": "Sitong Chen",
+        "email": "12212653@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Beiqianyi Li",
+        "name_zh": "Beiqianyi Li",
+        "email": "sc12214@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Cuilin He",
+        "name_zh": "Cuilin He",
+        "email": "mc36413@connect.um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Dongyang Li",
+        "name_zh": "Dongyang Li",
+        "email": "lidy2023@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Mingyang Wu",
+        "name_zh": "Mingyang Wu",
+        "email": "12312813@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xinke Shen",
+        "name_zh": "Xinke Shen",
+        "email": "shenxk@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "Song Wang",
+        "name_zh": "Song Wang",
+        "email": "12332575@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xuetao Wei",
+        "name_zh": "Xuetao Wei",
+        "email": "weixt@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xindi Wang",
+        "name_zh": "Xindi Wang",
+        "email": "sandywang.rest@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "AI Research Institute, iFLYTEK Co., LTD,",
+            "name_zh": "AI Research Institute, iFLYTEK Co., LTD,"
+          }
+        ]
+      },
+      {
+        "name_en": "Haiyan Wu",
+        "name_zh": "Haiyan Wu",
+        "email": "evawu2008@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Quanying Liu",
+        "name_zh": "Quanying Liu",
+        "email": "liuqy@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      " natural language processing",
+      "semantic decoding"
+    ],
+    "keywords_zh": [
+      "EEG",
+      " natural language processing",
+      "semantic decoding"
+    ],
+    "publication_date": "2025-07-28T03:39:43.257Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 97754.75,
+    "file_size_bytes": 102503284224,
+    "download_count": 25408,
+    "visit_count": 3547,
+    "url": "https://www.scidb.cn/en/detail?id=68d4e43119a491453e1c1328",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67a41f4c27843a4e47683a4e",
+    "doi": "10.57760/sciencedb.20611",
+    "cstr": "31253.11.sciencedb.20611",
+    "pid": "",
+    "title": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "title_en": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "title_zh": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
+    "description": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
+    "authors": [
+      {
+        "name_en": "Sitong Chen",
+        "name_zh": "Sitong Chen",
+        "email": "12212653@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Beiqianyi Li",
+        "name_zh": "Beiqianyi Li",
+        "email": "sc12214@um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Cuilin He",
+        "name_zh": "Cuilin He",
+        "email": "mc36413@connect.um.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Dongyang Li",
+        "name_zh": "Dongyang Li",
+        "email": "lidy2023@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Mingyang Wu",
+        "name_zh": "Mingyang Wu",
+        "email": "12312813@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xinke Shen",
+        "name_zh": "Xinke Shen",
+        "email": "shenxk@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Song Wang",
+        "name_zh": "Song Wang",
+        "email": "12332575@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xuetao Wei",
+        "name_zh": "Xuetao Wei",
+        "email": "weixt@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xindi Wang",
+        "name_zh": "Xindi Wang",
+        "email": "sandywang.rest@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "AI Research Institute, iFLYTEK Co., LTD,",
+            "name_zh": "AI Research Institute, iFLYTEK Co., LTD,"
+          }
+        ]
+      },
+      {
+        "name_en": "Haiyan Wu",
+        "name_zh": "Haiyan Wu",
+        "email": "evawu2008@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "University of Macau",
+            "name_zh": "University of Macau",
+            "ror_id": "https://ror.org/01r4q9n85"
+          }
+        ]
+      },
+      {
+        "name_en": "Quanying Liu",
+        "name_zh": "Quanying Liu",
+        "email": "liuqy@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Southern University of Science and Technology",
+            "name_zh": "Southern University of Science and Technology",
+            "ror_id": "https://ror.org/049tv2d57"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      " natural language processing",
+      "Semantic Decoding"
+    ],
+    "keywords_zh": [
+      "EEG",
+      " natural language processing",
+      "Semantic Decoding"
+    ],
+    "publication_date": "2025-02-14T07:00:39.792Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 104940.05,
+    "file_size_bytes": 110037613972,
+    "download_count": 2712,
+    "visit_count": 3848,
+    "url": "https://www.scidb.cn/en/detail?id=67a41f4c27843a4e47683a4e",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67cc1763cfa1d066cfad71fb",
+    "doi": "10.57760/sciencedb.14025",
+    "cstr": "31253.11.sciencedb.14025",
+    "pid": "",
+    "title": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
+    "title_en": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
+    "title_zh": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
+    "description": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
+    "description_en": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
+    "description_zh": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
+    "authors": [
+      {
+        "name_en": "Xin Xu",
+        "name_zh": "Xin Xu",
+        "email": "12211762@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Xinke Shen",
+        "name_zh": "Xinke Shen",
+        "email": "shenxk@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Xuyang Chen",
+        "name_zh": "Xuyang Chen",
+        "email": "12010103@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Qingzhu Zhang",
+        "name_zh": "Qingzhu Zhang",
+        "email": "zhangqingzhu2002@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Sitian Wang",
+        "name_zh": "Sitian Wang",
+        "email": "12312453@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Yihan Li",
+        "name_zh": "Yihan Li",
+        "email": "12312832@mail.sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Zongsheng Li",
+        "name_zh": "Zongsheng Li",
+        "email": "223010111@link.cuhk.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          },
+          {
+            "name_en": "School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China",
+            "name_zh": "School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Dan Zhang",
+        "name_zh": "Dan Zhang",
+        "email": "dzhang@tsinghua.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China",
+            "name_zh": "Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Mingming Zhang",
+        "name_zh": "Mingming Zhang",
+        "email": "zhangmm@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Quanying Liu",
+        "name_zh": "Quanying Liu",
+        "email": "liuqy@sustech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
+            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Affective computing",
+      "multiple-context",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "Affective computing",
+      "multiple-context",
+      "EEG"
+    ],
+    "publication_date": "2024-09-27T12:59:29.012Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 70711.78,
+    "file_size_bytes": 74146673499,
+    "download_count": 90342,
+    "visit_count": 4002,
+    "url": "https://www.scidb.cn/en/detail?id=67cc1763cfa1d066cfad71fb",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "664adaeb8355d83c98b50e5d",
+    "doi": "10.57760/sciencedb.08758",
+    "cstr": "31253.11.sciencedb.08758",
+    "pid": "",
+    "title": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
+    "title_en": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
+    "title_zh": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
+    "description": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
+    "description_en": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
+    "description_zh": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
+    "authors": [
+      {
+        "name_en": "Cen Chenghong",
+        "name_zh": "Cen Chenghong",
+        "email": "cen_chenghong@m.scnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "South China Normal University",
+            "name_zh": "华南师范大学",
+            "ror_id": "https://ror.org/01kq0pv72"
+          }
+        ]
+      },
+      {
+        "name_en": "Li Kang",
+        "name_zh": "Li Kang",
+        "email": "Kang.Li@zu.ac.ae",
+        "affiliations": [
+          {
+            "name_en": "Zayed University",
+            "name_zh": "Zayed University"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Jing",
+        "name_zh": "Xu Jing",
+        "email": "xujing9602@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Macau University of Science and Technology",
+            "name_zh": "Macau University of Science and Technology"
+          }
+        ]
+      },
+      {
+        "name_en": "Huang Guanghui",
+        "name_zh": "Huang Guanghui",
+        "email": "Ghhuang1@must.edu.mo",
+        "affiliations": [
+          {
+            "name_en": "Macau University of Science and Technology",
+            "name_zh": "Macau University of Science and Technology",
+            "ror_id": "https://ror.org/03jqs2n27"
+          }
+        ]
+      },
+      {
+        "name_en": "Jiang Tan",
+        "name_zh": "Jiang Tan",
+        "email": "jiangtan@scnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "South China Normal University",
+            "name_zh": "华南师范大学",
+            "ror_id": "https://ror.org/01kq0pv72"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "PANAS SCALE",
+      "EEG DATA ANALYSIS",
+      "COGNITIVE EMOTION",
+      "IMMERSIVE ART THERAPY"
+    ],
+    "keywords_zh": [
+      "PANAS SCALE",
+      "EEG DATA ANALYSIS",
+      "COGNITIVE EMOTION",
+      "IMMERSIVE ART THERAPY"
+    ],
+    "publication_date": "2024-05-20T06:55:10.809Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 659.24,
+    "file_size_bytes": 691264130,
+    "download_count": 25388,
+    "visit_count": 1974,
+    "url": "https://www.scidb.cn/en/detail?id=664adaeb8355d83c98b50e5d",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6332b4603d498241dcc008f3",
+    "doi": "10.57760/sciencedb.j00052.00029",
+    "cstr": "31253.11.sciencedb.j00052.00029",
+    "pid": "21.86116.6/sciencedb.j00052.00029",
+    "title": "The neurophysiological mechanism of brand personality attracting consumers: Evidence from <span style='background-color:yellow'>EEG</span> and GSR（SPSS data)",
+    "title_en": "The neurophysiological mechanism of brand personality attracting consumers: Evidence from <span style='background-color:yellow'>EEG</span> and GSR（SPSS data)",
+    "title_zh": "品牌个性吸引消费者的神经生理机制——来自<span style='background-color:yellow'>EEG</span>和GSR的证据（SPSS 数据）",
+    "description": "<p>EEG GSR SAM data exported from imotion software, and the data of Big Five Personality Scale filled in by subjects with questionnaire stars&nbsp;Label information&nbsp;peak_ count_ Xx=peak number&nbsp;APA_ Xx=peak amplitude&nbsp;xx_ Valence=SAM self-assessment questionnaire potency&nbsp;xx_ Dominance=dominated by SAM self-assessment questionnaire&nbsp;xx_ Arousal=SAM self-assessment questionnaire wake-up&nbsp;LR_ xx_ Alpha=PAI α&nbsp;LR_ xx_ Beta=PAI β&nbsp;LR_ xx_ Gamma=PAI γ&nbsp;Xx Brand personality&nbsp;Xx=Com: capability&nbsp;Xx=Sin: Sincerity&nbsp;Xx=Rug: rough&nbsp;Xx=Sop: sophisticated&nbsp;Xx=Exi: excited&nbsp;</p>",
+    "description_en": "<p>EEG GSR SAM data exported from imotion software, and the data of Big Five Personality Scale filled in by subjects with questionnaire stars&nbsp;Label information&nbsp;peak_ count_ Xx=peak number&nbsp;APA_ Xx=peak amplitude&nbsp;xx_ Valence=SAM self-assessment questionnaire potency&nbsp;xx_ Dominance=dominated by SAM self-assessment questionnaire&nbsp;xx_ Arousal=SAM self-assessment questionnaire wake-up&nbsp;LR_ xx_ Alpha=PAI α&nbsp;LR_ xx_ Beta=PAI β&nbsp;LR_ xx_ Gamma=PAI γ&nbsp;Xx Brand personality&nbsp;Xx=Com: capability&nbsp;Xx=Sin: Sincerity&nbsp;Xx=Rug: rough&nbsp;Xx=Sop: sophisticated&nbsp;Xx=Exi: excited&nbsp;</p>",
+    "description_zh": "<p>EEG 、GSR、SAM数据，从imotion软件中导出，脑电图设备使用Neuroelectic公司的Enobio20脑电系统，按国际10-20系统的20导电极帽记录EEG，以右耳夹电极为参考电极，设备采样率为500hz。皮肤电GSR设备使用Shimmer公司的Shimmer3 GSR+，采样率为51.2Hz。</p><p>标签：</p><p>peak_ count_ Xx=尖峰次数</p><p>APA_ Xx=尖峰振幅</p><p>xx_ Valence=SAM 问卷-效价</p><p>&nbsp;xx_ Dominance=SAM问卷-支配</p><p>xx_ Arousal=SAM问卷-唤醒</p><p>LR_ xx_ Alpha=PAI α&nbsp;</p><p>LR_ xx_ Beta=PAI β&nbsp;</p><p>LR_ xx_ Gamma=PAI γ&nbsp;</p><p><br></p><p>XX-品牌个性</p><p>Xx=Com: 能力</p><p>Xx=Sin: 真诚</p><p>Xx=Rug: 粗犷</p><p>Xx=Sop: 老练</p><p>&nbsp;Xx=Exi: 兴奋</p>",
+    "authors": [
+      {
+        "name_en": "xu zhi wei",
+        "name_zh": "许志炜",
+        "email": "zwxu@whu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Hubei University",
+            "name_zh": "Hubei University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "brand personality",
+      "neuromarketing",
+      "EEG",
+      "GSR",
+      "circumplex model of affect"
+    ],
+    "keywords_zh": [
+      "品牌个性",
+      "神经营销学",
+      "EEG",
+      "GSR",
+      "情绪环状模型"
+    ],
+    "publication_date": "2023-08-31T01:07:14.096Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 12344,
+    "download_count": 21,
+    "visit_count": 610,
+    "url": "https://www.scidb.cn/en/detail?id=6332b4603d498241dcc008f3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66993089ab1be93f47ea6b7e",
+    "doi": "10.57760/sciencedb.psych.00207",
+    "cstr": "31253.11.sciencedb.psych.00207",
+    "pid": "",
+    "title": "Behavioral and <span style='background-color:yellow'>EEG</span> results of emotional discrimination task (high-altitude and low-altitude)",
+    "title_en": "Behavioral and <span style='background-color:yellow'>EEG</span> results of emotional discrimination task (high-altitude and low-altitude)",
+    "title_zh": "情绪判断任务行为学及脑电数据（高原人群和平原人群）",
+    "description": "<p>Participants were all college undergraduates with normal or corrected vision, right-handed, no history of neurological or psychiatric disorders or other chronic physical conditions, no use of drugs (psychiatric or otherwise) in the last 2 days, and no irregular lifestyle. The high-altitude group consisted of 25 subjects from Tibet University (3658m above sea level), all of whom grew up in the plain, entered the plateau for the first time and lived there for more than 2 years after adulthood. Excluding 1 subjects with missing EEG data and 2 subjects with damaged data, 22 subjects were enrolled ultimately (12 males and 10 females, age ranged between 20 and 24 years, average age as 21.91±0.97 years). The 30 members of the low-altitude group are from universities in Beijing (52m above sea level), who have never been to the plateau. 6 subjects were excluded for excessive disturbance of EEG data, and 24 subjects were finally enrolled (11 males and 13 females, age ranged between 19 and 24 years, average age as 21.43±1.24 years).</p><p>Emotional face recognition task was adopted. Stimulations were selected from Chinese Affective Face Picture System (Gong et al., 2011), including 3 males and 3 females, each with 3 expressions (angry, happy, neutral), a total of 18 emotional face pictures. A white cross (0.82°×0.82°) was presented in the center of the black background as the fixation point, with the presentation time random between 1200-1600ms. Then, the facial picture appears until subjects press the key and enter the next trial. Subjects are demanded to judge the gender of faces accurately as fast as possible. The position of the buttons was balanced between the subjects, with half of the subjects pressing the left button for a male face, the right button for a female face, and the other half doing the opposite. The experiment is consisted of practice and formal test. During the practice, each picture was presented once in a random order, with a total of 18 trials. The formal experiment was divided into 2 phases, with a short rest in between, 144 trials in each phase, a total of 288 trials, and each picture was repeated 16 times.</p><p>Electroencephalography (EEG) was collected using the Neuroscan recording and analysis system and the elastic cap with 64 Ag/AgCl electrodes cap of International 10-20 standard system (Neuroscan Inc., USA). The referenced electrode was the left mastoid and the grounding electrode was located at the midpoint of the Fz and FPz. The vertical electrooculogram (VEOG) was recorded with a pair of electrodes placed above and below the left eye, while the horizontal electrooculogram (HEOG) was recorded with another pair of electrodes, located 1 cm from the outer canthus of each eye. The resistance of scalp was kept under 5kΩ. Signals were amplified with a Neuroscan SynamPs-2 amplifier (Neuroscan Inc., USA) which included a 0.05-100 Hz bandpass filter with the sampling frequency as 500Hz.</p><p>Behavioral results could be opened with E-prime3 and EEG results could be opened with EEGLAB.</p>",
+    "description_en": "<p>Participants were all college undergraduates with normal or corrected vision, right-handed, no history of neurological or psychiatric disorders or other chronic physical conditions, no use of drugs (psychiatric or otherwise) in the last 2 days, and no irregular lifestyle. The high-altitude group consisted of 25 subjects from Tibet University (3658m above sea level), all of whom grew up in the plain, entered the plateau for the first time and lived there for more than 2 years after adulthood. Excluding 1 subjects with missing EEG data and 2 subjects with damaged data, 22 subjects were enrolled ultimately (12 males and 10 females, age ranged between 20 and 24 years, average age as 21.91±0.97 years). The 30 members of the low-altitude group are from universities in Beijing (52m above sea level), who have never been to the plateau. 6 subjects were excluded for excessive disturbance of EEG data, and 24 subjects were finally enrolled (11 males and 13 females, age ranged between 19 and 24 years, average age as 21.43±1.24 years).</p><p>Emotional face recognition task was adopted. Stimulations were selected from Chinese Affective Face Picture System (Gong et al., 2011), including 3 males and 3 females, each with 3 expressions (angry, happy, neutral), a total of 18 emotional face pictures. A white cross (0.82°×0.82°) was presented in the center of the black background as the fixation point, with the presentation time random between 1200-1600ms. Then, the facial picture appears until subjects press the key and enter the next trial. Subjects are demanded to judge the gender of faces accurately as fast as possible. The position of the buttons was balanced between the subjects, with half of the subjects pressing the left button for a male face, the right button for a female face, and the other half doing the opposite. The experiment is consisted of practice and formal test. During the practice, each picture was presented once in a random order, with a total of 18 trials. The formal experiment was divided into 2 phases, with a short rest in between, 144 trials in each phase, a total of 288 trials, and each picture was repeated 16 times.</p><p>Electroencephalography (EEG) was collected using the Neuroscan recording and analysis system and the elastic cap with 64 Ag/AgCl electrodes cap of International 10-20 standard system (Neuroscan Inc., USA). The referenced electrode was the left mastoid and the grounding electrode was located at the midpoint of the Fz and FPz. The vertical electrooculogram (VEOG) was recorded with a pair of electrodes placed above and below the left eye, while the horizontal electrooculogram (HEOG) was recorded with another pair of electrodes, located 1 cm from the outer canthus of each eye. The resistance of scalp was kept under 5kΩ. Signals were amplified with a Neuroscan SynamPs-2 amplifier (Neuroscan Inc., USA) which included a 0.05-100 Hz bandpass filter with the sampling frequency as 500Hz.</p><p>Behavioral results could be opened with E-prime3 and EEG results could be opened with EEGLAB.</p>",
+    "description_zh": "<pre class=\"ql-syntax\" spellcheck=\"false\">被试均为大学本科生，视力正常或矫正视力正常，右利手，没有神经或精神疾病或其他慢性生理疾病史，近2天内没有使用药物(精神或其他)以及没有不规律生活方式。高原组25人来自西藏大学（海拔3658 m），均生长在平原，成年后首次进入高原并居住满2年以上。排除脑电数据缺失被试1人、数据损坏被试2人，最终入组22人（男/女：12/10人，年龄范围20-24岁，平均年龄21.91±0.97岁）。平原组30人来自北京各高校（海拔52 m），从未到过高原。排除脑电数据干扰过多被试6人，最终入组24人（男/女：11/13人，年龄范围19-24岁，平均年龄21.43±1.24岁）。\n采用情绪面孔判断任务。刺激材料来自中国面孔情绪图片库，选取3男3女，每人各3种表情（愤怒，愉快，中性），总计18张情绪面孔图片。每个试次开始时，在黑色背景中心呈现0.82°×0.82°视角的白色十字注视点，呈现时间在1200-1600ms随机，随后呈现面孔图片，按键后消失，进入下一个试次。要求被试尽可能快速准确地判断面孔性别。按键位置在被试间平衡，一半被试看到男性面孔按左键，女性面孔按右键，另一半被试与之相反。实验包括练习和正式测试。练习时每张图片随机呈现1次，共18个试次。正式实验分2组进行，中间短暂休息，每组144个试次，共计288个试次，每张图片重复16次（8次*2组）。每个试次约2s，正式实验时间约10min。\n脑电采集使用Neuroscan记录与分析系统，按国际10-20标准系统扩展的64导Ag / AgCl电极帽(Neuroscan Inc., USA)记录脑电(electroencephalography, EEG）。电极在线参考为左侧乳突&nbsp; ，接地电极位于Fz和FPz电极连线中点。由放置在左眼上方和下方的一对电极记录垂直眼电(VEOG)，水平眼电(HEOG)由另一对位于距每只眼睛外眦1cm处的电极记录，头皮电阻保持在5kΩ以下。由一个包括0.05–100 Hz带通滤波器的Neuroscan synamps 2放大器(Neuroscan Inc., USA)放大信号，采样频率为500Hz。\n行为学结果用E-prime3打开，脑电结果用EEGLAB打开。\n</pre>",
+    "authors": [
+      {
+        "name_en": "Cai Yudian",
+        "name_zh": "蔡雨点",
+        "email": "caiyd@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Chinese Academy of Sciences",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Yan",
+        "name_zh": "王妍",
+        "email": "wangyan@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Chinese Academy of Sciences",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "An Xin",
+        "name_zh": "安心",
+        "email": "370138916@qq.com",
+        "affiliations": [
+          {
+            "name_en": "National Defence University",
+            "name_zh": "National Defence University",
+            "ror_id": "https://ror.org/045vxh980"
+          }
+        ]
+      },
+      {
+        "name_en": "Ma Hailin",
+        "name_zh": "马海林",
+        "email": "83976475@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Tibet University",
+            "name_zh": "西藏大学",
+            "ror_id": "https://ror.org/05petvd47"
+          }
+        ]
+      },
+      {
+        "name_en": "Dai Shan",
+        "name_zh": "代杉",
+        "email": "rachelds@163.com",
+        "affiliations": [
+          {
+            "name_en": "Chinese Academy of Sciences",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "high-altitude",
+      "facial recognition",
+      "emotion",
+      "ERP"
+    ],
+    "keywords_zh": [
+      "面孔识别",
+      "情绪",
+      "高原",
+      "ERP"
+    ],
+    "publication_date": "2024-07-23T14:34:35.443Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC 4.0",
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+    "url": "https://www.scidb.cn/en/detail?id=66993089ab1be93f47ea6b7e",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6284bc2d9f0a08024767f8e5",
+    "doi": "10.57760/sciencedb.o00115.00009",
+    "cstr": "31253.11.sciencedb.o00115.00009",
+    "pid": "21.86116.6/sciencedb.o00115.00009",
+    "title": "Effects of active and passive suppression of facial expressions on subjective experience and <span style='background-color:yellow'>EEG</span> responses",
+    "title_en": "Effects of active and passive suppression of facial expressions on subjective experience and <span style='background-color:yellow'>EEG</span> responses",
+    "title_zh": "主动和被动抑制面部表情对主观体验和脑电生理反应的影响",
+    "description": "<p>Inhibition of the effect of facial expression on subjective experience and brain electrophysiological response&nbsp;</p>",
+    "description_en": "<p>Inhibition of the effect of facial expression on subjective experience and brain electrophysiological response&nbsp;</p>",
+    "description_zh": "<p>抑制面部表情对主观体验和脑电生理反应的影响原始数据</p>",
+    "authors": [
+      {
+        "name_en": "wang cai feng",
+        "name_zh": "王彩凤",
+        "email": "2056784455@qq.com",
+        "affiliations": [
+          {
+            "name_en": "辽宁师范大学",
+            "name_zh": "辽宁师范大学"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "expressive suppression",
+      "external performance",
+      "subjective experience",
+      "physiological response"
+    ],
+    "keywords_zh": [
+      "表达抑制",
+      "外部表现",
+      "主观体验",
+      "生理反应"
+    ],
+    "publication_date": "2022-05-23T14:22:07.877Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
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+    "url": "https://www.scidb.cn/en/detail?id=6284bc2d9f0a08024767f8e5",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "61ce5f7f5b86483bca618b94",
+    "doi": "10.11922/sciencedb.01421",
+    "cstr": "31253.11.sciencedb.01421",
+    "pid": "21.86116.6/sciencedb.01421",
+    "title": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
+    "title_en": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
+    "title_zh": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
+    "description": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
+    "description_en": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
+    "description_zh": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
+    "authors": [
+      {
+        "name_en": "Wu Yi Jia",
+        "name_zh": "Wu Yi Jia",
+        "email": "wuyijia@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Fudan University",
+            "name_zh": "Fudan University",
+            "ror_id": "https://ror.org/013q1eq08"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "neuroscience",
+      "computer science",
+      "human health"
+    ],
+    "keywords_zh": [
+      "neuroscience",
+      "computer science",
+      "human health"
+    ],
+    "publication_date": "2022-01-04T09:57:11.566Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 2243.85,
+    "file_size_bytes": 2352844150,
+    "download_count": 544,
+    "visit_count": 685,
+    "url": "https://www.scidb.cn/en/detail?id=61ce5f7f5b86483bca618b94",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "62bd105b70c5b75f4c7a6aca",
+    "doi": "10.1038/s41597-020-0535-2",
+    "cstr": "",
+    "pid": "",
+    "title": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
+    "title_en": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
+    "title_zh": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
+    "description": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
+    "description_en": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
+    "description_zh": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
+    "authors": [
+      {
+        "name_en": "Ma Xuelin",
+        "name_zh": "Ma Xuelin",
+        "affiliations": [
+          {
+            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "ror_id": "https://ror.org/022c3hy66"
+          },
+          {
+            "name_en": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
+            "name_zh": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Qiu Shuang",
+        "name_zh": "Qiu Shuang",
+        "email": "shuang.qiu@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "He Huiguang",
+        "name_zh": "He Huiguang",
+        "email": "huiguang.he@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
+            "name_zh": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences",
+            "name_zh": "The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Brain-computer interface",
+      "motor imagery"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Brain-computer interface",
+      "motor imagery"
+    ],
+    "publication_date": "2020-06-19T00:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 4385,
+    "download_count": 0,
+    "visit_count": 41,
+    "url": "https://www.scidb.cn/en/detail?id=62bd105b70c5b75f4c7a6aca",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6624cc48e112562dadfeb47e",
+    "doi": "10.57760/sciencedb.18262",
+    "cstr": "31253.11.sciencedb.18262",
+    "pid": "",
+    "title": "Pre-stimulus alpha band power and phase influence visual awareness--<span style='background-color:yellow'>EEG</span> dataset",
+    "title_en": "Pre-stimulus alpha band power and phase influence visual awareness--<span style='background-color:yellow'>EEG</span> dataset",
+    "title_zh": "刺激前α频段的能量与相位对视觉意识的预测作用<span style='background-color:yellow'>EEG</span>数据包",
+    "description": "<p>Experimental materials: Experimental materials include threshold stimulus, super-threshold stimulus and blank stimulus. The threshold stimulus is a low-contrast sinusoidal Gabor patch at the threshold level, tilted vertically to the left or right by 45°, with Michelson contrast ratios of 0.01, 0.02 and 0.03, respectively (Michelson contrast is calculated by: The brightest brightness minus the darkest brightness divided by the sum of the two), the presentation time and contrast are determined in the measurement threshold stage. The super-threshold stimulus is the stimulus with higher contrast than the threshold stimulus (Michelson's contrast is 0.04), and the blank stimulus is the non-target stimulus. The experimental material is made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2.</p><p>The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen is 1024x768 pixels, and the screen refresh rate is 60Hz (1 refresh cycle is about 17ms). The subject is about 100cm away from the screen.</p><p>EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead is grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode is a reference electrode. The sampling rate is 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_en": "<p>Experimental materials: Experimental materials include threshold stimulus, super-threshold stimulus and blank stimulus. The threshold stimulus is a low-contrast sinusoidal Gabor patch at the threshold level, tilted vertically to the left or right by 45°, with Michelson contrast ratios of 0.01, 0.02 and 0.03, respectively (Michelson contrast is calculated by: The brightest brightness minus the darkest brightness divided by the sum of the two), the presentation time and contrast are determined in the measurement threshold stage. The super-threshold stimulus is the stimulus with higher contrast than the threshold stimulus (Michelson's contrast is 0.04), and the blank stimulus is the non-target stimulus. The experimental material is made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2.</p><p>The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen is 1024x768 pixels, and the screen refresh rate is 60Hz (1 refresh cycle is about 17ms). The subject is about 100cm away from the screen.</p><p>EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead is grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode is a reference electrode. The sampling rate is 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_zh": "<p>实验材料：实验材料包括阈限刺激，阈上刺激和空白刺激。阈限刺激是处于阈限水平的低对比度正弦曲线Gabort patch，垂直向左或者向右倾斜45°，迈克尔逊对比度分别为0.01，0.02和0.03（迈克尔逊对比度计算方法：最亮的亮度减去最暗的亮度除以二者之和），呈现时间和对比度在测定阈限阶段进行确定。阈上刺激是比阈限刺激更高对比度（迈克逊对比度为0.04）的刺激，空白刺激即为无目标刺激，实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。</p><p>实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕100cm左右。</p><p>使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
+    "authors": [
+      {
+        "name_en": "Li Xiaoxiao",
+        "name_zh": "李笑笑",
+        "email": "1421269185@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Tianjin Normal University",
+            "name_zh": "天津师范大学",
+            "ror_id": "https://ror.org/05x2td559"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Alpha band",
+      "Visual awareness",
+      "Time-frequency analysis"
+    ],
+    "keywords_zh": [
+      "α频段",
+      "视觉意识",
+      "时频分析"
+    ],
+    "publication_date": "2024-04-25T03:02:52.899Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 31339.76,
+    "file_size_bytes": 32862119838,
+    "download_count": 1,
+    "visit_count": 227,
+    "url": "https://www.scidb.cn/en/detail?id=6624cc48e112562dadfeb47e",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6653e0c9c958f37781bd21ff",
+    "doi": "10.57760/sciencedb.18327",
+    "cstr": "31253.11.sciencedb.18327",
+    "pid": "",
+    "title": "The impact of social anxiety and emotional faces on executive function in college students: an <span style='background-color:yellow'>EEG</span> dataset",
+    "title_en": "The impact of social anxiety and emotional faces on executive function in college students: an <span style='background-color:yellow'>EEG</span> dataset",
+    "title_zh": "社交焦虑和情绪面孔对大学生执行功能的影响的脑电数据集",
+    "description": "<p>Using the LASA scale, university students were divided into high social anxiety group and low social anxiety group, as well as the <span style=\"color: rgb(51, 51, 51);\">Chinese Facial Affective Picture System</span> (CFAPS)</p><p><br></p><p>Measurement of the effects of social anxiety and emotional faces on the three subcomponents of executive function (inhibitory control, working memory, and cognitive flexibility) in college students</p><p><br></p><p><br></p><p>Inhibition control: Gonogo paradigm measurement</p><p><br></p><p>Independent variables: 2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (Go/No go)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Positive Go Accuracy, Negative Go Accuracy, Total Go Accuracy, Positive nogo Accuracy, Negative nogo Accuracy, Total nogo Accuracy, Positive go Response Time, Negative go Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Fz electrode positive N1 latency, Fz electrode positive N1 amplitude, Fz electrode negative N1 latency, Fz electrode negative N1 amplitude, Fz electrode positive P2 latency, Fz electrode positive P2 amplitude, Fz electrode negative P2 latency, Fz electrode negative P2 amplitude</p><p><br></p><p>Working Memory: N-back Paradigm Measurement</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 cognitive load (1-back/2-back)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), 1Back Positive Face Accuracy, 1Back Negative Face Accuracy, 1Back Total Accuracy, 1Back Positive Face Response Time, 1Back Negative Face Response Time, 1Back Total Response Time, 2Back Positive Face Accuracy, 2Back Negative Face Accuracy, 2Back Total Accuracy, 2Back Positive Face Response Time, 2Back Negative Face Response Time, and 2Back Total Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: ID, Group 1 (Low Social Anxiety Group), 2 (High Social Anxiety Group), 1bFz electrode positive N1 latency, 1bFz positive N1 amplitude, 1bFz electrode negative N1 latency, 1bFz electrode negative N1 amplitude, 1bFz electrode positive P2 latency, 1bFz electrode positive P2 amplitude, 1bFz electrode negative P2 latency, 1bFz electrode negative P2 amplitude, 2bFz electrode positive N1 latency, 2bFz electrode positive N1 amplitude, 2bFz electrode negative N1 amplitude N1 latency, 2bFz electrode negative N1 amplitude, 2bFz electrode positive P2 latency, 2bFz electrode positive P2 amplitude, 2bFz electrode negative P2 latency, 2bFz electrode negative P2 amplitude</p><p><br></p><p>Cognitive Flexibility: Measurement of Task Switching Paradigm</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (repetition/conversion)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), EXP1 Positive Face Accuracy, EXP1 Negative Face Accuracy, EXP1 Total Accuracy, EXP1 Positive Face Response Time, EXP1 Negative Face Response Time, EXP1 Total Response Time, EXP2 Positive Face Accuracy, EXP2 Negative Face Accuracy, EXP2 Total Accuracy, EXP2 Positive Face Response Time, EXP2 Negative Face Response Time, EXP2 Total Response Time, EXP3 Positive Face Accuracy, EXP3 Negative Face Accuracy, EXP3 Total Accuracy, EXP3 Positive Face Accuracy Reaction Time, EXP3 Negative Face Reaction Time, EXP3 Total Reaction Time, Repetitive Condition Positive Face Accuracy, Repetitive Condition Negative Face Accuracy, Repetitive Condition Positive Face Reaction Time, Repetitive Condition Negative Face Reaction Time, Repetitive Condition Total Accuracy, Repetitive Condition Total Reaction Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Repetitive Fz Electrode Positive N1 Latency, Repetitive Fz Electrode Positive N1 Amplitude, Repetitive Fz Electrode Negative N1 Latency, Repetitive Fz Electrode Negative N1 Amplitude, Repetitive Fz Electrode Positive P2 Latency, Repetitive Fz Electrode Negative P2 Latency, Repetitive Fz Electrode Negative P2 Amplitude, Conversion Fz Electrode Positive N1 Latency, Conversion Fz Electrode Positive N1 Amplitude, Conversion Fz Electrode Negative N1 Latency, Conversion Fz Electrode Point negative N1 amplitude, conversion Fz electrode point positive P2 latency, conversion Fz electrode point positive P2 amplitude, conversion Fz electrode point negative P2 latency, conversion Fz electrode point negative P2 amplitude</p>",
+    "description_en": "<p>Using the LASA scale, university students were divided into high social anxiety group and low social anxiety group, as well as the <span style=\"color: rgb(51, 51, 51);\">Chinese Facial Affective Picture System</span> (CFAPS)</p><p><br></p><p>Measurement of the effects of social anxiety and emotional faces on the three subcomponents of executive function (inhibitory control, working memory, and cognitive flexibility) in college students</p><p><br></p><p><br></p><p>Inhibition control: Gonogo paradigm measurement</p><p><br></p><p>Independent variables: 2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (Go/No go)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Positive Go Accuracy, Negative Go Accuracy, Total Go Accuracy, Positive nogo Accuracy, Negative nogo Accuracy, Total nogo Accuracy, Positive go Response Time, Negative go Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Fz electrode positive N1 latency, Fz electrode positive N1 amplitude, Fz electrode negative N1 latency, Fz electrode negative N1 amplitude, Fz electrode positive P2 latency, Fz electrode positive P2 amplitude, Fz electrode negative P2 latency, Fz electrode negative P2 amplitude</p><p><br></p><p>Working Memory: N-back Paradigm Measurement</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 cognitive load (1-back/2-back)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), 1Back Positive Face Accuracy, 1Back Negative Face Accuracy, 1Back Total Accuracy, 1Back Positive Face Response Time, 1Back Negative Face Response Time, 1Back Total Response Time, 2Back Positive Face Accuracy, 2Back Negative Face Accuracy, 2Back Total Accuracy, 2Back Positive Face Response Time, 2Back Negative Face Response Time, and 2Back Total Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: ID, Group 1 (Low Social Anxiety Group), 2 (High Social Anxiety Group), 1bFz electrode positive N1 latency, 1bFz positive N1 amplitude, 1bFz electrode negative N1 latency, 1bFz electrode negative N1 amplitude, 1bFz electrode positive P2 latency, 1bFz electrode positive P2 amplitude, 1bFz electrode negative P2 latency, 1bFz electrode negative P2 amplitude, 2bFz electrode positive N1 latency, 2bFz electrode positive N1 amplitude, 2bFz electrode negative N1 amplitude N1 latency, 2bFz electrode negative N1 amplitude, 2bFz electrode positive P2 latency, 2bFz electrode positive P2 amplitude, 2bFz electrode negative P2 latency, 2bFz electrode negative P2 amplitude</p><p><br></p><p>Cognitive Flexibility: Measurement of Task Switching Paradigm</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (repetition/conversion)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), EXP1 Positive Face Accuracy, EXP1 Negative Face Accuracy, EXP1 Total Accuracy, EXP1 Positive Face Response Time, EXP1 Negative Face Response Time, EXP1 Total Response Time, EXP2 Positive Face Accuracy, EXP2 Negative Face Accuracy, EXP2 Total Accuracy, EXP2 Positive Face Response Time, EXP2 Negative Face Response Time, EXP2 Total Response Time, EXP3 Positive Face Accuracy, EXP3 Negative Face Accuracy, EXP3 Total Accuracy, EXP3 Positive Face Accuracy Reaction Time, EXP3 Negative Face Reaction Time, EXP3 Total Reaction Time, Repetitive Condition Positive Face Accuracy, Repetitive Condition Negative Face Accuracy, Repetitive Condition Positive Face Reaction Time, Repetitive Condition Negative Face Reaction Time, Repetitive Condition Total Accuracy, Repetitive Condition Total Reaction Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Repetitive Fz Electrode Positive N1 Latency, Repetitive Fz Electrode Positive N1 Amplitude, Repetitive Fz Electrode Negative N1 Latency, Repetitive Fz Electrode Negative N1 Amplitude, Repetitive Fz Electrode Positive P2 Latency, Repetitive Fz Electrode Negative P2 Latency, Repetitive Fz Electrode Negative P2 Amplitude, Conversion Fz Electrode Positive N1 Latency, Conversion Fz Electrode Positive N1 Amplitude, Conversion Fz Electrode Negative N1 Latency, Conversion Fz Electrode Point negative N1 amplitude, conversion Fz electrode point positive P2 latency, conversion Fz electrode point positive P2 amplitude, conversion Fz electrode point negative P2 latency, conversion Fz electrode point negative P2 amplitude</p>",
+    "description_zh": "<p>采用LASA量表将大学生分为高社交焦虑组和低社交焦虑组、中国化情绪面孔系统（CFAPS)</p><p>分别测量社交焦虑和情绪面孔对大学生执行功能的三个子成分（抑制控制、工作记忆、认知灵活性）的影响</p><p><br></p><p>抑制控制：Gonogo范式 测量</p><p>自变量：2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2条件（Go/No-go）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、积极Go正确率、消极Go正确率、Go总正确率、积极nogo正确率、消极nogo正确率、nogo总正确率、积极go反应时、消极go反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、Fz电极点积极N1潜伏期、Fz电极点积极N1波幅、Fz电极点消极N1潜伏期、Fz电极点消极N1波幅、Fz电极点积极P2潜伏期、Fz电极点积极P2波幅、Fz电极点消极P2潜伏期、Fz电极点消极P2波幅</p><p><br></p><p>工作记忆：N-back范式测量</p><p>2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2认知负荷（1-back/2-back）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、1back积极面孔正确率、1back消极面孔正确率、1back总正确率、1back积极面孔反应时、1back消极面孔反应时、1back总反应时、2back积极面孔正确率、2back消极面孔正确率、2back总正确率、2back积极面孔反应时、2back消极面孔反应时、2back总反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、1bFz电极点积极N1潜伏期、1bFz积极N1波幅、1bFz电极点消极N1潜伏期、1bFz电极点消极N1波幅、1bFz电极点积极P2潜伏期、1bFz电极点积极P2波幅、1bFz电极点消极P2潜伏期、1bFz电极点消极P2波幅、2bFz电极点积极N1潜伏期、2bFz电极点积极N1波幅、2bFz电极点消极N1潜伏期、2bFz电极点消极N1波幅、2bFz电极点积极P2潜伏期、2bFz电极点积极P2波幅、2bFz电极点消极P2潜伏期、2bFz电极点消极P2波幅</p><p><br></p><p>认知灵活性：任务转换范式测量</p><p>2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2条件（重复/转换）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、EXP1积极面孔正确率、EXP1消极面孔正确率、EXP1总正确率、EXP1积极面孔反应时、EXP1消极面孔反应时、EXP1总反应时、EXP2积极面孔正确率、EXP2消极面孔正确率、EXP2总正确率、EXP2积极面孔反应时、EXP2消极面孔反应时、EXP2总反应时、EXP3积极面孔正确率、EXP3消极面孔正确率、EXP3总正确率、EXP3积极面孔反应时、EXP3消极面孔反应时、EXP3总反应时、重复条件积极面孔正确率、重复条件消极面孔正确率、重复条件积极面孔反应时、重复条件消极面孔反应时、重复条件总正确率、重复条件总反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、重复Fz电极点积极N1潜伏期、重复Fz电极点积极N1波幅、重复Fz电极点消极N1潜伏期、重复Fz电极点消极N1波幅、重复Fz电极点积极P2潜伏期、重复Fz电极点积极P2波幅、重复Fz电极点消极P2潜伏期、重复Fz电极点消极P2波幅、转换Fz电极点积极N1潜伏期、转换Fz电极点积极N1波幅、转换Fz电极点消极N1潜伏期、转换Fz电极点消极N1波幅、转换Fz电极点积极P2潜伏期、转换Fz电极点积极P2波幅、转换Fz电极点消极P2潜伏期、转换Fz电极点消极P2波幅</p>",
+    "authors": [
+      {
+        "name_en": "Yan Zhixiong",
+        "name_zh": "Yan Zhixiong",
+        "email": "yanzx@nnnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Nanning Normal University",
+            "name_zh": "Nanning Normal University",
+            "ror_id": "sdb_custom"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "social anxiety",
+      "emotional face",
+      "executive function"
+    ],
+    "keywords_zh": [
+      "社交焦虑",
+      "情绪面孔",
+      "执行功能"
+    ],
+    "publication_date": "2024-05-06T06:41:01.026Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 8041.95,
+    "file_size_bytes": 8432597404,
+    "download_count": 183,
+    "visit_count": 1073,
+    "url": "https://www.scidb.cn/en/detail?id=6653e0c9c958f37781bd21ff",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6838a23bb9dc7d0bec20b4b9",
+    "doi": "10.57760/sciencedb.25915",
+    "cstr": "31253.11.sciencedb.25915",
+    "pid": "",
+    "title": "The Neural Mechanisms of Mental Fatigue Under Cognitive Load: A Field Study Based on <span style='background-color:yellow'>EEG</span> Entropy and Spectral Analysis",
+    "title_en": "The Neural Mechanisms of Mental Fatigue Under Cognitive Load: A Field Study Based on <span style='background-color:yellow'>EEG</span> Entropy and Spectral Analysis",
+    "title_zh": "认知负荷对心理疲劳的神经机制：基于脑电熵和频谱的田野实验研究",
+    "description": "<p>This dataset was collected from a 20-day field experiment conducted at an oil and gas drilling site in Southwest China. The study aimed to investigate the effects of different levels of cognitive workload on mental fatigue among operators in high-risk working environments. A within-subject design was employed, and multi-channel electroencephalogram (EEG) data were collected from eight drillers under high, low, and no cognitive workload conditions. Each participant underwent repeated measurements across two rotating shift cycles. EEG data were acquired using wearable devices at a sampling rate of 128 Hz, with each recording session lasting 10 minutes.</p>",
+    "description_en": "<p>This dataset was collected from a 20-day field experiment conducted at an oil and gas drilling site in Southwest China. The study aimed to investigate the effects of different levels of cognitive workload on mental fatigue among operators in high-risk working environments. A within-subject design was employed, and multi-channel electroencephalogram (EEG) data were collected from eight drillers under high, low, and no cognitive workload conditions. Each participant underwent repeated measurements across two rotating shift cycles. EEG data were acquired using wearable devices at a sampling rate of 128 Hz, with each recording session lasting 10 minutes.</p>",
+    "description_zh": "<p>本数据集来源于一项在中国西南地区油气钻井现场开展的20天田野实验，旨在探讨高危作业环境中不同认知负荷水平对操作工心理疲劳的影响。实验采用被试内设计，采集8名钻井司钻在高、低及无认知负荷条件下的多通道脑电信号数据（EEG），每名被试在两个轮班周期内接受重复测量。数据采集设备为可穿戴脑电，采样频率为128Hz，采集时长为每次10分钟</p>",
+    "authors": [
+      {
+        "name_en": "qing xin",
+        "name_zh": "青鑫",
+        "email": "1395852156@qq.com",
+        "affiliations": [
+          {
+            "name_en": "SWPU",
+            "name_zh": "SWPU",
+            "ror_id": "sdb_custom"
+          }
+        ]
+      },
+      {
+        "name_en": "SuHao",
+        "name_zh": "苏昊",
+        "email": "shenyuren007@sina.com",
+        "affiliations": [
+          {
+            "name_en": "西南石油大学",
+            "name_zh": "西南石油大学"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "mental_fatigue",
+      "cognitive_load",
+      "eeg"
+    ],
+    "keywords_zh": [
+      "认知负荷",
+      "心理疲劳",
+      "脑电图"
+    ],
+    "publication_date": "2025-05-30T07:13:30.255Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "ODC-BY",
+    "file_size_mb": 3368.83,
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+    "visit_count": 291,
+    "url": "https://www.scidb.cn/en/detail?id=6838a23bb9dc7d0bec20b4b9",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66cc6988038a964b489bdf1a",
+    "doi": "10.57760/sciencedb.12479",
+    "cstr": "31253.11.sciencedb.12479",
+    "pid": "",
+    "title": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
+    "title_en": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
+    "title_zh": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
+    "description": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
+    "description_en": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
+    "description_zh": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
+    "authors": [
+      {
+        "name_en": "Yuan Gao",
+        "name_zh": "Yuan Gao",
+        "email": "gaoyuan171717@163.com",
+        "affiliations": [
+          {
+            "name_en": "North China University of Science and Technology",
+            "name_zh": "North China University of Science and Technology",
+            "ror_id": "sdb_custom"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Third-party punishment",
+      "Group size",
+      "Gain and loss contexts",
+      "Fairness",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "Third-party punishment",
+      "Group size",
+      "Gain and loss contexts",
+      "Fairness",
+      "EEG"
+    ],
+    "publication_date": "2024-08-28T09:11:52.785Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "url": "https://www.scidb.cn/en/detail?id=66cc6988038a964b489bdf1a",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "65b4ce08b3373114e622b5d3",
+    "doi": "10.57760/sciencedb.15681",
+    "cstr": "31253.11.sciencedb.15681",
+    "pid": "",
+    "title": "MI-BCI-2023-data",
+    "title_en": "MI-BCI-2023-data",
+    "title_zh": "MI-BCI-2023-data",
+    "description": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
+    "description_en": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
+    "description_zh": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
+    "authors": [
+      {
+        "name_en": "Tianyu Lin",
+        "name_zh": "Tianyu Lin",
+        "email": "1078467506@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation, Chinese Academy of Sciences",
+            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Motor imagery",
+      "non-invasive bci"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Motor imagery",
+      "non-invasive bci"
+    ],
+    "publication_date": "2024-01-29T01:12:35.964Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "url": "https://www.scidb.cn/en/detail?id=65b4ce08b3373114e622b5d3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "653f70ff37cb1b0197fabb97",
+    "doi": "10.57760/sciencedb.psych.00185",
+    "cstr": "31253.11.sciencedb.psych.00185",
+    "pid": "",
+    "title": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
+    "title_en": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
+    "title_zh": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
+    "description": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
+    "description_en": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
+    "description_zh": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
+    "authors": [
+      {
+        "name_en": "liansheng yao",
+        "name_zh": "liansheng yao",
+        "email": "lianshengyao@yeah.net",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      },
+      {
+        "name_en": "Qiufang Fu",
+        "name_zh": "Qiufang Fu",
+        "email": "fuqf@paych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "stimuli",
+      "object representation"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "stimuli",
+      "object representation"
+    ],
+    "publication_date": "2023-10-30T06:45:08.391Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 20646.59,
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+    "download_count": 12,
+    "visit_count": 293,
+    "url": "https://www.scidb.cn/en/detail?id=653f70ff37cb1b0197fabb97",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "627e1bbf9f0a08024767f878",
+    "doi": "10.57760/sciencedb.01763",
+    "cstr": "31253.11.sciencedb.01763",
+    "pid": "21.86116.6/sciencedb.01763",
+    "title": "Decoding of the neural representation of the visual RGB colour model",
+    "title_en": "Decoding of the neural representation of the visual RGB colour model",
+    "title_zh": "Decoding of the neural representation of the visual RGB colour model",
+    "description": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
+    "description_en": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
+    "description_zh": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
+    "authors": [
+      {
+        "name_en": "Wu Yi Jia",
+        "name_zh": "Wu Yi Jia",
+        "email": "wuyijia@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Fudan University",
+            "name_zh": "Fudan University",
+            "ror_id": "https://ror.org/013q1eq08"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "computer science",
+      "visual",
+      "colour",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "computer science",
+      "visual",
+      "colour",
+      "EEG"
+    ],
+    "publication_date": "2022-06-07T05:21:57.745Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 4148.02,
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+    "download_count": 265,
+    "visit_count": 1236,
+    "url": "https://www.scidb.cn/en/detail?id=627e1bbf9f0a08024767f878",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "64410294692b1255d27a18c8",
+    "doi": "10.57760/sciencedb.07960",
+    "cstr": "31253.11.sciencedb.07960",
+    "pid": "21.86116.6/sciencedb.07960",
+    "title": "Data and MATLAB Scripts",
+    "title_en": "Data and MATLAB Scripts",
+    "title_zh": "Data and MATLAB Scripts",
+    "description": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
+    "description_en": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
+    "description_zh": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
+    "authors": [
+      {
+        "name_en": "Zhiwei Xu",
+        "name_zh": "许志炜",
+        "email": "zwxu@whu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Hubei University",
+            "name_zh": "Hubei University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "CODE",
+      "MATLAB"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "CODE",
+      "MATLAB"
+    ],
+    "publication_date": "2023-04-14T02:29:55.915Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 1635.95,
+    "file_size_bytes": 1715421757,
+    "download_count": 250,
+    "visit_count": 2710,
+    "url": "https://www.scidb.cn/en/detail?id=64410294692b1255d27a18c8",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "649e4461c23e806c83e36bd7",
+    "doi": "10.57760/sciencedb.09267",
+    "cstr": "31253.11.sciencedb.09267",
+    "pid": "",
+    "title": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
+    "title_en": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
+    "title_zh": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
+    "description": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
+    "description_en": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
+    "description_zh": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
+    "authors": [
+      {
+        "name_en": "jiangyibo",
+        "name_zh": "jiangyibo",
+        "email": "psy_jiangyb@163.com",
+        "affiliations": [
+          {
+            "name_en": "Liaoning Normal University",
+            "name_zh": "辽宁师范大学",
+            "ror_id": "https://ror.org/04c3cgg32"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "psychology ",
+      "EEG",
+      "participants"
+    ],
+    "keywords_zh": [
+      "psychology ",
+      "EEG",
+      "participants"
+    ],
+    "publication_date": "2023-07-11T09:13:34.493Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.03,
+    "file_size_bytes": 35840,
+    "download_count": 18,
+    "visit_count": 959,
+    "url": "https://www.scidb.cn/en/detail?id=649e4461c23e806c83e36bd7",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "62f3b7501e8fab63528a3cb7",
+    "doi": "10.57760/sciencedb.02167",
+    "cstr": "31253.11.sciencedb.02167",
+    "pid": "21.86116.6/sciencedb.02167",
+    "title": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
+    "title_en": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
+    "title_zh": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
+    "description": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
+    "description_en": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
+    "description_zh": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
+    "authors": [
+      {
+        "name_en": "Wu Yi Jia",
+        "name_zh": "Wu Yi Jia",
+        "email": "wuyijia@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Fudan University",
+            "name_zh": "Fudan University",
+            "ror_id": "https://ror.org/013q1eq08"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Reconstructing",
+      "decoding",
+      "visual",
+      "color",
+      "machine learning",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "Reconstructing",
+      "decoding",
+      "visual",
+      "color",
+      "machine learning",
+      "EEG"
+    ],
+    "publication_date": "2022-08-11T01:29:20.172Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 1676.86,
+    "file_size_bytes": 1758313148,
+    "download_count": 11,
+    "visit_count": 1438,
+    "url": "https://www.scidb.cn/en/detail?id=62f3b7501e8fab63528a3cb7",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "689d88633c764c24f971e59b",
+    "doi": "10.57760/sciencedb.29131",
+    "cstr": "31253.11.sciencedb.29131",
+    "pid": "",
+    "title": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
+    "title_en": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
+    "title_zh": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
+    "description": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
+    "description_en": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
+    "description_zh": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
+    "authors": [
+      {
+        "name_en": "Weiheng Yao",
+        "name_zh": "Weiheng Yao",
+        "email": "wh.yao@siat.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Shenzhen Institutes of Advanced Technology",
+            "name_zh": "中国科学院深圳先进技术研究院",
+            "ror_id": "https://ror.org/04gh4er46"
+          }
+        ]
+      },
+      {
+        "name_en": "Xuhang Chen",
+        "name_zh": "Xuhang Chen",
+        "email": "xx.chen2@siat.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Shenzhen Institutes of Advanced Technology",
+            "name_zh": "中国科学院深圳先进技术研究院",
+            "ror_id": "https://ror.org/04gh4er46"
+          }
+        ]
+      },
+      {
+        "name_en": "Shuqiang Wang",
+        "name_zh": "Shuqiang Wang",
+        "email": "sq.wang@siat.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Shenzhen Institutes of Advanced Technology",
+            "name_zh": "中国科学院深圳先进技术研究院",
+            "ror_id": "https://ror.org/04gh4er46"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "brain-computer interface",
+      "braindecoding",
+      "generativeai"
+    ],
+    "keywords_zh": [
+      "brain-computer interface",
+      "braindecoding",
+      "generativeai"
+    ],
+    "publication_date": "2025-08-15T01:15:42.507Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 9683.8,
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+    "download_count": 0,
+    "visit_count": 112,
+    "url": "https://www.scidb.cn/en/detail?id=689d88633c764c24f971e59b",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "681a1af1aa991804f7636e80",
+    "doi": "10.57760/sciencedb.17992",
+    "cstr": "31253.11.sciencedb.17992",
+    "pid": "",
+    "title": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
+    "title_en": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
+    "title_zh": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
+    "description": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
+    "authors": [
+      {
+        "name_en": "WU JUN",
+        "name_zh": "WU JUN",
+        "email": "941911346@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Shenzhen University",
+            "name_zh": "深圳大学",
+            "ror_id": "https://ror.org/01vy4gh70"
+          }
+        ]
+      },
+      {
+        "name_en": "CUI FANG",
+        "name_zh": "CUI FANG",
+        "email": "cuifang0826@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Shenzhen University",
+            "name_zh": "Shenzhen University",
+            "ror_id": "https://ror.org/01vy4gh70"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Moral Judgment",
+      "Prosociality",
+      "Fairness",
+      "Social Status",
+      "Event-Related Potentials",
+      "FRN"
+    ],
+    "keywords_zh": [
+      "Moral Judgment",
+      "Prosociality",
+      "Fairness",
+      "Social Status",
+      "Event-Related Potentials",
+      "FRN"
+    ],
+    "publication_date": "2024-12-06T06:32:49.116Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 5490.47,
+    "file_size_bytes": 5757178758,
+    "download_count": 4,
+    "visit_count": 184,
+    "url": "https://www.scidb.cn/en/detail?id=681a1af1aa991804f7636e80",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "63a5b1e9f4cc512f6fba2c4f",
+    "doi": "10.57760/sciencedb.06986",
+    "cstr": "31253.11.sciencedb.06986",
+    "pid": "21.86116.6/sciencedb.06986",
+    "title": "Effects of Group Size on Third-party Punishments 2022 data set",
+    "title_en": "Effects of Group Size on Third-party Punishments 2022 data set",
+    "title_zh": "群体规模对第三方惩罚的影响2022年数据集",
+    "description": "<p>In April 2022, EEG data was collected in the EEG Laboratory of North China University of Science and Technology. To explore the neural mechanism of the influence of group size on third party punishment.</p>",
+    "description_en": "<p>In April 2022, EEG data was collected in the EEG Laboratory of North China University of Science and Technology. To explore the neural mechanism of the influence of group size on third party punishment.</p>",
+    "description_zh": "<p>2022年4月，在华北理工大学脑电实验室收集脑电数据。为探究群体规模影响第三方惩罚的神经机制。</p>",
+    "authors": [
+      {
+        "name_en": "Yuan Gao",
+        "name_zh": "高媛",
+        "email": "gaoyuan171717@163.com",
+        "affiliations": [
+          {
+            "name_en": "North China University of Science and Technology",
+            "name_zh": "North China University of Science and Technology"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Third-party punishment",
+      "Group size",
+      "Gain and loss contexts"
+    ],
+    "keywords_zh": [
+      "第三方惩罚",
+      "群体规模",
+      "得失情境"
+    ],
+    "publication_date": "2022-12-26T01:38:36.857Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 20906.59,
+    "file_size_bytes": 21922144500,
+    "download_count": 0,
+    "visit_count": 69,
+    "url": "https://www.scidb.cn/en/detail?id=63a5b1e9f4cc512f6fba2c4f",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "665822bd6db6702daf1dbf65",
+    "doi": "10.57760/sciencedb.j00133.00276",
+    "cstr": "31253.11.sciencedb.j00133.00276",
+    "pid": "",
+    "title": "Dataset of Service Providers’ Participation Motivations in Skill-Based Crowdsourcing",
+    "title_en": "Dataset of Service Providers’ Participation Motivations in Skill-Based Crowdsourcing",
+    "title_zh": "技能众包服务商参与动机数据集",
+    "description": "<p>This dataset covers all successfully completed bidding tasks from epwk.com from November 2017 to November 2022, with a total of 2385 service provider information (involving 15641 corresponding bidding documents). The main row and column labels in the data table have the following meanings:&nbsp;1. Bidding frequency (bid): The number of tasks participated in, that is, the number of tasks each service provider participated in the bidding process;&nbsp;2. Hobbies(hob): Skill task similarity, constructing a TF-IDF model based on service provider skill labels and task titles, and calculating text similarity;&nbsp;3. Competency (com): past task similarity, building a BERT fine-tuning model based on all task titles participated by the service provider, and calculating text similarity;&nbsp;4. Bonus stimulation (bon): The reward amount, the average reward amount for service providers participating in tasks;&nbsp;5. Task attraction (att): task detail, task requirements, and the number of text characters supplemented by the requirements;&nbsp;6. Ability exercise (abi): Service provider capabilities, service provider capability product level;&nbsp;7. Reputation and Reputation (rep): Service Provider Credit, Service Provider Credit Rating;&nbsp;8. Social recognition (soc): positive review rate, service provider positive review rate;&nbsp;9. Task difficulty (dif): The ratio of the number of bidders to the number of viewers, and the ratio of the number of bidders and viewers participating in the task by the service provider.&nbsp;</p>",
+    "description_en": "<p>This dataset covers all successfully completed bidding tasks from epwk.com from November 2017 to November 2022, with a total of 2385 service provider information (involving 15641 corresponding bidding documents). The main row and column labels in the data table have the following meanings:&nbsp;1. Bidding frequency (bid): The number of tasks participated in, that is, the number of tasks each service provider participated in the bidding process;&nbsp;2. Hobbies(hob): Skill task similarity, constructing a TF-IDF model based on service provider skill labels and task titles, and calculating text similarity;&nbsp;3. Competency (com): past task similarity, building a BERT fine-tuning model based on all task titles participated by the service provider, and calculating text similarity;&nbsp;4. Bonus stimulation (bon): The reward amount, the average reward amount for service providers participating in tasks;&nbsp;5. Task attraction (att): task detail, task requirements, and the number of text characters supplemented by the requirements;&nbsp;6. Ability exercise (abi): Service provider capabilities, service provider capability product level;&nbsp;7. Reputation and Reputation (rep): Service Provider Credit, Service Provider Credit Rating;&nbsp;8. Social recognition (soc): positive review rate, service provider positive review rate;&nbsp;9. Task difficulty (dif): The ratio of the number of bidders to the number of viewers, and the ratio of the number of bidders and viewers participating in the task by the service provider.&nbsp;</p>",
+    "description_zh": "<p><span style=\"color: black;\">本数据集涵盖了2017年11月至2022年11月5年间一品威客平台中所有圆满完成的招投标任务，共包含2385位服务商信息（涉及与之对应的</span><span style=\"color: rgb(0, 0, 0);\">15641条标书），</span><span style=\"color: black;\">其中数据表主要行列标签含义如下：</span></p><p><span style=\"color: black;\">1.</span>投标次数（bid）：参与的任务数量，即每位服务商参与招投标的任务数量；</p><p>2.兴趣爱好（hob）：技能任务相似度，基于服务商技能标签和任务标题构建TF-IDF模型，计算文本相似度；</p><p>3.胜任能力（com）：过往任务相似度，基于服务商参与的所有任务标题构建BERT微调模型，计算文本相似度；</p><p>4.奖金刺激（bon）：悬赏金额，服务商参与任务的悬赏金额均值；</p><p>5.任务吸引（att）：任务详细度，任务需求与需求补充的文本字符数；</p><p>6.能力锻炼（abi）：服务商能力，服务商能力品级数；</p><p>7.声望信誉（rep）：服务商信用，服务商信用等级；</p><p>8.社交认可（soc）：好评率，服务商好评率；</p><p>9.任务难度（dif）：投标人数与浏览人数的比例，服务商参与任务的投标人数与浏览人数之比。</p><p><br></p>",
+    "authors": [
+      {
+        "name_en": "Wang Xiaolun",
+        "name_zh": "王筱纶",
+        "email": "w_xl@nuaa.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Nanjing University of Aeronautics and Astronautics",
+            "name_zh": "Nanjing University of Aeronautics and Astronautics",
+            "ror_id": "https://ror.org/01scyh794"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Skill-based crowdsourcing",
+      " Participation motivation",
+      "Service provider",
+      " Machine learning"
+    ],
+    "keywords_zh": [
+      "技能共享",
+      "参与动机",
+      "服务商",
+      "机器学习"
+    ],
+    "publication_date": "2024-07-26T08:47:32.409Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "ODC-BY",
+    "file_size_mb": 1.19,
+    "file_size_bytes": 1244160,
+    "download_count": 56,
+    "visit_count": 705,
+    "url": "https://www.scidb.cn/en/detail?id=665822bd6db6702daf1dbf65",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "60b9c3adae98d4289fceed46",
+    "doi": "10.11922/sciencedb.00832",
+    "cstr": "31253.11.sciencedb.00832",
+    "pid": "21.86116.6/sciencedb.00832",
+    "title": "SVM and Randfores Code for DoA monitoring",
+    "title_en": "SVM and Randfores Code for DoA monitoring",
+    "title_zh": "SVM and Randfores Code for DoA monitoring",
+    "description": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
+    "description_en": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
+    "description_zh": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
+    "authors": [
+      {
+        "name_en": "Guozheng Wang",
+        "name_zh": "Guozheng Wang",
+        "affiliations": [
+          {
+            "name_en": "Zhejiang university",
+            "name_zh": "Zhejiang university",
+            "ror_id": "https://ror.org/00a2xv884"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Anesthetic depth monitoring",
+      " auditory stimulation",
+      " consciousness",
+      " electrooculogram",
+      " sevoflurane"
+    ],
+    "keywords_zh": [
+      "Anesthetic depth monitoring",
+      " auditory stimulation",
+      " consciousness",
+      " electrooculogram",
+      " sevoflurane"
+    ],
+    "publication_date": "2021-05-26T08:53:42.809Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 12812,
+    "download_count": 24,
+    "visit_count": 768,
+    "url": "https://www.scidb.cn/en/detail?id=60b9c3adae98d4289fceed46",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6242bffbe9abd34325de4caf",
+    "doi": "10.11922/sciencedb.j00052.00001",
+    "cstr": "31253.11.sciencedb.j00052.00001",
+    "pid": "21.86116.6/sciencedb.j00052.00001",
+    "title": "empathy",
+    "title_en": "empathy",
+    "title_zh": "共情数据",
+    "description": "<p>EEG study data on empathy and musical emotion recognition</p>",
+    "description_en": "<p>EEG study data on empathy and musical emotion recognition</p>",
+    "description_zh": "<p>关于共情与音乐情绪识别的脑电研究数据</p>",
+    "authors": [
+      {
+        "name_en": "Yang Jimei",
+        "name_zh": "杨集梅",
+        "email": "157553855@qq.com",
+        "affiliations": [
+          {
+            "name_en": "西南大学",
+            "name_zh": "西南大学"
+          }
+        ]
+      },
+      {
+        "name_en": "Zheng Maoping",
+        "name_zh": "郑茂平",
+        "email": "zhengmpxlx@126.com",
+        "affiliations": [
+          {
+            "name_en": "西南大学",
+            "name_zh": "西南大学"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "empathy",
+      "Chinese national music",
+      "emotion recognition"
+    ],
+    "keywords_zh": [
+      "共情",
+      "中国民族音乐",
+      "情绪识别"
+    ],
+    "publication_date": "2022-04-01T08:20:44.896Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 8309,
+    "download_count": 44,
+    "visit_count": 2590,
+    "url": "https://www.scidb.cn/en/detail?id=6242bffbe9abd34325de4caf",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66c7406cb7542f64ded99762",
+    "doi": "10.57760/sciencedb.12355",
+    "cstr": "31253.11.sciencedb.12355",
+    "pid": "",
+    "title": "Transcranial alternating current stimulation and creativity",
+    "title_en": "Transcranial alternating current stimulation and creativity",
+    "title_zh": "经颅交流电刺激与创造性",
+    "description": "<p>Collection Period: March 2024 to April 2024</p><p>Acquisition Location: Qufu Normal University</p><p>Behavioral data: Eprime 2.0.10 acquisition, organized in csv</p><p>EEG data: Borecon Newsen W wireless EEG acquisition device, 16-lead, sampling rate 500Hz, preprocessed using MATLAB EEGLAB toolbox, time-frequency analysis (wavelet transform) using MATLAB Fieldtrip toolbox</p><p>Data may contain errors: due to the configurable electrodes of the EEG acquisition device, only regions of interest were acquired (AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
+    "description_en": "<p>Collection Period: March 2024 to April 2024</p><p>Acquisition Location: Qufu Normal University</p><p>Behavioral data: Eprime 2.0.10 acquisition, organized in csv</p><p>EEG data: Borecon Newsen W wireless EEG acquisition device, 16-lead, sampling rate 500Hz, preprocessed using MATLAB EEGLAB toolbox, time-frequency analysis (wavelet transform) using MATLAB Fieldtrip toolbox</p><p>Data may contain errors: due to the configurable electrodes of the EEG acquisition device, only regions of interest were acquired (AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
+    "description_zh": "<p>采集时间：2024年3月至2024年4月</p><p>采集地点：曲阜师范大学</p><p>行为数据：Eprime2.0.10采集，整理在csv中</p><p>EEG数据：博睿康Newsen W无线脑电采集设备，16导，采样率500Hz，使用MATLAB EEGLAB toolbox进行预处理，使用MATLAB Fieldtrip toolbox进行时频分析（小波变换）</p><p>数据可能存在误差：由于脑电采集设备的电极可设置，因此只采集了感兴趣的区域(AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
+    "authors": [
+      {
+        "name_en": "Zhou Runze",
+        "name_zh": "周润泽",
+        "email": "creapsychojoe@163.com",
+        "affiliations": [
+          {
+            "name_en": "Southwest University",
+            "name_zh": "西南大学",
+            "ror_id": "https://ror.org/01kj4z117"
+          },
+          {
+            "name_en": "Qufu Normal University",
+            "name_zh": "曲阜师范大学",
+            "ror_id": "https://ror.org/03ceheh96"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Chunlei",
+        "name_zh": "刘春雷",
+        "email": "liuchunlei@qfnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Qufu Normal University",
+            "name_zh": "曲阜师范大学",
+            "ror_id": "https://ror.org/03ceheh96"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "transcranial alternating current stimulation",
+      "creative thinking",
+      "alpha oscillation"
+    ],
+    "keywords_zh": [
+      "经颅电刺激",
+      "创造性思维",
+      "alpha神经振荡"
+    ],
+    "publication_date": "2024-08-23T07:49:53.567Z",
+    "created": "",
+    "modified": "",
+    "status": "",
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+    "source": "scidb"
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+    "pid": "",
+    "title": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
+    "title_en": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
+    "title_zh": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
+    "description": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
+    "description_en": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
+    "description_zh": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
+    "authors": [
+      {
+        "name_en": "Chen Jieru",
+        "name_zh": "Chen Jieru",
+        "email": "chenjr@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Wenjie",
+        "name_zh": "Liu Wenjie",
+        "email": "wenjieliupsy@126.com",
+        "affiliations": [
+          {
+            "name_en": "Beijing Huilongguan Hospital",
+            "name_zh": "Beijing Huilongguan Hospital"
+          }
+        ]
+      },
+      {
+        "name_en": "Tan Shiqi",
+        "name_zh": "Tan Shiqi",
+        "email": "tansq@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Yuan Xiangyong",
+        "name_zh": "Yuan Xiangyong",
+        "email": "yuanxy@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Jiang Yi",
+        "name_zh": "Jiang Yi",
+        "email": "yijiang@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "audiovisual integration",
+      "selective attention",
+      "rhythm",
+      "steady-state visual evoked potential",
+      "inter-trial phase coherence"
+    ],
+    "keywords_zh": [
+      "audiovisual integration",
+      "selective attention",
+      "rhythm",
+      "steady-state visual evoked potential",
+      "inter-trial phase coherence"
+    ],
+    "publication_date": "2025-09-25T06:29:46.293Z",
+    "created": "",
+    "modified": "",
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+    "url": "https://www.scidb.cn/en/detail?id=68d16f3afae9e04d156b14f3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66487c324e7538442ce796af",
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+    "cstr": "31253.11.sciencedb.02109",
+    "pid": "21.86116.6/sciencedb.02109",
+    "title": "Motor inhibition impacts the motor interference effect of dangerous objects",
+    "title_en": "Motor inhibition impacts the motor interference effect of dangerous objects",
+    "title_zh": "Motor inhibition impacts the motor interference effect of dangerous objects",
+    "description": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "description_en": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "description_zh": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "authors": [
+      {
+        "name_en": "Peng Liu",
+        "name_zh": "Peng Liu",
+        "email": "liupeng@nwu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Northwest University",
+            "name_zh": "Northwest University",
+            "ror_id": "https://ror.org/00y7snj24"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "motor interference effect",
+      "dangerous objects",
+      "motor inhibition",
+      "danger evaluation"
+    ],
+    "keywords_zh": [
+      "motor interference effect",
+      "dangerous objects",
+      "motor inhibition",
+      "danger evaluation"
+    ],
+    "publication_date": "2022-08-02T09:08:20.475Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "url": "https://www.scidb.cn/en/detail?id=66487c324e7538442ce796af",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "677f4e0ea51e4d6139abfaf2",
+    "doi": "10.57760/sciencedb.11364",
+    "cstr": "31253.11.sciencedb.11364",
+    "pid": "",
+    "title": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
+    "title_en": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
+    "title_zh": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
+    "description": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
+    "description_en": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
+    "description_zh": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
+    "authors": [
+      {
+        "name_en": "Yue Lupeng",
+        "name_zh": "Yue Lupeng",
+        "email": "yuelp@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology",
+            "name_zh": "Institute of Psychology",
+            "ror_id": "https://ror.org/034dn0836"
+          }
+        ]
+      },
+      {
+        "name_en": "Hu Li",
+        "name_zh": "Hu Li",
+        "email": "huli@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Gamma band oscillations (GBOs)",
+      "Pain",
+      "Parvalbumin (PV) positive interneurons",
+      "Primary somatosensory cortex (S1)",
+      "Calcium imaging",
+      "Optogenetics"
+    ],
+    "keywords_zh": [
+      "Gamma band oscillations (GBOs)",
+      "Pain",
+      "Parvalbumin (PV) positive interneurons",
+      "Primary somatosensory cortex (S1)",
+      "Calcium imaging",
+      "Optogenetics"
+    ],
+    "publication_date": "2024-08-06T03:15:00.613Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
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+    "download_count": 41,
+    "visit_count": 1464,
+    "url": "https://www.scidb.cn/en/detail?id=677f4e0ea51e4d6139abfaf2",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "633c098f1eb2ba6997fb4a78",
+    "doi": "10.57760/sciencedb.j00052.00033",
+    "cstr": "31253.11.sciencedb.j00052.00033",
+    "pid": "21.86116.6/sciencedb.j00052.00033",
+    "title": "The role of norm understanding in third-party punishment and its time course",
+    "title_en": "The role of norm understanding in third-party punishment and its time course",
+    "title_zh": "The role of norm understanding in third-party punishment and its time course",
+    "description": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
+    "description_en": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
+    "description_zh": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
+    "authors": [
+      {
+        "name_en": "Dongjie Xie",
+        "name_zh": "Dongjie Xie",
+        "email": "xie_dongjie@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guangdong Institute of Public Administration",
+            "name_zh": "Guangdong Institute of Public Administration"
+          }
+        ]
+      },
+      {
+        "name_en": "Yanjie Su",
+        "name_zh": "Yanjie Su",
+        "email": "yjsu@pku.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Peking University",
+            "name_zh": "Peking University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "third-party punishment",
+      "descriptive norm",
+      "injunctive norm",
+      "norm understanding",
+      "event-related potential"
+    ],
+    "keywords_zh": [
+      "third-party punishment",
+      "descriptive norm",
+      "injunctive norm",
+      "norm understanding",
+      "event-related potential"
+    ],
+    "publication_date": "2023-08-31T01:07:19.091Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.04,
+    "file_size_bytes": 37192,
+    "download_count": 31,
+    "visit_count": 1398,
+    "url": "https://www.scidb.cn/en/detail?id=633c098f1eb2ba6997fb4a78",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "653ba4bf844a5137567bc803",
+    "doi": "10.57760/sciencedb.12791",
+    "cstr": "31253.11.sciencedb.12791",
+    "pid": "",
+    "title": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
+    "title_en": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
+    "title_zh": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
+    "description": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
+    "description_en": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
+    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
+    "authors": [
+      {
+        "name_en": "Yan Zhixiong",
+        "name_zh": "Yan Zhixiong",
+        "email": "yanzx@nnnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Nanning Normal University",
+            "name_zh": "Nanning Normal University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "gaze-cueing ",
+      "interpersonal competence ",
+      "N170 ",
+      "anterior directing attention negativity ",
+      "late positive potential ",
+      "event-related potentials ",
+      "ERPs"
+    ],
+    "keywords_zh": [
+      "gaze-cueing ",
+      "interpersonal competence ",
+      "N170 ",
+      "anterior directing attention negativity ",
+      "late positive potential ",
+      "event-related potentials ",
+      "ERPs"
+    ],
+    "publication_date": "2023-11-13T11:18:20.674Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 4732.03,
+    "file_size_bytes": 4961893877,
+    "download_count": 14,
+    "visit_count": 291,
+    "url": "https://www.scidb.cn/en/detail?id=653ba4bf844a5137567bc803",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "686751b46501574b34a055d9",
+    "doi": "10.57760/sciencedb.12128",
+    "cstr": "31253.11.sciencedb.12128",
+    "pid": "",
+    "title": "CuBiAAD-v2",
+    "title_en": "CuBiAAD-v2",
+    "title_zh": "CuBiAAD-v2",
+    "description": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "description_en": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "description_zh": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "authors": [
+      {
+        "name_en": "Keren Shi",
+        "name_zh": "Keren Shi",
+        "email": "skr@alu.uestc.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "University of Electronic Science and Technology of China",
+            "name_zh": "电子科技大学",
+            "ror_id": "https://ror.org/04qr3zq92"
+          }
+        ]
+      },
+      {
+        "name_en": "Xue Yuan",
+        "name_zh": "Xue Yuan",
+        "email": "yuanxue_@stu.scu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "West China Hospital of Sichuan University",
+            "name_zh": "West China Hospital of Sichuan University",
+            "ror_id": "https://ror.org/007mrxy13"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Liu",
+        "name_zh": "Xu Liu",
+        "email": "liuxu_007@stu.scu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "West China Hospital of Sichuan University",
+            "name_zh": "West China Hospital of Sichuan University",
+            "ror_id": "https://ror.org/007mrxy13"
+          }
+        ]
+      },
+      {
+        "name_en": "Ruiting Dai",
+        "name_zh": "Ruiting Dai",
+        "email": "rtdai@uestc.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "University of Electronic Science and Technology of China",
+            "name_zh": "电子科技大学",
+            "ror_id": "https://ror.org/04qr3zq92"
+          }
+        ]
+      },
+      {
+        "name_en": "Na Li",
+        "name_zh": "Na Li",
+        "email": "lina56@stu.scu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "West China Hospital of Sichuan University",
+            "name_zh": "West China Hospital of Sichuan University",
+            "ror_id": "https://ror.org/007mrxy13"
+          }
+        ]
+      },
+      {
+        "name_en": "Yunfa Fu",
+        "name_zh": "Yunfa Fu",
+        "email": "fyf@ynu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Kunming University of Science and Technology",
+            "name_zh": "昆明理工大学",
+            "ror_id": "https://ror.org/00xyeez13"
+          }
+        ]
+      },
+      {
+        "name_en": "Ning Jiang",
+        "name_zh": "Ning Jiang",
+        "email": "jiangning21@wchscu.cn",
+        "affiliations": [
+          {
+            "name_en": "West China Hospital of Sichuan University",
+            "name_zh": "West China Hospital of Sichuan University",
+            "ror_id": "https://ror.org/007mrxy13"
+          }
+        ]
+      },
+      {
+        "name_en": "Jiayuan He",
+        "name_zh": "Jiayuan He",
+        "email": "jiayuan.he@wchscu.cn",
+        "affiliations": [
+          {
+            "name_en": "West China Hospital of Sichuan University",
+            "name_zh": "West China Hospital of Sichuan University",
+            "ror_id": "https://ror.org/007mrxy13"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "BCI",
+      "EEG",
+      "fnirs",
+      "AAD",
+      "bimodal"
+    ],
+    "keywords_zh": [
+      "BCI",
+      "EEG",
+      "fnirs",
+      "AAD",
+      "bimodal"
+    ],
+    "publication_date": "2024-08-23T06:44:28.631Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 2922.22,
+    "file_size_bytes": 3064171934,
+    "download_count": 389,
+    "visit_count": 1162,
+    "url": "https://www.scidb.cn/en/detail?id=686751b46501574b34a055d9",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "65d7ea38e07c37345b652b55",
+    "doi": "10.57760/sciencedb.13699",
+    "cstr": "31253.11.sciencedb.13699",
+    "pid": "",
+    "title": "Behavior and ERP data of geometric shapes and social role priming effects based on the global-local paradigm",
+    "title_en": "Behavior and ERP data of geometric shapes and social role priming effects based on the global-local paradigm",
+    "title_zh": "基于整体——局部范式的图形与社会角色启动效应的行为及ERP数据",
+    "description": "<p>EEG data are recorded using the EEG system produced by the American EGI company, utilizing a 64-channel electrode cap extended from the international 10-20 system. The Cz electrode site serves as the online reference electrode, with a sampling rate of 1000Hz, and all electrode impedances are maintained below 50kΩ. This experiment used real-time recording of continuous EEG data, which was subsequently analyzed offline by the EEGlab toolbox based on Matlab R2019a software (Delorme &amp; Makeig, 2004) .</p><p>EEG data were analyzed using EEGlab with binaural mastoid as a re-reference, and offline processing was performed using 0.1-30 Hz bandpass filtering. Artifacts such as electro-oculogram, electro-myography, and drift were removed using independent component analysis, and trials with amplitudes exceeding a threshold of ±100 μV were automatically rejected. Data segmentation is conducted over a period spanning 200 ms before stimulus presentation and 800 ms post-presentation, with baseline correction applied using the average amplitude of the 200 ms pre-stimulus period. The 800 ms following stimulus onset is utilized for the time-course analysis of ERPs. Signals from 9 electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4) were taken for superimposed averaging according to a previous study (Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; Zhao Guangping et al., 2023).&nbsp;Finally, the EEG data are analyzed using repeated-measures Analysis of Variance (ANOVA).</p><p><br></p><p>In the behavioral data, the \"age\" column is highlighted in yellow to indicate male gender.</p><p><br></p><p><br></p><p><br></p><p>In ERP data, sort according to the order of n170, p200, n400, as well as the two conditions in the paper</p>",
+    "description_en": "<p>EEG data are recorded using the EEG system produced by the American EGI company, utilizing a 64-channel electrode cap extended from the international 10-20 system. The Cz electrode site serves as the online reference electrode, with a sampling rate of 1000Hz, and all electrode impedances are maintained below 50kΩ. This experiment used real-time recording of continuous EEG data, which was subsequently analyzed offline by the EEGlab toolbox based on Matlab R2019a software (Delorme &amp; Makeig, 2004) .</p><p>EEG data were analyzed using EEGlab with binaural mastoid as a re-reference, and offline processing was performed using 0.1-30 Hz bandpass filtering. Artifacts such as electro-oculogram, electro-myography, and drift were removed using independent component analysis, and trials with amplitudes exceeding a threshold of ±100 μV were automatically rejected. Data segmentation is conducted over a period spanning 200 ms before stimulus presentation and 800 ms post-presentation, with baseline correction applied using the average amplitude of the 200 ms pre-stimulus period. The 800 ms following stimulus onset is utilized for the time-course analysis of ERPs. Signals from 9 electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4) were taken for superimposed averaging according to a previous study (Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; Zhao Guangping et al., 2023).&nbsp;Finally, the EEG data are analyzed using repeated-measures Analysis of Variance (ANOVA).</p><p><br></p><p>In the behavioral data, the \"age\" column is highlighted in yellow to indicate male gender.</p><p><br></p><p><br></p><p><br></p><p>In ERP data, sort according to the order of n170, p200, n400, as well as the two conditions in the paper</p>",
+    "description_zh": "<p>采用美国 EGI 公司生产的EEG系统记录与分析数据，按国际10−20系统扩展的64导电极帽在线记录EEG数据。以Cz电极点为在线参考电极，采样率为1000Hz，所有电极阻抗维持在50kΩ以下。本实验采用实时记录连续脑电数据，随后通过基于Matlab R2019a软件的EEGlab工具包进行离线分析(Delorme &amp; Makeig, 2004)。</p><p>使用EEGlab对脑电数据进行分析，以双耳乳突作为重参考，离线处理采用0.1~30Hz带通滤波。使用独立成分分析去除眼电、肌电、漂移等伪迹，自动剔除振幅超过±100μV的试次。以刺激呈现前200ms和呈现后800ms进行分段，并以刺激前200ms的平均波幅进行基线校正，刺激后800ms为ERPs分析时程。根据前人研究采取了9个电极(F3、Fz、F4、C3、Cz、C4、P3、Pz、P4)的信号进行叠加平均(Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; 赵广平 等人, 2023)。最后采用重复测量方差分析对脑电数据进行分析。</p><p><br></p><p>行为数据中，“age”一列，标黄表示男性。</p><p>erp数据中，按照n170，p200，n400的顺序，以及论文中两个条件而排序</p>",
+    "authors": [
+      {
+        "name_en": "Guo Yi'an",
+        "name_zh": "郭易安",
+        "email": "189047937@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Zhangzhou Normal University",
+            "name_zh": "闽南师范大学",
+            "ror_id": "https://ror.org/02vj1vm13"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "response time",
+      " ERP",
+      "matlab"
+    ],
+    "keywords_zh": [
+      "反应时",
+      "ERP",
+      "matlab"
+    ],
+    "publication_date": "2023-11-28T04:24:51.434Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 0.09,
+    "file_size_bytes": 97704,
+    "download_count": 16,
+    "visit_count": 216,
+    "url": "https://www.scidb.cn/en/detail?id=65d7ea38e07c37345b652b55",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6863bdc5a7d5e55cbdbbbfab",
+    "doi": "10.57760/sciencedb.27389",
+    "cstr": "31253.11.sciencedb.27389",
+    "pid": "",
+    "title": "CuBiAAD",
+    "title_en": "CuBiAAD",
+    "title_zh": "CuBiAAD",
+    "description": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "description_en": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "description_zh": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
+    "authors": [
+      {
+        "name_en": "Keren Shi",
+        "name_zh": "Keren Shi",
+        "email": "skr@alu.uestc.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Sichuan University",
+            "name_zh": "Sichuan University",
+            "ror_id": "https://ror.org/011ashp19"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "AAD",
+      "fNIRS"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "AAD",
+      "fNIRS"
+    ],
+    "publication_date": "2025-07-11T03:19:57.720Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 2922.22,
+    "file_size_bytes": 3064171934,
+    "download_count": 0,
+    "visit_count": 97,
+    "url": "https://www.scidb.cn/en/detail?id=6863bdc5a7d5e55cbdbbbfab",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6627dd403e08f2716205c955",
+    "doi": "10.57760/sciencedb.18350",
+    "cstr": "31253.11.sciencedb.18350",
+    "pid": "",
+    "title": "Prestimulus power and phase of α and β influence sensitivity and criterion",
+    "title_en": "Prestimulus power and phase of α and β influence sensitivity and criterion",
+    "title_zh": "刺激前α以及β频段的能量与相位对感受性和判断标准的预测作用",
+    "description": "<p>The experimental material was sinusoidal Gabor patch with a Michelson contrast ratio of 0.03 and a vertical tilt of 45° to the left or right. Blank stimulus was no target. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m<sup>2</sup>. The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen was 1024x768 pixels, and the screen refresh rate was 60Hz (1 refresh cycle was about 17ms). The participant was about 80cm away from the screen. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_en": "<p>The experimental material was sinusoidal Gabor patch with a Michelson contrast ratio of 0.03 and a vertical tilt of 45° to the left or right. Blank stimulus was no target. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m<sup>2</sup>. The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen was 1024x768 pixels, and the screen refresh rate was 60Hz (1 refresh cycle was about 17ms). The participant was about 80cm away from the screen. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_zh": "<p>实验材料为正弦曲线<em>Gabor patch</em>，迈克尔逊对比度为0.03，垂直向左或者向右倾斜45°。空白刺激即为无目标刺激。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕80cm左右。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
+    "authors": [
+      {
+        "name_en": "li xiao xiao",
+        "name_zh": "李笑笑",
+        "email": "1421269185@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Tianjin Normal University",
+            "name_zh": "天津师范大学",
+            "ror_id": "https://ror.org/05x2td559"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "alpha",
+      "beta",
+      "visual awareness"
+    ],
+    "keywords_zh": [
+      "α频段",
+      "β频段",
+      "视觉意识"
+    ],
+    "publication_date": "2024-06-17T16:19:07.493Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 26860.94,
+    "file_size_bytes": 28165734617,
+    "download_count": 0,
+    "visit_count": 707,
+    "url": "https://www.scidb.cn/en/detail?id=6627dd403e08f2716205c955",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68db287a19a491453e1c15ac",
+    "doi": "10.57760/sciencedb.psych.00769",
+    "cstr": "31253.11.sciencedb.psych.00769",
+    "pid": "",
+    "title": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
+    "title_en": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
+    "title_zh": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
+    "description": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
+    "description_en": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
+    "description_zh": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
+    "authors": [
+      {
+        "name_en": "Chunlei Liu",
+        "name_zh": "Chunlei Liu",
+        "email": "liuchunlei@qfnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Qufu Normal University",
+            "name_zh": "Qufu Normal University"
+          }
+        ]
+      },
+      {
+        "name_en": "Runze Zhou",
+        "name_zh": "Runze Zhou",
+        "email": "271324336@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Qufu Normal University",
+            "name_zh": "Qufu Normal University"
+          },
+          {
+            "name_en": "Southwest University",
+            "name_zh": "西南大学",
+            "ror_id": "https://ror.org/01kj4z117"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      " α-tACS",
+      "creative thinking",
+      "Alternative Uses Task (AUT)"
+    ],
+    "keywords_zh": [
+      " α-tACS",
+      "creative thinking",
+      "Alternative Uses Task (AUT)"
+    ],
+    "publication_date": "2025-10-04T00:33:22.455Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 863.25,
+    "file_size_bytes": 905184609,
+    "download_count": 208,
+    "visit_count": 76,
+    "url": "https://www.scidb.cn/en/detail?id=68db287a19a491453e1c15ac",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6862a43c7fbf00385d589248",
+    "doi": "10.57760/sciencedb.27339",
+    "cstr": "31253.11.sciencedb.27339",
+    "pid": "",
+    "title": "UNITE example dataset",
+    "title_en": "UNITE example dataset",
+    "title_zh": "UNITE example dataset",
+    "description": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
+    "authors": [
+      {
+        "name_en": "Wei Zhang",
+        "name_zh": "Wei Zhang",
+        "email": "2101111787@stu.pku.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory, Beijing, China",
+            "name_zh": "Changping Laboratory, Beijing, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Shenshen Li",
+        "name_zh": "Shenshen Li",
+        "email": "ssli@pku.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          },
+          {
+            "name_en": "College of Future Technology, Peking University",
+            "name_zh": "College of Future Technology, Peking University"
+          }
+        ]
+      },
+      {
+        "name_en": "Ning An",
+        "name_zh": "Ning An",
+        "email": "anning@cpl.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          }
+        ]
+      },
+      {
+        "name_en": "Jianting Huang",
+        "name_zh": "Jianting Huang",
+        "email": "jianting.huang@pku.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          }
+        ]
+      },
+      {
+        "name_en": "Danhong Wang",
+        "name_zh": "Danhong Wang",
+        "email": "wangdanhong@cpl.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          }
+        ]
+      },
+      {
+        "name_en": "Jianxun Ren",
+        "name_zh": "Jianxun Ren",
+        "email": "davidren555@outlook.com",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          }
+        ]
+      },
+      {
+        "name_en": "Hesheng Liu",
+        "name_zh": "Hesheng Liu",
+        "email": "liuhesheng@cpl.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Changping Laboratory",
+            "name_zh": "Changping Laboratory"
+          },
+          {
+            "name_en": "Biomedical Pioneering Innovation Center, Peking University",
+            "name_zh": "Biomedical Pioneering Innovation Center, Peking University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "stroke ",
+      "aphasia",
+      "cognition",
+      "motor"
+    ],
+    "keywords_zh": [
+      "stroke ",
+      "aphasia",
+      "cognition",
+      "motor"
+    ],
+    "publication_date": "2025-07-08T03:02:42.167Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 322.31,
+    "file_size_bytes": 337968898,
+    "download_count": 8,
+    "visit_count": 285,
+    "url": "https://www.scidb.cn/en/detail?id=6862a43c7fbf00385d589248",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "648dc7c1cd978c23074a645f",
+    "doi": "10.57760/sciencedb.09015",
+    "cstr": "31253.11.sciencedb.09015",
+    "pid": "",
+    "title": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: Evidence from event-related potentials",
+    "title_en": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: Evidence from event-related potentials",
+    "title_zh": "在不同水平社交能力人群中眼部注视线索的事件相关电位数据集",
+    "description": "<p>This data set is the behavioral and Electrophysiology data of eye gaze cue experiment in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes two files, namely (1) Behavioral date: subjects' behavioral data results and (2) EEG date: subjects' EEG raw data.&nbsp;The file (1) Behavioral date presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), number (subject number), age (subject age), gender (1 is male, 2 is female), and different experimental conditions (direct vision, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction time under that condition.&nbsp;File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</p>",
+    "description_en": "<p>This data set is the behavioral and Electrophysiology data of eye gaze cue experiment in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes two files, namely (1) Behavioral date: subjects' behavioral data results and (2) EEG date: subjects' EEG raw data.&nbsp;The file (1) Behavioral date presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), number (subject number), age (subject age), gender (1 is male, 2 is female), and different experimental conditions (direct vision, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction time under that condition.&nbsp;File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</p>",
+    "description_zh": "<p>该数据集是在不同水平社交能力人群中眼部注视线索实验的行为和电生理学数据。我们采用点探测范式和事件相关电位技术，探索了大学生在眼部注视线索处理上的时间特征。数据集包括两个文件，分别是（1）Behavioral date：被试的行为数据结果和（2）EEG date：被试的EEG原始数据。</p><p>文件（1）Behavioral date呈现的是被试的行为数据结果，包含12横行和43竖列。在第一横行中我们列明了被试的分组group（1为高社交能力组，2为低社交能力组）、number（被试编号）、age（被试年龄）、gender（1为性别男，2为性别女）和不同的实验条件（直视、斜视、倒置、箭头、斜视一致/不一致、箭头一致/不一致）等，每个条件下的竖列中的数值为被试在该条件下的反应时。</p><p>文件（2）EEG date为实验的电生理学原始数据。文件中共132个项目。其中H开头的项目为高社交能力组的被试，L开头的为低社交能力组的被试。</p>",
+    "authors": [
+      {
+        "name_en": "shanlinglv",
+        "name_zh": "shanlinglv",
+        "email": "lvshanling@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Nanning Normal University",
+            "name_zh": "南宁师范大学",
+            "ror_id": "https://ror.org/04dx82x73"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "gaze-cueing",
+      "interpersonal competence",
+      "N170",
+      "anterior directing attention negativity",
+      "late positive potential",
+      "event-related potentials",
+      "ERPs"
+    ],
+    "keywords_zh": [
+      "眼部线索",
+      "社交能力",
+      "N170",
+      "ADAN",
+      "晚期正电位",
+      "事件相关电位"
+    ],
+    "publication_date": "2023-06-26T07:53:20.424Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 4730.44,
+    "file_size_bytes": 4960231021,
+    "download_count": 29,
+    "visit_count": 351,
+    "url": "https://www.scidb.cn/en/detail?id=648dc7c1cd978c23074a645f",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67c5b86ecfa1d066cfad6fd8",
+    "doi": "10.57760/sciencedb.21680",
+    "cstr": "31253.11.sciencedb.21680",
+    "pid": "",
+    "title": "Effect of Color Hue on the Experience of Movement Flow",
+    "title_en": "Effect of Color Hue on the Experience of Movement Flow",
+    "title_zh": "The Effect of Color Hue on the Experience of Movement Flow",
+    "description": "<p>The experiments were conducted in the laboratory using the motion of a treadmill to detect the state of the motion flow. A tablet screen placed on the treadmill displays one of the three typical and often studied color progress bars (red, yellow, and green). A combination of subjective and objective measurements was used in the experiment. Subjective experience was measured using the Simplified Flow State Scale-2 (S FSS-2). Objective data came from participants' EEG brain waves. One-way repeated measures were used for the within-group experiment. Participants were asked to visit the laboratory three times under different conditions, each time walking briskly for 10 minutes. To minimize the effects of fatigue and circadian rhythms, each participant was scheduled to walk at the same time on a different day. The experimental order of the different participants was based on a Latin square design.</p>",
+    "description_en": "<p>The experiments were conducted in the laboratory using the motion of a treadmill to detect the state of the motion flow. A tablet screen placed on the treadmill displays one of the three typical and often studied color progress bars (red, yellow, and green). A combination of subjective and objective measurements was used in the experiment. Subjective experience was measured using the Simplified Flow State Scale-2 (S FSS-2). Objective data came from participants' EEG brain waves. One-way repeated measures were used for the within-group experiment. Participants were asked to visit the laboratory three times under different conditions, each time walking briskly for 10 minutes. To minimize the effects of fatigue and circadian rhythms, each participant was scheduled to walk at the same time on a different day. The experimental order of the different participants was based on a Latin square design.</p>",
+    "description_zh": "<p>实验在实验室中进行，利用跑步机的运动来检测运动流的状态。放置在跑步机上的平板电脑屏幕显示三种典型的、经常被研究的颜色进度条（红、黄、绿）之一。实验中采用了主观和客观测量相结合的方法。主观体验是使用简化流动状态量表-2（S FSS-2）来测量的。客观数据来自参与者的脑电图脑电波。组内实验采用了单因子重复测量法。要求参与者在不同条件下参观实验室三次，每次快走 10 分钟。为了减少疲劳和昼夜节律的影响，每位参与者都被安排在不同一天的同一时间进行步行。不同参与者的实验顺序采用拉丁方阵设计。</p>",
+    "authors": [
+      {
+        "name_en": "Huang Xueqin",
+        "name_zh": "黄雪芹",
+        "email": "1022201075@tju.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Tianjin University",
+            "name_zh": "Tianjin University",
+            "ror_id": "https://ror.org/012tb2g32"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "progress bars",
+      "flow experience",
+      "EEG",
+      " color hue"
+    ],
+    "keywords_zh": [
+      "color hue",
+      "movement flow",
+      "EEG",
+      "flow experience",
+      "progress bars"
+    ],
+    "publication_date": "2025-04-14T02:48:01.071Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC 4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 17916,
+    "download_count": 5,
+    "visit_count": 121,
+    "url": "https://www.scidb.cn/en/detail?id=67c5b86ecfa1d066cfad6fd8",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "63145e688608590738eb4f6c",
+    "doi": "10.57760/sciencedb.j00052.00013",
+    "cstr": "31253.11.sciencedb.j00052.00013",
+    "pid": "21.86116.6/sciencedb.j00052.00013",
+    "title": "Acute psychological stress impairs attention disengagement toward threat-related stimuli",
+    "title_en": "Acute psychological stress impairs attention disengagement toward threat-related stimuli",
+    "title_zh": "急性应激损害对威胁刺激的注意解除",
+    "description": "<p>This repository contains data and code for the paper on Acta Psychologica Sinica entitled \"Acute psychological stress impairs attention disengagement toward threat-related stimuli\".</p>",
+    "description_en": "<p>This repository contains data and code for the paper on Acta Psychologica Sinica entitled \"Acute psychological stress impairs attention disengagement toward threat-related stimuli\".</p>",
+    "description_zh": "<p>本数据集为2020年1月发表在《心理学报》的论文原始数据。主要有以下内容：(1)基于E-prime 2.0编制和运行的点探测任务程序脚本;(2)EEG原始数据和分析脚本。</p>",
+    "authors": [
+      {
+        "name_en": "Luo Yu",
+        "name_zh": "罗禹",
+        "email": "yuluo@gznu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Nian Jingqing",
+        "name_zh": "念靖晴",
+        "email": "nianjingqing@126.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Bao Wei",
+        "name_zh": "鲍未",
+        "email": "baoweibow@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhang Jingjing",
+        "name_zh": "张静静",
+        "email": "583187483@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhao Shouying",
+        "name_zh": "赵守盈",
+        "email": "zhaoshouying@126.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Pan Yun",
+        "name_zh": "潘运",
+        "email": "Panyun129@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Shuang",
+        "name_zh": "许爽",
+        "email": "1162127430@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhang Yu",
+        "name_zh": "张禹",
+        "email": "yuzhang331@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "acute stress",
+      "attention engagement",
+      "attention disengagement",
+      "N2pc",
+      "SPCN"
+    ],
+    "keywords_zh": [
+      "急性应激",
+      "注意定向",
+      "注意解除",
+      "N2pc",
+      "SPCN"
+    ],
+    "publication_date": "2022-08-30T07:36:03.881Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 18427.95,
+    "file_size_bytes": 19323102333,
+    "download_count": 491,
+    "visit_count": 730,
+    "url": "https://www.scidb.cn/en/detail?id=63145e688608590738eb4f6c",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6625e721c70b375c9c533d5b",
+    "doi": "10.57760/sciencedb.17657",
+    "cstr": "31253.11.sciencedb.17657",
+    "pid": "",
+    "title": "Electrophysiological study of social anxiety levels on eye cue processing in the Trust Game paradigm",
+    "title_en": "Electrophysiological study of social anxiety levels on eye cue processing in the Trust Game paradigm",
+    "title_zh": "信任博弈范式下社交焦虑水平对眼部线索加工的脑电研究数据集",
+    "description": "<p>This dataset is behavioral and electrophysiological data from experiments using eye cue material in a trust game paradigm among individuals with different levels of social anxiety. This study explored the temporal characteristics of eye cue processing in college students with different levels of social anxiety using the Trust Game paradigm and event-related potential techniques. The dataset consists of three files, namely (1) behavioral results: subjects' behavioral data results and (2) EEG data: subjects' EEG raw data.</p><p>File (1) Behavioral Results presents the behavioral data results of the subjects and contains 8 horizontal rows and 469 vertical columns. In the first horizontal row the subjects' anxiety level grouping (high anxiety group and low anxiety group), subject number, ocular cue condition (all conditions, straight anger, squinted anger, straight sadness, squinted sadness, straight calmness, squinted calmness, straight happiness, squinted happiness), number of investments (i.e., number of times the subject chose to trust), number of retentions (i.e., number of times the subject chose to distrust), total (the total number of choices the subject made during the experiment), and reaction time (the total number of choices the subject total number of choices made by the subjects in the experiment), reaction time and trust rate (calculated as the number of investments/total).</p><p>File (2) EEG date is the electrophysiological raw data of the experiment. There are 156 items in the file. The items beginning with H are for subjects in the high social anxiety group, and those beginning with L are for subjects in the low social anxiety group.</p>",
+    "description_en": "<p>This dataset is behavioral and electrophysiological data from experiments using eye cue material in a trust game paradigm among individuals with different levels of social anxiety. This study explored the temporal characteristics of eye cue processing in college students with different levels of social anxiety using the Trust Game paradigm and event-related potential techniques. The dataset consists of three files, namely (1) behavioral results: subjects' behavioral data results and (2) EEG data: subjects' EEG raw data.</p><p>File (1) Behavioral Results presents the behavioral data results of the subjects and contains 8 horizontal rows and 469 vertical columns. In the first horizontal row the subjects' anxiety level grouping (high anxiety group and low anxiety group), subject number, ocular cue condition (all conditions, straight anger, squinted anger, straight sadness, squinted sadness, straight calmness, squinted calmness, straight happiness, squinted happiness), number of investments (i.e., number of times the subject chose to trust), number of retentions (i.e., number of times the subject chose to distrust), total (the total number of choices the subject made during the experiment), and reaction time (the total number of choices the subject total number of choices made by the subjects in the experiment), reaction time and trust rate (calculated as the number of investments/total).</p><p>File (2) EEG date is the electrophysiological raw data of the experiment. There are 156 items in the file. The items beginning with H are for subjects in the high social anxiety group, and those beginning with L are for subjects in the low social anxiety group.</p>",
+    "description_zh": "<p>该数据集是在不同水平社交焦虑水平个体中使用眼部线索材料在信任博弈范式下进行实验的行为和电生理学数据。本研究采用信任博弈范式和事件相关电位技术，探索了不同社交焦虑水平大学生在眼部线索处理上的时间特征。数据集包括三个文件，分别是（1）行为结果：被试的行为数据结果和（2）EEG date：被试的EEG原始数据。</p><p>文件（1）行为结果呈现的是被试的行为数据结果，包含8横行和469竖列。在第一横行中列明了被试的焦虑水平分组（高焦虑组和低焦虑组）、被试编号、眼部线索条件（所有条件、直视愤怒、斜视愤怒、直视悲伤、斜视悲伤、直视平静、斜视平静、直视快乐、斜视快乐）、投资数（即被试选择信任的次数）、保留数（即被试选择不信任的次数）、总计（被试在实验中做出的选择总数）、反应时和信任率（计算方法为投资数/总计）。</p><p>文件（2）EEG date为实验的电生理学原始数据。文件中共156个项目。其中H开头的项目为高社交焦虑组的被试，L开头的为低社交焦虑组的被试。</p>",
+    "authors": [
+      {
+        "name_en": "shanlinglv",
+        "name_zh": "shanlinglv",
+        "email": "461676235@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Nanning Normal University",
+            "name_zh": "南宁师范大学",
+            "ror_id": "https://ror.org/04dx82x73"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "social anxiety",
+      "eye cues",
+      "trust game",
+      "ERP"
+    ],
+    "keywords_zh": [
+      "社交焦虑",
+      "眼部线索",
+      "信任博弈",
+      "事件相关电位"
+    ],
+    "publication_date": "2024-04-10T01:07:57.616Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-ND 4.0",
+    "file_size_mb": 11655.55,
+    "file_size_bytes": 12221734939,
+    "download_count": 59,
+    "visit_count": 895,
+    "url": "https://www.scidb.cn/en/detail?id=6625e721c70b375c9c533d5b",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68c3c45626b8ca4876f6e3c3",
+    "doi": "10.57760/sciencedb.27618",
+    "cstr": "31253.11.sciencedb.27618",
+    "pid": "",
+    "title": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
+    "title_en": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
+    "title_zh": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
+    "description": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
+    "authors": [
+      {
+        "name_en": "Haobo Zhang",
+        "name_zh": "Haobo Zhang",
+        "email": "22301050292@m.fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "HuaShan Hospital",
+            "name_zh": "复旦大学附属华山医院",
+            "ror_id": "https://www.huashan.org.cn/"
+          }
+        ]
+      },
+      {
+        "name_en": "Daohan Zhang",
+        "name_zh": "Daohan Zhang",
+        "email": "20301050208@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "HuaShan Hospital",
+            "name_zh": "复旦大学附属华山医院",
+            "ror_id": "https://www.huashan.org.cn/"
+          }
+        ]
+      },
+      {
+        "name_en": "Jinsong Wu",
+        "name_zh": "Jinsong Wu",
+        "email": "wujinsong@huashan.org.cn",
+        "affiliations": [
+          {
+            "name_en": "HuaShan Hospital",
+            "name_zh": "复旦大学附属华山医院",
+            "ror_id": "https://www.huashan.org.cn/"
+          }
+        ]
+      },
+      {
+        "name_en": "Yuanning Li",
+        "name_zh": "Yuanning Li",
+        "email": "liyn2@shanghaitech.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "ShanghaiTech University",
+            "name_zh": "ShanghaiTech University",
+            "ror_id": "https://ror.org/030bhh786"
+          }
+        ]
+      },
+      {
+        "name_en": "Junfeng Lu",
+        "name_zh": "Junfeng Lu",
+        "email": "junfeng_lu@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "HuaShan Hospital",
+            "name_zh": "复旦大学附属华山医院",
+            "ror_id": "https://www.huashan.org.cn/"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "neural encoding",
+      "cognitive neuroscience",
+      "ECoG",
+      "Mandarin",
+      "speech perception"
+    ],
+    "keywords_zh": [
+      "neural encoding",
+      "cognitive neuroscience",
+      "ECoG",
+      "Mandarin",
+      "speech perception"
+    ],
+    "publication_date": "2025-09-12T03:32:36.797Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 5637.41,
+    "file_size_bytes": 5911250493,
+    "download_count": 1020,
+    "visit_count": 1145,
+    "url": "https://www.scidb.cn/en/detail?id=68c3c45626b8ca4876f6e3c3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "678f973027843a4e4768394b",
+    "doi": "10.57760/sciencedb.20263",
+    "cstr": "31253.11.sciencedb.20263",
+    "pid": "",
+    "title": "ERP and fMRI datasets of addition and subtraction approximation operations",
+    "title_en": "ERP and fMRI datasets of addition and subtraction approximation operations",
+    "title_zh": "加减法近似运算ERP和fMRI数据集",
+    "description": "<p>Data collection period: 2022-2023.</p><p><br></p><p>EEG data recording and processing</p><p><br></p><p>Preprocessing. EEGLAB and ERPLAB toolboxes were used, including (1) loading electrode position files (2) using the mean values of the left and right mastoids (M1, M2 electrodes) as a re-reference; (3) applying 0. 1 Hz high-pass filtering and 30 Hz low-pass filtering to all electrode data; (4) setting up Eventlist in ERPLAB according to different experimental conditions and assigning it to the corresponding Bin; (5) Calculate the weights of artefacts such as corrected electrooculograms using FastICA; (6) Perform automatic bad-conductance detection of the data; (7) Artefact detection; (8) Stacked average the preprocessed EEG data using ERPLAB toolkit to obtain the ERP results.&nbsp;</p><p><br></p><p>ERP data analysis. The ERP results were obtained by overlay averaging the preprocessed EEG data using the ERPLAB toolkit and low-pass filtering the overlay averaged data at 20 Hz. According to topographic maps and previous studies, it was found that attentional shifts on the mental digit line mainly activate the inferior parietal cortex [6,13], so we selected (P3, PZ, P4, PO3, POZ, PO4), and performed 2 (arithmetic operation: subtraction, addition) × 3 (direction of the result: less than, equal to, or greater than) repeated-measures ANOVA, and p-values of the ANOVA were corrected using the Green-house correction, Bonferroni-corrected. Bonferroni-corrected test was used for post hoc multiple comparisons.&nbsp;</p><p><br></p><p>Magnetic resonance data analysis</p><p><br></p><p>Preprocessing. The SPM12 toolbox was used, including: (1) conversion of DICOM data to NIFTI format; (2) time-layer correction of functional image data; (3) head-motion correction; (4) alignment of T1 structural images to the MNI standard space; (5) alignment of structural images to functional images; (6) normalisation of brain imaging data to the standard space by means of the EPI template included in the SPM and voxel resampling to 3 mm × 3 mm × 3 mm; (7) spatial smoothing of the functional image using Gaussian kernel function with FWHM set to 6 mm.&nbsp;</p><p><br></p><p>Analysis of fMRI data. Individual analyses were performed using a generalised linear model (GLM) to construct multiple linear regression matrices. The matrix was designed to include six types of regressions, including additive-less than, additive-equal to, additive-greater than, subtractive-less than, subtractive-equal to, and subtractive-greater than, with the six header The six head parameters were added as covariates in the analyses. Based on the contrast images generated by GLM, a 2 (arithmetic operation: addition, subtraction) × 3 (result direction: less than, equal to, greater than) repeated measures ANOVA was performed using Flexible Factorial Design, with the calibration thresholds set at voxel level p &lt; 0.001 uncorrected, and clump level p &lt; 0.05 FWE corrected. Signal values of significant peak interaction coordinates in ANOVA were extracted by Marsbar and analysed by 2 (arithmetic operations: addition, subtraction) × 3 (direction of results: less than, equal to, greater than) repeated measures ANOVA in SPSS.&nbsp;</p><p><br></p><p>Functional connectivity analyses . The signal values of the significant peak interaction coordinates in the ANOVA were extracted by the CONN toolkit to create spherical seed points with a radius of 6 mm, and their functional connectivity with the whole brain was examined, and the strength of connectivity between the seed points and different brain regions was examined by a 2 (arithmetic operation: addition, subtraction) × 3 (direction of the result: less than, equal to, or greater than) repeated-measurement ANOVA analysis, with the calibration threshold set at the voxel level of p &lt; 0.001 uncorrected, cluster level p &lt; 0.05 FWE corrected.</p><p><br></p><p>EEG and MRI correlation analysis</p><p><br></p><p>Characterisation similarities . This included (1) calculation of Representational Dissimilarity Matrices (RDM) for ERP using a continuous 10ms time window based on preprocessed EEG data; (2) calculation of RDM for fMRI based on preprocessed functional image data by means of a spherical searchlight with a radius of 3mm; and (3) The time points corresponding to the N1, P2, and P3b wave peaks were selected, and the Spearman correlation coefficient was used to construct the RSA characterisation similarity analysis by calculating the correlation between the RDM of each ERP modality and the RDM of the fMRI modality in a spherical searchlight with a radius of 3 mm. For the RSA corresponding to each time point, a temporal activity profile was obtained from the ERP data, and a spatial correlation map was obtained from the fMRI data, and the comparison results were compared. Spatial correlation maps were obtained from ERP data and fMRI data. Significance tests were performed to level the comparison results, and the calibration thresholds were set at p &lt; 0. 001 uncorrected for the voxel level and p &lt; 0. 05 FWE corrected for the cluster level.&nbsp;</p>",
+    "description_en": "<p>Data collection period: 2022-2023.</p><p><br></p><p>EEG data recording and processing</p><p><br></p><p>Preprocessing. EEGLAB and ERPLAB toolboxes were used, including (1) loading electrode position files (2) using the mean values of the left and right mastoids (M1, M2 electrodes) as a re-reference; (3) applying 0. 1 Hz high-pass filtering and 30 Hz low-pass filtering to all electrode data; (4) setting up Eventlist in ERPLAB according to different experimental conditions and assigning it to the corresponding Bin; (5) Calculate the weights of artefacts such as corrected electrooculograms using FastICA; (6) Perform automatic bad-conductance detection of the data; (7) Artefact detection; (8) Stacked average the preprocessed EEG data using ERPLAB toolkit to obtain the ERP results.&nbsp;</p><p><br></p><p>ERP data analysis. The ERP results were obtained by overlay averaging the preprocessed EEG data using the ERPLAB toolkit and low-pass filtering the overlay averaged data at 20 Hz. According to topographic maps and previous studies, it was found that attentional shifts on the mental digit line mainly activate the inferior parietal cortex [6,13], so we selected (P3, PZ, P4, PO3, POZ, PO4), and performed 2 (arithmetic operation: subtraction, addition) × 3 (direction of the result: less than, equal to, or greater than) repeated-measures ANOVA, and p-values of the ANOVA were corrected using the Green-house correction, Bonferroni-corrected. Bonferroni-corrected test was used for post hoc multiple comparisons.&nbsp;</p><p><br></p><p>Magnetic resonance data analysis</p><p><br></p><p>Preprocessing. The SPM12 toolbox was used, including: (1) conversion of DICOM data to NIFTI format; (2) time-layer correction of functional image data; (3) head-motion correction; (4) alignment of T1 structural images to the MNI standard space; (5) alignment of structural images to functional images; (6) normalisation of brain imaging data to the standard space by means of the EPI template included in the SPM and voxel resampling to 3 mm × 3 mm × 3 mm; (7) spatial smoothing of the functional image using Gaussian kernel function with FWHM set to 6 mm.&nbsp;</p><p><br></p><p>Analysis of fMRI data. Individual analyses were performed using a generalised linear model (GLM) to construct multiple linear regression matrices. The matrix was designed to include six types of regressions, including additive-less than, additive-equal to, additive-greater than, subtractive-less than, subtractive-equal to, and subtractive-greater than, with the six header The six head parameters were added as covariates in the analyses. Based on the contrast images generated by GLM, a 2 (arithmetic operation: addition, subtraction) × 3 (result direction: less than, equal to, greater than) repeated measures ANOVA was performed using Flexible Factorial Design, with the calibration thresholds set at voxel level p &lt; 0.001 uncorrected, and clump level p &lt; 0.05 FWE corrected. Signal values of significant peak interaction coordinates in ANOVA were extracted by Marsbar and analysed by 2 (arithmetic operations: addition, subtraction) × 3 (direction of results: less than, equal to, greater than) repeated measures ANOVA in SPSS.&nbsp;</p><p><br></p><p>Functional connectivity analyses . The signal values of the significant peak interaction coordinates in the ANOVA were extracted by the CONN toolkit to create spherical seed points with a radius of 6 mm, and their functional connectivity with the whole brain was examined, and the strength of connectivity between the seed points and different brain regions was examined by a 2 (arithmetic operation: addition, subtraction) × 3 (direction of the result: less than, equal to, or greater than) repeated-measurement ANOVA analysis, with the calibration threshold set at the voxel level of p &lt; 0.001 uncorrected, cluster level p &lt; 0.05 FWE corrected.</p><p><br></p><p>EEG and MRI correlation analysis</p><p><br></p><p>Characterisation similarities . This included (1) calculation of Representational Dissimilarity Matrices (RDM) for ERP using a continuous 10ms time window based on preprocessed EEG data; (2) calculation of RDM for fMRI based on preprocessed functional image data by means of a spherical searchlight with a radius of 3mm; and (3) The time points corresponding to the N1, P2, and P3b wave peaks were selected, and the Spearman correlation coefficient was used to construct the RSA characterisation similarity analysis by calculating the correlation between the RDM of each ERP modality and the RDM of the fMRI modality in a spherical searchlight with a radius of 3 mm. For the RSA corresponding to each time point, a temporal activity profile was obtained from the ERP data, and a spatial correlation map was obtained from the fMRI data, and the comparison results were compared. Spatial correlation maps were obtained from ERP data and fMRI data. Significance tests were performed to level the comparison results, and the calibration thresholds were set at p &lt; 0. 001 uncorrected for the voxel level and p &lt; 0. 05 FWE corrected for the cluster level.&nbsp;</p>",
+    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">数据采集时间：2022年-2023年。</span></p><p>脑电数据记录与处理</p><p>预处理. 采用EEGLAB和ERPLAB工具箱, 包括：(1)加载电极位置文件 (2)以左右乳突(M1, M2电极)的平均值作重参考; (3)对所有电极数据进行0. 1Hz高通滤波和30Hz低通滤波; (4)在ERPLAB中根据不同的实验条件设置Eventlist, 并分配给相应的Bin; (5)使用FastICA计算校正眼电等伪迹权重; (6)对数据进行自动坏导检测; (7) 伪迹检测; (8)利用ERPLAB工具包对预处理后的脑电数据进行叠加平均, 得到ERP结果.</p><p>ERP数据分析. 采用ERPLAB工具包对预处理后的脑电数据进行叠加平均, 并对叠加平均后的数据进行20Hz的低通滤波, 得到ERP结果. 根据地形图和以往研究, 发现心理数字线上的注意转移主要激活下顶叶皮层<sup>[6,13]</sup>, 故选取(P3, PZ, P4, PO3, POZ, PO4), 进行2(算术操作：减法、加法)×3(结果方向：小于、等于、大于)重复测量方差分析, 方差分析的<em>p</em>值采用Green-house 矫正, Bonferroni-corrected test被用于事后多重比较.</p><p>磁共振数据分析</p><p>预处理. 采用SPM12工具箱, 包括：(1)将DICOM数据转换为NIFTI格式; (2)<span style=\"color: black;\"> 对</span>功能像数据<span style=\"color: black;\">进行时间层校正</span>; (3)头动校正; (4)<span style=\"color: black;\"> </span>将T1结构像配准到MNI标准空间; (5)<span style=\"color: black;\"> </span>将结构像配准到功能像; (6)<span style=\"color: black;\"> 通过 SPM 自带 EPI 模板, 将脑成像数据归一化到标准空间, 并将体素重采样为3 mm</span> × <span style=\"color: black;\">3 mm</span> × <span style=\"color: black;\">3 mm</span>; (7)采用高斯核函数对功能像进行空间平滑处理, FWHM设置为6 mm.</p><p>fMRI数据分析. 个体分析采用广义线性模型(GLM)构建多重线性回归矩阵. 设计矩阵包括六种类型的回归量, 包括加法—小于、加法—等于、加法—大于、减法—小于、减法—等于、减法—大于, 并将6个头动参数作为协变量加入分析. 基于<span style=\"color: black;\">GLM 产生的 contrast images, </span>采用Flexible Factorial Design进行2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析, 校正阈限设置为体素水平<em>p</em> &lt; 0.001未校正, 团块水平<em>p</em> &lt; 0.05 FWE校正. 通过Marsbar提取方差分析中交互作用显著峰值坐标的信号值, 并在SPSS中进行2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析.</p><p>功能连接分析. 通过CONN工具包提取方差分析中交互作用显著峰值坐标的信号值创建半径为6mm的球形种子点, 考察其与全脑功能连接的情况, 并通过2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析分析考察种子点与不同脑区的连接强度, 校正阈限设置为体素水平<em>p</em> &lt; 0.001未校正, 团块水平<em>p</em> &lt; 0.05 FWE校正.</p><p>脑电、磁共振相关性分析</p><p>表征相似性. 包括：(1) 基于预处理后的脑电数据, 使用连续10ms时间窗口计算ERP的表征相异性矩阵(Representational Dissimilarity Matrices,&nbsp;RDM); (2)基于预处理后功能像数据, 通过半径为3mm的球形探照灯计算fMRI的 RDM; (3)选取N1、P2、P3b波峰所对应的时间点, 采用斯皮尔曼相关系数, 通过计算半径为3mm的球形探照灯内每个 ERP模态的RDM 和 fMRI模态的RDM 间的相关性, 建构RSA表征相似性分析, 对于各时间点对应的RSA, 根据 ERP 数据获得时间活动概况, 根据 fMRI 数据获得空间相关图, 对比较结果进行显著性检验平, 校正阈限设置为体素水平<em>p</em> &lt; 0. 001未校正, 团块水平<em>p</em> &lt; 0. 05 FWE校正.&nbsp;</p>",
+    "authors": [
+      {
+        "name_en": "jia liang zhi",
+        "name_zh": "贾良智",
+        "email": "2674952168@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Yun",
+        "name_zh": "Yun",
+        "email": "panyun129@163.pan",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "functional magnetic resonance imaging",
+      "event-related potential",
+      "representational similarity analysis"
+    ],
+    "keywords_zh": [
+      "功能性核磁成像",
+      "事件相关电位",
+      "表征相似性分析"
+    ],
+    "publication_date": "2025-01-26T12:01:41.653Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 41305.8,
+    "file_size_bytes": 43312265577,
+    "download_count": 1,
+    "visit_count": 55,
+    "url": "https://www.scidb.cn/en/detail?id=678f973027843a4e4768394b",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68cb5fc7dd2f5321a0f253cb",
+    "doi": "10.57760/sciencedb.23231",
+    "cstr": "31253.11.sciencedb.23231",
+    "pid": "",
+    "title": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
+    "title_en": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
+    "title_zh": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
+    "description": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
+    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
+    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
+    "authors": [
+      {
+        "name_en": "Huang Xin",
+        "name_zh": "Huang Xin",
+        "email": "hx11@pku.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Zhejiang University",
+            "name_zh": "Zhejiang University",
+            "ror_id": "https://ror.org/00a2xv884"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "emotion recognition",
+      "Multimodal",
+      "Dataset "
+    ],
+    "keywords_zh": [
+      "emotion recognition",
+      "Multimodal",
+      "Dataset "
+    ],
+    "publication_date": "2025-04-23T00:59:37.630Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 115903.23,
+    "file_size_bytes": 121533347163,
+    "download_count": 14516,
+    "visit_count": 2005,
+    "url": "https://www.scidb.cn/en/detail?id=68cb5fc7dd2f5321a0f253cb",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67175a23c9311b679d0adff0",
+    "doi": "10.57760/sciencedb.j00133.00406",
+    "cstr": "31253.11.sciencedb.j00133.00406",
+    "pid": "",
+    "title": "Enterprise Emergency Incident News Dataset",
+    "title_en": "Enterprise Emergency Incident News Dataset",
+    "title_zh": "企业突发事件新闻数据集",
+    "description": "<p>This dataset consists of three xlsx files:&nbsp;Weibo data.xlsx: The microblog data of the pickled Chinese cabbage event in the earthen pit contains 22303 pieces of data, each of which is a piece of microblog content, including 18 fields: id, bid, user_id, user nickname, microblog body, headline url, publishing location, Aite users, topics, forwarding numbers, comments, likes, publishing time, publishing tools, microblog image url, microblog video url, retweet_id, ip.&nbsp;News. xlsx: news data of the pickled Chinese cabbage event in the earthen pit, including 82 pieces of news data in total, each of which contains 5 fields: title, time, source, link, and news body&nbsp;Train data.xlsx: Enterprise Emergency News Training Dataset, consisting of 9 sub tables, each sub table is a case, and each case contains several news articles. The 9 cases contain a total of 113 news data, and each news article contains 5 fields: title, time, source, link, and news body.&nbsp;</p>",
+    "description_en": "<p>This dataset consists of three xlsx files:&nbsp;Weibo data.xlsx: The microblog data of the pickled Chinese cabbage event in the earthen pit contains 22303 pieces of data, each of which is a piece of microblog content, including 18 fields: id, bid, user_id, user nickname, microblog body, headline url, publishing location, Aite users, topics, forwarding numbers, comments, likes, publishing time, publishing tools, microblog image url, microblog video url, retweet_id, ip.&nbsp;News. xlsx: news data of the pickled Chinese cabbage event in the earthen pit, including 82 pieces of news data in total, each of which contains 5 fields: title, time, source, link, and news body&nbsp;Train data.xlsx: Enterprise Emergency News Training Dataset, consisting of 9 sub tables, each sub table is a case, and each case contains several news articles. The 9 cases contain a total of 113 news data, and each news article contains 5 fields: title, time, source, link, and news body.&nbsp;</p>",
+    "description_zh": "<p>本数据集一个包括三个xlsx文件：</p><p>weibo data.xlsx: 土坑酸菜事件微博数据，包含22303条数据，每条数据为一条微博内容，包含18个字段：id，bid，user_id，用户昵称，微博正文，头条文章url，发布位置，艾特用户，话题，转发数，评论数，点赞数，发布时间，发布工具，微博图片url，微博视频url，retweet_id，ip。</p><p>news.xlsx: 土坑酸菜事件新闻数据，一共包括82条新闻数据，每条数据包含5个字段：标题，时间，来源，链接，新闻正文</p><p>Train data.xlsx：企业突发事件新闻训练数据集，包含9个子表，每个子表为一个案例，每个案例包含新闻若干条，9个案例一共包含113条新闻数据，每条新闻包含5个字段：标题，时间，来源，链接，新闻正文。</p>",
+    "authors": [
+      {
+        "name_en": "19825000530",
+        "name_zh": "19825000530",
+        "email": "2319488315@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Nanjing University of Science and Technology",
+            "name_zh": "南京理工大学",
+            "ror_id": "https://ror.org/00xp9wg62"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Emergency",
+      "Incident",
+      "Dataset"
+    ],
+    "keywords_zh": [
+      "企业突发事件",
+      "新闻数据集",
+      "微博数据"
+    ],
+    "publication_date": "2024-12-12T09:02:42.107Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "ODbL",
+    "file_size_mb": 7.22,
+    "file_size_bytes": 7571617,
+    "download_count": 5,
+    "visit_count": 913,
+    "url": "https://www.scidb.cn/en/detail?id=67175a23c9311b679d0adff0",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6389603fab13cc64ab836201",
+    "doi": "10.11922/sciencedb.00740",
+    "cstr": "31253.11.sciencedb.00740",
+    "pid": "21.86116.6/sciencedb.00740",
+    "title": "Imaging Chinese Young Brains (I See Your Brain)",
+    "title_en": "Imaging Chinese Young Brains (I See Your Brain)",
+    "title_zh": "Imaging Chinese Young Brains (I See Your Brain)",
+    "description": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
+    "description_en": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
+    "description_zh": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
+    "authors": [
+      {
+        "name_en": "Peng Gao",
+        "name_zh": "Peng Gao",
+        "email": "smooo1973@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Collage of Information and Computer, Taiyuan University of Technology, Taiyuan, China",
+            "name_zh": "Collage of Information and Computer, Taiyuan University of Technology, Taiyuan, China",
+            "ror_id": "https://ror.org/03kv08d37"
+          }
+        ]
+      },
+      {
+        "name_en": "Hao-Ming Dong",
+        "name_zh": "Hao-Ming Dong",
+        "email": "donghaomingnd@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
+            "name_zh": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
+            "ror_id": "https://ror.org/03j7v5j15"
+          },
+          {
+            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
+            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Yin-Shan Wang",
+        "name_zh": "Yin-Shan Wang",
+        "email": "c137wys@bnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
+            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
+            "ror_id": "https://ror.org/022k4wk35"
+          },
+          {
+            "name_en": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
+            "name_zh": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China"
+          }
+        ]
+      },
+      {
+        "name_en": "Chun-Shui Yu",
+        "name_zh": "Chun-Shui Yu",
+        "email": "chunshuiyu@tmu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Tianjin Key Laboratory of Functional Imaging, Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China",
+            "name_zh": "Tianjin Key Laboratory of Functional Imaging, Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China"
+          },
+          {
+            "name_en": " CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China",
+            "name_zh": " CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China",
+            "ror_id": "https://ror.org/00vpwhm04"
+          }
+        ]
+      },
+      {
+        "name_en": "Xi-Nian Zuo",
+        "name_zh": "Xi-Nian Zuo",
+        "email": "xinian.zuo@bnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
+            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
+            "ror_id": "https://ror.org/022k4wk35"
+          },
+          {
+            "name_en": "Department of Psychology, University of CAS, Beijing 100049, China",
+            "name_zh": "Department of Psychology, University of CAS, Beijing 100049, China"
+          },
+          {
+            "name_en": "Institute of Children Health, Changzhou Children’s Hospital, Changzhou 213003, China",
+            "name_zh": "Institute of Children Health, Changzhou Children’s Hospital, Changzhou 213003, China"
+          },
+          {
+            "name_en": "Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China",
+            "name_zh": "Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China"
+          },
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China"
+          },
+          {
+            "name_en": "IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China",
+            "name_zh": "IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China"
+          },
+          {
+            "name_en": "Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China",
+            "name_zh": "Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Brain Imaging",
+      " Open Science",
+      " MRI",
+      " Connectome"
+    ],
+    "keywords_zh": [
+      "Brain Imaging",
+      " Open Science",
+      " MRI",
+      " Connectome"
+    ],
+    "publication_date": "2025-06-18T08:27:32.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "",
+    "file_size_mb": 63492.36,
+    "file_size_bytes": 66576568337,
+    "download_count": 595786,
+    "visit_count": 15472,
+    "url": "https://www.scidb.cn/en/detail?id=6389603fab13cc64ab836201",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68ea45d019a491453e1c19d1",
+    "doi": "10.57760/sciencedb.psych.00786",
+    "cstr": "31253.11.sciencedb.psych.00786",
+    "pid": "",
+    "title": "The Metaphorical Processing of Novel Classifier-Noun Collocations: Evidence from Event-Related Potentials",
+    "title_en": "The Metaphorical Processing of Novel Classifier-Noun Collocations: Evidence from Event-Related Potentials",
+    "title_zh": "量名新奇搭配的隐喻加工机制：来自ERPs的证据",
+    "description": "<p>We employed event-related potentials (ERPs) to investigate the metaphorical processing mechanism of novel classifier-noun collocations and the moderating role of individual print exposure. Using a within-subjects design, 34 native Chinese speakers (25 females; mean age 23.27 years, SD = 1.80) read three types of classifier-noun collocations embedded in sentences: literal collocations (e.g., <em>yidao dianguang</em>, \"a bolt of lightning\"), novel collocations (e.g., <em>yishan dianguang</em>, \"a flash of lightning\"), and anomalous collocations (e.g., <em>yibao dianguang</em>, \"a bag of lightning\"). Ninety experimental sentences were presented using rapid serial visual presentation (RSVP), with each word displayed for 400 ms followed by a 400 ms blank screen. EEG was recorded from 32 scalp electrodes. After the ERP session, participants completed the Author Recognition Test to assess their print exposure.</p>",
+    "description_en": "<p>We employed event-related potentials (ERPs) to investigate the metaphorical processing mechanism of novel classifier-noun collocations and the moderating role of individual print exposure. Using a within-subjects design, 34 native Chinese speakers (25 females; mean age 23.27 years, SD = 1.80) read three types of classifier-noun collocations embedded in sentences: literal collocations (e.g., <em>yidao dianguang</em>, \"a bolt of lightning\"), novel collocations (e.g., <em>yishan dianguang</em>, \"a flash of lightning\"), and anomalous collocations (e.g., <em>yibao dianguang</em>, \"a bag of lightning\"). Ninety experimental sentences were presented using rapid serial visual presentation (RSVP), with each word displayed for 400 ms followed by a 400 ms blank screen. EEG was recorded from 32 scalp electrodes. After the ERP session, participants completed the Author Recognition Test to assess their print exposure.</p>",
+    "description_zh": "<p>\t本研究采用事件相关电位(ERPs)技术，探讨新奇量名搭配的隐喻加工机制及个体阅读量的调节作用。实验采用单因素被试内设计，34名被试阅读三种类型的量名搭配：字面搭配（一道电光）、新奇搭配（一闪电光）和异常搭配（一包电光）。结果显示：(1)在早期加工阶段(300-500ms)，无论阅读量高低，新奇搭配和异常搭配均在额区诱发显著的N400效应，两者无显著差异，表明新奇搭配暂时被视为语义异常；(2)在晚期加工阶段(600-800ms)，阅读量对新奇搭配加工存在调节作用，高阅读量组的新奇搭配诱发显著的晚期负效应，而低阅读量组未见此效应。研究表明，新奇量名搭配的理解涉及两阶段加工：早期语义冲突检测和晚期隐喻映射，后者受个体阅读经验调节。</p>",
+    "authors": [
+      {
+        "name_en": "Xiaodong Xu",
+        "name_zh": "Xiaodong Xu",
+        "email": "412alix@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Nanjing Normal University",
+            "name_zh": "南京师范大学",
+            "ror_id": "https://ror.org/036trcv74"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "classifier-noun collocation",
+      "metaphorical processing",
+      "print exposure",
+      "N400",
+      "late negativity"
+    ],
+    "keywords_zh": [
+      "量名搭配",
+      "隐喻加工",
+      "阅读量",
+      "N400",
+      "晚期负效应"
+    ],
+    "publication_date": "2025-10-13T01:01:31.279Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 990.67,
+    "file_size_bytes": 1038788094,
+    "download_count": 3,
+    "visit_count": 22,
+    "url": "https://www.scidb.cn/en/detail?id=68ea45d019a491453e1c19d1",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "63353de93d498241dcc00903",
+    "doi": "10.57760/sciencedb.o00115.00049",
+    "cstr": "31253.11.sciencedb.o00115.00049",
+    "pid": "21.86116.6/sciencedb.o00115.00049",
+    "title": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
+    "title_en": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
+    "title_zh": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
+    "description": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
+    "description_en": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
+    "description_zh": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
+    "authors": [
+      {
+        "name_en": "Xin He",
+        "name_zh": "Xin He",
+        "email": "hex@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Weilin Liu",
+        "name_zh": "Weilin Liu",
+        "email": "1372593229@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Nan Qin",
+        "name_zh": "Nan Qin",
+        "email": "qinnanah@126.com",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Lili Lyu",
+        "name_zh": "Lili Lyu",
+        "email": "lllv@ion.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Xue Dong",
+        "name_zh": "Xue Dong",
+        "email": "dongx@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Min Bao",
+        "name_zh": "Min Bao",
+        "email": "baom@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "reward",
+      "selective attention",
+      "ERP"
+    ],
+    "keywords_zh": [
+      "reward",
+      "selective attention",
+      "ERP"
+    ],
+    "publication_date": "2022-09-16T15:30:45.330Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "ODbL",
+    "file_size_mb": 23516.52,
+    "file_size_bytes": 24658853287,
+    "download_count": 18,
+    "visit_count": 310,
+    "url": "https://www.scidb.cn/en/detail?id=63353de93d498241dcc00903",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67c19e52d732af307692239e",
+    "doi": "10.57760/sciencedb.psych.00262",
+    "cstr": "31253.11.sciencedb.psych.00262",
+    "pid": "",
+    "title": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
+    "title_en": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
+    "title_zh": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
+    "description": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
+    "description_en": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
+    "description_zh": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
+    "authors": [
+      {
+        "name_en": "liansheng yao",
+        "name_zh": "liansheng yao",
+        "email": "lianshengyao@yeah.net",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      },
+      {
+        "name_en": "qiufang fu",
+        "name_zh": "qiufang fu",
+        "email": "fuqf@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "scene representation",
+      "edge information",
+      "surface information",
+      "basic level",
+      "superordinate level"
+    ],
+    "keywords_zh": [
+      "scene representation",
+      "edge information",
+      "surface information",
+      "basic level",
+      "superordinate level"
+    ],
+    "publication_date": "2024-09-06T09:03:59.878Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 11770.0,
+    "file_size_bytes": 12341738656,
+    "download_count": 342,
+    "visit_count": 764,
+    "url": "https://www.scidb.cn/en/detail?id=67c19e52d732af307692239e",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "60b9f23881e63e42b94c22d4",
+    "doi": "10.11922/sciencedb.893",
+    "cstr": "31253.11.sciencedb.893",
+    "pid": "21.86101/sciencedb.893",
+    "title": "A dataset of China’s overseas power projects (2000–2019)",
+    "title_en": "A dataset of China’s overseas power projects (2000–2019)",
+    "title_zh": "2000–2019年中国海外电力项目信息数据集",
+    "description": "Power shortages are faced by developing countries in the Belt and Road region. Since the Belt and Road initiative was put forward, Chinese companies have invested and built a large number of electrical power projects in countries and areas with power shortages in this region. Due to the large number and wide distribution of projects and continuous increases in the number of new power projects, a large amount of project information has been generated. Accordingly, it is urgent to collect and summarize One Belt and One Road overseas power project information. In this study, web crawler technology was used to obtain overseas power project information. A One Belt and One Road dataset of overseas power projects was formed by further supplementing and improving the project information using documents from ministries, embassies, counselors of the ministry of economy and commerce, local news reports in Chinese and English, and case studies and field studies conducted by scholars and non-governmental organizations. The dataset includes information on 376 power projects from 80 countries in Asia, Africa, Europe, America, and Oceania. Each project’s information includes the project number, project name, construction status, enterprise name, installed capacity, continent, country, project category, and bid information. The collection and improvement of this dataset will help with understanding the distribution of One Belt, One Road overseas power projects, as well as development trends in overseas power investment and construction in recent years. This can provide a basis for China’s power companies to “go global” and become “One Belt, One Road” overseas. It also provides a reference for power project development planning and government department decision-making.",
+    "description_en": "Power shortages are faced by developing countries in the Belt and Road region. Since the Belt and Road initiative was put forward, Chinese companies have invested and built a large number of electrical power projects in countries and areas with power shortages in this region. Due to the large number and wide distribution of projects and continuous increases in the number of new power projects, a large amount of project information has been generated. Accordingly, it is urgent to collect and summarize One Belt and One Road overseas power project information. In this study, web crawler technology was used to obtain overseas power project information. A One Belt and One Road dataset of overseas power projects was formed by further supplementing and improving the project information using documents from ministries, embassies, counselors of the ministry of economy and commerce, local news reports in Chinese and English, and case studies and field studies conducted by scholars and non-governmental organizations. The dataset includes information on 376 power projects from 80 countries in Asia, Africa, Europe, America, and Oceania. Each project’s information includes the project number, project name, construction status, enterprise name, installed capacity, continent, country, project category, and bid information. The collection and improvement of this dataset will help with understanding the distribution of One Belt, One Road overseas power projects, as well as development trends in overseas power investment and construction in recent years. This can provide a basis for China’s power companies to “go global” and become “One Belt, One Road” overseas. It also provides a reference for power project development planning and government department decision-making.",
+    "description_zh": "电力短缺是“一带一路”区域广大发展中国家面临的问题之一。自“一带一路”倡议提出以来，中国企业在“一带一路”沿线电力短缺的国家和地区投资、建设了大量的电力项目，由于项目数量多，分布范围广泛，并且每年会不断增加新的电力项目，将形成大量的项目信息，亟需将“一带一路”海外电力项目信息进行收集汇总。本文利用网络爬虫的方法获取海外电力项目的信息，同时通过部委、使馆、经济商务部参赞机构文件、中文英文的当地新闻报道、学者和非政府组织进行的案例研究和实地研究中对项目信息进行进一步补充和完善，形成了“一带一路”海外电力项目数据集。数据集包括来自亚洲、非洲、欧洲、美洲、大洋洲80个国家的376个电力项目，每个项目信息包括项目编号、项目状态、央企集团、二级单位、项目名称、装机容量、所属地区、所在国家、项目类型、中标信息。数据集的收集和完善有助于了解“一带一路”海外电力项目的分布分局，以及近年来的海外电力投资建设发展态势，可为我国电力企业“走出去”提供依据，为“一带一路”海外电力项目发展规划、政府部门决策提供参考。",
+    "authors": [
+      {
+        "name_en": "蒋瑜",
+        "name_zh": "蒋瑜",
+        "affiliations": [
+          {
+            "name_en": "",
+            "name_zh": ""
+          }
+        ]
+      },
+      {
+        "name_en": "邬明权",
+        "name_zh": "邬明权",
+        "affiliations": [
+          {
+            "name_en": "",
+            "name_zh": ""
+          }
+        ]
+      },
+      {
+        "name_en": "黄长军",
+        "name_zh": "黄长军",
+        "affiliations": [
+          {
+            "name_en": "",
+            "name_zh": ""
+          }
+        ]
+      },
+      {
+        "name_en": "牛铮",
+        "name_zh": "牛铮",
+        "affiliations": [
+          {
+            "name_en": "",
+            "name_zh": ""
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Belt and Road Initiative",
+      " power projects",
+      " dataset",
+      "web crawler technology"
+    ],
+    "keywords_zh": [
+      "一带一路",
+      "电力项目",
+      "数据集",
+      "网络爬虫"
+    ],
+    "publication_date": "2019-09-23T13:12:37.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.07,
+    "file_size_bytes": 71377,
+    "download_count": 2012,
+    "visit_count": 13926,
+    "url": "https://www.scidb.cn/en/detail?id=60b9f23881e63e42b94c22d4",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "64bf82f502e62a3c98eeff55",
+    "doi": "10.57760/sciencedb.02116",
+    "cstr": "31253.11.sciencedb.02116",
+    "pid": "21.86116.6/sciencedb.02116",
+    "title": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
+    "title_en": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
+    "title_zh": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
+    "description": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
+    "description_en": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
+    "description_zh": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
+    "authors": [
+      {
+        "name_en": "Luo Yu",
+        "name_zh": "Luo Yu",
+        "email": "yuluo@gznu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "Guizhou Normal University"
+          }
+        ]
+      },
+      {
+        "name_en": "Nian Jingqing",
+        "name_zh": "Nian Jingqing",
+        "email": "nianjingqing@126.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "Guizhou Normal University",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Yang Run",
+        "name_zh": "Yang Run",
+        "email": "617674892@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Xie Jiao",
+        "name_zh": "Xie Jiao",
+        "email": "1759812273@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhang Yu",
+        "name_zh": "Zhang Yu",
+        "email": "yuzhang331@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guizhou Normal University",
+            "name_zh": "贵州师范大学",
+            "ror_id": "https://ror.org/02x1pa065"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Stress",
+      " attentional capature ",
+      "visual search",
+      "acute stress",
+      "N2pc",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "Stress",
+      " attentional capature ",
+      "visual search",
+      "acute stress",
+      "N2pc",
+      "EEG"
+    ],
+    "publication_date": "2022-08-03T02:49:14.234Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 61798.19,
+    "file_size_bytes": 64800100196,
+    "download_count": 6156,
+    "visit_count": 1791,
+    "url": "https://www.scidb.cn/en/detail?id=64bf82f502e62a3c98eeff55",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6414216494d8a763720bdf0a",
+    "doi": "10.57760/sciencedb.07756",
+    "cstr": "31253.11.sciencedb.07756",
+    "pid": "21.86116.6/sciencedb.07756",
+    "title": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
+    "title_en": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
+    "title_zh": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
+    "description": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
+    "description_en": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
+    "description_zh": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
+    "authors": [
+      {
+        "name_en": "Yanyan Li",
+        "name_zh": "Yanyan Li",
+        "email": "liyanyan@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institue of Psychology,  Chinese Academy of Sciences",
+            "name_zh": "Institue of Psychology,  Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Bo Zhang",
+        "name_zh": "Bo Zhang",
+        "email": "zhangbo_1008@163.com",
+        "affiliations": [
+          {
+            "name_en": "Key Laboratory of Brain Science, Zunyi Medical University",
+            "name_zh": "Key Laboratory of Brain Science, Zunyi Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Jing Wang",
+        "name_zh": "Jing Wang",
+        "email": "jennywj80@aliyun.com",
+        "affiliations": [
+          {
+            "name_en": " Department of Neurology, SanBo Brain Hospital, Capital Medical University",
+            "name_zh": " Department of Neurology, SanBo Brain Hospital, Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Yuri B. Saalmann",
+        "name_zh": "Yuri B. Saalmann",
+        "email": "saalmann@wisc.edu",
+        "affiliations": [
+          {
+            "name_en": "Department of Psychology, University of Wisconsin-Madison, Madison",
+            "name_zh": "Department of Psychology, University of Wisconsin-Madison, Madison"
+          }
+        ]
+      },
+      {
+        "name_en": "Mohsen Afrasiabi",
+        "name_zh": "Mohsen Afrasiabi",
+        "email": "afrasiabi@wisc.edu",
+        "affiliations": [
+          {
+            "name_en": "Department of Psychology, University of Wisconsin-Madison, Madison",
+            "name_zh": "Department of Psychology, University of Wisconsin-Madison, Madison"
+          }
+        ]
+      },
+      {
+        "name_en": "Haixiang Wang",
+        "name_zh": "Haixiang Wang",
+        "email": "haixiang321@126.com",
+        "affiliations": [
+          {
+            "name_en": "Epilepsy Center, Tsinghua University Yuquan Hospital",
+            "name_zh": "Epilepsy Center, Tsinghua University Yuquan Hospital"
+          }
+        ]
+      },
+      {
+        "name_en": "Changfei Shi",
+        "name_zh": "Changfei Shi",
+        "email": "shichangfei666@aliyun.com",
+        "affiliations": [
+          {
+            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
+            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Huanhuan Xiang",
+        "name_zh": "Huanhuan Xiang",
+        "email": "xianghh1010@aliyun.com",
+        "affiliations": [
+          {
+            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
+            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Mengyang Wang",
+        "name_zh": "Mengyang Wang",
+        "email": "mengyangwang@ccmu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
+            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Guoming Luan",
+        "name_zh": "Guoming Luan",
+        "email": "luangm@ccmu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Neurosurgery, SanBo Brain Hospital, Capital Medical University",
+            "name_zh": "Department of Neurosurgery, SanBo Brain Hospital, Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Robert T. Knight",
+        "name_zh": "Robert T. Knight",
+        "email": "rtknight@berkeley.edu",
+        "affiliations": [
+          {
+            "name_en": "Department of Psychology, University of California, Berkeley",
+            "name_zh": "Department of Psychology, University of California, Berkeley"
+          }
+        ]
+      },
+      {
+        "name_en": "Liang Wang",
+        "name_zh": "Liang Wang",
+        "email": "lwang@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
+            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Hallucinations",
+      "excitatory and inhibitory balance",
+      "electrical stimulation",
+      "information integration",
+      "intracranial EEG"
+    ],
+    "keywords_zh": [
+      "Hallucinations",
+      "excitatory and inhibitory balance",
+      "electrical stimulation",
+      "information integration",
+      "intracranial EEG"
+    ],
+    "publication_date": "2023-03-21T02:19:58.994Z",
+    "created": "",
+    "modified": "",
+    "status": "",
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+    "source": "scidb"
+  },
+  {
+    "dataset_id": "624756ac8105fb7f09ce902c",
+    "doi": "10.11922/sciencedb.01667",
+    "cstr": "31253.11.sciencedb.01667",
+    "pid": "21.86116.6/sciencedb.01667",
+    "title": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
+    "title_en": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
+    "title_zh": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
+    "description": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
+    "description_en": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
+    "description_zh": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
+    "authors": [
+      {
+        "name_en": "Drew Cappotto",
+        "name_zh": "Drew Cappotto",
+        "email": "drew.cappotto@my.cityu.edu.hk",
+        "affiliations": [
+          {
+            "name_en": "City University of Hong Kong",
+            "name_zh": "City University of Hong Kong"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Neuroscience",
+      "EEG",
+      "Auditory",
+      "Prediction"
+    ],
+    "keywords_zh": [
+      "Neuroscience",
+      "EEG",
+      "Auditory",
+      "Prediction"
+    ],
+    "publication_date": "2022-04-06T01:41:50.517Z",
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+    "modified": "",
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+    "url": "https://www.scidb.cn/en/detail?id=624756ac8105fb7f09ce902c",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "662cf17f3e08f2716205ca7b",
+    "doi": "10.57760/sciencedb.j00133.00448",
+    "cstr": "31253.11.sciencedb.j00133.00448",
+    "pid": "",
+    "title": "Preprocessed data for sleep staging experiment",
+    "title_en": "Preprocessed data for sleep staging experiment",
+    "title_zh": "睡眠分期实验预处理数据集",
+    "description": "<p>The compressed file contains preprocessed public data from sleep experiments in numpy format&nbsp;</p>",
+    "description_en": "<p>The compressed file contains preprocessed public data from sleep experiments in numpy format&nbsp;</p>",
+    "description_zh": "<p>压缩文件包含睡眠实验中预处理后的公开数据，数据格式为numpy格式</p>",
+    "authors": [
+      {
+        "name_en": "Jinbo",
+        "name_zh": "金博",
+        "email": "jinbo@dlut.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Dalian University of Technology",
+            "name_zh": "大连理工大学",
+            "ror_id": "https://ror.org/023hj5876"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhangjiawan",
+        "name_zh": "章嘉湾",
+        "email": "zhangjiawan@mail.dlut.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Dalian University of Technology",
+            "name_zh": "大连理工大学",
+            "ror_id": "https://ror.org/023hj5876"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Sleep Staging",
+      "Deep Learning",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "睡眠分期",
+      "深度学习",
+      "EEG"
+    ],
+    "publication_date": "2024-07-26T08:47:39.750Z",
+    "created": "",
+    "modified": "",
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+    "source": "scidb"
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+    "doi": "10.57760/sciencedb.ai.00010",
+    "cstr": "31253.11.sciencedb.ai.00010",
+    "pid": "",
+    "title": "MultiModal Vigilance dataset",
+    "title_en": "MultiModal Vigilance dataset",
+    "title_zh": "MultiModal Vigilance dataset",
+    "description": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
+    "description_en": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
+    "description_zh": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
+    "authors": [
+      {
+        "name_en": "Wei Wei",
+        "name_zh": "Wei Wei",
+        "email": "weiwei2018@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "Kangning Wang",
+        "name_zh": "Kangning Wang",
+        "email": "kangningwang_1205@163.com",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "Shuang Qiu",
+        "name_zh": "Shuang Qiu",
+        "email": "shuang.qiu@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      },
+      {
+        "name_en": "Huiguang He",
+        "name_zh": "Huiguang He",
+        "email": "huiguang.he@ia.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Institute of Automation",
+            "name_zh": "自动化研究所",
+            "ror_id": "https://ror.org/022c3hy66"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "multi-modality",
+      "EEG",
+      "vigilance",
+      "EOG",
+      "ECG",
+      "BCI"
+    ],
+    "keywords_zh": [
+      "multi-modality",
+      "EEG",
+      "vigilance",
+      "EOG",
+      "ECG",
+      "BCI"
+    ],
+    "publication_date": "2024-01-11T02:54:45.311Z",
+    "created": "",
+    "modified": "",
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+    "license": "CC BY 4.0",
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+    "url": "https://www.scidb.cn/en/detail?id=66ab23a955fe61355f459219",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68ff3c314874aace76ea463b",
+    "doi": "10.57760/sciencedb.30601",
+    "cstr": "31253.11.sciencedb.30601",
+    "pid": "",
+    "title": "Data for research.0960",
+    "title_en": "Data for research.0960",
+    "title_zh": "Data for research.0960",
+    "description": "<p>Data for research.0960</p>",
+    "description_en": "<p>Data for research.0960</p>",
+    "description_zh": "<p>Data for research.0960</p>",
+    "authors": [
+      {
+        "name_en": "Hangting Ye",
+        "name_zh": "Hangting Ye",
+        "email": "12118042@zju.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Women's Hospital, School of Medicine, Zhejiang University",
+            "name_zh": "浙江大学医学院附属妇产科医院",
+            "ror_id": "https://ror.org/042t7yh44"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "ECoG",
+      "single nueron"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "ECoG",
+      "single nueron"
+    ],
+    "publication_date": "2025-10-28T01:33:51.486Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "PDDL",
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+    "download_count": 0,
+    "visit_count": 23,
+    "url": "https://www.scidb.cn/en/detail?id=68ff3c314874aace76ea463b",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "67fd3df2ac9a663b1b555558",
+    "doi": "10.57760/sciencedb.psych.00239",
+    "cstr": "31253.11.sciencedb.psych.00239",
+    "pid": "",
+    "title": "Automatic processing of variability in multiple facial expressions",
+    "title_en": "Automatic processing of variability in multiple facial expressions",
+    "title_zh": "Automatic processing of variability in multiple facial expressions",
+    "description": "<p>The variability of multiple facial expressions can be extracted efficiently. However, whether processing emotional variability is automatic, and whether it is impacted by types of emotions, remains unresolved.&nbsp;</p><p>To answer these questions, we employed a passive oddball paradigm and recorded event-related potentials while participants did an attentional demanding task to detect the changes in the central fixation. A set of four faces was shown in the periphery, either displaying low (Experiment 1: SD = 21; Experiment 2: SD = 7) or high emotional variability (Experiment 1: SD = 36.12; Experiment 2: SD = 20.39), which was manipulated by changing the distance of emotional units among faces. In Experiment 1, the face set consisted of two angry and two happy faces, and the mean emotion was neutral (M = 50). In Experiment 2, all four faces were angry or happy, and the mean emotion was moderate anger (M = 25) or moderate happiness (M = 75). In Experiment 3, we additionally controlled for the range of emotional units in the set and used symmetrical distributions in both low (M = 50, SD = 35.67) and high variability conditions (M = 50, SD = 43.42). The two variability conditions had matched mean emotions and were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or vice versa. When deviant stimuli are embedded within a series of standard stimuli, the appearance of the deviant would disrupt the established statistics or the regularity, leading to the observation of the vMMN (visual mismatch negativity). The vMMN is considered an index of automatic change detection, evoked by any alteration in the sequence.&nbsp;</p><p>The results showed that in Experiment 1, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMN at both early (110-140 ms) and late (320-420 ms) time intervals. Further multivariate pattern analysis (MVPA) using all EEG channels showed that the brain could decode the standard and deviant stimuli before 100 ms under the conditions of both high and low emotional variability. In Experiment 2, when the mean emotion was angry, faces with low emotional variability elicited vMMN, whereas faces with high emotional variability elicited vMMP (visual mismatch positivity) in the time window of 320-420 ms. In contrast, when the mean emotion was happy, faces with both high and low emotional variability did not elicit significant vMMN in the 320-420 ms time window. Moreover, under all conditions of emotional variability, the standard and deviant stimuli could be successfully decoded at an early stage, but the decoding onset latency was significantly later in the low compared to the high variability condition, for both happy and angry emotions. In Experiment 3, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMP in the time window of 320-420 ms. The MVPA showed that the standard and deviant stimuli could be decoded during the early stage in both variability conditions, replicating the results of the previous experiments and excluding the potential confound of range and distribution.</p><p>Taken together, we found that the variability of multiple unattended facial expressions can be perceived automatically. Moreover, there is an advantage in the automatic processing of relatively higher emotional variability, and this advantage is also influenced by the valence of emotions.&nbsp;</p>",
+    "description_en": "<p>The variability of multiple facial expressions can be extracted efficiently. However, whether processing emotional variability is automatic, and whether it is impacted by types of emotions, remains unresolved.&nbsp;</p><p>To answer these questions, we employed a passive oddball paradigm and recorded event-related potentials while participants did an attentional demanding task to detect the changes in the central fixation. A set of four faces was shown in the periphery, either displaying low (Experiment 1: SD = 21; Experiment 2: SD = 7) or high emotional variability (Experiment 1: SD = 36.12; Experiment 2: SD = 20.39), which was manipulated by changing the distance of emotional units among faces. In Experiment 1, the face set consisted of two angry and two happy faces, and the mean emotion was neutral (M = 50). In Experiment 2, all four faces were angry or happy, and the mean emotion was moderate anger (M = 25) or moderate happiness (M = 75). In Experiment 3, we additionally controlled for the range of emotional units in the set and used symmetrical distributions in both low (M = 50, SD = 35.67) and high variability conditions (M = 50, SD = 43.42). The two variability conditions had matched mean emotions and were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or vice versa. When deviant stimuli are embedded within a series of standard stimuli, the appearance of the deviant would disrupt the established statistics or the regularity, leading to the observation of the vMMN (visual mismatch negativity). The vMMN is considered an index of automatic change detection, evoked by any alteration in the sequence.&nbsp;</p><p>The results showed that in Experiment 1, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMN at both early (110-140 ms) and late (320-420 ms) time intervals. Further multivariate pattern analysis (MVPA) using all EEG channels showed that the brain could decode the standard and deviant stimuli before 100 ms under the conditions of both high and low emotional variability. In Experiment 2, when the mean emotion was angry, faces with low emotional variability elicited vMMN, whereas faces with high emotional variability elicited vMMP (visual mismatch positivity) in the time window of 320-420 ms. In contrast, when the mean emotion was happy, faces with both high and low emotional variability did not elicit significant vMMN in the 320-420 ms time window. Moreover, under all conditions of emotional variability, the standard and deviant stimuli could be successfully decoded at an early stage, but the decoding onset latency was significantly later in the low compared to the high variability condition, for both happy and angry emotions. In Experiment 3, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMP in the time window of 320-420 ms. The MVPA showed that the standard and deviant stimuli could be decoded during the early stage in both variability conditions, replicating the results of the previous experiments and excluding the potential confound of range and distribution.</p><p>Taken together, we found that the variability of multiple unattended facial expressions can be perceived automatically. Moreover, there is an advantage in the automatic processing of relatively higher emotional variability, and this advantage is also influenced by the valence of emotions.&nbsp;</p>",
+    "description_zh": "<p>本研究通过反向oddball范式和视觉失匹配负波(vMMN)，考察个体能否自动加工多面孔情绪变异性信息，以及这一过程是否受情绪类型的影响。研究采用中央注视点辨别任务，而四张情绪面孔在外周视野同时呈现且与任务无关。情绪变异性通过改变面孔的情绪强度进行操纵。实验1结果发现，当整体情绪为中性时，低情绪变异性面孔不会诱发vMMN，而高情绪变异性面孔诱发了早期和晚期的vMMN。实验2进一步区分了愤怒和高兴两种情绪类型，结果发现，当面孔整体情绪为高兴时，高、低情绪变异性面孔并未诱发显著的vMMN。而当面孔整体情绪为愤怒时，高、低情绪变异性面孔在晚期分别诱发了视觉失匹配正波(vMMP)和vMMN。实验3控制了全距和分布形式，发现低情绪变异性面孔未诱发vMMN，而高情绪变异性面孔在晚期诱发vMMP。此外，在三个实验的所有条件下，全脑均能在早期解码出标准与偏差刺激。实验2还额外发现，低情绪变异性面孔解码时间较高情绪变异性面孔更晚。综上，大脑可以自动加工多张面孔的情绪变异性信息，对相对较高的情绪变异性存在自动加工优势，并受情绪类型的影响。</p>",
+    "authors": [
+      {
+        "name_en": "Zilong Chen",
+        "name_zh": "Zilong Chen",
+        "email": "chenzilong312@163.com",
+        "affiliations": [
+          {
+            "name_en": "Guangzhou University",
+            "name_zh": "广州大学",
+            "ror_id": "https://ror.org/05ar8rn06"
+          }
+        ]
+      },
+      {
+        "name_en": "Luyan Ji",
+        "name_zh": "Luyan Ji",
+        "email": "luyanji@gzhu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Guangzhou University",
+            "name_zh": "广州大学",
+            "ror_id": "https://ror.org/05ar8rn06"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Emotional variability",
+      "Ensemble encoding",
+      "Facial expressions",
+      "Visual mismatch negativity(vMMN)",
+      "Event-related potentials (ERPs)"
+    ],
+    "keywords_zh": [
+      "Emotional variability",
+      "Ensemble encoding",
+      "Facial expressions",
+      "Visual mismatch negativity(vMMN)",
+      "Event-related potentials (ERPs)"
+    ],
+    "publication_date": "2024-08-08T08:21:47.544Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 251.53,
+    "file_size_bytes": 263750751,
+    "download_count": 8450,
+    "visit_count": 906,
+    "url": "https://www.scidb.cn/en/detail?id=67fd3df2ac9a663b1b555558",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "684c3451918e1d0fc634c0ef",
+    "doi": "10.57760/sciencedb.25524",
+    "cstr": "31253.11.sciencedb.25524",
+    "pid": "",
+    "title": "MODIS MAIAC AOD daily grid data for China (GridMAIACAODChina)",
+    "title_en": "MODIS MAIAC AOD daily grid data for China (GridMAIACAODChina)",
+    "title_zh": "中国区域MODIS MAIAC AOD日合成值数据（GridMAIACAODChina）",
+    "description": "<p class=\"ql-align-justify\">The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file.</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>Please cite the following data and references when using the dataset:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p>",
+    "description_en": "<p class=\"ql-align-justify\">The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file.</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>Please cite the following data and references when using the dataset:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p>",
+    "description_zh": "<p class=\"ql-align-justify\"><strong>数据集描述：</strong></p><p class=\"ql-align-justify\">GridMAIACAODChina数据集基于MODIS Collection 6.1 MCD19A2产品生产，MCD19A2是联合Terra和Aqua卫星上的两颗MODIS传感器观测数据，使用MAIAC算法反演的气溶胶产品，主要包括气溶胶光学厚度（AOD）等参数。MCD19A2数据存储为HDF格式和正弦投影，需要对数据进行提取、数值缩放、坐标转换，转换为软件方便处理和识别的GeoTIFF格式等预处理。为了方便数据用户，我们处理了2000–2024年中国区域的MCD19A2产品的MAIAC AOD数据。处理步骤包括：（1）从HDF文件中提取550 nm AOD数据，乘以缩放系数0.001得到实际AOD值，使用质量保证波段对每个像元的AOD值进行筛选，只保留反演质量最佳的像元值，其余值设置为-999.0；（2）基于有效值计算每个正弦“块”的均值；（3）定义每个正弦“块”的投影；（4）对于每天的数据，镶嵌每个正弦“块”数据为一个数据，并输出数据；（5）重投影数据到WGS84坐标系，输出为GeoTIFF格式，使用LZW无损压缩减小文件量，同时写出元数据文件。</p><p class=\"ql-align-justify\">GridMAIACAODChina时间分辨率为1天，空间分辨率为0.01°（约为1 km），空间覆盖范围为东经70–140度，北纬0–55度，缩放因子为1（为实际AOD值），文件格式为GeoTIFF，坐标系统为WGS84，文件名为日期，包含的_info.txt文件为元数据文件。</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集评估：</strong></p><p class=\"ql-align-justify\">下图使用中国区域AERONET站点评估了MAIAC AOD在中国区域的精度，相关系数超过0.919，偏差为0.015–0.02，RMSE约为0.1，满足期望误差的比例约为67%，在中国区域的稳定性较差，不确定性变化超过0.065/10年。</p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><strong>&nbsp;</strong></p><p class=\"ql-align-justify\">下图显示了全球区域6个MODIS和VIIRS产品的验证和对比，其中MODIS的三个产品（DT、DB和MAIAC），MAIAC AOD的全球验证精度最高，偏差为0.001，RMSE为0.102，满足期望误差 ±(0.05+15%τ) 的比例为0.761，在全球46.8%的站点和8/10的区域优于MODIS DT和DB AOD。</p><p class=\"ql-align-justify\"><img 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5MkJEybMmjWrrKxs8uTJCCFNTc2ZM2cihH755ZcDBw4oKytLS0vDW102Co/H27BhA0JoyZIlGzduRAgtX74cVlOXL19WVVV98eLFkiVLlJSU6urqqqurqd3vLY54ajP9Y9Ofut7T1NQ0evToSZMmUc3CBQUFQ4cOFdN7IiIiEELHjh2TbEvQbEaOHFlRUeHo6AgL6PDw8LFjxzo6OsbFxYGJAlRv0HvAzwUBJefOnRMKhfPmzRs6dGhpaSmXy/3222/79euXlZXV1ta2YcOGLVu21NTULFq0SGRUAKsSDG9iPwA6MGGvWbNGXl5eZMNoamoaNWrUjBkzBAJBeHg4QmjcuHFVVVWamprE0UbqkpSUJCsr+8svv2CMXVxcEEIQzwSUiXOE5H/+/LlIQ8rIyIAZ18/P79mzZykpKWVlZVQ1BQwbI0eOpAZF1tfXg96zZ8+eLvWe3bt3izicPHlyW1ubmN4Dy449e/bIysqK3GeysrLW1taEK1gld4kYl8sFoWBvbx8TE4MQWrduHcYYNEhwlpmamnan97BYLBAuVlZWr1+/FvN49lDvmTJlyurVq4cMGbJo0aLKykoq2xkZGatWrerbty9CSElJKSwsjPoU0ikpKS4uLh0dHaC3wbQEGna/fv1EMVKgcJMXT58+jRAaNGgQuF3E9J7Q0FDQe7y9vckrdKInCKSnp/fu3XvLli1imS0tLadNm0b8lRhj8Jz2cJJgsVjjxo1DCK1evXrmzJmfffaZqakpsWXeuHFD1Jp3794NDQ0dN27c+vXrQ0JCwLuKMXZwcCBdl/h6MMYnT55ECIF9aPny5QoKCsXFxZWVlYqKip999llycrKFhQVC6MyZMxjjBw8ejB07NjEx8cGDBxCO9h56z9GjR2Ey1tPTQwjduXMHYwydjSp/PD09uxR6PdR7wsLCuqw74AAe5C7rXlFRAfaeysrKxsbGMWPGKCgoNDQ0PHz48MPrDnIAFsAgRc+ePcvhcKZPnz5x4sTw8PBbt24RJ7XIDwCrLy6XC6IeYwxRELGxsdAZvvnmGzabnZaWJiUlpaGhAT4EFRWV3r17x8bGMpnMwsJCkGxSUlJxcXEAy4IFC7hcLkhRX19fjHGXjYIxBs/s0aNHq6qq5OTkxo0bx2azi4uLhwwZsn79eqFQWF1dDf5Nse63YsWK7jyJYoPin/nzU9d76uvrhwwZMm3aNCJfwLciqfcwGAwiC8Tasq6uDrwPp06dsre3J0bjyspKc3NzY2NjKSmpGTNmQBFUvcfQ0BAhdO7cuba2NiUlpeHDh5eXlwsEghkzZpD4Hg6H4+/vf+PGjRkzZhDfPwxv4nYhdFpbWwcPHjx8+PCKiorm5uaxY8fKyMgwmUzQexYtWtTR0QHBJZ6entRaQHAxBKCAGrdjxw6RGgF6j5ghAWP8/PnzxMREjLGamhpCaMWKFefPn+fz+RDaQtxSQGro0KHEOIExLi8vHzlyJJmnxfxcGGOIAl62bBmJlSEE8/PzYZGdlJQE2UaOHCnmFeoSMQ6HAwAmJCRERUUhhDZu3CgS6+BjAo0BxJZkfA8AdeHCBXBmWVhY5OXlUdHrod5jYGDg5eXVr1+/Pn36UN1kubm5GGMej/fkyZM5c+bACoxKH9I3b95MSEhobGz8/fffRZPZ9OnTwa3GZDIhZlnMLAcTz7Jlyzo6OoRCoZje4+vrCxam5ORkybLoO29BIDk5GSG0e/duap7i4uK1a9cSGx48AhUE5n5q5i7TYO+RkpIKDQ3duXMnQmj27NnEsb5jxw5Y01tZWVlYWMAyY8CAAd9//z2fz4eYOfhCgjr3//jjj6LuBP5T8NVGRUVVVVXJy8uPGDGCyWSKxbSJbIe3b9/W1dUlFXxXe49AIIiJiblx48bmzZsRQmZmZhhjoqIR+ePj49Pa2tqd0OsSH+rNly9fysrKvnfdQdI2NTWNHTtWVlYWPiD98Lrfvn2beMPd3NwQQr/++mt+fv7gwYOVlZWvXr1q2XmBQRdMdGIhBzA7xMXFpaWlgbTp6OjIyMjo1auXKKoB5KGysrKqqirpGCDZpKWl8/PzIyMjEUKbN2+GoFWEEMj5LhuFWNZtbGyqq6sHDhz45Zdftre3P336FCG0dOlSMpFB4BG1+wUFBRE/O7Vd/i3pT13vAXvPlClTqCbrLv1c8fHx3ek9GGNw6AwYMAB88yIfFvhNDh48mJGR0bt375kzZ0KfoMY1E32Fw+F88cUXQ4cOhRjhmTNnSktL5+TkCIXCEydOTJo0KTs7G2J6wD8Fog0WFhhjQofH402ePFlJSamoqIjFYo0aNUpFRYXNZpN1AIfDAb1HLPLaz88PIQQGGIj8EH3tTChL6j2hoaGg9zx58gQ+7HJwcJBUU2pra7/88ktiuQUEwsLC+vbtO2zYMPik4uXLl9S4ZjKLi1RGKkFwabm5uRF/M2hdffr0oWohHR0dYohBUBTRe+Lj46l6DziDbt++jTG+c+cOMe9LDuDMzEwFBQWEEDGzkTzw4sKFC0ERIcofccP99ttvJK4ZjOEjRowA8QdWAbKXQUNDw+TJk8ePHw+kSBGlpaV79uwxNDQ8evTowYMHFRUVyToeY3z8+HFw4ZH8XC53/vz5ojBbMGUJBALQe4jpDnrsjz/+SOIbyLt04u0IgL1n27ZtJFtLS8uJEydAvWhrayOR/mA46aHeU19fD/aemJiY+vr6b775BiGkpaUFpYDHmRj5Ghsb+Xy+jY3N2LFjjx8/vmrVqqNHj4IQo/p64KsCGDJLly5FCDEYjMrKSojtra6uBr0HTMVhYWFjx441Nzf38fEhRb9rbK+Njc3o0aPDwsLAWQb9DfQe8AyC/PHx8elS6JGBQLDtLvEhdVdVVa2oqGCz2WPHjlVRUWlqaoqIiPjwuoPeA6tHcO2JQmQqKyuHDx9OHNNcLrepqUm0/AO9R2z9SfSegoICkfNr9uzZ7e3tycnJUlJSixcvBnk4dOjQCRMmkNhTLpcL6+S8vDzQezZt2kT0HpDzXTYK0Xusra2pek9kZKS0tDSRz8LOC6xTpPs1NDTQek93PfNfcJ/P569bt2706NHUj5UgrnnUqFFEp8YYP3nyBL5X77JWubm5AwcOnDdvHukNYCnR1dW1sbGRkpIaMWIEDGno2dDdQV85f/68aOOfvXv3IoSePn3a3Nz81VdfQSxRSkpKr169Jk6c6OfnB9Ent27dAm80WAW8vLw4HM7Ro0cRQkDH2NhYWlo6ISEhOztbTk4OPtgBJx3Ye8D+CQFGpC4sFmvSpEkLFiwQ3TE2Nu7Xrx8IcaAMizaSGWPs4+MDkeAtLS1z5swZM2YMmPezs7OVlJSkpKRCQkIg/+PHjxUUFFasWAGR0S0tLVu3bu3fvz8x/sfHx/fu3XvYsGGwaQ18G/Xrr7/CxP/69WsIggb/94MHDyZNmkQ+Ldm5c+emTZsI5uD0kZKSkkSMz+eDnyshIQGkw/r16zHGoBGCcQu+utq5cye13am13r59O0KIGgIPT8EIPGfOHJBoYjwTsxmolU1NTeCy3LBhA3zRZmBgsGTJEgCwoaFhzpw5v/32G7VcPp9/584dokVhjOHL+Q0bNhCTz/Lly+Xl5cmeSS4uLr17996+fTtEh3A4nGXLliGEQMPLz88f3nm9ePGCWhCd7gkCNTU148aNW7FiBflY2sjISBQyMmvWrO+++27KlCnEdQie08ePH/eEbFVV1ejRo0VmHujqT548GTBgQJ8+fWxsbMiQHzx4sL29vbu7e2BgIMbY09PTzs6OwWDk5+eTIkDvgYBCT09P+B6TzWZPnDhx8eLFHA6nsrJy4MCBYO+B4fb7779jjKGH3Lx5EwyKP/zwA4vFYrPZKioqCgoKPdmOqLGxccSIESLLgYuLCwQd7tu3DzYqA7dUR0cHyB8QgJJCz9XVtYcOFA8Pj3etO5fLLS0tHThw4JAhQ/Lz83NzcxUVFT9W3THGoPeAQnz58mUSDwqmr5UrV0ZERJibm8N6D3xMpKtA84FuCsEMGzduHD58eG1tLYSW3r9/H2P8+vVrJSWl8ePHk8AMLpc7ffr0fv36FRYWgpzfsGED8WH5+/u3t7dLNgp8RAxtYWNjU9Xp55owYUJTU1NrayuYCdeuXRsREWFmZlZZWenl5SWy3pHuRyLrSa/7dyU+dXsPxvju3bsKCgok9tDd3X3z5s3y8vKKioq7du0i/mZzc/N+/fqRGVeymeEbCnLf09NTUVFx/vz5jx8/Xrp0qaqqamxsbHR09NixYwcMGKClpZWcnDx37lwZGZlly5aVlJRUVFSsWrVq/vz5Tk5OS5cu/fnnn+3t7UtLS0WfyyorK9+4ceP69ev9+/eH3UEaGxtXrVqlpKTk6OiYn58/e/ZsoFNcXNza2nrkyJHVq1evW7dOR0cH9Lljx47JyMhMnDjR2tp6xowZ0tLSGhoaYt/PJyQkrFixYseOHQsXLiTGm3nz5klLSy9dupQsYUHazp49e8mSJRASe/v27cOHD2OMY2JifvrpJ3l5+YEDB65evZrsT5OSkqKjo/PLL78cPXpUXV1dTU2NRFUHBwevXLlSTk5u0KBBO3bsOHDgwIYNGx49egSz9evXr9XV1QcPHvz5558vXLgwMjLy8ePHFzovQ0PDffv2GRoaUsMp4GO3+fPnSyIWERExatSoAQMG/Pbbb7q6urKysuPHj4egvytXrowZM8bU1PTYsWPz5s0zNjbu7hOngICAzZs3A2+kob28vObMmTNgwAAlJSVDQ8OYmBhNTU3gedGiRYGBgffu3Zs4caKsrOwXX3xx+fJlcOePHj0aopjr6+sfPHhgYmJy5swZQ0NDDQ2NixcvUosQTa7GxsbKysqamprwMUhMTMzGjRtF8R+ysrJ79+6FlV9DQ8PNmzc3bdqk13mtXLny3r17sD9keXm5oaGhqqqqrKzs3LlzjYyM1q9ff/z4cepkSapDJ3qCgLq6+ldffQX6sYuLi5ycnKysrLS0dJ8+fZSVlUkU//79+7/44ovu1GhqQWJdHSw0V69elZaWHjx4sLW1NZfLvXv37ogRIwYMGHDixAmBQFBeXj5u3LhFixbp6elpa2vr6+vHxcWx2Ww1NbX+/ft/8803sHS5devW4sWLf/75582bN4NH9f79+/Ly8kOHDv3jjz82bNggIyOzcOFCFotlYmLy2Wef/fTTT0FBQV9++eX06dMLCwttbW0HDx6sqKgIYYhUniXTPB5PXV1dXl7e0NDQ1dV10KBBP/30E3zoJCl/BAJBaWmpmNB7+PAh2dRUkj65U1ZW9q51B/NqcXHxli1bjh49umvXrvXr1xsaGsLa4/Llyx9Yd6L3rFq16s6dO1999dXhw4dBxhYUFGzfvl1GRmbChAmg1IaHh0+ePHnAgAGbNm0i+8HGxMRMmDBhwIAB6urqLS0tpaWl27ZtU1NTW7p06cWLF7lcrlAoPHPmjIKCAsg3WO89f/5cVVVVTk7u+vXrBgYG/fv3nzx5srOzM8j5gwcPtra2amhoUBtFTU2NzWYXFBSAoWjnzp1XrlxRUFBQ+r/vYDIyMlasWNG3b9+JEydCV+zo6Lhz5w7pfmJzB2mXf0uC1nvebKz3zTffkI+xGxoaqqqqGjuvyspKGIQCgWDJkiUknqbL1uVyuVTDg1AobGpqgv7R3t4O6nlLSwuLxWpsbKyrq2ttba2rq4M0GKg7Ojpqa2tbW1vb29vJUpK8KxAIwLgNpZP7HA5HjI5QKKyrq6utrYWVk1AoZLFYDQ0NLBarvr4eGKivr5dcV8FakKwkOBwOk8mkcghFNzQ01NbW1tTUgHmDx+NBNdlsdnV1dSdyjTU1NWJbzdbX15eXlzOZTFI1kR+tqamJvFJVVUUAh4I4HE51dXVDQ0NjY2NtbW1zczO8y+FwKioqqqurqaTgFZHThyBDRQyQBxAALhaLBY0rFAobGxtZLBaXy6W6OwlBkhAKhfAKuQOBn7W1tY2NjQALgEB4BvRIiUwmE6UlUEAAACAASURBVGBns9mQv6Ojg9e542VLSwvgQw01g50woeGqq6uBPTabXVNTQ0qE14Gltra2ioqKyspKqubU0dFRXV1dX1/f2NjIZDLLy8thQ05qLej0OyGQkpIybNgwsK6x2WzAtrHzampqghYU7eYMrpOeUJbs6qLtDzo6OqBnEs2JzWY3NTWRLgQGSIhPhw82c3JygI26ujriwWxsbKyqqiL9pLGxsb7zYrFYpGfyeDyBQNDU1ER6I3QhyAwDR6xndlmvjo6O5uZmgUAgFAqbm5tBMrxF/nQp9LqkTL3JZrPfr+4CgQDC3ZhMJpGQsNPYh9cd7D2HDh2CsUmFi8fjNTY2klHZ0tICyNfW1hKZQ50doBX4fD6MXKg7keT19fV1dXVAn0g2qmyHbgN/hUJhl41CJg5W59XQ0FBfX0/6DPQ9wjAwQO1+1Ob416VpvedNk0VFRU2fPv0tG5I+fPhwzZo1RPr865qZZphGgEbg4yJgY2OzZMkS2O5BkrJoMXDy5Mm9e/d2qZpL5n+PO0VFRfPnz3d3d8/pvB48ePDll19SPyB4D5r/llf+mXUHpyG9F+g/vxfRes9/2ighIUF0rBL1c2vSeE+fPj1z5oyYP4U8pRM0AjQCnyYCvr6+v//+u9iaGKCwtbW9efNml48+Fla1tbX79u3T19c/efLkmTNnrly58ulsxfQPrHtTU9PmzZulpaVnzpwJzqyP1dA0nY+OAK33/H+Qtre3U7/cIw/a2trA6kju0AkaARoBGgGMcWtrK3idxNBoaWnp8r5Ytg/8KRAI6urqysvLKyoqiIfiA2n+W17/p9Wdz+eD64rJZEIAwL8FyU+QT1rv+QQbna4yjQCNAI0AjQCNwCeKAK33fKINT1ebRoBGgEaARoBG4BNEgNZ7PsFGp6tMI0AjQCNAI0Aj8IkiQOs979PwAoEgNTU1Pj4+NTW1u9BFHo9XUlJC9fGzWKzIyMiEhISkpKTo6Ojw8PCIiIjY2Fiyf8P7sNKDd/Lz87vbkKYHb/8nS3t7e0Ln9d/a9KW8vDwuLi45Ofm9v6pra2uLj49/8eJFRkbGR/nK5qMT7Hlz0DlpBAgClZWVGRkZVFFDHn2sRH19fVxcXGJiIuw++rHI/hV0JEelGD7Nzc2xsbFpaWl8Pr+joyMpKSkxMfFfGh1VWloaExOTkJBQUVHxV4D5v0qT1nvep2XhQBwFBYW37GSYkJDw3XffwckvUEZFRcX58+fl5OQQQhoaGtbW1teuXRs/fjxsFvw+fHS+09bWlpqa2l3kdV1d3YQJE0aOHEnl5D3KamtrgzODNm/e/JdK2O54S0lJgUPLyUbP3eXs7j6bzYa988ePH/9RNrD56AS745y+TyPwFgTWr1/fq1evmJiYt+T5wEcVFRXTp08nx578KTWBQMBgMMhOYH+a/yNmoI5K2NKTig+TyVyzZs2WLVuUlZXNzc319fVXr149duzYv+jj87q6ulevXn3E2omRSktLg122T58+LfaoJz9LSkpgl/yeZP5fykPrPe/ZmnDuATk9VJIKHEVua2tLfdTQ0ABHkcM27RwOJzU1FTZKp2Z7p/TDhw/fMmgFAsHdu3dv3rzZnWLU87JcXV0RQmpqav8VvYfsow9H2fecbWrOiooKOH6PnG5Dffoe6Y9O8D14oF/5xBHw9PQ8derUe9tBe4genCcleVRfl6/HxMSoqamJ7VzaZc6/4qbYqKTiAwcw29ra3r59+/r167KysqdOnbK2tnZ2dv4rODl+/LjkIT8ft6B79+4hhLo7TfktZbW1tW3bto163OFbMv+PPaL1njcNWldXFx0dHRcXByd7CwSCly9fvnjxIi8vr62tjcFgUK2INTU1ogMZMjMzZ86c2Z3eU1lZCUdkr1u3jrprJ4vFgqMHPT09uVyuv78/h8OJj48X61VtbW3gVCooKODxeOnp6ZWVlZCnoKAgNDQU9hlqa2szMzMbMGDAunXrYmNjRUuZpKSkhIQEeCsjI6OysjIvL+/ly5c5OTnEH1ddXR0WFga7ttfX1yckJCQmJqalpbW1taWlpSUkJMCWxGVlZZGRkUlJSUR4kXOe4aQ6BoORkpLCZrPJfqOkFqWlpeHh4VlZWVD3vLy8hISEFy9elJaWNjQ0xMTEwH75GGNqdfh8PoPBePHiRWZmJp/Pz8vLE1uLUI+yxxiLcUg8caJjGZqampKTk6lGneLi4tjY2ISEBAUFhS+//LJLvefjEuzo6GAwGJGRkWKugcLCwrCwsNTUVNjKFmPM5XITEhJiY2NJGwkEgtzc3IiIiLS0NMhGHA0sFqu6ujolJUVy52iCP534GxBobm6O6bzIzl5CoTAnJycsLAwOYBKdPQnO2dTUVA6Hk5mZCd0eVjtgfyWdtri4OCUlJSEhAQ5cA/7FOqTkACksLCwrK0tJScnKyoLe3tDQEBkZWVpaSj6wl+yHubm58fHx6enpXC43MzOTOMGBfkpKiuiYVclBra6ujhC6e/fuq1evXr58Sd3ygypShEKhj4/PiBEjvv7666dPnzIYjMTExISEhNzc3IKCgri4uISEhNevX1dUVERFRYGLXygUpqenR0REUFW35ubm6OjolJQUWGV1x7NYQ4sN86ampsrKSsCnqampra3tl19+QQgdP34cY3z16lXRQa0bNmyAcsXY6KEQJi0oJnaYTCYcbqivr89gMKjLThjLSUlJpaWlcXFxBEwWixUfH5+YmFhVVdXa2spgMKBNJVsQY8zhcBid17Vr10QbdsPJsjBt5ebm1tXVASkCKZfLTUxMjIyMBMwLCwu3bt3au3fvGzduwF6XeXl5sbGxgJIYqv97P2m9B4eHh4vOlLl9+/bXX3/93Xffwemh586dQwjNmjVr3759ffv2nTp1KqgamZmZU6ZM+fnnn8+dOweH8pADvKidw8vL6+rVq/Ly8p9//nlCQgJ5RPQem87LyMiIPKImWltb4fzbZcuWXbhwQUpKateuXRhjBweHuXPnnjt3bs6cOSEhIW1tbZqamgihH374wcbGprS09MCBAwgheKtXr167d+/28fHp16+fgoICg8HAGEdHR8+ePfv3339fsGCBjY1NSUnJF198AWcv83i8s2fPbt68WXROsp2d3f79+69cuTJs2LC1a9fW1dVB6WDvwRjfv39/9erVjx49On78OPU4dIxxQEDA119/ferUqblz5168eBFjbG5uLjrSVUZG5unTp9XV1QsWLAAzGLU6sOy4ceNG7969x48fb2ZmpqCgMHPmTKrfnar32NraUjmEUya2bduGENq0adOWLVuAVVAOAgMDhw8ffuTIEcBn0qRJknrPxyXY3t6uq6u7aNEiAwODWbNmkfPIXF1dZ8+efezYsX79+u3evRtjzGaz1dXVt23bpqWl9fPPP4OcOn/+vJGR0YkTJ+Tl5fX19THGlZWV4GjYt28fHLQudnYptf/Q6b8ageLi4pUrV2pra8+cOVNFRQVmjtu3b0+bNu23336bOnUqeGOTk5OHDh0qLS194sQJBQWFwYMHOzg4HDp0qHfv3l9//XVFRUVHRwd02h9++OHu3bsTJkzYsGED7P4i1iFhDFIHiKKi4ty5c6Ojo0eOHIkQcnd353K5v/zyy5EjR+7du2dqaioUCkVnEVD7ISyx3NzcpKSkYEQoKioOHToUjqe1trbublCTs7v37Nljbm6upKSko6MDGglVpFhbWwsEAjiNdezYsWZmZjY2NoqKinAg7osXL5SUlBBCfn5+SUlJ06dPDw0NFbWUsbHx8uXLjx07tmjRIkCysLBw2bJlenp6opN3jYyMBAKBu7t7lzxTG1pymLPZbAaDAf5xDw+PhoaGhQsXIoR2795dXV2to6ODEPrxxx/h8EEqG7m5uRwOpydCmMfjSYqdjo6OsrIyON1z69at0DSEVeI0/OWXX0xMTIYOHQrnG1ZWVq5YsQIhpK2tDSe2wulJ1BYEScLn8w8cODB+/Pg7d+7Mnj2b6D3gx1+0aFFWVhYsvME6zmazNTU11dTUNm3aJCMjExwcnJGRAYfg6urqhoWFhYeHL1682MLC4vTp03AqKuH2fzLxqes9QqFQTU1NSUmpoqICTiEGsyQc2a2oqBgcHAyn6Xp5ecHgl5OTy8nJgcOTZWRkJPWe1tbWS5cutba2rly5EiFEdWMRvWf9+vUzZszYv39/d73q+fPnUlJSSkpKISEht27d8vLyysnJ+eyzz+DQ9W3bts2aNQtj7ObmhhAifi6xtzw8PNrb21VVVeXl5TMyMlpbWydPnrxlyxaMsYmJyeDBg5lMprOzM0IIYoy8vb2LiooaGxsnT548d+7cpqamdevWiQ5ShpPAib0HYzxv3rwFCxZUV1fn5uZSt+qHo6pXr16NMQYDLJzcu3r1aoTQ8+fP29ra7O3tMcaZmZmS1SkqKho+fDhC6NGjR05OTrdu3aIGIIPe4+Xl1dLS0iWHoGB9/fXXcXFxkydP7tOnT3Z2dmtr6/Tp07/66is2mx0TE9O7d++JEyeK6T0sFquHBLOysnpCELBydnbm8/lz5syZMmVKc3NzcXGxgoKCjo4OxvjGjRuXLl3CGJ8/f15eXj4nJ6egoGDIkCFWVlavX7/+/PPP9+/f39DQAKfYVlVViYwH4GhYv359eHi4jIzM+PHjxWrRXV+i7390BHbu3Dls2LCqqqrExMS9e/cWFxczGAwZGZkTJ05gjPfu3SsvLw9TOExLNjY2Dx8+RAh98803CQkJGzduRAjB9srQabW0tMgKwdbWtrseXlhYKDZAMMbLly9HCPn6+qanp/fp0+f27dstLS0vX77EGMPZ7NR+CCfiDRkypFevXvfv3wehB16S77//vstBDeiBvef27dsYY21tbYRQQEBAe3u7mEipra3NyspCCC1cuBBWHdevX0cIwYHKcN64q6trXl4eLH58fHx69+7t6+vb1tb27bffampqYow3b948efJkNpv9+PFjGRmZ6OhoJpMpxjNVrsJJf90Nc4iAAQ3AyMgIIQRnm4PjHoSnGBt79uzBGIeEhPRECFtYWCCEqGIHYnqoZYn1QBjLAOb+/fv79u0bGxuLMb5z545IL/zuu+9EmqixsXFiYiIwSW3BlpaW58+fI4RgVamnp0f0nqioKITQggULOjo6QHOCaevGjRvS0tKxsbEVFRU6Ojqg2QAsEBamo6MzcuTI9PT08vJy6hHUYmz/z/z81PUejHFeXp6Xl1dISAjMzaD3hISEgB2Fw+GATcXHx6e9vX1451VeXi4UCrvzc8XFxYWFhWGMoctOmjSJOFyI3uPn55eRkXH58uXuYmWePXsmkoxz5swhbjIzMzOE0Pz583V1dZctW7ZmzZrm5mbQWsiBqZJvNTQ0DB8+HKRwbGystLT0xIkTDx48uGnTptmzZ2dkZNTV1Y0bN27YsGFJSUnu7u5gvk5OTvbx8QkICJg8eXKXeg+IyzFjxgQGBlIHA+hh+/btwxi7u7sjhEC3s7e3h/SLFy/gmGjJ6tTX1xcXFysrKysqKsJMT6UsOkaNau/pkkM4F1BHR4fP58+aNatfv365ubmvXr0CWcDlcsvLy+Xl5SX9XEKh8P0IVlRUdElw1apVoqUkdIM1a9YghFJTU62trRFCVDsNh8P58ccf+/Xrt2PHDk1NzWnTpl27do3P50dERAQGBjo6OiooKAwcOBDQgInH0tKypqamy0LF4KJ//kUIFBcXDxs2bOLEiVQP5okTJ8ANhDH+7bffyOQKx2IXFBTAnLR582aM8Z49exBCHh4e5BDvgwcPkiGjoaGBMU5JSZEcgwUFBZIDBMaFt7d3Y2PjN998A5ZO8L5J9sOEhISqqip5eXkVFZWqqirQukDv6W5QA4yk+4lO8Tx9+jRCyNLS8sWLF2IiJSsrKz09HYQn+OwKCgqGDh361VdfeXt7nzt3rm/fvlu2bHn06FFSUhJRodavX6+trf39998fPHgwNzd3xIgRQ4YM2bt3744dO7799lt/f//KysoueSZNzGAwyDAXG5VUuWFoaIgQgiglWJyA8ATbD2Fj3759QqEQZoG3C+GmpiY4losqdmAxTC2L8CkJJvSWP/74A2P8xx9/IIT27t1L8ku2IIPBOHXqFELIysoKYwylQwsCwwsWLOBwONDHvL29RQrc/PnzP//8c7EDKAGWiIgIjLGLi4uUlNSgQYP+6mgkUq//boLWe3BxcbG6urqpqSl0pnv37oGmD6OIw+Hs3r0bIeTj49PW1jZs2DAVFZWysjI+nz9jxowu43tOnjy5Y8eOEydO7N69u3///ggh8gkS0XtA5MXFxcE5vSQggPQG0GDWr19P7oD1mKg4oA/Beg6EJsZY8i2i9+Tk5ISGhsI6jPjmQeuCdYmenh6YuzHGCQkJGzZscHBw2LBhA0IoKipKzM/V1tYGo1pOTg6eAp8gSsCk4eLiAl+uYYxra2vHjBkzYsSIW7duwVceXVYHRGR3lgyq/OqSQ9B7dHV1eTwetE5eXh5I4R9//FEkC0pLS7uLa/64BIFVsJOB5EpMTAShpqamRtq0tbUV+CQYQot4e3uvXbs2KCjoyy+/FNl+qHrP/fv3q6qquqsFoUwn/joEXr9+PXjw4M8//xwsOlCQgYEBmVBPnjxJPn0i/TAiIgKcsBhjECmwFodOC+Malg3btm3DGCcmJkqOwS4HCHVcMBiMmTNnIoRWrlzJ5XKXLFlC1i3QD2NjY0HvUVVVrampgeUHzJrdDWqoIOg9oDHAVP3gwYOIiAgpKamFCxcSkYIxTk1NBf8RiVXS0NAA/3tubu6iRYvk5OR0dHQg5AW+EqWGS+fl5Q0aNEhVVbW0tJQ0YnFxsby8vCTPJENKSgoUKjnMqfhQdRGq3rNr1y7SfBhjGIaS4rRLqSUpdqh6j6WlJWGSJCTBvHbtGtF7qB9ndSlJjh8/TriF0qEFqRIe7D3e3t4CgWDGjBngXiQMkGUkyCiMsZmZ2WeffUbUKWrO/730p673tLe3z507d/DgwXl5eQcPHkQIgUsVhNSiRYt4PB7YJP38/DDGS5culZWVTU5OxhjPmDGjd+/e6enp1G6Rl5dnZmbGYrHKy8sbGhpgmK1fvx78NY2NjdTvueBFFxcXULqpdEBz37hxI7np7+8v6pTDhg3LyMjgcDiPHz/m8/lgR9HS0oJYgbCwMIQQ9S02m62qqqqgoJCdnZ2fn6+kpNSrVy9fX19QkiBcOjU1tV+/ftOnT4dgmsLCQiUlpZkzZ5aXly9YsAAh9PTpU2K+2rp1K5/PB4lvZ2eHELpw4QJhMisra+DAgeBKAz/Xw4cP4SmolQAvxliyOu3t7WDvmTBhAjWshxAHw6yfn19FRUWXHJLzkIVC4axZs6SlpV+/fl1VVTVq1Khx48Yxmczy8vKBAweOGzeOWOCAeHdVfm+Cly5dAl2Zx+PNmTNn5MiRdXV1YDYfOHAgBDPFxMTU19eDBFRXVxcFCuTm5iYnJ4eFhfXq1UtDQwOcGp999hmEIsICzsbGpra2duDAgZMmTSIh5wQiOvE3INDU1DR37lzwL0Msc15eXkBAAELo3Llz4Ofq168fBPbNnDlTWlq6oKAgOjoaIQRDA0zIMAypeg8M51u3blVWVnbZw4uKipSVlcUGCIwLb29vNptdXl7e1ta2ZcuWvn37lpaWQuiuj48P6Yc1NTWg94wYMYLJZIKPBoYwhDZKDmqAFDoq2BgMDAz69++fkZFRUFAgJlLq6upA7/n++++LioogXi0oKAghBPrcjRs3iJgFhztCaPbs2bW1tUwmMyQkhMlkgupmYmKCMY6Pj8/NzQV7jyTPpLnLysq6G+aAD3xCC7HG1tbWRKAdOnSIxDgTNkDigRpBFaeSUovH44Gg09PTI2IHxCN4oG7evFlVVUWNaybBUqDtHT58mDg9qcYbqJqYJBk1ahSLxYKv0o4dO0bshWfOnMEYJyUlicIJ5s2bV1lZCS5FmLagv61cubKhoaG6uhrWtyDbAwMDq6urc3JyBAJBdHT0wIEDqfUl8P6PJT51vae5ufmrr74CyzD0vzlz5jCZTCsrK+KM/+GHHxBC0LG8vb1lZGT27t377Nmz0aNHKyoqGhkZkUk6JSVl9uzZJ0+ehCDE1tZWEGrwekVFhbu7+5AhQ8AKEhsbGx4efuzYsVGjRqWlpVE7Fp/Ph8Dqb7/9Fr4BwRi3tbXBokRBQWHp0qVgIWAwGEpKSv379z9z5kxra+uFCxdE0y31rcjIyAEDBsjKyrq5uWGMz507169fP1GI8dy5c52cnKBQPp+/bNkyssjIzMyUk5OTkZHR1dXdunUrQmjnzp3t7e0gMubNm1dSUqKpqenk5OTm5jZ79mzQAgn/586dGzNmjJOT0+LFizdt2kT28IiLixs/fjyEHXRXHRcXl/79+w8aNAg8RIQmxm/MchMmTBDFR5uYmKSnpw8cOFCSw0OHDsHU8uLFCwhmhDpCbLipqamjo+Nnn302evRosOoR+t1V+b0JlpeXz58/X01NzcrKavTo0Q8ePMAYC4VCfX19KSmpAQMGzJs3z87ODkxrUK9x48bp6upyOBw/Pz+RtUDUT86ePQvRIZcuXWpubl60aBFC6NSpU/7+/r179x40aFBiYiKpAp34OxF4+vSpsrIyQmjChAl6eno1NTUdHR07duyYPXu2q6vr5MmTYU7KyclRUVFBCAUGBoIomDZtWlpaGoiUq1evkiX+ggULoqOj582bN378+KqqqpycHLExuGPHDh6P5+rqKjZAqONCpCJs27YtPj5eX19/27ZtQqGwoqJCsh+GhYVB/wkLC9u3bx9C6Keffmpra9u9e3d3gxqmaiUlpV9//dXX11cUPAfBJV2KlJqamqlTp4LcAElYWVk5efJkEEEFBQVjx45NSUmB9iorKwM0hg8fvnXrVvi4zMnJadCgQb169fr2228vX74sFArDw8PFeFZTUyPfPwIpyWFuYWFRXFz85ZdfQjQMm82GyCpNTU02m33mzBkw6ld2XlQ2YHfWPxXC4LKHBfOWLVvExI6bm1vv3r2VlZXJ2o90UVAid+/eHRISoqqqumLFCi6X29LSsmPHDli4kqDJLiVJXV3djBkzvvjiC9HHgxoaGr17916yZElaWlp9fT2ojDNmzICJBqatvLw8CHNWVVXdvn17ZmamiBNjY2OE0KRJk0Qa+dmzZ01NTUNCQubPn0+8E4Tb/73Ep673YIwZDMbDhw9hafLo0aOUlBShUOjl5WXVeT1+/PjBgwdWVlaurq7wbWdiYuLdu3fT0tJCQkIyMzOpH3yGhITc7bzAzllSUmLdeVlZWVlbW8fFxVlbW1tZWd2/f9/CwsKs87pz546joyPRnKCHNTY2Pnr0yNra+t69e1RTEJfLDQ4ONjMzoxqZoqKiHB0dmzovybdCQkIsLCwsLS3B4CkUCmNjY83MzKhkIZiAfCoPbj5HR8e6urri4mJra+vi4uKWlhZbW1tra2tLS0sGg1FcXPzy5UsxToB5oVAYFRX1xx9/eHl5EUM3xri9vT0yMpKEK8H329Tq8Hg8Ly8vS0tLCwsLWKFSx9vLly/Nzc2tra3t7e2bmprCw8MlOYTq29jYBAUFWVlZWVhYBAcHA5EnT55YWVnl5uYGBQUVFRVRw6UhQ0hIyMclKNqrSSS+7969C3EMUAqXy3327Nndu3eJeRk+yLe1tbWzswP7TUdHh5eXF0R6pqam2tjYNDU1lZeXW1paWltbOzo6+vr6WllZmZub03oPtYf8zemcnJz79+87ODiQw7e5XG5AQMDt27efP38O6/u0tDQYfYGBgZ6eniAHIiIi7t+/DyKF6D3a2trBwcH3798nzh2xDllUVARySWyAkHHh4OBQVlZWVFTk7+//6NEjwpVkPwwNDQWuAgIC7OzsrK2tbWxsmExmYWFhd4MaRERVVdXz589v374dHh5OAhMFAoGkSHn16pVN5+ei0CggE0DKdXR0xMXFUVWWhoYGNzc3Kysr6t6qmZmZlpaWPj4+MFS75BmUKtLuQqFQbJhjjNPS0kBuODo6ZmZmgkC+f/9+Zmamvb29tbW1hYUFLMbE2OihEG5ubu5O7AgEAj8/P19fX7JjBWEV9J4bN254eno6OjqCVYzMF/fu3aN+LiPZghhjJpNpY2Pj7++flpYWFhZGDNj5+fmWlpZRUVHR0dHm5uZk2qqsrLS3t6eCzGazHR0dYSIoLi7Ozc21srL6FD7mwhjTeg/pinSCRoBGgEbg70YAXBvgbfm7y6bL+y8hAD5rcLf9l1j4pIul9Z5PuvnpytMI0Aj8FxEQCATgJVm7di1Ecf0XmaGL/nsQaGpqgm2E9PX1iZ3m7ymaLgUQoPUeuifQCNAI0Aj8dxAgXpJ79+6Rryn/O6zQpf5dCJSVlVlaWkK0w3/rmOe/q67/0HJovecf2jA0WzQCNAI0AjQCNAI0Ah8dAVrv+eiQ0gRpBGgEaARoBGgEaAT+oQj8rXoPnGdJ/6URoBH430aASLu3VxO+7Ye/kjkJkQ9MEMocDqe1tZXL5ZJvkT6Q8l/9OuFcKBS2dV5cLpf6ReRfzcCH0CfMCwQCOOiUurfhh1D+G96lMt/R0cHrvP6Gcj9KEYR5Lpfb2trK4XD+LX3mo1S/J0RovYd0EjpBI0Aj8HEQ4HK5TCazvZ1D1WxAHkEBcF+0hwpRQUA0U4vvifzqSR5SYm5u7vXr101MTNLS0lpbW+vq6lgSV5c3WSxWXV0dk8mUyP6fG929VVtb2+UrdZ2X5KO6ujqy3xVVHYQThU1NTa9du1ZTU9PU1CT5LjDZ3f3umO+O87fU9y3MUzf1oDalq6vrjRs37t+/39LSUl9f3x2TXd5ndl5dPuqOeSaT+ZZHXZJisVjUrQVJnykrKzt37tz58+eDg4NFh7x2R7a7+91x0h2GLBaruz7zlvZtbGyk4r4ANwAAIABJREFUajaE+cLCwps3b169epXBYLxrh38LJ29h/j26mdgeLj0Z0R+eh9Z7qMOTTtMI0Ah8IAJvhNLNm9e3/bpdW+dAVk6OUCCAvWQ47e2NjU0CobCDy21oaGhra/Pw9AoNCQkNC+dyO96YNNrbBQIBKf7DpRtQAIJwpn1YWNjVq1djY2OzsrJKJK7S0tKkpKTXr1+XlpZSH5aWlhYWFmZnZ4vdLykpKS0tTUtLy8zMlHxUUlKSmZlZXFxMJQWvvHr1isFglJWViT0qKipiMBhkAqZCkZmZGRwcfPny5aysrC45KS4uTkpKKioqEqNZWlqan5+fk5MjyWFpaWlKSkpeXp7ko8LCwqysLMn7ZWVlycnJeXl5kqXk5OTk5eXh/7uozMfHx3t6epqZmb1+/VqSk9LS0oKCgqSkJMniSktLc3NzJVukpKSkqKgoOTm5S3i7fAUaMSEhoctXGAwG9UxA0mdER7AZGxufOnXq8ePHOTk5+fn5YkyWlpZmZmZ22ZrQAbpskezs7NTUVMkO8JZXcnNzU1NTxUqHVmAwGGS7JqIui46BKy0tDQsLu379enx8fHp6uliTibafLSsrS01NlWwRgDczM/OdXikuLu7yldLS0levXqWnp0syX1xcnJ6e/vcbAmm9hwxPOkEjQCPwoQjArKere9AvIMjW5r7R8RMW5vf279d+/vy50ZEjWlp7Xd3cTa5cOqSnn5iYdP369T27NdS2bj13/nxkROT1Gzf5AgFVcAO1D/xLqtTReTU3N9fV1XX30bjkhpZ/WjrQ/9Ns1AyCzot6B9JCoTA3N5dMA4RzoVDI5XJ5PB6cREE9D5VK5D2Yp77ewzSfz6daF8hbzc3N1K+TxJiH/YiLioq6O1zlPZgnxkLCw58muiulsrJSUu8RCoU8Hq+pqam1tbW9vf3169ekaagFddea1DwfnhYKhd3Vt6ioiPo9PEG+o6ODz+ez2ezm5maqSkplhs/nd0eWmu0D029hPi8vj7qJ5QcW1MPX/9F6D9SBtCKd+HAEyKTy4aRoCkT601AQBGDM6h06FBT8zNvLY9u2bYsXLd63b9+ZM79raWq5urgcOHjozG+nNmzcaGv76OBB3bPGv9s+sv/j9q3Vq1c7OL45OEWMVA8F2VuyEYKQwBg3Njbm5ORQyyJ5urxJfcpms7Ozs+vr69PS0hoaGlJSUnJycuAmxjg5OZnFYr2dCGGDkCV3eDxeRkYGmVypGYAm7O5dUVHRZRFd3hQjkp6e7u3tHRkZmZOTU11dXVVVVV1d3dLSkpSUBDoBNX+X6S5LwRg3NDSQQ3XE8pDWef36NexNLElZ7JUuMzAYDG9v7+joaNFxikwmE+wlYWFhvr6+sM+15Ftid7osRXRSbElJCaAKrFLfIswzGAxwylCf9iSNMa6pqcnJyRGdx1dUVJSUlOTt7R0XFycQCF6+fMlkMrvkqieUQQRlZGRAr+uO+ZaWllevXr13KRhjDocTEhLy/PnzhoaG0NBQb2/vnJwcHo/34sWL9vb296YsFAqzsrKAAsH5b0j8c/UegUDAYrHgoIMe9gA629sR4PP5LBYLfPBvz0k/7RkCmMfj1dXVde5D37M3/udzdQqtC+fObt68RVNTMy4u7sTx4zoHDgQFPr506XJ0dNSly1esrSy3b99+18zsiomJ7cOHR4yOxcVET58+o7i0jIg8olOSO++doEIORBoaGuAcAOqjnqRBZzp06NC9e/eWL1/+4MEDQ0PDWbNmeXl5nTp1qqioaPbs2ba2tu83DcDJLampqd3pPcB8Sef13kVoaWnp6+uLztc8e/ashobG/fv3/f39/fz8ZsyYAdaanuAgmQdjXF9fDyebAp/UPHAHY5yTk/Pe0zzGePv27QYGBgEBASdOnAgKCrKwsLh69aqamtrp06e1tLSgz1DL7WEaY5yfn19W9v/rfkCtra2NwWC8evWqrKwsPz8fTpzoIVmSTXSIdWBg4Lx583x8fExMTLZt23bmzJmgoKDq6upFixbdvn37/VoT6MPZhV3ae8ggamlpgbM4CEvvlMAYP336dNWqVb/99pu/v//y5cuvXr368uXLFy9eTJo0KT4+/r35FwgEaWlp1LAw0lX+0sQ/VO95s3JKStTS0kphMP7S+v97ib9Tx4UB8PzpE20dncLCon9vrf9Szt8DUi9Pj0OH9ErLyv9Sxv51xDmc9srKSlgct7DZJSUlb3w0LS1cLqetra2xsbG4uITH48NnPjU1NdevXT167DhUk81mNzQ0fsQqk2bl8XjNnReLxSopKXk/YS3STk6ePLlixYoDBw6sXbvW0dFx7dq169atO336tI2NzapVq7Zu3Qp2e1LuOyWozhTyIiy42Z1XRUXFh6gOe/bs0dbWfvLkye3bt9esWbN48eKgoKCDBw+uWLHC1NT0/TAB8dLc3FxYWEgajsp8S0sLm81uaWkpLi4GPxd52vOESG365ZdfDh48GBIS8ttvvwUGBlpZWZmYmGzYsGHdunVmZmYfwjzYvcSYxxgXFhaqq6vr6Oi4uLjAwZ/vUQrGODg4eHXndeXKlZ9//vn48eOxsbHe3t4rVqzYsGHDn9oICWOQoILG5/OTk5PBiib2lM/nNzc3g58LDnylvtjzNMa4oKDg9OnT+/fvd3R0XLx48ZUrVwoKCk6cOLFq1arDhw+/ByZQOmictL3nP23xRsEMfqyhofHseUh9fb2np6fH/12enp7Ozs4uLi4eHh4k7enp6eLi4uzs7OHh4eDg4O7u7ujo6Obm5ujo6OLi4uTk5OjoKDon2d3dHV6BtKOjo4eHBzxycnJy7rwc/+9ydnZ277xINiALBJ3+73J0dHRwcHB1dfX09IQMhCtXV1egCa84Ozs7OjoSZv6vQh7AJ1TH29vJ39/F0/MNV+7u7q6uzm5uLq6uLvb2Lo8eOfv4+NjZ2Tk6Onp5ecF3H//Bqwf/YYxdXZy19u5NTE6pqKggpUO5cA4z3HRycoLqEJwdHR3h/DxXV1cqUARSQBsqQsAHhKDWACq0GmSgthQBCog7ODgAQQ8PDwIpcOXs7Aw8kFYAylT+PTw8IA90GyAF/Ht4eAAdQs3b29vOzs7BwSEwMPBdF3MY4wf3rffu2/cqK6ugoMDd3Z2K6lvSpMUl87i6ukrehDaCHi42FqAJxF6B4eDk5ES97+bm1h1xGAVi/Hd2v66ZoZIlkMJN787LycnJ29v76dOnJEQXJHKXf1l1zFUrlj98ZP/GV1LPOqijs3v3nrLyN66cj3LB4MAYi85cNDY2vnDhQnR0dHZ2No/H68G4Ec/yptEfPFixYoW3t/f8+fMjIyNNTU1///337du3a2trX7x4cePGjSEhIe8xE2CMW1tbk5OTJe09GGM7O7vz58+bmJhkZGSAeiHOWQ9+i9xkmpqaRkZG/v7+58+f9/DwmD9/PjB/5cqVLVu2NDa+0Th7QEk8C4SNw0Hf0GqQAw4kNjExMTU1NTc3z87OhvOPxd/vwW+M8bZt206cOBEQEHDnzp39+/fv2bPH1tbWyMjI1tbWwMAA4lS6o0T6Emhp1GwY4+zsbOqpqPAUY8xisby9vX19fdPS0nJyct7bz+Xj42Npabl7924jI6Nff/310qVLvr6+u3fvPnv2rJqamp+fX3ewY4ybm5ufBAdHRkaWlpaWlZVBpA6V/9zcXEl7D3wDCB+jxcbGQvgw9a2epzHGoaGhx44d27Vr1+XLl9esWWNpaenq6rp58+br16+vXbu2oKCgO/7fUgr0DQaDQdt7/oMSxjguNsbAwOCRnX14eNjhw4djYmIiOq+oqChn5zcaQHTn5eLi4unpGRMTA7I+MjLy4cOHoaGhdnZ2QUFBjx498vT0hNn06NGj1tbW8fHxzs7Ovr6+z549s7W1DQ8Pt7W19ff3B93F1dXVrvNycnI6ePAgZLCxsYmIiLCzswsODrazs/Px8QH1xcHBwd7e/tGjRzdv3tTR0QkPDw8JCYHMbm5uPj4+vr6+MHl4eXkBfSBuZWWlra397NmzyM4rODjY1tY2MjIiJiZq40bH9ev9YmKiHj2yffbsmYeHu4+P9+bNntLSPtraLnp6e48dO378+PGpU6e+q1EaY/wkOPiwoWFg0GM3N9dz584TSCMjI+3t7QMDA6M6LwcHB39//5iYGFdXV29v79DQ0IcPH4aHhz98+BCAcnd3d3Nzs7e319PTc3Nzi42Ntbe3DwoKevz4sYODw9OnT+3s7Pz8/EDnc3Z2tre3t7Oze/TokY6OjkjUhoSEPHr0KCwszMbGBiD19vZ2dnZ2cnKyt7d3dHR89OjR1atXDQ0NY2Jinj59amtrGx0d7ejoGBAQAMA6ODh4e3s7ODg4OTnZ2dnZ29vfvHnT0NAwPDw8IiIiKirKz89PtC6JioqKjIyys3sUEfH4xYsIG5uHYWFhTk5OAQEBzs7OgYGBdnZ2O3fuvHjxora29rx58+B45/90wT/9r1OO+vn5HtA5EBUdbWlpaWJiQiCFvtrl38jISOh7Yk8jIyPDwsIOHDjg7+8fFRUl9jQqKgp6eHR0NDyKjIwMDQ3V0dHx8vIiN+FRVFSUl5eXi4sLoRMdHf3gwYNjx47FxcWJUYaftra2T548iYyMhJ8xMTEwl/xpjaKiouzs7AIDAyMjI2NiYkJDQ7dv337r1i1jY+MlS5aQr7KrqqqePnlC/j0JDg4ICKipqen0fWT/uu2XBw/tMMYR4aGHjxw9f9bYycWNTFQfmICWxBi/ePHiwoULV69effHiRWZm5nvrPSwWKz8/n81m5+XlNTc3l5WVtbS0ZGZmwpK6oqICPCZ/2oPEMrxd73F3d79w4cIff/zx6tWr95tmYL7Pzc0NCQlJSEgoLCxsbGwsLy/PzMysrq4WaRW5ubnvupoiVXiL3sPlcu/evWtqampra5uTk9NdcBIh1V0CtJOQkJDExMSmpqbQ0NCEhAQ2m11QUNDR0ZGWlvaWQBOBQAChRUwms6mpqbCwkFoKxjgrK0tS74E8pPtlZma+pQgqQbE0xriurq6qqqq2trakpOTVq1chISHx8fHZ2dkYY7gpoTdgIX7zD2Pc0tLq6Ojs6eXj7uEVExPn7x/4JvP//cMY5+XlSdp7MMaJiYmXLl0yMTFJTk5++fJlR8ebrybf4wJzY3h4eExMDJvNjo+PDw0NTU5OBqNpQUHBn9qruiwU9J7U1FRa7/kPPm+8xSxWdFQUh8MNCgq6c+cO6XzvnWhsbHwne1ptbW0P83d0dNTU1IBY6Ql7fD6/u/znzuFVq3Cn9vyG0vXreN8+/PIlfvaMc/bsg5s37QWCN98XHD16FMZMl/2py5sY46rKyri4OL5AYGtr6+Li0hNW356HyWSCjeTt2cjT2tpaspYlN7tMcDic7j5a6S4/GP8lnwoE2M4Oe3qKP2lubr57966Pjw9EHerp6b3rSvRNkGlp6YsXCRhjMzMzWLSJF/OOv5lMZk9sJEBVKBTW1NT0ENL29vbuIOqSx3fNDxEeN27ceP78Oca4vLxcX18fBCLG2M3GxhKhSITCEIpC6EqfPheuXGlsaoKirczNzP8fe98BH8V19TvYJLGT2HnOi+0vcRI7iQNuYBv7s8GATTMGAwZjegdJSAIJ9d6FekO9S6vee+9dQr33Leq9rrQrbZ37uHNXw7ASMghM/JLod3+jM3dmZ+6cuTPnP6d6+wEA8nKyVVXVjI0MomJiVxzVGjrJx0EkEvF4PD6fj9xLl0kacscfIdAYVnzeyc4fOcRDNovF4u7ubvKGUvcSCoU84u9J7Fzk8JZfAtpEPeNj0UgtgQAZeXB0BOS3xOfzeTweGc/1WAcnd15+98lhr3g3SewgFArt7eytLK3sbG2LCgu1NDQlL64lcDFM+HeTxyfPKBKJBAIB0iS1tLQ8iQWTPDiVIO8IeUYJIVrARbNEY/MXJgf6OtpaqmqqCkeG6MMD3UDEXto6C4AYeUyThyUPJRaLFxcXeTwewugrsojc+UcJdHxywNTVH/3tw3a457NFp9MfUc6SF/jkxM/Uv4fK3PT0dBcXFwAAmz07PDyCrlkkEjFZLKphgsVkzs3NiUSijo6OkpKSsvLyicnJrq7O4uLilhboyj46MlJRXt7Q0IisnrU1NaWlpeiLc8W7Qt7XFbdKdT7KlEIAnbg0iOOFQnxwEOdw4JOH/gDAxWL88mX83Xfx4mJ8fh7fsgXfuRPMzExbWdm8/HKItXUenz+blJR84MCBx9X3UFlKo9EiIiLQmFHGFHSxYpHofvoUHH1s3J9jIuJPsicRbyxFowBXImRRij3kBS5d6srbH+h9FJZSf0Ddn8pqPh+/cAFXV5d8ICE+DA4OmJqaxsfHFxUVLS4u0mi0w4cPr8FtguSOh4dHUlISWm1pbh4lNBnIDba2tg69N8fHxyorq4RCUUtz8/j4+Pz8fATUV4VMTE6iPYuLixcWYHDE/Px8Xl7ewiMESlCvmsqN5TQa2/L+VXoe6+AMBsPIyCgpKam8vHxxcdHDw+PEiRNk0HJ8YGA9hoGllv3CC7n5+ej4AID42JiMrOzsrMyS0lJdHR0FRcXRsXGSt09IUC8QHWp2dvZxPxuoByFpcmDkw0VuWgNBvBOE1HQm1IOQvBoYGHhcgE49zk9EU6PkEFuoJyIZ1d3dvTbdAPVoj0iLcJwvFhFNzBMJM7Iy4+Li4xMS8gsLIqIiF/g8gVjMF4n4YiFyDV6u70EX5e/v7+PjU1ZWRqfTnwT3POKw4W4A4OwifNgJH3HHR9zwUXcw4QXbpDcY8wBjnrATbnLFR71w4RSdwVxu50KnQ5zncDhrc+R/jDGvdVdSi0ZOkmdA/HxxD3nb0tPT3dzcAAD2drbZObnz83NMFksgEFhamBcWlyAejQwPffLJFidn1zk2e9/evTdu3DQ1NauoqDjwzTc3lZSOHD4UGBRsoK97+PB3x7//3t7BKSsjfcfOHepqat8dPYacUtd61x7pdwDgk5N4UBDe0wOntEAAl7W1+Ntv4+npEnmMQM/sLL55M45h+Cuv4J99hn/4IfD17bexMSssLA4IWFBW7vDw8PXwcNXU1HxYIO6PDggAEBQUFBUVRWiAhgIDA6HVqagYKmPHxz08PLncBcRVBr3r6pUrwyOjXV2dly9dUlRUvG1hUV1dfeXKZfnr1w0MjYZHRnS1tS5dvqSppcVisu44OZ4+ffrs2XMRkfDgPzqSJ9lhScsrOQa5uiIxP48PDODJyXhLCwRzHR0dt28bNzbWzc9zmpqaXFxcfHx81NXVUQKPNYwKAODh4ZGSkkLoKSdl5eQGBofo9O6KioqxsbEbigpd3XQAQElRwWuvv15ZWa2uqpKcmqaqonz12rUbNxTPX7goFuNx0VF/+MOrmdlQWZKcGP+bX/86MRkecA3jecY/AQDU1dWZmpq2t7dPT083Nzc7OjoGBARoaGiQ6vf4wMAaAvTgxDL9hRfSs7PJq4OlAETCxYUFvkCwwOXOzc0/xXcfFZSgw87OzqLIoydhFACgp6entLS0oqJiZmbm7t27j2cnXXZuAACy11D1Pcv50N/fv2ZT0bJzPrUOFMeOXkpozMvZjiwyzwb3AABapodvN2VZN+eiZtuad6e7xKmrxLY1z76jwKYlD/WbN2Y1zw4P9PZJ4R50FYuLiygIKzIysrGx8dkMHoqEmVx8wAofcsCH7IklIuyXVlGnLRh2FvLGq2vqyAeNxMdU4ueMe1pbW/+r75F+DgEA6enpfn5+szPTp8+cHhgcvG1udujbb4ODQ5MS4nV09dDsDAmmHT167OTJU21tbdu3f2FiahocHEynd3+580s//wAZmatWNjYqt5QTEpP7+3oPHDjo7eO9Z8/eO3ecdu3e3dvXT75/pU//lNYBwFtb8TfewNPScKEQl5HBPTzwmhp8/Xrc31+Ce8RiXE0Nv3gR37kTf+EF/G9/w7dvBy4uzfb2BllZ2bOzY/7+Cy+/fCc4OEwkEt6+fXvNH6zIRzIhIQEAEODv5+ruYWJoePTY99bW1oNDQ0o3bxSXliGu2tva/OmNN/wCAstLS1CwbnlFRUpy4r59+0tLS/fs2R0RGXX40OHM7BwdbU3lWyqjIyOnThw3MDKZmJh4Spxb+TAA4DweXlmJj41B7uE4zmbjs7P44iK+sAD1ZFpauLMz7C8vx7u7IdCUl4do0sUF1NeXaWkZnzpVlJc3PTc3bWFhkZqayuFw9PT01ixOAACenp6ZmZkwcCM9TUNTs7G+/vjx40pKSuUVd+3tbL28fQEAxUUFb/71zfMXLt64ccPTy+vAgQOjY+O8xYX9+/cXl5QpyF+/LicnK3f9nqvEdfj/+pVrMiLR/Tx+K/PiX9qL5kl+fr6RkVFJSQmbzR4bGzMzM8vJyeFwOPr6+ggKQI3OqriHlJHogNRXNtmzZkIsFnO5XKFQuLCwMDg4ODo6Oj4+3tvbu7annjqMguIiJdVbGjpaRWWl+sZGpeWSBwfts4Y7I2XnQhqghYUFlIECOYgMDg6uQTG5hsE81k9WtHPxeLzFxUUcx0cIK9LU1NSTxHM97ngqxlkXS0KvlYXLoFYeca0s/Fp5hAxqROe1svALJcFl48zJ0THk5ETeO2QhQrkiR0ZGJiYm1hzH/lgjhzsDgE/n4f3W+IAjPuggWUICNcclwh4fdAHCqb6+Aaq+RywWLywsCIVCLpeLJvzs7OyTxHM99vgf+Qf/jedamVUAgIyMjKCgoO7O9nMXLo5PTNhYW184f05JRa28rERWTp6Ypvihbw8cP/7Dxo0bAmm0bw584+bmkZyc3MNifvnll+fOn/vk00/HxiZuKCgkJCaPjY58e+iQp6fH3r1f+/v5ff311/mFRWt7A6484pV6kZxmsXAuF8pmPT2o++Hz8f37cWVl2GNjg2dk4L6++Ouv49u344aG4LXXwPbthYcOmRQXM+vqqlpbW0xNLTMyoEebWCx+QtwTEhKClBNqKip5BYUmRgb6BkZaGmp3nN3cXV1c3GBE6Nzc7LcHD+rq6h499n1Bfv7mzZvV1dVpQcGFBbkffPDBl19+efbc+d6+vsOHjwyPjOXlZB86dPieLVJN9ZZ/YNBPyk+k0dHWhjgmIkKCGmVl8T//Gf/gA3zjRtj+93/xP/4R9/HBv/kGGg3/93/xPXtAaCgeFZVsY2PBZPbLypZbWbXY2hrX1UFZtbCwoK+vv2bzAQDA29s7m1BgBAUGmJiYREWEaWrrjY6O9vb1ubm5mppbQEiUkSYrK6+jrfXK738fERm5Z8+eto7OkeHBvXv2xMbGbtjwzx9OnPjnPzekpqRs2rTp5MmTb771t8bmlp+UmSvN1kftQwOLjY11cHAYHh4uKyurq6vT19evrq4mrNJsAwODR8c9y89KRRhPQjc2NgYFBaWnp98zwOno6Ojr6xcVFXV3d5Nm2eWnfliPWCTi9I3O0QfnGEMc1kh7eW1KaHRKcFRtXmlSUCSrpoXDHJljDLHpgwvDUw87yMP6STdPUt8jFApRVEF5eXl0dLSOjo6ZmVlTU9OaQdvDTv3o/SQskJqW0JFgdJSav2dubi4xMTEoKKi1tdXV1dXY2NjZ2bmzs3MV14JHH8aP7gkAuDvWc60sXL4iavV2pSzs7nhPD505OHQ/G8XAwACKtKAm9Vlz3sIfHa30DhD35OL9Vkv4ZjniQT32+KAzEE61traTjnQAgMbGxuDg4IyMjLKyMkNDQz09veLi4tbW1rU58kuP7emtI8evxsbG//o1SzMV6Xu8vLxGh4cuXLxYX1//9ddfy8rKaGjrpKelqqlrwPldUbZnz9709AxzU5Njx47t3r1bVVXNzNwsOzv76NFjLFaPirLSbUsrNdVb33139OzZsyam5ilJidu2fWFsbPLlzi+LikulnmHpQTyldSSwEZpHWoreXnx8HIKhM2fww4ehTw+h7wFjY/iJE3FvvGH1yiv0/HwwNtZnampaViYx6t1zszE0NHwSfQ+NRouJiblnvVZVuVVQWKyrralnaGRooOfk7OLh5upK4J742OjNmzffuqX86aefqqmrQ4BYUFBf3xAdFXn6zNnsrMwvv/qqsbFpz969kdHRSjcVtXWg7k3+uqy7l89Pyk/ERn9/CB8HByW4JzAQv3QJV1DA9fVxVVW8tBT/5z+hzmzTJvx3v4OmlY8+EgQE0Dw8nEZGYHayxsYOU1OjsLCGmzfB0BDgcuc0NDSeBPd4enqmpqbCYOn8XDV1ja6uzhM//HD06LGs7Bw7G5ugkFC4qSBfW1dvcGDgi23byioqvT09Dxw4ePjwYac7zu5ursYmJvcGYGJs+L+ffXbb0npoaEhPR9vpDvTof0oT8GkeBoFFLy8vNzc3JMkaGxsNDQ3JjL3T09Oamprk6/hH9T3LB/ckWIf62+Hh4aioqJSUlN7eXh8fHzc3t9ra2vb29sfFPTCqZXou/QvFqNePxrxxIuaNE3F/PpX01rmkt87F/+V08lvn4v5yCvVH/OFQyQWIdCXayOXXtlIPiue6hx1J3MPn8xkMho+PT21tbXV1tYuLi5+fX0dHx5rj2Fc67WP0ISdfHo+HNApSxdRGRkaouGd+fh4Zkfv7+zMyMtzd3aOiorq6utb8lD3GQAnfRIh7SsPkyyNXb1dKQ+9phphddKqda2pqqqSkxMPDg81mT0xMjI+Ps9ns7u5uqkfpY43n8XYGAJ/KxXst8T57vN8eLlHrl1q1w/vvAOFkT08fqe/BcXx4eBjFvfb29vr5+bm4uDQ1NTU3N/9LcA/5JCKdLpUPKEzsv7iHyhMJDaOvs7Kio2FQq6aGeml5eX19XUlJSU9vr52NdVQ0FN4jw0PIrszlcurqaqurqjIzM7Ozs3p7+9rbO3g8/tTkZENDA4NOz87OLi4u5vF409PThYWFGRkZzS2tK5z1J+4i34cAQGVPWRmcww87AAAgAElEQVT0Ptm/H3/+edzBAXh6CoyMAn7/e48XXqjfsqUkNbXdxESvrq6OmEDQPQXH8Tt37jwJ7gkNDS0oKAAA+Hp7uXt6FeTnGRjoGxgYDgwOaqipIa+p6KjI3DzoeVqQl2dnZ6+lpamiomJhYVlSUhISGgadhGiByampdna2N27cML99e5JIzx8THVVaVv5Ti2rEQOQRNT0NbV5kDwDQ+GVggOAO1Ak9/zxYv56LYU66uuEtLTWBgdUnTlQqKekPDdErK8GePTiLBXBcYGFhiV58a7j5KKdLWRlUHc3NsW/evDkwODQ6CsXAzPS0srJSH5GJmLvAHSdy0k9MjHMXFnAc7+joaG5phfF942McLgdKPg6no6MDuU9yOPNrdjlaw1U8+k9Q6Ja1tXVMTEx5eXlTU1NJSYmxsTEp0pDqwtLSkowg+xfintTUVAsLi6amJmQ2Qp4oqD7Xo18yemvzJtlx/zgdiH0RhH0VhH1FI5ZUAvX7YZ/lHtR6XNyDahhR63Px+Xw/Pz8nJyekdRAKhWKx+AnjuR7rkqk7oyQufn5+7u4eY2Pj/v7+1GQ/y+1cY2NjdnZ2QUFBXC4XQUyxWPyE8VzU8axOE/oe1pXiULnSCNSuEwRaypWGk/2Xi0PKx1gTD9q5Wltbzc3Ns7Ozu7q6VFVVdXR04uLiWltbqZe8+gCeaCt0CM3BmbfxHlsxpaFVYmlHLG3wPicRb7yxqZnEPchCcvv27draWjThRSKYIHR51p8nGuGj/RhBeZS5Y2RkJCsri8FkUKXDf+O5VmYk0vegOPbOjvbmFqj2BwCICA3wzFKWLZKVaCt1iV5Y1B4peuUTP1oviWAebXfpvQDA+/vxv/wFuvvcvYv//vdAXn5m3z47DIvEMDGG4b/+dZmqqn5fH6xyzGDgN2/ibW3w6g0MDJ4E95B+zX29Pc4uLiKiCDb0a56YcLB3YBNepSQ8R7xdzjRkcaP2k6yWvs6nui4UShzDxWLoNfX553hoKB4WBm2FCgq4tjbu4AAdpzAMNbB//8iFC7e9vdN7esDiIn///ozXXzepq4MJS8Ri6G6FXtnq6uqk2H7c8VL9moknuRuFaMHIrLm5trY2VGUc8YrKpYf1UOfz4w7mp94fxaibmZkh6MzlcpOSkszNzakunyjhm4aGxs9B34NySjEYDIQmEe5ZQ7ki+Hk6xU7YcD4I+zIE27tKC8C+yDusC2/i4+jqkF9zQ0MDqe+Zn593dXWtqalhMBhkQc0+ogg8OUPWfLvJJ5c6IZcfjdwNGmpTkpxsre7YW/d0d5QW5MyzH0irLVWngsViubi4dHZ2UvUonZ2dT+j9vXyEK/ZA3DPKulIUIlcSvnq7VBhcPs7q7+mlmrRKS0tpNBoqSu9K/BUXF7e1ta052fSKg3xoJwDiiWwR3VzEtF1qNgRhI2IiAvVbi3ocxbzx1tZ2Ku5BWVgZDAaq8kRkAHqiOhUPHedDNlDnTElxifJNZV9v38L8Qtc7rhnp0AmS/PtvnYqVWYgyfKPgI6lHneDdyr96Nr0A4J2d0LuWkJ0/fk7yNUglOBz8wAEYvRUeDr78shfDTDEslchyMv3cc4VHjpgfOjSUlQUvvaEB//RTvKIC0s7Ozk+Ce8LCwpDQwnGcy+WiKGv02kWu9VIXQ05TdAvIpVS/1K9+ilUAcHt7/MgRGPAfE4N/+CFubo6XlECnKAzD/8//wV98EarNSNCDYW0ff2xCo+V6eeV1ds7JyyeeOmV99+40ugTijQ9xj0AgsLKyehK/Zn9/f6TvWS5FyHP9FAz5SY+JJiqS3QQNb3h9fb2RkVFxcWFxcTGfvxgdHe7k5CQlD35W+p6uri5zc/OoqCgST8w+vC7pKvxEuCd+w3ka9mUwtjcY2xtELKkE6vfHtuc+HPegpwbNE+rp0Nc5tS4pSrmLfMbJZ+2pxHOh0Krm5ua2tjY2m3337t3lDz7U6nH5rsUs+3yGYwHTsYDpUsy6U8hwKqDbZLY65nU7Fkj6bXLphd2Tc+zZri5Y7RX98fn86Ohoc3NzaiXwVeqSUlnx5DTMfDvKvFQQJFsUurzJUDov5tPKRpnDA4Po8UeDn56ednd3d3FxmZycxHEcFS3v7Ox8ZnYu4XiWoNNU0G0loFsJuq34dIJ4cMmnWwrodkAwyerppeIeOp1uYWERFRUlyVEEMx8+UV3Sx70dbMHsFG9yijc5LZjqn+qrarlbUJlX31VXXFvU3tc6I5ie4k9O8SfYglkc/Lcu6UrcRbgnLAwaVlba/q/suxdsqKKCf/EFPjf34991yChDDhdJlMREfMcOKK0xDLzzTuO77xq/9FIThi1iWC+GJRw+bMVksjduBBYW8PhiMTTo4Djkg4ODw5PEsYeGhubk5JDwhRwVhAA/Pz7fHx4R6BAXh1tbw7itpCR81y789Gn86lX84EHoEo5wDwF6UKaY0k8/Nf7oI/pLL3E3buy1tw8+eNA5I2NhagrMz9+/Zci9ztzc/AlxT3ExzAVAHe3/vzSan0Ih3tUFm0CAd3eDmRmQn599+7b5wED/zAyHwehRUfHQ1fUWCvlSF4702+bm5j8HO9fw8LCxsXF9fT0AYGJigslkjoyMrKE+F4l7Agncg0BPELYnCNtDwJ09wQQdhO31f7i+ByXBQqnwlkMfsVhMtXNBpcXduyYmJmNjY0KhcHR0lMVi9ff3ry2eCxBeL0iuAwByc7I11dXUVVU729sD/f0WuFxy0xLSBT1T3I/tit++nb/RMn+jRcFGy4J3rApR22hVCFct8zdaFvzFJNc8i77I5bDIjKvEscLCwjw9PXk8WJRtgPh7pvFcI4xLeTSZguDV24XcgLIRxuTYODWeSyAQODg4oOAPki0NDQ3PqE42AILRzMV2Y16nxWKXxSKx5HVZ8LrgKrnkdZrzumyAYKKtrYNUrCJ1rImJCTnhWSzWxMTEs4rnAmJcXDSaF98fntQflTQQlTIUkzGWmDGWmDYSnzmWmDYch/rj+yMKR3NwIGbQGf+NY5eWFMjOhfL3oPkn9bIgJyWVQPuQPdIHfUrrAOAsFkwJIxLdF6LLj42CuerroeNtXBzELpqa+K1beF4efuMGymYCMCwXw0x++cvKF19kEWI7AsPufPvt4uAgOHECv3MHHn+pAZHoSe1cNBoNqdCojCJHTvKNBEZkD7k/ufMzI9DlEwOAmp59+/A//Ql/6SX8uedI7Q5JINATj2FW//hH9Y4dQ7/7neDVVz1feikwOlrEYIANG/Do6Pu3DNm5ntCvmczfQ/JqORRA3CN5iPYkV58ZJ3/0RGjGpqTgx45BNHntGj42BrZuxT/7LPzOHbu2ttr09JHDh/nnzt3BsDAabYVIe2Tn+pn4NU9PTxsZGZWWlra2tsrLy2tqaubn56+hTgUF9+wMxvYFYfuIJdL67EPQh4BBe/2x7SvauQAAfX19MTExoSEhDDo9Li6uubmZnCfIHlFdXU3qpQAAbW1tWlpa/f39iYmJCgoK+vr6DQ0Na6tTIRSJZ7iCaa5ghitgL4qKyioTUtJjk1JrG1taOumzC7Af7bAgEKFp2TfN3X6n5EObwi32RVvsUCv+xK7oEwkt6XzPMt8unzk5Noqsh+QjkJaWdvv27bm5OScnp1u3btnb27e1ta3Zi+5H5y11BwBA+TDjQk7A1TzaNdRyg67mBV3LXVrNC7pGrJ7P9isbYXS1d1DtXDiOe3h4BAYGstnslJSUnJyclpYWBoOBIo+oJ/pJaAB4IxmcVgNOuzmnzZzTbkYsCbrNnNtmxoWd5pw2U06HlZg3IaXvuaf7MTExKS4u7ujoUFBQ0NXVLSgoaGpqWnOdCvIayTtLvrvITYgAOMQ9BSPZMT2h8b0RZIvrDSdpRMT0hBaMZnMXuPV1/xl1KkjeSbFsxVVYoDQ7OyMjA750eIssFovL5QxSyl/zFhcHB4cmJycbifvaw2IyidorA4MDCwvc5uYmhOJXPPiTd5LCeJVDAQB9d956C0rlS5fw2FgYYk347uC/+Q3AMBGGhWOYPYbNYFgdhjF++UuPXbv8Nm0SvPgioNFgtBeX+4CQRl60VH3P47I0MjISBRtzuZyenh4ul0MmwoYv3/n54ZGRwcEBxNK21tau7m6xWNzX3y8Si+fY7DX7wazCpdU3AQBDtExMoCPUt9/ir70mgTjr1i3HPQDDFjDMB8O8MGwewyoxjLl+vd1vfxuDYbirK+Bw8MhInE6XZqmzs/Oa38goJRL6wJpjs1HBJurn4+zszMjICI7j/f39c2x2c1PTzMxsb29PdzfM0d7U2Dg9PUPKv9VZ8eRbV5m0S9gaJpci4uDwv/4VV1UFjo7zmze7YVjQlStzn3xS8eabrN/9zlpXN01NDdVUWWFQYrHY2dmZLDbyL/RrHh4eNjIySk1NHRwcDA0NDQoKQjUm1xLPNcWO23DOH9uJ1DxB2B4a0UiCRqh8/LFtuYdheXk0ycjHE36Lj47c8XJPzs7oGRk0t7POL4U6QvJvcXGR6t+DSizp6Og0NzfX19cHBQWFhYV1dXWtIY4dpuscnTvmX33Ur/oY0X6g1Z0MbjgZ3HA8sO4HWv0xf9j/vV/1Ac/KyDpJRHffFHe7U8mH1gUf2xVusX1oe/d2nl0eY2ZqAhnfyctBdq7R0dH8/PygoKDk5OTu7u5nFsdePkQ/n+lzJTtg9XY2w7tsmN7DYFL9kHg8nqurq6en58DAgIGBwfXr16Oiourr65+NcxIOwOJQGrtRd67ZhN2Cmim72UTSUA9cNZpvsxAsjFZVP1CPfWhoyMzMLDk5eWhoCE341tbWlpaWx53wUk81Qu0NDQ3d3d3z8/MrVjuQ4J7h7OiekPjeiLgl6BPXA3EPsRoe1wPxUExPaP5wFo/Pa25u/vePY0fl62pqaqhekFL8pa4iOxeK5xodGf5m/9eRUdHWNjalpaWRUVGDQ0PBtEA1dU1vT49jR49FREReu3bV28c3OyvjhxMnGHS6ro62rp7BM5MoD44cvvRQGxiAYdVnz+Jubvgvfwll9h//CL2YMYyLYa4Y5odhg0QaWw6G2W3ZEtHbK/7uO4BhMMhr6eUpOTa6Fqp/D3ILWCDigx7lSgEApH9PV2f7/v37ExLjHZwcCwoLYmNjJyenbKwsTE3NYmNjFBQUAgICDn37bWR0TFxs1Lnz53k8vpGBgbW17aOciMqNJ6QBgGDlo48kfjxL7jsS9ENZBRg2hWFWGBaGYUMES6cwzHz9+jQVFbB5M5CTu39TyCE9Ff+egICA8nIYyFZTdXfv3n0xMdGubm65ubkJCQmTk1OG+roubh6d7W0nT55KSkz4/vtjrm7uQUE0GVm5iIiIo0e/8/DyhtKCHNNPQ6C51N2NM5nL5xU+PY27uuJNTdCGKCMDebt+Pf6LX4B160YwzBzDYjBsjGDpEIYZf/JJ0dwcEnBwrMtn6eLi4s8knuueTsXExAQ5tKERz87OrjGeawn3ILjzsKUfti2HwD3Qd14gHMioZIbmsiLyeiILmvyTS+6Etgant4VkFDmFtNLSeiILWBH5zLDcoexqIU9Arc8lFAoLCwsNDAxQURo0+MHBQYQmH2uOAAAaBmc/sCl836rgA+uCD6wLiYboB1b/bpbrUcJC5+qb4n7hWLTZKu9jm/xV2jvmOTa53QtcWCIU/RDpriIiIqytrdlLVdgAACwWS8oV7LGu4tF3BgCUDXafSfO8lOEraZkEgZZkZ4bv6VSPksEuKTvX8PCwi4sLjUZDSLSmpqavr+9Z5i3k9qdM1WlNNxhNNxguLQ2nGwyI1SWiXn+m0UzMG+/pvR/HDgBobW21sLDIJ+rAoNvxJPFc5A2F2sfWNjMTMzlZucq7VYoKipV3K6lb4XuA0PfkD2ZFMoJiWGExTNRCY5mhsQQdywyNYYbFMsMiGcG5gxn/EXYu5KZ39ChMonP+/PlHyUIBAEhLS7tX3hnWgBzoP33yxHdHvrt1S9XOxubMmbP3MhN8f+yojNx1S4vbFy5c0NDQuHDhgoWlZUd7u6K8/NDQyN2KiptKSnwBrMDy6M+M1J5S73SprSuuCgSw8Nb4ONRSlJXBYhSvvw6z550+jf/qV1CiPP88eO65MQy7TXgxMzEsD8MmCOmS9pvfgMxMUFkJ3VZSU6FEof4hlKOrq0umymhra7t8+fKxY8fs7Owexe0OAECj0ZDLVGdH2+HDhw4ePGBgCBP4XL161dXFdffu3ZpaOiwW09TEJDMz09TUxMzMvKamRlNTMzMz44tt2/QIKPkkLKVeEUlL8Zm8cNSfmYm/8w7+/vvLsQ7ZAwi/KGOi8GUbhlVgWP+6dQYYVvrii8DXFzpEm5vDs5FHRqdGSfZUVVVJPZbUw0yO8GEEqkuKSpxWVVZ8992Rbw4csLC01NHWlpOVc3G+s3fPHk1tnZs3b37zzTdubm5nz521sbEbHxtTVVV1dnY+deqkvaMTOunDTvFU+sViCG6++Qb/4QeYOpyo2gEPjBgSGgo5uW0bbmEB3cMxDH/xRfDiix0YZoBhVRjW+PzzNevWdWOYLobVfvopqK2FKZRoNPhzJhOmySYZiwpQq6qqkm4H/0J9T3Z2trq6Oul99YTxXLEbzvlhOwOxPas0XwruEcxwkj677I99HrRuJ23dzuDnvwr9xZ7g578Keu7L0F/sDn7+KxrR7499nvq5HGdiprEZ2iPIGejp6WlsbIyyIhF3ClrK+vsfO8U8AKBpcHaLXeFHtoVb7FZr71jkeZf1oAEQuKdgs1XOxzZ5H9vkfmyNlrnEah5cWsPOd8yzbHO759mzHR3t5Mg5HI6xsbGrqyv5iQsA6OzsXJtz0uPOfyhrBrpOJbtfSPVaat4EgZZkp9fJJNeSwa7+3j6qnauzs1NDQyMqKopaJLi5ufnZ1OeClvfepLFKtfEa3bEaXbishoRkeb9TZ7zOSLQ43tHRRa1TcS9buqamJhm58iTxXGJcvCDkcoUcrpCzKF7oZHbmFuWUVZVWN1SlZqf2DLEWxQuSrSKYmAPhnryBjIjuwGh6yCotopuW058mFMOCdP/m+p57ktLY2BhlzHNwcAgICFhddqJHKDs7G2WE6+vtdXR0cHdzlb0u397eVlRUXFxcZGNtLSd33dTUVFlJyc3dXUNDQ1NTi81mW1la3st5Y2JsrK2jwyVqPT7uk0Puz+NBv85H/wMARhh99BHu5IT/4Q8wVuvVV6XMMQDD2jHMiJDQYiJkfQDDTIh61dA3RUUFLyqChbq6u+/LEjQAxBMfHx+yFI66unpYWNjw8LCSkhI51x82WvTziIiIykoI1dtbW1xc3YwM9PUNjRqbmsrKygoL8vUN9NTU1FWUla/JyBYU5JuamhibmE5MTFpZWVVUVGioqWpoaj31+gliMXSWGhmB1wsADJHr6YEO4zgOlRApKXhICCzfsX49/vLL+K9/TWIdRCCHnmoMM8SwBkIngWNY5yuv6L/6Kly9cgXw+fBQQiEU/FS7IZIlOI47OjqSiu7Z2Vk/Pz9zc3OUA+NhzES/RSwNCgpCO9+tKPcP8NfSVLe2sW1oaCgvL8/Lzb3n6aJ8S8XH1/f8+fO3LSxUVFUsLC29PD1VVVXtbO1uKCo6ODqSI1nldE+yCQBovfrkE9zdHa+ogLGBnp4SbjMYMEQOYZ3nn0dzFbG0BMOMMayD0KiJCN4aEFMXPP88hPIYBv36kek2MfH+XEXo3NHR8VHsXIiB5KuAXH1aRE9Pj76+PqrLiI655niuxSl2zBLuCXg49PHBtmUv6XsEs5zkz2UCsR0h0P35oS0Q25G+VXFhit3S1kbFPQUFBWZmZqR7OABgbfFcMFfn4OzHNgUfWed/bFMAlTe2xBLSD7SN5jnepaS+h7PNPm+TRdZHVtkfWRLNKhvSktUc2GmVvdE0wyankzPHJrNWIj4HBAT4+vpSpdozq0sKcU9/5w/xLmeT3M8lusOlhPA4lwRXl/o9jsc7F/V3SsVzcblcKysrVHlGIBBwOBwej9fR0fFs/HsgNGclDJbeGqrQGqrQJJYEcVdrqQd1qg9X6gkXxhgMFhX3sFgsIyMj6oRfWzwXwAFfxCsazUkdiM8YTMwYTMwaSc4dT8ubSM8ZS82byMgaSU4n+lMH4ionysS4mMA9ouzetNAOv8hOWkQnDS67IPHAspMW2uGX3ZsixkXt7e3/5n7N9x4GExOTI0eOKCkp7du3Lzw8nHzZrfBCF4vpDAZfIMjJyUG4h8/nj4+PLywsoGRu6NESiURsNnt+jt3Y0CgW4wwGfXBoCAAwNjbG5/PJytgrHP8RugCAMvLKFdzK6v47/Ud/BwDEK0ZGuJ0dFNV//jP+P/9DymlSnBhgWBOGZRHmGAaG6WMYqlcNFUIuLrB++Dvv3K8/RZ4UXTUV91y9etXY2Dg0NPTEiROIUeTOUoRIJOru6haL8cjIiHtxIijYeGJyksPhUF+sfD5/ZmaGwaDX1tZOTEy0NDffs9SIRKLR0VGxWMwX8GeJvB1SB3+SVQCgtmDXLlxXVyKJa2qg23JsLFz94QeYhxBFqhOqsuUgEhBqM1NCQqdi2DSGNWKYwWuvMV55BaxfDwtWIDjF4cCzuLk9cDfRJKTiHiMjIx0dHX9//9OnT6+ilUSBYF2dXci/B+EeLpczPT3N5XLJEg2QzwsLbDY0C83OzCwsLDQ1Nc7Osvv7+rq6ugV8flNT02PlQ5NSjD0K59FPenpgKFx/P7z8hgbczw+qJAcGcA0N/Be/IKco8rUXE4YtKwxjYlgaYZC9Syh+ejAMrFsH786BA9DR6vXX8XXr4CTPz7/P1UfBPRlERCGq5Ori4ozSXQr4/NCQkPDwCDJdDZrwT7KcnJzU19fPyMiYn5/vIP6GhobWFs+1OMWO3nDOF9sZgO1ZpXlj27IexD0B2A4i3B0Ff+0JksTA3ycCsR1pWxV5M/PdDAYV9zQ3N6upqbW1tQ0NDXV1dXV2dvb19a1BZQJxz8DMx9Z5H1nlQbUNoadZWqIeyXKjWbZ3KUwuB6uUT3G22uZ8YJ7xoUXmim2zReZmi8wNxmnWWR0LHA7VzgXL6yYnGxoaTk9Pd3d30+l0JpP5tOK5yPnwsK8FWAa4t+P7GKfTcS5n4l1Px7mejnchmiuximiX0/Gux6IdC3vbpexcYrHYnfjjcDjOzs6Wlpbp6ent7e3PzEg3SY9lFSj2Fqut2HqK1XqKVXuKVPrKtHmcYan8Pfdi742NjVNTU1H93XvezaOjo2uI5wI44IkWswZTontC43rD43rDY4mGaGpPVE9I8Wg+xD0AiHFRJiuZ1uId1uYfSrSwNn/UQhHRDleDWr0zWEni/4R4LhRHGhoaamhomJyczOHABLUrvrVhNd2mxqNHjw4MDuXl5SUnJ6M9yen+WMSKp3iUThTYYmEBM+OtOFJSAiECLYVCGLG1fz9U9mAYlArr1iGhgryYozDMjpDNAgwbJXyZDTCsG8PAc8/hb74JC1ZMTeFZWXh4+AqZgRAfvL29SX1PXl6eoaGhioqKs7MzaalZfnXwRVBc+MMPJ+a5i1FRURUoEdCDoa2PztXlx19bD8k3sRgGuDk7Q5VPfj7MQHjkCDTzzc7CnvJyiH4ofjwkDTCMh2G+GOZOuDNzCJYWE7o05N8D00KyWPD2oezYDg54QcEDd5NIYCgmcc/i4uKlS5cyMzNZLJaiomJR0UPLt8Fgw7SU02fPLSzyQkNDkav4Kjyk6oekdnss7vF40AXnYX/oSsmtAEBt5d27UH8GAFShOTvDfI8MBtRE3r6Nnz+Pv/02BDFL7EWO4a6EbzgPw+YwbJIA6GYYNo50aevWQZXb1av4yZP4xx/jv/893tgo4TB5Xj6f7+Dg8FB9z69+FZuYiJjg4+UpJycnIyPLXVgsLiyQk7t+7eqVe2XjpFi05tWJiQlDQ8P8/PyRkRE9PT1lZeXc3NzGxsZHMQqTl4PuHcI9PthOf2zPKs3rQdyT8rlsALYTpflBQe8P4p59wVB1tDN9283p4bG7VZVSthVtbW0mk1lTU6OkpKSlpVVfX7+GYvII93xklfMhoaGR6Gyscj6CLZtYIjpng2mmV8kS7pnkbLXOfN809UPztA/N0zebp6GGVj80T9tslvahedo/DZNts9uHB/tReBp5p1JSUszNzefn5+Pi4pSVlW1sbJqbm9fgWUW9CziOCwSCzs7O7u7uoaGh+fn5vr4+qR3QzSrqafsu3O5ElNOJKEeiOZ2EhNNSj9NJYtORcNui3va25hZkPUSDF4lE7u7ufn5+o6OjpqamhoaG6enpzc2StMjLT/d0e+B3e0d0V5YcPU+ZnqdENEh05ymjRvQrd+feZBSqL84NNza1UPP3TE5Ompqa5ubmjoyMGBoaKisr5+fn19TULE/RtPqwJbhnICWGFRrXEw4ba2mJiKWeaGZIyUgewj0isSiNnuDf4E5r9A5q8qY1LS2bIQFbM+z0b3BPYyTMzc9VVVWReYbImfNTE9hPfQKp42dmZp46dUpbW/vEiRMoc/yKrCeiiuZ0dbRZPb15eXnnz5/v7e1dWOB6uLvZ2NgWFBZBxQOfz+VyAgP8M4l6kCwW0/nOHTqDmZmRHh4RyWDQnZ2dW9ugvXnFUzx6p5QgIX8IAPxcLiqCOqF7WIfDgR4kp07Bz+ivvyalCEkADGNjmDPhxUxf0u6UE6aZviXTDL53LzTEiESwwPjFi5K6m+QZ0fNcWVl58uRJhN9RHrlr165dvnzZ1NR0FfMz4XIxrqGuPjM7FxYWpqSkNDU1NTM95egA/6pralHxndnZGQ9393ICFbW1tji7uLR3dISHh6WmpXM5nNjY2PGJySdnKboiAPDkZFibbHwchvfr6uK//S3MvHzpEgwj8vWFOp4PPoA8eeUVuGlJMJMEIDxtrQnNRCeyv2BYBuE1NYlhALmQv/8+ZCmaBSTMklivJCIAACAASURBVGJpdnb2mTNnUKSJSCSysrK6fPmyrKzstWvXVsnvjnzOVFVV5zlcb28vVVVVDoczPDxkb2/n6OiEClAIBILJyQkXF5e6+gZ4s+pq3d3cmUxmSHBwOpG6NCkpkcV61NrgaPy3buGXL6+AiYlCB3hfH8TN5Ky/l2VqZgaat5DOcngYWqaioqCCTU0NWrvefhvy84UXSPPWEIZZEPqzRgTHMSyeiDecJ2cpCm7fswffsgW3tcULC+HRkpPvR8kBAOLi4i5cuID0WLAeO41W8+qr4M038TffBG++mfY//yN7/Xr/AKyVZmJsFBMbKyMj09c/wGTQtbW0vvlmf1AITNn1VP5GR0f19PSio6OHh4fj4uIiIyPb29sZDAZ1DjwKDfV2U+yoDWe9sR1+2O5Vmie2NZOi70n6XMYf2yEV84VWUVwYDaIoqO/hTs62tbdT9T11dXWqqqp3795tbW2Njo5OSEig0+lrSDRF4J7pDy0zN1tkfCjdMik9mf80TvUqpiPO907Of26Z/p5x0maT5E2mRDNJ3mSSvNkkebNpymbTlE2wP+Vt/QTrzFb2DNTrUG9ZbGysnp5eX19faWlpbGxsVlYWk8lcW0gU9bACgcDL0/OGomKAv39zUxMtMJC6dendAgpZrYdDrI6H28EWRjREk6tE56EgywJWy1D/APpoRIcSCATOzs52dnZzc3NisXhubm5xcZFOpz8uVn6UebV8Hxj90xbZknq1LVPxgZahAFfREhLyHTm3+JwRqXrs9xTzRkZGUVFRg4ODCQkJkZGRXV1dHR0dq8RzrfhKhzHUosWM3qSo7uAYeihsDGJJENH0ELInoptWNJQjwT24KLkj1qvGxa/Ow6/OnVhSCQntVeOc2BHDF/Db2/4D7FxqamooY56Xl5eDg8OK7JZ8FuN4cFDQyOhYRkaGubk5lDH9fceOfqehrq6kfCs8PNzM3Nzby0tVTU1JWWmWPeft6XlLSfn6dXkdbR01NbWsrCw9HR0XV7eHnWL5bFuxZ0VJifYEABZRf+01WE+0ogLfvRu6Lf/619AvZ+NGUjYjAmDYAJGLOZMQHg3Ep3MW0YNiZCT7//KXeGAgVG9s2YJ/9x2emXlfgC2dFMzPzysrK5Mho3JychkZGXw+X1lZGbnWPuRCgFAg8Pfz43AXg4KCvLy8oOd/S/ORI0dUVVQMDIz8/fzs7Ozs7e10dXW1tHWEIhEtwH/Pnr0mJsaqqqoy167FJ8QfPHCgq/uBAisrnuthnaQwXroWyMBr1yDQUVLC33gDJjSanobKnr/97X6w+jK4Q7K0k0CNZQRL6wgVWgShS5t/7jmwc6ckX4C6Oj46ire14Xw+zP2ISkZQR4hiDOXl5Un76ejoqKGhoZycHCqxTt2ZSsPanFyut7c3j8d3d3cPCQmBWUNKi48f/0FJSem2haWXl6eT0x0nJ8dbKipGRsYAAOc7TgcPHrSyttLV1dXQ0CwqKtqxfXvF3apVZqkU7AYAujoZGUFneTb7gemBzIXbt0MtDvVXQiFeVwftWQDgiYl4fDycY++9h+/ZA9tLL5H8BIQHjx6G1RIsrSJYSiPA+iIJejAMuli99RaESrW1khMNDOD/+AdE/Ev4ErrfKioqkm4H8cHBNe+/D7Zvx7/4Amzfnv7++2mZkoz1QbTAM2fO3FRSzsvLq66uMjMzPXL4SHUNrC70VP4mJib09PQcHByQghm5sa9B64BwT8SGs17YDl9s9yrNA9uasYR7+LOcxM9l/LAd1OAv5BNNIzyEUL8/tiNFYueiU3FPc3OzkpJSVFQUmVR97fFc/dObzdI2mUIlzSrtbYNkz0JJ2uXeifnPzJPfM4jbZJiwySgeLslGrholvK0dY5XWtMCVtnOlpKQoKyujOEd0K5lMJgrvoj5Hj0KLxGKeSMgTCQVi0fQcOzgsNDktNTYhgc5iNrY0L4oEfGIrTyQU4WIkQQrpzQf9zY/SrCjNkkJbHaVZHqVZH/AzzaM3TYyNk48/rAQkEnl4eOjo6FAtd8+sHjsUdk1hdfEXGpNll5oMlWgg+huSrjal3eDND7e0tpEBBCh1lpGRkYODA7LKIb9m0jWe5DZKQo203aj0G7kJEXDCCxdTWfFhnQGRXbT7rfM+HUH0h3b45fdnkvqe+JZI13J7z8o7nncf2lwr7GNbIsS4mMH4d89bCADw8fG5ePGiv7//yZMnf9QJF9n409LSHB0doUNfX+/pU6dOnjzl6x/g5eWpeOOmjbW1kZGRiooKncEsLSmWvXb12LHv9Q0MDAz0U1PTQkOCjU1M0fMmdUcfZRUAqMLp74fykipFiIdK0tPYCJX8b78NXXl+8xv8s8/g6jI5DQhBYkAsewi7DMCwOAyzJTRAMF59/XoY347cdc+ehUL6xg18eFgiRaijRZ4TWlpaZDzXzZs3fXx86HT65cuXySzM1J9QacTSwMBAJKRbW5qPH//h2LGjcXHxdna26urqBvr6dnZ2mppa4+PjLs5O12RkbO3sLC0svt73df/AkIG+bksrrBBGPeaj0ADAtL+OjhCCUKEkLDw8BSOMjIxwY2MYrH72LDSg+PrC8LdlnKRK6EIC9LRiWA+RA0mEYf4Y5vbcc3wMA7//PfTpMTGB5boEAtzLC3/3XYhNjx+HcEFq+NDtZnZWWVkZffCJxWJVVVVbW9vk5OQTJ05QM8uteKUioQgA4OrqGh8fD3FPSfHJkyePHz+empZubmqqra1jZmpiYmKqp68/y56zuG2uonJLXkHB0spaTU314oXzn376CdJtrHhw5GE2MXF/zIh76elw1jU13e9H01Ikggqzv/8dGkmJ/PoSbtPpEE0ePQqz8rz1FtzhQd4it7Mswrm+gwiLE2MYn7Ae+hHshbMUwyCg378f+um//DKurw/NbWg8AgGs2ULRq8HMyLdu3SJfxxD3vPsu2LYN37oVbN2a/u676YSalngpz0dGRLR3dHS0tzGZrPS0tMLCIsnHz9MAPsPDw/r6+uQnAYrnam5uXpHhq3Qu4Z4zXth2X2yXz8ObO/Z5+mFtNHYS9wRiu1cMAUPox4/APZyJmYamRiruQXGUKDUU4kk/8fe4zyCMY++f2mSS9IFR0iYTohknbTKW0B8YJ0KaWP2HbpzHfdwz95lJ/Hs6UZv0YjbpRW/Si/6AWD64GvMPjQjr1IY59iz5UkLXHhMTY2xsTHojwRxCHR3kKslqqZtM9pMEKjJqUJdm1pBp1pBp3php1ZZHtHzz5myLlhyzRthv2php2pDBmJtAByzobtrvaXTY1+ywr/lhX/MjfuaQ9iMa7DFDbZ+HYW53Y3/vA/W5+Hy+i4uLq6srqirf0tIyMDDAZDKfmb6ntz64MvJ0TezlmtjL1USDRJxkFfVXxV6sTZBbnBvq7x+g2rlQgnL0OkJzhsORrs8F50NDQ2xsbHp6+uTkZGBgwPIvAQL3LCR1xwS1eIe0+oW0+oa2+oVCAjaCkPTQmr1ye9JJ3BPdEOZYaOlSbLe8OaPOEnvHQqvI+hCh6D8jnovP58fExFhbWwcHB/9oAis0fdPS0pycYJTv+NhoaGhYR3u7p6fX2Nj42NjY0NCgmakJLSg4Pz+/uKjI0NDw7t27AQH+tvb25eXlerp6GZlEaSvyAXocAgBYCWHTJiiwORyoeiFlNo8H/U6EQnxoCKp5nnsOpuchk+lRJAqSFmkYZkZk6OnHsGxCnIQSaXvuf0P/6lf49evQ78TNDcaCUZGB1JAR7tHR0SH1PV1dXTdu3Lh06ZKbm9sqdi50HMRSGo0WGhoK/RZ7WFHRMU2NDf7+/uPjE+PjEywmQ09XNyExsbCwMC4uTl9fPyc3z8PN1dvHF8fx6KiIfiJppNSofnQVAHh127fj7e33r47Ph6CnoAC64GhowLDqwEAJ+PvtbyFXl/yiqEIaYJiQyPRoR2R67CJqmfExzAPDAjFM9KtfgZdflmQNQHXTAID2l5wc6OWjpCRhL3XACPfcunUL4Z6FhYWLFy+2EQVgVVVVs1B1NOoPHqQRS93c3NCLpq21JT09va62JjgkdIL46+rs1NLUTElNzc3JiYuLMzQ0LCwssra28vHxGx4eDqIFVtfUPUyMkQ7IaErQ6XhwMFTzzM1BcEzCDnLCIJzk5AR3278fFo8TCKB7+LlzVB5K0YAojRJA6HW4hLt9BeEs5YBhUcRkloAeDMO3boWxdadPwwQ/5EkRP9DqEg1xzwNx7MtxzxJjEQOllk8R94yNjdnY2CCrOjrL7Ozs8s/fB+/qCmsI94RvOOOBbffGdq3S3B7EPQmfy/hiO5b7QftTnKN9sR3JRDxXW8cDdq76+no7OztqDsyBgQGkmVhhiA/vgnKub3KTQdwH+rGbDOKIRhJxmwxiiQb7/6EZ6ZkPv23g+2GC/alh9DuaYe9rhxMtgrJEPeHva0f8TTXYMqmWOz8nZeeKj48PCAhALyV0QDqdjiQ0OVKRSDQ+Pj40NDQ7O3tPk00GvZM7oJmQNdhxoThYpjRc0soIglyWhV8rDb9WGna1NKx1ehidK6+zYa+r3kEPo4OeREOEh5GkZ2l1t4tOTmf9CCzPBQNi0B80pXl5ZWVlLS4uOjg4yMvLR0dHNzQ0rDg86lCfCg0AYFYHlAQfK484u0orCz9dGXNlfrq/praOVKwCAEZHRx0cHBobG5euBtbnQi80sgeWbU/L8PX2jYmK7eroSk5MJpVDaB/E9kXhQlx7hF+9W0CDV0CDZ0CDV2CDZyAkPANhD2yBDV6+dW6Z9GQS94RV0ayyTO1zLe1zLexyLOxzIAGXEsLCPtfCOss0pDJAJBZ1dnT+m8dzoQJvjY2NZmZm33//PdJ/rj5RqPXYqfeMpJECQ0xYL8gYEKQTJs2ZSKg8TLQQN1h6FOglzmJBTwg+Hwa/vPUW9DXm86Fu38QEunPu2wcVFSijzLp1UO1PQTwoKIZHePM4E56hAuK7WUyoJbyI2HXoyIyChzEMt7SEKCohQeK3IaWTIMeHdLD6+vpkXVIktiMjI2/cuLGKyxT1CEFBQZGRkSRbSGYiAnFPLBYBHBcJYeojMfGHtkoJJPKwVOJBESgRkMPDeHU1FMP9/TBaTUUFl5ODSp2KCtzbGxq5fvMbaHNZxkYqVwHhZuuEYQEEISTYKMCwOxgWQRhiYHz1qVNQaSQrKzkvur9L9hfYKfUnVY8d5XU8dOjQmTNnVFRUHiWCA+XvSUpKonKSNEwQMQ7wnKgHfc2TSmYqV0nekiMEACLs3l7JtcTGQlUNSjZNXtH8PJyftbUQU3I4sAEAVVwYBjmsrk4t1EplJqJRpkdrItMjF8MEBBs5RO7HRMTSB2c1BKPHj0N/LADgjHV0vO/WQxk2mJqaeqAe+8NxD/krKYLKzCehm5ubHRwcenp6JicnKysrq6ur15wCZ3GKHbbhjDu23QvbtUpzxT5Po+h74j+/5o1tX8UfyA/b7Y1tT9yqsDgz1814wM6Vlpbm6+s7OzvLZDKrq6vr6+t7e3vXpjJp6J14XyfyPZ3I93Ui39eltqil1aj3daP+phbqkduCGN47zv5EL2yDGu09jaB3ifaeRhBq7y4R72kEvaXsb5FYtfignUsoFPr5+SUmJsKM5E1NdXV1bW1tUvFcEEouLLi5utrb2fn5+VVVVa1YihEAkDPQcVlSXD1CrjScaBFypRHXUbl1oke2JFymJKxtCffkdtTvctTc76L7DWx6+110yfbNfVrvSwf17PbayQftXBMTEw4ODlVVVfPz89nZ2SkpKU1NTd3d3T/6YSk1h9e2CgDouuuX63uokHaigHaikNLQqmQZeLwk9BxnZoDJ7KHqe5qampycnNCEryb+hoaGGAyGEBcOLQwMcHsHuH2DC/1VXRW1jCr6RGdjX33nWPsgt2+AaP2cnkneBIwDAWBBsBDZFOx+18mrysWrkmiIqHKR9BCE213HlI4EEvfQynxNkvQsUo0lLW2JWOqxTDU2TdYLKPEW46L/CDuXk5PT3//+95MnTyIVzo9OC4R7PD09kfSdIzK6cDmcxcXFkeHhCcLHFj2iSGbw+TxUOg4JM75AIBQKkXPl7MzMilpKkQgKY/IPyeyxMXxyEr7ZR0dheNHcHAyHEQqh9WT9emjMOndOopmQkgpLqwDDRgjhEU3oJ5IwjEFIEaSWQKgIBg9/+imU9198ARUSNBqUaqSxgBwSlUC4x8TEhNT3+Pj4HDx4cN++fZaWliQrqD+RogEAQUFBsbGx8FBC4fz8PI7j8/Pzi4uLQ0OD1OcHBXux59hIVOM4PkukXmXPsXl8vkgkmpmZgTxa9jcwINGN4Ti8HGSmcXOD1xsQADm5e7dEGL/wAu7ggBsaQtPJsqw8VCGNDDFMwhCTQ2gjYgmn5kXCXBhHSujnnoO1t2ZmoEcLNZPesjHe7wAAukxpaWmR0XAoS0pGRoafnx/KZnZ/75UoAICnp2daWhqhjRPMz8+LxWKCpQuDgwOIaeStQadbXOQh3Dk7OyMkLGU83iJyPZGapWhCouXCAoQaqB4c6hEKYaTbiy/CWfTKKzAsa+NGmHjQwWFFT/DlLO0gYt+KMYyDYbGEQw+byJ+ZTrJ0aUrDDAv/9//C4q92djAiLDcXGtp27JCOj0NP4szMjLa29mp2riV9z0ochX2IY0++tLOz09DQGBsbu+eXqqenp6Kikpub29TUJMVnNIxVv46gX3PohjNu2HZPbNcqzeVB3BP3+TUvAvcsdwnyW/IT8sS2J21TnB4eq6yuIuO5ZmZmVFRUXFxcBAJBfn6+qqqqlpZWXV0d+c1Djrmzs7OlpQUFHJWVlS1PMwP1PT3j72mGvKsR8p7mA+39B1ffUg70yJGoCnrGZ7do0TYo+76r6gebCtEQTfao+r2p6GmZcHdkaJAaz9XW1nbt2rXk5GQcx4ODg9XV1a2trZubm1GRDeptbairT09LS0pI7O/tra+Fuk/yj7zArP62C3kBMoUhsoXBMoUhBBEiC4lgmQLJ6rXC4GsFwa1TErVNblvtTlvVvQ6aD22OcNN261vZbbXtra3UeK7U1FQZGRlkXiQH09bW9rghUWj8j7uEmdXKvNPdv872+S7b5yilLa36ws4s7yN5ASc5MwMMBpPU9+A4fs8dW11dfXJykslk3ssjcOvWrby8vLraulnuTOpAfKwkKD0saSg6aSgmcSAycTAqcSCSjE6P6gmumihDV70g4AZX+zkVWrsU21MaYcAqQWYs2O9YaJXQFLWEe4S+Be56MWpG8TrG8TpGREMEdakbo+5d6M6eY1dXV6MnkeTzMyCedTxXV1eXpaXlwYMHjx49+ijV9RDu8fDwgOFIExOnTp4sLC6+euWyt7e3goKimppaSWnZ8PBwb18fn3BbtbW10dLSnpvnjAwNqqioeHn7RkaEKygqFhUX6enpOru4ofcpOREBgGhj1y4YUoQESWoq/EQ+dgy6hlhZwUCYl1+GjjtGRnhHB3RiePddKGY0NaGYIUUChUAS+i7hfVJBCA8hUTxhEcNcMCwUaXrI/Z97DgKpH36AwmxmBua9HR+HKpCHyWyEe4yNjUnc4+fn99133+3atcvIyAi9McmrW5FAuAdlj2QxGadOnSovLz9z5oynp8fNmzfVNTQaGhsHBwf6+/txHO/u6pS/LkcLCgYAZGVmKCgqZmfn2FhZ2dja5WRnycvLVxCpyskToZSDp09DAHfsGNQEXLwIdWPt7RDufPopVJWpq+P+/lDHs2ULZOCLL0LQQzJkJQLZWXIJCd1MsJRHGLnYGGZNZJeBPH/1VSiVMQz6CdXVQb/dwsIVURk5WAmBgIimpiYZ0FFVVWViYvLVV1/dvHkTgWbp3zy4TsU9TQ31P/xworCw8MyZ015eXkpKSmpqau0dXQP9/QhCTU5OaGjArIZisbihvu7q1auJScnpqSkWFlYtLS03b9xIS4fV6KhnAACi8KwsCCJxHGp0hofhamcnnDPm5jBjwksvwWmMuPeLX0B0sn79KlxFKRVSCBwJcygQUHKWUEyaElVy79u20DHXrYOw9aOPoFHyyBEYDP/hh3h9PbRUCgTSfIZZeaantbS0fg64p7y8PDAwsKuri8FgJCUlZWZmIprK4RVh1vIdFqfYIRvOuGJfeGBfrdKcsc9SKfqemM+vemJfPMwfyJvwE/LAtiVsledOzra2389byOfzMzIygoODJyYmysrK0tPTc3JymMwHKkkhIZGYEGdiZGhrbdXZ3mZvazM3xyaFB7oKGEvIGn1X1f8dFf93VYmm5v+u2oM00fPXG17umRLw0TM287GG79s33Dcqeb6j5LlRyXOjMrF8cPWv110sYsvYszNUO9fk5GRUVFRiYuLCwkJqampubm5paSmTyZyemqoY77FrybvTVujUVninrdClo4hYFju1Fji3F91phf0OrQWuHcWjC5JiKJm9LWclxbYCJfW2cgKu5CzV3oJE4OVs/8vZ/i2TkuJi2S01X9xW2mWtustGbZeNKmyQlrSviNWvbFS3mt/IbK4eGhgkH38AAJ1ODw4OLiwsFIvFQqEQTY+nla9Z6tZITTN0rpYi9ySnXWnuB9PciOZ+MM39YKr7AbiKOt0Pprrtz/Q+Nj/d39nVTeIeAEBlZSWa8EwmMzExMTMzk8lkdrR3zPPnkntjo5khsFIEKwzWkWCFxrBg1YhYRLBCY1lhEQxa5ZgE93D5XL9SD6tMU9vs27bZt+2IRtIkYZlhHFkTivTZQrHILfuOasgNrXBVrQhVrXBV7QhIaBMN9hBNLeSmS5Yjj88j0STJlmdAPFPcQ75cxsfHIyIifjQZLto/PT0d4Z6hwcHdX30pJyf31Vdf3b5tfu78BXn5614+PqqqKtehAL6blpqioqpqYmxUVVOfnZnh4Oigpqoqd/260507Hp5eudlZN5WU0bc1OdUAgJ+tenrQmoBwT2QkjDDS14cR1H/9K8Q9r78OY1UwDGbWUVeHH9ZIEvzqV5JiWxRRTbpK2BDaiF4MKyKEyiKGOWJYJEFDifLcczDV2/PPQ/H8/ffwXMg8AQBMWvPWW5KEKOQ4SUIK9yAWTU9Pp6Sk3Lp1q6EBBkuTO69IUHFPR3vb1q1bZWVld+/eY2RkKHNNRlZGhkYLui4rq6SkzJ6biwwPs7C01NbWEghFhoaGHu6utvaO1VVVCgqKQ0PDBgb6kdEx1DMCAAOmsrNheLOKCjTQnD4N2XX1KgR2TCaMWl+/Hqp2TE1hCoCzZ1eRzWgTICCOK4Ea2URmwkqCjdOE11QOQcM9//hHCB+1tGBhkKkpGLWE/LFWZAK1Uwr34DiuoKCwZcuW48ePk76B1P2X01TcU1lR9vnnn8nKyu77+mtjY6Pr1+WvXLkSGhoqKyOjqa3d3NIcRKMZGhpqa2uPjI55erh5ennp6xvExsZoa2l7uLsd+/54WHgEevLRidC0LCiA+LuuDtoK77mUmZtDFLJ9O/7ll9DV5vXXIQo/dAjyYSWnKCkmA6Ioyh0iQw+HSKmAcmaOL+l+EHZ/4FcvvADvmoICdHTz84PuaH/6E9RTUn2uSc4g3+GfCe5Z/hqdnZ2VSkeJSiiUlZXl5uZOTU0lJSWtaEsicM9pF2ybB/aVO6VRVz2wr+5gn6VQcE/051fdsS+QP5AX4RiEllQPIXdsW9xW+UWYt/C+nYt8YVIvYXBwcGJ8nC/Ch2YXURuZ4zfS+3LKqmLTc1PySoJjk/unOMNsHtrK4QuJKQTqWSMblb02KHlAEEPAl3eUvDYqeW1UJpYQykD6L9dd3NKr0Rl7xqY/VPX8h7zzBkWXpea6QRE1F4KA/X++5mgeXSRl51px8LCw9Px8Qm/T2QLalZJQ2IrDrpRQWmnYlWLYeakoVK40gjUHs2ZAZxRm08kktwupZJUJb4JGS1R0wvt8itf5FK/mMVjEA36nNVd9ZqKw47byTgulHRbKOy2Ud95WQg12LrVPja+nN1ZOjj8Qz4WOgN4M4eHhXl5eJSUlT8XOhYDUwsICj8dDjh/kU0MSMOlAvkus7ReJd/YlOu1NvLOXWBK0E6QTYOe+eMfdKa6H2JO9dAaTqqcnB0/eAlSfiyvgxNEjwjoDUBpllE8ZZlJG8VlLREiHb8UwTFoGYw54HLdcJ+MEHdMkfbNlTdKZrG8QrxVS5g9dIwAQioSOKTYKPtduBSis0hR9ZOyTrURiEYP+7x7PtfQeJ+/Lj0hodNsyMzNRZufenp6bigq7d++SlZW1sLh9/sJFNVVVM3OLsLBQf3//3NwcV1eX69flFBUUCwoKk5MSb9y4oaWlraurK6+g4O3t4+XlpaCoyOEuUOU0cQoJ4qmrg4n8Z2ZgJpKvvoIv/V/+UqLYP39+eQgMlN+UT2okKroJYRxH2LYAhnUSAmaeMHglkBIa4aSvv4bggE6HocVHj0rCjMmcQHNz0t/QJPdQUYXl9diRBwn58DyMQP4rKPKrraXlypUrO3fsVFJS1tfTu379uqysrKubWyD8oy3yeDnZmZcvXzYwMGxtabGxsVZUVHB0cgoPD1NUUIiIiNTV1XX3gCZI6rmQnAYAqlv27oXl0//0J4jwjhyBxpff/Q4KbMSBw4eh+KSgRikasbRpybaFNBBtRJV1JKERppT86m9/g7L/7FmIYlcMhaMOkkqjtMvm5uakYyOHw8nNzZWVlT127NjyMAfqbxENAPD39y8pKYGBJ+VlMteu7ty5U0VV1UBfX15e4cqVKx6eXn5+fpGRkampKTY2NnJysqqqqjW1tX6+PvLy8tY2dhPjY8bGJtHRUVevXjE0MhIQblVoco6OQo9vPh86KQ8PQ3rfPshSDIM6HjU1qOZ5/nk4V1Hpt4fzE1lXUXSh4RIixzGshVD2DBOgB2FKqRsBYfprr0nUaWpqUBX6u9/hOjow1eTi4goTFQDA5XJv376N0APM3/Ov8++Rul8I25l2EQAAIABJREFUk1HdnNH7qK6xITox3ivAr6CsRENPu5MhyWGDtqJ30eIUO3gDxD1U0LNEf7lErIx7VvEH8sJ2uWHb4rcqzI9P1zXUk/FcUiNHY+jr6xse6K/sm/3SpWyXa/lut/LdbmV7Pe5+7V2937v6a8+q/d41u93KiFb+xZ3S1NZRdAn1zOENCi5vS4OY+/CFQDaub1x1cE2V1JvsGZ3efNP1bzL2b8s5vC3n8M/rDm/LOf7zuuPbco5ED0Fcd/zTJWvzqII59mxbayv5cl9x8B0dHVMTE8m9zRfzabJFoajJFYXKETSVkCkMUSyJ7FnCPand9cei7E/HuSw1Z4JAS0nnqVjnk7F3mkb70BgyGyu36MtsM1bYZqK47f+x9x3gTRxp/4KEyyV3uVzqhRR6EkIJkIBtahJIIIReDS7YuGHjXuRuyb33jovce++9d9zkKveOe++25J0/syMtwjaEu/u+3D3/L37mGd4ZZlez787u/PatVGmu8lzzoJZYUnVJd2cnt56LeK5HR0eNjIw0NTUjIiJqamrW2jVzL49Vb8J1OTA4OOjn5+fk5FRRUZGenh4fH7/2KABAZbpNsPHhcMtTzxWLk3jzZLgF7A8zOxFte3Z8uL2yqppb3rPqd5Efe3VV9ezyTHCDrzfd1YcrnCBBs2MM1rp70p3yejLRdc0uzlglmqoFymuEKL+kKAfIema7ErjHPNJI1EHggYvoAxdRKa4a9aDO+w4CphH6y8vLdDqdO5MJsX7+V4n/gLynrq5OXV1dUVHxaf6stbd87T1LSkpycHCAN29mprq6urKiora2trGhISIiIjomZniY7bWI2PS4rDQrO7uzo6Ozsys1Jbmurr67qys6OnpsfCwmJroaN3Hn/glikx4dhRkZ33kH2imjOMsbN8IX/cGDUDCzcSPbHuUFmwrAtVcoHk8tTtfjhrcAN5gwInQx3Ie/9RY0Pp2dhYHmtm+H+IDQbT0PJLjnC40eWCyWlpYWoeNHBvxJSUkJCQmvEhMM5SUNDoZChYmJ8epq+uPHj+vq6mtra0JDQ+JhdPNnQvKF+fmYmJjm5pa62tru7s7IqKihoaHkpKSi4uLW1paQkJCh4WHum8hkQq1HXx+MuGhpCS/qzTehoGL3bghK/vQnWP/tb6u3VW62cGiA29iG40EI23GT8Bp8ewYkEtqhkQ6RfaoDB6DbXVoaDF1z4gQ7NjG6uc+xb70GsqpRVlbmtu9BF9XZ2fkq4Ua47ZqHh4bpdHpFRXldXV1NTU1wcHBycvLs7BzxGK+ssNLSUsvLK5sYjO7u7qjIyJ7e3vn5eTq9ZnpqKjoqshkP/oZmCgDUrpqZQe90ISFo/a2hAb39EZc2boRCtVOnYPO1136Tq0gY6YcrB/tx9tbh3oWAROrAc1CwM6VwbsFzJ3zrLWg89O230JC5owNmDpGTY1s3r2UqCiKioqLy36DnIqZH3ILJycm29vaVxeXRypaR0saRMsbo46a+ghp6dGa2d1hVZHqmZ2h3XtXo46aRMsZwScN02xMu3HPHnnTUifT9S4otiZdb3hPKe98Rioh+WE819oMrrjJzIPFF8EnNj083tTSvi3uIyff29U6MDue3je0xzthrkrnfFJWs/aZZOE0QsP9Lw/TIarbep7LtyS5J650SEMFwARfUJDptNgubOsQXoZ/rHBzbL2OzTdR0p7j5TjGLnWLmXAU2d+E9mwWN9YMy52dn21qfgUWC7Yh16ITt7e0z09PRbZV3k93Z6qo0jq7qeeJeqqdkhk/HFPv1Htfw+LyP8bUAi2uBFtcCLK7jNWpeDySa5lf9zaufsJOLJVeVHFQX4dGWfFZ0ntG8OpI8ePmGfC+hsmh4cJA7fg/n6QPz8/OdnZ0DAwPItJywdEQXtbi4ODw8/DRA1NLSUl8f2ziS+8LZNGebgXbci8tp6dm+fkFR0fE0n4DwCBi1nP3lzXnvAwAep1j6UfYHG/MGG/Nxar4gY94gvMmuDY+EmZ+aGuns7X3CLe/hngBi++zsbBOjaWZxmlbh7lJi61Zq71pqv6p2K2F3OhRZZbSyY2vNLMwYRVAeeki8RHKj4C0t80jMKdmOxZH3GIbo3rW8LmJ3R8T2DqwRYYs3OfVdy+vUQG0mi9nS3PL/vz8XAMDOzk5LSysgIOAV7XtSU1ORES7380O8BQiCWKlEz4sIYlkAAD9YPTygWiQrC35D8/DAd/0bb8Av6Q0b4Cb9l7+w5TobNz63DXDtDcgjxg53pZ7GhTr9uIUEExf2ENYSbPXWhg0QRQkIYPLyEGb5+0MFkIMD5uKy/qczMVviAvFUnY5Enoqnjw2FQnnw4IG5ufkrCieCgoKI9Asv4hLe/6q2pZy5QfFVSAi00SaRoByCcJ/euhWiyTfeeLY9v1QdA3C3fzMSyY+Dddpwf3WUcV0LTw/ONkB5+23IxjNnoOgIhebD1wnMbpGTw/b0XsXDVU0UGuCpGIYIg4vA5ejo6MTEBOEVuOoo7iYAwNvbG6X+eCk/4X++fBkTh6PzozchmQz5iVbg5s0vTEr/WyztwA2WQzgRpBgkEgr5yCCRtEikhlXySK4Vzv7pBw8wBQUot0PaWGlpaO/PeVdz8wMum8XFRTMzs2d5Kv7T8h6UJKesrAxZmfT1P5lufRL66XUa6ZTfa6dhef1M0BtnQ978BdZvnQ94/Qzq9yLx5d9newwsjE35fslvRzrmSPqeq5xyJJ3iNCFhQ+KJ49JzhfCK2pP4cGnQKWcSKt/jBFKWwR570tFwPqlV/lwETwEAHR0dlZWV+fn5EItPjOW3jewzSv7GaFXmrFR2Fi0TmE7rG5OU3fpJkVVsvU9la98uMfMd98123TffCYsZXptzN3fdN98sYGgfW4DWYefA2D4py63CRjtEjHeImuwQMdnJKTtETXaKGO8UMdkhYrz5DlU/IG2tnouzhkFNTU1FRUVZWVlXV9fc7Gw4o+xapP3dWJe7sS4CeI1o7po/xlkkwb19YgjNJLqm+CcXnQuP9PFicOGR/kU2zSYuPtL/1V3/vDu1qpedZCOxsni/qvBhjfuHNcQOa4rBGtHPN/cqC8ZXFIwOj6zFPcT80RxWxS1EN8XFxcXbm9bW1ubo6PjCMKdzDdhULjaVj03lg6l85kTuZH/K9GAac6qANZGPTRXg/5WHTZdgKwvIoL80ycxb6ys//UN++t/6Ub9Fta8+m4D91G99qAeCjI9OjnQ0NDKIDwz0hkEvmbGxsfLycrRmOto7phamnHJtLdINkYc5pzbmELi3eYaJSSoloZadQ2Z6flonQF3UXkDS6Z6E0z2iRoQEp/Oe3R3rGHMO7lnW8VO/ZnSe3/wqv9kLyzWjX7V81FgrzP8Tei60Q1y6dEleXr68vJy4SdxP+Kq9ISkpCflzLS4uFBcVTU1Plz0uGxsf7+vrZTAYCwvzmVmZiYmJ/f0DaHVOTkxkZWVNz8zg8Wk6i4qKl5eXi4qKGhsZgwMDBfkFHIcv+NZG2S63b4coJDQUKp7OnIGWIshm+aUbCaE1qMQVBMjQZAE3YV7Bt5ARLmsJNmb68UcsMhIaZMjJQRvVujp25tFXlEyg1fzUXZ/w50I9NjY2oqKiVCp1XSiJ2MK93fr6+kZGRkLt9fR0SUnp9PRMYUHh3Px8a2trR2fn9PRUWmpqUlLS+MQEOnZwcDAnOwe5IDEYjeUVFQsLC0VFRb29ffX1dfSaGs4tg95Gzs4Qw73xBnQs2rz5GVh8QTyeZwPwjRahmTxc/FCCs3GaRJrkGIN34zs0kXEdHquoCOFOQQEMLePuzpaZAQBTqv31r+zMXMTqWpdAWvxV/lz29vb8/PwPHz5E7ierDlzLUldX16SkJChCGx8vLimZmBgvLCxcWFhsbIQZJScmxlNTUlJS0iYmJtGxT43V8vLylpaXYSq62tq6hoa2tlZoctvS2tbaWlJaymSy8FsGoeTnn6/i0j/VROrCdNzQHkl0JvCAmWgBt+A5cVtfAnrQU3DkCLStHhqC8xkdhZl3UXikVZxBTaRLUldXRwLI/wY9FwCgs7PTxMREQ0MjLS2ttqF+vKk79JNr3qSTPqQfucoPOI1q2O9B4ssVNUZ3Dcc9d2wh7vkBxzoE3OGGQQj3kNEhS5OzwbyidiQ+XD50yonEXZDQCPbYkfjCjz4YGxjmzs9F8BbqT0tKjI2NyWRyRUVlV3tLbvPgHt3ofXrR+/Vi9lNi2DUMP/is7KPEfKkVGVHeiWZS2dqz457RNmHDnfeMdnAV1NyO9+y8Z/Qxv559TB46pGNgdK+46RYByg4h/e142SGkj8p2IQPUs11I/+Nb2gYBqUOD/dz+XNyTDwsLMzIyMjWFlvv9fU/C6op/9TO9EWR1I8jqOl4TNNG8FmhxJ9SudYz9Vo+szD9pqfyznQYqZ+00iEJ0/mSr/pMtuaKLnSsjsaJwr+LdQ2r3DqmJHFIVgTWin2/ulr8TV17AaGhYq+dClgMjIxASoeyqHB9AgNDJzMxsaVl5Y2NTfQMjLi6hqBgme8YwosAnGPJhOBLr1Me6jLEuE6zLBHSbgl4z0GOGdZniBXZinUZYjy3GhHamAIDieGN3tR00nf3eOvu8YQ0Jmu4+vInq/d7aX/vpH54cbu/q7lkr7wEA9PX1WVtbq6urp6enV1dVj06OmCca6ESSCU8rSpQm8rfieF3BpkaYUngZ28Rwam5K1VP+tvlVQaubglY3BSxRfUvQChXYFLS6edP0kkmYAQf3MNW9lM/rnrlq8OtLyq+6Z9Q8FaemJ///9+dCW6+tra2WllZERMRa4QQAoIZOr62F0SOGhoaysrMXFhZSU1NRcOGB/n4+Xl6aj8/Ro3zZOblGhvp3BASbmxovXrxo7+BYWlYaFhYan5Dg6GB3X0yM5uP3NIOdtpaWhKRkUGDg6dOng0NCNDTU5RUUunvQBxDcI/X04Pb83nuwfPIJVHX5+kIrUU66opdsMCgjZhCJZIKrCZCqK5GT0mgR96/OxrcTuJcjNcTly/D7OCAAntbH55l0s6ICGm2sCr9LvDi4CeTPb2lpSfhzPRVX6OjomJiYhIWFofhm3OMxDCsrK21pgfLn7q6uvPx8FosVGBiQkQElmU2NDTw8vP7+/vv3f9PY2PhQ5oGKmnpZacnFCxcfPfIoLS0NDAhITU01MzURFhZOSk6dnp5SVJBXUla2trYRFhKm0WjQclxSsq0dyZahAcqePfBifwsyrstYgOf99iSRrHGrcKSXicZT1qPsZoZ4Gle2pOfdd6FIiWBjSQnMe79/P9uNa2gIpvtY1/pkFX+QcEJfX59b3kOhUMzMzKKiolZp9JEoqKioqKMTJtVqa20tKIQfx56enjk5MJVmWUnxoW+/8/L23v/NN909fXf5bxsamaSlpV6/cdPLy7ukpCQwMCA5OdnAQF9MTDwnN29ifFz2oYycvHx6erqoqIillbWUhIQeRX9qGgJ3AKArHPJT4xbAvDKNNK3OeOTlSXw1zuLpzFB2lFEcDDFeAno2bmRHVJKVhaBHTg6aGXl7Q/3vuhbNiLfIpEBfX/+/x74H3eW2trbCwsJ2mBm8Y6qlL+iT656kU954klEUV5A7liCi3UlHc7jkPT5f8tuSjjqQvn9JsSbxxHLJe4J4RW1JfLhACEmGiBqhJdi0JfGF8UnNPu/PRSxUZC/FYDAKCws7OztHhgZyGU++gh7pAXvJgXvIgXthCeAQgXtweo9awE4l3/BSNg6oaOneJkDZKkDZJkjdJkjZJkDFCZyGPezy4Q1Nu8hsfO2BjoHRr+8bfM6vvU1Ab9td3W0CqOhBgqv50Q11qm/S9NQktz8X9+SHh4eRyKejo2NyYiKgPPu0k9av7lRY3GB9AS+QdqNcwDvPu+ld9TRqGWY7pYeWZh/Rlz5lqrBeUUSdJ03kT5jIP25noMknlBfslr91QFkQlW+UhHAC1ezOA8qCX8rejH2c19/3hNufi3sZOzo6UqnU8PBwPEzAOJjOw0ZCsJEIbCQCjEZgY5FgLJI1HL44FIqNRaF+bCQUG0/FVvBI/xi20Bs+W68322gw22gISwN3TXRSZ5utV5bZuKcwVt9F6TMPja/WL5qw/5H6LpregdGBFjq9dq19DxJjd3R0FBQU9PT0NDGaRqdHtIJU5bykFLylFXGjY0UuAu+UUfSWlna/75vjjXg4NTcp6yx5iXr2utGFa4YXiBoSRheuG7I7L+j9RPHX5uCeZWU32dPqx8/rnH5JOa1+XMFVZnFpkdHI+D+h5yooKFBTU9PU1EQBJZ97PIYGFeTlFRQUp2dmmxgNYvdF09Izc3JyUD72np7u61evnv/ll1/O/xISEnrt6tWffv7Z09Pzxo0bNB/fvNxc/tu3paVl5GRlvby89Q0MpqenlVVUzc1Mra1tHz1yl5GWuXr1qhqZnJsL7U9RMqObN6GZ7aVL0BIFGfFs3w637a1bn5nfrrfHABKpB0c8QRytARO3QZniAB1/3GX9mV/MBx9Afdn16/Byp6aw8+ehsUtMDBv61NVBjycGY32tAcEiBByZTOajR48Iu2bkTCQoKKioqIjeO8R4KIlta5V+8EBTW2dpeflxWamwkFA1nR4YGOnuXgzDxjc0nDv3y88/n7146XJIcPAv586dP/8rZOn16yGhYcnJSVevXFZSUlZWUbaxtvLw9Ozs6FBVVXN0dLh06ZKsrJyRkZGFhfmZ06dLy6DoDgBo2bN167qY5uWdiFFPN2AqiRSPcxLFZV7mmElheITrRO4d+p13YDSBhgZ4uShOcXIylKUVFj4DlOtqYQj+IAIAsLy8bGtr29cHLSEQk8lksqysrI2NzarYuLh4pkZcXEzfwJDJZOXn5ggICrS1dwYHBz/1fodOpMVF58+dPXv27K3b/CEhoefOQt4+cne9c/duZFRUfHzc5ctsljo4OvoHBDU3Mcjq6ro6WuUVFZaWluXlZTdv3lBX12hqQtsVXBW/5ee/Lm8Jq3AKnk0dySCZuEEPyjC6hEPzHG6WrrfU4cnfeAO6PY6PYzdvwkQfHR0wBjSKFr2KmURzcXHR1tb2v0fPRdxrFOezq7d7qvWJ/ydX3WGyrR9eUlxIvFn3jfDlDeP3+Hx5G8c9pxxIJznllAOJKCcdSKesSYdjL6qhQ5YmZwN5RW1w3MMFleB4Rwie2IQtiS+UT/JFei7uyff19U2Oj+XUd+966PKFrMtXcm7IOYvjZA6ds3DPLNcv5Vy3PXAKK2QHX65o7t52R2crv/a2Ozqw3H2+5jQ/vKZmF8E2a+3oH/lahPrZLc2td7S28muzyx3treyCd97R/vCqGsUnYWF+jjubFbES0AOFuAH9uWZnaQUpR43lfrRU/dFS9TSnPm2pgpqI+N5c+ZytRtMAW0kXVJj+jboor+4DWPSk8BqnuZo8ulJHtCVLW9nXG/+44MuHN/Yp3N3/0rJL+npsae6L9FzwS6aszM/PLzc3l8FompmZZvb6LjF0lpr1OcUAEi0Gy804gfqbdJc7nDEWnsYFw4YbfDpz5bryVdYUZaKnM0+xt0SPuTCOGJUfreeo8KEbebub2nZYryXUtruqbvHU/npsoKWpuWWtvId7zczOzra2to5Oj8i6SQjZ3IJmN8jyBq/vcVvh2N3ht7jqluyIpjE5Oylhe+8nzZPndU7/iuMYVBOABjVPqx/X9FZFcYOXmctyTlInlL87o378DPk4uybjNKrVYecJpcMyDuJM1v8ZPVdOTs69e/eEhYVLSpBUkP2AAABampvIZLK6unpHV/fY6KilpUVAILREuXv3bnd390D/EyMjwwcPpIyMjHV0dKxtbHx9fRXk5UXv3zc2MS4sLK6rrW1paYkIDxMQFAwNC09PT7eytJCUlEpLT9fT1TE1M7OxtpKSkip7zN6kx8ehmzoSxrz//jMH9Re9/fF+JGzIxnVYKH0jIJFa8TTgaGsBJFIOnh2Tyb2jIM8vExP2llxbCyPZVFezgQ5h2cb9sliXhiLQ4uLr168TEAcAUF5erq2trauru0rPBe3jSkv0KBR1Tc2evr7BwQGKnm5mVpa/f/gPP0hPTo63tbZS9Cj3xcTMzM21NDVcXN2cnRyVFJVERUXhHlxRQafTW1rbvDw9hISE42LjMjIy9PWpSioqMdExD6SknF1cbGys5eTkOOoMiHu2bVt3G35JJ4olE4eDniYOcKzlGPSg/TuGRHLl4v+zs9248SzCNUI5r4J1uHkLvWSTk2/duoU2afSa1tTUVFBQsLOzWyVCAwDk5mRrwz+doeHhvt5eFWXl0rLHHh4e8vLyc3Nz9KpKfX19CQkJM3NzMpns4+NrbW0lryAvJi5ubW1TVVVFp9NbW1qcnR3FxMRSUlPT0tI0NNRV1cilpSUuLq5zc7NaWppy8vLd3Wyp5L+Ee5BVeChu0NPBYWk5x6AHsTQIz+yB6Gf8JBb/m29CN/V334WeXKdOsYMT4qFM2GuYm4eraABARESEgIAAin70cj0XesMixEnskQRB/O+/TKyd28TERF1D/WRLr88nV5zxIIQuMA7h97iJMTsgIW6GDGkHEk/GfZgXGeWpQLjHkQ10IMrBC0HApjXpSOzFZ3quQF4RLtyDxqOajXscSKdsIO6RelV/rr7enLqObaLmO8Qsdolb7hS33IUXnLDYJW6xU8xyJ6wtPhM2Cc1jByGsaOrayq+x5bbGVn7Nl5QPrqjYRWSg64W4557eZzfJW29rbr2t8aLy4RVlqk/8zNQUdzKEVWxHN7SxsXF8dMwjO+GA5n0+ijSfnjQf5QFeS8Mm5QFeIHFER+qUoVxjH5SqAgAC8lK+Vrz7LVn0WzVRdq2G00STLHpITeSAqkhJMzvYdHxZ/hcPru+V49+HijyHYPfcwftv75S6GlOa293Zta6eC10FygfQ3Ny8uDg/VOvclSvXXaDSnc9Vnm925Sr0l5uzlmfRqm4utC8KvFUSKvSSUhx8tzxaZnGW7befF6lt9/DvLiqfuah86qL8GSyIZjdhp7PSx480do0PtnR2db8I9yC2w/xc1fSRqWEhS/5L1HMvUT9dNfj1F50fbaLMEdsnZiaEzW+fUDr8o9rRH9WO4eXoj+RjsEk+infC+pjCIVV3edYKDO7PZC1L24vxyO0/qcxzUvnISZUjsEY0JPCicoRH7hsJ23tLS4v/V/y5rKysLC0t09PTCbUCWlvIndjaytLe3qHscVlCQry6unrZ48dPjcWoVOrKygqLxZqenpmbnZudnR0ZGWEymSwWa3R09MmTJ9BNADfowYV7y8hBZmJiYmZmemBgAMNWoD3dHDywr4/tmoGHVoOR9IgX/SsQAHeidsczGY1wthNkbDvIabbhkOi5LOvEmeXkoLKAyBvFvUNz02tfGUQPMkaRkZHh1nNpaWmZmZnFxMSsSnmGD56mUPS8vLxLS0rCw8O1tbRbWlv9/f0d8Uz1y8vLMzOzM7MzKDMOk8liMpmjI6O9vb0dHR1EYp3FxcW+vr6F+YXJycmJiYnh4WFsZaW3t3dhcXFwcGBsDH6j4A/Yv4B7AK7SssYTesxxeIjh4a3HOM0qfP+exZvP7tfrr2M//7xaOfiKbCT4id4LT548ERcXJyAOTC9aVKSjo2NgYLB2lQ4PD2lqagQGBRcVFYWEBGtpa/f29jnhzv9oDc9Ans5MT0+Pjo6yWCzm8vLIyAiHpTAoM1RbzM4+edK/tLQ4NTU1MT4+Oja2tLQ0OwNflOPj48R9xFM5/lPyHoRjenCnLcIqHClhmzl5cAGJVIALe2AOV2JxriI+/BBmEeHhgRZUeXns4ISvyF5oWtfVJSUlRYjfX+bHvrLS3tZGRLV+8qRvbUhfxLR/rV51r5G8p6Orc7K1z+uTKw6kY9yeWc64o5YjV21LOpL2PO6xIR21h5bI7IKgD6d50p50yop0JGYd3PMM8SDbIG7MZEPiDeWTnB+fYrQ0revPhRYqzNTd2zsxPpZNb/38ru5WAcp2QSqnULYL6m8XpOAFdm4ToG6+pR2ShT7zAMQ9t9S33CRvvaXOKQRNEOofXFK0C09HrO7sH/laWPezG6r4ePLWW3i5rc4mUPMW+cPLShRaHPTn4nL+52Y7Mfn29vbZmRmX1Kiv5PkPqd57rkCzm2c93ygL8WmIN/SwnbP8spN2ydzYryjwjaLAfk75RkkQ0d8o3t2vKLBP4e5e+TvFDGhuCACIK83dKXl1z8NbqOyVvbX34a09src5Pbdh8+Gt7RKXo0tyhgeHXmTXTEyeTqfPzkxXJ+tlel6EGSRoN4g8Erm+MKFENu0GyiCR6XWlNEpheREPuohhJXGUcPOjUTY/EiXa9sdo22fNKJsfI6xOJrpenZtmBx3IjVC3kX7LSfEjJ8UP8fojJ8WPnJU+ZDch8ZGj/Ptu5C3jg82NjKa1ds0E/5HSubm5eWhi8Br11x/Ujp5RP4GX42fUT/zEplEPrI8pfWsSREU8nJgZ5ze+dlh2/zHF744pfYfX3x5XwmlO85jid98+3CvvLE3Ie8RthA/K7OZTOLhu4cX7D8l8LWolwGQt/1/x54qMjLx165aEhATSCxC3By0vhG9Q9iL0/CcmJlpbW6PbgKxbCJogiGMXFxe58yKhfmLhEuNxgjU2Bk6cgI7Wq9746zXRdlJLIlFwp3RCa1COO22h/0XZo6h4ZsfntpO33oJKtKcJtK9dg9/QPT1sMQ/3tb8ijZQyampq3KmPTUxMbt26paamRgiBiLMhexTEVWxlBbGURqP5+cEQzGgYwTFuRrFZymLhLF3t2MU9Eh2O92B9feCV5T2IaSW4CXMuznOAa2FKOLotNGAAx5HdxA795z+zJXO7dsFAiJyLIK74nyaQEa6cnBy3H7uhoaGsrKypqekq3IMuHIVwRa5eiKWOjo7IVBwtUYKlxJJD00K3A8YWX5OHgbsH0fhJsMZGGIqgBhYzAAAgAElEQVR6vTW5thNxLBfnGLIKRyZTKM8osUo7cVtmhNTXnuRZj54eDLIgKAgjIr666T2a/PDwsLy8/HO4Z/duwMuL8fAAHp6k3bszsrIQc7IyMy5fvqSrR8EAeNLXK3b/vojIvcamZoJ1/yZBLAiESpOTk5uamrp6e0YbO10+/tmcdNCSdNiKdBjVRqRD5hwa7zliQtqTIKyzvLy8zGLODo492nbZlHTAknTEEh9mSTpsTDpkSvoW72F3GpP2hv+swGSy4CEjk7Qj/Kak/RZwPHuACelbY9Ih4gwWpMOmpP1+R4RnhsYbmxjr4h6YmHl4ODExsa2tbXx0JLWs7u+/yr93UeGDS4ofXFJ8/6LiBxcV3/tFBtKoB+/8y7mHvsmFAFvBVlgl9a3vX5B/71e59y/IvX9BHpX3zkm/96ss0Xz/gvyfT0uZBSTC+KMsZlPXk89vqP79F9kP0CG/wqPePfcAHgLPI//eBfn3f5V788wDdbeI6amp1hf7sTMYjOzs7Pb29unJKZuYgH8In90ucXm7xOVtqBa/tFXk/Da8ieotYhe/krlZ1daEsVYAAI9SIv8h/PM28UvbxC7BGhIXt9w7/6wpfmmr2MUt9y/k1laAlRWAYRGFGf8Q+nnr/Qtb7l/YyimfC51FNNH5ocBPofmpg/0Da+17iGe2sLCwpqamrQ2CtjhXfhelTzzIOx+Rd6DiorrDSWm7hzq7+Yi8w1X5szDLn2emRpaXWctLS0k0CbuH7zorfeKk+IkzLJvt5Tfbym52Vt7srPiJk+JmJ6VPHOU/9NLZNz7ctcxcWcFAeqCcyT2SldRGK8mNqLaQ2Ghyf4M17NkAOyU3WEqQ7OT+OtxX19nZTTxoxFsdPYaLi4vJycmt+F/fcM8J5cN7xLd/I/XFflh27Zf6Yu/9nfskIEH07BL5lOyhBDDAZLKGx4fOa5/eJfrpHokdeyS2o7JbZOvXYmx6jzgkdtzbfN9acGFxgbnMnF+Y5ze+tv3ex1+Lb/tafDusJbZ9JbL1K9GteM+23WLbvhbftuPe5puGl+YW5pqamogP7H/zSX/1w/8D8XvQxrCMZ84iXknrEugyiHzsExPjpqYmhoZG8QkJLs7OmZk5Xp6PwsMjPD08jI2NKyorzM1MLS2tKiqrkCiova1FW1urogqGMO7t6XZ3fzQ4OGRhYZ6UnJKclGhqZvS4vFNEBEKf39pUkNYgkkTS55gtoy1kFFdpER/NgERyws1TngM9GzbA7/XXXoOxnuvrYaLsF3n/rsuEVZ3IWo3Ix44W99DQUG1tbU1NzcuTaBLLwsfHB+X/G+h/ok+lGhkZ5ebmGhsbl5SW2tnapKSkODo6WFhaVVZW6VOplpZWDEYTk8lcYbHo1VXa2jrNLdBTtLGh3tfXr6W11djYKCU1LTYm2s7OurV1ZseO5y5/LW9ffx175x2wefP83//u+9FH5p999mTzZvD224ilfbguZoUDg5bxkI8FBOhBtib8/DBen40NNjf3P4N7JicnFRUVuXGPqqqqqalpZGTkKrtmdDvQy4WoUfyemBjo/Nne1qqnq2dmZpabl2eor19TU+Ngb5+ZmWVtaWlv7/j4cZm+PtXe3qGtvR3upCxWcXERhULpxbPcV1SUBwQGMZoYxsbGhUVF/n6+Dk42FZUTH//jN1jKYdcsieSOW4WPbNwINm0CGzag2DwlnLsA8DxcBniM5t8+p7Mz5uoKrcWLiv5p3DM6Oro6H/uhQ+DMGez0afDTT0kHDqiQyYND0EvZ0IDq6+crJibW0/dkaLD/9u2bV65caWQ0Ecv13ySIJwhFFVJSUsrOzm5ubakprQhXMgsSp0TJmYXLGEfLW0TJmQUrmEbIm8XgdLS8eZS8eYCYbqqjbyODQa+hN9TURZGtA8Up4dLGUXLmEbImMfLmQbJGwQ+NIx+ahkkbR8qaBUvqB4rpJpp5NDU3V1dXN9TWx1DsA8X0IuGvmMQoWETLmYcpmIfKm0Ja3jxa3iJGwTJInBJHcayvqW1obCBAMzFz9JhXVlbKyck1NjY2MxhpecVKFu4qlh7ajn7qtt46Tv6a9j5adp5aDr6QdvDRdvJXt6MpmLvGpucyGI11tbUFpeXKlo+ULN017GlaDj6a9jRtJ3+ypaumnTfZ1kvTnqZu561s9UjO1DU4Pq25qam2puZxFV3d1lPJ0l3bwVfd1gudWcvWU8uehmgd5wAtB195U1fvyMSGhvrubnbMQPRoEPNHek8KhdLR0dHY0BiSHKviYKrlakP1dNR9ZE/1dtbzcNBzs6N4Oep7O+l5OOjTnDVdrLVcrYsrHtfX1jEaGqMzUlQczDScLSnwEDuKp4O+t5OmgwXF01HT1Zri6aDlaq3uZKHuZJFTUlhfV9/Y0JCcm6XqaKYF/9dR191O38uZ4uFAcbXFj3XW87A3oDnrutupOJgl5WTW0OkcZT1cbtwzX15eNjEx8fPza29vr6+vTwgxC3SSifRUjfJSjfZWi6WRwz3JIY/UY33IMTR12OOjEewiG+NLaaivraqmMxiMlEi7IKcHoe4K0TS1CE+VaJpauKeav7NCDE011E0+mqYW6iYf7Pww3JNcV1NZTac3NbdmxLsFON6P8JSN9JKL9JKPpimEeyoEuyvF0BSiaYpR3vKxvkqh7g9C3WXpVcU1tXXc0IF78iMjI8rKypmZmU2Mpkp6pYEHRcuZbOhJoT7SMfLWN/CiGLhTDD0pRt76hl4UQ2+qoSdVw0HFK/JRc1MLvZpeW19r7mOq4ahKcdMx9KLoe+ga0agUFx2Kq46+hy7FXdvQi6LtrK7hoOIUYs9gMOj49doFWGs4qBp66lHd4a/AMz+i6LvrGXlT0a8Y0/S1nNRs/C3rG+oZDAaRUPzffNJf/fDfG/dgGNbe3l5ZWYnSPxF36EUEnp8r0d7eDmGXixd+lZKS0tDQFBESFBOX/PnnMzq6etLSD1RUVOPiYi9e+NXE1Ayl4tLV1dXS0jLQ1zcyNgUAJCbE3bhxMyE+XlVVTUVFpbyiQlNTNSw86YcffvPtD0ikQdxeh4Z7G6HteRKPnkd8QCOX4FjcBmX9E777LnbyJLRo5nqmXnTRL+tHuEdTU5M7buHS0tL09DQhd3nZ8biYgcA9DXW1Z8+ek5KUsLC0PPfzWQVFJT5eXgtzC0kJCS0t7djYmJ9++snOzj4nO1tRUcHIyEhbS0tLS9vRyRkAQPPyEBIW7u17EhoSJPVACg+NrRYdk7V79zoc+NOfoE/7zp3YV1+BQ4fAoUNt27YZff11xLFjy8eOgRMnwJ49oyTSMI5v0OGItz6cROsQm374IXQj4uWFapd/SvzwmwyZmppSUlLixj3Jycmmpqb29vaE8uslJyHiFo5PgNycvEuXLopLSFhZW39/6iRZXeP7U6esrGwEBQSpVP2goICLFy+6uLpmZWXJy8ubmJioqKgY6FO9aD4AAEd7u/tiEuPj4xBuWlnT6dXy8lLxSXWffroOSzlQBkMQh0RqfOstw6++Sti3j7VjB9i/H3zzzeCWLWOvvYY4icA9wIFRLBef1wf9f/sbDC/517/CcukSVCaOjsIYmysrL2HDs/+C2fRGR5WVlQnxe1RgYDk/P1BRwRQVgYpK0rVrtnZ2M7NQr2duZmpqanZfTKypuSUvJ0dcXEJaWjo65rnk9q/+Rls78tm0MIzJZJaXl6OP4xWOyK2+pal/cCAzP7e0qiIjL7u8piqzIKeijp5dmJdfChEfIcMjEMnA8GB9a1PXQF9ucWFtU0Pv0EBXb+/A0FBDyzO4tsL5Q1NqaGnq7X+SVZBbWl2RmZ/7mF6VWZBbUUsvrnicW1K4gue/RnYkxCVwzxwpuJHTL+qHskMms45eNTjQn5GWUkuvzsxIa6irTU9NbmE0piQlNDdBzybkjI1kk+jMba0tHW0tfd2dmWmpT3p7mhrqJ8bGOtvbhofYSpa111tXQ58YHU5PTa6lV2WmpzXU1WSkprQ1N2Wlp9XXscNYEMxBv7Jq8n197KylxOQX5uYb6xt6Orsy09KrKypzsrLr6DUZqWktjKa4mJhRPCAtyuRAsAXDsPqamoG+vrbmlozU1LmZ2Vp6zdLiYk11Nfrkg9eLsx1upfj9ncXHdLS3o1/JSEuvr6nNTEvvbGtPiosf6IP+YohFBNu5cQ9CnAwGAyU2Ia6xq7uvpbW9vLw6N6+osKgsN6+ouKQ8N7+ooqomPjF1AeYGAZz7z15oM7NzlVW1PX0DFZU1eflFY+OTVdV10N2kgs7EZVrch6DJ9PT0tXf20GvqcvKKC4sf5+UXl5ZV5eQW1dU3JSanT8/AaKjokVx38mizqKioQI8hMfmG+sbx0Ym87Lyq8qqcrJyqiuq83AJ6Jb0gt6C4EPq7rJ3J6MhYYz2jv7e/MK+woa6xp7unq6NrfHS8ob6R+GnielFPS1PL4MBgfk5+RVlFTlZuVXlVQW5BdWX149Ly/NwC5jKbRcThvyfxu+IehPpv374tKyt7584d5PfL/XispVFeUnd3aF7e09UpePeuqIiIq6ubAZXyww/fX7xw0dzSikajychIP4UC/Py3vbxpxcVFero6+vr62lpaRoaGhoZGQ8PDCwvzGuoaUVFRampqWppagYEBYuKirW3DfLwv2VHQhlGIK2JQdGDUs4JbMTdyCSEAiVSNS4OQp8w6e4mgIFTKvOK2sZYPRA9SlGhraxP2PTk5ORISEiIiIlpaWq+4Sfv6+oaEhAAA6mtrbt2+fffunfCICAV5uaNHj164cNHRycnT00NMTExfn3rzxs2goOD8/HxVPJ2ytpaWjq6ujY3N5MTk0GC/mppaU3OzoIBAVFSUppamptZq3PP661An9emn0H78+HHsxAlw4sTK3r2pn31GPXiw9tgxwMcHjh4Fx44tf/ZZHIn0TJmF8zYTh5tIpQhZuns39PY/e/ZZkB6CLf8O8aJ4zcRz+JsnR7gnLi5uZQXk5+YKCwvfxb23ZB/K8PHx3ebnt7N3cHNzFbhzR1dXR1j4XkREZE5OtrKysrmFhbKyirGRobOL2+TUVGtLi6qqWkdHh5urC1XfwMLcXI+i1di49I818p4NG6AE8Y03sHffBV99xdqyJfbjj6nffdd4/Dg4dgwcPw6OHl3cvTuSh6f/o4+IFQ5IpARcJEn0rLNQEZzasAEins8/h16NsrLYF19AxHn37st817m5hCQrqqqqz+EeAQGgro6pqgJ19aQbN9Iy2MazDfV1QoICPr5+yUmJmVlZBvpUZWXljk521BniLvzLBPfE0DZGbGzIP1xQUNDc3Pzhw4fe3t7IL9LGxsbZ2dnR0ZGdoA3fGtlH4QAlLy/v2rVr0tLSXl5ezs7OioqKurq6rq6uIqKi7FzouMgAHQINohcWREVFTU1NHz586OPjc+/ePTk5OSsrK1tb23v37omLi/c9YW/AxAyJSRLzRxzgbjY1NcEoHvb2ioqKHh4eN2/e1NDQsLe3d3d3Nzc3R6pwNJ4zE4jiXF1dr127pqSk5Ovra2VlJSgoSKPR5OTkTE3hJyI+cXgQ5xAYrOHKlSt2dnaysrI0Gu3GjRuamprIu5ufn19HR2eFBRMzcc+c6zTs+a6dfGxs7M2bN83MzPT09ExNTQUEBAwMDGxtbb29vc3NzdfNj7a0tCQrKyslJaWjoxMWFmZhYXHjxo34+PirV6+i6FkAj5dD/BYAICUl5caNG2ZmZlQq1czM7Nq1a/r6+o8ePVJRUbl8+bK3N3TYXjXzl08endzQ0FBBQUFdXd3V1VVZWVlYWNjExMTR0dHb29vZxYX1LM8MYic8qK+vj5+fX0xMzMTExM3NzcDAQEhIKDEx8dy5cyjONfom5p6Pubm5jIyMlpaWq6urqqqqiIiImZmZi4uLhITErVu3Cgth9lDi/qKJET1svnPAPTFsZmZGWFjY1NRURkYmMDBQSEhIUVHRDv9zcnKKjYXfGyjsEHsmOPAvKCi4fPmynJycp6enu7u7oqIimUx2c3MTEhKaxb9enjsEgKWlJWLB02g0YWFhOTk5BwcHS0tLAQEBUVFRlKSZmBUx+d+H+L1xj52dnZSUlK+vr7i4eEBAwNqbRNwtgiPJyUkXLjzq7QOTEyOxcbHd3V2+Pj4JCQnhYWExMdEFhXk+NJqlpUVZWYmTo72dnW1pWRmTubSywmxvazEwoD5+XJqfnzM/N5uQEDcyPGhvZ52WluLp4WZoqFdd3Xz06Iv2AGjCvGEDbdMmq02bBv/yF/Dxx9gHH4ANGxbx3FuLxNc2vkMP4dionQsJrd5R3nyT7RGz6gL/2SbCPYaGhgTukZaWplKpWVlZN27c4Kzal50VAODn54eUMn19vUlJye1tLYGB/ikpicFBAXFxsSUlhY8eudjZ2VZWPraxtrS3t2U0NaysLGMYq7amytBIv6q6sqAwf2ZmMi0tpbS0SFlFKTUtJTEh1tnFvrNzdu9e8M47EOvs2AFTdvPwwIiCvLwQ33z33diWLQ5btjgeOTKBgx7s6FHAw7Nw4gTzs88IdSESniHzcGQ8/hwzNTRgbs7ERAgiOU/9y673N/8P2f1pamoiUx5kOuro6GhkZJSWloZksC8/CQDA3d09NTUVZnJuac7NzW1tbvL3909OTgoJCYqPiysqzHd2dnBwcHj8uMTa2tLFxampGZqvsljMx2XF5uamDfW1xSXFI8NDKclJjMY6KyvzrKx0E2MDV1e7ktLhzZufrdI//QnCkX37sIMHwZEj4MiR4e3brXfufMTHN3v0KODjY7OUj4/Fy7t44gRhGwRIpFocmqOQ4s+xlGsxQ7izbRt27hx2+TKMvHDuHDRHk5CAEcz374dSn1fhObId1tLSInQHUN5z9y4gkzEVFUAmJ924kZQCI0ihLQeZ5SH19PLyMooO9z/1BnzJvYOBHjo6AgIC/Pz87O3t4+Li4uPjY2JinJ2dY2JiKioquKEDcR6ozWxvDwsL8/X19ff3p9FoQUFBPj4+Hh4ewcHB64Yl6+zsDAoKQr8SHx+fmJgYHR3t4uISFhbm6enp4OBA+BJy78HEL65LINfOkJAQNzc3d3f3+Pj45OTkkJAQBweHgoKCoqIi5OOz6ljk/unj4xMSEuLl5eXj4xMREeHu7u7r6xscHMzZw54dhMYHBwc/evTI2dk5MTExKSkpLCwMscjOzs7f35/Jtc0Td+3ZKdZQ6L7Hx8cHBQU5Ojr6+vomJCSkpqb6+fk5ODjQ6XQY0nNpadXugOBjenq6n59fWFiYh4eHr69vRESEg4NDcHBwQkLCqvFIipOcnBwcHOzg4ODv74+S+fj5+bm5ucXExBgbG3NHWCZmvvY83FeAPpNCQ0NDQkLs7OyioqISExNjY2Pd3d1pNFpDQ0NpKcxuxn0Iuqfj4+OJiYnu7u6I8/7+/qGhoY6OjkFBQeuaus7Pz4eGhgYFBaFfScD/PD090b0zMzOrq4Oea+iHXn3yXV1dAQEBPj4+9vb2SUlJ8fHx0dHRrq6uUVFR1dXVzc3Qrm7t5Ds6OoKDg9GCR0vF19fX3d09ICBgbWRX9AoNCAjw9fW1tbWNx/9iYmLQtXt4eNjZ2a274Lmv4n+V/r1xT39/f1BQkIyMjImJCfvDaBWPn2/CtLqpSVu2OOM+75AV3O+FhQUwOQnB6dwcxmQCFgs2mUywtATm58HiIiQWF8HCApifx5aWIbG4BMvyMjR9GxgA6/lzocgxJX/5C/XLL2MOHmQdPAiHnTgBvv22Y9OmeDyxEbEPIdMfC3HxguRkEBuLxcaCxESQlATi458RsbFYZBTGDpfIdT9XLVnuS+Ma9dwqRLhHT0+PwD1PzXpUVFTQNytyanuehatb3PnYcW0RZMX8POQJiwXrpSXIw9lZDHXi3IOsg9xbZBfUuYyzdHkZzM1BriKWXrwI/YCOH4cW3DjiwX74AZw9u3TgQOaePZQrVzLv38d++gnw8MAd+uDBhr170/buxf70J26WPg2DRCWT6xFL4+KwpCSQnAwSEiBXe3sBnQ7S0xHu4eYTe3kQbORmL+pc9V/EgKceWGpqakjPxWKxZGRk9PT00Kctyj6xmonPt7nzsa+eEN6enwcsFpzw1BRk8sIC5DD6W1piM3Zhnr1K8RWLobvAYoHaWnbCOBIJhhDcuxc7fhxqBo8eXdi9O+mTTyh79uTjrGYbDe/bV7lnT97RoxBofvklCkMFSKTh117TpVLbk5LgKkUsRas0KYm9XGNjYUCp8nIwOAimp0FTE4iLAyMwPxL0QKysBEVF8CrQmiEuk5ulhPIReaWpqam9Cu7hnAH/ruScl9PJaf+v/Ytw7crKyvz8PIrkzmKx5ufnken6i6ZBqAzm5+eRGTLKs430Mmsni9RM3L+CaGTmiN6Ea496eQ/xW0wmc3Z2FgV/Y7FYc3NzRHKVtWcg1jySdaELWV5eRv3EdREHEjogFotF/AqTyUS/srCwgEAPMf4VCeK0y8vL09PTiNvLy8vz8/Pov9blPPHYoctEY9DkuRV5xBwIFuGOqzPoTqFfAQDm3lp7vcSxLyGQQgfDsKWlpZmZGTSrBfyPAA1rDyc4z2Qy0c16GlkXTWldAxeCD4uLizMzM0jZh34FLdG1P/EqPeg8aPnNzs6iX0HLmGDv2vMQk19YWECQlFjw6/KQ+FxcWFhAYJp4rJaXl/+1Bb92Vv9yz++Ne9zc3Hx8fCQkJISEhFBKcILX6xK4nivZ3t4Dv8LnhgAA8zzExMCYvKqqICQEqj9yc8HoKKaqCiMvX72K8fODU6dgVN+15ccfYQLLt9/m/vAFr70G/vzntjfftN2yxYGHpxtpDY4dw44dg+qDAweGXn8dOVcTRwESyYdECnJygipRXC26XFdXUViYNTDQ1dBQmV+QOTTMzgv4pK8nMyOjq7t7emoqNze3orISpd2enp6urKhEr4++3t6m5pbFxcWqyoq5+fm21tb6+nruy0aaV255T2BgYFFRERIkrgqyx30gQXPLe6amYPfYGPbwIeTGDz/ARPTff4/98gugUICgIAzc8sMP4Hnuge+/h/2nTsGRP/yAnT6N/forEBDApKUxMTF4nsOHIfTh5cV+/hncuwcEBOpOnTI9d+6RvPyApibQ1ASiohD08PGBw4f7jxyZ+tvfuEEPCw8TkBQVxWbp0tJCVVVJYWHW0FBPbW15bm7WyAhMhgrDJbe1ZmRkDAwMjo6OPJV41dXVs3BN+ejISE1tLRK9NjTUo0yffX29s3NzPd3d1XQ699ckIe8hHDo8PT1FRUWVlJSkpaXXfr4TnCQIAICbmxuS93B1YvHxcBFmZ8NoilFRYGwMZtIIDsa++w7o6MDccMrKMGDm6dOrOIz9+CPs+f57NvHppzA57mefQfkZrsPC9u6t3LbN8OBB2tWrIyIi4P59yHlxcXDjxgoPT9+RI3PHjoFDhwjQs0wiWW7alJuWxmbpwsJMZWVxSUnu8HBfVVVJUVH29DQ7YFpnR1Nebubk5NjY2FBBYVZzCwPtLiMj/S0tcCmi793BgcGx0ZHq6ur5+fn6+rqe3l5ct4VVVaF4hmBycvIV5T0Ex1YR6Bb/Uf/BgT848AcH/mc58HvjHhcXl927d5ubm1tZWXl6ehLoeNUrj2jiMeVS/P2h1SfRib98YfTYlhZ2RuiuLridlJWhj1G4i3OL7l+BBq+/Dv7619EPPvD5/HODgwfL8d0Fag3wwtq3r/jw4b4tW7hNRJE6JhfFQbG1xZKSwtLSMlqam25cv2ZqZp6VlXnt6lUjIyM6nR1PwtrSQllFTUVV1dvL456IiLi4eFZ2TldXp6uLk6CQUHJK2tLiIllNTUFR0crSUkZG2s7ewdvLU1JSkl5XT1w+wj3m5uZEXlIZGZmTJ08i7TvbHIGbU2toGAQswD8lJSk4GBw8iJWUQKYRCUQRrzZswJSUsC+/fCEbUerWzZuhMmvfPrgfHzwI9SAHDsDd+uJF7NYtICkJJCX7zpxxPXHC7N69RlVVoKICDVtVVYGS0iIvb863344fOwYFP3/+M/FDgESKRubhoaELkRG0gsKi6urqixcv2trZR0dH3bp108LSqrWNnXqQrKaqoaGhrq6BIlJKSEiUl1d0tLfjiTWEKioqp6en7Gxt5GTl0tPSbt28WVlFv3uHX49CXVhc5GbpwsIChUJBeq6VlZXq6uqnwd3FxMTc3d2JD5c1jHzWAQDw8PDIYjtmsxchAJikJIzKXVAAc1qpqGC3b2PW1jCNxpEjUA/444/QQOfvf38mzlm1UDdsgMMOHoQgHllHHT8ODhzo3LnT8fhxKzGxVjU1QCZD3ZGaGlBWnrt7N1dZefLaNSRLg7nTN21CKzaQRPLftAnExc6EhtLKHpfn5mRfv3HDxdU1MiL8+rWrtra2KHkLk8lUUlTU0NDQ0dG1tLSUevBAXFy8rqGxqYlhoE8VF5eoxZeiqoqSja19YWGBgIBAdEyMmamJgqISBhYjIsBnn8HEGsgIl0KhvKK85xkruaj/2TfdH2f7gwN/cOAPDiAO/N64p6+vr7i4OCYmxs/Pb12nazQt9PZDdHJysofHc/IeJE53ccEOHWIbHPT3Q6PXjz7CkpOx4mJojsCdPn3DhvXTRb32Gvbmm+Cjj8Cnn458+mnEp5/qf/11Ig/PAi8vspyAEgvcAGV6//7CI0cW/vIXQizBbYMySiIBBwdQWpJ9/vx5W1tb/tu3XVzdKiorLl684OTs3Mnx7bQwM80vKLKzsZKTlxMSvicuJhYeGd3Y0KiloeHl7W1v7zg0OKBGVjc3N9PR0ZGTeygrJ0+n0zU0NAaHoXiD4AmLxeKW99TW1pqbm8fGxnp7e3MrTYnxa1kaGAjte86dg8YfTU1Qi4ES0b/5JhQqkEiQXe+9By1nuXfiN9+EOzRCOby8UI2FlFm8vJBRJ05gv/yCCQlBfx0NDSAjM3DpUm+iXhAAACAASURBVMDFi4bKytn29kw7O2BmBjQ1kXUHkJMbPnmyhIeHdewYd14LgKffMsZ9rUF4OCs1JfKnn392dnG5dv26v39AWmrq9evXPTw9R0bYUU3VyeSKyiptLU3Zhw8fSEnduyeckZlFr64mk8nmZqbhEVBkVFhQoKur9+TJExUV5cKiYgcHO0VFxfZ2aDZLsGhubk5LSwvJe5aXlx8+fCguLq6hoXHv3r38fJjVBI0kaoKliEDynrS01JUVQKez1yQAMI2GjAwbBunqQmZu2QLhTmkpFEny8mLbt0N7mnVzmW3cCDlz/DhcgbjpN3boUO+uXd7ffWcsLFysrs7CsQ6UGOEGM0Baul9I6LG6Ouv6dYirjh2DT8frrwMSKZ9EMieRFjdtAinJrKjIgF9++cXe3u7uXYHgkJDkpMSrV674+vmNjo3B8DaLi6qqqozGRm0tDRkZ6QfS0sLCwgUFhQUF+Zqamra2tlHR0OzRz9fHwsJyaWnJ0IAaEBhUX1sjLSMzPjE+NAQyM5HV+R/yHmJp/EH8wYE/OPDfxYHfG/e44H8CAgK3b99eV88VGxsdExOHAtpGR0dNTc+kpqYGBvrj8frYkY5ra+HrPjsb1pcuQW2XszN7h/7qK2iJyb1br6U3boSbzZYt4MABcPDg6FdfhW7ZYvDll9E8PJM//QQuXABXr2JXrmCXLoHjx8cPHWrHdx1Ca4BODvAk4fokUj1uywxsbVfS02MLCgu7OzsE7t61srJ+mlv77p07ZuZmFZWV6J472NmKS0hqamgGBfgLCkH7dl//gPLyck8PdyEh4YioqIyMDG0tTWkZmaSkJHExMTd3d4G7d6SlH46Mwm0ebbpI3mNhYUHY9wQFBRUXF9+/f19eXp7wxCYGs1jMAH//zEwYI258bCwqMorJZAUEBCQlpXh5gTffxNLSsIkJiGZQaJyPP4ZCiFVMe/11aOt65AgUPOBaP7bhDg8P3LzPnIFwR0YGI5OBtjaQkek/f5526ZKBvn5qZOR8fDyIiQExMVhsLObnB+TlhyUle8hkcPUqtEfZv59AVwAPDaDD8eoCwSHzCfHBVVXV9fX1165dd3BwjIgIFxISsrW1a2SwUw9qaqhLy8hQqfouzs4PpKQkJMRj4+IeP35saWEuIiqakJCQnZ316/lf1DW15hcWrK2sSkpLXZydHj58iLKKElx6qm+mUqmEXbO3t/f58+dTU1NNTEyIPGjE4Lm5OZq3dyHu8Dk0NBgdHb2ygnl6ehYUZHd3gx07YNpOAKCzvYMDVHXJymIVFZi8PNQ6/fwz9EdLSsLy87F33oG+4h98sBr3bNwIE5Hu3YtYDUHPwYM9O3d6HjhgdOdOlqrqIpkMcKcoTF0d09QEsrKDkpJP8B4gLQ3lbQiV4oGUnoUo3PQnEBM9HRMTUt/AKCzIu3v3roeHR2ho8B1+fgcHGE8INx1jKispysrJmZiaOTk6PHggLS4hERefUFxSbGRoICEplZiUVF9fX1CQ/8jDMy0tRV5eztHJycbGRl5RcQh3PEbrFNmOUKnUP+Q9/13v+z9m8wcH/uAAAL837nF0dPzmm2+gc7mRkZeXF7GdI7uB7q5OJfxvbGKyoa72yuXLPT19GRmZlpaeU1Pzt24Ba2u4o+TmQunCwACU65BIUDLx7rvY11/D+vPP2RILJJxYu3+//TbYuxf5FnXs2OHz6acGe/fGnDkzdfMmEBcHSkrwG11NDW7hGhrg/Pms/fur8Q9uqG7YtIkAPRhug/IsTaaNDfqsBywWq7W1paqqqv9Jf3tbG51OLysrDQoKDA0NbWhsrKmpGRkZmZ+fr62tbW5uWVpa6h/on52dZTQ1zc3ODg4ODg8PdXV1L8zPMxiM+fn55ubmuvp6FOaE2HdHRkYsLCyQ4T0AQEpKipeX18bGRkZGZpWeC0+iSVdWViGra8zNzRfm5127dmN2ft7fP+Sbb2K1tJa2bwfi4lAthRi1aRMUmxEBrDdsgBqojz5iq1qOHoVwh4cHAiAeHngLLl3CREWhgEdTE1CpmIpK47Vr7leuGFMo6WFh8zExIDISwh1o1h2JxcSAiAimgECyuHirsjL48Ud4to8/Jli6iIcoRPECYGdICJulS0tLDAajqqpqcHCwpaWZTqcXFBT4+/tHRkW1NDfX1NRMTk5NT0/R6fSOzs75hfnBwaGJiYm21ta5udne3l4Gg9HQ2Li4uDg2OrawuNje3t6FS+AQP9HCGxgYMDQ0RLiHxWLR6fSncIdKpbq6uhK+KgT/S0uK5eXldXR0F5eW01KSb9/mn5tfCAjwS0mJb29nFhRAloqIYGQyvMCoKKjJeustaPO0bx/ElP/4BxTkfPIJm+fbtkHwh/i/YQMc/PXX0J7sxAnAx7eyb1/tzp3OBw+a3LyZraS0qKoKZWYODpi/PxYejkVHg9BQpopKvLZ2N65GhPlujxxBqBS8/fYMiWRAItGRm+Hrr0O8hV56i4uLjY2N1XT60NAwjDZGp+fn5/n7+0VGRrW1t9Pp9JnZ2anJSXp1dXd39+zszNDw0OjoaHtHB54iZnRubm58YmJycrKutm5paam5pXkV4MYwrLe392nM62f52F/sz0XciFXEf+f7mVgGL58eWlcvGfPvD3jJyYn/esXZEuP/TeI3Lwqd/xWH/ZuTIQ5/FSb8zlMi5vYvE7/nhF+Fgf/yhfxHDvy9cc/MzEx5eXltbW01J9IU4im6i12dHUpKSioqKnX1DQvz84b6+h2dnSkp6fv383d1dRsbg9BQuBeurGCpqVhCAnz1u7vDOPokEjRG+cc/IIG0Bm++CT+aie1k0ybw4Yfg22/BkSMz+/eXbN3quG2b+cmTmXfvTsnKAiUlaHeiogKNWpSUoAGKvPy0isqyhMTi0aPsr+cDB6BbL35CQCLFkEhuHBp24rhn/Ts4Ozvb3t4O010tsHMzEeNefe1yc6m8vPzKlSsoJQUAoLq62srKKi0tzd/ff1X8HgBALb1KRUVVXUOzuaVlbnZGW1NrYnLa19f/7bdlVFXHvvwSppW4eJFb2QQv58svYX74nTvhbo1kPAjxEHBHSAiTlQVqakBHB1Cp4yoq+YKCtvfvW7u4FObnz1VWgsePQVkZFMV1d8PARYmJK4GBk2Fhy1pai2QylA/x8MDCwVjIPDwECc+Q41JEBHuTXsulqanJtra2rq6uZTz4FcHPVycIfqKTp6enX79+HWkJl5eXZWRkxMXF1dTU1uq5AAAlJUWKioraOjrtnZ3j4+NkNfLM7KyTk8vbbysePTr75AnIzsa8vKBoR1ERMzaGzNyxA9bvvgt5+847kKscDA0NgP78Z8iHTz7B9u6Ftt7Hj4PDh8e+/jrriy8sjx61ExEp19GZNzUFlpbA2RkLC4Oa3OZmrKeH1dw81d7OzMlZiIoCpqaYggI0NkeOcnx84K9/dSGRUIhC+LvcuGctSycnJ9raWru7u9f1zngVxq5iaUxMDD8//9TUFDr2JX7s3Ady06/yo68yZnp6OjExMS4uzg//I6R63MeOjo7GxsYmJCSEhob6+vqilcA9AKWJiIqKysnJQQ663JkBuEdmZ2f7+vr6+Pi8KMsYhmEZGRnBwcE0Gq2vj+30wH0G9DVYUlISFBREo9EIc/tVYwAAVVVVgYGBL/GOGRsbCwwMDAgIyM3NXXs46mlvb/fx8QkICCgoKHjRmNraWi8vL19f3/JydravVSPn5+ejoqLy8vLQnNGLaNWY6enp6OjouLi4iIgIPz8/QhbIPWx8fDw+Pj4iIiIsLCwwMJCIRsg9BtETExPIBxt5ZXMn5iQGDw4OxsbGRkdHo6ADa8+GAHp0dHRGRoa/v39AQAByPiLOgAgmk4nWj6+v77qXhtIHxcbGhoWF+fn5EWGr1p4nKysLjSEejVVjAAAFBQXoC3btf6Gevr4+f/yvkqNMWDuypqbGz8/P39+/trZ27f+inrKyMhr+R3xFrxo5NjaGmOPn5xcQELCWgQCAwcHBhISEqKio8PDwoKAg5Kq26jx9fX1xcXEhISGh+N+/5gO46pz/TvN3xT1r37bcrzmcXomICI+Ni6uqqhwdHS0sKJienklKSjIzs8EvEgkAYC0gANNdSUrCD9/gYPgZ/ac/QcSzcSOhN4HvelxiAbZtA4cPLx882P7FFyGff268d6/HpUv0Bw+Yqqpw59bQwHR0MCMjzMICs7TEzMyAtHTfpUuRqqqLkpLQtJmXF5pZ7N4NT4hvzJV4HBSURBN1ItwD03pOTU64ujg7O7uUlpa5uro4ODjW4aageB7vHENDw6jomNaWZitLKzc3tyf9/RCa1NbY2toNDQ0DgMXHxQYFBdXX1Tk7ORUUFIYEB1taWvX0wJcj4hUKwUkmk4nIIty3fxU/0dPo60vLyMiorKycnJzKyclhspheXj67dvmfOgWQBY+REdTFvP8+3BrfeAPCnePHoRHusWMQ9PDxsUUIp09jd+5gMjJQEqapCbS1F9TUGPfv+/LzG6mo+IaFNTa3YENDYGgYDAzCMjgEBgexwUGsvx94ejZpacWFhbG0tYGiIgw/yMcHjaDxCQASKYtEssEDI7EtqN57D8vKwgBgrqysDA0N2tjYuLq6FRcXQ866uLR1QKUMACA2JtrIyCg9I7OyssLCwoJGo42PT8C4JkVFLq5u09Mzk5OTXl5eTk7O+fn57o/cGU1NYaEhCQmJBD/RmpycnOTWEnp5eZ09ezYyMlJHR2c9Pdesp6dHXn5BeXn5xMREVmYWhq1YWTmdOBFlZQWd3chkTFsbGtcLC0P9IIkEYc1rr0Hc89prcE1++ilcrgiUv/UW9vnn4PBhJOCZ37+//osvvPbvNz53LkBausXaGvPxAcHBUFGYk4PV1mKdndjgIBgbA7m59NDQ1NFR0NUFXdNdXSHMOnMG4p5jx8DevQmvv+5IIj2L+sjBPZClXV2ddrY2Xl5eJaWl9nZ2bu7uvfgGjGFYRFiYkZFRXn5BWWmJhYWFn7//7CwMC5uTneXh6bUwvzA2Nurq6uLu5l6Qn+/u7t7a2urn55uYlISi0hKrdHR0VEFBgdgA/lO4Z25uzsnJSUJCQlNTU1paWk1NjU5n5ycnHpyZmZmSkhJ/f//AwEBFRUVlZWXCaYAYMzk5WVRU5OXl9TTqGJVKVVVVRQF8iQGI6O3ttbOzExUVVVFRedGe1N7erqmpqaysLCcn96I9qbGxMS4uTllZWUFBAYVpWfVDqBkbG6utrf0iBIY871CEOhqNtu4ZoAFcYaGkpKScnBzK2bfusNLSUhERkYcPHxJJ6FYNYzKZNBotODhYX19fVVWVO3sgMXJpacnNzc3Pz09JSYlMJrdxvBOIAUg96uPjQ6FQfvrpp2PHjr0IiiGZt5SUlLa29sOHD1VUVLizqaMTTkxMlJSUoIA3MjIyKioqayHL6OhoQUGBN/6nq6urqamJUgJzTwkG5WptNTQ0FBUVVVVV5c45zz2srq5OXl5eSUlJVVW1Hdcac/8vomtra8PCwpSVlclk8osgL/K3NTU1fQk4ePLkydP7rqCggAK2rf0h+G6MjZWSklJSUkpOTl53AAAgPT1dSEhITk4OWTGuHTYxMeHp6enr66unp6etrb0uBB8ZGfH29vby8pKVldXT0yMeee6z9ff3e3p6av4/8t4DvI0qax/3LrvAspVvKwssy8cuC0vYAikkoQVCIKRBaOnFacS99xr3HvcW9y7ZltUlW8W2ZMuy1eXebclqtty7pZlfrq4zOI4TYL/92Of//+bxk1xJo9HMuVc675zznvd4eb311ltvvvkmRtLYuM93Of5OcQ/s0pyRkZGSkmJnZ7cpOIH9YsLrx0ASi0XZuzfa1haUAUNG89wc6PkA3cbhwwCv/Pzn4M8S6QEFLN//PuhM9IMfoL/73crLL7c//zzu6aejnn02/vXXaefOGR0cUA8P1N8fjYpCMjMBciovR/B4pLoa6JpUVyMxMdN2drPu7qi1NQA9r70GEIAlMYFaWWmtrPysrIZh7gCeg5UVmpBgrq4u8vDwLCosPHr0SGxcPI/He//9A1FR0d0WJSgURcNCbmTdyvHx9g7w9z9x4uTpU6coVHpDQ0NQYIC7h0dObp5pbe3La1c/++ILNoeTkZ7m7evH4XBOnzmrBZDoLtwDyKd3OC5MJhO7c9qEezAb3llS6xGUoqIcK6u8X/0KhRGIZ54BfNtnnwUBs7/9DYCe114DMQmY0tqzBzl+HL16FXBKvLxQJ6eF8+eln39e8tlnkZcv37x5k9PUPDuqBkIvBr3ZqF2e0c7NaMDfuGZFo0XHx0HIp6zMWFGxkJODurmBSNLu3eD4v/gFxJG9lj6ak5hJH30USERWVS0V5Kf6+PgWFhQe/PDDtPQMErHmyOHDNxOToJO+3U/U0cGhqLjE8jPqZm198dSpUxwul8PleHp62tra0umgbru6qvLK1asKhcLdzdXPz9fFxfX0mTP9A6DPM7bkJicnbW1tN6oALC0tLS8vYzIbmwx7x57gCHCcmpq0Ywfe1RW1s1sXR/jRj9bJ9ZAk/stfgoQsXDD//CfItP7kJwCR79iB7tkz+/LLbf/934XPPx/x5pspJ082urrOxcai+fkoHg+SU80CpLcPGdOgWj1q0KNGg8kwttLdNTEysqjXo/39IJOYmrpeFb9rF/rKK7LHHw/63vfuUg9/+GGUSJzLyrwZEBCQkQ4Ue3Nyc6qrqj48+GFaerpWC3oUrFr0cHE4nIuLs5ub66XLl0+fPtXc0kKj0Tw9PZ2dXdgcLuQ12zs4yuXyS9YX8ZWVpcVFly9fWVhY3GhSvV5vZ2eHueT/FO5BECQpKen69et0Op3FYtHp9L6+PpVKNbphGxkZGRoaksvltZaNwWAMDAzcu8/g4KBYLK6oqKitrWUwGIODgxuOsT4cGRkRCARUKpXBYHR1dW06CNwJ7kMgEGg0Wk9Pz/32qa+vp1AoW54wPI5arW5rayORSPeeBnxGpVJ1d3eTSCSihfSmVqvv3VOlUvX09FAoFAKBIBaL77dPR0cHnU6nUqkSieR++8hkssrKSqhAeK+RR0dHVSqVVCqtq6uj0WhMJvNeI2P7cDic2NjYffv2iUQi7Lu2cbCwsBASEuLi4sJgMDgcDp1Ov/doIyMjsCcSnU5ns9lwyjZZe3h4eGhoqKWlpbi4mMViMZnM4eHhTfuMjo6OjIw0NDTQ6XQmk9nf33/vDqOjo8PDww0NDSQS6X5rY3R0dGhoiMlkwssfGhq6dzpGR0fHxsb4fD6dTt/SztBECoUCamzKZLItd1OpVEqlkkwmE4lEhUJxv32kUimDwaBaSHtb7jMyMiKRSMrLyx9wzsPDw/A4dDq9trZ2eHj43usaGhqSSCRsNjsoKOjAgQNYsmLjnH6X4+8a98TGxp48efLUqVPe3t7fUK+Zzaa+807itWvo1BTgNdfUIOHhIDXw3HPAMVsoKetY57/+C7VAn9mf/Wzk179u+d3vCixwJ2nbNvahQ+qrV0GAx8cHjYlBsrNBjoxMBqxeJhOhMwD/FI9fCwriZGYOVlWB9I2LC9BEsZR0ASf96KOwW3iEpXHmxsIu4LwTEpCC/JQLFy5kZKR/CmqObglaBIcOfZiekTE4CEqHUBQNDw0pr6gMDQl2c3c7dfr0tatXsm7ljo6O+Pn5REZGJSUlq1Uqm+tfXrh4kcGsvXLlMo/fXEOoLi2rwNwJ9LJra2ve3t4Y7rl06ZK7u3uKZYM3PZuc9KaHljr2PC+v0pMnAe751a8AyebVVwEWgX9QXnnHDvStt9CjRwH4c3KavnJl8IsveIcO5Rw6FGltnRodwyOR9R2dZoMBnZowzxoWl3TTq7px4JzValSrQXVaRGcwjEyXlzMlEj2dDtjNcXHANx89Cqz63HMQ9ExZWtxDevg6LIAqNSUliwnxwZcuXcnKzDx27KOSklIqhfzx8Y/z8vK1uvVGQk6OjiQyxd3NzdHB4dqX106fOlVDJPeCbg+u3l6eZRUVZrM5JzubRmNMTEwkJyUFBgZGRUW+8867vX2gEh7DPTMzM87OzhhJBfsGqlSq+1UdbrSqpZ4r+bHHaqytwVEhs37HDlDg9pOfAHKPlRXy8cdoRsZ6+9W//c186NDU3/428PLL9X/6U9bTT0e+/HLG558329jo3dyQkBC0oAAgHiIREYuRkREQOTPoTFOahQXdzNSQmlpaNtHdvTi+oNGCiFpLC1jMEREgRXv4MLp9u/7Xvw743vcGMRwJwRbAPTWzkZH+165dS7x588SJk5VVVTWE6o+OHSsuKTVYWMmrKyt2tnZ1dXUe7u4ODg7XbWxOnjxJYzAlErGbq2tAQCAe8OLXEm/ebBYIdVptWGhIQmJSi6D50qXLMHiJmdRoNLq4uGA3f/9B3BMTE+Pk5MTn8xcWFtbW1hYXF2ct2/z8/NzcHBzfFq6cn58fHR2dm5uDYnTw+Xv/HRsbW7Zs976EHWpoaGhlZWV+fv4B+wwPDy8vL99vH3g+Wq12dXX1fvvMzs5OTExotdoH7DA/Pz8zMzM6Orq0tHS/k/km+ywsLIxbNsx69x5tcXHRYDCMj49DkcB7d5idnV1aWtJatgfvo1arYfwAah9j30dsMDs76+Pj4+XlJbNoccFphbO5sLCwcVoXFhZUKhVU28Oe33hu8/Pzk5OTKpVqZWVleXl540vYeG5ubmpqCk7rlgeZnZ2Fz8PjYG/EBvDV+fn56elpnU4H1RqxVzcO5ubmNBqNVqu93wfNzs5CU2s0mgdPh9FoNBgMD95Hr9fPzMwsLCxsPIeNYzhlk5OT9zvnubm5paUljUZjNBphp8iNb4fjubm5xcXF0dHRlJQUGxubB4QwsSn+Xx1817jH19fXxcXl7Nmzbm5usDp9o/+4d4yiaF0d9Y9/THJyAprCzs7IY48BKbynn0bfeAM9fBggnocfXrWymrSyknzve9UPP5zxi1/EPPXUzRdeKN+2rWn7dsNbb6EXLwKZEz8/gE6Ki0Gte1MT0tmFDg2j6jHwp9WiGg3a0DAfFyfD4Vaqq9GQEOChIe7Zswd007S0ts61sire5E6gU4mLQ1ZWQFZFq9H4+HjfuBFCZzB8fbyDgoLq6xvm5uYWFhYqykq/vH49IzOrVShwcnIKDg7m8ZsaGhq4HJaLs0traxuDwchIT4+KiiosKDhx8iSDwSSSiLB2CbMMzHNt7M918eJFiHsSExO/Ie4pLs5PSys7dQp94QVAT37tNaDKCPs6WQbIW28tv/POxJEjrR99hHvnnbS3344+fjzZyQkfFyckkqZa29DhUXTSiM6NLy3rJsz9A2h7O9omRBobQJVdXR1Sz0XFYlStNvb18xskff0mCwMX8fdHrl4F8aRdu5A7WoWJlmZnd+HIF14ASbeSErNFynV2cBCwvsIjIigUqre3V2hoqFDYBr/86Wkptra2pWXltbUMNze3GyEhQqGQz+fXVFd7+/g2NzUJBILKSrxKPdbM5/n4eNexWCnJSVlZ2Wtr6x2FIJS8/eV0cXHBcM/q6iqPx7Ozs/vkk09gOgOz/5YDFEXj41N27CD296PLywD3/OAH6AsvAImEH/wAffRR9MMPUaVyeWpKv2ePwMqq/JFHkn//++jnnkt9/nnCSy+JduyYvnYNZA9dXdGICJC3rawEWa3+AUSnR4za5UXN5JrGEuqZMBi7OtobG1Dt2JLGqNGio6MgIHTrFiiMd3ZG33vP9Ic/RP34x433rlJLnssMftjn5tqVChdX19i4OCqF4uriEh4eLpcr4I/yzYQEOzt7Qg2RTqOApvQREYIWAZ/PLystCQwKEoKtFVeBMxjGWXV1Xt7eLA47OjoqKTkZ0tegfbbuz3WfPhVbmhQLpP3Pf/7Ulq2np2d1dXViYgJLiAwNDUF98//5R3ztETCzPGBPKC68atlWVlaglPOWSr4POMj/P17SaDRdXV2jo6Mb24xvvDSz2Tw0NKTT6fr7+xEEGRkZ6ezshGHvvr6+LSkmG9/+bxx/7ULdOK1wTtfWQK7533gO/1851OjoaG9vL4Sh/9lz/q5xD5VKdbZsHh4eMP99v1897JeCRqM+8wyo/ZLJ0F/8AsQeAgKGdu+WPvJIvZUV6aGH8p9+Ou6xx+J/8Yvc//5v2j//2blr18xrr5neeAP95BOAeGxsgLvNyEBKShAaHZFIkeERy92zbmlGOz+rmZ3TLfT36PlNPePjCI+HWspkAN3H2RnwMyxsCeSpp1ArK+6mNpl3klzgbn4jrxmqj2Ny4AP9/Xl5eQUFhTCVAL8nZrMZ/qJBgXbY5RokdBEECK6bAXcbvoSFJTCD3N4hICAAi/cwmcyOjo6enp6NGsQPsCqKosXF+du31+zaBSi0u3aZX3nF+OqrAy+/LH7hBc6LL9Zs356zY0fM22/HHzyYf+VKXWhoX17eLINplitQYDo9Mm+YX1bpzXIFwmGDuERaGiBGxcSs/8XGoqmp2tTUgYICdGR4xbg4NIxSqaC6280NkNAt9fDor36FWllVWlll3uuhoWFLStZTSNACq6urFpMCjXilXHYrO6usrGx2dhb73TGZTCZL31dLXhwYEDMydgRLqbYJvgUzERTZc3d3x3CPnZ3djh07Dh8+jMfjN9kfe9fGAYqiUVHpb77JKCsDNJ3PPjO99NLEBx/0X7woeuQRtpUV4emnsz/7LPavf0149tnCl15ib98+aGmnZd65E33/ffTMGdTBAay3xESQbyUSEUELoh5Dx3WmBc20WadDdVpUp1XJZENiMaD2jI+jmrFlzYROh3R1gcxsdDR4u709+sILhc8+W/iTn9yFI6E97/B71n9wzGbzHZOCJSeViG/dulVRgVtZWUWQ9R/lNWBRYEe4VuGSxjwx1gQArNi7N9inwt3dHcvAPiDeg/0CwmNgD/+NA7PZPDg4uLa29EVNXAAAIABJREFUlpaWxmazuVyuUCjE4XDl5eX/xk8BAkgrKwaDobW1VSQStba2MpnM0tJSIpHY0NDQ1NREo9Fyc3NramoaGxu7u7sVCoVcLheLxTweLy8vz8/PLz09PTs7u7KysqCgICwsLCoqKiIiIjY2lkql9vf3Dw4ODgwMbElA+bZXAWMby8vLJpNJq9W2t7ePjY2NjIxs4pTAjmnw4HC6EUtP+2/7cd92f7VavSW1/N7jjIyMGI3GmJiY4OBgb2/vnp6e/Px8uqX12707/8vPgMJbjUYoFMJppVAoZWVlJBIJ9kEjkUj5+fkkEqmpqQlOq0wmE4lE9fX16enpgYGBGRkZ2dnZVVVVubm5ISEhMTExYWFh8fHxdXV1sORlYGAA+6b8yycJ+2YsLy+vrKyYTCaVStXZ2alWq1UqFfadhQffCLywnhub9vmXT+MBbxweHv7O7jQecBrod1/HPjExERAQcO7cufj4+PuRJzb+hMKGus8+a52QkBcQkPLmm3GBgbH79iXZ2GT+5S+lVlaMH/xA8vjj6ueeW4Lybrt2gbjF4cPotWvAE3h5AWmfsjIQ45FI0JFRdEK/Oq+dWdUaEK0W1Y4BjzJuaKghNjXKjEZUJAJcn+pqgHtcXECTAUtbTfTxx/usrAKtrLZok4nFe+4YGpmYmID5EaPRODo6OjM7C9fWwsKCWq2en59fW1vVajXj4xOQHLK2Bm5DoTNeWFiYsTSPmBgfh7gHNpHBbAL1e0JDQzHcw+FwPv300xMnTtjb28O0ArbzlgNLf66CJ55wfPHF3KeeSn7uufhXX4176aXkt9/OOnCg4vjxWmtrWXCwJiZmJT8fdMXi8dGePlSrNi1NzK2NqJH2dpRBR3NzkZAbgLv7+ecgvvHOO4BS++67oMPFJ5+YL12q+/zzgZAQlMMxq9UT+rVmAVpSAvjjV6+uy9L85S9tVlY3oEThRgT55JMgN/Tb30L1bWBUk8lkMBhGR1Vzc/Pj4+MqlWp+fh6adG52Vq1WW4LYy2NqNcYmWV5exsYwBA0p3qurqwsLi/AlzDiwU4+vry+s9EEQpL6+3tnZ+fXXX3d3d7+3UyP2RmyAomhqarKTk9vOnblWVklvvhl37lz8D3+Y/MknWWFhFQ89VGdlpXj4Yd3zz6/9859gfcIeHbt2oceOIXZ2IFTj6goWKh4PChV7elC9HpnSLq3pJlCtBunvR1taVmtr6WFhY2VlqFSKqNWIVruq0Rv0pvYOsGL9/YHywrFj9U8/HbVz5+qPfvQ1uMdkWtPr9SqVen5+wWAwqFQquMxMJtPMzIx6bGwZxPyX1Gr19PQ0XNhLi4tTU+tjk8m0ZNngup2bnR0fH9+IflAUvd106bb8JvZrfi/uYdTWwiOPqVWeHh41RBJoZzg6EhQY6OziIr2jcn7na/U/+h+GByYmJpydnbu6unx8fI4cOXJbPywwMPBfPu6cZbstJKHRaNRqdXd3t0QicXZ2TklJcXV1/cc//rF3796TJ0+6ubl5enpmZmbm5uZmZGS4ubm5uroWFRVRLBuHwyESiXQ6ncFgVFdXNzY2Njc3t7W1CYVCSBIiEokWYn7yyZMnb9y4wWazt+RTw6tQKBRMJrO5ubmwsNDb27u6uprL5Wo0mqmpKZ1OB78ycE+pVFpQUFBVVXW7p3dgYOC5c+dgY10sL3mbPDc1NTU2NoYtABRF8Xi8n59fSkpKZWXl4OCgXq//X4pbqNXqbwjvVCpVc3NzfHx8YmKiu7u7jY0NgUCIjY2Fl/lt/0UQBCZ9YJ3X6Ohod3c3n8+3t7fPyMiwtbXdtm3b22+/febMGXd3dx8fn8zMzFu3bqWnpzs6Onp5eZWWllKpVBqNxmKxiEQik8mkUqlEIvE211MgEIhEIoFAQKFQ4FxnZ2cnJCR89tlnsbGxXC53yzJDeP6tra21tbV8Pj8zM9Pf359EIjU2No6PjxuNRux7B38nW1pacnNzKRQKh8Px8vK6cOFCXl4ei8XaOIlGo1GtVmP4w2Qy5eXlBQUFpaam0mi0oaGh8fFx7Gby2xrwwfsPDw9vWRH24Hf9b7z6Xcd7oqOjIyIiBgYGXF1dYWkA5jy2HFj6klL27r1GpUo0mr6oqPFLl9b8/ADv5Fe/WmeGbt8OyKGQiHPgAPCsTk6IpyfgO+flAeKOSAwCFRP61VndnElnANQTzRiiUqEajbG3d2V0FNFq5vULej0qkQCqBB6PBASAgxw4ABqJ79w5++ijgVagnfUW7gT6bCgsJJNKaTTa5UvWgYFBtylyF86f8/X1a7tDzctITzt37ry/fwCZRDx37ryNra1IJJmdmamqxJ09e1YklqIIEhke7urmTqVSrC9aV1UTiDXVvn4BlsbjX5FRTCbTRn6Pra0t7JDg4eFBodxVqXQ/k+bm5r7zjufp0/IjRwauXjXGxpoh19vdHQ0PR9PTAReHTEbFEuBh9WOrC4ZZk0qNSkQooRqEttzcANx5/XVQU/f446Ba6dFH1/8ef3z8xRdXX38dNIvy90fKytDe7tnxJZkcaPnAKNqHH6Kvvqr9/e99H3545F6Tvv02CAj95jeA2oKia8ABUMhnTp8JDQ0j1hAuXrgYfONGZ1c3/DIEBwVdvHgxOjqmsCDf2tra3sGhu7vbUoOQdeny5f7+geXlJW9vbx8fv7XVVT8fbwqVFhEWGhoWvrB4Fwn3Xn4PgiCdnZ2Ojo4SieRrQz4oiiYkJEREBCYmKp97bvCHP5zcscP85JMA4uzbB1j2Tz8NBvv3o3v2gBzfzp2AUHX4MExOId7ewKhVVUhTM6pWI1OGlRXdJKoaQZUKhMs11dRM5OaulZYieDxaXQ1gDpeLjI2ZtRqjbrmzC3SmCwhAr10bev55/+3bDbt3o3d3nVtnTf3gB4ilsGO5ic/D43EXLlyIjYsnEKrPnjkTFh42YGGhmU0mX29va+tLiUnJ+bk5l6wvOTu7DI+MGo0TyYk3r335pUoFSgtjY6KzsnLquWzrS5eamprs7WyjomOXl79qoA3zXJv5PV98gbq6Ik5OqKsr5eOPC4qK1kwg23gzIc7Nze3CxYvjxsmlxUUalXL06NEBC/H83/WTB3GP0Wj08PBgMBgnT548fvw4lUq9cePGt/qImZkZpVJJp9MrKytjY2Ozs7NnZmbi4uI8PDyysrIyMjK++OILf39/Z2fno0ePHjx40M/PLzU11d/fn0Ag5OXlOTk5hYSEwMCAUChUKpWyDZvCou4hubPBVxQKRW9vb3p6+qFDh+6tMtt08iKRKDg4+IMPPkhNTXV3d4+Li+vu7tbpdHNzc8vLy/DXAL7FbDYvLCxA8IogCGT/TE1NDQ4OwoZ0sLQKCzlDIKtWqzkcDg6HIxAIYWFhAQEB8fHx0JndLyEF8/JLS0smkwlrwgpCsybTptjSxmv5VrhHKBTGx8cnJwM9UltbWwKBkJCQsPFoXzuenJyUyWRUKhWPx0dGRpaWlhoMhtDQUG9v74KCgtjY2BMnTvj5+Tk5OR06dOjo0aNBQUEpKSl+fn5UKjU1NdXFxSUiIoLBYEDMunFa5ZZNatnEYrFEIoHTqlQq+/r6IiLCjh8/DsspHnCSt7vTe3t7Hz58ODs728nJKQNwRgHovK0yv7KyshGjwHatkMyEIMji4uLMzMzU1BSkmcOPmJsD/Vkx1zAzM9Pf389ms6urq8vLy29/IwIDA1NTU8fGxmZmZh4gkbBi2cxmM8zJwjOBs3y/a/m/i3s8PDx8fHyqqqouX778Dfk9DAYdj6+0xDnQyEj05z9Hf/YzIAH34ovAhcB6qx07gFzKpUtQfQeJjETy8wF8qW9A+gfQCf3agn4WIJ7REbSzA+Hz0dpatLZWVVKCDw1dHBpCddp5zbRej4rFAPeUlAAlXDs75M030d27TX/4Q6KVFf1eD41FKX77WzQ3F02ICz156jSXyzly+FBYeLhA2PL+gfdCQsP6+teLriPCQlmc+rjYaHt7+08///z06dNEMqWpqcnbyyM1NS0xKXlcr3NxdQ0JuREUHHT06JGU1DQcrtzW3gG6B+xnC0GQ290wsHjPtWvXTp06ZWdnt3//fgiAsDW95QBF0cLC4ueeqz9+HNS1eXsDBreHB6i+vnEDXDuBAIwgkSB6A6LRIstaI6pWIRw2kI4JCQEqMceOAbv/5jfrHvWOIVArq24rK+LvfmfauRP99FNQzF1ainZ3zo0vKjsA7gkLA00VjhxZeuqpsG3bBD//+dY48qGHQGMpAmHFx9vu6jUbBoP57rv7k5KSaVTKwYMfxMTE6nTrfUndXF1bWoTeXl52Njbnz58/cfIEm8NtaKh3c/e4ERxEJJM7O9o9PT1DQ0Ni4+Le278/ISH+0OHDgUFB4xOgLQNm0pWVlZCQEOyWa3Jysqam5syZM6dPn8aCQFsaEztCXl4Ol9t84ADANy+9hD78MFifO3eCMsBt20CrrOvXkU8+AYO9e4G64PHjQHXT0xO5eRPYnEpDxBLUoDUtGWaQwUGgfUQigRL2lBSZiwvt/HlzcDAaHIwkJADcU1UFWtPpdFO6xe4etKIC9fKa2707cOdOmSWStKnb7vos/fBhlEJZdHK86uziWlWFP3z4cFZWNplEfG///qTkFMhrXllednZ0UigU3p4eNtdtLlpfOnnqFL+pmUohe3l5RkRE1hDJKIqGhoaEhUeaTSYvD/ec3Fx3NzdvHz8YDcIMsri4GBIS8pVuYWlpm5cXmpWFpKait25R7O0vXbkC6/ICA/wqcLhLly9B7FVRVpKennm/n85/7XmIe0wm061bt2pra3k8nkgkKisrIxAI3/yA09PTFAolOTn56tWrJ0+eDA4Ojo6OzsvLKy0tvXnzpr29fXh4uIODQ2Rk5OXLl/38/Pz9/Z2cnLCg1/Lycn19PZPJVCgUnZ2dLS0tRUVFIpGovb1dfJ8N+ktBs+DYkSMfHfsI5mEffMJra2tqlWp2ZgY6pAdgC3ichYUFGo1WVFRUXV0NLQMTgjB+gKLo4OCgTCYjk8m1tbVNTU0UCiUjI4NEItFotKamJugsg4ODb926NTMzA0P4s7OzMGgNg9MikYhEIsXExFRWViqVSjab/bWBorGxsW+Y5wIB9ZmZsLAwpVLJZDK7u7thZdaDrbTx1fHxcQiVzp8/f+bMmdDQ0OjoaCh6FBMT4+joGBIS4uTkFBYWduXKlYCAAD8/P3t7+9DQUBgymZ+fr6urY7FY7e3tnZ2dDQ0NxcXFMplMoVDcZ1bFcFr5fO67775/+fL5LZWHNp4hiFWbTKNa7dzCApzWrw2zTU1NEYnEoqIiAoEAY0WYNBfMVvf29ioUCgKBwGazeTwegUDIzs6GtXhNTU2VlZVlZWU+Pj44HA5DP9PT07OW3AWKoktLS83NzdXV1dHR0RQKRS6XczicGcuq23TmGx+OjIz8H433NDQ0BAYGBgcHBwYGwhrFB7gTiGQpFOorrySw2aCthLU1yKVs3w7oyVeuQKYIaAkO8wUuLiCTkpkJ/AK3HlUoUb3GNG+YN2n1aF8v0twEKKBVVcB/4/GrlZXzJSULRUWoSARxj1aH8vmAUpqVBW7ELQxc9MUXq3/72/SHHgIE6jv+HTiSjT2/jh1DxsbQzg5lQkJ8cXHxZ599mp6eyeFyjh49mpySggUnIsPDA4OC3d3cYmNjzpw9Z2tzPS0ja6B/ICoy/Pp1m5zcPGEL4Ds7OTkFBPhfvXIlMiq6r6/X08t7ZXV1o5NeW1vz8vLCcE9XV9etW7d4PF5FRQWsD/xak+bl5f3lL8V2dkAHr6QEXGxICMiVwGJ+SDEZHka0OnRGMwcUeAYHQR1ddjaI9Fy5AjJVf//7V9J7FtOYLfI7c1ZWS88+C8IaZ8+Cg9bUIEPDRv1qixBoTvr7g9akf/97zp//XL59O3pHBxLY89FHQQgJBioeeQSprERXVsxtrYK4uLiioqKPPvo4v6CAUFX16aefZd+6NTQ8Ar9Ojg4O4RERrq5ugUEB16/bnDt7trKa0NPT4+vrc/XK5aqq6nou197eztXVNeRGyPvvHXBycrJ3sL9y5QqcFMxJz8zMODk5YfweGxubHTt2fPLJJ9XV1ZjlH2BVFEWzs5PDwwnbtgGmznvvAazz2mugnuvAATDYtQus1Q8+APLWNjYAnXt4AEnDjAwAb5qakNFRZM4wbx7ToOI2pBKPJCWivr5r9vam8+dnjh9f/uQT9LPPAFBycADQp7oaKEIC3LPU04PicOaDB1MPHiS+/TaQm/rnP7foNAJ1C2k0s6iNFxsXm5mZcfLkyYoKHK6i/Pjx4/kFBSr1GLg1X121uX49JibG09MzICDA1s7u7NkzNUSyXCbz8vSws7OvqiaMjY2x2ay4+AQ+n+fl5RkeEVlSXGxtvY5aMJNOTEzctjaWN6kqL2+Li0PJZCAyTaVSAwPpDAacRDyu4uOPPnJ1c6fT6Z1d3X6+vvWN9xXQ2/gz+s3HCIIolcrx8fGZmRkM3arV6oWFBZPJNDs7OzU19YBbW/hBVCoVRjtCQ0OLiorKy8srKiqSkpJqa2vFYnFERIS3t3dgYGBVVRWTyYyLi7t69Wp5ebm7u3tCQkJdXZ1KpVq1fJGx0+ZyuS4uLiQSSSqV3usjpVJpU1OTp6fnewffP+PjJO9sxxgYKysrDwA0C+ZVkyWDvri4ODU5CTEQ9qEbBwaDISUlxcPDQ6lUwnMzm809PT0lJSUcDicnJ6e4uDg5OTkxMTE2Nra2tlYkEonFYhiXksvlEomktLQ0Ozsb8le4XC6DweByuTBDl5mZOTY2ptfrR0ZGmEymm5tbbm4uk8kUCARarXZxcRHWAW08H2w8NDT0DfNcCoVibm4O5r7h20dHR2FgaXZ2dnp6GrIesSNvGiwvL8P0YmFhYVhYWElJSVlZWUVFRUJCAp/P5/F4ISEhfn5+MEpHp9NDQkIcHByKi4tdXV2TkpL4fL5Go8HmBR6cQCB4eXkxmUyZTHbvtEKI4OBg889/Or3wQg2Z3IcgIOqJoujy8vJ9Ac3cHFpXZ5qfR1F0fnFxcm5uZnFxY6QHHgH+Ozo6Gh0d7efn193dDc8N9louKSmpq6vLysqCSD0pKem2WEBDQ0NbW5tEIlEoFDKZTC6Xt7W1FRYW3rp1KyUlJT8/v76+HooFUKlUMpmcn58/NTWl0WiGh4crKys9PDyKiopYLFZLSwusHYPBxY3ng437+voeoNaI7fYdDL7rPFdbW9uFCxfweHxAQACLxfpap2Lpx85wciK2tqInT4Kejvb2IJbj5gbunnfvBn7F3h54EX9/QAstKQZ6d4p2VK8zzY8vrmn1aLsSYbOAe09NRWJi0PBwc2go68qVdl9fFI8HUYjaWlQzNq+d0WhRNgvgnpgYQO45cwbdtk36zDMhL700//3v3wV6NgIgKytQCs5gABIugpinp6dra2urqqoVSiWrrq66uprH4zc0NPB4PGFLCw6HE0skE+MGAoFAp9N1Ol1nZ6dqdJRAIGi1WtAxQCrlcLnGiQkikTg4OLSwsNDe3m4yQd4z8ClwTWRkZGBB7/T09MOHD3t7e58+fbq9/avO7ffz0yiK5uWV7t/f7O6OenuD2JizM/DEfn4gzFNZCdCPVAragGg15lWNHqQF+/oA2zYpCdjl/HngyV96CWv9iloQDwOKGj32GPr3vyOHDoE98/JQsXhZPz0yAgBnejri4YEeP87+4x9j9+xZ++1v7zLpQw8B6aDHHgNvvXIFGRuDWk2o2Ww2TkxQKZTq6ur29o5aBqOaQGjk8TgcTnNzM5/Hw+Fw3UAEZbSyspLFYut0up6ent7eHhqNrlKpBgYGWlpaePwmFEWHhga1Wm2bUMitb4AMaMxJm83muLg4qCSGIIhCoYiPj3///fdv67lBhsr9jIkdoagov7GxzdYW3bYNQBxvb7AybWwARWzHDjA+dQrY2dI7HQkKAixvHA6h05HuXlSvMy/oZ1DVKEhg5ecjQUHo1asrhw6RX35Z8+qrQMJ5xw4A9vfuBdAnNBSgws4Ok84wrl/r7EK9vEj796dYNDYB4Hz++U1huPWHGK8ZSEHqdBSg7UHq7OykUak1NTVNTc1cLrepqYnP41XgcAMDQ0NDg3g8vr6+QaPR9vb1dXV21tbVjY2NDQwMGAyGgYHBvt5eAoGg0+kYDEZTswDoFt4xE8xuxMbGYnftAPdERwOBRTweJZGo/v7UO3Jqa2urHDZbpR4bHBzUanXDI8Nzc+D3/d+7NTY2yuVy7Jhms3l2dlahULDZbBaLRaPRuBwuX9AslkkUckV7ezuUaVEoFCwWi0wm8/l8imUTCoU+Pj7Ozs45OTkcDgePxxcWFqakpDCZzKSkpODgYNBBpbLyxo0bnp6eSUlJ7u7uXl5eMTExsbGxrq6uMBMNT+M228zd3X3Pnj1lZWVKpRL6SJgKkUqlbW1txcXFnxw/fj3cP0xE65hfj3FCzvjCAhCTvHdTzU/l9QiI0qaiwqKMlLSY9JSbaam48vL29na9Xg9BAOT7T0xM9Pb2lpSUMBiMhoaGxsbG0dFROp1eXFzM4/GioqJCQkKysrKqqqoSEhK6urqkUqlEItnkyEUikVwub29vl8lkEomkra2tpaUFJnSgkg2LxeJwOAQCQSQS8fl8KpVKp9NpNBqPx6uvr6+rq2toaOjr6+vp6WloaGhtbe3o6Oju7qZQKF+b/YHXzmKxNuofrq2tTU5OisViNptdV1fHYDDquVyhoFWsbFcoFR0dHVDmRyaTQfINj8cjEokMBoPH4zk7O0MvzmazcThcQUFBSkoKi8WKjY0NCQkps2yBgYG+vr43b950cXHx9fWNjY2NiYlxcXG5fT+PzcXi4uKlS5f27dtHpVKxqI9EIoEGFIlEmZlphw+f+uMfiQ4OtJAQdG1t/a1Go3Fr8G0ymWJju2xsOq5cKS4pSU5IYB37qMjJqZJE6unuNhgM2LRaVF713d3dEItwOByBQKBSqWpqaiorK7lcbmRk5I0bNwos4suZmZm9vaCl0pbTqlCAb4FMJoNLsaWlRSKRiESimpoaJpMJvzVEIlEqlXK5XLpF2wmasaGhgclk8ni8wcHBnp4eLpcrEok6Ozu7urrIZPI3CW5hlvzfG3zXuOfGjRuJiYmvv/56cnIynGPMc8CLxFw7ds2W2PK6XnNAAIj3fP450OM/fRq5dg0IDQcEgEhERQUo15IpUK3GPGdYAHW/3V0oqw7JzQVMH0trbFDfdfbsypkzHadOLTk6okFBgPNMJqOjI3O6WfUYSqOB4wQEgFYVH3+sfeIJ3507VS++eJeH3gR64MOoKIhI1nEJdvIWeXt9fT23vr4Bo4Vir957sdhLGwd3vAn4H3oUDw8PLN7j7OxMJpP37dsHn9mw8/ox7v2UwsK8Y8eKPDzQ6GgkMBCAHkdHgBoJBAB68HhQJQSkljWmNY0e0WkRPh88m5wMQkPnz4PwxZ2oAoyDLVgyXKYf/Qj561+BGLOdHQBJ7DrzmGZCvypsRQsLET8/9PLl3j/9yX/Xrsk//QkqLW3hoWEDcxIJqCtBWLzRFHCsVo2y2awmftPi4r/Y+mODlcCnTN+t14wgSHNzc1FREdYlAO5/75lgz6SmAr3mN94APLOTJwE5CQpev/oq6GLm6goWIGyEEh0NYo7V1aDef3AIgJ4l/RQ6MgwkALKzEV9foNPwzjtzO3Z0bdtmfuUVkC3DZLO/+AJE0YhEdHhoVjev1aH5+fL9+4OdnWc//RSgo717QU+MLZfoww+vo3PsnDcORkeG2WxOc7Ng083rxn0ePN5k0vHxcUdHx7viPVvhnnsXJ3Yv9OCP+7av1tfXwy4TXV1dU1NTRqMRh8MVgpa9LPjTL5JJ+Qx2I57appA2NzdzOBwWi4XD4YqLi+vq6pqammBGoKWlhcViBQUFxcXFhYSEnDhxApJqU1NTz5075+3tTaVSS0tLc3Jy4uLiUlJS2traysrK4PPl5eVOTk75+fl8Ph9eOI/H++tf/5qRkdHR0QExhFAoZLPZdDqdQqEIBILOzs6+ocGGoc6p+blJoxGjo97v8vMqyz6L88tj1nDqWMxGbkwTOb2JTmXQb2XfYjAYbDa7tbWVwWBAcFZTU4PD4WpraysrK/F4PI/Ho9PpRUVFOBwuMTExJyenpKREqVTe6xc3oZ+ND7GdZTKFRAKgEkRy0PHL5XKIkCA2EggEkHzNZrNrampYLJZQKGxpaaHT6d8w3sNkMmss9XE9PT2zs7M6na60tLSwsLC+vh58sFgskSmJ+FR5eKBIJGmyTGttbS0OhystLeVwOHw+n81m8/n8Fos4p7+//82bN318fM6cOZNo2ZKSks6cOXPjxg0SiQSDWwkJCVlZWVKpNDc319fXl81m5+TkQMCEKXQTicRt27ZVVFRAOKtQKJqbm1ksFtWyCYXC/v7uigrt9etGuXzJaAQdG+83oeDrsLYmunDh5u9/LzxzhtPUPOzoNhocojt9jpWbf6ugoK6ujs1mQ0QCY5BkMrmsrIzFYpWXl0PjUCgUGMpKSUnJycmprKxsb2+HOGbj3GFjbBKxZ9YHEomku1ssk8F5hVMplUohaQm+SyKR8Pl8AoFQX1/PZrMJBAKXy21tbRUIBAwG4/9onisgIODixYtvvPFGXFwclpQB4ECvi4wIF7a2oihKItZkZd+SSqWRkZFDwyMsVh2NRpmZQdPTQaZg926QQvH2BuyRkBCgXFJWBn7QxRJUrTLPGhbWdONoTzfKZgNW8w1LzdEXXyAWTunE9u3Sl19Gdu9G33kHPX8e3IAnJCA1NWh/75x+fmgYpVCAfLPlphzdsSPthRe4r7+OWhT2NvuSRx4BqRkrKxD1eP55JCnJbDIBH7yyvMxmsUgkUm9vLwd8mYkjli6YKIr29vZUlJcrlcqBgQEgbEqhTs/MwqYKvb2+pdpAAAAgAElEQVR9lnWPdHV1Tk1Pd7QrRSLx3NxcXV1dT28v5gkwJ5GZmYnhHicnp7feeuu1116ztrbGoA9IzA/0W9qJ94DC9aLC4pLSFoEgJiZ2fGK8rKzi1Cmhnx9oJuruvu6PLZYA8Z7KShDf0W7EPfX14Nm0NODAL18G8Z7du5FnnkGtrNRYS3oIet5+G/nyS0DTJZORocEZw1JXN+ixEBEBoOTOndHbtklfe20z8fZ73/uqb8NvfwtCUE8+iRQUmFAUmHR+fp5GpVEolL6+3rraWjKZjOkWSqUSHK6ir7+/o70dh8MxmExQmru2xuFwYOJmTK1iMBgGg6G5uamjs1Ov07FYrElLXRLmp6F54+PjMeX4lJSUzz//3N7e/tKlS5ChAi3f2aEMCQkdHhlBzOZb2Vn4yqr6ek5cXNz8wnxeXtH+/eI9e9CdO0FU8pNPQLZr3z7k3DmwyiDi8fICS7KiAsFVAOaZegwx6lZW9JNodxcIiOXlId7e6OnTgzt29Pz1r+hTT6GPPQaSibCh1+7dAACdPAlIUhy2SaMf15mFrauff37Dzq7b3R19/33w+t69WKvXr1bs++8jn30GMrMUyhpiMenU1CSNBu68+/sH6HQamUweH5+Av7xtra14PH5waEguk+LxeDabY0aQ5aUlNput1elhWZxSoVhcXJyfnYX9YkkkEhTnxEyKIEBEID4+/mvjPRvfsmn8AE/wrV6CFfscDodGo5WXl/v7+0McU1paGhYWVl5eDlI2Mmkrl1f9jgN+x2UBsVaqALe5UqlULpfDtA7mvLFBW1tbc3Mzm81WKBTQhQgEAkFzM5fDyc7ODgwMvHDhQllZ2fz8fENDQ1paGoFAaG5uDgwM/PLLL4uKiuCKUqlUR44cycnJ6ezsVCgUFAoFhlj6+/uxTBafx7t44QIOh6uwbPfzkfPz82QyOTE+obGlSSqVkmtqCspLg2sKs9tYUqm0vKw8LS0NJqQoFAqTyWxqasKCENB1wettamoqLy+vr6+XyWStra2bPd83eywSS2QSEPgRiTeHiLADQEsqlUqYYYG+E0onFxUVTUxMbEoLbpr01dVVk8lEpVJZLBasSBIIBFwuF/bKIJFIAGNJFaWsnLCiL2/Zv9aRHC/t6NxyWmEkBgtZ8fh8LpcLQzUikai5uVkoELLqWBkZGT4+PhcvXqTRaIuLi3V1dSkpKXQ6vb6+3sfH5/r161jXiK6uroMHD0J4oVAoKisrs7OziUTi6Ogodl9BJhOtra+RSNUlJSUkEul+GS7j9HQ5Hp+RlDSYmSkVCqsp9OwblwWn9jU4f85vZhcVl6SnpaWnp8MyrtraWoFAgE0rjNbAHFZDQ0NFRQWfz//Xp9US7etNTZU2NIi2SuHBmd1yWnk8XkFBQUVFxW3ByQdP66ZZ/l96+F3He2CLpYKCgri4OBh2ht//qkqch7t7VHSM0Tjp4eHh6urW2NDo6+NNJJF5PK6v781r1+YOHkRtbYEXua1eGB0NqCOVleDWua4OdEIwGlaX9ZPo0CCgS1RWguCEnR0g/rz+OvLii6Ar6U9+Iv7RjzoeeQR95hlQTnO7pfu1ayDlUFWJ9nTPGRZ6+1AiEZB7XFxQe/vx558P2rlzfu9e9IknvnIh2J30T3+63mbyoYeQbdvQ/Hwzobq0sKhILpUe+vDDgMAgVh3r/fcP+Pn5y+5E18PDQj08vGxtbchkyqmTX0RFx4rEYjqNdsna2tXNbWV1ta+3+7333mOxOW6uLra29pkZGfv37+fWgwgq5hJQFL1NUAgMDMQ6ycFvpkgkolAosBYDmjQ355aXl3dKappqdMTd3c3L20cobLO3sxO2tpaX4196qfjSpSUXF9CCHv4FB6/HIXA4kOfC4j2oXoe0toKsTEEBSCg6OICY265doBr7ueea/+u/+h9/HH3ySRABeuMN0IQiMhKpqkLblUuG2ZFRAEmzs0GV9dmzQ3/4Q8Teveif/ww7inxl1e9/f50kvWsXiOQ9/jjy4YeoTLZYWJBVWVnd3Ny8b987kVFRVZX4Qx9+GBIailHFHe0d/PwDbGxsGAzGBx+8n3XrlrBVSKiusr5oHRgUjKIoh81ydnaKiY1LSrxpZ2+fmZ7+wQcfEEl3Vb3B9ilY95mFhYWzZ8/C4Lm7uztWImepPIr39HAvLCoZHRl2cXZy93AXtrZevXy5u6cnI6PwD38o3bVrZfdu9NVXAfaLjAQLMDwcAHRHRxBXy80FFiYQQPhsaAid1i6adZY8LJEIsoBeXugXX5j37Gl88skRC6Fs3UDf/z7yj38AoLl3LyCd3byJilqXDdOjo6Dr2cGDMV5eoIvInj3rcNTS/eMr21pZgRTw2bNAHZtQPVuQn06mUGk06sGDBxOTknEV5Qfe2x8ZFT0yMmoJJa5++eWXQUFBDo6OHDb74Acf5OYXAhpHSfG5c2cjo2OASVl17767X6PVJiUmXLtuy+fzjx45TLO0BMFWKYIgvb29GKUXRdH75bk2vmXT+N/yk2c2m8vLyxMTE8VisdlsXl5eHhgYEIlERqMRBv+bm5uBg1TIWxncoh0XC1+73FzLFVvuZ6EnhqQWzFtjP+4ShQze58LQhUQiUSrkPH5TXHy8sKUF3PZwOIWFhY2NjbGxsUQiMTc3t6yszNPTMzk5eeOlQW5QfX09l8sNDQ3FZBWxfVZXV2EuCaq8QEw5NDQE5ZdgABiPw4WFheXm5oJkU2trLZOJJxFiavEejbhwAZklEshlMhqNRqfRxGIxuF5LWgq7KIn4qwyWRCKRy+WQcnTf+37snVsNRGKJQtps6C4dUNLFFkoQZiIs/COVSvl8PpPJ5HA4paWlfD5fLBbDoAgej8/KysLj8QkJCfcT9l1YWMjJyUlPT4eNwObn52HKZmpqCofDVVdXC4VCmUymVHYkkUJzLm/3KLxSz6iSSACchWhgqxMXS6RSpbxdLldgly+RSDoUnXgWPjI+UqlQdvd0Q00mLpcbFxdHJpNhVzIXFxfYfgCbNQ6HExMTw+PxmExmeHg4RvDHdlhcXJTJJB0dnYODg1BZY8VsVnV2oKY1k0XVcGVlJTc/P87LK7ewUCqX88ViLqchiZhwhPiRdfKbYYzg1Ia0Dlk7iUKptcQs4ax9NWUSSZtSKbIIRInFgEwNKVlwLgAkVSrFWxHLtrQM2F8mGw4MHHNwGLOzkwgEIst74YRunNbGxkYYWSwtLRUIBHBaa2try8rK8vLycDhcfHz8xtQkZpDvcvBd456CggI2m00ikahUKmw5C500hUS8fv3LyKhouVzu7ubm4OBAJpMDAwPyCwp5vPoTJy55eo55eAAn7ewMggclJcCF4HCAETGqQie1S2adAYAeGg242aAgkADbtw95+WXkl79EH3pIb2W1YMEs6ymrp58GyZqTJ0FOC49Hu7vmDIudXaBMOD4ecXNDT54UPvtsxp49gCh6ryPB0A8k9xw4AI7Ba2QdOnToZmLi5599npaeLpaIDx86lJKaNjqqgjMaFRHewGsKDQmm0hmJN+Obha2TRmNxUWFISIiXp+f4xOTqynJIcFBJcckNy5aamhobExMSGrap46NMJjt9+jTW4iQoKAj2Cs7Ozr5NGIUgyRLjKbC1tUtJTWsRCJydnFzd3Oh0pqe7G7O2tqIC95vfuDs7T7m6oqmpACK6uIAQGrRqRQWwqlYL+okuaYyA39PRARIz5eVgJ0jx2bVL88orq6+9BsSeYVnd3r2gzbq/P6jhahGs6cYNepOwFa2oQIKDQfHy9u2UP/8Zt2MH+uMf3+WVoTF/+UsQrvjLXwAsGBhATCag4FiJr/jggw/S09KPffRxYVExk8n4+KOPbuXkTBhBNdZt3R0PNzeRSGxvbyeRSn19fXp6+8bGxpJuJqSlZ7i6ui4tLRv0Om9vr2aBsLOjw8HeITAo6OSpU4XFJZugZGNj45kzZ7DghK+vr4eHR2Zm5vHjx/v6QCgOrtLU1JTr16/n5gEGur29nYuray2z9nYlHb9ZkJub9fbbvkePLhw8CFqThoYCSzg7gwAZjPdkZoJLq6kBttTq4IrVoWNqhMEEVHw3N+T8ec2uXatPPolsBD2QkPyPf4DlevQoiM4VFKAd7bOGRWU76uxc9vHHRC8v9MgRwCLaswfUi2Gd3rFV+uMfg5Dk8eNoY6Oporzg0KHDiYmJJ06cxOErqRTy0SOHi0pKjJOTMFrp6gI6Lzo6OAwMDfv4+PT2D6jVqpiY6Js3E7x9fJdWVscNelcXVxKJdPD9A+cvXOzs6goLC9tYxA5tRafTz58/jwmH/Adxz61btzw8POrq6iBVa3p6Gir8xsbGFhQUcDicJouud3R8bLCzV6hPYDWphsfjUSgUGo3W2NgIMwKbnIFEKReQWW18gUQhg5hBIha3iMSeubQq8Trp3mw2r62ttbe3FxcXwzRKXl6eWCwuLS3dFO0fHBwkk8nBwcECgQAu7Af/u2Ja4/QrB436rvaO1ZUVo9H4/tHDweW3ahvr5XI5j8evKCurZtJsKTkXGgrd85O5bI5MKpXLAN75yi/euSSZWMIXCZvErdKtYjMikUQq+XZRH5FYopTcwT1iEXSKPB6Py+WyWCwGg0EikXA4nEgkUiqVmZmZ4eHhVCqVb9mgmK9UKi0uLnZ2dr5fn4q5ubn4+HhfX18+nw/FS+FK6+zsjIqKKi0tbWhoaG5ubmxovBEadinsjGeoK4VC5zU2wmnl8/lbTKtEomhpxecFcrmkdlmnVAJYyXKJvKKhzLPGNVuSsWACnCpYUSUSiUpLS4VCIYlEKi4uho3bNrFzuru7q6urg4KC7tfKdNMsm6USw6UvjTml/d29a2umMZ0u9O/bRj4+2F+QL+/sZHM41bjqUHqEXeRbMS47Lse/z2bRpR1dSmGLXCQSbSofk0hEcvlgVFRnWVnbvQWDEolIJuvOz5cIhQ+I3NxZIHf+l8lG/P3Vjo5jNjYSgUBiWUuNjY0wI0yj0UgkUkVFRWdnZ2tra3JycmRkJIPBgJJUsO2GSCTKzc11dHSEPNRNl/9dPvyucU9wcHBsbKynp2daWhpWxw76Bk9PFxYVtnd0tLe3C5qbSWRyf19/bm6uwTBOp9MjIpInJ9HiYuBO4uJANIdKBf/y+YAAq9UiK7oJdHAAxBYsyQJQNvPmm6DmyNJVS2tlhd8kkPfznwNH8dln4DYcj0f7+2YNi3IFEB++cQMEJ955J+vFF3l79gDOBOyvhHmRjYOf/Qxku17+GyqXmbhcZmdXt1ajvnTJOio6hkajXbpkHR4eLmgRwhhmRnral19e9/L2NoyPZ2aki6UypULB5XJtbWwio6It6lJTBfl5AoHwxo0gD08vCoXs6eGelX0LLgh4N4xadPx8fX2xPJednZ2bmxtsBUwiAfE36HgMBn1BQUH/wEB7ezuLVcdis9uVysLCorm5ucLCwuvXywcH0dhYkNdLSQFgxscH1FRDXjOJBCqMtFpkSrMA/lOpEAoFUHyio1FHR9TNrf/oUcJLL5lefx194w0Qh9i/H9jcwwO48Pp6ZEwzrV+SK0CGKzUVZLhOnzY/80zsK690/f73d5Glvvc9kJd54QUQzhgcBGBVKFxnNC8vLRGJNcMjo329vWfOnE1ISKiuqrpy9WpMTKxcroAmDQ4KtLO1CwwKnp6ejoyMGBgcbBEIOBz2lcuX0zMyeTxeeFjo4SNHaTRGRHi4f0BAgqUJQ2XVXVVaUJsE61MBa3eDg4MdHR2ZTCaMS0OTqlWjBQUFfX39nZ2ddDqtoZEnFomKiotXVpYTElKPHSPicGhSEqCTQf1lV1dgEiieWVUFkLpCAdQBdBrzsnYCHR4EJPyCAqAlcOFC1yuv0H7xC7Olw9xdwPCRR0AsDQZ7IiLQulrTmFavRVistY8+irh2TXX2LEDnr70GdoHt3zcu0SeeABz023pU09PomHqOQiGPjWnFolZra+u09HQ8Dn/J2vrmzZvdPT0IgiwvL/v6+Nja2oVHRi4tLUZFRg4MDjc3N9No1MuXL93KyWWz2ZOTU3HxcW1topxb2e4eniwWq+zuFnLQVlNTU3fp99yH17wpxrPx4b/ld9BsNicmJjo4ODQ2NkK3ZDabFxcXoabt6Cigw0dGRkZERNxWvTMYJxZBjyageDs5OXm7D+Xu3btvi8t1dHRsLLmSKGTNVXT8GzbUy2GtjU2iNhGII0jEHIHoWg7HNZfR1duPKYnL5fKqqiqRSCSTyUpLS/F4fEVFRUZGxr29FObn57EkyP2uHe6gWZy5ISB60YsI9XXLCLhF4A90eLJLvwz359axawg11dXVsjaRR3yEeyMugUNobW2VSiTlQm6xkC2548Xg/1KxpEnUmiigpQjoQrFo06tiELkR6LoqehWsu98HHj0ghyUSSeVSkOeyaNZIIHycnp6enJycmJjo7u4eHBxUKpVNTU14PJ5CocCkFZbrUavVWVlZ165du50Z3NIUc3NzQUFB7u7uEokEvstkMi0uLsLj9Pb2lpWVxcTEBAYGhtwImZmaXVm2zKtFzrS8vHzPnj15eXkboY9ELJZLFQUFPjHlrlElDuXcIo6QI5PIFRJFUl2CV5mHJ8FV3C1etDTfNZlMra2tBAIBZpRguXhxcXF+fv6mJA6CIPPz8/AbseWFwCdBmQWKosEBfduP0H/3aUxFXN/SELq8JD3llnSxqP/oIZ5AWF5WTmPQhNImJ5+3o0PeHz56UN7YpKDRjAcPGj77TFZfvxHBtLW3D0RGqm5r0F+92l5Tg0V94CSKZLLB8HDNtWujHh7itrYNwT6xXC5qb2+Du23+1xIT7E9MlNXXiy1hpIaGhq6uLjiter2+s7NzeHgYkpmKi4vZbPaaZcOmdWRkJDk5+dq1awqF4gHW+A5e+q5xT1xc3Ouvv75v374jR45gGYQHLws6nbp/f2JwMBoQgMTGggp2S1EIcBk6HVBdntPMoFotSG/h8WAPe3ugILxzJ/LkkyYrqxUrq2Urq8VNZeiPPw52OHECRIZqaswjI5OGFYEA0G8tUaXFbdtCX311cu9eUJ7z/e/f5YYwp/LjHyOwEvu2NItSCdYtxBx6vb6vr8/SE04/ODggkYiLiopKSkoHBgZHRobnLYUYsEXizMzM4uKiXq8HJZdTUysrK4uW1okz01MTRqPJZBoZGbn3TtpkMm3sz5WSknLixIlr165dtXQd3xTJ2HIN5efnEwhlbW3gen19AdSTSpH+fgACYVgChwMe2mBANBpkSWNRDW5tBeYtLzeFh686OCw6Oi7b2oImIDY2IKljZwdwU3o6QqGgfT0LBktxHBtMliXsgb77ruHpp0NffXXRAkS/sudvfgMAlVwOHLal1wcAPdgGT95kMmk0mr6+vqmpaa1WOzg4KBA05+fl4fGVarV6eHgYdoaam5szmUyTk5PLy0sazdjS0tLU1KTBYBgZGZmantbr9ZOTU2tra8PD62L8Gz9lY38us9mcnJzs4OBw7Nixt956C+YT4c5bGhM+mZycsm8f8cABQJmqrARr0NkZXHtMDDApibRevQVh+oJmEvT7sKhFobGxJgeH1f37F156afWHP7wLFcKl9tOfgmKuDz8EwZ6cHFQiXhyfU6nRvPzB/fsjrlxZtTQbAaDn+eeRhx76yrZWVkA96KmnANOotRVMLpG4vkrX1tbG1Or+/v7pmZkxDSjRam7mFxQUVFZWanU6S8tMIEIITWo0Ti4vL2s0mpVVgAbW1tYsHhrUGFogwhZd67foz/Ufwj0oisIuToODg9jtOCjwnppSKBRlZWWJiYkymQwskqmpe+eXwWD84x//cHZ2tuSGpCD/JZdJ5DJhXT3pVADT/SbTI5HpkSRqaRVLJXKphN3cVsdtqONwG+u5fD6/p6eHTCbj8XipRCS+o1nX3t5eWlqalpYGczT3fuiDnzGbTG2D3V68yi/ZRY6cUsKQbGplsXNG58erOuphY+vtllhT2iIR1TSwRC0tWQJmUgu9TSJqFLWENNUENhE4bYKNcR2JWNwmFhW2sEuEHOjnYHhm3UGKJe0yvrGncFBJBTjn7k0uEYhEW1TgY++FtKfo6Ggymby4uLixBm1+fr6+vr6pqammpkan02m1Wky+H0VR+DXv7e3Fpuxem6hUquHhYUxlEcr0GY1GsVhcWFiYmpqqVCrv1xihtLR027ZtgYGBoGRJIleA+m1Ru0KWz8lJyne8nnXIjeoSQg9itdTJJHKxVJrFTc9n55Ywinn1vOam5q6urqqqKiqZJgdzDilDUoVCkZeXl52dPTw8fO/Zfu0z5rVV1WlrxmNvRx146ZztM/aEzwn9Uo+TEv8f3/D4i7u3z6G2j/Yqs6rdfcc5GXjjB28bT5/pJuC1jraGTz6dfPfdobCwts5ObH5ECkV3QYH28uUxOzsZlyvaVIsnlapcXUd8fDRXr0oFAizbpVSKqqvbk5P7FIrNc71+ZImkraNDIpO1tLSEhYVxOByonYhd3fT0NJcLVn5NTc34+LhWq4XSl+v3jWr1yMhIX18fxl3D3vgdD75T3AOFQY1GY19fX0pKCsTymPvZcmDpU0E9fDjFywvI6+XlgUgPHg+gT3c3yBcYNGsmrQ4I61EowOFERoKgwZtvIjt2oD/5Se0dleXN7uSJJ0BWwNoapLXquSatXq81cbiQuYtevCh67rnE3bsBZ2JLcg/0RxvvzO/Uc20xfcvLy1ibYvjyg3HePYe4yzAw3uPv7w/r2OGhpqamKBSKp6fnN6xjLyjIJ5MrkpPRd94B0EStBmhjZQXEe/z8APG2qgrwkru7Eb0endQsmjVaVDMGuiUQCCQvr4HQUNC41aK9C9y7tzeAj7m5oM6ot2dBN6MdM7e2gdkICwN7nTuH/vWvdX/6U/4TT3w1Dz/9KeAy//rXoAJMIADVZL/7HXTMX13vPaZYf2JpaclgMExMTGD3x9/SpBuwlaVE7nb7azc3N0wXbnp6OjEx8b333isvL4df0a/OaasRiqK5uamBgeQTJwAFzcVl/c/JCeRkq6uBKQgEEMrS6lCjZgmE0IaHES4XLSlZ8vEhHDig3r4d/d3v7sIsGL7+9a/BWr14EViTTEaGh6cMKwol6u1NOXYMf/w4CEnu3o38+c/rKBx7349/DL4sOTnIM88A7H7gAPjM+5l0YQE0rDAajZgl4QB7eL83Ys9vNMwW/bn+c7gH3gn09vYODg4ODAxoNBqVStXa2trb2zs5OflgfRcURWtqary9vV3dXEUiEA7hl5NFLUKJUs4vJokEQuqlMPKZwLaGJqlMKpOKFVJxGbvVu6KZ3SwSt7UVFRWlp6exORwKXyyTAo4F5AyJxeLc3NyoqCiYRcVs+E0GRqMxLCoiqZHsKMBfrS++0lDs00q8xi2y5Zc7ckqv0HNcGitC2Hjv+gpcW32FkBvdTC4QsuRiCbWVT2xt3Ah6oDMD0MdCYpJsyHNh6TCRSKqUNt0LegaU9PGeonYZ/96oD/ZeGO2pqanB4/ExMTEbk1Zra2u1tbVUKhUml2H4bX5+XqvVrqysyOXyb1jP1d3dDV2pVqsdHh4WCoUDAwPT09Ob4i6bDLuyslJeXu7s7BzoHyRUNFWzuA4OkzW4kVpJtQfVNoWdFMUMD6L5kZqJUkWHrE3UJGuLYIaGMIOKGwolrZKcnJzs9KwyVklsXZRQ3CqXAjqU/P+x9x3gbVxXus5uNu0l2U28qU7suGyc4rjJcZPc5CbJli2rV6pZLCLF3ovEIvbeeyfBDhaQRO8ASfTG3hsAEmDvqPf5YqgxBFEl1r7k7beZDx95ZzDlzrkXc/455T8yWVdXV15+Xlxc3MzMjM0V77uq1mjzTx4t2P9F2Wu/TTv8mIvnY0cCXnptd+K+FxOfdfIlHf9J4PnLr/+sM+aJJP+XGlVeXsPeARmHqvFtsrlzp6evXYND+XWMFlwTyuU99fUyGs3aBbY9NBKJmMcbCw+XUamolUgmE7a2Kq5cUbu5TScnjygUttBn+1hLwJZYLK63LNHR0ajnATrKdTqEFwCJWEKIoZeWlrRa7ebmplgstvHw3lcs/492+LviHktO02BsbOwnn3xy9OhRJNHJ+nF5Z9uCe9q/+KIwNBQEBZlJJGiZaGuDuGdiwqxSgzmoRVTQ3UUkfo173nkHEi3/5CdjFnvP18oW0Qnf+Q7kldu3D+rkoiIgEW1olkfH4Et5VBR0yuzZU/6HP9D27AHPPGMbgYsqFaTx9NMwvelHP4Jx1nr96uLi4qxaFREeHhMTy2Kxw8PDb0ZGSiRSZPBwzU1ubu4FhUVIwZfwmzenp5XLS0uJCQl1Fs9LB5ft4+1dXFxaUlycl19gNpsbsQ0sFsfGhGMymSIjI9HZVlJS8sUXX7z77rtfcYmiTpk7JYlusfA1l7W2tiwtgdOnkdxmiHvkcigPV1fopikrg8E8eLx5bMysVIFl1Yp5VgPm5zcFgrGcHAPiDMvNhdlwqakQOlVXw1FRyE3qmXkNJJXBYrfT3q9eNb/7LnjyybTf/15ibc743vfMP/2p+emnoc6emIC2pUOHtjuDdNVkMn1VPmJ5eXl8fDzAPyAhIZFOp4WGhcbGxd1KfwMlxUUeHh41NbVkItHe3iExKWl+fkEzOxsWFkaiUC1FTvAeHh4tzbi83NyKyiq5VOIf4C+SSGxEurGxgdbn+gpZflVe4Gc/+9mpU6du3rz5gHzNqak52dmk0VEQGAh9W56eMJYZhzNTKFA2NTVmFgveo1IFFlXrSKg4wGJBevq6m9v4J5+YXn0V5m3ZTC90kh04APFlbi4Q8vWaxaFBc1sbOHo04ezZ3r17ITp/7rnbQA+CyP/t36DZaWkJwp3du2F/ADBZqHSX+3p7ggID09LTaXRaUFBAfHzCuIUK0mw25+Zke3h6NjY1t7fhHB0dMjKz1tfXJyfGQ6/fYLHhVBSLhC4wYuoAACAASURBVDExMQK+ICU5GdvUbDAYCgsL+vptsw5t63P9o3HPwMDAyMjIV6UqEG/I3/Q83dzc9PL2cv34eN2HbnWvO7a7xJNCsurfd+1oJAi5XXw6RyKXdvCFBfiuLoEor73LvpjVxhLIJOKuzs5mbH1KI8uugFVH5/M6OI2NTUWFhQ319R1cTkBAQHl5+X2Bl01XjQbD4OhIvpzxJbPCiYlx4VRfpJd9yapwYFU6sjFe3DpvTp0Lp/pLVmUEt5kj5EV2NId3NrcLuQqprFuhQD1Z22YKiQRWT7As2+/0d/yzRjYophmSk2f6quWSTpEVWkIPvbWbWGjhOeTz+RgMhkDAW7s8qFSqTVo+knlnNBrlcvkD4p7e3t6JiYmFhYXNzc2/yYpgNpu1s9rL1y59HPzhq4EuMdcrT/tgPo2M9inLL+bkVbEwDZx6mbxnMDdv5dyVzpriMFJ4JD4sj5bdLe3mdnblYsv9cT6ZjKx4YjSng9eAxWYXFbW041l0FsJFib6S2Qzf3VZNJtDCqy194dW4A47Nb/0g5/NHs4/9+zGvN145duPlsuft3Jye+7n8+Udk+/+1LfKxONXx0/1/OZgRLnUKXO0elMt6e1CbDTqsYkvkMgpr0KHZbkgkgp4e5FtksKRSIYkkc3RUOToq8/IG78Q96BlkYplYJO7u7uZyIU0Ui8VCb2pwcJDBYNhY6YxGo8Fg0Ol0EonkfylvYVJS0lNPPXX8+PGkpCRr3YNqZZuGRXW1vf12qpcXuHoVREVBk0BjI0Q4kKl/Fsyq9CYVDMGFfq+6OnNMDHB3137ySeef/2z6059uowRGtMh3vgP9AW+/DataxMZCK9H4+IJGJxKB8nKYR+zgsPn44xG7dk2/+Sb42c921kSoenrqKfPRo+bvfAdER5trawquXXMtLi769JNPwiNucjicDz/4IDQ0rH8AZpIDAKJuRlRV1/r5eONwrUGBAU24VpFIWFBY4O3j7enpubyysrqyEhZ6Iy0t3cfHx87ufFNT08FPD5SUVVgLCrH3WNfnyrbkKO3duzc8PBx5rNjI0GbVwlsIyfUBgPzU16+bZ2eh5NzcIGdeeDi0lyHUALW1UNpjY0CjAX3SUQGRYJ6ZAX39gE6HRgwkTLexEe7EZsPc91nN8sz60DAgk6GmDwqC6v/oUbBrl+axx8J/8Yu1Rx4B3/3utt/w29+GpRvOnjV3dEDUNT4OY9Bra7f9XAgVemJCgoeHZ0lx8fsffJCUnIJrad63b19MTCySo/5VWJiHu3tTU7Ojg30Hl+vgYM/icFlsVkZ66o3QMG9vb73BoNVofH198/NhZGtwcFBEePihQ4c6u3g2Iv0KXnl4eCD2HpPJJJPJmEwmnU6nUqnID9VGhjarlnrsGefONen1YHkZRoW7ukJ55udDNFhfD++LQIB2NZUaaFU6KMbOzrHsbKmLC2SwPnAARpv95Cc7zLYf/ADmHp4+DQeGgDcrlYsanUAAMjOVe/dGnThheu01GAyOhop/5zuw/ZOfwPO99hpscDhQpEolvOba6mpcbIy3t3dWZuann36anZ3T2Nj4/t73kpJTVGr4hqrX6VxcXPB4/NWrV2Vy+RV7eza3g81mJybEBwUGhoTcMBgM7m7Xrtg7JCcleXt7252/0IjFvv3WW2wuDMhFxbJDfa5/KO5B6rHbPI7Rh/WDNPRbuhbX2MxH3q75y8W63U6VT54s/fEBamyRpEcBk97F4mJi19FsRhGBx+oSeldy8GyBTCKSScRMvoTCFUZjO29WEhJT0itKiwMiYiisDnYnr6K89OLFizQa7UE6YLNPp2YskF0fXV+a00nwYNcEc7FenFpHVmVcBy6W1xrCwV5lYbzZtdldhKusqghKbTOLzGQyampq+Hw+EpXC4XAoFArCc4OqtB0bt0AMBEjWO0jFPIsJbHvbbd/BbTC3TCwW8/kCNptDIpIollcRvV6PlMK9h0nmwetUoH4uG/k84OrI0vDemHdfivqLQ+Jr1/z3/DHh2D43F796N7FU2tvdo2Ax1GfPlmY4jzs7JVESHZuv5DLyhL39ChZ7LCE+iZ4WmnuW8s6fmzz88ktKMB/uk1UVljKqC3ILLly48A1Cd0eWtRHuYQUvnC/4c2D9xy/kf/oE+c1HD4S+8kbun395rGb/oaWXvi3G/svncz97eus/f5x6/EL1787yT3h3ENqqGxoQ4YvFMJSKSqWyWCybwbIeOKR9x3iJ5HIhgSAvK+u3Bj02mFgmlrV14rpEXUKesKujC4vDivgixNKjVquVSuU9LMSTk5P/S+09er2ex+P5+Pjs3buXzYZs9OizcscGAIBIxO/fXxMcDNLToTuloAAae5CgZrUahvhsqBbAjMosFMD06bw84O3dc/p0r4XoFhZJevRRqAp+8ANolvnVryArMJJ55O8PQ0pFQr12cXoKZtVkZcG0o48+GvjNbxJeeQWWxXjxRZjM9etfw7jSb38bnubAAcjIcvo01ChPPQUp6b7zHVjGi0w21dWV+fj4lpSUHD9+vKq6hi/gf/bZZxUVlZNT2/lcUTcjysorvTw92RxuXEw0k9OxvLzciMXCZCsvr5HRsYnxsYCAgPm5+fj4uH3797u4uLz99lv+AUHIbxiRDyIxGIjaD2tzIpNMq9ViMJjLly9LpdC2tKMk0Y0w1auiAnnaCoUQmiiV8O/TT0NzV2YmjNVpboYfSz0PWMd+fh6QyGIFv9cwuwAlrpyGKAf9DA8Dtcqo1iyp1tVqQCLDhPfYWHjOc+fMu3eD55/n/epXuf/yL+Df/x0mYj//POQ+/OlP4VDa2UE7HZsN8UF5ObSIIN1HcE9mZqa/f0BpSfGRI0ebW1rw7a3HT5yoq2+Y1WgRmbi7udXV1dtfudLX1+ft7d3bB9lLi4sKgoNDvL19ppXKnm5FeMTNHoXczdXV18e3vLwiPi4uNBRWo7QWiF6vj4qKQk071g9NdLd7NCx1KgoZDMgKzWRu5/vfuAFxD8KHVFMD4Z1KBe09WrUezGlBXx8vNnbK2xu6b/fvh9Fmd6Ls734XYvSDB2Edr9JSIOBvzS339IKmJuDgQHrrrXLE2PPoo1/jpUOHoOnx1VehJ02hgMQCLBYUqeUD1tdWk5MSQ67fyMrKPGdnRyKRsdiGY0ePteBwSIocxD3OLk1NTU6OTkql0svLq39weHZ2Niszw8/XNzgk5KsKO56e7m7uHuHh4aGhNz45eNDFxXnXrl2FxaU2It3c3LRO3/1H5XMhQ2kymUZGRu4MJbYe6Pu2B9o5pX852/DOtao/nC367vuFj7xd95oDj8QQy2EGcSOD/3kmI76po5zcdbmQldzS0cri19F4Z/OYdTQ+htz5QQLBMae9vKbuA//cl5wSvcqZU/OrGg2sLnnfS1vvYDSZOmdHR5a1/NmxKgYxmFLlxql1Z9e4sKucuNUhuLK8mopWISeU2xTAafDtaHCkld/EV3u3lybWlWVnZfF4PKRUAolEwmAwra2t1iHbd2rHe265TXUKYKwP1JLIIRKxQNnXOCBto1DIGu3MoLydw6KILf4ga3eq9a2h7QevSzo6OnoPRYue8G4NMwDuHR6JIe8ker32WdLbTl5/DPP5S/3Fd5pZtSxmn1ykiC+74pOw9yrmyBnsibO4s/7Rb6t9A+aOHp87fUoT4iV8/1X6k/+n1vGdXm/v1F1/Drn4p6yWQPokQzur/VtB9uD8YJok+QYmN+PZm73P7ev/r9+Uf/yL1o9+2/Tuf6Sf+M8LbvuPHBxvff+G9N0L+l/9vP6T/yz77DHFrt9xr9orDh/Jy81FosfIZDKJRKqoKLetkiEWQ2/XraG5x5hKpUJr0COUyeC6XC4WiSUiiUwir+JUBbX6RbaFU+hU4iDRrc65nd8m5At7enqQlLq7iRqhC/nfiHusJ+jU1BTiBL2HOkH2JxBaX301uaEBrK3BfC4HB2iWaG2F7gMKxTw4ZNaoDSszq6ZZLZArVMXFy5GRwN8fODjAiAak/vWuXTDB97XXzHv3wjoIFy9CK0dhIeB1GVUz0xN6LheeMyQERqL87nfVTz/dHBMDBAKoqDo7Yfjp3BxU2PHxZoMBqhCjEUbx5ubCvotE5pUVuBEyua2tzWu1yUmJcfHxVCo1OSkpJiaWSqMrlUq1Wo1raQkKCqqoxBiNRjKJODY+IZFIFHJFelpaU3NLV2enQq4gUygGo6GsrLS4pGxhYYHBoDOZ0IqISgmh6/Dz80OCIpHtyFTT6WDOgvXO6FHWDcTeU1kJc7lNJngvOh0UyeHDMN7J0xOGz4aFQWlgMDDmu6BgksncVKuBVgtUKtOqasmsVptmZtGPeWZmQzWvVenUaiAUwtigiAjICXTtGiyd9vrr4Ikncv/t3ziPPAJ+/nNonDh0yCwUmn/xCzOTCeXW3m5+/HEYfhsYiKhntLOwZPT6+rpSOR0VGZmQkEClUBMTE+Pj4zncDuTJWFlZERQc1NjUbDQav9LWsxoNl8Pt6++LiY6iWcLr5HI5rEphNGRnZVZVVYuEgojwCLnitmoeCF+zm5sbWp8L7cEDNgAASUnp5883DsAqoTAn/do1CB8DAqAhra4Oop/OTtTPtTYplWwJBLDqfXIylNTnn0Oo8uc/b1NhIubEH/4Quq/eew+axVJTzQy6WaVST+nb2mER3A8+SH3rLdlrr4Fnn/26Gte3vw2x0759MKhoZATOVa0WDi46fUwm09r62sbGxsjwUFRUVFpaOpVKjYmJTkpKEgpFKqVyenq6tLQkKCgIjyeYTabm5uaZWQ2Hw1Yo5FGRkSwWu6Ojo7OzIyoqSiAQZGVlQu7NpaXGxkZFd4/1xAMAzN1Zn+sufM13E/I9HqB/61cPj3sMa5uSWEybRyI9ubyjidjuGFP55Emif3oXntZ4wJsRU9TOFV4oZL2TRLtayj6cxbhYyDpXwIxr7Didxwyu4QRUc56Lor50s72aKcVw+tNZ4zfa+oY09ynHsWHUbxhgbT7rZWFrPVVO9eDUe3Oxnp3Yy8yKq5xqBzbmS1aFExtziVbqXptdRGxq5DFyeKQEVktwezlvaggj44qG+6cmJ2k0Gp/PZ7FYQqGIz+fX19cj5ZkQXQijr2H8sjWhj62WtDEAIEBHKBKPKAgyCU8ATyEWieVdHYzBrgxVf/Py8vKcUjgpiBexqzjcjgfxRv3dcI9oRuDf5mdX9llqyPtRuOBI178Q3/jp8NGPk5tinndMPHxiOhgX515z9MMCu6NNx4/5fuYc9mGl83va5x5Xu7rX/84p1vH1k5FPJh5+tD7dacTNMTP+UCknFT+Btx6vO9s9c90DC/Ct1XqpH6n2r/C+4fPqzM+/Nf6rf0s+8fOILx9jvvGrxjf+j+zpHzB/8lz700eH9xzveO1KwGtlPnZHML5v00Ivaq65KTMyJqamKBSYcMdk0oaGZBwOLL6BGNzg4InFIqm0p7ERNu4JfVDMihwrlMu7GxrmDh3qqa0VyRQ8cZdCJk8kxvtW+V8nhAwuj/g0ezqlOPthPUUikdGwXWXM+qZs2v9L67FbP+AQiVhv2bFtsfcQoqObtfANHwb30Okwc6unB35oNHN3N6wZPq0GG9r1haHBhpSU5epqUFICvSzXrkF9fuQI1CsHD0IrjYsLDL5ISoIqndNhVKrWtOsiEaithbncrq7g4kX9rl3Rb745NQJrqG+r4QdpWEM6m8GeGB/DYDDV1TWzs19X2LHZB5XG3bajwkF2QOtzoduRDliv3q0NAMBgMGhQOQDm9XWYklVaChkgEUqjggKos6OjQWrqVGAgtq5uk88H09PQ1qNSmTUqnUal06p1WqSh0s3OGqeVoKMD+r6KiyFycnWFSXWW0lLrTz4Z8cQTC9/+NkCoZQ4fhj5GBwfz2hqU8MICxARdXdB+hGpopPM7SgMA0NfXU15W1tCAtYkPuHP/u42LtXAQTg7r+lzW3z5IG1IappQ8/rjAzg6QSDBciUiEwT3JyTD2iUyGCI/BMA8NQezYze9tqSjX91t4rCsroX3GwQGyaL72GnRpPfssNCT+/vfQxvj223ACh4XBWp7DQ+vaNakM1NeDGzcWXnghfPfu5b/+FXz/+5CwB6Fa+N734O779kH04+y8PXtt+n+niJAt3QpFRUVFU1PTPbwPdzsW2W59IQSdP0h9LuQo6zOg7Xtf7m/69uFxz8qwkn72ZtWuy9UvXmAVN9CTy2teuEgJz2t43zX/kd01fzrHqG7zre3ck0DzxXDeTqR9nsn4IIUeXMN1KWW/k0R/OYayO4kRRRyYW4dvTht6o2YVvqLcY1nf2myfUDSMSdcMMLcOXZRrS5Eigj+vOFBQ6scvu0Cv8OM1Xhe0+vAac/q5V7l19h3V16gVN3EVSdJm/8zY6MQEEpHM7WLTaVQajU6n0wQCoVQqozHaR0ZGsrKymEwmavKRSbpUfQ19MtaOUTu2COjWulAoGVKQN4dTp3uqKyoqqfjyMVntiLxpbUkJzDoAVock9bS2Qr3uPreM3uPfDfdU91emkNPPFH1eePH5jIKro1fOL5y/UuP29nOJx3Z7xv/uWHbwi80up84lvudfvvvTmMN/aN3zO+G+FytPX7z8LzlPnw457/eMo/tjF9LfkGrEYFI1Pi2nq5gmswm9kTsb3VpFOi8lpTOxf6EP/dYANkNayw+c+lT43A/Av35v8Dff7Xrh+6n7nqq6/LLqk/c0P/z31Gc+xP311ycfTTj4evsXPjFvvpzlVxnYwiCOBV/nNDVS2ZBgWioVcjjywEDx8PBoZmYaUlEL4RscSUiYuXBhPCoKDQO6NXR3/y+RiPn8xb17p52dV995ryg3zrHGs+HiF4vvvduITxieGwQ3o6WvPRkQe3BiQ4XeyL0b/8Q91g/Je7UBAK2tramp2/W5EAii10M1qVTCd1mZDPK+zMwa1WMLOpVar5wGcjn0mhQVQY0SEQEzlAIC4Dt4SAhUIenp5sZGwOHoNItarYlOB0ol1LvR0ZAj+PPPR0NDo7u79Tr4qLmtYzart31nWUGG3GQ0jo6MSGWyWY1mbGxUIpFqtXPIzvPz8xKJZH5+YU6rVSgUPT09COW8clq5tLSMHL68vLy5tTU/Pz87O6vX64eGhjZvN+EgGsXHxweJa76zG/fdYkk+Kiot3XZMAGAWi80nTkD+HXd3iAlzcsw3b4KAAH1W1kZCgq6qyoDHQ2hIpSIZXjCsCkZWaeBnZhaoZ8DgEAQ99fXQNYZkep09C0mGX3oJ/PrXwtDQVA8PGFx+7hx0bMXEQFRUVLSNcqxhpU3nEZno9fqBgQGZTKbVaoeHh2Uy2eLSErDkYc3MzEil0uXlFZVKKZfL+/r69JYqfxPj44hs9Trd+Pi40WTSzM6Ojo5OTU0NDAwgRaPQawEAFhcXXVxcHsbek5ycFh/fkJICU/vZbPPyMry7sjI4B6en4XysrQVcrm56enNqcmtDpYWeWQYDGseKi+HkvHABGscQ9PP669BBtW8fjJm/ccNcXQ16urdml2bUZiLJnJEBzp5hPf103ptvguefh3H3ly/DD2Ik8vWF183PhwDoq/K6iCUSvVMUBW5tbfX39ysUivn5+YGBfqlUitifzWazSqmUyWSrq6vTU5MKhWJgcNByuGliYhydiouLi0ajUavRzGo0W1tb4+PjJtPXpXORq2g0GldXVySt4158zRYiOKFQiJJGTk9NCoWivzUs9N4P3IfHPTAno5yU+/33Cx7Z3XwsSNDJa7t0s90ptvxXX9S9Zs/OqrqO7Xohmnq+gOVYwno3mXYgjf5xKv31eNrz0dTnoqh/iiBXi6YMFpaWe3cV/XZDr2ub6K7s7VBpYXkQdJHNKzO76xiqbJEmhzydHycoqxrCDCxx0zrbqDJWgaDKgVpWPSLpEDV3jqTUNAdkpKXxeJxxTd3AKMmSlgWLVsl72cPK6pm5bhosytaB2mz6ZIyloaKx7laRCDqt7q4Vbb4RyyR8dU85EVdSUZKlkibrhyNME6mmjYGt9XmDsnJzKGa6t8FkvL9JALnNvxvuWTNuejdcuObw89P+T5Sef37RzV914MP8yOMHmg/8NfaLz0/ITj3L2//DjrKfOoz84uXuZw5of/itkd9/v+23h39zKvl3blefcvsilhMwtfk3ZG/1zCvi6FHhhOsSpRgd0zWwdDkrrvbHf1r88SPgkW+BRx4h/eLJyr0HOj7y0rj6Lr7yR8zxXf6vnEhOOlf26Vncf7zEfOZJSmVFb1zC/LPPjR4+p+AyJXIpny9xcVE5OvaEhy8TiQQ+n2cxvMFBVNnbj0VEzFy8CEN4bBgObUbSahUyH8bELL3zTvexU45FDheOx1Y/9oe67+0FLh4gIkr3n49uPfqfhrfeMlvK/qD3co/G3WgF7nHI/6Ov/q75XNbP3wdsW+KaCUjdE+QQACClnqsrhD59fWYaDYyPg8pKKoslAAaTXq2FoQ2jozAZiUqFIbsiETRlYLHQOtTcDPVzd7dZq11Qb2q0YGAAqii9HlodSkvByZOtWGy9LeR5gL4CYF5bW2XQaU1NjefOng0ODiGRSSdPnvQPCBCKtud3Rlrql1fsvb1h6YNjR48EBAX39vUr5HInR8fIyCiTyTw1OXHu3Fk+XxB647qXlze2Eevm6pqTm2fdH2QeZGdno/E9D9C723ZB7D1cLgxGsagoKK30dBjZU1gI3V7T0+DUKdOFC20REX03b4L4eFgZDQnObW6GTDACge0Hh4PenPJymGrt6QmV+JtvQr9Vejp4+eWyP/6RHhYGQkIgwHrnHajcJya2Qc9tPbtjZXFhgUQiNTZijx49FhFxs6mx8fSpUzdCQ/tuhYqHBAU5ODoGBgU3NzXBkOe4uKHhIT6v6+KFC+kZmQCA+rrac3Z2OFyrs7NTTGxcKw53/PixSkyVjUhNJlNCQgJaj/2OjtxnAwCgtLRYoeBzODDCPSAAMhllZUGE5+kJoQ8WC2g0XVRUI4c7MjcH1lXLxlmNubcX4p7aWgg2PTzgnP7yS4hAT52CXlV3d1jbtbzczGYbVTPzs3qpDBQWQn6pd97Jeu65rhdeAB99BPFNUJBZKoVesg8/hCAKAMiU+POfQy5JrfY2OSPuJ3x7OwZTeerU6YSEBGxDw9EjR8IjIkbHxpCQeV9vbycnp/DwiFYcbt/+fUnJqZOTkwwGzc7OrrCoGAAgFPAuXrwolUpdnJ1zcvOKCwvPnTvH7bCNa9br9fHx8SiagfE9sbEwza+mBjQ1tQUFEclkZDK3t+EOHjzo4+tnMpm1Gs2lSxcdnZyGLBbX/5annsFgWF9fHxsbe8j4ng3lHPlgQM4jr5b+aB/JJ7XxgHfTFwGYZ88Qr0S20Lr2ZzDfSqTV0fgENv9SIevdJPpfIqmvJ9A/TqZ8GNeayRrRGaEZQGc09aphVb4HWTYM+g2DDtxCS9tZCwDMbqplY9Wj6mz+ZAWVQ+obbx0aoTC7ODR27uxKk2C2V2cCm/op5VLl5EKpQNI+oaZpdDnj2kqJlG9BM2K5gjusxAolFDYbFohAF6FI3CtlwrIGYj5UmPd0i6BHQaOCUCSViltb22RyBViTGMcS1vtjRmWYno6crcHopZ4og6YdGO/j10NlguAe5NUF3WjT2NqCxFFjY2MomrfZ4QFX8SLM2ZAn7QIed/Z6vOfjV5ZOnhn/46/bDz6TfeODc469tU+4nPpRfeJFyvzZU+s/+n7Vh492PPfDjud+hP/sj3uidp8qdVKvQ9DTN9dT3lO8onugkVXMyXvneqA7wbIgYFCrnx2Odt/60Y/Bv/6r4ZnfVl+ucv1C5mg/POjgPn/hfFdzTlJt+NKWduvyadlT39W+8Zzy6PHFXX9dfvzxnu/9oS2XJumRCQUib+8pZ+dpP7+hri4uMnQQ+kilUjp90ttbRqUOFBbKmExYoUIkkkqF6PCiDesxFVlIn3taWnDNLeJhSVC8e9AvD1L/42Ple+/2PPOM+tFHN3/xi40vvgACwQOKemJiYmlp6d7D+oCnesjd/mfgnqqqrxUVANAh4u0NzRIEApiYMHV2mrKzp6emoCN8ddm4ql7WLyyDBUsE7tKSWa+Dbgah0KxWmcYnt5Y3t2bmNzeMq2vmzU2oFaw+IC0tvq+v11op3kfjWb5GxiA87Max4ycYDPqhzz6Li08QCAT79u2LjYtDNAoAICYqksXpiIwIb23HpyQndnTxJ8YniooKIyJu+vn5Liwuz89p3FyvYTCYsPCI69dD6AxmXm5OcYltxKjZbE5KSnoY3FNRUUGhUNDbtJIAfIasrpo8PPQHDijd3Y3u7lAde3nBYCaELhLJyq6pgaYd9IMUGI+IgElMTk6QD7K52RwSAkJDN599NuKRR5RPPgnCwyEK/cMfoGWCRLpNH98pZEv9S91VJ8fLV+xJJNJHH+/Lzs0lENo//RTWWNBYvJ4AAB9vb5FI7HrNRSwWBwYGDAwO9/X2JiclZOfkeHt7b2xshoWGpqamBQeHhIWFBgYFjU9MRISHT01No/eOPDT1ev0Dpqzf2VXkDBkZBSdPsvug6RqyW/f3Q1dXeztC6AikUkgCUFw8LZOZZmfN0yqwploCaqVZKttGlPHx0DDp7Q1l7e0Ns7dSU6F8+XygVK5oNnv7oA8xKQk4Oy8++2z4yy8vnzwJ3n8f4ptf/hL6fFUq89bW9mSenYX5ccvLZmsrDABga2vz0sUL19zcG7ENnx/6vLiktBWH279/f05untZS+kO3teXp6dnb2+vu5jo8CqPsBwaHe3v7YmNiUpKTAwMDDUbjQH+vo6NTdXX1pwc/i42NdXVzT0pKzM7OtRHpxsbGzZs30bJEDTU1gsxMQKPBHy2D0RoZ6eziolKrAQChN65XVMKo/GmVupPL2b1nt39AQHcv/Bk+/GIymcrKyuLj4/l8/jd24aHdWOqbbN3jXPj7Y7jkOnyajgAAIABJREFUooYP3ev3XK3fc7X5cKCAyYlt6TqWw4xr7BxUSGKaunbH095Ool/JJVYS2MOquXXdtqmjrVttjxF3jW4XWkHPfN8GBIVajckEz9PT3cvlMmWKTrFEIBZKJRK+SCQUC8VEEk4iJ02OT25ubkokUp4QP67s3NjYXFgTTy5mKddy+4aJ20yDQrFUJpCIdyhbIRBKZZKu2f6aUQUeIh+RBBqIHsD2I5ZI6HR6SwtOrzfol6Rzg9X9nKS1qRYw17K50Gvc+htuWa1WV1dXR0dHI4kadwpnbW0tOzs7NTX1bgW87jzkHlvSu7Mvez32YdYu8uVPwXMvbD31TM3FVzvPvcPJxDrvIlW/Hbl66rCMTacf+BPt1Z8knfrlgegXhx2cxjsISh0MXZhZUyfwYxoHscWKbW79e1zL5iuTwTDX1QVDDQCQSuXS0rKhzCwFHi/u684vGaUweoR98oGoKKWjs3qwf9NkFtfVD+19X3XOzkAm6Xe9NPOj344++TLJLp7X2y+VCrq6JHl5wxLIpXi7rU4q5Q8OjsTFKZ2dVU5OUjZb1i+nsXqEYolUAtGPTAaZmndEP+LubgKJRKfTFgdVpA9jyS++VP/Si6onn5x54w2Tk5O5vgFYAL3Nfe24Oj09XV5eHhUVhRaX3HG3v8PG/wG4p62tLSUlBZEFonUAgNkxiYnAx2e2oYFCperweMDlQruF0QhTZjY2ATDD5wTUBgYDDCjVaoHBYNzUazUmlco0N3eb3rUofhATozp8OOpBeOruVH4AgImJiaLCQgym8szpUzm5eVQq9ejRI5mZWT29237chLgYT28fZ2fnwcGh+LgYdkfnxMQEnoC/dPmSv58/l8vd3NjIzEgjEIheXl4u11yyMjPeeustNmfbMHPr3oHRaETz2O/syX23AABKSkqQPHZ0ZwAgVc+lS+Ds2cnDhxkffQRiYmCVCQRfBgZC401aGjTqNDVBIwWSplRfv53PnpsLibK9vWFkz6lT0Evj4gL9htHR/SdOJCQlgdBQeDaDAZreoqMRAr1tJY32waYBzOaB/v68vLzKiopjx44Xl5RgsQ2nTp0qKCgcGYHGia/qS3h6uPsHBDo7OyuVSn8/v96+/v7+vqZG7MVLF8PCI8RicXJS4oXzF7Kys/Pz85xdXBrqG5KT4VyyvhYsk2KVx2791QO2AQCpqRkff9xkoV+H97W+Dqfi5KS5pQVkZY16eHBTUyFfT1sbtHXNzII51ZZepTWqZqHprLkZWn2qqyG6zMiAiL662ozDAT7frFKtzq4NDJra26E1LiAAfPaZ+Le/zXz+eRgV7ewMea4zMiAjooXpfru/KJC16T8AQKGQI9I4Z2dXXVNThcGcOHG8pLQUqSJnMBhcnK8GBQV5eHguLS74+fn1Dw719fVWYSphXdLYWIFA+FVi/40b10kkkr29vYeHR1BQkJ2dXUvrdmE45IqIYem2OhV1dQIKBczPw6pvS0ttVVUZGRnrGxsAgLjY6Bs3bly8dKmnt49Oo544cdLbyys3Px8Z4of/m5WV5eXlRSKREHveQ55wbXymj8ErKCvJcw1jJJUzs6rZFc1SuVQoEl0ppH+WwchvogWkl50t5H6exXo5ihBN6LP27kwsbJTxJhbWbaOV79srk3ljRcfVm5Tz83MkElkoFH/NlXyrvIREIhUIRDTLQqfROzq66DQWqwMnHsyi8SIGJxskUiFSL0sgENTVNZDJZLRcJRKtbGFYhrUpZvurxIOVDAVdIOYT+1rpCqolJlbcJe3sknTuCIMsKev88KhIBM7OaaYmR2UwvsewaDID4wNrR71eLxQKy8vLnZ2d+Xz+jpJZX1+PjY0NDg5mMBhI2bUdd3vAjQazgaSit4taa5LTFZ98ov3yy7Gzx0ZCb0j7FMMZmdI9J5mXPSkOzlcS9wf6/PnF/D+8WfGGE8NlbmsOOb/OqGsYqE3kx0pmv3ZdPeilGxoWDx0yx8dr1GoylSrq6YG0OpYqEAqFQCqxxJjLZHyhkMZgUKlUOpfbIZHQaLSx8+e7AwMG/+PJiTf39RNxQpncIv/O5uYaOp26w7DKZGNhYZM+Pst79iycPlYUyjrwZJf9yf4OUbdQLvfzm4yLG5XCWWy7SMTijg52THTi5sYaALr5Ksx6JQYQiQCHMwJwr1Cm20Wwvr7O5/MLCgqcnZ1R8rnbd/n7rf0PwD1EItG6ogX6YDUagUIxMjAwMDcH1QkeD+1AZvP2a+7yinlNuw60Gqh/LCWMLUgHfJWKpdfDj80CAEhOZgUGltgoRZvd7rEKB81s3lhf7+jgtre3DwwMdnZ2EAgELpdLIBBIJLJELGlvb0cYWsfHx5dXViYmJmZnNQK+YHhkZGR4eG1tbXpqamVltb+vVyaXj4wMt7e391hefNHrIlMjLy/vYew9GAwG4U61Oq15YQFcvAgee6z3/PnxlBQY9sThQMtNWhp03Dg7Q1dMcjL0iCFuQ4TBp7gY7hAUBC09SB7T8ePmL76A9MH/9V/AzQ3L5TYmJ4PWVphQ7eEBR6eyEh4CADTDubvDWOnbcQjaqW1D8MrKCoPBwOPxQ0PDHDabQCSy2ey2tjY6nS4SCfHt7ROTsJb48NDQ+vrG0BAk4eWwOUqlamh4eHZ2hsVira6u8HhdCkX3wsL8tFJpM8TIanJy8sP4uUpKSgUCyGPxlZtpawuKq7Bwu/9YrDw3V5mZCUpLt5P2IdW4Gtofl1UrkMNwcADG+pDJ0DPb0AChJY0GBvrB7MzqzOrMjJnBhODmxg3zlSvg+eer3nuP+sQTQCKBTrSPPoKQ6ZlnIOi/mxi/FqglKArGMy0s0Ok0Iok0MjLCYNDhLOVw8Hg8FKlYhMfjlSoYqzgM5+T64ODg3Pwcm82emZ0dHBzc2NgcGRnZ2NgQiUU9vX1zc1oGk4mG/iDXQlxmSUlJX/u56uoEZDKYmzMrlWBxsa2qikgiIZN5eGjQ0dGhpraOSiGLxOKM9DQvb2+UognZ5xv/1ev1lZWVeXl5DAbDuhLCNz4hcmBjU1N4eDiuBQffrC2LRCKOzq0IaZZNzq2MT05JphZbu1XvRZKP+jbzerZhOnLsluHBlcXX3dwyTs7r6wcncTQatavLErphq6G215HqECiOkUjFfaOUvMLk5uYmkUhq8XPBPfF4PIPBoFAo9fX1HA5HIBDwLIslxlnCUpDrRiqbhuoah6prRsuwQ9Wd0g6BhE/sayMMtvMlO3RAIpMx6XR8SIROO7+6vi6RSDfgC+j2srq6+iCZXMje09PTZWVl+fn51hTPt84E/y8uLhYUFOTn5wuFQpSs1XqHb9A2m83FlZVxAQGUxkYZjwcNJjKZqLODc/KM4CP7XoygS81LEMVdwJ/7qOSdl/L+1DjSjF5ly7ilXFWaUd8V+sW9G1tbGydP9ru4DL3zDguH46FjYzOyljwsiUQCGcMti6i7u7+iQnX+At7uLL2uVtDXJ5TJENzZ1tbGZDJJJFJ9fX1HRwc6rGK5XMZkrr788vJbu3VPPHbm+5Vnfol75XvSWD9JtjvV7/rMtWvT5eV9fX22xblk3fKaCtmBQ0JOp2llZUk+0K+/9RiBDpYHKCqHymBsbKy4uDgnJ0cmk6Eb/yGN/xm4By0zjj5Vh4aGUEJPk8m8uLidrwsAxDQWljbz6qrJbLKAHsthyEuwxSuxg4YAAJDJeDweZ6MUrXXGg7TvHMXl5SWkKuF9k4/uPBbZYn1dZEt+fv5D4h4e7zbuPguxW29b29Lrr4O6OjA6Ch2DaWkwGc7REVpxUlIguPHwgEglNhbaI4qLt403bm7QOePjA1lmjh2DoSk1NbBs15Ej4MMPK7BYzpUr2xFCx45Bq9Lu3TAACABIRnDo0HaVDOt7tGnfKZa5OS2Px+vu7kaKl925w7237Hj+h8Q9paWlYvE27tHrIRbp7DR0dvZQqWsmi5obH4d4hkiE7ioyGSYhqmfMMyrD5swC5MJWq6BtcmwMxqyNDJuV0/qZuVX16vAwxItVVVC2bm5QpL/9bb6HhwCLBZ98Ap2G1dXQeEYgIJZymzvbeXVH4Wg1GgGf393TY0S6e2snxIt3a+1e/20uZjQak5OT0R9pwx24B/1R25wUOY/NxodZ3dra2tjYGBoaQlgejMiLkOUy3/i0m5ubjU2NuXm5JBIJhlDAABc+r7Njcf223KvSEq63EyYzBaNWQ9fqQy4rG6NUKpHPFyDpV8h1rVXknVuQb8ViCZfTQSbTOJ1tUhlfJIQ1pTo6OjIzMwkEQno6pDPgcrkcDodsWWRSKV/OI/Xiad2U9v6WxqEach++o4dD6yWzelhcSqNIJhVb6lmKbxUdE0kkPD6f6uIje+PARFre3Pw8UsoNuWWz2byysmIdq44SN+8ok+npaaVSiZD87rgDwuy8tbU1PDyMPLH/W4Z1a2urHIMpKC6m0GliqaXYrKxTyGYsSKZvBVmB8eXRgw0HPIhOaQVumrkdarrt2OG7bhwbW3FyomZnixSKbUxjPaK3t63HVyiXy9hsOo9HJhCG7O37KyuFcrlUImEwGDk5Oe3t7enp6SwWC6lyTyQSaQyGXCTS+Huu7np56eD+0Rf2nv5O1anfUEfOOfQcd7z6OuPd9zcIhG4CsVcsFUkkYktBOqlYoejCtRy/6r/b9/Sxi71q1dLq6gpScgsRO1p/F1m997BOTEzMzc3pLctdBfJ3+eJ/AO5pa2tLS0tDxIrIZGVlpaWlZWlpCdmIQJlbj0uIgRoboRKxef1dXYUBPXdbAAB1dXUPQqV4jzMg3ZOIRampqZ1dvNXVldKSkrqGBpPJRCIScnJyNBqNRCzOzcnp64ckzqsrK7U1tSOj8HVwanKirq5ubGycRCJKJJLOzo7i4mLER2Z9RSSfC63Hbv3VA7YRP1d1dbW1SGdnZ6jU1vX1zXPnYGTuuXMwG8veHtYKPXoUpmgplRAGeXjAmFl/fxgEHRAAsQ5Sb9zHB656eUFM4+cHJW/5gBMnivbtE+uhUX/bESMSQf7I1tbt0TEY7tVr9CfA4bBTU1Pliu75+bnc3Bw8gWgymRobGopLShaXljhsdn5+waQlaker1VRVValnYAqMyWjksNnLy8vdCvnI6CiNRisrK0OkbX1Vm3rs1l89YBsAkJGR0dLSYjUhwcLC9Jdftp05A+sQmM3mgQHIqYPHQ88gFgvRzOQktHWpZ8CiekOv0hhVMyb1DJhRm9XqDdWiWmWamYEsiLW1kE/S3h6adl59FfziF+lvvNFnbw9+9zuIe0JCoGMRiexBe4tCfHQL2kBFSqfR0tLShoaGZ2fVWZmZVBp9Y2OjurqqorJiZWWFSqEUFhYiJM4zalVtbd38/AIAoLOD+xWbs0arqa+vn5yaFgoFuNZWxF2FXgKBSvPz815eXkjqHMznujvusT7Quo129eEbCF+zTVyzxQps0mq1MzMzD26KQDuzsrKiUCiGhoYoFIqiW0bAMyTi7Yo0Wq12fn5eoZAnJ6UEeZWmx5Ma6nGTkxOWH6/JoP8m9h4AgEQi43A4tqEbd6hGa+2IfGmxBAh5AuqEtm5gskUMq0fAYmEsFotIJAoEAjKZjHAJGgyG9vb2stJSEpkktsSICMQCvryro4tbhMvPo+RwMRU9bsHSzHxZbKqURO3s6mpvb8cT8FkZGezgmwOfnRv67Nw8g3srZheVFrDRiDarX+9naU1PT6OWQpuvbFbv5C00m81Go3F2dnZmZubeV7E5FbI6NzfX29vb09PDYNDEYpmvr6KjY5t41lLbeEEgEAddvxQR8BHt2GHGjVClJTBuXTO9qF/e8YT33mg2m3lSaQefL7GgZ2TsbEYQNd3dPtQwMrlLLB53de25fn365Ek5mSyEWA1GWVEoFD6fT6VSkTzN9fV1LLYeU1tVlsfqa8X1NmLHstNUfr69sTHSIwdb39/v8efWoMTuNz6VHjoiSoob5fI6Cfj2FgK+ydmdc+P0pxEnXznpXtf5tX0LvSlrCSM/KPSrOxv/zOeyfr7dq22pz4WvqIC1Giyxrnokh/YeUTgGA6xJZO3JQpQulQpLUNmAIfTaCBoQi6GDFt34gA1Eq+NwLYmJSU2NTdeuXausrMzNy0tKSkxOSa2uqiotKfHz88dUVgQEBbk4X7W3d2hqbpHL5IUFBVeuXImMjgEApKcmHz5ytKGh4dq1a56eniwO+8zp0xQq3aY/yGpCQsLD5LGXl0NCT0SkW1tbq6tLIyOGS5dMfD4k0vPzg06Tvj4oLg8PyIJkZwcTtSoroeHhxg0Ig2JjIfUjkwltGIGB0Ixx/TpESAcPQi7m4WHIMHn4MHj88UwHB1i5CREqAObVVRjQsrS0PRB3EzbMfNnaqq7CpKSk1tfVXnNzKy8vLywqjoyMyM8vqK6uzs7KjLh5MzcnOyw8/PKlS9euuZKpVIFQkJmRftX5akoqBMpCAe/d997j8/knTxyrqW3g83nHjh6VyhR3ilSn04WHh6N8zQ847uhuFr7mfAaDgYh0fX19ZWVlft6o0ZgWF2F0WXs7FAiVCkN3iEQYjoPDQSaq8XFo9VGpoM9LozJoVPp51YZWtTWjNs1qYPpbQwNk1MTjIQPDRx+ZL18G776b/K1vTb78Mjh1CmbGVVZCySMR+kh/AIDindsmT0D7CDXRxvp6WVlpZmZmFabSy9unsrKiorIyOCiwAMalVSXEx8UnJKYkJ0VHR5+3O+fm5s6E7HbC+LhYR0eH4tJyWKSzEWtnZxcVedPV1dXPP8DL0/PgZ5/13qIORy/2Vcj5+vp6WFjY13HN/2jccze+Zp1OJ5VKEfMnMnwP8veWqMHGxkZbG46AZ0aFNOekt8xp52dm1C0tLVgsdmlpaWx8vL1ZFOhZXphfWVpaLBZJS7K7KG39a2s6K1/BvS5oBka9SWMCkNOZzeZwuVwbpWirC++yLpFIGSxG7wiRLyJaKkvC/aRSqUKhYLPZBAIB5Rfe2txid7LLyMU8GU8sEkslUi6HS8ATRkZHRqZGa3MLuh08e/ce7tlzUBocSaXTS0pLK6owGol8OjBqJjlXVlG9aeGSuPOuEMPbndsRr6i1ZfHh89i3trZ4PN7dwqJ37AP6WLBUWZlrbW3y8hrz9x+7dm1RpdqYmppobGxsa2tbWFmd5XZJPjtE2fs+zd+/tAGrLiuRHt5TmH5ukccAaw/Gvt3fD27eBB0dJgBoHR1dvB38hncZyds3y+XSoqKJkyf7/fzEQgvr5K1hRdAPCuhXVpa9vXlfHBmMTlSJu2Xivj5mZweJw1qikMbTM06dZ71+uvO950hlz32c/2IouZ2SU1jQ2NKy7h+wUVeVm3AouPLapglG4925bFmWO7cjvGjW2//J34M+IWHDesKhsx9toPE9AAAGg4EkYKOH3HaiWys7alOVCiar77ggY5Obm2tNgrzjnjtutHTGlJaSfPrMmejoGE9vbw6bfeXLK1eu2LNgoIkyKSEhNT1jenLiwvkLGEylj7dXBCxKOp2UmBgdFRkcEqLT60lEQnR09LGjR+PiE3x9ffl8XnR0jMFCgml9UaSr9/VzofJBGtarSB47+qAnEAjd3dKxMXDsGIwNb2qCNh7EYLC4CHVqTAyEPiEh0EhDp8NAk7w8qL8zM2FAFY8H7UC9vTDux9ER+mLc3SGpkr8/+OAD49NPJ0VFIewj2zdxyxRkfU87tGGdio2N6yHB58+fj4y8GRoeTmhvs79i/+WXX/b09Y+OjoYEB9XWN4iF/KvOzuXlZW6urilp6ePj4xHhYemZmaFhYUtLS19evvTKK3+l0Zk5WRk1tQ1yqSQx6bYAeeTCiHDu6+eyliF6IPqrLi0tFYmgn8tkMrW2NgsEA05OICUF4kgcDlrIGhsh3ybCv7C1BZEQUv6srw/G38/MmOcXzHPzFkqkGTA8DNEhiQTlXFEBQYynp/mTT4C/v+6DD5KeflqjVsMQ4eHh28KZb/UK7nzmDAxss/4hQLPW8nJAgP+lS5eCQ4ITk5JxLc1OTlcvXbrU1z8wODDg7uZKJJIoZJKrq2tFecWVK18WFZeMj48HBQYmJsTHJybp9Ybx8TE/X5/09HQ3V9fjJ054+3jZ2dkhTA3Wo/gg8T2on8v6QOs2KtuHb9jw95gsC3pao9GIGI/RLfdtmEwmpNqiJdZkgUZlx0U0lhW1tzS3EAiEtrY2PB6PIInV1Y0OjnBwsL+xsaG6nOp0psbHCZsQge+Rqc2o7+Su1zNvGIe1m/VLOjYAhsEhKZfbcbcgkNv1oe2aWCzu6uKx2CyZTE6lUqUWwwCPxyORSBQKxcYFv6Cbxw/hGHKaWCxm0BksFgstL9DB5zVcvCrec7D3vS+kZ5yYBcV9IyMzs7NmALb6hpZmNFV1dfML0DR456LT6RYXd3YMoU975KhvhnsQVxd6Xb1e/zcNK2IoMhgMiD9Oq1VHRQ3Y26scHHqbmlpJJEinwmQykaxAg0IxXVoq7u/HYWpEH72ccOn1kSd+1Wt3whATCyzkb2g3dmio1eZ331157jnTrl2guXkqKkrA4YikUpsxuxvAtd4uFou5AkFXc7NIIGCw2VIpTNDr6uoiEokMBsM6Y1ynAz4+a15eEmdntVAoptGonZ2dtyyg5tr6zpefwGWeeS/0x2GSp96fcnfqGx3Trq5CUpOw8Bk6ox7XvLl+V9yDTg+bm7UZ1n/inq+fbyYTLEeAyAupqWs0GJABW11dNZqMRCIRh4NhN0ajcX5+Ho1iQ06BHIjKF2nY/EWQlWXPr69rvQ9y8tTU1NHR0R1Hznrn205hWUEOmbUw483Pz2VnZri4OF+/EcrhsN3d3X18/WQyWWBAwFXnq23t+OysLBcXl4LCQgaDQaNRHRzsMVVVFAq5sRHr4+NdV1fv6+OTlpbR1dVR39CA6lrrmwUA5Obm3i2+B3GE3ZrQ8LXbbDbr9fqtrS3Ey242m9G4ZqPRuLCwgNgq0bAHi/KGQOfyZWjpOXLEfPw4OH4cBpecPQvT6JqbYU5Wejrk0Gtv3wZJcXHQJhQWBnbvNj//PLCkya97esY/++xKM7SPIrtt29KQ+0LvDm2gskUAhHJ6emxsXDM7GxMd7Xz1anx8Io1KuerkFBoWJpGI3d3d3dxcqTRaQkK8y7VrlZgqKo1GJBAuX77c2tre0tLC5/Ovh4TIFT3tbTgajUEi4JmW0sE2o4lc/R58zUjVvc3NbbbZtTVIQ7Jlqf9siaOH9u2SkhKhUIgIf2lpYWUFgo99+6BJxtERhoe7ukKAaGFthkxSeXkwwa221tzSApmo2GxoB2IwzLOzYHAQQkwsFibNJSZuF4vdtcv85ZfAw2P1pz9NPH9+9daU/vqdwUp0EGOVlqJ3uf0NFKnRCEU6Pq5WqSLCw1yuuaRlZLa3tdrb20fHxPF5PHd3d3cPDyaTGRUZ6eTk1NTcTKVBSip7e3s8nkAkEnNycnz9/Ah4vK+PT3VNTXJSooeHx9DQdoyFVR+AwWBITExEvRX/cD+Xtb0HYVm0+aU/3Kp5YX7ZwgApYTKZBAIBi8XicLgpS20+ZCQs0wxw6GOirsnsRFZMCHFGdX+6F71pdnqhYWG9S2caVy42KvroolvZWzZq8r6rMpmUQqbgcLi2trauri7kHXJ6eho1CVhLQD4g43ZyuWxOV1eXdf7/xOQkl8EUHb/c986hPjsXvvd1WX2Tfm0NCQv7KignKCjoblaWjY2NewARVEoAgG+Ge6zPYH0v37htMplZLINY3C+XixkMRltbW0NDQ1tb2/wczORCvZVmqUTpdGbV02X0rN3qnj3A4u++10VXV812dpvPPmves0d/9KjY3X3KwwPGUN++CKXSHaupW+MexGhHoFJbW1sJBEJXVxedTufxeLOzs9YeKKQzGg24fl1ZWNjDYNAlku14cOSr8bGx2IzW3fZhjLf3d7x1fPjYqaXPP9MPDCD3ODgweP36jTuVI3Ls2tra3XAP8mhFRfFP3PP1s7ixseGaq+vE5NTC/Jyvj09pWXlhQX5oWDidTnNwdJR3d9Pp9Kqqqq6uLlSXo3J8kAZyJQDA2NgYGm2AHGj9O9HpdChzHfLt6OgoUov7vlfR6XTWuZQmCz6DYR0wQXppYxMmNWxubCCvQV+lCs9ZnBBbOh2sl7S2ZjAYtra2DAYDcrnNzU29Xm/zSmozgQoKCu7m54LltwoLfHx8FxaXJsbHXF1dsdim5KTExKRkfHv7VWfnyclJ5Nfb2dlpHWmI3KbRCDY2IBlyWxvAYEBxMaz0mp9vzs0FWVmwMhSBABQKmMZIJpszMoY5nDWEhZVIBM3NAIczx8eDsDDIWLOyshASknj9+lo3LIcFtrZAb+/wxsZd7cDWwwErnVkc57d6ZZybm0N6u7i4iMh2fX0NeYxuQYZr+Iq5tbVlMplWV1fNZrP1bEH0nM3roI1IU1JSUC2F6m+kSyajMTMjPTAoeG19va+3x/WaK661NSY6KjMruxFbf83NfVY7V1VV1d7ejkAfC06CNeISEmBhivR0UFoKgeCgxeM3NATCw0FWlhmDgU/IpiaY397YCD/NzaCgYIjL1VmyROEQREWBq1eBgwM4cgSeZGNjwcEhxYKvoGBMJpMlwWrn9zBEdOjfjY0N67dtOA/n55EbXFpaQnTbyurKyipUxpubm0tLS2azGYHLa2trZrNZp9Pp9XrkLQV50ul0WzamAvRyZrP5AeOaraVt3UZP9fANG3vPw58QOYP1jEXPuba2Njg4SCKRcDjcpCXTEACgVi7QaRBHzM1pdLoNGkkW6oPj0GA1nPstZoNp3WTWA7BO42TW1hUr5JB0DqmghPy3VNOyJDwjdUF3pGGx7GphsKOnp6fzeLy+vr6JiYnBwUHUw2Xdk8mpyabOBlInAb0FhJtAJpdvrazWXnIWXfWhZOdLcoqkzr6LtS0mi29Lo9E4ODjs379/ZCfayfVx8p74AAAgAElEQVT1detfJXq5O+NCvhnuQU/4kI0dh3V5ebmnpwePx7e0tKBovn0YV8Eu4wnE89q5RZVy89NPt1JSQFDQ/U0+y8vwiTAyYiooYL39tiA4WNrbaxmi7T8wbJlEkpFIkF4ZdUxa72FpI6E/UqmURCLl5OSIxeL+/v7JycnBwcE7n+0AmIlEjZ3dhKtrr1q9nYQPAFCpVGPDY93zXe8E3TgSEM45c3TlhRe5Xp46T0+ThSltbGzs1KlThw8fRkpq2oh3dXUVeSDbbEeG1VqY/8Q98PkG43V0WyHBwVGRN1ta29lMZsj1EHd3N08v77CwsLi42CtXrpCpNA6Hc/ny5fT09Lq6upqamrq6uuTk5Ozs7DrLkpKSkpmZ2dDQkJWVlZKSUlNTEx0djcFg4uLiCgsLY2Njs7KykpKSkpOTHRwcoqOjsVhscnJyTk5OeXl5TExMVVVVTExMQUFBXFzc4cOHY2JikMqXiYmJdnZ2CQkJVVVVUVFR1dXVcXFxxcXF8fHx2dnZyAmRPREaCW9v75qaGgwGk5aWVltbW1hYWGRZCgsL8/PzCwsLsyGFTH5mZmaWZUlPT6+pqamtrW1oaKioqMjMzKytrcVisVlZWWVlZfX19enpGRgMJj8/v6CgICcnJy8vLyEhISsrK8GyuLi4oIkMNqpia3PjK365oMBANofb0oSNT0g4f/58YGDQzZuRUVHRjo6OAqG4qbnZxcUlKysLEWltbW18fHxhYSEWW3vzZu0nnyTk5eW3tDTk5qYVFmZhsZiMjOjY2Cpn55iIiPwrVxKystKzstIvXYp/7bWLhw9nnDnT4O2dEBVVdPZscWJiQnNzmbd33Icf5tvbRx4/fjgnJzktLTEuLt7OLvbll8/m5WVXVlbGxsZWVVVFR0eXlJTExcVlZmampKQkJycnJCQkwWKuMX5+fgEBAXV1dRUVFWlpafX19Xl5eaWlpbm5uSUlJdnZ2YhI8/LyMjIysrKyMjMzkSmBiLSkuBidIRkZGeXl5XV1dWlpUObZ2dmlpaXp6en5+fnx8fGZmZnJycmxsbFubm47FsqFj3utxt/f78aNGyKJtLa6KjoqysHB4fr1G0HBwbGxMQ6Ojn0DA+Xl5S4uLvn5+bW1tTU1NdXVNdnZNcXFNQ0NNfn5NYWFNW++WePmVoPF1mAwtTk55VeuxFy4UOPr+/XHz682MrLqvffswsMLQ0Nr/fxq0tNrQkJqAgJqsrJqrl6tc3XNiYoKd3a+Ul9fl5BQU1xcW12NOXfuHHKnNVZLXV0d8nNAxhf91QQHB9fX1yM7QkHV1yO9ra+vR6RU39BQXw9/ZfX19ci0rKurq62trbfsWVdbW2dpIzvU1tbW1tVhsdiMjIzS0lLkVGgvKisrPT09UaT1/5W9x+YB/Y1X72E3mp+fLywsZLPZYrFILuuJDK67eim9oryGQoEJxtHRkUR81wOXbdjuoFI12dCA5XK5UotPxPLeLxZLJL1D9L4RgkgMM5DRnKA7G6jSpFAoTCaTwWAQiUQE0dpIQKfXDc8OcqbpuU2Zwu0sRbjLV+Vc5ubmNnW6iOs3xgYHW8mkvKzs3qxCXe/g6urq0tISw7K88sorRUVF6DmR3CvU/GA0Gm3khjiYjJYFOeofi3vQrqK3gDaUSmV+fj6MHBJJa0XVke3hV4sdUmuSqSQapr4h3tV11ssLWIx86CH3aWxuzlGp2JaWLh5vO01PJBLKZD3t7Wp7e5W9vby9HclRR4cPaVhbfZA2hUJhs9k0Sw22HfFlX5/+pZcM7u7Czz/vE4u3g7UtwUzzS3BZ9YxzZPaTsbjWFk9P7eXL5vLy5aUl7fw8YsL8y1/+gjhekDtChhWFNTu+VSJjisrzn7hnG/dYWHMS7e3tW3Ct7W1tTo6OYeERN25c9/f3x7W0+Pp45xcUGgwGNpvN5XIZDAaTyWSxWBTLQrUsZDKZQqFQqVQKhUImk6lUKolEolKpyHYymcxgMOh0OolEQpL6LD9MuAXdH90T3Ui3LMgEYrPZyPnRkyMHIgmfFArF4q6iUalUpG/o1ZEd0L8kEolMJpNIJCKRSCZ/fRTSc+QotNvIXVhfyPo8JBKJx+Mhb2nWoMfShhEd4aGhDo6OFCq1rrbGyckp4uZNPz+/sLDw5uZmH2+vZlzr2toai8VCRMpisZhMJipGMhkKkEKh0GjbMqRQ4BYymUokwm9IJAqDwaTRaEQiiclkMRgsCoVBpzNoNDqRiAwBhUgkE4lwkOh0OtLzW1KiMRgMG5Gi8kfkQ6VSb+0MRYr0zUYgqDCRQ5AOMyy8XuisQEWKfItODGTV+qJkMplIhDl0yG/YRqQwHHhjPSDA38XlGpPJxFRWODo6xMXF+/r4RNyMbG5quubiTGcwtVotOsFYLJZlrlIZDCqFQiUQqGQybNPgNIF/GQwqmUyi0ah0OrJK53DYLBaDTqdyuWzkcDaHRaUwmEwqh0NF/jIYFDIZipRCoeLxNAaMu2DS6XREpMiNo3/R6Q25zmCIBptOp6OzFN0NbaBSQrcgZ0ZX79G481hkVstkctSo9v8n7kGf2vdRTt/o6/n5eQwGU1RUBGlCS2lVZQwJJPuBaotCobS14cbGx+7hINjxmhqNBofDISUneTwen8fv6GBJ+6uHlfWKHqFYLLHRjjuuSiwLk8kcHx/f8SpTaxPCuc7J1fFOXgemEoP6uYxG4+joqM5obC8qkVfWrq+uzSLM6ZaXWIFAIJfLZTKZr69vWVkZItvl5WW9JccEvZBOp9vY2EBsh6ipCTHTLi8vI0c9JO5BFS160f/GxszMTGlpaWlxaUphUig+JKw9hC1kScUwOodAobSTyVNq9drq6t90xempKcQvLxKJeDxel0wmS06ecHBQubiMFBUJe3qszHs7DinciAwrjUZD6gzadEClAt7eGyEh82+8YW5qkjU21qD+TVjuY3x8a+X/svce4G0dV6Iw7fi9Tdl9SXazX7J53yZvkxc7WT+nrGOvEyeW5BJbkm11yWqkSEpik9jA3kmxd4qkSLH33gvYCZAAARK9sPcGFjQSAAkCBHDvr3tHuoIAkKIoWdLu7/n0UXPnnmlnBjPnniqnnL2w7u4pn59flcngpSUY5eH19vZOTk6SyWRXV1fMZHV9fR2rDjoCSusajWZ7extj/2i0WoVCgS3rN3TP/csFITbFYg6HK12TikSiQT5/VShcXJiPiIioqqpG3N6wWVlZWUvLK4UF+Tz+YD+VWlJSiukDGSyt8SMEQeXlZQRiDxJWcGw0MzNLsCjILyjg8VHRi3EFtASNCVUHQiW04PErqFG0SVi5XIao2owhAozOjo6KikqgiWwSGCtUKOT19Q1YLFKsfI8ZjUZTVlra0dkFLhXje3pJIOAPDkolEpFIxOFw1tdlI8PDISEhTU1NiOkKlZqXXyAQCHJzcycnp9rb26pra00xRU0PR61WF+TnMVlsxLCWxSgqLl5YmMvIuDtlpBqF1dfpdDXV1WDACgXwQbCjzadQuJJxN31xUQBBUG1NTWNTsw49VbHWTGZEIuQ+wA5okzC7FMpkspycbHLfI0GmMMTCMDw7OzM0PCwWicRiMZvNVigUXA47JCS4paWFx+f3kcmFhUVLAkFuTs7M7GxTU0NTM34Po74/IoVcnpubwx8agmGY0keuQNwZTN/NyACW+SaHrdNpy0tLenpJqBNCSUNDA3bWGMPPz89lZWWt3hPvQ1BlRXlHBxKi5LFpeXmpGY9/LNhOAFKJODsrm/4gON3LSffcc32+C952mtpeysHVq9PplEolgUCg0xFmDGaCzuVyaTRaG/olYaBA89jG19fXqVQq+DYgEomDg8OLghliT0tJSRmFQkFJK5S22vF+RC5IKpVKoVB2+tWvbq4ylgY2NZuabc3i4qI+GbEmk3W2tnGCYwau3BRw+FoIkqHqblNTU/n5+Xl5eWNjYwKBgMViIT8fGFar1QbEJRB/APVhwPtB/MHAsHJrC1uLp6R7tFrtvo+CXfAPwTA4i7RarVKpJHb3dA90k2i9XNZ91Rwej0ehUlEvtYhXJ4OJ79IykCGCD9Hu7u7e3t7RyUllSQktMLCspmbAyNrLmNkD6B4ymXxPqdFkvzIZHOC3cd1qjkCAtVqlQCDQX9ZVmYweG7vi5MS2stIUF6theAONaTw8MpKZmVlSUjKJJj6fjywfDG+jDkj0ZwSYdmBZdTrd/QwMK/XIoG/s2LE7BZF2GaT5uVkcztXN3X1dJktLTfHw9HS8edPbxycoOMjW5rr9jRtIOG59S5WHjT2SQ7zy8zjOzs44N5xSqYyOinRxdSkoKo6Pi4mMikY7fQQebJd1KeJ35OZNx4mp6fn5uePHjzHZiFsOQ1D0WquurHBydIyOiYVhuIdIuHTpkmAJCTlkDIyVwDBM66d+fvSL2TnEyzBWvscMDMPzczMfffRRRlb2TtUBPvV3P4fN9PD08AsIlMsV4aG3vLw8XV1dPT08wsJCzc0v+wUEqowD0JsaEGpSR3BwcAgKCr53WgX4+zs5OtbV1fn7+WShQSuNK8EwfM+HsuPNm/eCb4glUiaD/sXnX8ztMHckwGdujr2DfVo6Eo0ViThhaQ0IROOWsRIYhpsbG06cOLWJWhxg5XvMoAQc88DBg5XVNbujVH+jknqIzk7OwSGhWo0mKCgQ5+bm7Ozk5+cfHh5x+vSp8Mho4Jj/sWOAYbitpdnJ2TksLHxNKvHy9HB3d29r78C5uJSVV5ocDwzDHDbr5s2bbm7uKrWa0NXx5ZfH1tYRvRyD7sCAU27fxrnhCotKYBguKy2+bmOrVqMulQyg9R5hGK4oL7146TIa/sKwWT1A01nk59BDOPThh61t950yv0C6RywWz83NTU9P65u3YEupVqs3Nzf17wDs1f4yOp1WuKLQPHDKvLCwUF9fj/jCeUCNgEuLhUiokNTa2mpSGwbob5kcA4i0qv9qY2OTSqWCuBM7EzyISjSLw+0jk4lE4i7Ki8otxeQsF73g9Du5n+8iEouSU7N9A7Lz8kQi0Sy+k9TcSkB9BAOn2GKxmMfjcbhc9dKOgcoBxXO/RQhSzczrlEr19jYYlUAgmEUTxhAyHsfKyopAIJiZmTG5diqVau+fx8aNG5foxGLEo6v8vir6+Ph4U1MTj83jIC6wHxEvgmVFnJ6jruGNm9I/mfXfAk4Y+BWDcplK1dPb293dzWab4ORh1A+XzR3kDAKpiMlNDlqjjI1455VodCZipEAw3IzH021tO48eLS0pETEYS5cv8zIyiH19TU1NVCpVpVItLS0xeDw+kQhXVcGo0qr+4EH+EWmXSAQnJcGzs5uIqxSEATY/P7+4uDg3N2fAKzJu5+sueen8FsIwPDU54eR4E+fmPjQ0HBsb7evnZ29n6+/v7+nlVVZaZm9nT6MjhjOmT1y9UnA9uOJw7u4eMzMzISHBXl5edzOziosK/Pz8TV5LMAxLJWIcDufi4koikdVqdXhYWD8NiTer1/D9LHI3lJXicLjwiMiVlRUymeRgbw84ScbAWAkqN1F6e3mBIFNY+R4zMAwLFhdu3066cePmHq95GIb7KWR3NzcfH7+h4eGAAH8/X18nZ2dvb6+gwKDi4qJrV6/OPRqqc6fBwDBM6Op0dHQMCAycm5/z9fFxdXUtKSlNu3MnCiX+jCsCTpuTk5OHhzuXy5Oty3y8vQFzyCRwTnaWk5PT7eSUlZWVjo4OOzu7uXlEFG0MjJUAFRxPDw+ZzMTdj4HtlIFheGx0JDYm2sPLaxtl7e4EiZXDMNzV2eHo6OgXEDg7O+Pn5+fp6XXzxo2goKCg4ODCokIbW9tFARLqAauyUwaGYTy+0RWHuxUSOjjI93B38/b2rqmtjY+NvpP+SLBPrAUYhllMhpOTs4eH59TUtEQs8vbyEomQuI8YDMiA4+N2YoK3l1dGZtaiQNDY2GBvb7+2hvDbDID1HxFV3CWBl5fXYykk/VpYHobhQT4vNDTUPyAQUE4viu7R6XSxsbEuLi4NDQ0m43MBa8edrqJ9nL+M/rFb3vU8JrL6SqWyo6PD+HsdI014PF52draPjw+fzzeQeUEQ9NjrAfBOgGMzPp8PGLpY41jm/gXJZrPptIHmWiqducvtiGi1ayfW1O3bOiTopnFak0oLi4tLyssdnBxniOQZFz+iTwifxyMQiXV1dREREdnZ2V2E7jsubvN+keIB5vziAvisAtaOYFL6Kj7K0Yk5z1trDW3bKtXK6urm5iaTyUxNTbWzs+tFDTCNx3DPoCEwMNDb27ujowPzEaUPBrRP9EueKq/RLNrbT+NwiKkCBK2tr7e0tICoEfpED4ZwPp+flJQUHh4+OjpqgOo9LivYkCBUWWtrK9fI2gvri8filfeWxDVGslhsjGFmPNmVzeUkenQOK6VmHPmaMk5CkSivsLCguNjZ3X0d5zbg5z974vhQf397d3dtbW1oaGhJeXlXY2PV737Xc/78ekiIYGkJ25/b29uAd/iQ7tFq4eBgbVoa7OWlFItXhEKZTEaj0RISEm7cQMJIGw/geZa8jHSPVqMpLixsbGpms9n9VGpKSsr0zMzd9DQqtb+joy0nJwcQj9ghu1MGHAd5OTltbUj4PTKZnJySMj4+lpaWZhDsU78FGIbq62rLKypZLObC4iKlr2+nCwwI6ZISkwZoNBptoKG+PicnV4FyffUbNMiDH39XZ5dUiriyMHj72EfEy7NCnoE4yH8YUH33WsCJXMbddAKByGQyent60tPSp6enUlNSOBxeTU11aVnZ3u/7DYUiLT2ttxcJ6EMgdGdlZ4+NjiYlJXLQkCvGI0Hmu71dXFTY2NQ00N+/srJCIBCkqBsPk8Dzc7OJiQk8Hr+/v7+srLS0tOyxvCiggtPR0QHCVhg3u3sJDMPC1dWUlJQ+imk5l3F14AsHbCQGnd7d1ZmZlTU1NZWcfHtoaLispLi2tg6c7MZ1DUqAoOr27SQajUan09vaWvPy8gcH+YmJicPDoyZ3CGovpsrNye7s6gI2q4Tu7o0deF2A35Z0O4nPH+zv7y8qKqyurgGHlMFI9B/Rbabo7OwCB5n+q73kUVPkheTkZBYbkYc+1l+z/jQB/DMkRCYnJ1ks1sjICHBAoFarsfMa9AV6x/JPmVmYleakUsaGELdVy8vL5eXlQA3ZpIoGm80uKyuLioqqr68vKCjo7++fmpoCt5dJNsZOY4MgaH5+vrkZOTOxGxFkgIIzEtiJQZ8pSp4KvrYxC1yJIo2ZvIa3datyDU0LIf4aTKbR0dHGxsb6+vqk27fZOUUtxaVtHR3AvY2vr29dfX1jc3NuaET5yUs+lldT7tzp6elhsVjz8/NisVgikZDJZCyACeJDRLouKqhQDiG7HVgdzs/PDw4OMhiMnezedTrd8PAwj8cbHx8HYRNUKpW+2A7sn2e2i7Ra+a1bqy4u8F3kU2RmdraysvLhsqJL+xDtaMi2/Pz8uLi4+vr6oqIiBoMxOzsLhvdEy4p8NI6P4/F4rK+HvSBMJiaXxW2k1HvV4IJr/NunH0qljZdVsS3P52XfpscPLCGnnMnE4fGampurauoSbK1Gjh2mh0U0tbbi8fj29nZ/P7+W1tbaioqOzz+nHDmS98knaZmZfX19bDZbIBCIxWKhUEgmkx9Zr9payMUFMQnW6RSorfvc3Nzw8DCDwQC/RJNjeD6FLx3dg56qj5n7Xk5eDGantjAA44xBFWMArMQAUv/4xmBMZvYOaVwd69T41S4lWC3TmV1qPvrKdHW09FHAR56wWth59MhrvQcMEsvovdwt+7KhdLexPvoOm6lx5lHAR54w4JcTpdiVAzK78HsMFg7Id7Dq2DSfJgPiVACbfGO6B5joP037+nUhCFLIVDrEaSTiO6O1tRWjRVDZFiLuAoo+LCSkKZPH4/H5fA6HQ6FQMFOD1dVVExjQarTzY7AGcZBhMk1MTOh3h1yMqHSNwWIzWSw2h9OZdVtclQarHvqSwNxn6De4pVRuax7jHGF1dRXf0lIdESNuJwiFwvHx8eXl5ZGRkdDQUBKJJJFIR8bG+DRGTn4+sCkZGBhoQVN3d3d8fHxra6s+n0anUkF6Adv3HqdiZmYGaJYAVyD6s9DpdLvwP/Qh95Tf2IB7e4F8Z3h4uK2t7T4twmIx2Ah6GVwufRCJbc6k05lsNrKofD6bze7r66NSqQQCgUwmS1HPuYbdjY/DCQkw6hPI8BX6zOVyOzo6sV2ExGlHPPswuFwGn8vqY5A9ylyie8Km1hE3WiCp1WoD9iHydSdbHREPPwAx/f/i/GJlU6VNgV0yIWJidXZ8fEIikXA4nPDwcA6HsyIWTw8OCiorcwsLOzo7e3t7BwYGmpubgWVDbGxsV1eXFPNaqdMhLk8eSMQgCJqbm9vJ7YXp0XxtpS8n3fPI+f7NwzcY+AYD/6UxYJLf09raCo61hYU5ZyenqupalOLRpqWmenp6TUzuxbfNXs9Fff89xvSE8Sc4cKZqDLnX/h7AKZVKMplMpVIB04XUM1Bf3cvjsXuI/Vwuu7me1Ibvwxg0AIbD4fT29lKpJj7KtUMDWynuWi75QfOG/29ubra3dzA4XITcYbNZtAEWncbg8IZbqtn9FDaXV11TMzeNIBZMDdtUBg0plVt7ifi7IRLzAyJZ9h61uflJKSkEAgEoXPf1IUHcRkZGmdzBmbn5tbW1nJycpqamqqqq3NxcPB6/vLwslUrz8vLa2tqMXcKAxcJ85BiMzeARi89lbP1gclkNpE4Gre3xUSaTEYlEOp2O0K0s1kxw8KKzM7e7eyo2lt/UtGRjMx0RwUBfYVIwDodDJBL5fCRIziNpbQ1yd9/OzESceu3g2ECuUHR2tgwO0jkcJp9Pz8sb7ezkdnZyra2XK6sGh4d4pVWlQ3OIpQ6YssllRXxxbakgRIP8Mal7ttMh08Yh3z6iKOzunbskNPX09JDJ5PaOjsGJCfrg4OzKysLiYnp6OvDlmJ2d3d3dLRKJ5ufnMzMze3p69L2vgf60Wu34+LgxNfaY0Xw9r//b0j0AXTudXPrl2BGwE4bBNtrprcHXKrbnDDIYGNaOwRgM4F/Cx72jVB9Sf5r6c8fKTWb2Mn20NQRQv1n9/F4aebEwYLT6U8DGj11Ou5fs8e0ep/nY8eyxHQMwPbpHBAkW4TVpc0nJPb0WYHSTEB/r4eF5xdJSLJGKhKv/+e477h6eQqFIf2pPmdene3RanWrrvlo3CDehf1tgHa2srDwTxVihUNjZ2clms+h0RlpCS6BbbUled2RgQ35mZ4R/Q3xYE5VKBxFAMbFUe3s7cKGJDQZkIMnydlsRJNwttDudyWI3VjBZbPYAdSYvbrIsfQRfLYywnS66zWaxenp7B4eHgfNJTCqkr2pj0KPJR/3tqlpalY2Mr8vl09PTTCazurq6tra2uLi4p6dHIV1Z4zVqJFOKjQ2xWEwgEJhM5vT0dGFhYXNzsxxNRCKxqKhIrVZLJBJ9MmV2dlZfEGZyGKDwId2jUCDh7tCkRRO2rFh1nU4nEAh20ZXGIB+bmZ2d7erqYg4OjuTnL9nYzPr7y995ZyYsTP7uu1MxMUvW1kNVVQjvRy91dnbqs7jud6HRwNnZOhwO3sV2EtouKuU7Os1TKOz07Gl7e4Gd/dKxY+KEhMlLl1apVHZvD3Fqchpou2Oro4/Px05HH0Cr09aNVjdNNKytr09MTNJotIqKirq6uoLCQhqNpigulh88CCcnb8jlIomkvb19DE25ubldXV0bGxtSqbSlpaWmpkatVj/k/aAdTE1NfUP3GJyNz/IRhuHJyYnBIYSnR6fRKsrL6xsa5ufn8fjm8vLy4ZERNLgxKzc3d3x8Qi6X0ekMnU43ODi4sLDY1NhYUV7e3z/Q3dVVgybw5TE1OVFf3zAzOzs7M11XVzczOzs1Odna2qbZgUgHOwmb1cBAP2hnemoyNzeXRmdoNBoi6op6aBgZJwb5cmZgGB4a5APDk96enory8paWVsRWpa6usrISOAKh9JHz8vLnFxbFIiGXx9va2uLxeIsLC7W1tRUVFQwGo729raampq6uXi5H1PuHhgbrGxoQ9vjwMPCCOsjnEQhIdE/s16v/g8RQCnSkSL0kOWomMDTIz83NHRwa2lIq29pay8vLZ2YQxyQvJyaxUaF69+y5+QWNRtPd3VVeXt7e0TE/N1dbW1NdXS1A1aKJRGJBQcHKyurq6gqPx9/aUg4ODs7MzNTU1FRWVrFY7Obm5tra2iYQFB2COGx2Y2MjYlDD5dY3NEilaywWg9zXhxKHpglEDNvKzU0SiQQ+9DlsVk5Ozvj4+JZS2dTUVFlZKVjak5o2Njv9DAzDVZVVxA6qQqwVC7aUa1BFSd3169cWFpErPCjQv7SszNraGuUNSMtKy2xsbPIL74ciNt4A+ygBdA8as0U3NrxUV85VyJGwLdjc9YVrWPkum/CJxjAwMNDX18flcojd/VWlxO4OanRwY3MDubKE0FhL4nIfGHqhLlhaWlq4qKqcqS4gaFuNOFPfKa0JhZnBgmDLsfpidj9FdMtquiCB00+ZLL/LJ7YzWWwajUahUAQCwcbGBo1GA45ewErt1KRxOdCnAajDiCcMTIN6cFFuKRVDzeISq4Uan7KclC2Vur+/n0KhIErTW1trqP9DoH+5tra2vb29vLyMkSOAOtk7v2dbq9UpFKrYWI2rKzwwgAWlA0PSV7PDzgRstPvO6HQ6EomExBNls2eCgwU3bsx7es67uy86O0v/9rdFJycGl4vKGRHCh8ViNTU1AU+zJnpUKuGxMRPloGhbLU28E/r/7l67uuSMW77j0et9pNv+2kJk1IyFxcrt25NcLrO/n0qj0RYXFzc2NigUCnAW+qR0D7asD0IKPTIixN8SBG2Ojqp/97vp119f/8EPKAEBEAx3d3cPov74lUqlFE2o9qEauNUFKwYAACAASURBVIBfXl7GHApotdr5+flv6B79g/EZ52EYtrxifuTzL7bVqvPnztra2WdkZjY11B88dOjWrVtffnmsvb3t+PHjgYGBp06dbmvB/+63v+3q6nZ3d7+bnn7w0MHIyKjGxqaiwsI333wT5+a+tIyYpltbXvn8iy85XJ6Dnc3xEyd7iEQ7W9ujR4/W1CKeflZXV0gk0sDAgEKhGOjvJ5HJiE9PLhccnXMz02+88UZyappodfXUyRP+AQEnTp6oqan9/OhRPz+/48eP81ES7Rlj4dk1B1wZHT3ymdXV6yqV6sAHf/H08iosKiosyP/kk49Db4WcOn0G39x0/Phxfz8/c4sr1dVVb/z6N729vTccHNLvpH780cfx8fFtbe2pKcm/+c1vQkLD1mVyzfb2yRNfXrpsPjQ8fPLEMYsrVygUysWLF0+cONFL7kONHud6e5EwilKplEqloP+oYyDWA2LQRP+nf/rHuvqmqYnxzz//HF3KUw31DR9//JG/v/+JkycXFgXYMffsMPHMWoJheH1t7f0//wnn7ikUCj848IGfv39JSWnm3bQvvjzm5+dndfVaWWnJqVOnfXx8rK2vVZSXvf7Gb1hMprOLa1xszOEjRxMSEru6uhPiYt988824+IRNpXJNKvnow0M2trZcDufokcO2dg6k3p4L588fO3acg7oQnJubJRAIg4NDEokEObX7+/v6yJMPHC+Regk/+cmPu4k9HBbz6NGjQUGBp8+cbWpq/ODAAQ8Pj/MXLorQYCD7QAEMw5WV1e0ddJEYXhTA0jW4pBSPyblqqiuPHfvS3d2jo729m0AI8A8wNzfHt7Q9cu4+3YNOp5ucnJTL5dvb2+3NQ5H+7ZOjj/CTdDodsEkRiUQzMzMgUMzT9fmwtlgs7uhop1AoFDJtcIhDJg0015MG+unx4c0psS0D/XREDwdN6LdBO0YBPGzCKAdYGvrFkFKhrri94fixIMmDQyWN1ReLgyzGavPp3EEg9mJzOEBaodFoGAxGdnb2ToG09Js1zj+WHKTT6dk5uSUFOZP4xFFCaX5+/srKKo1Ga2xsXEbPUuMWurq6QMBB0N3Y2Jix/Mt4JIjx79SUWqfTjI2t2dhsJCYiQWH0ElhWjUazsrIyPz//0NpID2bf2SWBoI1I5FdWTqamMjic2cBA4blzM2Fhc35+AhubwYYGjN/DYDA6Ozv3omxkvKza+vrwH/s2HnC0+lVL3h2OzOZi1k18r3Xk8CiDQETMvDgcNuClKZVKCoWSlZUFomvve14mK+p0OiKRmJWfT7l6dfPNN2eOHavKzl6Ty8lkMhKjHtXpMV5WPB4/MzMDGoQgaHh4+BHFZ5M9PZfC/4ZyLuC258zZMydPnST3USyvWFyxtMrOySV0df35z++bX76Ec3MP8PcPvnXr3keev69PQEDAgQMHzp07d/Xa9ezsrAMHDgQEBBCJvRCku3jxEovNRW59tcrN1dXCwqKyusbL0+PqtWvR0dE3btwoLy0OCAiCYTjtTsqZs2cvX7qYnZNz9szZC+fP3wtzfe7c2eUVxINFelrK5cuXLa2si4uLzK9Yol5qsm1tba9YWW8qlT7eXmkZmS/5JU3s7jp77tzp06epAwN/++QjJyfn4pLSqoryv7z/voWFuZ9/oBvOtaik7B4JaGdn5+vr+/6f/3zpsvl1m+uZGemHDh26FRrKZLHX1iTnz3+1vILYx8pk69evXb2GuOpuvHHjhq2tXVRUpI9fQGpqctLtFLA0V69es7C4kpSYcP78hVMnTzrYO1hYXAHxzoKDAq5cuXLT0TE9LTUgMBCG4ajIcHsHB3sHBwilehubW15ylDY1Nnx1/vyZM2eoVOonn3zi4uJaU1tbmJ938OChixcvRMfEWFtZNrcg/m/ML18MCQl5++23LS0tHZ1dkhLj//bpZ4iV7Ni4bE1y6vRZKWqXvry0aGVlaWNjU1tXZ2tz/eZNx1shIbdCw2KiIwtQ5z0e7jh7e3tLS6vEhIQLF84fP37czt7O1s5OiwYGd3fDWZhbePv4JsbFxsTG3TPpwrk6u7m53XB01mo0Fy98RUTdJO6b7unooIvFsEAAra3BpaXNIB47+Ojv6+tbXl5ZXFgQiUQ8HpdGpxufoU95HtLpdMRXG6Tb3FRNTzwkenRoAiF7FxYWNjY2EN7aygpwNPykH807DVIqXcu80xjqW9vWQkmKwof61hO6qAVZncV5XQwmogqC2gCx6HR6T0+PsbmZcbNg2Abl6nHuUoTDbE40e4DCJRMXk315vV2IUjPqX4aIukrf2NiYmJjA4/GdnZ3InYREE0S+68CPxaDBJ3h8wIUaHx9PT09HLFzpdAYaOYxK7a+rq+vo6KBQKCQSSd9zD9hLIBApxuWanp4OCwvbI91DpVIlEsk2DGv7+nTJyTBqTgviTAMhpkwmW1xc3NraWl5eFgqFCoXiIWPjCaZnGnSNTh+/eHHCxUV07tycl9e8t7fg5k2Bvf3izZscIpHFZQ8O0tlsJo1G6+vr28teMrGs4lXWlYi0/+1ucWK8sHJK6HpT/d7vFsKD6Xw+h4uohHV3d4PglYODg8CR/bJQCI+MIF52dEhc0af6KaGGtwhnmsPJzMxkslg0NpvT28seGOilUmtrawkEAolEotPp+jQlJjmdnJwEcSRhGOZyuVFRUbt4jTKN4q+n9L8n3RMeduvgoUNHDh+2s3e4dOlicsodFovd3oK/cPFSYECAww3HkuKi8xcu8ni8c2fP3E5KcnHFhYYEvfGb32RnZ504cQJ8fGg0mhMnTzKZiCGuXCarrKxMS025YmldV1efmpJ8+vTpq1ev+Xh7xyckwTAcHxsTl5AYFOjvHxjo6eUTFxOdlZlpbWU5OTW9rVId/uzTc1999c4770RERBw/cYLN4TjY2YaHh586fZrc13fxwvmX+ZIGG++mg/3hI0cPHTzo4+t39PBnJaWlQ4NDebk5V69ec3Fx9g8IvpOS7HDTkc1inj59OiYmJjQs3NHxxtvvvpuelnrhwkUSiTQ7OydcXTl27MtZ1B+PWCSsqKwMv3XL0cm5uRkfGR52/vx5e3sHnKsL8LDn7uZSXl7h4uLs6ekeF5cQ4OdXVV391YULcsXG8vLSn/70nrm5xbvvvhsZGXnx0mUej2dhfjk2Lv7M6dP9/QOfHz0CPAXv45J+LlUQpFpaXD527Pif3/9zYGDQsWPHa2vrBgcH0++k3rjhaG9rExsXFxF2y8vHl9LXd+zYl4mJiaFhEZYW5h8c/DA+Pu7qdZs+ct/iokC4uvzF558LRUiUQcHiQk1Nja+Pl4enV0tLi7+vz6VLl52cnOztbJvwiBKxg709Ht/q7Ojo5u6emnrHHedaU1NjaWmpUmtmpiff+eMfLSws/vrBB7dCQiwtrXk83ulTJ5NvJ50+c7aHSPz888+HR0zb1T8WY4DfY5LueapDec9nIgRBQ0NDYrH4QVBbEGcaqQ8isIKWsBtxe3t7fX19Y2MD49LvuasdAdk0we3oFlJvf1M9qSSvm4Vq/NDpCNHDYbNIPUh4pt7eXn0eDDaeHRt99IVarZ7NCBX7nB1uqRlqqRGG2UyW32WyOSwuj9jTQ6VSAbaHhoZQH3cUtVqtpXdsZgUpZ5CV3d7WPNrenp9029p56vQwEkCzu7sbuGS87zQIpbmYTCaDwWCz2Xg8HlAAYM8AGmhkZITP5wsEAsAR2dra4nK5JlRhTA0H+D1CSJ9HqcXNzU1MpIK9UavVa2trGxsbxuI5U23voUwgWHNzG7ewmE2IW3G9KXC4waZSB+vr2T09bD53oJ8VGzvd38/t6enW52Zh49lDBwjIGoVb9nPnyi/iPZynJTZXpqKjRVYWPB6Czw4ikcFkgmXlcrkkEolKo+kmJrTe3orQUKioCILhJ+3u4aj6+zXOzitFRTSUugKuwBF/CFwu0jebzUADprJYrMbGRgaDgfiz1umAQyyNRsPn84eGhgQCAcC2QqHgcDjf2HM99rTcDwBK6Wvu3Ekdn5hYXVlOTEyIioxwdHT09fXr7OgoKS3bUCiioqJGx8aSk5MuX76clZ0zi6jaFUnEoojISAqV4ujk6OzsnJWNeENOS0u771lYp0u7k2pjY8Pn8zPuptvY2A6PjBYW5Nva2QO3y0hgqs7uxob6xqam6uqattYWMplckJ8vlkgX5ueSbydvbCo72tuqqmuqKisvX74cHh6xuroaFBRoa2t7NyNjj+5z9oORp66DuHKRyxPi41dWVycmxhISEoODgpycnMLDw5ubm2rr6tbX18LCwmZmZsJCb5mbm1dV1wzy+Y2NTfNzcxEREZQ+sr2dnaurS3VNrVwuv3MnVYwyRVWqrYiIcGcX17GxsZjoSGdnl5nZ2duJCW5u7iBwfU1VJYPBqCgvr62taWtrr6utpdPpWagyLJ/PvZuRqVara2uqW1rbsrMyzc3NU1LvLC8vubvjHBwcSkvLwNfVU8/+a2kA9dkjTUxMEonEfB43PiEhMDDA2dk5IjKytaWlvb1DLBaFh4fPzc0F+PtZWFi0tLZxOexmfMvw0GBCYmJXV6edvb2Li0szvnVDobidnAz0nBQKeWBggLuH5/jEREhwkLuH59zcfGREuI+Pr1yO+GIpKy3lcLjlZWU1NTVdXd2VFeV0BiM/vwCC4YF+akFhkVarLSku6uompKTcvnTpUm5e/vKSwNHR8cbNG7V19fvmn+1O9+yE4ofn71PngB37JpoMKC2NRgOuWy0a3ADbNiCEBTB9N/EV/uRDWl+Xd3YSuRwWkwG4O4j6B8rpYXa2UiP8G4ryWzs7O/VdmxgM9bF9QtLVjcygpTgX1kA/k8kcq8ln91O4vV2jLdUDbc39AzSgGNfX10cgENioayXtME1VnaoV32f5PLYL0wCScTUxfKjpNoVCAbyl+3I7o/84HE5tbS2DydrWQsBzLAzDKysr7e3tgBMA8C8QCPZI98zMzGg0Gn2kgRFiUaIMlhUYtwNyFmw80zPac6mMw+Vkp/dxxjtrB4boZGBfzuHyWAwmDrd448aEpeUwysN76CX5SZcVzknrsIgv/JX9cH3dcEWFxPIyMakyzpslvHqVEx3JQW3EVCpVTw8SrW9kfBwWCreDgmQ4HNz2FMJitRp2c6MFBMwcOzbQ08PicB4IYx8uKkbacjicyurq4dFRLUqqA+QtLCx0d3cDoS1Y1m/0e3Y66x6WYxvvYdGT5EB1sMMwr6ZYIViGnah+cMxhdbEMdiYaZ7DRYhmDWlg54MGCtxgT8klm9lSw+766DAasjyKsTTA1/ZlieWN4UOshJh8omWLTw5CDNYKhFBsM9kp/KfXBsNa+pgw2gCduXwch+oNoAgM2uFwxfUwwtQewD6tgABj+0XaghyhFudz66t3GKMUGoI9SkNePNAdKnniODyq8WLqHy+X29PSMjIw8VsECxBW6j+IHSwNcwGGse+O3+qjTf6u/wxF1rvV1ArEb30jOSm0fGMDMnJlcLqe2uu2Wb9Ugd8lkGG2DNnd/VE7wB1obmYgpO5PB4Y40V4jCrol8zo2lhQjm7mtaTExMPHRzrNNByh39E+7eF/ZWtaXsLrvN6iexMKP8h5ejYY7BZJHaalSLbBj7AWANPcggIREYDAKBsBP1o9VqaTQalUqdmJh4UGnH/4HAy+A1UP15+mUVrkkaO8hubgIra2Fl9QiPy+CyOERaN5PFcHGeuHyZHxoqV6v3y0gDg5bJNwN9h2OiWHwem8+hEGjmdusNf/ZK/yh1+vjxtQe28YODg11dXUCpGYkqykCiGjxN2qDRGJ9+OlJaiuho757YbAaNxra3V0ZFYaJG464XFhaYTCaBQHjhWj4vqZwLuEKuqq6enJo2Rt83JfpX3YPL5TH/I0L0hYXq6mqgcvQNDo0x8BgMGr0GapXV1dXP1ujaeGD/dUsAzl4g3aPT6RITE11cXPr6+vbI8NdoNIB2fCzaVSqVyYsTqwhc6oHHpSVBd3d37t1OP5fqNnwfl8tiMJh5GZ1t+P6urvaJcdOh0bGm9phRa7Q9JNIADXEtw2Cz+YS2xZxITkbUMJmAtQAkEdjj02e0Ol1zS1tfHwVjAABfRNgjdmkymCw2Y2C587aGFAMrHjKZsH0CBrO6upqZmXnjxg0SCYm8a5zkcvmtW7c8PT0xrSBjGIOSPQoNIQja2toy+AgxaEr/Q3pubiIvb8DuusTLZzo4aG5khFfbV+vd5H6HmITHt9bVbSgf4wDSoG3TjxubmwQymc3gIF6ZGGwPHwHuNLPjbQt5fi5WQatFuC3Y49Nn1FptU3Nz/wNJJeaLCCwutqZMJpM+ODiVkTFmaTl4/TpcXY11bbCsS0tLycnJ9vb2e181rKlnm3l56Z66muqDhz6kM5BAHmKxeEUviUQioVAICoRoWllZwTIAWCQSra6uAkihUCgSiaRSJOT7ysoKKARvsUd9SBGapFIp6AWrhTUIALC/YrFYKpWuoNbGABgMBvwFLWN/RSIRBg+mgI0EGwzIrK6u6rcjFAolEolUKpVIJBsbG9jnvtF1bLoAhuG76XcOHzk6NY189oFxYkgF6AKPWB5DqTEGMJQCJVBQBUxkJyyBJVhFk3GDGDJBRoImfZSCLgxQitUyQqlQoRCha4IsNzZIDLHYckgfJNTI2TTqdiqFYTgyIuzkyVMLC0jM6r2gVH8wBogyRik2fQwSwIC/YO5SqXQnlOqDiUQigNLl5WXQmjEqTMJjOwTsRoMdYvxbA6sMNqpSqQSoe7F0T3p6elBQUEtLCzAmeuwBugv/xqDu3iFRrw1DJHJvL5Hm41hdV9XL4bK6u6jBHg1uN9O6uh4SJQZd7ONxZVVIIhAQtR4Wi8Xh0vpIhF7Ss7l+dx7N8vIykUjEVED6+/vJZDKJRKLRaFwul8NBgneCRGfx2ORW2Vgv4jvggTa0Fk2gea1Wy+Vyc3NzfX19+/v7TfapUChiY2PDw8ORoDeYg2CToE9e+ETLymaxGXSyR1LD+3aZXu0+g2RyFD74LinjStYlIu1ZLuvs3GxVVyWHzeFyWXQ6J6eARyESNGrVk8/vCWrMzM729PRgy0qlUsGy0ul0Ho+H0bXAcm0Sh5N4eMAyGaY9p89p29raYjKZGRkZ3t7eJvw3PsGgngHoy0v3UCl9Pt7eGRmZXC7X0dExPj4+Dk0JCQkeHh7+/v4JaPLw8PD19U1MTERjQ3rGxsY6OTlFR0e7uroGBwe7uLj4+fm5u7u7ubldv37dx8cnMTHRw8MjICAgIiLCxcUlJibG2dk5ODjY3d3dG02urq44NJmbm/v5+UVHRzs5OcXExLi6ut66dcvV1dXf39/d3d3Dw8PNzQ2NYOqCWGZduRIbGxsVFeXs7BwbG+vt7e3v7x8QEODj4+Pp6env7+/m5ubl5eWKJkdHxytXrkRFRcWjKSwszMXFJTY2NiEhwd3dPSgoKC4uzsXFJSIiwsfHx9/f3w9N3t7eTk5O58+fNzc3/+KLL0Cw352uZONyGIbb29u8vb2rqmsJRKKbmxuG0vj4eBwOFxISAsaDw+GCgoISExO9vLz8/f2jo6OdnZ1jYmKcnJyCgoJwOJyPj4+3tzcOh7O2tg4ODk5ISMDhcLfQ5ObmBhAbEBDg6enpgSYcDufq6urs7Gxubh4SEhIZGQkw7+TkFBoa6uLi4uvri0G6u7s7Ozvb2Nhcu3YtISEhPDzc2dkZYCYkJAQgFofDBQQE4HA4Dw8PgFIHB4fr16/HxsbGxcUlJMQHBQW+956bpWV8YmK8q6traGhoXFycs7NTTEy0h4d7SEiIp6cniGtoa2trYWFx9uzZixcvAkVIY9SZLAE/vqamRkcnx/bOrrq6Ond3dwylaHfOYWFhoASMAWzdoKCge3YNLi4ukZGRYO/hcDgvLy9PT08cDmdlZQVqgQ0QHBzs4eERGhqKw+H8/f29vLzc3JDApW5ubi5ounz5cmhoaHh4uIuLC9h+4eHhrq6ufn5+Xl5eYCcD5F+9etXOzg7Rjw4NdXV1TUhIAKMCvyY3Nzewsd3d3cF62dnZXb9+HYw/Pj4+ICDAw8MjISEhNjbW2dk5PDwcbImYmBiw+jgcDqD06tWrFhYWJ0+etLKywkyyKyurd9JrNoneJ7p7djkIdTodh8NpaWlhMh8TjHOXRvb9CsjIwFz4fD6J1FNeSLQ5X5wYiS/J726sJddUdVRW1G1sIO6snlWC1sXC4gRWSx2xp7e7m0DsJT1zysB4qBKJpLm5mUqldnd3d3Z2UqnUoaEhEFuqrq6uq6sLI33G++q72ttEqCa+vngauFACnLa5ubn+/v6Ojo7xB34rDHpUKpVMJhOPx/P5fINGDCC/jkcgIwPLymKyesm9FvVnAro8be58yDv9xeANO78y15zWXO229hn23jBea5NvndqW3EPsJRC6BwZIis19CSh1up0cQxuPVigUtra2UiiULjT19/ePoKmxsbG2thaQRAwud7C2dun69f7iYjlq/2WwIliMv+np6f7+/paWlrm5OeO+nmfJy0v38HncwMBANoeLx+Pj4+P1kSKTyTBXqjKZDCi1bWxsyGQyCIKA60+pVKpSqSQSyebmpkwmk8vlnZ2dwHMUqK7RaACkRCIBNh0KNK2hSS6Xt7S0AHd8YrEYgiCpVAocUG5ubq6vr8tksvX1dQA8NTVVX1+vQdnjEokECdCjUAAlSoVCAbpbX1+Xy+VSqXRtbW1paam+vh7TNtje3sYOpvX1dSDjl0qlWq0Wa2d7e5tGo/n7+yOxWvv7z5w5AwwEdrozjMthGKaQSSEhIZNT03l5eYWFhfoofWDngpRhY5DL5ZubmzqdDkxKLBYDn2MAUVKpFI/HA/dioLpajbir0mg0UqkUoF2GJoAoqVTa1NQkFAp1Oh2IUyORSABKN9CodQAM/AURcIAaJnB5LpPJ1Go1QMja2hqIXyiTydbW1tbX1ycRH5KtmHhic1NZXLwG3L+tr0s1aEgjsNwy2ToIXgNBUFdXV2BgwPT0ZFdXx5kzZ4DprDHqdiqBYbizoz0oOHhpeSUlJbmmpkYfpVKpFLMGAjNFrfdlQDgC1hfsPalUKpPJFAqFRCJpamoC5BcAUKlUwKB6bW0NoHR9fV2hUKyhSSQS1dXVSaVSgFKdTicWi8F2AvYsAAz85fF43d3dwG4c7DfgMg7bnxsbG+toWltbk8lkQ0NDbXp6kaB3GFUXkkgkQGoANgZYfeCpDI/Hh4aGzszM1NbWXrhw4b62Aeq/54XQPcCL2uLi4tzc3NNrz+iv717y2AWp1WoplD4ajUanMSqKCd2d/VGBjWF+1Qw6a2vrYbSsvbT5WIoQmhpU3fGSEOpm5uZmZ2f3PevVZfnMlGQvQwKRj/v6+gYGBqamppaXl7FBrq2txcfHR0VFocwn3iS1UVTjOt9XZlK5Ry6X19fXKxSKpaUlsVi8sLCAHZLGwwDeDufn57G+jGG+phIs8temUkmmDLT3d6R23nZsx3H9bnAdHOmf/G2+vkn1gJX1TMag0qrusJOT2hLCuoKn56YX5nfDzG496nRwTg4SXXVvEjG5XN7b28tgMKanp/U9aC8vL4eEhKQkJ7N5PA6NJjI3ZwcHCy0tgeW8wQBWV1cbGhq2trYWFhbW19cxCy8DsOf5+JLSPRCawLXR3NycmJiIIWUvu1wfZqc8aFD/LdaFfgYAGP/Vh8Ga0gdDLwgDbdRHKoE5gr/gOsEG82g7SK2BAVJYWBDQUVhdXbW0tHxSukcfpbm5ucXFxdhojLvbbdzoxAzqYo9gIvqPBnn9+WLA2MT3AozBGNcyQCl6tkL31P4CA6G1NUgmg+3tIYw4aWpqjowMIxCYjo5wefnE9esWKytIDG39RvaS12gQS42UlJS6OsSDJTZBbJwgg43WOGMAiaEFK8eqYCX6GewtyGCP+jBYHnuLZbDu9Ev04fVnhOX1MwAYgqDKysqIiAgejwfEOlZWVi8D3QMYCVNTU/umADBs7DsjWFpsbMAX5RDSElpZTDaNRq2qwOObnlgOYhxn2+SQdIJpWPtUqh7KTXVJNj05iri6sldeFLYlDIakUqkmJiba29ubmps7W5tniXkbYsT3t8lUX1+fl5dHp9ONAzyZhMfiVJh8+3UXTk7OVKSEhNW6h7YGd9A76L1UvrX1/J07T9qvvh7YTnWFm8KKsVKpaq+UqOl2mpo0oaHayEi4FgmHt5e007Jubm4ODw+3d3Q04PG08HDJpUvbLYjLNJOpsLCwtLSUwWBgDAuTYM+t8KWmewAWmpqakpIQHzlLSwICEQQx0BEIhN7eHvoDlXXaQH9ubp4OghobGpqam3VabVVlhYenZ3tHh1qtLi8rCwsLKyktValUJFLv4NAwi8X09fUpLikBpNXC/FxsTExUVFT/AE2n0xUW5kdERubk5q2tr2M3GVj+vfyFYWh5GeJyoe1tzGe6YT2TzYJ7+sHVidSdmYGnpuDGxiZz8/CurhECgcDn84OCgs6cOQN4V4btPu4ZoDQ3N7e0tBSG4fHxMeoDCToEQZ2dHSIx8rvaVqsL8vMGBugSsTg1NXV2bn5lZTkyIiI8IlKwtLy4sJAQnxAVFU1nMBQKRX1D/fq6rKiwwC8ggPPAxX57W2t4ePjdjIy19fWujo7IyMjo6OiSklIQ1uNxw0Tem0TRXioCGIDMqiro7beh996DKiogJyfoiy+gqirE7Ky4uDgxMXFiYqSysueXv6QdPuxtaXkB8K723oU+oZCamlpff9/SG4ZhMok0NTVdXlaalpbG5d0PRkgmkYqKi5XKrRrU/B4NlsKsRQ8gch+ZyebAMFxfX0ejI4YYErEoLi42Mipq8dk5nn4mKNVHL9irqB2fuqgoKz09bXp6iEolkcl9bm5uV65cwQw3XpScC2x4/fhcz+1s1e9obnYxPLDM3bYqIQJfU9NUVVUpW9sdWgAAIABJREFUECxi7EB9yMfmAQ/psWBPCQDB8DBveYA8o2/Wt+82gVvI/v7+hsbGUTQsj8mm1tbWSktLKysrgbMlkzAGhS+W7tGVVeJxF2JTLoVV4hLLExsb8Csow9VgkHt5fD7LCovF2lu3dLduwULEeexTJgiC1tfXe3p7G1paph/cxcZtCoXCvLy8hoaG0dFRY48DxvDPoeSlpnvAvYLH4zMyMmAYjo2Jyi8oEotExcXFRz//vLSs1MHeQaFAZJwRoSHf+/t/4A8O/sfvf3vF6mpJSfHfPv20oKDgw0MHa2prjh7+NDY27ty5s/GJSb4+XvHx8UePfBZy65a1lTWHg3ybNjU2/Od7f0pLS/vwo4/IZMp7//mfiYlJx499mZScuo+rAoahmBjo3/4NEolM0D3gPja4PMD9sboKLS1BVCq0uYlURFzbWW7/y7/kJScn/OpXsuBgmM9nBgT4Dw8PR0dHAz+YT3RJP+gUzs/PB8wJDw+39o7OmenpsrIyxcZmSnJSyp00RByzvv7r1391xfpaU0PdK69+q7au3sL8ko+vb2CA/1fnLxYXFh48eCg1NfWDDw7gW1pOnDiRlpp66NCHEeHhnl7e4KD86uwpV1c3JydHG1t7Npt98cL5vx440NVNQEW/+xj1fqoIBFBYGGRjA6nVUFIS9P77kEgEazTKrKyUrKxM8PXf3d0VGhowOzsZGBj0pCpT2JgQl9zp6VjgBbFw1d7Orqam+le/ej02Nu6Go+PmJmLU4YFz/cm//JRGo735m19fvGyBBj+x+NnPfr6ysurp6R4dlyCViH75i387+9UFlVp9/Zo1zt3DDYcLDY8EBwHW3QvJwDDU3g6VlICdCbFYUEMDpEMdPW9tKXx84m7eLFQq1fX1UEJCQ2Tkrenp6cDAQLEYcaj4AvWaQe/Af49MhggZQclz/ltdVXfpTEB+Zk9vDyU3L3uPwaee8yANuoOg3UKBGQDv5VEoFGZnZzGZTJPAy8vLBQUFFRUVCoVCIBDoS1VMwoPCmZmZjY2NF7Wsfb6+nM+PEYKu5eEzC/OLMU+Juwx4l1f631G7gD3tK7kcfuCF+WmbQuvPzc2lp6cPjyKuLw0SBEHz8/NZWVmNjY1AzvWUKDJof9+P/wXonubm5tu3b+t02isW5gwmy+qKhYeHx5/+9N4AneFgb0fuo8IwHBcT/ea/v+nu7nHgwAE7ewdbW5vKGkTokJQQb2/vcPLkiaVVIYXce/LUaQ8P97sZmQnxcUeOHnVzd19Eoy3W19WaW1rDMGxvZxsXH//hoYO2dvYffPCXsorK/dE909MQkYhctxjzBrurWCxochIp12gQ4cv0NNTXBzGZEJ8PvfMOdPgw9E//hLjZHBqCs7I2zp1LyM3N7uvrKi0dz80lBAcHANm5j4/P09A9eXl55eXlCrns3LmvGAzGyRMnTp484ebhyaANWFpZ6SBYtr52+LO/HT58xN7W9p13342PT/j00882NjcVctnnRz4LDPC/6egMw7DNtashoWEXLlygUKkXL5w/e+ZsQUEh+uuFLpz/qpvYK1xZ/uCDD4QiSWZGmpu7B7pNMUx8jRkYhra2oI8/huzsEFQrlQjXB3UtIfTxCXNzK6uubl9cXKirqwsPj1AoEN0aFxcX/TCNTzQ4IOcC/B4Yhrs6O+xv3Ghvbfl/b73FYDItzC/z+EP3NlKAv+9/vP22h6fnu+/80d3Ta3Rs5PjxY1aWV+5mZt8KCbmdcqewINfK2vrYsS+bm/EffvSRWILw3raUSsQH6otI2O4FxLq7O3T0KIJPuRzy9IT+9V8hOzuYyVzy9Q02M2s6eLBjfn75F78otbKK295GghS6urpi0ooXzu+Znp5eX19/UWx2nJtrbEwCj8fv6GjDIhbt+9TeX0VjGZlarcZU4vbX5pPW0ld6069Lo9FycnK6u7uVqNn34uLi3ukemUwGauk3+HzyVra2Na6uE22dnd09S8s7Cu++1sEYLCsEQcDZ5tfaqX7jmHKhfiHIEwiEnJwcEokEDOxnZ2e/oXv2dJDDMNzS0lJcXKza2vrq3FkOl/fhwQMsFuvMqZOkPqqzkxO+FXFJGREeZml55ac//amVpfXNG47ePt7ePn5yufzaVevQ0LDjx75kcbh30+5YX73u7ekRGhaenZ1NJBI+PHQw7S7CSWqorz155twqEjT0ZG5e/oEDB8orKr083C2troL129NY9YDAPYFdG+ANoHX+8hfIwwO5PLhc6Ne/hl5/Hfr2t6Ef/Qj685+hV15B/pmZQd/7HvyjH62+8krY//k/+NBQHZm8hMdXxsSEg1tEp9PFxMTsT78HfFIUFhYijstWVy5eutzT0/OH3//ez88vKDhkkM+9dPmycksllYi/Onfu7NnT//E2ErsgPf3uJx9/TKMzGXTaoUMfxsXGXDa3WF5e+vKLo2np6adOnSotKy0qLs7Lzf79H/5DsIS45Th75nR5RVV3Z8dHH3+s3FKlJifa2Tvsg4jUQ+pjsgaMNJ0OIT3ZbKi4GEpMhOrq4I6Oqfj4wLt3SW+/rSkvXywry01JSQYnpkqlCg4Ofhp+T2ZmJhEVwiLsw4YGR2fX6amJ4ydOrK2vW1y+SOkfuCc99PHysLSy+vUbb1y7etXL2yc5KfGtt946cvjIJ3/71MnJMT4+8dSJ459+9tkf/vB7NzePo0eOEHpIxO7utPS7QEXyMfP/Gl6j7JyH7W5sQKjlAHTlCnTuHFRWBv+P/zH8+98HEQj04mIdi7WQmZmempqxuYkoPG1ubgYFBWF+53ahe7DjEusJK3lWmRcr59rY2Lhw4WJrKxJovbq6GjNieFaz22M7Op3OgOwzSfeIxeLnH0xgcnKSyWRiWswLCwt7pHteoJxrfn7+q6++6qXR6Gx2dXX1i4o8pdFo9Jd1J7pHKBTqg+1xzzwl2PDwMI/Hw2jrb+ge7Ih7TAaG4ebm5pQUJFAl4O7cTkqwtLQ8d/Ycm8O1uX59aBhhr2VmZNy9e/fy5UsFhYVBQcEzM9MXzn/1xRdfWl+9Njc7d+2q1bFjx0+dPs3l8ZOTEkvLyoKDAk6ePHn6zFn+4CAMw1QK+S9/+cvJkyfdPTylUumZ06cvXLhw7NjxktLyZ3tV63QQjYbweMbGoM8+gy5dQoQvP/kJQu787GfQW28hBNAPfgB/+9sTZmb+ZmbNP/sZ/fvfh37yk/wjRxKVyvt2HxqNxs/P7yn5PZWVldtq9b3451wuz8/X19zcvLqmtruz3ckJYeSsSyW+Pr5309Pd3T3j4+IIPT3lpaWffvrpkSNHi0pKKX2kQ4cOnTp1ytfff3Jy0tHJqbu76+SJE6dPn/YLCARRfnx9PI8cPoyEnapvhGG4tLgwPCLi2SLTYOtsbUEqFUJQAobE8DA0PIzkz52DPvsMnppiHzvmn5/f4ePDc3XdSk5Oyc3NVKuRGxoJxKFQ3PN08DT8ntTU1MZGZKYwDNNp/deuXZ+ZmXZxcVlYWLh06SIIZpKYEJeXn29ja1tQUBAWFnbzxo32zq6V5eUbDg4WVyzDwsKcnJzm5hd6e4h29vbl5WVHjhz95JNPamofqksbTPnrewQ6ahYWUEfHfZRCEMKV5PEQPmVODvzzn8O//nXfd7/r//rrnf39w9vbm7dvJxYXlwAMwDAslUrd3Nx25fc0AWCtRtPb27u4uIhsDxjWarWjo6PPVnLxYumee56aDxw40NHRQSAQgMc2pVI5PDz8kug6YEsGMnw+/zlzpHQ6nQHDZnp6eo+iwBdI90xMTHzwwQcDAwNtbW1jY2NIGEe5fHR09AWqzxsspf4jg8EA33X6hV9rXqPRGCzrxMTEN/yePR3agN9TUoKcp5UVZRmZWcCNoU6n43JYvn5+wPPVPVIAeKsEUXVAuMGlpSX1NnKxqVQqzDZ7e3sb+MhaWloC5w7QpQdGvIAyxczav457GlzMfD701VfQ4CAijmGzIS8vKDkZkX+9/Tb8u98NvPWW/3e+M/Gzny3/+7+P/vCHyX/7W1ZKigaHg6nU+9q+8fHxT0P3FBUVdXZ2Ilopd1Jr6+ohCAIm3EkJcY1NeDBrgCgNmoA/BolEIka1nnVaLUAXcMwAvtI2NjaWl5cBJHAICyI7gtY0Gs329vaelvwJgTA2z/Xr0NWrkFgMzcxAly9Df//3CCMNh4MPHoSPHOl4883gv/u7pYyMmU8/HfrXf40qLi4/fRquRMWYwLQ7NDT0afg9WVlZvb2IKzZUK1zl5eU1MoochQw6LTAwCOwrBKU6HYpYnUql3tq6r2uiVquVyq0tlQr73gWcaokE8XrwdWzCx+IYhiGBADp/HmptReierS1Em+e3v4Veew1KSoK7uuDvfa/5r38Nt7UVvvnmGInEDwsLBmQfaPmeMpNSqQwNDcX4PVVV1V1ddKkUXl5GDOvKy5s6OpBQ8zAM11RXffHlF45OTlsqNQzDvT2E3/7+96vChyHTAdjT/AX6PS/wQvL09Hzvvffs7OxAXAWpVFpSUgJ+dPrzAgwYkUj0RI53se9p/aaeJg9BEHDj+TSN7KUuGDk4LvSlM4uLi9jO2b2dF0j3QBBkbm7+/vvvg88bJDCwQFBeXo7ZMIKRg9lpNBrgD2X36ei/NfCCo/9qf3kIggwIkf2189haBsuKwc/Ozj5/ViLWu37meev36Pe9lwMd8Htu374NSJnFxUVM+UsqlQCtSVBi/Bf0hcGD7nYaAFauD7+XEe7lCjEWeIESGIYGBqBPP0V4P+++C3t6Qq+80mRmFvKtbzF+9jPVuXPK//2/4z09K8bGYIEA/utfkbsHuLQx0O/RH/wexgPn5eUBey65XLaEiqWQmeqg+fkFjDp5BF16viiwi00fvfpIMwbA3j52bE8EAMMIg2d6+r4aeHk55OODmG5ZWUH/638hcsOf/AT+4x+1P/1p6d//faSZGc/aWru2Jv7qq1tvv922ugq7ucHt7YCZgXyoubq66vN7nhSlKSkp+vZcwtVVcPyJxWKJRPoIMrGmH2wLvYKHZvD7RhpGCD4RMh+sGoIQtOuHmYUF6OJF6Mc/RkjzoiLY2lr905/mfutb8e++O/r55/CJEwt+foEUSg+2H8DIJRIJDofD+D0VFdXV1fSJCXhwEJqehjMymmxsrgMeT1BgQElZmZW11ezcvFgkdLC3/+STT4AVmz5m9p2n0Witra1DQ0MYWbnvpvZdcWNj486dO5aWllFRUVNTUzu1o9VqdTodn88Hm8c4XpjJivuzCzPZFFa4x6gOGPz+Mmq1Wn+fY40sLy/39/c3NTUZk4YARqPRkEik7u7unRwbYk19rRmxWBwTE2NtbZ2SkgI2s3F3IKiFSqXicrlA0rQXq3UIgvSRY9zsPkoMNIH20cIeq+j/0LD1vWfGsbCwQKVSm5ubX5SoFxv/c6V7AOuloKDA39+/trYWYGT30xmG4ba2thbUMYAx/DOhS3YfwNO/VamQz+UHdxyS0WiguTnktt7YgOLjoY8+glxcYHNz7Q9+kG9llfjhh4uvvdb1859Pffe7wZWV9wPqoj8V7EKCs7OzMf0e4JG2ubkZSMQfO2BE6oS6UsAuY6wK2BbY40uegWGIwYB++UuIQECwqlRCCgV05859rak33oDPn986eTL5H/8xy8xs/q23up2cRgMDfevqqIODsBrhLGBXOzLvpKSk+6IWCAntyWAwWlpawLX9WDzAMFxQUMDhIIboAFg/g+Uf287TA8AwpNUiG2x/CcOJTne/ERiGJiags2cRpbTcXPjKlfV//ucYM7Oiv/u7+VdfJf/oR8PXrvm2trKMHT7odLqkpCRMS6OsrDoqil5bC1dUQPX1sK9vc2Zmhhr1sp8QF+vp5XX16lUOh1tfV3v27Jk3Xn+9CeU7YufUvjM6ne727dvOzs4kEunroA+eaGA6nU6hUOjfBPrV9ctBXqfTCYXCJ+L96Df4Xze/tLSUmZnp4OBAJpNNzkImk4WFhXl5eYGo8iZhnlvh9vb2LstqPAyNRgPctxq/+u9dsrCwkJKScvPmTXBUvsDJPm+6JzMz09rauri4+OLFi0DUsvsBjWiJNjXp+2s2oH6McQcaBJcNeLt7F8/wLXZtYG3CMBQUBH34ITQ/j92yCIviF7+AHB0Rg6PXXoNeew3+9rfX/+EfYj/6KG92VnHkCGxmNvX97/vV1VEoFNjLC5HdgJbRv7BGo/H09ARyLhiGi4uLT506dfPmzcuXL2M3NzYA4wwMw7m5ucBfM4ZM/bsZy2M4xE5kDN642edZgvEkxGIoKwuKjUWUl996C7p5E8m//jrk5AS/887qa6/d+ta3qs3MlP/zf8IXLvB+8QvfGzeGDh2C//hHqKXl/nKAGa2vrzs5OWHxm+7evXv27Fl7e/srV64Afvvus4NhODn5vr/mnVCEbVTQlAFud28fe6u3DbCyhxnwNiEBunbN0HfUA3rsIbBBDoaRKiEhCFMHhhH8nDqFbNHTpxGW5MYG9MknsJnZkpkZYrplZqb89rfhV1+lnz/vOzo6ceAAHBHxEJ8AAyKRyMnJCeP3lJVVR0fT6+rgykqooQH282tub28FOJmfm/Vwd2/Gt/T2ENkcrkIuz8vLE4qejZxLp9PduXPHxcWlpaVlj6ITbKW+1gxgA2DipK2tLQxX+v1ubGw8c3mHfvsvYV6r1fJ4vJycnJs3byInoKkkl8sB3dPT0/OiFIpNjQsGYRMxcdLGxoaB2At40XwiOslkR//lClUqFYvFSktL+/9dXNJ7/B4vL6+cnJylpaVbt26lp6djV6zBKYw9wjDc2tpaXn5fv9h4sYEo0TgqMiZifGwXWF9PmYFhqKoKCgx85MqBYaigANE1ARctuJk2N6HSUujf/x0x3TIzg83M5s3MgszMWr/7XdL//b+Dr7029I//6HvxIlethpuaEON2jGbC7tTExESM7rG1te3q6tre3vby8sJELbvMBYbhoqIiELIAaxBQVAC9Ou39sDJ6b5E3AKWYBs8uXXx9rwC3bHERc3EEVVdD3/0u9MMfQq++iuDz1VeBNdzIq68GmJmRzMw6X3ll6oc/pP7zP/t9+9tTZ87Abm4IecRkPryngegwPDwc6PeoVCpra+v+/n6lUunq6gq0dnafEQwjHLi+vj4ETYgoEvwB6EQ4QGATojhE+r3/4gHc3lGq0yEuc9Tqh8wYtDXQJlRZichMv/c96NAh4Fnn4aj3QvdoNJCrK+Tri0gPKRTI2xuKjIR+9StEoae6GjYz45qZ+ZmZ0czM2n/843lLS8J3vhN45w5iu5eYiIgXgXcG0CWQSoeHh2OkhgHd4+/fjMc3Y2jBaj3EzLPLdXV1xcbGDg8PP1td6X0PUKPR0On03NzcvLw8EAkHdfyofeHsqH3P6JlXXFpaolAooaGhwDurcfs6na6hoSE5OXknAOMqX3eJSqWiUChZWVnFxcVAYv7NshrgfGFhgUAghIeHY2xgA4Dn9vi8+T1FRUWXL1+2tLQ0Nzfv6UF0Ah6ezaZyQM7V3IwckVKJOCYmemxstLq6antbrVKpREJhdFRURWVVXV0Nzg2Hb2mNi42prqntbG8PDw8nk0lhoaG3boWqUH1JU80/szIYRm7i3/0OuQCA2x4YRtzzMBjItzKZDGVkQF1dSIZOh6RSKCAA+od/QDg9ZmZ81HSLjhJAcjOzvl/+0jsycnJkBMGNUIh8dhcXP3JJwzCclZUF5Fz3fAy6uLicO3fOwcHh8OHDe0RpaWkpjUaDYXhxfi42Lm58bLSxsWkbTdNTUyEhIdXVNTXVVVGRkSw2KyQkuL2js6a6KjYujs1hh4eFNeMRf+TPDHdP0hAMIyLCP/wBiohApIeZmdB3vgPIR0D0wN/6FmxmRjYz83vlleHvfx9+7TWZmRnezCzwk09WQkLgrKz7JIL+8MGPLTExEXDL7nGh7e3tL168aG9vf+TIERaL9djJwjDiChJAjo2NJN1OHhocbGtr397eBl9+7W2tDY1N5WVl0dExm0olvrmpGd8yyOfl5uYNDfJDQ0OpqKH7LpgAAw4K+v/Y+w74Nq4j/ZUt6xzbaXaSi+0Un+3E53+SsyM7cs85uYvlJsmybEmkJKqQVKHYuyj2Toq9N5EUexE7wF4BggBIsPcKsFPsJNgB7Pvz7ZArCCyiHFlOLlr7Rz0s3u6+nfewMzvzzTekhQUGMEVG4huxtycDA3FjYQFb24cOkfv2rf3f3Hw7u21wECO+2ezbqwguJG8z9fbicOHCAqmrS16+jHsODGAb6Ngx8je/Qe++W0AQlgTRQ63SyV/8IvX//T/7J56YpEi/sU2/axeZkHDH+WUymaenJ52Vs43dI28Xrg9szVi8L89BiURy69YtoVD4HeKa4UYAXn39+vUTJ05ERESwWKzvyhSTUqkJIpGos7Ozo6Njbm5Otr7BdNCSB1gulJAbGxsbHx8fGhrq6+srLy9vaGgYGRmZmpoSCoWdnZ3T02uV76anp8fHx6enp3t7e0Ui0eDgIBQ0nJubUzg5WAbycBC4LuCab926tY0tCNEikUi08Zz04B9MQyqVtrS0eHt7q6ioxMXF8Xi8bYb9rQ5JKpXOzMzAdHR3d0MpQJhYhetCuvvExMTMzMwYtQ0MDPT09LDZ7KamplVn7dTUVDe1icXipaWl2dlZmNapqane3l6hUDg8PAzTurCwsHEKQJsoXFQkEsHlvnMX5oO2e6amplxcXFRVVUNDQ2FxbPOsB2kyGAzANQ8NDb737jvaurrmFhY+3l5qamo5OXnJSUkXL13u6uo0MND39fW1sbExM7t6xezKwYMHi4qL4+NinF1c76q3th/DTr4FjK2PzxrWhBo55rd97jm8p68PR2Eee4zcs4d8+mnynXfIxx7DGvqxx0oIwoogBARRRmkU/PF73xveswd99BE5PY0J4vT0AM68NgpwTly9epX294yOjnp7e+vp6RUUFOzEc4AQxjUnJCQghNpbW/74xz8aGxvZ2ds7OTlevqxZUFCYzWAYGBpcuWJ68NAhFpsdGhxsdtW8vb3t7NkzYWFhR7780tbeARb0TiRzH/tg7LUMA01eegmn/VtYYLDt44+vkR5RAkQEkU4QDgRR9+ijFc8/j3bvTiEIJ4KYOXsWrwJ5c4ceGCSg0rhmqiLKkJubm4GBAYvFoh2HdP+NDYSQv79/ZmYmQqiSV/H/fvc7I2MTFxcXWxsbPT39MlbZ8WNHnV3dmpsaT506WVpaeujgAf+AwKgbERcuXowIv375sqa2ju72F4KR29jgsKmREbaGKyqwjfKXv5CnT2OD+/p1bOgsLWGr+u23MbLbzAzb2Qjh5Xf4MDa75W8fLEiRCNs6JEn6+GDXztAQ+be/kW+9hZ2UH3xA7tmDKJdkHEFcI4gaguAThIwgYl56ye3IkblDh5CuLmaG3L8fp7uPjt4+P66zcSeueXu7Z6NINz5JFZ6h9/TxQeaxy2SyTR/r09PTTU1NFy5cMDQ0FAgE9/cGt5cGSZIDAwNTU1NLS0t9fX3Nzc2FhYUhISE3b97k8/kJCQlMJpNDbZWVlRwOB0Iz8/PzPT09NTU1hYWFmZmZZWVlJdSWn5+fmZmZnZ3N4XCKiooKCwtZLFZCQkJaWlpeXl52djaLxcrJySkoKCgtLS0pKYEE/rKyspSUlJKSkq6uru7u7s7OztHRUbFYvLKyAvWk5W9h57yFDyyfayvrYXp6urq6+uzZs5aWls3NmJ70W9o2LhiJRNLX1zczM7O4uCgSiZqbm7Ozs4ODgzMzM7lcbmxsbG5uLofDqaioEAgEFRUVkEMuFouBLSk/Pz8rK4vFYhVTW25ubnp6OhwC1ddZLFZsbCyDwcjJycnNzS0rK8vJySksLISVQE/rzZs32Wx2Z2dnT09PZ2fn2NjY3NwcXftZXhr/ovw9mFjZ3d3U1JTP56uqqt6kSiVt+siDnSCynJycmJgYhJCwp9vAwODsmdPHlU6EXw/T1tbJzGJkZmRc0tAoKMj38fUrKS4yNjYyNMTlEdTU1FJS05ycHFvbOh6A3bM+YPlHPyZ5++QT8osvSENDcu9e8t/+DfskHn8cYltSgoin1MkUQYxQoa50gnAmiGmCQN/7HvnKK5jjh34jp6UE9+Lu7k7bPYGBgaqqqi4uLseOHQMaCbrzxgaINDo6GkptN9TX6enpf/bpJ5e1tH19fYyMjIqLik6dPJGQmGhqYqKqqhYdHZOUmKipqcXlck1MjHV1dDU1NY2MTRYpvv+N5/9W9yCEfWnvv0+++SbOqf7xj7Ed+e//DnYPIohFgggmiECCWCKIfoIYJIgYgvAGfI+e3powN44QSnbb2trS/BZubm6XL192cHBQUlLq7+/ffv2ASENDQ8HZVs4uNTMz/etf/2J6xczDzc3cwtLVxemjv/3trKoap7zcyNBQS1v7Lx9+qKOr193dZWxkVMnnnzxxQktHF55rG4cnvwfWw/Iytk4sLfEdZWXhzDUrKxy58/bGTN8IkRERWCY/+xn56ac4IgbYHdk66zPECnk8fJJjx8jz58mPPsK2+C9/SaqqYrt8zx7yhRfIRx9Fu3fPEYQvQVwnCClBiAjiFkEE79oVsmvX8u7d6Pnn8ZLevRsPIDt77UIwWuAttLOz20mcS/4G5dvyT8y/s/1t2z0SiWRycnJsbAxcFwoqampqChibTE1NIyMji4uL3dzcent7v9lNDQ4OdnZ2VldX83i8qqqq9vb24uJiNpvNYrF4PF5FRQWoLoFAUFNTw+FwampqeDxeRkZGYWFhQUFBcXExh8OBY2tra6urq+vq6gQCAZ/Pr6ysZLFY7u7uXC63sLAQrBzYX7O+1dbW1q1vNTU1tdS2/mVNVVVVdXU17Ke/pfsIBILy8vIyaisuLs7Pzy8oKMjPz6+urm5ubu7q6qIdcv8gds/y8vLExARNK6AwrQMDAxEREVYajYlQAAAgAElEQVRWVubm5gkJCfn5+e7u7pBifK8zK5PJent7YVrLy8tramoaGxuLi4tZLFZZWRmPx+NwODk5OVwuF6aVzWbX19ez2ez09PTCwsL8/PySkhIOh8Pn8wUCAT2tVVVVfD6/qqqqqKjI29u7oqKisLCwuLi4tLQUprW2tlZ+mmBi6T0wrdXV1VVVVdCG/grtqqoqNpsN0wp2cD611dfXNzU1gdsJBPKva/cYGBjAa7GHhwdUWZd/0sm3KUlhJEReXh6DgSnOxsfH8vLzG+rrY+NwTU2KG23Cw909LS2jublJIKheWV7y8fLKzs5hs1guLi4dHR1MZjYYufJn/mbtrbwFW50N+tfXY9UCXDJPPgkRGUQQC5Q6CSGIJoIYoxwVMQThQRCL3/8+2rULKx5bW4wWgvf1O1/T8Y2HhITQcS4VFRVIajA3Nweio62HBGJD4IzFhBMD/cUlJRUcDiMLRxIRQvW1tfr6+impaVlZmV4+PnX1da4uLgWFRRnpaT4+vjXV1R4e7jl5eWuIla2udF/308YfNOLjMXKZIMgf/Qi70NYxUpOUXyeGihvOUHo6cPfu0AsXZMbG6MUXyYwMrP433cCs8fDwgDjX/Pz8yZMnATdgYmIiT0uz8fA1gVJxLig81N3VyeFwWGWleXkF1JllS0uLlXx+fn5BclKij4/v0PAwj1tRVFQ8NTWZnZ3d2dHh7u5e34D5MzeeX2EPvQhHRnDyGnycn8eOn8hI8uJF0tER75yawkmC//mfmBqKlh59KoTI4GDy0UexBWlgQD77LHbt/PCHIEn6L6LMcTuCSCaIFoKYJwgJQXhSpiQ23HftwlCqF17A1tKLL+LLzczcljDELzw8PHYY56LHJt+gZfv3N76Z3SOVYszN/Pz8yspKZ2dnVVVVc3NzR0fH6OjoyMjI5OQkRKmmp6fd3d3Pnz9vZWWVmppaVlbGYDBqamp6eno6Ojpqa2urqqouXbp0/vx5R0fHtLS0lpYWPT291NTUnd/X9PT00tJSc3NzRkYGvIIzmcz8/Pzs7OySkpKioiIwZeDlns/nczic0tJSFotVVFTEZrMrKytBV9EKjFZp1dQGH+vq6vLy8sLDw5lMJpfLBf0Hdgx0u+vfmpqa7fvAhWjNWltby+FwMjIygoKCXF1dV2u6gUy+PbsHcshXVlbm5uZWVlZaWloEAkFzc3N3dzc9rWC8Dg8P29raXrx40dbWlslklpaWMpnMhoYGoVDY3t5eX19fVlamrq5+4cIFV1dXJpPZ1NR0/vz5NZDfzqZ2fHx8cXGxtrY2PT09NjaWyWTm5ubm5+fn5OQoTCuXy+Xz+eXl5fS0gpUDJs5W0wpzV19fn5WVdePGjdzcXD6fT0/oXSdr+6mkv6XnlJ7W6upqNpudkpLi7+/v5uZGo3n+de2eioqKQ4cOqaioqKmpbfMmjRAaGRl2cXEZG5+AdQBaYavlpGCJy3eTf5L+PW0OB1eW2Eo30QqJGgnulp+Pi2J2dWHPxKOP4qpblL8HUX4IO4LIIohZgiggiPF1L4WEINAzz+CI2J//jN/dP/sMh3I2XhFQtLTd4+XlderUKScnp2PHjgGbxaa3CQ4zFxeXxaWVhIR4AVU+dxu50TKkuXug8/bhmE0v/Y13glSnpnAVTKkUB7lqanDm/wsvYPX82GO0p6eLQp+UUsIEkV4jiITf/x6lpqKiIgyIEQg2kSQMDO7U29sbFiRCyNbW9uzZs7a2tkpKSn19fVtZJAih1tZmNzf3FYkkOjp6Jym1mwocdu5cSghhIyMsjGxuxjc1N0dqaJA3buC8P3V1jLaRSHDyeX6+IsSeXqVHj+Ko1htv4EzDsDCMOfvv/6YtHvBHthDEVYLgUiItpqxzB4JI/9Wv0NmzaN++tdiikRE2rdrbcZCL9ifRUvXy8qIfef/IcS4asC8UCgsLC4uKimpqaiorK8vLy4uLi2/evJmXlxcXFxcSEuLp6RkVFZWenp6UlBQREZGRkdHQ0FBTUxMeHm5nZ1deXg4eF7A/SktL2Ww2j8fLyspKSUnJzc0tLy+vqqpKS0tLSEi4q2NgZWVlbGxsYGCgq6uruLgY0jsYDIa8x6Wurq6mpgb+0upHweNyT+qtqqoKHAb3dBStAu+1UVdXF01tnZ2d8/PzMBEYd7jj+lxbxbkgXQ5QAR0dHXl5eaWlpeC6gMhOUlJSQUFBVFQUTGtsbGxaWlpiYmJ4eHhOTk5jYyOfzw8ICHBxceFyuQrTWl5ezuVy09PTU1JS8vPzORzOarHh5OTktLS0u/IRLy8vj46O9vf3w6iys7Pj4+Pz8vLA10L/pZ1k/4zTWltbGxYWlpSU1NvbS2csIoT+Re0eeL6PjY1lZ2f7+vpugxhFCHW0tykrK4l6+woLC1cp/KempkhS1t/X19bWJs/5ODQ4ODmJOW1XVlZ6enoWF5fGx8eHh4enpqbgzWwrpXVPaoYkcSxARwerGdoQgQZC+M2bwcD8tsCeEhlJurtjkOmPfoT/PvEE1ihU4S1EEHUUiplNEGLKzTO3/g4t++Uv0bvv4lf248dJJhOfqqODHB6+fTlanQiFQmNjY5qwa3l5uaCgIDg4OCkpCcIKm94aQqi6qvLkyZMzs/Px8fGBgYHz8/NSqVQoFLa1tdOJlwihvr6+WbEYUnJ6enpkFJsznHl2Zoam3tr0KvdxJ0CmGhuxc+Kll3DsJj6efPVVBfW8hmKm0CfzlEjHCcKeMivRj3+MHn8cI4GOHsUQYPm5kx8nZb60GhgY0Hnsi4uLEClPSUkBrIN8f7qNECorLVFROT03vxAeHh4SErK0tLSystzd3dXZ2blEcQTB8usVieapgovz8/O9vb1isbi7u7u/v39yYkIk6t0JKou+KPUjItvasD0NUGKAPSFE3rqFA1VWVjgOCHjn6Wns+4EbX1m5jbg3NMQL0t4em9cvvEA+//xaNtw6RqqQSjBsotw8iCCGCcKWIIoIAl25gs988SKOw770Eh4GnJz+UdDjrK2tNTIyonOz/wHtnsXFxcHBwYmJiZycHD8/v7S0NHNzcy0trYiIiMrKyoqKisrKSoFAUF1dDaZAA7XV1dXROonP51dUVPB4PPD/g8EBiqquro7NZkdHR1+7ds3FxQXMYghbALyX1vFSqbSxsRFekeFneOvWrezs7KysLAhOgS+Hx+OBiXOvtsU/eP+4uLjY2NjQ0FB5Tpe/x+6Zm5sbHBwcHh5OT0/39/dPTU01NjbW0dGJj4/n8/lcLhemtaamRiAQ1NXVNTQ01NfXg+0I7hOY1srKSvgo7yCpra0tKyuLjIx0cXFxdXVNTEysr68vLi7mcrkwd/S0Li0t1dXVVVdXc7lcQLIKhUImk5lJbeCi4/P5sHjoC/2DT9ZOhldTg8OdN27cSEhICA0NBYJyeLf8F7V7EEKNjY3W1tafffbZkSNHIMJNPyjlG5SYSEd7+x6hKC8vT01NbXJycmRk+ISyko6OjpnZ1ZaWFsrK5qurqZmbW0plsqLCgi+PHLl+PcLCwvziRY0sBkNZScne8b7VhGpvx5lEoHUge6unZy1lpqsLBwv09THGYmkJa9ndu+XVM7xAI4LIpdwSvQTBo/KBZwnCjsLhIoJATz2FC3Wpq+NX8Js3sYo6eBDDSxU0CihpJSUlgPIghKampphM5tmzZ7/44gtYZPKSpNv41Wdl2dLCYmp69saNGyYmGKPT2dH+9ddHtbW0XN3cGhoa+Hx+SUnx6dOn3d09sVcpLPTsuXOxcfH6enr6BgazMzPn1dXy8iF8Q594u8ZWpsZ2x6x/B6USXnwRK3IXF4xWoUJa9F+A3CZRMhwliBKCaKVCMxYEUUwHYggCq2dNTXyGrQaDC7RxuUpKSrRzYmJiIj09/eTJk19//fU2xStwYa/ZWVPTK+K5OT9fX0tLS6lUKqjiH1c6rqWlGRAYVF9fX1lZWZCfr6SkFBgYjJmTroedUFZOSk62tLQ4dUolOTlJRUWFU8HduXUO6JyzZzG+e3mZbG3F9jFtfCBE9veTn3+Ogc+r1QaMjMjXX8c0PKuWtJUVridaXIwTu4KCsBg/+gjb5XJSRVQwK5IgXAligiByCKKbIIQEYUatWPSzn2F89HPP4aDYz36GEc3yhJzr84b/RQgVFxefOnVqmiI0RAh953ZPT08PhKVozTQzM5OSksLlcgcHB0NDQzMzMy0tLSMjI1tbW+Vfsrd51st3U3CQ0KgaT09PFxcXFovF4XCioqJcXV1jYmKSkpJ4PB5CqKenJ57a8vLykpKSWltbhUJhaWlpXFycu7u7tbV1eHh4ZGRkeXn5/yXVCCKtr6/39PS0t7cXiUR9fX3yYPDBwUE6Qgoqc6u/UEqM9u5AFaPExMTq6uqurq6goKCsrCwzM7ObN282NzfTFsw2cypvvG7sX1NTU1FRkZqaeu3aNXd398rKypKSksjISGdn56SkpLi4OKi/1traGhsbm5iYmJube/PmzY6Oju7u7ry8vISEBEdHRwcHh/Dw8KioKDB6th/MP9239fX1dnZ2vr6+vb29gByg5w6/Uc/O0h+/w8aDzufy8vJ69dVXjxw5cu3atbs+61tbW5eXVxgMBnQeGOj77NNPDhz43NvH18ba6vPPD5iamLi4uurr6Y3cGmVkZX7++Wcurtf0dHWVlJS6OjtdXVyamql0cPlH8j225TUKmCBdXZh4MCUFv3BD5UuZDFOnPPkkxoR+/DHWLn/4g4I6WSGISIJwJ4gZ6pV6gdLQlqCh5RQPNpgAibKwgAlUoDqS/JDBc6uvr9/S0gLrRktL6ze/+Y2ysvKNGze2FynOt2xuIUkED9PVquktzU1//ctfDx44EBEZqa+nd/ToUU1NzZCQYANDo/n5hfCwsEsaGgcOHrSxtbOysrS1s/vwww+Tb6ZsfxUYLWCQg4O3A9bceV+37RKI3YyMYL2urY29F7/4hbw8wY4EyG0Y5ZOQUVjmTkpDCygJ45yml1/GRz3//FqAkp5K+euCkp6amtLQ0KBZN86dO/df//VfR48evSsfkkQiaW7CSRw+Pj5JSUnYhOKw//a3vx364ovYuPhLFy8oKysbGRm5u3teuWKG6Q29PQ8dOpiXX5CRkRafkIhI0sTEODUtY4cihVuQSEgbGzIpCZsvjz2GgcngaJyZWQMX37pF2tmRFy5g38zbb2Mh7NmDl9Zjj+Es96eeIv/jP2gHDy1YRGHq3QgiikKIk5RIGwjChEKhYSvzySfxSXbvxnHYL7/EQViFgu20YFerCI+NjWlqatKE9N+t3UOSZGdn5/j4+NjYGGBfuFwug8Hw9vYuKyvLzc2NiIiAtBQAAt8XZQPumcbGxrq6uvLycsDZAH+JhYXFasS/tra2oKAgOzu7uLg4Ly+Pw+GUlZWVl5eDDwm8EWBa3Zfx/KOdBGyI4ODgrKys8vJygUDQ3d09ODg4Pj7e3t5+1zggPP3a29vFYvHIyAiLxSopKeFyuWlpab6+viwWKzs7G8xZFosFcKX7IoGampr6+vrGxsba2lo2tQkEgtzcXBsbG1tbW4FAUFVVBTUGALtdUVEB0woYnX+FaS0rKwsMDITALgDdYFpbW1vlYzUwg9/J3wdt9ywvL9fV1VlYWHz++efAQU4/Kzc2QCIMBsPTE7sfekW4wHVISLC1jS2LxUpNTcvJzlZXUzc2Nmaz2QH+/hcvXDAxMTHQ19fQ0MjPz7exsf07mXsgBzguDucG01pTSQnnEOnr42KiwChoZkYeOIB1CVgwP/nJbRq99ciLKwUIHaW4bpeo7C1zKisYnBY461hbG1dB+ulPMTIDDCy4ooJYwO7BZS8phh8cEOzoWMXqf/TRR4cPH74rXzOINCIiAlLkGhvqDI1MPD3cPTw9i4uLs7KykpOT1FRV7ezsqRyQNCWl455eXjra2rp6uiEhIQcPHvD0wrXSFEa18SPYLgcOYLfEpjcifwhCOIPa35/s7l7rnJODIylffYUdafJ2IdUGpkcbKpg1SLklEKWbrxJEGxg9BIHlee4cnimCwJ4z8B59/TVJYbLlL46dEzMzM7q6urTd09ra6u7u/uGHHyorK8Pz944D7vwAIvX19U1LS0MIsVmlNjbWrq7OgUEh+fn5DCYzIT7+1KlTzs4uPB7Xztb2woULsbFxLs5O3d09ubnZGhqXYuMxp8CdZ93k0/IyBjnBBiItKsLGjZYWOTGB6XY++AAbOhIJXo1U2iD5+99j2+jgQdLLC1s8Bw+S0dEYibyZSLspSoVCKm+rgBJjJUVUCJw9dxzy2mtrHOKbjJLaBa/durq6/whxLi6Xm5OTA5kmUNQpOzs7OTnZ2dn52rVrAQEBnp6eRUVFdLBje+24E7+LQh8a7wkNwI6w2WzIvoHAGUQHaAcSNOA8Cp4k+eHtxCqCi8oftbG9wz7bjATOucPx0OcBrJJAIOByuSUlJWVlZRwOh81mZ2ZmlpeXZ2Zm0imWCtpxZWWltLQU5hTS2YqLixkMRmJiooODg7u7u7+/v4+PD5vNrq2tra+vp6+48d7pkW/1Fb1/40noiGddXV1jYyOPxysvL6enFeJcVVVV8mlQtKg3nk3+Qtt8Sw/4fvWhr7tVgw7jbtWBdpJBBxCLQCDgcDglJSUsFguMv6ysLDabnZWVtUOjVmHS7+PHB2r3wLs1jH5sbAw8mVs9Otef77hOhbe3NwU3WeihyMeam1vWIRGy6upqoVDY19s7OnqLxWKNjY13tLdXV9fMzor7+vu3P7n8t5vqZoQwcOeZZ8jGxjV9PDODi0s8+igOH7zxBq6KoKSE34MJAvskfvxj3KCgPKAncMUJCtCDIy8EMUplAncRhCmVdrRm9BAEzgROTsYmgpoahqluowTB7rly5QrYPbRIZ2dnc3JyaK+v/K0ptIG/B+qSzonFvb29i4uLzc0tUNleKpVWVFQANVlPTw+Hw1mRSJoaG1ta2xBCo6Oj2+BdFC4kk2H4LcVVpvDNHR8BnqKkhCXm6YkDN+HhGEr13nsgQ4W/NHFwNSXSIYKYJIhaytPTRxs9v/89NqFkMrKhAZMcGhpikQ4NYfFCMS/5EYDdo6enR9clhSU6NTWVlZW1TZyLPglCiK5LOj01NTg4ODc319raBpBwycoKh8MZGbklFPYMDw+Xl5dPT8+IenslEml/fz+LzQZkAH22jQ3g2jl4cM1og7W6tIRvqrKS/N3vMNBneBjbzd//PvZBenhgob31Fo6ZBgXhsOkHH+D1+dZbOJYKxNbrpg+OsVL4ZbN1v04/hbjnUOt2CET6wx+SP/kJPucHH2A80Cef4CsqEALJDxv4e/T19b9zu4ckSW9vbxMTEwaDAVnWlesbqA3AWADso7CwEJQWd7ONR22FhYWVlZVVVVWbdcH7eDxeXl6eQCDg8Xhb9cHRz4ICSDOGPnByhf5FRUXbnIfH47HZ7JKSEoCtKBwLHyG3q7CwcJsBA5xl+z6Q4l5WVlZZWbnphbhcblVVVVlZGYvFghz7TbsJBIJSagPgC4gL7p1PbTweTyAQsFis69eva2trc7k4BLxxE4vFtra2lpaW+fn5gMGCxGzIXIO0fBhAZWUlTBlYVxtHRd8+uGo2dqBxzfn5+RC+3LQPdCsqKlK4ffllABnpJSUl9O1vPBWPxyuleI/kD1ToVllZCRb8NtMKfUpLS7eZMojTQWqYwiXgI/AdQFbgVgKEPmB90n1gTnk8Hvy+4GfFYrECAwO1tLQaGxs3zumD3POg7R54OI6MjPT19d31cQ9Knclk+vr63i+hyD+d6TZCGPQQEoI53GgDiNYuIhF+zx4fxxBRkWjtdXnPHlxo4umnb2tlOXOHBvSwqDfmVioTWEipkBaCuEIQ7bSGJggcd/j3f1/LTnrhBYxOvavdY25uTtclpU2fnfgMoHNkZOQ2pT+2F7X85aAnLcaNDVqY23wFfby8sPn42ms4IxrKTTz11G3Zrrt5SIJgUpBbEQVkHqbEyKMyj9Y0NDAkeXpiyC0AX2Zn12BY1Mg3kS3wFhoaGtJ2j8I9bhy8wh7gLYSM9+2lJ/+twlUUvpK/BKB2VFTwKuXxMOqrrAzzQqmpYQQPh4MNGktLDOJ59VVcTqunB6Pp33gDmyl79ihCo+QWKljeKRRGqp8gqinTHFEQZuv1Np6Fw4dxhvwPfoDJEtvbsTX5+efYo7bV/FIcE5OGhoY7tHvkly6WA3Xz8gL5e9qrtNEaGhq5ubkFBQW5ubk8Hg8sH1C0YMRUVVWVlJRkZmbm5eVBuu+6dbT2bxW1FRUVpaSkwHkUOsDH6urqkpKS9PT0vLw8QEZv7AYQ5rS0NGBk2dgBlHdeXl5mZqb8gBV6wpjT09MFAoHCV/CxqqqKw+FkZmZmZGSUlZVVVVVt2o3H4+Xk5KSlpbHZ7E37VFVVlZeXZ2VlMRgMSEnb9DylpaXp6ekMBiMvL48WsnxPPp9fWlrKYDBAzrSlIt+Hx+MB1WFoaOjp06f5fP6mUz8/P3/16lUDA4NCapOfMvq0cC8wHfn5+fJ96CtCn/z8/KSkpKKiotzcXPor+UZ1dTWwNebl5dHnl+9QWVkJU5+RkbF9HwaDwWQyt+kjEAjgWttMK5vNzqC2baaDy+VmZ2dnZGRwOJytprW0tDQrKwtYKzftA9ORkpKSnZ291Zi5XG5ZWVlmZiZMvYJY4GNFRQU453x9fU+fPt3UhGk7vsPtQds9FMIx4ejRoxcuXIA1Lf9839hGCDGZTOBrnpsTx8XFDY+MZKSnV1ZWhoWFxcTGjo7erl8olUjSUlP5VAWGifHxqBs3+voH2GxWdk5uX68oOiZatFlOMjy73dwwNGd2FgM/U1NxhvCZM/jVWSzGHgsGAyf6vvIKfpN++WVsprz99looYc8e7P5Zf3uGBqKwETEUo8woxSVTSsFQaikN3SVv9Dz5JLZ7nn8ew04vX8bv61evbqlOQFNKJBKarxn25ObmamlpGRoaboNrpmUr7++ZGB9PSEgcHR1NTEzk8XghISGJiUk0FhXY5+LjYpuaMZaov78vKip6YmKyo6NdIKgeGxuNj4/r7d0yzVvuindYG+DgmZnB1iRCZFMTzsHm87ER+cknmIoGCB43iHSOSvj3plLhZgmilOIOXuW5tqK8aLedZy+/TI6MkGfP4sw4AKDQduSmelqBrxlEmp6erqmpaWpqug3bgtwNYn8PEFMNDQ7GxycMDAwkJiTwebzgoKCU1DRI5oLf+czMdHR0VEdnF0Kou7srJiZmfHw8MzODy+MvLiwUFRbOzy+sq376CrgBg7ewwGvspz/FBs2qh+yHP8Q20IUL2B/z/vvYKJmYwOmEsCw//ZT88MM7Fucjj2ALe92OnKB4HYMJYoVym7GptZpDEI4EMSW/Sh97DPsydXTWSHogd2x8/I7hyX+4J75mcGHScGOZTHYfK5DLZLKEhISvvvrKw8PDwsLCzs6ODhDTj12ZTCYUCoODg8PDw21tbW1sbMBvSncAOqKuri5fX9+cnBxHR0cbG5tNQzBLS0sxMTEmJibW1tZbPdwXFhYCAgIcHR0tLS1pNgr5a2G8vFhcXFxsb29vZWVFZ24q9EEI5efnOzk5bYMVnZ6e9vLysra2Bn72jWdACFVVVV25csXW1haIZDftU1FRYWRkZG5unpODa9Rs3Obn5/38/PLz852dna2trbu7uzf2mZ2d9fPzCw0NdXJysre3B4YIhW5jY2PBwcHu7u6qqqpHjhzZihticXExODhYWVl5tWS1lZWVjY3NyMiIwqkkEkl7e3tAQEBgYKCdnZ2NjQ3Nokn3lEgkTU1NgYGBWVlZNjY2Dg4OtKVO9wF/sJ+fn6mpqY2NzVYVwaamppypzdbWlmbEkD8JQmh6eprJZDo5OdnZ2W0D3E5JSfH19aV/EQonAUJ5Z2dnGxsboJ/d2AHWBiz40tLSTTsANx5MK5Qt2thtZGQEnmx2dnZOTk6bjnlwcDAkJCQgIMDBwcHFxWXT1djT0xMUFOTs7Hz69OmjR49uJZ+NA/iW9nwHdk9YWJipqWlCQsIOgzLZ2dlhYWF4sgcH977+upe391tvvWVpaXnkyFcqp05Fx8SFhoa4u3tMTExW8XkXLl7Q1dNfXFyKib6hfv78lStXdHS08dteXr7m5cth4REKGgUiCOXluChEYCB+e9bWJpWVsd3z5Zc4VqKigjXovn1YkezZg1Xyo49iS+Xjj9ewI3eqZ/D09FGlEuIoDd2/rmO4VCymV16dEAQm6SksxLQ0kKcTH49pV2g9La9LoE3VtpQ5ODjIP74tLS2BEg0ykjYeJb8HIRQVFQVKurW56b/+6w++vr5//ONeMzMz5RMnTp48kZKS6uPt5efvv7C4mJeTffbsWbOrGJPr7nZN49LF0LAwfT1dO3tHb0+PL7788q6FpSiisDW8LdwXpOj/+c84LsNgYKTUs8+SX3+NlfFjj2FHhZxDgvactVL2TRZlPg6sizSPKg8+QYsUDNBHH8V2QHU15uzZyYYQWlhYuHr1Kh3SWjX4jIyM3NzcMjIyAJm7/XkQQkFBQfn5+YBr/t3vf+fp6fn6H/9obm5+8uQpJaXjDCbTw8M9JCREIpEkJ8VfvHTJytoWIeTs5Hjx4iVzCwttLS0jI+PExIT//d//HZuYVFil9NURwkFAuM3HHsMQ48cfxzmAAF7+yU8wgue11/D6hPS3X/8aB2Q3W6KICg5aEEQ+FdUCbxlU+XAiiDlapASB1/xPfoKTv5ydbxf52tSClBsnmp2dvXr1Kh3I34hrzs/Pg4daR3v7hfPnoyhO9pnpaRsrK0sr6wmKnOLvf+otLi42NTVlZmaGhITY2to6OTnJJ9bC+cVicV1dXVFRUVZWlgO1bSRTnp6erq2tzc3NDQsL8/X1dXZ2plkP5Ac5MDCwml5gZWXl5OREZx7Id8A0WkKhl5eXg4ODjRuT5kkAACAASURBVI3NxsFA5/b29tTUVGdnZzs7O5rNT+E8CKGSkpLVgirbYEUnJiZ8fX2tra23oUmsr6+3sLCwtraGZ8LGq+AEiJYWCwsLe3v7wsLCTTuAEo2IiPDx8VlVbxsNRzgKkvMdHBycnJw2NRylUmlubq6Xlxfc+0ZLBc4zOzvb0NAAmer2uMaO00a7Z2pqqr6+nsFgpKSkWFtbOzk5bbRpxsfH6+rqsrOzAwICfH193dzcII6vcI89PT2BgYHW1tbOzs5bTUdra6uzs7ODg4Ozs/Omt4YQampqSkpKghz4rW4NIZSenh4YGLiN3dPb2+vm5mZvbw/lpRVGCx/LysrMzc0dHBwAR7tpHx6Pd/XqVXt7e+Bc3dhnYWGByWSGhIR4e3tvxUY9NzfHZDKTk5NtbGw8PT03tXtmZmYYDIabm5uTk5OLi8vGimwbL/2t7vkO7B4/Pz8ghWtoaNjq+S7/AGUymX5+fphXpld0/OjXH374318cPmxvZ3fixCkdbW07Owc9PV01dfXBoaG83BxHRwcjY+PhkVtenh4enl6q584ZG5tYWVoUFJXciIywc3AEacqdH0M+X3sNgz/27cNcLxERGPsZEoK5ki0tsW9/1641c+dOfbypOiGpGIE5FYUB2ASbUiGrzgkLigdlzS0B0Qc6GYfWInSDHqFCA16Ozc3N5fE9jo6OX331lZGR0c79PRDnamluOvD55x988MHR40pmZlfU1c+rqal6enldvHBeU0t7ZnY2IS7W1tbO2MhoViw2v2rm4+3l6eVTVFjo5u5hTvX3DwzcfhIRwlTCSkqYBWB8HBM5KivjhCDwVXz8MdRnxX83qGcweiRUqr8VQXRQkiyiOJAQQWRS0Rlc1gMO/OMfsU3w6ac4BHPz5pp3REF6m34Ef4+BgQEd51qlgzIzMzt27NjO/T3+/v4Q5+JWlB888Pl///efT506ZW5+9fz5C2fOnPH08j6vrm5gaLSwuBTg5+Pr52toZDw+Pm5hYe7h4aly+rSTk7OxsXF9fb2RocHQyC0FkYK96OiIS3G98QbOzH/8cWwjfvghGReHS7Q+8wxew888sybVJ57ADiGAmt2J5gGRrhBEAiU9sMJzKAoARBCJBOFGJXPddp6BbC9fxrl1+/fjNLFtjHJavDuJcwUGBkKk+5qri42NzZmzZ0ZGx+bn5jIz0j/e/1FbR+d9fPDJZLLFxcWlpSV5FjWF80ul0oWFhbv2mZqampubW1jAVc0UzgAfl5eXl5aWoNLnph3gJ7ywsLDNYMDVuri4CHRZW51HKpVu30EikYjF4oWFBaCQ2fQ8Mplsgdq2cbNJJJKlpaXFxcXt+0xPT4vF4m2GJJFI5ufnQcibDgZcawsLC8vLy7A8tuoGv5HFxUWQ5KbdSJKEK24vyZWVlcnJyfn5+W2uSFFzYfLureYdyt1sv34QQnNzc9sPBrjothkJdBCLxYuLi9tPK9yRPEGAgpRWVlZgMNv0kUgksOa3unfqzRb/dmAuFC4BH+X70NVINu35YHZ+B3ZPQEDAiRMnzM3NgaiKflZu2kAI5eTkREdHI4SGh4b8/fysrCz9/ANiYqJ1dHWvXr3KzM4ZvXVrYHBQIpHeGhnW19MLDAwqZ5czGAwtLS0GM9vL08PWzj4vL9fUxPRmCuaGl78QQhhI6+CAk8YffxxDTJ54Av+/ezcOuygYOo8+qrhHTlUj6r05gCC8KIpbCRU7WKA65FO+ilFaQxMEjhocPoz535qadqRI6DGDv+fatWvy/h54XYuPj4d3ULrzpg2EUHR0dG5uLn717O728vI2MzMLDg0NDQnR19c3MTFlsTkjIyODg0MkSXZ1dly+rBEdHVNSUpKRnn75smZ1TW1TY0NCYlJpSbGmpmZuHnZyyF8ITDfYB+3sbBzFMzbGbDGDgxhoDCicZ57ZymcGNhCUSrhGEKGUm2eZEukiJcZkgnChBH5bQx8/jkcxN7cG0pIf0vZthNDS0pK1tbW8v8fAwMDR0TExMRHeEe96htDQ0JKSEvxK19Dg6+NjYWERGoZlamBgaGpqyuVXjgwPg13V1Nhw+fLlm6lpJSUliYkJ2jq6paUlFhbmXt6+s7MzgYEBE3f6e0CGtbVrYLI9e7D3BcpK7N9PurqumeavvYYX1e7d2MEDITCASckFYQHC3EuFX29QodhlKrAFfI/RBOFD7VwT6b/9G/nrX+OJePNNHIUcGcE+UfliFNvIBNS2jY0N7e+JjU1RV6+ys0PW1qSDAzp5kqGpeRkEbmNtFRUVfU71XFd3j0Sy0t/Xq6aqWlCEhflweyiBhxJ4KIH7LoHvwO7p6enx8PCwtbWF+Pc2T0+wrJlMZkBAwLpyXZPAKiebgnkOnRcXsAm8srwsk0kXFrBtDm8qEDKHg+WviBD2QMjTwyjYOnKWzUYcD62eEUE0UB6dbEorTxLEzXWqHiYVi5mkjZ5du/Cb+iOPYOX0859j99JdfTx3DhhJpVJLS0t5f09hYaGuru494XuSk5NBpLRMZDIZvEPQiwxEuhoDkkgly8vLlFUxD0fJqKoE+KWWeuuFEQLDMoeD44Pz8zi2NTyM3Tze3rh8ZmAgTtcyNsYFy37xC5zCJi/bO9ugoTlUZJBFiW6AEikYPdEUyfUCLVI49sMP8SgiIvAloBinvNy2acPaMDIykvf3MBgMHR2de/L3QBW5dVGs2YJisXijUxco+ZfxKpUBmmdxYQHepKHaw/pqx2tjbAwXSz9wAIvro4/I2Fjs4HFxIb/3Pbzn8cexrUNLb/furUxzEGkpxZzJp0TXQxAZlN0jpSxLP4JYlhfpk09iGqof/Qinbjk5YTh/ZuZObXSg0zQ2NqYjCzExKceOVRkY4BLuBgbo8GFmYSG2mBFC2YysQ4cOWVpZ5eRkZ2dnGxoanj59ml1eQa/DB9YgSUQBwtYuuLKyIqM2hQFIZLc9PVtVX8dMeusOIXgKwa8Jfjj0CUnM/37HBp3ldil2kPsKr7E73tRJmfy34DtRDJfI9QEolUwmW1lZueNUJIlIKX0qhTHj/VIZkrs7kNIdI8Fhe1xZhj7J/5nGzAwaHFS8G6lUCkKA9bAKwVRwjNGPBfpIGSkbnhuWyU0HOAIVxAinlUgksDBgNmEt4VMtLaGBAfqc0JAfDBxIH7WqO+hj6UeiwuH/Ch+/A7snODj4/PnzTk5Ozc2Y8I2WPr0y6D0wAbm5uSkpa1x5UuqHpPA7XFpampmZodeZ/IOD+mHjH7BEIoEFBI5Bau5xfhaLhZ/mG7iAbysSWqNs0UCUE+ImZdxAIAb436DaaCr1bj0rr06efhpn2fzgBzjH+M03cSb8vdo9kDVN53MhhGJiYlRVVfX19TetzwVipMWLEIqNjWWxWCB8+JnR6hZ2Li4uzszMSKVrjy38TJRI1s8jW1nBbbCE5GcQIcwi8/LL2CFhZIQF+9xz5J/+hD8++SSOznz6KRYsWH6bgZdB7FC3NZLy6AxSokMUffA4BWQOp0q63tbQUD+cINZqmXl7Yy/ayspONTQ8BaRSqYuLi7y/JzQ0VFVV1djYeFNc87oobuuk8PBwLhcz8NLUsfKrlCTJhYWF2dnZ2w81yiKH46VSKaze5eVlaMukeNHCKm1rw/bH889jczkgAAPtr19f88S8//4adcIWi5NexoiCmgVRzJnj6yIdo4gKpVQF+2BKtredZ08/veZSWi38TpK4gNdf/kKam+9UqrA8XFxcaCBkTEyKklKVkRHS1yeNjNCRI8ycnLVSuCRJNjQ0TE1Pj46OTk/PdHV2tLW3g4Qf6F8StTQOFzBaF+ZXKBR/f0xMTGFhYWBgYHNzc0JCAiyV8fnlqMpe4Th+ARCLxf7+/ikpKdHR0XV1dTjrYngYP21kUt4tYc14P5g+TCYzLCysrKysoqLCx8cnJiYG9NDk0nj7dMuidBEqUoWHh2dmZsbGxoaFhYEJLkPLc5KGZRlG7K6srMTHx5eUlBQWFubl5UVFRYWHhwcGBmZkZKwtquURcroKyTAoXiQSxcXFpaen+/j4NDc3x8TEAFc1Wp4jO/LQJC7/KZFImUxmYmIik8nMyMgIDQ2lExKlwpaVkpvk3AwuXTw46OXlBScBC146PTORlLnQvDZHCdTm5+dXU1OTkJAAMy5dXO6KKejLqiAleCWXlpZev349ISEhMDCwpqYmOTl5K5TMA53xe7/Y9DTy8EBXriBIxJ6dnY2JiSkoKAgNDY2Ojg4KCrpx40ZSUtL169fz8vJGR0eZTGZMTExLSwuLxYqMjIyOjqZ59ot7C90rXUr7ioDkMzo6Oj8/PyYmprS0NCcnp6WlJSMjQygUNjQ0xMfHR0VFiUSikpKSoKCg5OTkNVzw0hIKDUUGBqi8HB4XiYmJZWVlwcHBiYmJ3t7eQAUeFoY9z+Pj45GRkZhRjMGQSqVpaWlxcXFFRUXbRMruXTz/NEd8B3aPl5cXRQocslFJw8977dcllc7N4YcLg8FYBe1TiIEJNTXVyqqqyxoa9Q2NKcmJzi4ut0aGLl26ZGNnV1NbOzw8PDY2xior09DQqKwSUAGdqCtmZvX19ZaWFmnpGfHxsVeuXFlPAcNwk/ffx+/NGzAQtMLYvgGBGFeCiKBKrCMK1rOKwIV36zyCcKYCNLfVCUHgt/Mf/ACfdhWi4eBwb0YPPHklEomxsbF8nMvMzMzHx6ewsHAjyR5oIKkUWypSiQTCxhERERA67BUJ1dXP19bVqZxWGRoeDvT3D4+40dXZoa6u5ujk1NbeMTQ0ND4+wWRkamlrt7V3IJL08/Wxt3esqakxMTZmscoHB/otLCzn5vGzG1iDP/4Y393p05g2EPQxxA3vpptpCHMXBT1JplwRgJECGAoAUPyoWgq3RaqriytxXriAs7fa2tbKl9Km804akGehra1NRwlX9+jq6gYGBhYXF0N2m/x5oHIZGIWrc7GwiO/dz883ISFlago1NTaqqapV8vlnzpwZn5xydnK4mZJWV1d7/vz5a9fcOjo7gbo0JTlZV1+/t69PJpVec3VxvebG53MvXrxYWFjkdu2ao7Pz3Dxe/AhhiP2hQzid8A9/wGb6+++vSfWnP8V3TVd/e+SRTZcxLMUmyi7PokQqIwg2QQysr9IIgriuYPQA9OrXv8Y/DahJBwHEu1Ix0VKCR7mOjo68v2dTuweWNHWnd/zZav8dne7rB8mKLDO50cuxZKBvGiFUXl4eHh6ekZFhYGBga2t76tQpyD8S9E5eTqzNaca2yK1btzQ1NSMjI9XV1Q0MDE6ePJmeno59XcsLkR3cxK7qeckycHl7eXn5+fl5enpqamra2tqCpdI908EaKh5fxBmp9fX1+vr66urqmpqaHh4eVVVV+GEoG59cypmT1FPQ+0UrKys1NTVdXV0tLS0lJaWjR49qaGgkJCSAj4qcrSVHbqLlIShlc+bMGTU1NSsrq6ioqK+//roFaE6nRDKOJ9mDde309HRAQICfn9/Vq1fz8/M1NTXBB4yk0pXS1MWAK7JebNl0dnbq6uo6OjoqKSkBInOhtbPPyGYihUFS1rmPj4+Ojo6uru5qQQZlZWWQgLj3FvucK18vYHkKV/oLCQlRUlIyNDS8cOGCoaHhmTNnrl+/fl9n7wGdrKEBmZmhkBBsbyCEBAKBkpKSpqamu7u7nZ2djo6Ovb19QEBAREREbW3t1NRUUFCQlpZWamrqjRs3jI2NY2JiXF1dJyYmJKTEV+CZ0ZHmK/AikWxkeOTSpUsnTpwICgry9/e/ePHilStX1NTUoqKi2Gy2t7d3cnLy4OAgk8msqKgIDg729fVdIUk0NIT09fFoXF1hps6ePaujo2Nra+vm5nb+/HkXFxdvb+9Tp05paWl1dHQ4OjoGBwf7+PjweLxjx45lZGTY2tpuxZD0gAT6HV3mO7B76uvr/fz8VlE+CiBcyuhZ9nB38/DyIhGqqRHo6ug0NjUXFRVBtt7gwMD7772rq6Ozb98+Kj9L4+ChL8pKSz799FMPTy9WWdmlS5eumJnp6el5ennZOzguLi7o6uo5OtifOX3m8OHD18Mjerq7T59WaaT8TAhhFuY338RaZJvY1hbaGtQJm4ptcddViJQgqqh3aEQQzdRXdyQD79qFsRenT2MM74cf4oAF1IyktcUOG7jMU0SEvL9ntajQsWPH1NXV6eAXnArD6MSzttZW16/jRLaSoiJDI6Pevv7k5CQoCtva0rxv3z4dHZ0333yzvJx9Qlnp9JmzuTk5H+/fHxQcUpCfd+7cOWtrGwMDQ1sb6/CIiMGBfl09XVdXl2NHjx0/jutMOTna7//4k6kZqLqC88ZDQzE8/IknsBq+F4MSUdp3NVBoReCgIUh4gSpkBgCUCiq/eon6ak33f/75GsVzVxd2LP3ylySXu1OfBC1t+On5+vrSrh2EUEBAwLFjxzQ0NEQiEbxL0SIdG71lbnE1jiJZzmZkmV65Mj4xcfNmXExM/dGjiFXC2bdvr7a29jvvvieorj78xaGLGpfTUlM+++yziIjIXCo/ztra2kDfwNHRKSEhSdjTraenb2F+1cXVReW0Slx8/LmzZ5VPnBgdm6AGhvFnwBn4xBM4b4sGgD/22O22fKhLbsVCva1USqS0OS6mRAoRw0LKAySRFyl9+AsvYEbEDz7AOWIGBvjuNzhnaRFu0kAIeXt739Xfs8mR67se/CNxdmaxVwhixyl+FRUV7e3tfD6/sbGxp6eHtuFaRmaXKVfoysoKn8+vq6sbGBioqqrq6emB1YIQGl0UTyzNwS10dnZWVVU1NjYKBIKRkRGhUAgv2SuylcmlCQh1zczMdHZ29vX1AfMbjf1ckY3LyCXwWN+6dauhoaGtra29vb2urq6lpQVKYaz5e2TLaBl7mwBc1dvb29DQUFFRUVdXJxQKb6c7TYmQBPuEEELt7e01NTVCoRAYg6C2FF7tC2JZ/xqufHZ2lsfjwUnoFLbFjh6peO3uWlpaqqurgaZFKBTSEpjtGpwfXCMZGRkZ6ezs7OrqGhoaqq6u7u7u/if191Bpa8jXF1EvO2hoaEgoFAJvZE9PT39/f0VFRVNT0/j4+OzsLEmSQ0NDjY2NnZ2d7e3tfX19U1NTtbW1kPE0KB6IaAgbmcNTtrS01NfXB9NRX18vEolqamrgECgrNjY2NjEx0dvbK5PJhoeHa2tr1+Ib1dXIxQVRVfDGxsaEQmF1dTWHw+ns7Ozv76+qqqqpqWlsbKysrFxYWGhpaWlsbGxqahoaGmpvb5+ZmWltbaXnC5bEv8jf78DukZfs+iMO/ws12E2MTUxMjIdGbi0uzFtbWaSmpZeWllpYWExPT/f2itRUVd9//71Tp1Ts7R0OH/7y2LFjOjraJ06cTEq+yePxPD09Q8PCrK0sDQyNXK+5dbS36+np6evrGRgYamtr6+jqZmZmamtp5eVDKiZmOnnnHRxv2kCRt6ZWaR1wZwOiBqEEcY0qs4WoFJi2df8EBGUsCaJTQZ18/DEGuyQl4cJeXC6mQPwGG/0GRnvLtpIniLSSz7WysjIxvTI+MTk9PWWgr1fO4SYnJ3t6ei4uLbU0NSkrK7/19tuq6upWVpZHvjzy5eHDBgYYYJGZmcVisahS0jFmZlc0NS+Hh0fUVFdra2lduWKqp6urp6eveu6cmuq5t95+p64BeKhwUcyoKIxc/t731mis7xTdVoIFoXlRMSxI0ZolCCA6AgNIRFEf3ZIX6a5dOPrj6Yn18egoFqypKabsuyf1DFKqr6/X1tYG3QB7QKobJ4hy2hebX8X/icVz42NjGpcuNbe0RkVFWln5pKRIK9gclZMn9u3bd+mSxiq95HElpS+++EJfX0/9/IXs7OyyslJXVxeqLqyxvp5+bFw8n8+9fPmyro5OTGycr6+PsrLy5cuX1dXPt7ZCHAEj3196CSOi9uy5JwMd+MGvUaRHkJc+SRBQdAJE2kqZ5reRZ/RM7d6NaQXq67GZ9e67mElym2r2G0UEe/h8vq6uLl2fa6s411aHy8+C/Ap/2H4ogf8zErg/i3wdaPV/RiwP4EYeqN0DVXsCAwOvXr0aFRU1N4ffGOgHH/Ybjwxra2vp6evV1tYWFxUZGhrGxycWFxerq6tPTEyIZ2dZLFZRYWFFRUVGRkZbW/vQ0GBqamp4eHhAQGAzxa1HlfES+vv7t7W3V1VVVVRwbtyImpicvBEZyamoKCwsCI+ImJ7BcWva37Nnz87dEqAw2inK4FQ5Q6eGih3At1IqGbhIXkODRnnpJdLEBFPrikSYCuXNN3GCjJwAaEls14CCXMePH6fjxEtLS1BkEbjC5A/GzHhdnZcuXTS7erWuvp7JZOrr6RYWl8bFx5uami4sLExMjLNYLIpblpORmSkUiXp6elLT0kJCgoOCgkSiXliCzU1NwSEhHe3ttbW1hYUFiUlJo6O3QoKDq2tqpqeniouKxdRUAq3wf/7nVsbNpvtBaNW7dlkSRCElKNjDoQhmoD1HELYEsVZwFIT56qu4NKytLUbdIoSNrZdfxmlH/f3fRKSVlZXHjx+n67EvLCyUl5czmUwFZw88pxrq69TUVG1sbBvqGzIzMjU0NPhVAn//ABsbK5lMOjw0xGaxCgryyzmc9PSMwaGhttbWlJQUnC8XEtI/sAaJrKkWXA8P7+7pqaury8nOzshkdHd2BgYENDc3p6enJSfdBBAV8Dq++CIG9EBZUNo62boBQcAKyqwBGgUQY/F6JQpE0RJar6ev3zEvTz2FyTknJrAk9fQwQbNEsmX9UfnFJt9GCJWVlZ08eZLmwHxo9zyAp/nDSzyUwEMJ7EQCD9rucXBwOH/+PESCQ6kYqcLjsr6+vqGxsa+vr7Ojs7CwaH5+nslkQj32ndzPPfWZnMRsKFvrD4WvEGXoZFJRg2bKrEEUw9uCHOIEUcwo19e/vX2GXbuwNyIvD1ffXFnBL/GZmZgT5RvYPaucHAYGBjQr2rVr186cOXP16tWjR48C7aa8SEmS5PG4HR2dvb2itra24uLilZWViIgIunj7PUnsrp0HBtALL6zd9a5dd/VPYGLrXbvifv5zh9/+VviLX6AnnkBUHtzSuk0JoJ8ggkiRF+njj+PIC+DBFxcxpmd4GNMWHziAUT7fQKRTU1OXL1+m87lWc9rV1dWvXLmipKQEpWRokVI5MhIOp1xIbS3NzSUlmA7V19c3JeXmXeWz8w70u2Br6z3h7sH7qIAKn6VytaTUUgeryJMg8jaa5quwM2fnNcEymTiZy99/7SMtgZ004A1Hvh77Q7tn51P/sOdDCTyUwLcqgQdt9xQVFZ07d05XV/fSpUubKumNd0vjmiUrK7U1NWVlrPHxic6OjlEczuwZHBrq6uosL+eIxeLa2prS0tIhKqUC0ELVAgGAo0mS7Ovrk0ilk5OTY+MTExPjDY11Y2PSN9+8jZDd1gBCFCuPJ5X8MrOug6coqjdIkAENDRiUlY0a5ZFHyPT02yrknnK45DUNRPrlcc22trYXL14EPAqdpaVwiIJUaVzz4uIin88vZ5fPzMy0tLRMTU11dLSPjY01Nzfz+PzZ2dlKXFKnbHx8DfQwPz8nqK5eWsZoTYlE0tfbt7y8XFtbOzkxWV9XV1nFbWufevHFu4h0926sUJ99Fv385wNPPeX08svR7767/N576IMP0K9/PUSJFAA9IFIGgXlloH3bjoTcovZ2HIthMtcEy+Fg/8Q3sHump6flcc2mpqa6urp+fn7Hjh0DJlN5edIWibxUfXx8k5IwO9T8vJjL5VZwuTOzs81NTWKxuL2tbWJysq6uViConpmZgXKSk+t8xDPT0zU1NeDdWVxc7OvrX1paAhBAZ0dHW3tTWzsCEsJt1ydIBlHRVRuCoFHhiCB6qY8A6AExJhFEyPoavi1SgsDhrYgInBBXWorv+OJFTDIJ5UQUJLD9x3vCNW91KnnxPmw/lMBDCTyUwP2SwIO2ewBqp6OjY2ZmphDn2vTxJ5/PNTQ4cOzoUT09vStXzFROnXJ0djmjctLcwlL13FkdXd2szMyjR7+2sLRisdkF+flFRUXR0VEqp05BbYpsRubXXx8dHh5WPXc2PiHRz9fn7FmVomL+e+/dRUmvq4dKys2zWg0KQgYk5ZBYpKoawRkQQfRR5Ci3C2TKK6qnniIzMu5ZJW+UyUa7p7u7Oy0tTV1dPSgoiOZp2HggvQchFB4eHhODqSDbWlsOH/5SV1fHxfXaJ/v3B4WEfv7ZJ85OzmdOqxgZGTEYjAMHDtjZ23O5vGwmk81mhwQHKZ84mZKahhC6ERF+8ZIGi1V27tzZa27uUTduHP7icza7+be/3Vyku3ZhhMqzz5KvvYbefVf2hz8U/upXlq+/LnjvPfT22+idd8h33pH95Cdz68Bw0NCrAGcrYELatQvzJ/3whxhp+9VXmEYPIezmsbcn6+rWBPvNrEnIbdHW1gZ/D0Koq6srKSnp3LlzN27cACuHlt6mDYSQq6vv88+nFBWh2uqK48eOa2trX3Nz+9v//k/kjRuHDx308PA4fvy4mdnVxIT4L48ccb12jV/JZzAYbBbLy8vzzNmzefkFGEzt56Orp5+Xl6OiouLj7RsWGnL5shozp+X55zcXqdwCA2ckkxIXjQqXUVDxWTmCR0QQPIKwX08/vG30/PjH5KVLOLbF42E40SuvkKqquG7a9PSmd3yXnQ/tnvv1gH54nocSeCiB+y6BB2332Nranjlz5tKlSxoaGkFBQfL4nk0fpVCX1N/fH1Nc9Pd9cejgF18cDg4JNTbU37//4//5n/+55uEZ4O+no6MTGhry1ZEjvn7+XC730qWL2tralzU1IyLCLSytsCbrbD+vrn7r1qiXl2d4RISlpUWAn1dwaPSf/7y9RgEumRtUJlHfutGDqLytqvWPoKEXKQwK7LytTqC80e7dOOX4Xv0QWwlklZbKwsKCzmM3MjLy8vI6gywvtAAAIABJREFUd+6csrJyRQVme9v0QHonld4fGRqa1N6OmhoaP/nkk4MHDqSkpp1RUfnk008/+OCDkJBQT08PHV2d8PDwAwcOhkdEsMrKVE6dMjIy0tfX9/cPcL12DSHE51aYmpoEBgaslp26am7B5/P8/Hz6B8gXXlAU6SOP4NT93/yG3LuX/OAD9M47s//xHwG//KXH3r2jb72F3noLvfMO+t3v2K+/3vL007RZCUhn83V0MxbpL39JBgXhWGFDwx2Gzt3umL71zRtAxELXY18lyDc2NnZxcTlz5szJkyd3yCoeFOT/3HOM999HFeXszz775ItDh9LSM44fO3rgwIH9+/eHhl53cXHW0tb29/c7evRoTExsSXGxisopY2NjDY3LEdfDvHxwJZaiwsIrplcCAvw9Pb2srW1WizJaWZnx+GM//7miSOUsHlh+EwThtXu332OPTT/6KC3DAoLovnOVDhCE+XoS+x2r9KmnyPh4XNo9MJDs7CRLSrCnB0g1N5fatnuhToWRkRGdA/UdxrnokQKD131/ht7fE8p7E+mR0zthz/294rd0NvnB07RV9I18Sxe9X6elBy+/Zv7pBq8wYIWP90tW/4znedB2T2lp6ddffx0aGhoYGFhWVrYTJc1kMn19faE+l6WFZcrNm1bW1u5ubudUVbV1dHx8fR0cHC5f1nB2dlE9d87Dw6O0tLS4uJjNZsfFxpw8eSI4JJTFZs/PzVmam8/MihlZmTdTUvx8fVROnywpxc6GO1UIrQxAeYgoLpnYdSpb2CklcKK1mNIooHVIgqAxKPQZ1hqPP44bRkb30+6h67EjhBwcHPbu3RsbG2tmZgZVA+kf7aYNhNDNmxF/+lP8K68gDrvR2dkh6kakm7u7g739CWVlXT39oMAgezu7ixcueHl5nTql4uXlzeFU5Oflcbm866EhysonoqKiK7jc8bFbjo6OZaWlp06dDAwKTkiILygqGBu7je/53vdwGYrf/pb8/e8xmvvdd9F776E//KH5+ectXn898/BhUkkJHTmCw1tvvbX02mv1+/Ytfv/7MB2QgH2NIErWhYxl+MILuN7466/j4gx/p60jLxmFeuyrTzoLC4u9e/empqYaGhryeGtshPKHKLSpvHd/Y+PM+HjEofg2wq+HuXt4ONrbHz9+3NTU1M/Xz9ra+tzZs25ubqqqakFBQRhlX1DA4/F8fLzOnDmTnHyzskrQ093t6OSUm5N94uTJoKBgZaXjhsZ6/Mqpre0etGcP2rOn+qmnLF55pXDvXnLvXrR3L3rpJfTyy0svvli3Zw9N8IihVJSnB2gXFFcpQeDg4y9+gbmCoBiFTIbhzN9s21iP/Tu3eyAhIDw8PCEhAcqOYmL3DRvwRm7YjXdIJJJN929ziFQq3fQQiUSy1VfyyokWPpQgTUpKSk5OFovFJEluetqtzgn0wVsdsul9fbND5FUgPXiZTJaenh4XF5eTkyOlto0j2UYg249k46lgpja9qZWVla1ERFtmcAsweKAdj46Ojo2NXWNU2ux62w9+syPwWtpmJPd6CLj5aeHTgx8eHi4pKSkvL8/JySkowB7lTcWyzWA27Q8S3mr8Wx2yc+HTN/LtNR603QP8Ex4eHjY2NjNUXhX989i0Af4eHx8fCq+zMjo6KpFIhof7pqZuTU9PTExMzMzM9vUJ29rqpNKl/v7O6upKmqlCJlvu6WlZWBCPjw/KZJKZ2VGEpAsLM0tLs/Pz0/39bVNTsn37NioAtGsXevTR5UceyX7iCcsf/KD2qafQU0+hxx9Hu3aJCaJezgaCYxFBZL/yiuenn5L796P9+8mPP0Yff4wb+/ejgwfRl19irlsKg7I2j/Sjjb5lefsP2vJ76G5w4OpzVt7uEYlEdXV1AFWm2ajlD1FoI4QSEiLffjv+1Ck0NbU4MTkmkUhEItH4+Nj4+Njk5PjKiri/r7Orq2llZbG7u6WmplIsXsP3LC8v9PS0LC2Jx8eHZDKJWDxGyiTCntb5hVmxeFIqXRgeRi+9hJmFn3sOY8bffRczBbz9Nnr3XfT22/OvvJK0d6/tqVMdurrI0BAZGyNd3ck//7lx3z4ZZRJBJRAwLmNfe+3Gp5+CGMlDh9BXX6Hjx8meHiQQIKGQ9vfsSKQgAVqkdIPePzs7q6+vT+OaRSKRQCAICwuLj4+HB6KCDBU+4mL17v6vvpoZFITE4vm5ucmVleWREZF4bnx+fmJuDotUJGoTitolkoWe7uamJoF4bgqGvrgoFgpbJZL5yclhmXR5dnZMKl0WCtvm52d7etp6ehpbWud+9Sv0+OOYEumHP8SmyYsvkq++iv7wB/Tb304/91zMr37l/Kc/id57D2KFaN++sb17W957D73/Pvrxj+klinbtCnvjjaRPPlkTKazSjz/Gy/Wjj9C5c6SZGamtjctiUAPDEoYR0lKSlxu06b/yX8EqXU3ANDAw+Mfx9wAFc2xsrImJSUlJSU9PDwVMv+MP8KZ0dnaKRCL5L0QiUVdXV3Nzs8J+oKtpbGxsaWnZ+JVQKGxqatp4IZFI1NLS0tDQsPGQ7u7utrY2Wo3RywwhFBUVpaSkZGJi0tbW1tXVtfFYoVBYU1Oz6eU6Ojo2HaFIJKqtre3o6Nh4tu7u7q3ud9NDRCJRR0fHGo8wLKD10SOEcnNzfakXVOi28XJdXV01NTUb94tEOBujvb1941c9PT21tbWb3u9Wh3R3dwsEAvmZhbZIJGpqaqLXKr2YgVknKSnJ1tY2NjZWJBJ1dnYqHC4SiVpbW4F0Z+NXWy2Atra2urq63t7enR8CpE0b5SAUCltaWoCQVuEHOzQ0FBMTk56eDpXSBwcHNx1/Z2fnpqsRbq27u3vjIIFqaONg4BCFX5BIJOrr6wNCKWj39fXR54RD5IUPd/Gt/n3Qdo+tra2ysvLFixe1tLTkqm6t/0Q2/At1SaOiouSesCSXi3JykFi8ViJmbAzFx6O4OJSSikZHsbimpnCSc3IySk9HKSkoNRXdvEmmpaKbyXg/7MnKQlFRGMpA+3seeYR84gn09NPkz35W98wz9i+9FLZv38Tbb6N9+7C7Yt8+9OyzrQRRfie5LSKIFoKw8vTEHK/UhivmLC4uwJvN0tICSUpo3Ald22F5eVm+sAaNy1k7BVXEAG6Z/gpkgxCSyWSurq50nMvU1NTLy0tFReXkyZM7jnNFM5k5FO/Dmm5bGzsWHcrORmlpKDERJSaSFRVYpFlZiMnEBDkpKVik6em4Q3IylnBiIpmZiRgMVFKCysrItDRMKPzGGxhuTAmN/Otf0Zdfyv7yl/LXXrP6/PN4ff05KFZgYIBNnzNnat97T/DWW+i99zAZDzUXiBKyY3w8pmujNlxkjRKpTCqVSKULCOEqMyRJymSypaUlULRLS0u0SBFVt4iWGHSAIkR0QSX4lj6Wrku6+rJlZmbm7Ox88uTJ06dP7zDO5e8fsmtXybPP4nWYkYGXVnIyXnj0/2lpWIwgvYwMvJ8SIF6QsEpTUrAwU/4/d9cBHsV1rdfYxE6cvBQneXFi5zmxHWPiEvATphgbG9OLMUhICCQkRJVQ7w31gnrvHTXUe++o99572aK2q9VK2+c+zl5pWCSMie345ct895u9c2fm7syZO3P+e2oqys8nysowPSEAfXIypK//3/8ldu1aC6izbx8YSP397+Wvv271t7+l7trFXx+loDR8//36Dz/s2rMHYNCvfkWStOCFFzweJojF9ISZH6RiXsUZmlahIlofpWLJKAVtKY/HI+fBOH+TNEnJhDA4CQxJT0xSDodjbW1N5iX9d5D3cDgcFos1ODg4PT09PDyMXyWx1EIQxNLSEn5JpZqhime3eMiRrySucCULHqkbziIzfJHtmDh8Ph8nLSc7xFlNhEJhd3c3mTqAJCnOF4HD342Pj+OAC2SfuCISiSS5ZR4liiIPwFNw/Nd4Td7C8vIyTuEk9YGFR4/vl+yBrBAEwWazcWo5shH3hmmL6UBCB3wLi4uLs7Ozw8PDIyMjTCYgfulz8SBcWlqSpgZ5AJb3kJtkRSQSsdlsclO68k2nCIVCFoslksqlhc8CZcLkJE5Tg6+fpDxO1T45OYlj/XG53A0XSRAEl8vFQjjpa8B16QGAKY8pIxAIlpchkvVmUkifQnZIEASfz3/iv2B7RDJQljTlhULhwsICm83GaGN8fJwjiblOdkte5BPbceI26YPJIcTn87F5Ln70+E/J3qQpTBAEi8Wam5vDgRk5HA6dTl9YWCDJiBCEf8QiWEyQH2H9Y+Oe4uLiM2fOBAYGent74/zV5Ah7YgXLe7y8vDAtJHQn3N0h3vF770G2SxaL6O2Feerf/gaz4bt3iZISgs1eS3uJQwY/cY1jNGPTk1dfJd56C334IdqxY+ovf/F/4427O3f2SSbQaPduYs8etGsXa88e9t/+JpZWu0jqc5J4eoMeHmhsrJfDWWazmJYWZo6ODpWVlZaWFrZ2di2trfji01KTb966FRgc3NTYoKura2dnNzwCiXJqq6uNjYzHJHGBmxobwiMihwYHLS0sq2tq4uNiXVxcV1chEwKmD5ZVmpiYkLjHzs7u3XffDQsL09PTe0Y9V2RkZFxcHNknpqqvL9gIp6QApzx5EvQdv/gFRAq+fBki173wwlr85eeeg8pzzxEvvgjJoV57DaL6fvghMGZcMHvetw8ECYqKSEVl+LPP3A4d8lJXHzc2BqxjaAgZmjQ0FnR1V2/cEH/yCXDojz7CiTYRhYJDFNIT4oWjI118Pp9KndHT03VxcSktKTY3M3Nycu5bz98UGhysrqERn5BYUlKkr6/v5OxMpdHEYnF+bu4dK2vGLISLLSkpup+UPDQ0aG9v39zSEhd7Lyg4BLukkSRdWlrS0dEh5T2Wlpbbt2+Pi4vT1NTEcdyfODjJRoSQh4cvhZKOh8f77xPx8WuZRDG58Ah8/nkQaEmPRmlX/5deAqPvd94BMn700dp6506oy8hA2bULDKH270c7dvS89ZbzgQMBiorUS5dADKasTKiqIgWF+aNHuXjE7tkD0SO3bsV62AEK5c7WrczMLN7ISLdILB4c6Dc00Pfy9i4tLTEyNLh79y4efgRB+Pp4a9y+nZ6RUZCfr6dv4OruvrjIFImEqSnJtrZ2LBbEvspIT8vKyqmvq7W0vNPc3Ozp6REQGMTlAk4lSTo/P6+rq0tO456Ce8jPHHku2fKDVMjHRPa/tLSEcc+GXeRnfXM7uQsn++zt7cXBi8fHx3HcLKFQ+MQsMdJdQXSxickKyUKn00tKSvDnnjxGJBL19fVtxj343zE1JiQLSWryXPIKpVvIOhZdsFgsoVC4uLhIEMTCwoJAIOBwOHNzIPGl0WhsNntgYKBb4oSI/4s8HVdwI7mW3ostunDKxc3nkqeMjIx8k5j/iXck/RfSdbLDJ561+QLIc7/p+OnpaTIa9YZjyP8aGBjAOZTI3sjKhlM2t0syjawymczFxUWhUMhkMpeXl3t7e8mcgJtPIVvIyhP/BSE0OjqK0eTmG8f8AqOToaGhf5b40v+IldcdHR04mDgWgopEoomJCXLMkJeKxcV4mlVQWOTq6mZiaqqtrVNcUurr6x8XnwA9r4uUxyULSecfofKj4h78ZkJKo9VVnJL6EZnWa9JPDtfz8/Pv37+PHwCmlUgEgY8NDIh334WUCMePE8nJkLooNpbQ0QGbkosXiS+/fCTIISU60pXnn4cj338ftDD796Pdu2ffeSf+9dft/v736t27hbt3o48/hum1pLLy9tvJMjKMx91qcEZSVwoFQhR6eaGS4ky1q1fj4uNOnThha2dfXV196MuD1tY2ZJJFB3u7hMQkWxtrIyPDc7JyFy4oZOfklleUWViY2dna+QcGIoT8/XyuqF1lsVhOTo4mppLFxPhBTR05/nDF29ubxD0rKys4onl/fz8WaazTcu13M0ljY2PLysrIPiXPhTA3h0DSeXkAKGNiCC4X1HMUCvBpnPob17duBQz02mvAVj/+GKuxQBQhIwNY87PPIK3B1atIWxvduDF19Gj44cMOZmYtMTHie/dQdDRx7x5x/z7y8Fg4fvy+js7S2bNIRgZY+y9/iTn0ssSgqpVCQUn3BSnJUdeuXY+Pjz/45Zcenl7ZWZlHjx51dr47vZ4QWVdHJyMzy0BfT1dHW1lZSV5eoay8vLSkxMjIyMBAPysnByFkY33HytrOxdlJ8eKlyIjw48eO29jaLbEhsQYmEP40ODo6khO+5eXlvr6+6enpgYGBJ+q5NpPUzy/cxKTG0xO99Rbxl7/AULS2hpEpQR7fOBS3bgXt1SuvwCkffrgmJJORAcLu308cOgTg6auviDNnCHl5pKSEzpwZ3707eO9eZ1XVDiMjsbExgEh9fcLAAGloUM+eTdbQ4Hz6KQzdvXvh7XjuORyi8A6F0r91K8rJ4cVEB9+6pR4eFnry5MnAwKD09LSDX3zh6elFozNAlczna96+XVhYaGigr62tpaKiqnBBoba2Ljsr08TY2MzMrKCwCBGEjo62nb3T/Pz8zRvX4xPv11RXX716lS7pgSQpj8d7aHg3N7eWqeCbcI8k2dlqbk7O8MgITiFXUV5eXFxC8n5M6u+z3vw6MJnMhzn7NrQ/4yZ4MkZHKykpaWtr5+XlPQx3eevWrYGBgbKysiNHjmBJzIaupC8+Ly/bxNTI3d2lvaPV28ezvBzHjl87hM/nNzc3k/dO9oNN71ksFpPJnJycJAOLkwd8awUh9DBfwfHjxysrK21tbRcWFo4ePVpQUJCUlHTq1KmKiorjx48HBQXJy8s7ODjgJBIb+sR5Lerq6hoaGpaXl1taWkiRFf6w41Qb5M1Kn47fNYRQb2/vE0kkffC31sVi8czMDNbsCASCb8Ii39oPeQAOgo/zr+HrJ3fhW8ONbW1tz+KDLH0uWQevhdJSV1dXBQWF4uJiNzc3b2/v8+fPq6qq1tTUkN8i8vhnr+AnS04wyK6wECsmJiY0NNTd3T05OXlsbGxztsFn/CMJZJ+4evXq9evXnZ2dHzx4oKamdvXq1YyMDAUFBSsrq41Rc8UiQsQjRHyERJPjo/n5+dHRUZGREeVlZYmJCdXVVSCzF/HhAILA4ihy5PwIlR8b92CLPA6HMz09vZniGFEuSgShCCHMnHJzc3FeUoSIwUFICo3NO4RCgD5vvQUM+MMPiZkZSFWdmwtMVBrfbKhv2QJxb994A8xjsQ2EjMz8m28mvPqq9VtvZezaxcGN+/cD59i9G+3eLfz4Y76MzNLevaS1BO4fUSgJkpyOYI/i7YXycpMMDAwyszLl5GSjY2Lr6+tPnToVGRmFZ9IIIUcHu4io6DuWFubmFiqqVzRv3w4MCmUw6FZ3LO/csfL28V1cXJyamjA0NBoaGgoLCbl+/bqurp6xsVFtfYP0aH6o9DE2NsZxC0ldw+bXFZMXPIpnZ9nrMlU87iMjI7Ef+7pqA5B3RgZQ5k9/IlRVIf/2mTPEH/7wiJLPPQdw5w9/ALp99BHAnb17gT1jUcTBg4S8PPis6eoiExN069bMiRMRJ07YmpoWJSbyMjJQWhqRlgY6nfR0Ii1NGBy8qqXF0dVFBw9CV//93xj0IAolgELJkEhNUFKSID4uyNTULD09/aszX6ekpubkZMvJysYnJGIm/dD/XEdL6/79JH09PX19fW1tgD4ZmVnTU1MS1zPdxPv3V1ZWOtvbnJxdXF3uamvrXL9xXe3q1Zs3bnR190iTlCWJ30PKex7py9axkfRYRQjRGQzyC4hJGhDgm58P8Xuam8H+hkKB5CcfffQI90iLdl58kfjd70BCiRWCJDF374asKefOwSNQVyf09MDmxs4OOTsjPb3JEyeCT5+209Wt8PcXhoaikBAiIoKIjEShoWIHB5GW1oqOzgom6a5d8HQkMF0oycBVRKEgCe7hRoT73bljFRsbo6h4MTc3Nz0t7ezZc6mpabMSgCLg829r3E5Pz9DX19PX19PV01dSupSbX9Df12tgoG9qapqckrq6ulpSXHTXxbWvt9fW1trTy0dbS8vHzx+Hy3806ubmNuQlvXSpycQEgJqJCZKTy83Pz8ODNiEu9ryc3C11DR6fPzE2amFurqiomFtQ+EN9/shnh584DlvwRNZOHvlNFSzsOX/+PNbXNDc3X7t2TUtLa3Bw0MzM7PDhwwkJkoms1PlikZg2szQ+ujg5xpyeWGprHrwfnxMTmVpZ3pKTVd5Y3zM1sTQxtjg+ukinsoVCEZ69bHidMdgyNDR0dHTEphjk6JX6q6dVEUJtbW3bt29XVVW1sLDIyso6ceKEkZFRTEzM7t27r169euzYsbt37549ezY4OBgz0fXuwJwAP9zKygdJScmxsXFdXT2qqlfWEyyu7WUwGGQw1Q2XR1L+KfKe9b/7ll+EUFRU1NWrV+/evQsfVUdHzB2+5bSn7sZqxG+S9+BTsV38d8ZYOCCLubn52bOQXkldXd3GxiYlJSU1NdXQ0HADuZ56sRt3IoTGx8c3y3uwUXZcXNzdu3dNTEwiIiL6+/vxPW7s4hm2EUKhoaF2dnYEQSgpKSkoKJSUlHC53Pr6+kOHDj2MnftYaHuECGoLUWVPVDtDqXVFDe6o0R01eqA6V1TvhurdJLuciAeOxOJIT9/Af7ieq7Cw8Ouvv75w4cLhw4c3eMrAnIa9ZGpiYmpmzhcIBgf6lZSUZqi0kpKS/Px8yYcA/Gw/+ohoaVmTounrg7ft6ipkGOVyIczali2POM0GxPPSS2tKGRkZcCzauxf94x/Tb74Z+8YbNrt2pZ86xVZURJcvo6tXgeVoaRE3bqAdO+q3b6+RmEqgvXtBrbPeJ6JQaigUJ4mPDLggubuL5uenQOu5zPbz9fXy8q6oqPD38/X09CwvrxgeHh4bG8vLy7G1tU1LzxgcGLjr7Owf4N/Z2VlXX9/U1Ojk7NTV3V1dU8tiMYuKiwf6+52dnesbGrIyM4KDQ/gCgfSLgRAKDw/v7+/HH0cnJyecqwubCEiPYciPyKBraWo6OsM3oqmxXkVFdZXLi41NjIpqtrNDNTVrhyNEDA8T27cTx46B+OGDDx7JeHCYwe3bAfHs3QtFYqoMipgDB0AaIS9PaGsjY2Nkbo50dUfOnw89c8bWyqowMXElKwushWprIal4fz/AKXv7Ci+vdj8/pKcHz+uTT6AfiTwJUSjZFIo3hSKSSClQYqJgbm5CJBLPzc25urr6+PiUl5f7+nh7e/s8qIbEe+Pj44kJCXZ2dkXFJc1Njc7OzsEhIb29vY1NTeXlZV5e3h3tHa2tbXQataKyanR0xN3dva2tPSUlOSw8AidRxzePyejt7Y2tMrGgYnp6GqvepemJJ39joyPq6re8vLwhV1d52fXrN/gCnp9f7K1bbcrKyMAAaLJ1KyCbd96BkEXrYwYG50svAdzZufMx4Lh7N1Dy+HHIYH/7NsAdCwvCywvFxoLFj7d3/7VrgRcU7B0dyzMyeKWlYEdVWQmu5tnZRFqa2M6uxNu7z8ICcjPfvg3GQLt3gyBz61acwX4tROHWrSgrizc/PyWZCI67ubkGBQVVVlZ5eHj4+fs3NjZJzC+G4+Ji7ezsKquq6mprnZycw8PDIaFmS0tJUWFAQEB3d3dHZ+fIyHBNbW1dba27u3tlRYW1tVVEZBQZIJSkmJeXF5mX9N691CNHmpSV0aVLxOXL6Isvsq9fv4YJbnXHMik5We2K2vDIGAjtR4avXlXr7unDz+X7r8mn3N3dHR0dHRMT09HRMTIyQl7nP1V5ODfT1NT09fV1c3NLTk728gJ3PBMTk+PHj6uoqMjKymJpDfmnq6sCG8PCK2cTbijcvy6fdEsxRUs5U+ty5i3FVI1LabcUU67LJ91QSLpyNsHRrIS9tNLT82T7npSUFHt7ex8fn+7u7m9S0j3lRhBCNTU1pqamV65cuXjxoqamppKS0pEjR27fvq2kpLRjxw5NTU0rKysVFZXU1FQpeZJYTPDEBFdMcAnEG58Ynp4eGxsb7OntuJ8UPzs3gxAf70VIRKVSu7txnj54aCQFhELhfcmSkZExPDz8OKh6yiU/eRcIxf39lZSUfH19Jycn5eTkzp49i8WKTz7hGVoRQmNjY2T4LumLn5ubi4iIiIuLq6mpGRwcxEKmZ+hy4yEIoaysLF1dXQMDAxcXl7///e8ODg4mJiDT9/ODGBYbT/hntgcHBzfjHtwBj8dbWlricDgrKyujo6PYqOif6XvtWIRQUVGRiopKSkrK5cuX7e3tbWxs/Pz8YmJiTp06FRcXJzVmACQTI8VE8nkiXemp5RKRqogYndNU+n847rG3t3d2djY0NAwLC9uQp0ISRq8PhoKJ8QyVvjA/p6WpOTY+UVxcoqFxjUqFXFECAQh16uqIsDCgbXAwcJG2NmjU1weBBMljpCsvvwxT8I8+AgHPvn3o44/577/f9fbbgTt3Op45k3frFsvAYM3uxMAAZEgGBoSRETDm/fuH/vd/WTiq3jp7xpKJcUnyIxpp7uPuvtFAmPxeT06OJyUlpaSmkjyA3PXsFXKkIoRaWlouXbqEJ1sPM3ApKSkdOHBATk7uwoUL+LsjfXB9bY2Nra2JJC/p9OSEhroGi718717cV18Zfvzx/OuvgyQGS33m54mvvwZFIdawPP88EHPrVjDfwXoTiQBsTcazbx/os27eJIyNQcCjr8+9fbtVQcFHScnZ3r4iNXW1tBQ1NqLeXgiFR6cTs7NoYQFRqcL4+MHExFUnJ6SrS5w/DxxaYlqOJKEBrHCEva1bwQ4mPX2NpNKiZkyxgb6+hIT4zMys9bxgz07ItSNJEuHOHzr4KCsrYwm8QCCQlZU9dOiQnJyckpLSBsEAeBSXlVpZ3TE3N2cvc4YGBzXUNVZWV7y8QigUsxdfXL57F2lpAel++lMIOITNyF58EWRa77yzhngexpjyBaWvAAAgAElEQVSWkYF7//JLAEmqqoSmJow6CwvC2RkFBWGrZ46LS+ONmx4mpi4ZmTV9/dyxcTQ6iqanIVojjUYwGGh6GhUWCnx9B5KS+BYWSF+fuHABdI579hC//jWiUBok0HwtUvMLL6w5FeJ730Cynp7uxMTE7JwcgVC4Ydczbm4gaU5OzpUrV8jPcXR06rZtTevqY7RtW25CQrzEmBr5+vhoampeu3a9vr6ht7f3yOFDuvoGpND3Gf/9KYeR91tQUKChoaGvr19WVtbe3v7d5u5YfeDk5BQfH0+j0Xx9fYODg6uqqmpqagQCQUZGhrSVD1h1rAhMb+coHI1WPh0L5RQUpVOxSpLNtcqpWIUj0db6hbOMxfr6OlLcKE1SPp/PkyzT09P4SyK991vr2J2tpqZmcnIyOTk5Ozt7ZWWlqakpMjKypqYmJyenurq6trbWzc3Nw8NjHVchEcFmClMXhQlM4f1FYSJLnLwkTmZBSeKgNJY4aVGYKCmxq0Qrm72MT8TPgiT7ysqKhYWFkZGRu7t7V1fX2Big22+94CcegE9MSEjw9vY+c+aMvb39oUOH9u/fHxkZ+Z37xF+Atra2zXourNqDtME6OikpKW1tbdIP94lX+E2N2Pq4vLy8tLSUSqUmJyc3NDQ4OjpGRkaSkuNvOvcp7djNuaGhgXzRpOmAZ25zc3OLi4vYpu374B6xWJySkmJra9vR0bG6uhoYGOjj4zM5ORkSEuLl5fWYpRpCxGgpkXaRyLzy1KJKZFxGs12T0//pds137tw5cuTIwYMHjx8/Tnpp4ecKYVSWlkyMjSwsLCorqxbm5wMDAqamZ/Lz8+XljaanVySvEzDpoCDgvgIB4eOzFnTk0iUwENm6FYp0cvWXXgLLiV27wKd371700Uez27blvPPO3X37PJWU6vT1BYaGSFcX6egAv8dFTw9pa68oKtZpaa0ePox27QLT5r17ITGkBEvhnFy2FEoLCXok8p41Ji0Wi0dGhjs6Ombn5kZHRzs6OkjN6+LCQltb29zc/OrqaldX9+joGH7leDzu2NgYdmoYGYZlmbM8IbG9Hx0bHRkdlZ5kIIQg6rSaGinvMTQ0LCoqGh0dHRwc3DCsIdUrg6Z5W8PJybm6pmaJxfLy8Fhic6Kiojw87s7MCN5/H3l7A0kJguBwwDrn5EnIHbawADkp/+u/gFWTAh7MrT/+GBDPtWvI1BQZGRE3blC/+irl9GmH69f9PDzaMjPFDQ1oeBhYMp2BaHREpSE6DQ0NsXJyamk0flMTSkpCNjYQHfjzz4H3/+53iEJhSHAkDrIHdP7Vrx55/gsEgoGBgc7Orvn5+WGJyyW2zgN9E53W3t6+tLTEZi91dnZOTU1hgfzy8jL+ivF4PCwZmp6eHhwchMgH8wujo2OSSfna9wRLsFVVVfGUhcvl6urqVlVVwcR20wwPpoajIzduXPfw9KypqZmdnXNxceVyud5evhSKz4svEu+8gz76aE3M8+tfA2r87W9B+rJvH1ASawY//RQMd1RUCG1tCOxkbQ2pQKOjUVYWKi0Tp6TNWFndv3nL0dEpqLauc2FBvLSIFmaFc3TBLF1EoxEzVIJOR/39c8XF9R0d4vR0lJAA2d+0tUFNtmcPeLlv2TJFoZhRKDPkKCVxj8TEldvf39/d3T2/sCChbSfpXUKdmW5v7+BwOEtLrI6OjpkZKmZjSyzW1BTIwHhc7sAAyKVXOCtjY+NisZjBoI+PjwO3Xv9CY5sDNTU18nMcHZ367rtNe/bA27R7N3r33dziYizERQw63dHBobLqQVNjQ1FxkYuri5ub+8Dg4FOgzD+1i/y8cDicEclCo9Ee+0yvX/Yz/j7936U7wbjHXCvnwvGYy2fiyKIsVb/8FbQrHo+x0s9f4fCGh4c22/dIQ/8JySLN3qT/8Sn1p1/2hr2SfpBQzJwThM0KAuYEgbNSZU4QOCcIIlsYAp8VomZxEXLdkP2QV4Ih19jY2NTU1Pj4OFvKtI485tkr2Cve2Ng4LCwsJiamr6+vs7MzNjYW/++z9yN9JP6o4td/Qz8CgWBUsszNzY2MjEgL86R7eJa6NGXI+oa/e5Z+NhyDLW/IF016YGCVrpWVlY6OTlhY2MjIyPcRtklfs/SAJNsfXRjGPakXiYwrTy2qRPplgtE5PDoxObmWA5vs7V9a+VHte/BU6d69e87OzqmpqZufgcS+Z35xcZHFYvF4PPwtLizM2bLF65NP4OOOEBjexsYSTCbUXVwIS0vI/+DvD0qon/8c+M2WLcA4X34Z/fGPaMcOQDwSuFP59tuBO3Y4nDyZeOPGpIGB2MQEWVgAy7Gzg2JlBbNtS0vQ16ipjVy82KCri778cm32LCND6n3QE0MUengQIyM9VVVVTQ31CgrnLSzvFBUVysnJmpqaNbes+XN5e3qoXb1qaGQUHx+roHBBTU2tqrp6amoqMiJc8eLFisoqkVDo7+d79uuzkZGRioqKwSGhwUGBJ06cHBgEn1s8qjb7cw0PD0dFRbm7u+PIWo8Gn6QmyXJPZ7PZTCaTzxfgj05kZGRSUtzSEvrHPyDQzuHDYFPi7U3gVOoXL4JPXEoK8GbsnIXd+HftQl9+iZSVQcCjrj5z7lzxkSM+X3/tbGqaERlJy84mamrQwCBgHTqdmKMJmNQVFpWzzOAx51BlVU9pafv8PKqrA18nS0sQcuzeTezYgX7yExGFcpdCKSM59E9+ApK5qkphT09LTU3NgwdVp09/ZW/vkJGWel7+PJiK968xRTMTkxs3b1resQoMCLiscvnatWtt7e2TkxOenh7Kype7uns4y2xHB3s5ufPJycmKihf8A4Jua9yyvGO9Qc+1wZ+rt7c3PDzc09Nzg/yMfAQ0Gm2Zw1lYWODzBdgKDftzSTR0AKAdHGBA/uMfIDzDwHHXLiDmsWMgVLtxA5RZ5uaEqysKC4MoADU1qLBoKjSs0MHOw+6OXVpS1swoTbgkFDFX+fQFEW1WTGeI6QwhY45PneMzlthzgtLS9pqanu5ulJoKMwE9PfC8k1iao1/+ckViHt6ASYplTmDfk83v7m6ura3Nz8uVlZNzc3dPSUn++swZO3v7UcksXCQS6evp3Lqlbm/v6O/nq6KieuPGzf7+gdHREWcnxyugihplLi7csbRQUlJyc3O7efNmbl6eqoqy810XaW0sNtTT0dEhQf9m3FNQkItH9b/0A0e+ONJfahaLtW6bsuF1+YE3Me4x08q5cCz68lexuCh/FYvLZVw5DZsXjkVb6ecvs7l9fb2bcQ9CqLu7u7S0tKGhYXx8/HuqdZ75JpFQzJrjR87yg+f4IbO4CNYra5vBs/wQBj+AI65hsznDw2Ccjhfpf1lvgwwwmENL7/1n6/gbKP1An6UH8hrwidKnYGS2Wc+FjyFP7Ovr4/EeOS1K7yUvRrrbH6f+UORPRgfYQHnsxBcWFubj45ORkTE0NPT9jcqf6aZAz1VKpCgS6apPLSpE2mUxvbNvYPg/XM9VVVWlqKioq6t75cqVdWnqY5QkBxk5koqKcigUjxdeAPMFhICp/OpXhJISnHXpElhFTE0RGhrYdQVMjF9+GW3bhmRkVmVkJrdvL3njDd/333c5ejRGVbVTW3vZxAQ5OKCAAABP9++DI1hKChQcjSYykmFjM6+riwwNkbY2CCSwiej774PmRcJF8igU33XBD5YAQbuXlzg0xF1O7ryfr9/Zr7929/Bsamo6fuyYq5vb2Pgakr3r5Fj1oMbD3e327duKihcvKyunpWe2tbebGBuFhoa5ubkjhFqbm7x9fAsL8o4fP+YfENTd1enm5iGWCDEwmfA7L52X1MvL6+bNm35+fgoKChuSh5OnrFEVC0MQioyMDAy8RxCgGaFQwCr8xRehcuIEMM5f/xpcumRkwIF/zx6Qk336KTp4EMnKrqiojJ07l/fZZ16HD7uoqyf6+fampKyWlaHaWjQ0AnBnli5i0VZ49AUxnQFyHgad2tHOGhplMviMWUSjgdtdWBgIOc6dA9zz9tuIQomjUGKkOTQm7/37Ky53zRQUFIMCA06cOBkSElpYkH/q9ClfX7/ZuXl8R0YGhs3NrWZmphoa6mpqaoqKF8orKhobGw0NjRwdHJJSUhFCZaXFgUEhQoHA3s6ur6/f1tZGR1d3anqG5Ih4ViSdn8ve3l5bW9vT01NRUfExe731obp5lHp5+b7yStqLL6K33yaOHgWbJ2wFheMYyciAJEZZmdDTg2iNxsbI3R0lJRFJScuRkcNe3ll2dh6ONk4pUVGDTY28ySk0OYX6elF7G2pqRA+qUJXEoqeyErW3EyCQaWWPjM4xRPMLqL4e8ktYWQHuOX4cHt8HH6AXXgilUJIkJAXjs+efB3Xb1q0oPXXJ+o7BJSUlLy+Pc7KyMdExuTnZx44dDQ0Nm1+A0JR8Hk9fT7+3t9fUxET91q1r165fvHixpra+tKTYxMTEzd09NS0DLBUy0pOSk0NDg9WuXMGk1jc0nJVEDSCH3Nzjds1PwT3rRN34SxL5e1ak+8VdfR9/LunevrVO4h7FY9GXz6zhHhIAkTDo8jruYTE5bW2tT8Q9TU1N3t7ehoaGLS0tPw5oIwjAPbP8cAYvkMELlpSg9TWuBDP4QQx+MJ3nt0LUzkzT8PcHE1maOOQT7Ovr+w5KOumucJ18eZ+4ufl4giBoNNrs7OzCwgKXy91wDVgJtVnPRXaOr7+trW2DC9vq6urMzAyDweDxePPz899HY/XEa36WRoyJyQnGBsqQPWCftQ0KAXLvD1wB3FNCJF8g0lSeWi4TqUqI3jUxTcWfWXKc/KsrP7a8x8LCIjUVuJGHh8cG+54nkl4i1cz99a+9JV7eIOMZGgLlyyuvQLCZ/fvR88+j3/wGfFUoFOFPfrL0u9/1/fWvuW+/Hfo//+P22mtu27cny8n16OqCPsvYGBBPeLg00IFYfElJAHrS01Fi4oqpaaq9PU1XFwCBujroJj7+GNZ/+QsGPThNJhszaWkTIg8PcXd3Q2ZmVnl52cWLisHBIaWlJefPy/n5+Xd1gesQ3LKbq4GBoaGBYWhIkJraVT1dneDQ8O6ebh9vT1VV1Zh7sd09PWGhoYXFJakpyaoqKl7ePmGhwaVl5dJDGXuUmJqakn7s165dKyoqYjKZhoaG6emQMfSJlCQbEULx8VGvvhrv5gYWsjo6ACK3boV7vH8fnT0LJlD79gHW2bVL+NFHix980H3gQOYXXwR/9pnrsWMeysqZJiaDCQnC4mJUU4t6etH0DJpjiJmzPC6dKZqho8kJNDJEDPSj4eHF9vbksDDW0KCQvkijEdPTREEB4ekJZuNffgm46o9/rJLYoAhJ3PPqq5DJi0JBaWmClpaqnJycosKC8+flIyOj0tLSLikphYSGDa1PKw0NDExMTIxNTFxdXB4Cl+vXrqWmpXV2dtra2KiqqqampQ0MDni4u7W2dfT2dDk5313hLPt4e9+4fr1/ACRGmCbgObi0pKuriyd8XC5XRUWluroac+6KigrySJKGGyqS8ez3yisZH34IsYiUlQHSkXEQ9u1D8vJIRwc0g3p6glu3FkxMOszMMtTVgzQ1Xa1tvO4n5Ay29gjn5sAGqr0dFRQQaamAymNjASTiEh1NJCWh5GRGUVFKZMTS2PgcXTw9QxQXg7BTVxf0hvv2gQrplVfyKRTPx6NrgujOxga1NHOrq8sKCgrSUlOUL19OSEhMiI9XVLwYHR09MQX2zg8Du+loa5uamlpaWrq4OOvq6V+/fj09I7OludnSwvz6jRvZ2TkDAwNOTo7DIyPFRYWy52RDQ8ODAgNv3LwxMTlJEgrLe/T09MjP8f8j7sFXhdc4tBqTydxgtrXhgf5Qm2u4RzNb4WiU8lf3lE+vFaXT95QkdUklRvn0PYWjUXf08jjLvMHBAWncQ148lUqdmpqqqKgYGxt7zIb0h7rWJ/QDei4GN4zODaBxA+lQgqTWjzapXN9lUQ2LySaNDsnLxp8+Mvbd6OjoN4WQecL//wBN8I5jIxhPTy9TUzM/f/+Kigo7OzspBADXODMzs8GfC1+5dFzBwcFBaZswDDhMTEzMTM0qyiu8vLyysrLwjf8AF/7MXWz255ImvlAobGtr6+zsxJGafyRkhpB4pEScdEGcenm9qKxXLotTybqyOOUSooNdszToJCn/r6v82LjHzs7uxIkTV69ePXjwYGJi4reOEox7rlwJamoCIkxMoM8/h5QRv/0tev55kcQMdoRCefCb3yS8/LLnq6+6vvVW0LZtGe+9175797y8vODGDTBYNjCAeIaRkTjiMBiOVFSAb1F7O9HRAZXiYnFiIi8pSZiSwvf0BEmPgQE412DWtWcP8coriEKZl4QoHNgMeqTte4RCYWNDfX5+/sDgYFNjY0FBQfWDB3l5eQUFBe3tbUVFhWNjY+wlVmlpSW1d3fIyWAIuLCxUP6hmMpkjIyMzMzPs5WXu6mp5RTl9dnZ6ehq7oJNvAcY9VlZWJO6xt7c/ceKErKzsV1991SqJkUge/MQKQiguLvro0TQTEzQ+jsbGwIbps88A63z1lXDnzqUdOwa3bSt/++3Y7ds9PvrI/cCBkLNns69c6XF2Zt6/L8zPQ3X1qLsXTU+J5xmClTkOnzYvmphCXV2gxCouAi+jjAwiPZ2XkiJITuZXVyM6TUhlMOii4WEwo7azI65fJyT6x7Gf/tRSYtzzKEvan/4EoIFCIRIT10ymuFzugwcP8vPzh4dH6uvqCgsLq6oqs7OzS0pL2yQkpdJoc7OzhYWFzS2tLBZrdGyMSqM2NjYuzM9PTExMTU3yeDwWizVDBVOV5uamjo5O6YGHg6MYGhpi3PMwOK+FhcXJkyfPShZsR/VESpKNCCFPz8A338zHeDEqCp0/j2RkIM/87t1CeXnW9ev98vKlx45FHzvmLivrfvNm2N27uRWVfSNjLM7cKmJy0NwcGIEXFgIGj4qC0JxWVoSpKWFkBEUS6pFwdeXFxwtKS4U0Kp++SGeAJVZ6OoxtXV1CTg5Az/vv927Zckcqp/2aSPLKFXgmo6NrJOUsL1dVVRUVFY2NjVY/eFBYWPig+kFOTk5JaWl7e3thYeH8/AKdTpcM2o7FxcWx8fHJiYmmpuaFhYXJycmpySk+X0CdmamtrePxeA0N9b194H4lTRAmk2lsbEzGa/5/xD0jIyPZ2dktLS0dHR2BkqW5uXl0dJS82n9dBeMeU80shaORyqdjlNaL8ukYvElWFI5GWunlspdWeqX0XGKxuLW1NTs7u7u7mwyMOzk5iZ0N/3WXvd4z4B46N4y2GkBbDXxKoa76Lgurmcwl0ugQbnx1taKioqCgYHp6Oj4+PjIyMiEh4fv7c61f2zP8Yn8NCfOEHGHZOT7+ATHxCaUVlRHRMWtqWckxm/25FhYWCgoKKisrR0ZGQkJCgoKCysrKhoaGpNNBSFJlTwcHBfv5+pWWlEVFRtXVQqA1cnmGS/xhDtngzzUyMpKTk1NfX8/n85eWljQ0NG7evOnp6dnf3/8jIWaExMMlwvsKwpTLwhRlyfryk9bKwuRLYlpHT//Qf7iea3Z2trW1taWlpbOz81mAvyTcU+6LLzrs3Dnh5dVubl72i1+kbtkS/sILPq+84vGzn7lQKD7btiXs3FknIzO+dy9bYjoK6bFu3gRdla4usI+ICIgck5YGk+PubtCL0WjgZMRgQFlYQIWFjW5ulenpkH7BxWXN2vT8eazrgVyPL74oolDcNqTJfFzeA7IocsRLV1gsVmdnZ2dXF8ba0rtIXZ504xPqUm8Hxj1kfi4yfjmHw+FyuZtd2aVOXaviABiffhq4e/eEvHxrZmbx2bNJV6+G/uMfPn/+s8ef/+z6+ut++/cnXbjQqKMzaWKy4uGBQkNRRAQQp7wCjY8T8wzhyuyykDYrnphADfUoPw+lpxHh4YSXF+HsTDg6Imfn8ps3WywswDEpJQX19grp83MMUXsHir0HMWkuXUIyMst/+IMNhdK2AUf+6lfgfHf0KNHY+I0knZ+fa29v6+npwdMvaYp9E0k3tEuTZUNeUkxSDofDZrNXVyGTg/TBT6xjec9rr0Xt2DGhotJy5kzRm2/ef/PN4Pfe85aRcf/0U7dDh/xPnUo1NW2JiZlKSVltakI0GpqfR2z6CvhljY8SDx7AGI2OBsRjZASKWxUVQkGBkJUl5OSQiopYV7fw3LkuJydUW4MY9EXa6vQMKi4GYylzczj8wAG0a9fCb35jSaH0byDp88+D3dtPfgKI9JtG6dzcbHt7e29fH8lfpan6LHVpymB5j3R+rv9H3DM2NpYqWTo7Oy0sLIyNjSsrK9va2qTn7tIX/wPW13DP7Uz5o+GXTkcrnY5SOh116XS0pOBK1CVJi/zRCGv93FnGgrQ/F5/P7+npiYyMLC0tFYlEOLkNmVHhB7zOJ3aFCCQQMakrwTMc3xmO3wzH/7Gy8mhzmuO1JHjAYrGlcQ+LxWpubg4MDOzr64uJiTEyMvLx8enq6vpxJG0EQuKBNqG/kTDYHJfVQFNBmCUv2IwfZMYLsRCFWKzt8jckBltbO7ukRQ4MBqOysjIkJGRwcNDV1VVXVzczMxO8Uubne9md1bPltXNVtXOVNbMVtXOV9fMPamcra2cr66Gxqna2qnq2om2xUSAWIAh99AMs+AXEHW2YYAgEAml/LkISBjAtLS0+Ph47AObk5GRnZ2NXvh9N3iMaKuYnnBckKfGTlASSIqkowzoZGvlJUOffVyRoHTP0WenMbs/ytfmex/yo8p4NvEf6+X3T0EAIFRbm/fd/n9i/32fr1gg9vTRv75I//anh5ZcHDx9mHDrE+/OfQVxx4ABYonz8MUSq/fxzkPkbGoIyxdER1AWpqWD73NlJUKkEnQEeRjNUcIqh0SH5FI2G2tvHGxo4yckoPZ1wdQXVj54eceTImnHPG28gCiWWQonawE6kcQ/2YxcKBbOzjPi4uLCw8Na2tvj4uJCQkL7+AfyQmpsa3d3dS0rLpiYnAgMD4+LjsRZgdGQ4KjoaZwCoqX6QnZ07MzMTFxvX3z9QVlqalZ2N/VrJQS8SiezAVGVthi2SLEwmE4srvomSZDuS2Pds2yYnK+v33ntRFy5kvPdeWXh4s7z88KlTcyoq/CtXIN+ZtTVyc4MQMpIEUiCJGBwkaFQxm84hZmZQXy+gyIwM8KkzNwd63bwJ9lby8ujCBZGy8riKCk9bG/n5gXampUU4u8Cgi6trsAUuOn1a/Je/+P/ud5lbtjyS9Eis0cEs/fBhIjAQ4glhzQuVSg2PCI+IjGxra7t37154eAQ5OSgrKfFw96hvaOjv6wsICEhJScWe7V1dnQmJiSsrqxB2orCwuKSURqNFR0e3tbfn5eVWVFZKjz2E0MrKiqmpKUlASYY1Lo/Hk1bnkwTcXEGQl9Tr5z+/9MYbAXv2RB84kHH6dNm5cy3q6iPa2vM6OkJDQ7BfTkmBUl+PGAw0SxNxqQuIToO4Rvn5ECH77l1APKqq4C+H/bJ27wZfRBkZCL2gpDR686YwOBjV1oros3MMYUcHZJ1zcwPanzmDdu8Wv/66B4VSvHmU4hhCL7wAPgE4KPP4+GhEePi9e7Ht7e3h4WEREREzVBoepfn5eR4enm3tHd1dnf7+/ukZmdiWs6mxMSkpWZLfg8jOyqquqa1+8MDf37++viEzI6PqwYMNJF1aWjIzM/t3kPfU1NRYW1uXl5cLBAIqlUqn02dnZ7+PP9fmAfBNLRLcwze5nX7+SNilU5EX18ulUxF489KpSFw5fyTMUjdrgz+XUCjMysqytbUdGRnJysry9/cPCAh4YnKDb7qA79OOcc/0ctAU23uK7fuUMsn2YPGrFuaZA+sJZHDaivDwcBcXl6WlpVnJMj8///39uZ71jhAS1RXw1f6Xf3MfLoJb+wW3Plkv+8l2/pWPiNocmkTASXLT8fFxd3f3iIgISZ4cKoPBWFpaGh0d5Qv45bTChLHI5PF7yRNQUiZicUmdikuZiMWNiePRBTOZfBHvB8E9+M3icrk475hItJadEJNisz9XY2OjnZ0dTsAuzXMHBwefRdbwrBR+ynEICYeKV2NluYmX1svF9QrZcombeHE1/gJB6xyfnP4Pt+9paWlJSkry9va+ePEixtdPoR5+ZqWleaqqYebmEBZvdBTFxKBf/hJUXa+/DkBHUXFNG4WzhyoogLRGVxeYgZ0dERcHeoPycmJ0DBAPjSpepK6sUJlc6jyfvsAYpaenlY+Ori4soM5O0IJh3KOrCxPoTz6BnvfsQb/97QMKxYFCWYuDIg13fv7ztWCG3t6oo73O0cEhISH+9MmT1ja2FRUVhw8fvmNl3dWzZt/jaG/n6uZpaGDg4OAgd15e8cKF3Lz8hvp6ezvb27dvx8YlLLOXtDS1tLS13d3djxw57Ontk5OdJScnR2dAsH9ylIvFYnt7e1LPFRgY6Ofnp6KicvLkyWdUysTExLz5ZsYXX4B66+BB8HpTU0NaWpBcwsQE2doC9by9QUKWkgJMeWCAmJhASzSOkD5LjI0TZaUAJ11cwHn6yhUwHvn8c1Bc7d27ICNTsX27QEYGnTgBT8LSEoyGWlv4s0zqjLi4GIx7dHXRjh3Zf/2r71//ilOvr+licIzjX/wC7HC3bEEpqaLq6mIHB8eE+PhDhw67urlnpKcdO3bM0dFpfN1UXFdb28fHV0db28Lc7LLK5QuKFx5UPaivqzM3M7t29VpZecX01KT6rZsmxiYuLi6nTp+OjIzU0tJSUVUdn3jMGGV1ddXc3JzMU+Hp6RkfH9/T06OiovIskmFJON3gI0fyP/0UILiJCervB+BoaIj09ZGxMdx1Sgrg75oaHH1HLAE9VKBsdjYRGgpCsBs3wN1r716wM3/nHUgz8e67aOdOuoxM1bZtwnPnoKPQUNTcxJtlUqlEWRmobijW4KwAACAASURBVI2NCTU1cFp8553E55+P2Ax6yOH6wlaUlycqK811dHAMDws7eeqUn59/SnLyoUNfurm5TUsMvQUCvrq6emBgoL6enpmZqeqVKxcULzQ0NVeUl5maGKura9TU1o+ODF2/cUNLS7u8vPyqmlpKampISJDa1Wsrj2eRY7PZ5ubm/w64JyIiIisri0ajkXFxWCzWs7wpT/80PctejHuMNdLkDodcPBmheDJ8vUQorm1G4Ha5wyEWulkb4hYuLy8//BTU1dUtLi4GBwdra2vb2tq2t7djePEsF/B9jsG4Z5IVOMHymmD5PKWMsdxZ/KrpKVpnJ2iQ8TIyMuLi4jIwMMBikTmb0dDQEM6g+X0u7JnORUjUWMRT38/TOogLHypfkpuPGm/uI+oLJmZo5OuPEKqqqgoICJicnMQzH3xHOO15JaMkafxe6kRC2kR86kRCKqzXKmmwCSVpPLZwJucHwT1YC+/n55eQkFBdXZ2enr45/svg4CCmKr7OyMjI1NRUKpUqkCzZ2dmpqam1tbUTExPPOJF7Jgo/5SCEBINFnHvnVuIVV+IvriRcXIlX5EC5yIlfrydc5MQpcuLkxbQOKh2ylq6Nmx/l50eV9zx0qzMxMbG2tlZQULC0tPTx8SHZ+TfREMLEled+/HHg11+j7m40NATRmevrIb7tzp0gj/n8c0AnMjIAU5SUIHqhri5Y5/j6rlnzVD0gpqYRgy5mUTkC2hxBo4GEh8FA83PTbS3NJWWzNBGdAY4yknzjICLS04NZNwY9O3eOvfSSBYVCfyJHeeMNCIf4wgsoLBSFh3kpX76cn593Xk42Miq6tbXl9OnT4RGR2HUIIeTs6JBfUHzXyVFHR0ft6jUtzdtRMbFUKtXczNTH19fbx3dyclxPX9/O3s7IyPDqVTUHJ+fRkZHbGhqtbR0koTbb96ipqZmamurq6lpbW28Ok7+ZsBJ5T9Sbb6ZER6ObNyFgT1gYsE89vTXqaWmBqCwwEPh0aiqkfaUz0DxNIIZoPDPAb6OjIQCAjg4E3duzByIub9+OXnsNvfba8B//2LN9OwRp2bcPJEDW1iDv6ezgzrJHRwFW2tqiy5c7/vIXWxkZ9u9//0jY89OfQpYMCoX4/e8hVFJsLFpaElhbmd1S18jNyTnz9dfJKalFhQWycrLx8fHzC4v47dDX06usrDI2NtK8fVtbR+fKFdX8goKxsVF9A0Mba6v0jIz21lZjYyM7W5uQkFAvby89Pb27zk4HDx7slwjhMHE22Pesrq4qKipiD3ZNTc28PEinsJmM0i0IoaCggAMHco8cQSdPQhikmBiQ3ejoAPa7exduPCmJKCqCbCpUGlqmLhEMOgjQsrLgUFNTsKL//HOg5J//DBopSTpT9Nxz6Gc/63333f4PP0SysoAyY2NRT/fK7PLAIFhKOTgAQD94EL3/ft3LLztQKKsbRimOAImhz9atKDeXZ2VpoKOnf/9+oqKiYnZOTlZW5rlzZ1NSU3GcQD6fp6Ot09jQaGxkdFtDQ09f/8oV1dLyis6OdkiS4OSUkZndUFdramZubGTU0NDg5OTEXuY0NTRcv36DSqOThMIx8o2MjP4d7Jrr6+u9vb2TkpLIGEVMJvOJno/Sz/QHqWPcY6SRKnso6MKJ0CeXk9AueyjIQieDxeK0t7eRds0ikSgnJ8fT07OkpGRpaWlBskxNTW1wR/pBLnVzJ4hAfBFzbNF/ZNFjdMFrZNFrdNFrdGFt/WhzwWtkwXV+tXyDHzuLxbp3756vr29HB3y+8NLf37/ZCR8Pm/VDvuVd23ydT25BSNhYzL35CVfzC67mwaeU1Rt7iYaCkYlJaVXL+Ph4YGBgWFiYdGNHRwdnhVNBL74/Gp0yFveUcn80pmA6iy/+7vIekhoQpYxKtzCzdHf1KCooTohLDA0Old6LEOrp6ZHGPY2NjXjA4yzrmZmZHh4e2L7nO+epeDKRv6kVIf5A4VLUWfY9BXbshUcFb95TWIqVtN+TZ987L5xp6+79T89T8TC26RdffOHh4WFsbIwdu76JdLhdEt4739k5fmEBIty/9RZw4vx8sOI8fBjWOFfUJ5+A57CBwVoUuNBQEPMUFhJd3YB4FugCLoOJaDOQ2Wt4GI2NUVtaplpa0Nw8mptlU9k0Osh7JK7shI0NsH8FBZz+evnVV22few7SZJLz5g2Vn/wEYtPh4MIDA/1VVVV6erp29vZ5eXk6OtrWNjYPqmv4fL5YJIqJirqipubq5l5RXq6urm5iYpKfX1BVVZWdlXHz1s2KiorCoiIvL08LyzvZ2dm6urpR0dF+vj6mpqY4XhxJEIIgPDw8SHlPXFzc6dOnCwoKtLW1ccKKbyVpbGysp2dJRQWyswOeW14OVlA6OmuQ0ceHCAkhcnKAKbe2gjaQRhWvUBcRgw7yibQ0MOLR1ATQs3s3xPz5xS/Qli2T6/bJ6L/+a00Ed+UKON2lp6Ph4aU5bnMLioxE2tqM994z37lzZNu2x0j6wQfAwn/3O4A+L70E/yt5t4nu7i5MLmdn58zMDB0dbQdHx+ZWMM4QikTubq7Xrl0LDArOSE/T1tY2MjYur6h8UF0dEx2tratbXl5eXlbm6ODg6ORUUV5uZGiUnJzi7uZ618WVx+NLM2k+n29nZ0dO+FxdoVscth9/+L6VpJ6eYX/9a9WJE+jTT8ENq6AAhqKFBSBIPz+gWVIS2PDQ6IhBFYpodGJmGhSFCQkgltTWBpmZjIx0RjREoYxRKIsUCvrrXwFHyssDiMzIEI+NLc7yGxpRVBQAofPn0Y4d06+9Zv7yy49CFFIoa3GiX3oJgpj/6lfwpjw0fcsvANjc1taWn5+npanp6emZnp6moa7u7OzU3d0jEAhWVlYcHRyuX78eFR2dmpKkra1tamZWVl5RXV0dFhqsb2BYXV1TUlJiaWHu4enV1tqSnp7BYjGtrO84ODqx2cvSJF1dXbWzsyPzkv4/2vfQ6XRzc/OGBkhyh6ODMpnMJ4YnePpT/g57Me4xvJV89ssAheMhkhK8vpauBJ/7MsBMO53zeNxChFBjY6OlpaU0952QLCSpv8NVPeMpgHuEi8PzPoOzrkOz7kNz7rAmi9TmAMN5fqWMuciSzseOEEpISPD09MQyBqyjAa+Ox+MWPnQjEIlEeC9BEM9iTvcs1w8q8oailRufrNz+Yq1ofrGiuV7HjZJNzvW94vp82hwY8pN4QigUenp6JiUl4f8SCoUikWh4eJjP55VTixKGo5JHYpNGYpMfL2RL4nB0weR313MhAtFXqT2sjj5WVx+rq5/d08PsrB6trJt40Dhd1zHf2rcE7b2srn5WD1/Mn5yclI5b+FBfb2VlhdNACYXCubm56enp4eHhcUks3B9j5CDE7StcDD/DjJZ/ajnPjJQVzbTP0Gf/w+U9fX19bm5uaWlpLi4umM2QgxiPOWl9JG7Jy8s7diz8+HGkrg62nv/4B2SP+vvfQSSjowMxS65dg9myjg6w8IgIUCiUlhJ19WhqEqJu8RlMYmoK9fYA28nNRVlZ/MzMTEvLiago1NEhcTWanaWL+iUM/f590Mzo6xNffQVKtDff9P/zn9NfeOExDo1xz/PPQxxeCgWm6K++CnN60mKUz+ez2Ww+ny9hJKt9fT3hYWERkZHTM1Qej4ftRldXV7lcLkEQ2BiZy+OJRCLe+hohxFnhiMVigYDP5XKlRyr+djs4OJC4Jy8vz9bW1sPDg+Tc0iTF9TXarr/WsbExOTlZZmZIU3PNiBbLybBGJi0NZDp4TaeDUdQKlQmWKN3doJRJTgaUdPHiGuiRUGdZkk10HsPDrVvXUnmpqQFdCvLFMzMMmrCkBLm68vfuvfvBB5X790PecmkE+eqrED3o9dchMaqNDcjziosfkZTH47HZbJx7eWVlpaW5KTgoKC42bmFhERMQ2+gAvhSLuRIic3k8oVCITUElFqwEnv1gNfkGkm7QcwmFwoKCguDg4OTkZCyxeIyMko11Wq79engEv/FG0RdfgOI1Lo6YnAQiGRuD7VNMDIzJzEywWMLCHiDmwACA5fBwGG3y8kDMt99eQysSMjIplFSctuONNwCzXL5MuLmh8jI+fX5mBkRHbm6QFu3LL1dff93+j39sfv75x0YpjlWIKWxuDg/0978H10VylPK43IeqKKFQiAOENtTXhYSExCckLC8vS6y54SY5HI5AIMAjEwjL5YpEQoEAqCoQCjGLkiRU52IRBaYSfoX/ffRc8/PzlpaWDQ0NU1NTrq6ubm5u9fX1Q0NDPxSLJe96c2Ud9ySd/dJf4XigwvGgR+WYpH4sUP54kMKxoLMH/cx0Ulms5c7OTlLegxDq7Ow0Njaenp6mUqlDQ0MjIyOTk5ObRSab//r7t0A8J+HiIN2zj3a3n+bSR3Ptp7n0011gDeXRZi/VYZZdusyGcNjS70VGRoazs/Pq6mpqaqqrq+vDcKyDg4PSwiqgz+pqclJSSnJyeXl5dXV1RgYEiPpBLl7QUMS5/gnn9he4LEsqy7e/wBWOBrTD5rW94vqC4YlJctqDZeo+Pj5xcXFcLtfX19fFxaWgoKC/v395mV0+Uxg3GHF/OOb+EJSkoRior2/iSsJgZN5EBu+72vdAILeFhvjxyGQwGIpNHo9NmYxLn0lMm05Im05InYzD7Unj99ImE1l8Zn9fvzTuWVhYuHPnTnV1NaYk6anwo9n3wGPtLZgNPj0XLrehzD7WIjsbdlY43TYxNfMfbt+Dc7NpamoaGBg0NzfjB4PNV/v6+nDo29lZxig4ey/19HQThLioKO+118I+/BBdvw5yiNOnYfKqoAAWJl5eoEsxMABTk4gImGcXFqGWNmArnLkVMEYZHkaNDWvahIAAsafnkqur0MOD7++/ZvDc1UXQ6Uza6ugYyswE7xgTExB7HD2Ktm0rfvNNn7//HafJfIxJP/ccxIb+5BMIe/Ozn8GUWpLtAV55zC3m5+e5XO7KCmd+fp69xH4YHmZpaWmVu7q4uIhtZlksFpPJxAYHPInBGv5ekLxEJBJhXr7Zik0kEknb95SXlzs7O1tYWAQHB5OWuThSJyikJbBpCpbpxcVFjJbuxcRkZWWOjqLWVqCbtTVhawtM2tISfKjT00GilpkJQhfIMkEViqiSjFDFxeBc7uMDrBrHyHvlFaGENwspFAEpE3vhBUCmn30GaNTPD9XV8meZA4MoKxtdvnz/wIEoiecRkk72TqEADV9+GeREX30F+de+/PJRngqxWLy8zJ6fn8ccGkjKZrNYLDabzV0FkorF4hUOhyVZSFiJCYhhIkEQPB5PknmKWJEASukPK6azubk5ST0qlZqQkCAvL3/p0iWyUfKNXunp7eXzIU3s+Pg4nU6fn5sblIQCCg8P3r27SJK+A+jp4QFAXE+PuHcPJJTJyURDg8SLkCoSUhkEnQZIJDERIJ66OqQS/egjrOdDFAqfQlmmUESSCmCZN94AYmpoEGFhqLNjeW6luxfFx4NF0OXL6M03I7dtS5bEWXhsiGLE8/LLxKFD4LS4tASeYdIpz9hsICnG6PPz88skSbmrTCbzYeDm5eVlPG4xjlldXSXnJOQoFYpEkqezLBAINpB0aWnp38S+h06n37lzJycnp70dtHU2NjbV1dWdnZ3S7gLSF/8D1iVDi69/K/HMQZ/zxwLOH/NfLwHrmwHnj0L71we9zbRTF+aZjU2N0rinpaXFxMSks7PzIQTX1NQ0NTVtbW19YsTXH/CycVfwDREs9M64d005dk8590w7d089KtKbnZO2jKUSGpXR3b1my4i/ZikpKTY2NlQq1d/f38zMLDAwsKenZ82scx2Ac/n8wPDI6IT7vcOj2YXFfiFhQpEYPqS4fNe7Avv9+sKla3uX1A98S1HbLaov6OofkBaqCQQCX1/fgICA6elpc3NzU1PTnJycjo6OxcWFsumCe/1hCQNRCYNRa2tcGYhKXG+MHQjPGUv7PrinbaHp/lgMWAuNx6eOS9YT8ZL6+uZ4Qsp4XOZkMovHbG1pldZz0Wg0W1tbrE5ZXl4ODg4ODQ1NTU390YzKYdj35FMDTtJCz9FDz9Ekhb6pTg85Sws+w59qm6Yy/sPlPUwm097eXkVFxc/Pj/QjRQjV1dZcvnw5NDxCKBTaWFtpaWnn5+dfUVUtrwBdxYcf5nz2Gbp2jVhZIdhsYnYWGLOpKXz6fX1hzpyWRtTUouYWxJgRsmdXeZN01N1F1NcBG/fxAX2DhgbS0GiRlS09fhx83HV0gDvFxRE5OWh8jENnT06ivDwwXJE4gqEDB/h/+pPdrl0zEmeujRzlpz8FuEOhQJjjl14CPU92lqizs2FufoG5uKB1+7a5uUVxSYmWlqaJiWlTE8A7iJoTe0/t6tW7Lq5FhYWXLioaGZtMTEzOMuimJiYhIWFgEgEZACx7+wemJsdtbGzaWlsMDAzKKzY6H4nF4pCQENJltLq6+tKlS4qKiubm5qS5PsQ9ystRuaySmpa+usIx0NczMjbNy8tVVlbu6OxKS01pamqIioLwjCMjwIK1tIBVBweDcCIzEzBhcTGEGaTRCCaVA3x6eBjYZnw8tqSF/AvbtqEtWx5QKLUSxPNI2vDii2B7deQIkDIuDg0NLc1xa+pQRMTygQPWly6x9u5FH364lk7k6FEAsu+9R/zmN3DST38KejNFReL11yF5eFtb9fLyytTk5FW1qzY2tnl5eZqat+/csertW8tF7+nhcfXqNV8//8zMDFlZWTt7h9nZufGxMW1NzaSkFAhHNj11x/LOwOCgjbW1p6dXTfUDXT291rZ2jFDJ77tYLPb09CTDoqipqe3YsUNWVlY6DiRC6H5iwqVLl4qKSx+CVm1NTQvLOxkZ6crKyuMTk6mp8ZqazRoaKCiIuHMHUIqBAaAaHBM8O1sSPYGOWFQOoPLBQYB1Pj6AjLDk7I9/xIMMUShF67nfgKRbtgBFTp0CwVFGumhqhkETlZaB7kxTE33++fxrr1l98AFbErdz4yh9GANp2zbAW5KwPURZGaqpWWluquNyeb09XTdu3Lh71yU3N/fWzRs2NjZr3sVi8V1np2vXroWGhqWnpZ47J3vXxY3NXu7r7dW8rZmdA6ZOw8NDlpaWi0xWXU21r59/aUmxhsbtbonxvjSTwpoCHBdfYve9MT8XmacCwynpc/HTwW/N918/FO1YWlqWlpbieTyLxcKORdJjQPrff8A6lvfo3Yg//bmX7FE/2SO+68VP9oj0pu9Xn3sa307iLD+Wn0ssFtfW1pqYmIyPj7e1tUVERCQkJAwODpID9Qe81M1dAe7hL3RNuLSP23WM20sVh45xh45x+3ZYQ2kbs6IyCxcXHvPn4vF4SUlJ9vb2WLG1srKyvLwMrHeZQ0yPiOsLxI3FRFMJry5/OD1qrjR9pTZ/Mid+Jj+RaCoRNxaLG4rEzaUEZwkA0D+/gLCqvnBRbQ/z5mfMW5/BmiySzcX1xsUruwS1eRv0XAsLCz4+PmFhYVj1trS0tLKyMjI8wuPzisdzo3uC4/rCY3sjYnsj4vrCJXVYx/XhzYiY3tCskRSe8DFR/bPfBMh75hsTRqKSx+KSR8GQKHksNnksNmU0Lnk0VrIZJ9kVmz6eyBYsTU1OSct7RkZGbG1tS0pKcDgJc3NzIyOjsLCw/v5+chb37BfzHY4Ei8muvEnf49NBX0MJhPVU4FrlUT3wzFTAae5ka3ffwMT4+Pd/05+9hx/brtnDw8PKyqqjo0NDQ4NkKoAJ7t1zcLB3dHKm0egmJiYWFhbVD6p9vb0TEu9XVlb+7W9XPv547MoV9OABmNna2YF2Rl8fxDy5OSCW6O8nFuj81Vm2mEpDrc0oOws4w507ECBPVhZiHe7ZQ+zbNysjw9+7F+3fD1kStLTg/ORk1N3FZbAmJ1F+PjjW6OtD0s2dO4def9153z7hq68+YujSepktW4AlYXnPQ2vcgABUXpojLy8fFhaG81TUN9QfO3rU1dVteF326+TgUFxcZnXHMjcvz9nJoayyqru7JzQs1PLOHUNDQxZ7eWJsVP68XNWDGjsbK1lZOWtrq/Pnz2c8HgMUq/wvXrxIhka9ffs2dlk0MjIijXARQsEB/nfvunh6eQ0NDpibm1tbW7d3dNrb2pSVV6SkJMvK6tvZzbW0IC4XMrz6+wOITEgA4URKClTq6gjGLJqncsVUKkgMmppgB9bLnDwJwQN+8xtCYtPzSNKDCfSzn4HB+YUL8JwKC0VUGoMuLK9ARkb1n3/uf/w4GKuQSq6HGp6CAvB/xyKf118Hkd7//A/x9deor090PyFaXl4+OjrqxIkTwSEheXm5p0+d8vbxpTNm8RA30NOvrqnVUFdvam7W19fr6OxuaGjw8vRw9/AwMDAQCEUdHW1ycnL19fXKykrq6v/H3lvAx3FdC+POe02/vEKaNu1r2iYNM5liZscoW2a2Y5IsW5YsZosZLKYVM8tiBouZmXlJ0kqr3ZV2Vwv3y5m7Gq9kqJukee/f/7e/87s692jgzpk7c88cvKt6587Nm0qFRStTYBcUFFy5coVcpEdHR4OCghQVFZWUlJbU8nBCJydHe3v7oODQ/r5eQyMj4kO83UBfr7GpOSQkSE9P38iIY2eHHjwA9ycTExB6EhKgBc8eupRBkyzSp9DEOFxzdDRsoaICyp61a0F8JsRHMeFCLyKn2h/+AMy8dEnq6oqqKhcm53p6IduCra304kX06afFH34Y/Oc/y6boSy/JSlKQe2Op6e9/B65SqUgqFQUFBly8dIni73/y1KmQ0LCM9HSFwwr+FAou/SEUCDQ1NRsaGtTU1Hr7+rW1gKV1dXVw4XZ2JvdNJVJUU1115uy5pqZmZaUbGpra3t5eV65cqamDvKLkKxIhlJqaev36dTKW5zn+PVg3ifVzWAbCOs4Xf4s9f8vCwkIdHZ2cHFkZVFyW5OeM59JWiVLc9eD0AY9TS3B6v8ep/UR3vwfgBzwUdz8wUIuDeK4ucLTCVySVSkNDQ42MjEgvbFxJ6ufxTgULpnC6adCuvt+8od/iCbAkKbW9xuNT2XNzXOxfiAfP4XBsbW3t7e3xE4SJEM81x5FkBgtvbhDe3UnALqnGXvG93Yt3d0nu7RHf2y1UJeh3tgs19klGe36w3MOvzpu8sWlKZceUyk4CdsjhO6eBTvzr2gZhVfaKeK6enh4jI6OgoCAyBhAh1NXVNb8wnzuUEdLqG9EeEN5OiWinhAMSQHSBgruhbb6pvfE/Ru5pnKqN6Q9JGIxcBgOybiJBjx8ITx6K4SyyB/oH5PU9RUVF+vr6mZmZmOds4sfj8YaHh3+e/D2QAb8ta8jtwLC34rDXsWEvxSGvY4AT3SHvY8MAikD3OMIfbaT92/v3GBgY2Nvbl5aWampqUigU8sOus6P99m2V6JiYyspKN1dXI+P7GenpmpqaxeCbWvjee7bq6otmZhDJ6+QEL30KBVQPKSnS9nZCjc/gSSanUEcbOD6EhMCKi6OCN2xA778//9pr+b/+9cxvfoN++1v0+99DdeyNG0GrYGgojYxCrS3CydnRMchu4uuLg7nQRx+lfvzxw82bkXx1d3I5IZH/+A/skoFcXaXpaTHW1taFhQXnzp2NjIqurKw4fux4WFj44OAQnn/2tjaubh4aGvfqGxocHWxLyitnWKyM9HRV1Ts6Ojptbe0SidjT3S0+PkFFRXnv3r1a2tqqd1UtrazFEkLxS6wqOFJGS0uL9O+5efPmtWvX7t+/v2/fvtxc8FzFi0dVZYWKikp6RmZpaam1laWNrW1ycrK2tlZLa2t0dIyiYkBkpJTPR6mpoBSjUMBfBHt2YwcpyOs4iWZoC4hOkxWYSEyEaDcNDbRv38y6dbm//rWAtG2RHFm1Cpxzt22T3rwp9fFBNdWCybmhYZSVhU6fDt67t2LbNrR1Kzjx4D1eegl0PK+/jsufwW1JT4fUAH/7G0pIEERF+bs8eJCXl3vi5KmHD1My0tPOnT8fGxc/sVQkXFPjnq+fv4qKSv/AgK6uTmdXN41Gi46K1NTUMjQy6u3tFYvEZmam2TnZandVNTW11NTuqqvfe+DqtmKRnpiYuHv3Ll5OcJLc/Pz8pKQkExMT0lWcSCWVo3xLOSs7u7y8zNzM1NnFJT4uTkNTs69/wNfX38srvKQEubrKAtctLUFKLCsDD/HBQbAYMmmLEgYRxpWSAkF0urrSs2fhmgkf70ki985Kln7wAdj8tLVBehoZmZlcrKhEQUEg9x8/jv7+d+8vv2z43e+Wieavvw6C46pVoEL7wx9ArAoIkH70kdTBAU1Pz/v6enl6eScmxF++fCUrKzsxIf7c2bPJycl0BpNI5y9UVb0TFBT0fY5XBoOhq6vT09c/MTERFBigpnbX3MKir3+Qy+PeNzFJTEg8e+b0MYizSzK/b+Lu6bWCpUNDQ+rq6vLxXJ99Vr9lC3yAbN6MPvssq7AwDz8X7W2t165epVACcDct9aGllRXplICJP6Ztb2+3tLQkvxPwE0RKEqSs9q9AsJ1LUzniyE7nE/tcT+5zPbHPDSMniS5uT+xzPbLTSf9uDHuWKx/PhcVHNzc3ko24tPPPl3xIMF3XY1ndZVzdfb+m635Ntxx03QciAZWd+qOTWbOzK+uxe3p6JiQk4PxP+A5CPNfMrCQrVHBri0B9zxLsFajvJfC9wiVEcHeXQPug+EfIPQvVufRrGxjK22RwaztDefvjrhJ0mcrb6N+tF1RmDY6CKwA5zaanp62trSsqKkjbLkIIx3NlD6QFNHmGtPg9BwKbvZO7Y3+M3NPArInsCYrrC18OYfLd2N7QxP7ouUV2d1e3vNzT1tZmY2NDvrhmZ2exvmpgYGCFU/m/Ys5jjs22ZPY6f9vnfuR54KbQ53poYaRx7N++PldBQYG2tvbt27eNjY1xsgfMeshzxWKJRCKcJJfNnhOLxfj7F0g9twAAIABJREFUOzc3a/duTwMDKDdx7x58J2dng2nm0SMoNEEEyBAh1j3dsML4+8OaoKQEUcEffIB+9Sv00kvMVataCIcJ2WL76qvgjXz0KGwZFoaam/iT7NExlJkJgUpaWtJTp9CbbzqtW9f35ZcIV3eXX9ZXrYJA4zfegKX6t7+VVfQMDJTweNPg+7kwHxkR7ufvX11THR0V6e/vX1JS0tbW1tnZWVxc/MDVNT+/QCqV1tXW0OiM9vb2/v7++Pi48vKKurq62dnZ5qZGwv0ZKjNQJyaCg4Pa2ztWrChisdjY2JiUe7q6ury9vU1NTVNSUkg7F558LBZLJBbzeLyF+Xkuj7coFOJvr5CQUEXF2D6iRBUOKjI3B7mnsxNyBMTHQz32iQkplYbtMgwoipacDP4oxsZQaOrbb8dXr+74P/9n2XpL8uiDD8BFWVcX5KmOjrkpQVMzCg5e2LPH6vhx+saNaP16mZUQ7/HLX4L7ykcfSd9/H+K53n0XHMZfeQUlJy/yeJC4aHZ2lkKhBAQE1NTUhIeFBQQElJdXtLS0dHd3Z2VmgPWqskoqlZaVlrFmZurq6kZGRkNDQlpaWmugEgivurqawWAkJSVkZWWPjY5SKJTBoeEVLOVwOGR9LolEQqFQ1NTUjIyMjI2N5QN/JBIJizUtEAjn5+eBq4RzOk5R4+ToraeXJhajiAiYpaGhYIGNiQGDbFkZJAen0aQcHL5eWwssdnAAD6BDh0AKePddHL3Vv0KOfPVVmbLHwQFVVgiZM6Oj4Flubw/CJ2HksvrySx7pd//SS8C6X/0KK48gk8CJE+AhLhKBlbKmBtXVCThzUH+UTqcFUAJCQkLr6moDAwMCAwOra2ra2to6OjrS0lLd3dzr6huQVFpeVsZizdTU1g4MDAQHB7d3dNZUV3M43PLy8rk5zhx7tra2rrm5yd/fn0aHtIfka/Sp9bk+/rieyMIIkZIffZTl6urC5UEImL2draOj87Vr12h0Bn9hQeOe2sVLV0j3LHIp+sHIzMyMgYFBUVGRVCqdn5/n8/ksFutnEx3meYJ7SqGHtzsc3+tyfK/zk3CMoB/ebq9zJ5LH5Q8ODpD6Hqxj0NTUHB4eHh8fT01NTUxM/NmqaoORTjBV2WFW1qpX1mZY3mZAAEagLQOKYXmbYUmL9iAtbY7NlRcuiVDcdFNTUw6Hw+PxBALB95XvwM41vyDKDF24tWVBfc+TwCeJd3fxtQ/+GH3PfFXO+JX1Eze2TNzY+gQ8Jo5fXrtQkclYnrcQIeTp6RkQEIAd3fh8vlAoHBwcFAqF6T3JvnVuAfXeAfVeAA3eABhZ6vrVeSS0R/F/hJ2rjlFFWNNkhjPCgiazqZHWtKiuoLie8Dkhe4Wdi81mm5iYYCPA/Py8o6OjmZlZUFBQf3//z+YRz2rO6LDf0+VyqMvlcCe0T8LhLudDnU77uUN1fQND+Hn8wc/4P7vjz23nolKpurq6V69etbGxkasMBy9MYui4BXSJgnJzs/bu9bewgBRwOEI4Px9En85OSPE/SRPN09mQlzAtDWQiU1Nwdd69W/r11+iVV8ZWrRpZ4X2CvXLWrAG5x8BAGhGBWlt4k/M9vZCY2NISsurt2TP25pt2GzfC5zK5mssjamrgobFrF/z3vffAIdXVdeX4yTsxPj6WnJz8MCVlcgpW8R/wI5cTzBOxWGxhYUHKPdnZ2adPn75+/bqysvKKaU0uRfIIQigiIszcPJnJRIWFIO40NYGCrLkZNMrt7bBal5bCOk2jSWU5hfv7sXMPMjYevHaNhnNjP1UV9pvfgEx5+jSUwczKEk3QGXRJUTHS0urctMntwAEoWbVU3B6W59WrpW++CWz87jspjwfl0jw94e6dPy8VCIClpFgsz7e+vr74+PjMzMwnS3/Ib/YcfAVLv6/yYWBgQBq/8Vd1YGDgnTt3cIgKuT05Mx/PVOI0fn5++fnZdXVITQ1EZzU1UKHl5MgshgymlAEezYT4U1AAco+FBRjDiLzMvW+/zXxylr78ssyzx8AAPPb7+9lTi41N8AgYGYFH85df1r/zju8bb6CXXgKl48svw1Tctk36179C+NeePZCZKT8fUmq3tsJgZmZAAHoWS3u6uxITErKys+UV+89h4JP/IlmEZymbzZavzxUamvzLX9b/6lfo17+W/upX6De/yTQyMpycBCWTpYVZcEjI9Rs3sDza1tpy69atxUXRk6f4YRQajWZmZpadnU2n0x88eGBjY1NZWdnd3S0SieTH/ANwcjzky2rFQUDfwxOo3ww+uM1Gca+D4p4l2Osg6y4hB7fZ6KiGs6Znm+TqsRPl5BqMjIy6urpCQ0OvXLliaGjY1NSExYsV5/rJuzB4/mRJs1FRg1ZRo04xARiRb4sbdQrq1PsnUmk0+JAjeYIQSkpKsrKympmZSU9Pt7Ozo1Ao3T09tMmpxcxQrvIWMrRKPsDqcfiV6i6e5o/S93Arc4Yurh2+upmEoaubh6/JAfGvwQureRWZPYND2GsKj18gEGC/5rm5OX9/f0tLy6ysrK6uLvYcO7Ur0aPSyafazbfaDbcY8Vnq+lS7eVW6xDSH8UWQL/4H3BeEUA21IqjVZ8l8BqY0sKZ1yNvUAsLaKNGdITML0x3tHfL+PXQ63draGkfGCYXC6OhoBweHhISEgYEB/JH2A4b0T+2CEJpuTG+23tHqsL/Vfn+L/f5WhwOt0EJX1trvb7Xf12K3lzNYR4g9o/Iz51+N/9xyj4WFhaenJ4PB0NPTCwkJ+YfTAiGUk5O5c6enrS2KjkZlZbKkJ8XF4DBBpSEObQ4cRXt6YGEODAQ1w/Xr8A396afiVatyV60aX/EN/X0R0d/+FnKlnDoFDkBJSVLILiNobEIR4eDjcvs2+vzznI8+iv7qq5XKHrzArFoFueW+/BLSorz2GmievvsOIsvwrRKLRS0tzaWlZWNjY60tLSUlJWSZXxqVWlJSMjY2TqVSy8rKibqkPIRQb08P/lym0agNDQ1jY+MMOr2trV0oFNLptKfWJSXrc30fWHTt2jWcm8TAwCAhIeFFWBoeHnb+fFxsLCgnAgNhaXRwgJR6CIEMFBsLF0XIPWiBNoswVlqKYmPn9fSyjx9n7dwJTjpLrrjLZMN33pHu3QvLvrc3oUibGxmRpqejM2cit2zJ3rIFrVsny3CN5c/vvpOlA7C3h7NjuHMHskBLJDKWCvj8+rq6srLy8fHxpqam0tLSaRYoLRBCQ0ODJSWlTObk0NBQWWlpXV0dDmXvaG/HpoHR0dGGhkYqldrb20Ol0ubn59va2piEexD5JK+oz4UQsrOz27dv3549e7Ap9kVY6uHh4+SUTqHI5B5NTdA8pqeDf09bG1EMjiYS0ZnA5fx8oBJpe9D+/XMbNmS+9RZnxSz9xS/AB2rHDnBQ8/SU1lQtMqbHxiS5uRA5eO8e+D+9917gW2+VYmUPkeFa5lX1+9+Db9b3LurvvQdjYLNBVbd6NeSuItgLfJuf59XW1lZWVlKptPp64O3sUlLd/v6+0tLSqanpvt7e0tLShoZGiUQqEona29txKZXBwcHGxsbJqamRkeGBwUGxWDw2NvZkPBeLxZKvzxUSkrxqVf3SVaJXXsksKirAN7GoqEBRUdHW1j41JaWpuWWgr9fA0FAkgnjGn+THYrGMjY0LCwupVKqbm5utrW11dXVnZ+ePlHugLOXERFNTU01NDYfDaW5uYc9CYmJyXmFhaJ4nULsReGCL1dHdtkd32z0Btkd3AfHAFivtO2HT07NNTY3y+p7Ozk5tbe2+vj4ejzc2NtbX1/dzxrHzFiYL6vRyqtVza+4RoCHX3sutkXWzKm93jyRxufMr4tgzMjLMzc05HE5GRoadnV1QUFB3Tw+VOSnMDGHf3LQizIr9OPBqN/zr9vY5jf2ikR/u3zNXkdNzbnXv5Q29Vzb2XtnYh9vLgAMQSN+Vjd1nvuaUZXb1D8jbucRisbe3t7+/P4fDCQwMtLS0zMnJ6ejomGXPJrfFuhTbepQ5u5c6eZBQ5vwYL3VyfWQfUR/EX/zhck/VeBml0SOkxS+42Te42S+4ZakFBCCk2S+o2SeiLZA1P93W2iYv98zOzpqamublgR0Zh7LiqT44OPiz+fdM1qfVmW1tsP62wUYOcNf6Wxndam+91S7OYB2Vzvg31/dgZzGhUIjjrle8JuRfGRgn6rFn790bqKODKBRYSHAKuOxs6cgI+ExwsdwzNAT6nvBwUOEoKUm2bp399FPxf/6nZOlFu3Jt3rYN0v64uaGSEhFjikEXFxeD5kNLCx0/Ln3zTde1azv+/veVZpz/+A8It8ZJ4X7xC8ghFBAACpKoKJAVWlvr0tLTS0oenT51ysHBsbCo8PjxYzY2Ns0tsnSlzk4OWto66mrqOdk558+ds7a1a2tvLy8vu3HjupGxsUgkbm1pufrdFZcHrnfv3NbTN2xpaVY8plhX3yjPJRyTIh/HbmxsbGZm9v3nyNmzZ6uqquQ3fpKf+F0cGRlx6lSqvz+anwe1CoUC3lAZGeDg3NYGAiSWe6h0NEfjIjoVTUyIs7Jmw8LEFhYSHR2kqAheKWvXgpJBXg/2+98D8ehR8MBKTxePjM1MLjY0osBAwc6dtt9+S9+0CW3bBikA8E7/+Z8yfn72Ga7GBQuzVApLNZuNFhcXy8vLM7Oy8vPzFBUV3dw9Uh4mnz592tnFpW9AliZEV0fbwNBQQ0MzLzf30KFDvn7+7e3tBfl5V69etXdwhDjBysozZ057efs4Oznq6ulHR0Xu27f/yXiu+fl5Y2NjMoFHRETEuXPndu3aZW9v/yLRzoRW3P/QoTwdHRQZCfY9VVUQJaurQeaorwe5h0qTztKgCinUqkhIQA4Oi1pa7OPHxd98I12/Hr35JlhPf/ELgN/9DopUbNsG7sy2ttLUFDTQz2bya4hchcbG0vPn0YYN/Lfftvnb31h4gr/6KrhJrVoFaYC0tMBh+ttv4VH461/hbiIknZqSiiVIKBQUFxfn5uY+TE46c+aMn59/UmLiiRMn3NzdR4k88SKRSEMdghB1dHUL8vMPHTwYGBTS29ubnpb63XdXPLy8od5ZQd7Jkye8fXxv3Lzh5eWdl5tz6LDC3BxHfuKBY+PyOPYn5J6svLyspV0kQ0ODPN48m83m8XgikWhujkNqUH686EOn0w0NDe3t7b83ePGJ38zMjLz58qmPybOI8uOpr6+zs7M1MzOtqqowuW9cUQFFysgfKfeoXqd8u8n88E5reVBY3v12s7mGchCXyx8Y6JeXe+rr69XU1CIiIkiHp7ExWWbhZ43wp6JDFrEFZk6lZnqZSkbZnYxy1YzyOwRyBxCMA/1OasmNjqEE9iynqwuKBpK/xMRENTW10tLShYUFoVAoEAggb+H8Aj8taPr6hpnbO1lysKLLurV9Rn3fj5F7Zsuy2k993XHhm46L30BLIkS3c6nbfvILdkn6CjvX4uKih4eHrq5uX1/fwsKCQCAAOxcRzxXXEGmfZ+lSaAdQQLQE7izXdcy3Cq7yXxD+cLmnfOSRV80DSp0npd7zcVu3hBNE/zqPoAbf2QXW2PJ4Ljqdbmpqamdnh90eGAxGX1/fMPH72eQeRm1KhdGmavNdzwOzXVWm29n9tf/+fs2Dg4Oqqqrnzp0zMjJiE5+Yz39Ksdyzfz/IPdbW8J2ckADut2lp8P1KoyMWbQE0P+PjsG7Hx0vNzJCaWsvmzbkffoj++tenSC5//Ssoe06fhuD2hATU0zU/xevpRQkJYCJTV0fbtrHeesv666+5r7yybG/5RHDHjoGPxcsvQ9aV//5v6X//GTk5Sny9HY4ePerm5nb61ClvH9+GhnoFhcOeXl5kMSkHe9uy8ko7G6vMrBx31wdVNXUTE9SQkBAbGxsDA/3pGfaiUGhtZdU/MODr462mplZeWaWtpVlZVb20PACrcEIasi4p4atBNzIyunjxYmhoKBkU8xyuEnau8Pj4VB4om2BRjI6GxTI2FsxblZWwVGdkgDxHpaEpmlBMZyIGvcrdvdTGBj14ADXur10DuWfjRlief/tbWG9/8QvweFq9GlQUyspST0/waGaCt3hhITI1HdiwwfHgQdGmTeiLL8CL+dNPZTutXQuKj5ERuJs2NrKIa6yWWFiY19PTPX78hLe31/HjJ8LCI/Jys0+cOE4JCMDBR9/rLfR0dRsam+6pqzc1NRkbG/X09ff39bk9cPHzp+jo6AiEi5w5toWFxeQ0q7215d499bi4OF1d3cCg4BUsxXIPaecSCARUKjUpKUlZWflFaiEhhIKC/CmUvLAwZGwMcnBcHIjRaWkgpqelSUdHoD4KMJM5JW1qQomJKDy89MKF6lOnoJDZpk3AzK+/lsG6dWCmun4dpmh0NGprFUyyBwck6emgVtTUBGXPl182vfOO++9/L5uiv/ylrMrHf/0XuElpagIzJRLgp5OTtKREpumZm2Nr3Lt35szZBy4u589fiI6JyczMUFRUDAkLwyo0oUCgraX9vT5fU1NjYGjYyMiot3+gt7fX0cHe3d3NyNhYuCiampq0s7MvzM9TPHbMysqqurpKRUUF707OOiz3yNclfa7cI1Ps4fUST/KfUO5hMpmmpqY4nQk+BZvNxmZicsAvikikXI5gjs2fY/N53MXysurUlMyszLzKipqEhOT+vmEeRwj/neXP8yChEbZz3bnms3eTyaEdFod2mD+G7eZA2W4OsMN870YTDeWAOTavo6NdXu7BOYcaGhrm5uZmZ2dnZmbGx8dfpGbci17Rs7eDaOR5RnrJ3eTCmw8LlR4WKa+EQhklMf+71r5YLpe3wr8nNTXV1NQUP1aY81BJij03nxbE+G49eBkrATDB3ZjAlxCm8nbmzS1M1b2LI7Lqy88e5tP/Ax5mpZmNxz9vPrMW4CwBJCLXbVT8dOZR2tjy+lwikcjHx8fDwwOnjcWD7+qEeK7I6hCrDGP7bIvngE3mfUqp14+Re0oGC13LHLwrXb0rH5CtD+AkuHpVuPhXe8wusPr7ZFXP8DinpqasrKySkiCRh1Ao1NPTU1dX9/Ly6uvrW+EI8XTe/WgqJBCpfvhId32p8XYCtpUaby9bCdtKjbaVGm5m9db0DQ7/m+fvSUlJUVZWvnXrloGBQUvLsjQqT+U2Qig7O8vIyM/bG2lowJKckwO2qYQEMG0xJ6U0qgSyCdNp0vp6lJws8fMT6ejMXb0q2L0bffMNeEi88w58C7/2GuTD/fRTWJhPn4Zv4ZgY1NqyyGSNjYiLisCYoKEhPXIEffLJow8/DF69WmbkevVVMMSoqIBMtXUrJORbvVp68KD0+HFYXT77DHYsKZGODEu6u9ofPSqpram6fOmio5NzTk72hfPn7ezsGxub8HT09HBTvqWiqak1PjHh4eFWU9fQ2dmZl5d3S1nJwsKyrKy0rq7W0cmZy+H4+fqqqKj09Pa5uT2oratbsUh/L9yYm5uT/j36+vqbNm26evWqlpbWCy7SYWFhaWnwVCAknZ6GFdrODmx8ISGQ7qWuDtjb0ADyJJ2B+Ey2hEFnFxUtxsWBd4mpKYQXnToFS/WWLSD6ffIJmP02bwYVxcWLsFrHxaGujoUpTiOxxF+7lrp+fermzWjLFjAR/vrX4Lz8yivgy2xoiJdk0DP99rfgkjI8LBWLYcEQiUQNDQ1l5eVlZaWnT592d/dISky8fPmyy4MHZP4eIwMD1bt39fQNpqanLczN+/oH6uvqsrOzrl275vLgQXk57Ozp5TXDmra1sdHR1Y2NidHV1QlabmCF9zuXq6+vj1/QEolEVVV13bp1169f19bWfhHFAELIz883NzdrZgZSLYyOgo4H54dKTSU8bFrAbkinSYS0KUSjLtbUSBIS2A4OIm1tsFpdvgzGQRLOnpXeuQPyYGQkBBvSpyeZksoqSK1kbAw6oB070CefRL7zTuEvf4l+9StQD/3qV6AY2rgRpMmXXoJ7Eh4ODj1KSsBnS0uZ3CMUCuvr6ioqKrIyMy5duuRHocTFxpw7d9bd3X2AiDoUi0Xamprqamr3Tc14PK6FuXn/wGBtbU1yUtJ3313x9PIur6gsyM8LD49g0mkqKipa2jojIyMW5uZPyj2zs7MGBgZkINLz5Z5nPfv4wfmRLY1Gs7KyKicqxuNDzc7O4vprTz3vs4ig/+AKbQzzNa491FFK1VZK0VXJ0L+dqX87U08lw+BOlu6tdG2lFG2lVLUrSQHuoHkl5B7+7ateezYYHtx2n4QD2+8fILoHtt0/sM304Lb7ezYY3lP2Z89yW1tb5eWe+vp6CwuLsbExXLDC2tq6tbVVlmxJbqD4XPhFIf+6kNvkn0bB/sujJ+ffis2+Ep9zNT7nalzO1fhcoiW6QMwFenTm+ebuKAZjckWdioSEBDc3NxxDhEfY09s7PcvmpAaNXlo7fmMrCWMEPnZjKwnj1zaNq+wSDnf9sMtBCE09yqg+8mntydXPh2qFj6eL04bHJ+T9e4RCoZeXV1xcHKlmI3Jnt/N4vNDyQOMkXYtUY4AUI4sUYwDAH3dNk/W9ClznhfM/ePCFvXkOBVaujxxcizHYP4E4PCiy9yxxYc1P93T3kA8aQohGo9na2paVlRERmouFhYVJSUnl5eXDw8Mvomv4pyfKEztAtoXKpDzN1YV6mwv1N0NLAu7KiJsKdL5h9VSPTdD+zeWe4ODgtWvXKigo6OrqvkgKDYRQUVGOhka0qSkUYlxclFZVQWqe2lpwgxUIpDOz0immWDA1h9iz/OrqbHt7prc3srVFd+6AbHL0KKwGa9eCs+2RI0BRVgYzQEgIGuwXMaZnGMKqahQYCIv1xYtgcFi3zvuPf6zX10f29rBIv/wyfEAPDsLKweFIZ2elfD54/v7Hf4AQRpSakK3c+MGWSCS9PT119fXjExN9vb0NDQ011TWxsbEJCQkdHZ2NjQ10ogrM1NTUwgKfyWTOzXHAzX5yisFgzMzMsKbBc2V4aKivrx8e3amp+fllylL8ILm4uJByT15enqqq6qFDh1RUVF4kSoXQ90S6ueVPTMDBWCxQTvT1gdBjagohcllZ4IIyPAwZXwYG2EkR8dyRIdTfDwFvDx9KHzwACVFbG1I2r1sHuorvKz9t3AjeJVeugH+Vj4+0uFhMY7AYi2VlKCgI7d7tuHdv35YtULuKTNO8fTsUc+DzSe6BfHD4MEhQHR1AJL/4F4XC9nbIIkOj0bq7uxsbG8vLy6Oioh6mpPT29DQ0NuJnnslgCIVCKpXK5XHBA5E9R6PR2Gz2zMyMVCrt6uoaH5/gcOaamppwYC35tOKvIisrK2znkkqlGRkZt27dOnDggKamJg4qJDd+KkLoewLr60udnKB+aGAgKMwMDWX+YwUFYJ/t74d8mxwGf6qnJzMqciEvDyUmgtRsagoStLq6DNTUYH66ukpjY1FNlYRGn5lc7OiEye/kBGq5I0fQxo2Cv/3N5tVXx19+GfIsvPYamMjeew+yA7z8svTVV0GgzMiAyrLYnnj9uozJJEv5fH5bW1tDA8zGjo72hoaG8rLSyMiohw8f9g/0NzQ0sOfmEEJMJlMgEExMTHC53K7OTg6HS6fTZ2ZY+L9DQ0PjExNSqfT7AlgrfGUIF6J5Kysr/H2JEPoflHvGx8cdHBy6ING2kMlkslisKXBOgjxpT72bzyKCfMwRaN54eG5/2CWFCBIuE/jlI5FAOQz003tDnMwgTSKISjy+8hX3Xev19m012rfFaP8WaAHfSuAEsm+L0c71endvenO5CyvsXFVVVa6urjMzMw0NDc7Ozl5eXp2dncPDywISpVIprrwtIoqHrPC1etbl/EM6KO24tNjsaxFp5yLTz0emX5CD8wQFE8+Hppyo7wifnJzu6gIxhfzFxcVFRETw+fzJycmZmRmiLNood2FhJpnSd+7r/u82DXy3CdqrSwhBGcCUy98M3twhGPrhcg/jUXrJwY/KFL8oU/yy7BgBGCFbAik58AGzKJXJmpGvz4XLUxQWFi4uLjIYjO+d1bhc7tDgkEAgCCj21o1WN47XWQFGchT9GA3XbId5AaFR/4eMfmIDSJnRmW2VYeKQY/kcsMs2d8m3neZNTYxPyPv3jI2NOTk5kZI9eTt+zjoVo+UJmXe/yNH8BkCLhA05mhugK2u/yb63drqnamzi371OBYvFSktLO3PmzK5du7A37hM3fRkBJ02xtQ1rbUVENAoslg0NODIF3uaLi1DpCEoiStHM8EhX+ZLnM87Ep6kJ9oIrV+AzWU0Nvnx9fOBbuLZWwqBP04UzsxDGpaUFH9hnziA/P5aurlVYGCcuDmVkgA81zp/C4z1enhGScrkQqNzb+3g5wYMmZ5g8srCwAEUiJibk81jIb0Diy9T9JHX52xnbuRwcHMh8zXV1dffv31dUVLxy5cqLvM0JuSfirbeyHMEB5vF1lZfLDIjYzpWTg4aGUFc3s6m6DfI1TzJBrxUfL3Oi0tICHxasqPjmG4jHvnYNVBc2NpAKEgqwzw0MoMwM5OIyummT/d69ks2bQUD64APQ93zyifTjj2UMXGIdaJ6cncGUw2LBqJbocoxYQrlc7tjYGJVG+6mCj76vlWZmZkbKPThJroKCgpKS0ovYFBBCAQEBLi5FUVFQLtTFBcDEBHL0uLqCWJmYCBGITU1ocgr1tA4P1dej0VEwKyYlgRrHywumJQZvb5hbGRnSlmYJlTZLX+jth6no7Q2z9OJFCAJfvXrgl790fuklENP/9CcI/l+7FkScNWughMtHH4FwGRAAt8vfH1JS5eU95icp+izxUvaXy+WMjY19H/r0rA1WbE92ye1lN4z4g1VoZmZmOH7kf1buCQgIcHJy4vP5Q0NDtra2JiYmpaWlPT09K2Q1+fE/Fcdyj7bywwuHQq8ci7ii+BT4jiCe3R/ibAHZcrG+R/mS6461Wns363/7bNh47d3sAAAgAElEQVSxVuvuDS/W9GyzXD32770xzM3NsdZhcXERV8ekUqlLuTRhmPiFkJWVFRsbm5KSMjExERER8ZOkaYEEjxxaeOqloKQTwcmngpNOBSefBiT5NODQlREp8UeqW4J43HlcQANf+ODgoI6ODn7Jf1/g08zMzNPTs6enhzHFmkr0bz/5ReeFbx7DRTmcoHecW9v13daFwR8u99CK0vP3vV94+LPCw5/LQGEJwRSim7f3PXpBat/QsLy+p6SkRE9PDz6iuFwPDw9DQ8O0tLTurm42m+2b76EeqqITqf4c0Ai745hm/WPknqyWNKMkHZlWCeuWHrcmmG6eYmibYcGYpXd2dMrLPQEBAbgsGtaal5SUFBYW1tTUjI6O4hqxT53ePyERIk5K41OUP01XWwugDpCBW9zF7d01aXe+muyqZE5NyzuV4/nzL21/7nguW1vb119/fe/evWpqaq2t4PD7fHYjhLKysgMDIcMh3hYv1XgvLIKkp6Os7NHW1m4eSyidnJYODaHBQdALRUaCEiMtDVaehARZm5GBmholVDqbPj88jHJzQbtQUwMriro6srSsDwjwI9Lqw9rMZoMiBAfCyI+TFBeWExGHwxkeGvL19fH19a2pqfX18fb09MIJeBBCj4qLrKwsHz5MaWwA3bW7u8fk1JRQIAgMDMgvgNgWoVCYkvJwbHw8MyM9KjpGSuxS37DSr1kikVhaWpL6HjU1tXfeeefgwYNKSkovqEKLiAj383s4CUmPYUXs7oZkSG5u4JCbkABZkJqaUH5+X0XF0NQUmpxEfNo0BM11d8s8VigUMBRqa4N2R1UVrClqapDo2sgI1uyaagmNzmYKampRcjJSUsrZvj1271506hSYwlatArnnzBnYj8t9vB4jBM7U778P+bjJSUG4ibD7+vrc3Nz8/f2rq6q8vb18/fywUQYhlPIw2draKr+goLyszMTEhBIQyOXxOBy2p4d7dQ3U3+7r7fHx8Wlvb6f4+/n4+lZWVvr4+DyZv0c+jl0sFispKb333nsKCgq3b99+QVHSw8PvnXdyIiNRTw8E4Rsbg7KHQgFrFYUCQk9amtTPr6OlZWxmBnGnhJLJKYjtGhyUZTbMzoaNsrNB29beLh0ZkTCnZpkCKlX6qATEJltb4O7x41IFBbR2bfwbb6S89Rb68EOw4r79NijJrKwgDdKuXaD4+ctfwKg7MACcxEBOVKIcykxHe7ubm1twSEhNTQ3wlkIZx9o/qTQhLs7K2rr4UUlRYb6JiUlIaBiUdJ5kenp6Yn/wttYWTy+v/v7+0NCQQqL4Q1pq6pMsZbPZhoaG/xvknqioKF1d3dbW1s7OTh/iV1dX19HRsULuId+zWJggOUYiMn2PUtK5g0GXj4ZeOhqKWxK5TFAuHw09vS/QyTwfH5DH49+86LJtjcaeTbqPYaPuno26ezbpQAuI7rY1GqrXPaanZhobH8dzcblcV1dXMzMz+SV5hPgRr0Pi3iIkEUsCA0J9fQICKCHV1fXOTm4L81AhAd98qfQfvGPJC1yBwFThUIOSzvjGKvjHKfrFHfWPU8TgF3eU7PrFHfWK2l/ZFMCe5cjbuYaHh01NTf38/BYWFiIiIoKCgqKjo6FUAnOSHu9Xd/STpjNrMTSeWdsI+BqMyNpTXzdf3DQ/AAW/VgyMFLXlb9mKbSDarjAtY/e7WQc+yTrw6XMgfefbE/kPe5bHc1VVVenp6aWmpk5NTXl7e/v5+RUXF3d2dM7Mznhkudzyu6YWpKIepKIeqKIedEs9SEUtUEWNQIAYpHKHcsM6wYzHh/RUKwb2Il3IV9mQrBWlZhivYxivTbZGj7s6hvE6BnFaZkmG9FlqW1u7vNwTGxsrX/4yLCzs9u3bHh4eIHQS1oYXGcOP2QYhNFgSm3jjo4d3vlqCr5eQJcrtrx7e/jL51ueMjoqe/sF/83iu7u7ujIyM9PT05OTkFzTK5OXl4RzzT70TCKGeHuThUTwyMiaRICZdzGJJF2c54PHT2oLycqW5ROa46SmQYug0EX1qkTYpYPMnJqAwhZsbeJYgBGuQjQ36+OPAoiKoh8Vmg7Mt4WjyeBl+6gDIhzAnJ1vfwCAuLu7oEQVnZ5fysrL9+761t3fo7unFz6eNlaUfJUBTUyMjM0vjnnpUTFxdfX1ERLi6mrqmptb8Ar+3t2f//v3p6emEJ9q9R49KjikeDQ2PXPHwIISCg4NJfQ+Hw5mcnKTRaFQqdUXx9qcOGCEUGxvb1ISrhsHVxcZC8fDpabCMqKpKAwNRaanQySk/Pp5JpSIqdh4H/xQiXwAusxkdDcozPT3QQmhrQwbI+/chP3FGBhoZFjBmxsckpWUoOFi6Y4frpk2dCgpIRQW8o775BhxQ3nsP9D1+fjI7F8FDwIeHScsXEovFcTExJiYmsbGx+/cf8PbxTU1NUTh82NXVbWwps+o9dfWw8HBlZeWysrLLly5l5+aVlZUFUvx1dXX19Q0kEqmTk+M9jXte3j7R0VGaWtrenp779u9/mLKs5jO+Ox4eHnh1kUqlc3NzTCYTs5SsIvdUZmIiQig0NCIvr4XHQwIBuPJMT4PWis3GLRIK0dQULyMjhzXNkkhAcwkaS7EYdufzwWQrDyIR+CSLYF0WiWT/4XDgUHQ6mpgQmZra19bC0zM4CJ7LQ0Myvo2PgzYUd4eHpULhsqlLfPwthoeHmZqaBgcFHjt2LDAoODk58eDBg94+PjiTwveZLVVv34mJibl9+3ZDY+OlS5dy8wsqKso9PTw1NTTMLSxFIpGFuZmJyX2lmzf09PSh6EdhwY7t28srl3nf46pGHh4eZOmP/0E7Fx6MWCwWCoV8ogbw7Ozsk6YikUjE5/Pn5uYkEgmuVL/ijhNyD//ejbjT+/wvKAReOBwoazGisNRVCDyxx9fBTFYWg8fj37jguPVrtd0btXZveCZs/Vrt9lVXLmeZnQu/W3CwQm9v76NHj7CXxuTklBTx+JJOArrmFzv6hwt6hwoGxx4NjT/qHy6cF3XyJV3EfztE0hn0g0QfkHvmJvxij3tE7POKOOgZedBLDuS7rqG7Sut9ecvj2DH3xETl2u/jBhYXF8Vi8fDwMHeBPxbtXXbow6rjX1ce/6qSaKsIpOr41xgqj39dofh59dlvuP3LsraSd2RxcREnDl1cXORyuSSdRCAFV35K8va/p+z9IHXvhykY9iwhS5TUvR8mbfnbaG7SCjsXfuWKxWKRSIQHL5FIBgYGBAK+80O7q+4Xbvlcu+V97XHrcxXwJbjucck02pD7I+SepOp41SAl7XA1LYC7sjYMEO2lrmaYqn6UFoNNX2HnwipAPG1EIlFbW1tTUxPOgPCz6Xv6H8XEXHk/QenzBKUvCFiBEN2bn8df/4TRUc6cYv0763tIEeE5cjo5cTGCEMrMzHR3d1+x9uP/isViKpUqEIBdm7jZ4ELBYMBSIWTPi8Zpwuk5iVAoZTLFEzQxZNeQ8ualU5NQcqqrG6okTU3BkfB3U0cH/+JFGyp1mhjesjVjxahWdPHlBAUGKN+6lZmRfvLkiYDAwNramiMKh/38/YeHZRXX7GxtkpJTjI0Mi4ofuTg7llfWcLncpKREAwOD73M5Dg+PIiR1cXaKjYmFCsDGxsrKSjt37jCzsMLHxyfFqktDQ0MyIIVk5lNZtGKo+BaEhYVGR0e3tKCsLGlFBWRnNjGRBeQbGoqcnamDg5LMTEl2NhoeBn4yGNIFGktKp0HcXF4eePngkhaeniDx6OmBdSoxEaxgJY8IZQ+/rh6VlyNn56ktW6xPnuRu3oz+/GcQqkxMwArT0QF5aHbtgrBu/FlaVgY6J4zjQYpEiw4OdhqaWikpKUePHo2OjsnKyjxx4mRoaBhZn0tLQzM7O+e2ikp7e7u+vn5Xd+/09HRggL+tnZ2urj6DzrC0MDc3N6cEBAYHBdbU1Hq4u127dp0SGCjPKxx8pKmpScZz/QCWent7paeDOCX7yl5StGBpg06nSSQg5cifV3ZrnthhxczDjmVFRSBLIYRGR0ccHGwXF4VPO5eMgeQh5e8+ODkKBVYWFvr6BpGREWfPnktKTk5LTTl+7FhUdIysPpdQoK6mXlxcfPeu6tjYhL6efm/fwNTUlJenh4WFucl90/GJCSMjQ2dnF3Mzc0sLy0OHDt25rbJp08bwyGj5S4PEZdPTWlpapLvl/6zcI39DcQZw0vuBfKwqKytjYmKCg4OHhoYcHR1JIZjkIcg9c/y71yJP7PWCsuoHfc8c9IGy6hhZas8e9FXc5WF7PwOflMfjX7/osPlr1V0bNHYuB3nK5q9VVa4+mJ3lyPs14wmDj5OTk6OhoaGtrd3Y2NjXOyiUjDCFXgACb6bQe0ZCmZFQWBJ/ltifJaEwhUBkCrwZQvcFcccPlntm2ONeEQouwbtcQ/Y+BxwDtpbUedNodPm8heTgSYT4Ru1hzc4ORXnl7X23UOHzQoUv5NrPCVzWFhz6tPjEmrm+p8g9YokkNDQ0IyMjLS2tsqrKx8fnySfr+8RmI3kpsZv/mrDz3YSd78lgxxKCKUQ3ZsMbI9mJI+MT8kuv/AHJydPe1s7lcW0TLM87nbzqduFpcB4TLzqfNgjT5i78cH1PXHnUDe9LqpSbqv4EYISiRFCUMPG23w3N4Dv0WVpvTy/5oOHHUP5hJPnf09Pzs/k19xVFR1x4O+baJxiil5CYa5+QePTVj6OufEBvLxsZG8ffISSr/9XIz2rnIt8gL44Qcey5sbGxT72R3d3dmZmZ2OUeH1MikS4sSKdZ8M3NoEvoTKi7KZ2e4tLm5pYq+wqFqKAAktguQqgpFGTo6ZH29iINjZbPPvMgUuK9+AAfb0lU/55oaGgwNb1va2uXl5d338TE2tqmpLSMOTnJYrHiYmPV1NS8vH0EAkFyclJPX39jY2N9fYO1tVVEZNSj4iIWi5WZkdHV00Oh+Nk7OI5PTCQnJaampctfO8ZdXV1JuefxCF4MQwjFxETl5RXq6qING6CUweAguIOEhyM6HZWWNjo7FyYmgonq4UNwc56ZkbLnpAyaCALaaVTQjNXWgpSDFT+44HhCAnSzstDQIIfOZTBAnEpORnfuPLpwIeTcOYQrUfzmNxC0paAA6oyFBRB6sMoDIfAOOn8e5yp8fBnw8hoZqa2t0dXVc3B0zMrKMjYxtrd3qKmpZTLB2dDXx/uehkZoeIRQKAwPC5uAhJClLS3NhgYGqanpJSUl+Xl5enp6LS2tCQnxzMnJgrw8Q0PDJ1MDLC4u2tjYkP49j0fwYhjh1xyEAyjk98BPb3V1NQ4m+mEWB4TAJ3r/fsgGRKTxzElMTJSfEvJnfBInRUnyVT40NFhWWmpgYODm7p6VlaWnq+vo6NjY1Dw5OclgMDzc3DQ0NGNj48ViUXh4+ASVVlJSUlNTY2hokJ2dU1JampWVqa9vUFdX5+joGBUdw2AyAgMp2KpInh1KHCws2NjY/G/wayZHRS4AbDabVJeSb9gJ6nhGVqqPr2dnVxslwHd4eID8F+Y2xDfN8W9fCVHc/eDUfvfl4HZyv/vJJaLCdmcb4xS8O4/Hv3bObtOXt3euV9+xXo0A9R2Aq++Ergzf9OXtW985s9nczs4OMp6LHDkk8K2pKS4uXtL3sISSUSrXG4DnM8H1oRIwwZMhJHGC68Fb7PwRcs+YW8g+B/8tTgHbMThSZIhcd5utz/rCSjcuhyfv30MOnmQ7Qqi/v392jtMX7pG2463M/R+vgKz9n2Tu/xi3Gd9+mHPk69leSAANhyJleYKtxeUVYbHxUQlJj6pq/ENCF0WQ3fvxNsQHxmBOcvj6N6K2/j1qy9tRW+UAd5eIYWv/NJiVMEFn4Mcf37WnDr6rq4s3z7OMvn/CWuGCw8kLDifPP6M9ZXtUO0Cdu7AsqdWKY5LPozwd4wihyOLQi86nbnhcfg5cc7942+cGjUUdHBiUr89FHhDHc6Wmpvr7+2dkZECREM4zh0Tu9eMRhFB3QWTImTcjrnwIcHkJVuIfhF14h9paSqUz/p3juX4AQwm/5jyyzDh5BJFI9H1mgvn5+RVmCITAI6ewEOSe2VkIdKdSpXyeSCySiAirAj4Clyuzp0gk4KmykSgN+fLL+RQKRHeTZ/lnEfkXJYkPDQ2EhIREREQyiNKPJP3FEflh4L2CgoJexJVHfkcSx3au2tq6yUnU3w/mvMVFKZeLOjpERkaijg5uUpIoKAg9egR+x9nZIBdOTkIuH5zDEJQ/NBpUtYiLA7fczEwQIePiwI12fFxKo03ThVQaysuD1DPHjsV6ehYeOQIiVGgo1P14/XXp1avyLyjZuEZHwViz4vcsFrW3tQYEUOLi4nAy62dthunka5d80ay4xXgzd3f3Jz/xV4znWV3CISy8qQkSFpDb4JeOSCTicrkSiYSk/7MIQhC3ODQknZ8HuSc0NLTqBbJTyp9lxcmfyq7mpsagoKCEhES8hJDbrOAeScdXuqK7/KQSd3f3/yV2LnwVOLBobGyMwWCMjY3yOMKU2Lbo4MbY0Ka4UGjD/asp7sUhPhVB3uUxIY1xQG+KDKivLoX4KULuWVC+5H9oh+0xXF9ieUsQHRX3OuzfYm1pCJnTcTzX1XM2G75Q3r5Wdds6GWxfp7p93d1t61S3r727nSBu+EJZ6YoDlzMvH88lz098NFxBhUZlLoiG+6ceDEw9GJh2HZh2G5hyBSCRKTfAp137J53m+G3y01L+mM/HIQXO7KhLwE5bn/V2fhvtfTfa+22wg5YAAiG6Gyw9viqocOFwePLSJHlwqL04A9FSNBptdHSUt7DQGeIav+kvSbs/ANj1QdLu95MBAEna/UEytO8n7ngnZf9nMz3E4BGS9LeKkrxEqf6iVH9xKmXxod9M1ANWpAs72pUb5yFOpYhSKaIUf1GKn4Q5Ds8JQgPZSUFr/hi68W+hG998DJsIHLcEPfCrP/RnxDNZM2RifXl2SSSSqampiYkJFos1NDTEF/BNwgwUzL49aX3kpLUC0a5AgHjUfL+6jwrnGXLP4uKiUCjE5leSS/IIxAHkB5ywOXzR6TQBpy46nZJDZN3zjievuV6kgk/wsngu8lAIIT6f7+rqampq6u/vD85VNJr81ZFb/rQIyD154YEn3gg5/27I+XdDz78beoFoiS6mwL/OvRN85q2J5pLO7l7sRklO8n818v8BfU9OTk5MTAx5txBCAoEgNTUVqyVX3DDs6cxkgs9sRQU4yQoEkKm2vR1WavIn/xHc1gZLdnk5MjNLf/QI/IvJzf4phLxVQqFwbHyMzxfgSGAWa4Z48ll4WRUKhRMTE9j4SuQmZmP/SkjELBKJxeKFhQUejyeVSnF62RXjwd0fqe+JiorCVeuWvpEQjzeXkPCwvJwVGws5egoLAXp6QK2TlwfJDGk0RKeJobImjQqOPmNj4OaMS03l54PoMzSEGHQ+jcVkAquTkqQuLujbbyO+/bb21i00Nwd+z6tWgalruecyHOnoUXBqfpLxJEv5/IWxsTEcvUWn09hEduDJyUk6nYFDpuk0Oq5rIZVI2LOzWM7ANkGxWDw7OysWSxYXF+c4cyv4iVdEoVBobW39E+p7sKEnKSnpp/rAwsxBCPn6+r5I4is8dRECe+KdO2D5JY+AubqwMD82NgaMkkjoNFk5FAadjqvIcbkcOp0hkxQlkrmlFKPgfU+oSTmcOcJdZmFFlb2l8/6v0/dgZYOdnZ2ZmVlRUVF/fy91fFbpTOzJPcFn94cCHAg9dyDswqGI8wfDLxyKOLsvFGB/6NHtAR72JZhpnLmF62c9vt1sehiSED6GwzssDu+wPES0h3dY7P7G2FQPDH+E3LPw3Vnrbz5X2rrmzta1Mti29s42jAPx9ta1d775XEnpiiPU52pqelLfg7kqIX7j4+N0OpMrGGgesmoZsm4ZtlkGQzYtQ48pzYMW05xl4viLv9NgDs+O2vttsfD4yspzjZXXGmgxeBHdJcp9109ySx05HB7OH4YvnDwRaC8iI52cnFxcXLq6upjTrLYg17C1f8I6mOitb0dvfYeAt6MJrQymRG16K273x9NdROwLQov5MfNXV8/f2oyBr7JVcHub4PY2vspW/u2t88oEXXnTvNJGcWctlnt6MxN9v/g9Zd1fKOveINq/UNZjBNqAdW9Q1v+Fsu4vPp+92pce17e8Phc5jefn5z08PMzMzOLj47u6umZnZw2Ddb412H7EdP8R033QmsmQozIKEA8Y7brtcZ0z//S3TWNj48OHD/v6+tLS0p58HeGHLiDb9/D9vaesj5wCAUvWysQsG6CctD5y3PLwBYdTY5OjHcvjueQ5LxQK+/v7aTQa9mn72fI1d+aE+Sr+MfDMW4Fn3gogWozLtwGn36Sc/Mt48yM6c/L/6XvIuwYIEc+Vhf178OOEVegjIyPP+obGa7lIBKvvyIhMtdDdLcsKgxAYU8bHwUt0aSUAU05nJ4qOjqqvX5YkcNlQ/lGHTqcPDAwwmQxnJ0dtbe3gkNCqysp79+6Z3DdtaKg3MNDX0NB4mJrmYG+noqKSkZnF5XLra2uuX7sWGw+fhgL+gqamRvGjR0ZGhv6UgKSE+HPnL3QQed/lz4zXciMjox9j5woNDcWipEgk7elBU1OIw2FTqRMIQQxcYqK0vBwiqQcHIUtkZibIQFSqdHIasSYXxVNToP+ZnAR9WlcXhCNVV4PJijUtmZziTs5T6UDIyICkzUeOULZv7zA2Ri0t0nPnINq9sPAx2+fmIJKuuhqMXGVlMjp5sWKxeHx8HJLEjI9bmJtp62jHJSTm5+XeVbtrZ+9QX1enqampqaWVlZ1tamIM6fDLKubmOI+KC69c+S43D6JpZmdY6kAv09XVDQ4JDQ0OunVLZYhwtyLPgufY3NycvH+P/H9fBEcIeXt7p6SAaQMfEDuR/GAF0rNOihDy8PBYUSr1WRsTIwFfcwMDrCuSisWi8fGxoaHhsdGR+ybGOjo6GZlZqSkP795Vc3Z5UFZWqqGpoampVVBQqKerq6GhUd/QyJnjZGakX7t2raISEvFRqeMqKioN9fVqd1WTkh+GBgepqanTCOlTfhj/2/x78E3hcDhFRUURERHNzc09vd20iZk7F6POHQy4eCTookKQrFUgcLI9EnRyj5+X0yP88pmbm//utPPub/T2bzUCIPLxLGsJ+vY1OibaoXgXHo9/+azV+s9vbl1ze8taeVCB7hqVrWsBWff5DaXL9hDH3tz8pNyD7VwRERF+fn5dXV00KoPF6Spu1nnUrPuoWe/p0AT0okZN6nTNUxdX+fv1VBxu4syIjfc3pq6fmrt98Rwwcno/u8RuZobdsxTDIX9G+Prv7k5LS0tOTu7t7WVOTTdRnClfvha84a/BG/4WsuFvwQSEbFzWDVr3Rti296a6ILct6C3y46aubZhW2UHAzmmVnUv4DgInure2TytvE3bCOxzqHmYkeH72qu/qP/mu/u/ngMfHv+lJi+1fHsdOMgQhVFdXFxUVVVFR0dkJ8Vy6Afd2am88YLTrgOHO/YY7DxjuAjACILu7dbfcfHBlbknuweMh256untrq2oHegfRU8GGQ/+HzwudNhuce/a2EaLVPAQQsaEnkyH3AD5nsPWl1ZJQxsiKOnRw8nvZ8Pn9+fn5hYWFgYOCn+gyTP8WTOEKoIyfUW+E1/5N/eRIoJPHEG77H/jTeXDxBo/8/fc8yNmI7V1YWlPIB1eXAQH19PcaXbfe0jrxSh8QRAguXnR3EFZNyT1yc9N13kZVVUE/Pkjn5aQd8Fo14yCUuzk6nTp22srLS1tXt6up0d/e4q6paUV3NZs8+eODs7ePLZNCv37jp5OSockslLi6hqanJ3s7OydHR1NQMIRQVEb5r9x53N9ejR4+6e3j4+vrcvKlU/AiCy+TPi689MDDwR9q56og00HNzaO/eDgoFnAeJH2TY8/CArDNmZmDhys8HSmoqKHdmZyF9s2Cagxig/AHJcWYGJB7CmRwx6PPMuckp1NUFQk98PDIzQwcOuB84MKagAKmz//73lQl7Skshpt3UFG7E8qsEeXdhYd5AX//cufMW5mZW1rYN9fUPXF1v3VLu7R9kMOj3jY0epqS3tzbfUb1rbW19+/bt9IzMysoqS3MzP39/CwtLkEU83Xfu3NXe0ZmW8lAdyk4Zurm7RSz3wCXFlJ/KzoUQampqGh7ukzFU/ub9aFwsFru5uWFl9QqOPevYJG8JMw1bV0f74sWLBgYGrm4eVZWVHu4eN27c6B8YolInNO6pFxYVlz4qvqumZm5mduvWrcLCooqKSkMjQzdX1weuEFtgb2dz6OBhRwe74ydOBoeEjgwPX79+vaPzKUlW8FBfxM4lm3pL10PyjaT/SESeM/hQbDZ7bGyUQWPfOBtwfI/bqQMezwGF7S7u9rl4x7m5+YsnbLavvbdno/bujdp7CMDIboKC201fqRloBuBdeLyFy2cs1312ffOaWwQob15za8sa5S1EdwvgQF/32fUbl2wIO9fAU+We3t5ef39/KyurlpaW4aFR+lRrYsH1xIKbSQU3kwqVoJUhSkkFSkmFN6EtuBGf/93Q+Mp3iDxDnoMTcs+wpedqY+f3TVw+vu/y0X2Xj+8/+Oj+g48J+MgEEOjqO7yV9ciGQZ/E3uL4wuWPjCmQkXV4mLfAr/N18Prkt/5r/rwEb/iv+bMf0fVbA7j/mjf8vv5j4Ka3Jzub8b4z2dE9Z77qkysvCvhlqDb6GC5t6L30zXwbyHkgbKXHu330G88vX/f68nXPr14nkD96fvlHry//6PnV69ACvO76wX91pURPLs9b+NTBDw4M8gX8ez53Nt9bvVtv827dzdDqbQEE8C0you7mrZrrv3M8PzcPdWqRFDEWaAPc3kFuP4buqfauqfZ2RkvnVDtJHOD2jfAGRZJFeBci5JniulVz/T6DHc+BvXrbjtzfNxzaNz8AACAASURBVMocmZigysexk4PHgV2RkZEeHh4hISFDQ0M/m19ze3awx6Hf+B7/7+eAz7E/eR/9w2hj0TiV/v87fQ8xRR9/JeOufJubm5ueLvPtra2t/THGCDwhJBLQA01OPpZ7xsaQpqbkyhWfkRHIkow3IwdGTqNnIXiXvLxcX1/f3Nzce/fuubq6Kioes7Gxtndw8PDwCAsL1dLS9vXxNjA08vHx0dfXu6eh0T8wGBYWeuP6dRsb25bWFgcHux07tmvraKvfu6eurh4VFXXfxNjbF5IJyZ8Xj4pCoTxH7iG5Ry7q5LVgJCYmpr6+uq0N5eSI09OrCgqmMzMRjwdezHl54GczMQGVFgoLwcLV1ARamf5+6dwcyDmcWRF3RiDgCKR8PpivZmYwLM5w2DOiOQ4YwaqqoAZ7bq7g+HFnA4NZQ0N04wYusgmKH3xBCIEgVVwsy4Utf4142ItCYXJyEoUSkJGRoa6ubmdre+bsOUsLC1c3d08vzwAKxcjYxMXZ0dLK2t3DQ0PjnompWV9/v5+P9+3bd1zd3Kqrq83NTbdt2+rh6UXx97urpqajo33nzp2CwuInWSqVSh88ePAc9QzJwBUsJVkdFhbW0ACpARYXF6urK+Li2GlpiDC9rbiyf9BdMTb5rbGm093dlcGYDQlBra1YXlw2PeS3J3GclTsgAHV3LzxMjg8MDEx5+FBTU9PK0uLS5Sum9++7uXt4enm5u7laWlrZWFs5ODi4u7kp3bzh8sCtv7/f2cnxtoqKH4VSVVWlr6e3bdt2M1PTO6qqOjq6cfHxyspK5RWVK4aNtZIPHjzAGfaW5y1Eq1ahV17Jys/PxtzjcbmRkZGtbeDJQeRnL0zPyHwWn/E2/1RL8gEjWBXX3d1FHWddPO56YJulwi4bhZ020D4N9mwwc7KSOSnPsXnnFM03f31nyUMZ/JS3P/ZWVttOOC+v/1xZ754vHiSPt3DpjPnaz65uWq28eY0MNq1R3rRGafMa5U2rlTetUd68WnntZ9duXLKdnZlrbXtcp0J+5GKxmM/ns9nsiYmJmRn2GL3BP16REn88IOHEs+G4b+zh7kHQfcofisRJNpIUeQTyxc8Mmbt9ZuDwdyPndwGc5IDoGjoBXcfuz+lF5vPz/CGi2gk+rPyhMA7xXL29LDa71sfe9YP/8vzqjxi8vvojCZ5LuMfnv/dd/zdmh0zuGU8NLznwYfmxL8uPfbUSFJeIR78oO/rFTLOsQkhXepzLh6+4f/47ty9ew+D+xWvun78GrRzi/N7/6UqJHp2grojnWjF+hFB7O8Rz3fVSXqf6+Tat9ds0nwZa67dprd+g9tVFu1NsHsg9Uqm0lFoY1u0X1RsU1RsY1RsY3Rcc3RdCQDBBBHpET0BifxRvEULAEEKuSU7fqH25U2fjTu1ngM7G7Zrf7DfcMcIc7uvte6pfM36Iamtrg4KCYmJi+vr6nszfg0+H2xWX/Kyu/C5PnVpQ0yMryG3/K96Kf3gOeB39vafCqyMNBf/m+Xue5CNRk2EwIyOTzxeIxeLc3Jzevr729rbi4kcsFis9PX18glpYWJiRkTE5OfkP8x2vuB8rujiBxAoi7nK56OjRxfXr3aamxsgNhEKhfHEWkv4kgq+LpA/098fFxbW2tQkE/PS01MKiYolEUlFRnhCfMMfhdna0x8cnjI1P9PT2MBnM9PT04ZGRrq6uxcXFvr5e5uRkRXl5fX3D0NBgSkrqLOFXQR6ZRAICAp4l9yCEujo7cnPzxGIJf2EhIyN9dGy8tramuqaWRqOmp6exWKyEhISmpipLS7q+vhghlJSErlwBa1dICAoIQIuLcJ6+PmRpCVFd1dWoqAi1tKC5OcRioZkZNM1CvHkooLXIZEqmJtH0NJphCVi8aRaan0dMJsrNRW5uKCNjbuPGB8ePcw8cQB99hD75BP3ud8LAQGx8Jy/l6cgKlra1tsbFxvX29fN43MSEhOqaWolEkp+fl5aWNj+/UF9Xl5z8kEajd3Z1MRn01NQUBoPR2dkpEot7e3omxsczMjJ6+/oHBwcys7JX+O2Sp3d1dX2W3IMQam5qLCiEDLxcLic9PZ1KpVVWVDQ2NY+NjaalpXE4sHLX1tbSaDQ8Z27dQpcvIwlkV/jnfrjIwLP2YbPZ3t7uk5PCtWtRVBRs9fztyeP09QH/8/MfM7+psSE+Pn50dIw9OxsfH9fQ2CgUCLOyMjOzshYW+FWVlSmp/5e974CL6sr+fzGJm83mF5PdTbKbbP4x2U0xRiU2LIm9i72iQUUQ6X1g6L0OQxXpRQQsKIJRRIqA0lU6wwxlZpjCUKbPUKa+v2+ueTsZRmSNmmx23mc+cN9959577veWd969555TMDQ0jOyqMJg3btwYHBzs6uqSyWSdHR0iobDoZhGJ1P3gwf1bxcXo+gRaHPjQjIyMROWelJTLEHQPgiQQNApB0pkz8wsK8gF9Zka6kZHR6dPmY+MTPSTi0aNHjX74oab28Ve7ep7PFtaYdlTWufg9Pd1M2si+Lb6rFzuuX+a8fhnm8W/548C6x5HOK+bbBnk/VtZ55Dd0/w7PJd+YqpRyzJG/36r+Ijo6//4t/MrY0ToWcCsWjR3d7/PtnGPL9EyX6Zn89DP96fZx4Ns5x4yPBALTfyiekzkHJxwZdBaZXheauDws8buwpO/Ck74PU/3Ckx8HkFNXSMx3QWeXtBC0KJGoFI/E+fn5IyMjt27d0mqcE5F7OGRP/OeY4L+5hn3kGqrl56KKdAh493qZl4D/M7uFWpnv7u7hC4V1cYHhn74WNfedn36zouYiEslPt0gg8qv/i/32g8GOx84NSZdSc5f+7dLqz9AfOJp+SXUi/fHfVZ9e+n42q/EeQL6z8ELYpzPxX/0ffs7bEXPexqt+kaq/6C1+ztshs2d25GfTWSww/EFarcyD81zmMcYLzL9YZqunb6u3zHbBz3+PYxZazjkYuAuVewpbr4bc9o0oC1L7AUfu/44JLw2Ir4oSjKuWiGAYlxeiZ/7lCruFK+y+nfR7HLnMRm+t83LKIPlJ57lALeRyOXj3kclkDf0e5GVBIJSXl9NotOvXr2scEtIAAdyCXZfi4uKBgYGCggKtFuNgGG69kYzf8Hrs9lnqvziDn93GbHs7esuf+h+WsoaGf892CzVwVImKCqDvcq+mtr291fz0aTc3NywW6+joFImP2LhxU3NrW8WdO4GBgVevXgV+Urhcbnd3N5VK5XA4XC63p6eHTCYDpa2enh4Oh9PR0TEyMkIgEBgMRmdnJ5lMJpFIKvOyGTU1NSKRiERCtMfBq5HNZnd2dtLpjOrqdisrx8bGehKpm0AgdHd3JyYmNjY2cjic9vZ2DoeDvABU/qGoVCqRSCSRSF2qi0Ag1NbW3rx5E/j96e3tFQgELBZraGiIxWKBUwxMJpPBYAwMDFCpVJrqolKpXJXDGqFQyFZ5C+JyuUKhkE6nDw8PC4VCKpXK4/EQX3kMRn9/P5VK7erqolAoXV1dRCIxLCxMq36P6n0j9/LwMDMza+8k3KuqsLKydHV1dXJydPfwCAwMNNixs7+fduXKlZiYyLCw60VFnMpKdkUFp6mpq6iIUVzMaWvjlpYSHz6k1dfzWlt7iERKW9vwnTsdbW3shoaOnh56V1dXT08vgdDb1tYVH3e26WH78JCgra2LTGb29jJra4mtrYNFRYTqamZdXYO1tXNdXUd9PfHePUJ1NcHL60xCAiEzc7iqqpPLZbe3t7NYLAKBQKFQuru7iUQigUAAfysrK2/fvs3j8YaGhh4ZvBeJREwmExysAH8HBgYYDAaTiTh2odFo/f39NBqNj3jj4olEItWBHRqPxxMIBP39/RwOh8/nU6lUcMtischkcn9/P4FA6OvrI5FIBALB399fq9wDZB0XjLOlpVUfmVpcdNPCwsLb29vJ0dHd3d3L02P/gYNDwyNZWVlRUVElJSWIkzUOu7eXU1vbyWINcFQX6I08Ho9EItFotOFhxIMj+MtgMIhEYm9vb09PD4FAOHv2bF9fH4/H6+zsHBwcpNPp3d3dLBarq6uLRqPV19djMM5kMrmmpqu5mdDZ2RETE9Pd3T00NEQgEEZGRjo6OlBIe3p60C7a3k64fPnO7duVQqFgcHCQTCYLhSI6nc5mI6dBUEiZqotKpdLpdBRSHg+BlMVC1qIBpBQK0jl5fB61vx+xUU6hDA4O9vb20mi0rq6uvr4+IpHY2dnp7++Pyj3FxeVr1wasXZu0bl3C2rXJ69d7Ojrag9etj5dX3pWrJiYmdMbAnbJSd09vXHh4ekbWs0k5k1OpTztyuZxEIjGZTDqdxqCNbF7luHDOyaXzToGf/rxTS74x1Z93Sn/eqaXfPI6c95mRl2sqyFbAF+/chJn/+Q+L5p5Y9PWJx3+/Pr7o6+NIGIk0XjT3xNezDW3MI0ESsWj80C6PuZ8dWTjnuNrv2MKvji38+t8xcz87YnTQVygc7e7u1ir3gNU+IpHY39/P5fC7yRWuoZ9gw2erVmI+c8N95ob71DXsU1UA3H7mhpvtHPzh/baL4KMcRQYsAzzyXmdvb19cXOzl5aXutBWQAZoRTi82/EN7/z85BL7t+NPP3v9th4B/3zoGvm3t/fq12xixeAx4FURzQJEHrgb7+vooVKp4bLw6yjfob1DYZzPDPp35+O+nM0M+nRn+2eNf2GczQz95FT/nz6w2ZA0V+fDIiIv89PW4r2ep/2LmgNt3VJHvxM15O/ar/6PVIgu6iIpJfnbQ36Gw2a+Fffr4F/rpayGzXw/79HVVzOvgUeAHr7TnnRtic7Se5wI4UKnUgYGBvr6+sfGxExFHPjf+eL75F//+nf58ntnnqtvP55sjga9MPtnps5kv5iHIw8qoQtze4O1HcHvRn2H4HsOwPYaqGPD3UNiu02dOsAUjgPmgC37/Mv6HnsWXCyy+WGCO/Oabfzn/9BfILYix+GLe6X/q2y4gD/bRaXSt+1wAf3C8BuhKglMIoAhQtWvXrgUFBV27ds3JyQksGoF4dRoQRnsRYk/E3f3GjRsYDAY1yK5B01KYEPYdFLnhNbXf6xHrX1fdPv6LX/8qfi3U/+D2wODw/5B+D7IYLpV4eXn6eHvlFxSWltx2c3NzcLB3cnLy8fFJS0v38/VJSEyiUChYLDY2NjYyMhKPx8fGxrq7u/v6+kZERODxeA8PD29vb2DN3cPDIyIiwsnJKSwszMXFJSgoCIPB+Pv7e3p6YrFYZ2fnoKCg6Ohob29vf3//kJAQDAaDw+EwGExAgL+Hh6uNjaW7u7uLi4ubm5u7u7uNjY2/vz8ej3d2dsbhcCBDFxcXX19fLBYL5DNXV1c3NzdHR0cnJ6fo6OjIyEgsFovD4by9vf38/Pz9/b29vb28vHx9fT08PLy8vNxUFxaL9fDwwKmuiIiIkJAQNze3CNXlgZj8CYiIiMBisaGhoT4+Pn5+fojpPR8fLBbr4+Pj6uqKwWD8/PzUffSozy+SiXEsFuvi4lJ+pyLv8sXAoKCTJiZYrKt/gH96RoaXp2felfzm5mZPT3c3t1g7O7ytbZSLS4y3l6uJSYCNTYSfX4SZGdbJyd/ZOdLLy8PX18fTM8zKCuPpGWFri/H2DnBxxfr6+rm7ezk5udg7OAcGhoWFR7m5efr5BXl7B1pbu2KxIWZmLk5OAVisi7W1pY+Ph4eHq4+Pu7c31snJ9sSJ4EOHcNbWmIiIcCcnp+DgYBcXFx8fH4AnAF/VDRxcXFxiYmJwOBwWiwUNHRgYCNrOw8PDz8/PQ3UBSF1dXb29vSMiInA4XEREhL+/v4eHBx6Pj4iIcHd3DwkJweFwrq6u4eHhnp6egYGBIAdXV1cvLy/QNx418djYz1zAorMGh8N2dXHx8PCoravPPp/l6+NraWXp7uHh4emRm5vr4Ohwu6SssrLSw90jLi4Oj8dHRUXFxUW7uWGCgoJAs7q4uAQGBkZGRrq5ufn5+YWGhmIwmLCwMAwGExgYiMVi/fx8PT09XFQWLHE4XGRkpLu7e2hoqL+/v5ubW0hIiKurq6+vr6urq6WlpZeXl6enK9KCWKyNjU1oaCjo8OHh4RgMBoUUwV11YbFYd3c3DMbe3R0ZR2FhYW5ubng8HuTs5eUF4AKQenp6uru7u7m5qUOKx+PBOALVwWKxKKQ4HM7d3T04ONjd3d3Pzw+FFIwI9AtSoVBIpaNSqein36hYLAYH9BITzp44ceKU2em6uvrbxcVGPxgZ/fDDnUrEm/RzudRHxyMHk05OTuXl5SQS8cH9VsN9rtvWW+3eZr9ri+0eA8c92x0NNlvs2mq3x8Bx9zaHvdsd92533Lja3M/7LIVCJhK7iF2kE0c8tq6z2LXFdvdW+11b7fZud9yxydJgk+WuLXY7VZE7NllvWn3axRHf39/f1UUgErvNjH03rzXftVVFsN1+93b7nVusDTZb7N3uuHub/e5t9nu2O2xea25lFtjVRWppaUG9zqGcg1dRc3OznZ1dW1tbd3ffvbqr2OBF7iGLvHH6nmFLffDLvXD6GF99zzB9H/wKL5w+EhO+1CVwQfGdNAqlHwhMVNVFo9EYDAaZTL5161ZRUVFBQUF7ezuDwaDRaICASqWqhNfuto46b9xKbNACr/AlHqGLvCOWeuMWY3yXuAUv9olY6hmGxHiFL3b2n5tz1Z1E6u3ufqzWBt6RKP8wDOfn5/v7+xNJpO7e3puRQcH6c8LXfItftzhi3cKoDUsi1i3yX7MEv35R1IYl+HWLojYtDV+9IGLD8paq8u7evj5qf2lKbMjyb3Br9KI2LMavXxS5YVH0pqV+q7+N2rA4Yu23kaq/4asXhK9e0FhU0NNH7qNQ7+ZmBi+fi1v9LX7dIvxaJOeI9Yv9QCnrlzyOWbMwWH9+VXZ6q+qDAe1v6pxLpdLQ0NDs7GxiF7G7u9sGb77WZsU25/XbnDdsx2w0cN20zXHDVtv1Bq4bDVw2bsds3OG6eYPt98f9DbtIBPCx6p3ksdVx/W7s9r3uO/a4bd/vsXOPq4GB/eZ9njt3uW7b54H8NcBsPupzoLWzhUggMunMkMzANdbLtzlv2IYBpWzc7rRhi+16AxekxO2YDTtcN212WLMDs7m+ua6ttU2gciQM+EeZBwEYhjMzM3E4HBlxd93dr/qK7u/vp9PpTCazubn55s2bt27dKi4upqsuYG6ASqWqOnAXmUym/nQBgra2tsLCwhs3bhQXF/f19aFf5mgSSj/9zqX48H3fRB1ZEPPDwpij38YdWxTzw8LgQ4uif1gYd2xxNBKzOOqIXsTBrxtL8ghdpJ90FtEWeLGBX/kcOwzDd8rK/PwDGhsbampqwsPDrl//8Vr+VW9vnzvl5SEhwdXVNYODQzKZjEKh8Ph8NptNIpHGVMbmJ1QXcEQ8MTGBBuRy+cQEsmsGNqoYDAabw1EoFKOjo4hWnUiMqHcJBBKpRCaTAUrg80+JOA9ATpKPjDw+IC2Xy9lsNrASBDKUyWSAGPEioLqoVOro6KhSqaDT6TQ6HZQOLDRIJBLAFfoXJFcoFGw2WywWgypIJI85AbWQSCQTKhPUIDmaiXrRTCaDNTio1Q4eMsVcyQvHRdx/8KCqqsrf37+isiozIyMwOLisvDw0NPRhU/PQ8LBkQkLr7xufELHZLAq1VyKRKpUShWJCKp1QKGRyuVQun1DVVQpiZLIJheLxeimtv18g4CuVSqEQcWYpEgn7yL3iUbFMJlUoEMtKqhwkMplcobqXy+UsFmtkhK3a7JYKBMMSyQSoNfiLQgrQo1KpExKJXC6nUiksFksdUhRJjYQypNVGxsbGJkMKTD0BSFGEQQOhkEolElp/PzCyN3nWUCqVOdnno2NiGhrqqyqr/P39qqtrEhMTcHj87eJbwcEhnQTC8PCwRCLp6+sVi8VMJvJSkapMTAF+1HlAAP2pfUG8TCajUilClbl9Po9Ho9EFAj5YlAbIqFdWJUBIFQoFk8kE32dSqWR4CCldA1KQFuiFUKlUcEsmk4eGhwGkaOngEYrtT11bNjIyMj4+jkIKOEeZRyGdnA8YWchKG5eLTr5Pmsm4XE5MdHRDw/3GxgbkcG/BtXNZWU/ajnxSJlPEow0KzJlcuHCBQqFIJBKRSMTl8rgcHrmPPMBkEQhd3aSejvYOErG7tbW9q4vY1tbe3tbBZnP5fL5YLBYIBI+TcHkMOpNCodLpzNaWtp7uXgadSaMxmMwBMpnC4/I5HMT3uFgsEggEYrGYx+XzuPy+PgqDwSQQkFd/RwciD4FSurt7Ozu7RkY4PB5fLBYB4RtUB+UcyD0DAwPnz5/n8XhjY2NCkYDHZ/P47L4+0gCL3klo6+0jdXS09vV1N7c86OsjtbQ86COT+AK2UMgXiR5zIhaLR0dHBwYGwII0QdVvh4aG+Hz+wMDAyMjI6Cgij4rFYpBEJBbx+Gw+n91HJrEGGZ2dbX19pPaOFiT/1ocUSk9XVzsoRSBAFgXRlarJck9zc/O1a9fGx8fFo6NCPp/PGWEPDVJ6uxlUSld7Wy+J2NnW2kcitjx8QO7pbmpsYNL6BVyOSCRELpFIKBDwOSM89jClt5vFoFP7epruN44MscjdJO7IcF83iT00KOCy+ewRkEKVRsjnsNmDLHIPidFPJbS39ZGI7a0tfd1IKTQKuaO1mdFPFXDYQqFAJBID2yIayIPbW7du1dbWSlU+MXh8LpfPpTH6qTRqT18PgUjoInV1EDoIxM629lZiN/Fh88OhkUEunwv6DNIB+Fw2lz04zOru62YM0LuIhJbWFtYQcsvhsYk9xBHuCJvL5vDYYhHSzcRiMV/A4/K5dCadSqP2kntBKZ2Eji4SobWthUwlt3W0soZYXD5XKBKpXkD/3sJW7zag59TV1RUVFQFbYqAPsNlssF4OBJfe3t6BgQECgdDf39/a2or2BBX2ItAl0O0IBoNBIBBAnxGLxWAdHdCIxWKQRCjkC3gjI0MDVHIPvZ/c1dnW003o7Gjt7e5qabpPo/Z1trcMMPoFPLZIiNR3miolUwzz/+jRryz3gFZRN7iMbCsIBS4uzv6BQXK54sb1QtNTpy5evGhjbZ2SlhYU6O+McREj2iXTuoYGB+3sbIOCg2EYvpibY2Jqequ42NXVNSPziavoEonE19fHCYPhC4SsAabxiROdBOKTCmt6cP/EiRM5uchK8rlzmdY2NhwusrY59dXR3mZkdIypMiE1NaXWpxz2yOFDh87naNqw1uju6tgymXQbG5uY2DhE9s9IN7ewvHjxorm5+YULF52dnEJDw6ff7Xp7e8xOnTqbkAjDcMLZeGtrm5KSUmsrq8Ifb2jlFrF+xmY7OTr4+vlLpFJCZ8exY8eZA6wnEVfcKTt2zKio+LZCoYiJjnRxdR1VLcM8iR7EV1bcMTU1BTaTpqbU+rSvt2fXrt03ipBjO5NhBEiCR6q/iNoOqavLytIyJS0NOXQaf8bKxubixQsWFuZXrl61tDCPO/NYrVVrcRqRhI52k5MnM89lwbASjwu3d3QqKyszP326rPzxir0GPQzDrAEmstITFg7DcENdrbHxSaHoiYPi5o3rxsYn71XXyGTS8LBQbx9fmQzR65r6Krr5o7W1rcqUxNSE2p+2tDTt27ev6m71kyBFcdae/vnFogWh7aiet0KhcHFxuXAhF4NxTk9Pt7S08PT0jIzEJyUl4fH4nByVFpV6AlW4urraysoyJCQ4JSXl3LnMqKjIxMSE69eve3p6TKJ9HOHp6Xn+/HkMBpORkWFjY+3mho2IwJ09G29nZ+vg4MDmAPc4P0utzjnKPBqAYXh4eMT8tMWVK1ednZzT0zNMTEwDA4PwEfj09Ax/v4D79x9vEv0sUxhOTU21s7MLCwtLSUnJyspydnYuKSkJCgrKyHh89l6Dns1mW1hY5uVdsbd3OHcu6+RJk6Cg4MjIqKioaBMT00dOSzTowe1Tma+tq7O1s0tLz/D28YmMijK3sAhXrdaey8ry9vHRugDwyIeaK9YtOCQkAo9Pz8i8nJdnaWXV2tZuYWkF/DdP5qSpqcnG1jbz3DkfH9/omJjjJ06EhYefiY8PDgkxNDxy9epjJTONhFqZV6dJTUkNCw0LDQk9c+aMt5e3tZU1DoeLPxOflpaGC8NJJoAbGfUUMJPJPH36dHBQcExMTEpKCtIZnDGEToLxCWN0O/hnCWA4K+t8cHBwBC4i/ky8r4+vrY0tPgIffybe3d3d2Ni4tfXxUQCNVBrMq3cYlLK4uNjFxSU+Pj44ODg8PPzUqVPh4eHR0dGP+oCPj4+61ws0yfj4uJOTk7e3Nw6HS0lJuXDhgpmZGYlEMjExUfdHi9IjrrgrK52cMWnp6f4BgRH4SHMLC1wEPjY2ztvHx9j4ZEkJYi3vV7l+fbkHtAraVCq13E43ZAnfjcFgRkbgoqNjDh3cHxMb6+aGxUfiHRwdSd3/PnWFJpwcQIxe1Nb4+fljsa58gSDA3x8fEZ6Ree7G9UI/vwAAt0YqZCoZYrm7e7i7uzW3tI2PjTk62Dc+0G74CzGem54aHh4WFByCOE6iUiwtLHp6+6ae6GEYptP6LS0sgN8uDQaeeguSu2GxgYHBEpUG8tRJQDUr75QHh4R4eHoymQO+Pt7RUZFGx45HRkZ6eHpG4vEuLi4jbGTanTor0FI3f7zujVw+o6NjHh6evn6+169fz87Kiok7ozUHZKO9vc0DwdSNzmA+sow3Rd2BDBEQEBAZhZya7iJ0WlhaAG9cU/AGirC0tBSJxFp5mCItqFRHR5urCyY6Jlah+mp6Kj0Mw0U3b3j7+Hh6eomEAm9vn9DQ0KNHDBMSE93d3cPDwj08vLiIP7hpQZp/5XJIaLiPrx+DwVDtb4ZU3r2XEH8mLeOc1hxgGK6tqfb08sa6uopE4t6ebgtzCzZHpUzwc9ZVra8MDw2Jio46m4icrH748IGlQITMpgAAIABJREFUpZVI9eXwc9qf3SGUD+7b2dk/9gDws4dPvwHJnZydEpMfH+d+eppJFM9rQpyU8b8jYBgmk8lYLDYgIMDFxSUnJycsLCw6Ojo8PDwtLa2oqKihoWFyEyBqm62tYIc0NTUV2MP18fHx9/d3d3ef7PEUnN92d3cPCAjAYDC5ublgjx6Hw509exaLxTo5OYFzvIAztOL/ZlRbCFksv3MH7Lz7+voC24CRkZFhYWH5+fnXrl0DRgQ0kgKLaA4ODv7+/mlpaSEhIZ6enmCjMzQ0dLKaKgzD1dXVWCwWbNbn5OSAF2RERERcXJydnV1wcDC6m4lyPhk0dTYAWUZGBtjxj4iISElJwePxkZGRoaGhJSUlV69e1crJ2NhYUlKSq6srSOLn5wc2vl1cXLKykE9Z9VLA0M7JyQFb4VFRUWlpaUFBQZGRkbGxsRERESYmJuqpps+8SCQKDQ319fXFYDCZmZlnzpwJV10xMTEVFRVavWgDJZuIiAgbG5u4uLizZ8/6+PiALWYMBqPVxc34+DjYl0c+1DMy4uLiwC5zXFycv7+/ubl5Tc2/z1FOk3mAiUKhSEpKAuof8fHxSUlJoaGhUVFRkZGRZWVl+fn5YOlLHUxgLjghIcHS0jIqKiopKSkgIADoDLi6uubnI+KjOj0oKDk5GTRxTExMcnIyDodDhOXo6KCgIGtra/VU6vy/hPBvQu5RxwuYNra3t/P186+trUtPSzM7bXY+O9vK0iI2Ni77/HlbW9vuHsR1jnoqrWEYhgdZA9bWVsHBwY2NjQln401NTfPzr/l4e0dFx4CG0Uioat1xL08PZ4xLWVn5wMBAUmJis0qs1qAEyR80Nhw/fjwtLb2+ri41JdnaxqaPjJi0n0yMxiAWbyWSiHBcP40+NSWaRD0AKuXn6+PnHyCRqrzSqD/WFgbOLM3NzfD4yJqampjYGAtLy+zsbDMzs7T0jMSEBAcHxxE2ZzrMIOe8SERTU5OY2LiG+vpIPN7KyrqgoBDj7Jyekak1B8QKyMiwg52tr59fRUXlwAArEo9/Ut2RY8ylJceOHbt0Ke/e3buxMTGOjo5Dw8Nac0brijgW5fPCw8KeWe7pJhHdsdjIqGiFan0DzflJASCTmZqaxJ2Jr6+rw0fgrK2tz58/b3ba7Ny583Exsa6ubtOXe9raWk4Yn0hJSb13725wUKCdvX1BQYGDg/3lPO1eU2AYHmAyrKwsw8LC7927N8Bk4iMiwELjZIZhGL5eeO3kyZMFBQV3796NwIW7umLFo8ji0GRiNAa0Wng4bjqyNZoKDcAw3NnRhsFgEpKSwCyGPpp+4HlNf1OUCJSFR0dH+Xw+k8kcGhqSSCRgPZ/D4YyrLq1ATUxMALvqAwMDw8PDo6OjAoEAGFifrCUGShkbGxMIBEwmc3BwcGJiQigUAu8HXC53aGhIDjzVqXhFKz4F52D+EYlEEomEzWYDrX+JRMLlcvv7+0dVdurU81TPamxsDOxn0Wg0LpcLchCpLnA8UJ1YqVSCHXkOB7E4z2azQSl0Oh1ou4O1AZAE5VwraOrZKhQKgUAwPj4+NDTEYDD4fL5EIhkeRuz2SiQSsPGhTg/CjxbnAPMCgQBsr0xMTLBYLICnVnqBQDAxMTE8PMxkMtFS6HT66Ogog8FQtzY+febBhtfY2BiLxRoYGABog91DqVQKdp0mMyOTycbGEPvmoL1EItHExAToDFpdyoNtKbQU4H8elCIUCplMJipugs6A8j+5aI0YuVwuFAonJiaGhoaYTCYaZjAYwNG9Bj24BeCDDVAajSYQIBbbBwcHx8fHhULhZDPCCoUCzRmADxqCTqerlAGY6iMFZf7lBH5zcg9oQtVBmMfb6hQKRa6Q9/dTR0fHVAPjPxAXVBZmGeDklEAgoFAoUqm0p6d3ihck2JdhDQ6qdujFQmTYIIuWT+oKFApZJBJxOBxw+uZJlOrJFQoFn8/XOsWok00R7uvtA0fcp6BRf4SsEtFpfD4yNYuEImp/v1wuB1oODAaiKjQdttEM+/v7QZVV9t9oMqmU1E0CVgZQGvUAsi/DYrHZbA6bMzaG2CB5Ut3BwWwymTI2NgbmWeCJQj03reHHzig0PFFpJZ0UCYzNdHd3i8X/wXKRSprs5wsEHA5HKBDQaDQA6YRE0t9P1ap1PqnknyIUCiqFMjo6yuVy+XwemNFI3d1Av/Unop/9h2FYZceFC17PfD7/SS85lZw9QaFQxsfGuFzuk9SYfpa76kYuk/H4/GfzKwbasbu7W31qm1zE1DEvZwbUlaJDQIfA/xoCv0W5R0N6Ba9k9YaZerrUeIomB9mifzXI1G/Vy3qqQIDmD1Kp5zNF+KnZTpEWxWdqGo2nkyulHqNBPPUtmhDl5KnV0Ujy1PynnzOa1VN5QCm1BgCHWh89KXJy06vHPCmV1ni09MkBrfRoN0ZLfBIZiEfxR/Ofmh5NNR2yJ9E8lxbR4Fx3q0NAh4AOgV+IwG9U7nnSTDr9eAQXFTUK0ORX6U8kiE6H+osETQICoFCNSPR2miyhZaGKopP5mWZWvxYZioZ63SeH0XpNE1J1ejTJdOqoBunjFkSzUn80nax+LZrJkKrjqVEdtJeiNOoBUAWNDNEcpllBFDc05/80h2kWNB0ylAddQIeADgEdAs8Rgd+t3KNUKsGOo1wuB5vZCoUC7ByDqRzscQIa9b/ggOv4xMT4+LhEIhkbG0N3EMDBXaVSKVGdsgYnitFXhfpUriXyJ9u9SiWy6wm2MGQy2ejoqPpxNvVMfmthgBI47Q8OQyoUiomJCfR4nVQqFYqEgG11SCfGx8Fm9tjYGIAUfQrsCCiVyomJCYVCoTIt+jOlJRRJNADyh1WH+MFIAE0JdPGkUukv2Vt5yZg/CVJQBbBbBFQQgC/ux71aoUDOA4vFAEx1SJVKpEUA8WNIVafSNdDTWk2V0UtkXwuGYXR0AHW0sfHx6eSgNdtnjnyO05wuKx0COgR0CKAI/D7lHhiGkxPPhoSGwTAcGOhvYWEREBhUVVV5ytTUztYuJCSMy+V6e3ocOXIkPDyCRCRiXFyYAwOJiYnFxbdNTE1cXFzOZ2dHRUXu2rnTwtIKHNFKS002Nzevb2jMPp912ty8uqYmJTnJ2PgkULJG3woAWeCDCkQiWo2jYnNz88oqxBRbdFSk4RFDjIsrhUx2dHA4aWwcEBA4NvYrvFem/0IClQoJDkpJTVMqFA4OtnZ2dvjIqLLSUmPjE3Z2dmfiz7JYLHt7uyNHjiSnpLY0PcRi3ahUamxc7O3iW8YnTri5uV26fNnf32/Xrl2OTs5DiC0fODoSb21j29beHhMVCQyy4cJDbWxswQEuFL3HkP60UAZezOyRYUNDQ0IXUSaT+fv6HjlyJCAwqK+vz+yUqYmJSVRUNDAlMv06vmRKRMiQyz3c3S5cujw6Kraxsbazs4uOib1186axsbG1jXVG5jk6rd/CwuLokaPnc3LvN9Rj3dwHB1lnExIKruWfND6Jxbrl51/zcHfbs2cP1s2dy+NLJZIAP18HB8cuIjE8NMTewQExmuzr6+joxEXNK6jBqKoygi6AlE7r/+GHH/ppDLFIiHV1NTQ0xEXgKX19x44ZmZiYJCUnIwLRExTdXgR6oN11f3UI6BDQIfB8Efgdyj0q/zvcAwf2bd6yhTkwYHjooKOTc37+tdLbtzZv2Zp1LnP37t2hoSEWFpYjIyPGx4+diTvzr3/+08/P39HRMSHh7Lp16+Lj4+vq6oeGBrdt336r+LbqraDcvWvn4cOGnZ2E/fv2GB0/cbu42NTENMDfPxyHh2G45HaxjY2Ni4trQ0MjxtnZ3s7O3Q3r7eMDbM9U3ilfuWKFq5t7VWXFrl27h4ZHPNxcvby8Dx4+TCAQThw/diUfcXz4Il4ezyVPcHh+29atBw4eYjCZq75fGRAQUFRUnHM+a8+ePZmZGbt27wnw9/Py9mENMI8cMYyKivzggw/wkdFW1pbxcbFbtmxJSUltaWsjEbu2bNnysLkVWGla/f13Vja2XV1da9estrG1u3Xz5unT5u5uWGBdKTcn297B3tfPv6Ki3MnJwc7Ozg2LjcBHguWxixdyFyzQw0dFFxbkHzt+YnhkxMrS3D8g4OjRo6Tu7p0GBsDB03Op/ovIBPHR2NW5du2a48YnSSTS6tWrQkLDSkvLUpOTjh41ij8Td+z4CWcnRxw+ikIm79u7JyYm5t13/5yUnGTv4BAeFrpz9+60tDQiiUQkdG7atIlARExMMRmMpUuWYFxc79+/v3LlCg9P7/yreTY2tg72dgXXEetKWecy7Wxtg4JD7pSX29vZOjg6Ojs7x6t838IwnJqctHDRwpS09Nzs86dOm7PZbCOjH8LCwo4aGbW3t23fvq2lrf1l9tLnO9PpctMhoENAhwBA4Pcp91y7ekV/2bLvv//ubEKiiYmxm5v7reLiqoo769avX7t2TWBwGD4C5+Xt88h+qyvG2dPTy9T01MED+9esW5+Rkb5l69aU1NSW1laFQn748OGm5hYYhkdHER9+IUGB1rZ2169fDwkO2rdvn7W1TU72eR9fPxiG8RHhrm7uDvZ2AUFBFpaW7m6ugYGBRwwNwbF2C3Ozbdu3r1y58syZM8dPGMMwnJR41tLK6tjxE6OjY64umOSU9Jf5RvlPX+QwDMefiV21apX+0qXZObnbt20JCwuvqKi8cvnSxo0bVq5ckZya7uXhnpKG2D07bWaGVflP2LZ1q8GOHSnJSTt37szKyurp6eVwRg4e2E9nDgDfkHl5l92wrp5e3jdu3kAcXe3f7+XtExcbExsXD8Ows6N9dEzMaTMzD3c3V6yblZVlXNyZAwcPCYUiuUy2f9/evXv3rt+wIQIX7ubhhSzsBfjZ2NqeNj8tkyuOH/uh6Nbt3zikoSFBmzZtWrRo0cVLF3fs2BEVHV1dXZOVmb5p8+Zly/TzruZbW1leLbgOw8qjhod9ff1Om5tv3Ljh4OEjMdFRBw8Z5ubk0OgMLntk9+69bA4XGAjJv5rv6GAXEBh08+ZNjLPTgQMHgoJDQkOCgJVLC3PzlJRUs1OmWDest7ePicnJuLgzx48fn5BIJRPjWzZv2r179959+wID/YNDQh+1kb2tjYuLi7mllWRiwvDQwbv3nm6E8D/tWlPQ6+ZoHQI6BHQIvAgEfm9yD4KRUmlvZ4OPjMrMzDAyMjpy5Iidvf2ZM/F5eZcwLtiW5uYDBw/eKS/fv2+fnZ2doaHh7eJiV6z73cqK71atzs3N2bJlS0hIyOW8KwqF4vjx4y0q4z1jo6N+vr6mpibRMbHBwUFIIDrG3t5ux44dN24WIZaLE84mJqdEhIWG43ABAYFnz8Tm5OTY29n20+hMBn3fvn3XCq/b2tgEh4SamZ2ytbPbvXtPSUnp7t27bG1tDY8coVD6f7MvaZX1kdHjx40yMrOiIvGmp8x27jRw9/BIT09PSkwMDAyquXf3ByOjktvFu3bvtrGxMTttfvVKXpRqJWbt+vXnz2Vu3749IiLi9u3SkeEhI6OjNAYi93A5bKyLi6mJSVJSso+3t4nJycSkZFOTk3v37atraIRh2N/Xu/D6dS9Pz8AA/4SExNCQ4B9/vHHa3HxsbLyluWnvvn23bt0yO2UaExt39MgRe3v7g4cOlZWVbd60yc7O7gejYyMj07JINMV798U9QiRpsfDoEcPs3NyggACz06d3797l5e2dkZGRcDY+Ni6+tOS2ianZraKi3bt321hb2drZX71yOfZMfFpq8jaDHUmJCXv27gsPD6+sustB9vuOjLARuYdBpz2ygHfS2Dg9Pd3D3f3kSePUtNTjJ47v3bu3k9D1SFPHDYstLS3z9PTw8/dLS0v39fa6ceOGvb2dTKG8d7fS0PDI7eLi48ePn4mPP3TwoL29/dEfjCorKtauXWdtY33K7LTwmcxCPjOML2K+0+WpQ0CHgA6B35vco1QqEb9OFKpEIlEqlRQKpaenp1F1DQ0NDbAQ9wgUCpnP5zPo9PLy8sGhofHxcSZzQKFQ9Pf383j8ttbWuro6IomkVNm8QTVMqVRqTW2tTCbrp1Jra+tkMhmTyXzw4AEw+c9GLs7w8PDwyPDg4BB7ZITH5Q4gpqWkIpGQRkdsDgE/7VwO5055OZmMGCXqJpHq6xumaaLmmd8fvzAh0GztI5OBYji5j9zdTWpoaGhqah4cHBwcGoKVyt6enrGxMXJf3507d4BH9KGhIalUSu1HHHc3NzfV1dWRKRSZTEaj0yQSKRh4JCLxwYMHcoWCRCTev/9AoVCQyX0tra1A3WRwcJDP5w8inu0H2SPsoaEhgYDPYDAUCgWPywU2h7hcBPOhwcHy8nImkymVSjs7OhobG9mc367QA2o3NjbWT0OE3YmJccQnPJFYV1/X0tIyPDzMZiPu5IBf7m4SqaKiUiAUioTCoeGRifFxOp3O4XAePnxYW1tLo9Nlclk/rR+19NPR0d7U1CyXyTva25ubm5VKJYlE6ujoBKo5LBZLKBSyBlmDQ0PA4hSwpKdUKkdGRoaGEPuQ7JERNofDZDIe+coYHEQasa2t7f6DB3yV48Nf2Jf+o+S62VmHgA4BHQIvAoHfodwD3itghp0MGfoUfTQ5BjxC45+UlUYO6K1GcvVbdRqQP/r0P3olvHxidDlKvQoo8+CpxiMUQDT+pyo/Zl89uXoOKOA/0T/OQCNDjafqOaDcvnygpl8iyiSKDxqYXDUNrDQo0UI1yNB8ngQpSq+BLZq/egAt5eUE1IvWhXUI6BDQIfC8EHipcs/zYlqXjw4BHQI6BHQI6BDQIaBD4BkQ0Mk9zwCaLokOAR0COgR0COgQ0CHwX4mATu75r2w2HdM6BHQI6BDQIaBDQIfAMyCgk3ueATRdEh0COgR0COgQ0CGgQ+C/EgGd3PNf2Ww6pnUI6BDQIaBDQIeADoFnQEAn9zwDaLokOgR0COgQ0CGgQ0CHwH8lAjq557+y2XRM6xDQIaBDQIeADgEdAs+AgE7ueQbQniVJU1MTDocbHx/v7Ow8ceLEl19+uWfPnocPHyJek7Kyrl+//qRMi4uLT506ZW5uHhkZeenSpaampidRTh0vEomAjZacnJzFixdXVlZqpX/kEd3d3X3evHmHDx/m8XhaaaYfKRKJgDst4OsbuBaffvL/Lsq6urply5ZlZCDOOnSXDoEXikBxcXFycrJ6ETgcztfXV6lUxsTE1NfXqz8C4a6uLhMTk2XLli1atOjQoUOWlpYmJiZLly719fWdTPxCY8rKypYvX66vrw8mwBdaFshcfSKaTnH19fVTj2WZTDY6OqqyZT92/PjxI0eOCIXC6eT8vGiebaLWzVEAf53c87z64VT5lJWV7dixo7Ozk8FgREREJCQkHD16FIKgr776SiAQ8Hi8/fv343C4yVmUlpZ+9tlnra2II8/29vYlS5ZkZ2dPJntqTHt7e2hoKBBBwsPD33jjjStXrjwpVW1tLQRBc+fOHR5GDPg+86VeKAzDN27cyMzMfObcfvsJb968+ac//cnf3/+3z6qOw/9qBMAEMjCA+HsBV0lJCQRBx48fh2GYRCKtXbtW66fUwMDAJ5988mjmuXjxospW+AQejz99+vRP2byk/xMTE3p6ehAE3blz5yUUqTERTafEp47lhISE8vJy4GdQX19/0aJFIyMj08n5OdI8w0T91Ho9R/Z+y1np5J4X3jr9/f1fffXVtWuIx3WpVApcCpDJ5Pfff3/lypXgo4FAIHz88cc3biBOs9UvS0vL9957r7u7G0RWVlbevo242/yPLiqVumLFCjMzM5BKqVRKpY89RWjNp6mpCYKgefPm/ZKRrFFoXV3dJ598EhcXp7XE302kVCpFTST/biqlq8hvCoGqqqpPP/2UQCCgXLFYrO3bt0MQZGJiAiILCgpmz57d1YU4ZVO/uFzuv/71r0dfNXl5eTAM19fX83i83NxcdZqXEJZIJIsXL4Yg6Emrzs+RB42JaPo5TzGWL1y48P777wO5B4ZhuVwOZvXpZ/5cKJ9top6iXs+Fq/+KTHRyzwtvJltb288//3xoaAiUpFAoamtrd+zY8e6775aWlqLFHz16dMmSJRrjx8vLC4KgL774IikpicfjyWSyiYkJLpdrZGT0zTff2NnZeXh4rFu37sKFCyAfKpUaGxtramr6/fffgw2XwcHBrVu3QhD09ddfYzCY8vJyAwOD+fPng0VyHo+Xnp5ua2u7atWqgIAAIIRpHU6/pNCbN2/Onz8fgqCVK1cGBgZOTEyIxWIvL68jR46YmpoCqS41NXXJkiXr1q2rra01MzPbvXt3VVXVtm3b5s+fn5mZ6erqqq+vj040KGgwDLe0tBw/jrje3Lp1a2Mj4tB0bGwsMDDw4MGDhw4dunnzJuIDVYXY3Llz4+LiPD09lyxZcuPGDQqFcvjw4Y0bN7a1tcEwnJycrKent2XLFjweb2BgYGtry+Uizj4nJiauXr3q6uq6adMmc3Nz8JFdWlq6Zs2aJUuWFBUVBQYGrl69+s6dOyYmJvPnz8disTAMd3V1nThxws/P7+LFiwwGA4bhwcFBGxubw4cPHzt2rLm5GYZhIpG4efPmp1ZQvbK68P84AgqFYt26ddu2bZPL5QCK0dHR9PT0wMBACIJOnjwJIgUCwbfffmtsbKwBF4fDAXJPYWFhd3d3VFSUXC4Ho55IJO7atWvevHlJSUlZWVn6+vqFhYVZWVnqo3LHjh3A757G+JLL5d7e3np6eoaGho889+3cudPc3Fz946qoqOjw4cPbtm374Ycf6HS6XC5fvHjxjBkzsrKyjh07tmXLlr6+PsBqRUWFj4/Prl279u7d297eDoaJBmP5+flKpRIdlRYWFujS18DAgLW19cGDB1etWlVYWDg8PIzOfg4ODkwmE4bhxMREQ0PDo0eP3r17F4ZhjbG8Zs2aKcbywMDArVu33nvvPQiCtm3blpqampKSsnjx4o0bN4J5TCwW+/j4HD58eNWqVTExMVqnAlBTjbl3bGzs0Repu7v7119/bWlpmZubu2zZMg8PD/BG0CBWn6j19PS6uroOHjz4zTffbNq0qaOjo6mpac2aNVZWVgKBIDQ01NTU9Ny5c9XV1Xw+/9ixY1PMURq95Xd8q5N7XmzjDg8P/+tf/9q4cSMq0MhksqysrLlz50IQpKen19+POKeEYTg4OPiNN94Ab26UJyaTuWLFCkh1zZ8/v6gI8f0OwzAej4cgaPv27WKx2NbWFoKgqqoqqVS6dOnS1atXczicNWvWQBD04MEDGIazs7MhCLK0tARpzc3NIQjy8vJ65MHb1NT0ww8/ZLFYIJOsrCwYhrXKPb+wUAcHBwiCEhISgFMqIOTJ5XJ7e/vly5dLJBIej/ftt98CPk+cOPHVV1/R6fQTJ05AEPT99987OTlBELR48eLx8XFQC/C3r6/vww8/xGAwPT09f//7321tbR8pEnl5ef31r3/t6upKSUl56623gOgDEPvuu+98fHxmzZr1l7/8BYvFGhkZQRAE1vkpFMrbb789a9as2tra6urqmTNngvgzZ8688sorDx48SEtLgyDI3t4eVMHQ0BCCoL1797q4uHz88cf19fXnzp0DMTAMGxgYGBkZTUxMJCYmgjl9z549CxYs4PP5NjY2H330EfgWB9udU1RQvbK6sA6B5ubmN998E4PBoFBcuHChubn5woUL6nLPo23xffv2ffLJJyyVJ2aUGJV7du7cuXjxYo0dLiA86evrBwQEfPLJJ0lJSWKxWGNUMhgMPz8/9fEFVqk7Ojr++te/vvnmm97e3ps3b16/fv3ExAQot6ys7K233srNzS0tLf3Tn/4EvseWLFkChh4Y44CTiooKCIIyMzPr6+shCFq/fj3IQYOxtLS0lJSUyaNybGxs+fLlmzZt4nK5+vr6a9eunTz7BQcHf/jhh3Q6PTMz85NPPiGTyY92Bqc/lnt7e2EYXrduHbpYNTg4+P7777/zzjvgY8bU1HT27NnDw8PGxsYff/wxl8vduXMnOhWA5KBSWudesF/517/+FYPBzJs3D4KgkpKSp07UjybPwsLCR8QrVqwAmWMwmI6OjoKCgo8//ri3t7eysvLq1avg607rHKXOGMjh9/1XJ/e82PZtbm6GIOjgwYMaxXA4HPAhEh4eDh4lJiZCEHTu3DkNypGREWdn5/fffx+CoFdeeQWIJtHR0RAE2djYwDB8+fJlCIKcnJwUCoWrq6uTk1Nvb+8jdUV0ZJ4/f15d7gEiiI+PDwzD8fHxRkZGfX19x48fhyAoNjZ2CrnnlxcK5J6WlpZZs2Z9//33KSkp+/fvf+2115qamiYmJlauXAlBEFA8Ghsbg2EYcBUYGDgyMjJr1qzPP/9cQ9Xa19cXXbQXi8VyuZxCobz//vuLFi0aHR3t6OiYMWPG7t27YRgGzLu5uSmVyiVLlsyYMaOtra2mpgaCIEDQ39//zjvv/OMf/xgYGBAKhZ999tmHH34oEAiKiop27dr18OHDsLAwVIUChmEzMzMIggCM4PMLyJf79+8Hbx0Iguzs7ADDd+7cmTFjxrFjx9D2AgnBpD9FBTU6g+72fxwB0MdCQ0MBDg8fPiwoKIBhOCcnR32fC+2f9+/fV0cMlXvS0tISExPt7OzUn8bGxkIQtGvXrkczyfj4uFKpFIlE6qNSKpWCDfrJ44tMJn/44Yd/+MMf2tvblUolGL9gD2jfvn3/93//B1ZVRSKRTCZD97mqqqqAkgoYg21tbbt37y5UXRAELVy4EKgkajAGlAV37typMSqvXLny6AsRqGmPj4+DUak++42MjHz99deffvrpmTNn7OzsIAg6f/48itU0x7KG3MPj8T766KN33nmHQCB0dXW9/fbb+/btUyqVMpkMHOPYu3ev+lSAAq517i0rK4MgaMGCBQKB4NSpU+jkppUY/UBls9lgtpzcMLkEAAAgAElEQVQxY8a9e/d6enqAcHnt2jUIgpYsWYLquU8xR6GM/S8EdHLPi23lhw8fQhA0ecEZhuHGxsYZM2a4ubkBDsDMBSQPlKfGxkawUNTT0wOEgKVLl8IwHBMTg4oyly5dgiAIvG7FYvHZs2fDw8NXrVoFFoFgGFYf+TAMq8s9Mpns6tWrvr6+IPMzZ848Ve4B60bPViiQe4qKisCnSUVFRU1NTXNz8+joqFgsXr58+auvvtrS0oJWH3CVmprKYrFmzZr15Zdf8vl89CkMwz/88AMEQepqQ/fv338k8K1Zs0Ymk7W3t8+YMWP+/PkoYn5+fkqlcvHixTNnzuzp6amsrNSQez766CMGgyEQCP75z3/OnDkTrF3fu3fPXXWpN6WpqSmqHwpYAjiDhmhtbf3yyy/BYtUjKSorKwuCIGtraxiGAXRGRkYwDAO5Z4oKqldWF9YhADpSfHw8gOLEiRMfffTR119//fe//x2CoDfeeAN95OjoiH75oLihcs+VK1fGxsby8/OBxiEQU8Cs4urqitILhUKNUfmk8UUmkz/44IPZs2drfJmMjo4uXLjwtddee7SBhWaLyj11dXVVVVVgDAIRh0gkenh44HC4V199dfHixUBbbjJjSqVy8qgEXyaoIiMoTn326+np+fOf//yPf/yjoKCgpqbm/v37QIVx+mMZqCuor/egcg+RSARSy3fffSeRSNDKtrS0oFMBqu0Aw7DWuRfksGbNGolEYmxsjH4HaiVG5R5Qi/r6+j/+8Y/79+9PT08Hi09SqRR8nv3pT38C35NPmqPUGUM5/x0HdHLPi21cAoHwxhtvHDhwABTz448/BgUFge+A1tbWP//5z6ieMljvQactQB8WFlZbWwvCEolk2bJlc+fOhWEYfABprPdIpdI9e/bMmTOHwWDs2LEDgiCQFn3pgkkETIjgqwiLxb733nstLS3e3t4QBCUmJsIwDNaoJus1a13vmWah4OsqKSkJhuHq6uo333zz448/ptFojz62BgYGeDwekHv+8Ic/gO9CUGUg9yQnJw8ODmqVe4AMt2fPHqBMIBAISCQSWO8ZGxsDq+V79ux5ktyDzrkwDGtd7xEKhefOnXvrrbcuX778SECEIMjU1BTwBr7G1I/Fod9SYAbv7+/fs2cPEMvu3bv3yiuvgPUesF+mvt4zRQVBWbq/OgQAAgUFBRAEhYSEgNuKiorY2NjExMRjx46BL3vUzsXp06chCAIqLCh6qF4z6LfgfdnU1FRdXY3OKp6enii9SCRavny5+qgkk8nvvffeokWL0PEFlmrIZPLf/va3zz//XOPLRCqVbt68Gd1Yl8vlHA5nfHwc6DXX1dXdvXsX/faorq7+4IMPPDw8Ojs7X3nllSVLlgBOwHSnzlhGRsbkUQlG1jfffAMOovJ4PKVSCd704JODRqN99tlnr732WlVVFVD7A4pB0x/LeDwehmGgRQAyQeWerq6u+/fvv/XWW2+//TY4ny8SiQQCAZhbwFQQERGBYqt17gVyz+rVqycmJk6ePAlBUH5+/iMtQ63EGhO1Uqk0MDAAa0vo6QqpVBobGztz5sxvv/0WhuHc3FzwkawxR6kzhnL4Ow7o5J4X27g8Hm/evHkbNmwAeoiHDx9+tAyzdu3ahISELVu22NjYoB00ODj41VdfBWMJ5cne3v6bb74Bc1lvb+/ChQuB6ABEEAMDg4mJCfDur6mpaWtrgyDoo48+ioyM1NfXhyAI6AFcv3790XfDN998Ex0dPTY2ZmVlBTZoRkdH//a3v/3xj3/09fU9dOgQBEE7d+5Ep7PJ59ifudCJiYmgoKBHs/C6detycnKEQiH4YFqzZg0Oh4uOjpbJZFwuV09P77XXXlM36QHUX5KSkqhU6ptvvjl79myN75KHDx8CHUMjI6OwsLD09HSlUmlhYfGXv/yFSCQmJSXNnDkTHOgNCQmBIMjb21sikcybN2/GjBmtra3FxcWPtKAMDAzA3PTuu+/OmjWrvr6+oaHh9ddfBzoHq1evhiDIwsIC6BjNnTsX7IWDpszJyUEbKzk5Geydy+VyV1fXrq6u7u7uDz/8MDs7W6lUrl69Guj3WFlZvffee+A8zlMriGauC+gQAEq+s2bNcnZ21kADvN3V15X379//wQcfoOqDgH5gYGD27NkQBIGVXRiGHz58aGBgAPZBgBqNuvIQh8OZPCrNzc3Vx1dhYSEMwx0dHX/+859nz54N9lzU2bt8+fLrr7/+6quvOjs7+/v737p1SyaTgXPsNTU1t2/ffvSFtmnTJhiGwdfRzp07w8PDX3vttXfffRcc+JrMGFjPVh+VZDKZw+EsXLgQKAbh8XgcDqdQKH788Ucw+8XExPD5fDD7ffXVV4+WlAICAsB8Mv2xDPbFDhw4AEHQ4cOHb9++zWazgX4PWKg+ePAgBEFz5syJiIjw9fWl0+lYLBadCkByIHJNnnvHx8eB3LNq1arR0VGgdZSdnT39ibq4uPj1118HuhAwDOfl5YEldkNDw40bN8IwnJKSonWOQhlTb7jfcVgn97zwxnV0dETPczU1NR04cGDOnDkrV668ePEiqvoHtmwWLVqkobdbUFBQXFwcHh5+6tQpU1PTvLw8IKcDEeTAgQPR0dHr1q1LTU2FYVgkEpmamq5evbqgoCAvL2/BggUgXiqV2tvbf//99/fu3evu7t6yZcuCBQv27dtHJBLDwsKWLl2akJBQXV29YsUKDw8PsVjs4eGhp6e3fPlyDRN8z1YoMNExNDS0b9++Xbt2kUgkGIZpNJqNjc2XX35pamoKPs5SU1P19fUXLlxoa2sLlsq7urrAcafTp08HBQUtXrx4yZIlQDtPvc0qKyu3b98+Z86cgIAAsFwvEAgCAwMPHTp04MABQM/lcg0NDefPn3/kyJGMjIyVK1fq6emFhIRgMJgFCxasXbu2tbWVwWC8++67H3zwAdA6Mjc3B6c/Lly48EjPwMvLq7W11cDA4OjRo1wut7y8fM2aNd9+++3hw4d7enpgGOZwOMeOHdPT09uwYUNZWVlOTk5WVpaxsXFUVBTYqaTT6TY2NuAgCXjNoOe5pq6gemV1YR0CGzZsUD/PBQApKipasGABajsKnOcCe6koYqjdwoULF27bts3S0tLc3HzTpk0bN24cHBzs6enZtWuXnp7e5s2b0VWiyaPy0Ub55PEFznMtWrRIX18/NDRU/SQXOASQm5v73XffzZ8/H7ySy8rKVqxYoaen5+XlZW9vr6ent2rVqpaWlvv3769YseLUqVPt7e0WFhYbNmzo6enp7u7evXu3BmOTRyWQYIhEopGR0Zdffnnq1CkQg85+YCISCATBwcHz58/fvn17R0cHep7rPxrLra2tq1evNjMz43K5qampi1WXn58fDMPDw8OOjo5z587duXMnkUiEYTgzM1NjKgCYBAUFaZ1758+fv3bt2vj4+K1bt86fP9/a2npsbCw4OFgrscZEPTQ0dPLkSdTuWm1tbWZmpqenp5mZGZlMFggEU89RaFf53Qd0cs8Lb2IajfbFF19MbbKPSCT+v//3/ybb73kSc0AEAYu3T6J57vG/SqHPvRZPypBKpQK9Zjqd/iQaXbwOgV8dgbt37/7jH/9oaGiYgpPr169rtd8zRRLdo/9qBIAeektLS0pKyn91RV4O8zq552XgXFFRsXr1aqBrNrk8Npt98uRJ9IzGZILJMeBUtoWFxeRHLy7mVyn0xVVHI2cKhfLWW2999NFHVCpV45HuVofAbwqBpKQkAwMDsB45mTGwcgP0QiY/1cX8/hCQyWS7du06fPhwTk4OsF3y+6vj862RTu55vng+Mbfm5mYcDvfIqtVkiqysLLBHPvmR1hhghW/BggWbNm36jxJqzW2akb9KodPk7bmQJScnL1y4UF9f38PDQ2O38bnkr8tEh8BzROD27dsa/rlA5hMTEzExMXV1dc+xLF1Wv30EEhMTly9fDsxw//a5/dU51Mk9v3oT6BjQIaBDQIeADgEdAjoEXhICOrnnJQGtK0aHgA4BHQI6BHQI6BD41RHQyT2/ehPoGNAhoENAh4AOAR0COgReEgI6ueclAa0rRoeADoHfKwLKSZfWmk6i+h+N0IHzpIb/H0dGa/VfRKRO7nkRqOry1CGgQ+B/CAGN1xgMw2Njo1VVlSKRuL+/v6GhkUDobGtDvIv/j1wagKjfAiOH7R2dCoWiuvpeH5l87949oVD0P4IMMN6jDggahmF4dHS0sqpSJBb391MbG+8TOjvbVU7p/3fAeTk11ck9LwdnXSk6BHQI/G4RQF9dIADDcEN9nYW5efmdCnJfL8YF4+7u7u8fwOXyHj582Kh21dXV1dfXq0VoCdbV1TU0NKAP1MNoZG1t7eR49ZiGhoba2lqUXmugvr5eg6bhpwul18qwRkFNTU3AgqgGLMA2/fj4eGBAQEBAoEQqS0tLdXZycnJyLr9TSafT6urq0IIAwxo5NzY2asSoJ0HTqgfq6+vVaUCF1AkaGho0KqVeBEqpFWH0KQhobSn13Orq6oB5sMnIAHAe3G+0MLcoKy8nk/ucnJywWGxQUDBfIHjw4IFGPuqV0mADoKTBMEiunglAeOru98zdRqOlNFoBMKzeFg0NDU1NTep+zV7oZKGTe14ovLrMdQjoEPj9I6DxGoNhuL2t1cbG+scfb9y7W+Xt7e3p6RkVHV1bVxcWFlZeXn5bdZWWll64cOHSpUulpaUgpry8PCgo6OzZs2VlZShNVlZWQUFBSUkJGoPFYq9evYqmKi4uzsjIKCoqAjQlJSXXr1/HYDA//vgjGnPjxo309PTi4mKQSVlZ2YULF7BYLLi9fft2SUlJYWHhuXPn0ILKy8szMjICAgLUmbl8+fLFixfRosvLy8PDw+Pi4tQrFRwcDJw2aMACXu0yqRQfgQsKDm5vbz97Nt7Hx9vZ2fn+g6a4uNhr166hpd+6dSsjIwNlr7S09OrVqy4uLigzJSUl165dO3fuHMpMWVlZcnKyn58fSlNaWpqXl5eTk4PSVFRUREdHR0VFqTOcnZ2tjmdpaamvr29WVhZKU1JSkpmZWVhYiOJ569YtDAZz+fJlNOdbt26lp6drtIKLi8vNmzdBqtLS0itXrqAeQrSC09HeZmNtU1h4/d69u56enh4e7rGxcQ0NDSEhISgzoNuot8KdO3f8/f0TExPVKz6527i7u+fl5aEMFxcXT2bY2dlZo9uot0JZWVlubq6LiwvakUpLSwsKCjS6TWpqamBgoDrDk7sNDoeLjY29c+cO2quDg4OBNf+XMF/o5J6XALKuCB0COgR+zwhMfofJ5XImkykUioaHh1ksFo/HEwgEd+/eRf0QPwkOMpkM3IU+iQB46VJ3cTOZUiaTdXZ2aviL0CATiURdXV2of0CNp+CWx+NRKBStj9BIBoOhYUHxxo0bwHreZFhAcTwud2RkRMDn02g0kUg0wGSOj49nZmZOXanx8fHOzk60XK0BLpdLJpO1PkIjaTQai8VCb7UGent7NRysapApFIqOjg6t9thQSolEQiAQgGchECkQCNLT00GMNnBguVym6jZCVbcZ5HK5AoGgpqamuLgYzVZrgE6nA/c+Wp+CSBKJNDXCMpmso6Nj6m4jFAq7urqmKOWRn7XpdBsajTa52zy1facud/pPdXLP9LHSUeoQ0CGgQ0ALAtreYcrJdJWVlefOnYNhmEqhAMe0gKbp4cOhoWEQHhoabm1puVddA8PwyMjwtWv558+fv3o1n8Vi3bpVlJWVVXTrVk9vT1PTw9zc3MuX84ZHRsZGR2/euHH+/PnbJSXgxSaXyxkMhlKp5PF4dapLKBSi/LBYg3w+n8UaYDKZWuUekUjE4XBgGBaLxSPDjxkbHhp8+PDh5Jcih8MBvkgBCMD55f3796dWZEGZAQG5XI7H4wcHB2EYfvDgQUdHB6m7G4ZhuVxeX18PtH/Gx8dpNFpHe9uDBw9hGKZQKHl5l8+fP3/jZtHQ4GBhYUF2dnZBYSGDwWhsbMzOzi4sLBSLxR3t7dk5OVfy8q5duyYSIVpEQ0NDQLLU2moAEDqdDoifRKNQKPr6+p60nQdSSaVSCoWiUChQZMhkcnR0NKjyk3LWQAaG4aqqKuDXrK+3l0hE/BuC68H9+2w20kwwDJeXlzW3tMhksuqamgmJhE6jJSYmXrx0SaSylCsWiSoqKgiELuAeu66+HsCr3kYA7b6+PplMNgVvYrGYQqE8iQCgJxQKGQyGeuYa9DAMs1gsjW6TnJwMPKaBGr3Qvy9b7lEqlRKJRCaTSaVSmdqF3oKnak9+FpRIJCjlzx6obqRS6YtLPp3MJ7OExvzC5FPX66mZg648RU9SKBRS1aXRNCjaUzPwS9plKuZVnUQy8QIaXSqVqX5Ij1F1SHCLtBfomaq/U/GmatpfUnHQN7S+eNCWUm8X9b40zXZB2xRNqx6Y+qlMJpu60X9h8qmhm/rpU3mbOvlTOZ/8dkdbRGtAY1oHbQoGnVwuR0ff3bt3gaNcezu7ktKy4aHBu3fvjo2NpaelhIbhQM5cLvfggX3vv//B4NBIcdHNud98ExERkZiYVFNT/cXnn4eFhRsYbLd3cLS3s9m6bftps1P7Dhysq6394vPPQ0NDN25Yn5SMuGcSi8VXr14tLy8/e/bsxo0bN2/enJqaeufOnaqqKrFY5OToGBUd7ePjc+ny5Qu5FxobG69du1ZRUXHhYu7ly5fz86/6+vnlXrjw448/Xrt2LSkp+XZJCYs1EB4WdjX/6r27VXl5VxobG3NycwqvF168eCEtLT0vL6/w+o9SqQxUITc3d4r1HqVSKZfLwZqHRCJRKpUKuVwyMREfHy8Wi2ur7zlhMIEB/tbWtoQugkKpCPDzzblwEYZhiUTST6Vu2rB+3ny9CYk0OipSX39ZVFTU+ezsH68Xzp37TVRU1MoVKzIzzxkePnTkqNHhw4dOm1vU1lQfPnRw9uxPI/B4sITDYrGAy9LJrQZiYBju7+9/qtzT09MzOjo6xdtdKpX29fWpyz0DAwMJCQmge2gtHYbhKbqNpaVFdU3tqPj/c/fmwW0daZ7g/tMRG7Hzx+4fu7E7M7FR07sxM11TdkdXd11dZZftcvkq22X5lGVbKlmSddm6RYkSRYqkRFK8JPG+T4AECRD3DQIgiPu+7/smwQMHSZAggJdbiSfBNG3JNdPV1bGVgcDLl5nvvczvy3zfl7/vy3ybi+LFjY3Nnu7Ojs4ulOYfH/zw+RdedLvdv3n5t2az5a0337x46fKHH3xw8vQZAACdSv6bv/mbhqZ7UO1biv/tf/rB+x9+hF5Yrgb6aI/Hg1agnL43gnYtn8+3N3FfHP1y7VPcmNDmR6PRvdonAACLxf7V4j2ZTIZCobDZbB6Px9oTUAsog8HA4/FMJnNPztdRNpuNw+HodDqbzf469XGMzWZTqVQCgcDhcNhPCDMzM+jl5fyy/ZjD4VCpVDKZzOFwmKXAYrHKD0JvPjc3V04pPfbrarDZ7NlZPINBf1ydbxzLdfvm5d8og8fj6fQnXo7H4xkMxjcueHzyvTdnMBhyufzpr/JUKsV9HHg8HlpPNps9Pz/PZrPpdDqBQHhS5dlsNoFAoFKp31kAvXx6eprFguRis9nMMmFLbGAymQQCgclklpmCRh6xhsudmcVTaXQOl8tkwWv3dQ86nY7H4x8/ms36Zt/gcDizs7Nol0MJxkQrwOGweVw2l0uh00lUKmd+nsPjcXg8NpfDgekcGOdwaTQabmaGzeE8JvY3jmw2G4/H02i0x0/fn/vNun0jl8Vi0Wg0lUq1FwZH30R7/xOJBAfW5RFH0FugjGKVSDEzM7P/vo/P2Ww2iUQik8lPqh6RSERz91G+xB82k8lECbsvF+ULl8slk8k0Gg0dbugz9z4IvfxxXfYf0dx9rCwXQgn7pJqzWCwGgzE7O1suvy+CXk4ikfbWp1yGzWZTKBQikfiduSwWi0qlSqUQbvnTw7df/eFQ8M6dO16fT6/TdnR0DA4MTE3jFHKFQCCIRiNvv/322urqnfq6A++889W5Cxaz+cCBd1HwQKtWvfrqq8ePHRscGhEK5p999tnbt2sHh4Z0Wu0zzzzT19f3+dGjVVXVX509MzIKoaMXX3hheGTkx//wD/X19S+99BJuFg8A2NjYmJmZYTAYHR0dr7722uuvv97f389kMgUCgV6vO3nq1OfHPj9//vzNqqoTJ45fvnz57t2Gioqr3V2d165fv3njRuWNG3W1tTU1NRcvXLx2vbK9vX1mBldTU0On0x88uF9ZWVldXf3RRx9dq6ycnpp69913D338ceWNGyi0AKUsna7VQkhmH1nK8n4Ki8FgpnK5nYGB/insVGNjgz8YxGAwxULhdk31+CT24f32Xz333McHD3Z29fC4nKOfH0d5MTM99cGHH374wQcc7nx/b8/Pfv7zuvp6AoHIYjL/6Sc/GRkdfeed309P4z777FOhSLy8FP+nf/zHcCQmEQl//86BQqGI3mRpaan8xfInsTgUCj3dhoUgiMfjQVn2pJvk83mfz7d3gKfT6dHR0SfZuQAA4XDoLuw2fr1O29XV1d/fPzU9rVQqBQKB3+975/fvxOOxY58fPXz4cENjk06n++CDD3O5XQDAl2dP/1///t83t7R88umnXV2d7xx4FwAQi4RffOFFh9N1+uTJM2fOvPG73wEAJsfHDh8+/Ls33jBb9hsNC4UCivc8qUWo3vO9ps9MJvO9X3eOxWL77Ll0Ov2vVu/J5XJCoZBAIAwNDVGpVNrjMDw8TCAQaDQahUKhPjk8KZdGozEYDDqdjioHTCYTPS3dHiYymUw6nU6hfP1E9MlkMnlubm56ehqHw83MzODxeBaLKeDPz/N4DAaDSoX1IZdCKUIhk9FTCpkCfxQqDZahUskUCpQxFAqJTEZ/ZHLpYXQ6hUpllGpAo9GoNFopl0IiU0gUCplKJVNpJBqdTGdAGUKjw0qW1C/KN0Pp3l9TZm/m49p9nbuPfiQSyWQyPaUro3MpPp8/PDyMxWJHRkYoFAqdTieTyUODg5SSeHsS5alUKqwwlUKBx28E2F4qlc5goA1jMll0BmQMl8WkQkrA1jKZkGt7e8I+vjCI+HkynkHAMUlzfBZdyGLMQ7dNWon+1BI7IM0fnVIgPUt8oVJpkJIUKpUIaUct/SDNWYQZLoU4h52aHhrBj45TJibwI2ME+BudHhkjzOJnMVNEPGFmdIxMJFFYLHKpV9EYDAqNRqJQSaVbUUpML3HqcTeg0al0OoVGhxWAHHzcT8iwQ+8nTYlOJBLpe3Hd7e1tHo/X399PIpEe9Q86fWJiAovFonR7Cl++wYw9JyiF6QwGtTRq0MECu+hjTtFL46XU/785XqhUEplMIBCmSwGHw01PT5PJlNKwg5fDzrBn/KK9tMScR+MF1gL2ChqFQiWRSOjgQquGdjl02KITDzgYSn2jXHe0R5VOYSMYDDi09ySWC8Jq7K3J1xl/QoxMJv/3vn8R5JFVCykFAIB4UXT69CkKlWYy6Gtqajo6HtbW1S0uimtqaihk0quvvmYwGOrr6r48e/aFF1+SSMQvvPCi3mBYWlqqq6v5bz/60e9///bzL7w40N/3s5/9bHR0dI5I5LBZ//W//OeDBz/6ux/+kM8XHvr4YGtb+1I89tOf/uRe872/f/aZ7u7uTw59fOzEF+Fw2Ov1qtXqcDis1+uxkxjsJMZsNkej0XA4rNVoDEaDTCqZnZ1Fp3mLi4sMBkMqlcplssbGJqFQxOfPi8Xi+fl5Gp3O5nAUCqXP71cqFQQCQaVW0Wh0kWiBQqGIxWKdTovFTtFoND5f4PX6AoGAyWS6evUq+g1mlBooccqUyWa3btdUV1fXJFYSHQ8f9PT0Xrx4cUG02NTURKfRPv3kEAY7feP6tasV1zks5nPP/5pOo7322userzcWi334wQc///nPX/j18x9+9PGdurqXX/7txMQEi8WenBh/9tlnD7zz+2ee/fs/rhV6443X50gUk8Hwdz/8oUKlnsJMvPjSb3yl4Pf7tVqtTqfz+/1oyrf//bCxSpvN9pQyPp9PIpE4HI6nlHG73VKp1OPxlB+NwWBaWh4Be2WClBVEAIBcKjl16jSJTDEa9LdrajoePqy/c0cskd6qqprBTb9z4F0KmfTLX/4yFAoJhUKhQPDCCy+YLdZoNPLJJx/fuHHzH3/8Dz/+x580NTa++NJvlhMJlVLxi1/8AofD/eAHP/jwww//43/4DxwO97VXfvvKK688+8wzVyuux+Pxvc33eDwSiaRc4b1Z5SbYbDaZTPbtrHKK3++3WCxKpfIplPH7/Wq12mAwBAIBnw92G4PBcPXqVYfD8XQ59efK/cvZuXK5HJlMZjKZIpGooqLi0CefwKlVSdHh8/lffPHFyZMnUdCF9uRQehPCv7Jmg8IJFAplenp6cnKyoaGhFobbly9fbmlpHRwcGhoabG1tuXHjRlNDwzRmYnRkpK+vr62trbW19cyZMxUVFXfu3Onq6mpvb7969Wrl9etXr1zufviAOIvr6+udHB+b53LkUolcImYyGRw2SyYWycQinUxk4rPC8xSPfN6uXLSpxC6DKmAzuCxGn8Pqd9kDbrvPYdXKpdIFgVGjFHBYmLFRIn6GRprzGtVBgzyglQS0ix6F0DNP9hHHfBSsnUdWCTksGkUo4GtUStGCEC7gKM1J2WwWpwRkUGk0KOEpFBaLyeWwFwR8kUAgWVzgsNkwC6poUMDsox+RSDQajU/qMel0mkAgiESiycnJ115//e7dhl/96jksFovCSG++9XZjYyOLBbE0GPbdmkZj0SgiGlFAmWOU9JiSYgNVGZQvdBoVNz01i52819T0sK2lvamhqrKyt+PByNDQ2PDg6MPmprqa1pbmKczE8NBQf39/b2dnx/32U6dOVVZcKS1s6R3qeth8q7JRovQAACAASURBVOLuzYq6qxeGWurYE71cAnaBgteLOAohV7YgEPHnRVyWcoHrFrMCdGxoZiAi5brVi3Yh077AcitFfhnPKWT4lEK/RhJQLawo+U42nt3Vxr9/D3/75vDH73a//VbT8y9OvPZq//MvjP7urd7nX2p68ZV7P/ox5pXXcB8fWrz81WL1NcVoh5M1E9WKoxaNUb7Ap1PZEBVhQQ6xWFw6hYGbpkxNMcZGxIQpGZMkWxDIFhd4XC6byWAz6Qw65E5J5H9NQsLc3FP00UwmMzMzIxAIhoeHX3vtNRKJxOFwKBQKn8+vr69/7733UDXoWwzZl0Av6dJ0BlQ6oZrAYrFpNDqZQsFNT89Mjt+9c6eurvb27ZqKq1fb2toGBwdHhwZ6H7TVVt+qq6vr7u7uK4UH9++3tjR/cfJkVeX1hrt3e3t7m5ubSwuQr1y+cL7mVtX9+w96enunsBgWk0ErKSvoHIDFYkpEQqGAr1Yo1Er54oKAz+MJeJxFIV8mgWeSxQUujwthQDZrfHJydGxsamqqt7f33r17/f39k5OTZbWGBjs/mYz2QSp1lkjCTE1jp3FQl2WzodYFhwCcgVBQ1ZNCIdNoqB6MqqFw5KDTmG9PgPaQ7U/RR/eNJlS05/PQ0IPGl+Kxjo5OlUo1PTXV3dPD5XKJRKJYvEgkEl1OxzsHDqjVqn/+xc/Pnzv31ttvq9XqAwcOpFLpdCr1uzdef9jZxefPv/7aq1euXvnJT39Sce1a1a1bDAb95Zd/u7W19eXZ08dOnLhw/tzzv37ho48+OvKHoyql8u/+7r9evnLljTdev3nrVqFQyG5teZ3u3ezObn63AIoFAK3Yxd18AboY5Le2Nnd3d7e2toLBYLFYRCuM1hw9LRQKW1tZFDdCfW5yuVwyCZ2RUesP2nzUYrVcCgCA7ccBg8HsxXtQA0oZ5AAAUMhk/CxepVLW19fz+YLxsTGHwzU8PLy6snLz+jUMdvp+W+vLL7985PDh2vq7gvn5zw4fQRAkHAr++te/xk5NM2i0l176zYXz53/5y19er7ze1HQPi5k8+PGhYrHw5u/eaGlr//jgwTffevvtt946f/ESAIBFp7351ttb2ezOzk4ulwuFQpFIJJfLPa7v/uPOzo7X611bW3tKmWw2a7fbk8nkzs7O/usfn29ubjqdzmw2iybkcjmv19vf348SHCkFlDh7uk28s7NTqVJNT0/19PRyOGwSiSyVyubm5swm44ED7wYCgd+//fYf/vCHM2e/VCrk773/fjqTQZDi50f/QKLQBvp6/tP/8/96PN4PP3j/4MGDr776ak1tbfO9xs8OH1GpVCdPfvHiiy++8sqr83z+4EDfCy++uJxY2dvGra0tu92+ubn5uAX7jzs7O+vr606nc3/GnvNcLre6uurz+fbeeU8+jOZyuUAgEI1Gy2Wy2SwGg9nr9LZviP15T/9yes/29nZVVVVbWxuNSnvuuee6OzumpzAHDhx48803+3p7MeNjP//5z0vYNZNGo0Lk5Fvyu/xqolAog4ODWCwWh8M1NjZ2d3e3tLS89957J0+ePP750Reef+7TQ4euXL589OjRmzdv3KmrO3fu3OlTpzoePsTj8QQCYe5xQAEeIpFILc1T5+bmCHNzExMTvb29773//o3K6y6TbnM1jmRT+cxawGVPRALI5jrYSmWi/rnxIdxAV19bU+W50+TxfrNsIR0PpZYi+cw6yKbAzsZuZlUvW2SS8Hadam4a0950d55JcegVuyEHcCqAS7ntUKzrBLtaHhDMANKgq7+h5/pXHbcqptobJfMspWQh4LQmg+7N5XA6FkgvR1MRX0DOdyoWPFbTejyyuZ7Y3Uzms+liNp1ZifkteotG7rFbzQYts4RUoeSiUqkzs7MqtepJ/WZtbe3atWsjQ0OXzpz67IMD06NDP/jBD1579dX333+PRSHeunblxNEj81wO1GVK+M2jafRjZjBpVCGNNE+Zo1EhwjE0MDCFxU5NTTc0NAz09Q50dxz64L2aikvnzpz67YsvnD5+tK6m+vixYzVVN1ob7zbVVV+9fKm7qwtfCnNzcyQikUicw+FweDwe5QuZTJ4lQKYxSYShvp6DH74/3NEWc1myfivi0xXD9p2AbcdvQlaCIBlN29Vz3a2E0f6Bhy3XzhynjffZ5ML1gDPl1BU8GsSnBx7VjkujIk2yxrrsQhp+sKP95iXeWLdl/P7GYG32/pXN1iuxpsvGuguhtspo1ankiXeslz65f+jNni//gKu/phhpN+N6klpeZqYjPd6eYmNSWmHSrs6ISaG5UT+duCrj5+0asOwFm6vIVnIrEUk5NGmHZjngcipEAvIcirKQqTQmg8Ek4Q0a5ZP48scFLxcvXuzr6zty5MixY8cYDMaxY8eef/758+fP43C4n/70px0dHRwO5zEfvuNIL7GMQafSKOTR/p4ZLESJmpuahrs62u7UfvbRe7WXzp4++ulLv37u1PGj1TcrP//8WOX167erb1VcPHf69KmOjo6ZmZlZPB4Ol9KoweFwhMd8oVAoc3NzcDhhJ++3t7355pu3Kiu5LIaIP0+lUllMhlQssuh1K9Egkk2HPA7C1CRmdKiv6+Glr85QZzBWtWw9ElgL+XYS0YhZs+QwJrw20nB3T1vzFBZTdfPGiRMnWlqah4eHKBAyZLJZLAqJiJueohDnFsh453jPTE1l5ZnjVee/aqy83t90h4DDamVis04llyzyOUyZSCCXLErxUxLSrFyyaDXq3U570OcO+r0ep81pNnBLdu3voBqNhsfjUbH9JNZ8O313dxeLxVZXV6MGsrJgKxaLhccBALC4uMhkMgEAp06elMoVGo2ay+M5HA7s5ER1zW0UduVyuGtr6wAAj8dtNBoXFhZoNCqTxYrFYiajCUGQjUwatQgwmQw+X7Cxubmzs70oElGpVIlEsr2zA7e/y22zzBL1srNeN/Uup+5dTl2Tfka74tateXaRfMPdO2Pj423tbfN8/qJ40e/3S8QSm82+IIQOQDKZtKuri05nKBUKiURKJpP1BuPW5uaVK5ehJGazRCKRGm4no3CVXI+Xl5f3mY1IJFLZvwcAYLPZ6urqent7Ud9qBEGQkrJVKBSKhUf0ye3uDg0N7ebzXA774qXLsVhcoZAzGIxcLtfW2tw/OAS9vBOJ8rIms9lsNpnm52Fn4/MFsVgMhei8Xi9fILBaLbQSNXZ2cgCA9bU1s9lcVu/+De1cyWRyaGiobPna3d3FYDBP6Tb5QgEAIJFImEwmUiwe+/yo3miKRSLT09PxpeXJ8dHGkssOAEAhl/l8/p3tbbVavZvPp5JJ/Owsh8vN5/M2qzUag+vXzGZz2ZZUKOQ1Gs1eb/e9fs3f7uHlFNSvuXz6nZH/MTsXkUj878VZv/Ppf0riX07vyeVypc0DuE6r6cXnf+Uww2lzffXNt994/ejhTyMB//O//Gcei55NroS9TpVMwufxUHUE/pcmeSQSsbOj48KF84cOHTpy5EhFxdWmpqbTp04eOXz41KlTJ459fvXSxUvnvqq6dvX2zRu3b1VhR4eCLuscDlt76+bk8JBkQcCgUVH/obJRjF4KNNTCVIKR5ufnGxsbX/7Nb/QKCShsg2waZNPIZgrJpsF2Bp5uZ/IbSadB3dneQiPMinlMCYucjnh2UwmQXgHJONhYA1spWDK3CfJbIJ/dzaythLw+u9lj1jmUi1sBG0gvFVdDSNSWdukyAXvMqg5KWEYqltZ/n93Twhq4Pz/Ry8MMeHUym05V3FwD27AaYC0MklF42+2NRMDtsxp0SmnQbQu6rEmvFVny5FIrNpMBpRilND9e4PN9Dls8GikUH5m393WLTCaDm5kR8jjH3n1zor7CzCU++8yPyMTZd9/63Z1blQts+sO7NZmlUGo5mgj7fBaDHJIRakBwbg0RIDqLRp4aGay4fOnop4fOHz9y5+a19uZ7Z8+e+erUF/U3r188e7rq+rUbV6/U3LjefKfu7u2a6ckxj91EwmFb7tZTZjBy8QKNQiaU/Lq+zReUNQwGY35+vub27Tdff9Vj1YNcBiz7wJIHrATgL7UMMisgncivxy2Kxa57d5izWBGbJqNMZUKO3bUYWPYiPh3i1SIeNeJUgrAFrAZANplbCa849F6N2CvhODhzWb0AOCRFpxR4FBumhS2TaFlI8BAGDL111Iuf4098Sqs6S793k995xzfTbelvKCjpwKUAIXMxZEWSUbC7WdzZWA55Aw6LQSWLuq1epTCpWwAB486SP2IzaATchXkelUJeIM+aFJJkyLMSDuxjR/k0k8lgsVgGg/Hyyy/faWi8cbPqRz/6UW39naN/OEoikZ977rlLly9DS0RpaKAmNgqNToWG1xJX6HTyzPRoy93aU384+u7b1z8/1Hizov1O7ZefHbz4+adXjh0+duB3t44fOvPZwc+PHK64fOnG9Wv4qUmnxTA1PnK3uoowjVGJF+gUEnS9YrFKowQaldDI3vHCm5+/devWi8/9Ujw+YNVpbCaD02JaiQTy6VWwswGHzMb67nrcKBd1NNbOE6cEtDkNh5IOOPOZNbCxApVRLadgEOwu+UDMiSx5kXRiORK0m3U6hUQjE4t57GW7fjPq3Qi7kza1f240TsOGuAR3X6Os7iL+0umR37/Rf/oovbdV0N3kk3HNC8yMQ70bdeWTy9tey67PXEwvI9vp5YA74rYa1fLlkDfitiokojkymViyRpcB0pKiCM2Utu9bqVtmExpxu92VlZUMBqO2thbK9VLYVwYAsLCwMDIyAgAw6HUqFVzxhAaBgO/xPlp9vbQEVy8/ztlzLJnSUOEdCoV2clCiowFN3BtPbmda5dM9Flqrce6f5r76GfH8fROxx0qf8PANduOnhw4d/uyzc+fPXa+sPHLkyNWrEPO+dPlS073GqqqqaxUVFy9drK6pvlVdff78+S/PnW9obJyanj556tTi4mJzY0NVVdX1a9fePXCgu6f3jzpcWY0ot7q3txddz4VWqb29HYPB1NbWLi4uloGNR1V/fCgUCvfu3YvFYvn8Lp1O396G2hsqiRkMxsrK6iO/5mDwSXdAy6fT6b1r1MtVQq9CCfUv9GsGANo0i8Xi/4Bfs9vtbmtrQ6sKAHC5XDdv3qTT6bdv30arupeV5WLlbqNWqXQ6fTmdx+UGgyH09Ind5nFpBEH8fj/q5VkmS/mh6HPz+fy/oV9zd3f399r9H7fmX3r8y+k95Zp67OZXXnox4Hb0dT748uSJw4c+Pnzo4FI0/NILzy/weQAUQD6LbG9srScCbodcKuGwOXweVzbPIuOwpGls+93aO7duNNTXtTbfq6+r7evpPnny5Injx69eutj1oL2/435jzc3O1nsPmxsXuczlsA/Jbea2UuuxYDrqX434yLO4jo4OWknRoX0zMJlMDAZTV1d76OBHzXfrs+tL8K2NajBbKbBV0jw216FysxKEgjOzBLWQZAyk4yDhBwkviDpAevnRVaULkZ0Nt8XQfb+Fgsc5TLqIz5Vcjvot+rDbZlGKrSrJPIM8PTpAnp5IhpwrXmvU50x4rIWYe8ej17PmMP0dgw9bfTpZRCdedpo8BpVFKly1q7a8BruYgxvpb22oF7HpehEvYjeCbCrscSwIBDwuRyrgO9Vyn9O+mUwUtzOriSV06lDmwr5Ifnf34PvvkXpaLFziz//pxyGf58r5ry6cP6eUSYZ7u5D8Ntjdgr+djd30yno0ELXrVQvzYj5XyaNLSRgREdPd0tBZd+NBc0Pfg9aBB60c4mxjzc2aiksP7jUMdj0c7u5orL3V03bvYUujmM9dCvuKua2d1ErKY86E3asBiAd0dXagkvWbbIGeW5OTk3V1tZ98fLC9qWEz6oVazmYSpBPIShhZDSJLHiRoQvx6xK8HERuI2eBpMgyWnMiSCyy5QcAAgmYQtiEBIwhZQMwFVkMek7q7vZk8i3NaDNGgNxly++S8kF5iFHEc8nk2YWpqbJgyi0l7DKsObcSuX9Uv5B2KrFmsnh3C3rs1UF/pFTPDYnpCzfeqRAY+I22Rpewqh4g12dnSUXlO8vC2ZmYgwsMDj2Yr7FBJFqWUWRd+NKaT5myK4nost5laS8CFu08JyWTyF7/4RWdD/YUvzzz3s59JOcyenh4Wg/7Sr3997uxZHp9PptFpNBqLQl5gUARUIpfJQK2iJPwMY6yXNTXSVn299uKZ7obaB7dvtJ8/2Xqn9tpXpy8eP3L7xKeNlRUNF89+dfSzlvrq5rpqxTxzJRJACjtbLtPyQMuGjLFsls1MjPR0dbJKblj7+EKn0+dmcU136j9+70B77c3NeAD2/O0M2MmAzXX4S8aRiAMJmkHACGIOkPCAkBkOk4QHxF1g2Q0iNqj3xFxgLQo2VuCEIbPi0Uh7m+row51exfySQ7fu0AQlrKhBYpXMW8VcHnF6pvc+eaI/oxUuLzKiWtGKhLbrVG6pOcqR9onaK6O3zgdo41ExbVnBdUvnTZy5tJyRNkks5Ans3ev3z/5BOvxAN9BsZuAX+VwRmy7m0Dk0KnTUo9FJBCKVRqcT5xzft0/MPpatr6/fuXPn7NmzGAwGzdrN5Uo76OQTiWW7wxGNRmOxmFgsLiMW++5QPo1EIt+t95RLABAIBJ6+EUshX4j4g+ii5SEXb9wjKF8di8c8Ho9eryORyDweTyqVarRa8eKixWp12O33mu4pVTAYjUbUnVYml3s8no2NjT8K6dK2QBaH0+FyOYUCocfjRZeF78N7qFRq2c4FAGAymV9++eW1a9cCgUdafiQSCYfDcAscszmRSDjsjuzW1tjo6NPdhFHzU7kh3xlJJpPf61G7tLS0z6P227cqr2P/dpbCk7VGcwAgX69j/3ahUsrT/ZrRfW7QboPFYtF75MrdZnnZ4XBGI5FYLC6TyVgs1hMe8ig5EomgWw88pZjf73/6hsh/Lr/mjY0NdB37UyoTj8f3cYFKpf4V4j0oCYqFvF62+Ntf/0qvkt28duW3L73w4bu/P3jgbSGL9uvnfiXksuI+Z9ioiTuM6aXwdmplLRY0qhVyIc8kFWrEQqdRMz7Y19/RRhztXSDj2LiJ8c621ntNbfcau9qbuVTi3NTE9Ytf4SfHxvu779ZUjQ/2TY+Pkmen06txkN8AO+nUcvjy+a+OHz9OIpHQ6R2qA6HY0vkLFz56/72bFZfTawko5rcz+cxaJh4CmVWo7iz7kZAFytSAEayGUktBt0EpZZJYBKyATpon42NOcy6zlkuvItuZ4haEiJKJGHkGixvp14gFMY8jn1pRi+ZdRg2DODvS83AOM8Ymzup5lMJ6vCQwNsDmGgR1tjOba0vrsVA2Ed50aXe9+pRFHlPzd316p4ihImMiCl5cLzFwiGGNSM8iuCUcp4QdNatDKqFTzHWbdDH1YphLiDhNqwHPksvitJqfrvcghd0Tnx58cPWMi0/8ybM/PPLe2//ww/88/KB5YqDn2sVzIZshYtPHHMa4155eCu+sRFJBR0AjNvMolkWugs9x6xVDHa0Tnc0c7ICSOi2lzzJwE5ODfWO9XcNd98VcxuzEyK1rVygTA6M9Ha2Nd7Gjw3M4DGlmamttCeQ3wW5mKeS9ePb02TOn5+aIKF/opUCj0YhE0oXz5z89+FFtVWVqbRnCXZvrxbVI0qooBowQyHGroNITNCMeNYi7131mh5Qro+HZeIyAPDNPIcQchtxaNLcWA8lYIeYuroaSsQAdN4Ef7dMsCmJuW3YlouWSvTo5ZbRnsKWePDVBnhwxSAXFkiSGrF8Pg7AdJGObieh6wLnl1mUMorxHm7TKl5TzuyGrmU/RkyciMlZMJTBwSGGjUkuddi9Q3UJqxCCLqead7NmokLyjmV/WS0NWXcJlirmtDsv3+Jtvpdbe+MVPJq6ftnFnn3/2h2/97B9e+eefiDF9z//j3zdcuyKYm+KM9tJa7lLOnY7216+SB7ykSQ0Dv0idkTGJMh7DZdQMdLSNPbjHmR7WsebUuIH+1oaOu7cHblcMNdyaGhmYbL5d/dUJztz0YEfbvZrK0b5u/BSGOTW+qeADjwYse6Je+5dfHLt04TyJDD280cGCRohE4qVzZ/9w8L2mmhvbyWU4XrLpQmYt7bMW/QbIDp8e8WggxuZRg4BhzaF2SDhS2ixnZoI7M75ImYmZ5LtRd241BjZWi+lEMb2SXApTMSNzHQ3aB7di9LGMnq+ZGfTpFXODDwcbqknDXfThhwbWTMGjAXE3WPGDiBW4FSDmyoRc6xZ51ixKK5h5/XxSzVtaIO3a5QbyhGGmL7JACi7SjJTJMBun6L5nG77vGL7vmer30zC2vgYbpls90c/uacfV3lzsajS23jQs8J/yyv521sbGxoMHD6qqqvB4uJwKIjo63enTZ8RSmc1ivlEKra2tYrFkYnw89eSQyWRcLlcoFEpDV5/vDul02mq1JhKJ784upa6trZns9rVUKr29vbOysrO6ms5upzY3UxsbG5sbGxuZbDa7ubnp9XjQrQqKxUI+v7u9vZ1KpXZ2drLZLNzPIbebTCaDwWDJuSeZSqWy2Wwut1P65XbzcAuIra2tcDgSDAYzmUwKOiilk8nkyMiITqcDj12dRCK4Y3V9fT26T10ul2tqamxsurezvdPf13ujsvLK5csyuWJ4aMjj8aD3+XbT0ul0IpEwm83fziqnpNPpcDjsdDqfQr1MJuPxePx+/1PKpNNph8MRi8X2lsmkU7vZdDK98eu68OGe+NZG2mw2xZeXN3d28ltb6Wy2XI1yZHV11WKxJJOQdKlUKpPJmEym3l4IkqGh3G0IBAKaYjToT58+LZZIrRZz5fXr169fu3//gVyhGBsb21uZ1DdDJpNxOp3BYPApZVKplNVqXVlZ+eal3zhbKxkE19bWvpG65wSF02w22560/dF0Oh2NRl0u11Mqk06n3W633+8vd5u1tbWRkZHv3RER/JnCXxrvKe7m/DrJ8U8P3m9tTidiU2PDvfdbpMSJyXvVx95/O2MSbc3PrvU3bsvoK/rFgE6mFbLEdELIpl/1mANG1Wo06NApMd3tPNwofrins6VxbnxgbnKkpf5Wz4NWrULCo1OImFHCxBCPSZ3DYbvaWywa+chg3/BAn1Qk2FyJAbBLnp166YXn8aU17XQ6g0KhQAeS2Zl5NkM0z1EuCv1Oa24zmUut7CRXilsp6LKTCCLLfiRiR5b90JK1k14KetqbGiZGBl1WYzjgi0f8sYBbJuTpZIthh9lnN2vEAgWfI+NzVGKhTrqgErCsKumy3yXl0E0KMQM/xZqZdOlVuxvr24kwnBxvrJawpQzIZsD2BrRqbayA1RAEMAImiFL4dMCtBG7VrlNV9GiKHnXRqwExJ9hcBcloLuraCjt2lvxxt8WmUybMCuPcsJeF3bFIo1KG26QrFL9jI7U9vQgZa61/59WX9PIFi1JMwIy21Vz3iWiVJw+3193Y9RuzNtm2RbLtMayY5UGVQEXBLBAxEZNyxaELmFSrAZdVypvoebhAnpnue9DTfBc/0jc3OXL3ZkXvw/t6pVTApOKxE4RpDIdKouFx/Q/b7QZ1b8f9qbEhlVS0vRQAO6mZybHXXn6RNDuDOkeTydCDZG52lsekLwp4KvGCz2Xb2VjPpRK51CqSWd1djSBxLxK2QRG77C+ZumJxr+3O7VtTE6NeizESDMSjoajPKeEyDPLFoM3otZuVAraCgpVRcSoOWScWqIVsq0q85DBKKbNmpYQ23seZGXfqVbubqe2VMEj4kbXII/QiUzJilmAM4DcAnw7x6aCtLWIHXi1wynccihJfNBB2WglAFXZzLbcc2Iq4d5b9QYPcoVMt2bQm9qxnfm7HtBiSsd0mKB6eFrY37507fvjdt8J6idug6ul8QB3uYgy0v/vKiwEpa8ckzBoXNlTsdTknTsW46FPivhbxzHDCqV9z6ANmzarXZpXOj3c0C2dHsZ3Nva13pnvbZ3rvt9+4/OBOtV7IJA10dNfewHc1Ewc7H96t6X/Q6lRLBlobcJhxFZu44zODwvZof8+7b7xKxE2VlnwxyHBJ19wcHs9nUEU8pmZREHQ7cpvJ7fXlXGYNyabzK6GSuqOBhkWfDoStYNkV1i423LiMHR0M2ozhgCce8kW9TiGTYlEsRmyGoM2g4FLFhDEFl6YSC3SL8xouxb7IjmjE4plhq0pCHu1hTY141Iu5lXA2ZAdLXpBKgMwKshaFrl3pBHSqWwlCRvh0kP4BA/AbELca2KU5u6zg1Ra92kdwYCoOkvEdn2nTa9lZCngX2B4ZPy5jW0mjPiY2KyTE6eNO3ddGqKdx53Ge2+2+ffu2Wq2+e/cu6t2s0ajOnj0DnZNo1JtVVQ137zbduyeRSKurq1ER5f+uEAwGNRqN2WwOBALflQ/TgsGgQgF9a55YJhDwut0qFsur1Qao1M0PPtj46CM/gxHQ68NGo1qhOHXqFAaLnefxqCU3eQxmsq6ubhqHEwqFKpUKh8MRSSQWi6XT64lEIgSENOrqmprRsTHRwoJMJhOLxYsiEY/HhevVdToymSyRSEKhEFq3P+5QfOvWLXQ9F0qe9vZ2Lpd7//591M6V29lpbLhbV1dvtzluVFY+ePDg0qWLUpm8ubmZwWCg9/l22wOBgMvlkslkT2y13x8IBCwWi1qtfkqZYDCo1+sNBkMwGPz2U9CUQCCgVqvtdnswAO8UDgaWokGPLzDLd3zQbPl3J3z/22kfRuTXmvQOjyfS3Jz+4IPgwIA/HN53Q4/Ho1AofD4fmh4MBmdmZlpbWx/3GmjnqqurU6lUd+/eRcE5vU5z9swZMpnCoNMqb9y8U1/f1tYmVyiqqqpcLteT6ox2G5PJ9JSGBwIBeQm6e0oZn88nl8u9Xu++hpRPA4GAw+FQKBTllG9HAoGAzWbTaDRPeVAgENDpdEajEW1RMBi0WCzo0CgT/MnlGAAAIABJREFU51818hfVexAE2d7OOjUS6sTA6cOHRCyqRS1fYNMF5Bls30M5GQtCJuAzQpOERwNUdGCXQWA8ZAKmBSClQDx8JQS42J3um5mZh+FFWsqhLoZt2179mt+2HvaF3TY+jdhy4/JQe1PAbdPKxJj+roBWbKDPnvnow5aGO5urcYDkHCbdiSOH8aV9WWZwuN6uTjppLuyx726sASS3Eo+0NNSzCDgJj6WVigobq9CeshqGoP2yH6xFCtmUw2LAY8YdRk1xZ9Nh1EjmWanlEAC59ViIR8JTsaOLDKJXK4kZZdlEGGynQCYBttYh/r+dAbtbSy5z0KjYXY9nl4Lwfb2dQVYj0HCTWQWpOFiLIjEP4tUjdllRQUXMCzBiEhZEswUZuaikA48a8eoQrwbCTusxkAggERuIe6A7bSoOb5jLgK01sBYBS+5tm9TAwNkM2qeoPcViMbedVXFINy992dV4W8ogmlQysYBLnRrtbKgO6yUgGX70i1ihpF8LQ30raIBixiGDmlkyBjyaHcFMmjUepI+njWIkbM1aZQnDYirqDbqs83Rye13V4IOWeMgrF3Kx3e0hs9qqVVy78FXPg/ZsIgp2syat8uznh5lEPIfDxeFwfT3dLCop5LHnN9cBkluOh5tqbzGnRxV8tlElL24kId02k2ArCbXDVLy4nTbrVHOYUYdJW8ht2QwahZCbigdALpOwaTjTY/TxXjFpyiNmRdXzOyXwpkTtEEgvIREP4rXEbNqIXppP+LdCDqi4xN1QqVryIjEXshpCVoKo1QbCGF4tKtQRlwpxq4sOWVHFgJzy6WCKR4O41RCCClmgH1LM/YhoyWjJvuMBMce2VWqgT9l1T/Q3L/kQINmtDRmbfPn0sfGHzVI+16yWS9mUm+dOkQcfgoAOQAXLA6JWGA8ZIAQStgCPGjgUUPBHLBAOidpyGnaKORpiYZPGxULQmLVIVgyiNZM4KOcwhjtbLpwYunbKNN1P7Hs40dkSVc4bGLgLp46Ptt3Z0syDbFIlE188foQ6O8Xh8rBYzEB3J5tGjnodha0UAPnliP9ebTVnZlzGpZuUYpBaAjEXpEPAiHi1wK8vLHuNctHscK/PZtzdTJkVIqWAtb0WByAf89i5eAx1rFfJpXrVophOtBP3wa67kQC5km/QzgbIbYadlqjDkE+tZNeXwc4m2NkqDckQEnPCzr8WRfxGxCYtGgWIVVKQzBWlpKKKURBgC9zxAh8DTAKICJpFkClQ+zEgQRPswHE3WI/Ajr3ihYQKGkHYvGmVGZkzduP36aPffCsXCgU6nV5RUUEikdCcnZ1tvV4fj8cNBr1Go/F43IFgUCKRcLncb166/ywajX7vBweCweDT7VzFZDLZ1FR48KBw6dLWj3+c/fGPC1evFh8+BENDUbu9orLyypUr9fX1IyPDR+HKoLMXL164dOniyZMnr1de7+joqLp5s6am5vr1ytra2v7+gQcP7g8MDavh8qu6+vr669euHz58pKW55crly9cqrh04cGCqtK9guSUMBmOvnSscDre2ttbW1qJLw+BWyz6f2+0OBAJisdjvDxgMxo2NjYmJ8ac3amdn53u/QZFKpfZ9/aBcq3LkT7Rzofv3IAC4Vou94u1z+M06Vpao3WSp1ii8oE1gWCERtoeGdl96KfvMM7tHjoDSar7yU9CFWujWxuXEvfv3oN5LNBrt2rVrZDIZLVPuNkaDQaPRut3uYDAkk8m+9/Mm0Wj0e+1cgUDge+1cPp8PVdzLdd4X2dzcLNsr92WVTzc2Nv4ULqD7NZevKvtcl1P+9SJ/Ob1nZ2eHSCRx2ayUW1/waGJKrl/OcUs5cxPDLpO+WMgBZHtrLe4xa91O25pLv6YX5UJ26Arg1RTl1HzvzaKMiujY+fG7Rdx9QO8HCiqiZSM2CdSNtlaWHXrW5IBHzgvK2CLCOI86R5nFiZgUzuwEc3yg6dIFj9UACtkSrLKuFAv7e3vGxsbv1NfbLQbov7xTglg214uppbDTFLZqow7jpscIQuaEbiHr1ee9urxTuW6SDnY9EHCYS2E/yGcdRi2PgndppMlYIBn1gfXoulURNityodJrNAh1OCgm3WqQCEB1YckDVlGlwQj9HsIlNWIlhMTcIO5BwhbErSpKiAVyT1HNLBoFxcXZwsJMUcUsKulFHRexSRCHvCR0NYjfANajEBmCOEQMSfgLUceaUx80KtV8llmxuLGeSPpsYQljATckFX1t4N/XmVKpFGFuTsjnZTy6QtAYlrFDco5TyiMO9/mMWiSfA/lsZiXqkgrdJk3SqV21q3ajTrDkgMupDPyCaBZxyAoORXK6ozj3EMiIQMNC+JOISQilctgc1QrZmEG/ThJUCxdI03IugzAxImDRWGQClzLX03I3aDdCU1dmFWRT8oX5saEBzOTkvcZGu8UECjtQh8umQMloGHTbwg5DwmOFjuER27pDu7US2YnYigHDknq+v61JyGasLIXz6WW3Vi6kkxwy/rpTs26SgLBlRSuISOh5pxIEIBiA+LSIB/1pgE+XcyijPHLRowUBPYQo/BDLgQVKIAG01NilkAVCHJSdPl3RLkPMC0WT8GtgA2qiWqiPulQQmQsaoVOLVwu82rxHs6oTBJV8FYtoEtA3Q86kTRmSMITYfqlgfh87yqepVGoahxPwedvLgfySJ+wwR+x6C58+03I3aNWDwibYWE67DAYSzquXpizSdS0/71ICtwJWdZFQFE4XbRLEsohoOdtqdlbJAjoOsoCDfCkJ+LCCyxrrCSjmA2L6/FiXnDRJGesTzE0xhx+yhzvaKr5aD9oh5aFmuSbl0DC9HbiRgeamJr/LBnYfj5eNteJaJGRWhYyKZbtuyw0VnaR+YdMqy7k1ea92WcPvrrsupBFWl0L57YxZtsCem47YDWtRf3IpBLaSK35nxGUurkVB1A51Neh+DikJh8lmCmKf2UzJPzoNUbd0AqavhEDQgvh1iFNe1HELQlyB3l9gjxalxKKWXZjHFqj9xYWZooJaFM0WddyiR424VEXLIvBqYJ/0a2HEpy24lCv6hYCcqyZPWufJmwFr0iQJLlKF04MKCfTA/dPD9vb27Ozs8ePHZ2ZmUPsOUnJD3ncHkUiEw8Gth5EnBABA2VHjCUUgauvz+ba3t590H1Tierze/M5OcWWlcKeu2NhQXF1F8nmQzyfX1yYnJ2il3T7Hx8ZGRkYYTHQJLXlqalooXHA4HFw2W6fXzeHxnx/7fGZmdmV1BYPFMJgMVmnnLbhjCAZrMpl5PO7s7Gx3V5dCCdV3tMIAgPHx8b3ruex2e319/fnz58u7JO8jC1rhhw8fog4f39lwdJ28x+MpP+jbxVCPmVAIuvp+O7dcva/9mksuyl+XRD2WS5VLxEO+xCbJtHt5NHKu1TzeLw0R2IXZKdDfCbrug677xdHh6MBAVijcbWrKvvba7vg48q2Hfnu/5nA43NX1aIdltEWzs7PHjh1DzaPlGu6jj0gkmp6efnqjwuHw3s8+fN2oxzEAgNfrRTXLx2nfOD7qNv92+zWPjY39Ffo17+zs1NfXP7jftmyUFNzqnFMBAnqfiMaZGrLKBBaVxG3W9rffmxrqo8zNOgxqq06pFgtow13rCiZYxBUGqop9NxFSR54+VNRy84yJonCmOI+BPzWrYOA7uYSYVlgIGKGvcWY57rHJhPO72xmf3TiLnRSzaXIWOb+xBlJxJGwDmaWIy0zBjQtpBCSzDNYjSGoJbCThyqBkDN4haoempZLcSpvEtgWGQcDYtMujCs69qmsWxeKy27QVdnJnx9lTwyOt9Yv4MauQXvAbwGoQrIXgNLTkggBBAjj91UPkIOIoiUYI/sNI2IYkAiXwQAPFp4FflJILpO4Crrkwex8iPVDE6hGbFPp+rkM8H4m6kLAVFcYgvZRLLscdhojdELDqHEqRmU/1iugWAY3Qd39xbiJsUgYkrC2bbN206DE/0c61urpadevW0ED/ilFc9KhzTiXi13sWqNyhBxY+06pTusy6rvYWbF8nBT/jMarNSrFWwGSO96YtUuBRFsVzRdFsQU7dFuDzUkqMgcuZFhGjoCAjIWZR0SSwMKbiWmExaATr4WIqvhL0yJjknbgnYNESsON8GlHJJhUyq2AtjESdIL0UsmjmJofkrDnoIb4WgVbFdAJaALfSqMcVRBQgazQZs9QiYmkYs3mP1iuktFRdscsXVlyGDY+ejRmgj/cON9WIcYM21kzRpYQIolEAoTL0V7JSIS4lRAK82qJHkzWJiyWdBoFaEarEaBGvpmheQJSMAmesyBwskLuLRiEEcjwaqIDaxI8YisI/qFknaMy6tXHdYlgn9ktYznmSgYELSVgWzhyus2kBPxrVLgTEjE2bbFUn9FoM+95x5dPV1dWKioqBvl44TwiYdiMOkIo7tHIeZtS8wLHqVXadsqOuauh2NYOI9xuVVilfN09jDLZtqNlAPFuYaS1wxgq8iaKGlfdqCyjOYREVzSLEqwFerZ1DiMjZiEsJ9FzEvBBVzmuZhN2gycojUoc757GDah6tAF2p1iB+k4wGbUbK1JiUSwPZdbAeQ+IeiIeFLFBHCVtA2Ay1yZKZKWOWWLgELQ2761a7FigN1y7Y5MJUzJ9JRLnTw6TBh+MdzWLqrH2RCXFKuCDAByJ2qO74DZC23pKBLGB6jMfEH60tyKZBZhUJ2SDxvbqieaHImyjM3i9gmwrTzQX2CGISwmutYkQ/D8v4oQoLsVKrGGqiIdOWTR5TC8JacUAjsi+yDXx6yKozi3m4rmYJnRC16UMyNhwvxkWX5Yn7XZUZtDfyRx+OCxcuvPzyy+gy9e9UetAPLX2vAPsz6T27Pq+nUID7CS1l8suZR1+QQOuMTui3t7fD4fCTqopKZYlE4i195hPdqmdvk8vx1VIoS2UAwNjY2F69h0KhvPHGG6dOnSqvY0evRR+N/ufz+YcPH6L+0d+Qxo9P/lX0npWVYioF9RW0QsU82FjbiK/IbelqjO9E5WLL0V71xcbsgwcAO4ZQKQWlsuj1FhMJJJcrIogrFNra3ga5XHF5GSkU/hS9JxQKdXZ2lkmXSqXOnz//0ksvoSjgk3ghEommpqbKFH5Mkq+PpY2e/3+v94yOjv4V6j2FQoEwN8dls+DKDr8e+HRbVlkhYEgYFuhDDyY7WyiYIdLEcD69AnY30VUhqUSk6tqVY2+94uqrAZONW/VnNlovFkfq880XcreO5R5U7LZd2r19PFv5h3x/1SahM8CaCguIcQVn3SyxCOmLTHJ2NYZsrCb8rnQiql0UCFm0rfUlCLqELPBlHbOCCHxxl97ddqiFrEXgf8yNhO2oOARBg4ww9sVnBxkTfQUvfHtGVfyGK2ebb16SU7ALUwNGGlZBwW451dAMl4yDzBqIOuE716lAAhZk2YesR+EkdS0MX+vQzRO+1qFTyJIXJIKwsE8HAkbEqYS4jpQMtR/2KOJQgLANrEagi0nMWfJgSEObzsYqSMaLy36nfN4hoKZtsrRVlrLAl/WSat6vWkiHXGoGIarkbrs1JdRBX/Co18O+J/n3bG1tTU/jFheEUNUriYptu6IYNC4bFmhjXROdrdTxfip2tJBZgaa69TBYD6UinpsXv/zyg9/5se1AgCnQByEQwpss8LGZ+dld8VxRy4ETbvYIwhtNCfF+ASmyQI5LmUnjomWRI2YQt+0KJGhcMoiTfruMRZbx2dklP7QBRWywzjEbCJsgX3wGuLZ5NYzAleqrYGMNSSeQZU/RD00SwqmBs3/4hI0dKpbwlYR+4eaZz1uvnzezZ5kjHXryhIIwtmOVQI0H9jc9VHHcaijpoaGqZKsqKT1QWHog6gNZ4ysJS3TFOzSL6BCrBNpNFDRoZ2SPIAZBSSsq2XFKphyUoSWUyLDrUtn5JJeAAplilaXMknWTOK6eD8o4KyaxjDCypBHkIKpkAH59zq1ejzzxY9dbW1sYDFa8IICQp09X9Gq3nGok4Y079KSJocmeh8TBDubMRHErCQ2pyRhYD6+GvRUXvjr/7qvBuV4gwBaInXnCwwK1ryiagWCVhgX5ouUgeh5wK9NGkZc/F+ET4ixMkjdtpE8tkKcRv77o0yZsmlTUJ5wdlwu5ueQShBXDNhC1gYQbhM2P8JiAETr4+0ru5LDbQH0FuhuHzYyRzguff8Id6ypCZ3NDXC+qPHWk+dY1k4gtnBpQEEY0HCL0aVsJguUAUur/cCoSNIGgCQkaH9HzkUO0BlI75oJL9pb9yHoUzhY8EHKDWrVoBrJmHlPkYxGjAGJ4kImoFbisueqAT593q81cgneRnvZb00FnJuhci4WWfHa/fH7dJBLNDK8GXbupFWj88ukKHm0i5H+6N1xZdO0V4SaTSSQSoSmJ5aXOrs5INBrw+yilT7jQaHSFXP5nsXN973qu3Xze5vSmt3ZzeeTMeOICJrFbQDZ3ijsFsLK62tbWKpXKlpaWzCbT6uqq3W5ns9kOh3N9fX07u20xm60229Lycj6f9/v98Xg8GAi0t7fz+QKnw5lIJLLZbCKRQP1RvnM91147FyrLV1dXGQxGLBZDiUOD27tTAQBkMlmr1XZ1dUYiUQwGg4JY+2hbPv0T13N970oiaOdKpRC1Ovv22zsnTiALQsBlFCfHPLX3+y+Nf3VbWTW7Ok0yLSkNIOqH063HelG5JqgK4v0Xf6cCveHebrO8vNTV2RmJxgJ+P41GZzIYFCpNqVT+Wexcf671XH+Knet7ufDt9Vx/nXauR50GKW56dCtaQcYiXdcvaOg4HW1KSpqMmxVIyAbfqn4D9AyI2JElH0jHtzfTVZXXKg6+M/GTZ8Z+8LcP/+P/vfjMf/H94tnMhy9un303d+voTuOXO7c+32n8MnfjSL7h9O7w7fW+W86empm2WgePiITMiJIOFDTgkBVCpoXZUdLkcCbshVNYKFZLQq68s4tXC6UsKsxKPhwgYHAKyPTJgYd1N5hDD9wCUkLD19OncB2NAw1V4umBhHp+ScYoeDUgaEACJiTqgIoOlAclQwnqTwDBHivIrCFLPtQpBwlZILTgNxbtcgRdxLsef4RtbKxB34WABapEETtM3N6A6s5WyeKwGkZCVuhknV4RzI4KxjtFuKFNR8l249fvutRhg9wm4S5rhTmncsMs3vVoNyzSTask6rE9Se+BYxj6kuQ3HOoVrWDDKk1o+Fr6tJE5IyNNLOkWoEEtZIYtgtoAXJ4DwpaNsKu64lLFoXcC1GGgoMA5N641P1hdeHAl33Yh336hMHN/c6Am3nCxwMMgvMkcfWANd9/cU4drrnbxScCjQWQkYJwHHkXerxdgB5jTY+tOLQiZSqqJ6tHj0CcGjNC9JuZEoHUD6o4gYDCx8JSxvoe1lcKpIQ8XH1fx9DTsxL2aoaZqOWEkruQmxBTo6VLCZoCvtL59PQapvRoEEQfiUEIyxlxg2YeErBC3CNuQmKdgVSFhJ+IzIg4F4jfBRLiCz1y6jxraGd3QfQe6oKWWIPwQdT2Cf4JmELHnPRruRK8INySbG9uyy6FNLWgowQx8l5ASV3G3bPJtmzznUG5ZZRmzJOqx732Zfkc8v5u2q1Z0QsgX9byaPGmgYWVkzIpZBp8L/eFKOkdJnwZroVQ8eOPa1apTR6OTzQDTWHhwuTDZWCB3F9gjBUp3gdxVYA0hMkpRRkZkpKKeu2teXGeOmbHdxOZq5/wcCBoRpxyqZau+3YCBO9bNmRlPLoXBkhd2XbRXw3lCCZJBMSR0SV0A6qkgaLBwCITBzs7qq/MjnR4BeVnNM9Cwo3evD96+qsQPL6vm4yJyyZnGBP2OQxYk4oDLJINmEHNDHShU8lJHpwePnIRKKmYUroeHY9NvgMMWjaD4UBnAQ1E3OK94XD10SuPTFdwa1mgXNCySMPnlABxWW8mtqMu3QHUsslZ99q2IZ9uj33FpsjbZplUWdH8fX77JqlQqhZpgUImIfqfizOnTLDbHajHdrq3t6+utr78jkUiqq6s9Hk84HA5+V4DfkdBqLRZLKBT6rnyYFg6HlUqlx+N5UplIOGi0B073OS6MR967H/5fPvf8u2OeDzvCFyeiN2eX2ALZJ4cOXr1aceXK1daWluPHj3322eEPP/zwxBdfnPzii4sXLlZUVFy4cOH06dNXr1acP3+uo7Ozt6fn4Mcfj4+Pf3X2y7Nfnj1/Hu6gJlxYiMVioVDIYDCYTCa0ReFw2OFw3Lx5s/ydimKx6HA49u7yvLW1dbsGhmw2e/9+2zQOd+b0aZlMce/ePQaDEY1Gv7PhoVDI4/HI5fIntToYDIZCIavVqtFonlQmFILUMxiMBr8/2XAX+fv/Bn7+Y+fZC1M1E9eqhJ/cFNdhzDJzMBGPmI0al88bisWCT+BUMBhE3YSf9KxgMOjz+RQKRSAQQFsUiUTwePxev+ZkMun1wr0Ayt1GLpWePnOazmBYrZa6urqeru47dxtkcllVVdXTu41OpzObzU/qVyhxFAqF1+t9SoX9fr9cLkf909E67/sPhUIul0uhUOxL33saDoftdvtTuIBWZm+Fw+GwzWa7cePGX+F3KkApIIX8mkXhE9GW1fwNs2TLrihAnNwMJUTJqFSSrCgoUtpgN72cS62omIT3n33m2v/6v0/9H/9n9f/0PzP/y9/ma07km77Kj97dJfXt4rs3711JHvzN5mf/H3nvAR3HcaaL6u55+97mvX7r9fra63VYrTfYctBalmXJsrJkZYnKlCiRFAUxipkUMwEQIEiAAAEQOYMAkYGJAAbAAJMxOeecZzAzmJw73K3u4RAEg2Tv3j3n6c2ZA9T09HRXV1VXff3/3//9T+Vq9qHk1uTVC/yy3b7xVlS+ADNHIqMN6r5L0qlexzKts+oMnzwKniy1HIxDswC4GviMaRZjMeqYpQGfMa1S9/KcdpEw29tM6qib6W5Qz4zG5QzgTXMqISsWOQIwAbDwA0OCnodaxKhDDqghRiFiEqAGPjA22GSIU4v6rGDKdulBXHQyAkw4kRWgWZIIg3ccKEFjKkEx8DfkAZxlwK4A5n3sJ1GwNgCbhASs3FaJU7Aw3XMF8FeAxUgWlC4Jpq6uKli4OQ21SPJGYUzFjsgYNq3iLrgH9AyUW5GxLAzSinAhqmSldHzIgvWLFZCXb7QPXrbK0Fgg5TFzxvuObXmX2X4Rmu6EBqugtpP5uv35i3uyJz7M1+wNlO5QfvR6qv0sPHQBJjWj5Jbk6GXGxSMh2gAqW4Cl82EOSUO9JqEOO9jUvotnmSNdqFEAbBKscVhBB1daPLVTDZa660saapXa2FQjg0zpbJhsqJq9UqWmDseVLAgYGOR48A5m1MEMAFouqmEDp0kmjibC8Kov47Ej8VU0j4lSgsYPASsdaOoIkoyiYR/iMQFFmST2ld8CuFkBB/a2AwlEmxzYJ5IRJLwCrzjQaAB4MIHNAzudge/iTs90N4SkS3jXr4jpYuLAqpyB9wtiFucNwqSGG5Iu2bRfEMeO5rN+KdPKJAVE2P2i4wPjlhPgsDX9cr2hrFI0Hsiu+ubHrh77YAP7/H6orzx/rgS6Vg1QDgOjvCwN4WYSaPEavDQEL5NQxnCCQ+D3NQS4VNQmg3XcEH9aQxuXUoYsDFJvdSmPPAxgn5wOCymgX3TcG6i0wGoSg2s3A3aUnTujXyJPttSQWutm22uU1CFg1rUC/Ad8bXi8FY7SMB4VQJBhD7D9hL2AwQPkf+LAPrpiQ/wWzO/sA3cKCKPzgW4KuhCg3ulFV91QwAn29Fsw5KTAPJhYU9zAZ9jQxT/a5TY2ldp9JWDRgDiDdMxrNQjoM8mgFxgyQVCeKKvjpzTcsGzJrFXi89WX/Gs0Gt95551Tp05NTEzgP7HbbOXl5QsLdLfbPTc3T6VQOru6mExmX19f4j9Sot/hlUwmjUaj0+m8yz6JREKj0QSDwTvuk4ivhiMihd4TSvWzIkeHAkeGAkPciC+c9UVyFou1urq6ubnlypUrdbW1paVlbVg2H+xj3dDQEIvF6urspFKpDQ0Nmzd/1N3dw+NxaTSaRqNubm6mTk+3tbVVVp43Wyx4MLwVC6RKJpMgRj6RiEajnZ2deBw73hSlpaW7d+++cOECbgNAEPjq1f62tjaz2SKRSDgcTm1trUQqw7aY8ePc2jyJRAIPC7/1q+KWRCLhcrkMBsOtLROLxROJWCoZT6WSDrfLN02MHT0ifGPH+Q0XNzeYSmcSfCcSTmahbCKTBJH+Or3e5/PdepziuWKxmEqlWllZucs+4XBYrVZHo1H8V8lkUqPRtLS0FP1ZBoPh3XffPX36dJEO77CDYTM3P+/xuGm0WTKZ1N3Tw+Fwent773IifNjguVSLNby1oFarQ6HQrdvxLYlEIhKJKJXKcDh8l338fr9Go7nTDvgY8Hq9t+2F4q8SiYTJZLLb7cVhE4lEOjs7v4J5KgozCJTDCIzS4tp8Y20rLnKFAkbdXbGCOdFr0PDmSre+x3jnefUn7wRqjqPSGUS1hJgxi7dVhqg5uYZj0GQzLFsADibRNKpYRA1CyKa0zI+x+5tG6isH6yq44z3k1pq6/SUWBgnVcWDZAsydgrmTN5bYtWstPk1jcTF2NmWo/ryDOwMiQdw61KWF/VbYpUW1PMSmQH0W1KlOqDnOpakgl6ydGc4qllA1CzEK/PRRL4eS1/OBycdnATYhuwqEhoW9SMSPBJyQVQ3casWJHmAdTPMNGHhWQTR7Jg4wUMCJrNgg9gSsXEJVTFS5BOmXzUsk+kCrm09DrJKYku3hzcZUHJxmUWxVHP0E7aYvxD3A8QdYn8AlBIAUtpIVj7O+ANT/dKhTKaUOn9z8ln+iGSW1wLQeeKYHnuuHKR3wXB9M7ch3l+Z7ymD6IKJcREQzCI+Aaliohp3XcvWUQc5wx1hzdffFs7zxXmrjufOffexZGEV5UzBnHHBUAUuREWQoAAAgAElEQVQDC8Ap2Jmur+54v9hlqFtlWiSONlU7JCzEq8fRAARoIqD+eIVRiySlXXZwpgMSpn55MWtVIBZFymHyamVelQBoxsQCQG3ZIi24UVyYVcmpxhhaYmB+cGpg3TKyYgMwCJcYiAWAtzETT+vVab0aTYZBD1pliFmCmkR5NdvKINH6mvyiBdQqjSpYTg5gWWFkaswKiOF7FKNShVwFld47rq/5LPD9FfpFUryu9d0B2kQERlcIhIwhETd/jni65IOViWZ0pgd4IQ2YCQ1nzxgFsHQO0XIBiV7LzWl5gHdvl6MmIWQW62eGWQMtQw3nhxvOs4Y7Kd2NFQd2eueGUMYQLKQi4H4h3O7sBXAMovxcKsPC5ERrjVu0iPjNQJ/QJoXx8HLr9X6xSuMqros77RMt6pfp+QgAInAs4Dep/SoBuB2SYQA0w96CpkNiFbg4o9hzQmQF9prgVXfCY8nHwxjrOQZAklMNpCktEkA8cmkLMBR/IAHeK5GLMz3RXONTC9BVV8zvcho1Sa8V7IwbaPFxhRmH/HbL7+Xncrlc/f39Go2mSKrFwvFAEgYY00nHvUJMJvO/RLfQarXe3SUEXFQWM87vQREEvG95pVKpouPpli/Bhng8NjU19YXS1bfmqSCRSMVEHwiCjI2NsVgsvV6PW30QkKYCvCAIwusFQflsNtv9RbqF6XS6aB25bYVxXvOddAtXYtAwPwGiyabmO944u7tadXA8NqWGgsnbHOz/XD72rq4ufEigKOpwOAYHB7Va7d2HDZvN/jK6hevCo269Kjxn1q3bi1v+q3QL/7A8FSQS6aupW5hMJrPpJOAw3gIvbj+TWjGDtkmExQObjdTBDJeA6lmoVwcCoFZsIMYYMCsxnqOCASzhBhGiARM6sL74zUGT0q0U2Hhzww3na47v54x2BySLJhYFrEOYvC++5+3Pjs2DkFEYljNXZUztzLBfMJd26oM6cUi8qJgeW7rakpQsgDD7oD3r0BjoBOFU/0zn5dmOurCcgRr4pukBzlj3TH1FkEuLc2cCE11A98VjBKYCtx7xGgBnQskEqjw+A5DhwZItgInYJAacBqcKoCW7CsRpR1eQqVaodi/CGkvzpwPjna5lmk8wzxrtkk4PK2fHAuJFwK6wSjA7EwYRjAJEz0NMwrxBELQb74J7kslkJpVEHaov2y/4Kov5m2CbzDDZmRbOAlaplovquIBYqmUDyKLjwQsD0GwvQKKAQSwEBS0bNfL9UpZLQLcxyKONVTXH93PHuoMyhp5BTkkWEJzQbRKBhfnO4wQ2icJyRkjGkJIH43Zt0m0OSpYC9FH5cDN3tCsmW0KtIKgqq+cb6EQRaYjcWjPfVRfXCdFVp4E1vdhRPXNmb/TaFcfZfbpzh1A+EZHNo3Y5LJ8HEU8uVVTKjAw1ZKjdsJiGyulpMS2u5gGiVdAOjD1eEzA8rHqQsA/4hlQM1CrLSOghxoSTNxsQ01lD7TLqsHYBOEYhE4hyAiBDNg+rGCAcTLGEKBYzen7QUbByF6eetYVEMpVLJ1Gn+s7tAEb+TaMXI7igbl3GrjYvEiANu4CDrwNBYKoxCqGFQQBfAJtHksfGObDEWKVeId3FItuYpIG6itqT+/nEgaBOpFsiZlQsFHChsNFl4N90xus/xzdCRmFEzgxJl0Ska1G3OeWzBRWcEJskutbMnehNapcBhrPJ0gahZm5cMNE7090439uYMErQkFPJnO1uqOa3XwB2UwkNYY1BDjXkM2PWIA+AnkEH6lAj0vnI8nQc0HS0HoPaZdLBYR8asOWsCkiLRTuCu0aJyheQZRJqlWZk9KBg1smb9QsXWCOdUsqQmjkTtmqAwwuLtVznF8sbBD67+TZIYW3f3Fx2OBxtbW0UCuXq1atrs5MW/Rf47ktLSx0dINXUDUrqzaX/uniunNlqAumvcoikzycd9MP5GxfEYjEtVuv83Fxj4xWhUNjR0WEwGNPptMlkHBkZ8fr9FrNpaHg4kQQvHBvdXM0bn26bp6KpqUkgKAg0QBA0MTExOjo6MDCAJ68oWjvwAv4Xz1OBBz/fOPqa0n+G14zDvhZ69Bs7LfvaHdtKBXUzAWMIvT4lrjkNVkRR1G63F1Ha+q+xz39YngqDwXDp0qXi2LHZbO3t7VQqdWBg4D85bL4C8VxfzTwV6XR6fHyCQiImjRiBdO10aZGCeO/bLHIiEL6EUQdgn9k+0IjMD4CHM6caWE3iIfD8HQ+iARvwMlhlgPyIBRLDRgGs4aJeY9Rh4JBGFfOEgGQxoV3O4xRXB8a/wSuwxiReCOdZY+cA5GsVR8emBQzyVZNiVS/T0EkO7iyxvU400U3rvswa6YpoluNmqZY+JSRc9QnpVhbVzaWaWdSMQ+NlkozUQe611hX6aI5HTooB9RLW8VAbSJWQ0/FtPc1LB/ayP3rXc2xHeKAeZk4giiXgfZPMwkoGrOUgOi4smkYtkpxwNnJ+R3boUlrFMs9P2OfH0tpl/lAboalKRh2JSOmYhQZbAgEBBTeTiBCjADWLM9plj0l3/SYv3nSFQjgcBnnTaLNJI0bfWdsvX1RGLZKEmutjEoA5R7mUEc6uUq7CJiwwB3f66HkIFiAGEA9G3EbULNQmD8tZ9OFu3cJkULyYLPaLUwHsGQY+GAlr+wUjHa81dYAG0fNVc+N+GWtVwQnLWZr5STuLTGyvFY110noamUNtccl8TM3VzQxLiANe3oyVSXZyps2c2axR6KZdM463sy+fclV+ZvvgZc3ZoykxI8Wcik8PoFourOEkZ67ZL5zVnj5iPLid1d2mnxlTTfSxp0ZMdIJDzMgJp1HAsgJE7IyOHxxpyPCpKT1fP9npoPZn1CxWx0Vyfbl0uCXGJRY8OwAxCGDFIqxi4sgDNYuTGp7PrF/fH9c/h8PhgYHBuZnpvF11vUOv21SA1RC7X4Ct0QweAOyYlwcQ1AwAljnVcbfFzSSiAjJm1xECfH9jYEtgMQ0WUNet96hdtipdWhho1SwQQtKlgg/aLgekKzw2CgtYu460RLBRCMDljcMC5BSVM40McsCsDptVAQVHRydYWBRCS41sqme66zJjqDOhXY5quIrZMQV1yLNMszLJLu4MoHxZJTYWZXKwb7yzOSRcSHFJIRbJJmLHtAIAYlxakNRCTodmerPTPYnpXgNzRrE0w6FRuItzVjHXLBfFpfQ8rqJk4Oc4xPBATY5PSWp4htlRJ4uc1QsYA63UznoZdTim5V+PmS80aRHJoWZx2ih2Wkw3YML1HrnL///gTGzevPmDDz6orKzEd4OgvNvtzuehfC4fxcSXo9EYi8Uik8l3OQ6Oe74wT8UX2nsysfzyVYtyLMBv8U7tMBJ2mvitXtV4UE+JhLzhDz7YuHvPnpqaS/X1l7d98sm1wcGzZ88eO368oaHhwP79E5NTDof92LFjHq/vywix3GrvGR8fx+O58CttaWl5++23t2zZUkzatYq9UBSNxYBytMfjSacz7e3t69Jkrmuo/4y9R+FDfnTI8mcf6p6tdOnt3kQ0sO7g6z7+H7L3hEKh9vb2IvL7D6bLli1bNm3adGPY5LFhA0H5XC4WjUXC4VgsxmazSSTSuhqu++hyuf6/bu8ZHx//Ctp7crnc8eMn2luawxoBVIijKU46mGiHRYJ6dIAajC97VimIugq5gWnEBACQbrInTB8DRpF4CFAuokGMDYNFOQHlOpCcEgFxUhbAjMnE5cy54frzTt4MoERgETRr187iTIfhLeCAyBiEfvFiVMEqMidQiySmZJl482jIkXYbFfMEM4MUUbBllGH3Mm2sqXp+oHX2atvBks2Htrwrpw4nZeykih2WLSX1y6hVkqQN6FvOsutPWfpr0Pl+hE8GmGa2F2ZNoV6z8+jurj/767p7/p++P/rTK//v382/8Hi2/FOY2ALzqYhsoUChdWkQBTNcupfz1pNT7z+73HBGNdQcJrSjiwP5xaHlpjLZZA/qUiJSGqbrc/OjP4ZaUIskrmKZVdKCTXnd7YJNPcePH29tboqo+QD53R3r4IRT3PFUEKcRa6gjIQ4FZY9n+dQwiwJcLStWvBMBq8ZnAdJEZgmICfIY0YhXSpsara8E7jkQhwUim27ql6Jl4npN0lqeTzAfU94IGscuim1cJCAOZdYklVOGTYuEpHRROtphY5DGGqvoXbWkS6f3vf/GiY/elI+1xTmUCGMqskxJSubjtEFfW5mh/hj7wiFVU5lquFvQdjm4873s2U/SLWdC8+O2is9nXn1pdMeuyYvV5PNVtIF+fm9rpPdSdH5UOUeQTQ1ml6lgoCqY6j1b+594eOil3yyc+kzRXRMerUenO/LUDl7dCelEN6pmIMwRQIgpXBFwwAGH2vV+iciZVs0d+T2rq6vHjh9va22OGSQwHpVtxhi+dhVg69sVqEuPUV6AzDegzNtkAIolMBVHuzzrMetp43ERDdUCHQTEKge3jM9aQC1FB+L1RkZtMtFIx8SVix7h3I375TYeRox7Z5HANkXWpvSL6HElpzhmUKvUL5hzyThoLpVecUrJg1YGIaJkLU/0Onmzww3n5wZap9rq9n38wdmdm9XUa1mTKKXj43o/qFWa13GpA11N1VXamVEle8HEmcvK6EACWzofXJ5NLY6GB2rVXXXqiR7nSItsnmzjzaf0gqRFblletHDn8qJphDmKUDv95w8svPYY6cMXue0XlIONUUonypvIMYbZjWVq6jCg38kXABC8fu1rC6hFHFVyTBrVLTfK3TZEo9ErV64cPny4t7cX30+tVO7etUsklkjEwnPnKpqbmy7V1rLY7PGxsXw+n7nDK5fLWa1Wv9+fzWbvsEsml8vhSQDutE82m03Ek0q+PmCNuWRh6uemmRMmtzyy6kjEPFmJUFR1oaq8vLy0rOzYsWNXGhvPnDk9MjbG5XKnCFNHjx6dmiL09/XuP3AgHIkEg0E8meVdKuN0Ot1udy6Xw/fJZrODg4NrdQuXlpbOnDlz+PBhO5ZVNJ/PX7pUU3OpFoLgqqqqwcHBgwcOCsWS7u5un89XPM66M2azWTx9xJ2uOpMBLeP3+3FvDv5zBMrlYbhfnP9oO3X4k2om38Uzp20Oh2dNhTNZkKtj7emy2azZbAqFQrncnXohm85ktTpDJBrP5dE8jGZi5viKdN1xEomEXq9Pp9P4wXO5nNPpXIt7isOmr68PHzYatXr3rl1CkVgiFp+rqGxsaLh8uX55eXl0dPQLh43X671T62Uy4CpxV+NdGjCVSul0OmD+v8Mrm82urq4aDIY7fA8253K5YDBosVjuUplcLme32z0eT3EffNh8Nfk9HC5XIhahPsO6B00w75iEgC6KB+zYFCDgAsTLBNBkFOjWWMHqGBTT9YsEOOgCmq2pKMYCBop21+nAUWD7WfWAj6koiuZGejsWB1tRNyZ4c7s5DlTDIgHPxwEMNgVdCYMoYZQDCxOmWQfsCgaBaYm4PN4jmOrXzAw750dd9AnVZBf/aqNkoDGvYhgmO8/s2lJ+YJec0CcfbBL3XIoPN0bOH071X8rP9XknWvTNZyNDdTC5FYTVTNTnrpyAFidS0YB6dJD51puTT/9OtXO7jTyelbPggRqEMYa4DajXDAJ6nWpYxcx3ViY2PMJ67hfLW1/gn9xGOV5i6Tqnazm7ULGP0VzhF8xhoUY4E7yII28qYH6uO/J7YBjm8ngiAR/1aEGY1e0bCjsg1lbAAYfzJ7A9Uas0IJjTzo7BEhoqngVdkIphCSnVoGGjAcBSCrmBdkA8BLgacGbqatfCYCvIHrrOR7Pu1MDkA1ACbBIl1JyYig1SDeAntYghk0g7Ny6c7BUNtxhnhp0siuZaq7Djory1TN5ekZ/tVrecOfXBa+Ufvq5srxC1nOPVn4ZofehMd7zvfKj2oLdqt6LhlHGgKdV4ItdTCZN70zVH810VaR7BOz3CKT8pqDxp6LpkHWpOU7tCV8qixH7gn3Wo8moWouGgnInAmc+Y3/y7nh98i/3Kr2XbXxk/uNnRe17fcnb23L6F5sqIeB6AOcBtvz2UBHY4/XLIeUc/Vz6f53C5MrEQeHUB7gFxTCCuyqUD74Ad48KHChz5RAg0L546Nx4CfmGL2LNMMzPIiFsL9owFcHY8MBQVzDY3DRLUqRprrOKO96DugrJD4e7Ag9VxvItXw2vEOchINJiwKBIm2Zp+kaQ1PMMikT9DkMwR9Etku3DRI2XLpsfEQy3KqZ68hiW81lK699PKgzvV5AHZcKt4sCmLwV/gdLNKdbRx8VR/VM5KG0Q5uwokMTUKYencKmvKPjdmHGqWEwa5M0QGlWiSLGf1AuBlXnUDGpZViqhZELktX7EzuuXZhZceEu54TVK2c+TzEm9/lb759Fz5XlpDeQSw/vFY9zsO9bxR6Ps9+T04PeWJJ54YGRnBF7BlLqekpGRsbMJutdTW1VVWVJSVlbNY7M8//1yr1dpsNsvtXjabTSAQKBQKq9V6u+/BNqvVyuVy9Xr9XfYxmU1cHsdsNdmcVp3Yo5N4bE6L1W612a16g97r87ndLp1Ox2KzXS5QcLncbrfb4XRaLBaz2axWqw0Gg8ViUSqVAoHgLieyWq1isVgqleJXZLPZlErlwYMHi/FcKIqq1epNmzZt3rwZN0ik0+nSs2dPnDjh8/vGx8caGxt27969xGRVVlZOTk46HI7bXrjVatXr9Ww2++6VUSiVQoHA47TZrBa/x7Yws1xSKjj9bqPn89MBmcDjsXtdNqkEvPAKW20uq91tMmotVrvF6rBYHVab02Z38wVSrc5id/ptDr/NuQL+Onzgbfda7R6L1WE2GdnMeY1SoFdxJLSTsr5vaeiHLXbv2sobjUYOh2M2m/GNVqt1YGDgwoULRQSdy+U6OzufeOKJYlo3AX/5k09KRkZGHXZbTU1NWWlpZcV5Hpd3+PBhrVZ7p2vHE2vIZLI7jSt82HA4nC8YNiYTh8MxmUxrr2Jt2Wq1arVaLpe7duO6stVqValUfD7/TrXFKyMUCosVttlscrn88OHDev0d7d/FRvsvKfz36TUXqgtDwEt10/qKKYw51SCsI+LDFWsKUAbHNIkIsmIFYmsWiZU3J2XQEl47XIA7WHQ3iMQJo+komI6Tq2guCadjPr1CvUAA0xz2tA2eGs3iQkxQQbwOE2vxmsBvkxEgxRZyoW49YEe6dbBdlVZzEIskbxI5GQT30mRQxkgrlhBqR+7cLs/ZndK6o8FrNVlSa3joknm40ckgJBaGTN3n1XVHs+d3uJ/7le+DF9DGI+hMV25pJDlSD5GaQSDxwHmEMYqqWdBEc5A/7w/6E7FVBM8sEXZnrKqITQ9HVgDas6qA2C6PlC/9JLX5udlf/kj59pO+i3topbuqPnrt0pbXlruqDfMTGR1/raPhVtQC9IIN/IDtbvwe0DVQvuA6uY4tbjoUbrGwSIBRLRVDVkGsGeLS4PgVtcks7GkFdSQuocMgW6QdiNrhcWrRlYJaUiqM5lJIMuKRL2sWCFEFE8A1LGtScdW86YzFGGkc6AB/CrBmARkbFQexSvNGkXOJ4ObNGpcXjRK+XipYGBmaG+gWtlaEW07AnaeD/VWa1nLPQHViosHcVaFoOKmY6Jdfa3XveS97cCNau9fWXUOZHDd3XbJea0ovjuYFlGDlPtOW1+PUq1a1OMomZejXEPE0yiOgZgkS9uBjDFbzoJleeODCyqu/FXz/u7Vf+xvOkw8ESz8mndxR8dFrDSVv8ZrKDKTetHwJWA1BnW9jhAPOL7M4o1sOOoD+7N1eUA48DOD3CxZHBgi8ITcAl7i0wY0wwOtPAqkoiG8CTDKdVcqTM2mJoAe/X65LSbmB4Q2EU4EwK0y+WeMWMzTzk3EliA1EzGJgkQVZUNzgQQJwhCVgeADtRD94qCie3WcGh/IYELsqid0vOYPAI6RbFULe0sIyc1HE4ziUQtU8Ybm1MjzZDJNbV4YvGyY7vUxCYrbf2HpWO3TFr5P5NFgQg0WM2qVhJdsqYOiWGS4FPyJjruokOcY42lceInTMz1AJFODYWpyhWMQcUL2gE4uL9CHyRXhhEJpoyO59A9r4uPCpf9dtecnfdppwauf5LW/UH9ouHO000idzBuHd7xcs2k7gs/1+/J5oNLp79+7NmzfPzxcEuOPx+Pz8vNFoDIVCNrtdpVTI5QoOh4MTVDFe723+FPk9RfLvrTsBZi7Ga16/DyZIAWNbc7m82erEydSZmNYjPpVLFrRzUBTVarXBYFAmlY6Ojloslrm5ObvdAciX2SybzYrFYl6vh8ViZzLAxIIHYd1aDXzLWn4PDIOToyi6Lh87kUh88cUXq6uri2nbFXIZXyDweL1er8flcs3Pz4fD4Z6eHjw1xG3PhSBIKpUymUzrrhonSRfvoEg4bLU5Fc5cIgP1LKc/3j07/5uN8HNPoVbQoTiheK1eczZmszM/9crOQ7lkJuHORC2pVW1qVWVRUVcsszHnTNgyFjT0BrTNfkWNR1rmEnzu5O51cHY5ubuU1PctzBIH+1PltX9arrvHK6vCyezF+mN2IzPObcdPjSdtLfKao9Horl27tmzZsrBQyIMbj8Xm5ucNBsPq6qrNZlPI5XKFgsfjfeGwcTqduNp18ezrCggCEshnMpl1DVjcDUGQfD5vMplyuVxx47oCgiDxeNxisazbvvYjiqJ4gth1rbFuH7fbvbbC+LD5Cvq5CkMTygErPVgSMNFbYIGXAfYusBNE0HgYKcR1Fw05ILQbWbGDWdipQV3aoE0/T5wwKqUgjiOFqdeD1KEhn0qgpRPZpFE9b1HPZ9i4s8Dyj/N+LBLIIoMsUiyJlRVQIrwm1KUrBIpjgniAJBGwo0EHmOKxR8+Ykp3SLfsFNP/iOCqZRblTyFQT1H4yf2xj7tj7uStHVmoPWuo/d134LFxeEuipdPZeUF46bLt0AG04EN36ouje74vv/xfV7rfyEw3obBdCaYXaT2V3vpLatzF2bp/lgR+Zf/dYhk4C2YjySZCNIbGSiwYDXlcapEF15q/WwOxxRDIPtZ2Ees4vHdvX8OSv4KuVMUrndPWJKGMc1fOuIwb8yfX2Jh8M9wi+HO5R3IxH19gDgOkFW8U9BmC/iWHJv1bsQPkG00pBnZqATkId6jNLOGjUjxhFQHw5Fc37LG4uVTc7sjjWbxIwrVKukUFGHXKQDgIDNLAJiN2tRzy3xV4YjIDM4riam1EyndT+oJRhUUkZi/TpmRkCkUgkkQgkEmOaPN9Rz688uNRQQR4eYAx3q2tPCI9u47deYMxSidMz5AsXrz3yCO2FRz2d5z3d1fyBjqGefnp3u6GrjvTS00fu+WP+g/fH+q8AWBCwoMkAUAWMg0wgaDYBOQzhHRtzl48kBy5pn/21/e2np1599sLDP0O7zkTHLpPP7I6T21HpHIKD7DtdBY7kviTuyWWv41ER6AKHqiD4tN7eufZ+iYAHgLAPjwYPWPXTkyMmlQzksQcJ4PxoNJB3al1sinp6mD3cqVsiOyVsE3sWtssK/WISQQ4QfoiFFmJxVXhyVmBJjaGpKBLFfM3xELhfbAqgR2UGkWtZPd/DoRikfB6HQyIDyTUCgUAkEKhk0ixxcvZq53JnzUJv89zoAGtiQDd0Zbmp1MGdM6gUy0xGwqaxDbcZak+H5SybjMehUVmUKdXspHWJnLqwP7v56fyR91dGW51SdsIsjxgl2RVwt4JKxoOQUZE+/GG+/TQ01ZQ79j5auX15+zutrz6BUttWiW2UK5VJNYinA5AOzz1S8N/dzhQHHnXEPtvvx+9Jp9Otra0HDx4kYnJ8OBMWn/GKZA5cr3ltRoJbCbNF3HN37vNt8lSgKBKPw14vfrpsKqhdOumVnXctH5B2fV3U+uduwRGvvCqgbkzGfB9/vO3EyZM1l2ouVFXt3LmrpaW58nxlTU1NS3PLZ5/tIZIpdpv10KFDTpc7Ho9fDz6/tbJgy1rcU7zqgYGBtXrNYrH41KlT586dw+09axukiFfy+XxjYyNObLrtmdbymou/KrZwwO9zOmyhYABKRTqpup987tg1GK4eMAfe25R/8P7o9k9zsTiup4yiqNfnXwlGwPLsoMh6v7dce4924t+d3D12Vomd9Ymdtc3J3aOZ32HjHfPJL/hV9QFd56p5KOIgxtzzCR87GZSkw9pM3GbUShKxIIqiuZTXyTsY0K1nrN+ap8Lr9TY3NxdbIJVKtbS0HDx4EGd9FRtwHR2eyWTiCdtv2zL40b4CvOarV69+BfWaC4MVhvJeC+zSglSCdiWWSxmLCi6YdsJgqk1GwASNm2GK2zGpGyToQuKry4tzPS2NHrMOwWf/VFjPXzIuElYldBuDGFOy9HSCYmbMzZ0BCyoOs1xawG/wGIDBH0/6k8Sej+MhoP8BxPKtIHAGE2EDCZsMAh15QNBZ7RprzveUw5f3Zfa9lTnwNtT0OVR/AGo6CjceQjpOZVtOZA+9De1/E6rcgV6t1FzcTz+3N9NyAjq12fP0A4N/+83xh39s2Pai5vCmUN1huONU7sBb+l//lPftv7c98CP3Yz83/fuPLO+/5aws9U+Nxui03BI1IaJnVkGwLqxgIioGzBpDSK2oaNZCn1wu2wPRB+1LhKCYDmKIMHmejIYLNN9W7MD64lQDaz8u2oZTSQCxSZG3yANfHMeehyzy2/N7LFLQhsDDokd8ZqAzFAtiajdY3gA/wJHAABPy0GfIVztbPVYDyCtpEkM+i26J7OBQg8I5y+JUUsNV08aVs+Ne4dzaUPMvBj24NJ9FihoFSRHNSuzmNZXN9zTRaTMUChUovwLMU3gRSWQihUqYmiIRpohkyuT0rKCljvfxhonKz5k0kGCINDd3tb6x+8f/vPz8byQv/3rx6P7xkdGJKcLl1948/43vCJ992PLKo4Tv/MPo409wD+w2XeuNz1Hz8xOIYBqoFK44M3lfaT8AACAASURBVH210LXqTP2R4O43Vyr3WjrK+ec+y5NarD1VQfooahQAnopRmNbgadTWYMebYdCXtvfkIYcaxiX+HGpwvxQa/2agUzR/Fgsgm1UAXfXkokE6lXi1s8VnwxSMXDrYoVTNjjm50yuieRuTnNAuaxdJ0umRAMa7x24ZzPgaCwLkBKIHMANPKgayeK7YcEFzYFJyqIG0o1kCuMDaZS35qrT/8uJwD5VMJhAI1zsE/McgKXl6mkomEUhEIugXIlkyOyXvvEBrOs+iz8/OTIv43MWetsWXH9ad2q1tKHNNdDGmRmfIxJWeGrRsOzTVnKs5kN/xSu5ESbT6ZGqgCaaNwQtDiISGJkKIy5jvvwiN1ua7y5DKHehYnaG/VlJzNM8ctdAnIwomHtQGspGoOGDyCTpA5V1acL/fLHKIgPtF9vvae/R6fXl5uVarPXPmzF3SOjIYDJzJgdzh9QfiHhSFg8HMzp2pZ57JU6lgJc7EdML+VSsxZOzXEZ+QdP9NQNcZcVBS3gWFlH/o8JF9+/aePVt69mxpeXk5kPNpaRkaGqKQSQcOHJiYnJqcmNh/4GAimYrFCvaeO9T39rins7OzSGFGUbS6uprH412+fJlOB1kCi6v+WvgCQdClS5fwxKW3PReOe6wY3zycgBzBnD2Yc63m5fasP5Q4UNr8wLsXT548SZFG/uW47Z5tvheOSfKVZ3mNl3e/9OLH774nVWmKp/N77Vb5NdfyXsvCRpfwpGrkfid3H5xLwrkYlE/CUAZF8nanN564XYD79aMgCGI0WZLJFLgisBGB4dy6mt+KeywWy+XLl68fA9XpdBUVFWq1+syZM7hxDjtU8ftC4csMm68A7mlvb/9q4p7V1dV4LArSKceCsM8C+8ywQw2v2EBW7VQUSJaBWRvzOhWn72IBS04JGAzpWD6+ukQlEjqvhCxaNJNAIn73MlDCBY90NhnqkDt5MxQgM9hoZ1EQixhYtvGQeKBMqARsnugKoBAVpDuKixNmeLBKAaeYOxUhtDovHYAuH8zseSO/7/XAk79Y+e3P8mXbQNLQmS64pxSgn5Zj0OnN+erPoCtH4Ys705XbJRV7rRf3Zg9vDD32U/G/3Nv6998e+vcfEu+/l7PhN9aD7zgOv8t67qHFf/xuctfrSNnW2ObnDb/+qfqhn1z7h+83/t3fh/ZuQgcvoF4zaISADdVzUQM/KaB6liamzx+Wd1eHxfSwgg2ZRLAJpFiCzNK0TQse7uNgkUPwLFdBJ5Y33o2GXKAQ8eVXfUGvu6grv/6uQtFQaDUeCaNO4E/JY9RmQKS97lsB269L64IDRv1YN0WxpJWrAEcG7SCSLujI+8y0kX5iT/Oq04T6LRmT1DLZAYuomGyxHHXIbSzKXF8TtbvByiQXnr+LaAB3peEfcWYPsAUWUiyB2HgVC2KOIqTmcMcZZvslAG6IYA3Fl1UyiUgpfCKBF5kM3iQSiUIlXh0QH9qx/Nm7uvP7tDNjNAqJOj3dtX9f5/0/bXr8t2OP/pr54tPLrz9z5Sc/abv3XvjCbuTUh4H3nx398b/0f+vb5/76b2u/9fehfR+g7SeBBI7PBCix45fR3rJk42FvTyWtbK+0vSLMIUcFM3k9H8a8WpBJBAwM1y8Nb9J18A7j99wtjh1BkFBoNRGLgnBFuwoCbyUMKC82DIjEMSHvO98v+I2D3Vnp8MrM1PjsyNVVqwbI/dnk5sWpLCZDAJxcdrmZQZzpvULrbbKzKFjeVozi5sAeA0AWOStIYIcnlChc1Jr4eZsMViyirJHweCOjr3mWvBaFYn1x/Q/WH4UPRBKJQiGraZO8ms9VHRdY8zNLCzTO4jzlxD7yS49MfvyG9PhW9ekS1dkdy1tfVZ7agwipELUd6qtIH9oY3vQ7w8M/F9z/42ztIZjaAeSzfSZYSEVHatBrF+LdZY7+atqZ3crBhrBkMaZk4+0PZAmNooxTB6x3GCgEDmWPHqB2G5ZwA7D9AAE8b5H6nPbbrkC33jv4lmw2+x/P8adOncLtPSiKRiLhsbGx1dWw2WScnp7mcblLDAaPy52bm7vTQfDtf1g+dlilyjz8cPa++3InToAEkxBssRVueTgfjzkoUHYVP346nUqlUul02m638/kCPL12CstyCkGQ2+1JJBJer9fpdMIwHIvF7q7xg6LoysoK7rAoXtf09PRaXrNIJDp16tTFixeL+di5HA6bw4FhiEwmyeXy8fFxj8czMDCQyWaKB7m1AOczfLmhnxN/9oLvF6f9j53zbGpZebjUWUkIPl8m+9MPNb84wNx6ll7x6vmaV2sOvlraVF55tur8E8+/+rsXX5ydqEnGg3BcHVLXGGjvWDmHk34mko8DwJILQknbutPh0pHrNq79iLuNUimAe+70whN9rMV50Wi0p6en6OfKZDJNTU2nTp0qhmuBYTM6Gg5HTEbDzOwsh8NZXFpaXl7+MulNvkw+9lwud6fa4vnh/9vysXu93nUBaFQq9Svo50qn093d3RMTEym/AxACQBZDjPrg1udsSpuY5TVpECyvONDPBbP2db7CjQLG5gH8zXDKZ/PxaSYmNeoyw7GAm0HMyulZo9DGnnZwqAuDrZOtNYTWGvrVVi1tAgio2GUFvz6+nOOr7NoyHmijX0aUTJg+mJtqNFfti+1+Pfnu47GXHgy/8pD+x/9k+/m/ZktezF8+lDu3HTqzDa3aiY7XgZyItXtzZSWZT16EK7Y7d2+QbnshuPl3Kw/fJ/jH71X/ydfq/vLry//4PeXGpyOlW9DO08GGo+QNj9HefhKp2g6f3JQ/8Gak5JVr3//Oib/4S9WJHeh8P6pkIrKlHG1AXfEZs/oYu3K/sbvKPtluXZzCQp8wlIaHefvMSCIMY0LDBcgI2B5R8AaFMHjiDzozfrvH6YAg+LYjfnV1taOjg0Iipo2Stc++QPFPw7OyqD7xElizQVsJgeEn4ABKKmEfcD46gawfeG4GuVdBGvO0btnLpxmZ1JjLlPdZbBNtELU9q2JYmWQ3Y2qhr2m8pYbQXrd4rV2/OAWkunGoahKBQHec02PGckkKp2EBBZbOwctEaLoLIrUAdtRgFVSzm7N7I2F8jEihEIEhgQyAD5lMJZMo4NPatRVbYinUyZ6exdYG1uUK7rO/NDRVzs1SiWSwOA+PjhImJkmb3g4+9VPko6fcm57u/9WPaM8/lHrrscyHT6e3vmB97N/L/vJ/bv+zv9Qe/xRt+Rye7oIXBnNDl5R731rc9Ax71xvWltOOa3VW+iQwX92UzRTwkAB2vA59bi0AiQQN12e5I48vFAo1NzeTiVMQHuGImyVMItShils1NikvYAXsZgBK8McGYJXBuh7/i+OeFDYS0tGkz+YT0TULhJRLjziV1smOLI+YNQgsDJJ7eZbW1zTVUkNoraX1NZkXgSwhQD/FewTEV+IDYM0VAT81SFOPSGnQTA86UMmtPjY1Pk4mgx4pvIl4AXQKkUikzc4uLdJvwCIymTwzy7hSpSx5RTHQOj09TSaTaFQyZWyYvzAtq9wfL92KXj3nqdoz+OqTnGMl+bFa6GolOlCRuXyA8rN/rvtf3zBdPoVyxxHWGMydyvRUKPe+zdi5gX38Y0tXhWuyzcYkYWMS96hiNXfr0PgqkORORkGjRQNoxId4TZirTo05uGWoWZLRLbssht8L98RisYsXL545c6bIa+awWZ+WlMzS5nq6u/YfOHD4yJGqqgscDreqqgqPZHHd7uXxeGQymU6nc7vdt/sebPN4PCKRyGazFfZxu13BYHx83PfWW9b33w/u2pWYm3OsrAixdA0ul8vt8Xq8AZercEC32+3xeHw+n8PhkEqlHo/H7/d7PIBn43K5fD6f1+v1eb1+n8/j8RgMBrlcXvwW32ftX7fbrVKp1Go1vo/H4zGbzRUVFbheM77k0+n0Q4cOXbhwAY/nSqVSZ06fLisvt1gt+/buPXP69PYd24UicW1drYgl96/4XYW6rD2Py+tzu73uZyqsf7zF880S2c8/I393O//rJbq/2Ob89m7Ndz61fudj1daKpfy5cvSXD6Q23Nt74E2xzj63sHh41yuDpd9k1P/RfPvD187fd2LHLw/vL6HNMyLRZGgVpImg0RZmaQVKzcr1l0KhsFgsOBNoZWXF7/ff2iMCgcBisdy6vVhvu90uEomcTie+xePxTE9P19XVFefhaDSKD5sir5m/zCspKZmeme3u7tq/f/+Rw4cvXLzI5fEqKyvtdvudzuXxeORyuVarvdMOYBi43TcNm2It1xQcDodAIHA4HGu23VR0u91ms1ksFt+09eYPHo/HaDTKZLK7DxuFQqHRaIrDxmQyVVRUfAXzVKRSqYaGhraW5hU5B74liDSmBBIseYsMoxUbAPMmsoImMKCDxwThk/uqG3HpwIOvTQ5bJKtypml2JCFnBkV087UGB4sspIwA9qJRGJIu0XoaRhurJltqlNMj2vmJgGgBKsZp47gBz1WpZiMGPiynw6wxmNaTp7Tne0qRxoO6ba+Yfn2f64Ef83/wvdq/+lvC17/p/fXPwi89nHj9EdeGx4a/853FX9wXOPBhrnRb/syWVMlLqbcfTb31aOClXykevU/5yqPeDU9xv/3tjr/6286/+kb993+g3P5qvvU4RO1Ah2uC+96UvfVYtnRLdutz8U9eFj/yM9lP7w1teER8fJuHR4VnetG+c772UvG+dxaObo6O1aOqpYSGp6aNg/YB6aIUIEHjih1NrCKpWCLgzUVDwFoWDxWcILEQQCdeE0jmZRHHVWyLWn4n/Z5AIFBbW9ff270iYxZsPBYJQCEaNmIGv9XQxnJGYWEVLC7kwPeEpVkFWwrUXcgINBJRmywsXTRRriaEs0HmpOlqrWX6mnioVd95LrcwsLI0QW06T2ysGK0+JR3rNC8RvYL5PI8ItJ7ZE7BwGmZPQLPdEKkVIraAR/yJeqjlGHTlSP7iruS2F+Kv/Grmtd+NT5FIZApuOiASSTMzM2QKZWmGyl5anAJ+LgqBTJmiUAlkMoFCJYyOEUdGGaPDow88QH7gZ5yhXgJ1mjw7S52jTTZflm54LLP1Oaj8Y/R8ie/tx+d/9APLv93re/DHgVcfof3T9xa+823nIz8V7XzD3lOVbz6JXNgdrDvI3vXG4v4Pkn0VKK07KpjRzk3iHO214AY2iSNy5q3QBzYV8BBqkUTkzLvkqQgEAufOVfT3dscNYti4BnBgiGpVtmRcJEJYAjJAVgsBDxR21/iBcwqPhQQ+XCuICrQp81Zwv/iE8w4mKanmBBbHzX3VVvqklNBvXCLkTSK/cIHS3TjZemmypUZGHdIvTAVEC3CRDVOwwOEdDXShYOk8zBiGprvyEw3Q5X3ohe3Lp/dMEUlErF8A7sFMcWQiiTI5TiAQpjALz8zszCQoEwlEIpVMXiRNLpEJ/DeeFL/+DHN0gEChUmfnaPPzhI4rmhNbof4KcL90n13d/orowxdy3aVIze5E1W7Jcw8pfv7PoY1P8o5/6qCNQNeAmWel6bjg0AcLx7elpppQ+XxMydbOTxZ6BJCyFTgPCV2xxl1myGsGZG23DjyAWWWIXQHhWWkL+eRZZs3vl6fiP9Iebdmy5bnnnmOxWPiqptdpDx86NDMzOzgwcObM2cHBgYaGBgaDWV9f7/f7g8Fg4HavUCikVqvNZvNtdgiFAuFwIBgMRSIylcrtdoN9QqFAIuGfmEicOhXxeFai0ZjDkThzxtvWJlGp/Hc4SyAQCAaDLpdLJpPdrhaFbXgQO57c4E67hUIhvV5vMBhCoRB+WLfbXVNTU8xTgSBIQ0PDiy++eOzYsWQSeI4gCGq6cqW2tk4gEJw6daq3t/fixYucZW5DaVf3Zq6MbF+NBENh8A6GgqFV8F6NBFfdKxevrnyjRPXp6WPkxn/itPxV/YXne6qeLDv9yVDZo7R332afu1e592fxA5vMh/9O3Po/5JQN6oX9SspGA/kxecf/pez/dmKFP0UgP/n0i08++cyePXvAYtTWVlZW9swzzzz11FM1NTV0Op3BYAgEApVKxWKxtFqty+VqaADhZtXV1bf2mkQiwVVz7tQ4Xq9XKpXi9rBAIBAKhRgMRn19fRH3GI3Gbdu2Pffcc2w2G99o0OkOHTpEoU5fGxw4e/bswMDVxitXWGxOXV2tf+ULho3JBGLv71QZwGTHsMiddggEAn6/XyqV+v3+O+0TDAadTqdcLr/TDvgYsNlsKpXqNmP4+s9CoZBWqy1WGB+NNTU1Wu3vlxev2JK/b+G/L54rn89rtFqFTArZFbgLADIKixMrWFbxuRU3LQC3lAJfuYFUScABYjfwedwK5EzwN5bUneOhj7m4sxpCn4dP085PSEmDKyI6bBGntDyQ/VQ0H1eyV2UMC4NkZ1PBEmXEnlNVDEQ8Ay8OgpSNc33QdAdEuAL3V/hq9tsPb8yXbTW8+5Txp//s//V9qnu/P/H1/yX7l3tXHv6J+6H7Vh6/X/rTfz35x3+9854/b/izr/l+9yv45Ie5E5uyO19Ovft46p3HbFtfZjz7S+Onb668+pjt1UclG54yHNyYq90LTzRAY3VQw0F4z2v2t34bPflhcvOzkifv977+CLTjJbR6t6vl1OLJne7qw0jT8Syh2XrtcmB+OK9ho3aFjUWhdTdkjCLUrQUaRWEvWN5W3WgskPTZMwE34Ku6tIDi49YX7PYYyMPjue6i1wyyxmi1KrkMgCoszgi0rZ4PtJ6xZWA94ilCn1sKeKfgv0rKGe7pfgexWz3S4mUSDFPd/MbTgaFamNqRmGyKTjSGRi5Hxhoi5A7TaIu97yJCaoGITYU3oQkkOm06CnWXQlcO509uyu/d4Hz/SeWDP3L8+F7Bb39hqT9rb7/g7r5oL91rbSj1NJVaeuucvZeUo93ToyOUkZGFa33ipgusoZ6F4YGl4X7h0b3KfR+L93x46a+/NvfZJzw2QzjYJfx8u+X9Z1LvPgad3ASd2Qyd+CD+2iOS+//V9syvnI/dz/35D62//Vlyw2/Qg285y0qubn3TeugD9Mi7merd5vay0Gh9duoKyptUz4wuDrQgRevI9Ta5E+7BwvKBxQ73c90ljj2RSCiUSrVChnoK+uZFAnghk931cxWsdAXVKxmwzWDDAORFKRB4RbBJCECYRZJQsT3caSeTpJno8nCo+rkxGXkwsDyD6Hkp+VJMzowo2BEFa1XO1M1PrIgWgB3LBIAOEIgSzcCLQ/DiNWjhKkTtgCYbkb5zrtISz/434TMfmsr36JcojoVxt5RtX6bbRCy7UqQVsNyLhGU6bYZMpM9QuYs0CWuBOT9LoZApVKqOPZdUskz1pxmP/oRxuYq5QNPNjGtbKx3nduQHKmFCE4C8F3fld75seffx1ERDvOOM+q0nVt56DNq7Ab2839VRdm3HB74L+9FrF9KEJlPvxVX6aE7BQO0yC4NM620qGOFAHlNcelGM2BRRFS8PJKex9KVrbHJFkPoHxLGHQqGWlpampqapqSl8CoYhKBQM4hmswuFINBpNJJMsFotGm737HH1HP1cqhXq9KAyjkYhdKs3mckCGGIZhPj9fXo6EQjcOm05Dra3B8+fhWOzGxltKqVTKYrGs9cLcsgsIzME1lG/9qrjlVt3CtfnYURSdm5traGhoamqyWq34r+LxeDQSTaVSq6uryWRyxb+ShpKTtSzyLjv1kEU5GggaUqvWTC4Bp0L5kDHtU6bE7d7u440jDf+m7rpH0X2PuOseec89ys571D33KK/8sarhLyRt95gGf2xifCoauU/Q/Eeu5X3poDAbM0LZcNg6ZVv6GEWzTqd7586dmzdvHhsbczqdRqNxdHT0pZdeevHFFxsaGigUCq4r3d3VXXeptqW5uaOj4/3333/kkUd27dq1jrb1Jf1ceOhTsa2i0Wh3d3fRzxUMBltaWpqbm4vDBoKgYDCYTKYSiUQ4gg2bRILD4XzhsHG5XF/o5/qvysde7Mfida0rFOnw67av/fj/u3zshTh2LGxq7SPyzWXsARdnmYBIIozn4VABwseN6R5bP7BwXGBpMApDIpD12smd1cyOZgwC1CxMy+ge/pxPMAe+lS7Z2BTUJERUTJg5CpFbMe9JE0RsBkHmo7VQ2wm4/OPUmc3x4x/kz22jvPqI9Kc/TG94JPLCg5HfPZB8+aHo8w867v9X1nf/oeFPvzb5d98if/cfRD/5YeLDZ+GKEuhcCVS6JX/sfeTcJ9HJ1tnXnyE+80vjp6/ajm50Hns/Xb4Nbj2BeWrOQ9W78nte873yK9eJbfELexy7N6BV2+GWz5PDlxy95wOD1Yy978p3bEhdOBBaGrfzaLgnyLRE2P7uhsG6c+AjUDmSY/BCCltlcQ0P8F6xxONgiQJTuQgyAmiIr69fKo4dviWOfS3h5uY2X9cFt/0IlHz1vDytN0tuW2ESdDPDNlKvuf9SerIBpbYnxxvc/Rd9Vy9mJxui4w2GtvI86IUW8CY2Q+0noXPboHPb8ofeyWz7XXrj4+nXf427Gn0P3Ud8+gFf41F0tAa5VgVf2osMnke7zkI95XDTkcyVz72XjutP7Ir1X0DaTmamWmLUvtTSBNT0eebSXk/jmYYf/GPdt7+9+Przhtcfcb30QOSNR/KfvJA7uSm74+XMxsfjLz6oeeDfnIc2+Q+/7934JHL4Hbh0S6bhkPXKSc9g9eKONyTvP+M9silC7bFdrYGJV1DFgmpubMsbL0001wCm+fVWQi0S2CyOKljFpbT41Rpo+PvHsYPj32T4KR52TQHfAR8GBTvcmm/FePI1yCTKGYV+4YKBPmWiT5rok6h8HuVNJIUzDkpfkD4C6XgrfJqbQ0UUi7CQCoDOTHfhfiE0QxMNUOdpqOkIXL41dWZz9NOX8ntfp5ZsCMz0guOYxRi/WwZruSmdAJEtZNWcCG86wSVB+mXUKMjo+E69Ui3iRSR0lDmcme0T7Hxn9qNXFKW77Mc3eQ+9laosQVqPQw2HoHMfQ5Wf5A+86Xn1IU/7OW//xdDBd9DL++DuM7nxy7buysBwLWXPe8bynfBYnXN2IKxgAZ+jXa6eHftow8uEtlqc0Yy3AMiepucnlKwbgZDXdYlg8AxW4LSBPBW/Zxy7zWarr68fGxvr6elZl3Bg7VzPZDPqTrYFVTksEAp8g9z8wsOjYrFogSyL/QMet3Qqe+zz1NNPZ6urM1u2xJ9/Ps9g5Lq6Mlu3Zg4eBGFcaw+Fovl8fqWrI19eWvgKP86ac6EomkwmzWYzDMNrNt9UxFhKkT8gnqu1tbXIa4YgaHh4uLe3t6ura3l5Gb/ktW2CA68ckukun5r81EjeZybsBH8pB8z0cjvtlG36qIX4mYWwS8s6t13c9H+Lr9yjOvcnxs//Vlf619qLf244/vXloV8F7BQ96bEVzeVIHLJbTX5lXcyzeNNZoCwCw/k8lEwmLRaL3+/Hv00kElIpEPQpSkXj/k2X07UaDsdj8Zqamm3btu3atYvP5+M/wdvoD8tTYTKZamtrixWzWCwNDQ0TExN3HzYMNqP2VJt6OBJxZnJJCPs5qGaxt1AU9bgc4RAQoS5uXFdAURSPY7/TPoAWhsWx5/P5db8tfkRRNJFI4HC5uHFdYW0c+7qvih9RFC3GseMbURRtaWn5avKaQYdBOQz3CDGqLOZTx2V1QGDFdeYKoP5gZfwvtpCDLYVCYerH6b3FPfFEDTmTyDA7wh5o0S8S7JQ+y3grseHc8kSflk6c66qXYurGqIEPnlx5BJBFnNAE3qQWaPQS1HQEqtkNnfoQPv0RWvmJ8r0nFh/+ceS130Sf+YXvwR8v//AHk9/81sDffHP4G9/q/fo3r/7NN20P/Ci35TkwNdcfhC7ugsq3Quc+gandeel8rPms5pPXrB8+o9z2ivX87lzbCXisDuAecit09Xzu5Iexbc8b33tKtOctfe1hlHAFJjYnyR2OofrVsfqeXe+pKvcs7X5X3nAqIprLYqp9EvK1muP73335WfAIbgcZ42GTMI85TWBMjghM3FhzYbY0EWQQQAYBoD9bxLkvq99z5zj2td3xRWWo4JSRAHy5cBVhj6EmYc4kVkx0cxrOGtrLLJ3nDN0XpquOLnfXqAcuz1w4Krl8AqW2I+RWoO5IbAaUqUufQeVbc4fezm5/KfPx79LvP5F45SHfQ/fFXniQ/+hPGO+9mL+0D2k8hPaWod1l+QufhXe8kTj5MdpTivRXwqQWVDQNcydBGnYtB+ER4MGqxHB9oLNK//7Lc0/9xrDjXcmxT1y7X0vvfwM68xFU/nH+1IfZna+kP3zK+8TPFc/9ivfJS66jG9GaPXD32cxEg7W70jZY0/Pxa8YjG+c3vyCr/Tw63pAhtkYFMxLKcNm+7Zte/V1czSkoEplEOcMNLWDMqIkBUAxH5o1CfAtqATySL9bvyWVA2OMN/yyG+9foPhfH/xcWbjh58XGC1SdnFMqow9zRLuNEu2O0UTPWTqo9LRlt18+Nk1trNMQ+VEpD2GPAJkrEBKiATa4ZmmwATqjGw1D9Qfj8p/kDb+VLXpS8+mvxye0QtRtljyOyeURIRRcHUdYwQmlFmKMgH62Bj2o5MMhlJoXtCjBOlEuIbAFljSUmrhhPf+puK5VdPGQ7sy3XdBTuLYOuHIEu7oQqS/KnPwpvfc6y/XX+qRJb7SGU2ASTW1OUDtfQZc9YQ8uuDwwNJ7gnS4TNZWn5Ukq3HFexxcSBM5+VfLjhxZSGB06HySVATg3s1GL2SyFkEsJWGchj6lQjTnXeiD0qAJ1JUd4g/H1xj9vtLikp+eijj6qqgIgLPpUnk0lcEyWTTmezWRiGlVL1lfdnpvfbBV1ejzyRChdJpgC34Mvh4EDfyTPnqtqJFk9CMcbN119G45GkxZ747WPp++9HfvNQ5sFfZh54IPPee+nf/jbzk/uyBw4AI1AB2QAbEDgSgK82cwAAIABJREFUArHUdtcsEy49A0kkkFqNxGL4UllcdFOplNlsvru9JxIp4J7ir24t+Hy+ojAP/u3IyMjaPBWdnZ0bN27cunVr0fmFq/piIVqpfD6fSqVy+WxrQ+fCeStxl4W014y/iXvMxD1mwk4Taa957pRz+oxroPmcufbxxBvP5CcmU0cOh3/3y4m336YvDAGpmHwczqyEw2Gn0wWqUYSWt9TY7/MHAyD+/LavAdPiewuVG6hn+/VAignO5+FcPugPNF5uoJIpeHMBCRwENhqNuOfutsfBYQSOLIs7BAKBtra2Ypu7XK6SkpItW7bcdtik00BkGUZgqUje9MEseY+NetjCbXAZaKtRVyYVySNwYcygKCrUeqsJLkcoD18fAdhJb+yAouiXyUtqMpnWWbaKlccLiUTiC+09f1he0uHh4a8gr7nQfFAOqM3iIMYiQ+xy2CoFC/YNQw4mxg8m5WJIkQKQoMHTmBD46XHhn+sPZ2um+8LqkjWJpNMjxKYqWuXhBIeUWRqDNZyMSeIX0VUjbZaZa2HpIsgObZEATg+PCM/3QZQ2gEgITdDIJej8p1AZWHHhdx7VP/Ezzs9+yP7H743+z29U/cnXdtzzF1vv+fOP/sdf1HztG4pnHlrd8nL+4NtQw0GI1JIfuZS/uBsu/zg7Uu8Y6sqpWMjSUPr0FtvJkgy5FSa1QOQ2sHhg63q+tzxcti2w5zXZkU3LFw/mKO2IgILol1dFC+G5a0s1xxStZd6rFyNDl1AFHbHJVDMjyukR+mBb/eefObnTqEOe1vKyOj5oN9AOYtgsSeuFkF2VNQpzNgXityAubUrLj6u4kFmSNwi+TBw7luOpgCnxVr3VXLGmtdcA05uRUOFXuFqxjosJFoPWzptEUsq1ibJ9s2V7UxxibnEIks1n1JyAaF7aedHeVb567RKw+uCGn5FaqPEIdGEnVPkpVPEJdPqj7I5X0u88mnrj4cCDP+J87zuyR34S3PBbxzO/lP3qvonv/UPNX/3NyHe/633/Oajyf3P3HmByXNeZKB2e932f117tk9Z6sr1rra21+Syv1rakVZZIUVQWJdKUGCTmiEAEEkQgQCQiAxOQBpMxGEzOOc90mJnOOeeq6py7q3Oo8HSqenp6AkDK7/nb/VjffD3V1bdu3br31j2nzvnPf94key6T/TUsOgSgQr1XiKtvE50XsxO3CdEkhaiyiNZRdyp39lXy0i5gJbh+AApc3pk//UrwtR+HX3hU9PrPtKd30MPANlkcq/N3XokPXps9udveeMrXfgHvv0pP1pOLnaqJLuPc4FTrtabj+33CGRpT50yivJnJL8b0CWGVZo3gVSlaxAWzmHXvpg2CpHaFtMmYvKQfyltYBPy4xwxZ1oMoHQ9QHjMTzbeNLWd9dBhTHzM3SvY/yl6y/5Ueq7XHh0aUGbNENt49UnNq6cKBrGS6oJinIKeb0C+a0XZdx7gjuIZfNAvB8LMyBE4uFmMOI1VHDN8grr5TPPlifudjxLMPmX/4z8izD+cu7Cpc2Zc/tzN44NfmF38cPfEqcecsOdVMisdJXi+g6ASjpGiU5PYQUy0kp5uUz1JaHmESFs1ipPdGfvAaeIRH64iROvALd1wgGo6GT70SOfq8/P1X5deOkZMN5EwrYVj2CabjvIGZ6vetLef8dy/F+6/RWnBwy8e6zIsjE81Xb54+ElFyaUSZ0gsItwm4wdJxOuYt+u05r4OIB/PRAJmIQDwpHsz6UNyuowKOYtjj97g3SIxNa/+Wr4VCQalUXrp0qbGxkf3RYbe99957BqNpcWH+3NnzdTdvNrU0m/W2ttdWJvehI7ut4/ts8ydR2R0/uhpPRwrFPEnRFJErnDpZ9ZnH737qJfG33jP8y2td139yaHZf3ZHj/NY3m+hDb81fG/Wfuoy/tSun0eSOvpf83g/y/f0ERVFEkaaKjMpD0jRVJMhf1qBvDyVom4l+6gnqa1+ijh2mSQhtKBJEsVgsEkQikbBagaQOvm63EQQRjUadTifBnnKPMl4vEBCSYE2BjSTJoaGhynguv98/MjLyzjvvOBwOlsuuuamxqbkllUyeO3e2ubn59KnTer2ho6s96I4Ib3mn33OMv20bf8c2ts86ddjOvYSp+/ypcC7lz7gwD51I0d4gTdMmqfzNHz32i188vshdJmiqUCRIkg6HwxiGMQ1mbnNLm0mSdHpcmM+dKGYShQxJkXg+jeD+VZ++07JUpRrcv1r/D31v/H3Pa/tX6i8oexoMk2OoUBlxmCPOuub6zva7uUwW1CqCtFttyUSS7ZxtO5FJ9WqDLBjMRhBEMBhsaWkp+7nYaXPx4sWmpqaKaXPEYDQuzM+fY/JU3G67rVFpG16fm9iLju+zje2xje6yzh5FFk9jholIFMsm/XmapvZ3Bf/oRfs/HXW9dSc4KM2A3kcSFEUWi+tNs9lsmUzmXqNJEEQul7NaIUPtlm4rHSAIAsdxVje6T5loNLo2CtuXIgjC5XL5/f7ytGHz134881QwQ0tBanFUC8wZfjuALq2SokWy6WUUlmy3EahpgbAkAsu91wKUG14rQx/CoBHXUh2BZLVJS7EbDjlhlUIibvWSfWEwj6hp9RJk2LZKaI+RdBtDC32emS77/ACJApwFMLkWMSmfoxRzpHAE1uK+auLOGeLageL7LxX3Pel/5qHBL/19/4P/dfi//MXUf/7Lu5/69Bt/8MeXP/ln+DPfJff8orDjZ4VDzwIYZewW0V9L3DmbrX4HH2knNRxqrM7XeDLYU0vLZ0jlAqVcoHTLpGKWWhkkFtrVN0/Eemuo6RZ8qo1QLVFWYKPO2uSirnpl3SnxlUPR3pr4aENmodM337PcWT/XcjWuFyDLkwHZAo2qwGbgUEBualRN+aw0HiRjPiLiKYbdBB4k4kEy7Cp6zFmnifCYi5gu7EZhVbjPRha35mMnrFLCsh2320ZFZ13crh9nwBNrhMXwws38RDvk+fm7xvZqQjFHc7rBDGAW0m5dUcuNTLa5+m4i7ZdJXi+12FnyqvTXED1XiLtnicajRO3+4sVdhcO/ybzxWPwnXzN96f9Z/vu/nvurvxz89Kfv/uknz/2fn3j19/64+j99OvzYN/I7f1448mzxg1eIK2+BzaD7EtFb5Tj9VmRhGILwXXpsacTfcIIaqAHn5sgNGLuRG2R/Tb7rkuqDnfnGY4WWE8neKoCwzLZRvL60eErWfFl86YD0/D68tzraV5seuu7sqhV33pq/UxdV89GVqZiKRzmUm6cxqiadOsJnK2LaosdMQP4TWcEkyhog1Xz+w/Kxw3BRFPBzgmAOAL4N4uaURYuY2Jr+gmU5YhlBWesppoUHDVh22NhJhrWPBdIxfJ7suLDZuEjDimWouahfoQ3LlGEFnheHvCibCY83O0eakYUB2qUFS4lVQikXSMEIqC/cXtA/eq4QbaeJ1pPFmreJ4y/59zzpePmH5ie/aX7sK+j3v7jyub8a+8LfyH7y1cKR54jzO4tVe2FEGt8HZXS8nhSNUXo+eGNReAXyznVHe6qoyUZysYMUDEMyO9EoudhZmL0jqzmSH7pWGLuVnGgipZOUSUA7FLheoOxtlFw/Ia8+FO2/ho81pBa7XXM9vM56Tmd9RLPsWJ4MyzmUQ170WBjGqbWcNmmcSsXJZLSAh8k07FAZvIBHstEAncaJVMzv9fxOek84HH7yySe//e1vCwQC9iFb5vN27NgxMjrW1Fi/Z8+eN3fsOHvu7MrqyoUdzbMH3aOM3jO+1za62zq627p0Flu+jkkmTaIbrtPv9R0592przbdaqr48fOPvfna04ciBnQ++zDly4/rtSdGfH0qeHzJLJHzEicysoq9eUFgdNpvVKlNb5sTY8KLsWp/gdA/yYr33379q/8Qbjjeb3Dd+U9P90Jurj/xGdf66anwa0WldmN3jtNvMepFg2Wa13Gtjo3JEIpHNZrtPGalUKpPJ2DI2m02lUh08eLAyT8WdO3e+/OUvHzhwgA2izmTSJ08cP3X6tFqlOnDgwGuvvfrOO+/MLyxeuHBheHTIbrNrZUbxnFa6oBOO6WRcnckAuGkbYu0RTZ4ba7rJHxhX8qYUvEv9Td9899l/vP7CY8NHT4jbDWaj3WpTqpRCodBqXb8ru82G2RHMjqDMnx91zyj5j02eeHzug6fmzp6Rdp5V9JyWdnyw1Hp9sXtcwtEadOdW7749WqtSqzgqQcfK+KX5O6d5bedUPefVvb+o27+n+2Lj6tC4gjuyOKXWaZ0O1O3AXHYUtTnsVpvdakNsDvbPbDItLy+zuG+4BZvtzp07lXkq2GnzjW98g/UA0jQtFKy8uWPH0PBwU2PDrl0733jjjcuXLwtEgn1vvrtw3ja22z66xza2l/nbYxt9yzZ5wLF0xrHcjn5jt+9TO0x/9qbhD150fuWw7FSvY15krRu329eGzmq1Li8vGwyGys7ZNKyQumR52WQybTpe/mq1WrVa7crKSvnI1h2r1apWqzeNwqZiVqtVLBbL5XK2MTabTaFQHD58+GOYp4IkyaWlJalUmsUjRNQHi3g6BoEnQYTCtGn9atGhYhZohhc4YKdiPobDkKHzySQhXqnEUYvTqTgVD1IxHxVxQyiv1wwI6CAS0wlSBlgQaUQRkMzZhprJBcAlkHNtkPPBAlhd2qWj3TpDT4NtsJVgefpZPDUDISIggEVGapYIXi8x2UB0XSRvnwIow4Wd2Tcey/zqO6lfPYQ98qWpLz6Ife+L0Yf/KfnzrxOHniYv7QARe/1dYug6JRynp2+TS13emU4/Z5C0iBhSECYhJStyGHsVxhlOqhhtjEUvsWoBqjItDM1cO9O974W+Q6/6Zjuk196PrY56FvuX71zTzg/qZvvHmmr94nnwpGBqOuICvTAZBSqXfIpIxYkEwzKXjEBGgmSkgIdjPmc25vdi6L3i2LPZ7MLiokImzdoUm8W2XZ7WC8CA8VGwPpimNHxsOLpTCzfOIKMjKl7GCO4G2iJyd9c6Oq+SU83EBACZKfEEpeHAuDi1tFvHv3pG0VhFMdggSrVAiidIySQhny0sdIB2MtkICkrneejtt5/M7fhZ8pnvhr73JfsX/tb63/9W/vefm/jbzyJf/QfPPz0Y+MZ/L+x/kri4gzjzKkCv2s9nhhuyYy2EYdUz1xPoqgIL3OxtgttDisZI2QzEkQlHC/wBpL8uv9hJSSZpySSpWiQtEsiK5VQb5oemLh6++8JPJg6+7Ou8Iq0+HBqqCw3cXGytNS6NKid7Zu/c9ApnixYp3CaTaZXGNED0h/uJsIeApHJJyCTlgpj/tEWe0q1mjEKfw3QvXTSbzc7OzSmUikIqBgYJYLeKQeI2O+QmY8xI6940UD0RJbA5oxqAvQcxYJgEhkOGBTTqoWJ+KuKhwi4mx4gHaAhivqhuNc08L5RD4eWPoVN3yZUBcrKRmGyiZNOkeJxSL9GqeUo+o7p+wrowBBEGLAc6M5MJu5wwrJCGZVLDIXg9LDaLHLlZ7DhfuPZu4dyO4oFfFXY85n/6EdH3/jH9xNdzz36nsPMx8Aufe404/waA1vXL8PphFhEajnv8dnCsBeaDcp4hB2ceGQa1Rlgktvn+go5HGZZpO7zhlKYWppJP9i7cONP8/E9nz7ztGmpQ3zwZnWxzjd8W9bdo5waUE10zbTd8Mg4ZD0L4ei4F3ZhlqMKyCYK19JQZwtLxVMibDLjz8bALQ38nvYeiKJvNdvLkyZaWFnZAY9FIx90OlUotWF0dGh5eXFzgcDgrgtWpmcmgNr9c7Zp42z76lm1kl7X0uds6sss1/e6w4Ppf6JsfMLQ+YLr9gLb1D0RNf7LS+KeD1x9crf+PX90/8qsjl1uu/eiFm44jvdHaO01vnT7wUkvySH/qQFdob21vZ81Xh2bH5o103Xz8ndu2k4Oh28vJqRVfw7Xl853Yu1dU75xYOVyjqRr2dvAj4/LoosbtTlJ5gqQpgjEXMZ9UkSyZhMDe43JBvovtX9vBbERA3HvFiztJkgMDA5X2nlQqNTo6umfPHhQt0eQsLS1OT0+bTKaB/n6BQNDV1eX1+W/fvh2NRgmSIKgiSRPlP4IAExVNUl1Wzl/3vPRfOp77ztiBrwzv+bveV744/NbD04c/0/HMFdUAdDtJRSNRzOmElAhFkiQImqS9ibDAqxf5DGKvUeDR3zUvvi9se2jowGe7XriuHbXEPfFcqsDUD/5BkqYJKlPMWRFbKpEsHaGAlDBPFvBC2pOPjej5T5zdeXD65nHhnVPSjivqgXbLwjQqkfst7kQwlIppQw5dGNGFkFgSd9jtZYsaQRB+v7+5ubnsLNs6bSKRcEfHXaVSJRCsDo+MLCzMc7hcoUg0PjlGZGhMgIubvPMn0bG9oPSMvmUd3W0b3W2b2G3tfq+vvebrR868VnXpX06e+eUnd7i+edr9ubcRiy8LtwTGOMJut+dz97T3sHlPbTbbh9p7WI6f+0yJWCz2oWZCj8dTOW0IghgYGPgY2nuy2ezAwODk5ITf6SjiERpW8yiswmEn619nOd9gBXcowNjjMsCrqksPJMsRN/Dup9bYnNnMo2XCEmDjiNLpqFUuQAWzNKok7HL9UEvybjV5aTcx0VAcukEudALjnI5Pm4SEmtP9/v72PS9rR9qw5YmCWcyKZwY0w2QqRVSkSUjw+0ueqclGMK0P1BBN75MX38wcf17+9EPot//H7P/95yOf+XPvj78ef/oR4sizxN3z5OowkBqbJO7OmoBsEdxz22kMQI1jFOUtkk2hUpAG1SFf7W8V1p/reutZ2e3LuhsnsoN1yOCt2Zba6mP7V/put1d90HPt/HB9Fa+7KSCZT5qkOacx5bLEEFM+7Ga0SeBxoZJRMhUn0/FE0BPAQOm/F29hLBbr7eubn5sJKPhFi4Q1zLAmHBYV8VG9Xet3KoMb91oZNUhGoyr93KBbMEOjqpxRqG+9nB2ph1Ad1qU11URMNpD8flq9RMhnWnc/1/DyU6a71dh0R94iBYwqRPapCOU8YM/H6uATPJI3ibbT5PV3yfNvEO8/V9j9eOG1H4ef+Ib4O1+I/Mu3PF/+vPXz/y394g+KR35dPPECcfXd4vDNgnKJMAqQO5dCdy9Qix0AQDGugtMHLB8lgkTSJk/pVgAvVaJMZND0jOqWs8pWuxtEJ3cO7Pil9tYpVe2R/MBVR+v5qboLtUf3L3XW91w931FzZqi+SjzS6ZMspgyCnFmStCrDOlHeoaQ9Jogz99lJp67oNhEuQ1wn9InmHHr1vfSeeDx+t6Njfn4uFvCQiTAV9YGlk8kqujYuW1yNJcUavIqQ1ctlgBD3IAr5HJikbwzh4RrHTzZplgucQshrW7BI1L31heGb5NA1Rn1hAObjDaRwjDIJCR2/9eUnOo7s0o7e9Ynn8ibQg1nsNuMvhlQzpI5HzDBoOVBPG8r+XLJ6T+zEi/LnHs0+993MU99OPvHN/K6fF489B/pofy2uWy3a5JRFjI40h+e7aIsI5sz6RGIBf7AgpPRCWB/KJl72PQFRpM1iXneT9MTOgbeeUTV8YG04lR+8aukAIqJrJw7O3LnZf/NS943LQ3dbJAsTAasuHXDlY4FEwI373flkdO1VKk6lYkQiSiSicZ8rgNjslnvyKt1rvEiS7OvrY0V7GcCxqTCfz2eJDYs5MoZmsVVc1RNcqXHPn0In37FzL7rH9upWm6rV/X+/UveHM5f/w/CZT63W/h/K5j8S1n9CcP2Bscuf0jQ9IG76xGjHy7yeHygaHpi+/pcXLz0vntphWnhec/tTiroH/OK3ijEFWYx7XN61R56iKQASFWg6kKFNfmJZGem6La0+PHjo5bvvvjt7sMnxwVSqcTU7rsoo0RwSLMTTDEqEplNJPORn4DLlO1nDzZThqGV8T/lIf39/OU8F2xV2u31kZKQs78uVlXcKhUJDQwOO44yJk61pwydN07li/unps9+fOvr5vjeeWbjwo6ljh8WtypDtNV7tEVHrsk9H0XQ0GrUi9mOy9td5V1/nXxtwLH+g6Px83+tf6H/z2cWL17Sjs265BjVzLbIjkttZIl9qAAOKKl+PpmmX05lMMqyGFAU213X8FZxhN1lrL1ePjY35UhET7l7yqDutSzWa4QvK3kPi1q+P7P/CwI6XeTV4NuWwQyqrcs+Ew+EyvqfcG2yWNPbGyx1SubO8vLwe8FWgMpGCR5l0ihOr1zz8KtfsMf/csSbFrT/VNj6gbPp9a/MD16t/8Zk9vj/bhX7iDc87naFuQcLohTt1Ox06ZwrPgEpfvtnyzv9yXHN/f//HE9/DuJWLdCFNp/FCxF+MeMHYw65lDjkIXRaXwMBWaMD3MA4s+GTyUfvt8AK9TtO3kao/g9O5JGAgHIqUQWia7i30XCM+eJXovZI9/kru+CvE4FViqYOUThOK+YiSG9OLwqoVt2LZrZN5JQtZo2j96iUHjZwyCaH8ZCMI2vF62Ok4T9XuDb75U+kXHxz6j39W9UefmPvTT6Ff+wJ55S1yvgPSCGTxgE5qm+4GewyzgpM2WdogYDSJkveHtMu28VOUlnIlrhfaRlo4e5/mHnjO03RaVXvUON6unOqtfm+fbqaf2900115Xf+bY0t26mHwxoOQZ5of1c4MDt6r4vS05vx0SfoUYKpcw5LQnYoFM0B0O+NYWwcoHqrRfKBSIQh7SdNhkOaOosFEhK1agdMsKIqsY3fPTsUEk0wz1ADsuxoHmwkInySo9vVVAzwN4kTpisbO4PBjlDcVUvIh03iledOmkfulSzgzjAonitVxyZZAB2DI60wTDZNhxnqjeSwIk69Xiaz9GHv2i4dEvBb7zj4bPfTby3X8svPnTYtVecrwxp111zk+6BHNoZzUlGgN2Ika+pg3lHGfQYNAnYBS2gc7QqCoqX0Taryy//eulwy9hrWcNtUcMty+vdNY1nnhHPze40FE/117XeOYor+1qTDjpW53UTfcaFwb7rl8QDLTmLFLatWYAY1xUhE2W1K1GXKXI3m1GBcIrCLJYoJNhKubL2ZTFjT5HBj29AY+1YThYBI9NyuSEZ7J+4pDvluG3XHtwckkq4qIdypxZrBm5AwMxdB30UfZz7BYx1ULMtRP8vtBsR1w87eOP+RR8l2jeL13MmcQbnxclpV8hhaMAAGIseVDPVBPRX0Nefdt34Gls9+PZ134SfeSfMo9/rXDgV0TzcUI6VUQ1tFODcYaccz3AHM08L4w1a8PDCP5W5unYcINrz0tEzXd0XVvc9S+ys3sdTWe0N45bpjqX+2/fvnTSMDfA722Zar1Wf/aYsL81Lpz0ShaMnHHd8sxAW9PK3ASVTcByVOZ7zCSK8WAu6A78jn6ufD7f1tZ28OBBDgfCiMpybtOwcrnc27dvMwfXzUnFHJnw5T2qZDFBiWdcL0zeembm6JMNTz11/qlvXXrxu9VPVrX9UiGY1S4d54xdma7/9sK1T54+/JuWS09OXf73ncf+6Oq5nfmYnkg5YvZew+D/dArecgnfRrkvmriHU4FVspgqt4Ei0oxRp3QgH/YFJkcTDc2Og6fFR6+N1c03jjhPTyUPDCbe7Ym81xe6OhNvnPePClyOYDGeJvJMlA9N05OqpNDKYFwYHWir3lNfX8/Gc7GiXafTHT9+vKqqqqzWsC1gf2X3CYK4dOkSyw1dlsQbdoBYmZiQcXQx7JikTRwwCfwGQxSjKCpHFFRh+8PjB1/kVv167vzR5eZfzJ76zN1nn1k432FZnHXJX+XWPDR+MJApMVYH/P5QMJgn7xe1hGFYWe/Z0AzmC8RPeb1nPvhgbmYDK0GRJMJZ/Lpu9LNdL8y55DQJIVSVeo/Vaq2uri6PSD6fv3379sGDB7lc7v2nTWtra6lAhQpWzJGFNJEMFg3Dfv3UHd3gV9Wtf6jv/1tp/8NTXS/xVwYXpy7tbdb9u5ccb7T4neGiWG372nFbwxLkJtv2pv7XxnPdunXr4xvPRRIA1on6yFiAwoNAn+o2wtLsMpAohCmBigOE8fK8mVlYmdd9kFI+K2gVcT9Y+ytM0xv3GRpWtzFnFDlXpjLSebLzAjHZWOy4VKw7TPTXUvIZSFsRcZY8aNkkGL0ziajT5pEsbF1V2dWW1PJI2TRAfybBOEHcPkmdeMH05HewR7/s/cFXIj/7RvbdX5NDVzMjt5CpYSKdwOR8G3eMxtSsYCAZJAfsQwZHPe2zFVFN2rTRSVGxrNOoOm8UqGqPcg68ILlycK7qmHq6TznVN9N2UzvTj/AnzIvDDu6Yub9e3XXDL+do5kcmWq7KxjpsnNG5u7csvPEcGBiMYCnBNDSmS5ulfsx+L97C0kNIFGlMS9mAeLBSnoEqsJE0b7PAq2j51g6sPALWLIsU4YxmZTPkVBOwMA9dI3qvkHN3SIB1i2mnjg7YgK07lyRTcSKT8Ji1ATmHiZOCZLGgmpiEpGKOWBkCesOFuwDB6bxAXNxZPPFCYcfPUr/4uuqbXwj95nvJf/l25pmHix+8SvbXpAdvuEe6ExadeqQ9vDJOOyGlFKvlwBxbaz9tl+dNoqzxnnm1aESZk05pzu9b3P+s/OLbU6f26EbahKN3l7oa9TN9KH/Csjhk7rpmaTytaDgb5A1Jh9omW67Lx7ts/Imp1us27njBKmNjvkB9ZHyIfoelvAhuv1PIwaPhtRCorpK9EMao/JLA3ELBItmYEINViZinycZYzuL+snmj9NSk4qBUYVrCKkGWhnOro9RMK2iWrCY61wZ6jHqJMglpOxBXgt0oE6dSEb9RwRjwNpBKkA4FmH9sMjZcgJhnFKmJBrL3cvHCDtOuX2SOPV9482eF3T+HYL3xelLHpxE17dSox+56xHPwvDD0mIRVWhkQB8nyzGLIQrM2UpWTCnoSVeUU88orh5aPvCqtOTJTdVS3MCSf6Fq4W29cGHZwxy1Lo4a5AfcQbLTqAAAgAElEQVRct+JODTLWLu5tnmy9ppjotvLG5/va7RpZAQ9DupWoFzyDMV/GafKg9u2H4x5HLRbL4cOHf5t/6sSJEyxqNZvNCkXCdDoTCPiVSpXDYTeZzKurq7OzGyTl1vpOqzr+ovuFrw0c+H7/kb8ZfP2rEwfFmBWEHbMtcZaffuJb3//BD6/dbO1sb2i+uufk6XO5XMluQeajZAGkWiqGqJYumeZfdXCeD6jOpYPiiLXbPvskyWRmYKvKZLN2lwsqzmVolZzubKOrL5LN9dklXswZMIepeVOhYSl6otd5rD96sCd8qCdycSLWvpL4whHsyaveAlFq01b+nvHxcdbPxV7o8uXLw8PD58+fZ6U7TdMWi8VkBosaMP+63BKJJBKJtLe33z/tQzaXRRzwnpAl8kWSDedmrwCfpxSdn+545h1+vdCucaVDL3GrDTEn+7Mh5pz3KMtFQVELQcj3fTaXy8XqPfcpYzabb9261d3dvSn5QyyfbDbNpIs5okhs5e9pbW0t45rNZvN7773322TsJ06cYLVAmDZCYToD00alVtvtdqPJJBQKZ2Zm7tOSIk0ZMDfiQbCRg+673zbe+RNN0+8b7/4J0v/ZA23G/2un58/fcr7UGPjWaecDv7Y+eBDTuNYMXRsrJQjwhf1/j+f61/H3jI+PfzztPdDJRBEgDqwny2sBwEEQJREVgaoBuew2QehK1ENHPQnMnHQBYheWJHhVjTH0zQxe4Z56TxxcPEEMSIq1K86VGXA8abmklkvMtFKyWZCdbgOsbkxaaVj9wXqEB6y6sKKcjaGMyVUSdhZkwLLjSCmG6pDgdlMtJzwHn4ld3kt3nAfVqvFYsedKfqY9yJkgnMaoy+pYnizYZFmTGCQBY0gAo0XIxWTDDlIeE+hA263jZT5G0irNGFYD4lnDTD+v7/ZwY81IQ/XArcs2/rhLMB1SLMUkM47pruX2Gyh/QjTSwe1uiiq5LsHsWGONdrqfDSwCJhWHMm9XfBS9BzKXMfQw0AbW+MGwIlWEAslphyLB8Npt8tCty2CQx2tWupIWy+qyYFChESUum/dwhinZDKVfJmUzxHgDYL0RUGtKjMNp8NNlPM58JOAxayIqCHte7ysQrkqAYbEYWw2HEI0BuXPT+8WLu/O//q77p/8zcGYHfecDiLK+vIvovJibaAlOdhFGQVjB9YjnsmZx3sSYExxAgLle83bDUf6VLQmGKw03LZ4KcIYM4x383pahW1fGm2sH6y7aF4edvLEwbyjO7TePtK7cue5cmVrub13oqM+YxHbe+GTrNe3cQMYkKlqlzLjIM0ZR4EP1nmKedhpom5RReiAmiwGbSwBvDnSC8EnbQJ3CNcsJzTIoB+xNsbHuiBqonlAtOLwSoY0vCWzWF3jxoO2ymIrrF85Sq8Pg/lNziIW7JOBsGJcZgIfU8GBCarYYmUm4NaKoYgnmAGuLZQadtK61ij3LLKRUi2AuXeqi2j4Ivf9C8Nb7VOcFov0sQKEnm0gtj83vEZQu+MTzMC4WCfumUWafWu//e4zO2rgoCYs4pV/xiWZ1M32c3pbRxpqxxpqh+iobfwJbmYxp+HE1zzLXP1171sEbFw62cboaU0ahlTc22lhj4k5kzNKCVQZRAh5TPuzxOH83fE88Hj9z5szrr7/e1dUFikQuNzMztWvnLi6X39XZcfDdgwfffbe6pkYoEjU2NsYZPrr4dls6mZKYVD/sO/g/BnZ8eWjPryZOPzJykO/VRlN4AI9kMhkOh/Orp379q189JRQKc7liLJ66datuaWkpm80y1SbjOJ7NZheXeL9+7tVXXntTJpp3a1uld/5OfOP3la2fjDsnkjF3IplLpoqBYEir1UIrEol4Oh3PZOJud4zPj96qix57L1N9heZzElYj6rTjhQISTCitEb42+EQ1+scvmj71hmVIFM6m8EQiYbfbEQRJJBLsfUUikRs3blTmqZidnX3zzTcPHjzodIIigqLIiRPHL1685PG433nnnTNnzr76yitare7mzRs6nS6ZTG7XMcDgFwwGNRoNjuMJHMc3FkonUhqP9dcz5/UOC2Z1JJNJLOiJxWN4HAom8UQqUaqWbbDD4WAbvLGa0jccx41Go9frvU+ZeDyu0+kCgUBbW1ttba3f789kMuz5OB5PMG2MRCJarTYWi7HHE4mETCa7efMmvbZtnTZzszM7d+7kcLhdnXcPHTz07oF3qqprRGJxfX39vaZNCk/ags6fz33w0Pjhx1v3tB36yUjnQ5rb/0nV8klp038QSHlia0Kow+zu6Ple04E2+6F25Llrtg8Gg3pXOpvGM0k8gcezaejUaDSq0Wii0Sjb4K2fOI77/X6dTrdlBNbLJhIJj8djMpnu03tMLKEVRdHytAmHwzdv3vwY8jWvjTVFhpxFlxHXi1J2NRXzg8knEQJG5lSUZDzuBJNzispAYAWdTmw2zt9H6YGfcDoeBDuHXeZcmcJRIx1wwHJmAk8T7TGD5lFZQy4RsOmdUg7ltUCKDPgz0F4rA4kIMBl8AnTMR4VcpRB6VE2ZRfRsm/fGe7GlPlrPB6t+fy251A0xJk4trVmKu8yoZCksX/TppPqlcQALOxRQfyIE+eShPRoasjxWiPO1ZZ0lYKTsQHwHKApIs6qh3VoSUeQskphm2SuZX7h9ld/VONpQRdgVionumIaftUiCCk5cBUy1Ce1KyiCMq/kRBQckk0NRtCsiHuw+9h54cSOLBKouWiQxFS/FRFnDi/uaUYFg0qAWLcA9QznkhTKdTFnKrtH8Q897TNDPbhPtt4PvzCFnCIfWnUfowmDCrKT9VlBDAWTDKDEuPWC0y6OTgXFBV2cYewxj2wBxzohh8BMx+gpLfQnQVym5OkyM3ixe3OV+5cfxyTaK00WM3iC6LhHTLZA10yqmVIuZAIYIgMTSLZyzcEbL9Mdl4XqfnaJljVvcAam4YFBcWtqlLToUeYs0rln2Sean6y+v9LVO376RtctlY10RJTdrkQSU3JiKRzs1STUPl8zEZfNh6SIAn+3ynFkcdd/PzwWPDEkQQYx0GeIGScIooVENTGZMT2Na2mcl3SbKqSs6jWTUR/odBKqmgwj8sT0JD1eE+bvX2wKTXDaEsVMRW55MgZapBvOqVVJSelAVPA7suCQgFsFnkCMr08AbjqqBItypZXLLmAGHF0CY41qmJxVQD6Ki1EvkeIOv8WR0sY/i9xJTzeR8OyWdYnsb0usiKgtnLChb9GpFJu74ZpV67dHYdnSKLN+gHRKiQa+WhkZDOOQ5qzSk4vlkizNN1bzupvm26xm7XD7ZE1bzM2ZxWMnF1XwKUyV1K2kNP6ZYiiq5jKVZUXTqgx5nyZqxtmzd/38ikaitrT158mRvby/Dyeauq7u5f//+0bHx1pbmQ4cONzU3nb9wgcfjXbhwAUEQt9vt2m7zeDxqpUprMpwUtx8RtHgCvkuSni/0vPFQ39vVgp6A14dhmN1uX11dtdlsvyUN8vl8Vqv1g9OnNWpNwO/3eDyBQMDv99fW1j700HcefvjhkdGJWCIfcMpQ7iuazv/sEuxRDv9cPPKySdphNWskcq3bG3a7PWxb3F6vOxh0RyIuhyO6ukp0d0ePHbO+/bazrc2rVPiDvjAeVqFxnj7G0cWUlqDX43K7PRqNRsfkzWATUdlstrNnz1bGcy0tLZ06derMmTNut5uiqJmZ6SNHjly8eEksFr/77oHr166fOnVycXGppqaGy+V6vd7tOgZSXDkcDrFYfJ+uQ7wus8msUCigZzxet2u9k8v7Hg80WKvVejylu956ObfbLZfLLRbLva7FniISiRAECYfD09PTR48elUgkwWCw1JPM+KIoyqa7Yg96vd7JyclK3kIcx3+bruvEiRNsWjePx11ff2vf3n3Dw6O3W1sOHTrU1NR48dIl/vLKuXPnUBTdvj1ud8DmuNRy9LP9r/zd8M7Pjr71Wv3b47PVc6Ypr6leP/Y9ee9DmPBYIBBWK8QhH4pHvKjT1b0afrHOufuWVqhBTHbn3Tm4WSZVrRjDsK19Ur4vm80mkUjuVcDlcnk8HrMZRmH71jJnejwelUql1+vZUXC73Var9eOZn6tYLC4vr8hl0owPLUZ9qaA7E/VTrAqSidOZOJmKFfBwIujxOiwhpz0b8UMYS6WO8hH3Mzgs0059Wr+KyXi4D4PoFZcB1AifjfGUreEb0nE6l3SrxRmXBQI9koC2LoXAlJI7MhjhNF6EhOcBClOz8iCv5nimOopWsG1AQBAQkHDhT8en9Xy/SemWcWIavmZ5QTg1mNILIEw3iELgVQCB7AEeC+TPCjgAplp6aWZkeUnPYEwm5eXeJoUXelQZUizxhzrUvFm/TlxMhGTTgwh/wsoZcwpnwYTAGkXYeH6X1rU6yb1zvchgcsPyJYtKVqwguapcxDOZDI/PVyhkWau8YBYl9KKsHVDJtNcKoUyImnIbsxZJXMP3i2dDssWcUcikV1uzlDh1TMYMH8jaiItOhiFPe4bpYcaNAta7mA8C7kDPAzxsSreCSnkJH0ZHPDSqpqxiClHB0FQOdyHt0UmiSsbJhShppw4Ux6gXqvJaYTSZmHDAHrH5GawSSsPJz7RhzedgUAQjYLEQjdHyGQrclLORxWG/We2SLEVUfNncqGp+JA1Mg+va2LZi9f4HabuMRhRh+eJKf5ueN+3RSnJhr3RmGBNMWQWLyOoMo1qVTCYglbVL7vEWbksNJGRFlH7pgkEhqRyLyv1MJrPE4agV8oLXTsQCyaA3HfaBqZKN0koB60wuFop7sQBmD7uQfCxIlQ2ZFbN3DctcMee3PkdRD41p0roVRDCfNMtAKUdUYMtBNZARBcqzwCCIhPLppUGbDhzEiTD8lIrAIwNBBmBtpVNxMuYn/A4ILoM0fGCLKqqXHKO3iw4lZRSQqgXAVzEIMBpQVkqA2QlnI1qRZGlaOtFdSp/OZkIFgxZj2WLZvNgHZNPnJg5rmxSUQo8xIFsQ9LVq5kfCenE24lXODFo5ox6txL46kzeLS0MPxJ4K2iFD5vp4nbdYjFFIvqRTSCvH4kP3rVbr8ePHhULh2bNnWSBdsViw2+3RaDQUCjpdroDfHwgEV1ZWPkpibTwejxVSoSx4rELZ+KMT7/1V13Oy0LpL1Ol05nLrqcuFQuHt1tt4MpnOpHU63a1bt2pqak6dOnXp0qWLFy9OTk7mcgWaJjJRDUXmiVwk4Rz1Sg87lp4zLu1NuGfJfEWOi423mohEfMvLdH9/8fJloraWnJqi3RgT9bRejk3ntP6dpst5KtiD1dUAAb5y5Uo5eVkwGPR4PNFoFEXRaDRqs9pS6XR7e3vlTVVWyO7ncjmWAWjrT+UjsViMJZguH9m64/f7NyWQ31rmd83HrtVqL1y4oNVuSOvG5mMve7VYIuPKPBVms/nUqVOrq6vr06YA0yYSiYZDIZfbFWCciAKB4EOmTSjif/aXpw/8bN/pX37l7itf6Xz9vw3ubr5zkW69Ym36lKj2AbfoEE3TCOqqdMmlctSANLW/M/xqc/DBg5jZX6RpEkU+3M9Vjsvb2m/skY/o59qUj31ycvJj6OfKZDI1tbXNTU2IhJdxWfLJKMEG6KZB48nHQ+xn3OfMx8OpkDfiRtIhHwRmb12mP/wIDkpMGMtbJB7pUtRpgxSekKcTeINANrMJmTM4lU14jcpshIE+AHahvMRvkBMl/SzmpQN2iH8xSzxSTtFjpX02RqMCdxjkgAyiPs6we3UqYxR6JIsJH+ZVrhTM4oJNTjh1dALuseJ2GMkRBY0BOHgiHtpvB00ILCU20IcY1Yd2G3I+exIz6XlTHrOOyiagMclIMeBwSDgpp9ln09mWpylMBy/cQbToUBXN4rh2xbowlDQKaZdWPdkzOz5yD7UHmL6uVFXdbW+zL0/nTaKcVV50m+mYlw5hhMtYjHipVDQTdOKooeixJjAL7tAnEQPp1APW2KWHfl6HhYKaWHGDjCBkXYpAuGdhw5FYxAYmmAmpRFnURLt04K9xqKCqtXEhMwmfRpTF9CB340GKzTNfjuDL4GyqeaCAYkKcSvLVKHBxRwhMSwcctFNXMEvSiiXKZ6O9Fi9/wiWYyZpETtF83I+5JYukXQ6YGAa1zbhL1qxKlWKVddlUHmG9SIxrKYfqUohWPTcSQMwwLqkYnQwXYwGHUpD1Y16z1sEfLw2iHViA82ZxUsM3zw2AKuxUSYbbZ8aH77V2hMPhM2fPdt5pCyj5Oa+tkIgQbPxRBoc3hHiISsfTYV/c5yKS0UTAFfc5s9Hgv/J5Yfsz4MiZxVbeREgvpSOuUj4Zv62kxabjdDZRTMdcRlUeD4NDOQ1hUGsq0YbnBfzIeAgIJpxa4AGySZHV2QKmAzWXVdAdCphgHquHP+YRzhBWKIA7dE7hLGmTFa2QRoPEtPC2EEBA5Q27aI+FchvgD0LV9IxN0QhWWGAnYjN4gEWQ9lpyUX8q6NbMDiGChbTXTngthag34ccQtSSXjKIaiUsws0Z/pS0yztyYmmddHAL4uVsnHe2cn5q417hse5wkyenp6YMHD7JxNyxodGtJHo/X0dGxLaq0fMp6oiUIJAKr0xCyunP5xhFRqyGKWXFPjix4UFcBVBnYZEHL80uXv3171+M1e/fv3dfQ0GAymYrFIoZhJEkmk8nBwcGqqiqDYXPGx2QUNYpb3JJDCOdFj/Qo7potZIEPkN3YS8dx3OX3Q4OzWcJsLg4M5C9fzldVFaemikND2V27yLExYGtm4DLlW2htba3ENbtcrqqqqlOnTvnZqtYiwtYuBf+LxWJNTQ2bPmIr5LaMfbHZgOdz2wJsGTaC+v5l2Czr9y9zf1wz8DWT63zN7KU9Hs/ly5d/C9Ypd2CBUWIqcc0YhlXmJSVJcmpq6tChQ8PDsA6UO7CyZ2ia/pBpw5TOd3QWXnnVN94nv/TeibO/+fzgjgMfPFO36yc1l78vG/yKoe/zIVOD3WYImLsKGR97LZYi3OjJ/+Uex+/9xvLTKx6OPmk024A1oNyajX39b5qnoqWl5WOIa87lcp2dna0tLV69POE0Z6OBkjIBq2c0FfQUGO4ZOJiB18p8PJyNBApxBpSwQV3YuMLeSwdihXEimLYrvRYdnU9BGLBFTHnNIF/hpTlSiIe8Jo3bqAZuD2YR/xCxARxCCTBsOBQxJTdsVoFIYLKl0lEfnUvmXCbjbF8B6G5FbqUg6nIEFVzaoQjrxRkfpE8H0VXZYFaQAzVLHGpOM+oam3k+5mOi931hl8OiVbqthmzUTxdSzOkMfBtQ4SE67CT9DqtoyS7hUekYmY7T8UDWaVqeGUWUApKBauKo0WrQ3ou3MBKJdHR23m2/45JxIWUjcCEybjintoioMgFXieYkk2ApZIioLxMNFOJBICAIu7YXe5X3WNpn2gzoUVDvaEwTlC25JBwq7AZtzyoFCxPYhyJ0MpyPh1xGtUs8D4WzSSqNE4mN/bY2SUqnhF20z8oSV0aU3LBOzKhfkYhSoO3vJWMBEtMapnuyZknBKkFl/JjLHlaAJSmi4CS1kHYewuvASGAGL6fLCC30WsAEFfMBgYLfQbuN4O5x6llWa9ptDJmUdp3SIhUk/S4mRJHRmNmxyybomJdMxQxCjmN5grRJCRsDjTKKVoY67CuzAKlxGaImhcOo37TGlb8yqS4berq7I24k4UULeLhs/iSS8LwA214qXnqIMngRD+dgXLaAeLYZi22fIGYSxryYeFG+OJHyYdClFgl0AtQAj2QuGsD0Cq9Fy8wEoPsDx/Q962cC5oFr0Uo55GEFJ2zXA4ya0WAYK6A3Z1fblkYKVknGJPJpxCGrNqFdpRxK3CxPumxUyEniIea5iG3Qp1nqLxacl4wA+C/sLDn44n6PzWhViP1qYUIrMK5ybIsTRgHHbtCKlnlui55MRshUxCjkoIoVEg8R8RAdDySc5pWpEadKRMX9FB6Muew2yz15lcoDVLmTyWTa29tffvnlgQEgkmEFIUEAkTJbjH3p5/F4rCNsozRZ/wbBxmsJJstHCZLME4WXuFUP9rz61eF9k05pn2phBpXMueQTmPgDedc/D+7+m4FX/u7gT98/eYK9XDkwh/1qNpurq6u7uroqsbqZTNaOAD1jMROIYeNu8SGE85xH8l4cmyRypdCnOJ5wu1xwR+U/giCs1kJ/f+Z738v8wz/kf/5zn80WjEJ5tsE0Td+9e7ccx07TtFKpfP/99/fv389aWVilodwzLINzoVC4fv06awAo33jlDpPUIvu/rd7D3g6O4zdv3uzp6WGhwVv1Ho/HU4nvYafNSy+9NDg4eJ9pw+fze3ogF0dlh1Tu0zQ9vbjY3NA4OjG5OjY1eObo1LOPvnrxqU+Mv/7a8FkqEc/jFmTxWWnLZxQtn8jGQQNmT6dpyhcrDkiSQ9JkjyhxYiD8/HV73ULUHiynkKu8Dkzmf7v8XO3t7R9DvYemaZ/fD6k9CpkNa2UyCjRrLIU8HizEAux7LZWGF0pW6BYrFv0N595zzWXeRIHtLRnHzAGHEcSnzwY+EZ+FrSGMWkzcCb94vuizf8Q614rFaJ+tgGqdSkFpOQbvGDh3YkaJjTtKOhR5k8gpX3ZrJRkzQ9ID8e3JkoqQYsHU28qeioOg/OFEOq6RCMIejM6nNiz94FAACxarGxGJkEm64kMYbx3zXp6OBNKRAA3GIXDSRYKB+8SxRyIRq8VKZ3EKD5c0iXgAbE4IkEFTAZQMOYl4gMBDDETJSUXcRDxIZRJFPHxfyVdxO6WRYowEILGidjFXsTAJBE4+KzMudpoZ8aBFa+aMB6SLYInBNGDX+RCtFyLyoHOiXhrVFCxSt0oAUyibIBORfNBD+R1xFdfGHStYpUWb1CHjo3IwwrEMNOtRQoiSCiDrl2Mtf2ULE9uGVJRRcx3FmF82N5GO+Ag8XIwFyxoJnU14EVvYqmX8fXghEbGKlvyqZSpgp71m2mVMBNyZMHiOqFQ0j4cjwVJ+xEohWpaUHo/H7XbRxcx6/WsTnj1CpWLF+Jo+xFhPiUSETEbZz61nrU3greMSZyxqcTqfdhqU4unhlNNKe82gAQdRMO1kEj6LzsyZ8EsXiZImtE0l29XPcGv5rCmzTCPk6zXqqAcBY1IQoxAlruKiK1M5szhvlrrVIkyxkjMIwCYU8wPHILwJfJSrMFZGmAMJOp+aHR4wcaZQhRBdnkaWZ2QzY/zFeZ1aMT8/x19ajCNGOpfI4SG9ZCXiwUozJ5vAg55sLATPSypeTMUCPu+axrJ1ZLY5Eo/HDxw48Oijj7L0PDRNBwP+ixcvopjTZrV0dfWMjY329vWvrn6Yw4LRe6KMGrHpMqKg6cG+1/YI6s8pe785/s5fd7/4o+n3m4wzuig665E/P/zB+yM3ds7XLnpUBAWUfW4HpGsoV5LL5aampqqqqpRyRSwcjQRC8UgUQZBK/YPIReLOSa/0KMp90Ss7mvJMR4IOj297L1jx+vXcd75DXrwIrpjwhnRXExMTlbyFExMTjz322GuvvRaPg+eOpun+/r5uBgjV09OzsrJSU12NIGhnZ2c2C+Hx99pyuZzd/iFxdkx+LlDU7rN9RD9XpY64tTaWcnBrAFqhUOju7m5oaEgmIYXF1niutra2sufrt5jyt99++5FHHpmYKNkXAzBtLmCY026z9vT0jgwN9Q8MiESiDw0DPHfu3EMPP/yDH/zgez/60SsvvpQandQJZx9dPLF7/rImAnSRYUu78s6nZXW/F7awTApb74kmSUKstrdwY3vaQ0f7o4v6bDK7Gef2UfJzfUQ/1yZv48TExMfQz1XqZoqCl8VNkoxllY0HwJQS9VIRN7hXgigIdeansmWoch0EobupnopVknkbBpZbn92YCbpBLuIhOoiCRykdJzO4VSFIWeUQ711CMHyUFbZcJkYkI06zNhcPltQRkA04sjoDaGLGkp/WC3DNCrirECXlMoL8iPkY74wbJHRFU++zT6XjWqkw7MWA7Zc9pWQiAvJfgEo49QAbSkaSEZ9BIWZfxKEka0NiTikmIiF/mcRsm+kOhyhqXcxkcBDbQQwwNJiWCQhSUwE75dQXEA3jvNCBkSaEASv0xhsBsYFH1jwg5e5a36FY52YuiUp4Dhmf4TKIgC8D1dHJSC4Z0/PnChZQSsCPhqiYGCLG3rDJg7bxutCMDA7WrwDiNKgg4QDbUZl4IYDap7vT2mUG5S1P6QVJLQOmLiOoAFgN4GjwgQJmZaOrbsOFGBtGOk4kY2oBLxX00Llkyo0WGZEJbqBkbGVuTsGdS7ssoBHmkplYQCfiEWAec4ENCeITGddtBs/FguEA2Jzvt1EkBTde6R5d70y28xnOvQ3u4K0aD5WOw8vDhnvZUE9Jf80lnXqlZnmRYtVNFkuUwfOpmG5lAfgR3AbmeblPF22otjQ9mOdUKV6dnpx0W/RASIFpCZvMOteXMQpoBFBfSe1KUseMC6KiQk4YiPsZk7a7CmM0FXLmrNJlVCkIaES4VoAYtdzFxeGR0dXZcekqTykWoBYjXUgH3Qhi0Kw/JhWzi0hEftc8FexLsc1mYxMO+P2+qqqq3bt3j41PGA36EydPnj139vTp03z+8tGjR00mE4ZhyHYbhmFSqVSr1aIoWvk7iqAOh+PCcofUrAm7fde5PQ92vzKs5ETcfjfmtDscBqsp4PIJjPJ35+tOyTr6lQutnMFJNd+JOX1Ojxdz+1yeaCCskijeO3X86T0v/2rXC9VtdVK5rPIqCIKhmAdz+W1mlXqlSTL6smHqV2bOXrP0ttWoQJ1+zOlHUCecgqKIzeZYXUXtDqUKNvaOMAzT6XSHDh0q45pZvSqRSPD5fL/fT1HU7MzM3j17Tp48mUgkbtXdrKqq2rd37/zC4qWLF0dHR51Opv6NzWIuiFosFoFAcK+uY8vodDqpVLqp9yorwzBMoVCUGxcu5F0AACAASURBVFz5U3kfRVGxWGw0Gu9TD4IgbLqGTWVQFHW5XO3t7RcuXGAzeDgcDrZmDMO6u7svX77MPu9lQ0rltLl6tXbXrl0jI6Mmk/H94++fPnXq3LlzAoGAzeGw6VpstSiKYhh25syZJ5544vHHH//JT37yy6eeut3Z2TMwUHX3Vt/S4CMjB17kVR8SdyxxBiXDL6tGfmZf3m+36lFXAHX6EGR9KtrtdrFo1eN02OxIz4JhZ512Z6u/etzLkVo9LsTvQZ0YamLi6st9tXUHRVG9Xn//UUBRVC6Xq9Xq8rTRarWHDh0ymX43O+v9Vs77/vbAfX/9N/iRokhIjrN1HY+VVjo8CEmI4v6SfA0zKyAsggyI8t4L9yYBXPqawTGTvgRHCDtB9mTANaBaWfQalWxc7vYnfuiFsolk0OO1m2DpzCboXDKEmAMyJriXQRvQDnBtbADGoloA8UC4jeujXjSbiHixiBsBvSebYDMK5WLBYgL8QQB08DtAi8J02YjPZTUyL6ylviWSUdZp+BH1HjCwbZA0OB12gcYGHh8rSLsgCmRLNjYZKgR2gTJXwvesy6HtBnf91xJYJJd0C+fTVgUYmXx20oewjErqlSWXjMdgToGzGxrgd1ARDx2HNAub1eVtxyibwP0un83I9hidTwWtWp94tgxhZnCsG8fFLgdqY4BL6xnGhI8g1LOJmM8ZcaN0JpHze4hEpBAPFxJh3Oucb70t4CzKRYJCPExnE4VE1GnSESEmQUQmTuFBMoDAjWQTH03vocB3eV+95yPOpQ8ZF0ZxLCYimEmXCHlBawTDZJTOp4rpuFrE99oMEAkPZrB/1V8uEfO5NFJRMuRjtI1YSC8JiGfptahGGJf18EAYEZjbv+uNZ/CE3x1BzDTuo3E/6bNnHTpUJV5eWvBIOWreXN/AoNWoowvpZNDrR6xUprQWUalYSfPL4P8KvYem6WQyubS05GK8QslkUiQUVlVV83h8r9fDX14eGhzq7esTCASdnZ3pdDp1jy2TybCBWlvLpFOpQi6fSadzmazOoB+y8JPpZDoFVaVT6Wwqk0ql8uksmS8uuJX/3L/rv3Y8//DIu9Wy/uuKoevK4ZuqkTrV6B3j/NsrDZ9rf/5v2p/fNXrFaXVsvRA0LZ1NZ6lsgY6GMJuyExMcMkz90sbb5zcP4VF3KpVJZwrpXDGVz6czSYzZMhloQCqVSiQSd+7cKcexs3qPRqORSCRsUnoUcdy9e7e9vd1ud2i1GsHqatWVKgTFGhsbURTdvj2pVDqdjkajBoOBvcq2n+l02uv1smnSty2QSqUymQyCIE6n814XYk+0WCzBYPA+ZZLJpF6vD4VC25YhSVIulx87dmx2drZQKGTWNrPZXF9fz/ZJWSPkcDhshD9MG5GoqqqKw4G4Nh6PN9Df398/KBKJOjo6tr0Q29p0Oq1mNq1WK5FIFHL5vr17v/mNb7y2Y3eku/fo8Sf/tP+Fd8TNmM2VTBP5fM6vu2GcfjriFrhVDX7reDqTZ+vBcdxgMMTj8Uw6ReRTVCGDhfO3efFXGrD9bc5JWSQUS8WjYbvVdK/uhbmTTvv9fpvNdv8GOxwOp9NZnjY4jt+5c+djmKdiTYdi7D33XzpZrYgxY1AQl4tC6BOYFj6CQFqrmUhEAKyTTTgtjN6TjtMec85lRtTioFltEPHg14qXvM2rOR78UClLpeNmlczvsITdiNdm9Mq56/mtUCBNAcQlLOuQDAEMJEF0jUPlI99IBs9EAw69OuZzhlwOt9XosRk9dpNZLddKhX6UYfmLQZhPEIEgODqDVzqeyGQ0Hw8XU7GQ/358zaWhAU/cFpHGeuVK0g44Amg/E5LmMoAdCAgSLWsmmS3nbqmNSsULjF+GTMe9OmnOZQZfXsTvU0m9Fq1fJzcKOEUPRESX9DkwATpZ3iPAhYARIvphJpk4lUlYNAq3zRRx2T1GFaRn2o6CuRQMD3ySargco0qy9sVt+mHTvTB6j9tijHlQh0LqsZncNpPXbtYKV5Z6Op0mbdTO2HuyCa/NEPM5aTxIxHyl+ZYB00sxFcsnIuEAgEbvt7H20U1X/zf4yrqS8/GQ06IvWUGyiRhm8xnVfodFJxcS8LB85Hm7tYUZnEhG7Tql06wPoTavRedT8Ei7jKFll4NlETKLmRgwtQQMbwye/aMOR/ly2UQi4EaMWjzgCTkdbqMGJoDdpJeuSqaHjQqxRatOhACK5zbrY34XPC8V0DEYl0SEyOC/q72Hpmkul/ujH/3o/Pnz5dEkmI11arCffD5/enq6XGDbHbfbva2fq7IwiiBlXHPl8fJ+jWb4S8N7uF6NLGQVBowrAT3fp+V6NUte9axd+uz4mS+M7PrZ7QP1Q+2FzHpcWPn08k4cT7q9QPFHFuIJ94xP9j7KfcEjOZT0LmbCUmTh+ZR/ORiKBoPrgGiapkdHRyv9XL9N4r1///5f/vKXrDIEtbHbmhuOIIh8Pt/a2ppOp8uX3rqTzWb///JzbWrw1ms5nc5Uap3qemuBe/m5KkuiKHry5Mnl5eVUKoXjOEVROI6X87Gzeg+Xy/3hD3944cKF8okbpg3TRSsrK1NTU+UC2+54vd5YLFb+6erVqz/96U+ffeWVsV1v2X/6yO7aF45PXLqlnCCLJddn2FivaP53svo/iFjay2fdi7ewQNACa+7UcHRHW6h2KihQY+VTtt1JJBLsC8C2v7IHvV7vJj/X6Ojox9DPVSwWJycnl5f51LbytbxybdjBAf8IqIsg87cVs3nPhZj1czFR8WbGxhCjg0gu5JbPjvptemAG2qT0JNkkjgwcAZgS4Vrgr4H2MCt+CepRId0zeDLoibrRuN+Fe+wkG6iCqiGGJeYFORr1gYUp4qZ9FhaiWylQSx6fDfdbrrwsY/BiIuKxGgOINQ4ZE0PQbAB9h2ZGBxcnRuA2I66E34ka1AzdUbkG8PvkEpGZsSGrThn0e8tO5U1zMZ1Oj42NiYTCdVfa9k1iamZV0lQcYqxcTHCNSw++Njb9U1ku3ltGsupmIuDxGhUQHJeK0YVMJuiWTw+FUAvYNkrh0DEqgICL0GOmYwFweUAeNwMEcAUQCl+L8GKHhr1ueUAzeCLoiXjQuNuOG5hQ6krDGztMbPJU1tIDweEMioiBllNMgGHlSK3ts9FqpSlXwMNeuymA2eIBN6CJYz46Gc7GQyP93auLM2QqRqVjCb8L0aupZJiOuOFGWHNaBs/FQzPD/Q69OuS/p58rlUoNDw+LxSKayP7OZo/7jOA9fgIgXTqe8LvBhMlO9UI6HXDJpoeCqJXcOvk31FPqE5jSjN2IebjKc3htTmbwVNATcSFxrzOBmcCJiWkhyo/lB8IZetKYn454QdndYkdkIw/WxmKtzg3NgMlTxCMuiyGI2fGAG4iYGVtsLh7q72oXchfoHEDBcL8TMWogSUXl6Rk8HfHPjQ+jZl3A59n0mHzoVx6Pt2/fvsbGxgrQKJzEijf2dC6X29zcXC5Q9nSUd7bFNZd/ZXdomnY4HCwUZtNP5QKeZOiOYqq4plhsarw2ggzYl/VR9IXBM/uGquz+Eq/xptpomo7H45vCwovZEO6atUw+Jqv/Y/H1P0x6lgLBkt5TvnpdXV1lPBeO45cvX96/f7/FAqH45Q5hd9hPgiAuXrzo8UC3b2pGudps9n9rXPOmZrPqnU6n+y3M68UXX3zjjTecTqfD4SjnqWBvnMPh7Nu3r6mpqXzj7GCVe4lVqdl8t5suUf4KCcVcrjIqHOBlwSCCIC6X61pt7ZHvfrf2h9+3vvvuy4Nnr6iGNG6LLRGIR7T2+aelt/7ANvvzfKqEiCrD4cs1szts5BdMzkjxDi/60k3b4Z7wtDodS68ByCpOYMP1Wb2n4vCGXYbjylPGubM3e/PmzY8hrjmbzVZX19xtby/gG7AIG5aejcsQFfXCu7jLCPgPtxmod0oaCRMcBOHc0fu9EWYTITcSwGysRPdbtIhOGcJsgAoqy8jKKzJCF9JWBxwAQYi4SZ+NCKBM1ncnuJO2wdgyibLZXPFBFJoa8TDwnWjp5RgERpyOM8wr8ZINCTLJRz1EzF8R3lWhwKViJcMG205W5GQYyE55pWaipuN+F2bUSngLZqU0EXDDEp+MlYQrI36oLL68MDM11GcxGTbj09bWwlgsduVKVWdHR6Hixfeeg8J2VyIMr+ZeC/giXQZwD7GOMBanBdSRPhgsFqvOCsLKfs7gAcwesemAviUedGkUTqMm6DCXxC1bEjrNTwPrkhKcayyjDNDpmimnnkTURVQL+wEHMPpE3KAWV14xzQwxGIeCEP8M4ehKkLIIY1dA1XSYyXniswEbkN9Bh91AHR5wUEGEjHiL+BanaioGoWdhF6izcJugfYLSlmA00WQUKKOYOHMqFY17nTatUrq8ZFLJcC+TK81nhQTpKWa6ZhOFVJw7MTQ92OOwWdfGYfP/SCRy8eLFzs5OOpf+kOGo7Fs2yontE4Dbl+cDC/6tmGYllbFCgcgmMJMuw3I6pOMujQLVKUNOOwCMKp+X9TrBAQedHw8wXREm/A4SPHqe0ohAdF50fUKysDPWL5zBocNdBtCY2QrLJQFexpxV9romQjSTZxdch5tuFu6XWQfKxxmkHTSYjb5kdeJsggITncumUUh4Cxa1vIQAK5/FPi+55ML/y957dreVZQti9jd/9Gcv+4v9ln+Al+0ZL/fMLM9afjPjN6+73+s8HauruitIqiCVQkkqUUwSlShRDGLOOSCDRAYIkARBgiAJAiByzjmTiNe1zwGvIABkVfXr6tevps7Sog7uPfeEvc+9e58d2XQug2Ixvw6WU4uYRr/z+XxHR8elS5ew3w1yzM6bzeZCoZDP58KRcDgccns8Gxsbfx55j91+caibUrHostqLZ4f7RlMmciendrt92Sr7A+cpzbQBucvrSiKRwLxIzZ3iSdCrvHcw/j8mPbxgKFYjPqmR9+zt7f3+979vaWkBpxZUQsGg3w8W/bFoNJlMWq3WeDw+Njb2LZP35PP5YDDIYDC+973v/e3f/q3RaMxkMqS8hyCIXC738OHDN7ZNPm9B2yaXy0UikVAo5HZ7Njc3v1Te43a7a8LhYFDTGYy/+9GPfvyb33Le/WDr7//Tvxn8zd8s/OE34ieZQo4gSgkX27fXbBP9MumBPBjnyXuqsX+STdusNoXltJ0WfX8s+JIbN/oLNcQllfpO3nMGs3K53N8/sLy0lI4EG9AV+ADF3jjXZpNEMgxu534rRLixqkClEvcDhQM2SA8XPXpwP8b6o5ATeA5MWrBYIpfe21zfk8sIIu/Qa2S8tUzIh+Q3Vd/6Nz984FKUCEIeeGslF3clKyf8hLzTYHTis8CHnhRpYJKAf4Lk4HUQmgohx54mCQjWDJNPx8oRL4iCsAtuyIGEQ2EgAFEvrAUymR+CKxOEjXaBuCtoL/vNoE5KBCrMRNQLYqRMNBsNbYr4Yg6rgBcClDgMhAR99y2agzXailGtsllM5/mx5/P54eHh2bm5dDjQwF28Gj64jl3J/BY8N0CBU4N1XmWnBuYf9QBLZDsE96h4kAg6kCykyuH5NCWXCHTb64TfaNuTrrPplfhJtWPFKiBKhMDKx34IxlI4zKMFZbFFWRoQT6OCDPBWFbBB2AkItkG8AoqYj3Bq4V8lxVugjC3ZERMJvI4biZEghuQ+sEEeA2gkwyipSDqOGFkPeLdhO2j7PvB5ETf8cx+jLaEHZ++QA0ZHIaqJRCAb8mwIODIhD6IPgCQDxf5G3FgpmzCq9/hMik617bRZz96P2v9PTk76+/tXlpcz0WAVf1y/dTErU2FrYGlBG+HWw/5x62FHJUOAgrAbJI4hB9pUKLlvPFCxqMMMUDZB5NIbAo7hUEkUspajg20BJx30IKvq+kGRDRl+y6JeYEwhajMwqUWzshKtEYeWdmrgDU0EX1vNk8OlImCzBSloUPwC3FtlD4B3XjnsrBwe4CQQBAs/HOkgE4W3DGz+IuBgGLAgjHgQypDZOMR/R7G/SWUZvHdRIhtPB92iVYZMwKnEXSTNlU7AL8F4qFqlLFl1apsF4sR89ZJIJL5wR7p58yYOvFssFrc2Ny5durSt2FEqd9ofPOh++fL585dyZKiRTqdT55RMJoPtey5uc3x8HIlEzukjlU6n4/E4NtQ4r00mnQ6FQvpjfS5zYo147m+O39wYOo45iVwxm87gp9LptM/ns1gsmUzlyuve0tl0Jhf2qBJhi93hdDgcuA227xkbG6u279nZ2eno6Lh37x4WHUXC4YcPHjx+8rhYLLa3t1EolPfee9dgNA0NDdlstgZjoVHT6XQkEtFqtRdAJp1Oe71es9l8QZtMJkPa97xezpu1dDptNBoDgcAF/SSTSWzfc0GbWCxmsVi+iNbY1NTU3t5us9mMRiNp34PFaTMzMzdu3FhaWsJsx7ZcfunSJfm2QrWnfNjxqOvF8+cvunZ2diYnJy8YCG8bHGuxeinpdDoQCJjNZovHExGLif/jX8+/9/2/Wfnjr2TPzekAkT45KZQzJ6Wo71C39nOPsiWdiumOgQet7qS6nk6ng8GgQX+cO0lnMymbLzEmCvxhwP7hmJu1nw4nTokCGIfp7SGb1ZQ+20jVPeB6JpOxWCzYyiqVgh2bSCTGxsa+nfY90WjU43ETuUzcB5r1mqNbORWBhBU4OEcqBnFTINSvHwhP2A1kz2cGKmU/BNNaTAJxegTbAZBeyx44Hzk1EGolhuIQnqY6Ox4IOSyikLFoDsLYOriGuJKnTCxlgfOrCwTvIQf0BkHqUGBivwWZ9J6FtrOrQSCUDAMDhCUNkFPMi1Q/hlLQDnQu6in7LcWAvRj2IDFVEBYS98O5P+aFEy2E8kNMg0sLYzkgaykQb3tVKDanBi6itGVlN/gVg7ok7odl4gmkwlGP3avbR4QBadaSYeI0lQx5DrZl2xKB324uZRPhC+17ksmk3WEvZxLJgPtcvhAzlJk4DITiJoPZDSQaOwTDZw+yzMAR5Kz7ME9E8MDyGntLQQoFK8AhEydy6cftLZs8dsmjNym3kkEPHM1r8AIe44iBg1SRsA3AXsp+UOF+SFcsnIWq8hMsdWDPJMAxEBAESHFDgGw78DQlj54I2mALeY1FtwHiF8AG84L1tEcPKQ68BiKAbLfBC+8AkBKwAvxRHSXWOEBbAvl/OY8grZhFhXYdxMNEM1TDIy4d4T6O2I1+wyEEaIh4gPZDLKJkMuhVbki2xPyIz5VLxSLBi+x7ICBcMJhLRFIB5JBYAyIs2sERnBMh2PNhJJX0IRYN3gUt4k2tFY6touxTAWpgUftozmpg2sLgOFnKxFvv3VVuSUvZhH5/NxXynqv6TMeRDtdDJMKwn31mImDB2V4BPlY0BAx39r7gVzKDnor50YsDf4FTjHrBpy8RQoKfaCEZQU6aiH3HojXM8eAXDdSdLghFAY6ZZuDnELQhHinkxNgHb82oF97KkB3idaFXBubm0cODwIj7QnaTT4uSE8PbhwJj5tJxv3tvQ6JYF8a8zlI2AfnYa86wFzJBsVjs0aNHN2/exPIel9P59OnTjz76eIVCddhtr169Wl5abGlplaxL7969azAYsBtLvS+Mw+FQKpUajaah5w5u73A45HK50Wi8oI3FYpHL5ThzVv0o2PXJYDDI5XJImuFwehyu5V3+W/SHzbxho93idbpxG61Wq1Qqz5ut3el1ON37++DRhds4HA6dTnfr1i3Sn4sgCC6Xe+PGjfv377tcrnK5TKfTr1271tLa6vP5qTQKg8H4/O5dvkD46NEjJpPpcrnOmzD257pg1Xa7HftznTdhm83mcDiqJ3zeWDs7O1/qz7W5uXkxFsxmM3ZA8/l8Xq/X6XTOzc09ffqU3ErktsFRnVwu1/POzg8//HB5ecXhsPf19S0szLe1tclkG7dvf2Y0Gs9bF942arW6HjgOh8Ptcjn9fp9cnvv+D8L/8W+b7v2Oee3tT8ausluuhLr7vG6v0+23Wq1WefMB/YcS3ozd4QeHPYfTbn/t6oUBZbdjfy5I0AF6NIct6LXbbFbGhrmNGnq739k8pW6eNfw/99SKnV2ns/Zx/BTGQo0/l0ajuXXr1rfZn6tC5Eh5SfXXPBXBdB3sIcy7EOcefciAn7Crge8Bf6KzDAkk8SPdQMiK3wIuxInw9U8+sh6rkyHf0e4WfMRPU7X8FiYbIMOIwZcx6ICxHEfoL2JEIIsqHPFhaBRcH6gIDuDr1AFHgpMQAXuEvvIe/YldU3IconmqCmYI1IsIzCGIrBzqnHE3rFGUnBroEDFz8BG3q4Fa4KWRBiiWPRBRRDwEJLWwAfnEjJETVEtlr5kIueDDHbCCoQxSNwAhCbuIdDTmd6m2ZHG/izhN5iPeoMt+QX4ueA/LpSrOo4YlRcCJInKVhPTdsNKADSgWDr/r0lYW2Nh8GDGpeGnuYyITiwc8n1/7OHCsSrjMkKjhJEnkqvACWpUk4guDoDEJO4E7xByV7QDtBGQtjg3Ggdt4M8OrWQmhq8Ex/hAIvA1JiSyqottwajkkKjlTlUUcqdl2gCC/X7btA14OpcCyuHSACNsByK78KJcIyv6Bhj4EZZn9ECIc+pGYATKfBJA4Sl126UE2GXYCmix7wA66jytSSVC0HRAxX8RtU/IYCb8TosgEXUFPxbSC/BTWVcqAF+xdn8Keegg7wIbGylEfLBBzz3hbukAeCQiqQAYxH9VvCtTRRiUriFMhApaQw3z3008iXoffYTlUbCC8vBk1Cr+q+GUBVZoH+FrzHuzGSmoLJTLnRxm+qgfF2A85gFl3aCCDm9cIvLtLW4p4T+w6OCQEbETAWgi5SimkngPBlYMI2U+thxGdAoRYETd8AYIOIoiSYGD5H7wIRpDCwtt0UPYcQ7OQExipbAJ4UBvaBi4tPOsxAAPqPoa8MRCjQV0JXhD3h132/S1pOgDvTiER8nncX4ftIRKJxBeWPU1NTWQglpOTE4VC4XQ4E/G4z+c3Gg16g2FDJvuShAPnx++p3hj2L9NzFQoFq9V6cWLtbDZrtVqr7Ui8If9n1O7f0Ns2QpXgzufpuaonU5+PncxTgTuXyWRtbW2PHz/G8ZoJgjCZjBqNNhgMRsJhv8+nUChwPvZvQfyeashgLFQbVmKpBnklkUgMDAzUbxuHw5FIJHxen0GvNxgMW1tbXxq/hwx3WT2Bmrprd7eo0a5Raaymlug//uCjW//5rf4/XpJ20xxy3DLlEWsZfx8xzUBu3aQln/XW9IDjFpL6ypq7gWSxh5/87/9o/W9/aRgWV0Jf1rQhf/p8vhq75m93/J4iEbIjMQmyHYaUWEgDUrFiSUIgu3gARBo+U4XMe42I8mHf40ZMT/UXFtdtB0TI9kVIm5/++EdqxaZdfxTzucqZuE2nBp9wLGpClAMoSiJIhGxgeuw1wJnVA85EkLYdx8KPITG+aQdR0H2UsfxsDhCYRwfM0GtWTAlsgc8E/AoWQeH52IHpgVwKKE6JXsIEZqjC5SBhFcgt6pyrzUoIEIxTZTk18KV2H8MxF77+dsR/nB2p4YStBVqC0lWW3fqCx5T3W4EZ8hpz9qOA3Vy8+ABbKgGZD7tBg1DJU4ZSEOAo0nAch1zcIHcB0Q5iQ/1WwBHJBVbyKCEuB68ac4dk+iTzLtCndDjssb39q5+bD7ZNmv1k2FfKxC3aA3B6ysTQSd2FRH1e4Oe8epwpFkaBfJzYRQ5lvAL+A9EtGEtJgAjwDC84ZhJcP4MPCsdcAkPsY5g8mh5O2oqfgqxh2i2diFEgU1LgxCM4tWpFbrELD6LMFUjQpQYKirhS4JjRPyIZAWuwCiqVFW0pQi4MZz88sWmKtgOICu0+PjHvg/HZxaVUACSGPUiW4wY9LyThQnG9kyEQmaC3CRCBbJhgyShzGVD0yrZE++r1Fq0CFEYTBqDP4DYcXXrndx6LXqPaSQQ9pXTMdKRKIKenCk+Mtah+K6AGxI1B4HicWsSbGkBFaD8qg6DFhhJd7UFgHvKf4xCy9voMIFcDHRx6cUB5bYBAUB49ZKzzGICn9FlgH0LUxEPCpU0cyvQSdkU65TODjhjzW8D3oMMGAixwYMAWQ0BteJdBKukGO7NEqBxywdECKyUxfh1qQA3aIYTtgLDu59zGotcE0Ylc2rxT57FbvxbfQxDE3t7e5cuXcT72M2tQQG01Y7G+vj43N0dasL5h6ol+/LnsmvP5inVR/RDk3LCuoVQqkW0I8NsqrghZ/6nr0p3NUW8+nk1lvO5zbY3x0vx+yFRBLoogiImJiep4zblc7smTJ3fv3iWjLddv+UKh0NXVVW3oSs6KnPC/LLvmcrlcH6/Z5XL19PTg5WPoKZXKK1eu/NO3TbVdcw3oSABaHI58sXhyevqiv99w+WPrB7/732lX/qf5326HDGinEsUSYdTvW0W/NXP/8Wjuf016IOdGdW8Xx2uGt8B68t5o4LevXK3L7gIYjFU//bre0K55fHz8W2jXDC9VqVwu5rEGpKIrAVIN5By5rQaA7EHgYxR+F+T2SDSNLC3guAZkD7mF4+915VNOHl5JigtfQ8jl6Te9eNLxjz/8wdMHrbbjo2w0oNqWBRwWOMgC6xMj/Jaidb/oOvYdbJxY4bsMfAPY7qSA9kPgRDCvQUyGGj6ytsOCeQ8xYcCmoMA2SKeAaCGQGTwlMMXVg+cRycrAR/aMrUFZ1nN6RcVyCD/i1BJwdEaZ219zEkhlAJ5H+8Bd2Y+Q7MdBBOwgAQLKcUbS7ECDy9a9olNb9FkhKJxxJ6ffLkHGCWPetBNymC6Q98C3r5CDrKtuHZgqI2sVEHsEbUBooz5kbOEiUlFQSUQ9cKQO2iGyQMyLsm5pKsIVj75sPQAKZFUBWj1GIIpAY5Aoy6wEhUgcsm087Wj/4ff//sWjhy7jcTYS2N2QhJxWMLlIx6Bz+2HRymwgUQAAIABJREFUuJM3KLwq6an9iLCDnIbwm1G0aBfsFo8eLKnDIO4CAmY7KNoOYQ7AoOzClUrSLrQlnJDOqew5BmWH3wJUE0REytfSO7SdCMgVqjrVb5N4AcmEzwKE2aGGHtx6zDBV/oJgCXRJwOaCUTMktQXzGiwGw1u0EsgAsoUXHCAVw+sqQaLZg1O9POy8iO8BipTPwWwhO6wBhTm2gUQqaAdAQS6zypmhHA8AQNzHYAcWAU0QQANxQrBk4JKRfNGGbNQwv4iVxXizQf7avZLP3NF878f/8IOXTx/57OZ0xK+QiSPVMTMzsVLQkXfpPUe7+QDE4SzHvEQ6DK8Szv0S8xF+E+E35ZzapE4R1O249jejBpX7YMusklt2pZaddcvepvlgN25WA1Pr1BAWlEY0ZEdirRBiqZGpFp4eyhtatu2f6ORlsxL4RdA8al6/a6DjPgJUmnYJpLQCdz+XDv7CwkEqCRiHw4P5bIfsYcFP2aIsWfaKoANVYbxAvliPvmDa9dstX5fvUSqVP/vZz7AWo5rXqabxf+V8Dzlti8n83tPP/sPAlWH1qseHXA5f06w3agRB1PM94+Pj1f5c5XK5tbX15z//OfZSJkephkyhUHjx4kU1//TGMOjM9i3ge5xOZz3f85Of/AQ7eTWEDM7PVc0Y1UAGP/WV+B6LBZvDa5Eucrqr552J+79fvNUq6PGfJsDAqFCw2r1B/Ziy/79TdP83gaOXJIuGB72Y70Ft4Il0KulHkuz6qZL9eDy1/lxjY2PfQr6nUCgsLC7yOWslyIKkgW8T9tYBCoEIPFBxFZjxYovFmBe+aD4jEBUs6vdZ4AMHDIcJogljNsh9jFIVqqs+hfu4DvkvHZp11vJ4X9ehXAppFrLIdDodRXzDMeE8KvktRac2ZNg/CUKOp5zHlIv6kJ4FTCZhXOzGBTIhB0RhAaNXG3gDBWwVowG3HjgSrB1zQ2onOLki0csbZBJrairiBCQ5AAk8suyxKEHmH7SDlD5oAxLrQvwHxMg5AKILh2Bkb+tDqcixkTUo3dB5F5NYdH4tmZUohhCWfKC7tv2CcSd8Pt+TTqfn5ubEAj4cdt16gDm2pKmwcciExaoCNgilcSiH3WBGGvUgPx1gH4HKug1wF2xZ3OAeFUG8EbbCjvlBShR2InflIFDrZPA0HhSv0ib7e9Q7WyWU5BKYUaxqdKghg4TbULCrg7rdU7eBcKhPLQd5n7WyDcB1H42SRsEbg3Yi7i8FnaBR8pown0rEfWW/GaQgUTfYCZFGMMkwWH54DMA5+S0VQogFBsiyCvLPOxH8wT5JgyMRgN4EmdYCWv1gHgRtPAZksBUA8ZvPRCSCr5OkvtZUVpKxQ65NlACV3BKEZe/UoAg7z427n0qlpqan10UCwmcErRmI+rAEdB9YnIgbLJMg7FAQHAZPksh+Gb07cTgwwE+vEZRQDg1wq+koqKXCoAAqx/xl2GYg2wApCGgDwXCKsO+fWPf5tIXpwR7NtqwIXgIRePuwkCkZhKiYiWApEw96HIVUjMilAi5bIghBDlF850gpHkyZ9n3anXUBl7fGZrPZdDpjbW2NwWRR6XQqnVH5R6PzOWzVjjzhNJ14LfmgMx7ypSCBXQQWFbKDkM95dCY02oeK57hsPzp1GzNWNeAu7ISTEtiS2+CnR481ZeApGQ+CvBCSdrnhDQVJD5JQWvZAb47yoQJnj9S1JeB4dtF7dHZqgvdl1+/4enxPsVjs7Oy8fPkydkgGT6VYdGhwyOvza46OpqammQwGn8ff3Nz8a9ZzVTMiJ8nMxMzUv731iz+uPd1PVPywqhuQ9S/Vc6nV6vfee+/GjRvHxxX1GZfLWeNw8rnc3Nwch8OdnJhwuz3T09N/Fj1XjeM9OU+y8ufKU2GxWOrzVJCj4GSr9XkqqvOxFwqFx48fX758GUc3wA5uw0NDfn/g6Eg9Mz3DoNP5AsFXyVPxVfRcNpstl8thL/ef/uxn3/u3/6578FUhdyKW0a4JnjsyIaJUtlgshWIxE9y28n9k4f69R3Ezl3wD+18xT4XH7a4GRX39vxY918nJydNnz3q7X0a12yVsZoEVE+ARbQIqFbDB9yjiBqkGxOdFugOvET6FkH3JgwQth+DhBT5QWILtwC7f5ZgXTHM8IGWBHrwGZBGiAnWV97jkM4B5aSJABExEwFzAxjQ2dd5nQWa8McQPgRXFaSyQCnoq0qAsdmM5C0OCDZKwbzCuY31QIlgh+eC1G4GFxAPA+lRO0qpKsnGslwE9C6iu0GHUAYa04EptBO4qg2x740EwyMUevKkIyFRCTiDP2DsGCBUyKsJW3h5DReSDeUdS2oTNchEnRFiUGc2m/Vh9np4rHA4/evRobGQ4rpGX8CHbawS6GLACIXfpgNxiy2uLCmYeQulIcUyB9JnmCzIwIJkHRBZAKSOw1S1YhCBeE/RlZ/562CkvZAFPnCywQUTATIQdcPJGS8i7TRA5OuYDQgjiJVfa78qc+VcD94MTouFgvjirBnYLguFIh21UwZCsuF4nQGLkNYJzlt8M4ii7GmEHWTHbDpAb1BEKEWQFehnzw1ikVjQRKoOOKVmRbSSCIIIK2sr2A+BCUmHYgShV+JlcAcn/MFIwb0r+taoI20FSLXPqj+q/CPhKOBxubm4ZHxnKmvdLph0QjQCEw0TECVxdJlZKR2Num0mrjngcp4lIEQIhRnOJCLAgwAwlgF8JOyEWDpl8g/RIP0nCnIMOYImSIdA0gQQRrGQI91HZBcIhYCZCNiLqyTt1JwFHOhpMhv3FdCzosgdd9lTYF/G71kVCPmct5nPZDDq1SqnYkPI4q2wWi0ZnUKk0FoslEQp5XC6dzmAwmLgwUJVGp1OoNAGXs7O1ua/cFfD5ewq51aAzqlWHig2NQmY93HFoVTHTofNgy67a1MklCql4XSwU8rlOvbqUhpCk4AZxkijE/Sch9M6eoJgR5B5A7yMwQLBGZB0fghNLOQNBhvIRL+wErwHETm4NYd8nbPsolvde6khm02vOw8t517e2tq5du4b9uSKRcP+rvsuXLq2ucUwmw927d65/er2trU0qlbW3t2NPFkej4nQ6sV2z0wlOUg2L0+nc3t42mUwN7+KLYKUql1ut1vPaOJ1Ok8m0vb19XgOXyxX0BxjL1F98+PbfvfrwhY6usRuDLp/L4ap+xOl0HhwcHB4eulxw3el0GgyGlpaWarvmTCbz7NmzpqYmHJV4Y0N29erV5ubmVDo9MjL86aefXr16lccXdnZ28vl8t9td3T9ZxxOWy+UXQwYbYl/QxuVy4Qlf0AZnC9Hr9Re0sdvtW1tbRqPxgjZWq3V7G0yA8SpcLheNRvsCFAQqWE6zsbFx7do17M8VCYeHBgc/uHSJxV41m0y3b9++du3ao45HW1vy1tbWC7aNy+Xa29s7Ojq6YDIOh2N7e9tsNrvdYIr+9ttv/8M//MOD9nav1xf2BpZEK+/xn+3qVRqFKuT0ef1Rh9Npd9gOpb0HlP/o2n9hdzic7iDG7wXbBu+B4+NjpVJ5wWScTqdKpVKr1eS20ev1bW1t30K75nw+Pzo2Nj87XXCioCwVN1ctfM0hKg9yNMXeVZjhwPY32KQABOnIbTURANE6aRWEDS2xjD2DzCHxFUSP4diXjkKc31SYKJ6kQz7DvuJQJgjo94l0pJwIJv2uQhyFAMZPZePlZAiizMFPMvBJvRMveeXM/hfH9cEEEty7gkBFPPqKGUTACq5MKNIMCIqCdtQ5LAfYi6ADhsOqN5SMAtQ9+SyYYKOQa5B0HRiFACg1TpNEJlo5Fp+gCCWQth383cDSFuw0lUisgpzgIm5Q2zk1Ob3cb9Wfp+dKJpNDQ8M0KgVyb5mVQPOALzwAX+4Y8pZPRQCSMR9AEmRgKNNZHGAL5ATcbaKg3IGFVKEAGBpkFURGYcFAJhEEOs0oUcimQ55jxfq+ZC1gPASjYK8x7rFXElphh/x0rBTzAjmv7qG6jpnR6is19dcNYkgc5QIfMa8e+C1sPoUtpgM2kJSEnGVsYgKh8ypJSAAd+TTg5QQhHdvII18wQAeJLAhOg4xvwJrkjFsFNR9Y20DiNmQJToBeUp/Vbvmt58aJSaVSXV0vmXQacFc+UzEZTgS9bpvZ57A6LQa1Srkj31xbWxPweQfKnQ3pumJrQ7YuWReJDvd27SZ9zOfMRAL5VAyi8+FgNnhuKHxOCYuITtHqzrYfKMuQKxzgJRVK+R3a3c09mVjMWRXxuWtrqzQKRb4h5XDWWCzW2urqKhLnMJhM1e6OSMCnUqnA3jCYVBqNzWLt7yocJv2GVMJkMtksFmZ6WEwmZw0EQXweV8Dn0+gMNpstEgpYTNQAeCMWe3WVxWIzmEwWiy0WCSUikUgo2N2WS8RiJpPJ5XK4XK5qV2HUavZ35HaT3mrUm491xXQ8B9GWo2Ajf5ospWPZaBAS1CBMlTLxTDTocVgtx9pD5a5mX7UhXT9U7jrMeqdZ7zZpffqDpNOYtmvzDk3erPTaTF9XzyWTyX7961+3trYSBJFIxIVCQUtLq1gsnpwYf/iwY3R0dHx8Ymtra2xsLJVKJZPJRKOSSqVMJpPb7T6vQSKRSKVSOp0uFApd0CYWi2m12mg02mgQuJZMJoPBoFarPa8BHigSiWgO1K96+v7Qeet9/vM5vTgUi5ykM8mzkkqlbDab3W5PpVK423g8Pjg4SPqxY/fslpaWX/ziFziIs06n6+3tHRweUii2b4IL93J7W5veYHj16pXBYMAuzfWzSiaT4XBYo9FcsOpkMul2u41G4wVtUqmUxWIhJ1w/EF6FwWDw+Xx4UQ3bxONxjUYTDAYvGCsajWq12ng8jntIpVJHR0d9fX01fM+vfvUrvG3icbRtmltEYvHU5MTDhx0jI8OTU1Pb29ujo6MXbxvMgV0wGex4H4lEkslkHGztfX6/f3p6ur+/P55IFE/zWz7dz1ZudzL6p7jjVrM2l81l0tlMpphJuFzbt3SrvwhaJenMaSAQ0ul0DWFCLtPr9RqNxgugl0qlzGazw+Egt00sFhsaGiIlghhE39zfv2h+rkgkEgwE4MMEzqgm+BfxVMj/mTMtSAtIopWKgNlmJW8okru8ETMNn/tRLLjXhO3s8YqhdILIpQrpmEl78PRBW9PtW6OD/Ra91m0zmtWqTNgLvAUWG7wRPgRlJnotQjjrk5wYrmBGp/oiJJFGz56miVy6fJrKhL0+h8VrN+dTUSKfQSqJOEAAExtETQvpWCrkS4d9UY/DadYbdeoj1Y7fYclG/FGf02E6Djqt6bD/JBZ0mPUhjyPqc4bdNp/dDBFpk5FyPkPkMpXjbwzSm+c9xpLfgkRoEGWn4DoOeZznxe9Bn2kUnSwVQkgxg9LKayoHzKAB8ZqAocHhUnDkFVhvDLhPHK2ucvEskmQ1NABZiFslL1bQhM7lp8liKqpV7bQ3N7Xdv0eZn4n4XHGvIx9wAJSAhToDO0Z6DYLIPusrpFSDvIX5Esxhn6bKp6m01+oxHwNe0nHiJA70HksLUhGoIBezfCJ0Eg1kwr6Ix+4wHZuPj7QHe36rIRPyRsHT1xTzu/PJaC4eCrlt8YAn5rFHPbaQ25oIeoqxQDkThVVk40TURfgMZZc2bzkogZDpEPS5Tu2pXRv2XBTxPRQOh4LBmN9l0h7JtzaFQsHqKpvJZNLpdBqdRqPRGAwGl8Ph83jAW7DZbBabRocbDCZDLBIK+Dz5hlS1o7AadEG33W7Sm3RHIY/DZtApFdsm3ZHdeJwMesvZRDYWzEYDpUw8GfL5nDajTr2/JW2++9mVSx+0t7Utr1AYDCZwNEwmHhTYG1SYLNbqKptKo62trnI5UFgsllIh9zutxVQ04nVwOZytDan+6NCkO9rfVcBPmZTL4chlMpGAL+Tz4ak1YKSYTBgC0hEtL8/OztJoNAGPJxGJFFtb25sbR/t7moM9zYFqXSLe3tygrFCoFAqDTpuenp4YH5+fneVzVg93FXvyza11iVqltBl0Ah6PyaDv724rNqV8zhqVQpmbn2exWOtiEZ48FkIxGAwqjcpisXk8Hp/H25RK9CqF235uXKWG3+J8Pq9Wq/v6+qanp7ExJ45Kh3IzZTKZTD6fJwhCKpVyOBAg7oLylfJUfAV/LovFUp2PvX7Een+u+jaJRMLv95eKRcEqr+VR+x1uf+vRwmG0ovgoEcAc1uu5mExmdZ4Kv9/P5/Pv3LkTCECsQoIg8vl8oVA4OTnFkMnn86enp2NjYxerjf7F5alo6M81OjpK+nPlcrnDw8NXr15h8x0MnFwuly8UstksuW2+YrjLhnELcZ/4r9VqxXqu6ouzs7OTk5N4Sg8OF/6HuV//zfRvno3fkYeNtpQ/VTgpIOOqtH/TKXkrfvQ0HffbnRU8VvdTXU+lUn9Cngomk/ktzFNRgUu5DCcw7ApbFYoeNAhYilMtHkjHkGnCm+GVK4dXRJxAIoL+FbJwHMdnccyOIOJXysaT0cA6b+39P/7h5o2bAi5nb2ebRqNTqbRV9irkt4LHkWSlEmv4jNZiBQfwJRmikIVcAUUkg8mlcah7IpsopmNFFI0Ghs6lytlE0GP3Oy3RoMdlNhzublOXF8dGhqkrK9NTkyuLC+tCwcGuXKXYNB0dZGLBdMTvNB3vbsmYNOrK4gKfs8pbW6UsL01OjA8PDbEYdBGfu8pkjA4PDw/0z0xN0ijLiwsLc7MzTBqVy2LQqVQBd427ymZTl7clfLGQLxXxA257OuQ+8TvyYQ/MHCI4Rwqp6Jfn5yqXACmkTgd0SSASQwIwcJkG7FQUf2cgSpwFSMTsBeAF/zsTMOTSMAecSb7SOQiEyplEKRNPRAI8Ju3Kpfdbmu7tbUoMGrVQwOessg4UWxDeKRM7gzMarprFxLKKAsZLFrBzmiIAL4ibRHmgIN9FLgOj51KlTDzgtgXdtpjP4TRoDhSbbNrKyODAysry9OQEbWVJKhIcKhV725tGzUE2HkpF/C7z8bZsnU5ZoVNWBJw13trq8uLi9OTU6MgIk0EX8bhsBn1sdGR8bHRpYZ5FpzJo1KWFhVU6lb/KYjNouIFwjXW8u7m7Kd3bkkbd1mzIkwt7ihDBLwpsVipyGguFfQ08Rau/INlsViQSYUYHcx74b0VphHgFOqlDOrvKYDDW1lYZDAY8SKPhyzQajc6gr7LZTCRcAXpPpUmEQs3+nkQkFIuEezvbAh6XTqf393S/+9Zv733++fz8PJvNZp5Ja866f+N/qUS8IZVKRCLMfm1KpVGfOxsPHSh3d7flHA5HyOdtyaQb6xIuZ22VzeZxuZjLYTKZFMrKwsLi4tLS7Ozs2NhYZ2fns2fPpqen+/r6+vv7p6an5ubmZmdmFubn9UeHDqPuaG+HQaXMTE2NjY0tLS7QqdSpycmBgYHBwaGJycmV5aW5udnu7u7unp7hoaHxsfGpqanBoaHJqanp6enpmZm5ubnR0dHu7u6xsbFZVJaWlpZRodFoq6urDAYTK+C+bvy0L2KZvP322++88879+/cxCcFajGpU4oQDo6Oj53q5IAJDGmqUzylfJU/Fn+bPVTNgTZ4Ko97Q09nVvTj6uWrqvc2eP653vTyiAd9T58/16tWrnZ0dcu29vb1vvfXW73//+83NTZIpJO/iSrFYfPz48bcpT0VDfy6TyUTmqcB4fOedd959993m5uZ/4rb5WnbNJKIxOhYWFm7dutV6v5kh5vxH4f2HH/5/a+//ZFg29cjAbFXNtqlmh3Rr13emfs5v/2jm3ykZ/8F2AD6J521jfJD+E/JU9PX1fQvtmjGkCMiziLylkNVwIRUtVBgdrDNCEUpwhqZsokLPcumTWKgQ8pRzaaJ4ko0G4wFvIugLeRwBpy3mc7vM+iPVjmZfmQx6MtFAASW7LmUTiYB7sKfr4ytXbt643t//6gEq8B1Hhc5gcNbW9ncV+qPDeMBdRIMWcYKhHEhrSifJWMB7sCtXbEiEHDafTeevsdh0Gm+VKeKtCThsLpvFYTEEnNXNdRFvlSnkcaYmJqYnJxcX5ufn5mg0GnxTV1bwQXZlZYVCgSBdL1++fNj+YH52hsNm0igUCpVKWVkBHQGdjmT7LDi4o0nSGQz0j0mhUtHjVDhwQzsGA0gRi8FgUKjU5eVlKpUyPTMzPj5Op1GYNAqDsiQV8mUivt9uIk7ThWQ45PdefPgDvGSRZTFYRZzp+DCzgpkezAjiK1gBB+KrBOjXMhGimM0no4mgNx70htyOZMh7mowEXDaXSRewmVJBbyYezIOeLlU+SUQ9ttH+3qsffXjns8+GBgcfdzwcHehlsdk0GiyNTgcZxj4oMo4y8WARpUirpBbB2qXTVCLs21dsbq6LJAKegMMWcFZXmXSpgCsT8cQ8Dg/hRcTjKDbWpUIeb5U9PjY6Pze3sgSYodFoQOnexAudwejq6mpra12an+WwYBJUKnUFkEOFSYHOBQpmAugIBXQGnUKhLK+sUKhUrNwBKQJmKZhMQDaFwmbQFufnJicn6VTKGoMm5KzKZRK5TBJy2YjT1FfJx57NZgUCAZ1Ox5v24r945yAFFBtUUWtrHA5n9azgTUWlUvEJDyQrKytLS0tUKpUF/BN9cWHhxvXrb731u/uffz44ONja2vrixYvV1dUvGZTFkorFG1IJl8tZXFwaGBwcGOgfGx3p6urq7x/o7e0dGBgYHh4eGhoaHBwcGxsdHRnB9ampqVev+kdQmZ2dpdMBnjQaDc+TRqNRAbCMF11dHQ8eLM3PM2nwptARahCoYV5sNguvDz/FgveCQaPBi4XeEhadTseIA3EYC37iUTAQJicn4VgxMDA+Pt7X1zc3N4fbfF2+J5VKPX36dGZmhslk4g9dsVgMBAKlUimZTODIv4lEQiaT4Qblc8o/rx97zaRq+B7IOZ9OLc8udPZ2/Z8zH/wvS2/zvPsN/blWVlaq/dglEklnZ+fs7Cy278F0MZ5IlEolr9ebyWRCoWAqnR4aGkomkxdQ02+BP1c4HB4aGiJ54mQy+fTp09nZWQaD8Xrb+PG2AUVkOp2KxxMbGxtfum3+NL4Hz+QLe6a/+7u/+9f/6l9NT0ytW5RBiYAQy4jWB6WtrWjxxJYO7oVMU0bB/0b58H9evsxQz9m473i2r58mKjLR+m2TSCT+BL5neXn52ynvAQCVSsDNgL4jSpwkg257wGEF8oklK0hCUDxNF9LxfDIS9ToCDotJe2jRqnlMKhfxFspN6YFia1MkoNOox4f7zOWFn/74Rx988MGjR4+X5ueoS4tymUR3oFQptkS8tXt3bn/04ZXW1taVlZVHjx7du3tbJBKCvB5RMiYTbBFoNBqHs6aQb5p1mrDbEfU6HWa9em+HsrzwsqtrbGx0YWFhGsrU7Nzc9MzM7PzcwuLiwiKcU+cXFmZnZ+fn52fn5hYXl4DusYAdwUdkRCvPLBvQoZnFYvX29PT29i4uLeFpkDNBk2rwB3eCPuhwl6yQTfGVClVmMqlU2vDISPfLrqGBV2olZLUsJCMXy3vKUErAj5LqQrJykkRSkzSRyxRPQWOYT4SjENrObNQdOoxaIWWOy6SIBNyDHfm+fAOSZgh4DqOOvjT/u1//+pOPPhodHKCvLFGXFuRShJdt0Do03b390Ycftj94sLKy8uDBg8ftLRvrEpJ0IUEFg8vlCPi8HfmWy2JMBDwnsaDPYdnf2WZSV8aGB8fHxxbm52dmZqenp+fm5mZmZuYXFpaWlkB8sLw8v7A4Oze3sLAwPw8CBQA14KUCsxq8IJQxu7u7+/r6lldWkE4HWlY3qzz55n/VqKnBC/rJIhkmJNugDg8Pv+rtHR0asOoOiXzmNB4KB87NS0qgks1mhUJhPd/DZrMxf8Nmr5KMwtzc3NTU1Ojo6NjY2JMnT54+ffr8+XNM13t7e6enp6lU6r179374wx9euXLlyZMnPT09z58/n5qaWmMzFxfmlhbm7925femDDx52dNBotPv379++fXttbQ0vGvMNJACq17swPz8/P/fi+fOnT5+OjY1hdExPTwM6FhaWl5cREwhM5PLy8tJZWVlZIVdBQpLczHg/43PCwOAghUJpODR5sbqCe6u+QtbJgUgekcFg9Pf3P378uKmpaWJigockXl+X7ymXX5sD4bpOq/n4449VqoODfVVLa2vz/fv9/QMKheILFGBJQL5RKRaLDocjFAqhxF6NWuTzOIlSOp0+rw1SIZ0YjcaTk5PGXSA1UzKZNJlMoFU5pxSLxWg06nQ6i8UiblJCRSHb+u2zT//foSujwyPK3d1QKOTz+cg2xWJxYWGB1HNVQwZ2dLmczWS6Xjzv6np5enrS19v7/PmLS5cumcyWsbGxUChE9lMzqUKhgNNHnLfqPIJMOBy22+0Xt/F4PNUTrhkI6+BsNlssFjtvMvl8PpfLYUOiC8bKZrPVEC4Wiz6fb3h4GMOkjArABBV88VirRdtm/2Bf1d7e3tR079Wr/p2dHQqF8qXbJhAIXDBhnHM0k8nUTLhUKtnt9suXL7/zzjs3btwYHRoOo7zuJY+n+OxZaWaWyIGKligTLw+p93cnP9+ZHVStRc0Ldv4vQ/rRUvG0UALFJVnwtrHb7RdMplgsulwuv99PtgF374WFr/veVWD39f/7y9n35PP5qenpVTa7CJmAUvhf+SRZPk0RpdOgx65Vqzx2M4u63NHeOjYyTFleWmUxWQz6wvwclbIyPjbe09vb090zPj4+OzMzPjo6OzPDXV1dXlrs6nqxtLSExNQgIGEwGFNTUzMzM93osz4zPT05OTk2NjY4OCgWCrY2ZOA8srOt2JKtiwQMBlhWwgFxZUUmER3t7fI5awMDg909PaNjY4uLi+hzjz+SbHTghzqrquCfNVSB/M7WV3Bf1WSjvs2ffIXFYtFo9Fe9vWIu22oy5NOxYjp2Eg8Fz5f3pFIp/LkvZ5Hu9LSoAAAgAElEQVTGEMxC0b9CpnyaCnsdxuMjl83EWF58/LB9bGSYRlkRcDkYL7SVlbHx8Zfd3T29vVOTkzNTU+NjY4sLC5xVNm15afAVsHeYEcQ6lynQOMx0d3e/ePFiBgmoMF4kIsH+rkKtVABeNta3BGuKTalKsSUWCpTbW7qDPf3BnkTAGx0d6e7uHh4ZWVpaIsFOVqpwUlv9KvD8RvHCZrNXVlZedj3flootxmOEl2gmGriA70kmk6OjoywWSyaTVfM9bDZ7bW1tbm5ufHycsrLS1tb2wQcfYN0N8N+zsxMTE9PT0yMjI729vX19fePj4yMjIwMDA5OTkwsLC1NTUy9evKBQKKtgPgyFwWBMT0/Nz811dXU9f/ECLGYmJsbHxwcGBqhUEDHivYrZFSxEYTAYKysrZ+cBMI3EA62srJxxY5XXBGOnFh9nv//Z8YKXf//+/Y6OjqmpKbQHACBf176yXFUIgnA47G2trVeuXKFSaesSSVPTvZGR4ab796VS6a1btzQajcViMTUqFotFoVAcHByYzeZG9+GaxWLZ3NzU6XQXtDEYDDKZzGAwnNeJ2WzW6XQymey8BiaTyWw2Hx4ebm9v18zW7Xavb0j/4b/85N/839972PFQrVbv7e3hNlar9fDw8MaNG6Q/VxVggDUsFPLLy0tXLl9pboY0pe1trZOTkzdv3uDxBR0dHRQKxWazNZwSOeELVn3ehKs7tFgsSqWSnHD1LbJuNpvlcvnR0dEFY5lMJplMdjEW9Hq9TCYzGo24Z4vFMjk5+QWHTaBSDRnM9Dgc9o6HDy9fvryyQpGuS5qa7o8MDTc3t8g2Nq5fv/6l22Z/f/+CCZvN5o2NjePj4/o2RqNRh8rh4eHg4ODjx48fPXr0qLPz8bNntkePYp9/btjasrjdGoPOZDKqNUef8QZuyiY3lXyn9JqG9eNDBdVi95nNVrxMs9msVqux7xgJ0pqKxWLZ2dlRqVTkttnf379165bBgCIoYgB9k3//cnzPyclJc3Pzi64u2bpYp963GHRW47HNpN9YF1OWFibGx65+8sn9+02XLl1qbm5eXFyigjkCMmZAkpLV1dU1VMgPKxNJrVlgWVmxi2SiwmKxhoeHb9y48Yc//OHZs2d9fX137969d+/e5OTkxMQ4k8mgUCgDA/2Tk+ODA/29vb0MBuOLqBtN9+7dvHmzo6Pj5cvu6elpJpO5ivgb3Oe/lL8sFuvJkyftDx709/WOjQxvb25sb26IBbyDfdV5uygSiXz++ecDAwOydYlOvW81HluNx3azXioSTE+MTY6PfXbrZktz8/sfvN/+4MHi4tKZZWuFHJJ4AQqH6AayfqWzwC8HGMRq0A0ODn766ad//OMfMV5u3759//79SVRYTBaVSh0cGJianBge7B8cHBDx1hbnZp49eXzj+vUnjx93vXw5PTODxQA13VYP8ddZZ7FYjx49etDePjjQPz46gvEi5HEPDw/Ow0s4HL5582Zvby+Px6PT6Vibw+FwRkZGsKjm6tWr9+7de//99x89eoRVQph1wy9INV7wsxhoWMRSDUAGgzEwMHD16tX333//6dOnr169un37dktLy/j4+PT09Orq6tLS0itUnj9/3t/fz2Qye3t7m5ubr1+/3tHR0dPTM4/shTEj9dcJ//NmxWAwOjo6cBaFgYEBzKUtLS1hd6TzUFN/vVxVcOAWn8/LZrMNRqNQwKfTGTtQdmUyGU6sXTynlMtlrLAolUrnNCmWy2Wr1ZrNZs9rUyqVcrmc2WzO5XLndVIqldLpNM4Yf16bcrkcj8fdbne5XK5uA7otn+/9d9/74Q9/ODU1FYvFAoEA2YYgCBqNVi3vqYINyJXT6bRie1u+taXVaGZmZ/b2VDwez+v1TUxMpFIpsp/qEYvFYqlUymazZrP5vFUXiwCZSCTicDgubuP1eqsnXDMQHgtnijhvMsVisVAomEymdDp9wVinp6cQDqdQwEOUSqVIJELaNb8JlgpT6PN5WWyW0WgUCYUMBnNHodjZ2d3c3GKz2dgzrn62eOEQdyAYvGDCpVLJguIWNpwwnkyhUHA4HJFIpKWl5Xvf+95//sEPXG43oVCc3r2bl8uxSDOdTLttTr5r79r2qDxoyvokdtGvfQfP8ifhMmR0L+Ft43Q6L5hMuVz2eDzVEy6XyzQa7Vuo58rlcjMzM7Ozs1wud3llZWRk5OXLl8PDQ+3t7Z/d/uz5ixd37tx58ODBy5cvFxcXv9SqgHlhIc+jU1NT4+Pj/f39t27deuedd5qbm2dmZvr7+58/f975/HlXV9fw8DApDXrV3z87N7e8vEyagl44yF/jTQaDgSX2k5OTo6Ojk1NTy8vLi4uL+PhV/73GuvaZmRk4qa+tLS8vDw8Pd3d3Dw0Nt7S23r1791ln5527dx88fNjV1YWFatUk8+uCAONlbm5uenp6dHR0YGDgxo0b77zzTltb28zMzKtXr54/f/6ss/PFixfDw8MTE5PDIyOv+qHMz88vLQHL9XVH/Ctpz2Kx2tvbW9vaJicnR0ZGJicnl1dWFhcXDw8PGyIFbClSqUGk3xGLxRQKZXh4uLe3d2xs7AuX16ampufPn9+5c+fx48fd3d3Ly8v/9PdleXmZVJP19/dfv3797bfffvDgwfz8fOV96ewEYdvw8BRSpQ2iMj8/v7y8jG1o/kpA/bWmwWAwHjx40NLSgg2rZ2dnqagcHZ0bV6khvsp1pWGz9fV1CoVCgLancfmrte+pni5m7Nxut91uj8fjgUCgOs4yQRDz8/OkfU/1g7jeEDL5fL6vry+GNCz1j2BxyLfAvicQCJD52M9bZj18pFLpl26bP9m+h5wGRivOJfIF6//973//Zz/9KQ4/WPJ6cw8eFKamiEIhncnYrODQZ0n67uxODhvF2dNY5OilVfDLhIuLJ/+n2ffMz89/C/kegiCy2WwgEGCz2UNDQz09PZ2dnS0tLR999FFbW1tzc/PLly+pVCo+NSI5D7OmYBk7E/mw1NzCP/FT+C8pGMAVJpM5h6xAsLgefFvo9KWlJfzJxm3wQfliut5wYuSUzrv7pQ2qZ16/NPJuw/7Ji0jFx2CxWIuLi1jlMT8/Pzk5OTQ0hMPG179R+Eo+n/f5fHQ6vRovH3/8cXt7e3Nzc29vL8YLuYqaGV48PfIp3IxEBxLUgc/R7OzszMwM9qyho4JZHNyy2gSkZlzyJ9ZWkD+rK+Tcqi9+9frFj+O75AJrusXowHdxSxIvCwsLExMTQ0NDmDych5dMJuP3+2k02tDQUHd3d2dnZ3Nz8yeffNLW1tbU1NTX10elUrH9DTmT6jmQk294l2yJ79bjZQaVxcVFzNlc/L7UD1F/hRyRhNh5bc67TvaAG5zXjLxOVsgHyaGrK8gObH56enp+fn5iYmJkZOSCc0JDZJUblfqWEolkfHz8Yr7H4/FEo5DTsVGXcI0gCJvNhkMbN2yDCRjOS9qwAe4E+7GXSnBAb1iq7ZrrG5CrCwQC1Xm1CIIYHBwk81TUP4hHxwskOykWi0+fPvWhnBjnPUL6sTdsgLuNxWJfalFbk1ijvjeCIJxOZyqVugALpVLJbDZnMpkL2uTzeRyvGQ8BvILF8vz5c7zq+nHJZiRYcOUrbhvsx35Bt1arFeepaNgGy5Nw+IN4PO5yuXp7e7FfXpkgyicn+YmJfEcHhD/yejG0T4v5oeO1OztTjnSsGD+2r7/tkl8tpF2pdNblgnjNDQfCz3q93uoJEwQxMDDwrfXnymaza2s45lnFSoZ5ZkCACSH+lDc8v7LZ7OXlZWzfwDyn0Gg00hKzpsna2hr2VsV2oDjkSXUbTHQbDs3EQVNoEIK2+hGyvrq6Cv4756jGMFEhfVXIp8jK6uoquNVc2DnWXJCPkBU2m81ggA8Ljp6C14VHJDtUqVR4t9W8UeTPdDotEAhIJgN3jh/HXVGpVOwcRI5LVthsNp78eZBnsVgrKysN75Lkdg2Fs8N9VoMRP4v3BjlidQWc2igUcqXVt0BZifBCcsw1d/FT54kr8F0KhdJw5mTn580Na52qNyQLFXJ1dDr94OBcPRdGTSKR4PP5uCty3+JXBiMLT6/hpsWGa0wms+H8WWAKBhGAGj5LcqUcDgdPGE+eBCB+vP4lqm5QbYxMXscV/Hj1iqob4B2Fbe+qr5PP4vflvMfx3fOWhp9Crv5gsk2+KeQyGQwGVtNgFHyVv+U3C0pRlGIwGPF4wmw2CYRClWpPqdzb+Ap5Kux2eyQSuXhQk8mECdh5zQqFgsFguDgfOzYTvvizEI/HSSes88byoVJ9F2I4KZXEOWSPIAilclexs1MqlThrHJ1Ot7a2GolEp6amLl7U6enpl8bzjUQidru9ejL1dbfbTUYSqr+Lr9jt9kQCUladV0qlkl6vT6fT5zXAYYqMRmM1hKvzsb+5ZeAXuW0SiYTZZBIKRXuwa/bkcvmX5mO32+016c3rJ4aNrOuvk1eKxaJer8expgiCwKzPG0GVdndPm5sjQshXSpa9qOXO/qQodEwQpaRtzrv5llc74XJBOtsLisfj8fv91Q2+tfnYsdaZzWYLBAKdTueqKm632+Vy7e7uYuKtUCicbxa3263X6yG2GJ8vEomsVuub950ej2dra4vFYm1ubjocEDG9urjdbovFwmQyeTyeyWRyuVzVd51Op9vtFovFHA5HqVTW33W5XDqdjsfj2e32mgfxs/v7+2w2WyKR1DdwuVwWi4XL5bLZ7OPj4/rOHQ4HdnCVy+X1d+12u1Kp5PF4a2tr29vbNaO7XK6jo6P19fWVlRWRSGSxWGp6sNvtcrlcLBbrdDp8jqzeariezWaNRqMQFTzDKsy4vF6vQqHAdGJnZ6emf4/Ho9fr+Xz+2tqaRCKx2Ww1M8R4odPpW1tbDfFiMBhYLJZAIDgPL1wul8fjfRFbvWZop9Ppcrm0Wi2Xy60ZFP90uVwqlYrJZIrF4vqhXS6X2WzGzJxer6/v3Ol0SiQQbnhnZ6dh/wqFgs/nczicerDgDSMQCCgUilgsrgEL3hJyuVwikeh0ung8Xo8UHPhOo9HweDyRSGQ0GvEMMWrI9wXzo3t7ezXzd7vdR0dHUqkUb0ur1VrTwOPxSCQSLpfbcNe53e7j42MGgyEWi+vfNQwNoVC4traGcxTUwAdvSx6PVzMoiZfd3V1sst3wfTGbzaurqxwOx2Aw1Pdgt9slEsnq6mrDV9XhcCiVSnxIqAeL0+nUarUCgYBGo62vr9eAxeVyGY1G8n3BbtUNUVNzsZqGgY4ymZybnXnvvXd5fIHFYrl69er1T6+3t7fLZBtPnjwRi8V8VIRCIXZ2EwqF+IpYLH748OHAwIBIJCLbzM3NMZlMgUBAXrlz5w6VSiWv8Pn8mZkZDoeDr+ADzM2bN/GXls/nCwSCtbW1mZkZ3AP+hM7Pz9+5c4e8IhAIWCzW/Pw82a1IJJqYmGhvbycnjPfz8vJy9YSfPXvW09NDthEKhR0dHQ3tmkFsUCqp1Yc3b9y439xycnI6OjJ85crlTz7+RCSW9PT0MBgMcnQulzs9PY0/+HgJVCr11q1b5NACgQBL8clHxGLx8PBwW1sbCT2BQEClUhcXF8mnJBJJV1fXy5cvyTZCoXBxcZFOp5P9iESi1tbWqakpso1AIJibmyPhyefzuVzurVu3qkGBJ8zlcjFI8fRu3bpVjRdwbnhZm+wTbx6CIJLJ5MLC/Lvvvsfl8mxWy8cffXT12rWOjkdy+fajR4+qIYwCcaxUL6qtrW1oaKhmwvi7Sm6bu3fvrqy8fgpvGy6XixeON8CNGzfIZUokknv37j179qzSrUAgkMlYY2PMf//v7aOjIrGYx+cLBUL+Gqd/cuSH/Z88Wp9Z31DwVhcnH/xf4ukf72xxuTzYtA23TSfSm0skEnJ6Dx8+/FK+tua9+5N//uXsmguFglKpHB0dvXnz5tWrV2s81rCh0+Tk5OzsLDazrTmsZDIZnM24E9l/1CefS6VS/f39f/jDH2ZmZqpZbBI0W1tbV69e/cLQp/5ZgiAikcgXvoL37t3DEVfJp8iK0+lsbW09j0QdHR11dHR0d3djVTf5FK4YDIZbt25dkH+kv7//rbfewkrcmmdzuRyFQllcXPz888/HxsZq7hIEIZfLZ2dnHz9+3NbWZrXWxplNJpODg4N3795tamqSSqX1j+dyOblcPjg4+PDhw6amJrlcXt0G4wU7ATU3NzOZzFKpVN0gnU5vbGxMTEzcu3fv+fPn9ZkFM5nM5OTku+++29PTU4NTgiBKpZJAILh///6DBw8aGvP7/X6wOm9qEolE1eOSdZVKNTw8fB6JOjw8/OSTT847SrpcrmvXrn366ac22xu593DnpVJpbm7uww8/HBsbq99R2PFyYWHh8ePHk5OT5HzIysHBwdOnT1tbW1+9elUfSjUSiUxOTn7yyScffvhhQyufbDarUqkeP358/fr1R48eYb072Tk+jS0uLnZ0dFy9epXP51ffwt9QLIz5IkN4U1MTtpyobhMKhfr6+m7fvj08PFwfxbVcLi8vL3/66acvXrxoeDj2+/1jY2NXrlyp2TDkEBKJhAzJT14kK1qt9saNG8+ePasfGsdzw2ZMDeXe6XS6u7v71q1bVCq1WCySfeJKsVikUCjYOlv45sEUN1Aqlf39/Z9//vnDhw/rz+sul2t2dvbhw4cXLK1mxBrBBgph7J+envr000/Z7FWxUNjS0tLe1tbd05NMpaVSqUwmE4vF+JjBQwXXxWIx3BKJhEKhVCoVoYpIJMKEE7eRSCQymUwoFEokEqlUii+KRCJMvfDPdem6VCpFREq2vr6OLwqFQogheVakUNaFQsHGxoZEIsGXMROPxxUKhXieIpGInDCmfzweD7fHt4RCIa7gBwUCwfr6ekMlEWhSCsUNmaylpfXZs2cH+wc93d0PHz64c+fuweGRXq8Xi8XkovCEycmgCcN6Yd5ni6qeMJ4DXgs5YcwZ4JODUCisQFgMRSaTkRDm8XgCgQD/xLcwhDGo8QIxFqrbCAQCclYYIFwu9wzAQsk6FAwNBGq4g0/dNRumjAreNjMzM59e+5TJYknEoub791tbW/te9afT6fX19epF1W8bESrkthEKhdVbgtw2YnHttiEnvL4O20YoFG5sbJAQlkql5F7FwBdvbkoEAsvGhlQiEaCHBQKBkMeXrq/zlTLRulgq25LKto721lS76yIxbC1y25AAx4Ct3zb15KP+XfuzXPnL8T25XM7r9ZrNZhkqNbLcUqkUDofNZrPNZpPJZFqttobSpNNpn8+3v78vlUqVSuUbwjcEiVQqhe/qdLqaZzF9tVqt6+vrm5ub9R87giDC4fDR0dHGxobJZGoI2UQisbOz0/AzjYN3bW9v7+3tNWyQTCa3trY2NzfPk7jodDqxWGw0NsjWVCqVPB7PxsbG5uZmQ/faaDRqs9mUSqVcLq9fWqlUMhgMCoVia2sLh0OtWd3p6anX6zUYDEqlcmNjo4Zvw3ixWq0ajWZra6ue+qZSqWAwuL29LRaLVSpVoVCo6T+dTh8cHGxsbBweHtbwTAT4tRaw16hcLm8oWA4EAnt7e5ubm+fJ20Oh0MHBQf24eBoYdGq1up5AEgSRyWRkqJzHzmJP1PNOIQ6HQ45KQ44tmUxqNJrd3V2lUlkvwC+VSlarFVPBGpjjmZ+cnASDQbVajYeomWG5XA6HwzgrpFQqrZftJ5NJp9N5eHgok8ka7ttEIqFSqba2tjQaTT1eisWiVqvFz9a/azils1KplEqlDTcVQRBer/cCKxmPxyOVSvf39xviBVx+FAqZTNbwfSmXy/hVrd+NGHRYULexsVEPFswRYqHO7u5uHqWPwE/hv9jqZXd3d319vSHDV92YrGPSRf7FphKBgD+dTodR8fm8ra2t+weHNqu1s7PT96aEH/fjdruePXtmtzv0x9runt5orIEU8OhI3dXVlUim7Dbr0tIyOYHqilgkHB4ZgXyIIyMCYYPTQrlcnp2ZplCoudzp48eP9g8aG9fPz88f6w0+r+fJkyfg11NXcqenIyPDwVDo+FjX3d1dzdyToKivIBQkotFoMpn0eLz5XG5xYb67pzeRTExOjK+uNs7jsbICsUbT6fTY6KhYIqmbC3GSzfa/erW+vk4QxMrKst3hrG8Ti8U6OzuPNFqjQd/V1ZVOZ+vbWC3mJ0+fhCNRHpfT2/eq4f60WizjExMEQSwuLiwtrdR3AsdRUD6wi8XC5OREDRbqYYKvVG+bCGybiNfjaW1pOVQfWS2WZ52dfn+DBBFuF9o2DuexTtvT0xNrtG0ODw+6urqSyZTdbltZAcv6+iISCYeGR3K5/OjoiEjcAMJ429Bo9NPTkwcdjw7UjW3/FxfndXqD2+15/OQJtvKpGev09GRkZDgUCh/rdC+7X1Zvm5qW39zPvxzf882t4buev4PAdxD4DgL/jBBoSMmq56PVaK5e/WRmbm5tlX3lypXNza2aQz9BEOsS8ZXLVzhcvuH4+PPP74UjtdbNBEEsLMx/+OFHcvl28/17v/z1b9AQrweHg0Q+/+J5561bn2l1x7/5za9fdIFW5XULbESSTt+5c7ultdVqs/70Jz9ZWFisaYPPjf2v+iTrUi5n7dKlSyIx8BM1/ZyenrS2tB7rDSaj8c7du4FgsKZNdfvqejVkCILo6+25deuzL7JYNH1+t+PR41MUKI9sD3xSItHcfL+p6b7b4+nufslksWoGIgjCbrddu3a1f3Do8PDwe9/7nngdZNtkJ3hFGs3R5SuX5+bnJ8ZGf/nLX5pM5vo2DDrt0qVL0o2tF8873/r92ylkwVPTj8Ggb2pqCgYC9+7du9/cnMkA/1TTRiQSdXV35/P5/ld9S8vAG1U3uKBeDRz14eHVTz5ZXl5hMRgffvjh1hYI46ufRdtGcuny5VUO9/j4+F5TU6TOKJ4giJnp6Y8++kixs9N8v+m3v/sdHoLsB50/890vX16/fl2tVv/qV/+lp7e3fqAvQmXeuXP7YUeHxWL+8Y9+tIT4J7ITXCEIYmhoQCCW8Lic9957jy8AS6DqNgQBHGpLS7PRZDIaDbfv3AkGQ2Sb6rV/o/Xv+J5vFLzfdf4dBL6DwLcfAtVf9vo6svhJ9Pf3C4XCrS15X19vPYtAEITP6+np6VEodmZnZnp7ek9OTkl6QBIVjeaop7dvd3d3Y0M2P78Agro3x8P80/DwiFajmZ2ZpqMECNVNcJ9LKNz8vkrV19uz28gGmSAIvf5Ytb+vUGy/eNHldoOZanU/mJPY3d1Vq4+Wlha7Xnan68h/TfuGPyFd67pkeHhYrVZPTIxxebyagfDuoVOp8/MLim35gwftqn3Ij1HdG0EQpycnoyMj7NXV/f392dkZne64vk0sFuvt7V2XSjc3N1++fImlbjX9WC3mrq4ulUpFp9HGxydOTnP1/aTTabFEoj8+xgl0axpgyASDwY3NzX3VXmtLi0IB2cqqB/oqdczw9b/ql0jWNzdkPT09wdBrFgH3AIJVj6e3p0exrZibnent7c2enNSMRRCE+vCgt69vV6mUyaSLi4vFItgqVM8Bb5uh4eGjo6O52VkmC8IF1TQol8tLS4sUClWpVPb0dO/tQUy46jZ44Xq9fmd3V6FQdHW9aOhbRxDEzs6OVqtdmJ9/0dWVrvKJ+4t9Kb7je/5ioP5uoO8g8B0Evp0QqPn6n/cT04kLXMfL53uV4z7JHjAcGw6Eb5VQadjg6168eCyyt4vWRTZqVCEXhQlnoybg64TNFdBfCFF4XsEt60ky2R7PEzcjL5IV8joG4Hn9nHed7AevBY9VKp8bLKC6/Xn1Lx3rNeTP2T+4B1Kd3XAgvPBS+Z8216quSUhWXatUX6+oVH49eSSMxE9903+/43u+aQh/1/93EPgOAt9yCNR/2euvkN96snJeGwysmrv1EMSUlWx8QYOaltU9V08G12v6uYAXIfsh50Be+bqV6kEbPlvdoHrO5PTqG5DLqV5+TbPqn+S4Nf2Tj1eYr6pnyEfOq+C25939KtfJyZCV+qfIlda3qZpspfp6OaijCxpU36oZtHqg6tHJR0i81DxY/RM3rr9CdvLNVb7je7452H7X83cQ+A4C/1VAoPrb/Wev4+DdiWQymUxoNBqPx+Nw2I1GY7FUsttsOBhdKpVKJZOBQECr1TocDp1OhyPHRKMRp9MVj8cdDkcymcSBARuiJHd6igPA2O12nNDg6Oj/Z+89o9tKsnPRf/7jH/7hu966Xl7v2nOXPWOP53r8/Px8x53ULbV6Wuqemc7daqnV6m7lRIlUpEgx5xxBEiQAAiASiZzBAIAgSGSAyEQGGAAwAkwgCeCcN4UjYdBKk3r6eXm9WmeRdepU3FUH9Z29d+1tXlkFloS+8xF9hxWm0+nl5eVk8sDv9zuczpXlZbvdtr6xsbOzY7FYNjc3NzY29vb2NjbW3UChxO3xeC0Wy+rqajqV8vt8sVg8FArFYrHFhcUXHCZaXV09ODhIJBImk2k3sRcKhRwORy6j4jsc0XdVFaLCD1bNZtxmsy0uLgUCYNmk02k/WDb+/f29zc3N7e3tSCRitVofLxt3xp/GamgeLJtQKBTfjIfD3zK0k7t+9vf2VleB9C2zbHx7iYTZbF7NGKD6AwaSW/OfNP7/Ae7J4s3swLLYMPuOZR/lRrJ0zE3MjSMZclNy4y8onn30dN+eriE3JRvP1pBNeTrygu791uIvzvDip8/E479j97JT81s7/3SFuSkvIOyLa37UeQiCn8MF/SPH/p0Uzx1pbvwFQ0OyvYAs2XqyeZ7u6tMp2VLZ+rPFn3j04nftxU+z7f6RlT+veLb+p/ucTXle2WzP/8gM2YZ+l0i2w3+KSEbzt6OsvJJKIZ8+fZo2TLt29eqFCxd7+3qrqiqrq6obGupv3MzH4XDFRQ8uX7kmFouvXLlcW1f3a5FQcZEN2zUAACAASURBVNGDpubmpsb60rJyFApVcOvW8sqq3+/j8fiuOZdEIhEIhGaLFYbhifGxb86es1otDx48qKmpbmluvnkzv6ysfP35viP+FIP9veqEYXhxceHjjz8yGgznz59rbm1tb287d+5cSUlpe3tbWVlZW1vb2bPfYHHYlpbmM19+iUb3U8jkTz/7zOV2G/W6kydPCQWCM2fOUCjU69evM1msvb29iYlxjVarz5iAGh8f39nZ3d9LXLp4kccXovt6yyvKW1pa7t29W1T0gC8Q5C6236vn30NmRGG8vKKKRiGfOXOGzmBcOH/u4sWLPb29VRWV1dXVTU2NeTdukEhDd27funz5ilgyevHi+aZmYE66sPBec2trU2NDWVkFCtVdUFCwsrLi9/sFAoHL5ZJIxEKh0GIFy2Z8bPTc+fNWi+V+4f3amtrWlpZrV6/+wcvmd3nXvpM83zfu2d3dValUOp3OYDBoMwGJ6/V6g8Gg0WhkMplKpUIePfFXp9PJZDK1Wv1EOnKr0+mmp6flcrlOp3tmBq1WOzExgbT+dAa9Xq9SqV5QXK1WS6XS57Wu1Wpf3PPp6WmZTPaCvr24OGLj7pnFdTqdSqWanJx8elBIikajUavVTx+lzl1A8XgcmYhsExqNJjtNCGWeV79Op5PL5TMzM9myuTmR7o2Pjz/zqTajASeXy59HWJ1ONz4xoZye1up0Gq0WuXLrV6vVLyCsTqeTSqVKpfJ5rSuVSoVCkVthNo6YBpiYmHheWWRBPm/gWq1WpVK9oG8qlQr5asydiCfia2trWq0WeVmMRqPJZDIajdn+aDSaiYkJjUaT7XNuRAfUGBVTU1PZ/E88nZycfN5TZF5kMllukdy4Xq9XKBQvICzyLucWyY0jT18w6TKZ7MWVS6XS3Apz48i8PG9oOp0OmfRnkkWr1c7MzDzTotITU5N7+6fbxmAYXl9b++zTTz/+5JOiogd5N26Oj4999dWZz0+erKqqpNKGOWz22W++OXXq5AcffnD7VkHhg6JgKNTX2zOWsV2ExQ7cuXMHjUYXFORfuHChrLTE4/X19vbm5d24efNmXt6Ns2e/Ka+o+LXXzzu3bx9/5528vDwsFsflsE589hltmF5S8tBqs/+n3d1hGMYPYt94443C+4VfnflqhM5obWn54IMPiouL7969q9Vo8q7nnT939vg7x/PzC86cOSMUicJLi1XVNTAMG42GouKiuvr6xoaGb7755kFRUU9vn8Nmu379esGtW/n5N69evXrmq68MRpNGPXPkyJGz587lFxS43J7z58/fv39/fGy0oaHxPzNlwLL57NNPPvn0wYPC/PwCmUx68uTnJ0+erK6pJlEobDbr/Plzn336yYkTn1+7dvXBgweBYLCrs0OumIJhGIsduHv3LhqNvnHzxpWrVx88KPJ6vaiurjt37uTl5V27euXixUulZeUQBN25VXD8nXfy8/PR/QM8LufEZyeGiKSiB0VW6x+ybHLfqT9p/PvGPXt7e6Ojo1QqFfHGxeVy6XQ6CoVCo9G9vb1cLndkZITJZLKeE17wlM1mIwURe/NZbwAcDofL5fKABwk+ncFgs9ncx0GY8ZjB4wFP4AwGA3FJiHjpQtz3IP4fkL4wmUzEZelzusZ6um+I4ws2G/iVZ7PZdDo9t8In6kGKI0WeeMRisZDW2Wz204+yT5/5iMViMRiMsbGxZxoWyq6t3d1dkUjU0dFBIpE4mcDlcrFYbF9fH5fLffHY2Ww2Ynb2mR1A+px1JYHMC5iUzLxweTwul8vIzAsyU1weL3deOGw2l8ngs1liAU8o4CNTx2Sx6dkJYzKHR0aYLBaDyRxhselszgiLM8Jk0TMXg8mi0IaH6YwRBhNcTOYIk8XIXHQmi8liM5gsOovNEQjYQiFHJOR+6xKxhYJhNpvJFzA43BEWm8FmM0BDvxloZuA59795AmIMxovWzMjIyAss3CBTs729LRAIWltb6XR6R0fHw4cP29vbEe9ySFOIL7NvN/voDllyDAbjeU8RsiNOMH7jF4/D4fF4yDQxGAxkMfAyAZkXZD0wmUw6nZ51/IJkQ9Z5trnnvS/Iknj6ae77gryML35fnveysFjgZaTT6c97XxgMBtL5bFdzI3Q6/Y/xx/7dYiAYhuecTjKFIpFI6uvr6usbWEyWSCSWTkwIhaLOzo629naZdEI1M4NCoYhEQn19vdFokslkVpsNhuGRYRoGg2Eymd3d3TQqta29fW9vX6GYmhgfHxoaEoslfD5PJBFvbGzg8XipVEokEru7u1rb2ibGgTXRIRIpmbEM+d0O6ruqDYYhMpmkUEyhUN1VVVUYDJZBZxiNRhqVxufzKsrLuTyedGKCy+EMDg7W1dWSyeT5+Xk2mwPDsMftamho4PF4XZ2dfB6/vr5eq9OHl5aEQhGHzcmYwx4dodN9Pp9EIhYIhVQKeYg0VFFZIZZIUN3d1dU1DqfzPzPucTrsZDJZIhbX1tY2NjZx2Bw+nz8+PiYUgWXT3t6umAQfRWg0GofD1tfXm0yzE+NjTuccDMPDNCogJpOJ6umh0ajt7R2JvT1gKlMuJxKJIpFQKBQJhaJYLLtshro6O9vb26XSiYcPi0kkcjIJDIr+vhOd3Y/+1JHvD/ekUimDwTA7O6tUKm/evPnll1+KxWKRSNTf3//v//7vpaWlb7/9NolEeqa3IO7jwMtskwDHPA4ITEF+ptlsNoFAGBgY6OnpaW5uRqPRBAKBQiZhMJgeFArV3TWIxeIJBNzgYF9fX09PT0lJSVlZWXNzc2dnZ1tbW0lJSXFxcUlJSWNDA5FIHB4eplAouT7As60jPo+QbQDZMJAuPdq5H/cW+dlls9kMBoNKpSLgIDcPj8d74hap53fxkPq4kUf/c/v2xCM2m61UKp820YasrUQiodFonE4ngUA4fPgwjUZDHJmNjo6iUKijR4/SaLQXT0puc4+nBWAX4EAq47CMwWDg8XgCHo/DDCC+1olE4uDgIKa/H9OPRqP76MM0MomEw+H60eg+VHdRUVF5eXlTU1M3CoXt7+trrGqpKG4tvkfp7dBPCPQK6YSQOzkmkYDlI+JyeQIeT8RlTwi4ag5NMzJkZFIMYzzDKE8j5GjlY9pRnkrEUUslmnGRRsxV8xlKCU8p4k6LuHIRV8Ic5g/04qtrhsorMQ9Kegof9hWW9N5/2Hf/Yd+9YmxRCbe+TtbVMUPF6wUMOZsxKuCLhQIuX8ARCDl8AYcn4HB5bB6fxeGyALplgSvj9Rbsyhwum8tjcjgsNoeN4LwcYrFYrCesliMzkp2XmZkZu93e09Pzi1/8ore398c//vGnn3767rvvNjU1icVipKYXzHtOUwD3I+/M4+XK42S+MXCDg/39/d2o7uaWlv6BATyBgMXhenp7u7tRnV1dWByOQCDgcLje3t6urq7CwsKqqqqmpqaenp7GxsYHDx6AN6i8rKW1jZwJxKGhDMwCBABIAsB0EEGcnICmAcgSsDmAGhwO5zFgBNmYLBY9A0cYDCaNNjw0NESlApeooIYMWZG3CRRBbjM1sNhsxMMWl8vNYBekxexfkPvxlYttMnVm+pb7IYRQjMPhPM9SZe4E5cahZwX49//df1Y14MAL0hYEQXt7e9kPmHQ6fXBwkL1FmkskEvsZY4xQRi6cTqdTjwNiJhRJz+08ck4qm36QCYibFKTpZ/bqO0lE+vxHEgopnkwmd3d3kQ4jf5/gcCeTyUQigQwT6Tzyk3hwcABBEGKfMEuEXPrkECGd2AM1pFIpxFDqd0KEJyr5I6mRrQ3pdmZyoUQid9lAmWVzkB0jBEGJRAKhBkKB333ZZCsBqyZjtPbg4CCHYtnu/E6RbG1/6sj3h3sSiURpaWlrayuHw/nZz35WV1eHxWIPHz586NChv/3bv+3p6Xn33XdramokEgn3+UEg4AOoKRIhn4NMJhOPx5PJZAwGc/Xq1Tt37pw5ffq1V1/78IMPL1y4ePHixbKyMlRX5528vJPH3mq6e4NJwmGwWDwej8kExFFLf38/JRPQaHRPT09lZWXV3bvffPXVufPn29vbiUQi7zHYQvrFyzh07O7u7unpaWlpuX79elVVFQqFGh4eJpPJCFOHx+Ox2ezW1tZ79+61tLQUFBT88pe/vHnzZkNDA51ORzwjslgsKpU6PDyMfF4Th4bu3rtXVFxcX9+AxeGGKBQWh8t5DPCeJgnoFS8TMlsawjh5OhvydS6RSHJ/H3NX1draWmFhIQaDuXTp8nvvf8Dl8b44ffqll166ceMGm8P5j5deelhSIhSJmCwWM/N9nQvUHjWXGQAyLwh3gU6nY7FYCplExmEuX7p8//7901+ceuvom6c+/+zSpUvnL1ysqCivrqq6U3Dz0oVz9XV1QwT84OBgZovF4rAYxHXLwMAAjUYjk8k9Pb3dKDAvleVleZcudDbWzionVoLurdXFWDhoN2jC5pm4dXo3YN+a5msHmhw8smQI3V/1YJqOd0+PxayqNePkvlufmlPDXt2uZUo6hKJ31etYhK7KB7fOfjFYelv44Lr/zgXNyV9JvviAd+oD6on3JGc+Gf/VEePLP9Wcfqfj1PvYWxen+uq96NpAV8WGRuyjYjz4bs8o0yPlecfZsyO4SR5zZkLsUsn8eqXPMBNwOfwuR9BmMiulk2KeckIiHxVxuFyEegh3hEqj6Q3ABsYzw+rqakFBQW9v7+nTpy9mwp/92Z/l5xd8/fXX7733nlAoAgxOFouZwVigwmfNPXAvKgQzw6DTOWzABSHg8cM06iB24Gbe9ZLi4q++/OL1Q4dOfPZp/o28vKuXG2qrm+tqb1y/9vWZ003VlQQcFpt5XwYGBjAYTFNTU1dXFzIvQ0NDfX193d3dVdVVDZXl35z+orjoAZEwKBYJlIrJqUl55gKRaeWUSChAobpxWExLQ93NvLzeznYqiSgWCYV8YI9fMTmpnFJMjI2iujubGuuxmIE7t299/dWXdTU1RAJ+ekqhlE1MjYvHxSIhlyMbH1XKJmbkEyQ8tvj+3fqqioFe1MjIsIDPV04ppifl079pHenDo7+gninFzJRiOnMpJ2VTcun0lGJSLkdAYZZ+w8PDv5UP98SUQU8FsHsh1+NHf/B+hrT1xJace/uoBQCzHvXrcZvgHolnO4wUzM2A5MnmzGbITcnm/24j2b35D24r2/ncAWaHnNvbXDLmZsjWkE3MVvXEo1zK/MEdzu3S0/Fs09lHf3BDuePNrTY3nlGbfLxoMusnS4RsJLee3F69gDjZbL9vJNu3P3Xke8U9zc3N/f39JBLpn//5nwcHB99///233nqrrKzsb/7mb9Bo9OXLl6uqqp6He3iZbR6DxVXX1Ny7f//KlSsA06BQZ8+evXLlSn5+/jvHj3/99ddnzpx58803v/r6q7v37ubl5ZWWlt7Mv3nj+rXa6uqR4WEmk/lr1IW4mObz+aIMwy7LMQK+DEWiibExIrr/8BtvVFdXZ9gJ3CcCgnuqqqo+/PDDS5cuXbhw4datWwMDA1QqFfm0zebncDgIO53H41MolKGhIRIJMJ+GR0b4AgEinwI8AYCreIM4XE1NTXlZWU1VZVF+Xt29/JoHd3nDFDaDDlgFIA8Q13F5wI/9I9kQk8Gkj4Bx0UfoIyOIlCHbejbCYrEmJiaeh3vW19cbGxuJROIv3n23sfRBY3XlP/7w769/c/pHP/gfnCHsxdMnLl0477CYvGa9VT01MykFvrLBVzTYacGV8VRPGBysqqy6fefu1atXKyoq2travvnm7PXr18tLHn70wQe38m/eupF35uSJWzfzqisq6murWTRya11V4d3bLS0tAh5w587mcBAWETIvyNQgiBBBVDKZrLml5eibb84aNHByF9oIw1ur8NYKtBKCV+fhWATeWdta8tHx/dcunm2qrWyrreAP9ixZ1Bshd2phDvIZIK8ecmshx/TBnGrHqU4tOPYjnmWbJmSQR+T00FDb3hQT1nL3VZykTpDSCSA154De7W4vHL19hnjina6jr+E+O4b56gNFTUEcVbreXgTx+uFpNmyRwlohPKeGV0LQ9sbWxvJOfG19fXlrO7buMu3NcNLO6YOFuaDNpJ6ZHhsfZ3F5HLGEJxCOifgeF7Cu9sywvr5eXV1DGsQdP/pmY31dR1vLP/7Dj0Q8dmd729E3j1gMOvW00mExeWxm9ZScz+NlmPNgYoCINnNxudxhGrW3u6uqpKiq8HZ/V3tvd1fhjSutddWttZWXz37VVltRVXz/mzOnaytKsd1tPc11Eupgb0t9c00lj0rUTY6LQK0coUCA+H4H7DWRCOEYISl8gVCpkDH7Oz889qbNqIWTu6mdOLT7+NqJQTsxeDe2EQ5RCdj8q5dJ/d0ttZWjI0NLbls8Mp/aXEtvr6c3ltIrofT2xsH2xl5sObkbT+zE18KhBb9nMej12c37q4vwamg3GjqIBvbCXnhrZW910WOYlgz1iUkYIY1I6OkkoFqneCPbi/5YdAHe34ITm+Da34b3t6HEZno3FlsNb20sr0YW42vRWHRhd3UpvbWW2o7Z1SoGicRiMAC7LrOmRQKB0/HceXnmZEHfDhk/HtHubtRSOGy1mAlEghC87+xnlv0vmfhtenzrDoZhAR98s6WSB0QiAXEmuri49F+SDs8c1LfIkXMDlk002o3qXgpHrBYzkUgU8HmI5cln1vP/J/7BFPj+cA8EQZubm1tbW79WZPnpT3+Kx+OPHTt26tQpHA73gx/8oH9gIO/GjarqasnoKMLG4OYEwCDh8QYH0O311TeuXPjs008uXLx4Mz//zt27t2/f/uyzz06dOnnq1BeXr1z98ssvvwLw58uTJ07U1tVzM4KkgYEBIPMiEvF4fGNjIwGPl0gkCHrIaQREOVzuEB5/4fSXr7/+OgaDQeQ7T+fkZXRQRgDkANwaDpeLuI9+Zk4Oi1VXW1tUVNzc3NLR0YHOID8mg0GmUKm0YVR3V3N9XVVJsWmcaxnnKBgEHZ/mG2duqHhmche/u7ak4Dq2v5cxTGMymZQhwvAQnslgsHk8dH//QEfL/asX6+vqcD1dRCyamcFQudQD/clwnqanp5/nwSqdToNZWV879Yu3WQ+vDT3M+/GPflhdfP/K+W/mneayuwWVxYUwvAdvLsOxcHJzdXkhYDPpFeNiPo8nFAgmRDwueZDY1Vx4/dK5M6dv3br1sKiwqbqyta76s88AZrp3K7/o3u3K4sK6suLWuqq68lK9SgklE4ntDZtJPzsj81t0VpWCgEGTyCSRWJzBd9+aFoR5hsfjT548eeHs16t+B7y9BsdX4FgUjvrhqA9enIMXnZDfBAdm0yHzmmVq26FOh+f2vHp4wQov2mCfHvLqII8O/PXq4aAVXnSuuwxMMoFBIU6Ni2f16pBVGzXItr3GVbtmw67SjfFFNLyQNrigkwZU4xoe3cylLU0JVrQSDa2f0lTeU34/qBRuzE7uePTzDuOiRZtadCQCVodcSOtqarlwRttcquyrX+AOwubx+BRPPibhsTn0/v5JBtVLx4d1ilWLZm1W+7xXN51Ob21trSxHjh19k8di6FRThw+9uh5ZYtOHP/jVLw62Y3ByFz7Ygfc2k+uR9chCwD1nMeimJ2UcDofP40v4HDGHyRuh1JcX37t5va2uBtXahOvtam+ou513vezOzfJ7t7qa6+srShsqSlBtzXUVpSb1dDqxtbu5bjdpndqpgFkzq5shYDH0EVpWrPbtieGOi/gS1sity+dvXDq3tuAHOAMAnTi8swFvrsArITA7y3447EqHXetuY2LelY5F9pf98HoIjnrgeTu8EgRX1Peo4N5mLDrPHSGPskY0k+MWvSo4Z10LzO2E/euBudUFv1Y+KuEx+Sx6NOT1WQ06pcwxq4t6bcses0bIoPR1YrvaFl3WeHR+Zy0SctmXAu7kTmx/Y9mqmaYP4boaa2bGhNOjgnDADSc2Zqmkzq8voh+Wi5hM9bRSIx83qZS2WeN8KPi8eXlmOvTtAMPwlEJ+6dIlHl8QDAZu375948aNsrLyQDA4NDSEyATJZDKFQsFisTgcjkKhIIkUCgWDwQwODuamDAwMEAiEbCkymdzf359bD5lMRqPRT6eQSKRsKRKJ1N/fn70lk8lEIhFhdWcTESWBbNNkMhmPxw8MDOSm4HCAB5hNQYaQ22GkNuRI/LepAu5gGN7d3SktAWF7a5tEJFw4f/7WrQKRZFShUOQOk0QiodHobN/IZPLQ0FBuCoVCIRAIuUNAUjAYTG73EO5+bgrgYeYMAREXDA4OZtuiUCgDAwOIJAFJpFAo/f39RCIxm+dpmiMdfoLmaDQ6N4VAIPzaHQcMOCvPCBmvXspLly5xONxQMJBRPM+rqqqaX1jIbfp5ywaPx2eHiSwSoObxeGkhKbn1PG8IuWN8etkMDQ3l0pxMJj8xC9llk62HQqE8c9nkrnwSiUSlUp/wQvjMd+07Sfz+cA/S3f1EYqi//6c//SkKhaqqqvrJT37y2qHXf/A//s/uluYTn3xcUvRAwGWPDNPIVCqDyQLqERwug8UmUyh9/f0MPHqKS2spL2ooLyktKenvasf1dOEx6KKi4vt371QU3qqrqqysrvn6yy9IZbfQD/OLC653Njc2Njb29qF5j7k7BAL+6zNfFhYWstnsXJjC4/EkEklFRcXRN48cffMICoXi8/mI7AARXXGfCiIRkDVgMJiuzo7ujvbe7m6E6QL6jaAPANf47fU1jbcutzQ10hkMnkBAIQ1NMkkkVGtV8f2m8iJ8aw0f2zEnY6f9BjhghEOzcNAEh8y7HqN7SuxVjVrFw0t6qW9arBPR5zVjBgauu6ZMSCWIR0jktlpOQxG7p1kw1M8j4zh0GpdOo1NJwyPDLKCax6YND9NG6HQGUygSPSHtfnL1HCTyPvll/blPFmjtn7z+szMf/eru5bNBh/Gzjz+qq66ILAZXw/PrkYWNyHxqJw4n4tsLc2GzyqUcs0+NGmXiOZW0u6ako76qoaGhtbVloK93oLenrrqytbaqrrxETMIyB/tuXr9CGRzo62yrLH1IxA5Mjom8czZoLw6vz8Nrfqty/OI3X1XXVHMyerWPiJ2J83i8O3fv/fKXvzj31ZceuxleX4CXA+mIb9uphtwayKN9fGWQjc8AL9p2HDN+BU8noBrEdMsoI+GbTQct6aAFXnTCfiMcdh3MO5jo1pbyYpNSerC6kI765mdnzDIhnziAaW8c6e8k97ZL+YwF3xy8tQLvrMNbEYCf1kIxr9Wtls6Ns5x88oZhYmFaGJoRr9vVE5QBExs/L+csKjjCrjp2wRXBtS+1TcUO9mBULVmeoLuGOt2jLFnZA+eVz7d7Svcdqi2/3W9QPzkR377f3t7++c9/XlVRQaWQD7/xutmga6qv++iD9z0up9s6G/E61haD6+HQRmQ+vRuD9ja316Nmg1arkFknR01Kucts6O9qGybi+MwR5YTYMC1Xy8dH8FhSf29/e5NcxJ/gscoL70pYw71tTajWJgmXOSOf8M3Z4Z01MC97MYtede3S+e7O9qxSeUblH3whSEdH0Y1Vpz/96PqlCwveOcBf2VpLRwM7XhMUskLzdnB5DVDQAnl1gOxrwZ2g3aeV60aZWgnDoRzdm5+DVoLQ5ipg122vwVvLe7FlPp3a39Vu1c0cbK4mN1dDDrNNp+LRSJiudhoBNzKEnxALQ945gPn2twH42wN8nfXwgttmdjutjilxzKFZcNmCTvN6ODjGY5plwiWLJmTWiukkKXdEzBrRT074HBaXUsYuL0Vdv4FpbJaOjhl0aqlEJBGLhQKBQa//9jz8lrsnNjEYhoOBQEVFuXRCNjY22t7e3tPTQxwaUkwqelAop9NptVptNpvVap2ZmdFqtQ6Hw2azWSwWh8MxMDDA4/EcDofVakXSFQrF7Ozs3JzTarVYrVaXy9XV1aVWq61Wq91uR+pB/MtaMsFqtc7Ozra1tRmNRqQhp9Nps9kmJyeRDBaLBXGE3Nramk2xWq0Gg0GhUFitViTR4XAg+pcOhwNJsVqtarV6ZmYG6S3SYSKRSKPRsnkcDkdXV5dWCwD9E2R5LCdKE/B4dB/aZDL19fVisdi62hq329PR0aHVapFBIUOQy+VI/y0WMHCVStXW1ma327OU0ev1MzMzLpcL6Z7L5RIKhf39/QhZbBkiG4GrDXW2wx6Ph0aj0el0h8Nht9sdDgfitlmn02UHjijVSSQSu92eHTjiRjebZ3Z2trGxEXGhjeSxWCxyudxsNmeLaLVa4HbeZEJKWa1WpVLZ1tb2mA5Pkgcsm2CgoqIi4xp+rL29HYVCkUhkpVKJylk2DocDOYqYrdbj8aDRaA6H43K5kESHw6FUKg0GA0IKiwW4u+/s7ETWic1mm5ubs9vtcrl8dnY222GdTtfQ0KDX67M1z87OIrOA5LHb7TKZrKWlJTtkq9WKHO1EiiBLQigUYjCY7JKw2WzPXDZUKtXpdGZr7uzstGbOxv+Wl+27ePy9457dnXH28Ifv/eLc+QsTExOtLS0tFSVyUu8MY/Cdw4cmmYToJMfdUzdH7jayiXLuyAi2F9/ROErFGcRMKZOilkrIvR2lNy7TupsE+B50XWl7xYP7d29fv3KpvODqSE9LR0N1Q2khvauuraq0/F5BR9VD4VB/6e2btdXViJKyWCy+f+/u//Uv/zIwMIBw7IF4SygEqg8EQlVVVUF+fndXJ4fL5QsEfIEAOXH2hPpERkGHV1pSUlx4j0ElzSjkOpVyRjrKHSYrxLxJsYDNYhIJePwAmoRGoduaMC11g12tI/2d+jEuvqPRK+eMDvXhGsvVjMGYdWpeJYYXrNCiE15fhDci8PoitOiENlegrRU4ZIbnVLBpHM7s7rBzGjLL1lWCmGEiPivfmOHBZmnarUn7jZuWqSXt+IpZ6ZgUCsg40xhnjIxh9bWZ/ML4oQAAIABJREFU2cRZFl7Opu3sAr8tzw2pfXzxjfcPv7w+Oxk1THKGBuTUASUd+/PXX5UyqUta2YpK4hEzo6rRJeNUUCGQj+DZJKxRpTDr1dopedCkGiX1lxdc621rbqqvfVB4/2FRUXdrY/71yw2VZSI6lYXt6e1oGezrxvd0ohprHhbeq6+ru3D+vEwiiLhMqbAHXl9A1ZS+9tLPhoaGspIUHp8PNFtHaD1tLQMdzWa5MBX1wAs2OGhJu7U79plH0iuvHkT8RjhoSniMmNa6/rbGSSHHpJmxGLQa+diUkOMxzKwG3XGf1TlKd4wx1SyihNgrIfVLyBi1mDNvnBZQ8UGnlY3rofV12HTTUad+ec4I78bgrXVoaw38XbDD0QB8kIDjy7BHA/sMcGAW8hvTHi3sUietk2vTvLhJumGU7pjlUNi5G/btry5EnUb3zIRdrVBges38kYhqYrq3wcjArOjGPZLhWS1wNPiCAKXTNWUlpz4/MWcxWnUzToPqg3eOtVaVznsdbrs56nOuLwXXl0IrIe+i3egzqVVSiZA14jRqA3azcUbhd1hEbEZ9VcW4gCviMAkY9ACqY7Cns7m+pq6qQibm8xg0Cq6fRsCRcAMDqM4hTJ9UxK8tL1HJxqOLwfT2Bry/3Vxb9eGv3mUzRhC2qyATGHS6iMsi9naSMb12kx5KJhCAmF4P7wI8qgWsNUS26NHBQeO2x4jp6+7v6ZqRjZt0WpNerVcpJkV8v1W/uhjcis7PqWUWmVAzIZKMECXDBAmDrJ0c85m1Qjpl3uscJuJoBNyc2bC8FFxeCgG4s7MBWETgAhFoF8BxeHsdXg0C5t9KIL3kgudtSd/shlkRt0ytz07u+MxwbCm1G0/txlcXg/N+99JCQDUlm5KNW6ZlIgp+gsuwSnk6CVuv/S149Ikpe3IHyzA2kslkKpXa398/ODhA1GYnJydFIuBvfH9/byNjFweG4Xg8vrkZ39raQuoMhUIejzeVAjrLkUgYKeh2uRDn86lUKhwOT01NIZ7qY7HY3h6o32azRaK/cdYdiUSmpqYQT9c7OzuBgN/r9SIOv7I9393dDQQCyB6cTXwisrW19QJTh0jmlUzILcjhcPQZ4Pg0WZAURNM6mUzu7e+l0+lkMrm/v4/FYnd3gYPP1dVVZNTA69ZeIkuo3d1dvV6fIWYyEokggvtgMPBrf5wQBLS/o9Go0Wh0u92Iyb6d3V0Igux2+0KOM/m1tTW1Wu31ehFZpN/vDwWD8/PzT9BhcXFxO+ORNHdcuXEIgnw+H9Lh3PTceDKZ9Pv9uTXH43EsFvsCtV9E9zyVSh3s7x9k1g/gHU5NCYVC4NEzkYjF41ubmzabDVkAiE76ysqKz+/f2NhYXl5O7AG3bltbW0j/t7a2djIOsBKJhM1qRbj+O9vbs7OzsVg8t7eIN3i/3/88yQCSeWdnJxj8LdzQra2tpaXfIriMRCJPeGLncDi2zCHEJ3r1p7j9vnHPwf7e7ASP2lF/89wZXB8K19/LGcKoBQyPUszAdKdii/CCEzZPwm4N5NYmF52JqH/HrU+ZZZBjOhWY3feaUrPj0TFqgNqpxLVqGdiIZiwwStMzsJoRzJqSO0UbqM+/1Hz/JolIHOhFVd3L900w/GLy9dMnznz1NYFAkEgkXZ2d7779FoUE9lcOh4NCoRoaGvr6+nA4HHJutrDwPqa/T8hhEfGDbA44/pfLGRIKhXQ6vbGujjjQsxTyb4SDNt3M1loUhg8iAbeISeNQ8Axc7zhtUMMhBZTihMewE7DvBa2JgDm9YN/3m3wyjopFXFBJotrxtM8AB2ehgBl88oIv5lUoaIX8RihggZwzKU5PaqQtRW1MizApwUBquDmFr4UVdNivh706eG4G/PXqgewGYRdFAYBILzrhgOnArT1wa2Cvbss4ETBOI6c8nreGDvYSBgGtqegWFdVinuBap8flnOHB5komuhWyK2H9KGwcT1mmIBUnOUbat8i3rMp1p37fb912GfcX59Ih26bXquAxRumkEUy3RsjwTwk1XCoW1WlSSqMLAYVEUFJ4t7e9JRKY0wtGMBV37ZMi0tDQ5fPnakuLNoMOOOKU07DvHnubSqMJhAImk4nu7SENYrXyibDTBM1bVz2zhPY6JYeSYS/J0kBcZYbBzmqAfEY4MAtvLK77rfyhAQaFuLocnve67HoVmJf0ntukE9OI07zhMQpWzR6ySEaiDsP+2mJyI7oXW07EVpJbG4n4ql03bZwQrAfn1kJesJsC/aH1xzvrBhyPAsGN3wyZxtMKOmQcTRtG07PStJyWkuDS3F7YIIG9etihhKaZkFuTDpjhpTl4bTG9spDe2jjYCCdX56GVQMKlPbApknMz83L2/Bw4bPyCcHCwr5uSNVaW8ih4h1EzIxstLy4UM4fhvS04uQP+7mV0WeLLyfXIfmxle2N5cz2a2l5PbYSTmyupnRi0tbpk0wVMMzLmkGWMFbFrg6Zp9Th/VqdaXghOCLlN1eW4PtTKYmBGNkZAd68uBU0aZdGtG90tDVvLizC0L2DRz399RiwS8oAmFoOA7mFRh4wzkxH/HLwTW10MErADGqXcrJl2m/XQ6jxYz0CwmFmWPgM8b1lxaNkkLJ85shxenPc6rXr13uY6nE44jNpxDkMhZCvEfJVUYlErlgPug/hKcnN1b3NtN756sL2RiK9YdapZlWJ1wbe+GEREe/B2RpqW2IS3N6B4FFwRHxQwQx495DWkTeNpvTgto6YnSNAMB/Zo4aAFrJaQFd5cAxO6vQHHI3AsDO+sQfFoemsVXltIhj1QLJxam1/x2OYD/hdMytOPnrnBP51NJpNhMBgYhltbmllsLmKb5+zZbyhUysOHJcgbOjEx/t//6r+PS2UkEvFHP/qHOZerqbHh6NGjb//857X1DRbz7MsvvXTs2LFjx487nHP5+TdZHM6D+/fef//9N998c3pGldnOI4cPHz569OjRN4/K5JN9PT0vv/zyl6dPf37y5MrqKsKGycibdr1ebzqdfmbnkd06FoshoOEFeSKRyHKOJ3YYhlEo1PP4PUg9T1MmlUrV1dVFo1Gfx3Pr1q39/YPt7e2DZHJ+PnTj5o1YHOzQFvPs3/3d39GG6XLpxA9/+MPpGRVmAH3kzSPHjh0rLCr2ej2HXnv1V79679Drr2t1+rqa6kE8sb6u9r333jt69KhACODmZjz+wfvvHX3rrddff53OAJ+mr7766r/+67/eu1+4mcGd2e4Fg0EEiT5v4Ol02u12I5DieXkODg5yKQzDsNvtbm5uRob/vFJPEye7bBrq68bGpefOnb106fKNmzcjEYB0F+ZD//IvP71fWDwzrfyHf/gHDhe4FD35+Wdff3MOhuGaqor2jg4YhvOuX/3522+n0+lwOPzxRx8dP378528fm3MBjJgddTKZ9Hg8yWTyBX3b3t5GwNwL8sTj8Wd6JM0WAe5Ul5ZyhaHAymJ3939Bfk8ymdRkTOGJREIBi47r7WprbcVjBqi97QoR72B7A/yO728nE5v7u5uptUUo4oE3waYFL4dSYspOfw1kV6Z9hvQkLS3GQcJ+eIIIT1JhNRe2yYFowzbFaSnDd7b0VZRU5V+vqKxoqSrrqq0Yrrw7dv/C1XfebGppBQc3MqG4uKipsVEgEDQ3N1dVVSGsIETBmcVi1dfW0kgECZ9NwOMRk0LDwyMI9OFwON1dXaiOdplECKUSi343m4RXCZm7a5GMoGHLY9KMsYdDpum03wQv2uF5K9iSA7PwggOOeuGQDQ5Zkx590qOHA0AfBfKbwO91YBZa9qetU+kpFqQRpEc60koGZJ5IMzpT9PYUtzfN7U2PEdOCgfQ0C5pTQS41ZJ0Ev+aLTvCD7jeC2oKm/Tn1in5iRTceUgoBGAqYNkwyMb57GI95npxrZ2dnRqWyzpo2nNod+7SGhTdwh+bGGFwsSi8VpbeW4UQ8vRtLRBd2lzwHGlFSSgEbvJyaoDTv9j2EcJWQcRS2SLeneXoxRyrmyyVCn4Sxo+LHTYpU2B0wa2gDKL2IPk4eoKGaVIIR2gCK1tM6OYIfHRm6fe2yWaeCN5bgkGXfpcG0VPf0dHN4vJaWlvFRUSw6n14OADFTLLK3HhYPE5UcqkrIWNSOw35j3KzYcaqhgAn2GXZsSjmbwh2mmHWq9N72kscp44yYFKO7a5GDzVVoa8UipKr5w8vm6aQPsIXAvAB0OAuvhgDE2Y3vr4QP5p1QYBZedMARD1Aeiq9ASy4o7Em7tWDhaYQpSnNKxU5r+QDojOJTwoHU5HB6cgTcKkbSzmnIKod0wrReDLhBbg3snIZDFnjBvjWnX51VLKpHw6YpKOJJeQ1LKgm3r1kmAZ9xzww7OztKpdJkNMxbdAvGKdkIXi9hOabHBcwRo1oJZeAOtLe1Fwf4IBlbBhrEiNxnZwOo1MzbwNYe9WTXBuxQwBYZ7NPDETe86ttwGfkkrF5Il1MHKH0dM7IxOplIw6JlXPoEn1Vy77bfboIyqjm70eBQT/swiSARi7vaW2fkY1uLHig8B0fd8LI/sbI4zmMZJgSOqdElkxIOmDYtih3bNMDuPsOOfVo6jGdTiRaTLp1MBGxGCYM6Z1DvxdfSO7HUTsyuV81MSNYWAsmdGHywC2Dcbhxcic1sJLm1nk5sPkJ4CI9newOOLUNhD7Q0lzJI0hNkyCyD7ApIL04bR1NSSmqClFYy0you5FAiCAwKWoE2GKg8BlhTERe86Ix5LWshz7zbFgl5U4l4YmPF57ThsQNSqfSZk/K8xOwPejaSSqUikQiipBWNRre2tjY2NhQKhUgk2thYf++99xYXl+pqa35tf/nH//RPep3uzJendXrgZlwo4P/5n//5w9KygoL8//5Xf0WlUH72s5/NLyyGgv5XXnllaIh49K2fh0LzD4sKb9zMP3v2LJ4wdPz4sVu37qDRfXYHsCizMB965ZVXVGoNfhD7wUcf1VRVffTRxyPDtFdfedUf+M2X+u7urs/ny+VGPD26LO55+lE2JYt7sikMBkP3LB/vCHFgGI5lAgRBCFslGolsbW0NDAwkkwdtLS31jc3m2dnTp09fz8tbXFw8f+6sZGwchmG9TvN//Lf/dj3vRkVZ6V/+5V+SM5QxGE3x2MbLL780gMEcOXw4HI5Ulped+fqbwnt3O7tRn5/49OKlS+i+PsQg0/ra2uE3XldOz7CZ9JdffmV//6AH1fX12XNZ9lJ2CKFQKMuByybmRiAI8ng8v5Xfg+CebMH19fX+/n6E5tmlkhvJWTab0eVloHi5vqFUKkUiUTQa+fjjj8fHx//6r/+6o7OrqLBwiESBYdjn9fzP//mDI0ffqqmu+ou/+IsRBstptx06dOiVV19ze7z3796ura1bXJh/88iRl156Sas3NDXUX7h4CYZhkVDo9fqyfUP4PQjuyU18Io7gnicSn7jd3NxEcM8T6bm3WdyTTWQwGP8F+T2JRKKioqKpqYnH47PYwO4cl8vr70M3lRfb9KrV8PzOWphJI7c2NjQ11Au5LKmQOzMp9btsUCIGeBgzvLSKB5nGIOfM3qw8aRyDbIq0ipMWY9M6AeyYsgiogzgsm8fnC4UMOgM90D/c3x1zqFXk3vpb18ofFHa1t4nEIm5G9YbL5XV2dpaVlhYUFCC217g5QSgUIoZnkAOuTAZjmEbFYrFA94pCvXT2a4dJu7m6BO+siWn4qrISel+bSkA3jLKTC3Ngs9laBj+sUR/QbwjMZjRqtXDYA0X9IO43gq3Iq88wdUyPfpR9hrRxLNVTnBqsTFGbkzUXUyPtaa0Q/HaP4tN6CajHb4KsCsilghzTEJAdWOCtlc0lf8RtCc8qvVOCOSlHxqbyqARKdxO+tUYjYatFTD6NSKeRuRz283DP6urqw4clA329G9YZyGvYc6gg+9QsC4/vanaadCvh+XhkntLfg26qH+rrdMn5llGma5wVEJHgGSYkxqaIdSlCTYrZmZZgDzT8VdOkUT6qk/AmOAy7TJRw6Uxihn+KD4RTUU/cZ42Y1SzSoHZGqZFJmLheBh5tnRTB4TnIawC4zWdScamtNWWk/q7k+gK8tvCIjGCHc8GROSD4C5rg4CzsN+7YZ9acepeMe+DWOkbpt65cDM5Z9+Kru9HgJJPExXZz8L3TfLpFKoAWHPCSCw7ZAZrxGYH2ScAMZDEeLRz2QlE/vLqwZdenF1xwyApFfdBKCAq7QDa3Lq0Xp5idqZ4HB6XfJItOAQyq5adVnBS3J6UYyehK69MWedo4ljZLgW71vDXlN8XNio3ZSQSAuqXsBQXPxCOR22okg92hGYlbylk1SEPTQqdJl33tn4isrq7eu3+/u7tLOzkxOzVumhxdME6N0XA0HNrvsCwvBjdXw5xhUl9XO4046LOZFuasoTlb0OeCtlfgkAVoPgHkkZEALjjh5QDk0aUtUgCa/bPwktMxxvJP8gAwii1tzs8FbUb15MR6eF45JuAOkxj4/oBqDF60QT49PG9JuLRa/jCmtZ6G7U1vrW4E5sJ2Y9SijlrUy87ZtaB7P2AGUxyYhb36hH1mxSB1STl7bp19nHnj0oWA236wG9+JrUqZJDq6XcwemR7lOfQzgPWSOXL1COU8klvFMtLecIbTlpFkAUVpRKT1+HZtAXIo05PDaSkpxWhPDdWkNTzwgsxl3g6fEbB/ot6MapEezPK8Hd7ZSO/EYtHFjcjC6kIg6LS61LJ5rdQ4OT482M+h04xalWJUMC3hi1nDWjVgnPzuIXfrQnY1u816/fp1nU5vNhlLSkoePnzYjepRq9UsFmvWZHzvvfd1Ws3hw284nM5Dhw7Nmi35N65jcXgYhllMxuHDR956661f/upX77xzvKW55fU3juwD51C777/3q86Ojl/84lcbsTgBjzvx+cmvznyJwxPUqpmbN2++9NJLBOJQRrXI99prr5nM1mml4tjbb9dUVf3sP166fevWG2+8YTTNQhCEiN7i8bjL5UIMAu0/KxwcHKysrAQCAUQO9aws+8lkcmFhYWlpKTfP0NDQ8+RciBynpbm5ubllb2+vq7OzsbHp8qXLbrcHg8EsLy9fvHCeLxBdvnD+408+PX78GG2YXl9b09AEeCTTyqk33njj+PHjb7997O233+7qQr3+xpHoMnAO9eH779XV1R0/diwWi9OHqe/+4pcF+TeaWlpNRuP9e/dee/XVjs5uGIZXlqOH33hdbzDarOZ/+7d/29zaRveizp67AEEwYgkJGePBwYHP51tfX89NfGL4e3t7TqczHo+/IM/Ozs7c3FyWwslkMhAI9PX1PQ/3wDBst1mvXbum1elnTcay8vLi4qKubpRGo2EymRq16sMPP/q1BPPNI4e1ej2qu+PevUIYhh1221uPwtG33jpKZzAbG+re+cUvjxw5XFldW1pS3NzShulHH3rjjXfffbfg9t27twtq6+qBPHFlZX19I9v/g4OD3d1dp9O5u7v7xGCztwcHBxsbGy6XK5vydOTg4GBtbQ2Rlz39FElJJpPBYDAcDmeXzd7e3tDQ0Avsmf3uL+PvkvP7k3MdHBwg+n1SqTSjGslBTALShoe7ujq7W5uwqM6rly9jMZgROp3JYlFptEEc9vQXp7pry/Y8BvDdbB6HNNy0mptW8wArW0pJ8dApSlOa0QmNDi5S2mSkfiGFwB6hcXncASx2sK1+Wy9OujU6EcOhEDPRbajuLiC0eqR1zAOnfr+t3ZwDfjKHgTkcgUCAxWAK8q72obqAkTQuF4cZ6G6o7m+o8GtkuO62ro62wZYq9wRzy6GGwm540QWY7T4j2HXm7WC7DXvgRRccycCgDPM/g3UMAMogChCIeopxLDXcnhooS5Gb0qMEyCAB2UJ2yGcCOcMecFp7OQivL0ELc3DImvAYPJpJmZDLZNAZw7RBzAAOM0AaxDiUYhmL3Ivqzph342R0wzlTU1PPs1sYi8XqGxqHKWQoaIF9+pRHv+9Upf2G9dlJ5cggBdVM62tvLSuM2PXbfnPSZ9x2qDwy7tXTn/WW302o+ZAIlyLVp0j1SWxFmo2C1TzIrlgzyo28YQ8Tl5TgF/l43wQjqpZszWmTAbNfLeWQ8RKRMOKbU4wKeGwmBYv2q8YAmslwrSC/cdelOfDqATQEJ7AMkM8AJBRhDzgovuCAvPq0RwfPW4w8cu2DgnEKBvYbEg6VdZzdVVXMwnYvmKaYvU2qESytuzHitu5Fg1A0AECnWws4BAEzOEAUiwAoELJC/lmEqQMiywFwsGh9EY5FoXkb5NGAtjSCFL462ZCXrL2aar+d4vRCNgVkn0obRwG2ABNtgoJmEHcAfmTcqZtXja0ZZasG6ZIKQByPjOOTc5a041JKf2halPTokh6g57vv0qyGgJ7BM0MsFquqrh4mD8HhuZRHt+/WJZyqVMC0bpuR0YnkgR4yurur8sGSeXpnOZTcWt9ZWZqb1V+7dIHY3bofsICjbS41wNngrz4NALch7VIhOBv2GcJ6qWtmLGqc3A7NJZcDc1PiaSEL3t9OxFcdRp3faeZRiYGZUcBH9OrggCntNyZcmgOPHvYZFvQKIZ+XsWUAzn6z2Gzz1DgcmE2BmTLPMAbrSwplTBIcmN2xT1sm2N3VJewhbMgxKyH2TdKwdBx6yTe3H1uBd74NaLLgZnMFXs/BPdn0R5E4YMV5DOCrYIKSVtDTOiFYveuLYE4X56CIFwgod+NAUrkSgpdc0OZqbHlp0WFacxpWPbaIyzw3KfTIuX6NLBp0j7JoSw7jfnxlPxZNhSwHPmMkFPiNVZNnTs+3E5GdDElD4jPT05cuXWJzuHK5rKSkBIfDlpeXyycnS0pK+Dzu228fm5TLXnrpP1pbW//+7/9eJBJ//dWZblTv8vIyqrvrvffef/3Qq59+euL4sWM4HO7IkcMFt24V5N987dAhPH7wxz/+x+Li4tdefaWhofHTTz+pqKo+ceKzpqbmo28euXzlWiQSUamm/+mf/unmTWAzLO9G/u2C/NcOvd7X1/eTn/ykF90/Pz/v8Xi8Xq/dbp+amvI8P3i9XrPZjGjDPC+X1+vV6XR6vd7n83k8Hp/PZzKZ7t69ixhAysLBXMpkPoDLS0pKA4FgZUU5cWiosPC+ZHSsrq5WwOefO/tNL7r/6uVLrx069P4H77PZnPybeVU1tcsrKyM06tvH3j5+7OevvfbaB++/39WF+uW775w/f6Hk4cP/53//71+fVPrRD39YVlZ+6LXXCguLrly6cA84rzhTXVP78YcfnDz1xfzCgnnW9G//9n9fu573yccffXrixNJSuKqy/ONPTwSCQa/3N0P0er0zMzNWq9Wbm/qb549iCoXCbre/IM/c3NzU1JTb7UYK+Hw+AoHQ0NCQpcbTy0arUV+6dJnFZk/KZaWlpVgsprKyUjE1VVxcTKePHDt23Gq1vPTyy2MTE+VlJfkFt6PR6Nio+MiRIyc/P/HyK69+cerUgwcPXn7ppStXr169euWll1/5/PMTN2/cOHL48Llz5y9euPC/fvK/6upqf/az/2hsaHj5lVdotJGMPtmjEblcLoVC4cookz01XJDg9XptNptSqXzmUyQRWTYqleoFlPF6vRqNxmg0ZpeN0WgsLCycmwPWor+H8P3hHhiGt7e34/G4VCpF7N8zGIy+vr7W1tbi4uKuzg4ymcyg04UCASJR4vF4IpFoYADz2isv19/LW6f0+PPOO776ZL8xL9VyI9lVmGq6nmrOA596+KoUvQ3i96X4/dtCnBdbN91V2VxTxSf07TumgQqqRwdvhBIBM7mjvqejFeCtjAkcXibkYp0n4jweb3h4mNDZcv/aBXI/SsxhsDlcMgFfXnDtxpefYZqrxxikKTZl16kG20PIDIXdyMb8iIuT4e4A1BKywWuLmUfgHHWGAwQUEUAko/6Z2dSD8GoIivjBJj1vh5cDcGwZ/HwDln4UnAreBLZqQBPLAXh7Vckmo+oqCJh+Op3O4/F5fAGXxwPqKW4Td4RCIoFTsll8qVAonqetBkHQ1tb23s52OmA+cKpSHt2WRWkbZejYQ+y+llk+Zd00uevWZRgts0B84zPAC1bbGPOTd9+uv3pmX0pODdbt3vp8v/ir/Ydndisupzrvb/SWreAb99Ele70lmyLCjla8LBpaJDS6B2rFfU1aAc2qnJiSCKalozqlPKSTs9CtKiZh362HF2xAWQdcGXM7CEwEosBZgFEe8c/0sN+4MCNm97ffvXJOMoRe1U0cuLVz44zK6+ceXj8vwnfPTTD9E8ykWw8ve+GoF1pdADRcDQEB0EYYmPxZXwKEXQ4AlhJAq06wcYY98EYY5ASn4meBqlnEC0BSEOhaAbhjHIWskwCHzduAdsjKPOAoBMyAh7fkgpd9BwELu7dZRkZ7ZNx9txZZFVv26eVZxbwOnAKLzU7u2JSQT592axOOmeUAkK8/M4B52d7e296CgtakSw15dJvWafs4W8vCc/rbbBL6mlGWmFPDwYxuU9gNZHOJ2KxO9eEH77eX3tuTEGF83X7HgySxPoWrTJKb0hOk1ORI2jqZdijBCTifYd+l257TLqrH3JP8MWKPXioCqsGxCOBWHmytLwXoOLRWMLIfzMhqgQ6ZEaiRAYVuo1stz5hwAp8HLA5nXCTYcarhoGlJPTrc3/Xw9k0RnbJmVh64NM4JdsXt64U3Lkuogx6l2G2YSSEH3XdzWDhPIBtE1JWbuL0Bb2bUrZA3YieWeSlWQeJGGBzxS2wCBax4FKQgL85OHNpchsBwVlPbMfoQfopNmZPz0kCO6YYX7Ttz6gWbIeRzebSKuG1mNzgHbW9Ai86kWxcO+H4v3IOoyywsLKRSKWQz297eHh8b83i9k5NyiWTUYDCYTCbF1NTY2NjeXuLzEye8Pr9IJHxQVITq6fn1hnHq1CmvDygVqVUqOp0hkYgnJxVMBiMSibrm5m4VFBTcujUpdpMDAAAgAElEQVQ351pejlZVVV28eHFgALO/v88YGbHbnXKZ9NLFS5VVVSsrQH1nZ3u7pbnp/Pnzbe0du7uJmWnl/fv3CwsL8XjC3v5+drH9LnKueDy+uLiYLfLMSDQaRfR7sk95PF4uvweG4XA4jBxRRohjMhl1ep3T4WQwGWazWSaThiMRAoEAwzCRMPiwtGx1Zbm8vKy5pWV1ZeWrM2cQCaDP68VgsJNymUAk4vN4LrcnvLT0oLDw+vXrRtPs9vZWY2NDXl5eQ0Pjzs6uWCTM4DHd1atXix8+XMio2e7v7fX19l65cqWiomI5wyjSazUcDlC0eiLMz89/J3Iun8+Xa5vxmXrN4XA4nlFgQrbIsbExr9enUExKRkcNer3JNDs9PT02Nra1Gf/s0098Ph+FQl4KR/KuXxOPjmXYNssjIyNjY2NUKk0xOcnlcXG4wX1gPDmJxWIJBCKHy8VisDu7u8vRSF9fr8fjZdBHzp07iycQESck2bGnUimv1/u8nQLJ9jvKuXJ1ybP150bC4TCi35NN5PF4/wXlXMjw9vf32Ww2CoXC4/FDQ0NEIpFKpSLm6Z5QHwZ7NpcrFAoHcbjie3cevvKv1ndfmf/l4f2aq6mSL5N3Pk1WnE31PgBClpGWVH9JClue7iuC+WhYhImR6wkl+Q4RFfxMW2SwURzTj60aZQsqyWBLFa6/VygUPgFxnnnL5/OJBAIB1d7VWCsk9IoJPWw82j0pWNGNb6hFG+bJgzl1KrMfPPqMznApYL8R4VJALjVsV8AuNeTSAP4Bspf7DAAArS3Aq/OAG7/kBp+nm6uZYylxeG8bTmyB79TtdXhrDfwF38TIFYNXghkegwHeXNkKuQIzkuG+dgaZiMA4NotNJWAFHDaDyURs3rDZbBKJRKPR+Hz+8+wWPlp2qWTEILdLRhZnxFHtWFgztmVVQoGMKkxmt3uM5DLKqhmhhk8pwjaUtV46GW5/kKq8sHXlg717pxL3TidufnJw50S6+kKqNX/77um11vsJXHWCjYJJtcu4KmrVnQWV2KedXJEyljmYoFK8bJAGFXxCS6VVQofnrYBWNgXYmAEufKQeC6BGxkIPIleCgyazkDbFIDB6mhU07Ghfk55FWJzibczK4ybZpkWx71SDIr7MRIAJMgGS7u8CHsBODBB2JwbojBib2Y4BCgNSb4DEXXAeOzMjGeXZeBSIXZb9gNsU8SLwC15bfKR9EovCG0vQSgBAqJUQFLJtmCddUhYX3bJukmeAgjFmVhh55NC0KOUBSuhpry7hUMVMcnCkywJUOl4UUgfzWqlNPLwwIw6rxxZVkk3LVMpvTAPeWMbqASIABWJTEwy64fNpZX1VRW0fvL5Wfn7vm2Omw/++fvuLNKY0xUalxwjpKUZaTkvPsFP8/jS9DTKNwSFzRDPK6apd1MmARtqiIx20bJinlp0mv8VA6Ov6f8l77+i2rjNf9K313vzxZt7c9eate6dPZpK5SWaSmTiJM4mTXMdxnDh27LhErpEt2WqWLEuymiVRskiRoiiRYu+9FxAdOKhEISpBAgRAovcOAgTBTqKd8rxxJAhmk5I77fqehQVunrPPOfvss3H2d77v9/1+VrUMizpRmwo2SjMWZcYkg03ylFM7LRNlKRvAT4fOYHg1MixsMfDJCuYoobVWzmeNEQe0Y/SI27YYdK8GnKsecypgRWJeLOZ50L04lCdn4uD4G/y+gJt1f+RvLAE7Ff/ktm7exwBtroJbeW9r9havL4F91+LAmRe0YPHAcshtUitYZMKyxwIs3Tl7XC+Z4lEjTiMS8wF3smt602deMqnmNSK7Ub/XTdm2bWVlpaKioqioaHh4OB8ouqWiRCIhkUgYhpGJo5ws0havYLWYGhob8Qlybm4uP5Mo3yWAHzkrQh7azX2L10cQJBQKbYet5LdtY2MjH3WLbltwIM7vgWseGhrK4XuAZLdQWFxcfPPmTdyE2nJFeA+k0+nGxsbl5ZWF2Pzdu3fxpCQMwwJ+f2VlFW6upVIpv9+f69Itx8H/XV1djWVB1rlquQJ+fSDaFYutra7mOjPXJ7kOyOaT/5vgmsPhcHNzM95U/JvL5d64cWPvzpFKpfiwGR4eEonGcTh8eXn58sqDq5iLRFazF4Vfb+5i8y/T5XKBYZNn0ecuGW/MI+KacVhY/r75ZTxF8aG45lAotAXXPDg4+IW1e5LJpEgkyqWF47YOlPW+MHdZAHkgjVZ2/eqn7+0LfHoMvvROqvI8wmxHON2IioEapYiMiLDaYWI1zGxNTUAbs1JkRrQxyU7bJrGgedkgV5O6mG1VHTXlI91tbbeulZ052t3Rhueo73LOB6uzbidOT3dXXWnhJLV3WScGL/HzTvDsnneD+EvIBtLOAY5nOm2bRFzTq7OytFUFUAVGSYbZuj42BJvluF8HEJmEbOChjyek4NMweNavgtdW4JaIgOf1gwd6FuOJhwPWl1CLCtgE9knUJMMCxmWPeaClrqOlic3mQBAEaMe6urbrDdHpdJlMttuD8t5zIZOGPTOwU4NkTQ1wjcB6u292fK6gQYNWgLbxz27ap0ariq/++ul4w+XMrQ/TtefhtkKY1oRA7TDUCo9UpGsubLZcTxGqMnIyiEpMMjcM0oxZCVsm5gUEdfddRnMFo71mljtKLLtcf+5YfFqIWZQAi+pQg8sEca77bbhvxNxb4wF5cFrmUO/dEi2HuG7XYGErwP0AAiSA/gHV3DosZEOi3oTPnJkPLAc98ErWvkyupRYjm9EAshbHUqvoxiJgf16aA8k+8TCIjODOtsUQ8NLFAwm/BV6O5mFQchnUuMci65CL+QEmOpud51ew+b1NYRUf882kHWqvnB1S8VDgKcFblcXcuKeTVlXM68w9l3csJBKbU3KJlM+RC3gKIW9CPKYU8eUCnkzAM8hF0dmJhF0DIDh4WNA9DbxWEfu6dXK4qrjwzV8t3Dq5eOOjzb67qEmaBYdlGY8ck6hFAQsGEdEgahjPYoCmN61q2G/G5t1Rq15JH6a2VPL7mqehYVJTRV3R5QXdODYjWJ/kqPiQlEWTsekKIBzByYlj0OiMCfEY7NLiwcopxtBgS71eMb4RjwIfUmIV5GEthUHIac4B8MWLYXhtKbkwl16OrUQCIGF+YxmL+ZNeY8KHOzsjILC74AMuHNxOzdk9a1nWyvXF7C2L3TNAHxhDWeQyeG1YBO8VARPwxjk12Jw9bJqm9bQGp6VY0Ji2q71KfkQjxPwzqFu3YVMHtDKjclwuGpPw2Tbb78bXbLfbL1y4AEHQtWvX0Oyy490cHx/v6wMgHhRFEgmcVwLMQunsgu8SDAbj8fiOk3HuyG63G98dX4NPWrld8APmJ+bgR85Vxuv/29k97e3teD4Xft47d+58Zg4WFhbiaPFca/O7CIbhysrKSCSCZ2vnLLZ0Op1M3vNRJRIJPJl/y4XkX/jS0hJuG+XqbLl23PkUzeb85+psKfzb2T1ut7umpibXJAzDSktLCQTC9evXZTJZfofkl8fHx3t7e/GUdfy+AwqAbL463nJgIAYCeFo43r1brgg3dVwuV26vXKflauLQq/xhk9uUK+AeqX8ju6etre0LmM+F38hMJiORSLbPzcw9F6APzeYQuprjUhqmE6CR7PvinOMeN5pjCtVwkQk6YlNtOnVL5qlUNp0VizjjLpNLrwpOS0cbyk8fe7/yZtFge3NF0af9XZ2PaPeAd1k6nUalDna0+lU8LOZGgjY46ola9bPSMYDGtamAM2De7ZKxaT0tgv5WcmPFinoM0/HjIiKv7ubY7cvrwqGMhrehEWBODeo3Am/Byvw9Z8PGEro0BwIxqws4hhcggaIedDmKxnzgyb40B7iJN5ZRnxEm1SBQGzYjQsWDy3qplM/ubqzu6+zo6ent6+sjEAi48teWvtxbl/TeDwzJAEhvls74gamRszm2F7K+K8yrS7u1svaKZV4/JiejM0LMMI6a5ahjErWpkBkhTKxGBu+gs2JgKnn09zDdQUvUMOlSCX1K3nBVSdGZD4QDLe5xBqe7IS5nYFo+AM04NcD6ybd7trUBdqjXZmUKUvey14yuRJNOXUYn8I0RneNMxKbCskFGxDvjUYuVzFFORy23tzk558bWF8J2g3K4baziepzZFxtt9/TUIwoqgMy7pgFSfhLC7JOYRwurmHPjjKhJE5uRr+ilm7Zp/HYg8z5kMXwvxX1zFUQnHWoA53JqMvbJiFoQVnLGB1uCE9yAkhNS8RJWFbDDnGqASXdqEBswXjG3Nmmb3APfg9+XZDI5JhACRXU6EDfN/+BCp2Iex6AQRWeUsEOTjUAB0BgWdW3YNQpi16ZeDAJhAUM2AmsGZrffgAaMAJ+kZgMnR8wPwEnemSxj8lLE43CZZv2W2Z6GqtJPzogGW90SJtRZB8x9YBNrzRNiKoNBZTCB0mreOPtMyo3HYa9ZAK0R7FCv6MfltOGVWBhNricXQnDU49ap3BopsKezIcJ0zG+bEMmYo3zSIJc0lF5bwpbmwpN8xUi7jjmAeXSIVoBoBWAk+Ax4twOsW9QDeMPn3ajfgC34gPG9GAa/lAU/+PkA39scuhgG4eCYF9hY9klA1e3WZcyKyNRYaII31t8UmuD5ZKyYRrhhmUg4pn0a2bRkjM9mAbXUbA9TqHvpxeZPSLlyIpFoamo6dOiQQABCD9lX3mUiiRRfWrJYzAwGQy6TTUyoFAoFn8/P7bVjIRQK5RhrdqyAYZjX690tTQHfJZPJuN3unPWw43E2Nze3sMtsr7a6uvpQIpZoNLolYMFisXJxLgzDtFrtyZMnb968maOcUSoUCrkyk07TabTJySk6nRaLxQYGBva+qGQy6XZ/Lv9oe4OXlpYeGmGJRCJbGrz9OIFAIN/rtr0CiqIul+uh+Vwej2ePOBeGYWq1+vjx42VlZXgccGV5mUQkLi0tW8wmJpMplUpVk5MqlepRhg1uLm9vam6Nx+PZ2+sPw7Db7X5onMvr9eaOuWNhbW3toXdhbm5uC38Pi8X6Avp70um0Orvk+3vynpwPKUJMpo4+AJ5iQWsW4LIEohLrS9hSBKxxTgPGFEeWTc49nXGoMwYpZlPBUY9Xpxzuau1sbhjoah8dHWVCQJZrR/uAeS/V60FLIAiiUKn0kf5JEUsvYi3ZtBa1QibkU/u7xJShwaZqBW1o06HF4t5Vm1pAGiQMDfa1N1OGB7ViLuzWBcS0/pZ6el1pvO9Ohtu3OU4GMBGbCjzKV6JIwJzxGjGbGlFC6Kz0How3O32CavYp8CJun0JULEBpuBLBOF1YZyE2yQxPiXQsggii9vf39ddXdDdUNzQ20ncHaNPp9D1wzevr63K5Ylangz2zv4Pdk7VCQAK5RTXH7EI1HFTDTStom3I6gNM6NaD9rmkAP9ewsxBvE7hHs2LEJMX8s6mAxTDO1rEJetaIY5yxPCMDM2LQCIyVLA436615EOcCM3qe3YO5tWn7lGUcmptRBCd4m3aNS8m3C6mivoYpYgerq26WTcg41FhgdkE3bhVSvTK2kUf2ytgBrTzjNbjG6TOskamW2/NVnzjffWn2/PH4JDejpKFaHmDfsU8kNMJFen+4uzredNPKGp3l02RMsoANeTWyFcvU0rRo3TkLvBFr8yD6Jqdgajbi1nrEVDd3eEnNnyU0c5tuCwdbQyoeaAawETWAlUDLB1fnUKPWCWD3WFUx367+no2NDYlEMjU1NT4+vtt7AhBHy+rAsyBILuQ6p8Y3HXggcmbdqJgbGwH96daCgeSdAR+nGsC3PTNZC0wNsMMr8yDeGnWDMbY6n1xemJmUa7nkWT7FOc5cM8oBritoBF4cN/DlwG7tlJhPva+RnlVnY4AXg6y4VVCn2DQrHAp+ZEYZnFWtRYMevcom54mG2qY4ZKinySRhp+c92HIoatY4xXS3jG3ikzwytm92Co0HXFKWgU3QMoeSVhWiYsNqLmi5Rw9eb9w60H6fAdGMIdNCVC9EZ0RgkPhN9+zpsB0UgJ00m8Wi6dFZMWaSws5pr5LrlkKLMzIde2Ssv1k40OqRc0I6hUYq4LFZNDqDQqMDfbj7zisa7Xe2eyKRyO3bt/v6+hoaGvCZQKmQnzh+nMcXuJyOq1evXrx4sezWLalUdvfu3VAoFI1GIzst8/PzBoPB4XB8ltm003awbn5+XqvV+v3+3Q4SjUbD4bBGowmHw7sdJBqN+v3+6enp3SrgJ3K5XAaDYe/GmM1mq9WK18EPW1ZWhueN476HkZGR9vb2zwQNDQYDDoS6UVR040bxxsZGT3fX+XPnzp8/JxSJa2pq5HJ5LBbbsUm5Bu/dGKfTOTs7u3cdnKZ5jzrRaFSv13s8nj3qRCIRjUazx12IRCKhUGh6enpubg6/olgsNjY2tsXfMzg42NXVdevWLXzKV6kmjh8/zuXyXE7HlcuXz50/X15erlRO3Llz56HDxm6379HgaDSq1WoDgcBuwyYSiTzKsPF6vVqtdsd7hK+cn593uVwzMzN7nGh+ft5oNNpsNrzB0WjU5/OVlZVZrYCI4d9h+ffDNSeTyYKCgqqqKpwb8IFx8WglCIImRzszZuW9F7vlKPCXAOf2KrYcQYNm8NoKpsZsBpBvFou4luw64WhPT1PdKHCEsFmse4jp3U5Ip9OJRCKeZYbXgSBolEhUC9nYRgyLudUSQU9vH4lMaW1qIBGGh7taa8tKJGz6SFdLWcF5Rmd9QCeLGSescp5NLU2HHZh3RkgdbrhTqrv1MUauR8YJgO5FTkZsqpTbgAgHk71lmZJjmU/eShQcStReRaREVCdAZ4TA7rFOgId4yIYFrBk+wdNTPVZwYqa6wK8SsuhUKpXKZLG7O1qbSq9TqZTt0Kj8a6RQQIXdLP2lpaWCgoKWxsZF4wQCUDX340rbC/lY4+xWzK1NWFUePjEhJSGjVQlO96KACKC+QKAge5yACQQBcf+ND9DKYRFXxKASDLbqmIMJiwrEpLIEPA/Ou+0sKdvk6qw0kzWk8GrA3jIrnBNCbGkOmfeax6h2AWXdqJASOt0yFqurntFZqyT3dt4uqr9yRj7ctqEfX9WJgxP8iIqbkFLihHp7b6X45sfShjJ1a4Os+o654ON0SyHSc2uVP2KqKxs8fLTz7QNDlwtEzY10BpNBIk+11huoA0IOJKMR1/kjgERgVhKtL1F+fIhz8KXZrhovfzRMakwzWzE5cfzOBWlnFeBP0o8BRh/8ipz3+Yvvd93yjMRjnt3tRx6Pxy9fvlxXV8fj8Xaze/C7zGAyhVzOtGRMJxd5TTNJL9Aj2zAp3NzhjEMNwGT+rNE55wRhO4Az0wHu6YUgiBDhwaOVGLYSnfM4hHSilk1MWCYwr37BqPJr5f5pWf4noJX5tAq9UqJVydUqhVwiVk9O6FQKnUw0LeLEDBML08KwRYetL8ALQeM42znOWDbI+L2NoSkBo6eZ1Fo9KYA66u7WlV6fZA5vuPRr1im/ejxsUGEhy6Z10iakigZb5yZBiPB+tqMGIMfnHIgC2rh2Ym3/s6kjLyTP74f7b6OTEDCp8bxIgwTE8txaVCdIjNTaaq8pKq/YWQMeOXtOQoMNEsw5JRloniD3pBzTvmm5XibUSMamJQKdVDgjE87IRSoxn5UNuI+OjuLuit1uzfb1n8VWCgsLaTRadXU1/n5vtZgvXDjP4/MH+vsKi27U19c1NjYqFMr6+vpoNLqwsBDbaYnH4yaTyeVy7VYhFostLCzodLpgMLhbnYWFhUgkotVqcU/MTucBBwkGgzqdbset+MqFhQWPx2M0Gnc7USwWi8fjNpvNbrfH43G8beFwuKqqKt/uAdqI/f3l5eV4r8Iw3NjQUFlVPamaPHfubG9fb8GVKzOzhrq6Oo1Gs7i4uGOTFhYWQqGQTqfbozELCwtut/uhDbZarTabbe/jGAwGn8+3Rx3c+sSjSzs2OBaLzc3N6XS6+fl5vEI8HpfL5fVZFkE8zIRzPA4NDVVUVOj1AFJms1ovnD/P5XIHBvqLim7U1tY0t7TiAh0PHTYOh2OPBsdiMZ1Oh7Pm7NZg3DbC/WE71llYWPD7/Xq9fset+MqFhQWv12swGPZoTDweN5vNDocjN2xCoVBVVZXld9QD3v5LfMQ1/352D4qiPT09FApFLpc/eLfKn5/3LkMQabDfIYGwxDJADOSAkHhhLY4u+MCbq98IPParMQxLsUlDhVc+gVisHF3h3mdgMBi4+t2DaQaCKBQKm0F1GXWGSfloRxO1r4MxOkgd7O5qbe5priMMD3fU1xx4Y99vXnpBSuqdU/E9UtaamIzwhmANHwsYjSKI2lYXbruBkWsBAyGpFu67BSto0WlJgDuy0VWSvH546aM3Ni4eSNZdQam1CL8PNUgAttSqBG+uESesES3s+7n0xR82HP5te9kN8q3LfPIQBDHJPe3kxgr2cO/eF4XH6fbw96TT6d6+PhqVjIUsu8eVsjgbtyZrXxpBqjAQXcq6Fjy6gJLrHiNh4hF4EgIJUGtxEH3Ao1orUeCWW5oD65ejADuMJGkjfbS2Kixk/FzSFm4nbfkGvgrg2onrxHGt+B72yA1SqROWiRk+2aXg20V0I4/kGmd4ZCwtY9DMHhH3tywqIWFbxZHXXjr33ltacqeP0e0kNGR0Y9gUc5nUGK644Kk4O9tYaCZ3RQdr40MN8aZbqyd+s37zpGaMQbtb0Xb0eG/hDSqNTqVQWQwarbFhqr4iqWCtG+QxPgGxqLBZ8VrlFfmXv9T5N3/O+Pl3pEdfHis6tUqsWyfX6+qvyZpLI5MgAxy4VRzA77X9g+H4nt39PYlEoq+vj0wmy2SyBwNyp5vNYDJ5HJZeJlh0GgB74ZwT3EeP1iWFQmoRFstKfq7MowCLvQruwmIImDs4tuze72gFgxOkoX5uTyMWACzYmGs6rFOwoWw8KyuTh4vlUWl0hUScWY5im0vI+tJS2L8a8qDzPoAcd6oxj3ZJN26TcZyTYpOIOcshuCRMt5ytoQ/MULrE3TWLWhGtperY269d+uA9o5DuV/HdUnbKocEck5hjEnZqnGKGmU9e1I3fR2hpASGhXYMl1xCNeOPieytvPLNWcGStqzwxyYWtE7DPgITtAK4kHEBEQwi7c7P0dPDpx1g//ibn7Duyyiv8trubvP4NapO+t2p8qDU2LQJBNNc04G90Z1nO3dNrlknzhFgq4OI4w9/D34Nh2MzMTElJCZFIxKc0BEHi8XhiczO+AJa1tdVEIimTyR4lYPGfJ871iPlcW8JGW+JcKysrg4ODRUVFuZDZ2tra6ura6uoqYHRcX1tcXEwkEj09PfcxTztPXl/IONfKykpvb29JSQmObbo3bBKJeBwMnNXV1WQiqVAoeDzezp1yf20oFHqUONfeKM//2DjXFzafC0XRRCKxh99+p6f6g3UMBoM+3GdRitGNJUDeeu+RnUVErsyDXFbAPR8Gm1LrqcUIh9DX3dnxiEYPfhogrp5dcHOBifu+R/o19EEXZzhAbUsw21KMthipqa+mnEIiMllsiE7rbm6orqocGh6mDPZT2+sX7pyJ73tq7uCvEUojyutO0ZoyPSVw9w248RN4oAyRkjHPTEoPlCNdUjas5idlTMwkRaegRf6Indq3MqMAHiy/CTD36MaSDYUrz33f//qT9FvXR4jEmksf1316sejjE92lBYTmasLgALQnKvyhdg/+w0HSKWDKbMXTaLMzKMBYZF+sZ0HW9/I8+I758LR8zDUNOzXWcaZVDKW0AiweAOniWWQSuhjOOuRWwMrNZXBf1pdWowEFRPTK2ZjvPmfjDmZB1jXiNwBryQOIHwHaBkg+AQMC5Gpl8bMBBccmphulPPPEuF4yNqMcH6eOTLfcXKc1Y+KBDahNU3PVMFC7yu42dpZN1RS4xigBUtvG3XNYwyfJilN6Ug+PA00MtIeIbamxgY2RmmTxsdXqy2YWgcliU0dGmCw2i0ab7W9epLSuShmIzwwwth4tMsFAR2uC+57Vf+mvR//mL9U/ecz38Wv9Zw90nT1Yffg15p3LE4NN62bl7sDwrL3oArjmBf+ucS78vqxvbAiEQoDuYYDBmP2AAfrgV5EtgWRyOoPD4Zhn9YmgA5/UMw6NVcx0KfhI2PEgQ3BpDhimeBYV+KVsZFMIV1ejQTmTFFRygbnmyrJiLoZsszomE8o/GYPBgJjMoNsO+AYTK1hqHcBrsnjtlC1r4bl1fgXHzCd55OwV00RGJ0xDnfbuu9ONRRu0JkzYt8bvV/fX66m9yyq2idGnofQkhUMIrQFRUjHnFOICQWrkHm+TDvXNoFYlOsnGZFRsMRgPONUQXcigyjl0AQeaUUpjbms66gU7igdgUg1ccXrhhR9Gnvim7if/HDr7lquhoOfKif5LH1Sd2M9pKVeNtG9OsrBZMaD9zNo9S6bJGbmQx8mCe+5fJ51O/13505aXlz8jrXn55ZdxHSU0u9yfkh78lUgk3d3duGGE19nyDdiWd8E152oCit5tuObcVjy0lE6nHwpQ/bfDNbe2tuZ0KjAMY7PZr7322pkzZ/BsbbyFDzolW4JhuKKiAjeM8q8lV8bxzttxzbkK+GG345rzK+B15ubm/qNwzU6nc0uci0ql7tu379y5cznJiy09g2HY+Ph4T0/P3sMmH9e85ZLxfzEMy8c1b6/zH45rbmlp+cLimpPJ5O9t9+CSERTCsFYhifnd90wfwOoRx6KepG1q2TbtNWlDbtvynF8m4DJpVJALll3o2QUvP+I3iUSmMSEGlWIidaBGCTbJRCdoCKsNIVSmRyrmRmqNraXCulJp5aezjcXSpnJ+dzO7q8HSVY7UnVs/9uvgk9+O/+bJueITMLEKa76MdN+Au27At0/AvWWolh868rrzjRfX6V1Y3IuthpMBa8Jvsek1U2r15gqYlhA5G1HQUKsq012KdN5YrbvceeI9ApFIJPdn/HkAACAASURBVI5eu3iusbqCQiLS6UD5/aHL3vieez8zOAMChfl2jzMbX8gGrQBweyEI3DbxELaxAgDXCwF03oMHsIDrwqP3GrSTHOqCVYNFHKhJASQOEqtYYnXNa47p5Q6NIuI0xXwOu5SDE9vgRgzs0IBYTM70uRfk0gCjKmACSePAsfQA6IO6ptdNStSjXVCx53WykM0oHR+HWCwQjWJCNCqFO9In76nz9Fcq2u8KOutnBhu8XWXGmoKp9rtjLAaTQVdcvWB6+5euS+/r2GQO4LBkMEnEif42LUSU7X+l57/8v463X3bQBiV8NovHo0IsnUwUMyhd+qlM2AG7DfGbl9JtRTC7Z/XYKwtvPgs9/q36734jXXshOFDRemq/uetOQiuAHZrszL2Dmyd3pdij2T2JzU2pWMSBmDw2i8eGeCyIy2FzOEBXbgfrJwuykQrH5mcVwGfj1iYtE26VcJJFXnCZsdQaQDHP2YE3NLG2Oh+a97ndppk5FxA3tSvG0lnCITRryK67Z9Ir8c3FeaVMigOoc8OMRqNJx0Xri9H0Sjy9vJBejmEBM+LUrBsBDj2mYM1P8gDVoV2FGSUgqkuuS5Ab4iNV/p7boY6brorzwebr4a5bodEGY8/dGH8IEfQDBbqh8rWRqriYCNgT/MDnhCgZMKsLVZAWC465fvDN5asnk7MTphm1QCQYG+PL5FK/15VYiWPJtbTHsnLjTKb+UrqpYOO3T8OnXrW99TTn1z+Ch8uDg9VNFz6w9lQk1VzEoQboOhsgb8w4AaJZzONws9TsuavD3xN+V7snkUh0d3cfOHBgYGAgNz+trq4iWfUrnBJ3M5GQSqVUKjVXAd22fDHsHgKBkJ/HjjMZnjx5Endp4BZMLi8pk8ng2pktLS05jp9tHQMSknbM58qv+ZkK1X9yu2d+fr61tRU3v/BvjUZz8eLFz0gGcZ8ZiiJ5wwbItW4mEjKZ7KHD5gtg9xAIhC8grhmfX1OplEgk2ttvz9x9wZ/1Xe1tA13tq9EgllwFWd/LYf+kQEQaYBOHGuvrqCSilM/hMSijRCJObLP78XbdwmAwuttbO2qrhT0Na7xedKwPsAQRq2FGM0xvRGiNGKsdZTQvtxetl3+EtRbAXUWbQ+WG8rOCa8fh8pPw9YOJ956zPfbVtud+MXvjVLzkePLOSazsA/TDFzMnX4l/+HbopaecLz7leu6nsZqyRdnYskG9GfKt+xxRu3EtNoetLyFCIjLNQ60qhNGCKcgJi3KwvmKUSCSSSB2dnVmiQqA1RqPRts9/W67q0eyeNOzRg4khZ4I4NWjYAXKPI25gxKzGsHgAUA2BlOMlbD0OILFLcwAPCyj7POnlGINEIA/2ri0AthtgQq0ueE06u5jhElHlI20RjWCGR7aJGIBtb7ckecB9bACfrNcBNCZguY/zALx5m5YJj5g+wxhQ9jVMingsNgcYfw/8HxADYjGYEJ1IoFIodIhNYXHUTRWCs++31layOGwGxKJzud3FJdQnHzPfLVA13pY315JpDAqb13/2wq1/+g7lu98yP/HPk49/S3fmmKquQjLSPyPguEWskJK/FvYgjpnls++ne+6k+8vXT/5m/fbpYE8579oJhN8T5Q0EqW1Ak8ujh52adA6djXNX4hnsub7Npp0/ir8HheFVv2PJqFoyKJfNU8tu07LXtui1GbRqFgRt/x3hjh8ui+WclMAePebVJ9wzjJF+cn/XxmI2FT+rtOqe1ThVYodyTEUfDGtEZqXQKuMnrSrcWtq0Tk1KBONikWx8fGyMv2U44f+KBALpuFgiFsulkmWnMWmbtIpoJmKbp+dOEmpHRAPwyF3wYTTDUCvCagO0oozmeNGxjY9eQW4eS5Uc3ag6Jys4qq29ijFb4L5SrPiQ4YUfis+/F6W1xaU0WC9EOT1o5elE9SeSr39V+N/+zP5PXzH/9IeBu8U2ykhEo0wHXNgcIFXC0hspszb+/quZ6+8lr723eeQFrPSDQP0VyafHUX7vnIgcUbCxGQEGEgVAzDQ7wjVph2bJNLlkUnk00jEOK28IgfzN39Xuwecw3IOCl91O55UrV8xWq9Ew09La2tvT29Xdo1KpIAjChZBwTfIt3yiK4hMYgiBbNuX+xTOJNjY2dquDIEgymXQ6nalUKrfXlgIuHIb7hLZsyv2LIAieHrXbiWAYxnUuI5EIiqL4jiiKUqnUXD4X3ht4Zjiez4UgSEd7W1tbO4qiTU3NYwLB1YICj9fX19e3srKy27kQBNnY2HA6nbnmbS+gKBqPx/1+/24HgbOskqFQKL/B24+DIIjP59ujMTAMZzIZh8Oxtra2x7mSySROmYOfAkGQWCzW2dmJI8DQ7IJPiz6fDw9uul2uK1cuW8CwmW1r7+ju6uzs6p6ammIyAbni9qbia1AU9fv98/PzezQGhmFcUGy3OgiCpFIpp9OZTCZ3OxGCALMMTxXco87S0pLP59vtRPiwwWHaW4bN7/q72+4be8Q1/374HrxB6XQ65+/Jf2fdLcFq+zOXwWSSSaTmynJCfw8QyUqtY6uRaR5laGiICQGfPEhQp5I66qrKbt8eGhrKuXy2H2qvNRBEJQxP1BZtUhpQehNMrYcrT2cKD8Ht14FaE9QKvmmNKNSKkGuBQhC9CSNUbtZf5BV/bLl4AD7zanL/00u//uHgP3696Ge/aP7pD3hvPDt9/C31/l/PvvBD93e/nmm/iYgH54/+xvKDx6DHvsH++TOmt15D2m6i0lF0zgUEGtdiWDYVCDOOY1alijrQ2dI0SiT29vYSiUS85QwGg0QibZ/8tlzXQ+0e8PiC06jfgDjUGft908epAVQra3FAKYSjyGP+ewWcJg6nHVpfBkjzbOhkIegh9ncLaMTNxQgWMCUDVrsky6Lk0QH8ctDol7NFPQ3CwdbFGSkArubZAQ/KwOXzOQcP2ASkpnSoWQHLyauMNtlgK8ijzsZ9chcL5UpMkJiHm7wMFltAGNbeuCAoPjM10iERjtGYEIvJGDhxiPXzx7tffUZ45j1B0SdjxZdqf/ZM55M/yXQUwWUnfK88Tf3Kl7u//JWRx74leeanm9c/xEYrQbRocxk1SFBeF0asQnuK16CO8ZYyU8etFQUjNMFNGGQ4AinjUK8bFVkJNj0acQMAigcEjx5c46PZPQiKplOpxbAPibqwoBkIqc57wfdiCE2uhX0uHo+XD8PPdUC2ZxhmzQQS9WBzjrlJPqGhQkInbC6EsKgb9pssPFJCk/XKAInWWY+COzbSrSB2Lekl2fijdlYupAARl3ybMnd4UKDTs2lcdDqNTvdrZZhVsSSnueoKUp2FSNMluP8WfOcEoFPvKYHJteD3wmiGybVI/cVMwf5M+3W46wZSdChe9tFEw43YYAXSehUtOhh++cneb3yNe+IN6vsvWko+9NcVeKvOG998VvflLy298UzqzD7/U99VfflviF/+cudjj0t/+Vyq6CTK7gLiLUshlNmO3j6O3TqG3j6x3FksLD5l6727pGSGVdyUfQpnsEzbJtcBWbYWaJUEjBmX1jUl4XNYW5ymv4fdMzc3B0FQOBzOEe/KpdITx09QqDSnw1ZeXt7f33f9eqFEKvvkk09MJpM7q+rg3La43e7JycmZmRnX7goJbrdbqVRaLJY96tjt9nyFhG3nAYIDj6JTYTAYpqamdmstLkwxPT2t1WrxOm63e2Zm5vz58/k6FVwu12AwxGIxHFyyublRVFh47dNPFxeXuro6iSTi+bPnhKLxW7dukUgkr9e7vbW4QoLVapXL5Xs0JiessUcdt9s9nV32rvNQnQqXyyWXy/e+CzabLV/SwePx9Pb2lpcDbSzc5wfEQblcq9Wa65wJheL4ieMkMtnldNwpL+/p6S4quiGTK86fP282m3drMz5scrIPu3WgQqGwWq17DBuHwyGXy/fWqTCbzQqFYo+D4DmAe8ubuN3u7ToV58+f/wLmc6VSKYlEolAohEIh/naF2z14etfQ0BDIUXq0hcViEYYGm5saRTwOYP5YjxvYoyQSicFiUUgkCpHYXFtdcbOw+s6tivLy3r4+iAUW6GE4mPyTMyAW1N4Q6yxBOwsz5SeRuvPpi28lj/4KvnUMJlTC7HaEWIXQG2FeN8xqA491QNNXiXUVufvvUi+dih54dnPf/1h94Qeub3+d/Y2vml94YuLAS8MN9WQiiVZ7l3d4X6D8bLKjCG2/lm65tnnjA/1PHqv+f/7IVnIWozei9imQI+3ULnP7fPQu3UjzOLFvqL2pp6GGMDqafxV4B+bW5NuR+ddCo9H20KlYW1tjsdgqpQL2gjx2BFAfTSM4rbBdE7PpEwEbSF+KuAFmPOICZEJLkazXZxlQw93Di6wC3M/K3JxFp2cT3CpxanEuPe/1cYcRFR1xqBe048szUvFQG7npLrenYYLUE9UATfWtyVy4JQTgzEDN+8HHqkTUHJjXgzCaDG23INJIPmwL8Hoz6TxmPqFMXgdAkIQ8Ol56yXTpgI1LAmYxxCJRKL11NZSOFsEHby4efCZ5+S3roRe4bz5rv3QQubwfLTqUvPj24tGXGv70T0/+4R8Fik9hPcXoNAe1KhGoc+7mcWfRUU3xh+bOMl3zDTM0mLJNYV4d4tLivQdCRbjnzKNHww4YpGFvteSyuOaJPfLY19bWmEzmOFgkmknVus+CZZ1hWDyQiIXiIW96LT4f8knGxTsavjgSyDyjQxbnsKgzoOLPsEccirF00Ip4tG7ecIrTiajZMZ1keUYqHGylttdwe5pkxM4FrQjz6Vetaj6HnTUugQLXFssA71xAeMNgUJisWSETE/R5Wm+s99xEqz+Gyz6A734E3zgE33gfLv8Q7i6Ge0uQ0btIVxHSXQx+PrRG4DotP4mUHvWUf2yoKdisvQhffjt55IWZf/zK9PNPRD7aZzzz+vy1A5vtn9qKjktf/FHo8POZm0cyp15Ze+kJ13f+ofD/+qMLf/zHc5cPYUN3gDqpCoJ7boU/edt19nXNxXcN9dcMbbcszIHkPZRVVg3GpYW9s0hWlRaLhzY8Js34vYR8/EJy3xQqzWQ245PTI35/NtO88847JSUlDAYD32VhIdbT06PRTEcjEZ1OJ5fLORyuTCanUCgwDO+t1zg/P59Ti9xeEyfVXVtb260OLjBpt9v3Fph8FF3SeDzu8/l2O1EqBXRJP5NMz9clTafTWxLibt68ee7cuerq6hyuWTA2xmAwvF6vw+EwmYxDQ0Nuj7e7uzsWi+WEKrdceDqdXltbs9vtezcGF1Lduw4upLpHnXQ67fF4HqpLarPZHqpLmq/8imu4trcDRxeGYfj3jRs3Lly4UFNTgwcB8WEzpVZHoxGtTiuTSrhcnkKpJJPJew8br9cbiUR26z28Mx0Ox/r6+paOzf2bBtq3CZvNtvewWV5edjgcub22F3BdUq/Xu0cPZzIZv9+fr0uaSqUIBMIX0N+zublZXl5eV1eHq4HmzUtMBoMxMDDQ0NCwBwnNlvr42zyZOOoyarGNeFAloPd20giDU8yRoZ7Ogf7+3s6OyqIrhZcu3Cm5QRjsHxwYyOpYfc4pkH/MXBnKLnQ2m116dfnSb9HC91LnX19999m1159KHXshU/geXHk6XfXxxifvpktPooS7wOLh9wDrZ+A2QqxJ0xuFNy8pTh3c/OClyI+/5f7W10P//FXVl/8K2vc8fXQE4nAYXD6ts3nsyKuLF9+CL7yF3Ti8ePZ15rF3OyvKh+sr4mISoP5jtm2yuziln7Qe39/TVEcik5lMZm9XF4VCyVk5uQZTqdTtL/35NhCFQuFwOLvlscdisbLbt3t7uuf1ss/he7JBgfAkf4Y1ApAfOYsEJGrpsYgTJKj7TUCH1W8EKGzvLAwEWYGs+pyK55Mw0259ZGw0Mng3KCDOsAkuMS2kFpjGyIS6MlprFan5rnOcEZ0W3XsFB/qjWTFXtw61yJFZMTLNA3IKoiFYOARzu5HB20jduczd0+ILH5AZEB1i0ZgQHWLTIej+xPw5/w8jz8ylc3mU4WHSE4/rDr8tYNLoTAjImQnFNCJBfuz1xEevYOUnsIoPXcd+PfbEN5ef+37ywC/gTw+af/mDsb/768nvfV1dcDQhHkZJNejQnaW6T0SHfkU9+qq38VpGQkjoxW4JtN2sATgk8wSYZQFk6nOeHtzrg7m1y7PSPfLYPyP1Kiwq6uroMExI9dKxOb085VC7p+V2jdKpUXhUAss4C4s4ltwmPoe1I9ALjAEm06WfwsIOQInp1bvGGVG1AHbr5iTUCKEmJKXPiFhOLiEA9Rg5BGJT+WjDHVpHnYs3EuYPu1RC04TYpBQblWIhl73F9oGYTI1EYJYLjGxScLQZHbxjKT6RrLsIl59MXd6fOPFS+txrcMlh8J5Q/mG69Njmx29krrwLUG6497T9eqb0KFJ8eL3mnP7iu4EbH2SuHtj4zY/XfvX9zdeenHj8G7rTb6V7SzBGEwK1eouOTr7yo83Dzy8++/35p74t/vu/Hf2LP1f+4BuqTw4kuF1IdzFSfz5WeEh6+AXy4Zd8fXcRg2TTpPDKOSABPkvzDbD2UQ9QQQlYEk5dJmAK6hQGpdiokpiUInCNCvBtUorNSrFGyLYZZx7R4sGrhUIhDoeztLTk9XrR7JLbPRfowTBMIpXS6fTcph0LwWAwx++3Y4V8XPNuFTKZzEOFlh5Fn2t5efmhBHQ4009+S4hEYg7fgyAIl8t1u91zc3P4deV3SG6vdDrd2tqa85bl1ucXcvie/JVbyktLS7hCwpb1+f/Ozc1tERTL34qX/X7/o+hz4WDk7bvja/C7kM9buLCwkK/HjmEYh8Px+Xzbb3p+L+EZnbudBV+P8xrsXcftdu82C+A74rGwh/IWejxASG6PZXV19aF3Ac+ozz/I6OjoFxDfk0gkaDTa6Ogon78VMQBlF+Cw2fJwzc3tny/gk/o9Jzx5dHIMCpmmZ2gDLgXPJKIN9HRRqVQGkzk4ONDV2TXS20kY7O/s7GhtbR0cHNw7oAZBEIFAGBgYYHC4wuKC5Xd/lv7w5Y0Dv4z/4nurr/44c+HNzKcHkaL3MtcOmP7hK4K//suZXzyRuXsGYTQB64dQCROqUGazq7Vk6Op57+VDid88ufizx8xPfLPtVy8OdXZCOVEwDg+6XRw//wZ8+hX3/mcFJVfoRBJnTEAijIxVXY90lmHV5xKNVxS1xeTWRhqVCrHZHCZ9qL2JQadBEID15BYIgsjZZYs9lG/37B3nWlxchFgsHocNRO+zxk0Ok5vlutVumJS5Ndk5OzuLOzVgRv/8B3aocfwE7NREpbQAfySs4MyMtPqlDAtrUN9blVJQES0vSG5ykVsN7BG7iOqRMk1j5JhWjDmmAK2zloeq2Qi3G4ayXjRmMzxSAXcWIi0F0cJD0cO/TL/z9Ozbz431do1DVD1lQDLSx6cQpcIxNpvNpxLZDDqdxaZC2b9UCojE0OlUJiQcHhBV3Bo78Bb9S38pLb3GEotpFAqnsWryo3eWj/4KuXEoc/MI/OmB5MGfW37+3dhzP1jZ9xPjCz8yPfnt9VefxM697i3+gHv9o/lbp7D6i2u9pYaqT6LD1TCvF7PI7ePQJLUPC+DcUQ+AzIhTszwj+Xy/3duK+4Qw93RiT97CeDxOplAgJjNu1wM19SwU1z4pybIKM2YVooRlAmihuLVRg4rHYW9BH+MjhM5gjHGgRZMKcesQoAM/HVFyAiJKZEo4Q+32TfCsQrqF0JTi9yHjowFah4vQYGT22+jddnq3iUtYmZVhXuB400gEOSkuXDiPxWQsqrjYNCfaXbpY9iFy+3iw8HC67ARS/mHyyoH1g89lrr4DHD+3jiElhzY/fGXiq39H/+u/nnj6X8DvpfkyXHMWbrgID5RlSNWuirO6j/bFi44hH768cWaf+8iLUyd/u0qoRtntMKsN6S2Fbx93vPfc8q/+xfi1rwj/+98av//Nlee+j51+1Vt9YfzOxY3WT7H2T5frPrHcPj1HaYGnuZh/1q/kKWiDQFYsZEPnnCB/bWkORBtjvlWHLuMF+YlZJsaszCrAgE8DuQ/AzahdM6sCbnv+E/mhZY/Hc/Pmzaampvr6enzayJ+3cruPj4//b5XPhUNS+vr67t69W1FRIZfLc36O/AJe7X+TfK7q6urceMAwrLu7u6KiorKyUqlUbumTXLVHGTZfAFxzc3PzFzOfC0GAKo1YvLNnfsvMnZvadyvgrhsGk0mlUekUModGVgs5M2K2QcJlE/qoNBogKsxyFdLAbMgZHiF0dHTsfRYIgkgkErB7WGzqhZO+p/5p5Vc/WN33k9Th5+BP3oKvvweffW3xtZ96n/mX6Is/drz8tP+NZzKF74O5mVAJM1tgQiXSU7whGBy7eZH30dv+8/snPnqHWXaDSaU+MHqYTCaLRR/o9TQVplqv8y+fpkIsZlZNnTQ8RGiqEd+9Hjj1Onbl7WVCLZdCoGdZpOlU6scfHi27dWs7XIlIJO4YIsyZPnvbPeDHhmFwOrk1n+s++ObhLDv3a+bwK/j8kbZPrSgYcWpLVEK38Mk21qCjrzJJqcc47SjUkhIOJEVDmJK8LiHZye2wZBRhd8DMZpjRArAgjGaQltxSAEDiN95HSg4vnHlt7pUfbu77kfhXTyw0f5om1cGstmTd5cRQlV/OnuZSg71Vzo672r7m2c4a13BTZLDORe+1EDpULKpvsB7uu7k02tD3+Lc4T31ffOOy7vKR4NEXNj56GSkGPonUyZeT+3+28dIT/peemL97Yb74hPvlH8Efv4aUHE63XQ12FDtai6c/eS9afBwdvL3I7QsLRuFpgI8x8sjFZ49bBJR8uFJWfHR6Ub+b3QOMmIfmc+ET5+ramkAg8E1L8buA+WaNShGZSmMyIa8WpMqDrvbqXNMKBnNnXyaNzpgaH4OdOFcNEFRP2iZX9ONxFSc6JbCK6BYBzckaQnndGLsNhVoSvJ4UtwtTQzGNKKTi47fSpZaSaQDsg7vWQNgLggJQP0ZvWGq9tnj13czVd5RFJ1bJdSitASDeBu/AQ+XAsrl5ZOPoC0u//UX4wPMTr/3CfuEQ0lcKNo2ClwQ4i3deFozICo6qT70eufmBt/Ha3K0PU81XUHY7km0POngbLTsWvbw/9PKPZ777D7ZXf4Ke3QcX7IfrLoTai92dpVMlpxZar2Ok6iCrb2lWBkTZ/LNWEeP66Q+sQio2Zwe0C+uLICfRNwu79SsGBQxIIrZEHkEKHuaaTjnUCgHXaDLlZp1HKXxGuv/RRx+9//77Dff5mjc3NqQSydra+vLyssVitVqtFotV/mhELP+p+Htywand+mG7TkW+HjuGYf39/e+8886HH36Ye5s3m014D9tstnA4JJfLl5eX+/r6/tfi73G63Rubm7t1C54WvkUJZIseO273vPPOO2fOnMGhLRvr6xKJZH19Y2V52Wq1WS1g7CiVyn8V/h6v1/tQf8+/lk4FrkG7R+ds16n4wvL3YBiWSqVyuObdDJpHXL/FOcRgQlQKlUGlKFlkaLCbTCID1pPRoe6aO0PtTeTBvtb62ubm5u12w5bTQVl1eA6HW1/4KePHj2Vef3Ljjac23nxqff/PY898z/+jb7m/+TXaf/0z7RPfTpx+Hbl2IPPpe5muYoDfZLXBQ7cRALR0bYoJY1eO8ipviGuKBxprtobYIIhMIklb7oYrP6HcLoG4PPAOTaf3dHZQGMxb5091fnpx6sirMxcOsolDWZ8Fvaur6/B7B77//X/p6u7GxeQZ4MUfTEM4vGOLPZfbiufl7sFbeG9obs9j32bN5MyavQs5JweYqlV0lNeV0gkXDYpZPnW2r3p1tHqVVOscrIHuXpM2ljhIraqaq7bmQgxqQZjNwHZktgDnGakGbroMAiU3j2Qu/zZz/T34+sHUb59Ovv4/xp79nu7maYxWh1DqUHYbJupDRmvSDVewzkK0qzhDqMaojZheiJlk2KwY03AyBmlKOISO9aLcbtvBV9g/+rbh7Wf1F971nX9zvfB9pPQoXHokU/Re+vRvNt76afAX37Ucec1a+EG85BhacxbpuJ6gNlo7St1DVX1Hf2O8uF93+aCtvTRCbspouCnbpBYaPrH/taNvvgpCgfenUjwz/wFCPNeTToD7Af0D+GOATsVD+XsSiYREMq4UclN2EGrEfDOrNo1UJOByuBKxaM1j3rSrN21Ti6apMe7OLh8Gg8liQXN6xYZ1at02tZlNa8I8wLGRtk8tzkh17FEjNLSp5q4z26z91dSqG6rBZg9nSEbo9EhZmA8o1MYNEyIeh89hC7hsDgtkD9Aglq6vcaPy7HrztY2K0+itY/wP9xmaCjFuJ/ghUBvg5gL4yjuZ4y8u/+ZJ79PfS53Zh1V9hDKbQFCY0wHqMFuQsV6ghuacipKbtGfedNw9b2krdjZe2xwoQ1jtCXbXOtSxRqxP1l+MX3nX/dqT5ld+GKk4gzVfAm8X5HpXf2V4tLb1+JszlRfNDdd0A7WrclrKKE/bJjWM4aNv7Tux/zWgExK0YPNeJGxHPHrYMZVP/J0byRk7uC+YRxfQyrgsptX6u+mSrq6uNjU1HTx4sLGxEf9BTapUxz74QDwu0U6rrxQUVFRU3LxZKpXJmpubcENhfqdlYWHBaDS6XK5YLLbTdrAuFovp9fpgMLhHBZwpGA/o7FgtFosFAgG9Xr/jVnxlLBbzeDwmk2nvxuToj/G2hcPh+vr6fL5muVx+5cqVAwcO4LpOyUSipPhGcUlJOp2+ebO4sanx9Jkz4xJZc3MzTse8Y5NisVgwGPxM6mvvxrhcLqPRuEedeHzB5bA67LY96sRiMYPB4PV696gTjceN09MLQX88vuudCofDOp0uGo3iV7SwsKBQKLbwNUskkkuXLr3//vs+nw/DMI166tixY0KRWKfTXrlSUFZWVnb7zsSEqrGx8aHD565OpAAAIABJREFUxuFw7NHg+fl5vV4fCoV27F58JU7zvfewwfma9zgIDrHa+y4sLCyYzWa73b6wsIAPm1Ao1NDQ8AXka8YfB/+T/D35NsoWu+feJhaLRqX0tDbdLi6qLL7uE9OCtM4NDX9BPaZjDRPaG4dGRqh0Rj4qNv+YnyvzeINXCya+9d/nHvvq7Fe+RPiT/3r3//7jT/6PP6j+wz9h/MVfaf7+78JPPrbxzs8z1w7Co7UwrQHuK4VpDaiSjgYtmEOdmOKuCYeD7SXUvi4m63PBKSaTSabRaH1dtPNHei59xKBScLjS0OAQncGsvXqxq66qp+x2x8ULdEAvw+zr66urq6uuqjr42ssDnW1sDoeWXXIeHQaDQaFQcAsp1y00Go1MJuMrfz+7B89Oys0Nj1jI2T1ADcooRc1ykN3t1S/qx8W9DYRPP2KVfLwwwZ4fG17TiSKzSg2LIL1bsDham2I0o6xWgP/A/T3EGrjuAnznQ5AZVHoULjmc+uCFxGs/Tr72Y90vHw+//1zm0jvrlw9Gj73qefaJiW99zfTsj5CaSyi5BpESEA0XFg4hOgFiECP8XuCQY7ahGi4mpSwquBYJT9FTv1FxCr1zAr57CmBv75yAy09kig+tHPyF98Uf8A8+P1tzBaODxL00rTEyWLFMriNfORZoK5q9/bG3rRjjdaP2SYeEYRNQRxvKC88czxI069K2ybRtEoCagfNAgzjUSctExj6FeHSwSweAydk1m2Yl4nwk3kIknVx3za6YJ2G3HqiHBkxo0JLwGFbt06t27bp9WiXmC7hsEY+Dayx8bgDn/TPGYYu4bCGXIxNwV80qPKAJuPt8M5FpsWC4g3i3UNBUtqCXxbTiDac2OqtU0YcmqX1LeknaoUbdunXr5KplctU6qZMJcTgRh0oWjQ4JSSNyQu9G2YeZs/uMp9+MtV5Hu4rQOydTR14I/PCb1u/8w/Kvfpg58nzy+K/TN47A1EZUw0MUVGDdMpoRbjegY3ZrsVnxhmB4U8X0j9SuC4fR8RF0kmkWMgUMqoBFdw3Ubd48EjzyvPCNpzRlH6P0RpjelGK2RVk9S9QmUuFpf9+d2fprno6bGLsjIx6xQQPOccZQ7a2rJw8v6SS4iZnxzCA+I4CmuXWwQ5O0TWUcatg1nXXzaFCnOmnXbNo0m9bJRfNk2Ou69z7waH8SicSdO3eef/75HB2zYXbm1OnTVCot4Pf19/fX1tTcraxUKJRlZWUejycUCgV3WkKhkF6vt1qtu1UIBoOhUEij0Xg8np0OcG+d3+9Xq9U4cGTHaqFQyO12q9XqHbfiK0OhkN1un5mZ2bsxxuwSDofxtrlcrtLS0lw+F65L+uqrrxYUFODwnXQ6XQmswJten08kEg4ODp49d049PV1VVSUWi+fm5nZrksfjUavVezfGZrPhs/uOBwmHgh5f+FSDWm+wLsbC85HQjp9YNGQy6HweZyy6S53FWJRKWvvFTwnnatWu6By49B0Wn8+nVqsDgQC+LRwOM5nMXJwLd+hOTk6+/PLL165dwzvHaJg9deoUmUwJBgO9vb1VlZXVNTUqlaq0tNTr9eKdvP1M4XBYr9dbLJY9OicYDGo0Gq/Xu3333Bp82Pj9/tyaLYVQKOR0OjUazZb1+f/iw2aPu4CPk9nZWbPZnBs2Dofj1q1bX8B8LvwBst3f8/vJdeU90rcWIQii0aiD/f3D3W1LpglMMIBKiIBEP+ZO+C0y2gh7uHdocGDrbtv+h1hAs7D33IXav/tq0Ze+du5r3/r4Bz8pfu/Q23/15aL/8v8tvPzjzMmXkwd/kTqzD+F2I8IBkKXC6USko1jAjAWtmGMyoeGO9wCwNvPzMQgWi9XR0TE0NMykkimN1bjdgyN1hvp7B9tbqVUlPDqJygAZShQymdzZ1FZ5u6GhobOhZqivm8Vmk8nAcMr5eBgMBpVKBfKQ2QX3A1GpVAKBgDt+Hsnu8c4CS+U+CBfAQexTYFa478bYZvfkRQpw9C7wZGQl0nK4n2xOFuCgA8oJuoRdPccdMA43wjYVNsVCLUrMN4vNWVdVbO9wrZvYPEdsxFR0gO/hdMCcTuA2AKipSrjtGkCEVH2cPvt66tBzK68/aXnm24JvfIX15b8Z/NM/I//XP73xB3986v/8o4GvfmXlyK+RitNw9ceg/vAdmN4I0xoRSj1MqATqYEA0I6jhM209FRi1Dh6tAsoh5FoQoBypSI3cNRceS946vlh1dq77FsJqAzdURlqWMywDNZPVBTNNhUl6c5LehLDa4hKKjNQjHmqzCKh61khcN465dUmr6p4Qqd+EhW3wnCMTtGd8xkzIkYp64XgIjXo23LNrFhXi1AB/z+46FfcmXDgDdFfCdjRoBayD3lk0ZAU2UJbDGnVNayQCkHCes3a3DWN8BR6iwtVDQzMqzG8EsTafAWBffIYNy0SU3m6jdsF+E1CcCNuwqAuLutYskx4p0yNhRtQC4PjJSpOalGIc68OAsuhyiM2jU1erLyLnXl/74EXviVfsbz9jfO5f1E/8k/DLXxr5k/8GfeVv3K89hVR8BMJbjGaY2wU+UCvC70XG+lDdWDbJfxqzTSxpBFE5E4w3hxpzarTjfCD8zmRZqL2WyoupzsKllqvBntvI+AgiHsZ0Y1EVx05sUTeWzPZWJZgtKag9w2iOi4ii4TbJSIdZQFXTB+NaMebWAkMTp4aKurO+H2d63pcJWlMBCxywZPxGZN63YdOs2qaxoAX2GSI+N0i5eeRlfX39+vXrhw8fztk9mUwmEAgsLi5ubmyur6/H4/GlpSW5XJ6rsNuxH0WP3efz7S1d/u+px46/r+dfzhadCpFItH///tu3b+fkLOILC9FodGV1ZWNjY3NzMxAIbG5u9vX17X1Rj6JTsby8vHeEZVC5/oeHnBWsJak1ITRtisyb45aE1Ao+CkdC6UhMOJJqT5qlCigsyzpfWudNzfjTM8GMYR4zLmDGecwcQU1av+XIRfVP9n3z3Yky7kb+teeXM5nMQ+NcfD7/nXfeuXPnDg76fjBsNsGwWcgOG6VS+SjD5qE6FXiOVX4Lt5T/FXUq9r4LGIZFIpHPUjfyG/DF1GPHMAz3wm2Pc+E8NA97dO/yRM9bjUd8AJU+BBGpVEJf17qKjYkGEBUTdesys3JUxUIdU+s6pZJLIxFHgXRXlvIn7xifK0IsiMnjdbd39HV1E0aJNDqNKxL2dnTePHps9qePJw78Mvn+L9OnX4Wbr8C9pTClHrXIQZaTS4sZpRtyqpzcTyGRcF9O/nFZLFZra+vA4CDIqWZzchVYLNZATxel/jbr9qWuipL+oaGuxtoJ+nBocmy4uXZwaHh4eKS2tnZoaAiCIDb7gcwqlF1w1w4Imd2Hh+NKqwwGYw+7B0EQr9cbjcyhYTvwKPiN2RSYbDa7U7NuUqTtU/eSuXLxGrwA8t6B0DcWsoG5OeIEWe5zIM8LmH0BM+AzDJhQJ2Bkxp1AmFu7wB+0kNsRORlV0hEJATUrwBTumsbskymHWjfUGqAPgCiMXYValMiMCFGzEQ0no6AiY30wsxXYKN0lSM251Ce/nTv4y9Dz3w8+/Z3wT75t+eev0f/yL5v//M/tj//j4tPfSbz7M4CrBe6cj+CWAqDcJBpG1VxsKRzXyzwDVWlKHR5kAcc3jAOldAU1PcWyjDYneb2YeAiTEFC9AAinA/tgVs0YFNbfoF48rKm85Ou5Y2q54eONBJVcBaFzySBzSph61siaUZ5NZ5sF2UOrcSCGBRir1+DVOLwG9LCQDaArh64uJBfCqYg35bcsBHbNjIBhGHja5+ex1Xl0KQJguVE3cFe4pjN2dcKqgh1qzKPXK0TA0ZdF3mQ5dRjgn/zRllfGB0bYbcfWYuhCIB2yAxeIU4M5ppb4QzaoHzFlc/pAsjeQSQE2kEeX8My6xplrBhnAMHn1BoUIt7Rwc4vBZI5BjDWoE3jaGi9n2q4v3zkTP/x87Lc/C/3ie4Z/+irzL/6c9bMfparPI+UnAaNP30146A7cdhVw/PB6gG6rRYnZgKBpUAYBHNJ9W1knFWTtOcgm4VooHanZcUzLwwwi4KZyARJLxKNTUvvHO6rIty5rGgq99C5LT0VARHaIGVrGUHxW6pJCWubQulEO6KMWg0AvBYjELYH7srmSWVlAV2JASw6wkC+jK7FEyLkZ8cHxcDTk/53snqWlpZqamsrKyt7eXgCYyy74Mz0f4Dw+Pt7f35+rgFfL//5i8DV3dHTk61RwOJzq6urS0lKXC3jR8jsk11EwDFdVVeG53Pkdkiv/z/A14zcitgJ3ji8/fs33Zx86//6cu5QWbxhbquMt1XAXqzjxKk68AgKfO8z4bcZiQb+7kBgpZa2WcDeKoZXift/1G+PXDnZ+eor0aZOpcMB/vcXynY9m/+CQ+2/PuE3B5I73NJ1Ou1wuBEFyl+nxeGpra/MHBovFwjsHT5LKdU6uANIAJZKHDpsvAK65vb39C4hr3tzc7OnpGRoaotFoFAqFmYXrMu8vIANr18f1/Up7/mWxWF3ZBQKcdayh9mbjcCvaewsRjwBfukuLGOWIiol5ZrCVCH+w/crF8/19fRQSKRcgyGFltpwH6AJwOCzczmAwqCxWR+Gn3icfCz/+D5Ef/3Pq5EuZwoMgb2W0enlWsRmPYBHnCq9PPNhOplJzNk3+MfGssa2gn2yHkCmUxso7goqCksP7O7q6BbU3MPno/Diltar88oXzRYXXL168WFh4veP/5+5NoOM4znNRZXlJTvzuTd59L/fmnCTvJXFy7dhJHC+J7DixZNlyHFveYluWZcuWrJ0iJW6SSIokRIIEd3ABiIUACALEvs4KDIABMFhmBrPv+9I9092zL92zL708VzcwBAGQlBQn9x716TNT011TVV1V3fX1/3///7e3dXV2jgyPgLVHKOT6Uyy+43aWuxY+nz/EbhKJ5F6MtkQi0dnZyefxEgEfGfIApLKBbyivrsKqA2pH7kpwPojBa7SVjnjBqhz106iTDtqZTAy49kmgwNFz0GKWjKBKEPip6la5eDdz07doyU1ycYCU3qa0Eko+AXRhrG/DnvrDV57+Hjp9O2dcBK59YLD00mwgScoso+QTpKgNUEMErdTIJablbfrkL+i6Z8m3f1x94/vxH31Z/y+fS7/4TfSLfxf6p09V9n6/8sb3qkd/Wr30BinqoAzzjFcTE95Eb1+oADHDLOVkQ2hxHhFZT4mUX58yykiPGsi9QPh01ocQu8TillXZwI3pE2/073ka6T1vaD9N66Zj073CG43NJw7N97X3NZ4Sd13TCvr9K6K0TUlhLioeKEaD1WySKRI06yUZLK55nMymy9l0OoomsQDs9XAPwe2fyWTy+vXrfN5kNhGmQSQWP8AirP9DThRHskGmomaFWyXzqJY8KplHp/Sa9R6dYnEe+EfZPOW49DrugXxsrLSkTT4f0CwyARPpVjlnxwrqKVopoIFWS8dWBKKzMbCZIaKD186OXKxD5FM5syxpVXrUS171klm+MCUS8gXCOREvKwRcdUrYmhV2OkUD9oFW562rxKld9P4fIN/6vOLlH9Oijmrdc9VjPyO76sjGveT1t6mJ65RWQrvWGKciMj+CinurttWacJHx64FCDeA5oUsFnAwBravfENDLbQqZXSlLWJRMwBgzyOYHbkg6rwy9/ZK3+5xnepBBrYhSMt/Xdu34m3O9rf1XGqZvNRtnJnxaeSYEU0mMSkfyMRRg0FJ2PThrNkXn0mQmWU6F0yiEh5Eohm4fkfsc8fl8J06ckEqlDQ0Nm+2Wt/xlaWlpbGxsxzWSZrcPB+7p7+/n7Ni5y29qapqYmDhz5oxcLt987Zs7p1KpNjc31wzdud7Y/MnhHp8PxLPb6CsATVl8AA6w5hl34lSwha9jV3+sekWSfqEj0iLFLWhl1RJas4XL5Ob6t6YjyWg+lWBcTnp2mm6/zlw6y3S2M4tzNBakKhWGYSoUPbjoH5InR1VZX7S80ajNTaa3455QKNTS0sJVxiGbK1euiMXis2fPKpXKuxpB0wy4KLAtLy/X4t3eVcHGD4ZhPgS4p6+v70OIezj36hAELS4ucqvyFq8zNbaK8INu4+PjnC9jnkA41dtREPeQVw6Qkh6y7xx5+zytn6X0c7RdTrlUttkxtWBAJR5bGu1VzIqEk+P89xDwgWuXQCS63daq/e7XQp/5OPzJv8x88+HK7u+QN47R5kWwzuWIYjKyONgFsIhIxP2FE0S9l8uaEotv3u6baj6v2Pvjiyfr+lqvKS69w2s82X7t0qW6Q2fONLz77rvvHH77zTd2X2044VwSL4smhgf6+vr7m5ubGxsbt7tn5KDPffwWUhSFYVgiHqNCHhoCDJUthFxyc9CuDUi0CQBttWYHiyUeYbJJGjKD5RM25e2KokPJ+PV5u9IqGihLblHCNmphEDivG7tKCm8A6MOq1ewzo3bJiF902z4vCOpWcPMK6dFyCzwIbO5cIxcHWeIza+U+0QQUVX1nyKaD5IXXKod/4n3q0cgvvg5/9hOrf/Qnsa9+Nv/Uo9Wzu6iZHqA3CXsyTp1v+HpFO8MaLQNMU3aBQJibrgU4bASWPty+6RQDG+Elgaf3kuz4ywv1b7ja631NR7St9QsDHdfr3lJN9o40nRu6dubK8Tdne1ujqllEPg0viyKahYXRW87laSYOry+xIC46yiSDZcxNeE3xe8t7KIriQlTSRRDPtRoLkqgL+K3mWuXVUkApycUVZ42xfTogdUsEmJBLq1jhAe3q1m0D93hBmNhcupiKFsIQE7SWHEqDZIxyKmj7CvBaKefRWgnocL+eRmzVgNW8IHSqluwLAs/KDKJfyVjljF+XtilnpsU8gWBuSphbGqdB5JbrMUGXSCiYFE/xp6YjfZfpc6+Uj/zE++r3Emf2FJ/8Uu6pR0tv/Th/5Bmy/xylEgHxFWRIGmQQiHIPYopxV1dlRYw13ONckwEVm09HwibF0uIEj8/jC3yaJQbS05DBu8DzzU/MXj+tOrvP1X/NNdFhGOuYu93aefa4YvwW78bl242nGo+/tTDWG3cZYY0ssDaHaWVLwgmHahnELOOcj7PyHoaIlpLhfAyLhrD3Je+hafrWrVvHjh2bn5/n1ioMRc40nPH7IYVC3nz9ek9vz8jI6NramkQi4TLc63O7K5ftOWEYvr9KiPMcQ5L3W9vfi/+e9xiPfYs7HIFAUItTwTCMx+M5evTo9evXN0JQ0YODA4NDQ8VC4erVq3y+oOF0QyAQvH379v3d4ZRKRQ73cB1SqdLxDEnRTKkKxipGkJ5oNUukIyGkQjIDCiJGVB1R6vRU5tX+9MBaPp5Z741YNIynYtt7FaCocpnGUHp1JXXtWvFMA9nURAn4jMvJEMSW/DRNowFvqfgAey6/378ZB2+353I6nUeOHGlpaeG0VCiCnGlo8EOwfHW1tbXt1q1bQ8MjarX6vUybLWqjLQ1mGAaCoHu9/XKZSZJ8oNunXC73QP892Wz2A/jvEQgENYu/7Y3/1R75XxanYrv3wvcODrY+zjd+i9gNsIYnJuSLc5R1hRK2A7gzd7t67SDVd5ZWi4EmIupnMlEmG2PyKTKFlfGo327kjb9X70HAQkoyw3v3cPSb/xD5/N/EH/273C++QXafoFRCUHIlj9j1XTfagA6L3QQCAccv3mgmCAA0NjZW+7klIRaLJyYm+k8fvfnmHl7Tha4rF7u7u0cH+8+dONbV1TU6MtJ982b9qdM3O9otU8MVvw7Sy5saL9SfPHn+3Llr166NjIxwPopELOoSiUSTk5PLy8v390bFkFUaNlMeTdm11eAFyD82IQDWEd9dWOGus1xOzEXHA4CMAqQmgBcCut2vLzrXvDJBdZVHLw5SSn516CIwdR5ppDTTwHsK5gLjUiKYIlHJpgrpOGRUxQ3rJtygFr8BBJU0LwL91/IoMPsStQMYNHaFbDpIHftZ/idftn3z4cgT/xj64qdSX/1Mad+TABgtjVKoiyllTFMj0VUBEzRzNsyUV5u33nFNBGyYXSrgFGfzxW6kGb+BcchxUaf88AsLB57xttbJzx2MSIeNktGp7qaIeg5TSpzSSWV/q7GvGRL25q1y42TPfF/rbM91Df+2TjhY8BqZqJcJuUD5LB0qb5PH4HvKe9bvc5pmihk6k6ykoyQRZ/AIG6hVRwUswEvkOhlr3S8i0C2G3XTIrVpemJwEYSa2yHw43BOG/WC9L2a4SOxMCqn6DZ5VSRUyA0ToUZOzvaSogzZIgRKTiIFY7qUMUwHSkWoOz6eiAZs+ppfhDtXMFODAzU1P5dxaxrrIqATxZQHAPQIBXyiM8ruY628yZ14qvfiv1qcfK+75XvG5r1UO/6TSWUfN9wFoBbxcmsySUVQ1ywTWfSCRHjU3ChzuEQiENvk8UOoFzBRiWZMBT0ICgcCvXQGh4/16xqNKrfCWTu9TH3vJ2XF6rfFQeLrPPjsu7Lqa0C+iyhnb7PhS92X3WDu0Mp1DPeaZsdnelpneFhXvtkkqKKAeEHsumwYqsHiQSYdLqWgYQ94X7olEIlx0SR6Pxw3cyrJs165dQpG4s+PG/gMH6urebTjdsLICLJscDgcMw/6dNhiG1Wq1yWSCIGin8+AYDMMKhcLlct0rDwRBXq93dXWViyOxYzkQBDmdTrlcvuNZ7iAEQVarVaPR1FqLBqHg3Q2HIEin0xkMBi4PDMMWi+Xtt9/ebM81PDz86quvnjx5kjOJ/yW4qaurO1lfb7Pb9u/ff/x43b59e5eWls+cPSsUCYJI8F5NCvg9R7pNs5qA3++LoP7uWfhHV+HRJeiNW8F4yH+gB/7amYBszSKaV92cg/5wt//p9vjuk8rRb7yRunQlgsLBeAQKheBwWG+16u12yGLxra76EQTsZgvE4+du3qxeupSqq/M2NGi7uhwqFYQgEIZBKOoPBPzbRmR1dfU+o+D3+z0eDxfSgbsiGIb7+vouXLjAzRBO3tPX1/fyyy/X19dz2FEhX921axefzwfTZt/+uuN1DWfOLstX6vae8ehQGNl5VsAwrNFo7j9tIAhSKBRut/te04YLgsEF1rjXEEAQ5HA4FArFvTL4/X5u2qjV6vtUBMOwVqs1Go21aWM2mw8fPuxyudafe//BX//ZuGdHey7Bhkl2DQEAgg4IwXlPcFDLuWMCcKUnJ2CdnFmdoD1qsEzO91NKPniFjXiZbAq8fBdwENO0mGEq+TweF/EAL3hLaduPrGeYnh5tvx45+OPKK0/E//Vh4gePkE1v0mt8QOzIpeIOzVjfLaFIXHOrs0WUxV3vlro2/xSJwX+HW5tErY2DlxsaTr777vFjp0++21B39NL5M52dXYODg+MTk8NXzs63nB3pahvpaL566cLw8HBLS8vhw4ebmpo2s8V5PJ5MJuOC49xzOm2yY+cMrSkgUdCux1u4s/zrCfMKILJsHLk7wfomBqRmVk/EsZthVurDCY38+qhqNq6cZvRzlENOKfjAmHllDATkQh3rnJg8zo4OwVQLMdgd08vAa32tOr+e8umrPh3t0QKfztppSjFJzvaQo1cqdc9Vn308+O2HI0efo+pfBHZ2DS+Rt8+W+J0pmYhBXUHVfFS3QPv1QJzAihbumJ6x5bNG5hvhyWo11hJ+PeNUFqVD0Eirc6RtrvmUsOPycPM5wY3Lty+dNIgHIxpp3rhAyMZUt67IOy6ahf0LA+3K8VsFl0o+1j3ZetGzJKzcoTrp8g5F2P+g+5ymWEFRmsZjTDrKFAg64id9ehJ1suI0Aw0BfhUdsGTMKzmvkSFiJcyrkknnpXOL8/Mz0+vgm5tdAoFAMj0VDviziUgmHq7t+aAzop1PWYHKifbrKcsSGBTXGsA9eKwmEQH3SwFnKvkY4o/WcI9AMDs1FbUoM041YVdFzWvLUsnC7PTizDQiHc+Kb+E9Z3MnXwq99p1UyzvU7TNk17sAp0q6KdMC7VYxsBFaEUc08yBoKBewNmilWN2iRQlCwfP5ArNKkUW8GcRD+K3q5QXuoE25yMR8gJQGMuvzckFgdsQxOzrXcUnYfmms5cJke+PtxlNG8VBYPVfUSAhRp+Zm48pgh2FqeGmoQzXZm7cr5GPd4y0XIMVsFXNTMRggvEyilI5hyPvj9wQCgZdeeuk73/nO4uIid4uFQ6GrV68qlcqZGUlnZ5dEIhkdHVtZXhmfGCeparlc2nHjIiTEohGKrOyQoVqtVKuVStnlcuE4Xr5HKeVyOZ/PO53OfD6/QyHsoXK5jAMPMc5isXifPLFYDIKgaqVSqZRosrRkxZ1IdnPTKpUKgiAYhoEcGyUPDg5y8h6uK9rb27/73e++8847NXfMk5MTg4NDZrO5vb1dMjPT1dVptzs6OjscykAhWyrvdOkUWTZA2T/a7fv7Y8GnmsP7ekOffAv+3Rexj+7z/f5Lvh9dQ/9kL/qRl7FHT3hfveH76H74Iy9AHzsagU83Mp//u9LnH87W1+cGB3OTk8W5udD0dHhqKvvzn+OPPpK91Fi43kkcP5y91liVSikUrZZKZZr2BINJHK9d1PYuKhaLdrs9nU7faxRKpVIul9vcw+VyGUGQLf6ar1+//sQTT9TX12cyGcD2DYNpI1co52Znu7q6JNPTY2NjBq2p6SdTM+9AYRdepSvb+6dSqfj9/lAodJ/GlEolp9OZyWTulYebNg6H4/7TJpVKuVyu7R1SO1Iul+OJxC8DrIKKKpUSRYHPu7dKpQLD8OZpUyqVBgcHP4RxKrh7oFwuc34LJyYmOE7PZkzAvY+K2I3P53O0lc2A4L2n+Xz+4uxMKegAb9iQkbVUYiU9G0wLJo+DvUAU09FV6TSf916jgwHisFgsHOyD204yI43VK/vJxn2kuANwY0NOJuIlUcfqNP9WT+/w8Mjg4ACInsBu76Xxd+UUA3+GnNhmbGRkeHCpDwKBAAAgAElEQVRgdKBPJBC0t7bevNHW0tw0PjZ2u6N9sL9fyJu8dbMTRCITifr7+zs6OsbHx0dGRrj+5N03Phc3LgxZpYC8R12wK0rONcoLmMgAFrAcFxb9AO8mjF9PejTrQpENrcRmUAKcH6IOELkCsXFWSCCoewoD8dVZPgrpUUMLk2WvnoFNtFdHmRdplwr4mkti65ogblyKmUwU8SulIEj4uoBkQ8jEwSlO/MNGIaDdatq6TI5cpg49HXr2X5KDV5jpTuAZr/0dkn+dMs1TbrCiF0I+eE0aUUtTxuWwRgoUOg9Q4W3UyFKJwWX6gWKFgVmLdNhQ9eliugVsbSaqnc9YV+e6rq5O9K3x+gvONc3kbcK8Etcu2ObGkwYZHTQhihlkVZy1rmasgMXC+HQFuzIRfJC9NE3TuTSTTRYToXIqwhAxwByCzXTIQ2MuCrGDaKl5nEmHKj59ORpgihkqmypGAqUYQsRCi9K5LSxnkUg0NzuzeZdIJIaV+bJLFVTOVmArA2I7sJq+kAcIe/Lp9TtlY1zSoYBfMUt51bhtbWZKzBcIRELh7LR4blo8Oy32aVbKPmPRBbwKaZakM2KRRCwyDXclzu+JDV2hZ24CPwXDF8nJZkDuAbpLRcmt9i8J4rpFwqkJaxeZgIXyaZmgBbXp1w3mp6bm5mZBmyVTkingEoLHFyzPz5TDXqCcZXlmFPDfaALxRyFDxatN6hZCa7Nh1VzWJp/pv7EqHNFM3s7b5crxWxmbPKKes8+Np4wyCjIEVsRR9VzGspJ3quiAlYkHKqlIJIS+L3lPMpkcHBzk8XiTk5M0u3HUE4rdaJrmlB1LK0uCSRGVrzE31m++zV+REBqOJqD4TiqqdDofTpA0UFjc38Xfe4xT4fP5OKnD5gZsTueyRCyCkjSTK1FYmvx8XfD1nq0aou1xKjbHY+douZwzWO5tnusf9nOd8EtRVIUqdV8Y4+/xGvqiZHmnvqfot3tT//Vl5E93qf96z9zvv+j66D7Pc6c6Hqt3PPwu8sk31H/7dNunftx8cUh/qfHq4z8++PO3WpZ9lXTDxcw//H3mRz+iZEuUco2cmaEmJ/HOTvzdE+V//FLpM1+o7N2XlDW6xr9UygDmEBg19vM/KE5FKpXq7OzkJgPX84uLi1zneDzrol9utlAsG5rLadE7br+8JnodnquDw5Y8Wdmhf/73ilORzSIYBjqTICi9nsZxrm83f26PUzExMfEh1HPRNJ1OpxOJxPz8/MTExPDw8OjoKLcwC9mNk4Lw+fyBgQHOAJs7/oE/BXy+z2GhskkmEaBhE7AbSgTveojncaaUDThMo8NDW0zNt1S6TfAjkI4PZGxKxjgHnuMT1yjNFO0EKhLOjmlNOj00NDQ6NDDc18O6edtS3vv8KQKRVVlM0ycR8NeW5gvp2Ax/sqenZ211aUk6w+PxOWeGQpYcPTU1NTY2dvnyZS7e2f3lPSRJohiWiEZI2FxyqZMWRc5jBJHYoxAIj4pH6RRGBqwlrz7htWZg1oga6Aoh4AY3BoM8CQSkuUQ2Cexlsinwye0gVHuGSYUZ1Mlqu3Qp7XzAoCBRBxNygkULBH73MvnUXUNTzgWsehC/ArPTwDTMA3QQCQR43Q25GczJDqhu3QcdWKcNtGm+PHAhcKOedKsplZAC9tJd1JoAyC0gA6GS4kFPQCXFrSsayYRmeiIPSCqbJEk1uc57TYDgBoCs7dWaZic1U5MW2Wwa9S+O9KRNS+bFKVgxQ8NGJmAEqBFYgBsZqywk6dPxbnMIMmeTh3zOzc+CzWmSJBEUTSYS1VS4GrSlEG8mioIuImJMJsHk03Q2SaUjpUQogUCZCELGYOCVOE+AszlApg56Xaxx39bJxl/fgPNlvkDA4/M9Ji2DWlMGGWpWk6kwk0TpoJ0Oue8aEQ73lHOow4Sp55i4P2LXiUXrxoMCASA48/gCj36NifkBOgxalPMzk3wQuFQpHIFvXyzPDwBaOvDc00bN3KJWxyk7gIAlpwpeFuEWhWpBsjY9SbpVwCIy4iW8llmAq4ALK66pNXN9gUAwPSWOozBgkoV9ICQ7IH0bWLTtAj6aY75ywGKc46ln+R6zNoYGlvlDMf2SV6eANEtUxMMgFpIdaDAWQVNQMW0QDbDuHI0l2IL4PDusLZuH5+60z+c7cODA2bNnr1y5wrFqdsQTWo3u+qt8WX1I3xtxSVKoNhM2ZasliiuM+4sbCn3/gvebF8PpPOWAMpgL88UAkZYJwpVnfvLKs0OztlIIgTncA9i87N/AZ21jGJKswn5ftVqtHaslNlZ2plgooIF13EPTdLlKZ4tUPFP1RMpqb3HKmL+9mjnPjxy6DR8bTRzoj32pHvntZz3//RXfqguQWrgCOYNkTkdTO9LS0qJWq7mLIkmyra3t2LFjJ0+evEecCtDTZbowdnFZ+BrEe82j64kg2mzYlEv6imFLLuYsRMw5Oz81dtLU1HRg+vofz7T8f3WnfzZx/VPu7v8zsnzaO/dDCb/jxae/3HfsD7qvv/PkU7/42uP/sv/1p+Id10Tf/u7Pv/LVnqvNwHsymeHGNJJMR6Jo/vyb8R9+0t36GXX3f9G0/1ZIV5eLqshSiuufIBLOZnO1y6z1Xi1BUZTH4+EISbWDWxLbec1ut/vSpUub505zc/PRo0fr6+s5XvOO00avNd74xfzUvoDgda9wn2+lEY06OOP59Qp/ZbxmivL6fNVKZcuF3KmGYXL5vD8Q4GjkWz9pmq5WmXKZiEYRp5OOREqHDxc+97nS22/TpdL6XGXLYhgGw7Aa34u76uvXr38Iec3FYrG9vf3mzZtjY2MTExOcIobDPTW9D+C19PcPDQ319fUNDQ39e428REKhgKdckRVSMYaIAnOYACuHIOJgedh4jgesegGfJ7zvthn3AM4Eb9Iwx6/GgkzQTluXKa0E6BpiEBPxFVcmTKNdgvERweQE4nGoVxYnxncwZb9vbVtPgv4RCCbHx+anhLlkpEIkmHIuG0FkM1N+pzXkdw8PDNR4rDUoeeXKldHR0ampqf7+vmtXr+Zy4DbeviWTyZaWluHBQVQlpXy6ashDpcNs/xB0Lk3lgEgsGw4k/I5sDIsG/QTiLYR8QAhRYPsQaAzZnevP+32m6TgC8IpfT5iWodUpzKgqQHYGtQMcE/XfJVooZaMuUw62MxyQAsXWakkzObD80/EAG0OelcqwznbLxvmgpB+UFrDSTgXtUtK2FaAPCnsTazPQspgwrwZV0gjkhFULrLZrQ6KzroZjWcOcPGnLJydz2nQQyGxQB4Xa9VOjDq2yhCcqeIIp4BnMb12eTSO+oEUT08xtOEDSkh4t7dEUjPP6yVt5h4IJmtSTvaIJYN2z45ZIJC5fvjw6Mpx2G6pBB5mOUuuiSoLO41Q2TRcIIhSIQa5cPBSDvUQYqeBxNk+aKeC5RHhxQTo5yavNh9p7BQd7uEnG5/PnpdJ8MgbMuRFbyrgUUC1U4ijwFRRyA83j3QNKFYiww5jHfEw567FbxieAdrh2dwgEAp/DwhTSHEJdW5zl8wU8gVA1K4JmBqu2FRB8jdsNUsq0wHhUIDjrsihvV0DK2WjQ55HPgvhliJ2BjZRPv7Y0vzku2OYbg8fnG3UamoiCuvAojVjZFxsEaK5ZuzmDYtmuVxXxBJlLM6VsCoUMclk2GfFZjUnED8hkXDBd4A7AgFvleuFAxadjIm6tcFAiekD00C1DlkwmGxsb33zzzcbGRu5UpVy22WzlSoXAca/XG4mEMQxzWX19L6tEb0C83R7+bo/gDa9wr3f1ChoyZ1NwEYgaKLqej//aM8gfvOp96Sbxhf2e7z4j/OmNtD/DJMcEU4899xs/9XzjUhSC/GUSgKGqVFo+dIjcABm1VvkixVWtg6F3EBqVqkwYJ21odcFC9Mz5++XZazP4aX7qyEhyX3/8raHksbHkaX7yuhQfVOYmlPFZDQrFyVSOnrcVxjW5UVXWgbE4bKMyzh/xxi/wvYXXLJFIjh8/vmfPHrPZzGXDUJTjvbpcrlQqCZRB1UJrfd/kLo9ov5//uhcs8Ht94oN+0X6f+KBPuA8S7nXJL/ybqechY/dD+u6HzDcfstx4SNv1kO7GQ4au39Te/Mzyjd8xdf/a0uQLb7/5g4b9fyW/+Dv+d3763E9+9Ni/Pv7mgeecsz+DF5+2qW4bpUcskuedsz9BtQcilrM4KogYGmwjn4gYTmKaQ4Gl5zDVm2nfbb9tLpdJbb6oLWmapr1eb+FBcSq285pv3ry5Wd4jFouPHTu2d+9eTtRRZqdNpVLF8bTP5wuHw6FwWLGmGOkQLZ+O8l8D3cLf7Zl60++cSlbyd8YXCYXi6fSWRm75ef+4pHS5XPF6vQ5HlSRrgOauEmiaKZfzGBbUaGiXizYYKJWKkslIsZgcHq729FQ7OqrNzWRjY/70afzUKbKpqfTEE8W//dvSD39Ib2vbdnkPn8//EMp78vn8xYsXL1++PDg4ODw8zHFfarhndHSUe3pyRl4ikWhkZITzzrf5Yfd+0wIghBdgXgcgZiYQoO0KWMBSypFISlmqQADGAA/gnhr8enAtAoGEP5GAnOANm4gxYS+QJBUzVAJRD7RODNzm8fnSaXEmgiiW5idZu/2aC8EHF353DkAAn5xYmJUEnFa6wBKSOMCRTQAZSYEgsynN6nJvTw/Xfu6z40Y7MHRnOc5DQ0MjIyP3MgCJx+OXL1/u6uzwqxazDlXJq6PCPiCwySTJOFKIoWQuTedxmgVATIEopaL5RITM3C2euXt13LJY3vUzm2BSIQaxRdTS2YnRlN/FEBEgrUEdQD60MS5EBIFtepqFO+wyv706AiAhIs6kw0wCA8o1oIfSRZWStFPLFHEGD+ftmqzbzBRwMuxzzk3kbArSqwlqZJloMKpdYCBDyiDLmoHTnapXByZGEgOCq3iAiQXoqA8k4iCON1DbwSaAmwF0ttBBC4Pac0EXbDcHndYU6gcW0axLGCaPV4lECY8zRLSYCJkWRHH9Ao3YKcROQ8a0Q2OUjPuWRBXIzCBWTLNgN2jverJs+hGPx+tPnbrVfTPiMmX8thJso9NR0D+5NEUkcvEQlQODQrEAlM6li8lIMRGuEgmuD3PxsN9lD3hcVqMehJXg8edmZ70OK+R2QC6732mTLcyzji75IPY4wAoEuC9ivrBaihuXmQQKYCg3ItzgFgimlEtHMdiqowsADafDiMdugdwOj90yMz3N3cury0tWk95i1Fv0aumsBHjU5As0S9KoRpqxKYCXBMgIhpt1gVhwqj0yYcm1RrrVmEmJh4Mpi5z2GzIuHWFZZVC7XycHN+bddwT3i+UqTSewAIDdeJSGTEBKlApl46Gg0xp02/AIsj4uG+2n2THKJyJ2vTru1DKQkcRcdNBKONYMkjFEs5gJupxGrWRyVKNe2zQUD05WKpWFhYX9+/f39fVxuY0G/a5duxTKtbHRkQMHDr7zzjsXL13U64yNz4yKXocE3NL+hlfwhpe/xyva75s+BLlEieWm2L8etO8+cWDXu0c++wb/lbq3PrV35m/2a77c4Pv2fsP396780ynkr98KtuzvZpovMmpV9VvfLPzN3xSeeSafz+cr5UoxT1fzVLX4VHPoZ00ef6Ss8haFhlyXLH2WH3tnAN3XHXi1I/D6zeBbt5F3+uG6PkevLCrWJhSOlAVKe1AiFM/gRLZSypPlPFkppOIRBPaWS4VCPl8u5atlsBeBR731LZ/PwzAcCAQKrJ+9fD6fzWZv3ry5mdccCARaWloOHDjA+bKrlMvnzp65cOFiNBZ96623jh8/fvDAwTWVur2tXdyo4+8GoEfA9ozgdS//dS9/t5e/xzO51zuze8G7/+/tF3/Hfua/6m/8Brr7z8K7Port/2PL+d8KP/mnwdf/UH/t19Hjn3Od/jPjzYcM3b9pEX1ntvurguYvzF7576prDxn7/7Sz/vO7fvyXLz/zJa9DWSxkC2WqUCTzhXI66shlM/lCCY/7opA0oL1gEDxtET0JK95K+UeLuKuQS+eLVKFI5fOlXC6fy+Wy2azNZk0kEvk8+LnjhuO43W7PZDLc2UKh4HA42traOPEG9wnDcHNz88GDBznfRWaTcdeuV+UK5ejI8P79+w8fPnzlylWlcq1voDfixZeaId5uF38PgMu81zyrV1FYjhcLxXKxgtptSYsFkIooKlet5gqFXD6fK5XA58bGEZI2HwFnisUcSeZIstTWVnjkEXzfPtJkItVqAKknJ0u9vbnWVuLKFfzCBfzUKfzkyWR9PXb8ONHYiDc1EZ2dxOBgRiDIzM9nlMqs0Zh1OHI+X8Tp9Doc+Uwmu7iYPnQoI5WC9mzaOFvCYDBYmzaZTKarq+tDyO+haVqv1y8sLMhksomJiS1G7LwNM/La6yO3fnMin9oL5U4PwAccmxKLAjYjcP6GuYBvEoyV3heIaibptuhnxUL+xPgDitjpNI83qZfNMEUCKHHwCFAuFDN0yG2amRgfHx+fGFetLESDXuHEGOdfZ8v17lTkzsd4E+MBlxXgto2V9Q6M4JalYqaaS68uSsfHxni8dWr28NBgb29PDczJ5fJ78Zqr1arH41EqFXQxW0lFaVaHAnAPHgUhjeIBOo5QeJTKJkmWD07l8GomCWROeZAA6+57Bz0crCmAJTaoWZIIJzOoF9gowab1oNkFohDHvJoVj3w259azCp0NydyOtdSkTdkU8Gjs1+UdSptcmotjuViITrOeb9KhEupyLolKbBAJv24VtWoLrF19yaliOUx6wHZPhdYFV+viqw05VoFg0W18/TJZ+ROdS5sUS5jfTeVxTsDAnaXzuNVksBv0wINRgcjGQ9YlScZnptIRJhGkiDhsMyT8DkBPJuLlVCQZjdxrUS2VSkaj0WDQM6VcOR2hUyFgcJRJMESCisFkAqGTCEXEq5kkkL3lcToL8FA5HWdd0aTAuJQzlUxSr1pjY/SKEa8bNLWUZSq5EOybEosmebxl2WIRMJfZTs4kmIgnaljBXfpNXOZ1GlwuEfLo5W75XDHoAHo0bijZAovpmHR2lrtJOcUZsCXbsBKY5Au0S/NZ80rEsMIkgqwp/rpb8Aps9ivnig5lzioP2fWI3ZC3A38HFbeGhExMzF+CzMtz01soSrX7hMfjKVZXyiwEp8NeAJ1TmFWrTIeDYFy4i9o8bbgXnlI2FUEtq9K8fY3C3GD6JVFIr0g5dUw2gfo989I5m91+r3HZ8XgqlXr22Wcff/zxhYUFLoNGrXrllVcmefyOG+379u0/cODAuXPnlleWTxw9ZRfHVhrRxYbAXB08cxRgIP5rHt5u7/jL8NSBaXnT39p6HpLf+H11+29bux7quPiV0auf+Oiu1c/tHR9fVCE+9XLn91565nzvY3tzhw5hz71GfeUR6vmfxc7Ue9bUCxasReL70VXn7z7v/cjz/qevhy6Ikm3z6Z7F8OCCT7DiWtV5rU6/zw9jSACDPUatMhYKRLBACIUxBEaCcDAABwIwtwUCAZvNptVqA7VD62fufAUCAYPBYDQag8EgDMPBYNButx85cmRznIqhoaFHHnlk7969nFaoVCqdPnXqxMl61drawQMHDh48uG/fvhW54sLF82Kx2DWTWGwISt6BZo5CkiPQwqnAWmtI0Yq5DJHAgIL458fzjzxcfuSRzHc/nXzi4dbHvt33lW8m3nohO9Jfeuq59KvfzHW3lb769ei//an90p94nYsoEkCD/qBrwT/3pEf6zLmLrd/9/tM/fPIni7LlSCQKQxsXEgzBMHvZATSARJBQUqMzmQ3LXn1/SHc6sPKikf/9Nd5LFkWH16EOBNEglgggYYVyze1236dzfD6fUqmEoPVqgsHgyMhITc/F4Z7e3t5HH3304MGDnIW5Xqt55ZVXxscnOjtuvPHGG/v27QOh2pXKI0eO+GBvEAl4VCFDf1T4hlewB0AfwRveuWuO2Ytu5Nm3Kl/+Krp3r7e5xT8yBvshOBiE9HpwhRvDp1xb8waDgXAYxjCwRyIwgkBGo39iwtfQQHz1q8VPf7r82GOVpqbKwEB5YoLg8cICQUAs9s/NQXI5ZDDALpfLbl9TqQIYBkejYI9E4HB4fccwGEUDGGZ3OjU6XSAYhEMhkCcc3ujo9e9AIKDT6cxmc23aWK3WI0eOfDjjVFAUVSgUtvtrFt69cYhn8zEOFdUk9typGlTanLOWXpcksbSdsE0Hnm5BG+BRBq3gvZYsmjWKsZHh8fEPAnqEIAg8b0UqoXKsaRj3bC1ng/pVweBtEdCvCWamhPMz06w9b61R7zshEot7um+ikJup5HaAFxyNhmUpFfH4jAiwork6RCC6qohLPzgeO1DLVplillVvra9zTCYBNB0xGPRY2AsUH0ScDPuBFAT4Iwa6QuBPj1tLNlYXOpeuEmAl5hbj7W2mWB9xTCkHmTQSsTATAXIyQChJBJlCppLDHatzGZOs6lYxXg2gawDlzn2hz3rVnLgCIqOQdW01AbvDsLeaATQjMhWCpONJrZRj88T0SxHtIsfFBoxpwBF2AU/TceQu8cbGFW2/BCaPV7KpZYkIctqqRPLO+lrMELGQRDItl0oyQReZSTKlLB4OWpVLQP0EyDc4azqeBYkCUUrHE9Hwjusod5AErFiSKec25Baso+FcAuDsdAj4IMYcQECVTwOhYwFniChLk0oBOVmRKKeimjUlj8ebnppCfG7QjcUMU87GUXhuZobP589KpmMIzFTynNQQlBMPRF0mAvGx1o7sTGCbWilkrApZxq6kPWpQLxFZbxIoMIdCXj6fP8m6puDx+ev7hoKKLxAszE7jVkVYu1hNhpkYRIc9QGwWcvtXJWnjEuCJw/qEaSVhXAbuDIAoSM/5sWRCjqBJBZS8G8VuwUB8gcCgUVezaSafolMYg0eXZsQFPH5nULhxLBBMOgTeebhRqOQRn9uvXgKOJ6IQgLyJINC3JjGmnMOjKAz57zMuO56iKKqjo2N4eJg7WywUVGsqDMMgCDKZTH6/z8PalnMBB8gKVSlQhVQlGy3bRQndrcjimaCyJSLYa12se9584bdsnQ9Zuj+i7/yIue0hZcvv3Gh8TN7+31Ynn7YN/4Wl7SHsqX8+/uLED6+En7wctS5bJpfCDUcle4/J335d3Pqtk41PNZ4bRd/u8y07d1Ztcy3M5wt+P8TSZ+kdN4Zhav57dszArdw1PReXh2GYzXEquDxOp/PYsWMouu4N0uPxWG22SCSi02kxDFOrNZlMtqenhyMtlXNkLl7OxSuZULmUISlg/wYoUFbEXXf49fNvvLJ6s13Yd+WnDU9/+sovHt7zA7PWxDAMlUwysXQmlws1Xq7+4pXKvIRm1olTgKZDlasFlMDxWCwml8tPnDjBxQHdcl01wjmCoBy/B5RMlctZCIfHw7q6wMoLqGJ3zNZEYAsep6FQLG6fDLUyOTMraqOLuf7s7u6u6bm4zjGbzSdOnOCM/MG0Ua1hGAZDkMls9vl8Xq93Szx2mqTDxuzSeUS4z8d7zcPb5eHtCclekkK7Pxn/4V9W/uFLxa/8U+XIW5Xjx8tPPFHt6qq10B8KFcVi8t13SaWSMpvJiYnqlSvVCxfI7m5aoahKJMXdu6Pt7SRFcUSoHbVdwH8PDNeucXuCYZhMJsONNTjLrFPBNudkGCYcDm/h93yY47GXSqWFhYXau6DwHtsDM3CSjNoyv70Y7hRfIJCIBKUExmTidNACGAAxiCngeDi4ODPNm5wUAZDyQTaBUCgWTKJAEsO5QslSufSaVDw+vu6uEKC3DRTyQSpY/49ofGzU77DcWWZA6IONnXujzaWZFMZkk1a9ZrNUqdaHD8Q97I1BUzl8g0GyseBlE2CJQhxAnZQDKzeFOtf990R8IHhCEtS7BRlwyOZeuGf9eCkLKaUe5QJwbxgN0qkwkJyVskGPw6taZBAbx70AQiAizkIf/C72z71ACSuqiQd90YCnTLDEjnKOwCBkSVAzOgPL6mY6M2QAHN6AGdCAQLyC9ya+KhCxoC+Jwuy4AMkQCzUyRBSbnpycFvCls9OJEIB0lVzab9KQufUuBZQpjqYDcE/s/riHfeaS6RiWjqB4NISHgngIxkMBHIPTflvabUjb1XjQjUcxPOjFg+40ZMcxP4768TCSiqAapZILJWHR6zKxUDqM4FEsGUZWlmQ8Hl8kFNpNeiIWSkfQTAxj5XZpMgphNn0ZZ5VlAPGw0qwCgXodsEFe8BnTHiMoH7QH4/ZsIozBPtni/PLiwt374jTrwgo4dJ4WZ3ymuMcC7NfKOU6YlA0HUPV8zqFK29bStjXCriLs62nuCLg6py5hU+sVK0tc4bLFmmCpdk/x+QK/y75+UxSIiN+dDgG7NiB/ZYk+YGgKGSDxjXPUbwAKc6lYyKqmYAuAvAmEDnlIzM2kUCqTTIURLBisrRbvMZFOpzs6OrjVi2a37X9cWloaGhribrfNZ8kKXcxUKznKJ88er7/44lOPnzn+aafsZTwwHbdetk1+Xtn8kKnjIaPo5/7F53Sdv+tp/7h3ZfzhY+bvHzr3/dZ421J5NUhjOSbX8E71kY8zD3+WUi2jeKxKVsGSv9PGMEw+n98cRWF7LoZhcBy/s4BtzwEWNRBoaQuvube3l/PXzP2DYRilUikUCmuN2XztXLparV67du0enve4VZgRBlSfmtzz15N7vjh96EuzRz868fJfTb5yTXybiN/htWTwDIaFmXKZK5YDFluqS6VSSqXy7NmzXu+6DVetYSQNQAqgCQeRbDa7cfxOAWSZKCStSU8fuvYmUIStvp5wdOTjOrIM2pCP69Iwr7bSb+c1oyja3AxI1hslg7rm5ubu3znLy8vctKmNAKB2FSlYTuh7I6obSfEBnrrpU5pbv2Y/+3vu439gP/EH2Uc+X/rcF4qf+0Lx+09WhoYqnZ0UhgXF4syjjxY+/VyXSR8AACAASURBVOnCt75VaW2tzs1Rfj+dy3H4BpRZKrkhqLITHb42lLlczu/311qyPcHBO46/tf1srZztvObe3t4PIa+ZG+xKpfJAeY/wV7EB0QeLPBZmJVXOYQ8RJcPeQjJCFgne6NDoyIjog4IeroECPn9eMlXJJEvpWD4ZMWqUvEkQf+NXuwkEAs3qElUkiulYHIFcJr16ddmwJk9icAkHyjUgpUggxTi6vCAFlGuWq1RrAwhAJrhffC5uXMCLDksI3QJiQOHrdFpWcZAOM2EPE/HSIRcdDwLXw5wkg3uNvhcc2XR8Xd5TzIRMyrzfAhBDFC0nwrkYlokEVfOSfNgPKDthLzAByxPgXRyPgQSQLW2wfDZgxNbWssKJag73281EFM0nwulwMKCRlTcc9tTQz3qC5SYDTjrqAO/92zDcDuVz11LM4BEkCnvLeDwTw6IBL+Z1Yj6n26w3aVSQy243GTMxIDxIonAMdm+hKLFKqFyJSDwQ95RKpaUl2ZRYJJmekkxPr++SacmUGKS5T3AKGHjfyTA9PT09xUF/kUgokWz8ERye4tC4SCSckUimp0CxkNvB6SvLqQjqAoMCLrxA5BORYipKxDCVbC4fdGiW58VisUQiYSsClU5NiZcXpCXEXY2jFSJVIZKbd8XKMlB3CYCVe8ZrrKTCkM2UjYdy8RAeRoIGedWtwgxyiUg0PSUG7d++i0XTIqHfZiaz6QqRrGZSRBRblslqLH7u/UcsFlsNenCbl3PZGIa6rGU8AWj4sDfkd6FeJ+SwRAPeChslDVxaKROBvXGTnIFNZNgH5GeA1xVk0lE6lwKiIDYewvqt8R6+cBw/e/bsmTNn+vv7Ny9pW/66tLS0OQO9aePMiIhy7nvid/9n74ufbX/t+dde5o0Lk8mc3WZ58dnvnX7tj8Tj12mGyUflTl23cuDhueY/XLz+f13p6y9E5SRuJKAh28jHkZf/vPrUi+UTZ0N9I2S+WFuD2dX8Dgb6D8U93d3dNdzDMIxEIjl16lRDQ0OQRZM7YpFqtXrlypVoNFrrvU19A5IMw5Sq5Sen6r/I3//wxBtPTB8/bxz9weypV+RNpwyDF4xjw76lbtdsk35SAQHxT4UiqxSAfdFC2pVGXWk0lEty5YTDYRzHPR7PqVOn3G735jFypZG98rY98pYTS92ZfA333GlLLTNN026XJR5YiDvagvI9PumPfNIf6jr/m3/h57VL2I57AoHAtWvXuEK4xojF4tOnTzc0NNSAQq2KWmLHacMCWvBRzjCOVcvg+Bd1Nx4ydT1k6HlouvX/XXzpa6l9/5x47tHE658vf/4f85/5bOkXz8fPn8+/+GLhC18sX7nCYZ31T/b6WJM30uv1Vv8X4Z6bN29+CHEPTdOhUAjDMJlMdh85jfBXtHHByfl8vnp1CTzTASszW8nE1xZmVual/AcZcL2XVgDizvjY2opMsbw4Oz0FIl28l7+9zzx8gUAs4KtXl+dnpnkTE2KRiAc0CryO1uvjA7fB23O1wJBFu0E7uQ11CQSCjhvtgwMDCoXiPvweu90O7rrSTqq0TZDlLhCQY/3bJjF2zcCAV708fgeX3ONfdC5dwQFRppCMYjYdjbqAfKVayMQw1eQgYtUXUsA1HygKjwL7uFgAmBRlEpzBPJ1kDeZTIaBr4wi83Mhyf6lVWsyEIXfQbcM8trBZWXWtATfEwCDrTrT5dakViO0AgepACXe0aSyfekPoVSt2c6JAFJIRxGXDPA7Yac1GMTKTpLKpJAb3dLStyaRAL1nJl4gE5DBXsinQMzVQVSCKqahWLkuEAsnYPfk9lUrFbrd7PJ7FxcWanpdD85s+N3xDbTpUS9YmmqDmQoo9VzvOBncTeGxmugDo2HQ2WYwEgi7b+hBU8qkIol2ZR9z2aiYZgTwikQhYkm/aJnl8i3IJ6KQQG6sl5AAT6E86n1pbXgSwRwDitYSsQEEWdRgQmwGz6aJGBYh369dX/MZV2cIk54N5U8m1JJ/Pn5HMRIIQEK2xd3EuEVmVLU5uwvecZtyoVZfxRIlIwA5LyOdEvY5sCKISKIVHk357Z0uTZlUGbpZyrkAkfHpFFVh3sl4SUiEOWGcjQcPaChHDIu8zToXb7d6/f//ExMTRo0e59SydTnV2dUZiMZvVMjwyMjMzI5mZUSqVc3NztfVsx0SfTfoXYy98puuFfzj7sy+0v/Tl3tefvLDvqR/85F++/u2GhtMcCwRBQyHjFfOt/9va+3uo6lBIV4ep9ln6/0zT8pCr5xPR1UNJ9eVw/XOFI6+Ry8tMGZhfUdVcNrxMU3dMsQqFwpZo4dvbk8lkOAnW9lO1I9vjsU9NTW32W3ju3Lmenp5jx47JZDLuX9K5WcnsbLVaHejvl8vlvb09oVCov7///lEUmCo9op5ZCJv6vYuj0EqZquLl3Msr1/7H7R9/Z/bEjxbO/PnQcx8dfG6/ov355cs/mmv4ofT0gF/2lqrzb0Zf+eTIyzdds1ztsUg0nQC2WkggeOrdkxdPn+ts7wikIxrc2+QQPszf+8f9P/0+v86fCtWucXsChF3yw4XCup6rhDsg6ZPall8PLL1Qy7xjPPZfxjOp6bkYhmloaOjv7z969OjKygrDMOl0qquzMxqLWy2W0dEx8GYimVGpVPePxx4nmMea+D/ue4zf/f+oO37DcOP/UHb8pmXoj6wD/0XY+K3I158rP/wFuv8WnEhVUgRlNTI7qecY4P6A9Pv99/fsn8vlYBiuXeOOiWw2+wHisU9NTX0I7bmKxWJbGwg4Mj8//5+Ae7iH+8TYWNDjAOCgmCkTiRDskS/MDQ8NicTA+9kDtxrJ+l45RSzR5wPLjWqqqHuVzx3n8Xjj4+Pca3oNWg2PjIwND3lsJq/N5DTrp0XAZmdLOSKx+EZb2832VsXqyr2C9SSTyba2tvHxMZLzxrt5db9/OpsEsh/MtS6YYRVh60vm/f9YIBIoHHebGfcak01k4qFowOs3qqsghvkG+OCUdxEvHUcAaRd1MIC1GgKqiniQ8hvIgA2AoXgASIOAiyCCRV3AxR+HY1jbN4KJ+YEjPp8W2Puwfo3XJT0BM/gZtDHJEPDSi0eBkImIAV1eJlHFE2AV5yjb4JNt1bpfIlbmxBrWlTMpEGi9BIJ7Az0gHmHyRDqKBRxm1Gv3281Bly0W8AEuFCh8w3tCgSALxIxgUiriI/A947Enk8nm5ub+/v7l5eVJHksUZuWXLPIQfkDt7Kb5IRAI5+dmYLedLmboIqunKxCY256NsfzuYiYbwyIBH2TRVRJIIYEtgreFuyYYUPWKRFGzko3TbqIxJxkC40VH/MCtYsSnXJRyjgf5fD7sdjBplIl4ADcojTGYgw2yoWUCJtRtA3JKFiJt+OkBcKm28wD0kYRgH0XEgClZLpHzm7XKVZFoUyBe1sfP8uJCBPZW4kEyFgBKOjzCxCEmAsKDpBAvZDdjPpffbg7YjbhVAUK9JjE6idJRCIxOgaCKxDR/YmFKGEaRHR/o9zpYKpVaW1uff/55qVTKMEwiEW9rbdmze/fUtKT7Zuf+AwcPHzp85szZldXVkydP+nw+FEWDO20YimoN+hdGTz88suevxl/++MgLHx147qULb09LJFPS2ZaW1qampsXFxfHxCYPZ+c6bz9fv/nhnZ0coSmChqNe2oB75hmP5Ha912rZyYWXyOV3bI55zf4eN/yxqveYWf93Y+1dBlwONpBE0giCo1+tVqVQ7tWL9GIIgDodDr9ffq7XBYBBBEJPJZLFYuDwoirpcruPHj2/mNRuNxt27d58+fZr110yvrKwcOLD/dMMZt8u9f9/+d+vq9uzZvaZSX7hwYXZ2FsOwHZuEIIjX59WoNREsDAcDgWAAQZAoGp4yrzw++ZbKZXT7vd8T1b0qujiyJH5b2von/c98Y/LwhcXbratjj/Pefnhkz77F6802wYJXs6BZXdGtmTx2D+I/fLn+U8997eG6p77Vvvfnne/cmh/vUgl+NneudXn09YWmDrfEHHRF0XAIwZAgsqVha2trXq8XQZBgEAmiYRSL2hQdpoV6BAlxOSEIUqlUgUCA+4mi6OTk5BZes1arffXVV8+ePZvL5VKpZMeNG6+9tlsknrp5s/PAgQOHDh06e+6cQqF89913fT4fW9eWVgRRFHF74QPHzv/dxSOfGdn9g5Hv1vd/VtX1W9Km/yFv+b3xa5+Y76z3n/uYQHhTo9MHNKcdCz8PYolwKJSIBPtkcYU1EdqYjDAMr62tQRAERhYNI0Dfe9eGIIjH41Gr1XcdvfsHgiBOp1Ov1+/YWi4vgiAGg8FqtdamzS8ZzXV1dR9CXjNJkkePHm1paZment7yAN30NP5VJkGwi5Fhj0UPJApRVCGTDg4OAL7vewM9XFO4V8lfZbM2yuLeaDd+PfB760rHXcj4+Hh3542uttZ7EahvdXWOD/XPSiRlNozw9gd3sVg8depUV1dnNh6+FykHIIntdlu5FKCKQ2bAkAC+8tKsTiq6kZOzh2L/uOW/xYzHYkCtwAF0zK5fmp2OI37OQAxUxO2sgwAaj9DpMFPI0GngXQaQYVEHeEf36SpuNeVmA2f6DSxJ1gPsuQDhmnMxzNbOCVpiEA3paczJZBNAMccxtSM+EJIJBI0PAg4TCPtgBG5jkhiTjtARH2ccBNBMxAf+G2FDIgStAC3FIBYnsfQm4NGY9S3EdRGw9koyxWws6JsTjCsXpaVMEhSSDjPpCMhZIJhyLgx75oQ89dK8z33POBXZbPb06dNt7e0zM5KFmSmX2RAO+EKwLxL0I363SaeZAyZUNRj8wPmzNYNAIFiclxq0as3aml6lTCAwU87Z9aoI5GHKuQjkUcmkIb+TxIGHaP3K/GbVElcWwD1ikUo2p1tZ0K4saGSzEeMq5ddVfSyDyqtVLqy3UCAQLMtkOrVKu7ygW5XpVIo07GRgA/DTHTBVE1gkCKGwF3NbMb8bg30Y5MbsesxhCjkMZu0ax2uWSKY18mXdmlKnWDGsLmoUK1NAZ3dXD/D5fLFIqFlZ1C1LdYoV3Zpcp14L2vSVZJjJROkEEva7xBNja1IxEzQDxz9g1FhXFDmgI4sEvMKxIZNqFfbfYX5sv2W2HwmFQmfPnh0fH7969Sr71p6enZl5t65ufmFhYnzs3Lnzo2OjN250LC2vdHR0pFIpHMfTO20EQbhdrlgoOuBaeEpy+oys93HR4Yf7X3t0/MBt+xxDMz2dXV9+5Mvf/va36+rqvvdvT/7jPz12qr6+kM/jBIFnitlcIUskgqmUOxqTGYxIKlosYkRo1jP9bfWNX9c2/5rl6uchQ3M0akpnEpF4ymL3pNOZnRoCjnHkHi4mxr3yEATBRQEjCIL7SyKRaGlp2WzH3t/fPzAwcP78eZMJaKAsFnNz07X29natVtNwpmFkZOTK5ctqjbalpcVms3HlbK8Ox/FoNGo2m3HQd2Dn8uA47kthWSJTIHLWBGQLeBAPjGeI1xWtGszJ5CtUvjwb0EogdTpLLIZMp9T935g4/LmBXf8wsOtZyblfDNf/4PRrr9Yd4Av4Q7391y5duXjxYmNb08DN3iX58mVp32PNr+waOTulXMhlslniTl+lUimz2RyJRO4MJY4T2TJO5GqNj8fjFosllUpxRwiC0Gg0169f5+YPJxfs6ekZGho6f/681WrN5bK/lOvUvVsnlc6vT5uRkc7OzlW5vL29/T7TJpMhAj7XiRuaj507/LGWV/78xq5vDH/n387Uf+HoYPu1L5i7fsPU/VDrpUen2x+2tj603P5HXjPPAwcEWuJjr+vbhkYLOSJNFHAil0pnzBZHMkXgmUIcsybCzjQOhjWLZ3IE2LMEkP9ZrdbaNW5PcNPG6XTe6ZltmcA8d7v9fn9t2sRisZaWFofDsf3m+o848p8an8tisSgUipWVlcHBQeF//DYtkdSfOCEYG2aYqsusH+zvu79T5l9Vi0SbaENbnstcFZsz/HsrFQECxNjQwJaKWFDFE/HGB273DA8Pr66u3kvPxcXp1Wg0VJ7IxtB7qniAR5/EZmUQk0sD0IDYgctBIOxJA1visG99gQdBNKMgHXKBVT9PAGeDnDinkm9vvrYg4jEhh0suDbrtwKRoM7Ral7WwnJ50GAhyioDoA8zdOc+Bm10qc0dgE+Amh9nQCpkEqDGbArXHYACYvFo6wK5zUT8TcjJBKwA6cYQhEuCNPxZgEAeIDY7Y6BgMxEgBC1iSYTMIchK00X4jEBdxrqV9OoCQgBMa4OuZwZx30B4RB/R5YG2eZIgobNU7DRpAOuFkV+zlV4gE7LLK52c8NlM+Gb2PnothGLvdbrGYUZs+ZVMxRJgpZ8t4LOCyqpWKqY2ot/+e+cMTCMYneVNTYthlBw6ZyrmmS+fli3NMJW/XqTC/iylnmCIB2wzrXpl3qow1XBfw+EA8g+hX1lWK7ADVcA/HwuGxcdR5wPKL79SsMDDrZTviB/I2IsokEWBRFYOAQChPgBGMeJk4TCUxq1rBmcdzDodYezHBfd6deHzQnppZmVAgkC8vwXZz3mdi4hDstDqMWgYPg0qBdBAowUt4POC0yudnfTZjmUi8Xz0XgiDHjh0bGxu7evUqt54B4kW5TJJkFUTUqlQqZYahl5dXZmZm7v8cR1EUT6dz1SKcjVQp0kuEvjp15C+GnrtsmdCnfZcEN//lyW8/9W8/VMmVt2/1NF642FB/SqfW1MoskpXj2tsP8974+4ndfd71YGGAoXz1BeiVj6S/+Ynoa38dqPvLYO9XEPkRj2WkWknU/rs9UbPn2n6qdqRmz1U7IhQKN8dj7+zs7OnpOX/+PCcEAjwStk+qJFmpVKrVarFYLJfL3d3d9w++USqVfL4HxHVJp9McUSZXKVapO279am1jGGYZNf95/893yZvTlVwpXyQS6Rye4UatUq3g6bTL6Zrk8Qb6+q9eaPz6N77xheefeGbi5HWn2Ju5o/n6wH4LN9tzMQzT1tZ2+/btCxcurDt1vHvalMtlmqblcvkD47GHwiEsEFS7TWNS0eHbl56cOvuJkb0fG33zr/vf3N/79cWbv33owrGvHV24funbI5f/oqFxr2L8Jw3nXhy8/LeCls9Y7ItMXkemdbm4wWNdKKTscXuLdeB/EgFARQcKwULagWNOHIkW05zHps39uT2dzWZrtnvbz3JHavZctQxCofBDqOdijVOoXC4nk8kmJ99HMKydHrbv9dirL7+0trzAlHNzU4Dw+6sEHPduQs3NtEgk4rEhe7bkvc8je0vO9/QTBGG9uz/ZGOz9Pd0SsYBzjX1/3MO6F6/QhQzQNNUkLtsTRAxgAmDBzkpxUiEuZASwqWY9/gHcE7AyQO4CglcAM3g2/DiDOkD8SNTBaoLSpVT0+OG3/AZlzmeyquXrpmo1DRenM0pHAE5KoOCPITcT9rLOA80A+sBGEMcUrKxaAD4ARtEDWBP2AvgStIJmgABhDmCoxUbUAp4Jffqylw2wxbpmrnq0ZZcaIJuwF/CaISMJGYuQGUQTi3gBToKMQLQDglUZQKUQcLUHdszJhD0s7tGBK0oEgBF+2AfQD4t7gHolHWECZjLir8aDYFFPYeAS8AhTykQRaGlaiHgddClbTscToXXL3trNvzkB7NjJKhX2pN0G1GM3atULczMC1mL8LinHe5olO2SaEgm1qzIi4ALW75XcL626jh16C/W7comwVbvGOcn0u+zA9yCALPwN4MKhCg5YrKcnePyZKXHeqWZgzgodfCoWZsYn+RsoBOTkGsHnC6SSqbxbxwSMdMxfRZ0gtBYEZD8kzCLOsBtoxAImBjJRfmMeMvt08hmx+I7z6R2u5n6HOLs2qWTq/2fvPaDbOrK0wd5zeuef3Z2d6fPPzu6cs+fs/jN/T7TcPR3sv3t7xtPntHvc0W21O7idZVuyJVk5ULIkKjBnMWcSIEESJAGQiMwBJMBMgAAjiAwiBwIEiBzeut6lnmEESu52J4/qPFGFevdV1btVr959t+693/r8lF0r91l0Ubf9wHQMD0VhUsunRweNqu2oby/q23PabciS9pMkmUyWnZ3d29uL4RanB9IPnieqmZycbMb9imNpEoZher3e4XAQl2AY1qWYbNocLJXSv9h17An6u2/0Zz9bc+r0RMVNUZt4V3V1ou5bNSfeE1ReHK3OnessWKXliXu+xbr4/cFbBdLeXDG1boM7btlQT47uHT+OVTXGFDvhKaGPXGUtel2Z87Sq6d90g2/aNxp9u6uREILGhAS3/+v5c9XW1gJOBTDB5/N96I50+/ZtsPlI4Az8jEQihYWFBEEyezAM8/v94IGVfBZKQNMG1tPEKCQQYxhms1gzJ5pWdj8WqoDoFdy+0WgMBAJyufz4O++8/tIrDU2NU9b1G4vkMmnflnMnhqFafw2cCrlcTkT0hha9Xm9nZ2dWVhbELSS6QWQwDHuUabOzsxPvDbewvHik7KUnm954pz//COvSs7Rj50YKn+op+7umO0d7rpAXTUvaiXHG11bqP7fV+bezvMsVVWdFg1fti9e2h98zCd7dIP31Ss3n1MNHzdL7PtN04Xztkd6TR+gX2DuisD+oUqkTuBr/89f256qpqfkM2jXDfAI89gTlBPu3kJDM0dd39IUXNqVim1F3EJyQxUwZ/fXTlYfixZqEzSwOLpHUVFf3/QYu9MncSu5/f39/Z2cniHr9/f1TU1OH6HvQ0EQjSO3hMGC7+gNzGcCFeCCOxJCRCtoAQm4vuEgU2zUimUC5hIQDJIXgYoFyCTcZxsURpQg3JV7GS5ZQaBb9FubQmVWyd986ZlatmRTrRqUM8+25rYYgbu+MmkaKIis6do3oi1+3gYQYwJFQLOFYp5sQ8BdvfRkZGBnlB1Y7IAxpV5EwBPmD/ixH1JIwSEVqCYIZV6+gnj/AncA0EveqYJlFicgXEbYliuuDAsygbTXQ9CBiHKodh5fHwB4WITnYYhYV6iHeDSTc7DuRIKVcwpQiTCtFp5B0JUeXGGQB647boIxa1JhV7VNJ0KbSoSkUDC7PCgcRxhsKMxg/tZKnwaOXsFjsIR53Y2bCtjpn21iwbEtCLot2c/XKmVM+p02v2jYotjC/W7u9OTU5MT0xKhgfFkyMCaf4KDM2JMT/CibHBGMjgtEhwcjA9MigVDDuWJu1rc7a1vBjdVY8NTY1OoQIRoeEo0P80SFis5bFYkln+faNBatMbNkS2bZEto0ldOHmsm1r2SZbsa4v7G3MYdoVp3hSMtCDaVZcG3MLE8PouX4gPz36/R64fbHZk6NDSwL+5vyUXbURMyvQqKFdzk2/Zs2t3cIcOxGL2qFY02vT2l2lHC6Xy3Xx4sXnn38e1DkxHIh0d9cRxZMfj3AcCAanpqbAYzllJVCo1+t3dz+GkBDGnaudAc9z3BtnhTVj5pW3BPf/745Xfj6aWyrtu7LQdIR56h/qXv36G9+7VHhLKluPBSO1i313KOUb0rVtu27SJG3eGspepd0Vd93f4l4Skc+LyFck1HWr0SCVhzclnplOy8BVLe8VzcQx08wHLhE5pJJi7n0sirk8+ztWBEQKfmHI6xMcw+JuIFnfQ6fT4/25mEzmCy+8cO7cOYAcxzAMRe71othCoOnZdToDgUBjYyNBEFf9R1m/3//o+p6PLkvKWcwWq9WWVPyxAq1Wu7+/H41GXS6Xw+FoamwcHRyOxKIjetGNBVKRhLbh1OqUmgBu1xxD7EmRwuFwAk4FqqqpCWQa+Eun048ePXrp0iUI6hiNRh0fTRv//r4nEAgIBAIWi5WigbiiBFxSxYbs/dKbx4quUFspZ+Zq769zF/VbPyad+3vaWz+k3Sja7P7OyJ0vMc692f0D9uBL6j1z/oT623V80toie2NiVLEzICFR2v+xivvqHfab02PHREM/K6N86TTpKzWT19Y0s1rNR1Ee0HTA/8X1BcXvAa1bfGFCPhmngkajfTb1Pci98Hflx85Gxgfcnx49evuDa13kVkCiyMvNra+ri7fvAbmETqd/Wm+Uh67FCJisD3lk/VaFP7QjgFtlg2f7w+WeSBiJL+oVFOUIwW7sIBnIvoOED4gUjHaO7LiJrhEzq5CSAyE6bSBFiGblQCMCDlNImFhEVSHZAhd6QMhQLaMQiEZ5zLt76t3j58+cpnVR9p02v8s2ymUa5JvIkd5lxbU1q1H1SkSx7FoVRlS4gAJ6Hd3qAdABDi9woI9BeoLVKA59gJvKAmo6vg+FlD14RrF40CWtFNmxQhSiB0IPITOFFUtB2TwOPo/8v5DUggI2alF0O40EaXd2NlA9SAjD92hUYjz6iw69Pg0y5BqGQDN28JDESweCF8K0RyjuETwTlC+HlCtR5TKmXApszdq0D7Ej8QcCI6OjaLY8dGJ9cgIOB+F9jg3ytub5fuWKX7Vy+tgr1y5fZlA7Am6Hd9c6yGIY1XJkF6XfREyzqqMaaXh73ikVhBW4vAt7fzvrmEa6uzY7xOOy2Mh7i81Bf60rQuyBcIkplvyaNf7EWLxNEgcn40BiI+kdHagGDpvFHh4cUIpmQ4rliGo5gMfXjihERolQMDrYx2T2IdPnT8AVBGXK5czxx5SiWefGfFC9inY87VpkKW/XBbeXQvLlqHolrJZapUK96iHyaMIi7vf7Ozo63nrrrfb2dji1sb5+4cIF8Yp0RbxcUFTU2NBQUVEpEAj7+voikUgwTQqHwxqNxmKxoI2xBykUDIWCoWg4Mq4Vb9iQK82EdO6HzA/ku/pQMBQIBS/M1D9/8513Xn6zvPQ+uZVUXl5+LSPjhz/44Ys//alahQS4WDTm9fuMHvuCaevmHOmv2176d9YV/a5Zo9IEIpFQBAsFowGvZ9+ptsnpGv7F7Y7vyiv/zVT1iq0lx8ZiYttazINA0oNwRKPBcDgYDAVxm0G9xWIwGMKoBKVQKNTT0xO/z/XhDs7tLnc3ggAAIABJREFU27dPnTplMqEonYFAoKL8fnlFZTQaLSku7u3tzcjIEItXyGSyzWaLv/EHDDio1u12y2Sy+MKEfDgctlqtarU6XSXBYDAcDu/s7BgNRqLDCZXAT5VKtbu7C9tw0WjU4/GUlJSAU5XX7xvTia/NNl8dqxNZkBt8LIz2NJOT1+vd3t4G2Q44YzAYEuQesVgM4GUQxG9zY+P8hfNisUSyslJYVFxXW1ddXTM3N8dgMKIofmLqBI5jJpOJuPFAIODz+vw+X3Vl1UsnXj935UJ5YclP33r5ybxfvvTWaxQK5W5v1d93vv1qZ06dlFsk6r44U3mk+8TfUd/6esfN/1bU8hZncMkmVezqrD7/vj8Q9e8LZdxlUYVyNkPI/omw/yfqxTuBHR7mUcei4XAMC0WxUDgWDKE5EAqFHA7H4aMQCoW0Wq3R+NEowLT5bOJUbG9vKxSK340fO5vN5vF4JBLpVy+9dPytY5Xl5b29vXQ6PWGLDcyWW1tbu7u7kZvuwxbTBP3NJ3/XoCtg/+u3JGlB/B54lcCL4fC4haFQaHZ2TraxhkQKlQiBTyGLFnwXCWxZAJpKLUFKC4c+ZtiOKZawnU3MqkNRTwxb6DUPUo5Bhr6htbh0opHi20C4TOCyxiwahNFtVuBaJZ12Q3zvRkbW9cvz40NBu8G7aw7v2VD0RZsWCV4qBLQe3F5Qz496NeuYUhRVLKEQc2hHSY5vrqGdKaRKQfbIkphZEUE6HinqiXEbme+gfa51JMnpt2L2nZgFxQjG/b+QJIfe4tpVJNaocYntYNcMWeNGFEs4cgUusRm2DmycDZsxhAavR7Y+SI0Ewhy6a0y7ijiAohlpkXYKgOLj4yLi9FH5InLbVqJgxFFcOsRUywHZPIrjlyYFAoHZ2VmJRPKpx7tCkjfuFTYyOLA5N+WR4SGSEba8WD7FyTx/siDz+jJ/OGjTe3fNEcAtsWrQ6FuUUa00JF9WCweDuk2EbyVfRDuMSEtnXp+f7sdVMQg+HVerWjeXEWQ9EvLwJsyKTfFSvNchCydL9xdVwmLPT4y4tpaj2lW0F6aRYCqRXyVVLQvmJ4a4XA54gT3iYwi47iwWa3hwcHZqYn12wrK+6N4Wh3dQ4EoU0FIrwXRrEZXIrFWm/oRPM1jw7e71emGnZkeny8vLO3PmDKOvX6NWlpaW5uXlZWdnT09PZ2RkrK+vq1QqRaqkUqnm5+clEgkE6k0g0arUGpVapVLNCGemxXNKpVKlVKpVKtGGRCQRr62uKZVKrVYrk8mKCoueffbZH/3oR3fu3Ons7JRIJAgIVKszafXbiu03RgqvzjZeH6sbmRrXqTUKpUKJkkqp0qq0JpXWJFduSRfp62OZKtpPt/OP7Fw54rj0Q8e9q8qiUnknXTEpUG7IVGqdQqNSDg8vMRhiFJAa3ZFKpZJIJJcvXyb8uYAz0WjUZDLt7+9HIpH+vr5z587duXPXbrf39nRXVlaeO3d+YpKfl5dLp9M1Gk3CXcNPpVK5tbUlEAhScoagkUql8/Pz6dgLPVxeXhaJRIfQKJXKubm5tTXET6hZo9FsbGzk5eV1d3fr9Xq9RqdUKBpGe27MtX4w2chcHNOq1Fq1RvWAHq6SyWQCgQDy0HR7e3thYSFMohiewPzDbDYHAgG9fqewsOD998/Q6HStVlNUXJSdlZWXnz8zM3v58uWNjY10fYZpIxaLCQI0nCqVwWCoq6v793995j+e/W5HRwedRmdwmMNDQz3U7rfffvubZ4++f/6MSWvY0elUCuVtQcu/My/9672lz7+m+fIVDW1UoddojTo035RKlUKlVaqNWq1le2OdPdTSOHblXte/DrC+r596d5V3Znm8bF08opDLVBqDWmNYXducm19QoikBHUkcU5VKtbCwQHRYpVKJxeKMjAyZLK2fR5on79cs/t3ZNYMfe3Nz8+/Sjx3FbxsYaG9v7+zoZDKZPB4PfUqmSkwmk8Ph0Gi0nt84nmGq6n+fZYfLPYD7TWknB5X4phWSdZA5C7LtRViP+Gv+I82NGOlsEDwnvndj3EbSBv5Nj+1s4NbEDqSzAc2H60EwHvDnsu8g/ZBBhkyGrSrMprRJpjyyJRT6xShDlkDqlQcbT8uoNmTWI0cGQzsbbu2216g+UDg59OjUrgEpopzGmF2L9FLqFfRidllwjZEcmRx57OgvKK6Q1LKDiyZKBNyNBCMEFIo0VejWcGshxRKCMUHWPFI8gvMaEpX2nQjky7gds2qQGKddw5U6RiQAoa0u3LlMvoAkqn0nkrHkCw8UQjjfgHv4348FiUZywLJvc9as/ljYtPiH2GazFRQUtLa28vn8T1FEBjjP2alJ3cbKvm4T25FiOsmB6ZJajBk3Y8Z1q2TKszWHaaUIB8O4FTNs4qKtKGrfiflcMacRcxqQIOjY8Zo0fusOFtr3ueyTSJdz4OgOArd9R4l5rDGXCdm/uyyYVeVRiEcGuI8ex5zFZvcxWUODg+vL865tccyqwsEoTJhdHdZI7JuL6wvCieHBT/p0gZV0Xz9y/hof4s2ODW7OjJtWhK71ueA2klCNKvknknswDNNoNBUVFTMzM/Ams9tsXB5vY3PTbrcrFMql5aWlpaVHMVBN3ueKnxWQ12g0h4e6UavVbW1to6OjKysrNBqtrKyso6PjQ+Qs2Ox2BFFEvmGD6Ay/Sr5nSK6fKNnzeNRayZ6JZxBeVVO+oyv7H9bs73vzLvlyr+/0v7xXfzn47A/2v/vd4PIycUkCHjvIPQwGo729HcI6BwKBhfk5oVCIBAi9XqPRcLlcu91BJpNhrye+qvj8J7Jrjr8wIW82m0G5klAe/3Nn5yBec3yh0+ksKiqan59HOp5YbEel9ezvz1g37i535Ii7lm2KSOwjZAwMN99O2Ofa29tLsGuORqO9vb0dHR1gnWOzWnk83sbGpgNNG8XS4uLysmh2dnZgYCC+J8n5hH0ugoDP52dkZNy9i6RMUAjB/Lx7+873n33ulZdfaW9vNxqQvbYl6BRYFZRRU6fA1Tnjvt5tP9tu40m8/tDHHoX9fa9Oawhj2IxDm7nYfk9QsrjW5FwrNc28r50+blzOdKqo9p0Fvf4gxk80vO91iGOxRBvzZLvmzyYeeyAQKC0traio+O15hqdb/nh44nK5DAajr6+vp6cHlC69vb0J6h8mk5lQkq7ORy/n4OnR6aFvhHxGZIgaoMLkcoIgIcNgMAYHB9MtlLu7u0XFxdSuDufaLJJykPUuDpdt2EZ7N7hKA73mcSMbTLeGPG7sO5jThFQ7ZmVMJ8X0W0iaOYh37EQ2Lo4dXAdgRdKJ24pMXvbxwH17ViSLIElCgRC5d1YxnTRmkrk3ZlzSKZd0Gm0kqVd2t5ZDO3jAHjAncppCRmXYaUP17FkQQic4h3vs2J41tmdBm01GOYRgOYgQiNAtHDGbFr0m93eRFQ4SgFCklpgWCXPovhSLGGzSKZZj2/OxrZmDe0d6IAnyaQd/dRSP0Y7uwqFH23zI7dmCIv1YlMiZy7CFggBBwF+7Fm2KOXbQphhshxFbaWAbpFpGAgRuFIWpRG7plHZrlVihEjIulys7O5tEIg0/gPxMGNZf7+eB3MOfWJoTLgin5idH5ifiDv7o/MTI0tT40tT4In+UP8Ca4DLHOf2IZnx4jMeanRHMC/hzgun52Zn5WeGMUCAUTC/MzU5PTSb3Z3qKvzA/Oz83Mz83i+hnBPP88UEu2gs7JMH0jicAMWV4cHBuRoBqmxHMC6cXpieX+WOLAj5/dJiLXwOXxD8XkIcK48uJylkstNPXj4PGs9nsoQHu9Ojg5ABnVbKSMBwP/SkUCn/wgx/k5OQkUMKLHwr5fD6dTsfwt2YsVYq3a051HpVhGKZSqcD1KR1NJBLR6XREyC6PxzM7O1tfX3///v2hoSHpimRocGiUN9TBZ16cbxSY1qB7CbUd4FQYDiDkojH//p7Eqqjf5n1HVPcX8w3/xX7zqP/LT/u/8pXo0BAYAEH3urq64u17PoRlvHr16tGjR1dWErmK3w1qPBQK1dTUOJ0I7SGhG/Dzk9o1p6wEumcymQ4JDA00Wq02DqfioDIMw6xWa35+PuziEXbN0Vhs3rJ1d4lyb6ljzrJFSD/hcDgeCQRAqRLw2EOh0IULF1588cUEk17oBozL1NTUQ6cNYdeccONgYRaNRlGgRaUyEAiA3KPFQWflCsXU1FRpaWlbW1tvd09ne/vk2BBhwrWqD9/tc55qtfYt+30PAl4CTgV0LByNCiyyW6Ke4jXeukMR9Rq9+iGLtEgxcXx94FeGxRu2zQYZ51n5wPOxaJiYIXBryTgVnZ2dCUyAVn4bf393+h5AaREIBHw+/xNtzLN/4wRqnuLi4uPHj586daqtrQ0Wwd9qNzgcDshbfbg1D/dB4uCJuCcIw8PlwtKNdFEsFgshuj8ATAXbZLgargXhDOQzLpd7iBILWunv75+eng6HUdT25BSLxbZkMolkBWFlwytfJUYBb5AT0wpSYMCOkhb3pTIgp3RkgmPVYCg83TZScujWkTzhNCG5BHljOZD3uHYV32baRK5YexZcXMBD3ew7kYWNbg3tEFlVewoxrbGi8OblhtICm0YW2jUGjMqgfQf3jXLEnOaYE3cLt+kOvOhxOPQHghTuWu9GIs7BWdS6E7eMxl2UnWYEubWPR9Bx6HHXrS3kLG3XxIybfr0sYFYjVyarCumcUHhoDZKfHAjNDXnO+11YYC/m2wu5HV6XzWM3uqxGv8OE4NDdyCwp4rKgENK7er9JFbBowjubAb3MZzdEkYylwXZ3UNC8nXVsQ4Btz6MdLuUy0hIhc+8ltF8jX7Q/+CpKOS4QEe63sc+FXvbgonXg8k24aOFeV1xuL41+6cqVV197/fzFS9TuHiaL3c9k9fXjocLBsasfd+868PNKbXCNjMw+Sgd4pemEHmIm9+GJ8yDhMx+eCzYTecsjSYXJYjH6+ml0Ri+N1ttLo9F6+/oYRHPw6MFuMti3MfAEOl3ieSEkIVgHCBmI0df/SfHYY7EYlUo9d+5cfX098eZOHlM+n08mkwmCWFL6VOQeECPiAQegJ5FIRK/XDwwMXLly5bvf/e6zzz47PjRii3huLrfRlNPIOBf8lB706uP4XB/Z7gY9Oq3wnIj01861rmhtk7W42GlECgO4DsOwxsbGxUXkWk/IENevX798+TJEpSPe6JCBv5FIpLi4GAyAHrT/sf//EOQe6KrJZMrPz5dKpTs7OwcKKlx8C0cjS7btHBH15gKpQNyTLerkqed1Kg3IHHCtUqm8f/8+DAdRcg1PgJgBhUBA/J2cnHzotEkn98RXSMg98YUYhvl8PiaT+f3vf//b3/72lStXBAKBVCpVq9V2q9ntcq5p3Reb1l/IlZT2KdV6h9/n1WoO8LlgTgQioXGD5MYCuVTav+1GSCMet0ujkOxp+zZoX529/7ltzo9iMYR1SowohmHJck99ff1nU+6JxWJ+v/9TX8fZ6ROPxxsYGGhubj579uzx48fz8/M7Ozs/kUaHg6cHQsvH/idEFkIoAakFutPb2wsfWC0tLfV4olAoHR0dXV1dvb29UG1/fz+VSiWRSK2tre3t7SQ81dTU3L9/v6mpiUKhkMnkurq66upqMplM0LThiUwmt+OpoaGht7cXtGiwgvf39zMYDMJ8+/B9LrQ8YVgkFECOWjYd2kJCEoAa2XtaVMiE2apB3t0opq0dP3BPK9DiQFCfXQMSKVxm3JtpF4kduFR0sPFklKMAP4Dc7jIj8yDkEebAQp7VBeHtG9cvXrjA7aONDA0MD49MjI/J1leRTcmuHql2bBokuPjc6HKAACNCIAY8yAEeYZW7MR8eNNm/dxCUxetEAYECOHpr0IMF3FGnKWzVRq1qn2bDuL26JJxk03uGB3ksBn1OwFdsSg0qmU4pQ3iWeLVeq0G1tbY4M704My1Znp+f5vPYzB5qZzuJNDE8KJ4TCicnGL3dTFr3KI+zMD3BZdDoPd1THIaAyxjp7xVNjy/OCVfFC6btVa14RiEc8ZuQyXNwZyuCe7SBzixg1thNh+0ywJI0NvbR/lH6mf4pnAFxYXBgoKKi4u233z579mxNTU1vb+8nqhqeBULDSjwaKZ8jYtL29fV1dHRUV1ffv3+fRCJVVVU1NTV1dnZ2dHRQKBR4XlgsVm9vL4VCgXgwFAqFRCI1Njbev3+/qqqqtbWVTCaTSKTq6uq6urrm5mYynpqbm9va2kgkUktLC1xSX1/f3d0N0hVszIFbA3x7sNlsJpP5Se0rg8FgfX399evXORwOhqd9j4fNZrtce7u7juVlkVgsnpufn5mZORxwAN4HoPaAelL+1Wq18OGe8iyxw0LoexLIWCzW97///eeff/7atWtcJlumkhdLaXXbA4E4FAu45BCcCp99KehBRtMWl8tq/1gQoHg8dgzDJBJJRkZGaWkpAXmxvLQ0v7CAYZhYLFKpVRwO22yxUCiUw28qEAioVB9zPk+4L/Bjf2jkmEfc59rfT4tmr9PpCgoKxsfHU35MDhtEf9/9zj90vzNtXDPo9PFCRvI+l0gkysjIKC8vBz92Dz5t9vbcu7sOsUgsEi3PzqH0KNMmIfxBMnM0Gk06r16z2Xz8+PGf/exnd+/eHRkZ6e3tbW9vb2pqqqmpaSM1F2Rnfvt7P/vH57NfKpPXjblWtrTJlQej4THjygeLrRUbrHWz2m5GsRiiYbdFWqSfOReLJX51m0ymeMd7DMO4XO5n1p8L/NgJO4BHWVI5HA44ZxFCB+dBYrORjTCOkzgAyxaLxYKTXC63v7+/trY2Ozv76tWrGRkZmZmZLS0tg4ODUOHhTROVMBgMKpUKS2dXVxeJRKJQKLAit7e3t7W1deCJTCb39PQ0NzdXVVWRSKSmpqauri4KhdLa2orCn+DLek9PD41Ga29vLygoIJFIbW1tzc3N3d3dXXjqiUssFgt25Wg0GlwLNL29vbBkgwk2jUbr7u4GgamlpaWhoaGpqamlpaW1tbWjo4NKpQKfHyr3YBiGRcIojp/bgdl2UDxi2NBByAwOtJeE5Bglcl/3uQ80NygajQcpV+w6bN8R87kCuxZ02PR+hzmwa0HOWRFfZM/qNqhRrBTvLjpwA9h9i1YwNkxubirOz83JycnOukfr6oAo3v1MJpfLFUzxt8SLduVGBEXUBfwHHCbd70bRcYL70YDbplfrlDKVbE2+viJfX9mQLCvXV+SiOYVkeX1RuC5eVG6uauUb8g3JhkTEZPSyGL0jA5y+3m4Wi9nR0dna2spkMntptM7OTjqdzuVwmhsb62trpibG5gX8seHBvj4GjDIDjziAbIGR1S7SVvT19dHpjN5eWkcHejF3dXUx+vq6e3r6+pkI/wQFbUJN9FC7+nrR6LS0tIzwOMtTY1vieZ1s3axTB/FtOCT3GB6Ch+Dz+T6R3APzFiQYED5AOifmPHwJEO94eIKAvre3t6io6MaNG1euXLl3715OTk5bW9vAwMDhTwpRA8jZnZ2dIH80NzfDzATpvKOjA+Z8Z2cnhUJpb2/v6empra2tqalpaGggkUg9PT1tbW0UCoXNZtPpdCqVCo4I1dXVBQUFLS0tTU1N7e3tvb29XV1d3d3d8fi7cNegWGUwGF1dXR0dHT09PWiI8U8CIGCxWFQqFR5bqLAJTyQSqauri0qlwhfRryH3uFyu1tbWa9euAW622+3uplJPnTo9OjYuXl7KzLxdUVGen58/Oztz//59g8FgNptNqZLFYlldXZXL5ekIYI9GLBbrdLpDaAwGg0gkMhgMyY2YzebNzU0+nz82NjY8PNzX15d1915lWfnx5tuXp+q1dqPLtmuxWKAhpVK5uroKPz9eldlidZjMFqhta2uLuESn0xUXF8fHaxYKhTk5Obdu3QLfZqlEcu369axshDWWnX2P2t39ztvviMQr5eXlc3NzVqv14w0d/DKbzTqdbnl5+ZC7NpvNcrkcwiinrARuan19nehwSjKz2SyVSlUqVbq27Hb73NzclStXpFLp7u5ufCVmk9lhsZ0dqzwzWmk1WkQiEXHWYrFMTExARG/sgTJseno6Nzf37t27RqPR6/XSe3tPnTo1MjK2IhbduXu3rLQ0Lz9/dm4OpMZ0/YFpI5PJ0hGYTCaz2SwWi3d2dlLS6PV6qVS6vLwsFAohMIHJZNLpdGo1smsmk8kvPP+jnx/9YWZ28Y1m0S9KZPcH91YUjj2HeddmgbmMbtxq1xn0XeKht9j5hfPUdZPSZXPt2lwmo85kMhJ8gM6sra0Ro2CxWLRabUlJyWcQpwK9XPF93HT6HkK+GRhAQgwszf39/TQaDcziYAHt7OyENRF0G62trefOnbtw4UJZWVlzc3NtbW13dzedTgex48033/zFL35x9uxZKpVaW1t7+vRpWAHTLeVwFl5yHR0dFRUVpaWlIE/U1NQ0Nzc3NTWRSCToCXxQgvalpaWlo6MDluPe3l4GgwHvGx6PBy8GuKOBgYGampo7d+40NTURZkbEzULrhJ4GfsZrlRLeYfH0LDzBW7mVRKqpqcnNzS0qKgIJ6fC4hRh6CjHM70GSDR6cMOaxB3fNMZcNCTpe18G+D8J09GD+vYhjx28zuK0mr1WvXBRo18Wa7XWNbF21KZWvrUiWF/VK2QJ/7O6dzFs3rldXVU4MIbMJ+dqKw6jVyNbXRQs3M67+8hc/v3TpMoPBKCgouHL58uDQ4AEeEx6Xrx+XIeZmhIrNNZtifX9HEXPbAha1TbkhXZwdYPVTyKRuahelvY1MIpHJ5Kbmpvb2tvZ2CoWCguKDMoza1UUmkykUSk9vb38/k8FAEXBAMgYmAwPZHA6LzS4oKMjJyW2nUFA3cPtcGJdDpgoQEFXFa/tg9jIfAIIymSwajdbc0lxdVVVRUry1sohQS3fNdvOB8QQaglQpEAikk3s4eOLxeIODg9BcX18fGOZ3d3c3NjaCnEGlUjs7O7u6uvr6+rhcblFR0VtvvXX69Onc3Nyqqqr79++3t7fTaDQymdzY2Pj6668///zz169fZ7FYWVlZGRkZh8g90AH4wEBAuQ9ULyBS1NbWksnk5uZm+FQAAbEdH6G2tjYymQwSJzxrMCgI3h3HkCHmPIfDKSgoKCoqIiK8JzwC8aMD/Ym/Fh69eBpiTInnhU6n19fXl5SUXL9+vbGxkcvl/hpyj9PpvHfv3uXLlzs7O5EKxGKmtLdfvZrR38/c0Wm7e3pYLGZJSQl/aqqiogLevuZUyWq1gtxjsSCRImWyWq0ikUin0x1CYzAYlpeXjUZjyhpsNpvL5TKZTCsrK06n02q1yra2WLT+twuu/LTzg6EVgcNkdTqddrtdqVRKpVKr1ZqyHrPZbLVaNzY2tra2gMZisej1+pKSkni5h8vlXrx4MTMzEyIKTk9PZWdlFRUXK1Uq/uTEyOhIXm7u9LSgsrJiYWHBZrOlbMtiseh0ug/RrA65a4vFAnLPITTQ4c3NzUNuymKxrKysqNXqQ2h2d3cHBgY++OADiUTicDji+2wzWxUmrcKsMxmNy8vL8JoHXiXLPSwW6+LFi3fu3DEajXa7rZNCuXrlal9f/87ODrWb2t/PKC0rmxYI7t+/f/i0kUqlMpnskBu3WJAQhkDNUk0ti8XicDhsNhuIy5YHCUBnTSaTUqXSaDSLC3MFObdLK2op0/bjDTsXmzYFkh2nw+qwmXEZ2Gy1WNwOp0qjIi1yz05UZU+SpBrZnt1ls1gPqsTprFYryD3EtNnZ2SkrK/sM4lTEYjGRSLS2tpZg3wPfmsQyRKFQSkpKGhsbYWcHNnqam5tzc5FuIDc39/79++Xl5cXFxXV1dRQKpays7J133ikrK4MltRPXjcPbrrGxsbCwsL6+Hk7l5+eXlpYSCysLf72BsAV/2Wx2T09PZ2dnU1NTdXV1RQUCkYl/BUJXibddfAaWbKJyosL4NRfyhFR0CE3yVY9ewuPxaqqrs7Ky6urqurq6Hir3BINBgVCIFIxBL7LO8TiQaYvXGUMZV2TfGdqzh/edMqmI19c7PsgV8idmpyZGh3j9dNoQj1NdXZWXm1dSVNRJIbeRW9taW3qondMT492dHdeuXq2pqUGWSnhi9vfTenuY/f0tzc2lpSWtLS3t7ZS2traioiJ2P2NVtLS0MDsrmBZM8acmJ4RTU4Ip/sDAANL9SFe0W2uiGX4/lVJTVVlVVdVGbmPhqheEtcblor+4p178mw/YCwIB5A9neH9/H9h/PDqrH50SXAWrqyr5o8N61TYCIwt6gnt2e3o8dr/fLxAIxGLx1NQUTEJoDuxgCLOZ+vr6e/fuwdYPyBn19fVNTU0FBQX37t3LysqCzaOSkpKGhoaOjo6ysrKzZ882NTWByQudTgcdJJVKra6uLioqamxspFDQuOTn55NIJIJ1EOKB+AkSNqgh6+vrq6urKysrKRQKiB2EVol4LuIfFiJ/+IiAHAmRrh5K+ehjEU8JT0dOTk5xcTGFQgEdEovF+qTr7/7+fnd3940bN3g8HoanaDRiMpv29vZ8Pr/f79/d3d3b25uenn6UDYuH7nNpNJrDt4QgYl66fS7oYTIee2jfL1CuvD1UfL763sTgqM1q9Xg8D3V9So5byOFw4uP3SCSSoqKigoIC8OeCeL4Oh8PtdvsDQb/frzcY/H4/iUT6VHAqPq19LrBrBl4l/43FYgaDYWlpqbCwELaokmmS4xam3OcCyR74HIlEzGbz3p4bTRq/f9fh2NtzC4XCh8KbGAyGh+5zqdXqdN4t0PlIJKJUKlNu3hF3t7u729LcWFdVuiSScKWhcx3OTPquSBNE9jsPksfjsRut/miIqZ3LWGhp2ho0+51RLBaKRcIPHLuS/bk4HM5ncJ/L7/dXVlY2NzePjo7CPg68k7q6ulpbWz+0cs/MzMzLyzt58uSZM2dIJBKDwaDRaLBxzo80AAAgAElEQVTaggs66O3BLBE+bel0OoPBgJURaoMllUQi3bt37/z58zdv3iwrKysoKKitrQXEBtgyo9PpoLwhk8k8Hq+jowMmX319PYVCoVKpOP452mz61BdcDp7iF99PMc/hcHq7u+9d/+DevXt5eXmgdjrcrtnhcGRnZ7e0NMvWpDa9xm3Ru61Gt9Woka1Ll+YH2P0lRQX1NdVnz5y5cuVKS2trD+4Eh48LsjMFyeMBozhMFovOYPT09CIYy4M9R8RDDq5Tqa9vyMjIePfdd7Ozs+sbGvLy8qqqqnAb2/6RoaHJ8TE2s29kgNtP72XQepYX5oZ4nLbWlqqKCko7uaGxsZ1CoTMYMIKfItOgKujkp14tVMjlcpubmwsL8knNDV2UNuXmumJzTbK8IN/awtKkD9fB27dvNzQ0jI4iIHRCVqBQKM3NzZWVldeuXcvKyjpx4gSoGcBIhdB2oPgNeIq3F2bg3CM0XnDLLBarsbHx2rVrJ0+eRONSX5+fn19TU8NkMmH+sFgs2K2rq6sjk8kDAwMtLS2FhYX5+fnV1dX19fVgMwdNf+oM/K2OC4fDaW5uzsrKunHjxv3792Ea0+n0ZM+jNKP0UfHc3Nwvf/nLyspK7IH95kfnHuT4fH5raytBEEtKvyW75qR20DvK6/XGexvFHvhWWYN7Z8cqX6u4WldZU1NVMzIyEu9enlAVhmGgySBuCsOw+vp6wq4Zqs3MzDx+/DhAUBENAVfgZyQSKSoqAgOghCbg5x+IXTPRt2g0KpfLg8Hg8vJyfn4+mKoQZyETCoXiOYxhmFKpLCsri7/xUCh07dq1EydOgElZAnOAcnJykkQiERxOaAUuOcSuGeihdRCXk2uASsLhcLw5fDIZTBuDwaBUqaqqKrvaW40mK387fIFiu9xpndryhSNoank8eybjQUznvaCXphK8O1X+wtDdHw1kcnXIriulXXNdXd1n0K7Z7/cXFxdXVVXBN1ZPTw+YKzY2Nubm5ra1tV29evXu3bs3b95sbGwcGBjg4ImdJsHZ+L8EIYfD6erqqq6uvnv3bmVlZW1t7b17986cOXPp0qWcnBwajdbY2JiTk1OJp/LycgqFUlNTU1xcnJubW1JSUldX19nZCZobos4/lgyPxyssLDx3/jwozEBE6OvrO8SP3eFwgLaMw0YyDINBp/X2DvB4lRUVd9B7t/6DDz64dy8rMzOzvZ3Cw8flcG4QA8fBDbMIYg6H09PTU1dXV1xc3NDQUFNTk5OT8/7777/33nt5eXk9PT01NbV5+Xmgn0BKnba26urqkpKSnJyc6poaEolEp9MT6iQq/wPPcDicjIyM27dvwyYpjU5nsdg9vb0EUiOWlJxOZ05ODplMHh8fZzKZXV1dnZ0IfbakpOT27duNjY03btzIzc29fft2Z2cnPC+HMyHduICas7a2trS0FDQ3OTk5p0+ffvfddwsLCxkMRk1NTUFBQXV1dXl5eXV1dXt7e1VVFfE5QSaT/0jHBcniLNa1a9cyMzMpuM9BH27LxWAwDkAikwblkIL5+fnTp0/X1NQATSwW83g84MnhdrsDgUAkEpmamurr6zv8BUbgc8XSpIf6sSf7cyXXlFLuATIkYURDNTJe0Urv7OJ8Q01deXk5jU5XqVQprWKT5R4qlRrvx+71eouLi0+fPk1EpQNlRiwWczqdoVBo3+MJBoO1tbV/4H7s8WwEucfn82EYNjMzU1paure3lzCyyXKP1Wqtr68HIQP+ut3uwsLCM2fOENBjbg8CSfX7/W6Px+/3hyOR6enph06b35ncQ/ixRyIRPp9fUlzEnxgNBENzylAG1Xa+3Tq+7lvV7mfR9KG4kD0rduU/9Rz/Lue6xYdCFaSUe6hU6mdQ34Nh2Orq6vz8PIfDaWpqKi0tzcnJycrKOnnyZEZGRnZ2dmlpKZlMhg2vdK+3dOXsjyf4xiVkFx6P19bWlpOTU1ZW1tnZCRY5FNzoGECsCGtoWAphD+XjVf7R/Orv76+vryeRSM3NzS0tLWCExOVyD9FwWiyWtbU1JpPZ2NRUVFSUm5t769atEydOXP/gg1uZmSUlJZ2dnY/I+cPZxOFwBgYGeDwejDKPx2tvby8rK6utre3o6GhvbwcrEBKJRKPRiK1PYq8kXR/SlR/emUc8e3jlh5+NbwJUjOBYRCYjA5fW1lZ4PcBCkPxXq9Vubm4ymczm5uaioqK8vLzMzMz33nvvxo0bt27dKi0thX3M+FZ+vTyMy8DAAMx8GJeSkhLQ5YDxPlhNwbhAK7/fcfn17jThKg6Hw2AwmpqawMkADI/IZPIh8mjyMIH/VHFx8YULFzo6OoBApVRev359Y3NTtLyUm5tXXl7R3NIiFAq5XC6GYZE0KRaL6XQ6u90ejUbTkERisZhKpfL5fOloANAAtBHpKolGEUS0QqFISRCLIr9jqpJ/Wdggt+jczr2x8bEqPE1MTNhsNplMtrq6ajabY7EY2K/EYjGoKhaL9fX1xe9zicXiEydOZGVlAbpWOBxqamxoaGwMBALVeLp79+7GxmZ7e/ve3t4hN+X1ehUKRTqCSARxxuFw6HS6w2nA7InocEoOaDSaQzoTiUTC4bBcLvd4PNEoilU4MTFRVlYGijGiwkAgoFAoQqEQlESjUZvN1tzcDJeA3LO4uPjuu+/m5uaq1cg5TqVSXr9+bWNza3lpMTcv935ZGYlEnpubA1g3ouaEDEwbm812+E0plUq/35+OOY84bdxut0qlikQicBd2u729vb38fplKgUItL6n8d+m2f8va+YvjymHpPsJ9jESikQgWw0oldJpiCiZ/LBbT6/UWi4XocCwWYzAYn9SPMuXD+CiFv9P4PRgeKmBwcBBsXHp6ekBp39zcDK7aHA6nrw+ZWRChOAg7BsjArla6s/04+Hn8WSLPwvdc6HQ6bIoR71SCAHnr4EEL40tStp5QCD9hUyDlKaKQwfgoxAhR+KlcnnDjYBjR19fX+yCNjo4eIvfAuPB4PMDx6OvrA6vw7u5uKpUKmyPwKQxWqMmdh1GDWCkJZ4GxUAlxCoYYzFZA1oQ+QzmMDpGHHZyUTUOFMCuIyuMzLBbrkDkDTUAP468i8kwmEwIpESXxGTabDWOasm+wRQsuQtAE3CP494EB8kPfr263m8fjgVsfg4FczGAfNn5cCDuY+L7BPAcXp3TjQuwRExcS4wLPCxg8wXDEDwqQQeUp750YdKLmhAw8L4dce/g6AMOa7nLgMHAgod34sQa2gKgHdwd76z09PWCW+ygLKEGzsLDw8ssvg77HoN/Jzcl9//33+5nMpcWF69c/+OD69ays7Kmp6atXrx6OU7GwsJAOp0KBJ5VKJRQKNzc3lR9HRYCz8Hd7e1sgEAAuUHw5kVcqlRsbG4fAPigVCpNmp32Gc2ywYFAqdJhsGrVmbGysuro6JyfnxRdf/N73vldeXr6zs7O0tETAPqjVaolEcvHiRQKnAoTCe/fuvf3223I5Qj1jMvvPnHn/zp07BoOhrKz01q2bFy6cHxkbz8vLo9FomvQ4FZubmwKBQKVKDfGhwDERpFLpbwOnguAbkVEqlQKBgBgFnU4Hjrrb29tqtRrIAKdCLpfDT3CMisepALSyzMzMd44f12g0ZrOpoCD/9OnTDEbf4uJCRkbGtYyMvLz8mdnZS5cuPRSnYmVl5ZApoVQqhULh1tbWITSPOG2EQiFRiVqt1ul0g4ODeXn5dXV1yi3pisx0JEP9VycVL5ZoZHKlCof6UCqVm3KZTI6AqhQ4nkk8ToVarRaLxZcuXfrM+nN5PJ7JyUmBQDAzMzOLp/n5eTxCAfojFAp5PN7Y2BicnYlLQDwwMDAxMTE9PT07Oxt3EmXn5ub4fP7w8DBUnnB2ZmZmfn5+YGBgcnJSKBQmn52bm5uYmBgaGkpuGoinp6cHBwdTXjszMyMQCHg8Hp/PT+4YVDg+Pj4wMJDuLFiuTUxMpCSYmZkZGxsbHBxMeeOzs7OTk5M8Hm96ejr5vmZmZoRC4dTU1P7+PnxhECs1kYnFYjabjY8nWMXEeBLhaWVlRSAQcLnciYkJoVAoEomW45JIJFpaWhoYGBgZGZmamlpaWoo7ibJisZjP53M4HKFQmHBqGQfKWVxc5PF4ExMTydfC5Vwud2RkZHZ2NqFpuHxubo7H4yXXDCUzMzNcLnd0dDQlwdLS0ujo6NDQ0MLCQkqC8fFxLpc7Ozub8uzs7CyPxxsdHZ2ZmUnu2+Li4sTEBIfD4fP5KW9NIBAsLS3t7e2l3D6A0TEajZOTk3w+f3FxEYZDLBZDRiQSzczMsFisiYmJmZmZhB6KRKLFxcWRkRGYNskdWFlZGR0dHRkZSdl5uJzL5U5OTiZfC5wfGhoaHR2dm5tLaBrOwuOWfArOTk1N8Xi8qamplARLS0s8Hm9kZCRd03w+n8fjJc9GqG1ubo7L5Q4PD6e8tYWFhbGxMWg9oX6YzPCUeTyew+2CiccHMhCIpbi4GF5mcvl2K4kkkUj6+xhtbW2DAwPDIyPCmRkGgxHBcUkDqVIoFAJc0mAwmOo8KguFQqBpSEcTDAYBEdPr9aarJBgMulyu7e3tdATQkNfpEWwsXZptYCpnouEwRPLd3t5+4YUXnnnmmcbGRjDvNRqNH9qpQFXBYLC7uzte3xONRhsaGk6ePAm7hw6HYwB33BOJlsvLy6emptrIZIPJ1NLSYrPZDrkpApc0XZ9DoZDVagUQj0NoEC5pXIeTKYPBIIFLmnwWSvx+v0wmc7lc0GH4qmQymbW1tX6/H7ixv78PuKRwSSgU2tnZScAlDQaDNTU1p06d2tzcxPfOtltbW1dWJMz+vnYKBT0FY2Nzc3N0Oj0cDqfrDEwbs9mcjnuBQCAYDMrl8v39/UNofD6fTCY7fNo4nU65XJ7Qk1gsFggEBgYGCvLzB8f4EqVjcdOosob2fQFiHocQkO3Br1AopNPp4kcBps1nUN8Ti8W2t7fBXKCqqio5AtXe3h7YLDc0NPT394MajVhcotHo0tISm82m0WhtbW1ECCyCIBwOc7ncsrIyHo+X8kWiUqlqamqqq6tTmt8Hg8HZ2dnq6mpQRBPVEhm73d7U1JTOyN9qtXZ2dn7oKp/S/cHj8UDsH4DvIeokMiMjI5WVlR9aG6R005ifnwdvWw6Hk2xsr9fr+/v7wYcfPEWJamEJBoVBWVkZKEvjz2IYFo1GV1dXofMVFRUSiSSBwOl0MhiMurq6ioqK8fHxBOEpFAotLS0BBlBLS0uyR0YkEvmw3du3b1MolJQvkuXlZXCdA01vQusej6e7u5tAPko4i2HYxsYGlUpNF2RsfX09Nze3r68vpbrLZDIV4gkiiyRUDj3PyspiMBgJp8CEAsyNa2tr+/v7kwmkUmldXR1AIyV76DgcDhqNlofDVab0HgqHw1tbWw0NDYWFhQ0NDVptYqwwu93OYrGqqqry8vKEQmFCB0Kh0Die6uvry8vL3W53AoHb7aZSqWVlZb29vSmfl6mpKXDpApvNhMv39vbodHpWVlY6ldXCwgKFQkm4ivi5sbFRWFjY0tKSsumdnZ2qqqqKigrQEBBXQcbr9ba1tZWWlo6MjCSsEjAuPB6PQqGUlpamfNxEIhGJRCoqKqqvrwf7jPj64Wmqra3Nycl5dOtmt9s9ODiYn5/f0tISX1tCfmpqKuUzGE/2KPhcBE5F/IXxeUBISPm4EWQ+n0+lQoF3iZLkjMvlspmsztD+nSVKw+ZAGMef2t/fHxwc7Ovrg3mb7M9Fp9Pj7XvEYnFXV9e5c+cO8bQKhUINDQ3JszS+S36/H3bK4gsT8k6nM+WzHE9mMpkIz7L48vi8TqdLt6QAWSwWUygUyfOHRqO1tLTAtEz253I4HCAsYg/i9ywvL3d2dl69ehUQbeP7QOSnp6dZLBbxM2VGr9enfE7jiVUqVcplkKCJRCJg10yUJGf29/dTLtRAaTabW1tbKstLNtYS3yMJVRmNxoR3JY1G+wza9/h8PolEMjQ0VFBQUFhYCGZcBC8ikYhGo5mYmBgfHy8sLOzu7k54aF0u1+LiIp1Or62tbW1tTX6Ednd3KRRKVlYWk8lMfvtiGCYUCrOysoqKilLOMJPJxOVyi4uLwYKM6BiRsVqtVVVVYLxGFBIZk8kERkuA/EKUQ8btdre0tJSVlU1NoQ3O5DQ8PJyTk0On01P2XKFQgIUHnU5PnrhOp3N8fLy2tra4uDj57RgKhQQCAYVCAfeu5KY9Ho9EIhkfH6dSqc3NzYTtIVBGIhG5XA54iiQSSSgUJiyUEA+DzWY3NzfT6fRkuc1utzMYjOrqahaLlTCm4KMBUdjb2tpSjgvIyi0tLfAFmdx/uVzOZrOTFyCg3NzcrK+vHxwcTPl+dTgc9fX1DQ0NyWI0LExjY2M1NcilJbldDMMWFxdhT3BsbCyZwGg0DgwMQJSa5AU9FAoJhcKampr6+vrkbwCACNjc3GSxWM3NzR0dHQnMiUajCLZwaYnJZNbV1SXbCVksFlA4dXSgkMfJi7jBYOjp6WlpaRkYGEgWpoPBIJfLbWho6O7uThbaMAyDcamtrU23VEmlUiaTmcwWKJHL5XV1dTQaLeW4WK1W2AHXaA7QDePriUQi4IMmEAgSZiOQiUSijo6OdHPGYDDw+XyIVZj8uDmdztnZ2a6urpqamkfXuqvV6l/96ldnz5790DUMmJmyY5OTk83NzTC1YqnS79efK6FHMAl39Hr0nEaClWvM7OUuVyAxinGyXXNtbS34c8FwlJWVvfTSS+fPnxcIBMT7Pj4DNh+FhYUwyRO6AT//MP25wKCH6DB8RlLwMBARPCX4c8nl8tLSUmALzJDCwsKXX3750qVL8OmSbtqAPE00lJDBMOx3bNec0AHiJ9za3NzcnTt3GAwGsewQBJBJaddcU1PzGfTnAo4g6/39fa/Xm/wKhNnv8/lAFwf08X9jsdj+/r7T6UyndAmFQj6fL97rMuFyL55SNg0ODumahgnt8/lSzksMw4LBoMfjOYTA6/Xu7+8nv2Cgh4FAwIen+A4T+Wg06nQ6XS5Xurc7tJ6Oq+FwGFpPlpmIJmKxWCgUCofDKW+QOJuOdaEQ0mGCaSFRJ5EBI7jkT3MgiMViYTwR9AkZOJuuaehbwiXEzwhugZiO7TDl0nUbTBOieCIqjM/EYrEgntLdGrQeCoXScTUcDgNz4quNzxPMObyGdMwBfXu6s9B6urNw+4dPicM7fwjbYUzT8Y2463hWxOcP7zm4w4Rwe9L4q4g8jEu6cYcZdQhbiHqIDGjXZmZmCO1XwO+fn5v3+fwQQlAhl29tbT1iIJaUgibRFmC/J39gxBP8evF74muA/N7eXrzAzdTMXl9oVbvN8ZTJ+h42mx2/zyWXy4eHh6enp4mPRoVCvr0tj0YiCwvzGo1atCxyOBwfuvSmW9+guUfEY0/+JI7vLTjeJ2gaEghAjEj3lgFigPlM2eFoNEomk2k0WjgcVqvV8ZN8b2+P0AbBE721tTU6OkowB02b+Xm/328w6EVikUIu39zcmp2dHRwcTO5kfMnvLH7P4foe6JLX61Wr1Ww2u7S0NJ3SNDl+D5vNTvcRFX+nn0r+d23X/Kl0+nEljznwmAOPOfCHw4EEqRTZOvC4J997b2KSv7WxfutW5vXr10pKSgEU3W63O53O3VTJ5XJtbW1pNJp0BLu7u06nE3yp0tE4nU6bzSaVSm02W6pGUJnT6TSZTACzcAiNTqfb2tqChpy7u/69/ZHt+bdGi1mauQmDdMm45XK6wG7X5XJBtaAXJ+I1J3AmFotqNZrMzFvZObl+f6CutjYnN/fdd0+sra/XVFevrq5CPcldcjqdZrNZIpGku2toXavVbmxsHELjch10+BCa3d3djY0NvV5/CI3D4VhZWTGZTMk0cAtlZWVdXV0ymcyJf7JC9xYWFqqrq2HeEpoPYhoHAoHhwcH33ntvfHxic2P99u3Ma9cyiktK5+fna2trHQ5HclvAKJg2arU6HQGQSaVSi8WSzFuixGazSSSSw6eNwWBYXV0lLknOOJ1OnU4nk8l8Pt/6+joEA0Mop3a71Wp1OBy7u7sul0smk6lUKmLaWCyW6urqlDv+BH8+xcxjuedTZObjqh5z4DEH/jNyIF6Nj2GY0WCoq6s9f+4cm8Oh9fZc/+AGldqVk5vL5/Ozs7PVarXBYNCnSgaDQSwWb25upiPQ6/UGg2FxcVGlUqWq4KBMq9XOz89rtdp0NCj0nFK5sLCQjgAakslkYrE4vjNOs61jdfhvqW/+LfVYi4RnM1qkUunq6qrRaIRL5HL5rVu3CH1PAmcikfDw8PD1a9dy8/JXViTnz5/v7um5e+f26PhEYWHhyMiIyWRK2SWDwaBSqebm5uI7k0BpMBg2NzcBaSHhFPHTaDRChw+pR6/XLy0tyeXyw2nm5uaUSmVKGuBGaWlpZWWlEne7A2NqJpNZVFQET0g8Z0A6NBqN9fV1586eY7HYtN6eD27c6Orqys/L509N3blz55BpYzQaRSLR+vp6ys4Q976wsKBWq4mfyRmtVjs3N3f4tFEoFI8ybZaXlw0Gg8ViMRqN4+Pj165d++Uvf/nKK68IBIIPMVANBsPKysr6+jowymg0bm9vZ2ZmPpZ7/jOuno/v+TEHHnPgj5EDye+wcDgEPkFKpVIul+/s7FittulpwUNxKh7FrvkRcSoO2WeE0BUPtWtO2OeCoQlGw1dmGy/M1PvCQQzDQJMRP2rg4J3OjAnhl5nNRqPRYrGsr68ZjUalUrm/v8/hcA7ZiAeL9fhNt/gWibzH40lpYUkQYBgGipP4kuS8xWJJuYdFUEIEmsN3Gz0eT01NTUlJCZlMBkuy/f19NpsNUk7CnIHCxGmj27FarVKpZHZ2lmg6ZcZqtbpcrpSniEKj0Xj4lIhGo3q9/vAdXr/fn9IakmgFLEYSLMcnJydPnjx54cIFwiYatJ7xV01PT6e0dIyn+bTyj/U9nxYnH9fzmAOPOfCflAPJ7zB4jSWwg8/nt7e3pxMI4BK9Xg9ASynrBBrCnyslDYgIjwI4EG91m1wV2DWDuczHzmKxKO7YlXB3j38+5sAfCwceyz1/LCP1uJ+POfCYA3+gHPiYWID/SNlRPp/f3d0dL/cAGXE52iMzGhPgGpJptFot6A/iL4zPg0Ut2KTHl8fnfT6fRqOJRqNEIQhVxE8AEIWP+/jClCJdyvt9XPiYA3+YHHgs9/xhjsvjXj3mwGMO/NFwgBALIINhmNVqKSkp1up0UqmkobGRQacxGAyBQJDgmAPezvH3KZVKEyLQJLvULSwsJHitJtAEg8GZmZn4baNkt0en0zk3NxcvxESj0YR9EKvVurq6Gt89QDmIL3mcf8yBPzoOPJZ7/uiG7HGHH3PgMQf+sDgQL/fgntKm0tKS999/n8Xm6HTaK5cvnz17Njcvb3FpKSsrq7u7m4Knzs7O8vLyysrKDjzGEoVC6e3tvXnzZlFREWD4UCiUzs5OwPEFmo6ODiqVevHixYaGBgAtoVAogJBAIpGg2q6uLjKZfO7cOTKZDIAzHR0dra2tBQUF7e3tQEOldtfW1l68eLGrq4uoubGxsbi4mOgMlUq9f//+9Q8+6O7pIWgAknZ+fv4PawAe9+YxBz4JBx7LPZ+EW49pH3PgMQcecyCJA7G4hGGYx+OZn58rKSmZFgjGxkfr6+vb29qqa6qDwaDRhPyJ9Hr9Dp7Ap4bIS6VSo8mkVChWJBK9wUCUQ2ZnZ0ejUa+urlqtVqPRqFKpiPL4CpVKxfr6utVqlW9vb25uxZ8iKpTJthQKhdlsXl1d1Wg0KWlUKpVWowXvcZ1OR7S1s7Ozu7ubxIPHBY858EfDgd+z3BOLxXxej8+z5993P8rh87jjFbMPZXMoFDIajclRWd1uNxGWCkU523P4bEaf3fTww2b07Vqj0Qg0HY1GTSYTEZWS6I/X6z3cuj4QCBiNxnhFNIZhbrdbr9cThbFoxGfUeFVrPvXGQw9EZtQSMbJS3rjX6zUYDIf7IPh8PpPJlKDuhuAZBOejoZBva8UrFvgkMw89vOJpv0ZOXAvuAAlRet1u90N9Ddxut8lkSvY1cOMJOB/2+7yzk97JQe/U8MOPiQG/EmEIQ9rf3zcajcn1e73ew+PIOZ1Oq9VK3CDEQnQ6nXa73e0+mK5Bl8vO5dhZ/XY286GHjdm3r0AIjpDcbrfZbCZGFsOhRaB+l8sVX/7gioP/7XY7WMjGl+/u7gIMMhR6zWZlD03dQ1P30h9y9NBUvXR3XABll8tltVoTOuByucxmczIbiT7EYjGr1RrP0nA47HQ6HXginkqLUi2kUGc7ex96zHT0zFJpTqOJaCJlJmGYYrGYy+UCFrlcrvjhS3n54YVxYs9BFqI+QoDEWCxmNOg/uH59fGJSIpFczbgq2/5ofImaN9bXrl65KpFKZVubd+/dNZktxCkiMzkxfv36dYPRODs7U1RSQpTHZ7o6O3Jzc3edzsybN+n0vvhTkA+HQqUlJbV19fse9/unT09M8pNpMAwrLS1ZXBZtbqxdunx5dW09Jc3jwngOhEKhxcXF1dXVhIcinuaPLq/T6QQCQfJK8pvciNlsFggEhwBrRCKR+CXiN2krFosRATOJen7fck80ur9rC7sdEc/uoxxehyVhSkUiEZPJRMgKxI3B6+HevXtPPvnku+++Gw8UYLfbX3zxxdraWiCORiJe7WZ4ez4sX3z4sb0QUEmCgQBcW1lZeeTIkVdffTUe88vj8Rw7duz27dvxnYnP+3y+c+fOHTly5Nq1a4QEsLa29uMf//jLX/7y5cuXYfM+HPDv8xkhRmWIWfvwg1Hhne6H2kKh0L1795544olTp04RNw6h9L/0pS+99dZb6Sac3W5/4403nnzyyZK4JXV+fv473/nO17/+dQixj4JTu12++tuhu2+Gco4//Lj9+n5fUyQaBU2Z2l8AACAASURBVP/G8+fPP/HEE/E3vrm5+eMf//hrX/taSUlJwuASTFOr1S+++OKXv/xlEolEFMKH9c9//vO8vDwo9JsNnnePen/2/3l/+czDj6P/Y6+tGtCJHA7H66+/fuTIEcCVJJoIhULvvffehQsXiJKEzOrq6nPPPfe1r30tHpNhamrqmWeeefrpp0+ePAky8d76muLL/6z4+79R/NMXH3L843+X/91/M7c0QUMfAlwfPXr0S1/6Ujzwk1gs/o//+I+nn3769ddfTxd5VigUPvPMM9/61rcmJyeJPvP5/GefffbrX/96T08PFBrHxhlf+EvmF/6K+V//r4ccX/irvv/6fyo7qXChRCJ57rnnvvrVr8Zjk62srPz4xz/+6le/2tjYSDSakOFwOE899dSzzz5LYJ5LpdLnnnvuG9/4xle+8pXLly8D/TSJ8ub/9F9O/M//+4k/+fPDj+Of/7P3/uwvpYOj4MdksSQuEQDi9txzzz311FN0Oh3q393dPXbs2NNPP/3UU0/94he/+A0X2WS5BwQpKMcwbGtz4/z5820UCpvFfP/MmfmFBQxHaCIuREA6gukz77/PGxiY5vM/uHHD7UGIEAQBVMig086ePTc2Pn4789Ybbx6LbwXykUi4qrLi2rXry8uiE8ffKSpCslFCJV6v99bNm/eyslal0ldefpna3ZtAA1XRaL3j45NsVv+p06empg8gJoiqEoYVftpstvLy8gsXLrBYrOXl5XSTE8Ow6urqN9544/bt2x6P50NY+Ndeey07O5tOp/N4vJQ1Jxfu7e11dXVdunQpKytLKpUSkbKTKZNLbDZbQUHB7du3ieURwzAOh/Pmm2/m5+cnfycn15CyJBKJXL58+ejRo+nWsZRXwRdvbm7uW2+9defOnaKionfeeefs2bPz8/Mfhu154403AN8j3bWHlPf09Bw7duzUqVPp8JEOuZY4JRKJvvnNb6bD6iHI4jOBQABM5uML4/MKheLZZ5+NXzriz4bD4ampKYVCAUF9zp07R6wV0Wi0q6vr9OnTRDwImUyWmZkZT+P1euvr68+fP0+ApYyOjiaIPr9vuScW9TntsX0n5nU9yuF32ogpFQgEJiYmXnrppePHj4tEoubm5paWloaGBmKMZ2ZmnnjiidbW1q997WtU6sF6rdFofvKTn/zJn/xJYWEh8DoSifh0W5hiMaZcfoRjKaRZBblnc3PzySefbG1tfeaZZ+7fvw+1mc3m11577U//9E/Pnj0bP5bxeTabDa/wI0eOjI+Pw6mqqqpjx44NDAx88YtfhEENBfy+KUa0vzLCrn3oEe2v8AsO5B6hUPjEE09QKJSnnnqqo6MD6u/u7n711VeHh4efeOIJojC+VxiGNTY2fvOb32xpafnnf/5nYh1paGgoKCjIzs7+xje+Aa+HoNsVqL8dvftmJOf4Q4/o7de9fY0g97BYrCeffJJCoXz5y18mMK3odHpmZmZjY+Pf/M3fpIu9kZWV9d3vfre+vv7JJ5+Mx2wqKyv73Oc+d/36dbgRv8ngPfET/4vf8P/iXx9+vPCUm1wFck9LS8tTTz3V2tp65MgRQI2GCjs6Oj7/+c+/9tprCYwifp48efLnP/95SUnJU089RegqsrOzv/rVrwIqNWg+3Otrqif/UfXF/1f1D3/7kOPv/0b53/8fS3MDNJGdnf3ss8/W1NT8y7/8C3Hjzc3N//RP/9TS0jI/P59O4j969OjJkyczMjK+973vQR/29/efe+65y5cvNzY21tbWQqFxdKzvz77A/vO/ZH/hrx5y/PlfMv/i/1B2dEHHTp48+dJLL+Xn53/jG98A1eaHUG6vvPLK22+/TaVSS0pKCIGe4BXIqd/+9rczMzPfeOOND78N4BQAzH1o0fLFL36REJimW9vf/NznT3zufz3xuf/t8OP45/6Xd//0C9JBhKHm8/mOHTv26quvCgSC+A6cOHHiV7/6VUFBwdNPPw2SKOAQczicb33rW++///4hCqr4/qfLE9JAygyu+wmNjo2urq7KtmVDQ8MJuE4gZ3g8CNx0Wy4fGx1lsdl+P/q4iq8QwzCT0TA8NCSXy2H7Kfax84gWw7D/v70rgWoyyfO+t+/t7uzs7Oz2TM9MT7+23e52vFhtQVoUFbQbuUXkEBUl3Hc4EoRGUBs8MCDhEFREURAEUc5whzMSISKXXA055DDhSLhykoNvE/7tN1mkZdqx327vy/fqSaWsqq/qX19V/f5H1X9o8Pv6+vrh4WFqc3N7e8eySiAPrbW1paWVTqdXV1e/fKlyeav+IsjDYbOZDObQ0GB5eTlsG+p53iSFXC7H4XAlJSWTk5MFBQWnTp1SRxXL8jOZzB07dgDzWVhYGBgYCG4+31JEvYampiYrK6u8vDy4/sfc3PzevXvqGVaNJyUlffzxx6gLtpGREW1t7YMHD6pzsKtW8maGGzduXLt27c30VVMqKyt1dHSGhoaePXv2u9/9Drw4h4eHJyUlLZO+r1oVmoHD4ejr6xcXF79dxo/mXzGivFv52LFjb3q8XjEzJLa2tv4YpoEMMpnM2dn5x27ryczMbGpqkslkzc3NExMTxcXFZmZmsPUQiUQcDvfy5UtfX9/Ozk6FQvFmHgaDMTg4WF9fb25uDovA8+fPUVQADfil4h4Gg2FhYaGrq3vx4sX+/v7y8nJra2sHBwdra2v0IvCMjIxdu3ZNTk4ePXr0zJkz0GEWi1VSUnLgwIGLFy9Cyjvjnqqqqr/85S/KW+F9fX1dXV2htrGxMRKJdPToUR8fH0gBiTefzxeLxbCgXL582dzcfGpqysDAAJ0kcE1WaWmplpYWuGdbwj2FiqJr8tIbqwZFUbL4STEMc3p6uq6urvKacDs7u9DQUGiGWCyWy+VUKnX9+vXoRyCVSqFhkCcgIACDwXA4HG1t7fx8FRcICyKLxfr666+DgoIAdC7hnvOKKGf5JY9Vg+K8k/C1vOfSpUumpqZCodDQ0BDtOHjMOXLkiIODA7r9SCQSPp8P01Umkx0/fjwkJITFYn322WctLS3QMCqVamdn9/XXX/8V94y/Enpai213i4/uWz1Y75x/Le8JDg4+deqU0ke6lpYW6vpYea+rjY2NtbU1ukODc0TUiZtQKDQxMUlISOjr6/vwww/RS9X8/Pw+++wzCwuL9PR0GHEV7tm2mfmX/2Ru+mKVsPFzxvp1E3dU8hKlD6CTJ0/icLjBwUEtLS2049HR0Z9++qmpqWlsbCy6u4vFYj6fD0skj8fT09O7f/9+aWnphg0bAJANDg5u3LjRxMTk5MmTKPfGrq0r/LcPVIjngz+uEv79w+L/+EHeI5FI4O1UKlVLSwvuIpuamtLV1TU0NDxx4gQqVlEpskUiPp8PIzsyMqKlpUUmk5OTkw0NDdWX4+TkZEdHR3SJp2Tcd17zj55r/tVzzW/eHjzW/NrrVx8A7kEQpL29PSoqSltb29bWFhZWmUx28ODBK1euPHv2bPPmzehWhyBIXV3dgQMHlp2fgq/rJ/2rDgh+LK5e4TvnUa/kTbAC1b6vPPDporUtazOajkYEAoGFhQUcWBOLxS0tLQMDA25ubrCaRUVFLXMtfOnSJWdnZxqNBixQYWGhra3t7OxsRkaGq6vr1NRUTk6Ok5PTshvwEATp7e3dvXs3+g0jCFJQUACVk0gkX1/flJQUHx+f2dnZubm51NRULy+vGzduoAwz0C07O9vMzAwMtAUCwfXr1y0tLQE8tba2BgcHYzCY3t7e6OjoyMhIHo8XHx8PC6BIJLp69aqTk1NwcHB1dfXQkEpl2d7eHhwcfP78eUtLS7hjsLW1FYvFJiUl+fj49PT0XLhwQb0e9DtHqVdZWeng4CASiQgEwpdffjk4ONjY2FheXi6XyzMyMrBYrJLHO3/+fG9vr6OjY11d3YsXLzw9PQsKChAE6enp8fX1dXZ2JhKJT58+RYWX/f393t7e6uPY1NTk4eHB4/FEIlFYWNjo6GhUVJR6w8BpXU5Ojre3d3x8vEwmo1AoLi4uTCYzMjISxOFAHycnJ9SJtUgkunfvnpOT05UrVzo7O/fs2XPo0CF11+MoNby9vRkMRn9/v7OzM7rao0RAEKSxsTEkJEQ9RSgUwuLc39//zTffwMUKERER6EINmoRTp06hmFUgEBQVFSUnJ0M9nZ2dSUlJ6nX+UnHP0NDQrqUnNTVVCSDq6+vd3d19fX3d3d1R4H/79u3du3dPTU05ODigWyN03sLCIjo6GuLvjHsqKyvXr18/PT3t5+envjUqL+Hw8PDw8vJCCV1fX29kZITD4YDXvHjxooWFBZfLNTAwUB8PMpm8devWjIwMKPjOuCctLU1XV1coFNrb258+fRptRnt7u66uLnpROoIg+fn5BgYG58+fh0XBz8/PxcUFcA+qCkEQhMvlxsTEGBgYwCR/Z9wTHR1tZmYmFAoNDAzQjxLEAKmpqbt370ZXxtTUVFSKJpPJlCvC6dOnWSzW559/DsuKWCzet2+fh4eH5dIDIgfxu+KewMBADAajjnsWFhaOHDlib2+PwWD09fVha1QoFNHR0QYGBpmZmTDZjI2Nk5KSAPfADW8KheLp06cvXrxQHrf54osvhodVzPS74R6FQnH8+HE8Hj80NKSlpQUdX1xcbG9vf/78eVVV1Z///GcQyynd/eLx+P3791dVVcF47dy5MycnB3AP8NADAwPr1q2LjY2Ni4vbt28fJL4b7hGJRCYmJnFxcSBSBXgxMTGhpaV1+vTpBw8ebNy4EUxfeTyel5eXsbExeIwfHh7esmVLbW1tcnKygYEBintmZmYMDQ3VecR3xj1jY2OpqalfffWVgYHB4OAgKL+MjIwIBEJra+vmzZv7+vrQGYHBYMLDw9Gf7xxZhgl+jp/v0Db1DQ8t/pa2ASBAc0Lkx/IvywY/6+rqtm/ffu3aNXh1c3OzsbExlUqVSCROTk7LPIZ2dHSsX7++rKwMyip9Gnh4eAgEgoGBAU9Pz0ePHnV2dlpZWamPF+T09vZ2d3dXbwCHwxGLxdevX/fx8ZFKpUQi0draWi6X+/r6EolEuVxub28PDs+h1Pj4eGlpqb+//6NHjxAEefjwYUVFhYmJycDAQENDw9GjR2dmZvB4fFlZWW1tLQ6HI5FIVCrV0tJSIpEQCAQPDw/Qi23cuJFKpdbW1p46dYrL5ZaUlOzYsUMikdTW1trb209NTZHJ5K+++kokElVUVKjXg8qG0V5kZWV9++23NTU1OTk5lpaW9fX1qampcrk8JCQEVBNubm7wrYaFhSUnJ7e0tBAIhAsXLiwsLJiamhYWqgy5jh8/7uDggErNr1+//t1336GvWFhYKCoqMjIympiYYLFYsGeVlZWpN0wsFt+6dSs8PHxkZOTMmTN8Pv/69etBQUG1tbUlJSWurq51dXUODg5AH0BdSn/heDw+OTmZxWLZ2dlNTk5isViUCUQQpKamRp0aYrH4/v37Fy5cQBuGRqRSqa2tLaoDgfS6ujrQ8p89ezYiIgJmtK2tLcq9AwMDeeRyOZFI3L59u3LvQ2/cVton+fv7o29BEOSXinsQBJmZmcnPzzc2NnZ3dyeTyd9++21ERERoaCgqqCCTyZs3by4rK9PT08vMzGQymeD+Q6FQmJqaoh/EO+Oezs7OjRs3lpSUwKo6MjLS1dUFxHV1dfX09EQJ3dfXRyAQHjx4AAt9bm6utrY2iUTatm1beXn5999/Pzo62tbW9sknn2AwmOfPn8Mu/oOeqzBZXnJ91aAoTBK/tu+pqanZvHlzZWWlvr5+RkYGOIih0+kbNmw4fPhwa2srat9Do9EuX75cVFQE61RCQoKhoWFpaemmTZuoVGpPj+oW+YSEhJSUlPr6+nXr1lEoFLDvkdw4q/jOSX7RbdWgOHcS1XPl5eVt27atqqpq+/btZWVlg4ODIyMjmZmZMTExbW1t69evh9mLIEhtbe2lS5dQJW5oaKilpeXjx483bdrU39/f1dU1MDBw4sSJQ4cOffTRR2vXroXFUaXncrcSH9ET2+1ZPVjpzt9LBj1XcnKyvr5+WVnZli1baDRa/9Lj7+9vaWn5xRdf/OlPf4I5tri4WFxcfPnyZXQNxWAwzs7OGRkZW7duHR8fb2trGx8fT0xMLC8vLygo2LBhA8iH53teMLU2MD9fqxL5vD2sX8f47BNUz3XmzJlDhw7l5eVt3bp1YGCgo6ODw+Gkp6fn5eU1NDR8+umn4O5YLpfn5eXFxMTAbSsymczY2Dg0NDQmJmb//v3T09M0Gm10dHT37t0xMTE3b97ctWsXMIVscm3Br/+95DcflPz296uE33xQ9G+/Q/VcTk5OLi4uN2/e1NHRYbPZz549m5iYsLS0xOPx5eXl69atAzZdIBDcvXs3Li4OZEIzMzN6enpEItHPz+/EiRM8Hg/AXF1d3bZt2zicv9omU+5kOa35B/c1v3Jf8y9vD25r/tnjn37bXVGNsn1mZmb5+fko14sgyLFjx9zd3W/evPnll19yOJzm5mY+nz86Oqqtra1u/4RO2J8a+TFw8F7SEQQZHh4eHBwafvmyuLi4o6ODQmmqqqriTU/X19fVkGvHRkcZDOboyEhnRweJVPbs2bOSkpKq6mqFQtHf3/e8vX1ocLClpYXJZD5taVHIfziTsayPPB4XuI7aWjKZXMtmswsKCjo7VQvair1YVlwqlQJv0N/fb2BgANaNQ0NDR44cEQqF3d3dOBxOXcghlUoLCws///xz1HiloaEBh8MhCJKbm6unp8dgMJhM5pEjR5ZZZgiFQlAiL2sAg8FAhQEJCQlpaWkUCsXMzEwmk0mlUisrKzJZZQEGT09PD5VKTUlJAYfhBQUF7e3tJiYmPB7P0dGxoqIiLy8vODh4YWEhIiLC1tZWIBBUVVUBK+vu7g6XbgcFBeFwOKlUamJiAgvCnTt3sFisQqGwsLCAlIqKCjBZCw8PR+tR3xpetwiJj48/ceLE3bt3h4aGjIyMoqKixsbG6urqbG1tgW6BgYG1tbUCgUBPTy89XWX/5+vrSyKRxsbGjIyMlBaKTCZz7969KD2Vl0j5+PioC0UQBCkvL4e3FxQUxMfHKzGEesN8fHzYbLaFhQWo1AE3ODg4QJGEhISoqCgXF5fKykqgD6A3CoVib29Pp9NDQkIePnzIYDACAwPRfonF4jepgcVi6+vr0TxopK2tzdjYWH3mtrW1ZWVlSZceExMTmK1tbW27du1CpTuQB9X4T05OPnz4UEdHp7HxB4P9tLQ0dfXL/wnco7JrnuPJ56f/liBUnaVSWciij1Ao7O3tRRlHNB1UElgsVkdH5+jRo/Pz8zExMVFRUTCN3dzcUD3LD3bNQzQZvW31MPRMzOoG+x7l53ju3DkdHR1LS8vR0VGwpQIAER4eHhkZqd4Y9fjMzMzJkye1tbWBvwkNDb19+3ZsbOzatWv19PQOHDhApVJV2jGJREAplhalLpSmrRqkRSnC5lLQegiFQuj48ePHuVzu5cuXCQSC0v5p7dq1urq6+vr6P2Y8ODo6amNjo6OjExYWNj8/7+3t/ejRIzKZrK+vr6enFx4eDhZ/C/w5YXq09KL7whWf1UO0q6A4A+x7ZmZmHB0dtbW1vby8BAJBWFjYrVu3nj9/rhRUKDWS6lbY6uQCQa65ubmOjk5cXNz09DQGgwHBhlKNcvr0aRhW1YiPs/m+9oJjhgLHb1YP9nvnsm8C7hkdHbWystLW1g4PDxcIBN7e3jk5OfCpxMXFBQQErMg6Iwjy5MmTffv26erqZmZmcjico0ePdnR0ZGRk7Ny5c8+ePampqdCR+f4++lfbGf+1ifGl1iph2xa61obxe3egYG9v78GDB7W1tWNjY+fm5hwdHclkcnFxsZ6e3p49ey5cuIDquZZRrLS0dNeuXXp6euXl5bD9MJnMoqKi3UsPah7Orm8o+sPHpR99Uvrxp6uEjz4p/vNa5pINLHR8//79O3bsyMjIULoQOnLkSFdXV1NTk76+/q5du1Bzt2WtQhDk3r17urq6e/fuffr0KY1Gs7a2np+fLy8vt7e3V7cnbc584P6Pv/X9lw99f/2HtwefX/3e/4OPe2pUdnILCwv9/f0on4e+nUqlGhoa7ty5886dO2w2+/Dhw93d3UNDQxYWFgwGA832zpEVkcF7SYQmRUac8fH1v3Mn3cPDk0Qq9fT0dPfwjIqOupaamnYrLSgw0MbGNubKFTwuGI8PaWtri4w8cydDdQIAFxR4hUA4f+5c2LfhFy9exOPxI6NjzU8oBAKhoqIiMTHxypUrxSWlKtFv3gPLQ4fKy8oIhNhbaWkBAYFEYmJERMTwyOiK0GcZrV6pXLHegMS2trbt27cLhcIHDx7AMhgbG6v+SQiFQhKJ1N7eHhISEhMTA6VSUlIAT2CxWBAG5OTkAH+v/i65XG5qaoqeSgHuH/QsYLowPT1tZGTU3d199epVqLy9vf2bb75RN7JuaGgYHh7Ozc01MjICscqtW7ewWCyLxdqyZQsOhystLZ2cnJTJZBYWFiDViIyMBLPIjo4OKysrDAZz9uxZkUhUXV1tZ2e3dFPl5J49e3Jzczs6OlyXzB4WFxdPnTp1//59BEHMzc2X1aPeKaWbMywWq6ury+PxOjo6/vjHP4K4PSwsDFjBnp4epaXB3NwchUIxMjKam5vjcrkgREEQhEgkHj58GIPBqEtMgQ6oPyw+n7+4uBgZGZmTkyORSHx8fICrVG9Yfn5+VVUVBoOBEZfJZK9evdq7dy9tyQzf1dUVGNfg4ODS0lIUdsTHx2/dujUlJQUU8QUFBeqj09LSsowaQqHQyclpRRekt2/fdnNzQynT2dlJIpFgiePz+TY2NiDBPXPmDGq/oZ4HLahkgUJCQlBR4smTJ1EtEOT535b3IIhEJJQI+RKR4G8JYpHgx3Yg9T6jcYlEQqfTQbs0Pz+Psg5zc3PohaeLi4sS/qx4ZupvDXPTKPaSyWRKeAviGT6fjwJV/tKDNuPNiEgkotPpsNDPzs4KBIK5ubnJyUnO0gMwblGhEE+xxWN08Svm6mGMLpliL772mwMdBzw+NzcHh72npqbGlzwCvrkxoC2cn5+n0+mAnaenp4FKExMTDAYDZdcUMpmY9b14oFP8fffqob9TwhlBR00oFKp3HFrI4/GGhoZQwI42Rj0yMzPDZDLlcrlCoeDxeOgeKRAI0KGUSySi7uei509F7S2rh7ZmydhL9BVKVEGn02GOoR0HEcKbQmm0FKx3w8PDi4uLcrmcx+NBDUtXrbxEey3j82efUGaaGmYojauHxnrRqGqzgYfH4zEYDNC7ox3ncDhMJhMdkdd5/8dfcM4M5mVcLhcyv3r1St1EUczjjZHJr8h1r2pXC+S6MXKdgM1G3zE5OQmnNhQKBZfLheEDETo6QdDMaGRxcXF4eBhEOwsLC1wuV6FQiMVidG5CTt4Yu7O8qruyZvVQUd1dVTs/xUVfsWJkYmLi5UvViMjlci6XK5VK4fz8W5q6Yj0rJr4XiLNiJQiCjI4M29vZHT9+wt/fH4fHV5RXYLH+Xt5eCURicvK1mzdu4HA4lUD00KGw0yERkZF0OuNW2k3lmS8EQbKzsvz9/e9mZARgse4eHqGhoSOjY2lpad99952fn1/k2bN4PO7ipUsikcjH28vWzs7b24sYT0y/lebt5UVMSDwTEc5gsv4W3EOj0Q4cOECj0Xg8HoFAcHFxQRAEj8ffvn27r091li0lJUUgEMjl8ubm5pCQEBCNZ2dn79ixA8xEnJycrl+/zuPxbG1tYbcOCAggEomzs7PXrl1TRy2FhYU6OjqFhYXj4+ODgyqzaw6Hc/XqVTwez+Vy79+/v2fPnhcvXmRlZQUFBTGZTEdHxwcPfrDHh88vMjJydna2qalp06ZNfX19XC7X0NAwNTV1enoahAoMBiM3N7enp0dpIikWiyUSiY2NDZzZLCkpOX36NHqDQ1lZmbm5OZ1OLyoqMjExefz4cVlZmY2Nzfj4OJlMNjAwKC4u7unpAVgP9ZBIpJGRkdTUVHV23cXFBZRuRUVFbm5uMFtdXFzS09PZbLZSIm5hYTE5OXnp0iUwYK2pqbGzs4N5hMfjHz9+jC6DgAUTExMPHDjAYrEmJibq6uqio6Onp6ePHTtGJpNpNJqjo2NlZWVXV5d6w8rLyykUiomJifK81ZMnTyYmJkgkEmiIwKC2pqYGpU92djY0Micnx8HBYXx8vKamhkajRUZGwjjCTCGRSOrUUKpHmpubra2t29vb2Wx2SkqKOhEyMjLAhEgmkz169OjatWtjY2Pt7e2wOISGhhYWFpaVlZmZmQHqUs/D5/PZbHZwcDCFQmGxWImJiaDKp1Aopqamy87h/y/jnhUXEU2ihgIaCmgo8AuiwIqQ5b0kIgiiUqB3d4+NjhYXF2VnZ9fXN/T29tHp9K7ublJpaWFBIZPJYLKYTygUCqUpKyurf2Dg+4GBsVevVFaiDQ119fWtra3V1dUdHR0lpaTFxcXBwcG+vr6Wlpaenp6urs6enp75+blnbW3jnPHWVlppaUlhYRGLxbx9O72ZqnIDvmJHlo0OnU7v7++/f/9+QEBAYmIisApwIbVYLM7MzAS5xdDQEJwCGR4ehus2nJ2dw8PDuVxubGxsY2Mjh8MBAQyiukAovrq6ms/n+/v7q9uLIAhCpVLxeHxAQEBaWhpYkr169SokJCQvL+/ly5cEAgF4mOTk5ODgYHVVpkgkioqKcnV1JZPJoJiWSCSgvgkNDZ2amqqrq/Pz87t7965SwADgSQngBALB2bNnX7x4MTc3V1RUhMVi7ezsXF1de3p6FhYWoqOjk5KSpqenk5KSurq6JBLJhQsXkpOTQeVNo9GW1aO0TabT6YGBgSjztrCw0NTUBAigo6MDjALBXDooKKixsbGjoyMhIYHL5WZlZfX2qu5SolAosbGxAoGAwWDEtn8YTwAAAhBJREFUx8e7ubkdOnSIQCAA+qFQKG5ubl5eXrilx9PTE6Q7RCIxNzdXLBYTicTW1tbu7m6QsUEH4SjrvXv3lKfxAZhWVVVBQRaLFRkZOTk5WVtbC/QBbh84w7i4uJCQEFDt5efnFxYWouyEUCh8kxpEIlEikXA4nGW4p6GhAcRjg4ODfn5+wcHBgYGBN27cAICltGg+d+6ccvhAo7osj0KhEAqFoaGhoN0D3l4qlYaFhal/APDdanDPsvmr+amhgIYCGgr8NAqsiAzeV+JPa8rr3PD217/++heVQf416a2xH+vFWwu9z/9sbGxE7fzeZ70/vS6FQuHt7Q2mfgqFIj4+Xv1wxk+q7/Hjx+hFIT+p4LLMyuu2MRgM7PFsNtvd3R1Q0bJsv5SfYLz4vlorlUorKirQszLq1Wpwjzo1NHENBTQU0FBAQwENBVamQHZ29okTJwICAqKionJyctADIivn/vlT5+bmcDicq6srHo9PTExcdhLq53//+39Da2vrMp3Uu71DaW/e0tICB5DfrEGDe96kiSZFQwENBTQU0FBAQ4EVKDAzMzM2NjY+Pv52e8QVSv48STKZbHx8fGxsbHp6+qcK836eFv1dtYIF599VxVJhldnua7cKb9amwT1v0kSToqGAhgIaCmgooKGAhgL/PymwRiAQwGEfzb8aCmgooKGAhgIaCmgooKHA/28K/Dd19B3nSDvi5AAAAABJRU5ErkJggg==\"></p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集应用：</strong></p><p class=\"ql-align-justify\">GridMAIACAODChina为长时序1 km AOD数据集，可以用于气溶胶时空分布和变化趋势分析，捕捉和分析各类气溶胶事件，与其他产品对比分析；环境健康（如颗粒物浓度估算，污染暴露估算等）、气候变化（如辐射估算、辐射平衡等）、定量遥感（如大气校正、痕量气体和温室气体反演支持等）。</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集使用请引用以下数据和参考文献:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><br></p>",
+    "authors": [
+      {
+        "name_en": "Su Xin",
+        "name_zh": "宿鑫",
+        "email": "xxin@cug.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "China University of Geosciences",
+            "name_zh": "中国地质大学",
+            "ror_id": "https://ror.org/04gcegc37"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "MODIS AOD",
+      "气溶胶光学厚度",
+      "大气气溶胶",
+      "气溶胶",
+      "MAIAC AOD",
+      "MAC19A2",
+      " MODIS",
+      "大气污染"
+    ],
+    "keywords_zh": [
+      "MODIS AOD",
+      "气溶胶光学厚度",
+      "大气气溶胶",
+      "气溶胶",
+      "MAIAC AOD",
+      "MCD19A2",
+      " MODIS",
+      "大气污染"
+    ],
+    "publication_date": "2025-05-29T01:13:49.587Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 140495.25,
+    "file_size_bytes": 147319943214,
+    "download_count": 4649,
+    "visit_count": 4752,
+    "url": "https://www.scidb.cn/en/detail?id=684c3451918e1d0fc634c0ef",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "675da34cfec5a156275fe9a1",
+    "doi": "10.57760/sciencedb.18626",
+    "cstr": "31253.11.sciencedb.18626",
+    "pid": "",
+    "title": "Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays",
+    "title_en": "Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays",
+    "title_zh": "超大规模MIMO阵列可视区域空间分布数据集",
+    "description": "<p class=\"ql-align-justify\"><strong style=\"color: rgb(51, 51, 51);\">Abstract:</strong><span style=\"color: rgb(51, 51, 51);\">&nbsp;The Visibility Region (VR) information can be used to reduce the complexity in transmission design of EXtremely Large-scale massive Multiple-Input Multiple-Output (XL-MIMO) systems. Existing theoretical analysis and transmission design are mostly based on simplified VR models. In order to evaluate and analyze the performance of XL-MIMO in realistic propagation scenarios, this paper discloses a VR spatial distribution dataset for XL-MIMO systems, which is constructed by steps including environmental parameter setting, ray tracing simulation, field strength data preprocessing and VR determination. For typical urban scenarios, the dataset establishes the connections between user locations, field strength data, and VR data, with a total number of hundreds of millions of data entries. Furthermore, the VR distribution is visualized and analyzed, and a VR-based XL-MIMO user access protocol is taken as an example usecase, with its performance being evaluated with the proposed VR dataset.</span></p>",
+    "description_en": "<p class=\"ql-align-justify\"><strong style=\"color: rgb(51, 51, 51);\">Abstract:</strong><span style=\"color: rgb(51, 51, 51);\">&nbsp;The Visibility Region (VR) information can be used to reduce the complexity in transmission design of EXtremely Large-scale massive Multiple-Input Multiple-Output (XL-MIMO) systems. Existing theoretical analysis and transmission design are mostly based on simplified VR models. In order to evaluate and analyze the performance of XL-MIMO in realistic propagation scenarios, this paper discloses a VR spatial distribution dataset for XL-MIMO systems, which is constructed by steps including environmental parameter setting, ray tracing simulation, field strength data preprocessing and VR determination. For typical urban scenarios, the dataset establishes the connections between user locations, field strength data, and VR data, with a total number of hundreds of millions of data entries. Furthermore, the VR distribution is visualized and analyzed, and a VR-based XL-MIMO user access protocol is taken as an example usecase, with its performance being evaluated with the proposed VR dataset.</span></p>",
+    "description_zh": "<p><strong>数据集说明</strong></p><p><strong style=\"color: rgb(51, 51, 51);\"><span class=\"ql-cursor\"></span>数据主编:&nbsp;</strong><span style=\"color: rgb(51, 51, 51);\">南通大学、南京邮电大学、东南大学研究团队</span></p><p><br></p><p>阵列可视区域（VR）是超大规模MIMO（XL-MIMO）系统的重要信道特性，可用于系统性能分析或简化传输设计。本数据集提供了真实城市场景下的XL-MIMO VR空间分布数据，亦可自定义VR设置实现更多数据生成。</p><p><br></p><p><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">本数据集包含XL-MIMO可视区域数据集VRD，以及用于按需构建VRD的XL-MIMO阵列场强空间分布基础数据集。通过自定义环境参数信息、射线追踪仿真、天线场强数据预处理等步骤得到XL-MIMO阵列场强空间分布基础数据集，在此基础上，基于可自定义的VR判定准则生成数据集VRD。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-center\"><img 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\"></p><p class=\"ql-align-center\"><br></p><p class=\"ql-align-justify\">具体地，阵列场强空间分布基础数据集由以下步骤生成：（1）建立真实城市场景三维模型，并在建筑表面设置高（Site 1）、低（Site 2）两个典型XL-MIMO阵列部署站点（图1(a)），通过射线追踪获取各站点中每一阵列天线在目标区域内的场强分布（图1(b)(c)）；（2）在目标区域进行用户位置采样（图2(a)），对每一位置，将其对应的各发送天线场强按阵列部署形态记录为矩阵存储（图2(b)，2(c)）。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-center\"><br></p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">XL-MIMO天线场强空间分布基础数据集可用于按需灵活构建VRD。如图3所示，以site2为例，选择值为70%、80%和90%的能量集中度应用子阵列能量集中度VR判定准则进行计算与数据提取，绘制该准则下的对应三个位置的VR可视图。在应用中，亦可自定义子阵列形态与数目。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">VRD可应用于基于VR的XL-MIMO传输设计，验证并评估其在真实传播场景下的性能。图4为VRD数据应用到XL-MIMO系统多用户接入协议性能评估的示例。</p><p class=\"ql-align-center\"><img 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\"></p><p>使用声明：超大规模MIMO阵列可视区域空间分布数据集来源于国家自然科学基金(62001254, 62171240)；江苏省高校自然科学基金(22KJB510039)</p><p><br></p><p>若在论文、学术报告中使用该数据集，请标注数据集引用方式：</p><p><br></p><p><strong>引用本文：</strong>高锐锋, 苗艳春, 陈颖, 王珏, 张军, 韩瑜, 金石. 超大规模MIMO阵列可视区域空间分布数据集[J]. 电子与信息学报, 2024, 46(8): 3063-3072.&nbsp;doi:&nbsp;<a href=\"http://dx.doi.org/10.11999/JEIT231273\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(47, 160, 230);\">10.11999/JEIT231273</a></p><p><br></p><p><strong>Citation: </strong>GAO Ruifeng, MIAO Yanchun, CHEN Ying, WANG Jue, ZHANG Jun, HAN Yu, JIN Shi. Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays[J].&nbsp;<em>Journal of Electronics &amp; Information Technology</em>, 2024, 46(8): 3063-3072.&nbsp;doi:&nbsp;<a href=\"http://dx.doi.org/10.11999/JEIT231273\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(47, 160, 230);\">10.11999/JEIT231273</a></p>",
+    "authors": [
+      {
+        "name_en": "dian zi yu xin xi xue bao",
+        "name_zh": "电子与信息学报",
+        "email": "jeit@aircas.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "中国科学院空天信息创新研究院",
+            "name_zh": "中国科学院空天信息创新研究院",
+            "ror_id": "https://ror.org/0419fj215"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Extremely large-scale massive Multiple-Input Multiple-Output (MIMO)",
+      "Visibility region",
+      "Ray tracing",
+      "Energy concentration",
+      "Subarrays "
+    ],
+    "keywords_zh": [
+      "超大规模MIMO",
+      "可视区域",
+      "射线追踪",
+      "能量集中度",
+      "阵列 "
+    ],
+    "publication_date": "2024-12-31T07:24:48.267Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 1147.07,
+    "file_size_bytes": 1202793451,
+    "download_count": 35,
+    "visit_count": 1036,
+    "url": "https://www.scidb.cn/en/detail?id=675da34cfec5a156275fe9a1",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68708f0c0e9dcc59702a25cf",
+    "doi": "10.57760/sciencedb.Fastro.00028",
+    "cstr": "31253.11.sciencedb.Fastro.00028",
+    "pid": "",
+    "title": "Sky Coverage and Pulsar Search of CRAFTS",
+    "title_en": "Sky Coverage and Pulsar Search of CRAFTS",
+    "title_zh": "Sky Coverage and Pulsar Search of CRAFTS",
+    "description": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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\"></p><p 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\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
+    "description_en": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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\"></p><p 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\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
+    "description_zh": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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\"></p><p 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GLtKLRx6pDA8kc4X4MCwX2G3kZXwLPSWac0mPVsNZqzRvJ3SXHJ9Ido6DGWW50hh8SPZW3WirtREa3qtgxV/14JzuKam6xJAee2ANZaLnjHijKL7lZJ1e/QNUjz9EZ/ikrQ4Plg6t+yUvrZyE9/xo9tvx006l8stv3zRRRehF11+Zvl+fKP0EdppZQXKYlknWHEntNq+XfdVo6Nq+CF3rFZgt0MH5uxHKoRki001mxh1aXVcrJ4UkbZxFYt0TePF7DybCOHDpbRdTDv5lEJdjB4+5Tkuun65U6OhR0Jv4FCikG85j5X87gW/3HLmmla1Hema2NQwDAMCfbXr1Vp5fv+hI4uzU7Di5sKR5vzBevlIY2G6vjgfBQ0VhrYVSjSvbAZmDCtej2v4unXxNt6BG+O3fBo9HrfabA0CBgGDgEHgKY2AIcBP6e4zlTcIGAQMAoKA5lOyheViSnrw4MEe0T1wgP34EKUuGjYyI/enM/n8wGhxZG1mcG1qcE12eLwwOJkdmswMjWcHRjPZUmR7rcjGkVWzN+ElwoCwSV6iUJoJ9Uhjj83ECttYU9mjODq3vtz3So1ry4WYB8flxSyIfVxkucJW70uzhCHSLskBxZJDaBL6XjGbZl9iLUOEI+UHM3sfGF+7vjQwxCVcYbmaQY/qOoE8OypmnISl2oGFh/DGYTcDdbasNdnwknFKlUSJ+vepbaRuKto9H+2uOoeraq5lo2HeOOgMpcPLxlxsmHV9V5YsDdPp9B4a33v//ffDfjdu3Hj33XdD8yYmJuJiV1NmnBPWqqksnUl9+JFehRpj0Ez8LSJQw41xM26iNO6qasdqtKOGb7dxPA6tbmijXhYzbNCV3uC39J30nPyON9JrCVt5ohZWGTcsCiW2B9M2bsbYeNNBSfy3NfElJ1p8Ka+HDhipQNltH59nKmCX29Fiyyq3VbNrV9tWDQ22L3bdUlXJq1KqU7RrxXDWr0wPBkfmpjFlkBSPf3TLxO6KyyYKF4j1WTHcOE6cJILa8ljWqwEz7g6zNQgYBAwCBoGnCgJHRcJ8qlTa1NMgYBAwCJxvCPRIwVKzOUSgR7Lft2/f3mWJM6h5CTiE4E6oIfwk165di6B/5ZVXIuLHEj/HM0Fh1s/vmFWVjmrjb4tRq5gW84MKWHgM+55D6GB9SQiOZp6c1yGMOYQqacbCeeGsmj9CQtjRmfUZDoVgyUkxr9X/2Ig6EeKEz3BsDCwaVEWAKGJp2fyQX5iVMBorECNeySaRkJWjQ2jJVamw7aQ8KsnyQEQ5tr2kFeWdgbSTcHGgtQo56BlXiKMUjaThbA6rCg0VPRSSKBWnK/6GoSTVnm5aWyNFuCxN2qSeq0u6TdIu1i2yd85G++vuvnJU6dppW20YdiZgvxNeLqGBOV6Jp02rlg+DYws59kz/4f0b4zxsOUOvaizJRVV7tZVZBW3knE66haRQWelpKYhWe1hKY86NX3FTjKhVtatq7ajWYa7E1obWdhdVrfS7EGG5x3Jgp11YtM+Roxq6+63YuxgvYuJshYWUXcKIOuMWkwGg6WkLsf/mHxAm8DpOOuM5ymP6hW6lN3l6RLjsaluVm1a5ZUHOq13bilLDueTmwdGkfeGz1uG2fFRi9gdj/hUTQ/fee+/NN9/MefE9ZmLF8wjKxZvCtMKmTZs2b94c70CMURofVZw+iME89rw5YxAwCBgEDALnMgJGA3wu946pm0HAIHAeIdCnKCvazCqusNxdu3Y98sgjMdXlkISml/BCZMbnsy+yI7XHiTO4QbI4zTHrzQiZ2T0fzHdQmorHLPq3BGGlJKgyJq6wR9GvQj/EWlVIiISbstnRZ2A0woljtiQnlw6gJkfXu8d/oK/6vFYVQqHEAxYixT/MpCEzkCXZkfV+xA2VOMbwK0hUx0fNCCtGvyfnUfShcsQclyBPWuln+9qCl8jIGNamcAWGYUdhMSfxiptti3jLawZg62qhYePfesFAsqtUpeUPZ7lOUCjRMuNzS+2vGAtGchLheEl1fXQzjnekq88FRWSpB6f9w01v30JUC9ysG8B+J1PhpZMeGmaxJz/e7WdyjkGiXbJrFFKtViFsKIEZG1g+0+Nxbxy3fG58+OGH45DLx81wSif1WKVLheXKj/SFeBqjqm1iSt1Vi200xgo9bStwtPuxXsBJKK3cAD0WTi1TJfqXLkTPuRC0DGNpK+dFUOLBDH7aForibBJWzLCkjnKjHlqid+Yf+0yOMGZg41xHsZwiC4b8ZI0fd0zDVrxoGE2wdvSRI0f279/Pa7VsNmkvRhP9V4zVmzYtSxfoBOYnisJ1oqcfUx1zwiBgEDAIGASeaAQMAX6iETfPMwgYBM5zBFbI3300OA+lQQSH6O7evTtmvOyj1EU9RTbWWcWnMVZJxaJ4Xz3Vl8JPQoH6D4JuoHGFOcAlNCOJr+gTkIf4ivAaUrwVp9vebpxXqMijSS7GGR89p/f62eKr+jA+p5/fO8uvZRnliP/8QJUhNj5BjGFWgcL4Fltcttjfis9qYDVQPPqwZYn2PJYXL9ZKSw1lWTTIq3ejajOYLOL7azVZTthWxaRHqxt+kEsmhJJ1oVjw3ujZaxShoXnoKqBb3nWqGVoPHAmPNN295YiIX4VEgOXz2lx40ZhHdKgYrlWUuQKyxzikBlA1FpQiH7QNR1b0/DwF++fjxnyKa0wGdk7DUvpEtelNAdBt+gFiBNDrQvqNRA/KiGB2g8WQ6T7Uv7DiSpufqNpmxWN60GbpY/TnYvTOP6HD+tYeL+aMjAKmMJKeSuNU7KlSWsJi81NMiZY4l3SSejKmP3gYMlpLzI2MYz1m+RXXSHewfsBjb2LQyMfcE28flJi3cs+ePWxJHBIuLl4JmQkm3sGYDG/Vv7ZcsHX9+rW5XP5EXX+i849dLZPDIGAQMAgYBB4nBAwBfpyANMUYBAwCBoHVIYB4TYq5Lko5Usx1Ib0I1rEpJj6KKJdiwZotBaPIfdGLXnTJJZes7iGrzCVUYZVZn9hswlpWkmpNZojbVOuGUKn5plrsOJFy4cbzDdX1g00juPdaC02/mHY9FvCJ8B0Nc/Beyyo3/aGsR078Wgtp7+CiP1LwsrJWcPd5G5OekCSIyWNCIRMHmupFVV/Y73TL3bsgOupSOto44KwvhBeOuhiJr6KoxwHOBx54IJvNQsBO8jhGWvwkdu655x5MeVlCKD5zkrtWX7m4/BMVtXSV8mRSQyZetMK2zcLRoisWP150xXj2EnkL1XHsXcx0BU7IAnRc9x6J7fFk2w6xco8VxaWUgg8PZhz8igtJiZ4NJUaTrKcfuC2+s3d0okquvrFxThpVr9d5W++77z5Wb2KKqtlsvOnVP1+5/Qe3ffJvpvzOQioRjI0NbN4ytmXLhq3btl104bYLL9y4YQOdRR0er2qcarVNfoOAQcAgYBDoI2AIcB8Ks2MQMAgYBB5PBBCUSci72FiiR4LoPvTQQ5rwyg7WlYTw5XksdtrTIG2NdUhyhO8umr1+bVgf9Tvf+Q7lXHfddZhinp8yNGBqQIRDCaFSTs0P95XVTEOsjZsBnCocyia41u762WQCVWKtHRTTXjMQxeNk3t23GI5kndCyDpfDbRMJNMKbh8LNA71YGKtBVRSbCl9f6/4j0ZGWe6AcBZEznIk2lOzNJWJridG1tnx+TC7d79vT31klAWZ6BcLGY/AM74d32rZt20mCRZ9+nU5651IPgpF0pcYoJrkEu7JiC2o8e/FLX2R2o6VqXbsVworF6F1mHuSOXn55r5as7lHjY8NPh6MlLqbUELbTWXuAIFtJhe10QvpVsnOv3p60io91kSaQVpTDmWj33tZH/sj7yX3tdqfjdxudTiXoHukE05MTiz//2m/cefe3b/0OHvgXXnghxuokdkjo7ftF9XceqwrmukHAIGAQMAicKQKGAJ8pguZ+g4BB4DxHAPF3OQKQDZRCkJMdO3bEjHfnzp0wWLJhw4wQHIu/F198cSwEo5E7VvZdcYZ7p6enb7/9dkwu4cAEql2RYXkFTntfaMmyprCrKYqci3fOxkNXX9slnKlLv5YSr/jBmWCqwcq3VqXVLWUkalOlHZbSbjfCATgYLyQOLkS5HB6k6khFbRpxd8+Gw3l7JOsW3ODq9a4QpLh5vVYep0ZLj4Z5RwttdL/RVNM+VBEyNpFTkwW1bcheW8LaWmzFdWG9Eo9T1uN36tChQ4yH4eHhfgOOLZuaExQNU14uEQ8Zy4KY93LX8hmWY288G2f6MB6n8F6vxj0r6DGRQXjnlk/caVZmsgm1Nd/CtViCabEwEnbv2FfLwNRjVu4WnisFC90VSoyZtMrgS5y2BnP2cMZhpeJ8UhbEIir1UvdIDy7ty739dFxIqT8O2LzaV111Fe9gnBmjAMvvdr5xi/d/fdRVAVpuPQTiUlWL+Nd//vHKBRf86003xV+DePIr7hG6I/4I8E3AuIO0efNmvhL9arBz3Josz2D2DQIGAYOAQeBUETAE+FQRM/kNAgaB8xSB44rvWDKjYUMmjhOkFy9BVLs4Z6LF3bp1q6h7lhQ+G7QZJOXEQu0pibbchRciVpcxB0Z0fty7od9AXUMoCOwSYV5WPorF+VOq8ONevWMLjCt8qB7tmHGIhzRV9SeKSZ/ljmvhxIB7qKJyWgG4ezG4aCTxyFwwlLcbbYU6ceuol7TDKyaEF62mUfpBmO/a043woRnrUMM+UoNtOWuKajKrLhqzxnMEEQOmxyHqVb8XHrNiq89J1cjc9wE+Fslz+QzqVV1/aKvE3MapWFzBu7Ie0kJTVVtW3XeagSiQQ+UwZOP5EeHDel9TZAnnlnThw3DgCKvp4axE2CrElDgObC4QxLfovaVJETnQ6LHFQ+GOO+4AyWuuuQblrb6iTQKaze7/+VLqs5+VMXB0wtyg/f73Z179KjuV6l/puz/0KfHu3buxpsbEAzPpzZs3w4SfsZQ4XLHUM+U85tjoP8vsGAQMAgYBg8CxCJhlkI7FxJwxCBgEDALHRwDtLhQ35roPPvggC7GihYuVOYSnQmx9+ctfHguukF6UbNDgfkFaotbSOXtwKTZadbx6WRZRmFsg2Ol0ul/sMTu60FgVppei4ZGnllhrqDxvLZSDubJXzFnDQ/bQiJ1+VHw/tdLOYm4aSWNlnVg0emgLWWuHxs43okzSabNUbAuLaG/PQgTLPVjxbVdiBdd8e+sQy/yojUWxlV197XjYoVr08JxzuKaONCTI8PpShPr30jFnKCOa375WcfVlnignvAjOgxP4iTLE51c/ck5ezjl/Vd4aupbgzixdlUzYBfT9OWrNDI3DKlmsitTqWnhlV1rRQlPWCsaOmoWvOr4ViEcwOR1eHM60Qmuh4xyockYlHEXw8CwqYqyms/ZwDhVxxIwJsbV0uGm5TY+P3iAhEtb27fdx8uqrr+r7UUtB/HcdVchH9AcjkWN9R6yZRh3sFgtkkNJkeEoiXNkVOsWH1I1Y05h4LP+24PIwOzvLLbzsuHnHXxW+MJdeeilK4xVa4rgcszUIGAQMAgaBVSJgNMCrBMpkMwgYBM4jBGJqCrNl1VCUriTWC2WLogZRlWWHkIARSfuKGnYewzVXmBr/YUldRGZlY6nrQN9EquefSMY94fhEKMdV4iqenDGvJhQwemBZqXfZrXE2/GMtWWpXEsUjk+t1YeITJ9jK6rssN+RHO3e2//VfnW9/123WA8fpXn5F7pd/ybviCrtU0jF3T3D7k3CatuI7qn58KKj7yak6q/5aQ1nnkfnwohFZlTebYmEep94Nixmn0rAyadVsOSOFaE3eG812L5/Qq/8uoyXHtkA4jMwnAKXaVwl2z7sHq9FMC6vpcP2AM5FVl004RWIwCz07Vvl3bHmrOkOjmFuBAEN7VnXD6jKhXUTxCJsire6OcyVXPKTjQS69QdIHjPr40rKKMtSVKF1ZJZgFinWELQKklVuqETgECWeKRFTE8cSQvH1CXzkmSLjLIsIOYbSsfEIN5Sx8xQczFiriHLbUUbD9vvsOHjiYSCWec801TGzJTTrx7sHAxcv//vva7/9gZn6WC2KNoPXOjIvGunWZv/i4u22rrvXxB8kxrZCiOYnCuT/Xxg6jAgNsXn9s1+HAMOjLlxKzb3wTqAxJ18tsDAIGAYOAQeBkCBgCfDJ0zDWDgEHgaY9AX/pkB/kSJUyf7iJxstYuCLB6Ki67fYkT7e7Q0NCxyJxI+qTkSDWi+jej+k1WZ6cNa0tttvOv9AqvVPaAFoqPZrHHFr10pl9bdNHf/e538ed85jOfeZQ5tATahWMvqqiM9shyRpSd1asY8ZwTVXCJSCgVPrK7+RefyN79Y1RmiO9+0PGD0Fq/MfnHH3avvspNnhp3QljHARVvybNEulQUHRKbZLcRWXtmg40jLl6+OHmiGyy3iMxMRGg7m2IZJImQFHTtfDraMuDlUv5VazxZQeex2IKwLTCxot0L0d5Fe2+FlYTdpBNuGHImM9Elk05RdIUQKP1vqY9O7zc9C1wwKW7HaRxT2LVr17IPselHrjq9kuO7+iPnMVt9Jk950u/VzaTf9LyFnvrhgAhbqIjrvqzAtNCK5hvE2WJlJgmvFcB9hc32hoIsLKw5MeOfkOIp4cNRQjUO7nxgIp+47uqLxobyBKCWG2SOSd4obpDnLVY6X/2q/enPprodypIhyENzafe9v5146Y1OQRwWTgl5XepRcHIGShx/oGRO7r772O9/oJiPi+kwnynoMaH14sed0kOPep45MAgYBAwCT18EDAF++vataZlBwCBwDALLxUrIBubErA2DbyRbBEqiBKHgRcGC7y7SZJ/xsnhMf6HduMgTi5UIw1r6liA+vbwq2Okf/m23eYttEyNHa34t1u9xgswzExOfstNXYSYpd2mTTqRpRGci68gP68GIkpMLwmtZMZWrxP4Rjut3739gx+zsHOxoy5bN69avdR3srf2B6OZU/X/Z7TudqIKZZ5Da4OZf18q/PXDGPdSUBMsVPkUdApYJ0vXzKC6qNyjVst3uf/yr97efcVUEXRbm4Mu/dCbdevGNqd/9XW9slFtO3Pa4vY9uWdIJzfnmzZuPO1/waL5T3AMYasE/wlzdeSCqRc7eeeh6lEk5C41oNO9MVSPWxam2sTwFZ7KLhXLSC7YNe/lE+KxJJyckmXawPX7qjxM0zDvngv01FvsNq10v5YRbBtH9RpdOuDiUSjW0EvEkRR3/Acec5YmMRqZgpFBNq+IsmzZtOnmYq2NKOs0T0G/6Cw/zuAKnWcqTfVu/405UEUY1oycI8SJW1a610CJSGobT0GMCiTvdkPHiyothi6kFiKRShKRSnVYjm02xzBIuxINpayRnj4jJNGsR2ynWfLaUo6Jwdi744Q+Dr/ynvWcP3uDRhRckX/Va7+pnO6NojCWd+SChkOUNxERl//79fLhi+5Tt27fv1iYqdCL283y+mB0jXteVV17JF2zF01cc6gqajUHAIGAQOF8QMAT4fOlp006DwHmIwHJhkeYj3xOw6q677opJ709+8pNyucx5CAaSIvIiCd6LgheNZSwgnrqYiAAttIpHi5Mve9Ehf+9rE/49ci5IRbMJK7LdUV8l21wM3U3W+pu63jOE4kbW/dPdmaYbCu/FtFLi3FICO1pqFw7MMeyv2+k02y3XTSCiT08fCQI/mUitncy8YvRvtjh/79hNB73Wo+TOqdmX32f9/YJ1peeoknNgjfqnUet7SWfKdvJR8gq7fE3rlgV7cDz33Gvan/xk9gfft7x0GMIRmCIQ/mi7TmtktPT3n3W3bqPOj4lJH3ZmE2ICjH04N67m3jjbcbf9YvVVsFV3PrIw5xfKQWKmEg2VrLmqXcriCArRxyhVERJJ+Kl0iJ12gi3DLtatz5ywBtKidNfk91GMVjxRP0tCLj044x+ueXsWxXk456hNQ/ZkPrhsLJHQBqcr7jqTQ55IqPB4ZSwWzWIEYndAgdhCMyPzmJifyaPje3kXoE+8Asz1PAGPO/MKn3kJYC7JZqgQbtpqsPxSx5ptqtla+MjB+anZqnK9dRs3M2dVqzKdZDMTlEwkZVBpF+I0IbUS0QB8OO9AiQfSqhi23HJFNWrMJal83h0YtLIpGY5nXtcTl0AL4ovsYBiC3UrMh/m+wY1xlKA3cdngE0eCD7NlVmV5eIL49vOk008MpLliEDAInEcIGAJ8HnW2aapB4HxDII5ZhYI31vESswo3SOR79CFIgc961rPYoh4hOLO2Mo3hObmw2pM1ddZezuWnYgUNaiSJyRSpIPSzC+9J1f4/1mVpbx/qfLHkHsxCLLsj7ewbF7PPq1p2MG+9/DvB/2mrHJreqZqqtWShWtH36q0It1A9BHS4ERof1hj1O/BSpFX0PCMjQ9DiWrXealRfv+ETr1n/9y58uZuqbU93d6ecQlC8yndGa7DhOXXpv5W/vD5123X5j5Ss/SxOjECvi4Yrplr/U2rNv3H4Nb/S+PAfZn50mzs6GSVdP0D9Kcm23VYhX/rc59yLL6Lhjykoc8uRI9DygHqi0sRCOzaBxlNxhSL9lAYkxTKFgc0ndNpxnXIr+udv3pMeWb9ALGfP6sB3CXgUWh3FYry2L7MF4hFKG9O2tWFIDaWtZ4zZo1mqT8M1/z3x4+VZkXpgOjhY9/aUI+InFRJi+bw+H1486nkwGt35vV8nLuc0rvDos+ED/Jg1OT8JsLwCcV9qgND5Hjhw8KFdjyw2/MhNT266KFUaRT982z07qh1sMVzLSSThwZlUNpvPpvV6RfIaSUitjKfGc2ptSY3lsA5wHDcqJJ1li2z1XpyjnveYvXIGGRhIvIN79uyJv35sIcaE2uI8FhnM9MUfQLYYvPSn/M7ggeZWg4BBwCDwlEHARIF+ynSVqahB4PFCIBaM/uM//uOmm24iiDH8BLXA29/+9thMjqskCMznPvc5GCNepq985Stf9apXHeuISDbYyL/927+xtu1LX/pSsax98hKV4eEQJCLo/ngpETmm0Wig67jgggvguq9+9auR9pD8jrv0rpAlKWN5WjqGLunyOSYTtsI+NDO0YIgdGJf8KNYmZc3Slu+0uhGXOhFmlnbO3vPK5Fdgue29xZk/H0xVC2JdibdnI9v6ZFL9gcpdVS7at07P3b2r8wJf2dWm3QhEoBYFJikIsGv2PNE21mqdwO+y4zppjzBcqOm8xGxdMtuJ/IaRh3567ZcdnHbL2b0fH7LuL3gqhWC/UGiOvNMrXlcetre/PP8ra1IPJhWs2w6pyQwK4NAZITxyN/uyWW/+jsh5o9q6LfrWLSqxwCJOtpviiVQ2l0lFW7faOQm8K9VfzhjiU8dsmWiIg2PTLyg2221Rdy+PnXvMHas6QTnYCeuVbBM7ZlVHuQuVKDNod5hriIgCFHUjifrrAzH9xQRvZCU8a10Jra+6cESzX2E7/Bw/6TEgnd5GG3+E5YVdvH+7oVNMRRsHnQ2F8MLRxKNxvY9fxpmelarrwXamBa3ifh7EJEWtViMv3cSWjou9js/SWtOrqNQTl4W3iVEi77Z+0fmascpR/MV4xpaNeNIyx8QV3tlnj22amqseWmgcnGscqZQbbS9S44nMJO9srdbABCOdzrRTqVrX3lmmRMVSw9mEyid9JlxGC8zQsOSSnU1I+HH9AulPpby7kpkG62qccFieNiLMN23T6Q1veAON5IuClweaYZkRvPtu/gT89V//NeeJSn3ZZZexthOJvwV8Lfv6YT1bdNrPNzcaBAwCBoFzFAGjAT5HO8ZUyyBwlhBA3EHS/cVf/MX/+q//Im4KQh42coh9X/jCF1772teKRKgUvmQ/9VM/hSB4ww03wJAxGH7LW97yD//wD32pKK4bOVn156d/+qcxKn7BC17wjne84wmTlnh0XAdEOhgRhJcmkJDtkOCh4lu2bHm2Toh0MF5I/vK6Ld/v44x8qAuNRVSxNxYeG/DDAipWO7SaXavhq3qHGLM2jBdepM8rVlthfZ0gEltlH9tj7aNLWWFkXZX7p18efzM+hYc+vTa4eRSdqwi9XJKnKvuZ5c1/coAYsv90+CP/NPv7rLfbQsFLJCTR8cYmzCqdTuVzeXKjzEFMhgyzogq9hNiO4M5W+JitXjf6Jz878jEC8xz85Hj7m4NcQMQGB1KUa639xGFvbY3yQ7x/O4nZLw93v54bCRNRddFfX81+KPLWdlhjJgw/rA49r/G2t2XLi96mjX4i02j7YeAnCtnk77w39aqfcVcXy6ffO7EJ9KZNm/o+wMdFXsA4aeoXSOcSIGpsYnLnnD/TSm7fdShKDqSzeaDODQxZXkJ0447jB5HnOiznlPacNYVwrOBcOKjWl3DWpIepwgmZhn5QxKKy90+HR+ouul/0gMW0sN8LSsGmIQ/dsr5dyjlplU//InVA2U/HYfl8+qWs7k6exTwFhrIrskP8YEQnAWpF/qfiYX9Q8T2kvXzcGK7f+ta3sFNAIxqvMxQjgBE9DdSDhz270w1my5XATkbJ3HxTfe+uh4+U2x3l+XbCTWYy6Wwuk83l87zXYafteEneWOJLZ5PWQCoay4u9NMYIxZSdZF5LzDBiMnziQfn4gdtvclwkh8SogwkvTRj+GNt7LvG2QoP5fPLxZAsgjMb4lqf3kHj8kDYlGQQMAuc6AkYDfK73kKmfQeDxRQCh5/d+7/e+/vWv/+M//uOb3vSmuHBMhWMdHVehEGiDEcF/9KMfQY/J8IEPfOCTn/wkXPf1r3/9cgFofn4ew7nf+q3fIjbpb/zGb/zar/3a8quPY7WXy23sY8UH16V6yG1wb1bLRHglZC5C2wc+8P5rrnkOps1DQ8NCVTThkZoI7+RXj7eIZlDsY+U4iFQ71BTXJzBs1PCtWkc1O3azqxqh3Q5lNdGYA8OHMbMlvya3Ep5K3Ad5DEXFT0KBKPIsvzgtgm3RO2zLs9xgDzaRQonFvJnLodwS7vFUN6PS9YHUIY4xa+502hEewLaNghc304THDw6ZIdVPEbaYSgv1lafxVAitjmSsXCfamrmTxVzCcq79oxx3EpGHfowkqnDoVL3K9zJjr6+S3YmsuX8e9v95HGaYwNe4EXm3RY33qdL/m7QKbcv5sjP58uRHPlL7v/+sBN/2QmygO/lc8pfe4Lzg+nknl+mKVWeMnoB6AqF9xXkOV5yRHjmVRFOmpqboLnGRtdWtdz28b6Fbbjud5IATNtrtBtMFqTzadSfpea2un0x63W5ATKzxrBrPO5sGwvVFCXtFaKJ4HBz78P4Yawb2fVPB4bZzYEF1Ldb4DTaU3K2D0cYBXUJvDElhZymBVWwxfqLyAQGqRphoMpwhsJSAMQFclx0mF1j0Cwd41IZxsX1Mll4fMV9YlnqkSK5Sk/jXssvn7G7cLgYVn469e/fy+Yr9IGh435JlObBLnc1vGfH4Aa8fH2Gai1dwy4B1cWnj7Hx1ar52cKE+VV5crLOg0lBucHOz6z+y49784PjgOA4FqXbLmm87uxd5ZyO92FI4nLWYmhnLhARsixcfXnqQoCmJF11/tthdXh997XQ2KwrhED9zmkyKi+Mt44vKp/XOO+/EAuhjH/sYWMWfVsjwc57znKuvvro/mRXfsqLM06mWuccgYBAwCDzhCBgC/IRDbh5oEHjyEECaQV+KLvdXf/VXUQL3K4K5I4lDMiD9QC9/93d/F1EY4YYzH/7wh7nlM5/5zC/8wi8sF3dwxcRv8Itf/CJE9IUvfOHyS/2ST2+Hhy6/EYkfY+wf6nT77bfj1Ybid2xsDHvmX//1X0dHAfVFTUEFNCODlMrtmpNKMbFdK9wVotvxoyZENxCWiy6XLYy3JQFg7aYPDWalUFkZBcvGkNVvtC5XS6M9WV9XqyemCgV1FOvguE6IVhb1oudYuPwlPTRBdtq1XE9NJvJoZbkrURTqK/UT0izkGR7t5HzbQ6/LciwlqBm63EIuiyMrWl6yYssrj5CWqNjnlAfHbRSywaX4quN4WCl7C2QParbT1cvbOq6HD6LMZoTw9WhBFwN/r+ebX88n4M4kbhCTTDu7UzW+6+RfSWTqvZUdt+af/dLE5z7Xvv/+iBDTmeLQNc9U2y6slEZ2zglxv2RMDeNG25PQBd6TJBRH6N6hWCfJs5pLVFbClUl3hHsPVmeJ/1VYa2Nm2m4VR9ZkS0NofdFVpzy32QnSSa/VCfIpbyStJotqQ1FtGoDR0Qkyg6B7s/fM/jCLxw1TEzXf2j4VHm66B8oSfHskpzagPR7GsdNlrmE1VX0C8uB08D//8z+f/exnl6p9+s/Uw6nHt1utFgWhC4UHapTkJRTUOKvfR0ZM/0myJ5DKL/22yVt25vXRBZ6VTdzXcQ1jqo9tC03mDCsGxcp29lcYucRVOW67erYXllXK54u53AUbJoGgGwYVfBVCx8tYi3XnS/fN1Mr1crvmuwk3nUuns+iG8SBuYEvSdacbasdshEl9zlOlVDCatzCWHkrbxbRFZOk4gNtSDwD1o+A/vgAtbx1eMKSf+Zmf4RFMshBiGj7MnwO+8MyB8g4CFLOiz33uc6+99lq28XRJvz7Li+qfNDsGAYOAQeAcRMAQ4HOwU0yVDAJnEYGvfOUrOMpCZZEIkW+QcggBtdzYktVluXTjjTf2KwGHYfr/e9/7HtaSy3VT0Jsvf/nLX/rSl66//vrYfLp/yxnuUAHUvHDdmPRipIebInI5xswve9nLELxImzdv1pK6kHZ5XMxPtHYSDW03iFpdBa2t+1alrVgRp94J64HDISuC4qzbCV0WuPXFUBkuCs+HCepiRJDXKl35LR6lUB9UrFAo4bcsDepZ+oflT5BTFT/oc/hJJ5xMQhEYNukqhFpqNddQ+fCKoJHyom72hmr57pwVilErRsWiBLad0g0t5XaICb27cyX8CqaRTsGg4dK0SLPfXqWkcaLslQr2doQw6zOaijiL4eQGItMOB1a+41QIVCuZRYaGnXusheRrSdpqz1jWvBs5hDfmoutms75TcZUT7XDVz3KH3/rS54P1M/lffkPmjb/o+4HtJv1kalfN3rErHMdq03aJC3XlGlkAZjUJOoEH4mpynjxP3PVAemi+8h/fe3hg8zOTA1sGQqu98z5iVTMP0emGyYTb6oTJpNvyg1zKG0wHawe8dXl1wVBsvknrVrIIpgegQLEqlassinOf+P16BxejwHLG89aagrpkxBrLa+a88u6TV/ksXgUHEj2sO3k1D5IXZOktkfESD4aYU8XNki1DpjfEetlR+HI+5GE4pet3JL6Zy7LD4JSJEF2TR3ny8rmRpbL1b32BvX6KeyQ+7Bcsh/JAnVbdwDj7Y2xj0MgEqYMDMy+zefNmlguKl8w9k2fF9zI/gwHC6OBAtVoL/eqoF7z08mG8FqrtytR8qxmmarWk3yoMbLmYObgGHv1+wOc0slwm3RY69l5iTtt8TBSRpYey1mTRGclakOFswvJkskH+kXhWjI6GbNnuY7T+2Mt9mOOvxaMZ4uaAD87ApPiPBR//hx9+OP4g/+AHP8BlhllItMHMP/I1hg+jH5bodEvG0o8WZ/YMAgYBg8C5h4AhwOden5gaGQTOJgI49CL44PeL1R8xongUiwCh4333u98dyy6PPPIIJ/GCi2sRC0OIQXjHYSG5nM9waWJi4r3vfe8Z1jcWxBCwqA80GxkL6rt79+64fBzRfv+DH0TGevbVV+XzqKljmY+t6FFDS/khhFahv0WvstjmR1XaWDI7za5T6yq4Ln65KHW7kQ3X5S5ZsxPJXbb8E1GeA9YHQnLTFNdKuAr9bSZhw2Yznp3xxGQxl7RyKYRRh1ivkF7ypDwnJWvvCnEVGrBUs2onOrAYzdX9O/a2282xN6y9Ylv2juLza7UdqdbXBp0gCfF0oKUvmB17TZPnz4Zbd9RfmCIss1RF9G2i+2UX0ttTO4oGWJ+0tcevLOcrDdD1R28JL93bue6y7H/ZuU7hFdXuPxOtKmFhZi2MxokmWoXrOjFqiYIduB3l2xg4p5xEPp12horRQsUZhpIDqOU2gvaP78q85mdbifQDTWffNBGwHJYOxgB7utYdLyZ9y9sx4z97XdzuM+z51d6upzWsVmDtmA6bodeqBaW8aCaddCaRTLb8MOE5zU6UTDrtTpBNu6Wk2jCQWJPtbh1JCHnVqPJ7Rbr55pv/6q/+6j//8yYiGC12ou1T6lDDPVRRuBFPFtTavHrGqE1E3xV3PSmHvCPMAWk7cAu/TeatYCPUhEWSsFCNX9ITVUzTScisWCJoHsxveRFgVDJVxDxCEImdf1TIrr/ygXkCiWE7j087V+WVgQCTXQiwvluGpoxUGYGcYChi+8A4ZYaIqSKcpHmVXLGMsDFFAH3eFE7i75rg/fJsnZl75Q3UJcS1pqg4Sckx1ZP68ZSlxy1lOIXfgNbHDehGR0eJegBW6DA5z7fruPreU3jAyqxSV6qMJ20bdbptEy2a1mTT3sRA9oILttSbmClYpVGn3FZ3PTi9/eCRWZWKktlkqpBKE1I66yW8rm/Xuu5UM3pgTubXWLhrIM1754zlnMG0yqcchDZxspDuI/VwO/kAkIyPJpoeH/R+SVGckvmJuP6PZu3vUT6TpJjKk3CQ4TyDEM1wzIc/9alP/cmf/Al/PlAIw4Svu+665z//+fwFAV5uJPXLMTsGAYOAQeAcQcAQ4HOkI0w1DAJPBAIIOixLw5Pe+ta3vvnNb/785z+PVP2nf/qn73vf+5BvMCfmEqFQ2WIR3Rdc2EEJzL2oTVbUsp9nxfmTHPblL/IQZwttDKT31ltvRZyKnYqRsQis9bznPQ+VwqbNm2JaLoK7bYlLaqjEO7drVTqq0lKLLasqXrs2Z+qB6vragDn0kOxx0bNYtkTrQbWgiMAt1NF1VVLMlUPky3TSynp2FqKbiPJJkS8huvkk8VqF+qY8iC6+si52zjqJyC710D9aXpRn6HBXwi52TbcWOsn5jv3AkfChRw7P+WllpevN9/7xFe/KeOW1b59pvqBVvStFKKrcZc3ila0oQfCszE1Tv9O2xmz0shBeHITF+FlwpWgkXdnTEAgHphKi09XnpUkoP1nrlmV/3Ie7P1+J/mHQeWTo9Yuzvt39RsFrZSUMz9bG2K/POwMNXWflDXXTV3eC29PIvPV2N5VLJgZH/FKUfjHhpFXYKahLrx9+9Vv9iy96YNGud718zptZ9EdL7mDaIVJwoxsWEm7dd2caajJPBXq4xOicvS2YT03P3v7w7KGqWmjbqXqrs38PwBQHJ5xMjlmIrh9B0fGNzWTcgYTaOOSsyYQXjyV0x51QAGcoSrLshXb04BF1oOFMVaRRawsK3e9lo/ZQRkN+wgLOXouPUzLTQ3/5l3/Zv/AHf/AHjA18Mn//93+/f/JEOzJoaanN0lzEcsMgQtW6eLyrti9GEJz0QxsmDD0jpDmkF+MI/NUDXMllmomFshgvMtBlzPH/0X6PB4A20cdWQmz1ecWECTNcmU7SDgJwY6HEEOCEvHROOiFGE7xcGPqmect4H8WqghdNjCx0ifKqyn9pzymMMelMbtD9hX8vEY/x7+Ujwxku8a3jCwPvJRp5HBUvzikPeTxSXBoPikMnMElBl61btx41KSexYhgYkLaAIkGwhi7OXzI4MD1XmVpYWOy61VayuZDesO2yRuAyAcEMBBEAWHyrFVqzbbVrEd8KxaepmAon8tZEwR7I2KUkU3WgQ2tl0K6+Bb5SB8t+MePkUy6BBeLbe2CfuJTlWNEcGkVUCBJ3xNMx//7v/87HnC85TjEoh8GZWUuYMN4xWO70TYeWl3Pip5krBgGDgEHg7CJgCPDZxdeUbhA4pxBA+IjljxtuuIEFMOJ9ZGhm7j/+8Y8TxSqes6fOiDikOEN8eNoNoZzl96I6wIIO0ktCHY0NKhFTMaF793ve84LnP5+wo5psixiL0qodqAZuunDdjjXfjBbbdrWDMTNKErS+KK9sNJsinWv9JRIzSeu5uF156HW9SMRul2jAqHDtDMzWC4tJC9mxgPyXEKLLpXSCiKxQXZEitXalX1/hPzppEVME6yUDRCECEhMIU8aZufIdu6uH5huHam5+/ALlpQggXHEHQ2yiPW+P/aovHKy+ed0f5ZyZzKXl7DOgBj1Euqrwn7Mf+GHtjaLpFd7L04X9Ar8gr/XAwti1elnYBdRXn5cQ1QpSYbFAUgJ67DottenW+odeUfrNhNccf8tc+KqGf8ix81ZyPXFou1R7qrV1ND3luo3Rd1QPzCl3XyGlUgSb9geC5Fu67kaWKXKC/M9l3vWh1sDovoY6UIH5hGM5b3wgUW0GQzk3n3HKomYVHd5sHUFcKnpWU3/k0No9C9Gin1zoWpnRLUjwECfmOHCrZmHWdhhBpVrdMJdyS57aMOxMZMKLJvS0BXU8avTJwMalHE94as5aX5R8z4MP71105oKsn90MgVtbVGtz6rJxdyB91ht4SugxMURQIm4hgh0+mTEBXrLf7pUkiMW90mu1HAPUYjtaaCoUj80uSl2nxQ8OAj7O56obsOKxi8Mz6x6TMwgJ88Y7heUzWl8HMkwJ/Kc82WVfIJS9OOmn9QaC/JLxicI9HqtyxHDBSIIzkFt4MYOcM1onjAEF7x2vp817mrHD0Xw0lmW9ZRWxmLXPtI7H5FQaqiyrB+nZH4rvdWe/kY/uULHKokzexSbNdDFevtBOnFohYxiY0N18fHBk3bFjB7jBylasy9X/3PXadlq/KCR2eo9LowLLfeCppGb29uDg8MDg8GXYsERhpVKdWyi3/WjdZhcDlvt3z97x4N7AK+GBn8gUMpkcsHWJMN92yh21vyofBNZYKqUUbsOTrLGUtUspeQViKntMrY/uK4vYB+GuiucsMjERsThTKRMNZljoS0A+Ft5enx5d6AqgaCCqdeZMiSsBecezBvsdyD+j9A//8A8Jr8glLKVZKYAEK9bLmPVKXFHU0c8xRwYBg4BB4GwhYAjw2ULWlGsQODcRiGN4sq4vkkcsfEA44cN48+J2i7AYZ8BGmihTNCGWdTkkMy5eq2nUMvFYsnOIJIpmgPT973+fcFacRPR83nXP++jrPgrpfdazrnS9JGwSBVSjG+2rCN1daIULDavatSuoqtqirepEDnoqiW4sMr7ww1gcd6GCECEkabRMnpV1VVaW3LQKCRQmQYmgMhmnkESjyw92yzBduT2mtjECRzcqFqmPOteT9/uSpDTKOlJp3X7n9numCbWcmAkKXTvPj1VBbiekFv7BQ/jJCmu1rO8t/sqhzqU/O/qpSwu3Zu0yp7pR8eHms785+66Huy9E9SX1EeIQc2DKhgAL1xX9tfBefmJizKRAyH/4BMv84ITMaSxUozCgug+235iww+tzH826B9yhhjvEhAATA15gpx7pvubHjQ9c2PqLq4e/nJhobP7zsHFH19+bskph9jkNZ6KJVN60Lj6Yev/uWfWTex7yu+1MMj2x5RnNyE072K/CjsScFV0rjIidehcsBP+jYDorB1i5W7DfZmrML42kU05YfQBL+NzIJJWBqoEVHpLdIMwlXJwnNwy7E+nwGeOuhGyW3pV/KxKBf9/1rndJF9pREHjvfuf725FKj25504f+dkPRw/j5snGcnGPAV9z6pB3SFGhD7JhAmDpoVbx/nAEso1QGbCtUCw1xRK+07a5yWnouCacAlvJC5YuClyW+0PpqZa9+pZg6wgpB7KKFB2mbZwGRF1OKk86WV07Pvcih5sNyiZOy5Rf3SRJPA12iYK/Py9xU3BdC0xjY0GD9dggrZqHmbPCSC9zNnu912tQbU+l2tnCgET08J4HO4cZ4HxTSYp3BW5xN2NhrYLvMyyIAAEAASURBVKAh+n29iPHC/MICli3zc92uz4fl2udey2Px2ti4cSNfMyaIQIlqcQYajE4YJox+OA58wHmoGpwZA+nloRCOA6w0+tRSfz6xf1sMghzKHluU3g5OKPFXl4cOpVVmjV1sW7MLB/n61cqp6lyymyhObthGRHo6hZvAtt7F5sU+WFP3TYW4DfN9G8tZE0V7JGszcYNGXQMuz9G9YtWC6KFpiYs+nLOwmpGJPpvlst0FJhY7jluOPMcvpp2BjBrM2MwMyjdSV5CupIPptJMnak4sCfKgDcaGiJAQb3vb2zhksgnTHj77pL/7u7/76Ec/ytDF+wYmjGYY/XD8V6Zf+OMCe780s2MQMAgYBE6EgCHAJ0LGnDcIPD0RuOSSSxAy4lUu+y1EH4IgiLDCmdh+Dw/DeCfOwyF51q1b17/l5DuUFpPe73znO0TVYu1W8iOA4h7GskmxhxgBpvDdxXR5V9VabAbzTUivKHhFRu9ikGnDh/HdFQksJrsiaSOUORBd/AzxMEwlolxCLJZzSVVMRYNptBkWOg1MmpGSUx7CJYKclhm1MMftFCHSveixNDuSwleVEJ3RHSFmk7DNthLpB+cSDy0m6onxVCaftnIJJ5XClDQiYo0wbXkujBF1jxYla/Y1X174fLFyeMibgtMuhmOVcI2ysEHWl3UjAU3IhuYGmiYglYpxKQo0DmXpJFx7kZg9lq4RqV68MyPysPwtXsUi4m8P3rxv4eqt6nPbcrflE7PtMHuksaU7/I5dwUsW7GQi+9eTVnLS/pKb7hRe2LJeCAoUD11xG+qiH4R/H3QvtHL+2jW52fnFpOqOFZNl1nIJ2g/t2uNl8+OlrJPJO6jM0x5RsmPCsyrsziSTsg7Vwn2LzqFaVOk46NuTiUQnCEuYPaPKxLI2ghWID3AuGW0ZdsdT4aUTGHbyyBPK7AzjT3ziE0D+re/e9sUv/fur3/nH9Qi+n95UIvBVePmEuBCfSZXP9r28vyS6m+0xzxJyO98Mj9SsxQ7zLC5xzlEqNjoRwdb8SGJlM1mgmFGRUSXvgidTLLBefpiAYbbBkQhuwKdNMBiRUFahtyThQWLkLNMOmkLJW6T5EceaJ5NFAy+2+TpTDCR0WleYIjryZkhRYpBNsqOthebPbvPWVo50b7t18fa7wlojsWFt6SU3bL7ootZQ6WsPood2mXOB7aINxnY66aDtjND25xPhoT07W5XZlB1wspTOrllbWDc5Hj9zcHCgVxV5TO81JMY1Ua9I8DTtWGCxCDBRD6gRZBUWGhtI41ktN51Zguk985nPXGUZy7tycmJ8cmIsCMLFxfLsHFS93A7mr7j0wnLTevjw4k92TlmZATedT6RyNIHFyghdXgusqUZ07ywvpyomI3TpawouZLiUljP7a+rrO8Odc24maQ1n6Hc6Pcwzp4BRTMLBuBp0fJWYY6GmJh0o8BYz1jCLFWeYScTQJEa035RjR13/0qM7tIjEHxpCJJJAmOCLzDXEZBh76b/5m78Bc1THTL++6EUv4o8CazJxP3c9WorZMwgYBAwCZwcB+SN6dko2pRoEDALnHAK870RUJqQzqxzF5pRUEZUqkUvgqMSJQV659957WVXoHe94x6c//WlkEW5BM7xly5YXv/jFX/3qV48rncSfEbZTRw7f+t3vffvb34b27tq1KwwDgmZd/6LrEXFwBZtYsxafw1onWuzY8w01LzaZDsreeidq+U5H4XnI44S7aHGVT5PQLCgsrm6E0kH5I1xX5LZoIGMNZeyBlI0YJxohcSAUndJxkuaOK8+LiPXYchYtwn4PW0q0Rqg1cGzjNoQ2ACyOjP1wb9S1rYdmIL02nAIeiWSJsSICo479IuQSgoa8z9NoEf69siPSpD6HayXgww3iLXanWneGeocfiuMShqnEIpJ9LRdCllFxs8CRUF+Qch3N9DRvdgSfZnl+/5EpaE/a9ieKanBopO0M2V563YCDj+V0HQV68+riV9ZHn82pB12rhS65Y03uDV5Tyb6raW9caHQH80wv2PW2hJKiJ6ZZwKVbObDv4EJb5Z12N0qGrjuSdy+7+MIXX5CSrloSWI87MPT109lIk/TYm21E98+qAzVnpmZXOmE26S5MzyQR5JMFLGN9vWYyIcUGE9GmEXs8E1456aUZMVqEfnSzrAoCnBboZ5rR5//3Vz/3vz7zjj/7ssplN5ectbng8okE6q84++PbomVVONNdiAT8LXaq1BRUhhWjhZmjIzU1U4/ayuMtK+MeLxwYO2eL1b8YZw4TQoxCnHt55wPGjzY80HMZGDwLMppUy4unMRIaK6pgDnvzUPqKXCUxJuUeNvqXziO3yb1SELu8vnqww5jlNeSf5slab88l3oecFbz7efaFi/vm/+pv/e/e5vEwCmSJ7Gxu8DffHr3slV+dzXxjd1Lah6VFyDJDHRX6A/nM2NAgrg3N6oLyOwOFzGgpU8olMAxmTV00mUxhYAOS48uQlDXJGKhgFA8LqZIMWv1Lt4fQBuiESbzjMQLY6xJajGzgxDB4IkeCRlQ6VGq5lPgAYOvB0e69e+++98FWaFfDdCNKoxm20oOZ0ig9pZsomEubZBINJblMC4LA9/aq3Ytux7dTMoNg55PEO+BDqsP7Ee2P2UMd+CCdZEQsoaSLY+VyHLYH0xZLFoOqXom8l0HXkGet5Ksghus1Uwn8KVmOm27XUns0sKxmx/QofyyYISW8YmxHDRPmjwXK4Vglfmz5jxZh9gwCBgGDwBkgINLtGdxubjUIGASeYgjA4l75ylcSZvm///u/ccfiEKJLJGciYGGiRmP4JhCDCnNlMiCIoBYm7CdrHXHIEkSxTNP/brCD4Ehm5BgSimKI3pYtm6+/XgSZ51//ooHRCZbbhT7NNqy5hlXpOixKVO+otgTdwZ5ZS2xL8p7wQhsNj6wklCYCKvw2rfDXHcyoIdFmYMmsEGpx2UUdpG8SbqlFtcdBSO03Crdk1LxIwChY2P/GN75B6aVSCQUFdpJIZnDgTmTduY9lg3GYxDDYkeC36FVcbdtJ3WzkZghwT/LuDxG+trSYjy5MWmILSZRdAUEi7kaKcEQBKxRhnko4ogjzVGYECPqFqapWhkOMuREWEeH9yxMhEFGr3fVb1cjvbN28FtfoaqPVqcxumijZqXy147KGykTRqbXVdC0aLRJF1malHxx6Nw74GTVVbc6zMKnjTB5pF1Mu+jS7QWNQ2mDC7bOyscS4nqr6Y0UIt7V/vpG1WocXWq1GA95x/XMuf+76RKNep+sxoQccEnakqM4AZ/kgiff7CKxyR/cFEyXWfYfV4ZZ1cNFGjZlKOd1OkEq42MmnPaeD6bloyGE79oYRNZmKrlzrZoBFOkGPi+M9jJIBjiVYH5xRX/va1//lHz/9no//7y3j+fF8dMWkl8N2VDON4916Lp7DBCCwVaUZHaio+bbbCe2FZrjYsluh2Dy3mVJiaV/pEdzpYZBCgCGEYBBAJ2WySWuAZUzqcajRwTpW01VNVoX6Mh3BLeK72pcXZCRr2YEzmtjKJTmhT8ZTOcJ/9e28oZHklk7RJxzegtia9zmT7V9a3wz+8YvtL3wJ+1leGu4hE9+GMF9c+9d/tv/Cq//ga9VKl0WtpWcowvNcrACyGdYdEx94ee9shWe6RNIi3LR8PYT1YVsBH86KRlQRMop3QVhfwspCifUUle7Oo8YJLztfM+b7mAHEHBouh7oSG13WGF8RFPDJGgqAw/QHobxEMTw/X642fSd11XOvn23ZBxa6+2cabroYuQk6UHqF1wBPjUZ01yFefPl68Nnku6FjjxGHDEsZAmvxmdXxEfAQkU+uhNoiOBazSJQBOnGv0pOMGs+JWJyJJZqwuNYfN+HkK15w6b6ltOLS0une735Odnbv3h3/BYEMYzrEhxcPbZgwCc0wrjf9ovo7K0ozhwYBg4BB4JQQkD9Qp3SDyWwQMAg8pRHglWeVjp/7uZ/DF5fYV4RIJWbJS1/6UnyA48iocYZXvOIVsNmLL75Y2/zOsU4S4Uy08CGCLDoTAll985vfvOVbt9y//X6k8M0XXADpff711z/72heUxtfNt+25RjTXJPoOjJdIUZrxiuchMlWPnohMTZhmvVAKdBeNjfjrprFkhu46KHjRSyCNpT3sexHVtTAWQx+L50d3w2kLRvE3kIkAAuSg6SURLZaTeKnhm8dDEIiRwGL/wPgp8tFUqNB6R7GgeJQo/WjdpNq66npHdiXBwdjKjSKp9twsOQXj5UeoC4vT+BKwV5yfsQa3XOIVsVYNhyj3KrVurV7rNBt20KaowHK3bd2yfihF6fh8sszw2kEXC/CZekhknQ2DkELr8KJfyrmTiSm7e2dBPYK+pW5dvL9zeTE7ho3pbMMfQzFkWbWWP5BJ4BMbdDGSdA/VgrG8zDagCl5T9A5Wg6GMS9iwiXx02Zi7MD9/2223wROkLVphC/tlViU2ZUREx2weRVBsayrN1ulkPRV3huAjLb1nilV53f2LWPPCdR1sd9NJlxA+maTb7uDvLeMAZ91NQ9ZERl0xCb3Rw6Q/wpaeGP/WvSZ/8I7UwwfnrH0Ve7bcCduLl2weXZNTl094MH9ynqx6Rxf4pBwJQjKUaIcMm9lGuL+sKr7T8K25OqHRWc2Ila6hl/Ac0Rp2fZY0QgsqneCHYjrAaEAVzP2MG2gwQ9IRJHtGBzBVmJIcS4cCMHM5FMYDhUEJoY2HsIxgcRqWrtKJyujeE8YkhUtmqaUUoq9Qqj7DOXEM0C2w3nhp8JJw6vBvvMc9cFiGkeSQm7idZhbe8ib1trd/7E718HwqwXeAZZREbS01kOIlt3SXVBbbbD4mQvBwfGA1JjzVI1moLGbF2n6kkLJQ8m8Ztkdwc2VpL9eO8LJw7BQR8KSQOMnz4zFAvKwffP/7BGTmGLdzaPCaNZNM9GhkqF8vW+++J/CXxkgq0G616/X66MgIMOzZd+CHd93fJLKdW/TTg1Z6yErmmePYtaD2V2y+IRLlWy8Fh7U7+MJwsZrhKyEx8PEZYW6R0IBiUIMtCbNgiqj4uJPkksy14ZkS7VqI5hsWr9tozhrPqXUlm3jUhEknHJ3w4OOmHqpL0B43z9JJ3e+icsd0CBoMHyZKYqwZZj7ixhtv5I8Uvid8T5bukN9xTy0/Y/YNAgYBg8BqEJA/SKvJZ/IYBAwCTxsEeOvRdWDPjLUzRIgFhzBvxmNtuTCBXHXTTTdBklF9sNbFs571LKwu7/jxj2+55VvfuuWWO350e7PVRiB80UtuuO6FL7nqeS8qjK2D7s407HILZV1EjBZCtviBIjpTLE6DXixQu4ikBLZxVSGNOGUNpNUI9sw5Yb95FiVK4uIrEpUITcsrdNbQBw1MH1H10ED2wQEdL2ofNMArhK2TVqH/IX1saY+nMIOABSD+b8vj7qwon2wUChNA1YnOXNY3btl1356eLz+4c1c7TLXtZCpdHBvMXbC20Ok6c82QADajBY8oR7OL3ZFSopRwan5Ua0STA0QaWiw2P3VR4gsZdZAHiW81hrJq2+Hk70w7r5+qJbgR2Ocb/kg20YqiLnG6Mt7esr+u5LVDVW+psaLzyEy4eZSBorYNhsH83j179sZrzAAXunEmDkjYh2OdC5iYDMR4QoMZRcRwIkBRHKF3RUv7h7rFHIWd0Ll7Kpiue7sXFMtcYZXdUUHG9ZroflNOi1jUeC5GijGzeQCJPLxi0in2wl6dEH+Np5qqRTvmrP0VVW5gNB9sGXDX5HEbloDDT8hw67f1NHc0RGISL9R3UaHnr/jWTC2q+w6mztBdVHPJhKwLhVmBp10DsH+G1RJsScKGhTgDKw+3bzgwhgphRBAsWK7jiss6hcOqgyisleeSuYFEijhumosKPYbsam0wHFhoMP+E+AjvFSIqpJcU7/Nb8uhTDGC6hCOoNmepCqrowA8ZIVHo//b16WsWd+//lV93601dgNwZz5jQHe6N12c/+IG/3ZG+/aDNoPKSaa6SxH1ZF65fENmDavGrd5UPByjIKe1Agb4XUmdHP3VB9LzJcKBR89oNsZ9Ipzul4nSU3jPXTSUTQzkWFhK9MV8haKEMIxX5Qcj815HDUzOzs1Sbk7izMh3Gg+LanjtjhlcvXvapXF6E6HYir+PkNl9+9e4q/vP2kSoTkTYrabcCoqaLgQncH9sTLEpoJlMlBLqTZc/18m8sPgwIBDbgpWAVdEbKrnnrQI3V1FkFHcNyuVrCKidjjWdxNrbWlJyxLHOXvdBZ+g2k3D42+kSM16q3IMykJO7ZrD9/yy23wIeZoOQzgkIYMkxCRcw46T9j1QWbjAYBg4BBQBCQP0gGCYOAQeC8QuC4b/0KSSLOgwjCfLxoem+5BX8tYkEXigPXXPf8a1/w4qtfcOPYhotmm+68MF4bO9uGttRF3kVQFuEHWdgOiQZFfGbW/MTcDn0C2t2BTDSaJRipXcTCmbCuCTFsjEUkEZp6grScWFGlx6WPaBfhrFBro+llS1Au6BnNhADHvBetL2rM+FlnowKUTB2g3AB7xRVXnJwAwy9Q0WHxePjwkfnZ2RfecH3Xcqcr3R9tP5AoDpaKQ3gDztSDdttZMyS6miO1EMa8doBVbeyZij9UcDOOXQlUKpi92nvnePifrIlDr6AqE82ZTS6yJw8577mt/UelXA4GPFP1xwuJGs61kcpn3Eem/W1jySNVIs2yrKs9BWMeIrRX9Ny19r0//iFryhBQCv9wBNMVvYOhJgxfM+IaJBmEyYDMunnzZnbuvPNOnKuxl44TXUDqQ9GN1L1HgiN1b1c5qnbENJd/ScdhFeJMijjBYTLlMFkB299QsiaF/bpFiSX2GGMGPebhmuh+DyxaDFrXjS4YcCbz4veLAYLE29ZKbKIi3XzzzcjZxENa0agn8VD+UNM+4ZkRDgX7FsJyx8XFd7quar6NaUA3iEQljp7cZ5UoJ5P0UP2i/GUsozollBmkSOyfMWONZLls2ChKdZsgYko82OG3YjiOUpQnEcHuoe254YnMwAjUiFYzYsmmuW5cB3lLyahrhNGyzqMPuJ1LXOQ7gFk/zsYYXqdSElWMEM0L5QqG13Krfs+5853XF24Ipw7+2ru8mXluW24sQL7U617lvfOdn7zP+/H+gPhVuVyePDyCwikE1TVbKiGHVE74uK6YKLG1alhfl+hxofULV4QvH2pmH7h37l++0t3+oB0GyQu3DLz2VdY1V+90hv97J6YoLvrPPNNwnqwwRLRkMUjBZDotTgEqDGanZzA8vuLKKxknzA/iRcIIIWx+PKfDSXn8k5d6wBNL3+/Ozs3zfWu3m9de+xzs3vceWfje3Ts7ycGaM1C3B+YaNisP87nuBhjGi5c4kyB8CxgYdIteoQoyzBLNGI3Ldxul++Gqw0deUCXSvk18Qeyl+XTjkEKgLGsoqYbykGG1YUCrhbPcK+7EdI0G5XSQ6TcnRpTpEhYZ5s8Q6Yc//CETuMymMW+LWvglL3kJUc0MGX7yhp55skHgKYmAiQL9lOw2U2mDwJkgcCJZDZlDBGHLnp9f+PZ3vv31m29G2ti3b38ylbn48mf9/K/85pXPe8mGS66qhclyy9neshoPY1bHIqISzAZ1EMITUieGiPjgJfEu84TiivtuRtarHM6wtAzeZXiWYqgqAuzyhPQYy9MxjeHS6QhNy0s8ep+GwXux/cbHD00ChnacQaSG7UC9YLwodvp3nAiffobT3uGhSHLcHhNCWGL8LPTwK8qkhgSdpsJEJoMrgkYqnWrW6wMDg/nh9KYXbSs3o33lCEF246DHokQHK9FkwVpbdMrtcA6P34IzUUrM1rqpkjPgWWvDvxxRX0G8DeaLzf/OqR2ZyA0Tz27mXlp3M+114afG1aWt6JdZBEfCbttifozDZJtYzyzBZGF0rYazzq6ZxuLcEafiXHXp+oyXuPzyK5PJRJ+1LgeNZtKifhRxDsEZzoCNfdxMDplMYQKCQ24kw8TE+LXXPhf+cmhq+sf7KvPhwEw7WbdSQeih7PUcr9UNskmv2Q7RAPu+NZC11xSt0Vx06bhmvzLuVg6q+FmaK8m1qVqI7nffolpsof4MNw86a2C/4wlcInUteiOOyiBex920olOejEOq0+N2AqPPilDhbMOphN5MNap2HVbD9qG+SSeZhPoSRI5QyR5RsjthgOsvryV0mMmMJMQ3wOAX6su76aaTCusMzjP3JAburuOriJWt3JAYZ0AJGxYzaShlhP2vG4mmjat0D6OBCFWyE+tI0SIqNLnAi2suFW23W81mS3S82p5Cd4lN54IwFIVyXPFoAH9Wx2XjEIqsPTyQvfbZwX99Q+ClE3SHQW67CWv4udfMp7LV0BoazEmMOf2VqNdqKHjTeIQTD11npxViAK1HgPSlNEHonLB29izv0vHW9WNR6vY79n/kY6lKjQ5njiCYu+vwndtHf+cdG1/+quFc+ocHCaauUk7ElBz8TWIQaC1oIcFiudFwDhf6ieELxms+3rNWtV7tdNq7Htm565FduWxuzRrsRdaUSgzv+D3ojaUncsDED2aQJBLJNRjnTE7GT6cbk0FjzF7wmzMjluvbyTA73CmNtjKj0zV3ui7uwc3A6eBhEcrK6hgOtH3WWEIPz5wXsRjEWp7zujT0+DariqM9rstsBXpjlaqLO3G+LF8MlMDDmWgsp9YUwvUlZzRnM+kpnSQflbgECpOd5Z+LuJ4rtssz0CgMc7B/Jn3wgx/kS9JzwLnlln/5l3/hW8qnhvgUWCoRtKI/Gbe8hBWFm0ODgEHAIGA0wGYMGATOawSQLeL2I0bcc889Xyfd/HVMnTtdf92mC1HzXv7cGzddeV2YGiy3VBUjOnzJULD0XHmFuyBMollKEbYKzUnGHkypkawaydujMN60yqUc4tMgnWrxZ1Wiz5n3R79R0EiIFioaRG9IF3yeS3jxoT3Aajc+/0TKSTBezM771aOlPB2uiCp4xUnYL45wnISla+fDNaimhUDEvEP3GspceO+eMqwVla/aX/YnS17GdRY6QdZxkUqbaN8Cazz14FXdFyesxWA6O/fRscxMiUhP6HwcLwqeUS793oyTbs+Fl/8kdUtLDZQbak3J3TPvrx305usQCDwD7Yf2Ljrd6f3zajTVHC5lX/fCi9YU6Uqt49H1WU2XxQ2k/vEOBAmeiXI4TnDjTRvX41b6tTseuntnfTpMNaIsIw2u4jhJfC9TKa9Wb7c7LZo+lHXXFlA3uVeu9dZgt8r46onXx68I1Ii1lB6as/eJ7tdBf7xxyF2XCy6dTKQFURmW1IopEnaoz9e+9jU8wIGdQ3pHcqy6mcevweme5eWkYug4mYs4tBjtr1iNEHNWlMAwFontzIJYlN3xVdITBS9nUL0lHJfljnDPTXsecEGMoaupBJyQocL8C9pvcXcmmDRDiLdTnOzRoQbR4tx0fWGmVZkvDA4nMjk3QSAoOzM4ghIwCuGuBBd2oUDVWq2LQzZa5VCciFHAYos/MjLMI6uNxmKlKtpYmLVwXh7lZdGiSh9JzwsTkq20jH+lZPC+a+zxfdunP/T/uPv2Cl3VqevYude9JvfWt3y7O/Kl7Yw0bmPkoL6OKtVqPH8EKcIuGr4nHxcpjAfIb3Z6dAt1uUbvrVeHL40OH3rfhxyi9OlH6O+WPAlXgXWf+eSDoxf+6a1coa/h2QrnWEyCiRYWB5/H7hefWEx/WYuYOHzwYVlhKBn51dnq/HRlYSYKuvDMl73sp1KpdFz/Fdsna/xQjfh1A7E4xgEW3cwguMnUi298WT2wDy76Dxz8/9l7DzBJruPOM015X9Xe93iPgccABAEQFI1EihRJUY4StUtpJa3ct5LuVrfUrrzubs/sUaLc6VtpdaREI1oQJECCIkASAOGBATDe9LR31d3lfZr7xcvuRmMwM5gBBgOaSgyqs7Iyn4n3MjP+LyL+UV5xYjNlfalMxISJmzQOBYBh2PiEkE8JVIlF+UhQohoiEblY2RG2+A7gWOCRkEleOszCIQ1nn04VLTyS0vtibjpi8o5YHd21ks6S0kV+9XrknUx38I7GRwmqQgKG8SjBqQcCC8AwBBae2F9H4V9kj9qntSXQlsCVl0AbAF95mbdrbEvg9ZTAuvag1ERtMbv4ja9/46tf+yo6xPz8Yiia2HfjbXsPvHXnzW8Jd42QmLfaIo0KZgE0SwEBXCUatw7FDlwpyqsZG2/I7Y4ZPVECesWHUCFeTubc1dNXOyy655oK9NrIwOsdOBMPQIy98DCBalCJoGXip/X8HF7lV14x8gA5tYOyUN2Gh4e90GugOPgc+lM2UrAQLktrCcAmsJYNGMElZ7V2fRyX687heRikDeDifN4ezIgxmVDejCQ0IteUfU3gr7fav0MBub/s93+zG/Md9s+mZQT4g+fjB2eTP7rouOFn/V8+XrsVy188rE8s25s7fcezdtwtHRmf0zEounokkbpmNNOTDN04jLOt6LIK1XiyfOWfXkeUWysA1T6+6BxerC9X9CLGTdcplSt0fHR4qEHaX7s5NTMX9Vkps5YxywPBSsrXfMMtN3eo9KFPPvkkgmK9AKO0t3mBx0zBmWLryJIOeszXfAHd2ZTRBmLW3j7spt6MXp2Tn//858HkVLcuW/bf+973grLOEv4r7+1FX7neBu685ZoztmQXW74sYfYlt2pLQirJ7mPClOZA5yQEV0T/YlrFBRrHVkmR7Ws1mRVCBMWtiPBwYPWTOAicD5iWbF5yFa7RgGfXIXKYg87yzGSztFzMZ5M9Q4ViCYDDjR/u6NdZNNCMVCaFSEHWc9l8uY7zh4+k1yq/kizi+IMkK5LyQTdiHWR6qPt/HfTIw0BQk3zwowKr7DtvGGh8cLfbM3l66XOfrzz6tFOuBod70z/8dvOO206Ge//8MafUgmdJwn6Bx2qjsy2x0ouzt3SEVpkGcc1sdEMkpwoXfEZdhtv6w9v14eceWvi1D5NATA4J/F1toGU4mf/0O6V3vOcPH9QWm1g0xdNbbXI5lZIVmZUFD92Bh1WgLBQGkklIWAzgjmL1qbbiJ8HY3tF4wCjllk+eOD40gHN0P3h4YnJSCNKTydVS5a6hCa/D5s0oPiuVaqlcJtswkhifmDz47POaPxxMdDnR7ryWmK2YM0VjpW5Cq4YpWMzCkKWpDOfK81yG15OM14+NnWEWKW4tnIC8NFSSlT0ZYr1AooUHkvpgArMw1IbKQfosGbzStwM9YuPRevToUYIX2LAP89gfGRl561vfChK+8847131PqPP1kv9Z3W1/bUugLYHXVwJtF+jXV/7t2tsSeB0k0Gw2nn7qGRSFr339vqefetpxzP4tO6/+oQ/suOmtA3tuLDuhimVO4yG5JDQ5tE9W+XWCMAG96C7Cu9sZZnXf7UkYHWFSYhikJpJEK2drFhtVoyvUTdRiEh0TNIs+RHOALqhBwCH22QhYvULtOE81YA4ijVHX2AEAoxnTTpycDx06hH2G4zhjY7IGAHMCEbPnKUYO0x3v10xI39apHc0KT3IsrJNUGc9z0BHxnxiMUfZD2lGwjdaIuIcDWO5FW9SEA4i/8D/bz0T0d3JiPeyeKtRvHUxoKzUnaNpY+8ERoUgQJXhrX2c90Lm1m9y7xkDSEspcMehdhk1Ai+oJyyvzRXcsZ0zXSIYTb7r1ro4olstwNApBESl8AkG9Wffv2tSXDjQ7/LXhSDwKNVi9Ho5EKAADF/LEqrwuFgresmXL7j27Z4v2vY9NHs/Wq1ooqDu9UTtXbg70+gIDuzkHMMAqCcsQbEwVjjAix48f37VrF6sS7HOcg6/XxsLD2Io9J36q/rmCXbQkBZShO2GYwJqWTzOI267ZDuDPZxrVVgunZ3yYa3UL42wkEtRtI18qQeWEjQ4mswYg2cUd2kimOy3Lrtaq8HiDRkk3zVqD7hIH6kv3jRrBmG3VdUQf7bXMgK35mpqv4WrlUlCrsX4C8k6bEZLiiEMCAuNqECpzzbZb+FczBHKUh4d6dMgQb1j24ivgU01CKKmpVz+25P/MCesdO3b2/uqvdn4IYnPLCkRK0dihRuiTT1hLTEwBpUxWcb0WT3dxbPbpvgSxq8S7OkTzOuJjIhVTHBBYKpaS+WAL6U7E8LcKJSIPvJUkdVjq5n+5E3I5v9OCYNwkDRO3hPzM5KeRcpPglQDIJyiA4gkIwHsCx2AoqTER4y8dkrxKxJD3EC47fQyzsFtZqlSy2vH58eizpwa705XSyt7de1KJpPcYVVW/Ph9qWORug5COfzSCDsJNNzzYz3Jhc3ncXR6Pm4H9yY733nTNdNkeW3Ini8ZCScs3jWoTm7DEDDccvSU528T1nRIYdVkjUIudfGVkOEy0edXiQcQMkYUDwolh+I8HzGSIFVKnL6b3xKzRlNYVk8x2Erx/1jopBy5lo0ds3gNz7969pLiHYhDGCl5wmIX/7u/+jkUxiB5BwliG9+zZwzOW8y+lhva5bQm0JfB9KIHX89X+fSjOdpfaEviulICom4p4CR/gr3zlK/d97Wtz89lwsqN/+3Vv+MDvbz/wlu6t+6sWrDnueAXNAA1QfJx9OphKkmSQjDcd1ljFJ+9Fd1QcmwFCHlfzRtX2CnedHlEjn0BHjL2gHUibwbpoP6z3w0+Dn7OXs+QKN+zC1XnN9s7BkIWixicKGR7OxBHidou6dkn6GWi0Jwa5sZar6/Gwmc234oFA2GfWmk6AbJ5o/crrVZRMGydY7Fue8scn3qvYbCR6EkHiUkvYJlbW42MrWnUh2be1KxWptAL7dm0RqGOLtT9qtPoSl/utoSSyVHVPkK+lrC3k3dOzM6jYptHtB1oEAi3HDpo+MgCno/7OsDGSCu7qjPcnULzZvL6giBtYezD7sJG5GpHyCYKdgfN5RV+yIw2ybTlO0qxG67mIWy1kA5omAJjlhmeffVZKWtu4kKBrBoIpdN9998FVRuEozWzsAJJHR0c5F35a3NS9g57+zcBt27aNn6D+YlGDg97GEXYYXE6gr+Pj4xzxNr6ywSrMsghH8FDgWu8gAKNg+aYrIYyxc2V7eqU2t1yEyQqzPo7L/Eqan0xXFzl8nErp+dMnI4kMsgCqYSbFozmWSHJ/VMoFDLalYpkeMNKMPhiyFQj29QiLWItg4BCRwiYIE6nlF+bxo27agXjYzY5P9GQ6G1qmGBzU6D2e8LLqoQH/OF1hHkG8kp8MO7TMIQuHaHjaAsFARyYV8hPoTyppMVN7WEMZb2WiSfwErAGCLTEvk+DaJD3PbFn/1MHW1u5IXywWiPigkT95xj21pDtGqDsiTPK2d5WYsbnQEPAFbjd94G1QdK0lCIxRZ/BZsMAHAG9t+Lzos+L2NvOW09Pba2MBb1k8MlbbhMRJq0tA8mB/2fSV60BUz81FKLwUlpbZxXDwiUUbMIzYqa4JHKdGSaEsUvUy6+IRI9nL/UYkMOxP9zn1nFsrHj6Tz08tHM86731XL+uGpCBWmbbkrvwu2XjssDEkudzK/AJuM3OaVdmSMTdntF3xwonxBX2wO2dHx3P6dIlc7kYRsnEsw5aEAXsBw4BhBoguyYSQ/9TG6GhAZa3e0EoQ94GEFct0xGee8rs4SOMrRC6lnpg9ktJ640IVsZbB+1UJhpFlvHj+k+fvne98J8+BL37xi8BgvGn+9E//lPhhbl6y3PMTpA+wP7wwE15Vte2L2xJoS+B7TwKXW5X53pNAu8VtCXx/SsBTo9ELz4yP3fOVe7/8lbsfeuhhTEPdQ9t23fGBH7nhbd07bypZ5uxyfq5l52dK6XRS9HxhPbHxbU6FoTNx0T57EnpXRE+FDGJKJejzrE30QDm0rvmc9ftr8ZWuobFBoQTMAPqCWDiC1RTlBm2eDBn4Y67X+92j4ngjQrOJVcM9khxIICK8oDFTg47WUy5deoOFyqgv5uTq4BPRO1m8IOsRUmFssINXne2wGBnBlrm96TwiZmHFdMavOrzdwT1VB0DhBsZy3UuL0/Nj+UIrANTEATngx5VaI/vRYtEdSjP67mBKxxd2Tcldl/Er2ZHquQ6haFqxYR/JajMl7fRMcWwh79McfyRGLh/IiTGW+8l+ZDmdYX8qjLqsb8s4ZCRen2+euPhk7YNxx2rptYZiPdar8bzrT/ZuSzmjHfpgxNrdAy6D/mnV0RXwySV4pOMdgEKMJRkIigXYGywmFTo0Bznf22GyeeWz7ML0U81f7T6OuB4AZgkGb8zVo+oPzcMJn+GGvwdr/3rt3jlQ+IhfAIHKs3Ozs9MIxXL9C61Qzgq3zGTGtCuWkS9bVUkU5NZcMLAfMjXMoDg/s2hVrhTNVsUf6AvA4oSFl8EOGMloZIVg3FI5EQ0P9veS+ijox8/V79n9G46NA3woHCIWVKYGuXPJiNQVkSjNVrlVXdl23a7OjnTVSHxzTsI7JbkufcDyqWF2xsXAyUQ0HhEY9BJBMxHGB0TDCn386Eoxt0i2qv17d7OW88ISBSBYwUivvwwNG5/8ExOrYFpFR2zhmW8Tc5EMG90x7Zo+GzbySsOA441E4uWmUWq6uO4Dm3HylvBUOM35FD9+UuboddOpCOe03ag1gcYmlm74u8SybT6z0BjBIfmGa6yHHhNALjgXX2mZgPrmkeDe3WcqRt0FNVMyd49Ob1WzwMFi2qaRnAmW5zpv3oDomEGCh4kitnTxm5fLBOnhch42A81cC/s0Gd+axmB52b/81ROxoJuKhdP4awx3DGai8TCjJHJRjVDykW+eYLxj8vmabtJotTEHOjo6+cdDibUjpiKCWZqdWZk8hYU+Eond1NXzxr7eipkcW7HIKjxT1PMNWNklVFiBYc8sjMzE7E6xLAzIc0KKEWGDhAHMpKcusg6Ff35VKKaJqU54afAiwuU+lHIHEi7vGl40PNA2dny9nRsPnm/fO5kbk6CSJ554gqfrBz/4QeJKuOlYbWT9l+1v/uZvuFvvvPNOkDB42ON750Lv2vOV3D7elkBbAt9PElDhMt9PHWr3pS2BH1QJbNTFsYPx7r/7y3ff85V7jhw5agYio3sObLnxbaM3vjXWu4MMvUqNEwUVk18+n7MaZbzUtvalN/fGUD2hOBLfZuGvIoJOfApFH3n9tIONXaMVgBBM2fSRRqHfYOlFgyFW9vX1Vj3fvPMaD8QC97KhlnHm5s2bN3o4vxrRrpbfch+fcgnlnM7bvTEiefWlktWbMOdLzmD44C32m/1auTURz/5hT6KUbGi2DKxp1wcLvX+0qMcr2daW//r0P4T8sYYR2zTYofniPiI6LbADyAWDnbGt04wH7OsH8D29nHOBxpcs57kZZ7xgP3liab5YDxtmNJXuTMXqlji1EixIzh4QUSZkb0kbmzL25rRYmy4gMcpEg59V+X7Hcxp8UUHDHukwh2G9wvNZLl1VrzmREcFnHhhM3iNgAEODoowLPdbjjcso64ML6AUVSygsFFKK6JjzvY3LvVBD5idprjjoXSXN0TQAMAVC6002F8zITFcOei3xCuRrsVioY4mt62N5s2IH5ipOseVvYbqGC800K/WmHzddzQCOY+OEz5lyMfXPTY/b9cbAth3NpiQBJsq5Ad7VsLnpYxNTMGANDfbGo2F2MGSSGRjeKuJ3ESLmygCJkXQ3EgSKGCQBCplOo5itrczecv1+gvlrjvvf7q/FIyY2uv643hOHdttUbEYK3qzCtRfgCj0dGxsD/NN3MtPgj0qXN0qbPoosBBZ5G9+8A6vfvV/kEEKTRQ5xfuZkHJKRJv/wAC837FJdyzecYt3O10jPo+VqWqGmVUnqY5ulWqtYruKsS9Q6kD8cDmHz7Q83fvuA0Td2ZO5P/qtzYgy6asmDTeH9XYP/5X/K7bvxo88bx5aDwDapWapUzzwQr8K+dEGm1Nr+auvkoNd4aaDy01b9Uq1vlLJ6q8o6DabgMCsNkSDWdbpjOi3iCzpjvuHu2Ja+xECSZR3yLcnyokw+6b8nG4He6sAV/aDts/lWrqWR15c45/zS4sLc3OLiUtNqIQvm/a133FaSlOPu+Ip9OqdPFbRsWS9AnSWJuEg+7MJELmsTrDuovohIuV29cX2hK0IxLTNQHKRdDOPxgINNmJdOb1QbTMpCGw0guIbFOiUORsATywtFnG/PG5TJyUlWmrhDmYdQDLIwun4+J+D0AQz+8pe/zM3IXL3uuuve8Y53AIY3nnnxNa6X3N5pS6Atge8hCaw/wb+H2txualsCbQmsSmBNAxNlDiWsUCiSsvdLX/rifff96+JiNpru23ztnZtu+JHhq2/To53w/pKhVxEyC7QgNy9xa+LbHHb16mJh5iRq2dvvvJkUIJS+pod5FUn5V1ghWOuaNACwQZQa2APciMMeP5GEE3sdfqqYzjzccoWb58nlnJ8bW+6dwGKEZy0ED9BmdDJ6IQK9aK3unBV5B6U6XXxfv3Om1TL80zkLQjIQIhzOfQlzImcNJZ1d9q/1af9DRzediFfuSjYPB3wRV99fTbynaKQr+HLePffhR1d+ngDbmh7tiGvZopGKOLkaJj4NPLYpreHAeVWPJHCmMlHkZb69qs2TUs12n5u1JivGd56fzhadSCyUSKSjkQDRqoQ0A3iw78XW0O9oytnWwew872z0ygSqAPuPqIxHKzUwnjua0Ydj9m6FfjFLKYVcIAurEhiF6Mbtt9+OHfhihgPVGezKIF7MyS8VEHibHMhXX301GNgrwWuzOhOrrjaRs8eLOgl+p3Mahl9SHwdxJraxNBJu6oMJDP4x0BuRr1qzll+cxxva5ziNVj0YiuO/oYXCyZRkdmo2MI6KQ/vs9ByeHUMjAz7TD0LG2go/FtgYBjRixaFtjwf0qGlnwhp5lVNho7SyND0zde0118isQkSEYSvkxhf5LljG25EHztok8P7KJ8IvForPHDyYL+RHR0b3XXUVjxz6SMUASD7JssPGBxidgZQLNkwmBZcwuoojN2BeuVjLDo8tGXd2FOZcq1dKkHIkdNepYmBs6LmavVzBY7x+cjpfN+JOIFonhNXWrh+wf3RYSy9OlB94qPHsIfI0hbfviL3ltpXBLXdP+T57xG8TIGzgTIG0xLUbMdNvIWsSv2shFKCZVCX1S/XiRuHt8ql2BFIrmYiVWG5KOeqsTB6KJrpCmT4WGgTOOU2nWXWsekC3WW7ozcS29KdTWNRDTk/cFLt6UIM5n6rX+yilbtxWxXXe3zeee/H7qmNy+ieer59c9pPKCJaHwQR5fSH2d6qFlYW5WUSzf/9eejUzM7uQXUx19fpiHXMV/eSyM5nXZwp6AbNwS6+BhEm4BXWii5FcRMKwyZgiT1qthKVkJfvIBE4BP7ZynxbxS+qpNAzSeB5FWId1RsjUHYVTWrGWv7QzL8wckcZ6FwhhuP/++zlCxC8uNt4L4qyrvZMJOiDvAUiY83Hc4GTihN/97nfzQFh3yeHCC4zGWcW2v7Yl0JbA94oE2gD4e2Wk2u1sS+AlEhANkve4m13M3nPPV+666+5v3P+NSrXVMbRz641vHb3hbT3br2v5o+geaCDocSgfYcVihbLbFcHfTOuJ6+wnAgT0arlcngowhfEJlRSvf1FbPHVTtAv5/0puVI2NFw9noCNuw7ihUjvOorATrbXqSjfpIrtP89hoM58kW+IqItCINQX3Apw22sQussCLOQ0K6IfHW5buwzLTGxMHURwUyQZ8Gttul69YnLzV92+6fA8xomLcsv3i6Al7rRjVzEn9g19a+T874glyIIWDbqWukz0HPikf1lcbrVQb7TD7QmI+VV6gl2cqACrAQs/N2VNVc3xJmy81a41GOBTFLFltWeTvaYldVdwQwAajHcZowt7R6TEsnXvcUakROB/zZfdoVhsvuKBfv26PZoyhuL23Z932qxRwgdb2ww8/TBwvcBTF9yJ13FcPgDE67d+//8UAWO7ksqWdWLSyTf9CiUy/kogV+QT9AmWhq0YUAC5S5zb4im+vq5dLBauasyynWauZ8Hn7o5jU4sm0GZXoX2J7kS8+5CyKkdY7HI7hJAF1FmmRcGBORiRHd9R0O6LidJrG7K5WNWgGi01srC6tzroX5OIJeF348pXHgjQdV2RNJ/kw/NIwxmO6J/78xNhUT2+/7vOrceQESWXEqauoeWMx55zfcoJUAa0XMBKCLXi2MCcKAZWJmzEh0JKjiBhjFjgwrspXFZKrgDrXALbZ9JamF2rWV+5/qmwH9m/t2TfabVYreq3GUmDLH8ya0a+fbj085W84OEsL2RWADf4DWScAonNz6A4uzYLDsViSEwtYTJvkuKwE8FfaKBgZPIwE1Q9y3Ose95oLAA4nuwPJ3tUO4Sat+mVZDbdVT0SD6Xgo4nN1u54IGZ3xAAbhTNDpjZPry2D5Ccs8D2e6g/V+tTJV+AvDIvVfhs27d+qO9ttfaSzVgtz1wncI+0MIs7/bBxhOmkBilkhoz8GnniRDOVLgpuruxgOnP5jsWGn4Tq84Y8vuRInQCVOSVFvyAMGDg6hpJVLaKWuv/EOeskag1lQ8YfETDxyhFhMGaZVVOOx2BDQ8kvri7qY0TAdgY6ldpC8bMlkXgxzhecErA58g9nlr4KZxkata9B3HnAcffNAzC09MTPAehFPgPe95D9RZlHNOCC1NaG9tCbQl8D0rgTYA/p4dunbDf4AlwAublz3v6bvvvhuSj4cf/g5MrX1brt184B1bDvxoYnBrS8J1RWHyY/AxjWjAwbxDIoq+BMoEdgaUYLQQWY5nEz1igzBBvw888ADIjXgw9OB1FWPDKa/VrlLCBMZQKR2EgghvUvZRQXByJm7WI7XyTnv5hp3VsfO1mtPYNorgfGde8LjXqo2uzrT2jjvueOlFL9/yl17zckeqlvvIJLla9VOL1mjGR9JmQAJgEurg0U7j5ELjuo7H9rc+FDIn5KEvneWvUXUHjrR+8bT7m0u1cDpqLJexPrmFBmF4Gg7m8B/blraly0j67OuGIBym4aJ+XpYNaqunxisPHp51E0NVO0gYN4Yzn9+oN/DvNeAqAntEfWYKz+cOYzhh7ezy0AdtOO9QAarnK87RRW0qry3VwC3OSIc+ErV39/jOHD/W19+PY/P65fg5ky6FeUV8ID1aP/7S3iEw9GnPdx0zEcHGnpLNcsal3iCgbtwy8dVHpfZqpHAw12JZO7ns5lrGTM4ptPRyzQ4Cem2QmBsKAHodP4zOJDXSNI7Xaw4O6oyEbYGQjcXZiWa1PrhtJ0mAMRRHfL4mIbGkLzJNzMDUmF0kTVZzx6bB7kQAj4+I3yG8vyemk05ZAJ3CEdJr1SBvGnMV3zY6jqrzBLRwAjG3cEpXGlq5IfTIuL8SCCoMwOBAaZdsYvYVeKMXC/m5+dlNmzcjthcsdt5JL/+pmqMGXD6onSLW7mvvr7p9xQYMpKI72BIDPjzG9ZDpBn34uUBu3Tj6/MHc8grXE9jMIlpHZxd4rNR0Ds/WhXet7FssQ3vuazpmHcwMw5PD6oPAYMSgzJiKfEss2JISSf0TH3JZj1Gt4o9qlyc8OebZtxFCvbgUCMYI1ZB8TKq/3qfalacOFGIA7EopD7tZJGCkYqHORAh39ERIqKFSQTLM6d1RDbJ3WOgw3XOJ/Lt8d6LXEmk687zm/vbdVqHlZ6YxkHA48yiQYN0QS1E4gPASwSzMyqlm1POFpbns/Hy1UkOwyXTqtttupwjmAx7pEwX7dNadIFq4pBWapsqlp0GzJ0iYOSpzTgzCHhJW36QVclRNEYZS6KNNapd0U4QKM28RQn/MBgkr0iwWPuT9hTi8OUnoPj7P2H4x3nJ/eSMh5akhktLPv62fzCnsExPBW/VLX/oSC5fc7HfeeeeP/diP4SNNrA2lXUyB56+q/UtbAm0JfLdIoA2Av1tGot2OtgQuJAE0C/Widxz3yOHDd33pri998a5nnn1ON8MDe27ZdPM7tlz/dtL22grTohKEDFQHWa3vijq9CXcwbhBShYXBS98qWsbZmyiWKAu8/uHpQQOA4wdbJcoiXsfe+vflfvHjAylmSKUO0rmWiyds7uTMzHSqa1/v4LWu5j9x7AQd6uvrBwCf1V4lDS6WAqTlalvXLNlBe1v/yo/IRJ3CmeuHXxACh1a/vNJOIjectE+ePAmsoiI0MNARdkXP1VlV/Vp9KO3NXWk4z8wYREWfXLR2dvvHV6yeBPhE4vF6Ags9rT/bFfhsQF8R65USOn9W9Ovvzf21Hd6Xr6Fq6/kq4MqtN0gxo1k2+qWGOagv4pDsakfaGUoCfy9Km7xwP73WAg++c3Tugedn5hrRYKInGE+hCoMr6k2XYEiJ/sWsZ5rpsLM1o/cnrH3dGPlkkNbH+sW1qFI1bUGhX7H9VoT/aHNGH4pau/sAjyi1BzG6bhwOrgHNAmXRcS887JyJqyRLG1TK3cF9AZ0V+6zIXKSJ6cWtXZ2CFEufAJNjy/ZUycjW3bmC2O3pfsxPFlZxfhbDrwsO0asNOxyEw1jiUAUSN11Jz4SXhO3Ul+er1UJmaGfEb2L0BbbBjYw9lmtDAR1K8aDewre2M+4nirufJDRxk3xmZ0lyo2iB5BAHEM/spQ1THFWwQ2uFhkPwbbFOIyXvtPhIa9qZ0yeSyXSms9uLquWId0uzo+5O98zYqeziot/v27x5S0c6DWrhMQPCeel0kpta/qcAAZyyrwqTO1bdokzajc327tn1+5mz1jd+EvMrzwH+Fz9bp1oqLMxOVUt5v271dqauv3p3MoozOJiZc+U2KTadxZI9X9Lmy85cSV8sE2NsksoHrmP6jnG4KSzQJNwSL26aJ4BfSLKEIBoiaFYcwcOAOhkwKVIhM2kQ1m+6QWdUz73mSvvUP/ldetqyrWar5RBqq5NmjKhsX0c8EguakYDgT/Cw+KgHHcyhPTFWhRz43nnIU4za1v6q0tSXDUdWz3mZP8rgrU8Vrb99ws3XyLYlSxukPiKnEWURQgszomcWxi6NWRh+RAyzAwk90CrWcwvRaGjz6CidPHHiWCFf7B8Yiqc6qq65UNVPZe3TBWMmr6/U9TKJheGRxkFaSkZ+0neFhEWM8kWeMdJ4uTsEIWvQnuMdHSYZQUCLi0XaJeF8f9QdznhcFQYrQydPnBgbO8Ow9HR3X3PNtUHynUl3L1kIXr1yqdpOnDgBDL7rrrsef/xxGnPgwAG8o9/1rnfxTvQa6X2und7+25ZAWwLfSxJoA+DvpdFqt/UHTQKiD6qNHTwnP/uZz33xrs+fOnXGDCZGr33T5pvePXzd7YFED2ldURjwDQv53XRQ64xKvqIh4RHBjCA8NyidFKSIOV9ehNSFZkKw4pEjR/AoQ8XfuXMnqvAFXvbr7bzAOS+tmFrEiGW37MI9Rumv9fqzumWUC6lYJu/G3mh0/gctfLP4HkocnrRfqUdqh49VwaztibopNYgJSp2otF9RgTjAIaUKoZiq5KRyongryhdPRxIkIvqWaF/q1xc+vIrk9xcpVOtdRkQgKM4nvc1zzz0HxAL3gn49Rq6XlvdCyZdpj+GiaWJyWfblUeKL7kincWrB2tFjnsi6WxLZ64wPdrkPIAbhM3PIu+k6vpb0x/WdaP70I9ZfFFvRqN+BbpcMN0TuoYmi2NO6oOFu7dY7A841/STCOYdsLrUHSmguiv6/PnnqqfHSkp0Idwy4vihWO8YG/T8UxAIs6WQYdTA5uVgGYtZVeC+Dj18yMuu1UyydWao6hxeRgw4rD8lxNqd17EV7e32kguJn1nQ8AMxVTDwpThXofa4Xdc6d9bHmV4bYiwH2zryYyzeWifnXmzDKuOpWLPfYgr3U8M0WrZWKr0COXwlC1QnWjQZ9NVISBUxSHzEaWDKrTSccUI7Nmg4krvMr7rmamy+sBDU9kkqDKAKYf2GtdvBfJRGBS01KAABAAElEQVQR1jOtM6En/SAWZzipJwL4DAM3GFw+XjLT1542zI3DRw7HEvHegaFCFTjEPwKSAXKedZcrvbtKcN2p40dSqUxnbw/Ij0cQzAK0DdsdGFv8k3FXNt3p6ZlTR49w/24aHd6ze6e4Msudd3YDPEExmeWek/+V3yxZdgR5ikMyKBSkatkuImk5BuAfyiV4sNnnJ5bCFPRU2FquliLkeaCMpfIYwM+8XJ6ZmcHJ5eqrr0J2zHRwHbl8WXYB3UX8+D7AOi79o6JC3ZkrWnMlF9JjPhdLEKobWImhesKt13Yk0MD75/n3Aowp07MPs9ToIzWzTEy6CWO3QHrvGYMZmZaI+NURYYta20CEktoY4nHHikUjYdINi8s3q0IGZFFRBYYZRJYzeM7jEoxxGNNoKoQHuPeMl6eaN75rRV7sXyUfgqjtg3PuTElfqhnLFbfQJBwdh3aiEtwmsdCO9I7bMywtgReAnPBuV1TvjTi8biRaOKI/++QTSwvzrA+MT0zt2bObRNwdXd1EBYOEWeg5jYN0wVisaHB6kytYHKQlYJiBlpuMyY9YxNFkrdXsYC6WkUKehswr3nFRsQmLT1N32Am2Vlor4zG72B0z9+/b0d/b40lAFbFezFpxl/5X2qXWv4gTBgnjG8UtDFcW3tHvf//7d+zY4RW5ocmXXkf7irYE2hJ4PSTQBsCvh9TbdbYlcB4J8K7d+AsOjAefOfiFz372O488MjlDLGxl641v23TgxwaufoMRTKIsiAJn6ritdkZc9I+hpJdTUYe3RumqogF4JSqAyGv6YnUCWoKmTj4YHK0hmrrllls2Noz9jUXheIblEw8xD/Wddeb617N6p+LsmvPP/XpX4NOmUUU7XJy4c/bkLVf/0J+h9lh60uj+UyP9S9hXvBIwvtAmuuNUyk697pTLzckJZOCMT6Kv2gsLzsKSDhEyaqhtt8YnJCwPF0ZUSAAB2I79nl4tmUAELBnow31aIhEYHnZSGV9Pt6+jw0wlNZNEp56Wv97qc+/gI4pbLKAXlfotb3kLeAbFCDnA6MsFG4Vz7usv31GkivL63Lydrflm8qjl0Le6pQYcNvpUrvGujt8adv6BHrem4+UvJuuHQr6gA/1V5j0lI1PB6fihyv92wv31csMktBKzDOq6mF5E6STuzs0Etav79WTQ06pfVaOVhq3BjXTfI4dP5IyK2YHPQsXyB/2STsayNAyxjaZDJCcDhJf+aNoYiNr7+0x8WdfU+rMbsDaj3KWa8/yCM1008SVWLdcH4tZVvb6xE8eq1TrdZ0Q42RsXdjDn3nDDDV6g+9mFXvD7q4kBRgLTU7BAP7V//9XDIyPLVedYVtyep3I2PLqlmhUO+lpgYEJbNaPhOkGfWW9aoYAPDIyDL7EM9ZYFm7DVBIkA2/jVxpma8Ne6TapkE+ALMgyTg9d0kwGnO27GAo74rCYNQkkLhfyTTzy5devW0dFRuiiiWH3arKJZDhJxgOW4ZjnPHB6r6+FAott2DV8wRK5cdbY8PhgMWVEgtsKwYdGbOXOsryszMtgT9QvF9Ivgl5zKFWxOvlB4+qmnS6VKMpW88YYbcB1Xx1/2A0S3Wqeculogf/jnWQxXG88tLsRLwr3EJ9625LUmLBlzutgwxdIoiNtrDPmuLZM7XXPJOrWUWx4aHEym0gK+VL9A7CG/HhPXX4HEcBSLH7VOmZiIWzNFZwZ7ZhGacZ0wV4KHGw5WTUHCwGCYtmGfZp85hjUeaTCdPfAmuJpWi0hWp62ak+Jo7W3SOJ6Jgr5Zo7G9hT8yc1cqJYBwOBRkZMW12+9KDiGoy/zkoII5WWd1wwPDQugQZOHjJUsLayI836KD1KwmA3Lib8OBWJtAdG26oC1Uhec519Cx/JdbbrUpUqWDFClmYaTkwzuaekkUT9o8UpTpCRJX5eaf/M63ErFIMpkYHBiEZpkq8F6A9G6pAgZ2WJubKBD5bxSbODtodQWwkZ7QEhCbwVNYJZTyJCNyU0OnnkuSjyrodyOmEwm4tXw2bDSGO4L7RjNbOkxYu3DsJ1WbOn31KvnjPdRk3WH1l9WSL+UPooHd/Z577vnCF75AeA5O1zD5v+997/vxH/9x1ojXS17fuZSy2+e2JdCWwJWWwNqT+ErX266vLYG2BM4hAXFwFHOHffDpZ774+c8/9cUv9IyduW1o5Mc+9cnH3dS9Mx1uIMFlKLgoZziDYQEgd+JA0uiIsB6PqVQ0nLPe8J5m88reylwLVMBi5jkh4+KLYyThwZS2sUDUAn4i64kXG3mOjqlDlEZR2Wx2enY+Gg5u27HVLfy9Nv9bhtbQWoHms8lnD344W7n5ztvfF9qXdc2Wq8Vqhd+zvm3qc1PG1Iw9MeZOTZPJxayWTVx1bcf0FFbUW1Ey0XMkuMxTlsTGJzovCjTHRbNd1aDkO9qQa6AWK2XI0Y0W2VODYXt01E0mtf37/Dfd7L/xhsDoiBvElY7/VvUvJVr8Zkvk2MBCzgIBhkpMvrAoYSeXgl+PDaninPnouE3YLyxKvSljqQS5t7uwXLXzD/zq9l8I6PnWVGzpD3sD+YgjdhRCGR17U773D7N6olx0tn6m9O2ijQVPDL90lQLRPjuDzmDa2JKyR9MY7C5qUeACvadYYQHWtOmV2qceONII9ZnJ3nzVCeLWyzgrP9IWFMdQN9lOV9gYSmlDMfeqfjMi9qCzZvRqPaqpMr65uvv8vDtZ1BbLYmkbSRuDMXtfn4+1ITz5QTv0CLsfxDYME0eOHTvGOsUdd9yBM/MF2nzOn8hjxL3AXXC+Vp3zKu8g039qeuaZp566av9+LTU0ltOW6tpsgWBUAWw4MFeagoEx4fpNOJnEtE8u21rDQkri2aycbFkjkK+E+7qOOlmMxsxMeLPBxhjtMY51x42kH09RZzhlRsn7JHeAzo0M5TWlrlNes8+oCDxQnvEkWz54dGJsernCiKiAXnADv/UObY6n0txKIcONBXFDFUrwhelJnIpX7y8pQsrBNXRjlDUN9kpX3Xfh1jp06DDyf+Mb3/gaUcGtytn749Xq4q0tqBjHcvmnwpX5BB7DG3d6bAK2fOYfIzo42J9MCH/Y+nTzGk/XyISMoRhIDBgGdIUDZE42MFouVay5oj1VIBuQwzjmGuBhP3HXJCtWxmodY3VThf7KHIT/idRjmIiFxEuce5V05I5jh2/yV+Qtf9WgyF/Gudao4yfD5TxtxP2e/2XVgzhnzKFEeuMvLe7B0Ed7btIQPfBewCwMgZyyplIa/+QWVpNWKlwtX+1t/JDf1DqRNzfgNis17KWqNgfsL2mLVWO5StZuYSkXX31htxIdEpyvcgogHzfmNpKBVnfUaSyeGsoEIZDeOtizZbNQMT/73LOwj/cP9qTTHchnqYZp3T2eJQWaxyBNqLAiUROzMM8isfwzsbE5KwuwJxlpHgs9VEpKMHzFTQ1fCdZ3dKZlRvF19cOYlSJuwu2IeJmWvQt52Is9/hXcthvl4+1TO+gX+ujPfOYzG5Hwe9/7Xjga6elLL2kfaUugLYHvNgm0AfB324i02/ODKAFeqGxoKU89/dRdn/3cY1/4Yv/4+BtN/95IJBVK+6KRzr/4Pxauu/X/O4gPZAAukN6oPZQyMPmmwniFrSts7CiMdvlEKK1a27AOff3rXwfBwu2EHQkv3/U3falYOnHyBADYi43kio16huqci+ILCMFqWictidZKp7vecGC0Nf4m0x3XysH6n4WcR8KPHvjqSqT/5m+9O3nr04nfQ/1vtg7HrA+Ug02gvcBcwCoYrakHmr4AppJWOGX7TCvRoaUTLX+o1T2QCLmxMOHDum94yA2FBHNBcAs3ED68KJHj43qjJtomoZKnzwRKRWt2Xq9WffVaABOSWF8E7dJnym8M9urXXafffnvozW8ObNuqGUSKClf2gw9+mx4B9WF1xtv5ypt81wZk9S84iYSxRxZJiOpO5ZyhDuP4dNktz2NX/JHBf3jn4H9DS8z+3/3GIxlbUgshElErsVcFfmY+/f4FjMCfL909bv0QYMcbbrqJp+32LqM72Nrfj0Mr8mA81xTQs6q/iK8UC/ip1WtuIPrsnDuWs7LVAB7L5IgFyImKa2qNloWBq2k5nVGDRKDDMe2qPgOjIrrv+aqmWfQ9X3cOzWuTJW2hBBB0htP6cNTe2++LKLux1zoa4LlAA88AgRjtb775ZjyZz1fyhfu0JqVLFggza3pq4tGnD8cH9+npfgJNYR0jny2onfmN26sYdVt2KMDyjpBgSaypowX8OmZhQC9+0Zwp1Nx4iWMWBwG7GMzNet2GEysM153pdqW1tE8H+o5kjBgpeBWwUkMng8vCzcGDB1myofvezctBfCqyFXux5BLyWhf/adzjtdnJsVAknuno8pt2RyLYFfPBFB0OSDsV1tWKpXJLJeJmRYBlBRYXuIqlgQsEVHtyYyaA4NgnAJvzacYrG4ULj9H6r16lCgFyjCFTs0bF5WLGhDZsIV8+dnJ8ai6HH3U0nuofHg1FYxKXKkVIX7kt1D4XysXcDUSdstCANFhcAHnyTxjLIHyu2jMFa6pgTBac6Tz+wz7GttkycPFFyMTVi5UYrxSGTR4yssxEUi/+4XzhVSLClXrYpKnsqwU8rhJ+Y0RHLcgrFo15Ls+IDgd/poRKqCs5dVV7hLOqO4xxGNZDvSOqdYTEtVtupRduJtUnr6pzfa7Jjd+8jktmI3wWFivudMFZqJAKmIxTer6BTRgHaRYUsFqL80Vt4aTPrmCmJuA8FjAyYb0zpB24bvtwR/jgd77lNCpUDNf7QN/gwEB/PJkAYxdqoGv9RNY6nTemCi5518otcZAWNjKJuObRL89sImF4+1gWXHCyvOmlKUKMrM/hnxDysUjBiNB3FyQsjFnCHe32J4xMhBNYw5MuX5bJtkE4moeEP/vZz957773w5JF4CYMwZmHPJnxZqjvX+LSPtSXQlsCrlUAbAL9aCbavb0vgFUuA9yg8ozjqHnr++U//y788cc9X+o8cv9UXuDoUSYTjwTCJScX71/Fp6T/7A+fNbzm0ovUlydCIynWORWZPzX3FjXnZC2msZ+mFEIh9gN/o6Cg7qLAoJaVyKZlIsU8g6YgklZHyxIYlcXbOkeeenz11PDQ7F8gXwgvznUePRVHptk50/M8zqH61v4gG/668lLn1wR/6Isl3dh356PYjf+T8STDyo1XiCsd+f/ds40Chs38lM1zsHrLMQDORqodjWK1qsQ7J3+Ize8VKKQl79vS6u7p9cVR/ZbQUhXVNgxPtTylzonuyj1YFFGxhFSLGMV89M2afOGnMz1mPPuY++1xkKUt6VQmGdfW6318/cIvv53828eM/7kSjOMH2dPfAHrzOkfv6ajlMoEPz1nzNf2bFEjChawvzS0vLi9FY+me3/MEO/z9qVnjm3w0GCwnVYZV+RAbHsffle/54Cr7tb1T+4pnGr0ApjMEIswvurptSbmfIvWaAqNFVa8Yr6yM1UhNQ58knn6jaRnLLgek6mYrRnhXVM/jAjws7diRMnTqabUfUGBBmHe2aXvyuGTrVH2ntizbmlTewBM0+P+dMlPW5AgREEuY6FMNr2ocqvKruquvQmwHArFaw/nLmzJlNmzbhu8gvr6xTL2rKRXxRYEfOY+fkxNxXHjnZMbKz5u/KN8ibitZOLKut/L7F6sUOXsiRAAHAJOwVr38AmN8waywQiIM0GBi8BBuWRWA0wJhCwyETiujehNEJ5iFlVMbAGKj6LwZw+kgIg9Qud6PzxOOPL8wv7Nm3b/OmzfmWM1dwlivAM5N1CEbaA19M+7nTh4e6k3u3DSbIkCSy9u6kjb1dPfL888+DqOEDW//tYqQKbPj2t78NAIaC20MyF3PVehWXsuP1Sa1rebvq4rP6g6COHDueXVq56pprU53dOOVWlYUTYzhBqjBiq/kmDzX5H1mqv4KN2YMaGtoqwcOgPozk+I6b/LBUsibzzkTRmcpps0WtZPnF7IxjtgQzS25n8LBF/iOeMTxqVsmlGVweacoiv3anMpTMHEHCXAgGdhxW32i/BaN4ywrgJMCyoExm4YviH0tWMCCKyRq7qMr0nsFXCM6qpMwQFjKIqKds6YfXG7UnX7yntvrq3blrv/B3XWCuhVm4bi9WcV7ADxyzsLZSYyZrpZpOKq9ardkC6+M6tHAq3dGVSqeEwSseIDl5V9iJtFbc0rxdmjecJk+eXbt2b9+6jZcgleNsX2qwlqedWGqNLRuYhSm23IQST6/het1oNcSMLjAWR/sAiyhCDSHjonqiBkIczsU/HMM4HfdswjBmDcTszR16f1zSffFgUJv64z0kvAMvFsXqsYv4sy4oPCy++tWvYhPmkyU2kPBP/uRP/sRP/MSWLVs8wW4U70UU3D6lLYG2BF5bCbQB8Gsr33bpbQmcJYH19yXHT506xfvy05/+1LGjJ/773/6/b/b78v/pjyKhCOkVcdrlVY02TOYQKx6O/dVH/Ndcrd7+nmniRcrKWVW8dl+l8RDJVMq0HMsPzqWjo6OE80n4a60WjURJ1WkaxubNw1Y+X3rmUO30yfDyiv74Y9rERGtqOlIp+y3cjQV/onA7vx0I/dum0TKXftIfGXcP7/+j05t/CSNssnJq93O/rN+wMPwHc65pf2Lmo9/I/4IrPoOoOvwTCeAXh94X94u3Z9iHvytIyo34tL09hNqJRuQB4AuIgtV64pZR3NdxLHF3nC8XV2r140ebjz1eu+vu0NNPhvN5n5ji9HJfv/Ebvx7/lV8mLxNQUyrZoDJeoK7X9Cfogp+YgqGn+dTp/K5tnbmCnkk4s9nmQGf4Kvc3rwr+LQB49leGfMtxWutNPxEiWufVK71/MIla/a+1v3iq+itYF/EthHWnK2IPJY1tGfDkq3J+pi42nMUPPfd8zTZbqU1m15apvEG8LrCXiMoArMUESkq2FdL06JmY0Rdzh5LuVd24LyrnzfMIjmIhSCq1jGdJI1zUZ4ui0A+kyPdrXdXnU+maVvVcrwDOB3Ex4o899hjmSi9RyhUbO9VauXNWau6DR5aePj7p79hkRjPlOs6req0JdjIwReKOjcmLyY2pDrZnDsJ6FQyYNj61PmY7zM92JCzxwOjqNL7ZciJB3BK0DEzvMTMdsEc7iMPEwdbrmYw19ylf8NdYlYPj1OrV+7/5nULTGL7qxqYWAbvJT6vSglBAeIYlevz0qXQiio+Dd+H5PqnCA8ADAwPnO+ecxwFy2KJxCcFn5JprroE+gNOu2Ii8tEl0hI2k0JiyaQbeLsePHdu8dXMoEsUCWWk64OFqkxw/4vorfr8Kc/Ig8x4AoGBEyGoEiwhQD+CHD6tWNKgRiS1MzhCbu9pisTWRcybz2ngeO6pZs311YpVbHpMWdFAklxIwLAuivAAEDwtjGet7grZXrdDq6aQeb5xUq9YwC9MCmBcCAWLAVxGhSFKQMK8PfApgBMAzGUDIP6cjqneEYIuAJVEnaiYeYrEL6Og1n1pWV7teKp+zjiArnpTcubjuL+PJDE9YjmhhPVuDOUyihZHV1MlnA6nBQKwLA/Uag7RGYuGuiNYVcoLNZaM6d2Dflr7OOA1gVZFlmv6+PtMnGZjKTXe+6p5eck8sa5N5dzbfmsnhwG4YvrBr+hkRqZ+H2Jp39PrdzmFkhSAQINzRYhIPailWASK4TWmDcTKKw41HgmUEq4pQU18N4noZZ/X1Er6qSSRBQ553tGcTvvHGG3/qp34Km/ClLhJdQsXtU9sSaEvg0iWwyi5z6Re2r2hLoC2BS5CA0hjkfHYWFhbwmPrUpz71+ONPGP74puvf8vbf/f2h298Wby7Wb7zRf+QYvrYsb/P+5i0OGUjtTbf5+ntQjZV6Iq/tK6Yprjf7ha7qWiweJ+qVDElLS0uoyCgOs1OTj9x3322jo+aTT7nHjuZOnnAnZ0KNWqLZ8NQMGF0s3cyle2rJ7kIkPbH3tsX+LQdu/vh+7T7HCBbC3cuDvXP9b/WoiiqRoUJsV6JEzh6UGSBZjbhfZbIQRYU9wCfso5mo1hl2t2TAve41/R6R8As6zQsNPs8eOi6Ji85lyNW1aGg2lRrfsaP2q/3hxr8ZmZvvevgh/d57k7Mz7oc/XPjUp0Mf/UjoljfQDE8+V2w4NnZlfWjGFqpHT80eX8Bt1reUDQYjCcsyItFIDd1U2yn+tb6G76qa9s04l8gChqiIcIW5wWuqrEI4mn/Z2ooVCkssliEQVl/c1xmyhhK8HV6VUghPGOhocmJC9wejg1c1I31nlu1cQ5ZxGnhjY8BUWM6vSzBhR8yARIeA9l2dMGBBRbaxr+fYr7T05+at6bI5XxQbXH9SH4zbsF5ByfPSaxkgonYxywC39u/fj/XoHCW+NofU7cpdr08X7VPL+mI9sGBFY00z5COtkZh5yWlUFZOvWW14fFeQKLEiAN8VGYDxhbaCmLTqjuVzxQJMjKmkBbYxy8ZDvoTf6k2aqYA+krL7kyZgWB4w0n9xcVjv0OpUATO72kIj2OraMzs5HynrsRhgmf9cv+50RYVQIBkSum8uj+/AbHVRcAgD1yuQJ5dAjATmgWqeHDPbtm2DUPd1uY88KVE1G6ZsJUGXCP8z4+NE+g+NDG/bvi0TDuFF7CFFrPSs3VRaTrXhgtPKLa3W4tYR5CvyktU9Q0JnSebc0hZ4Vin4Sj7bZNDc2WXeMCRx3cW6O523QcLjOY1/FQswLKmtgNYtyA8sAcPioQ0llMazU633AYZ59ElpMkI8DyPhkM80cYxm/ZH/WXkMBCVAWHrkCrVywyaqlpsaXmXMwsKaFs478YBBpK4A0TA3WgvWKLL74iQMSvfeL2revDB5pLRzb+BMg2dvJK4PxoxmrwZbOA7SU3l7vmzAdOVb0rU4wdMuSwbcrdDysbLDXJ4Sp3EzFexOR7oqM9pg0U4HakcnspGJmecPHe7r7RscGsyk0/HaYmpp8mf37c+3AieXfU9P6EvN8HLNh2BrFgsHwthHjAD/kAf/eC+w0W5uAItcUrid27JaARpf9hnhsjMR0I6HzI4FtzfuDsZaOEoMxKHvUvNdLpUHo/yVbeO+d+SiPtUkkqz1nhd0Pp+HO/qTn/zkf1Qb624gYRIpefECF1Vi+6S2BNoSeM0ksKrAvWbltwtuS6AtAQG9BHPxOvzSXXd/6lOffuCBBxwtOLzvjdtvf9/oTT8Simdw2drbab1jxAkeOlj/yF+GJ8b8moOuA8lS87rrwr/+740dO1nnF1HKG17e9xve1q+NhJUOIC1n82pVIbJ8wR3PWlpxVpZrD3xLP3REGzu1fPjwfC6/p9mMOahsJDMySaaT7xwsxDtLiczErjfM9+3KR9JzW66qa8FYQTctH16Ft6U+eWv608SgZv97dzU7Ojf6K5YWRHNn61m8p2vgnsz7lvn29aXfOFy7XYO0Cre6bl8qKc6xnVFnS9JIRchzg5Mu8qBZL9o8NfFFh178BWvP2NgYCS3W1Xd8C3FG9MhXH330Uc8+vGnTpkQqRT7WxunT5f/rfw9/7BOBVqMaibkf/YvEBz4g8WeEsIIoX/PxeHHrMTe5Tr1Wf/7EyW8dy5ftSN7IbBnosHyxdMTNVYxYyC7Wjf7A0XfHfyjkLjnziYU/7gzMJDFOQQWDKFu7V/r/c1aP1FasPf+w/HXX1wX9L2y3mzJaT8i5bsAUL2I6paba2XVf3PdHH3lkfmEunszER/YvWInTKwRJ6g0mkO36/QYpf1nlYRpA4J2M+DA7Q12zs1MbSqzaJDdCOK9CmYlqoKuW+9ysPVkxpnMc0PtSqzHDcQUBz9doLgcqYC57NZ26uK6vnoWkaTGz6vSyM1nS58r69EprPleNRsMBP1HnLmxGOLJCnox1F3wL3OUTF2jsvVxJ5GfQb5IMiZxGuH+CflD2my2ywuLF6vTGgS7EOtpbM0IVtnECMrfzuRxVlysVpmY0HsXntmbGS3qigTUZS6Nr+3Cp0O24nyBJOJOgDlY33obuXcxDRo2IXPMKROpdi+/xU089hXEeII3XKCEUG5rw+uzSMDY8XE6cOF4uV5gwmzZtAqKvPyg2NAvpAlZJuG3DZFZueGAPKy4Pb6bx6lOBB9fq6IiMce0hPRUEzpBU6dEQDw9trtA6kwMMkxFaW6kbeEjUm05DcjtJoiDY6y3MrTbFQcclNFokHFY+8zJE0gDLFgdhSRwdwDuagxhRV+sUeCiV8gjnTMzCeMrAu67Ys0iSp8X8eFvAnuXCI81M6AzrOL2zJiULjiBiGn6uwUU+8sNLNo4y28G6i1V3OmfPlo2FingyEypcahqkj1YM0kLMz1MbuzS8YmQVToXslL9hVhZ81cWwVQyZLaYzLxnYAfZffdUIbA6aUW452YrcR8eW3fGcvlw1C5IxGwdpAboquJquCnc0ywRiL/ceH16oBB5DkkVJIqWplDxSVNoRdgkSxt9kU9rA6yHCLfdCdyhJie2FI5e8t1FELHl/7nOfY8n7kUceYQ3uh3/4h0HCb3/72z3qxFdf1yU3rn1BWwJtCfBk23iXtgXSlkBbApdXAtxfpMbBIeqfP/HPX733q+Tu6d1x7Y7bfmrzLe+Md/Tx0sWM2Rsj4wt6v44a6qtVnempxqOPu2OndZ/f2LUzcN21et8gaR+kYZf9VbmmmL2o10q38T7QmST4DFhbrbamZmoPf8c48rzz5NPakSOBSjlgW2hKnFnTjdPRjlA8PdO368iOAwvJgUKqt7p9f9UXcXQTGC9N539UKswseWf3c/VMQXE461giqYFfJRWs/JVPpXeJ1sd3WGP8tKIZdCsHfLUdgd6U1icufNrOblm8X3P4FOm8qBfn+YLvJbmL+JEd0oGyWo9Q8R5EcSQ6FO3Ey9iBGxuKL1/Xi1GicCv3fc36D78dP3Wi6fM1/svvJ3/3d8n9QgkohNLDK7gB17/5wAOn807R6NQTw244iW6HYQTHAbRMUum0LORuvyH84etCf84Q2kvR/N3x1tEwwMd/fTn9w0U9XLf14FcKHznR+gXcbDEQdcesobi5t9vtjip3ylfUJe+dgkxYQZianAj37xorBk6ukEvWKKk8t5K2p0XGWiF5Ij48Hsbg3Nqc8W3JOJvTWIlktpxTkKpkp2brz89bM0UDwIDlrTfpjkbdvX1GUgJf1WLIiy/mKvAV6GUdwFz22+jFFb7wjaoR7LGsNV/1TxaslZpZqNkhvwGIBfQyZxQjN+cDigzy2cKl1BT6K0Avzs9wtwGlwCq4i1tBn4+USAHTxLc2HnKHErD+Ols7MJuDXNWdszZYVIprBsKnXAITGppZaITmK1Yk3Z1Kdgg0cF2f5mRidmfASkaMiD+gjJdXTCovkg9f8MU4dOgQpEEeHvB+vvjWeEhPlqDYzj1xvCIv4VPNNKFcgjbsxIkTPChw0j5w4IDUsCbn9eI4mYeDqll4oLDgs2ZRaoIDNf6RNwiDsADQ1U/OXHtayEOD/FWSQwgsCh5mrHM160zOGlsxxlbshbKZy5Ubesj1hVjCgIYK71+mBPHD4Exhk1ZgmJUMoKpEi8MUJc8ivcTCh65DSGbi47wqE8Hq0n7VVs4HEJJNQHykfS7rIAQMQx/VFXZBhgNJHTycgfbMD+2Z1+mLkqwnNwXAAeEGdvKlqjtT0MgdpXizxEG60NBV3iPJ6sz8J1M0xmdwOOzltcJsoJFP+apdQWvvUGrv9uF4JDw7M9vb1yu51mWMJSPxQsU9tWIfz2oTOW2Rp0rDqEniK4kgEMYsbMKkj0cm3lCphssdIv9YsdSgRYez2gvYJiq4i9RNMW0krY2kcP6H31v5sXv9Xh/jV7ezJhaNV8+n1UY6cbgksQb/3M/93K233uqtyr10ar26attXtyXQlsCFJNAGwBeSTvu3tgRegQQI42IF2tGcZ5565uMf/6d/+ey/zC8s9W++6rof+vGr7nyfkx5BR+mO6iSr2JTSBuJ6KmIG4DZe1d0w7GmAZpc0P2zhkB4Kqpe3fHsFjTnfJQLnRB1i86r2vlGF1CY6EjrW0nLlW99wnz3cuu9fzeWsf3baR3ZKuU6v+kJLqe5cov/ojpuzqf5q9/DitpuKoWjJH4Wth2SimKsJCQah4qFHzwLYLFZVCtHSwk1tx7HG0KQthmCpzGuK6iHle754miBkQBxe0NUeo3qbv2eLDr3naNrdlAFDrZonlFguQTIbATC4CIgLpxe+0B4SJsMTIVvgYU86FL4mJPnLUgBis6YnSz/zgfijj9K21l/9VeIXf1EwBarZZR0gGZkXb16T+AScK+ppd3x67ulp15fpPb2k+wMu6Dca0CoNUe+AtvDwEKQXNBZ/JPJro8G7BKOjFooSCE4UQ72tB56u/tIDpT+xiNEzsKs7mzvNkZizo0tOu9T55jWPJk9MjHd39YQjYabyTFk7mtXOLGvZOrqvRaZYJGpb1f7wmbA2RrrOkjZi+reMZGKbUvb2DjFrycCyIdANo0rh3gHosp6bb02W/BMrwEOjN+4Mx919fdA+IX254Kxmy4Wu+9BDD4Gybrvtto0rGlLLa7MhaIU1yJvqHF2wF+u+yZyba8J2a0MqVm3aoZCJv2sQM3jLCZmS7kW6DRiyJQ0y2X2jQR8BwIATATrCf2vULSsa8oUMeH0x02lDcW00I88NcYZVlXkdV+tVchdTIusL3zl4aq7kdA1vV8q/eJUENRtXZ2K87Xrp4Ycf7u7svpYlNrW9NsJ4mVIZnY1nQLPH9PaiJc8eSq9XnmTVNSrmVia5mjHnGv6NRb+ifZqH18DY2Bje0Z6DNMHk3H0bJaYGm4/V+bo+c9VxnTuNocQoWsQK2nDxl7bVoqF4VHOFZG7zRlHkgKGSWzgdFAKnSEi8JO5//MhyK54P9MwUoJjyC9JTOYdh0hJaackJRCkehxaBM2LW506vVMs4dFA6FnV8DVj6EWGqlilxCwaXWSLTQoKNCdPloRH28SCQpL4wSGEWZpr1xSSvb0dk1TdeFcE1q389iW4cpo2jyf3oSYapWLedFZVLabKgzREqXNGgei5gMG9oxSoNlQzSPNOb1ULYtLoTwf50sC9pDsScUHNp9uRBwPlgd+fg0EB3V7e4sMizDCQMqBYkfIInTF4nRROMWTVJ/qzoo2kKCeG5rVTIDIRbaoBUo6TX3G6CvYUqjOjooC428KjYhOH/64/a3UluvrNnjDxhXnLw7JMu+N2TD5+s+HhIeAxOvpGRn/rpn/7Az/7srp071NUyuS5YTPvHtgTaErgMEmgD4MsgxHYRbQlsfPHjO/eJT37iEx//xOEjRyKZvmvvfM/1b/uZvm3XAOWiQb077I4II6WwcYIPxWJ1rredUlNe5dv2QsNCg0X7wZggqAgljTAxx55faC5kG488aj/6mDY+aTzzeKjZDJIYRdergXDVH5nfcs1M346jHZuPb79xOZwphVKWPxTy+1Cx0LSkI0qd9TqEpgLXFQzAaJBASpbwgzCWwutCtzA+OO7grLXl+UaozpWeDr/aX69hrCGI06dhV/aZ3W8OhOLarh7hL+Fszxf8nHK7UJ/Vb+sjhZsoDmngYcqheYODg5s2bfJg8AVK5nJwnVMsFN/3/tQDDxS7u8LffsC/dRdapzJdvmz9r+SE9TbjSkfMJMw3b37zm30+cyJvncz5YJrN1cWsoZCARA+ipvEpNSk1l2y4NwX/fE/4YzF9ARdacklhls85256s/eaTpZ8yfDF8cUmZs7VT7w3bV/ebwFAp4lJmn9dCgn6xbBA/2dvdd8OBa7NV/ci8dirnLtT1fBm8hz5sbfHfe2v6L4d9z5h6Da3UckNl/apK5Nf7B38MmCcpkc5lexFNGp9IVz80Z08WjbE8yV2NnrBDuuCrulHQVYvPJVoaBnpB3QRTQTh8gZE919Wv8JiShpNraKDfbMOYXNYKLb0K4XPQXMqX6qVSNJHMpKIE/eLbXGtAiC1sz9CPiUlcAAlcwQ4/AXpZOWo1IYDTon6Jex9M6pmAva3bSIe4u5Rx7sVtVFVjDNQXyvZEzj1x+gzj2D+6lTvdZ7QGEjqhwrAicRDzJusC3AU33XQTkfAUc2WE8+L2vugbTSJJFU8M/I1fmkXGm2NcIJMBc2e57DaYQoYWJoAV4kDV/NemD17VlA04v//++0mmhbf2eiTnxdTpjQuQlERGFQWGiU3FZZpobuCfPInV/bqGe5gGrkmeZ7+eHT/e2xXdvmWI+O2pleaZFffkkjZZ0Ct2oLaWzZhoWIKHsT7DKEcJ3Ay4OhM83II03MKPRoY2Eo4E/D4BxWubelpLhWqHvypHsTBvCRJW9FGyKCbxJgKGnaEkPkp6OmKoxHsKT3pXc/3LbPR+9aHOAla+6rIo8/xE8cRCBXarguXTAnF/OFUjGzbGW43Gk2xZUgonQ1rCbPrqK77aQlzLp81GMmpuGuzfvn0HbjfcKBRKO6CRgzHr5JJzYomAap38TNDjESdcb5FaTHwoqJw2yttW3jRMfjmgdjiO8wWRCDrBzMITFnLjNgmdJw9cs31bb3AwpsVJLyYS5H/p58WM9csIQ/3szSherf/w+Qfu/ty/fPurX6hVCjfceOMHf+5n3//+93tLLV45l6vGi2lV+5y2BH6gJNAGwD9Qw93u7GskAXmdYRm4++67P/7xj99//wO6L7z75h++/m3v33HjW8xAiDXmnhjpSUxcrRIhycTAf7yX5S/ba/yK8961UpW8x2VTFQLkNLdadxuN0rcfxDfLfeQ7zr1f9TerERw0IRER2qrgma3XTg3undh+0/jI/ly8OxvvxCN5uVwBqUSCZsAfZIFd/M1UR5SeIUqC2PFWKxL7CcY3MBvIkfQqaPlkPYmEMdiJ3pUoudufaXYsYwSRK7yrVptJCHTcTr/TZ24L7em1ccoNovJTstQgJgivF2sXSX1cKD+sFqNa8JIPpIHSj/JKSDa4yAs+hMrrAvlL18tQDaMA2VpHjzbueFN0eTn/kz+Z+ed/ku6sGq7XT39hZ7VHHEBGauNsb8/r8gunnmsPYIBl7OjRo9lslgsHBgf27tmr+QKPT9krDf3ksvg9ov76cHsWr0JMfyIG+U/JhDzIdcvu9M93mE90+U4LOmruyTrX5lpdIeyNlhUL+Ei92xd1ruk3EgEuEvWZis7VlnMfQyC4iT7xxBNIFde+G264vuULPzvrnCkYcwQBkgEGa2+rcWfio7cm/9eAVqZl3hwRTVTMnGEn88v+7j/GOeClYlTdkODywwvWRNEcW3Esx+yI2CMpfV83vMc09LwAmPEl5J6+vOlNb5LwyEvp1Lm7+jJHpWdIY6FiH8u62bpEKRea3AWkmCJbmGs1KrPz2e6uDuiFyXUEvbDeLJF0d/OOvRBlQ4hF3xAJiWqgBWZRo072o5AvbGh9cQ2eqoGEjfsDhuENXVbavJpX9I7VGVh5z6zYJKGlJfnlLK3p7OqAXWwgsRrXvd4DJtWDDz6IWO64444rGRq93oCzdpAb8+fJJ59k1HA5hh16o8WeX73znVLZPXW68dC3tFNjGthlzx7/G95gDg/pofClzdqzqr+Ir/Co8dBgHYpzIb723LYvclKtt1/VI53xWJSLdQcwjHEY0iyWQeTBJkmSc/VqlTML+WzQF4zGYlhoB3s7epLhaFgeHVO51qkV52RWYz2oYuFK4APyETMM5GuJZVjcpClIHgwkdratOIxnKqsbD5PVJcQX+qvudNq3dsfTABykcdLHPVvSHfsdbKSxgNsV1btC0K1pxJ9jFo6z7MkNpfov814KXPv7QuHq6OrYya8s4U1NTZYbrZodbPjidqLfCnbBdJWrGwVZFIAxi/S/0kccHCROWFL7amGnZjaXA7XF/pjzo3delybtr9Xi7cMLxasZV4lyy52ruCeyzollF+/olZqvqOKExTtaMjALWqbBq0iYCuRKWkYfEBQO4Trpq/ytvLPC/bi7Ox7oS7gjcW1TBk8TLaKirr1uXeSIeyef79ObD4WGPVdyp7Olb3/tnq/d9c8HH/kmzlLveMc7cI1+y1veckUeWedrYPt4WwLf5xKQV/X3eRfb3WtL4DWTALcPVq+HH3roY//0T1/4/BfyhXLf5t37bv3RW9/37yOZDNl6ie8dyQhJJovZUOyiIsj9Ji/c9ftOgMCa4nE5G6pubWoRndir0XvToxnVDx2yp2ashx6xPvMps16NZlc8a2vNDGR7t+SjPaf23z7ds326f8dc9+aiFqxhSmhZkUgo7PfTcNux/T6f9+gA9IFYsPRyXLQhCWv0eiNVKw2MPmN3AAO06gKEW+FwCABMY7BPcHLM8d36zWoUWw5FrF6LHCQZSOGdgaFrfGQTiaKXALPB2oYORkBFIFIL/s6ApHw0wAmiwYhsPRXuHPjNG6n5+XkSw6D3v/GNb0S3IE6yj8QbKn2IGpaXGQfpErV4Y0dW5I98NPwff7sejgSfPRgY3XRO/h5vFNSFsoucUcGUZKTCl62Ua7BRo3PjVsB+V1fXrl27kqkkF47l7LG87/SKVW4IZ7KIXtWAFFB/gcSYgyCGpUKpF/kL9ZSJfiwhuBYcSAbWIqYvBnw4n4cz5s4OIktlONUclcGjlovZqABJglsajdrg4BC8Yg3Nd3DOnijo+G0uVqjCbFr2tbHPvif1yz69rllm6bFY82A4QMrhffXwTWUtXHNdv9vzEV/63625ykvN0nTZ4MPRji+0xov+08sw5Qr52aaktqdb71Wxypxxztai60OwRK4dmJ9HR0fPdxrHL9fGKg8323TBPr2izVf06bJTrIq/gwy5mgSlYnVxebGrsyOdSHhx18WV5dLSdMfgdpaFiB0IBn3ewHHTQIIVDRqERw6ljEzI3tGpdwKF1bSREVc71KiOsJylVy2N+bBcJqnv6pIULGPyCEoDfeWmOGtDvKAR7ggY3ZlX55ThWZe8pl+94Wa9jFFjrQd3jBtuuMFzyqBefpXZUKk0H324+f/8VXB22mo2SBpnGVpt557wb/2GuXcvkQCc8tp1xGsCbUNuYHUeHZs2bUJ068+QC8rHu3r9FCaFN73lCHsYh8tNJ193S3Xt9JmZ5VyetUQLFxWee/KM0rv7h2OJpOE6PP0SIScV0mMhrKCQQlmnlp1TS+5UgTngr+AGrDiQsQyD+jCBWgJ6FU8yaLhex3kkGMS3wONal+fu2iYPEf6TW5+D6pMHi+QW9qKFya8bdFkjy4QxC7NkJuzr3RHhzcKOyunqstWZxijIkCkfcoLSWWHh8cUZBw8+s7CwyALHQH9/prOTPuYaRDtrkzkH0qzFsrbS0PJV4dAWmzAPKWkAQcLKJkxaKV9zpMMPgbOTn6xmp7YP94yMDCaiUdVoaTsNLzYcMjOdWLRPrOgTeR1eMSKHaywTECQsNmHpJsCZ9wZiUc2m4Y5VL9M8vVFhSaqzdzQa8uOI3p2OdUkmYXdzRuM2hDGb140nGZ5LFCBP14t+VIqE1jYks7ZLIm4tW27Nl9yxM9P3f/nzX7/rE2eOH+rt7f6Jn3g/SJiVIM485/tlvYT2TlsCbQlcqgTaAPhSJdY+vy0BIUdBCqCpj33sY//4j/948uTJ3r6Bkavfuvf2d6b7NkUjof07N7FsTLQefs6eOsBblpflays7732qtBbe0N77lTc9EEhvtezscvmee/XDx+2nnww+9ghBrgT0YubNJ3tK/sT0zuvnU0NPv+EnZtODDX+sqt7rLSBr0xYnOgrTzVg4AEWtwrfy3BA1gP/Z0ABYWff25buoIJzA+937VKfID/gtekdkvR2K2lLZ0pz+svnuQ0ZAgoE9zUvO9LbyzbpzALIk0eYxm7NyT6yaEJnyDxJbbBQ0AzYXlegSQ4GkfAwZUBkjaFrn6SXUiBV6YmICV1gsS6gR6F7orCjWXi2vQH2hTK61c9nizv3x5YXSH/5J8j//L8TfrbZ7wx+EL9+I+FzOakvZZr4cyCSdzi6zo0NXCUvOV7tXBZeyg18on7t376blqmwXGuQnpp1szZjIEdwmwkSi6hKB2HzlkxFxbIe8zM2WjYIONwwjgzBh6JH8Ok3WEeS6RNDe1ukbilm7ewBJq0JTtVzsB4jlG9/4BkLevXvX5s1b8B94dtYaLxkTeW2p7MDCgxNvJJD/xa639/sOao3A7Ee6zccyZOJkFSUUcJp7lkO/saxH65YxHNjyiG6KO6630SM6g+XmVNYeK5rkBSVbTDIEV5a+q5N0pjgLS4vXTj/7L3coiX9xWGW948qojyw2jS9bJ4u+uSJumXqxQs4bF39myIiIySTQMV8sZ5eWMh2ZdDzmtJo8ShqN8sr8zMDI9oarRYJ+WyNYADpokiTrYb/WHXH64gbGecYI2IM01if2uojE2dVlrcGezJPoCI5hdZLmdEaczR0kocXzYnVmniUrpMuoYSH3AqTJFHXWCWcL9Ip8p1U8YEGY3LBE22K698ZOJgPT+tSp6od/Lzp+Gupj1oYikSgyQS7V22+P/M5vGf19ciucf0q8+h7QCgrhkzWpY8eOITRYA9ZrXN+51IqkdzJu8mZhFRHjMLHipbrz1LPHbX882T1A2AJDr4oV/MYe52PTxWM5FRRv4XhYXOjPrFinlrRjWWe+YlYtn2dNbYGHHVbEYP6zWcok2MVQSYbxyQ/A1rCK4kSML96kOuri06uYEOVVJOzDMEuWY7HNdsXcnogzmNJ7IxoO0rwh1HtO52GLqZyN1TGmWWdn5y233IJ8GDUeRy++H6lZgnQxhs+WSaTkwpc+X9Jzdf6xKGDA5NbkZ2zC3qPe76ZDmlXNmpXFpFHuDDS2dce3be4b6OmR6qWxnEuaYiNfc6bL4jlyekXHFyPXNMnnDD8CuFqQsPjL0HsJOtCtRnH6KAQFoGMJvNEdXiO+QGhg0w7clSDYgxusJ64Nx4R5jrhoOi5PSyWYVzzo69JWgy/iLzSdxYK7ULKPHHr2sXs+cc8XP8XaAQD4Qx/6EMTRPMqo69VXt15ve6ctgR9kCbQB8A/y6Lf7fgkSUK8oOR8j5n333ff3f//3pLlnVftd73rXL3zoQ/uvv+F/3HcUP8atvWHhtZL4Xk9v8DQHufCyv7fWmySlq3f+2o7yScbSe2bcPjVWe/wJ52P/6KuWY0tZdBxWrQuhzOzInsX08OO3/cx03/aVWE9Z9zck6FYoRgVYalqpht1XEClau08id2EEhTKEN/6q9rXaHVFKUTfkQl7g8imeZgAwr03yUl/9t3pETpN3uA5vp+VY9vVTzu0TkphHTAh4wyoHXE7hukqPNvsjEUeMH+j1gnhRVERfQf+DBFW4tVQ+D78ksYS0RJw+XTvidzMErUUVcQvsx7YLPKvVqz7TT6Dv1q1bL0PyCdiL+c/Vcj//bxP//PHyHbcl7/tXiFw8+W/8ZIzccsl67tD/z957AEZ2Vff/r0wvmhmN+kpaaau32WtjG5tiY0iI4Q8BbDBgShIMaT8CSf6Q8v9hSP6EUBIggXQIhIT+S0yvBrMYsLHBZe3tRdrVqkujGU0vr/w+5z5pVtu167XBoLfamTfv3XfLuffde84953xP7XOf9f30IcOqW6GQe+01gZffYm7aZISw+D0+Qniw2acTExOIJQMDAySAlVywhVOsHTTeM22PFc39s4Q/AeuK54QToydIjF+ZKH5FEwm1RPoilA4bGQG/iTzccNGcwwVaIYLK1m1UHLJTE3GuWIWFueRzUn2WtuWk82ZVuU5tYWrb29tg3PdM2cNiqKzNllCRoGPGq1DvN+98XedNyMK5L3eU/6ObZgdNJ4wnHzXV9PqLxkIvnWZkut3/6U+8QrpRyCsN4ws43ENZYyjjlpB+/TYBPC9pE2W1ItzZKgzviE8y7KPHOJ5U/8fjJwJapuL8YNg+kDWyZQ2b61rVjYQE9ZpewOnXrVaOTc+s6moPmr7pY/tRbSOKSDPBOS/O6L7o+iueXpFAR4LNCxhvR8hdl8b+mcFOMtVi1UnyznGor7mqc3jGLjR80v3qKlQaTBuo6dRTC/0qd0456ET6DiUwkb2butZTUj3RF7yhhTM5ryqxgr3iabBTqVS//tXge/8GHbdlIcphHx727lZDId/ffdD/lCvUsF8g1ONabyqJ+Q+inWenvX//fmQ8z3tz+S9Rs4ZL36bmRU52794bT7S0dXXPV535ij5fRZPJZCtvvTRSTaYiNAtkoIbFcjLsJMLghBv5unNktrF/VsNneKZqVsRG2sHmBuviqm3VqjafzKJ+04xFgWNTb5PKUTKT2cQbYfIqcqg5W86kQAXWZfqUgTRzL1GFQLHGQDomsYVxU++O6ccOPjo9NgIKPdM5ceO7Oju7u7twPDl1F3ghUylk4WARwTB4uoRDrz1RNCeKAMjruaqWr+vVugQ9ou34CET9WtQHjGLJqGbDznzaLG3uiz/7mi1RZGBVS+qpGiGACHNl+1he24UknDUm5vUcMZkaEkIJLDHiS8neoO6adg0qONViee5YvHOdZvgZSwjBmIKHfLIbRRtbwlo6AlaWNpBw1rYanTECp3uvpdBO3jf2ir0dgMXmLOd7kdjUWOqCwfZs0WY3wXQaX/va1z/2sY/deeedIJm95CUvue2220CNZrL1sr2Akbac+qykWaHALwMFTsOx/TI0e6WNKxRYJgWW8iVoetH3fvKTn8SoEjvP973vfa985Sthelj2AG65dnVw+0a/xHVckCJkRXyCDhRP1ZJWqQjjEgyy+s989rPal78a/M6dqHlbgMvV9Ezr6pE11x7a8ozx9NrdVzxvJtxa1syqhmJQty3PTA7FgMSaRAFFgFbD8AcCwPMEvCVWJE8l64q4C+cn8Wv4DesgGkgu8scNpb9YEIlpuyebkZ4bHjPe5KokQ80XMn3r5tmRJ6EBHMzMgFZL+Dr2NkI1nnYCGc3JWo0Uwi+4TSA7UQoZydY7WmHYHNMy/IBYKZnZ1O1IEKUEQCZmwdL3TxQAFF3b1YIevr+vH7VE/+DqUFDCGnktekxdQ2WkbY5x/TO1T39Sf+gRgLu1WOw0eTYazoGDtfe8LzoxhjUjomkAwn33ruKx8cjtf65t2CiKWmmBUNV7nLGEzTPCGxwPfoZ0QQSMHxJ47JzmElRzpqTjZYo6lE5QGhehMJyXZVs+06w3HALtonKEavjIoUoKB+SiiZIans+SiEfE1IkHfZ0xuzWgbW438K9ePlm8qsL9EyEG61m4MSzJpZd1/dCMdaxgAtGcrRAPBv9D9NyCddwX3WdqNbj06v3hkIYZIZsXdCLjRcaF/tOY/pI5Ama51Uc1EYChhlxn3B6dbxzOmkfm7BIxjn1Of0rfkLT7Whb1TB7JTvxsUhJp5Prrr0fXtPymnZjTef+iI9Nh7Sm9yBsWHutzRQdX+XJN6F+t2fhxThVd3qh63U60RuOdA6GgWZkvVrLT6Z7+uUnN748wXlIhA7P/1YSiCYF3ZQIcreovg+T4oQYLkELDGYm2yiaDGkQYR9jEVFsVp6vlOJ7+zGcgYHF4iVUfLuupM+d3Ee54lcEz38uLSHJYcICMJYFuRidq5ZIn/XG3XCnLhKDjB2HUpiZBeSJC7kWowTKyoJJsgHJANMCxeBeQgcFaw1LDe2GXSX+vqDMljsVwPQnigBqJGYR/Y4JmF6VQtcC9m69oZYRhpcWEBmw2VRy9WjImS5DEZgOlLeJfv1ELXmrMlayDM7V9s4wWLWsFy3WzHgJizSyDzuwadUymHSRVCS+s3koel9dPTXHeHC5XFuvJNzOxjkN7vQHmvFhWT+u2SdB6q5IIm33t8bao5hTagn59fXd800BbRyK6oBX2sjjxU0bbiQOVXdbWkAng/7pWQhy5vDJHcs54wZwsSSDxHE7CNbfc0IQCmEuY8WgoXtXr8/WCVQmU9jirMwFB1AAAQABJREFUk0AmjPQkQv09HWCqU3F85jujRkfU2doRyJSdQ1lnv0jC+gTA0YRQwtbaUvGWzbDgsWuEKmdpCiL6Spt5W1005xolEsApVNEkmve8e3jW2Dnl9satNYQwTJkgZlNteRBSnf/RHM+gAPI8Gxk9CW+Dz0ToffGLX8xmEOZmHJ/61Kc2btz42te+FtNoVgdvrjvT4Dn/iqw8sUKBXyIKrAjAv0SdvdLUC6MA+rcvfOELH//4x++++240Ei9/+ct/67d+C+e05i4swoff7+tpbxED4Qta/85ZsSZPf1JKVmiJmTQ2Wvv+DvuBR9zCvHHplsgznp6+dNv893ZU8eldvW061f3A02/dv/rSmZbeousjvBJiCZ6UIruid0KaFW9DfuCRK+E0JCgpiCCYYLKik0LJIlgvE4GDLXNBZlbirpJ4hSFTgrAs/sI8yCMe6ySyMAuzZCJ8BKzBAqsjiknFJsAxtJXddJHaGNWQPXGZf259gOg9jVVG6z21ZNb047K1e258sI6OJRYFxxTRCf4W3a/IdfwTQUkyRwJ20bnjM5bHWG66VM7PGpXptnhED6WO5eyernXEfUHGIyEVUtVUT13oh9dWMjMHB8nNVy41ZmfM0wnAdj5f+8rXwhMjSOuYlBP+FWtY6hE5sKd2145QD6hT4tDrHRgXwEMTK5L8CcgE0mwzdK0kUGREmzE0axVsH9bF7FdABfS6iHii8hVJD3YWU1sCgdimj1g7to8QI6bOCRpgz7AWwbhuueGALxFwOmLGmhR2jOctMyCT3H///bhBQk+ggKR6uj6Sw/JZJzxvtgZ7TfRTQlkj7Lko6l2tJHTHNKFKR4AxSz9Kd3BDhksVPpVtDX5W5aI6SH4sbx+a85FhwfKFTdH9rkH6bSVAidDiLAfvLBUDbq35kp4l8cW9RSP6W/Sbtvn3Tdt7ZtzhbB1SY3Ye9hsAQcfCvkoVH/hAue6mkklA6CIxe36uEY1Fcv5oMBKmLzpibk9M721xiI2s4Hylgp4ua7GqMupnkWoyepkeFtqLNWxH1OIRVFJcUG/bYvIzf0MlDu4z5IDx40XjOHPyJ/ROs2KYQyMAzOfnr9iyRWuJ+wNBtOYgEXBQW0jBuLdN3RdvEULI2JAWPWEH9WRdwL539+7dXkw1zEzWrFmDbOw14bHUZGBgoPk4YwDNbwTI4hi6R2RW0MIxG7YRBXNVp1I3MZNefDGQ6/AHdseLYCUT6cdY32pc2ct0oY/OoRbGhMQdLfjKUT9yHbsKmATnipUq24460IYmciM2P4w9yOmRcmGaXawKm6FcB2O6bjVQgrN1isTMVDNXM0qGEc1TYnci1FWraVPH3FXzxO4SAK14AN0oz8mzi310+s6CbjQW3Spo5/xhBDFfc/AT9nTC+Mri1osyvFgHMcvN4LGj4zsfrBXdybr76LRbGLfi5tiq2LHtA21b1/Uko2FVjInH9Kq40RM3rux2p0suFjR7Z6zhnDFbYZcKMVgk4YphgDKAnpnoUOJi4Qm17CjgSg3atu1iRA1Y13RZjxWM4ay7Z0branEH4tbatNkZ1xWIoMzwi4eUfPpGLqY47XczB0jBsXr16ttvv/3P/uzPcIfB+uyd73znX/7lX954442wIs973vPYJD1tJisXVyiwQoGzUIB5jHdz5VihwAoFjlPAeyn4hPH6yEc+8slPfhLYpGuuuQbro5e+9KUqBCucQXOFagphx3NYevf41Qs9oyaK/eV5vikXmRO4zwZwn1qxbB8+VP6zPw0RuEhiYLiO6S9svSzxN+8tDFzy0Xsq99ndRS1QRT8gPmCim5VPUQ2iwhWpSSRczgQUxIFTV1hTSFXCfyi1LUo6MShbcN9F6YlHoRJ6mTikWt78oSQSaoZwLDyTHMKHejIOZ+qC8Dw8zEXEbHiEiF/bMty44kBjvsOZuipSSsmzCHGkCdX1tp2llr16rqXxwBXzDc1tT7WkUglM9QhxiR4VEBeAYUiLXTTW5tQVklj1ciE3XSmVLMfoTwUGVrUlU61Ug8ZEfNa6djCEvCosVOdCO0SekxI1zdq719l2KcK38+AD0W3bTsqQatkjR6t//JbIkaNwdOAkwxP7BadHCFe+4qrgO99hdHZBKgRXlEjssGD2zABD9EUp5+UmpFSHjAINttXZN6sPzbnzNQOQJBoHMTEzJniRsnM2GmBAkx2hdOpOUHx97aAPi1GxhUbxW6xbYX66YrW4oc1YFbe3dOILLERqFnRSK076yZDhdQDwmeZgUo7pLOwnGUyWnV3TgDA5mbJJpBNB2NHRnDgMKeJobY985hXp22AjC//WE97RgS8fByNFWZKblaunQ28apZ+cjvf7Um+imRAXDnUXILdZN1PDPNtBHbQmaW1ohy0/R22pIcI5nocE/vVCtp7UhMfvp/SR113UETCeugNm9U8nnOmCmFyaqKIaGpsCbE9gWMmuBAjP44f3zx7dk+xaEwDmx3S7kiHwrq7Z3L2xN73Q8apvyFmarYYOit/DGWeyiP4fQwjGkhb3WWvbjHSYnlh4SI2aZgbnbjHATj/+8Y/hs7epYbzMwXDufB9zCtrNxpDAYs3OPO2pVyWOjTXe+ieRPAO50bDq0XBEKI2fwcbNkXf9hTmwhl8/k8pLB7nuyMgIjsG8xYjEz1LY2o+ZACdloGZcNbqW3mDYoc3NVexsRctV2KkEVp9pb3EAoMxVTiYBzSHST2tYa4kILhT2GuzRHJx1c1X/XLkxX6qjDkWuxvoXhxOMgBhPArsg4YXV2BNMPrS/qL79EL1SrdI1pGObCamZHTextmCGNwg/LlGs8UYhthAOtKmI0yGWw3pvQuuM6sT1VcI1LaCOzPeLc9zSJi2uLfK6Lx7oqbMV7KLdo3PueMmYKig9cFVXkjB7IjIZEsAJb2ijUTDq8zG93GZWtvbFrtrS29MaU0hdHk3kk4yByxotiEfJoYx2JGcQnbjUMGoNjGtMthqZYGmP53pDLUnPH1MTF/nJViwK85jfaQnqqbDbEdF6WrRWZ2p9R2R1V5KVtEl9qb4QcPHCYnPO95sB5j3C5IYqmE35Rx99FOsbxODXve51vLwyiZ+Wludb0kr6FQr8ElBgRQP8S9DJK01cNgUUD0NgoNqXvvSlf/3XfyVGCFaUrC4cGLZ52Zy6wJx6ZdkFLj8h3IywMDjNVu+9r373D+pf/qL/ZS9LPPu68l//Vezee3HdLQXClUBLoyXVmGvM//OnOv7q7f2X93zxx340uxIVA5lCNLeIHKLClT9ZzRFoWc5FSRgSRkc4ETgn8bNCPOYuS77IyCLYquVfhOEFPlzVXVZbab9it+Bl1BIPF6IEYTgA/qkH1HXSoatBgoEx4rM1qHXP12Y2m1OXRq2AsBokoACaWQ64k1eGy2kn+XBjY/uqYqAeDollNrLNzEzGthp4ThJVGOBcnACj4RCKtWI+Mzk+xeZ9OJq4vC/+/1yWypWIMGHXlWqxZPt2TToDSWxoFxGBlk/7M6UUDkdxmTRRNfDUhOw6WKVivVbxCGTZ8FSi5DSJZVMDDxlCi+yOLI+6ksGGtyq6I09veeq4qjjO0QzaHvg2CRXr95mSH2UbuKYDm2TUay7qMW7hXCq2x1UnFEIwdjydeQkrXNFGOqmwviqpJQP2hnZxJoMJlZ461+GxX6DaPPjgg7g+ovjF/lkeIrJU3dk3o4Feky/7smUbeC/ZorFxfAWCi82Vxqz91IqbjurT0RfMNx4NudkW6Wr1vxor+l+QUwMtbkSuVyR15qr6nlkHEOm5mhkwiP1j9rZY69uU9lyYzzMeVHJ6eppKQkyEkDOme3xuHO8yRc54wLi0S1/TauyasndOuLMVHUhf2xIoMtRmAvhct1vSHcCBxaMBX3muKxXqTwfXtwd6kwFvW+R4NYVW0tf4GB+YAQxMtOoUQrTYvgQ4zyowqrzRVOHcXXk828Uz/DPxM8f6ADYaYKfFyz/7b5qDgotgxbseffTY+ES0o0u/9RW1j35CJwqON2hBwGpJBl/zSr1NQcRdSOsvQjM9skM9rKCx42Ac8hbzyZSFWEgBF9Yvp9RM9e4pbeRC2NTDMRP/WybtUsPOVXCdddFV1m32v2TjlDQN18xUtUzVMec04ip1RH2XtAGjoB8rWAemjT3T/qEZe7pg5es12wr4WRJ0Hx4TJbTJ8lrK9qnpSggA4rmzEIQI7e7H9rzpjyqt5DqWKRUbxwctb2izBsYmeiRvxPETDrqpMBEB3Z4owOZ6RxRMaSZytl8XxeETWrs4jpc0FoIChI6XwaY2ohzZYwV0wthIG1MlfopiFuzoal2vOGBctwaJLWxXMvXiyOH8pM9Y34F9hN4ecZWllnpTNBHOE0FjY9rktRqZd3ZNAjfgjM/r+boADapIwjJJgwOJnM4uAJTEY0gEYKGn0xaubwKbPWLgtTtRdEZHjOrMXH+7ddm6KBtSaJtp8mL1F79PaOP5/fCGEIOKye1Nb3rTH/zBH7ARiYfwhz70IRyyUAj/zu/8DsGTGHik9BKfXwErqVco8MtEAVGd/DK1d6WtKxQ4DQWab8Hw8PBHP/rRT3ziE+DHXnvttSwnN910Ew5dT9hagqzprZNIp3DAC5IJnEetkb/zu/bQsPWRfw+PDQeLhXIgHLrjc9gsz7zujfDRlUgSnEykIbhFLIrrprnq79519Mpn/u4XnYIbku1/2bqWtx1lIIfS2klRLOr4SbGcs58tPLaIxHzgWqaYJkmsBGVFNnlAknisCTKd7N6TTv2JcMtd+VTXKGKxCXILhQQ2zzBbRHBMBEET1TpMLZq18l3oxWitx6QpxkKyFUQrgXcuiYm2nfaL+C7RfQBUrjaqpWKxhBUuBfs0p7M92drWjjXp2OiRNb2p7nQrOuHOiEYAqtaIfixHSAyMvXlUqtUbt9enBfWTH96HXD7/QzhbHOF273K2bwci23nowdjWrSdlA+3sqcni298euPc+mo+wSvPQjKKiDZjBygt/LfzGNx/IzDZqtUsvu4xnpV/UcWI+0gVyV3P3z9igKx+cdvOW6GSoAZIzEYZgeGwJ/stOhih+MWIE+RltCIw3hgIoy2uAwLLPYWi1hgtCWwdRqePaZd0a5oUQW2UPP6codGLZS39RD+Kg7tixg4sgS8Hoq/53i5azc8wdymsTeWO6ZOEIAI/IiBPTayL2ItsbIMfYz0/+6Rr3HxhkznDM+lzCOBhhsNiDFd/LcuaGIiPOir/C3/PvOAjP19ydEw4xXSbKOmNmsFVbHXe2dZITT8tQosKnrasQ3LaxEkSdDvLzE4Z9tZRKS89Vz0llGdzw6w+MWY+MW6PZmuzbBAO0gdjYbH/EQ3pb0LFnDw22+Z+5fS0O0uqFkqGgBsVCc/FWGBKPX28wMyj0pL+xvt1EAaWosUAS9dTSWizrnKqChoXmHNt7pj6eubB8llXY+SeiejR4eHgoEPCnG5b+4EPW176mDR0GMtvdtNH3kpt9l12mp1Kq2uceyedf/nk8IRVdfJc5h6Sgi+G3eaZdrfPI+qxJvVIliRo1fHMFw91i3Z2rCIAWGMsWwPkLr44k95KaGjpMEU0TERM18r6Jyt178gfmnEBLV9kypucLtZrekIUDsUpi+eHEgS0JamGGnZrhZeFga5UcF4bg8Sp4hbAHKburQb8bVXF9E0E9HcIo2u2Ouf1JpiNglmXXdLFK6ptMvHG98GvhyyOv+iGvALGjMmV7PK8PK8SsqbLaH6wahTqOHtSKcvG6t1sjZoLAxUalPH1kW2/sqvXp/rYogfSUSEvtKFpyY+d3quQczDi7p7HdMKYrZrGGal1jBwAMbZZOILRJynPtodprrtCvbrXitWIAcG3DVwqHh2qh/7prf0lLrepb1R5xVrVoAyl3bQr3Y5MNX1UKpKLQk1t6YhPP45dHDSLbf+Yzn2HLHvCItWvX3nbbbWzZ8yJ7GZ2WjOdRxkrSFQr8glJgRQD+Be3YlWYtgwLNpRR11je/+U3Wj29961tojW699dbf/u3fBuaKleNxXzy8pXCxtigEWYiFMUaYYVmdzeT+5wv67oPaV74QykwHgE3W3KmOtYcHL7//xt967Wuv8O/47vRfvM9t1GT59pZxdtM5N8zWP//D8otuue3bxkQJKYeYEIhsUozIkSL6CXNB6xAwMPHyGBduqGwWanO6tksSuS6immThWc+SsdoR55IKj7QkAy9nnOGI+gpnHws46BUE31JJ+HJX8qHJYjiH5TXSuE9M6sRHlA7CORkVitg6I2zxR1WVxsDvs4vzuZmZ8UrDaG/vWt3VGg+7u/YcmSlWYaV6EmYqHkjGwuu6oltWRYmuuX/aQTGpmu4MJp3BFFqLBQosVPY8v6gaVZ7/0leiN7+kEghGjx4xFxmOpTk5pXLju99x3v3eYK1eqZUDWBUGUe451Ujc+Mvbq5u33PPAA+yweMCepyM4mUEG6dy5qoZYeHRemypLZCOCeXLLsVx4q2oNfY1PUK/w4fPplZoEOgJpCX7Vi3sECBOyKDkFg2Z7wB5sMze22v0JhGjK9Lpoaa1PPm++KdxgkwjBEoWhSuRUHe2RcftI3jiWNaYgsSmqaboKHpEQWiCIo6lsi7prUsb6lsm1tVv99R/Q2+i89UpIDOtjdXSicNcN/+X+1Z93fYNYZu4cd0YK2nhetilWJ/XVLfa2bqygVUXPWlvqCaAxrpiDg4OY8krbzpr+5HY+br+l/4g55GoPHJj4yk/HK7E1idZEnVhHQT1m6t1xUYs5mYNr2yODA/3eyFysi/Q+7yzYP4emnbxAPcsdAvz2i+KX3r5obaSce++9F1voK6+8Uu1unHtgLFbyifgWMmgahtC8L2F26XIEvS3LrBGN66kWF1MHUSQel8GeiDqdqww26TAjYjuGOjMgm2Bj53ruYt5XdBMrHjZQClUbTWmujIqYSD/M2XJAV9wZ2NsqlfJWKevUizG9sbq/J9m3bt+09sO92WGQCEFtD0ZBLQSxEFdYxE52ZVk4mK5VnDrGLL0j+aleWsiWr+b7x3XOmdQRBUNIwj4tSkjhkN4acjpVoN3eJI7NOvjnSqEsWS3n5fVGBWVXbXe2hBbXJRjYeEnLlDAFB+1Zx9m+LgpcQZYyQRCrZKNarcVXGWz1X7k2vbmvpS2K9YRXeVnaqCfLGUsGhta7p+z9s4Sa4+0ziw29ZGnlGo5HbipYf9sNxrZ6RvvJfflv76hNTrPeJJ5xjf/ZzxoJpf/mu6MTpSA4dt1tnZ2tEQmeFHfWtRurCJ4EAp7QZrmtk7TLO6ADC+U999zzL//yL1/84hc5Bz2LHfynP/3p3s4L2SyHnssrbSXVCgV+ESiwIgD/IvTiShsujAKsGWCWAOyMERG2f5dddtkb3vAGpN9E4jgu0YXlvPynvPVbpWdRZPHFy8oq33d/Y8ePGl/+QuDQwUh+nmWZ3ffRnnVHVl/54OXPPTBw1dHWfp/rvPdX9VX3fW/s/73dZ7FBjz5QuBKUgCyvfHW+88/yv3L9n35rMmv556qJkpu2NRy3ZBVUa7AswsgUIgOpE1gASvduefVfTOj98j7Zy18QfcmFysseuqo3Eq3iIuQ3KkUpRXbXhd0BAwmtNEoGMEhaY4LToiReUR9yIspnRCIRmcT32GPKpC1KWjVEZYqITq6SPfwTys7CfG5uLmPVal2helt7W6K9O2+b5QoAqVm/U56Zr2eKjahRj5m1ttbUNdsGN3eZM5ncI6M1LZwCQQeR7NIuNx0RRut0bVza3jOe03bqU3z3eyJve1tu3Zr0rkf1QOjU1Ojk3anp2le/7H7y0/rslOELgpdWTqbN216rPf26I8V8wB9o2jyf+rh3haLAmXlgzJ4omYfmNGyYRcdr2fjcgYSDvTPGz4AMBwM+mDMKhCVFBibIJ3wq8DS+ANbWmCXLzkgSRJl2vT9mb+1UOonlUQC1Ks6N/f39TV6KiqlaEVmkMTQPSrM7I3C8hOu0sYlUIOEaAbQA6OqI4o/tDrQ4W7tNkM7sqT/XC1/QjQraGel7snEijfhzA13v0n0bq5boftFyj+fkVi8Pxt0t3UZUNmnUUDsTjdR1sK+Iaovi+4YbbsCg96xpn8ibMlRkOGva0LGJO+45rLdv1MNJYLkR2vpatY6ws6ldN6p5NjXiYn68tKXS9cdg6+f0OsNf5RPz1zd0mEkUWCpTRRi58xgPOhQcLPTnkA4C4q/+GDN8nB5Xbx5hkCw8ITva2r3ZwStrGWPkcarU6bOVd8SysIgeHh5mTkMA3rp16xNsmS8TqRp/fKjxIxsxmXzpyGTO9rfYvhhuIw8//Gi1WmYNYf8sFg5H4/G2ZKyvo7UtQgz5ys5DYw8dLY5WIhmHjYakZoZqWDgTVViEYXGuYRpnv5LQRDJRK0EXcqhBv0AWZdqiXgC1xjCV80IL6oRPi4DbH9AIq4tOuCOqg0qAJMyJSMLKwOjsI9sbDN7bpQrAidedxkk4a4/k9ckilhcGMj9OwiIJszPMzrJd0xqlgFuJmrXuuH7dtt41aR8nrFCyCnnUUhVH3ZupCAb17knn4ByhiY28oEbbr9pu3xjMFv7zY4VPf9lwLJS7Nr5Jmh159rPSb3nTA27rO742ARx5S7on1hKPB5x0RCNA1KqEtjblrknq6Sig5bzkohS+6AcEwX4NvGiwsg4fPsx4e/3rX/+qV71KxaA6Oy0vel1WMlyhwM81BVYE4J/r7lmp3ONBARgRsv3pT3/64Q9/+I477uD85ptvZq8UmKuL6qx1trqzSi1wJWKYjBbDdmt26cEH7PseavzHx0MjQ6ECVq5GMdwy0bv1/mtuPtK2bv/6p8/5whXXbyl3XjBQ33aD9Sv1kSO//cf6xCRiLEFe8b4y/SGwsJze6uYPbnLiO109Qz1yjY57pq/92PAb9paegjEayyB/sM+Ir7IkKslVqssPxSqpU7kjv0kjp3ID7kZUvvz0WHSp+gLHo3hzScfKLv+4hV+xT7S+sDhgohCYF6aHZJKJyhs1L+yGiLhyXXKEFhxKKsaY1+NshY9CQMZXFTQlkh08POLW8u0ha3NfauOarmw9ULFElAWherLgoOfsTxGYxDk8WbIqxdXtwXSqtTVsNcYePTo6daQac81wOBIDo+Rpg7FV3cppkIfV4bVz8dc5vlVNtexvvrblU58p3vhrya98RdTTpxy0CDWtPZd1Dx+q3He/kcsZWA5fts3p7nbiCSJNeXLaWYqWgjQX+NMDOXNo2sk2gC6TSwi06HhR/4q9MfE8BVQJNHLkYQ0ONxAy6/iJ+kkMn+oShBZkF8Bv+lNGX9TZ3uvzgJ+97j+l1scvUBIcPC8LXrWosLCv4x7klT7BJHvWPpjTD80SZhNYcTGeRwPcoEg634eznNsWM4C9WRNzLu01o9LrVLzmVu63C99y6ntl3AXXmdHnmtFrXS0MP71zyhnJA3wlNsMYEIKEvL3bjIkgBoXUoDletdOcocV6+OGH0V4ODg4uJ/1psngcLnk9SMaw5t/fNf3wvqHBNWvT7R1Vy+IN6IrYmzt8BLJWr5i8HmL/wWskh0CvH5i1MiV2jeQKW2E9cTyi/YKDLEnluFgtlVGlaVhRog9kN/DnBw5atfL4h1dPIjwfOTLc17/6sksvlXn7MVCB/R303tg1XPRNE6+qVB3cOPCKkNhx9X/Oc54jJi7LGM/H23xBZ83SeZpVb3x8LJejIsRZyzcadUbXxo2b1m7YUKg5e4YnkPQMfzwYicm0u7gRKm+o4cTB2HcKmYmRiZnsVMWf9/eUQ12Zuq9cN4jJVLeMKrOETRgBWRowTEESZkr3TIWkaBlWi0dz2HonDGvUyBISXDARkYSTQYni3hkFrlnrS2IZYXKdt0KWGTlkzJ9IusVWei+QSsQHViXFGsGTiCLujBV1EKQJzAZcdqGGX4YgQQKn4doVLJIGO1pwKk4HqgOt+vqOYJeYK6utOSlTCuV/zXbJal/G2TelF2rV39istey4a+xtf43VDcU3RX72nVN/8Gr7plf/7e7wQzN4+YjVCqQIm1osJE2TGMIxIpw5a9qMvjixhWm9R4iTGrXYjPP/XiQHzi+N7373u9i1ff3rX2fP5dWvfvXv//7ve4j9JxLw/MtYeWKFAr8QFFgRgH8hunGlEcuggLcwAHBFTKN/+Id/APIUjRZy7+te97r29nYvgydsYaAyjivhbzGoKvz4PvsrX3X+547g7FSoUkIZOpVePbz68h8+57bxRM+hzo1AltTgQBy34QD8KzF4WXLxb7qhr/YnlzXiOz6d+84Housr/nCjMqeXdoEk1FjzR/MmSl8BexbpUVrnuvNu5/v2vu3zo693iSLkiZcLS7cs8ye3XSyx+cctUfnKIg8vJFaGcsi6LcyBKGy9B0kjeYhxJt8acq/PdAE+CRlOOgpPhWjEQ/Ig8q0ne/OspzFAoic7npUiRCqWvX/yktwMB/YIOblSATso2BNz7dJcyl/u72pb1RpqDevE3wB9BKxgJQZj8+ZM5RESjHBIH8va4mmcIA+txc60uLNjmerDE3YNR1jN2dRh3vScp1JZeF+2zAH+4cBbr6n4OpkgJF1yIHMCwzO3/fLk3n2Ft//vxNv/4rQCMFQS4nJYll2uiMbW70dfb6hoy55Hq3f/TJ88XqzbD4y6KDQmShIDE7hgVDWMBFS71TKoV6KHoRh+IvQiGMsIsYnhbFSqxF0SMhIuhRCdnXF7dVzf3qOniER09uap2lB3bDjvu+8+wh11dnZiGeuFZeI60u5ozt2T0YZmtZmqXmBoujj9gkGtBomO2OymgeSJOoSl3d5lADZDoykTnbRiKb1BSU2RbjkHRcYFKepo3ocyGQ0VzoF9CXkQB1eVnkc5OePRJLN34u1knTH1E3uDKjHegOfdM+2MTmeHDh0eHBzcPNC6scMHTltbTMyYqVGzgV4T6MO5Cl7f3sCmE9njsDekgQ7ivWFQCDEvejukqrIDtZBz8+RiFUTmSNcMJ6BrH0vm5HPSyPRe2wvLE1Fh586dGGK0trZerJaelI+QVWFEI2xTEHc58ewpLqzOJ+Xv/aQI74QWiZibzyPvEj8ZLHSKw82H67wa2GMj7WPohJson7IWqXcM+wuGXLaMw7BWd9lGlKWDoeBlypRPtPn5qWPrVrf2r+oYmavfc7AyUgpM1vxEYCpLLCXkYdmAE/GSIEYm5j94s6gtUS+ThdpJHampN+rVqawzYh1tamGw0MGODiIuAhygdUdtFKcYD3fGDGKnqdUHgp175CtSSLtYILIVZyzvDs9p+M9PYx1d0Ynoy0SKcy/tC5guW7SNwmzALq5Oh7b0xjaviva26Pjp4KrBa6ZqKpTlP+sLoY9bi7Pz7/rb+rd3MFPh8MF1dVu1aONg54f+9r9y7Z96BEMcWR9l25fXFUsoMC/8ggSGGNwWwS7aXZvW1qRAkGbRI/uL/EY3BwPGO4jBKITn5uaAyHrjG9/43Oc+96KPPW/grXyuUOBJRIGfUxunJxEFV6r6c06B5jKAkMMawEoA1gsul5/97Gd//dd/HYZeltNlLKgX2sxm+QsZwAqj9XRr1equ/cUPfcDYfyC0c2fQRiQzxno3TSV7f3LNy3eve+pwei078nh1ujV8t9i+FzwPEXMQasVJyTYc/e5h65WDX7n2qg+2Xn0E8YNcRaJ25oXZMInYq9ePtGR3BnisZUsjsr7QYky/bcv/nq+l7szcoqRRjwVZrJis4R4XzolQRC398skdL2ory3nzgGiyYstDSg5WJ6buEDqDPW/ATnw+B5gTdtOBJ2WBR/ZB2QsrwGPkwwWxzxXoJrbb4cokN+RyCpZCyE3i/XLLzszOz85lQz77hivWbmjTy9Xkho50ELZCfLVgHbTeuJjMHZmzRgsIWnq41Tias3oMX1/KHJmz5suE/TBKvtS6ntYNG9zOKTEoxVY27od7kbIys7MYKEqZotA22CxHDL7iiitgqaE7h8crSDtPOFw7lzNHjlpUmEhIIsWd/uA6TP+uXbtp4OVXPoV6B6V5Z0q+NBOhPI08MIvzp4GNcaUmiFbEFvKLzOQAI43nI9bOgC3j5lav2AC31msWtsfEMSlV7YiEQRKNejhotATsVTFjbSsBkLyiz1EHykahyj4RHoxg2+ISD3FoJvXjM1N2D2SBYNVmib1cFUN3+piNBbY5qDPSbGvU1x4CoFjf0g7OKk/JxgiHlC243F7p9J50ImoUlMmjRd9IVpya2yMW6t8tnWKUqJ5REp+cnf4QMqFfLZXoOwaQjKGfj8OrGJXDn/DArG5hciFwdPaqeGNrl/hwd8X5oLZSZY+2HoHhzCHFkRxYdLwSvNUaVgwbO0xUSepgED0uhyLewra4R0+Kubj0JFtm4MfoDUuVUKUSetezTcD1EZhoiQm8RHoX4kNaNYKaxOKXd+2kIeK10euvi95kL0OK4D3yaog4im8wVqmbNm3ygrgun8jNSjYb5cnS5MC0hp4ZO3ZOuOulbGtrQwDm5UWrj4qbbb5mdPGFQvlSpIqgho1jhEzUABCzbN7xubIGDpbMxaLIBcMwllx9ySx4BONuxHAimYevjQdivb1Zve3RaX103l9ABq5LVGH+0Anjzw/wNMuBRBVmIAshpNZL+kROvWmF/JGfceVFSUu5U34tlneHQkYi47ZH9K6Ig0IYubQ9auDNe1L3SaanGaiyVYQlUXvEbAu7l3RoxE4/mnP5m0ISLiMJi2l0qY6EzJTVAr51ZrK2b2b+h4cKG3pim7sjg2mDiNzI5JK55O+2Bo1U0HGKxIKbdcMaSNdVx1HGL2K3wQJSzmR8lkXEY5ZpyAjGHQDRUADwMTAQa5YOgD/KdvTJbGjun0PR7Q4miSFMdGKXdinaHG+cLIbHf0k1ln80RxSj7q1vfeuLXvSir371q//93//9ghe8AEg2tMHohNkH8TJsJl5+/ispVyjwZKfAigD8ZO/BlfqfgwIwAcSQ/Kd/+qfPf/7zzPK33HLL//pf/wsMWxiCczz52G4313jFhSheDAagVKrsGyq//z36yETwJz9O2o26Gcwkuw5cct2Pr7rpkU3X5fVISfOziQ6slQrbq7R5rqkwmYVvgEuWQwSIxmv7PvJU812mMS/sCTKIAOSw2NZhsm3NmL4zPvn3XWYDY0lzMlBO3BgZ/J1sKDD/R5s/8MA9z8ppHcjLi8uerLHk6v1UCy5LNyuvYotEtBFa8BMRTsrmp3JgEnlYLdFcRiJDVQtTnwoSYAPPT9H6hsTESw6hhmLwRXMsSkBRLZAt8iuZwBqJcEUBonTgJwwT9nRONpvPzWVSej5pmmt6Un3hxhp2yz2OQMpdkAaoRMBw17f5YiH7ANyZaQyk/Mdyjf6EvyflH8k0YiEjoJsj2Uayy9ebFCVAIJE0NItAF1Gfvnb9+s6urqbCBPaR4Jae0Iv+5Ec/+hEKE0Ti5oGU5Q0e4vZGytUyt699KmK9aujJH7To6NGjBJTGkBj9kt1ogFsrVFvGoYaQO5a3M1Xz2LxVtdCAOCL9gupcEwdgw3QrhPkNmrW6RYWJuFutEcwZCGhBLOWkBMxSwIAXa/ERd8RYFbV7CaWsGNFzlo/QTtuxmIBb4pCxQa2lTk6+ru2d1sZyxCjSCmU8kAWYzEL6xeyZLRtXj4dhE+3eVn1DGjWO2tVYLE8GjrR+gQKwjEjAwxkc9sSRGHfEVNjuSxib2rVWsNtI5tX2XARje4uIIDi8DQ4OLhb1c/HNUMcd8UhWB3NIKmRYPf4yoNYq4hhkOLFh6j2p2dr+mfp0RSKJcpv+A0u2TwIdyQv3xBwAibErBJL2EwmIcL5NQ3S8+uqrUd6Ojo4yVgn73DTfUFkx4zCdMIbE6oBtBG/Q8Up6p3xNjI83GLjqwBKESYAHiW59Yj7nW68zppcJV/U4LxdlMjMwbvEswGj/jM+ccgNxl50pDmYqKswnynB0eoi1TAK4KrARgE0T8xV9h7iL9EselEu7TslMLqi3zLsj44v/wPSlw7502LVbtXID4wUrU2afix0c9l0FNZ4N2bm6UY2t2jMzE5wZ7m2dvXVTvy+RPDBZf2iK0e4vWr5iw643/JiEIAVjvkNAcmRg/mSXU5YSrzeY88XfYeGHqgzLH5YhqGeLNS1Q0UMB8PDcI0F9T8YFLguXgYEUuHEC4oBgSVa8It5mqNeGhZYsvFkLbwzND9OzLXpPi7ut28VZhtnmWMHgJFM1shW3WMOVxo+fSLZWyefqo/OFQ7PuQHesL2aBQs+MRKwjNsupIKsW1k16ZzoeQHVsADVYaTg44BCdGyutls4O8AmLBZt5momXyZZ1sAFSg1quMEYC3rJRdZG6UbPPBnSiEA9ltV0zLlB/69q03oTs4Xrbgqohi6N2advO/5w3hTEGtOcrXvEK9jQxin7LW97yjne84zWvec3v/d7vMb2ff5YrT6xQ4ElPgYW93id9O1YasEKBEykAe4G5F+F8CZGHfmDVqlVYO992221YcnoJPUbkxIcu1i9huhb0OcSHYFWsViv33tf4wpcad/8ofHBXqF6vBiIlM/DjZ7720U3PfHTTDXN6qKwFkHgtx1DLPxIFTJtIm/yDP+CEQ314J9aVsR9+/NpXRvQ5t+af/GLCvjvhK+m+q+bbfy/j+LCP1uqjLXt/L2VUoraPwEP43drtvzvW9aIMeFpvfuRTO6ZfIku5rOhK7FQn8iHMIX+w3dIIT1gSSXXhTylv1XOoZ5VkI2m4yzKfiuDsJBAmIdNJh4ktDHfisTn0hmJ0XCyWwUVSZnL4jNFGOFRRP8hBhoSsoF+I3FOrN7KTRwJaASFoXU8i2d5z9WAY1aXH7pzad4pO0hSCWOyZRvozKw1gmep9ieB81QH+pCeOtbXzlFU6Rmj3jzpVQFA1fXOb1dPiqeBUDdQHdUUA9hwg8dlDEoDFhOmUNgj7o2M+gOoml83Wfve32//ni/PXPj165zf9qGK5p8go9VCJYVLhzmdnZ+FNiSPNTrxKIjRfzkEmeI3+dNwenddHS0apYgUDREKGegKCVami+NXRYqMzDSADA88K5jJhoiwn5MOiGFQWXYXhdfCpW53S+2L25at8gqW8WMmz14FxizYJ7tmz2OQp6gMJyra7c8w+Mk/sUGM27/gCGmbdSL1woxaG0Lowhe1hZ22buSHlDLSyAyJFnqksHjySa+zPmMMZwimZ8YC1rtXcnHa6EzwjQ/AszzbzpHcIzoRqEREIpn85jzSffVxP0IQfnm2g2ZZdI8wozMbmNj0Z0Ex2L7xaLqGMIq+Tq7l7px14cciNsBExGxs7jFYGlyKF96ackZoXqTHUBMEM03dmS5ARyPWxk5SdlLGxMbLiBP0kqifyhC9nZj5z5l5zvVadvtFCNNdlg4mssPVdSgB5WNzRUcUxQYmUJTOOsmKQSQfZyXEPHRluYLeAbW+1Sg6mwv1at24dwZZ4Wr3EtP0sFTihQ5rVPX1dl1SOOvN+HVQHJxiEIwajnm2SggSqAhLUmmkEyZZkngc+YjOO7iTwEiOrI+tiscLGHI/QkFM9mZvZLqnCOU69CkgdoJt8ip5zruSgm8WiuOZCH5m3keTHxkbnMrMQqz0RfcZVmzriPkyp905ZOye1Qxk9b/lKdWIRi3aXWGzMXeS8oBP2ie6XzGWl80o6tVJyQ6yIMVSOBtyoH1cOCd4LZlVn1MZ+GODoVEjsgNQgPQfhVaNUjuAkaMo0el7DSXhCIgmjmBUhH/znCnBZ1WpLSyQRIj4TqBuFtZ2RwZS2rlXvajHEJ7lcrX/jm+b73ucnsBQtAGHbEazpedv13/abzstf/eH9/nuPhVmDUBFLAlkf5SWSeoqriPpJDD+JFadFfGLqkowAhocA7KxtxS7aaIuC1SFbZhfQdzx10tHsTbaK2NVlMQL1E2s44D/xRyCAMCGFPdf0kx5c+blCgV9gCqxogH+BO/eXtGnM9UzxoCCCcTU0NHTt0572qU9++sUvegG7u1q1Yk9NabiHRmPisanWlouywEBr0XwJy8RaDtclR3Z61t2z3xgeafz1X0VnJqLlSs3nzwcTdz371p9c/vwjnVsmIm1lF6he9Gb4PerYNSs8IzEiFaFXchH+Q3gh1lB2ysVWTJZQQLN+Y/1HwsasbptDH0qXvtPh8SnxernNtDC74oFgbz7xa6HiF1D+CZehO2b2my09L8hppn1ZyyM7Zl8EXyGFyBLLn+TLH0pFFmkMnmVVF0xMeZTFW9ZvzMnELFnSe/KwOicV8TBQQKO4w9qZmDe4fQkRJBKtlAGjJhyP5GaKMVjQh6RGfhq8A/hMRM1BP1Bu6LAdqAuoEsJP0PSP5qvpePIZl/b0paNssYMX4vUUlTz1EKLIDTwk9XLKGcq6YZ+O+rlg2fGQOVtEqWaCUDJTbMRSJmFXy2zIg0xTd7tVq72R0My2Cf+TTqeRqegG+DxP2QIzGo1HYV+P7d4d+f4P2rBuXbf60J13BomEFI3AoXrKHM8EEStHZGmsPbEfxmJT6rfITTfLOt0JBdL/qEbdgxlrvmZO4vqLuEsoowYIz6Itr1SdUNCs4h0ecFH8litWKIweGK82lwBIk0UL3TtUjTlOOqZ3RNz2sLup04cy+HTFHb8mA26JLTHV5id1pjflBl3mCGs7XjYI+TtXdnxBDeN9hqVPF7UzwwYvPpz3+lpNIn8MtiKPn/GQHR5NGy/Yh+ewVHeKthn1WYMpfW3Kk35F5Dvjw0tuUGec3NikYH/hvNSVSBSe7ehFNAZZeD+FWGiz9b3T9dmKX5HOjfjsLV0+bCOPt0qdySuu3nC09hhGHs64GI1CcbaGQHrb0OHDg2BJc5+IU3oc0RctIupEBLBmQNHHUjbdxLtADphCcM7uJOdNykMiGinDj6EmHb9wwfuW6/LmkIR3ApHW0asIVbV6JiPTbjE/MDbWOHC40NGqzWTqB/aYkRY9GHYyU05u3rQtezbri0dcf8DJ5n0dKbtQdm3LTLU0MvmOTRvclmR9ampcc5PxlvjqAd/G9fWjR610SsSyZBLFmWzvgShO+VIpeka9ENReelLeU+krVTfMGeS+HAtd5iVd+KFueB9cp+1o3hB92SMjGAEDGItu7nriK/i9yCpMI4i7NJ9ZheveVhR7B6BXNA1SvImlmbf3uFDrMR5koJqy0GzNADcAsZM/NLqEFCbiN5bDeii4bu368qpVo6MjAGKNFfxjRSAPcdw1Lu0xUeTun27snND2z2qFup/tPCZizIBrDu4SoltmRmLGEEUuFFRVlrXKI6Gqv5rVxXi47OpVS88Z2mzZIcIQPr2JkLFn2iV+WF/c7leScAyRcbHhi50ivdWkhDqXjTyusinVHjZlYuwwp4vWEUyjsxpwWbMET6q4+Vq41MBWyJ2ebyAM75+qdCcCgx2IwT4UwhvToZanXl193o3uN77hc2x2V2R1CmnxK68Nv/BXpmOwF9ZAws7VTYT/cg0FuOz5CkFpqaGz6qEThgFhNQW+geDDhYY+U9Wmg+5IwTiUcVFTr06669PuqhZ2k5uDSdqhRlqzQcs98YggDVcHPwcHB9/znvfcfvvtn/70p7GPQwbevn37H/7hH77sZS+7aENoubVbSbdCgZ8NBdRc8LMpeqXUFQpcTAp4kzucxD/+4z9+5CMfQVzB6eWP/uiPsJHD45PwoLXv7dAP7Idf0S7ZFHz29fqaQRGDWVGWLJCPpUJwYoscnFt65FHr3vsOf+jDkWNH1tTriJIzrat3bb7hnutePRbrOtq6GgtW2AgcYJEo4BIQL/gUnleJPlRDrVSyUMuSJz9xAqaEBiEoOE/qx77znF9tM4bzu1qOvGW15iiXUK6/bLr/9ROoOr0csvcnR/68CxBKxF9RebSWtv7nMc1f++zE7/z1vg/RcrUcK1PkBYdbRFkRdyVCjphsgT4FowaP4om+klIJwxI9WMnAXBGdioi4mt2Fea3m8KjcwhNYIYJiLMbmPbigSG580hIxFFPetshLaDRhImECUM1kcvOHjo7jZXbFhnZsuK9YZYZFWqAIOZbZNeTc0Jydx5yc5UP/Npm3sB2dzoOGpScjRtLvXNFrHs3ZQ1kxn24NN7Z3UU3+nbsAb4AJYQkB7Dp7PvJv7W/8o4ThTHz6MzO9XUWxQ6wgMcKbIln98Ic/ZBeGlKhuEIBRzqA0blofnNqWE8uXouhlxKE9s2As27M102mga9AjfqNUpy1ihAiMayAMELSMG66UkbcMA7DTQg0ZWC9W3HBIaw1gGe7iO7pVeKkFQerEso7XxWsgarqHHnpoy5YtcEjNlOqWGO/tmbaHc7BoeNChEjeVAEn5uKnbhF/mDEPBNWkNE99Lu88RohYqzVadXZP6cM6ZqxIQyBlM6+uT9rq0DGbG3TK7HI0ioY8Qq5797GcjDDTrfLxhZzhDY7x3716cMD3V2RlSne9lj4qC3rxr0s7XCbklr1IyYG3uNNmUgWJUGGUjijuvqjxAb0PNQxl6nM0vISMs9WDC7Wv1IhEtv03nW9szpqdSqIbuvvtubGivv/56avAYK+HRhfIwiGDDAieUZobNE9IwHdDvfEM2BATcKJ161Ro6XN27xzk0ZOTmtUMH3SNH3dmMUS76iiXSApCNepSpUx6VnQO1VyiCKjdFnGYPTjYnsStlXpLXRW7Mc6o7xLvjAsLIAcfuNow0ikGRS+gEmR+oTD3davuC5rrVRjTemJ4xN6xxjID/isvcSEvg6qcEVmG6bBjJhBQE7rnM4KoOvIqqOmekr7qBiIvcy54a+nYuUGxfXx+eusjG6LSHh4cZz9lsFh8L3kckXuaQUzNsUu/UW4/TFeoptRXTJL1YI/ou3sJ6kRAE/FZ0Gx4arlSrq/tWpZNRkOfbo9j/u/un649MaPtmjFzDV63rVVt26FgBGzhNLETLkzB4rBC8AHQlhchuh/TiyQc9hNhMdD10wgBH43RDuKYuJOGk29ditAOXtSA0SkfLKnfWQzVHBl6h7k7knUNzOOgaEwXiriMJy3RaqCIC1w3XCmFFFTV62yKbOv2/ukZvzU5qP/yB/YMdxuSMk0oZ1zw18JwbjNWrLdM/WXIOzBBG2DmYBbvBFE9jBH7xjpaqMMTU6innDBr2hZmaqSXXFVAWIQM1NMDdUYAAHXDv+pOCBagIQW+fhiCS0TIOWsqkxz4UA6mZDxeZir/zne+8//3vv+uuu7DIwEeMyEnNwdZMuYwSVpKsUODJRIEVDfCTqbdW6noSBbyV2LtI9I4PfvCDQFvBWf7mb/7mm970JvbLmbvdcsm6/yf1D/59dGJctudZVn96f3nH9wJ//If+q66Srf3zP5aWy9OySkvGLC+1+f/zOfsb3wp++UsB145aTrEl/eCaLXc9/48Pdm8aSfRXReLVrDoQvgL2Iyu/CL0iAKvFXtZrYS7kmw9Zv4WlUpcapbl6YSbasxE2oz00FzdnWbPnd4XQC3spkWbtORVNRTa4pVqRXh4nMiu/4Ez8/p6G5rfhL/L1LoA9uIrylt130etK4AfBC4GxYDNb4kCIWo9PWZVx1IXnkFwkZ080kTrxiOIoZfFmHR/PYwErSzvsh3oKTa9I1MreU9qJlhj1L/hYsYCGZyhZoPIlck/AKYwMH5nJzgcso7st8PS+APQJIh8r0kre53PQst6UPj9DBCahgmVpkZCZLVnJiFlsgI6sKawRchSYFpWx4o7PVYSMJdeFfbfqNYhhffVrZccuXXZ5/Oqr+ru7GXXYdnMQ8xOlGapjzJ7hNlAdw+aSN4yFJwBj9sgVWFvkLj7hbjmaYCReLaAMOuqhOXeq4OYbPsybg0HDz/iq4+IL6hVwYoY/CCaWg5Ez0YawtQP8eTTnVBpG1SLuJcn0qOPijI383x91elo8YnrZe60+ocHekEYyQR+F0O75DS6moDoMUv1I1jqWN4ezWrZGU9llsTB7xngBy32MRzFu7IiZfUmnOwJ+FdLv4tNLvpe+ONm6u2dKO5oHddb0m/bqVnMgbq1tk70XGVtS4OmyWJKbSuUODQ2x90B4D4h54s3T/1paBy+H5pULZvXU298szi00tN2Tdkl88oWbbwvZGztNFGgc4D89+OCDCDkeHpL3DA6E+6btuRqSGi+xC6r3hnYTVlvNVxABepybFM3iL8oJpGBM4jV67NgxhiuC2WPM1qOtR+qFT5Uj7zhdrVrIJOgY1WrhgYfcsWPWwX36Pfe7w0d9JbRy0yHLQmUn86U3PDSjEvQ1cKd3nUZbu41BdUe70dnhFspzjuUEQtHeXrCKfBvWmW1t5UzObInqvKFz82ZX2smLBni+Vq3NzombNRhsM9Mt0bhrNQq79wp++vCQkW41snP20TGzVAhUMsbEKJN1BJH4gZ/Sjc6nPsnkR0y2YmuqUa4ZWzc5pVJw26WNRMK88nK8HeyqFb3ycsMXAEAvn8uViLErR6lcrvC5afPmHqKgAW538CACMGTAnZ/Ry0vHnEAc5sHBQbTu2KHwmgNhBenYN2E/gpRLRZfH2CMX/LjXlYxI/nBYTQR9A61OuaFlSvZsGXFRAvDkC7lde3Ktqdbe/v7JIr4aWAYFXr6Npur7puoPTWoHpgnOB3y0XllEzEI/zNYiC1DIRBJWy40a+9LlJx6sgEiSmBwDyDznc2f8ornFKWMvTsIxtytqEdCoN6aBwweD6w02LwM1o5z8KtEcSqBbE4QjbjPWpvXZkjMy7w5nUWhrMyUjUwkUaoH5SqNUaRydb0yWyjPVFKAMz13fue7GF0ave5bp1HXQNEHgi8dYL7EO74sboFtd0WMcnXcenajvyxhjeX2+zvYlJtauwsoWKiL3WsA8Wiy+ss4yZ7JagR9WoF0Vd4JG5Y39GaR6ZRfdqmNdJfZcxw+vK7hycqOOJ1lyRuoTJ3a5x0UmfHzIAYhm8v+7v/u7t7/97e9+97s9JmpgYMAjoFfSksxWTlco8KSnwIoA/KTvwl/yBsAZoP9B9P32t7+NnRgmPW94wxsQPzyuURimiYn6v38shvQrlBI9AGxmbPRo8eP/6evv0/sHlrd2nEpmxbapxcgpFvOf+pR2cKzx2f+Mzc0WrMbBQOzgxqc/OHj1/kuekYt1F7WAa6TdOkGMRNMrjq+OwDSTqeSihF2P3UdHcUJ9kAIklVqhUUsgywqPjI2z8MqsoLqflVuJwKTU9MregFsL6cGyekiFgDCBDY5wz/bX0zfnXeLUuJH95afFgiL3qmBFLMOIqWKHhsQIHyBio/AECLpwIcIzkMCTRdHremQkHSIxKUnINXWIA6oPS2uB/ZBlVfKhbnhAN2iWSMLs99ccE/HAKKKztmMBPeyrZ6dGQLpC84W+4KmXDnanYshAqDQVxb1Pdbq8D8ql0HRUD2ZsyooG2H13YmFcguV5VD0Vy4bV8A7BNZXD+/SuneMTr2A0vf7pmfKOu6GA9pzry3PZRHuHH+P2fB4Ggk9MyBBv0NjAOiASIwMjCTeRVxmxCGyofZCTvcIQnp///OdzzrNofiLhSDASGq0kJq3IWMFXtAJYO9cwMBYVOnssNkbOhPyl8pyAC41qHXSWyTyiqAkbWrLEXJzAy6mIfnmXCfDVujb6lkN6ySvx1E+qiiSJwzPKyauuuoo3aDGxGuI4Vxe1Q1nz6JyTrRkY9cl2iQQ9YotENjgQOgh61Bl3e6Lu5k5gWmVknFoKTDwarcGB1UVb2zNljxWNTEmUZUQnXhUTc19xyF4s+NTHT71CtVGoIjx4u12nJjjtFbqD6/QCn/SOl+Yx6YEXuVLe5lxVND9VIHCggaYRtnddG5FFF8fcIkdOKxVn6WZx+p2yyzYyAo13W/xIy74WcUFVGZyGil59n4hPthVQTjZJdDGKdHk7EOoglOz8WQ27VLZHRip3fd/+0TMlQy4AAEAASURBVA+No8PuyLFQJmOqOREhiH91hlk0Whoc1AbXGGvXaUnA3Lp9l1wS6exE5GDe0FsSMuP4hGQc1fFxthjI+4qnXJ7uWUUfqH7wbh7/nBsa8luN5PoNHoGb4Y9cG6x99HT0l+PO5WyrjuLXHp9iM8ma5IVt2AeP4GNjzkzVv/c9AO2C2Zyxc2egUtd27QnJTqbe8BuAMBRX99eQhTZtnEX63XBJrS1Va19V7kzjgNPgtZHpWtu2ZQvl8L4zgNmh5Ar7a4cOHSLEMXOCZznizSGe9ThTx/EG/OzP1BuuyIczL5rYeMDEcLdsOeva1hyeaN998Chxd7K5HLb0fb19Ndc3WXQDut0e8b1yqwh/SMIPjoOObrDHh1gIkF/VNrEG4hxtPTY7gOqJCZIUoZY3NWSa7WbjV/Z3Lf7cYlXLlkEf0BFZifG2f8bpbNFXxRrAZXXGAcBfeP2E7KrCzUy8k4URomYeAMC6Yzr65M0dgpV1eM4ZLYDCBWR0IFsNzJctLLcni3q26t8zWdvQam/tTRGiKWIYKVfrcHTAsRFmeXlZS1NhDf/kS9rMSWx5Zuxd09bwHMCBLEl6oa6JpzFWO+KWznQqTuqYgzEImARZ5hsEaqpr2ao+UxQ5/FDW7Z0WvOj1ab2zhemddngz8+nac1LzlvGT8jmwy/iP//iPd77zncSJxD0Y0+ibbroJu2hWBO4uI5uVJCsUeDJRwFuDn0w1XqnrCgWgAGwBPAGY/oi+sDsgiLz5zW8G4fAkLZDbqNe+813zHX8BR4WlnDAd6pDHgRV5z3tC1z0TzcDC1WV/8TgMnGNZlYcerX76M9rXvhofPszTLGcH1j7l/t6nfOtpt0wHk7kG0BjoUiMgFemxTj2UEE2vPEv9F5YvtYhJtbzVTKqwWEm5SDqn4dbh1x23nLPKpVBbH5m1GLNfv/HXu/37q0PJ/W/uCtQE/oSXGf6h508n08+aYTlFwzB3b3LyL3qA3NHi9fbXZDueNwcK1d7qs///g5919QRSEUsv65rabUfWFQ8ldUEWO9ZXWRLJVlgQTqSCklipgpF/kFtknVcC8ILRqizksvhL66SVHCIgiUjMdc7UJSy0AXlmmUeLOJstTI+PIzttWdORTqaAN35qnz8kctBjOiDyo1PWdNkHs5Ir41NqHpi21rUTPMjd3mWDyLJnRkStWKBx9Solq0lDl3XQLqTazLvfc+z226Op1jV7dpkdnfiSeng23ENXhr0iYiQ04+dpM+U6oxe5ggPFEHzthg0bSIl6EChjHspa4ZF6fKZOHJBAd0+/3+fDAfjY6DRevD4Tn1I3GgrCYgH5HSEMEq6/DZCZTTzW2GJgeIQMmyZvStcv7fZd2a3Dm9K1wjKd7qAyHAcOHEB3jdIJ0CPPk1Z1/kI/ZmvOzgn38Jw2VSZCSQMNDQOkYSGTG9gOYsWPsqUr6qxJ6tu6wI9V3aeKO6lIGjg5OXHJtu2PTFhH8kBbC0s7iESTsLZ3A24NudRYO109T3uNmnMdjRP7C16FT5ts6UWojYDhyRLNPkIHgvk6n0tTLv/cqwZVmS3bYLA1hI/lRbBWJ3SUUR5/6+WGQhVQetztBgYG8IImhPXBGZ0dId5edsW6Iu6Gdny61bDxOP/lV+Jip1xKW/JeJnnPXgusBRApnempyg9+YN39A/3+nxhDQ75iOSBgQhJ01dbNajLp9q8yNm/Ttm71Da4zNw76kmkfcYNNBZGnaMMr4A1n9UuRjxouls3WEuGR6GLozPvYrDktYo/JU6Vywk9vqGPmLSYY5NjsSJnv6BE+mUfpHa1Rb1RRGpex7SWEXaVaqfuqhU3bLq0ODeezmeHvfCeULwYLpfAD9/mRioeP8v77c3lcQsRSmOnbMBpMypddhgWQ7wXPw4M/cv2z6tlc9GlXu4GwGfJM96UFTAiAz7HvgAyMfT47aFRqKb5XszmLzf05+vbo560AeAEcHJl5YM/RmXx909btERyOZI+HN1Y2F8K6m4o6mCuzOAIr8NCYe3BuATEL62i0u0BIIBBCM4CjsRviGekGOciEaUM6xhsDcsG7KsGHxcCHuL5ANLeGmIs0duUGEk4/MM5iGk2GLEPNkSIPnnqoVkimFILFVKasFMJz2lgBr2A9U9XQcot0Cma4Q8hiox1X8USgPaqhqgUoC7z9ZAgPIDnISgYSxlmuS/Sjoayzc9I5OKtNlHwYV5cauK6gygZGUBZZtVZK22gXDfE2gZlKQj4t5id8HbHl9O6YhnfxxjbmTAAdSSilnNqEC76y2IMar8nHP/5xJGHMggDCADXaix58cYu74HquPLhCgcdOgfNm/R97kSs5rFDggingzc6obsC4wmUFpJAbbrjha1/7GtY7HvPanJ2RUpAECBpjT04HRJ/Ar4V1QlY2RDIgcKamMJA9uwCslsGFNYxlWzJlQcsXs//4z/bOB0Nf+mpc8CzMw2uuenD7jfsGn3po4MpMJEkqEHFrI4cAyfW1DsjzOJxJGF+1WKvaqFVbUeL06xd8D3ddrV6uzw5xhrcsr2ttdth0nVwkfs/UM2/qPRhZXe582XzmM4Zph5BH/U5g/HPB9NOCTrDKo6nt5eD7x1A6RwYcI151DGfe6fk/Y7f7zRZ4AHgKShY1Jkuv/BYCiVWzUjKrC+pD7onGORogmazNwkGIMCvpvToKUbxTtrFpqoBmKblXRfclnTAr7EGo+EZ04tT0XCKRjIXMge54Z0tvQ4/MOW7KIMoF/nZCLJLz74IPHgVXc7osvsfYm6mqApgsNt6ihBeOlENhDUm1l1fSYkKEV+dzn2afP3jLyzCwZKTdf//9xFBh8wXTVg8xyBuHS0ajRx92+SVaMwX6g/5AMJjAdFNaS0KpQ1dX5403Pm+2WP7J0Xom6xQLRq1CyF8D3W+pUqtUS7ZoLvEZc2ZtCOyk0q261oL79UimBs6XrYfqth32m7GolvbXN7WHLkm7AMNI/rRWjSd5XshxQpO5hWab+gPDgyK0eZemkbZiO+ASg0Q9UzHmwKry+2VogncaEOtrJO5EyJcO4q5mrG8D/VtkSHKgwd6xpFy5hrAnMFol81iejta7Em5HzNnaSWY8JoNxmQfZIhuwq0L6pnb91GdJxkF9OLy7POJtN7D7ABTqwMCAt2vmZXVqDme/4vHhpKEUHP8A+0EPLzYZrrsurfeK5flxUi80TtUE5v5o1jo6zy4Gv3k1tdWt2kByUVe/5KmzV+Dxu+tRzKOtoiKbWULt5RySXvH9DBXaz5/bqFVBRrjvHueOL+v3/ThYq0XoQUOr676631+//HLtmdf5Lr/cXNXbynkw5M0yXlnQzSOifKqzJlW964uX1cBzXd5B9nFAsUahepLxNhtPGF+QHqdxPrFAppke6g9diFSMQUENCbda433D97Ojo5McaMujOx8ZHx+jwrJTQdNcPRwJbYrGQtu21bI5kAuC0VA4FEoEQyFumIaVnWkMHanNZg0a/oUv+EoFd8eP9OHRRHbG2fkQWxxsIwUct5RI1gbXBJ5/o7l1i9G3OrBmIJRIXXnVVUAr7961e9ejuwg6dMklm9krQUDyXmSq/fN7LPQHy4gbMbXLBju2rm4fn53HwjtTssbnqpn5AiRF1KsQG6lgjhc1duuA4t/aLXBQj05YD4xpwzm90GDvUkyFaxYAy1odtD8ww30ynysqeG+07C1QoJpCPfFYMuEPqTLrc2fLesyPk4V2MANYNC61jTWtZk+LTSg9mWzkUe+NlEo354dF2sp9TKNYMjqjJlH9tnS4+PgcmrNBv59AIVxmYzEyU6jlqvX5KWd8rtKWCI60hJgBViXcwYRNDOGumMG0pkgi1lIdEZ2IxNs6jJG8u3PS3jejHwP+oI6mF9NoNjFdao7uF7mXGURgG5lKxNJLECJrDQIIa7MViSg+kjcOZLT+hL2+3RhIYoK+0HbVHFVtr8zFlpzXt0cHXl+2hIBQwR/4jjvu+MAHPvDCF76Q7aQ/+ZM/ufnmm5kHlj8VnFfpK4lXKPBEUmBFAH4iqb1S1oVTQBgqZSMKwBVuKsCTMiP/13/9V9M4Z+kCphILm6njXZhMwMXjP9ZcFNS6x9Lp0xMtAkx81kMlFhlF/tWswve+37jjf4wvfSmWzdiGPx+K3fuUX//Jtl99+NLn5/1hsWAS4VbkPQLtsHID5oQ9HVfkcVlR+VILIoUufp9QvjTTuyF8Fqd6MB7t2sQlu5SxSnOJzrXsoCN/fuLI71/f+d02/9GeWzORDVb2u+F6xhcarPU/v0jcBsUqEfrQimyF2yMr2mtO1Ld9Yuy9h2vXQitPHUutKETUudRb1U44XCElLZGL6lN40aDfGUwJ38CDwj1I5YQL4ZMslGZbBEuETMz0ZCFH4JfGSn6INVjk+kzZKpiezc1lMXG0WoKOL9qeKbqJYHRtymiPSCTZ7iQzEvU4LWlOoNO5frhh2R2n5rhbe2mJvyRV4VAisZzATvHZpLhcOsvh9QcgyTvuiu49SJinltfcisxGEbDI8NAEofWkhaVDsZmf1++4zDrFMta3IlmGgyaeh0iyi4noFtOvT9ciTjQGh+jTdNBNgYpBmx8K+Lu7eg3DqTesagPvUpBF6yiZ8IIcydnVuoVGomGXGc54l1tWKR6o9ifWdsV8kxOTGFWi2l164I3sbRh5MiTcDFhx6FGbBhTe60Yd6667d9IZLeoTovu1kFNR/ML9Gz5c2V3d1FpMIx3UetPaupRDrCkhpwyQhUHstQyTY0RNxDxiex6ZaUyOHyCGsBGKbFrb1wtn2WmEZSGiv+Tfcg6vegSDoc4EgEHffqankHZQbhP2o9k0emeptTPXl/48Uz5nvs4op3+M0YIDNhgDnuGEt/YlHW4HTrwyAE9+lAui9ZpuTJfoRrntN5z1nW4X4VAUFU5+4Gf6G3JBQ6xskCq9eKGnHd5L6ggBpPuZcTCScfL5yg9+aH/ms/r3d/gzc0HHgkZ1n4lJs/HM68xnPGMmlQr193VfdpnEBGfiFEeKk49TryxNsThW5VrznJjbqK3wrueip0T1WHYcE5SIW8OxmR0QzIz5iX++9yzYdYwor4HetOD3B0SHrOlt7W3+AMHOFg6GDWeqv/RkKrk9tV2KV4f3eCAe8/cOMgfS3ugtL2c/1smCWe0Uv/FNPTtr//gnvLvavt3moaHwnt3Bhx6wJdCRW+zqtDTTf+tLY8XK1TfffHj3I6kbXxCNRZjAldwtxjpnp8ZiLX4238df4IVq4h+h9XWA3eUAH10aPTQ5Pj00fTTdM5hMt3vTMY4qo3lBdSZY32CrcVWfWaw5D0/UHxrVRgq+vIWpsKHEYGRCggmLTCjW0SZvmiwvijDylsmJfMniS985lpEHg6Ouz4Gu7DewIk6GffsyTlfc6IsL2nxHHNh52ZA8laDHabxIa75Rt65rdYkwP1e2j+bcw1kQ7I2ZUnSqEJrN17Ll+tHZeq7im4kHRotguetdM1o/oX3TEto3JnAUMqD4JJ9LZLY0J/vdvTOgAGpY1mQqnl00BuRM4yIGyxYCVgOlUqMwHW/vA/WLDc6ag324niu7UyGi0OkH5lzEYJyW16f01ij4Y7LZJB+L1b7gQdCkAKsGhnUvf/nL77zzzve+97233norxghog1/1qlcx/sm/mfKCy1p5cIUCPysKrAjAPyvKr5S7LAo0GRo0bP/8z/+MQQ579rfccstb3/pWDE2ZfE87/3LRk1gB7PRt2lDt7opMTCrZRzFnwpMYtb5V4Q3rtBPtn5vFLVZOcUGgG01MZz/09+7DO6M7vhdynIbu27ntVx/a9mv3XvOymXCqagQQCYTz50OVULEwXNbdcMoh2sLiguSVLTmfZn2SmzzLHRY+tSTLMrbAQQORy+Uai77uC4SQksAImbE3/+3+d/1/m9/S4ptouWYmcbUnOop/MXmN1rbfPfvSzS33rQoe4EqmNvBg8bk/nntxwemllipbMWCG5UT+woeTDAFkxmoLDZTsQGOCJSpckc7E1FnXwj730k4iE4rgxk9VWSosFmIoAQXZC9EXa1iUX7gqA+ah4iLWGzYRIHBMxQV3br6Yz83q9VrM1KPYNIZT8EZr00Z3jAXf3ox9sieUn446QrTzO8DxUrUU9lcIInSRC9J4RErO+I9pmVxZXs6qZ2GvnMb7/rbFsdLbLj+i+7dY9YA/CAPNcfZNcYYGvuL24aH697+v7z8AFZ3NG4PXXWcMrkEgk4Gn6jtWcKYr5njWyVuIAmLbHMIuvEFoHDcc9VWqbjTix+OsUrNbcAjGLr6OqM8mT9ysYFZJsM5GImhsTrmXJM11aZHvMddEr+VZ/HrDm7fDM2aD9Qf8E0tLBEjvgN0B90i50IsJHHLu0bw5VAiM5JB+eWnE6RfAHmhpo8n1a+CNxSNaf0objDqrk+z+0Ep5B4S+QtkF0iJsY2XK6Dg2Xhkv1+e1qC+itbWECHqJ9AuOzjK7YGlH0SgAq8F0of5Lr3vni1WQX95580qzVqc+tfwrzdx4hDE/jC43hxKXVwoIK2dzJ7aXqlbMNNKvHkEWZgc4/j2zRriVHRr4bz3st7Z2ipsiWV0E1nX5bVh2SsYGWxiY2wwMDDBCTnpO9tZUG9UnN9n6IgRcrrLjruq/fdTc9WhketrHroxulGPR2rat2tOeGX3Vq6KXbBAkX10f3XdACwcFCVcoyTy0MHhOKmXpT3qwmQrhlv0eDlS7DDPkW1JiaY8VNMMbadb75BGshPhEmMc6mjTYGPMsV3iKNFzhfHBwkE84e5rpfdJ2r2ja7p2QwDtpfp58ZXH0M2eLgCbpQRR0zbYOXpyW3/gNxoG0Vd4it3HsqFuvzX/4HwOhQOPzd/gdOz41aX/ww2FQ3v/9o1ttt/KNb9hGoPjSF5dypfbX/4YRiblhpHpvXDWrcPzk5Mocv/MzO6NKytHVvXzrBnAfjoyMVscz2WxrsmedL9pKp3vNqVrmsRxIfrjyals7/M8c1GdK9kNj1s4xY6JsFurMcE6tYdYEVALYP3GiAShRed/QNCGzDJ3FV0gtgwaTP4ZPpMdoGVipqYBxLK8dIn7SDKjR1kDKAGYZ4Hoy8UYUVW2enI5eBvgUbGxh7bypA4toe2jWEYVwITxdDE7mga/yZyturuJMuI2JYuDIvLE34/QDYZVyVhPaNyKuQBws7Ox59cV1dgyv6MYu2t05YaE6niz9X/beA86Oqzr8n/J62161knbVZUtI7iaxAYMNJoRiQk0CtoHQYlJofyCEUIypIRBiHAiQ0A0YYuIYU1ywDW7YlmwVSytpJa20vb99/b2Z+X/PvW+f3hatJVmG/D7sWJ6dN3Pn3nPPvXPv6ceXyhOMGp2wR24k9lC3mCtlJgpuh1Nk44L5l+mE5JMtlZTFIxmzP2UdnHCeSHir6kprGs22KA4U5RVPt6Sak1bpl94FFlwwpfBxDnDC8YIXvIAv6P7774cNfutb33rttdfiG/zGN75RR9WiwHHeXrq9hIH/uxg4tpH834VxCbI/YAxAo0Dm4uhLSAaomauvvhqzHLKtniBKWPS9qcnC7b8sfeH6SCbF5iH7I2ld43H7nX8XeM5zzHiieu2GcpM9VHHPbDRuzkv++L9Lt97mu+XmaAbfR/9o3bLHtz7/vrOu2L32jzJ2EA6BhZ9Xyof6gfHSVFbyoEhmI0UfC324+CHNijqWgkikRSjNJgkkaleHI2VTc1NjxdRIbcc6+F82ObbSgOVujt//lys+sSH6QNBICpmFC5nX+NuJP/3R0LtH3S4JfeXlbehsC5fRoHC5RHhWhmTQ2yQ3koDPtofVMVGLtFgd/0MyMeB2RaMwYD40t9CIlhXxFbcs4wkwCaV+7FB9BHSUwPDAJYT0Qp14xFuW3A95VOAiZt/f09s7OJHzfMF4XXNTQ3ttgOYQpRPe9AUb/C1RYfZUd58UU8daXuSKYZzIOY8OkJ7HOzBUWt0W2NNfXNvsIz3Gue3enhGcr8QlfGOjAwlCk9Vz4HjVQo1QbWH37uIFF2LAueO9/9/R88/fvHnTiYRf4kWXXL0PP5j/3BeiRwZwJ0SGgWVzdsWK4Dvf5TtnK0Q37YK0R456h6bwELOmxPgZiYNE7RJXWzJFuw7ZcwgbBMeFtCBfdIjODRVIJFV0I5hKlgpGa73dEnLOaPC2LvNBazKNaBq6Bw4BKh8mQZ+J0UWXCbiFchh6iGsKAACFUaiSyQl4f3X3PftHC32FxEgxNuVGHc8XCgeaGhvAQjqTKxF8xnRwq+uMOytr3C1tvqa6WngJaqhGIDVX7vROle7fM759X1/Tqi3kL+lqMDY1klkUGctJDzp14iwNn4MBCJlU5w8fBXSMKzwqUT6zYsDSUEyfNYSsLRzAPP/16i4seF3h+rAq6BkjOLbocul5yBZutlaaOvaZyHIiX7esK4MTmQf2TfrjdeEgoem8uqCzsYVQcEzCk0bCgoA9HTfBEtwvAdLWrFmjxY7VrfCU7ino3eKRo/mHHyn84EbrZz+PpNM+1yva5nRtg/+Kl1nPf37k4ovt+nrtciI8q5pyPT09DAryI+pkKnIwdnpEKICYg0nLBYs/B9dMToQpFP7Nb36DMJS5LaOoWFkqOeecc3ikJ7auB6MMXQYDDT5VamBDoUVqpi20uxRAdHUKc6AaCYtcy/cw+6OYXxjsIR5y01kjn0s/9DAx5fK3/dxKTbn33hecnCSQNEiBAyrU1hVbmwNnneOsXRO6/E/8y1rJscYsEkabKtg9ZNf4vzyVZIVBBrFnzx4SFvLJrNlwRqxl5WjanM6ZLChl0NV8whygJuA1xb14mDzhxUf6vB393mjRj6kLmYSRqxJByiE1nEW4LEOkp0qhLGiQRY8lnabAtFSqfkrTXLPfkQEhFvBq8RAOGa1xoyPu4qjfFrd0GmE9Vk82HwREFgEy7Y1kvEMTMLESf4uOkDxpeDI3QVQuy6qJ+OvjwfqQSbbhtoRBhDA8hAlhJfJsAQqQlbzQsNJF3IzdxwfRCRu4RUzB4uZR9hqpqbHsaF9i+UZL+YjQKisFXYUAEDpBjJg84lnWhiTTe7uYXhvrG01i/uMIrZvgrNcius+3xqeEA8iT9W7+DD12h3qI9fiZz3zmhz/8IXYW2Ei//e1v1zLTY4WWrpYw8P8CBo5RJ/8vQLsE4x8QBqBpoLo++9nP4u6LvRmxnZE4YrEGCk58+WaxFvXEyFjxsUeK/3ObuR91qG2sXxt48YvtLWcZdcL9yqarNl4Ky44JlYpNYzKTvPmW0g9/EPvlrZBxOV9g+zkvefTMSx+88JXjVtg1VTgWXlOHvAUFA/vnmgUH3s8gzyFWTPq+PCsXlMvywQ5dvoI9lIJqo4L1FTWoJmfY59jhJJMQO5iFvoSQz454hNoE2EAYLCJhaGq/mW4L7V0V3hWxk1PFlgPprWPOSssWpgl2F+IAMblicYWzRed57J+feCFexO+RnlTfJNaIlCfrr5iZWRAVmFUBpwYVJlyuqyj7mc4IbaGuxUWaMoriZ20xcyViZprjWePQ0OSB3qGOlSt9gQhRqUYzkuWovQYXKfridiSc1fV0S41GpdKncAE841ln26CFze3BkdLKZt/+odKGFh+W2Od2WI/0QbPDyzvntuNApSlG3cXFmpTp4RQnXvmqmp/cMtTRtv9zn1953nkrV6zgHZlEix7y6sGD+Q9+ONy9Gxoml82QQCjgxzndS2/ZEv7QB+2VKwlTvbPP2T9tHh43wQ8pfyVYLONuEcK6JES6hT9zMWDZxFrDDpkESOjf0YdEfTDJTBUJdt0ckmy6m5tFTaEZS5qYDxo34Wegh7D+xe+X74vPDd6AQyu+HNd5ePfh7UNG75Q1kgukS3DRXiwSTNTWMCMmppNTE8mEL9/gSzf5kivDmbBRuPxPX+yzbahb8rjAWsBXVM6rV3UVw3U7hoz7t+07NDi2Zu36zgZrXR32gX40beGQpC8CAM7zMTn/DsCjnSZFLV5qmLku+BZ0HpGuKnVqJAAPka7Ao0ZIBTPzm9AFFjmrdz1kBvtHSkdTwr4y82N2cVObj3A1Sop1bErIAIiACKdE0h15eZl7fFNuWxQvPh+JyxQAx8ov0u7v5RGdhYe84447YEGfOy/ZsmTEcpwMwp2vfM3/P7cEpyaIVlDA9761xXzeZf6rrwpu2pQLhQqEYyghiCkSMY4wyHjn6r5g2oO2lvGiCchoJiTXBEUHJ2TSZi5RTI+UHiYENBgpcBNzZaQ5aGiZadp+gfmgH2HbDKhwuVp/C6+L5oq5rXngBXF4CnNgwXpO5ab6QNkG9IahfEfos/BX4D332HZn30Fnx+NWT7f7yzvQVgcxCJHF1cyuWmOs7LSe/1wvGIw8/4X+9hYzQqApOQQM6uPvjDThVAA7/juVb4ci5eaOX3jOE/0u0eDJwr1l65ZoJMLWOV1wJrLWaNpLFpDXygfCIVsoSZ6JMiCxrIyA3+geLDzUTwolc6rohz/ENDqHFl2EBy4SW7bIAJtWucu0o78p8KsQoX4qo3RJ/hfwuYgI40GjPkimYq8thhLV6Kg1GyKW+iTLq4SGZMGz6oj6uHHOynuE9CNk9JGk2Z/0eseyQ1P5QslyLTsaCdZFg3jqNobJ0mSuqHFXN2IdjV203ugrQNIRNLpE0XO3Hyk+uuvgVMFO49uSzRdM1mf28lC8eSWkAMAIYWBjjCMdgwxAmoxIsSYIGwz3661IkPvdXE6ULBKxgT81H4CWBZ+PSzsyLNijE7mph4+SCE9hg7/zne/wAaITJvHkgrLIE6lzqcwSBn4vGFhigH8vaF9q9LgY0MsrMXWxtGFthaa55ppr3va2tyFr5J1T2GvLu18u6yQnzVRW5LaRmF2bMImzIva6YgUH6a0DrxKidPrOewo/+4XxnW/GUimzWBhoXv3IOS+594JX9bSdkbUxh2Pr0Wye7M7sLLyLry9auaLaiVGM5URXqDq4IF80w/rOMMiK7hfJrih+eQMGGIJatL6mGAniEoeelvgf0AFs8FJI1cBT2hDhv8i1kQoL10pJ4XXhaYmwIrwu3JFHSljsMbnAtAxpMcJvEg0hBY/AAMOG+RAJyD46+9Boq75XvrPgEFQ2xZkXoNzcgwcP7j+w/+KLLopGI+Qfmso6R5MesUMwhpT4zymHqJjLE3ZcZWJujxHZEqaeChQWZio6tb/QjkMp0k7Y0yWvb8JtrbWOjLtntPrCJmyw9dgQiMNsvfRM+HGQJ5ich4BKw4ou5bFjuJlf3W2+8IW+Umnw2msbr7kGYl0kJ8d/VdcBcjyUV7ffbnz4o0HxSPYyuazPZ0O5o8st4Pr4yesCz/rjg9MW6SIPDHujeQFIWNyATZQplAyk0YR9YKYSXAfbP1g4ZgIaYIghaDWiSBM3G/V+U9Bc3eStqzFWNzKJFkajADPD/WK99sxnPhPuV48p94VeVrhIFrxH+92ecXMwY46lUZOK2ANmG7Nd8FEqeTWBYlPQaQtnN9TmfW7WKTobNq5ndsIAE+UY9kNYGngd+GbPXb/pvAGr9dCk9+C2vYXM2PL6SKtvqi2YIe5xe/uy888/D0ThqYvAC30sbCoHF9BVcDtco8uFdNM3uc9x+PBhlEiXXnopqmysc4Ff5APq0NdUCCT0CF4Ib0+KwQvxSJvIcqE6WxnjE7rgLcGPiNRYLTzs6PcOO4MZzf0aCX9xMxNsxoNVFaa0XivEH3Zg2iP6q4SFlylOxGxMxyFmZfbowicExLxCp9CReXWUb2i0SA8REKkzF4wgeGWS9PT07NyxY1nHsqaWVnwWioxsqRSenIht2+7+538Gtm8Plooly0o1NyfXrRm75NL05s3rzzuvVSUiuuuuX6UImatml2rMrMNvdutWWrz33nsZTZZ3hhXhCw0x7hdddBGNwrJCZPNTH5rX5aPj5/G6sOB9EJ6cmnzggQdhmM88c9Oq1V2zP1qec+hzpYLKVy1f0jHApZh+VCmvL7j5lIZSyanKX98MPFQolasmcb4ws/v3dm972LvzvlAx23Dbz/FIjhztFSskz822tUlU9je+wYvF41e+jgFjrsvOIIM6A7L0pHw8lSknIHkenxUDQTSBU6uqet4iDXn00Uc2rN+wbMXydNEYSbkjaS9dQtIH8IJ9xcrD8rlNEXTCBhzv4/3Fh/oIf+BLFnGxwUnYRNZJKHF4YWL9Y9DEdimAqb2SL1ZQqflhjQJZ1cW1B9N79kFCRteEvLqQ1x43V8TFFRlXYe6DvmmZt0Y8Eddzprqz1V3QI5VzzSHsosdcjJ97p5zDo9nB8XyagIV2iHBpyDTjQY/cSM0Rd3nCwD2YEFb1YfF9V6REeZwwKhlKFh56oh877e7BbP9E1gk2kBaekI5WtBmVN1SAZn3pH91k3wc2KoElRpwNp90QIeIX7sHehkZzZZ0ZJIR5vgCEmMzwOXd0LKdHiIc4yrPhlP7o7jMNvvCFL3z1q19lrUBL8c53vlOzwdWIOqXql15awsDTjoFToQOedqCWGvjDw0BlL4EOJgn7jTfeCOv7rne9681vfjMGb099MdX1q41QSAo2xBlSAFJP9h9UEpmf/E/xBz8K3ntHANLOsnedecn+tmf8/E//djDS6KJ8lR20/Kb8VfGEiVeh+V74Ggd5PWkJuZD9SEqWD93gDBGiWGZ5TqdgKnhI8+Jqi1JXbWzC8RLhw1SJEGGAJUOvLgmpA+mt2HXBiMQ0RmGLEVTQJ7s4WlzR6AqLS9YEI+JjUzf4lwga0QCZIcyIX7ZJaVpOtCwc9wyUp+FvZRDZaLFChDOBQoKH0UQST5EMkAQY+mA8Jw6T5LYlgFNT1K6PCEfRia9UA5CBhqcKFU0dmXK6x3yjOXci48ZDBA4x1jfZdcESCDk0JSx/fbB0VjvYnT1Y89BQ6RTDP3b1VbXf/nZy69m1v/k1YcHwkp5XfIEb0vFsJvv9H/o//zmUYOBdDbouiVN3wH3vu+2XXPHAsLV33B7OEPBZ9NMR20qJn5socov5ko1GnmocUl8YGDy7RGDzE9/L8XA788ONmfURs7PRWxlzt7Rj06zn9wJoBBikS+he5nC/GhrVWSfrWI/3O/vwx0tCjOKLbFsOqXqIZIZJuUNws7qwic5kdZ23uZW4psJslztTxVhSFbwvSbymc95jo7B/waMpI59OLYsW20OZznDBcfMwyeSh6ezs5HVYHRgh6DPhnJVdKxzv5ZdfDl+EsghNYGVWCD5dl7DVL3vZy8goi2aSmaYBgGuiGBQYs44L6oRPxuQV21c4K55SG1bTXGAazVLDReXQL2oOGftMwOAOhy4Ab8aKxMRMpdPkriKEHpk5R7LQyPTeWlbr39TqCyAiUVmFeQt4ODPQRC/DtPfQhHMANadHquf08GDvH61vPGNFvQ+7xqC4mCIv4OBC4b981s519BROnrN+SgEOHgEPd+Ac4BK5QwF9pps6Djn44ajc54IDBThQIRTQ6nHuaFRzQZww8EYlqNYxLeYO1xyUb21pOf+CCxiY2++6K5/J4/IfcIz44NGWX97ZdM89sakpoqIVA+HJ9WuHL7ts/IJz3VCEac3wkUaYOoGTDFvACfJZEPSZ8FTabHLnzp0Q4l1dXRrPFNaHYE8vuOo3P2eePPnfqhcV3njDM6enRQ+MH8A555zdvmzZjBxSnh2nRrkv5jjlQ/NP8mPmBTHAkXrKH4AedJ6fBKjlup/sj+LXpBALCFO6t/fIs87eEgz6p79zoy8azH/r+3YxF37wQeGOHDe9ussZHfNddZUTicfffLWBwLeuTqYjoj4FKvBVgH6ylhd+DoZ11rTOzs6TGpoFq8NJgcxPTMW6ujps7Osb6thDp3PusHDCCJRluZZNWo0AwiMCUhCWuT4mTsKPHC1uO2oO5wJJMusSJqposinjPcuw4UKi8yfJy3owjzXPbyaYDCWWTfCTbKPRoBkLeg2YE0eN9phLDuHOGmNioJe9eE3Z64q+Lja4auKJYQibDvK+nnHj8KTbM5RKO+G8FSJXNEKKSFDUzhhgoxBeniCqFnbRFsmKZdEW8KQJ6gE0tMqPHRq7/ZHDufozBzOBybyZKpoqbZLMOvYDVju6qekHfgoVQaYDWwJ2kASBnEmtBGiocevd0UC2vwaqAi8lmbLSDh+m9js4hpJTulJdNjDiIDQpUVoYxDe96U1EyWpvb9edOaVal15awsDvAgMLhA/5XTS71MYSBmYwoBdQfrGhXnfddbC+0KDve9/74H41Cbj4ljNTzYn8FU8pJWo9VhgCL7t3X/aWnxof+0gknw163kjtsic2XHz3xVfv7jpnOkC4Ebaj8p6n9z50OkigyfDLmcCMmD1DnUJnEWaJR2LJpo+Zv7xctXmXn7HZ8e3JBoYKV8JNCZerPHvdkLCp2GipME5gB/5CkVy8Qj3EASa9BGpbtLswtFyH/B4MXgK9rs+oCXt1EbRzBskJo0ErpNInKujLXSjDxi8hAMq/TuMfrBaxrYUuZ9S6urqwttKaN5rgDvw7cD6jzR5IOvvHQYG1qsHonXDYswG4N2kkwmazxG19SoeeUTk4NpNQyWL4TaQoKCHGHuKDUCgCi+HWoviknUWRoKtKJqcxpvRNjPpu+V/hCF7/l0YwsOh7c+En0o4vkfCISc6AYzJQLELuwwnIbEEHUVMLkTeSsSZyZhLj8BDuvG6mBJ1n5UqUcP1BHxpgxyJgEgQQ8WIxn8YLzoGZsPzEvfKgBVvibmPI29isCCk9U6ugqHxl3IPugTUiYxPMUtXHVS5CVLM9w6XelDU4bYynMTrwwY2iXMH0ulACBCsawn0Ous1c22CQ1UPjsEy8qVGmCY1i7O9yRmDfVGk0bfelDLyuNy6LrUy4z2irZ6rLfK6CEzddDnlX0aqcoaXAEndYB1AJ8lPzxlzATSFk4XWKQcmh2eOmPuDcdOxfXuQOj+B1MYKF6+MOFepGuQ+/zeu6Eh5xsPJwcIGAALZT3SufQNq5554LaL2HD+/e19OXjSZd0Z/Qi7i/8PyXXYR7+dRU+u67f6Xhn+mat+Xsc9LBlsFp344ndsE5BwynI5Tu33Okf6/b2tZ+/nnnUwmqb1Y/qU0hhBqAEzNgZgi8/V133aWBqICKpyvOq9yEj6Vr+qk+4yRSYYDpBTcrdXKBDTB1ghYIVl2emxz6pr4DtvXk5Cxz1LZ1slzbZ5+9ZevUyIBzy0/jN95Y070vlM9RfaqtxbvyythVb2hfvoxczqo+BhHBgSx7GgA4YY0W/VM3xJmb8MlatVu5WbmgKn1NMY7KfcG6rrrqVvmyzDxI/AVsJhAVIaAkKD1rddGNNKy/cN++gwNe4/AgsjhxWlH/lPWP4oUYTcUfSP3SuAglHcCgK/LllsMiwGm49BP3EL1Ki50OH6LY7FRtMLMAng/oAncq/Z3/jIr5DxQwCmdsOmPNmtU+rHf4deWVyKfqr3yDR2biRx41BvtS3/ke3hDh235hfulLBAzI3HA9n4313nc5oxOJd7zVC0T97W3MCeluNUZVk4sAUAFp9kDI7Tl3TqSSSm36AgONpqYmpiuyKly7sQo544wNdZFIbUg2CGJKoVYlTjLxsQEZ19l0yTg4ha+smwhaz1oZ/JP1ZvdY8aEjpZ1D/mTBB5eYKxEWgUTlEo1CnHp8EjVaJqWaUGpk9BBzQ35hU15ExVoieIcx4TcGU7h++PaPu60JsgA7y2vNjhJBqgFWFpzq2Tens6o6dnYPsSBq2I2NxtGkua8pgV00Gb9HUt7BgYnxrH86GJoIBsjS1J8yD0x6e0aNlQlCWFkdCbZ1DZGMN9EBNzXZvrVWXZe9a7C0Y8TsnbKncsIGpwsucSWRuSvRuVAgGAhhBQYnz4WOkjWZc0dTeCZbjcH6tlhsbY0Rzg7G/U7XyhX0hO9Ojb+cZG6d6qExgIz74x//OPFZPvrRj6IN/spXvoI2mEilS2zwqeJ16b3fBQbU9/y7aGipjSUMLIwBCFZUUmh9v/e976HPecUrXoFTIvoN6FpiriASnrPHLFzLk95VdA2bp2g2RKpPUKJMbvtjuev/w7rjl5Hx4YLlO7Dmwkc2X37vs68aDjcUSfMiRJDs7sI2y4ciCXW0vhdySmX6EaaXB9gsyUHl/OQVvZuoGBf6idb6yj4ju60Ip9m3oJkkypSKOMUFFstkbFUBiWCmhVmkQmqiPpgMGJ4Q0TswaQ6Ivy6WTvBvcVhfJNZRjzAbGD4lgmYEPSFNKpTJVj1zVHCob+rnUvdpPaicjLhwJpDgCPI13TynBYFJIWE85+0acomIiwTh0Hh+RR1hsYyo7ZzTQSrgpwoY5O22fncsbx8YRf2L3bVYhbXG7JX1paNjFjbYBGvausytoyVNE82BcuYns6W3t3fHrh0tNXVdN1xfc+MP0q2t8ccetRqaZWgWfXemDvkLpV3asyf/nveFBvrgwHLZPD7AuC4y0pnlK/yf+NTR5q5b9hgjOeIqW1lHJgODXyy4MBRMWIJsM7CkQ3JKJgpFsTVwSX5rORBu5NXA9Tfsrq43N7d4zRHGH7jmIpCh4YCBRAU3/6kAqAYGOc6+Ead70to/5o2mDDGbw7sYspMZXDJtpqhlNoe9riZznWShhKpcgHaq0NZEribl74Ep+9A47IaJlx0albPbRS4DThYCoxpnC1wDJcOhdZ4wfjDw8E7c7Ozs5GKBF8AvUbvJGqVCK8m373laIgMqYIy5Uzl4xIzVZoHMYQrwiJv6jBl/S2sb2O4bGn1w3+Qk3K98ym5dqLgqVtp8xnq4RXhstMq8AiQiCkNMhgggurIUrAODvYd7fV66zUpmJ4dr62ow5UWlrHl+miMosYZfo4XaYFY5Y/7N8sjN6oO1kXcpDw9P73hESX0GJ6yiPIJz5uC+PvRTmFsugLDyln6qm55zlt6rxYz+0HEjn03d9JPCP30wfPRIwHELwWBu02bfW94ce+UrrHiNvMusm8VjzBpiGuWgdY5KQ9zRkDBA1fcrBfQFxWSKqgbUHTEsR5oBilk9cAog8B65zWBgOBdKRNDFEgdgMJYx4YTln/JP4YI+SYY2xJfiUsAyoEMVSguybquuls/SazGS0R1DKBm0S1itt8SsqMX3h4EG2WsI+Sv8MRnL4H6xQRV7HJWkJ+jDYgKrHLxXTBgw1nmtxyw3Ig1xySF901/RIhhQJcsnystBR0rOPffegygHY3IdHUOqYsbmstndu90jvYWbfmz0HLQf3+4LhoJTU5l4BHTZr/vLUiBY96EPyiYUjQvXroaFKrkUWBY9MI5AfEaRysCBO/gcjNgR4nCGF1q0guM+pB5qRn6KxcdMuDUpzH36hV53OOMMT3tTeQuHGtlLAZjOsjmabkPUZVwY322YRh/1eqf803lr2kHUSFQOJoOskwxNCImF6iajLVOozBEfA0laYsNlsArJYDEZDXqh4lRTxOhsrRFV7bLa5U0Jkd3NHIsPmapMwjQMTrsHJlw2o529U0cn8hnHl0dN648Egz78kvDdrZftyVgR99Y14iSMIhoSRXZyPn8WOr5otMr9097OYXfHkHdwzJzIi+13Kk/ISSawjlgpnxv9ggLQVmNAqSatkQhBIcCTG6HU4eXxwiXnrm0k6qR0QXVEJt+xHs307KT/MnbYJiD7BmD8Gm666SZmCNpgNBlIDxdH1Ek3tvTCEgZOBwZkLzwd9SzVsYSBk8OA7Gqe19PTg9b3u9/9LhunNnhGewMtiO0icW6okaUfVkpTeyfXwEKlaRHWyJ2azvzmgcy73hk9cjRYyCQjif76tT96/Sef6Ng6GUiIYyV7itgjSxV8HlrlqwyrhAFW9JPcRH0nBnaVY4YBVi/xdpkRVns1GxNl2W9V4GUVyAqNAf+I1ojxEqGtNCGkqFPZ3vlNZIsghs2EecSJ14+Cl1ggFulza4Mu0Xfrw2JGFQ/ZxJGSrRJwZBtjozkNm1mlT4tfgE9p2HXHx8c12Y3ejGPxWGX6Ld6DfCQwkuPaqZI7liqtqBUP07V17grJBiw9Wbz14z2lfiih+3pLaccHA9Ycx6XKhD9E+bysxj2cJDqYGbaLF6yEKD0uuqgERgvDvL6jfXbAd0Y20/wXf+krudkb/q32TX8ltPjJ0A3U5k4lC7/4ufvFL4ay6Wwu77P9Aduficfsv/9b5+Ln/nzQv3sskCYbi+mF/Dj6OnCdMMmkQYLnZErmiyV0sEwLTIcJDc2QF0ouxs8wzA0Rb12DQaaNNfXKa20e6jTC+dbQNOL0q5Wc1dhjFFUZFyft3SPG3jET3e80IVkgrYhRhv4Xk1+lMGiKWqvqjTVx94w2xDfCdFXXo69lTgiHbnWPlvZPWASGoTfNUYJFm2e1YwIA7tSEnf/mond0L+644w6WCCYbZrowe/DzvKRdfBd8W7815xFgL3h/TrHZPwVJ5OHcNeiM5zSeCTlT3NiCRIFHx3o0Uzlx4OH/nYkcinBBU02wcGaLf2zgyCOPPLZ16zOAWdc/U77c2pyf1TBUP1JV6lGrLnIq17qqWW/SIdYvlkrWP1jGXC51003udZ+IHjhAXzPxROay59W8593hc87GElPIZ2aCemVWJbN/QB/jLqhtsCtPqkdhATBmysmyrfqKwCdbcEkSM02EeRVrULucsCaj7oPTIHlMoYR00sRzHm8DmFy1UAsnrC5k3abRbDY3MDjc2NREADZtswNvTmt0Qv2vGQL5VV4hTK8pULxkjb3MTUcnR4sT47bPb9Q3pGvqu7P2b4/asGRixaN9WHzE5xflMPywLPIY7IiFrYo/7CMcgxdRprnhgLDHsgbBPB9rSa5O8GDh5aNGccoFRv4bN25EXc+7wA3G9JfpplOlgcFSd3fu1luMnXvs7Y+G2MOIMt3ZWcrm/H/9FicUq3nDG0jqbdbWy/e80BddDQ/cKSs8d1jzEVvoiNxs0MhWsEdAkauVftWvnOC1ngycmSdw0Vq2xbTBVF5BJcPBOCLvIL7DUIpFkmFlLZfBUtMPxDotMUyj7cFk6aE+Z/tRYyTvT+VRCJMLYtKzQ56PnHFiDcSiSm30VY27GmiBkptyzf/55HAxOcLWHLJKOBPVRgM4Ca9b3vKMVXVdtWZjjG2XlhdeAKWmmWNmhouUnNCGhyfc3QPFnUemjowX0o4/h8g6HKcWNnrY4IYwNjUYLWMUbXTVmyjAy1RIuTZxKhlKozF2Hx8wSAI8miVtkkkwMIyrxd9ETMlEEMN04oNkagm/z20EMUJFeFbyEGHnzzpjFd4rcNoESpwRRerJMgP0Cf+ldxxq1phIRojMz8pMomCmAZwwRtFf+tKXsDfBkQ3qDqrgSWfXCbe8VHAJA6cBA6dAB5yGVpeq+EPGACsm3cfYCa3v17/+dfZOAicQ5gqVSAUtlCE6KGww+9/FF1+MHpg7rKR6gz/xZZS3oG/YHRH6Q827Y5Njn/2M/cs7wzu3sX+NJlofvPDlD1zw2v3LNk2zO6JFEeDQ0ApBApT47InKF6IKXYNifZUPjZBQ0oe5h9BY6r42WxY+lo0FgTHcLKo0Ccvs9+lsQ9BDhPQgtC+knZA/vKYKi8jWLzGr0PGSlUFiWoSMuqBZE3KaY2Zj2KyLoOmVNEiys3HIHn4MjhPHzLF3TvVK4wA5BXQPZ4xUoVQqiFkEkkoZiGs8dfdOQGaYR6ecRMSq8RGdq3heB3FMBKxFKjkO1NQtqEzm3d/240NlkmKxMWKQw/bMFl8iUEI6nsyhrTGWJUobGhSdMI/g0+Ax8R7dti2bydTV1Z61ZUvxyqvi/3NL8pzz6n59pxmQqMUnBZwCy3VHJkrbf1v6yU/dvd3COKzfYL/0ReaZ5z7uxn/Vg35DWF9mAa7RAYnw7OY9zN19KH7RVgX9WEHTNzzD7QLaWIJ4+SxI/IaYubqOwC3OljZ41TIdU4033R1UheSuQL2JeYWmWTUC5fuQScoh8ojHhtzuMZtgxRNZF8NQFFulojiboSti/jfErZUJb2WNd3Y7Wnp5UTVUbrQ8D4WmlI/o0KQLI31g1EHdURv0VteTR9ok5SYtVYOnmj6hE7Xq4M+sBvSC7sBDaj/SU67zSRuWD1rNKD5RouzsHHQmixjAspoYbZHS+uYZnMvHK3jQ2OYvVpo7B5zpkp7IXmO4tLGFiGvG0aN9BAlDZdfZ2fmkrf+OC8wAL6I91Rm3NDCc+v53jK//Z3TPPmZINlHrXvn6veeeN1aTeN7zLiEGk1q31IDqq+NDjNE12nuMtxcc/co81NWAdPDJNf7N6bw7nff4h5NnvmgUDZtFmEDIBaIfkXSthF8lPp9wtiy/ou/F/xMTCXVHBGEygGpoFI/OWiyLMVrTgSFC4ZnNzY0hccNWc1jsd9gB1ChyQ16UBzBdUV/hNZvszon+9M9+Nn3zrc7QCPX41q2u/8tXuudd+HAhcVs3m4awQ0gy+YZZZziLIk6YJC5ELyesl4QzJA+UKIcJ3ABbInY9fuXM4peoS5GARCXkLQUQZw1CNVr57sofkR4vPopHH30UeRA7Ka7v1eItXlaH/JWtLZtzpybTd97p7Nvv3PyTyJ49ZCLmfqZjOfZQ/o98yGpui7/oRTLjxThFIJf3RLQql3rgKpMEfS/rCdOYO/om7tzwP5gnyGtqLVpwrHm6yKGr0gXghPlYYIaxhpCIg9QJHNIbli1zMlcamjZwEi6ITRVjp+2vUHvi3uKR34goDbsGig8ccfePWz0HdnuBJjPeJgphMdlysbKBE1YsLPXKsKuapWUmDLOF3ZzL1NABxq5p2ap4gF3YkEBZCWNZ3FtVby5LWFGEkZUxKveW1/ToSVXUoKqeuWZvKrDreTv7so8fnhpMm/5Ei7JqpktiOyBRqSNkEXeW1yBqZNkk2gKWN6w3ei5I/+k7OQ72jXnb+729o+ZQ1mKngw3OFBH6iHQRgEWuomzH2FKU4JQukaLcoRe1sNlRQ6Jw1blN9lRbbbChJlYN8+KjVukPAmK+6CNHjuhIikhh0P1Wm31Rkm2UHJawwVwTJhobaVZvcLF4ExpZS+clDDzdGFAE/9PdyFL9Sxio2gcQDZLciDURES+s7zve8Q727PkLol5n2de1TxqC5wceeIAsF+y43NEYnf9WNaZ1DRKaCiZ2997UN75n3vLjeM8+AgqNRptu+7P3//bMy47Wk36G/UJtWjMnKhGfMUcno1eOvmxjUL1ajatEnpSh/jkA6K1Ob9NCpMD04hum3MOggVD2EpYDRQGeoEKv2XrXFXIGLYGEZVZxOLCJQldZEyZKkwS9bIkTcwhlr2QM0hTJnEaru6yhmnOn8hOTSHYpzujYMX2sFNaI4ic1w1QgZYDa0AUq7+qn+qcuz/6HyZN2pETqD42CRq66/JNeg1MS/WzvcyeLPrJZDCeLEhjI87a0kaFRaJrFe7pg/Qo27/CUs3/M1zftQDSL5aFlrKm3G6LFqbSFIxlShy1tiNuV8mUWsSJVUgNdu/322xG4YKG6fsOGwgMPeM99Lq6The9/L/byl5+I4H8ObBoqOefy3uSUkZGgTV40WojVPZH23dvjEmyZeUKOGGT4ENKFvINrH4bx6HsJg8W4o8RQuS49eACUT6hj0Xc1R0h6gf+Yd/YyG7XSfM2qahem6+i2bdtQ18A3QqBUY1UXABgSHW3vL0Ep4pY2RsJscEPu4aL4pUN8wVE0xOxlUberzjurzYKKmt+W7rKucJAQ3CNmz7hLJo+4TzIzndmA+l2ZtgspV00gzkHVwj91tTr37wUXXICGYfv27cxVCO6FXzhNd4UJYlkg/Jhr7uovTZUk3y/Uc3vUXd+k9TOzZqmC02XWoSjOkIQbMDxvWaxIuiNl1WBpmh4GGOCrB+I0wfuUqgF4kWoIA+R5+Uz66//lffZfokcO0eUsgdzf/vb4X7/dam87eLDLPbVWAABAAElEQVR3584d6HmIm/WkXaAmviPOkMIIPdEAAyJvschUv6taBlfi/JF33CScWg5ZlZEumAXDYjXOFbBzVlpfeGDyLoltM8mrScQqHK8waUApn46qVc0vYWBonarhjORg5CTYnvBKuCHkcsMj4wDT1NhIviTKslAI2yMoFN5b/U8F8u/ZXaWLguP5//xG5sabYRopKSPLNhEKNf3ju7MXPe/Gg76eSazx3SIB3gNhsUmVxoUJ4X9WetZuZj95spVmGG8X4Yq5ZuXHe1wHMpRcdBLTwSG4A64uMFfktokGLPg0/eWwTwlg6vOpYI+OYa6PrQqsCMYRz2WlUnpgBeCsk8IB/ZI+OiPDaIazP73V7D3i3X9fsPsADsNFyyw+Y3Mhm4t++pNmfWPkvLPppydqQtnqVCT4coVUVWGAsSuBCZ/VkmFgwzxnqZlTYM5PGR51VPeL3Z9+IQqnR1TIlNNd02VUP9yCa42lnYFpZgvu2uIkrIUnwBy2SFRuIjiezDk/vmtPdyaWCa9IFYieQD45SVjokKReAmpIvkDpI2c13IJjaiHcFvKXoR6qjLV0MrsYJgJMwqPWhcgz53YgfGwgc5JRHwbj8unwj4rUherMvJPqJieXeNF9ScLCl44m/UenvQMDSeDHQ5sUULCsMUJSSzBnryNurKp1V9Y4xfGBFR3NkUhFScAgioT3wKT7WJ+zZ9jsz1iTZA/OmbDBebERF1GP/saQXiqXK6E4AI8lnajXkjo4Zlhj3RtaQhdvWUHUay2rU12QPix4ADoHkT6IW8bnrHUSSLW01GP+K7o8I/jpT3/6y1/+MhQC8bHIG6xJuMpYz39x6c4SBn4HGFhigH8HSF5qQjCg180vfvGLGMbgHMIiiFUM9OsJLoIY2KBpRHJPecjfrq4uHE2rmTRFGSi6ZGYrlVYdd/rue/M/+HHgG18Jslob5o5nvOD+81/92y2XDYfq2a2ghsTFV5EU1IDNHxQVWyM0FjoE7DihoviHEahsqxzH2RpUL6CzZAMUVYOLZN2Bdwj47EiIeCWiDRC+V22ybKsUgtMTiscvMRvj4r7rkZCwLizuOq0JYkERzgquRpjemTbl75OiC2IIWvPgwYNwp+xSQ0NDiGkRzXJG9AADTAFNkkp31EGdjA6XXEBhcGCRvm7dOmyWoHHPOussGCfuVJqmMMQWcZ4ZDnYyWF/GovK0XOkJ/IEOoCo0nzuGwZl5YKLQUeuHFumqKa2uhwPTkoQTqKiqiBDDMNVDznjG98RoCXVKKmeiDcA0rjZcmiQIkWmEbef85ez3GuQZ7M5UolEBusKhEEaSXiE/+dKXxm6/PXn2OY2/vtcLBHnhZDur66QFwXL5DwZsLuFPdg56w1nSeAipHghaaCdcoluRAKkoJA72AhggIHsJ2D7CYJE+ih6RXwgYomGrNeyQsWNrC6SS4Iqpp6uvnGkXYQd8I2N64YUXVg+iLqMAI6OPua2/1DNpHibr7zQBpk1JKIIbAKST0oPUROyWCG7GBhmGsYJWbc3Fm+4c6J/IY6Hn7Z8kFKqN2+SaBmutqDKEhgdE/Z1WIDzBC+DE8JJwUKivde5fyC+6A8V/smNxgi2q7gCtTCeMmXcMIkzRmjmvI1Za0wTJKx2iNg3AzMi6Ezl35xDmuEJPImpZXut01ZHjmhVEhodvEMOWrq4u9FpPG+Qn3MVywcr0lE67+czUV7/m/cdXo0/stlxvumO59VdXxa98I8G1YeDACNl877zjDubipc97nnaoXqQ9HK1ZKOb0lFEjLBZTtrxUmyy2zmSO3N241Hrpgjh5IvHB1Jl/2YIFQY9mi7ALyIOwbRZwJciWCCD4JR8DZz4T7rOOgHYYYcXSKMDgdsRJmElODcxn1MKs8xy5fGEqOQ1PUFtT6/f70O/BdsqAV/qjhp98aW/carbsemjoHe/3F7IYX2BNRBH1v+esW9P8z594KLjsJ09YbrEwfHhvXXuXP4KH9rGKqNSR4kqAyZ4iW468zRRRnImkpdWW0jAncFnCDys7IDYIMZn2S1R/iVEckHj+sMTCsMnUY/7JHz1+yFYYDr0UU7v0RBdRfyrF5EK2Mwn9pRg+wxkfd8fHpv/5s9bgqLdtW2SgHySyBjmXPDdfLNR84XPECg9tOEM4qqoDpheZGqJS2FQkhsDAJ8l2oP2V+E6fdG5UVSZdgCqQMZ0tGWHDolqmEAIL2tq8eXNlr2HMpZfSTa7MdNEbmXaJX5UV9xGFHcOYGhsp5FKERPYXpmsTkd5UYPuAM+A0ps14pkiIRHEaR9INMhFPI2jgNTERUJjT8ovc5BADGa5rUcFABKu4PJBSQXjIMKyv0UHe3RokgyYxJhiXSqfmzHl9X42UIB5aAyAhNkZS6HLdO7cdwQZqygu7vhrPH8P0jBHWqX0bCeYcyDnDT1xyzqr1y2qrAmRIxwES2SXxwLb1ebtGvP6kb0KCRUvcR7YSpgf1yFIllImwvsrkHpm7hJmI+l1vtLulLnzGmuVrlVF0W4K1XwOuO6JBrfRJHL/RQ2D9zi2+4s7OTqLxMdDVlNix0lVXAIqsHH+3b37zm9Bv73//+6+++uqTlZhX1bd0uYSB04CBJQb4NCBxqYrFMcDaR3gY5H9IAdk13/CGN3zgAx+oeAotuE/Mr5BKYNtgg2HtqISfbLHPfvaz2S91Yaif8potFI5FbNzMrbcWrv9y4IHfBAu5ZCC645yX/nbTpfef+4qMHVI7v2ydwrOqzVJpEhTrW5KwKGLqLPFR1FYoRfif49j2phvlLEQB+4x42sj2aZKRxisiagWuGKyvX0I6UQ1PgVCxDpi94dMriYsSOPH6SQxowvQ2x9xWjJyj6Htt9ANSl26z0pK6U0EXdXKAE6KGwOVCIrAzcaGDiAizP+/gXV7hrC/0c66rC6pa5zRMvCV/V1fXFVdc8YIXvAD2g90OA1R4YJhkdKRzSJbq2ha/pi0KEN7jgUOlvGcPpBwokEayIkmCIsCapSNavKrKU5CMRujXvU4qb+0dI++xm85b65rM2mAJ/U+OMMaGofItybRRXS93H0oLKwO0GZgRzkwqYdBTN/848Ko/R4Tu/uD7sZe8VDU0C2f0Aok41oCLbOfST01VlQH1sN585KhxYMqQYMsZYixjPWnm8ySxxPucCJ9u0A4woTF79mEOjUBFglEhwmeKwgPDXWFObKxthLd01tRpl2npULn6mT/QK3fccQdnuF+tLJ0FulBhzHFrz0iRwFc9o+YwJgJ8O6jMoNGF5ZCwz2SNbow68LEbG9FFqOApgrq5banR9Aheva3fOzhFPkymCXmtzFVxZ2OrprsWeGsG0sX+UjMkF7og8Iy2YU72jvmQLFbXCT9T5LWUzjjurn5XQpXLAuMuSzjrGmBYFOuhyEqQAYSKVzIQajwx7BU8sOQRYWlVPQwwhRc+nibIF27suHcFdCaYsGdeKXv3vbm/+dvInifoUi5e477tbTXvfa+VSFTPLHq6f99+FhyYWMK8L94LxG2MGo3DJsH8d3Z1cu0X39E4TAtc7kQaAZA7nTOLHp+nxPXBrRG+lyhWEPB49sKfcIFqlvkPWnHDJv5tpohfgAgomZ8Yy+A/yVCogPx8x8JaiAJMKb4Ys+mCClWlhC8yTor/ExbYNfK5PIDh0FtbB8dmkjFOfUTlBZA/FG8JF1+3rmR979u5L30dxlFl2FEMtBT1Cj5f2w2fO7ThvC8+gOTKHT/cXbNsVTgSZ9VH3yubkuIy8fIAKtYTaYBvRz1RwMiCLD95II/EQIXVAHtpYYklnpbYS+MUI6JSJS0lERrKYUIewtfBhkWCYodPI9RCBfyPXHL7Y9s3nbEpFsNseNbsm2lHNSaDqpuVn2AFG6fcwR63fyB53SeDU+O+Pd3+1HQ+EsYdw3jbW4zmtsQ73ib2L8EAKwQzRHrCocDmV7UPsPRPnpzoQXmcmWFxsYyofkcDzAqm5bn4OaMK1jWrJmYvq0i8cU7OlMi5PZGxioY5NNCfTU2BlFwhF/HZzYlAQ8xX29y6cyLwUK83mA2wTWB8hDuxmBU4WNm4Slqtd3VBjvRSkCNtzpAYchsdPzwzQ8CcqQ+TQBi3GndtAyY5FmJr1XNeV7ipwoPujlSoSlAvaId2kHxO+0bv7x4dyFhJL+4FajxfJEdabdNF5BGzcoWh7s3rV6xrT+C7i3U0NgJSiVQDqMCIL4B5JOls73cfHyb0tDWZkyBt6Rz2FEiITCgKoU8Q3iB2QVudHndzSZksuWQ8ZNUn4o0Rb01HwxntkfXNVltc0ssL8BRQgPIJsydqtS1hulnZu2aL8J50rHXHqRTDASJF//CHP+zs7PzHf/zH17zmNceJy0jZpWMJA08vBmTnfnpbWKr9DxUDemohG/7Wt75FiHx419e+9rUseYTrACVPumLOR1tlrsIAo9ukBlK5UAy6CgYbdSVZJhFrO8TA3LZr+p1/V/PYNn8xP9jQsa/znJtf+/Ge2q685Rfdq57yanuCkIFfIgUCXr5CafG6IgbZlKS5qq1rPjzsEfxjR0GKD7kDa2URKLqYg2oJh/wofsWLUjYS7ssF+yXmzZiqsmUmAm5DxKoPOW0JJMdmjb8wMXx0xbLloZB2SFP7T1XrAMNBlyFuEAHg9IVzFGcMQTV9qcHTWKUksnmskvCQhJ7ggD1jx4JfZRsjwjZSWy2y1XVCMuoXkbJTOTQr1C2sICwu7DSopgD1U5gzMng8eQjWzSvQK9Xt6uuTPVPtntEiOSEg+EbS+ACbuCpd2InjEy0o9J1MjfAtg2ln17BFzExy6iLzRhixrsmO+p2cCpriM5xzO9ClKKNCVTMAcNBxes3F+eefz1ziCb0mhEzyshfWPHR/8sUvqf3xTYSE0nOnGixeqab8FgeWaSUkDwlsBtG42j3jHjwnvmp48/q99PrY3Svtn7UEe1BqjebW7sk9/0jxWWknSheYp6mpVCheg7QdZVdTBOs7b0UCc3GR7x8PUcAG6wj5uKC+kafU25ss7h4mYBVOyBI0Vehg0VdiYuqijEEl1hiFsPOtqS2ta8Sin/m+8EFlOdfF8bUnaR+ZkDKkSlqVKJ5JbhzRNmmesRpzC9cz/y41I3AhwDjSlksuuUR6e/ITY361i99RyCElsrkDV94iC4uM/Iqa0irhfuHdwNGxvqjCxKdx9wwj0BH0MWRrG412+CDNW84ATMlKu7+DXlTaOt6FhscrFfO7d2f/9d/83/lWsFjMNjV6b3xT6KqrA6tXkV1Fixgr0PIKMwofAe5ceumlLDWVR/NbqfQXM0jWk7PO2sqiCPs6kjbGMtiKw6VYaVx8cxLXClYE9hKfXjS9ivSmbQntmyt4aLTQAxOY24YPJnSCGPeQxZi/+NAyRUG6EPgAwFolZs9isiOoBvisY0zloP9FUKHYALEF1aPHUGYzKZZH0QDjHhkRLlJqmRlbwGiKlF7YVnC/+nXnm9+BvwI8xWZKX2HASziI/st1Q1su+vgdE6TemR7rCydaDH8Iw9qmBtwd9cSR6ioDzxQAUulCGQqpSs0LKSIchyrKiVKA6sPfmADsqChFOSzxtNAMi4G05H53o34PQWoiYBHsl2RysEa9hw7u3LHL57O3bj2rvb1tkdGRhmcORkqU0xItA8U6FiBm9uHfusND2S/9e+Cxx/yi9HNLZ2zOusXwNdcYiUT0z/5MdjyAE2MmgfjEl8GZNo/9pYYFGWBdQtePHRM8mMjVXJe5pHexOb3TJYEGQ/rhaXcgZZA0CHX3gb07ErWNja3tjHgi6MDjRUPmnsHCfUeMPaMWxh0Y2zNPiip5Ev2HDcblhMkjI8UkUCMCMDSnm9CTAGICdT3K+YTfbSCHXwyjZXdVg0nAvwQhrMrf/nHXTCpUtUn1zIXJTPHBPQMPdI8N5Qn0t2LaCRw+2MOmzMOgV4xFA3UhuzHqnbd59foWP7G4xAP52GSVjwGxUd+U99igs2PIPDhpMe1TJWM6Lx40tCWEioyWUZoe9LJJEqJBruCuLsJbv9fW2rKsObEsLvEUNzaboMgtFPr7+/DzheKCisBxg0pAviYeNDY4n9ShsQfd8qEPfei2227DxOwjH/nIi1/84lMWo59U60uFlzBQjYHKx1x9c+l6CQNPFQMscyyUt9xyyz/8wz8g83vJS17CMoetLPXO2bFOtqWZ7YcNA1JBiIX7739wZHgICqajoan17l97v7wjes8dPtebiNTdfdlb7njWG47WtBckUQWbjZA+7DRCGyH+x9q5JIk0yFYijmSYPeNG5NiOkErAtaAMW8tchaeVf0oSTNRokjJKHiM2TKcYxO5Z07tSABcjiBX5p8gUEjaYjSG3vcZoi1nEBGIPo6VcJvPEnifWrd8Qi0alV9L6sQPjz//93/+FAYDp1QreyjONDcgCeDZiUSBc6OzsJG42WjLuLIjqBW/OqbD6J5ZLcNr//d//feedd5KvRVMAuGNhxfTnf/7nmD9RePE6K7UteEEXSFMBMnFqfbBXRifgFc5dESAL7slWS1WMK7kiBtK+vSPYsFuoiVpjXnvMhGrEr5Y50xB0t7TDvmiqXugPGH74KwwIoYNR/zZhzg3+TawA3PTnvxh+77sLwYD109siz7q4Wp0iTc0cmvKr9oM6HuRi6Gzg/eXiIrt3lLigVINRutEW7r409oG1kdt9Xm6GACZdU7A784KfTV036qxOJadSg4dbujaCHrL+YndHMKpz2u2w8tya0xyVIhJCsg5zomGcU4CbtEsHRrLuzn6jewI+3Mbxku8CglZ8Nk1HyD/XI944YZ/RmesgW7oehZ6ZzktV3BA13c6h0sEp6yBJj1yztcbrirtbcU5maPkc5kNwrIKFr6oxjGwCS0iopYr69+TrW7iVOXeZIXJH/rj4Cj4+BPcrQdq4Qa7O1Vr3KxOeQvI/QOoX+lNe9ygcGRwvhKa7sQkvBnrOaM+sBVKvARGPUTpmnCflIalePW0nACYYPkMC9FRaOtgzfe3H/d/7frCUc4hM/rzL4p/7rH/9Ot1LStCjOdim0z09PZxZbY5HuVYwqeEeHhnuPtjfsXbTSNpLFu08CYRz7nQWlS+Rrqwi4QbFuBnJguSH41zE/MA1YIxTJVoQSCXpOkGbxKpTSHkKweRi+gwHUhsmrYuY6LLyyiPJ38shgyEe/4hmyAdLRiIJxYw3gYmKVfniiq+ujC4fNsgoEiSPCFXllUHGTiaCEbKcZ9R7xk9vKV73aWZ2CQttPSFY3j0jm0i0fuVfdsQ6vnL7Qaw08BngC4dPJtzwxjM2uuTpVkJVFiO8HPgu6BVuq4AkklaZVzK1QLA+0yDYprPSthxqq5opIQy89Eg2GsUPYwyC4a6ElcaeiC2GyNLohEmJlxzp6z+0N2CU1q/uPHPjBuH+ZRilIVVt+aJ6WMsTWT0uK7iVxtMrwj8VJv/9Bv/QcOmmm4JDg7br5lhYnvOcnG3V/fNncFsNrl3H62nSXPv9pEzXrVRXrmpd4MRbMHhIyXkmXifhMPaxXHOhd5bKO9XgIZwl4BYmYLiU6zB4lbYqmAOHdBXLc5a1gaR3/6M74jXN9a3LqFCjAEFia8xtilvkGX6w13mQkNFZf4r8uiU7izScBOwuHkyksGJQ1fQQwUAZCj5pXb8eJnZ5Zg0qerIS1oeQTnrLEh7hErrqLDKli9sAbcoIS8sVUHXXZM7LoYFihLzJTOHxQ+NJq6E3ae860DuSLJEsEDmmHUlEg2Gs9Fcub2mJWytQODeaNFEb1C/rZqhKHLj6pkmYVNoxYB2YtMYxqZAoWaQKowPijaVkWi7h6TPD+4LhQKyhkyq4z+Qhyhchsup82Xh+MJA5EjczZJlivVrZ2dkqXhvHQBWoT/UAjxz33XcfbPDdd9+NgRIhUQl3quubg6JTbWTpvSUMPAkGZj7oJym29HgJAyeEARY1XQ5u7X3ve98999yDxSxLG1o17lcEhydU13EKlRsQKoFL2TxS0+kjBw7kvvL1pjt+1XTwCcS9U4HY7S/6uwe2vrh72SZX7ObKB6s370Bs4QyWw7FJfH2VwSfGa5D8BiZrFjJwsVxTCquZ92SD4UWh4qldtkPx2iJGEeodIgVZRikS8sfCAbUFSVmUcpiuQZHESNUbJgEP4azMhrDXUYNSyGiISipU0YjNHHjKIf/GpLCiU2XDg+0kmR5ML9ZfFKjglpfY9WHV8M7FIlQbhWo7otOC4RmgNIYlFSHWp7C+3OeanFXINQCGXQoDSGyZ4Ld59NQ3LYjyoeliyG8TChXtuQRSVfttBZ4nvQCqvOs+0OtO5ux94xJmBlZuXZMVJa2uUEbg3DuziYyRVCxkLuUxgMRFFhED0SnBJH5Ncp9/6JEGjqb/6OLY0SPJt7217otfkCgwVfDwLibTKOS5wPKcFyHIeJd6YKSrS1aDLS0W3Uf7CEzqDWTsaZQOlrUs0PPKutc0+XYwnSwnWJpEb21YtViFo2wy+pxz/mvgu8lsw8CRntaVG+rjIVSyRAfd2uI1RKQXc9qiCaD69a9/DQWJ83aFB54Fhuo74VIeGXAOjJOm0h5JS6AUGIaCi301BL7YejZH7WV17uq4t2WZHRbHOujoY5O2UiHUPF/i/rHSvgnf/lFM0K1aIKwxEDTUinjkpMdR10xH+AoqBrTMbf114Pr79HGPMk1oGO7XsXYMFFIFMYqldytqnFUNomxX6D6GBCXRMCSezRgLiKh6AqZzRrOJfYcuNGd00FzxRf++o0BDdYvAgmTBqe982/vIR6PDw0XbzFz0x6F3/E3sT/+UMGtzwFZIOHYCQxzVZaqvdTkKcAE2kTOOptz+qdL4dMEMxaZzuEkL34vNczrPGizfKUwrfC8mxEXkkeK1KGyw62K6THByQbqwtWAUpsRFNEM4IlKjw/WRFB2HWNYKsVbAtQSGENUrEfRgXQgmJLS+yDFZrl3FNMID48NJW/hDyhouPLOKFVQs5B+4/8FwOHThBecqnbbAzggy6VnVacI70JP60D/F9u534Lv5j6UAnhZ3l5e+NPKWt/QFEt1jpXS20L33cF1LhxWKkjWnIR6kN2Jh5BDHy1UabHSM4sWAils9QqettI5qS2JXQgjAlqYYT1mFjh0alwKPgKT+CksFRuD5SRIuHVcxpVEOa2bYKqUHDh8wS6nljbELz1rfmAiBHzUpqQPo1VpWtaAda2v2VXkcmTFsnfv3OSNjuW9908as/We/CKZSxWiYmBLWX7/daWmve/ubGSfHJkSe2j51C7o2aXN2veoXlSNgxYar+hkTAH3j8eLb8QrrG9slDDMlV6xYgVzseGa0NAs2QeqBw305M5y1a1IFW2YYyGNesJsbbm3EaY/ZwaDxWH/xvkOl/ZPB6bwtHsKSUksCLijmVqyIKS//y3tMbtW9GbjVkDGjyLEkUbLwaSJ0ZQcRCmvcVXVWa0K5bfOarIayShz/kKr5D3eAwaTz0wcP7h0upI3Q2PB4oGVd3hdHDBRUOn9J7RtFDGqsJWdSg0VcrhmKQhBNDUywoVTp8WHv8X5z/wSRwEy+v7Rw+BRQ0iLTzAx2+wL+WBNiLOkVSMEROoqFO05jxcm2SH5zW+iPNjZ3NWlBKxWDu4UG8vj9WfwJRNgvfvELfOKgMV760pdiKghRoV85vQ0tDsbS0z9MDCwxwH+Y4/509ZrFmxAv//RP//SDH/wAdo7lDOMWWTJPx6IpO8PsA8q7ePhw5s57cp/6VN3hg6ZT6l5z4T2bLr//wlePN6woWjbxWpCYiwZBHdgB4RIjdIawvobfmzq79r6Lmn65InrQcYJ7M+t/1X/ZztR5EDwY/lU3JYSvMPBi3YjGgH2OSBjoAgwXuhHdgoXEGgKdp/AuYudc1vcSMYgMKN5y4Xu9hqjo66hX753sp47roB6HpqNrbOqwlzC93EEmygbP/QoMBMp+5jOfqZlezl1dXTR3WrBaaWL+BVBBryNrR0IP14HBOTweN6Hgr7322ltvvZXdC+XzV7/61Ve96lVPHRg1vtJlhR9B+InXqekrajg67WDSdmTSGc3htOliUb6uQag+p1gYHxtZ1lz/zFUIH6hZxpfy6BXBeWdnJ1SU1mUpMDD0cqbe/Z74F/91uqEp+sBv/Cs7AUa/pRFFMaymYc+4iw65Mhwo4cESN3WxOWdsOLcNQGPZvZPG6LRHYhyfm395419tCfwAuir7eM34N+Klw2EUF8FVhforJ0NnTsPF/3b69V/d865iOldTV88s6mr0XbShaWWdKH/nN8Rg3X///RitYTCMbGJ+Aem4+BK72weKQHJ00hxOiYOlMEV8FJCwQjMSWMtqi0G9GVvbyFGpvwA+g/n94j0PhQMJhA+MEU3aguxfU2dtavZasN2WAxDmv6WeLHoCw+TO0XlHQS8H1vu8gY5ofjSvRWs6iYdq9PHQdncMuNMF5WlnuJ0EuSE+uaBNEMB/er5RL0a7vZMO8cMwGId+DFruphYTYlRKyr+5A/T7YoD1rK4gAg4ue/vt+Q9+OP7wb6GV0xs3Bj/9qdCll5nEqZfP70SHjJnPqoVgYmaEwYzmGwUzE1lkK5g6I1Wx0a0R44psRnJd9FJZB1dellmSnBPsh/nG6spCSusYr7Ii8tH6fTbscf+kOO6jtoXuR/2FYQjLLN829sDCAPs9FlWeKi7aCJouiriyuyyvCD9MFGW4DlidGRgrWJCL8sykyUcefaS/f6C1teW8886b4VJknPlflopsrvTow7kvfil6YD8TAI4Y79/8RX8c+as3WWvWkLObivKF3K6du1etXhOvSfBR8DXhw4x5C5GW2HdIUEzSJjE7Ik6vaxEHWJ7KfWLgyZaEBXhBWGX8cWSTIogRmxQMM9gAAtTFAogwYQIOf+QG7DnbBBfqKWDDq0uvwQysvuFOjQ0UM1NnrF3RVh9GM1wTIsYE6XwIQoHOXPVedY/x1jOEC2ml6qjMHMWbz7TkGen7fmOMjWauv8G/c0dobAwgCpvPhNWP/9u/sMrFL7pIckRXH6rmObVTOeb0OmJFT08P26i2o6m2XqmuQ19rkIj1iOUOskveYm+qBMeqLq/QdOwGmnkiQg8mvdGMVdJhwMp4BGn48bqNcbtvqnj/4dK2fmu8QA5hi0BZOeyixXDHRbgQFCcQlmrBg5ZTzIAkI6DuIgSQjA/k3SWsGlJvQjCirV1Tb3bUWNqsSY3hHEwcA1LPN4HL8IbGJ+9//ODOI9N7Dg1GOs/213fCnGPSzMRQnLZED2mKuctrTKJY0UR9RBMumnoRgom5IzH5h73tA8b+cXM8x5co4iemHE+d6SEfmRkjNagDIGhg9eMiAubrc2K225iwSTTQQV7iOndDk9USq0SO1NAetwvHOnNiVywjBMfCSBDrGJIGYza4oM/OiVW2VGoJAyeKgSUG+EQxtVRucQywGyDH/dSnPnXDDTegn8Gy5aqrrkIVpt+av60uXtv8pyzWNCF7g+z9wiaVhkdTX/6a+a3/ihw+LFtFU+cvnveWOy9+42gwwWYFZYCBHCl/JCCzDdHkJ+UBat6iKH4lhUZX6LH3b/rQ+bW/8psZaU4oLjdr1N/a/8pP7f3AVJFkhlIJazwPkJyj70UnQbhDiDTk/g7hQA0Xe7lIMGRjakpsYeJzkrY3SCo/VHNeS4SoGCIDJt18BGWEtDHroG5AgQFDA4mq/N5770XTi+WqLsRTNHgwvc997nPheEn9om29qqt46litrq1yDZphojiga/ft24dEA1lGV1dXpTkZCM9DO01mP1gUiJWvfe1rf/EXfwGLUqnkFC6oc85blRbn3J//E+KMUWLEYS9HMr7dI062BKEMLeI2hgxshn2F7OEDey45d/UGrM8pqYKEackIvKKOXEq1jDdQQDTktz1UfM6loUw684lP1r7nPbrFang0tNzhotr5rXK/Gkh1U7p3aMLZNWoT9nM0LdoHKOAVgcfe3HyZj1SOe2uGPtxip3FDZLJItaVEatlHh32rp3Ju3bUPfbkvs6I55u+osVfWei+5eJOI7KsBUu0xox588EH8LXGQQ83I8zlFNCRQq7uHUdhaPWPecErpQYTCQ7gj3sg0HgtZbWFvTYOxqRkbcoZV2hL8VPVK95ROjWWdHQPGgQmDPBwhG8M8a2MDKlPI33LxOTBU1bHYpa4f2gh3U4hdvgVYel7Q90+tzsXaU8+oPC1Rr5wpFI1qThHilRDWol1RvdEnBQOcv3Fwwjk8KTol8BC2S5tbrBpiFlUdc+D8vTDAQFv5tOR6air19W/4rv1oMDmZSSTcv3lH9K+v8Tc1V6CeA3PlfvUF1TDZ7rrrLvSlBCNUr7AsCwOMKyyxbfuTRqpkoUaD702mzZyLcIRcNW4WsSEfoMlMY6oZ7BDLG2zyoSkeA2klKlmM8Im7JDArc19bFLb4+hISOeCpZdYCx1gbk4yapVUFRpbAvFGfqJFnsF/pMUCprxroGbyq2+XuyMDK51b5dsrujmrAAWLmbc/NZPE9LeAZ23PQxhf/zE2+LZut1nY4dT3RWTO7u7s7qxL1zWtR5gn/UAtLbGoVnjojjLGJOICgR+Q3RvVH1HFhlRVjLImOJTGBcMggVmuJidEon4HALbUJ3hXq6ZGqX/oJn4baHDbJcAo17FIkH7Zxf0XiIJa6OqVtrfDDcGWMnvRSXtY9Ka8s8kPfo7Uyumb/8fI5N5ebuv7ffCNjxZt+FB4ZQSXtlorOq19TbG6u++AHpOJ4DJGFakDBSQ1yV47qais+wPrmTBFVbt5Jl2HIQDg7FFskzhELvaIKyoPqpgTDQ9MO0pk0ASsBSoZFcOkznaaI0ZIQr+aHeov39Rp9qUASF3TJnCSjgOFEwI+HsBI/y1v0QfYdqhAEyln+8DptIoyIKXqgMew2x4zlcW91o7kyYXFTrydqdpVRUd1FqXcG4VTV2zdw+32PDhXjk1aDVbsyZ0THc+LZi8cvTTCaCDWakbPXuWvqTAJxwQbbYp7P+sXXwOzAEsoeSpd2DSN+NfePmWM5G5d77I+yBXE0gKBiAYNG8dskrVAeXPImwdiIPV42il4hqmZvQ5PZFBEJFO8I3qTDnOXiqRx6bLDGIksIOTIhJMiWdM011yBeX2hMn0pTS+8uYeAYBpYY4GO4WLo6BQzolQslAHwvps5cECGJ7L7wwNR2uhYv3YraFVjOJShK8ktf8b72H7F9u/P+6JHGVb94yd/v6nomF6KJU45VsuGh5irlSSBJjhnEvTC9niGkKrtVZ3DHvz/zys7AThJa2E7AmQ6aOMSEcwY6Y8N+aOLyv33k31NuK9sUG5XyFlPWTWJsJs5XEOWZbAb2PhIMSDwS4jT6ybIjW0Vz1GiLY/jEGcoMaqxMnFRQQV840ByiRyU8GPY/GNByhwKcwRt+TViME2UK9Z1SrRzbXSqVnMJIneArAhxphLZv5wxVgWIE1TQidj2a3KTv6IS7uroAFRUo3t24JVOAjmBw+xR54BMEcn4xAGNLJtzrw33eYJowmPZgsgR9gDYegzQshdsimekjT7ziOWfWx0L5QgnkI18499xzdVXViKUqCLjJP3tF4tZbk5vPrLnrXrsO2+YFyBT9LuWrGeD5sHFHwPPc8Tz8ubF/1BzC9RHgXJRa5jMCX7qi8e+ZmkOf7ij9Bv83meZamyAz9ZKRtr/r58Z3j3ziod5Lztu6vqspcI4YJJfp/ArkCgMGA4dJIe7fdE2z95UCUq2CBO3f4Ul395h5YNgYykqYUDg5YaclLQfh07AgtZqwYW40NoobmwpGttC3TItTUxP945lxq/VA0hiZFnHD2nprTZ2zpomvZKF3FAwnfqIJ9ELMN/qCil7Hzzvx10++pPC0O/pzQ3msaBkyLAyxfEbkxW2WgWP1ARjE8IEx58iUjSbJdHFqKG1qtQikpLpdVfTYS3L1e2SA6Rs6/txP/qf4sY9Hdj4OiNPPe17iS9fbXZ0YvyswZ00W+iieh7bNlz7rgSrKU/7yHeE8j2wC/RsrMx/gwJQ7nPZynh9edzyNwb+Zd83JbCmdh+sjqJWNry8MMCwj6iR0f0HDq4/iMSsMEn6/1CqEuB+ZJaG0Ufm6cLbEIce/BBEkfpsxiSBI2CdRHWP3Kyt0mRZXTAi9kr8KxJM80SO0kfglsjgj8iNkYKXXPKK3UivfSjYP1we0ZjhoBkJaG6hLapxQqvLi8UDQFaqngCsd4B/yAMykiQCfLbhpiXSNwSqBsiVbjyTsgUMuCicGh4wRtZhSi2JZfBawo6ZNNSCyTgkYCg+6WgU2tktecnI8ErLbGmvgeCWGFkGkJZo0zLBbHyExrFkTloVFlh3pgbx33IOqFaalLbmWP3D/3uh46is3+O9/KHjkMB9NfllH3ikmvnejFwoFt26hWr1ByAjNrp56YIBheDBpPm6j8x5I6+QwT6fZKLlg+BDI6jABi8OvX2S2jWedgSQiPMJ/oLhVM4kqTTcecDsSZjxs7Roq3HvI2z9ijhf9WYbDkQUT0Q02CJgSYTlM3/2MXRn/8rICkxbKfRQ2EmcotMFoayMeAtg19R4hrHBcV1GyWHpn42J2NzWoSIu69x98vLu3ef15R3PhPsLsZxAtmdM5YcuxO4uHqNBtDgvtQf2E38dBxgdvLrMRfp6u4Z3uYVb98OHUvunY/lHraLI0Op0vuEQG5UNCjM8nJosc/xA58Q+4+LpUlC+DwCWEmiPwxLoGg0jUhC+RFZKPVWBfDP7ZvTnuL91NHqME/tjHPoZIHVsAdMII1lmCFkfRcStderCEgUUxcMw9ctFiSw+XMDAXA3rBgkH60Y9+hAsHhN3rXve6D3/4wyey/cyt6zi/dROyxamDNdxLTk/e/NPSh95fMzyEEq979UU/feHbH9ry4qQlhIjsyGLIyWophKwIzt1AgU0Gyg/LZ71Lm1gvT79v80c6A7uQkqcfrjvyzUT+ICJZN7Ilt+IN06HOyQtqf/7mVTf866EP+bBs01kZxXSZLI952x/C81ey+yZixH6E9cXckfCbLVGjJUpSVrstRi5f4U7ERWxm7+GCvnAQCvuOO+74+c9/TkwphAWVZR16C4fel7/85Zdffrney9W7p2FfUZg7oRPgUY4gPTBRcLZYmcIAQ6/A3FbgpADFxEJRcexdXV0333wzsSvYtF796lfzog5hckLtne5CTI+BpFs0bKLLEmYWI0PownTezY8drfUXamtEdD0y0Hckm93x+GOxeIL9lY5oLlHDojAgpEL61v+N3PYzgh8FPvoxsybORr8oiSIZEVGDL9ghjVUeFT2iXpWOpq3JvJXMEbnHNP0Qu05LTR8po81iJNuDVyP4VeSEomKFNN4fRjRPkKC28EA85GCEdkaTFVYSeMpWjws/YVf4DNFm48w8h2iogAEZNJ7x9k8YvePeeNHKYXAJuYPFGyU8VFkoDoiF6y2vtVZG3RX1KkQbVc8+ZmrzptLFh7uHgx2tI9NIityVdWZH3FnTqLlfPgHdmdkvn/AvgUgxwPQFYRDE8Qm/esoFGWdvRWNgeqCYde0Z7lcEZ5W+ABK1Y0t5YNQ5khRFDstI1F/a1GbHlFGpDMpT6vcpA7/Aixpa+Wpdr7C/O/2x68I//H68VErW1Frve2/tW94qKY4EXI4FVhvYCczOtTSzUjt1zRT2EEkc6TvavW+/HWvsTWLzTM5eeyzrTqT5AEXrywXRd7CbQYHJwcJMqH4HDSasrM9LxGWphCPGIJ/AVGGc/8XQ1IwFUZ1JwlswGkFvGYJJk0hXEaVr1fidh2MN/wK9qEC++AUDx1eM8BH/eSxfOjo6KkuxfGlq3LmwIiEjIpZNZS6zarDnfI+LNKcmiWwQGpMAzT/h/H1GDM4jLBuIiHqxK3YkAjaum+jPyREljDHxijnLfQPtMYpiMa6WgI6KHyasoxj3KsZcmDDgFk4Ms2pCnE1NFsbTUw11iXDADhFv3+exmBBzIaGiZ4kuMeI0qPS2iBhmHMFnTw/NvCo0a1zLMgVePCO4boOx3gtdeB6RwKa++z1zYDD/7W+FDw27f/InpUIhf/Xr3ebWune9W6zVQyxrvDFrzpHiiIpkkiy+2grSyocuqVcGXoSFZudiDUSIrKM5LFYVBi8Gm4KNuRbyhYHp0lDS4KsXGsLEXdb3xAiZqNyOmO8tF5ijqdKvDxUe6jcmssHpAqp75jllCJcgBueJKKysRClnCJBiKHwJ7yg99MC8UcwT+M2YzBojGbM/7R2aIJURPHBpVT2iRgk9MtMh+TsHZn7y3IcEcP2adatXWj4/TPvjh5MPdE801LZP5AM4GiTz5kTG0OfhrHFoytw3jkV0YU2DrTnVQq4wPDo82D9ALLqA3/6LZ172xKj3MBGwh31TbpDM29hFsxfkldWALHbA7OL6LmwwobOQtpBNcDxL1Eb3yJRB2Ih1jSQFsIn4JfNLDs56HuhJoe6dzKnSa6S3119//Tve8Q4Mod/4xjd+4Qtf+MxnPnPJJZfoApViJ1P3UtklDCyMgSUGeGG8LN1dHANCy3gezpPvec97MN8lPSwGsew6p10BqBdX4bfw9bn5Z8UvfC6241E8w/asPZ+Mvnc9+/UT/hoJ7ayJTgFa1l/tc6V8fSXNLHH7oTXUhity2mcktv9R3V0s8snHa/Zf2xTMRCXTKurih6L7Dvo2fq7ga069bMVNNw9cPW10QpEgXsU/N1soAAW2T/FoSNx78KfC1DlqtsScroTZUWs2RH1BkYfyn6I6RHgquwNcFjrSbdu2oe/FzhkzOe5zoIGEdSSQMiQXgR/gXuA5j8dH6VeepjP9ombMybDBhonlAi9WWFksaTWc8FSUQferz7qw7ghWqd///veZAPD2OAZ//vOf5+nvY5eSmB9Y807D8ZZMjIaxeGew0XsExUjMtN1CU4090NfXvW8fvTj7nHOht+bACemC3MTK5Uvv+4doqTT+guc3Xn65MIhPdpCactEigt7946XhjD2a9MZyxAyX0DhmCXoXjyzoaRs7BDtK4y5efJQGQmCTaRTFiZ3pD2ke7WytJfZVHbpJQfAxsCisW2f+4L7IYKkoPscK6KcUoiqCu+wacfsm7fG8kco4SPzhxMUWlRoRFTleU9xcHjfao+6aZjJRLTyUQl6LWR3WdIWxrO0laYHAp0Zb1FvfTFCehd/SYJzUmUlF+DeUQp2dndVdPqlKTrZwbcCEmx1Pl1bUofuVvsxGJR03iPh1FO9sGSwv4S9uavVFlR1pua3ZL8wBgEWSKvUQz3l02n+qqSHzOnfbbbk3v7VmaCBv28nX/nnik9dZ7R0MlNbgzGm3MqMq9/Wd2ZiQuuO1Nb5E+2+PZscTaTMYHU0Ttsogs1GhCBtGUmuCM1lOlm9TrpnHhGSOWG5NLZbKsMFi6GzbJJrm5MSJmxDGc9LGuTdkuokwutbJ9rpwQw2u5HOmlOC3CsdVl7MfVOA/8QvkffiboEvkooIH6fisWVABYHbTJ96MzCvZII5zSLXSa+zqfbg9k2mWwiLbxe4Ag6ZMHrECa53kjiLNT7pklrnioqS0RUUJMyaboDKfBu2qNau+vnZqejqdyQ2OjNfWJgI4CqOKJ9yAuFKTUYmGPLY2BLs1Itgt0Wh92KiLwCFTbEbqwadf1WkwU/5V/ouxMy7dXvz1VzLtwldf6UxOpD58rf/ee8Nf/wZ7zNRNPyrkizU3fteLRIgdDdPPvsyHpNFbqew4aFnsNo0ijoFMwKLqV7/6Fct7Z2cnLyxYZ/mmgC7GD1Gft6bWXFHrjRGtLelNFviuKYJFjH1wikRxDmzki9bbL9xgPNSb+/Uhsy8TQPWaK9mkFypILmsXdTpGCuAQIwYM2uEeRfggYggqknlEAHDkF3Dak3lzLG0MZcxDky6RGjtrSqvrjdY4op9Z86Ey92a6ILD6/RJUsDFs1WUPr3SGxkf7o4nO5oaW0axInUh6xNfHlJjI4ORsHJ6y9o0ZTfa4M37Qmx4kex7zgEAlNdEYiuKWTv+ZTeaOEXtbn7dv3JnIWdN5i/zbQIiBBnIpNLzMImyCIIHYI7ySOe54qSIZhl08qI8mvQPj7oZG8sPbEEKMYvWsAMhTPvTQoBKApIS2JNnE85///CuuuAIDQ/ZZjZby8J1yG0svLmFAYUB9mku4WMLASWIAjR9evt/4xjfYcj796U+/6EUvku1iZis8ycoWLM5aLdwjmha0VJP/dr3xnW+Hdz1OqJSxSP2tr/zgr7deMRxv4Sf7t1g1s/oqxQTS1kJR635JFImuUuToclBdGTz3mq7r/mbNR5F17n1/h7Otln1amd1JFZRJvGpo2RsGYKo/vOemh1N/UigWc7k8nAFkWUMi0hAN1IYJJUJmHa8t7nXWm+0xTJ3Zu4BBkwhqJ5D2DIwDYXqJk0ysjhkQTGwFMRXGwvmFL3whvMppRZo0egoHsBFqCNYXhQ9ML7Z/+BtXACP+Fncoo6NhVdfPTaQeKOg++MEP4v6NsggB/ILBSKrfejqugeRo0t0zZvaMu0MpYzAtznKQ1Q0oNELehma70Uwd2vYLjAWj0QjKdtS/Ml9nz9j/n73zALCrqvP/La/3Nz0zmWQmnSQEkSIqTQFl17ZWsIINFLAX7AXURUVxEVb9r311LesqqGtZBFHpRTqEtElmJtPfzOv1lv/nd+/My2RmMplJAlJyk7zcd9/p59xzfvX7Y72wBNJf/lL0Yx8v+/y+u+70rl9Pa2V57Z1y4V2gYRA/RIh9YEjdPG6N5fWyrEvFgxpETC6to2K/f23Da3SlNvbL1tIPO4T6cOqSbJoZedtA04vGDM1zn36NN3zahjZxbJYmTaOWKAeyD43HdG32jBZKKyRKpH3/YA2s5t4Mrr+CH4s1Kup8MeTDptcGn1xvj1jdSeuZ7TrmkU6/pboZF9X19fVTyNZRc2eqovnDiYCyps3/wmd2RtAaSA/myDWjkP1+pc0IjFiZiP9ZhIekzAVUShIRh9EL50aYVefr5AdCoC2jtYEcLhPyPO5wv64X5fRk+7pHHIaACfaPa0bJ+8pywM8ZQFlquVzqrW8L/e533mo1t25t4MtfDp1+OiDLIhGkBXM1Ao9rLEGol08WleuIgXjFxTl31xKAgijNdmeU/rH8vY9sUyPNiaZONLsVsSmwvT4dHhj/QmxeRwugOhngI0QDaB11/EMZW/S9WDkjeRR75iC4CaDsWhEvtrig5avYcyJPePC++xtbmpcsaWeg52rmAQ/MgjLSTUR+iPl4sx7/2mc00Rlz51Bx1IqsTFYfLBYyM7iUfMXMV9RMBeYHm3P8WtViBQtqMaIW22ksqIUfJsyT+B7DAGfyBXafaCwcQJuNszByEKc+Uc3CBju4YnziqhrzW1j1NwathiASXhUzaSZOxCZ72jfZqj0P5ODmIfsKz5yEJo0oZ3/4I3XXLuuX1/r7eqxwFOck+4J3Wk1NDRe9C8YO1wtpw9S7Nq386QXv/573C3RooqYhwuAUAwdhIZYj7pKm4VTA6kSyAGj5aAEUfrDvJy8sfiN+qyNGgF/locHajT3Gtgl/tqJVhNFVazVCS5h+rModYoAXgeDVyGTdrYQi6j3iCcOHXTFShnhQULIoE5g9jJbbowIW7a51msSF/xGeVuwVU62Q/3mO5GjXrp2PPPooTG9OT1rJFTk9MVzQYK0BTi8y2ardGvMTISmEJ/54z8qEumlpeOOKpnw2DQLAhg3rhdYRIaYyXDAfHMY3GKEeUlGNhZSvWFh6Uw+eztIcp8FsGBKcwLlH6R3386oiJ7WXJ3EMVpDMIsaSgZiaQUl6KC7m8cc//jEEBlAdYI5cfPHFnAUzBuRQ1HO4jKfjCBzWAD8dZ/2A+8zO67r7outjp/vCF75w4YUXukhXh2Tj48iUY10OIjk6zFyhcusd+fdeFO/ZqVjGUGPXX854x40nvHYgukTYBOe4lGAe8MBiRydgm7A9wGyCe0VRJmJL50hz0k52mkcdoX6xTzaDla1YLiH/Bf6B88LZu1W78ihufAiArWbvTrRPVq1GHMiGZKAtHgBqAh8eoHG7kkpnnCi+gn017ZLzgSEaT42jFIXvvfnmmyGhGBn2a9jCM844A4eW00477Qm4fYM/wYUiGlErqmmE6MQ9cgPf1zuIIg7anQsKA5BG18YM+pjuvPOd77ziiitA8Pr2t7+NPXw9y2N9w2jLYnFcsGCACzXE2MpEmfUgEwNXGfFj4aYSmaYzqe1S1VAoeOKJJ84w6aw3UgiL/n7lClFiV9/+ltAR69zp3e/adpohxUxL6RJOsgKhQbeMKf056CotX7NYXtKgKnbPNkg62wrHjcdXN3k2J1+UKW/22nc2EtJFhDLohJ+TSp6RwxYyr6y3fM9c3SK+UE4tUpd7UTVcCisNjT2WzzRgWhskiTirijECvJDjs1rQB7LYy4nmDQ0cLWYRi+ulqiQCemPABFD0iGYt6hI7k5XU/2O46Zes54maf7BsDxfzpq01RvRlUf2IJVhGUNFer0Q95wHcsNXwArLSoHgOIPsBZnEG2N02hC9wp1GGUeh56MVHx2qDeSeoOCGvfNWNbV6iyyy8LoZuxgQtPO/0lMy7e80sUOZHdiGZKdMs/vq3lU99JvnIgxWfL/eqVyWu+rre2DRZjkPaztl0iE4XjQ92gpK4p82OAYjQ8zBRw3l7ZwajXH0wa25PB0eUZjVr61EWFfo/vVo1zIrlA/mPN7CGc6kFso5gTOGCbAJbpWMEHfYDmmDB9xI7LuYzYa6aBSxQEAbr3RTOwiG4608ezxv6ftttt7EIsdPhjDsks3bA7Wck5Gx0LvlPOFZhYFh6BAEOejxgTzDz/MVPOFMxYYYxiCXilED+1kSzVxB7aZSTWikcjgX1kdSErBynEJaKs2GKpSvB2LNV9gusf4llAPAYEFl4YmuIJOI+Kxm0EE80hcwGiWhAtD+a4bJ4NGbP1DFWKPsp3VmMuFh4+Zt45zv4alx4QXV4pPSvX/De+LfA169i65n4n/+ularJ//qRCmPT3okmlM2IvG5fZw+7u+z5nLnypzKAYcZpS1gdTjGW7kIYYLcytwAIgoRfTbT4UKTvztaGsloBQRHxERU1W9Wyo+D8GUtj+kXP9fVO1P6607h3UM3WPBWJAK0C6D2aJ3qweKpHgiJeJNScwUaPUGiyR85eDIyJoL5pmQpOyOpo0e7NqNvTyvKYubpRaY9hBEFyFe4XTn716tWu4GlaI/HY1bpXrGjv6MBoa2dvX2l8LB7r1qxguWQXi5hn+0qWZ9yDmhpvgnA8uT4V0bczU2klUsk525UsHpYQFlJI8FvDnvWtxoND5t2D1o5xdcJBihaQLd5YDggHIQDUSEgsNnicxEWqYqpZjtoSeBZ2H9rgFPEFMZnBiIDiKZmLDpObFXJQF9ZM55xzDt5haIAxh0ajQGyR1772tXNaOR1UTYczP/1G4LAG+Ok354vvsVBTQgLav//977F5BnQRhOdLLrkE2pTns4+oxdcwmaNeEWxn4fd/LF56aez++/GQGWhb8adT3/63k980FGgQ60s5HeSMJRvUPeZGSDw5uYl5hFkmRKqQbVPb8IzGcBZ9ev17X9/xDdX03X/ucm0s7MCoUB6bNeVq3hNGuj65Gzn7lx752p9GXuNXjUTA7mqNrmwJdUSJhqKCbgUoi1u8u79DKlELxDpAUD/60Y8YJY4ut14cyd7whje8+tWvJk5DfaDqNzPa9jh/pdmo8rD04yxh5OGj6pwhqmCsT10YYbdV7tRwD3EMbYHmH/V1vcH8ii03bD9ZMIx/3Do41SprtKjcP6zsTNsDeXUoa4EwhVllzF9rC2nLE7XVDfqapDk4OAyXOI8+xzKMsde8OnHtrwsdSyN33qY3t7EkFrLAaQbEFiMD9nJ9TNwbwzIf3hNCtAAAQABJREFUGrK3phUi5YxkbTyFYaQQvEPBsGhQjfg8xlGBn76i6UJdKalVX/bOSOmBECRE8Oh87JiC6athNLrV+//a2l+N/MUhRfaqAQoPl0XWHgsMym92c6eGCIs4k0hFsOKjRazECRFlQf4g6cG8watoAZ/aGrC7W5SNDQoOwBCzsydReCp56WwwSO8fENjn3oERIzN06glHrm0h1IfL2k0Rens180C+0HKYJkYVy4LZjTmQEg8iD43BPGDbmNGX80AKM24Jf23DEk9AOr2ILtMdeCoYKpwdFpVxRttpz+yX1E3DTzJJuVz2K5d7v/ilQM1Ix6P+738v9KIXCyz6FG8xo8DpX50C5AHkNYPf1dXl/or2m5Aq/RNKxtQGcyADGZmKTih1Qnm1JQgQI0BN7Jx+nyahbfAhdFRIVSh98W5F3yuBi+ICFqj5VXRlRlNEbQrruKA4ZLLs5+yoGKEwRNxzw+7kMjBEheXebcYh/KSnaJbYAKllxnTwEz6lgAyj+gbl60lBcMsIyuSztSBAVtHSE2k5U1bTFTNLGFi8HqoqLtl5fD4rbJIabJijKEaMjEoYttc5U/mP482RCCPAY1px2Qh6ASrDP1ncScDN4kxsChHsQME2OOpH7sHpuf+9kuKlZfytla1cPvP9HyhbN9vX/zWwa6eRSBrFouerX9a6VoeffzIssBzGYqE/8+WiECwUgPrDkAceePZimOyEogBm5p5oLCcesn5mlzY7u/vELQRSgjWfyiO+RKwgdtGyQtkFkV2qADvbS+KIXM2bemq39Wmpsg/HbOxpxHK45kCU+8WwHD1wuSYWSbCTFOCMq1RCFSJ4d7TBILqJR1XQXhIDJho2WG2PI6EsPfLwI6tXr4lFY1Kzc9V7x/HN5s9xxsENCmNqnOja3pQRTetNRX9zQQlnqmKnDbOKMDOglsK1FMKLBj3fHqo8Y2UTNjuRULChoXGyWBYNhFbOum/YvFf2dp1lk8G6npBLeJE5fK8r6INnBsCCVUGL0BxgKUDkC+QvHcTMa7CPaAEoC9sOfpUWL3zA3WbM+VnvMpTnRz7ykWuvvRavsa985Su4KlD+IaliznoPP3zKj8BhDfBTfooPtoPu7sPWA+v7m9/8BnE4QjjgkSh3MVuPc7LK+Tx5uXnlqZy1wtRyYbDH6ZK7+ZbalV8PX3OtX7FGI43/+5rP/n3daT1NXbINOye8ewZxi0mnIH+gzQVFiE+OE44sCpo6LaTQ+sVD5/B5NLcBV0uE0fHn5gu/CRDsROhZMVbCF6kSO7FAgF5TCRn+9fi3+Kq5kJV+zhLv0SuCqHx7tm1JZf1KY2M0KpA8tBxeF08V9L1cHEXuw2QyebZzsUejUOXhYsaq3uLH5MadUD537NgBhYde98gjj4SSqDO0/IRe11Xs11swNV/OBDiTOaNHwEHDAGMmDSv4+AbxQ8Wp9mVqJQPgWXsCGgTvX7EgqAFq6jHT2x/ctnx9o7d1xZz8IR10B4Sb8m23hH73hyo8wue/oLUsTrgDLVKXekiZzh9uBnLKQEHtz4AXpcIosFxRrWDHTLBTAKe8Xqtm6H8vvqIh23ty9HKvLx979kT4xHHeAocEhWCM7/R8LNLwLw3i+rvXRbOhge666y64X2zU6d2MGZlKLbOVLtvbUggIxAOtVBXGV+CGcGCDuuW10uyET1maVLsi5tIE9JLM8vSL7/JKOY/zhvrwsNmf1yYqAszbGNcIFLQ06pEf5f3ak68+sPto2J6UM+7qGVmHMB4zfv1HfYWoa43pIwLvpCcDtfVtcL+LbgvOIyAnIydyV+Oi809broySO1DuJ0UxBSw8NrPqQ/fmXnlWpKcHMjRz9tmxz1/iWda18FlwU0qxk7MpfAuqrZ0TVhqtb97aNmqmy0Ss9WKKqXmR//lKBtazCorSStUGZd3nuPxiFIoqK+TTiZreELQTYXFnxeEdvMAWAckHU99ZWdQii3SyMkB9sUaR7ji7K0IQbuq70wGM2PxZMHXBxntOPSGOiLxceLLgw8KRt/ABnL/Gx/BXZymIMlYOSwVjE1+AtSrvpRjl1ix2SJTDwCY59tISD3ZL72AlV0s2t1m63wnFJGYOImCW91mmB/MQVH94GjM9RJ8iDBVSDOYRV+EEnLDE/DOaQgoBYxPADot8es9UShHOrNY/3DfG9gbspC/xvveRvLpje21XT/XfrvRff4P3Xe+rqfbEOW+wyrXEFy/TmppsnY2FMqWA+o0U61xOd+Vu+tTU71kzbgIAOBBzcMwhj3Yz1tO4X2d/OgmkQsRUBPIliBHa2j5g3vJKTfSmalXR4YoRA+EpfcaqwBlr7Nt7K7f0KH0FH/YSpo7Zv844g6sQCYhbNfJGLNSA7xbIbgqWTjnNxhHGVEnp2C5pIyULcIHNA9nOiL00iheBXigUcZuARnEMYQDez6ZS46nUGFIATm3MymDyj37m0Tt7ehASVWvGvY/sCC9tGre1XYLHrhKFG844nasOT2QHfVrcU+sNmLvz6WUYRXdpyQa6jrmzCPQxL1oa0/BG3thi3jtk3Deg9mT0cZ9AkQsb7PgzwPfSaphqblxtMD8h9sLxeLyoDhXxl8Yc2gCpEXNu0eWLgb2sAf7svSzk8QKv+mThAwyVdcMNNxAk6eSTT0YzjCmiq4ahqHqyBRZ7ONnhEXii0BaHZ+IJOALu4QEFgHsn8jY8amB9AfuFKl1sax2OiQ2Qv/zjKHUOxKlS+EJdorgdTKU++L7w/10fzYylIs13nPi6/z3zfX2RdoIbSRrZ48gjJyzsbpmDWZyaxDJHDJ6dQ2WqyDn+p1JKQYp5+9ipaaMz7u1d8rpcz1a/8miCKCYcaaZe9T1vouHkAufBiLVpSfOxmyKBFY2xpkBbMuTzezyQYlu3bOWT0uEPGYe//OUv3/ve9yBq3bGCigKwAc73JS95yT/cam6OIXDGmeeQmHj8EreZRs6J28xcc81ZAgwJAWlms8ennnoqJxB2knfccQfdnzPvY/MQSz/IcX20AMKTihU0RLkHmz24zVxmbKKnxVNORgTuZb4D0jZr4xOlt54fq1XSxx6ffNXLaaq7LN02z5nXnfTpndr7CcaEIIXYcL+5mlYBlgvlmFevYUIqyCISW6dWgytmVkL/l/pAb/mokxNXdXj/7rPyvCNVJTRWO2Y08AEtfPIKxPXSgT1VuRWJ4D+VAncd2/U5W0gGikLP89Cw1ZeDEtKJ/ei6EYNoxluFpyVgRW1BfWnCWho21wDgLAVNq2lPnVIY790jw8ZQUcMIFu3MyiXRFh8Bk0SF5+SZmRHdC6+JKwPaq6T9fUE6QxK4xCcIA+wOLzF+N7RZAxOV1S1+uLc5u7y/nk3+vq/52m92pB6MKtldFDr2Z+6xSvf5/LIzFkvjl33Oc9W3Etl0obXV/sD74u9+j4aZu7t1Tl9D+60JD+doVPd6U2WrZ9wGvRxv3s2jtfGSB+Wh4FqxVjyqz7KTEVEKjY1n2BOTyRh8L46+hDIKePSwB8dyOx5UfQoKLgsFFwyDo0ra215gWsPqogGWNzR9e3v7Alq66CR7v6qS3X3iDhRfZVRVFVGFywPDfkwPjLTo+h6XDNLiuSrijRZm2K9HfXaHWPLbBMLNlkAss6Nl65HiaHki37xkuR6JYLJLBHU44VLNBoAaZg1NsmtsxfmLmIMgTJkS3qESwi3kUUNewLolqrCjwKy1hBRM2RtCGj6uDh8up3J956q3jkbWjVkCq1f7Vq4MPec55kQq/YlLPPfd7f3l//onRgoP3Fst1QJfvkxLNgdOOBbjBbbNSqVIcSJqtBVkjhQIEzjjMJo9AAALgzGJ4ImTGjZ4v+mnStgznKzYJBKcgFVIKoPZ2khOhbeFZmBwgJsyzerRHd4zVwdOXqHc11/5c4+yPe0p13DoReurFcpqoWwRwhq7aHDVMJPGTB2tKh1x2WDuoH6wlM6WFSzVsyVl82BfwlMjPNiSsDWY6W+Ne0IeOxmLbn70UccTQfTt6H55O6BG2GCjkeiRR25iAWOyoZQzhW23tLR1dXWu2pn1AoU1lNfG/bFsNFo11In0YKGQCbavU322WlAqg8bqpBbDwkbmSTY0GP6umL4kqh/ZjGOwhYF3f1YdJ3KGQGSJyoGhYDdB6AXwOIcYuOw03iQ0WA0ZgQb+1nBO701baxuNdc06QZVd0k3WgSxN+XfAl8yHquJERqz7b37zmxghEk2DyCPnn38+BjV0n18PuPDDGZ+GIyAimqdhtw93eSEjwN7661//GmEb1rCg0uPbiazxwLYYlhn8LTEhh3PG0jjBBtmqJrlo2RcxXs7mc//xA/sLl0Qy6Xwgdt/RL/7VKz66rWGVIaJJhIiTNDmL1YG5ghB3wvGh52NnRRUmJ8jk5jq50ZLPWdqi3OWYdE5jXZdwvgHdfNOyK97a+VlNAyokmPpLqPRQWPWaoeOKjcfnFR0tXXx74Hu+xIvao3jjuGfDZPHZbA6NAUHqrr/+erSdGDQykhxFeJZy4ZqCQtVFcj6wgVrIvBxMGqhnPHhBpablAEEBYXWo7EtZLZCqmGXiqwNSxePTfXfqHxw2+3L6lhELmTcBSE3DAt0pooyr6aHmQOnVJ63ubksKxbWPw1EWp21lv/rV4EcurkST3r9cH9h4VF1gDcEB2YRT2WyyiWzwn6wHZoRkjK3L6SE7WLKkrWYr9w6YWyeU/owmiFNQDTCQBkSDRNUxgMrFsUwccUVngjM6gEEeo9QQ6G3wDMBCqP6uSLizM+E7tl2D0HSkN3tmnqoZanwUeSVZePNwiYiHAL7aNgGcqTqas+BUyYt5Kv7EvDt8aQ5D7pgrE8pRHUgNeFnmFG+RSWwrHhoxtqe1nSkBV++I2t0x5RkdOMUJM1N/AeutJA8rjRbOtgyvp5nzhsV53XXXUebpp5/+D8FFn92qqd7RJ2eDcXaVqS1ndvJ9Pjn4OMBoR7HLrVdAw5jEaCy6etUac3gg/Zbzon/6Iw8zRx3d8LP/0rtWOitZJIjun3rG/d7Q01wN1ldAX1Nle/OoMVLwVE3AnFlFMt8eDRrdg+lzFZmOaQz096seb2fHEr+PxW4htGkMEa1XCeoWOrT2qGDk0oapkZRteZ42ULvLALtx9eZJeWA/wREhByQvN7w+rpslljuzVcEwWoCxIXRAxgcbPH+zD6wxj3MuZxGzHmQDgIXbvmvw9nsezhve5s4V/kR7BjPpio1ymMBLYEqj7oMZZhthyZPBnTMpgVMV915VwWEVtbAEFsYaFk44oDQErBZx6pYF4CNYrpNpnnGTsp0CwRpGfFm64fra3fdqv/pV4N6/g09ZRQl8yaVWwOd77dlbd+0y5DDfwzuGgsHZeP6zx5O3Bpkvc802zqnngiDOTjb/EyE0pGZsQJSRnDWQsfOoehV1Q7PVFmHbxCSHYwDzbe8DI5Ubt5sPp7zFioeDgEBgeGmZloWwAGtnChECBpQsJkC6LmPDX5kP54FulsCE8xiZ0sCjyzuaTnnmanyDo3a2Z8vDTVjkY7jc2OhC081oMKOYGh9/4P4Hspm0N+Bbtmp9LbRkS0rdlVWHChpxuYcGdxdymcblG7BjbwxqbcS6S1pHNCpEcIwKAFf97ZSWlUx1V9r8+27sotXBnI71EJpegnIhBgWzWrTB0mwbkyGycssCged3gmkpbWF7WVxZ22yva9QSOIqQVpaO/DtUFycvoZLQymzcuBH3YIwT51ljh6rSw+U8lUZAR3zyVOrP4b4c/Aiwh1IIGpi3vOUtWJgce+yxRPrFwxNOieeL2mIoSshm7IVsYt9Zt/fZfx8UkyoQpNhpOUw4UaxCKf/7P2X+5eXhX1/jK5f/fvQ//+Q1n/vFme8fDjdjm+xE4JQ6aRPKXpFAI5xG9ys2zxxHLtSk0ebZdkbb/76o7RfPbryxLdCbLYfzFsA5sqED9iNySg3AIWIeEsGR4L3aruKRMW92Rfh+1VuNrCpFn5uJn5ALdpZtDc4jajdf0tT++sYArizOpi27toiKtm7d9o1vfIM997e//S2GvjA8qE/BJ4QfBgUKsTSSAsaNT2yh0c9AVx2kp9/Bz+aMErAEQ0OLzBgiAEWuyzgtak5nFFj/SiE//elPOZPQk7io4PWfHosbhxahYJuYutsnEHILmsiYBFwBYFmtZEas4lh7RHn+cSvXE6JKDl/5M+MSX3F5ZFe3bqmde663QrDKN4fPfZND2k2mZh57enqgjGczwORkDfDJqwEDzA3JAgEfBpV+f2DnhNGTRRauEBsG00AUwJA6YniMrb6FIbQsqhrKataYJh5ikBO2z1swmlJmV827MuJvXJ7QNrRgZzhJaMxoPmIXmsRo8zl7BqlL3i4FFTQsq9sMMYukaroMUomDbwIMqdYUVAB129CqJqFk5f2eUQ/dgjIjk907Ye7IqLtSSlVRwdZeHlc2LMEfnt8nc83OiRsngwMPLKUs+IJL5CXq7u5+fG3p99M+p3fuhyyoOdbTfgqQn9kZeEdQSR2wTa/LrUEBo/uCN1u5ek1TQ1M8Fqn88pelV58dvf8eYIutL36p4eqv682tMjFySdWT/8nt3Jez8wtnDyUPpELPuPnomJKq6FtT5r396kQV0SCkOrSvzXJuiIjJK7Dq2M+j6WV9VKpVs1xojAU74l6WLniBrSF7ZaOyplkncAuwCZNoC1OV77c99A4qf1Gum1Nl7/9/eCEAxmFrXdEVrzn3bnUzMjPgjDbaNi5+2m+zZ2R/Yn6tr2NMNxoTkY6meHG018oOA/F4xPIGnHsJdsDOE8V81wdnC86WRjgcZHfsBGK8K5sp/wviBlrBkoGGUFCRANBKV5SUYCOpoPEP5y2AqWH90BPKhufk5tP5KwvNKUdGiFHlG1shcXq8K1cFTzpJP/MF1eefVjJqUAi+6/7o//W1pV29wRtuaHrBCwOJeL5UXLVqdWNTI1uuz43BPu/EIJrE/pmDD7khAs2DMS2h10hy4gGtNaahUddtozMp0E80f2R4FFPkdtjriOe4Zb71TWy3tWxJhD6wizCNyI8I1Mcw+KFGgAbTNHyM3f7z9lEyR1CxWCpUDZIVyrVMesIONRuBplTFU1YCy7uXr1i2pKmBEFYSiZprdqeRCCCF93g9Y6NjqeGB5c3RDcsTmMEHNMyOCL/NjGmqLy7oaPIXyCt1NK8SRYnSwj4OJtrjTIWi8s5i3L6yUe9MYOJhEsqbX/AQolasA8Sl2Zk3WH7h5PkCEhheZASsNsTVHBg2DLBTRdIBd8cOQBIn3VzNlvyLvJBVvexlL0MhDPYKsUh27tx5wgknzCkXWGTBh5M/XUbgsAn002WmF9hPdmHogK9+9auXXXYZJBoA9AA4cSjN3mfnL9ChvyUJG+JI0cICc8c4jiKyuT4wbK5tVkBNYLPM/v66ylVXR6//v4Ri97Ws/fkbv3jv6udkPHH2Vaf8ySOS7bUs/jM4oggbLNEFhFjjVzukjp6/6qqzl/0oru8G4EoYVcuTVtp+3nPud3suKKotRNpASo29IkgePo5wjm1Vrdix7/Vf2ls9+qUt/97qe1i3algzWUpQCT5La/6IFjrFMdCatI9AUYDbyc9//nP8T+oq31e+8pVAOhOgxdVQ0ZwNGza4RBXYG1xsx11dXfCZ/ISekE/YgOnqrMUO6fwDPs+vMlQOq8bxz5zSJPSZsE88PLRtgKoAAQuycp7GHLqfZIXQsd6sQDqlsLySqLaoVrEnsKJBpUnzPXP98nXtGD+TChJEyIuZtTtCd80yCu94VyyTza1eFb30szOSMXqMEp9c9ez1ceMM5uI5040EZMrHzIKe2JHR+ifMbEUnki9m+oDiouO1qjbUE9su+mIqCmCEIGIcFTENBn52TUHbwSJNeu2lDcqyBBgztJzaWOpyuW2AZIco51l3d7fzeK4PeQPVoRwgvVp/2kqVhdLCW1pz6BuEUGhtiWbR4FS0Mmk34dUnl4zV7OIYw+Gism1C6xu3wBeN+qzOuL22WSNEiiTe8yHfqIdXxjXQxS6A/YT3gueQJpChU12RlLMvt4MM5n56NzvnY/ykPijzt/8xboUULxaP0ag7yHyNRcJ2tZL51GcCV3wtYBu5NWt8V14Vev6pQo1OrhrJteALgDZAnq2elF009eGiNZLHcd1Tsmu2jWMBVgsOArBfqWKKY6hBnwfIZ4Dagl5taVN0ojwSq44sjS9vDVodCVDocDaXkau3ZMbLNX+r2D/nT3Awv2KuwkUJ4CCwMuEZ9lUajWfb5CKBvEOO0Wm9R/vK9UR+PqvxKrz9SSediGVp1AcWsbY8LnCSyBbHYWBK1nhJ2FqcVMVGl+hKhB2uIYmWYObEuXL3RfyCiob8RCha5CH4CYtOGOtoPyGFYafNtrB40jaGPcFJoypGaPKtmt0eBtrbuVxfuiz0gjMQmWe+/R+VP13vufuuSM+u0oMPVPyh4Gc/GWpr9bS1i0jGuSb/28e4UwVkDIjKyNQQQiE0ZCoRX/JQ2jFVyD5yTz6ekQzRX1tYbaWfkx3hf1ohDSGlx7ZWN3rgHndn7Rt2mHf32xMVH0BabL0EGSpkjYDX8ms1j2GUKyYexYGgnMhcQsHoMLAcCR4zmDQDTT1pbbzEW+npzTE71qomuyshKvepet188um2kC0C6QDxw4Bu6VzajrHPUW0CMY0R0KOxRF+GgEkqMwuDOlLETwcemLjEhPZVuhLWEc3K8oSK4dtUodgf2RubPV1xe9MS4+5+6+FhaxCYaDDVqiCoEeqMA00CBVex4hbBLhwy+BtKyhCT6UxJGSvKCGxrMI5q1jqTug8hytS8T1Wx538mhYteuB3Z88Ncd6QhMUElCYLwrW9969Of/jSaCXQ2b37zmxkBp4x6L+bKf/jZ034EDjPAT/sl4AwA+4g7EHB6WDvv2LEDlSYbCjzbQnai+iDWy3FLxWDmkTFz84gyUsbjUc4Gr262RZRS1TQLY9nPfNb/g/9MVqujoYY/vfiDN57wmoF4p8Q3nLoguzmGiUaAS5KIUsFRxFOXXx21Mv+FtbHLj3738xquQRKNQ45qypmAe1pS7X/7ysvWJR79zMNX1tQm+F4MsVAHs19y3GGDimEe2Bq3959c1s44oX1wbUNfPOzX/StVL2QQrnQcQlINomL4XoSLKG3cruFsCarzm970Jlid6Tss93WKCgoJBhg+0KWZKAjPHBxuOWuhtBhSnqNSQHQtdUyNPPeLGmrSL+SifNqDJSp8CNiJZOnq6uLzsajLbY+rFHXvH8tPmSGcbMcKnqGClTOJu6hWLMvvFavLpU3Na5taOpNEFpVVR8p99JfhsfO/+33wpr9WPJrvK19VEzIppIY4gg1j6NzuoJB0z9Tu7m6XZppeZn0SqQWaEAzqLWMW+Cjpqg7aKnjPeIMRD4apJiAIcnRZhKqGSthxoxIIEiISwVzgXG+ZWgJ70YTWHjC7kpAhTuPJ6Rz2VApjCewzZBwK/HpLpM17rsnmZCrWlpTSl7ZTZb2I6oGgF15wp0UggG0D4OrJkAKb3RW2gX2WaoT73euqi7FQ7GwetaCQcjUcOw30eyuTOPu576orYdgrI2oWl+llQBxxUIafQQ5fiDMw0iKyoCN9jFR/ezX0H/HFXYpM0j7W5ILbJPlto3dX9s1vjvztJlPXcm84N/q5z2kCzj9zKucsdHJ+mXsnOV9R4m1LGWh9c2V7IIvDJ/6KosBpCnvGC0YiqBPLlycEdwMDQaCeDTXg04Kq3RQlRk54W8705XdtaljSSNgcWR2yfp0btz3OKpuzKbMeHuzgzCpwxgO3/Pqby6/z1MhPpGQrQMDHRgrNzas3T/oZdT3xv9IXBHkACzmSNTmVbKPWEPQmg8oKJ9TwRMlMldDmCaJShr8CKM05rgk/XEMJDMMsog7+GexhEibHSpdFhRhEgexTo1475nDCyaDZGlFawwro34FJL3qGh+Uha8NZLnInbK28IGyUomONX3CReuFFxZtuKV73R/PnP/NveXT87e8ct8zQN/7dTjZEn/c8MDmYH2e3nCxKSpP8ey73K+eva+7O3n7LLbcgC8aIxk00I/2enPPdiZUZRZXLJSpEBsrIuab1nBeBALSE1hlT3nSUfeZq4687Crf2eUbKXkMAEytjBUSi2A9bYQFON2OBULFKP9gjIWOQK8igBLs3ItbFIh1bdMZ8rGgP5rVdWXtZDLwGdXlSfOyn77/1LnADsUFoAKftdm/Prh07e/B/7l7VsHXcxqajL6MAkTVWEmzwUQnRpyLmGMmrvVl7VcJ0UP09js4WzbD0EcnIM9u8KxLm5jb7zv7a5jEtVdSyxB0s26Beo6KQyBiYgRiS2k9YCQU8DglSzRnthHrSB3L2qqSxsZU4GviiORuCzLQ0sN5sRo9jF9LIpY6cxs/34WZkyIjHiU7iwx/+8AUXXPCDH/zgqquucvteL3m+Ug7/9nQdgcMM8NN15mf1G5r1ox/9KA4VsEkcDG4InAPePhAH7powHhgSBIWKxNlljzObgwox09f48+q3v1O6+upYf18tFLr7pJf/8JWX9kY6awJENQlAy8bIvkusOVfS7NreiHpXmu3unWLO+daVVz+v4dcce7XhyMBP48X7gpZZDR9d7nhd2ducPanxV2/u3vCzwY/IaeJQm+iB4Y4iHtMqjPhqmdZA4fi2jqNXPjPkOXq66yO7MCIAcL+ANXbpeIiD5z//+bC+mNzsi4ivj5VrMgeL644xpblBgzA/RvYMpuju3bthYDDXIQGMMe5J7qnsqvXcXIfqkxoh2vhEa8ShO6e57KGqyx2B+jgcqmL3VQ6LAaPcguEZL1mjWROjrNHxdDIS6u6MNEWw3LOWJYSBnL89aiZtfOiDQdPInHp64wtOd5LLQU7hTJx7ydfJRbfnZkarmG7IIDfjjhQwUcAFKxJuF3ELshtUuyDeOqDlXgdJU9hgfMVw/RX0NvEQZsVjnpYM6S0hsyVkrWnVRcm7Nw1HM5hNFgx024yf6u1xCCeBRn9kFOArFd1vumyI3Rpq55oAX8lLZloJgm5G7faQtbrZ43r9yg8zLqfXWMNuHjbxAYP8JeuyBCSduTyJY8EewmVGPvRprjL80UcfhdzEJJAEUIQzks35lQAndLO7u3vOX5/sD/HfA0+4Lho7wO44c9yMpeqWzbl/eUVsd3+lsan60Y8mL7qQlcQMU6z82+8l+yllkdZis92VtmB6C6Y+mLbSVaKJWshlgGquSPBqe1nSS0CjGmjPWEMiwTTwygTNgbWqJkNaSDPbYtayjY3bHuxLDfY1x9c6lddbUb/Zb5se7wTs7a53z34r5o1jZXIiEAcOO5p9vYD7LeeJmYDuuEcbfcTJHDcEQhhwarBjhAB/9qhLIsLcZgldW7RBfkKFiFNoumLDAkkspSrK4SlgDll+cljD/1RLQE8rKQrxKiGfiscECuF4UGkUTthGg0pIYTELYWhFRi5eGIwP/5wTW5g7MWVw9t3QSc+1TnxO7Q1v8G7frn/jW5hDq+96jyebmXjzOeqK1bEL3qlFgvg8OaGFnMmhBXOtO+c3wdBCluHKpnklDyDIllsO7QYmbevWrfVpZdPjnn2PbZC9mnPfubLrW1ue//x1N/fUfvNgfqBGAHYf+IAE7EOsr/lpKW7z6FG1UkWQoi22V0ZEoLYojA0byG7G2QGLBm85r/fDBsfNNY3KsiRKWreje/V2qnnMp1Iql4qFwm233kqTnrHhiBVJ77Yxc+u4sLuwwSjtYYNHaoIKnnIsonuFWa3BYwODgssD48jF3BCM4IQOdWVSfXDEuguEi5Q94dERj6LqANyRE4QTDYGZxF7CV4LTDaQtBNOmhKFGdEJdsMFrmqrrmjzEZHKaO7PNSD9d87T6eC7kBoEUGogf/vCH55577rvf/W7ilvH5yU9+kgW8kOyH0zw9R+AwA/z0nPc9vWZ3hHAnei3ARZhWfu1rXzvvvPPgxPakWOQdGjDsah4esrZOaGLzDFcLRouP/VTZ0GTFtt5beMVZkeF+j6nkTzvF984LPce8eOJ2xSg5tsmy58sF+Q5SYqkqN2jOhBsWVsb9URKwd7Z6el7X+SPgcmsjsa0XN5uDcTG3MyuF3ZFtD5RXX2Z5mnIvW/KdP4+dNWqu8utmzK8Rs8FTyynZgaQ2sXFZ+JRj1jdExe5IvGIcI2FG4/rrr4f1vfHGG10bTmh3jMAZHJeIl7oXf5HXzY4OAfEwxyGMKMVQHZBUkBrcs4MjsuXURPYJw+yeXjyv3yyqWkrmgpcAqYtKMU7GQvtgpnUhteMXTTLXJHgh6Q8uDbQX4X+BI8bNTE3njbFMDuCQREgNagA7qR0xW7Cj5r9MK3vZl0LbewqJZOzqq9E21EebG2aB3BBJDz/8MOyca3e6r/Lcg5YxJ0zOrqzWl7aIowjJYBp4oYv/GwwDZQL+zBMu+BQ8kOF+eQjjyQ3sbsivJX1WRxQ5kUq0GFn0e1+Qawiq4H6Zzb1/mfZNrCTsLSPGQE4n8vAEHsi6Lp5f0IZUKjFlrMaQpy1ktePv14pNmhCZM2uaLA/FjvXoiNVf1IeypFI6Y+qSkLkW2GcnAWXR/Gl1yy1P3JXm/srCrhPWM1LO+ZXesWJdf0sIcV4H7mfXMmfeJ/5DaLsDIO+m98vZBPG8rGS/8131wxdHysVcd3fwRz9MPuvZ7JQilXBZ2ul55rsXdPKtY+atu4yGmB8EWsJEE6ylXDN8GGHqSqVsouwF26dYM4PiCGiB8AwEdkAjiq/dHAIm0ATgqjMOGpZSjbXnxoYd7c3MVTFfEx6X31hU1MNCmr6WuF84QhuJkQsTO519lT7CS7gFPi7Nf/wq4bXlgqnD0gQeGJGNO2KcvwQMbwTbLGCvbAAZC+toI1UQtSQ2JnC5mJzkqoIgDU4SUB3sNCwtWY+ATotOmGBsghw+LGywjW0O2OBomJuDtdaojk4YeHDZVqa2lL0XEN9kP8SMxbdmjXf12sipp9TGRkvf+0Hthz8M/uxner6YveM2PDyiX/6SB+MsjL4ch2WohtkDh/wC8A5k0Gi8CXCFeRfRHFAYckZPXxuzM+7rCW80EbPoJoZCHO7u5gxCIdb19VUHMBwC0Ea/9tIjfCevTN6607ppp9FX8ANoYlmEBsARFzcZLMbNWBCDIK0E1pT7IsvuLO80f5CaIoAoVnG3JuCfMpgXdKvl49baJmVZXLASZ58Zbo8wF8KmBlQ56I2RkVE6e1xnU3eDvWXU2pbWXG1wBm0wUgzgwYUNVgZzompemzDWNGtNYXH8pfuOOAIFvtbUpa1usu4bMO8ZsLZnCBoMsQd2msT5Q2KCEI7Gw8ZjDC9oF6aaMbEUgHtX0zQbmOgJa32LsqoRCAmajH7C2dVk8mfeUOnCJ4WUuATfeeedl19+ObFLAK+58sorzzzzzKeYvca+1uHh54sdgcMgWIsdsadUenZnJJcAXLFN/PM///M111yDntPdLNhKuPbbW2fDcvcsPi1wIx8asW7tU7al9aoAJNp+zeqO28cvtdeObta+dCki21Auk1/eZX/18tiln/cfubE1rnXE7QcGzIKFRZTIPsGcZCPOO/YzDmbP5ClWb4+AMNjK81r/8JIlP8ZSaPf3Gyp3Jz14G2MIrRB50tYLwapZjR1X9KvFnuoz89YmvI86YkqjNu5Lb1kbyf3TsV3POXpNyE+EXqePtoLh5S9/+UuG4oorrkD9y8gALYgEkShHBPVx2RtnSPY/JjMGzc1V/4T1Rd8Lr+t2B3YRdRAnKF+hORB/wnF1dXXxFQAh2FdEyC4r7prgUribcUYts7+iKsQFCFYE8Ta+T/VpnZ3yUD3h1EGhjZL8lFNOWWAjF1u1UAFcIhKxto5Zo2XxL3p0KD+RybNKGuORlUtCS6MQVeYRLRKlhdma3RJZtHCBllW+5Sb9onfDiRof/3jgpS8ldT0xN+49UiF4TngwV3lefz695U5KaVjBsB8cVnZhdVzCNkzaCe6IQORi6oZLFzpYx/mWuRAEEVUQ2sT1F9cpn+g+GkJKd6O2Mi4oca4+1m2D9NgWg3xIN5YK4m2mtf6T25L6mwjESl/aENjnCQVUsBrVYEWHcAjXLDhjzY4FPE0hs0sQtjQnvPDkez69QGkTdSpKz4TVk9F6JkCy0VrCFuZ8m5ZgBb3nLZiea/qYuPeoy1CvuTaHpJw/sZuFxMjy3ZSYS6CZ4e1bSMbZtT/BnyywU8wCw41gkRtZuMxNLjdx4UXhy76IYCP77OfEfnut/4j1LF4EgPUFv2eGZo2CFEFJonOzM1Xlhi2123brqapv57iIa0o4ErBuvZg6G16gXmFra6aH5cveauJioAPeA1QSwD+NfnRQ9vpWT0tIgAa5SL20Y2l9c5tV8z/yAScdhjAwrjOGna/uNX/j3DS8ufBOLEuEfUhqWNs8nz/jk/FXtju6BmeIsZJ7Wjm9mOqp9FnFnCru19uiRFDTl4QxXbEaAirYwhGfOAAHvSqoB6wZZ691BonVJkHXxEoWTWOW4LGwQ2WscLXRIsa39mgRY1oJ04AWUexh3Is6WffOJdVLNAfHm8nr9cQS/lNODlx4QaGpqRyNeO+7L3zbreVf/SL/H9/3nPgcs1TS43EnTASZpIj6TNEvlignL7soGONs7MwmPeVEmDMooFu7+ymvjnNRGulZURzT5IWxRFqHgJKHMMCAfXCU88lW1t3dDU71EevXt7a1uFmDHn1Vo/6s5Z6mYDVTxPiC3d7ipEAciiYWr1qvR8Im8c6JeRDNF8bWaT/SAecLeCjo2/lL+vGyOlJSMkVG10LHLirj+kQ5d27H2UhdkQ3HWX9ff3NTY2sisjSps6tjRw3CGfBXIoqFAHM0umhuEWqMlO1UARWuCRCag+I+OS+0OO5Xuxs9yxI26N+Ad+NeJlbQOANbfHVaTYNwTpu6p3cguaAKhrTDwIRJJ2YSWuIoSAGKsqt31+7dA6OjY4ZRRTkPIcHAMkeMYX3i3JGf59NNSRYoECyib731VmJSoJDHZ4GiFl7OPFUc/umpNAIHruh7Ko3C060v7iYOnhP6XjADOO0Ip+Yi9y56j+AolD0aZ12lL2s/MGT1plU8x9j/oLebg9YRLcqaUMn+1lXmF78SzKdrmqf4gYsT771QbW1zDkei+9nP6tDtZ6tfudkcKerIjzkFUUGI8Ng5axypJuXJzi8b+9T2vjK0lagJqukv3h/CeMo2S/zqprKq1dzdXtX0ENPoyNiOvM/siKkrEsqKhvjYzugRq7tD4RAGqEII4gKaz3//+9//7Gc/i9cuXzkUGQo8SRAPo7liQBY9JvteTzOK4ivEPZebg9rhgWF33WScoBzMsD18dRvGJo7hNKQJSnvsmWeTX25KtzQIFw4SuF9X4+SWue+mHewvMIqgJVOKe8oebHH7zC+rhkuwRovqSMbsGy2OTeSJX9jYGFsS84R1OxlGH6UieWE5OH/nKEuwkIu50vkXxY1q+rknNr7vvSwrltsU5bAnC+sBSndfdu970one1do2ag+hEilJOESQn6H4HIk4USI0XCkpHERnENEhomAYIA4wKEVYjj8uUZFaourSuNIWNIlIIXQES3naheHAPffcA/19zDHHuMty2o/OLcnlTTQmSup2bNuwYq1oZWKG0AhkQrbofmkS1TX4DcyYVyRt+BZpopAfsy9GwwYAaUda6x0H/9MT85lLY9qaFqhbSTyjebPzu09QjOzrp9nPoR1Z2Az47J9kyBZc6ezsT5wnvNEYFAAlAGW8wFa564AlIeixcL+AzL/29ZHbbjH8/toH3t/40Y9aDj4/U4IMhRXAtdfSmbMafDxt9ZFR45YeO2340yUHrddkzRmRoAc4Z6yfQ14vPDCSFp9XN2CGPTpau0TIRF8XUq32GKtId2AF91oM7kyxiUGGSkv2XsZztuVJ9JDusAmjB0bLdNddd5166qks16deH5lEjIaYQaSoRA1AYciuvlc3ZYW5/0ByUlsj8FEqkd1yJQPQe0zAcC5Nl7VMGZR+YdXABIb5QbrN8uQvviJmTYy80mWiKNmhIjphFTOxHSHQ+LDfsZZEKFOD6ZJNWVFgjWLRWCwem7awXXoAmsAbe/t59tvOq9x1R+nPfza//4Pwji2VF55pFoqlL16mr1gRPuMMYBh4OQwXhsFZbewzrliZPnZ1daHlpqeunM5dwNI9Z+nWv3LD6QzHi0gaqwFOVfarv/71rxTlJubJ9JOCReJUNflBaZTAeY6KmF2RjkQ86mkrfM9Zrt3eW7lhh9Kb82hlUwdBQgmP5zW/14r4TWKJGaZeqhIW3m3IngFwuGW7DLNaUScw3MgqfRl1ecJY26guTYBXx8AxRHtq547jg6pRBcOuNzUKpDnUV2dUb4vYqzLWw6NKT0YZyKo473B24OkND0xo33QBna2yM22tb7RWNOrs/5TqDA7Bva1VSa09pm5sMe/oNx4Y1gYLwFwLW07AjqrgrchRBnq8oUqocIhCQitVClhE2yicx7GILljrkuYRrSr8OYkxYRkdKyOScImWBXrNTHbS+a++SunpH/7wB3z6cAxmLkByeeMb38gIkKqeZnrGw/dPwxGYg9R4Go7C06rL7KNcbPfAXBEZj0/iibP1u1vDwoeCQqYS25mKjeJ386gyUREajCviM4GW3dSqRO+7vXz5l6K//R3ot9njj49/81uFZMIbjYYgvGWDhgsQbVhjWNnUrlz7iFWsapyjFM554ezeslmJyNc5OGVTdzggES2znYLdzB8dBoO9PMQzyyBEC3D/PgUTUqeGpK/WOPboxtbYsV1LUVB0PWOj00AZBNRT8L34jXCq8RXlwCte8YoPfOADkKeLHQ2nzAP5mL4Xcw91VS9l5cqVWN66Jy6fXCiHXU6MMxirLQ5vl392P9EkQ41xtrnECtkx4nU7Mr2WevmH8IbRw5AbnpyK6pgih7D8PUXJqhO0k50ALNe0VE3tw/xO8zQ2EIlCQ4PaFlExBlgSY0FMHf57MrvcgXyypnK/uiaydXMpFA5fcYWNJmdasvot3WGQ52fp6TvpWYn9Gbu/AAFhpUuqxD0yZNSB+8D0UiB0Od1tq0ywXx4jaCcTamGk/twZSkNEbw3iTqk0qRNjo3Zbq6srqDdEIi2hrECowSrd89S5c7rjvB4CxKpvHjV3ZSV6TbYkUX9FEAWFplqCiykvGuSR3hFBA0xDyD+j39Ib5zkckbJ5zMbTrGCg8jWXJdWVDbjxu6jaM5ow91fp/4IvKkbkD1OBdyW0KdIfskNiIgCCaaQY1rNLoS64yCdiQsSOvMh8LmRwnIUlH1z8h6lC8aabi+eek+jtLcST6teuiL/+dUywyEvqEzn3kLuL1C1JPjM1+8Ztta0ZDw6H48DYsFIsoy2meVVcEMXUGa1vxTT8Pm8F1le3Qn5PwmuRIOLBEMBY3gAdjAxn7srQtaINQ4BYF+3tqfjxvaPbCOa4qNYVr7CFcs9LXTeoWWyLmDhsZdnl2FoXQqBPH/qFTPpi2/NYpKedNBsZDccNrD4v5nRD8T29EInb1MVWqdgNIb2B2Fd4flasYTjhAt7C9gQwBHiZVtFY2oWKBBYGIoQ/rB4Uwsj+cCEe15SQRwNqPuq1UC0mQkpzwGqN4nhsN0d0Avow1NEYh+P0JTfZELYZ/gaPe1bguGfVzjqrtqu38vWrvH/8Q+ADHxSb4Q+9x9QDyQ++d+fQSCabdQsYEsEyu4qNbRS7Cvsqy5UJpde8m/zmQhjQN5heVIgcuAjK+cntLcc0HBpNIjtriRK4uOHX+nTvGSXnofscbrleiNMXjdCMp3T7Tlim3LG78ru7Ug8OjEY6NtaCnpqhjxasgFeJ+S1cm3GgKVYIwAuNI39Et+5oVhlAzKHRqUrMobKyu6D1CZizgVF0ewzIxenzM8n4QSfQX7d5qKx5Izas3wCmA6O9Mm0CdrgzrQ3kcYUQ0G+QsfIw2BV1rKwOZJUVaeOIFths3S8MtntpIV3Z0KJjtbS+FTa49tConiqB/igBn1Bm4PnDSUPqMup9TEvQUKsqDS5b4hiM+no0r4AUs64lubbFA258KjVGCzFQnz6AU3Ut4n+yM0HnnHPOC1/4wve///1vfetbf/KTn1x99dWcI8zFQRa+iHYcTvoEHoHDDPATeHIem6ahFUTri5srbB6erhiHHOheIDsxnh4YSd4/BGQoXh/C/eKH2R43j2xVu61U7vwPqNddFx9P5dettS+8qOm8t6q6b/Cee9q9/nCIwwyrPqIFGg+NKVvHVLyJMK7D/0TK5XDgAxtRERoKrytshENPC0lPSD3N7i+tg/fFqCp6TCG/K6pUywr0GyZSmgZ8YeDYigIrbeuVcbvd2qnnWzzapAyb7Q/HW4L3YvgNnU1dnGegCH7kIx+pHwyPzdgvrlQ6gliUi8OAnDSby50sPiHCoKQRJ7swXSQ4+uij4X6xHcLEC60+1Cf9IjvlLK7iA0oNlUCruFAd8HlAZSwoE+tirEBcH89wligddiLZAEgPppqtETXmtZNBe0WD2tfTAyO3atWqOUukcdVdO62PfRztaO2Vr4psOkooqANvMy3CaF/dMW7vTmPcRYxEWb0oMhGC+8wibr81NVwh4q+FJSkmYaIHkep0xUANpylhvxrz8daoaxrt4nA2T4JZDDCiDThDurPvdorhGeRLH0EdC4jtTWJT8iqB0wvqFhUiZmqJaO0Ruz1srgP4Sno8c5pYYPLW2SZGHAJ8lQfqU4GK6kwK8FVnDC6eLDNzzTnIB/AQ/E+oQyhLDBRdDQArmTXs8lGoBQ6gzKdAFmzkZdZrtYl/+zfvJz8RqxnZpZ2BH/9n8DknOnO4/+lgUlmiMrNsuYr9yIhx004rZfhyBYlqU7Msll884q1Ck3ptH4qoihkQv1+7ZphhPzS6jUc9kTyTfswT1GQAU8f5KmXPgedkL5rPU/3xmhikJ64Axa0Q9Ts3sDco6w6mCZye7usz+yWaXSy8N2ubGv/hEoHZbdvXE7dfnCMnnXQSEzpPN5Hr2bk8fwXpnnAL0ZgeiRAmNx5QVzXiGGzCyI0WlNGCBreDThgNoWMHC8IlBrfCILE0oQSAC87X7AmAo71KpKD2+dSGtJUMqYACZlK2N640KjghyzJ2VjL/yb1c7o7Ed9v2da3wLlsROuFZVnoi85nPKffcHfjWd0Lp8dz110fi0Yarr9TiyZ7enQmB2iCulRp0wgEyQdBFcLlcrBYX5wxvUnfPQbbLGcqmxAw6rG4MjTHjRs0LtHChYVRBFvezrjSuC1B4xU5Z5l8dCv7ep/YGqrvyWlEhdqNG5IuxPH4HgqICRJZgo1QxI0dvgDyMbjtjIMUKs+qG3k2DFJ3TkMZ2J43VTSoSYcdhBd6eBstsO0MmdzQD0QYkBJ8cLmiG1zZ5OuJWz7j1cMomgj1QW6CdoQpGq1+oIsWwR0ra7hyWdMbGZhXr9ymnY+IaMN3K8R1ad8K6f4SYT8aWcW3Ci+ZfsLswiua4QQ2PCQCHFMbzHCSGqaadAB9IWjEEGCpqvWl7QysacA8TKQ096MvtKXKcH//4x3i3AYuFQRz6Hog9V1px0DUcLuDJPQKHGeAn9/wtvPVswVw4RZx//vnbt2+H2QPYaf6Dbf7C2U7HytZDQ+aWlJqpiViPbbkhIHgMmxKGesMfS+e9I55KlYPB3GvPDn32El93t3tOSbGywVmABz46ZoCn/8iosn1ctMdol8JeC4spUjq6K+F+hWoT8g26XL7iNenVxJzm0fzxKWtZk7aj5RW53D1edXOIn0hO1Bl9darpJRlaVDGaK+bqo49cv7yri2o5fsAvwRgGm2cEn+yPaNWwkEFMyMEmDXtiXzTYbSCtPe6447iH0MQ4lmMbchOIC440vJhw8uFiukkP48Qswwq6akzE2Dxx1chuafUyD6br1IU1AZ+w5ZyjB1PUfvOiMd0+av79kd6CvzmV8yHwCPoxQxQITRS/TQGJU5phzcx1iDprSZQO+Y99MjYwlGtfEv30Jyxdjuf91ruvBCxOKJIto+YgwRUrar5ogWcVU8fX+K99ZuxXDZ4eTakUzLbNpdP+nnvdcG0Vwnv+OuDPKh5QkH8Jv4ReXBYlVKZnh8OpUFe9/ZDO3Hd3dzNT9YfTG+M2nRcEUdTuvD6QQetiEplR3LIwjOAGXYGigC+NUmVJ1F7bQgASnszZZR7a4JhsGTH7C9pgVkCV2hPK0hCWdW6ci+k1L+iel45VyqqbXxDD+wjp6aq46anbWZ7AAD/WK2pB3fhHJJLJcP4qhezE+y+OfPe7YMNmXvaSxFVf97R1MEQLbZTMPyvKytbsv/TUtqa8uZoHchZPE00x2qMoZlSjbIUCarlsadg8E7WrCv4zcaqJ3YpJKvpeU2zmI4Jlvt9K2QTgHHCUxfOxTuLvN9djlAAWzt3baQ+NYS0xbgcvTHEHn7WNrhuFlVvsPF2AzWBtP6YMMDAWOKMS6mYRC2OeFjs/URQvoJuKAYRTwhyaF9mtQnZZvDj6+qrX32D+7W/q+ITV2qyddJL/lFMI4Ss+rIqdFLmJDmhWrmzBRw0XwMiElSL2LIZjAhGMNSzW0WgKHTEt7qYCl4VW06epO7MjfrsITpLPAtB+5IGdGUIobVhLwGkW5hw2PtIq9jw8Z30BvbW94eqrgBnM/tcPzev/qvzut4lsrvT28wHC97/rHXanNYFc2FaYO3K5pyfddDdY1gmSOKLLHnvssUzZ8573PFffu7/R2ufvyNl37GBrl4sqQN7iEy7axVlkexxPpaBdKvniM9prp7UX7u3P3tSvj9qNRTnBRYAKRFYANjgIggP6c7VUEeW5szcIseQWi5YYW2gUtjj2j5cVzJEwBVqdsFc3EU/e4tiRdNMu5vG5z30uQCGYbGDo3rm0c8PGDSGfb0MrPkT2lnGi6Jl9E8pgUZ0oos6FH1YyOAbjsF3QdmeVNY3GEU0o/Jk4aQH/qKA1rD6/y7M2ad05ZN67mxnUJjyaRMxCfQ1GmaguREAs8PIegANUTKKqRbyObWC0aPxw3m5SWpNauIFA0O4im9bgA76lpyC5oOSH7v3Qhz4E2ss3v/lNhBfzH0kHXN3hjE+WETgMgvVkmamDaie7LWzSxz72MURfyMOuvfbas88+25WBLXyTgX6aaoSI9B5Nmbf32lsm9LIhCDs+r7E6oTxrmbJqbEv5wnf6vvglP4zZ8cd7rroq9qEP6g2JwZHhibHxdCYNHn/FsB8dyP9tc+b+8dA9Qx5wCDkLQV8I+IBXEY1EzXTs+twtW3Z44eWE9dVBYlD8XoXQgoonFrALG+N/00LlhmdZhhfZouVrN6Kn55vePqElyqRIa29ZuenCluZm9mcOb4AB8QO5/fbbYRRhfT/xiU9897vfZVuEHqJ895rq4xPu/6kGOofN1Bd2cBpP1zjCmWXAHiGA6JpL9mE3y0OMuOCKIb/oEuccMYFx1oW1gGBiVTAUnO6UR8oZfebhjCfzfP3MZz5DsS94wQvOOuusRWWcp8y9f3IbaPdOlP9059btE7XRnK6FYvgXIeFuj2OZKYCiq5OmxzbTmQypkdlDW1AIo+Q2CfAsmMHcz34a+MK/QhEoV1/lP/FknWW2+BY7r8PkiPVMmDsyIFvi/YtRgtaqPvi61rc/O/KtBn1bSB0PKpmo3r/Sd/MR0d8WrJbh6noE4cBfMeQw881BfWncjhjj4dJQeny0WCrVqlXIZTT5tByr8rvvvhvvdOQX8JBzNlNagndZ0dqcUnam7NEyrsVCZiEuYv7oMP+HfJgU2sublPVNKsi98krtXRY9cSZb8tEdCJedE5ahqK1he2nC3tRGyA5KI88iloQ7fUhnULsRJor27z2hk9/ceWXxsIwxKaxrV/iZVWOAc0AAAEAASURBVMokQoNKxYuves7q/rEPsdpArQ2/BOU9f0sccDSZR3tsOP2KV4d/fY3l8VTf957Gq6/W4g4wr4yIMx37nhMZW3deFQWknN8+VNmZDYw5npmQoRG/3Rrx4PCLOTPEK+hE/oAAPvM+sCMC9rusUWv0W8sT5voWT8LnLigpbp65kHlSVfRpzKbL8vF1/p4+pr+y6tghuXinOPIYee5dtnxRDXNXqdtU957srG0881mlbLkUzvPZF1l4SBpGA1mk+7Xe5UW1oZ5rzhuEm6guOd8PYZlURGlcdAEEPmyD4eUYQ3ZUqAFxUejtLV55pf/aa5XeXk8uF0iNaXfcXUqN6evWapw4klX4Lv4FPGIgvTSut0eUxhBBGZRoQKISBj1KwKuKZSxWKixXx/OJJYNrbR4SolTNG2q2VM4bvqzhJahb3saaBj9UGEKHhXVDRDjtpJHSYGpjxXErKkdTP+LI0KtfaR577FBHh+e662KPPNT3m98Y/3NtMeAtTIy3H7mJKmWKTKtjafuKFSuR2qDep2dMGbpfDkdOT/cEkd5MXXNOwb4ekp2p55WnhRy4bObcI5fx+X00uVwp79ixPZ/OFitFjN7KuWxMyx7VYq9fFsmWjBKwnorJG4qdE/pzGhv02vwVhSpfGDUZMfknu4EcBiJEAGYMbzJCB6dKNlY8mB8HfYJi5bTQmTonL0PU2NjQ0tqSnciMjA737e7v7Fzq0T1MCtF6iVMV8lsAWEDhIfpCzluqimtMiRmRoMQwxmJ2BIqVSHKlaBl/ztV4AP0zymQMngmchnYfCy2WkGBAQuY5KTXDFgktKn0LeApTLdBm4jBRuBbKVmVTivjxDyft5A42udc5HVjshztpvPgvfvGLgZD86U9/CvwNLywhP92ZXWyBh9M/NUZgborkqdG3w71wzwPGgbi+uEDAI33qU5/64Ac/ODeIzv7Hi82ILQvYRvu+IXtbSs0bU2BXIWNDi7I6XNJ+//vaO96RyGeL/oDxb1+Nv/YsPekYC6FWqtRK5SLZx0ueXUOVXQV1x4SZ81sFVex8ACGMeizkjniYhH3KlbdU86YTn8U52Dg+2T/xOSPAujDAYr7Jxqj9ZuT8Tv8DJzX/Sm3IL3lLgSccBLYGFjQ2QR4z+prmJZ9W1PDOXbuQ/IFxjViXMeF4I+IxIoDpitD99/6JmgJCEwaDzR14JPhemsnJOl3VUF8G/MRZDkUIqQQ5CLXET6hreMhPcCAoEOA3pl+ulGR2150J2PMYRhqxAt8Rlu95ekjvHNrGypdrv7vl4aGc4gstLauE3rUDXp3gLFGfhm7TW57o2zYAEeDWDKvPDRQhfeSGzso1Omp/8pNey8ic/bqGs86W0/gAj1ZyyjVesneltX4BnYKAU8J2z+vb39rquVdsIoqhwjavXVH93YavqZhU+l7V8J6yGdpSeVnNFsUzUUCS4dqSsN4dwfIBFzJP1VmiruQCIszVq2O45a5Vt8aZn7aSN4h7pPSDPl0whgdT8WSz7iXaNTyUqnnE84qKljWoyyMW6j6hBmYW4RIlPFVHitbOjEaYioqlJ7zmkri2sUklwMZcmWaVMvWAYZ66lWF37+s3s4viJ9fOENvU6b92dXWxsOtFPZVu6PL0ns7umrACll269W+lc94a27WzHArb7KhvPFfCatVzzp7IOQqy0HqB8NybQ4IYGBo1mVls/1kJpK0Q3Mivl8q4+2oIFStVM+TTQx4TYBtcMZuDBrA34Und/55qZ1cy4wl+EOwnmNvw9s346fH8Wh+n6ZXO+bC+OKen5Lxgn+QJN+yxLFG4FxzUAednY2SPZe8FTcNlb+CU+Inn3MAY8xA2m2LZbGF+eKNhgNl7+ZUNlnXu8sNY4vKVfZvnWNiSYM7m0YZ9PacKNgoS8Eka956vh5ayp2TwF3HRhy1kqyc8kq57rEKuesOfgzffRj9F0EgjaYhmBW+4sbbpSPVl/6IBvStP3WUqHlJY4iYCGthOKxo1uDvAnwdxNwU1sKTwlyhKWEejE4aFszQ7kMQRFAbPGt35iBFqUEIthl+tpu2hCnJDpTlSI2IcfFrIx4JmnxFOmyOMwec8KhQQ/BaYEfaQTUdtCj7/tFJL22BLc9vISPuPfhodGfRdckk+FCsvW2bHY4mjnvGMZz4D3THMm3sRA4KpQVWLgANmdfp5OpliMf+xiXExU9i5IBtCGujOJk94zQP+0NGbjqID6dTYQO+udZuOFtcuZ0s+vk25s7f4fzs9fQVRp/t1onOrQ1mjwVNpxlla96Q9PoJOwURy2lCCFCtcsIRQgvVFbQuc1VhJ2Z23d6bNtYJiReQhEpFc6uYCoiqqKs/etGHbjp6apnm9foEUFSmCQpTm5pC3O25uHkGCUx4yKuMVc8z0jZUi+aoHbhzARUqWmEatSleSDYTaJ+ca3+YjGrXOoHlkkojBlfvGvOCiZZnfCty4gu00LLFpmWBi6UBUeOGC1XxJrTgA1BlUwQWlN2Md1WJ2NXgceyXhrd1BW8zA75XWzX766aczpyg/uKAJcYVz7dgPsvC9ajr85UkyAocZ4CfJRC2+me72xiGAzwOhfcDBIzAd2zrv+aJedZExTlGwYKVsSxkPDIHdJ5D9bHVB3VzZIGBX8QfuqHzu85Hr/qR79PHjn5X4xjd8R25A8FdveOeyTiydtqaMOx58KKW3DKtNYyG1xI6rgYNvt4TttU0W6qkjW+wlcSLmGZffzKnuaO4AtVLhkGF1hPVlybI7I5IGPTIaSPxu7DJP8BnHhL/pUwcw52P3B2bIVjuUxgu9Def39U38679eDMwVBw8DAiME/w/uF6eRVPykvSbPLqf9sK/42qGCgMaqz2z9hiTT72H+3U6jGuUkhjirs7h8hdpDLVwfFX5yY+ixijDEhURzLyqCUKsn4wYtJWm4QZ0+/fmhuHcXnxyrxWL5N3+9bwzFb2Sp4W3QTa2KhN8w2xMEKrRjXuvIjoRmSGBnlGwMkUt8M9c8cUeMz8yHL47t6s02NEUuvQQ9rHNgT67vxbcW9CAxfu7L6hNVrQhtopkvaPxam34vazR/Z9PY/4vpo7RHNwOl0IuzzWenvN70ixs+d/XACYrW5tOJ+mgvi3lWNFmd0ZjVkkCehHsCJGxXVxdNdUcV83Uo4+mT6LbT7RH3eLo/QqjeggodOZbOFzOjOLhhQ8HLI0STqTSALx2z2kL26gYhFHl79oypU9ZUUQKmAvAVnsy5mh7STVx/1yRswnUufmRkwCFCyci6ovHcw0XwtW5ROb1MVNwsRdbwdD6fXJCe05Mt5H6qL3st+4VkfHzSwAuxC/Ee7au6evtZDLlrfmm/8dx4pZRt7Qj85D8Dzz1RVuwCrvqmzUSjVNk8Wp2oeAZz1liRXQ9xhtEQ9gJ2BdPL1IrHb0CvoVbzajGPQliU5qge8RgrGzXIX9HBLP6CYYB/YDNh43XVnosv4xDn6O7udkusj3C9AlYm6xMTX+yZERMTzZVP3kQ2PXfdksXNxSfL0r2vZz+AmxmvM195XVntCA5gjHkREGLypvAJdc6Cwa4HPnnOith4iTdb/wninnsSu2R9/flB3tBCjgP8btiU2F2hJdCeebK52k03eWoCNs+YYIFVM8F6xvTKtG6+STntDOynZ9TrdpxR5PxoCHoEsgE/4ZI15HDCqaKNnzAeoagZYYbLVatGvDtQPxCgW7gTC2xSSlMGs2ZQM4NKOeypBM1cRCmcsGHp2s4m6ITNmx/J59l2AEBC6RpweVeXLVt9xDpt/RHY+xgXXGAPDU+8/TxvPlN76ctoTO1jH1ZWrom88lV4WNUbzCywjDkTmYjpMz5j7urpF3gzKzsryur9xCf8//PfWdPO1arDAZhHAtaB4innFj4zHW85r+fVF/xpuzZQ8Go1NaTZr//3d63Zfmvv6uP/+z3f8gVBqrMJnyG6VfkzpTO17aoBHgTAY4ywmipqAzlle9pc16R2JyX8e3nz5tLXrzav+0Owfzd1tXkD1RNPLH764+Hjjt+yZeuStiWAgwGhuMTK+a6+VP3pL/zpcUC4CuGG209943+/6rOE+mM6EPsClDVQUFY1EHpQRawmMQeYAKyt0qnMi16+dGQ09rJXrXznp28bsHvwdPMQMVhkHJhuPe+B37zg5h/mok2/eOlHR5qXI9kD5QuBMoGOseKeKCujeXV1xtzYoi6Jimxi71lgvA9kb2L8mVY0wC9/+cvPO+88ljFIqBdddBHLe9bULHBKDyd7so7AXlTsk7UTh9u9jxEAv/Ftb3sbDh7IuvB0hW/ZR8J5HwuxLDtaqmQ9MGhvSWmFmih+QbdtiZgb2pR1Cbv6ve/UPn9pbHC4kEiqn7u0+c1vxo1sajeR/MiFBzLmg6PKljH17gE7C3x+SMeJBbiLhN9c0YDiVwNWYXUThJdQ52dv8t+9u/jnXSECqWN7SbANrHckPCC/CZCguE3GgfwNKcvjHaGmD5iJ1918w+Vh32AkHOlce2YoeVKuEPzyp7789a9/HZtDWgLjxzb30pe+FHZoqmHz9voJ/yPqCKzRYH2hnPaF9jS7E/W+s91zQWDV02A+DbQpBCs0n3vBibliAsaQVVTPy0MYFU4OyAvOJOjFP/7xj5QDw4n9WL3AQ3sDfXX73x/qTRueeEcg3DowIcchXmbg90S8Cr5hnQktThhERRY5mhl+hZR028C9SNpVmzgZgZ/8DN5Q//xnCX/BanLonQM5R92XYttYbaBISEOMwQR0KqYPbApfw7lc2Rkd+2qjpwgyuZDLnmK48otgOmEkXzze7Hn4iMhf7im8psFvL01qHRGrw9HFTdntSZPJgvYMbQb2wPMSshiUadtTprj+5uzxgqVq2JRhZSf40vQOI7NkBDbGXhJW17aKoZpLNMzuMMODX8PDQ3gya6OgWGvSts6o1Z5wAja547jgT5YKEhbYiXoOeAnukZu4UF715+4NzNKJJ544j/prRvr5v7paO8qcP9k/5Fd0fVz7rJo5Y9ZYrKaRv+rf1Y9/IlgtT5z5T4lvfUNvX+qYtEuC2dMnT6dfbjkK2ITWjnFirnr70hZCDWL8EiQM44NqFWBnvhqEOIJtMJyvaNKWxlU8gtujgF15CNnFPMqbQ42Tip3pdezznsRcbE2oQA/w0Nln2Qf+A9sdmekOywM1Jowc5hU4QO7YsQN3A8xh2Ppml05H2O7oBSwlHpuQzvDzCKTYAOGLYD6RVfEQCED2TDR7rnSDDZO+c1EpCkmy8C7wkHLIxb6NPpndiSdkRBpCObQBeYHr+DCjGZRDRrZWUrKqkWJTKSWD0sRaoiUu7iAV0QvuyU6WGYUckq+8objFwmP37+7v393XRTzzkZFaDVBnmXFMTvjLAY0xgZKaUOX53GvVtYumSaxkhNrNYbsprK9tIhSwNZizh/MQG+pYwRwvGBjZZjDN9wbKNcvPawF+YcXqm5iAJfZpRkAzgpoa8wRqO5VR0+iI2p1r18f8nmgoHMDQ3ZHS0jCpSFV8fOUW+qG9A+Foy80325XKyGvOCmzbHPj0JejuCz09Vjwee/tbFccBi/OC85FpYs1woeoHCpupP5iRRFo9Y1/CYg3Dusixx2IMDQK1mS+EIaKkyZZZKfn+43ueamXcq562MnjcMuumnuqNO0zvzv4T/n5NvJxpnBj61bb3F5dvjARQDwAqBuiJgENPyjilELqrFgy8G2Ct1XSFERaE55Xj5toWJfHH6/zf+0554yblZS9VPH7zwQdi1/1f5fZbh399zaPj6e1bt61bt76jKZk+66z4jTcWnnuSdvwxFiEaH3xo6fDDy+P2oOCZaejwixLPGcRv8LHsdY3GmmY1QWgsyxz/1Kea7rrDZ5pabmTFCh0y767d1t1D9mBWS3u0wPjw2dde8ssXXdwx1vO6n118+fk/rlqq3yN+FiVTqQo+H4GIbQ4jhCPrmwygK6KiYmZNMWLsSs4gHehksCqIFYxFA8aAoOHgFfjtb38bB5wDLe9wviflCBxmgJ+U0zZ/o+FbEGnj7/qFL3yBUxMsB/CB588y/69VGyrKhvvtzxOySJRIQfQDDcozWu3EaF/pnAuDN1zPaZH7pzOj//Y1T/fKSYrJIZ64z9XsraPmQ2PaQ6P2jpQ9ZDUSs8CvKkmfBbgoIeA2NtpYUDeFXfsjNnANq57T1wTuHQLWwYPvENCSAmKIWbNGTrHnRJ3VGVNWN1prmzWAdu+5Y8fd21YsW3ri8445TQ9HrrzqG1/+8uUDA7vZ5rq6uj7zmc/gmOpqAufv6RP/V05JzPCwwYNBghyBJtuXZuAA+kKB0GdcM/JC2MGfQKVxQefxiVrPJShpDKTkz372MxoGyXv99ddD7XV3d7u2rNCaTAFPZvA2LlEyo5Z9fHWOcflNS3ata7TyaW/rzjHTq+pVDkNit0Q9GD8nvTZuwPsgtySzmHzlstWLPxY1a+nTzmg451we1ouWFIu+1KGC2ZvXd2dswqhCJVcts937YEhJcUanr4t4SgGxyRfhDxWxej25/403vjBj+mpdnpv6Qq8GtrotYK5qYlFPMhi8LHC8jCRZoNEhbTF+puR9NQ0iYDBnigF2iiDM/VhYWEZZs63c+CCyC49Hb1+2tNln41mwtgnlntPdufoMKYFvw5Yxa7DkGcygSFDbY/DMtTVNAHbSmH02YF8N4zkzjkiFG1S7rFVkNPO8gKwH1/V3ngIX/hOMBHvgDEJz4dn/USmZd5otr4ZRHX/v+yL/8R2mcuKfXtry8x/jmytErfNngc2rWNZ2JhQc7xKxrATWW3CtAmqpLJpe9uZKxcLauUqkX58n7AXgym4NaVG/uaYJC9U9et/FvKp7Ne0QTuhe5S7mC+NJcl4lQAFhYG666SYYTuTCnI/uW8avbgfhGNlI4U9YqN3O9gUryz0yQZ6TGF5onjcREwbOWZhPVLWzG0gzYBo5idhIZ/zqzjif8K5w4zCxKJ9d9hgREluri5/ktt/tDsS6WwjtcY1czjjjDOoFL4A3jjbQVN4+t4MHPH0z2ln/SqWQE5wRHe1L7fGUr6M9ODLKplKqlBhDH1IGNgxVqTY3KV5fPdfsG7d5fMI/c5qUOFlKRUzGjunqKtase7fuHu3bpRoBrxkI4fEb9uI95YP6ADpfVLuwvdQmuCBgJSsBbbeh5AYljG1zqLlDV9oJCCHQDs5L49S9Zxz+P3vnAWdHVTb8KbfXvXdrdlN2U0ghCSC9SlFfQRAE1FeKUlQQXxGlKCoiFrB/0qSIIF1QRGkWVJoIRBMgvZdN2V5urzPz/c+c3cklu0l2NyGgvwxkdu6ZM6ef5zz9kQAQKtHnxdK9/uk/mJl8/1ev0uFZXPstHBCnnniiNGvf6uu/pwKYBewV0Jj5Re4NqxdOOmTSDlbC0J5WpgDSuSpTGCuEvfEzz1TOPCOM1rF8hyUv/p+vvjpYLPYd976Gz1/M3o+4tZP28RzZbK298tehQnrxnONnLn7xiFd/80Dj7HTRjPoxqNaQE6BFjI4xWJRc2gJ8C9TJdjZWUlN54X6sO6VsTFszxh0w8c/PTzjyQKTl2MqAWPRcdXXsxv9XeOiRg7/+tYWLFi9dvMh67LdNzz+XuPKK+uu+TXA/5PF61vRuTO6b00JJFJUtESeJQFYpEeYKaTBerFqT1r415ab5f/fedU/f+Z+K330PTQHkTIkLQe4+NcZrm4wlHYY7kTZ196I5/7Opc+VBrz8pDnNsjEtiPu34BYL6LdDgovCI1pWxNibNOQ3WpBiC2tHAwbeM9Vt+sCRgXd1yyy1SFAxn50c/+tEFF1xgL6zhjsm3fL33x3/DCOwlgP8bZtHpA8cJF2c8Fr8wt4h+du2110oe/NYDwMm93QcbAQfIUJYiYvwu7CDGr45SigZCr5oNQRN6dXq4WLrjttLNt4Q3tuZra4sXXBD71rWWy4vEi0yAXdA5TCI3JowlndbSbnV5F5xdPV1WPLEJuHCo8RtTa5U5tcrsOouQpDh2hu4FUKNGuqbXWNplre5TJlXrvRnB6aM00EJUoGM+/BkSk0PZJ2Yh0WoMuja1rnttyVJqOuzgg2bPmfPU009973vfW75sBfWDASP6ZiiknHM0I7DdoXlnXzAhEJ/MLOcxnUIhbQe6lKNt6vbGh0qhdUHdhmJvVMFXYAPY+/H8oQ99iJ+INcAvZe1Y4UqZBiVA/4C4gBaDQ1Am8haykciryqqdZ7H6BKNXoUDcchhuX285aIUC/X1oT+FCQ/gFqQ7DFTar/VpLtUoIU+fUkmIQ2sDn8o5tXN8NPwi++XrOHwh9/3oVUmDgpZ1jFDfaRalqtmyt7lIx/U0UdOIwYTaFgZZX7WbtY99U2iKoX+hK4VDINtAK4uUl4zKL+HspzqzpFVqCija9FgOnt9TNoqV8BgEBO7wGkG9nQJx87Cx7UwiFwFU91oaE2oMzVZj/pFObIKCKmksYIAohc1xrieLIl7ERiMM2lyhK7FZrYwI/q9qGfjyWuGqCRmMQnpTHbcsAt/lkhD9pNtNNZrkY5OwP/ZbOwiWRnJehPR2af3sp9qRsfUlRlSm7UvLWQnfHE/QMmDRuhJAsyVbJJcpSzRXyy5cswV176WtfD/3qVzhpy37+0pobrlc8Qod/yNRt25qBpc7iYcsUhVZ8b1FvSxhoKoJWkuhCRlSwglLwizKAG++7VsDnxvkCoaFDbmNCFG/k5Nq25LH9pncQbPSXyQXRHFsho/pKzjh3lhxawSitwCj805/+BOkLMVm5BliZkKOgvMj00LCAjYi+K1tv2HZWfjhse6iRb7H+kAcNeSo/4S0/qQiisTJdFgW2zQMgFNqba9jyMWQFkAINUNKGNsYTFSl0CiVtnmFBEtSAkmVFHPdcxD6lRzQJ0yeKhehyqnYehq1rp4l8TmttUxrLDEe0ow4vvb6I2IcCJtrnPn/K+Dw65hgrEoYEBViyDJgR6HNB6OZyDLI8SpDAQ1IyNQJO28s3GApObm4OutV96vzmBLfl9eXVYH85mFF8CaJ2lV3pkgZd5NaDMOzslY7LTOH6GGNXP0F6shq+tTYkrDq/VR/GrkStj2iEF7ZB/QD0c4CgWOa8cHm0qDv285/jnam/5Vv66hWex590Pfdcct4rxlHHVH31K1ogYOoa84vzpPkLFrCuOHzhS0r+76gGc2hmkSIcNYhRH2gY4yWaqyZ+/WDo5puSk1uq7rlT8Qrtd5IZ5ahSGv/XR9NVscyPbu39xHsPnff7v33kK71u3GUp6byBj+igT/GY+FVWi8I3on0ESgamRfwkC8iQx9cUoX3zamfTkZMi1pR2xLZq1A/rTfOe9dHCzTe53nyjrqEuHjtm5asvRf74x0x9g/bpz4jQw5wtlhLxu2ZPr65NGcu6rFX91pZ+TG/UnpyIlpRBzpzHllvNdiTiX77S2u890c9dot79KweiMLMHjHM1VRnTqo03wvWlpvHfuOU0vVD88+HnpEwRVAmeBs0um/iIZpkJf+ClvEWoNqIc99put6bVFGfXuWoCLDXQRbFoho6qGM2RXfJbNCngNH3pS1+65JJLnnrqKRxEw/zalWJHVvneXO/8COwlgN/5OdhdLeBA4KS54447cPhE5Nhnn32WCH5jYFUKoGIDTqDt+oT5ZpvVmtDwvA+08bjKU6uUuQ1qfbq9/+jTwksWwpNNfPyjgcu+VHXQwTAROfAEoLalXrDuQMKWdilLutV1vXAHNcx44MtWuc3xMXNOnTq7zpxRo4chmcUQCCq3PWUu6lRW92ibEzjjwY2hAG+0hqjrBBWsCSqNQbO5imBxanNMxYqXZqK8pmv6AfvPRcfspJNOIs4T48DhdNlll11++eWc/QCyPQ/LaIOc1t1YNWUiJUB8AemIfJVwFGCWu7F82eCh9x1UQZNA4NDU5QHkDx/j/AQh4xP5FUgYJJzEe2g2wkCZTi0SmeMnvZDUEXc6RQkSb/b5vOBtyf7Mq6++5va5qqcfhR1jv4hDWFRQLcSJhmXU+/RxERyiWDXEwBJLZeCS2Ak/nKTCkiWem29BpGZc9ZUQkTxEZufl4Gcj+CvQE+xqLXNllxCv9eK+JWsgi4BsKJTVvBVlB0ABu2tBMwSZKjyyaUJj34PBcjivuvFOooazxgFV3Vp1nVvE19y2GWC6ElceFh2328hmNHAKuqQT5WfaQHRNpXriFLZQLpnsa1tf3dTi0t21wvRXHR804JrzwdCKBopSTISEa3uULX1msewKITEOazNqEbrQFRq3bfNGMEjDZKF2FkllG/gp80GlQAPjm0QSzMN8PLIkaC3KIS/rjcIRO/MMnTkGQ+KRVTiWXHBzli9fDolCwwa+HxwHAWB7epJf/FLtq/8swfH58U+qPneRkHEJKLiTWRBDKcoBm9Y2p401PTAu9dakgblKpmiggFHAWt5U/To0cNnndZWKho7FL16gA2ZdmAcDHQG8XolrLN0a/hum46WXXgJYQTDs1oK3VuesIh6gBv/yl7/88Y9/hEXI7ANGZD5eAZ2am5tx44RQF3nplClTmAJAjWzVLrZNfi51ZakRKCeVaJzCeZCx3Le2e8jTDtoAYGQAnZ7yqXyGkwKwhYzEySVAAyReGjDDJMXPLdl+8pOfAEJRkz7iiCMIeEMb8OkAz9QBj7IVO6jaaWZl7YOJKHL5tCOOyr65yPfiP1g2uPjFC0dZcZVO/Z/gYUfqfj9u+F588QXaA5VLCbKilpYWSQDTNhrDrueSDxwWFA7UrK2p4X+b4hNRf/oy5pa0iQkrRsL44CTmLRbCKXxlofMiUA5IJmgkEXK2T1d6vGqnV9mYUtf71fqQ0Ri2xkeUKj/ogmg4N5ohWyLqkvuGVa8rVXiFMIzUQw8rSxd7/9+N6r8WpO66u3DqyfHv34A3r6p4/Jijj0aSzzJ76R//OOTgg+Usi0J34ZKu1WUBYpDtBuXmz9f+79Kc1+O77z534wQhyBbbEvTKTL7wUnjVmtT55x553JSuj53ZdNddc1b+fd7cUzgOzbLekzOq07lqn1mj62BPMLzgLghv0aJkcQjQ3aLu6lM8OaysUV3OqW1pc3PCnFZjTY7rpWWrfZaRnDqZQ8vj9000NW8i2X7AAelVq5Snn1aLRSUUDn3oJF9jIydLbUib2Gcs7bTW9SttabU7LQx3u7JWoWge++gPPBs3/vuaP8b7fXj3sv+jZwPjXu/X4i1aS0xbftNdbS+92apEF9bMCZbUnK2/jfiXrByfCOAx5OHbfMnqMMAG1VSOCH96e8pCakJsJ7zAiGnb5YtmcUYQEIRQSdDA7LVbb7319NNPlwU7S2WX69lbwLtuBPYSwO+6KRltg5yTCUUvbPqffvrpCy644Mc//jFH/mi3rlMUbUDAtbgD4S28PTAwoU1E6AIozxmBnPWbR5JXfjWS7E9Hq7TvfLvqvE9q/iCHm2D32oC6bGmtifLSDmVpj4ostyODNwsBvwnoVxsqz6rVZtUqc3F2haKWDdc5kzIlc3mngaB4PeHXM8IhJL5bECC7NELAq3G/1QAeX6XMqFGm16h4JU2nUp6ICI6CSSGuGvFr9cwzz4DxQPDj3hndb2SAo+3+aEd+B/lBwUH+kPPsehucSeEBjAfeP5rGIHCUvL3C5Sfcx8D+2EGntvfqiSeeoCVwWyRCCUHr5GxububZaQ/oms3vF+9Zn0SDgCpmrLiDwFEI8gpegUG++OKLLCUYKmZ73ChEUs35RG++PR/ktC6WcIhSwm14S1yv8ppVXnVKDWtvoBZR9NCrXMpcfkUkl+7bb/+aK76EwEWgQUOz7TBF9EIsV4FGbU4auOUUCFkOFgCMGBztClKlrTizoBCssTd2Wn9hajHf7sm97o1uCfvAAZKJ8v/0Wx6QEa3vVwvd1s3B885Rp+9j2Rpx9F2OEtwEZDusakeIvbVRA40WDSC20aqeUlsaz0bC9FfEihRqbxZcdNoIrRMLaw0hqyGMRZbAIoZ2V7LPeYGF2PIuZVNa6S9CE1nssskxoybAJNqbc2v1Y3yCjEdncuhShA4EgwTzgK0DQgxBMsYKBj9DygSqzS+5xuTzblSRGKxnR3/lJJJDTMMOL7A8JzMKoCDy+dUrE5+7JL5hQz/yuh/8sPrii9gAtjfWHRXkFMLKZBGKVZHSMUrH+jdnaOWS6Xe7csWyz+PCYxw4MV7Ti8UyobMjuHqO6pjQT6gy0MGxbcZ31ugdNWSYd9CEjD9Qi6mB6NrpmAxTxHBJFV1WUHeHAgT444YAHqgDXiCugJNoCEPuYuzHM3DJoXiHK3X3pLGqkTxDbUpbht3VZRpXWZR8hr1IOlAUxJ0HhgUoCvBESslQ0BIgM+MDSPmNffEVwwJggQuAOwxE32jzIuyqHM8djALQG0CNIJeLh1K5zOdqY5P/4s8X9t+v9PxL2ANrDXXeE47zHnSIVV8DdSJjzzpULnscWtdh/WCfJa0ktumd/ClaZSv1eFXgmAabZp8aC+IHZwedGQLOWdgJp/OC7s2WFDTa2VAgDERAJ9YEbrSCWaXLq2xGqwW96KDVFDJQHKsL63iJcPprw0V7LG3QKnwf6+7oJz9plXKpOXOsNeu0G673P/VM6vE/lA89LHrHzb66cbi/XrJ06fq1a3EzAfiSRVVOzQ4GcAevnCbRuOKWzbmzzwlm07k77wwecjAng/2hFOMqxQceAp57//csVVf8Z33CuuvOk9581HvS+xZ0etKqibT7vJ9/Zr9Ff7ORMfusskG/LIJywNP+/IEvPHb617PE482r6ZKF3nJ/DgpW6W3tmf2Tn+KWNHDOpwyhx2dYq9fgjzTc2R0+62w3MErT3YV89hvRwu13VJ1+Om60plXr8BcIF7y8W23tV9vQiM6oU9988aBnfvHSiRe9FDtw1qbVc4jLXCJykkUogcFuCn7xlJirfm7Vysaji5vMrg7FncVHtJoS4YKFg2j+RxnJKInIlyJEloULNAUbHyKZ02DI7E0pY249oR9gLzudE10V/43+kjOILjRy/osvvhiLufPPPx/+EYcXbd71+R19i/Z+sSdGYCuquidq21vH2zMCEH6wvS+66CKOqEcffRTe1S7sWKETSbR6BL+rehQCZrD7vaoxMWa+p16t7dqQ+dJXQ4/9nvCy/UceFf3FnZ6pUwWZYgNZcUYLi19jGTrPXSqaz+v61P6iiHLkdSlVHrM5Zs2u1+bUWtNribQhPgJ0IUxD/XJxh7WqVwMLB4DiUAHbX17j3KjKRyRSk4By0+MK1sJ1fvic5rKlS9H+QgEYIgrFlQceeIDzGAwbVRasOCCidqH7u2eGIOpQ7sJhxlC8fwwVML/0iAt0AXxFss93XA5ID5pawxqk7fjD0b5FkIvUhUPiE5/4BC0c9nOZzp3RcIQPiCO4ZH4+B0XmksMFqgqFj+50Ipksbqwul3xt1W3+pNFHCK4MMX7Thln2qeWedMoTKPg6M+MKNTNs79Zge5A9CEzA80C2KEeQEHh+/tlPA889V3B5A9d/z/KNyamxaKikGdEiM9f0EnPI7LOVGmi8iJGLgbrZe2jsYZeSA8XyNmb4P8oXn/AX56dyd+rl2ZmqsyBAFLM3oL2qhfJ/z+US/q9cpU9sdsaNZbNw4UIGYcf2kygVbhGmVnprQgSiYK+YhGiUSIOm+8OxqpCr2m81hdUZtbpfkMRD54WWsfksHNGt6MDxld6VFiM1IW41hqyJVaBYw34lp2t0dwqC8Bj6DePGwmb98ICC6NAMo0qhFqFCWltLadKTNtaboypht2SmdsoZbsCHKX4AcbOsVStWKi+9mLruO76+vjU11bHvfqd04MFx9otg7Oy0NIphfk0s8VZ0mt0FF9yZnqyORxymHV3UXMlE5JsvEOhIN7AcsHCer8PNbAxD/Zan1+gxH5ru/AcmOdCiYdo6piRWMuJfiDFoYOi0MZUxzEe0EykcYB+6l5A8AH8yMeZUB9w49thjUQUiJhw1jnAihqljrEkAZ/qLnBD1YwfWjbWwkX4nu8kdfJ2LM+L444/nY6I/MFCcBU8++SQPqEdBnHPRQoaO4WIkOUbZfdB1J554Ig9sSUniyjs0XnNzM0VJlXLyO0PKw7SpUz3BQH5ioxY5MXDMsURfEHZKeDj3B/BAIGCIoh533HHb64ZT1LAZBt/K5Q25Z4U86tRqtTlu9WbLbSlFuMvKqr1ZRXiNLgjyCcfCQjUakacpsAgEwj1uSGU0y9SNfqu2n7g+xqSI1hBR8b7JopdFU7tdFzBqYP2rbn/0nHNR3c6ecoqxpdU861z/iy8U9pmV+tBJnnPPnX7YYY3jGqriwpxb1LbzHTps/96SKMEx7unMQinxmc9G1q9LXvrF2k99SsAAmwJmh1NVubNDe/LJbHNL7LDDmYrAge/pmrZP7fNPXHDbj4+aHPvTCnNZr2vRkad3TD0E4rFIUAK0jbD1gbtmqnmCDgm1YWXtlAOxziFoNSp+eVMplXH+rxZS+cPvvCqyZHHr6Z8MH3Q0KsaYBZfa22llZMmS/o+c7PvZz9r6E7233Dr13ntLn/9c8YjDXYSDVjTkE+8Zp40PG8u6rdX429vSd9KDX+mqn/L7k65KpdVASoCwvpT6xlpjVr3ZGMHlnhxkAdlCbu2AcVpTtDw1bs3bXFrd68IJFurZuIAhSjkMZZDEgh06GE9p8AKxj+vOKhh0JAvCtUFXWplRV55VowWFcyx5Df4d+DmKP4wzFyyzxx9/XOpR4jLgnnvuYWuMopS9Wf+jRmAvAfwfNV1DGgv8BePHkd1tt93GmYcjOxCOIblGnoAnWGVtT/n1dhRjCM8GWLFi3vKMGmtOtWnd/8vCj34c3dCai0TNb3695vwLlWhEHhkADsAp+bcky4s6rMXdGoHj2jMarFngXNQtDHJmoPNcq+xXBxcWilnCc2FgvLTTXNatbexD8EsIBK1QEp4eCc+BNQuM24aQORmyuQ6dZ4RbZj6b+/eCBX29vRA5kPo///nPOc6pHYkZ7DpGgOeR93b35pRnoSyTZ9kSJ3FsDeNzae4FZi+lvjsmjZweOfU6KW/TA8MO2wU53qmnnrorVYAsOvgiEgOEGxs3bnCVfemOKsXw7zOzaSUS196yrrk8Ab9eLjUG89Uub7WaiuL8ctDkGGoKb8MDI49rEU33+vxH+X2ua7/DOmw7+cPFCS2e1rVet5/1w0VF0GZDx2o7kyUWLRTj8i5hLosyNu5JwaM0l1IuqS41eWb95fv7f43dryGiVrMABHJkuQvuQ4r6vgRXxRAT9NKTuT8ayYYIEOb79+vl+Qv0+gZFCD/JbSHLYrphW4DFDjOYg7ILdMBX9qgb+wUvPC88sNpSaFAeUyGkZGh8II6LuCp1csyMY2E8/IYQqbC6VveaUL+bk4K4bwhbjX4TZ5tbPSAN04hdSqKPyOgYBUqRz9J63GGF7FLp74KPWTmon6D9ASdup81hDoTppGU2LltifOOa+mxmdcuUOY8/qrVMEdHcbNx3p4UApIG9oIMrO6zekr6xz0yW9QyRjdx6oWwgSEGlkOi+fo8rXzJ8Hi2ow09UIXqbQsT4xY5e1iDWw3aW/c6bsIMckgDGZf2uEMAsFS5qgT0ELYdZL0rO7BTZYOg9TCewekW/17ZN3UFz3vZX6BtjUbx27VqMdWnV217fkArkQDnJNpuxHqawZEKx3YhgBGY/b948OKT8ZCQxq3nssce++c1vwjfkJOWgoRf4IOAnnzc3N1MaysmcQRJswlvk4lmOv9cX6LH62/t6fB5fS30LQNWpfTvAZ+v7ETyJlWlfA0c7OGtdwFUXEC4wpUAYr9E4N+7LgU5oiBBh/RAZFzpLen4CCenXVILQ4khpi8/akLTwoDk+qjRFcFYMfiI2AEXLDTBQlUBrkAdrfjSS9tuvNH9B/l+vlj/zOc+f/uz5/RPZEz/g+vgnzA++T4vXYb7B8QfErtR7GixkVH/ZxoZwfHXNN6LP/r3/hONrv/0t2iCLAPDLh/Tjvw+mU5kvfF4nqjA99Lo9H/t45NvXJX7z6L5f/L8ZNdqCLYVnwqcsTLqzJQ4bggaLiABVfgvXofzM4SYaLXXb28ogJ0PFYMmVzL/v3ktnv/zQ/MNOnX/eD6Ot+vSYuU+NqgaIYqBkq6JVP79Tr4m5NW/3qaeEN2yY+Oxfk7/7ffySiwFeYg1YSl1Ixb38xIiZv/1HDW2rHrv2YT0esQpaAt9litqfhb7FZ6SKB6wZtSqnkuyYPepWvd91bAuOYMx/bRI+ottT7gSnaRFWgFoi+JZGhGALmxbcaGEYzKykMWM2MAwW8v9OIgYnzdkNyvioTVoLPYBRDfswmWEMoVEIA+tTn/oU7JtrrrnmyiuvdJCTYT7Ym/QfOwJ7CeD/1KmzUQILvuw555yD16vvf//7X/ziF8em4mUfmQLCJovKm21w8oA+Or8JVD4hpOw3TmnxldLfusZ78626aSRmzAz/+gHPLBRbYMmBpgmAQwmoHq3sLC3s0pZ2Kmt6iA6nE8HPqysR/NzGlFl1ytx6zDZ0vzDb4MgRKqMb+oxFHaDgIpZ6b1ZDl6lk4gVBhAWu9orANpNiuL+39qnV8H9IFZ2d3a8veAP1K4gcbJw4wmkzx/BNN910wbvDdx+UGDQYrQI5444dGscDaITUVSNlVBddRp+NbuIalHLAqwDNOyiB/BzGsmrIDDEptkYoq4I2iINqt16UTy3o1lEqYfTegveMsqKhbRNkkmXVh6b2ZQK66epYXu6r14njQqjJSNBf6/NOjIdnxdXZDc31AclSFigLSDakOLIL/AmV88VcMedGLfYrX60q5DtnzVr5vx/Nr14hfFSJ5omTG9fWUpyOpSJjyyixnLhz4fWEi3zMKTkFncxSt5R1fWVMf4lP2J03PGILlMPWmhnRf070PH+A/7cq2ZO+7idDNceXXONSkDGCstRMLVQAD1Czvt77wr5XcTREKjINo7xkpXXcCYIAtiyQZny9ojbf0tJCvUPHhAEnHdwO0/q2lNqT1xLEPXIJHK6M8p+msHfA+uJ+bWJUawqbE6tIsHGnipmHWJJ4FTk708qmhLDzL5t6lb/cGFJn1es+scbsEaKyt+FCHuXEekEwCJ1Al1nnjPzuqs1RTdxdBY6qHHYrhr47+ISFxPgilEH5BeSu/9ZbPdd8M5DJdB1/fPXVX7GaJ4eFG3ayVEzbkOJYDDIHK2xjwiLWUW9BRyshY6hFhL0+VzZv4OS5UC67cN7q0vKGEfbqQOMJUT2q44BQq8UjzUCx9pbY3fCBslnDzAU8O6maAYgYuqqH9GxrguijzUlEeokoBqElwFDuRzJB1CHpRW6JzR6KtfKzUZW/tabd9CRrR7OXFQ7wh3rcFSsY+r7TdpEHUAnMl8MitYupnV0GGCSdiwdYTh/4wAcAazSJWeCZi2+hgTmwaDYAEJVp+BT4suaiXgApGs4I0gF9qEwjHOYaZnhJIuJ6w7h0IrVh/YaJqHKwquyGD5N5p/0ZSYbBVYt3K5jjE6swmDLakiZujbvTChLCBDq9hYFIs/YeUgumlbe9NKE1jXVAOxbCSYt41yhFT4yaNUEMrXDVQLkDRdtbQfRBEHfQYFOneCZPLh99TG7BgtTlV3oXLHD/6S+p004zTji2f1xjj6InM+lDDzwIpSNRxCj3kbOLEc723P3L0M03p1omxn75Sy2AOfRAe5gp9oFilMr33e8yTdfDDyWffoZYV4SF0lMp6EPr/vsh2hS3ekiTt+W5Xy9/YaWQJXAoCMdXCqcDXlT8KH9zfBja8smH/Gu/EzgHODg4oQLF3Jm/+sKx/3zo1QM/fP+Fd5rFUG27ugUd46Q1owp3EEp2/7mR6qpkSV2aqWo64BjtkAX6n59Nv/qq+xOfiFRFLHnsWKpbUVt8ubZf327U1e639rWJK17Hrs3b183Y1qx5fe7dN7Tvd8Q/Dj+Wmdq3zpwM91roLtv4jGqBnexTrTWElZZq87VNpWWdmj8nvK5C60Kxw6SDkQc+id8LImKiblUyMM2D06H0839O68hZM2vK+9a5ol7A4QCOJCZ0YPxGsqq25rFXtAhpAZ8IrhB+ZJ977jn8zMFo4xXX1qx7n/7DR2AvAbyrEwhkkkXwgAYj5xCYHPxRmejsFicbxxWJHCpkcN46jXCyOSnby8ORhtU+fp5hsXN6YWVHzqGZnXKGf5DQ1z6wtqTMBUQq79cKuB+1rKDLhMO6f73qa12x9Itfmvj8c4rXk7308uprrzG9+PEX1C+XjSxj5WsubrcWQ/12WW0pDY0a3qLAPC4EsFNn1lpzGtSGEJi1qA8Qhc+eZR1Y/GrrE7iGFi58MBHhHfKqKr9aF1TGh5Wp1dh4oP8MkSEucGXp/+l3v/vdb3/7W7oPzxXiH5/PEskYdd+HH5FdSmX6QDgoglmWz3bbRwcxnTWAUTfmoKwoWPIoP4+kg2De0FE0gMyUA3XBMwhoS0vLLnVsOx9zQjAvkI7YXY+kedspZiDZ6Ti/kTaccPz73vxtHuoX0tFYaWzyYOCtQzCoVrku7GoMKzUhoxa7IltSJougp1yiHMH+Fysq+/Pb3PNezbm8odtuOWzfuXkwRRsjlHihJNrJj+2cILnx2TK4nVGglQQwQiewSUEeu915PbS+EO0yYlZwnIJ/Z3PLiTU3HxR60K922iI7IdjUvfguda+/NhQ7Nhg+KuupLaueooo6haVtuSPgfa5K8+B9Nwde5fUSVcKA+EAhgomDmQXoQGq0AzYH9P+aLvSftTZ4RhlDE1ZVOIBBAiymm7e1IX0cpr9BY4aIrsTYDF17AyloDK7sLm9I6LgeCeoWmoH7VFtwmuxPhn41MEej/eOMp9MS7ALArSkHbJs74AslzOEl3qOtbDA/WL58HFr7YJbd/JeK5IrigYXEJWlgSA7m1Ok7tfLsYhkzL1C/hpW66We+b16rF/KJU06J3nrrlvY2O+4U2QbQuB00lCVH5MzVXQa6NlsyVmfKwpwbvFYnuFG+HPC6CPPrc7tKhuGxtLBbrY0Y4/woP5en1brYN3ZjxM3+fwf17OorCCf8k3GNkEfmzBqfwOj85S9/iUax3Jucm2gkIumF7sWHs9wplcO7q23d5e9pDI3kOOZQptnHHnusQ5yPqmzKYRxYRawrLk4BLh447CTPDskthC6JjIwsmXQ4ejzDgpGKMIwPpyQrkHUoRxXMhDDCpDgXoi05gBDDnB3Ys3DuYFCNcJgDlwt1ULoAJYyCFWJhnNVVSjudZbTP9H2aW5opVlK/NIMa346p2UrW2HUTuKg2oNUE1CllhIfo5rARtG5bNRoQh+EoqAWqMVzoReNBWjjKcqEXTXRZZXNCWdePeXBpQlRtCOMBYYAMthsu2y4AKOcI9gGu8RODjeODJ56YefKZ9HXf8v/jJffjv591xhmbqqs6jz7mxVzu4IMOqaoSQYNH22s+gRhM//MV9xVXFPEK9sBD7sZGaf4g2i0u4X85+8ab/vkLEpOby0e/dxA+iKMONY/AokWpfy8IHY6yrln401Mz/v4c30Aym4JlzNkjflEImTlMyydqqw86Pl3Uy6rpLZVOuev/PvjqQ6/OOOHui+5MeMJqVs0VlGReS2UtMzSjmbBEWbwHWM+tMRZ36I3RwMHuWkrGb8eSl18+8uijwviaEcXTXs44JVFdG8tnxz/5C+G5SzD5QBHNus2r3X+6+29aYPU+xyVyeiei+GR5Zh0UL2cfDROHDn8iHvXQ8S5YEv9GGry53JrQEzrCGHSeUS9kW1EN3mRMly7iYkL95xARG1Ze0MlowmudKeGkpiVu2VrWtpah6PhYLjmDLHuCiZ5wwglEEmH933nnnSeffDLFjXZ+x9KCvd/skRHYSwDvhmGG7j3vvPPwwQgxALcVnjfeJq+77jpOi8qtAmOVgLQcipwfHFRQbnihGFo9wQDhOXFifec739mePjNnFR53Eb5BAd54442OV4mhpe0gBYhjX+gLKSu6ywvb1c60hnMdQpjXBE387M2qVrO33Jj/8U/Njo7cpEnaVVdWfRZv+IAe4S1CXshsV/UYC7uU5Z3q8m50mPViWXXrVtRjAonm1Ctz6qwZtS4Ev0BeaoQfua7fWNKurELwK5w6EucNXR3BRQwh+EXnOWihujmnVoVHKCLECExRVEUfX3jhhYceeggpGT9ReEM1BY8FO6AWZAv32J255viXc4rEAwQCDnolrjCqloCLEPqCMhF0SO3ByrW0vaJYe9L0ERcdSNVabLr3bdLe4djGWSJ3MCqEHiNp3naaLSaY9cEFwod9GkQgbfbovs2vZ1TFjZhM26C5p+tZl4gshLJTzGfV+KwpcQ26bxAV2Fo2LRGsbxbbiuWlb3zNZ1qZCz8ZP+wIRdej4vCyV9TgAuYzUlhOCM+hW2xsU8hMGElZIgtPop4YJq1NaJvwQaqaQY8V1Ho+3nDpLP9THPCUYWMY4qZ5i1VndHomVrf9sKr/kUhVTdk9Mxf5cjdi2kCjkMVxkIPw0PCy5dLwt2lLPkFMsT6Cx+zwzrb2RxYtBsiC7l2fVjcmlP6sqAqTMaqG+kWYSBOifr3GZ4hYi/UQQTvAwyxih6zAg3RGx9scLITxMWVClA/dlZXurmdGTyLiFMhQQwVJQggjeTYIkJC5lgzB3VKjmN/BC8iMiQS1yBoHk9+Wv4AmLDKcokGbeIYxhJMCp0lsFmZZ0OdCdcDsu+LKwB23qzieveiz8R/8mJBEc2prRgYxxG7JlNVlHeWegkuYo+e1DJxLrETgp+BA1a3n8XTlwfmzoIHDON5H7dmtToojARZuVrmcVokfb+eF/gu9dgitnVbFHiScOEq5hCSR0J6mAgbBPiF9OVv5ybXTct6pDLQNFg8gEVAGlweAzLxL8AKEYTtwkQceHy3EAxwyWFIklcudPJJsJpEjb5tegBhIApg9xZCimUyKvByQxWijw0wi22qb5UQK4txtypQ/KZYLbhSt5cK4moahcM4DOAn0PI4JaTbdAbGBAQElTO0QCSTKC9jFh2y6RYsWoRU8Nsp/2LbtLFHAdHg6k2M6AuHujLU5ZbYlhUfi7oyGIDGRV5FGlnETKDQv8LgJVWziALnXq3ZklS1JlQDCTUFjQpXSiHmw4Kk6q4uNZhNnNvUo7FA1T/Ajp4U+fHLikUfyt93mfe7vM3t74qtW946fMD+Z3PfQw+obG8VpwCU+FfeK0rbth9jG9slX3LSxcN75AYJF3XJj4OADqYeRrMwNhZx/8IEwlr1fvrL2ok+Dhdmfksnqu/9B7/nnpx+8L3zYIQCWCffcLdii4rWazBT+sjz/7y1unGYD8xEFF8qWHhD6YDgWNYrlD/z2+6e++vCilkPuu+SOlDcq5BkYBpcVvC4TsdIITH1P07TaxYtef3HtHxKTYOVmc8VD/vZX9P7yh74/2jA+Eg5nlixRNm/Rpk3xT56iBvwzly3m6KXLcOJ6estd85fFTj9y1dFnPHr+j9vLvmTGSpcFTYv6Ejy7WTVlvFQQbsBurTjR6NX4sFY9DR/RhAsuv9mudWZ1UESiK5Xg7unCPxYev42ihbUwUfroTX9BhHODBk7mCchktacFGRxHAiU4EXbJA7e3/Kh8sYNndhm4AWwgaOAzzjgDrPuGG27Ygwt7B03b+2o3jMBeAng3DCKHFrQK6lgQP2BaiN3uuusujgdOL/hGVACPFv8TKB01NzdDGHNC4J0C15RY45BSCR+BevhhhzZGJPWFL3wBc/xt2kcGWL/QvbBp0crA89AYKECApg12oRNMAugtbMdtldpfQtSmeFQ0Jy28GkzwZLs/enb42b+UyoZ52KGe678XPea9NEZAEZsHi/oNSsuL2k0EuYs6LXwApovwANWA26wLGrOw+K1BfZrQ59LKjLJN8i/uLC/v0tas7uS1AABAAElEQVQlNOx28N9TgDeLJYvLivvV2qAyMWLNrLZmNui1/gEBSHdXt3Rw9elPfxrTLyrn0CW671VXXVU5btuM0n/6T2YZChYMBikHSMkIu8OACLrRZvZDSaL/jJMwvt3mKB1haTvNhl4iGCrZWKi7Mhc0zyyLdZVJ51775+uouDfWdY9rHNezNp/vFFqa6IZ5C1YVZ/I43acb41z6hIAyKWgRIZcgD5aOUFgiGVtPOEEAl43U1V+vSqaS06dXfe+76IHuoEe0H0yRS9Kf/JSDxgP44tSpU1imK7qMdJeW7y5niWetlo+J3DQz8DR6acWOSGaNGj08BfGReNwXOkxxNWaDB/dEztDS98WN7oB7mabm+81gyT8e9Q+FmMZ4vobWzk2ZW7Xfvu3dvYuX/QOMGbSSFsI6AWlmHmV7eOCqqavFFCpZUpa0lTYktL68jiMQSyU+pYi/ieSaB/zMxbzK+CoNhyIxD4gEO27rgFT0HUGxuqqr1J7VwRHB6xoj2rhAeWo1Tj3flgvJNrg+SPmwpQuR0dt2MYmQE2/T+t+m1XhBl5JnfBPAe7rgggvIsI0vegF4cTSLxmW5BPXrv+N20MTsly6LX3udGgww/rob5ZphZ21rbULSr2BjYi7tMPuKrtY+M1XSsvmyByvfouFx64ZVVg3N49IB3UGPK+Yl7ByxTrG+02I+W/K7tbA98cRKpprtgQhmB1jHvoN+Q+R73333IWlhzchZg5D7zGc+89GPfhQumyxnT7S4oo7KxeM8Q81CnUpqlp3LEcwrWJYyRd7JQI9kr0GgOa+dz3mArQaVSz0c9LBKZTbukpSVQl36i+4PdwkE5CuHlYPid0UzbZJjUPbInpK0KAU6lcoqKj/ZwTOZYTSQ4cwzz6SFMNyJLgHvHrfSUPWYXN1///1UgboN/nJxKM3s8CyrQF+Go4EtcMQRR5BnVPXuoEnbe8VuEZuKAbBzcGCMCwl1s2Tc3Jzkf6Mjq2H921cgiI4w8oK64+LMQPMFahDtM9xodWZVgKEwDw4Zk6Jwi4RfJamwJusVJ4x4Ejfx5HJXnX2O+b8fT95zb+m3j9W8Nq/+78/3vvFGvmmC8dC9eiBC+Yw9MHrgaJKlDL0zPWxnght9+crY+nWJKVPM1s09139/m4yl2uqG//1f5dHfpP3ByIdPFjzfwYZQfvTkE5PxKvW3j1nf/4GG8wgPKieiAEjZiM97+pGRmV3FJ5aXV/R6sgUwNOFWykwroYBxwrzHTv39D03d09cy59jnH7B1TzBEVnpj41845hzIztWq/0+nXfWp2z4T+dw5k8/8TjYQPewfD0x++Zl1k9+z5MDTY9Foa9IK33xL8O67E9+7wX/Vl8Vc23rg1E4Tqhs93n5hzeELuWINoVxWM3JKpqAWSrjdEvGcezJCaD+z3pwYFV7o6YvdcaK1KXPqXePCBP4w520qremFn6ulcfddVHBSA92LyJh5LGu4nxSyk3wZRobQiE4gCs6pKCTOqS9PiYmgBnLKxPawix7bDcY0K/9nP/sZQheQ9gcffBB7NNHHXSp1bG3Z+9XuHIEdIYW7s57/6rI4zPCFUNlFmEaQu+gOcZaTDpBDDsDRBWMbFSZSjj/+eKSXGBhw3ld+yDPnDQpUHLG8rXwlIKVp4uwK2g9KG76slPWNdROCeintaWtBG/ZjekFAaivkIhqkMPqNdG/pPfWM7jfmr8eu5cuXuk87daPb27rgdTLhJAMpFlByc8J6vdNC8IvP5/YszquEjkrMRUQNa3aDNrfOmFmnhwatzHBSuzFhvNmhru52Ecagi8j1eRG0AHOOiE+NBy30NqfELHSeiW0DQ5BO0VkYz3AWQCghtBgW8ADIYKToknc+1o5XDurb9QwKAupGC5m1kbdT4j3k54J1AgoleY38HGFDh805bOIIC9xeNvqFvAtJKcqriLm2l20k6VC///5D55q/Yp/kMqxZnIIvPYPrpj7hS8r0CgMn8A1LmTW/iNIxI8GgdinlXk15RS+0HGke/PE65F0CCxo4Pu06LSXz1FOhp54pEg/3+uu1WPVbBuGtw/mWV4MtfmsiyhHW+qROaJmeZD7Rtb7G7z0seg90jNEZ3vSNmtCMknp4Cj017WUr9Ssz/Au/3pCrOSWV+WOs3OcyhBK34OlDeQpincvrLx9wUOzSz1oNEzeuWpkv5OXUM6qoL2I9KFtBG+T6ed/7TrD8/iVbSs/OW95jBJJlYajrwvxXURj/SARrMUPNJYxyCklSxlDWdbl1lw72KUkyOHTMFJg0F8O3JW1sSLk39IHrqPV+sfVm1eFhTta5lV3y1hEYeLtb/tApWY7s4BhYeDtuBuUDP8nDqMpnaBJ+Iv56+zoFrcJFLQjKIBgQkQ3byBI6+F1dhWuvC957j6W58jdcV3XZ5UIrwL5G0jzGrj1trujGBRrUr5EuCz/PwseVHeCXiEd+tw7TksUW9kIJwI7UagPl6XXC6I5FOFRjYth27q5EOcVMB+pRzMWwdqSIeaGBsW2BpQvhxCcsXUL7MIZweCH8Rjg4zroatvG8dYaX9cDa4E7D5B1IC2uVD2kn3EMSnQtgjvyZV+gs4KZOLi1ZGneOcviMlAbjm59cfEge6oIA5hlyUcJJypekLHdHiAQ+QAlsTLq5jR4E+wJDxGH74nSk8q2TKB8q75XZdvrslCNz0mwuYkzw8/bbb+csJu4gBDCyawhdWPkQBrSfmTruuONwHQTTB541nthh+hMOin5tU+BOGzC6DMOVDjAjqHWkVptcrXYky5uSAD2lKyNcRsOFlwF1SkJIiXmwUhBRhc0+t4a/aAy4MA9u6DMmRpQJVZrwkiUU17YSnG9pm65FL7zAOv+85O13KM8/F3z2r/FFC1MfOqU8dWr85ptMXGHbQBVoN1wbB0oSe9LSytl0tiqm9/W6brxRVPjWKzdtn250Q0rl/EdOw/cyLyWtKBY0oSJj1ZnTTvf/4Ymuvzxbd/pH7LpExfIfvJlZNa5pR7lfay3+eUVpQ9oLC67ssjI5Ndud6A3H0aKaPe+JuYjGbX1kQ1PWTzzgxfeeLbKZygsHfzTU2/n+P/zgsh9/hOpA2RZOPvj3n7+N5jZsJh6VdWgpVBOuam/dkN+4ZeL4JvuAHqicMxmgk47Hw3XBgyfgIxq7YqUzoyayah/0ahF+BK7LzLacMjOORrRKyA+YgIw2XaDZdQHt2MnqpCpr3kZzfpvZkdETLi2Jg2hUp4QhiRAFA9+FVTCTZKhJImCVBZGMj+jurNWRMog5EvMzeYITUYkdiEpGfIkxtixW+BVXXIG+0tlnnw2rGtxeBgq2R3vEZe3N+C4bATG177Im/Tc0h1OcQ5EdgrUq/YGfDYuUIL2ojLJhuBh2TgtkuZz9nIWkyG6TjvtQbO45OOGtonchM/MWpArfdBRIOYT5hZx2vnKGjM85ermzXYd9O0gnEC1AWdNrLGhX2pKq7bRWqQkaqD3vGzMLv/uN+aUrQj3drTW1wV/+Qtn/gE3tXXV1Mb9PeHdEIIn/quXd5UWdqtBk7kPzRMNExIcAymdNrxM6z/s34FtCKtqxvIxUWV/RUV7Spa7t09qzIsav4MIKwa8S9+GLQp1QZe5bbe1br1f5xJEBuAJNhN+MiSkGYCAQ1Isa8M033/yfYoPBFDiTMnQinFfOg8wP3sCSAI2G4yhfjeRbpxDngdJAyLjAtJzE3fsAnQYXBozw8ssvZ8WOrZ2ySTQ2nUo//8iK/nmNatY2KBKHldCzl//JRSuexdPAKzWaPeRs3+TDgyJawgDtK7aVPe5WecP6zKGHh7q7e089pfaR3yjwbOQByNcjvoS8jgtbo7L5783Gyh7CdKldPanuzes+MudfZzRcgl5/2+1N5WfqzCn9E36CnXAp84RPu84sfcwd+koGcqPtp+Otl+OuY3prL9tI/eXMRYXFh6r5vHvSBH3mdL2+0dD1l158KZVJHfveY6W4no3PMpDyJVtfkoTyhIkT1vRhY6/8c1lXT85twjsqEypMINlw3/yBQH1QzXWuihbbJwbSHuGpWowSMhnEkvRg/fr14KyiL9huKf7WfLTDCNc2TYsFlJaIWdjyRi1Gom4N0TQoOBfLT+odAJ0gnmWivLOiwIYpSS4wcHQuUfKQCz0XWk7y2rVraaQsEFgHaCKRDmJtyE/AIL3gInFg9mjmIDwcUuqIEoCBWG5LIsQpk/bDvOM+oiLGmolh+drXvgZtMMD6tMtx2sBD1/IVuc9cOG7evLKqla//TuSyywX6JhfnDjsuC4Frub6vvL5fsyP9ok/IMsBJjIopqNer5/OGz6eXkAN71KAOEMbbs9kSw2pgQO2Z5uzi2I5kYJz+OplZCUw3iwfFqMoGMFxIR2H1whqWX6EvjfNVGJ0AfCcnr8gp7zwwiRTFWyTGbBYm2rl4JQNoc1yy8Jx0GsAz9sOSKMXGVS5OZ0DgJgPQ+AkEBqzJllMjtfAJNCopUOkwZFnA8qINPACuWcbkhBqUDUOJg43DvuAnKwHmON4EYIjTNVmsc3c66KS8+x/oqWwkfWSE4cgDW9B5JpEu8xbOPnQ7omOwIHY9kmQGird7urMDDRWtxdFCV87YkrA2J4XOc3cGGlhLIIHs7oYG9objbCvoUNiuPrca9UGJWdUBqz6oTIgQF12NB4RqrsgiTgQBqWRfqIHB4GAiHVWc9FNPq2++Xv7Bj3zFYn72nNKBB9bccpPFQhW+Erd+JUoavGQbWd3CDEySoIOv3vJXUJ78E8HuYONR2tbBFKeUQPtsch55qH0aVsDPgSpE4WqqbP1tdfG5tXhwdOcJGl8yUCVye9SYH8s3CEs1W2CsFLzUZN24jRb1CGG5ZY1LdtZuWobJTTYQ2zhhZtrtQwyLyS6jNNFfrir1lHpXVbswuWqcM3uOZnNTaaEYHnYQ+sqqXlL01X3G0k5LIIHCazchf0UXI16G18D/IvKP2fVKiy22tf2kDgw1WoqEuVrYabyyEcUlXD8KozmoXBR7cFhhjwV60QKZlAEPSMFAqSZoNYbVqXFzbh3GdCLssOBiDE6cqHhMF93B8B4MHNMA1KFxPbsHdBzG1NK9H41oBPZKgEc0TCPJxN5wsuGvkmdwO5nCmcpbToJKZJGf6EhzoKLm6nwI1MDlOqQvJ6tUGpTFcsbAYYU5/cADD/C2shznW/nA4Q1x4sSXf8tboIBNROQNZVFHeXGHcCSL3YVLEWrPB4yzmsr96au/57/1NrdV7j/2uKYb/5975mxkSqg0RSNV0WgYUCuAUbtBoKNl7UQiFf6ugCpBD0EFjDl12pw6Y3YDJpoATjEaQG0CrC9uKy8naktS7YT/mkdjU2itIJ0AejaE1WkxXGQB+Fxu3GPZVzKRRL6Niy/MwMBa6CyuCAA6CLu2wv23dOxd92NU7ZRTzKzh4xSkgW4iFqCEURVSOQR8CO7FVZm4G59pMEr+rDS4+6Cqu1gymO1jv3u8pJQPu6Cq/Xlf+2LMV1k4/LMZ6OLoGvgnksUStsIzcu+9MBoax5G3lXiQzRDer0rl/i98MdrTnWhujt/6c8vF+Sjwh1G3UyxhEBPOXQOvyxs2d8FUxhOlR7V8xpuikaa7vBhjI8Vq9ZbWBzyT+/wfLKde0KwFBAh2K+6it7Gca+qNndXDdiAahT7r08GZ0wWdp2OxmQu6XGgTptIpUHYmXTYPZFHii6JYqhCX2ZY20XzenDb9sYaaCG0SuBr/wesCM8JvXLXfnDR74qzqxoBaNMpmqYznI8MpBwy+ubkZcjpdsjp7XRk9UDTcHheasZ7GQHZtsqsDAbLNI5c1wlyT9CoGtJz3LKfBlgiHapKoRk9bYr1ysbFJWW8IfEAIIC2galgeaG2QyCsuFCJQFkWbnEQEYqx2slERZAOriLf0E2Ibekbml3cKd3phD8W2t8q2Oe9IhHyizRQIzGQ3IcQjkTKdjgz7oSxhx6/I4xTi1Oh8yAOIEYNfmUcsWhtPNfv6tW98tenVeflwUPnpjZFPni1duzg1Vn7llDmYKPDflV2lLWnXlrTZlUKNUCx/nMGYBqwurVgwoIG54xE56rImxfSI25hep9XAX7UxP1kOd6e6bdrPT15xH6xx2/fDfghFysDKrJIc5XNGgHQuiENmmeOMFcVcw81kLniLgcbzzz+PjytAvayOXYBW8Ec+8hHIRVg2LAlYLZQA5SzHk2zygljFwpYaUcdlmZHoNJRPJAFMSzBhlek0W0BDXac0UniQ6jmSjuWnbJ7MzBKlBPlK3p3eoXlEC2U25y5HjLvkWtIY2iCfaQBbjwUM5Qw/iE9kZufb/8QHpwtol+DQDp4CQ80swPFhr2EJ/0/7QphPSCpmCk4ExD9gwZkmp4S3t/sD1Yj1jHpLQ0Cv81uT4yZk8EYhhzQRRao9feky2rXxgq1oBEMRt8O4XIJHjyZtdwpbYuGnc1KkjJcs/Auyym3ERpTJRQ32drF/6krwwx9WPvSh1Nz9k88957/9Du/SJYl584yjjo7f9DOQGJRPWKYDX8rvB9cDy9O2fh1MHZpv4EPwpiGXOAeEAY98IQdZ3gcaN9BEEcUAgHDqTO+hE80nlxXmb9aTlqsUdmXLCMbLQb8W9iGwVYslBW0+TlEuuzqhRrwlMm7LrAGmPOkkYVCdyFl5EYvIXReqjzVE+rtW9q3u7U+8euiB7/EHhbDEHh+IWeEcGydsM6td44IGOimwkjckrM40+CcxnEUhGWHBKyI2b8GRFSArYCt72S1HLo1k5YgJrqYI6tDG/M1qW8qVcIFJqgWslHVcX0OYW2YRUbAgiYG8hAnEd3ROSpizSkfGmCOEK8yxJOlF18Z20SNABzqJCGNgdILYs+ArWXVjK3bvV+/UCOwlgHfbyLe2tqIXwRHLGf/3v/8dnWeMl2TpIIuApG0YwPInZ2QlAUx+9pg8wuW3FHjvvfdeeumlWB2gU8Sh4pzHwzZdQr1hX0matK9gvbHFXNalZQ0R6yigl6fVKO9B7XnzhvQ5nwzP/1fR405e8oXY176mVcUECBssC7f5G5PlN9pNnNTDyevK6jmDgL0WrufhtKH2vH+9MbVa99pkCyUXTWt1twh0tKpfb7f9XWWx2ERlRYfnh+BXRCCYWavMrdPguQowDgvPxr2gewEuMJipnPMVDSs8c9DrirYMtum/5S9CA2gG8HUpiNjxFL/jnQYfZVJoxllnncWC3MX20Fl7L1j7Htoy6yBlydPJRX+w1IJP+AuvQBhYUewNy1WefpK1/2k1mkcc/IJrLji7Wyk0njOPPhr+0zNlggDfcANRdoWVeUU5o2otxMLmlNFKpNyE0bqls0RB0DamaWCCK/AASFmOasVd8nc/HB53dUb1lCLf96ZfITaRyOE5vC9yoq5XZSDmzfh5bh8K3kLBH/Jv1er14xrqV69ZA3kAu4qSQB9B/fm5tYX0zxJ2a6u6ldaElcgq+ZJlIOAr5rUQ/lAEVuV3K1VeEfV3Zq27KeKXu/WtO8UCcefYBjNY0mFUx/R0r0X87okIBgOlGQ3+WSefRJfQ5hYyMvuCaJFtgBhgagaTxV/nFVQrxDApACjEudAzpMh6SYRsZlrpC/0COYYkQPra0tICMYBAjNVuT6bKHsc2EtJIEtXkRHLLK76iKHl///vfT6VsjWeffZZXFCsvMrBZQD5IhBRHGEgKF2+5w1BADAWxjfYN9DYNIBHrRJmBftFaWg6FL/OTzkVRNAZCnQfaBmbvpPPAJ2D8vIKYQUuWBy75FQ+MhqTVwfX5KRkHIoei0H5fwF9asz550QXRF/6RCwaT3/6u75QPFfoTyBCjRFNHFdNmCsj8dFw+UCY0GO2kNHQ11/So7QU3diu9BTdqgIGAp1gyUW9n4mArYlqcSRHv10LHccqkWK3XbAxmkps7+1n8sEwEL0cIUTlHJDUC71WmkCgfINvkqcQRxhyR7ryiC9JGHSIWNWCZLr+iqZCF6C/A0UCQy+zzVg4LD8AHSaxCZ5IIycqMP/fcc+SELORbOoiCMYcgu4C5YMzlVEoJLZ/IJcfn8iKP1JXgW3jEKBeQIl9RlMMuYc1DdzmfONPEA5c0cJWDvM2dPbhNyqh+UjjrhIuvaA+jwREGqU/HGVv6QoZRFfhuziyG0nZrx4b64Q9/yHr46U9/igkAAgCQFnYlF35M2Pjs4osvvpiV4EzQHu8XwS1QadaidUpz3CJ4LBa/aq/SX1S0KjtsEjHV0aewST8QlXza6tOVKFSZMA/W1vbjoKQMmG0ICfkmC5v22zO5dTZhs+KMIfzhD4dOOql95vT+f77S/OtHXOvX9q9aqX745Ogll6iaoELFyWFfDN3A0zZ/tpO8Ta7t/WQWAFDA3iHlU7HGadUYVC880HvExPITS3Or+n1Z/ELrbmS/BE6LBRGfKlCSUKSEGqKXtLGimaLfFCs2OJ1FUGwohaxwl1WNFlJszubExv62zd6VGw4/QPCn7K7KDg+0pcqvHT5BHx8uL+pCAxFyV1hfo0LYlcGls4KTiz7ssTN4YFWmxPH3IUbKHi1EuNbUKq0ugEa0+WpraVmP7tUJnAnDAm4vbjWgbaGHcawvHERDnxcM4l0Jr/jZgtpXoHysghXbvE7MAFMnbmO66An7+rLLLgMeSnVoeD3bM3gZUw17P9pzI7CXAN5tYw2nE7jPgQe5C74Ff9QBQDIcqzzInfrkT/nKSax8AMoAyAh09Itf/IKddssttzhiosps8lkKT3jmgTaAYfPMYSNrseG1gNltKWP+FmVtn1o0BUcy5jNm1yr71ivuBf9OnXV2uLU1XV3jf/ih6ve+1waVomxERbhRdfv8SzpLizq0xZ3Kym4rUXTh/9nvUmr95qx6dU6ditozXmRtvxEcD3gjsIR/rB51fZ8Cqw9PfTisB+oIV88BQLBw9Ty3Xplag+BX1MJFI8G6vvvd78JHAB0Ey4Hsx9+VRCacwZSZ39k76C+DDKK8663iuALvB6fkASKhubn5ne3aSGqH6wm7B7yTCSL/mAeBxS/R3IaGepBypN8UNu3EQLbaXP3LolbEYNVpDotXBIs4/GL35EMDW9OF0ZDIZAvYYLFbZm9v8ZprvESV+MTHq0//CC/hrjuljOTBPnFFoYjWcLTLOb2p30qUtHjzLPTcjFK6Z+O6fv09hkoohrL3gFxhXZVwEfqveM+DpeqzejV3PnIMdDkFqMEWnDWDKrjKoZPdNVeLttitpRmmURbEnmVJl9eMA6QFu7WSAGZH4CRueZeF6nVbd3r5qrXjJs8sZtN9nZsbp80WXr0sC/EvzksnhKymiG3Ga3MFKrtJIWJwCDvUj9tnbWO/ULirC5sNAWV6nRtLCVqEVxFcMwn/SzZy43wuTYidn5UPEEtccurZDuD3WHkg3CMPdxAC1rO80F6BqGhpaZFvUTQlP5gxwjESySPT+RD4JlPYXKTTcO7QMLJe3jqJPMhn+QpCF6pJ9NS+SAQMOq+gHmUVzkKlFt5SizSv4COZmTu9kAQwRoySQiNRZiBdEsDQxgMq5YOvKPmEE04A3jKPCMBkaRQq9BIVdfrMGc0eT+ZT51a99mouFFl74fkbJ6Ib/zLZIoPOkCiTmGekOI2kUspklDBTfeHV11uz3pQV6Cn5s6YXgVV9XTWKwD6P3taZKOVyrBSm0auVXHrOCuuTIpEp1a72zanFS5aKIumBbJMdd0qS6HRQpskaqY5jSxLAHB+AYtIrLycz3WTvc/FWPsg54llqLclEeZeqBHwLq2Lt2rWwhpH6sgD4Fgh/3nnnQRcxWTQJtq9ToFMvH+Itkp+VcyRbwn17ntV4JYlh+ZXzOQ/Ot3vsgU0Nbc+C4YIYfkfa4HSWAWGzMDjsiN3eEgqE0YblFxemMcA3qF9cV65btw7jYU4NhgKpAHoiXCzs3d4Ap5vDPdhGMNBJxMbr7y719dWb2r6xQkc635tdY+YVv7em4KtK5U2Eh7BwoO6QTOIvCoPhPq+KcLI9rW1I4qMYLydKPTGTJBlo1zTYEduElQrc7sbPXpw78piFLS3VS5dP/P3jxusLeh5+0HXhZyKf/KSFPjUyX0lGvg0LklMVsMaSqzxKBgZEVofvARVTNc+0GteL6wp/XqW0Z8ViMEwdr1RoBsUCatSPi2g1XeDEs/sJ4SwgCH1jBYlBtElgUSqK0lDLEJ/pgl4bbDa80XZveHVfeVIUjhwvgd5CcUtuPP4ixW6O6dUhfE+Yy7vwv43fZuTtChrRaCZmiVxVFG7J2tIGoUDqQohTqHjglEfp+rDxalNImbfF+NcmY3OKUBE0kg/hPAg4Jzxdl4RVMOxxdLnhHdOwTEmFVMbYuCOLlqKKcbjTnMGJGxieEf6RX7GG0V7BXuPUU08FTf3qV78KYBxbgSOsd2+23T4Cewng3TakILKYrVIcAOj666+/+uqreSCgEVtCknDISSorkz/ZM5WJ8lke2xDSuBeCNIL0xQWF3Frb22CgLBwz8nPygI/yzGnU3NwsEoVdh7Wu14T63ZJSy7bac0PQRPDbEjEKOPS/9LJwKpmcMjXyxBPuadOgfiXAsoGdrvhD89uMpV36wg6zNaEB74BKYbc1IWoS4Hf/WmvfcTpxJu1PhGMJhGZvtEEnq5tSKp4nMhhsoMqoCmRdxPjF1XONObcBRRdRC62lFhDHRx55BPfXEiVFIECQJykJ316XRb/eoQvLLngTElHexSbQd3x9UQgMRUlvOP2Vy4BXTsou1rXrn9MkLjTSaRJuz1nzY2ubLAdBEHQL7BUKAQnmmZXqjrck9BDiTeExaivtyipBIlwKRm3lX7nWKvrDkSa0gUvlxFevjrRuStfURL/9HXsZU7ZcyxW5d/jorHxCguH5uS2t9uUJVY3NJsZNWlE4b7PWF4/MlnFw3lp9WmrTG159LZ66zczvagsbXdWf7vXXZW1BAiqXHsPVqMY/64l/TlFDkHRdnZ0gV3QWrgc0Biucoahszlt/YjZvtGWEh9LerECckD1D+LmgLSD1DbMm7GoKGQ0Ba2qt8LW+nZ7SIbM1CTcKGhg+lBbyGU1hBT/tdpgxBzkZaMV2Cqls43afnW8dsEZ3wHQR0zlULhSRzelQ8IfU0tJSWRavuCpTnGdAKP51nJ/bPLB35LhV3skDWSXJJ5555VyyMZAB2Cs6ifLBkS5K6bdMlJ87XYCeB7+Uic5dAnlmBpknX5FuvxIVx92eNBYcUL9NTe3XfKO1pr6uvo4xIYNTJvU6yhTOMBJ6Gi5MsqRbNfsES75UWvWbHjwQxjAbVt0+H/JSqz4WTfl8fq87rJYbIkbEhTsZ17QaIeoB/kuKiwKdS6LF/Dz22GNlIm2WD86s4YURqaxMpJE8iM7YFzBKgqnBhK1/KYcPt/4efGI05JmI/Q6Qk9KQQsM7O//88yVLF/dRkEYMRWVF8msnxXkYLFX8HTZx2Aw7zVn51e56bm5upiiqZpuj3yH1HWAb7a7yx1YOeAKaBTvgHYytWNlT51uEvVyYa9Hr5cuXEzwGjQOAHkc87BI2CzI0bMSOP/54+eHbPUED5dsLWfJtIJka4ANGXAVVa0+W0m4rqZm9GfyrCwfFWaIH2yRz0SBYo4XPTpSie9CnTSqbEnjOF2TwuIi022KriWubLkzZd6Yr/KlV/5qv7zM1tmlL+KEHystX9954s+/WmwKHHK64hT9C+SEbZJtvZfrY7hQ1bGmDVJ9oKCVTKcp675vie0+T+dSK4iuteqKAkgbRkhgNq8qrhnyKx0/IKCInwc8FoImvKkoWIE6QpzZ7FXFxr9CIBseLL+zC57YVNzvrXKlD57QIFwdwgSsahToKEfcOaNQaQ9bCTuEcC1XzjrSFVnN3VsmWNRTRe/NKV1qZXWcAyry64O4xa9SI5dGkKhdxOidEsAouLe1Se120XIS2KglvCBDctmq0ZnnpDFYwBvbGlIlMW+srmD1pa+44y3Y6jZdEChyYAkoe1SXHAUiIMTDxSr/97W+jDn3PPfewsyqGaFRF7s38DozA8IzVd6Ah/11VIltAkgBCj6AMNAuRJnxuCLyPfexjsqNAH/YMDo1RjgKxq9wzvOKCNkDqC07w8MMPg6VVZhh2qCAgJUXNQQuLF+yNbJw0Nk5joT63tMMA1hBUDVCEF+dJUfXARqsh05W+4grfbx9FBJQ64qj4Y49o8eqtgFIAN3RUzDc2W4t6hMurjgzBAyAE1JjXmEaY3wb1gHpzajUSAYnwmYSihGZ4s1Nd2wsPD9sSKw/swt+V26r2ExLJao4q+9UTGRgNlkHwrygM1Ne//nWYxHQcdJmwOowM47bTXg87FG9fIs2TheOnFwJYOk0hZQztdIric4qCIpIIcWVRIIvwNd5VFiY0GzUHHLTSbCzYcYpY2WA5ODu9UwgX8gFkUPQadURM/sCAQQ3zpvVGh6v3r6X8i7ikZOFwPjHmgvoV9K+iT/1g7pBz4lRhr56tpxcFGoqVfewx/ayz+LB4002Rz15kE9A0cGu2nbaNDBQlboq6oV8EuF7Vrban8DEivIGiKWyU8v2dm2P1499Xe+OJketVLW8mA31PhnNvBtG/CszNxj+c0EJI5Fxt2qdDtadFq+YS8tOul8BMxuLFi4QRM601DDRyQUP7+/uhB0hBLIYIThIhEyaOd7vchHZ4o81ctinbmdUwrmpfuyje0GwaxVw6U9UwPuTTmiKeuVPjB41TwVfsZbi1p3YvRLW0OlmyHnmzvDmpozKNf5fpcXVGnTkpKlT5JFZhN2/UN0hxGk9FqK0CdghaTuOhgmAMOWPOWy4xB4OzwE+2D7rHgDWpmTzqivfUBzR1m6pkL4amy2y8la+kyJ0ZMTD9TqcS534q8vTTuXDY/fjj3ZOnvP76gv33219y97b5cLA6seZFIZbSmTOXd1o9BX1jr5Eu6aj3uFx6oWT4PFq+YPr8Oh7Egz4t6jImxPUqDxZ0wgWDKEfcBv8Nlrsn/8qhQJcH5ViUmFjtpCBCx2sAFi4OHU4ir9j+/924I4cypCCTzgbZk7Pg1CWng58oXzDUUtTPT2djOjl37wP1sgbwZ4mGCw8YMkAPS0sH8BOaQcgM6GSwIKlHsGeaZMNFdeWqFW63pwU9FOHcxELTh7g+HXg6zIqwSSJ6MHZcmOLYYIBpQ8Mt5BE+F/Aa1RBUsUqdXKU0RJAGs9HEqWHTVANDKjRYLKu3p5szDseH6d88av7q3tA/Xyk0NaKgHHnwAe++sxWvULsRk7BroJhBc6Axgww05mSBy8Dkwq0bdoppmz3RABnBaV7eW3picWlFrw8/EYi/sW6Dh1vt17xuYe6LlLVIJjlqorWMiSTaRSGin5ri08yQV8sJW1y1NqDkultjVvfcJs9Jh88IetHzs2ERENL+YKBqxEUGkhJjeaeypp9QCwN6y4xn1GdRyISoMj2uzG5QcUhmlyAKkd/Cht6UtOZtLv9rk7YppcG2SBcsQltBA2MJTG5NMYkVDPkt9puq+DWrCt3DELY/1pxaAh+4QgOyp4GGDRQ8yj+i75YFGQw7D4YjYhv0+ESFg+fdKMvbm32PjsBeAnhXh5vVTxHbLHeAEdprWHWifYcUQsYD4NSHV+TUR1httg2gCqGH8zml8S0GlhCEhNF78MEHYTKN/Mjkc4yKwaQRUFCmbBsQbWGbieoyapwAhqBt9HvAOC2wbmnxokuCr7yMhVzh85+Pffs61SUgpv2V6BQ6JOv6jIXt6uJOa1m32pPD27NC4LW6gDGjToWO3b8BmlbonggYqAhloTfbUWtRcFLaASc1rxgATVwveJWagDU+Aiwz+YS4o7RDwCT74iwkrDHsYeqFoEKZBKc1EojIDM7gyJ/v4B1yFIyfBjDCcBxAI2gzQoxKjH8kzeMrLrqMEEycjtuBlUwlNPCcOXO2l2Ekde3GPLLZzBHrGerlpZdeclCWkddCIWTGMhMSCKkXi5xzmp8gxBMmTVzZWV7R4+r9RdrXIWK2sEhyNSX40nqnjkEQh6evIXfq9+Oa0JtnVAaWEPkEtpHo7z/0sMjqlYnTzqx+5CF0eiszjLaFcItf32yt6DY3Z3QOdS5bTMaSFh2Ae+NTkx+OX3FA+CEdwzHRKxcogaYJD8xY/G52nZML/3hqPQJrGjqA8opcNrrAQqL7bFKAA8Q/rCteIRflpySAW1qaS6prwSZzWXdp/hurk7mCWcqletrx/oy/KgakqXlGPKBMawx84KCWCSg/VwyF7CkFUhlIDD7bn1paWNzjJZITIuTDW9SZMWvfBpfdsK0DOPLxcXKyBdDqlJ2SiTSDJbFjvQCZnzuZuZzS/jse7DEXq1HgiizLVLr33LMjTz9TwLT7l3eHTz+DkFdsaiDG9oh/RkYQvuJSt6TK8F86c8ScE3o0IuqHoqMejqpduWSi/1culgM+V7WvPL7KVe01ZtZrQbEWBtabXcg7cJNTzPLARTAu4iXpCyLOMzpN0LojP9Hegda/bVXKYXGK38OLHy4zbFbagBiWtSf5rUBgYI7TpLfvAeD28ssvwykDOIDSIPNHFIwsmtOfJjEUiILxEA5rFcN+51h5W4eIelGUA97a/EdhqsCuSxUNBLw4XIAMht2P7g/OmcCgkC4KUGU7ZgT4Y8lFzB7I4HEhlKLN5pjaEOFQkuCMP9uCNerq6ujsWrp4fHuHeePN/vnz83W1xWAo/Jtfu3Gi4UWTd0DCOeYpYLsxmGCPlbWz0Rhwud1oA2+5I1oYWgvpKBL/o7XwpxV03IPcAuQtZxgBl1rlF1RlDn9XRQWxKaxomMGoRtuFSEglUufWmT6XBhGLCzH4BiEPvL/OYLm3JVo+7fCpE9FmFh/YZKzzx4aSeLDC7ndJu7WqV21NKu3ESSoIVxsBjxK3R3hyTNm3TplWrfmFVbA4h/kjyHQwsZKypNN4udVc0anj0pUYV8BJAChdFMiuBTGMH24hFuYLhD/gokIJMWrOiClzGtXGMOwo0ZHKQbP7NYobQ0du2DrwceDtIsiRgSF3pcxRVL836y6MwF4CeBcGz/5UghVATOVy54xB/6elpQWzLl7Bk0PPk4MffFHqvOEIBIU3YtljE1X5IXxiYh1hLYMUFB/rkoc38ibSmAoCWACC3jxqzybayLkyQMCKChGBgvqx8vILxuf/L7hiebZlmnbjj4L/c6LQFxVQWEAKBFR5U13ZabzeqS3pUFb3QDyDzWl43GkMl+c26AC7/cdpmGTYbUM2hxKLsbBNWd4jwuh1Z4CVgm+KP18kTvBK8TK9b51JWLaoRyJn4kOqIQwy/WV8OIewByPOOOJQDkupJ8ZYjZa2HPlYjSEnOqvMLB9y2NB4SavAv3CMIUdYJtI/FgZFIQ9B/lm5ACpLeLcRwByfLFdcy9BIokF+8IMflCdrZZt3+kwhHBIc1VC80NIS/YIABicL1o4nLlfrirLn4YJu6GXd2DTRpR7taaqy4q9nkv+01LLX0grv/6a3dhpIG8Mml5+oE5Pavq98Jfyzn2XDUf+8V/Qp09AT3mljhs3AzEKPLthiLEdnOKl1QzfCToafDP2B+2VxrorzH4zJpycPD957eNWtcW0jLpnh4puqq7/UuCp/vlZ36dymsO3GQ2wrpyIKp/uwUSB6URIBJvCKRIm+ADHgqpBSMs1F7eUVvdqGfmVzZwaC28hn+js2xRubc5lkurftkEMOmxxXcGo6G+Vn0aKtVci6KJNRYWO+ttF4fqOrM4HhgzWj2jhgnPqeJoxFBQ+qcgCdFo78QTab/GxYhPkoREgtVvDXXSx55G14t+Vk0BlYRkZc/f19554T+vNfCl6vdv99gdNOQxoxkpEBt0OdYWO/ua5X25K12pPCOS0zbCO3IHUsRNuIBSzTrdUFjXERfVzQnFaDA0LGgxq27ot3ZHzoOtwxvGHj8IwGIIzCJeS5554LuHvH2/aODIislGFhm8P5Yo9DBO7hoWCH4qaESmmGvNOqKVOmwJjYM2MC1xg5MCMgjX1oA+fg3/72N1SKZFRh2QwiHR533HHgP4CRMZwvI+8L44CRP7VIkGt/KLYv7CccQW9KmusTCs47cRaNOm4yJ8g/wj2yve1/lltXQ24r5lfjfqU+BH/fbK7SxoUJJ0fP5AYUdJENDxCill984cVkKjF5yrTpoVBxyaLilV8NrlxeCIYyc+Y0/PEZFbfJItaS/aE9Qfa3o7jJ1cUH4JDMNXwEebY6w0gGxC2IZIZlqfNWABZF6clZTy4vvLLJnShqhOAo4V/PMKN+gooTeQifUgpKxYZQz3Iu0c0ab+mLR+sdKWtVj4I+M9rLyQLhFxRXoV8rdNd5cx/cr+mQaTV+tI7sLtrEqSiBX4A7xhySG5+pS7vVdX0EbbZ6ssI1NNIVEMi6ACSrNb1aIJB1QXG68KHgEoozDh0btTVhzNts/msTH+r9eaFBjekvJzWfQ9/iIcsDDQxHXcyccBvJlKGNOCUmvG2hjegT55XdLNGisV+scNRbiGdOJBdkXYz8bil27A3a++XORkBH13Rnefa+38kIEABA+m3C0gnD3aeffhqTXfSdiBEPjsseAI4jJ4TYg+UJYUwegvrwfPfdd6MTJTcJ8Ad9aaA/mqUUeNVVV7F/qHi0WwieLpoYbGfAWVvaem0TUiwN9RV2f73fPGC8sl+dajzysHrJ5/0b1qcPOshzzy8DRx8rhEpbQYCRKqlvtBElWHtji7WmV0uVwP+1kNecEjcPaVIPaTT2b3QFkXjROmL2WtaqbgKlKou7gEQqBwbMQiBTABdZ8EdRYqlWDhpvzW1wI52wIZ44gwHTUPjEFudEBFjjNBJrPRIhhnkLSo2UjIftKfDsZErenteQK+BwXDSSGpDg8czMVgzdTiqmR/QX1QAp+ce79TZQkgx0nAOMs5lBIDPEEs/UOKqKdtKOMb2Gdr3wwguh37D0Q62RXo+8406FdIfuwztA9gvNa6dbrNhgJLKsw1yfVPJvlHytStZnrtzf2zfXXR9XWmq0Aw7zTNzH2rK8YORcrnC5cTaaY2JrMFyCr82CeeTXrq99nbTSN68JnnyKIAm3rmen8p08UBY5OFfX9hpr+9WNCbU7g+9QcWZTB0WiQsxhzQX9CAVJ7NXW4sELUmeuK7x3dd+kNzsOe633nBc3nZ23DiWeoVJIYzXs8bzFshH+DsGu4KTAN2HxOMx4xhamWCxWBdbCzt3Qb67p1zf0EbLSwoWArrvAQgrZRKh6HN0NaaXpLbXY0s+ud9nWB8OTVfRmfdL8+2qlM6UXTPhQbFt9br2Krzp52o9hiLYZQSAbF8Q8Uk1wehh2/CTP9kpm6NDARBglGVvby7ZNLf8ZPwVGZtOmrEha3N3Vdfzx0VdeLfj82v33h047fWhnt0mxUTrRVxDNNT0mejRtKastraXwWKhrRQOE0qZexGwDztWwRyU6Sx0hNKNQv5gjCuhqr/rdgMyNbcxZxswvGj3gFZyALHJsfe+66y7UXOVmB6AhKoES49U23R9bjf9ZX3HAwfpks2DYwk7ZkyPASQqjlgvgg4tszlyeOdH2WBsADrQBdQAAHeYS0sQJChxGKvbA8ENZEnAHWB4EvgIf4JiANIV7smOQMuYFQMepVLIgB88KsXEA9xC31QEMTHCYZAXcBAe24NtjkgU5hRKcfRSgCqSgJ5wpEotbOIuC7upDVpw3Nd30YwhiE2eiMHtPUld9Q0NXR1dHR/v/Z+894Oy6qkPvU26vc6dXaUaSJVnFvcADEsCYxGCMwfACJoBt4pBCyUcIEJJgePBIfmACBkMSYvoXDIZgbBzA9ARDApZxkSVZsro0vdyZ29s55/3X2TNXV9M0TWM7mWP5zrnn7rP32nX1tcp+f8dznuN7+cuKz7q8dN/3Qr0nsl//Wu6RR/1XXIl6k0CPvCH/T8G08A4yUFxsMaYY5II4kq/UU60K0gIadS7hiyBNTae/57d5Ea0mM2UIP7qL5wzZLvNFJ+jT+EeU5YqgQzVUogjmtQta7Wu2eDrjaDjAmKRnE0Vt3tILmt/2hCdy9tB4yQiREwy9rrwx2akpyGBTUTI3ReBvNS+JFSCZ3QHE/RiNLkG5ISlRC+ObjfE1Uaw4CN0apB4aSgSMdXWikEdwTChvAQhpuTDqQII4QWZNStKMYG1xCS6UqVk0xvmKFQ3owpm7F7DL3ykA1cP5P4UgECrEBndj1wltQwyghx56iIXNk6kuzl/H2q9PzQisMcArMO6o8j7zmc/AzcK4fu5zn0OiyRHz6U9/+tWvfnV19RNThKOWXzFvxiEKfgbzsJe+9KXV5jEQIqoQCBL+mRteVFe1wEJuqq+w34+OO7/q1Y6M68jCTLKeRJ1LO7QtCS13662+977XOz6WfsnV4c/f4d+6jWNdHCWgqqQNeySvP3TSfnjQeLhXO54mWxLhbhFzVkhTfmm7dlmntqWJ4LEcEVC6OF3opAXe1W88OSKeGESJIBAfB3jMT45frTvunNfkPGudvqGOwIMcKvwkNBwaPwYHA2/aQ9z7ox/9iJwlLnaWACEYSjFcfFXCaTolcD0Nrskp0XW4ONCMK2iQZwsBjV5TDCM0IgcihcXMG0H4rBp+5OJwFByjHKlcSDS4Z/GArRfY1kLgWWwZ4CeeJzILXsTIB3Xf0oABKzN0kF+nTa7uwPIdS5v9o47/P4tjIXPP5cHRJntjg7G+Dirf6mkw463Ef/aODeSHD1a2vCDsLtjJkbeGh8uveW1wfCJ9zTWJj31UJz6yTMuC5mXGONgE88Dg/+goCTBYzIKIZW+IC7D4AKOMRVYkPLcjAS25Ck5ktHzO/rFtB4Y3FiodiVCwK1yKmsVcLutO2qlZYzaJaUfMM8hfZn+a7INhicWizDWE1F78CMbQPJCZA1zuamzK5YnUWDTRhOdSva+0fV1iR4sJCwRal87O6AYP0mX7nn3WQMZD9uKIUbl8nXFuA8oKl3lf+vicasmtQz5YrvC0bAeAVw9PFTr9jvUPZcBnT08Pv1D49N+f8d9kkdCJwcGR3/ndhj17s4mE56v/EnrZ1aqnfOIJiQSEXTBTniUCRc0i2e/+kXJvyjiWJnyLni7iyWbmy1DVJkmPvF5MoCWzdcyLM5uBTHNrk94ZF/WGq11mPJ+CIWUzcCGnI4b/jTfeiPwOyhuLVpQhhL1QPa0uDDhAzk84n/+WC2CeFcwIcOCzzeEAGS5YFLUq5nllBX+qjj+CCbAJaEVxRyvYxBmrAoVxIfyi7xRWIPHJSYg10Kte9SoUvwDGgoH7xXwANpiciFgTIGvmLQBWb52xoQUWqAJQA0t1nLD7hafS2+Nag98O+LDv1YgapWJXwf26WWUNEIIwaUWdIExpYYM1DIDZsx7iQcB+ymEw2QgJz9o62kZHRpl9sXHbvCmwfad53SutSy71fPHL/sf35r54R/7Ycf9zLne8WHLILubNBXZEFVblWWAImKqnMT9BUUBYcurCAENcEWaFe5BRbZiVSSgnW8Tn1rh0nSdkFobTIDjd4+gkSU7lRYka9KPbkIlwk2oDpebRrGu2GRsTDqd/Y1hrixC2ymWDBXcRJMNrmyEzXJcpG4wPtlPxgMH4yFHlNief6kbDcFprh4v2YvAihlc8h3fNlYhxhQ2juCKT06hQxtlYC4rAD9Q4yeITUwbfuraI7jNsXJYwBIf3ZZoqGGZJO5CdArBSsvNaoWxIkqSyni4ZmTwnqhULIt+WAVdQcbPw68iRI0i1kNfwLin9IF9JFIw/MMQtYh3qWUKdC299reSSR0C4kSW/vPYiI6AGkCMGygbpJscKcR24pmEXVQwbCaIcc+7AP4CE3G3JLw4mQH/yJ3+CYo0UEby7hN2i6lczUtG0J0crD/eRMZzzivDL9vp6m0g57UFr7K1vD3/5S1CsuZvfHP3QB714saoz2n2T+94Uil9tzyB+vzAAJrmOiCnfHBJbkfNabDyHm8Mi4HMXjY1p0KP99p5h/dgE0Z4dEAChEzhi6iREBGl+nR3NzvmtRgTPYqmfD8EHSAHe8pa3cAQjHsNWhHDZynJSAY80AULqaR5IABwG/IixFz5TaoLwnoX+oKdEZ4XDV12u/aydR5YTpyoGS9UCC2+u+spK3bCwsdaGLoFSIULbNOZt/lZUp6hBUTB8pSNcVZUXUZoePmkfGHOOs4H2Wb3r/EUfwR41QoxsbtAu7dCxHZB3ELtXjMd/ONJ5frCuPSgRH8HuleLEn74j+rl/SjU1xB7c5e3sZI1Rs8h0FnxNHoLEa7Gdh3qt/W4Ac4J/wvaCYEXcjc+T4FR4YFnEsgeUWpi/yOzB5o5NbgxSWxuj+y9eH9nYvc5d8AKB9NQ9ZlH7M4A4OHR3dxNJuwpdddKpqGRrAHBg1OjNaOwpeggAIHJMB2gDXqg5qhMXZFuTtr5OMeDSjvw3dSkFJHzUvfvyjw8HhnM2kTMvW69vb3R2NrvkgoiuqtBNvbaMvwp+PtX8zlUTBTB+IVQ+p1814vFchZ9Zz6srmaViDw2MXXVV3e7dqUQD+ah9F1w0xZpKnxgBRABQSKyBqT5O4mD+YGO5d6gymPMcmbDG80Y2b+MemMPR12+iawp7jULFQr8U8zhdjXqDF6dfs0FCXsnsy/pc9UtNPQQ98ly4X24AgcPttttuu/jii6chQX6iPAuAQeAYUfTiqoP8VDZI9+FPiHyJJQhhn+YJAHGWoAQAHF9pFxl9bROrs3jUaqm2qzDCtKYpw8UQES4LjQKSNYrxCvo0DKbgkBVHoSqZ9m615hW6EUjYWOxpxPqkkDySdKCpBrJwuYYwuiUbdaNgM7c9uMKonyiDWmNQ4kuvj+GiYjSEsWyWStwiTqlUBgUgAWf77zjvfI503apk//0B6+GHPO/9a/jCQjhs/O3fRW94vWF6Bceo6/QTfvLhbH9cgKdecl/nCWOI1JWxqv0VgQJCqJkDWD3KqAW005+p3LO7tGvITwQ+5qFABhHHToR1xAGMSbZAjAmyK5c/dKWnTSS9kwCTrp78l48P2kfGjb4MYbSxCnTgWuNmOVAeed7WxvPavAQPm8LQ8l4NbEiXCXzgPD4oePB4iuBYBJQhI6AOe9wQEr/rjURgbXbI9EZuEREBy8UiARPbiI8fGbB+eRwjGmO0AH8rXC6SCLT6DADwQdB6VSoJHhpixN4WEWIDH72dzciUqc3VIU/O2dQUuG1M+6jCfPjwYVgA/ByrBfByR6ADKYuaR3mNVQenWmbt5ikfgTUN8HKngGXNBaZHcglHpMIazUT8qhgsH/wtnA+qEtUwyhOVQwy/ebjfZRuGcR5JwOffwP2SikXX/aazpdF+VqfR7KuMveUt4S9+iU1b/OD/if3NLUYsyuYWxYGAgtcZR4a1q097BO53QOKOUlXYK4nvLmo3LmnHjNkkkrNLagsTcDLj/Pqk8/iQcXScTL9GpiyYwGc4LRGtIwbf4lzeQZhoM+wePKqz6XQGxzC8IyACUPkS4JosakprpArwiYSSX5/mYjOV2YU5rYI9z031lKQMsm3EH1B+9BoqRP1UW49aJ+oTcQl0kjJYUk/maeXs/aSAvOuuuxBq0gqx/okaUgvzQppmnWPjwMzS91O7Q8gL+f/AsHUiIwEwDo1r4/Weilfcq3Cp6q7XN9cTtQJ8pZgI4WpbNoUCEcS/gvZYctm7/tV/y18hCbY/8rfB579gsuDCpuY0yKWfzpPD1uGUQaqwkYwFsyjcr+Svlhu3OWGDEewLQ8pD2feiDea3aNBsChHkXG8xU0IG1UnsTXWpVhgBVH+gSdhgFEFXXnnlqYUxtY6ItPLEUOVoykABOJzB9kxaheygRVpCLdAY0Xvq9PVR+5wmqIdqA1PvT/ZHzLQf7C0/1O8dyQrnvqXJPpdYmm0eQe7uSzX88mljsLQvU3BMA2OWyhD/wSPh/XbX3QAAQABJREFUGMJJOMvPz9xHLtkoH+PJ0d/9nfhju7N1jeH77wtceHEt90v/2NQsAAxAIEBVd1n/7iYQ27zdg5WhnOdo0hovmNmC4/ObHIfhgCePmYHPyJUrUb+HOC7dTUarz9nZZrrRv7EBlGuVB0+tXj7Je4w5DymOkAni0YPjz8c+9jEV7nhWqHhIdAO2wCqrQFd5fGZtjr6zj7EGV6E6oBZkW6/uBW4Ff6k2OZQ4k2s1gWcVFrqvLpYNCl70Zlg484RG1ae64b67u/uFL3whyYSRurJZYBrhMeCKsbPDR4y3WDxVIqr67koDT8XqMnAorfMbxExCuB/2YpSheUzhqRC0wpiBIsABog0ua7gKow3OFYlLrI0WtHzJJpByQLxO2OjouiVXNsMOs+T3ufYyuMls6PFffln5hc/PO7b/V792Hn4o/dnPO9u2ehDmCpMoAwQcC+ndFMDyt1qegQLtKoM1NinCR0aP82dWKbZQjVMXJwsM4QVdZnukMpiySVCENADLp7Swo3bIjz+t9GtHo3XFJqHzqtMH0PVBAoPpITS9FBH+VqI094/nkunCyRFsqwnr7GB7DMVI56ovSsswtAYEABpdWie1AkZXAhPkaK6o5yqiCkYamCoaWMREg8KKu12VY5Cqgh4NVTBe2RAIOYK+yeihRtZxZqZaLtHeSxhL4ZtBjvkyVtB8kvuKau2gD+tFQbsClfpP7me/2DvYcvKpxDTsa+4pysDiZQBJj8KDBGAYuyG74bkL4exVrT19SkZgjQF+SoZ9slGIIdyl8A0mJRJOlbWK0CWABflFIIHH+isPDRjkrGO3RU17e4t9WadZV0qPXvvK2N33WIZZ/sTH4m99O2EBJg8M99iC9iKY3m8G9IcJZDWij5eQWgoTsiFhX9KpX9runN9uRjzqqMKmWjs4Zu3q1fcOGycmyJPkGmrqOtxyc1STeFdNzrO79E0Jj2spM7ntce9Rga85BcjwdO+995KraeaJwCGipAAzf1rCmDxNXlH+zNAZ0P3gHuSCzDUSEwXeXD3l+VNu+awgBGET3QHC/aqrriLhO0TbXDDPOuCI8KFg0GYzuYqfrxaDEhrOageSEvri8JiNC5UsMkfb1GB21undEWsznB6NuRhJcBKXYDRK4dzj2MnR/O+/LjQ2nnnVdXUf+jDGz4IDp0pXWznTjXCwXINZZ/+oZPDC9deS+kHbxNIQkOApqVaxwSKgBiZeEM2EPPR5nOaABrt+bqNDAgi/PxCORKaBgSEfpBsMMLQvjlgQJUjlqV9kPW5ltNaXsp9MGkfG8D3GiAtRu+ieoZkoBjD1IRO3gq6ovbNNXH9lh8/oKeUA6ljK+uFhySpBWrLWcIXAV+e3kqlb+ihsu/t39T8gDgh+xhqAApsJ+erDsyItMkFTl22PjidvvCn+i1/k6huD3/+OH+7XZX+nCsjfmQwwU8LzvOXsHrCH8p4jY9ZEycwWK/4AKY4sf9CTK8IDY7CH85u3Pmj1JIzWgLWjzQxJFAaZzadqMDGEQaCJAyc3EHlogHH3xbafg662y9PuOQOJfI6gcwlytGlVPUO/wn+CEcACDFqVF12dvsiRMXXRImcRFkmgpNVpvdoKGIE1wAjAP1QlvNVfuVEwwkjAKCIrxGSMdYXsjNQJCNEwjcYKCVU2cUNm5eJqq1qRe7XLkHaSY6w9bhKZKezFHkfCfML5wUphuwFaoC34LNSkLhusp8ilVNTG8jDGsMGwalLCND2w9AiAKFwlDDCe8a1bF7jyyvJLr6ocPBp+8D+te76T+trXfS97qREFldDCErc5Iwm+Bt1wYQiNSB2xC/cLl3rA9XbGvBd16k6liC5XUgJ6CIXlWg57tLjfevX5ZrsYBspZSHN8uqyjQ7iptpiRCFi47KoB1E1fqmxM5O3+sQy+HaB7v8eGZRVRr7xXfVfqwEy6MWw0RjQf6B5+1RUxwK+65tCicUkXxNrcazqxgEuRuG8DBERAUwiY9bDfZuRLErJLjNXL+ASDxplFMj+B4KkTEy9HL1p4BWMUjUrfmChKroR4CJc9apJlKKDMcbF3EOKgz0esAI3EDRejrZw+IN5wAGGRE/CckriEzH8wztHI2uOzOAJrDPBZHNx5quawxAwMH2DM4bB/hrtYLFOhKmdDy4182BwNj/RZjwxqqRIngFPnc3a02hd3mKGh/pErXpR48MFsMKB//nOx17+BDDFTHAwklEb4e2ivRwb0h/rtQ2OmG/JKI5jBlibnonYdRS4503xCaMmFieaeocpD/cZ+wv2lxOmXCFtYmMQCWmtE66lzzmtxLu/EGUO1IG/RWbx8r7nmGlAXLkB4QUMqgQDmP1zc1p7xH/QdJIcgEO6R/hLnDGKRsxI8xMnIPeQg16z9ZIgYLn56ygeKJYqJI2DcfvvtKsX0wkGC1oHtwbIdrI+RPy/WvOsQ3XbPoEW4KdS/WJeJaYGhs5DaYxZWSTtaDeVuNIWGJl/lj6x8R0t94AOR734vm6gLf/VOT0OjWqM19c86rtMfyh4gh7DlkInhSFIbzhN/UiqH84TJxOWSX8GUPHGVvYJBRQWsHoJbHQ07t3UJvSdubWz0JOJ1ELVAMg0M4p3efffdiIGQFuOjhd6MCzE8YyLNaw4GdfgdHIP9lvyTMLIOPklUI40aBOE00DD31GnbWkQXAWC8VdsEK83tmJMt2/fuFddfFBEBj3XZOs82cf0VOQLcr7zlllv9D1Y7BDf+zxDctZCvPiTLb5GxZhgZc7V4mD57LJl805vj3/1OMRI177gj/IIr1PRM62ktAzw1ZRrM7e4Ba6hoHh0habORK1p+r6dYtAy7UChZ4YCvULGZ9MYQ2Zs5WivbWj0S8FkW4uQ6WH6PFl4DOxqCD/PU1772tZxsdBBjPzTA5LBBrkc907o8rWYwHT7AxOlBFTbX0Tftlf9OX9XgoJFDRzRNGrj63eQsAhkBySo3zSDQd4UBWQ9K5D0PDPAS+E2w3lALd3d3g1BQCBNTGiMyMClYEvUm9VDD/GtvniYW9pNsOE7luoAJG5wI2ET69KsQWYaB3ym4QjCDsGroKkUVDLcGM0w0h1TBJndE0I+Zs/zO4VEsln7xwANgRjEJJMQW5zNZicmm/vzfKlxysfWzn0ePHsnd92+5/U/4r3gRex3eTR3xS+4mEgSGa7HHr0haJaapsaPN211vDacrGBXTTUI8pgq6Xy9feY5BUiiBamr85YtoWIEaP2oTVTAJEVwLZFJt+vOWMZ6zBsezOdvbPzB8/PjRjV3NHuJtuYdZTe8EYxHnoi2GqpnX3Z+JYkXssbKOBy+a23SJKAkwtBLIyhUKU4ZBEvyI5x1BuRpDhMKyiHrFE6ucz6UnHE/Qtb3W0frwVCypxPeEOFtIIXX8jcl4gm4Zphrdsgy4tOt2SDp12gWFBgZn9SLHgbnt6enhHu5XCWWYUG7QGbDZ8XjC/guC/3/giXfakD3NvqwxwE/BhEBAkA+JwJicR9/73vdwlGercC0JFPY7mxTqWftNX2X3kJkh3ZHmNASdi1olU5Gv7+TI7/xO/f4nMvUNwe9+J/Ti38X4g/2sLkqS4+7RPvuhQe1hMtAkPfkK1j4S9WFbi3Npm355p7Oh3o0QLeA52YrzcJ/96IBxYEzrz+jjea1C+D6cfkNOexTHDO3CNkJtEQlQHX3SIzoLu4uHM3wgFA+BAVRssKX2d0mD9FS8pKhbxf2i7oBv5KxUPC1IiHMQ8T+YG2YJceysAE5O0hIXxqxVLvohvYDYxU4B/S0TR3DyRUlqeB1KBVUnfcczcJq0npVLvOWjKc+RlH14mDAbYC4zqBPySttYR1ANqz0qjjtcLmqU5cSN+13uc9+9z/uOd7IBKn/zvtDVVyvA3MJuwcV8gAr3D9uHJ/TejD6aRcQtNm+Siwbe02Ucke/TMiyxy/QIEC5LLJNcH0LL6nTHnO1t5KEBi4voh6vavloJuLUzjNg+EOaUjYAnPPdwv4puw6pi7wBDoQ1kzGRWWV8bAICAGooJRIxIGw3zpoTTFYcAUy2caqLaFvXcf6B4aNyXzAsddmmntjVh4StVhYl3FDzqlVo4q5WcpRt2AYRCd3e3EutMb4VjbJYOTS/1NPl+ClIVF3wsOfYHNyfuu7cUi+l3/HP42lcwsGrMpwFcZYAlEavUYkPJYbYzUvAcGYX71QslvNTEuo+I2n0H9/tw+/YFEX+wxjrixrqYvYXY4fLiVP01K21aWyv7VS0bPtnRrGRkYSiUONYIiUf6OpioyUU5LzxqvalPmJZnOjnIemZAFnUkyszpOuQyphC1E6TGpPbJ2bsHbBATaIhPGGCwEnuTjsyFiVYcEjrLoMEDIxcmeArGUCI0nGPl8Lx6ob1E60tWLW5ATLt27SIzH7IYHMtRsmFoSkmgVZ9nA2wFCTVzChOOuD2u1/sdOCWfz/Hrjsq9Z1uwVAICytKJTLbv+FHbG0+XydCjTxQIAmWHXTYYVTAK2cGBAQQB8MAsCQW2EYn6tp9rvuyawuZzPHd+zfvII/lv3JU9ciz4ohcKQ6ZGaUk9ZJCr+3SBg6P6O/mp4Y6k4wqH/+9I2i6AmuDKTe+x4VI0aLei83Bxn1uzwFd9129q8MBEaRZ3aELBeX2W7h3LlkezWODbEyheY/Vhnx4SNYvbw+qbbmfhbBHgNgRtxpzXOVeRL0gY5zLKGxxD0LSjcnfCPs0NNyMCBi7q8OpIfsWUGvY7h136+GgpNWzGmsnkBHTQFrgX8Q8Cl/L4HxXKDlEnhbsuSuz9gJcplpJcbqemfzBliPzYPgiS+I1J5Gt1Hnmi+oHxAgERCIvw7W9/G35YTn73p+nVrX1f9RFYY4BXb8gVAUF7xHlGHcphRKRoLHwgAmAMqnDMtdmqBdRNtTa+YmbzYJ+1Z8jMWXKCtAbti9o1cqY5e3ZP/NZvJ070TjQ1hX90v/+CS0Q0RmDbqe08XpSQP48MGQ/3aSdTZsGSjOdNQWwsNWp4VieJwkVAplpMFp1dfdZjg/qhpCHJORC8abpPtxvCmGWSptzBT5gUwQF5Y/IVEC3pHwmRDRtMzA+SxxL9hdoW2EfV7jPxU80O5AVaPgYBh2fs/aD7VYhLtB+ICUHhfF01mmPhw1i7tHgLp76vf/3rLFF4tlkjtFXLz5xWLIIgTcAKJD1inasC1fLJor1vFJ0nelcnXREjJdOxuxtMwod0xEj653MREy9NrVeXeRN5D+gqNZ57zfXB4aHU1S9N3HqrJKmoKbbgzgos/DeQdvaP6ceU7bHCwuBY9KVsJ8BybaWon2Us6FfsptxOGFrIqyFs6qnXdzTrCfC8bJbpAEPkkTsK2bDCjqiCKQEiBFOKtMm9Do9WnkyZvUkd118yQmAAjeJXeG/E5pbTFMEg3FkfdbY2K2R9ehMu0UA1dOTh/squfs9wVvbm5nr73CZ9RyvmG5NURXVYgIcOMCOYZEtdSxm6amULvWH9EyVBBf+rvuOOo3wDxOmXTPuqQDa94TN/V2DLUkRVPzqWvPlN8fv+rVwX0/7pHyPXvcr1Ypuc2Wl1YQfOmEOGKv4nXXYe67eF+x0T7reMuZ4XdzWHpWFV7Hxq1PT7GhNRQtES+mVTnUM4dHeRybCszqwp+FV/kdgSweGGG27A+pQDAeNnQrwoIc6igAH3cZKwHRb11rSRfDp8xaYJNgyKdrEdUeVhO1FmsiOqZ+PqdAoLFEw3+QR4ViM3yDdpGjHl6gBAK4wAByCnIlYh8MCKeThj67zFBfN80UUXYV+KMwVHCmNIzCGSJ0FQ0RdQKmYmqioKn7HOJRRwoRArJLAOtm8dGEX7sPh1iEWMbzBB2lEFI4vkqpRz6eSwE2zMWCacFbl8kkUNG2Dgwq+hrampXCkPDAxhDTbJAzMy8s8w6+t8F11UPP9Ca/Nmzz3f9j7yaO4HP7Bam7wbNnK8VHu18A4qmBcrrKkdHLctHavvrc2ezc0OeZKIjAXjWnA8jw1qA6ny+ga0q7VvVM8owkkSHswQXa6JRbI46NochI6ZyuTSuWLJiI5mi7ZVqY/6XOmeVDLVNfEG4jAlsobLykqWEREMi+kiAa6EsyVJUrqIOTTP7LogflDcCN/K8UwlBE1AFVwXsCdS6XSuEI4383PREnU9GBbsbgmqleK8AG+MmqdQgQHWUyWgkChZNSBx4FcBO9VTVh1f0PROwXyalBkKkFX68pe//Etf+hLCGuL/IQGv6eCpetbuVnkE1hjgVR5wjYzBN910E9owoh+zWzCAQaCO8JJNovZPdQudCTJ2sVCNyBQfPGmTuKWIq4WudYXsC7pIGm7Yv96VfcnL4iNjE9s2x370Q+85W1163lVouS+O5a1dJ51Hh4xH+9E7kduN4PWYMVsXtusXtziXdRmNQRgQOWlpZTgnhfcMGkcmjMEsZ4Q89+P6GNY74hquj5d3aufUS64j4Q/k6NEIYgyKgunlzH3HO94B+6TCMCy4g9TxDL6U7rfK/TII1c4g8YX4U4LA6sOn1Q307j333AMlgX7g+uuvR1dAauubb7551rmDLaTYNO2u6g414O9KEgtY/Zp3ZemCfvYO2sdTxvG0fjIpa4ylWR904H67E/qOJgPbpxqJsrsKZVlREjszK/2Rj4Xv/lYhGgve+S+e1naxD3NXnWp3gZ+CRTVie+h7hmzSDg3lJSQGuEtxnuKAK1ywVOymdtXFJhrcKzbJUCh8aI0hInVp59Q560hFo147HQwYHhX2U1H8DCxpYCB5wYICpHTGGcmxf41jY/ZwQSPUBwoE2qEyS7zK9Do0zBGnK6af14ZRlssTn96Eqoe6+jLWDw44g1kd36rmYOWiDoOsv0QTMUTY7Y6dFJULU3zk1kwZPnWsQ+7V87P6KTPkXjNaYaAFPBnZchmakVIiqlMPT4d8xrur/WASVpk6918hm/zTtyTu/napLu585h8ir/7fbhcF+lkvpABQP+GwuDakSkQ6rQwXPEfH7EzJqDDfkFqWXc5n8qkJ/hSyqZiPIPxOnZPb1upbV+91SV81inO1MGuzy33IuiWD1ytf+UpMeFjShCbC5vlNb3rTEji3SejdP8sF66l+v3qS05slwALbCecGpkAYurQaltAorzBr2MGigGUqmdlzzz2Xe86BWiS1tJoX9RZdBhIkQZw/HI8Lb91dO8I/Y1ZDdOi3v/3t4FM6QnpFQi3isIO3BRutp6fnrI6qAoNPjtdEULg7sj/i0UrkUckbDI9HGp5iMZdJRWKJskYyMy1TkVhQE1nx81f+qz0dLRWrPDA4ODx0Sg/s1sxRb3i3bPb/r2cVL720nEpFf/AD+yc/S99zLzmj9Pp6jsulYb1FzdG0wlOAaQ0B46IuM6CXT447lqRjMAcz5m96y3G/TZ4C11ibLSFYmRrct+QGCpNcCfCipUx6tP9YOTOWzeVTudLxk33J8XQS4W8wGvOTeNk9+qdeFObUvbym0UIcMj9oETZUUCHRrVwGWPJxZjCHLunFsoVIIgAlKqDLRyFfSA4NhJ1sCFRf4RseaBnNKjveYNlFtZSRMGaOJL7iFSiTfAlHbiEGsiQKLlvxIHw7v5zqjtRdc6HPQHikCAD1GLkSuxtBM2uSPQ4VhMgGVxFySRIjkHWL6gv4a+pYu30KRmCNAV6lQed0Zku8733v+6u/+is8fhGcoxVEDwAC4Dk+Uchiu7u72RLcIxCFNlW6wXk2CaTzaF4jFPMTo2ZRZFh2V9y+sNPZFDMK3/9+8eUvi6RTyS1b63/8I7OjSyVrYcepTTeYJeCz88iQ9ugAzK0J9xsw7Y4oqmP9ojb74k6PmDG75w5/+lMVUgo/7qY7GsrJ0cDGDXvs5ohOpt+dTc7lXRJygANJPDhdRRm+Yei4sPSmF7fccgtRvriZpy8rMg0MMmcNSH0ee6oVaWghlXD84ebNLKtkA7V9B1sD4epwHQsBdWYZoEVzy/pkoZLXGtoIwneuUaWnuLbi4lvtIxOh6mQZQ+5Di/C1+qv66diEhTCFEOKHRuySLBvNqzkbm4T77Yk73QlKgY7kP3W5NU5Wm/vxT823vhUdWekv3iUKN3dVT6t/8rUz/EG9qu8bKh+cwKhBJ+8RrXlMyXvkcrkClWiAXeacT+54Amsqt7ZTHzbbozapj7a1SLy3mQAwDujK0ACzBqBxqYEysL5c1cIFy3l0kHzd9lDOxJSLijG0ZvwwuobfhhTAIqM7YRBYriFIR10xytSYTHZOAHSylnbf3kpv1kd4zKBRvnydSST29qibEnlqGKvzgsSNSeHwQUXPLJ/tpUi76MpQOiHjU8faJOTuH9Gwo0cdn3COHinu2lXZ+4Sdz4ldWsAvYgY5bJ5Olzvarr+aA22VfNd7ol/+UhlHwM9+NvTqV8uKmbHUZ4V+oiRRr0YKJrpf6DYc23R0IyjuNW1spC8zMVTJZQh/3uAncUiyOZDd1BYL+sW3ZPK/WSs9Ow+RfxGpEcUvslpIvQ9/+MPYP2PSoqZmaROE0SAcNbhvNbWOKzI8LGbIWcy/0d+CrOk+F/eQv7MKAedpFN6PcWAzcnO6iHCel1bgJwAGVC5aB11ydHPv9mO19xqNsqJgv7mpdqz2vvpwnhuOL+yiEdRyrlKMcwY2GHNTQkazurq7uzno5nl92T9NQs5xXR/U8AWL+ByvVuk/tLeQGixlJY1TemI8PzFaSI2bkfrR4dETx4/q4WYM98iLmy1b69qbvdiRjI4yEdMMZARhmKZv0zme5z0327Fee/zRyGO78//5i9zeJ/wvfKEh0eZoHcZMdeLUGC67U/NWIGeg6EU3NXq2Nuu9yVKmIOGeC7a5e9AZTpXWNRhBUZsC/TSQEBY4dWRXrvfEY9FwXSPC5OGxpB1tLXji3nhTxvZOFG1MlqKYRarD1AVEGnS7Cv7jdShPvI1Qo4OKwZU5S/IwE3sMc+iJkkEEDcJSSmQs9y08b4ZGhivlQtisEEeDuBi5fM7C3zocA7eX4HfdaAocvaiClf0ikmeiEhJuWvjqspFxzaEBSXVmWqf4yt7nEgjd/hLhnOA+cL+QRkqzhbwJlzfoKHRCUFaEhmbNzwiJ4oK79rGKI7DGAK/GYHMCsg3+8A//EHepD3zgA6x+hW+gQaE+yRjR09OjmGGQKw6TeAlCaqClAT/BGCPp5AJQdheEooKYm6G89mCvfWAUMRhnhQQNuqxT645ouW9903r96yOFUuqKFzb+8PtGfaO7Lflg/wpr0pfG8pl0R9ruAX00z0FCqCF7XZ1NoL9LWuCBPSH38FIt8ll29AN4SCadIZRLhMLHHMXntES19XFtJ1Gmu8wmSY/Ec5czcBwC3hIQBek4SB2rJHL3cTq4rSvYz+KnCiu1msREbWeqDIYMUTSK3QvHHPfT+g4+XgVxQC1gC7wHfkhSLlYsukEsHvG2QrtLEM5XvELcGmetB1oQQhALLlWASngdBEBhBoGH6qp9lxCOe4cduL5j43oS3Ck8kLYuYXTFtHU407aaeAMJV1vTIr5TPIAzdTK57PW/F+rtzz7/t2Of+qTh87tc8mmFa9uaee9Ok6BUautNk3/YAAyS7rrzBPDSLP/TIIVYucAiMT6ZR1cBh6KOh2G/zrKHUd/ZYqJldSd5+vgwMuh7kXdccsklbGEB0b2kt9K8sN9PDJXIe9SfMoj8rLho0DA7SSHUtqDWVa9viFvd9exJVyLAi6e3I2OiaT85VNg/5hsV11/7wg7jnIS9tQmFoZRVPBPvcaRglwHBjScqeic4c55ghQhszPJZXZPIwjjNMFlU/ZLuu5c7DY7V31f6t+9YH/2k7/7ve3/x88qPflTu7zPbO3WySU1SHe7kTL31VP11V4FqnMArzsTf3xr56Mdsn7/8wf8bf9ObmDR+Y4bnAo/XUxOpw0cP52zPk0kvut/jruWzJSk6CIAO/yvmfqFIvKmpra21JWqPbupqeMmzt2zf0B4gvpSsSzX5czYxV9NLeA60XLt374ZoIwYebDDLGP8dzvbTFvMSqsY0tFKhZg4KDsl5RmxJdZ/1l9hEXNg9clSC2bnhgomdzr3MC4jqNRwahDKvY7W7aljSPYRkCXEIMBFYZnH/VM1CFRhoHsyhYRUWBYx6nU9oKuSM4CkslQgxzVdQGGwwHlgMLCQBJNaiaqbwAq+pLiA/Nciu0Rgy26IoIe14NBLweaxyIZZoNgMRIxAxA6FSMZfLZOxIE/bD2SLBsXAM1mKNDZs3dNbHsZMSrES7U3XKjpfeRWOBZ12ukyppwwbta3f5f/2rUu+JQl+f/6ILKS/KYHlLSi4Q5mUVc1sBMPBhPGBcuo7EusXjSYeIMKCuvozn0d5iPKy1hAFG+sJ1CjBhWvWQ1+hsCCTCJoPh2BVfvAVzGFIrW7o/R5joklauWHHiWpm8PlnDZBVyAJLrSHIsRbwiqXZrN3AbgftFG1wkBLeYQwt2hVUGpfr8vuamZv7xRK/krnjWzo72etMfyWP9IKepgdmzmEOLG7Ao7QHVRbQ64V0LJSheTUWclhjgQazApuBx+8UJxibiNIBu50YZOSLLQEaGUcPmzZvPP//87u7uqnU0xACxVMC8RMZh6yG4kTlbnVlzAV77qB2BNQa4djRW/l7REJCb0BCQDjgAkDdCrfjaT+iJqpASPIqqjeMbggMBEtpgdhexRigPM5wvFLxetDoO3O+uXvugy/0St70n4Vza6XSGjcynP+P50z/xVaz0i17U8K1v6eHTOBB2+MkJm2S/+Gw8Pog7iokELuTldeviDuPCFvv8NtMnkrfJS40IMR7qw3qeiPNezKSJ+y+Wzz0JjfKXdpoxzH3YwO4e5jgg3h3mspD+7H/Uhng+qLpWfnBrauQo4aJ1NFocMUgWIE34fRq1XfPGyt8y11TKXGP0Ahan10Ays++TI/t0PfKI1024QkgHunPrrbcqBumf//mfIdSAvHbUGGE4ZOg/5prjnkXL+mQW4KMYAUgQCkDd8krti1QL1to3ZJ1MG8cnyDkkSldKxH1ofYm4ZmBOXx8Qe6Pa1+QtakEwa1vJ998S/fY9pXDYf+ed3i6hntVVC9sC7qVZIsA9PmQfxvU3L/JjcKYrqJF2uAcNshNoWvCkuycEDEN0k17Hagx61jVqWxJOW1TQJVdto/KOpsH1gRSxd0L6O60AiJv/elPWk+PmsaQznBcDK4m6hSGWcgy2JQlEW1xfF7W2t3ol3Ihq5bR2pE3q2TtU/uUJ72BOEPnGuLOtycAbX4QI7iVY3r3gctkRTBDzy6wxWShPeOgS8MlpepjJd5b9hy7RFhpgNsUspp5An8qW7v+u+Q93+HOpSr7AaeKzbfPI0fzQkHfnDgPHWrcD0wdw2YAtrQLmlR45GOF/8jbfX7+PI6b8wQ/G/r8/gwCfuQxmNjE8Mvjrx54crCTyZpx5nyiJTBO3NlPDb9AW70Gb9Cpm2C97wUn3X9wT7WyMqj0k9ctQTE3nzNpX7ol00bbRoWG2h0oN1u7d7343mcCVd+Xy5wKUBxXIadnT0wOyWznAV6Mm5HosZvYLMh0ORvgudHcgncntthgQ2ICMM3JbXoJDW/7ALqZxDZXUU8v9VqEFWeDKC7UDIcSQ8nyxQ6EGn0/WKjwGWVg5eDmBqZNAWaRdROCCzg36SlEFi62/CupcN1UA2KUQUeubo+uaI5jw5FIT7V3ryYzHwVEqFkr5XKWU9/hD2SyKU4ts30QwJWFSoUKWJCJp2XsefSRRX18rY6rWbDQ2+S67rNizwV7f4fvs57Uf/yj/+O5KIOzbvFE4ODkeTsdDc8G6vOdyBlUvNyz2xnrP5hanP1maKIrAMm+be3pJg1zqqff4TA6samkBT32B4awP6AGS9eZznV1dxMROZ0tExrL0AKFYCTGD3pVESiQiFvmzi4GBWlVFFTTTFNabQkiFhYSgTpyu3chYEn87V9IzRT1fciIBSc/pHpsi7snl8p1tra1hTKkddNG5ItSISC2JAo3zEZVQE/egfeTN/AePTATpQtlQqa0KtlUfMHxu5uETJ3sP7N8PJj106BAyZWgh1pVCcLC7nAmcD4qSr+m83FLsJS95CfMLD4yRIDb8HIBu08ublbW3Fz8Cawzw4sdswW9AQ1CWvXH11Vdj9vCv//qvSo02z1rnJzAiCADuERszPpFcQj2jpaEquJFf/dd/HT9+8uhw7qFe59AEoeW9iLi668nWq3WG9dQ/fNL7F+9BoZZ90431X/wCUW4UDawgAZrj4/aDxHw+NEHumYmyny2P7ndDg30xfr9tzvYW8otOHlC1veQgiHi1c5rNbU14FzstYTvhs85v1S5o94ihizpyacPR3v/+92PtDEkNnXTXXXc95znPYbfP09/aVpZzD4mGHB0yAjwKM8YNlxLJL6fame+qkVTPa/ulnnMIYvuN/I8ZnMtgeGadT6snUA/ocmFcf/zjHzOD9Ovv/u7vkFMqfFwLKqwvpgp0FvaG5whrlOATa0AM0em+ytwIu6UIDpfjo6AzkMGagFBq+sFRu2yZRGHCAGxTo9FdpxFLmZDFLEG5XAzEX9lF8kf+Fh54wHzr22Eaiu/968irXiFOOe7llljUBwo8bd+gRUS3gZyRzIH0DDI60gj/WNHgKblHhiJfeCgidvH7dQs0RMyuuLMh6myWqFRSehLmGhAg7jGFYvPu2LFDjYD8SI0Ula44xNjA95i0w6MFE1SN9k/QoESfBifrkZCb9yguuaDQMItS2EXz1RaqS3Ekb//bfm0wI1ZbjV7n4i5zR4tW53OhOh0udgQkLxd7hOMFJA0PzA0zDh3MyTNbP6oNLuVGAQmaxx4EKR77YkYTjnbyRPG2TwfHx6BkihWcyQ1sSaWzfX2Vc8/1rO+WCGen930poCz4nerAzgBVzZ1YBuS+/GXvn/05Mdtyf/jmug/cAjmm1L8yuTOuaoWsnf5kbteRrBVuHi4FJ8rQXk5F4l7pZceGaMYl0+/F+93eWG82BKzOQLa5LsKsuZDMVvWMtpb5oAoqTg2E7v/oRz/KYc5G/ta3vkUSGgXJLMOypFaR8HJiQCzCTy6pgqfmJbrPdoZm5WJVw7DRBY7HJWA6NZJsCjSfDEXP6soCaL16PTVDWdMqkMD/ExIFsQjUjpIm1Py+iFvVKaTP+B+98Y1vJODoYdeeDsEu2uAf/vCH+Dxz4qkzmcKLqHrBRVW1eKLGPCUnn+xqazSd0nDvQSs7jgbYtux8eqyQSZaKFStYh+qSoCqoPTGKPnR8oPfE8YnRgY72NhZYtUFVoWAkwwjs3On/7edn4N3sSuR791d+9avs4KBn81Y9FhVmzr3cbi2la+oEEAQ1dVGVixPnqG3qMTGuLu6CGi2eSOIV7LE1ozdlPtZXaIjCpsp5Xr3kXg27rjfGwtt7GmFTQ8FgoViAQSWoTdkgVZL49IIiUdvGg+jV3bPXxZ2Cj92LG8I+t0Z1kglPPpiMjIXpsqT2RXObLTo+045J5iOH+IM0W1cXh2cG2s6YFvbZZA0s2SLChsnGD4WLL4jbMc5C2SvoWAMr4RVsT+QqfSO5sWyhPuonItfBJ5/s7e2HvEH4xc4Fy/PJNHEh0uLYnEkyuUAKscD1vOc9DyIZdxJYA2xqqIeHqsDa56qNwBoDfHaHmpOXdEcwY6h/WfELxJHuBhEsC3JV3K+7NUhIU0Q/0J+uPDxg7B+xxiay6YmxizbEn91ltPjt9Gc+HXjne6DQ8zf+QcOnb9e9ZAuVy+2h2I8eGyN/r/bIydIvd+0m+L4v1hQLGZsbtIvb9Es6tC2NaJm4pvOr8kwu0otz3BjNYWNjg7GpgdhXwi1LefcwSybHb7rpJkJ80dx11133xS9+EdsPhWPO7hC7tUO7Q9ZDiMCCQk4hOOAecq0WhawUGPAM8HgzDyyYCmI+8xN4l6NNhuyZdgGzErgwhjfeeCMSU6LdYP2oVNnTesPYsjihWhgKSkJVoBXhIQc6n4R9BgHgAUtVLGN5V1hHhzQQjw85R5Pa0ZQznIXJlFHqjGtdEaOrjszVJnEy1CKs4jnVLijZyWZyv//G0Mnjmf/1nPp/+gwpBQGYSxVY1Ce1HZ+wDiSNE+M6tscw0vBYmD8JZnY1ze6d4ELLxa00gr2Xjb0ERl9BoyXodNdhqj2XpxMlbYg5/J9ZCdMNI114ETM/PmgfmzCGMp4xid7OfhcZtiieTR2jiuYAeY/Ynk5rBOpBoOKa1keKl2zn354on0h58B/Ge/nZ6/Rz6kmVpLb+9PLV16G22S8IKZThFjzwzPVcLbzMG+wCEJMjLkEhM0srEHH7Dxjf/CZmLAyuhY5aOGAh5LBOqbS1G+ftNAIqXPCc3VkmhLWvs0SxIkGaw0aYOeCyhoma9vVv6G95m79USL3hxsZPf0r3YAHvzs58AApJmSrZu47kD48WcmZDxQhgukh+Smi7im15PSZWfAGfGfcS6pkAhBbGOOvaGiNh0SvWQni271m6mIH83u/9HrI8JuKP/uiPCASAZmNlD3M6xSmB1JJTAm7kbHfqLNXPPuJwm32pLKxJNQ4ckuzB5XB9C2vt6VuKcVBIHFkAOgMksJwVywSXOpkdqnrDG95AhiToARxSME3CrgGDFH4FZ/G5zFZme13qVDWbHrMhHu5pCTdEzaa6uvqGRo/pKRdzDZ1bPJFGXzSOVJVITritEsoYTtjyRTOWZ3g0NT4+vL69RcIyuQCq2vjkm3x6PMEXvMDz4henTMNz4MnwvfdmDh8qlSu+c7eJ0w6l1H+zATf/M863oZx9YLgCUyppDcTB+FR35npXQBJVsL65wdzY5JxMlolpj0F4xvI91mvnSqWeBshFqYz8BpRUl/qL2UtTyGiMaNFwoFSxEPjZupcUm3j2Fksa8REmFbniBcgp6r46+UcAQ8PcGjVjfhtzaVd2rVUkkrO8VXRzGqEKrlh2IqTHwiG4X15Vb2OGTVhp3LYJR43SGPMvai8RHVrxwKIKxiOsUi6L3eVEBta3OJEpkM/Z8YYJ0t/VUnfOhp5zt57b3tGBDEuJj2UYXKUXKhkYY/V15qCp7mOPAJFMTCzOWywl2f4zS649OasjsMYAn63hhYxA/wP3y1q///77If7Uol9qe7KvwpGQL9E+4l2XNIlbEyEk7Pq4/bs7Ek0BO3Xbp/zv+Uto+L6rXlJ6//txniCnumoRkg7u91CysmtAf2zQId3o8NAQDo5NLS3bWrwXdOiXtWsbya4hLcgbc0CoKpNfUU+hK+OUVS/wOTGRQs5KgDsKoTe44447VtmwitMHNgNsh0geYgJOjHsIrLm7M0cvF/AYoge5BkRbtXLh6woF6EV4YLwcie9XHZkF1Pd0LELeI0L2Q4LcfffdcHHVnk6DlQKMPLwNPsAqyBOJGflKwEMWANp4pgNJBHOhXgRDHRyRyM9HJ7RDo6wfwdJRr72hzuhucLY16I1hHrj4u6YlKcdis+3ku/4ids+38+GI////sqfrlMscg881C3NVU8m0W1iRx4c0FNEjeZ1sCly0IAiafyJkF7mOaH3de6CUPMCwwIZESm8IaT0N+pZGzRVsn9oF1SZYD0hJIO5BgcgI3MqlmFxSrdRFAuTDE8Zx2G/wvJtXSRISuu3SKsbPXQn04fY5jcCi+j9Vg6rH/WQ8f3msvHvYN5KDtHDObyESu3NuM4zwZKG5Jo6tAfdLIXSzjNsslsk1rSzzlnFAMIQgAA0wVU0HCSLj+Inid75jFwsMGj0S8XkFsqNMAmR7+w7PRRdLNCx5bapXBO10L8WSTa9weeBSGwwwAp2Z9qjuvGmZr33D+JM/9mfT6Ze9PPEPtxvBkCydMwAB5A455/YM2CcmrIPDmD1GcXqht8gQEWFI4t+KFfaSQd3paTSagtYFrR7UR65W+VSvl9ezM79NBzGG//M//3M8/9nRW7du/eY3v/nHf/zHamedoYtnrn56CcRqrA1ljMBvK17/9PbOwndOObjf5QMP7ctoUI9aY8/EoViR0VUya5TA2JaDd1ZE5sJgcvX09Lz0pS/FioEpIz6FssjDLpq1jaPmonDHonpqktk+QDoNJ+43Ouv9dWFDKxVJwNzS1ukhHa5jggzk0NN0ScBTIuaw5gnG0hWzf0w84NZ3NooJsaCjyXOAvqgDRzZMJBJ4wRWlLVtK2bzvBz807747G/Hr0bhntsgjCwGb5QcY3z1QOTJuon3F2S0iaS3lknbnuPhJXaxfgEugCu40rUqld1wclyzdc3TcfGKw2F7nxAN0Q7AT5aXOyRtKoYwVdrQh4ubR5YS3nWS2krUMN7yzpPklG3AMYDATmg4JommnPmg0RAhhBS7HWkrUuW5GX1cVXNbSZUI621G/FnRNHGlYKiHuJhmGo/gqk47QzhRsxJGWbRVw5CIaFmJYXc8SM7pURk5vehBc+E1/2BOMlNwkSeDQxjq/33UYnDYwqGHAILUk4rQC1a+sPRRjGCZgYoOh6HJEadU6124WPgJrDPDCx2oRJTlHsLfB0J/TFpMbhLtTB8QiKpkqKuSye1ZoRLjB7/fJMd02fKGAsXNd5MrtieaAlr79U773vtfjOL1X/e7BN7yxb3Tk2NETiKA6uzq9Hg9HyRP92d2Dnl8/OfLYwcHRsVQ5M+S3c90Jc0M4c0m7saUtJG4a8m++Y07BM1lOCsqlaCa4X+IuIssnxjUWs+At9at6ZVGfVAhJBPm7NJyE8BhOWJ0jwLCophdYGPCwbq093SDc/+u//gucDYmPUJl6zlLTC4RwOcXU+L/uda8D+yK+IX/VXFRIdYoZEPoOs8c4QLtAuDD+IABMo7koVijkM5kcUYdHCxoRpw4l7cOjTt4ykLqi99vQZJD6iBhsmxvdNATu0qILwnESlAKAJCSyXXxol/42MX6u/N2HIq94JTRBdZzRtWJkgdTjjMPu8rF4+wgrcniCwFc6KRlpR2LMYdgKGeKuXB5xL2pIlhDkiQRkFt6U1hvCBoGvumMWSb+QBMF2zlxkcGf4m2HhCRPFalTToWCTzmj2SN7aN6of6M32jo1rnrBqlxYkIKWGrNpsDduEVT+vFX67Sn6cakckWgCl2ceS1o+OmsMZp+wYnVHr/BZjZ5sh1l5n2s6IJICEmQU8BBbYHDJ3cJ5q5yqAV+oT03hs2zZv2ZIQ175TvVCbhOEziqXiL34RKGQ8hpfwax4C4KNTxfzP9DvXXuM5d6smKtbT9hR+ffiDzBphbslgu1Mjb7OY0Vozd1NVCcysDmY/9x//Yd94QzA1MX7tKxJ3fFbHopseuedMTcem3pM1rFYx5u727kHSXHkODhYH0ziwR02vD2KsXLb9XpOIL2GfGcXvt0HyV53f5vGLm5nsMFkRp43aqcpX9o7uIw0h0RFxg2gXjQSJjpDbSuvutbLNqWpZdVgisPzOUhMrDvO0ChXYfE57vtivqgYWHqJVaAZeX36di4XhKS+vBhOJIeiVe/AIPMZKQaXGE2ks7jykTeIAATdhmUKOA0KT4gwC+uAwXNlhlx6pDshfcVvF8jZiFsr5dEdHEyyWsGM6GkiMZqUomx2PU/TAZjCaLRl94/n+4UxjQx1crUIDVCYVudVyI4eLofs2bvBfd206m7Mz46Gvfr1w993l+oTekNDjIpqRV6agUF9n/XRxCr84+0bsb+3FJ5nQx0STQpJrRyeDUcl7bk2TfZq1HrcIoaeMrU2edfX28WGUqwL7eMV89ATBWitdCbC8OhVVbVKrqpjRaI6acVCT5pzs7YOAcMxAumxgIEVeIhhyuNNY0MA8Si735JVX1RcNX1/Moc2A6cqzlUzBjQtdhPutSKT9bFnMoYmthTTdbZRPzLq0zOgJK9mXHh8aSRfyRZGtEooQK25E8eKO4/X5AwHUK35MJA2zaFMbDtvYVzvFMjy5RuIrFwQdnp+liywe4odPpFp8csTNQ8oyNYiGcQMmS+hXv/pVWAbW/0Lmy21y7WO5I7DGAC93BKe9r6go/CfhCZHuoPtdJpUmBJDQu1qyINzvE27UK49u90jMZ70t5KQ++RnvX77HtO3czW9q+9znmtetr6tLsGOhZXu6exCGPd6fufOnBx8+Wf7NgZN9B/dkxntDVro1WG50hhrK/R11nvZWOIcFs6ycHmxQl/UFKrY6gis0PHC/MEugluX7vqIWoFpw1RIOAsgpNMBng47nIEMrwomGeA/eBhaCTxh1ZR7Mczq+c+fOs9H0tDV2tr9+4hOfQAPMqU0Ms4VwlQwF4gA6zhrA3oxXEL6ACaAw2A6MTJohy6TrGpv2DTvoPI+m9P6UiGkRu7RFtK4o8Z+1C1pNyd3nrkPVwSqatSk7Npa+7tWhgf7MJZdHb7sNazlhPKdK0BZcHO1WR2bulcNegm+0D4/rJ4QBFtda6BLcbnmXLrgsi8NmADPKXuaxm0VX8eF1Ya0tjMG2vbOF2B54CIm0eQqNyx2VYFSsNAzwk6wN7AVQg2MiVSXmUDjv6XeNwPsnBvoHI3XN0izsLOE4TOJbYg9mIZza0ewgSqcj6qp2Td3QWLqsfWdvuS/rKRSdqNe6tMsrqZICRjqTBoUri6xpb1W/IrMgUBmAKQkFYiMu7iG+aa5abMk3DB08JPOSJ+1iodSFKK6tnR5KhTD6qt6plmwT47WK8che9CQof1lFXGjbCxdeGLjuVWZT46mZngKI84Hdp47W0yubKrH4v+xu2A81Jmx2dKHccxON4aHKeDvF/ftKN7wxfPLkxPNfUPeVLxuJhEuJufMzV3O8p9nM1O6BykheYj6P5iwM+X2BUCToL1ds/FTK5Uo44EX3S9Srpkj5vBav3/UoVsPDWK3AfMwFHr1i5bnX9773vWuvvZZVATuK1wMBWhQpNjVLc1exjF9Yb1z/Dc7MZYzBqVeJ2IQMQqGwszrsp5p8Wt4hS8UmpXpgAuOKjAaVqAvUhtflDTfc0NPTQwQj6A0wHXnvWYdYPXByrlSLMrqqyck/spVjYf+53a04toQ9js+LZxlRRZGtSewlYcgcgxvRA/sinBtGIF7UAjigBn1oL0VaWAvbZN0cMYT+euEVvle8IvnzX/iTY/677srsesg5Z4u3o93Fk1JQgJn3kjNO0x44bj/U7ybXrcgn9sMI7wKmExZVMADOV5WCR32CuhqD+oVdZqZQHEgDtlFxPPtHjZNj+fUNxI6holPG1W6lk5RlXUDviOD5W0mOJovFgukLEheaf7Cd8MAYNhMgEF5XIYXaFrnHj4ksxHWkzxOxNSenXoa/FfWvVrR0ImOliw45kCqZZN+JYwGXr2VIHvz1rmxyMOEptUS9gUhE90W8XlJLmKWKWKMhkKUmPKFEPM8/cRWWOFtE6qJCmGrmxWWqNbAqoRPQHICbqJYbLiVfBra5xp6fIF2UwBHbySuvvHIhRNdcta09X9QIrDHAixquMxRWxAQnKUEIcZ7EwEZxcfOs/nlqlNrYNS59kioSutneP2yi6mEPEi93kvv9xCc8f/WXHkLAv/nmxMc/rvv82NvE6+paW1vWretCPHh83HrwpLU/6T2aD1UCjf6mdfX1zeHS4MWbGm94+XOvfM55hDP1SMrh01zwzwiw6umvfvWrF7/4xSi7IFjf9a53YTsHNXPGd2ftcpUO41dODT4ZOlVyIRVWX6cjIM6FvDIrGPM85FCDt4Es5obmIJe5h7tTXYZqBG0LB/VMvugXqlTc/+gXpuy4Ti1wJBkKDno4YTX4cCawxBDQWJOiFV+/vru1peXwuHZ0woPr78FhuFBRUwY8FYj+DQkch7SWiPBFtc1V5xTqYPxD/zd2z92FaDz0ja95cEdkU+g6LTILYB3YISQRNM09vJ8SSZw+D4rMFyQ0XnDIaH1kVB/KO4jeaYXKhNF07aZc/C4GVOKRyiN+UAUMnYiUjUFJU7y9SQT5qAT3j5RJ5GiYGvo6iVPpQg/Xh+8DqRc2bdyEJhwxMBQVN8j7aYm3nhiuHJ0wCII9MDJeKRdj9Y1u2EkJLm3gmBA2esh7VOesq4MxV1VOdkU67d6C3GHY7z9QPjThH03mho7vffaWxPZms7seeyz78OFjuC3Bcp8+Aqd9Y6yQE7Fo2bkMGnPEfdWR6bSiS/3C1Bw9coS4I22N9bFSWevvq5w8oWXSECaGP+Cq16V7VO/4/Z7GllLAZx08UinkgY3g1KXLLvf90R96NvZM5boUOGBHgRbWmhln9hlb5h1BgzDMblVLBXayciU4oBXmHD6EiUMpRHQWfi4+sa/wmuvDe/dmL7ok/OWveNjs0qL8m63lU+uNQCyP9Vvofo8m0QPrXq8nHEtEQn7COQS8Jm57Yb+nDu4X3W+4cl6zCFbmqnQ5vZv1XXdpa4zhBz/4QUydWbqsBJgBjEVXgSmtHbja+1lB/Z/wkFMCZgw9MJkRGZD/mWNCr6toFKkcS3R+Wd4SFoY7tJKjAVcdgkUTqhMnTMyI7rvvvp/97GdgQJ5DSKiaV34WJNoDcZvQduqkdoe3JIkQ6BD+SuIwCY8J0WeQg0f3hRwzmC3rxHeYIF6xjUGyqzSegoyztAqejFo4FLrhDc5zn5d76KHgo4+Vv/zlUne32dxqRsXbZfICqbgNyNF1+kWzeUv73j57IA+zanAPA4yDDlmFJC+RJcGoCMk8Vc9sx97pFfIN++0drZ6WcPnIaKWIHZVjcBI+3FuuD9nNUdhVtzYFkpx66jth8PUNLaFYUB8cGs4Xiv5AsOh4iInlZuXFK5ixsuuCU5LUyTqE4+WiF2DnpgiW1pM4mXBWZAkemciNjGeODSQPHx883juQS4/UR4OQKJzfqC56Nm48f/vWCze3djUGUPTmSmBqAahANCxlC+ZmCYa+YwAY9DIZksg8bMFai27Z1C3stlARQw1C/DAV4CnCYnFPE4oomjE2px4wiRQjRC7WN+QNxUiBKDbVmT1Vbu1upUdgjQFe4RElcO7rX/962EK8pyAxl1O70Luyt202/EP99r5hdiMmQUTfsS/tQHWmZ7/+dc873uktlzJ//ObEx/5eJURl27hvCW1zfMImXNa+Mc+JXKSkhT1+X33Yd06D01g5eWl37LLt3Qi3MF0mLASqD4yCQL1wPuAbrjNCjtEvBhuYvIJIYH3f+c53soeXs2lRQMFH8Ql1CyVKB5R6aiHAAC1kMZ9V3HlG+BdbACIbyRxcLscZCk2MAzndlLJa8cBnr+nFgrrk8ow52apRYMIOQQcv0CSMNYP/M0OB2F5JUtQyYBKHhocb6utZIeDvJ0a0o0n70BgSWZeBdeyeRnNdTF9fJxlrxTFIeAkXm7kdECaUR7aTf+A/tD/7M0KFlN9/S/CV12KVxGOKQK+zdJXBKlwQN1w8h5GrrYcnghld7Fi2nd2DFVx/h3JGSlLmYuNEk1igTd4L/oT6oAEX0zMgQq1g66xpDWF9fYKUvE6Pq/nrS1mYcz94XPKKlRzn0JjTN1HO2trQ4HCpWD7v/J0oPDvaO1gwwDO1NmxScO/pKz32ZH/f6EQ+VyxXCpVSqVzIVSolrzdYHzE70IfH7B0tiqWbpAgAX11qdDBXe3Sg8mCvFw023Fqznry0p+68rqBLnRijoyPMwjwMMIMDPEgruNg1DFpPTw/3bLRp4zbV7FL+UiHJFxN4Gh84kP3iF/QvfMX89r2ln/3M6h/Umxr0eBy7c9WcDHA0Zm7cqD/7cnvLFvuSS81Xvtp3zTWe7vW6D3s1fpcLIDgZEEKhVEcIxYwjKeMe/m2ZJ4/qHquUQeOicpYunvzxungoGMJJuTw0mHnd78cefji7eYvnjs8Gt21jdbjLQsE1fXzc9SYf2Yr9GDEXip5jow4mhfiYSdQrj162Kvi6Kd1vnQ+XcqM5dIr75cXZ653eznK/s7w59jnGCXNFiwQv5Aa6bXVaV9AzoeS/geDjXF1uf57J78O+pWMAAEAASURBVDPmrEDOUrg+Dl7W4WrOwtNw5DiXsCxjg4NWps7PFQaTEWaosdR729vexn4HhRHD4qc//eknP/lJaA9ssJcs0J8LUFqsXpxrrtWuHvc7AQ9J4F3OGLcI4jC5SAj2FpYvV7L7hieGUljyBlCE4LYaQcM5hSipjbakTvWJ/0hXl++mG1MnjnuOnfDddVf2zq95rn25HglLagHXYknKu/+dDqScV/1Z+/6DDgha1YZkNqdCSVUIpyyOuCQ3xh5boSXV9OmVnPZNFQDU9qixo90cThVH8qJNLTrGY/1aplDoRgDrQjWtKo5W6IGOxlBHwjfY35/LFwPBsG14Jwqw6PCcerZol21J8wuDLb2Z+uBA41CdGJ9IjQ6WxvpGB3sTDfV40zCcI8k0auQKX/yRQKyhqa1rXWtjLCSCeBhg9Eb4AXPf4OZw9nucTEHSGpP5AX9gZkSNL7bRaNSBDahJm1gkS7BrEc3gYJ5dHzICPqIZGmAQ7LA6OzohGs/I/aohowFErpzAGI2y/IghihRM/bT2efZGYI0BXrGxhSDDaeqmm27CkAwyAvqvuqvZljCKaMMg1BbRnkuLI4d7uN/eM2QWKpwk9vq4c3G71hkzM9/4hvHWt/pzmdRrXttw+6d1P9QvBdyzwKX2e9PWQwP67kH0XYgP8WfQsLLb3mJe3OnZ2WC3YMzZiheKB6TLBcEHOwH/CT3EPuQCTpLcADP0JdVO28agCvwWMBGEJyRiCvhDKWyrACyim1NFIWcZJWhcaHrgEavZdBqifIFyBBTRAMlZNlXfyv8FDXNxuoGbodhQ9JGimcnF7pfnK9/e6tbIAibkFYms6NHHP/5xYjMwm/NPKCV568EHH2RAMB5DIlBbnp8y6RRr3vD6Hh8qo/Y8PmGcEBZVEDGppLviGqrO81owBlNYbHpzpCCw0snsK6+LDAymLr+07lO3U1V1nUO7oGfmYrWwZgioyL0iHKtgsISYo3gszs6AnT40Wjk87jk5QeRnkeuCyYT1xRhb3JLojUAmIl73nhu4Xx7yX2PIaI/Y62LazhYpjOETsayPjTtpy4tH8clxLVl09o6Yvzlu/Xzf0FDeaFm/xcqPtTTE0CBOAeMQimPPoP3kYP7Y4HiaoBxlct7SuF0uFnEHa2yINbvBpXcwIBIxczoXxN4WEDVtIG/fv9/pTZYKxVJcz68Pjl+yIW7l0yPDIwie6TIjz7blhmWpRBK8NesFncfGZ9z4dQrOWQsu+iG1iUH5k/sH/vp94V/8p7+MWqHkLxY9Tz5Z2H/QQ4LfhnqanKyX0qGgp6XVu2WrZ8c2c8N6I1Gnm+JcVQsVpwHLSXnnwroTcI57Znza6bRoWGteYIRZzFROzYrOszPZ9Nv/LHb//bh0++76RvCSi11LRDlsp6CveX/yVuYKd77d6H7z5tExmXryTLICocXyhZLPY0IwEfAF7re7QUdDsqMF25UpXn+eimc2tdQnLBJUXs9//vORfhJ86G/+5m8gvDj55+vWUtua5z0wDso3ppWje5WbroUKdAMrTvdZS7XPV/mexQxuxXqiu7v7vwFOWc7oQUGBahEHYOuBGPHsrQ1qZtKRaP/BH/wBxAZMC44AP//5z/EN5nn1wDlLAICDMKBtixmk5PGhDZZ0G3LqiRmKHEASwXgklU9mS6iCy5p/vISs3yYuPmZH/Dr94hkXwR2ufqnnNa8d+/bdAQbwH/8xe/hg8MUvtuSM4Wd5SdDe6dMD9tsz5Pz6pESUAP0IMnQ/UXXmJRGuziGGVzBhGmJTimjqOb2O6d/cAtIWeTQvaPeGjPKRJP5Ggu+PpT0HBkpd9fD/kzCplyfrdA/f5liguzE01H+MfcF6KDt6Jo8AXctbZqZoZCtWyIP3TN4vfLABAYk6B6qV8BCDQyOZ9FjYKJ7T1RgK+lHaEnLMB5frDTtA4Q3aZBu2MN6y60KQAXK5rfPHifq09qhkYs+XLHoN8DgnERvMhVLuoRnEylAigEjmYVIuFTC0LlHGjqPS9xj5XA4/F8wwp6qdPixzfcemCetRPCghwEg+19PTs9ga5qp57fmsI7DGAM86LIt+CDGB+f6b3/xmzBi+8IUv1HK/qi42J8JdRbrNX7s6mFxKlwRl2qP91uMDBiYcbL+OuHZRm9hGFh/4hfZHb/aPjGRe9or4P3waExd1FCr6mFOLVKs4cuwZ0vYO6uNwv7Krna2N2vlt2uWd5qVb29YRH8vr43yHcYX8Zadh/MaNOu4hmqkKTSDoB+oECunIkSNwp3STkwg/BxwVSFfA/Z133gnnQ38hICCml0PE8C4AcFEPPDnJhLhfeIUQUhReILc8/xTM+isnkTqMmEcghEqGYGIEnv3sZ6+IBeasja7mQ5YojihwTUgfb7vtNsgv1d+5YFCLjXwS4BsIFOX/XFuY1dXU1EisHxIOEQfy6Lj+5KjkuLUNO2BoGxox9CWfltMRUw2dhgXZBS4Xao9/5KPRu7+VjcUi99zraW9jGyioZDKmLsWxIItRMPO4CgbdYd0CHigdL/q9I86RMY2YyZh4CfzURuNiUewSGy51AHrjEeVhV4CBAiQbrA/bPXET7pd7/IH3DJbB36RxwnvLZzgek6ghJumUhiayKL2zBcuX6IDZIYtNLDjpXEDI3ycGKodTxmgxUPAmoolGGCJou5Z150bqm8IYJIecnnpjSz1JHVz9Yk0vVHfcAXdwIb5vX+lExjh08InyxMimaGZDneYvTRw6eOjQ4UPsRDYpEgE4YVYpCJWrdkCqI6NuKI+Wg5map8y0VxbyVdVmp1Lpr95pfP8HBBBhoMlbiTwc0ss7MlqKhI1zt6KGrtbGVMjUEsGa8PVQJu5Xfq0Cxo3i5zmd6BrEMUIoWOIV3320wqAFghInjKUx8ZfvjXz+84VoxPOVrwR/67eAQgCtAazaBW6QCKmvEEzofocK5pExh4wgFdHpOBZ+0ZnMyMgQJHdDNBxzo161iuWzpHN3+39q6dZWu4L3atuySG699VbMPRCUkI2DGKQ4PjDCXCvY1kKqokUcX5nHaeKzhby7gmUUrwWCZjmtYLWLrYrWkeVhhcsiBzWv/nQsFuAFllerjsIL7JEqxnQwFBAh7EfswxfybrWhhbelSlI5F7iPjYBNBL7BLEtYETzaSOZHFiWoi0XVSeEzXrRIGTY+et3GkKcZ/1OP7UMya0jYRaxFcPDg1PP5/NlCeTxdwgaQuAHjMKIFwZBkKgIBTbZSs3OlWo6pWCR8w5vKO7Y4d9/j3/fExOc+53R2+bZuBbdNvqOad9/nCXjtVydKyaKXWA3ofgHLhU3EfQh5ifkEN0iArnSRDENW2K+RRkgwpFuI9uRmtmvyB448XeupNzc3aidGC6mylzdSJc9DvZWQ1yKn5tT7UqHA5SJn7uKRwPbultYGWFYQiIgDktniaKY8Mp4+dnJs94EjhHs9b8t6hosz7dix4/X1CdB9z/r1W7aeu3Pbue0NIeJWojpiUKkSKaSbIlg+cQyGpUfZQmjuamAtdbQHPFpHzEwESdiM5Q6g4fGEF7FLDkifxS6aECYiJ5Dw3ZhD6/yKhALJeNjntCbCba0t0gPVl9mGZdZndBzS91WvehW2D+RgxxQfsnyxlcxa89rDWUdgjQGedVgW8VAduJ/97Gff8pa33HjjjXwiTVdEZ+1ZDI0OYoPWrFZdc/hUn8kNm0a9iOnF4wP2Y0P6eEk4hLaofWGrtrHeY+1/svTa14SOH8/8zlWxL9zhIYSmnBhTB4imDeech/o0srzsGUQxJZ6HEa+zuYnUGvolbY44Ckp5qbP2guxDpYYIHNQrYOg69CUYiCfsSQ4XWFyQEKQnwYHhh8EHuInyBG2JcswD7NoOql5Rj7qZ/7MKCcXgxPjEClc9nOdFWqRpaBcYCd6CiuIV7iEj6M48Ly7nJ0aJLhNAklmG+2VwqG2B3VxOu2fvXbXe8AMkkRWdwiFKDf4ZW8Q+jcAtDAge79P4EBkQmXrwh7OXhEPjztExe7wIEtJgbtY16N0RYl/Z25sNyfYydZ3WIrjqJz/0vOVtHtsuvesvI9ddK4ZRsy0n1iQsXy3MqkfUxtpAndLahscm+WURq2sjWYnqgb0ZNEcmOUoZ3fSBm6kZCgCIJTaXaxSNBAAUh4iZ5EzrE8amhN0Zp6BzdMI6MmGeGINi0EM+WB6naJsjOWGcgQ8vVoylN3W1drWGNzWIyH0SZgd0ax8Zs/rTEv0DxXMxm7UrhWhdE/e4/q6P6etilXNmswafGhaHAJcPHC3uG/XRC38w/KwtDds6gg1mBof/jRs3YstHSEmOGnYl2lHoAGZTOjjboKk62SbTJm6qreX+ZSTtkZHsHZ8PjI2ESO1oib+1R50tDiPmeJ/3XCyfFXgyRjOvuSHnLGJmEXmo15cLa837QMGOZtxYQgRiGf/g/wn8/cehnip/++Hw9a8VGF2Zifyd87KF+x0sD+W8R0dd7rcMBSVBRfsO7yPmQtHSGhLRlqjP9fu1djaz/txVMl+dcza2qB/UvsDSBy9fsqHyLvZKOJ51d3dL184+ADOhRQQDNuHQVrkSZhY4q0+qBwUAcJqxohAGqRZXfzTcGRBLSKDCABJEvPownKXRRi8H/bNwcTZg0HdOJygKGFF4YAySwe9nBI+hozxnmiIGzlh+ZgEahfJhX8B+UBXyU7iRn/zkJ+ya5z73uar8Cs6LmnRVYdCjt0aMeBBVsAZLBgICNZLVFlYK29p8sZTKEZjJW7Q9uSIiV7IHE0dAGFHwiQDm1qIq5J63iBbp3bpde/X/LoyPRX75S+enPxv/5S9DV1/tiCGV+4bgOtUnImI4IwXHRbEiGyYtOVweKBCURxFwI0GP3QDIBumaiCZlG5U6yUskVSj4VUUzP92f3XKSJEm/oMPMFQtEwdAcD3Gm9g3pQ5nixkZJMShBKflwL6Yyl81B1yVHBvVi6py2xIljx1OZfCqdyRXt8bxdsJA9Bz3BeCgcbAibQb9v48YNXV3r2MVsIrJiqnZDEh1aR07tuvVKR0RnSyRnSz5x9y2UnWjACeGR43bE/SPWYc1hozFMogrH1XsjNRA9MIp5IQwUhSAEgowNptFUQpwtFMIq1jQuyq5s4gwjM+tYsXSJxo81/kc+8hHs2kDrFJt/hGfWs/ZkISOwxgAvZJTmLKPQJzEz3/rWt95888233347VsHsPUV38homxKjIUE7CpyFr54YLYn0ecaaqkzNg37D1yKA+muN0cxqDzgVtzrlNHuvQ/txrrg89sS+9ZWv4K19BJ8YZ6W4O9R4xftD92nuG9ccHnZE83K8R9DibGm1SpFzU6mxsQNPANee2lB+mLhAwGAu+Ak1sTw9hAjaCA6666ioUwlAtt9xyC6oD6AYUgJCMkKRQMJSH/gZtwCLSzPwonHYElhkX/AxUCMM4V4HaN/ACZUg5JeW4zOWAEDqGdxVfWltype5hqDD6pWtwvwg7wJfzwDk5K0/78wuB9w033ACNQjAz9MB0aiHD9Zvf/AZ9+CWXXDJtPUuvBS9o2A3tG7QI+3yM4M/jwvhRbSKo9cS1noSzo1mPIrI4fTVWR8zOpnOvfV1ooC99xRV1n/oU4YPmGme1Sqm5WoDlh9ie3Yd9L7Ib1uTIyFg+ndrUkSC/7uYGfUPC7ghVisOH2+pDiYjPb9rgK5A5SB/EDujErAJ+tmF9xOiIOj1RZ1urIHvikTwxoh8d0/K2EYT7ha8zzWRBS2XLueSwXSkG9FLMLHbW6Z3+fNgs40SqoGIX41/UWWdEfXbYa8F+E1bEsgxfOIYVVkvI6ayzz2v1EviZbtT2RWgPlyTg43Cy8rNjxkAWNGziOHp+h29ns5EaG2GHYvug1jzrn8WJKQftqmshU7niZQTk5HjqG3d5kli9i6k8/YANJsevLINI1PPiF2nx2LTZXyAY7Dtkc/SO8upzgS+esVh1+QFy5guf9733FhT1hXe/u+7P34kIBchpcrYWT72Hnd6ewcpwzntorJIqk/NEwqTxFp59+YkxrOcwDe+oC27tDLaGKvikKFJ0tjrPCOziCigQOTARV+F2y2p597vfTQpKBFir0PqssNIul9C4ySSOJPPji1lrWOZDdiBDAZMDAFTFiQE2UZwwgC2z8qW9Du6Dgmcoqq8/VZBUAVjazaktgdfGwADrDTRRrWqBnWIceJEJgnyCB66+PtcN5wz0CRgBGmyBTdRWxevonLEbggiBDyEMJJZo0DbwwP/+7/+OzIipQTahTlpeXEITtc3V3FOTOs20uN9si+IebHs9HBZYRMuRg+WU4fUUC6VsoQjS4P+MZKN10kW0tW6mIuHEJi9Vk/oCfWg2NPhe8ILyRReXv/mN6N592QceyGfSgUsvQcIw9QZ/xQOZyDJ4I4PnOL2pBD0wYl1uQUAcfTwmNBcSbYI/ZcgDVIQltqIBjH5V05zlriFVTaWn3QpKkP8RCm9v8TSGKgdHy5bttXVtIGM83ldsj0s6X5p78sCTeLTt3bsXsqT3ZO/wyCjmTOvbGvoOPWYVc7FoDOwWCEVtf9gXivvDCQnIbElGojDxxKYuNZ6qI3C/hByLeEWWIFmSMF2uwMxj100gK1hWiWXll2DOU4PojiCEHfG0WmN0kHHG65huGuh+EaNzpEPiuLZiDJeIAMBq8NVog3PCVFPSrg8j3J+CZurvQhYMZVj2GJNig1nLAwvgLmBTla39Xe4IrDHAyx1BCAhy/1x//fUf/vCHYQYguJE+wkjAjHGAwkhwVnIcg2hpCZ0MdCpfeTjXUpYTQrMPjlkPDyAYYwPpdUHnvFa4BVMbT+LxG9/1m+zGc7xf+SJWwmxRkRK6NDIvpssODsOPD+mPDTrDORKiGSGv3ZOwsXy+sM1xQw3RrFyL6rZ6hcOI4F7QT8CPsTf3IAM6iKVWT08PZcDcUKW4MOFCg+E0cn2MhGGGEQq4Nqhio8IQUUwBoD6nQcJD2FdMi2f9dVphvjKeNMoFFQU83d3d3MOfL5CFm1nhGZ8wrWifyPfLKXzGVpRnNSO2wO6csfWzVIDEvwjpkXyTAKmK3c/YFlaL0DRwXzN6565izemdsA6O60fGtYMjDkEgwRNew9rcYBBNamPCWV+HIlauaQ2BbR27knrPX4fvuzcfCvu/+i+kV5qHU3LrOK0eVhpzxExBhMEMgzK597kUWMRnwIji5LM+YfryA3jPXtIT2d6EMbbWVUdMKdChBrUBxwYFFfJoTQGnJ65vbyUKNAtYI5vr8Ql9NE80TocMFqbHmCjqoxlUuZnRgV4H9OyrtEa0ZjNb7ynQjxpGVPpK1JPuOnNbk7m1Sd/cFur+f+y9B4BmVXnwf8vb6/S6szuzs52t7AICSjWxJGBi7KAIMeofbMGu0aB+RsUGGk2MiWLQFUUiCOgXFfUDlqbAAtvLbJve317vvf/fuWfm7rszszPvzM4WdS/LO7ece85zzj3n6ed5GkIBl4hrsqhKX1VDUFBh9ZDdccbEln6RHs14UX3QznuUzmtRt7mpRTunVg27jKHBIewk9E++AuvG/GfKOTWcrpMCEZufftrX2+2y+QXAYL3YCEstrF+nX3qJHhRhAunvbCHkFVGVfcz23RnLowIZGR6NP3Cf+13v8eayyfffXPmpT7GXWjbH7+QabF6f+ya+gjv6iz0p9v0qsYKOryDcXiaZymfiVr6QiA+7VGO0Z1dLxFq1ILq2icgNJ6sXUwJJ5EKy1oGTmTDkPYK/BxtPLnmK77BCMfGBScpH+/MFIR+OiQQRgeMEY4DQoD5comGZ8kPPV7vT1CPbBTDoJrwEx+mCZBogy3wEHuagL2gWICuMKud0Rx7lVEJJJgYIjSBV5cxV6kfYZiLNWQCGwwGFotaHhWNuMCVgddD+c4muhM0C3/rWt2AzYDbmfZI4wwJdrA6odQHVa8eIRh0Nz4TUReAKcnZ4fR4RfxjTZVFBEEUGRhJm/zBuybZEa1fD4PLX/hG40ud3LV9mXvziXEWV754f648+ljl4WF2zWq2ICvFNHIJkI7Bh9oz6icglQj3xNncxe+KHbZ8KSc+2oFro9fD7JUkvsf1cOruCaUtIv3aborrJx/gjSgrAPLmRWm14T1eiL1EYTcZ7hjJPdxYiIXdLRO/p6RoaGoaEgabgSdps6wsLszIcTA8e0oq52roa8p0QoYq+syUY0ytGaZy0idEV8YJRgVpAwmE3JYaBAYTuw0uLnpLTXZi4cYQey5CUElIr8BPISrhqOwcVwDA0RdSAlwxMYkswn0B4bxWhQGK0kXsZEBFKS9QrBGmAwccHGRhv7aoAcbB5Bg1Xu7vo1BD8gFP5NCdALmXgZ5999gtf+AL7y3Dporzo0dlj/kbgrAA897EEs4MKif9EAglkYFTIGJqoDkTJCQe2U7Awy5gDlEr51tZWzqWAcfypbHUmrGd61M64RvC5iNtYW6esYw9jIj7y2tdFt2xJEw/gJ3f7zjvPxjiCf7L7gFrO2tpNuCykX7M3qbPL0adZC6NIv+r6emtlnU1I57R+JFG5/PLLoQowB3QZL2j0oLJ3LGlJnNDRInxCFeg42ArDAhQLiRfGggLAiTSIKxGCMeKxtM4xVtA2aDxNlH6J8U6V3pv6nCZsJsEF2ZMslOQYyq9h6non3QVCviDVQk0Q+eg7RWZsBZQHHwMRnbHkpAZP0Q36df/995P2EwjZx47LzYygOkPBUDPmk8dB0B8F2qzs6CfhrdURU5AYucM/0gjB/RP5GduXcN0qmZBiBtjzgJ/C9heUG29C3Zq/5Z+Dr361eNc+yhwUvhE8kCQ2WOzZps45U1e+zlRBXcJsZGayscjMpguZxEJSUpBIydBF1sG8GfRpC8PqylqLmMwtUatR7MvFAGscIo6XSGKM45lQixOabjAJdVOHY6mCpi9rb22KuKr1xKuv3LCgqUECUNpHCDIw0G+2BpMmsbVCW1mvtxMIxGM2C/9q0cmp+mgVFP3Xe/L74u5YGh9tc+NCdXkVAeHxKXbX1taxiJwX6ThIxrmcqrZTdA+2ib0I+h+2wpPItUPmRV1zFfwB/bo3u5evUgiIXMYiOjXgMusE6o7HSXzUe++9ife8t44wfH91VdXXvyYC7Isv59gHpoRIRA3dPWD0JV0do+YoKSJzloeM7aY1OtCZGR1JJoZEKs3cUPrgC3UB4y/PW0YqYPszTfnFp2xijjfpGge6rWuvvRYDF5Y0Njfi4UnrNgBzrHa+XgONoCCGdsxNaJkMBp11bk7fQZ4K8hwKgTHA1a2trVAuSBivT/+iU/9JOoFEPvXUU1SOoHV6ITmRDiJMQu7Btyx/WCDmnjwHX0G4y+8XOI0PBCQSjUz5Im2hRgGxo0+B6wDt80HB+cyu8rtAKxBr9PVAC+G+4IILcEzgDnuDidCLyxuOqfTinnvuIVov1Z6MRWT3DmEMcU7Dd5cY0W6X2LODFtHlcoM/iSeAMZYdqYigbMolMDJRiMkVki2aYS+BkalAoBSJWuWFfa64Wxd6XvKSVFW1umtX4De/Tu/elRtJ+DZssJMSqv0p4/CIGfarVV5h9sQrypbdkOs0zDciiAFrYmxhach4iN9iP20em7DCZtmoH+32WNNywFmGfC/5LRhMolHyjWALOShH5oh43+FG96hRKIwUQzpuWG7/wZinP5E9f3njquVLFrctBlPBTPIRUe/yCus0Egwk+g4UUoO15PDzeA1Vy+aVeNbKI8EWxRZcBiYawPQ6BonouL2WuQYqwi6w0RrnLukyLsJc04u8EGgZTBFmjDA3fuGMLd6TlagMBWGxtAqfUigaJGdG4mUYcEbn1yYJYkswikTOeSS2BFOnSObMiYW/GxZy6pIzE5FeDk45v0xd3PG2bt3KfmDmnuSiy3nxbJkyR+CsAFzmQE0sxsK+4447brrpJjyfCZ4J8QZpgihBwWzDwxkYGUkuWrnwkIcxC1PMXlPiZ0KNMNNCT6RYQxnrD13KgRHhXxHQreX11oZG1a8Yo//wtujPf54Khzw/3Oy75BKhfhpXy/EWm/tf6DW2DWgv9BOT1oVeiggFCyLGugZlfYO1hhCjrE7534SGZ7oEiyEqXH311QQ9hkH59Kc//cY3vpGb6ESx/UIw8BdibUMw6KDUAoDgEI8ZAYaCYq2trZII4S/Nwbv4FDEg0CfIIeNGeaDAy4hL7sOGIktT85Qa39KhKx1MyBKIkkPenKlbs3gOwJQGJFA2lcMhcTl9K/IVijE4dIeJ4bTHi5xTgEGjzml8AZxXTt4J05hxI+YHQF5xxRV4MUjw4CGgWHxuyXY4AMh+8fkItwg1kipw+YpThhMxlS18+M1DMRX/Z+JOiRHEl8FntlYSCUNZVacT+1FSGPkiBQDGprCmkkrFr/7bUE9ncv2Gym/9O1GRxHDbYy4Lz/hLcfsNoY0Se4Dt5FXOTRYp95mKxSIWO7NQJG+uoQWje4f1g0PqIOTcRBGuLK5SV9dYK+tdlX4IuzWcVXb1awdHTdzPcH42RBwMbTiL45PFHvRYLFHp97Y3Ruq9mSZ/tn0ByQDt9Sa+9jGLXQJmd0GMAOwKm5RwtRKlj+2jwAn223jhPt9nPNmtDyZUPEmW15jnMIYNIickB97aTp3i2j7s+k/zj0aw48oaw6cZO/YouQxRTMADaWxr177BfflfaJVROS7AexoBtRe3Pc4KNjc8YHuq8rnut73D1d3tufSK8He/pVVU4vRmZtJWNoODPDko5QeVYI99I5uj2jdU7EzqhDkdzeqpjALPhvEEi3cwHPVX1jTU1hvp/rbaqFYYecWF5yxZvHDyFz8Z48BS5EDDRaY6sDQbN371q19BoWjr9I6801lwYPkqReet6U+gWTgiwT1PSUScd+21In7ABhAvpE00SvKmU+a0nIBaCTMJiYSGsmoA6bSAcYKNMphoSNEDQjIgzcgznNMvPoqk1OXU73wOPBcwiGESnFJ4htQyz+WnhPXigHjRuuQ9ymmotAxkEcJBCCLndWYp7tDXX389O4Fxh+7o6Pj5z3+ORIdIwzeS787rZxJfHMyOX259SLCCAtvriL4aCXhG4imSA3m8bpHkNi+SAyG8kWYc/2QclUJoHAXtkGjt6DKnRoip54ILzHVrMi7V+7Ofu//vg2mStC9oLFbW7OhRhgv6UML0eZC6lfqQFvHgs0OLICqR9QeTqUB91CpQoMCFeEuJXcGGigk6nTN0K5dLjg4OwImJiGVgG+KJSDdAxpPJzDQQhlzbCsrcaGpqXrl08RUbmpcuCPam3ArBmQmUkPbs6cu31mhR0QvRojhs5pWLcCRCRImR3iNGcmBBU63u8iDK4pgdy5lAkhN2YGF6rQyw6dcWcu1BsFePqAXQEUfrw5pXuHNhvLVwbCZ+FQGx4vHRIVgyT7iAHsGvsnNQ4mcBgqKigEByxqLLSwk7QxKvs00aLgDFBC0R3pNAXAJiFdlYyMDwFIjW6YJJAFqM87hvFo1CTfVRAVgCZNc/9Q8FmH5sR2fPHTIwkdjA2zO+NXVdZ+9ONQJnBeCpRmXae6wZnpPxCNGXbZPkrWaOMvPF5FdVRDi0lVAveenUBPYE4/Pr3Jl4YiMr8Mgfus29g0pe0d26tbLa3Ngk3Cbjn/lM4Fv/UUDL9J3vBK+6ioakr6RYbewus5Td/cUX+rXtfRjctJyJS4ZVHxS23zX1CMC611YKlqzniY0f75rOwhmQ8QjZD60tXBS+shAVcBm8Ah1H6EVYkicURijiHIFW6vJtOIVrNCc0QQ3SoQVVFjQDpgcRGqUABbAAsOVDSsXgSkgdeBOJiKe8KP2oUSVSzB5poQyecCBk0rpsaMKjE78EElS/9BqySkOyX9NUi3yFtRyOCowv/eE5Z6BKIaQAj5gtJwnmacBzHvHJ2PT78MMPwyniEukAA9i4sjP4fE2nsDxhKJ588km+FIQfYnYc4K2epLFvBNFX3T9oEbuS7TM4P7fX6G0VyuJKkUrXdiA6SpttkZlZYqkQs698OfSjuzLhsO+uH2kLF4p1ZR8TICnnkvdYdKXDzlsQYLoG/Mw0FPyti1qraqr3EiA6pvWm1XjSJCRVW1RriRpLGQDRtCCx23vZzGzFsmJhEstLc6F41kbSBhzJ8EhcKeSXLIgur/M2B7L1/mINWnbxnk0PJwEqHh3nOLaszW0oSn/a+t9dZl9Kx8uj1m+ub9ZWN2hhwaiPjc2xb52GK4kVZcOAJS9F54NBfdFiZf2aXGWF0bjAImT6m6/VL7nMVVMrWBr7OA3gljY5btSAeQa3+Il39fjjmad+nwhG/N/8qm9hmzYwYD6/Nf+/vzYee9Lo6cEXWkFhZ1skxqqBFbTUjlhx15H0ns6RlBpJZIter1YomMweESpNUbweVzHWZfTsrnClz11cE/S54rE4OiZQ5XREoRTOOZ3zIViqcO1s1QF5slVn8+bNLPYzYuTtHk2AhMs5dXTsJWceQiwkkYK48GzGaikG1Z6s8jsRYOb8rhwTZiN7X6nkj9QITC+Y2wwpB1Md/gfSyYTH+Ys7XMrxmfHTOMPIaHAgViFIT35LStqQMHgwCsBpgOcd3sOpZPoTdKPItEBLK0jULBlmER/CUfXCw8C9kPqRmUZH8KTAfwqNEiZih3pOhm36Ric/pYbSA9G3Rkhflg8vFBfh9JQE0eSJBGVZXrHNR0RgQoRDDEYAJmoxzroRTMHC8CgOqpK/Y3USh3JRq/eVr4wT7aK3L/Cze9NbHh+saE5UNRluT5GAjkkT0S7i1yr9FmIwkqTAjIJBVfPsD0EdS+cNIgMiYpKXKNc3lOweGN1/qHtnR3fXkSOJgZ5FC4nP76YA4wmPhwIafg9tAhvHILsSDJgHDuYDlo7GoLamUescKYzkseqwlc+1tbMY8hqNEQRPWxAt6QIfmvkT9GobVrQEXIbIlSi24CqJjBDIhXN4zkoVrbCIUy1HQNBhSYtl0+xdqg1pbAmmL7DzeHDlikp8ZCg2OqqF6tI5MZ5BjzKuRxgbNogxmX7rgyKeFjZnBoAXiyb/pLs4/mrCMm8LwfDkwh0atqF/cGj37j0j/X0u1BT5fLc9h2Gi6AX1is8z7UEZeDD2A7N7BRmY8A1tbW3lvDhtrWcfjo3AWQF4LlPh7rvvhqWAmcAFGj2WUwVoAbOeXNWlc5Rzih2P0XFoNl4lW3uNnQNq2iQDpkWcno3NaqVfzT30kPbBD+lIop/8ZPTtbxebD8ZXM+ooWj8wYjzXrxL4as8wmidB8GsD5voGZW29sqFJQ362ceDMi83piHPCQr3uuusQjRD8/vu///uGG24A9dAd+gIBA8FxApnhwJuUwtAGzmUZpxJ5wlulBxVCVCgsGRSqwlYs7cbS6QVKA+GXZBLjM/IYXmEIjQjGeFXB1UHkqBlRjTGXgvFsqd0ECKe5ZPMPDjzAg8tTOa0g62KC4Msi4jIsfH3O+aU7DIJsCPIJanOo5jStn7xHxHDGi4HRQ7WBW7sDG3ONUYVfmSwAs1WYoUBuxK+YrzYlbHj+kCa3g92/MYQ3W4+rKosqlFacn6PK6gaXnXVAjIPTImcQImZz7onHzHe9253P5T7wwfCb3iin7tFiU7Z3/JuM+QTpl6rkwUuwSqxWpushsVeZNMXqQFoEFGkMWwsi1poGF1uwAAAyf2DQOBDXu2NinxP76tH44vzM1l84g2yuwGysDmvrFlW3RM3F0WIhl2Fi08rx4SrzCa2bOUv5+c7CwZQbE3Qub72oTV9VPeaSLZtwEMh8tFgmYFMUY86zQhlMuaLRZx04cFCotyJhjQiu69Z7LrrItelcfXG7HgraZoWSrz9FfafqFrOOBTA6YvX1aocO+VBy1dSFXnW1/69eGVqxiiyamR9sVv7tW75nnnHteEF//KnMM8+qzQ0ajqm2Y4KctCRd3z+s7To0cKQ/ZvoqfV4XuTpd2GIMeCNroHNfhcdsrfV7CyMXrGisCPkRtUAFIE8mJ1P05HUVO9XrXvc6orszN2655RZYKIeVP3mNzqFmsCUR9cCWLJw5vF76Cm41oFzoEatSLkMu5ZwsLTbhnAI2Lz41QptQ+NRcwiKj+QUPQxYn4+FTA8N8tYJICX6gNlTDfGtmI2uOcyZ/+V3jayKOUhVIBro5ATbq5AAZSiOEZEK4M6HY9JfMQAqwMJkz8CG0yDlLBm7EedFuR2VH2FVXXQURR2CGPWD3EHph7sDYzLZRp+bjnwglGoxcfZiMHiap4oJ+T5pgyFk2pWrYdHkRpz92sUKV2M4qE/a6NIRAxLGxyH2ycigKCE/s6FX1gytfNLjh0sLz28JHDkR/+F18rPPrNhbcHqgaxuSRlIEQKKJAhbX0cN/w0GAiAbeViqWySZIyFQo+H57MxI42EqksOm7F5dN8YV+kqq6+sbmO9H9wBzpTF00EVIDvxRfhc8vRO7anAsKgR1vfDNrMd8aEYIk9FRYikSu017JPZuwj2n/FOVS7rq7e1gvoIXcBqRMtAG/hn0yWJnbhog7AHdrrIq4VG4sEEbeJjX1it43mGGm2wid4DlsGVlIikWfWCtSwF1iGxfLoZjQgBHLZPvVTSwTbeFjxeQgNbWRxahKZmYRtXETIlGGxbBmYPiCW41+NnKy4Aro/zIyqDLhamhsBHuRf/jxhcFgj+ELjI/mVr3zlkksucTwOjh3Gs1ezHoGzAvDshgz0it8LVlCmIy7QzMsJyA4ZCTpKpRPuT9uMWISslh19xW0DagwdmKUsiJobGxSCARZ378m94U3+gb7Yq15V+ZWvks+bmm35lyrhw62ehPlcr7ptSNk1oKREwiRwlkHMnjW16sZm4UYiFv/s4BkDFhL18Y9/nAxPNPixj30MSUk0bQe7giRMEH7gNsCJYDoKTNvZKR7KV/gFP4IlQRDUg3wrpV9egA5xh1/aBXHwFSBLUgBGMGaPBL/E6ILbg2PArIfkBnhgHLyYeH0C9zMrCGkLeZuQwkCF+m3yF5+iP3b4a8gnyk4IKraIVatWSamewjA0QAhg6Jh5ChPAORDSoymrOnk3aRrvGqRZ4uyTvBoY+OKMJPCg/EYG5oPyWTmn74DBUGDHZrSZ4WyOovyEkaQApYjXtHugeDAmEv92DEEghJUy6jGw+rZWaivrlCqyAI9PkmNrIJlAJvb6N0QOHYxt3Fj5zW+qgsTOcfbOOG5Am8kSUjKcVTzbBxTytQ5khV9XbZAAXdryWoU9urazlzWUNncNK2xmzltawCO04yjiR9IigCSbo+KJmG5kVrREl9f6zqlXjXSCAB54NxzbtRnBmaqAPaBPHCk8P+AaSpHSTGkIiahXIAjc3jxu/LJwRhMaeeBh5OehxamgKPMeE4bJg76fFcorhPHEyEnoDlYiTiAqu5TRnfFB2VhmAyqQ2KxRRZmwlFUMO4aYrhzdXdlfPJD+8u3Z225Lf+e/Cs9s9axc4d+wPqC78g886Nu82VM0gJRBhslxxUZyO3frGzdp1VWwkaZJuEEmvNKdVA4PJEcSmeq6WiwwPo+ex5hC5+FAY91Lm8JLmqMvP39Ja3P94MAgC6qlpYVfyRGWBe5sCjFxOHDbYeMi3Dmi1Pe+9713vvOdk9fsbGo9iWXBPGg5aQDNGr9znslgVCYeCBbExQggL3EO1oIinEToT07VTB0qRpxjNOZGWE8OXHOpFUUPXwRxkV/ehyYiBnNA4ktly2mqZhA4IKOQeOyKiFXTrB2qnebp8Vqhfqgwq5IDqkcNbW1trB1u8mjyWzyCgCIJQ+jxoiLfB/6ArGs+1gSt6+R3Z3eH1vkP1ktVqvwutucQIDoc8o+OjuSzBZ3E6boukJkq9rISegMDJp7AxIXKjO8Kls2JXkCQhcSn9CTNI6PaIV/t85e/adRXEd71XM1jD3l/8T/dqzd0xOJdXb37Dnf2jOYrKyL4YA93HYj3HzLyWYh5ygyQzs7rwfYsNHcEeYBn8wb9LnfI7Q+7fSHFF8yaQlxHfrZHjdZmXNGihwi0K+vcNYH8nsEiLpCm6uqKafsHcotrtKAQggHb7qTdGfqCRHpg//7OfTvWLmuCycD+ymOU7zE0xabYIE18LM0qVgRs4VS8NU4iBTanQrYEK34l232oIxUfzmdwBi8Q9mFkeDCdMYruUApliGVWB+wIZGJ0eUe8hWGZmCAhj4XGIVeAC0dcF1mCbcuUsP0Cptg+LRh0NU8eJXfQ8gST8aSmFFcvWxgNCSaWp3Zlolg5B68w2XApJ9fMS1/6Uozqs3q9nCb+DMucFYBn8dHhlEBzZOgC5d11112TMaxYHuPHLOplnahmxwjb/LT+FBkz1IaAsrbBJEukyr6BV/9dePfuxPJl0c2b9YooT4U8IZYhq8wcyalbe6ztgwjP6mhO6KDYb4CMsbZOPbfJqg1wZwygWcFjs08Wbt7kyeBF9oiyfwzaM1aX/WdChdxDLpJq1wmPyrksrXnCOa9DgSBp8NbwMXBIbW1tUvrlEVwCUrEUjLlE8Y9cB9tNJdBXuVcHozGmY3xPIMAAKSkuykzKy7Y4cQ7uOOeMA/IqwejBPvg48WLpU6fY5BOnWugo8AC2vMMvUNE0jBoH9fMuJ8APYJPrOUl35PdFm/iDH/wAkZ7IlgwpsDHD4S1s0FDsEoJN6OkpzKcHEs5RciPnsBeFz8HTyeDRn/6kuXtE2z9q2c7PYk7iztBeqy0m9VGFiWzJi87h1EAxZInkV78a2Lw55/P77/kfva1VhAy2D6fY/J6gUnG5Pc/3m2Rp6k9p8bRRG8JJW1sYMUgYJtaOZWYtcrqaZFys9GktkWLEI7TNAxl1OAPIKsr4QjZb5THPW1rXXmU0hl3oNfi+0CcHVHrgnM/yxOqMm7/arw0kNXzDQprSUmV43RorfVe/+uThYl8aqmz5XbpHbFgSYzXL+uehuJzDVITyHnmDqQJiZBniKVBdU42uWn7Byb+SOZoHCOZehVh95sBg/u4fe75zp//gAaWrmzRNUcvMPP+c+8XEWbCKX/+aN5G0eR++tt0SppNkPM+u8uUrFLdnJGc+d+hwqu83WuoRb2GHbmV7yKeTy6VyuYg/gIxNvO5gcbCp0nVBW5R9ZX29fQQCxB4iVSQn45PJ1f3Tn/70Na95DcsZzxrOiWQrP8HcR+tkvsmcYWsoCwdoTwRO3oUGQSkgGdSGAQrhhGVeppR1Mrs4u7rlxEDEkvZMSXxnV8WZVBoqw4eAanNgwoUgonVlCZRPUukNY8I8gRbjaQKaRQaWNyd0VEq/Uz6aUHLypTP3oPs8ZSI5d6YszE16dOWVVyKcoGlCBU9WbTwE165dy9yb5t3JtU1zR1AagX3EP/73uTCNaARexg48PDCUy+dCgZDIXCAkPLHlgsBUuEMThIlMRUmyBmtWyCtIKRptEMJA/2B3b+/2fX27u4Z3HBnOuMJPN5z/u3V/u+7Jn9b1Haz7xT1GvtDXttLl9waCkYI7mswYzTWBFW2NF6xu1cPVh9NBze13e3BOPgqXrYEVjsQ0KpIS5RWckEGuFQRCpZT4dOKPc4CjYIRQvrM2x0aJAnYnmyL6qnrl4GA+USA5oTKSdW/rytVFlZoAmlPBTsjy/FLJYWHw6GHj8bnLF4hQqigqLQJZabGslS5q7MUlSnbBNKr8xAYDnjESKSj7OCguJY8vtJ8/RRNvPc0bQs1dVPxFTxDzeioPC2RhBxahxWQv7DeprTFMHBNiPiMGC0EX8btAl1FSwCkJzSh2FwEut1CU54pWMk4WZ8MfqSTQNLXR/oQxGYdoir+yy8x8fKFxuf/3f/93eHIWVPk1TFHp2VuCNT17lDECksljFyjz7/zzz0f6ZS6e8OSTtdK8OZBSt/ezbZJckWqlp4j1aWmNS81nh294R3Trc8loJHDXZlezUI2PHUJwEnnAt/Uae4ax/VqjWTRObNxHbFZW1Kir65TGEItMLJzxd8r9K8H63e9+d+ONN3KO2ZPYSDIZ9zRVQKqneXoij6bpAuCBQDmc+rkDlpevQCxhp+DF5YEdAAFYWhgojz8JemgIM6ySlIqx3vOUGrhPAV7HYAI7BaJh+won3OSpbGsaqGQB+YsmuPSSc2g/BycojGm6tbV1QoFTc4l88tnPfpbu8JWR7WV36C+OzQCAnh6tAaPHPHfgQfSlPHQdDbdzU55YWE7tKnDj3zdkdsbVnriCL4N4qlpNFUqjj03pyrIaqCa3GMOxackZdXKBqJB7fIvy6c9QTfGmG4Mb1kDuZOXz/ut8RGomqFVvSkNoH0lb4aBeG0BtZLH6IJciwYGido/mFkVdKwKGKzuKJbDo82SqA8+PWI8cUlNFHdXvolp/S8QfUVKJzsM7Os2icKg1MENROV+/ra1ttvCLGSZAZM+Q8ut9xZGsN1E0jaLV1KARnzNnQqd1ggxnCq6hw+azPeqCcP6l7a72KoXchGVOy9mCNE15lCa4SPBLrymGfYZZBJtFD1g407x42h+JcYa76Tio/M9PtZFhq6sTc7oZieq1df4CcV1G6JM2OOjMQk6ECUIo+E3l0EErm8u6i8mef3+Reoe37pDLzFuNWk7DnX71fQeuHVH+pqfroK+Q8Ie1am++yhjZs2MANhFUIw/Z/dKpOC/fjgr5FnfeeSeZ6sCEF198MbZfJuG8VH5SPxkyHh4EYJ5SfD7bFtGH8jqDIPsrzXezreTMKQ95uuyyy6Qp+MyBag6QONPvBCc89aBTQwCWWlpo9ARgnIYm3J/t5WQaN2UNsjk6hcTLTjGCFV1//fWocrDRcYL/BYKKfHG+ALNrE6Zg0unVBEN+o+L5nrwW0YeypFqAKBDTEQpRSKTMIc04pBd2qKnH9fQrL2jHuEokv9///knMk0J2VM0aK2z6ooq/0JX17Qq0fPzmX1y15b/+7pe3vehn328/vHPoc/8xUt8IpR7MudKF0OJalUhOfclChU94HsWQ+giEQbpAQcIBClIp/iLsDSRJcY8wTGxqM5EtrKrXg3wlUeYYkQ92Qjro2T2SxN6WgPF/DLtvuth1z/PpZ3p9eBkPFXzf/UPulcuKl7ZhgQAD29ZVu7a1a1azWaq3p3f3C89s3LAp5Lb8bkItan1JdSRtHrZEYt5cQcvlTfYYV/vs2Wc3wtt2u+jLQiva/e2LtfC2I+reYbO2pTuhDqeUWAbfZrGJN48xuSjiblR6hQ1avkVP2Am8rsHl9xSDHuPpbnI86nFVSP5YfD2kUKYkaZncyNz0XY0XlLy7zusxSe+SKxrnNusVHslMMi/o0hg0svbj/VIS5PbAAw+gcCEuD9YduMd5nVfHa/lP9v5ZC/DMn1aibASGV7ziFW1tbcw/yOqJT7uxpaQQV119lsBXw+wWYK+Agfi6vlH36ebIJz4VueM7iGjuH2z2XfxiYQ2z17xchSyw7b3mjkFle5/SldTwuxDZRCsI+KyuqzOFpCGwjVzkM/dxQgnspSww0BP+PHj1YPGDBk9fm2jMPiZUdVIvx9s85i9Mg7yGOiLToidGZkOW4wBfoB6mL3xTpFzkT0piucJOC+OFiEt5AMYZjw1p+FSzvQcPOuqBlZSPOEH3zFtw+TN2jWJouCfMFgkb7yKNA6rcA8zNGWubrwL0HRYZ2kwHmc9s7XakdAc2iBMqcAR1IHTaZRyYD4zDcSYDM9raM0jkZ41/HUMmrrrQmgpPsa1Sb61WyXNLmnvpweDUKbuN9GsM9CeueUuopyt54cXRb39bdYtPw+GUPAknROtVUR4RpHogzZ4orS5gtlVpK2twM5OrzUrkTeKOhAd6lId+ldt8V+b+B80XnsMtu7Gpsr4+uH+gGMARefRwhRa/aHmVT7fIoAOlhzlD9kPOYUZxzL4XYiSJNvLw/sKuEc9QysSna2GV2hQkZCUbwHTAjqXZqGVpLpGggjRicCEBd7HCL7IKnZoDcZeFwypgOrF2ZB+5w5riJiwgcwxlCoMw++6fih5IrG5mM/nf/Nb7m98WOjpQ9ecCfrOikuwa8CPqxS92hcOFX/3KXSDno83cMbg2q8Lf4qo1rvPbiwPvqiz8h9dCSMb0wNy2EIOrvJ3n1T2Z8zYXXZsWNUfa6oJNvuyC2khDfQOLnXGTeNUR85D6wEXz4jNJp0BQ7Fv5yEc+wiS89NJLf/azn+GMcGZ+ggmfGchx0mEcpNltwtPZXk6JwWZbyektz1fjANlKMFhQzvnpBazM1pmNIAQKTwCb+c9XxumJ3pVZ1YRiECZoukOb5lzPhGqdSyqEcHOUWTPFOEB90FMUynAIREDAW+q+++6jsxgPWPhUThmnicknEiM5xZzLySW5I/dCjwz0eQpxVz41OjyUL+QJSkmCpEQyMxJPEJs0mysmckW2s+bZAu8NkzDJ79GrQ/5FCxe0L168fMXydSvazl2xwOtx7+jDYVjrDlTubHvRobZN7bsfbdn1jP+Zx0CJ2XPWQxkLFgEvrHje2NlPnC3V4zGxYSLlChlYSKSi+472lZtswUUAJi8RjaYL7Fu28Iie0Hs84zAC4wXgdFCWEHUJ8VI5hxgcrsKBIZJP6obi2jOkDCVzS2vdPLLVkLKgymQYGh7qHxgo5nPnLK7HKguHjGIaiVxkh8opBdJKYJfOWwG3Qpoo+xuMvSv/8GWw7+pGWstnq2tqYa0x4Yo8TwVh07Y7InI+4fMcGtvCTCW8KqRW0jXXsk9YNeMZGtVRmhP5n9ZtF2jiYxEa2maDRKhad151C4HcTlhV4eVzCDojDmcIZjqhLAuHXO6oOH/84x+/9rWvPZF1NFNrf/rPzwrAZX1jzBq4kCE7kfxt3swaQgK2cpb6nAh8JRKQ6qrZXmUR+Iro86lf/ML10Y+ggyp+/l+C115DqHV7oYBl7NcUZf+wuW2QwFfkJtXy5OZQ1IaQsbZRW10rgvdIEVCya2X1sKQQrAOGbnJIgLUJ7XDFFVeUI+yVVHCmnMoRc37pBfZMKbxxE7yJvVfKxjIqtfTeBHq8zkDNnMCtwpYhHvMLbeMOpi0yACE67t27F84VHx4kZAZKWko5h++EdDmtTDMW1CllJICZpti8PwI8hF70GgCJLwM51icDALPFQb/kp6dT8OgIwJB2RuZ4zPpAytw9rB4YVXYPogkWu5I8qrmkVkMvs7hCWTLmTXa0syU03or/8y3Rn92braj2ff9O96IWm1sQn27eu+9UyKrZ3lc8MKoRpiuRNWtDitilHDH5le2i+kWb7uvuHL39G4Xv/0g7eNjVN1DYc3D0d48wFPUrWnNua++hnqZgYVmNtmpRbSRagbKDscL3EqaHjWSzJU5jA2J3et+w8fARTNNKLKdF/FZLWPG6TQKLxDKCF9HcYAKVGEvVQb0hbC4KW8trSBF5MsfLGTj7BP0ISwDhilmExII4x2RGp0OeBlaWFIDl1DqpH/FYoGZ5BZOUz1mPPlq89z6suTmvT6mrx3GtSBSzfMFsX+xZu7awa5env09YfTn4Ef9UovFbr3+VEvmeN3c3uwTMWGD0waqRe6tST4WKhIJpwucw2ep7zApfUlO1YuOiYC7WV1nJ9g3y3AaZG4h5jJvkiamVS8bQkYdn2YdjisNTkqOO/fyM+Q033MDqnpP+5Zg6T80FALOm8CBlKEDLJz5nqI1pKVWcp6YLJ68VUC7+ShAjKNTJa+Vk1IwcCNhgRadyviwc1GwRo/M6J9SA6Ms84RzSPB++eKXVn9A5sEEx4RUJmAJ7ACMBx4hTNGwDfNSMsxr8D9mlGAfjJmuAx+AEloNAJFQu8QbpoFgsg0PDsdioWkiE9bxWzJB1LhIJCaFK091eXygU8AdCvkDYG6iAzxTRoQ01FIk01UXJputh5zDFiM+gscEHUydinxZX3Xtrlu9b8iLVra968sHgs49rFcHiwsUFbwAh9/fstusnlideviTatQLujiueAABAAElEQVRecJ8wkwrNn6A8tlhqS3QwqUSiIgRUHnfoAt7IPCcjrtiLizsehAM3eL4dncUvj3OwIgiQXpeOPlJla4V7UaWxb6AgUiypek9C3zOQXVKjh2z7DoV5g5kAOh1giPv7A37fwgZ8jCGOwjTNUCLEoj4WYmdRJCwkg0MUMOxZVNoWoCPJ10S8TbV++BZ2Y0FeZa5jakCmFUl9bYsuO6IlmRW6UBti7tSGSPmuxAnzTGhoERdahIamHEUIkYW5Hm9ovgvDReZkEi/h28U/JOoIqZ5Ep4/peClgk89pkzlAyFIcoQlIhAyMpmZysbN3yhmBswLwDKPEEoXVA6MhAPzyl7+cF9rsNEkMq72DxW396kgGnGK1VJgbGkhAqud2bi+87o2+0ZHE37228vOf08YCX4kFJxRbltqXNrb2KrsGLbYCsuOC9VzpN8+p10lbuq5JC4iAAUK1JBqaxcoSxWHf3/CGN9BTUC35jbETQmwmICZR7k/lkF0Dh0JH4ZlktxBNQcqS4WC/d5u95ViKuIwPr4BxGBYKgM3B4zD9POWS3X2SVuE/jPUYQzo1IwhRLWwuE4lZROUCuaoqbAF8gDOQp2aQaRoICeGGmoPd7FiKAGBy0wwFBAngJXhoQ4h9BfsFh42QA71xwJYnkAwI3rY+sZmW3b/9hHugWkttjioLIxa5f9fYubjspsSMlK5ELC5BGC0zt+Ux6/03Q/Pzn/l04G//RuhMJVmQc3hCYyd8aS8iNEfFjpjWGVMH0yY0rClsLgyjddY9Nt0yFbMnUQhlM+n77iv++F62MUNqoan4l7kMM719u3/NiqrFLfuPdDZV6K3CI0Rj9zmgYfSAnDNEk0e1LMDF0KBrN+/fafam9VgW545ie7Va6RPkEz39MEkIYZMMVO9E5NKrfcaiSrKdyYgjYmzn2G5ZwB0txEzA8Iv+3pkkTGzmBtwtqIPZxfRGEpbwMOucN08NeE5zxzsBDGzs5PHId+xz3Xsv69+3aJFg7lQlGCDoqzdvGL5Xv1qpqMw/9aSrIAxZNkYl3omav+Qlnpe3qoV/UbEr9IR7PllvbKnWOkPGAV9mSyAXVwIbc241F9G6qhtfVxNw9ff1Y/GSaiOEGSkAS7RArQway825nNv4MMJ8EQI+P/jgg+Ciz3/+85/73Of4EDbUs6QBoqun4QCjsnCQWjnmNggO0LzOtJSU6wSrcuo8jSd0AZkBW2JrayvnHKcRmFk1jUyCXqxUAJ7V69MXRstGbhh0HMgDZ9SYAAxQQV4JFYn4irAKnGzdxAcNBhIeALaBAvSOb8qXhU9gFwk8AyIuG6MoI9kJgtiBHECz6J3hLljOLA2pKQNj4IrV1tq2bOnSFcuXk0033nsgG++vJsBwNEx8Kk13FZHphBAmUgSlcmq+aGEIZXtwsmAGPAr/7E1Glt+jVfqsoKAvxPvQseseCTc+t+SSVLCytWtb8wN367FBpa4pXtP42BGrO65hC/W5LJLfIpJiw6QN9sHSFyHJCYPwGHXnSkh6pAIiR7G9N5j0vBV+1aUYuULW6/HBBBB0im9HZ2GoJmrVBQML/6Gy9XdVo3ZkJBPLEuiCfL+ubd35xiheWhSgMbEWGBloDbwZW9V0DY9rrU7oASDWYkc0nEksa7KhF3MueZI0y2AX7pjcaS8l+UNYL9wSwpBUmDJsyDaTAvAIwCgORF/s6GI4XdELyRgJAGwgwh5iZavszaYhXoG/w+xMaGghK7OlC0afcJX2cKMKyAmbvEW12YLi95gkrBLdsIGYfsKXPoUZIxz07bffjj0GGfhPm0sv7fj8np8VgKcbT7gKUBXhDVC2ockjtBKlZztTp2yAmsEZvUlja5/WFReBr2oC5po6ZXG1Sy/k4m9+a2T7ttFV51TetVkV0YYEFhNLXSx2oguYW7uV3cPWtj52YggWJ+wxV9Zpa+uU9Y1qNS6bHALKsfLisrwDoL7+9a+zqOAebrvttjM5amh5HSqrFEMlD6c0siv5gUCp7H8GO4Nc+JVPIefINpAoaToG4SL9Sl8pxDnIEm4CWF0ozyViAHhKUiwZIQOaLSVkZhReTDKcPUQO4gcthDmGi+UtqB1VOfBMPgHgyTfLucMnxkcLB29Cd6A+pDuTqxobDvsPdSK3Azx9ZP4DHitCeoPL5uTrTOY9Q8ZBPJ9jyoFB4U6MA1LEW2iv0hbbTsU1IsKXLRVLyG3wkSmFS0NsJH7Vq8L9/bHzzq/62r+i3mf6SRjK6dHcygznTPRHB4Y0wlmx/EiaTYTqVXUajklioYkgmQYkKhIfSPzbd9xDw0LvJJL+od4lUbHmJmCGW/dvXJNVjXqfoZsENU3wBfnuUHQmwJQDWw6oBHSGnfjV/sKBUc9gRkvmiq3VOlHxCD3NsIxktUQG8qq40GJbAmm01qjLKq2miLpvz35YK3hN+UXKaWu2ZZg8zExmKQ3Bk3HQU6Yroq+zQACA9cIOOjlJHGAwhjD5EY+dO7NtfX7L0xdS5WS3Px+oqY1vfT7oD+uRoAmPZphevw+HG3PTBe4XX6g3NpltrYWePnM0ziTPRaLGX7/cfc11Zuj/uvIP4Xkz+I1abWelamGRx9EQF3SXddCvt+c8zSmXMhiufY2q14ITOODS6LsjADN6cL34jKAxYTwRFThnGBnS2faUvrBIUVaSj5TBxzXurW99K23JY7a1ncbyICVYfAAA8hMH44+u+8frMjOHSYJOlrlxpgl7k2FmNkImOMAM2PcAHsTFDOeEY3L5Od9hquOax7AwbRz8M+faynyR3k0oyTSTN8GNdJmOoxOn41xCMUmWSffReREVgmCT7NuENCDTwjZQD8t2+/btlIeqckkvQBQwHojHvIVM2NbWhnsae0lwKYLZkLwEJSkmPYwoCdqhcHVV5XD3ATM51NJQFfRBxsT0N4r2ZlTOFCQuBYdkZDl8kgmPDL6K+DDJig21SHQNQRwPkTgJMIGCVU2qvufaLkz7qxqHOhY88Uv/gz/Jrlz3RKYurvmILJUuaOTWdLlFPEiyDXtdOtZRBsa2eQrlPmMkABAxFRTswIigiM2I32ymJSZzfU1lRbQCIyvIkK7REXpKpwSY44d43e4CVYVd6vpmTzKT7krAGBAaw/t8D7E4jJaoMCkL9th2CoAxYxyogJH3uZFIFa/Y66YQyxldQDxr0HekUxyzifxQESB+td2AaJEKjh5+XakLw8HAweELLQbQTqokZFohA5N4SjEqA9jA7WP8ZZ9OeiQVL+t0zsiYKk7UKKwLdmgUyhmEyYIBEloDESaaeohTjYtZJq94dREqjKcUG+/9zH8pzLTfuHHjrbfeCveIScOGZhY1zNzGn0GJswLw1B9ZYjRUSmTBJRwUO6nYBztfM0xWjh7u2R6rY5j02Swbc2WtipXMbZlDH7g58pN7sh6f90d3eVasEMtGoAKbAxer0Xqhx9w+rO7sUwjew/L26MrSaouoV+vqrQVRkIGNE8RCmMVikCDhZ/WWt7wF9EGvSQw7X/2deojPvLuQJVhSaCrjwKdHXwvZhrmXQoUcjQm/IFzIsLwpiFB1NdQLxIRkC4WDYjn8CsZensLeURt8jBSVKcwwENID1a/M2ME5cgISOK/DLkBQCTzOTZhjCkBWoRm0KOm93GE1q4HE+ZmQZgD85S9/mQA5nEzzOrMCIsBeJhrF1sdQYKLhl2FB2KN1SbegpYNkghnSOkaI/KykDHyX8Pkxl+L8HFHaifxs70i3ieIYlqdVWqZ6xTRG3vO+6G9/m66qCt9/rybCZc+OEkwD/+RH9jy3Cpb6Qp95cETrz6rJjEneo0VV6uKouQjnZ+zWxBIvWtv7rIBHrYgPJX/wY5cIiobpj00+wnYvtiAR5SLkq3jpFYtXtiv5jMfrO2fVqsamJgaEL8v3nX5gJwMm79hDRC4086lOV2/aiufYYqQ0Q4x1Uhpi+1WHk6aOYoRQn0W1OqQ2R5RFIWVVHXyA1jfQ53a5mW/Hq3xe7jMhsVHAtyG/0UdOmJPwfNLwy9yewOM648AUYvIw4Z078wLPHCpBiSHeMs3Yl24t3v417yte7r70kuzhwzqJc4qYSQzdF8hccKH/hus1clmFQnpzk37hi9RLLzEvv8T3uteoF74kVtNQGPk3v7VbTQWH/rNSz+OBxoZ35gU7viwPTE4w79uY0tSCFbxS8yxntUosQbMgFtYUCgL4YJxOgsEQqwkuFuQAimD+zJaVZ0oSs4D4FMRoZPDZG4YkfNoHeQ7fZQLMEy7nUOHJeMVGIKLiUwweAg+0iRXU2tp66lsXHZ7NAQ2FZoEZpBYYLMFBF9CizqaaGcqCaVk4NAQuQiVN6dl+FOdrztCS/ZiVCxLjoC/orKHINE2nWLDUg+6JMDEybojsPotRojtwMkIsJUknwUdkYzDLnP35vMiAEKNEZk7asGEDRB/Ogft0hAN0Ck6gj2APuAt583i/VBUIBvu7D6mZ0ZXtDdQhBgRSjBgG1wjJAPmYFhJXTrgli1S32YIR9mle+BcF66XaGCFrpun3WORCRn5DVtzRtPrJTa9qObCtYeRI4313utPpvasuyrs8mDR5HWWwF5lZ7G61MK5ywI4CHn8l7ynImbhE7rXyuEPbsjfiNwZkQlin06mY4CsabIIvumUP81Q/CNuasrLeQ3r1fUPUrhUU165+YMgvqXWhv+Yd8b5dA7oG7O3oX/0+b22I/EmI9CJNETJwIocztjAI45WdN0xCQ4skwfbBu0LFTT32f3iB1Yc18rgzeowd4itvIQZjB84KmzAPjCqCYQtm4eisY180MnDIi6JBbL3Gq5pRKRSlHRgDu3DeRAa2Q0QLjQCqBJyrqdClGtWBcVDG4bH/TvcDzEyYtrY2oplCgom7VgrMdG+efTY+Akcj3IzfOft3bARAah/84AdJIAFL8ZKXvETqluZrdNAq7ew3Do6wANAksdVBWV0nYqMnf3qf/zvfK6CI+8qXQxdeaK9GsURZZ/b6JGAPCZNUwvbgi8KCYtE0h42l1dqyKnNhlZDE5BKeHk4H70uUIQuD09/85jcj1bCoPvOZz8xvf6eHZ8JTBzzul0I4odi8X0JmoCJYOwEA3S1yLK1DfspsaDKopXeQEDhkVaUd5A7GZBhiRp4DAUOeSAsw0qZUKoOJKSlfPPfcc6GmXOIihdAO2BxQTQ6aQFXMI+5DquVNquIEegxH8slPfpKneGddf/31ToWccADtBMC4iXKReiDStAgwKEcoA4Q8Gh8ZK2tYeweN7oTWk1BGczwR5Laxwqrxaw0R9gALtT+Vj5Eano8fTO/Mk0/4N/8gzzz+1C3uJcunKjVeep7+kqa4Y7jYm2TrrzKSItO9Vh9S6vwWzhcSQtbavsECCckgby0ur1YV1ROj7FAQoy+28QhNt24WXERLgsaiALfHTTibTdHF2QENzUTK3XLAHE7jq6bhLdYcUQNeoUdPZIlLieMZSn2xuyjsJyK02RBUlleZyOWC/aBjFryLOGXxnqT1ywJZvXo1TaABwbuPBNc0RMflwX1gQBJDuuMRN5m3HNzkl6eAJ0tOkJN5dAoOwKAVZh1hUpP/c6/rM/8CE5ceGAhefoXa3GQ8/gQRoeE0jbXnhNZvUhY0kXRdFA8EtZaA1txClo2RotKdULbuL16gF6pcFnHeiEsGg6Qh9GIBhr1RMYlYSpYxEW0RlEq0WDL1bS1YJbHCVXajRVnFGiOJxgRsM4cRoEd4lFx99dX8Yh36yU9+ctlllzHCc6jqTHgF9IKhDGwm59iZANIEGJjAaCf5WKW7VyaUmfdLPigLCiSMbhS5a/IOlHlv8QQrRIoDWipBsYt9T8qBfNYTrHbC6wwLoqMMw8EvLcoFPqFY6aVcGrIYvyAlPih0DSTGGqQkjhgomiUJlsSXV1hTPIJ6ohkvrY1zZgLSLGWYDxzQWcii/HV0keeccw7z+dprr2Vz2Q9/+EP4yW9+85vsMkMSlvZPKU5LfmNC/eVfAsOC5ua4nb5huOOFC9ZvIjnffp96xG31pdhkJ3yIqK2oqEMZ5FVhCk7hT5Qzl9eoC6J4p2AvNVfVuar8JtGS/9Bt7B1Se5Jan1J3y3vueflTd137k49f/rv/atv5//7jfT86Ut1WNLWhtEi6WxPC5mlhxRGGWBv/AwknIH3UyTbGBYcK43N3QiQuISEQtuhkvrAgEGoRaosxZFX67ewKxrpun4uKgPDKNm9DqPjD5wrxohsq/PAh73Ay/cYN/ghWIIizjffAIagUn3ryKZh2vkV7je5xm4jccNddmoC5NwYR10SE56LYclgbFEZjDsFsOyeqQqit1fUur26Az6HxMDFDKVyphRYceRhJuGAU1zfpxPcSHeVlmw3AlHtuo+7VEfJJa0q3tYSQ/xW3jgyvIe7CRsAyyJEZxZnLYouTVTS0oplfWc+2bBsQCUd5v7gYoIj56Ec/yvx/97vfLd63x6G8t//cS80zSvpTGk6cgUk5/cUvfhEP+/nlJtkweWDE2Des4sCMYq45ap1Ta0W9mtHbY9z8QV82Hb/mzVVvu16w1KxJsJa9MDkfSJu7hpX9o2rHsElUAxZutd9aVq21V5nLayEvQsM0vopn+BSsPLli5WqBErztbW9DPQmh/d73vjc3bmyGJst+DPnBYRKToxQCy37vRAtKSoYMCSWDhCOXniRUMqFaKaZOCT0gEY4bnA6dllSZX4eywkzzrryPD5WUeGU94EQ2F5XWCf9B1l+U01B69gqyp5d3pWzML1Vxn1lBbcx2CgMkBgcqQebZtGkThamNauEPINuyZsojdqGU6U7rJPE6MoJORhCDsKvYHHK1RJUllUrwODiGd5VMKv/Rj4bz+dilV1S87W22oDBGhEohn8dzVshIxiJIdVfMGs1oXl2t8pmNIW1pjYILE0uNAt0Jqyvl7oqLBba6IhC59AL/Dw+wYVQ8VXVCZ2ULZkKxKi+5yB0VjluMnujLfByIRb/ZXxjIeWOo53PFpXV61G8SjwnBijtYJwFBx6NQUaIea0GFQsy80cHOvYMjctQg/HwdgGltbXW0LfMB11gdTAkO5gPXkp3lfAJuhGtEB48wRkmKoYjp6OhwYEC7xDkTGPcK5+YpOznS2UkMbdjx3FNPK9dd5zEKib++uvovX4Yzn2vJMr15oZLNoDxQiP5qB4AVXZAflkmtmoMZdXuPeSSp9abcg962drakhbKuhYa2F5HXQPpFb0koFIwVyqoUlgPT8mruRfSO2WEPhjgVnbXUbb/sX/sKAs6f6GzHpoTPDtIvWkuysOByIoddtPJHeDCCSCBM4DNWAIZQgmbLzJEzv1+AJYMAzH7RM1wAZgaCEh2siKpLYoz5HQ1ZGxOGSHv46OFIzEehUTn/oZiYhaFloCPnwErsKI55yn2KScUcgjQGWOpk+oGvJD6ncrAcwFOGc7gCPoEk1khWtMU5N3mLRl/84hcfr4OUlI8g5VdeeSWMFg6rEFYE4y996Uuvf/3rJW093uvl3wcMSDOCejaXD2jG+iZ3tZ/E9SpCWq8L2U9NkKQH12cV+VPJxW1BtKgmCtZItgADSXAonFjIMBz2kfReqQmaW3uUrrg2nPH+ZNO1W1pf8rlv/u3yzhc++IVXPnTJDfdd9YGiolNPekRBogugHZTuvmA/G8lRF9yAwKA2cSQ8Mo5XAymRIQmjKBkEU1XqyoYa2zLNO+JAFYgl83jMp6hPJcir+8YXmXc+k+1KkXVB3T7k/+bj+es2uptCgs9gBPjKuJSjeUEPyxY2yOXCqOYXMjAhJAkurfWlrd4EekuVpI4Fw1zTYLZEpsieQFVw1MtrdZ/L8KLg1ElvpAymlXhBKyQsrMo4fedNc0OTnR7JRuW8QmfdQnKGtTBo8RnSI0G7cwqRn9GRunRhV2ek/BSCogh9N5uNhaN1wXLlLWNNnYteiVEr75At3nzzzWAGzHVwrdIXury3z5Y6mwd4qjnAJEY5x3x617ve9d73vpdJNlWpWd+TiADV2FBG2TOo9cFEqwr8d3s1rsu6WcwnP/Th4JFDcVKs3Po5eDLRgEAfLBMBQNowdwwgOWv7hoxUgdVOVHdzcQ1MsLK6Vifwlb0Gy4IKhA4nymqRG64AjB2/uHnTUzAyWWHLqmVeCwEDIhw0CbcfftGJSno2r43MXBlCIL5JYBMI5MylT0kJPgpkWFLiCQ2WsokMIJ91fI4pqA/QZTCSUjzmBE9mNgdS2z/90z9Bv+ljaW1kt6bvPH300UchQpL2UxuCN1VJCs1HQUXCyCAtIzIQRhKoMprvSBx5Eo9irF0CeWtWsblabwxZTSGrOWKLv8cuICAtFA0jn8t86lPBLU+mvT7/p//ZtraVQjSf52IRCWFDmOtI1ARRH81rpExsiGjNUa05YtYGbPKpmATJ2I97RQzappCTzFsR1V75itgTjwcef1yD/Pp8/mDQ5fNrL/vrwIsv0vwBamUdnSCs9lcTbMLWnsL+Ue9wmuQNRkNEJbKdVSTnhDaYJO+RCBNPGXi2atxyI0pTEMrtzrjqQiERZQ3dBJ+VT8O5ZMtOEKrpX2eqcAA5BydOYen3KBELN5lUra2tnHAffQqMJufzxfBRFSwsLhJ0fDqTss2Q8aUy6ZTL7bESifR110Vy+fjLXla1+U7FDerkP4w4AeJfUWfpAb/ItKGL8by2k1TtOa0nLUzxB/WXblL+U9OzVa+PDX/R78qTOAtGkLxUlto+6rkoC9I23ItdPuGO4RysT6K2du0deuH+wLKLTF8FnJ+K5p51NGEYnVeOd0J5wlIQ8xkVA6o6tJZIv8cr/Mdyn4+A6oSZDJJB93fmgM1oTwDGuVM6+SeUmd9LTKnsw4IsTlPtqYdqGmB4BB0pc3wcyCdUyH3omrTTQsU4wB5o0KgWLTn2cO6wfND9/fa3v6U5HEF5hAjEbltOSquFtZACMBiDOpFsqYoDAVWiTZpmKUnNuxRxqYFDggQqKyW4E+As85K2sEwi/X7gAx/4/ve///73v/8LX/gCTCZcB7SVtmj3REzBjACThAHhBMS1qMpdESTMkhnykpNWHcQUjMQlwiOTtVztT7GfVssU1WSO7TZF4sjUEBJEscJu/dxmq8KnVAaUZzpJlKDvG9F2Rxff/A93X/2H77/1oS+96pe3hTLxB1/x7qFwLTta2RaLUZQdQ3Z+YTYV22YYoQtnVMYXjv2Xn1Fs0SYhMwmmpaWQP+s0rDhyebHqIRPHE4DFXkDqQ8QNa++60P/9rekdgz4uu1O+bzyRu2aDtXLMBVJBCwCtQQ8Ll7t+/XpGtTagvaiFbUSGm3QpOpmKlYEEE0skEcwZJol526uEPUlsbwJ4oBz75uK6lbx4LoivhWaTvUbs9sKLuz8lxHsDYd4wz22yiNQlXLDsKCe8jPVgea3K5l5efLKTynRyihAPDEYeIRzjOed+okYQV1MhAwVfRIyAXWF+XaPbr+MuZXdWgDQGyjQTjG+NoQ7VGFsXH3roIcwV5bw1TYV/Po9s9vTPp7tl9JSZiFqdmUQGc6RBZtK8TiYrZ5J6tHh4VC2qItkvNpyVVcIOHL/ta74f/igPg3bbl5Xakii7AiMJIXjfgHmQELsj1mCG8gr+ma0V6rIKZXm1RS4yejYrOKWwxCucPPXUUx/72Mc4x0vnHe94x2yrKmNQyyqC8QoV4AQaI1HjrLpWVmPHFqIVpD5+kRwgHljPJKU8ttRpuCq/45S0aZ4Ako7AInBIiLmEIcDqi7QAPYD0ckealOEepJrcKSxNwYLRsA9GA9GCynkFioKtCVZDROwAd+MNpPtr11zZmTAPj5oHeuLk6yM7Qq0vbwzkM/lcU3O9pogEGD3dPfxC+6kNydmtu4diQ7Gf3tf4jX+DWpq33OK78CJBdIS0YdNJCff4b/mDMP6GGAHn3H5dLBBprO5N6oMpZShj1Pj1+qBV7yc/E3RNFICW7xs0B+ASckptwNxUY+b37CUnbPjDH07f81PzN781hkdJ0uJ77asrrnyZ2txoL7lyfS4ceKY8gXj2p43HDqvDKRGrEzVwQ1gPuMW+oXhGjWVMAp0I93PFigb0Kp/SGBS+5RBJZiwHdcILwjw5kueUrczjTRqasi02yNEKj+RXg7+UxZg2LLEpXzkRqOBlSbUCx3M8eenoTJAzIpMZuvHG6P4ONp+F/+0bBLySW8iAYZpphpP/zl6jP6v3jlgwi1g8diUvPhS6tE3/38B5o9ZHrMSPK9Qej+kz1I1Zz2uHtQAOhrpWfSMBXEqrBZUjfe9+dFRN1B15Lrn0UrE64MtnOwJ0CvsGRArpF9UVG/vZZFja0GwrPHPKM0PAM8wiZvUZ1SO2voMzGXkOeE1g42DigdJOwejRFu0im8m2OOfOlO2yaYWhO74UMeVLc7wJGNO/iZFTFpAlYTY4GEYOiBEzn15AgPBLkjehOZzwy0YeqDBvYd2FeFGJ0xZdQ8bjDsKSFBqpBCJFASggv1wi3La1tUFxOKSIywkaBAnMZZddJk8m/zp0/3jDO/mV8u84dUJkv/Od73z4wx/GJozzNippBOC///u/R3RxiHj51U4oSX9piHEYjcWJxhj1us9r1ir9BilwD4uAVbhAs4uVSAh8OygLnsAiRVAir2EfXlFNTgG8v0xEuKU1roivWO3VHjlsdsa1jKZ21C77zys/fKB+6dt/ceurf/7FxoG9v7rkhmfW/CWzoGBoyawZ8MGXIgpCpgBBZAnms3GDtuSEtQFDvQvbKaJAZQoa6XnX1BPEkaCO0MCxg9KcUXj8xtFz8cAi7Ktyw6bAz3bkHj2sw0jHCp7/eqr46jW5CxdgrBVNY8+H3LBOYWzwjuGViMc8f4ELpTY+Sx7N6kviUIlCXBOmYMTgIk6UIrwGtTvtjp2w7sLqi3Qh0GK4heiSm5CsSP1J4fjNhiR2Vq9rNpqDgi2HL5IMAVFAW6vwkxJM/mOHi+xixjSQzZsMPOkKaRSDOBGzYHroUTqndiEAI5ALw3Lx3AZXEBdvURuDOcMhgWR6owNF+8PutkceeeTkeS/OAM0f2+NTgbv/iMaEhYdljCy44F/mk2T95w9+MdcPDBukHk0aoGqzJWqtQFHksozDh5XbbmctFD78/sjfEMKENscXv0AGak/C7BhVD40qnTHbsUQhbalJfN22aqtVyM8l5Y8PLjVBXSQRohTnsI8c//iP/wgRWrt2LS7f4F9n/R+/pvl8IpEdNQIYCAv0DWnkJrIWwHDgd3RSQaItmoahRGsIaYR7gAo6hHA+u3qq6iodLjm8pDtibOkaXtBwbBRgnFFpT4CIwkjI3GQ+QD/gGBgZp0xrayv2B1suLuQRnAukESJWk9abtI6MMLWFNkXX8n4jrmdjihVzGXD2EWgPIbWZZqIe4kMTOVI1qwwz8IUv+nLZwRWrD64+x3psi8vldWNAs/kV2ErmADDA4oDZhdhsH6xHLiWv40BVeiI7LruMMxslYTXsSwEe/kuH42pn3BzJqbh7RQNGUxjnZ9WtWbl8DtG8K4kvN1HZKWytbTD9Q70jn7uNuMD1b3tz4Jpr9Ne/XmiiXC7ovFVXrbJ5n0UqKi4FYXbnAjZh1hXxpX+7rziUcyXyajZfXFHnwvm5wAZSrBk51MyUMVEse1VNOj+31ahBKbajRhYqMgqMHaVff/ze/P+d3IqEAdGFNetEKy39IvJ88otzAM7p7+STo7WNf5fu3u6B/kExSAUj/9nPJn52/xFdb/zi56pbWmxmZ+wTyqoc8JyaCf65o78wkHd3DluxgmCvYB99nooHBz99bd2hSnV36NzR4LqUknYpcC2+PNpMEQYu+AZP9LqjwAhW0G4Ix7nuKtVyHf59duklYuOcYOhmM4eoB6mAqFcsEEIVkPfIUTeUNvdHek5f6CCzSDoLnDm9gCKADCVeAnOibAI2Z7bMDU5njpVTlWwL9IvIRIj1ydhbwsCsALdPD09puxNK0srxnjqPGAc5FPgEcRNJVbYIYOhGpSgrfxFxZUwKokNh9mT0eJH6OUDO5DIFn0MacGAGDG5KYKjTCWoFDZLGWErKwyHN0COEYW5CHKiNt9ChyxoQgOFnnNrkifNLSef8tJwAAAdkDq3KHXfcQcYy7NUMArppuY2ToaDAHGBz3mIoHn/8sfr6uk2bzsPquKLahT9RxGOFRhS/2xpIaTGZvMi2TPYmxuIbpwpaLAv1waMQR2kUxFqoxepJWluOiEgfWkFLeYI/23Btyh1805b/vOCp+1p3Pfrdd29+dunFOdWF+hjzjt+FwxrSniBsogO2OCx6K/DvGA7knDBUhQTZgLAGK8PxZI3Rs7DCXcwRPaqI9zLv8ZWPq8SxUSYJdF99jqcmVHhgh5lX3ITF+vELxdFM9mVLAEHRXfr555/3/PMvsOFlbBhV3a+TJVTHKosc61bU7rQ6miH1k9jehFUWh/hz6l3IxmOgQ1g57B+E6pqAcv4CJGcYdw3C25e0CNIxIO3AQmo1zUajJSJ8tPhyNkqn63pLxLp0EduPzUcOFbWkK66IENwYwEnigKybzpODSsGzmj1OpGLGMRs6jxrHMovnNrlCiMdCSyEgcD7rWF8m/aEAI0aYcVzxkV8gEFK7NKng2RvHjMBZAXhsOCTyxQfjNa95DZPpnnvucfSFxwzYXC/s+tWhNFt/FZYN+qHaoLK0SqnD99IsJv7xg5He3lj74qoPfgi3IdsTYxwwAgUVCLFrkWG1YwhdnVieYY+1FOm3wlpVTai6MWeJckADuWCKkZ2FXCHts9WZoHnQD7L+soRmXGnltDKrMpBM7Bi8wuDzC+KGTHIADIQNdMNNAD5JgMmhgPYga8FyHY+rmFWPzqjCdBDn9m9/+9tAhaZj5cqV04wkj+SAENASBQRpDOG0ZHl+QakcsneIgoNpo5dk1P3WYaRf4g9XVpEeYFWtuqSydkk0t7zaDPpF7iNQOnYA2DIU+5lsBis/ifC0O76rHT7SHw72fvB9XamUj11B4we8FEBix0AfwRSVrUuoOL/wwgsBiUs4BngpyfrY0rGL+9IbGbaMdxH44ZyYSPZT3RcM7R8yuuJi432uYDSF9AVRbUHYJE0rHNiO7dub25YeiAW640be0sMeo8mnms/sM59/AQVv1wc+4du00btiiScSzA8MZra9UHPTO/0XXQgTB0gStnHwZ6ZVTsmxE8H1WU935w/HPKNpM14wm6J4hZnFgur2KDg/k79BGDgUi5wWFWG1Jaw0h4qNIfJQcEh1szhra2uTi0VcnKaDL4ITB3oWKR6UQoHqxJGKS+/P7Zwxl0hDKlawEaGQYehB2ja/YNcqORhFCQWCZq1KzMChzZvdP/8FDn6uL3+p8pVXS5wi3NBsDQIThtcdVwhZBch274AxkHYdGrbiBj5vwhnH69HSOVPzbdjcf+dfV3+ixfX/dD2rhtnmxbYuIn9WWpU3uavfZ6m2Z854D1kCqXRKzXrzg4Rctfp2G/mE4YFnsj/keKnp/sqZRvYy1PwIQth+iXr1pyT90nkwDCKN9COYbixO7TOWOZ7qtMl8g0Ih8h07T+YODVQPZHWUTbdrclDKZPSCfzjhG5AqJSZ3Sk6A4Hj3KUad/JaKqTa9FYn35Aqlg+QWcgrwlHMQL0gIUB9++GFHiGXJIGoix0ptBUib5S/rl/DwojzhXRC7FFadX1mSrTpYPsHS3JePJFaXLxLxkZPSOuV9fnmRw7nEGozLK7H32AswZXmn5BlyApCM3tvf/nbU7v/wD//w+9//Hj01gS3f+ta3EtaOL3giveBTVlRU9vT0HTggMkrwzRuCethrEuc5NIry1OzOqKNpRFAEXZGzZygtKA620FROS+bNlfVqjV+IcqQ46kuC80iVZKVECiJhLv71mr97vv3SD9zzvsu2//y9t171wzfftuWCv4v5IoiR5Bn2ArlbRAXks/EDOwsPILV89GjcGsyGKWsgSfwnNeHS+guerGEuDLk0Nc/X5wMJijfVUUr1XKp+aataHSj8YGshVUSPrf9yrz6cTr12jZ/cuowtMjC89NhaEHSWPcDW6nrd7zLZYqgNKT0qg2B1sdMIQ65BnOri2gaeijfGGhqbe2LNVHjNTQtQtRTZSAxwfSklmVOIqkVhimMpYKFgP0d+5aWx/yylIaxdvIitMcWHDxW6VJFnHsmZFMGYqhl2FAHYgamNkWH7lZkU48T4sUA3NMKH2FBMNQ6T71EDOAoZ+PLLL2cK3X333YzhiUyhyU386d05KwAf/aYga9AQstBvfvObE9/Xd7Te8TOSX+8etHB+NiwUbMXFFdbSStRmRvrOO/0PPpAjJv7XbrOCiKBy7Y2/JjKsFg+N6h0xa5gIPPYabquy2iqVZTVKSKQtncUBoYKeQcCgFrCkkFJiEvI+QeRIq31aVgssBZt5gAGsIzO5SZEYUCV5Q0qHnwbakwQeLEVHRwfyNuE0ZjGUfyRFEezJ58xHJyCEDAE9I+Bwn0i/iDHweccbczz59wyqnTG1C3fiLDNW4OymiFUXVJqj6jlEeISKCPWv2Aa2sGWhbDSVTO3cs9O75bHEPf9DmoCBa96Sb1+2urICZs7ht5icsjAA8EWc+5IPg6rJp8wWJAqmCieSIXOELiYMnBnCM00ztegCvFfrxr/oSbkODWW3HegKuQ2XJ+Xuz/qj6UGXsnTZCtI27RuwnjnU352CVqmuENruYPZQp1UgarHuIiDVI1uULVuoyiwkBZnu7jbzRQ3/WXvelo4S8JReSmiP/yuoZ3fSfOqI3p8lPaMedBUaya9gi0WxtDaSRsjXcduAQlcE9ZqAUReyltSgwhaowKmWFkt5Qef+KT7hY7GEJ0MCeHwCjnmEh2C8TAlq5uBbh8LhusoqK8eGKgPuifbsjBOCs4hEouFwJLdjW9/tt4eNQsU73ln5zv/PFnvFuw5IrBQgLBVsGOIDI4WulH5w1EygN0Ho1cV7KFD8Xj2bM7yBdU8Yd8X9W5aFHnEXDyjwft61evTlqr6ErWYq8U3YcDZ+ILEfPnI4FF/iEsnb4SUDnS+kF18cPuZDjhc+3l+yraCiRfolUsOvf/1r5nxpF4731h/LffoC+4ujLyt3luvoj6WLE+FEdwOOmiAATyjEUDgHdBApEfyM0ztjhfxMDaw7MB6KP1YEJSGdnIMekVfZGiAxJ78cyIfSuLplyxapQnLaomakXC5ZCFgm5X05u/gcvAs/TYvoiPmV5zSBzsjZfUDlQMJTR47lXNbTZh9OW6UnFJbKhdKbznn505tpg94WyLEcQjucGs7wE8aW7sOGPfDAA+xluPfee+E/EWA+/elPn4gNhg9EklhpWEYY5mAcCEiJgFfhLwbdmnfU6mNHa0bYJMXsUrXRnJUlxCMBovNQIqKrGm1V+oGY+fBB4SrMvo+Qz3IVTHLZEvy5P1D1yTf91833feiqp37wljvefd5v7vj6R+8b9FVA/HBsxm0qhInWluRoF5bWxnL8iL9wC+Ke8KcS4amL3qAeDA6xEzjd3ei1hMeyKHQUM0/zBcHGa+rcN15QvOPp7ECWHczmk92+RCZ3zbmeiIc2BPplPrNNBiUL2BJJFbCW1mhuO7QVtnEo7TAycFyk7c2LuFbF9Q1InkIrKqEtbZ1wYpuayQxhYARmS3C3ouA3PphBlMWrWQTWyhvGkmrIDx0UzI/dc60+YL1kEUvGfORg4YgiHMdSBQsDvIekeZawAwc8IsIWfSZWdl8CdQOhoVG8Gxub9aiHCFmCXpSCcbxzBg3f7+9+97vEG//EJz5BKlOmwfEKn73PCJwVgMU0kNTl85//PAp1PJ9ZKvM7OVjpLAicn9nEmyZhpGo2s323VviKmAc7ix/9qJ+ghTf8fcXLXmbPcznXBVCAgX/pwZh2JKZ0j1ING9ashpDRVqG3VhoLI9IKNAtgEfMojcDAUuFA48glZI/VMota5rUoHKd0u0VugSRjLYSoOwSYpqDiUF+gnddmxyrD0RcHXeg01AJKfDKaOF11ylmN1ZfdoXAtP/rRjxx2ZBqQeAsdEL8In9DmCSXtOSmMYB1DVm9K7U0pncPMWRwa7MjPEY3Iz+2VSggkL+jcMQeSnjfoXVFZnfrXb1RaZteFF634P//80MOPt7Uu4uMy+By4J8BUEWcVZSqbt2oI6WSrcoXsJOimWBEclL/88ss5AR7mBvNZ8mfcwXaBeIAdic5yH1cok420gerDCZVIXYmiGg54q12pOo/S5MumRofi+C41LehPGiP59M4DXXlTDwU8xZSaXxn0hYK4NgkfJXpCC8ThViyfRn1KwespZpMhv3CDRF9A1AAoTemBSA/7AgDMZIBkJOVTToANqx0vwrnm8ph33f+7V+tLeRNZM5031jYI52e2JEEFIa4kEYQSCoHOTeRnYmvrS6pNO3uhGF37c/B37DhJa2S8+pn/8pmkxWbmoidWgp7K7YWZTHrv3o6lS5cEwardPbktj6p9A2Zl1LtqtbqgEXsiIijBSor7O1J/9apAX19yxcq2L/wftsvbzM3RAWQk5ejZQ8o0EyE6+5OEXdA741Ysp8czZMiEv6Gc5XVrpNCs8rmqA2ZtJFBV/Rf+yF/aHeKjiO8iN70JK4KYsOObCISZQO9+rqhZ+BeyCUw99HS27cKgmNiCYRKv2a8LGwlmE1io0g8KYPfff/+b3vQmNIbsGCRMo6Pxsd/90/mBhzsTOjNhcUmQwCpSMzvhKZcsbX5Z8qW/XPIRpWMRhIwlzx2QFQcnHPIRSG/nzp3yvnzELyKu3PSLVRDLKoVlzVTICXpb1PTslwZdAxuqQCrhhJLYh5GouUN0DwgcgMkDFOQMLOIuUiLd4ab85ZJqqRwBEqLMTXnwrsRdvIv+EXMlGI++cMl6B2nTKQ42m1CnU/+pP2E5gHXR6TOSYCE6wnHqwZhDi2gQOG666aZrrrmGjcF33XUXfnkYhD/+8Y+z02HOHUERCWfFHHjmmWcwb/ClUEmzX661whX1WRGPsd+jeeMiMnOMmH3QVlyYiwrZHLDxpk1CPbExuPjQAaMn4Rbp3fjcLsXnJqOPqWXVlKnlVM9Xr771ofNe96lvv3l559M33/rXd7z1X/cu3ADay1saCDPoE8mEiDqNFdbeCSwwHKhXKH1tUo4YyZyOZZnaCJ9KX1ob0grtaZEUVxSyCf2M4wn2XBR13XiR8t9PZw7GoMj6rmHPt57IvfU8b61fSI99A/1Dg0PPPvvsBRecLzC/0NUTQIdoJEURs5A4VJoIvdGDB7Iw5LIdydzQiL2XYmOYmT82GOIOSm+yH+laEeCJ/N+VUGI5a0QEkBFRqg0YBau4vIa0R8iwDvukElX7JQvF3uPfHSgciOF/jdwr7O0+JDBLTeVNQogJl2dFKCB6kxgBaEtnozb7t6NeOVx8IEEqZhwQXKBvueUWrB0opJhRc54/Mzb0J1DgrAA89hHhLUh++6EPfQjdycnAm6NZa/8IYfdoTq31m8vIksoiJ6fChz4UHhhILGqr+PxnhdFgHGWzEsRiMIgXbXaOKgdGrayJmGFF3VZ7pdYatZYLPRNytWDlyjycyinPemXnCaQCsnH77beDHEufllnhvBST7UqEKH8hq9iRoL6l9ctH3JlHOKlz27Zt8A3r1q2T2tZ5rLwU+NN1TmYUQk3SqVtvvRXF/4y9Y0AwpsHfwAMdn5uxCIR4JK4eiQvn56LY70Poi+LCaq05pDaGSesltZklmFqeMllNJf+Zz1Tu39dbXe1933sslzeeEN7vzoH4yt5dX8CHL6rf62OrMcG0mPZMCVuHPFan0xFO4M+c1znhEvGSAzUKTwMBhFjlYC7aEzNiedLVqCtb63GdWF5pLKvRn3tum1HI9gwnO0etzng3TlRBvVi/oN2rWzHVW3HOCquqRhkdQQJmsbmIQ1XMC5qNb8KyJRkZMsNeSjSE1I1kIplXLmFeAYbx7Ojo4BF3nAmMhhvweIrH4KFDnXuyFbvSDcOGN1P01vnzlb56EVQzn3lh72DMJF4G+jLhSFblKa6sql0QUWuDrmefeYbaJFPLL/XD5uIQy1pmAOFNnUecsI6w5NMcxplSbhiQ5FNOeDq/h6zT6fKMlc8WBsozJaiWIG4VVRVBxv2JP+S//W3foUM6oUksLVNX677uLe4r/8KqrtDyxcS73hvtPJxqb2/+yd1aICyQCGyQ0KiIoA+MGCeMD6OH/ACOaWlpKriCewYUESsloybTRUy+uKuR5hGcW8grYZ8e9BVawu5FUYMILjZaOjqMOEIP7Itv/SkJN486sRULWi7bnBsgeYdgwWA3+5/1/PJfmP8wpTBqLCPBJSre/Nq/9dcuCQsXOVtDDWwcGIiuv/56viC2X6Tf07JdZcbvOGUBgBfdLVkCk4tN+VS+OLnwbO9QDyw2c0SeQOwkcUFsY+3w6P9n7z3g47rKhO9bps9oZtQlW7Ik23GL05wQkg0hhDQghJDQdvPBEkJdll6WDkvn99JZQskCL7AEdiGhsyGUhQAJbQkEO8FVrrLVRtJoern3vv/nHul6rGZJlpzE/q6TqzPnnvKc/vTjPUwqte+xByJE9eIJkBeeC6QgPQ9FQYxa7OoN6VUrWSWxegAV0wx1sYJHrNJYDh2qIICZCSnZJSBUqF1J6sjFJ/YTAnxlXUOdKiqUN8uWotCAgABmaQMVzfG+sqsAIa0gI2Qw8RRFFt6U5j0gx154SgCanGdKepWGSIqFTUnXEUMn0IEqrKxjZsw1pfxl+knVdAjdwhHW3d0NQb5MFS15sarT6Fj4Dp/73Oeuvvpq3K9imIaXO4gZiGEmwOI6lgnAcYMBNnxY3Iu6bDXZaBpCzvkrfdDAsYABDYb7ejSBUVFkocLeTeWRT3KsOAMF/X92GehqGVxgC4En6oc6LrYSYc0oaVxLX/IH/9hx8T+/5qfvvP2Fj+n97as+94J7Lrjxe09/S9nwIxTNFsXGlcOUHnOPb3Zc/rk7L0DI/FE7A7ciaeWMlnDinHj/26ed2WqvrIP/d8zJPmO3T3ad1hQ0X/pY4/Y/l7YOhdhID2RDn/1t6ZYL/B1xa93aM8ZSo4gBdu7chdMEtxxZDHCTg50OZr3Q92zBtFquCLYQXyPLtc5vNxpw7S9doqTBE5inYCOGc3a7z29UceSMF09jXB8r4t0axiXJDYjXqlPe1IzIXHShWXzSUvHCpV3UIRX9cl9lzyj4jCsHrtL5QoMXYQAEuHBYcuAfGxtjWfqwSTXrghVmMqSOq+PTv6pDYKPAF3v5y1/OvjSHEcGMXXpaRR5DY5xWLfcay76D5gw6J3jkg3HCBFJzyEuw6ABLRfKizOCgL2px+yi3kEcMu7PBWdMIzuQUf/yT0A++X+Xu9Fs/rcUnXBQCz0SNjrM3ZUNp7E9rIwXi2H3sVfXOqqS+ttERJQ3WykTSBfyhfBrIoYWmBNn+6Z/+CYn3UjV5AXBMS8rxz7lLNAGUgjjjMUbiJ7gCqIBiOUOYKeJhWu5FRoA9gC6cek7zGGUQuOc973kEnvCEJ6AFDQ503D6CBkBZCJxJoUe1s0KmpTvbSpaOQ/JD48IqHikqqazTGtfawjhL1NY3zVCN5JV14BR+86vAN76Oi6DCy1+ebm4YcQ3GGFlwQdJgfwnjoxnShatvGfrRw+z3iWQcd1MqO0XUgjRjc4T2FfLS4bRDFa+np2vXsFUYNgbzOurE9WGjLWa3hpzVjWgw6Rs3noHi0/0H86MHegONq1ZFI0G/Hvb7sRP+02C5pbWz8cXPG/3E5yJWOYTnMNdLo12XiLz0xUbHqlhclCl4wEGvuuoqFeZNQ5QeIGGgveSSS0CR1aOQZpj9sjM4YMbNqWp4pC9mc6SWcVJSbY+aKGWxqg8XzQIkHnVa+MywuFc5YmeaA8nVTWHETOhMgnd6NSLqIQ9VEAM5hzKk+qS6HaENGBUJ6GRESV4uFQA84CclurWATTLmiXpA6BW6AEpNjUR6XyEhlNYGY7fXvb1DfYU8YwmzmpQKIpDQZC8j2fmqdIzJCBpNDA8JAIY3ODQ/aYj3yf0uL1B8EgAnBfImRsFP7vb6pL1rV/ljn4ikBsXBGjrrmhMdGiz+26eryaR56aVjt90W+/lP86FQ6ze+4V93Bjx/MqshoBDaovTqqZRa3LBTcMydA/Zw0ejLOpm8YwbF6JfLG8uWKOEEAzruZFYn/e2xypp62Yh5VG+7QVkozWvqLnhG4Df/ns0eBCEjhlQgMe4qEvkug8VVJL7UdpAmPvCV+WhFO0qPuynZ0INqD5mkvarAz3/+86hysA1ySKEnyZY1pUZ+qj7xskxAcizZ6fXblGQq8fR3bTIVVqNJWM03At6jyDAKYULyVY0UX4nhE9OegDJ59bKoNIp0ZEozYdQnwgwHY6GkkWQEg2d28VVl4Q3LEjdCfCIXk9CLVyU88YlPpFKq414Q0qiHT/QAUxdKiRjoWHSOiCSsega6kYefHD1wh9UnlZcEVMfeSCSMEpWeNx3C46VkDvOTZG60BBRlSCEsNPZVYng41NSaIqN6UEGii1g7KoEqXwHGJwWD9+ZOOxB6VjR7nUcz85WigJy86lET28vlBfjqhacH5v5KegaFhwBdxEpXnTm9nJMfA+SQeWh3w9d+uEy6Ft1q1e28n/GMZwA81yPhlPSLX/wisxT+NfNZlXzc0fEAUCkhgQhgF63imSEE4EXjnRifT4mwhbMlfCMfwRA3x5VIKL7wT8el8wFkoaPGYEnEt5C+OKzC2ZTt44ZbfF84iTDXAukQrsWK1tuw5n3P+fxT77/9uT/72A3//XEzGPzxFS8ZC9dj/42cU/PbuJ6mUtl3qd2tH5CARKYh/7EFuurHY0bE8EW3pZyCbRcq2ppGw1T2tC7oczecr1GffvMFwe9tLfy6DzspfagY/MzvS8/b4tvYaJ635Txs13fu2MFigSGluoKaOU4v7NRwUgU2gDgX7zx4eIZYRekKMvi8dqvV9e2sOg0zJOyj3LwGG/SZyGhM7ALE9SG0+mhRS6NBLkJghN4It8obWv2wP1Veab/47tEv7MCPtKYjBx71awUNXehy1QmaBscWDrHhF4g8QURf4mtaMusiB75gpdEotl+cfaob3PKkzJkfNh84KeyZz3rWs+CkeE2eOfVpHPv/E8CiMsQsQdcId3wcmXMvswVOFVkPzOLDaWv/OHuBLPb2uLaxXuwHtJF08dWv5dLb9A1Pb7jqSvaCY6uWO7L3pZGzuZ6fBT2yGyNobmiIfzsTilV0nGUwG7Sc+si6wTDAPDA1Obbe2TItbzwwwK3k8aqB6wmeRzwmHIyLwvnAVr0EJx6gcHCOOXjhJ17Fw1UCOyeXWtF1YEhshWyI84EEJBL8D9Upha0em0WOLvbmPSP2QM7oz9sHR13EXXcgzxCCrUxqq+udmFivTp+Wcu7ZmfHCK14Tr5bTlz3x4PlbMiNjw4N/oAqQFYUsXnnV1cFwKDA4Wvnj74v/+S1n314kNU53j+/p1+kXPU5vbQTblBNhhvKPQqq+uqcO562V4spWPD+ntdES1wCiiWS3xfS1TTrmN5xNzLHD485oNTyU1RsTwbpoCP1m/HyMFpzRrLG2PrLpyqsaY3XFb3/PPrCXexCKqzbU3fT/BS6+CFqNKlVdU+DhJxwcBRDtUvTeUfjckAue1dDa3jdY9if8YZ9hlyobmv3NcauKl0gOYCPUuqIdLSrADPq05hgaH9p5bXpIaDftiiuuYKS8B2YWNoEKDBBoRpBPigIhAAzqE8Pa09ND1V5GAvSAgo0RByknRiWAGCasPkHPsEkSz6NivAbyCdpDRao3aQBAEcAwoWsJdRIAnuqQ3t5eMirAvDLxpkOYSQsKS4CiVAICyNDUKQ49Ax2o4nmjJH/WqlXlX9wTGR4ulYqVUnkiA2yP0wAAQABJREFUC6OjZXLf+lbDqh5z+w6mX99N/zCIs717fkUClQZcGTERew7ahrQdOp/FQgwG4Sk8iCc7D6StgVR+PJurIpc1nSEsx02LfXpdZ1NXgxF1spkDu+4/ONFLqsw1a9aoBtJ2ymy7Vt/302i1t0Vz2LiQQLI6gEwkA9JqXhPiYITWlZ7HW5uuD+w+uG3f/fJRPbQdAQ7uYWk1tiqvetWr6Dce4kkAwAq1PXjwIIIvYlS8eivBI3QjOHTtJ76CFqv+hJSCSiSGR6UhHo0YwnQIj4pXn2gjzBR2Y/gXWCp6Zaq2I2pQpCwol5IKkpdPvDle0VAgPUDCbiZQ+/CVjZ3uYitQRamvZGRaqv6EXJxis+oBxlxV6h7kVbMdGlKVAKh0kdpe1Cfeis1KApSZmave19pPgMSJU/uJsDp9oCphQ3h11QJMWDGGalvnhUG+ebyfKCrTBNrFo6gU71NtoLZ8FU+unp4ehlXxIGoTE1Yk9JTIJfw5BR71c0rkEla3oKIAg/XLJGSMvOmxoBIeCYlpBWsQITBuOz760Y+iGgCCev3113/hC19gEi4IQopiCdReLalGyt19pKxVCV88aEUDTnTEwbJmIGuMc0ey4KJasaztS2MMrIWw+mVjwQsjV/6i48tmiDDGdGJYB+F4SoNs1na0rjt05Vv7Ex0v/skHn/PNdzUP7P/+ta/a17qeglyfT1xEzEYA7PKiXt5UIWGhGPnLRoG9D04f8Uel4xmrakEZVtY3I2jlq2R03zO/JhulYyj19LPC8XD5x7u4HsnIVkJf+mPl78+pbGkLbDl/y2/vu5edEOYCOwBZhJ2uafVB44IOjRMYtiknJUqaw7IdsmuJEvOWdqs9hhxYxNZ/vXvwnKtb0VRmmwEOOGHrm/EIzc0X0izKwp55vCQoP0VziKALvbkV+N3GSntlJ8R5JVbEul69p7e8B8057IFFF1qQE5LI/cC40TblLmUkDQM5upyqBFe5YKVT7x7/ACZwz/lQFzsnuvQ4hUYLGr+J3pE9Z77T7uPpTgBzduKFD7HJb37zGw4ntZCWaha4C0HLV51do9qRcZnfDSF7dZIbjFjUVvptb4/v3Z1tbo1/+KNyy6dsCbJ23Nq5JEzfNYJaJuJfJ1eVBRD2QWDoq+La6ibuQ5HUi4OTRfiNb3wDq0UaC4txJlJncQUvcS6GA9wFaEFuwN1BVpaqAsoEucdYqKenZ8kH3QOSWrBlhXKAQb6088qrYrYAVX/84x9Hpke9H/vYx9RFFLMlro1nl5zdAE92da7OE5WEjLN/ROx8SmNDbNZda5s7otqKmN0Rn5k9IZO16qTf9Z66vz2Yr0vWffrTXRgSiW9RmwMJab9CUkOhgDY+Xv7lz+1PfzYIxYXBkk/3b9ta2rmz9PJc8NqnavX4+J3ntNcbGpIcgbuGnL60NsbdQhV7ZUzrSOJFGUYSikgcS3q+qu8e1aCBK5o/FAAiGLrGeN4ooRepmf+9q1Je03jOlVckL9qCoZuYCnGTZH3SwFhu0ctvsrvRlfrDwfLBbADVqfGS1ZX0NeD5GRNkv4EKcwYVNFOa71SdRBDLar0rznXfE2i90ogD/YVgYKx5QPigKxhuUH/QHQJETlYlf4kBofeuBpnyiZ8QS9NzqYwUqJhEblWydrzsLB/sUYknUgnNWKfKmoA0EAO1kkDSKDYWnyA7mWyqQPUGcgDgE/GgsLWfCHvnN+QQlJL3FQUBo1xxdu8mK0jD0clBCsr661Y9mza7u0af+9wj115bKrq2bm5mPjL/ePNAcsMNVEQ+GoBHytFqEs8wOL4ysoVyLpfHBK5gifEbdihxf7mjHi0FqzNa+dNDB706VYWeOx8UWCBR2LdD5/mspnTugU5fAX9XUyeODBRTOlS6+AWhnosT6fGxwwePgHopwHhzPduHP/xh+AhQnlC/tJ2SVV10l4cQQ5EiEqwdQRXmzRAobVtK46d6e22nZMokXn3y6iXAiNDt6pN6e9XxCXlm7SfCTEKodHoS6pEVXfvVG3dyKWLPK5AyeWgRsxoiWcVD8yNlhaJWVDrAKMccfFXpVYA3GXvcR7WrFn6+UiYooPpESpXe+4l2qJfe+6RiOBZh4kyJVJ8AQFHCM371CpwtABicC3Qs3TVbmuPG0y1ez0xJrHhPUyKX6Se9xNG8TIUvulil87no7NMzMmQwXxgvtfFOT7AcMcwu3N2xLd96661Ig/FNg3EQxzoML2bgQmukCewPlKBOBwpXmwHxiZB+/goDxxfhAPYyeiBrwP+F9huv6n3jdskRF4xIiSEO2b1Q8y1DDXJ62lrQz41K7BKYBBsoN5Z8gf+8+EUVw7z5fz5xxT1f7Nj7p9te9639yZVVw8Rz1njJiQVxAOUSdBOHuLtbT8QICUyj2Pjcq4lEkRgKsGhVNrX63eP6uC12dzYYjbp+1RnBWLD0nW1OCTLSNr/xZyt7ZunSVclNGzbv2buH7U6xwNzqZUeu8+vnd+AtxMJhCJobg5i95MRBF9Rn1bG3tNodWHWVnd0/1VesKTWvh5RXwIiq0RmNpl+3GA9QCv7BcE+X6CjhccLUxL305jacZlGJO2RkdMQV2WNWIj639L1VbUQIX/w/c72L6ELDYqjYXHao1IpKlokfCpecJhVyYGhgumm+ow83Fudq1157LcbkKBG4g37cbjy9Epy+BDArn4cN5Tvf+c7Xv/51VGGXfH64Neh7RqEZhFrwo/yccNY0Qc3a+Xvu9X35S1XwiA99wOzqZIrLoprEjvjbN86VLRoW9kPibEK+rkiQ3eiqdxrhyC3qAR7ygaBAFBF+9rOffd111y2qpJORafpwTI9ZNBzcSYjMhOxg8Isu5LgZkY+Bep5kDTFGlqMOA28CGLSjBQ2cc3edmhgQAJAupJwxMVMHtRws0g+P61xYN1pCFdSp5Edb6oOtYactjk9y9nR3BrsvKpXjErMYd2LnfvHz0K23ovysvfVN/nVr14gzDnFvBgEMyaTIBpSOtIF++z/+M1Io4m6Fa5PK5QpX14fKpcztX/Ofd64vEZ+nzTtVIwrYkbKO5Lj+l6sObOx5muvslpDd0yj6pZBsnLW7hquDOTPrhFav2+RD+0jHIQfWUBC/HEUYJpn/e0hzVobPagk3dAEvB5zrDERC6v/jToGpCdyulnV4KFP94xGxvMpUdHjwTRFoVzRhzXROHylYXIkMfGictURMxL9t6Gw3TFX6YJhAgimQEx11d9oLs9/DjaYP4vQYDzjvkxfwPhFQJIeKUVPF+0qNHrkLxUK8kuapBIS9lFMCouxRo+5R+xViaQ4U9jGPeUxtYtTDtIFBHJIwpIFQIAhRwdBCJgGoVU3ncbpgGZdd1vaaV7cH8RFam/VoWGmwDwz0R6KRVDUSTul4K0S9H358U2OioSlhVSDPjFLZToRhoDjMok1t3NOYuOZJT6KU2j7xVFQuvfRSN56hlr+llPG7fy+M9grL361YFgmzFPQvsbZ4yYvrEitFRJJIxK+6+goSMBBkQ8qKa0YYB5dffjnXWoB/q7q8YfKGG2V1j8/lfVUB+pN93os82mw3pKTEXjm1X3vcpzbGC0P2qIxejAqAYSPjnZ2JJiJrnim51E/mkjdh2BmY0sBMk9XX6XSdahFvFZixzNpPKuzFeAEv45SYKem9ZAS8lF6g9utxw9ipwghQWsT05GwdMls5XqVMhinzwfs0W96ljWfr5oDjYfNZ2pIXXRo9oPqEN1SrOtEWXZqXsbe3l/Gabcvyki1tgLZQKb58MRTnng48emCghK+s173udVTEV5551khvgPPAo4SBOEWWAEEFRnlOqz8ZsCJ+Z5/J1UciCD00Vi1BTsIoxKNVGdGla6Eq25mG1q496RYLu7yGiDiyQtJbsp07L7zloe4L3v61l50x8NCb33L+V1/0+d9fcH1Zx0mykSk6dSEu4EU6igMLCnYpaZFvCt6A6hERBKE8x7GntVC61svYBrtXE0GBCypBewVVnqPV8g38+JJVgViI65FKxWoIS5vvPlgtlctPXNvV2dWBYdTEqpEDXe20KIFr56+A/K/AiwO2/qwnB5bphEJV6Ei5NOLff3+xeT3qXcDrPrDjHKe7ASm4oDbi8h9TI+Foq8aRl+/WWW0mWtY02s0owHMjMZa9ZDG0yg50qEUXml4Ve2AdBxYVqAUd0TH1cA0FNDCNxk4YCcSFKw1oYBd+KWeOruATya688kp0PN/+9rczhVCtnyO9atDp9jaxej3d2uy1Fx/xt9xyCzePv+Y1r5kRA/BSLjowWrK3igxKlCja65wzW/S2GNIwLf/WN0e3bi1cfXXsg+9DS0Uc3biPqqhk2Q8NaDtH9e1DThbxr6NzBdnGJgPH9Oe0gOQek3iesLlrRkN68P73v5+dFA40cmAUwChrniU8XMngEoD9LBXzlX6A0sMlIAxdBA4UPs8eUB1IJxw3vZcS2TWYHHatquuOm3FJehiBD5xjNDCxPcNxDkz6+dQL9YLeKXlnmxJsvlzitT+t701re4bZmdnZNbMw0tNgbOyq39SsNUckUvjE3oSaPCbs7HjupudF+49krrgq8alPca+QpHM9wSDq6e7uBlQBEgXWP/7R/6O7hJDm1OG/KrdT+ijQzBfKGzaaZ6wh73yaQ0+mCvb2FNfYaBDA0EetUaer3mAB4otCsWOPZOzeUYMbbhzLiISojpsADTxhIDeG+OT0qA+bLXUOZkIr6+zf3XcvCbC385ZqTTsXNm70Cu4ff7i9cjgbwGoIveO1TfgTFtUv/I4MFzFAEgUw3YczbbM+gs6IsakNLjUCTvdfTW0MLuQNlAOYDaqPcHOI4fs8u6impPkGKXnGh/wMJRMeCQPwkEaBMWPixUVOL5AYEZ+i7t13yPfAA5hxSQfxsAKRfB7qq55/XuCJV/nP2myE8Gcyw+OVSSAaq8tZ/p0p41DWGSlwKYgtqumuXCIU4O5opyFqrIgZ7XH9zFbxj4KlJy3lgej1HmJVNV6M3y+ecoNxY3BHdfyAJ6V2+0c03aymM531l2PrAeTkFekiD8WyR+EDliWJnTZer1gjbm3HvNT2RY3UO/0h3mvgDI13v2JAC79MrfoZ00yPnF4mMephAqD87NGxRE7PPp8YBhDA2KLZPGdLP1nno+OvdygALh2ODEpJxWmjYiHRzIW2BBNcqBrUJRaRd6F1zZYefgcs9YXS8LOVtoTxqE5ALkJAKi2GxZXsjRpq6oyX2l0p6qR1uKoI9habAPQ8Z/Qvf/lL+GLw7NT8mT8kjBFMfw56JgybiMrIe+Lhntuw/CfcYbx1OOb2AV38Twp9KjbAXF3LMeqSeNKXcjqLvbDsWdDCfpc9C71n4dsv2vazC29qGTl01oH7N+36tb9Y2Ln+cbarNgyNJ1rTwv0mo+jsEHD7U8oUGpKudU1E8LePM2oUIXljmZwIGRGpwq1Q0s78TDZGymyOOHEnvWsEqTJe983dOJiuVta1Ilu1C4USnakUoUmpcgFUcwyonAoK2HjrtA05neVmIxzQ6mP3FAr7A/lMadMVMRo8kUkBr2k4ao4G5NTm7MarCOpCRUh3DnQh40GVrEZ82KnWSodJO+G2N0cN7hzOlO18hcuDyajRyYgG+IzDPjrMxU1FEI3mOfYzlIZqWmMYqyjSgEcI4DN3hBur2oUiPfdj40rt6U9/ulqnc+eao8BT79NpSgCzr7GjgV5w18symP66PH9hdGkPDlZ3DxkFm4lur2u0N7QYTN3s974dfP8HLJhHH/4/wQ3iosDdUCc2W/ac3pS9fVTfnXL6MnCBROF5TZOzsUnb3KLXR2QBwSqTuTjX5D9mrqqi4amjFgvLA/03nKTfeOONaoUck/QR9gMI4ZEvCfWrOoG2Y6KG0AzPIgtiD6MnSS7wleNuH6g+9vb2QpO41/lYvAkjDfZkZcvUx6qBn/jEJzD6BUj8Z7D3HXeIycWDSjx4DHoQ3qkwCSQfJTiYc3aO6L0j+p4hJzWaKgz3VbNDK8OlllClXh+N22M+02CkvM5RxK8coLYz+trXxn98dy4SDn/9G772Fe4pJ3OXxIgOQL7B4/lpV6uVvzzgu+931QrX6YkXHbLzcCGeYxva+ef5Np7JdUleFQLWtEeBi0vFbf3avlFtqGDiz6MlZnQ1aGvQoUhydknV+ar20ICzN+0UymYoQGWO3zCGS3quKDVyQqPf1RzWeuqNjc1aPnW4d9+BZDw+G3dgGhRzRAh6ce+ByoMj/lRWGy07XUmjJQbR63BzQqpsjGfFSZMPMC29MeqsSRqrG6yOOLgBjwymV7T8cB+mJRMM2BSqR5yX5mQG4LkAALjaSQaAKWHUxfN/+XNgLO1KVUGTHGvwCLejRt7xDv2sszCHmnAoOlPHyBR1Jyu2KluP2EcK2kAGjybVsN/EM6dCFKtVJxbRGwNaZ72+rkFbocjV+faz1GoV7T/8R84qiw6gu3lLlYgHdMfMj5U3XBV2Hb2554CA78CjfM5zngP7DLtfzJ6nrcoTHVi1TCgFRJAtEYGkKnFxY4ceNYeL4vehBQ0pTphpWbshLAhiWACYH5NFqc8sDqoF1bh8ib2uhvFKbyseAdXR7YRZMh41slAYkCQr5sWi+3mhNar0Xov4ifiX0fEUqR45IwWQEOdMQmjgRUPFoc/xzTDxphDmOUsSbEQdWIvrvYXmol4eOEFPfepTeWMSzE6L9IJBV3b1fD1umaRhpvEgAoFFBQ1MlikZORmjfr2Je9k0bf+o9UA/PG53sxIuozxQdLzRv5YKxY8ffQyzUcJYQuElQdF2yHALhvmnMy5LJ5ou+/MP1u6/329XD3afXfShwGJA00IwCwk5KfslQBnuCUes7MawMPkfYrJYwSEzfragXS2kxxFcREo+edxGe79UXO1bOOjDB3ae2xU5kPbnRUnZ2DtmZArFNY2+e3/zq0MHDsKno0NUWaogoGqKgqJbZYsTWdqbwZE/+3VRd35ZRtO7ktNWbNEi9Uc1Z92jQxB4NMkxiiYXtwHLlcIVlwYWAtiABkYZrpGSXUPHSSjhy+vNdUbY72RLDhJgufsXkS+OP2ib23xcBUElEIYhT4ECkoVyODZcutyf5CJSbndNFjnTX3Zj2CVMmO9///voA9LkKeM+U6bTJe40JYA5e0AvuEnvxz/+MUyR5ZgQIP79Getvw/gVEGXRVUntrFYnETB1pIK33BLuP5L7h5sSb3wjvHvmt6x7Fw1DnMHln9uGnN5RbfswPDCWpNMStRD/ntGo4RJAeEKyPGTyL+hh74bwwyRg69at4Mpf+9rXTvKpuSBoly8xXHN4Hz09PRhIuB0/36rgnoLbcdIfNxfkFOQuZyRbFWHE14Q5NRXLdr71LSod7nxuvvlmKsUDFoYf1HtcaKmHQxGKnfMA+6Ip6V3q18lbzkP9NtJUvGJwWZ9dwVtSZUXS3xoqcfHvuV3RsA8pfQzuwDFQy7TW8j/4gf/t70CUZn3y49Frr52YvDLlxcgQuRbviVxUNj5m/exnIhUl5+TDKVv16eaNN/rXrnYZpLNOfTcP56+zJ2WJ7ysuOczbyYixIu50xRzUVoWJ7HKmdgxY+7Ji7QPHF38egJAp66msqD/xAAjcWaidnrjdU29u27qtWCig2MkgTgK1iL9AR/U40Lb/pxe9Jn28DMKBXYMeQrvMxIekOZTHDElujbCqGvJGHOatqnM2ovQhXC8Xu5ipWk445hg0EoGZvp+MOKYc6q/M8BNBNxcKqJqrSD+1WNRcvbo4PGgPphi8ci6fTcQj732vedFFvpZWGVE17WasQCYNWI724BFroGhys9doCb+gZqFih3DODfudGeI3GgKi6raqzlojuuhugTOWNkMkg+70PZjf90uwN3Z7/iuFG+1KkWNBRNZ22Wg8w6prP1osqxifNyDfmMv+6Ec/WhCfbob6Z4riBIThxaEAZs8b0RZv8FmIsZmSHyeO7Q5iDMSaMkkKkUCYKQF7S3p+4Q+5oKjB1dR0WlwhC692uXJgn0/P0C00ip7hXIAwU73EBjh125w3FLAJOJUYNbbuk9xFTE5ODd4Qh4qSJwwM82EQz7t9i0wIGDzsRXAH6HD0lkF1FlcW3mHgLTJkZOf0Z8h40CKeIJwWV+iictEi1iaW8HhABB581+G2hj5Hx5VP8ymSZEBOFoTAHGRTMBm+8lAOXizro8YPtluHM3hCViVzHsqex1boSjjZJVw7VBh4YhQsRzXfwE1DhtWZKD9lrX3DJvuJGwKRczaOtnQ2/vneLX+5O54bHmpdNx5rJDFyTt4wfFWdoLgKCZbTS8g9tVmLHg8kH6JUoYSF9oMAdurc63kVWArgGdtOEfDP+wf6u1oaz+0J7R4u57kEUdcOZn0j2dJZK0MD/UfSI6MdnR0cmjXliHAXSjWEHFhIWUwXhQYO9jtNO7i/XTZwf6zcfibOtgRj9x7goZB4AD/PyGm1km1AyiK7xrIXsCGJQWi40aEpivIbmVzwySDKnyIHDvmcbNHKVqXD3SuRxfkl6WCLCw1M3+MqTITh4oeMApENQKgH5PQQICjHg2TGAMONBgFu1UB9YaMcN/2MhZySkacjAczxg2EVlwDhJE1dMLAcEwJXrg/2a7tHNLwIYFyxsREprkz+9Cc+Ef3G10vx+vC/3+ZrFeVYVbvMYRYt9z0M27tHMLbUhnKyQ2A8sA7qt0k7u9WoE3YVyY8z3afPVLYSfD6xaX72s58l/Na3vnX+++b00h69MSB50P9sB1gSHrvxHb9NHIRkmXJszJiNg4pkPMw0zkuwWMKgmIsYuBnLnzGSYaUuHB6AlyCFg+E3zxoBEm0xeobDlZ6ZDqTMySF7b9pA+bk3BXlpGMFIU31sU2eyycic1Rk+b734EpuS152jenWgv/SMZ0YQfVxzdeJDH1IKzNOrUC1C9Mnd8qXe3tDgYMAn1pLwD0LhsN9nVs49P/CMG/QkJsq1x9UMPQHjCWeMf0tp+0YcbnCFtkb5uTuJ2qqcT2TgvObK+11jxr4RJMsoP1uOHFHGUAEWLOxVThQ9yW1JqEzH7c1tZimfhWmCiGyhHJNa4Bgd9ZOD/IcPVvBKPVbxwUZY3WTCcef6IU610byc9Jx5tDAUNJpDFmBvklUvW8NsnUaxTEswvIXO51rwTjwMeAjrIFcoag5QT7yiqSWozdAfMJpbzPPP1y+6sHzeeb6rnhj9h5u0jRv9nR0iTz8eSIz5rkHrUMHYO6Kly2KQVqk6UL/IH7gWC+QsEbS7G4z2iL2xdYIF6Q3oPNors+7BHxbG9sn2bftzwbMPnv/8QEtnqH9nQaty7QX6BpVVW+CtSMq9e/dirMUqxsgW2S8Tbzn6k90MlXXIAwgzKFVwYsLUvjgtFSgxaDBGH2YW2DmsIsInyFlmOuEaQLV9OXqAxp60B94QXdTd3U23IMSjq7Hex2abTpuybS4IJFY9FBqCZUpeHOdiQdXVJobu5bjh7ID6ZS2w+RAGniVXVaitdKFhgKF/UMiaUeA5n9KYw4wRD5QDs5Hb3Rm+h1eGBjCc8qxTWGOINFAVQc9LaXDMZ5nAk4JpAseK9TU9PTEcoL2j9lfvR+/XhEYVl9AQvpB7k6khUStVDVmsWCq5FBh2Ivznd6ynbrBevNm5KDS6oTDQkx1YmzBij90SuOSy8l//sukPd3X0PdS74ZKxaNIlBiWLUHeTwyDFyz/BgSU8eWLyExqYA1qUii1Yk04cJ9Ru2kmIJotw/zIbGSwWBay9Qg7WvW1Wiqsi+SMlf6YiNxb154y8Huipq6SGB8hRq9WF1Bhtb0Cqj5pB07KqqCLLTYTx7ZXGFFSoMJjz2dL6J+AeD2paoJTDRW3cdIYBfW7WBWykuEiPOcpLXBOFEBgEw0EOTHK7MYaOMyFBY2S754o4w2mOyeVSmbJVqAgNjCgY2hsamCooRG5UcyvDGBt/0SSAwLaEnJZcbm+pktziZnkxb5nMH/jABxByoPc6WfksqU+b6MWwex+9naOwll//+tfvfe97X//616MCTVuWYyqwkI6M2305PSf3hFlIclZzpxlrO5e1vvQFWTivf31Q3G4Jw0um8ESf2lwmdiij9WcdLllF6xNP680xbWWd0xHTmpQP2Mmk8x8FWo2sG3Wgb37zmxxXeILF5nn+2U+llGAhOK0F5wNdmM+403VqJ6UTyEUWLNMIQ+/BYJ6tBBWvJpvqvdlSLm3fcmUCogZ48J/5zGdqt/U5agFIjkNwVixIpwioXfhlbg5mLVTxD2VshGOo53Ag+fRyV4Ovo86BeGyOcCjIpKxto7gmItKqZN/3/rpDB9MtrfHPfUb3u+pGs0PDoeK0tgZv+cfceDqyYxd1uwxmo7imO/iim81WRHnUNduj+hvGrb4rVT2cwYaTy+Xt9rixMql34PkZL+pSoM2dDbClDqdt9FvDfrSuNeyXxvJ4PKpCikDf4KI9GXDaEtxDaGIFus0dcaWKOVvd84h3RkbHDh08lKpbu3ugsGPPgVDbhs6mABfcV0qOL6CP5MxMuQpCIO6vuOM0qK9MGqsS+O4S8fjci7625+cBydIn8ab6yYfE3T2ZN/gkDerYtrU2+7bvtPr7CiPjoXPPdg3N5mivAM6/Q2n7UF7vG3G4kQLSF6QGkW++bEWCJtMDu7jOpNEUtDa2on8/URqCFBZOT08PW8ocUi8qYBojRzj0APc2+6Kr8qufnO8dOaj76jdcFWtdG/j1F3K5A/6+B5AJCgrJdVb4JsSwEwfFX/nKV5DYLFOXUrISi4EsItpi+QMmbZmjs2b75I3+lAQnAjl5Zyt2Si2P5J9wFVnLMgeYoLZNPwNtbSefSBdRFNnZl2DPcb4zFYlRYnyqOMGSj9urUFwcMbQLTXXqAqsmCz+Pm3E5EtTW6zWcABACJ8wdHsKLqFqVNmP5iyjtxLMoeNhwQF8hZXHti0b05Zdf/rKXvezd73435XvNn60uVj0E84TTjZkSMV9/uqcyXlbCFi0SdEplUQZmn3R3S6H58J6frtg4TxatFSFZuRXcuXJD9fqOQnLbQ6mv/Vf/X7faxbLR0tx43ZPNa68qfeH23he9cN3ffvP691zxb2+/a3frGRjlolxjlbS6kOs6SshJmTzqL4ufo1540bIZcyBqIzBbRPtX1IAhL9c0Si4AconIyU15sjngM6w1AZfLdXP5UpFr8+wXXdB6+18LvaNhXCRuG4oU6lb3BNK7d+1pbW3h8ju3HG18sPSHr4+WR5Ayk9vQyma7ZTbYWmhMqF8h87m16HDoB+8aNTErFnBVpKb7y2deF+w+F2VSpz1ubqEBHCzyGCN5G/XmwYx0lPSWbp3dxr3ydLOHzBi4id6ywifXDzvVXZoPC+xchWuQuD8Z546cRE404FLMqCFUxRiNUUYG79OrW1aYKJF5Rc02+ir+pS99KW6PXvGKVyD+gZUzW2IX7NPlddpJgDFZecpTnsKN9kiA50kFLWIuoPCwDQvDEYwBjMawvaFJW5UU/f/MbZ+N/uc3S4lk+NZ/M5I4/lGcHamBFQPRsHOounvE2JXSUnkWuB7wWRsaTVb7WW0694PJI+toYQ9EL5xCSCMwKnLiVR8a+HSb/exnPLQaGcWCmO7wEaGB0VijG0FieAjDWuYgOW4fkgBu8TwlsQsb1GNT07T77rvvRS96EeC9+tWv5mav48LmFYAsCMs0NkR6ZhrmxKW4zGR775jY0w7khPqF54jac1ed1t2IWNVMxMIzEQBgfE7ujjtRfqYi6wPvD19xlUji5ICecwJjSVzf4Dv33HJ7eyURr/as1p7ypMDzn2+u34AfoznEvywf1hD/9Y7Y+9Jm35g+UIDoNfA811mnbW6FshWeK2fF9iH7UMYYzAgjFtKXIwbl55GMLS6NXL0pGKudDVpPnba6wQRvRXWCnuFm1BMUsaKWu/PQ+F+zTUfGSqmR8ea2xu44FDhsdo2Ll4ZzXLPDaaczyxqjvhV1DsrPm1oU1Cz6OTvNG8uHL6A0YBUP5ThDvLRAuh0jgy8seWf83z5det7zCj+6S//7ZwRXyxmvnhnrlOmiOVxcgb4Avs2HisZ4kSkh9zaCnEUDPpCoZERvjzttEe2sNgM/qFIOJYIG5XLQwAhS2A3mJFMFExp8qLjnf7TuJ1Yv/6f65MpAU1MLKuvsKg0rY2dcEsrlC8M7tJZ1esEYueKKKzHww00rGhzLyqdX2yC7E8giEmCkW+yKi5YiKk9aEBvgndBgBKSTTuwBKoqln+dA1k+shmXPDYuEdYH4l7ODqUKAh92Sn0wAnhPsJbJzEqEyAHNWsTCokSqWpP/n6B3q5VEJlA0wwiV+1sbPkX05PsHHYX+edn5pdDKf6JATnEWgAWxu8zn0l6N1U8pU/cwlc1yczlxCooNyH4uF66nnPqRURmYgySgTtEGV7I0mMcN5+9//6KRK0GMTD3ZUWKJyozwjrP5zsVADXV8y4pMVVLUrXnr5uVrLgw8cfPN79Z27fOWKH23dsfH8H/7iFIuBxz32T49/Vunnv+pO7Tn3jz9M16/c37lJ5KG2ieMo2G6czaAWgg1Tp7wFVVNTbJLFqRWq4gUK/5HwuNmjG7D4kUyT6Sah5S+sATQsGHR04DtXreru7m5paY0E9LPbfX3p4mBBlMtSlVDGitSx/Y+muronhOHBqNm+PtL/t+LwjkB51O9kDF9OD+JEE2oXyAQo+Vse95XH/MUxf3nULI76uQHqoucmOs4Gn6fPpBFc+FQXdOQqY/xgob8tsmt0ofGSxU98YlUb0IVWTXUbQKGiCx0zgz5nvGhx7ylOsJGQV8XxmBTKBRkYSREEAuTDdAIes+S2ZLHVcv1FU4T6V9MPtUHpJl1Hf54rb+6++26MgTkCiKlNcxqGTyMCmLXNc8stt+AbkIuhWSQnOPyUxiHNpPHKIUbNof1j1vaUPlrEosDuacD619Bxw753j3XTP/rLpfKrXhl6+o1iD+auXi8XfmsfShlYWu4Z4WJSFpLTIbfLOOsbta6ErCuvonnOVFUymx0SS7xecTpyvwhyQrX9zbOQR10yrz+97iIGPSjOCQ6wOSS3M7aUExTmMQ+YItnRWyNMgMRe+TNm5Cv45UKrm7GoOSJVY8HFYeuAizC+X/3qV6fjATOWoPKCu/T09DA3mMy11oYg7qgqbR+0oCfF8/MIe6XM2LqQtbZeXw3126yvSKKhfJQR4PU8e3eld3flH58fTo9mnvWc5PvfJ5f8kPk4cxgaBmZqgBPMXHeG/8ILg4+71HfuFqON2+fF6mbuDqeNqaKzfVjHfn64CIsUhxZY8BpnNnMaTez0R7L27rSxf1S8NUZCKPBxz64+nONKPtF0wqOHKD/HbG7bhuAJyBLUwpEwewUr6Li1z9bJqFIhEk9nSnf+8cBgJdGXypdz4xs66hPBcrnEmR7kxh0Y25x5yNdjATwM2z1J46xWHceSi6t0RkiWNRIMDMVOJQg6aTDLbju55TJSxXt/bb/kJeFiyXrDG+LPfb5w52eab8Kbl7nI4Do4vtrWb/fnjf6MBquemzAZEFTOwDbKuB4NG40RuzOOIzQuYWY2yPxnmcBmYj8BJ2YjhWj0cOuZG+5oO+4bWX9lePNT6mD14xQ6FotCNiPsRYgWCJmd54TiK+3tf+n7+5c8eeeunUy2b3/729xaMXNpSz2KnhOshVbnLXYggphXdC+7AXgnMQstbXqzwOx/+9vfQjdC2p14adPLPwkx7P/sqGywkPGwQeFrEOZAATvnvSS9xPRjEsK/oEB6yRtNr3XL3XUMEwSVao5X6ckP7Ny5U4FR217CTMju7m5YVAqk2q8LApJZPZ9Df0FlnmBi2sJeAQ2MbQ53eXKhIJc+cGG7Ulaao6XqE6wZeLsohytCaGJX1LRfH6r+aj+cbpHCuiIDoZEwxmNLlHsKJ8rli4DPwUZGiLTrNtgX+0eHPn6rs7tX4QlQg4Kzanpxx67klnN8Z65/R/L66Fh6y+571u9/gANv79oLLN0Q906WFjTlHl2BgfLdktlC1D/2GcEK5Cta0Gh4CeGNk0tY7I1RMYgimSRW/0vKiYddGgKYgxsmkcAKZmEY2DSlxsuH83JPXs6IjZVD1zy2KxHBd4lbAu6sw1r3hWEzXB7YhVsqkAh1VLj5JYmAKGXJf0he7cZzxzuvGVt1doPfh/qMm8L9BMpRFxSdbTANaGBXF1rU09z/6E+rMQYBSlJVkhRJN0LNBnzOWNEuVg0yiudnMQOWc4euhl9Pl7rhCXtgyGnAQIMa+lnKmuw7Ek1/+MocxuoTxQFYZhDDc6efXsKpFyNqgafPg9T3zjvvRBCKgtmJjz2Y0AMPPNDd3c02NNmHMgsLln1gTB/Ogk+DP+HBVRDZg/sG8u/7wLpMOnvGusTb3iKOW9wJ62Ykl6yzA6M2gqnDGfQc4PY4YZ+1sk5sEbuTMu1l5i/2ATthfyT3G9/4xlOb+qWNUKqcymx8Xm+xh+IIHiyEfvAi5xOYMknUzymR8ylnWdMwD1/1qlchB2Cjx6U573lCSEZSMh84AqdDiEZCP36kMmbfuAPFiE0Kk9SvOZCUK+s0lIrbYqIANaUuNP5QWWI259//odih/dmu7tj73yOmvzKBjzOFgUWBIacaCKKLI0oMPz06ZzqgEzE2R8uulN2X1dNlo1AGPGNlgntrMJWB48vyEldevSPOkXGxbopgqFMVcd9Y3uCWBc5YjiPsmuIBe0XcWNvAFTgCr2Eayn5s1mqP96FQLDz00IOwbv90sPTQ7kN9mcOlstNQF8wc9pVMJxQJB1o3ZEoWhxyeRbiQSW6ajeudCas+7OnbHq+Oh/s7VAqiP5zNTJkMJw0uIcaOHM7//XPj+cLopY9rfsubYT3OOt/UXAK3gDQdsEaKiAUcLj0KBoxC0Q6GTLSgDcuJBIxEwOlImKsSVmsMXEVmL3gD/FN2EqXOivYEFAgKqPDFYHVNYlpH2y07u6adfU1jICLaxVMmMmWikgKh3rEleMubXrljx876hnrsfpdV9utCdPR1ItObDYSHzgfX5A1VT7mKKXC0gsWG4OJBVsFloOSHa14tFvaJfMwHHuDH/ggBey178QRL9rLTMyjv0POcekQSYEToNMKQ39CEXsrlCFD7Sb4ad6GtAEImErlACZiZi5tIi8u1UFAXl579B2keA42LI3x53HzzzahDv+ENb6C0OcBmTrJpM09QDGS3UVWzWeHccveQ5l44pBU0p4hzCs5G2UpFYTcW1HNlu8pOyJLkH8rPOEjGcsi2VzfogfHR3AMPhtx4tgQy0d+Ijv22nv/jH5PnXhBtS37i2vccbOl62Q/e86xvv9OJRO+56FnjwRgoQ7qoR0NcxAAg4hAL0IWwFlrTRQpcl5iCBHMjcZEPQpmjDo0d7GZ44wprkO35GAQDsGFNqtGXJrh7cdg0btoSMP5c/PNAmIjxcPs3Hyrd8lg7DnFMawyMDtEtdmBWtq0r/uaLhcJBDI5JeLRk6oazaiHGDVmrn6qdeVl0by+2xDUJqAtkybHb6/Qt7ciogVS4/yN5dM3QHYKNClqBgKt6pmh40b1u97ujhUfo81dgBsx2CkvcdIrcCYxLLSeIsFt0oW3hibuS6IJlDLg+O3Gp7TOqqFVjusWgSFlHYXFLPvbFtQL4AOIyVNwA8Rz78bT7dbpIgFnwGKvgXAQLK26FZrK58+2ExltxmGAucsjVFIQGpr1TxL/cZY3rFG4Yx5GVXj6wb/Rt72gul6z3vDd00SXMUQUAgDFhWdNcn73dFf/2jsjN48xhKOczEP82EHA3brLMPbVrgCBIyRyKkOiQgh/84AfxGQjj+ZOf/CR75Ym3/diqHlm/8B+DRAKWrdfD4B+MPjFc80Okil8o0KC8IDGK0b7QvMuaHp0WbNqp4uMf/zjuMQjMs4Ecfvib5QQFrcejCRMGBgFzBnqYYyNf0bjCes+Itjdtp/JwGIUT21XvdMa5HAgBqS+AWpQYWR6z3crZhO/E++7T3/IWvGA7t3469LhLmc2cY27HH5N4tm4hkWoCgJFGsajnbhRn5b7RKvccHBw34D0lQ3qr8vzcLt60KI+DaOeQfTBj9GW4eB4jTxuaM1syU1lLjiMqtG3x/JzUuhOYGAnBg+S2Ulk82qRax1GOlDwfSPzuUPBAzgy1nxVJNiYjzjlnb2psaar6G7n6iGMQJMC2uN7AbI3bON9SzpYE7mO7V5X5SHtDB7LEEEOhHHEyYTs6mXLZwSuvru/dM9rT1XjXD426hNtzMuOYtojFmN5Hd2l3PiDv7x22D+Z8+0fsdAV7M71ascNBrj6yArDiDS0ZQAceBkoV3/syId29lwJhorNM0LlA4g32T7FEMlEJzCQHE/QR3EQKEFTIZsVxjSebCfQJb3Yq0NA3velN3/3enT6/ya0E+KIjJbm8nlTk5ZRI7+sJBqAKeBZXOOOOuTI6tyDT9AkBHjp8qSg9Opmxg0Snr2o75ASbvBzZabVX7HRQsUFdJgJYVYruDyJQugsCGEqPnzyAsVQD4TXtERWgz+E9sQxZRMxAKH8OLzqBFcqh5oFKMiYq2gQcaqdqh4DYoAJGk9lA2F5g0l199dVMuelT0esWlBHAlJR19IRwW3P2pZ2f7dZtdGCgsgQvlV2IqS0B150gPqIFs5R4+SQHO8kc67ozrJXpI+Pf/hGHvlvFBNEoCbj4d3WXedFj7x0K7sxHH1x1QdXn33ho20W/v8OslA+uOb/oh0mE30HcQcnNt1Qm1SsC2NW6cQukOhckXYSfJbleCF9QsqPK9UICyxRMRLZQ2uh1guoKwMI+flObfyxX7B8Xvn+q6tvRl7P6t7e1NnL9utp32bWjjb41F4fGhovjhwR/cWFQLx1/0IWYtnPjoaG64cz4SF3IGB0ePDIwkHKd29X2Of66on7xCy2iYPEL7Tr0UrrQUNvIgaMgH/QYmSZ2fOhY8BCOoHQen1iYPePvSsyhaR4YGOzGgG8CGoTDFIumOax/7IGbo2ijS0E1oM4cxL3iL37xC26E4a4Qhn4+WWYu6NEfe3SbePS3Za4WcDy/8IUvRFTLLaky1eYxS2YrjuXPbsvCY8MljSIeCAiCpTPR9cNj+mBeFivXeLYEyoV0oaDZqa993UynB1paQ5c/IZfPeagSkEAxsKHsT9v9OfzTisN3npDPQhLVGtVXTapeHn9eS76JByABD6kvCAT0AxfDEvPa176WA2Ayyan2V7blyUeF1RucALM6kDwsnxWqN5lqYX/xvriwDMufmgbiyhXxL1Vdd911ODmY58QmI92yd+9eMAMwBvAGZguPS3A6oVA4GA7tHOYiBL0/rx9JC7eXrbU+ZK2IamiEwpcJu+o7wqc59iGhPZrK3vyC+lJx7O8uaUDVX9JMS3dsrtl+gV5zhs0hpHKHWMZ9vOT0jhmHEOUVNe4kTET0jpixpskOC5UOmPZQVjucNbiCCI2tUNDizODcGSvi+Bl7Gznb6yLoHjttYW1tE+evcH3xILp16zZc2s4BwGyQe/HI1+uSDQ8cqICQFp1YONG2wpduKBf9gYBl+PJyrBlVy0b+HAyYdaFKR9S3ttlx/VV7ZTyiAwyBkjgt997ijvVEV6h5TgzDiaOFsbe8pX7b1lyyvv7Hd5vNsk5rF4LCj5VqruSXgsQTuHBDxqwxy6gUbdvvBH0migOhgK9qWcmg2Vnv1IctuXmuZvaylzIheXDniw6z0rpU5pcTkM38B3BkCVAzZwfLTRHMrEEi3/Wud91xx51ISz73udu4rKIWclVYb28vGZV6+czFLzZ2el0LKokRV1BB90LMo+JLdojVBRUyR2IEOHzlCEN/eI5kj5BP7J+oleJtdQq0dDK9dCJHz3EbCLkLBwrnRowCUwudWLIwV4+b8dGeALyORSQL2pXx0tWE1c/apoHl0y2cJkzRE5zztcWehDCmFhD5bDiIMWaDXMXDf+RyE4Qc73vf+771rW+xyXzqU59iJsyYi0imB7Jf+AIoxz3+8Y/nBISdfX+fNV6ReYOwF8JVOGPgtBi5Qni5cho2QzSTuOQHM1Q564VQ5W325yw90ag1xrX+EfY5VwtanGMJ6Wxowe6VHHg5LkTxmUUrfNsVbyoEYi/42QefdtcnkmNHvvyiT4/5o2AYOOOI+rWgn3xCzVI5BQnmAVEMjkxdbmMIIYjuz0Alc7pzBlTPaveFkN3yoWbnn7HhIl5mjzKc55wT1s387/twR2XsGHb25oK6ueOyC133yOSUsgxf1Equ0Psm9n9VvLQH8+hSwE41NeK28HC1qKWHL1jXzs15qNLVTglFNqND5HpuVvu/jqArW7aP5AQ6OtM0KuuboHalyRN5dYNOuHAlOtp2tdfaT/eWnEIZP9LiJAQX0IWyE8YnlnQ7AnlnICv4TQAFMqPCjYl0gys0l66bEeuiYjhBX/rSl3CFhTdcLOZqO60W/tMhfFoQwKwMhGMsddhjnjXIiYwulAN4jJo3cNF4CG/evBlu0+Hxan+Ba7t1v+60RrWEmTuw/2BkbHTsC18KOM7gM2/QsvnWkZRHACswMiVnKKMP553BLLSGmNy3xrAH0NpjTh3iYxd5WijAbPdgfuz4yH7R2YP4RzdmcUUttOqHJT1CHvAAqiYALqLcNaOXSIDDD7dnU/CShQL5SOs6ZjWH3DOf+Uw1yvA4FgQhcxhZAfowaNDRFfCM6SuFwnJq7U9b+0cquw5k9pcSeGeCHDR1qytprEg4nQm7JSob/YzVwXcZf/Nbkvv25Rqb4l+4zYAJzT484048ywBMR1+8mJlrBPVxNMj1/qw+hvJzyW6tM3Gc3lZnwRPl1ITDCqt494h9OMtt8mYwYFtVbnbVh0uiUyTnEESqYdf7aZ3e1WgrBTBO20OH+uBznfiOcf+h6r6MD7/Txaq1KqI3+Q1fxSjDWT5SsJIrka1j24PDDCHa474VCTupLrlf1KqfpVOXNxoShTGCXOENSgpqxbMcVbKQWc4QnJ5uJ5hK5rvfDnzl/6LNb37yU35B/ek4wYOnAFATo2fKzp5hbSCjpZgDOduHF05QOk1sgAtlqyFidsTsxpC2qQWj3ZnnuSrcm5BeYEqlChfxIukWUFK6CzKJjsISh2WLxS/UL9jqC15ws1dODbRCNquf6k1pXjKv5JMfAAbOOx6gYgtiyz3xlTKlFWpGwV4BuZ/y6RH4k35gZKFVpsNGL02PXMIYKgWpRZoHmQSlTXWPhBmyhA2crSiP9wSvH9qPZ8aUCCdgYsKxgk2guJmPlv4Bz9yxYwfnMiM7Y9O8SFqE3BsbN5xjQdiA63Lj68tf/vJXvvKVKs30JqOwQ8m4/4DFtnbN2uGC/cBhIavE2ZKjR32O4J7w7CRGaGBEkXKYO1oQH8SGU8SejITiBdr43z7tik3xhqc8aeT/fiPAvYJiMCUUMldBlJrbon93yX4rkCtr+BHMc4uSZXz18f882ND92jtec/EfvtPUt+sz/3LH4cQKaLc8l5ELjS07uJDO8hbKN5fqr1bLibYuanRloVq2IjQw0IrNlV49p9XHbbqK8Pb6ZLYARYLTPHszVHPpvr5gPFo3WEr+ds/e1e2HVq3qFKJdbIQdzdIP/AmP1xNUEjVRl0DGLdNjut9qyPjNITuTTw1vMhMrsbkiy+SjeltFrEqI02e6if5D1WisILrQ0MBs+zgJwjvuGY2K+iW7e3hpoP3a+SvNMrz5Xux8MY5CF9rl9hrYBmsuDSydS6m5ktYvF3PIIAVMa22DeHGk9jlwLj5y9Hz4wx/GWyqCE7RiJ6E+7f6e4gSwwhhQA8ZHPBvBE57whDknxnyHv6enh5I58iEyEQXACOcnnG8Q8UPjOMvFZ7xZH3VWxJ3uxganpWHsIx/xZ9IjK1ae9ba3+0Q1d2K6y3JxeWQHx6oDBQMhVcEW1lfYwIJRb4sq31fzhcpLBzAgDcDGvg+o119/PZ+e9rSnKYa6l+wUC4BQQvrSKOg66BYVBhtAwglpd4q5fWeIebDlgH0LMXDbbbehjTlPIQMZ6SLOPHAmpsf0aZCpYCur7xnIPdR70GlOCCdWc1bVo49gr4zqaxqFLzzloUwVk/vOnZGvfAWtYv3d/2quFdJ6EY9nrc04KjVRCoFnNKNwiYoPjsFSNWAejeUdHBc1R6zWkL62YcLvFqzq3lR1sOAb4f4Aw/ZzaBpOrmiO5UTuCuCYMHHvX1vCbg1bK+pcfVddh/UOPwXMUjQ7Fv54HTJQcH5/BPN+rRKsb1sVro84ZzRzNdPGvsMjA6PpjsZO/HngJKs+5muO2i1hp6cBnSg5CBde58OTg5YihGEu0VGE4aQwFZdWXcLrTLZcxd7yYspIR9/y1mC+PH7z8xPPfqb0mzr6cbmfyaDgQKcwnZjwSOeYxnV18br6hr8N2KmyfhijLIx+gxC96JWxKevcmxWPmDgha47pG5ocLo6ebRgg9hAr4WRlxjk520io00cBz/u73/0uCCuT/HWvex2o6pSzCZh5KIoEJIb4JwyOyzNb+csX73X4FCCXr0ZK5sBiT2MHWNZaTrBwr2e8cmpjTkJ3UQXaDZwCMOLpsZNQo9fShQaWtme8ls5drEoGAgClpxC2eR6UC23dEqZXLfIaqEr2mjkl3quXeB6Un3Fzg5Hn97//fbYXmDJYWLA/k52vtYmJwWkWmxisEyiivxy2h0tiDgSlRUISo4jUENXxyaSVwUvFk5O4y+AbV7YhHw6IZBIvkuyc9+zTn7Y+9phnXFs40p+/+xcBkglhrOEZouuNr0i3d961h5PWx/Gn5bWCrper5t2bn7q/Y9NHPnnNxoN/fuHnX/yVf/zUgbY1ZClWRPjJxT8AItgwxUCGc2MS1KAQuHI4ik4XV/JWNJwXkoqGIC49Z4XJpYHu2cmL52hj3Z/yYi/ltAoGZWdHkv2Ms+Bwlu/tM67dEE0OjT/44LaWlmaXuyoljB+pjh+E0idI3zl1neV0v8/EQY/jcAtS+3B1R9xA/SAwbm4dNHHuzm15Qh+7IHh1E6DHuuvJ5GAuLVSspqeLTrpk412bT+TxG1Z3vavHLTAq4LX6oPHYlbiStsv7RP5OrpJ4sBFlaPTXoINDYtwliFcGGjgLLY3uG/ckV7uTPuoiqYzU7M/NN9+M1zT0By+99FLO67kTz17Mo/vLqW8DzPF5ww03gD1zscSSMGKZKOwm4D2w89k7OHsggP1IlDStL2vtGtaHC+DVNtaSm1vMEEh4MV98yUtLo2PmS1/c9vQb0JMQzo9SspC5Lp5IdwxjwajvxvmzWP8iYeMOUn1tPT5sxRZxoVOT2Y+lGbxDfAL/6le/+uIXvwjO9F//9V81zroe3bN2RujBRxUPGEyRHmBn5ydDg1NEMPJTjCnOPn777bdjzU5X/Mu//AucvAUd6lhEo7UITkAXqdkFXREOi4Uz59mD/ZV9aaN3uLy3byyUbEFPpzFs4fuqp0E/swXzWtjCMoFrRwH2Jn1e3r2z8g/PDWUyYzfegOdnMWRZ+OylWHSP0ftCrESZTGNYGIThYnhyv9qqx8v23wa1PWPVkZxvfHQwmB88sye5sVlvCHEYyakzVLB3juj7R3Edyf0EjlXl7DSHCuKeEcqCdsTCQtu7np/RpKJdsuKgl0AL6KIZRTq1AMwSlqXNdWj/vaO8c8SH4THunlfW+1c3o2yLiyV9IF3MZDPhRCPnHicZ8sbuBmdzq1Envqcn/81S9CMqmr5CjNDT08MMJMyWiKoFg7UcQMLkGk6luNzTJ/gc3JF85mUvr/vd7zObNgVuu22okK+LxryFwMxh2igykvUCbNWqxcZ9MB86kvftG7XHi4w0l0VbIe77ha9uaGGfWR+0uhuM1XG7M4FJ1dR57jWKWjgCEMvQUkr24o8bgDUJobj5qrwAAEAASURBVMK6Q2YFCgJJj6AG74xknFIOPYmaBpMQjI2GE+Ch3oXWeFyQ5pmApaQgnAIn2dk36IqlpcyphQJRHobFPL3GecJ8cpJBVnHXDsaoTDY2UgKMFB0ChnDSIAfPoV61Ek9apYvoXthSiGHhHy0tkKwRiP85TnlwNhhhdBHL5xHOJqBXWe8IfrFvYq+AgmVSwcvjyAbJ4Zm76/iKxsSNN97I8uHmi5/97Gfbtm1jHU0nckjJ3X9tba1QvxlL//ZWK12RExsA+MTDEcYrAs/YsfjAHgoZhtax+0moUGxmRSwM1eoYu4ZKm9fXd55/Zt3Z67WGOl93Z/SaJ7b90y3pdWf9YCBw1w5YjOC+GPcJiWghtnWMVLDhT2devXnvfefu/m3L6P7hpjWp+jZbhJxyNPtNLT8yQC3k494EYsKxhChDCwFO7SIfRmCMpy70ozlqoRXxC40nS8BWLRDIjn2YA7g+YW2CwtMiUIQzWoy2SPmqDdGQz+w/0o/TyhUrFDXo7PxFdnhbkLK0QHXzM53HvaS++Yzqke2lagEWpO6zrfEzgtmy5gvFDH8I/1YxnxPHwY57fAsAk93odqVcMEFHYrWL2lkZuxvbwZgZdAuEBMlZbviw3ymzkbrZyClHD544E7ixM5yRHBlBG8SWWCqASEBNVMP9lRvWmTDi0xEFafCwuoDjXnvhVnts871f8k3XL7vsMlSQsBBEkdA7N700p0PgYeAln+Ru5dYfJMDYfC9OkjM3tMyhiQRMPhD3MWcoL6svEdFWRhyESdiol3/zm/D+fWP+YOyp17mLopYxJXsBbmmH8vpQwclXZO0yJG0xrSWKA1KZ6rWp5wbG+8qOyQPeAOGnVPzx9sYe5yU4TQIgl6xqHraVU6zJHI2oOYHWI9h/z3ves6DWgciCgnA69vT0eBlVGDp2b6r4wO7hfSln55Eit9UXhwcMv90QCHXEm7viTotcBjM55yczC2bMJm1X8297R6z/SKanp/6jHxFupOvQcTLVAv6i2AYmR4a//e1vnFXoixKeDb2mGhxY5YsGdkFx1JLMcnvEQR1J1ce5uDulYydTqBphX9Wucg8NtgZGHjcaBlc7yCma8NvtCWN10onhH0M4U/LQRcwcFtFscENfIVIAVAiho/vA0dTokml/7a/0pgN9o3ahqjdH9NZoFYdj/QOHUnk9lc3j8iN1YBd3Ma1oTHasWNUZcxrCk30rHF+FZxwt8ZEcAgFluQEhXQcurqyCwdUYtZk6Z2FNYZ4zE3hTODlh3+BE0zDtFT//Zez7P8hHI8HPfDYfiw7t3dPe0uppX4Nk85Ae1UcGCyMIaOb94/aRlHlwzMqVBAUBm8PxVaFkcUsk2EMsaK1Kmm2hSleDeI6d7aGlIFIcKLPNydkyevFQv7DeadGTn/xkVKCJn95L8CuV1QbMIBqu1ICXhIfrgbGgAHsOTZ5xRUBdLKioeSamT04mDTlPqKYnY5igWBgjVgFzXo3R9AGdnnEJY5ge0OFMlUe4ujhdBFtqCRuuivJ0oecoGW4muzrc1Ud4F6kmsGUplgrbKScRBDCrDIyOA3GONnqfOJXe8Y53QO1//vOfR9OEuYEZIHjg9GkZjdVBVf613zlScOWqrl9iJjMpeQhwlNaHTayEXGmlhj6G3L4jzGV8L+EtH6tUIY53jYXf8NPSC7Y0XXzxlXWPucTnVMv+4J+t0A+3W7/cj2DZBykHTSseOkIcu3ZWLkxxdjZu+tCzPvnin3z0cX/6UUP/nltfe0dvC/qVZskykDiPHtoBTMG6BjZqmja4j/sUNJjy8cY2DkihA8WzlNPPlSsiGwZU7IH9Yvl0vEeaRiZNC+rGY1ZGICe7e7qYG/R5sVgSVrut778f+tQXaSte8uJY8zrcQdvtZ0ee8u7gb7881v+/4dCIs9KuWmHUjwJYVyHIxZQXjyIr6tyrewHumIfv9tpG8fchOwVy4IwO8TxaENwJ1nHfeBo/QlyMJ0AB3GRebCEvXmGULfvefXIZML1Q4SYoUWPDn5Y0AnspRgz4xwoiPcObNc538fHZjDT8eA+YFbMCL+Lf/OY3n/Oc5wDH8XKcat/nMVMetU1mmsH6QhUE5Wf8ntVMqqVpEmdzjV8lZzQPHWtkKtRjN0e0hkDpga3bNqxZW/o/H8HRe/2TL6//u4unVSw3gx3J2MN5HQ9YIqTAhWbYaYxqzWGtKbLI0QHtu/zyywEP9uFdd91FpXh7W/LmT2vLIyVCIaYgqRg/c9TB+zyV2s6sRs6ARzfkorQOBp6H8c9zAOgNlF4gUTgjyaL2W4rlGStpvSl97+Hx3WktV8RRbrlYyqwO63G9vCLasAaqwNuY3cokz0TAynz9m8HvfS/vM4b++RW+ZH2dbMvzhOiYZICn6ArKJkzrFJzHJKr5EQ34Lu12WuvsB/rtgYOVhK+6tomtX+rmhNs7ylX3+mCOswFTW1Huypb0sZwl9w84FkzTlpjRHtfaQvbKxNEDA/KG/sFEao6qmWCk4T1ldsnh5qptjRWs+w7qh0atrOULGVZ9SGtDnbwatALJatWJxHyIgH2xuvqwryPpbw5zk4RIn3nA0mD5r1+/3v119CW97SLZQPXIOavgtYGigZ/RacBKb4BD8BAG40TYcrQBiw1RJnJ4yEXIjJFUKhFPwOc3tz5ovP2dWJs4b35L8O8uLoodsowgXaTqUeOifqq40ZKzb8TgLuhM0Rzn/lJoaAd7KjsS5Ppfpz5id9QZ9aHquhaMsueClfbidJcBAr+cK91M39idoJHwV0d34TYCF+4z8ggAnlHmoQwS0PaHS/DrNYLOZ+dRvepFEpgeU/v1RMKMnYz4yAhtp9+Wr6ITAZK8SuGfzmEVoBE2TxLlBCudkp2FRi8hJGRHWjRfZkqZS/jTW5WqTFYQ5Jw6uU58WOdTAmnoH3xeLFajZwk74/hF0Tnc10o6Jv/vfvc72MHeZUXHz+ymUH2C31MuhoDX9tOf/hQKB9Vo9E347vWYkLGana9qP/nryGBK5757oSvV7ifnr9g/cZ4RQKiIwZBof3G2VoUG5q9LA2uxgGgja3IxT+iDv7ZahIEYDPkCXPyTyqMGFYSXi/Zv3tLlVi7Up/1guRQJFWgWfdqfOi9O3/ChYGX8gl2/efV7r/7Uu3/aW+/qQttm0Qg1N9RHEg35dApt4HhjK+CawbCc7QDpwsrUghSExy0AgXVolbPbfVH32gqvpeyfHNaS0lUPRA0BRgxfEwk2cGHpS6N0gz5nToZCATCH7GBl7IDedUnhoufWB+pAxTlWhPsfTuiXv6Lpbz8fv/+OSusRs7Q+PJDFtRUKRCKzpVoo/NaI26/Sjaor5Q8kOufT+mYUmLm/CSzBODzuZKt6uoAutB4cM2MDek+X1RhWWt7Q5/IAWlvc/rsVuC9x7utD3G7i9IobHE0useAOqopY/2JFTGJkw5iAiSdttJN89oUdmPBQAF+AawIMt8iJl4pkVtxxxx1IUyAZlKShNs0pHz5lVaCZ6xxISPbBIVB+5j3jJDiRAaZAYd24cwv79l3D9p5RjPiNeNBaW89FqdXhwVRk587wBz9Uxb36bZ83V3Ux0Y/W6AaHctauUf1AWjs4ykQVDkxXvbamQV/fgDhIFKimC9yOlnBsSJ0xvMlEezkFb731VjY+zmNsRBUudWyOU/MXqBLYAPIKBHQEPC3fU6C1aoi59AgX9uzUTOxzzjlHzcB5tk6VwLxVCKU7wSZohiq3JQ/YB7hGyNc0ojWZZrCSy67fsHZjT8vmnkYu3HNNImXWHlOjW6K1/0Dl2c+KZHMj11y970lPau3oVJpax6ScJ4g1yUAoKQfExYtT8KufArz7pEdHR/ZtT9pDzYFind/ODx8+3D9YKRaqoeSulNY7ylZgRoI22Ds0+UieGxTQIJKzJREwm8NOV1zf3I58mKZJcRQO/oGkCwIYTrCKUTXW1s4JqvBdEnvwTCTj2HH0u3aW94z49o/rCIda64y1TdWAZuLyomjEHX9UqIlyobNjbVtDdH1bdGOrlsBfmHteQlJyTntXCtUCANWkpOJeparGE3nTKPADVUttXfMsE1Jw69atsJnA0oAZjI13d3c3625J5gBgABVzgIdWowLd2bWqMRio/OPzYwcPpi+/LPPKV4/ms5lxSNqKUnsGv8H2wWsLJIHfb/gjddtgkRRAO/TRUjVimiVRezaCPiQNsCfMtqiO08GzRQvd3Xhr92q3L7zR52RBxxVSx9tUvbrm7jRKADawDSylN27c+JOf/AQx7xy8DMg/JgPm6JQP4j7PWuaGYaFfmRs8QA5Tg/5nFAhPgWTKz4VWMUd6eLiY86DCwym2fLXMAcBxPwGVAoxeQmsdOI9urdOm0HFLW1wCBQOHHSynOdSAF1f4kuRSPCNYe/Btgfb3v/89GwXraLlntbdmaQX1TmGjqIFbkgYubSFqQHmzoSGWZDdD9utFzrMulZ4dBhoYCTCKkNjBkRcyT21cJFCcQe47/O8H8/lSBVM+01TEHiQcp5GU4eoiCYmIUpd4WFLVy72BELNCYZGE65H4K6xfzchWA4cyvv1p/0DBj6JVEEsoOVR5uMWHN5mgEnU2XsKiheNow5GGP2285pztv+xO7T3rD9/dH67bH05WsmM2WzQQaE5h9LA/FIk1d/qD3BEkmlrkkvNSShZQ4WWXKxDmBqZGrMQm0YU+SvtxBKD5zJSDs0wGiGHCbK2trW3SQlffmD8KbRYQbav3/rFV5/m33NBghqhF8Hz1CPyG1rTGt+pMX2rfeGR1pOIgykacC7CGBW2LuCuC9rIANgGeCsoPCnIaogiDbXoGTe+RwYH0YF96ZKhQLpWLxaHUcCU7Gg2Z4TA0tOSXR9PjYQq0K5YzWsT3EIqlBnJgV9mbQqQzSUzJbMwUyygAA6ODJ4uAyHTdMvg708M3xCHIUZghqM2TRCo8bZ5FyhgfFf3DoLLPgmS4ivXLOKhsELmKNljAtF3nxutk0G6NoBAKR0Yr3XlnxLZzj7mw5cKLmFnHoA5k09B80EZzGorTFn52HT3kt5ojRlPIaYnL0MgKXkhfY4nEOscNoNL9u/vuu8kNj4d9fyHFPLrTsoA5NljPoALr1q17dDdmGvTf+c53uNUZpBm27jXXXDMH9jwtq0SgIwdWjSdMT0qgUARmWu8IjpSNvhzmslgCibA3YFQ6Er6OhNGTsBvDTG22RnDfY/Vk2J6LxfSLXxQfSaW7u+z3f+DItgfPm7HuhUdiyD09EwArT0seiYhQbUVnB+Clx8aKhVxbOz5jdSMY2DPsHM45hYoJQxRlKW4EHisYGXE+AatW1Ibqgk57gpvG7AS6WZNLjY884BzTqyaGw9VTxyUZ6ri8YUZAzxCYyILuyWBl35hvpIhLNo3jsDVaifq4A4KVbqbzsG/JosMGrotoK5J6e7zaEhEpH8cnhRCgjQwTpTG+3kjxk688asgm6jrhP+CmWAGBOkPbLKIwKDpygchiXit0v+siAdJ3EUXNJ4t4FrWssbe9Pf7AA9mOlZGPfGwsiNtRNMsE+wLRofdqFoXMWVBAvm0bqKTKvr4xO1PCW6aJA/Ag89vikkYnGjSTIautzsDzgthcTQ7jdHggAtXQ0Ew0OdlnCFA+7+mJp8fAv3j+859/zz33QDyjezbdKm9KFsrHFh1Rs9KqnfL15PzE7baS59NG+DIwfagXjVNaDQFDV9f09tJDBFFHvWqOLX3pS1oi/cC++jAetUyqJW3QUhZG56AoDksaCoR9G5SMn7yXso5ZymJDYN1RF7s0SWBcMqXhUzCBZ8nxCIqGA4ue14xcp3lCyfJhc0bHFa0x3v/6r//KlqIuTFLnSLHq/LLX9kWTejGVHs82N+HX3ZV1uhSVbKCTJw7nJn6TjbCYNhlQnOLPmXtoAYQkeLJAHYe7eYUcRS1KwENfSTPTmCYFhIIkIf+RAGdQfEZ3Nw5tifzZMBAg90VbXvOqH77hzjdc+79ff9Z3P2AX0vefey3UcZEdHR1rUfWRagRmtyhvx50MiBwYXWgXGISj1jkrfJM+seRKMK5K5hszYe/evbBrlT8RgXvaA3MT5c0tZ57X0Ir6EkfHlDT85BAyk2uMqzuatqaw4OUkR/ptIO5mnusmesj2+SsNWj0Ng5dOC+jOmW0+PDxDq+eSwUPVGMZm49mRAb+vrhSL5rRuTUTQbq0TVYNyndEoFlzoe/+537EKyHyNctXBnst2TIy/omhyC1hYAuvDWfgUht9EO9o+bwU08EQh0xoqEfQAvH7mA35kbrrpJnglMyY7VSNPWQKYPe6d73wnXkaQ7C/f4LnqjjK9+lFjLogBOqZkY/sf6i9pw7oWHB/b9807jmhO6IqrGzE4nLYaYBqlCsZwSRvCcbQ7fZujekPEaYqoe1YXBjjoCKIYcBSVDcnMvffey/yGTFpYQY/y1Bx4kCj0BlpDsPRm3OMejU2kXezdHGOQRhdccMH73ve+BTWNk4MS4I9wBiCdqyWrOKjwFLV/3Dg47uwdc8oOHobQ1/GtXtnUFnc6onZXgyj/uJ2m3kf7j5TZr3wles+vtpPh9a8fGBlh7963d+9+92jh1FlCWaWqlVbTA5A6HhDBYKilRbSrqrBRK0VaBwy9KWuwyMriYHBCfq5wwMevMQrxaULEir+Ipqi/tc5qC9urElD7bvvI5mpJsYiUCHHGHgZ5IhmIFFxkEHQaWJuMMuAH399njxRMTIiaonh0tzuSwvrNcmVgwfIFdJt7mEKxru5V/nLaSOfWrF2JptPo2AioCa2gcArEvzdvsLTu7m4GztPgIgFIpCKPFXlQWztfT9oDnDwQJ+FICGcqSvfbrX3qJJknSJRGq0H4ppOUTDMevHeuWrWyevfPgrf9e9UfMN7z7tg5Z69zZyaSJVYHlwwxHJTj1shfRhrEwD40ZvfnzQNjVqbKrYziOyUUNIolC11yYI0FcLhgrohVV8TFl+FsD/kQYbGxqLkyODAge7YhNwPPlqU2niHDHSsMWXCvz3zmM1dddVXt1znCtOiY1TpH0mX4xFgoQgVhLHS4Etkp42QsjNCa41mGaieKpGqmN5N/+apYkpIBEtyXnWdJSlt0IWwUalPydBMWXdTSZmQO442SjQueDm86iokEmwwuEnOJ6b18mxgbda3BArXDPwWARwUBDD8FJa8THwsmJ1e/ggxzc8SXv/xl3CL8x398tb2tHUr3oWH7vv346/D5gpFKKZPP4U4h6uKqshcK3emeju5fsTKFrEuGFRHqFOUs1sVzluyzutxPW+W2P8nIzsvDCyu/8RJumeRmJNk5xZMTb8oT+9U6aOAyzEtc8WvDweTHnv7BXDj+7F9/tv3uz3xp1eafRRqD8ZZgXZPZ1InlsMDjZpUKCUkF8pcQZDEkI5Ys+EN2OfRMqCq60FDsUu3k/eRMBsLMRsVSFCimPTBRc9nMgzu3Pa7lca7s99gUE/VKWwJBfXMb6AQmTSJxRQ17OOeggezX7IEj+zY3apvX97hQui+KmfjL7Xr22W0mSEzVTgSiySNkLGTzeigb6UgHnd6slqizQ8KucTVMyeWIk+f1Tb5SxcpVre3D0Bo6JtsVEAnwGUfOskhQjjqwHRxTD+UMXDgGTTvos89qhSSXTphjicGWvf3221/96lcjDVZIxbFtPmV/nZoEMMcAY8negaxsmYdO5iY+3AYy+khOFmF9TN/Y1tnd4EMbb/CHP+rKprX6xvjznys+66ZNwcGsnSoaaDWgt8BHbgNtDus4wsEoUVaKu7DnCT8rGbeBKMshyVHiX7z/gaux0Surj3mWcwok40xFa4hljJhCrXk6B/yY45bnUdpAmgACzT5F6+DY4b+etsyxo83YTCzEOP4RUxzbDw4GLLuGnL601pfR03hxk4mqdTZH1zZG8I28tgW3CrKDeppMtYVXHnpIf9e7MWkpPe364jnnaWXhv6BcBAbGGcO5C+QLhVOVPyUX5ahHfSWsaGCSqUe+yqLhCHDGyxq6xwfH0WUyosEqh7Th07FwhlOLnBVnlGG/rz5YbYsaa5q4QZ4iFVdZzmV4Z7AJ0BODElN11b5p0f8j7zwA7KjKxT/l9rt7t/dNspsGSQjSQaSjPERRQX0WkCegT58KKILYULGBYgFB8QlWBCvlCYg+VHgqKIKUAAIhvWf77t29fWb+v2/O7mRy793Nze4mEP5DuHvmzCnfad/52vmO8gNET0JrYpm2c2fKVry636pmOxriDqfCgqbAnGothz2IoY3kTXYsSkMDHI7FmmOROmtrnZHEMSZrHUIQT3UAgJINMpFhol2KkAWhrVmzhl8XUB1FihTi+iAgjR+8ysOUBp9PdYomQA9MgZQGe0YbKyyHvGQEydB9kDXo0Q88cBmYrMLspclAWaqZRZ8Ez7qnYRMjI6kLPxS08sl3nF1/1lkkA2Z35F2aypcNMYdrrWAPZbQXhp1tw9pwzhzLylFw+gzHVyh+M3m7IWbMS2gNHP1tRENPD08qMKcinD7IGBQKmXSKjuI0myDuXQ2BAu8G96Fvzz33XARYCnIfvDuCTGxseXinb/nFsJah4WGyMWRwC3uUYdgBhxtiWvIQRDFCkz014y5bXVTO9F5pNTwwa40+3Ds1Tg9Ocr0UwMPuSeEusMe0GzK7GRk4Ji2UCcWyL4O7WONQKcxtZU2AgYOaYLNbr780hkatQSJhvJFgshUiuUOz+lIYNT+oReHZAk+Vg7ujrq4uFH04hT3yyKM4XVzX2varJ7JjdgxvNOFgVSGbZa3FY5AWauMWBnMcJHx6cAOwWeio1Zpi4r6YuxK5T25IC9n4YRb0LClxn0Bnp/NCvSKiVssWtSQXzlVHRAmkWE5OITkFbJSFB0b+yPDAv+Kocnus9cbXfDyUz57+8I/O/9Y7Np9zzbr6Dphry4iN2hYKWdPFt1j4SnVSp4ysVCzUMgCKa2V4YLJgSsVhZY8HFrBcHELA2+BUt/j7nBgkeu1t7djKIY/u7u6Won2P+yKVuY8urGxbIC/XFYmX5p4xvReHI7i3lKO6mQULuVlgIq2C0c0Gp4DJ10FtOGK0yMXlL4O6nbPN/jEYXSOM8ta00BKjKyaT2zLJhlH3slYjbWkQNAWbS6hc39e4y8b3lyZXKMe42cntlEze6RkV3S+neaJBa2GDa/zgVl36ozqE85LsSldccQVug0vTvFxjpk+pvDR7RC0v2IO77rqLc5Ls1qVTfFYhZ4Ji1mgPZJxcwWB544d9YWd9IqjldH39rbck8HL+ljMTc7tkGk88CkiwA3dhD6W0QTHbgOIUIVl9FAtqqxaswDxmXe/INJG55K8qDb4IEhlawTP65SZ00uLoHBpiD3dCCUwvagSaCvoE61kPzQEOIk8MDot5lRnAqbqdAvZo38IMoLAFbNry0Y9+9KGHHmI0b7zxRqzIdqteBS0zhMCEP/DxFmCpv7qvwJ2oGD/jrxjPz2DdWDDXUROYm9Dn1Nq1YRcFuxyINx8d2YGQ5RZGP3xhdX/vwPwFDRd/uNBQF0pn2DmYh9AWCuwZ9HFxVsg7zxgSXRwPKfz0U1NTY21tDZvBC32FbWNGOoMhkNyhFww5Q2MGezDrDPdX7IS1Ia0jYXQmHLktaedlBvz0rWdf7QeitM9VJxJPwA07PWmHqrcNY8ysddYG6iPscIiKjb5R10cF9IG4J3Caq5BzGTUZuyFMDcAoF4krdhcSBMYSKs2rjkFXFlzQkQwiFlxqJvtnuB/OCsMoRiBGSUxFLBAV8Dn223UxDAetrq6u4gzIis355oQxt84U1xu781ACLJ90n0scEVZQ0WoaKBhWpppLYqVTybe8vbp3++CChY3XXou7D2/skLYwZF6HcPoDjdP+S/ZHtvhcT2F4LLA95YylrGjIRIgOhUJgLG81cvS3yqmLaktaTMgIFw97c9ytV9FrEnQpC7qrv8fZ0mOjCq5JBObM1Zqb9HgcyL3BctPu+FGN+u1vf8t1ZcS+/e1vV7rfydKThlWvrI5VXuQsJIbphQFmCUDBlxXN7KhyVkMKTgWJKhjpjJI9EclEZboSz4QEwlmtebwwMAnTDCbqRbQu3hPtmqJMr7enmCRlszNDsH4Cg6kZsrvZy5Y580gU+LC+NArRPDIUkBh7NGdGcAJH4ajjZl5FaQlUx8JhKSljGWSysNx0CMuHHfDRRx8Fkc6KcrW06pdmjJoMxx13HIfqjzrqqE2bNi5evN/37/xjIHhIbagwkguk4VOD1bjwT6Wz8bhc6i6oECLUJUUhbpe3Fo6fazRYqVAuDXWabYluWRq+89nMXzaGUgUDB02kB4nCdJEPHgw2jwCqYFjVvGaMZK0aROKCSAWpg4Zl5xEeWK4nZFfmDc55S3XzN994ZXys/7h/3fuBH17w84t++vTy16Ihytrs4NwzBPJ2uVtBuRQjRShtM0VRM2aYmQLKWEHISMOxhT6w3eS2X7WmoEwOPnjXJ7SYmWBglhJEowg6VU1S245HoJc3FK3aga0BUeeKYZnTO1roGzZCKcd0zBWb0we2BTkFLTdAFReCswntoHb2PuGex9pbB3OBlOX0pNR1vkK6LG0W/1ZuPrcq7MZN/aB2M2M7XG2P8fNwWsuQHcdg6LttcYgNE8IY0JepnINimR0yHIQJt+bg49PdSv2l7WiMpsE4YKCEXeE555yzu55l/OXsW+E9smO9uF3AfnzxxRdj7MHZ15I5N/ugMal6xP7ZwD9mfdhpiXANF7YLTmbNGm3NmlzAiL//vSyUIkjINZwuDGd1rsMeyoBoRO3QGNNrotyBhCoYOMdnfCUQs7ax6gHXY/SrqBB4A2ztyHv66ad7JSgUoF6L4PHSvAwC7KzQAexztMXfZP/rzJsPfqTD0X3t0R6DE4BLYcNGn899BjQHJHXqqadOo1JEJMCMSAgqxG2+oEO2ou1Je1NSR/27fsDOwf2K1wkuoA7CGGC721WLq4UyU9HlC5yhK6+qfuAvnLGtvuHbLa88mjm7dctWpiIcmjJDoIqZd7XXWG9YUSNgooySn0/KlklVhC00/zYMFralTbZA4AlzoKmAus/oz4htEupFpNUN0UBzld0Yo5ksNQAcbyDdC3EGuQYrBS/qxXsA+APsi5gYeLW7nxxYbNS/25LGcA7bMLu5SotH9ILlJDPaUEYUpGx11Bi20+ltm7NpLdGIK6z8yudfIDvNYbv1V+GFgYSKAA9ijl/PgstLMI0AZTJMqkyMh3FhpRgM2M7KS1PyiHh1YnWvlXZC64ad0XThwA45UT3ZQ43eJ6+H4esQ9Kh4V7Ihog0WMvQKYyNZ+M8pDH3q8uqnnxytqa/51S/0qmp4YlUR5ZCSh5SS2D3rBb1LplX99kAusHHYHs05GFqms4VgmBs6LGiF2nCgJlpojhsLG7Rqpcoth3VZOMDW3tEOTZdf+a/cLT/XH3zITg7YoVh60cLg2e8IHvUqvQ4PVV6zdgQUPHDjaH2BB2MczuBhObwjRbkQOBwxBF+g2nHmxwVOO08zyaOaScDrQ4md5PES872S9GWL6erqUvIFmBnPJhmUgmaY9Mih4FTLZpx2JGw2k0HtaMy0/68YYFYE6M7DohX2IVgRbSojwsaxh+QRFULiT8amo7ZjjGuQmIB2cMgErtsljvUXsrth5rzSNqsJj8qXEgjTpVQNrmAaM2mVJHF3C9/T6b0FO+3VqiD0ylFtV6Wh4cRMAE74sX8+fu4Zrzn70m8uOf7slf32cNbMFMJmrN4SdzSCCsGkgtUIO/aruq2TGnOJDatGfv/AyLMr6crQssWLX/Pq9yzpqonk7loZyjnoM91sCDUEL+v4KEa8Cl8q6lo4URi2rI03adGJgqUxn0b67u4GOMSKhWD1JBZTpL5IzWfP+dFnf3LuyU/f86EbL7j71A/ffdoHOVeMNHMk41RH8fUMwyxCZylJRlU2CML8DyVNfLag9aKDxXGXDhNoL2/Dio0UZCqHpsnne0jDrGBugKjhgSdjmFVBUrcjFyiKOtdxhvv7Nm/d0ptGCuAUxvT7Hnp2XZ1z1LLOeZ0tCj5VzwQYTl3YWN5mZ8VnVe2GpN6fslHk9owZIeyrTfQQhe46ePdxEsw1aNK4bfiQVpPr63NrcLtlalk05+I6hCdXkJNW4kzTJe2wPMerC/KIsIFy2G7kkkWBloS+1rpBFQXfxK0EOJf505/+9NLBHsWwzur7y5AB/spXvsKu+Zvf/MbTBsxqjxUVpnNGv5/bjzLif52jES1c/yJCL9v5zd1LHDu3/7LoUsScJTOOQwJj8MCQxVpeDkeIC6y6GPetcQMwBfCML2g3vIsfRPLQB+yXPGoqP/DAA+w0UCT4wfYys1CxrGMDeGkifQ/OGQYYd2WnR3sxsqIfKJAwRJtSXPC1lKDc3UrpcIiMPc0AK6jAwrgoAHhOaHCGZ3oTGyaHTuCQpL+lY3nnhX5nw4i2KamN5JARspHonVwLFLPaq4z9GjEcdjeoiQks+4wbwU/+6efMb36d+39zl16cOOFEsWFy5MoWLIQhL8pgWX/Fux+mQEUO0gpoF6ooo6TVxRnj6mFt6zCHfs14yCrkHC4MGEnj7ohd2IIHZq/FTV1bFV4lDGyEiuBkgVA+JMIuAYQLLQKAnXjdQL4P3jvlDKXtmphRG7VzWQfd4mjOwOcwm1OQ3VizEwGnMRKa3+BUmVYhjyGtnCL2thxqLyrZDwyjXwSz/2vlYQpRlofo1sgFTuB1d0tGy8pRur5csH9EqBr26gZuQdwV7qKHqdFfF3OG6U08VmcUqEQb0Me++aaP3nVP5Ac3FnAy8vWrw8sOdAzM2bl7cacWI5NShQMCoU2D+S2cbB+0khb+OWXuhsOBbNYKByGM9HjQbq8NzElYbVVgYD6OU2/+EgESmQhopKO9zd60MfOtb1c9uYJz5lgScLmHs3JV+qvXaJ+KBI89lh70Z/TCnGo+//zz4UlOPvnkm266SaEj72vZgOoZGuJ1EVJdpciigYwXBzrIyHhVrj2DAQABwnuUrXGXkUCi+E+gYtBVPyObY7AUsp05Ri2FgSkBg40yE7BZ76UJXsYxjLhiGne3jdAACE2YsQR2N++eSM/MUTI1LPmZ/KAvXsF1/HrTew/Vq6RIUEfwe0whT0BDvd3d3bA3mL0gXdoTtc+wTGBG7qa2oZn0EisIoQMzwS88YgiQ3v75z3/+r/96/80/ufWHX/zAkY/+7fRLv/1cn92XDWYLOgeIoGBroy4vSUt0bX5N/oTmQvyJJ7d86Wpz21YD3ModPP94dOQP/9f+iUvOPODQ9YO5FX0Ru4AKFMQu2BT+jfxodFH0svW6jxhOixY3jJcmxb5y/ld8YpE+EtLiQfYFewSmOK+PmvEvnv29dX++4fzfXvHaP36rt3O/fyx/jaUbWdA4e2tEeEK3HilYgqL7FasrIbWFIeZmJsyARTWqm7ZpFJa1qLNOLiAV/HR1dbEZgWkh83aBZqXBWiKsH9zqpDMwAHM3J42t27YPWU59omW0WsNKfK4AKbue/N3xkNNpiRvLW/DiqHN2lyt+IWBGs/ZW2PaAnO9FWttevVMectdF9MM6jFTBzq0tbHMCHK/K4hRaWHzIDAT9ctmyVOPg9VMuRkJHjYuyIzpNkfNO8tBP4NhrrrkGP1g/+clPzjvvvEkSvqyiX1YMMHszctNvfOMb3DOB9mCvDJTDqfdBmFgHYtqui9oNcRO9hA4a+O09GJFYZ7xJnJSXgAJS6E8J9zuQFhkYgrOaqNiH1IScKmar+5TmKilGIpi4YLeTTjoJZKcSQJ3gaJQwhi5++w3ilSOTvc8AMzQgdPDInmCNVKupAioTBobzRV6rqRRKggR8RY0DLUsYMfn0yDUKUXWpAtWrFzmTjcorVpXMrzLfYkxRHIGM0AVBa+LF0b+T+XPtMkx26A+XKgJkaQjq0Bd6rc2j5jbRAHPAVLaPqkiB21A7a40FdVYCRkFwr28mqqxIcxEAXHBRbXJ0ZNmBNZ+4zMTPAo8ufJRyRLRLeKaXQOp3n6LsxDHGGEph/Lw9ZY5ktaB7BIldYThjDKUL+EXEYZFjofcz2mrYVDj86Vpa7FwQ84dx3C3akTuYkLgtWbJ0KIccwdw84ozgZxj3HuJIWw+GtYFRfTDFzcPiJA/PjU3VxtxEfHFd9BUdga2bNw8PDaPhp1JpgrucWSbC+/kmmwcjyweB9Exmmr9Yrxx/pFdXJQHhxiPxjYPAChqDq7c6EqJUnyIvtB02AtCdfpZGcbzkguZghkP6j4MkoyqcaX7dGvtjlwXHUiPvOqf+rLPFOzapd+Z+ieDqbwg+mceO888nn193/1O9GbNx6fF2VUs4gOgB9YIVjhiFHFYAWket3hyyuhvcM+AlMPv7BHrdTqcyf384uuIplCFy0QS/Mt+d6NhI6vY7AsuW2u3tquFerwIP04k72MG6jNpPf/pT5GXIboiv8IFJoJdoK+yrYnopHJwGNqAEFhqorJKiyMXaV5KOStJPnQbsSgLVP8xVljzl+7urKDuf1FeS8RR9LX1Vib14qlOsC/GVZPcy7nMBr+GlgQobzhbPiLBrMPF2C4nt6b5StAfzWUnZ9o7tseo0hSoIE1AdSxghDgx5hctnT3dOafkgSZAGI1jhuJeWoGLodggJTtMUkQ0Uy7LFpuzQQw9D7/fw7368ZcML/3XdPSuHtJ6Umc4429OZghVs4DpdAw5VO3yOUTvQt+3b3w1s3SqMnGA6FrIT2bCp94abGq7sPGVh29ph5JFmxsLHlXzjgRPDLVY6K5wf0k1RU7JHa8ZQzuYIEkeRXCSik16enBYNalViDg/KN9AeDwcTNx/7Qdb8e+753Huuf1f2k3c/Ne+QvGHkLKyp7QSuKtzecU9jyXFi/OyMiz/dDQD0PFYQM2C0wpDgSJ6XckRGoBfwpu5YvjJbjjzySJAPk9aFbxc/dEh91Dh0Lq6owqF+Y2xscCRpJfXaXk1fO2rXDdtzaqi8tF5BiHNrxBsFhs057qYYMZI5jQYGk3KJUSiAC0+Lg1pulwKX/OX/1rh5WLuNAuPvG3HaJebNYv/MxfaOPpaT05SIGOhJ/GDgmxolMJL3OKeOO/SILALZOMv2AMdz3vzmN3/6059+4xvfyC5cNs0uOmKf+vxyY4AxEEUg/fGPf3yyAZ7d0QET9I7CAMt8wqU7nmjhXlkJ+fXrAytWZI1A5PTXyXH84lohkR2MJJP8y7AeSQDzrNdGtKY42VnyJZRdcQnj78xlHl7Y9ni8+arsn0844QRiVIKiArxIL0tRgll/BaFDzCml06wXrgpEUwppiP8PRaIR6Sk8n3jiCQyfZk4TsJ2glKBkqEn6EB0IYTCFUoMoMGb+i9ARD94US0Nw5IZ7M7AwgZmgJLZ8Hj9sz24a/ssTWzL1i9cOMkNN16zIXlAHf6i1VVn4Rna3uqLJK3pe2Mj0ddclHvy/TLwq/M2r9ZgoZ5j2bEKQqopa9Vc0u2FmLOdVYBGLioVN2jqCYYXZMyx7XDTC6RotUzAG07bB7oejRraouNEQs1qizvx6aR0x/uZBOMJdwIwpckHpKzzFbFF13qsrpICx1Vb12XhfHMrqqazdXG3URXBuZ6MsTGaFXxMZL3eBRPSakMUB4AVctMyuJWt3/PFWogp4rxPfy+9Y3tepA8xViCG4L3TL6BKVnEhVgTAIerTCbb6olmQ6tWognLdR/2J5ZS1sguaRzi9K5n/1MI8X6bXU+0RARY7HZFMjl328bvXq5KLFNV+7WnyITbJ/I/wSF2WO9sjjj+WCDa1L59ojTjZQw4XP3HsUQv8f0nMZuzZutFQ73PexxHXw5gHggUQAwRlnKQkggYJYLGA/8vjjNj7eAhNHFt2xYz0YK1c6I8NaW3tRu5k/eJ2B26fPubJbIR9mAoha0eJl6/XDAMLEEo8YjO3V4qU0yFllKl/JkHld6hXrxeyydi+LP+DPRdh79QL+xF4YzIxo47TTTvNipg6gw2RZMWNJhnSJvYPO5Bj8Lhfj1MW+9L8iwYf5UWPEalUzEHTHDKwEeJY2kkmys09Vkn5Pp/Emm6qIOczsVeGpJ8zMAVPlAwABNgu1WLxKWYNHH330zGuZxRKK+sorWcV7kHvxkwUqKUeVxnGhCy644LDDDzv5pJM3PP3X77z/1Wd/4SeBqu6NeSeZyveD2QwTO6YqPTsvblornreffwFf/65ukcoF/SEFtJ9+Vlu7evEhbY1huVZdzwnVkBdpumywcJ6xsI6HZ14KvKv9Fp9POQvrX9chhpSTx2+HWy48sOiBRbzI3UjGUCjxs1e9r3vjUyc/def7v/y671562xOLjqGUbMEcSVvVUYM9VFhxldm1FRac4ZLE0j/CDXJEUWwsIcyDhrWoAZUwH3ZNYAO/J6IFEakeU79Ssu9Ru5277eltcXN5q5V1nB6OPrH1I0owxJ4Zm3B0uc0YxvkyEhwv0HEWNhjpgoWPTKcg6rOMpXOHInwATsWipn1Ih+Epb90sQtXPqw0cmS+MZe0nthmuLTkm5TaSd0pN55xYmPNB1ObywGM4yjJiph0JFg5oxQskfVS+ByicK5Ewnfjyl7/8ta99TUa7GOSdG7CPv718GGCGinOSWD7j5B0Sdg8PG7XJyKcLTl9G53YT8EBtTIOuJRITx/Rv/ieWzyUXzK9dcsDONLaaL6I3Rk00nMX+mYnoBDSrLmpgRMEx4KIVojKU/QUIyIK//e1vmPdgJ+Y1GYoN6SZZvGtUoSFIqYBGTaoUCDCKbANlS56tyPFu8hXnxXjQ+j5OP0ix0PQoSSB/S7fYWayUfVQRYQp+FfYoUSqi81WamTQQhAuNi2IQrdE999xDv+Cc4N3vfrdXETGVN4rSeMirQHIlptpQ1l7T72weyWR0J1Uwmc/M6Hl1IoJpizt4xHVtjMrMRtjc9J/vB00CQvaiC+tOOklMX8ntbkTTH8LKcqomeGIUgVqWoiB6ZM9rBu0tSR1+M4ZnSs58BnUO53NIgd2XzTasO1Uhra3aXFCPl0WyFC81uuiUU05BtEEtdC/SDYyuJ/OnSgIeN6U0fuNgvj8V4Dq0oTGsOYyaiIMXimDYGEmxB8vdSwFMRPJ2VchoT5jzEjbmHrQYTDU9Y4Si3gISFaP6p+ireoWjQHaDeILJoMwivCyleEAKlLJU3xaXp6oDpdz2u79nou2L99+fWTG/QUNhUJzUfVfplcyIBUKcWiYEEPH4YcZyR4k2VBaqZ7omb/px1Z13psKhyI03BRoaJFLRPfKVMuR/9SjOkPff//VJq7HBqesOR0SOjkgiEtZxnckR8HjMqA8jgzAXNTpxaJOJvEV/4VHV6kYa5fKrej6XwcTNCriOvEH+aax3hKpyCnGucZdnoiwFPLZIf/zjHykEww3lm5CWgqAUkEXVlb76u0VBQrFEKsD8X0vzejEIdDj7wKsC6fHHHyeMHA2O2ksz7QAuJyoBgzT0oRr3CutiQpKLiUp6ViUuixQDXGH2fTcZCAfkxmCxnbFUkcQRVqM/RaPU4KoE9BuWX8p6lvhKBmiKkmf+CTZeeS6gKAwZmJAE8DhQinNmXlfZEkCwytuW/yvdojqNmal21Re9owCPjqK7CDDb+UVmBFTgw2k4veOCeq/bKRMREgUiH/R3u2ryUUce9fDDD5/xpjetff6Rn37ybad/5Jvz5xyNf4pkLjeUDrECa2pMhMnZrdsCOxAthckD/jM5A7WlJ3yIXVflZEbFgyaR+ERE5SvlM3t1JxoGA9Pf8GmCsdEp644xnHFqOA+MLbTsn9hCoyaWDJGQLn6h8eHsGGOW1h+pueLsm8I/TB377L3n3fDe//7Qj56ZfyRXveMTy8hw/61wtgCipjohFMv4xBqf+a7KH+dbgAmRwqeAYc2vhx1WnrncLU61pNwv8IOCwJ/IoRCXgHjLpZqIo25pu6hz8bOYWdQaH9C3Z42hjA3hxa28nAo+okOrQUwgqYqqFtejS5vNHPcLYAttc6uww4XJQ2ltC5yzYUQDhYPaoSDGJy09zAPRtrgRwa41lsU1SHAQ7S+6NxubZx1T84zLA0tPa07KQrFvi0F1yKgK5efXK6OnCch9fym2q6sLf6ucJD3//PP3liGtD4K9G3w5MMAKkYEyLrnkkle+8pXveMc79k4fMrH6UxaOrFhezO+akF0XY2nher1g3X6nHF1/3euh72Sy7/wgHcP+eeuIs2HYSWY5osi5XzGYpIQqufyrNMfO+X1v0OjsKJ7/GPUFL3+KvuQqFxVD5/CojgLjq0j16itsjwRhSpXhMegYOGHFqRcKzJOuzUqt4CnO+YChlMKEZVxULNX5UX/R1wpfKRYChYf0a9asoSeV8ao/O9pajI2nsWNRiH9EaAueQrjLmsg3vOENzG2Pg/WqY+gZaEXreJFFAbKz+XEQES2fKMfkwTtF/qGnt//98dUDViKp59mjEDY2YhQaxzGytqABSyRwfUknupmd1GjqYx+rS6eGjz+u7mOXyf1ekrC4w920e/5nYktmV1nVa/WmzcE0x37kRL0ZxI7IGE7ZePHiVIJtaYlqo6PaaY/jm2pcFuCHT3U+tKYiQPlEjHq8ZP55BTv3zDPPyCckCPnAIw+t2Dxi9tnx2vYFrGV8S2OXNISb97R7Wz3WTXnu9w60xq3mmDO3dhyAWbTBgx9jhkAt+YFUrVDwF8VP/Uk2c/p2slEVwgZ3l+mebLihLkbChlihNcFuQiXl86gVChOugFFcGZPcr3wmr2eqR/kqpbV5o/25z0DJWJddFn/lUVQhopkdkAk94SYd10wQ7h2zetKaNWJH6k28jmF15976a7P9UyZgdtYEOqo4+itXdqhaSn/BGMp+BJTF6dlgY1N4vyWBB/8CSYGXasBDEibZDd2a06kl1PFpKU1Bfvvtt19xxRW8IkfH3wwpiVfU9hSVloJRFMMQ8xRFTvGKfEGteoSezBCFKzwDmSky7vITrdglJAw6aJ+iGHeazx6kilUyjsn6gXjFn4PcOKgJzAwEvBO9t0tWcJdgv5QT0HDlVIJ+gwEGOUzhC6CoISB5ctFFFEK/kZ0EiFp2OUZF5cz6K4OuODE1/2e9/KkLnGyOqVxs4pAN8DZqT5+6qL3wlRFUa4RxpDrCwD9FExSq8QDzp2SNQ/BQIPsUIoApVhxIGAHB3ffc84EPfPCBB+6/4ysfePNl39mv/ch/bcYbdNYwo71JC5FwrKEe/gorZ4VtVaUgW5jWQF1NjpuKLChYqAExeWTzyHA/sOudmTToaaPogbNyOhB8DQHMf1jnik8sBJQubgT4cVvovIZf6DhngHCHj++MnD5shj//rh9c9f13HvXCH8/56cU3v+2qZ/Y7BrOurEgm7Zh7zxCbAhnkP0HJ8kd1jmwWhhys7UnqaHsgBrjJqb1GvCv7NhGvC8sEQP6cqOe4eHd3N2X6O9mfWpok9Ur/LGjAnVhIfH4M6VuT2mDG5u4JzuVyudGhHQEuQCpSwLplitcrrj5KYzXGP8fAtjQvJyURseLG2YgE5BgztA1NG9+16HmNi5ECqUJhNAeKNay0MUKegFyMhEMyzKpxAc2ex79UTts2RhWuz+2Q3SpuLyZ9YIA5Bsy1BSgUSTRR36Tp990PLwcGWPU+il/Mw/7yl7/shT3Spc1krYkiN4OoSsf+uSEq0iwiC2vXBR79J5gj9OYzy06dVB79m75+yNk0JAffY6ZeHdJrInIH0lSzsmSWocZBRugRat73++67j/C4NaA7feHTiAGrKu+LM7Gk9WqpMAD3q8yxWKCgYxUGZp4KS6gkGds/eAq2s6z9LaOgNDCVFDWTNGq4yw56hcViK6jIBQjWt771rdizMVjXX389dExpsZDm9OcuGWAmCVup1+HZXP7uPz++YkNy9caesapofmgl+DgcDh5y+NKOGq0rgfsr0AKYnKeEPXDsoS9eWfvYkxms2b/2Na1abHoq3Ukq7ILdTiZA9o06W1LGlmGA0avD3Eig5XVhhtmQ82i/Nfyr6w1huykmtsfCBpU8zE+oRkzZmaWKame3o9+YV6SFMStCLJAXyio1OTb23HN9Zt08ThqZab02qtfEnWyOY/4mh3nY7B18NWKAhIQraLdUGYsa2YZLqp9xhEIFZTV7cJu0CLIYeZl7RFansVRIizCtBFGUVo4IWXTkstmWfhyfHc9uHs1rHAENRTR7kVyiO9VOyezFUIUuBQyICbpO6X5LZ7VXn0zBXDb5vguqBwdHTjy+5iMXQ+8Q5803ApImlbKHR/TUKCSRg61yNDpSiHYsP7Y3Hx1LWyHs3iw7m9NiIeTrYqfD0V/upprfuBtjwDQwquKR449J/+8fqrZuyll2JpcNGGYwHMri4uz01xuuUMyDHBHkBz7wAVqKQcF73/teBTbomv6nE8DDLMZpaP7pK3UbllfRLgOMLw/JmAC0YuanCndZoz8BCEp5o1CRv/rVr5CB0orzzjtvtxgzZFLYNJF3Vlh3P4QvmzCCZhY164uHmQbJDuKaemvYC21nrJUUiemHEBZZDNvZXqi3wirA6ixG0NFLhAGGTQUbgy7Y1sHnEC1TLxPG2muph0hVAB07n1jyFIUYhQZ6CbwsXoAaccrwox/9EGR1331/uO2q97/2A1d2dbxyXdK2CsG+dHDNqN22eH+rozW0aYsQvhOPbBHz5oQWLd6Km0kxe9aquDRRt1Deyr6RF1toly3UsZ2OhzCG0sUQenwDkc0xmXX1wBPbTB5+ms84E8GJBjwwxkrwkXm9J1L3xXd8+yN3XnbSijvPueXj3z//+ue7DqaSdJ5d18G6WDhTmG85oCXlCz8MAPDQ6J2FIYfZxqRb0rAX49IB11MTdU40ptxf1TNogFFsQF4qOUK5hBNx1IRFp25zuCZrWZyMKlgGlwMPpDSkrxFT+NhXtJmuJHYiy8RfMsYDzoEteqaArxAuCDY4vku4NyU3+kaCRjRsLayjY/1bMgbS+vJW+F5r8AUxf6N+lO2ctCJZxsLhnBz8IQeyCHEKPcYhTSMetFCz14Zlhy99GD/w7Re+8AXuQ8KoVl3aV5rs5RHzMmGA4RYYMBgGjq17AyMrYXKBjZesKDAFmtg5JfIteyCrj4lBH7eTcTk4AbGsSN91dxy6bf7CugkFrCqTxeiiDjziOP1pezRvZFmbIi+z6+PchKbXRWXNVvjQNJgl9jwwVxGWBKFTCPbPFbelwjqnkwyswUNOoIXsUzzDdAqaJI87yA4GP3xH/bvXmsyOAqekgAIGiDOPa0Lmqgy9aC9s0tQgISBgvJBQQBBTDvbbjCl8L6fZoWmoBVXShOZWaiONqpRf8jLz/TGldQEYhq+QHYgG2E8s29o6nM1FO/KRwSS+A3NWtKHZNIIL22NoJturnIXK+NmrYyJALTzphx4MXXcN1yRkL/lI7BUHy47jnsGZSPUi/GU/xnvECwM2rrywNoqGRHoeCuo9YxonYVhdZkAzHZhPp63W6K5DYFwW7WsMGZaW3d3ddJQS09AYCAhGgQAD5JnWq0ay6Fz9DBf3cQ24YdXU5sOBhiq8QdqY2gZDZm+SOxssXE8iguaSwKao2VardVbbdbtp5eH1KX3thUsH2vtUGgBUGTz3Hk7mJBOMaQOrT3zRjs4MQUiPVO7JLfmmKr2jxhBPXiJB3om5FdLB0Wu0sZiejYcj3ZMbP3vAADDbKq8KWUF6Aon3tTQg9lzs2dd9K/qHe7OJmuinPxNQt+y4ZAttIYuN38/t2ziaa919t7N2HXRNYb/F5utf37z8oEVz6jasccJBI5OzsYQPG2L1kIgFW+JWQ9hZ3GxiD19aaWkMFTEfpAOhl7rnhz58QfK/bzCfX0lmRAipeJXztrdEjj3WqJamyeMaXJx11lmcNAED440/9Ov7AABAAElEQVTCMzxRMKPJxHDgoIMOKiuqUGXM+q+qenaLpUw4fHAdisrJZiNoR520ROxCww877DAmnvKhUAkwdDssHEyvmjnwdRjXVJJxd9OwIgAPZKuEBbubfU+kBxGxRiovWR13YkTQmXd1dTHHwPygryKxXeUFvvxS3n///Zdeeqm/XezgiAmYZqxxMPwvfvELbyYzvXnFfIPpd9dddxUhf38hlYcp89prr+VYE2wVVsdeXYCBwOJLX/rSbbfdxnYDRQfvscsjytA8GIjBokAGYPkIwXDyySd7ZXpQsa/h5opXXMNyZoGqvU9FAWizG2+8kcR33HHHzVece/blN7U0HzpoBbRAzQOrswcsaWl637k9V30DT4T4kxTXWLqWq4q1vO/cZH3zY+sKCEPVidy43JyoyAJOA2v4eVZQsRFisZyCYBYvIpQgW4uNLXTWqoVLEzZNCGTRE+dFjxoJoljiLwdwOT2kranvvu70L0JyHPfUXf957Tu//uUHN0caYXhHs2LnGwlwLQANEiaXvYPNg0plC5H8wpqzbwyn5Uwyyl+MLkNtuN1BZjupRtfrHCYAUkssBehw71Ch97U4QBtohwOL6yxrMdN5OydEFzwwdIKOkBh6kFt5l7RgwExK/nch21GKgSeg5S1c44QJtIgP4Fo5S4Vfz+AgfL6WCDotVbSPaqRF8PR0Jsep5iS4bZGKpA+x9E7nLBgKTNbT3GwZ5oilaAJQvg8qPhwFe8A6BIdYLgssBZU83FeP3uUTn/jEiSee6G1hJan2+Yh9ngGWqe6amYE44IEJqxg1Mt/85jdBEJWMEpPg3HPP9V8aNEUuElPLQNpOZg2Wd0h3EiGtLsYS5lKYvH3HHTKj3vAGB3+jvrnFemc5ckIYrzz9o8aIeAvgQBeH/m1MR6qCTgJhjkxgaULZSekHCYyJ2hMKA/bSS6za/ve//52Uhx9+uBevMoJjICCmoXnw17tbYQ8A/6B4kZUURUbwF1nUUzYLn8DsTADoJMJl08x6ZNGOiPJQMb0AACQ81Aje3KWNK7QXG5jgaBelQfDRZIxPYMagXb761a9iP1kEPOwx5BqRUJNw3TiMIcxp0iJ7OdXnmEXAYENwuz3DxMvc9/fnNowYWOBnnUiyZy02zK3NdV1NrXNrtP0bRU5Z2ocyCszdwf70OeclMrmRo4+pu+wTTGfxegHYRfDt3VfgWt9v92SMvhQGFDasjlgeZYzhNMyniQEsdy/UVhktCa0tYnfUTML+uufTABwTRNYIEgeajEMaelVpUSbZA5y0pa0b0Ln3L5gz847dEMJ2i4NDmF7rHP4JcDSigAUuJ/wD9TELJ3kcQGLLooun0UlQtEyzrq4uxUOqEpgMUL2E4fvhRhR3wULw68qU1IkETBhmGq8kQ0VQOtAMZv9A3+pBLWk2JAe1gbH8ge244WByFpFNwO9omeGu2MjyTpNj1QBQrrTxVvo/lYbpaiQO8MOgJu8ryDH9yKPG57/AfMxcfHGtuwoUeqRQIb5Ak4MDmd/dG7jxB9G862kUOdS69f1/frjuis8uO/zIVdWBVcNGPIxDUaELopFAXVBMvxbUg28F3gpHAeKYRzJUVZlHHB7t7MyseEJfvyFfk4gddJAzr4szta7KQcYUK5eLLrqIJYmu9ZZbbqHPaR2cG58YHcL8Ml5yfng2HgpUxXj9NlmpyDt2iYsmyztZPFiOCQluKUrgQQUL193dzVcmHloUFhccJjOQmcwcZj/idQrISaNkfwqxq24sqmtWXgEJZTXT7yXCANMnRfvL1M0s7UO6GhEMD4u99OvUpe2Jr3uf9ihtBasYW19vcqoEbL6YDYLqgZBP/r664YYb/vnPfxIDQ4gDDhKUlll5DIUjlUAihlTCXwslsBz+7d/+Des8DEYYOEjWj3zkIzCiuI4vAklVR+SaNWuOOeYYSI7TTz8dOuHee+997Wtfe+edd+Jnzl84K+i6665jDVIU19ugxpxM7KhyMVvUFTi/+uWvfvyZd73tkm8fdNzb1iftp7aFH2gNnHzUMS1XVQ/ffk/uObm4Prx0v8YzT08vO+CRZPj5AY4cGbC1wvriYlp8ywhqEoxdwC80kRLB3odPLFSUvBABXhaS2DHxiVXPzi0YVJ6CpYEfSRSBKlY8sMMFP/oLjft95a1fr0r2H7jx7//1pdf+98W3bazrpFS0UJTMbbcuUhcemGIpB0hUvfIip2E1jMLgfrkQgN/D28UlR9keFiB8D1YMEFH0OZsvOM0t1PfZFxzfodi9HDxiOM3a9m22mY835XBqleZaJkSxRtR0YiEu+BW21GuyW4aC1sGT1pKmPA6GuN+Xf9hvJ7M2VzqhQI4G7SNDeiIkPaXICBrFiesHVtmpnN4Qc7hvYv8m69BObV7CQBCxdcxesaWwKRmSE1nS4VpfCkbaoGOrQoWl4hBrp4feUO/MHLxhnXTSSchrmPxFgO6UZ19+KWr+PtkUdtZvfetbYBDuPYctZIKqB+YQlpitt8JWsZ1XyADLLHHsfvwEcLIfzUZYqw1zxl1csdv9A4HHH+c+6vCb3shq9K8uNbOwjBzKOn0pazQrtDj2G9hMVrkMsGiG5KHIXaNaZOok5ZB60dSESmAvJxJbmqJPvO5NtYPblh0/YN7pkRdPPfUUTEhZB87ecmWseXZUtudDRX0LZlRMLIpuaBfFNYFEJgPEg5wEKuzFgHEQ3BCPyBZbwaKKiEdorZhemB+PAUYOUsQAkxLuWpkDKL87HFbfnAwEWhdxR9BQst8xRqsb5rXM7dq/PT6nVu+qFT5N9izfMw4bMYX88GWfTKxfN9bYVP397+mhsMh4mdOurZMvx14LjncYx+k3jupbh5AlIzAWoyNuPWBhIlqGG4HdhAWqC9vNMX1+k7qsqaiJ4/0PsaioNKUUpXR6nhH0OElieNSthriQlnHjKHiflQ7VhZsigxmnocqsi7EPceORmcT6Szwz2mZQrK6quHK22pjfaLlmR2TeqZMr7DLGGmwGDP70sI5ADqjEQ1oxJQizzD2w1fxRufiFAWNWqEj165bmfnd34/U9Qyu3ae3zRLiOJZV7wKoUWoEBVAPKmt9YTT6KIqo0nR9UFQYJ4GTSr5iibuz9oL2UEYRK5hSymcsvrx5LJY87sfayS8ZpGbfppJe6bNvauDFz0w/qsxn2dWkXLDm3II7199zw3eau7rnVTVvGgqmsFQsFmAnVAa2jzuAMNk7IYKpl9pYCJ1SFop12fPN34AjK/fq62MmvDsDZmgbnzQeSIxGrt6W1RQ0LOw5qHDLjRIQ2MmToDaA7mUiKl2OTwicNghX0PDvqmG4IVEBn7JKzpQl0u5oSpPeN+3Qqdrt/PKO/c/zFQizSfMV9kZ4tCfNvtChkIxn8Br8M99QW3V6BngZ4OuBOnmeyhiggJ8+3x794Dd/dmsBgTC2kdWovQE7KEOxuIXsiPYC9iLSHahEuUX74wx/6W8cE4Drcu+++G4oR5af3iXgEx1zEgBaB+yO4vvs//uM/vK/TCFAgqABGArEj5AF8tVcIn+BRqeWDH/wg+mHiIUSh69C/QQD4jb+8LARQzXEoAJjPP/98Xh977DGMHz/0oQ+BbfxUB90OeYwHTVSXaLPBPMpJir8of5iJR1fAAyO5u/uue3721fefpeUWv+a8lQP6r1boQ/vFT3nF0XWLl4VzKe6Cy4er+uI1j2zX/7weEamwndACrrZVxKXwwArBgqv5B08r2wsHVN3zwEjf2ajVJ/6KT6ysxjFACCYVyRFWca6j4bhYji+xk47YRkazN1bNvfw/f37NN05etOHJs27+yA/Ovb4n0QIu57SRIbsVZIDYPEtV8gi2kf8n0Do7xUAKtROGWVbItA5px4RYNnI3SfkdgU90Kf2GB0Gw99QduKMzZUc2zNxoVzwUCzg4P2B7Gs7YW8a4kkLO9MZDNk6h8RQkzZdsqrdcgDUb9jidK3DIOWvBA+PtC5m+EzRFZ8ttRod2wEhLT/EfnfTntYWne2AbjKZo7qwD9UMThZrRgcAAJJFt1dUeu6x+RVq/61+50UIQkHK204tTaFP8vFRHCl21DIjsXZyQYQ9VokmkMAAE0cik+vznP//v//7vk8lNdrR33wy9HBhgBBVMUC6cgCCDQJQZ7+6yGIQonuSd73wnSCcSCXd2zgkFUWhgk+je0u1OOsaemc2VaB5dWMlQohCD5E1muVoUBtiuj3K1FiXpmWf+Fc5mUrF4Yin0DbCMT2spk+9YzeUxfuZAwrhreBYDFyBhGcLiF92VJPFlkdfyD0QkExRt1U5VaBrEFkJNEN8RRxxRlLMoZdHXPfpK1dPe/1xENinRxqCTAOHui9s6P0HPbIQEmRoeJhu2al7TFGlIIWiHOD3IvD3++OM/+9nPli0EEkcdrOJsJxSwChNZOoIQQBTY3NosSFdzNgxZ21PBASuwBQ+DwWggXN3Y0b1wTqKzTm+vsrvqhK3zT1hVIFMdfmHszjtjP/oxnhe1z302gGzF3fAqm6qlcM1CjIu0uX9bW4P6N8ml8FgWYSyAn0O9f0xLZoAUtxOy8Ynxc40xtwY3dXSC2mCKAaCXQB1gCb+ml84v6n9KWz9UwK3FgkajOWoMpOTg8faUnTaq2EfZ0th+8bfUm9S4pEFObWs6vq9aqvTWGi7u48p7wbdFZRaDspvvOKlWRwxgMKBdlM9q2l5aDJFIkTAbYbGUOxJG47grSl/fX7C1APZpVaZIqcdxUnFxzBQHoop+cztJUFZFaMttfpHdtb9sF0fKshi+/obY/z2QDoYjV31BD2Jk41bg1sHaoRXN1YnsY09EMIJw57Y7cTnrTRMKzso12tZtXcvn/HWzFQ2HsIJuiOl4d6sLFxapE+BTjAHdoOHfi8PDeoLzUi5Wl9YJQeNUVcVFgWOarCxoRORcjeEGtzkC2a233spdZSRT7hhJQCSLGlkkv0jooJ+ggBFSdHV18WnmDwJfCOtdMsCqImoHNU1GUlcODD2gzgiAe8FUKIHJSyf47ZNV271uZqtCVQXtSNuhyOkQICk7S8uCAavMfqf08GUTTDsSzpwOpBWMGoIkJS7fC/cjTBvgqTMyChwcpS0IO3iYHjSNrp4611746s2EvVDXZFUUwUAvsYrhbJkA3AYEfeJNSD7BBIJnUNj++Mc/xgQa7DoTj7gUCHMLnfm73/0OU2c/hHz6+c9/TgxmXwoAtiFoAFzoIUoDMH9iFYZLgWkHmWO0SKMoAQUv6wuXRTDtRSZjZ5xxBleRd3d3QyGw7kpLK4qhQGYRx/VRQX/729++9eqLznLsxa8+f9Wgec9z5t/WZg+bUzu3pg7jxu1D9qoX7L4MnC77DWAIDob5ZDFRCDtQFIZLlI7C4XFTCjwtmwSInAM5+IVWPrGoXfTA6Ikd/Ok4NRHJSBpych5YSGLLhuuDBwYtOzmDU00bos1fOP/Wq/77jEMfvWekpuW2Mz7dU9PK59G0nohpOJ3mAQDZfV1NrPsq2xNlYrSW0+USlgC3E3EtkFE4sJ0jMkV9UOaVTRYsVKpjKJPUjZooUm+KOU21Gn6Yueopb8vFSD0c6MVbBf86ODDl7jcCrP/RacXi5kAyb6ULXG6k4xQaJTr0RiigRwacqmAB31fYu9HlawcLT23HI6MZtApvPUA/WB/QHvhbzy2/zq1Zz9dQ95zGt7/xiGNOjBxYf+tjVpZusJwR4NBNfA/hbXvzmues5NZUKlsoCBoEENqoGGCmwX+6z/e+9z1MEvzwvWzC+zYDzMxGFYZ87pOf/OSb3vQmRgUZBtZlIAgoM4+WPfbYYzngccDSpTUYWwz1F7Ztw3tJoHOu0dyAR46NGzeRi6IYflhHRh00pDDRFMM8VtDGspxWJ4mNQQJXYLtWgk7uTw9EmEZHHKHX17mLfeeJrTnJtDaQ5v6kgBjy6w6mIrVRuf67TtxFKypvimp3fGJXU2rGHVFuCOE6fyGF/eRIUZp94pVxVHQJ0LJFMaYEwEGQkn74kdRCgcEuQgUWoxF/ur0SZhZVSNgxwRg+0jNRIVMwUKRd7MfYINBS9lo2v8mkbhCCbGbkhQxl6mJoTeN49TeR3lM+iqklGo09+fgTI3lj1UgoX7/f5mGN8+fsS0hKO1vizXFnTrWzfyP3BLkFlKrGOFPT12d9+jNRxxo686317xGR8154oKHpHDRX5RejbPyYHxcwfuaADcsvEsJvhMaJIM7Ysx+g9APXN1aZzVVOc8SeWzd+vqAs5JgE04FF21uJesoZzeG5XbecwDPb7UxtYeuovn0MYRZnqeXiX64+YpNLp/UR9hlT9M9AlYga9RG7JSo8sxxSKsIHZaHxRQIVkx/yizi1HJBwEQZNKRMPf+cw/5lFRTOBxMTggUbxJKwgGCEiUbrCVIAkVfqBgaGBoX7mRi/3s2nZrRtW7d+gjQ43TGFYUdYog5IrfGgO3U7rFACY8wk7jUPp5qb8o/8IfvkqAwH2FZ9OHH6kzGxfv5EeHox7rnCph/iacqB05K4rM2A5eB41QrrJKWcOe3PhYS7HdZEmAsqGqLOkyYTycBH1VDDiLuXZXm3zmDUvkT+sM4RlnUIs/NJd9DwAKLBBs15BXEeHBoZ4yE0uUWQgaBGKUEhPhbLg/UgMW8IQTM8WxqurKECloH10fUpTyteymBCc4O2JRSXs1iudr5he+oFHhUFK7DjwDMRQmvtFvF4rYGg1CUBo5CWGwG5xZTSnq6uLjLP+MOUACWipQiYVc6kEl856pXu6QDX6YDPMxJCQwlDt6Rr3xfIZbrzGoBRF9Qqj65cisaCww4INwK6QOQy3+YMf/ODqq69WzVRzu+wS8/qBiQRm9tKwPLENec973oNWpogBBi1g44bYQskxKYFcJ5xwAulBKaUMMCWjQGYtw/F6yJ8s8L3/8z//w/G3IgYYXTFn76ETXv3qV3vweHBOFiAlZmggLvj2n19zyalDA4e947Lnep2BbPSulSLqrQ1h5yyqW7S9qGpF5yucp9Tg8cBwoNwzB4ZGOSm7NVZRrrKTNBxPFVvoDLlc0sVlAPnK3UjYPFMYDxRyFpdZnGnVbLlmJYae2eYwCRcFrWhZ/rW3Xnfhbz5+8h9uMnK5H597TTIQZ+kOp5z6GNmpkv5nz1AtdvlgV0FNdRSLu2l8LKMJg58MBfJLm0NTEQcuMOCryvc75LMQdeRjjgGFkXoh1G/VaC35eP22JPS/g2ieA72RoHVwh8GFESWPEAoRw1neLOd3cvjTBuCUhqVT7yj22zjUdOIRq7tWmH1u+GupNscK4sDlgPCodu8f+75+vZPNB21TRue5Vb1fvLbhouSS089YVK/fv1I8c7Ox5vKxaDARGtQBrNnK18ZCdXW1bGc8IGo1kIiEOIv+wAMPoGI899xzXwoEdklHzTRiJ15ipoW9GPmx7gC/X3jhhapyEXtGIgrjq92X+GOPP2ZR90Jn08bcPXfn7rjb6Os1w+FcZ7v+1rear3plPBbfumULK4WtAv/JkBEcamIGQCZii6Isx/jlgfTxCIhhDgAX5PppbqnGY3s105mHU+gPP8TfwrKlE/hAprJ8ch9ehjNytD0csBNCi2GIb+EzAMtT7KgreQAMqCAcIQgAphSjKZNXkCkAV1LgSzYN/c+IKPDgDHkIs094qk66gs0D8gt1ln/3enFbVMI1lQeHgVP4FC0KowkDTDqk0ZAstAVLLcV4lI6vPwZs5TXcH09RzA10TQTgYPoHB5ra2rdtx8oltG1U35IUiWso0dhcn+D05pwanXtxq9xraYsKYbqKfLJgD11wYfXqVaOJ2porv4jvW4otSknMrD9oY2DSuCbH2+b9VbCNjWacjUl98zA3HBnVUaeQ0bWQg391V5LpcMNrOCROI7ide1Gj3LXgz14UVue3leBTffIa6EpoJY6dY3U/Ckax0ooazqil4fuqj4t/0+zNRk3M4v5gbh3ozcs19EaAPZuNxk4EtdYao7sWqW2Zja4IjLKvQMKexCdFrMM80CGKpyJSwclaIIZNmoAHub809H4AA0pkvTDZQJJ89ZgQyKmn//VUIFa/fkg4SiykG8L5RBjeWPiByR64HUQwzEAQY9lKJ8uo4slCt1O1ek2OjSJ/ZM+uduzsRz9WPTyQfM1r6i64qHSq0dKu7vlaNmN1tlsc7g1HGBo4LazfEZMzXlkQdXPTWAHDugAnU6qDVmfCnFNr16tDJjvPBHqMB3oFBi0cjoCcV/baA3kDPf/T24xV/flXLzLb41B5ZJMpRGKvXarVxNAQlDbMWLzRfOc73wFCf4f4w17emQRAjGrG0mMMK0iDlQJihAFGIMg09kQ5fmi9Gr3I6QFGLercL3MJO0xWqFcylB8xXvn/+7//C4oGHiYJtCAwAzmTUCWYRu00llzTyOhBWBRAMk4MICEhYtN82fCKdBFzAO6OPn/ZNKpo7GbyykTCwPivf/3rqaee+vWvf91Dp5TJJ/S0zGSYT3bY17/+9SBM9MAwrh7FhX748ssvnwIApNJ/+tOfSMBUZ3a9733vY9VArJZOXSXcVNJMVSBp2LvJyPCVVsFXlQVayEtPQNm6q5NxXi4SU47nyLe0di+lP+Alg+dvb++49JJL/vCTq4JB46C3XPJsX344w7kS0dmCa+GB5eAfHKfad1xuluwTPLBgzDh378Aii/8pLSVekUUzS0JQajQM/0Y5wpQqrMr1PyNZaGOYWIGc7QceWJAvpjQmpwUNUiKkyhnaH5acinzzkl9eeOxfb0lFqm9519V5Xe4ZxjCTQ4VSmTDb/C952RRhiSXobo9Uli5oCK85CRw0TDwzL+RyYKCStGUeLx45AjLNLvckcJl0E1Fsr2rLZkdgs2ZneUW1XpMKrB4TtTbyetw7c+Q5GjQ4DHxAM1C4UIoCjAcYFBiYIOFJS08hVbc1LjMezjnpPMcnjbXDGpdKxIxsTcBe1BQZzlkjyczBzWasr3fjjT8JZbCYpkvZxgPSXDb979/addSRr+xc+JdVFnZxo9s2Zu2qoaoqDgPPa1o4r23RK8TTh6raBcH9Ua3G0RrmABwynXrC78i2T4X2YQaY5YEy5Gc/+xnidjXbvGlaNAQm3tcGh7O/+nX49tvjSKMiYcba2bQ1d/23cJzW8drX7r9kCSsYwbx/C4TjKkJAYEMlRRsaHnl4xZY16dphKxSKGXoWzXMoFolqo2PBfzxqGUb4tNPwkofnUHdZy7RWsGGTkMw5eQupkBiBgEW4CpypjAu7CFb54/O+CPziV1oNbOxqimsq+gx7TExZ5XBRypf4K3sAFDZAIj2FG1QKbUW4e5Cj/2caKBvgyUbfSzyNAIVDwCEPnlqdTgJ2zd1iA/zQEqYixMN4vyCMwYkyX/enKQs8pnoepVuUAN6GTZ3uIoByb1hvzEUDyTFnXZ+c1uF/vD/MawrPqcYvseW6hmKWKrQ7URKTVdaJk/zZz2K332EFdPM73wp0d+/pQ790xQQE43+9GK9DiGHDfW4g3ztqjuaMEN43sOmOOANj5mgKkz85CwSV3RzWufMGySinDLy8RYWrV6x51a42WTI2lG2jdn+GlcvatRurtM3iSCyHDBvmrzpks5q1MLZV5sgYzocdjtmiV2qOBvDZ2BRxcKc8Wcll4fFHMo6KuIGWZTYy6H5yzUs5tXmbwhWw0Epg5EmRVHZ6MxpL5BNz22urt6xbi1D8pMMXT6EsVXQAsjZmF3OVme+BsVsBBTO1Yxw4p6MTIB2rMHDJJdWPPJJpaam67ltc3VvI5YpWFj0pFFcoHH3FQYPtHZgnZPPiLEQOlSEZwM3I4Yfl21u2jHKbotkY0ObVmHVRq6tWJBeuaV4xjCxeCEc6GYPrVQOF/mxg06DFJdKhgPZcH7cKO6+alz+8kxNYkFPjUoxuFoL7ADxoitN9cFCwedAKULpqrFl9CLk88Q3x8IrgCjpfbVjFcFT8DjOphIOUBgCEldETBfBJoU2vMEyOlfSQZhJJYrKw04E6CExjWqos/rxeIexKCkUzzZgb2DwzVYhh3jKINJwEyCvLTmAP4LIBJhsbAaaeU6Pisnkni/TAVgn8LZosyyzGU50qrQiM2aoCGgBy5SUrB6f5zAqma1kyZrY6oWw5VA1Dy6k3xB8QkGo2AgzxSqCMvpeMHJ3jl9l75plnfve7373nnnuUmSGRMJ/qa9nyiVT7MgXy4EwLP1u//vWvaWmFY63E3+qgwWRVFMUrMYci//yfKqzRn8ULk/eCCz40ODjwxS996d7vXxk0A8vO+PCzvdawZqaxaYZCwKkVXiIEKQoNQ3oeFYAHlsa7CKY6JLJg9MAgIIwtwdU8sHtwfpGQlslxmbDIl6H2iM7ZxmjGqo5QgNAforCFB+YgrvyzamMuUZ3TMRb6/ZLT4m8YvuRnHzju77/YMv8V9x17NmdcuXZoNG0nxj1VUYsQNpSENTXQUCCbgNDfcOM5jasisH/GxBpGtL1616bQSPeQPoB1MZ6SNguEZR5QHA8fSIzQxN2+nWbOa22yMRrNWOKNuZcbiQKiRUcHNr9WDJMobRzCiSK5wC+uZefhWKQmRicMj2aH04Wh4UJ/T27z6uGnA4MH1GdPO+XYZc3GwFC+NeTkn1+Jek8kEtiku7Jgtiz+mcPJ3LPPt79mcX1t3DYjTrKXOc8xOA6LRczg6kEIJKu7XnyIKUHvRP0CUnd3N7sbDDD2TcyxyZrsZdm3Avs2AwzrC07BTN0jMsr2PhRKYe1q7Z57gnClLk3PZGPBBjDK//kv9SOPEFtT100IbhK8EnAsjBgPBQVbOA8BdnQ1/CPJ0dWb+5Gi4HzODqRW9g4MPD92wnEnhNavDaTSmUBgGzTIv14IR0Iw1SBQSB8ClIz9AYcVkeKkIM1EvKXHg+ItoAp1FSvAW6weEDsHWB4gOKgZiIDJkCnnQsnEmatx3LNzCfvQm9q5XYwgBp+MkX/tEc+IIKGgb2d+qm2KbqHDIS6nprpAizAnFep+i+pieoBibr75Zi5d4BPyNqyg/S0tSu+9kkYhWS/GH6B/oMjHxkYPPeywXnxHjGrcO71+0MmwANiJuL2v1miLOS3VzmK5wVXQr8quQvzKRoHV0ob1ziWXGrY19pa31r3l36dDL/vBqiDM0lEn8ZTRO/3P6mbpKdJEzQfA2zSS708FYEpZyfGwaH3TWXNQzjZraGlZTvVRsyFuNYb1BQ1ihTRZzapAOlOQQNmHyjQbs6u1A/SJlMN1ZQCJU6t5CRCCdFgqr6/sL/SkzdG8YxkolLCE15GMxSP5Zrn4VzZvNl/p4vI7ZtmKJXKymVAUX/RaVFzpVxVD2xWzBHXSN1ooYNUWRVRfmFfHISXKKN9p5IIy49Q6uZiBXlFTQFsEj3r1MqIh4fhla3sbXZn564OhG24oQOt84xuh7oXbenu2b92q9I3kUoPlZmcmanZne/TCC0e+fq3WP5C3bJyFQObkFyxuOf/swUTLP5/B+DnQVOXgoXD/Jiz8KX6nFnmlTfSPs32UqxTNLUP2cIGbq+TmIyRIA5nCfWvM1f2ZY+eH5ySkDgDwdhwK4QAOpwQpBLNJEO9EaRoYTB3JVu0lCw+Ux8x5EtCRwkioqkCD2Kp1d3ezUnAdD5cI8a2GleqoGmZbBbA0IaCIGBYUjDE8EgEFnvr1+oRXryH+BP4wbUEo7I9hEfFQCBp1virRJK/opUHUcN27LNNfWlEYhMCjGl70aYavdAssh9qjpy7K65+ZNERVge0P056xKxqCqQGo/CuUN0NAemCeObSV11t5SiXR25sMsBo+7ISh5tlQuGJQ7aFIhbgkieWAOI9BweYZH3WHHHKIaguHGuCW4YphgFUJGHrwVNJSZPQYIaNGVsyzN3/I6w+XFsVXnt0aOJWltKiZxDCLwG/IB2/+yc23f/eKTHLk4Hd+8vnB4LAe4HoeaAQtYscwbvIBSlBB7sWx6YkzZ7ngiCA30uEtn7+yGYrvZ+GBHUx42MvpFH6y2C/nsI4U/pJ/7OccheVbDBZcs9EDE839QvC6dx387/VjPRfc9vEzf/HZdKz2wYNfz8HhDAVl7OoIVw0BguJ7pQ+oimpRIVMJYYrGHy040hzEha0eCdgNlCzR1FX+AZ/D02KciOh2CjSueqCoiHjAOKDVwbczFxLjEozDwNuT7CUIbnOFVKa7qdqVIztPPrkCux7FdBTyOQCeM69rUfPSdF7f1pftz6ZtGBdDD4SryDASyaNRr40Yh80DEdtONhV2vXFD8KDkKCB0wGpcVHFaPpU2MVDNWWlrTDxnsVtm0wKDHo4EAvEwp6+1BsHlQs8UQc4NW9gkwgPjlabo077+ug8zwFBgOA/A2SY77i6GIZex//VcOIO/FndsZVnxQN/rwb7ewvqN4rDVFcd6K5bPbIc8JTwGi0erbWza/8Ca/Fabq667YpmlVdFgIR2NhDMPPBC37LGmlvVjo+lVcmxPPRzpdHUdzqOPP//EZmdjvrp/tMrSsZHEJzq63whngMXyhmmLd64pnyLrl9K0SKeI9AjT0gT7XAyD4h8XD350AtB50FiKvPPiX5QAGH969UL9PPTQQ+zHjD+mStiZzEpzkGezqbe2tEUT9dwTt2VE25p0+jES5tH1xlihNW501uj7N+juvbhleljcVxSs0cs/l+jvTc7tqrn2Wg6Ylkk3vWZPngt2CHtO77uy6UJMoBhgN97mBP66QX3zCJtgoDpcyObETRKXcmeRLSPvNIXhrA7B3psLGsSFhlda2QB0mOLlJut5NqH1A4WMzZknhNbWvFonZNlm33Zty/ZCX0+wtk7v7Ghva3xs0Fzbx7I2qB/ZbW1Eb6sxO6rk4l8QzzjWKQtBZZEwG2zAkwFZSRlQMwj1PKaFViMl4aQtcwJrqG3rn01ZZig/3L18ucySySFG4oPsiUGB3/NcB8HeTI+UZ8TZ8gUBJkfSH/tYdaEwcuZb6t/yZogUUdruTAUyLUWc4ZJeRnU89KrDzabPjt7318LKdXgOiS5f1njSicNt8+7fZOX0SKPcmmjMb7DF/rzcLKAh6KDcxWtv7htZPTC2NRVIhprHcqgmxNMzx0IjYVzTFVYORTc+UTiiwz66m1PB2vat4vkJ0dvvf/97XMVQAuaC3ERSySjMPI03NPC6wM8oQNNDP7FhEaPskKkFPRVsJ3pXHl5BCEwAxaLDLaOkJXHZIcM1FMttajU1MMC67DXuRe3yMMAz773SEuiE7u7u0vjSGPqNDq/8NGBpCV4M5hgIkmCAvZhZD7A/AjDrdOqhnPV6X8oFwr2g0YXB+P73v4+SQ4HKMqGLUC0wKD/60Y9Yzsw0Dtl6DWGGYM8P0lMiLRxY4m7K+1oaYPlwkyrx+F6mTER4sNAqGcuTtcMrUxo1sppL7EF+RIczZ9KAq/2RXi1eFi+GAJy2yuKPnJUw8+ejH/0o+w4Cvt/88Kp0PnfUOZ9ZNaQPO2YKYhNWUrcjnEx1LZkVnuXmXjA0tSuki3aXRDi4chW67KJOJocrDbGCJo1cRRSUu3+JFwYV9K6LiTJMbrV7lISCSIzuFM4alSnq5Oow1Am3H+m4Rr7j0He1DGw58/++/e7rz0t96u7HFhyFghmBdTDL6RhKEsGzVEPRsg3zj8tK6Vd5BfaRjLbd1KODTjRgH9zB3UUCANCW7To6gUHhwioGyJs8ZVMSyepjGrC7IXlkDkCZz+3qWtpUGBrNvjDUO5QKb7e0LWZ2QyC5MjT8tuMWd7dzDk4nMbshoiuQOdn5bcTYs9kcyzojbbFQrGogLXLeSEALVulmg7ZuyF7YYLTXhPIZKz9vjsY9E1hLS9/i6AseWKDLI3WdN6enoG9e+1xeC2Bz6uTS2TFxGGw1zYmGGmKDQi8d0ekoYsntm/FmkQZjzPPOOw9f5RwK2BPyx/GaXow/+yQD7E5eDYKD/fu9733vZJPV608x/3fZQi9GAkiacJuCaETccshareRx5VOixXXMSDiqJwytq6XqkHktrFMhyR58CPosdPppR596alquexx/lEELk3toLDs4xjHF0GgahXAexJEO2LFAhLtDN2zciNciCBRFxzDvCWBCxi8LCZ0YAYT9UGyUpsiOssgRuT4N2bVQoJLWvjTScHcfeKdolGk7CAXUDNk962BSOOJ55caAgMeSsU2y/j1IoISEcHcPZ5JF6S0ZQRgML83UsEHzY9B+1llnUQWyZ+wnK8w4dbEAwzbPb2tn++pBfVhPbB3TNwyh+ZXtIG4W5tWa3Po7r8Zqigf9yG6nYrmE/Qc/jPziZzmUZ1/4rNEknvHLbws7ZZvpCzNfnSqEdIP7ZfSLaHQ0c2v68qhbOU4f1DHNlqO/I2mjP23jW0IsqQpOQ7XRlnDaY1ZLFShOre5JYUdpBlFy0kknMb5loefywK2jcmKfIjoTdjyf1595pu/7N2f+8Vi8kOVqI3O/pYnzzj7owEOG5kb/sRFTLrnTuz5ut4Sd7oYdNw+DDljC7GrTG2V6xn9KuSyoU0eyjhQvpJIBhjpsxkzWtm6df1DnUFpvrO5qadrhNcebHkwn4IdQIy9YCNqdqe7ZDdIuwnBc7NyVt44y4bVYaLABVVWxkc9cEX/8n+mOztAnPj6SFFYH8xt+VaWUDFoT7hdRt9NfGLlDG7nPya8za82aM+dYgX/LhU/Jh2pWB2KPbnLWDojvsY5asz0mtm2qvaW/wAwpDO5GZ//8xmRvNrgRsq6hPhqP5DJOIMS1HEY6U4hFApmchfXEQ5sDz/Vk3nxgiPPKqDmgg7mGhBIgdrn+t5KGg58ROmAtAiXtT09XQFSB23eLwqAQdgeVi6GEiGfhM8HUKDNhSptcSQxCDXp7JlwTVn+0jkapNiIpYLZUUnXZNBSyRxngspWWjWTCsCNMmwGmQ/zFqi7yIlVf+RPMMMzKwpESwvfpWSfNsPay2WksvCVUPl8JsIkrcWfR3lo27wwjVdU4vgJ94XKZy128DifAUsJOgY0ATS87ThF7w5aE6yk4QNxlUQ6XJyGzngIedB7gBFKCWlmh3CXjJQYJE8ZlAIsCBhgiloWGHyySqU9QBUBCGngtL5c/wIACMMdG/JHcvzhFFn/KaYSxFccwDWXDVVd95b6ffrOxpXX/133wX/1wpUH0SlzNp0ccWDLMjJUmiSrYK5nssn0IzykGUDy14mqELVokmJkC/qIlkgQYA8vZXiyhXUNoItlr0Xmyd3K9gqSAn7QdVMcUFg9ybFDKJjsu//uiDdefdnl1sufUf/7i3V97y/Bn/rSqdT9UyGNYBJnwg0Loy7NDE4ylr6AmqUV00np/yubcDPgpFLAPajPdyyL4WPyoLKBZCAa0L4wOI8WeyBCPE/rZLNNYuRVAU0UyxlTlon6KIy/ncfrqrCftkXwwwS31BSNYiMTsRP3GVLjDhp5xjj76aFAlM9AFehxOIF3S4ozmMVoVJTZXRtEV3MO6bsjBzVhNxOJGpQCs77yuyCuPtP7vQc5/0Wv0NQen0e6FDjvE6V60YlCLtC0Oa0amb0M0FqmpaxYOKBgeTDN2nMemnMLyliD9pDrc3/5LL70UJTDG/J/61KdUW/xf993wPskA090gTQ5v4Cm+EmbDCQXNrnl58I5t4dFT1pAsMJEIZaNVwc4O7YXVuzGEjpbMYcaMqgkLfq7Sdjg6AN/LKYf8yufFiGD5AfGqav6VlrlwybKResPeZo/2c7FZATuElgbxRMd6tl2Cg7XErsAuy9ICG8IAs34gsDhD4pWGIB/PbJjoIMvn64YNGy2LY3JhWOdwKKzOjXR0dKqF5+XyB/ahGQyokGJ+4FWYeLYojm4WcUelKacXA0eqmFsCjIUKFxGUUEIYnHv9DP1KXRCj3d3du6yUXDyInNmGGe6urq5f/vKXkK0zGRoKVPUCMHxFMBTMhRo3DxkbR5z1/Ux9cZvPVJ3bgFMfp7VKW9gwfuhFVSook3VBIaL8tbMrV+qXf8a07bHzzq99+ztB4O662WXLdp3Ag1MC7sYgm4MrohXSAKztjriiBhhf9epaLvHR6Rmzt4+aW5NoewN1ESeD/1/HGMzhLBJ3D1g06Yk4Xtk5f+ssbMCBJGWr4t2iS36AgSXDyJbKjKiR5Fzft2qAzTeIVVE8aLdh2fzC2m1fvDrwwto4e0UhF7IDxlNP9Xz+qqYrP3/Y0oNf6LWS+QAHe1q5+LdB47oIr04YOXQInmWdF195YCbTg1qKsvOq2C2m99ZtW5tra7o6PGsaNUrOQFrcaSbCspuyzSv5GkQb85wCobDBQggpkP6ggmC/V2IpNXZTt4sKICM2bNqQGhlrbGnc9ovbCjd8h9scGj/3OWPRgjXPr3LJJ4FZrSzIxMWLF2G1bWcetLZeHMw95fpWYQKBKx8N5H+bMk/5fc+XVqcWprJGTZXZmrDrQoUFjSKAIJG7NnaMhYJNkZiWVfjNX54I1M0L2HVV1agmtFzWCUUNDNAoPBLmnLm4KsQjQTZnDZvh+1ZaHZbdGM5++KIL8QO1bNlSzMMUyTJ1k/lKF4HP+S1NCT6ZTARTmljFKE6McVH8AwiECcZMLquYheAGM6xZs4YuFZGHpiFgYqRYX/CraryJLA0QWTRzVO1T/CKa9H8tMaTyf6woTIGAqvBwRRlmNZHXJ16pXkzlPaOyMFgMAWE1BxCAUgL4R9HNXvmzEsDcnU4Dv6mqdxdUBUPluSqE2dtbwRXe3op8v8LsM0mGw+dHHnkESw08WtEuf9NggOEhMYqGbznxxBPxqEzXeXXBGMO9cHJYXVb0/ve/H9WL93WyAOVzVa+n+1XJsPNCe4zEkFeqYGJTHWco8MCKRy4FEgeG+fq6171OZQG7QmmwrsFXJIAB7u7ufvjhhz2NNMv5jjvuYBZ5/q5Uxtn6pWocYp922mksZCyif/b1y84JRpac8p7nlE+sHFuso0VQS8qGzmTjVVDtONZ18a/ijHVsoXFkBd+J0ylYXHSxQgHwD49QxGIeDNqfIGQ0rl3QKdYtAPKDrZ6b3qkhCs0cwoqXw8M6vpEHg4mr33Ztw8Cmw9Y9+I6bP3rTf353c10nJY9mnQCXAwvhI8eOpRr3EQilWqkYYSrAciIX6bnwgWZ+vwa9kM9zbzpMM1MUkSJrlh5WD2F6g+Fjv0OOo9QelKbKBPGqhYyBNNiYEeFhbqsAyWC9l8+pyh2z31M95vphvYcbK5AGxLVtWW3toLWwgQMybnMVeNLwcR64MWouabJGOUIsx3MsXDyOZuweTEgHNLQYODHlGshAa3P9f767NznqPL4CvyjSk1ijHbCk5YLzN1c3/n6FlQlUo+LGEVbWMWsiMCsUjvBX701Jl74Q4rqpghzsElpworPcHmOD4CAASmD8DdMuN+7l8FOGtXiJN0stDhz3QQ/hW298Hk8JNLKRwJIlmVccGnjsH/C9kMvMe/hXfDhrJ59gtLVpq9ZMWcBOH1nZrCsYYGKjIa3adYDKhHFSY8G1GygzuGQJr24dO2d0NGYtF1tzyl+uHoPEMgN1CYOFHw2ZtZ0doGCVgR1CLTZEQTQQ0grZGzHeOoSE8pDE6lWrkskhnMWrpcInJq8JN6Dr0DesXpYf5fCrHvA4n9h4KA3SBxTMsxOg5V7IUi76RYjzGk7dFRKd04ASRM9DRvoQkUTZOwDRtED6k4bdESZWyWUr6UwFDxpOtiuUSGyBmGO5nhKmAemOLMwQoGV8EZGk06nqlvnrRnHno8EAjxTEmBTpD4rflqjTltCXNHFjXhm6lmGG6hfh/Ln/UdvfPzKvq/pzl3PBzI5qZieEpNe2R0e1VFrmbSSiV2PQA5A7MC89qVjfiQoBy8G/IRf/bh7DKWKw2rQwi4oEjW1jBpcBMENNUQdriYDWVqN31TqIiqU4fiZ5mEs0lIUAb0C6oqQud25vHLZHc3hT1DDqmlOr6Tgr/v395ur1rHCWTUGk1gF2YrN3IHn73XXzFsyrrd40qrdVac0xq0nONow//nnrhYtqnEi7l/6q2hUwQh1IB4x3lhuJhbm+stdJW/qchDWvDu1xFV6IAI7jJ0hYIIlYAsqGUxVCZ0KoIZZi+/eKKm0MiXnAcpARhUwBXwpmT1/vpy+vswqpM85InH0WZMIBy2VlQWGgrlGrzC1Qt7P/tDeeZdrbhD1OR3Lbgpz6DjbnnHC2JvvbV4UH1idviUc7mkJ2Ywzv34YcZpY2TTTMBw0FAgYRAxln2wjWYs5ImButuEMLoaSTydo4589ZRjaX59IeLNgQxWRHBws9PaM1+kgsc/2VVyKIjESiELjQPVO011fnjqBKrwDYEevjPyspUKUBsUMZe5btlFY2Lzsm9BzdTqVwX/zySkoCPORiTBVdzu5Az4PTiMTEZorTbn7IvXBR7UWvXrLdClAIYCjwditjJYnBAHTLFMJ0uoJ5SFEAQOcoiQyE4O5aZKDZQ+SEpANOHskRGBtimqZNWz2uBg7AynYyxQIkcjdatzMu3UWvUCxcOq3bE8QuSIMHCCBO6Niye+su4JvWZ0TM+NqgH04++WQ23NIeYxe49957iccmi93Hn4DBIhdeKlFFcOcihAdPJVCUdjvF0r2KtFMlwFJymB9XQ7fccgu6etgMHG4deeSR3oVG4BmOEPNKgCxUzblifN/AIeOPmjFCL41OCD+a07ZNmKItqh9oCGCjuEZqyQ1JN1910dtymcWnX7CypzCYNcfyEBdOHTs5FxEoBau7fdKJ5BImU6FhIYy5+BPJMaeA4YS1rFg+C6MMgsa8GX6Uk8IInjnCyp5LgrGMY0Q0pCOyQ8mGi19oUTRH5J52ZJ8QEnDFel8g8cX3/vzabxx/wFP3veXXX/rpO780EK/jSryRLB6eZHVAbrhQsHwF+3FGiRFW+1Q6lcHsuLcvv8lIPWeOzA31NQYyp7z2FHz4kA7lvDcTFE3S1dUFQ0tXk501AvpV/C0BT/AHGcZT3Ku0gDu6TXw7B1Mcx4UdKOicQ+4ZRVDOZaj4xLVaq2Qj9md0a5eYOTXGcKbA8ZyMZeaS+BLjThl7e8CID2m1eJNuC5phI7Bs/+bPfyr18D9STz+Pki24dHHk8CN76tp++oyzYSyCHEAGSoOFNuBE8PLoQgQ7I/7AIkFuzeBCVrtGlOA7YFDNxwye0wE33XQTdwJ7HeKHc18Mzzpduzc6gcNLiOIYBtBoRSMBqdraGvqv88ZuKESffJwbeIGSa4Lzxx8bfdvbjYYGkTpV/GDfiPs4McZwxH8VDnjcrHpu7QYzNZaOBKNI6YjaMX/Gi0a+Bb3OXR1ZVAwuQsBmARfxYRxpyWE3mjKeB+TIw9aocrK6ikyYwCmsJPerc+ArlveNZVduz+ewdShkNBOzN4xDRHnFLov0V4VVUaBOxWaz8eD1gfUsiNw0WbrUiFZZqQ5ApgpHq3g+EeBRhZT9BXgXqjIfvXaV+TatKBg8hLUwnEoNNa0ypsrkAUyLVKO8GH821XXEEOCXYSqbzJ9FhSmTQeEaBsX93nrrreypFeYtLc2LAVnTM9A6Q0MDBSOcjrb3jWibU9r2EaUHFfTanNDn1urz6x0ur5aMTCJvosqsFKUn0pGhb1xT/chjmWgk+uMfmi0dYO0ipOxVulsB1Zkyd0dH7VWrs3+633h+JfIobf/FoRNP1hcsMJnzE3XRIQgFVN+qWlhBcL/9mUBfykGOiRSJnXMkrQ+lLM7T4w8XLw+NcbMlwcW/TmeNQuL+FpYBVhm672R3qqCUjsFZnbZxiA7in9MYQ1Po6OmxzFNP40YiYCBdlRsQ+ceeypacWfFMIpOaW1vH/fWtMX1xw7jYADYDYQelQvjyqyhpVpPy21kGpomoCUAm3oFjAkXsiAIyd2NXMWUT+BOXDbNtY73sbd5uGsGJGwYKY5ZI9TeN2HiTqgmP03zMNIDHZwzVwQyTUcFAGBEMQ+ZdjTMZPHQ7hBQVJeqqI5nYs9d8raO/pzBv7oE3fldE8Tp4SealGn0f5hm1tn4sYG9DgZC6vy75q4S1PSwes7pSDe8ZDC8d7gw+dGLtNx/OXtleG+xIWE24DKXTJpkCCmZEKs/3OflQYtiOcqlQNGzk8nbe4J4JM5UrhIPG0MBAz1DP3P2WZ3MoiEPNdXUdsdyfbvvpb/7nTsr+6le/Ug45j3fzZM0f/+z+gdNQrCa9ytampgcMbeXaYNC44ihoEVjR113+eiQMFctAE4Apol7kquD24kQv1Xd2LpAbbaykV3erEZh80yFF4+gvQU0VFeMP+9NMFvanh9MGeEhnUAE4h3GHSvajuMkKmSyecihzJ/S1c1I+0TTwD1YbCpJKeo+UqLaUCHjn8mb65tXudcv/Y+9M4CsrqoR/l7evSV466aTT3UmnF5p9V0GEAWXUccAFUHAdtxk3Rh3Ub3QE94XBGYZBxwEVV0QEcRx1xgWGTWXft6b3Tnens+clb3/v3vv9657k9s17LyHdNAz9/b4L/VK3bi2nqk6dOqfOqVNezLMt+pnyo7KjUqgxrHzTtD//+c+xGGcfxG8d7aU8//zzUdIiA7BYe5H7GvBa7c94wgknIFICFRpgEvBAP6+55poF5iY3JiKYISp79kSotXGI4y/2gIehTlu3bv3CF74AYF/58ld/+q+fOqtcO+z1H90wauMzv1hRcqkeU3a8LJhYPrEu+ZYmtYryH61jFcGZs55nPecVllh9IKAMnpFpSxoaZQg3bDK4we902W7FRlktCEowxjGUUl7p3GCEpysclcDsmhz63R5pv+yNV1z8g/e+9LZv72hf/tMzPljUDYRYXEmko8bExCT7TdQu+EZhtEKpW12dMyonijSCkVDQaI2EV7RxypFlQ7lfZXT4hbsjPfST2ephLHOKNN6rgm/2aRqpOCy1guv4ZVzfoXN5LxJB1dbxnTlU0MKTaNRokanOIZPKBcBfDkvi2nZzsmxxz2KpaowW1L3KYwUEV/YCjFSs1tdimtFIeeVKfUlX5IyibTmlUOSpYvB3T9p37xRHJHq+4qSW9rIxUcBzCsKwErfVoEyXkYGNLSE7PcRZaG7WUIPowssf9bAkYQbPvdDve9/7kPwl8mD/PSgFYHbcGTD2ohbZ+wrpwqHAoYfFPvn3tUcfqaHvNYPGoeuj69ciGOsmzthnxngRBaK44IS9UeLGSXahAursuIvUuCB/ig2WWlt70D0tOUe0cMvFl13FdS6qTvO7NaJnCJkaN3D5zCSbg8DCj8jEPJT54KKs2iZjAhQCrTvwCWQ7iZjW26KFcAfkUhkKWrduHewOFJ+Zz0OAcuCW+MQ0Zr+TGHnY2GYphQOT6rEC8u+109swH+Iim09s3EIL5IEiQBqoCJAoCnkbIInkIYG3tAvYzdu277GwzrCJIsnve+59ywEbSlsWzkOXSq8unMz/Fet9tnvJxVH2s88+e1+z+4vyhxXN1rRVq9dMRXq2Tkd2j+vbxrESYqOPe3GtFW3GigT3HtVWcPuWZJtFfDej+gGrS3feGfzyl6GMlb+9sOWkk9yt8Nl0kmu/fildwGNjpnbf/ZXL/jk2NFgtlVCsGffdm7v1tshHP6yf8BLMAdUa4ZrXgpA+JszhsMquaX1nFsw3WPkqVaXynaygstatmk0Y6SUVsTujOm4hsP2ZmWYLQoumC/T2c5AukKoK1udN43bFdnvP4F5fbboWyFhOjV3rCoZInD9SqymH/RGAqQt7KU5ZsJ/VnQquSHM9uJpo0B7mBbo1anHfdO+U/jMKwMxB2FDYZfhXNrPmR3hVcqlYmpicRCjdV1yiCcxT1IMK2tmnUi0PThR25JKKUDlad9pJudKvtEIEZlZE8mLGTyS/bL4wWWgU8wWem0++sZstd/Yv1fWt6qN36P/8g7e3/+b3y3Qz8dGPaakWdZBs9qEQaY67OeNYhTvM0t0gc+mu9yx9lQAAQABJREFUlspVXU7JrJYKwVBU29Qy/OVg91ftUNf04ZHrx6IXtoaWrWpVS75a3mdLq/tLrwH5xpFathp00qsqRS0aMIplKxLieIxTrNRioUCRrXLG1DRRCKO46EomVqQTreUd3/v2tyDInIziKGBdDQgkyJa9vb2L5BKQXWXrAZrGpqfse0I866BdzCuQLCw214FKmf4YQTD65KGHHmInFFxaTKXPTxrgfE5VhYLY87WFMeLhK0sPC6Vg/nyJG+OlcH55QBsvQErCjekXHwN9YM31k6+6vHwCG5llbBYzSfmdu89Vl/x5fQXV95VYPRv4sF5Ga7JACeAYcxDyBUvTmOytb30rx3ob4/c1BmUvOODPRSdgU41rLq4OBgamHkbRfq4DJ9J+43/SQB+QmRFFHnjgAV4xu8WHCAF/sQc8DIVnqwi7a440w+bBw/zPdV9rW7J07UvfrGTgCpcgcCMCwq3r10oZnIlqScGl0Jx/CkC1IYkKmJuK7CIb7rw6VeXySn1DNkalZJe5yUEtA8qMl81mh1Ov6i5DhDJbnbKpcXlw3qiGdMvUlD+eaDANP87/t/We9s1X//2H/+OS1/3isuFI229OPM8JBCtWEJtNxhSzSBO+gCUF97NqjxV7ZCX7IQbzP1ACZjymRVJaqF3jyCRIwNB0L+sWdr2uP8mIuSX94HG5dQkaX71yaFV3ylxTquUqOm3fbaMH1iJ5LT6JQFE7uiuoXHG5nTW3EB07Zy49ylcxJuXwF66k0QbbI3lcmXLfso7bC1xvoo7ZkNN35WK7c9rmMYejwuwtZBLa8LS6GhjRF96Fq2DpzHxZS6AeZ1hgZrhCuaiuZdoS0ltjGGOrW//8+ERXXHTRRTgexj6fuTAXsIP17Rk4+xdgs7Cy40427NEXb+wxNjw8mEwyzfRwUDvyaHvdetplsPfDqcjhEcLwl4umHVx5YrHbhMrHNGB28Uo3I73aTz6pDDvW9qMKbOw3Zguuz/GZV1W+dWewm9O/QROLCJcyKLLgx7c5ZSBzYj2FCCpaF76RerqiPTlUeWrE2JVXZ5KRpdmBkzKkOfyCtTweQfeaydrDzpa/Doiy9xVDR9g4ZGYEZpGcPWYdmYQYsQQTOg4thjkgL7SAcym01CuHAJY8WIkQyRUd5CUxD8Sdh1xiQgZxp0yJh5oAMBnlIaMfSMKABAmmRVgl1X16Ll4X4C286thK4PFe/QHQFewCVI9K0mnf+MY3OINEMqyeWMNoqT/LPoXpHx40bxTLAxJt3rIVa5anJ81SavXWCUyGoKVs1lgrM+bSqI12dO0SMEX1Knui8E+9vb2qq12MUrGFQvHjn0hVytlTXpr59KfVLQF8lEHYfzBVmyQ30Np79lSvvjq2ZyeLEIdz8MnIwpcc2F341vfN5b36ypVqoWzoE1bEjWPaSEHDBV0kACo5EPHRPJf+WfjKMKBktlJr96SMnpTDxQANBSgY6h6AoQfApTrxD/JAytG8M6JcHXHJsLU0ZeyeZvLWevD2vrbffuB+tSPt+jiBG2E/TW0Yr+2rhTnNYLRHrOUtSF8zrUClwISiRXBXoK6EqbqxjVRKPG0jAMKAPEjLTHkmCCLl/AKwuvSYKUliJhTTp66Z+/E6nSvedv/mjtVHsz0XC1R7W9mddzAGfvyxxwRILJ9RQRCm97AcQbuCkI9gAPfGXIbnZoLPJwDTcHLxgFYTGzdvuvTSowu52CteseSv3gbFUt1Guap3HZrDozoKtKGL87dzz5FuB4v/mbYrRKqWkUwZskzFsv+daP+r6bAxtCLw0LIly2f2H1SS5g+1DBeqGO/lihWQCi4AO+doJFiq1IKY35s2uIELaChguVYN2sVozW4LBZeFqu+44G1j4xMoDGEBG4tm+BCT6IdGARhQeeqyQGFoIPHIJ+AhtIJwU9yoy/hsXul86l08qkB1QTAwWYjzs6l6v/P6u+6A9A9rHDpYimV+QTyREoEN+ZBNhANSvrSUovCIg9jMFGaGin8jPrFkgCoQhN7eXkn5XPwKdoHJ1MVRHUZwAQGYrsC6m26RrmZGo/QDKswKQJgDDt5zZMA1H5wu07EQx0urF2gmBJlnvsIXGQ8yNK2CeBhalMxNy6FeGTU/WhJG8cAjWfyfmhby7CMxyAKTMQ/s6+v7h3/4B/AEnfMNV3zsDbrZ/5LzkLWUDIz2FlvoqI5aCdPnWo1j3moFpW8JMMvi8RjNgQpmxye4FrRa4ebeQNU21A62yxmSL4y/BdZAuFdBRLS+jo0MzCYsRFviLCcI3cdnhGnaeCt0Slqeikzj5ye8o39o0wW3/svbfv0Ve8nyu497NQ0vVbkrMdaKjkgtLpQxUwidptYWxTvwo9iOyaIdChiRKe52so7qNtUhKpcZaOy9rVu3YsEHf7t4ScRfCM3khM5U2crjUchSh5BHiwbOtLn8qSVs9SN/uvD4syjAHT0T1de11fBDhOF3jSPBto4gMJTDN5iTDtnHdwcQKJZnAoNFpdQt2srXEU/M1JIRe6pkwOXDSiF105cYzZU5PBRiP0IxMOjH0SpzGDgZ4RZJzhDBRM3hJTBM4CDA5ZdfDqIyiM8Dys1p/nPwshA5eA6qe7ZFgvrozdjy5Cg2ZT3jAEiCU172ssVUTGLKf6YyHXZflJtx5D1czAVs5DUKVxP8qaeYQ4F162f0uWrmzDxKfaGhsMJCU91phqmyQmSc15pOyFDlEJ7vASQeJhu/MJQUxVTF6mOo5DwwYG2aNLN4RVdzhTP/hipYPYrW8Efa0rRFXiTFsTDD1sgOt8rsOHBvqphmD8wuD4QMEgVXhHaLgNiRsqLziRiJl1+kYimGNO755L2F9vb2wt3yzqJLAwkAFQ/AQO5PP/10wmw6wjHI0iW/8GFUwZJMMjCBX4lnQvLwureChhAFEqd6cLZzGpLMiZD0c6KavSyQjCYj9ni6FPrtF7/4BYojYOAIJb5zAHiB7M1qaxLHKSBKrlrVifFJFHdb8pFJKz2e06e5LZ7GGs6SuNGRcJa16Ie04+aBElQ/ABuMkRQHPAq1bDt7ySWJ++7LhaPxSy+DHSCdSqr+HYBH9Ts4s/Hp4MYtHJ1VzrbUTABAdhvt4JNPWFipLe0cHFFaWbhSfjGppfpUOjFmJ8eKBruYrBzRELSb00EayxWG0CwD6OkyMbM9brdFnJVti2VT6Hns2cCohiHgJJKzeYzNUc72DjqTI5nkuvFSYPektWp1YukrT5+65TZ9Akd2zHv06yZTvhiLLP3Lv6gmU/mCs6aN3TG1mtJe1YGz09sL0Gp/uK5nt2zZAiavWrVK4tXIzD5emOyzYQXqE7trIeXoQzEMs/ELVTFbXv3f2bzOnukq2/md7mmsfhz1qVllsHElJ9UxA2FcuAkTftqddibnfpmzfX19xDdYU8+txe0U4Ade/pu46QZz0+ZUT3fbFVfoUc4/uV3m5pjbRSzFXJ84oD5XwtM7auGqaXGfIZRPbRlwxt0sbwlzzh2F7YrUEAYCbkEzDfIX5UYpIJgyTJtDo6XV3KO4NLRxyrl/t4bZMxcglapWpZDP7dk2VnPQ/DOYucGnWlOBcCxx1TXfuuvuu2DjrrrqKr8sMdt1irx4tE4ivdrZC4MauC6vE3wi3vvk76Omkf4Ezz6McO4d3vZXJwBj2sfIwuBCWoVAIb9BpTmM3ZR9f/bwPGMJAIawytSA3fTI6TPmWjgBG0xsVchAkJIwv0hldVp06RM+0VFQ2v3oAWCmWGGUWcRZv+hbpgnd60ehBaD1YJA00EYR12kC669LJNXVDyLueuUAMOsvJI4WsRxLvFeUf9y9LKSkgaShfJAE8Ei28Krq5d2nQNPa96mE5yIxnSlK4Jl9t9k6DiC0TYtqGjlbufrbmKAxxp++adgb+qZf6yLpCpn75IJPEL4OHGMacl8J+3ScQ2bP7rvXXHPtpR941fsKsUNfVS7HSjWzgNcpPZqJmpVScWxigoVj7wLmbjAFWB7R+lbL0O6oHrJZrnXl7AZuQNFlkBYqHsT3BMuPssNy2VqDs8GY7yIfhpWbKD4pWs/Gdwy/xo6WDiNmG06VNSJ01as+nSpOnHXXd1/3qy8NrjxiR3sPvLGy+DU1jHvc3XVhumb7Qxlsq5WH9aimGWN5G/0ojmnj4doh7a7+YHYF93cROiHyQ5HqUMWfZsGwWlXXd5jTFRtnYBiHcqB3tKB0vIkQWnS7nUuJ3bVyTiGwGo6zsjUwobTHBmd3h3KqfzhIvDuvJyeNligwB8m7NlOdrpgFDl1aTsVSrEg6YrKuYb7KiBDAQTRLagH1ge064nI5Ci5bQqmwfVJvCdknLDfChjpgBAMiyMYvNhScPOf45Mtf/vI5gB2cLweZAAzLwrEHTkogaD1jh7O6vPjFL8ZvwTOmlAQnn3zyYlK6yKq4HNy+4yVF2Vmqx8E7Dnx8LZNhCjcrR7mrrdom849D/kIRgqY7J9lJkSLmEb+hMvAf6H/cxVKxw8Dw1LC2LWuO5ZR/IrKrWFWo3rm0c+vGp7Zv39YMhuZxlI/21ROABdebJ52NFVGTXKLlEAEYnkkOmM2mmvOXCaOENPdhMebx1n7kbUitUFjiCVC+gAGjAHgUNEurZqYi3BsxKJxRN/GVxPIQLxcYwKOzklGOJx7DCogRHWWyuhMvX+WXVZ+AV8sc0N3y5/tUl3K+V8nOISI2zwAAIH/yk5/QY4A9X5ZFxlPCMvcuqGKhODw69fju0ogVC3V37RoDI3Tb0HEWtSJlrkhpq9L2ElC24ZlpGlz+TT8PfePfFD5d/KnI8ce7+3+Cqg159iuCgm32HvcM25U8N8ILyjLiljKB0jhoXtkzHHT9b7P6Ek8kmI8QXwmEd5Y1Lv7luhqc/pcqzD5jrKyzl8RCiBkFPppTIbs7bnDxr7pqYdEPg96o8GFhHphw8pZaFgO2HYga47mAuku5aD48XMv0r098/EO5K69yBgaxzqJDq23tHe95u3bcsaNOKBVwMvi1ASUb6AAoJ/OlKXQegoH/aIqYEUwrttuRmojhFVDJyDydNTegA5V99V2PDezI4ba7FrIqLTt2cPtfMBBcQF3ctHaJpNtHhka47+fx7VMooSdHRzI4IsCVV1ypYQFAThKi+0U7BLftiQpAhfqXBlIOv1CGeWtRezIKqdhtKT/6eNu/XY0q1/jUxYH+VTIV5p8PgJBW/YrTMdMqVvNqnB2jVqUAnd3weAtnS9RWSjScVJsq9E7DEAhU6gRZvmgN7q49+Fhp2zYjEu065ugly1esWNf+2y01dtPxrGZEEnrPqo5AcHxoMGwXjj98TXfSfuKWn1z+z/8M/BhxIP/7Jy+RdAskjodaEBcZPhIw02XgiIRY0TlcCuqNtcAjv729vR5J9MfvU9gr2Q9bYwl8Ba9I7Jn2+NNAT8AErruEnDLKXpmSRl4XLt9f2oEK03tMB1DrQAnAtF1WPZAZFp9NHED12uW1GiUt8TKh5jNqWLiNkosCWcvoW3YeIWuIEAvn8n9lmoNRDAdLBvHQRkogQDzFSthDM8nowU/TQEV5ZU1k15g2cvZYZqu/FsLSt+AwSyRCO6Nfl+D/7VeWZq47AjFka/5/q7He2O03AJTAIKq11bIYaNFDMOVlx4R4eZhNwrMx3PhVIVJWXn7JztYJOz4UdeeddxID2ghgmzdvZj1ivqAMBLtwnf3Lf73ozV/sWdp61LAWK3N1XMWY1K0YjmwwpiAp/5TtrfrDLqrsDHd1csICaq3lqxwhxmOzzt2/qIgUgYc1Q0qrKR8cSmBVvaAyIc6xusWUny1S4dfKKdfw/urEgtjuakmSciQQcT2Q+OZrPtuSHznt0V+99Zq/+deP3pgNRPmIwXBrTMnePG4BFKJKlkYRAkSWJhxTDRe00CR3AqsTVcviszy+Srz3YVKDJ8xodgTopb0fFhFStatHR2e7vh3NuYF3a04hT5Xt4YIRncQZlZ3scVyF997iZDmjP7g0au2SANrjQkkvV3WcOMIIjeWdAVNLhQyUt0tjRm9rKFuocjY7X9XxmaK882rakoQOG1Nj1dTMSsWKqiuYlSG0GUUFTPHq7A9yOE65toaNTLS6roNjwj4AdP3MM8/E9OnKK6+En5/pxr3fD75QE274BdsIupuNB/SBuIBmTi0Gzl//+tdM6cWkBCObrgd1eUE+3F+BqeydsJ8UY39cyZ7KXZ29dRvuc/XuTpfxcnl7j5VzcYhtGAudhfKxCw+oCnZdMKuzhSpm/odNJj729fW5SVReRU5UrVQNAZnJqyxJ2QlLKd/F0PH5y1NfvDnvJfPHzM5P72PzAJOfMyF8gzUhC/uChFk54JIbS5Du9RTC/hLhCXiI8cMgCZCN8e7g0WWoLefTqAI+gMQ4caFqIeWs62QR/oAAaj0SQ8RJxkMMzKUIwMRjjy3le79sf4jemx0TaD3MBA8w8wunIuo4xBK05RLpfjc9bgwYqFE+gZy8YrVOJ8CgUAX6bcJ0F3cniPT7m9/8RpYWgc0DwwuQ3v/J/+qFCfCQRVKCWU/tyu0umtVk6+5JDEO5FRfxrNKbCXYlnZ6E3dfG2oPr5QJ8JBmlVx9//DGGMRwJn9C1zPnAB0Pc9XrKKZkLPww1dIGZwS4PsP0OMAb8j77UaUnpAcz/8Z+sGDi6iy1hvuB+N9CW5gCWmHWB9gzEmjWr2eB8ZLe1p2Rg0iO31HHcZbioZ7lCQO3+qAvFEmlO3uodCauT0zCLe+g0MIERFFyazcSUUtYUmD0btUqxgkdEZ+d4ZTyYG58ySmVzWza4LZPoe8mp6f7+0kOP6jt3Gy2pJccdW+zpGw0mRnL20csw/uJp0m+slzyzFdX/BZMFWwSX4Fnha0Ez+od+AFqEZ0bNN4NUFVNVZ/soJinQlloyZNYqnM9wd47ri595V83jcX/46w7yXlAx7BgcHt40VhsrEGlPTwz2hkLjo6FO8WswU4byrgdgdN1shHIWArsMwEwBwUnvU11AcLVaq264+67ce94Xmc4aJ56457D1m++6G9ORU045pS69vAI2T9k8NgbkgXLijGr5RlwuOFatFHRXb2Zq/NQxYK7pcSNyOB+4uwxLLwcdZiyqBfaCqpAwVyw/eN/Uv3zTfmIL2AMpzYZDqfNeu+K8805d0fXfm2GVrFAIX/0pqEs6GYtXCt2t0dDkk5+95GJG553vfCfbWHXNlAnF7BaZBMojlgWC4XWNkry0yCuEwAK4UZd9gVdQBfkKlPZKni8xBJPEC1cKhOAhJdAWfoX6gZAL52pao5Bi8j4jYE2zEymNAvfmS7Cv8UACGpMLqOTXg42GE8PqBj7Tn168VFH3KpH7+usf/aZ5BQapi96j4eCepGTtEHEd0R2pRsJ1hXAuBn0dcjs4CaGT6gQ561J6r43taozxEh/wgLRXin0+6/UaQqXgGIINXcqg+zcr/bB56fc7IK1rLJNR5pN8ZdRYEZihPLJSEy+bJpjFsSnj/0T4yCOPZDuSEm6++WbJ6JWPuZxgCEuq7OZ4kNNeEYApgWKpAoSHmDMvPErO7ID/4ZcYaD5W0HSObMJSI17BAA+7tl//099+7Lu3bS/Hd0zjPhBnS5wR0tPpFhZoZaOoHkV6pWolbSnpV82yhBLDoOUEOWCIItc9BoMDqoCy0YWgk4oFzrUX07kYyVROoZWdJXQb1/3Iq8jAnG5MRdRdwWTH4HlXrPMHL//EuoGHj3jkf17708/9+E1f4toWLgaeKthpLHvhnqlOzXG3s4XXUmuMLIgGXqmwSY6oi5HsVMhJYsU4w2fvXSsBl26Bk4S7o/MV9DMsE8HFPmpM08E1pUq+bGIIvSen4+gEFmZL2EiP1g7rxLbb7Rd/yYDg6Kmgs7ZdnypreTxdWWwNOJzi4cTWtqyWCNvp5Rr64zUdBh5SMEovu+plRAV08y0xa7JghoJofZUXbigfxznxqsV9wu6DtTmOtZSTbS5txqQO40HV+NkHNMD7Epfesx/Hlvds9MH6d7HM4gukfV//+teRYTBDXww84BbTmKdpYjUDFGoJZu8d4KaJ/ZFFi+0WNXHxIR7mehTFbjrsx+o7d8HRh9YfqiamD2PcvAqL8VqONFbj0CPcNdYams1mFOfwXVds8wIAT8nmHAwHDy1SRI2bnJC4CfMm+fhVPgBUxYm04rC5/8gPc9Mw3Awkj0/CtIkZFbU0nIdsmltFojWVrS+KAjYJ74eFmFcBhXhhCTDfIMc88sqaBL2WDXU+eQdg6BbWe7729vZKSkgSO1WsBzyyhBCQTzSQrxLp/XqrHfDL+gGv4Oa2pV3kZfscsVYKkV+Eatkyh5vEgaQHP/Ag7qLqgQUhfM8998C+fOUrXyE7tX/605/2WBnZQRREpUUEWGZk3x3YGBoivYfyvSoAgJJnXyFl+kjRGHOS2AiXrGRVDFc0ZS3TGUP61dgydI+rqxVO9B5sBsPXJlOp7GQ2YNlTl1ySHB/JLe9pu+5afe41nv4mP8sw7jtChxxS6l4e2jOgLCYqVQyIuYSeGZVfuTyyeg3bv2opUdWAzurfnimkX30IFk75vrKQ9tgYzsKUBrR8LscF2Ol4pD1stYe1/gwbxCrrbLeoUhZ40HExBbAoodM1hCX2UCbG7OmcHU/2pNLxdOTRbcOT03n2RHc8sXm84LRl0umeVffurI60Rro6VredudK0cONVGQnGd05zvZqzvk2PKwGwHo0Fhvni5SvcNggmYdE/gCTgDw5RhM0Ft+eWoJiG7VljWf+hkILa9FhLZWDN6jUwKTOLddOWgzQz5Im/ivPwl0netdxVPmgFJ/KDOza//EWH9XLkqKE5xLD2+wVgqiIS3Abnm1brRapBhfwZeuLH17dvfGpqaafx0Y+09/UHshNy4NBLWRfI17Qn8n92qL4y7mzLvCG7ZyDk3Jd2yoYecOywFX3daOzYaa6G1sMv0XLttc0P1O65jyuLzLZM8LhjjHVr9Xa1K8fA0GR7YPvYpf8S2sjGYhCaDH8WhQX54fV6a7r3decuTUSqWogTK5xOj8EEVe22oN4Tr7ztLe9jtrJTht6DSVQHHq/soDFh4Q6Z5tAooRvUyURDZ0IC6Amk8sYbb8QaFg6SPmws5FnGsF7AmkDfPJq5rwVSgtA9MgK5rAuQRxoCHtJ7jDvLxL4WiyBNUXIAfl/zkp7aaRGUkO7dj+wLZ4HqNu0uqLosZ+6kUZizcDkLfyU7oggV0b2L16zOV6mLy6pCSeC9ejAQz0M8KxRqA0gKVSMPy+rpJWsaIBcqYlm8GktumuWARLLkMVOYGotnQg5IvV4hNBYhEwEYUgy99eIbA/QtkdI5MAwiowI/g8svn8RFMLOJoojxHhLAt9C3BOAZYDPkE6880AQxb+R8KdPNq5fqEFZFAIaXYI7L4MovYFCOwMMo8wgjIXyFLPd8ZUxBPPkEBSMgNJzsoAeaBuaXFOLVKwHPQTrV8VCX2P6QkYZwkgtfXAMDmz53/ks+f+ODNTu5m9Vc3bCG0yk7FYEj9iaOF1CLEP9BkNm4T4aV7KpsIzXc2bDoq/9CTm1lxmpX53a1XdO1jSNmXguTisPAbRFleKWIua5jGq0uY0AGNtWNR7jDcI8ZGveueNE1r/rUhddfdMqd1+7sP+bWE87hmBU3u+fKdgL/P27lwEVzyOzCosIupEodmi1pCNWxKeyfakd3B9QhIAXs3ofWMRwQRvYUkErqVsO96RYMUQgNWdsenCxaHOVFxT1RUJ5HEFC5i6g1XuuO1x+1VUAoSLhq0VndanPMDQdAFRyUONpUyRnO6zuyeiZiHbY0gDZ4TaY26TroxvEVR6VY79X1H5aypMOnBsjqXgrLxTT0rM15TAGWbQh2wHdMOuko2wr1iugLLrgADhZPTJdddtmCjTsIPjZZxV+wULPRgkYX9S/z9gAAKZMMVFL4v9jySIv6l/+ZeuoMcHBmPjuVklGtqP2jWKJJcUpNq67tUhtUylGRQmDqFD9rsxbQTWBgOuK4Qsii95msiNCIwGREhHaBV9PWFYD1tnblmX1qauZsp5erMQCvhvEG8UJ/JQw5a0w5XwyEVVZTiiIN5HW+lAcqHnZEtLiLKVCof2NKaFZfX19jvMR4OiiXGqofiJR8Yt3iDKS3khHwPiEJY5/GSHkLIT1Del7ZdgXsL3/5ywQA6aMf/Si0kiVWbOpY5ITFlKKojqI4HkyNsIwiVPPJe7DqZw0j2R//+EcAoEAWLaTmohYe0juHa1DsMC7y7YpS4aVD1XClFq1aSXSkIVyRqWWS5RPWnIwoHBH/lnYsxYi0+r3vh6+7Th2T+dznjI6l6rpsbcYgnKoBhhqlE/y/8skfs0B4phPJ07XMfOcFpX/6t2hRoZ+LwXYhmQq+/e1GZxfYLYVgPcXhTnY3N01og9jt2EYsjAM2JxTSh0omVtDokKeGdqbbWpOtHd1Jc0Wbzf0BqrjZ8ZJy5vul92g+nBZdgT9H++kNpetv0O6+h3Pn+XDMeMlJmXNff9radf+zZXh0eCLavabDsjsTQaWCNgOPDmm3bChGQ1bQKj12z20r1x69ZlXP6pZid2ofpk8dYLAgIleAD4wp6MHOlycTNmuUvidfmyyqO4phMVa06qVhRVrqim14pXfgA9Clzy53vhSUFAsbR3XpDxWsqbDVlVJHs3zfVVDwX/bj6j4t5hVQ2Si0tu9oveFnYYwUPvbx6f5VbWj+gwZbALRdjhnX1Qtp3TRa21NZMZ3/yKmxj5vxctcnhnIP5guPG2bETBxXDq/OYdNgaRk9cGHt1zdb3/luZGoyhLuPoF6+8UbtgjeFXnu2vtQ1Oi2WCnffbT79NCY5uAJl+4WNBKqDt8rf8KslZ5y+fsmKLWM1nNmjb0sE7P7VXb3x9p9+9/K77voTc5N7IDzD77r2CuQIjY888gij6dFD5o4IPHyCnoN1fPK40rpCntNXIEEZCPGhFoDhFWMQwgDjDShHYT0hEwykySQAG6FU0F4RCInZ14e6eOqGdZ8KYZ7C2Ysuep8yPmNiREQeLxlwEmbqYRQKrRapA8sjIgnLUV4v8eIDtH0+zKEQ1g66ne5l7ksv8UskdlUsIiTgFeGQXx4OY8sWDGuZH5GAXAQq0hNmqtJdskYw6JRMBy5SABb5jboW38BnnxKYkRhFlqsrTQalLrLxFYBJySOQE6AT6EbaLj3G8ge7IkSMeB5Sij4TFggjL+YFxSJhiokEulPZCPjDH/4AYKSXAgn09fUJN8LZB6Qgr0ayI6y+4hWvIEBzGEHAIEwWZh8rTm9vLwlIDyngEwPEA6bxywCRkofdNJFRiSQgYyefICYvfelLJZ5fHr7y8JUysVuWZI2/EKXGSIkhI898X/3xAO9/JQw1u/fee/GQNDI88PWPvP4dX/1Z1UpYDp4UFDzY62KxRTKKb8ZEwBorTXA6orCdc7wIwbizOiRdOvfIwPq4FauUMOYuBYPbrcD1jxV+szVCydmy1Yavp9kCuUjJZYZhiZ3WiFJs5pClbe1nJ7ylNTfyN7+4+JwffWq0fcXjfS9isDGRYl2IsI6LzbMqBej4N9MBfGBQ0JZwwxBGyACfHquhPyBJXQ/R5/QGqhc2RsWGsa5nFvNK10RN/dAOAwEY4b9aU7cijSHHBhz2BbgwCQPvpgNDk/vbzfGShWUcu8MTefcAc1EJ7amIkUnYXQlzeTowlq9NlbhySR+eVmozsCQZxXmKpRxfmSa0JYhR3owhtPLCLRIRh4qHimZq0slE7fVLXIPTWSjgHvECzU20OAPfJ3lhMb3xPKc5OARgIR/YWrA84P+5bqLylQf6zuqywPKsBF6eGVxXXr93Z23lUdbUMlG2oECnpmg2Z0QQMrG2rykzZgd92oyNPsYVg0OBMu7WA+Hl3TPTxF+YK+6yNYV5hlJDujwX044k+HqDAXPhUtNOcf9qYu592DNmxa1bONEbky5goExm+nIWQs1NyLylmV3LeykIMxX6ZOEWUSwPNZEY4ivqCHLtrXvBUNPCm0YuWMw+fGSuQnGowqsFaFlRZMViceJVNk3hCZiZXrK6OuaLXzgZuVi0eOqSyStEQXRfJPP3IYsoZP2HP/yhuPLi7MTb3vY2VlA6XDLS7fBe3npMQFYyvoLMLC3yiV+1PLjnefhEFbS6yp6Letj8C2yYMKaD1p5KelpLYtxcKlWDRjlk5a1qPjs9PpJ1tNWvAFeYJnfddZfKo2kDA9u3bhu4447bX5RuM7/xb2XMtl90HHrW9t/+DpGangYSGF8wkMRsP8ENE8MjKy5LNdp42gtLAVsvn7xfDGKBn4pY40nD430y47Hgy86opTKF//yF/dTTnLQ11h4SPOs15pFHacm42h9SkwAPwEvwgfHUcHWiZEwWmCY2l8WzxE0X8NdvhYJcfYRtcBVa35nUMzFreQrdK5nn9D+QUzW/jQ+YY9nKBFTthG7dUvrKZbFNT9XK1lSpFglXtf/+dXbL0y2fvviwnpU3P852UijTEuhIOqWyMuFABf30o3fbhWn8WFul6vDWR8LjTwTazWO7/iwUZwtsfx76k4eGS/eCwIy4jBTY5ads7urMzrezdZyBVI1ui9krkuFRu41Obt5aH0SshQ/trHYkjZWtprhK9n1U9KQlrB+zPNyutaj9NaEHvkJhH4EKztufa/FhqBPelkc/8IHWsaHJNWvNc143+tgTu1qHrBq69NKGDRtoPo2F/2PgpPkAsXvKHi2a3ICVLfyVYY2elLo8FJhKnDCZOEHxL4qSMguMDj1zqfNwxL7qqmgBlwt2uVqOBaLhUq76vR9Wu5aGXn6mFo9xVqG2cYdl4UmQpR9rHJhjxQNBRO3REXt8LNG6nOt/cxWrLRboTBuZsPX0fb+75OJLaCNGHN7Fm3VN9tAM4Jk1Ml7SBMLCHqELIgb+FcIrIjGF0EZmEEMPm+sVUlf4Aq9kFzwBn0kGWaAQprkwzUg7dWVCRelnchHgk8i6fj6G+c5XyOl//Md/cFMA+3q8SjKwi9FZAJjGT5AsyYuWjOoEPMpBrq4DrDFvYwy5aCbw7EfextK8mLrSIHRQLUQXZBVAlVGDUDMN6xZir4TFBKgFyOkHykcU8ZYAyYsExT09qFb89sxkISXjy2ABBjgDLhEJVJILMsvjrx0RjvIlBukXmRnuHDRg2gI/w+FPXBcGvGeMqUuwmFeKlZK9X1pE82kFMXQ1r/JI5yB7y1YgkDMKsx/VX7BaTHbBcGRUYmRllDRck0EbCYsZMAFVsdsosFo6lk0NuhqwvUGHlIkADHJCf/hEFurlIdzf3y+zg6/UBf7zAAa/zFlpPkNAae4X9UO7ZPrzlZ5nEskn5hS8FtvismdBFpSuUkLjL/OOpzGeGLpOBt1rQtNk+xS5yKJIJv1J31I+LZUYsJRdPzp544O3//zSvzn7k99heZooBYsVWFylp8FjzgyZrgcLlFPzGbLCbb0MGOUe1VZ691Hasskd5f+6q/Dgo06lGlzde8yfnbr8qL7ulsp3HuQ6BnO6XEsj66qFSdk/VdBpakGICkOSCnOASc85etEJ3vSid68cfPLV9137tn995z9+9Y/DwVZbt3Jlg7vzqNFtiyrCbYX664b54VGmxXilCps2Rx3xStUZd4Vml+WWFPz29fVB2/300/u0qACtd5uQielrWq2piqZUtZY+WdK4X2Z71sIj9JFL93Iwc4cJh6bOIcqVtMOazuWM+SqG0OqmjATKW4TniI3LFwyhx8v2dIUD1eZkSY0aUh9SPUd/4RzAVjRziCE1ZQhtxXGg4tIAIsfzzi7ccU1oGEJ34EXanUcy3O9973txxvTTn/6U+6jngrSoRr9wEh0cAjD9Bflmy4ELyv37nV4/QpjYyQYXPbrjfdobUOMHriHDgl72U8P2hjEDXW5XUj+q006qKzkUJi78VDnE6xjuSX0lAAcVh8ij67mcAb9lhrVwMwGJgmGwFDXW0+HaqSucrgR57QheoN3j/kJTVEnuQV4XA/cystD0vUjmFsXyB+fGWUP0F0CtMqkHh/LOiv5+QiwPssq68U1+vAL3Vu2m8uKb5Jk/ihVr/zLOX2T9F+Bk6YIJwO+83z6KZQlNKV/BAbgEHnKCBvtPkupr3vu+yDZKMulYVojPf/7zv/rVr4jkyl/OABPgkUIJsEDyeHV4w8EnUH0+bpti8UbAwXMeboh+Yk9t8InB7Tsmx/SIFo5wm0AsVO3LJJbFE73x4vq2JZA16qRMBHhObgjT0NLatmLF8gSbLh/7+8TERHTt6vD7PtCSUS6M4Su4eRpgWLMFNkw3Ya0EcgESZlR4ETbO2QInGV/5JGmwFGUUAI+Nc+F9vab19fUdecQRxkknaWvX6dNTZLMi0WIs+tTGp7lTlw1seocrbZ94/MlpO7gxnxgsRktOMBmy9JY2/JJOVrXRXQPcz4W5s1XM5kcr0wEEwYC9pI9jCWA+4gT9wwMk/MJ8CDtFq+EIJZJfYRNbW1vsqVz5dzdHNz2FXQWHrmi2aoujJTdtLf3Xb6NvfmtHJl4MmS0hXDWqu5eGprksodbatkxrLVZLtfLoxhVdnYcvi61qD4RdlsjrBK/J3vguMgB45IXbFoYbhOeRSLcE5axix7hVqqltFFO3+tuMOJL3jOw9g11eXX4waNvW8VrRCW2fcrKF8jE9YS448h6qkHA6mUwf6l6w4fsqn5hiYJEA5mVcZEBBwp2HV17e8rtb0HYlv3dNNaXMSHLTigmmEBhEhglGCgFYJaZ29nqqiPr2YN7kXNl0NXRb7hO7qiccF/9Gb+j+oK58pNWcTDlwSsG8oMM+vnTLFbGi8n6uNkPcx2DLH69rv/198EUvwitAsIzLNeQ/zpXVdFThJDUDpqG22VEdmEH2BAzcsaQjgbZIrTNqRoq7PvKh99eqtde+9rV//dd/TZFeL81UMPcP+Iaw5NGfxsQyHP54dAhoj2myvyT/qPkT+9NIGGtJaIgn9pARFp9fJqCftkhihCgeCCmn15i/dHVdgcLWM3GIh9GXJRU64EksCwNTVxoLN3RbIslISwnTOXVuLIVu03WNhVO1mswuPMwCReJr6OdnZI+66g7IK10HEiIX0VHUKwIwLD5kRAZFfv11CdiN8ZKGr94niCHbhRRF3/pLkHb5yycXD/WSjG4EDHLBDHi5vDKJ8cLsrSAd8Yomn7Gjx6gINOCVaUsMnyiNXlWLh8uU8EszhUhCIdm0JY33iQD7sHQ4AaYncMpXSUB1srnDJyw4iPQeOvAkiLx7dAgTWXJ5D5HQeUHOW2+9lXgwlpKJp+dZ01luCBOg1QS8hybIokMT6EbipZeAnwBVSwzNBGAi5QGv6AcpBIABjHgi5au3E0EPADCR2KEwmzhHQG8L/lM4Cy6/HiT+ACsajz+GsCRmlRQLAmLYCQLzaQJVSOL5Cqwr6oX2SucwoDASEDpgk1ZAvn75y1+efvrp9/zuxr5Djjj89Rc9OWpN2JwHVmdMDC4HNmeUUL7muEutovLqCRpaKqqnQsW/OjKwYtfTO//xX5x7H2JR5nPx93eM3/T75Z+68E1Hn7hhxLljVwQf0anq5Gkdt7woc3NPZAfusQYK6+6ZeNX26onJYLSinELjVcvYHWv/5l9+btnQpsN23n/udz9x9Xu+XrGD3B2cLTotjAPE310m+HHHVs1THgkj8xUq6IGNgUk7EdQTYUeZmLkGVwIwDQd5PPxxMzbHEEnf5HcmOXy83ZfBr6eNpy7UuSif+R/Be3sY+ZPNffquHvmoHeCXxPX+lmq2pM5dl5WPIQ0peqhgJrJ2e6SGIXTS1A9pU6eFuQwZn2GIPDQ2bGpV0y5zbRR3x6A4oycco1KDgeJiGvfQNvZQMFp5YyDgtIbtFuURGvBnwIV6Yyl59dVXo845SHFYxmIOCW4yPC+YKG4P52DVu9/97qYQLW4M1OCVLWNg2npol71p3BjN65mE3pnkKG/TUptEImFiLgefBDIggooAzCSqDA7FwKsl7WYS/Y+aTXWZ1Sxz7NXtzmldWnh6MlwqUBCqDjuRDodnVEak2Z2tZStKo9sZtzriwR0DOyDBdUwMM4GZifSLAAD+unIK/uuUZA87193TC9vPWoIbZFk76yBpfIWXrVuMG9MsHFMH4cKJ9+8raxvaXUgMq5e/BPZuZfsW1opP822a+rM8b2GYjA9+8INIv9TI1UeEQdSFcdX/1R+ug1k+wakjrO7K1YbKkdGiPmnF4+2dU0V1rVxPxujNOGszwWO7kzgzmFmjsH5JJt3zPHSk2pNhVyb7pa+Obdk8HY52XfXvq156iismK+wlBY8HA85sMfciRvgb2BRJQwJQlE1uj++RgCcA9Pb2esyWfELkUfAgSna2O+0ZjkANDw9tfuThDU9tYE2VlsIm7h4b31ZM7qkGpmpGWM+N686StpYsrrCKtdHBHaXCFMq7hFmZLpW2DU+lsm2HruwKhRLwLrB6XncBMBwMJuVUiGyMNC6fpF00NoSFZz7Pvb65ySmWzEKlyuyrca0OJuCBoHX/ffFzz1vRkfrDpu3WBDpDo6IFJqoR5mDbyn4KMcvTT2z5U9Rc2plWdkSPP/EkS0lvbx99QkXoKOgoknkP8WJbCDBoGIiHGZKvBGTCMpuYv7ChXi4CsE2y0MKhMji5mr59nKNPaoBWtOKlA3jraY7XCXQmQgjMCqM/VrSG8u4lTZqzJKU0oF4yf4Bi/a/+ME2Y3wOkkD5/mTPlqCiX16hu3WR+7XKdfZuLPxc+7gRwE9SCttNe+gRZjk7r6+sb2DmwfFmPS0h1tDxdKWOyWN5SNqKhYLkS2FA8c0f1z0x7cHl0T2ug0hLv7InE1CHsUtnauq1SLsHbgmy0Asda7AzCG5u4RSiVJo14KhQLHn34xHVmBDqO0IuY58onmBuE1q62Wtt2T1sYI8YD9rJ0YEXK+tT7P7Fr564jjzry3//932Fn/V3RNMw4ii1041e2tFAOo5Rr/NQ0hg5BqBCOv2kCLxLcwASRVxSk7AWj7PLYMi8NAW9Y1dx2h8MfKSnBFj4xZ3kFD5E0eGWNQB7wskjKxfwy+0TWRaRBmJGwJwB4JWzdupVI4ae9SAkgWYFy9BuvWNl4TahLdgBfmYZMUhrLmoIcJaMpO+9EIsbwKw+Vgma0URCDrUDpPfnKJxZW2ddAlhPBklnPTiJLFblEsIRegf9EMnbsStBLZIdKi+DEdKDrGAUJQAfIK9ZGKEgxLCIxFXm/7DwyzRm42267DeCpBdmSXxJAOsAlbLmZZYyvp0XkE+N7hutaBZQDhrpOpgkURTJQVyi/19sAIwIwbUcuYhB5yM4vWSQZncBy4H5Rn2Sbw2M5ZNDpcFpBegqhWwQ2bKPofMkov14u0kPViaRAecjLK7+8vsx38yWvAob8Urj/1R9mEGUcocBemZLde/WnX2SYfmN8aZ10Hab1bNlTIE2TwhdZzgsnGaMJQoJX/glL57/05JN/fO2PMdK8/uuficTb+v/8PRuGrT3Dw1aQe4VaUXJCaRtbQf94kVgmv6TP7KqMjf7gx9q9D+HvRi0BLr8d2LN7x2Vf771i+RsOW3n3QHVV7NHPHfmpY1r+ELTLyqOOpr0k/euzu66+e+Ls7+38TC26El4dbh3pe3N65TWvufiLV51zwl0/2br+xP962Tsxu6zYRq5sJRVrNLNwKijc1RCcIqyg4q9uTJTwCGXEp+zkiHVUFwcfmzwMKB0ijGiTz4uK4lYkB2NjzvHitgo/XuhsR4p6dMpOhfXWqIME3liM6k5HY+d9rFibrBiFGYfP2njRjk9rmyMBlLdLkwarZ1+hmi2aHMwezrPnyzxxYmG9VsR/KE6IDJgeDgCjTueiqWDE9TLkjlS25AyF9K3T2pJxa137TNMFaVECYwiN5kPWnUbYDoqYRUt+/9utueaaa/CfceKJJ/oBAUdZA6D1QjpZYFzuUDkihlDOEhdSqVnEyXjQ4qkR58kRbSBrIGeC4wwpPOCsEkTNAH/5jeEq53jZAVKWA3BVSKGz6R02ldwZ1KwE9YELBpJmlEtCH3987Pobxx54zK4Wk6v7E6/7C/2UlzgcQHJp93hZHyooY5F4sJYOlrEqYWVSOjEf4XCRXscFF1s1iME1/OYZDk6EsH9mV7e1Y0VrW8vE+AR8A2thYxMaY1jAXNgbv7yAYmAE4Tz6+vpYPj2w6BYgl1+JlI46IM2hEE7vsCssC7xX6SIDZH/Xu9517bXXsjBw7vdLX/qSrNaLzL5wMiW9uv8Njufu3pDdntV3jpUn80yF3VXHTKW4Gy+2LGX2tzmYs7pYPYuoM+XySn69+Mc/mV/8IsHAu98VP+Vlgv8esnkBMjGheDyo/H0O0wDp9xL7O58mzz2zzUzEjsi2cll9fNLMTWuGGUile92TdZm2jCSmhI6O9iNPOjM0Zmij1lJbT0VrrGdVG8/+2M7pqw47HiVNS8So7n748BXplx7S1hKPyh4/gwW77HGEBDyEgQvs7e31PrnHa234UNQrRq5QqtYweVKnQdFCqCsKObGv29NFVOwxHG3ZxakCxtqBbE3L1Uq0qzNo2FUtbOCAYsrM7RndnRMBjy5aurQLGAjAL4K00jPSLWzQiADMbg5fpRslAd2LRRzgwZ7CDUtzvA7H8IH7T3m944478qXyjlJi2opCiUJ6rWVtWm9XXihBV8okQIHyUCaKC6qGh/79zb+LxtObCsmizVY7vpIr60/q1zR1nhwnbQInuSQ7LC+meoTZUEDTIqXJJ9ourozQtCMheFlUGk3DpoAdO7Br08Yt1Sr+itDpkU+BlEwkupcuyX310sTI6MSadTgfe+yPf4ShZ1wg47INh93yVG6aGnuWL6+U8kY2r2ez4XJ5WSTR1tLS1xb71RNT05VAuWZzUC8WXjKip6emtyY6yttdT4FL4nHseku56YjLfKPlUasDe43o9UMMmD5ZDY5Z9rpjjg+ferJ12x8jwYjCRpAS5w6RUPtbzskl2554upwIJbpbtKRTuPU/sPP6Kd342c9+ll/EAwQhegBeFtNN1TC3aRJgItAWwgg5dXICeeEaScCwIiOJjstLxhAQQ0a6lH7wMBZhQFRhJEDAqCuTV8QPcjF2pKSHSUYJyFE8oBlwgk7ALODxlVckH3LxS8+TGNacjCKrkACxmXlBgayk4MCWLVsQTYkHGWga2jNgo0tF+UY8jxSOzEDP8MonshMpn/il1Yiv1EUT5IiKl4sAJqbUTpnIgVTq5aIE5B+WcmKAAfovZUoCgBdaAeYIhF5GApjCShtxlMCrPJKd6np7e4lBhUWnzX5Uf5mzHK2kN4BETvzC4tMhIvrSBIaGPv/9739PYkqThzCQ0FeAhDhK71HC7EeNMk877TReYZHZxCcxJdA/lMzoUB0a+E9+8pPSBDKyyUJiPuHvUwRgho8uJSO9RAcKhkj5dBpoQ2Iyyq+HOfQkGRkylNjARo0gEuUgV4MYvFIO8eSSjIK3FEtj2emQSPejKpymCVQoSHnl8T55+yzQBLpdAOMXBIN0UBEDQUXwMN4nf4CiOM9MDAHaSIvgefr6+lh2JcZLXPdKS73GemkaA+RqjHzGGFr3jGn2KQH9wNTjIRfTn2ZSBe3lFa6VLmKO7B+o+wTGgUosY8qUhILNWad0/Q3nvOHRxx79whe++IN//OhnDz+uv+OY8V1T5WqkmGjL6nYL18a7zZ6nsay+ziFtRnT3yPCtd6rNWdVDopBVd1pYO3YVH3hg3ZnLX5x5+AtHvqcn/KTyBFuL1CZNDgEH0qWQOXVK648ykT2XbvqOFl0Kr64WcVu/c/Xp33/Vp/76l5ecedOl29e89Mmla2zdLNXwNcWliVSjwOF/mdUA6IIH5iicZBt1tGCHTSMV5oYha2ULTP8cpGJGs4CCjdCrxeDkPKOgIGgJ26vb7IkK1/OqW5FwxDUWMHaE7JawdlS3stlueIDECRvOmiXGRAXxVcN2KVfTylUH4+2BrNOGIXRMj6oEwfGilcUQuqZkbISYAG7VQvo057TZXzANDEICmoEuGA9YeISmKygaQ2hOI8eDyNJcoaR8j80++tlnn80uFWa5eGWaZzRn076A/+7laF+wQIKC7IlybQxuhOjour6GyrP1IsCzPAgbAcH1mqO2cZR+lFNktXsHtKcnjGFux2ATROkL0KAiOmKNPAehvbyNAbWrhPUh2iF1hFyVqx6QRaYOr80ILrUDRorbTh++b+jir+jDY5guB0GwRx6tbni8NPb28LlvNJdkKIk5D7hUQC0wwVBM1l1/k2XuAQBnKnATr2z3lUiBGK8Ms+HgMQXp7V8zMX4Pd+S+4x3vIK8s8/SJvxwXbvUjkU0/eWn+dwMuwdTgnABS9AB+eDzI4S+9LWcv0p9yP8JULbU3zcsnVjLqgurV1cjA4SGAo7+scxdddFFT1G1aZmMktYDkNI0Gzq0FCxb9kYHSxp3jA3lz20g+X645JXbytExL++FdiZVpZzlqSRcjm+J3DU73fR9IcU7ypJN7PnuJiwt7E86tawZP/OB5CbyAfPW/+sN8xeEuCwr11m69w/71fxo7dmJ9Wu1fFT7vnNCRR8Fewz4GQGXDiLV375w29uDSWgsmk7jDxrpYn5jGk6HiIeIxdQ9CR8zWy7GjettWdC+VeUF14DmPH04vzFLNbiVpZExhAYvFQjKZsovlWt/K+MYtAXUJotqnT8TC+ITGNaK1slcLxaJG/OjD1lLjUN4OTOsdpobMVC1rrQljWTR92HmvP+2oLoRkimXc+RV+kXpRjFMaAYnnE6yDwMOWAfAQIw8JPK4LZpSddREnvK9embDFu7JWKB/LKKfyzqpEKZOaKZPCxfpRclERtcOJgqWqUlvjhlscZhgBQLKWhIuG8oSgHrqCh1zySsCDExZHVFLyid7jAQbYaAgvPK7Ez/7qsNvJlgRkbPuOLfmcd2ONWt27u5e1PPZo5HvfGwwEt5z3humt27bv2AFWM16MOxIUgsekewEJCH/Eqj7nwScmfnpd/o/3m6znS5dEzjhj5atf/dq1nd/4w9Bk3oAMtxiFpZ2h0088vLfVeOjBB2GdV556qnncsdpddzOKDAVXe8Nasv+JU0D70MOsQORPG0a3j5XecXJXz4UfnEYSuPlOKztJd5h9fUve9ebq0cffuau4eU/p2NWJ9og1ufG+D73/fTSNK3/pE1RqjJH4SkXAuP32270eIw3dgl5OtIVcJYoIOtsnoL0DbuO+jhg6DZmTXyx0kIJk8WKnEioNicPpHSUw2SmNXBTiIfODDz4owjCFSL2wzlhMgBgINmx8SCSSA+sgHfia17yGcuC2sXrgkxQI2NSCGhD0A8FwXUMVlODpLqiOVoCNW7ZsYQVhUATx8JdO29lMgdaBTkiPHhjSTNT4IiiSUc6kSDz1ApKsYgAmTRBgSEBRYCzIBpxk5Ct4SO1gFzEEEAz4BT34pRVMDcAgO4CJAEyLwE+KItL7JbHUDvZ6kZJA5qNESlFeRuqSXNQCVMSzbcGDEEuYxJJL9kOJkYdIGg7qEk83SuLZj7onWNIJov0GNmAG7aVw+vPv/u7vaDXiBF5OuF4LBCDSU/tDsuhbr0CvUuqlOh6Bue6XURODI7ZpQC3EfvqWB3kVNp1CwBkE3bpcvAJYX19fYzwx5KIV/k/ECNYR6fWPJKCfQS32Lxhckvlz1YXrvkqBEln3qe61rhzvdZHJvPRNA+AqXYS6+Bnhb5rdHwk8MpqIwexRMr5+oRF7DWaHh3v+jC/MMM1hjICZvScoGFjtdbh8uuSSizdt3oQq+B8vPPszP7p3WdwaLKNj5AyqEeCCigii/xx8kFxK3oIhdypJvA8ODQVLHFFV3IKS1ZRsiiBqhNis3D4Yt6a+cPQXe4JPGVZw8q6WXT+IOcHe4uQAAEAASURBVDu5x9YOH1rpec9kdNXU+tjNr+v8+g8GP4vtG5SAm5NKRuAnJ7+3e3TrWX/67gX/fP7lX7htJJhis3u65ASiyk2XWvqkGQo03mf6XvEVHBlQNsn29kkDNSyOkVvDCiYFsPswW5mGjCMTDYT3emPm8yL+SBYFgqazlo0VrBx2fBV1f+9YUeO23hQ62Kzdk54ROOYUqfrNWBKz+9O1bCmAEriCx1CDhd7Gm/SOKb1t1Dq0E4MmbU1GQ8HGfcuVqoYITPciRJSrNse0UQirixaVIl2Hi8TBiuu2jJI1boJ0PULrLIjHLFOuawRaMPaNb3zjdddd98UvftHjFuYAdjC8HAQCMN2IGo1Of9Ob3tTYpaKmYB1V3M/KlXUEWqV3cCFrYRvMPSXZKjfEwBSqO8Rg0dj6QYAE+1ECN5bcNAY1MnZzSqhAfp4VgMEaa3APRdRi8/mAVTRDHx+euua6yNhEgcVUScx4IjewWi58//rA0cc66VQQNygKwxUWciR90N2bZ1kVhJsDDxxqkDMVLmFwlSzsj1Vrdom9H0s7/oQXP3jvPeJ7kLwseyy6HiM1p5yD5AX4obZMOca3SW+4rVjAtGmfWgkdQGYQFoowCznrFiXAOgjD5JXGJ7QBrGd+tyV8JddXv/pVBGDCMDTofoF5PrC90hYIsBKzZMKaeGkUYI6+YaRaCGeMZW1Tu7DD25ovTHauXLcyEzy03V7b6qxrxz6gHrHJSCEKw2x7+mMfTz71eC6Taf3XK1BzeIU/RwGq5rHHR2s/u8n44Y8i7B0oJbSj3f2n4mOPRz/1icyLT9pZLOYLeWxRBx2LW52mKmYoYJvcZhzhbjqO7nNqQE2QiuV0JvSlacOshDvxVKyaCYs/w6E+I/wyFvQn5oR0hZFOhc84PX/rn0ytxOCZwKS6zeCQauuf/1klkSjjLKCmc5FgqWIyaVEpIqVjsJQOOEtbzePWH5LhpodmQ4z6hYJodSNIcNg8/niWT5AcxBM8F8GDacv6OpPMLWftYYdP7bTWWop0ZyK1o7r2nuFFbObxl4ncwn48o51IJsxYamSPlQ7oqw85qqfFWd+uTr3Kg35GgPRA9dAVAivHRPkkX1GOITjh4xReH1toSkDBSiMVYbR2hqvXW7sf0mvDx3RHrPDxZvIvHaOTFRrCGUQj/K53clXlxJmnLzn7deuXLeP4byqdgiO/6aabrr/+eiZasVRsz2TW9faa23eMfOaS9J4R8uKtytq2q/qdH5W3bO/68IVvPGHpjx8ppoPmSvyptmr9HRFoeGdHB71nppKRV/755N33RJ94jJPBQKS6nl3CNevir3vtaKxlqIxPtthvN1X/fO3qpe9/f/r882q7dhvRmNHdNd2SeXgqes/O6c50vCvldEcrb/zER/CUjmqLmw8RISgJCsBDP0AN6rqaSOFrCUC06wYX5aF0NcKSSEqIcL29vUSSHoID+ZI5TvkISAhUfAIT4C0YRNKwxvlrZIAQpKmFVQ8xD2FGagQwQRh+SYOUJcMnAwp2ITciXlIa8jOWovQ5NYqcRhpqFFAhazQZNtdbUvlK4UBFmWL2JmUSQ4DmEOABYZDq5ZP8yr4kcwFdKJIeaYiXT0i27KEQpoEiHzILmJjsCBDPdjZ2TKQHDBFcAQZLYPpNEvOJfoP7lNJ45SEsPQCc3HBW90nS8MuweuG6ALsVPHWR8krJTf0ysFtNggXWIAZX7HIZLDwtsWR4XKMUiBKYjkLW9a6ckRo9EboRHn/T6r7SdvCBXmUfgU9gHbOVV1nLiCHsz7JAUf5kjeG6jP5iJezF1KVsLIoYupfB9a90TZM9D5EQE46NMCIySfe7xoVb7e+ifeqo/YbnQGVkgjBYzF9vJkrJtJf5hyHD7bfdsWvXzs+85cVv+uTVqWDX2EgR58Z2Z5dhpOIhbphz1x59ZgUij8tWw5ngAsoJpBJoxBN4ssLJpIb8aWOaq1lY8DhGazJs3dMauJWcUw8mtn55SaAcRVJmFXQe1LZebK65zA4szZ/W/uNfDr/H5s48ZTiG7lgfDWe+e+bHjtj0h77Bp19905d+fN4XqhwVdIxciZuBZ+GY7R3e1fSQf+qjPlVxIkVtR9ZJhKzjumHUFe8iDwPHxIdiQ5ARgGfL2Oe/VINkgeXqug4Nu+upqrrdF7cXOLWKZZ3WkN4Wdw8huyTOV7rKB6e3KhMcL1jZosGh6/EiS7KOpLErqLVEnc6k3R7Tu1OBvjxCMopivcJ2t2qkE4/qNbYc2XpgZ9O2Agbcll4o20EsEhQphUwovsu9YdjoSNRWtnD+coaAcwD4G9/4xm9/+1u0wT54DqbgwSEA/+AHP2Alk51Lf+8yPrx6hIOwxHhp5NPANJtAxo6sPVVCFeN+VNir9j24GFqdpHXLIbeXcb4A52yV1xQl+iI/uy7oVKWaOaV8NgSW9agbxNyYhhIce8+Q9cQTapNFPaLoVaeJzVy+/NDD0cMOR6WLYy2hDOPZHH6AOIrkLZOSTX5pJr7RsYKG8wOL0ZnRC0oDXEUDbBx53ImgLUwqRBw65fWPF6jrJX/JL8wwvBpMVW9v73yQzxe/f81hX5MaJS8b8zyE4T49XlB6Em4Sfk44M69vSck1oRdffDEx55577re//W34y/0Dz1+mACMxlMb4bp+wdk8HcM+wdVSbKBs4u4c/b08HuuNqw29dB94L66cDhZBXqVI0Z/q6a8M3XF81g/plXw0deeT+QShQLfDrNWGmfNRym7c419+AgnV2snHvTgQD58K1P+F+4P6+fqblQNbeOapz9REX1CSj+IC0LbwfFbHTsdnlAdu5Iq8lanFD7fEvXptE5a3apX4X+QAVfCEUn0eBEQmbR51gvvcd1e/80MhyWYhVs81sNJp8+5vt408YrAaGc1oorE1mde4GUMclTHZJ7ba40dmiLYtrbVw573bsfLU/Y99KL4Fmwp7yigaPfSsKRK6Al5WSiWfgtk9iqh2AigQ1a1VGbZj5y6dd6C7IBdaRiymDdMotVwMDu0qhjs6eGBmx5+5rVaexELTg8ChfShAwyOUv0KtdYOAXaY0EZKT3EJZcoACsZk390B79XNDaqUiy5qBBs6u/sia/Y3RcrKfOM7TQxIV/m3z8icKynuzZZ9tTkzgCRvoFThqLbMB2MiV/7bKvrV639pBlPaUrr1gykauYUc6csJYrXgS8vf1O++QXrTnz1UcsQ/41elu0o7vV/j0gt7S2woWEojGzb1XrJ/9P6YafOXfe7oxnC4kW5/hjk288r9K39r5B3Q61dMXNobLzi021QzszyzsyqRXruB9xT87ZvEffjL19e2ZVRu+I2tf++z898vDDiA14fmbpEUNN6QQMv+HR/TFe56CJhSnkqI7Iil48ARRi0Ar6E4mUfQ2RM5FM6AG0u/xC6kWwZBBFlEICZDSFhhMj+ylemRTFKFAOdIkCkau9T14AMOq25+QTe2oEUEz5x9rLRYCGUyP4U1cpn6iXeH9iL0xpSNfeqwQU3roqaECVPQIvnuYjivOKuCtbA0iSEFXCCNJCWulSYBCJlJ7hlXgaLpC7k3h2h2huxSTg69y4mbf5Wi2fF/ha94k5C0jkooHoWqUtjEWj1CoZpR+a1uIlaPpVIvfvl5KlcDqfNYvpJtsQrHHEM5r+Qdm/Kvy5EO8ZIFpKJFIBiE0tjOZ8Y+HlVVC6JjxezCIDUpckpoRF5lo4majyGN/5UH3h7E2/0gPsSghlYKOnKeaA2E3zvtAigZNZSf+AS40bFrFo7I47bj/99DO2bd1y248ufeX7L8Xj/kTZRD8ZKuM6R+NCh8aH5b6mB0by1XU9XS2HrY499og6cOgyuJVwaLJojwZiieOOsrRfGHpRt0I7f5w2y5xYdcefZGizRlNDvyovf9dUyhhcHnlsIr8ShTPb5aAjNwBvbln9zdd9+XPfftNpv7lq+9qTbz/6L1g4OAxcqFgx5URjzqNe1bLDP4XJwDFRdFCZcutE24S1OjMnPRSYToDys4Q9mxEU7IW9wSP0pHvBLw4b82V7rKgPTNmto86RS6lXNVXAc2FUCzb/IiY+hvTxioNVM8r2EuaAVW284Axktbawle7Bu6O9Bj9bRRyA4WfLQMqldRQXDaKcpwQlRbsEirXcQA+M+2thBaoW6gdtV8jZNIbPLXUtE30CqOzcsQh+//vfP+ussxQkLlAEDqLnIBCA0WSi3eWczALdStezuELKG9OAvNwOCSJzdxHCLxIsj4wUhBlLZjFmJq4xb2MMdhgiQrvq371Z1DxR5SpxVP1ReyRzc8O9TedCtWqZvS9lFKnQmF/+kMWezGKzR05Ig7sV5uwaHA1r6jL6uaXsfQMLOStMi5HekeGx2AQpq5bBhDnp9FfSFUiMN954I0ZrstqxcU5m6C/P3lIOkhD80PHHHy8s4/MAMvyusH2YtVApDA2V+nci6FsoHQ/8BL9iaAfTw9rGNaGYtEFHsHj8zne+4zFq+wE2nIqY9EsViOVgSKatLRaPjRadrVl9W9bZNc2tcXokgCf7UDppdke1o3vMQ9rUbXiNNbq4r1DP2rZVu+jjHLybfv25ree/VUU9Nw9MP9uiKJ1ASCapUy5ajz4eLSjnT1Ih2O7eKaY5WzZaO3cFeldxiGX7hGoXp3eSUbtctiMhczCnXPyje8PsH1Uj/hi7U8aKNL4ZZXNhn+HntB79edppp3LbcLbsFBKtLWe9IXToodW7768ODxvtbZkTX1zu798dbN0wqmj9ZEHPVrRQkCP3iEtGS8RkYzUTsVe11Yug+92RYtIJ74jGr7e3d+48VVSFkrHm2j3FyCqi0Z3C95US/mQ1knqRaUFaliVhwZGEQcVyqfRoeWcxmEi0pCETq1otpTN1ffxAYfwSCxw8DOvCrCrID567aWYQCppVm/imMfR/AphWsTdYjjg5LjGsGaGybm9zBt+vWfnSlqMnvve9MXrrQx+01/RzbQXbc8gMiG2yr0TVWOcGw6He5cu03LT1wMNsUdQOP1xf2RsOhirDw+WHHzdHRst33x857fSeVGhibChc5prELukaWiqegZR2/tDDYh/ssi94k5ad1pNxrroqJDN37nYeGwuwyMMWRCJcHRG4Z4fzUIDbI8phM8jlY6UqfkFDHXGbc1NT2+7/gmvBwVzu6+tjGsKw0sNMZ9GkMfE9HJael1+2LZArXDaiHicR7RgduF4IMn0oA4R0itKV/qTbKUHK5BNaTQqhixCA5SyoVwtkQQgORdFpoDE2wPtKG5sC71VBgPFFFQlgTdviT7mYMK0ASPYQ/agFDPQnDyWw9SOegUhJ58hqhWAmu95QDyG/ZCcXCRZT6fOQBvomJJq6GBRZakFFGdymANAWZhy/dV+ZCGy10AkU2ChR1CVe/CsLGfCQnv7Hhp8BxWRAskMoCNDJoLTEHJBfgGd2gJkME7jN+D4jsj37euESmSZzaeb+lwrAdAs2OMz6A4L/AgpdIUYclAmhEGzhkx9zZDrsP+jPS04ZUPEC3RTVoWaYkNx66y2HHnr4hgfuOPLeXx53wacfG+ZQa4Crbs2iE4jDuLKksRwJJwzcrGVGa7DEdYZGoDV0/rlTf3tXdGhQueiPRcOtbbiQCL/1/FxPX9DeTVLHCpS3BGX3k8zcIMzRKQopPh3gskTNqLYFBxB6MYJuierI0bAN2IHetu4VPz3tw2++5Wtn//DvN687aVc0w/KRr2Jophz6zJJsFyQGyRV9hSoT5gJU5MCdQZTAWlvcznCBqvvQGzxYqbD1A5Fn7Zb4/f4FjJWtOjyeklQrCJ/GeFFPBO2BKb0z4SxNzIJZX4HekdBXpK1JlMBVbWhKw8A0W9ZGuFJ42uyYtFa1mdyN1N9qTxY1hORyVXnzZQSiQR1pmTNY0FaUwKz/DAzsFp2riJQaJAynHdys7pzSNo7ZR2Ec4+obGGUsHDF1ZEnylEP1QL2w3+up8AsNWtDuJz/5CcRovgvTBEdZF70zM14T5BOCJ5IlPCzKJ1RkGFSoW0aV5pVRVCy1YqEVczkfVnnlqYBsbCLnwsiB/uAPjxJ33TCzjEtC4bEULzPnwcOobi5ps7j9PDutUkhWtY+itmH07qV5LXjXlkowbE5VnSg+ym2cv0VY/ucU43vh/DrXn8QC6gBzDRMP2omlH5ey4hMrkezvX4XxGN50EIDJJF3hy32QBVm2m+o3notmQMtgtlj86DRmOPQd8buuDzmqx24fCRBXoHo8JMCOlJszucuRErAJwW0brADh/QaSBVLUgPATMBPURVHJRMwORrkJYGdW21PQd3PBr9q4q3a1xle0x47o0lelnOUtrlns3JqBlgmA11unUJx6/4dSXLG4bHnqystR6MxNuN/wNskI5LLPzTcAUP3FvUd71zwFEAQW3A0xP3ERh6PR8dpo2UTgRN+Lchd6nM2re9sD3BuGHbLltCW4+NfJhGvLWzDi3U/Y4VwBKRqLMv02jVqoGjtTyWXHHB9ZfwSu5Eq6OWREN09pQ2NchID7GTNXZVGpQSsCeJ2ybNxCLE3pfWl1FP/ZP7RCdcFsWwjz+Irlhf8YPn3zGAu9Uv9GzVq0NrF9W6G3t9fL6MviDyoCEzGNcMQua3Y6hEdlNeKqREUHIRzKKkYK4WAqSOuxyP5SCLtwqTtURBpxYVRLqFV5UBv+HKeHtGKs+KuUfkvCKAa1ZM05dTr8l+NaqOQMf6bw6ZZIsTB04ksyr3lNS7HIjIY5hkYxuaQ68SmVaWtNt7Y5drXctzr14Y8YHW3KYoE6ODaCZdfNN09t3Zmwa10pM78nhzLFJd1zwFQIAUp3dOBTW4Fm61snrQ079afG9ULJCYdMi30YtBBh5TKwyHmncKxEIs2JhgOpoI1pfU+i+OaPXEi3cPQXyyNBFXRZIC+iAgRBdZw7JamKpYeHGOlAxFGhDMQIWN7oyAqFqhN77zPOOEMUyOQlAYavc9ow98UrXKKRFYUsIGOQlzDaqn2VYcjCvp4H29wKZ96EtV04TdOMjZFs4dGNXp80JqAWqYjtA1oke46NyUAbesyz421M8DzHiGE8laLDZ8/iGQ0gpQfglVlZ6kClaZQgyFb36dm80l0ibLNFwoDCUWAtj+IOLPWOFj+b8hvz0jqGkr0e2GL09rJhJIPbmHhfY+hAjBdoiOxQe9khSkwH7/WABEBCZj0jIjsIYB2v0FsI17MvH+lR+gR/AfA23i7kgeqoZw/hM5bgwdw0JQ1ZvnwFOoDzz3/Tjd/68rK1R/UfcxaXj07j4QkzxaLTFlWcC6TdZUsUQ4xvjZf1BZZMDec2PR1f2Rf753/Mf/u72oMPGbgD6Fwafc87ky879WErVdkZ+vMOpWsy4o6G60AoLew9QHAXIrbQCXaoEZBx7xTj2kLWa3xEpUIYQhtWxcob4R+d/oF1O+89/unbz/n2h676wHeLOtoDTsw6rfiUdB/WYVFhES8Tll81LrqmbkUqaruyGgUeuwwXmAp2GTLQnlMMBwgJlTS+ul0bLbrdpTS6HMTVdmIIHXYyUQNWSHFOqt0zMLuAKxXu2rbARMHCaDlf1rF2ruqYQytjus1RrSNhp0L6ihZjOGcTWSjrWEqTH0qUjDhT+B/EABuui7sulP8xEljxiLgIQ/GnZQvaYFBrm9K7k05HXDpG42Jari6H78XfkPTewfV7INi357LF8BwIwIgTC+yJwnOQDC5qLjYIWJjPYfaseBEl/ypxU1CW2cKBLsznlDurfWuB4myQf+fkAh3UycFKlV+OiFHR3DLdmd6x1DzlZP2XvymTkiQopXUz5NSK3V1txx63kfstdyBxaRh5rkhr/Sv6T1rRDzo2a5QqG8iRgdEDs09TxchDx4DaJC8u1HMl/YxXvgrm8tZbb73yyiu3bNlCegjuXJAOjjf6FeYJWOEP+J2vNw5sY5rW4o/EiBF8gwmG72GZFA7+W9/6Fq5igQSHvdB9OHt/lv2AkHWRh4yoK9kDQlkHznEA9UFu/c2Ze/LatjFWEqUStSuljmjuuGXJNa3aOpy/ztNRSL9sBuX+6fLk735XQV/9L18zl6ijbs8SzsamqemgpokSmfilo3hURcGwsawHYsr0AP/VQ0gtZXY5Eo10deYsY7DAZWB2MZdPh6tFM43MOQkV5+6ximMGDTxopINWe1xfnWFpo0y2odRck4rcEp+5OZAL1Cx0KZudu6btLCd7NWP3lAO1HypFh6eju6edXSNTRnFi9aqVsbAxkmd5sCPMLyT4st6aMPGI2Bm2sMOtowMCwH78ekNAgM0X4Vm9SJfYWJwFGisqSsKI97Xq7PS6OgTVfFokroBgBHlFcwjjy+Ny5HooHDr2yHWBcGQcb5YBozg9xbUH2MyP5I3WfDlgYmttc9sa6ReAnCoonDQEfCwgA0xZVwe0cccOln6UjtzSCacYDEfMimH9PFHO6pG3jzjGePSEgnFv+ojrfmT2LJ967DE2jMFtUlKUHDeAY6b2t77lbbFE3CkVWz74Pn18PHf9jc5dd7EzYqxZG3n9WelXnpkyzWo4DNfU22piBO4BvLevZqNACTiYTWPW1qnAlknFE0TDZpGjXJDOkFYu2bhVwyyoUrZDYUiongiwc6QvjVufv+iD9957H6NwxRVXoFWG4QZC1KHIrijo2FWkLqAVsJn7UAA2/nEZRc0QBOrlPkzSIGzIPagCkYypdDK/fh0giWWCzMI+g8PQkEaNEMXykJJ9N7729fV5uRYfQABrlMH82Rv70/+VMM3kIUDK+RJLAskoabwsdaXJFgBp2AdhbspqhZAGJvtNuElAkwXJ60rgVQHkgrRw0xozNo2RouTTfA30N2rhlPIVhIeYYw/saexdkGeO5vKVZLRO4GdqPMuGeOBRC8XyirIRxJNiCfjbtZj2SisW+PUX6CVrGul93dcA+yNwgwjA0nVk9yCXGKo7IDVSBVMeJbAIwLJq8LuvADem94Pnhb1AY/oXZgwA0+HgFYjEUwekfD3nnDfccst7rr7q6iv//m1fuemRlS0rN2U5emMUdR0/hkmuqFBX2vOrHF4lQ9VD0o7+x0f2fOqL6XPOav+LM1Of/6zJbiW7tKGozeVeqdaHn6yVsiecueQaPVBuOaWY/VlKrX/qgbd3Krrd/rI8NpaWndg0vbYjVjhkidkacbgi6P4Ba9tU0K7qO+NdP3z5363b9sAx9//yVf915c9f9WGYcax8OWacENdPwpm4v8LIz0SwnKiDtVzP66RyemaiukYZQs/UD06ibXqWE1Za4rI0yuXy6lZ9qoj0q3FDb7akj4TwZMSNRLW1S+htgJpturTfPRkXC2qrWp1x7rWo6Ng0cUMUulw8Qu/Oak/HrGM7uQvBWoOlNGeMyxhCYwBFOdwlgSLNKltqZ5yxYEQIcEtwxOKuGXeAUK3XuEZH3zXpbIrYXMvETRIk7evrg93FFdb/F4Bnx+6A/sXhCtZQHKqcjzqwWN5www14WxUhpKFyhAMRNpXxs5qvok5gzF0VLsOsTBjVU49PElv36+LHTGr+SFaQRe/v483aPoAxINosiffoMilV6a1tibecN7pzT+3Bh4NMVlWMXcy0Jz/016Wu5fcN/F/23gPOruI8+D7l9rZ3e1PZVUX03owlQHRjGzCOPxtswL0QOy+OG8aQOHbiQGzjQGyDHYzBxAS/FNeYKnoHUSXUdiWttmnLvXt7O+e8/zmzOnt09+5qRXGU75cDujtnzpRnZp6Zedo8wwXzXuxB4XCE+1nFgjjj0jEKmaHtVpS7bvzCiJ9cnAJGl1BCxFXUS4p66tkf+Mm/XguFxD1dbtnBDEUJUPbZB7kazDzH6vYoX3/bmwClUnNFk3wpnDnG+WyTXV1dN998M97wGCyOQ6D7Zct8610tS5BYJHSAYJGloAMcyuocCOkdw4cvMlOEc1azvxI1yota/Sta0PWBENOQ2cY2sufuvlv/5+9RWuJTn2g+4z2ThLar42y8tFNPRcrS9qJBaH1BPFGAPRNeefUVJqE/4F+x/wr/wYdkuxZEtm4X6ywH6oXcCHBVPO0aLW3weDszVs7QldwoJ0AX1seHMlpaLMy6ggvIihkNaZ0xtTMs5KC75t8koJJ0rrIXdeagAz0xuVy2XK6gmsAl3lYYN0RhitIUtMYKSn9S6UmqIyklhMqxnM2XyqrHmyvpdDOSUV3XdJ8S81jYGnU36Y79lVOL3fN2s536JqGb6x/wTdpCOxkoHAqgYmq9o6wtguWOB4zWiGcgKflV8Zl/aHVESnv8INqoH6JEThk6GpsGCkS9CYRvbB1MZvOvDVXwIr59aOPi1iAK7UVLlqLXhbyT9vYkRs0ipU4SEmxTWYox4YNVczHAlFdUcmuEhGJH0PtEI2uh7eAEYkFsyPqjjcbpGbUz7X2Xmgp8IT6vk+F2HgYLVhAJnQSb+L7+vng0vmB+u7V5c/r/XBZ9+RUqEO1/6YXCn/6kX3V56Pzzc+Hg8CsbmkMVjtFNTLwIRwRD6FZHOKWN5K3+nI48BQU+B0byFRNxRoE7AEwr4Ofol8kFj3hALOXNuojeFrHq/OZLa+761a23crj25z/7mfROZLdDGIOgzpKGXlj10wOyRqkM57W7uxv4aQsdhfkfnV/TKozSaLIsk1+nKxDjkhE1AkINyd/ylWJ5nDQyIHM5bSTSXU5V4nfuFTxhjqMClQYyNStiHWDp5hM4A8B4FSJMi6RfLncWSqP5xCBZAAmlH2lEDyyk7mWfQsgOC0QaetjpBKcHqI4ELMjuwt90GADgVJGU7bEEkk0XVchcDpBVhTjxBNipJWdFQ9jvSAm+oaqtuftUlTPTK8VCHTGL6Sj6H4NnSgO76GoC7qktSyAx7QWNpYxmpmJnj3caRfkgs4O9TrwzUrOXU/XVye7EyxiGWx4HoFhmJQ8JWD/dNI+TZW8DGFTTdfDATGR2eSe7G5g31xynKAKAOn0s3An25TAqcUgg1knGejqcsnM4FNbb2/PA/Q9879NnXHn72mw50G8qxZKZ5siuxwxzu5HIKViu5qgWLKazTz7nySSzN9+auv+hyInv8i9aiJKn2D+cfGNLx2V/3c2lvhtPGLfmNWjbOj+czG3Ryq80qgZyYW4DLcfOTDSekGE/HDUPO/eQ5QfUWeFy2ocTDK/3r5ZE1uwwfvlSOVEIPrrs5F+e/Y3P3XXFygd/9trhp21s25+LTbGdxBCa63BtYMTKDFUA6cPuw6Ym3xl6vHHBTwa8kN8qB2Lrdy3PNFbOVokebx0xUAt0NVgjWSNRFD6MEOCOo3xOq/EgHQV7LCCqeoCS2ufV6TuzxnhOS+EROisIAq7zHcyqDUm9PVzpjHoaQhaW0uM5caJqWPivFEKImF9P5LjNlVYgT6+ITcpSOX7sDVEorSEVBtXWTk4jp9W2pNktDn8JGHBOzEUnyN/ZH996q6ta9E6/Vott3un69qp8xvKuu+5iGTrppJPcGSWGyRgZdv/uPga2kZ/QNgnNL+MFMk+qnuxXlMBYQds4LgQae3zAMP4TswJtGqkdJPQHhD01ai4odRE/+QGinI1n//3FGUiMOq3Fyxr/7mv5hx/PvvSqVSz5lnbHVq8qdy/eWAriX06AZ5sk7hgcqVc8ubZ6rPMlkk0HjPh4AK2U4sUXF46sPR6kXlQPTV8wrSX7HzF/Qdf2bVuRDvzN3/wN/cPeADnyP1EPzK7Mnj0LmTW9c96WGBBpJppmdxxTrrvuOm76pZOxmbztttvY0qoSvEV4oK5CgSBk+2Da2ppSt49bfSkljat7oUu1WoJKV2fDsvp63NxzNW7NukBxEZ9IVr72tVg+P7by3clzzuUaUH+N6wftlLIUe+23gzLSQXf5ecZfVk8EUnQIOspCoVgvPH+gdgCZNWteZ+DTn81cf31kYAcrLC3g+HL+0IMDF1yQCtX3JJXhLPOE63k9+UIpk1ewzPGiesWAiYvEQnpz0ESgu8iWvFZ1MgQcTCBmhFVgwVoAjztekNoqh98i2xI4lEKUKaZuxK9sTmrofjEcwjViuGLuTI5NjIb1ePcEXqc4aYC0u2y1RLXWmNUewgXXpAG2UDTaa4K9Xe5aEkTXzbW73ABXNcr5NDBhZJEDYPOVGvcbg6+Pcl+T4CqgP0gDiSYNGllwYFMhASF8ZV67QAcSIQNcsmTRpnFjQVTdsmH9tr7+gxYf3lwXgM4jPYlZJWAwCHfVcoCEXhTPzy4gWbOSSjmJ/L3So2sF5Hew6hb3SUkRn8fyljap/nbFnOetP+oj9tkhcVUV5VAFAeCB0CfMoMA9Ll+2XMPh2Nho6Y47I9u3C7JI2A5wYbsW5Ojsv99iHHO8tiK+rLM+l04M9PVRAgyS01jKsR+Brpz46hk1EzkdbyLZouH36pxnzRdR/OLlzIANDvv0ovAPoXHPVcxXacRITEt/4spv0a8f+asPnnPuuXQF6yqPLB+YeQAYfS80q9QLUREx2EkidCAMp4FKBDwkQU3Gia6jY6UCkIw2tFM/UPPwfjDAfHJ18lQCd+idIJ3dINUEwJ3AAUZGTk8PRSh7Cd0mXSSNYmqS+0xeiGnKwYaQdrFpUjh4SHanRsrnkXdQSyaNvoJvBAGcNATk48BGFie8twH0z2SvOY5VRU1n6d0JAImR5RfcIJ7GgicEKFk0SVXlvsxXOgHpiZSqSHraXc5ehakCvprSmCMyI1jHKMADE+NEOmWyybJ6AKSMcQK8AqGTbPYAw8fsgBRmykg5jkzPUCLaqJJOzl5U1VcobAoBEnoJUKXBCB0lVcGQNxCKcqRm2rWdAmkacFIUU9uJnB4AD08//XQ09ph1SBwjI4dEyMjQYBIy926ZXrgTM/3snvNp3w8wo0EzLDWkSmB6hxADJvzyl7fQXWP9vff84NKzLrsRVnPMEIeBPdxCFELHKOhy1lG/hzsWFLDQNpHU9L6B3K13ZG0+S1DwoaieLwYatfFS923bPv+F7iv1aH75t8fHHs/nX/Nzt33dMZn6QzOWB01SnR77yru42I8jM48/k0mkfJ2tLatXvX//gztOrPvHBwvjZf//PfZTK7a+dMqLt593+5XXf+nXE4KDFraT9SFUYgL3QXnxH7ZhgvkTD/OA/yGwgD/BgdikUu9TDuvEG+XkWk0apFcgJ4qTtzJ2djeKGedXLfxajaGtLSG9FQbY1Ns/YW0OVo5sxzauqhL7XSj28AgNl25xyRNmH+mKuC8Ty220xw3jVlOYe4OVxY36SM4Y46t9e7Atf+BaYCstrOGQvkkuRmjz2Ci5cUa0HpNV1uS8MpBSeoIKx9CQm/Occ845sBgYPX3iE5+oAmjff51t/v+3Q8/U4rjUWWed5cgR3SDhDZKllp2VrQWC47HHHuMrCordZVHwpBJx7XO/pJh8A9uFFxNQXKzt1Zjkrme3sJwURAnFssAJESEeUQtTSNg227EijocmMB/s2SPu1+pJeuuauuPvbwufcTaJKwHfRCTy+mhl+4TtitoUB5XLRiWTTA97mWb1TZMXfMrCqn5hgDWvBRmHw3ThmhzbHJR/3GudzptJj/7+c8+9/tpr8YPFIXUaCUnxNp1PqALjnX1lfCVhxDJqD9U7W91elc72iffaH/7wh1dffTVDzBlgeluStntVzuyJafXChQsoH6uVjWMW50+Gs8oIbqSwG9bNiGZ01msL65SuenOeuBCoxkNeEWsYicsvj/VsyzY2RH96w7bx8ZnwQaSGf8lnuaYTIS2XxMjbh2oUPUMU5CkDBwHR0dFuJJKefF4d45C718qzwzRoxx0dbPl24aknzS29wgn7AfsHjjk639TxxBsDa/vyw+OZJi62xeWzpm7d1p8sVPzhuhjXX1lqzGO0RfVF9SaH5Of+QCpJctnJkk5zO6hl+CK4d477mYXc7IevfzOZEfz2QG8PdwZwfWyplN+07nVPs1H2eBuaF3LuPxpUG/wms1K6X6ZA+kp0Vz6HHzs1nRbhSIQ7YTQ4Jbu+miNif5nzDwa3ZXV7kqVFzID5Db5Wj7iNM5US7oIkIQtj5i6OdCLp5GOP/9S7lTc9w1nuOyAFXsSMgFaZN39+W7sQHLB/Q1NKylXSkTAYkMV8gm7mYaVlWF2kOa1EEMCxZM3El7fw8ccihAkaQmS2S2pg465wltvSIv76JlJDU7iFEcAlOSJsgqkFowajonhHJ0pPP+tvagT3jFxW6N69+N+M4M2k9MJz4f1WxBZ0bt6ck0R2LU+BYhnuHS8nS56hlInxWNCH8bPh5/IqjJ+L+BDWUYpxBaLXp+PVjA0ezyuebP/HL/kw2wqdedFFF0lTZ3YTqHmeyb6c4Q9Lgdyk6BlaRJhGyXbNkEOMJQNDP8Fmi4ZXKmwWzBpJ38+el/Rkr9XwmWrbu3gAo/yZ8gAkCeTqAbSSFaHJ07PQCnFwg7VrfBzWi3GfieWA0ZU+zNDpIdOR9DQsDbgn9cYMgdTCuRdYvrK9wpzwCzz0JLVQqQSJwEzVzdS0Nxc/veHucvhKD4BXjC/xvOLsTQZw+i0RRnJucpkC/Zhis5fpLn+msCwBuQydI6sGDM7l0r0MBKr72asgF3kxc9irPmQUyMXAVdFsEGkMzVthgOkoOSkA2wkj+JNdBwNMp7EYzt4op69md3YgR4dfOSI0h5Jlh4CogDHHWpzqZgm8jUXNUss79AkGGHRFTgSCMSgz1cIM/eMf/8jdZmt++x/x9kWH/NXlhYqRLug5nD4XxOFbOyeEq2J4fNrCBbovwAJushNUKl4vklzWc8tsbrRiEbbpOK4xwx/f6RtoKf1UD+SaV2et1fCvwtQTYXrRakqFvx3Irkhcd33mzw+xzjP/jBe07P0P1V/y4aPOO///Ozx24zPmuDd628l/vWz7c/uvvff0//rXu9/zZQzBsA3CEBrXkvbaJxfAyWWQYQIksX8BiqrhlCTo1fozVlOisqgB6yLxhYcNlCWRycWseRtGVlVaQlB3ZcS4eMMawRmKfSPRjpTeHjHYsyREu3U7gCAQD6gL4+Z4gYPQSg6nQwjd8GKVVfsyekvCWNbAtcDW4rg4z8y1wHjAEmaE2OZ4FZxiVLh8yvaGBYnAbo2DaG6+EGaFojIrUzITBZU7ZXvGlQNbhf0sazUOX1FV/i8DLProbXyQvSHCxDXi9DJZyp9++mmpqYBQIxnMMMnc54XsXOJgwBSbyuG5SYZV/LG5X8HCCoGTPcCTyD69vl0xqIuhG2021745Ws4IKFOsB0GzXMYqc600l4tyY7c4KyI3HtQh3K2CCglO9Zkd5UTR2xjiLB5u5Yyoz+QuMjAPMRjIB99RLOGmVI3U1TMVBZeOQGeGx+/hjmx8ZWljBW7NrtC0oEcvlo1UXi2E9VPf+4F/+9cfcWMHXBlCGiCh0yTVAn7LfXeGgveVaACG4OaXHehtWE3e7mYBGNyvvOaXw370c02jx7dSLVXY2ZH/WTiQGMgqA1mVC5CwowTHEH90NXqC5aQvX1xU3ywXKdLLvpI5QWnbzlhJ3XJL8Kabdmpq5dLPaQF/3+Bw3halkx6GB/Zjsi4WQ3yS9/SU176ojoyajQ2+ww/XF3Wr9Y2TZ0FmpoxdLcXleaa+Lqb09xfve7C85kF1aJhKzUMO85/3fn2/FepB+6vdXUqpyASqeD1lj++Rl3esG8qOpw2rmITAjkWjXD2fKuc0YQRhlCtGU8TTHtUag+XOqHAbKx9gRp2CBJpX6HLYCfSfhCGGJBcnG8WvDMhc7e1t2LiWveH9hc1VTi/k9IoV9wSj80Klcild528MqeVCQfd4jZKHU9YB3c+VY5lsJmaZbe2x+XVGmPvT2A/sQs2RMeOF50t33mVs3AjFbS1a5DnnHP3oo3QUL3ZfvWnUZShELQp22pWiOPXPwRtjv85IUBc2mYbRx6SWrAIkCL6jYSGg2KqoZ7sQIcmW+ECJW8bN8cRECS5hYqQhCNue3jkywjIIg8GywM5NCbQMsPnlEArcCxOQGqUSBlYEJlke2kxyW9PERFugy5ff6V9hFNoV/1iAkvFwhfsClr9kanvsENqgKcHFihqlFsqp+dBVIOGWRHlR3JNPjCYSeQ9nxiNRPRxkn7ctBdDcqsbQTqVU4MYZFn+GG2pDql6BWerN7GYioTf705zrNqEAqB7VfdDnKXH5lolbO70gmGE8QIAzRjyiYfxc7zd6X3r62WeepvlManqVZtID0PFwWfQDBvPOOKKvq6L20LPJMzgwGOAhJksksLGjZltFpPxKKxBNkoWFjvJpDks0KA3jLXHYqXTGgt7UB7qO8ad1VewN1QEYPBLTR86g6cXDPEimncSYV8iGoMbfo+ZtelFODEXx8AqbQfMlDsMy0Y1SuOPACeQ8xEvmhNEhPShKB/ILipJSEgYMitsw3qnLHQB42sIUAMOdMYWWQNZDMuYXSmbKJwzTKCFxZ59jmKZBmVAXA80C1d3dTQPJ69TolCM703l9EwFKoN/AIkl7wGzL5ZEaGSCwWioMoNEpnMSkhHwiLNNDMAAtKWFWsb1iR3N6fhZgqsDmtSrGyevEy+F24mcPkJihlGpYBCK0RU43pzSy71WBs1dHsSA23Qg20gmMO7I2UIIpwzhKNHBXPXtp/z/+Sp8zKKgomHFyj6jZWJJxhA1b6C996Ut/+Pd/XnrYuxZ2rdo8hsMaTqjieMnEOzEDCIOX0H3tq45L3/k7zwRWSJL6gTaHQVPrzjw5E4k35I3vnKy3FkqR7MVmrskK/0bTt1gahIQ3q9S/MHrs2uxnzt/vcOveu7P/9aAXvo5VhV9uPC0UE7/4j/bly0854oTfRsoDmcDaziPvOeEzn7vnmysfumndwavXzT8UMW6+xDWBCvbFkMtC92wvSrJRk/y9WKU4n4hnZrM/pcT8SmME15JEiu/ML0RdLBoEZK639iu87S5u8o7mKsmCBg/Myd7xgjKYMnFq1RSWmgAhcq2qBXlAV4M+kjUTOHwumhNF/GBbiYI2lLK2BfFipUR9VmdcX5ApYymdFt6w2JjZOdWIT1jA0ePoH9DG8Qu3D9Mb9Qseif+4MRgtNG60tuENqw5X2GTTzjvvvCuuuIK9bxYcqIJwH3ndpzXAaNUR75188snTB5gYTpyzZLMk3XPPPZiRcA6ByOkciCT77O62h5iQGGjxj+AuxJl83eOowP2C5tAIcKocKoaaFIQwG8a8+RW+ZDJGOqsHg+VSCQUCS6SEHB/9pDIhpKMrOK27M6dxhw0gBL2eA5qEU3WKxRcXp38BqCTu59K8oXBWcAfTUNsFIsBzFep4QRxvIxo5DZMWPheZTb6idu9/1MpVqx5es+anP/1pd3c3kAAP5CwpmZy768ldhe4zQfYethwsMyFQEDSyGwEamzHbj+zV/0ZI6UmgYs5///vfBxhc4XEGuKaOmlZIsN8czEI8gxswS1m/0xjkNGNKOL7iYgCcWbEWLWjSWwJmfqg/lxj0HHiiJdSpuzB6CrcFshc4S/nNb3osc+yMM7zvP680kQLdJOEIeLvE/GIWQPGV77+38otbgokknCdbB5dyah/7qO+sM7VmaUo3VUXVEDg0AQU1Nzf5Eonibb8O3H2XV5gRidsezAfvz7/2qu+KKzyHHaqKuzeEP3T+35Eyk0qYS5/mLz+0JSoc9A8P9VWypeauxdioYn2MhAhzo/owF9nRRlGtuz8l6SZrd8LSgw4pIZf5labCUHU0Nhar84eilUzBs3VDac3Dxvr1FdzILVncuPqk0xdyJLbhxXXbc+nszp0jeuMyrVjI5rOlilHntzxmviEQnl83JXm1UhPlRx9Wrr3OGB1mrEKRqPnSS6U3Xi9//ovqWadpdXHXOAiw9/KhqWj+zXW9O1OZTFf3sq46/D/vVgatpiv45eEDpOE0+yu8WAjZBalYAUdy1nhO7evZODY8sCDG2WAvJDL8M3ndHpuc7oXNwKCaFQNOA/E2r6RH4iuBKBQLE6lcx7wPKvnn1Fjec9FY+QbFLCCKYWn05Yojga+oahxeQlVj5wuTLhd+7tYM4d48svyAg3HNFfJp+v4r6v72i+NrHtVeeNEveF9xFwZUEOeSvE1NiBqBgcUBBIbt4Zdi0TRKBhiqiSvhNo0KRyAc6sYMDGeBiPYLZaygPUhSUPxSBRIlSgwFtLjHag0r/kL/VZd/HdPoCz52AaYcKHlQ9UgIqQsUctwsU1cVp+duFMkYAhnjjncaC/qhCXHUmKxsjBdNgDXiAT9ZMagaNJYD6mR8ewMsX7A67JtOu9zlz141ZlYkAE7wAbZQtnd2NklyLNP5PXelMgyCsYnD3tB7aIMZCMnqOCnR9T355JNSM0+PQXyDCWSRD0QYLZKse5US0imhKkDnI4NgIBzwaJ2zjJBYhqty7fHV6UMawsPu4BQF/tTcLEBgdMKSsd9j+bMkcBh40tAtsjmMFxMZCpUpI887SAiBTeIAwhewAoqLfmA0HfgpRIaZBXTFTPpPGHu+8pBenjqhZLgjRBLESMkOA0rYGSDCNR8GFKkWQih3V1Da9MQyEvDAZNnD09M4MSQDYWgjMbSFgCQq6PbpjSKS9AgLmCZgIDhPn9CHsisooSY8Tl3/IwJOW95Kc9CNM32k2n+mVsu++uxnP4uo5d/+7d9uuPzif7rn9UwxiIwSbQ9Oa6BduVSoYHqfHSqfMX9py998bue1P/EkxjWPoIBKuupbfVLofe/LxKIHePOxLRtGb//Nzkee4IYJX1Nj7PRjm9536FD9wm8/3vHI6KJPHqGE88mdf17DQSrIc4E0bHuCVle9hUp6zaPxgw85tK1uaAvzWv/1yk8fuO2ZU16869xfXzl86a2joTh5MgXLG4Jiwe/HFD0F9kl23GljrggPzK2Eyha/cXAHFkqCfGLlgQGG9qjJAO9th8t+i3gUtLWjXIlUVtDWpooYRauDaWXbuLkcrcfu88J5DepWd70yKpxdaVibl02UZDh/5hSx0jNmHtSue1Wru0GnWDheEhSg9SwFl7p+3Sqy54qC6AwBcgm5sw/vI4yFsBRn6xzPC+Fyz5hVh02Pqpx99tlf+cpXHnjggQ9+8IMOAE5H7cuBfZQBlogCAwwt4tAKTj/KLmZtJYYVil8WSh5oC3fvC3pQ/GN2wVpi8cdduV4iYB04+Tv5jc9i3MU0gauFGhdYLDJNPe4yhQYYIzfmjgCRKTKZ2gr5UfJ6ygaXiOotTayVXV1dFCFJNIgzwDM1z/okXlgt7i4ClcmO1hYPd3Uem6kWPDmPWSzkvL4A13NkS4LNkeS+/an6Bzgbw9r2FLb4FsYkuspqwsFQDDk4MW+Oe7Tz/upDMMCPP/EEEm5IK2S9/FKKmzKgHfQhex40SnUFtd7l0Mgv7p6plfYtxSHaQNWAXotdB6aXMMWxd85+2qpmlcDMVker3Q2vmXKPkbL5yDvxevf73/+e11m4X0qDXKPb9x5mCp7ELf5sGSsPZj07EsrWhFkydW6ixedZa0xrDZodrIw7BlvruSp1Si8qWyFBBa/NXCb/yU9y71Fy3rzuf/1ROd5oGuUNb2yY19wsrSjtyuzkpRK30Fo/+XkolyqXsD6CdVVCqYnCz35eaW3xnniiGhBk3CwP3ALIpnm0ec2tldfWVe78v3rJMH2gv9ggWD6DQ0P5//xPtWuBp02gIq3LG0pvwhrLGv39W1vaWnKB5rDXkxEuCitgJKpXZi/uGVvj6sKIUjfJQ03NUfBWdi+iemg7p6th2KR5MHohEBVCkOoCQQ4ViI2RS+L1da8lr7km0tsLVKah5J58IfGnB9suv+zoo44ZT9ZvGUQvHWROeSMcPdZ83kBQSXJMFMeMGPTaCwULiWIODVd++atQnsuSaAtjxiah+PPl5E0/9x50oBKrs1eTKWhn6bqan1gBesY5FsHRiErEW+6IcexmsjRoWahVSDoyMkf4lWHmsptDk8vKGyPFkI+rnvTeMQR33gWLlu23qP2ANv9TTzzR1tYq1UF0Edu2nNQQrJKWpVgGFHqRX2qBNmW5oG9h0gSNyJko1rX4BZXkf+rl57xHJMwrS6VH/aXRkNJR9B2T0efnWGDLvmN8dR+p2cBdWMqQWFoy1ZIYM/DgBeF+xEF1RxxceG3dyA9+jDvooMcXxmVKPOw76ghvLLY8Xr/ffvv97ne/Y0ZzKJSSAdtGY4LWtoSJ20/OSqWLwlU+d0gEMefiyt9SxedDeGSKc00+rVRWoC0641yuaP70767u7d2KzhDPz1JjNh1aCersix7LvtNv00sgxl6EdjlOsZ3bs1mwx0GO06sMAdoDzsvVzPvWI6d6e1dZ7hhgkMATyVizApMKXAKjnFYTkDuyZCTAAZDQ+bqr1N3+8rWq1bt9tl9AXcho6gXTGAWph4SR6+7u5ru7fLqLGJIRkPHkAidZYmCBAJXFGZBYu2YHzN1wpxxZF1y9DZTC7oNZhGSnZczcf1mO6KIq7p2KZF01y+GTWHLf8iNFYAwlUj8kBSyD7EEvvvgieyh8PiNLRfQw9TB8QCh3AfoNDIRCoNvpXklZsZ7TpfQtaZgXyBfk9V3TYWQ4qJGMfBLLvT065JXjJSuVYSKnZ3fHsPiwkgMJ5bjjZRgyr6qXaA5jPT3l9Bi4cWkwwidqkQwwc1Za17vTE8NDi7g8GeDpRppG1ZLNno487rz/g8KMNS2ipW8aZvLS/6xas5dAGkbzym9d+eijj7766uu/v+4rp/z19blCZdTAEFrxciwnLIy91vbrrdHQYatWt3Z15h550ty6w4qGm446XDviiERjGyZvdX29O676B2vLVu5G4io9pT+RvunZ3GZP09dOPevQ9sceUJuDlr9czA0OB1AjiaOJk9slezNi4HL/QNAwmyMlCCacJGet0C2nfm3RjtcOfOW+0//0o9vP/5YJ/Wyo+EcMC3pqkgaDF7S3XbGX0hBBmnGESsUrsoVoPh7S2lKVjpjA1fqGevpTCn14nf4wrZiG2BHMkRYF02A35tV7BjOVRE6dKHFWS5wExh1MfUhrj+G4kSZOttFdHVEdMW1+2hCHgUvqaE6QJsmicDLaF1Q764ymoN4S1uZFjZ05FY/QxZxdjmVFAoQpSXA6kF5CCcwBqKIZs91u2VpEDW9YeALry1jzMhXs8qAfEK7Br8EAu2HY98P7KANMx4FDzz77LOKiPXYiTBHrOKszy5OT2MYbgb8htXJYjNuJlEV1vr4JcyJfmSj4xVl7MTFsu0DQGQQyDCuVtUp56DArFFQjUayEndKcAIbKcKQiOf57xAUgEPVCNIS71bI/qOczahZnVsK0iXWBkgigXYFYhIyAZ/ZlDD/SLCY6px1EdgU9BAiGfYLXxuJKuYTcyR8MYImBaAweOO6vsQcAD/OQemIBcSNoXVCbwKF5ib1HxcULBn7MkNaItvL0c+d1fqd/YOCOO+5AQkMuCZXTHBlgEWRHQcZZFT/TK/s6+m2Q3lGSzJTyrcTD2EAIQgzBQ9KN0LuUVnNHnEstzz//PD22evXquSSeKQ0DyieIgPPPP3/NmjUU+IUvfAElMINrD8dUPpmSdwIyLH+rkk1lqBGiLsbY6ksb21Nab5KAkuAmC5YkXavzmPPD2oJ6tdGaqGiF+vo2CpheuMDvijHxtcsjL7+UCwYjd9456vEN925B01UqisPzSCvJxe7e4m+hPqHPvP/BYD7LlDCMCvSKRL5gsZC+737P4Ycp/lahUN61qUioZdNkGERiZff5vVohX3zheb9h4BUOswmxQtsTjjft5bVqIqm0tjErgRCWbLzgyVrhSCiKpyyfV08gklRjkXjItmjAL4XWGrKafJWFDcIMGLSXdYkixSwQjxsGGQmpx0M8QijQRpoKU2Amm+4fGJuvK9lf3Bza0gudVqwoTB3Y4sDISPL6G5v+ZeHR+3X3FRt8DQXECpFoPJ16+ZimAABAAElEQVRMRL3FqFaKeiCahjmGDQ9QVxezKqXyhg3qtq0lceLBoBaIKjEtYZuHh4rr1gWXLmFCAoMDp4RW/jow1/wqGzU4UZnIiRUG0VgXnsUxQUInn8ux3HV1dVGCtC6RBRKG7IZwPOKII1wVWRiTD2S9alZ5aaDk93lh4OP19Yd2xDn5DMzQjrJzXFkUFGtQw8QAG4o+KEUqZRoy98F/VgwoHroXwhpnXOvWj/qsL3dqVwS1Ler8TPSCtBgl8ESICLSK5yBv5w2qxmXa1ZgjyrdjhY/t/sHivfelfv17ZbAvhpq7tcXz3rNCxx7T8pUvjVz1HVbBSlDzX3ShvnSJ4hHe8W3QgE6c5nBBzsWJSt+EMpg2c2WNYSlZGrwuzq6QZWDIjokx537xe4az4bqIh6sR437zyT/e/rOf/4xCMH6mN0BgioXhlz0AH0g/wwmQgK9sN3x11bhbsLu7e7f3aS/OoPNFluOU5gScTyR20ru/Tit1LyKQB1EmND15aCADSskw3vzCgtJqWTv7Lw9hdGtz5C72AohpSRlESREygwCJDZ0kMKLTbeqgL5GHklgSmqxRjAj8EpATIBdhViG2j9nJeiaslHRIsRETBxgoDXZXdrXT85Q5986XuUhPQ3jI68QQBmwYTuKni/X5+rY8VC13SQkzjeIVGKiRGOYykxd7ddnbNXcuwCO9FIUQliW4YXO3SMZTssR8Jgsn1xg4B2fkoLCJs4ZAY0io3KU5YVmsfJVhJ8bJRcBN5jl55xgAMKmaY+MDi6Q+oIqdpiinOtoO/OwgzJcqRpHdE5STJcyx9reSzOkKN3hvpUCZlxWAuTb7TJmlFjoKRML6ci6kIImbmpu4NvaCCy586O5bFyw/quuki3PDSqqEYlPLFMwYNztY+p83WDs7AscsPSzWtcJX4R4fLesPbsp5UkOl4yOFxB13qBs3g54kZf8VY4W/w8eeqpxw7CFnvm9xPfa6WiWo++L1RiIlSA97qeaHDYNdVI83GtiQaT52mIohzru+2HHIr8746ld+fem71tyy9rjz3ug8CLIEd1MopaHT6fZdi73YxClBDgSR7Dolg8PA5gDX8/qsBsyIdEHwo5nDAgJxEn0yWf2uRYCeJDszi18e2bFOmpr9TLLx8QTmUYsaPaN5RLpqoYTCFntmDKGtHr91SIfQyNmtrC4AamlRg7oTK+g8ttNWsYLKTVwLjKfPnnE13mlyyKi7URvOC//S3J/KhUk0EoOxIN5aSmJXpcV2z2l4lETvIg5Hi+7Aq5ZiG1QrvQG8amnsp+95z3tuuukm6IHps6karH3pfd9lgO+//3466swzz7TxYxJX7K7bbaxZxXDDjVRPEiuybydTG2Vz55i1abOxcTNUWv3S5fXd3U0HNv92U2FH1s+ZeUFeK6hPOZubMXq2lB5+VN20gbug1f2XeVedqEHKhEJu2opiYQhQvoEYiEY4NC/EnXYXYvZsRiPebCrX2+c7+lh7rkhYxBS0myBwlEu6hLEHWcQcEPQhWmmKxbIGn7KYHWGe4/H6/b5AgQsqDW0ib8QR8NRGbwEGajUc9iRLqi9l5blpxqujVaZEcH2iaEbD8U9//vNXfvNyDHQvvPBCucyBo+xScjucBHEv/8jm7GWmvU7Ots3SzPIqKRVJEu11KW93BmjEj3zkI5w/Z6Xj1t+vf/3rUqpdVQ80nKR+BIdga89IALXBJjq33gNZBJ4hYtw8pmxPKiNZdTiFkYGwlfco5QUN3va4Mj9sxkrJEYwI6uAuajzwsdk//C74sxuQVlp/d5X/8EPbDKWlrQ12DTYGzYBbrCDmVSGnbNsqVn172SMCJBWSQ9yMb92mFgvoSt38p6ySltJMKE6aJskLCigPDfoyuQCiARf2itLAWkQ1uRw8DJvIaNbcOlraMpLWNC9HczPZrDeQwcUXRwBUbqjROfnKuRSDCxIWNQrVqy2sktVW/wKAs6lUf9v1TjeMpQqBUEzpeV198SWvjuDLhDlCFMW0wYeYB272tXWR9vmIj2P1Ldn0eKWML8WUqhuRsNEeVZPj4xRGS+tinAg2S4lxI58TPWafphNUIze1+MX98Sa8hFggZpm/CugEpSg11btgnPo7mpi478lNeQyxFK0+UOnb+BqOs2EJ2FnZX6mLSSE13rzCkSKTAvGkrtspReiQhXReQ8rGJhfBT7iqtEVYWPBcZcFKgJZOYicgD8HyCqWIzkfSN3DFSK8pnwNd8FF8hTRkMYlFw6pyZMH/H17lFmXiLtXcqeLNytCLmVAm+P7Gpd9VtU46mJvanPKdAANKFxnjY9l7flu88RYNEWCeE7ve6ECf+eMbiqYRxAjowx+0dg4FzzzTd/RhekPDjv7+fBbFshgFqGppGwLAIABXx20cMRIlbSynZvKWz4/NM24IWV1xOgoI3GDsAftgmUMBPeY1uc4qZox898pvMk0vvvii9773vcxrJing0aWyjeA2rzIs2S0H+DcRgP1AL4doT6rdnBIgzUEh+tOJkQFGGbkGIqrprGBVyjm+SpsUuo5HcoA0mQWE9ZZaZGMZdDYLiUjssDVLhtDhHHjN1a9m+tkj6fPu7m56gK0c1qKrqwvwamaR6MryxXAz+vQPKSUfwmZBW5D+zAUqlmU5ppRAvRRIOYDh1vcyMWdqfk3YnEgKdMIyQIxsEXhF+E2zHFXFzvJKLfSVZIbpEAaLGNYHBATw/NN7mJaCkwwrCmRAhbvDYlma/jLHGRTy0ldSREKBb65nZgEY3IM/J4FUF4OEjCbwAOosueb+iUZJ5KEfKFkSGHvMTg+w7oFp0uTQSc9URUL0F2OAqReM7enpoUZHuOAA898YoFerlrLZgfnABz7wyqsvf+cf/vHX133jgljb/APP3DrOHbwK4lWvRzgWLlueZ/v1V4bKrVF/zB/AXfdQ1kwW1NOXeUK5zNiza71cBS/8xQo/3uxrzH9sDzJPPxs+ZfWy5sj6ETPTGomtPCbZu42FG1N+ktlEjML1X03vPjbrCw0kzLoAFWlp9LiGfu9hf3XmYz8/ovepU//84/6PXpPyRQxFy+IeIoQFtViFdl+KBBssHvsPHOkYnpnxhjVuLG8WrCgLCLjBYNmJpn6kzFEusITlxNwju0g5W7dtXdTd3Vwfnx8zx1DnlpWdWZWbjTgy3RdS2rLoYGuajYhWN4b0BVEO+qI61nZyJZKFslcob3dklI6U1VnH9QdKV1TBRantDYvrVwV9B0/EpokrIkQFEEi4KyJjpmg1YPIldl1hssrZIsrZnrXaJ8rd9V4Y4Guuuea5555zH6Saavy+GtqnGWC06vPmddL3k7gmRgF8BMmEnIIuBc/k6TW0EyAW1CSR7IuN2EJD0fRsyd98q77mETOdQ6w8EQ4FTz2t4+KPvXfZgtteKU8Ic2g15jdaobleeKH0wx9EBodwaUnJ3meezj68JnDZ33oOPVgJ7Lb943oc0lbobJk4JhIg9k6VXaF/cNAXCoQhdrMpgSDAwT9LmOzCYzj7cUQwwHh7t3loWqLpyHJKnNE38dGC/b2GIxe9DR4ZzTBm0iY+xxdOnkSjxNpPa1TtS1kRv5nj+hGocBRZXjQe4LRVF9DP/9infnDN1SzfXE6LdR9FSL0fMmB2QUlvsbExzRCB85W1lQ6ULaiqj8KlbFjOZHKxL5JGMnU1s1SVsFevsjp2LNGfk6I40a9zfygBrkC2EZgBmJ2M7BAfM7VxlsIZZbJz2gEJNwPKyepLLrkEwGpmgZxCCuh8kmefWBmnq9qcNDIgFhiB7iC9OGuxbsQYTGlDOXUrlqs4izAtr2l0N6mN3kJH2Le8Rd+8boLzGpI/cYpCncbahXin3Lu1/NWv+U0jdfZ7G//6Urhn235QxyFzd3c3i697/WVSKbqXS6QFowsMKtQSayDY6fEHPGbAh/xUgudUJAPQrLTXbRpHMtPjtTpht4W3LlGIvZEwfym3Eq8P1UUJ54qll7emN2wf7RvK4DSJq9hhUTZsTo1m1NaFi30hj1nGG5bWwc1DQQ5qisWqdnfbcDDXeKpg49VW6TAXBQys75uHs/t1NBhDoz6cTKqcDhUHRGEO4bT5H562PDSkcVSUe3fq4sFwRA2El7Y0LGlSIpltTaHSCtsSgWKFDYnuDcyfX6nDk3SFJYghA10rlTKlsdf62lsK5fKmlzd1dXVVDZADJJPRPQS74oWel+JHy776jkX1ipqZ2NkVM7oWon4XNLqcgCRmE5VNZi6DioRBbKnbYfTtMjhfbWQq4igULgJOX+ZNFytIUhbWw+wTpx577HEUuKveqb8OdUuxJJC1UB2TiPL5SkWkhoNiTcBMg7ClLDStg7SmrxnJl9JfvdT3Sn//Qe8uffGLTfo8Pgrut/bgMTKmuX1H9j/v1LnAgpNICl4MAE0L+9TSXXf5V65suOwLRjbnjdep9m3G2MxAvVImhBew8SbgFkuEOThhJooaR8qzRW5sRlgO2sL5413Z9AXVCvciloQLaG5ADgU4PqC1Bso/uuJbfTt2MEbXXvsj5jUzj6GEykeUI3VNEJ0sj45p/UxTXsAwh4e20mP8VqWV9BCMKI/7E9Ux3GTB9w8M6lunfaX3MhCGAvHmJQuUfegedEbZPZtYRcFVyQM4PVALdd2w73WYZkpxxiw55Y7AGk5D6BCkA7CpBMgi9cCyJ2cpQX6iHNkVrF0MMSKJ6Rn3uFxTlNxlmBeIMHhlMZQrv1jDdzndAJEoileZXnagfHU6U0L1Nv4ypu41WZYsNc9y466qiw5xlKswog4Fz1ygf0gMqMAs9zLEMXCGVSXwygxi4lAUKd1NA5fmgi0MARklNhKmhMnZPb2mtxzjBm+WwljcYHQdBliOmju9jJljae6MexumIsbCGaO9ze6kpxwObIMDjAjrAKgr7eHZKKuEp06WWQKy+fxSDqvx7P0gv37j699Yv+6NO++6e80t3/3ovxyfDkVKljeHMWTR8mEgydJu4qPBty0p5guP3Mi4rcA0K0oxL4zEjAo+onEMYeL3RKRQKpks5BH88+M96pYDw4ed857Mi68Yr65nFxQbD6iL/umsU7Sjj34l5RnI6Vz9EC6bnBYsWMqEFfjBR3/6wx+cdvSaX6w7/JSHDj8XYqEoYDBD4uQWDMck8tvgiDAB+xfjTTxImeG0gi/rlpjS4Bd2K3IpkInlL7OJW9NlX5FRTiImC2f+3clmDmNIhw5AGcaFRwFmFeJQTRS4F5PjY0oz13QIsfYkYE4hkujiwt6hnIX2GPF3piwYhEROw0JqW8Bqjpp+TV1Yrw5ksZRGZ4bXaHEpME/Qh35dFMrZUYgi2B5aWjCsAEeroaRwA4YtZMEaTqu9QQVL7KOOOhKzsvvuu+9/GWCn/998AHR56KGHPvShD9lFiKOwfSljMCO43tZgaWEDaiXxkIwJTADuF9ySYbFBQvyNjBVvuS10/33YSgg7CcUKFCrqHx6YUPW2z3/2sM76x7YxjNrSBowVB0s/uyEyOKAJnZsoGD1ThNtibr5Z/+YV6nzOa00qLvjm0UFEbg5VIXKhkYvYMdvP2HiiraNT7elR162XMfyC6CziklKUkWE/Fss4FzdRPbHOkwAj50JJDWoqZx1RcBXwJi9q0SgZLEdaA0JOttYpd/cA93FH/ZWGEHb5+HA3/ba5Kfce4C8uV1RS3thlX/nKVd+84pZbbuG2LrZ5mZvuYjGV4gNJjUF88KkK4N2rUpi3LHNEArl0pEEYXnoue1tVUXt8BUKOi7BGcA5c6hX3mGV6AgwXsdYmnjYC8+OPP04YsgCYpyeeKQZIeDhw+OlPfxoaC5C++93vnnzyyRTIUzMXNJkkZCGGQE7Ia5KRcab0TiFOceiyXh+qDGZ04bRgFH8NeLAyYSY7GrVy//qhbM95F7yH1RnPlOhJq/X5mLHgN7FSTn/uc7He3ty8+dGrr4E5A+FkRTQHwkLSFhIk8BiO2UDvetQRhS1bfMWCxjF1DyYGnF1n7VOsQw/BxZOtr5sqxAHbKVYGSIFlrXb8Cfnf/ymcGFXtPUhMFXHxr6WffJIVx5+WunFH4rWN/WZ8WfsiFnFl25bNnlB9KN4MIYk6lvTRoB4PWHh6W4pD5arK7FcaIkdWjMSu1lUlnOQZaIKCVMjYnjAXt5p6PGZwvTB+gZElC8iQq4lVHqj4lIPBEtICn+lT4j6tPmQ1+pVmj24J8cAkIELapOveRUsrhx+mvfiiPScQLHiRMUAn5vc/ILx8heH1trS2So7UgYp8TlgGZAwTEDYDVlmOC/Kv4bwvWh9i627w5KOe8VhdHeyBbDIZmYwAQ42wMegVUZiw9IFsoChaJhLMXzDfG4j0iaCAuS2sIMFlh57HgQxBD4hnJs5cNtMNKmEUzrJGfnmIAWaWEcrhlf5NgTWVRutXG8q37BgL+LWrPlNStIH+AeqnE4QOE0CoebILbQhYxovlwvo3tERa7qyMPd+FvZZh+XZm8lt6/EuWedtjkmOnHkfrwhFNKDaornAkgmk3Z8l6xSVhVq7kyRmVgILvNK1YEkSV5sd1tAlEBg5Di0ZdiONPRoPP6F275pe//CWL3o9//GPwRDYKTo/+ZM2Rr5NQyjY6LwJFp8ZRNH8vn6osVa8ULpdl+cuwAhVrr6y0KvHcayYjjSW9xDF+5atTwkyNonbWUrcS7E3D4NQ1PUBjAQBCfPonJwYkJyxlQIy+RGAJDAuse7d1stQMQC2gAXZwiXqrWlT1WrMQGUnnwE5IqhchgoQfvppRk+wKUPGK/BRclZBLlhL4pyv2gYTElFO1bswCQM1PNeGnWGaxW7Th5HXSAwAyIBYWYAM92Mgk2QAfiJ2dlAQ5aENziCQNnU9R/NaUGlBjzUqd2mVeyVSzfNE/XV1dskx3mrcrjHJlLkXRJzSNaUKHyPSgDYsDYQKgqxxHunQ6zzOX8ueShuFwJ+PVHeOMmjvNHsOQJdAMrHgs4LROYpozpnvMXpUAPMdgk/HFvV/Vp5qvgUDwJz/5yfPPPbfh1edeuesHB3/oykypkjQ8OLLxFStxVm3hb0lsFTSdlVXsy7hxKSD+D/rmLVDH18HTist6bCs5kkGiBBfMr+i+NGepDN8Nz2e/cVz3gu9cnrr7DxOPPVOZSAU6WmJnnBJYfdLGQMsdTxt5Uw9q6IfYYhSjwq5ubmhY/rtVn/v4n/7+lF99e/1+q3aEmuGtcUTC3iE4bIgHexSoc7fOt5uH/Ta3E+ENq9dvxTu4v3G3NLIH6GSJ2ywU4A9SFebXLGNHLUw3GBxwjA5gQSAjkQvCTaMRD7R9IY0OVhvLW4NZa0eqwvEoh0+RNTqF42R3YUwdywkdL/cpQoBh6ozWekdWbU8a3Q1etBvdcWUkp3JCuFBWK6LzUdThOgtuWXQ/EmThjEwRDIVf3JNs7+Ao++x7iQcz1vaEsaTJu2rlShjgq666CgCc2iUw++yvWLP2tYdhhmkBS0499VRQr2Aom0bKzw9ws7PWHFbCnfQ/MgqxizOBpQ0n85lFU4aJRz9b7tls3Hc/gQoWjuhqYYEIqlbuT/fWve89y5c1PrkVC3ilzWeVNrzh3bQFnZmtBxJrDfbS/NHXvlzp3erraEO64gwndWPvzv+MMEcIoNJEdWThVPDy/azHHy++8moE6b68SJpvuz8hn+LHjVYl78GkXgvRUnQdGPzFI6KcYjY1miw3xDl+LCyZwdeipacLZgNHI+wHMMiyq0gCIhf/OIaO1R9u31AD4esFf6c4zsWr20gG70GeD1/y+X+/8cZt27Z/+cuX3Xrrr5hI5GLvZwYyD9nkoKQJd3d37yp5xr+kYUKy3qFsZ8VnDQUeuUtJwJyOmrGIOX9gy+GBQGHtmGOxrs6ZnIHs3MBM/BNPPAHlhOmmA/AcAZFl3n777R//+MchQKG3CNNvMrtTYxWE9InsFoDnYYOpSjBT7RaW9baicuNIuT+v75hQtia5vMULFtOkpqjSxhW5kbLPMMAKysQfL1BN27pMOODx7/5jdM1DZYb2R9d7ly6ZxKFdFcM1ySAkp0AjgcYm0prYJRdZJ51Y6unJ3nt/eO1a1Ib8l17YFTztNC2OoxGZVqRn75TnDhgmVmr4LiKh8CSh4/F7zaWL9M98svCTGwPCgzdzSi3rntLxxwXf+z6tPoaDoq1pD7Y30WZfc9hTMvjoyZresuZnwgkqvYIpjgXjs6AOyS6SWIvT7JAjkwytAEE8cEHof2aSi8tul8PE4YCecSuXLw6n8u3dC/NdXdqGzZz9R74m1L+a0ANXmpqCBx6YLHuTCKW9GneUIWVoj2gL4qYv7y+LmT/1sDkobS3ej1+SS0ywZnmZ2oCu6PkFC8Of/ZTa2hIIBdtrOS1HeIRKgYIgH+k3aYoJFYXiBR7j4EMOZm73jlO5WOX8eqU9bhVSKEMmfb3Q82SEqmbceWgjk5FuB+X4xAmKUCiMK/mxgpYvVIqmODgNU97VoGXSqS2bthxw0AGKT2h954KTzCAQGAhZkHHdQdjBeUpgNeABd1iW0K5vGjd9fYMNV1yVYDX91Kc0dGKmMpIYjYSEXlqIIEAyIWoh6+SDIBkX3EaeIwMFcSAEobOt88RFoKCBuL4iVwB+ERYoKgISbAkGv6xFnKv0NjZsHjMw9BpNQyoZYZ+G7ytEjTC9eL9kqffZtx8Jj+I+LeIx2sNaoyfxyf/zJUr46Ec/esYZZ4jyp1ZXAZ58ZVzsNoqYqoc+YTF0VoOqr2/xlWUZgQiNBQyYT5Q2vLKCAQ8xshOcKtyQV31y0rgDpGFhYDTdkU6YQQev3GXyySlWxjuvTq43F3DXQhg8keJOdy1OGioFZucqGmpkD5LkO2GHHyb9HsFjH8R+gbroB+bOTF3hNMqBgRgKd7/KNDIGDpOHBDBITEaYYQkJmmFmPYprACY9aMPv9EJkUaA0bKTTLhn5tvyyaLAPAuH00qqAoXbHtFVCwvJCWwjLFskSaCbQwk/K/c79yV3FTPGzp5lLLncJcwzvVbH0FReOsK/Jwukl99jJMNvfHKt+c8lwniwVD9QOFkkTA8hdUHfuBbrHl20UBGOvQffLelLlqm3uZcqUDD2FQIHTDxINZi+B/mciXP0v/3LxRRfdccPVrcuPXHDg2YUxC/4qW9G9FSMCC8xiz0QTf6GJhDPYrRNqrquu7sxTxl7f4IFW8bEZC5Mu/lXCwfhpJ233Rdjf8Uq1djD4naeynz9yUdclH+/48Ic8sAM+TzYQe7EQ/OVT5kAuGPKgDuBElRnxqxhgourkxpV7jr/k9Cdu6hracMoff3T7B/4OA2uun+cwMBcdsffY0NRoFjQAOxrK2FBOHcip7RmjI6onk4nt2/uYPo54i+WF1YmmsK5SCjQMPVajOFcUTK8z6EiZkbIBx8IljcN5RdwQWVQzZTWZV4dSyrag2RbV0c3a+gl7p5wqhx1Tm18vnGKMZcmlpUriVhGcBA1llG0TSnudhavP9jq9c8IczcJUCzaYBy0g6jo0cIS5ClIUDY+jCOaC05p2d9AaOH9lOIN60uyo0089dfVll/0tRpcM7lT9+3ZoX2SA6bE1Dz/sD/jf9a7jUbK/Nmy8PKxtmxAnrRvCYDtDMkmFOwsZATm9IY8gknCPXu7p8RbzeE+F6kXGQyyMG2sXJGehZ1N0xcFYdHpRqLFyDQyhKyuUsIcUg81843JJCmHaGMNDsNCaZ2qVoWJu30V/S0XYsW3o6SuESUy0mli8OK0qxXXr67N5PSo2uelPwCPsPCup4XLS0BqWiOO62FGbnOvEDYBZyaezmWJjQxR9NrOqZIgrqjH6r0cJLQlAu0R2blgOphaNJoIv+LvaOm7Wh5WcITxgYe8IrcxtH5CDmbIy6gl/7Zvf/MJnPnP//Q/cdttt7373u+kuSDfysoA6U9TpzOlgyxgSMG/pZynDZmOQrA5fpd0XatU9FjJT4dPjJVeAKnX6p5liWC8GBwehOeR2BTAsxwAJzOz6vDoAz1TC9Hiw4Mtf/jIKIgKojn/zm9+wrrHl8wp9Q3qqkIva9LzEUClPzU8zRY4nkhsHM0l/R9+42ZfmzIYO74ghSkStNKnZRtZPjVOslmQ+qdrNEIq5Ae6imnvofv/V38cUIn/JJU1nnwXd6K7ODRKJRfnlsjm6s9LXp+zoxzeov3ux5wtLKi+9krn1Vgzpghd8WF+2VEiCXKWAh5wndCJkmFWeTqZ8oWOO1ftXn2LMm59/4nF1e78ZDmiHHRo8+liMkodGx9BvD40U0xUrkNyZKXo8XmQ9StajYOgqzuWWlfqI3hKxGgPmvDhtFgswBgtgrLu91A78DgyzBOiVvolKtuwplIqb+kYPO3ZR8JKLx799jcWNUMLhIS3TDL8v/ImL8i0dr3GuWkF5aDaEgYHL7kx8PHrqUAlOdaPdhyocv+fgQ7S/v6r46CPKhk3CHGXZ0uDKVaW2VsIRe/ynQwURLBkMdgtwVbYIjJIpmdGJvDHCKWmx6Vi4OmsLtBjNXNKrS16LrRH+GQpGasPIBR3PQwAqpK4u3tbetnG08tq4PjhhNNdZ0BML6oyQx5OysYPy3QgwHTwnhmSyCjhAJhe7eFdXl/OVALs4tJSIsRCBm/mC2nL9Na3FXPCYExJfvIobBH2FzLLF3eZERkmnlLFxA0OXaB2X6oqb37DSVxAelxtxNdY13/RiHFNGRVs2BDbQciHgCfn8XfOhdaiBNzf6EQMBt4uUt0azFtfLDU4o6Qo7hQnTC+vLQloomkF44FyRG5ti8Sjx0YDeWac3hco3XPNduEpWGPyyUBrTWZ6JBbFBKuY4kZD7s+h2uFUOidg7xAAj+4O8xlCZmUUn8wpUvDIKLETTaV+IJCIlrwLksz9gXU0rGNnHctCpDvYGRQRF0SfgAH1CAvJKJefsVcz9KyVjycyvnMuYOVALzZR8OOVI+g/mjX7gk2OOzqfu7m6nIgk848iazOPEzx5g9skJOHsyvsop4JTMXANhqJQaAV7akbIAyplYVRpEIWwGGMveTTORP0poq5K9o6/UyHzhFxhqVsR0YI2ln+XuJrdg0JtGkZ6MPDUzvr2R4DB9+Jepa4+Qg4fuOUUHckaaEWReQGzgIu4vACdoz4iAY0hLHYkPgO0R+KoEDCjEG8DzMKf4hXKTRhBvuhVkpBzQmy2JZ47MD7nO/8AHHnrwQY6S3f79r371pndNBBv6sTrG9asqrkrBVQUeZtlWbMAETbM9qfZUfAesOinatyN3x+85LIwMyV9RMtFo2xc/nluy4sHtRrLsgz0rG9qLQ5Ev/bFw1ILAiiYOBanZlLlpVHmDK+QV5LDiTqAi0mUf7kXQeXLJAt6htP5w+7+f849X3HLJcff/4pXjz3+54xBo61xZcNQcswECifqyo2iy07cMA9cCc5kQXql6fTiFsqDQt/b2sk461LWTeO4Bqc9jC8BnhJyDwEA/dNWbO/Ma2mm4A24G5nT0UEbvSxpLBWlY84HF5SilNZLnJDAtgo+ln83xggrvivJ2WZPmh7WuV4bywqqZm5nhP/hP+OtVuBJJzHy4KGG6yrHTiso9yezedk34yhKepQfSGgCcdPIprJBPPvkkrjRqwrEPRs51k/gLg/7Iww8feeSRGSvwwnbrtRFtKItlnerxIwkSF1qCeu5lGFxkVeKXp1gqor8VNlICZaFOIZ00AxFPsYQHIpxGiQml2ZchWSC9ajKo8ZjGWT7EIOxwRVxiwhZSgFXmoizcuO2uy0XcE2ByYhZPYrLjkBQDZUvJQbq3temW6k2nuchoJgaYfFG/OKMb8hkY/InLmTS4VnhgpVQ2K8WcH7pY90C3CStoE6N8rocxcdTmPj8HPQThddbZZ+cU/1jKWNqKW1e1PWqiPRtJG3ihhZfA0YvHI04Cc2V2xKOfet5Hj7zxxueef+EHP/gB2MnOzfEnh3pmfXc29bkMtOxqd0pipCjUHfkWwwBJCdLCbY5FAQMcRU1lIJT63kJIo6D/uOP7z3/+M6vecccdd9111yGHgwYFHihClnsCrE2zcOlu+mxOrbCUbSPpVzj+0tQK2o+kYfzwTYovByNu7Ox//vFKyOxqVOC8cToPDOxeKK9AGiCUS3KpXCxv3lz88EfrivmxE97d9P0fMhdmqRq0VwvF8obXS7/8leeZZ7wVvP2pmZZm70cv9J640nfaqWKpb2ri4E1VIQwN7h+JRD0FSwa1KvcGYiQkQjZTX+898gj94APFHWQYYgSCGh7Oi6VXN/VvSni2JSpIqyq58eFUxcd1Q/OX61xYh77Oq6Kpq/MY+Cha2lDjCIAcGiaQBIllV5J0YDJkgQOGG2DsY7dPiGUDo8N4oDxu+MNHH9XwvW8lfvOHyrr1CE/NpQuj55xVOeSYTUr8pUHEXiZlRbwmVifdDaiI3YWJMG2UcWowoC9fHpg3Xy3gQ17wTGokjEABkVt1nl3vtvRK0A0gOdskAZqAfg/qBAJ0S0/vpjHFirX7vP6or9IRRfMy0962q0TXX7audMkaSOvM/UTFEy4YzQGj0csNRiXwmc4BbeRcYFLwyuPKvVsQwMQqZV9OwweIMJkYOJloxDAd+DowMFgytXWjWmfPlqZ7bsXx9/hHPs39FXixP7ylpbKpt/SnPypPPKaNJ62GJmvlcfopp3kXdWvBICeOtk9ouYjStXip5/ijlCeegu9B8y8WYQ9roaG8+wR9YZcQEO7+UCm0IL+ZjCDjdo5NbE7lBjKehN6EAbiRTzfOX1AqmigJmBZ5jGEqucTQVo9/RTwSaA4a2IH3v/7Eddf+CI39tddei3Ea7aKlEotk59BL1Fmzc6hRglMVqJl4d8D34k1KFuCUgAqGE6jgQyQtXpP2RTVEFnkado7V7BHguffJHGusmQww4AypC7yCImd2wMkTySPTM016enponbzyyul2vjppnJLx0UAnUKAT4w5QBR1FDDOOX8nSsxWSZXpR7oyE4czdPcyqy+QlnjIBSYadnRQApEiLFklRKQ2E6+CVLEhRSe+ukRL4JKEizOYi8ZDNBfCqUpJAxrjjq6Ct+UrHUvVMgmDJ93Z3dzO5qFRioEQ5KkIS5AiDaAvMP2BIgElPw0FLEHVvQZoOJzXWtKOenvIvE8P4ssEhF5Dig79Mpe5apLwJfKCfmR08fHVwwJ1y9jCLtlAk2g8CdB7KpFGz55rLV0mn0UvANncEuPrqq9EojPT3PH/X9w/+yLfTRXPc8JYqOtbOnqBwGgH1QzNZblkJ0J7eu7EcPqBtwUUXB486Iv/0s57xtNLRsvCEEzJdS57IR36/HjE2hxSFr1kTZbIZemCb+cBWQYbATeMsVjysGIJ0xyOG4jW4ZNHidh/IbwzBOA98/yHvPehdHz//sRvPueGLW666N6OhpFWzRWSzaEQFJJNbfo0eUVMFc8ynDOZVWMGOBnE1FNOZLHadUxnAIgxY5FoxFTtDyJ1XhG2ao7POMy9VGccPVolKFY4ED2dM3AB11ilotkVP7fbwSrcYaBF2pCv4Up1Ax1vgUmQFB0ODWa1JZMT3GDcqqZ0TlbGMuOIowdZHPhgrcXSIhoujkUx5PCCRE8UAHovEd9v0FS30Tr+yPaUc27UEcpRrw/6XAd5tBPb2heUGKcKnPvPZobzGCKWwjEP1busuGBbEQvbQTCEWmAFlwAO2DQ2iWxpdsmiRf8miDEZ30GeYSIhztRxrh7bSzHAosHRpf0XngLtuquOGVrf/foWWptDICMUypkJ7qqEaNssLFgYXdTGhqpCY2eXDFMji5mgl3tS6cL4XREm8NFa3uDsKoQYjmxyDYahqNYUgFsLJrZExNLPsscrF1Ch+jbRQIJ0PZkMWkVzCFAk3MBWZr1xIjZAGV2+5kC7u4PLTBAEgOAdsZHxpoFJQfei268IYXWjYMODtpjGiFNN6Abe2GJGqwkdXuoR1tOFN6df+5IaTTzge45nLL7/8q1/9Kp3GIwrkXMEug94qmGd6ZWuEf2YrZUrIPV7+MnCS/ZD82EzZ5xgP0UM5Up04Sxbgr/oqY+SvbCMJMBWWQFYlrvlKXh7s8C+++GJkDTSK49P/9E//RAlyv0cGQSdISf/spiwOADUrciKpTgwFqr+iuWXcGs6qSljdkUA+I3ytqUalu9nb6o23tJ9wQIc23CeussDZAKw4/BLbGMhPUbKQ0b6+4U9+6oBkYmJRd/zmm1CEzc48IVSy+vtK114Xfu11gdYWmGN5h3cWrv+3cjjkW32yHsLdkT05bHDtWkSoqmm8OjG2KscSfocTCSuZMFNpNeRX4w1whlRgen3BeYdEomYEQ+PRHa1dyyJBfSCljWQM/MBxGyDmgY1RC6Vra6jSGPLs2NEP52a3T7A9tJfaYRclgUgYUoyHAJZ4u2/nk8DSuVy2VLaPIi9b2NFWZ706xl3FoaUHHxNdvMwrWB3LCIRz0djaMfXZAUP16MWyYL/bYhydZe+cTVDI0sQWI2ReLruPXTvcVL8BnnycXpKvtAJNAkQ5sKL59mrezf1jOwuBrmWV+d2LuKpe8t75XL5/oL+rq4t5B8pBxYKW0NOE5XSzm2pCoQaCfkyRsf9l1Qyr5caIZ2GsvGnjerF22A96Hv7CUaxcuXK6tkqAgUlyPs9WzS96LWhx8A1dB87Pee3u7kbwAeIBAPMf/BkZ3dmb0IxUKXrVF8rFfPaEVaOnnYfV1LLOqH9Hb/HaHwZfWivss+ioTNr41dbES6/VXf41dfHiraPcgaEObevv7Hi15TOBZEOutKlsrFc8psdgs33XcXWf+Bg3zLnRb7IN9m1kBSQOigIkG1/t9TQt25ZU9OY6v2aOZFPN3E7sU/Mlw+9Buqnm0vai6tXCutEW0zoixQ9c9iXWP1yS4mlCjgg9Cf7IApHpyLBTnROgf/jKQwz9T16u6yMMyT4X5pNeZRBR41BOFSY4VRCQn0gDHwULxAOGs/jwoIByp3xz4VmqdhfIbHL6BByo2ScgCVQ1EM6xTHf5MkxGiYcUBdWIEolekp/oAXd6+cq6hy7rmGOOcbjN6WncMe4wJch1g82LeMLUDia708wlTC5Hdbxt2zYgR4xCRgdgZyFivQJP5CdkGeyShPndtURM1cZEY1mT7wQokzCsBXuN1N3JXmIxZApTnVPFVBF7CgG2FCLUTAhaMo5QsZLfk7pBJyV5nTDNlPQ9AR7S85WFCAjdyZz0exV46yXsVXV7TIyy94UXXkDCu3jxYicxywV95by+cwHZG7Kf3bXsVS9JYb1jOrF27VppAu0u8K2EJQMslRZzLwd8u+mmm84999x7fnHd8qNPWzB/VWGUbUT1VFThgArllaCJxAbL5sV2lij6/vO10rsXNKw4ZlXosKP9FbPg1XZYwft2GH9Yp+ZwiGXvMqgui7CCFaFDxqoKmh5VE8LWuO1Nxy5M2F16Kia3unAnS8hrYW4JfZCyAneu+vxR6+/t2vriCY/96t5Vn2AlLmKJaZi2gmyqZXQ+IzL1zkqicCWShaVxsx9vWD5OWNPn7KRVaxRLDZjjzlgz7AwuAXrJVQj6AKsrLrxhcSlmrsghSrxhaUMZc1uysl8TR53E5rFbmaLBgorgoC9X/ia57qgknOzCv9g8MKp1a0Uz4gYNb1hDeWu0gHpZuIAmH0ayPt0oGpwlgnCjZHFECf9bAWR3ol95hJkqhtCDGW37hLVy1cpHHnl0t9r37ZfZCLv/LsjZ3kZG4SePf6nfTOUdTBD8AaNg09a7mWKKQdg15Nj+cdWYB/Zx3sLAued5f3MnDpUNrrWDq4SPUK3wB84udnSu5ypExYMFxYYxY17HAs/HLi5ef30AcgoJhxBymMVw1HfxhWpbq122G59wrKIKlgJrZwMeWMeeXiiUFdW3YEElGosmkvkXXvYvqUGjsAv27ejn0jOrqOhlozJRzBtM2JZcwJ8tabCuWHfHguLWI2SpOLnjWDIGITlTXF3NOQSoPTQtzLqx0QT66ZHBHYNjuWRivGH1qtaojpEDSuC8pScyXH3J2X2OwFVQ7KIQH06q0RYtPu/Av/v7b3/j61+/8847oV8d73NO181xuGV6uXmznSNulxmJx80dYWgX5zB2zTKdhWOmqkkAdQLdsPvMr1mYiCQxHoD45eEVXoJVhoftii2ZGCoiIMMiw6wPtfNwc/Kll14K2UE5V1xxxZVXXkkh8B5SHE5RhPfInM9aT/VHbhXcvH143c5SX8LYmbIGhjcaSF8CkVC8ZUGjpzloLmoIHdwWagmp2cQwAEBtQ3g5RBgws/aZViX145/6n3m25PF6//lqvbvbjbvVVdrkGgrBwjPPBF5fx5IuthqZAdveQjF7992+Qw9TBAMsHnoAQueBBx5YvXq1ZLllPKszJJoMT/2C4T1bir/9rbnmEQ88cCiiHHG4/4PnaSsOHC37x1m1M6it0SsLewquocP+mQtaUf/C80dDaqMfy39jcaOwy9iFJwKyXWGheJeVcj4KJkHCI0dnCga5cypY+4il326biaRzcELdmba2p73PWoVFjfGWSCOTbpwL4gcVjIsgiRF31vk9dZgzBVRYUBcMU2VP9tOulWfqw96EGERaAS2LPSoE5frX1zV3zGsOLJiHCMvjaQwajUEU76LR5YpwNsOGCuIx+2gyNDTHRLu7u11joTU1NY7mzWQOpSnKc60xbPmVSlvMV79sOcXAqMC5kYVKYaVmghTiG2UXdrYkw26TWUBGgKBS2GZmJRw7kn6GAHkE6APnx2UM2dfXDbQ1PdIfO+x7N+R1b71fiZeL5iNrgmvX2uslohwkImIbDT73bOGB+31Nrbl8oaNw86LQL2IT27iPqOUT+CTxFoeCmbUL/Eu+EDjkcL2zQ+X4t2vcHZhhyAnj2WHz9iE11BbpXFFfh8rZyBscUvIaRoVbLkK6Mjw6zJ1UlXLOoLmZcT/GBRntuuu+99rrrzc3NeGxgwkuy5SoxTxyqnCQzYmRAUZKKsCZhpIHJn4u3C/JGCzXeFUVXP0KAIAnr6WBAWb0eUjkBgwyC44IsPnEgiwtltHducmsuTSquu7da+GrLMQGAU/aU/OdHmDJdZs+usGbXmxVzCyJwTpaTfkEQH7QEhjg+mDFCQOJQxcSTxrZOZQP6yhpcZbHqmWf6SMxh6WMI/co2KsSVIHHq+RaqYL9BV2u5CUAQ3aC7BYnl9McGeAr001+xVsBM+jQQw91ElcFHP4K/gRJFomdBHKUJQNMmUxefp26nGRvLgD+yO6ipbQRFJLoPUv5dBpCcKqj21mF6FI3vr05MPbZXGARvQ0euiFk9+dxx7zTYfqcJdeRDe1VdVJeCcCMKW2ReWcZ370qnMRAxXTYKwZY1o6jH3yL3nDDjdd94xPfvvOlsXCESxm46pYDr/CxmD5C87P0kJhlmmC25P2vzdaTXqMpGgpoZqqsbhrBW6RfePXEbSd7vM0yB73i/At2cTQWDQKbNabORY+CZTUPryjVONKDBokjjVwNiLwbQ2gcTr3RvPzeoy785J+/e+KDN6898n1D4TZqzxYtfwgAprYGp3+c/qRMyPWJgj6Qs5oTZkNTw7at4vasN70wyioYdLleCbBtYoPR4+aXeWFrJGOmixoXAmN9zZVIOya0eXWWfSMSsEw9k0QdzgWj3vZweSyjpvwiF61Bubgzo3F2aV5cJWOD32rxlkeCfg4YowSmtVQo7mLI04XQZWgfRV9iIcj1CiEYcbpajA3lqFw6M5BSDz1u1d133c3Syh4noZ2CY58M7XMMMOiK+tfjC1XaDhnJC3kGOGxjLIoq/sdDlcDCmp1LJBSwwHiMV2NR3wfPr/h9yh/+pIxNeHRPLlznO+fs4PvP2eJvePkNWCUGztoypu7fEuk+6eRKPJr9wx+1TVtQ5hSXLdfPeZ9+iHB7WzVqIFPIq0GyMzFxBA2NBSr4/GpHe3swVlc66STr7jtLf/i98qEPSpjt38kymptbmpqbYZhHnticMIwl8xah34YmRBJTsrSJJC7KzXmxUMqC+0VCDPdjX7SNq/GM0tUA8Zp95pln7MaB39ZYz8ubtk+UKkaqcmIia3Ab9YK4NpQ2m6JqMYUDd4MbAAW9iS87Ux1O4wlH//CnLr3v3nvXPPwQ9yGh9+jq6pqpJ6taPdMrS54Ue0OdQBR2d3czBHukJxggFkq5YkoAnBXZXRHXOdaMd6eRYSoFEnZuKdqHKuIhcnrK2WNkdVBRXGgG00uBmB7h8opT05JKhuxgv5eGYbMXNfevThtLit6bVBPl0Ei+0J/KK6E6TpZYmgclZGtAWRCHnzGbbVWkTRWLhRwqELKDxoIPXPTFa+m++4s//jHL0ujnPhNeuZLDXphtzU4ocUpSWf8Gvs3FVTWTVjb2So+0b8s2M5XUrHlix6AOkM8mAfmVr/zSz5BlkjITiexHOM7f0Ze7/voIrDh3C3OlEFfQrHkos/GN4De/FT3gkGTK4KhmfdSzM+VDN8cZ4KKJg0INT9XseDGP2lqnIrAM29pPxxwOuhChu0MXUrXsPRhC0Gn6iNtfOfqv9YwypURZxfT464OjxXD7WN5fzJuG1/fioLB9Fea2XPtUNjHu8Hs1plWAi5fqlPlRK2LDsKtlb+dfAJa8EHAydqCW7vNw52EwXucrl0vZ9MLuOKI+sd6Jf6L/nV+wNF8QR1XtoREDIyyuSax6hu2+pWvigcpBLTDCNMDEUTjNZ4KAw9QLPoM5sNBSy0QHSgwX1ex6KJwEEPryVx6tJBcwi4kmWFmrIRwxCkVu1WgsFBv95Zbvfbcjb+xoatMsNey3fLlM6blnJd3APlkqlwGbxRnlbvHJp7ynr1xh/EPY/ztdiCeBH8/4Hpyg+TrL8c6nzPDBWvtZXDTFPJaNJC8wOHQ2r7SFEgfGyhldGR9IbN6y2e8Pgns4NBzZiiq5JJS/ipGD2LEMj1qyUn3I/bf1b73pZzeCk/989dWoNKejDZtIFY9KV/CQUiZGwCeJEq4qQByAYyr6TLJDuzpvr/9Svswjq3Dy8wpu01gkEZDgPDYsIrEcMsQTEBy8Eg8KgUiEAcbpKFkUSy5iCyCvKt+pl2TuT8RTKUNPbxDPrzS8hDVChoIW0Z1RVgEYzqouY2b/dUpw6gVsNhHqkhkBALDhrgGDMF97enrgRiTDz+iDt04VQEXz5StyGR6Kpb1uNSkxPE4WCYA7xvnkDsCE08nEkBKKVlL5bHyyQ4hH/QUauLM4Yadwpy4nxkkjA0680y1VCXiVCy+/pOGXh0gyOnmnZ3HHyJLhhegrmG3JDpGA/kSmQIAEPIQpcPqC4C6KcFWlVa9Vid/GVyBk1QIZoDT+MpWC1fQGU+8vU91MfQVK84mGz5SgKp6OqorhVUYyKZAETf/6pmPoHwSm7vk4x6JYlC688MInn3j81dde//2/fm3Vpdfj9jVherDSJRAP4v0KolvMWdEa/opDLepEWUsmMOjCmaylc0a3pJagYOwbDe0mqjgMwkyX46wYc9pNZoPRMhgEQbvvggxDaPwr4tDHp3GNKD59hBK4Ymh3rL501cu/W7bluZVP3X7XKX9d1rh2WMF5RNgvWBGKk2hQhQwUSxUTeWNnWt0RVOZHGhVrG4LSvbIJ3wXa1N+qWuQHj6otaLDs65HF5Ub5isblf+iEueFi/2YmhaAXnGY6ZQlHmPUqmoAxLlLCFZap5g1xn/BgFmNDY78Wz8jwcHks2R7db9S+aQmbcMqhJxFBc4KNXVj8o1xVOIjmHim6Um7NXNiJOhp45q84jj2Z24DxG+fUuy8H9kEG2Hr66adaF+znjzZy4Iy+2zWaML6QqtBKUhI0fXwn+xmMIcXA8HBbS0vwgousU1ZbvVtB2oaF3aXmlu3e+B/XlfNcbyLpMEvdOGLVz483rFrlO+RQWApG2OIIH842AsJQoQr/YChDXhzGQjEruYqGY/ZsUYkHtDY8lJhW+YTjlbvu0teuZS7K+8mcsZflADSneWIBxCqazgTN4Jha49QCUz1o6QF/sLU+gLvXPM6scH/q5ZZgA3dtbWE9kTOaGhsx2KNABK4vvvjiGaee2Lk9u2lgtJDNrx8wD+v0w+q011UKlmcsh+tX0XeViun3ebjqhTu+uPi7L+X9f+ydd6BfRZX4b/v2/n29JHkvPaETkI5UxQoiim0VK4gu+1N3QRFXXRdcXfuiuAjK2kFQFGkLUkNNAhgghPT+evn2dsvvM3deLl++r+Ql4C5/7IV839y5M2fOnDkzc87MmTPf/eGPTz72qP7+gXPOOef+++9vkPM8bGcZQPWSQgDSCQow0PY5a0rILL0zVtYfRkKkxuRYLntDK+a2Bvur6VAiMU9PTw8JEP4w0URfYnwncrosM8Szk3zRRRch1zJocv3SDTfcwJqrVyngI4GxWYf0cGDwpysarwM7Cn69ZUFFt0f795iaE2vuxhw4EbA7o+q8tNITN+ck8b4nniVLlnZ3z0EUq1TKzc0tMCkrc9lcZmjl45lPfTpQKdeOO1579/lrH3sMBf7oo48Weu30j1gj4WynHMlelgxbCOEbnYFdaC/cCew6CuIX0VOeamMyFtSfRGq7VKo++ljoidVYOLhO9V3dmov49uzK//5Pgc45TfFUfswK+SOLli3tz+FLEONnA92IA8ipqNEe49ohqyshZmgPuDedezF8rU8gXxt+GcH782amJk7ts3YZZx1J15a1+0/gMlhbH644W8ac54dFL8b6msP5uHbntkHsZlkSZhjpTuL/390Yb4D76r6qCptctKYRiu/MKT3QmWO6o9vDWpwNPyxiKA1hl4rT9/llo4a+xvHFnt7eCF4zd/U5u3fZoyNaMql0ds1LtdB9+nIOFwCKyVDQUNy6xsO+Lh2EGClFIQrzEM8eIxI8RUhBH/mSUtgIwtIS5pcrPhSH8oPRAUojPMPGrJPJWy+sy914k/nUWnXnNnYB9PPf1fqOc41Ydc3W4Y5QUmOFMIO9OkM4RGShkFuaHOZO7rVVMnlf+ccB9Y8s91nZyNgd0epzYZydB44op96Q1YIVLf8Tu3CoGv+QwJ9/isIqm+x9csUnm81s3bJ1oKAM5c2cYWaz25RaER+iUKZYzAdj6SguBzUjEIr7Alp2eKjvr3856MgzjpyffP9bLioUi/SLD33oQ1TThf3SDzHUV1bZi0UBw7wFnQfgJJAaGnQjjByJqjkZjpe3IYD6BA3ZLq7fGSANYwufvLWe+lzEMy4RI0thGYgwSpfcMOzp6UE3ZgSmZUGbQbU+rxcmgVzs8GK8AOoQGSercDADZz3kOX9BkZdfUMwgAPMAhHZhNCAljqwg0ewttCEgRVCup/F6E4rEjRJxBcIDcJqeV7l9SkEQAYqhDHu16O3tBSCvfGXukBMTfC4rPnmBjAkIbxGzaThIysQEZLoPJcoDmZ7+AARp/+lhMl2Ams5Se/FSMshLtYe2Y4rE7wPVAQf4EGQoiNIx5trnorOHEj2aVgMmdfc2MCGstIuGE2gRubXrZZk5QNHQdvYIzAxtNl9pZYZE5uX/GQtkUKJ29HH4TXZ5iSTLPQwL3mrsbDB/hWmkBt6w1jwzTPiHXKRhjQNWke6jaW5pVDJz3tl/lZ2IFpl9lvqUtOMXr/jSBz/4wZV33Hjoae+cs+C08rCNUSRO6oPYHovlfVGCHKrZg2Q+cMNCH5MaWiyAOodrK+HdiscdBrBtxnoZ0UXcW4CeTJ6KpXHbsFxVZ9xHo5DesEJ+Tmjh3glDaOZZbchI33LG33/2139/4i3fWn3EWVubF1E+vqNclU+MMA2PrL6MxN0ENsZDeSUeS4XEuknj/NKQ90Bf1eaIMPkcKOicBB4tObkSe7nqnrAzJ+kkXloVbACvtkWM9oiJSak4Ccweryb8PA/hxjmndidF1VJBNRJ1+opqtqS5MgQXGgAAQABJREFUm8BC54/5lTHX+bCuIxKKs6SstZeqTkQsxdAGtAdwxEVKyzsXxpLNHJX6PwW4gfSzf1Uff/yJ3oPPwP7TXW8RQ7zMDBfjrhbxFOvlfYJjOiSnFtfUlpS1cCm3Wg/X1PWj5rPbnYEKigT3FnHGXmVjiOuBOb/enAopwfCEqLUXulf03gi+48ZZOKnDxAJ/aPhAw9+M+5U+oviOWGHC8Tt3O5kxpallKt4nld0SC+4s0NEcFlfgQX4yJbutqX1RLxbXmp9rZ1THzyWlNZiNS5K0XBU3a0pzZMIjK8WxRrtj2zahRxu+HVuZCB0tGz9hxZKepDGYNztiamkMow474NPRfuHakqXszFhBnxFN9Pz8V7955zlnI8997GMfwwMBAu7kanr1nX1gf4Gw8ofc4M3BiFPYsjKVMqMwzSCLS7Vzf8HOHmGZkrKY2CgLIYxLzNgYR57j9X3ve9+1117bsIviAfekHy9mvwOSa1wWYXTeNGzuyess4HHrLyMaxqIsiwRUc07cwEVBZ9Ra3CKYRT6gCvXQT1hzQRgSMBDCNMP54dW9mbEtnZ2JK6+cc9RRxsZNPiwg9nafvbkb/6qBkLN4sfWXezmlKUYyHnDjL4fl5s7zJ+Ls0HIJ0x/+cCtmpTIzCwQEEAve9a53yWaS8fIXkiqFkr1mjVPjeIjQvIjHglf8ws+rV/sL+fZwYqyiY8eXL2vY22CoLtiVKvu1pN9qCWkLm1jeB3cXn73Q4Y36/RwZzXrEZBz25uBcgbpDbN6IpyVkdjW16iMZffeL5vbdAG/p6eltbl3YlPjL5tpgkYuQVPxvUUuQwQ3dmt1cM146c3EwGRD6J+IOVXa1LwnvVfiVxAEQOmI8mQh3z7MzYtjj6Hcr1mruzC/FazoIihD9BRwQYeVmV3cqZT23Nn/Dz+1Vq3GQbwdC+YXLIx96/5zDj0w2R9NBV+vfS0LqC9+eeOKJUoJHgEYHk3qC5HPqKI8iy4ohJaMkUF/SI3xzBA7VAiKQpqu7qzkYrj77dP7LX9P6+rVCubpru9gy/PbO4vBw7OMfD1UKA5v7l8yfq3a0Wbu2oP/qrCc4Olf7ggYr5VYHpry/45i4PRzZ/s9NgZ3uFRGqr/KMNbQm1HJZnxOp2kPfcuLvUBXh8WXSo8aisZ5Fi0d22c0L0tnBYirZ7jN8WsCXHxlz1GKqrTWbK+Ldj8NPpbIdDerJgJbwVX/6/a89veYpuOgnP/nJJJj7iKCxZHtRBZkUPWRKl3v1gBBAUV9ZNZCrRTQcCjBt2qAAS+vlKRVgsEW4pFAYgMFKpqlXpWgUVg/hkMmaGAij0vBJ8q1bA/EDNB6JJyMJjDFZAeYrKWUaGECynNx9RbCW24bgxmTEK9mpKejV133mMPwGKXp6ejwFuCE9GEpUvXgZQ13oAhQnOVl+9QhCGhIAUyIPSmh90p7fg0NAUrU+ZsqwnJUkzYFM7zuAbS4JGXNrj55TluVFYl8wOSWlo4cTT9WYpBgMZXMT72XcZ0ByAllovvrExEwusT7BdGGGjoYlD+DQOozJKDb7hdt0RUwX/zcF3lAo8yzdBKJBfMrloQnos7RCQ8pX/XVyu3gxoDFzcSDMoEEahggSyzBVoDozZzyAr2AlH/rIPrOzZAZupAcx1nTQl3j+/Ofb7/3Zle+7asVoKMr6M6dbjbLij9TdxCsI706WDEvuyCT2gx2Fc0IhE7cXwnaSfV03hRB82KzKYUUnFGBBKOQbzCwDhurpD2i8yFUGF87rTsKPlMINFA7nY+899Ny33veTg3evOuOe6/7rPf9WVcVNHHjD4upgIejvfcC/oQnQszPooj41hN+ptu7ent6GBHuzvsK/DhdB9aT0/oI4f5sv40lX5XBlf0HZPm4d0uqKinV4eoWxDzAvoQ4WlNGSymXC+O7jQC8GpDsG8ilFbVLLimOm9ULMVkKqkTHC7IAwbQOJTXIckzEfAMpVeTU8DbFw4J4wFPPE+NBIVIsOh/1LDl6BAuyV+BoPeJzwWsFzdHR406bNx7zncvgXYsPdLOAQgrHY5kIhxOoHy15aeAbG4hOCj5C9qZaq9pW5TFXcf7UjYwgxXHCt8DBHCSxnYGvB9dCi/i8xtvs2FQPxASnZ9eSMITx+qpSxvLklv6Ozo51hxb94YZkNAdxXrHsudOLrwXmqBSBt6cLuTMDeMOzEw8LUHlNq9rVLFsOGjZFGIqgXapwRRpxUfX7hrp07rJPCnIPrTASKzNxMpcuWLS2Ua325XYnuJVTH8dNvhUaNkQMLXZyfxEKE+zWFAYmCnm8XTIM7UThVsfz4M7/2r1/74uWX33rrrVddddUXv/hFUfVpKisIMYsHQXDyEntDPgYLRGoEKQIMeQjTSBJympT6ZD0OXNuLeISTnvrIBoCTX5GBEIn2Vztl2RuUbr755iuvvJJxGSEDb884ZgA+pfMJnJnReZVCAxMhQzzPgU1+wpLBHbzF4qQwm3Folx05ddu4s7vIMh5ehGO6n0vdrN5moyNud0WUg1qFSbesr0cQZl8G9GhEuJWib2S/9e/J++/D13n0Hz9XaGnyoUAbYmmDtQTRj9xnMsVEdDgUPPbY4l13RrdsEhi523XkgJONc96i4NRRnP/Qjz/+OFoEIZiTbOw5IO/SdjyTYRJDTzUzeXaNpUYNhtyigxMLLtarsVZpW4mQUeq3OcQzlFWLVcz11RDrtbad9uPOjWu9uPvaHZoE6i+VMFk9AH0p2b+UyA3RagINrj4aN4sM3Ko4UdzbpAcrFQuv008+Vbz7PnX9BjsdT573zt63v+W0he23r3cwvKhUxQ2BtHq1VmUMeWE8WF5nntzLXcS+deueb8fSo6OjoawDeJXoYVqJMkmnYN8M//NZU9m08UUsLCOp9KJ2/DLaA4NDOueU/H44DXkL8YUATHjY4YcV8sV4LKTv2JX/xrdiO7YxJtZM03CqwWeezu3ambzqqy0rjmTaErixXSuWzRn2GPHEPSh0VV4J0wHr1R4oKTeCKEvqDKCHnQjxcLvs46CKLhcOhbRCNvebm9QdO1kSjI0OsqRYiUQLsWTh1j/HTz3pxJPPsFBulReMc7fZp+1QgraZNarrAs7KeDAb0v1h7Qwca46D0fBNYXVzuKaw56zh34MbFkLrkvn7C9G3jhrmVqu4xgmdhn4FwlK5ggggw6pdPJ4YrgarAX2sOsZlWvOSSbwGAsPnEw4WxOr++MCIrsUSzfiV1s1cKm4U+tb/5Ec/pN/hfZR1E2DO/EAxKaUxApCSlkIdQpWCgJLzca1HvGxN6DklNL6Snb7jJahPL8P1Gb0YLz0MINVsRid2AuXGFzB5IIWXjAlo8kAK/ogj/Pb09ACZzktZ8ABKS31BMix/ASt3jRhhAC7tqymacVLiSSTsQRgFDFUNBRs9Fn2eWYnNJdbm+ATCsI2Hm8wof71yvcj6GDapIC/MJvPKXxBmpQY6yJSyy4MnEGSMVxCvhOWr/PVKaQjM/NVLTCnMVvJVwHVbWf56aWYTmH1GLyV1gZ48wMf+gkaUzjXggbvvvhsdBoLPpmjg8Hgp4RNeAcKvV5b8Cv/wlfDsKzhlSjosnPnq7tCCLeMSbSHrAnPCeKDKmqDkB6+Cf4sA4yTzPkOQpF59ERKfKelQn4ywTCkjZ5NepsS2jpGHMH2QX2kQRP+aDXndw0lsqyh0WIYsRm8J82/xC3qIbbArh9eAP3MFGcbp6RCEZPyC3mc+85kHHnjg6SdWHvPgL+eednGmYnFprdiz5eI6YWc2AVDQEAlKvENPaCqqwlp5DBtmzhKajil2fEUk6bgeJYD3YpbwxSQoBC9OIHI/PABdtU6kwf2VbgoFmLONIZ9NiXT4IV/8+nf+69d/+PYj7v3Z42/46HPppaRETQj7FR/47q3dFHVk3sGumC2rGjdT5A5b5rjeH/dBDYHu/j3MNXo67LRHTQyYuYgILyr84uS5P6fOS1pxFFZJpZeBFYi3RvWWiJnK2+NlnUuPaIDBcVyDbKkOqYd1IIZZo7u3VoZMo9YcD80bQ+93JTDMv7GbRYxg5hVnrTQ2XzSOVUcgB7jYVmakLxxoH46mFx5y9L2/u4b5jhHgZYW/Jl9ecwrw00+vhSuXHnyEq/7C5RwYg3Jib4oWhZXRn5BuuCVlH/QUHURMVjSg38AfrYZGzEoQc7ImQItWA6DJDWA1OJtkxO8LJkW6ELk9jN1/EOKWsBxbtPksoxErg0ZzS/XQQ8KrVhf+cFvghNdPrI7sRdTrLZjmxX12PKQkK9pI3nTQCmxrz3ApiQ20rkWCSqAoeimusJDcC6bNEfOSre3JiAu74EBGMR7Qt4eHOrgrJsSSpI/l3L6c2R3T26PqYNYux3V0aZbEMIKsVc2Q38Ckc1jVgiWFu5re+4lLnnj00T/++c//8i//gjEepoCiZrOp/t66NPxlBtrnJMSMu3LlSqZYmgGpgrkEQ2jGQeZ1Zhd6C2uB7Eph7UYCjC0Zu/cXJXCQtmqgB5AZsvPVqwKFXnbZZcwrRCIXsg8MTerz0pmlAixzMSHxVaolHpDZBxh6vcRmpcYKR1JVl6SMQtVes5MVioCCTaBl9TQZrSG7K6Yub2NUYuQR3AStpBrAZMP8N2/uPMPvyxfy2spHAz/4AU1oXvHlgRNP2LN+c5mTmS4FsJYnI1Z805lOiQ4xryf4mUuK/3m974XncMDIglM5kdLed77vpJPVaAywdA3MLIFD3TE+RAlE3q0nkVcjGVB9Ad+CucG/Rui8rPQSiWDFcMmN3E73HC4MGSkJJwrCH0ORe8U0vCyyNpkK6Pj6Svsd7v2in4neWNcjZyiuoXT5yoyFdcburLjvHq+JbU7Z2D3C3oRRqzUde0TL647Krnwse/0vCtf+LIzH93e/d1E6+tywrnG9YBV1vTCyZ1vTnCXpsD8ZNWpiyEcIFtq+xzn7i89kJNk6o0FhJOR7dLaBUYXdpUKxEPA5Udtk5XV4CE4T5sreUgt9ZMuWLZAlGom1BP35u+4y1r9gcm0uyobC+hdLYao2PFK89fb4goVqSzNtB8LsQEodEhzQT6SKQn+UVaivCGHJNqQkDOeTDKGTbgvjIYfBdag9Pt3QBgfM1WtQvP35UaNYgEfwoY1nHm5fLz/xdPLYQ5TyD5XSz42urNpJK0I91Xe4Zr8lW/qvhNJ1QWDZFpeW2Bbq6oJycQcLNmFuGgyIKtjGX2Pqm8cUtaZUXjB9J4squw8ooWvxG/AF5iyOcdXTYNYqmEgv4mLDIE7UGPJC8a55EdPC3MDCMcvwwJ6YWk4n7KULe6797hXjmczixYsuuOACCVC2JgDl6+RfJDMpd5IGDZAHOZJlAhqFGB5GKtRF2Tu87BKsVJy8X6mweWlkAOBSEGRrjhjGIvLCD/UutSiFTxKmzAUofCC///3v57wG8XAF8fRKVEeWqPiVWYhkSKTj04i0GoMtafgkx2rqQryELGtBmHJBuJ7gMgxK8p4zUrJwwyoAmMMSDAiMjagiYA63wM+UDhw02BlmBCBQCulJSV7QoBbgxiwAkmBIdj7Jh5S8yg1zlBDqDjMjZ0N2qkDGhkVPaSYNcKkgyTqixoMPbeep1nvBT/EXmGTnV34jLBsR/VMufEyR528Q5TWih8kBF8JKFi0ls8NpckBgPVEagBAvy2Kc8YaaAyjLw7Mh4FVkOphe+plT0tzovRJbxiIewlwqMwOnTVfi/sb39vYyhyKooIRTLgjDV3CFtxK0T0sQWSINQVeaje7qYcjYSxZe5QAiw0R6CaYLTEnMKSOng7Bf8QyJjAn0X4izz1LoznJlB3kPMvb09FDWt7/97QsvvPD6r3/hu68/rzvWutUSN6Ewqbk38boarOBTMRrSMydKcdVgeipr6Dhz5gQvKhtHUoEmBk2VzWGuUHVsNw+romBWrDpYR7sGdSIZW01oamw44/E4HlDLNeEg2rGUNfOOuXfFu9/25C/fev1lGy+9CQM1bJgKVQyMBQZu6ROF8Md7RKEIHkVcTqiljPnM81uX9rRS09nQxAMyc8AlgEiiOwg2+H9WxotsAgtFJldld9feOa4sbxUaUz0cN5eY3BGtehPKYEEbK3J+2L3yNxgLJ5bH0pyIHoz7C0sW9nYU1VV9+gvjTr4iEvBgHSc2gTGcFWDdKMRRVgR8Qg9i9kdAqphGpqInFxwxNDTMNN3b2+uhWo/Jayr8mlOAn3p6jT+STHT2inFOEBqDc0gutjAE+3IWEX/lrv3ybOmIyRDCIfbJtIbDvUMKbmEEKBc4unTFVLmJF86gc+0bpkinYAjBhhWn7iv0FiF7ESd4Qtzee/rpzqrVzhNPCvdWEzeYNEJlp7clqvSXtZDGyhNytZrLVDJj2Y5EU7rDhwxHp83XxC5Hmet8+bU1fKxFdXteSufsseQqiXDcb/vCuK4VJNo8qrBsBryFLWp2j90R03ZmlIo4BuzDVwwbIxXT6s+oWIBsz/iuueHXe950+qonn8QFH4IC1/wwtb/q/OpS66XqIyZSCuM42iYjIBMwQwPG2KyqSukESY5hFCmKPAdgpSPxRwRkpmSNYOZJgmRMZtg54/KKEsmL2TNh5MUGKQc40mk2gzviIILgzJBfqvC0IccucgtbX/XZ52vIcNF4yyEHn9gyJ31k7Ddra8WaPq9ZqIJzUtrSZlwZAUWoXjwgjLhMwOU27oocVzKOvWlj5GMfSZQrYyedlL7k4mWK2ju3hzQIpnIZnlZg5iZmuoe7uFQ87n65s7Z+Y3n3LjUaDixdprEK4N43WJ8L4Ym9CCmsA3ZKhiFSjUV8J51SvefeQB6TSNH7WLhnf66oaJE3n27Gk0NDdsU28jXUSm4Itjl8CzQux25N4Itf3NsuKsgzTRHy45S/ZHS7sejP20Yp01iSUlLZAWflw6Ubf2s/+jif7YOWBT9yQfyU4/RQYPgH3x2+7b96zlx6aNtRz/RxB1QQJyOFomWZJsfm02ElZBWTXPGTZfAQPmBpAspFyj8wqUv2CERqegHcjjSDDIro3z9WztvOnAWLuuctXNZih6z8ls2bDz5omXsnuagopoZIYJQrty4tx9a4h3PdC0L3xYOXu89vYr3Neh9LfS8+qxbyCgqw+yDUSmmDfkG3kmGKlrs9Mk3DL0IJEh79AoQpGhNcXlE8Djr4oM2bt3S0tqTxupbLquWif3QEyliptBlN6BXGKs3JDmljXw2pNwm/+7bPUttUPeHY7BKPKIli+NNcT/EI97ipKk7w7fiHBmMfCJT+Ghr9Tc3YkkKB96sGY6e75klLWug5rGohMkJ5plUp7zIdPD9kj9X0kZKmxdIdqWYYifsJ8YzDfceFcjbk89tWDUGkLd3UEq4taQ9vfPxP9917L9X80Y9+JE2+aQKGPug/Q3fu7e2lsag7q3XgQKMjqxFDFyAXnA6JJEtMJiDbrbK5SSaPXqOESEWuPjHQSEBKHvosg8x0u9Ny9UHmlemRv6X2CwSpDRIgO3jSWKiLJAZPxgGkdhZcoKRcWJTZ+SoD3isxMIZEAF2XLDIMWAZMpFWJP7YDaL98JT3cRXFwJgfUUYzlUEk8Wfid8oFiEJBC+QoQIMPbwAFPL738ChAoT1judpKRPkiN0HIpVKq46B5eC8pCSQ8Dwy1Ak3AYNiEIw6D0WOaV4gVkFn4lKGrqaYy0PgXxiaan+jPUS5YFzNmkmTmZh5gMsM9Ju0BzHhhG2sLIZVkoP/PwDgSYnGSgx+IXAZYMyAJANGFWRmSbejiTjGe/0JNIQjHZIlCMGPiBX1rHU7Nlsil/wYSpf+ZzpNziI3HD4J+VGgkWNpgSoKwCn+rrJVN6MVNmnBxJemgl4yUCXhpZileWFz9dgNEMaDzTJZgcTwPxUASdnYwIUfuVXQIE/wapZnJBrzAGrJhcGL7oetM1ilcECWQaujz8LI8VsKL3q1/96qGHHr7unz/6gW/+caToICSULCdbMdPCoYPctYISQqJAWgWau4ZGEwtLXY6qcnYJ385V4UkTiViUpmtOwMfagRhgycAP2gM6MDK8TIAQw2ppuSZUg6DOzpbNTRQlTOqc4B3Hf/SkZ/+84MWVr3vm3gcPfwsgSqbCJjD7x41NIMoSwoptVkd2bSSYC9hRs3jz3etOWt62ZNHChmMCArNX4dGaxCawhZdcLu/FqpmznBzE3YPT3JQSEZfTTMFmVLslqrWE7YGQ2DQeR/LV9KKtj4l7YbUkHsWC4fag0lU0B0v25qEdVqjJH4kjgIbYY+fYsCM2gQUxqSznawa5A5hToJCVKw5Hdpm5WLgVv0ZcG9bb2/sqVPFvDGLqseNvXOhM4J9+6qnO3iUlJwS3YrdAG7rXYbMDI/Zt2UASJ90Rj4Sjq9k+KMBI4Gi+dAYycrMv5wSEWo1tsGXjyIqDghhohmYHEuaP+rms1Mr0by1UTF9cnRvh7NxOti8A2XX6GeY3v+Vbs9rcucPo6RVi+BSDHdeTKOFxtiyUREgbKdH9qraj14wAxlblmpUKawXhUd3C0sCPK6yqNVrUWqK+3RmLS1nEkE5vU1BPmpOpFHcpje2q5U1926jYl+5Oas1hDALZJ1ezFXusbJimOFCK+TjOV4uWs2vU9rfom/L+m/5w2zvf9qannnrmsss+z1zyyveBp2wPugoiC5/kL/M3YYZIyILoxqCMwIRKScxtt92GdMskx/4AV2vuc1KfsjgikWuRqBhVp0sg41evXo3592OPPcYrs+mXvvQlTHfAp6G9Gl6pDukbImcuqOErEJxCvrZ6VeXHP7VefFFc/2xp/bgO+Oj7lp/1prcuST66Q2kNKPOS2qKEjf8zd6AXAxn5PMOnbdu3c6n0kuVLlEx2/IQTk3v6cl0dqZ//lx4Vh3vi7rUNbHogzO1zZZq6AFnFkc+8ef7ObqeGO2pV9QfdlSdGSzFteA8zHIdIaRqRZQrG3pvQ59MOWm594mPl625QqwPsKXP4s6j7/e86O3DaKdusML4ouKoHl4koEZjpM28xKHN2vTlgd8VFlcUGzCuj80jBGi7pbTEnWRq37rpD/8lPQpxMy2XQ1pRHHys984z9rcuix+/xLxryJ3Oqfv68TOv5LSc/NH7BruJBLCVruu23ik2G7Yzv2F7EW6R4UAl4qDvz2QEs0EjqIIOimSBhI80HgwG/P7hl65Z1e4r+ePecBUviAbs1amzeODgwOHiIYLQJ8iMdepsziPeqKSyRmMQxb2bJjIQMlhO6kMblbMJLs3yAIDVe0EbZQBLaJ0ug1OFw/p577iEL/ejXv/41AQwNcGIfT8QfffTRM0494/iO9mIwrGe2KyYCAz43sRTQYvGUjb1xzxbdWUnpzmhk7Pbe+AXXaekuZ3SzPXSdFv+d40NEeXhC+hCVw19gJXh0uWtpbeway3kizqEwfXEZ0zHho8O34P3vej9VuPPOO13tyDEMncWJgbI+Wjb6x/FmyQ65T9hiGYplOKWKVStndq57KhhNxaJRqFDN7IGdfBX/j7/3LWiCH8FTTjlFyoLoYLQCzOypTxMk2/uHctEWGLhYsEP6ZJSg0VGleJDYGLX2JpziL3l7enr4gDLAgEZeSqHpMaKu5xzWIKRDaZQZkOnt7YXBplQDAEihUo9liGPAlKyIHIkCQ3aKkGOmVCNJL0VMKkvzESZefgIrvqJzSrwRVmAJuItIGQOzkUUmBm3ieeWBwQhLgGylSg2KQkGAWvBJKmYeHAmt4ZevUhykFqwRkBf+ZALasmWLLIJ60TUkNOkkgniAECNpRd25np36zrCTBvJyKxhQQGZmgfJS02vAx3uF/rS1NDMGsmwmlhVoI5kRZvASTxcA1ZmrLzPOMplMDEAQIAv8w0MktaMt4EPCLGrIdpeJJ/+S3SMj/EMtyAIvUS8mShqxAWE4lgEK9pD8MxngdDEwhswCQB4vPF36+niYwWPO+ngvDEDZT+XMTmK41Ps6OYD6TdVgkvpPcumWjECrj59NGPqDJLyKOkp6FoLZjpYMOZvs/7tpZMf8W+MAa9F54R/Jb7MpjoaQozEB2otDeaeffvrzTz646YHfzT3mPflhq1DWyvikxBsW4xh2t0L5FJObOyTw6prgYhzKyi/DNd6whI6gImDQwEJRVhUuLsXEjGzIFG67s+PlhJg9UZpJJYyWlQrnbyxF2F4FhOyNilGz9TU9x/5lxXnnPXztqbf/YO1Bp2QM0f3xn5ziYj1UPheYi4aoqJxvqUs4JqxXaia719zl0VJSQ8mUYJi/wSPqOC+h7Mmro0WWCYQ76EzVGSpwta+1tGWK6yuoPmyMIjA3afcX8J6FjgDpcAeNRbSGKDZAxVhbZ3ZIqLsLSsDKlqyYILjjYJzFHR0V1yiaWruUVHIFDMNr4hIpDkgb1ZxVC4aiCLNPPf3Uee88D+K/xp/XkAIMiXnWrn2u96CTYS04qgxbi6uG3KnP5TdcqCBJl0Ub7MfD1SYYQVdtneuB0QNxLsddvAIqgiSe0zGWs9S8u+86C6ACMcwnOAacioSqSq2i2vlSgas5/e6Rp9CRhxebmkMjQ4U7b09e9CnRWad6OG+Q8JtNITVT0YZymFjhvtSf4W5rrj8N6WapkO0b1NJzAj6jwpY3VxHa6mjB3q2pXUmVZSqBvOi8zngFtZvbg5ViXph2j5b0eFjZNKwc2q2PlzgQ4efqkSzu4qEs8il5GBosbdsol8oafUrq9nvve8dbznr8sSc+/vGPM/fzSweWnD0V1gcSR8nsfiCykBnIzFtMYEy9zCVMTvJmdiYkuWYsC2BWJjDzpD4ZFVHFvY8MezH1NaJ0lHBsv7///e9Lee60007DBPqMM87Ym3vav8zoYAU0INfDnDbDVB/ENS1btlS+c7W2fQd3AWG0w+AeHBsd+961rYnkitPPZNkiGgjMi1tz067pQt0oQqETs4XjPPLoI+Mjw8tvvjGxcVMhlfT/4jda9xyX217Gch6qBJiw2U5hLwX616Mm68LSnhjh/MK2SoIQv3tlX2l7BpMgAbCMvXbtWuAgNEt86qHJ7Foq4TvrTc7CRdXHH6/tGhDeuo5eoSxbNhRve36POVrCWgb7TOGkDeOlSBDfV3ZLEN9X8LN4MBVDDTuAdVPRL1jYctTNo8yJelvINtbvrP76t/5amV0hLtBEZYRRrIVOsPNKRS8GOmyuX6IrBZ3sIZFNSwK3/nHLBXf2nZMMhqojO0Zr1YPbjINdVQctCLFYao8zy15uDab9QQiDkdj1RUrIs9eMb61kZ0ck7g+Et2144Q0runU1jrqFSjM9j4kzTXYwoCxaHPjrM2I10FVOOJpLqZi35BcsCoSC7igxgQatzzMtTi//AGJIIfRQFGYauoeNr3QT5gasSmzdum1g987nnl29ovfc+FvOyj23lmMaarrJCgSoiD/o01Px0Im7FJWh0Z+/piP8hn/SCoHyQ3fVfvozc/fG0HeNwLKqOJiuxFFOMRNzar7cxkB8ScWJ5FMX2ZmdGgbnoTfiCsys6XON8Ap4DKkdBOE0XWfCUp9dv6nPbCoE27gEsmYVFgbXzgvfl9C3cOCr31y8QTul2NGebO3hCorWRPCIpXPnJeybrv4yo9DcOXM5/Tslx76cAC97Q+zGnIQBitaH/6EGEOgOeDEgHVRicJNh6IbaIFuNNFJrQgFDAabXoHIgIPIK8zAMyuYgMQGAAJAiGJF4lQMgaeinDTxw6aWXcqZX5uWGNh7Cn//859HGJXz5iVwgQ6G80pUY8Ti5TYcl8mV1m/HFJbjAU47e8hdNlRZhFAU9ckMQklEcr3RxqaLMCHXiI2B5eCEvkOXSDPQBIL2DLXpIwQgAcIhDSrlyCnyoRw9CL5WKdwN9JHQZyS8PEIiUSMrwBAYz/gETOQHRdhBWbjbOmGPiIyq93GafITHjJxbj7JYDeYZkDZ+oC4MGwKEG4wNf5a54Q7LZvHLBAdAk/UnvBYjklSZAjZlZZZJZYFqaQyq6ZIRKklDUDlLTa2ZGBiAwkgQlw7Q1WWRjSWSmhACzkYYsM6QBMk9DdpYMKOIAphXK4tw17XXqqac2wJzNK2srGCCQEtZlk5xBgDBjBT10NtllGsaf2Sf+X0kpuwzDF0PBDE1TjxvDaf3rcccd95n/99lvfPPfHvjNdy868c3NoRiOqSr4mlUUXM/qYttKLI4LLZiwuD1PWIfCtuKLxransJrEewcCgLj8DtBixxJvWLiwEgKwEEMFw6PHciXS3i0vlbOQCteRciUSzlc44WjiT6vG3pj6h1MvOWPVzQvWPXjopkceXnomai/HTWp+tMEJOC48+SM6D1af4eYO0KvkxkpjOX+iO6PYza0tdclexSAl2s0RvTNsDobESWC2c7PcCVx02ATm6hAWhSc9rubKTcJRvS1s9ge1WBkdREgKubKSTrVHW9srtoKdaXNUaw1V4wFFM4SvLFes4mYptSr2AoTnXnfNQYu09rAEH2YreOfzWqw92ZIM+Z2uhYc+t/Z5GkpsX762nyko9L+IMJPctm1bV5x9ca4i7BC4fhruFascgsPFLxbLbGziF2r2SNIPaQQ2gTG5ko52sDoWCpYQIUVDcmoM+wfWdZrDnBSgHLeofRSg4hh27ryuypjic8q5kfGmlramRBS06I3WWWcqv/yV+sDDzoUXu5PvFLCw0WiNKoNlLZjFp7SoD9MI7uPyMb3F5wwPlnzmuKF07t66NdrSnYiGixVzSNPSEayazQVJHcL0Z208v+3KKJmCzRFajiJ3J3wvjuJRycF/zpYRc1GzP9tndae0zSOib7MJjKiGbyQcsuRq2g78mrOZnA/fePOt5779rU89tebiiy9m3xX/T5OXhKeowKyjmKiYp5k/mFYRxbi6A6mO9Xi2mJDGmFFoIL4ypfFJDvEMoMTIwXTW5QhJdN26daQnLzClzSHTs1zRJ5JPzIg4vkb7RZkhDTPQ1772NWwCSYa8RQI5s05XKCgdwNzZAM3OFcoPr1R37OKoCQuNGCAwJomirero729tOfbowzvaKxVzSQt8+jI+B2EPFNcW0TFSv/pl6Oe/ZL3OvvwLoZOPp5dQc9LIdJ4s/lIuC7fGwqjVi5kceKmMum9kYUF9Qp5Qle3bd2zdthVC44lDjI3uU48eEVSqwGRw2CH6kmUYypadQMEXGjSNVVurPt2P52cODhg4VPfruJiO+pz2hNYZx8sinVUApEQeCXm63ykTuFYjHP01c6YR4roy26ytf94nLJhVLC5wOcYsWOzUIt/zKV3Y6eEWoy3/APdx+4yFltFbCuij5y68xjGSzwy8cfnieUd2+ZrDwkO4LAtRb79k1ikxx4ZNxnM8OxqPGb5AIb40YNH+Fa3Qp1eLI6MmpdB8cKNXxwbyYvhsNDeZp59avPMeX3aERT1BM+DimiIYCDIEJRtFKyDQE6dTUbyCENHQvZPpVDKeSKVTvT3zO2MRo4pTsHLVWa+pT7zl8GcCvkf99kORc+eX1x9e+/PT3AvE+iIXI5nJRPTT52qJLwu6rg1riZOwVS0/stL+6tcQQcpHmcElXNAGb6Dz9ahKp2PcpfpMfyiQWx+MHZR3ooXQpx0jrahJnFOyOnK23W9edfkXN+ze9ezatSOjCOXdXA7XP17JBLXRnKKbm89v+uqS4J8NtcAgIjbBVfX1sdRj6ukPZ64I+bWY38S7v5Hf+R/f/S4946v/8pWenh5qihzMr+zyaDjQBOIg8TNYTW4yIhmXjj/+eMRWrGqRWWkXGkgqhAxWjGxSsWTkpH/RTRqGUOhJWQwyJIaXUCCJ4ZWALI6JDxEZvQjFgxg5drHFOlmF+OIXvwgaFM1wzSLFu9/9bsLoIYx+VIFKeXziBWTLUhwPwL14wvITaICwxKT+FwWeh9phcSrjySvDcgyXkVCDLdb6sVGChQ7gJmlbD1aGIQVaHHovKhNpgAD9QQO+hsKEaRfmC69cSRPUP5RAiEkLzqwPyGqCCYYwmOaSHnwk6YDJMhZVIA0JYHjqSACwICyVE2gl5yYSy+rU001iNeWvTDzlJy+SNLNJ5qX3Ag04NLx6yWYIwP+0F2wMs4ED9WWzVy4wwWz1c66L48QIPF1BNArDFMSkNdmaZgYHeH1GIMOcFDoZJUkBGAAEgM8rvUB6XAOm1zUmZySxZ2bf8BUg1EjOU5L9aFzSg1V915ZFT1epBpjylcTwg5w9CQOBLi/nhdnAYUFHFor2C2tJOnsLB1OW2BBJKXLnuSH+NfVKvcATZpg9Vg3U4/ULl192zTU/Wv/cM9sf+d3c4z8yVrZyJQ2tTDivMoRVKDM484t7IlIoBmjFPAjefMI1UCSoYhHFPhk6LexLAjowe2CVGttdoi/LRREmGJxmoe4iqJDR1Sw07ECjPht/Wtz6i2kwU9Wm9PxbTvvEh+/85ht+dtnzXzt2OBDDh06x4vi5+c6tJH9eklNcYUio5qKxHQytw7Fofy47lDPbY+wryByzp81sUopdre6kuqugDpWVXAUjbXFwF0Po3eP2Iq4idgd8V6+ZgCaQ4ECo4nQl1L6CMlIRNwljkMk5ULbThkp2f84xhzcXS/lCzkgHGTN3DQ/2YeSV7pjv9wXdi5CEsCFAu562RKUhle1gxYpGxUQ/d/Ghzz98M4T9W1R4NkSZfZrXlgLMjFguV6Ndy0fLDDH0BcHaE0Tkj7gDiS14cXHR7GsoIUR8Cl7Cg+xWVNzj7/Qe9k3czU4WirDsh3WQnvYWNi14t7uK4S8ZVlnqgKlZnWIHKVexmgSqYhdNZy/xl79SHniIlXwnnpiGC9SWqBEZrXGHcMzHoXsc1WHzp3EtdTbPJpzFPeXsLlSK2RbN5gZAtB1MnUfy2IHAVnbfuDKK6YItLAC5CqyV/RS6sirOA+BWiQ49lOeaYc4ZUiWtI44+oHEMmA03cSQYrwA1O+PoO0ctPa07vtTNt91+0QUfuPu/7+XaW6a022+/naG2YWCaliIzfpBApC0ZEyFpEXFYXGeWYtaU8RIAswv3Pcowkx/TFclmhN34UcqpxDJLcRAF8dGb9uTcs3XrVuycf/Ob35CGSYhboDhzQoCZm8mSnS7iJ0+9Mi+fqIusDuEDfxgY0W7XbXTd9uJuwR09Ja/DgNt2+Eq59pa2UApjBUp0h/a6wuBaOdw2J9MHl/LdN/7OZ5njf/d36U9/mtOr9AqvY4CqJ9R6VfAgeTH1NaoPeyllANpKzR/C7ti+fU73HKb/jvZ2uoxYjqUfvTwDoGA+uvOcnp7+SnSkHNpdQMxkNZG7uHwDeWe07K4mmg6O2uJhozli4fuKI+7k4kgwlYRDQJKmBDCCAs9k9EjDVgN2BFKeIJu7LilWFMK62hG2uKoORxFOrqBhpovPJ7/PSSSt7Lj+yZDanWcmVapv1HIfrV39+YQvPKKb1nHZtgsHfcHSm7qvGysdPrdp6dzWsAZU9wEBUGKHByaRGx0vr/RLbx55iWpAW756CWjk/rzqw2mRSGn5a9lHHnkYbkQlgHuxTSU9CzQNhnwSE65s0hcv8//jJcUf/ac+PKgwgDBKpBLGBz+orzhaDQSBCAJeWYTpUw34eEizeESXQbtAZERsrVWr2oL57YlEbTxTuPmO4prHwmfsTB29QVPGU1Jrc3ZiQ9bzzy3Zs99ceGyOncsEFi6NnnGq1lNxcnnkFHNnyDj+VK1cNn/3+1gTlwzavtOHOSqEHRsNZWa2OcULfR2DivF0sKcoFtTBnmX+hbjDdxRLrz4Wy/70weD8bNNb33zCMceOG9pdd9/9yCOPfepTF4f9vp3jOc3X997Wi9t9q8Soy2QhLmlQFaPmV0ZPbb+5K77tmocurNWwomi98tLLYCs8Nr/nPe+R9UX6h8iSMoxL0ARqe6SoD3jkokXoBTILkUi0sAEpkeDRWiEdX+lxqBYAZLvVAwLNWfIgnlx8oiC0X77CtJ6UTxqu2wU4qho7RaxTkGAySkBg25BPqGr0CLRENkxQfRm7sJqWw6nX3CSWODCYg5j3KiPlL3vO6HsN3CU/kZ7+Bar09J6eHl6Ra0GPgqgp5sR0BFJSHHoL64moLuADzqQkcvPmzQAHwnS6CvRkCgA3uRiB0gsNKQtoEgF0b14JS91VmoWTixhZd4ZuiQC/U9aOeB6wpYIgQ4uj6IISkZSL9uJ+VxjTWH2QaBMjT656nQ61cHJDyIzeL/Wl1wBfxtA6FAdA2Yvrk8kBjcREggxZSOZh4qUkMLkd679Sd4kwv/Xxk8MeHD6R2JtVwQT602rwhhw/SSBXGSARKWlBfiG15EYPcj1AIuUrVYaRvE+0HWTnK6VMpw7xCcpL5iQj6x2UBUrk8mYur1Av4BUxXcUxDZD6vCxdnknmzLOsOHjCYMwaFATZpS8AD/jMARIzbtDdJOPBrpPTgx71opSGLVCxktSEhCi8eEBtOXRMzj5zzHRVnjnX/9hX0KMX9/b20qavpNBoNMY1JZdccsnVX/3c9+9+e1e0aYuJbyonr+F5Rzi7YkpH/RWML1RhMfhDdkpHDCGAH9mSYQdx5myLg410SlIiG3FVkonlp8uwxJG5wPUTIaFAumKTWjVtblmp6SoediMBhZVau6oUFf8dx3zkrEd+PmfPhkM3PvTAwW+yVAMpGrtMtkknwImeRZh//E+cePcFo5HWudxoOV4tb+gvtMZSU08wr4BSE/zgKC0RvT1k9wecrB8H1EKJGC7Y3Jw6NyX8YAskxQQpiEPXhocZV+meuMttDVp9fiVr4HJIaPu5sj2I5px1FsSToXA4nsKLyq4mJWYGQ4UazoiEhIl+m68AUFTYhSoGWGFXG0kpRgiJi+OlTXOX7tq9e3xsfGYx6RVU/VXL+tpSgDEkQ47vWLAMf4U4NZG1lMxNGCZjCQeXbjhtFmzsNsEEE0xPEMGOirBqUArillElbws7CqHqTqxQ0Em4zpctKVe7EIn3CZNEsaAWUu1wQB3HC1WqM2f63YIEU4RPPLnsMwKjo+U1a8KnnubiT46J6hAiyH/YX7AJPFYVe8h0PL/PR7fauG5LJal2xnCnWbUHXswP7hj0+VvmLefCILwEDxWURERfs6vaFA6E/c5IvpaK+sbEwXcngeF0rXpKr4+d4fGKiv/bu1505qZ1ThQnAkY1zMEAbkUS2m+1Zvvd3/GqvnmUQ8W4GE7+4pY//sc3rrryqq9jX3f00UejCZ9wwgkCVUH2Osxl1Cv+RaTAk6oH2QtIwAhSPPtbCDBlLqZVHrmvK4Ew63z961+/+uqrmfwoC2tnjpogpEpBhImNvKQn8ZQzInfYDg4McEpOznz1iDVgXv/JC8MAL4VhbHjYwO2ghhMY3BO6BBaszC24lpCx1STam5dhbwAQInIvKMynF37r26FSIbNoSeoH3xVHdvemnPyXUQ8RWQpbfEXVASWkjelk0wYIso5SKBH7P5qeDIdaKW98zBwfsyBKc4sWCnq4edlBdkfGGbR8Lw6zNhhoSyjlikl3y1XQNiz2E3FsweQVNWzG4p60cEExNDRev+3DgEDpCCuexCCJKVGiRjQo1/+4JdpKJmcPjdT27LFqZqyjNdbUWkwnOa1f6+5E16kNDQfbWvS21nyzFT0Tt+9qcUvciHxMr4YioUgFf03FiHlfYKTJTH9gMG7sPjT9SG/6DKGXAd0lO+SCkiyXSBwaMCEVWEnECCPd8suyDr/eI7N4r9zCG4jEx8aCG9av4+hqS1gvDO5A/kZ+QlpCapcitRTdGvLC4Xt27xbua044KbZwvrn2eat/sNyc9h92qD5nnppKTsx4bmEIbXQBFoCwhvAkYA8NGaAsHmRHZGL0tGAgkOBY787txW/8m/34o/EPjcdfl0cnUi3DzhlWTjPSnBsvO/6B+Ir7Yidc6gSuqBiYMwcy42tbHfxvmNV4LTZ3Tm183D+0x6mUTRy2dbJYKG45ZyrWinblhYKv+V9yz/y/+CG7VD+fXERYTcyHK/cmtN8nWnIVte/B4n332BdelP7why684EOcfUHhTAZrK/zjTcpXOvyrxURQCBZ/q1kPcmZG1Q73hT+sKZ25RZE1F5509ybnyg1PPvDHW/+AKvKv//qv3p6eVFCpJhJ/b2/vdDTxSCSJj9yApbFUh7yGlmkQu+kHgGrIQjK6NDtjbF3yCRMbxhxeEYI9MZE0oMdDKWi/vE6nr3rAZYDGkqbXZIQt2UmjBVmooghipPYINK+ghuy80n3AnKGPZJO/wu18RU+QsjtpKBGZhhqhFFEWBaEhA58HfYBCiSQZD5DhOpQfOoLsBUTWFwFYhmu0aJRntAXOwlAcWhDsJ2kFPXnIAsHJW08T2QSkp3RKkVbBEnhDKUTKoQPc8LbIprEEXo8JXYlKsfRDZamRbESGR5lmyhmhPrsMcyIUyZIwCFALHsJ0N0+35BVU5dgrk0lNm3afchyGbmj+DAIe05LLe+RygPc6c4DxhKLrKwLn04/Q7eE3iE/dgQA9pSoOEWSr0dCS1FBPFiHJy2ACTBkDKJRMaEjTk0ymlAzjYSUj6yEQJiME55w2CJCAtRVq2pDRg+AFUC/BSjKGF1kfoIm9dRMASo0ahNHnZTJQpRVoGjhhv8QMOBA8Wdqg9Mls5uEANVgRO+SQQ7wYAjL9lHSoTzZD+JXknQHsq/uJgVRWfAb67LNE8n7kIx/55S9/+eQTT/7kS584/8rfDxUsNms4KYZNqBZgmxcWEId+BVe6Fs5kgT7uApTKvTycT2QHGO+2CBsTo467CezjLIXYbHLndKYLi5sXhesfl7lVJhfXwRDrunbUL5xFV1g+x7dOat6DK85//1++d/LtV69ZfmpeDVuK7irPUk4ThVMpCnLbaGKUw7CLU42VArc+BnZlue7UasK3515O2CcR9isBeHQlxUngMWxIUdpNoc7gWkjoAjuea2/rkMItMOk+dAQMMBkN2DLr4PxwUeEqVnbXbUVcvDxeUoaLyvx0uivKerrVtmnATrAv0dSPUaa73sAmIvchi7UH6CaWIYSGbWt6pG0ukWXurKkq6Z6lOFd68cUNxx137H5V5H8+8WtLAWZKiKdaIonUSOZlk+VeumDbYGP/jPWCMEB3TSH2fpr5r4N3KE2x/Hi2cS8LwVszNg9SuuV8POe4sRxgvZqLOGDRmWHxFZbHhCARtqPC8Rp3TbZwESWrSvRKlXMK3d2VY48LrXwod8PPgiefIjhkYgCcACz0X/E4XVFtV07pbonb/thAUTPLphKIWD51+7YXaqVMurkpHvLnxoYNfQP73m3zFpUsgwPD6bCRN62IT09EODZcxfJ5y5izK2MvaAoUypjO6o/vYvuMIUDbMWovaPZli9WWmA/tl2PuNdPyY1pYNf0+g622rKJvHbHnpfUXhrS///xXmBC4jW3Lli1nnnkmdsK4hvbkgAnUX8EfuhzbF8z3wICAPK8A2GyzMvPh5/l73/ue1FuY8z772c+yuCjRYLxmzEKK4nXKBV1ZDJuS/QODiXiCs3tuDPtbYrBz6zCbWojxglGXPc2do3YLR0+WH1S662E4UMZjzoP2iytf46Alaizu7qdOTR5RLobc2fHqV7+SGhgstLeHf34DrocEEu4APCVdmC4QpPjE8IcIy3I49Z26gCnzT0SKGi9fvkzH9+9zz+X/9CdjyxZD02sL5mtvf5t+0CFaMuleMCYuhnGNHy1cTezenR2s1FhqmdOWwJNUOGjszqh5XMf5NGg5tGdHWzLCnUrNQas9xlgklpDlsXCkeWQsifZkccedaVy0hCmUe5BsoL9630PWzTfpu/ew6GMm0/qb3pB45znKnB5j+fLKkiXaps1m34CvvSVwZFyN9dFbzeIJ4fZ55Ycf9mtGvoJfKQ7bq4X7ok3vzqj+0hE9u8Pcu835CVf4Q4JEQOShVH6l/EfvkGREVEXWr9/3k3tKDZsn5CWe6rS3t9GOc+fO2zTKZKq3tLWZxVyHMfJMPo8nNsRxFDNKPOmkk5DnKIL60itRMxAWaUR++frihg3Qp8Y2Wld3aE6PnyvdAoYWjmBLIpAU5h8TG5u0CNqvR0YINly0NZsZvpRKJqTcitTL1EhFkN2RIA897BBndDjz618bq9boK6rJc4RDBnskXPwPxfiLZTS1VRJO9Yjh6CcsJVxQyldbwdfr4cO5ULBvyG5JzVPs9eEjK1qbr7auqBVKTv+Qgm9OMbqy4iG41drsExcsGN3V69pLoZq9ohI4UzHaBrBiG1+lhQZVu6lobs34iqZRKZe+823ldSsii5fCV4sXLFy3YX1bZGN75SHRG0rB7D8qocftoLsgam9S86uqsevCerq0MHJXPHXRcedewtB8zjnnnHbaaZTLU8/5E714FmMRehpt4e3ZSlD8wrHI2YjyMADaDvIxTUMv4xPqBC1FcQR4KIt+x2iD/I3cL1eUPDj7FUAtueuuuxjcaFPAwhLAp2iaD8meImBRqQbsF9iZE1OQTMB4gkTFK+o3zqjQMCmdWkuHgqhV1Br1hsSQhYEXrQaE6zVYryAimRfkJwJQkrw8XgIZqG8yYjwFWKx/uZoY6hODORBmzsjXBlDEQCvRPMEg9eKZDKQB5pSv9EQowCc6EXxCVyUMzPrEoC0HBKn79fT0zDAOAw1VDdV0MsKTY+pLmRym+zOc1qvZ0BkgdHkSwzaSfxjNet0VHNYgUOZp1roRQ9iHgz/0IQttCrcDgUiOMhGD9otOTh8hDHC6CQMdYWjLJ5YeCDPDyvGc8AE/LI+CFaSbEgIowQY8fGUcAxPZEHAUsx6RZKfWTDH4NpdcNCWcKSNpTeBDLrr8lAlkpOwmpJwyDQ3qUXXKBDNE0ky0i1TpZ0j2WvgEEaajwGzQow9+89+/eeYZb1i3+v7ytlWdba8rWA7bVEWcLQYVg1uKBJVdyzOhdrr/Y+soeFOE6ckBNoF9whsWRnZyBkcMD/vw1yTUOGEcLXbQaoFa/6LkgKGZI+XWUauragfLJnesss/MnRRKmfOMJpa9+i2nXHTSX29d+NcHjnvk5rtP/CCqH9cFY/KBXyhZHYGNCLr/3GES6LwEwpHm5oWmz9mRsYXrLBK9MsrI4hp+KaiN7dyIOPobKiv5GgowV8M4e7JKwBZHEurT720XKKB2xNWWcYet4zFuWjXFAWnhQ6uINsHmnM1GYXNExaU2t72iJJf4LIiGq11xYlTsogvBRKhhWMkhVLiEVfNVu6V5Ab6G0eaO+z8FuJ70+wxjmpLunN+X4dT1S+PH3gYTuZE/sT3AHIKDu+zmzuahwbDRjAZoLtFCrN2x2+/XNYtNKdf6n2bnsPvoWGG331rYlSb9bB6SNYfUeEDvUyxs7gsmVtB2Aks+uMLQfR/+sP3ww86f77AG+nxi7R8WnAJwJKg3BWpjEfLqQ/mqEdADSORKLZrOxkp6qn2eL5IvGumKrYU1fB7qpbI1qhupiMM+ms1dnCqn1TFMsJuDykhJHcxzd6g2UjYrVW5O02IBe7Si4w5ubtKXzdfmJH32iJ1RuHOYHWC0XzRh4SA6q2ibh+2etPrsgHLW+R89/Mgj/+4D79+8aTPeVm655RY8RbEhLKe92ZBl5jSzGfpZk2YeZTadcu6fGb78ijglj1my5ctOLxMhLMTshd77uc99DumEZIgXTOHMizNUrX7gMLhuqHhHrfysbY1oerMaPloPn4z0Sxpv4JseN8GpjJAbh+ztOdVOhjpPOH70rvv05zbAEwymWPVgAqDEo9Hz3qGn5eS6d0TdC5Tvoix+LTN3xZfjf74NXzHqVV8PHLUCPhYjEClf6jR7s7l/qb6cNZk+EY7Rrxoks5elnvZFdMQwrobXPG1+59uhviHOkXAkVln/YvnJVaHPX6qfeLITDYMh4lGFPShF2T5m9+Uyu3Nj3GDga49qhm+04HCpNY7NhRmCruh2Kao5TYH0/NBItUsAAEAASURBVGZxdCybyyGySMGddXQkp8lCPEhAB1QIZC9BEEfNjo5FajXz7rv0a64L28L3Iu4cfdyFe/1Pc+VK5GMX6G0dvks+Wdy+w7f2GWvnHnMF1wqKbhpfeLJdrli33cnhIFykV6slQ/erBcMu6pof26FR6uI9jOaooGyM9PT0INtJ8Y79QKZqIqXs6yWeIYDITr1kAk7dYKcEAyVTLekmTrWOI13RNAhtVA2VlfaS7Ep62JWyiMeYFusJpEmYnIf+Mjg8snDRoonZd2/ZZEfCQxsBPR7IRe9D+CNy+56Bx7fk4/GkUR1a0pHt3y1ufAExikalQYIHfpKTrsVM9S9/wX1g4M1Fx+DGQH/uS3b8cRYS8YCU92tNyq+VXEmJX2ao2pA68J+qeZUvFFaNeNl4Q7S6wZcs2Or3/Z0fKdUqfo5f1ZzyLl94cQUuUmu+0u/N0Cfmq53tASOo98eLK7PB02uC4KqZfL2lnFLE00jtaV/5+476rBrKFWorHw11dOafez56WgRrWSN/P5ZudKryHVr0SbLtneNxyLFNK93oRC5WDTX3p2s/uqdvT3tHx3e+8529hHnpL6SA1LOUgyEdJGJoQstFjpd8iAoqNT1aAR0DTQPoJJDMiRrQoMsRQ/oZuIVBT0rqL2E5VYjSAcVDgLIoFz6BrwjvszqwEHVhAxDAMAa/7F7yS9ejdAI8KNIgKRmVEQNNjEi+ol3Xw4ddScZDJDApnb4PTXjQfMANXiXMhgN5vcUgEAAyPYgAYNkPR/UCH5gZzUQSViCx94GHZUr5K6MZtylChokHGoubsoMAYW/Wib8yI3oLCbxP9dBkJBkn5/XSzxxg0oGYkjjeINaQBeD1/ACfkLI+zWSU+OpFerh5MXz1Iuvh7DPMUAAQEGaepfWhP4H6XICtL4VPjAySOYmnmeRXvBhgccCEAihGbH6ZaBhGgE8kuWh9GEOGIRExvLIsQoDE/KKRUhbpaf0Z5mJS7u8DhhJJMtIucAs8Bn/Si+k4kv+h/2SLgOkKAn8A0n/5bSA7MVSHhQPyAh/2piDCcjCvTyxJMV0RM8czBcDn9PR6gDNn+R/+Ch1gJ+rOMi5t+krwPOnEk84664233fanO3561Tv/+dcD/hDDDA5TCtwcERL7jwg8DB9iCKE5xH6keypYnGsSmnAMd02mTYc0uTDQHRBIheGdX7PKXPnrWL3hv35y4X+c2PJIShvA+hcfjC/kXnfjjo+uK5xVxahTw2hULVSVKpvAlr0tOe8vR73ng//9jeMe/MUTx503rkeYxNgi9nPMSGAhjK/Bg/rWKZvEEKHnqjYumjHAzFadZIA0chPl1W0ZNcAmcMzak9PGyk6hpuWLlVHd6fcpsZLTapoM1EzxdHOGaApmviAM0VpbWtsj1p6QuEIJkczmmpgKuq46XHTGy046qC2ZPzdcChRHRWS5IGoEedlCq5UV4WLXxzU1QvWHwEihQqVml4WrF6LxSLIFkWlyT3l1q/3Kob12doAhrjh+E2k7tCSOsgtav7x6IgEeodnXcI8BO/SvlyeY7o12EScHMNkv2mJdB/Ngwq4dNV9wEC2MJfoGx9NmeVGXGLVn8YCMkw6jl9rYxAuGM7FPdhIU4yIVPfttOTyLDA3k/3hb4pMXwjRTPaKvdiTVjQNZzfanIoGxMmbMFiba8ViHplfSLW1WJRdIprkw2DC4DKmGU+iiafePq3ObjHy5mgj4UKGHCmZr3JevmYM5bQgXz4q2oFndNW5mKwaO7DKqNpC1mhM+9oqbE/7hAYvDhWxmiX1g4ROLnXC76CibR5w5SdUa09vmHnbfQ49c9tn/d+Nvf4vB3sknn8yWKZ5X5Kqq4PQDfWReOVUw1TE+TgkJoR+V9cCWOQHOA9g//elPV155JToAYSYhzvri59mTwMCEEQFBny07RKKenp4p6wWo7du3sekUUVbOD/57aHwjvd0glj+jhmkcpnV8VQ+fQRylTAnBrSD4iHMom4at7Xl924i9K2Oe0T2v4/OfG7nuhsojqwNKjZHIXtgb+8j7fUcdoQZf5o2GzFCcX7deYtmycNMt/mv/E3OU/Gc+mz7/XSPDw5zu7urskCndEht/PMrXf0AQYUxkJ4ehsD5+pjCCa39f7ac3hDNIuKZdM3F7pOqGb3go+6MfJyHjosWIGlwVS5V3jFaf2PmCmuju7kx1xp1i2eY4Chevc4RBMcRN12yAR3SrJeLrjjlx7gFWFBxrMSJLicSjpxeoR0yK+8g0/QP9iXTK6O8v3/SHiMVV8BgyQWy2bSlOqd36R+uUU2pHtfoPOyr2kx+X7vzvyrNPqwuzjnoPCwmKtmP8madC579D6+wKYdbz4qbiQyv91fV6BMHaKJVjhUw2mZg4zsR+CIK+1HaQnxBkBSNomlSKYCdUKb6ySM8cw8YO5AVzqRHBYzyigcRQJJqTvIw8o3nw5QCNU8gOLWg2lVS6q1ucokTfQAtCXpcWeiKx6xmYUmBm+NaTVimdxNDB5ha1ifXll+gEwjQxkitiKN0K3erpZ56huV/sLzmRdmyOM1mr6ZDmYi6DyMJDcfJWD/CncS0W36rFYNrvP4itLbX8lB55Au8LwmexWq0Q4LCU8oeS/cGA1sXth3dp+Q9FjjiOJcBtmTN79Nujvi1a+RbFv0b7nFZ8VlV6AuGjhEN7KFDd7CtXFsYPXqpGk+rrT8o+dn3yC6Naa14sDDgY6bCmZ6Ny+4+u+n4czF7uj6608AyGbGPjAPmggxJzOs0cVza7rjeeFLOFMMcS58KQZJBB1BobTrauaFY6LpyfXfypT04p5iL+on29RK9JIUl5fgVejkNrIjejBMoYIqGq3OxleERPo8UhI7+sIPB1MutCXjhBbohNKk2kn5xlcrIGyCAD10neAxmWadDGSSPVDBiVpwEsAjq8RBo4hOwyzCthmVKysawmnCxVTSoLv8mJgMQ8WPTQL4i599574Vi2teFGYDKAo0KDEss0VJneKmUvb8NTFsovmDAR8KA/UKgE24CtpxdJfDh+TAD4NAdVI8yqBLWmCKrMYI6OISHI9BImv6hYXpgAqhe9gwDJoIAMez3LS+kBqceKSNY70IgkbflEDMv3DAKQi0/MXxJt4uszsiLA+CAtEomv/yRLhCCyggSIoV7A4UHP9BJDXhq6wcjWQ5gAeWkI2FuSlIYADRRUPtEErCpCcMKgSgMBTeYlDQFKgUXpLw3zgvxKAtqXKpCGMIMVKRkP0f2oNXkJy09S3QUTBp96S2PgeLwHBIkhkYxsXgWJr3/4KtmbSIrmVdaFImbIRdeGbmSRYMGEcqkyAQ+H+lKmC5NYfoJWMPx0GiwVgZIkluklVeEHHg/ydBX0EkwZkADrP3kxBwawHtSrHpZDIgOF5LEDg0+9qCMOWe+77/4nH7zzdavv6D7iPDRJNNKirobx8aGK647EWUZmFP7YGHCJPQC2uJhYaTGfjstbnL9OOM2VTUhidoaqtvmG1t9++eArUjqLgK5HaUWNKOUVidsPPuSh3++55MY9/2RrSNaOOAls4nGW8U+787gPvOWha3rXP3rYhodWLjuLzsleMZfIYCTmairCKFUIx+JN/BUhxhbFyZdrg6rVGg3sHncSbdJq2v3+6v6oantcb8o4iZw6boj7Dkt92XKfvTjtpPyDff39mzZt5BIEuYjD6AGSmqG1NLdiBd2cU/sL3LoqXNKYjoaiPiR2j510wEk34XFX6edscE7sEmMiTrsUqlBVHLFWsVidUOcZ5dwau36acNY7t6fn/xTg/WhhWJaBlTlp2UHniCELhpVcVDcru+CEc3P24mmDuDh1uzfRPopCWHLiQY6dcJuz8IbF7hPDoGBd/JpwHYdps24xDkfLRnz5pDUNbOwNlUSAE7Zov2q2qAznlfkpth0EShiyWq8/xXfTjeo99ygXfpxjk1MBESmTPmfPhmeLfiT/JWOYGnDoRtNGa4HuxFzsRUtlzKSVEVPLl60ABydVcY1L1lCyReHROotjgLKVjhrZcq07aaDEsr3Gucp1/SZn1vHL1RY3alUL60Nc7QYDvmf31IJ+H/oSHnZQtPllB5ji4GnsH7aPYkeB6bS/oKe+e+0N7zj33Ev/6VI0wG984xtcBMpNGx/+8IeZU0Xt3DpOVaPGOG+krs/FpE4/7OnpaUzttrWcEZnYJn+dMqa+CGadX/ziF4ybSELEM/+9973v/cpXviJlUy+7l0VWxBupG+JJD08GK79P61/XdbZukbI1xzQQz9lH182nrJ1/Z3deY8Tege8dD/jLAwIkhytwyr0ti0W6uL9024jGbaVvXLCs5fP/5B/oM/f0+xJhX89cvaMDx19kryev5G+hdSAsMv0//ZTz95/2WbXc297e9OUvwlfFQqlcQXDsIAk5X1564xtkZ/Fe1hc6I6xL2bQxXd27RxPi8FBlrn8xuGsHqhami+VyCW6lTOgc3LmzunlLaP4CZCI6L4us28aE2YwTUtu5nt5UImENy47xEutWymjfwGB+NBkwUkHmtMH87syjL46zHyIrLktEZqovug4jQR9pgYkw1D8wEAkGzN27IsNDdGbcuFbx9mhXmQihhZUr5p/5a3XBsnEjtGDR0nBbpzX0ds3Z7lhPGdqgpd/TdOzZjtVk58bDqWR4wQLnLacX91zq+LbSCGOF5SiIKMCUgnROoVCPXyRLRCiEPPoCIjgL3mikyMHEIIgjRkMklAFZl63uEgx1QUAkMZGxeKQ5nqgMjyq1asoIHNkUHaypQ9lcls1xx+nu6qZRENQQKwGCnMcrEiTKMFMaQj+l85XeATJIn4QpnZTIpDgmFmiPjSIsopMgiEA0pFIamlfQ27WrrxXH0cEgFx7USgN0/OaQvXvHVowYqSCgKIIehBzPlt14Njs3HKrpvpKW8QfRe23rRcvPnCcYmiUMjoBgQWb7OT60RY11quFYdfDJpxJLDudWlHXrSyPRCw/vuj7h26jqW0NvdNQzVVxbwRg4L6eX+xZXWr4VUeKbbEa1E5qjx+R8LTkhiVj+3Pd8vqctu1XT36r7TympkXL0y6HSBarR1UlmfddurmZKLFtiNrVr44LjhbpMx9SEai2OijIxc+E76+HCqYeQSOb1zDnvvHfJ5vA4ijaCelI+rmetyWHIK/VJPtG4vCINoUKgSCAQ07I8fMJknUZhTEM+hv7QkDS0C5+8QgnT9WTvI8zjfWpAT36Vv/JTfUxDGP6kRLaG2CCCPbjeHAxBBgxJSR1hzoYsfJXm+tSIOReVnlKgCQwmd/DkWiGQ0dWpIDUFAqwIKTx8CMBUPNSClCTwXmE50EAIRh2iLL7y0GclSqSE+BRKAC6FVgxK0GqyuQcJ6h9KpCzAEjj99NMBwjgP38reBxHoYhTBSCKJDFdDFlR0cKiHI8NUSsZzDhkIzBHEk7chJTRBh5kcz8Y4pt1k9BpRZuzt7UXLpQmgrWSS+gSEeYCG+spXwl5xkrD88pV4KAb+dHMUUcmoXmIC9WEJQWaXYUpn5GFsl6szRPJVjkWMJOAsdWMGB9iVdoeGQiZ2i4YmfGWzV5bCJ6BJsOBDe5ELNsBnG5FUgVaQX71fMtIuQAZ5GenhxieaRvIejQWhWB+ZDMED5QUYCSnXe4UHADVdy8pkUlbxslAvyuWp53nv6wwBqs/ATlmQDqXaq0tDFr7Ke6cZRanadMsToO1lnA6Ul0AG4ASpTkvRCIYkntrRuRpSvhZeZZ+CVV45MtD8H/7hkqu+ftUtV3/1C795R7842euvmNwMbOOEGUmbGUMMoraw7ZTXxrO2IQ4HayrublFfOYSFT6YqyRBTxOwjFONjE4989ZDL43o/DhlzG+OZJ8JsrEUOKiVXlAO+/Pkd3xmrtNyXuSiCKyy/RlkVC6NRZ1ty/i1n/uMnbvvnN95wxdqvnzyuc+eLytHZuDCDFjORwAWMxOq2V3Ui1ZHRsbxaa493DkSUXgqaet/Hy9I4Kbz0YZqQx0Uo/O0RZXdY+MeNN7X5nCaceIyVtgXjqTltqSWLF+F/FhZCL2XsZayW8Lh3ozlkYp49XlZGSwJ1joONFtWBgrMg7YQNPR7gfhwlFVaHy2wLC7GKK3VY+q5hMi12Ctzbld2FJuGcTFdSQScdchYvXrDlxbUebtPgPnX0bPqIl+bAivAKnk5w9xL8zwWQ80ZHx1KdvWKWkxadgpWQmOApZBkp04orqtm8ZSmIby7TzQZDwf5cXMTVzyEfDYYhBBsmwly5XMiW89mcYzUb2THd2rB1JyfguepcTvnTgxbcDmKtEQ2Y/XkLzZN94GyFtueTUKB9555r/e532j3/be3YrvX00kPIIv7VPVSBuw5TvlIwqlcNm2uBcX/NOXK/HrbCkfGq2TV3XjISwPagBp+p4oqzSNAolJ1+ik7o24c4S0kL2nHue7HsrrhqZ/RCxSwrxlAG50TGYN5qi+iVmg03FzPsURvhmpnmThPTCmL/bOEXWmMnT3jDZofHsvcU9HzN6sb4ccg+9PVvf3LN6d/6xr9xgBYh6VOf+hSByy+//Pzzz5djXF09ZgqiLeANGPmGGWKmdHu/0UXh6YbZa+/Haf8ib11//fXciimlVSSGs88++4orrmArBmg85IStQIb5jwADNBM/cyp14StiH78UTaQn6xAzf55jbvuhYeWVmq/ycMK6L6YO6hpOt0/LBl6f1fUxq++zdvAw3be4HjPgC4YTg4ng0m2j1raMun3M6S/p20ZrmuFfPaDvzlRPW9y2aGlr+rDDqrUKa2oIWS4QwdceNNEV5IN2sHVL8exzY2NjmcVLk9deMzAyjrYTjYrjdkgS7F3Ianp5GwJ8RZaSrfAS2IZEk14hCEI/Np847+ayPGs4g5ZZZEXU9cVK8kqlChMGDL+VGcdaiHqjdmwdrY3XjKwVSGs6ZtFUjxUiLC80HR62Q0iohiqGXbW6qBWzBgvZnYZD6yMgBR2ETnhg5hohZ2zcsOGw5cvMCvK3zcggRmCNuY9LjbRiGcfuqlKtsTV676Zqpjc83xeKxKO+TLdTPVkP3qLpG2v5L4xekTXW9DvcanPCkc2XzoksfAItqqotKftPDonWm3ASKwmDMERfQCKBwaS/FiRFSEQTEMmE3dvbC0ch5CFugnxPTw8ZSczgxv4k3rbCHEJa/4L55BPmzj472RQ85ug5y5bOP6SHpQRKW7V6FW2EZCMtIJB1eFatWvW2t70NCVUAsW0kdUYngOOQDMfpyGRIuhBNbl7RWOjkSJ+oKwiXIANhQSAai3V2qrrPt220wrZ5OZ/LDO4atEZWZce2bd9OiUCG/5G0pOS9p69/yUGHGMcfn7vvtwk8N6NcJpjchQsRE9v1WJSbI6wKZ0ccIw6POgw6QoWqcj/N7q6OjtFa1+07Fr/14AdixZs1ZxBn4Th/dpz2WulwPfSYqo1rsUfs7GonGw90FjWnYLESiZuTPyYitztapqA8b1YeKBU/GY58rKSly+Y/dIQPOdEeHVEHBpWAH6sdx7+UVSj0Xvto3XqwpsN3oo/jQpFLzxXjZN6YnJ3NO5Xvf+8Hhrg9WDywPVtACNzycmOoNDODkQXKIMczgqHi8kqYWt5///3Q6k1vepOn4gIHXQsaytbnK6zCIENrEkCLQLubXBbpoTbcIj/RxLQjdvW8wttoszNvUIsquRYu+IKmajQ0zUejMxoQL/1Fu0kaf1iChCUkc/KN4nhABl0UnFFHvQyA5ZP3Ol2gIQ36M0XArmiJIIPTQVjUy9uQmHhi5AjspZkhQF/jgUqMDygbEBCa05RkQS9CU5LaEVSF8uBAe00JjRbhoYJAgBo09GTEyEijUJA3KUwGBTJAIB4CQj0KlTFgAmJSkCBSokFANjrpCcMeYAh8HmJAgEd2ZLmcAYPJLXfwrAfCKzxGYjCnIFhajhj16IEzIyoxtAK1gGKyCODAJ8yDoCSBgz9E4ytE89Y7eCUl7UilCPMw9NERyIUKLWNQ86gO8aQhL8jQImBFZznmmGPoLLSLl5gsfJIZZZhaNER6XxsCckGQSMY3tG5JIoprSDbDqyxalsgvrzJmhizyE4nlvvEM6b1PJOYhI/0XqtJ83ievIAgI0SCXFzNzgFagawOHACllGNaCY2fO+L/yFcQoF+Z8haVLunFVx3XXXTfct3V03T2d888ar7AJ7JQMLeLYHPQV8z0Dv7R/ZvqU3CSkWbRgTDvtMHoa19jaGg60aHMU5oBS+OSSf09qfYpp7PxFevjmZqPKJq6R02sjRw/3Xop1c+k9c/5j1dib/z9z9wEg2VEdCrvjdM9M9+SZzbszq12tAkIJCQnJSEIIMAbZIj6CSHoEYUAGg21MMBgMGIPBPHK2kMHCYLABYVAWCghQAGWtVpvD5Nzd0/H/7pS2aVZCBsz7///ubm/dulWnTp1z6tQ5Fcv1Yb5AZ5sFp7qZuP7tyhOf/czrP79m/70n3PaDKx/3x86WNmPFoY0u7IjKfsjma+U4nLTNUtl6zGiXomnVTQPS/TePtkkt/CbKvxUQLNZ2Lx87uuiwlQ7zAJW2+PxMYqaSPKorH+HY0gCbSFrivbYrbivWZCGaBDYM7fjruWjZdmL/fO2wXouc42u6YnuiWeKGoXNuiFnfTLLuMDpEdtiVheUsA9CRwB5Q6XfOOsp1ZM/uy0gsFdeK5G8S1oKoI808aMVHzELhaERNN/4R0/wmkb81cr8J0N8pTYMaJaQrVq+rLBUtkC3teWDvfTdNbL1teu82hP7Dt16cctC2a4xcneqqq3LC2tTv/Od3NY+wZEi3bYryOc95TnNAMdDxQx/60H/8x3/Mzs5s2HTE01900XF/cA5LM562ft2uP71KvVxaZLrdfONXrzmwtf68P37iaSc5leOQKgBF8xKaX51LjLstumO64YD1QiU2W7HjMcbNlow0dD79qTPr1+Z37ln44pd73v2uyPqKfOblHiC0gGWRjAawU+X29lQjZ94sNV2oRhMTicTYfI2XsHGgb6FYXdnFO43PlqodmdSSs62csVlLPDBaTWfaCpWq3QWdGarAyXWJDp1+IzUxU0m3pa1uK5oRKlplGuMmz84lTHDPV5L1hepALrVUcZq/jTHaJ0oivGOBk5Y9zjQSxYmGzfFmiMdTnW98x3tf/JLz/+497/3mN79l6OhlL3sZr5Ib7EIRLkqzFR1CrkNeWXKUvsjwq1UQXz2EGB0wKW+FQ3VC5uGSDcmQLPxK4+Ee/Pu//7urfR1eLQGAHDx3Y1500UXGeh+eHgJ6aBmllJdloL9vWg+sBCPcpr+WEyBKvTr12XRtH3mZ+bfu3PdWpmuJJQvRC+21L3UVpve3nzeRiu+vTn0+ueLvSeZyLpAjRUwh+PHL+902a61t3SFnOyfryVRbtE43zotou3OsUarEN+Sr+fI0ITToAqsAp0mQgCqYvJP5V766e/++hdWrcpd+7Yqf3Q5/Rs/w8LAekXDC3GuwyJfhPPIPCmhowAajTVhZlJT1gc1CW3OiKlNMrkhmeRJDAzXjJXgRVUK7jMaldEGcovjgkOXQUhlH3LuYpC6HNh7pKIX5Yq0zkxxbNLJo6UEiYwNwonNVX9eGgUbswPy6wVyt6uauOPyV5VdxgBCAR1F/AUPNJLIwUun02jWFXGdigg0X1SVtm+ryCg9rkfObD5/JtDtC47K7akeuSG7q6l7R1z2Qe0919J5k/c5U9y2DH8iW7kpaY5c5/MZ4lxZjKKl7Ovk6Owzc7MH+I678f/jceOON7Lk/+qM/opqZL2Y5ggwrkckbqGpsNcx7qJFHysDB7h6barvq07PFyy+vffpzZs9ddE4BTF369ezzn9V9/otj69eFMYXTTz9dpTQZRTP7BFiQ3GmzEIjD8iO0kAFZAtIOpWBD+6Qsr+SZv2HO2Vc+ucSAEMZcrrNzcPUte7Y5wivf23fcljWzeyonnXAcr5tdy5+nQhXBB/Pa09dz4jHHDLzgeVN33FLae2dmJJ59QqLQ1+icSsb6elyDWHeqc61WPiLdd4RzzeOF7en2DTb41kvlcmciefcD++upjp2Fi47OvujApW8cePJoMrevEZ9OZn+6PKCpqVq27Aye8agpaitLbcWvDqau7Ev0LtUqteTCYtpB3Z8tVM7uSG9c7D4r5YiNwle+53LjlP23tm8kTovVe1PJqcwzagtXxXO3RKdwaKf87MKZidwzojXbjJNs/7mr1qyZmZ1xoZRyuASkC48ONqsgR7/2ly5C7UBSwknjEbkI3+UmE5yHkJmsEgPNJDRAe9QZzbfddpvmKYumasZMrtaS4IBHRivCemmvinOmFAjYR0MGn6Q1yyOGwQ+YyCKBMAkkmQSGDnzELMGS9onvJGWTGkokS80sIKu7iqiOSLXzWFEvTCaJVrNGnO3QWlUhoK0K8oJMnCgoqqlZShO+ADHmImo7ympOr0lPhlWkCV9eMhlwgzxFfc0112AlT5sbAEOVVUTonbULGYO/F8pqFt0EKD5EahqytyZrTRPiW3+5jqGCSIF96ou5WO/xSUrxSOohbBxsAiDSq2RNOIGGpsT1PrA1iCbc/NoaUJb6Bh9SGBGaXwNTtHGSGbowAZSBgIBkqgY9gVBTGKqaMM2PdMbUggdlAQjVEfB0jBk8n/KUpwAoI0NcoWEy03AbaNiE74wWkiMB9mET7QcsqSOB0giHciUIktnEQaBJXiqUhDRffXqUR7kBJemFm3V5lCy/7hNBVW7rVyJKtMhSKzJqEcgYfr0irHF2RdvgekhKhEIKvABWvahcFEaloA1ay5LR0CQ12yqfrQkeHuZShsEspWgpMobSW3F4eK7/r2Jg2yoAAQ0xHrUmXfhI3pDo1+GP4EFBSSClDvdLX/rS5Zd89AXvO2Ofuz3L1ZJVTqV0ZsChm+nI7IrGnghWtBNXcQqyryNlVphizLjetpGu1N3y4KP+YV3b/cd03ayrmP5ZburrQ3ayRVms/G0kqz8Z2HdpY+0rSn2pHcd1X3v9/Eh7OloFPc+BriYsztzeO3zjUec854YvnHjjpTcf/7T5lHmnRLHSyFlwtGxSRsVH/ywUhlYUUqyzetwNNFeqTi6mHFK1rnf56KzI+F9OECU69MFow6m/rQOssO6MK5HqPXOx6Ux8dilhLjfmNKyFpGObXTrjQROU99tSZMJJV70d3A2OjAmwqDeeLyWmCnVbMze42ygOZqI/W+/JxCfTLjeOxhE6UrFiMloEbkEuvYIF9IWpBxPsNqhOONA3t356xjKyuWZbw9aLL75YC9IXGxU1UHvVVVfhSsSYgw/19c53vtPZQ9QLJIm6U3te+MIXNhEGxHyA84mCwU812er41Kc+9RA4ZICkyQuBv/mbvznllFNaSzlYWvT//38c4LiJCNLS3jOwd/f9A6uHr/jUn++946q2rOVAFWc+Jap6d5SKTjZ3aLOVCR/6x4+97a/+Ao3OP/98NfnWt76FUpw0HppXFabozzvvvOuuu04CXaMEb73gT/7qQ5/dfMYLFqquYOF+xNpyXX2dXTuv+erMgf1nP+OFn/7cx8575tMecWgNTSOCHXwCQY2F9GVqlgcsznI165OLsXJ/NAoSJe7oTL7ilY13/U3sEx+vXfjq5Mo10Rm5LcwODYCWTDXia/vjc20Jp7335+KTi9Gd3c5zdab56FzMMmajLD3txlpSjrflcPAyTDBYIF0tW8iXKNbqe2biIwNJ+34XlxwvVF3Znd43U7XOrFaN2w22rtsB19ZFRIctsVUXqsn6XMW24XI5WgttEA1WdEdVAQbNjC/UDSPFZwu1NV2pO0frfb2bP/HlS/7sTbf89V+9FTEJ1mtf+1pbak2xIuwZZ5wRxjIPkbAmuQRYGIwDPYdewav+RhbaEC35Xa1+jq+hlw0wJZD48ssv12aYhrJbiY2n2olBjX/+539u+r0H2RLjq5x11llsXPg0UaJPreJ2JhZzk71llOR1r3sdp0KfJ2Uzr8ANN9zg+Ovjjj/uz9/0RpM3iYUfRMsoD7S3fb874b6q5UWVuJusJ1KX9TdOn48PFhoLV8RXsDPCoEmkCimIoBF3TdUemI3vnm7wfh+cwiw0N0lV78/GVufivfGZxd37aivi83Z+x6IJPQjwQ9hDTZRUgR/umXv72zqvu7ZksuhjHyvwgm67nbWhs/Hr0akwNRBN4fI2K96E0xrQLiQLfQwFgSbhjt8I8YNdSGv6g2GHSKRTWw4vbj688wEmZq1ksCaZSlonZFjq8KMyh22SoFJvOFNtsuhct0S7AYNoJVJiaqExWVgqc5lyPY1MtiuT6u6s9suXKW29LzqjiP3HYsYR6lJTJRUHCz30f0h6IO8XWZiG+r7EqlX1M85ufPPbKG+TNnVp4ZPzAhLHHZc4+sjdCwntJpZxzFv9vvHkUEflOYevq22/MFV4R/uWWYtsO05WSsQxQyz1xZ5Y79sSnc8a3faL7p48V5dAfv/732duknzSbp0bW0cGkhYmWOgcQoWSrHA2MfORqONIIOZDvIBupVa7667qxz/p8qpazDB5VGJHpbz0z18prFmde/azGmZpl8/FkcVD2rURpSuOwGsIzGj08coMYkUBqacUA7FglLOndQ+sLmOC0pAlX4mHjGjy5Kc+5d7x+GLhF/t3b3/MMUe1VYw4RMeHMNNZ5PpFMEkU5NXlJzf/rC2Veenznj389x+tzn4oHvtmbKCUfntn+dPtmWqfw8PK09NLq+O5dydibdzeZGn+1J5Tjt0+PmUt9Y4Ds+09Q/0rV990354tp9w7dO79seT4svLDkkJUcU/Uhk6pJQdStStiju3Ymm67eiAlUvkrU4352fj8Yrpeq9weSx3GVx4vXP/DwhU/6HzTn5fzXTaYzs+3ddfOHUp8JdFRyn8oW/pWonFLHYvjpyfyT6HWMT929U9XPvsFf7lQsNk5MdD/0AwhYx2tkBdhURgiDPdgW6NnhFhL88F0fe2Xv/zlJzzhCfwZuhph2SV8LXA4GB6NDpEJA5kkwGJAQG19vOw4ODw8HAE9+OhcjHGAdjAiUhiapOw//OEPv/Od71CVbL6QoImSxHD2ygrHLIKnvQSzQLyvIWUIYyIhgW3I1SyIJMCQetFwpAQK8l5pAKWLlJKYmQcWGBkZQRa/4ARQfiULLo0ipEHA8InAQEZjDB61cKCt4QMlcqv4BsoiaU1kBJRI2NBHGpCbn8ihxIpuxgggsrFL9AQcDtoaQun98QLO0JAFEQg/0pmBxKlAMYODsnio+sDoANYAltYqWWjOQf1qQQoCR5siIVIKa318cmG+H6J5ArXhL4wRCtWLQSNwjQwAHkQiuKyGTvQ1IMBZFqXArdnxBWgBq+avryAoV+MNnRSyqCwaQlt8kCsEDJOugIApcdOfx5TQycJQMpCbBaEGOJgIDh0S8EFDSxuM2nzxi19Ud5Vy/qWBG5pBRsKjgmgl72WXXYaqsjM9X/ziFwf0cNyoBNWtLCJEklEbg0ziEQzthatviC1IDmQw0SEj1mI87WlPU1nIN+t+SKD1E5iekKA1/pAsj/IKGU9rAq0v8DpEYitpVAoZC/EKIqK+Mt+DWDYpGXAgIdRIcIDxyKNNBWiHpBTZjAkJ/tvfUERrrt+t4v9tQb+XBPgLvVZ6AkuiXMZBtJDaV7J3wQUXvOc970G0QwpFfGrzSU96kpSf+9znnv3sZ3NgLrnkkuuvueqpd/xgZuvMFV/54ML4XoPwa484+ezX/n33huOiaSU3I/Fu0dZUTqIxfvvVN1z60WxH7omvfG9790jZmLtbUaqRX7oxf182pi0nZq/MJ2om4aNBfIp/eTA/tXBdR/zFbe6A2JS/5arpl1qnZrOkU36YE41KzAq275x90VNv/tqWn35vy/77b1l7nKwc4Pa26BDTg70HJJb/xABWuegiBv/PFWpznamZkqNq6+u6+J+HSnsrf1FPy22N+Y04zuxsOAortnc2NlF0PmW8WI+vWLOlnkuOzdfXdUdoaMsPHxVNJWKrnCDdEZ8sxZzsxSZQKf7zeLkxXWgMdthblFiZr+9diI0XYpwmVlYmHc+U3ciTsDK37j4dRSesl46Eu7h8A3Omfz2HImyFaLL4wgsvpBwgQENqMq2VUlkcP/fcczUxydjw0rDYX/rSl4o38SYx2dAk2VfMxU9/+tOUxkc+8hH3O1BHBKZZigBoPBR+is76uc99Lqu+qTRakwn//8cBjjmuM9nWUck4M2DUATUnPueiJ77s3b3rDrv6o6+956b/XB7iiWxGl30YeJgvld//vr/XB6gbTYo63H0jc+Z7XeQjBgk0G4dziHGMk6q+4aKLnnDKqZ947199/AlPt54va6+ikyot50skDuzaOnLCE7vXH7P5MSfs2Ll9KLrjISrQo7sKfTPqe9UNRFQ7aODCZygf597sn4uWByzWkm6gXpM3uREtD8i//rWzn/pkbvTAwr9+rfvP3sT6i4ZKPC3Cr2OwjmB4IL+jXF2VS7jhaaJYbYuneETjC/XubKKbqV+p93da+Vzj/BtpqZQamfYkH5gUAgYvx8nec6DGQSmUa5lMslapr+hJTM3Xyja6x+K3H4g5cCjDK6jVnX3F0inG0pPzFXcjudEbno0C86W+qj+Xz9Ys+rdobGwmPr2UWpisD3XEluLpmX31wXXHX/rt7139w8u+eslX/usHP9A+/+WSS/7lXy5Zt2692WC9oEFKNEei5RpGP2yLoAfF6zvJq+5Tj8gIwKBwK8Mhcim7JspcCPEyfvvb39YeGDd6cT391772NZqUHx4YEcoiBs94xjN0w9oMaxUyGh6mB2QAcQYsz9lKPMNLfOkPfOADxObVr361psVKgCSjQRGMnk9+8pP0ss77c5/70oWvObdWnbDTvPxgJl3t0C1yuTCQRESaphCvbmtPr3Bc7f5G3cS5m3gL1eItscXr67UD8UT/bOzYPXN/sGeuZ7TYcNdUIpmquMUuXe9ui6/uiq/rifXU4vXCVFf7KqsyE9WKWquO6odKQVvAef2U5cJHP5b9xCeVWXn72+tnnHG/vaaTE6YUmfI6Zvhr56rA3GwaRgHIw38R1tibeBVnpiCdgkZ+1dYMufRD6BPCSolY09F52KqV6f/98sUPf7RjfJIepOpZVoXBVW2vON9srKvjnfw8WUmOzpmCSxjNcWajEfs5inIpNjOxf00ub3a2M51Y05la01Pv6lg3N9/Tns2qiIagX/QwIlul6OFVkJRo8RXNqW7eOOyqvWxXd8fznjdnFd8V17QtN9mCtbAnnTRw0YVTfSvu2B4NACXrNu3H7UZ2BnW6UipcN7b47f7Oszqyj5vNHVZz/HN5IjH3k7YHvll93JdPHxpeMb84P2hj/tAQ+0zPTbewO909w0a06EA3T5gDj2DLgiQ57Cqa4dprr7VtPpBUgmZd6gvztWuvyZVtKdJ31PzBWcuHGpWSo/I6aPCO3KbNm0gXhrImf/SjH7EPUAYOmg/6kHn00XYUHRoU85qoV6vEeB6PNmzctH3HrpWDu+dmpgvFSKo1GTaxDoN4/+Cq6x+cjE8c2DU7Nd5eLy7MO3Vs18L8gpFHkC18MNyuXD2TIqamp2+++cdTM1Mve+GLVqx7Xap+b6ZxZ9tZC/UjG+WbG+X7C7FVya6nWQi+ZLC7snBC/uS3LLRnJ/btynX1zNXjp24eWb1icN/2yxMzb2kkFyLrpL6uVl7t9H27guNJ26G1pv3J3r+rzt6SrhUa9VTN+TSNtK40lcmaWor19KRq9Wr7Pnm1u9Ebr139lr9oO/OskommsTH+83jl/O6OmUz1P5O5YvYlifj5pDEa40dymva2ewcenHrJhmioh6eRDEa87hO5kAWbtm3bFswviquphXitONs6HacJBIeZA2NMAX00HCJq1ANHmLzcBqP1QUqpOIoR++TCd2zySylJw3tRihhjJR5w0DxS/st7a7kB733ve83FoTw32BIbhxe8/vWvtwVXBZQS2qk+yHl+0nDw9P3Gv+HG2g4yxnOTUjhoM2gE+VQEv1TRJIF5wYtTcf0az8dDaKFBdcgoAUJxVGRRF8Vxj8WEASlVADmEQ321CMpc9QNJuUZKhKqyHEOoUJQEAc0NYsLNtV4EOOSVEq0oGXpbD6usoNKxJiTw28RfGJ7IiG5SigcckdURMcGhPMV7YKsgdRRWRzWSMTAiUKlZOiKA4yu++A2Gu1o3Sz8kIDEMVUc1fcIaBNfwm8k0eWhAST8iYJAiCJgEWl+oOHxgq/HqzqT0CViVQn9hdVF9DVAVYIJ6WKMWoQi8VqLScQ1A1QfNJ2RRNSpRWBZpZCFRGBH6FPEBT5yVC7W5Z9IQ45AXHMgQaf0jHLi+0uhkv/CFL2CZYzXIPAwB0b9YZ0cSOMYiL730UqMbn/jEJwJNoIFE+msD0/p3JDXnY18StYkFhERlQ10EdMpoyMk34SNZiPfrkwcoTzOyGfjtp8WaWaNAK0yl/Mq3g/IGT2NMfHUcDJKJoVoZbgqIkYuyVWVt4RAgGjvmSoAOPgWmICkdDixq+yQACEpKgI8kthWrQ1BqfZWYYLTG/G5hMkCuyHzg2u8G5BFzqQimg3xIOzJXQQA++MEPUraKJle4T4qY6LJ4mtCI5ate9Sq/KIlQ4rVltv373//+9170Ults1p74h8e/7B+X5qbu/uZ7Lv3r817ysauyKzZyYaNL/xhL0UzO0vWXfPjcd3ymsHfHDRf/3Vlv/jx3zqIwGpmj1pmcX56GSlQs08XxqGgR0bkW0WbLxWS9mohnDKXa3hsd9ZRN1nMZZyPHSvbU1OMP9I1cd/x5T//pV874z3+848IvOgJUuVxrrnLU+xCwCPoyzEiQI5fUHJUvbG8ngE4t1nkKq7sSdiy3egGh+kQijJtoa3jkbALxOD48PBwS/De/kTjHB3Opvo5afr6RaWuUygn3N3BrR1222h1OCItgQKsVlJfVXakHZxq5dH0mmXByWHmpMOXUq3y7NdsDTj6JNfgmfdl6Vzo+nXAvlAEHO4Gjw5gMDThnO5OOmS13/XKlkbQ931lZbd1mceLMj7B+JCo0HqcrNCvtCE8DAq2sN3ZPb7zyla+01zLEP/3pT9feqRcOcEhvdpNU6FzCnI3rWknUW97yFv1aKCIk86uDYNKYA6DeNbdmF99MEAK/7GwO+fD/+mvjwL799hlagajoSnFxYPNJFjXEMjkeGjEKFCGltFallhibnp2fm/7Dp57dNJeR1Uw3ZU2MmAiAaHW+8nOEZbcy7DUXXvinf/q6O266cu3jz3PTVzrawE1iG0c84Rnf/9hF+++8qTi3b+ORxwRJDhQgi7gYwn6b48Q6iQisKbtOZzRXe7KJyWJyqlgfm62vykdmnAmgRld3/PnPjf/Tx2Jf+krjwtfFXAAbdZ2/8lAH3js7MhtzzkmPdRUaq/LJfbOWZnOWEqPz0XqJ4X5nOFdWd6fKM/H5Yr0tm1gqOcY5wTVGD7O4lXI13ZZ0pUu2zTnSNkDGhrKN41bGHhyvz1dTdx6oTy8mBrrq+TarvutptyE3al2d6Yq9wYWYtdabEhPnHte/sbeSKRXNrsVSbYurOm+fqtwzmrAreLpYHcwllhqp/bPVWn7DRz/9xVS99M1/+8YlX7n41ttuYxT+wz/8w4c//GE9uvGbk08++cwzz9RiCZzeVz/BENFh4w4zCPNIpPpix68zdMKu3UAjgqs7N1hIG2obInWW4ROA5klMQYPjQBS/QUIk1vW6/cjwj65FYuW+613vMnf3ve99TzLmGu38mc98xlSeDowLQWAgqSDmr5bpVaO9/372RDI6IY1y1YJspE44hTaKqFQNgkW8T7ZVo1FHetPoVOmntQN/lSzenIgvGZG0PaU3lj0ytmV/5Z03jz+NUe80smRbLJ9sGP9b3RNb01Hb0t++a6mTiait6hqbSiFUMPwqr3jlVal3vsMtr7PPe07yf7/SIjOGiMS6Qw+7nP2kew5G3kNUDer1kZTsQ+2oEc2066RpEw9P2LwrCMEKDEXjFLJggR6dFvYpiu9oT598auJve0o/vLx2992828rRR2fPeXLy8E3x9qxdvrun6/c/OFZO9/b3ZAoWP2cTewltodaW1iJiqDjUnVrZWe/K1jZ0J9sSK7QjUqEvhA8HTBh3oka+LCR+Qzgq+mCMQ1oO37Q54xyjiYna2GiUYOXqxPrVXW94XfWcs+v33OtEqM6Nh3Ue85jZ3lVX7XKpXbI9lVisVnbuOMBv3dTbXS+Xa4uFtqX2hW+Wd316IT800DvU11ZP9qayx+aSVvYqltJRdxOArvowYmJIUkEW25N2/bHrwZALSmxH4o2SbKavf/3rxNIKBSQNFoAsnmXcY4mlpfquaAFYtFe5Wi+WCi4ZjFqxXRgP3F92AkJiH6HavSu6KFgW0y/y6hENcIJGQnCBVSrMcAm2MslRVl9/b3dXT+R5VjJL6f59U4WVff2bNveZadSP8t7BmZ6eueLGK5echWeypVa689ab1qxZvWf3rrnZGU0ANFYLLvBngoSUl5aCr/jJz3z6cU84a/2qNx2b/sv+oYnkisXEuYtWnkWjHxFz0pXG42ID74/lj35g2z1rV29YOTjUmFvMlOaTe3YPZT+SiC3Eau317jfEa89NTDll0qjP3mrlo8nsDfHEjvnpj9SL3b3ZvW3rK1Op2exCt/XrFtobLbJboJotJY+MBiKryYF1b/37xuAWd48WFxeHR0ZmZqd3bi+0bfhcY+4J1YmPJRr7EzF+giUayWK185Jvt138nb4P/Z9zov3l1FwtWkALV3UfGRnBVoYp8Ta0gc746FPgEW2s+kiqqxZJfQmb10IZjCOfpoJ14Ux/Lqg+mNLDl5BLT0HhUCMkgZIRL1lwJrVK2cmGjin4SGGWNRSKxYTKbBinlzfIVv6X5YcmdOCCNA81veX7isBhPnJvDLhYq6Jv0valgbZHoaoWfHspSU5wwzRkpeMy1UHpyeWBKnwIm6YXEhBmekAFA2JeAUc0r00ShbJCDGg8LmG9AOGkUeHg1WN3uuwCXFxEC1q9ldRaDQuPvCk6FMH/hz+AMMcjNJc9jPgoHQ0ZNNIbxUBer1IylIk3OvNLwdEMCbxcah1GiDQQ9GQGiGx9xEusIBUEk2/J7UQfPVdIxjELAfCl8cBN0UYPA7RAkGZ9vSoaE0FWTejx7mDFgAsTpM3SJaOxvSrUL7ThzysWxiMAeVmkDm5isEYdAylCesKmCGqTEgiKQkXkCuGQS8bm04ohxLwCDpTeEMtch6srUQpe+CWHeCqvtk9QjT5L9rGPfQxZ1EUMfUjOda8kEw5vfvObHTlJN9p2pMrmdgxA66mZpD4RMBB8eutb3woyEqmsJ+DGK/ZVlc3eQCnIQPgUSIdxzVo0A83qNGMeJRB4JMEj5mJUaOC+hmQYLUwG1DTAFBZAfxyhHlUHSuREDPlUETIcGIcFYkDzqiwSgstiMEVe6WX0SUtvohQ0AAXSOqIUyv11v9qC59d9Fd8E/oj1bWYkQvQhz6GV5s2v/8MAGSMPhwChsjQEWAXEODasRKMn3FpKNSQOyBM2uBEzMtMEosM187Fjx87Vw0e+5L0X3zPVsVBJnLHhsO+/7ck/+/qHT7/wE5bc8ED5nvR4PZqfqHXkV8RWVp2+4K4YdwK3WcVcs7u1MVpaHU1ExWrZTZXq7dE0V+T+opx/bh5YU044mDMWHy2t4f1Gw+VWODlQOh0zm1qvxh3iefWJzznrtn/fdOfVG8e337visNTyKmgHUD1k2kcoRECXu5QozD6PYNctz4zt2DtWHJ3e1L+lr13kQ6qyWU0yY0jRa1DI5FNYs2omePRAoK3TKVfmGrvnEuNOdXZo9lJ8odSYLMXnSnVOykMQWkpezhXvSDeGOmp723kxMU7AwvRoebq+snfDuGnkarwjFXd7zmBHvbc9ZW65HI7CSsdTlkA7Cqseb082VuRNvUfnvFgFXanGE52DsUQ6KK5QqIJ0EMJNKT2kOqG+Zsik9PhK61IyDPKQUoDSoC7CFI40mo81AmaDaWa9QxOgTy49tcuDejFFTCabnw4J/KbEPSTb/4XX+P59+9ud6DNt/Cy5ODUajfsn4quGj3xIUJbliuSSV3Kc6hhYuXbjHXf8guoJ9gEtQ4nTJqgGPfE0mnHK4AwHhE8//TT03/rzm0ZOPS8Rq3MX3X7C1u3edMKz3/3V6syes089YaacGVDIskUhF62HiAKBl0E5Ni0SyTQwl1BPLkXrB6YXYxMdydlSdOd1VKLxsJe/rPTpz3Tcc7dJ4PxLX6pqUXzLg20bNqzPMp1MRN99Qzyzqiu3YTrV4HBWKtX5WmOmkhifb/R22qxed8KWeaNF23eTTj9lqBvmSUrG+62UHavgRKsKv6Iza+VG47gVyeNW1L/+86pVGAa3xueTjVzNxLfrGoeto3RTQi1+YK6xvrv6oqNXrFkcjX3/56VbflKdL2TXr+t50hNPP2zTipGua7bXCtXEbXc8mIgVtZ+OROKHt+zMZWNHP/6sa1594bb77zUJfOml/7pz5y4GhyUrHsJHTTN6OKKqxdBkOjAWdRhEnH2mnRPl5lBC6GbYBHpBuo/1xr5hNnmMSTMOWqgVTZDyTi1gJtz679BOJNCoqHVF0Ok8B5af3ohuxX3zdT6ZTgmqRIsyzm1XialgJ3sFQ8cnpoMOXnduqTx77rOf/Uwj3hXLrI8tjaW31Mor6p1zXS4zhSTuK7eUX0hvsp3X8P5wvXhLY8+rU/X90VqQepLBXrfAM1Hqid/5zPwFpd6/u2L65alUujNRX9ObXJWPreuoHb0qkSF6D1OCzcpG8t5olG67tXL++R1LpZlTT+3/7Oe3j49xkILpo5dlAqqjLhk92SiJaKwRxOWuQO4o/MgPQqEPbYvU2MHgQCK0xTJV88iGUH61I5zVCgK10bnRlUsdf3zysE2xQlF949n2eHdX3NReLL51suqowF179q9cl+1I249uyLOxe8feSr26VLJnxFaYfaXquDt0Ng6vdxBdK4IKHRkZ4Www7whPwCFgz5Lw2hyuxqn2wkLl6qsLl369/uCOjGONuwfSz/3j3J+cl/2DM0qPO8Xk4mI8vaOY+PH9tdmyW4Eb1kRY2hAtnW4kzcCWE+nspmHTjrMTdhV19nWvzZqWNUlZXVoaWJHp7bV9BXmRyMp5OoRPSw6Ry3Iao4/f+MY3DFIG3Ig0sRFm4hhxQ1gqAkmbtgXCki4quFqvJTqyBkd1GFy89ky2XrGm1LRnzLRgPGHolGk7iZt4GtYpCfNJKTSGFJsVWF2j9qIsvyDji+KIAaerM9drmwO/+r5t9xfXDC4VosW3jHvDOoyP/v6+fDqWidUPTOwrL0zsK9SPODLytWRXitYHQzhrqgI4riJRBRuNO+68a7slzdX4+p8unvcXh530tKVsY9RQEJekkh4pJs6dqfzJ+Hhj98+vtiPrmKMekyiVNYDZu+8pjl+89oxtWFz86dr0+rPjaztjBheWCrXi+nj8bbHam+PJuzoa182kz2zEXJdVyry4lP6KIxSccRUJcD1Rqzx5Mru+ZFK3mvqDA7Ge+sxkYSG6i3B5EC06uSBWb0/1XRTreWF98UfV0j2xxtKtd8w9/yWf3Tu2+A8fevuGtatWr1mHqlqHxFw71WSDogYZQzoCT65ItATs1Kajggh2FsniExqYnpISCzic8goHM0UCAXqMQiAkgGAQgCSHAsR0XRJWAkJpOKcqCDDXQj9FH9I8snNE2dYWpDAOnvWsZ/GrwRE2pG3qzFkGTbEHB87wpyHNEqtOxKBfffCO7xescHBIpleRfBK/WjTExPNhFM2M0LWRIkgCQwxsytIfBcjkTUwr+FCp1hh5VcdggSoDpS6+CitX9TUNLVcaYSX6hCDzz5HnAABAAElEQVTB3A/ARXK3SKCU4GCrpo2GCCulryK5Z9DTL8iOtj4JyA6yAEUNSTrKK2MIj7RZTh0CknyEleYRH0TwYLSvSkdkxXnCfIWCHp4rRDY/wVbnginNGHmVyOpQfVj51TCDNRLSSKw65AehDFwGzhIGyYKyVaiK+4UYugmoL8nUDworjhGCcRB2xBSFTB7E+4rCrfKgOIkPcYZFBmkEnKMepJR4UCnqYoWL9NqFV5QkGzgF5ne/+12YGM6gnFmZxNIwjZXPGAfgu9/9bh6vKRpmqK+gWewqr1eISWBMXHdsWkIuxCHe4hWEVhww3o5SNB+ME998cBOtmq+/c4DSVlnkfUQIGAFhn4IkYAG50kwC5WWEv69NTS6MznDzCV+wj+CFZLAVE9x4cLBeO/UJ0UIkUPjFuAcE9xVBAISbrVv40Z+AzKOnURZhoPYfRfIfHcL//Osj4tkcUwvwCYCVBZSeBh6UT4gnQiTKwtfQzEMkgOT8JS95yd/+7XuSyUZHo9TT3um44sz6k/o2HHPPDd8989UfbiSz9EKkG0xLZDuPOP2Zl/7lubTIE1/+LnPCuYyNqQ03Q9ieunXx6LmauaSdK54xO31lLjkdTZUtO63xpVRx/XPnGzzlWObn86eZ50wt36LUlop1ODur4tKfuh2yPzviSfeuP/bEbT8++u7L7x86zJxI2THRllga4Cez0YxahEbkqkTWYaMt1TY0MGj3mG05i5Waq1v2seejqZnoaVU02uzIyIhIwkO08FE4eB8Cv/mzMu9+h8aBhfj+xYmlQqmvY/VUMbZ/3qyDCTjYHXSDWyBCY1Uuvns2PtrmKhkHatSiA4PKtmHGRhfqI86+iDXW5BM75hq5hdjsUlQ1fn02UVtavuBG1aYW4tl0NG3Q3hYdVFxOtrd1RFdXtBTy3wSpIHKra2OWa2hSs0sZqKZ/g5pl5NBOdFFrq6FePvWpT9G6hzjAjqgkY3oTjfFRCv69OcBUs8XGzomBq5lY439BRbaW7ZNuydA1XWCC7ldZ2zgwNtq/anjNyOZ7t27rWTmcdoY3+UB5zIlkirljM2w1kc5YuLtQTbzlAx9/3xteZDApDJObnEFuA0VUmDajn2AH6G8iIAf7MzaH9jC+d5eJqfkl24DjC6Y8XU3t5oyB9R0rhmdj9YnFxsb+5aN9lg+BoEODUOrSwIF5qNFBmBFmBG7PnH3hka9oCGTvXLR0OVoNFmtkjn5M4X89L3vxV6of/GDsBS+wAfJXs0cenSdEJkvjiXpmYHB9uSuxfbLicDtm/4HZaFlyV9ZCi9pgR2qxzJh3iRPvt877NevbnnXXUTVlXrde3zBoLWc9b0XC8tU7R62Ibxpq3Lyv1tGWtLh/YpFrUBtsT08s1lbnE4uTM7G5meefvGrdzL7xf/ynyrU3tMfNiqecibnwvf/quei1m846a2yo6+7xWLK903CBaemJ6anBgXwylRmtxn+yp94/dPifvf29b3/3u37645tvuP66y777/TvvvHux4P7UOdsmPaFefgk0+4ni053odQSan0gOUjE+qO9g1ix/wvXoQYFcrvO444+dn53/+S9+EXYd89PkYgfoPg8yIroykdaQhYzpUI3O6oF0tDp7acjkMrwIE12UTpqDLb7ZvbEz2P1ve9vbdIe+9vX179k9vqLzmcmlWxM9hcTzZioXpxNF42sRmEq6nHj+dKzHPT/JWO7k2v6/SNUPxOqp6i1dhe931/am6vl66uzFnrNnMm2zfzz03geLx43XHre2N7k6V1+Tqyemtu2cjdaDcYCZs1q1ijN9QrMHP/oSq5fV6MUvzo9NzG3elP/C51lAsWWLFKGkCUYDqsoI5wgt2fQBkWMpGP2NnohAkZWvRaAMyxvxxYXOGIn0OmxQVQ6kkDLK8ese34BNpXVKscj6ih6HUe/es7t/3VHjxeSBGcow3p+Pu4Wroy3l7j5riRzEOHtge7It09fdOZRr9HdU+zsJwC8VsWal9OVaB5C/8stG1HJ5ayG2MTtXueLyxCc+kysV5pcKFuFkSgcqn/rs/Mx850tfvD/dd9V9M7fed/+KkcfY5C6LnQKMykKpagu8gan7tu4ZG+zdfOopE1/6l8bOSnt+hU0QTkx0/NVSrJY65ymNbpd5VliBrD30Cd44eSNg+iRqmlds/D4MVfKI9NYk5zWveY1hF/YuNagna1ZArsgUdlNRW1v1hOPL11wfzUkyZ7Optvb8QmGuVKwkTj6p3tlRmLKhNFqnSnO62goPJqdnbrtra1fv4AmPPYrSJ9LkmcYnJ+YAFYEbWH/gwL7xyanRhca+0YmZiQMdVmcnKjwBmhBndSpYDLEtmzdizez47vHJsdWrVllKoCyNCDTKDZEF1EUWbYHeY6WNjjkUup7YtT85OXb00/9w1cmvp3vjpQfri6OprvW1zFH1zpVXfuc7XV2ui+o69rhjs23WjlkUXet/3HGN6n/qCWPlTPkTY7HUO+tPf3r8uOOzK1YaIlraWyrPrux6/F3JGDE5vBgfbk9uz52zsNSzf+a7ndm5TMP2gD8oZM+Ydd9YLT6QWvn6mfsWR0Y2bjrM5GQk1jyksfGx0fFxF0fFEiuS+efE8gZrSq//6zN2Hagsj74dE93JYAt6Zye/VxNjZwf9EDoIv00eqa++NnyVUkvRPRli0F4s1TMXQc+E0QeeGEboWYCVSwBtQ1MivQxodNMTG7/gcxISAc2T3Y/UEpBhv0AhNYH3K0wRSWM6Gt+DjEHstNNOswYed4KTE1CFM8TgQzjt9IEnaJxAX0GjFsQI821kJAAET8vy0BVEBW7iWZyqJjE9IF591Yice/RE8IE5sMgFlNGToC7Y66rsCcpBAkOZlDYDJZBXdtUfGRkBjcjBEATZZVEWTKi48AsN1fQJhmEwAiW9miVGAYnxAoZAwUfpYsQr0Ro5ZZFk5CKxGh2AFCCATEY8Uq71GtAI5FI7BgDKSI/+YCI4/D2AE2+8w1CRfGZ6GEygAjHlkkw4FKeVQcMrBEInpRcLCUTCLXwFSiOFlWS+UrYS+wQa+gQ/Nhj3YfxXBbEMWz3ED56+ggYmbP3KSwBUTXZkoYUQRDVRTPVZUGEAV0YE5OkFtKGhaNlbH2DJIYASww1xlMjjFcBcvxKz38gD+QFZub5KyXtZRjBNH0rD3AoCLwwTHatdIdAL8GFoHEcPG6wawkCwSRQplUs8UCKBJcYUKfnhHgMlO9nz6/EKydbWKr3IQ55AUiIE4UM+hVfNE3l/nQNMPn0CWVnIom1SqqEFiYQYIAJkQ+cYmgPRIrpkTMXhGaSFJMiFYtjURMPXVpy9YpxHZBBCnGom/n0FlIJoQcAeDpMoEk7xWCylMEzwQlNtRfXhGX+rGJCpBYTSVwL+iHmlcaSL0psUE4ML+lCrlmxhay6Jh7PqkJllcWrsefDe8bsuX3ns/5oqNcxtDmw64d4rbpuf3NU+RNrrrnuIimvEHnvunx7xlBe7myLR1k6YsoZm4/WMWaV6fLq29oejz3ru6n9qW1nc+M6xnZ/pqT3g1K5EfGBpzflzvU+Yw/J7Fk7/+fTj9WLVesrSZRPIzqhtszbY4jCn6iQyV558Pgf4iBu/3XXWBbMNU2DRIbhmPtXWkTEuJ2B8kZ1IqC2sTpokS0Vj8A3uRs22xf0zlXX5uo1gBsajdAdlWx0RDSmIE30lHNVmuTlE9fqNn1ybKzbq3ZmGRdDzdlMtuZCyMboYO6y/0RaRKOB2KLj+XCLTmG8sFmrOTKksMRrHRic7yu7H6djQ3aXFdnfE3W9knXO2GLcTWOfKgXIOtgOJVX8x1Vjn/E09UeS3mERPZvP9dNShxfz6d42RKiADVmvyXXVV5q74sZYJBBIFd1pza4XBXPFqJKU1MoR/nVpoTfl7cIBxiNYjuFqUHUpwNRNiqar+m3w3uatUKV/+8pdfcMEF1Lc9ysb/Wr9ieUf3ZttoMSi6pDm1bKBEFr2HjCSLczOzk/vWbHzMUiXhsLLHHn/Ka1/3+g9+4H2WBUpBudj2GfwcBemBRNJKrUVoSBR9qbDY1R6fLWoYkZICejkNprpWtzFfdZuRE8843tGGlgAnQuHgs5z44MuylOfbYn3ZmtufC3afLzZ6M9Eu5Q5nW2kJiVj7m/689LWvt299YPGyyzrPe5acZNATJDv8BpgZ57bn49lcfK5YH3CorV2mDqZKpyyETicaG/vS04vVdd2p6lTDYulsOlngY2SSS8VKNpOq2IdgTYIjshKJxXK1s82AU/KmXbUHJmNbBhNbxx27w1uO7vWeLNRW5pKlSr0nWXns4cnhxuz8N/69esW1cCgtbzptTyfa5mfn/ulTgxvXHX348feM1XqGVkG6UlraPzNRygxOp3KqPLYUL8UTYzYZxGN9hz/h/ONOe/2b/7pWnLv37rt1JNdec92dd92pV2ZnUGG0m1atskrB/WUC/PInVD98xSMa+ZhjHnvkUUceddQRxx5/wmFbjt4/PvvRf/h7DrA+kk6kPWWWrKlAverF9YimTXzVSRsoAhY0PVnozkN5IhXEbmCCgCYyxFDZXkkR04GQBAsmueql1fmvpioPpM+YqK1cWrq6s7InHl+3kDxzIX2Ek3V5wodHa2Qq95PZylWDlX/uTS211aqJ6li8cH9ncVdi9avG8rEDTxv8/I+WTlzR2Vifbxy9IlnNdtuVzeUN1pLi4BDQII1BQKIZ7ZdfkL9/6/xgf+Xjn9gZS5V+cef8YrRYTr+rD6MIQvXpzRWrVmKQ2ZbG9Ey8sEDwYp25WF8v/562453aMctGxI5gGClLLga6Qlj5qEFVidQnHTJYi4AjIyOsWGiF7h+dWQMSB3r6ZbMVii732bFvJjr8uSNdnzowlmnP7lmoVzrXrB7esDi/uP32a3q7e0aGTzlyVeeJa6zu/2V2ECg79YKJFkdUgqXIWmLdthakLlG7ObC//rVvZJ2Kl0zFE+noggMLMaxG/85/1J54euaI3j3zjdJSwn5467VrLrjeem9vX3+iIz++6/7FbKZRmPpBY3DV6VuG3vKGmbfscvv72J4HSouGOxN9z3n2GicKOgWHMpqcDK4Fcb322muDYUrAGMTkB+lQksAghU/MaDFMPQ4PWwp3/EI+wne5pU9OTJYKhTWnnDZz4o9j1/+IcZdqj8aDqkuN+dUr1pz3rMl69Zvf+Lfe3h4L03ksdH15qThfy87XolW1d2zdbSYTWJ/QR4CRocNg/LHb2O6j03eks/m1wz0O0hoe6opVCiuGhnCTkOAs3KBBzNjxi/Oz5aVyPh8RVgvylagDqFnhghoREtX0qtWwPtza1VavPukpT77gzW/un5s98IkvL157rUPGsoN9Heec03P++U8752z7oPoHBtotQL3pxqWvfz32izuMBrf96d62o4wo5CunP63jG1fFv/5v+7/yNfO5DO1UvZw7cT52EoulNjU9P5F41VH9H04nx9tPrmROno+7atvFE0ltU1PNxVe8L9b22EbsLlUIrQP9x6NjV/XmkYEVJETkRz7yUT4SwdaDqggOEiq18wkp/JL24EKooIwcML/SS6mywSIkdRwYrgI6Y5BcQMnoFU1QTIuQgG8gi7y8JsAREJsIA5igsW4lliVMqfnVZslJcIAVGh65iDr3wCtXDRxuj2RqZPLtyiuv5GSGHk12EsWg5F1b00UyAffIGHoouWAFYWWFdi0xgLQcvkPVLJ9X1ZeMQQ8CDGWXUbtTd7Kh7tJQmBCAD2he/bYasggSCK7upEutxYR2ijIgMzvUS6sJ8dxaGFL+YqgRlLT0US2EoaSCCkVhLm6giZqSQHSQXQyFAFVgAYeqgox+8uFNHwFFAQKC5nLBR+sz3ElNiVG7EKl0rUalUAZA8/wgh8erNIQBGpIByLcRCSylJ+BRR91ZoJVXtfCLIPwixIlSLHtr6Aymr0oBhOBB2LwEPkpJHpCR8EisFIVCGxmlMfwhI1biERZrg6rPV0QTiSWQkn5GUjIpLJmwyvoKpl+RqCQXzjbdqsAjX1sfzFqWmiolJl7FwW+mVCKmkOcwKEDpOX9Id2A4CYZMTGNkctE5fkMuvyr4gx/8IGgYwiwl0iFakBkJjNlJT6FhemgjXgOtmIsBEzEeXEO3ECaoHmFq9tcZssiI7K02gPQgBwitv83IZmXD19ZXYWImHjF5uWQslIvITQTCAM3w8DAmSq85S48jxPsQgHgEf8kUTSAlkB4fgwyEcsNvyPgb/mIfBiFjk9EyKsITAuFXSxEAv1mEGDKMiSGlr1q9XwIQUBL+fT2op3uiQFr1RgC+jGmDwJjoc3YMOWkWamKMgFmX15oLHKqJCAXnR/ZLP/6+t3712XvmnV+TjHcM2bxre3rHSmZYNDIf2QKR8xVv6+hhfzutzi5H3US0PdUhiVVzsKmv7H3DY7tv3ZL7Ue6o6aP/cXFpNFOtJDtWLiUyJRAmK8P/9MA7K3ETXeaCXSPcKKdc/MPxc/tMgsK1HO3edY+dzXZt2HbL4bt/8dO1j7NBc8lCLh3bcoe07P1iCjz0T9GZyeXC3PS+neaGDf6mO+O333lfeX/96LU9w4dtxKVAgSanvNJytOLD45u0evQAiCvzsb45C5vjk7HqkktzKgnTthOF+uqcHvNXTK8mKJ55vjEbm5mszpkOiK75GD+wL7VQX92xcm593vJP3e3Kjsbe9sjHcX2JjNl0Il2OZtfRzWVR5tj43j7Ycea2z87ufl1hE/5/G8BcusVaqiuuuALTqQLSThOGsSfZQ6/dfA0Aw2v41FpEKz1b4w8J/x4cYBAd2qGDsYCKG+z1ZS97GQ/eOn7eSCseNCy9YICQinFqkQq3fnUhw4q1p1pOEEvaYhZZNg89EXkd0KpJI65f+91j1Ur1ra952b0/++GnP/XpZz37WdqMpYkGDyzH0rp0+UGzo+BBKNH/tIDHVTTWrAOVSSc60gnnpEctxxlS8VixmpxdaowtGj4x5y/6l88hevaXHyKBSqzpqo+VEmNzdXb0fCW2Z7a6uR9tlZLIHHnUFEvxsu8t/vVfdTz1afGWg+9QALY6+CDuJatuKjvu3bZ7upBMD5/UluirODSpVp+px7pK7hCrrepKTc8t5KyxT/cu2vGbSRZZouaBo134kI/ZgTncbylGlQ/sTPPtU40Hp2IDnbHNKxLbx6oD3ek5I1oNla84kKqtt//YkUb3ws4dl1+BgJaFRrrUjSbVaONxcm5+4Uc/zo8cOWQrvF6JHtHjMl/t46snpsux2bIT0mu5TKw7G1+spsZKJkONhuU7N57y5CMe/ycvflVHW9wQ0eTE+M7tD27TqT7wABXMn7/3nnvT6VQul8d9Kz9HRkZMOw8ODfQPDObyXbv3jg4fdvjajZs5MMauFpbit486hX5w5LjTY41/ohpw1gqrpvERGAFSsD4tb9anWqyoWwrSpfvRW7RqVVl8RfzW3ldHy7VjgrBIdH4KkoyHG1/9mfrulyTru1NHzCaPnGuvO1bMOnSKNVZJDqfWfKo2+q4Ih/ls8d86UqX2oo7GJz+JZPH7fcWzF9o3zR7ZftXO1NRQ94A5+b52gyKrLJAplZa0c+aL4eQIwvJDg9MgjWpl+tWvzt90Y8HYxpe+VDvhhJkHtu7esye6MJjeWX4YrI7CyudzjKFVHF0HJ/z45tp3vpt4cJtrMRpbtqT/+JmpE05K9PXOTDva4KEGpdaeUJaMwhqLVyAPsXRDGnSDYQizEXVRjACJxcgb7MJKrX7PnvGF1EoXYjtoIpdts0yhuBCbMcbUVlpweGLZTOd0X3u6J1kYSMXaE/YURJonMEiA4ciLEwBQiXpBYQYTjgh48EXpSlTb8s6d6fEDy7WILuiL1hwhWrKamo+VmPubjqotWXXZqMzP7dl538Da4TZHNS6VO811TY91rFnvGL1vf/d7HY2ZZ5x49JZLvjx79XXc2dxAf/6002JHHfnT/aPf/+pXjzj8cJoqDCtqnqY4tFBjjYyGYOuIFGCxUQtUH/0LPVlotmBnMFMC8maZIl2UiO/dt/+ZT31G/0WvnR/qr1x1TTleKXfmUqc9Yc3zn1feMHzdtdeObByh/Y1T4EhXV/fEZGP/eKF/aMXuHQ8mqgs9mXb2mU9sAgYQD8F+e5Izaz68Vs/3r8zk+zcf87h4afrAvTf1rFjD8UA3kox0UMIyeyzZZDLynMWwmCHGJqMSsdiv+LXr1s5Mz06Mj+07sM/CrVo8NTa9kJwef93rXtuzuLj1DW9q27EjnWS4pzIuuvz6pbN79/X/zTsbh4243av8g+9V3vQX+fGJYHyVntiWPhp7Sv1/+bKpW3f0TE1Hh5stLTmKLGtz/Sr2ARshmcyurGeefOU9c8eu+cbKnp3WqTXaIrvFwvBa+sjY4DvufGBNb+/+zs5oUTGmozCtHixLVdO6VVMDxwIbs5XgaABGuQpKhl8YQaK0bguoWFFeCTA4HizT2GUk+ehAQckinuBJj91GB1AYW5Hdo6BQrmRh+ExieSUABzGVCyVSITHxACFgiF/yeiBvHk+JukL4sA+0YjE+gWz/iASUEuVDwEQSPItdVYpbpaahm9BsYegr9IJfIaxeXqGNvxIjCMT8stFZEmb2YGLtgOJINc+BFEFDccxfAb9kQEpioBZILUywKecgSLIA6xUyipPMKyILePUVfOnZK7wF1Ydh8GegzQMUjwvIJTvKyCggGZwRATWMWVCG3D9f4cahEgAZhuDAkHcX4EMVwfmxzGJ5JcNlgu0rLvhVcZyVRQNUkDQc44AtgBiHJoRfKSgJK6UHe0ungGIQA19K2Ap41e4kgwkdhQKS+aoUv+ERhhWpkF1x0sDZK0mDQ5Bb4hHMfQl41OQtuE/Dw8OBGkFdqIu8vpIBzFKLQGHtV6THq8ri0cHCIwdYSl6HSLmg7RO0mwmagVBiqK9kwakQGeqiaJ9oeEdmhOyUm/UsqoY++IiqQCmiFTjKiGSz4rIWJBzEEm2JvZRGH0TC0G/ADfFDLjGtD3KFBogC8uK4r+C0pgkQUEykxKghLOBVLQJi8ISDGOzzKfQsxJJ+awXVDCOgSnkNnA24IXh4bSYTMBzgEVBTZYUKQql16CGkVxctUVjDhFjQAAEgzzCk+R1+VQfXwCFprdnpELwTA6vABQFDRWQvJAutPrRlvEATS9CDTmuF83sJE3U0gapAK8DAO3rPemay6hi/wC/xcH7HO97h/NEwvNLM5ZPHa2iSKDm6e+vM1h+vWv0Hs/bqprI6Efv+DKfXpDLH6llulzLVvVraSe0wDk2oxd1Ym4hu9qiufdt9n3nTyNtO7vsvmyAzqyvOL43Sx5P3Lpzw4a3v31Y8RedEWiNDjvFiY52l1+l4W8Llt3Hex11rTvzJEWefc/s3H3/Nxbeff3w15gTH0vb7t23YeFg6TTksq7kIlQisv5Mz87V0bmhwoDQ/U6zNZwfW2mbZvzIyog55QmU1ybDEwNcIym/5zM7OjO0+ML23xt13SNfYzq0l07aF/PruVStzLevufgVsROQjN66eT6/KjMXuuHe7Qe2eVSM9HY1YPn5godGTZZfGnfjb6yqPtth0KTp1DKWdLmabdKluEXhsoeQq1ghba6GLBTfs9NJ1v1LIo76wfM4880yCQRgoOqSw18yST7JqqQg6BHGlh1vB6He8HtIcWhM8evih5vHoiR7lKyxpRsuPdWC8X1iKMflml4ijdNW/1cdQAere6mh9jz2crWDZ/dNTU5u7e+zoWzlylH3qrV+FK4WFWsI2PQHbwOL33HnTLdf95xv+7I3nv+T8ICIm/fSadLeBc/PmtI/WQnEHkQppQj/d3TfITM9nG5Yrt7clnNCjiVDj0ixWGqz2HfsXCnv28ICpV49xXM2PNQP5AKeJ2/JrVOW+jqRDS/s64/sWXEdaPdAWX99jzjWC6aaYrg++r3DllbkHdsx/9ZL8Ba9sApGR2iLrgIvE6a58+8q1A/eMJ6pd6WwybSF0pNjNms5V+fS5Uq2yMGt2dmh91wHXcC9Vs+kUi5t7R/4iQ7+W2DlV38AHrtWmlmqji9Ex5dHtSh2xI1emzSTXYwl7gAfyaaaL6eKutobDadumpqkGvoteBUoE3XiWXX713fu6qkvtqfZojpyOUZHBlawqK5gkpF+cMm13gfnqhdnR3q5cdz7L6W2vJiL/edktcex1W7Ivs27gmOGThrZsb9RrmzdvYrI7jLoz18l0sgGSR25Zpc30dmiMTk5tK1bKc7n5MctWo0OqLaD1t1hWr4eoHmG4rBTCr9fwgclIqKhd+zNDfxNS0sL6yGaykDj0Fq0KWkZ2STAxQfZE2TXw9ic0hr9THnuvK5GSjUUn8oDQqHct5c9JD749mV5XK+/mDZd3puIzefetm7oyJIZrlpunEumFu7Ltm2eziamNXVMbhkyTkbPoz0GsI3SUFS1LiULLDHAE4rvenfvXf3MeYfWd7+p+ytM6E43ex510/PGRd8RXZJGE/YoRUyIFG4/NzpR/cEXs05/pNLtXXmpz+PfMzeVf3FN98xviZz6Jebhjx3aSrI5mSJRD5PR/7Bt1pG2BZZwdYmpE+CyjEwKH/MqoUejM1GWyWHPw1VIuWc/0DOaZj+XhTZsma10di7X50e3uMy0uTnUkoxa3sP/B+WT7nmqvJcGtxdEY+ma6zLIRuLHU0URzNukRytUGKT5ce+55f+Ik/pqDyAwGVAxFqH6tVCtEx5tLulhYsitgYdZFYuWddz145/ULR53e1b9CS5zYs8OtOu36p/7Vmd33Xnzpf913993PPO2Yzeedt4qhpqds75xbWnr/m/7c1IeemAoO4kGJaZj8AeLBHg34EDaeLWORfWPWkbXEwpBGPGNIALa0DTmkmtFqYWHxqutuvH/H6Cte9IJVL3/50Auf78pj26cT3d1LnR0//vntP77pJqte2SvgoABrc2KufMddWzNt6cW5ye6MYw/Sp5zyeG1HAnxEn1tvvcWUC6rMLBTT3atou7ZM9uSjNuyrVuEAJRjiEY5DErXhwwpk7Ylk5DFYCRJ/APfVVAx7slhYGh/ds3Z15fFHFo7esn+x2PaTny32t5+4sqt38jOfSm57UIPIZLJtaRfz1qKhl/vvK//4xtymw+o2k7/7fbmJSe1+eeww3rgtmXius0EWakvfanvFy2P/8FFNojPTboV2rX0xeSYfgwPcfdQJ/2vvgbbvX9Z+364XPfX03s7EHQbNJuesWHncluP/t8sg7r33ymOPzfGgUF4VTNYx4Ch2vYme0oNozCY7x3QrSHfBBReor2aiUmQbKcTL6xOaqD7blAuhOWCxeKMtnCj9BbsZfD4kjvuKgxS+A71l1CsTTv4Vaz7Yr1gMFGNUWcgIoINenF/KPia3OK5QGGIQ4PSP7B6WPfYpNJSl+QgHgxtruO7Yyj3gDIe2CbIEMvKEQdNGQFZlyciV6vgaHiKBrcJ4AjEGvSqAw+gX4/EJoVREvACmq4JkqAGyehk7VhcTzqqgoREeFIA8kZZdua0+DJw5bCIVgSPBbMUUxAQf5VFSdraBV0o1NAfNR8X1yzKKEWYeAC4MIHwCnhCQhTOsRLa+qoHMg1UFBNe/o6fSFSqNSMjLiyaaG1phEAoQbIxTllLwQtXUJcg53LiLWKbbRUZEhhiYfikiAIHy68ELtJIeOwJuIMgigSrI4hHjFfEVBGEYqjJnEncQWb0Qma+OyLKoiMpCUukSQ4mwiQdBFQL+ykV/r9QOLd10w9SLkAQuwF9Gw1tUECkNrCfzvgKFyyoOjnBUjeUnhJUVXiEmrC6qFmJCYyEPhuEkRjE74V/xilc4eAbOaqoK4j0B4ZArLA7CHdgSBl8Vrfrg4wvqhRJRzziC4qRUd2kCx8NXFUdGtQOzmV7LDUW0/iIyCBgKH2Gf9BEBWtjGHBJjgQBQnma4FU5rGG6e1pjfKqzKYWijNReme5RORNU62BWtCX6rMDghvbJCIMQ0X3FNNbUUtMXHoBkCi5vpm0CaRTezN2N+L4Ege5BphRZKpytsWkTtsF0oJJDSUbVE3fnhYppYaY8ET0YNUKRnZGTkwW3bLv/Xj5/716fmHJZY5/y4+z3lhiMmVGSXoo+1ddHxQU5EiWiFcCgjr328ZngZmY62mixufNd9Xzip++rT+i9b236PQyt2L4xcOXHOFQeeUk8OuPB2GQ0wogONqyZ+U9ZgxhwE5dzjesVKyeS3z37taXd878ir/nXjH15038AmouZS0pjZu2UPUH5/MQ3ycDKlF215yXW5faK01Kiku2uZmKWuUaqoqF95NCKtRnMICupXvv1mL3Yd93fnhqv18WJhbqlSTOfrLNdG+9hcpdgX73Sz0yM8Eb42Cfdkqx3ZuF1jjCvTkAuV+kyxMb7Y2NRvuV2jK5Poy9Tz2Vh7wcFaTtiKLUYH8EWVYPMW2GPGrePRmlBbTdKdHOC7IgocFNpHKLYlymkCmraTRAmzLB73lXItPY7Qo/2Cl2RsK8iSBHKHoa7fuX39Tx1gGFjwg2emfENdljGPe3V8gk884ZY6xpy+y76kGkxfaJ+//ETKZ+cT7d2s+UhaoypGLPFvuZqN6f27k7lerxP7HhC17+c/Nn8wtH4LPWfiMfLPDm5r1DcAS5kODw+zMFoZEMbaN1sEFY/ZVWupc1vKldn834eYVDKxaWWv2TDrs9pjOiGNkDZBZZ3KL7F9WIin55zxqUpicqE6UzIJbEN5dXNfRF4VSR9+xMKLXtDxxS8VP/DBhvMhevsDC7VMgZGRERa/lEavzTO5hHbNZO3BmeTW8fqKPPeVO+p2zLSdA9Y45+NJ/WdPdGGSvEknsDNdBTV2ziT6FGrxXZPmgVPVYm3ORdiafzw6263bArx8YnqUfKfH56qDziOOEscSLi7vzCVrCzY5OF3MiUHiCS13Ip3vWoonSk7RigYDDBKk8n1Digg9SsRo1TYbX6tOHthXb6wtJTriBUqjRllkUsaBLB2xNSLaLm/qf2yyYVH3Di2j4U7dWGPMFgIopRBIs4m2d9cdSZ2crudSxfTcRN2dT0q2kc9WDFOiLihds3aN7la3egj54WP1AetT+3H+pB6doUkLU6l+mYBcFHzUfzcz6t2ZR6CpRIgMAen1uxSQSDGR8CBt21GZNRfXq1trxTvjtdFGcjDRfmwqvQmFtPplFRbt/LBahvdbjrxfMCM/gKaJdnmgXKOxZSiZ7xDhdfn78uqjpl21HPvQx4WLL8k6MzPRKL7ywr43vTGSb+OY1hIsr2n0q09lmTHfs9GkPX5U63v2Ni75SrYSHc/HMiBWRCU5NzH/uS/kWJMrhn50ww3qwh6VnUUVrBZZvbaSJYL2sEf/ZPcXIjOzAGFyyYJETpoZGR52y+rOeybmq7F837roNOJsbPzA3omF+lwyalxje7aXF2eSDTd7VTOusmZOF+NzU722IlBzoSh0RnYP4PDBRCXqI1mBVB526zWV7koi3UhCz7Budbkzn1pcnmZ31Ve0wDUhuyO5OjZuXEqmk7m+5Nz+2fmp8uL83IH7re5YnNr34O03FBZm2tPxfFeuULQSonr5VVd94SN/+9ILXvWXb37jrt2R42o2gV4ymwElaBASGCIX+4zfguwsTra1SIY7ahAq+y9oFfNdEOb5SIMv4hln0nCruM0krbhUTbf3/ez2O/aNT598zOazzzrNetbi7PS+++7Zs3fv2OiY3k7zhwMTVviwzUfsmSzUl0p33P5je5Pn2uJd+fwtt9yqaFRi4mDBLbf8bHJyipvQSOdWDrYVLY8f35Gu98/MzjAK4U9IeDX8JXynyoIVS1cjLNxYFUApFExEZbir6UBf4Q0v3HXOKbPZdHQGD9q+4o8TlfKt8do37r/xJldY68hNuri8w9hQe3sH2lZv/3nsBS9cuvFGl4TIsvwsD+hcV6yNtTs1Olb458xjM4X2pepMTRtp9FU6X1bMbDFz29g7e8yt37klOpo+nXZz07//l8P9O48/4YwN6zbUy5Xbbt1mJg/luSjBxcIR4o3UukPmPhagsBJR3rF2qnP22Wfz3NQXB1VT+0Ir6ZEUbdnNiKDKUqqvR7xfrxjnVwIuq0jwvSqOx6J06JFAAFn25BahrAvFAoIqIxx8vf76653eQYB9YvJiAUwIg54Inlyd0GYlIBvcM2XpVnRS0FMFKsv4nbKuueYaTUwuVYC8XMoFgYRDlWhpFAQSHB2TSgWK+8VipYhRKIRDZeEfGC09lylk0f+iGFAwJ9LaMvxhRfZUR0r1EiMe8UmvZJBEEKVw3ogW+VF9AQCVS6iUqNaoh0Ti9Wi4IL3f4PPzjRUBH8jDTcCAgmkfZYlHCsirGtyQV33141g8MjKCFCigCBVBH0TwKyUE0CdUH5JSwlAdoQ1mQAMoTFQpoLCeMlGWkSZM5GZLBmf4c/kgAyW1QPYAM9QXJkbfUBUo8c2vwiArxTybcJi/RUB8DNwBltACqFzsNmQpDA3IK11K9AcBfAkUjeCqoyB10WaVDrgurFkimisIznq0ILcYrWpqBIJPSCSxR9EoFtKLP+TxCdeQQokepUgQBMMv+qOzgLaDUAaeLITWq0pDDGCFm8ptwlS6MAI2+2XoeZWSxPqkpn6pTfUSwHrI4xGnEcc9EPYJcXwNj5iDwUP/V0cIEyrwqQIpYUsS8DHkCtBCWagNPkY/CsBDC4hFs0l4qhU8/BPCElSERYHfCubDQf22MbSW+npkRAFNTEDjRWSYECqvZEnrwIjAi0Ai7PZJRq8SCGM6/v7fwx9zlUJQ/TYfpRsi4Q4oXQ+rvTQR0L9TeoTN2GVIL6WAw9WcSMorBop4gGA45u1ve/uNV3zv+W/cNZQbLs3ut9y50kkJNKKbgPWNpm1czeiek2gCRk0ZX5GFb8leZ9qBEz41OM1S1xqdN8780Y3TT18suZmwsRRznUGKBW3ipysTQUMnr9Eq6ETcvTPZdN1y0fkyzxAisXvXHHffuuMeu+OWw+65euvJK8sVC419KiXAIPi2DS27FKGOfuWJtCeyxNyyG5spxZanVaFyqA9swBF3sJVg03LLmX67n47OjvWd2Y7+2O7ZvWML8+n+1ZHN2RH7+R2/WJFcedxhvxams4UGc7H8aDFRWUwlo47ViaG2W/KJporVIZMYrn3NNfrm4/vSrvdrTEbmpH2XFotHJZTr7ouq2V6K9laG5ru7Z/dFbf83fEg1ImlZzaaHbl4JDD2pR6aLiAFtLFlTeLwKs0V/w1IOSRZJ6v/wMcwJAnXZCscrLMOn1niqyt1NrTEhTIObmaxncu5WfkjxUHCM/khAuCDxgXUbK7HM/MyBFeuPsORvaf2R3Io777qDB2jPKiDIxG4QCKNxiGioybQA54dRG0oxlkCwzjv3KUS7U1Nw1Gh0T5dJyMjti6ZHDeVWY8VGJtZpW2WysT+6NJIUUru0fwDy8N9IXTdig/lUfrYy0JHYV3Dbdc2Zq2u7G+3WI0fuZCL31rcufOtbHTt3PPie97f/+RtWr1mv+KCbgr4IYPUWyhrp7SBwK3uTTqKbtSi8lnIo5NzidG0+3h2fG8zGd+7Yk29P14vpVHsff0e3TCLYLCTU+EGhEZ90Je1SvKcjPlWIijduNbfkJPRyviNtK1u5nhydbXR3NMaLjXK+q/OUkxtXXkeC25LRSgZtnmdkx3/fyScutHXOFI0sas6RK0u/cLH4vcpBsMjxRrhlaQxSG91vGmc/mYu2MQIYaimiwPiuXUznQsfaCcd7R8rFEy0sWVFA+kjTRI/VcYvxyVo2WW2rlqIWZWY1m6geuya+2XT+xlNec9q3Vqxb2zv00L2XEdEjVzPaAW8EEemMIekamRF6ca3C46vRd8vjDYUwTKNSlm04OldzgjMpZYgQntC1CBhMCq8PtcMIiDq3JdJHL5bW7dq35/Atm+cWFmZm9g1vWF+o2ldyWD62PbO+Otq3GDvQZTjFen2ZnAQQz1Y7jzEIkWykBnP2q0ToLOO0jAZRDN5UqH00l2ld9FVX1N/85+asZ847r/+jHzLrruwo3/LDFmQhaVb6P30eRSnaZEr5rrsz0zORQEezaibnKvaC+5vdvaexa/cGZyzFTqNYEerUU09ly3KA1ReW6qi/Z5CFDiZQDE1Ips5eWYFEdDGaBHFl8ZBYS9lF2mG5faJ4+11bp4uVrlgjGys+eP+u7v7BQiJrklJ9B1a5Y6MnFy/NFPdsXN3/uGM2kgjeJgtPoRHyD/ULUfV0zzBkwjIcrVqEIaxgq0Xo/gW6BgdpCafZ1s54YuOy74NfrVrMH/m/RnnKRx/Zvmnzzmlnid83+cDt2nOms2d2dO/knh3tPWYYMqRidmzn+PY5gjywZqTUVn/gZ5WFuenJqcm52VkrlBiSgel+rWehkW011PsiNWYFujFwUQluPByIBZ8EbZFEAvOHDGWej4fra5ZVY+Fj3b1t974D+4dWrqmUKz+67pqF2fG0U8E7OxVBwyCFa4fIYTqdGh7Z8OCDOx7cvX/brol9O+/fs2NrZyY1ZX2WUb99+1gJ7EtaVInbt+/I5Uypds6Xa7u3bzUBMNTTRs7RlgnO+CMkmAV/FiduQkwRLEKFojYVzUwMykft7BrpSE+++1X7R1ZGhzzHaoZcjJFFhoTSXNvZ80eZif8T7eA1/V5cLNjjXXSyN0qVK5Gxs2eX80ZsIrdrClcp7fbZ2OIHa/kP2ic035b9WPK92YX7ol1JucfWEn2GjepLjQ0PTJ53yy0/1+IgVigu9GZ6rK0e6O1vb88677ZarS05DCweJxUW8SqKPxA8Ye6Klu4V6ZDCwh9ig55nnnkmCxU6WrfayUJoyZX0Wg3LdWRkBCmIExpiK6kOKtcnufQdBE+LAFzjUpbSCaFP2M1L0RPra5UVWgT3gwxw7UJPJ6USVUfeqEaFAn6BrzgIoLkGFUpXI4yAG2yD5sEsyWQPoPwqWgLx2ghU1c5X5UKANeCreDFGmUEWhpXBZTYxOWdf0i1kgGUZrF6gTCMTITJJhqEhC9IpFFYsZlVThHI5sUQFZGlIjl5V34c+ZkdJFwTQgSGry1YLUgR/BcGBJKObSKVIBqyM0ACTQAJunEh1PCg8PDwsGZxJI04pSx0D8rBSRxUE2YOMuCwNknp8VRdIyqK5AaUKBBhwoNRC3fEFJnAQLyPEpJFXEYhjxIoiJfniEYckyCsXDNFEuVqNeBVUhMoilwRIpDhtXE2bj/QhjFwySiOlKsOZxBID9AlZ2PQkAfIoSerIQ5BeKaGNVg5MBkQaqkPAwAdB4i42hSpwHDRgERwQYhl+USlgEvBXfWiDKa9cTWxDQK096EaTSB+0AU5J30wZoJF/LQK5UBJK8AHNTgrL8kNK1CYSSpEdK1FSvL4V9aRHfK+GWuRCXoJB2kHGZfEaFAT0zn4hjMianlw+CQS9JBweuTwq69ensJSXIEEAwfFUxkNqKjEhQauDMH6L/2F+CLSQGR0wDs7UiASQaQXqq8gwdCU+fCW6cBN+RICt2VvDOEv88FqukNGrSHA85F/tmkW0ZgyJmzHECWGbr5glTD4RrRn5ew9gEDRg24QMW/LP8id4dv+G9WvNr0TReJlP+BUisVVA66Mi5KXMKRz+sIZ59jlnX3n5Vd/5/AfPft3/md152//D3J0HWHqVBcK/+7117619667eqtLppJOQHRLWECDsoiyKgg5EcPx0RGaYYQZHHQSdUYcZUWAUh4/hEwackXWIoiKrCAQSErKQdLbeu6prr1u36u7b93vvSco2AWSEP3iTvnXe857znGc7z3nOnhmcGBjba1I3kY7WJPKWiI5DSVG0XNEccDRJox3D/5idj820Q5sTjinGysizSrj0032NXKe+lyehgaR2fDCatxCMuh7cYff6uAnJgL5jeurWUrfjpeTQbRdef+mpW4b/5n+0dl++7aireGxRI2j7ZjK1e/ZwNAHdB+qHz9PuRHdnFkYn8yNj263eei1uJ21rLLqgOCr4Oz8R8lI8phZ/pxz9hAAmRwZ6Fo45iqhhQXIrZUdhoqFjEvmHEJPiH8D0HtGedNIVJttNObx7JhprMYLZim/Uuyvb8UnLoQ39FxMGCLgcpaobg6NLcGweNnlhFSdGbTXig9GC0a7OQipX3HZrw/eMf2htzSiwFcEOUCHDysxLsBj09rrrrvvMZz5DK1QrkKFqmbQRLo1aH9n/658fQAcYEorVeJxbeHilwedG/gOOn/sh0vVtQolnhzar7WHXTsVjpQduWXngVo5U6exRmn3si/+7Yzl+qzVz8LJmbT07sWdk1/n/50Pve+IVh1/x8pdRUxfW2VSALyYBAmx3SDiO6+d+7uecLYaD5tY/9KEP+Xr1JQfvXm0vVRNbdQMb1kuoIcru6WvQAdP6G9XYaqFXa3bWV9bXNza0agCy7yo2wRDDYwihPglT/weGEuVWbKVm2UC8XEieKbXPj3bBRKqWnJ3rvvY1yf/6+/UP/EnuZT8Wn9mrRgaLRsABYa0+eRcHixbzHJ50TlWvVrAJIXV0venm3s3yRr2caBV4hd2ZxNZ8qTWloU31tm3OpQsueEmqmQ4GirZXWo1w32psdMCATXxpq5OMJwcS9gBnzf1u15uVeiU/NLZRS3z9ROuSy8fHfvonVo48aMlFBKfvVLVcZfTjL4pddPHJSqLsWhNmgiGIzveNXK6ob8e49OLV1YVT991eHBpNZge2S+v1WjTkPzyxK57OANWobjMuPctxOc1uNXEDTS/Zn6sO5EZMY7n6Va8fo+NmX0fPUj0noEVtxki6/Yy51HRtLXXvqe27vtlbW05eeVn3sisTNvNoa/tVi4vm8CHSIWVm/fOf/zxHR3u8IyO7zd1b6HhwfT+R7Kl919rOnUX4phqCj+irquWBDe+EXd4BEjA21mDaD4ZO0N0qrZd27f/Wcm+0+5KLu1+IFeujryqvvCuVqhaMClj8nkx3ci9bTe9TO5wS80JHDKB0x9g9CjLT4n/7Aps3/mxha7N0yeNG/uiPTB/3zVQ/XwQl2rTsAYVPGdyLKB+elrdazVrd6INOQ3+QWGLqak1sy4W3cQyLfB2fMIe3wcPQPTNSwKfhtfgNBO78css0nIEJ9PM5z3mOT7wx1odPj71eb/rExzebicVa7rZv3Jafmk21N5bONKulhaH9j6vbn56NNVvJkX3nD2dTWw99vdzants1rERoLC0vBV8QEK+Yb3Ucj40oVS4YkmbABA66c9ZF8y2QQzTayGde9/Shl79sq7zd+eKXKCLt3Lae/4pLx3/pFxfzI1/82kJl+czKmQcdIz82OVPebOirDw1NMNIbKwtjxaGSDly3V9s4Ozk2mkxn7rrzDvtv4RN5hf3mmWh0MLhxnCc6wGcVj3U0XyuubeaXYAVHh5MNK/gHnDENY2X3qntzxx13SGkD9rETp+/61gNb5S3j8KlEd3tt4W+W5icnJiVmPPmyMsoChxMnTppE3yhXyp3swsL88sLpbrtZ79r4HU2nSMbVwxl+vE4XjrEtLm0ob24PRFtkE6PFYsepX/3OLQSilqxW067AkwIoCKpaC54uiaMLFSJ5peJL68u/+evt2d0btpk0jw0u/M9Y9S7XFPcGnpmYeWUqXqjvfVWseiTb+Vt9Y1MKaceqwYfGpfftja51mt7V7lmNYvGTG3kNXbIPsYHPNMu/MzD47wvxdCm1vzXM7+0vGlFxa71D841/E88dvPqqmSdcc40bQamZW6YImn/J5wac64znRIAQTYkEwTFFGopCDVKd8U0riGojnqFTobcTlvKKRCB1MqUjL0sOoAFNHiSZgqk4klUjSEop2EIJpfQLMnsipeUPIOAVWeMndgV8dOr49I6JVooYmvzWt74VthRDQ6OeMjKokJ7e0iXpfeXIkrseZmRoFhY0lHTelAiKtOLgzM7OIk1bRmRIAxZ8ySSApDD4fj2SARum4xACmvpFdUM/RDJU8x3VIF8RzrHQPoKPCmD9olEAnpSBDsgiElugigNYJBeAgWpw9AAJJRAbEAsIY7gOm6qBwxKH9MBigpVTHvaK1GTHfFUY2lpkdltPRrecCGyhEkm9aXWAjED0whzbxcNQ7UOOZODrhkmGjVDdYQJhyRIpc6kUhj+ghBx0kSwkMV+5CgUBP72KDIoUGda+aVUuVEkNvUAJe4wygMnukRH2EqtkvsIBfGFIhlED5UoWzAL1CAMxlEc/EzMJSDKQ8U1xupf4hlFwwDqKh+dI5hjIi2/CPslIGQQIxZFptBFKkMcEuVBNrD7pRczNzUmmLGBF4jYMxcBw51H9eRriFYEoVWbnU0gcPFGR1A8Dg3GGGHUCioviTsGQBQSGSH9GiRCADOBuf7jxxhuJjLgB1NoSikotBuuwXS5SIxHkEw0GogUzA0xFEOgOSiHgq1l0dRlPxMDKL+aAD6xCH5U+vOKnr9/203eJfBS7zk0JoBKpa0jzqJSYIAGKdrKglOxIYSfmewz024ITxLRThMorjFH4oEpiNUweBQ17mXcKthOPfOLzSgMprbovHBzOHcg7iX9QAcoP1LkdYO24yq7SUYYdfdvBH2nhIMAdBMxkvOY1r2FOmQXaDmEDSewkH/7n//nPf/azn/vaZ/7PocufunbsrgPXvapiMstmQKVGDryJFSsKHcaM8V6iQlg5b9blmpa0hplr2Wr3ov5p1A+M64Im7BR0rRU3tc+UWnRpok18GrMIHm/BasSmnX2pXj7bcy+u9s9O4E9f99of/fL7rnvorgcqS18/cK27BTPDu8dHzXmpsIZ+VLqoT9lvuw1lRqDcb89U2MJVaSY2m8mNWncqug4jwpJBwB+/rBxSVBkMhHmwiiqLZCFlCPh9rATBoYSyqBHoPTgzstwZOrPVcKPDZq2bbnWWy+3l9a182rq2gomDHVB9Qp0i1Pvaifb9K23X2dgsjDnibQvdqJtR65ngtbfR/t5SvbWxnTDjbQymq5dsFW0imkTEZj1nAQ6n7gJLWK1G99UFfYCbIQzDXgpldmhjMCOsMVmLfOUrX+nEAXc+MxFWEKsCzkVjXn72Z3+W6AO9Dvd5xjOe4QwguzM0pg6I1urx7Qn7sdz4e+q+c+gcFnznRN/9izovATrPTRZeA+Xnxn+nMMHjdiuWb3STxG6Z+qnbPvvlD/2nSIuiaa3Y59//m9Q1PzLz+Fe+UVQvPfCK3/zQn7/9F9/w+tf/uze8nnJRF1VLj3fH5GkMOEavfvWr9Sdhop145jOf6fB915AMpeInG2ZNc5w21YCo/a/SGMSo9Uz6xy11mC+3tipbptNCk8y+K1dVDML4doTEp4cSpzbbJoEXtxNL0SRwfGZY7eyPRfV6g6/75c2PfCx94uT6f/69mY9eazGnRiXADNAgrFuiNiB6OBufG+X5OtK5c2A4fbIXn5iNxkcrK/PZXik/c8F5g8lyvT2RTZxa71XUSL1fvcyUq4C71WTi6KLluIllR1Lne7uHExvV5kQx0221J4eTza3N5ZUlR7ptWfA9Nf25U+3nHrpo4j+9ufyRj1W+cVt3q5bcv3f0Bc/NXH/dA5nBm487393qazuQ44O5uEUQ6mRX7bdUJBmv1bc3Fo4a0h3ZdaDbcrA0PKyA1tW1r7ezdOqovnEqq6cKNwcV9LL1FXUmlx9KuwTVlUv61X0rRQZhEriyvjg+NJLL5lmoXLz9tLnkzMbplff8962PfCK5ueEgpcr4OMRm3vjG7GWXJrLR0nQb5wyZX3/99fpmNpZwaEx6kJF+7w033ICx3ALh97znPXwRB6gaBHWNMOfAkunAdm1qRFS3y7fjoIRWMziREkQW9JGHCjlJ+dAhLYprn1Olxc5aLXHv5otHBv9kT/yWwaetJKeam3852JpPZiZ7+WdsFR+/8YsTgQAAQABJREFUzco2U3vSY79oFUCky5Gl6f99BGa0ZieKic42qf74y4fOLm3O7B36xMeSkxY1/YMcoYbDJ1SuUO+kiabdZqbtM2lU3OERHT4R8TzS5Y6eU2F6ymw66pjUc5eXcCm4ETuGdQf4I3hFhOuHYA5nZSfynIAFGonNTrad4cIfmNy1ezCTbqRSg6mDtZ7DY7vNhjEKoxjJwVQvne8VDu7PJqM7LUGg9vwzVUkrzrMPhHBwRYaehmUtvCLc1guFNqHAQfNg9J00o2NbB0dHXveLvWc9s3bXXayDqbrE4y49m8m//1N/e/vR9erafHSOcL26cvohRmVrfXWrtOoyoY2V5db4hPMqDlx0VeXsg6nu1kUXX3LkW3fyzhlrZcFN0aF/AgFOJ0Osc8LTgqpicQxW7IlfM2OGTlRJ+JNI8IO1XsKSedWo08bdDrrMDBmXarabWoV2Y8vS+3ot2gBpfbK8AoCz4xgyZIvp/j3xwvjW0oYrF+wcdcTHdsWZeFHfjxBlAV8jp+h2u6Wg/EC+urlazKY21zdPnRhdWDhtHSsPW2LJYA4HLYpXEqcbqozawedWHK/IV2E+01WHk897qksgOtX7hhbeFG+d7Tg/o9esb94VK92eu+QPBrrZ+t7XZh/6Ui0dT1uuU9mudC3yHxsfuO7p3Uwi96Qnl8bHRtfXkkZtVGpGgC3gUyxfGyv88lL1f2daXxnMbVpw0+zOPLR88R0PXXHBJZfNzU46hIpppRj0jSAwHz6whSplwBnm19AVX5mKajVpAhLQxbGjvdxBh0rgIUXloGtf0aLy0jEtKOUhU2TqbKjyXjHB8AEpcxFwEleB5RdqO7BCgKOGM+4DJxHdDHLUYVMuHgIrC/S0JloZtck5F5phygCOgqQBTXYx0JZeXlSgS6SU8ipFRtjCE4Z8WQFdCKCoNzwRzo7hidMiERsGm0AQ1ovQf6OrygqPIoCNumj9HpGyvCINM/2GlLBSsyizT365R4rAB2mwAiYo1clHIwZiEVDCKFW0V2mwl/JjhQcmaLSuChBIQh4o1BksUFNQjRVhiEGWMJChQ4UutGMLuajpHCBKSLiyKF0AK5QCYblQwUqTEciQkUWtJ2vIg4APDAIFUDrSvBIHBPQN9DfkgiEcxAMVInUkxOiZyC6LSHT59dANYP1KjKUwxx/KJjtaVGeRioahxEjANMwhVvGg8ewBxASYU0g8oWZwQwtJSUlXhcHHOsn8Kki1RZRK6lUpQSe1PoDjDDHhPP5IE/ozjAxOUhvcEPZQGxyTUrmUB4ZqEOfBL24H8QnDGdUkGLSF4DwhTNl0ZSWmVMoVaRAKHMvlgtuGvRpK2HJPabv0eiPGlSin3W1yQeOd73wn8q1jxzf6BisHO3NzzfL963/9r8F04rfhSw4rOx8eRKFCemzHAeRjS1By6QMmAjt47sQoro9+hL+wX5Ka659J9tjEARTalRLCfneSCf/THloEiKqxk30HvZ0YgZ1I3NvB+dwE3za8k+tRX0N8QP7cNI8lB1t2vF9AJFAvPML0iprRlsfmelRx3/8rK0pXKecOqJ/5mZ9RU9Ri57PskAATCqYxfSxKOzE7AcpjHb7Tdj/28Y/Sg431jff/7r/KDo897qVvbLbjTlZ0ii3y9R10/CJVIYTIqYr+Rq98ezMrtuO5DymRNKdj3stEZVionEtFg7r87r4XGrfmkodryyv8YSulV/vwsvHozKB0tb/fMJY4Pbjntouf/bzbPvSEe75wxwXXlZ2ikxvsIhsG8gVYCFCOGSRRSjNRLBBPVNvOkXLFbmyqEBXhq0rBLKi5bIhH7fZLmmrHDhMkU6OlZAAR5fWxD+PJWKmM5L9vcvB0o/fQ6aMbK9u6N5OF+Pzi6q3d1Zlij9Gw5172R8TRWyvXP3XHwm1nY8ubdYs7y8snefkuEEiMjlebcafeWgo6mU88tNY+sZHY5M6jqr97UUHRMkXctjahF680dM6MFCTGhqPhVGsrdZEDnoYRLakTZiT98tj9qlPhK50xPsJueGDlQYX+sE5v4IBf9kefztRmmJJRuzn2zEuA8E/4/QF0gLVtCsb3c4sPr4z4uZHfJWwEMNpnmsrQWAqta3TZS15/yfNfbQ1CbWuj3aoNjs/03APcblXM/NQqkZIP73nd2z85XDna27g/3m5assh5wiMP3tEkzoH5Xu2KgVKzMRokw0i+OpN4q5HeWIsPjF8UOcr9Y4mMGelY0E2VRH/SgMdKLnnNtU9i8Mrl6NA83ScAvxMJwPrkltj9I9EG4PVq58SpswPt/ERh6OLJaEEI9/r2226/6DWvab7lN+N/+alv/fG7O0952ujYqFYTA7mqEIY221oqKe6YLJPTU7vyObf+WvJt4YHNwFZuWFlxdrOzt+bUqu7YQMq6mH0jyVMbsYqjmfV+o3lgJ573VqrR8MxwJu5ML1ybLmacIMQ3XSq169amxpJbW9uuzel0p29e4Oimn3nocaNv2Dda3dKLbiUz1YHcnVvpj37pxGq9F+15SOX2Hzxc1navnt47OztUtLfXYQCtmemZ7gWHJ6ZnStutwrjVHZM6xlFt9ifRQXa7tlWvOGOMJdIFNqPtjtBYOpsqZhy8F3Es2q4add4Zpqg+pRMdO4c5++rSwanYvnrJEoeld77TPmCJVfnGxlr9E5+olsoXv/sPYwcNkG9q5tkInQ0PGApiGjxaYr/9XAkDijfeeCNr6xZNMfTkgx/8IBdHWBbZA+e98mmYIRIJecWESP6HAKv00IMPVCvVciu1WEmMHUgvbjs0fmR57A9+fv/PTiUeyF+4lb+wHO+key5xMcASnRQ9mdr99njqYERt9DzyN7w9/NvtlrZKL3vZ6EMPVkZH8x//sIWwkdmkyY8kg+EjwWiwyad+Y8x+sVLpzEUXle10uv02b1ImbclIG5hLtK++KnZgn2PPreJEr/pI01glniL/RkoOELCR0e4PB+KeBGI0ln75bZwMLgvHiCfHIs/OzmpKmeBWo3XLN+9ZKHUsL7Bxs3Tm7rMjk8WhodHzr1248+tpa2mm9xTHdw2lWsV8olhIzV510cyuqeUVvYI0t0+JytI2Yyl7TXaWvsCKywK4R3EQ4Dvq/HDgYCKsLkcL/rPDn/jzT73iJ1567WVXDT/+qlqtudmoP1hqf+qrt33r7js3F8/gT3FkVEd3a20xOVDMFYZbtcpqZbsVTxiP4WA3tzcL+YHe5vILnvfsVr3y7ne/O0hcKbhkzcjnPvc5JPPSrr/+elabNVM68j3MtHhMgLw6izn8XVRIwD7AUECkT9xl6c+cXdpz8HInFk1MTKZj7jkruoN3bm4OvcqSRaHSw1kpxPrQQ8eWNhv5oXEjx2TSajWimquaaFL7C8kEMBBPEglnz04KyzU9TWNjI6PRotP1tQ3qSoGVjs+YCRk9Cq4qKfM4pRdDsgjBcL4FmM97uibf2Y6ZY+9oJU7b+9R21L71XOxi88utlU/nJn40Vjiv1ZtNdx+gXp1qfbs9Nj3+Cz+/tXvvwn3HLtm3P/trv1761V8dqtecDhnNCXcTW3v3Dvy7NzbHn/zHH1sYHH7BgeHq+bN7CyP7arlKauCYoZ+777r7+InjC/3zw+AJSSoKPWrmFW66IhQSl+wqxE8PVD0IIQIU8ctDOxouIAhEicdVcgFNgP0XdqKGBtXAAb3iQKgFAlJilHaUEqoRmAms4rCRRPSuoSQLsB76KRK7NCV+9YIksHIVwjpdVqAYnObkMTXcFF/1suSCOS06fvw4pdJtUJav4pGgCoPjVZb/+B//o2SWV0DY2Ip2QWMPw1ARaAhaICl9qKqYoFwJmCxdSt1IhCAHpYQuGUFLCQcAjQsoS0BPmFZozlRwONBV6RlAJAtopDSRfCNIUm/QPML0BOGUCn8wnFuGLYpDNT2UFyamcfSfcY+M4Bm6f8Zr4K9ccOi87Po8OE+yGKKyKEXDqmhhGMIW7coyiCkGaX1p93TtFEF8SJMYx9Q4JEODuZbeGCh6fYWAXHCTDHNgompjHbrEY0ggFgfgSbWwTr0L6OG5KiYXbhunkBKlQEkpnkp4VYQEWGTIQInQM8+JHMgLQwzanA3oKRF1NATD6TOG4IM9jfABB7QwmIW9PgELGRjCh1AwSilY5Bd1WAQI4FinS6yfqSxqyRYpWhfCJ8VhNRWFNjhyqUFyKQsaAUPQdh7IYwLxiZEYBBbpHe94hz4S0+dVw+qTRRmWHRpWQBFazNIol8eJA+CrMq9//et/7Md+zMIrvKUJujpIsG3YtIwYWSiGsWaKh0UAwiTgAAJGsfyGTnAA2krxieqGqeOg3jgfGiNKCCvCAkcW2gttlCoUNwJMAQoAAZ/EeBX2CGv0aVdIFiCH8Lf9DQAVTfrnJg5SDsB3MtI0IvMKPYmppTC2oxQcMQHaTvoA1uu5kMNXkPFZOACkeBBQKDUOCYS1GgQaXn84f8ndA7dAvgAzzgyijj6ci3OopOfGhLAKS6tRShU9GGWczkAn3XA+JQaCref0xnd8ZH5obrFqXDm21eq4rJb3zudjFo2/Rved8O378w66owbKybKQjTe6sbRTV5uRbKLE8ejAGvfZCkc7CHm2sXit1XHYq0IiGVlmZX9vp5ftRit+B9yuaz2jbcO95K2Hr3/2bR859I2/mXne61btNXZVT8tCa4RHYo/+RTPA0UoWJ3adI/B41cbaWm/VDsSOjYeSRIvOVGcQGGG6TdA0QcUHxBPYgp/BecMfCULkzqed16BvgA4NJEZznQP793UHIdatLh1ND46M7h07PJMaiOZIHs7qj1XZXzvVvPl4s1Q3WRUBcMYorBJWlpuEa3U3a3GH6Trk9auneme3THBHW3/Rh1eSm063zJ9rxjPdbnZGBpJ6DRz9ZtNa/b8fLXrve9+7Q8sjpUdKIhziKQlLwvoxLEyifjJVPzeLsBkvhxyzz9SJ3aBp5ybYAfs9BiKZfZ9P6HZqF8+F4xVa7NS5kd8l7GAnX7P92UJb0i2nrjdrpZVlAziRIsWSayuLrlapb1WKY5ORUsW7S2dPx8qx4u7xqy64fizT0nqdywhNPpXi6DCsRi5VJ4xmSZn1fXsPpF0HerZ7sh7baMUr0QQZjTV0ZH4iGjGqNqxhThrkcAzNdDEa84Ob3/B8Fyqk2j0YP7vZtVP8VGVzfrW7a3LEDUkjuTiDODU9tfu6p5757OcKf/el7Lv+2+ALXlCYnoYegJoBzoQAoUJS4yT8pCdee+G+A/VmpzkSXfdit/N2M161ECM/etLOdLv2kp1sxpRvZ2bUcuvoTmoVvt2J6WaarXXRsbu/oj59LNXqtCezsQePnXZptdbVRr3k9orTpHPF8eLoxNcWsg+uNS+eGto7NDyaTWzUO3efqN95fL1U7xSH9jpDoRNPr9i114zmDTYa8a12J736rRsuHjs8XczPPq4bTzt191tnN45v2WJRdNIdK2Q5s1n5bEFnuYB95dJaxiLAXfsjZY8MVMRTcux3g6WNdrckjDu5dU2daTt3ID43HEsur9b/4vP75y6KMhkQGki4YUywUd5o338kuW+fMNbxIayU4MSoM/whjTpvQJh5DS0Hb8PwtpYbk/GWrxyGoCKwjzyhHj7y9g/+aoSCbTLbks4VeyMHuvWklei2K691U414+mTrqned/PCPTP325cXPZGMla73R1e0NNPNPSk+/JZG7xnhNoHoHbsQH/yIudB1nufmvXj96yzfqlOE9705ddiVN20kZAhRDp0UDAJl+A9A3GfgX661v1XJjk7n/57Vrb11KnzohQc0duU47vPqakV/456nJXYvLS/wPj7aEccEoRhZzwOQ4KouPEhgFMv5wmwT8SoOfwgyu7LyNa655wq23foP12a41j59dX1gprays5jPpwtDw4NDwyeMn2iMXpqzG6LTS2YGWUcRTdyTGB550ycxgJrFZ3uTzz83NcRE402jRwoHJVqiVfFnapSBuBE9Xg8dfJEo1F0UaTgKC8NZ25e4HTq5vlj/+qc8/cPRkMZ/r5cf+7pa7py+9YWG9Pjixd2L/hbXtTVu665Xy5IFLjJqMzswxc2eO3lmrd8cOXFAYHBofHUrqBy9tZVKTztnXQYIDU8uGYAW3gyuJ21xS/hYjC7HQR9XqaMz46JDnBcpC8bCU1xXIUc2lFONXDGfX1eI6yBtrS7T6vIsOVbbL4o07yKgs7OUnKSW4+2PjYwo9u7g8PbPf3uZcJlpzS0AID78cI2GRpEOOhBLC+hXw1GQSFqwwEJ7MLz4DiHW4qlxYgSANTeDpqgWySL9VLs3tiSxwr5Kq3NEs2M/BcOhcRWdeGlXqbHypO/WiWCfd3nr1M//6Hfe0N0q5qd1DT35K007l+/74S1/6yk/91EuThnv+2SsTf/tl9zhz0LqjxWf/h39/yeOv2azVX/Xi5ztxztljLmTONuszie0LhocdSF06sG+gkDd+oLNnPgp6OKxjYDqU7vGwYY402ylRxy1QfxlMyVCNyZA3BwVt/MRGJOuD4Q/GqrCaABnBkd4vBaNUAWzfQ0uxG8FJJXfbGjW6lF9xvnKa9WBxjBpASaFUgpS1Kdo76qq+0BP6qSCJ5YUGHx2eGI7JQHmVF6o+kRQRYzh/neiJI3Qs4UO9fX3LW97iuF04w59qmbREDlBMGSZI7FEjqIr0EFOPgPXAYXZ2VgyxKgIrpMQ6k7Swogl0G5ni5dXnQR0cgKWrSveq0mFRIAGlAKJCGvVUKSiFLeMpvQBQNALtoOmhKRqZKojiUOoTtGEoBrGYI43HJxjCFlhZqK5CsRR/0BuQDCJThEEE+gltuSQDjYpSV7P06NJPgzzpKxelASWWRNEgUwnxzBr7b0AB/k5BV8EBgTOAqiTzRazIpB5kCiUCpQPKCs0x5BUkgRgs0jHDEBqCvYqjh0BBQDJggy6BiTMhC30gUKQ5dQJ1qAaH7Dx4Yick3SBZeALFx2UDoY1kbMHzwEZCl1FKJUrpl2aCrOKjVEcXYqgGIcRITAHkAkQuhoWuIg2e7JV6ESy8ZOGBOUkp1CsmqFlWnFr+YLyPJviKmVQdbpjjlXRQinCTeIpmWGSkpRpTYQyHDASUTpONPmM7xF772tfKqCCJ0ehXJEywzqAGJpAs5sgIZwE9JRwTxkmJPRBQKQSQ5hOtICNl4VvgfPgEZ0CwFyGKo6v4j0ua/qB4XrECDrgnmd8I+nd+4IDbPPJzk+CDVwWdG0mXJIYbnVGugKqhOEUjVsrAK6yme74SB7X09VwgIYxYlWUnHgKoRu+O7LBrp+LvJPteAhgbZP29JP4+06AR4aSDCQHUxz72sf8rmLaneR6VBTSNtRUKKhoTsbW5UUx1pgZj6/VuvRl3hutIzkJOflcvzL6aI+r7R5zq6JQcnwjP+cYp/dfojGgd3cj/8vD6nQhkFysli+Lch9qKuzrYPFzkpOpL80tdydGJ2VKby8RSTv7ksHYSX7z6x1/x2d8/ePaeK+7929NPfIVtb07AMKscnS8UTTlHWgtcYcDiSUdk7DyuFI5t1dpnF8vLI0N7RzK8RX0cOgn3nURew7MTQ/FoyM7ruQFViXqLYclVZ/VL2cbwJy2vGMqstRLNai+6ELibq8YHLFkOmtyH4Nij7t1L7SObhdTUoYleYnt9sbK5Pr7PnE3wWmO1ZnQU0fJWamG7p+fRbla35k+nRmdTiYHozCCXGUYMdAhRNO5Qc/USfuvLxI1Mqb+uYIzKeRQt/aIfjt8JC0CMtu8o/LmfQhgcmsA6PfbTPyHmB9ABNlyncbUwho3W6kCCkHgwDJ8W63vEydkwOrW5gVwn3uW/Op04X5yYLkQ2YmtlodWoTOw91FcOCpVobK2WVxdHJnf1amubtc7plVJuLH7f/Q9QaQ6WTu+jCsUyzafIUCHzRcek5c/rdd21s1SJlftb59UPqqcIlUX12HZQW7V7ZqvnVDQiyWajQe5Hgf12r9YFx/ePxXWeLfjcbMVL1d6RxfoVu6I1WqNjIwXL0l73L2q3fePg8ZNb73537m3/2QQowOysyQEArR3S2oWw1iudTFywK96Y71aHXL+TbJbaQ2NjneaoTPMb7UQvNTPsnqFohnX/cOL0Zr8P7M7uAZdFxcYG7HDWZ9ZVa48MpB5a2dT3adQ2y4um/hyXFbelYenEt7Y3Z4rDI6Xhqa+cNn7m9ODu5KADenJTE73aVqkwOpRP5eqNbt25VnrgtW6+2nrC/u4Lrjg0VT6buPMb20eOWJ+593EX7T5woDQ2/uGvPHD8xEq+aAFGN9VrW33aja9Fm+SN4xjWiNqPh/nYtw6YyljFV8+eckIvMtZL69snj09NTbveaejwpa2z88lSSXbQGnYJJVMD6Uxb52a70b7v/vg1T2JquQtaNc4BWBoYItYEav55/6HdxVVipZPaAI/WlB3pG4hoQTvhyihGGxYcR46v1pcmAxWaYZPqTmx1Nth6qVxLDJXb+VPrG/PLW5O7dyeG7XKOVjcvtS74q/J77+88dNnInROpM9PjE8mBy3NZMjXShurIrv6DJ7J08LLuoFv6l/9m+IMfcq976y3/oXT51Usnjl1wwYWPSo9MiGn1eTx9OBE4xtkUyWe+fv/U5NgVF1458Xv/ZfvzX2gfOdLTd7r80uKzbkgdmIv3h/q4mEwkNw7hmti5uTnQVFiE8w+YzmBxNFo2ufFm+Gr8HvWaEmrMOCs8G+m1JVdddcWevfv/9u5jmcGzI+mpTGFsa/nE1N7Zsen9jfTm+sJDuaHx4YmZofFdw6l6Mjt38a7caL41MjLJneQXatQJi9MAMmR4VMRkSE/RRIA6TIGMCgsBLhFk+GTmCubOm1WLvnXPkdu/9UCjXnMV4v13le8/fmJ030Wr2+3mwF1n7r+9MDg6tud8q8E7FScO19MTeyweqW9vJjK5lWNHNHhJtcEJ46uxYrppS/BRx/03rUyOa1bNSJC4BXt6ShjFwuI5v19nhjJ4+uRHd29gCI6xeKoqtLXHyIE2TsqFBF+NOFux4qq8zUo5n9iEw+jocN0tQ9GEbou7BiAyOUP4wFXVMwHZJVYRB9qtTr284iLgbic3MGAjMuYrlLrKIj1FhTOfG56qAB6KhDwp22mcyqQGhwabtUaYEZLLo1xAFE3hgZLdK7Tt8tjedoqvnRbRoilHtx9825trx0qlz326fcddSYtyelblaOaiR0v38+/4/P2notUNiXvv6x25L9JgL/He7bfdIjewkVL3Ry17Sysffdcffv5Zz8qemXfrjpuTRmYPJGljs730R/8t4Syxa68pPvNZV5x33me+/rXjx47DDUWWZmAF75DbR2Mhib20lHOJTMKiFR58Q7iepIk4IlBP9RwoElVRvnlUDCEFfA4mQhhw6iQ+fOIcYwWVk4vHoDowDl4xjZYqjhcLMjSoIqGIV4pGjZGht3hIN8RDyegMnPWgxENJt0o8sPoJiqBOckkAGRpl7IkyI1DPQZ+fV4pADr1PzL4eBWum4wQCkeFnmA0jXOqBcDAVZ9+UKhxU1yfxRg3QSNDSo1QdB8evPj+skIB7OnIMI+WBFS5JLBJA5CMHcwDEB+kpJGIZUuTrbQZBBEYpDhAQghUNeIKmUsMfpdiiM8xi4CdQwXApQunMV+iBEBP20ih8wDEIU0Ws+MIXviBjiNeXQBG20/kgONiCiSdSQgkmaGFPAqqhVwYTueCgIMhIDAfMxwc0EgEgGAsrj3KpR+ADQuTVA5QRKPF0QBHQUGhQQvxBtewf+MAHaCYmoEhZEMZqc7NgEpMsvlJR1ZNA1UowIeyX8tAxHWm5dEqBVbpX0sRetCiCVQGQBWaU9PeQQLgkiBXwx3xgQXMmGa6yWrJQAGAhjFc4CUnVwSpBKPkKCIAeBEIMhyUGAbY4IwGKqDrzZbKXuMFHGsSkwS74kBFW0zok4LY0PBb1SF54KhGNwj5x+SADq9D9FoPJYSTFLyZTfiRABnASAQR1IGA1huAPrcNAGWErGTTULAFihW0Y22JmxUiAOQIetIAWwuIBx3l1HN+AEs+84LOAmJDsH/0NCPyjyWijB2OD82bUgP54ZCQgGBKKMNq/CyhlBeMMbcmIkjKoUASxQ+N3yf6PfsJezz+aTAKYeBT6Ty6XolIYmkCrd0oEk4aASeI7kY8N9Av/B6WLCcnk1WjSRqr44he/5H/97//1oXe+9Wd+76/drWH5qMW3DX2tWMKAk86YJ+q6mvbVGeOUR3scI0iOh865rtEgfSfWis5t0vOMZokd8mwOTmMWIWAfWS/hpJB01JGNXiFg8aVpAZ62yVzH/bCgeqLleP4Lj3/5oT9/62Vf+cjnnvzTDmzEuXqnVzQBhAhZQY8umYmghMdKyI2FE0NTuzd78aNnT52/+4I9I+BFve7w4A+dZxl2GAUjuk2Z2UxaoWapYhRDnd0ZykGvGBAiyvthZQI5XoiNZKN+uwUq1nLXu8mtRmy1YolclBhS/pex0U3kUnEXnNaiucAIkX6zHvWJsMDst2PAhvK2V0Y3IRVcBtGrRJsgYzYGu4oSh6M+MOYJcnq2m92xrJ3SkaAJfYe0CO4P2fP9doDxGkVvetObHMP7hje84Xd/93fFvPnNb9aOWk6zI8J/lOqWTZ86wKlkIxFzf0mq2i7oRCVMJDxi4ywhII1oUEIJUU2Op7ONZr5uI2KTCMweW4AceXWhZaU0O2H2lK7s4NDXtfhUMVHUn8zENjPRUUwU1H5gNUXlUUrFuWfV2GTNQVnd4WLhkkse7slHtePbPYEPtIWaaiRKy92RdGeptH7LnWvp1WNnL5ianczD/YEHHzwVSwy+4IXdj3904I/evXT1Vc4U3jezb3r3tE6nehjUOoxZhnJGMvFDU7HWsktfFJ46rt+bdnxldNHNfDk6aXfPcHT+lpp+YDhxctOBb7GBVHy6aINBZ6KYWN/u1jup9Up3JD80OJkstHdlE6nl1eWB8T1WOw+a8dUH6CYyFiy4Dc3tmw67syy83c0le1OFxEg+6Xil1dO2PDknoKdXnyodff5TD+5bOLr0h+/p3GzP8HoynSsPDg49/5m7X/PqF121P5EeuOLQzL6huD3HiVRmox1/aLV7x/2LemVtBTymNiBheGx6cGTc+IWFpLlCcmLmwICFDfZLJJLR+orIPERSZ35wn2kxwpiIjt2LJiI0wOoYA8GJxDdtORdEm8cRZEd25KVZDW2SXAy0B3v5B3RDGu6OJlNAKwuUrxSG9oa2U+GTExMb9d7Z5dhGI8W+NXMZo21DsVS0V4Td6XR2jybGU5nB0YtruYuGdrNNoWWO9OJhlYmQP1d5iIxZbW68+TcGP/iBTjxZ+fe/MvLLr9+Yn39U+pAHbhGs/sP14eJE1DkNa709uPciV2p8fb50vj7Wj7803+2ksgNdJ7VkspWN9fayTaTRoabYggPgYFewlcEZ0ssN+uZTgM+qcnwVoUScCYn9Yo6Th10tdGJlq52cbPbMbm25EXZwdGb2/MNHT86fOfbA+MyB8b2HOo364n1fXd6c313s7r34Sesrq3o4/FH+LleJaFRSTgPPMji+Riv4x6SJKA9fgT+tqYMGlGQh1tWl1RPHTpj7hSbpLx1/sFIcXF1bz++hcQPZXF6nozi5N0WL1pfq1S2GwjaejZXFrfWl2QufkCoMJbJDg8XhofGR86fy2XbpL47cDj6UkKldwQ2tDrlTAL6IfovmVlvLXaMJmMMp4dBQCe44/PGHHwl/nAnes1dAQJO+WCiaA7cCIlNpj0/tWjx93K77SrWiD0wViYPe2g9jcoBD5mE0gOKqwgexjTPRNcJYER2zjNr+Aks4QMxHRXOXFSSBvDCEc7Xm/sBkrd7YrLYG89FKAfKSETLC6OIjyghDeYOzBZPqtq574o3/5o1MUS/+gXi6NvqE1PDVL5l+yYvXvvA3C297e2LLMTzpkSdHTWi3nd6yHyg6MCQ1Mlx42uMnkr1oVufvH6vFjlXSuQOL2cJXbrutaprwv79v44MfsJ7bLeGNRLzzlKcWXvXKyec+/+Qvv65789daH/vE9G+99emPv/qB+x+AJ3EEVLXxMMR/EgnaqKNLFbnIBrDMlNJhacwy4QlFwk8SIZcgNZ/Qa+hETyw48fLKomcbPPUgcY6+SoFLXglXcZjj0S+VXbk8frzV6yAU46ryhk6g4igznkus7kjMx33Sk56ED5SWCysGWAxnSUCAIUEA6Jem0XMwESKNQhUtzBDBkDTB1MWFEnLUdKA0prgBVX0qNUUCfQk+kF9apBLJSAM1PeQLPmRwg7tPIWUXHyTOK/XqkSZkwT3+Oj7otCsO5lQCerpnc3NzSEOyDkwQTTCSsJU36F4YcBSmukB5fIKYAO1FvnjYQh6vIOMrDhAxWig8lkoJc/AxCk/otgTIxwflKh2NIqGkdJ/8Yr4AUPKyEhRDSmHdJPyBud4Xm6ZEFOGPXx0JFRlPFEFAxKEiSwkN0IgACWQh5k//9E9F6i2jXZ8W/uRCCUHDRqIEEBApNTcw1yWDGz2hDyK90oFgHOCmPw8OriJWMmWhC0DMwSXci4xdOg2UT9Kr/r4GTBQURCAZbEGGhpT0mdFQU3xlQEgWtoDgs3INrxhY8TgTFGckgySUwMS6EJBGGAIoJSNywT2IkWzQWCWiHT8JjrYoFIuEA9vxUycWG1lC0pFRGiwlNfxBL2RomvQ0k0IqjlLJBX8VUxbcICBdWSylDFICJaV2QRGQhJ5kIiGGq3grJTzVX1iJBFBK6YUxChMC8ooQg704BgLIoIVH/LmBnddHvj/8Fxr0zUtQcgogDMnARjXLCkyv3yl7gEKIHmmIlWrBXxhY0MCXJoQFYC5ByEUiCqW9XhXhF40hIPzdS5QAnO+SZufrY9PsfALEAzcbEKBhmCPE/BN+kRkKOrc4okTv7OwsgOfGnws/qKXSz01AxKSpltFDzJT+9a//5Y989CMP3n1bZ+XB8cIF2w6mavccQZxJJ1KW5/bB03cs6e+6xRkLeyPX0wpCFSLb6dXj0bXA0fmx3G5d6/6qRXdJ7CBjm66dfkCJk4Ajam9myy7iRHcgm6h1EtY4ul3l9kPXb2feNnz87r3lxWPD+xz/2Gj18qkoY5St7/5tb1ecKT80NJyGnHWO9e1qaS01NNJt2qKYcPBrNlKohx081TngEMZNdvhAeVRPKg2ogF8VTcqQkX3zeNUC0j0VLVJ3yweijZBtV0DhzK65SxIDcTcwWdm6fySaVuozKjqLZzzXc31RzixxzQm2ecscAw793950sXf1TOySiZhDheYznYGUC1yMJHTSlm7yxuPcYHf/unXE4oho5bltwGM587QRM6M6CNjDOn4O1B+O4N93C78ffG688Ubm23mYNluCo7WwM8R+y0j+39sTsVHLUdpsZ8uOV2lao1vsjxCPjTo/xkSK+ZA+pIiXtvCZtBjl907uLwwmzhy/2Y2NjztoK3K0loxysIPUKLQHao4qF+wXXSEPXiXE4p3G2Qfv3agWSvXCZst17epBd9TNGQM5dWV1s7xdqlWWa0vHK3P5KtwY+jChrQXSPASyooEbXcdMmmemBwu+Rn9xcbm6trW+2Ch1C53U8N7RPcmJA6NTsW987SvF/IBp1vZP/PjK0Yd2ffOb6V/59e3fekt5eGjtyLrm6oJDh1h2zUMw3AZZo4an3Tp+9zfWq5lyfaBUs+I5t1rN5IoFI1lwvu9MeX6+MplzXbgZxBQ2bvUKg2O7DF2NpBvHFrnjDkCPbcWSpc1WbeHePXOHkqN7p1KZLVsC6o3E+DBe5OOd9Q0rwXKjQzk93gWeSl39tooi1V1bxKqRou7D+GalqjvxoqtmdjuJ6x1/GPvbL5v7NSTu+DCzFe1P/oXzT/b84s+9+skzaSd5nF60j8ruz112Xk9N7R6cuOk2/seSIbRQG7BrcnICAyvV+kapHBmaXrdace/S0FajNTw6vt7pTe7Z1Rof6s2fZcD0SlT/LVefW2uRzhcfd0kzmbC2Dbs085pMQtFgEL32mNA//vGPkxTXR5NMWMwHV89XnoSUAqwqF1lL7KumOgiXI0iTte7ZrPMqDb1/NZMduPLKKzqFyaOr28fL3TPV5GA5vtYe7o1lllZLYyPDdGPPWGLt2D3ZYm+jnBguVu6dR0x3Ztfu2bk5wAHkGQSFCb8cvquuuhJR67/+H/Jv/wOEP/Tc5y0/5am9r3x5ZXl9bu4Al5fzx4XFfL6LXJol+qxmUWyRzhZ2ktl6K3einDYlfubE0fb6mZtjJetmZdTz2i5vGz5YW1vVBxkZG9u/bx8XU9XgvWEXd0SAV8FoqhqaOqqrfZImYCilsEJ5b9ok3hVkzGkeP3FsvVQ5vlLNTl+wVKqOFrJjIzO33fqFr3/t641ErjCxt5UZ0dlrNbcuO3xJOtlINSo3/93nT5w8pV4olD6DybJzqrAFt/msBARnr0rR0/DVDJ4wYj1mpAcHi3tm9nCiHTCeHp7SHVxdOG2UMze5b3r4vF5uaHt5sd1s7T10ZSeZPvXAnVtLJ3PDo6lkrlxr1htVOyINcey+6OqhmYvzser8rX9eK+wub5h0qtEEXhQO0B/dntD2wJBLrQlR5eFMnWgL/fHLr4KSBFLqReASoeBYZE76YyhA6R/jqkZVq/fgCT0Nc9B1HfKV5fZKdKqErQADWM1Lk5cPSldFAgWCVz4QgPgDFDsgUm5NHU9I0dxoX+WFFZQwjc7Dh8OhIpw4frxn0DA1UK+61tvKq+i4WtmpkADIEgcPj+hRh8meX/+1X3nx056WPnlT7GDWEZWNpd8p/eEnRl/y6qlnvzC/a/f6W97aOH9j1wu1dLH7j+ZWNpz0jorYO9984GXPOZGKRZvfghkUYIG7W+ml92zfsfulL7r9ts7ZpcU/+INctzm4Z1+SA336lJvrtiqbhX/x+uGf/Kn133t7+siRhf/6+7ve/ntPvPaJ7/0f78WQvgmNTjsThltgvoEqnpOaa/Iq8AHJ6gi6UER7BVRnvMIir+aUPPRKlUcvYsUTCvWj9j4FyD55Db6ygiQQBhA0cudG8N1JGWPxEPc8AqGOKM4jPXUVANkn3QBtDevBC5FRJEGTEXKUBWH6RrXAp/aEayJUdvVRXvSKFymXlCJ1AOiAQslOu6CDARqKpNQD0bliu0LTZlUwGqWEj2kofJAFuwQC/riqZvmKjTSNuhJVsADSoEgdp1rUzysOgB8GpGwwAVavWw2VC4FAwYqHiqXEoaAgMoSIRKmMWCelT4HzpkkDaWLAVzQIsjMpSgRc3waXcAPtREPbyQ7b1U3YYo51GfiGJ5SW8lt8gQk0QRo9H8WF2gRJYcsjjTXIyOqCpkQSx1hiQhoMDaBIT0ywAhw+pB/8BKWQEbljl6/whyq+EajmWADTxAtgCwRkRL4iJCMjPQcByimBZEQgvZTgAEsW6GVGII/bofJqeoKlRRrvxaAAtNHF/qAl6LnEsiPKJxCCQrryCgREKVEp0PAJBKaDIER+7nOfM7CCpShFOFSxWucWl7CFJuAznMGk+R6JMY0fQkw0TesJcwARAjKOAUhGBIeH+AM9YSvDJYMhYkEDnJgQSHvtXpEGKxQNJg7jjwCR8ZccUWn3PhIgLLFIJ6JBW3FabbyCNr6BjM/iBWAlMthkuicGcCoqAH+MhYCKJtIDc8hAG2fAlAuvYIJX9hmSu7ojF+lTMzgIoA5vAREODyCGh2gjdsH/kejoL6zQDjgSsI6CkRFMvKophsOUKBnNUa4A5DFKQE2RC2k4DGfGX6RHjHi2DmRyl5E4hMNXvz5hlIDq6dNOvAD8USFAgdXlcz9BBp5iZJHx3E/ChiGohACLRDQCkKQDCJGeIomBGHEI7DxgqmWUChvVd/EwhzNl8wmvQkoyomk7uTAWw0O/+tvCNN4HjnJV2J1cApChAD4JG/2hjcJ79+w9eeLE7Z/5X7ue+Uvp+ITbeiqWTq2WzINFYwz9uyh1XcemRpNd9x51NtY3TVpqq1w8sl3P1DvJXiJrVkYfuF6PlrYlY5mHVzpF5dgGHB/JmrPo1oWirnS3ney1au1oAnlgKG3aud2tl9e/mRt+YHwus3DPwDc/u3H1y3LZnLOSO7FkpbTVaLeig41ijoButJ1JG4uPj42I2Dh7rPLAHSO754rJ2pdv3ly5tzmcadkZgUZaZPAoUAqJ8DAFNNkvhaFLKhfLr/LiNl6pLJKxIRRMwC917etA78qrrrJ8zF0wywsnN2v5zXZ6nVtvmOtsdaw3ft5eN4PEjtx3xOXvq43M4pYreLKr9j+5AnjK6fHuOmlorIeS7UJnvXFy+/YznXvuP1kdOn9zO+kk7VNH76i2U/VYtjBz2MKyjGkqU+OJtDmhUqWRadeatchxRUvU2PywPj+ADjCNZ1bcNOPwGB0SlKob6pX4/0uqe/XVM7XYUIvG9dpr80flH8geHBqf7oxMLCyvRKzsj3LF3XAyudd+0Wzc/uzY2laj2ypPOdm5Fx364mF6GA62UqOiVjOCnh18wlBgBG17qVMdt6y+3c5b3afa5LNNasXmiFtrNOO1RocTlivlbHrvt9woonPajECaPszUzO7z9+5PrJUc+KSe7Y8nd110+LxGff4vv1qNF+uWGoxPJocnlholcw3Ws1jG2UylT974qqEHHxqZPz37vvd3n/jErUa9tLa2MjISrEOAz5RH9PavgXagUdoof2u43BrIx7NuP02518hyzrrl1m66aY8la6nooqL4cKFTSO+qdjic9UJvq9TMZOJJtb3RMZvtRCs9+YFys9CplZKxqvEAp14bDMvEu5afq6z2/VbrnRX328Rj+WS+Z3cFrubye3ZPj1fLJ8unr9gzmLjj3s5XvurkPNd9paLF3ZlozLbXbHzyU1Mv/1HLyxf+8F1n//yv49tbWYetn3dw5J/9xMVPua564eDHblvqxiOPgWR7mYw9IkaGbJ6PqkokFbTaCRxv1Cxm6R1fix8Ynx75yZ9Yfdt/jS4vtjED9k6jTyfTP3YDy/1Qf4SeW8A6sPtMLXNshJghZkc0ZuwmHgZVVCi7oD3GUgbUrzbSb2icQptNplxPqiKvgcN0KjN7YDaTH3pwtXNmOzFfiZ1cd+5xYW25khlKuwiGkhjtmR3LDabbZ1eOdGqp4VTBIlfQkTM8qG8cVQGtr1L8wpBfKG4gl5eg/K7/ln/77yN6/klPPfLiHz1lVxV96tkwNgoNjhGiNJbBroHjmyZWqxMphl2FS+UTtc5WubK2erazvborY1lm6+SJ41SI8kvcb9uiFWgsJvkE1wF1aoRWxC9NZjc12OqLR9sPTzwJuq0Z45hCQ6OLk/fcYzGeydjx9XaxsbYcSw0dfNyesUxHN2/f+Zdb818Y2m0WcnL2kq3S8lRu7MlPfWKydGrpyJcWopWfVVrCxYEV5GGCFTAhAh4AoVB4/EEaetVZ4vOVmGQhTealUCycPDO/VamncwNjU7uWzxwrmTI9cSY5ONmpnWlsby6evDeVHigMjxaHJjdO31ctd9KpYn7SjbXlRLe9eIKDEi3/HylmNhdPnO2tVbdK4Hv4cLAiF228ARFlMSDY4hHmqPEPuEFSQk8y/MEWLAq+C3fBA0lUQN5X7EPXtrnBsd3pbBFG5Y2lgYz6GPXBQnEEJDto+EA0eAIHMJUYEhCNGJ88IOMJwNgCq/DJqwSwkktYMhwWk84P2yJfqWzjI1BqhAd8YSlDQSB4lZ7Er73mmuc/9em5b9xy5E3vOPTWzMRzmwOzG+lfuKP8Z0daA68bfPx5yd/eW7hoLZa1HTj3n99rxBd98V/9pdmXP8eWk4Zht/otudht7V4mnnpKMnNxLTZc3/0Lq199y59QfgdgJC46XDh/1sRo5dhRk4NFC0k+/snYS18+dv3Tz/z+23WDknd+0wbZA/sOnDx1crAYHcADT+RgBZ3ksqPa4VI4jHYuuHh+AG3hcKNX3ZceIRKgDq8EVBNhKdUF0gwaRTpUmkYFJuC/jBKDLyVQ3Hps0Vuz9FQRRIO9ku3du1cpPDYOHB9UxfFweXnSOKlfSlfZFr+cSHUt1HdfRQLi1SfKA0LAWbkqte4f31ca+gMOnwaBKqBXqMKEPyQBVWEN4OMr5NVWoGBLrNIEivQrEIVMKUUKwHN2dlZeZUGVfdN/AyS0LCAAjskABmiUX0ACnRbqhJ98ZbRzrEEO+3vZAdYATOz1VdFIC2yHm5Ey2XlvfC+CU2sAQQsOqD7KBc0vtMXoPuE5znvAJCNIhsoIK0wQDzLmAMsbFhP4iWlgSgk+IIglC1kwSlgAJhCAM5YKBzPilbYjWenQ8BqEK1KMsgBRHPzl8kpSHqVrXLAaHMBtU7exS0bVH/LwAUe50oCMewbFGBC46SkBCz2CkIzyEC7a5dqROCCwxUDplahcuiEsPb4RAW6DjzNEw/xCDw44ybZQciUqBaUKop8wDM2ZlDREx1J6pcMKab6qCOQlFw+N+KAh0idphCkJzUEaDIVNLOt9Eb0WRDLaSJfI1LiAQumSjFIqUV7Ic80lw0wMhJJIWGEgSakORKZcdUcaTGADZdTXxVjqBxSqxQuHV/CRT80wR17JCEIvlODkxTTNB1oojFyye1ANBxh6kC+NLLJ7pPGKvbIgFg4wgaEi0BUqyNzcHKo9sMUEeRUqAE8c0LGUTC6giEZeoCQIeQWUjqUCIFBUQEJ182o4g0rIKCwjpgXdQwIhitQ6Qw+jhAOqCiU1jwShoYGqr+BjgoBHvMTQ8MnjE8ghAO1QXEgZfiXwVUGP+iQe30JGVHikJ1C1BiES4wy0HwszZAkwpRQgcaKnijSWmMQARaDIQR3V9QpgaHSExYeMwh5sgZ5JCL8SU7MQH36xXbsADoQBoTDib7jhWe9973tv/uuP/PR1ryrmJtxvVG12XU+QS0S7KZPRUVbcMMc7j7CXhKVGROX4x/3kQbXTyawW0wpeE15dg/suCY5rxvo+mwkMWyHr3U7GHle+Z8RAmySl6fCyi3nn3UbLRXvNiqPtv3jVT94w/xtX3vLJmy95HkPTi2ec19NoGXwnVgVG3OKcn7nv5sbYtJNo65XttbNWl2ybmM22p9vrtZnsw0sDkI920g8MDORTSHqLmUyEX2Ki2CJxm7BCGvzHGfzBPdoewtZ4gjOWc9ztervVbbYGGvzuVivXqKyWC3ORwrpDkaxXWrFUaWFxeaPXmbrcOdnRFwu9tMKNykxueZczsEoNHslAJja1b7q1kdyoLOYLQ914Md7NRLyLuvkWjeo0RFOJ9Y4jvkxYwS1Sgx/m5wfQAQ7kYTQr7/mnUUu9MH3P5OByLtsyQxs157msaXb7qU35pRJ7ds+wAY+UFc27mmOh39ut7syhyw+PXnLDpeNOMw8ch4wWmhrxAx6LknjpDNX8+ItuuHOhe2Q9fqKU2KiRonriqq6o9zI1bp/t2GQ+dt54/PBY+2J7S/vOhIyz/RMdAyYGiKxr7D54f+2mm7pfv93ShPjc/tSzn7Pv6de99ief/+WjW5Xs7sVKat6hPOn4vguueMrVhxkUuShMtVCs/8IvDH79a40vfHbi+S968nXXHe7vdYEeIwJ+wDOTyb7wBS+k2RZ03LvcOltJn1jrrLZT8+ttJ8vZ7JdKDhaT3dHhxO5CbyjnFPL2YCFxZqOTGnEt6OChZGrB7t+GutCNbTqHRkfY8kX3oQ43G+3hyXHrOizzdsaOjX11lb6VriQnxlzli4lRve0O5tyfhmPRhonpkYFiots4+pBJ41h/8CxtCbXbUFIa0YwBs86pM8lctvOZL4+Y6S6ORHb3xOnNt71r6FcyV9/wrJUrZ8+UkfawHBGFE8Vi4fyDhyQU76Qck/t79kWHvx1d75w3XjzvR54/mYxv/ulHkwtnHCw9MDkWe/bTB3/6p+MTk2OJpPMn+U8Sa5tBU/MZdE4YBvLkOBOERQEA1wZrJKRhaq0Qw1uRPs31Gz+vffsYHUGp4TEDCQKjmcwPHlmNHd9OLm52Su3h4v4LpgszZTdROch6YjqfbO8Zzow4Z28kOX3FhU84PLFv93S08KVf7UMDSY46UaYxIcMqCUREx3rlD/7P1Jv+ndOuN1/8kv1/8v7d0f1V0Rr34DdoQbkstgIiLeJS/1hI3sAll9jYnOfEzC9v7Hrc04e76eWzJ7OJQ/nKyeP337W4FB3tywmQXZthskjR2mNuEy8BZL6aQVZoMLIaUYn5kRSS487fMpQIvaDYfpWLJ9JgjowLZxe/edut6cJYvhuf6o1ud+K15ROrmdSufbPd7KXFdmHuiscv3H/v+IHzN88kU1tnymfuv2g6tdhtB5MNGZICkIMiBnDFPetZzxLWnolkwXkkyuKmCPMmcUzMnj17dTDzxcHTq7X8YHXT2qGl3tTkrtG9B/Pjs2ub5aGJGfObkzPnufa36hDa+7+GlamOjTRWPixur5xppVMdjUJla2pyamp8btclhy69INrWqA0m5cArcscZ/opWFjLYDoGApzDPAx/gLICTWnrCxTeKBIguEJYi0GM4XNgmwtGJKdW92WmX1lavveqKbvPg6kp0Si1x8H6QSRs1YDpdhmw0exQYH8hOGA5hjAAfJIOJSGFIavmIhg6Ll8yvT145Q77a4hRLDzhEvdWoG7EaGY5O64U2VfcbJKtoyCPHq1Je+pKXuBjq6Nt+b2Crcvy3E4XZYu6CSvq8+vivMoj/pZfoFq4yYs42Zk9s/MitR251KL4joG+4tmKQ0IagrT/ZNfDOB6ixUzwafxzbujE3+EvtWKFRfF41/qlYYs+eS//oXdGJI1ZDOQ/jn5+p/X8fyNx0U+Oeu/PPes6glYFnzmhs2/OL45deZv0L663aUlQ6QCGDdLT3mENM/DCYEwG0TX+p7F7d/evV7L1f3gAvmQ6rv1Ta+gKeHBWSRYAvSwNlAQrngeWBgaksogxeBVnjqkgP/hDHc5/73KC3oOkbU1oSl1gWhQYfWn9DRtInekKkA3SJnohhTIymURhgVTrdObIwMcKr1kGihA5oVBZVh2SYZuETC6MFnlKajcEQYWUFz1tiKhHOUlIukh0YAyt+JxplRBHFoGnqOPvGFNANAUwIckd4UCRgJfZVRjNyviIW63R+sAgtSNB/Q4IOAMgSKM5DNPDn3IfJYd0nmmYuDntDh5C+QRJ6ypJLzZIdcOX6RQtWBF9NgNHGbZ9giNin9S+BxA01C0CaH8wU6xFKN8Kuk4al7Bt9piqKRojmXl4o4SFr6R4pLjUkP/zhD8OWOVXdyBQb/TL+yAdQmChD50oM4aIOzkQMMQkUAQE+qFebhFW3T3/605BRLgugLKwGE6MshWM0DNMonQFn38KADiqoJULQiC3QIHSagCf6TlQX8kqkJx7MFwMHgpZexzuoJffXJ1jhLS4hH27oUrSwyGAHiFt9oX4aR7LAfxjSW4hZKYClCpILtrJLQPpGfKCBQAojJpAPLORhHqQGOGwllkx2yCNfofgAIP6ouXSAzqCFZNUFiqSS4h7twl5hiek2ZJQuDVoIxaJiRoxAVYfAFhACWAgE2wUl2GIdNBAiPckqi9pAhn6qUBC2StZrYAjVldcjDbBgYjs9NB1Kt2kFbRGJBOyVxkNeXkMYK2igYSP4iwFNEcZiSFaYKQjJ8AdA9R0yhpVpgllTqqUCSqD6o24HphiYq9QwRA7glJ+AaC884YM0gpAM4T/yIz8Sitj5VVYIK1H/HxrghJhApjAkwdnJEgIhoxIh89hPAT2GiN5CQGICApD6ue/K4CPxoehRGQNb6EPAE4dpHdrVIHJElPQqI+DUjzJ4BQRwzSvgqlKYXg5g8Y2XAoJP0lASGekVNQgJxFNdYdoYCITk+9///vnjDxXai5Ojc647iaVzQ4XM9GC056k2P7AAAEAASURBVNd6Qj62vpnOhEkV13Ls3bdLT9bUAB/faqvVarxcjy4ZNZWfx0bNU6/bUimjnFG31VNv9ax2LqQL+O6DHkcxbROsgPiYvb6VdtXpUvdNH3xqOjNz+p7ZxPZ8N6cnaE+lhWCyyGeGx0oFw34OXq5XywQ/PDE1NDqeyRaqm2fPu+iK6y4/cOWuaERAdVDFcIZ2Ufg+CtEPVuCD2kTuDIJkIuk//mBLSIZjtDqEd37JCC2jxdQzrr3yrpXeqZI7CGOjA7GDo93sWOSF6+qo++Cjd/DO+fSDpdb4jAuKo85YPDYzmrn0kr3PPXigGNEfQQXtZLm7dbTq7MsD5x3a6AyUG9HB1z7bVo3tCNfpzmbyxYFMezRaVbGDYZT/h+/5QXaAvx/qUtHi+NiumT3ZsQtOn1yIdRvFmUPDOT2Fvu7pZwZteljcxEG5SKnX0nUrjvYK0ZHiB1z+3FcIEmWAVKTwMDoqs8Yj2ItHRNIzA7l3PLbWTKxUe+WG2dl+1yRMxljdlIhttXur1dhqPlXrJkwwRwTS5/5WXUV4M+vpIN36f/qdYrRVqaaGpM09Hbm/fnZ+9BU/84QLpu83Z9jollvxoWxxZNeFDuNJ6NUjKhkfftWrTn/yUys3faL5a28pTM2k9s8++MCDvg0NDoUaHopQarA1TpKNr6/OjJ1nmUF3jSKn5kvtpI21re42jS3bv5C0J2GikC5VWwdGUwubLvC2orGbrK2mXB5VjY3kbb5ar5XXS4n43tnzh6fGnbjMPkwWk7atO0TaCW8OnXb8nR0RRh4g4OStTZPMtW4h1yvmtIi7HQeWjvY2RpszTYrXLdG2uyJaEdTheWN/b8slVVaLhIWR0dJ2G7ZKf/pnE4+/6ryh6RPrjWQqG1UnCfo8lNeLBZbi4jrSes4JF4ZbrZL4uxPt+IGp/S95ye6nPaXx4FErXIrn749Pz9T77r4x4X7e6M4eLQ1zwMRrWTXz7K8Wlykhes4ZZQhdO6LXtPAeeFpMNnOjPYOG9psFAY1sD1bO11FKppPL270Hl2LlZuJMqbvRSixutut2UhSHextLyBweiO1yl3h9vRCvZCuxufGYU4as8QaEZ7BjmwCnhCIJ0cmDHta18vGb4v/yDVm3vT3veePve190SiDqs9FRsRSVNwMCDHkMMs7OzmqhkQAC+x1VhHhioWGbhr0bvdrm+jWXz3W2prbWJqDvFCXpZQdH6y4LPgDIbvK/+Z2spEfbg3btDW9P84Z8BakdURFo6z8w50tpmXBG5J490/OL+2upkY3tJTvhBqPTmFKVSvnokfuqrpibu3xDFaiWuvXqwYMX7B+cPTgSry0d0V3XJKMFAsf6W4DggBZtpIaQ0wC+1lEbyeEQJkdoBL+ZB8nBhfbZswudWPbSJz8rf+TuE0fvsbf80mueX9hz+Pj82sLqrXsuvjbayxNP1bY3Upmk4dyhyX1t0/GJNIZP7NpTXltMpfPpRC3va31z3zTmLBYK0TwDbVGuNhuL9Jr4N3SJ3zM3Nxe8ImzEEyylS7jhq4cfJj0SyMtXDRI/gGSFBawnsaZoYveBw1dcrXN68v47Z6aL934r2u1GPyXmw2nFvfLVOp02vXAtcH9tSDJpTKs/ls8Dxi6JpeHQk5S8ENAukgvcAPGVcPseYbRKMGXvztBYtHaiY6eSM2YWNkuRp+hkQVca2KbBG0hapWTMQjXevZsDSs6XHD7cOXosdvIU01HcznZ+e7TximTu6bXYgDPlGy6FoGzdxXzl1PVD1/zaz974F7/xG2/tD/fKC9xPJc6sJ7v3J9jMeC/XiDXfV69dXMg+u5UYiMbaXc7Y+Mxnu9+41d7N3uGLcs9+5uC//bfl/S60U937Z0e6dL1Wt53YAN3MzB4eJC+HXHDbw9WmpbwB3jzfmhZxoegP54//CnmvaMEE8qItMqoywuQlsQ4DMjENc7ySNc3HXkVIhoeyaxR8ZQpYDM6o9OBwNfQGAw64LUZZQfpqCmTkJQjilgYQnUOaTKak76ZxcmHAWQBf8R+f/cKNbgMV+g/qJiSVG3oX1Ik7y8sRIyU3PSCMfJGwBS1YA0DkBQcfVByVhdOvXKrLrKGFXygXo8ejojwoUn8lVhzIEoDjKx2Gs+qPNEX7Ss18BRNPwGcw8RlkFhIofFCWAB7CNtgKWZgafAB5p3lFLJjqu64UPqAX8pimvpMUclQ3MI/3T40C6kMf+hD0wOedE00oS5hiYwJjLoAEYOEmLCVsv/jFL3LWxYCAEAhAQ6EePAzuo194ygsTD6x8UrthhXBCZHCEjXGo1BhLQ6AKQ7mkBz9Ak4xS6Zqq8mTKKOGqxEjGAfhQAADlDYKjY/gGjiwQYyWoDT5QIa/SKwvfZCcgrPYVNxAul+Ew2QlCJMtJUsJSoo4FUIQ6Ag4D/oxnPANLsc6jKwVJFBn3F37Vq16Fk+KVIgsTx46BjC5sxDE8MbACE2Si3QiOeLxVKOXxS4cVSmpQkgvTxEtDgoQCIHzUFxAgQ5p4izRNiWQUHmIyAg4HaokDSFZVkUBkssAKSwOZ6PUohbwoJ5gghH6UgkgBXYwhRmERDgMoGQT+6q/+SuJQLyxSECYF5AcIEABfWC74U3JIwkQpesIqkUZHXjHGj5ROWMGiSiYX+B4QvHqCgoVXvz7txO8kxkDYMk0IR2lIILG8IbwTI4uwTx4sJWhpsIVo1N/wSRGPyigXEYiUK0Ku/3gN0OQS9vgaCnrsrxwSnBsvb4iRi4aoSpiPBBpOeaTEfJ8elWsHguyhwtJ2ydQXgRAjTcBElWHlvNIKcmdAhHeQD6BUBJ+EqQehyBIIwVKRuEqyLO25GRkBnXZLCRbv+bt9z3vSqYRTWmMOLLEH1VX0/YOfIR/5Wy40sDdQk6NpDX6pBQCaSOt4+Zpassjx187ZVZiKV9rRCUMRS20SbmvV3A4Y9TSk4hJHZz5J2dxonl2pV5vVhQe7rfotic7jsyNzq6cnbr3pvsPPyR4YrLcSwwO8/Kib4h/4yMgXRjotXEo2K9XBsfFeMqsJXCs5DTpl1ZA9625FVQyS2ZO5/gXXO5LFLvyhur6qCBLQ2HPlgl2PEu5O3owtvoX40FY8n41zYV2yU20lS3VHdEZ3JssIpjlxE8XFTK+e7GxiFS1icHKJfCpuhVyyvyYjwq3Xc6rWoNVe5rmdmNuzI7rXiA4eixx4+y7plwV1OgRYF02i9xe1+f2hfX5gHeDvk8JsJkvN0on2YLQA2WxnLBpLsIM90qH+f/3q/ZhSIt0yCbxac+ZTb19/Y3dIEzo8IUzAmh8m1XMOhMgSTA44x7c7lndwmUuDoiN5dXD7x6fbNR+NZ5TdAV3vndzoPG5K71pFeMQgRoB63bX11oc/XDw7T40iNKkTT9x6w4/fZAH+zNOetliut84e6xTPW4znhnLZr9594gmHpty8KRniir/1ls4tN49a+faWt7bf/cf7Lo9sHz32y5RoFFltRhlcBUczhI2ti3eluoutzlgqtt5JjCRPR2diJdvNbj0dhWPdaEfozGB6o9rePZwcqMSXDFft2T/BWY3F77jn3j17d/cGRrbrsWos06x0XRTmJDeXnuXSiUImXm70BlKdgxOp7Xp3y/LG6Oi4aKKHlmw3EoP51rMvnxzMdWOHz7f8bqBRJ7VsLt2OpSoMg6Oz7D7dM1M9tRjtDOY5Q1psFEi0T5yOrywP7hmtb5aHp/b2e7sRVY/sDzAGFVXYQsHchdOCTNlF7K0mkp890Tl/dHBucsgNN6xVspjttar2Ol42clnEcon6I4WaEOHwimnMJTvLX5mdnWU1Iun0EzPu0hvyZ03Yev6QZFpfjVBIAGPV3BFjZ1Y689uJ9Xp8oWT8wt3LVpNWmtV6wvEG7dZosjHYWC2kYgO90lS8YS6an2uzmuYfDnyjPi7RDzlqvAV0G9jOu++6K/2Vm+O/+qaDlUr58U8Y+h//rwPKNS871mon405MCECPJhh88brcypUjQmTr7iqm87Ha7dG5yjXeBnL4QNx33kloMADUHqsCiA2IARUqgkZa04I/Mu6UuxOQjG/E3QkxF154UT27ezU+fqrxzeRy5fzzDm7X6oP1ylK5Vp1fblks1Kqlc8PFXHpu1/De4d7+4uan/i5a1MedUpAunLKwnRuE/5xI8TRcg+cVAoqDIerm5ubM8HBoMNPXVqs5OjZ525HT22c3a5XazK497Vp5eW1ja/G2TnpoZHyXRblGAczlgNCuN1LpZK4wWhie0DHNDgxvb6wZas0PFJKNrXZjK9NJb5eqzvSmepp2/kTf+4oOeoGDpkVLzFkUHz7hm+act8TxAl/rTgq8c5E33XQTrWNnuKT8VCnhLF5/J55ILS7M3/y5vxyd3L1/evjQeQdsojK/pTOv4wEOl5Eq6nQNDw1X3EuYKw6NTCQTtnBnbCnABCn5Q3DjiPAb8IFZ4B5hkYCyYOLhK4hpNOxjjHe27WRiPdLNes3hFDSx7WCQXs9UeHS9WNwsR6+QLxrYQyxKsTfyU7NZu/CdkNFqxiMvpFrs/fdU7ZPN2CXN1HS7d+11qQtfePJt7x1enB/8g9JLXvzi3/6d36lVW5r1diwfH/6p2OEvKFoNjxwLi0Da3cqfd+LPfthIJo1c/NZb8+0uTzL2N58u/9mfDfzObxV/8me0l73qdmNxveC6pdnzLdR54KEHpAl+MO9c004cfEHVB5NxmAurs8HL99BwtNMlKflqOrpBcxClyvPmef9cLu44gBxcfNZbQB2fOEj5/2fuPuB1vcoC0X+97t5P36fl5KQSCCWRErpIky5CFGGcUYo6g8MoDuroeB38XXUcFUUHUIoMV0MoojCYhBIEQoohlfST0/fZvXy93P/61snH9pwkese59+bNybvXt961nvW0tdbzrMqwVjtomhgAsZFyXnHFFcx0tpcsxsU5ltFtU0E8WhLJCIJ7TDRaDLWDJR39AdKMboCvigCNbseJHcaK7CoR8SmLRBAFz6gAJpZ99YkHi0YkUEs6RsoMUEiaTyYpaiALqp/3vOcZC0CLt2oFYdDQy5oHXAUHH0DaKyABtLGCt4NpfqIOcFSLjFmwgumvLLzyCclRwSiJGqohlQwokRoNvodk0sNTpE9wZoopWkB6+AAOJgkiCiZcNdiCg14BDYLmyAMOCUpGrDIKe3NHFUQuMsLWxBq+4aQqieeoi1VATKQdHBA0JtKjFBoqIIaQFPga3th8qbySeZCAjaHe9a59VsvojxEW5apTikYabhO9ySugJFaiohFr+oWaYRE4SgeK4mm75KJpmKBcCXhTAGI4pUWR+g5J4pBAxVcuCNoTwEkTrzBEk4hMnKddUIWeIhQquyyxaYqcjLyVTBZNvUgCpTDaH+yVkhxlIWtV209ggVIWP4GwIsMRSD+VG+uCk+R8omAoApm6eiAMc1RjkXhTo3iLmZA0UEgnCRSeGmppfJIGT6CHTDFKV0SspHgigLEIh5I6Szr0Bxz4Q1s9IlA4IBldMSOJ68fpv2oFVTBloT+kQLJoRJpuTjxiZSEv8OEgi0ETXz2+xoC3LBJQLRgqTgxssY4sYhql4E8/PcnSt1gQtsDZJ2xBDtzoOekAFdPjBiaAQIuwCC2AIxY+EUIfLG3RZXiLQTuAUcEkoz9EAxSR4Qzp9HMJoMWji0G+xkeMlPCXMerJ5sSPFT4DGck2x+CwGMIiPnoYhwweC1SMhxJiUR1/qi9iUGR+EgNFgg9hj3h0IRxpZ8MkGo94OgABtPcRkzEy/wyGSGDy+Zq/u+bLV3/y3a981zCLrm3tV9eltRx2Z6iGRaUseouTglWf4uyxRYU4aJYzlrPhBOOMFc5h0jIsFbaAN5ftVq1x7tmuMpsddvNt2t7KXu9GCLboBVMnkxsYGlhPdoa27odtsjBw37mX/+DNnx27+1v3veDtDoViRTqZtxC8l4SbUCoLx6qV6kAhPzjqBJNg7i2ePLznKc8rjU5Pzm5fbbtvNVFdr5gl6Ngr3OlgI2KpB+WMTRAm0A18wB889CnyUHxkcp9dfd7GmMB2e4+LiaG8w3G7jDlOTaWZ2GimWPhjBZdTrhI3PKvNVCHZPfrQnfPrNlKNjGzdHe5qqncXq93xUuzQQ62zGnRsKL9v994j9cJapZtpmrBSL9i2oeTeWdDBTw4jBVUrwO10DGe1hur6hHyeKA5w0P5ucplnNmYhQynVdU6bu6d7l0o/Cvt6LO01grjqyKy1emqhllqtuXG3N+wS+B2cCo+QjlBjsXv37vgzfDud15RIasdIZ6GenK901qqu7gkfZDKe4UwsNdYU//G1xGghtdxojwYnXb3opekl6y4tNr9zoylIdS0C15qDn3L10M03py99yr6x/C1JE5aczdLh5Wb7xNK2iYFzyu4ECyrVnppa+/fvnvyPv1x0GMz73lf+4heSudOtA2g03js+oUxFmGFOds6bznSPN9Nj2QcWrW9wA3CH0a+iuv3loSVn1WTs6DcDvFZtl/KpXfnE0aVMhcHaaE250zifaObsckz7ajH0YqVj0GvQvn4mdavj5qQt9ry3HBOfHCimqo3ORs3tYSmzuIPpxg8fzOycP1y59VBpeqL17GdX/+yjbmBKjY26m6Xk0pFOsvAjr+sODlW+fpWdLlHlG6Vi9geelr34gonx0dTs9mErh7Ndd2MHSjAwCKL3x1R6ZX11fm5q5+7BgZL48C940N1WO3X3fOqeBUvIC5C8aEvr8ulU9EYw0POIKOOv8JPvR9y6ZFG6KF1R/IaTWGooJP7UKOvYoimjUQ5NnLNqndq1+9zb5uyszpxYbZ+qJpcqiQWNYjqzurTYqa3ZUDozkCq1V0q1lUwn+ayn7tkzHToq5s7U5MSMhfqbHlQwZifYMdrB9TWX4IzcdEPjl987rrG79GnDn/5UenqLT/1HD8Q+0LqBxhSIFkz8Wi6X2FW4enKjc3I9t7Iwd/zwA9n26hbHJ5nwyOVYMPpFq6OZtnrumDcyR4tJk1lpkSf94vTi/S68H4lL/fDmQK2b3ciOn1ruZgemt26bHprcllpZaE7sGixNtdJfy2vL9xzU60yVLLlJ7B1LTJdGn/GMp5rK90STHXpsQSjp9bFdN0lSvmrl2YusND2uHt1XpgBWQJt5J8sNtz9wz1L21JGTgzt3XTCTPXpq8ejJdSv886Wp0fLgll376o3OkXtu5GGwyvKDY+4pOHXsAZ3g4IgDBVadR9esLbc31lZy3Yn0ntzUxOT0lHk0tEOGHcamUSKrHd/Y3NiixZidnYW2dX3W17EwGHBYijkqOMcGkxmI3i4OISzs1TkxfSgeM6hab5mJtZjC8orpA5bmNs4/7/yZLdN6NUaksQAAUUrE+WLpyIJ89VqjOTo2dsHBc8hIAvjAjcKDFs01xWEgSxffsIiNjjO+Yh0NHx+fPHz0WKvZyJnqtbsylZ4Yn4CqI4Ry+bRFUAf3piYci9dK3HvY4vNzd+zco9RvfOPv51eWt+zc1skXhkoWz0+Ye7K5KfNwpnNPvZ0pdC9+U2roedvfs3vx/b9V/c6NQ694WblUrlYsD6Yk+dTAVPGlL9r4yIfcWBUsoNBEpZKuVg/7hMMTWlE0OC6ztyRn6MTR1V/9z4N/8bHsjl0rH/torlVvj4xvf+97akPDX/3s544eO04iiCJ6Hh2q0aUuWBvJ7MN//jAlwRwzkwTBR+XW4qfWQDzjleAojE+UikToFSOMtap2W7nKN4jMB1MC0CTmdrJcJSB6LlxsclU0aBAQILgNGTF0FUUSsLA/+9nPsr2oCgjURpNCV32FhiIQzEyJ5iw9F+MNGRnJFI0USXHwFEmUfLDdu3dDiT4oEc78TLkghkDEwoR2wSRmoXKsLvyht0jmYVInrogSqZ+8kMc6YOmSSEpCE7R1qBCOHi/HRmJvnzjhAj7hpLJ4Vt7sWvBBxihIGlbQR1N47YzEXA4IoyXyHCH4zzmHM/dDufQTLVKqR2aWcAZd4AS16K2NhKdaBkmKDWG+jfTcGCt+o/+MWGjIhXAiA4oEwcElOMgiAQxxG//hrH1TOkZpQkHGarmMUCguuoU+KSvWeuxFLPFxfUmWdgGlcikFQ8BBL7AKghgIdEOtV7RcKLW6mN9lFhqfFSeB7HCLBCoXWGjLjjN4BSA8MRM5yCQ+OgCOsBZGMpChJJLygwOC6mnoB+b4HPVfofSEa0G76JI04CgaEEgqWmJhRBnKUTu0DJQfHEiinerCQXrZUSFANyCpaMw0cSo9wf3Ij/wIQX/84x+XhqtG+aENK3mjTmI1zpM7PCPDYYgWKs0lVhwcwDSugQkEgXzK+apXvQo/VTdKIlLpERn2vXhs9xM+JA6yEmEOZozUbKpBcFPLkCmecEHACphQbEVAVcYIVoBQ5IWPGAL1pu3iZcQlBVE/ZKr4BCcZYjETZAAl9qDCIyDGJ7VDWOkezPSTsEK6XqSeApD4FWRZYEuToR3T9N8QQKOf0iAcmRBQ18QghEwhCUMke2KuPkoxS0QshiXAE+kjQ2L6/7X3ZgiA+7k55rFgQgbnfUWFoQo1HeF+io/Z+8iL7IHcbO88FtR/Vjxob37zm3/hF37xvrvuWLzvptHpZ81VE9VWstByIbBp8J4gg2Pmys/gifYc395spxXRLsHOdkxu2bTKGQ6T5mFKqnd/Ets0dGVBQLjACSz2tEAaSmdY0YB7V3c5WaiVUvVuutFd6o7NHt3/4tJ3/3bXfTdvqy0/PBQWVDuvxsbBlMRNly80HHTDv26agW2nc6VyfWM1nS+Nj7rO7UhuI9EeqHSOHV1adrBfcG4xU52iAHhL2Txw0VRSMJH4rMIaacUmrZn2/5/k11gpZeaoaC+0s2/mjuSqmZ3DW1ySOlYIw5paFRCcQPzg0vLOnTvaqya6w0iQq0+5P4vcImoWWBIeE2ZjpfT4aHl+wVWv4VRtg9txkh3nsNE/vht/uF53YHWXPRIzPjHfTxQHWEuEQQ/e/9CubZcNMt2sPTDf0gzu2WbG8VHoce8cll58r7Gi4ev1zuHji1uKpYu3lXqLDk5nijZNGOFIJjW1GkEftObaoF79DEC4haNL7XG7ZxtJAx5hLp8U+dHW/nWsuu26Sneh2jm02B1mn3wfHWXbyFtxw2bGsmaDQ3F1Ss8SkqyztOJaoVymu300VSt1j1cbfM5GJXHvfHfHTBiMUYwx9rF3/ex6cbDzcz+T++pXl//0j4d/+p1mbWgbXYebt8aaDRQNLBXzlltuNe/nqJuZ4Z1mdZILneRY6iijM826beUzmYeXmtPDGeNI24cNeFkz09kzmTGNuWZBYtdQUCY7lAq32JTSdv8aMqi1UrWNzkDWAglneHWWqu3BXObUOts9MVRITHF8HWte6162M7mjsrDyZx9f/erXd//GL5Xf9hPVUrH6yb/KzS9aOt4652DxNa/K/eBLWqtrta9cb86a6ds9uG/i7T+ZmxxLNhsdizyXVyaHUm98yvi3T1bvPLqRLY5wDoPTGVoeI3WtRm09jB0F+QXma25UPEN2YS7DKguvdNqEetEpW+UzB1ZPy7v3RzfGUIs9Vk/K3/+ovdDT+83G0vfopOMwZ2jrOOFd90gljqyU1hvJw8udlXb65ErbvmhKZxDR5Uz5dGvrUGb10O0Hdk2fs3Py/Km0BQvwdKMqHdM2nVGcggIyvXsdk8e7nS9+aeQ3fqO4srqyZ/fQJ/8is3Vnrx8P6PUzCmiNA8W9px/fS2KNevdeJx2EIcBOurXeWXjgzoWT6z2fORpDsd1EnXZN0bE3ivD7HXaE7K0ZZWo8olr96GAhsQj91hZTpDvuuK2dyByplVqDO+wIGBkf2zJ4XipX+s51f72eKLeyJn3Ta6aROomhUvLCp18wVexM0Sf7kc4Nq85087Bik7GQGCXaceYRaxVpitZ8i0Qy+4yxyMdjV7GiGE+U33pPmxSPrSV2XXRZY2KlNL230lwsph+cgfXoTudJuPa2MDKdcZ7E8GQYyG031lcXbPh9xiXnXXRgdtfUcLv+3MPHjt/wD7fc9K1vdZrr5oJnpmeYPpwBSgKx173udR/72MdYKhCAD2QgoH2ACbOMTBnlTBO9DlqYXPjM94jOgBitihimKnI4EjIuhkVNqVq9du5Flz75gv3pcMpG5+InXcSyYRthLHNN0SZLLApwkmQmPzgxMl2rb7RWwgpPestglZjdRo46P4hBA4sUCk9vcmFqMLBsd2d7TU1PlwdG51bXWe560FK5kM+OXnJJWAp4923XvfftmRddnhgsrOuYaFG9NXDr/d/50GcOlUcvKhXy3739zoOXXz79sh8a+4fbnC6XqK12jp5IbjhCvFu59LLhmW3Vm2925vzEu39u5bY7NS9RIWlnprveWr4js/cZud+5cuO638tu0WF2W99LtidSnVQ4TdRj8N9Mbse0Z8u9ZTrDjB2Wzbu+V9i54/i3b8n/0Esm3vTGxrnnX/NtzsW3mk331gSvAD+VwqpjB2jAuQf8FvF4wuA2xCOB8GWXXcZ8V6P1HaTJ5DVzxZC1qxCvhLkNAjRNXo4HPTRzRXbkOzs7iznsTja9gshd7aCfAGIviZOUeNzWRKja5CI9WUBMJxJtXOkhBjJnTylwuOaaa3bv3k1etJp3BCX16yMf+YgY3rJyCVfvQ3wmsoR5Aq9//et5+LaSigeWGa04aiaNXLRLZcRJYciAhnZqKTufRwA5FJXPQ2fUl6uuukpnIb3qg0UIV6fwUACGVB0OFo5awqAsWg0giiRAoCKoJcwhgDR0GWuI02v0ULUVg1ESY7gA6cgCgtK1M7LAR6OK58LYJQEOgwlV7IW/n75ioFlHBzsrCBM4VKohDOGGe5Y1ah8oP9GgDlc5/PQ8ygjONF8k+eKM1htAGuKnOgJ5fJCeEIlbrcQKmCAWzpxGrIYMOepV8UcMwpUrsaKJAJ6KiD55bAOlpGNoMd/rE+0C0yOx7HL5CgeyRg4u2Z0OIPFhmqExPKFI+KMgkSDDTXaEEy6Wag2wRaSUUc2kR4jiNCawBV88htAoBZGyGW+0KwsJaKEzGA4BHCNlGuuNagzRsglgb1Qw0okjPtIoGtMM5SAQS9UIX8HBTKXQHxLx06OFITsw9ReIUke0PCiCAwQQgg/8bbnUYkQBBQhFlQulyMElMoUJbRSQkl4pDkOgLRLwqLfUiUyBlYumIY1+eiNTLqyIqh4E8MgDw2g9SoNRHlmEbbWFISb4KS3MAYQVZJDAsSdNAImeCCSDHoVBrMSUtt9dyq7QWBogcMOuaDbESAlQgQ+AEz2RgQZbBGqOMD8mO/utCOoqXjJvcLRv7BOcPyMxhNU1GCoaQPAN1vR9/jMS/wt/UkWDwhGxfxIUnFVMiKE3vv3cnAvaUFVZKEOsj5u/nh2mORq0M+IpA66eEekn0b/61a/6i0/8xS3XXv3kt1zOFqt1GYpmzuzETVix3FvnbNevWeAwGssZDmuiOWwG18LVtR3H6DbTCadcgMYWNWmWM5/ZkiGkZxPWXOKT59TRITaZFaJJy6zc5unS1aL7FsKscvADHtx+3vzA9DmLR3ccu/vQ4BaR1VZ3MNi2yaGpHcPT9Kdz4r47y0MTA2NT9bGJBxs3hK7UMZbV2nI6OZLt7N+5Z2n+qKF4akkHaCahG7+jaViq1uu8NAu0l3VEY2M9ehztiuyCt66LEzqS7ZayyWIm4YqXStUtMN3lSrI7Gpa4awq4Pc58HiylMqMjK+nkontvwjLmbrWRWmuESePSaTc2GOejpSR3WgwHmKkf1lEHO93i5xDGc7a6ZJ3eGSUW1EZMnpjvx6yc/x+j65RXTKyuLVi8l8/krebnFTXC4gSTtEGNIj7NasUBaqNbdvWGHfo4Jter7XtPHZ4e2HVgplQ0RuGL6kiqvUegp2zhZz9PP8AV3jHsflfrXU0CG8QIXyh7LNo+y+V650RvEpgbHG7zDRdohYfcLWnqjI3mTi2oOpo/pVgdEdSB+KcnG51wbONkOfHt275xeLl7YqMzPjlz/9GF5sbCgcnsntkdpmuMyZR/8q2N228d/+M/bv3Kr1UufNK97rJDfQ9nnZxOQmuux9LCMh00taH25jLFoVzypMqbOWQt9Gj22HLD1gcerOmv48s2y/LZus4DMLu7ttHcPpJdrnaSB8/nzXJqtg1nVmvtpU53YjDjxuNKg5OfNN9byifS9ezxNaSnBt1kXU0sVdpOfhsrdi+ZTKa/e3/1b79YrDePvv+/bXn328tvvjL5qlc2Dx+1ymHAnYEzW6uuWnW10t49ydvvau3dNf0f/q2LP9c+9vHO5//axeOJLTuqlzx59HWvfebs/qWTayfr5JwLLU0QbvhjXnhlbXVlbUOFLIRjCcJ4FbvZGdOlXCKfcQ9bZ2HDVHfOsNM/+WjBGQr9BgI/NSIUQIWPefU6uvnTcNi19e79p5rLjczcRtvtUWuN5Mk1WmhCspNyD1enO1JMTBayo8XO0EhmptS+ZGvaBHgvexhWBDZaNpsRi1rbawy6za9fX/vVX82vrS7bv+fUnF076ckZDyRjJ60POKsXtDgnceeJdiO04amp6annP3X/0fuat9fWUaFNZEzrfWEiL9NN68lm2txPn1FW/Ann0Lb/4wctunlxVM522RK1GN5RTRbnVow9JXdOFNcb20cKmfzkHlcOu766Xm8PjG3ZunPnzEDbQWj7xlI33/gdRi/DCBAiYKDo7Gd7OzO179pxXbhPkW9iyIJRq/PT7jOzKIDETBM19kvfuHW12tko5BbnF3KDkw+v1jYW6w7iLLumL+Goq7DTxOEqI8Pjjjaurs2Xq/Nv+OEX/8CemeFGJbO41Cp0uhcfeOXll97x8pf8yR//Aci0AovgxlyLdKtT2KWisUSVzmyCCf3BVWH2GW4wFs0UMdSkxLE3vvGNEuiTWDm6dsjroZlNdOyuex64+fa75hdXt2/fMVTMPnT/g7LLS2qaiGjVUZbVlSx34b7D89M79mdy7eHywOTMMHU3Wc/gU9+BYj3I5RFm3UaZEisE+IH6RSwK9pb7SIzRNNtDo2N2FnZbNXeOuihr29Tap/5re//2Y6FrD9rmnyG5tacduHvPO479lw8tGef7zOc//5LnP3/rO99Z/d3/mrnzzuTJxcSGA85S6/sPDPziezjv9ff9UuOSJ5X/9b8eftFWq6/CiFWwDPT8zdTK77RrY/nzry2cVw9GRjdcIZ5sh5VnkbFGKlMT47bmt2vGsBMM2HS+YMLF0rO9/+XXG4nkWqfzzVtu4ZJhO+agF+16dwRq9DR3COQziI/E8mQ4Rb7yQ1QWWqSakCntAoEiKVeMBLSORyEjI4mtIFecrSJrViyZipeY70oZVBxyZJ8RZTReAecYqB0gSMZeZ5rDh90PLGRgAqCHsUhXmWikTH9IimJwA0gNEMJSFjhqor5AWYxpxakRIMMcWBml5PCobjBXnFJ8EhbvJCc+FQhU8Q1veAP4xmIIXSWFiQQqkbdc3oqDGz3xM+Lj/CdqTLcx4VOf+lQ0nmxUlhKH/cQoX2VhbAmIwQSUQhJzBPATmSqmEmGFOrVVdtjiIdPWwz8UT4K45ycll1F1MEghhmeIFnyAqp9yEbFWSxg5QAlgIBqhAU8kQN4jvYwilcv0h54iFBRJhrlcMa9kspNgBIJ8kKVXjzBTSrphKpscEUgZIuuijhEWDKFtvp10FEcJMcHcLK8MH6LcMYHIMF/iqBuAkLJICNNAtinOwE12svBTMvFx7ENiKiqS8y899DAWS+EWf8KTWHGGasFBfTeCoyzsgjyFURGklAB83EApdcJVqks6PmEdsICgkZIzHryjFsUi0CslGk3DSgmOeiQGuySDCTmSHaIghi4aKwwxCTwQduacYRGSlYyAiAOjJMYWCdBOrHgurMGEhqEchEMJUUrHCsXhEj4oUXZ0KSJAf6SnAM0nYlWcomEOMV/VMnAAIREBMZjsUZyftIKjpUTxsitUhUKpvkYpwpJFFZIAthZ1awp8MtVPbXCSGlBpaiA73kp29oNAkRGf/lf4y0VFiUwlxRYz3jAHp09aP/HmAAJxAwnkK7Es8BEpLHJzSpLSEsYYX4WJBv7K2pzsf0sYfN3fY4GiFZhM6OSyOY1cmIZemGsGqQeGU34/ERX5tjn9Y4XP4G1M1teBM3Ip1FmAn/zk//jmNX/9zB9730hh2OJe5zw5likbVkGHI11Ct+TI57CtNUSyrMN//LVOkgcbzsAK85U9XeK8pROFdJeL23PhJGO2mhbqWhFJrSTS1JoOMb2ZCrZHolAebtgGx4iavfTonksP3nxo7x1f/eY5zwYVbpL1dlMGOMz5Tqs5d+S+jVXXbidGprcunjjM2UinsyPb9w/mLQ+8n5FQr9XoqiqAyaTMrlAl8RAD8RbbKYwqgL10PlaiM1RlM4t80lxYnr97z97RQrccVkGbmOLQdqqcnQbyu4776WWxbMv5Xpl0IWyEVpW5OUYQHPS11ujyHZyhEpPRy9FiciDHl+4y31PdhoEphrDEoXI4YczcYSdcy9you2st1N/NKD3Rwk8UB3hwYIiNlmkbYqdtBhK4b7ZWh124RhoIkkz8byFBo7oaFvEbaOjZCrGd4BXZNbrSSFi2unvUzGeQljZCWykvI0NHon3RXlCas+pYZ3ooPbLaGucrNhNhIXRwvRKWtivWDlj6axeopaf3L3VGi9lMcG/9U5eMoIwnnvXMztWfozGUPFYufysjQ8XLLksNDU90U9XDDz/5wNbyfHd0I3l8PTG/3hoslU9sJPbQE53lyblMLjP6vvct/d01o7a7vPFHt151VXqfrQVVi4O04LCl+npBFNHmyclw1lFPjbrnTzupyeHNmaTTsJLZ4yvtDVpr00IyPVc1dtVtJlIjTe5udrXaKmaS+8ZTTsbaSKYrrQ4NHsyn5yvtbjZZzqdWqmqFCXDryY1UJxxeU1mr2dbjSNlWK2mPGkuwdsddJT+MmznW7xd/PfeCHyg942klO4XaHRd3rBUHv3xP5QXbt4z/27fP/cGfTrz2ZQ4hWv2V3yhcd212eCg5Nm4rVfOzXzh5211b/8t/ftGl537u7o7CuWknDoW+k8BUlBMP3snaaG7dsffcJ5E+ls4ONc6fTk+kG5lmiyfaSIeD0aTXiAQJnGZFjx//+EU9tCOb406ePLGwsHThheeLHBocOP/AOThLPbRTDy61Dq+m1p2tHXb8phfX25bHYGyr5WSvcAbu9GBqMNfdMpiYMlDTzl2wrZg/7f0G5QSQTaDP1jaxXaiZ0sX3GoSAa+Wav8u98x2uK13ZvWfsy19O79r5WJizS2iFWZGIeQQOhHUB98y11zrB9y/mUgenMhvzxw126PjZebpefTBzUKPJUBOm7fIy9XRmjIxYWTZzQ/hRI8XL61Ho0aPHDh95ODewZb24xaIMO6CH8p2N1eV2vX7X4WpmYDqxOl9ZW8/l8mOTE6N2hmwrbx9xC5d5USZguGMQDmwLm7WUFYvTTfZNLryyYBL+kun4zU5gIORZokbiCTpXHD6+3h6Y3lvtDq6vf3N+ab61tlw0jDo0TDvyQxM2i2dz4T4wHUjBIQyJ9pUve+azd4ynvvnVkx//v7oPPNRs1FMzkxM//iNPfd4Lh9/9szffejsTTUeiQuldGBYmCtg9GKjbZjbxZ8gRDoSInyYKNCMCjFd4chJgSEVZsRCuNxoYtf+c/Reed5Dxvr9UZHQ/54Lzl17xsi9cc90Xv/SlY0OlU/PzBnRBtmiWNWZ20S2UjN7RkbFt+y/84MeuIu75Y0de8JzLRgYL993/AEsnFmfWC2J4hWMCOhLY4iFLS7OgdIYvJFmTusPJ6ZlyMTuzczdD+NSJI5rDQr7273/s6L7ti+FmsdaeZPW5DZvWB6vd0W8nU3eOD6y+91/Pfe+BqRMLJ3lLqQPnlH/llxtfu7719a+kGs3uhecPPe+5JvnX/+RPBm+9beOBh1I/+sbE+RdYZB7UxvqUtdDepVLfTBggN8YcThIwSmTk3IBlaDC1wBopurxqRIz6+Wca06xgrZouFBud9kPV2srS8p1333X99d9gmKKI6hpQYJGzkjXXdAPJqjCG4wAucSeIQ4BKMzF9ZWPJCCW+QXQpWfx0SRXwBic2niIlY16Yp2Kh8rhYZubEGOXYK8wA5WQCwqaUkc0BJkO/p7PBsIObhxBBgBKVpj/ygqwCQkYTDQFhfQ1L3fAKB0AaAE3USKZcyCgL5Pio6UhQQ+GGWGqmeefD+KpEoLwhg2QF7e4d5gwxShitfGyhTspVojASAKEMPCIaAlVahLE0hLpGJfeVIkXrShYFsaJkpMMSyygsMdVSLmikYChN1ZALK+DDlkUCVwHyAioFZDha4tECZz9hqPrIovoL+6TWaNCgig/IRI6UuA0gWeijcUytRIhqJSXFhqQAZPAnsguSjG94wkQub7UYcKgSjZQ+QRV8j69IU4OEuTc0h1mJLQL0R0bOMDSkR7WiPXxdEKiEZCQFJbIgU7qEKDyRRrlEEPUHY5ULmjTSSwBDD2zRDivUoUJZ3kiDKh2I4sMK/QXSICklbGFCmviJjZYiKwtYMO2/oEgy0iUOHuAggMw5h0DkjOoPE2yknOLhoB4B6ysFAMdkO9LQixClwF8TBwelU12yIEHcdnQWzitLRqqiIPJCIPQog1xgohR8b5FQxSvwpRcWg1i50AK+n4oAX0uOEPoGlHgwsUIWAYyiqFHEGjdgCchdU4oD2VeRJOgnVGkjUQoAK7sHSjBHI2H5qY4owoNRcSxANYSe4iDjwYGQrefBykvbFQFnaXDmgQceAFyzENNEBlI5AQyXXTwB4STmxDTx7ZM04CgaBImxiF5tThPDksW2Bc/FIJZ2ReT9jFoUU8bihCEJPQ1ObDpwQzL1SxExpp8yZvSOmJ8Bp//1nwycDXBzFvgThDaNxDfHw0edijGYSbg0ASYRmX7gUYHHNPL6enaCs2M2l2t88F3v+pnDDz24+vB3wyroii17iVoqaTEaLeEnOIBGNxT2BOuTektze36aDf+ZQtZxUF0jztY5k2H0BNl7DL7gxDXtcCV3OwrDtHBwCsL5t2FiLFwXHG5c6OZs8Usziju1bvqbF7/02f/wmR3f++Zgt7mSclIOJ7ATrEdTqdYwJ5KF4THNQa442K7b8LdYHhyxbFPpJjbCYuxad8vWGRsVYwWk5FRIK6EFxk8somA6iNiM0F7sjXx4fP6oO2ur61jRXHVTsDPl3O5rrWXq3u/d2TrZ3Z7csn82zrd37QwrDo7cMucYIDzhvVP4tBVqlUZyEW5hOtu/UA0YWgPZdjnH2aiuHr0/NXEOH9j8lV2YntDx46BhiIq7mkr92rpZak+c8BPFAS4W3baSSjaMexyyEtUW7eL0foMeFNTEfeAX57IngcBhuhjEEZr4jeV5N1QHftcTVkHfmtxIbctsmw4rTzRGscnTq2mCtYl6EYGzuO8S586u4dRyWO+etAyXa6MsK5q5tRTUr5VK51Qm6Z6lo6vtncNEfLoJtmCg9OrXbBw5VrzxRueWdHoHyFWHhtM/+ZPp/eckc5bthdIuOLj/nFT5thPt679zW3Z4ujM8lhhOmJEtJzs3fOcGrdvTn/60oc99duN5LyyfONp+69sGPn91ZnLK4lEI26fSo7+P9ek2AvXZVOfgdDa/0LaaI7fG5EzZv75UiyMFCasY60vNxnDWDsltI6q1DqO5ayxr3+/8WgKTLRSZLKdGC4n5DXunQ41er5lm7GosqounmtX1dPFA3e7XWseebNFpN4mFNq3dbXJHm42rPle76vMLWRsOM5O/+auNK6bmKrlvzyefevCSqff/Ws625L/+bPH229yT2M3nmqmsddQW7xYfOrx21efG3rFtdnToznmjDNmRMVcHdd3S6mrWgdGpatshRqH7zKc7T93WPlhqDBw9XL3p5ubhY2kbNy99UuqcfV2jAIbYQn3ss+UxA/3mVfKSdadoWDiVPjHXnj/VHBpKzWxpjE52Erm5tc7xjcR6K0z8WjsThgDDWuOEUS7jIwPp9o7R1Gi+c85Eanr3vp6ChAFvOzG8YxvEhGKIaK00yjEmLLxJdKrXXtN6/RvG19fM1Y584Qtp9/T2etM+xjD0gCNGwJt96d1TPUmDI/3AQmfBwcYhY3f/dHm4Myo9vjGDZNFPR3tLuZo8XSarS1hDGcH2y/onA3JFHGJKWvHQkmN1UvhjNe/0QOreB1Zra4u33Xm/i+xWF04ODo2cXDxVXz0xfunThnYe3GbpQac7P28uLhzmqf/WoDNcIkM04uYEoBd/wtnmTAWxA1hpnEPI60pVW5pvRcD9c81aO1N1Y0G7wrdynIIDJ2a27li1bqHhWiAXYuc06I5TVa+hrTo8w/b3v//a0vt/v9QMy8aKBmyOHGv8n7/PDtr70pftfOUrHPGtRAgwZfQxOhUmJkx07XhFoFinm2GriddosOfYr9DWqbM5CIVlzz7jvzmESiOzb+uW0uGjG1/46/Wbb25u1HJ79u561cvf/JIXHti766Mf/ziLk63AB0Dj7t6pS8qCajZfWO4O/cCLX330wXttjC8X3JO8TMEPnHMOppmmNjXBzrCe85prrmGYMuZkhAPueaCBXfBnIou0mHl4oDQ7PT49PfVQOXvkyLHztt27d9scZequXbL+37c1P/3p3Opqy/mYTztn4Ocvzu65ZbQ49+KnN66741llhy9849tjlz01/+IXF55+ubnkVLPVePj+jT/8YP76rzvwsVjdaM2dyj+5MFx2HbFmOrH4l/nkU7KuFAy2Ai3sZjtOiuysxrF0Y8mK9Y8JvLFe2ZhfcCQCmbp5btW9qU+6eLVeZxaj7iUveQlVJ3GyiKzGq2gli0Qy6xZ1hCU9Lfrd3/1dYU6yMQXmo9EKJjI1i6269B7CJXeRrAdmtypAqQTY2RGUrsFgB4C4CiaZKouYcJWYiIYaEJZIYXD85HIDwhdVx/30gKaUaMpLSclJTUtOVVRJxjRhwQ0E5GgW2DES+0TNPMBiCPExwWNdoHWyiOT8EK7ESkGLeMzxxHotQCFhonTQFMf/Ubo3/KWXSxOkOH61papWFItkcF9//fWYxk2yUJ/Oy/685z3vRS96keEYSyH4PwZoaHVsefSYAHqrDqCpCMLKZforRXYYGj8yACGSD6MIIoA8TDAcQ7ACNITLwmqHDCA8ZwURnNIR643PfFdZyB3m2IXDOEkoRI8/6hrgYpQrL/zBsbSY/wyyvNBTHJcgDlHhj/RmerEdhlDy8KtxL8pOERIQQeSnsOx+QgA0GqKS0grokYu8RA8UuqJFoThoIAfnDZEIUGNAYnoB8MkOKIEoZawTj2NGQwS0gZgDMpiYBmEBKbGCliJcAiXKLtlrXvMaWbSNvqrvmCBAdeHmibTAEAQkKEJzJIztahMMJYhhWoTthAUygIZ7AEGsLH5KRu7CeOgRE7+SoFJIgYg9BARPzNE4QxUDMU0p5EscER9Mph5KidpI4lRR4ymjUvyEBtXyppNYqo4Ai2TDVb5iPqwAVAvggBwp8YGqKBQVYqg9DOMTdUbRfmKpIjSbNMSCCz+pDXYRmbKkicliRnA8gCsLQGFtwuYE2Egt5VU0VYy5YBUDZ7xjRtLUbqMo/twMrZ8ejZCMWg0+PZmdnaW9EqAOMwUkiOlBAA3/qRkFE4nhmEYuIjUCmjtsiYn7bzzBAUxDdT/yXxjooxTh+NmPgaSnD188WYuhCQQd4/nMAiJVFmT2E8dATBZrxxmfHv8ngDTnzW9+0x/90R9dc9VHXvzuZ5m8dAkt59OF9Q6vYpoZk+15v8GBMNGrTZUrTA93wwpezHbOscOhe55d9PC6JhlsCKnO3Z8Z3ZEujViOqoMO3i/JhGHccDiRWWJzpwMZY8K1meL3Lhy58UmjN7ab+ZH7bpluHF0pzpoc5VdbKc2vXlpZ5TZv32pxAfYka2srtfWlodFp47sAWmVdg2vJUaoc5IL6pRuiG5DzVpHloVqqJ5kSN4W0hAEVfSsx1uLNvOpLJ0QSjln9iZFdM+maybLGHEuqMDxaGE7UXchy+gnTRcPZ9MCyyd5wp5G5dNmQsF53qS9LNgx89yCFg1gdGW0+GXXMY85YJlwilQjXJZ/eBhyWczZ7dfxs3B4p8Qnx94niABMn469Zc4lcabWVHeym8Z+O8ruaNiAmmqcO34O7bC0bTi0kwDznyU3u2FOtLHcbdMVihe7cysZAqj7Q7W6ZdABd2CkRWweNiBbE89gtQtIU38Ry61TZmdKplXBEeDgLOvoblg3zPJwRfdRkVDY5OZAo9Qa/KAQPKbl3T+nd/84hnt1bbnGeanPXzuxznp22+3HEJNVpGfs7XkpfurVz3vP315NZZ2XwoEp8xDBIyto0i5vM7t/X/uiHq29409A9d6/++FtTH/toqI4eRPfaF7SoBqch+gNo19F23f3Ou820VU/DVGFQKhtcOHWJ5cYvPrLcqpdtjUjYmTk+mF2rNEuZ1N6psLt1pZ5mZGkXtg4ZaEgsO+rJueeJVKXesTtAI6GtCNagbZyrydVkduacveFaUsf12NPbG7dOp9oOxWsNFQs7tp1smS1P3TXvOLH2M3dNzdaXOrfdnRuxOFMpNsSbtXcmQCCl/p2bBqsb0+WR71nq6FLiyWlj4LWVxfrG8vD45EYzYSCKoPeNNc8rVJ2ZfOL3P5g7dpx9ZO3p+kcLzR973fDrX5N2sP6mZvf7bDkrpDnWW+OlK2a2j4y37ryt9rG/SH77BgMDG46nPnhu6corpy550rAVrSfDJluNo/O2DXxYb2Cxx1g+NVI0i54ez7fPtek3rHAPD431UCpayeKhY5oq7NFxShFUNSRy49FnO2/98fJGZeW88wauuiq3ezbEn/XIzlAQDaa8DDhh7d227cEcObraOeL4Ld8S3Yly64KpiXRST5k01K3nZnzMzs76qFNkzEXLg3Xo2VwOlp7WqE2xAeSjPTrmIxatVTbWW9m5pY1jR+8gl71bx1aKU/v27HpoYfuu4p7Fh+9KpnMjkzPnXPrcVOX4zEh28cFbr7ud05VaXQlnsX7xi18Eh32mTY8Moe2bdVh3Dn+fCAjrOCHqrLkaIjt54qQlOnfOte65696jp77DENlYXWq6gOPUsfsKpe17zk8VBgyZZAvZ0uBkaXx6aHTEPPDTduUG1w8tfuR/ZNpG/cOgj5pbGHZuf7v5qb/MP/3pxd59DJFqvIIMuweSMPFTPKsO5kTJjND9MF69o+OEvSTCQmWLMILZZ3RldmZr967bT/yH9w4ePZZpNvLZfP3+BxdvuHHwZ3/6mVc8Z/HlL2/03B4Ego8K0omlHF1YTaZOjU1OZfbsG2oe21gPd7qE6tWbn2ETECvm2CNqJaSGi9fHULNamLJBW3fFluLCcQkIGkrah8Wl+UuedDE/Ymzs1hc+86RlWt1GefVXFgrXfrdoUrjTMS7d+fpta/fkhj5Rzsysv/wFiYcr2zKtxuKXvzw8PVm59bvN//k3xfHp5Ilj3e/eVlxatCwvURzojo0kygPddAYtrpmiL23TG2nHGASjQisdTIv2OrmryPX5fGEsNJ+Shf0LeXchmadvueGpVSqO/pt/05rZcnz+lLVeHFRs50SZn4xeAWeA2YQ/dIDRqSp5sMXUFl/3b/7mb/xE5lve8habYHESW2SJqhVlqlBWPnmxgDGTJYFXUmKmeLxlPVBIxWEmSz3qPluZ5Qoy4YrBUjULhFg6X5eFiuHUePfu3WDSCmtBwaeoUqqtjFG6QV4kwvuVkW1KmWkRMwWZoEWlQg7qlMgWFM8/4c6hQnYPKmDIfoVwnFFRLqzgxhdlH0sTKwuJg0x11RqYWGzszUuBLepmA33ZAABAAElEQVRwTOn9FgAaPXGEbeSUEB8ggA9Qkl0VgIkiZEQO60pe+IOPk/gmpUj4iAGB/Y0V1mPzLjCfYwYlCSADbTUIHGjHhkh6JGvcME1MNP2pLkqZvNpPAezSMgAurCzckBJkb0+UKbAwJDWl+0R2YjAcfJAxTf0SVrQA6tAIn+hF44Pl3OQCYVwiRCLA+Zhd26tOgSk9sJgc2yjMJE2EXHHFFTxGrIAAOdJbTYQaKgtV4UEJYIjSozrBnzsKSFQkSuJBmowxECVo7ENKXrpPhGKBOpzhT9mgIS+64oAO6ZBCREZTDzIqlKVcROEbrYCnsLyooMy4RItwXjwmo1ShSsdnGbGRILBFDRKjUPWOQmJdBItYyPD3aA5egU8lFBQ9GVkUhHBoR5LjGyExADEwJVNZZAFEQb7GHgoJqjzRIARPwEEg+LCFGIlDVenSkxTdgBUB4RWqkaAIovcmdG8x8SdK/ZSGIBSn0KgSYMamxtf4EKISoYcu0lec9OoCOOqCNBBTYiw6ZkHR6cyP/Qfm2EKBo3Q2J3z87BHzfnp88GgYN+fqh/tMjunPjhdDeUgcUX2Y/8IAZqo7gOAVBGI9wkBqdjZkEoFDtLeF6W2/TskS00e0Iy3kqDYJaxA2x58N+ewY6VVPDvBDt39nMFUfLtg+mTYhxnOL81j2FAVHIhwlEypMKpzCrMcKBwnpu1yMaClfLR7UIZ05hUTw65qyW9hkBTQ14zf3zL3wDST9qMt+EuliqrW39L137/nPTx+9Np9Y5ZAmLkmXO8231N/2yeP/8bbKFeEQTJ1gWB3Kze7RGpph66MT4Okt9Z9Opbazr53OD0/tMtW8cOrYjd+5CXvpJ3X65je/KQPuWUkhIF7LKYCNlDNaiUwRqity86P9UW3FEJxHddM1pzrliZldhxf4N/ns0FRmuFMJE4l9uzCcPeQAr4G8Od6kPcxU3nR6JRys7We7HFYfnq7gmdZGqmF1eE1ckx1tkl3TklPp+GWJrA2hDv+qrU49sgN/M25PqPATxwFOjI6NLy0v2puaKw8PDo8tbriNhtJ0rdcdyiZKg+HmZr5Mfa3pQi2eI9+Jlza+rTcm126efPD23NjOgW1Dg9NdHmGwyh6RFnXRAsbG/Wzu9xTTJSHdXWPJxeAHdjfqtJOvYSIu+IFWv1MT50UtZVILpcRDi52DExQ41ORQRDaXtgBvZirxoheHapW1tnjAcEosyPztueceMLvdOXKocOhI4sgx59tM7dmb2jKTCReThPbabSXg+Fe84rkbn/jzyg+/avBb31r6mXcd+OjH3FlCDXsYhjFmTx//UHTQxuBsuQC5kG1nTiUK2WRm0Rpyy6GtVebIhQ7v1IZTsrn04agwXko61VmvhIOymKknVhPcXpvds+nu1uH0ynr11PyyieyhdK1qnGv5hPWTqXypWijfMd90clH2B57RufYr6ZTJn4JBtuDA54v51/xwfWbrA0uWQoflj6eq2fuWmlsynY0VezNdyxa416s5nWyOA5+0RCzlqlLbCTUMoXFSBXuD/7ZspDgtobnisVw0mbYkdO63f780F7ZY94aWOrlqo/5nn6zPTBVe+kMu1jxdHQP8x3zYGSwD2rDPAPyJY6u/+f7c7eZ5kk2S4sHc9N35B35r4jffd965T/5GcD5D66lz1MRND2bKme7Wwc5kMb1lsL1v3AVNoZReLx9Kxlttk4CGicXATtVxounE8XBRMLs7+5VrEj/+E+6LWrv44vLHP56P48e9/AHQpkcnPTs7KwIcfUZo0QIEp4V3nMJ3/6IGPJQ9mG3aPX782NH77r0vWk4icYxlJkBVdEh6Pu1djPf+/hPbuqA0PYGEwGM+CLGxsj48ft/aovXzidLYTNY4famQS51a7x49MVevNU0omNSrrK1mxjesjHYl8shAJpkLLkQqHawQDGEz9Xv3viafXSpOau41+vETPdh34Jw7TraGU/XVWx8ojm3tZhbr1XAx8tjEFjXb2f3FdnfLroOOVAzHBzj9omlwyiXY6fYdhy2d0DoQhEbCYr1COmWzedEa/nvuzV588Rmlq4DK9WBdNCX5ANoKItArR7HisEcMk5ENx+xD3UFn6q4sZTfW5z/8kZFjRwvl8tKKSeeGlmegXlv/wz+auuD8Fzz3ijvu/p5T26LzzC5ROuvc0NK9SymH8zabjR1bRp9/7ku/+pXrmIz6KvYTMllmzAJWIO9IZ8auYo4z2dk0cGOo0Wo2MasRJiakmHGnltYq60vjY+MGeiYnyvt3BWY27urkrjuc5veaqtWqacrsfTpZb1yfzL62s3Wyue/cGcgkVhbay0uFman2F76kZctNTyU1JVu2hCFAZ1fu2FbctbOdSncq+rWw/+Q9X53/1deGzpwf3Hv3hnx6DZLFIprt9Q1DTo6Iz5Te1Mn+/c6VxVb5/POzr3pl8ilPa44MbS2XzAyby2j25maRQ+cRYsaSIY4/rHN2PG6gF+sIgjPwgQ98QFmOkxVDl1Q6WVQcKPZwOP0iRAD7ygYCuwozpVRb5QKZrPkYPnljr5zYjplAySi7mKgD4tkc/DdZdve2z3B+pKQMksmu6pFOjESFUqSUFy1wJj6SYkzHgohYLjWCKAlXlZfYV90Tk1d9URwgwtCAA3yix+gncftEN6QXoLcgU0XQ1J1IvHoki09clz5b5NVWKE4aThF7FD+5EwBqKCTmvGE1INDmDwMuL94CAiXkS091IYmKyBaNDOrgGcFCA1hfka+4KDusxijJZIGzR/pYikgQxLPeIAANAlLXlGtcCZd8hRJyIl3xzccmXPGKUx08mI9jpIOf8mImsxtYYU8/r9ZMBbe6Fcl0wISkpcLmbwnOFgzqgQpogCavBwmy+xkZK4a3hs+wlRcECWTBGYyNPupmVIkYZG2FBbqQNKcqJa1Wuoy4hFcAio/aqywYctEVF2N8lRj+EQEE4gmYhOJh+8Z+RwJ0ecsOf6XghhiJPRiFXcqFuXLVGhTBjY+H/CuvvJKgQYM5umIt6HNMdvEeOiZSSqCU64354kUKwxxkUibrmBJ85XJ6aRqZyqIIopcAGjxzGgWarzLSEw0d9soCeexCReQ/853WgS8XmICABnN6KyO/WulwkCB07qGpD7dh+Qmgeqp6SoNLcOsT1Q+A1g9HGvE58jCynXAVp1IrHbax3ZaGAhtgogPQ6EPYHIBAFNnmSGHMifzBCsAdxgZnaKBXiycBMZGv2o1jPmnP8UE82qGEXcIK1RpECNCLX0ETiIUqXfj/jQe3MR9isQgKHPFX484uDsdERsnCVmJSiIp9RmJAxHjL4ok/z0jz+D/hwzkE/MSRQ63Fh4YK58xvcNu6tYYpynDZb1BQuhometh1ujp7GoNLGlzdlKnd4OmZNzZkK53Ws13bMCVlHjks37NWOLHcTHWbhQG9as9eDfuKgVC1tpf/4d/seOt0+l6dn5lmV8iY8DClsqv4rZ/d8+aPHPrt69d/VGtlC6Pi01kDwcEgsdE4VyiNzOxg5CgxmMCu0m1nNpxKVXOIRrhTTZOiaqgRmlw1UUtCT1Aa20wM0dzR29gIRD05g0vSR9Go1xSvF+5WDS3VE7l00sGutWbHLUWO/ql3Ow75IoTo0YzmHbgTZoDDKAFn33x1J1mxvdQ60IHTJqPaOn/00OKxenUhlUt1VheO4Ha1nSlvO8+VqFxKYw4u2OhWl1T2MxB7ov18ojjA+MLgXljbqK8vJ00bDI2ZiG/VaWXYm9rJ50oTW6VprC/X1tfKo5PJbOjy6XD0AoNOJ5LWRS7XwvZXq3x7p1Wdbg50ipubPEkf9ZkcSE0ttxfLSQMeIATN5OGFQRRB84fJlVri6EqinE1NDSQmbCeJ4AMSyVR5MFEOy+Q96lh49/oGCm8VfPOOOxof/vPUd25I1mvtVK6za0fhx3408eznJN07Yglxry3rZU2VX/CCjQ9+oP22nx792y+tvP3tpQ99KFEsq8IRWi9NgBwD/bff0+UUb+32OYfcZI4sdrKjmZPrrY3egTRcUGfUHFlojw0mrXAeLyanhrK1uh0KidmJ7FqtPbemvqft9U+0qsXmqUw7WVmZXz91cmznuQ1rwLvmf8rX3tfZ/6Tx3T/9Y3OtRvWr1+uUEu3GRrE09uqXDbz+NYfzo3cfSaR49B0DZ52V9W5tLJHavq397Zugq4XrySm2RV3Tpu18fhUAdD1CQ6Y4OLplNpFyVLV1Vp3xcnfEaXXXfa0wt4CbYaLa6EKolZ2U+fnPfyHxrMs5wI/kfry/GkeDqdBIVyrVr36tdM/9RilY847yM9NrJC9podSnPjP4nn17JwcXj9ji7Mjc1kSB99t2grfT8/aNd2YGNZI9SZ/FfGXrhFhvWnn9pZ8nTp5wBNnIddeN/qdfK9Yrq0+5dOgzn7EMy9I0vQIaTmvOJqz1fx4RsQPWYK2tr/Ew29mpu1yvYKO2Ti7VdAOWe76arbbTNhSKtGiL9CHRDTHanQitHx8DQQy42BvPDLXn0WiREhCtaqFUuHOu08x3luorU1tmdo+njJv4uLDeckNLLp9LZ1InHj6aT9Sy48m904Plro2hg0vLq2jU7LLzOBigqXqW6Qowv0wQRUzOeGvKX/GKV8RePAwDJ7rmf1eb+UVnFk/tHpjYnh8Yv/dbX5jatseeCDdgbqyvth1aOLPVfGTLMmhCs+LImcD4X2uElU89KrLGn4wzhdZch6fVr+lwNheNUrMB+hvMNLmqD7b9WIehibds0mo6yFubR7jkYr2oxDgTrZNGo5lNZTsnj9e+/o1w5W44pMpugKy2SlsxYhP/jTeMvuYN86fmD22EGybf8pa3MHkZBJ/73OfPPf/8Awf3rVRTlVq9cvzu++fDEkRplMs3gAADjj0nhjQZ7lyUmNdyaIylZjCPrhELW085NuPwluHjh+57+OjJ4XJx3IZsF3sR9prDGdEcq15cWt+rc5XAB03UyRNuFe+MXfKUxWu/Mvna16Te/KbWn/1Z4vAh+xAMwNCDpksAX//arutJ12sjxdzE9OTx+fl7T1a6FvpplnpapCe3X8RwkuoJLE9bVXWL08SW1PCbFhJv2D2Y/fVOeU9yZMhuYcUPD48y5lh7URaqJ4Xh3viJ4bgd7S1kIh8r2O5XX321eJb6O9/5TuljAmoTdQbf+saTcCi7Z/2rIOSFYyyGWJZCo/XAVQCft4DJPkVHgqFJBxi+oOEqoYPDlWXQQ8wTvYgIlpiiSyCepQLVuFSVkszOzgLF+wUhmuDvf//7rUCmA5RN+j4+ciGKlROpph4CCmXBRJ8kNiksb3DEU1S4IV9AYmHERtK8QUOOendGPG77BFvQEM6ikl0ToUpKb2o3mvuwkhFLI2nYiy0UT9HSS+mTSDFIEKNomCuXZba7d4oymBwkHICqLbiYiRychLAsHo4NHoLP05aGtpOIsHKFuUnaLvDxM0okkgZnrQo4sgBFJVjV8OF/Uoa4IJz/oJqAQ9wcSGBBw3+kqSMwQQU+AMuzwnOJcRhKgOO2NKDRlj4zhSEvu/pO4ooDRBaNs/SYbIBA/GY8Y174+Kos2IqBsDeUiABWHAkERs+ZexOz+ITAKGskyB4ZG79Kj7FisI7QY6Q3/tM0AbLg79Fb2ErMesZhwiIIlOK5QGSIBHiCvb6iXaTiZPcWRqAEPkGetiAcWxBoJkqMnzjPYYvaS5Mh7Cc0lALnKB2gFCE7RqELxyAvpZ+UR5qoS6ARqOJIXDKR8JERb1GkcSNQ8dGjBpz4LOOXnoKBIzHGGhpAC/gCdj7DJBICMqrhLBdoj/PQn6jYsngUijqaE5uCSCkm0HC0K5ozHMX6ODDP/oQJgIuXFwmYI0yaiFW6MHJgToIIUVDUtJgemZgjLIBAbRGeSAAUTRbv0VkApQn1CRwx4KgjArQU8JDoX/AQn+YLAJhDQIvxODDhBs8ox8cpE3qqfMQW4VpL1Y32qo9ygfA4eTd/UgX00ddee93N116974d/0ZyRcdqma1W1jdzSjj1ZzpNJWgsml66QV+dPOG3Sesk008rDOnAgFUewvbZ4rNOo1KoWaXZb63PN7oJdYLXyblddBKzC5rckM6jYmX/zln83nfkeg3T9jpG5q8uVB5JDY62J564UX1IvpZeu3PneI/eev9S5KMvgCu42wwwWQTRurygMjJv9ZZmHibyu24bhbKVx4kKy703nUmbqoY3SREdiDZDFgLcKrmqoUP2YMwKqME6KpMCUNoYz660TR7ru6uysMwoc8pzi/BupzoeLR09ze7iQLmfcYOzgEEdZhe7dYu+NBgc4sXWgx8DeYNMFF+yvjTQbh+tzdz08sW1nrlXMNVI11yoj1BFE7Hr3rhqO33fmgd5n4Pn/+88nigNMJYnTWqRULm/khUWVzyZTDkthTQXTtuvQJswKAgkqFBzjnsyCaReZmHWZUDprCevCeufQsiXH3x891kvFNI//puA7x1MLzQTv1yHAui1DUrowNpuVxpylDbvc6k4J7t433x3ezt3rIdGrqL1q3NOXUAav+fQTbBXXS/7275Tvua/VaTXazSH2+NFDtf/6+93iQPaKZ+fzxY31YG+FahhITJfeeOXKQw8Xf+3/GPqrv1rO5ob++A/TRUaGssJgp4b4jGGVmM135zY/eVvqnrmmYaQTa/b0p1dzXW6wmRZnB1vUa+vvWrbt7rKVuotqMsOlxGqlZXxq72RmpdI1s5cdHTFMTIdz6dsS9dV9B8+rNFJNpwg4lLWR+6vb5n/k4gMzv/Sesde/snbv/Q7BKJ1/sDW7/4Hy2NW3t+Y2mKtuJE7kk917N9YvnSxufd4VC1+8NlvZ6GGoVQ7VwrnVAy95YX1g6OSJblyNgmgfXMGSzY1YXp3OlgwjmZFOOHfj/od4ybwZYsYa1kTggiXHDx8bWLP4Kvx6hNNn/o0Nq1hNTL5AqcylLtVuvrXoWmNotG1lTmkF/JWk/t3vjlY3tg2M3JxqTw+nS6n05EBnyqr4fOfAZNoOkB70xyyL7WIHXWmgdOLY3NLy/HkHz1v73d/NvO+Xs63G6lOfPnzVVa74NEVy6tS8lGejfFqC6Ol1XcqiCWb47rj7wdZYNzM8o2CnEhycSpeDI+yk8dAW60eN7PQQO/3qw9GLb47vDeAkko1mZ3Ghu7zU2ag4ZTUxOpwaGesN2P2jzuYRHLoWDpxct/UXq9LTg51KrTtUyh5a6HDvc7n8wNbdY9tnR4vpV1y660l7xovVlUSlmirmk/vPqSZS9h5kM2ndkv5Stx3x1GX2MdyMnkg2lidG6mt1GV/9zm1HltsnqhbtV9onj8wMjYTbby25zjQMXjo80cxyrd5xHtj8iSNmeMemWcz5lXZnePsME75br1hLkEmHJRSOOmu6gKzZyW3faqPVGUX7CbFoh+k2mCOWj4rRf0OJac7Y0hUx11AhgY4ndlGDQ4O23XYferBIJWGcyzjgQt1BuLl/Y8rrp5YsJbj0KU9+eNPYuQaBBK0ROn/GHeO1heUHRscOh4nqROa+BxoHzj1vdCRMmm3btv2mm27k+irdidNMCgY00TBSWTlaAFa+kW/dJFvt/gcfnGgVqvXm8PRO57pvnxw6dvT++eXsWDmZ259cG0qW1/E2dLgg8FItyc1eqIIlVjcyX776y8/a/uRLL7vs1B/+wfDJU+WffEvF4a6Wiy8tMFTbF52XfdOViec8147VfCOZW1n69M///E0f/JPsZcfT++2rKCRbb6vfdWLjho9O/MRgO9NKtDLtrzkOrFp8sanh5JPO0XKsJnPfaOf/tH3kX7W/csjAU+HCizo7d1hO8L177rF40TWg3B32OtuXwYq3uI0o9Or+iYBhpNH7xCc+Afef+7mfc5YVhdGp9+Uonn9oISXR9CM5IcLkxTRnBId6l0hoP8VY6hlNc3OAyorKyUIFk3MSfUJWo6JpnFyzs7M+yUsu7HIIKNG+WQacNExzbzGMEtKRkg3KRhRPqXQ9fhKZT1SLXkmsRCT4yuwDH3V03uMnwhm1vgIYp4YoAGzZMXwYhr5P4lEBK3nPeBg60daJ8YD0EygdsSCokniCUp6DxMqVBlaKiHY5wxo3MAcr6L9cSICkr97Gp/gG3DDS6QNHCwliDrWMkWRBmuJBJlNuRvSvoE0c0JBYGsRCEm7eDF8+ldJRJwYckegVgKSwooUVDRNwxMTqoHYoF8LwFA9mNMElgCqAZIdkyPhKzTCQXFAHuIC3EtUpAU/EP5ZOATwgwIdKaBP89EkywGGFuv5PMTGvgkicrCkMlAwE4CfHCbchxpkBhMZKrNyYxTva/XyAiEMfE2CVAgdZ5AVB4qhs4qFND/GBw+MTFZIeWDoGPW62jCSI5wDSLlJDMl8akj5hmncsy9wmIXL2UBpjVCJ+ta5NZaSEclFvYdKEA04CAm2JgfUV1UhWOolr/KXhOYPvK9yIm5hkEU8Q8FQraYJHjCxAYRGwqg/1wED0QgBAAfoJCBJwUonGUpWIam8EYoU04PAzZSRcnPGIefznDA8ZVqSDRZigOJrmJ/yhqoio3rHKbAYrlwf30HL2Vyll9whwy6W0g0OYQFVDrBAWSXYYTpp+qmiRHP2OCefYRon37N69W2KKTXY2hiBfJNy8Ddmg3Vd1hMZG7YqiDDn/Bc8ZQPw8I2YzbNyj9tEBxg2c3Dyu1E8pGeowAVdBkwa3Y0YjdP1kjx+QEb0vfOELr73uujtvuPZJr3tPMZPaaIU1T864MRUZ5l2tegxQ2EE83WA29g6yCLMvbHv9onFWexl7X9ID07M01sUSi0cP5YdnuoURzkaNR2JqIbQNwY413Pvkyc/O5m9hua58ffTw70ymK7lgkBmO+Lu59XuzAz/XGEqc+KHpD3z4xB+yGGQLJcrOlA0eDOPTpkUzpaLC70aTk9l1kYvGxTbGkG7Tcwar0dv/eManM+I3p1TQoB2OWYuqSrlWQMPORJ6tO27GwtU2p0u0sbncuzDJJHCrN6rtyOhwQWyNjRMcm8jwUiE7OcSD6BasHs8VaxnzlQxMLO6dfR2GxjvVNSutQkPxRH6eKA6w1Xludrnl3lvdnds0ChN2+XYT5clE3nn3XQv6XcYTThrNl4fcqZW2ETMILGgCbQ5PdnzbXr8c47RQSc5vJJeqpjpFnO6f/pkykGVLqbsyZGgkcXItbKylvdSIp0RXs8n0fLVjTmmkkHpoqblvzGwGV72vjqd7681lGWBpXP8NS3nVs56KGVgBKVmobFSu/kzm4gsnLUVbCWOK/QEzlXHoF37B+qfir/36wCc/udpqj3zoTx3qaqe+vuFRmxIlYoF3PuVo6Mzgatt8ZTmXPrJiUXTmxGqz7qbicDGaBsD8cGukaL9ucmmjOzOUdhb8WqWVTyX2TWVXNtrHV2pmfdvhKrRmOdUcKGdqrXalna1a6JuY+tQ9jadsm9p7wejAJc9Qp+e7mZvm2tff2q51nQzXMWTBdzEvUFtPXn/v8svOOTDw9n+18IH/nrXmuceUZjZdfu0rM89+1u310m0n7KNgkWOvY+XDyFEIJzK50e1WhK841xtR2YKqGo6xDY2C/8M7tHhpS73D9oXTtbYH/NFeAaTFLXNrZugSU1qdWpOP7b4yJ32Fk9O6cWiu0+V4d7q2iu4czpTTne0jqaFcd9do188wY9VTsUeDfzqO7PSjwc/UGjTaD/3iL3X+2+/tarfWn/uC4T//UGbLTA/TcGiBJl5vHbSqB/RsmKxSGgVSu5u54/D8aGdmGwvNBYATLqM6TW8ul4eRruJRgURNOANyd6PSvvOO2l/+ZfrGm7uOjRocSDz7ityrXpneszeZP3N42CzAP9xx11Jm+1JqpJOf2rKlVnakWbiDqmtNQadt+KBZ5uM1K2976aX7uiupb3y18j+/xAQb2LKl8OxnjTzj8le9/IeOz51iNzCnmHSM+EfF6kwkkd17Tqy115rZpY1GOzeyY9+O44cf0nts3XNerjTcqppNtWZnoGwhftbQmCMvjPhkDHznMu5wTmzbsjPznMuT//NagjNuVQhH5nH+Eq0LLszvPbBeqQ71TIRYUB8rdgPzWhfG3WJSWAXNHCEm8exCZg0ThIkWBRfz2mjo8uq2nWmDdiOvppyd7vrtpoqTrltYoLGaHEdPeWhwanLKCHOcumnU1alCOutQ46vK839Qbt6a7oYjIs+3lXl2f6L0s+v1i3fs3DU8NMhopjDKgkNfYVR/Zhn9YNxQ3wMHDuq6G+lSPVFKZBvbt21/+fMuXTx5ZGNt/cFjs/u3nUpPNLJXFjp/0gxarCcPY0mp1g9mBy5khqZuuj2ff2jh47/12zs/9N+3veNdp75+/WjpksKb3pR69auaJ09li6X87M764OD9c3MuaJpynNUnPzH5t3/z8ocPNy+vlK+AtrMyCp1n/Fjr1dfntt+WbKfXf6eQ/Hwl/ZvF/HMqiXZ+8Zrnd2fuS3bvSW18uv57N2TuZB3mahOjmSuvzDz/hXtnZ5k/7EJKgr1o5G49/PDh3btno+3Og4q20Z//+Z/Tot27dzuolpGttfRgQtw9SIhsKdmjKclGZ1RxeBiF0S7sN61S8kvJUfZoiONK1AFSxtsoaLajhbhs8b5xzOJXbflRIhlt9Bn/JeaUMrKV6OReoH7qp34KcD/pjywMYqs6ISmL9KQJrFlixDJwNeaIYu+yhoFFO5Si/ScecMNYzP3QGPQe8D2CMRDfEXmRkSKl9GPoG57wBqNNzIFBoGQi2dYeTJO4n74/amZBMvcDXdH7VRCweKtqqBSbS98cjogRXCwO4UokXORHIfJJCBQCfpJI5BiAOAYZyRDLrCc4/g+nUX0kcbk2U9ovERwsgiRyeEQwVG2JW62BPAki3woOTEYgrAD/+7//e9k1sEZAVCKeEn879qf8SQGgIvw+kyEQs/BMuGektjkB9zL+jG+sJjhC55nQEPARBRMlUh6I2cyPOR7peT7e8CeFWBw8BTBNKX2hRMicPYqhmkAyJrY+RUbspaJxkgc3oivoJ5aSVyyIbxzVOLY/+OkB5Ctf+QqF5EEpQhrIy0Iu3C0xxEc0MiIEe2GLk+CjgnrDEMPhQ3ARW/HxkRfwyHNhLIUGtGHrJwg+oQU/iSkiplrBBFjVREWIELw9BIouXIWYEv0UjqUDRYjSAOgB36NodV8WHPD0YDzeS8b4WUaSwhmS8ojkWuus4yBO9D8ljn1uZOwZcJGgaaIwZHrGJz9jQUo541OMicJV/TGKmkVO0n9heh6z9CEgXIx2zFv6iK2vQNE0kdhC22GOUTHv/8Y3EWBUROaxwGoV+58oALn0f/YDICCBb6y+a3tji03ZJIik9VP+cwKumXjve3/p6EP3D6fXhwrDS82wcdcSX/awlct4wwBgYYYVvb17evSD4nTKZlfs6uLfmgdy4UuwQp2rk0nke46ygXkHQdNoR4EzVdnuQZDSdBo/MP63qUQjsVE8+eHBbDVcUiCZo/C7yXzyY5XmiwvZ8+rnD34tc2QpkRlVqskehNiHxDPXB4fjSXoeQdSIBhvbPtu2s3g6g71Df4lSfYwV5GwOUMjHF0E/Cwhav97PsLA5OLfWxKXN6yadBuu+qFX7PTfZ0fAayvMdjOOHTc20qtFMWvlnVSz+uGk5Qnay9Or8ajk/YoE3DodZMr4Q8xeNUpjScIfj2kIc9Okj8wQMPFEcYEq1dcvWjeVrV+t8r/RE2eliiXwusxEW1Tl23IbUbtFaASsMh8fCWM0jT9ADDW74Gd6OaHN2zvFK59CS66os3z2d9J+pLmrH3vHuQrW9VEg6SapSUyvCMW6m3Gh4GFLKpK3UP7LStkVustQdcSlPIqlHV4fjKpRH8OohBKdqpXvX3a4v5qxEVGwBCf4XN9jJeKtre/bM7ty1M3S98TNqVIxUeuQX3ruSTBf+038q/+Wn5lPtyQ9+OFEOA6Wezc3o2XRxFXYNp4cL4dacUi51dKVtoaYNzKc21HLrnC3tSC83kqv11kQpVbHdN9ueHErb3bpeabi6Or1wv8bDMdAOU5o7ZM9VJ1Msb5k9B8L8kUoz+7d3rK8euXfX/r1A3XvPXYWZc9vZcPpiGAtoN1eO38fjyHTbVx+pTwztevLzXzi1f0/lG99uHzmWHhkdvvyp7fPPe7Aw+dd3dJfMPffO5SMiHm1vciocpK7lMeR+91xjY2sp9+SLKl++zvRKT7hY1rtuJZPKXHywMzgSNnRsejazJXCpd3rBsbXO0VVHW2cKmfZorpA5cE71mze6KcqgoFrabjX5MLl8MbdvT6dUIN8tg52Zcmq40DowkXbRcg/MPyplU4Gng9CPsvOnXGms/9IvdP7uOnq4+OofnvzQh53v9/ADD0rKVtDHO0wlGpeP2j2TppaUTWwF6d1H11er6ebxQ5Yf7Jsubh3c2sdDB4zY2BkHzqsCj/Z8nyFWu9/zvcqv/frgoUMWtrjeyVn43as/vXHoofJ7fj65Zy9WANBPzx87NN/ojiVPNrrZUnHvWHG57jjlxJG55WVXAyTq1U5ibXHupRcM7bHn4JN/0fn4x5O1jayj0QqF2tev33jhC0bf9TMj+78/QfRo2D16HEvrxNz8yc7Y0ura0WNHWumFxtq4HeMchfzgiPtG0uP78hRH5+OKum7bgGWj1cy0i0OZLv29Yy5xwJTtT1y5tLrSveVWh+e1jEh20pUD55bf9VOVwfIdt33X8q1ox0ezBh4YiKXMfbbjO97xDkteoxXC1GDk6fKliROPZyBtIVrXZsVLLk58cY6hZ62V45NTuYJruit2PT3t6U7Xuf+++20MZhXZbchIyuayW2ZGh6rvTx77dCpZD0xXPFcyvbRt6IZG56dT3X+fyf9EOh2GVKLnxijsixiSukafKNLk1MTu2dnLn/XMC55b++xnrj720H17d2296Px9tzbJp/v127Y/46IHhgrz5X/V2hjNtT5WS5hzwqaX50fe5p5EE/uD9z7wrJn1zxxaXvqtP/yjd773vdte/spMvdIdKCd27cpfdLHx38Mnjn/lS19ilD93Yqr6pS/WPnXVuNuWpqbqN81139JOZOvt1B+nM/nslodVgfqN5eQn1zsvzpee37JsZ+PQ8OAlP5uo3tYt/mIyXU88Zbltw2k2V55fqn/gg81yMWdiOdS+LD9hYmJ8amqajRvGKnqqaOEGR0KYdf7pT39a4K1vfatKpKnUo+OkMNGw9sSwHb0ZvpKJwVRpPNHgFhkfaTzCzGgGLhNTiX21j2noQJx/wPM+2wmCSkjskVG8GIVynFirIPAuKBUfkrWn/ur4VXBOhXPgzNL7xLbjgNn7p9o+//nPdzjz7OxsdEVkARAo/UjEQZo4k8akFmbjIkS56IW/n9SVySgXMzciyVjnSOiDUB0pii4opws5CpKMWewTCJCHIQ9NfCw9Aumzwk9lxciIkjcckCyShYoPMTF2abKIQwLwxbOPxZBCzygMPoyUfBsMxARs0QySOFASeMiRO4RFwF5xxRVSxpk0GPZL2YxJlIUjwURyQbEFISCrFEDhm4ysagjQhzj7RCLwhABfDr1YJAuRYRpuUDDIE4fiIrHgYCa3M2oUiqLPo0TigCq+oSsm9o6sAJwDTx9AVgSeqPVS8gcQZW9FTA9DKfvOs7I8kvkKvieqaEyMM0oURrVHQVEE2i5PdBflFU8T0Ig68HEVUXAGjY/EzycaTFColBE+/iABz6WJZSFWYmEkKAVWAhpAaokWwJGjfcYlwBWKKGlkAQonpREPmiIk81VKbjOUfPXQQ6orS9SWWKi3ZBAWiJhgnYIg6ScSSDAyB3CyJlwNoDS4IY1hJtLpg/pfDiiLu9j3GJEQQSnC08eNLCJW8Ws/2eZyN0dGijZ/7euYSMzvO/yoizFIpv8K8pVMsSsqYQRCtQhFOLIxigbMzQ3d5kI3hyOE/6fvPjkRlIL6qns2qH7isz+dHQMgGj2o8KACvWcn++fEaHgHBwZPHj9y+PYbhna9MLvebbQSjbByM8y7kmWY6rDQId5SGwbETbSFFb4SsAz5CYx8O2PZ3r1Wz9qxcL5NWPEcHn2SRWQt58vSU2ZeIbU+lT8iuvJwunsi3W2FQ2GDQ91KrGXCVYONG5O58xKDqaWRzIlGanLrlmn+blBxqYA098vKlT+oWbg1AyaWtbm41ETrYLhxNxw40iv6UV74tlklHiXFI1ExZUysLD7GUD5M8JpMs+janJBp5416wOT7bVmiExzgrGWGJrIgnHR+dcOmyHZyo97Ou0e49wT1W18cHt+679yDD6+kM2F78//N3pvAeVZUh/53/e1r/3pfZrpnh9nYN0U2FUERAcWgaFDRRE00JsYskoTEmOQlmkR5iUtcEkFxCU9ckIgG2VRwGAaYYQZm65npnt67f/v+u/e+b93qvvOb7p6mweH/+L/Puwy/rlu3llOnTlWdU+fUKRdbNigSHjB9ar1STDPlzsHyMv37chGAQU9XVydeZ6uWVedAbfsK8A951CoObs0hiFLNCgShUrENMzdjz4YgIv65GxCCPHMVBd3meFibKVut3GlFH/Lx+R63UJEsZDj9cbtQR5BWy1XGBgKdCk/FgXoOPTKSipYyw0njgrN30j69R7jIEtQ/N2M210Nxws2SIHpBHmwwkZKgEJgEyYnrtxn2Qp3nNUk0hIEqjiPEPvaxrOqYf/WJ4jf/cyhXjt1yi2VorEOwPtTC8szc0VydF2bQJ/3OGT36gWmkXy1ecUY4uuzXUYQiBVM8zImuG9yZli03ShGdLYOoXxhFcyGw1dXLoZmujjbcOkXaV+IAnRuMgBdpA9dwSNSpQDhtrEQsxh2VT1PjAcUI6mKQ2FzSrFjsy8L94ASoXPvSL3Ov2ZK6cNWWttWnmMTqyrgZ2jGl3Pekla37BBKOPW4vibYj14n7P2j3kZqy6RUXFM9+WNu2XXwRBhiArWodieCbr9GSC8/2yAKFqQaH+0fzjfE8R/yNfM2aynPnsL1ibbjlskun777HnEwje7s0A+JtHGe3XH2lxYGcktYXUVYkrN6EgUWH6AkBoftzDNQFIbG5KA6d2rlM4/d/b8VPf3YU+8Z33Nj+P2/T2LB3rzwhj1zjWfthF+ZxAM0lMlFPTU0+M5QdTteqFQ5oWeWxfTU9OjPtRw4BXChNcjaskZLhWHI2BBsqZs8YtQa3b+MCOuBE5avGE2owEHxiR/XhR3wdncbcpYXARlum8rXxsl3P1Ktmoz2MPwk9FjD3HU4PHz7CJIcyHde+Tn5sVchv/Oqp+re/E0YrbPrKlgVTwtGb+v331zdtDF7/VjAMzHKLurmNJwpDBPfc88NHn9rfseZMNm5GR4Y0zT5qOYlkZwoj5kiq6jd7kq0cXIGIS+nRzPSEyQakouWnp/LZaQZoo637Z8HIa1euSd3yscYTT9rP7a7h02L9+vDZ59rt7T9/9JdHh4/CTkmMNQ+6ZpBQECFIgOfvf//7jE7skPn1eKOmlIxlXWtrT9x889jgsIkXaBwjK1YoGFATyfD736v09VXKpVRriqLocVgH0Xem1mbc1WZ+h0MyTtVX+1W0vlvVuS5+a9W3MW9q+TbtH0anOsesszkIOTJyFLaVGQMeWjLcrIs8lAZfaBroke0duwf3jGNXWQ6GA+GA89CDD23bth2BKV/o/K9tr7zulT/TzWz4rXX1Wp9d0NQwNyCUVRzuOsE9U9dP/viZLnQvqZbhsvONex5av7Jnw0DHQGsKsTKXTmN8iLn7Mzt3vfVtN+q57Mz3f+iUK3n8tQb9vkxb7SHFvKxu+A/byl/QA8yy1Rnb+Mtg+HU17gd0ar7C41var26dvmcscaGpt9W1PtwfMDSFg/UAvXLPj4PnnLdm9RpmM4x8N23sa2upDh/8diraho52zdpVzzyz55c//0WqtR1VFc2EkKAuEoMKWF6EKHhrOgWemB6R7KO0GQY54AoJh16bR3sgjcT0gux6+C0KoUwkVRh90oNq9JwIDNRIdU19LfwMSQmK8hkmlEBGaIm6yMWQhGvnlyxIYjDosAjogTGnf/3rXw9He8cdd7DzhedkaA8zSLKTkTDFEgAkaqRYCR6/1IIQIuUQfpnqiSEZdcHtkf22226jlve///0SSGJ4ZBgumRYxPwAhwLDjQ3bk4X5XxQfMoItywCHMNDB7EwjlkwwJClolC0IgJVALekIJpARPKmqoBSEHzlVG8gsyiURo57Q8r7IJBBCBaB0CGwgHqwhjFM5WDjI/2mbAALGCnk0TSPjKL2LViZgnSuAT8FMyVCEHJvVKSY9IQOUXDFApexy0hYr4BY10BL9IUEBC26XUDSSyNHLJh4aw5/K2t70NSiAGxJJeClqgDikXGUDWMpdDEBXCGA/C7Z133gmEOMemXkmfJJNVkExmcbtLTOakh1bZX/OKag4w2GkFmGQekPM/YNMpIIGGEOYBgcAGhJRJmF9QMY/yKROASea1FGKTLWquToYhCYiHhwCV0jV0NAXSLxg2gxNGCpBL2AAM+KmRTSUAowQ+gVj6URpOyy4gXo4O1Pv0PqXJ8QU+gZmv4JyWQjyEKU1+JQxV0F+UCTJ5hfbALW0kTItISaD5YXTM65rmr8sPQzCU49UlM0JmINCDTc4DyPZ0EMCThlbIUcxssBAMwGYUeDCABx5e6QgohzaCNKiLiQJMDgwM8ImtHC89AQiYBDKGloJzsoAToPWSASG5FmLGS/CCAsyHAMYcInEuf5cogeYzbzDJLN+MmV0ASQNLFLvEJ0ACA2+65urbb7/9qUfuOXPda7D8wrcrTCQ2uX5WI6H1gXMRPrF4YP24EoEIeHNhVWoIFZcrlorjq0KY4NUwoz0b2Xnidh8EOmI4ChskNTMwl61I5hW+Hj7VDIh7lhpOHTtI0+f4o75KDlUKtZAV2Dg7GeKCFsyebeRt6hecJ/yiK5iLU5aI30wPGCSX6062itfV2SliiSa/uE80Kxbg1JiC2oBJDSNLnDyXG2pZOBCVCAAqNRZQgybqRoEqPGfCKgMb2oV81cEIce5R2Q4I6ZxU1dAVc5QaDTd7BxhvuiesUZCMY5rH8JlL/zL9e9wa/38QRvDe1d3VqBa1UlZJdGQrwlcT8OCRrIYUIJTAKGDFhdSCBucAPX40kgo9vDB5zde0sbxyeMZJ9iB2iZ5c/kPh3Qmdo7PZEMShp7kcFPIU1vri0LpQQupOpqSMcgmYX9s3VlmVQiTAnlCcTZITIsvDseqCPhUnBA89JACzhHtSnwmTxOC06/2rlRjuTIUXHxrCRC9zyUbRSg4vxj/2R5mG4//k37T+6J7y+Fj4k3+r45HVnTS9WfhYXZQ7xwMBMiS9vs1Ihe19U1bEZ6ALxYgas5CxYoPVmOJpWNUwxnM2PrM5Ml2oqlg+tEfxdKNPjNVqeBtqjfWiy8L9ddnmK+exGcOMelYvPOGFg5aS0KL+KrcfM1P4ddMKaBHma2ECnUmPlFu6+56c0Xdn6l3RQNTP8MaFmJOtm9yHxCH7uamAIUcOjkPA24ndselKgZOBiVTHI4P1no3dHX/44czXv1l54GGzWLINLXjaxshNbzc2b2U5pb/FNOI+Al0of1gmi/ZoXuEyZJSXeBSfLFrluopLv+np/A9KhetWD3T+6UfHPnVbY2TEYHRjsxFPJn/rXfp5Z4/ZelR3VnWABPqamQtSmy28GcPzwrOk6Nh4qBx+13vyDz5kGab5Ox90brhh1959wMfUDAPBegaXwOoFN8DSSN/BUvDJY0e8YoOhcKhztVHMJNCalAsbVvdu7U8yoTcDA6mwEQ5ry0Y7RUkuzSvh+IAQduxsxrr3XgzoaY8oqFqxi2XcZepRp/bEk+qVV3BnhaQcRIhSubFzsl6omqNTQ37tiB4121etqVf8TjDR1o8ptJKfnizk0uefuS7q5BoPP9LqEzuK6NLRc3KtkM9kP94qPvwL5YrX4/aKsXDccDgeOPnmdp0I0pvxZEeqx9l4waWtB++64bzt/ckR0y6MFtpHg9cf1a5JV5MYgyC/c70xUghXx+BSrjgzHg2FtUAc8TMcie2dYXavX7RqZdeVnfprL4FKcDCN3H/o8BCbEAP9/UhQkofw6qVqNHWMTTqIMIpH2DK+wtPAuyM2LOwmAa6ick0Kf7pP3dz+d3+bv+ee8lNPq6V8ce2q6FVXmVvPqIVDeBaGNaH34YPPP/980F+vDNqHbmeZsAuh6mfafM/GOWGDO5raj8qFy4zoTXiaLyS1Lyvh8yenGpxug2uka2TvuJWKH+CBczJN5G11JO8geK89datTGD91NfcORNetQ0vc9cCDD0yOj+XDb4pU/l63nlV9NaVFsGiKYzaUlftyN3zne2onR9+JuPCyoURboVLftf9obnrMqlUgLbg3aKytrbVY5gbOFj09488U6oaPaSGjKn4jpHyjXe1wzI1pTcG3LV1nR19XZNXnPxysF+5sCdY3MempO/ao54gZWPjZd52airYA98GDTiGbD4equaMXbtzWUvgntTi80qijrncO94QSb1mz+q22dT6+2P7gDz5CFhRonHmep4eHI8fMnsIRw0AyDDe9BqPMAIEFF409/mF+ll1M//IFzhVVJDwr2WFhpThBOfQ4fCp0SwL4S7YavWJg7DhvRr1IiQw9WHMKJC+4QiChNFLSNTCgEA++djEoBSQGO3IaYx9HJhRIMnhE6I3ySY+cTAPpU8QqAkgIzAxUQRgeFwEJ4iELsjSvHiQyQNWypQBPDG2hWMLQMNllGNkekKgX4GkpDyCBCmJIjxgDtHCrAEAW0kDzFAtvDemSmAbCBPOJFpER1SsVkV4SNjCDKNY+dJUDAwOgha9I0SAHe2+2a0E1pYEcbL9plJwA6SBqAQYC8L7AScmkARU0n5LBgKyIsDe/yTRS0uAXkOSCC1roNeCXIpbEDCVDG1JqIiWRJEMqIA3VIWMAABggo0zPL+XLsAzwKwOMBcD2ki0aAHiw5H0aHBykfJAATbIVAoooSoIBzLIJXuIlAoi1fKVw5ExGJd1Bl7GUQHg0jQf00ijqArf0Mq1DnOAVbJDRIw8pYEBvxNMcsvAJ8GTVZGTLzxPYSIPHIwCGcigf/EN+SGiADTxUIdFCjWCSHoe2ScMwgaophK/EkxEkUyPSnaRbmQsk0DWQitdqiRbvVwa8r80Bcj0vS01bmrO86DADgWdedrYDIEgvEnGd6hiAdDFg00AoioFDArrAw6eXfpkBkDyvakoG+VQEDci9EuYEhrmc/eZhjNflC5/PCxI0Jsfa86aUCZjrcCfJrvHyYYCQGCaSYpdZy8JkXCn/ta997clfPHjxb9VDplGqw6yKC3uF72bETURbl4lylVUMdYaBWIdYuFACC80KwrAwGBXSL8sT+GYbjVs+0ZqiIKVrkRUdH9IvAqFSVSJj5e6VvicDfardVtbG4pg2q66UXfOHOZHlOxOWrVqwWrKNjgZSIiwv0ifspoCbSUYIxyLkyFVRMLIYJGNpDJst7KPFx5P/0CxcKYW4wgXzR+GBS+W0I25xuR0mMit8UCkJdL/RwF4anIAQsZ6Ly5A0rpLherIjRw6TqI5pndOYOrIvM6MUKxEz1AM6BdzCYleAX0MAdiymqZPfjJNa4stFAKZRYpZEeuVynXAyV3G4ghW6w78rrpW4D4lDuGVhkEnciRAwSzaQcBoKLWMIrfZwzDUsCO5EeRbGkxSWc3VKy9edmZrDLdWYyyNECBbFFb8t5Edk4Io6OFEdmn5mpNU0aulAIMiqT2kMZngCr1g1HDEuurD4k/si8N+MKneEwSRWMcd941Xw+5yCe+zRx7gqSc5lXkYCjDU2ORMf/1OMw4w/v0XfsePp37551V3/K7V2HSeSn69JotWkwRt2rNc8lLY45dsSVIfzlt80OPiewdOtgeGx0JnjdGy44IQ0gfNcXQ1rDaOhBwNMAHaZ619VtSeGbKoyTrLcEVVxDh3BcwaqQvzpqPnxQ9SCc6KetRvY4bLQ/Vh2serUTA4zIDJjHGGOF5G66TcMUIQNBvW504zoFmLd7uFmNSkGqw2TO9yqeGOv6/6fj9df1bc29uEPpt7x1sbUtBYNq22djZaWw3WzpWy1B9mJg6dHMrdR+08U7MmCUnF0TAayJXuSiwwsA6sSaAmdIbtrjzyXbYv6Ljjn/N5/WVV6/DHr6AguRMLnnGH1rJzWzMLU2JZTVgmuQTz09NIPUNOf7jzG5Ljt8fJbfiM1OoyCz/zkJ8qXX1GzG6lW7F6wJxW+MWFf4AXhZVnbJMvF6giT504voidlSQTGi/ZUI9ndn/KFwrX89MYVyfVrV88DBcZFetGAX2zOO28hlJ+cUskulZF+kZvEngcPndmo21OTaqALBbXYmZxti93b1/vcZB1vf889cziWbF/TEy2U65FA4NCMkqtxraK/jKJfsQN+MxFQ/OyjTrq+gIU9jzDSc0eIoAZ1YpwLHNgyEvuuz0uqrkkqajHg2D80YWrpLfpHTz3zh7pa4GA23hnXpPY4yiOjja88mP+7ZyuvcBoGp4CP5tPtwRVwJIXpSewRUu2tcNFsaWBBhD3O4XSjvSfoi4irR4YOHuzu6S6V8oVicerQYdAukKAo0jc1czQyAE6GSSnj8TUKzDysqYxKFmbC8tPxvyxbTiZfyQd8yf61sffeHLBqwp4Jx1zhGEaxRiGjheMMepkLjhN9V2/ivhZlBqqv3R0P7EmxSAiJESWAEqr8TK9utHznp4PO01oyMzXtQ/6MRo8doBJjRdCd+AMfxicMVWJdq8KqHs9lInn9tK2nyu0G6mKjbf+Bwe/et/7SS77a23FAKT1sVYYdNaGFz8mrl37vgZ3Og58N1cut607xXXZ1eWQ8FoljJNLVajB9Ic+Ew6FDh49wFPkVr+nBx5gTKKASRWGJpRb/2LLTJgPTn2iLXKUkr24YEWRgto3YggpYh331u1qCO+O1SwzYDLtPVzvYH9SsSexnXEwy0tlDZzKla2tDmzr/rj38LDsCNMsdCTAag3b6U1Hl+1HfH/3rFx+G6YfDftOb3gRzyRzLwIFbgguHFYa3hpmmd6T4J5lRNnEYTYgBSBpw8ADFhh0PAfhU5CsC8pFhhiQazvvvv98lYA3RF+6TjAxbbh7++Mc/LmVmskgyAB6YPIQQaoHZ5ZV4drVIAFT8SrkCOCmZVznegY0uY3WQ5cDFInYiJ9Ao8kJjYu/J1YXKEiiEbRqkCIQfwtR48OBBwpTAZEIayJUqHnvsMQYOJC3rIguaTykjEcmEAyUAHkQuuWdI3ZNLkVj4RALaQnbJgEqWhXoBDBYWCEkgW0dLwQkjjtppizdl8coDzZALvh/M0zVMd3QN+wJ0DfGASneQDBTBxNMQSuMThYBeWgcm0TJRI7IZRVGvZKChZ4l2txJxMhOOnBjZv7SFeBpCLrIQpi56mQDVUThNI54AAMuO4Cu7AwhmVEe7+ERp8peWcraWXykuItKDatDC1gPwUxTFAhu/ACypBXzyysMrYBCgUZDrwMAAr+SlXh7ZO25CIZRKaMEwMTSf2mWf0liJXuKJlOnlr3yl1dSIiQqkC0mDXr6CUsCjUdAYuKV2YmSBDBDIxitHhqkdeZVIYJafwIAXJgaw5SRJN9GhlEnJBMAkfcrQINDf308ukEx7oRnaSEZ+wT9EwgN4wAwmQTivBGQH0Xxi6H3SUyz0jJxMGJRSsgzz6j0eHiiQrWTgpF7v67yA/EQWL9cSieflbX49US72s5qTyTBbAwRABTsgoAX883qiEhZmb46RBACS5TTe/Amk0elYK4BkWkcfUQX9vrCihTHN5Swn7GGvObEXuXT5EACogPCa8y4dpkA5MS6dbOmvzKjQxujQQSc7HvP3pis47uHwKubKbBALp8Zi1XRYuzZ14AAAQABJREFUn5kPZxVa9JI4YGnb7OGbONjkMnuYG5LhdUZs03A7kSA1sV+LRCduVBGPWPUd38PTrzsr/hMtXGi9MTD52YBSxlGqWAHLaOyuDwU3Yk+tPlt4ZcFpKaaLeavQ09WOMaYAAp4Xv1uumEiG/Iy4hSHZ3oOQiaoGgJm2hPnhyX7cEoXSzoesDWeixGDX0SlSY0FMnMceNGdhU8E3EE5VmERpMCx9ua4VuOFRFacSSAqbw65BMuqLltWQ5YPvQrktbGX5BEPObmZ6WDeEc/hj5b4sQy8jAXjFipXQI9eomANRra6w34AxOqsAOtS6MEYXRgg1G8ejQmhizCw6IKVkhaY2V+UmFeXIjNMakoYHy0W/LDkV4urXeqGm0fHjnAZG5GVcoFwWw0lwuVjP+3Vfa2qtFXOKRyYadevMs85kPmJiaq5JSM19K32/95HCl7/k27XH51MbmlpLtmk3/oZ5wXm48glijOGIEzs0p3lmccOipWzYRn//Q8XebusDvxMcPDLzjt8M//uXA6efsbRoIY478Ighq/p1ZV1K64zY+6etqKlPlp1ATon79XTJQlOKezoOcIFn7rQewxG0ZkcDatRs0ZKxyYLeGoLi0SRYHHPg2uXumK4l1I5Af77CXUfVkaMj8fZu3YdnDupiOJOdE/ZqIB4KmSsi+ApzwRCoE2f/BTzuf3PDGyhduZf1V0we7pJvlNm/qGGL7dN8I1nf3aXaBq7H7ExFe220fzgnK+c0NPy49EKe5w6n6YLCzW8cUeBCo1zVnka7iQ8iW8cKulCxq+7tp+A2EIqHwvFHJuqThcwF/V19194QsDmo6xQsO5yMdhvayh7BrTZ3Aa8nfkQTBHbtRvG737duvjlayJbaOzs+/y/hK95waHjIqVS7uzpladTO/josF0whbChr2Amme3u6rDw3CUEYFJ2M+FsHUnhxoJJ5UEmekngYFPgJueUMMwFnQ+RxD+dy8ZiPdfqaAWVyXILswg0rx+mCmtbXy1F7L0vDF5uuFAeHhzj1sYqD+KFod1wby9qjk9PlfFbvXB3k+At3fplmXDg6Vrm7zC1NKCFplJRv6GIUSdyPDdxeyUsHUJjgLzRXqg9Njf3ulT/ZFEWwx0WaXp8JKHiWaK2p/nK3+fgbEjc1srfvL76C3ZcgF2irNp6tuY9J3JxliQAO3Ljavj2qdkY0jqtQPQbb8KzQoWn44rE4Xqtg1+ChgUdypeCN8Oc//3mQTAdxy85HPvIR9hfoNfAs5at5wIN22BEiCYzlufm3fFRxWmORzT0hJ5fnrunKt77t7D+gm35r62nqBefF8WvSkqRAhGG1/KSGG/JaoPYLE25aLKa0oiG2mdjuqvwi4DuPOYaNt92KfTq4Pa7r3f0e0Isp8RiHkw190mKTieuslajhRI6ZJwl4aTWEUa0OKFrSjL3FiV7HbgKaHKekV/3t69uy9X3PclVZ5dWvPlyuc3OOZvpWdsUifrY5QghRra1tWJcageBM1sa4a2U8aW7aqGzbjt0YIDOeYcF8xUD6G53BMz8QOPXfdPtpxdGn/j0Q/HFKs311u2IfGmSjL/n2jGrWyNXYHoSJMIIYSJh0TGP9Oi1uhisfj4X2CP/UmVDlF35nRFdarcD5Nb2j6Nee7db/+v77hI6U7oCBRiJC/mGCpQSkCymqiaa6j0QUXQYbzS8cPDFw1fzCGEkBmKYh9pAc2QMJAVGfrzyUyTiiWfzySuciLCEGE0NiIpFOkT2I4SvsHY8U5ChZggHxU+bAwIAERv6SmAASDmBDYM2faAtSIlmAFlApDVDpMkQF5A0qomTEV5hd5g1YasRypB3YSs70Yq0AhCCB5QalB1mwskZFKTrX9aUM90yLKBCokDTIiFgFmy6FKxTpWI7QQBKzlSAx0wwwYQYC3DbNlPQvRTXAIzGQM1rBMF9JSaXUBQwQGwUCIZISEjhNpgl0HPAjmwEqQhcjjrZQJlABA7/glrYgtzMx8gmcIORQO2ghLBHYjDfZFipCmqI5UgKkmeSVyQCVhsswUKGfpAr6RYoNfAISctFrUvpqbj7pUTuzFpOFEtDvITHSCmZv2iXlZ9JQu5xA+ERRAMlDsXKbgDCohswk6jzgycXjvQK/hJNIypTdATCeAExKkAwNQHhUBFqgXsoknizUQpiGE5AxJAbzvNIvwE9nEQ/ZILPRR5RPYgCjs4iXfUexhBc+XplkobspDRggSx4SA5IsXDYHgRxkek2j42gar1RNP0L8EiQAkFRECZAEFEIaRvFCSU/CI2EgzMCnOvqXMcIjvz7vL0DSR5T/vCmXmcBrIOmBh5mfAB1HAMwTlsgEbInhZRY7LxlUytMc6dVLyTx8kjHUAsk1pzy5Ycas1OgwPCn57rvv5hfWhWdhRRIkSVQMH+AEG7wSAEgJ9sJcJzemv78fKj185Eh5Yl+ku5dDOehU6wY+/wBAjDvWTXbsQaE7AkQMTCkjEqaFM7EwoOhgcHaFACyZUuRAgW9XomC8kbJhcwZRMKosFQ+NX3NNz+2r/NuSl6TNhD12d0Q7rOopJfWaUvy1dcewy3b7DybeL7zbUKIw1xfcCKycq4JGUIROeHW41KtRKyltPbA01QaG0MjAdhwV7Ul/aAyCu1B+lHMTR7TUqXi2hs3DMDOPKxK3OgGhaDLuYB2cHPl1xGORDTm5isMvRBfTkGsccxcX2G1Y25/DTnZCqeQV4ZjF5f9kGfX0YXcdm29DcdKb9WsWuPgM+GsW+iKyM0iY40BZbvxwC/ce2Uq61IgJVb2Co+0i/pWEEphDuY4/IPYZXGISc8GJRhfG9NNl9WhZ6c7ZvXHEV/HIXM8PHoU6zuoWLgeCMVOrGELj8ZSdUTGU2DKnUoXhwSYTTqlLhjNVMlcmhLXkYiuKo0bC5llnGj29tb171JExOxb2b9ig9q5Q3eVEbJVpx93q0QwegDBgMNMIX3899/olbnpPYveuxhVvKH399vBlr5aNOp5Hbs4922C31Wrcr53WpY4WGkHXPdhkAefvaiKkYePN4RvuJpuVUW09U8bG2wlz4Yyi0fCAbseDOL6Gc9WqQhLG6VWoPay1J6pmeayjO4L+DeMNBhKe99gKYsbA6NSQG0Wig8Qsw68Y0/xxJyMBpRsp9tNEjJCLCfMfJ2yIQ1DR2OpAarb1g1mV48qcyw+47uo5bVNtWAfT3NvE7U7CMJtTvrmyw01lNQdfBVh0KMjAGK6I9gsSkRhCI61EQ+boWPne6anzTlsbCeiN3IRZTp/VsdFN8vzUISd6D8VqvTbzV38Z+NQ/YgqWP2Vj4Pav+k87XRwMOZ7QJInCPUAerCsuGhapK1tTdo+z/yfwhBbzjNUJp3OdO3l6Fc4GWN1h1OAM+vv7WQgRVwAMhgMOaV5Sp1GvDo8E4yHzmjdXf/UrnI3IBKIhjsLFLObFl2iRqAsh05yyb4o7tOujU3mfqcSC4up2vNBxE3y1TMc3dHHJLsxQb28UTb+iJWLa+efXfvFzn4vroF94qUZG45Snc/55TjR0bKDOA2vBK2bJK1aufPCpw6dl/sfGvmcZfdXhyMyXU9Vn8HynqCk7+paZ+KWZmDpyUehjB0Y+l6nEQ0YjbGXT43m4kJbWFC2rIhUH1JYAbiTsvji7NlUoUa7KsFnocuEtaCk8K2wZIEjuWcIi+0j+EkOAp5k5bgYZ/AucuzvFw9P16sRzICO0qk2PW+UH77c/9/nwzEyjwgUwmvPww6WHHgp8+HdXbNiAxyyWjfbWKQUOB3MpjOag+TlCkH91oYQW0qJPr+MbL+AqDN2qxRhihOCa/kjO6onic1qsn51xnWGXKWn97X6jIg5YEokteq1ahfnjgSlPxBO09/DhQ909vS2pFNZYuzL+vgNPR/IZo6tj5M3vCex8tm/VhtX9PRtS3C8tLhiUdN7Z3XmUcxNB1TatUsyKvP36qT379QxudQUownM7k/NrX61vvbIcioTT7+McU+o6FvO88njYLpu29pRtf9QIPcagru2PWNvwnVMQ29+a1ognzKvf2PD9Us/dS0xtb7z8mYR/JsjhqUDIX7u3bv5W2jhzuiU8eN1rYp+9PXDrrbciySAoQuQ0cCHTLPcyYPT5iiiIZIVARUMwOW5mKIkhL62TiCJML5OFh6FESnh9KiIe8RhuG/mHxPfddx8iH8i8/PLLSU+A9NAVYxAhDYmFNPD6RHqliRLnHgQtT5ablwBpgQcpFJkBTpoy4a1pC7INAgNLA5MGMPOKDMYvFb3uda+79NJLqRoXxyDkpptuoh7SyF0wYuA4ZZNlMxkdyDCQgVhoXAiJnxfgdd7DWoawRDkIKuCKVxJA9jIZrZCFeLmADah4pRXgio1/AjRkYGAAwIgBmUQCCWnIKzcEkbFhsmFbSUwV9BrA85XHK7k5QL1yu4puQn5meJJrHkoRxhDJoASsY4AKuZHeBL0ATxdTBULdxRdfDMZ45uUlPW7GwRjj9Bvf+MaVV14pLZ/JIqcRmgNsUhFHsc2wNYcl/PMKR0yS0qCUHnkFTjAMoaJbRqnbXIIMkwa6Am8UJYVPL828wolHd0rbmdNAKcVKyGV6kC+FJS9AdgRjiUyvzHkB8AD9gzrmOlLKwQXSgIQGsjtAAh5ykUBgU9PAD0gmhlzgH5s+CTYkxMilIXxiL4leA1TCEioCPCxeUC/0BrbpCBnJLx2H5OxtcHjxSwcAADF76TQv9Ctl0vUMJXrQE+YJy4poHSTR3KIXWv7CPqUEUC23lqidV6R6KW+DwEXTv9BKT5SePpKTLTtZwCDDRJ4oPXIyPiNIycAkDVtawMmcwzRyoiwnNx7aZraHSnf+4r833nAJFw1WOAQs3KzCkwrbN2GjxNLKD9MgE7jcxEWhpWhsmsPa80Uw20JCZmXmACIAihWaGJc9xU+VGnBwhsxZQiendf7PA3//p+tubjUOhs+cWXN6ltuUuGDBvSRYsyv+7T85d6h/C0slYrOrrxd8nTwfS8E6+lMXjGoxU8yMtg4IywI0rBhaw7vGObglYDy5jysridtSbQ4z13T8WovbQMVVwHUaLo78iTrdqZf7UAKGgmdcluv89Fitkus6ZS2WlRhUcssUvSwhQ5+MkyVOYmFeDtJgfEEjBoUgvzh5uNfduTu5bTjppb1cBGAaxqzHJFlOH4YOEXdLXKpRs8OwKIo4m8qpYPoH+sAGs3l/hM5YOBFAv2goMiiBs8pBv9MWxX+3S1KC+Jf7YAK8qlXNW6qwfa9jLs8OEgaLbIIICwZEr2oxfyRTqWYcrczeUQGeiemPVZ+heKwOgKNmdmpXrfT39XLOCdrnrBLDSqah1RgrIswcy3J8SKQTHKcWufpNxn2dlXe+K7Z/f+nqa7If/3j0o7+n+AIQr7DPJtVcmccX4L2JLa7eqN4eVoYyDc64p8LcaWxhbpEMmelKgxukMIoAWI5bU12+LgRLtGthn1poaOMFJGErFmT8cHMSOkWHO4SrYMGxcSuXxIsPN9W6jrIrbLxhCN0Q5TBZMGsw1ilWDHiBDIEP8YeGibYJ4alazE4fPbxyzQZw4RgILoVWf6k3FcbfLw+p6riLtbgNC1vbOlJw2OTKUWWsZNE1WDgzcTRsdPXofi380gqCQK0mVNK0A32azQnkaFBDUxbz2T09Yb9jchHUyhbNwptPGpvs2cMZHqZOFAByIYjQIAihUMi8/wOhb38bypq+9NLkV75k9K2UbWIhl5zBvHJoC6s7v83x7mxi5+vqM5y+tE2kBUPjyl81GdCUgFBKL3xgGVmW4BpZceEbJEe+eO/jlCqXKw8fDpx/bvF9v219+UvBMhu6sC1qES3Thz+kbT6VS59pDlz0wRkrVzeGs0hu5hlb1zRsIxHQD0xZXJUe8OkliACvy4Ya0a1UWBgUmKiDzzu3fOlrtZ/8mHv36FSBH0o+bXP4ysu1GDdPiq5eCP/CmEgkXOH25dDIRZ07+GplgqN/3R4Yi4uBxJmYMSf/Oe4gcyKXTPf5dly54lfxzreFtRY2W4qOufNIZj937piCAEKa2hbVeqIWjqN3YR45hxS5mc16zAY2HBXsLDzZwqmDwQjjuDRfCEQo6EA7i+LT+0dto7xi1WrMebf0m8r+fcoXvhydnmG8cqUf3WwyDT31ZOEbXw984IMTjp3JZNPBSHeEY8l1s1/z7YXPUyvC+oOlEafLirNaKBYggaHhWrxHuDLmVRCdsKNmLVd2PDcyXarvdVRcr0ZC04FCEZakr60jHsFDYI/YQlKsPbv3bN/+BP6raOnE+OR37/6eIEtVvbJNnOk9nHP8ucKKO/4tqdrZt7x9X0uPbg7iyHnTqnazMuVEonAwUC8alHKl+tR4w5foaiihvQV142mnJ2/9o5n/+Ia1+7lGpaom4tE3vDZ67TUzoeiOfZvOSVzdatylJsuRd1eVd+SAVzPZCTnA1GTbvfntp5vqcChUMMLh2qp+452/aZy1tVH8As68nbq/8qVoJJ1kCispZQxS/Plw9Uu2vrpEae+4WlGTv9PbK24HBRFSGHCHjIuYuR/GFFOoPBGARMHQgHcnEnkA8XIu1XF/50hjNpJkiFXsTyEAM7j++I//2Et93XXXUSMCA76dwMy8jDIZNIM842VphpD0rAvEEOAX4QRZRbKSjF8qxfZB8tB8hYHjfiBGtCRCKI22NLPyZOGB/CgTGkYIJBfIob3MLcwMRMKjw48imdOV0DxyCOVIANDPMBYQC5mjkEspxIO5OUAJPMQgrlCCFF0QQmg+WdDl8gk8IwVROAl4leVLfRGQYJFISppMS8lOGk+EIxLCBjxKA2w6DqgAiXiysyMgpdZmeOaFqYtWkL75tJFMQ6X0O9WxCoMKXkHXcSvynLJuXpngEPGGAkkvZQwCyB7kpctIzKRBvV4uapdN9mJkgB6B6V84vYAuOATSyEIgM0pgZ0TmonZEKTplXmmLvpKRKvht/gqeebwYvkI5PMRIyJGgwDPN4QH/ctWQ6bGiByq6tblMwsQQD2B0isQGkfQakQjtBORMKPEM2gnIEuQvr1Qk20tHyLr4JFN6oMoASIbmcZnODCz3s+YlWM6rrKs5pYyR8DTHv4gw+xdQCHhjrpBbOXLQMfApn5bKyBdR8hJZgB/kM0vINOyLEQCBDB85vpbI++t8gjLlJgWtpqNxZ0AbF6LXq4Lm9/f388qiwy9THNOF1+lespcuAHiXXXbZ977/vR2/vP/sGy2hNsP/CfIoBnU6LjegOkc41nU5OMf154MUCnOJjgVtFtwrFlhCd8GwcqyUv9EZU/DJfDSnDOW511AsxIiLllApC5UKP88ULvjrfd94T98nTo08ZKjoQDGkxAtUKDPZlf7T4cLUeOwTCob+GBUpuE6BjxF+oqkDZ7HlscP7WBCoLjt1dPros8FkN6NH7+4oxTtcg+Rj88zJwhinOOUuba1Qgq3IFSYLFaNkKJ2RDm6ZRaoSLoZ5aJuj4g1XmEDj9Eugh3OpFsw8p0GzNS6+EawIQxUKwbYUbz7imKpu00TQJ+YkzLvwTDExeMrAqpMF/EtXzstIAKaRrHyP7d0X1O2iZWATjyoS/2nIWLDdpiYs8qGYEieBkd6alqJm7DBEZzsBC6Kqk/Fp40XrSNpak6J7l8uO04nQOP+3h9W+sFWs64hzY3nGkJAzuQ4LGz4MfeER69VSPe+kzEg0ENw3MpPyCQM2FhgJkguJIAj3EfcIi9u15Mvc6gXArNbMMjLRwt/ZQlyIAmefp//wB9l33RR79FfKX/zZ9KO/bPna15xE4jiJamERgOBWJ0aVw76OvbpF746rRzIWAgPCzEzBwpSqEtSFYXPFxosr6VG0Cj6eI38VJVOy2RAK+xWMwbmZylQbEVON+IKr160LoehjlqnhAs+VcLG41lDEMZphZUGXaC4LJiN/9teFgR8BM07nBWhqOaiPlrTVbQaGqruHRpQcro862CPDwFV0AxpwA6mbpZS73WzMtgvVBgpndL/Cd3wN5TPHhrFuYbyKXpZiFySCnM6R45BfuLGOB6xURMfZXdyXGEhoLWEXtgBXyQilBE3g93kfCkd0ZPKs/vyX5ffeHD2wHwvrwnvf1/rpT6uCCwRU0SzJvC4sDb4QVefC+LKl7B61yg2OkYAWmyt/OblNMtlr89LLSDhggbdymbpYDuel8V5ZlgNrVmb/8Xu+ZGvkxhsbF1xYeOR+fWrG7u0NvvoybdOmsmlMDB5aNTAwmi3/4vEDowUsL2xwNTZ4BKug59LFmZpw4A0x+Ez9yLM7wwGtrS+V6u3qjglDVr27O/S+91TXrqo8+LA+PuEkYsoFZ0dfe6Xa3y+MfsSIm2X4Fm2L/Iof0+lMetdo3d94LuQbRf0//dOQcTQs6M/NVmc71vLn74rFLkStVz0t9nPr8Jbynr0UHV+3pmtg9ekh/70HqobuT4SUuNHoSSL3q/I4GXwn8/7AwABY4oEjhO9naW9mFj10sWZ/+tOf9l5PFICx42E3d9fg0WxJHVgX6grXInajtmN7cGpcgAylygmKxQSi/Pmj2ttuaF8tWHwleJmt/BeHX42rMo3PBnXcoYs7ezDX1iupQuhytieshtqT6LooGILt9qYsutiaKeF/3ra4b95hU0/csyLVAgxGRqwLbSE3vS838dOokbVi0ULeNz4xGvCtSKTEdaMkYIN6LNuI/OR/JXbvqCSSmXe+3xeMrVi9gautOqP6mHA/h55BMDoUPpGr5gpOW8yeqTTwgVFtRFadfk5y9Vof3s45Vd7aUo4khlTjwHCpvSXaiN3qBDZZU7cZ2pTiE9ptRoqt+Mr26WbH30Xe0ateOlwfG9FjSX1Vv9rarsZ1dRqnQao96A+PtbjHStzexhLMLinjRvkpM3RxaXW/c03sLNzLdnIbteu5ila4RDG/cxAe4NSJRYhlRqWL6W6GyaLpSczTXI6o23WfiwwJC/6tb30LYuD+4TvuuIMwhcDhwZ0TQEaifBLDgxIAV0TCvvMQ8B7GJvwxr5J55bSqLB9SZIFA6kO44hOvDGEgh7WV0hqR1CVzEUmP8MonKuJVtpFiKY0qkNUl243chYhOSg8A4ESepN+l5El6+UkCDOSLzkUyjUwsU3oFymnN+8TX5gRe+TI95csAghMjkfFCwJXIxGWnrHeEaZQU5Dg8SXuBX/YXGb2AVzsBqpO7VyQGGPhyFHFE0lOU35ySTgQ5YIMEdDQClQQVRRaVzgNVZiSBfHgFVLAquwnpUT7EU6/sI0iL10XLIQHzvCyz+RcxiYcYKW3K2YlXdHpQDnscyMCASgy9RrcSkH2N0EUYfNIor0YJAPHy8eJpAqiTyKdkdkBIwFfJk7CdwZaQ1OY1ZyQXyJwta+4PuUAsb3QW4i4bIpJg0KLTd2eddZZMiCTPWJO0QQy5KI2AxDNhkEYkAMtPxC+KPS+jLJbuk53LZgQlSISAWAYdKWWahb8MKwYdCegyqmbQ8QtszdL+wlzLjAEkSgO9HoNHQ6CxZlpdArZl1jIvGVVgLkEkTaP7sGPyUD0v5Ul8PVErThRP1XSr3DJjJJIM6QiyYb7ytr1OIniLFkWll1xyCSzn1NhRs8EVtclMFT9YaGLgV9kyEEyt4PJcKRSxTmgshSgrF2qEDGF6qDeUvkjpqlONzQklWK/Q3WXT92xe+/pTxcfHgniWjiP6Ug4UjpbOUQ6WT/+nQ7eviu3s1rf7ndGZWvyZzBbrOf/vbbu2Vd/TUp7Kmm2twdorVgU39Fg4fuWA3qG0MsQtY5VCMBRVdB9sMbc1BQwfN6w2LFcDjO8UIZif5Ie20B38NkqoA616IVMs0Ga93teJGgv/StJ8EByBFG57Cah1+GkOt8C/0+Bq3SpX7UJFKJGADSLkYZyHxGlhzp1xkTL7CyAZ1DocHi6MDa67/NqT3IaXoLjjlo2XoPwXViRba//14Ddh7UqWhk6vUNcxiEf9S0cEfThQFRNrrSHuBMalk6uKEzF0qjcyZYBYOgkObKbshAvq4aDSEVXcjls+PGJoIL9h2JipOPmoUna06SLjBFFcaBc5qdnW1y+qb9jp0Z29LSt8HW1relTXQoCa5WibX91CsiYhE6jcAPaYhvnZxLsYdrTKXLM6ec9/pf/8Fv8XvpD40Y9mXn9F7EtfMTdsgGqFkttdgcSfRR/JlLuyJw6k1qX0vrgzlLFHdaUtgi20M8XR+ICBUUS2IpwnUyctFYOd+xUYJGUun4HWxfn4nE/VsrXpw/t6BtZEY6EgNySZqJG4Pw1FHYOamxs40sBesQu0/GHuoSTRN6IpbGkyWCq400GsLVfxnJArIo7VdE3fvGlLz8oeJgGys/UgRFwcWTEChdArrDW4napSxxBa6JmZyph+RT3C1kUgmNIDPifqx4pDwwdgImSnwmpYVxN+a0VcS4XQect0JBSrvqSZxRFGrMAqOBDAI8OjT85/85vK7/5uPJsrhkP2LbckPvL7wjO4m0AWsmiB8yJnS8VrguXsHLULFlsB5LbXtChdEXezHChP/EgeC86gmfMTXeU+7DcIHMOKgMSu3vA1V2e/+IXQxa8KnHtO5J3vFs2mq9rb1Vi8ms1EQhEufj6QdmqBBP5KwuFKjEOc4RCWQXY9GuAmeJ9ZzKar5Xx338qOSKMvGVrdxm3xbk1wM9wZ++Y3K699DXZvDrdH4SEcWWtWJheJ2MCGwiUvJcHzft1eU6q1yv7hzNCMk9IPsz0KIdeew8xG7DeJoS31+BDiuN8p+OxEtbbnkelb9znprFjtAv7EVW/ofNc7rliz4v4Dtc6In50d14O/jnaCiuCE+EUMYIVGAwMPAasH9ym5luapg2Tz+siDszngIlngmYu182Vx+lhTrJ6EoXDj9zBK/Br2+S6Bi+PH/BODFuvnyWm7r5+d0Vx9Q9E+I6b9ytyaq79frX435hwxGpg8ra0G3zajdhZUXExFb0ikTkXDJ2hZPPxCqdpQxursWckUhIdle3RXR0drWxtecMSuNofZ69mvOemvhsv7z19RUfqMUiOxY/fAz57ZEE1sBRWJZPLoyMjOwZEjk9VLv/KvHJuuvv3GsUQPg6att3dVlFu5cSzUK+vDwraltbXhtPXGNK7KUcVRet/jR5WfTmXWt9gb+vpxVZ6pKqOTpQMHD5yzZf3pa7qxelKUP7ZD11qVn1Yyj05NDFVrCSN5eb6+Kb+bK8ynewYG4pu24DsyX6vNTE6uDEU5ISx2k7hwq1JXhf7bhrO08DHo6LyXRjSEV8ylzzhrk2KsBZel4gnNZMAR6zHcIQGWeXY9XvWqVwnMLfacqJfhNfvdh9kYakGA5MAtkdwLDf8N500VlCeFTOKF10b3WbRASpDMH5wrvKCkRsk6I+0wKGRekiHwMJB5pXwIVZaP8IOYAbHxytiRVQCY5MKJf+Mb30gk0pHMS3YEBmYDAIO2qY4SkIpZU8jFV+KpnTIJ8yu3TjxWnshFHxhZIOTTwjYiJSJNNc8/JCMSa16yeOkBVXLGfEUkYwzC0AMqOEH6pWkovUEUVsfAKUelVx15Gb8gkIYAP/Fk55UhTPmUQF7SIEA2g8EnIhnjnoTsFThvk4J4UkpUSJEJzJAdMN71rnfRClTcPJ4GXu5okICHvAsfSpORCxPIGJmg+SvI4ZGSKnlJwCPFUQLENIcX1rgwBhKWkeBW4hOk0S5pFC2pC3oGdYwXuS9GFdQl8UCreYCwGUgKhJDobgKQK59kmL6QdXmJJczUIsU2vkKiUMXAwIBMufAQKVkgDIl/0jB4gYdfHrqVeF6RuvlEgGEoy1n0l/RyoMlBJwcgtS+a+IVGMvTIIlder72ykHmvL7TkJdLLkkERAYnbl66uRcGAJ180vjnSAwkI2caCVMA5e1Xebl1z4pcuzM5OOByZxPP89JFQJIF82+BgVAMJAoIW1QpuzxbGVmKWBJ1i3YSpE6Ks34TDV7e0V96zxewsjioPPVV+4in2tOPrT3nVeWeuu6D3i7vKd++FkGB8KIa5iNNqQktUVUND1bOeKJ07UdDyuWKpONWul4finW0zhyLb73vFtddc1h9ur5cDtTQrZD3kL7XFnmzR7zwCv1DB6Ydd58i0VuEQLS41Cjhtbcft7OwkclIxxaCW5yzGprM7xw/0rlytV7CJRIckHP1yhrFFGJLOTmsYP9eK6YmDR6fzQu8NR39k7zNO1EnW4qe0Dwg23k3JRIW7LK5HRcfuurgUCigucqiXMsWZ0XWuUc9JbcTJL+xlJwCXMpO+RpbrNWEJsF5Il1ACwwLbUgmMPS0MZanKMVSxRyJEs1kBRdCM139wjJKGqnUlU+VKWGVwytrczUo+28HPi0iRTvSxioXxujYM5TXMa+v4VWpgU4EZmBCDhW0D2x6mliuhPNFay2pgwt7ShWdzt5YFVR0D7/jqWZlYTiDQ46PnvzFOKJIpBgeLyU9/Kr95S+VDH04++njxkosqn/lM7M3Xi6u4jm0FzM/O+/EACPi49Hhdq92XVMcyjRFDbQvr2I1PceDW0Bje+JQqVfFc4zrKEtOFQDivNUfliDXCa46biYpKUeO4vLCvpnpDtZCBcaGO7TRGEaauMZaYbMSurxg3x0Fl1Z2hA/sZXUI5XKvu3fkE4kJ6Otu7ekP+SBpzd9UM40uJNsHwiHobTGcqW3pwiwhGc2UJEQOBCbGb62mxb+YaJ8g6YNgtITUZ1PyanQpyta+W9KOTnkcnXiFzhbl/5UrTHCXqIDabmfnoxwJfv8NXb2Q3rI3c+U3fxs3ubh2onde45tyLhQEY+d9Wnxmv5+rCFJbXgYQ4ry4kvud7WIYBBzWFZGtILmFG9GXvga0QLrOBo8EZhRYMmOedi1+dyo9/OvWFL6v9K4LnnuNftZq1IWAYralWevXZiUbRCjaC3Qm/0q5lDw0e2HrqisEZ9kTtFkOrVJ0QzrQa1fZUYlW7vjphxenUWXISewnci4WnXUEeYvdQrA0eMoAK5obfRQVggCZxNJ4Mdkfa43r56FNuux0/Fkga1uCUTdeL4yUkCyKS+3EVodhTRX+lWte4x8oKEvjBvQVV6fjAB85a2VKsWL0xuX0wH4PogWH7BgcHGWuAhGIBIAl7oM7LIPE518zjPgI1Gy/DaTvZ2gnLBXVFOTPN1k0iyi1pbAKQGr5NqD0weGL3FAdT0RD7qOBM02Kl4J8GGx8yrcP6OWlza8HOsm/kKPGaqrF2KFXfxYenrogrk+hV5mATWMKrH1OZixKnN6YOjbBq01J+EcCnaiO/ZeR/zNYB56+hcfofhe4rt0xuXTueT1yt+jdNjom7cEatYP+OH7bsfbqUSube/t6qa4HB3SkrOwSPKAlPNhwnf3i9G2ixghGFs/e20tjYbh4YCdrVGis/VwIOZZ2RTJ0h1RXDDZfIXa1WZrKJvfu2pGd69z63OxJF6E4l4oJrrJVrXEPVv3Jlf39/qrWVM9sa9yMabU59SO/S6pGarxS1UdvUGwYHzTUnPZMJrWZrDBTGDH8HExDWlCEo4ATPHKIEi4yQA/JJ6EWeINNx0V53y1j5CsZg5dEfQjnIYEsz383FUTUsoFS1IdohqknjbdIg+yH98onSoEYqwtAUgNGCIqVQo5SrSYOKGJOo5mJJyStZ0EUjmiI4kYtkMo10UwxBQtVwnzzEs6wgwhGgXg9+WS9TMiwR6WX2hb+0QooT8pPEiQzzCTa3WZfoxUsRa2FpXiHkpSgeCSHpAQzzdeBHYJMimZcdKQhogVMKwHKPAxmMS5VAqbwSRn7yygeZbAoAHhWBJVDqlbYwQEq8PQMMn0gPGISZH6QVLmFAAga5y4DwJuWfEy3WcPzAtrTWq3nTAcJgv4aqEdjofVpKGAKW/Q7loF+dRwMLm9AcQxO8VyrioQlgWI4L7yv1QjagGjHYSw8tEWbaAWkEyEirgQr0Mmx5pWnEEyOzUBolo9amFhkjvzK1UnJzpxBPdi+Xl9iLp6VSliYZsq4Ud9kfoRMZCJCu9F3XnHHRsBx0FEKBgOENukUTLzPSg1xCCD14aIQavfAyS3vRyagI2puH1Rdd2vIzQg/LT0xKiJ/O8uD0sMenlxpXDJyzzjrzZ/c/8MQj/736qq1wpBVLyMAswoKRgINjGAgfFPJWU9hIFJwsoMJ+CqVlm1r9zS1G79SBqX+8rfLzX2k1PBFpee2nyrqVPX/y++/dtHFwpjZWEY6AhNjMjryQhHEdDWGLO0eFRA1HW6mUtMZ4vKd96tAl68O9fdXE/oOZu76X2bkHhyz6is7kddeec+YZvkvX33bnQ+iYCtNHOXEGg0AhtWoNK9eqxZ2vzqzI/oJQv2RiiXxg9Zs6RqB1obNFyyiOAXMhMNdJAIAco6SEk0/FI30rVlQzyszUTL1WjrR3R+OKGRNnJJufkB82EiZfmJGDGTLCkDSmDsPQr1svbiV8qTu9GZgXET7h4vciyvr1s2Bjo9qV7PB+X09bBVNyR8XzGJbM3F4FZkOcZ8QqAdMgS4NKOKP7vA97GpjvTujqcFBrL9hduPsRksYLebjiIqSuSFhlKm041Tye4AT1w4lj6wE7jo7HwFlI3TiawS+0EZqy1rWyIbLcB/qQ680yM9B8Mf50I/aumyoDq/Lvuzk2eKTyzpsyjzwa/+Qn5MXCsqjlUJ4oDf89ujOQ0nuT6kS+wWZBS1AvNZSZEqcClFoQK2jcKaN9Rd3qWo2wVVzCJxjHoFF2YT5dyYm7y5wwghaDSteQe2k/YfhpcC32IphjxLXkKMZnmWwBIZMSjnZDfXytVYpDQ0/7An6c9SrBlkOT+erRtMpd5G39ZsRE9GWW4sg+pYhsQvQVZOAWzh/Q7gS5ldsn1JJooqJBGwfRUZ8eUK22SKMnpod8fDmWYzmoZu3HkcP0zPQZp58heER0Ebt35W78zcTTT3NYJPuG1yc//wWtk4uOmAVdSU3O9K6AspzySYPHv2fGGpmq4B4Ary9mDSRdzwvLyC+XYfakZS/TNvGIGbiqFcvWocN4Y9IPDXGBie9VF5qnbjS2nh5cszpUxu2wpgT9Db9vZHxyINVC9qmiM1rUR/NWxdI4H26XraCpT+TsYkNIb9z7GvRrhYaFU+WuhJowrZVJVhbafeyhENF6t+2ii47/eizdIiGxITE4XsyWfRBertBXdWJBdSb4inzmp3EVAFTXlTOXblk1/7mcXq2h5K/uVaulClQAJYgZtlEt/PC/Im943crTUwU/FgrHzWkI3nDArHkwmtLOELEBzlviDZtDb6meBx28LIXDgc2LJ+Pw8NCew9N7p9mH0TheNDNYeGrUPmXdBv200xrRWFBoRdAMW4wADvizf2rjfLWzOxSLctkZwklnz9msev7sn4X1nbRIbeOWYrFsWErAiVyrpf6qvl+4FfUeMMtKO5SlJPzD2XHT4sh9sbU9yGVQYvelUhv7Q7N4DxfoqoVAfXtYHfYpiYZ2dtloK0ZDB0P2rWbnD6fGuFMuHk0kVv34P4N2I3fVmyY71gkzDDxpReyARJocIooixIZows88MzXR2H+wNjSkr9+QXLdmdZvVyJSV9KQdja9NhAud0QMjLT0tgCF6nctr4DthMiLR+MWXvravqyOCZXO17g+H1WBgJlecEJdmiV0BMWg4Hx2+VEvvUNuL2kUV5b+jZgOr8oZQPmlqZWOl+0zh9MUOne2o8Xn0JoA+wUNvYu7oqexOkGqRaG48QpyTfDadjrkpQs773EeO7EXyvKgo2GjEaeQrKUS5CBGGlGhOqIipRlImYobUyMnXhVURT2L8vvCJ9mJYK8+v8ooEKGUwKiLc399PJPWCHIpF2QhTKwW5ExE/6Rd9AIlxBA0D8KIJFo2kUqmFRpKBQjBM5VVCQnqwLTXVCMC0CDw0N5mwh3/CzZInMDS/elVTBRVJmfl52XcwILcVwA+aUvYTKRbZiZKpl8ayyciMwWwgN0GWFm7pCERW2SkePM0BmtC8ZQlDLnuZ5YbqZJgu87IQ2YwNL/6FBuYVQnOI8WRXGk6Bkg9p1pcyT7IIennxckQyplMESwkAoi8jpRkYUAcpgnz6VzaENhIgnmRCIm+Slr2MUi1MyieffBL8yN0HvnpVzwt7Gf8/CIAr9gugAbnaNq8Ii5LfSwESeGDAMsBfisJPYpl0OvTPXjODSA5beWyEUSNH2Umsa2FRYAl99QM/e6CeGQ67npls4RFGR2PCosPCBNPKFjQrjOCX+HXtG+FTkV59qrV1hdJdTWe/+vX6w9uELxY23nFHibubvQdG/+Gz3f/wybds7rjtURxb+GD74UHJRzkUyXIP3wZnpYfj4VAcFrSUXOHbmtt84VY8gw3f+rf+9Ayea0hmjY5M7Xg68Xu/u/l1r738oi2PHWqY/lA5P53sHOC6F0RIdH51fArVmBVfCBu1EBcniGGdhjNPxsKWqRtV4bUHS1t4PC5Dmlv8Z3PGw75kiz+mqNlciX0wM5TkDlphFMhRQyEFSz5PDZq4xULXJc4fwouATFaw6vhewuvWu2e+TgDJyyT6OGbx/zhMkK/pM3JDe2J952HnjPc0PI/NlOwI+kSQD6LdO4HhCFEC+0NQDI/oCWjL/T3+R3STuOs5U7WO5mzcILcEbVQWJGqeWI/Ps8gbw2OgRctU6nlLqzjaVE54TINe6ly2I/pbT3X2EzNV1vS07cPREUJCwk2xPHGImYIHkJYDldtal/g0PXjpJeaDD2Y+8MHwvfcEP/8v6ccfD3/uNv/mLe7xSySReSQ9v2kScSIdlSsqWr3euNkdVzIVayTnxHH5G8UPGR6h7QJXN2GX6HAA2y5XGxMjeIDEzhOFupUZOwLZM1eY/acydJUGpcq+QE7kdnCOztvYHCMSM1+Ae3Fc3tU0IRZzZMDWhUtJztbUg+2xFesdzT99dF+hUHWCHZ2tHTWfH6lb9BfmGS74btH8UCY7T+xmYecsXNibjh0KcHhViwU0v2rFfBZ34XREjFnnZyKvLMAtZRk/rNlwisybwgkAKoB/+6L+15+M57LlSKT+l7e2feCDLOZuS6U4jmEN3Dtzgy72RURVsu8XqRSBiM/4VNszZmWqtENMPT2RxpoUm2jLfViGYd2kXoI84FDk5PqvYqny4/vqH/+TMMBTE46FPpusfeh3Qu++WevpFn6j3MeuVKKxKGhli3T/lJ2uclgAJ9tOS1ibtgLJjr40F4DVrICf0+8W/1rb21t9ibhfWdMKnbiUJQua+3WpaO7FhQemCrQRxaIoNa6E4UfZpvXoHKC52+znTz1XDfbkfR3T9srBwjmnRO8LbcqXrpuqfDfFHUhivwNfzqvT8etzQh9cDhUfCSJfgkWhLYTdrzc4M8EliaHTTo/HhetwHg/vYEmyLDDW1A6HCjzISCzJs0gTyRd50DzAj0qNU/NncqFO12Z8HQE1PTnBXVGnrhEXPrPPrK9dW7v+uuodd/oaNexEhC2vY5ZSLaF3vl1paaUhGJO720Jqydk4qdy2tueoU7hXrR3iXIXj31gzXmObZyp1n4KGtVqVMgM7HYZpcBJkpui2yVF746qhav0rVzCCeBqlB7T8XUIVPBSv3damD/uduurzm+W7i/Z7p3xnzyiNJxvTX7S0q5gDkxMHI08/XsXNzw3vLmJ1z+a3YnVxHHdurhA4ceyWAPteJWv/geI/f8b4xS/tV74iuHmz/czO8re/o+zcVc+XMkYgfOmlbb9xbefqdbopfOwDHGqfLVs3IzVXqiUlV+TO1vq2bcrhQ42WpHHaGS3r1oVX9onFU0w30KFjJG+0sv+h21P+35iqBBrqz2K1qUaDHYHzyiuvrzoG++DBqvk23EmncVXnR44Oe5TT3CPzwoDBMy/yeV/pa8mvUwU8ujyu6eVCCyclRi9miQBywl133fXhD38YuY7S+IWnF4gFS5I65zJLMVJGSsHDS0DvQwNzCef/pUwpSMj0/HrlE0CMlCVD/JL+ScAo4FeacTIGeeYXuox3sAR3izAgy19GDpGEhiBHEaBRyFQ8TFwQNrpuJExmCTR+oEjOaehgkakII8SSRQKMuhJhg7ZI0Yjsso0uLXljneQv+KFMqeWWOASxVO1JuUwX9AsCPw2XmvZfv0YPRGqEUGVdOFIGOVKjKLuSZKAI8fvXrJFaFirqaQ7l01hJ1TSNWoBEIsGDEPCkBSydjvpX2ibQ9aDIS3OiANsQPHylTDoRTEpIaFFzFlkjvzJl8ycZBv/AuTB+6RiyLGz10llO9BXpFz05G6nI5wy6F0T5Jyrz/9Z4Fvrt27czfkE+PQ7q5P6InNxe6lZDw5iEMM9OHD0Q1Bp+wyhgTgyriidoOEjXh4+QVIW+Bo+SQiIWKh1XHkaJckpKCx4dH73vfl8NsQPuVHjBQe3DLrH19J76ric3Xfi6EL6JHJfxg79Dq0OVFOaaYqEFhRFFq4Hp13OrznzT5nVGpTz55a/501jiwIuJs1yG6cfj6PS//Xv3GRtfddrWI5x38gUnDj7tD0YdmBlVmFVj58hyL3ygymX1JGONI/HBU09Zt21cMcoO3mdoHqfwOGAoNBhiP1081MxJQczacL0E5Aj8/EGXjkugiZlcZzw416HCSROoE16g8dSj4yFM45aFicPPdLa3tYvzWS/35+UlALMU9fX25Id3JVEhgve6oMFSQ8/VcOQrCAIfPPWSoAtkT3SS7nHOY2Qi+qqJw+AD/UZMrqLOmPpIwTmQtje0wny9gCVTFogG8JQ29KIYOAgL0+mSEBlRVgi/0BhVcAAcS/qqPaNrR3AKrXMg1sKBlnuI+PkpALAfffRRfi+44AJSNzdhYebmr2ThdqWW73wn+6lP6X/7N/Ftj5UveGXxxncmP/U/ON7JyGxCxsKSRIwsrTkZBJ8M6MkAx/6U6YI9XrAySEEOt2BzAl7J607VZyQ3rUf4LxZKI0ePhFv7FLE1hDgMLYFYgXUe9sjYbUNnni67RplC8yjYtEhAi4hrbr0uEIFqjcMQZhXPYriOZRcJlwAGAdWUVt9MDGJk4rDexlIFY3i/iWGzqAsdPF7dEXopFrkhZFitYbsjqkWQNISWyaWN5W1DuFC7IoXr9xKmDUkp4NfqQ8OZ97439uBDQcxQu3uC//G1yEUXujdizTKdouMnxtEXW2OTakvcN7DK6ezUsLs7RpiiWFm++8umjLN7oj5dxbrbYg7sjtTXtrItAFI8tDQlXywI43LFFVd4O9AyGwrgxjN7rI/+QZxLWYTfMbFFGcnNlP/+76t9vYG33KBEIUox/cPB8DDLH5i2MpY+khXKTK4OypbqqVj4QD1QKqno49nsQHmOIML5+YFWX1fYSglfgYuC2UyYAmKmQphUGs4HGSZyfiLFwUFd2TYzuF8K28m23vtLfz4Qfiqgj7W+dbpyRrn4WEAt68b6Sue5BSWIdwht5rsBYyZhcPQEIVgekvQFUDSa4pYu4YNB0FYTQXthIJGzNt0qMep9kq/L/20YYS7USip6IZMeaPO1twln3aKDfT7/tdfU29pK997X2PusuDbq3HP9V19lr19fqNeUTI0a0YSg5YCh5LaWQuMSJXipL86ZQ/xJ6EMHD2XSRyiJZHASPBTJUUYY09Eci7i4HiysN9oYPxKTgsBsJfttXSkrjUDt35PBo1G0R5ZVUhVfsBypflWz19S0RMHKfqe9+93Z6XDLF24JlQvZ086aWnemKk7fK6mggg8td+S6P5haTEw5lZKdzZf/5V+jv9pWTYSDv/kurVTI/8EfB3Y+pbWkgi0tesmq3/29wvBQ4pY/UletEgyAi3mxoVF7zsg96ozvyf7gYePRdGO/opvBsv9bypuuDt349im/78iRIVQZEIXqO1Vp/wt77KNaoBK8ftK5KqtPq2yIqTEEP7pLn6pdPzrct6nFYjMFJCxtywpO5MPIfV4zVJmSOiAGuYtEGAFYGnnC4zKyJHkQT+AFseCwy3feeefNN99MLQwBhipjjbAsUFYtf5c4RYyAOsdkNOeYDUuzWK9AoKUK+epFkpSw9+oFvOIWxnifaLUMyzTe68IExCxRjkxPW6RSFImIJR71IHuLDARsXBkIKH4R/0AU0oVEFLnoFDSuXr3sRhFJAikA014OZkuXUbIKUtKVUs1IJyKrSDNaunKZ+yaU4zWkudUSBspBS+mBJyv1fpEnpbgOx096WsQnBHt23Jau3avRKwpFNJIwIjcxYpfiRe1TeKXJALidF8MYYU5uluU8VDeDRAKZBmSC3uZR0JyMvPQXexnUIjcs6GhaAVroa4YDHSdV8QTmQcJrc1FgWELrRWIez7MwV3MMAIBtOdZkRgYdT3OaFxr2EOJlPP/882kmePNi/l+gGQNgDPqHYJB+GZsQDMTcPEibE78UYbqeKQU+c/eO7e804AadNMeDMBGzxI0+YrWETXX3enlx2WMUNwAifP1gwJzyK/WRERMrOZIhssK/Mvcz18ClaUp1cDh8QS0WUKfLiHmSysRZPLR0dauB0kNnW1psKwlvr5NtA/HTDGtyov70Li5DEJqfuYcifbl8eftTLQMbIwE1F0vGugZgVcUa7N4hBM+MFx4AlHXM5Ttpf6kItS38M+I6wCLWIvxWGiqImnXs4gKDdys8acNTRZOt4XgS4Dj5iDHmnr2HAmu5bm12GwtH0AjJaKGE6gdVhTgkbeeHnzl1E27qTxrML11B82fGl66m5y1Z9vfmTZsfO7BrBbosUIqAKYQodbqk4AoL7haND8pglMNQTAk7AdyfUq6gZVn8MZQzGilQzmKc1Z0p28Gcgk/gLvhsHEC5C/yyKEyMG1E4twGtbxF3jeIwDUdchTrSBiQjBolQVUNMusqtufBNuInCCsJnaHE/ZCwBO26Wn42a+wMYLEgs3izbL2h6nW2gzxf7kz8qX3Be9kMfiezZHfzql2ae2Bb7ylfMzZtdvMzS4bIa6w5CgU8OP+MUJ+Z0xXQuH5oq2tOwxAGFS5wwz8jXfMWKmjXsekZp7YhoZrCOSyrbEVovTDiEglPISCCGOSTPpADnLmL4w2FgOyLtfGf7SqDIDEVaV6zTdc4SslEGK4TIjXxT8+n4HkZ7jJEz/rOxcKYExGAFo4uw347g4RmzZ8Q5w24N4sdLjWGF7U50LmoZjDRkrv/msL30X1IPHRl6Zs9u9jny09Ox3bvu++JX/Ll0p+lb8e53J//6E1oChzSkcqmO+aNQbOzYXv/3r+k7d+qNOh5yyiv7jZveYVzwCj0hXGh6D7ZAPT3d7e0dIGrPeGOqIqyyNVvrCDfWtdFKusnFyLLhZWamcEnnshY7X6p9766A2HcXpQikY6qO8qdeK/7HHcqrL1ejYVGJCz5ZJgvOWFEdyXJjns51znQfdyOPYQtd0xs4UOT2Gy6XrVvRgNaFkOavr055M6TXrBMGJA/HZ47YIb14VnPEyNEHy5IpVZ8dsbltO2tzcfNUwdat+GnfTf/jG1v+MKwNBzbUgusxIhCe58BVgy2O4uumf3AwKi6Lch02sEmLnMS+aTTsWzdgoMIWdDZbviR4WBYYYpghXmG/GF8EJOoWgg5gJGbZ5hPcGxIRfC3pYcvQxsj0VMEml1DuqtxNzZQi1jZRF6MGmb+ru/HqV/vOO99XZSGz9FC4EYlOZDPTw0eR2GEKgQcTQQpsSbUcPDhIXu7m7e+P0fsrent73ANXKMGQE6SqRLKMVIqqFiG5M8YqNUcnoiNrTvlJ4XRr1DD3Y6/EElR17bNcPMwY5W1m8HK8pR8pOdXkzHTiR3eJo8a33DojfFIymuzuhFj7XbQJvNmVcmnf/tDKnsbhw/7Hf4VLC2XrBWoqXrz9jvCuJ2hmI5M24lE/oxFbj6d31O7/qb/zRj0iHEI6tScaY3+mlh5RtRLcQOI3HO06f+lxv/UtOzhk1u76z3pXV+Kq1+OgmcNduaUAAEAASURBVLQNvN9xJkp9ixUuOfl/VrVRNVQ2OYxBz3Lmoh4eK75lWruRXXopycwi3523ZxEuoxb8kh6xyvPBs+D7cRHsPCJ9EUXbcR/NQxsvvPBCFJIynejZZTxkX5gKa0AIDyl30UIWRlJIM/lBKnITRPDyLgGfCCSkrEXNIwcGBuZBhVANj7ioHNKcksUIHAK5lEZYmFDhAh4g8Yv4yghiKPX39y9sRXM5hGUCOeK8MAGGP4OLWYKmkYxioXmpL5IlnHHGGQTozYMHD2KaLiUx+YnBeM4558iw9wvMDBzELbT3JGCU8QnFHeYeXpolAkh3CzEDPHJF5neJ/Rd4fSn00gqqkGHoCg0quWQk8Yviiq+gFyTIimgFkj9jX74uAfCL+ySBgczApzcNetTeXKYHLV6soGQkGWIAj1lxoYUFTWjGAMIwWmW6GKGRWqCiJbDnVUr5dIH3KgMeGPPi571Ck57Wd5lZ5pUw75VmMgQoSi4HbMEQBmnzNNjzci3xujQZLJHx/0ef5AbQvF2bk9Idy0ECFbEziJ/SzEy6UZ4I+nr0Ela+asWqh8SFaC47zLKH1IpjEVZcoZNh4Ran2DgczGFhPRwUei03tiEu8GRFN+vwpFgghgINTSuwN4tV9dyiAPvBjAjHGxSn/0gt7ipkDrASXQFOTQ7uN9ijF0d+qIRqhTwipE7+ZXK63QgYZiAcbe1d6+qhRRNRNiBclN0zEJJgvLqWg4HlpcFoWYGjBmBYANzu0FB4C7yFCR2CAE48eAVD1ILh43wz++msykID3ICXEHOcR8wIZXjeQVRGnIa359ScZlvZod2bL7pKaCJkWS/j35eRAAyW6GyOXP7oZ18MCJeyQdwpCZ+gbCs2tGwZwVUglCuqaihgoTSOvFUxbCYosrpIFkQmsS3pRv7CRLNrk6koozn7OZ9zJjdfYtk/p+6X6U/4O1ue+N4eVVbW0HGo8Jg1BATuGUNfyV0+OgEkdihGS5e4kRRrbZVDBVu6tIi4u0fQ/NIPKwrMMatL8ybr0lnkVxooCleN4EUXB7Y9mv+Xf1X+8q8STz5ZufCi4ltviPzNJ4zWFgw9BJO+jMcja9dGkQxifyFkKCviOs6Z2HfIlK2ZspItOcUwdxcZelpriXJg3+YIAddWCRfNQiYRZ6SRpph6ijU7H3BjmCmYAzg4jdswrEBcYIBdzCwIJFiioGxFqtCUtnVrGvXqUZRLpq8TXxpMGQi9BkKvhcI/jM2zD6cFru7X57SEUGEhBsNvC2hdZDQjuzm8jPZTBI6uCvlkPG4MHrS//NXszx/pQ8hoaev8539MvfWtoqfnBrXAFcv+4IHap/45MjIkPP7W6yHd8A0Olj71Ge3WmHbuuUhOHkrdWQMLGWf3eH2mgtKNidhpDzd6w7U9u57F0mxWvbBskGFisDJid5xVWeCT/6slew8OlJmWj83RwgsTONy7V6k0nSzF7NxSDkw56YqaKYNVC29hOeyhFYPXQr0RFKIvIrEV8HFxFEYB6kBSY9IEQ8vBo9sRIqFsvuiYOby5kWLHE/bi8cHMRFHcwpWupblpuFKr6ebaw8Z1X5tc84r4P631/cyv4juRe7ki4431hcBv9bVeHH/j7da372bjgLIxOmb5snFL/vrLjf4B3gFulrTcqqkUjQRSDdobeGL4MBhuhtgS3BjDEAZUthHg4af5lSy1G2nla+wBu141HOXsU7o7o2IKlc0kkMsXtj311IrevpZkkqurK/VaHtZpZATTLPh76IUu4xemyu/zy5NvcLosojMz0zDrmBZRFPMAxMDjIW1tyuhNKJPZeodb3dxsR3NZb8sQbTWnOUXu8xOXf9Dh5QrOEliyGgpm447Gtci5Qjn4/XtqQbN2zkUz57xKKQuHgFFu8Eal73YNfDioQ/FrRoJqIGTtfNocPORUas5VKQX19QMP6MLnH1ZfllOrGz64AVX4CNi+Q736GgcXX8WfWCPvMS2h+1JISQczFxiV4PlVZb1S/LSqPRdRfnq/75KL/W0dwq5seMh+Zo+ze49dK+urP2hunHTCu7hUecf24XsfyD/0RPLv/uk98VgCz89YzDKyYKbhcSkWgvc2I0Rd7uPhv/nVi/TQOJu66Q9dIE0luX0X7WJ/fz8fn1fjRBoKRxokAGMly0cAuOWWW4iXysDf/u3fJgGKr8997nNSmJkHBinnxVAa5IGwIcvn939zdx4AllXl4b/t9fem77Sd2Z1Z2ErZZel9EZAYEBFLNKGoaOxJjCXFlhgT9f+PGjVRo6JJ1BiUqBjEBAEFBBEQWGBhe9/p/fX3bvn/vntm7r6dtm+GQTf/y/Lm3HNP+c453znna+c7ynIYpK3kDImf9swsRyWYGQ8kiuGcVsK0VzoczrPSiBSkDZpMeGbJ00qY9ZW+Urw3MCjpEn2OdAxpBWIyypxWbNBFMz+p8lV6kvHKHooUgDDowa+6s2dagbNCpSJn9gx5q9yOqVG540LzyfCpcFAXeEurA4FgEB8EGGVQRWn1g0jVqOrhDzJWE2Atqix5pkBE1R4UxQKIHIFIcJuZGLCpYALjyAbEo1oN2uDKi4EgpWKJKYS6yFtZY1ByZeC4CSoTq/A0OIlUMYsoalrhjIgSPynMpy1wd3C/i2aA6Sh6hgVhGn84rd7/1a90EfOI6UD/M7VnCkpe7NaxdK/oXMGuvefpJ6IbOiB8IUQ5tiqSYbXNsR+ylKF7ACeJhTgVZa84xBrOyz0IOPfX2PiEjvB3eVZ4TyvEY6lNG/eVQz0TbhNXIck3/3Ygjr7JtimErJB0ULIwsWjCU60Tw9trmptdGG9ZnOQRzBR2m+Qhs7O9YFrZEsUYViTq75eoTzEzxr0R93qSVhIr3sbPvYQ/XFWDn1pf30s7uTAcHphKbWzBpCl+TTiUReHkwMhAVeGHvZjOoF7hGqVm25FR9lOxjGB4x6Uw6J04GUfjIchyo/3Z0Z4zNm9WBS0h3C9GUScWA0wLN595RjkzVBg+YDSuZ3ToU/gohCJQ6vC6KL1QA9LjuO3mwX+33NcMFglOTiHajH5iu+bjSIGMRk1GaxizVzcwUgt5hF+hFLlBN1dwSrUoyERXBnrD7KEKFodYEnARBA1mRfaBqsnsc05vt9A3++DNVx1EANskxHeVO+7MsmQ6h6M173lP8ZyzR9/yttTundGvfy39y4dCH/9Y4uqrNUt8ElSzK6CLhk/gqahC4Gdiw/+0Jq2WJMuFzimF8UJoVcP6CdtARQwTjM9mZEi4bi6w4nCzNsS4g4oMVQ/08JSOiRu0I3pTXJgyiZJnsiJGiBFhCcDzgBUxM6ECVMyKmiZO+TIVWVzghLG1wMkz/ur1Ynp1Zwuadn+BkJFUhS1sTP3qJ6GYGiFeT+nuynzz33If+kh6ZCRhmKuuuLzpK18Kd3SJItLPEvw4E9nSPfckeoQu51Ff+Y1NjGXvvNNYt9aItqJIZAf1v+JpoLz/QGakYHHQnQ5tjdtrm81CBk+bKLwDQPyyqvhBMI+WA23VUXqdI8uoU2SYjy0NtiUR49K3oFRmxK6h8rhj9Yy7SBOW1xpDIxPD/YfdmpVZREwRs1DkADAuc5Dtic/hZTGnLcXqSLH8m9YNQamzB1AdzKC5KcGra1vZpK0oTnh7xnbWN9ZHa5rCaP65Cs/RJqxNPxy+tTZ8uMHq8zCk0lvHnZVcKvvaU8z2G16f5VqmH94V4ups3XLqaqIv/93k6683m5p8ZBCpBAMl+5vCuskFXbYfFERQZsy1eSYC0Kr+hCqFZFHXdQbp2a5wRsXpYxqQDDkdTUnxWURFvvNSZcfBtcYHDh063NPrOPbydtEgsWGAA1A/jBesBa8IL6CoeNC9wIPBpTP96SXoSDoRwotclb1J0+KmtrJBHTuvHISwbrXo9p5Im52Ju9GiuPqAdCM748TRJ7NLpFKeFm+rSdZfc7F53aVWe7NZEwmF7L6MzsiKTGPyEbRh5TK4/JMi0umQf+2HDj8Mlz06ihNLTgUhqgIYHtCGPjbTOc0pa/Zhu/ePwk6fuOlyz8t+M23ds93jwO+rzfCZea9hIvo2b+R95fC+3eF8xrSLpb17R//xn5KPPo5/Xp8/1/KrVkbe9UF904YPffl199778ze84RXxWDKVTCFFo8foPeYRyyOQMnxTAB/zlwSKYIVAZzjoWz6j8ESscEy6ihdaEZCz2KVTcldXF5EVSeYLoplknezu7laJyMgIUjUBYEaKoRh1pUeicL7yiSzEH7vAHq0F9FNWvtBwFDIN/Y6m+82GYFxFteKbODKJ0Ayrpi0UClrHQy64QfqHIQtKIH5WsRSYXCkJUumRidCrOG2Cwg7Gi5RMHzoWaBVXRuLga1DRixGorAXGT4ktQFq4QaY2eIL9yzwMMCAx1ioXawilMfr8wpcqy+ElhFmBevbZZx+3TPqQh2QsBvyqVxUT5GUgWNaQXwT4zMpGDJCDLfSAunZOVRrkWsIAa4LS0AIYATXrmWuLw89KwMA67LeJQZXNcICfyDHB/Mo0CwrTG0AbLDgLyvu/IrFaH+g3piFh1l6e3zDk4CGWI0yl4uiR+pBmuW5WuF2fNJBtkyNDsL+oT4Q+4AMrBKpfRLW8Pt1vr+hsbn7z64/8zWfC+QKGCxATZEHx23LDqzLLV92z3y5oUa4+EjZXSqOV/A/RSnZxgyyOoCkSOXisfn/vxKYzVkQuPs9+8FE/tSQSODissaI1tnHTATuUhu6TNUooqvzECMeMlnessH1vu8T7/He1m5FUUdVDgZ4c7hVOym8/E1wuFsXdKYrsqc0Petsn+NnvoXDov8zYcNEZCxeMmnp7bGx8fHQEuJllrLjok6O4HBO7VxEEZHue8ezimWeeKc094Z+jNPEJAuqmTUgO7LF9W+MN6+m/sInyVuiyvK0P5Vyuq6VbcQeNfWEZnavcKonLFgZLdEqoPzgaSIC2HNv7gpWwbUN5NzKhYTrbFPMaon7S6gZJsEY6SDSN61vMvGOjlC67wusK+nLTpwN7hi4YlbULMzycEctMDC6tXpvrl2DRZZ5Mh0pKVI8SlbE+zi/pn0o+y18ssGk3/0UuuiT66CPj//IN4yN/lXp+W/k1rxm+5LLkP3wa07cp8wvwWbpxllI07fnnn4ewQOJ79Csp/cb7xYsgAOFBKmzwr8P3is7kyZQc7k3hX64MbywyCzlAIcuNvq4VThhlmFh3oOlCnoRlxdQYCUsJKPzzpUfcp+svB0zIfscujHYmnXiEA70ctcersxHHaEtnQyoc7O0Jr2xV8ACbNMWH8CjM1YXIxLYk+YVspRA39/DDhbe+I7X9eZEdLmtKvPWtLR/8SzMsR/gwbwlKlXWP5IWs99yOMrw+ohHxpw8dU+IXMxB9+3Zspxh11uJ0ZoKFkUtyf/HMkXQZutxduWp957LIahzBkhi7Gy7dnbw4B/7jqJI5qG7WALISBhFuSjFsAn884V50kX3nf4l1hM8GUhxnoel077wLvFQS2ACcZR1B5kDe7B138WrQViM8E/6uRtKFSAzxg+vC6YSNQonrjqy2JFe9eSf7oKpRmxWYeSJnUn6AwSa0f1wraNZIXtSkMTPEqW+ORsN1IzFAjBKJhEbLnRPOCmxruAA8IjfteP+1s3z12o62m29KXfXS0t7dmDZEVnXh3MtsapRtaHzCG+q3+wb0sm20NHvctVNXm8sjjkKxJnSSsoqci4NSTaBLIaAJKxZUhada5+EFYFCcUcnod9SyhRxFCbgvWC9SdixfDmbi8ZihWbtm7b79+6D1IXB5GDJFTfIJPQCz/qjwYqoO/sJsHFvvVMfL+B2t0d+ALS1+sVf8pVFf1LZktXtSXJ5tg5HiOs4trU/Xnw6PARKsb8jYoexEbvee8r76+OrRjg5crdanMDmuLJFwTUIbH3NzhdDppxYx0OKsf28f/r3ck7qdA/u5NUJjJMIh7HI4r8+0cTqWW2HLGfl8uLyH9bh08KJo29/qfV+ynt1mbNMLP7edP49Hr82ZnZnou1gSDxjmn5YHVhZ+nTUfeSxkJ7nLis7E3UZy7/70F7/ovv8DN/3BDUeO9Lz1D9+cTKVgG3hoM36DGTUVruinY4JwDor8BcF5VJgOn4cBPib/Yl+ChRQ8/8IXvsB6ctttt33wgx+8/vrrUZoxvtDNijdTYiCW1mNW12PrVVgHwoAhkJLT0eDYxEv+BorC31KswlLCtA5yFoQMmjlrpXR4EB+kRBcKi9vV1RXEkAwVt9KQ00b6CuaQjAzuXH3CHJlp3Q1jCWelZpCqFwVywE5jwUEtcEQwMEqiFMD2Ygeojkedg6Uu+pMmwxPKLqOWXqbY1KIBwCQgnt4mwAJCYr7yq0og8GIDPE/5mDGDuiQAJMXGEwaHmVAqHvIXCKcBSfPFkiUWo3UMAThMgpka5nnqXdAnOHAl9qKHqU71G7OGSgEv6OoFlTlrYuYj8YtQ3k7rHwoJYqoHjywQZqww+J+rPtesDVloJFUzmxjN47LudD6L8FwTeaH1Ljq92t/79u9oE9+skHEiD0doLZbIQvvIkR/UD4TYI6FU4X9JBRG6Y0hrq+X00sXL/zo88K3vlnbt0VzHbG9rvf4V5pVbHvWSP9jGNGYP5McTpQ6zU0oRFpoAhUipFIVOKBQ6fNcDG9dd13bz6/cPjenbd0liU/jp8rKGZX/0h9nmticP22UHm1GqlnIcnHkWsaQUVgJvqvAy4m2UDxU7/qL75NiMSqXELRVs7JDotJLTixrHgP0O8pcnvx1wtiELm01hj5s6uiKWu7xWKw1uXd6xHB9XlOmTwb4XaFIKUyHqrNzBJ2trk0p0e2y9J+LbicUAM9na2ts4Kpk9+Hj0jNfiTg2GFlkFNgzgxHhRr41ibY/VrBEPy12U7BdlsZjHqlbntiRUjlwQ4lvD8kn2EtXlilNitAolHUPow2NeMuSeDV/qJ61uWCYLo1iMB9a14K/c45JhTgGP5kRKhPADBi8MsoZEI8IdpVwjJJ6PORvQ525oM0OCacIDz3woWk1aFpqZX6uMUfABntSRqql917vzF10y8Tcfi/74rrqf31s494LsDa+v/ehHjZY24V8X8kzymUGWabnpEFPDa2xTTOl0RV3EYCGhQCHMZBZtMMeDcY7nnzRgWNAXMxb+vBaoxZ4Ztotf00PewT8OFaxPdnGtUl2KaUVCVgFfaRzAECwKfJFncnTUSzW/k/kkJ/NYRrCMz9uP/U349v+sKxZysWj+Va9e97a31a5fb4SQlLBiHjt2sp756OU6ZRdDFgGP/2D1ZQiQmsFWUyrsUEcHxtHYG9/9yE4z1dqB/srTOptMfKoV0hMO1qRle2VdI2cvB0ZGh0eHT9lwiip4/lbQYLUZK6bLb4VmJpLRl12VvfPH1v33i/CBh842jPTyldE/vMWoa5AYz03b2t4RjXP1owUjFXFqTTeNQX/JmSjp0XyRPs/CD2NOEDI5aN2UMLrr4YFl1Bfcy3Nkoaz+nDdRsPrTzpGDh4YP79FDqRWNzXlR97mRsFksietp3KHky+Vw2YiEDRgxjBIyTuj2Z8qbljectLIx1b0evh5fLrjG6gB1+vtLP/u5+73bzSMQbW42VeO89PLkda8MwwabRk9PLyarqCYYHVqB3hXKbGaLKhsIFaXUGkEkY35knAEGVTkhU2rmcjYfBaRXNS245mHr1q1QKvhAZDonkwmEO9i3Q7go/g2KjdPgJDjrrLPYniEl0f0qfgNmADqDMiEi+aQssVkZRM2lEF4GYfKRhoBxujYRuiHu/VvY6E39/ihWE+5Pk47N/dmus2m89qa0FkbIHNaz1xY++/HS97/P+fpSW0c2WWNde3Xt77/e6lohKCLIzOP3jRXW0xMTjz5ec/bm/Mt+J3znnd7Dv9JueWP4+leXHnw4yn0StXUelxY6XFjmFUNR6/LLwCHv8I8RomsDUdxuRf88al5wvnv7Dyw8u5eM9BeLkZdgeFOouRgKEkLhQTP7gHmOkfh0fOIbpciTdUgNcSNNqyO7do8++ujGzWf+3d/93WlT974GPawCwetkL1T8gddVRpiwvqgHqzwDHBRw2WWXVUmmw88oxgaCmy5TemnQSWUHxxSDBz7wiZTIEwkrEaei0VF1KkY3qF0FgtZRAnqnrq4u4oPIaYlfjFfqUqsKhCyoqOAMlHvUCGaioK6MCcAA28FkjGMDpp0SKCdIoAL0GJ1AmEAQVvzhzJbOjFEZVQmAESSgk5lEPL29vfhSVrKGWTt5GjzBK8AwNykTRAoiFxqA97v00kvJBWA//elPKQ0JCLCBM9yySyQa3UDmBeeGjIB6iedX1UUWcAarkKBpC4Vh/vRUBFoyQViCZGGpWMGmZWTJUjp51ihAUiQKg0vnwxsjT6SxCqthDomnKNKDHi95yUsoimnIo3CY13kqmlbvgl5ZyXnIgut1Bk4Z7TN3kI/gd21JKgXnKVmx2YtggIENYOhA1S4E4gotmSmMdfWNBYUU2lefZalSMrUZ4uMywEy6APmXqupFlKPs88eGekI6FpqggEwutBPiOAQlGJgofkCYb/I/NJqwrhILA2f96Dknc1LNhRdc0nL6RnNiDLMHra5+OJF6PJP47IP2hCPeCqD5QWe4YClaqEK1K2NJJzSivJiYRZu5tJv59GcjH/mb9r/9aPa++4pbnzWxD13b3XT5FfmuVY+lYzsGhQ+XAmQJ8MvB3slFg8a9TeJuCsddL8rja3chs2Gs4IFRf3OlK8wL7AxrkE9sSwP5H9NLXFv7ChklREDn59n50pEjfc2QVdJtZJF7WOQaJN82DHnD+N6nmHoLwu0XpZnVFbqAGVhdgS8olY+s7nnnnX/fU79a9go8KkGwiesjjl+DrBy+Hcg6nXXIWcTtWIFzp77khYPpoLEgJM6KC25dTLDJf/wBnXohEYb+o3mxa69LwwOI8yGFY4L9VT5MFU+rjxirlzkw4IhGHEeHFecoudwrDYHoGJhOQIOWDFTWKJ3FyNDoL69rsfCM5e9ySt94TI0QT9DHbDlVQnH8ZByq3bQp9t3v5e756fifvCeJ351bv569/T/tt9xS/+cf9Oo4W1uJ7hry+IAuYamFFKAKlnu1sc1Xnd8hirdXTcIvGPwIx+I1jmfLghCMgiw6M4vypxyJ5NPRz7E6P2WQl3OI5d5eeBsd8PikXNfQb5AXM8ucJwbu+2gtPhmmZXMTX/on7TOfSw0NYOw1umlT4itfad68EZ9DU3hRkWOyaP9LJKKvXR19+mkUwDbXBZXL+Hv1v+vZk1d58RRNQw6dtb39veW0bTZHoolkqjXlruc6oonx6J5dpUceLR86YidikfPOblq/ob67e7I7VSnzNGNKGq0k09LHPHDeK7vjf//32S/+g3HHj81sxomEnfMujPzJH1sbNyFXgAMpa/rOQTtdtnrSeO2Xm7Gf3voMqvXD43LVsz1wcLDXSYQiVveaGtPqqDEawoj9QJapnpgXpON+VHQek+XgsDchR8o58VKKYx0awam7aKHxPFEsOzEOBCNhclwsbHJlG/93oRCX63gc/3b18KOH7Kf6UCdqjVGP+8k2NHt0Zvmee8wvfMmyc3IjHZ04PFT812+WuG3l5jeyM+WLnKqNYMC2y3/gjvDOonjguWBWdBVfZaL43cv57Z60BNlJO3H9FshkqM7vHtU6yYIpSigEokI7Qm5CO0ILgqvqqayRr1D/igGAxoLUoCiWAnYXwqScB8jDPUegOHdlTk6FP7y2+D49moviS/nq8dKQEa7RjDqubGPfR+z88vEvPx/+9rcEP1Z05XN57rYqf/PbhWgocfMbdN+FNZYLe/buRZi4sqsz5un5b/xL7YZVNe9/cxoPVXfen3vo4djFl2T/6I+Kt98eScYRfmNc7YST7s2vC52xWQulbacXW7fyzmh0+3Bx+3PRs8+auPIl1t13I42zXmlpSbkonAdv4p6Z0twxwyto7Zna97rFfw7FHm32l32MrMazz2+vOf+Cq6++mt6rXJMhQFU/BD1c+ZWSp71KZXNEqk8zfznONDNy1hjIa0ZTfaJepSbFckepPjCVxEExB4BZPIEWNgOiHNJQQchshVJnfOETWBmmlR+0jgCcUnd3NwkIz9q6aXmX5JXVXqE96z+YSaMYiMqSsWkM5kVlPGFwGHYXaHmmfVIxqhX0s+pqJAVwyOqWnWnpj/uq2AloLEqmWB7FVRIPA8x0gzc7biEzEzCsjNQLYYCBJCgWjAUHgIR5DaiKgaw0CmW7VwwVbDC9p5g3Zj1NoJDKooIylyQAPKgTAU+xr7OWSe0k4OEr7C7oqgR8yDgQuRLGKAOagYeUHMYhGYnR1c8Ee2bMrDUuLlIVPhPlVGlB/AuBgV4CJX72s58xF2jjIopi6BlfpDP0JEUpHKiynKAJQf+omCB7ZYIgMki86EBlsdM6k9dpFQUg0dJpnxYNwKIzKs8az2596mrPDgsHLLsg/CoKXzQdogf22VbKJx7IfR5ZaHLUYQXX+o+t7v211gUdy5Y3tsDh9We0nz9pP3JIz7hwv7KPwZ0KIYuaC21yCJ8dsK3SJSzVipHmMiDEwUdausLf/VJfqmXoE59ve+0N8VdmQ1CJoVhvOPlor/tED4wnAjytlM8M9x6Qxrpsqm7Pnu1uUq8t1JzZxu0qEr20jxopDFLRWsH8wLUKnSVqZ9S8vspE2qYWMfz4uhwz9Blg8mFWiB8s+s4aHBoBnwPxIvwUIuyE5dTE7ahn/9fuX7/mLa9bWrBfvNJOLAaYdoKv559/3vfv/HjYnigYDbC+EJr4K+MYMKxmtmziDasmKoYGqSiXlwpnDHLLoMH/4v3F0SK2KGllDIOh9PtPzUykMFxse4jTpCGjlpONyUmpTZVdDK3r/+e1Jg1Ic5uzr3InqZEvY+LL5bRi64y5LwCjKkL6PZTXdKzlMQ0wHJR+83Q3+x9YJVC/AIonWH2k+XQIx9Ku+p3YE5dOfO0r7if/b2qgX/v7z47/+3eMG26Kv/udVmsb81m6TdOg1FE7IBiGOgEMpcSAOlE79Dydo7pjRgI1hVR0EJZBoXAFZACqv3bQRUcfNUtJCUHDbqHkbLzmcnkSwaiTQJ38USmP5qwmhBzEl4HR0XZmIvPN72j/8JnEvr2sahMdndZHPtrw+681IuItGTnhXOUJ8PAWqZrQ5Vfmf/5gfGDQL5XkdCdX1cZDL79Gr+OmTW+0iNcrN2/7FiKe21HjnNxgaqMTpfvudf/5a9GxwbL4vC9pd/53+dqXR268QeuMC481d9UBSIqbgqRWnQBIBLjgKHTqBvOvP+X90XvKfQOR2nqMgfXmJsgZRoqJcmDEHS1Yh8foTqOzntPa7kkndQ9MuBGkN309iYaWeq5HKruJcAhGHRduq5fhaUpEfUv3eFxIO1Z0Dw/kMlncqGulopiveWWukxIPPZxF4R8e1gplG4ugSMiEJeY0AdwvfrmiIcMyzLyN9zurNubVhNyWhOntHLC/+59Jp5TFxsO3YcYqiSXC+c8f2Bdd1NJ90ihHVjTBGfAZ3Ia+x4pyftFJgJ9+98pWyXlpTPjpirDutdb668aMfiExTeAXAQ10IQEeKE5OkTFeTChoRyXqEgGqritXPRiPQZKiE4ZQhkEiHqoLA9r5+xxfl9zenDXr8tobOP64yv2opfcayXwkyTLE5gybmnITb9T3Xhn+wVviHECor5lAra25Eeg4y3Du+on30quMpkYf2YxoKNqT4UKq0upGPfFKZHq3hOsKNX9Rct+ayA39u9Z2evxdb3WvuKLw2KOFw31WW2v4kvOi6081mho0rwcBkOij5QamfOEb3wr91UeTf/5nuEMv7b2t4U2jvr9CrKXrDf2rWvhk5+DjE/e/p/Y6jlPlIjcapT2JyAjG+d7A0GAkEU9igeAzXfRb0Hx19I4YsB0isqurKxidIE0QIDtDELwuVSCAB02IklQypoyXOrQSMIqQgBywZCiffvpp+EkYYMWbBdlBPBZbEqhGBQ0hAfgJ/6MCMIf33HMPhfMosjIogRYFuZaqdaociqUWVl2AVNVVVlQZDuolmUrJgkIkYRxBwSYFiZ944gni6QcaEkQG2QnMGlmZYGZYMcBBn5OAeplEdPjMxPPHKOBVGhUOYhYBWGVdbOhspkxkuER2MWZ95VfCagGnOrAI7kipW+lGhv4FVj2tommvdBSVMkcWWkuQnp5XCxfoCjKjcp8pKIfTC9JPA+DFeGVJAwZWWgqnw/kFh/mlb+fh86uBhFb4tEeOchbRIrKog9yIh1i7WA2UAKiaqkmDfE3JuBkybIJ4iKTDA8IMzAFhXowVD0kHAFMd6xJElzqtwLCqY/wz4aeBoDTxC+2lpZpxCiTsgEAGblez3GLEQlPG4oCfJzfC+uSbKYsCTdk9setA5gl1M6XPFetSc/ewuWcE4a2YRxe10HBO5/YTFJxiKKi2OlabsV63XEwuP5k4382GUITs6SxAnIiDOBiLt1FwfseBrzzittSHltXU1cm9kt7hCSfjYPIozm4gyVDxJevEfUkxnylm00nuHEro0WQMafvMHl6aGLqDxccS01qhZnSxA0eVyL2n/hPQNOLkEq+vmEALKD5RytG6jq6To+X9wRCTOh4yNje5Z9RLKTnHG7pmCyrMpQH1xS9lHo7sxa98throWa7DdQrjTt+z3vKLUAJztpZxwlAWIovjlnIlUgTmRM5w46A4VxLd76RNg8/McbJcNPLC/k6vQCLwa182RgouRxCjIblCNrGYPhAU7663sEpxbAMnaUO2wa4F08tEoRIJ4ENN7tfisKWhcboHs1jPXtcc8q1jyT39YZljLYNQCHBreopFvcu8jMdq3/XH7o03TXzus9oX/inV06t96v9kv/F17/3vTd50s9nILaYeNCVSXlY6lrktW7ZArFAbm8ei6pwvE5ZgLKDz8x7kpyswIUNJcskll6jiWOIxbmGtRAMDd4GcLxBBzVffzG9MZ1ezM+P5b/+7/ZnPpvbto4dyiYRz0xtr/uL9Rlsb/Da1sCTNzFoZI6uIZZlrTrb+9E+zt37F2smFp/gS9PLtnaGbbwxtPANedDjPjUfc8COXZa1au25Vg3ZyoyUc/M7t3pf+GV9ZrmPYTpmSwtgT3PHDUntL5PpXG7WYvB7/AU/YBdnm6auA8pNTKFbIaGvWWpZZG5gCrKMi4lTFITA6kNYGs266bNTH0K9y4NbTYxh9s9gPy5oYS4Tj2DvrqZC9LGF21XEDsC/2JH81MB0fanykaYfGvMO9g9ue3DmSHmMiwTQd2Plk/NDOeP2ypo41dQ0NBfyAlzn3a+WLDg74oyGLA8kYHMO2lWzugsYfNdPWq4trK+p1r1goH9gX6RO1CQ9jJ8PH5qW7VqlY2rYtdvKatvZ25LTQ5bAukAsLF+RzCR4+5Cld8KI1xTV6s0xh+pkH6oQRgZyF3WVSo+liwKEXiQGHgW3aiUpiFOTBrzSAJkyNWhA/LUCK3rRn1XGXtFWK32A1X+KO3+5mH9Ltfseo8WIbh4tb6pMXlbfdFhkZlKMRjY34qqcQuByWIQwE7P37jPVrjHAkzGHvrhUHMdKyn3bsdyavF1/ifnW6sUxLNW11Mq/X6v8sdP5bzDPPjJTKnARGVqFFIXcgIuoMs1VzRo3VZStl1g8P5v7xs+E33JJ4041x/Wk3/CATTuZcuWX8jkfNrrHkKadpP16dG90efwtHVrLaJRPaDxO5fG60bK8775xkYzP0K2OklHhwU9BV8BKqNwiD8PO7omEd45nWVy/8FbaB5RH6FeofOo8BYtLxzFTkBnWhLGWhA3LwQZHmFMJXKEteISUrTX6wjVQ6f5WeLEhqYJzgfnlUmagKoZ4VpxTUsuQBgEQSyhZA645bOCOCMlAlA3LYfgwfAJ5ZQBvBfOQF9NVMmGEn+Hrc8mdNAANMXYraVgkAA0NTDp7xzDMis5YGRimWiWIJKB4D9At4jFlzHTeSLZU0tP24KUlAc9Qv3cJTTZZFpwGNyVt9L1VONxBSRKe+p18KCXjpmaQCvfcCO7D6BtJ7CBZpF9LDIBcLL2EQ74X3JyWD0r+x5gRNIACxpBhm0JJ1gEWDyErMB3shh/D8pFCoMu8LDIO6aoYq2VZleGbJJP7Zz37GBgfBtlBI6FsKrGa1mVnvtBiqVkt0KZPtPbA73LyZBBj9QYoju1Y8p3AGwvXJF3FGyqbOJiY2pLAVvlmyUFHwsbAdsg2i1uIwpsxj0sHukhMFmxx7g8gQ7pqsGD2TVy7CJYrCPX2sppEqY4f3hl17z1hs35iOvD4B5ytVkQm+W4pk8002tEBVuMNeMZ9PNbTEk2Y0xclB+uT4NMC05lf5CojcuaLOAFMLokv6p4wiRjrGf/jryaEzzLA5aAac+LUaGuyxxvVYTak+5cIm0NVshZ0rVpjZdP2hvsK2Z4vDozX1dfbgwDnnHN/HXpWgvtjJlp7DeeEQ47uipiY59PzDyc4LC9im23okhEmhViyhrOfErzmccVq48xLfprjMEW9YYJRfrQwg4jq5HqkmpuOdGIz0Ty9ObjAKNtCWk8C4Fz6UNmr67Y3tiEJAfrIuhMaHJ/f0NU0W2irO/ALAYEazTXF9LDywaIBdzsUgXEGxNchcGkbvCD1RXteMEmYSXmoMNkhWASgJNvJTTz1VJsiCgJm705la0ipmZn1D/Uc/Vr7lLZl/+Yb3pa+mBga8D/xF5lP/13jDzeFb3hQ5aU0ID5rQxb6f0soVdu6y5/wC/EqVoajYOdPN/YHVEC4FSpFumbm5zp1vli8eaxhLnN/TdIVXKmbv+GH545/AQxirUT4WL7/+dcn3vie8Zt3RTp8DEfzBkkWQYtRyaKRS1gXnmatWOM/t8AYGnfra2Pp1evtyLZXsTbu7h7GNR1CDiMZb32J11hLW3HSm+NCD8YkJYaZQyhpwE1y6DA66hbvvcS+/Uq+t8VdjhmIOOKZayUyho3gmIwQu4dBkoRZBjPyTdx+9kSnuGsRRmdGfdqNWeW0Ti3E5nrJ2DMJueMNH9mYz6daVIThm7tNdXmfVhcuddSLQXcIHuA6O2VmuuzHr4+3doXwaRytWTG9oaDOsaCQOb4tTcTtiWnk2X/TAEdS/LrMLxS+Ox/HizgW06IFro/qylFYf4oiyqRU8bi72bCRhMoD0BvQZBhn4kYjGorQ6kkyuaWtlF4ck4iuqGMRMC/LVAfs2kPFybJfMYd3hSjC/b+fsGABQTIsiHaA1IcIgDpQBoTqkVJkZqMB2UJ300Bn8gvMwEr75A8N3zAMXxJUELCETefvw0NDg08/X1NU6DWar1dm87ANaI2eixciCjXtw245kXPfyaTqwGI4UNcO2S5zQcOxSEckcGxjjIduxtGYQXtrua7dvsbznqc9Nx/NP6gbC6nW61YV/jFF9+KNuVi/0XBg6aaUFik7Jnzw9riWv0EZ3mu258lWj7p3h6HO7xj/04fBLz0r83lO4C8EeDHl3/s4R63s/Km0+w9t0VvTt78z8zXujr3KMxoJxesb5kds3PNx40436mvVWIt53+DATXy0diu0EnmCdVH1R+XrcaXJM9y32BZBQdil1bmUZQDIrAHBi0AfqEwMKkRrADGfIIDLElQww3BfULWp/AjDD0NwIUNAd0Q9sCizIFAVfSmAmM1kJz5KEgRzGdWqE5yuSViijaFhHOHYmF7nAZ8CmCUBOOSr/tF6a2ZPzVXPsN/pT1XJstKDkIrgUcEwd+VFTT4WXsJNZauiQufCEbunq6prWkBf1lXGho1iOqqylcv9V4owgI8DzBK+/3QBLKNo/+pn5BbvI5rhUsIEMl19++QtsHd2IKKF6uYOqLqCgWDFYH4LZ9AKBqSY7exYPKVm7kOSyHM2TC6SCjGBSL6LPWe4oGRHGPOVX/wk0YEI9++y2Ynow3CraWHYg2EkoI7G5koIgyyEWfAbXJ4+IkW1QVhUihRfgn1hRSQCracz72CNlq9RsWplj70SNIRfo5tME0bg5IWTy0B4+Y+snzhgRPuFHxORIpGiPhQylSOGUqUh0IkKW+gwx5fJRyBeoNlh1uXHXEVH1XIuGNGJRD+33uVy87WBai4NRSsG2GRetYtoN/cHKIAXLXqVHOCdMh8ibdIlvQg6vLy5+VOXRWEQbHis+dH/6n2919vdmc/mBzPDw8lbIK5XgxP89ERlgMPi8c87d9vz9nS9/L7oM0AmEE1dJ4g1LnAnjrSdZ0nBOC1uTiHKRqVD9DJJglKCsODQrZwXvsU1v8E9tyIAyiD7++zgHF+3yJck1pyPl7kb0/AsbLKmIi381bQO3kDCdmDWu1o9TaG719G/ZprJCLg8JjiQnX/JQwQADB+5Jix/pwCcWRANaDoG5UGCNw+YEFETstyQrXeViJFMZMVtnZ+2HPuK+7W3jf/dJ81vfTgwPOZ/+7HP/9OXstddErr7Grq0dGhx4euvTnIaCgGPxrSxhQR2EnhYyRS3ftJtlQ8n5CEPhQXNQGsQclMHMKlQMgk+4aFZeJQENaocrqB4w6XPQhKHn79Dw+Je/pH/nP6K7diVcLx+LlH//D+Lv+9PE6tXioQ8bl6COuQOCRqhLBc38IFcl4wyz+ySzc0WIe2LQ+4dCmMTsH3P3j7FwyPxChLauSWtJskL6ucol88Bhu1QgP0BRreJ8pMCeHj2bFlT2QZ4biskvan8KOlDhpHw7iswSogdYX3cNuhO2eWjUaa1xLuow69ysxWVHtrNiWfTM1thdubrefCszBofb3HSVtJzVyzhAI1LRitIm613cH6YnTGzvhJxByCNJXLacHjCsxODhHYn6tsb2DjxghaOmU3LzZTsesnI4gvD1vfjEoudiWO2WHdMS33UpU0uZ5ZSemRjVo5FQpL113AxH8nKAFtjoT9pFz+KsWO/qIgVdy7SCUociZ5YphU/Qb8dtDlvTEY4sszl6WnNCzLPJ4lc1e9bKkhWtyW9lpMrG6KsA/AaTQh2lA+F7e3uYF5Bx0HCV1CeJyUIM6WnRYDaHG2x42bhRlwiXkUJziuiJg15bQ7g9gVDE6xvNt7UaNWvXFyJRmC3dCuNtm3MbXIIUtrR8stZatVK3OGnE3uv1pp0O9zO17nMMU+nxRvsbiWgv181hU26XXlGKvynrRQva8CcGP9DU9Io/NG58HQco/C6gG0yz8W3uxPcMdzB6/VCppWzflzL7Cm7fIUPD8ZWs0s5oLHx/Qzgajz6/q/zow+Gzz0v89adL2Y+Emw4b9dr48sbw71wbfvnLjJaWWTuUUUPkTNvVGoKuj85kHTiulfispVUfGQyQ6vkgo4pnhQxipgUAr5KDYsjwC0IaFjSYWPAQKadSrLFIVubllVPqNJYmd3d3g6s8JOB0Je0NUioAZmJUkGARAapWZ5upmuzsSoAN3gZGSapSfqk3qBpEVfp2AuxlUMAEptUeJK6MnzWyMsE8YYzMgVZVhAYMkNhfSI+QgnlEgC0DyOcpofITUgYeYjiGAH9SqfBUySif3Xly3lXmrC4cKPDnSj6zx+ZKuSTx8Cpwv9X3f2VKZATgJxYZShXJRkxXVyZYEggXVwhgqEFXv/Rq9Tgwf42UswjByrQyKWR+yxQ1v1SuoEtVYNonXhkITumTWO1lzALCoCjjEuSdBsCCXoNCgqqDmFnLUQqPaaTarCl/A5EsWdue3TbSe6h5HVDLmUS4O3WQix1X5LFCvBEhtI3fQCEPiYUNEHoU5Sw7ok+rQ1OxkEzSU2xlhYnsyKFEUixMyZPt3wc3YTY21CaXc08FTATaDn7gruE+hOUu5CyPKxnC8NLECEetavb/KGAonE0gkqgPRXEbAYMg/DlaPYFUki3tM1mmJbbNAjAVCPvCuWVPGJlJr0h+3b6NNPo8AOAOzXDD8u7auJEyR8ulfd3d3SAbXoXLjzyE95zkGParKCqcI/nCmgsvgCaYH1uWtkkvpLQTkQGmPZduufSBT3y20SqOchSwxEFtRClyaBvlCHgpVyJlnGi9bHGcEBZDaOzuBUkFj+l6DnZPFPUEKOlo6bJWA+VQgUioPmBGsf0cyLvWGHdYGamY2yw3gkwmWsjg4e3JOKWVY8By3TZWE4MZcZuGlYRdzKJVa1p+UgQGyYX09waYi8MK4+x1rdyN5JOzoZASCkLbsZ2wnFG7ELgvzuOfO/DMpub6z3za/cs/S3/jX+2vfT21b+/gbbcNfu/2sabGNVdcHmtuGdG8g+FwbX0d01v1nS83ErQmQnp6tkeWkGOfIAYhIkuk+ghvrM6WYAuqWLhjM02+sZhyyg55PARE5YiwxPPMzHIU0qlvClR84BefeSb3xS/pP/qv5NAgEx19ytjVV8f/4s9imzfLmsVCNXej+OS3QgrzikV3fEzPF2TxiyWxVYaJl9oohLvvUPhgOePqO4ft3owIzogP6/b6FgMX2QpBpTQkgvEYeOz/J2WrXqICrrx0uYDILy9IL+XP9shY+CwBNB8yo8ouUskn8dn/05P2+rNmz4TTWetc3OwkjuwtPPCLiW3PwW1aa1a1X3bZaza2PzIc2tqfDZX1xnhiRZ1WJzcPzTXUswE0d1zQwH0jTt4N9aVttLiRkJUpYAOJOW4If+G018TCuYgs1bCKzHHxiVUuyczCJ1YWF1kWvrBwEI2XO3NZjRdxstm+g/t0r6mpoaW1PfSyK/X//GG5XEgmE5ZpceaH882FTWfETzlFLsGm+zUNMhfJC4ylopKBamanVTZCgU3McM5BdsB8ZWhwCSb9Mg/7W1EEqMtpTxLDVECyUC9bDSwuMewfqCmY+8pKtru7m+rgN0fHn0P+BXOlCLiZQCIaa9aa2aAf3/9UMoHJcMupa9pChWHcrBwaLT1wQHv23v0N4fxL1iQHJozWrc/WtjaXXvUq49dPcp02pZVx8R0OcSDDvP5avW05YgKag62+Xepb5tyOibR9oG7k8021pbgRR4uMYtr27nHyKT12Q1lPjjVfmLC+f5uz7mTjyiuNGD5ypGf10Ela26fdI+8wrYnwpUOhS8bjtikuuCw8S0I0hMY/l0wN4K+7DEfl/QvOyezo+ee7FvbrvW5jc/iv/nq0bG4fHF7Xkh6XC4AQIw/fd999dKQi6eBM4MeY9fA58IFKeViNlUowglWOV8XQTQYZuOAgHKUxZHwABsaoGvKaLIw7WdSSrsDgl4fsfEWrXOlSCw/hoCiaYRZGGgslR6uplPVTVQcwaiGFCWHWzwR40TFgJoUDEg+FME34pf+BISgTbpwFufJeIhrCV5VLZVQxRJKRMoO8CwqoosgSlFaZnUjFNJKMPUL1MJF0JsnoKCRHqrsqc80aDsqfv8YdO3Yw7RSfPGs5c0UG5c+VYGnj528FddFXDMo82+788JAX/FRyKFLSut9wA+cHjxOzzE1aB97On7L6r3QpTabYKjFqrpKr6Sj4WKbYrL7EUJBWznfWJQyaguFWYbh0jh7MBcCi4yn2uBSpspuoXhoC5OyGSm6l5q/i5FnVqauavpqnOaw87Pjpsf7l4hATXhQaiHVKqAChtmBQRdvq0wT+WifrHXwFV7j4vK6/osHESi54DIM1zAdIaN94fSQaS6IfGOuxITaau+FquS+DAuCmOeUoilLYDscZS9aX9HAkPZpK9/fWrKJm/5FapDKBSz1SEZukGYmGjShVAhuGrrAwUv3RZFPJj/1L1iBiQZ3GXaQQNL7SR2y/6SGfAQ4KkwBMMuo8ulDMtQUStNgexmClqUXdnRgr/viuRCab58gZNuGu87SdP+/SLZK5sonyfoI+S7ZMLG37tmzZ8qEPf/TI9l9bKy9BUCFugvBuhSDF5Bgwo6DnbGMk63BHCyjCkWDUi2U5VS5QgBNoaFDDYQIdDen5kosqP4JxAqjpP2Cowi0urR3JawdH3bipxZfj8kcw/Lg4pwpRv5AxBLDsP7VFx4E5WicQBVtoHAeJVEesoJkMYJL4USiW9CFQR+afWdZcsuCKjcWae04oBKk/WyyrAKqDF0mQNjnn/SaCzvqy1toPfMB7+9us736v5stfsZ95ZufgQPg7313947v0s88JvfOdXtdKryYlZH/QMcdDa4gzHoaAPYNf6DaaRhs5UaaMTrmLldYpErZyQVf9WfkLFQjAcMuVIvn5JvnUWsCC4vup1d3Rkcz3f1D+1jdjj/+6toBaQ59obNLfcFP4da9uOP1MfFVPlhasRZXVTwvDao6NOE9tLf/ox+bO56WC9aeEX/Fy69SNeh3enuUBFfIOh36d8YK/7mp4EbBPaTVqxXpxEveAAeMz48zN5n0/C8nREbdYKEbCsrmipC1s3Gwma30MnFfSMAUb6+zdd9/N/nTxxRdPxR37V2YEJ361fUPeSBGxpH1uixbd+dzwpz4b2bOblcxmI/3pPaM/uqvtwx84d8Pmgwd67Ew53rZqRR0iBsCupmuOrXHON3ekoA3kzMGcU3CYjW6+5EQilrmsPRKLhSPRctnm0C+VlovCoaH+9a9EMkjGJU2YQPOK20QOn8ZDeMAyTmmuqY+cSvrBwQE7Fk/+wY05loaf3GXAw4muPWSfeWb0bW8xRFEpizdtAaOYhrAukEcgZ3WtE6w6NC54z8RtiNs1TNqqH8h0Htg5ZeWl8kGlEWAKsP0HXFwsHhsZHt3ek35y/1j38sZ0oYQfdVjmhobGytoUzLRi7549+ui+vuFtiBK2ZfdiQ89OfFDr3DeR7M2Fd9mRrf2FTi3b/YO/dj/03pr3/MnE7T9wfnovV5Ri+F5IxPVXvCLyylcguqZw2tWX5gq33WFtBCoh80DMzMRKIS9iu87AgI55ueOU/r0cvz6sxQvGmoJ+T9l+4H7rvPO8GNUqDEH18OpMU9gb+Fjc2IEEUguxUbLKisDNGQ87u7C+ZhhsrK7CxYJz67+m996ZfMM+HI3kwmv+5e5f/fS++zafcVZbRyfwQA8xHRggwmA4HcjAoTwhACfMV3g/wpXdMk8YdTELDlKGedLM84nqFG0HJICkwizRYNE8uSo/3X///VDPaobSLkYcdWKQANj4yiscPoNLM/lFLMLa+NRTT6GpIwGqHqap6pAg45IH6FL4RoqlOrR8OFlQgE2raC4wWOQhvivHRQl2p2Wv8pV9kB1kLo2ZsnHt6uqiUrgFQKJ/mFYwCQwNVdDPVVZUmYyFdNYmV6Y58cNq51X9MBNalr6rrroKZJ756f+PGJABRQIrxlI1hwIfeOABpi1EaXVbxuJrFipxxs1hFEe90+wIWBlOO+00PjEX4JlV+MUAjzIVtTZ/q6DTmPssVvMnq/xKFuXwXEUiYyKAjGlBp5MqCwzCIuBGneuVoOjZoNjq2eXYftiQWBumOFshCyQKUgtCTPoYhhD1lVAP2I6RFL6UnCwlQj/IdgQdZ7l6TK67FSNm7jtRLlKkEkhCkctRCBjjamPJltGa5qZ0HzZalIBVIPZrlC63xVCOD5APsDDAgECURPIPV0euxgXikuQo3Rg0bnpAmb1Uj/A+kmDbDANM+QAuTRfuF5Nu33hbKhBQOLwnjyQTth1+hmQcWo7WJZvhiWmlli+Yu/ZwxQZXKHHy1LFzTxvW2y++aLKE6ZCeiO8nKAN85ubNjQ11/U/f277qIlTrDv5FYSeRWPgGpWWPa2a50ddIhLWY6Eo9PEKP5hlJEa8wMCRDM4fLNYTvaPrTeDePixG14FfwyJsxlsfTt7F/AgNLZ1N7KKx46CBN1YFUWD+11XSoAhFpJj00DmuDJb+Rn0iHY7i59RI1tURhmz2Qk6MFXMjEnbGntBlJC554kphj4cY8iXVBnaqquvLFJhTs97RUquZNb9ZvuHHsiV+bn/9C4b//J57OxO65277v3gxE3lVXWa/+vehF53G3ME4EZLmQySGPzNCarQ3AAABAAElEQVTgRUVxxLlcRoHDm5LqqbAi42SyyRIizksgNQgwUipyKvcxf1F9kBLmYe5kwXBOgkRXUigO9XL3PVi47TvmnT9OjI8yUyGox9afYrzhxiR3n7a1w4QyTvNUfQwc/oubyZZ//nP3k5+O9h5m2gvo6GoefMD78IesLVvMRJyeGSvq2/vdjKP4arcu7GxotZCt+MtJRZGxqHXWufkLL9Qf+kVl/2VbWmOvuk5v4HQ7ieftmqnC2HIglFkB2TgrSc/J7373lNBIDzjjjtkz7pyz3EiN90985euJ3TuYQeChBcprXqR/cOAfvtj26U+97NxV+4bKp3bE/ZPzNFLGa6q2F/QXgmvfMMdoDY4ewLOFOdMLf2U7YY6RhDC2FoamVHZCHFrA4LBkx8MmrwX0wFyJVIRxMsjCVUl1EauZ078RpympDrDIiB8e6F+7alXi3e8sbLk0v3MnU8s4uTuGbKJ9OZcIV8JNcwJhdjVNowvHCvisZl+TfbIDW4Qp/K8sdv4wZLq6mwHzVyh7DHdpI5QowiACPGTHm97eQ33PHUgfGszoscbU7oPLEohK4tMYYFURkENqrFu7eo3rjgyP4TEBMsiN1u1JtyMnjCRSOAHMYWL/i/u7H3lw7COZ0B0/Sbz9bdo1v1viFLShh9eusjpWGo1NCLhZAnBDjmfBBm9E98R81D5iQYRFDMTwjpvFAyauuD192HMzET2mu7FCNj0R6+3X8VXrUwXotQcGh/AIkiucni58Trcf0/O/joTGksnuqLm/3rzbbMmnbkmY33bCJTl2SGudVCZ+wR5dzzhe6Kv/Mfa+j7yXFe9b3/q2suzgpDQTX52URr/HkgjHSy9V8o2qH6r5ZUWqktxnElEFLFDlVIKRUMQlY4eCEdPlaiqdlkYNMZE0ECcxygaVSMaRlZCHTyh/qBe+mkg+AUxQCM1XkXhUJgFP8GlpA6qWaWUSyWKudDV0JrDBYpEGaCvZRfBZNSTIrppAdp4gkuy0lF5liCvjgwQqABOOOnpWBpgSYEjYF5Q+lnopRw0x4WB2TyvwuK+Mr5KtBCmpiP1LiGmfqKDVSivOIC66lqDwFy8AU0FvzHNiE+nAQgUEdMWzzz6rugLIEdYr+Jkav5WuAB6GA9MJ1g3wUM1xIkE52q4WCgBTuPECu5rSWA+hzQSPKzD5BRZbmR3IeYjhlyqCuT/PMJGMBvKrFqvfyiioJgAz/Y/aAxZ9QWAwuxVXz4JPUaxy/C6oBAXAzF+F/OV8GhIBw05FgKFZFR/NfjfLLi5b32Sf+3/Z4P1IlUK4AxkLifR536AWImSg4AD1MPlxuAE/LKyHuNnCRHpgYnQs0thFZk76hrkaMzPmNrFTOgOHdlutTSl0MAoKVZGUJSpWP07WSdgYupQrb6bTjQEExwZYlNhQqhFSVObjplJoPAy5hVn36xYOK9hzpGNopzhRgmCAbcd+W0DzdDMcbWhpE8xUacDDCCs5dIM7wlm19etaFytrrgTvNxY+ERlgkC4ciUAo/PK5n2947YfK4l5brKD5hxkkHp4xyoMyxxCaa4FxSCPdL9caeRkxnEdmIice+cXNFVebCtJrRrro1HLsRQQ6MrD8B37zj0kxmMUwz0SLyzm6tctwA7XgzpdZ4nl1Uf000QO7e7cf0LMe1zUh6xkb7kOexESIxMWMhJPzMB5DWU6Sc6m07vQ4sM01Mo9EIwRRy64/L8u3YNjmyTDZF4rHiYSTZ53d8fGP5998i93fN/7Nb1sPPhAfHDT/7d/K3/rWxLIW48ot2oWXpK57hdHQ6CAl41i/r9/xu1HwXz0sajyEIe5ZxzFynvoyy1/pt7kfvrK/QmNNS8aUFfZVxFEsMgwlIj2HTcMZHCg89XTp+9/3fvKTZF8/tzkx2tm6WvvKKyM33dxw2aV6VN0wJFWSa+6aj36RqnjYWI8ctv/py/Fd2zUEtLJi6XreiGS3lT//j+a6td6qk3oy2u5htyx2JSIFaY97q5u5+XmWBtIcs6M99o63ljravHvut4cGOC/onnFG7LWvMTasZzk5Wn0VIbYQiEXYKsiyaR0lgGvuvlFntOjfe+R5XdzjsPWg9sSTgnucURH+Fs4TuZ5mPL+j/NzzrS9dEWmz6mIWSIhKSt11UQUUcybxu4+v4kRqvGQMTKD+xUmOmy/i4MrAtzMzgYuOSiWXIwGcw+ZUMvdZlEp20XZCYROT85JtYwJdRBPIJUmmmQw5qH9X1otolPYyQLQAqgdyQF/eEcNc88ILZesKR1AWyzgf+5CF3RqRAYKD42qoFPCHx+kfNguvJoL1NQsm0dOLPbYSeQMG/z+SwuviC0NuEYQgY+dgjiPkIg3A5At5Xki5smtl60kbuuv14VDnwEhv88mnb2i2cKwxs2QVE0y0J594oqOj3QxZ+0qNlt6ezjssjGhmLbdw/uM/injuTmvFF+8zrz6l/uL1TS2nbcShoMHthVKwYCeNwVsbxsq2xj0GstPprUVdS4AayL/lUJQYp7tevabXiN+s8qgJN+Q11GNYzmqLdAmKc7C/T1ZWLAy0xMEjq8vljnA41hntiHr9MXNPRNsTuWzY7SoWf5nQRyx3eSF0cdZYlkP+WAhf9LVvy2VRN954s0LgyRl3bLMhuBVpiyiN6nilG7u6uhTxd2xaeaMQ0vPLo16VPI5c9DnPzCzEgBh4kWXJCpQts6acNXJagdSrdJgEYB35ZdBJA+NHE+D9pqWf9koVgIpuU5GDlTWqcGXMtLyLfg3KBEthZVVfqdJYYehzwqShLcrXLjqH4Ax2kLeydnrg3nvvveiii9SOoD4xLvv27cOoZybFRsmV2VU4iFRVE6lY8UpCmTR8BRkUGLMCM7PkyhiWOxo4U38FiwVlr1LCt/NQOBhSWXtlOUsVrmz1UpVJORQLS89cYK7NNXfmqk4ZtJOXPmHoQWNSgiRzpZ8Zv7SNYrZC+mNZRkMAid2KGhkdmAE1ZDBUcljkBT/Koo01YRF4VWXlMPPYtgT9s3XrVsJsTzPdJaoCKyEhzISdGV9l1UuSjCE455xzAvirLJNJpOaRgv+423GVxZJMFhx8oQ30ik0yBJnPwvo7gegw4fjgNxFnyzYumxyAy5ZASrHc83cH2bxdZHzy1d8tyC15+GVnRLWVaGiDABYSk+yyQRLiK4Z8juEKHwKFathFzLKscp5f/oM3wOmVz5oAo7AqZAYev0L/R1rocWBGtKl8py5/ZZPoGU/Q2ypN8FqJGzMyHY1grYRlF45ItMzSMJqAEjh4pKnYhAMxygfOUQs0YjouLpkkkyT0ElGXWyQOH8SnEecDnyvmN11xhV+Y/zko6wQOnIgMsOquK6+84o4739/oDWVCLVBeoBqm/BhCY6aPyT33IYElubLFYeAWKH9Nx3kPVDK6HzCRoQIbkV6gTyrgLiuiwXZmi1oNvqcEpRlMRk/xQGCrNZBzUYRFhAe2V9b5NygFCFnd4Mkc87SGqHZam+5t3rB/wjk4VNq1a09Lx8lwv0w4U5yvcnOG3I0EVz6UEf6xzC04nrOh2WiM6VhCAzhSbTYVqIQFbS3VwThLKsBWqArwLEarTjoJZ3zSQb/3unLPkdwdd7hY/D70cGqgT/v2d/Rv3ZZ77/uKp51mvuo668ILE+tP8VIJ+A/Kpb+Z2X5hUt7R2Sgd7fe2/JGHNf04TVOTkL7QdbY3skzB6OdXP7I6+SUXi4Xde8oPPVT+n/+27r47UijE6FddK1pW5sLzQ6//g8TLr/ZvNpLVS8pRra0oqZqgSDi3PRfZ9ixaL9ILnSbLFiccXfexx7wdu4sdK3cN40yP2cRq4nTXaR31mMjMBrnUx7oSMledHL35Fu2669zRtBGPeQ2NRkOdOlE8S3vngBK42PvZPrHMhKapTKWGYCDrHRk3+jPi/Lk57sYtt9TbE+LmJS41lyaIHYv0CwpJLmrtORxz8Pwcp5NgM2a1xaqsopowRVEF83fvmJMt66MFA6/ptN42uL3QSYTMvGPzgmEzuiV0OcifSgUb0+gSQBbdaMSAVWYbwluapI8azSm9PurWx48SXlhM8QCMNCQU9hA7+JDxNiuEsCKPPfYYeMg527nSqIyY/WTKHAAWgwEGbXmNuL6Q0Bwlz6huElMw9FXQkYAaIR/Z7Bk7IMFJXqymIWa6sOM7B73RnKGHkqlIKF0Ak2gjyWdpRWWkj4ysGOaeIX3UcIqOgXl3puCsGDt8wZ5HDMP66ZY39xbjX3nc/eHz3pnt3pUnW6e36b6URUrGNAv/zxSS1tYVvJa4caT28nz/3ZpTwEZKOCFObohc/jrDi+bRzrs7OLsdM849z8OIw28wIqqNmzYRhB7F0vvaa6+BHoJQxpdeW+c54djXy0duCtuHjJMmzFUTIjQT0ZlMoHLknKePvGnX7jfCFr7xjTf7hckPtHWwgMAmoT+H8VAMLawjifnKa5B+1gCQkEt9QrnHQxgT2ZlMTlCXCvCrAqSv7GeqVlOMr5Xxs9ZOJBa5MIpBUf/zP/9DJK6wAqHStELwn1wZQ3jmCREIesV4zFXpksSDnNNOEtJwYmgLghs4qK6uLsALyO6ZlZKSAWJdQtKkRA+kCboiCKuYylajbVOcDGwMS5A6QUOTA6U3vQqaYRdAV3A0+pFHHoEfPvfcc9kpNm7cOBOS+WMCkACVdincCCLJ293dzSsPQwkqss4Qnqfh81e3oK8ocmk4isdqcgGVagIB1m06X3UjQ8l8qexhBNNMjS1btgRSnmrKJ40aAnqbqURHqb6qMm+QDD9/MCdVNirIVRmggTNfQQYFD7pEyBh13qHSPKEyy4LCVAcDTAeq8oPaK7t0QQXOmpi1BXadT8iMQO/AlfqsiadFgpY8RC4tSNNqmeeVeqHoKj0FzJM4+DQrtLNGBlmqD4BjMAC7duy4Gu2sf36VDUcIHrUKUY2/dSlOAGUunB2sL1kkXjhcFiuVBlpSmZUKsyDZ+Sf7v3DPfnn8EWUb1BN3K3DEB2tqiXGKZdMcxLHn2OHIcK+TTZMbHY1bzju5NNZwJqeHcMnhkxNkltGT5lGt0AqijFXf5m4z6xWowndwhjVWLZUs1FXaQtMt0BfUKv8J9wt44mIpqJB4QJBkwir70QIbC7vPnPuz0KypC7/8muJjvzaHR0tF+8ly+eqXXim98b/nOXEZ4JdedZXmvHv0+fuWbfo9GEUOYBV9N2WilBe0xsJY7kBCrZQoyfFdkJJLfccwMJaxYQzkT4Rbd10NHhj2OFti63IjanQEee2k3tcYPmTpxQm7ZSzfsW8sgXPweNh3iDU1A6ocSh+FwXy3MWZt7NAMbuEulYdgJbhXOxyByxXMwi0tlyRxHhiGwIItcTHQxsCbt7WNbnuNjAU6T5meajpUWfdSJDtao4/Z0L6hjhWhd71be/vbnd7ezA/v0B58yLv7J8mJXPTRR7RHf+XoxmhdjbF2vXbeOfr55yTOPNfoaIf3ULCwT7DvigqGKc1g+GWqT2jD1Ogcu5HxMUjkj5CfT0XCKkCK+xOVNJ5WKOa3bSs/+KDzi4e0J5+MHT4SdcsxX8SWjkeNVav1G/4g9rKXNaxd63LK1y/1Bc5IcUs/OKhnM5MwTgIIihhmqZjv6Yt6Ttiw8o4e0e21zdqyONAGzZlleGSBBQ+a6rWmetNfmP0hp9zj09bBHqzKhUqG+2WTZgflkxpHlQaDiJ2D2ngJQwk9anrNtWCaF62tzyIBFd9HpJL0CBNY5aCUQg1NVoR4KViVEASOooeqtdrfyWIOj3NRES64PORW3FKcL9pRYXFdJFYxy8rbdshlhDVuPMKfhGd6MMkRLJ85BlzCBJqbxsSlEsbaibAn6t86X1LrwzATsJkx04BFuwVYkObT4oPXSaBlCMX5M2IrFou44S5Lqk2QhAzu8XBKNjDvucPpaCzGnVJRMb7ylEAHcoGNCjH/+PiYmVjVl6+tixvhLJ78zOGc+C+oqY+1JPXACj0ArDIQNLO7uzsWi4bKUTsd7R/CjNwzQ0a2/8jq//786lJ6b9cZT3adR43scEM59+Ee69Ejzpq63O+dEdvUKvz8aM7FnwKt6cs39VnXrfK+GO5I1//xwPBXaqPZZKymJu9OeFd4yTeKm0F3NO49Hi1fcVnthReaiZhCcQWJ6jR+eZ2MEXBNPXZBaOVdpcFP6um7TO5SksUAXX6HV//6UP27v/iR93H3E8Y+XV1dQesChSHlMFjEK4oZ7oiL4uhDxYgG6WcNBDQxbBJrkSphLiWDUvKwzlAU9IRqyzSdj5D8CznhBseInpOikLZAlCsv0ACAHTWerjhti/VmJeRzwRakoTcUlRzEVAYUzCpG9X/l1+rDs+YFfh6qQBVMAvp/1mSqFpLxINxRh/qI5JVfmGcINSW5oBzCxMNWUXIAHpEQdryqrypcWRdDAA8GAOQCNwgonjwooTJxEDlPAN0vXBNMHSDBSHDuGqjAQLqaohSvq+BHrFMJ6jxlLsknAKOx1fOKwM9Z0KBqGkUYjh3kDyIJsO6xGkyLrEwwa3har/I6LWbWXDMjGVDkWTPjq49BLMIBdUZEIRL8PJAwTMrWTI1R5fpASrKATjNNyaqsVB37V/ICGHgGJVigqizhuMmAn/UKUBkaxp1JQeC4uVSCxQ1ElYVXkwyw6V41UxYHDFtYNRVVnwY0YMXhzBTnV/knXKVQoixDitmUkiZJbCLkG8Q5ccojpCxYKoHfHD8sH4/u+eq7n00SUmw5lx0cOZiFYknD6drZ/t266fVooU344hrpTffux6IZd9DZ9HA+M0JJTZ2rwjgo8jXAAHUULL8iIGW/lornftitgqWSuaDCx7UtCsoTva4oo9mvpXYeWO6jJtB+OsDDJEy05YSk/cInw4hBue3bf6iluYkjWuaGDc4H3pe99eujD/7qmWTikxddKF072blBbSdu4MRlgLu7ujlwtfuXP7ny0tePF4RnZHg4HV70DaF9t1Ie6gsIvqGMHqnz8FkLYxyPaGh6ecB6kIhlhOOM6RKkts5Z30zeMRM4QHXbraevqP/8usT9caOXxLYb7y2f8mzh7YdHrw2b0UibW4PHLZ+Yq3LoBEMmRx4nutapLU6hqPdy803KGEXFDAGK3YBImbCDwKM1VtyQ/OLLWndQgmF7YGTL9qpG+LyKeVZl3UubTOaDj78Kia2w3rGyFk74He90h4fzTz7l/OAO92f3hQ8eiI+NW798WP/lw+4/6NwR4Szv8LhM4rQNxpln622tjaeeatY3+oewcQ7gL+eqQFYj6SmWDTk9MdlvU3NdGs8Hhk+uknI49vf8zx7MHjq8trZOO7DX3bZN377TObAvMTISRufFhMQiDs+znDo491zruuvqrvpd9KiwpX4VopumfCl7qiKJX/jDBus2NuF8TZY6vzxZS6Vkr4ikoLUZ/TcnJWpMueQ55TMuYlcyx+OvDqov/Nb7wE6lVf0x9Tb3X7X9s7UrDoFdkzDr/lQOcaO/fcBNO+bhMdZeo7MBa1su4DWS3d1uZ4e2/zAdLUe6ZdNleFy3uSF+6nruGNv2xK9FyCkDoXPmkAD0xDwnyqZqnOuvi0XuQ0/tLIaa02ZDiRtuMVa0DE6LRLjxqOyg6Y1asLiO+OUPaQW8PYdN20Hv6Ijlc9Fh5octbgN2UiGzJWU0RhxkTAKejO1iHjgNOkptGPPlR+Lmev3sZ3497bVyZmZBK/t4tvjgkzuXr1zTM57Y2K7VxUKVAyRI7ml9GU+rDQ/mtL7xMr6QuO+puammu752OXeLTAJHKmFTUzEWDGaNWLdMfZIUSHyxI989ruUcK1tm3OVwQq2dP2Prf4Orj597/YQZZ3Ojz0MajrERJJjbhmMfu9e+YIX30tUIFjkwYiLZRci43XxPrfdgk/5M4pzR+IZS4flRLB7iXbrZhttz7G9imSfOtW6+NnHplfgq8TXUx4ACDaSwEajoqGQywSk84a1DayLtX3WdPre4w3XGdG4sjq1BEd5zpO/2229n533Xu94VEHzTenja62SX+OUH4ZkBcikiGPSGK0BIpJQkM1OqmEpPwphUqEg0igFUxMwFyaxlkhgegwcA4JqgSwIAiGGq8rugAmetpTISvR/UeXd393EZ6cpciwgfF2ya9r3vfQ++FA08IoB77rmHGDAf2S6EsqoRiQMMG2EYvICrpGRGDcNv4hEToMPETKOyOsIomug9xRbCSsEMq54kksGqTFxN04ABdSg8IagLhKABJcPqKG6nsgQSVCJD5acTJEzP8ADM3r176ee5VmzWPRTLNGehfaWaSUbs3kHs32KrlWUEDAASMcIMYsW6OgtcyGIQcCzCQICywC4YYGpR+mTU7C/2/JqlASd2FHMQT5xorbmEeXGQzj98iyiTZZBd0ikVoSXRgfhbKXumHI2EgpMQ1L0QcorJFHcw8iJfZHdl35QdViLlP9nkCEvU5IOpMEURJbwz2djiOOLY1mEV9VFvVJ9IJxs6U4ZXhzm057ZEYrWdayh//NCOmvqWWG2dABDiFIyoPihHKEWpA1zjV0hf+BpVtQ9gRcVTAPCXfZ+HPKxa2MuwVFZ8PH6QSv2OoAbIQ6kaCCG6VU6/KwBFLn70//kw0mrhzDEPdIfGR+pqU7F4XK+tD19wQeikk4cefHD5j38cjS7gfrXjQ/nipzhBGWAGgDG5+uqrv/Clf70sn4mHkljTi2GAi+Uzh4FxYiYuYeEq4Su5A2kg7bbVCoPJZUZlbhJ1fBKZQhBZGF7MwlhaJBk4iM3knIsbf/S61vfVGIcZcTUDDGO8K/zLzvCTT5d+8dzo34bMxJltFveOLrz/BVlB5sa4ee7KZDR2ypFxzUwbHPrFmh5dkq+SFBziViek4eGQifU1OhscYqHQzpfKa5utgZ5DB/bv50zFomWWCwd7/hz+3Gf/X9YUf+mV2pWX49G1fORQ/qmthUce0371K2/bs7HR8ej+/fr+vdrDD2na1+BsuWrIhtNobrbbO4z6Or2mBlNkOxU32zuQtskcx/dxZ6dbLthH+oT65iQn1hXjI3pfv47rEUo7eATXqJ3jo1axHOKUL3PU71yMT7Bwzq7s0i88zzr/AvOccxvWr9O461U6XtBGLRiTywZxfr75Wzj/Vz0cip5yyvi6U2u3PTOVUsChqtJZm2tXry5zWUjcXl4X4m5xtcJOJVvKv+imoOa7/RvYUIUphRUUCeQarUZ3wR6vqEnkAruH7eESR3+domd01NI3Lj28f9xNNCxLveXm0U98LoymRdY5ukvXY6HYzTfpKzrQw3Z2iGIKOgkiVWksqxcrzmgtvaQ/d3h01+4D4baUl/JgZXMFrjUyHBxS454dFhfvz45B/GBfL+xSvH4ZToussOmW3BJMchiTacy1Dc4Jc1S+NqytqJ+0x5G9ouJhG4aWBWAIFHoDvR/bQ8CPVSQU5z2koYEkI35yra9M4YcZx/4JfMuLFCWk222pBa8G3Kwr5lGIUUNubnysZ98w5q/KtlOq9rS6ppaDxZCWnvBCMRTjIzk5I9QYM5qiGIcHajE3XdK29iG/K7ckte4GNMSUCeRH2z9a9A6MWQNZvIt5RimbLmqn7X+kdezIoWTD3ae8TKMrMU3Ak3bMpK/xRc/haHTq9xxwHz4Ez2Cf3+mtbETsZxxJd/yw9LXfqf3T5davjFQ6djZtx1xLJpCjNxmR96Re8dpyJDlUzKcK+ZoZ3sVgSBQq0mTQssKMFlA5BNJuxTnRIJDL1PG0r3z1q4wa7sGu4OBQFQ9lohtZKDbONb6VFUI9ABDA4DK6u7tbMc9LSJZVA0MlPCoMSGAp7eU5bgkkVravM8tZqhhgwLhUcVlzlQnrBcyo4xgpuA7CHDQAK5iJ2J+zh7DIs1KhX120Go1mwl3D1cC4MlK8IrNQpgFUgaDhuH0VAA9GoXnjAU5Gnw2XElgcZmopFVseZHyRAgwibVHGKSzvSBDguKiLblc4OVe9NJm8wVdeeXitjOSVNvIsyJAhKFMFGMGZ2Ss3JlXvtFzoaUlDJKOPaAPEIExXL4KRZlZ2dXWRHb6XhwWfsQvWeZCNT0v7bN68WW21lZ0ZhGdt76IBAPGQXCxtmYsGppqM9AOGuIiflFSimiy/gTTgFVwdWgHXLZk6x6VkbsA7KAbT716fUJRZM7kjTWksfLkkBs4V26tQlRVA+/suEWrGCQUlJKeJm58Ul1poZhYrFzNaW45quZaTjb0P1efHTCsmcmYoLfQl4QhwwHOrIiWzAMRfIRX4QzJ+YBIEtqlkFfUvTZCC0RdSpdTtb/FQ30qtcrQCMX72e0uigN4Hjb4RQQLZyOovMolktsn59507XvKylx3N+78kJIziifnQ8ddcc80nPvmpsV2PNay5DFVQGWthj9O8GtotlFbwv+Cb7egcDM6U9fGcVxcXG4ZU1BwTrhKtoz9MHAY2vYgnDrGSYb01/PjrWt6X0g95rlnYVZt5JOoV9MjqYuoC7EQLG8O3FgpN+8c+HNWd09r1iH/4L0CB43aUYKyfCOyoi+kbW8RZONMHl1GDWTTAcgAYHlhM7dlJDDx7ufj0GsHXrWugnCkDkl22JgqjIyP9/Xg4XHncGn8TCdQklL70W4f0LBS2ulbVdK3SrnulTFnuAdvxXPbRx7ETdn/9hPbk1tD4mJUvxEbHNP7t3MH6AUssrBaPUL/+vIZ7FL/SHuqnccNbJpIKEUjIGW7Ni/vpWCZ4KxtGOlFrrFnjtLcZm043T14TPufsJAaEHPgU2Ejil0y5FO+rXqdi/O5RH/1g8CNASHIeNWjql9djR1uKl0R6R2viL/48/WfvT/b0iuSOWF0fX7Ey8b73aC1NIcNY2YBxvRQyW21Swgt/IC6hjdQiBQVJgBhM3TCnVJSEKeY+wOD2ZrzejDGYdiaKRmNcULHA9bsR64leW2+LnnrBRU0fi6dvvyP//HZ8kFvrulPXXxM593yjrhYREuQgoFIO5IUKLwJy1b38jpfdI8PlkdHx2hYuucPtsyc8rQ1ni+cq/A+78VAIrS96S88uZIo2PGHZlePB2EITz8RAi1gs2dzt2xAth0uF2ijc/LRBEgDpDTqHDlHQQh+j6AgIIxXJr98/GvEkRoFAmuDT0YAMOtDpPVhJ0wbdbUnJBWxUezTNsSGKZbuFToJURbgzMZ7h1pp9PdB8Xi6XXVljp8dGodTJhAiDjiUAavcN2QcmMgbnIZpPwiNmdpxWO8moubxWz+dytCjhGwD3TrA3mXnXHMmVuhoEOAUJ0ljK4eW5fifvWaN5N5dJF4YOFYqFsx74N+5Bvr/zzOfyJWOkL9HSJYeTEDohgMD5NhcUhtH5GmMFL++F79zltR4qXrkuMlHWJkqn3Tb8vVPj/7Em8sOW0E7D4CbmtmzowpbOW4zI6Wya3JtljXvYdTNPJuGY6go1c4L5EwT8PldiC3II9870BE9vvfVWsr7jHe8IdIBTJc3+F3Zlmtnw7OmOjYXOJuOxcdPfIMpR/cFn8oHEKjA90WLft2zZstis4kqQOajONM5aiMJnPgWBIBz0/6wZFxcJ+8EzT17mFIpoEkDKw5ngOhglMKuTslANQFKB4HVmgdDTMxktlQwOHIkJPLCadIwXKamCuTxPgTOrIAZel4cAs5Jy6GfWVZWysqjK8KzlLGEkdUEZUCCBIFxN+SQmGX0C9sKQqCwqMsjOokeC+XnpIPGsgWkFqjSVG9PMXNQIMigNKqwvI6X6/LizcmZRxATNDMKVyUDOAEJWYzCQrwgRgEFZMrPUzNwUKkuYFqY0RDm4bePECp9Yk5FQqPPVaMJnCkqmZV/oK+DxLDTXbzc9M5FeCixcfrvAqNqR8rC/lLl/iJ1T2DyhQH3OUu2ebOTCdsJjyvYpuylvhH2WTtKpSEkm6YQIkDR+2slPZFJsLJFCsUpKGEMx7cTBrWxvrpGLJ7C+C2MxzJEz0umWT+ROEqcUKYnIKjNX1en/kcyS3AduMl5qneNhNjGVwHCBtOqHpPBHsEtszHLeGFAgeo7JLs0V80C/3UKJe9pQ7wFrpJSYcNY3aEeQZA3004iTVp38H7fd9slPf3bfPjmPcEwZJ/zLcSiD3y78Z511VmfH8u0P3vGSTVsKtlH2xBAalC7hR8o3JEZHy5KE4j5va0N5dIpaksOCul4T1ca4rV0U+LSAwUV/guLeK5YLr+n+XK0B92sNfrcp/b16047BNOR1b/SukY6/HArV5zZFvrpz/NV7zFPDlrOuBRfT0gegF/UwuuqZq1uCwffxSquPehvbHbMXAzA0IGKqzX080PfwSz5lj8c45FLol0yMtOEBOBSVrzVrtbZRd29f3yAM8ELRei7AXkj8ZKMqZimlTU5iFaqpiZ91rnbW2SLjkjWg7BWyhed34i6jeOiAfuCQmc/bh45ouazX22f5exLDhjtlbyLNEA3X1g6XHc6gWhz5TMSN5W1eLGau6NK6uzgipjW3PHPwQM3JJ5126RbEHg4TElmZL5nyK5/64e/UIiLB4z7+6iKLDGgCkkBz+CS6YmKD3NJMWQU9LVkTuuRi/dZbc7f/wH1mK2a8xuYzaq69Vl+3Tufm3klBWTD+QQFLEJDa/ScI8KaoQ/Z4GGAIGnQsPn4KdzFW0vYMatzJNJDR45bXWmPmC6V4NLxvxC6Uww/tL5c6U6eed1HilNMaCllpL/cSN9bjiIum8lB4ZUUqRgGwgF8f5r179j62e3TPYHo069h9PcbwYKFot3WtjiSS0EzhkIXIh9t9w5aB22caiTBIeGPL4EazItcCw/oiFvLkGHBNxNHyY+Vcv6GdXglSJagzwQu+BllwogOBSI/hYwbyaHYGWPqbE7MOh3IJsE8sr1H8m/RMUNS06iAuYXEVr3Jw/97DE85+TrwyHP0HJ4xw/+jgsuZmCFMluYDGfX77Ljtac1JzC7IhzjYPZxHrWfVxL2Xhrz505OAR6LZ169Zy6GNAdCcMDEOJ1E/K9EdJIgkXsHXvJ7uIz8KxlNO86uSRQ5sObnWN8O7ffXeqfUMJ2DmTH8YBAesNd1MbRXhhG1m0ls/ZFIXXsNq4dWjMiYUIemNu3eOFd/46dwsuwOqibndjYkUi2hrBuEFaDGfSGo1KyK9eBeb/pcfoN1gjyFPIO9ZGXrfveB5SktdXv/rVc3Xp/2PvPQAsPaoD3T/dfG/fzj3dk7onaGakGUVjIaGERDQGjLQmGQMLLLZZP799i732Pvs9+9n4GYOfFwfAXmOzRgSDbaKIAiywhEAoj8LkntgznbtvTv//73f+0/3PndthenpmhGafS6O/61Y8VXWq6pw6p061FLvCZM25yKLUdnPgQj80nGo5NhPQC5OtIgQAVkHoh3gb1qghC3uA8CeffBK80gSgN1lIxqHYKuoNq1uFRwFgQqmwlPMgDEexLiHRYlKEkOOBBTorbJxH4BYFgwEdChyx2M6FG6H81Yn+QqgoiuqU+20OXBSAixooesyBJjMybXoPzdJzrW6ZLKxRlHY+DHAIjA53+DP0aDh92JwglPYzWDRq1eeqYS14UKWhCgpk46NdnGERCBsWiiJZPzk/IlAHFM1w/LCsehaDfyWOfQpcBTd0p2DJAv2U71WWfiWFrDDNTxbxVgjkwmQMAcsm+lYLo35SIQwQx0iJZCLJSzBKqgY0lFJSsqPDEgjpo04OZJvxVaNk25yL52+w+TW3RwKCFLIJ818geEHbK9WWjHagZgZrYperEBB+rcyGJ7pdgzt4SzRILVSEABPANkdbwEoKVJQl/5Ms+C6otxmGwM8BzYKwlQTQyjNaJfVJg8MaxQNBGTDJkhi4HCeCSd9IlO2m4aAXGeWwhqaYX/j8F66//qf7eWH0UnPPawaYefXa177mM/94zxt//QOVRKzicllXTnIQzjACGNSJ8SoSZmPlbUKzgiJ0jrcwObNA4gpNaRTkGEhGEboPVjkRNTP+kZ1t9zHKxd2Z/Oc6Iw30ZuF/iGz4e7vGPuH1/6dqzJgYML/+xOwVsKmO3djew5PRghfstXAdqhR61lEmg+JRNmpfPdCwRrn6KOe6EyWYeLFFBKc4OzGV6ejg1EXM3TQMgD1V4P1rqyeZmPL76idmr5bbj0IsSnU6jU9j51lBeE4TBPOZBgaWtc2okYnEX/AC2FTs5JyezkAE3xuQ79ImmJ5cni6Gi8ufON521dUUwtUi3q2aWxFkMaBEv/4v9x0rlneIJD+41iuTcZUu6EvpT5nsGLUanWgcPYbSXrS/xxpYa3b3YO9WOxxggjrmul+O7rIdzjXXJAcHzULJQys3kTTbszAEokpyMR17MJqZwCFHPXAOe/YAG5yDPi1LzXTgd77z7VQq/cIXvrDmB1d/PfsEV38tc0On1ag1WKfGCo18zebVobgTefBo40DS3tTZ1dfe2RGz4lGzJyF2npodW/5KOIfmLGf6TRievUfGTkxW8iW/xpOv06NGLJ1MpkF3Tuk5E+JaL4rNE+OjhenxqGWVeYI1Yo0dfpY53t3X76Ta0deNRxzeBGYG9masRNVvR74r3FwwI4Kll0qZmHSRBmIVlt4gEBqLjVntACODapbpkRJBIuZhliEEQdMTMxQjCgi9CQ9r1dq6eazQX6chmfsNXgls5mU7tudHvPUd7v6nHu2KNqK+G8m28wz4E5jYMXxIwC1btuZqXtmIpuIx9r2r1hpPneQkjLfczDUZPbeba+N4wau5Urtjub3pAIxgXgTDJZUdmarPNCJYlUe0G406hXrilh9/ua8y++jaXU9uucWKxqIwuXLpQl6ZqjdQOfHjFveOKxvarVq7P1F2x3JGfwf8MSuqvMMcCd6gSjgJsbjv8Pyy2ZvVu0FKJChg0syw1fOelu6ZDw7+QoBCl0Cd60h99KMfhW1DvXC1+/cZha/ih4LRnBEk4RLBcm1oTn0x/dw+QIhBDQCJ7ijqDPjZfZrROKyfLYmVAbRHvEZn6sUZlSKGaVbhae6fFfYJh0osGqA31bFAwYrcdtttvGuAPyyNopp045eEa9Eaw0I0W0ualp9LFr1YBH0Lh4NsUAuhIpATf9iN51P4YhUuHhbWAgA4EoUhi2dYELp8emYfix4HEAvyrSaAVZe1l5ygH99wY2J8AYOTWdAVZj4ESVtEyjBkNbXO5+G0Ebxihad2eF3Vr6F1IQPMaHLtnORMH5xekgwHdL6YJf8qtN/73veo5eabb9bjGMqhWPaOC9KEJeu+RCLoIg6RcXTIWY+0nss2McqwbQWUvMoljGFp1UwnABY6+jT3O7+FBVsqyeR3sL9B/TH/5Cf/y2DPp9T4uV9zSbR85gCoyC5usdzJ5V6zmsLcpuViDnYOXUSKGlSvOc74CqUc1s8PiQw+Z6Rq/XEeeKjtnKtFqwuqxXu6sdJ6/S3ybiPT3Y/p256uujG7p7e3p1OsQprowN93333vf//7W4G7FH4/rxlg0Ph1d77uL//yo4Xhh3sGbyy76D9jShzDLRjLEZ1+OElOeKKOUXGxs2wWXXMs767JQmBjTlYkHsWGGESS0xhQ0jK2ZvbHDdE/nHkgYdaj6F2CQAF5KAhfeSjpF6N2stwVeXJysmHL06IWFnk2dVKATB7cCsdUMUh5qFTEuXoNNrp4PZdZ6U+XTTjzerWaHx+JJZOxZKrREAtADa4bRs2JvLwZU430FPK17++duGFbt1gUBsuCqaPFrhCG5zhZMBUDAPnIEhP4afG8V/pODgGC35xfeP5IpcJPtq6G5Rw9McJU416wytCC7KwIdLu5Zk0/hh+59dUUtfrGgQn0poHC6oMP1e/+VPTIoYjr1uNR98ab4m9+vb39coOXh3UJkPVA1jhAHZ7EsH29N4b2bHsEkS8KpRbXai3YPN60WT00K8jJLNDdl45iv4dW42CIQLKy68AGC8lrmURhpmv/mJerO8en5L7A+g46UN68Q/l/omBVMK0csVEnjkYjExW3OGYWGn5vzL9mvRgqmhuvAB78lIlbAXRLJ7FsNzPQGXWmRwruiVPZngEnIxc4U8l4oPkvUnz4MXhiv561mKb+NNM5k+1CTxp8SMv1YJe3keQluvxYzTbWpksofoyekvtjlBMeOdMh7DtQYwidQp6WJuC0l5i2xELRklG2KAxxBW5p0P1cxcO8PN3HbfWBLAcyUtqi6el2OG2qYHT4YvmWr5lqr3mdndEDV/feM5g91hZ36sa6XOMFXeuwkejEIhFOMuBsTcxKYwMr4WVjzuV93sm8f2oy55eqo1XU9kqNeuPkqdGnTrlVpy2ZyvSkjFhw1DKHnAE0IOfTo36u5hexk43lMNfvrRdueeDjIMcT17yyYMXchsjVMYrJjWtukoL8a2Ll1++Mbs748WqJlbQedWbs+L6pxv5pMMTE7Db3Gxo1nlNyU1GnM+EnbSzbz/H/1Fkql0+OjPT19CTAQNnuI1aMV4XnekdmyxIdRbfgtA+hIz/72c+S8ld+5VdA5kU79jkI5OkISGeFqvm4ZHG9gAsEECiKDIqzAOpdqq+QSKOxqbFy0zGXIzHzcSEDDIbrCQINgQFmoQi5uPOEFw4chpYld2Gli5YMtExAJhdzDZqYJuCHH0Y0B/CI5oCfyYIw8DxpZQ65kP7deuutlDM4OEiTYVEWBWnlgXQarjk9E5mRgplH/RXeaYWd0FzCefoZ1pCXO8+iwux0VCiMDQNX7WHEdRFm6QNdw41J8ZaDG11ym8vnLPL8zlXnCqMKhp4foBYaEDRqoWSCqQHKkYAVhvSMoALWDM/yftAV7GW2nmvG5Yv9XykWkTg9rDoLz592sdqgX1bhHZaJcSvO+/bCXArdB4i6jQc/2NeFGmTLCsg80QKWzYvjHBXGClnElhUkkQwqmZVmStCckzTilXKrvDcj13tFgZmACHeNDC/SaAQZ5RBZSpH/tYAALMkr7gwcUyA14iJ9qWIOcHyMofC3cj5wpguaIgnkXxCr/YEIj9za9q985Sss9a95zWvOzHpp/HpeM8B04Y033Lh2bf8PvvG5X/itG9FzrjSErcVqDm+DIgHGaCqmURk4kaFiHKthzcCyFhudSZY8IxU3eYO6hnlIQS8Z5LhdQuOdX408jxFBWYMDMoiCfAxxFdtNpp/ETHSlXDPGiwa9I5RztbIOlicgo9l6gYqFVVfVsw2y4hNWuPyd/bY1iizL5kFUq2RPlwR9kOoJE49mr8cybbo1BNrGVBGr0e25+uzuU9Vo2rusy+pFbCb4J4zKJeHOmMzzEAvs9PO8Y85AtPELgom+Vb+MgqTSZPr1Obfm0FovmcxHzZdyjn8pWBYjDCI/8oj7wQ8mS3kWvXKlnOA05bvfLYxPpP7rb5Z7ey3H4folCdE1OFVwh6eMCscudfvwydy6nlR/0jVq1e7OrmIBG8EhtOcIyoqTg2ns7jQcuRB0Bqetuq9TAIF6nN/V2Y0xwKeO5abc9pN5Xq+1ulNuW8xB5Zid4MRUo+xi/ZcLKvy0PRG98iywm42YW3rQiRVQZAmcd+fZyVrMWMVKdm3wZjwrNeVgIyLT0zewvgzLa8ijvhW3gXS3AZseTXcOZGrwW/VqxPfiHTDJhIt1aC6slutugllRPiXPewdU7qmTpxgXaFOlvQBVj0UgwqC/Ub9U1oKXZqAgQ2oPm1hqeUWbpvfB2LaXUBc0j+fQGonQKW3RRkcT+zffQ6f/MjpKHCuPHfi9sVJuyPqTPu/u2MAk3Rrss063nfHrb3J6f9uwOqp1v3twF3c0KGhdRnSyeAu4M+F1me7oCRgGUdLiSaRj+fEjk7WOLiOTTPa3gbxsrsouBqVy+FpxUbCeKPrYxmfpmK0YNz39rfUTwxPZvoMvfjcqLHJg6HmZOM8su7W6sau78darIutyx91vP1b48aNeqRLbvH7zi28Z2DjUnUr86JiNMQWTx6gcm4tL8MvZhLUmrZcD5poci0QHMFb2xGPVZ/ZYuYK/boAXfqy164yMiJVOT+/5HoIXYmj4BRicFunThXfffTc8MP3/hje8gShwT8MZ0wuCe/OVn+Uv/IDS6KzqcFM6jhcbAHgq3PKQcW9ZE3DzEDRGiLp8+osRCz4zoahdu2WpKprXDU2DLJr+hM9B+ZlCdMekn0EDrtrS9vNkgOGsYM6Zd1THUcVFPa2gdTRh0bY3N/xi4MwFZyqYgPQbo6nHgos26pwCF25MIao0d06zPzy1PKeKzjMxo7O6JrNrcI7DK9MhAKgOsfmGP//NA2VC30KTtHQFs4b5ztxcXc+3lLaKnwy6kGaSMyBsTlM384UtDNHEC/cw2aHCgoLsC9JAkxARqO3ItVmkwcJVVCtQmyx8QZ4ghbAb8ouPZtEo+UoBi8N0Os1F8gGKMLeL1w77pP0YVs6Y9g/0MbhBE8zPfe5zXFYdGhoKE1xCnuc7A8wLEnfeeednPvfPv/F7H8i3RWGAMQHNfUFUoBH5cm0OveiYLe81oyHaMK1q3ZiEB3Y87F0xbMHLwGIpmiFBhjpZXYc2NA//xjdVi/djcxUDTDyCymEGCOBba+p2mrL92UYvVmeQ046ZXPs+ccyc3tpp9KY54Z6amhSKFgKF884VDnNQvIVUcdeaSHy8NjU6Vsi7DrZtfbeUmyr505Be6U6MQEHcskkhr0GwEqtFOqbc5OEZs+J5G8rm5i5LVLEDFJWGXfqO423VSkL2gigGSSbzqnmzDJsIt4Pj53k1fH52y2Sfnq5//svJEncrWaBEfUsexsXy1u7d9R8+aP3MqyNxByVnTAQdmubLm3L0uM8Fyfbezo3dtleYPH7y5MBAf1e8KwTyonq04XRO2D/zXRFghG92dXY8NXzq2RPFSEd2osR7uW5/xilW68m4c3SqMTU98d1P/KlXK8k5CwoPKP2bCB7NmO0hVwwaN9c7FIvy8G/+5m+ulqxUAOVi6pFpI9/wZ8r+2PHDsWxHW7aD53x5CaguuoUeJ0FwuTDlmCjGagSWsTAlKbJKdo2am4zwSjBiSDfp2N3Z+Oahndestcoz4ydPjlx1NU/rneHmu+KMQP0R9hucMIwxgdA00N9s2A8++CB+PVkIc8qBFEZTGsZ4CVyUE8+1XKkQvGupM8whl8q05MOHhyvV2tDgkOtPde5/W9r7hqiWIBsVCxhseK5tzhgzf+2WHjTa3uy3/XxPdmAGM9GW38lFbRkDnN3f093fA1KZ+4eP757K+d1bUUNOtxvpmEiJCdeK59HA23PKFeNYuWq55MVSiahbv/kHn4FPfuKnX3c8MyADjWydS/lymQIz67Wf32VumDg0+aGPVr73ANdIMGpV/45X/erXu9/7nl033Dzakdo3jdltdnCf2z2ZOKYT3N75+8+if89LYKdGav/wD5XP/lMPsIgA2yitG4z90judG28wM4vwdTC68FGATR9CH6gw8xOf+AQh73rXu0AzwikXYQvf55g+1qMTIKF2qr4gVxN1gM7zS5/AMbIZ4FF31gLhPbgHu9ppe7p4RkF/hB5+qh9ITqdr8iEF4piDAFhfepKFnZMFphhKsMAzODioadUCE/6w5KUK1PSLfskLFoWi0VWUsFSxGs4ZDbS71qJnsjDwBGpFyrqHlcKKf//734f4u+CYo1UABg4eDOn0+Y8srbj33nu558y9g0U74ZwCw07QXMCpHrQGdL6DDHSgIgYnOBfvbIvOWZ4p5SjhnE4TwrZwTopfT97x0OSzHl2dUx9e6onpENASY2ALG8K4YwwPSwThmcjCNBcvBHxgCcKASDKV4AGD5or0BzufCELmI/AgDW6Wf87nIQZv8A1Tk2vOL9yspmBpJF2MQ/06too47pVEtVSWFdxPtbH/Q2dKpqBcKTFID1Kx9RMhtQcCVg0ngfxbYr2dh/q8/4YASV0wQALz0oUK1PKPeWChldkTD/RikSt897vf/eM//mMyXnSAlwZu1THPdwaYXn/jG9/4F3/54ZFnvj94+UtKVR9rWChCIzZFEdqriwAV9MLGVVQe1xV8Qjt6tGBG280or2AbflvchJMRStQwjpW3TDQG+5293S8pTH+9GB/lcltApAoDVOu4a9aPVn0vsr/04sDYkoVEJZ3sd6Pd9XZjunRsc18CSzag7vIL7sLBEMyQh7D9Ld3O8eFcxXYbplFLIEHJeXak7pfT3RjIQTOT68FuFDbe8Hr6ernVfCxv8qwxgu7ZintZj9kepyDk2Ze8a5kq0EY4dpeWcNpJiKwS3NUOKELWtYVpVtQdDH/gpLjZWXPfHrGKy0oV8DzIyahDOJannjXveEk1nd431hgr2BjolXB5D40rnd6GdrESNA6xF5S2SkjmADnnPwhVBgcHm+Un0i/SOX4s2z1SSZnTZVQhENxtbHcqrP4xZzLvzVatR7/xD/d/7r9JWyUxoPMvYPuXAOGGG254+ctfvkTk2YNZI49OexXPOZVroJRrGW66ZzCTwvpSrT0h19+nZj1eOmL+opcbidq1KhaZzPbuNaLwzDEPj9kGl1FRjeckqydtd4o020BdPtg1zgRAGxTcOUS/TslExgWKJ9S14yfhSjSrwBaWFdEWBDokLPRT0zhKz4zMsrzIuCdstzu13AoZZtSOJbPPAd34n2b8bwKlN5Yu3heNvrAeX18Eh3Sbs2u7zYnfNKb/Zlv/+92O15TlkXCUj0ScNV+a7ERTxWoFIxoVGV7uxPelkRLTempQJ56qb+4Z9zlCK0xPlfLTvnX59lPP7trzvYYV+cF1P9cwHZRXENbGeTIO81emf9NGY11jNv/ZLze+/0BIFsQsMzY2OfWhv+5dN3TNhm2HIfksLkB6WJbsTPqdMSMVXMGYqzZfrN73vdrf/X0CK1oJMYUVjcXTR44W/+LDdl+/uWsnxj/mUs7/QWYCzU3/qE4ppPCPf/zj3bt309h3v/vdpAq6bj71/M/5rjgdfsF9YRXNAISBF7y6sEBk3VyHBlfBwDBwoQfCgrMDyEfmOxCeFTAQ+4JQ59QF5QrvjcOPXgnnkji466VgILwlSkNgjJmGlMNJE0XBERGOTimg4pZQvljYE2eEwJ3iznX/PaOIxX4wKKwGXBkFZvg39hou3Wi386VGVgzGQu+UhgXQIjLyDUMuuIfaOR1mBp0/A6xnT+GqeAFBBbZwY8KvF3boRlBXDwcv+Hg1A78oAxYmaMHMMHwZD6JL0IAEzFMQFcUr2Dm2D850VlHaMhVd6lEcqUCPsTiE3QK6ho0ikJ9hSJgmTLC8J8xIsnPNy4Tl+BBYqgjEWnckoX6AMtxK8cu/wHwNNElAYyg1KHpYAZCSNkwfgh2EkEJ2ZfmDhmk8Fq2adZX4mn60USfU4SEFChUGk/+lgnknUBAYMJUEy0+J0gRSKv/mA+fzXLC/0GFSvvD9UmfQbH4v5kKgA3iCREEzZIg+//nPs1q+/vWvXyzjJRC2HHn3fACf0wZexN28aehLn/3MH3/kpdxa5OkUGEIMQfMAEkNX5jIwlDTv7Fp+DNluA7VVjEL7J3Pu+nYxpwT5l44ZhQovz1plo/s74297U///bXWUh353/Pjf1BrPpCyucnbXOt84034r1J9xvHHds8U71CgQCJvn6pwRO1F1eeK6uysyJAYkBFmWwJWz9Bn8yUuu3zFaaDx+tPTw7gNrezfNuHHsP3PWj6Sm4fo2FnFdVLXtKjyAY00WPQQySLkLGavY8Ddk/cEOFLMFZdXJNFklLPNF/KT/srUwhdgvlyHgOIt94IEHBgcHz/89RhZTups7vNValckv6wC3fHFVzJDxqHTRcb0Hnjx86FSed6u2bL/CsezOZH1zl5OZs4RkZtrahoaGWrqNvJI9cC1RF+onFFio0zu3MAaIUPWMw/mIEW8/NukOdniDnSzcsFVWqe6fLHJtnrOiGiH9Q1dc/qJX83gYugzdac6MaDr5QeXTuMzLNAjkV03SBWupX6gbtfVJBgAAQABJREFUJ3LmbNkr1rgm2jDt2M6N2Vu3+N2OH6kVeZ6stDE+PFPfPWZNVbgG7KKUx5FWIoktZKMaPA6MaWhUduNRO5s0Mo6/sZ15jLJ3cmjdOq9cpY8xzcSpKR0e9K3AD6VyunPQKz7Teup8SgMBhZ7og2zQ5RA3MMBNA4QtZXOEF4LpF9Pob7MiyMtXduTU3tHOtuu7J4yZv0MG642nyn+WTb6jYm0sSDfXYo3DttXjOZ0wtabtHnBPvMNZ/8ls8hUyBPO6zQonyadqsUTcaW8zHcNL2X5fGv3v043VffHITGO6Hpks+LyYFLGdqu++4PEvZqqFZze9YPfQDdyqdjjXQ6UEq3Jc+nDdXb1GJjd7+JvfjrEhS61yN4SjB3b7yKnRykMPdm4YilkO53CsoTD/mYi1pk2ACxzYgoXomcqXvmxVqzxANw+qSCmTo6cqD9wf37TJbMuEXa3ZII/mCpAu5Tay/ZGPfISQV7ziFZD1zGuV/SJnIyMUJ1EQ0EpDhxmbPRQSVqH+5pDmlOpfJjaMQhSsaBCWvLCcMIRc+M+aUpNprubEtBRN4GbutzmW9M3lh4buFqbRkObEIYShh9gwY7M/TBBWhydMCVGLHyKSLPAwOB3EsMYwpZYj8rXAcDGPHqEYpVOPvJRARhLr4LLI41dpKmjQXONSsCl4zdWxTvNTD7PwrDyjFqUAh9+wZLh0oCWcAxraoj/hFdE84iQCFgsWLsRkcmnG8Ev6sHxACstftUdLbs4ehqyifM3LWkfeZTbZ5urOyd+8MYWTlxNthglkWAXALbUv0/bzL7ylLn6CqGAs4mt6jO7SGxwtyQAJp7VfDBhaqnu+/aTtgMQFDQ6AbrvttvB0hnDWczpEpzwdyOrBz2X0JbUoSlvYjWRng+Cs8Fybz1yWhYJLN6gfJ3XfnNvI5kiGoERhPc+g5GEoYJuZvzqHJaP4+DtXhmTTgCBoLk4RQUsOWiEFs8s7lSJ6ZAAhq7Bsn1RHFllplaoIEgsXQyh+FVAIUAFUkvQiOmlS2PkBCBLQWiFNkbZolIA5n4Ag8X/6059+8YtfHOpSzcdeMn+f7wwwHcnG85a3/MKf/umffujPPrS1I1WpCUPI279yGViet4Z6FjNFIJDDoYst8lKo2GLdPpX3+uV+nYVCayOClVQp7Jvj//6Kth9cnfxqbHBm6++XqtMxl8uK3XWR/Zp+zl/3+bHfL/mdKAEGL1dDHNqTZTDSqueM7LjZP+D2IhMBLc6YOSsab0F3dDwNyFnnqjX2iWE30W7FKs5U1ePZULAJnOLqoVCrwsBgXS2XLxbt/j5EwVXXzlc91B2nS42t3VY2FswdweA5z4ogeF4mgsg46zap5CnnsufPAMtcxgLNhg2JQpEliZNCeN9YLG5FoogrjS1b607MiEe717QxUm1Rf1OX152KiPwtcHQ3wkT+tfQlCy5yLQxmXBAKQ4BsWmxa6gp/goYg6N6x+mwtYrav60zZ/VkzgV10F7bHOjYtR0UcArGigia9Q5e/4pf+n209jRvX2qnctJ2bxqKRmUn57R1Y2YI1odh77rkHBjgsfxUe1G6HJ3mF237q2X3ThTqM0g1bO+7YEsmO7is9/EjtyDHu7bVddcVVO3b2b+6+70jjRM7hICtmWzyGjY0urGvD+qKvy21hOl149ZTLA/PIjzCh09i7t3Z8xMeqNYZJNg7anR3c7lpBP51uB8imzBXDxOrPFttiPeUUVpcx2Q5naLoDGemTFU6wdCopdxgK37C9CTq88o105HrP2TTNVld7IlP9YDV2oFH52Ujmt1lZZLLbvJF88reiQ9ebFs+9zGGXbjVorMQ61g50sF54Vw8YKZtbHvNmpoKmBLuSuftYZXKiOjFl1HnluV5Lz45t/+EXiqb3o1vePGPEwGxspdESTI7F5b24Rpawk1N2Tp4VDs4+OIvwg2fj2Hqt+tGTaFBnEtHZWcyGO9lkI257GFMIdu1gwYOgyOXM48cDzWfOEwPTV6JBESQ5dBDmppGUS184wOQYRfdXvjjISvgEyCPMX/HzHe94B+J3pFKQUPyEyuQ2O1lIRgh9TqAOGx6iVIhEduWjwijCdcYhpaFAzRV+9fYs9JDeAA9z4QEN9P48qwoJOIBD11QTQM8pg6oUmAaGX2TaNBBQOUBphpMEHMHoZQ1YXBaEMAvwwE0hQSKEusiFdFe5Jk1DmfCElInEVTPSCXQO5CO6ozSQGpGdkrgZG4ETbo0QUtIzLbEgNmXSXWFGwNDs0KO6ogIGlTZnhJkZGhoihKjh4WFqpxYFmEAOjygTyHWMCMFpmaFokYHQNpIRXppBoXV0NRwRDyMBMOHkUgVj8jKCfIGtBRLSUBcOD63giyMXKcmibeGLI1xhCJIs+VmYkhAtWccCsAV3uTofvC3MUCLAhASn1XRLWC5wfv3rXwcSclHC/fffT+1k4Uwn7IQw8ao9rMMhCgGDsmHoWgPJShq7sF6QGWhVJLsw9vkcosMEJqyu4efaNHB1x44digwsC/gXrRfjWyCzTupzreJ/gfT0D0sEZxy6MmuLCGTRUD+dhlYFYwfGLsMAL9MVHJaxrK2CAVZUT6QS/evXj03o5qr7GDsdk5bNdG5z0doBUmzkCofKEslHguVH8AeaRLY8TGPp9jyXR9MEN6XYpxuNUzPTVgzThgl2fLJGfLdz9hT+6XWbNSebIhXRLbABc0Vh/kd2flKJUEZ2ZQFEGBlNplVdjC9Ngx8CTmmWVEfFZxj70EoJBSSBX+QA0h+SMJCW4923bw8mCT/+8Y9r4kvxewkwwHTrW97yi3/wB3/4xS98/hff/u/z1VqpbrNVcYvWLbE1CuYI08sbl3LYxP1GrwE91zByVe4Y+t0pGeFU3EBQAudc8Ts/dPgjbx34v27q+GLMmo10NbB140GOGvbxxtVfPPW+A7VbBCF8o1bOT48d6123Bbutk2Wrs2fbjOPvHjV29fIeCVwFpa5uxAXPO5L29Vt6yjGzzfWOTBkcL0/C3Vpc/UIIjC63PM5J8eguzhaL3dnMRBETr06x5hWyVr5hbGxz13dgNHoeN1cHyE80F2sBqxtUER52d5bOw4cPM36QXEpENkMHeQFJivgOUu98OUwGrr3TedWrqvsPxDn8ECV4UShw/Uh97drMbbeNoqtuWv19yY1Zq7+N0QimvYzG2Z2spOfnJH9QiBYkq+Wibo5BMA9N18bKzkjeM+PZoU6/O2lj+CoWtQ9Nge2BDg+PwcMlBwdJQ53eLX1+9Nknal/4kvnjh5x8oc7zfTffGP+5nzM3bcaa06JVnVMgppsmytZ4wfVi7Um/urHbuH1LOv3MY2Mf/Ih1aL8jD9k601E7/oqXrXnn229ct/abB70cgmLLwDIWHBPSYLducnuHN2k7En7aRu3cQkDvPvtU6b//rf/ADzOOw/SspDPma18dvesue+2AsPjLgqiDgiYhOAYiKUkNjtG3UJZkDUcNAwAnZmHoxP5zX9pjVsqCH7glB2K+6n37DyViztr0QUx5Gb5dPexnf7VASf5IovYb5eQ4nKbnPQbFHTEiFa8aNWN1p74nN/qPVftO2ZSpKbiAzVZ4bKo2Knr2ftqszxj1GZTCfQu0hzKDeTh48CAkeM51fvR0/anDExMVrNpgUz266dB3/LG9I3byoY031rhAjWKL5WHD2YnFaiha22a+4trJOM8CRwOBFasLOiYKPgXyEDS29MdPjR+bqMdtt21NtLd/DRor+2nYoUPQ4ju2b9/guTPFgl0qxGJJyxIGD1KDL1oSiJgxgrn70UfWr9+g58Fcj4T5IZb+o2l8YckQEsJyqBD+29/+dtirMKi33HILD6gwwXlTQXPx1QTXXXednlNg2wz+inB1lEldqAjxE55TH2IhUGPJ+7M/+7MMN4TUww8/PJdnvsxrrrlGGWByaSvCBBC1ygADFbGEaxPwUCaQ0LN0CAdeGqXhpKFMXbvQIm4ukyhYF6WV6Wp+Ai0ubD5lAidR6IezypFAy+SZGfoEEhxO8l//9V9DOlITACQkOCmhRHk2tgUYqqNM4KTTiApj8XA3Txlg9NIh8YPIuQ9Dc/PNN/ODMhksJkizuPVlL3uZwvmDH/xAYSAlo0wtDB/8P/j5ne98h6YRTrcz3XCA/brXvY4Q+pNpSFtoeNjGl770pfCNlPCtb30rDCQxDiMR+sAvw6fHEyQg7/DwMN174403koa6gJNAdYTgedGLXkSZYBqdpiHz8ebg4KDq5DOywENRYUbwHOseNIHBoufpIhxEPMMNzBxu0hWUyWPLLKm0i/UENpV+pkBlkuk0FXGH1ZGRbuEn4aCuhlMgHgCjfPw0qiUXUTQwlD0iT1O/Tpywl9RD4oVOOyoM5ycFwopcQBY9LHxRD91CFy0ada6BIBiIunPnTkXFc82+ivSMDsCztizTwxS7fOwq6n0uszDjOMTRAxHFxnOqHVQH/xnl5rxgOy/JUQ7LOOScLlzNCRZWwWxiHdblXWMX9qqGLF9Oc8lQlWhjxZNIkbg/wlJDZEAjCK+HX2h3RLOItZTHlSBJNM/6ii9ICWWh4WSXIKkkSEkBAs5cLl48QnVN7PecpkTQ6XRjSbKUM91y4CxWlgUS5Rv4LRwnTsJkMQxOuIMw0gkXHFzalBQXxWk3CPxBu4IaqXWOHgirlHgSALS0Xv4EoyBwEvzJT36S/e61r31tmP6S81yYFepiNxsdpFtuufnvP3H329/+tu29kWLdRb0T+h7BL9wLIwH9iB3fWsPn9SM0VeFmYIcR6E2XRQU6G5c7vumE5ZX8is/rL/1/dfTD3x578wu7vz6UeNLxS6PVoaeLL3lk9uWRaE8koKc59IEq5ZIihctzHxhcrVpiomdKznd2mu4aFBOJm0OIc+0AMx5P7Nw2xNn78GSDE93j0LO2zXU+sB5KEmoV9j7ZlsrN5irlSi2dZiqXal6phqzbzJXdSsOeKLlbuyyYBMAIZpKi5rlCskj6+Vk9PzVW2cZFSm4JgrZgqUIUA1VBpUouL+R+deFjiYQ0ROrCkq0hLaUt/3NubWOtYWVJJSO33FLNzeY//VlrapZXgkAad+umtv/47vr6jc/uO7l27Zrta7ndokXKgrSUA2xoF74Qr6RhU4FYwQMVtUIgg94+XbwI1nxjYnLyxPHjV1999emIM32aiweGjs2CNhhtMjJRfwA9+Uo1GY+i/5+vWyjkcowiz2sFCv1wNTetNaKH9tff/4HU4cMNt16r1VNcUPnnL5SGj6b+y28YmwZllVuVC5R6xDrdoUkfi9NjRTPW1p3pMm7e7LWd2jv1Jx+JHj1m2pgNZF760Xq9es/X7M72tW9967r29IEpXjzyebKehiMC5ClarEPHVFU7aaQtr37kaOFDfxl56MfMxFKFue5HyyXz05+p8TDMG97AO3t0O1sv5BF9DkapcEblM3wJ5AsJBYLhB+sg4ulAckHfE8gGzM+nnto9Xjb3zcTk8pDhWslSdG3H5k2b6Q/4GcRopAnLhDxC84co+AcOQYk6euxIPJJ44eWHtmPECj2OTXUrWwHU8heN+CnftbBfb3tiHpIwq/RYNHUtnGht6uiXnxyFh5GdmCYgRMI01fGpysGDByh8IFp46iQiL/ZPr6OzE0WJoaEhGGDslg+XOyZqfY32zVHX9oozdrVy1/CDV/veI5f91J7sBp5QYr8FXBYTRNP8QinmwFT92s6uxDW76g88DItPPKu/2ML33XrU7vjpayfs6Mj0FPxs3KzFDJO3l0mj1H+1WhmbGFvT2+dceVX0e9+LYgESxEJ8FxXxHbJm59qrDZTYeXZ4/hgF9kxPGWgIafgy03/v934PD4ZVWdI1UL/0p3rITlSYRQPDNWFwcJBR01iNgoXgJ47qGFY8Gq4egMeDjAKWrzkKvyoC4IHD1MmLXx2TVz2Qdy0TmcK1FsqELZ/PIX+JChlUOLeWMkM5CQ0kZcsdYGUVSAPzqRnh90BO+DEOPmgjjBbdQvPJC7JpvWGZsMFarEbpV8sknGUkzKIZOWRUD+Ur6RkmCHkMmEDYSGrX9mp6HSa+WHgOs7Asw9rRdi0Wqbt2oDLPAKkTk1zUxVKvBYbZ9ScjRW8zv6hIo/iGA8EMpVsIUUeaEE7FT801Hz/XP/xkKQgD8ZCMWvjiAI8TWPpHk0Gyw+LSw8BJFF/y4gAPpp30TD2+wM8ExEM4Kz+LCYhE/9MhBMJC6/VR/DpS7Fa33XYbfhiGRx55JAzHg7v99tvBbcrk1EDbTkqNokBYPmD71Kc+RV10L1F6aAI+KPMPVsCoE04nhF9QXblcPfjQKMqkLco/42fIdDEMM7L4aM/AadMVYTge0iujjofZpxVpOCE4QtTT/AVg7XD9NkeFfjIuH6spz1pOWOCF8jAWOHAsXCVCSOgBxoufQAVO0qv4QXJF4wsFwHNQDqOMvgmLQ/NQrrxe1X9pZlzJy4CCSHi0N5hE+nOZYuGicc0JoKAAjBDCGQUwHD8rwMpFwcEAyWSvof7EfguFzBzh3Rfl5mQLBmNhHJQbDULZgSVaTISKNJivJBPNPylAuAwpRnxy4Cs+KQAXrFiVSj0Of+BEfLkALKkcz03lxzH4ac1OotjGc5SQ9rrRB9mCj0wAqWneyURi06X+4MXD+eCL8BfwtWeAleKp2IYSkIoIIAQggIU0IiIOKCJJRZMBmRg6oFGvf/KTn7rrrrvC3VlyX2ruEmCABUl8/+1vf/s73vGOPXv2cS3q8l6bW74QzTz+wXMdkIz8q9ZNbgLzNhIMMC+CuHVGTbSjuUYbcfyUI3xFJmE1SnDHmICJ76ncvvf4bbaBtNUoVZm3NkRhzPU6kn7Ubwhao2ENKmBZS7QjmAvOVBHMEM4Y/HZ7PfgNMGIVIy4YFEwv7lpt7bTaooi/zGQUtWdjpmJh6It6AQYDrnYiVqtW3brrxB0AiTr+aFHe/CzWMPNr5GvwPB7PFMcRYAPYBXP+RAmRETwVkzaY+MzKAOYLVkNQEMQHND07JUf4kE1DQ0PLlK9EHhQACLDUZqOTWQtpBVgWGhYvUWep89xVZ4/zmruiV19Xe/KpKEKtgQFn11Vef78Zj16zbU1nJqWysdZCFoOPI0ylWUmspBKpOAc969JPsmCxkcMbP89t3ZKsqPGkn+F9WOHZNAHfRcFAZeDAmAmjOzLLO1sGktJDh49NjI9u3LZrvBytYVAKm2qI/hwugsrSDj+TLucaX/1q6sghWd5YwIOSwZ3EYw9Xv/+9WF9POMgLe1JAPdMBGG0ENppfLJUwKnY07x+ftkdy1nTZj8aSUbPeYxnVBx9y9x/gjrugfODYFKKWmfvqN/tf/bMDidj9JyZdISxBdyow+9b0If513PzeJw+Y2dLgVduMxx+v3f8AWr7RuNzEI3u5XLGqleKnP9N9082nMunHn3gC5FEmB2ZVt08AU3hBM4YDGkWPpYEZyoZyIO7ZYtlu2VzpipGRU3tno3kX5sfNOI1CbabQFry8FJB9Si8CfgCAzF91wE1FBKaTGdi/ujuE+Jd3EBI3V7k2IfvFAbhhruFK2yM3Wb6DMS+r9mg0sY1eqHVmqzs6tkE2UIhgtWlOFFwrloTxRhZ+jbwfLpJcYiGgtUb4LuiCY3vstlw8nnN4ua1QL/fOHL/20a8xfD+445d5hZhjMVQXuOYtKxgbNkolrvnkePRlg91973jL0X1HrKnJKCNiW7SwZkWyb7zT2HHF8Wq0s39wXcwe6vAHMx6Gx0AQRHCBFA7LZA03V+h+21vLw8NttXK1gvWyeiqT5Bpx5brrEjffYnW2X93VOT/IImZUgMMvGrAQ+jTn13/91xGWhuF4VFcZD0MD3d8cpX4dzZBtXpgA4j6k71tiQQBlqlvC9WdY9cJYMAS3MJwQsIi1a9EoAuEel4pilEFLFj3qpVHNyaAXkSVqCOiKIw2IB3sJrwUkdA5ZCIchac4LCbIUFUKZyxCOy7QduSWuGbzQT5nh5WQCAYaZFTLVL3jBCzTl448/jgxNZdQasoz9YfozzBhWFHoWRQmNpQlLtQI4X/nKV4aF4KHnw59Up+sDX44bWLphBrSHYSDBpTBWPcqNUyY2AjVEWWgwmWbqcIBmKjYkAQs4X3BPoxggPTXQvPolFnhIQBM0fZiRRUlBJQ0OwDSL7gsaxfIFehDOT43FE7JnsCgk1to1Taj/DN+uivGk1wRUpwwwJ3qoIWj54RceHvSjZOxIU6lm4ctc5gxFkQGFCCTquljxxdFjmMimEDqKUwNNr1F86SvtUhXCh+F4AIbuAmaAoTqQH0chfIkNW0EChSSEEw8h2tjmwHP1czLCMwEIMBeuG+BJqNzBmqzLMiMbQnWudT2X6Rf2jHaghi/szEVhIzE7PocvTHw9l1mYjKKWItJITAmK7ZqRn4q0DC4Z+eoJDocLVKR+Fr2FtSwVEjRHPoFmn8wO1lno42C5BT2grKB+CMMjWzRJhTgUElECgzWCQIkMqlDWWOAOOGM5qReKirRBMXWMXfkuZ8HwioTJP9OMN2rZ/DjEwOzazS5tggiTzHLeLESEpAoqlZLIIDI2AgEBOgkZXEAwSMRSbTzPcOFv4IGlBdQB2NIkVMbkKR25ASZA8mEJC/hkkRtJTwnrK39Ice+932IZgS8Lfp0nOD+x7JcAA6x9gw7Ve9/7Xoz0/MkHP9iVMLd1+xjOOXVqpN3urDvYlhVsgmMVE+QuFqFFoFSto0FsFhvWaM4baEfNGINw6B4bCFoRF8sYGkhkBJuxnjXLS69oIPNmSb5aH98L3Rkgnz92bD9FcrTTN7gD1Xc4QxCAW4tkpMK1czxwgEXnOohkCnCvL+OmY+bTY34yYh+bcqcde5qngB2xD9SWSo+Xq+VKyY6k4WdQ845YmLn2T9bMsmdNVjxaN1nyBjv8gTZLRdLnj44I6Q5OeVyiTjpuT8rvStmQwrJ8yKwIJkrQNXNz51xbvSA9xBP7DUeJy0MO5cEOzZRjI1yK0qVsCAL2WrbkWEwVemWYgwVRoOfI4FShMZFHqdTsSbX3XX5VfNsOTG9Dz9qJCDctaWQqyns5wCIZV+J0j2SlZiOnFewKVLfM6q9lymIr3WkYhYJ7cLh233fMPQdYY7zLLovf/uLMxg20MRR2QQBprqavPAhbN7yj07JSbeiQ9RpeFy3Xg6MlJy7cZrnCgzaiPuwiCwaHuR1QKlYeeahSqZEawoKzI6MkCxx95T36iP/KVwZ7g4lyIBsbWxSOFiEYoV7oGygq2Srmdy/2JDUWzYXAHz74Qw6RhouZnJ8Yr6Vqvr1hw9pU2nYaldLwsOchg5RTc0Uc5IVAY01NuxMTmaF+bKGz6MqC7FtRjoKgGk2/J+0XZsrtSUde4kZLituMhnCJoIGUwnboGUluhI6Pdm4QuWhIf0OAsmWSkn0UBwnFVo1YhnEBYPykREBHMmJJNsc5mP7VL7zJP8legIq1t6vX701J9gBmUcXELYoSCEyUxO/t65V90eys+n0x83h0g2j/yhzpwwqV8MDFHX7yrawqPLOW8A5gnRlO2E9m+reu3S47kGyPdIE3WuDkIoa1+f6UO9Rjgc+qUwAw0H/AT7KckazaMThlKrT8Ijblr3/q22vyJ091DT6+5VYWCkwkgPQJLGwj+0UbXZ5Qs47MOvce835m25WDH/jdiU/9s/H0E3WenFo/kH7Vy52X3jGS6HpymD3OwHh+Nsbb48HNJwkQwMAut+FNFIt9V10Z/53fnv3EJ93dj8sLcozx7S+Ov/7nzXUbdEmVXlpi6nDDHJTmMOLWW29t6cxFOUYwDcIIp/jfkgWoLkUHAoOK8HvLA6+NZfT1NEcT6/kafCklLJ/9OYhl3VZpJ1OAMeIckEphA1ilW0aq5eeisC2TJowCHzjeYjowGc/agdQSZgxrDEPw6EqiUSAYBbI+aAL8uDBXs4dpqOrxAMMi+epXv5r2htT5omis2UmMay4q9ANJizaBRlEFHk4NKHZR/opFTLlWUmpiVmyFnIbccccdGk4gHr7K3lMmSzpHLYTgNE24YbEewviFUeoJG0h1sm/MF4iHMhVaTUlsWCl7okYxZJxcq1+/pGFnUQZYrxI0x8JUY+wDpPrGN74BU01iYlG0oVHwxliPx8NK+Oijj+KRZXre0Vd0FOmJ4ksw7dJIZo0u1LCswBOG46H/VdJLjSxQpAexl1p2AJtOoHDIDHLpvqNMWnMTnrd+Boj9GvBYW/jSjfQhPRBuoCuBXPuHrlgqMaOgutBLJYCWCC81sK+xTQMGR3WQPWDF4OAgPczQE0UgUUuVs2i4XJPx/b41A5F4Es4yQB/YNtnLhNsN+NqALhQiDClWwD6QQGxGQrsF27F4A3JFlEyFTieeK4mFWTMaN3hdSek3wuQ4oAZRAg7k0d/jd5AewKgO1rcWS5MX3kH0qQgUIkES6S4pLRP4AmowSEdjod4QW0jygCpYtI3nGYjMJeB+heUGBPoF5sibre/fXbjs5m5CBK6ASRZQ+cUn+F+6UYA2P/axjzFJOQ08T0h+stkvGQaYLf/Nb37z3Xff/b73vY81d33WLpWrTzUmeeHEiCa9IssvRJ48C+z5DcutoK3sO1x+sxsGPLBxatZfmzUR60EzohGtDyMxtILVcB2C8hbyZJABdb5o2/oU5621cnFmvL2bS4YUJgygILdhToqgjpxMFSaEu649oBCJFrQ4Zye5fDsdNa4dMA5M1rDFdCLvYspripdTLDOeEPM3uUKJ5gseQvVztQDDuRF7stAo1xzUd7uS2PfC4ld9ExrRcm1R0gXgrAYeGsZN41LN4iGoAqLmWePIrJFwGt1JoyvJu8r0hRDxwnXNVSQLRxCy0g+rJxwvFUE54ViOWf7Yb86aH0kUq2GLVtLpXEG7KRZRVbBDE0OQzHMU5lEEGCsYyEu57w0OoPk5UjDG8l57whrMRtvkuoiMvgwyTQv6+nTJy/oYGgaRhlA1a7ceXTfnIHzxn9TF3a5HHq5/4L/FTp7gLjJP4ha++93CPV/L/s7/2XvlVbt3P3XkyDDa8liRoX9oFF94e6og7VOP/HCiaB6fzsQt48njsLleWzo7jTJrAZlguZAvSSvBA/ilWdnwGCYLWw2zuaLc2xSQGLlypRIsxBm7UDbRjQgg5YSbESEBxAHtUuChq5h3Id0AlQBVRNNoO0v/hk0bj+YSXdl4peBkg8uzGGOHE0MAEcm0mZEEXUpKWW3ZZlBlll+2k4zy0PfWLUN1nu6J8VoPDA+iY7s7YfRm06/aeWMqYrq5Yi2ZjCbjgApFBUUjbeGowuGefMx2YolMG8JGKTyAHioHx08FGxoRcgc/Oz0dyJIN/To4OKi0F+Gai3k9W4+hSgGIaaexJsstBC3gjHkd1jIXZxjUpccxXHlC/bd/YLtZ+s/+xG9hfErTRF5llA6b1k856bs8o73Ehlv4UiqyU576lgPl+LVBPXOV8Wdrt5MpNsby7pqMLM4c9yjTi5/eqxwrM6j76gNlN5mrioGr3Mnhtnj2ZcMPcf71yA1vGEl2ctqBFgzLCqcOUc5EGi5PLjtRB7uY//i0N1mJ3LXjmp7fHkxVCly38JKxarp72Ercu7eB2B7r2u0xeUmrN00/A470qtKgdP7ExLjX1dN13bXRTRutyRkvX4gO9Fu9vVZnJyo02l6ADJDrjH6jEFD3q1/9KmkQyoWjo1mW+YKHkM7LSA6Xyfv8jIJ6xoWw0TM47ZDmblHehhBd7qAXoepU1KbJyIW2P/OR2decMSz5YntYAZSHBDEYXPUDT1gvzbzgjLruHcsIhMPaz8kDa7qKboRtZmU+p4rOKbEOa6B/sXg+EjAKzXFgBT81Iy3CzzkFTB3sDYEaTmDIiochYUY2F5yWqbEaRQiDq4x6S7gmDhkeTR/mgtEiPTe9CWEFDl2IG0iJw0D1KM5THVJ6NiNKYBFASA6O0edaO182IE0PUwcGavkAg4clC7QkTQgG4azVfDlFYjZpIcTiYS8APKLgjR966CE8OKJQYcDQMRcBgIef3CfnS4cDGI6zGA5iVL0ClXImYxhFLKBqdYChgusgk3yoEae1tHyXCm9Jdj4/gYfmawk0Rw8mQJVzYoBRfoEYCEdwKXiWaQ6bpmYHAMZRlU043ViqqHMKh7Wm6ngqDZFehy8QpSyoaag7imFjFJoQolUIEdg5CYNWEs5XSFtQQtJBEQivDDXoCYM8xwHWZ0di6U6re04cDaUIz0uXJqIQMDY2icgSlAip3og0Slx64kwFzpmCUP2RkqUqqZ06g19w4IxDAEJQMfQR1A2H/fDMAsESqEKB5+N4PlYYGJFHQy/QW1Tqn9xdKz9tbL2JSSEUGp3ATSq0awV6+SUC8EDr1T95YuRrX/v6Bz7wgZb153xA+onkvTQYYJ1I73znOz/84Q//0z/901ve8hbb8Db3OBvS7mwCXIERFZlszJz46bavXd/5zXWJowzYsdLQfZOvfGDslWW/o1A3T+a8te1ihRn5UlvMzFVgJhlVwT4Y4wSWjzCshdYz6oiR9qJjpJBKmePRNOR7jLEPWANBAtBlooxaPDXCMWBx2hvqkNOdwAVIvbKRPL06BMs0N0639UR4KDVmm5kojxgb08LINFiYMObKOSbELuQsJKuotiKqFrsz3kied6HsXNnrbbNmakZ/qjHYgeRWTnRWBsUiqTj64kUaLlq7YhtMEL/UsI7k+OfHTLc9YXQkzI4EpmVlguqiskgpSwcpK8WuxkLMfsAUgieBvGNHYetlM0PbauHaSneFG/PCshkWtkDC+TIw0Pw1t84TWdNlc6JkwBLyGs88qLLC8bpMVxIFcrM9GePoS2j2YMIHtLsMdTjgWpesEEEVutdSi3qI1bUbLgvpNGnYS4hVB8HKms4mjc0YqBAC8WvU0NDQFVfs9EZP5f/8I9G9z5RYEQMUA7Hihw/VPvbx5O/+Djvo3r176vUGh80KAF8yBv1gVkrlrFu9Pls/XsN8UTweNfNmpuFUqqVSj5A7oAD/TK7+TqH2wPrLYGGXdeNQcnycKOCnlxJxue7IyVB1/TojgoKlkFNQJFgPalnaaAuO2BZHZzFYg1t3TZzyMyUjwewRS+boYviTuUZ9Yyx97ZUzn/l8ZP6eD13EQIBV9rZtfmfvJBaw0AWOMnBIPpnWLpOto81ckzZSEUE+Ox61LttWtSNRp8E00NpBG/aU8kA/NtJFnyhwNJa/+tUQvuzTODxwkvQ8TWCAaBqMPXjI21r4uVAHM4rolT2HDWqgTQ6BcWFRoac5ED9tgSDD4accJ+Ikkyk/8e5SbX8s97e2iaTdiFxRjn6Y41yMD5imG81/L14ZrXe/BwUObGX3Fmsv6gqmGLVRCN9UxNvcbg3yhFuwL0Fq4IhAwkb5Q0NDJdf70SN1FOAboubMNSdve3F4x4FHkPs/cP3rRGDLjmabCeYqYnfQCZPFoGvdRc9lqux8dY/z1GjjVVs7LuvqFs2XqnfwhDtWMatelCU0aVvtKb9TZrdsDYwUEwO6EGThJwkmJ6f2TU0QunVoU+cVXYV6LVcqzzzz7I4d6FwIWUCfgK6gPSwcP0MHKQkFSR/80i/9UnN/hgkW9VDaouGXbmBL26GbeXIWjk6xKGwXyTSloDoDEThwTAMJoWfQYiUXKBHmei49zHpdkFn34LJQhAG25vFq5vNXAhh52QVgXWiRtnphLrQiQTP6YWHU+YTAruDOqYRwIM4p17kmXr6WhbFhiHroUtY9ehVejl01VDIPk4XwhCGhZ2FUGKKehSkXhodj2rIaNBe1lAY7aVAt5sscAdM4u9SVXPNCM6gVhuai1A96vOQlL1HeWDdcvro6kQDsQqKue7HGhkNP+Ug12RpY8Uive7eiIg2BYiEXHnWcS4bHH8xE5SrpE2KphV1GmWqO8EKmWsEjzU033aRMPpYCCQRgalHHrFHJKhOBgZsPlr9sWMqmKgmhqwGlqdPCl/mSjFi4fT1PBDC0S1ZxB1jLUT2IparTNEvFEk7f4vDQTLp6UfabQT9rOYtWwUgRzhsqwr9B5kuigLyT8w5ZOoIh0g+/lFYWyZGMrHAEBJFMOESJ4xRfyEOsWEpJFEExUlzgSJhOodKD4gMiDGFuZdc0jEypGC/nq5F4sW8D2Rus3BA6wn0Hm6rAQ7DCRs1iCY/K4DVJyN7PAfx8DXMVXdg/KOTBwghnG4ABbw5so495lWGrNuPFO4QCIhb6QRhgAVq6gjREAOHHP/53bD0wYhcWque+tHNb9J97+JprhEpg4eAZSb6wE6DhULd9cGZ0qjBpFsyr2k+8ef3vDiV+bHGDV7DUuCz+o9s6v/DEmlv/v71/dKi2K1czrRzULRhrxjnWiZn5KurSAbrytKAovjPQFreIhYiswkSBI8wKwX7WKMrkD1grZzi+iXktzKlz/AN+uJ6L9BV+MJgrp4/Am4Ff3g/mMwEArD9tp2Pc7fSSUfvEjDtpR6aEAOLZWtAuEJHJ1JTzGTuCxSADo1+5il+smiUeKi74hZqNqHNtm7G2w0pAQYcOuPlvZa49bv/UOqSm3mjR45XRQl30J2UCcF3QcEZLxmgJUD3unSJLD/65CZ5DD6a0dJLWI1+Z7i110n0sdjhoJignthzOAiGv2TDYjegEFkQ2pIUMsJZD1/PcCUd4bZk2/EEd1CTc7xNPPsmNJ4yE5avmwQf35rg6bUQ270A2iGxMdjo6jfd1YDA6YvWIm496bmmqkR+TrbDRqLOZqVQQUyUc6ekeI0yi66IthlYYdfHyAefHOlgKD1mwjIqfXZM69DxVo/gODg6yyrNRQbdRDh6axnLPZiZ1wZTs25ccHrZiCe0tKZklhjuizz5VP3RozW23vfSlL+E0Odwg8SihzHjc8dI76EzcWNHbPW4cnzXGi2ayMpbP5Th/7u9ATUsUaTa0+1Pt6blBybQ5r7jDfOaZKPwMpxc1nz2VA4NaLBW57TYOGIICRX8bF7ZCPcDWEsJPGQLOBXzj4BTtc3h4jJNFzjRKVTcZtUqefSjvXbX9Svtnbve+cq+s8TJ/ZD2tp1Ltb70zn+04NCJZYrw9a3KpAePPka6Uy9vcG9qpLugV7gPv2tm46cbI/Q9QAuYWWZBREq5gR/muu6weuaa4OGwLcI/6VUeRoxZyIVqBMczl4ChRPDaqwvb6PPzbm8Yg08K2LhIS1kvJQbTkwx73AfOPos6V69y/SBr7LRN1e+knZnh9xrR6vd7/VJVH1/yo0fmf09ldC8CXHZpDGVlp5uMoX5zU4R+ZcosuD/dO5wp1nv8FQ7c/+pWiWzzcu/VAx2aIqaA6sUhpOjZnZIwkGFSYGDOjUc/s4KbERN7eO+McmHRBD6yUoEhClbEox38oS3u8+cXrU9StreOrAjfkGFzi3bxlcGNt3be+fS96G5u2bO7u6mLmJhP9IREJiMwFxVKBd9791V/9Fd7bb7+d2TQftuRf5p3K7SmKRKwVfMFJqgr7fMnMz+8I2oX+S4vIcW5sl4actoc9vDCxhjyXPdNSVwhSS/jSDVo8hm0diV9YWnMiAlliSKBcd3PU+fvPE+zzB+DCltDSgew+YJ0qURP13DS2BYbmBjYD0OxvTtPsJ40m029z1KJ+koXsbksCoFLyoyVcfzIrEXEPDw/DAMN7c6YTJqNMLBSSnV0ehwciIZySJIZ+AD9DF26gLFmDg4NhuHrYdimZcljfKCcoUsokEMJAGWA4alwIgEYpsYGEUxXCoQfUUR2nG8BPIWprDdh00eBLkzmmJ4qFlBmk4WAFflZa1mpaRyyuubql/CxfdC/tIsEKR6SlqOZcVKo/mwM1PVVoLS3Zz/oTpUJaMrjlMmh5uLigXeyngMt/xIiBFTGJJRussnbSfHhXKBkdBmKEmhdJLV+vWpj1aqVAub/uVWZq01xG9KNtPSL2Nc2o48Qx2skChWIhxctebyTrM4latZhOlWMpYIDD1BNlqkT4CrMgcPC/pBZA5GplcGEfKkT02yRekpy1sWECmsA0Bx+Ujg3DWzxBbxh1T5hb6Xut3HSdkjkzbBl159iT5S23poOKEf8KlgrdJk6YZAh9t17927/9u9e//vXg1cJRa6nuef7zUmKA6etf/dVffcMb3oB5Q/RtGD2UPy/vhauI9ThP/Luh/9LpHBKcqSbKJ2AT7di6uhUpX5v91vuunHjv4586Wd8S8MBuBuPQMxN9Gy7zYz5qhMH8kwkAD8wX9ll0Z9B8sDKpns2uhRlpfgWoKknBcMEG8CaH2KRcbJRA/QR37bb1mRFmDZEyCXR2ncPoh5iUiVi7Bvz2aREFp4oe6tAzVXMmD03p1Lj8yAscCIF5pRSZptiVE00Gjosmin4hYiLznIpbhQY3XeuofK9rs7kzLBNJOI8VAaNgwP1konba8QfbfTjh6WIDO8OzPOgtZ0DCpXFgVGqYpYKBIjFWZKN2HYtZsO5cZk7HrBh3PGVqSafJ8oN3vkfCNZb9eDDQRGU5hujRi6YkUx6STJK3ycnIuI2Z6Rmum2InibdPSDIychI7CZgN45Hkw2NGrlw1bfRyve6+daXyOFY+8489BnuDXDFiuN0p8+de+sJ0zJ4cnXzwoR+GZQeD6rND6MLByS4bA4EsJbqLhEPDBsOGEe4reJTQBzaiECRqFBnx4MJt+I47YFbFhZUKS1Qu8z4p4kgzGTDA83GUBq1XPXkSkwScs8pxzOl8c4mAbq5/fa8nZW1teM+e4gFbM5ttozPLpSInCBTck/Q641Y8IrXhTE4qb7i5+vMj3j9/3i5hogmO0qgmM8a73mbv2olJX2qeh2JlfyU5slN3puoEL3WxX3C72I9GAix1nIePV9Zu6+p5xztLHT2Fr3/DHp/Gupq9bWvHW9/gX/dTe0qxkVkscCOO9hzuqdaxf97o5jXjtJuc06iV6Wn39cfe/R/K8aSJCLFGK61id6fDG0gcPbQtfq1OoacrIDhYpvkJVbGwde0dnRhan+U2Yx6tP+FTe9M8SkzyBT2uJS79VWEF86xYM6YqKdd+96R1187svanJ37P9QxTHqVWsoxbpBH4ASfid/0e09z2mcQZH14QhrQCAS2AjW+mecbdYtScnZ4r5HLtqm1/a/PjXxj3/3qtfPu3ZnEZEHHlIsM4oYEcLS/ainmq5pelyOY5CbcziTTg3zloVwa6eK5bzo8jDvRrvRkTsLLJf0+1Eh6TJKVRzX8Pq7Opa07cGsQxLT1tbVruXJuHCTAt/8sgNJbzpTW9qamOYvNUDcaZ3SjVC/XA+2smtqS/O77A5KwF45SAgeOGCIkr7KiBlqSEvX6qjIkZ50eqapWT0vIo4yAV6oxVMCSynKlFZOSQXKiVVLzzvoHBaxASEhA2XwUVrDPu5OTYMbO4NPZdcnrxrLuT/z36QBEyjB0AVPBwrM0x4OG5WTFu0hy9gj3FeppjJXIaG5oYIhXPefa56AeQCsaH32FXPH7xmdFpYmvYJYJMMOJsTq59vCEYzVtO3uIUFEkI4k31hlBaFpJpK1dFLTGfIBk3M+SyzXhlm/YZVM92I0tN5jcKvsRQF/AQ21whxpXpqKJuETDWIAaMInqh2Okw15/sh24wHR0ZlQTn3p3wN5M42eRG/My5U11xR6NfuaoklcGEIpwwKeZhXPVpCS+AKf3J+Qcr27n4M2Arxyw8ZOPHO0cGiMAopG4h0JRB+V2TFQXoEuZJHBE7CDovek18vuZUCTC8NMHmDJXiZIZIWNWKoM3hgyhdeMYCP6ii6c2o8CuNsd5dEsY5isJghRAD/iaxY1E8DujionDCFD2kEKpFog0Zlq6ekVjIgqGHJDyJ9xgu6eskUEiHFYiOYNzvgbYEDugeoaoeqFup4hnXk4dqWWwIweZUDYiVQlhbAgRvK1vT/5WtfPHHi+Hve855la7k0Is8gdJ7nIDNSPDmFAOGLX/zinXfeySR//PHHNq7t27gl7p34WKd/CByb+dfOsf+RdU/I+Y61wVv77pnsdTODscfetemP/2jvh6tudLZmV8pu2kOOJLavuFlbCJ7VCFCNV5RkklQMuwp2CPNoT1d81H1FQyDARRBZuRhlg0+dPJXHbE98A4hS8Rs7e20UDgOcPWfcbep8njK2rPyxzYl0xmlri1v7T+RPTI6XEm3QmtinDiAxEWjCSiAEhtlAzsMdVhB1pGjkGhacakfSytfNkZy7IWuuabPQEtel59yWFS5EGxYMeabdWN/uY1lnuuJOlY2Zssc7THQQMEtjuejv2uMVY5yb19I/AONBQiejKJabyQgnCwBJb7JWCBQcosLKInSFooVP41QSOulHP/oRx1eMKQsxmyU7Jc+ToPyZLxQeefgRsUCL9hEqGbzw7EX2juSzY1x/jvzLg0fgz7HaxXIWdKCXTXF8ZmTa2uUxnGq5zfGyCbM9ZqNVzjOtWPdh/qcy6e07tqFYri7gVe1wA7v99ts1pKWv+MlijWsaqdNeznTZlmjRorRguKudzkA/2OibtnHX2pF1NOhIoRqlT7k+63T3yJ31szvBx/VZ6+WXGV/f6xWjsd6+vjSCMsvIRBrcI+1J1NdkSEK5Qclr+2O/8Bb3mmt4ctSdniqt6Ytef4N92WW8v3z2qhZLUfOM4WmD+/OjRTlTZPkuNRpxzmbYElyvbse/dbB666b1vW97a/9rX92YGrWjcb+rp9TW+XjR2X1SJPYcl2D6EKE7DFhv0kta3nre/g3YUR9j1jxkwqubg4PWr/1v/p0/V+WxaEyibtlkDvSbWbl/3zJMIYyEc1YNHaZ3dKFXwAi1/wElTTL89F2tVj00MpNPtuuRbH+bVB0W0uyZ27zJE/AqzVHUxWDK2uEbozmoD2HfnUh7puP1ZvYGd/LDbv4ep3EcPK0b2dHyzqLz5h29v8ABRLA1zg09WZZaNSh/aGgIAKar8vDVeMlLrRmK93rFijd438eunToSyXQff+X/7iTTnI6xYcfiPKfs0aVyaYIHrzhfQCG7YfIOFTi1tg09Fx9NUmBG4QWZMca8eXcNtpnjEiTwnPmxETY3ED+bH5urYjJMCH3LHG5Ow+6rNDeBzGWoEMAeHBxE2e87wduweFZ4AZiK0HikHArkQEr1Lxalk5oBuLB+FhwoQrXLcmFLZhwpHHF6WCyEKX6Wo0VpZaLoyTAxNKteC4diZgiUzWBcflIMMLq1uBC8Zs+hQ4eQjC2vLUl65GAHDx6kW2gRP/XtZSZsy/VX5i+x/8YAN/fwUn56kqWPWDCN7ZWzErZU1j20rojSm6toR7NbXSS0CQHQVUKBWWQfXKoBTeFM/Ody7nOzl62BJagJhNV7m2duSylELRPLkTquJYv+hA7Ux8/159zGFPygo7BMyaDT7eE3nDKwzVBcRIWxKosmK4mZhkyxcBoSyOrNlxBWQp2b/NQQiDRdornbwhLE2kVRSlDRdSgN0TR2AVYqDdQvaQCmeTRD2LTkC/Ll5SQIqmgiwyuAUIaoUwnDK6Qpq6iIWfkLa8tuKxswsfOLK6QkwUHC4CvMqoiFYx1rrXbD8bziyT2J9o5Mbx9ELxskdklQH2yLcnOI67L0oZAGwS6uzC3EOnZXPFSx2PqlqoC0oDYqkUpD7le8ZAQogRZyHREXYUtQIufTSQID/0PMc1lQ1MMDKgQevvqMHJoQN/aMV897mIKl64I7wPLOjsIOjI7pfeGT//3mm25CkT7oz/MB5ief91JigOktFtBf/uVf/v3f//33/eEfrB1Yy0CCJR3W44b3L3jKj7fN/Hl3tJKseWVRVj/mHPt/7cgH3MSWmdu6v/L3h37taHkbBF+1wg1Rv6eBPBVdaKg/hGEyN+TaN6IZGDWDQx4M6oJ/ls9bw6jNJGFK5GmiOfQWPBF75aBosW6eyjNJoCCF1ty5xkwjcJMzoGAiCITn6GQmGLOzMz09kWvW2YenGwkzPTV6bBL23WmvVjmIM0Ff5GYIeZguclLEBX4IXHn5BjLXP1XFLJY1U8aAM8/kmMdy7jrY4LQcLIUwLUXlN8MKVa8ZZDFA+Gwba1LWmpTUWKx7s5XGTMmfLrnlBjqWcdoLm8peK8eY8g6pEGd41/IsJK93GO4zTz9JkImdddgiHqIyjMF16y7f0Zfz3aPTjRMnhnnwiY7DZASXKVkmoz2NcqJRKtvHKlma5zrI17EdbAe15P39U6zmvRu2AJmNiiemdom0HL9WHT9+oC/l7rx2Q2dyCA68lZDnmmUyte2y7Yy4rHVNTkMgFFrCm5Is6aWxqE5BsS3KAC/MRsWeHY1ctr3c358aGSEBHYIDxfhW1q6Lb9tqRrhcutxACY7JOirouKXTesU242t7PLsdYtRPmO6GDhseeGuP801JJY1lV5TSuzrMF15vX3mVzRSgimQKlWKWw3Nq9Xxi/8gMk8XhqKUu7JxRhY+N8pCvG+M5X04ZPXe6Fr1nT31bV7q/I9XWu46V/1TZP3JCrpSTAYzFRjWnIxC/EcvF5PiaNi6gWhh/9sZGG08/i2q7US76m4bit9xkX3F58sqrZNeaN7k032206uwOgo/LWmE6xqtYKuaLlQOjpd5BprbfEXczvJlGijNYXAkIKmLSB//JR9CKXg2i5CMxwc3n0SICWK8r4fNQmeyv9ka77/2R3v86W5p4dhQmtLNmt189QAuDK85SlYy5boVSjg6oFLmIOzDRqPjRqXIDa39R28HGx/WPfinm1fftevHRZD8nQRxAGByO8WhbRFSayzOjjXKeYWdauSJBO1BwGh2967H8whNZnJ2x1rFioK/OasdbcRxG9Ii+fFO75ucItEt4CZyDHg6MIKPxCPwBzJBlSs0gboIZhvNBiqJ0JLYbWApuvvnmZa4CNrcWColpSAhkE9+QSJrHuua0F9F/8apjI1OdF2Qp6LwMDg6ybjSj0zKtYt3DkYCnrck1NDS0TOJLIorhhi6n+bBJIGmgvSLHky3As8bCrRHbEv5vPxf2AEcSsE8gMLI7uBqmKpYd6FKOJJiJdDVRCAM5k7pIDDATn3MramE1wEaUnmFdvAm1sAdWEaLdEpznLH6gs4oyn4MsCnZYEQs1LvypHk3D8rvUCozBEU4nGSAYXTAEx3TT1ZsSuCrMT+Ym6MTNZArRO8xEMXlZzYiCfyYX2dkUtFI2XNhj/NSuIXxvueUWMJNk3/3udwkHVNBSvxTLlgEMHEBQWnOUpiF7Cwo1lxxWQSBbWIULccIAywkvp/E8XUdm2dmQ2nKFD3pHvBKATIl9lw/kCpY1iIAzlD0edV8XFSqR7gY54Qc44hdOgVwE43iuhT0X+1lcaoLfllJFO8+PsaOaRmlgsGQ61M3xugiKg31eahe9YsRq1C5kg+QhTFSM5WkFbHug/ikXoVbg6BBGRA8HGSOWUD0eZRTCfVOLmeu6QNwCSQC3zxVQaAgA40mK0mE36nMTjfPI2NEniltu4vgArWzE4rxiAYDCLSPgOrD74acf+cE//MOnVwDaJZCkdY95noMMYr/rXe96//vf/9GP/NX73veHa/r6ksm4X/oBr2EaXqR4TzbeiDXQDhaMQnjvWqX0+JeKG96bT1jTV6Xvf2Y/T9gJzlVtY9+epzsSdiSR7RgY5Oil6Oq1O9kYMOlGGtO1arzvhSREjqW9jgSPlwp6gOH1cq5ewxg02p5VzL1OjI8XpvxKT7bmxRDG7lhjdsUDulYw/txcy/SOmP6WTqcn6efGU08cmoxWpzOZnumyx4VlCHYRuGEKK3jrVe5eInyGYa/DP9iFOheDjXLDnCoZ3QkzBxs801gPG5yBMNZFQCbeUo6jO86MmU58WdpwkB2qvHTo0PCJEyMN3gBlPXSRMzG5IzfcejsXqkfGc7v3P1MX01mByrgQ82ZXZ3c8nmSC80IzRuGdaAzaHLgRwM7abX9dEzoAAEAASURBVPumwcCOxIZr1/de7tiItM2D+55ZM7Cus7cf4vcUFwAtf+NVPx0MmiwXAE21jz3+GJN8oL+/uweL7Qj7eUHKyMT89piV4smXrVekEjxLLusuDZTFcAm3MEpDFoYvUUDrckyycASXL0SgY40bWOu87S2VD304XsLqt1RCC8upjPO2X7TWYENoSchDeDRF0DXypvQrtyMH5izS4gggZZvbe60Eyxsd5Ptf+MIXVFtVOjPIJmu1LL44+ZAOcjws+ewe7hQ1fHSYZ+s+WvqyrnKvmLmELNGxsdnGcHIEwqJJwBPj/rNTJnuoY8vOICavok69xjVUnIHJsljU7k5xsdzFwDsbTuP4scrH745885vRagWs5pCj/I+fj/zHX4nccAPEskLLLoL8VjmH5t5Gp0AlvWwMpFHtOxg2EFjvAEMFUsKmTZvY6ycL7qHpUs8QeCyiUXa3BRuPdBYdyEHPkal6bxt6BJwMsR0ID3zaydrgTZXQffA3ZL1kYSYyVvAaNXk1IZP127OT1UzeFI6zLVHPprhmLEuN9nwwBFIL/8ufxRyJ2bCenUDP36ui0e83ipziVae3H91NWQ/d8KYymgSBhTwUVzCQhVCXbTkwAQLxwAMHsgHbEbMjjsKBPIyEzjOnFYmILeeBDQ8PFvhSttuRaCWe6EC6ke4KOxkhCa4FTHZcHIGIbRHfIXjX3uZQTGmdlStNaUU0GQ9OPZSMv6XSC/6Tulj0aC+Owln6VHgFmgmmXiBHQ7SvKA8/q2uzRuXylWgnAKcmew76ZHl4WmIBDASg3zScycg+gh8lTBBxUWihdDkuIQ3TFvk2ij/sDy3F8hNJ8h133LEw/N9CWnqguZOh0VkGBwcH6fwwWYg8hIT+5lxhylV7Fi1t0cBVV3HBM9IVUD6wDcD5PAe1pe0rgXaFaVjlFFWaEUMNMTKp0VWBK+YwJVwMdwUOeFgwyaI8sIIHkyyv1geOvOrRk01SUgKBcG6akSycxbBHAyf6IKrYojAQwmp5++23kwbFIg7+WDGaHWqhrM/EstcQDsKD1/0btqKeJlQJmpxcacRmjzyOgJNtRehDQX4oTDG2LIsp7K7HpSF+SQy/xYfoRr6QUASaRqIdFTYgpzSkPMQkeLWCooJ4dgw5zSa5ZQyc3Bfh+UfTrHMeLpmFnQ74XykNRxjJuSwo15GDwgkjlA8SYPRKg/r4nN2xbKrWN0npUppP+Zx5tTDAxALb0985efQHkVrdbNTsHtfpREht+DFe3ygL9ysiPsN/4nO1/f8ySocVqna6Zm/Ckjby/GzDfWHyc3f/2abNg6jinh2sSyHF6TXxUoBWMAe66m1ve9tf/83H/sOvvXfjwAAY0ygcZcwQqBlHPaNeYwoypCAoirawuuWDWPxF+7bRlZrKDgxhcaBRmkUqUs+sryeNbEbkMEmeYKl65UCqw0/QBGO2HI+U53ShMRPE/G7wZE5UdnCu/hZ4IQl8JRANgsL0aB6aHgyPJpC6YF76ih5eMYGjFrwWJ5hN6rM7PWAjHYvF2PjY1PQ0c2PDxo0vvXpg+uTh2dLI2g3dCduCnM2V6jCMNFHocKzMuT7STqxYIxmGCYEPbxSmJk7OdgxsLtXMsVKjM2aOFmoJv3hZf2oga/u1ytHDw1VI3yrmHKq1ujC63LTRuyL333+/HinRWF0cWGKUAa5WK7O5aQiUiBNJxzFaxPOJ0d6k2Zcy16WSA9H13MJ1La4r21UPGaDD/Hd99CutXVfsZBYJrMH/815+8ZioGNIjjspYH1nLHB5ykd6TMOk1WYgklrkKZb++JzM5PpZsTA72d6bQbY4iFFd2hOUuYsag4CWv5gd+WiGFXBzHSs0KzrpP8VB+ykPSV4vScE0gALBvtKWjt764nu0offke48BeTg7cbVsjr3mtdeXVLPlNiVfilSUdHti4zPvH+54+brovesmuDrnP6m/ZehnAML64sxZE/6uC3PIpmW/sLAcnGlUjcnKWc1PE72YFPjaG+Feu1jAHg/NRG1vEcGKcmtJDph2Ft4AHrhbzI/uHB7Zs4xgSG+dMIqxIdKbNtSkzzpnkVK52zz3xr3/N9r1qo87cwXRT8vjhyp9/2O5fY8F6yaPXYpaTvRBtqxbmgbkD9pIAAPhC09AW2gUa6KAoPuAHsOyaTX3VOFMmbfOimIh/ZRNSbAq6QA5/A1QcybvHi5ETRb8vXr+iP7oQpeCJOS/anLFiB/fXvvwV7+EfWzPTXl+fdcutkVe8tLtvwynH5eY8BtsZenEsFvlZTL2L7hNHNm0Zbgtwo7+p5DnMJS2wYO1sooD+s0jHOWIoVtxrnv7+dZWp8Y6NTw6+gF4MjnXdeIwbHKg9cxPbtNp6OMRL2PbY7DOxBBL4jdv6PEeer0JbxKrW9cI2zwW7rJJtCasnhYH0/8nee8BHdtWH/rfMnT4jjbq0RdLuuu7a6zXuFeOGY4gDJLRHSyDkk0ILCfAejyQQSPknEJLwSAwkEAIvcSAhhBIXAsYG3LGN2xavtH1Xqz693PL//s6RxlppVVfS7vrlej26c+fcU37nd8759V993UyggBhzKG6Qt9RKeqGD9ZXFDb9qqZkGPsimIf+1r32NIxm9CglFJmpc8B9Y6HkNaBdc2UILQuRpppdBoSXj4mZaENqF1jV7uTro6kVmPqn/dNwbUJrtV03KCzNy3JKr/JBtkKmnY1yQpPDAdAAEmMqD1btUHzWFeag/6w/rxabezP3r1JL/fQ9TsW3bNiCP5znQALyIGLQEULM62iimbr6xBIjpKdMvzpwaqHAqp8zMn5bQ1sq9Qg85OH7wgx9AYV522WUr19DpUvPM+cJ+ns2QnRDMmfmrPlunrnFY5Zk8mB4+hV+mGFq+Qj5xWAD8+rs9PT0QokKvTF7sdfpFdmYUy0yWvnhIT1BfQzRSww9/+EOeYALNKT6ULY8fPEoKmAQRZW2ohTxpFonWqi5eEtk7MnrRak1sn8KawsSj74SWEg5VNiQhdPhGc7AR0aa1kRjWi0IS0GVqcTBWFodLGA6bkvzjR3S7bcP98MJj7d34uMmyUx2V6mhDscCKruAbYlb1o/pDXVC+6JPxXVIFZoJZ1XHsB9JDLnqI8wgwn41+U3UF576sKxLKP/J/q1YhkhC7N+oSGKg7+U7vqiPR8qg8RQUcDVBxB8NIv68g/ebuJ+/99qc++cc4/J1aR86xAFn4t9OMAdYDIxTW7bff/pef/YcPfOA9nYRSNzPy3PQTrSFv3CnXIrC9Yvlv2lh8hjMVsJf/il4mHG8IG2YOA+hy3g835FD9+2T/QPRjEL0JPC5BbCvGCWwkwLLmoSoSVdoYO7Cr0NjY0dlB9ONkU0eiqR1N6sih520z1Ni1XglurMEcOhgby18kT2fV3J4mLceW9TK5xuaZmvp+gQVIJDwRao9FGotGLujJ7Nh7qMHtSre2Hs4ZQyEnWwnKZdAVN+YabrcjJSRK6p9pZ9IJPGsP7n7GNaIHWJQYTIbKsQALT89zzj047sWCyu4d+2Imxt8YRLERsBXYmouji7C77CbsKXUxW90Yhjg0eOeyd0wbCeAjp84ZG3p5LmtKUzNyz9YgHoZYWuKgWMEvFFtxjzQ5Yl6CiEJEFRrm8prR09mYaginwgR1wLKZDFWSpCpsy45APDAEEHD4443tD/y4r93OdqVaj90hlNxMqpHluTpLFPEbsJI2VcIGJX2cSDmoHx73c5KREIlO+IornM1b8JsETZ1YwsqkceIURFww3y7btcIyds0zmpwr1xt9e4fJjm2aOBGZv3DbbVA8mqavdwZeBUKfvXKaSSGaUq24q5ec5cYcKLgjJXu46JKIi9OBY0PZI2AUZHOkgSEifhWxq5gKiU6YbLQsS0vcUzk1MN/lLXAbDjkSslqSAdvx2gysp+mPjvp33UNAJ7U7Iyj1kbZG7WjkyKHyQ49YPT1mUrk1TyGq2P3pp4YYgmcuvqK6ZNTacJeTlPNbn5T1gxb7hXDTmk76ZQQdaU8MKwTmxyKOQsta4A/kpAVWOhmGFLDVVz4mL6ww0uFQpG/n2B/9SdOuHdgqcJpGCjm/b3dh9/PR33p3d+vaXUNeO6p5jtVs1t+5s/Kd73rP/8zdNJ66JRY0jAbReBA520q9wo5fYZg6NJqSb6l1tHNQ7J+zZbfqW/j8x2vVl/3w75DdPrv1+kPxZjFnwtiLtTXhH0GIK4ypgLaXq3IK8wrnuteSRCQByIMwNhjEyvI9kjsjkkhETHCuReI/CxREesDsoUGWFYowENMS8hliuiEkBBOKZSObwzRUgTzSftcI7OGCuIANyduZnbe//e3sIXOgtJ5BytfL6Jv610kwr/hfFgUIzMWqQZKlVbUMdnkbZrwMjdWHBGdplc9G6CxvP5dQm45VBgDJvsNRooWqC6kHbo3yMw8X/S6WtECMJbz6KLGQzp+aZVihXMgj9MFEiA0OLC0xBOvYHjkI6Dl4WKc9ljAQZBzI7rFzrm+tuhJm6jSyKGZP4yivRwNZAhxelK/Uz1YkzixqCcypKJP6js1xwBOW7cyFOfPJTBBBeXJNfc4a59JP6q3or+wPOIDQIjM1yR3X6qjb09MDnos6lGO6obXkwbWi7JUQGPlskRQSonWQzkuMIOjbtpZWqONyuTQ6Ok58jHE3kvdCFSFerAiyRQ48zj6oUy7hW4XI0CQmRyhfwHYGqIDBCQmhIpJt3iHkatv4QcjaA2dcJs6TqH+FsYQVlhrkMFenHD/QQXgTISvoEf5S8OWhADsxIUX4bwHXTAjPfFKvhq5CuW+4Nj6SDG//Wj58ICIibQYnbU00xx8IUT6AFOrpfMIc3BYa7HU6Y8F/3v7ZTGP8LW99iwxBD7te9el5c/oxwMwunpavec1r7vjiZ1/xxneG18caohf4IIxds64qRPY1QtXhZFpBdiE+cm7DjfC5ZMSJP5M7X1xnLXLhhIgOAWLj6Is6BexsVoRZEics1ErwMoITggZEb4IrLhsk0kTe45HcZYw3o+haBZUFT5h1scGFaBc6FaQYKkryaBhg2irUame1hiKgubDYC7o4q/SCh46EXkSuw2usc0h5LEmTiJqOPlWyG0r5UFtHd9puGA1b2/v2432LWSlDZheCqUhBoTfEa27BK2fRTaLnQl7jWBl0QZWIvWOQsF7kv01Gei/lprs51J6KkB9Ikx168cy0b6z3ftoJV38+9UaDRRYJQyfLrKxqE8GButQeIYtHczcK0FNfXtM9UUz+6JXJjWgy1XOpu7kxc9ONN3CKz7HUVeEV/6ADOhQEAguiI/b09MwWuGK2rsh4YlHcHBmgwhJ5sNRLQARqnrO2JXdoz8ED+5syEkWDTmpE4p4DDA0AOAZS0Wfoey3a4NSnGNcCm0bL2zeKg7x5NE/sRNSGdsF3YyFkKLBNvnC5vuT1RQVLjXhno5zkqzBnkp3Wq5VLuP64FaJt4/0bRMLh5rjVhfevmmdvZNgcOArTy7vgM6OSA6/mwYOYe/cZFbdgFjgAtZECtBcnGT2HUdH9nzYKvurxci6iiwBtent7GSYPBwtBmYRB+JqaXmdqrkU6WvRLWHMLeL2OtD6t+fbCRScRHYfxKf72dxPPbceEAf6eNuA62R2c+35U23Zh5pW3rWtw4Dz9cs194nH3T/7MMnY7bxt2tuQCW9wtDLIFl77nj32ukrwp3PEpI7Sh3gYu1ruGTOLPVZEIsSlVg3WFoxv6HyqGnB9f+1bU6ij3kRCxNWEBTxZxkSwgfZDUX9Y4Dh7JlnA83JIQvpaIdNWaRxdiYYdkxNg/J8J2JkbiZS+FyYA0SdetocH9o4fvTVhPpczDCBIPPbMxG2xrad/a1bWG6XjkkUegUdivXgCBYUCa6Cf9/f2Q1JBKzI5Oy3HrrbdOLTnzHuaZLUh7t878ddWeaESiOS3VgrSCgl/G1gEamnBQFxiylwqlFZH8yYu9piH5Yl9fufL1js28mbtRyqPnqat6ZhZGsQO3RoyfmT/995PZIMAuB44hWeDiHvaAQwoJBfdPP/00gkJt3Trb6wt8DjJzmiyw8ClbjC0IJFyzZs0p28OT2DEQBlEdHeC05X5qT5Cks0+ukDyuvo3oFvnKtsk1c9tEoEMQQcRkEDn42r3kimuezTVWCzjtElYqaG1uqtR8iUkFgQJV4XsSNoZQzyitlBKm7LuVGvFc0c1Ao5LcyIEa4hfxaxZidYLmx8gLahoI0BM4/mql6lpGlcA2UCsc+EJBWVEvv6n/cfjo8bb2TNQt4w8Gg+BZKm6QGgel4BY4yMVaDtaZyiHUJPsR3G9kIodKnfqdCuy57rXKao4SQpII5W2VWkLFVzaO31/JPOeFlAm2dGDygieHJQZYpS53/IrEYEwc3IojB7/3jX/83ff9RnrRxomT9Z56f08/BljD8AMf+MAdd1x01zf/b+R1v3J+x7XR0EbH2xG5fryy3wr9uD1cAW2taqSSunk4c3UeTNtZuGBXbouKdRWEkg1NhEtVEp2yC8tKlcID8wc1CDRjETmRLDuhm8NEAodIxHjCDzAUzuZLXiVoSIST+MyBJWF4MMmTJGS+phwNgiSj8ITMN9GMFavelnY7EUYxS4kJHKNalhxnuT4zNMuEUyv7L0+4+AnbTgivN77xjaxqeBWi8OlNp1ws2mapy3TS0VY/2SD+wG1OxAlla6GSGy0Ydm50vDx6aO/Y0Wp+JExim8IQtsrRVGOyGe8/m5TFAyRMcq3hspkKp92wURm3Dha9rqTflsL6FGmVUPL1rspika90faLz3MoaX9Al5SbKTr6vhE31Z1LZZBlupCF5Q//V9/XH9ZrUG3r70zBZeIdU/afgh4aoDEwuNcDJL/rRPJ8ao5DAKniarW3tTtjBVRtBBtTPNPggTAGjpB3T1PZv3HBycKjM0wwzpNCD+dkzxrbuHM6zGgh/TLQJL0JUNo/cXbYOL0H/OThQ/2LnC+uLjEa81AnKsms77SBAhUkePtyH7jwaCZ+57RwJ/twgjKVIrsIxfubNGsYa6qxlvXh+MeKGa5jcW8HBA4cK+axGEzQPvEXnp0XQ5UVoPvBE11Afmv4qvClptEgkFJjP9/XH/ZFrurdhzQ7bxjUBsck3UUEfyk3Ia5pxlCUe24yL8cJYkiyh9ujDBAsQUHF8UoOCGMrV0mOPhG+4aR3eArR5dKD2xX+0/N3OBwbMrpwMpOoYI8hbTbPJN5xKOP8dd+/hUM/XAnudkrUSbY7AVjDA2DaLg1C+4m9+5u6GSmF/28bnujZzlBOFDCARGasq3knC5XLSI7kjogaq4Ei6Gc+ArjQqXyOEe7bYP6vo0PQZzbzpp6Io4ZUEW3psBm5/uvyh5uidtsH+KGPwSRQcdPjmu4PgN0MOIosw6UXV6F7YEercC+OgAJD8whe+ALPX09NDmsoZMDvmAcUWgoHHvHMafmE/B0ogrfaJPQ1HsIguI1xbxjkFQ1jRE2tzEb34f7coQhyMQuHodAwqDQjZkmRBL8Olq9KfVMcuzcWNnqPTaKYYAkQXNBiC4OUVeC0DlE+BKiBNER9rtMGCgItOIVXXxh36+SnQTekC5A0bRef6jZ4dKysbMsgi6JAIQWBtAtmi1BKlsBSVIxqkRfsQj8UT0Op20TLwe4RdlehZeA6bLmZoyt9KkBwXKjgBufxYNOwGoUqBiLqVXLXGeznXyfkxMjkSdzVeyZvXXGq94sOvuvbSm8MQW/ahsvHEoererNjAUS1VSAdEMyw8cLU0bhESNUp8bOF+o1hZKZ3ZgiltVZlhbNy4ceJu9j/QiOR+xTitYpsHt0ULrbWuB2rhsjAoQEN3C94IxrzpSj9/SWo/lE9F+Pcf/evnI5b7W+96N4z/7NWfZr/MT/KemgOC2H3FK2792hf+6qZfeNPTRxvPa/pI6Og7zWgx+o6h2tWlyLNhPNkazqtFz8Qd3CsamS/u/7Bhpy1PvGShEbH4JR8mbnLoSMquMVSUhUCoZyY2IcjnF6o2CwD8K40chpOkJN6BhdxYBf7SCdzOjb7RmMJosWUdeAPSgND17Z6v2YqJtClXcUfG3QNH8i/pSXa3ioKJmHiYBbLVwoHoo4IdRLvGIZrFRBN2BWsQiKTe3l72Yr1OIJiIOQ5BqS9ICgrQu7GKt3fYf9nGDjQ8+7LBcNk+kvWHrOSOAfRjQa1CdIHALRcRRGHJ2NyxFntjjCcjKuUJMaVzjj1eNeO22xg3Ryv2njE/Eyd0rdkUY7vQ7CZ2qr5ZEbdqCbEVjiBMZrh68dbHOwuGyBYzcwHPfDLldXlFrsm/+tv0r6paOsFxBcSIT7iMNNZEi4v/w8ShgVxCT+YD44K6Ajqhf2b708IUUAhNGicBBm/TdHQ0p4NzgoSkV+3p6dEWngvtOVMTGNlqcHjMytb8MQwoQHf4WuQ9JPEViSlMLyEixIpIyXnQV0p09JDKOouZVPv6XpGoulUCmzd1dpshJx0OGhNWVwMstFpyHFLNGWfLedEnfmqzjlAsKG6KUwwnIeeC88xYvHdDioaxyOrv7yearpaGzIQkGkjwpK+vj/6gowCUHOQAhwOwp7d7tGLkKuL94tfKocpwqVh6/vnnWWKwaroqWddE3SiwZ5jEysKBmYOrM60RexqOqmlC2UJC1xwR8iriuKyewSXCOmNkbGZzRPtSMPLcfQfNnU9abx+1uoSxrj4aq3zGc3ZJRAHzj98Qv/I5w37UqT1RG/iY0/m3cuwaJuHW25Kh3gxO0/5wyUoGtet++FX6/vTFt44ZMcRzCKzZzbEFIawm0cWwcxYNPBEPKuigCX1HkO1aQzwMnwz8OaR1+mU++ZcK+/hgk1BabWYYRu+q7XtdtPY0w5fYCmJGhnUWGvDDfvZ/u+ahUMcfR6OJUikHYOvWa3X4T2wQale88847+YrBDqyLgscxH/WS+qn+Wn9YrxA1Mg+1kcIx76/kF8aFxmOh62IBPamPDlQEbrwxc6QLqOa0KcL01cOGn3in2Qdg57S5yonXtow1nMqTiLckdIW2ZdBDJtBuvcNaPngioGBSnnnmGSrUSxUxPbVBrmAVVV+8J1L/ar5LMHYGotW/p13nVxpQzCnkFtQClAZboo6myQ4JdtE0uxnP9T1nMWfoSvdnjvqJX8Ovnd2bglC0RqYfSHyxrRNFjugCJMwVylyFsULMyEPmHS5UssAIc4xGSwTYbNBs0liwwRXwlj7NURknE6RPFcqAkrDByP1TdhipfaFMaBMVzSTkvuXS9Ppr32NWyqH7fxgdHU13drSfdfaG7paf5hL37cFB0h8aGhUqRFyQbYKhjh/em4glm9dtgDxAeh6TPDJyKUpb7heCkAssw1grGOEFkvcBmfl4e6jDVJlgFQeshwmwoF+snlAlJL6KhBBzc0ce+Obn3/mOd9QN4FUHT/uP05IB1jP9wQ9+8Oqrrr7rP/5t642vs8xXnd88EB75A9vOhTaPOQQohYyGuPXNgtH2L4c/0V9+KcGT0MOiAMFukwWB2yIpWMj3C35pHhjMaBYSUMwIoSzzmDUYZrSxI5puwUJ/bGBPxI57sTZkM2U7Mk7Idx9umZfAJtFPcZyAzuwFA0ePwjeKQ4DpJkNug1XKVY1rrGhPU6hcRrtL3NooxBw7BRc3GomwRMIeFc5WCHR48CkXT/B84AELFdNoollAmTU2NhD0uKGTZWg2RfBgNPeMeumIlSEGTq2nUAsd2NtfKZea12wQ1Zsjtv5ihkrdjJcYvCw1EqUW/HGTGNH2QD7IhI3GinW0YMRtrz0ZrM+EQsXx4ODRytNPekeOhjJNofM322vXBw1iKyudmcGoTunyatzisQM/A9CWxYjrBHvMqaA9306wnkW9Dj5MLc/X+hNgAgOMOdBMBpjzSRfjE3wDCadWMs+9OLoY/STjMcNHiH3FLknsK/LNOg5cLnGVEClxsbdz8IDIEl2JL4ox5itloqm0yH5KOUHzSDyZiK1tCBJ2Tat/detmU3Po9b9Y6dsVzaqoEiA+x1gsYt1wY2TbNjLk6ANWeGNlkcWINE5O7fwElh4LIl0AFSyAO5z1PQ67wGhrjDuDleGREYQCOMXxooYPOI6l944hY/v+4f49h664eHNTDFPhY9bmlBblTEUybLe3OYMDyhmXzYVg10RsFtsmgwQPDqYgAprayLDfmo9dhO43qD2b8H+3lsyrRFCYVO1bZ1755lr+nU6y38x9I2h5nxk+l4oGi9Z4GcbVJJY7obISQwfWH8QbIvyTS14Day7HvEn0N+yfMWFBwStxtoEzor6S5HMTL+tmPChU6iPCiomKmEQNGISQEDhsNsZMrFQSYQ7ewK2O+wMfCNeeZqKDXQ217ybd50JB1A1f7jqvGDcTJXP0c358ayJxbjY3BsSgjWYCHyELD8HAn/zkJ2xfr3rVq2aW0aADRSGtuIctpIxOm4H4hoO2DlsKAMn6Vll/vqI3dGZxS2O+3oD17FeMFz6BAUJE6q2epTobcOar8pT+fRkHxezr4ILHFaMABQpoNo99eBnbnQlfGtIP662gDWNaCYVbfzLzrZPyhK4ibadXml2hD9zXJQj82tvbe4IdA9rauQYfY4StYLKW4FMt9Z8sgND0EpCBhUmHl93+mc7UgXyyAFLvwJJvQHJOW6gFhsOGrx10yXUn3rbq4twk3B23GEJP3bqX3OKSX6RXvNvRfWbZEx5Pcb/wr9prSTF2OBMiL54ICQ2WUhwCRYLUICTH4UpmTNheKcYTNYEUEv6YfJG4YhGwi6NBWFfbcIJKMpGQYEMB4V5Nzt/Xn+e9LJN373k4/6lPZ704Vl2iSzuzp/23fu3yCy8a74w8tA+bLJd0LQGyfWnQJFCgEwpDUmMq9/zOnd6+wcNJ8QehFT7xU9AAB6tZXzOxiCcazWb+NA2MChdxwCIcDwZ6FDfjA65DKht16V9RaMj4jaD4lF9pp5iEQ3rsPz9nuqX3vve90yo83b+elgywBvqll1768pff/JXP/mnHpa/2sNhv/s0tay4KRv/SKN5HyDdm0DeSI86NP8m+q8/YhnbXLwZiDO2Kg24NbRD5Ywn/LTyw1Ce20CXoSLcljUmnIVGgrSBXQvrhKNd2YjfVENjEkim45TxxZYqlccMN29WYjYrEgyuOEcdVQsqBovyVzLT8Zd0R33bQd342aGer7pYLL4pPHNBq2anFp4cDrnPp+zk+oTjZjNDdkQmdKiY5ZZv4tee02msayNRit6TWjhQMqzRUzR1ONXdCPcOZQPICEzrIOidRCmKniiRCNhzHKlWCvC8K4eGyEbW9hjjW0ZY7kqs9+GD1b/8mfHAfcZxD0Ug5k3F+5S3h66432loBpFYWzdHVFf2JpdvT0wOFjdbuVJDazrv1rBA0OHjY+5C/Uj+4gfEPN2yaED2Espw7/Izsf1MwcN4e6k12IOcNl52RIgGNYWwJfo5uEFkpDo1wXgZYTzEJomBi5yzqQfx+1U8IWcUxFR4Y69yij3ER+eHJ0iR5d+CBSTJT74AZj4a2vST40IeKd/yr9+STZPoqN6Tjr7g18qpXWx2SAVV3W3V/Yu+uvzvthkWIYpweQaIRIxESDbtc+eqiR5X8Skh3myOVnUcO0w5wY3EhQuYIh7CLxqKD+NH7oZESuwT6T6s9RYxmaZEadEPTAGim0qEbbjJ37MIQHJhQyJaDLaiS5em6qwl5JINEH9vSWsIhw0HGZpW/GqTQDQMx+Z/8CW2G21L6btz5JTaPnF982Aqfg/0zkTnGS36ZxRuWVNiXPfGtdDV/aMPWPWvPwayqiogukPDadAy2Fpd+jmsJDECwMfyhOP0DnyRPlEHUzW6AzAIbFEytShQ2A+yfWxM4KIlyeM9z/9wTuRv/H/+ZRu/THbFijLEGOdP4dlB+3om9fzAULVWHPpNIfoahwJYANA2K+icwgTLm6z333INaYMOGDezV9V+n3cDSaBkNxDqThQkDMj4t2qgDeebNNLDX66yXnK1AveTq39A3kBCIsUi551NOiGOjv6x+r06XFudmgBkFhC/IjT3ISo8I7yQ8HqdGVdDb7ymIe/AnLATCGs+EybIsEJY5WyUDZ83SFg3phT+zuVV+0tfXB7fW29u78HYx/GHzrwsIFv7ivCUxYEG9jHz8FAHOvB2eWoDJRTSJpRWd5/QEsPVf0T1oXpeHnAII9Sh8ctW/9E17dSWb15QQABOARlQ+nK3QJKh6JdIULCRMJ0chB7n843SDHLYNfHnRiwoDzKnMC9SFyRZKs4njXlyXcNCtYRDme0gCOLONWrkw3NeaPMMz46iXYIPXJyq3dpvxRx6ufehD0cDJiVWVWHBZu/cM/PGnOv/8E1du2rz9aBDu6qDWcj7LP0TVQSLGEV0YGkhUgrhfjVqEyCzoYNesU7YazQBDwDARQBhoc7ELga4cr5Rh6wP4M08TfuJ5fcqgnKDMSm4A18PQ6FvDPhcqCF4Yu1cr4kZSQXkIXp5XzPzzvn+FicmZXRm87+u3//KvvA093LLsGy/052TfncYMMNTD//7IR666+pon77lj2y1v3i5WepdtXXNJxBzxqgeF8nTWOpWGtGWvRVerc10rrA5cE7U+1hGwuFCMGF7CE4K7tYokUarUqmsyJAIOKrnxUrFawUUcPDGCUrUadoqZsGhaYKRLnlsa3tvUtiYaSyaIYSSK25BXKRZzY+0t7ZoxFbzDAjvwh8qGPUx4LYvUnWe2Ga2Y+i+G8agjCcjHSoBGxFkReZtyVhFyXF/cEeT6vI7Q2rK/Z9QNNrSEvFxrMhj3Q3mxhpYYOZK6jKVLbFgGzHomZA5aIN+EJ2E5j6g4P5XAb1wfGM9uH//TP0uPZ4EZlpmO7yWGhyp/dXulqTl89dVWNCawfKHxyU6s4l84GaABKFAQTdNzLrwXk3uDiPcmrym3k49O2b/w//C9unsoIrhhfnEQYJfkfJqt2yAS0hZy9M1W4HjPBVQVL+gbwZDeOJKXsIcsE21qCxYBNZGaKsG/nC4SBZqNVGyhQT4+EZ0SlBhREcsxTlrp7rNikXB70kxYtc70lI2Iiqi5qcm56mrn7HOCkRGvVI4T1bml2coQJPqF2cF4G9eA43V1/mdHcuTKFZyPWuRJMO89dAhEgnpGyEp4J056BkL6qP4juUIJ6VE1FSbVfS5lOMWixakDzHHjpBiH0ERjavBmKum89Jpy/27/zrtMdhOlD0dJ6r/pDZGtF5oRpboGFuvWhK8+07f6rZoTehZ4qBPHMIrw59u2VQ/u8x8vB69xTLtq1PYCDAJuV1yTFErIo2sVv7Gc3/JfX0JlfOCaV2UySUKr58uyz4h/hzj3krZILKK9ilco1ywryi6HA29D2CE2gYQlI3I9kggYZduM2mYi4qO5b4sraQBpiu0HQgZ+xE7ta62xojC3bFYqV6Ed2ZEpP5iPvrRkVbdH7cNmENICl9nAjf0zmHbTTTfNPJj1K/zKJqb2MQNWmWJQUbA6mgGGcOSiJFIePjV6oweeQxUMiYDoZ8m7wWwDWZbnUJAoxtm1tNoEwmUlqO1l6eopWMm8DLDuMxi10p1nZ9AXa4m2+KRR9g0+uWZD9ZXu1cz6YcvhS1lcCxGsz3x9sU8Y+2JfOXXK0/m5T8wT6SoYguTrRGo4ue/iaMYOTOhZQDQ1SLvGK9YCBxqb26mwm8EH7t69G24u07WJExNpLykpHILhBmRUFBZYcQBKeyNMsHj1QMNw8qnUjRA2cmILM6ipW6I0UwWsrr6ggGAHETQrrppfEP1jSIkMHWspKCA+X7LObiqP1778lXRuPNfYoclKeGr4kcjRo4U7v9fwzp7uptgOwlSjPKhWScjKb7Ew3asWs4MkAtl6dtfLt23Fzgwag3OQExZeV7fPDcuZuSCOqWaPIT84R/gV8fEDDzwwlTemMJwzk8IEgYFa+KKWqIVooII3KEmMa0HyiNB1qDAS6ypX/moq1Wo/9E+j++/HXNaxCqHqvlq1xX7k6//H9Aof+N3f0d14MX1OoTtPt2Exr5dcctHLX37TPV/+8/NveO1Anjy4hmPYm9vbwpGJEOqZaHABPi8gaCBxeipVoqeaZYnG47j466GGKpeJzgYKCBFqhJDtQKsTx6c9jZomBGmYsJ2yG+JZctP5LHMWRowMWELdR3IsIXLe2nGoMxAYcVG1ViqMDSQzLfDWijuErkX8YhXcgCA6qOrQtRbdYFOT291I2BqFeArsizo8ECU+/PDDKIFnxJWhRllxTVG7odPuimc60qFqzEJ9dDRklGuhMchaghVJMF7JWqNtwgmQQ24hOgobBV+OBVlrwkvm8oW7707nshEboxDSYAcYbRBO2inls9/8TmjLVr9D4vWqS9bP5I3sFIsay8SrS/pDQ3As8C1seUsjeZkbJeADD3yfLERVohVYJomIojrKEROt/ltS91bnpd7eXlAM8gspLMwD5DXt6s2O52yg0EBwwic+KRwMIFf/iFvynSPjbk2pQslrBcEH94szi8hYRb4oF0eIsFeKy5KYxZLyiqDsIm1B1AKwEc0S8iEW9jG+XddI1GJBXfWqvK3vsakgH5exdq1gGs/U/5NlFv0XhQBIooy4YH0NchqxVBhSR9JY07EOewpM4IAY0qWWlubeDRubm5pGC+Wnnt3NqBKGv2U9Eaie31cLxaPRc889hxMFCRQOddP6wcDtNWuj73hHbds298EHyefkdXaFr7oyvPk8C1XJJI1otbWEymdbHopWw02ZoiPGyDOTiXzoA0TuKn3lq06oSuRsgYgVx1RqtGiOlkgbJcLr8ZLXc+C5NQd25mLJ525+LdZdyYjEdSez1EBWzJ5th7RR/PO94tjA4YH0mnOYnbYEli9e1CFFMHyyHw2LtBtxGCnQM1EzhUC7WsqVxeYrYh4QyOQj/h6JDqm7LEQAcnEMx55JBteNWEGtq7Xauf5mMp9Ng4D+ynAQS6EB5ustt9xy3DLzPuSkB3spBnrzqY98zvg5GGA8EtkTlrYbzNuf/y6wLBAANzRtVw/bvpBqOfWg52byciCJFpeAJOwr2g6TYnWqcSGVz1uGPuv6uaEVvfyRtrD7Qf3zunZ/xZxhGd2e5+3V3AWgiemq1tHNXfLEf2VJMkHLxfzTbaRdWH7q+BQL7x7cghYH6x1Di8/AhJloM7VOmmPzZ9Pg3Dzxg7JeM6ii77mRk1FdPFnGJuptrdwNwIG+YmY1o3VcHwQ9Ij3MlevJQmo+MjDwzDPPIobq3XxxHyn+cP6DKRBvPzFAg8rgP0gJITcgUfiANIEDJnYGv0tEaJgBjjqZOV7hnkhYijWQqZQ5pIQZhImykRtVOuQK8aXLuWzBK5UK5C9qWJMKwtWy8dRTFPds8eWVpoTSIB6XVd6xK1MpNyeS5pCMJtnY2tBMGgV/6EA/qVo61qxb1xi0tqCTk06CtFxTDzts2bh4kUlBvAW2sxFp4LMLYe7HE0QtGu0ppukT5oWDmAJKY8yJHdtTahirZTwvHhkm/BU6Cr/x4uCmX2kKJ0RpcdXbW/vOyT761bKRi4T2kPxw+IF///yvvv1X1q1dK51+cV2nMQOsJt7+6B989LLLLv/pd7588W3vPKgitQZuZX2q5pXybZ1doGyoms/ufGqM/MClFFNuujELRVQ4BTeHEhhsh9pDEYoTIYgOWU7GzLGq5Y+7nQ3JJNITZcE/VoSeVPbJIKbnJllVVS9vlPDolRS3EIoBTnc+jLKQsgrpNS+qsEUEQOQoOprD/Ngsu36xZo2V3bNbrcl0SnKCLvzSSmC2bK0E1gtAf060jLgpCDoaYm0NMXq+b8xriluj5SCRDwo1O19FtkQiVjEMqdbwCoabF80wn8ABdXADw6iVnL7dEa9i5nJuIScZRZ1wiOjnyWRoT7+RLxzKeqSQgXuR7CkRSG5Z5BPiNQAKlFb+YsickZhdoVGBOIZ1mQTCQtuWPmMcMDrm7dpZe+BB4+BhM522Lr7QOW+b2dGKwa5sjasyloX2+NhyjFdzvPrU56SfRjGgzISFuOGGG9j7FgucY5sSS6Ghinc4J7GvRiSEuiwNDgQOG9gyClO/nBcggQANJJBtGo0vIiWoRKx2JYmsrDJJdgcnzC7fgfrX9lD/yn4vMiO5dF36Xn2XByd+wfqKXlcq8keKyKHkVAwFXnuDEQ3hWbyN3kJ1cYQQVo0cS0NDg3tGh9ZtOlv6VhprNY+efdY5uDPzjyrUKpdPfcMTAa8aRgC7uWaN2dxqXHWV4ZG0KGwm0rjn8qseIa+YkajR+tLgyGeIOGndli5FNvrnbo7//Gus9Wvdhx6JP/Zo8IYSO4qkl4pcMFz0q76dLSMvxlLDKrruWY9+J+z7Bzdt25NYg4cGJ7ttuOtSTmssGCl742VrzPcryBgCrzJy0GhZa4VT7Q1i/wyLwBpH7lx1DbyCC+NZJMzJtvbWhI+Yn8OTsfZG2a8Qlgc1vJREHMiohICTmUGUGEJWJgs9hNs3Ego1bL7KtCngCoC5N4xvfetbaO0gm2CA1evycI6Lw56Tu7+/nymgPDcUhudB5o2ZA/eoII77en0Kpv468+FC+jC1hpW7h5pkUFpKtXKtnMo1s11jFMqiW2AnmTuAdlzim/2NqqhHzy8ZWbhHFMi1wMoXWAzTSq26pyFQFPaST7YUNhbue3p6eH5KzSnKn97eXohgDZkFDnMJxfTApx09S6hn6iuAmjNrsXUyEQjE6/WgKOMepaW2IK0/n3aD5TMHJcWWbE80rUL9lUMZeZy+ZzsiSiX3nNFz5Jg8bj0n9yHwxDaHfXgOEuKss8468U4CIoRZAA2Wb2kYSw0DA0c4hhua28l6UBzEzlmUtHgAQaHIqaUMmeQoUwcaBmBQI5xx/OyK968cY5zSUAKwi3zhV3kGpSysgZz6JEEMWcQOMvNjR7C4goBAiF8cHc2iWsNSrD1NSbSpJictyVOjkvGB77Q4GdUZIkna4js3sAs0odhx4CfkUTRkxjEyndzNpkG1DhY2Qy5OzHoByOArrrhCalGiOiDJ7qQ3TJhkQMpXjlc2Xoays9hUToer4VjrXr/qeKNnjxpn57f3Y4idSsRjiVRsw1Wp6Nra/Z8rO3vMJx78dCgoEnFJdUod7fVWT/+b05gB1sDfduEFv/SLr7nrK3/WtfXaAb9he1D5mYWf+XhPtPhzt7SEINPC4XiQO78l0+RaR11EHtHhqpOrWQXlBhzgxK4EQjgDQ/dhGQ+SV71gHGeAcb+rgQjoGDkExEnGAc/1WBbe0IGd2C5Q2Bw7mCtkC8k2uM1EKjEetmNWNRoE2SN7BVOdSGPrGrVu6KmsHlYjlDeGGSVSBFetXDU4q5lwU6IJVuts4gifG6n0GkDa+tBDDyGZm3PLFoK7KW43xqyxin9gzB+KWSOVYCRvjNXscsXIowFnHVombDDrHNqa1YjWifUXI0crHT56JMgWREgmFq3Vaj4XyjQba9YgIxguGv1jZqxoO6EgYvhEkU1HTf6lIhbKcIB2/EvtPYpQZhyyt3DJDnECF6Dg2GN3XmQdGuRGMDpSu+tu//NfiOZwc2Schved75RufXn0LW+DGxGj3tP5gj4DOHjHnYBeQk0TNkKGsXswGM0Wf7p9X8O6c3AfQJFIwGGEI4Q4ltBYaqNnTplkEAkfeJGbSAQsuYErREOD7RBnimar0mE3HbG7G8nBKy+tKJj1qqEJZp0T6mBWuEN49+Y4OY1kD0RJjt04h4qmmSKRsB+K5EfcDGeYRVz0UjDqkSqBenY8tx2zKM5N3oJJ4wlvQQHw+cIQMBomF3dMDJ7hJdXgZIx6nHznxk5fXR0iedvO2Ksrxs/fZlo3BqQa/ua/2Xd9N7j4cPSleeAY2GdaiUuPDhh4LJO0nL7jQ9FYq55x/9eodfetbxnzQhgykRUpbpuob2OO1cSSj7h5p7Z3zBssFr1KNiiNhSJG3IJZDWH/zGmOARe2XpIkvYwDEqnE21riZkR5ShM1OrsbPvM+I1ENbw3MJ0WxL3Ys6rgmtZx1Pk7mpFCMW+EziGVPPHtCgAALcT6W8cs8yvAM47vf/S6fr3zlKxfIFUCvc0hDAAFdoKplOnUJt9RIzbKBHOciQ6zWFfMWnLyeRAhNBGSU5ifozlPBQk93HZYJgpLP44zkRf1oAkemjFE/mW1a6wUpBhXOJj8Tl9Bw6lnGJQHM6enp4a1jFmO9lhO7oSHEFvQEfhszBG1lQJcQQ7PHYnivVcEn1sjyvK2hCtXLnrY8Na5KLbrbU5uqP5kXQ/RbMLo67AU7s5Z48nwmztSb0PVzPlL/svs3goRaCoNSDh6bIwYMWQnMrA9nJW5YXAgxWVazTQHPZ/tpsf1hHbFX62zDi31Xl9+5YwcERtuanrIRIqyPqICxUhaHQ/hMzigDF0CVa0KOYp4ivcdWCus0GFYUYLwrg0EnTFEk0HLIc4TrujnXOHV92E4yCDeuP4ezLuYWvJHdDV29NS9WUVL1PVm33BKPXbjN33/AC0Wpjtf4X2oxjfCWs0vRyMigal16IE/ZtUJEpMRlM6TCW4alIfltvmsq2Ov33LA7cdVVxywEnWiGQWEsM5YvVPf4/YVEsWCg1ztwecVO5JNjQ3sIagTlbnrNzS1XXnG522R7rzAO/fu+4f986N3verdWJs/Xo9Pv99ObAZZZD6zf+/3f/7dvbPvJN7/UetN7xUQxEq6kMk4TxgyYLAfEprrp5psoOVwynjzi7Ry2D+aMIzk8wcFLEcSUJFwqZnboPzEGDnCTg3qH181WzWA0aENBJClAzEzczJbEdD7Z1IFRPcFSi0f3JBobraZ15UqtWClAU+MGBGVpRk2YQBQuCo0FjxXlKyuB20LVxOW46ln5qleomRvSXm+LFRE+i5LzI71GMTZ6wk7OEbGwvh4oT+ebomamw4flPjhuDESCXNUcLHpjZULgWIWK0PMEn6a/48NDGIUXEnGrJeJu2GBnyeDColcKbtEqmx4WiRs3WMnk/jGvb1RMp6M2VpQGhpTxooVOLxy4YUciyqYiZjJsxh1D5TQTUAuwgZu2OJElDmm9kGWuR3z8T4YJIaJpkeOXmOWpNA/uVGvu9u3e5z8Xz5IuS00A8g63Zn/7u7U168Ovuc1sEAL61L8448GHmSc9GPLss8/CEizZPk0AJRcClACx0WC+mi/VIANFSMpjMX4WUwIBJpgiglaZV/Eahd2FB5Z7kbfyK3pgcIzTSNIFM3EpOxXy2lM4LiwC81VnTuQDI4hgrCwt0qeulIifuCBNdPhKYpliu9vR0ZkzohnBVSJRIaWyBkZ1QSPVkGa9YGLHUY2EldMFJOSq90nfqwfycPKXFwrIcLmsBrvtw/6hX7Otgh/+hFu8c+x7R2PZrPXrJWdr0XAIJxkz2j5cDdJjJT9bxitXtM/5grthzxMdA/uLTnTfeVezmkSqbflJfIaIheajVw/l8uOFQ8+32qEytiqhWm1oV8hsHD7Y2LVhU7WGXzB8suQDpzAWfwnbTIaI/8wsYgSDGYt70L28xb7DsvLh146X98eioylZpvhMELTjkjEiV4upWGSb42y4794fkUDxkosvMQpl4pTJPEajRAfArntv/57vfOc7QBUGWI12/g8YDC7wBCUeL07V9wJnns9RBapmZNsUoBjCb20Hq0MT8RDWiA3zFGGAoUsQl8BKzTGcF+tP2iuV0cHNMlMsNO5ZQfUwxbMNHLnG97//fSaRwH7TyrDpcVEbOMNPSDqmFViWrywN8IeLhtCJQV9icKRrnrr2l6WtZazkVO7bcYfJUmX96p/I8gDfyD078wKnFVzi0sgAPsCrzAsBUAtjOnaY44YKO24nF/gQtITppTOcFAwEKoUnC3z31CkGzp8uYhRAff/9P4I2aVt/dtGD2CDWDc8wQ4N0FSKWYx6LK6TX4rDFYSFOPeK0BAVMaFvkrpL0SBhWPsXAWVyCKSYl2VsCrLiihLQUi0kIGUs8LrnH85EI0hL/WWigxw9YhU3xzJvelL//x5yY1CP6YNt0SD60vqvlpuv32/H9xNWZPM10e8kWjFXhAkx875LhEyaLj8We+hLgBm6obDqpjBHCDhTP4YuTTQ2pM5tbzm+ptDhVJDVcolg2rSJhMr3sfzz+say7732//d56JcfWfdp/O/0W5EyQQ0+87W1v/fI//eObb3tXNdFuRQw3YeXj/s6R4Jy2AGtllDSgb0vMvKATJoxUIvBycloOgfLCAqMCJSQapgMwwAYRkjEU9Mh85Rs5no8HHSmDGMtUk46Z+bLhRSI+LpCkgPFJJRJDWYTlk5Nq9u2wXxnND+9NN6yLxB2iPdNVEF0oYEXyivxIVpPQsgN5knGZBdfM14zRintmiw2Pyq8LxDOKIZabCYo5nkj/w2aq1ejJGAdzXiZnZGskF/VxLyx4TqkaFAnZI8aRlTjbdCQev+XmsX/9egqLsslu4zBbaO5M3fbzZjJVHTHhnJUS3YhULPTnbCkw0UTPxj4cV0OUUeJZLK70XiyEl38QdzwMPGJojEPEoYWkFtADD/lPYHT8a9YfJouzw3HLJwYekJULBKC8TcP5fO2H98dz45jJSB16okAX3MW/d1dw43XGacIAM2oO/kmQTPzVD+GBiZUNrXkiMrxszdg3BsZakg4XKMH/Kvca2GAJJKUugbyaSYQcYl6M5YD4rIo2GNZXFMUwWQia4JktqyHsJcN2d4YCE1Cf6PRK/wmCQ1m8gbBRMJN2rTnOvNN/QSFMHInvqpcVCs9SWU5HrB9aEn5HS6ajuVFT2MCTsXFOaJoGIz2NgYvrOM2LEvgX3WqfN/RntlEyE/e3vhpY8FjCiblBymj9sJN69VE2CsMmARL2yOGwVXL9l9z59xG/dmDbTfvS68SISlz0yXIUckmtQLR2x0jEElkn5ktAv7HmmNXQ1tCSdCqlAkZREbhT5G7FfHl8FKP0qF+MJr3CyL59npFIpOA/GUU+2Jwzr28IvmV1550PHy7fkzOejxnIsi/Oxa7JmQ4uyXG79f3wGvCojeRX276j8tAD5q6+wA6Zm8+JwA+vXUMkbfTDGzdsuPbaaxcHmeOVnhd1CeemZ+Eb3/gGvUIYRDXMFzO1lNk5Xh+W6xl7VF02v1x1nkg9wIcLZgOObvF2NItrGWZDRzLjNRrV9zhxzMsAa4ZZW/TN1qRsOWq6+ZytzIk/V9CS7aJ+ATeYhBVttN7Wwm/QanKxEBDVsbPh1rHwd09iSRSkWpwBnFm/+n5psJ33LZpgpEiHAREWGXp7X7mx6+ZWrv7lrVn3FqmipqnmBeaJtI7hjxZfIsfE5Ad5BM0h8tCWHYuq+e6772bxn3fVTcUKwnciYIl5FLkNVKwqCFiQCo6YM5NahQegsCCBSH4DAnwKQwDLSvPsJCZu/2LVpdXAlCNcLo6A8jqcpF2+dJ15fovVZHVXQ+HHB6r/tds7UowNlUKPPvz8hg1r4n/56ZEv/ptL0Ga8CjkZzzu/7dd/eXz9xvv3+2VPuQzKLiV6ZqGB6IQgfAB5jN6IH1bogsCA1C/VUG4L7QAPg3QAoj2TjDTG4nWhHt0pwh8Xhp64/9sf/8RHMxkMy16c12nPAAumBsFHfu8jd9xxx8++8cmL3/7JXCU4IkQk+lgR42xpB/uYUMGq5qh5QRcmmSCwLAVR8BDQGTo3gLKE6CQJJlhOblCxSyBNi+db+VpAvtCWJAGWhQcm3gzeP7nhYc+t4ODnlnPe4G6qCzd2k5+k6EqCpRy/oOYK+0lEOtITIfLVoSxrTV/wCGOlgHDkeAVny1a+EnRnvN4myFdZCrq36pXjLAaGrCtBE8XZxk4xd7abyTblLf6HHd2YsXoazaG8vz/nZROh8WqAn2GuYpXia4pVv6WZVKBZO5lKf/JPC5/6G/OB+8IwxlDY55+XeP9ypl0sAABAAElEQVR7re51Fb6hKDUshAW4FKLly6FXUgwtHsWwz7C36M3ImQavC4sRkX8W+mLhMrHrlJDcARsTUakpLJVY5EkV3SBCA+GZRFk8sQs1RCVCkhryxKgnh3PMXwzC0d1pZ9djfpjlCyBk08HN0TqwTww/NMQpzFN+Y9oOD5hFrD1Pj6uOEtO6y3PE5zDAXEijZys27a1pXzkXnjlQeOqp3QeyHmZFsFx9u55hii0nunbDWZM4LbIbASkLC+ixclRKHuaMxcQJhKMw61RZlGNPEbSlbXjg1qSSEk1rbyW/1gzzaH6igc60CMbqqwzaC80hbC0sbjQc7U04rQ3BYNYl8a+YUMn/XBNIOJWaWRpUAYzJZtPyv4L4RbXBT5ulR22jIGLqoMFPXGw3v9uO3Tw4MvpUf6ncsJbdCdCyyjLV/Nqnf1KzjF03vnbER1CDTy8rmry+nhhxVElR6DuRUDSdyRWqkRRhsIaisXhLc1oWKGsPXKdwCUlXGSkG5ht8QxBALi3H4cQTEfm2bZcE3l/X9o+Hyj8y2/ORNxFZIUTHlHgDyWDcaP6fZoK4VmZ7qsF7/LHCX/91cv8hsILX3bvuym89P/qu33rJeeev6eq8+eabgepi4QN/qHDpBWng3DXwa72Avte0LAZ1WpvEPol0DNKKHsLkT3Wd4skqX1MxZ5Wbnq05usQ5ggRtDgaYMgATwKL8rEN7tgpnew4Hq312kMctygcYyphGdbyD2SpfrER4tnqO+7w+a3SDODRTmSWYNK7jvnUSHyLRgKlgTtG6a8w/iZ1ZeNPsvRQG2o8//rgYhCiTkIW/rksyR1g8LeQtJJ646YLSMMALKb+EMhpvkcqddsbP4M99993X09Oz0hpg7HK15zYbNRAGdfmE+10sAwyeF0kDadpN688cRHeFcxBJXlQgKkWeij8wDWiGk+NQ1LtiOKUof4mAJTbPzJec9GLkKcypHJsQMHxAsgrDDN0T9KYqb7vIPtMsJoujRhEWInLB2sxNGxv+4YnyA/vN5F/9ef7aDvtN7+z49EVe//PuyHikow0zhny6/YcD5o4h4buxxJaqRNusSAoUGBYpSIOIFSRoZoUuRebmK5LztSwjFWtwCG+gFBc9ofpPNQ1JX676n/vz3+9ev/a3fuM3NemzQp06udWechv30sDR2dH5/ve//w/+8E+uftWvGk3n5ir+YdBXphh0dc/pkJwgopdiXUWMl3SCgjCtMv2QtUMFLw9pSFgrlRqrQiqzkFg5ELUGahK5TKlmD+YCN+6TTBhWqbWtM93cli/7YHIk3ZJoWsu6JdSNCJCiET/eWvZsMlz7GEV4fjomZtgMSpbRBPHMF/XANCo187DrYYdMdOhx1xgteWe1mA3CNqu+18ntWYCCq9tjjz0GvXjNNddwJKuFe/yi039S9hsdKbstSXZibyBrHI0aedeEJx8twYtaXqk4/v0ftV1+UfKPP8aG5A0cjTVl8GiBjyntPexFm6teCp6WJqsEm1XbFnws6V4ZGU/K6olZYa8Rw3K4HIbOVkN2UtItKdaXBS+51/gq3LKECBD7WUAksJGBy18eXNAeNMd5uw479fOMD+yL2DcJlqMdSKaPd0Z5/UBkbsmUBMISbkRdepJg5nH+xLJOGHbV+1lqOPUfI8HlIEHIDZbOdgbXyTuGMxN0CIDyXsxp7EI8aZVLxbGxpuaOkI0MZOruIdzvxAXyCjilJgn+jOUzZwsPkMCi/jWtpiiet0h8hP/kmtniRD3L+kePcTCP6a+ofx3TbUvrk2Zi5lFDQcjCNW3dulV3KRUyUk2grRSYhgQwUdBMCzTMmzmOySGzOxl+/Ban+3rD3edV9yNuDjk9pkOwrghLIFuoHRoaDYW7CFHHGskX/PVH9zSOHSxHGg6deyUuHsCcHQrHewG0RVYzichNgt/mtk5yMcSN0PDhvYSJS7S2NDoh0lBhAh0Nh/xYojnRiyF0snQ4FQxs23KWnggZJq0CHbvLWf/PtcE/C8a/4viDxApBAOAbId851279X2bqVgRWlLOGB8f/4q9Se/dYjjBFQBjZVvLxx0pf/mrjG173xjf8j/POF03sYi8sXRf7Sr08TncgvP4K04vmkHs6hsZexwrm15PLAINgfX19PT09EPf1bp+Um6mrXndg6pNJFH2ha/hVwsGeCKug65y7lRfam7yjPCcdn3MwwDN7O/n2sv2FUwIzkTVPw59VaHpRYwBQ7PYEkaKfsHbaHx7dO5XQVba4U63D9dHVO7ZYDKnXUL+pV1V/ctwbgMOxiGgYWC3kFaQJFFusUI9Z4DpuB07Nhxr+SAfoHsBZ6U6yJ2sLBYRiJ+IDzNY6cOSIjVGSk8hXCNVOhBqoWY4zTi/Jb8RA4F8118kfIUuED0RrFWDADBUo0n34BDV+of3VC6IvFu0OSYDleFybrL3ncnPjyMHct745cM8P/aFhD8Piiy/a9MZfetfWM9J+uWdgh/nJO/6z+6bUdVetuXAdQXddw96b85/c5R8pCMkEPcEipHYhL9G/qUfQxmhiYw4q2QmChMfLe6kGDWxOyzXSPtE01DjGm4jh5abeKqPHSfOR+7/38H13fuXLXz4FzVuWESxTSdhlrHZVq2JL4nrPe97zhc9//u7P/cHrPv5VYpSiBw7yYs0A6pI+c0ubJYIVEBgtbjh4SRcEJZb5/AitDmlJQcFuMt6yGAIP0RG/EO6cOMk+Sq+Ca3hFC9auHa1RKBS1bSds5GIpyw5LlFfw2CcVsGEn4k4kXiagXA0PZMQ8IltKkyUY1lstqkkk4+8Eb4UlKQGlKtWg4OFjHKCM7W6o9TZRvSoggHxBEzINrLC+RDnChocLRd+0X+f8KouOizE3RqzG1qC32Roq+IdzwWgFpywY05RTLh74yCcaXn5jbPNZ4bPP8fCse+zJ7F33pn7plbUL4oMq1CIAoZsMjhBHwA/fTixjqRqIhlSuUQAMRV5hU2H8PCXTEjooWF+ALiy7jp0Dxc1zydTME5khufjDV/gl96puWGY9eVK37nn9UxUVNyFCgqHnRA/Aiq3/OvuNTIFNyYsudH9wb4h+1yuGqWDuX7I1SDWyS85ocPYqT8lfgM/ll18+L93D0Y6aBRsY9DwU5tKnIJjfNxYQ7rAUbk5isTw+Mj46mm5uZcHA2TK/6pJ5UXMsuApJoTUkoAJzrL9yL6eHIW45rWmzKeIf7tvut7W1t5EGYMUvPRbQ8HBWOkJ7LYkgKvxvfdYlVgo8iQ56qY1UBZUVAmgcm9pLCi/B83xqDZP3YDbiYNwoNtmhTao9mp1AxvGiWzNthNosNNJH4bG++d474tXKvjPO3pdqh48neQOnNfbPNWJOekFUfJJYcbIASWwYTbc2tncnozbGLGQzJw0V3D/x3kPkQHLZ/fxk1GgkHjzTImtONS67jaxV02px2v/IaPl1r/i4Ud1nECY/stmJbWP7RCQk/au5FeKa7tgO221o8k5qkDcL37s7fvPLrr7qiksuu3xymAv9OxPUC31TlZvqI9rb28szpp4UNbAuq0DJTeuqxjoeTh0U9n6oW0GeqQ+nvbhqX+kJrJHuJxY0mllCiDCVXq+Pot4r/eRE+s/yWZTwSAsy5mCA631b9pv68AEUKiZGfeLDX/ZOTquQqWQz56GOPMyNztIEAJclYO+05lbiK6t1Yef4UhqvzykacmLnalEUD+dFaWRAcL8ramuwlPGswDtYQqFRQCi8CjuVBnt9UhjNvBMxc8S8TrIfiI3Ono2JlvUlokEjkIWGF7WXxMHC/gcbIPmmKH3+igDeg3L3McPEJFg5AEv4EjmAsQ70hD4QckF9pSrhCYzaa7eYvdnBo3/1Ge+/7uPstAmJkS+Vv33Pnqee7fmTP3jH2V2p4sGyHX7U2tT/MzMa+Jk4aY08EmyigBCSl3bVP/4IySSch7DeaN3iDrJsdQ6rE3nmGE/kiQwC+TpckWKAqy6jIwiIsC3xsFJYSL8mrlKp8rf/3+9dctHFr33da5cwF5PVnAZ/XwwMsAYzwumP/9En3vLWX77h6XvtDS9jM8tVhCiHgxH+yws2d2JgQFkh8BKOdWEnFhHENMcIECWteTgv8eI0u4MrHW66DtGMScblmGTOhHDHSx6dhzdGeiFRDqMBScXCVRg/WSQiVQLBHJhqiYljEU26UpPFgwUFh2Yt7CUI8yZLXC0+6fHESqA7rEC0r+VsgGUCkZlzVXukVDuz2Woic81EyRdQUx5MuYjui8sEsjpsh2Z6gU4peMytiMFeuKRy3Bu60mZHihBBxhC5ksKNqVf+XO2Z7bXPfSnvVUs+NsyEXPfDr3i5c/Hlz1ecoaKJHzQvWg5aXALKGq5EEpOdS5S6yt6VJyjFFUUt0gVMITG/5A5ZHLI3jDbVlscY+Y+qZK70ePUfvfkczEkCp5aYtKVgJn9nXhxLWD098cQTRNBBgzezwLQnNC3txeLhSy8tX3mV9aP71ezILKJJK0ECvuoXzMkwJ9PePb2+MiNTydnZOg/ty+mOIJagWUhVFHeH5NPcNeRV/dCRcTwCWA0ECac+Tgj0umI0g2WsWykd2buzfe0mJ5agcuBKAT2b3MOP8UBPLeIRZKhN8SBhmd1N5v6jrjjnrNZFH8YrHosLRIMr70zXl/tED+g2VBcMMAe/ZoDVQFa2f1DVUKtAWxavoCSXmR0f23/oEMB9foDMRNaB4R2lqmnH02m78cwHvwVs+3/+HaMqcKWDAw+aXdcLh+xyDR9gibMNk5vDgRkRhuslcdpPpZgQfiJcF3QAm1kM63TLSjhBOhTvScnpO2WksgSZYflAzRvqtdK9qlfyoYQYfMoqIZerdfCwA8Uhkyjl5UX1d/zQwdT+g1uvvXYViKd63/SNHghMJlrW3t5ebHr1E1AR7mWxqptplS/2K5ML54b9xRTwLraOlS2PVpxL7YUGIjB9oz/rDcNKUYavAJAbKGPuOW0XxcHWa+MGaBBwaFExhwAjC2Sa6nVqnSt3D4LXK9dYxFct4Ks/P9Vu2MEAF+cgDAyGrEyZ5tnA/1Otq8ftD+DVttDH/fXEHyIr13HyqIoNn4sbrJTrxiNzNDFtdcxR8rT+iRzX9B/ycjVHAdLO4YUxd0+Yl3vvvZdTaOOWy7JV5QAMgUKYK+Fa5VwSklfE8YSnRfUhlcnvIn6GVkcDLMSIOr5EQs5RjKQY+oejjmSpvBlxSGlhrEl4WxoC964Hqv/1Y5XjAVkwnDPFfXvvweF/+UbbO94cXHfdwCPPDiVaYMCJ34ExqHL0QjYtPsbkhaQBcYxUHRCNENaomGqTysFBW0Zb0jWqXIFTA6dLv1T1sH+WrJSmCdmvvI6heeuxXKT5O77yxd3bn/y7e+89xTc66euJXS8eBhh0ecPrXn/73/7tP33qA2//P/eX7QQetrjjMs+Cx4RINbzzOuy4+HvKWmDit3WQwKWK8T3mhZxp+MSK0tGQnL2oNqueuKfCIePIWoV+BJXRA7MwxozOlInlQMu6bmhpnGbFp1zpbEBk8DmBVpiIqZ6FKhgzRYQuri/6mYYYxsCyxBRy0wW5kT/qP1iL4UJQrJk4Eo+WrfGK0U2A6Ca0zUJZznYhJeVs27lzJ9a/7FYnsmboBmBIOUaqkTh0ftDb3fo/f7t0593mA4+ERkbNtvbwy66JXHvNQKb1x9vdchVzyyCNcsnDowDSSMgabfCMSQeqYDYF+g08beQIsL+K78UOVzk/8APkNYOfdMJU7LFwyfoXGa3eAcyjhWB/NkAWQN/khVmAQX2kMQAIKMPx0ULUPS8opCba7eyK/uavl9d3BT+8LyTWLHHjwm2RX3qNc9Zmg5BoL4oLUMISgCSIulHvzAsZPWiAfTjnD5fIWR2MlmUCQXK43K61m5hVNQ9qs2ZW8AyV+ZItWw4YJebkMfig1b9q6mTuCIbVFDPSTiVhYV6O2NWD7KY5NEIrv9X6h3IcabLRp8N+Iw7o3B17aeNb6CGIIX5ZIKCOrWNx3+CRaA4eibNfNwcYiamXTiQrPoEJvDzOvZFU4Bi5ILLx4NNtR/pz4fiebdejwQXUbFfIjDlWuSeQM4GdYVMiIXukgLQBs2m7ubWNuZLcvy5htHCICiHulkDQIbsh6rWmk2etP26c8+mQ0aOqP5UTm00zmSK5kCMHe1CpVsIRBIwS+iybK2RSqbhKQbQ4cCxTabAOhOcTkAJPdgM+n3zySdRfq6lFxO4amxSEKSuP20sEXG9vLyBi+0Z0SKQx7HsB1LS6ENBw6YcwwBiWc9/T07NkBnha/Qv5ihciTesFspDyy1iGM6VuSI+JJjpzKl9lRFrUcJhBXKxhJPTkssPAAGt3ypmTu6iaXzSFwXPEgqxNqAWSpWlZzGz4DNDAPcDI8LEtZy1rQwltYb5cOEkrXLq25apzafNFN7Cf54JaWFhwmaW1M/0tRo3AlGv6Dwv7zob/k5/8BGr/nMtuzFYlpxEeWhEViASiHTYYAkZoSPnf91wsQOFdxeoQ+RaEDXQsr9AHOF6OOaaC05WzjH+0D02LkTB0zbpGM1bNZx98hFhYkhtGETxCSKjX8g881vPmXyhtOffooaBAPB+uwMeYVDzAqFS4C5lkngrpwY38J52j1bBJBCwzLTo6fpt+CXIo14YlU0qqVWNwrPDUz/rKmTP8gKC7khCR8LQEx5V+TKIfluQf/ehH3/jGN5I8SX54UV8vEhKfOQLFIPg++clPXnXVtc/d+Xfn3vauwbxJyhMyZw6Q4dUXnhbM39JhJyfC5cLEmls7ImGrxqamTXNNvIEVmsLTouDEUJ6XJPgbymIuExLTKlT9Q1kcU/0GfsB9HOtFO8iVsZuGK2C18OGTBJSitof22CiiB5awrlQYpCMSsUZhGxyFxiyFmfIeq0BCtB2uSZS2fDkYq5Bu19vQbJIrWEmsVOVqpBNvKvLujDPOIHkAQk3MgJfLakhgEIkYZ25MtL858epfIFhU4ERqieRwKPnMIT/uhJtTZsIhiFFQLAcFomfByqi42TgPVKA71XCAqWRXEXj4Ltk9ZbwTIgAeYkDLlsIjtg7hofgQXwuBhixFmU950zXM5wa8zoTbPl/AJOYQugRPLejO+clcBXyZCccJbeg13/rL5m23ERQ6FI2Q+shqypgYob+ILpCXoKCIvXVAXbUfy/A40aFuATi7HnQeerOhoUEUBSiNi27w/HisGoQPjTO3ZMkiUrqPV5nkP8KrBZ9SnObhfWtVwEjMYUIdMo8h0EavAvlkFtWGzvoTAyCzLWmO79+dThaeGeDQsUT2rqTvaO+10nXFQE6iAkwbBDHBN8JfibBF/jvmgqaB9V3hnkiLAPyYho99gnotkYghfQgl/fJwNt7ZlQ5bhWF3y/f/OepVD1x2857M2pAngmdU8tGwg2cvgW4iGEljWOEHEdOWXLpBQD6QRFQcMWD6MVVBpM0M4pPPYY+zbjpqNcYQ/LLjyTJc+AXysC+yPzhbzq1mMlYhhxKYUAA0B0FRKBZrPevt9d3xVFrqlK1g4XUvveRUkNbv9Q0SMcgjMA0kh39jCdTxnwI8Yd656g+X3gn1Zr31ej31J8vVRL3mE7nRnan3TVdVf6if8xXBIiYh/IqdEXip7+XAXMVrNQnxacPCKkQL6dg/YSP15gC2TCt2Sn1lE9PSn2m9OqXQb1rfVvMrml4OROJsYT13ySWXzMt0cUTqc4pOAkNEydywEE4kWsHM8T799NMg27ydmfnisj9ht0Qgq9W/q4kzJ9JWX99utNZONLnpouufzkPpE64P7xwOP5S8CIOVLaJiOoUbRZqPbgovIMOsepDuFhocdjzFxgojKAphhLxClcqM8z+Rovg1Gkaj5XtkzVRmnwJ5ngoDK1eVgFieayZjfRsucCWyjUS9Ufo2IWsnOV5Of7GC1IZxUgH5gUOkCyXHkp/AflIuMcuiPa46mQLG4tHQ29s7b8x8eW3GJby8YWQrEEKkQpTsjgT/Rb1HZpakbGYTZAADhvvFD+7jH/+41LEqZ/eMzq7egxcPA6xhdvHFl7ztbW/5p7//xNU3v9pMrR3IGTgxEsnZzxPyWwz9q4F3QYeFzAOshQjG7nBzWyhiu8hCENWIbbMKjgW6Wpgue5gOwoaJm5sEj/B9HANYTGQMQqlLGqDWuAQCghEOxc3xkitmooKwEvAWnIZt5WUo7xLaZ3wMfN4yEg6kJySnrCxZOnUMk3UnKw1ueaToF2v03BonTVHVXJfwNrQYKaIh84Jc9XdkcSITQoqJhPLET2XpwJQL1bjR2BBIbiFZvtgsu1m3I2Ulw0bFF2YmZvtjJbNMLGtgomQNxapJz/WGwhZDaB4o8skei24QIYVI0qgOaKAp5kcZtOwPMhhpXcIIq4ey+3A3XLbI29wU98Lk3Jnzwm4K6nYhhl4yS+qiTbplN2WMpkY1JdK0TMuxoJiz2VP9R8aCRIDTGtM4tAEIvDVk6TcMsNZpIA7H1hEVBw9z4/mq6+486ifXnz1UCXFSQO4yj1pDnx05Ui3lO9edeWTvc4iFgCPIPHioXyBpmGvOOA9LdzEeUtKZCTAqKWfM9FviVtOGjk0Z3AMsCEoIEWhKOrNkw6cFg9782c4Dw6VYOtMStdyWxIS6debrLCUeTnR75s/L9ATylOEzcOBPlYAdEHMhw+KTJ2wGSINskgf6ksitXAnifm3D498D2Psvu3nMc9ia0JrHbXx6if9s4cjpuS7e+6ypPOmqEFggk3Lx8nWI4O6rGNGwzJhgxMKojpFhiXNUC9lbpC218yxmaGqTs+mu8YY3VL/4xZBbFkEXa9qwRqveht99Xy6dzpCDYnUvxoWvI5+6WR3EBe6FwHjocHgI8vMrju4a3/Qs9/f3L6+HMJQKC43mdAxqSGfmlF16Gmm7BLCvEDiBA0TVtOODIdDD+naqe0vJVTYjZ8g0zSdNc60QBOaoVpvF0gekzMiXTyIrPkcnp/4ElHp7e+tP2O1XQaJXb+50uWHLZXnW0Xtu1GJ/0Icmmzb4D+tLeVb08g6Ws6C+dy1vzYutjUB3WIot+wAX241Flb/rrnuYlLO3Xe6HU6iRcL5D04RdsZymogeWUFYcrbKDyGQLB8jOgm5Wxb4V+2dFlEPDyH8cr4o0VeejUDgocrmHyDdrISfSva700MOqAqFbqYcP+ElE1TAVI+OV5y59FU1ADkXxroQ8EnG0JiooKfSvumS2lRIII1MrEVGJZqRBSUCjcidQeKI/+gX9qfdDfT833tbf4pViqcznyHi5VLNKxQpnJFq9uB2NkXtJWcPpaomqe/vtt3/sYx/Tgs56DS/Wm2VewycXTGADs/iHf/iH3/zmv//HZz/81o/9PZYPR9ADk+S2ZhzBABI9MPpYz9vagYetrAouUG1TSwiyEDTAQB9DXig3xDN2LcDws+IRaQbmT1xbMWDA8lnoSbhj1/SLkn2kPS3WnOB0Jm4RfRo/XlCdxULlYH4yYhJyrQwvjfm0LxmDYCT4h+Ei8h7dYYXE0hU6r+5ZA3apFoj2uGrmqsZo0RgiT1La687YWGXr9TYV1Ajp9VeNxKqSqb8v8V51j3VL35BS4Tpo9macngyOBOZowWtO2uwvODAzQPKUjpeNbNkoekEtEBdo+o8tN5x/tSYwV8BAEie+iDgPy2hlaSs+VGRpmr7Rn7IHqB/4KiYrQG/HcNDTZITnFLvTWy5oTYDA1sINXxcycopNwk0XX9hrC6n6VCqDHgyiFksBhLvwupz99A6qF79fIMCBByeA29hzzz3X2dledJqaE0beC4+UJFsVQhxOCJwFZCM3LKK1NVlma9cGHtSqlZGBg01tnVZE/C2VEwAVqxlmFgWWsv+zpFrTRsTyz11DpDiQ2ISYgA+BG1FlVgpSgnocfobx/OERP9yYSDW0ZUwVC+A4LdITjQlsEZoRXbm+Qe7QFg3xyT3N6RYnuxV0pZybtjR3t8efGfWeGzHXHNjZcrS/GI4c2Hqt7EjsMuJtIcbPDBAFLzfIrcOWNYo3vjpZRSMs7sGWBDUgPRJhtKoSIxqGORX1CYzPHkhzSxsjEwzbZL/8xnI4XP33f7MOHS4jUT/jzPRtt471bjATMb24J4ezGn8ZCCIw4Ik0EFSH4YSGw6cdzYAeI1Qvyg3uMamFkNVzrT/pn75ZGjSmDo/NR9sMa3IWtTN1YlKh9UWsPswv+Zz6ykm8p29cvVNYJt0Z7EHov94l9BOAuRDZ4rKPBSNkxHOkeoYJWfbKX2QVgsPTzj5mjev0GiajYGMELVcI36i/r6+P/faqq66q5z6dDUR0g9XKJwGKgS0LmWOLr7OVX+xzfRDot6i/7hO0jE3M3SW979XL8FUPuf7kFL/R/X9u+3MQFT1bLh0TbRP6LOT1BDNVRsdyxgkDylmLTorzUs0exUVQTwBa1xOfYeWZK7QnJ2lFyHhOBDXLpiQo4rTl9Z3Dxmgosf6Gl+X+8+5QkQSBchBTCSBybbPzlddT5eiufUM9bfJMogLJT1BFUkyIUvnKS5y78OOYjKIC4hM/xwRuWVH65RN+6NE91WjE5uv6Rkk1WiwW5HBXsnKkNtojA2HcsQQDFc960fSO7TuIi7nzEByKf+TgHlyoElFrc+c5kBBaLMDL4B6xhDEp5XPhlc/a6unww2m2M84LUpYudM/HP/6JX/+N37z1dW/dcN514NRAnoBYaGIlBShWemADnNh5HX470XikRoRBZk/Gjjo+xvBk4iIPEczTcJF1ouQ2rlhEICtCZwKdSVgsQkPDKMMVjFfs2pjfkcJ6ASMLMxk1HRSh5AGWWqlEaP8ommUc8FyzioNeTfwBIHpxCU6FDWwPJlaEWkKKBlM8MItRLVRSBJdqftE1x5Uf5kDB25gxyF0kjKdcomPRGyW7AONCSo30DtzVD1WZE/qY2ClU94TklX+EEDMTBEhWF1piFMLJsN2Vlo2o7DF8j/BjWeJaV8QCHG0wBDlGF9zzibwNmYIok8UEROrjkxfZSGCu5B8AkueytfBYbg3jUNZ/6pB/RS/yh/kvVC5YE1100UXa8Wn+F2hExqlaUqWl3RfXxQDRXaDNADiABTYA0raOJJoN45iXcFnoG4PQwUKYePyHxjhJ0P1KnGLCXZXzZU6HfLE8XvZ27R/LxMy2hoiEQMNuIpYIExQd5GBnZwYVJ6n3UKaRbT135Hkn3tbZnkxMkv711lcW0uCREYyVcNQnIVqh77nHX/LSLWrhvDDd0zoA8sApXX/99StHOELY6UyVuJNhUDfTeQHaj141NSQua0heGATffNpd84V/iLiVA5uv2N/ULb9CIJKTNxJCwYsYDudeAnnAU4fDeA7LYUvM5yQ7Fdp1EQpRXGQXWGXxJ2T4qaiZieLGO7GKp0Fg3q967rAMcFvarBtvSF1zpTWeQyfip1K7h4f/5dvfvuWWW7q7e+atZxkL0CUurWVFvgMDDJ5DJcBtEgcIZz8sHdBzaosDeCoK15V7kBSUpzP8Ok0RuoQesoh0HD4o5r6+vs2bN09ld7Fy5FpCtSf3FcClMXb1u4GNOl4tJz4vJ9hzhs/Mym6mDsUTrG2FXgfzyd1KV3UUgxVqZRWqRWjF8qxL9pe3ReRT2hUCmdQCZxPeAOKKwrO5Ci+5h4ikNUtDDZhkc3FzspzMOfg4j5CJsxMuEDJLHvgyvgjp++ijj2F62bT2XNQwUNrYHksIFyEnRecK+QIPiq6LQxBRvlYFQ3pipIhlqLL05DTjVxWQSnksCsVPBRRC92N55E3k3Xwl9P293us3bmx/16+NfPZ2K5sX6ibwq3gFvvrnmq64qPKzxwYHCjknzkbB4YrDEWowGal4FQq3DH0EYPmrqFwoT55JJgYcMxtiUpAAlmXDKVVoy1ufkSRJ/Xv6K2VxQeeqYwjHysJRkRbPOHNTrmz0QwcNHUg1r7Ui4baYE4vidcwQhc2h8i996UsPPvjg3XfffdI3WzXW1fhYCEOxGv1Y3jbe9rZf/ocvffGzH3/fV779I9tMIWI5QmzVil/1rIEittAWDC3/trT5axqgCRX/E/gdSbx50QOTolaSl6LePIIbnmUVMWZzhQ2oETOdsFi4BEuAVcmcUyX8TNU+MO614BIcRepExiPDiYHE1E+oN7nAYEwdYN0qvA6LCKFKLCBknARS9aBELTRsgoLSDQHDBN+nWDJod3wrB/N4MpuSp7dgjFQkEt3GJpPmppHwnBnEQIaZQde3vPDUtU02N/l3WhuKnY3ZQTRut8QVI6T8K4pVr1A1+DehEJaYehIID9NPT5wS0YeLPIJ/QIk9hp/UPwGJ8MYCEcxLDOLaTmtwtq/Q+xBMOAOT4UDzYLOVnO35LCOcrfhp8FwjFfHSfvrTn6JO0coo/ZDeE/GCY3jCt8QM9Y0YfsI6khVhJB4BmEADkHIhN3x4HzMylHMrgTW0p49oMGOdbRvWtkxs7BSSKdNoPEEm0gTzyMGTMku2X+rONEohWXAmZ7zmMFUZjf/yfHmhefjwodGx7K5hv1L1apV8cXj//r6I09szh+Af7hSLWTiiFY1EOu8wFTjl0CRG5fpIbcOuB4DcyKv/xyh+G8hvbewvxKQ5jP0zqdo8D/Uc3kmlimdbIQJYEhI6kaaEuAeHQyEYYFf4ZHIpscUF8ZDFItVzMW9PjltAzxPd+Gn/3jWdHY3dPQ0N6Xt/8IPfef/vHB08+sEPfvC4b63yQ9AJt1XII4hpIgPBD8PDaL4XnIdboD+gH9SwDmxD4f93zv6ZcwEo0FcDKH7SRuPoz7lHdkZEnGVfmzM7MPOJnh0kCCvvJTGz8WOegBt8PylAOKYfs38BVkh5YNVWTnI3e+On0y+IpYDVEsgkxArzhxdZJCSQRGvlXn9/P0ew3p1WeRdCIMhOju4EGhLaaarAbpGjOTnFkW8+/bMnidHZu+3K3WVRq6KmEqWU8JciDhZDZKElIUtgRGF8FUNsSvxnyBuiZPFcSgkpg3aKexyDJZhNo+XdeKZ/Xbe1JmlD0u/L+j89WHu4KXHZjTd1nrmxcO+PCvsPRRsaOq+4xDz3XG/3s8U//JOdb/xzDLpo2RE1lc4uoglZVb3SML9Al4rKzYqRjsj2k6RdNTzsPVF3wUF0EW5aOHlj44ZN9ArCHtkENIn2aOAgWzis2bWSidQI5IItjIkdjYYi8VgStTN8hFZgB6S/+PCHP/z617/+uuuuO5V3uYWPeiElX4QMMJMH+/qZz/7N5Zdd9o2//+Sb3/MR7CEgwa08Fvwy/YMFQSzkQJXAKLm1jc3KU12Q32iNhV6yRtJ2hR1yQ1ORNZhTjB3iGuUSDBvMghImOSRx4uDfMGxGuTRQwJTCb8YcGm2YFaQTBlFIiywzxRzA0aHQIe60pAhW8aV12jF4P9TIibCh0n+xWqQP6tIcseYiROZcwHAfVXDUHKsFYhFdMtamSBccYuUIQ6EuNnTMSvF8Y0uF1Ft9JIYKZ91rMw8GgtWr8MMxW+dzku0H0Rpgd/FwFlAQlhYXRzTDSBZwf1Em4gQNoBRv60+Bh3rRjktUsgVdaIFg8FB1Ii3Dspd3Vh8UC+ro6haC3Efxy84LJcdmyplH+xznWh/FzdnnnA2sMz2bR0cl8vNwGTwPRGqDlQ57dLJxzYbEcNEPG4Pj49mm9RuRS4wb5r6C3bX+nEgMOweZKXVNYLGaSkRGRF323JCxtoG9Xk4h1gQfdYqWzZ1zl15xAE9UsEx/qDkMgxiOjRSzcL8IWtY2JWi9XK4gdKfF4yIGywdBEsizCgwwpAaGnbMSrGodDBUD59Ch6M5nKqHQmT9/+RVh85mj/kgJ1x0RohGdEoE2Lk/An6VXIGUZN7ioccSz/RC9T+XiJhB0jUBVkifJSkZ8AlZl4tMlaEuAOoRaoZB76pnBtWvXbdmy+S/+4i+eeubp337f+xYunF5Co/O+QuuQqvU+oATu6emBsNMo19vbSw36kxsCICul7PJrZaEpWVbTcIxliGYY+cts6Dfv6Fa0AAjJ2UET2ipE3y+ZIGYB6t5OA8ICh8DrCC+AGDP4Asm4wJeXtdjS+r+sXZi/MsAFAwygTkcTA4ZH/7Hw1Nwg844jgxZLsZCXjIHHhRqUEgsTbnPeadUgpTMsB6pCdUwgSd6CrlguhAS3dScRTLNBoXo9bp9X9KFeaMgFOBaRks/ctVa09ROv/Otf/zo09lnbrjBTHYUBidEIvwuVLhwnFKkogkUVzPf/n733ALDjLA+1p51etu9qJa20K1mSJVtylY17L9ihGRNKIOVPAQLhBkghcPn5k0BywYTEBDChXEiMTUxCSCDYYCCuuAu5YtmS1aXt7fQ2M//zzrc7Pt56tEXSyoyt2e988/X69pcYkfhFJApEFBMbFdS7hC/lQd4MA/AJfCkJYLR1Q7L44YuDp7ijyZHhyouHYFWtW77y7I2tO/La/4yETlu/Jd65PoIjC93otiIHd/Wf8b73a+ncrq4zqZGDLxQQGNhjEUsXpW4PMhJcXB7hCRNBMjQl60Qvy0lX9CcOlXaOmGXb4vOegeLGtlAojMVmMddCIeC96kw+okGjajo7UnRgpEEhRygMdbawiY40dnlF1ozGfOhDH2JJf/azn5VB8objiKpYoolPQASYmWAiT9uyhRn93Of+7m1vvfH0FZswnwRQyLQiDAnyOZQTB0UgYyjfFSuVk1usAFISnthhLKCdvtwIB/AmYliGhtopCPOIZmBQGgFQUQkGB2YhYvhK3LqIyx+EHfKiJGkWRpzWpB5BwKJcLA/uj9V1FPQQOw0UF1ITIvjwgUG8kX+GuFQUN6hYxhKcEDdLiYjBimQhqq3IfhlfUmOLkS8jBT1bdHNFYyTnDhSMvqzdUaehJyDmsTTMsoYw4YO3D2Q4sW0oO+/oruNnn3mGi0FxF73GSxfkn+A7Y/3BMFjINBM4WpJHjgmvyyhjoLAouBatljEhXs4swZckHeeY2BSr9WEc4PDAvgAZnha7qLWwEyQdi4F9sXHjxmqnDlznnKeAHYy78MFK7s4+sxKKd48iloNoEDMi1gy9vHreDaAWUBIBCt0MhFi9zOtgARKG2Yn3LIxAs97UhPGBj67Wf3CfUxy16qykWdJSQ08/1U9RICdA/wyrXAKvfFTMAq7bxqbmUa2cLw72d3cn4qF1W1axrA4ePMCqmM5fNDR4mgcHmCuHZJMbQwlAZqSZ/9JizyL//MoxqPoF8VrDBIAbfejeSKkAbGKvWdc0YJ6/WssWcarsPt9jo9ITRP65jN9CyHYWZDJOl4CJf2BuZvQvRFgFcZWQgANy+GCWAyN88SDmH0WkYnLvqqqfPUh2oCXWD8QCfIU88MADTPu11147z2Jnr3jGFGCe1Rx+FhUwNNONiSzF2PSbxyc/7AdmLPsIPgLa+tCtnw1DXEhh0Bi1Bfz44yQAe0FxGBAJYZ3PtDhraDGH8Pe+970bb7xxzuq7YL/gHpOHsYbKZ0niHz4LPu+zVLxonxkrznN49UeZf7iAHYJsjQwCM8LswMqGVEThoGRj0knzq8mfcSQaeGopjCwMqSIGkZ5jnzuU5kEkrSV7LWnU8vPbdtRWIzXSLx4ayU7nYAQBpndLUXgeqV3Gbf3ZV4wUAacNjF8EDcGBFUACkAESzFU6Bm7KBHqcWazVlEUdD0hG0hArfGBYWXByzGWhwscvDWxKH8recceeH/7EGkkLvNPRvuxdbzv7ssu3Jxo+92DZCAaCZihb0PantIsfuO/Sg/v3dWwcjjYKLxkHSIKBA7gKN8uDdNRd44FJVEdVfPQMZaEO2RARp62PHHC3dxuDRT1q2diFTnoMrnHYuZbVNG0ampTO6yUz0rB6Q1mzgpYeDbrxgIAKrIQ777zz3/7t377yla9AO5NxeNU8JyYCzPQxix/96Ef+4z++9773vvd/7r3XgmerIZMgagDwgZHtHMkjJqiDlJZg4dr25lZARgHHmXwQ0VPbBIIMwT0yEELQrayr50WhnuzCsYScAoUJX5jiRkdMYemOWda1VEkvj2pNESeBkaxcNt5YCkVCmbyNGHMEb0movwoRB96opw8sysCApMhgYFRZpLKjohWsw0P2EsnLOxbVghxDLZDfHsy72LXOVAzQeGwU4d10db2zIgmorsFDA+uDuYfrivZ2YX4u9uMf3FSkwn7Myxvp5Q69sjmkGANAhfYg9LAxHJcPnFjsTGFnydkkL4HXa3nIDFDe2dkJDMeZfqxU12pp6lFLw5gAcGPegNmBt7lnz57NmzfDEIbVuXPXTtkurrZj554dvSUn0lBJrhELDSKLDi3HI8kwEzrGyTnGMYGuGVZAKBXeQuWdLVvdI46TwLSbHPly2HszzrEfSyTjDYH2hFFXKScTwVgsTpd9vBEav38Bg23SHr5C4/QTzHl8/EXI5trdm16xcjU30co6UX/t6uryeYOTy5f2a5piAiMLqiQIJiSj2Qwbg6nE1SZ8PaKfqrrpsrDoRXk+5y67+z9IY1x1VW8pwKXJTuiq07a0W+evcrYftH85VM46Jh7IsHRFKnLZdiUex9UReho2LGJUDWD/BnF6zk/NiQeMhqh3987vqlONBzkBZmVIH3roIUBwtt7FF1/s98ufCD9mus4uXjxV8wDbUcVk3ACuy/zX25SNp9Ip49WYTPd1yixHJ9Jv0kLN2sGDB7/zne9cccUVc0aAFQ9wMRBghhRiBHIBi6RoenSmTNWi5ouDnZ8cWf48Hs02LEhdNF6p8XN3c5IoHd0jkvacuRmQ6rhxFO2pllEiDSRjyiQjkBV3xyItRarA7RCMbqaylobN3M0av0Ju4FGJkRxk/QAsQQf3hbNqLOcYJmO4uKZ/9rOfAbCcduWbDxVAgIX3C/YIOAkxGGxXSUKrUQXnFIavpwQsqr9YxvJYUMR6WDKcKjHCDNjz1i36utLw8Ne+UfivO0MCIHn6wXsP9nzmZogfG1/7ayuazbt3RtJQnG0N+ejzt30XdbEXXnNDzsSOkEBAMMmAmlRYwCIhNPBB8Fm5pMXQEKCSGzbgkCGQZfRlnJ68li6bhZJRH6o0hM2GiIKkZIC5pNDNVrfYEQ24OhywSpuxTfhweDSle9DBkX+ux/efR9b5wAc+cMkll/zmb/7mEZV8AiQ+YRFg5iYajX/pS1+8+uqr//Hmz3/wQx8MWYgLiuvng4Y+kEPyAUe7skEE/yyD1jpb2gz89LI8AZYYl/XNmCavhA0tKvrA6P1q6OKKIx5SeHxguMGYaEPgGWoKwsxYpUGfvlBy+x18/7LyhaoEyl0fEdfBuPaFX4yWsJhHLglDkwcaFcQnHK4GoE6BioulKDcRRliaj5TwSpanB05RJi0oVLBu7WSKbqak9+Vc0OBDo5VVDRpqzMDuBw4eeOaZZxEEXiTATppQ9YC3QEFUEdARAYLZcvAV4cNUpXpF8OUj3uuU+uYNifcaT+uhwS8fAePRs//lmOAoB/yCj/crBHjCeHHnPf7448AWXOeYUuAIHuhHtmsw0ryuNWYMFVE1x+4lxr2FNCiW3Hh0J1UQOk665IQal0fFrJvwhcnLUsaCOtSXYtlW4jRk4H+2FdOXaKjf0NLYEq4YfQPJuuSyNgRNX55iblxwJ9U8Vg7GigjDdKqRPK8yTvP2NgpCE3l7z77D9fWNy1cu39jsjPYf5naf9YKHnwkVCYsgUyLA09S4GNHOQM6OH9gXe/Tegmlav/M7IzkQecbcaIoKGtwUMa84yTyn7D4/YG/bV96XRtDF4nTCfxJOzpHNgqyGsBMS7EJmM6G8GVGIvobTIAz4hXlYSMBtPPfeey8lXnXVVdUAK8QCSBtzkNpamMa9shRAatpZHcdBhC56dcyvwmoE5oyyLuwAgqsgsLpIGshgv/D3TgAEmDHnrEb+mfW8gMzJhZ3KWUuj8dCneOgLNzhbdcGlJNCiRE3svPPOq/GKeRlQ8Vqvfk6InLVftSSgzKN/SLLHORIZbXRouX85CS+44IKj34xaxmeGND/+8Y+BP1s71kdb12R6DdtxrKAWZgF5qCY8FLeCR0Hhr3hsFDivYgkS3BP/nVi32rxSX1HvRE0NpOCZ3sqj+4FoAF4LF60IBH+5I/WDuyOCrBLpgTsID5crQ9+6Y/lrtp7WtuKuFx0Y6FzEy0Z7NxzcXgxEnjv1MuzjUlMkAAMBJABUdwwXACGgSdzIAh0JHizNiQRARJ0o/weCA3kHFKOAeF0Qh5FaMoC5WY+jMN75GSDq8SST/1IJaLY2nLczJcOzpEPvnZBpYby2ISrsir/4i79AVOeuu+46OvjC5CYew5gTGQFmWC+99NJ3v/vdeHa+/vrXbjh5Y2g58rc2htlMQx/MYpZJz5ScShrMUy+KAXr3lDa3Nc5OAbgXwYUVCROHmXCGA0HR+4UbjDg0Q5ZD6l8sOYv4BIirFUAcGrOrOnyW0e49ulscQbWdUg/sZ32jjtfUsRauVgb1FjHOKpShCrxf2VUy9WzGMlZrxEWTbA9ErGMB/CeJALaHK3BAUYgEJcRmlKDsyEzZzJaddAgb13pvVhvM6y1xu2LEu9auP2XDek4A0i/GYU311Q+nJ3R6OR88apyyoQI5k7O1OtlRDgOFYwTr2LbhKHe5xuoQE+Dmg8QOqgm0QS5wq/6CbpZDmGcbyOLpAexXyDfMKWuNecVxVSovF0oF+iGewGw5l0WCR0gUYlIYrKsuhuyCyuSduJTrui1xPWzYXY3GwT7BiL2V/HIzkbdRcLZSf1LC8zOwZ1/OOXvI2y/YD0/LnoG6u7q9sTVeSA1I/KwPUqAQ/hUvwk/M8oZOwLbiAUpgzcMW4CvQOTF+sgUMcBAN5xzriQej5Upu40Z346m5XiZF0FmuLjkL5IRwEwFna7uxuS18147ytm57pCi+EzhSOHNKttiIDgbMgtjAEpdITBbiYQkxkbkwD5PIw+D86Ec/okQ2XXW53KzIMeKJtzpyhjDl+F+91ef/mmNAzRq8PvJDqmPKAIIJM7mISS9IFXNs2fGdrVqG/EhbyiB/+tOfhvChSFq33HIL+4UD+SMf+cgEAsSsJSNkcaRyFjMvoeqvKlwdM+t64LIDDAXJnDXlrF1b2AS0h10GPl9NflrYKpZ0acwyyxJLzlA/5yBQzaRzKcwJAzl+h42rVt22nNKgvlB7lyK89MMf/pAhPv2S142UzBIsKh0pJxczQB51Xti6wMEwaETMmbtTKPYeCO1oGxvz16wJN1ZS8Sx05XKpOfL6FcmH1hq3PFwIhqwGrZR96pcBGMo+yOAFAIP0w4edw4dWbOxw7JJhhzXLXXfoqZbRnkPL1u5dc4aab/AFUHBqA0GQOilFSNfC9xWugTyCk6NxmQzpzWF8yiACrQ9k8UWkx0IATlpjlOSSYz4LyLtOBUjAaAhqU3hpFTlWQ0f7F7wG20P/c889X/3qVz/zmc+8OhlFCwYGzWeSFikva45V9jd/8zdAZu9+9x/+7Gc/bcTG1XKgQxs2gGVgDQtVOvFV25cRW1ZwX/OutrHZXl2PKK7AYSycxrB21gojEqhELB1OLx71AohDF/Dtw/L1vAQjDl3GJpZkESOr8STMYN0p50BPnZhjBhowcA45U9PqowZLEKwBbmnRQO9XDK1LNYIviLNcBwPRqA+7mHHX8SeEnl40CMtZvso/2vPyXhwbMzZRhi4Unf7B0b3lVGPICbv55kjdoRQOrw801Sfr6uvY79KXSXnHipj3H/AoJciKFA3IgLpd1ME677LnVYBSZqMICIR0f/FGYF6tPLqZGQRwzjPOOAMO5/4D+9d0rWF4Co65f8TtaDUOjGB2EAFaRkyoL0LJZIXqzsAo3qywMqwvq9fQP0dxgJ0lJ7nrcFjjJDERNoNyxJOJs14k11l1QdNuiRmtYb0popudXZP5rt79G6YoAAsg44WVLhsZGRrJlHYeQiW2ks+l9LyGzd+TT95Yy8pklJBwVjMjN5V3D0FK5krDVYFbLnQ1NzqBYKpYHOgbYDw5CiSx7LMFeahTbr6Sq6VyWucDP4Zq7Z63dbCI63ATFjDaO8qXMpXRVM/hgmgPNEeNS7qsnqyNuXhEs/iKBrNYVndE8oU3kl3xsIG6EQQ7sVJX0yNHlNceSa3gB+bZ6+vL9DUQA0QEmUckbl6RXjoij1/CrNsQ0gw7l5NEZZz/mxp92rYfnrUZ86936ZYwz8FBexOQmktBKTUoSWO2v78GFntkEPxBEAnd0SkrQsgTZimNoZ00Egt8JGPJ1SLuAdPYX0tTFn4MIzmIeI5hAxawauiP81yEkxvDlQckgI4r9MojLZzraSkqx04eBD+GEVD7kTcPqxrs90iHxS/tWAWy2cx99z2AyGbnmZcP5aHyGoDinvwzMAwguiCfXJIqwA0ot5GodTmbWu2rV+iN+3cMffe/Dj2yrZLNhFd1NF539RUXX9xwRcu3nshqrljRwJnw2F3n9ZBrzxsiYH6YYOLQ1MaLLxLRex9nNA+cfsmwIQxjQ1isIubMvSfjLNef/CC7wsuBF7wYF9cxOENtjGiDOXuogKsXlCKdsOEii4eakuSZ98MIMBSjBS1TEnc2tAJhWMwSxQNuOZ97z3veg0DE+9///iU39fMeGCngREaAZaoBzROJL3/5y/ilvPnmmz/84Q8ng/pZK9DvraAah3Bgr26kSzZSB+DA8IHh6OZLerZUOrk5ABdF4D3NiFruae0IDFTCQRjIelQXxWC8BCP8rJfBY8W0DG5++Yk7r0RjK4p45WK+lBmuhFvyERjKdtCBDwb0CWlHw760VXACtpHX4RF7PGQlD+2BlqAW+AqGD1zBb3AFDyg2WsFBhPaFlzu2i9TEq30oyK3IWJijaBqP5kdCRkSzB0PaaLmStNIN4YErzjuNE8HDZVS+BX7TDIVRcIZSNOTnBRdbmluLaZhqEuqayNmee+65lOMN2tzKO6FyIVUI0xWAoHP1KhbPsFvfub75UNpBFyAUcHMV8fuuYARmNVW0SzjZwXCiUVkR15ti1t4Be6TEsY6QguOkejnsk/Vtgv4JUVNkGVhweAHDP3ZIt1c1i7VhPLDOOoLM1wJO0MjI6LP7R/tTcvy7hYydqvSm7agoIXvu9qZvjWoDjeEB3lXrmcWNekPd6GjpyafdXTvxNBg89ZSWzae2btgAZUChhdMXOYcvjJmTyrtWJhN+4mGkqqyrrhksyghDkmiKio9BoTHIzaquSRc3b2Wh7WpRw2lusXb3i4qHCc+3VA65xbpYoADjXmw/BhuiZPQw9praRSU81AIPX2AHoYkIXZtLfAyLfv7552+99VY4fqAcSmXOt+AK1wWgUwm6I25QC4eBMZ9MK6mppdMk8hEDeL8Asl1dXdMkPKrRTNwCrvaj2vTZKmPL3HTTTfQOi4wf/ehH/+RP/oQDh59HxP5lfFhFjz32GHjpdKjsdA0ho8K9p0wAmUZh41TB4lSLbQbG6fgWe0VhfuQxn0TVEhB+dVId8/a8Ypjm9IMuLCCvVY0PBxHCz8z13CTeT4BRnXIqOLSx3YDKD8Oy5PrIzN555139ff1NyzvXnn3Jtm6RoAT7xa6yAO8eBAiXyZPPEiAbpiw7nlsL6eJLVxpNu3fu+99/E3hpn26XUBpyBoYPPfVcw979W377t65aF86ZVvv6NWnygAKPjx3QNmTjcl2dvnxFT8qu6EGuwni5cPquezjN9y3brG5ITE8KtO7dzAI5AA+pi9MD49W9zQUMZI6ktHgAjho7em20GksVEwm7urBdH+RNVX7N4y04wr9SteuiuZat4EdGcArQkHBAj4fQzXT//M//rLen5/v/9V/HLVHvCLt7xMlPcASY8WAFXH755e9973s/8YlPoJ+G+c2w6W5ut0KWjc9epAEPp43RoqjIgdOKQWb89NhmrlQ5pc1kaSrOK6q865utyRmZRQAAQABJREFUZNiO49cngI4BgCVC1A67I68LWYWMqATjGRjPMYhDIroPkAgFCsS44pqlEVG6axBVehIg2wCODf4sPoGLhljVGleh9bBZPH+SCzVL8Gu8BNkaGgKCOY9vBm87yfYiILtLYFM9nGzmf+RShw7trgSMeF1n/3Cv3X8wbe69YMvqthhazd52lMxeYLy0I14y02dQDZv++zH4wighgYlUNrJP87RoegxavzhVMk3QLBCE3rNnz74Dh+z4iqGKyRboz2mwc7MIJ3gnNn9sWWX2UE4oPiUHqiTHuxEztZOajYMpuy/tJiNaZnA4EkmKsA+5yOMxB/mRCNiNEcxfVZKvULqcukuwWxd88axcvfqwYUcq1u4Xnt24uu7MTSvH+ZZTt2FCLD3HWi9ayldeeWU4HNKKpcpzzxa+9JXQs09p2ZxTKbuJRP6iCyO//3vaug0YypP+Tyhi7j/lvuTaxelU5PknowM9hUTSes35iEh5N6nbiMYSj1clUVjW4MdQbgwdRStJjiP56q6OFDd1Gm1BLeDkOS9KgXC+XEYQpuamSdHjj+tWdtjZR/TSHgclksBmI36ha7TyFWlwEBUCjJW6SgHHATfHM2oqjIePWhBgP9eCBKZcV1NGLkh1sxaCuDj3US1iCLMWddwmAOJUWKXCeCF8zK2/rCu4x/MRxp48REw9i5CHDY6RCGSGOzs7JyebEIPdBHjaRIIw8H7mmWfgHq9cuXKClsSEXEftJ3ccBtg5RRX56ajVu4Qq2r17N9x+CDGvWlh/ysmCJsjigUR4RMSpKYs6JpG3f/t2KMVnX/nrQ3kTU7IAKPj/83hXXIkCIfN4AoACmgAvCwtWdze0afWFoYHb/93csxcWrmCrCHICr7tu/x3/2XUuEnIXH8xUlp9yWuCs07THn/SuWrnewV1tw0i+/ppsfdvDu/AVLO4GV4/uXrd7eyYQffb0qzwpMQ3sAP4BImNj4JBgETRE/nHsgBhzrXKFRwJ6MqzVR0RIerBg9KYq4AJg721xIl0RqVugB7u56aKBh0SaQOWY3UoE9cfuu/vrX/s6fo8WQ9pigRq+6MXUDgktelMWrwKW3ac+9amf/vSnv/u7v8s9wWUc1LVNrVYkAGNLPE33ZAywX7BNPFAjilyEPVvW8xXn1Fa9KSoq7Kx8NtDyuIXCeixgY7QNj6Zh0+zPOilsVYEDsxXAWsGfXVjBmhUI1Ld1OnDT0MB39AxGnjNuruC0JgwsxLE54gHxJJzWNUxDkx1WMGLPIqAhW4SXiKHmXHG5RHtgTYNjUynmW70dJGQm2sROkuSSw5NUJQADWdNTBfdQyo1FWoZHCo+8lHei+Y6mSEdSW5YQprcHqKuKxpSMF2TkacmWLVt4L0hpC1UI7UEzCtVN5DNRMVVg2UIVvqTLWXvSSQcOHHqpJ+U2tZc080DKjgShyIiqDCRP1iTrPqg5+1PiBBigLxJi2Qthk3sCfLij3miOOKOYPwuaEaQbONiRnjZgQnpLVLeXJTGGWOlq8KiYXDBgT5UKbgan1KSqBkqALJkvjJQQOeflxI7AZnUeZ3oer7MFUpZHFK59yqgayBsQGWnJrq4up6e3+NWvJ57azrbLycWnBZ2K/sBDuXgs+ofv05e1eet+oRY/9yM3Mjbe3dYH76Gi/Ombc3Wt9qDQGcJGJalkvMY7I0eBBv1OKxUKRojJgYoht+zpbfY5TZX4YE/5qWeK+w+YiXjTaVv0rlVBvVbpYo+GLcrbmt1f7v+kNvqvlot9A1aBnDtla4XW/KeB+t9mTkEJaA6onWoUuK5S3cSQGIqguCVjPHnGmzzFX8gxSmUUeAURVmVKgJFfQPwH7YwF5CxN0YcaoljV04n6M2cMF2cUo4dNGniSx7y1NXRopiSsAXQL5zyDYKeUXrsOMNiyIrUwkjx4eCY7kz5/0ic6zEq1hypA75lBLpTjBPuljxxTvOc8zuQ9sR8WAxtKkX1P7J7W3jvGhP2FvglrewZDgEpNABrWzKd37fUuYErui8cee1w3rTVnXjaQF2gZa7WY3TFFo1BYnSKFDDwj0AkQiNyScm9p7oq4Fk6leh5+zALO5hHXgXCKwVjdQNFNPfRoYvPWxw46q05uWfXHf9j9918qb3saJ49kLgcDiTdeE73hzU9q8XteqqAphtTnRY/cEbULz5x+fXfTaq9CHC5QjdySqrMMtbTD+yUoKC3QgCHcqAUC7LZGuehx0quPlkxwkKTptiX0Fs9NpGAC83uoWnh7GJcuiCFeug9UhfZvabT/I3/8R5dccun73/9H86thaed+tSDAUHy/+c1vXnTRRRjEQiuYhQCYuKYBG1eVKCIT2Ec18LcpZrFwxoWf3rJtYJyZ/09uxskQODJoJuQhNxl0z1xhJUKVWNCIDLnISYcyDrwXy8ZNkjCBcZ0KNmsbVjBW79mBE98kGGD1HI6ZBRvzbm5DmE3phmEXR7VsSQvgYRjPXODAyGB7SPDYmgIBrnhW2k1xvFQEDS47mG4D+pW95eGwXkraJRtFdhny/dG4fHTNTAkFzda+4d77fzmw+aTlfTmredhtT7rL64wIkpJyHIxvydnWsOwi2+YK4ayc4Rw83sA11VTajEUuAPTnnnvurLPOkjGa97Ey24Atge/RSOS8yy77/gM7TESDQ+3cGTB8QV/FHzaLQw5sZwgLvnaA+8Nw7NYoKsCuxS3hncmFgZ4WayRe0QfMbC5TKO7OIXSUaGqLJOvBkFH6RcmkEw/VIv5M0bLcWD9ct+BCzEj1AE2YDpKBc4JTVWPF1elrC+MeTPYdO6GtIZzkqjnySUcwDDAX4LKrY2XpxV8GfwE8LSPDAoKlhekwdIDMe+53Xv8Gs7X1CPHrmTshg5bjPs+WE/ffBVFZv+aa4bwnl+4iLoWnNBnS6iI4qUbTxT27djZ2rOWsO5xyVyUrr2msRJ7a1v8Pt5gv7YYczcSlkuHwO240bnyzhRUfJkzu5epiJofl2nftQ+UDbw8UHkUbXCjXNvRDqB1lyz7g9H64VHxBN/4f2AiUpRSn1VCr00Cp2xGedfzBcxSfEG4bK4QFQGvmxjmc3A0VU6Pp1+myL0g8Z6nHkRhzyzShTEBSviLLymHFYNaO+00o5zj5iegyF+6cG4OyLnnVSqilEFYLDHZSMoyIvCqjejMQPf0lN2vhtEE1A9EGziVOBoh0E86xWQtZpAQsGA5MyCWqv4tUy5IulsMHph4U2PndKUt6DF7ReE4hHvyi88YhIsMy3fkMLRKiD2lekf84+EHL77vvvt7unlhTR8dpF27rdpBIjobQ4eJ+drksBckUpR3pmRBtgXnxOQohXMPmMzyrgpNKI8klgDOsUdLbJYU1lwZHY075YC7+f591/2Dzya2f/IT23LP5nS+hi5g4bXO5a/ULVssXH3RgGXDlNRYGt+68t2yGnzjvhrIpHAJYXEJ3F7KxB2F7gDqtZcykFt4C/ouUKMKkSQuBUHPnoN2dcpA8hcOVDJSaItjp8IS8Zruea5kHzGuliigAGyWkW3VxMVMXtD//8Q+Xcqmvfe2rnjr8LEBALbUs0TSvCgRYVp6ub9269WMf+9gnP/lJ5PTwTChxmC+PW2GRhXbClo57pJ4cGKmYSutNoxKMADMui1ysN69vMcMmwB9ZxMkX3GPE9OOmER4V++khS2znwigzUMITdq6wkbGERUq2X8gCK8ZaOgxeHRvNPVnWYqU1hqy/eBVWCG264AYr+ENy4APjsRNilIebCmLLxkEuAjYyODBfwbEjFVFhp8EeACud47+xDYYx/XqEEmXzkzEYjlnR5r7UyDP7UyPLm+qDLn7G9o867Ql7ecLE+hyJPA7ZGCAsBU2zFyC2QVznFpnuoDxuNwAN7urqAo2BuwLgoiCk47a1R6Fh3lIRV9gvDmJ7MJjKcEtgdVxDSMYTCbDBVioVd7D/UF8+mGxqQeivLcmCEtvCyDhADarYTntTKO7Etja5hZz9yAE96wY1DJxbFpsEI3OtCbPOdJYlRd6ARy1OAmrx+D+njJEM449KeSRLzrtnXDdb0UZQspd17ZyzZW0L5v7HH0DYbdu24VIPNs4MJfMJeBpTTOlUqpIv6nsOOKVc3hOApKQK5nPKYmYKYlTx0KHoaVs0Mzhew9z/+iPD+TGSc0IvPB17/qlcJBh/xzsGi9AfuBRdfANWPyoLvGLMlPGVdjF9pVL59FV6pGffwGduDu7lYAP7RQFYD+fz5u3frjTUm298k55MTLfZ/fK9S7xU6f24VXyEEXQONJTvihWft1yrEj7HDrx2RI8VjdGvPP3z3ZxZ+F3Dq5afVwVAfWsUroPtqbKAWoDDL8Y+nWG6JzR78X6CqyBajyYOOgiqFn/SJ1fqfzoeWj65ebPGzLPZ4LGK3TprRSoBuK5Cd2Fbge3MuoQ4AXhmLby6F6gW0yRks8lFWAUgWBwr4i8rBNlssH3uOEVsmrU7r6oEjA+PmsEaD6JXyfhgBATMdkqIiBGbPAh+ZPV2mJzsqMXQHswXA+qed907+wtm/4EXjEhdoK0dgr2Yq5R2iLwzyC2wi4R5GWIq0nbM/kJlQyxqtjRZkPhJgXMLYBNAe4/EG17eXjSDmaL9XH90MFO6bmPbKee3xs+9CPBovxl8ste54xnnQCrIIME223ho+8l7Hh9KND1zxnVAz5i/wsWuJRRiUU3yIBCvKQKACDDPHe39Q/sXLV8UcaVpA3m9JwXegey025Y0G8JibEg+zOPx52soiw6wkbfBIESQFQT44f++7f67/u2f//kb3EHzqmMezTtOsr5aEGA13H/+53+Oy2wEobc9sa2puZEVxvQ3hI2zVmjRgB3G3VEA1BcRYg260EDWhgEL2AmTNlO2N7UadSGYKEh4imXmlQkzHnIQS94bEImCsODAgv0W0JLXxYYzrFx4tuwEiGsB04EiBRrsaQW7o44Jal0PKxjruJqDxn59RLi7ZhlcVy+IcSxY0MJt4aGFbBvcJnlOkjTbFLNb8JMjQTcGuUs0GrztLQvZ227e3vcgV9lpyUS8WMj3jZSMUDkdDYzmtd6g25s1Doy6TTFnRUJrwaAOpmWlKjkpJmDA/i6aHDhOzkGv4bO8aCoQ5/333w85c1aoaJayjpvP/owc6USwLLLZ3HPdeZbBYLbw4uHeVavKpWiDFYmL7L6JzgzGzNze/hE30oCdw0TIjmADS4SfEYfm/hAz/Z2NTU3h5Gnt5vanX1jREnHqOnpTwtSCetOSFJNvnU3iBFvWpOccizeWaXhjDwl6MynBLatbDh8DIgVvIFeSgSeoZEdsnMO77nrStugWYBHKxNkdc6YWuEweXOg3vOENd95552te85pZJxM1v+eefRZPQtCx0OtBfogstJD283CbCkksKMr9sxZVcwJv0DABXXHi2x5m+JzTz8g1t5d7pGqo1w1KAXi8OMZqNJU+0FfO5RBU1/P5zPAg4tlug2blHng0vO8AZ5ZXopxa6CVFbDvzwx+FLr6Yo2G8jGn/0iun+JSe/h4Ua/tAovJ/WsOpGJRkTrLg4VD+hWDkQ31GNH/aqkexfXDWmWdMtiTEdqtxx6nFQHfUQ5uql8e0TVxqH+gdi5x3de9QNQfZoyuApJAaIdXxwHIkGZEEqhMvtR6/3F7VHX7X0h1kjkAvQVDZaLWk94v1a/FjXm5BVajGMqtySBD5FB6OMrJzmyCtQCRaNscQAcYAO23gpJL2/eqpGgG1EuBzMmVQPJXPv6rvUwT9xTO35TFFicdlFN1k6XJcK5Ll5M5CQsIYIW1n6EjMoUQaKAjHidQDDQOQgJKIRdqTz70KF74Q5WNQeIMe+MsJi+cVLmWgY7HbKLxf764WADdvuy/049ulse7Ky0dv/baBuR1dTHjAIyZNOR5NXnzRi1ro8Ij4UnppNPSFh+HTlpbVBSOWAwV8uBjqzwsyCeIcDJQve+QOy6nsOuf6gUgDEDtFYLNWTm2vPjm/PfjcAxDUmS8QERcxfhzqw0ZrTCjdwwWsNBsYvk2EnOVJ5J9JznjPE6iQLvMP98L4sETIFItFum6nDu7455v+7Dd+4x08HK1eS1+9r1cRAsx6YsN/4xvfOOecc9793vd869v/GrIU4qdj53lLuxkLliMB7P3ovSZC+YiD6qOIQ3sWnvNlN1N0NrRqq5IsSlneLM6GEJizWy+6kYbnLliP5F3QYANvqiUNzBlHwbBtURWEKIX8RTAA6OyxgkFuHaOURVXPbk4YUWH3imU2EmQLNuapIQUJlov8M5vH2zXyV5SBxbxNCSefnmIwfpsgI8WClK9wX9U0fzVLUykZ5SVEP0BtYItly25YlA10LM41pfWetJjmak3aKxKoNIPjyKPuALX9gEIUZKZOQ+Az4rnsl5ZsHm2mwRdffLESY/MHaKkHgJuZi5ol32TdsnL59+RLPU/vHS7YZl+qhG/cXz472LZ8VevKk+LJOhKhwMLCqY8G8rgC0NwmZOptZl2MOoiEtON0oN+r2eubBaXaO2Q7lp7KyeFKgqjltEBajdjN43iaumvlSvIeZUuGZk/2cwNHhcQsOeaLe1fhmWo1qrw1vFnvYsquN0U/padtCS0gm4BuqdVdQxnjSWgGyNszTz+Tt8t1G08pJ5MRDyaAMGYairHpplvaomtWsyVUdeNZ5/XXLRbcQn4FV+mNV5ZXfUGPxIbyBiIkdCARwnHCxMJf2rPvlwfzZdtCgzvX19dT6W9trguUkrmdO7nf5Pzg7IB0J4QuGQfr0EE3NaxrnRMLmuq3nf6J6WZdxyr/W30oFaUkzj9Pt0oL72goPpAJXVNY05E57eTYZZddzixPuFOpfKpSZ4qDLF0LqDpTEUvtG5Cl6jI7mrYjesBVBX6FDDADCPFuwqgutf6NtRe8EcvzzG8tfFe2PydbLSknjway7ot0QzEXLHLOKKwHYZ5a1TKZ7jO5SYsUQ2M4UZGYUCrKi1TL0i0WGgr2L2HR1yL8zLRit7yzs9OXRlm6HZ+55Zwn0H8ZnOkQWqx17Nmzxy9k9+7dhBmWrq4uP/KYBtzbbruNEzJS1x5ftXHfcAY1XsspBey8XdTNQAjMTnBegFm5/uB8yh0ooC0+TMvuY4e0czsSp7z5dfnd+8oPPiJYsrBr9XI0suIDvzfaddIPduOMBbRAbk+0fjJ25HAGTgDib2jSAtXLPcot2Jwd3rz3oYphPn3662xBuDGDIhrI5FOD491+UjX1Eq3uQuBsTAhFgk4ioDfHzBf6K4dTWBEykK1DbRL1MdwZzuHenDQd1Caw0FAevU43V9biEa3JLN784fcsb2v8/OdvPjHulEm9PrKIVxECzMCwqtjAX/ziF97xG+/8xN/+47vf94erG4TjxRIP6M6G5kA8CGFG2LkB00VxPFd2szg6SmODChECwm66YGMCFwYXhfE/BmdObg00RCvsjWhQD6dxMK3355w024CMytER2neuCEVg9NwSHUqH/YMwAvzhimMWRtx6HKViX9cTqEiGzbDjsF6LOPQ2RasBmecx41jezIq8BtwYPC3BCsbWa0X8GMOIpnZwFUhewqSWXStbVHYde9IK4OZNdqSoABhwp4tZDOVpo0UdiejGoNud1fYPuy3RcnvChBctnkZFfkNKAmTZs3fv2DHiuojNUAyH5iKBFxS+eI/fZkArELCFOGIWr7E1lcwtxQTxAIrRo5nzyAXARGpOf84tRDtWre/YPWCHirvaVp6Uts1wQ2MknoDBO9h7UMulcmUUPVmpJWs41T3oNLevcqL4+sXgub4yATfUWdXoxiz9QNq26paXila+Ikc/cg/tdUZQt9c0U5KsPRYi5yxQI2Fkj/EUjT4260ehtdVTQDI+kYxOkYx9Ot3dTJrpHm/Na0NZ8WxMGtRV2xM0RFrCpH/rW98CXoS3xk84wDjvoQFvf/vbZ1A0BWzaes5Wcabtrnbe+Ibyt/8VfwlsLyri1EDnx3rbW7Rl7VQhV6tX0XRtmyHeGw1vesol93Bv8emnKs/tcPIYzWuKnH1GYP3JvXKZCyG7PgyNmbD0SD1UumrtuoGIVioWd+zc1bX2pMOFQCIGkpq3goGKdF5KBhMGBkBNQ3Y2fpvMGk9+lC52ys1dDDovWPkcdqilNNaRncPOtOtsD4WuBroob1wfecMbX78gd6q/T1UHT/g3MwjCBj2IZQDGC8qHdSW1BUBs6P6sW3upDBEdhMjFHqylwVAE0Foi5Ry2lS8LXUtFR5RGnRUcJuQC7/WPsiMqZAETszYuv/xyRZ5ewGJPmKJw8sx6w8hTjUcTS5TnhOn+lB2Bu6ukq2bgB3AII9dAdhjFsECUlazj5CDypsi5+fM3c6ed/4bffPGlgwMZ7ijHyQ0PHhzlpmtc3hmOi9aW3JRI3Aj517sxdYxjAcMYqaL5L9vy79u6quOjHyo+8GDm0Sfs0azVtarhyksz6zbdm4ncucONhAwAb4GEYQOYqOxyDjsIZmZAgEXA0wBlOGP3gx19Lw3G215YdzbANXxk0AcPEfbqlitbYAIFF/BDCpSG6JEwnCe9MUrZWKgyDqXgLYN3aMuSbmMYI6PS8Cnn7kgiBQ0fyYtlaVGitDGvZfzs63/Z/dIvEIN9tV2y041bjWDQdNmXXjwL8i1v+fV777n3Hz/9/6077fxzzjlzQ6uJMyQWOituZdIQY8uWjXxCKKX3Z3W0c0FB+zOeDaqKniq7o8XKKa1mHWKPbETJ5LbFEYcWzXK0auOWmKEbyOlYxsohJAkabIvuLsiqUxbFd9BLUJWSbbOFcJuURzmPnVtCIFn0gaFEgVQ3RfHD5ATxW41BabjBYltLdpTU5u0LwuJ/GBTaFD4zLcyXXKx5wQ0WCFd07WXr0jYFPZDRrtjwb6OxCGZrBX7VdFxjY8U3FdT7C1ilZuPpB2EIB3Hf6rbAl/Z4WpFw5HQPe0HgBAlVbCHUeJEcbyuDcfDOTRdrWMjzXHjhhcfJaX6kAzXheoZTCh0UVgnzouZ65gJTJe2FPmw9BHsw+GYZuGs36hKlnJ1JZ5e3OZhhq48nRhy9MakNDQwlYtBjooJyGewRCJ9GMuA0xnWUyTuSFpSUfUN6MNGQzqMAL9baGsNuXcBYWccuoC3eSvVPf9dl+fmAmv9Vtbb6pwpXv2fuUfVXquRKUeaviMeEe5i7S9qgc+ujAQGxQKX/whe+QIA1gDj0DAgwaZTlJLe5KXzjDcVovPiD7xvdvSLk0dFhvfmNoUsvM+q4a8d7q0o/0re3U91Kyd75UuGfvhp49OEA5vJG+o1Dh1PnnBv/+8+uOPm04QBXr9EQE0x2Qm1ZGxqXhf085NUxLl8uBkfy5WIoGtpyauH7P8aUNwMA9ktOEQrn7l63Xq8bU7idraWcIhWF88I5DgUwMCKkBK5WUaiDmo5eOKeMo+FfhGe20mb/Xr0SZk99oqTwe82oqoeeAaQeZU549dniN2n+Y1xdrCrNj5muFj8B6QlPl2zKth1R4ilLqDGSio60bTWWXGMyNUosEp6j1usa23bMkzE4WOZDpALj2BCIZ2gPKbkXeCvSDDcFtyrpIXAwqifSwNJHeocuGIpF69evn65rxHMz8jAI6q2GYoYxPMqf7rnnvoMHDsUbWs+74X07RmORmOEM7q9viDY2NYP1GcEIRLaqmaPfXgNRUSzj7QVYRd/WY/3Vvfnf2rr8lOvfELvytbpTKZrW3kD8+y+h4quHmHpBX8kFII3LUqQs0YCCEwYkL6AOXKmQWXnDz74UcCqPX/f7g/FWkFZSBuEDyCNDK5V6NQso7l2iXiw/7KgFq0ncHaFoiUB1qoiZUTccclYk9fa4l9krZW4v6avUi9KanXSdc1u11Q36L/uce+78wZ3f+se//dtPnnfeeXMr+cTL9apDgJlCVvVNf3fTo48/9lcf+p0v/8d9uUodCG0iyLIRwktD2D1jpRUNViJBYef2mRBRxKjVMELRFZcNkCsZGFhe3yyCoBaWc4W5Igjkae1WY0RYwZFh0QqOoBWcczKGJ3zvyVGzbcBUUX+0DBfruACR4MaYrikgDl0irMWDdnMcQ3CitYfZWszZZQtOwZOIVq6G0QRWm5IFTqW0Cko0qHUQhrCB4jFWrN1YwIkE8fpLCtLKDlZd5idn38gIhtyDAsgKWxnAAodPSHe7IwFtpGh0Z5Ho1g/ntLoRkBl7WUIkNCw5Bz3ekQeTyR72HtlhY0EVsQTeNJ5B4F5EEo8LgBb73VkCrR9vIh5iMBREy7F9AlsVBBigGWsWePNTSSZ2avz8z9na8z1uQTO7s3amqCcizr6ymYgEGhuTg/193X2Da1a2FIxkqzjJNNKjqUAoGmtpF+RWpJLBnJz2Bhi8zvoWkG1316BTdI3DKbvMkga/MlxcH6FLv0pcH03xIKrH4M9KQAHuR9RK3btTlDJN1FgXdYT80aiB0SkRWOFiharRAMtVynLPPvvsFVdc8f3vf//cc8+lMIZumiIlWrrtOXDq7uluaW4N/fqb3Msvs/v7RJy4tU1rahZTUvPfB7JXNXtgqHTbbZGHHkSGKuu4VipnObb54nPZf/pa5GMfXd60at+wE8dgvbf3pXFen8nK0cShhgqTYZj5MkoWRqpkHi5rG84529h6ZuWJxxH/oMFkYI7y0brAjW8yGpvkBJFSZnpI4wY3yU0aLhgbCuY2UU7CvB927jkWHMM1txQw7edokY7Oy3fufAGLVyeddNJMJR7db2OrwpvHo1vz7LXRton7VNPg/SpYs0Z61uzVHGEKzha40As+iehhKq4pXebspVEEcJvHZp+ugRhgZ89iR3q6BMcwntMJe5DHsAGqakRYOf9ZM0d6Wh7zlh+dBsD+pSLcnM566bAmkU0gMcsSESFo/YTRj52ZNnp0erFQtajDkOsPGasapTDUgBxvq4uO3H77bRiW7dp8XiHSWsoi4uhULDcYClnh+Bjnh3uR20tAXHkkLACMDrgLlb9Qtu2yvWsk+JkHzFX1wdZ4IGg63Rnj2V5nsBhCWrPONGxhI3Frik0fwBtkQoH1EST22L/wsbTlI4dXDewajdU/dc4NDlVoWMMVXxBevR5MwGXpAckSI83iH83AjYsRD7pgHI0R8+ke5+CIncNypYVKcAWL0E2oREqqOT4MDtlFJXpwwN63t/zSPjD31SedxCmxz9l33XXXfPCDfyytWHKw+xzHY5Zs014/s+Rbyp8BFKOR2Lduve2CC87/u4994GP/8M+/6LZPbtLb4t6C1XSYn5vbA3XhcsLSX7JwvKkPFpA0djMl2K1uvqLnSw7hdK6yrsXEqJr3wE7TVzVYeLWuD9p7wkZsFHtrOrrEg/DHMJqDVRtPg5cSMM4OPQkUGJkH7MlAjmKbFEBiHURJncaIm4yAUThsvLqQEXE1vHqCMKPxWzQwjUN2dsfL6xdaVwEOsyFoMGzhEih6GUFudAx0C9YMadnIkPSMQH194+DIwODQUFtbCy7DPaib3YJPYwd6F+qNo6g9h4yhHBbqELY0DmXcZAA02O6sC8QSsbVdqwXmfnnrzGefHoMFpBqu3AKjwYXM4RJV9QFE5mJGtUndZMj7wVxFfwl4qLOzc/LIyoGINogL9mtnbRN3X4Npuy5qYhph5dp1Dsonhp0aHS3mUpUKeBGmH/R8EYOEZsmuNFpCNA0H3GJJhzwZNbTVdTgDw5i53Z/TRwq4zsY2hgNiuyKJ6UJ3bZMeeOXhrQ5kWgCbAs8uvFXMy+volS0mwdyhXlfDsTGWqikyHqiM+xKQCqiOcSOgYG7eoL7TtUEyVD1gzggOAEitW3eSVt9krVkjW1BEnfjv5Z1YlePIg2CRhw7q94P9woiQy9rIZynFSDTqTz5Tev6FukuX0x1azFTKHetVTrDk4ONecGLDCmw4dfMQYSF+W4/tLbWuX974ofel/+X24n0Ph8p5O6Bn13YFfuOt5taztegrbUlP014qMxOvtfv/j6FnA28ZLewJh4cTUjf6VlDuNvWGL6CR+s698ZK7cvfuPYzwguNO0zStpmgmjt1xPHg/8puL2CELaUqXrSwlpRjPBsFmz0KtLL/qGQL+JoVEBVmNlDNv0hmKmvJTe3s7BVI41DqmQ+EVM6AlnGaoHSINPvejYMp2LFAkU3M0Z2dyq9XsYM8P1wzwc2o2AzG5pBM2BtIAYmtKuWDWTqKXDk7Iw6oDVVCa5+q+mDXvEkoAP5wjEfFXRf2vpeUwz9m8taQ8OmlY+czsHXfcwdV0+Ts/3JcFbMZ2p47WIkReQGkgbUEBFRAiFkvkqiSOf8De+HYBkM7lC9CEI6EwhP0Xh8wnuoGOhJ8kWComGFBUJI/8FGFKGED49UVTEkPKqHrBc6IstIgufP7HzaOH97etP9TYQQxPBFcYyBF4ecfqlWgpVRIAh9MIQ+Q06yNuW1SM3Q4WsP+sF0twv/QVdUZrFG6zQBWSb66PXgG5f6nw7dudn9xXyeSKJaM3Gs5dds6PH3/sX/79O6Dax/bsmmu3FiXfqxEBZiC5ejduPPlLX/rSO9/1W5u+eeY17/rQI4edTU2VrkYUdWWtok/b1WDFw9h5dnYF9EBGH/TEodls/Vm3WDFy4lxLHyraG1uw5MaSF7INvGA4qGcst5oiFRGHxktIAIloHSdJWQMvR2CnOJ6BY+JiWQ86E2htAMuyhgu9Bs+9FA4igQtiNI0b4lrC8/cLahqMGCDGuZIWtEUoGpav2IhG1tMDg9W64Keg0GDIeD+ySa9FyghFi7F1b0dJp6LRSKkcT2UyQ8OjTQ0NEKtWJEpnLjdWxbSI7pY0vaegPXnY3jWEzkNgpKD1ZPV1DaWNDYbTP2iODEaHhx2cRjQ1GU31Wig8vkelZO8Zjxj/fXz+Bb8CuHz00UeffPJJzGJB3VxaxwGtBW9HOZDbGpVs3tzx/JwBmmQioLns6KsMlQKI1qNwUhc1MtgrF2olQgBiMbG9raUuFshXrETYQSAnGXbyydZI2CqX0PgVsks87DbGzLpgpaPeLDju7gFWmoHzOowjY+YNuYPmqNUaKWMBiwVRvRRgVnNj0QZYQDCXgNgIA+hjmGShVwhexIzejNx3PMuSHh48qQ7AmtWrVx8RaR8gAH4UFAewO1kt4pGXR7DfScXPNcKplA4dCgruYXATG8WiXipKDfGYjvrO3n1mubwsMWa0T9WhNh72AjznayhB2NGwlU25nCVMSl/B+p/9lUtWr6//Xx9Ivv0tTn+vGY8GV67UW9sok8v9lbM0bbON0MZy/TuM0a8ZK9OBv3ALP865OyJ6sFQ8uz901YgeKdpO6FO3lD9+05WDA0ODgwMIE7LFpi3u6H6AJHR0K5y9NqBPnpnTscZkmR2LR2FWC1szfQGpoExQa1QKQf5nHQEmDhyYlMdqHBZ2BBajNKgJmMpHCfm4ou8sRk/nVib3y6WXXsp6nvlmpHDWmMJ4WXL8ZEhZnyfewkOuG0EMRgPhBd41dpBkNaac2zTNIdett96KLtWKk06vW3Pmvn4TxZ9wQGvtXBsQo7bANJycAMccoIDyil/jobZYISnBcHLTyFUCJIPU8ohUM0b5Rc5LLkQgag3hTTl7PTxAGLoCqONOCQ8LZWFiwX8icZ2du/aeL5Pu/jd+IB3EUoODVR3LwFequpZlUQmpWiiJUpo8XgSyaTGMXSH/nHBxttqbcdOY83H0iGkvr9OXIf8sC7LW21kVPOGNhFru1n+J3v0THDUCEdHe3P6dn/inX3z87h+3tbRNSPwq//kqRYDVrL/tbW975JFHvvzZvwx2bG095fwcTN1i+eRWmLreIkQgLWLGVxqJYCU2jAcwrdfSRRzacbFazk4olI00ZrFK7rpGZ00jBrG8VQujSdfWNpkNUcSJ3X1hPZICBzYGc0gqunk8A1cQpQaJFUPq7ENRLTBdQbsdFivbxUBMuuIYxVE3FbKRQKZYpB7x+ot8BVzoAK6SUPpFFcFzlQRiI21VW01cnGl48y6ZuuIGIzgdLiERLWgwFbGtEsm6QqmUzWfDAfOqTZELWrWG4W77xV3FnsF4Q33LujVrVrdvbw7et6s8WrBCZuHMNjO2/6XR73w3/5OfBrJZMxa1zjwt/htvC27erCfjnnUBZdGEJgipTQ4g+f+4fqCXgwKBPSIiNdlz6XHd9PHGwcAEGQP7hQnMcdzV1TVZlNc708kAR1DElfuyVsFxsHYWDYvhNEieGKpCqB5spVDUmhvCqbz4N8qUxJBDCctqdXUc/0KZEaqluyJphXR7fSvG2ZwXB7ALbPalxW01usEI5kO85GtXEzvHm/2qJQBGpBBgWgvQpsKLwXunnf1Zu8iJL5eWg2Y+K1Jp5IwPm/zFnsf27durY2YOs6jB6KCCHzhwULEUvPGYOdORfxUP46hbI2EMBovvtZS4MAxjdzKEiIgLKUI368KCAI9dsNTghYbz7ELpJX+YKvQg+IvIumEa+0bM/yq6G5ob1q1tbNx0ahTTk6KhpOZG3qwQ756eqbUcUcG2jxdLu0O5e43lqfBvZ3Tbs7IneLrGAfPFWxMPPxNds2YtWkxIuaNgD7Yza7EzVTnpm9/O8SU9KUVVhJ+4Kk56Wv3TD9POKT/5haiA/5OMfpiAX051QPV98ldiFCw+3SfVmMlfKXxCmdU//bAK+C2hHGJ4CKiw/2lygFlTRl9VYrVB2KSdnZ2TE6syq+OJqf45Q7jGlGipUMhinBIztG1pfQL75TiFQ64W1dJq/KK2Vq1hVpqyIbeodS2Vwrl8lTsoxE9mJT8dz53CBdk3v/ENzrbTL7+hJyv8JDi0YbMipl45JwXV5a9SgZIDWqBe7wMWTLBaMpot4l/QNIMKXiojliIOR8UQrveg9oiBWMGEuScpCvZIwHKER+Vgg0N4yFywQPgb925bPbhnpK7t2S3XenkNpCbJJ3nESiaFeaA55Yw/UqTH/q1DSjTsxoLm833u/pEKXAe8TrbE3WYsY40JlNZ6nI6XLX9Z9vKnUq7s3BW8535we9qMOF46O/iNzPBVn/n0Baj+SsFzKVxKPhGfVy8CrG7iT3/607/4xfavf/y33/a5nxVWrAb6T5WcLa146EURT8AOTKGf3o7CupOElzvi9pg6EqTwV7EOjfUpDAnli/gNBrktY0wLw26wgmX1uzrWzLeu1PAHUxfWDgxrmAWCHzuMgeiSJsaxKmw8MacuWsEsU1O2LCguvoLBTNje+AQrobFQYquII1OPLw2fjfaIK6OsYcOFLhliUJoNrNBgtWlZ32gBChaNDWrYwoZZqLhZdJID2Klm6+rNTY3Aqacucy9tKsaefrL/lv9rP/t8AEFK7OSs7mh572+dfc6FhY7Iz/dXLl8TbOzfN/iZf9QefUKvIDTiqR3f+/DQjj1Nn/rfldPO7M3biYgFXA5cLYIb0LoWbnOxnwHL4NctLKuQOaVk8F6QGQI8al+r9bBU9rjqBXxUcDNYrDR7yvZ7fXP3DruHRw3MehwYdEw0XtFFL5votEBDCYfwuaPXRd1sQfDekVR2aGh01ep2CIcc6LmSGQ3a2ZK7qsEIm05ng4tUwkBe68kYRPbnUHpxi2UUxTUsQq9qsGNQQSe1BPYpD/HPPfccV+/i+auks91pZJaEBd0Sw13Q1MtxyoGaed7JgowcimGwr+G3yCpfuHU+VrURCHStLrQu03sOI81hpdOynZL1XLelaDS5aWMFBScu2Fe2HtLGSJ6myP1Nely1ecgwN58TtCwOk9Gi8VSPMZKzOxLuuasVA1kq3Ld3L3MBguGv/2lGgMMMvK091HFbeeDT+vCteIjTOLwE0rBsa9Utt9f/2U3br7v+NWDp7FNahxIpuugQaCgQ86EwVbxNJi+AMASSFbOFra1oN+orn4DjIUuRC+o+Q+3HEyCys7NTgbOsIuB+9ZV4AlTHV8KQtGAwEsP5pni/CB1ggZxmQHYRE4BRcQmtCuTNJ6U2/+CDDyq5X/8r9A5lBBVdCYpVWdRXunbllVfSU4p96KGHiKx+zjrrLCWPes899yitQv8ruwDeC7sVSHTCJ+RQrr76at6USWPIUj3PZ555pvKljPWaCWbkiEeehfQoMSoM1s9IgHbyib5D51Xx/nvTpk2KK7tt2zYaA5VKjRjlM0rKRTYj9thjj5HFf5gjFHdpJ9wk7FT78SpABxVsjawEBZLYT0CZHFYAnVTK1FOL+qTS4MKHn6wBloSK4XCm5awW1g8xhBV3TuXiJwHeC/4sUrEL2061GhlkRoZzaWELX+qlMTisPVY1hE61rmrvEePJrldnV+25lkRKNhdrm+NOqagsiXU+eWDpxfZt257Ytt0MRjdf81u7cibSUR5kK1C3UPo5FeS6kH9yS/PXowvTX2TfwJZxgQAJPxgKesixZ0qWVN4PwZ01B+6XOlmEfuhi61HkNEmCACYKhjAM4FEFjfLlv/iPcDn3VNflA8k27lzTrShzm1Ktl1+1QoLqH1UIh8hF1Lkxoq+MimmhwYLbmzVLZac+qq1s0JbHxVDlfKZGulssOS/uCpZKZe5bvDeU8j8c6Mm9+Yb3vv99lDyfwmVIT7jn1YsAq6kEf7j1W7deeMEFd37m99/wl9/LOzGYY6DBm1oqqxssXJ6QjL21pgENdS0ZRjzYPIxlrLyWKmLbWVwlsStAg1P4iynaG5r0znpj3ASQUF/WNpuNMfRpK/UjZiyFcHI+fXhfrLErEIqIZ2q4uA7QqtgoFwkKTPIYGhgKkCtiqRUbLrFRzmnpotMYwwWox+jRRYWAPQ8GAoMX5BbtX3Bg+MneZpduqQ0MlComsnRBswOYiQYNLqOWTHazY1nTtSdrsf07uj95U/BwN6r75JHO7t3b99d/3/bpurNOP6c/XeywjOIDj1Yefwy+HoA4NYjxaDw8dfeO3PGf0dWrB7Tm54fxgew0BN2mqN4QNUDROYjGDiC19QWCVq06YrQBcxRAVAuLAMv4oCJpWRdddBEAGSAy7nAAI6bUypN2H68PvQCBAUYEHuWpbqYPrzMRB1LunhEs+Rr7hmzOV9DC4TzSBC7rNhIUzV4s8rOKEERAFnpwNFvIZ3p7R5e3N2AlC69g2TKkHBaw2RQur0yKlP5LAzaCCIdGbdYqSzdoOe1xMxGyV9Sx3qfgKKozt6pJ8zriq7v5yrDYcksVDE/kx8Wb/Pg+eGWquf4CdAABQ4cKbIExr/Eiqe51dc2Tx4QJcBFOfsebC//wT7qd1vNZUSWqi+ONPPCWG53VnWIZjyLUXvLKAhrAVvRozq64DsSyoIiZaNC3eSByVxAlwWoZ167pRlvi8ZBdyOXAD8nFw5pnwYPMd3Z2sr9oJ1gZiKX6ypsYkKu1a9eg7vTCjucPHQKxvNK0NySDO6KBXsMKrT31TVb03G/f+TokYsBpf/SjH6lOoURHRqXL+vOf/xxgVHVcfQXFVT6xAN9B2EA7WbpsQ75C6mIbEgA7onnVw0UYxEkhwODGCllVBdJOwFZ6QRrQJ1RMiQeXA0NTRVGsQrk5TNR0qIy8FU5FRtVrAjz+V/WTwjkrqiP9vcYn8Dr1SSXm7UPPtNYPq68sGwI0nuYx5jTMz+WXSUC1VjVVJfC/IrdPfPUnvwoaw0N6/2t1gCXhf1Lxft8ZNIX6quwsbwIKh4cNy4JRGekmGWkJGAJvplXh23xVnwjQcoUAo5uKsgMxPGp8KJAlQcnVk6sy8r7uuuvoCFVDNfCzEA99AZKBwvEIUyaR/sPqhdxAetYSq454yudRgbPPPpsyaSfKC36k991gfap2sp1Z8ypSvYEH1EVARp7qjBTrT4R0bNJDAn/MJ3zkEzEzfFWfVLIJeaf8ydTwsCkmr8Ap079KItUwItgFCYzlcaS+kRl/FsCJNFb+kmPpsll8ctLS7ePHP/H/cmBf8uY/yIVb8wUBKUOmIzgqRFnZZPzjjuSqZDeO9ZJ4ABXEJ8sVZKCCeHNU6cAP0e0SySnyeGnhM+EXw/sle5FSlYoiacQfKkAwt43udqS7L3ri9pIRvP/1/6toiPlc1MRITCmEFZgrbZGWSCRh/od8HcRHTNCpC7lNCWtnf2XvCHqRIORuPGC3x42WuBzgc38ET0eY2jPug8OnMpJg5Ue6D/z3+nXfvuWL6iibe+EnaM5XOwLMMl/V0XH77bdfffW1D3/9o+e/93MHylhS1ZB2GCmUN7VY2KlSC7opop29wqoPVnYEjfCo1pvRhgsYXkYEWtitYLNYh8YazVCutL7FwkqQtwEEs4QVfPbyQGvUTka0kONkw2UroacQxjDIxQ7UYOEguuwxcj00GAszWMLBaLMALcIiRiK6lNJGLacpLtgvq5zNlgxrEYdKFRosmxOEGZav2v1jy1U2JEoOLmhwAFxaZKddetfR5taXcum7fmYcPCz7Um1UKnP1UDo9/J8/SKxb95qu5kB+OPP4E26hLI5QABKxIoCmMEiOaTnbnkyms26saUcfFDg3GTKS4gkZ49V2YwRBDhe2cNBja+PtSboh54B3OHjhGXaTf2T7afyY2uEDP+8MASUDQwJ4MrAlF6mWGRow/08MCFA1Lg0mFaUOXq0nbe8ahLViHBhBWMCNhbXhjIHyC4uKWWPBwAfmwEfbNBFzhrPGmpWNTz+fGh0ZTNYnQkETCWesDi9LWmGjwqrGR9fOITdXsfozCCBgpRwsWl/dYHBJ4Bzbs32lVv2k5ngRmP8BXJv62wLE6t3pik0rXDF/lcTSoncfzlCwP+OkYSSrf1bnUquOd2dnJ8xAEBjG3E88a0ZwD8AOdg9v9QBjKVwODhvoB5Hqa10yWX/BJZoZzn3tG8HdO0GJ7RWrjF+/0br82v12YOcDD128dXOyro6qf/KTnwCd08jRSmhfASdMTqS+bdOGNbkilqSHM+kRu4KuBVb0ZNfhXC100kl1EZ26MAGqWk5HmA54cWAmisAEMqDK9PvuYWIyoTBxK5Uy+IVmdo26a9JlRjm4Pnb+4e5u0GbOD7iXsEwZCoByuuYDkYwYXSOeR13ACvGgTIyW0wD4q+AbSkAAdIVkfCLN+eef72WSn2TkrUaMwAUXXMBbfVVvHy1Buk+5klblkAxEml6wQdjg8DyJ4fG/+gFsAahPk9+YbeOZHE8M7b/00kun/ETkOeecM90n4ilTsbsnpKGbMzRG2S2fkEX9RBR2ghsqf4lSJhM0ZS4ioQNWf4K+wKpQMTSSYinHf4hXKDdr5rLLLlPxRMrloGlqIRHAWx7LXn1Vn3yVe9BgSvA/qYCaYuK7urqIIQtrldVYfWIwj8wXn1QC3v68g8TC5Vbx1M7jzyyfkNrgJ1/9T+xftQ537typOut/BaVk7ZGSXGDOfhYCNBKOOmubE+C+++6jdmLUQ5jBV7aCWNKKiMAnlYZhUWIvNBIcXkXyVgG+qtb6bVCV+lVPGU8bGFKGi2QkmFxCdZcnFFj9c7HDfuNVCxe7OsqHmsPccdGwdKurO/otqa792IaRp4CAyxT4MM+xbc/camcG77777nvuuTcYTp527bu6Mwg/it1NVPwAs739PbbKPIjd2/JygXAciIGMVA57JiYWZ1UM6ZGAE3FLz2okbz6gS8x1JtuJysSlIP8LabpYNgSKhqCs6WHd/bV7bmnKjv5yy5U7u84SMTNX1AxVuT747f3mJUCvFMYfA0Mqwk7AZhARfTn94IgD4hC1jJX1RhvwvAdCeS2e24smwJ4OWuvWFqyAXSzu6Om+ORz60ne+U5/EmMLcyjzBc73aEWCml4sKIOBzn/vsH33ggytPPrXrit87lGG5a+mSmSram1qNlqgSrEBP3T1lmVUfrdSH9T0B7RCWsXKYaJaN1JcFMTCgEo3krYGCfXKjg60gUYgUdFUsXXUmtaaQEy472XpXj5dHbAvh6qG8mcUtEqQpG2UGUa8HQbVhBZtsaRc3SHCGASChVCF/DCO3kNJjltsYZ88Iag2wXxfWYw5tsAu2SV6sZFGOcIOlyeoRxJOf4MaCBmNN2tDiIcMqFkefec4BOvduYtnu4OEC5OruszuCpUJjg2kAVOTyEkUkG9gT72YrE2EXipDOYBh1p5HctgYDaClrIcONgQkHecNjdBIBF5lwFB4SYfzNesXIWaQAEb950kgVRUDFAkbDKOAnUBSQEKwbwnA7F5baDeJBr0EJUHgDdgHYohYGg/fSf5gvpyfj7hjE17Sxf9RmZWJevy/DotKhZ4iPd8BW142HkKHFu6w2mtXiEbtctNpamnr7BoYGBjtWtOL0q7NJD+nOhhaRHUDm+VDayJTd/gxXhYNNiOaomwjpKxNOg7jnmelhIyymtJ5cTn0ZLNHJRdae0IWCNLaaZmrVhG9qSQBkK0yVNxCDAuuBIegCSAvkEtiMfGKpKHSL5QqiVZ2LMLgKK5YAd7biv/l1AZmBIfATztUYdE6jOSYsa+OmTSuvurb+lFPdwwe4a+2OVcVY3V7bfPaFPZ2NcWPcpzEQPIA4B1e2GEuGhAlpRsOcFdA1omFs2FOWyQRzf7NzMSzJsHBkBUNJeF9K+pQG0H5QDjYXkDQ40iWXXEI5lKneKkCYdsL04/HbrwLsHeSoAcQp861vfauP9FYn83HO6kgVhj8DTMbuZpQmSMXDGgUPmZyFGNrjoeVTfnyZ++p/phcq7Af8T35A9dH/edQCU9Y7ZWQtTZqc0Y8h4IcnF1V94jGnjDwzQjKysDx4Jmchhlw+LWNyAsX/nxxPDEw5nik/sQ6VdMCUX2dwO4RUpxLspP08bDoe1S92yhVXXOFHqk8+MQUpWSQFVKR6+xQ6rgPoJmRkp6tPhFWZKkAk2DVh9RCm2YQhJCmkmgHkJ29GQ61wPvlItd/Ha6+9ViHVHBSsUqpQbwI0DzoRhXC8gFQTox4S0AWwcQLsIB7imSneKi8jqWZcNcCv6+gHaIA6KqlaNWlR20B1isaHihOjMaFGRZhgcBa1Dcdb4YhjoDbCgd/Z2TlhQI63ps7QHmaWB/NXbKpTL3y90bYpPwwdTRDgsKgGqdueJCrEHw+N5S3+L/T+kdxo1kaX1zQ9Dr8AxcK1AgQUihrXnJc8ii4iJXmFgPcCwEuEi0wodyt4LmtYayyOnr7nIYx/PnL+DblghD0fNPlfXTReKZJdjgJvyXtN865R7uWYpTVH9OVJDeuhhzPuaAVHh0407Kyu09uTYr9acs7nYYI5BtZtyF54ce9t/3JTIf2hf//ODA6f51PViZF36uvtxOjbEfXiPe95z5Pbt3/zix/53a512qpLD6UwJ6Pn0O/FznOz3tUINguRG3jK7UgGUGFPhirxYffAqNaXNdABRvkOoWgAULCCTJkYbSBX2dCMaKhQiFjluFHBuk3MtU5tdfaO7kxnzUDR6mjfOFRAbhP5ZAfvSuCo0LTKIrosivgifcH2M0Ui2jM9ZyDVAH6LBjKyqSAtYU/Vn12ajJgRx80VHfhybFRMvUs5GNmSzSub1jsX5AfZUWPgLXISGOTC0DvcZ48KANTM4UK4AiKMxIjtAomG1nYVHn+CxBwTFGMGsK4FPG0EV620Q5hNQvvB5GTggBgtyPESKmihoLiKjZh6PKTDr47BgAIxhksc1upCIsgdDoqUOKXJazzg/aB6+ZvNZIdHxAgKdycQtoItwEN86IRP838QlKJwqPIADbDUYMdxNQLoTwnKz7+6o1CCOv7l7NXQLdFe6Gcq9UMpGzttTTGjN4teKGKf2HVwoLBw+tfHtZGMkYhomTyWjgAcMdCmtTXXDw6l4SIOp+vXtoajAW15wmmKmgjUvqQKHHFMi0sFfENbljBipt3ZCK/x5YN+kXo63juWqUCWfi2yLV0bbB/zVzQjYFSSFv6ucekkeKxiwJIFcJY3OpaIbqpI/41sGNA8Zf70pz+F70SAR5UPY0chq3iPAF/14/nKOgFCpUzWJ1gxqyK0gpAAAEAASURBVMh/AEZVSr7CYgVW5pMPvIL4qcJpEsiAgK38Z4qjsgryxrlQxxPbYzFTP+XU/Kq1fTm7IaC/7qxopJBBp7YCHByLX3DBhVCqaeL2g5WmigmNLFMoYS8A01fxeCQaCwcDVknkvcBhjBUJrmes2aP6JF5VgQKpnQaDGyjcXiE5/FStqv2NgiiJ6U61NK8/RPR9yqJIAB+MrcdXJgiEHIICYaaAIZ0u15RFzRoJYau6bbOm/1UCzkCeYzsO/t6ZQzNYP2qjqbzsOx/drS6NZNSiJL2r4/0whAAe/6cfICM7RSlXq0h2N0Wpdcsbypc6WIhnefP22W4QfUDMVCRvFaC1qhz2kYrxvsjBRV4+UaayLUdAjQyRtE0h1ewdUD5VgkpAl2H4M4lsMUTK1cmj0GPq4kCDYkU5HIMg/348AfoF7YNCOANpg39kqcJVS/wG+JEqvvpndZjrFV1xej3d8UKB1MUhwFnkD0V1CbWHKQqJfcYKioPixk/IC7WRYenq6poQf6L+ZEAUcZYzkAFhZpd0T7u7D3/3u991NPPCt70P9i/Sl1B44ZpC5h1flrIDPNxTeip3uAdljmbyo9yOetgwx7ROoDfDBoClJICtB/fyhr8V8MAYL59YjYX9C9ZadLD/DEwOIwqAvLJ15z2b9jzaV7/i6XPfgrEOpFJQSKQ+SvDhbDXStMqDI7xhh/0bEu9HDWE3GjSf6bP3IkyHi42g0ZKotIj+ILqPqub5zpLR0hJ519v/+d6fnPuB97/2uuuluIUpeL4NOw7z/woBlklRR8M/3Pz5F1/c+a1P/M7bbvpxqWFddwbdSHzqGuAPwwV7U4uBSyT0AkifCCLVbDViGSvkxocd3AUN5AwljdwLK7iMgi4S1CYGWtc2uavqgXCFd0TGQr5g9nRvWrvyYNrcnzFHXS2Z0wcCuAu2rZKg3CWuH5F5FjQVaQ3BgQ2Ri8YjEmgwzFcMu8GXLdpihQip4/qYmOFl5/EORkyM1GXR9a8gla1kp0WyWph93gaVjspeAG+38x2h0KYNlce3c/8A8qPuDHIvaIzhhDdsLIWiz/UUNzfH2q64PHPn3aGRlHwTnFnGyjG1xOtfm48ld+8VC9WcC9zUQm7Fl5LjFnJIxHE2GeECfGDY1A56p9EgAtJ6FOlx08VccDyg41YH4Ux4xRHBh22veFpHC/WVSCx2rGS4FtVsEsR1BaOAFAFGgAADuyhkgKqX7MMUwfvVdwy4YFOHU/ZIXmtO6P1pPLnDDWYGMT+oI7ffFAP71aMhzeZwN5wG0xzK44Ba/PquWt740r7Ddm6oMboSE+hrmkQsaDeWnx2zLyUi95GQXigi/KyFdISfWaUyWmoTTR43dQlMjpfl4t0P1Z+IAR5SswAcBi8FQLD66RzXWX388ccB4EhcRjy34uzKROItq1Z3rG6JaXt2Prd/32Hv1B8rG8wKeJEaAQqR/lWx/OQB6qJ8FQMOxpIgxocLq5FVwgCFENTBeLFLRBqykxF46/rrr/cKk58q0q9CqSmqnxPeFMhTHdmTdcp7BvQPf7CUTmk/+F5/67q2sBntO1R59KH8I48Zw0Nu6zIL311bz0BNExsCMPbZL0DKAROvzuoyFxgAsrawcl0RJImGdA4KIVFgAK+5WVUHYERnfTCxus3V7Zk5jCAoCWBhqWRq7mApM0oTmLoTyoGdTgOIJAsiizyEwRMWnPakmJkTav/Vz+lGYG7LYLrS5hCvDgRwM0B2zucjbc8RpZ858QxfJ3yqRtv4VI3pqe7444C0P4//szoAYqZk+/1I8voVIZXNGaVuKw4ffPj5RAoOEIVUcxKShjcZVZMIkIwYHp/R7W9MyHlcfFTnNxKkGpfCxCjxb2pXjzoPsbWmhC/QeuDoUw+f2LYcgHyiHHjRvInhK5/IrgrnrQJ+76oDtA25caRppiRVVKecOUwVmIKjUgRPeKvEk+utjvGTzVzyUvxKN7lAf/GLX7BsmDtWwtLtrJqyv/rrT9KjUy98Y2jFafkhMVkTCjii6sT0yEqTWfLeKmIsnC+W+oayIMuBUBRRSjWVgLGINAt/SOUC7HWdqIJjvBQsH7TDBLR2EZ+GYySGZuFkJZ3yDXd/HpvQj1/+zsFQggKQNcM6pSqWEVZNFW6Rx1Meqw6lRVcMiDZH9RVJbTCDSw58oxrFMqwFrbMBIjUQPs0Za57KdaRvqdLrz1O/fPbuO+/qPeWUv/7TP51XiUfagiWY/lcI8Pik6VokGv7XO/71wgsv/OHf/OY7P3vniNk0mHVht+ZwzFtyhgvOqS0vCyoAUJ7UaDRF3caws3sYribLWh/FJ6ejjRTJpWM9KF00Botuf7ayvtlqwNCl5sJZw6rIsqbk6pXh9Vmc09j7U04sLRj1YEEbzDtFU6hNioULa5aNR4FBIUTxFhIU4tawYMGQM+L410XruA4bRRH8m8j6Z9Pi4hVhyFxRHKIIJgwDGTtbtgDH7GZ5oRrXrx824htee9Xw3f8TONQnctZiGt4TvA7H6t78a6lo/ZN73axtX73mpOYP/WHf579i9fepM6YUCSbeemPgogufL4YPp2EV62Basts9IRAwYfD0oCDTnrdk5MsEORdaGtYFPGReeSd2w+DDFt7LnYBewT42+sP4TPbcJpuCNo8dKaDrckz5RwO4th/2Zk598t/jszn2V8aE9AD/E84WjioFHANPAGkBtUCEVsRjDpGjcFWoo0o1c/7VeW2mMJnf7rT2wgC8X5z02lgsx7x+f1pcbYEO8RlHR+mCjjJ5Ki9Wx4lCdL8xag5mtUTEzRQgqboVK756RfuWTlxZV05pMRAxOJx2u3M6Vqb6cg6KxEhG4OUrGTbb45Xm6BgHo7pHhLl3QYRU1yAucHUpPJYB5wEQZNhpMWp4+PPwP5ELLOiaa67hE/DZww8/TAl+yYSB0hTfEtyYMqkCWKtiRJxQ0rLQoXGQf842LwtamCYXIEyBYsBkqiXAi4hEEs/DJ7KrKeBNAmX81o+pDijUkZYAOFI1GZVSImkI81Qn9sOqUv/nhID/lVnz7i53NF8JPf1YdHQ409iQ3HpuMzIUhw6mbvli7P4HIt5GcJ9/rvzzBwpvuiH8zncUGpcxyyzwQtmJhazMqAiKIKkC9otfcShQAMNi1cMyMETvbQTp41hVqkKv1xNaVeNPhkLJc1bLOROJqAsT5MPZU5bW5T18gnnFSpg58ZQl1Bjpj3CN6Y9CMpYipIdf8aWnG2pOAGRzGCV1PkyXbEnET1h+E35Wd2HyJz+GAKuFRx2DyvsRx5fKzi3Goz5VF0iYS+2yyy6rjmRs1U/SI1LO+QlurNBm3j7qDiKKWr46k/0EYMXsU7Y2gtxkp1V+pRSlcFdsLyvxb2oBraWRxHNyQnaELkbL+anQY85eAojAkIZWwShGMoU+qrNU9V29/VqqO0K4ugHqJzGQsJFtYX9VJ6ZwGkM59IgucxnxFWkyn4hQnfiECdPfp556CpIEmvzw/Jd6vw4ePPjfP/hvWLhbr3/X4aw4NEI4OeoBimMrhKsN+Ebe0lcVyYz3DY6UnEAwHPP4vWNgoEhBjgtIqpGBMQO6y7ICvOYPopf8zyaD/avknzH/jMrh6XseP6nnqZFYy2Nb3yRWKl10wQxBokTQ06vTi/Tawm+vbIpkvQUhQzuwf4GXnu4R9m/GNtEbqw9VVsSN1iROZ+Rqnv/DPn3dr72enfXTn/4MyTJvRBam5Pm37Tgs4VcI8NikKAwJmSikLC655NI7/+4Pfv0Tt/WZ/z977wFm11Xe/e5yep9eNBrNyJIlWXLAFZtuA6aXYJzLBQIBnoR2KTd0E0j4wgXCTXKBJDeE5Amh5MsXSnLjADEGm2ZjTHEhxk1l1EYaTTu97nZ/714z20fTNCONbEloWz6zzzprr736ev9vjZXr3hGM6yzM7UAO2oVdztYeIy66lrjN1Ttj3mUbcGvuZGb1gyENUTB+oRHLNF1tsuLWwMCIguuGiII7nZEOyFF/LiJ30o0+vEPH9Z6C05XXDpW0VEU8pOOht9AQ5651a06NGRiMYjMghNWI2iOgBVN84nyCPNHKqBhesypxmPBQnUvgnEbWZNgw0LVghTdaosncJPKwOOwV2bLwsfBo50S+8UjzLReODn/k/ROf+bz38EMAJgtKfsNA31vf0Nz+hB8c8XbPIO5zu3amLnnGtRs2X1D/6V3N8SPhXGfnFZe5Wy/YE+u87QH0YAm1Ksga02LZO4A9js2RZhiNPuOBLvP+TDhva33j1q4JaxvxckDadBqKK5EQruTZOCR6OO7jY2EJPh7B47xIjL2Q5rD7gMSmG8lqJIbHMAKgylPsSoYC2moj481+g/09a6nV5bd2AfZdKh+xPWBswz9GJgY9sVSW9U9D8IUu6LrQebRTbfk4Z35kFpfdOkYmSHR7Uzq60GyDzFecYGExjuc2PIrXGrBUUL/RClUdk/JCFTmhzBbDj/BKLN1tG1I4S9zajdaDjq7B3hniBhsH8xZG46UWlEQrYjqFyfoQEbe6NzEIgFXgEOeNwrfQNFBO4EmoH2YFurJQHkEPkoJChGo46RAozBlIIigSPgHAZICagdjCrREpwUU2ZarNr9dee62iluCwPDxpJ6poEGmZiIX7qw6cHm1c7BhM3k9RyxmRUmZQwxVuRkdHYZTs27dvBaPEFR5f5icZQFZ0seYN/ui7GAbpV1+Fk6VYsdy47dbwrd+fd+lFFfUohhD//m/WRTsiz3xWzAxL4F/WnUQAlm5jc2Bj4ruUqEtoK1jUjPuC99L5p0j/IQmnExiCdqdNdC/UJ9JdpgHk7IKXqq9BP1NdqaN/LZnznEzkiOGiaWqSn5NtPJVGQbIzeVQXnUo55+qziMdZMSMjI8E6oqXt90HDFyeyf6pfWbYLUGLwFDccglwqRS1SPu+9915S2JPRr1aomN2eGwYrODGBzfCzQJsgXg5TdRxwBFBn9G5gHbJ5BrXihq2eHQPM/Itf/IL8QGJqSAkkYoukDgi4IZwR/MRFOp/UgTxUhvKpGImqTFWgekpVXn2yHRGbTd1TFBOMexDCKW6A7a84A+9hN8CqgOGr7HTOwBquqUpf+tKXjh45khu4sPeJz3toFqYJ9KGgSiFnoVp8AohlIGUKbQ5VKjRPtdGo2V4omrT0EFCQH335CfZfckAyeURHSp6YE/8Kee7zdqBLQadus1JrhVtOlEgHuFdOa9aLfvx36Wb1jstefHBInGLABUJuzOv8Cjwq852rj1RF3sMRnIwRLdIg0FGl5U7g/goTS0vsAUc79P6UhzFjsC4o9qQvSMrrr7+eFfG1r30tkVhtrIqTft058OB5ALxwEDH5++KXv/w7r7jhR5//0PPf8aljpgGmPVrxhbq2VrS82Ya2o0c8ualZj4Ertr7dSbcz6owVjPGSuHcDkbY8rdgUy14kbMhppxu4DnI2d8a2br8wFp1ztR81tK2dof6U0y2iYPNI0ZuMYDorgLlsuA2J1yrSYOS9yIHF6lJM3EWb0QQ74oRD5LpEOxGV6YZrlhuu2NkmENnJMkfuGolpCfEU7aAyLdJgCedtSFGudsdYmHBk1198We9f/l/ew480J45GiN65fVs51/fD2chX7zddPVS2vC/ebT+4MXbtpot6RjbHUDY1QtOR6P0z+l27nbrEA/cAW0iOZRtC7oSKiWkMR+59RuZ/bIndHtY4aaDqjaaX21O95jvFDxXcnQYbEargLReXBDzFSQwnL4T5I+1CmIZ5KrrcJrrTbEB6JDHc8PSZSeHG+bwsF0fEvI2vZEPkTIegXg7Bzx7ETubbRfqboC84lhd43lDWSQtzjyz+T20DDteZ85WzkCMcVVWYx+pkbctyGm+ROfPGxQf2SbyShoEDx/LOgQKhtQxgcKGp+egXGaBcKA4QxyvPSMR0CwTrEvtXm0H7IOpMF4SjYhoRVI+TcW3/+IyW359/YHrn6EDoUPm+ll2KDnUMXXik6ByZnK4V84QI7g7XDkzWN8aq+6btbZs3UWFmI0SGjKZPpigoq+gt3q5c1LAvc6kMgbQB+SGXX0cZHcZLNZ8UmPT4b+Ar9+0/qQyUozLTtMmaOsY8xL9ydzovJYJGoAE4D1pxqi/0Gy1KWQ0neuetHNXGc66DWRQplYvf/2GCxXXczNUjVqv6ox+bl1+ZiXbiLY6JXWNVszEYqHJoJlYHnO9EPDTFCB9XesiH6cWgkvTnAtXr4KfV3wCAGXRsmLErbn+KpYSMBaKWjmpPX3zP8Kl5on6CAmZ6KFMRlaLGffGD50bKud26kx4jJaBbzlHWSRd7bjwIlsN8AEi5HBdv3ZuplHcoltUKea22XHYPUOjidyFsJJG9kadwjMc9Z5zCvezkoGWwMaxSBlfxSVVUNhJBzgGoZk9gY+HsUO+iNAVZKY0lQyKZr7jiCr6yBaFARKJ/sIRESh6J8BPHEGUiM+QrxwQwe3R0lBtqwo7NDkMh5/zqQ+rLsTsyMhJwPeixs/FiMmCh9tE/+Sj+Zv73G/9qvBpqOLiDlOAjEIFyNM6RDDJF/WHlL/OVU1GPxlLxVLTuRpwWvwoJyC+AXzEMFOfPc8QC/GFoTn7yhbVSLLaHYOD85NFZO2qlhikYavPKB773jHv+LZ/u/s7L3++zmQWEI13waRb/UV47d9AGf6VGRB1G/xld0cGM+dC0NZY3KjjogF8ftjbmQkO4tw/acAojxJp6zWtew6LAlcm6UJWnUJez5tHzAPi4oVLb4kte9Py/+ItPvfNd7x7ZMnz5y96B49ypulZEPxln6C0Um7VCw9vWZW3uCkWAZcAuTXy7XbEx3JWwEOomC7ggMmarWpUwvI42VRGrYGILF+qU423uiF1AxCAdc2LWjEz9TER74kB4Q9rdnXDHi/p4BR9XRlHcaLl1HVNeDyGPivTL0hVAiX0CHCOAIoFSRHkZua5BMFBEuHVHwxmVwOC45GF/YH2mYyaHFZizYYFFvRagWkIiGTc9qD80ZT9va++2K/uTnjNrmPsq2vd+Zf/yGM+hHophsNHQQ7eOmXcesgbTAGbxdQeqbzkIYkG/ItHFbyxbEMgXzWd+3RG79be73pTRj8D3kh3IDaF4G9NndyW/MRy/52vFLxxqXCWe93gA4CqegaQHwTA4KRDULr3pi7sRWddK8aTE0gT08oPvEgxNb/SoQb/AYLnh5eSXKvAyHp/j51Gkv6PIXy0S9gXI4GZyHH+xdbG9cijyFgh3LvX7/E56fO51+qb2SwpbcLPWI1k9ThOZQjBH9sy4hyoYxugYeKO00xFzDhyjT8XNWb3hJszWpGVsGerjqZlas3z0wIMtLHiZNQKU0C/o6d+woS+Vr2pedbJVnAw3pyJ1HES7R5oJPavpdTdf1zrTuFrThpNaTzwznPO25IiENMd9h3Hwohe9SFEtdCkj294cBYAX9x952rORof1r+/3in2q1arNl5bLpybLLzKcTIrrTkzrtGxpNA/Kh6ItYAwy8oJKL27jqFK9cdyLHJmL7xxrhUOy6ZzddJ2q14CuwSvwD0p++/pRm8RjHjhmWFY4L1wm5TglbA3FaKRxupr5sKz5pAI88GRFFaP/bo2TfKVabWfTTn/6UT4wGKaq9NKAveg1QwCcEwDzVPjGYNlxUH6IZeRE/QbmuuvfOmoy0js5hqwFCtPfbWdOA01xROod5deoMmtNczce6ePqEV0LdcsPSeMxmDsOhTPR5LzdcvBr4DbZcsgtUPcmjELJa0WBaHuQTlMv8Jw/zH4QG65lC0ABipWMDvCSoxjRaoWU+kd/yGfjC5EZhaRAyiJeSg3MH1jbmFVRD1Ye3qGqPjo5ySGFETTkCl32EzCeKRWq/4hXUGbQcFLW4mRS1OPEMSaG9XNSQZsJZVg0/Q+p2ktXQtQ996EMoG1zwxGckN19NHApIzTgWcyL+fRRxCh2kiD7/+GtZTYYVQsjRwmIASE6oUTkaUahEaiM6U0I5QXASxCgqohMIVj8N8tJDFZETtGE7PIurdx7PaM3n//RLYde+++qXH+zb6gdMEdeSfhXUfFCf8nq/ClIdv1SXSBm9Sa0/BeHtTlbMQ7NuFauliLEpR7qbOp49LY1a+8Wkffe7333zzTd//etfV25HzuRZuvb2na4nTju9eLoqftrKZd7gHvmtb30bKn5//X//0fDQyI6nvCRR1CdNBLPekTL6w2JhW2oYUw1rV3cIZWO1jliP23pCTOiOuDuWd4+EjKm6Do4FwaI7jQZopWVUmhpKzlNVd0untiGLhIYVxz+Qo9aXMjuSxuGUaESjIDFZ0dNhA2lzAVtN7JANTdzW6WKLj+CXT1YoUlN0nnHGi7NJYDC6IOBty8VAwofBURelaEAyr8DYE1CNJyp0P2qmyAMpDRg8Vkj+9U8dfANjgtuwMEEUxC5eqUw86+igI1AjVhANO4pMO4yKMpYSeL3Do1UYUa2gTjkLELcKRNU7Qgde3PmujH4YN1nV+zort8bsybDZ7+aeW4xdVE2H9r0g9a5/rP170+0D/PqSWwGBGC5ysRvRC1D6fqfodrM5fmhfx8DmaBJPA7JTCaLSBWNzg8dcvkjL5KKSZODyFczVHsanf5HLZq8Z8fokltX8A745EL8rHSqlx0WBwGD2ERxyUB+wDRlIVOWs4yfnNFxtClRnLTxs7jn7YWwveIs02Kd7SOdG1ZOTG0qCzZ3EfXv3VWtEzNT253UCcdVtrxHu7OwfJKL6A2PTs7N5ughsHDfsaTccF2Ornoqt9yX1o044m0Ixx6BDk2G97oS7suFi2Yp6jV2bupsd7rbM4LYLNx2crt75vbvr4w9uSozGER0bmZFU5oIeIxt2Lt0Q8hUA5rpIhkM0kcTOCnCIr9HTLUtHGDI7M5vedRH2yTqaATorSJzGzc2FBV25Tl/VfIBuw7UpxjZIPCCV1qVsltpsUzP3PhJxrUZHNjqw4VjNGaD0bIdZnJFNQs1E9QnfBp8CpoldE96eYUPUyuJvFtsD+SPyE2TB+KojeJWRiclCWd95zEDjXoWG414laL6arsxM5gByKvKo7goyLLhR+f36Cn2ibpjeipbl64L858ZXEAWmkvTbkuT+udHGU2kF/cMUwlh05clzKq84S58FKKLUClTD6dRj1jkBi4pggZxQy6mmq50fzWdu1LmmxLasZY4tlIOQSmERowT7ZGOZB6MQtCW4CX7iBvY0FzeU3J7OPfswF6Xh7JpnCeTGfqkKARsjCuYt6lLImROKI54HqRunMDtMUCblKADMcYwqCoVQFBdTkdP28ssv51naQv+TElyUxkXmoJwFNeSrqs/i9NORQjW4UFaHvzAyMvJYvvp0NIcyac7dv/zlt7/1rVA4/tQb3j7eCLdaulj/wtXlWJufEWQjczAQ1WqNMCLRZLaup2q+FCqoHvxiSF+OFqCpoGHffawfw1GVJqbFplV1WnXicFYbVsOpu9oUGbeWDlz68G21UPwnT31NS7zZSOBPiNi5YoIXKCmyX3Mhj4kbbOrpiNMZN4eyxsECIWDcSisE/E6b3kiHOZyh7FM6nFXbP/3pT//N3/wNn8ghzoFxf7Q7T/PdeQC8RAerCfRnf/Zn+w8c+NT73vC5/3XTxVuu2pc3sblHhItoFzlwxdYKLRMR2bZOb7QTUTAm8cINItrqk2KEDnb2xJz9JbzvokGN0NhrEoKojgzWK7V0NFGn69qminVhl9GBov48eQuiGO0we9PemGhEG+NlL10x8EREqKRi3WuGiLEktvuY/oJqULEGoIo4FEAYksCneL6BXwUMBiEDbgUGN/EUDSA3sJ7l4gN3U8TsJmJw3cI2GAwMYjFbbqjUigChDRN9a6/RsmCJcQEt2SbEYYAD9NUcG7d1ICaUurWSiTItombhnAF+EQWjlvyk1D90Gvt4z+y3uqv/1KXb4nvHfkSbvjOVectk+tp8f+S+S5L/emf17b6AECYcgms5hOhwpFxzkFg0tH1ULE4GBBKDeIHLPgCW3Qbi33dg4NPH/g4oBL7c+ChcNh1/Y5vbV7zDFf3+Y67R6/biYkv95EcL5GBWA82BRzrtJQoliahOwT/mqOawV3Xj11O81CalCuGl6uJrUD7v5UjmnOaU5ZMLMSPZSMQ+SqWQR6ECOHzKbxBxZCeLjcONRE0LExIv76bScQNPg8fKXiqTpMc8I8SErFjmaAKOsFG29f4k/JfIzu2jcEYKTbczhus1bQMGwLZmN4qh6oGSbm/viUUj7oN7Dj0waSFsnCi2coXZaF8XsbhG8fysudt7UACgx6ngXMe0N1Altac8mm8u+0n+aS/TL4JI2uhZmP7p5wxkguqcZPlLPtb+UtUQiB48r0IG0adLPnISifCvcEvW9+PviBD/qifXzNhsWe9JZfSnXWX8z33+QvTPaykaToRpXP0kO5GqliWSIc1mZ6ATJAe7iVgiyD4QC4keVwZDC/9ar1GgMMhKUBw3GJjx2d5FdAvUp2It+a9tmyXq+/Gf0JRoQgZpCJCZ8xC1pLQXu46VD971+N6cey069f5k2lAI5Pv5zlncmeBA/A6Ayh7LzlHvClbikq9Wv4IqEVAH1YZFyGnFbsDBSjr4mfOUexLRaGWBs4sqzEkiIJPP4Nklbxa/mhReTagIzkcs1+AoBXkQKQdaoEHlg2KvueYaDlMudbCymwXKJvAXeFCl84ngmu1I1Y173EoFhfAu0nG7pVoB7CSF9lKU+mQbpEqL3x6U0H4T1Lw9cU336kXK1hpeCZrep17mmipwOjLTqC998UtT09MXXf2Snktf8HA+jOyHmJqIT+Womyc/VEulBxDwNpocIshTHCOCwKnaQj9R0LJSl262dNxZzR0tkh1hsk+4k+gLUJDx6LVCrTyJ6WKj6TQt26lXIkbouts+m2uUfn7V9bs3Xwa1SAg/YppIaOCgEse1X0gS/xJ9zK4EUgcBCEcqxtisW7LRiNYHc26fRD86wbSfL2fpv2rQv/rVr77//e9/5zvf+ba3ve2E62jpgn5TU88D4GVHnpPmy1/60gue//w/fP0rbvru9zsGL9w9Y8TK2nQF5OAdKWm+jysJGzNdc4AEnSIK5hJfTRd2mb1Jr2fK2RvzcHA1XeURDHpd5G8NNKJFj1qfrblTNW1zzibIMIIagcEiBNVSIW9Xn74h6+6b0Q4mvaNlL1s1ZqIe8ueS6YdKMvAFLUGDUTW1bM8WiwXMYjEPht6VmCjET6IsERRjReyYwO9kGBgsTo8on20jHhL2FWaD0M1gcoS9hE3CZ2wL8aserjdajVYzFgXdyiVAR7xVIXVlJ5FQOhg2g4SB69Q5GtaYQ8DTsFbenvgO+4I1mS39SzbkzIUVledbsdkv5lKXN8xMeVfiv35Zf4vF9uR4eK2lDN5J5akYGACV3OIMXqkJLUwIW82uFpt2kwDhqY5u1LyBvtLBIspV4lzEwnz1cbrYAEshPvxVdZa8/MwmeKSk9yTYN0XtRJqj6yMjI+wdnHAIY3H4wQHst1X4vsDLO+64AwHX0572NOXcUhV0ip+cSZz64Fsu6Dw+OVDRJYPFDgLn+KQ+XFRDKu158KQ5SvmKQqkCFcHhKscqzda8jTsuaczo20LxpmaM5/W+MOGmdCYMIeXCZhIk0pnQ8jW9nxjrhmjg96e9mQrqPeJsDMdXnSkdnWe0iVCwx/5028ZcxI1e2K0Zlcl8ua6lhwaTobLZMzZzb2n2WDSe2NybwOXzcIebE5apdFh7n8A1oNoQEyTSOugGbiDaFmRrf+Qk7ulDZEQ8WCmXmeq/2n1kum5EY/GRvoyvj3R8nU7iBUs9QtM4U9HZU23hc3h4eKmMJ5+GAbBZs+O338bK1Z9zXb6JEodeS6eyL3h+/aEH4vfcg3m8sLBQBEAWcd2zI1dexW5UZfGL4bDH6mBG0CGYLaDfYYo9Ap7VWSi4xxNrg5Ov2VJPYk0HcAX/P+tZz1K/M4eZqNwzB0ZHRc8QkhcJ+QmNFeE0QSnyICsR8Qvgh3vWnXIxDZUMabu+U0hV+PznGdgDDDczCmRyBtbt8a0SS4A1xaHweFWDRcrhGJxQC6rBkLHzY+jLCCLvZSvgXOPIw/0hBwEnHRduJkHCAGAQI2gZExJONGwoOH9xebWgwFV+BUuzb6BtxIG+3COLdw9AAhfN4dxf8BQbu9rbaSk/8amYztyzKUEbBNiYM457VQLZlHfu9nexDaoY8kB0eHyc5sHFsajeQr9RPiNLfdqfXVCrVX5F7wbFb95ClL5TL22VLz2t2ejVv/3c52DmPuf1HzhcRnKjhUM4f/bFv0u9WJSPCrOYFyaz2YoVrrUkKAmabhCakEBQvByOsIzxEsrYYlsHzRxDiCRf5X9FQid6+vWufqeqWYceMSOJZNeGyw/f/ew9Pyxlun/wrN+3RDIkLrKU59elauEX5qsFEP2EYJ89SX1jVhsvEH/UyyP+dTC78y7oNDbmONFPadyZeD/60Y/e8IY34PvqU5/6FLNomfqcT166B84D4KX7RW0fII2vfeMb1157zWuuf9n3vv/97o19uSltf9ibAJHWtJmqW2vBJcI82Jupexd2CZTFmgBAxlrqiGmXDoV7UlZnXDuU14/ySN3AVA90WmwCnrVKUwx9Z2vG0SqGwc7GjCkKnD4Bz+OdMSO3wd1Q0YixdLTojVe9XARRMBrUXgUul+8fi6i/4tEKaTB+sBwfAwsYBkbLIicFmS5uerkhwjDRjJGAZhMwz4CSAgOjYp2v2xGMIkQ0jbctcDV+ZfE+UWdrb9ajUSFMuagUkliWPUbH9IyPIV0bbNZCQ5rXOajRxsPFnDlOrtoDRriSEgwureFRuQsVkpVHzMwVWi6819CqrhMGzMohJMtfamNxDkD24KG6XDDdFgcC+0u9XqzWSuD8cDKr4eJaqiL1EeVd0YX2hcOQ9wLFBRerQ8v/pjJKfl6PevCvp9x4yNvSiRtq2SM4MnkxZzPyXmj0gEynBEhwzi14vUSaJSYWx6Q0YtGl3kUyN2Tg4jDjPOZcBC/xqS4EuUpaCx7gfGovhvwKYDPNsHnj3FIQV91wKFIyJ6WKMRs8yItIR9J/qOAdrKdDCaNa9w4XvXRCuBuTGJCHJPyVrXmEmJut6emoVA9F+r60ROoKh8Th4XRN6yYOcB3P217E06uONtIVgvOyMRMf7TQONSOPTEzNNPbAppmsOIN9Xfv3PdIsHen3NnfvGB3OIkifm6hBrbgBAjFrVMqhQ4fUDe616cD2bGu9V/3MwUaH0BB6Fc9hFELvEaN7bGqWWZPOZAe3ZjmXlqjWWt+3VH7mCXhvgfofFVMDrebSUs+tIa2EisjBfamDe6xQKPLsa0C/hDgaK3o7hzbF3vvu5ndu0e+8yyH8Ul+v/vSnJ575zGLPwH5x9y1LrN6EYwT/B/4Uc0DOZw59VgvrPRpyYqGlrfXWULlFWZXePtgVfzzqx8AxDyQLDmbVgmImL3p0YQIzXAFg6GZWCnQhQ4yfbXobYnpd6MKFrzz//UztASYzVWPcz9QKPj71Yqvh7GBNcUBQA9VLj3FVlK7Hcq9m54cpRh6WM8OnsgGD77zzTj45Aqg5N4p3CdxFPrlv3z4wMIv9pBsCyYDAky0C1a3TRPrTEM4dVUOwrjrKF1eYbDBu6AQuWqQ+ORdUToYP7gCHRYCl2fEUAGajg0dAjwVHP3smfEMlOobryld6b8GKWHIUOKRQFuONoF+1oy6u51mUQqdxYddKp114+XVO365qOYTAldi/iF4hQPivvR/ITE6WCYRBNtuBhxMisEAb+9SZGAfBP260xIcONzq+USAMDSMFASrUjJBx3EG9C7Wim2XHq+Jbx5H4GQnNfeFP/6m7OHH3zmfdf9FThfzVHBFBCx38aI9SgaA+/j0/ISrQCZU6EHciofDhKqFVMJ+UCKAbUu5ACmA8N7UeLWUtd7QXfscNN9xw5ZVXfuELXwgm6lrK+E3Pe/6kWWkGMKF7e3pu+o+bCIz08pe+5Jbvfvepm3JdcWd33jsc1qZqBpaxh8samqVFiwAz3lTN3tFtdiZ8HpGGMydvtCPcncK7lTOW9w6W5BGcCeGyFQQLCKlYYEiAsTdd1Y7lbHhCKKyK9qK/sDCJZZF0JbWJtL2v4I1XUG3VsmhE44Wr7lWJUWajuQoGBnqJbTAwGDws0mA/VhDSZIxckAOz1OF7tXwxb9nCH5KbiWvpmPia4kJ/NBk2kBm2bLdmu+hF4zmxVLHrTaRLgFwxVwBpUg4lx6M6CF7sjfE+bXsoVwJ0Pc8ECRsOoj/ZL1zL57fNgxHZgmQX8nSRVvl0Otxi8YElnpzJz/+iB83OxGtMfeiCrSBat9U6tO+Bzt5N0QQumPgJ4AppL5rSIukVOon9DkJfLGOlhn5b5vcgKVWe4j916chFtXuksu6Wbl907aeTfy7D/B9GnEQOJ5xVcjyDWpXDSd6i9hdY10qDi3MOaSQXpx3WRxz87EdgZlWm2go5lYMDHsUqAAPnGecon1xyWvqIERY413wVjvtLOaoo6SCflYBCD5Pn4Wl7shYiabIsgaa70qbVdAlQNJTBEtepELNIg0dj4vmZfsVnG+6RmW/YOBNqC69soF+YLzBKYKYWavpwhw5s7kk6F3TitEwjhFLRimb6N+Rn9O5YcTZfuOSSy7YNptB439oLdUMNVX8fV1Xsfmk7Jz1sfhqrINCCk/u4B1b9hY5FegDNBJsAcAWo5tHxI+P37MtvvugiGpgy7S4/HPH8eK+66BUzLp4eQQqDwsjedtttNBNNAYpRw7RieUv8GJyhxYYXPbjXcFrNDRsym0ZLU5K52DKqupm44AL9d1/l/fZLIyx4Ao6n01Ys+dAEa9/EABgdw9mirAUmm+hUsBrFKR2Hsdg7pKKsl+AlS1TgJJLoBAWAR0dHg/ENqD1kHVBvLBaWhqLXV3hFe6dRf8ZXSYfgcXC/WD6zQlHnfzrbe4B5BZ5hgZ9XgV4wlJw+WBywWK699trHhcxtX6ftdQv2QxLb8wB0OShhGoLKOBk5FEAmbBEcrOwYDDGbJ7s6T9G0oJD2Etrfstw9+bHOxRnEurAgF7xlTZUhszrWFxSivgL4uWip0vyi7UGT6Q0sukmBEoDkoE/4iZOUB8l/++23k8LGSDYIBl5BB3K2kgdszE/qpWTgHvTLlsuL2ELXVPkl63wmJH73llv+/d//3dNDT3/t+4/VolhpifiXuAbQf0JeLqwjU44ezqTSoUjCJ7N98S8krihWYjko4iIhh1FJLIzj3irXPxKFV+NHDOG4FHNCCS+C9EivorCJ/1AENOHQjiP3Pe8nX65EE9967Sex/kX4Eg25vvh32YOV/ofojZpeOqLhcmWowzxctA8WCMMhipnZmLal29iUlVCUixqxsFHLfWfE2S1f/OIXQzoS9IiZcAqFLfeScz/9PAA+wRgzqxDiffOb/wmH7xXXX//Nb33ziqF4V9J9eEobwzOWH/h3pordrF5rGkcnpse7IhcPpy7oCsFMUrMbX1ZPGCDYl9s14x0o6rjRmq6JWjILrGrhuAjnWHqpiSmmgfhuJGtv7kJ7lUcFxVFERPOGc0ZfRj+Ud/cntCMV/Mjps1ER30E0UwLayxQFDEbSCwxWUZFAKSxmnGxhHgx2EhiMo2ZRkhRHWVXby1fFOAGf1WQTDKqxqvnH4oewduNmtNoARuNlRxSkgZi48uK8ijooeBuRkABHyixbOttFzHRSZgVj4ZJNENapxHZ7JlQPWzFeTDP4j73KizUSW0U5Nm8P1ileyhauG2Wi9gyClRT+yX7ga3EjwQTZUIAv0uLU90GvQHqyyzHB02KKQc1lr5Gukv85GiRFNkdJlV1S0ukWW58i/FrZyMad3qRMe3Kya+ApkaNFHvMvzhJ+AuLKGw0Dah6Qw+HEKf7MZz6Tnzil4Lpxw9BwkY1C2HZJ4Z7jB6JfHUt8BvCAX0dHR/lUFw/ytvlvUlRwv/wNeWA8GDMt9+FjTt0JMdwHiw7R5PqyoWrVGunUtnfrqVadgSCq7qwRO1Ry9s56xWYY9MsU5dggwtZkxUMvutygZ3B/JQoFg1kdLdmOiLWtG1m3i9C4Hhsc3ja0b8br6PYaBaxL80/YsSkd8nb2ajFhpC7BtqQJimkNrcM93XXq2uPtXbS4WxwvNFu1YqIHIMJtFcWArlyc86RTaAsKAjwOgcIn5w1NY3whQfjKtIF2YYbAxVdmYCfxIqor807TMADu+dmPIq7b3LGzZkaw4ecnkGwSXSvWRabTy3bKLPffUYA/ZZl8QQcE7lVNlrdwhRAIszbEgTta0H44scyJRbBrrjX1veWWW3js2c9+dvvUDcaLG1T66T06qj3Dym+CwUEGlZ8SuFH3Kz919v6q2nj21n/da842C58LVYInP/nJp1J4MA8p5ByYQjQH4SryQ5SKOWJOpWfW/VnqxlnJJ4udwkG5qDpzT21BI2yPAFT4oRyF7JMwCpFtgophnyENBq3xIB4T2WNheSA9ZkddZQ15kI2OA5oOYcKcIaO8cjUgBrjoiqCNtILKc6kU1Y10CyepShkZGeErpw+fdCl9Gxw0Dz74IOcO2XipEhGjcI6GPMQqN3Q+L+LitApqxQ2vCN4epAcpZ84N9YSm+tOPfYzIm09/xdvDm66ulVBb1GMhPLDOa/pJUx5tDl9gRptwChKJ2ZqGCRckMRSgEJA8gfgXklJOW2klPyBNxi8sv/sJCAxEqxmvGbwG39EQ1dC9oe6RjOG89FsfjritX1zxyrHBi3zVQwzNhBqFhGqnNhb0Jz9BVhEetTeB0ZlJeNTdM061GUHUtCFlDaaMnrRP7Up11napQWT00Q1kRn3zm9+EWby2Is7nnu+B8wB4vieW/8vMRsfmG9/4BuyW17zq1f/61X/d3h3uwdvzpLtvVh8v69N1nci9GJpaUzPTpXTVzByrOtu7tf60mBdApyJlHUjjncgbSLp78trBEpqlGv57yi0JyYteNObEOMcq1LXJunGk6ozmHBzE4T7On+usUB04jXx4IKsdzLuHihpa08cqWr6BcI9/PA7u1Vs4x3J14XJh6sAOIrHL4JbJFhkLSyJqxixgPnDs3DSMehWptZsKe9mEngjP4UaYUmh34Oc5E4PlBkrX6tj7hkKYiWLuS30qxJzRsH8gSJKXMstXZL99Ve6ro8lfR0OVpFtiV4gOVZMvKdT+rdt04ZbRftCtlb6hZHYRY8d4uP5MZFrsHGwQ4F6Jw0MQNlF1lJzgSEegLz/q4VjKjIaxVuULYFhwr98d8ptPH7OvgY/9T5oojqEpVPYzwQ5yyYPzN/ytOcaeGWKi4niA0OTugf0H4U/X6+pwEaXl0dFRnBuRk0MamkMKJYJuq8XBHOgUsdegcEKKOl3YgPw3yAdtULzbIGWFG0pe4dfgJ7XZ+V9lQA8U7EMF3dLCFcs9nHeZFd0ZHZLj6s3mqJMP/Xqs8cu7mYRef0/vFZd3bhxNDSQJj3S0DB9TdKGnylp3QiJyYQOTi7vTFW2DTEs9FbZ39oWZKmgNoSve0kKHioLp4V9OtvSuZCiFZD7jTh3Y/dD0FGrharSCSp6+GwYIQoeLVzBAHO0MKegayqdk60YsQyfiBL0fj4pzegGL6+JPQTUPFv8oE0QGYsnhoPMZfX6FGc+nIvLac0LPQdjhNhxSb7lCSF/xkpqBY51aK/WT70s9rns22wLMH9KRz8OTksf9qR3MGNGX9r/wGzJf3AFwydrGUJhFRSli5y9mwenVkpRSwuovSFg6R4XZWPAUFBhUF8oO0LWQuUh7FmRY7qsifymW8YXWp5Dlcp7V6ViSwzg4V1t30kPDuAOf+FyBmOPXoPz2ZRgkBjdoELBHMQmDlLP3hs0H6xIOGg6XlVv9uLSRrlbbI29HGEsNIc2RRo6MjKAYpVIAtzCOqT+xFWjI2NgYImIF7fhKCSA3lvwq6880YCvG9HF0dJTd5gzsk1U2ZEHN+coeqLZBSqBnMMVqn/O0OniEHmNrBRXTjaonwcaQqWTgSFI8elUIBbKjosjGnsNJiqUJX6FegM0rn+PBu1bZnPXLJsscn8boz/dt3HHlK987VjNF3zAiRj0QoXIctl18t1oEtRJTNeg04CsUDkZ/mAByDoIyIS15XNzHOi3Xwnssp6QdNRzTqniW7oUiZigC8YP7KzJbHrFaCIAEjSkqhxcf/sWTf31z04zd+ZTfaZhizAUpheajuL8CWy9xKcELxkduJur1Jg0CRo4XXKyZZusmB3025m3pMYezcw1Z2JglCjwuSc0H5P8veclL+EQ8g1r+4zdSx9XtbPzyKPl+Ntb+sawzu/lXvvIVwrv//u//wT/8wz90xUNXb9R6k87D097+IuSmwFd8O9t4Xp0BlCLR9bZ2tHb2hsLAUJ/AJtb2hT16b9rtnfHGisZ4EZVpyVmzRDdjuubhsA4Ekq8ZyOuOlt3RDqRzYvnKcpd1AjQNadt7cKfuHkAHu4xSNO61PB8G44tLog0j/hVFaAcYjNgWp1ICsEW1A3VqQywcWJ22I0JdNgdIe99ZtFeytJh4dfZSMT1MFt8bHvA7auolq1atl8LxdCJKHCFZ8FDUSGxF19odf/voey7NfAubU8rzvUv5wFRz+18zm++zSrdk7Rkj0mNnX1TKPl0c5Ex5o7dN/25dSpawRv4eIiJo4aaxPfnUPNkAw2Yo3D+6hfcJbudXASDSB6TwHPiH5vDFR5HQ+TCExSxW9h4K4xnJjitpu1KpEstNXZzTBL6t1fs8O3bZcOSRhx9piLxUyuKQwP0jB4M8qGkcqxwwHBKkw2PGzYai4xkC8gTZVObT/ymYBofeu6e8ong81ifLDmrMHSnmhT5R9J4yZGxuTlvf/nbxH78cKVWFOc5s/Mq/Jd78ui3XXVdKpVAHwq03HJPupIdZLzpnHQl9pqL1pvSepBEz7Yv7QgAtWKS/Pubic2yihKd+rZM8NW1kINcZzXTFsG8P/epggz0X8KPswZZsuDq2+Vzy17UmEmqIM149xbnOxVDvvOiicCxSD3UODPfyU3cKxu08z2PZF9CFzApONfyrFRxrv+YSTqFfN/uBiv5xKjNrwdOMPtEpeSOkuQp61C6BYTIguIDsgKQgQ8DIX1DIyl+pFpM233Qi5VJ0bG/LMGPPeOZRcYHMhQt3senlxj/hfHYYuXUWO/bhviM4XavBGZLFx6izzA1iHGKkwMmdiBAr20kp/++qvHX6ZEQw9KUwKNr2Iv1KaqwjeozeAADTLasHwKooCoEyCxSq28s/N+5pHde50Zb1bQVAiAJXWEfMK8AVE4ydeeVXs00xkc4NAIw7ZZYSotQTtnrlPjkdv4KgFEhjT0AmCYhF8YfaYtfK6xgChowbBgK2KUcq40IKxyvbJiCNnRN7FtiIcD3WdGTwLMxQxZE8He06c8pUm6qqTzvXjM4kUa0IPlk1gFt1PNGZsCZBxQwKFzdwH9RPdBoxkFWZdDgHHJMK12WKgQu3gq2JxMd9g+Kg/9znPgd6veKFr5s1+uoW1KIeN8Xxsmq1gu40nOlVKJZpV1dHNpaIQ+4ioSk3RQKEsS+PQfqi1UjgEkhInKqi/EwJkIyJqFub2M0pGsn0m50D4RCKjdC+LkQ48VPEyk/zslrrhts+21Gduv1pr7nvt64TCkEXIzK/AxdSC2qMhAKVfABdHYkXzp8R/x4qe3unqZiBAdNA2h1M4o7UF+wsIjnmClnxD419xStewbaAHhb0iRrNFZ84/+OyPbA+dOqyxZ8rP6hJhgT47//+73G5xi7/mc9+Jmya27sJ/OuEHp5pTlesqp4JtyqV0sN7DoxH9Xx/J2ym0W4vO2fUy4JhcegdMePSQZ1lsCehHSi6R8r6TEMv14ktRGgiHa/ORdGIlnhLx6raxhIwWO9LE9ZELqmGh12BvrPP3JjzDuadQ2UdYIMmdj6Kby2iJbkU0sRZNN6hCWkkNg9YHYhDLBAjdgusfEhiYp0BgwUhY3+LsofjNfFMhfVvFZrby8SRGENeC62djEerFfzMFmMx0wzHsBMG+lJK1iy8e+RtF6f+C/mt14xVHoxaeSO+0YmP1j3D1s1m5/OsrusKnh3WQ6hjCoYte71fPvipo80LRL4rENWNhRDGokSNM2jZesAmtA92Af/U0Sk+C8SVl58ue5nYAPsd4cNbfvKQ0CK8RVMG8TYIF7DLh7tx4yDPtGxvYqbod7u/FZqGGY0TvGpvwcgknIsveSJcUJihnAILWKHtVDtnNpbAaCWhwcUJJFV5TC8PjuWhvDde1BDMop0+XnDwVdabM8pVpPHexqyzJWVrt9/T+Nt/jDYa+NqH4UKgrHBhuv7Xn8sO9O+64mpmyKGS15NE1wCmg9OVNKfLmAEbAykjZlgX95swVuFh3H/MqtiR2ZoDL6YjpRWqxI72Bjti2Bdv7UWVXtu1c+fU5CQAGCkWXbRkV5C4jsfn6OgoZxxHO9s9ti4KF0WikXyDQ04MzpnPGzICEX3eyJIDo8aLk1L3rEfs6U9r5W+bTt73kh5347uMjreH0y8hXN+SD58wEeoBJUCEwJAdS3bICUqgWhoa6Vpo9wPRZq1B5IyNw6hDCxPK0zDhXjjbaISr1UTmiz92D6Z3gfnhA2QxJJCjl3/IfnVituHpHaabv9jWU3MSQ3fOYPbABQC4vaVKjgekgdhtTz9/z2TmYqqw55zMhDl3exB2CUR5+967oK1sBYCrkZGR5aCgTw/LQ9yovlUpZ2k/U3mmCkrCLLfVC0gXdNpj9hUuM9sgOjvgKC70dTmJEfbCsOAGWAXcBacxxBg70CK+ombFUULT1HaxmqrSJ6AjnCwyT0DOq3nkMc5DDZHYUz1Q5clNPEoAstKHdA5TfblCyIaqM8J2JgkGesGi4JRUB2XQcPpfAWAIGLCxQsU+Oq4zEDyuclIUibyObIrRzz1HMKOD+hX7FWMXVCa4Cd6yXjcoMf3zP/8zcynbt+UJL3v7r2ehZ4hVIciTVyhNRdquXlcu0wcVKhaJxvit0nRh8WPvRiEwxbEYIB/iX5/W1YxkRzSaooxQ9ShUUrxLmAi6GSboJ+FBoOyQG1VbHqqUnKUg56fe919PveemfHbglhe+w9bBSvhMES+z6tWqDgv7AdESh6/hKuvfTTl0Nr2xgjvTNDFC7kx427r0TR3K/85cOaq01XzyRgbila98JVyMm266SbmnWc2D5/Ms1wPnAfByPbNEOnP91a9+Nevt7W9/RyqV/sQnP85a6IobT+zTw63mGPjKcvINo+y0ilVnvJDp7YpPV7xMh79m/fL8KS9aGTgr6klpg7P2nrzESZoK69MNcC/UrWe3NPQuWcYIh6dq7tGaNpx2NncahNI2WJdSBEvVwxvWzv7wxg6kwS4O4ieq7hTBlmIGLqZLohQthsHKLRYWgvi8EzAsDnJEKRowjNwVJGz5zqIBycIvI2I4Tp0djyBPcXGUhcko2czuri4kXJVCvrMrl0vGxR7Ydp6e+peL0t+F0m4+nDn8VznvQFp3TeIEx64obnxX3kzXXcPFQ5YWEdGt48X2Ni/50thH9lrXKKqPdlALohALtY6rauK3UDexAZb9hM2Nt8iTnjc7PpbK5FIdXWit1IlL3rRaLatl25FwtLMjh+irUq0V8jPgHzoGCbds0wZhmfGnRfTjyNBAd1h8KIb4UFsVcOFIxcnmtScN92SI4SQsRflf/cpN+8WOw1nCCc2RhhsSgv6tnmzlRbL70ggK9z+WfEX76/z7ua3d317xXOXtnXGrdgj59nTFPVbx8F7WFdemisjj3XQcfooXLRer375Zb8jRxe4Mv0Nsvwk0Va5Xv/O92M6LY+Fsb8oo1kRI3pk0Ub/vggeZgaFgwUkB5eL14cEpu9An6Em2AABAAElEQVQKofyDsUpHUq82UHF3N3WEooQF7guhi0sjOBdRsoK7D0cAdgBn6oIWLfi6qGlrS6A0JWzn/OZJ3o6CE91C/K+jRZdoQMg9s1EniyOLZQsmu4yBsHpq37PH3xR2DtI3TE5GxPQso3GHd+QXzezvRvv/ElEw5s1LNgHaYjl8i3ADaQZPQUZQo2UrsvwPrAHUNzp+/D0TFs6Vl7UyHbCc/LlDBCO/QJbE3CWNqbZYqYJrLcuNhM16VZAvFaBF/iLCiIkNx42FjVTUOZkKzb9sub8qGCaSn+XIVuoCw4ixQ9FRKvNo/Zcr8ixLV5OKSp9E01AnIcQa60jZPJ9lLT9t1YWwAzKxmtirF7wk6G2Vrr4Gie1DwHwDNpANOp5PrE/5pEwu9exZ9KkaSLew7w0ODqqdUCW2N/kMaRFbHxD3Bz/4AQcllWQUmOdsFFQVp8RKtk9bmPMgQ1AxzA7ktxystAsWM3hvdHR05XYFHYKhOK974hOfyOfKjzxencP0C2ymVlMH1TRyBs2BNQDIX1nZgQ6EF8knUnSFfoPHF7xUrSnewhgt4BpwtgZPKcYElYeLwWJkXPgVGM8+DwMaFxiwtmmXumCArryDBcUuqMzKXzlGjx4d/+AHb4Qs/N/e99l9ZbOJoxlTi4eJVwQbd56m8omqKjFCSmWfRu1EKgPtCt2L8hryHs5HpSEIuke1TUhcnsWFVdTEg03KInSJq0cSnJxQgBHDxQMOByghMwHPuLuBuuppzlz/o7+GOPrZlS/dP3SxPzoSR3OOoFumGZzQCG8ycaMvhaRXlBnHy97uaa8KUR0xBrP2UEbrx+PoGi81Qxjr173udag9E/j3mmuu8asU0AZrLPF8dr8H1jwSv8n9ppb0m970JnaHD37wg8lk4sMf/jCJQwM9g/3dxZb7nZ88NNFMNlMbp0Sf2ZipuGN5ZzTrR7lt291UH0YNb2u32Z9xx6bdPSV3vIjGKYGODAhc+FV47iWIWbmBma4+XTGPVtxNGQvDYDQrWMoKBkOt56JGpl/bJNJg0Ygm2zSOpqNozOp4qK5a4heaoDjIDIHB4Bz8LVM4BwcwmKhLQAfZD1AO8YMGQ6v6EYZ1lEBwlBWueCkEwrFwZ0fnzOz0TD7fg4pmGDv++rU9/xJCk7ocH//zTvNoRioEsPBi7s+iRz5nbnzPEeIvjTe23lt6VkPv3V29/JHKVXUjA2B2ml4ibxe6cRLmYUhMii3cOfHUJ6riGuw3F5TLhSpnCltkGzGwM3FkqtYkzKy/2v0thk4gThA9kE3FY6FeUC7mwj7KRSgoHSyd6HmJeFKBT5xNKqky1Dg76u4pIhu7Vwzp2SjcAK65PpXbtovBZetBzwRrRgR9HL1Qrur3E+/vfjX8DVuUVFcJRXy8Jk8i3d0368zUaI6JV2cU5uES9GcwUHGPlnSC94LbK3Ut02NG6s383gO4D6d3QL++OjAyfwfpnzW232w1BtO5/XlhiHbGmUsSAGkA82rd2dlnYJGCQ+/d0/ZMLVS1xcIcF4UtutzVR7pQv3cv7GaO+Z0jg6PBk0b5iuP5oYceUvbSbb11Wm7V1t9eNMGrZ+ricJz/BvERfqKRkCGwHrTH/yDiHILN4hxK2nfH9Zrpba5FLmlo0YZZ/KIV6g71fJTV0P6i4B44xxV8DW7Um7EJV/x1qnqiugSPPnoDz6vZcA7d9cOYaaSve26RFYvyMmx4A42vRznu/gMCJbEQlhUgfCV5XZ3nOdnhBtBOlzWFFjQOz7gRBpZ66tGXrccdNC7vhXhdubFIe5DVQE4t2XXrUZHHswzIQV4/OjoaVEJNVDYuyE2GI0hffKNyrtx7i586t1M4Uuk05syS3YJTBjpW9RvYCbEVvYFKyAJlEzIocRY3lBOIts7GroPPrgwNWGhARwRitAI3P7T6TGiOGougJiArYBjLAcjEqseAE6bDH//xH3NSoDGE+RhYjsyMFw+qZzlHGCM2h9WzDnmQIxidXmxwVtAUCGr1GN+odrW/VKUsOaXbs3EP7wZ/ExyvKr29qMWFkEKnIbBl1dDnANHVvGLJPAG/iTLRZgpqxTiy4jjlVQoTDz4vwJilB7eCRAZOAWAk+bg8VKiYRHUxOsvtgUtWI3gvN6961at5+4WXPSey+SmVmgkdl4zyT9F1nHsQm6xurQrZUyjiqrS7qxMMDI2CZ1nEPxgACv0E2eRvwnVL+PscjlCoPpmHMJlT00RPkEwQRciBxHxYExMwSAvkRsDfqGE//df/9cQ9t0+me7738ht98a+eDDvCa/dpwqAV9FtwL6/VxZNWLuL0JIzhTv3ALE5/nGIzhPypK+pu6xaT4BUY9lLCoktNADQsgB64xcYS8wUveAG5jnvvoqfOJ6ymB84D4NX00qN51Jx7z3veA2H3p3/6p2zoH/jAB0hkIXVEjV1oJuMbOe7tL+gTFQ+KFuXG2YbTjebE4kswo5aJGLvQiM66e5LuAQyDS95Mg8DCOobBRLuxUPW0vFJDn6njN0sfrxCp1R3pMNCCltXsoyX0QDHfzQzow03tQME5UtKOVfWpqleIAYNFGgwUR2Vyzg0AXrKEwSWrHY8KBMIBLwEBI1QQN9G+cS8qHCxXOGdNTa+7OgLhGE6UIl12o1Cu1iId0bR5tCe8m12m9PNUaDJJ66UmorMpCLV+V8KejIf7KhiZfvnIx+teEhXnSNSLgs1CXmfB2zDu/KofxWh4BO7B6WYiZsYiJkBsamoSDWahWiQmG36qtJieoW9F1RMgC1kJJ8EMES03BtYNh6g2HGB0mNF+8btCHIDRDMHRc5f0Et0sf9TFPicO/7yaY+4vePGwtr3Lno/G5sO8+YzBXxlcw7jsssvuuOMOjnM41m0WsP5O62eVu0e/zT2Nh69C3cVO23Wt7X3stEE9guLlMX8YgxQP7sPBgjNeRsHbQCdnouLmq1ougT8k4mCJtXU6hvNnOJhaR0r2dcJLRRIxxhScjZwbYToG3maIDI4bwU2yiYc22Av4JJwqe6J3kAbZgn4l1jRV3jvLu8y6q+2fdeGzklKzdRTvkfoOZ124lVJp/385JTDB3bmT01rRo209G9R//W/of8XvVz11pIwINMTiSZgcMwL7l7v8jqVBjj31ZxF7HK9wre9061/rjjaJgW1gItDYUoi8Y8rMVZ2Zz7vZV5oRGL0LyzthG6EhVMXUJ5VZ4ZHFeeBoYFfwxQP7nqLrv3PxxTCh1TzKRNBMYEIvwOQewnleIatNuDzojPjDIvJniXAovyDLDsP+Fi0sX/95JTAmT6z6UpXHHJonkCSs0EwyME/gFi1HBq36nWdQxmDsqBMkGl/bU0hkUaCwitY3xuFnUL1PtirtrVt5rE/2DY8+B/R97nOf2/7GR39jR6rVAIHqV26gBfl1Mb6lEC6yKR4NKvqnu9rtlVzfe2quVGyYVNwooHJGrSawEBe4SDWcCiuUSyL3QGLGQvE1qD+UkhoLPoH0wGN+QjIM6AIyrbLrWHQMPc+ecPNZZYHrmw3JtgKHzEzY5Vw0dnR0dIFC8pIvpTeY5Ahy4azRRlAlOB+zaroR8MlMXsDrQbpOHgAz2+y6zAo1OtSN5QMLA/GvuqdFCgYjBqAhNI0FSFUZC9UQtRghCbh4VpWDfJJqk4d6QjIhLgYhM9A0UK3iJTuBxH/6p3+6666fm5HEtW/86JFmomlpoTBuWRFX+IIM/2yUY93TKpUy7+rq6oAUJAWgWyUUCw4+RZ6C/honILu0yH4giPz/OBhxPUV0QMPsJI4Dp6WIf7EH5B9Fgp9FViznpzZYL7zmPz4K1L35FR85mu4lawTrX6RG8mahche3wk8BCHgdca03rQ+lxdXOwYq+d1qCvCA63tBpb0jpfRL7l7ctJDOW6xD1Irr9bW97G9AXG0wMgNdlxJd7429U+lLA7DeqA9beWFYd1x/90R9xMn3kIx9hY/rDP/xDNSM7OzL9mE9kjP6088g0yhjgBwN1iCVfIuvMR2b4SsZZXFdC25CxiVtzQHxE4zLOKDQlcjdqzChCl1voNnvTNeCQNl5yEfkO5zDdlF3BX0mi7pGL6dk+YyTnHCx6BFuaqGozVTxFG2hW8yy2DXjGw1m0ZYs6NIsT3wBsFtyzItE7Q28WllmMvQB10VlEN7V432asScHJDTYOI6aFujHCDdWd/lQxJOhYs44KUBWHWj7o9EWzXrgVt6a1cL+XMQuxsC0Yy/dB3SK6WsMdOtTqPubVj1XKIeAhgcY1ozPnaAYeovPNEIHXkFtiu2gXj7luudDK00e1yQnh4BlG79DmRDqDoJhvlIrpMpGAxYCFUgQJ+luX9LXswn7XSo1kq/H7iEe4EAxww79JIgOF8V6G9gseklfCUZSFHhr6z8TlQ/7Atq4kfn55lCT1mb8HhILDwb3eTM1FpN9y6V0x4R51cOqrsh33Of8sicLyGC/Yh0t6CzVduMJVFzYK3vn7cloZ7YCaCH6pf6VhxMIe+s+FmjYTd5ALGE/4Le+R3fBBRZzOtk4HItKnJy//rVYk3myAhUJoPg9kiEpnhnV7V78O+uUEOzBrHywZTfxLz7iYwcAHwaX5cFbHHVpfwiKKdcsP2MjppSpNV3AkP/3pTw8ImuMacxq+qIGkw/2TAF+OHtGw/f72BtNzsaxXeC0nsudO6OWbGSV7byr01Z6QFeVg4/zDEVt8d2f9fzmxNzcMr+iU/sPslgjDJ3FBBHDS88kkOeHjUDbk8XUaaZPLMo/e98tEKV9NJeOXXgq3izlK/YhSJjbO7RcrDfMwDBWQ9frpsMnEQp6T3oW7ZIopAUjYBADDIUBfi3m3/hfqcBS6snGvmifr/+7Hu0S6V1EkfHIpKpDGnqsUCUQtE9Wfq6e36+nDBSR+8D5+AjLxld5G1kRsLSAQiUGGc/KGLReDZ1rKxSiw3M60JiOjRjIPHELhGXTEEQnk27dvH7VFmAnUwYsv94ClxTWHduLBNQ0chTBDrrrqKrbZtvN3TWWc3sywvdRKoR+4UewApZy8yhejaIbqOJ3DVOeYAIjSUhTg2WraSyADABU4imu009EV9LOqNmPEzqbuFQuG1ylhb1Af1iM1ZGNkoLlgf/CpmBo0gTmsKs/wcUMh2M4QzZjHYX9QGjkDWoIZ9dGPfhQTtxve95lm/2WEKSFbPOz4YUG5nesEKUrzuju78PyCJATAyyFYauHixEP8C3knVKmvElUTLzIgXWENKwSdiuoiLEapGgmLiH/RrJaS8SBbJ5CKI+ID4nrecOtnh4qH9wxf8osrX4w6NAQmUmioXKnQo0Mhd21zWwYJrJ6LGP0JfbjDeOiYuMjNtyQSaSribO8yRruUld/a9i769h3veMcXvvCFv/3bv0UFuu2NvP/8dUo9sBRJfkoF/qY8zL4ga7XVQheaVf3Od76TeTk4MOivD/e3+kJ9SWf/rEPAawL5skxO2C8gsOGs2Zf2hma9feLkWZvCyXNDrzQEuEpEoir34scIW99jVSyHnZGMs6ED+0ysbYXSVe/IIoju1fAgfbjgAqXIOVn3SnVxrIUsV2CwyTo3cBAF9MWBDgASC2HXd7BPZCNYYpDXvmPnBjYXgqfAyWSwaV8I50O4jHarSWsYpFwJ98GJp3VzZLpUgL0p3Ah3sdfpFStdKdUagFQPf7k5KPKo7W085kZaZm7WyfcJ3GWfAn2xn0H1Z3O4ERLbXfYvNLMdi/hLbj1/NJbIpDIZVJVxdMDr4OrhU4stjf1G3k3juTNEA5g9Dojern5IsX4ucip4LP6B5DHqqos+8MO6RqiYZBScdIKNhSPtKU95CvBPHTnSQkry+Q90D4xDBNrIWvN1McN2RM7sww9+412ema/ZCYR6S1yybyL1PVJEeu9he0v1UICfLPMGrzulI+2YLHhYr6RiWgMvVprWkUCtXS8S0TelF+vGTHe6+yUvnP35z7WxQ/QDElv0n+GIeBdujT332TOJZOGYMVPzNqb1jqQZMaxd/WZHVA4G0O9YMYRm4YFZyU+AqOkqgZHExrgjbG/vQYvWHT92jCNtQcAbRaqyL8MOUMaxJ+i7JVp9kknwhpq+IhLNJDg2PesPhEy9xZc6r9zmHtMrMMDOz1KRFuhXZZS/TCLjnrRWieppYnn/0ucKnwxoZEpw2NMbqkMW16Q9Bc1G6BtEHwz8vffed+eDR7Xb72hg+9TRdcvPf/nAtLdx+yVdPQNI7NufkntUuV22ArnhnIYfPStSMWYuv/nznBsZCQl7JppdwtVhvqznBY0CiUaJo6OjJyyXmYPbXuI0KOLphPnP/AyQtkoxVVUVPMYN9B/iDkZCCUNQDlJbBLQdm9FjtjROR+9hiNjX16fI+tNRPmXSb6wd5gmyRHbXdekuNDDXpZzT1OQTFkufgBwAOYAoGrIubaFM4ChTlAE9YQVWyEA5SAVZ2mqSg4EplhoC1HmKDZBPdJ5JIZ17VKMXqyuDkRh07EtXszPwRjZMekOdNer0WaGGj9dPgaSXyYzMM9BnXq4+tEuZVpGB3qCNdCnnAt0F9GUCgPZVrwYl8Aj39ABetVX+4Kd1vKGfkd5T4AMPPADcXdnWV72XjY4mc7VXg2cJ+cZUCS7GPeAIwDImnXnCqmer5NnPfOYzKNltecIzBq++YQzhATKDsJYS/WSfZvOLhuTmEXqAnTUaxRIMxEtsC7fSxHAPJzlyGIpyoCemv5YclHSZnPV0XMQkcqTp02NCnHGAYtLLIUnYhAp+cyCJJXSod+mhX173s69gtnbbC942lUHBgaglbtS3/qUsfwT8Y1aISEqfP2GplYcFmdGf9YYzonp5sKIdyCOU9qCsLuhwN6U9Yse0988J7ykcKuvd73735z//eTrnjW984wkfOZ9hTT2wtvFYU9HndmYWIWv+E5/4BCfKe9/7Xmbqu971LhJZLSAf0M9AKtSTRBHD7wb1uWyP8JwsIyjZmKFv7dYGs97+WXtvQcdHNKGSCGNTFlGwW3X0ekXsewtN/VjdmChr/SVnUxYbYPGoPE/rCtJLhbVtPaGNHdiOOqhVH6vpkzU9V+dBIDSRYB2sHSzXiLBH+KGDsQ0GEuFFAM0+oqeI2QROjwilhlxYQxFXBMUw0iSPa++pdY9Xhy9IzaSvaBzrKoYnc+xCvN7UQ1DjkStKoV5CLHl7ijtQqga1eUaEHQQH17makW5oOPvZVg03Ogk9LPsUJdebWCajw0nwNCMkHDzNjKe8aBrVamt2OhJOOokevJrUkDo3xLcW/YtCNIaOsn/BmEMoLLBXdiKkwhb4WHWnpMiYzHe/ZECCOv9VwOR4Cfm027I8YKG/t83/eNxgSTkkJBIqTpI3OTUVjREZKgnoxU4bvIq0v4lvJY2oVeQFXcpwMKzopXbGcLmsdyXmkJV09HyNKBdb3yMFd6IqUl9AOvvmZNlFZT2XIpNOQCycNbCBMgTVuh6LuYSnKtaFc9mf02eR8DvGvpKXHN2S+6MPVL78P1s//2Wr3vLSydhTnpx71Q31oZGfH3am6samnIFEMRGyYc0kcOygGWOzzoGiCe9jn6+4lI1rSFZ7kzqQO2M62/vRPKJ/gjpL16mLDuWih/ExwwEJQ3d0dJSvJM5nOS1/qQEdd6QoRxw160mhmc86W+mlzAL/BCyTSXcNvQT3IzivqKSUJJO7LiGtdbcsp+TcUK+tCbQdGQgKAmiLwSVZ3Bv+hDyuTFK4bv3Bbd+85a6u+34GEyfv2Q/84xdmqsb1b8j29famsXpaVBv8P7NAJ8YPWDqucQbqFVHEFf0LP0y2z6GmUV5U1MYUX2ql/jmuQqv78sMf/hCqBbo8MIZf4TlmCBIhKBtFua6Q82z5CRoXco0+R/+QOissAT6kpUqixUwIEDJLA7HYck2TgTvNS2a5V58wnbotyKNSTlOFYSvASqC7TqhWQP+v0KVBnVVYl+DrWXcD1YueBWcacGgdcQ5wC6B1igCYEtjlkPqCi6gnABhoCnJjTyAFeSAZcFFLygtf+ELV8ywWNgHgUPtAgGFUCNPVLATeyBJD4Amz6TRNwva6ndy9qlj72jlhVckglAvHk78VKJ4C9+qrqoa6V8UiNWX+09X0HtfJ1XPlp9pfHeRcMvGEv/KUEhcvXo80B7UOJiQcVS74JjfffPPXvva1SCx9ycvevG82NFMvE6AyTr4wQUQweMMdBt5JWvi5470wyyACkaxwRLYc5DroOYpKINsWhDAdinYbQiM+hWrlRLQtrTqb6ekRokod8lCqIQ1OMSUQOrhuSzwUsqe15uv/8yN9xcO3XvPGnz3pBqERDGIm+XJjoR5E7OFTHQt3SFLRm4PYG0AFOqffe8R96JhXaolnm1y0dWF3eFOnT6+sbs9Xw836Qr0Ui/q//Mu/fPOb3ywvXt3j5Dx/raYHzgPg1fTS0nmYi2xef/7nf87n+973Pg4qWDUkcqkHfDC09LOLUueXFT94ML3ci/pCg1l3/yxBhtFnxhswKEsv4wAWSaPFcvUqeI2OAWs1lKIPF5yNWW1jLoQNJ8vdJ6xEX4O4wVu7TFyxE9mVWGTAYLxSY05MCBn2C6TBrHxgsIoejN+jerlYnBpT2iIAxda++yklnsoleoaRBoNMhfr2Qs1w5t8Ov/K9F/63lqsNvTd/8C9s7zDBnkwz1oxcWhp806wXarXs7v9uvXqgry8U8YXKAOyYN7jXDkkEEL1ryks2jTpKnmwvOLfGHpM7YBVmz46HE2xu0DshmDh/2dV4Nf/L/iadi80xWel80X1GXCzpEt9VVLhFCAwcFs1sUYOR8jXRchH+HT0rG6YaH36RwlBXhjUQK2mZqLMhJ2Jk/2Lbkzv/AUlAVAhQb1ou/LyZUv0HP33YM6IX7thlRmIAK8G5/r4qINyvW9iwMgmtO2EQbQhFdV6uClb7Gg1i756tub7Jt+BMvhYaLtF98c2QTugpw8vXZEdGlxUBd6WJrBznzMS40lD16UjKxg8UTIe1jox+3wTpoSftfELy/RtyhbxdLIVyna3ObCXT9ZOj2uGScUGnASc1E3Eu7jNhQcLcGJu1D5RMeB9js+yxWi6pTZWordeXMVOGfdEAutSNWXD2PGda8fIVp1a6w7+gRXBwAgaGS72Yxz+fa93+corRYwTT88fU3pDlRubM3Igt+R5+g1sS6vcDBjW9kab3Y3nG59fIAwyH0+GEsphRu3ZoONBlWLKwFRKpBRgPyhJSHmovsItrfwTBIBcpnOJ0Ol3K21/7+jc99YVv6n75Mz47NX7Ja3/3Of/nh/dM64jj4yEHFnf74/69sLoxWWgiLjDJpbcs4bPIxV9xCycriA80+jMRjv4VO8d/bq0fkKE8guHZaqSCiBGwi0MeQues9UVnYH66U9FzDBxEP5+qXdxglMgnzFCcFeHdBwqV+i9JobJS0KcIhEVnYDOpEvQoeJ4WMVEhUjnaSGQ0A+nNelWbV1AUM4S+pfyVJyy/royQVa1WLmS9an76yqFPMLOHzTQ6OqpUSU9ld1U93F7b9pTV91XwFFMCiAvupZLo4rKbUThMH4SWyuyfVcBGp+Au5fMgwkywLilsejxIfh4knbODexYRwtLlakI2dlS2HbpiMY5qb9eZcw8jYDVsGiqM5ggXN3Qm0tEgsByLjj7hwFWNUp1PIm6foTYJerTk3rLuPcBBdoovWm5YSW8XLLPh/Mmf/Ana009+2Zu7Lv3tR6Yc23WQnThNaxbDMIIShUKdnR0zs3mmH25fiHvpEoaU2aV8XzVEkCPkIeSSyGy0OrZSUMI+OUaiZzXC1aNGVw6qUPUSdr8Yl3FsYhXohz4SIi9sOs+7618ue+Qnk90j33v+/1ELSXDEhMl5Kj43fTKUc1YoDr9d6uid63X8quRixkBGH+nwjpW8A0X8PxtUIxs3tnebmzIuuplyTq/iUsNNS1Es/bu/+7tPf/rTb33rWxWjZBVPn8+yhh44D4DX0FmLs6rl/clPfpLZiS40ExcMrNh4izOvNoWVJlAOH0V6dtAYyjnI6A4W8fAMdtWKKEWLNFgvYfRvafmmVqijFG0crmkHSw4+1odyBuLfYKWx4KCGN3UYgzkPH0iHSu6xmoFbJsxTCThMxHBky6z/pk3QYHHjHNE3IH5slQu23TDT/SxxKxypNlpQ1kRpCYcj8QhOB2LfmnjlNf23XJn5QXxHadtnW6Vfl+wZIz7iJrY2iPrretHvl9484V2t0K80CHMLz8weboAVqVy86W2o25NZIiHpTfxTC+YG1sofIKj4GhBow15jpPqG8OhUbjitStlzGunOXpGqA3R5RpS35fJRJxAUL14+cGVU8OfrbzVyJ5BYfNALGpBv8i+4KAcp2r68i0w1QUghAzgtBtJY4dDJiMSbNv7DkI2jigy4AHdjnZtK9o5wqP96956LdlwUEodMlMfOiqmtg5tuVJQz0RACat7mN50K+u+UukqHI+PFIhfXVBRIt2PrO1ujcC1LVGRdK9XcpmPAy0CLlQDuNCtL9B/XK9a0ZAxvWFoecbGr9aXEZGWi4GXjJo7T/r+91kW9vT3D/fQDAt7ZunfvbhTYQ6KEb2pdMX4Ng6LpoUdm7aOCfg1U9H30q0+VvFzCHciEE4a9s9+AbzJTrB7Yf0DGwL+Uz1soVEWQkUbHQtAgBsQXpYqKtBplNlXayX3Sh4eLHpOBOuFnIjMXj54enavk4mLlB6TxkQutyEjYfjhyVa1+az5xRBwUyaTRdMds6c/Pe2HOWGSm10hRyxa2uPiFKXhHg1ZDWAGhJhPv+AvyRSkP+8ne/v1jtEhP9YbHy7mD4lfZ7OgutQxHrITwvs7j1JDruHLKLWGmSDV1+CNwiyQHs0oy8UmrPA5sYQyh1S+/rfeFeyGKVDaZJyxbQRr6BPH4CTOfvRkYC5AhixuynlZA+67AHQAbK3h8JreXuYpXHmpIo5S2KvfAsHUHwOoVyLUoWSGBFbpl8ZpaIfPZ+xMAEsYi2+l6uXpCY5k1SIeASznFKZx7WDBrxZNI6hR8BbKCgQGlwDZOBHAaPFBSOCaYMBSLNWygu9s+aqApNcrMLrgq6j6AeUsOGcWiHUAhhFNaJapcspzHLJGqrpJhEfSMTxdIBYOUxRssY0f0V3aYSy655HQftUFfsYEH9+t+EzSWOYl7JzSic32bnvTaD++vod0X7o5HsxEnrLEa1OVMT8/QUUyher3BP0hAfKs5erjixKpuFKsvtivEwvxBiGKjDj0HWKXiqLOJra8gYskQQriCiRBsb01CB9dtA7oI6cW26b2v+OH/G3Jbd13x8gMDO8jPaYv+nRyucpzKDQe7P17cH3fl4kZP0htM4JzFeOiQ+8Axt2SFoiGtN24hgRjp8FU05aGFDx5XyvwXVgfoF5dXoN+3vOUt59HvfMes89/zAPhUO5RlDOL9+Mc/zhy98cYbYZzzyX2wvNf+goAKN7DX702Y2GTij3dfQTuETW/Zx67o3Friq7naEvd3CIdnG950WT9SwYeWO5x2N+IiK8JuMLfgWXVsAYP4QMIzVd05VBQ/W0iPp31EjUAYDd5KSw+b2O92IHStuMSCc5xYCtErEAy/m6xaxKoogrAPQKEbsYFPPfT/vGfrjVd0fcdI1jNX1tkWBOYSSdjL3f7fb/7+nrfjDj4RsYTLBhxFvbnpRkrCPoNMJw5r/0N2pCL2sQJ3DXzue0S7RfSbHzBbcSSxFCWyWnSh2ZvEzxbC12Y5EupDWEpmOcmBjP6RAWBAyswrEPn64l9S/f0K+Z7nzVQkCBvCQ8ktDwQD4r/C/8bdgRnn/qPalh4zI9GZqKUADbVdytbnt41kP0UfHBrCO9T05NSB3Q9ecvG2XJJwSjpmylE8TElm3iWFq7fxB24k/oqma8h4tarFNmxiUo08P1/Fo5V4LMwk6BWgL4rQuNHXk4ZWQ8LnEvVXWliuSy/1ZvV6U5tA8IuNbtSYESNhHf9YWL8Uyl5XInz/FMbe4GEKpNVAZXNDBn6osyHtbekKswHjh+yRSXuiZoLtQb8MGCF/Yax0xL0N2XAM9Ntn4JacBnRK0GcBiuB8uLNQS8i1Fp+7CIFxaAEZdP/992PrRe9L69fpgmhjjPv6xAiHLkR7H49u9B3DPYSJnySe4GKyyj89bXS8zpv8sJGpR98xU/uKZzwSNdyIlambLyhHrikyI63wtkj6uSco7kQ/Q/YRpARJCPhhsYgPbrqiJyATOd42X7CZGfLwjObd/t/xZn0gmcwNDWA9LmtI0zMx/jKL5tjVJDHYh8aP3HP/ZJVAha7NinyweO9hNBe6N8QyXUx78d8hShAYAIuZQMK3AD5Rldf8u5IAQ5+tZn+DWKFP0JZkVzwd2GnNtV+/B1D1XLIHSFwyPXizTzzNfVs5Z/DIY38zOjqqXgpVil7DCa0ZT6WGyNKRIipt2FMp55x5ln0P1IcwcAU2ylobq3Zmf24ep3O7pnJ4PCiH3Ywtjk+2MtKZMKBZas7uh+9iNLcBq7feeivYhXveguwXTgo5mUtsBSiG0EbODlLkGGs7lYMqkc7+D/ol544dO9hJgp/OvRsw83LDTT+Ae0G/dCCcRA7ic6z5P7nzJ+977/vx/PzSd/zFhN1NEAoErmL9i3tSrOrwfGGh+SwKU/RSJBJuNlu4v7Jsq2G5RcuroN6MKyxfgmJbiHJCDRutaTk+IQCt/DEMhOKmY2hOc/YIxDqOXDv7BtF/huLC91XVxgqKuBJG2rBedvs/bjv8q/t3POOm3/mwr9lHHXDKomansJfpeaox3/+KAuE7ITOQfBDgl+CR+sFZZ0/em6oTwVjrTHrb+zBAw9kqmVX++aeX+auGG9D7xS9+8a/+6q+U5vMyec8nn2oPnAfAp9qDPM/2rTAwOzuKHOxTH/vYxwSKLbWtn/B9batEiHyWF+aJgym9N2VsKrlj4trKOFrVCqIUDZoSB7DE/hbl2LA+05DwSxMl40DR25CxgMEAM4Xa1EmDmWkXIsq4wLCjRe9wydk3URmvWE0iiNdBlgQdMuPJDDamhoWHeNAvkAZBpSkMMiHJic8k/DMiXRb1iz70wOev6frGC4b+84LEQzqKmXb6vsKVNx197f5jT7t8yt06pSKysV8IVc82QXRS8V7lVyhz1MsctcHUqK74rdStRPPIpdFwN+BQ3Nki4ILKJ3oTveAjWxC42AlD6VMX4C4dL3BXkDncPtmXQAAOm5mvq6J2KY5WTEEYIZiC9ACXPCZ14T/54/+VjY1a1JCy2t7mrD2cQ4buj4Nsbvzkf4qis4Pvr0RES4a1XVcOP/zrQn7qgD7R2nwp4V6QTPqFy4mOBE6eAX7jXGGmjjtoWBVIhENI7NCjzjdcIl1RJcR0HSk89bv5MlrgmEB7CV1rtrSmjoRT4E8Nn8AadrmQAtpkCWSrDWThWXoTBVRrsGBBeMtwuH1pPELjAlHrSeN0QS82CLmudyeYOfaWTn0wTawria50/zG70Awh0N4/45qmhh9p4ip1xsyhrBnzwwITT0vN50f7StdBL2AYiJUF+JY8dPquXbsgYQF1YL9TtC5bsDSUlECVSbVw6ubQVpyWha0OxmDu8odp/suCv3OjDce3401W5Sd67Vv6xmLsfTVnJuo29ESP7cVwwcZ86QoN/Jmug7SPw5wLSlv5q+oNtFuh1Rb4AuFB1Z90ID2m7jFqYno0mnbunjtjtv3Gyy7NvPTldx5UzfF8A2CZ4u1XLJVJ5LSIZs7MTMVgcsRyKfS7o6IQT5nkpJd4BgYKKzTG/zJt5yZyezknfU/lAcC8K1DVO2FRkMjQLui4nmN025J4HsBPvC41Fsv1DNJOpOio8tIzy+V53NPnphMTaP5auVHzuU7mrxJOnsm9cTKtOoVn4Co+7WlPC3RtTqGkuUeBDYBVFi+7NGBSSRf5utaSqRLP8iCaLCxq5rDi93EP/cOKAOtyFigeGRJg0C+vU3YfSueZN4Jp1auDGRXcLK4PFcaohCNgsUR0ceazOmUxwzRoDt0I+kWMPzIyorgJK/RY8NTZcgNpceMHbyR6xfYnPTu264Uz5RBuaIhgn/G1v/1JyokpVF4ul1I8gpAw88XPSgH5QdUzGgZEIgcg9CEOPaCmLM8nCOkCJCBWPWZY6CdDFWp2g9gYJhqMiEwgdUX86wFToSgJH/nEg3c97/bPV2Kpm6+/sRxGIoFAyI2jPifEHItF1guv4T74JJFf0LnujOuDKYlRCqk7VtJ3T+J01ohHveG0M5I1h7NylK9m1CgObuDrX//6r3/964h/f+/3fm+VD5Lt/HUSPXAeAJ9Epy3xiJrcREXiGCBCEps+tsGcCquZ9EsUtzBJwCccq41Zoy9jTJScsbx3qKwfq4h/LBxiYZgKykMpGkd26EgjKJuoeuNlHRg8hDQ4i1KuCJNRo63Va8VioVgqbrlgK3b5/UnrwL13J62wbUW8ptdBNOFYHK8DtVR3tZ5phWK4leefKFsqP1jiCktDbxkVXDYG0+z+fvEPflJ6fdQohc16w0kTcMdyQ3ZCu3WHNpmxLz3QjNkAaNEG5kKMyye3/i7CVzRMhHpHDlwd8A5fmWjmoOC9MIgXLRhdbHftRqVamEFPOGITNaFZPrpHYFAilcj2sSsKHqZEFGMpkqLYBLFFllfxVSyB+WzgS9rDd5dKBsq37UTyjKIDAMU6ltWAw2TYHsw4HTGYAGIlghILomx4kSCKCKFmfLjuN0LrveIJd95VL5bzhC2Iktv3wE9V8YxVrHvI5IkC3RIwHAb3gsNxcFVseDgDI/B6UvwXaoBVlFpB26GIE/KMJiaduvgMxCy62sTW08imYERq+HCmJV0pQdUTJVSj9f6sVm54E2V2Xuqi4w4tGUaNWZTbkeBe0A1LUrz5b+81OlHg0V0shx84how9XLVcJk9chPM85fXEjb4s3s5E8xmVex9ASffRp81W/eDBQyjkhlHG1mc097Dn5jwdN4/s5vQ0U1v6HrEwQZIhidZFXUraefzFd4aoYtMJcgwyibD+lbGUl5/wkkxUVdPToaHPO0c/oFW+GjIaZq9oNMjUg0kR2mH2f9JIPMcvy3/DCUtdPkNAtgYNWbwDBNsCaxb1jQ2/+gXViFxzDcwstTw4jFMih3+0hcxTqhuOJXM9KVjdM4V8Kp6w071pFPdhS8EdmZOiSJOiEUzHZWW1l7B8ldfwC+QsGgE8sEp6lLYDbHDng/LkGl5zxmddPKZUWSUu+VN7g+hDaHqQwwlztj/1eN3DyllZQ/XUK4axK4yhtarjnvp7z7QSgh2Diilp57rMkOUKWS59uW5ZnB9czUU6GtEw/dmFOALgAcHiAbOB6EDySmno7rvvBsQGPA5AMng4+LrcG0mH60rJShV8cQVWePCx/Kl94Fau5HI5V36KHYM1gsoJOlbkXDnzY9nwU3wXvcGc+fjHP3H77XckcwMvu/GLD5T/f/beBMCSqjz0r+Xu+72990zPdM/KDiIIok8QEImKK3EPRJRo4hZXXkzUJJqXf4yJRo1BE/XFBPUlESNqXIiggooCcRiYYZh97X1f7l5V/993Tnf1pTd6uoehe5hiqK576uzn1He+/QsUHVdhR1CVunqhNkG7Ghrqhf8uZ7kQnRyIeA8drVoSuVflhCa2wQtCEfFdAJmqckHDxps6UIXzSmMjh3bEck3wi3EPibFc1cMPjpUX0z/qdFsLA2/59kcT1fyvL3z5w1ufrc/eRFhQSN7TwnTLqgP0Sohd+mhimOZlI25jQkIfPdLtPtrj9peDtJ9Vrnw25/DLpbq44HzpvQHhcMMNN3z3u98lHvLrXvc6qX4RZRes+PTLhWbgNAG80Owc1zu9U/GGBQ2MX2jkV5///OdRBFrmDq4pLl8RH6+OltQOGTwsTox7JoxBHGIVEOqKG3fQ6GIVQgsyGNtgo2vM2N1XDuZ7I5UhL9/rlIrqSzObGptzdblEJPTc8zpiiWTJCPx6+4FYay5vJgYn0Km2xivhsbI4yoKohizEZFSRweILCoYZ1qVoPuE3SxSkoTSr9SikwKBD6g3FCEXnBozH2gNDafOS3aWGMUVqaKCi51SNSsRhhPO1nb5zgr1nhVxkW4LIC2UiEk8CKkFrIVrF27NpFE3cGeH4XpSjowRUT5NHOoPBMcGKcZlAMfpDhQBK8gADlR61WR8XNoS0RHPc+SN9UDeBLwLaJjPDaVTGw7mo94wWaF1AqyKbJTf9kp4J+awfhUo3L774EijeihkcmXAIpI49CaQmcyVicwLPOjjcxnGRgw42XAPlddCDOEEYy+pQD2JYbXVcrEDyw/iEVDcJ/kwjSOkx5RzOWwwTJ1WcB4MTYibdmBBmRPeowbCaksbIBM4PzcY49Vu9o0YuZjWniISEw/3KloYgkJ9xskP29BFgyYb8PjqMBjXsT6t/zGtOorRjh83qWc1mVqhf5kH2mJ6ccrH86MP3X7Rl95rMT8zKHm9/tWKn3dCFgYa32rH/xQARdOv8HGNSUibZn1Vdj6rsOG9aYEghDn7uux/bhUn8gJmuhhpZXeI2NyZYFEjFRRGrKGdQCacYOLax5h+8/Juqo//uFbeb7Otguxm/Iph6uWHjgmXqwD3O3tZmZwZUW+IhCSkfwsDZSuNkUM4/mCjxHB4ZmwjueqjKttuwBW606DZgdBRk68oenr7UvPIxYp4gR66cyag2yG6X3cun4orbADn5DTcUsBMh5Zattobpupb4xOJC/XJCQ8Aj/NGDfcK6QJFf8IIXzCkvfcKyqzSD/xXQ/0XO0ood6eJF/UseAjaN6E1AZq/2uVryDPgF+bjwC4UrAfgOT8Zs8DH6HDq/0SU8oO3sc/GgUdnwyCfRfEaaR230HA1nEmkO/aDa+vWgsBCBAPaNhGsz6Gf/CwJ+4liLUrrg7JwrJAWaH1XwxShAwSnGXAiQyAQu0Hl/BsjDZkAjgA9EKMBT6yJS9N988q/tYPTVH/rCvokYrmY4CTniYzgOFVW48ujIKMwg5krrUzItHPwca6BACA8w+ALt4STl8FbHHyrNeEMVYYaIF8AAPRw4I/sBP5QdJP+hJ0fkjgoEcAAlMLAyDs2I6V77wL9euPfnXZm1X/+9TxetMAhfLOiKH0qpTSQoYDdq9qd3ouCUpgmnui5C3BZrY8bDxSy+rw4PmxMl8X21sc5YnzLAPwWhWvAs1ssNyfCa17yGOAtf+9rXXv7yl0+3dGot+ooazWkC+EQuh96yBK3GzQlK/AB69Pi1O8QT1QyUGNanfMnrM6HmlNE1Wj04bBwdx8EV0mCJ9Cs2/eXq6Hi1z3XqsiloY2K+DnePJQNOSyS3PhvY2BpvzIojFioBzGze1AFIgdjYUm+0tVnjKDiNGQMFvBB7g/iLLuO0SSyN8xUXZpv4goLmxL88pK+AFXGqhFFuFa98FbfiFGPRSDAAtYbJMcJBc6je/kkqft6e0pZjVUSv05fC3Gl3KG49eFakP+c1V4uXtQag7uC5DReMg4PGSAUyzQxF43XRDuhtr/cYXqob13UAIgUaodECdeyJpyisNGgRRWgNJUUcRq+kbyIbo6uidCUaMMAy+c/vCOsl8A9KVChdTTwjvzW3d6NKXbxovYqRzjsBqCK8ZrCMnRDKecdDdofaeaFsFyoYkBjjhfIwnhubmtHZxvVfvlJl0rBHoVbxQR00whiaVokOBw0LsQUJClln4F0VWSQ6q9htQkVOlExmD3KX7jIJiMhTMRetX9GXdk3cX2njXt42JIxiBQVm0cfGezMiYtkSOSsVNkMGKtxmWwY5sazOwaHq4RF8PNh9Ey7q8XVxGhUH1M0Zqz5mR2wJCywOpabWRs2Q/AgYRy9u++vGwE51eqgFcwa94n738A9Ldb8frv8wli+6ENgMIj56hfIbmBAC4anKlvIX+Rg0MNIDdIGoMxQMFV17YJzoTQwR61+Jc6Cl0IuvXU4qZhO3F7HL7NhzFP0Mx4StgzX6wmfT4huZzMkEap1ApgLZCEOQF1Onmf6r0vhUveCBvbHB/lI8Ebv68iMEOJSjErsj2ZeUmW5bXojPdl28vqkFfKh/VL+X+tmcfAJUi5I8n960hvh0FSfgaf/+/QwHjGQB5HVGM3yT89m2zch5yvzEZICJam9vX+0Dn9qzT+LK0ATXap+oEzVB+M/TqjSzbSiW3wTzvPjPdoHmqAfKlqs2DyQxdDspyCpxlEUAWzIgDdZ5aPe4WGCchSj9YjShLcNpsbatFfjM8cdptRgC+K677nr729/OGfeEaCGQFndlTAJfxwlhW6y0ecP24frrrzeswOWv/UNz89UT4xKUMRnyMhHW2ysUy8NDQ0wCBsCEQ6Lz6iQVDjEI3mjJA93FVyhHHpuDU4/3xQpeUUWqIieiIAuo7xHCV45UKxCKZFsSyUQsjNgCjzMYpokmI7grCOfWY9tuvONjpUDwq2/4+BEra1eroaAdV06bJyuTDShn8GQv5DyXFviZiXqNcaM17qZigQePetu7neEyvq+8hkj1jHoCcGi1NSm/8IWnFYjehx9++Pbbb7/mmmtOPWbHwsN/qt6eJoBP8MxrYI3uPhTmjTfe+MpXvvLf/u3fOB5OFBDnqzty5Ci1wZvnC12fCeDaqnPU2XFs7KGhYXPErRRx7B4suzaawtG4A4wI4gY43R5IhIyEXYgbPSHhbMXEoZUDUOns6vFcp0oMNcMcG+hDZL3ONLasaeoeFwEyRsXYr45AV5fFWTz/CJiGNi/mpoRfEzIYelisINhIEHjlsXwxFA7jYl40UtBGtsxC2HjojHDjiJNTcuDp6YZQMI0HOgL7ku617dVXbApmy2Ox4hhYfCUZzTenHh7x7jlIIwFNkolPAmXxC+EiSp+g/FSgLvFrAJwRmoE09Q9qUugb6QBaoiSrM1TlV7QIb9U/plPDJvqiL6Ei+vPGoaCdGxDCGdqbgOx4jULUzD8kbUJIS2P84y3icRtnDDsfOzoyUcoNBRLpLHUGmRFIX0LReBZzVahIJzSlLfbB6JPTN6w40ZQx8fHgIvTFYiQXh7VpDk2IwXUqKiQNoYggktOQvgFvdIIOmBml89w/IdRbA46gK0bvmJGNmc1JkSEnQ5Ut9crwG7dbVeOxPmewGCDixLFhEVA3pYwJrJEdoy1r4bI/GYD6DcS1367J4cvUPvboY4Y30BZ4f1P8EWbDmwhPPBK0CqHQ2qrdUbDsMXPg02Uk/XV/jDH41ARKQTygQPtBIC1eQjjV7PRfdiDacR0dHfv37yMoVPuGjn2D1ZgNLoV2d7UpqVucXLbpYgs+qcUVzonaFNw5RhXog3PMr6m1l/dLvfRJKbuv6qzP5sq0NjbqwLlOJFk8dpXaANMtkUAosuBjvwl4LqE57VzdeJciY4mNgfmT5K7pirBvhA+lXhg4zOVjqEoaXzArIFxqirDBUNfnh1pTqjiRFx3W1n3oPx/X8YxsBAkJnZzTOfaJ7OLKqIsPAVGehlAro0crtBfsKIglvncIJA3JazsqH8joKOkn8PSsrX9FPTNY1EaOHDkC2oBMdUX1bTGdASBo6pr14pkh+NQvxdvb27kj4ILw44GvA5ignVEz3hl0I1OBjBSvAWwMCGDyr9iLruq+zXhgEpbfZ3QBgLd8AkQ/Xn5tK60GuIQ3/97NvT19qaaN513//gMTuCYhVAfOL9Bdsorl0iDYrCe+yiMRMB51HDKpcsoJ43isbE2IGEYWAA1BDktQI/A0RLWCH3LhaBRWckgUwORsDIaTDeuJcaI8RKL5TDHUoOXcTBmFG3/wf1KVsZ9d8LJfbn5eHr2zotOQDIFMgeKhzM9qIoLmBBbEj/9EXCILDm6XDLngYy1Jc2O9TWjJ3X1GXyFQraKI56FV15HxlCmTHOULbAhOCsDgddddx+f/ve997znPgUF/+jpJM3CaAD7xE63B36te9SpO7t/+7d++9tpr//M///OEIH8+nOWbQd0IYqNULuF0pz1jmeOFnuLeYCDelKsr2MmSHS244YLoLUvMJKL5ogc7pgwpiU52dMxsHPbWxo3mtDc8MkTYH1BpPlEYmZxe9H9NS3ND3CYIEE6nsS/tLyINNrA3RhqMw608MmGJRSx2rbDcaAJd32ggWMQauViqFIsm0XHxUqX0NfH7kyx48fzjJW0KHgBHWkcqF1xuvmqtk9rz8NA3vzu0fQfkYGjrxtwrXnLxuecb65PfeJgQwUYkhNfelG2FoOWQ5OG9AN4dPeUfVCJdR0SLuFcgnVC+kxCHgfASf11kkMzqBQw/teQKiqknKAYBapMgSv01jSPjTmJEbE2ZGaYdwhfH+kRdgiIXyl9IYvkHC0DgoWHFG9YNFY/19A3gYiGdSsm0U+tU0DlVu6we/8RhNUQcMZVd8SiGxngiKmAalZ6BkhezTcL8sh6jeWC9m4qaAdvFlriStwizngh4SIOhyZEGQwAPTIgGTnu9mQyyxM66lNeeCTDxtNJTcPeh9uzYecc4PMQo3Ma0hUNsBNkb62wshNMhoX6jNCzzNzl4NXNGKp0K5/9v1HkQpoO3MzPx+bh3OJhKpt2AUzxvKPzmATNTMPs/Wwxdu/cgAZrkouCOHTtAWUBcOLk5tzRSqyb4iW+6hln5ZI1gH+DUTa9Za8qE6VPb21lF5k7wV3fu1ycolU66Y2Pe/n2Vu+4q79hZ4eM879zEC19gbdjkJXDsPT3J5GQPTxTdtffdy3diXXlV2cACnGS2iEiAZ/SIiYBXzXcne0K2q0QCV2Q8a412N6pesga8Qc3e9nDdQQ0zK5lR5/H+ZA51DKTa+I2LqQQFAeQ5sEVObTvPGXtY/9T3k7P9FrMWKyoPkwOpA4rJETlnx2CoQUdBI8359lRKZCpQIWZEnObsltW1Yebr7Xzp8y2c/liQgeP7Chk4Mfbmy7lC0sHBOO/oDAp0oGTEAOcZQDfbvJmdjFsj3hLIl/utt97KWcnGfuMb3yiLrZAO0vXFPOA3AeqXSTj33HOnkk+dv8wVZq7f+853zUD0LZ+8Y/u+rpJdjOaaYwFEAhYhjoaHRzi9Mhkdd02dbBx9gqSCfRljFXO0KDgYaRx5CDkgTYslhArgTbCBBdFDEMwxCo4kGn7Kkk0M0MRjhlcg8K94V5XDFDdXV27/zrMf+/F4MHHX9beEck2gs7ZRSURw1yonKD1Btg8BzHqFw4QADQZhZ6v14ozNRay1SW99WjxpHRi2dg26E0UvGkbz2d2YNtdlhLyasbgzVpGpYKFf8pKXgHv/8Ic/xB7kCYvMqOH0z+XMwGkCeDmzN29ZvenRZPjRj370spe97Morr/zOd77T3t6+8Mcwuzp9JJBOQfimOswArFOYqVDXpKNIs3XLFtsOtDbVveL5F8ZS6b68dxjb4DGjd9wkytFoyYZhVnbdEhwyYggLKStC3a6IeSxqNI56jckta9Jmyq7u3bWzY0N7MgG2oRBxw4wQQDhjtGWoxMVdcDfGxgUUrbEQJvQO7uPRi4aE8yCDy1U4cLiMgvoLjueL+PELBuOg5PzD+qKpp4pcCsIT3F1XrTz0CHw5Y7R4+dpwesf9hz/8V4GBITB7Wq92dnX98sGWW95+wZXX7GiK/OpYKO9g15q0QsmxETeIfXEQr1Q2cjLkzBDYAcg7EdZOzh8pEPHiOFpZhpAqdDGEAg8IvfVvgYu8n6IShMAwUAwGkko10nPz2EgF+W3UdusSWohMMiUUHS21qB9C/wrRYgdDTa2tR48eO3ysp9mxEsj9FOiTEVMINW3gqUXraCOjX42fXgt+px2CwDPH8iK8w5UCmqtIjobG6byRjktXkL27VSsRNWJhHJgZI2UjExbvXMMFZtPCiLcuhqDdS4eqBHBKE+sYk2zXODDgdo2ZsC4HC27XKGYwZixk9YwY0EXrc0invea4s6UhIFQSnWPA4R0ifwAAQABJREFU8jB5sdNaW+PlA9+no+5ArPi5TLArHIjQI9N2AoHfNOS/YcRu7rTsEW/83+sa3oseOn5KUPfVLB62JWTSgw8+iNmSVntb5J5HONDZ2Ukn9EISW5hQpAQ2PjbmFCTKFbJmpzU1SQnX9Heq34v7K5VTWP1Rlcjg9QQssp8LtMMKOQ/cX/rU38U7u+ADT0yMeb+6b+Jn90Y/+L7ARRcb0agemm5IYhVW3ODBvbChzbPPhKPkoBMv/Bo3xnBrLlVKFO/ZKmSAW8XmwZafzcneY6NO5pVNZISD4gI6zHJNjaumpuU+agkwPmmOqyK0tdGHBFkEkwCNWP48H1frJy0zK4ECG6ItWuRZC7gAQqCwtfZ+zADsIe6ycgqROmk9XFENMXykW4AOpHzMUm3feDXnz1NyuvRgOdkhpQChs2mn2qlY+c8QdVh014p//T5zNHDx8/Dhw6D7Mxzp6XlgSxB5i+8FsScGwCt8xeknI6XnegPrUdd+7P7Yoeo//OEP8xOCh/vHPvYx7iCEEMB+Hv1AbYBZCGA0n5/97GefYrbxepU5C17/+tej5/aaD362J9ZRKO42oxVMf5N4Z6460JwcbCL7DStP0AJNBSBw2DmGNVbCwygyWhuMjpMP7T/e4zalKn6h5Z/k9DxwV5SfFYEsCI5yaCpuMvCyQWAO7NeQ3BBdZV2x96ZvfihSrdz2O5/Yu/4CPH7iQSOLnrTGVKkngkI2cTcroN9cVB6JhOvrclQaD1cJUNoct1pT9gNdBNEkugeeTAlR6Z7RYm2pMyXAp+oPf2ZfelDgOch+4XTcfffd+mBd4Xt+9kBWdcppAvhJXD628iWXXILVB1v88ssvR71B+4RYYIvrr8LvEx8ewII7PgB5wJsiwBFoC90LnOWwIS4c8h8qhD+Vy4Upjtvn5oSBN+DDRDkSMhhrB4mLg/AWf8i4Ga6iyaxDB+fFj3QmYhwaMTOWkx8yWism9qeKOp3EqqkZ+ioTtvi3oc4dwI4Uvei8qEZjpIoj4jECqakgTIikMBKOBYOJoEhnxQWUOI4WynL9ALJDEc1WbHNnS6AzY1+2r5QuCEVqjwXsnYPdX/hqaHBY6BC5aN8NFItdf/+Vdeee8+y2rT8/6OHuh1MGSAe8wzlVyDULZagioQcgbOkrOqZw+DB7DQYk1BK0L7Wh1Y2vA5oR8MkNYhWqQRGsSGE1sa2alJdkK1SwKgGGyk91E21nSL+y4SIPlwpwqyBvVF1KOIwcTkgqlT8QCDe3tnZ2Huvu6Wm1rHgcRyZCLvOPLtIRYtEFgi72IQxULH5ViKNoCDGvNDhBUCusVkIGEmDmbQS/uSbCQAMbbnLi6QoKOWXDesC5tFkXNRuSKLcbEbO6Pme0pLAdZgSwNpzd/W7eCWAuemzEoc66uPAmiJaUixn4x8K3dnvGQ3NeDUSmWw3ocTev2mWVDzJZhftDoYEYdt+hgNKTViSW/UDc/e2IlcuHKtvWrK/H61lfXz/6e9qxLWEe2ZlIM2B1X3bZZXNiA49rbOoHZwB2v/xCXZY7DkVbDScYS+0clDOElltScHB5nqvHU5Us6q9aLKNcdqtIVDknQ2wXOUtV+pwTsqhqKd/VXfryl5NdQsaXqpVgKFQtVwJ795S/9JVA2zqjfb1/HNIa1gSR/ETg8D6ca9ibNg+VaF72USwo5P6sS+JIEz+bDGgOhIKBkiK0pirUpJSMgXiDMK+F8738iZrVCQz8SEPEMevNEySwK9ghYPkrXKfxCYax4Gt2DsMETgGuGSnm3xp5nbGjoHC4ZsD5BSs+ZV8iFmNyZmjAMlq4aXB4eYWeJJOpHY+DEGvy6dSbDjYDBA/jhXScwQtYdYMF4M9n3ao/hNqdr1P8McIg+/Wvf41GwIUXXghuM+Otn23lPIgldDrNiBBZ0/mOjo75+oxkD309ev4v//Iv2ACzpfUs1c6GHhdVoUsF6ID6nW8mV84M6J7UjmK+GSCnzsb6vvrVr+a73njBVenzX9qZF5Y9stJUEI/MYH92LpNhFyFp9aulTo1njRXdYbDNqoUtGmefcOWRCSvpCwIMjjx1hIr1WVzCKIDsyTkYxItqQOyBSSA8x0TFwrQKv5FJo/ruf37n2uGj/3PeVfdddr0jdnxuPCyoIZWTmxvW12KADe1ddVjicpljPUBbuMhyxvq6+rtbx9x7eur3lOHUR6CrMUY7s9HbmDJz6PLNj6rooUEaYAWNMeMdd9zB2bFAfj3Pp+8nfAZOE8AnfEofVyF7GjeGcHeggZEDYw98xRVXPC7HrB+ABmhdyADM4kEFECkAB6ENQJuo4VnPehaoJAIErYgIpJh1ZCIsxbuv25CwN+SdIyPukVGrb8LrJ25wycQQFNdNqF9iBVHAWXTJhDzuzRspM1CdSFvHrHXFKmQSIWSDNV+vfgwYVnPCasL9UtXtz3u94x5lB0vGSB4RsYQUB0fnXnAIRG4gsOIf7riyeO8dl2C+YxHzvk3BvfVBQFJ/yrp0b3lDnxNw7CM/G07s2IUTKP39C2gAqrlGYGCgtO2hNS/eUB+3BsomMMuFOkWOTRwmPP0p2AZgolcQc2JvTBGICdHgBtAJoSASYEgc8SQNrERuRgbMOaCPBVAKe1nslaQ18iNOs20XLW6MSfgJgGRl+B/wClwNBRyoTZGrARU1cISyVZbHrIDASoHQXiwSXdPSijBzmvBjOLaEJhLyndBzjpkvieAO/yDxCEAc5RkL1VY4lPEINcLLNAcmaM6Ix5BrE6cOfW+JiReMoH8OzWxkomZ7loBMuMh2kACvy+LIihG7E455YKDSM4Htd5B4S50j2J8ajWlzcFy8Xq3JirpOyHK21huNcekv1LIajYxoxuU5I5ZRZgLMfuhecgn7gWGrU8ELlHAobodzRBBC+0tmyb9kRnGrtnkzjHxY3RrK+28XfmDGOGkggxEOUFDREu7BIYzYZXgMdm1mki0rK7DUS7pbLjm9fZXHdhv790kQp61brA0b7foG8aShN/qSKsdaqLJnV3j3Xn9KRFsK3Yxg2Nn5aGXfPgzaxBh4qnLCXFnbtyVGh0qJROzSS/ATLowc8YA1c3hMKdyfCeIbThaWrYnaBb0lQYwBJkWJwiKCBxQV1zOyqWdWNNX0kv9q3gS46fHWAJGDgx++i1ObANa0HFJNTQCzmWdPlFo0vXSzXz69UtgPzAbn2oxhI2nhBCRRG+DxTDZEMacqAczoIPn4uKCmZkzFKfmTdYTAmz00jgzSQXWAEvozmZ1nlaZoJM0/EBmdHqC+1w6KGUD2i+Bkde127FdhjM4J8WpHxxf9vve97+67f2IG4s+8/v2P7j+QL4ncwsj3de/pBK3NtbRHElkmCoyDSx3ICv3ARqyK6a8p5mCiZIO3VMFgsAzCdIgEsivEkRIexmL6qCQRRj+i4CiEjvKKgtIi2CPnadSsvvYnn7lk749GI6kfvOyPBxONZI7awkHWB6fugN8HMAWuOH4/RaHaq4tZiYCVLTng6Ps6jR15c9SJx2PhtY3hzbngxrqps1p6OsfF8L7xjW/cfPPNsDkgCpi62TthjmKnk070DJwmgE/0jM5VHz4hoIGxCkbX/0tf+hK+zsmld7z+0LlD9+pTAenZgQMHyADQ5EREFIYwgcxQCKCeXCBY/JxTy2iqcb5lkdzi5hdG1Iace3jEPTbqdU9ACUvc4DEJHYwkycacFVVMyNdBTK0S7TtHjO6SUT/iNUW9lqTbmLTjQXwtK4ggZKBc3KMBqy1ltKbE7V7fuAP9PIhedElkwgQBgsAmFBPQqrNnmCC8LX0E1rU6m6xtZ4T7I1YYpWj8YQTMn5wZ7kuVnnWk3LPHjDvsQ1SDBZFXE8ITWsdmpX8k6FbDgXC1YMD0Q72T5hVqL76piHwEK64iVCX0nOofFaDsLHGFxTGWEL0QcLYF6YucXCAmJG9FD0QIVk2/KfJXBoZZCBYmyEs1CBRRNA73DeTnyB4t/CfHiFCMW2lmAQJdSgtZ6OJqWa0m1UMsJmLRePt6mqdfgGFeQbRjIkLX6Abqx5qzWa6YmHxCfodD+H4QGhXFHvJH8AMRkchGowUTvfFYDGke3qFhMXjE6a3PopLNyjp1cW9D1pb+gCbi42rUOTJklrwA/ccjmmSOy0nQNeIlAmZHg6gDpUKVMxvtpNLLoZjQtxSe6zLtrGvCp6gaDeWiU4TwL5RLLAzji0ZD1XDJzjHxhKVqEJIdz43JJF6v/JoYOfgcdy4S9bL6b+d7AP+Ds6NUJCjBhdK+cVQ5OmaBGpJuFCaHDHcpl1KXko+CMPPurt2FL30p/OtfE/wapks5GHFe9MLo619rrWsX7ohayqW0Ual43X0WulLiWwP+ApruDp9wtVJm2cs9PTRnBSbhLa0MV7zooT3Y61Y3b/CSqfFu0TzgSgn5Kr2QP7JF5eIPsb4nhy/8GWF781ZNMCvJDmTrebC6YcdAAOuVXeJk6SZn3eHEabg0Q3dxVsY5EoBjYHWQheC4wLE5cpxySbKFpzgTtYODvwNnEwt5kEX9gdS+ffo8YzbJ987RNvsgg/nFxVRgVMnbjo6OU3Va2CF6aGwGrlN1mLXjYs/PViHR8wCPAw9Aq/GjAL5p24fakc75jCKDNvOe8VaAhVKlRpEKltCqA5Io8APhF97DnFP33nPvZz/7OdMK/+4nvjWYu9ieKKcAkuPHOBPcQMSKRe2wYvzrg01OQA47we1AckbLgmFWRPwr1C/8apCQfAnlZyGDRVwATmJiQETEDaXsp45BRAXo3JGAEEZrQYKvYWd0du+O1//wk8FK5Z/e/c87tlwKrhbC/WSEY1QanbyoRrj/kwevvJOz10vHzPqoc+a6hvNbmh44XN61qyDBlwh9FCyf2xzbVOd2Hjrc19sr9gyNjbD19Zb2NzZb5VOf+tSHPvQhKIIvf/nL2lJsqsnTf0/qDDzdCWAQMk5ijA9hM/P1ctbChWKn+psVqMSCkA2sBRoVKx2OZP9t7VrpnPquKpjGP/kJxvNf//Vfb37zm3/nd34Hg5D3vOc95ETFC8ABXsgdKkL7f4MDCviD7qWIjxz4LVIKrJqLpv1Evxs65dixTgYlNInoE7vpsHFOo70xR9Bg9+iI0TUhCtJ4tBotu/h2hp0GQFGurdDJNAdD6EUbR8Nm/YibjbkteHhPQk3htgn4oqWrk61BKxCwNJ6V8N8EJe8fd6mWAMKEYsJIGEo4G4gfPtrfPFB6bIvdfXYal/VJwvUqx9GimGybezfYr3phKP8fmeKjG0PjO6l3cg4BZjxaXrClcdQKDOLHyYOKBRpBmwo8El8HvEcXGjJX5oE1ElVnNIdRe/YciW0jYBEfYBASQD50XKVquUGxsjCMRCZQJgjgyLPQEtQiHQOsyisknDJSJM0Vw+jN41TZzYUlqhtNSsh1CpNHdoe6pA6By0ITQoVDJ1dd1KExF0njIF/UnqUjReTXhoG+clgE4WTDbRivTOIkxVFgN6xiycsTCs8mGIA4dZjIizY3ejWIwYMBEzk5DgbXZ8wUIdqFyhIV9wODxriDWo7Zn3d7x5ABei0pkyhWRcdsTUhBbF3WpJwNObGpkuZldKr3+sfMO8yVFie4xSpvi1xSyt9RNDuTAQmmx5CZG9u7dMTOQA8Tr/ZZeJGgRhg3mneja2I+2cAy34bBlwXflGNg9l7VmXW22i40t7TIShhu1wjKUcjrzZBJYCdYGTyqM6g29+Ke9bDlMO3pLvzjP8Z/+Uu1UiYbJVQuGN/6dikUDr3pTYF6vNEusQk5kLOZKnJctYFoURqVXcmZXcGk27RqgS0nt5E+tJd9aW7awC0PmaysD+LiAWu6D9CcQKTWNa0wlfTF3EgcMuG6yHaWfSvHNL/Q/xe+UUzWZIH1XdyUzcqFp0qIN1b2eJ1g0UU6yh5A5RXm3SqSbNRuzvk28Kx5ks8BrTbQwdmvSOGgwe6LGNELI4tzlj2VElFy5nRDZj57YnXK0iZ/FU2RHiAeEzjouWbPwyoay3F1tXakehKQH0InMAmrjvDTA59N0s83IWgCcs14yyQA51GIuPjii5kBTswZGVbmT/8L9R/op/9cu8o6faC//6UveynpL3rbX1TanlcqBOwYfk/c0cG9XjCUbGjNZbMBjlEqUQMWWYa6OEMRD+D3pAhqJ6ed6OKBX2B2xzmIdR24FAce53sI5Wd8YAieI+geinWgQ4Tk4NdogaCVFoZEYICNlbEPfvlN2cLQD1/wpgcuuIb8nJsIHqSYqkshL6oOwTpI1h2iGjMe8uqjblva3FxndA26B8cCQ14qEHE7MuYFLd7GtNGSsLbtGe7p7cUUjmLob0I1AOhgbfATGfgHPvCBz372s+9973v/8i//kuWerl+3cvp+EmdgdXxpT96EvO51r4MuBbHTTbBT3/3ud99yyy21+5Jo3e985zvxzQAZjHIOzgygY/WX6XeMzx4g/gd/8AdwtdnZr3jFK/xX+oH8oERf/epXsealfsS8yIHR9aJOXsEE4gCgEp4hfbl4mFGD/7NW2uYn1j5AqGOsQIrgxgIupC487G3KWdBOXWMO0mCcJPVC7hY8pMFQv6hDQ54VXPwZejheGgyafXmLmGxHo7hZMuqj1eak25IklBKUt4ZOVAlcAlMXJWLEVqlsYH0W+wp3AKvjCVSjzdFSsC1efxDhV6BslcstmfqSZ+FZGpkzkmdlIWx3JZ0X3BTu6dto3ruT6hQ9KZQhPa+ubQ2ef+7hMbtzAqrEQrWXhgBldADwB5xEoKoHCBBkGhXkJf6wZeNJX/xgMWyhlkkXN9WSR2WHPFVgTg9DyXPJSHYBelDZ1MzYJgcplchPVQa3UKpSqRISXHJNQcfJ1QJikwgNxW+xGakUj3Z2Ol4LyC6ADkIU911UhtNsiHpy0ZZEQhJFa6MkjElxIQhnE0eFQHysVlrSRjpqh5C9WtXGmLk2Y+Kfn0mHEwC5e2jYJbAwzEc027vG3GrVyybwEW0i+KXazTmTgPJRq7qxwWyodaw02dnaLVP7zDzErcwb3b6H7XQx9O6R8X+opHsboXwZXOni3vDrBk2zWrbqg6k3Mlqu+fYqXBis1vmUcIjFDMyZDR4Q+tJUAmkEZxRmENVFQuGia2HDrFbBa0rK7q0lC1Wzi7qpXaFzIv4tlXftRvarJoAFZ2WFwpYd8YMfmS+8xqjLsV/m7OcTNmYGw2hTF9a1JTq7yVws5DFOwDIfa6W+XH2mfZPYr8tSCzXIV2AVK9G776yyU5/xTAJTYZXAK9hVyoHzdB/4kOkPE4FxAZeUN7X+s5i5T2k/q3kSXS/hvaAXMO+SPOEw5s+gXUCjgaIVfefPOPcb7BtxB6U5d3PnWHmp2hoFKSV7ePG9IzNynifMv7Rt9oTVrpYMHLU4ieRbWGAeOBOPa+ZXy9j9foItgAkAG6+44ooF5sHPf4o9aOCM7g/IFWy+q666arXQfrULsfiFmzMnk7B//37cocM448Rkz9dWvsKf8WdGn+kko4CAh7vHAxzSWoa4//Y5z7lsIp9v2vjMpstv7C7bxD3C4UU1PwyalIwn6upynA7qJJN9wTGnkDI58nBbNVKy0F4GcaIpsCYEGGVXVPY02icyDdIhYsOCHVEeXAWWOQQwys+ckxOYj+H5GbTNNWJ26e3/9p7NPbuO1nfcde37iwHRxhe5MYiXQmgEEVSXXi/1U8AUZnHoz+FLBQHD+jQ6iIHHRoxHut3REhiL3Zgsn1kfIB4SHbjgGRdu3bKVjY1ki/uhQ4dwnwEBzJ3YqDjE/du//VtoitV1Gk5Oyqn15+lOACOO+OM//uPzzjsPlAXPBJ/5zGc+8pGPcOh+8IMfZMfzHeGd4kUvehEaWWjqg/z93d/93e///u/zeSPIla+shpb40z/90/e///14OyCoA0VmKzaQGZ73X/3VX6ERTU5chuIMsKOjA1SAmnnL9YS7a4E89GdGcT9BlaJ23MMa69OBNWkPserREa9z1OwtSNhbvmEcO4Nk67i+oxLy1xi2DKx8u8e9VMTMjdh1Ma8xVm1GqJjANRQIu+4tk8AnL89splTQTmWs9gw+ot3BvNefCayra9r22OHeke7ARKGpuY0wwuIuSzWEx6ntXe5l5ybX33zNsWMPBQ8cRkoMSQKoqWbSre9560iu5fuPQHVGgEx4loa8hKyH9BSVZtRLlR2vbpu4QjjHEiJT/BkxDxYyWPzlAh+FRBd46uHbADyW2jXTTVHIUJvUI4SsQNypS57UL26TyZMQWUTQ6GpTQFv+QoTIG1VAPapFUPAYU541a9ceO9rZ3dW9dm1zNJbA8SAZ6SE9B0xCAsMIYGj0KhykZvS6LbSd6WESvkPGQpSHZhCGu9j6EgEoJgJchOBG74RzdNgYKsO1DDDPnWNesSQBk0JRYggjABcAjdDeKE5URnufdW5bEqeGNaObGuV8f5VQPHNjdfS/vNLdwc2jub8sV/aNe6O21VaNtJSgfoktbde9zwwt5A2YFuH4oNK8fft2XJtAA2t1hhk9ge7lEPUT+QaZkbpsznWjRcyckZabTlta1mq+7i6czvEMDZlKifdRrH/dvfvCmJKr1ZQqhfqVv6Gx0eqxLvuss/hEF65wgbdm69rg795Y/NTnIgVIeuqV47yQSKf+4G2VXK7ryOH29nZVnKjajj2RD/ccg3kRvuIKHHsol84GUXyDskeEcUOS31aJiIcS+4uwW9iuWyUQAaHaJYPsCf7yz0TXC66KGxGHaJOfvj+xflVLe6A+ZBSUbWtrA7Idb7XkB8HVneJ+vMWX1ufll2LzsCHRbzy1ybDlT9Rx1TC1NzFTWQgusUlOYYtxJoELsoc7XkJWyxdxXAu9cGYGTgbg/wMPPMAzOhFPt69Mz4BP/WIOCitk4UlbaW8h4eBZMBA2MM+soB6U30/9E9b2pz/96QMHDiZy6179ye8dLGRKZfyYuKU8Nm7lxrVbcnV1OFTxywpux/GmfoMujpUsTH859DgaOfAEdxKXpXLogQ6JwAHdORSYiXsk5eR/foaDIl0AuSu7qCgaYoYmDrHc//Xoj6/cfkfJCvzbmz99sGkDJ2/AxmxYhApykk5dCmPiN21STlK5oZfXGDPWJL01mcBDne72Lkz/EM0YjSH3/OYgnp8FSaOHmMIlEvAy4AWAhMLnYiioUL3yla8kSOTXv/51Hp6Gn/zU1K6gv093Avif/umfalcD2hUk9Stf+QpCWtLZtX/2Z38G2+b2229HgkEKWsqEp4NmxpHdDC4X2ZCNoPAAqoeeQ221tc/AiHe84x0dHR2Q0H/0R3/0rW99CwTrhHwMsJqgJfy2EL7xDHUNzjqVqAGIAausIWY3xLxNdUanCITFHTS+nVFdVpF+JUjaxNhQ7+hQw9qO4aIZyxv9YTMxijTSyEbNuojbmPCaElY2ZoUmtYlpYQp4CE1hJYJ2Iu21ESHNCZ/T1P7rh/djzxpMiuNotKOROeMoC2daxUroJ93VF249b+0nPzb2wztLD+10nEpwc0fmt15YWLf17oHQY302bDzoRNRcEKYBaSWuLysDfWtaVWEAAnwBXSZOsOwqvYE4RtpLGvFRIZ8AytCccods5lKgTKhEjCippiQVSDI/hBSS/+QSMkRlVnBQ6geS4ra5jE2wZWaiqCiTQ0hnPXBIbcmvLt0l3kTCsZaW1mOdnYePdre2NidTSQqIErh0GTLeZWg0JNC8LDA/EfKaMxbBAAhQRIZUGIdkJg6osU6BpsY4uXfc6RzF83YAmItObP+oM1Ym3p3RkCIOtIv+eTaC8bbMWNSuNmarI4WhkNGkWBMynMVdgHuE0xm77Sv5PTdFjXvNSCF4lhwCnDME4nGMjJG7JVj3DohTdQAtJBzjAEC9gjOewEj4b5vN8lTafzk6hgIF54R2k553vAePaPGvy2HDoaJ6zgzrh8WNQ+XC9ybGC+eee478YuW0le9UBbrGySUU1u2yLiuZCD7v+U4qW/jOd4qP7oR9UTr7rPB1L7LPu+Dw8PDDjzwCuw1xIhsGV96Bns5QYcwJhyNt61lERRTiu1Kmnq147OhRDCN0bwAs+zsHd/V5jWvWxdI5HGvxecrFZMgW1rnkFwQwJk96n4Nz6NmWnXsiLg5yqpm9gouvG4UXrD9gJq4iLejFj+50zsXPAF8leh8Y+8w4QxdfwymQE94KoAm50NJUKk6BGUDDAt4oSkDohixgJnMKjHS+IRDUQ/t8JmICUpD5sq3MdE4WjVuC8IBtgmrOqbrIwcEqI/gxrPAb/7//OFrMge1AqeJfJkAcSNdOJAUvAqXjfOY4o1ruCi3DT4o3WkRR0UBYok86xL88EAREdPrIptAviiPpVY6fJ2sABQorFzBUOpLHG6toxkFiryt0ve32W+Klsduv/8gD51wJpUxPcDIq+I1U5p+VgsSpaZeTWVrxDNCz+qi3Lm1urbcODTqP9htHxnBO6eGX9MxGd0vGbEw+jp7SJy8IPxryiLte/vKXs+HvvPNOECH9amUu69OqV49bsKfVyOccLGwb8DMY/7L9TZMHFKSBTUQb0vmRfrzhDW9A2PurX/3qec97Xm0l0MxvetObEHOx0RfWY6FmvGH9+Mc/RlOaSv793//9mc985nIwS90NGtUscxRRkHrpYxWCvLaT/rMi9MxUyEvVBTdkvZ5xEQjjJQtn0ZgHowXdW6jkK3nmAUKEWEew0AA9sQIquBJhPDsiqiB1UQe93KaEmY4Au6DRNAmhIYmGZCIrXpsOtT73DOpBlaVnvHykv2hkk5rSRne3fyJwT693Qdvm3I3N0VIJPeNSONwViv/0iPeTA2Y0YkeIZoQHaOx7RYEFAkxsPoBHQnNqwgGiQbWJWyml7ywQFOglTEkoTIe+CWADusrw5Y3QxvSVZHmnNKsV71BShABR8E7yUa2g/QIe5YXnEQmORpDclivV+pgmqVX9QmaIYrT0TG4sslQUicUx4MQYuKu7m81DPCG6IXWJSbAMBnIFCjYdhgdpixdrA3qPqEtGUxIJsBi+kguBfNe4MzBq5T3IfII5u70THr6jI2G8Qxn5ktk7SoxfCxX3eEgcJremjXUZuzjujdAV8YNILTwwBpp9wotuM0dMUMsR5+O56C+ygZ8Ype2WU3aCaSP2XDt7gxl5lqJ+qWoh6pfXtAjnCHs/9KMQd4Do6MR5OiFzTetHh9yyKzoGYdPF+bNaKHmxjEsGboYj1hlbS8FwtCwepaQlGavwS0r1DdF1a5FJ6Q9/SQ3JgtvocT33UvucsyITeSbRjMatbAarUFx9UCczgCScfHizjN774xCRFFpbY9nsxDADl33DlyVzBqc5k4U0pjPohMNit9LZxpARi8fJx4YtVtnxlJD/1T8e5ELSH1H8IahftAo7OjrgL+hXy7zTE1Sgube3ty+5Kjh0YHvgQ1u2bFncVlxyU0svyBjpIYQ6VWhDEjj3IDHAZ6xXVmy3lz7gp6IksV4g/8D4n7YEMKgwsiA+bcS/T8UKPPVt8pXBFSXOBUzSJ7Tqeuq7e6J7AJyB4cse4BMAwzyFeYIo+V999dVWIPS8N/zv4fQ5xHQsV8vZiJ2Kgp3FmAeNLcm5p4545Bwak+TXaAFvNWZR+XbheFXIk0TKEG8vCr+SExzvjyba1HIISmXoc0nYDhefqVQ+knfzFRtlQHLWO+Mf+sIbN/TueeD8F/7k8t+tEhTSgOkMy5i25dhV7XOXo1t1R7ok/TEtbInr4+aatLk+I/FN9gxZj/YSkBKvpWZb3N3aYG5pAEGePIj9zUIHuL7//e/fcMMNIOeIuziU/benH57yGThNAE/SBnol8NW8Z8+ed73rXRrRgT8Ho1q7tPWXip8848iklgAmP8rP99xzD7Jf6Bw/85wPZOaroJ57770XXYirrroK19AEBNONzllkMYlonHKRE0kL9cNSna9UTUPyxcI5a0vZrSk8Dbido+6xMaOvYMSKbnDCQBcXQqsoNoriIRktFEjWUN7qD5mJMThnyEJRC/Eaoh40W0NCUjTxqfB4ReepTsC2g1jMRq19Ox4p9vSffd75ofoGKO2hIqQ1RsjWrzrLiWAsHkwAbPoGnb393lA5lIsjL3Vx0wUjsCL2HgTmFWIYYCZyVCX+BVKJaS7EpwiIBQJCQzJA5HmkygvMiYGoAsfkWZGy0iex+BC4J5BPKGR5Ft0V6TmPGhbKL3miXaqQQQhAE5ktJG7QRt2UoHPKrZbUJo1RWHvYIru+YtDArWuJkhOwUXQWKjlgUlDsVZD4YfNJpWiuxoKVBnF2ZYqhNW1aEh8Pg2pU0EdFzSYIeT+SdxDUIwAkWl0u6RWKZu+4GbO99Vl4EAzCbYi669Je9+F9h3qI54S81kT6SpfAtBbvvLers4ttz6QWy4Ee56rB8NWWV2hprU8lGz0zDJWn5kNN09QY5/srs2FZF1100c9//nP4MhBm9GR2ZuYUzlGID8dzJ6pGp5jky3SuSYlljvyQZVBTLy9kKRa+YF2BXpAH+TONQn5TOkqcgk2bq1df6Xz/B7JJZElh2zgchParXmo0NUvVi6h8zqZVOdWxcMzGizr7cLKb8gfBL+cfSP+Ro0fWrluHS+fc7kfFA9ZVz0cInC/xeUlvEsSwVhXBieMikUkLhdG+qMvihswwixiz4wRLS8dVP/SGphR6YUHbjgZY9MlLduqJuwCMVEaMqyVXCUsOShJ6EgJ4yZWchILMGywb7kAammMLMb30nJQlb4/abutKTlRttTWv/Gc9q7B1OCUX7zpo5Y/reHsIOwkYtXXr1oV55cdb7SrKj+CXrwwsBQM0un1CvqyVP3z2v99JVP/gDjMDUL+rffjIjWbLWhgsJoQIhIqFUsuZl6277oN9hdBEqeyVJ6xQ2DLg8Ap2pfElsVbT6BOojxCe5qj4o8FXhsT74FJUKmcfcYwMJQ9WBzgWZAaBM+TU1DMLUYvbZzH9dV0VjsRSPiONuOm+8uf/dN7RB/vj9be//v/0ZlrIH7HdKD6yBLmQ/9RhKvib9GVqoThaAf24wmmOe+tSbnPCfqDTe+gYCndik1YXqZ63xjqzjkAbrKF/9kqHqY4Ls0pkYyD5t912G4aWM/JIvtPXUzcDcyCjT11nnpqWIRff/OY3w+yH68/nin/mP//zP9fbFB+29Ilvu3bX8pNE0LgZ3SUPSDzXjPQ5f+oKdXgkWscXF02jEa3lwLXNzVl8zsTZpWanzFlQUG8Jm2TkombQKYwd2VeecFrwoZd2R/p3iCveYCpVv3G8Ci0qIMKtuqWqNWx54bwVnxCZMB6McxEjG3HrokZDHJmwEQvhWE+DEO4aLshD69o13T3dj/zm/nPRdu3oQJ45VnaG895QMUA0pgnHQju06loNSS9VhvNnEe8U0hcbSBdpIFJTl3jAAgQrVYyCRdkaSIVhpCaJRUSs6BoBrBIQCSkxvq/IImF95Q2IrLoDqQBzen54FuhJ76BsfbAnR7KaLTUINUMaLIvRcMWwCTUUClgNQRG7AeYE2tG2Ap1CaFNcALYMmVeJKF4SwtGgGwm45fxYa2MWspliActJRZh2xOlEr0FNW8aIQ6yBgkvoZoK2I3dnwsfL3mDBGc8bOEGEbI6FxUJ7fNSMhQzMrVMROKluKuLghxDGKnB/WISHuOECxzBQyAd355p76edKhUYFPWVIpXwJy+poCEosFpCgRzE9D0Kd66HNVXxGGnPCR3HppZdSLRdvZbpkevX8TmaHOmpsamKtDg7izItV4nCqrJHYv9M5cSkBQQjWOKOJ2T9BLvUXqmmYzq4uNkA6m0m2rY3ceEMpEjN+/MPw2IRr2uVMg/Hql4ev/S1rEY6LZjc0Rwo9fjyhpHaFB64DTYsUUdQZSk5o704GZnVsqjguRtVsEzZsHKxAXXpy2FL6J/4/2AZqM4niAEGzZMuqmZHJlBa1axAcrGFVZ+tRA9NQnyYbfLHlK5hos45aSYVeR93DGaupE2fc2YpIpJED08WVjPfzsbBYdB4EHbIfztET8jRnjHSBn6i8Ig9hKhbIszJfsdx8VkwOn/Nilnu+UaD3y85E7recSuarfLWks6kYPuykp9sk+EADoIQ6jJxMx3M2rZb1nbOfeuzcOZuAAyz9xo0bybna9wD951ybMWSGCRv94x//+L59+5s3X/SSj33nWCk0RvjNaj4RcKOcdIIF4A9FzjV9iIkqHc/CX3dV6EcLnLCCYxjRVMZtimBYpbIIY3AiKaUFERGMaFK5T4hksQOCAKbGCvGVKmIqzIlpB9xzOx969Y/+Jlgtf/OtnzjQchb4IYLiJPEtlYhX0EMOVdW+4E4KvdEYCq0jB6qPYdBnbKq3d/W723udrnwwX3XqY8a5Td7mLOIf8Ct6JOX1xfBBv3Cpi3ALm8dPfOITywSbUxWf/nsiZ+A0ASzQB7xcm8xxwG/btg15Eccz0ywEhCFhLfwpJ7P+qV/56Ut+AKH82te+hqUxatXoitx6661a8rPkCikILcFwFl/DJDZNAdOOEMNjXQtQeXhs4ljfUCDd1D1mjlZD+YCDMQZhk4h4VKxgfQEVjBiWZ4moFA1aPQSwDaJT7WAcCyWci7kNuI+O40pagyegg1BNrc0tkWdfdv/992MSiUtAFMAyYTsT9pjuiiu2kSNFiGEXtwe4xsXSY6Ls5sv4zcLpn4mcrFzFcRRemDwPZVFlPYvXLrpRdiDOgT6iOyOSXkU6aCmxkMGAN5kOOWiEXFA/JY9wHRkLAE+9FximyQwBgQr8CUTThaSwXJITPW3mt2fMKJXdbMSoT6IiyWvJIOJcvHNhfhwwiWYUCpqRIL7HiEWM2rV35ODBge7OnNty/lnrM1E7HkLtWSoFRo+VXBz9D+TFL7eDqrMF7IbLCBnswvIEpsejgH4YBELOp0JGa52F7QrnQCpUbctaSI+hokQQaFkdHR36+Nk7thfB43Gh71Shleep4ZHxR9KZLDVIF5dxUafWTaBOKFjuM1RzJ+cWrbASlL+wJjif1qE7LCeinnZpHhoMdtViOgJzHa8q5ISHBcINs0U3AUFpt7dH3vxm98Uvrhw5jFp/uH2d2diC31693oupfOE8sg2muzyZV88ApDtn+DDq3eWy3X20Amv5nLPgcKPNRT78c4TQonr8taatjeno7JMc7Dxel1Hnkgb8OdNbm5AP5PG6jh7Kd+GoTCpk7Fw88JUd1x54fBcmf+mqlA3z9Hu9IoCsyd5Mv5njiTygfRDAxEPSmN8cmZ7qpNkD0Smz05fWU8UFWq3HLipRCDFmI7vHNRWsPvnZCcdV6hTLDAeEeLCn2KAWORw2ABCDSx8Kiyx1amSDNQmGicMLrIFQ/D5RUGUFTg4o6Mf/4uNf+9rXmzrOvvaWr3aXEkOjBdeppEJmcy4VxFOjXJyH4FpycnHjJAM34HCrVq2xikTTDBa9YsDlBASh4gWxA7XYQw5AyYj81gtN0sKShjQ3ZOFGVbSWR/KCQOJvhZ9nDB/70Bdf0zDe9/1r3/GrZ1yHfRWuSJE9QFuL76vJM1sa51KHpxyg6qcXDRJEw4D63VKHRp63e9DY349TLglF2Z52zqgzN9QJm74WUeEHSi6vf/3rf/GLX3zuc5/7vd/7PdqQZk5fK2wGVutJfLzTiKnJTTfdpAUjlH3xi1+M3FXvSFBDohPpCu+77z78YKGNTOgjMBWNNUIV+83xieiftVSx/3ZpD3BAkTljZvy2t71t9+7duJuG/F6OxGY5yC5sqkndabenMDZ83pktZdfrGat2jXu94wb6twNFyFS86pkTFTGFRRlT6EwJKewNmxaCTqKwJKCEw+ImKh3xstFqo8RSspJRiSQLDID4ee5zn8tUg04hCyL2nV4IDDyyUQ9xaLsRgCAcL0FyY41scEcHO18VJ9Wijw0FDiXsiiYMKtnot+AQX0VHRnwq8JQuuRJsCZ1t8assNp5K3Kuhq/AGRTIMzSkcRQAgEBjuH6SRwLtJOKw6OgmvhBgWMKkgLncNLimE2TNEN+aktNmUEmtnhgAdAjWsyFqy4BkbGpjQrK6Skxvn5pp/88ChYv/O0cPF1nPOnIDgL7kjhE0uEkcdHVdx90UoqdFidZwUR2B3NOThHJjxEsiOn8jYszEb1WtUXolL3IZHqukQR5M9Xto+PAmlkPyw7iw3G6BWlqibhqewf4ApFaCUClZbkihTTJ6JpEyeTlMP/KQergW67b9VGUVQLwJWO2A1NViNdfZZW2WZRe4+KXddoKoT9YrdtuvAseJ9j0T6uyqhYLCjYxAPWHJ2w0XSGvi1TZnxWIwPECmxOl9ld6IAr/JPzoceI9OEkRLPWze1Z8KIiF10LNFVgRFAdXzUtZUu4RlCl1gOFJyhSI96MF8xfgEXCa8ge3AHAi2trf78BVpCl05CEeaNCVzk0BbZH/BCJlPJvURnbpGlVk42/zNcWpcoDv3DxOIvZ2k1rOpSDJ/vCNQCJsJqXP1lTj7Dh/+F22dI3+c///lPH9kv88bY+fBxFoX4F+V/n62MKAV4uByHcNTMRRPsqBWyqegPQW7/9pN/E0s3Xnj9/x6NbewfKVQcJxOyWxuitugUixSCTmsQSLfVCGQIOPscKnnEH3Eq1sXHyvetwZxXEAFxfKVU5CSnkKweUuQolPHUFUQn0fZw/gyOhvZcvmqDGaIR11ru/ZNbr28fPPLYugu+f9178uEEEhHc3wiSJvMliIE0oH9N1SY/cR9qiewXT5wdadH4e6jH297pDZVszN8wNzuv1dzaaE2pbUlJquLCAQdEBIfjd7/73SuvvLKmytOPK2sGni4EMLMOWegTwJxA/jrUggwUNdFG/uIXvwj6CEWq1VQAWOxpPxs/KQvA8mtY5gM1Uz/sIhSiCA6Mo2lkwhgYk861hMqXVko3JGXpDVooShRK80jh1qWDa9M4iHa7RzFJJUSwO1iwhormRAkyGNJUqE1YbmDylRKerowhC7GnSUjbeMBMhsV3PJRwJqRkwjEL5weYi0AC4QMjncEDtrQ8NU75y/8YcqDNi3CVC9wfmhDh8KhIhrEZtorEQyqbtJuveCV+Qu56QgyjGorCDJLkquU4QaIBQ1EIgENgrF1noTYt8YcdSUH+S1MCS6GQleRUgKBklzmQF3REveeBLkkikFX1VErxiv9E5VpI6KhtZsJuOoJGtNCrOGCIBAjDS2AbyGGhu4C5dKxQiWw495JfPLD9x48O/Lp39/oNW4jfBOFXqngYguIbCd1mqkQvh+ACMeX+Go/ZyJWJmLc2badi4nmZyEANcW9NmrDMkG7SF/VPelx7seHZwLVbvfbtYp4RBS+HmTK7CeQeKJdyPEAGo/8GF0nmeurqH6+MlALwJTgeO+q0bFzeoknF9ziVy9DuzaFMFpAi+tWCavtyBhIn54gNjrpVEDVUPXtTdU/3ZSrlRP+dyI9v23Gg/Zc/DVWdiXPOjm/eMDGo28AbBw8z+wNowFm6EvnKZhP1e3awfKK1fZVSGEuzFbOpGPwQrQDC2p0oTWM4F+gD0z+I6uVMCSsO/wuSkh6ufPSXCVxgjy1tHkB22cCwDLSS0dIqOcmlwOTYAOw5Vg3+Lz6c+ZTYWkvgAlMPQAA9AvaA/5Ge5OGc8ObUYeKinMK5tgC7hGwwfPGEx86Hy7wa1eCXM3UMHxiC8hcPqOQsMFHLaWVllmXI2uU19D9LD9Ofc1nvf6hidJRwobyEr8kfLEcqLBUq8VOeqgdGStPf+vZ//sM//INhhy59zS3xZ75mpIx2WiAb9lqIPqLQXTmxxBGpoL6gURxtCslCx81E621QOTrJFqrn9Fb3N1g9YQkaXPFQvBPrX9AwEDGEAbhlFosv3IwQCskG1zKjQd4JlkjUX1xVcW7WOeO/+4O/PKtr+7FMy5ff/x896TWUTVjVSFCUrQTDUx0W7E46ppBfeaaD1OplYkZjwm1LGWuz9vYuZ9sxt7cYwPtMY8w7H9PfrJdVfiv1bMtYPA+ntjfffDMHx49+9KNTW8j/VO2xE9juNB14AitdgVXhcPJf//VfazvGTtU/a49hEjXKqMW8bGLwbOg00v1s/KQgfptra1vms/r2PMAiKhOvfe1rf+u3fuuTn/wkAYep1m93mU0cR3GAAUqq2Vw8JjHZdQeAFumQlaq3NtR5Q3mna8zrnTD6C9imSgBhXDoTalzsdQWwIRT1KmWMe7FLFDoQMhgRKNRaKuJBIqbDVYyN66OB8y66GEfHwBxQ+5GRMZTr/OZqe4uaSjJoIQ5sSQhYQgcG1/NQ45DEqGQLDYwc2EFNGkEx9DD2ycA+Qg1ZaL+IwbDoSIuaNJJqNwBdb5ohgC/u72VkahOwFSbhIKrdoi+tlKNlzaUfmtKQDSBQUkqIrQpaL+JKmjh4VGV5gaCxJmNtqROdGkAy0mn+MTMD404eXe4qpC+dpDBi3ljD5ot6Ht2zr6fYUzha37ymAL1ODyQupos8UkVxFQof6XXCNnIJE5ZBhO4aTiroNSZN3DAofVch4IVyn7xqKSJJAsFapp7hcR2o/gdF0zJTc12kg/eDBaLpQGgEIh/6eADZs9FAW6ZKUC4YH3VRdT6pwxI6RPPLoYTBxTXvaZHoAkOoHcVkt6a797h+MgTZjfP3f64xHV9aIp5oXLsme/QgRL7b3kEMQjQpdBWwSx7XG1LVG5gj2EDJR4JCgSWGAOQjp+xaNT8KjUDlXhQEgjXBnPQqzLcWx9VvlgxEjaq0MBlCCCqIGkjnDgLH3uWaodk+uwlqwOXpasF9T8jUzZ4E/aU8SZXPbm75Kajz6eWmz3yDXAwB3G6R32BtByhy+eWX+5zo2ler+hm/1vh6JTiihjZzLi6ThvoD3xHkn/6OVvWQj6vzjB2citgZ0IFwf7SVzXHVsKozM2qUCjH/4fDC+ymgkuFoOOCPy/855+bxs81+ID8flF98doaTnALh98H339Lb3XPZ626pu+KtIxXYnV5dMpCNBUFWwMQYOtwvjjE6zbOgZPwUrNMcKzjDBRuTNxxftQ27ibLXMeQejdlow4lmH2XkhBZrMqL+Voe78xMjiTVbQMjg/2L9C/KFeiAyknIF5Wd8llSev+M7r/zpF0aimS998FuHMq20hsp0PAr3QSoSXE01Tkf8CZSu0UHDSxNDMuquJ+5Rg7l/oPpIj3dkzMYsEjnEmQ3e1owxFaJClVAS/o9+9KOY++LalgCrp4Bvs5O8c05+c08XAlg+sFkXLFvO41psDM+x3/72t33hErKj6667Dnks55b2VQjCd9ttt7W3txPLa1Z9y0rQPeRg+MEPfvDe974X63kobewHtPxqzv4vq70FC9McM8M1IxeTiGy2MW7Xx4XA659w8FEMJYwZ8CAxhEuoKItMGEq4KqBNZJ5VzIZL4sCAAC0AQGw2ElCzYQ8DjHRIWQtHvH07H+w+cvCyZ120adPGKTAkQOhxrbOCApVwoQwJzT8YgEIJABahEERErIjwiYrQ4dgnVxQVKiSxiIXxnIzXaGS/lnIlrYCpMhUWqEoYHOVGi+bwJiWCCWlH/VOMD574pYgOeZKOoKGNZqroOFMBqtTiQHtXl9sUqa5vEJYgl2tYEkUJz9XSrrjOh3RBmoePKwljk9uSL3V6RjRShWQRm2EgMIbb1MlcwcisT7jpkB0KSO0ERqqLQ/qKQTU+D1XfpCdqinRrqsma2/I3zBJq0OTQbG+QNf2S6UO3gpy4REYXTvPCdQZi923KBfD8zDk2Tdarrch3wcaA7tLRtmsrXPj5eEcBZQ4LzNdPW7jyJbyFAVNfv6ZudIC9Y515BptkAidvwv7As/fcSwnrRHadrLcsPYr9auXBGvxJkleYPwXxFKLqAMECXtVCtiV0tbYIbhGYfFgq2v4TJJ6Q46yInl7cB/IMuFgMAVzbK7+G2rZWwjMd09043v2zEjp/wvuAs0Yd8HPXrl0cjpqtppH442rLn9UllD2uhk5aZn9EMAjuuusuJNuQ98yPTq/dPKSgO4b6N6CPOTxpPVwhDWnjF8aOmpvWfaidnBXSyRPeDX97wOflWMHsGeJf62RBeqHWxJ2LdlGM56MANmrvaIvpCZVDV/tNAJOZZArCW6mFsYup6kTloTMI+V/7utcOD49e/JK3Jp9905GBskWskFgwHRWsCoRJ4VZycknP9QkC8iOPGIK5w5jXlQVvJN7j5iEEvEbbgFNtILaBMlKT000gcyJIPZ5DDAezCjWM9Vk0INEpyviOLoFfETdE1Oievfen773tHeVw9I7rP7p7zdngdkiIwTwFLxMIjxxES331BLAlhamsXorpWV3cbMtZm3PuUMHbNWjsGjCxVotGzLZk9axG+4xGiSGsjmWpC9n+jTfe+N///d9488Htcy1nX9d++r4CZ+DpQgDPOfWoor31rW996UtfinMaoAbwCGNgWLmf+tSnAFUU4YP4yEc+8r3vfQ+ODtuac+uzn/0soAphMvmfDAgO5ILi/fznP4+EGRoYdalvfOMb7e3tT0Zbc87JAom1fUDdEn/Da9PBNSn0kJ1eIYPdPrEQtkYKCIRFSAvirshOAXJQpKIsDVgyoITF3JF7nFBAIRdfAp29gaHhyNhvDh0aLF98/uY4PqMktBBQUsEXuHYa1xcqQKCTAB1ZHVkg4GkwTChdu5HM8h+eohECYwFC/DcC+Xh4li4hfUU6DUWq4icBXiGJ0WMRb9Lw7UQ+rIIqiQ0xeYTRSJNUpRpSJIU8T19wMYcKiG0FKJOPnANFVLKtEnqdqG0rP70QvVJ51aiaEr1Jucea6jk/LANaAmKbJCgax6nEQ0HcXBETOBqyiWBne1YsVM0SGClmpsMir9bNq1XQ8zDdnxXyBHrHcT7DTHR23xgCHqoQCHBmYAGlEQLJxnkEHSj2qtOk3eziT2oKaASU3pPXRKlihqpGqvsomECvHWgqO/g856MgmFdMlLLUpqtpngMZVX+9b/S7CvtJvgJRBeNiMsENECcHA6gJ8GGQKt/LiRUx+ZiW+vpEuUD7GINjCMA8XokW1AJQFCdAJ1bBvmbalvvI5uTSOinLrWv1l9dfqF56dtuScTt2C+5/OM5QyFr9syIjYE4Qj3PXrq3hBDFADnHUXJmo2jEC6GCjkwhzDSBT++rp8AzjEnkDpC/czxkzc2oPn3WHM8iQdfjMWtIUEM3OYTPwluclfFb79+/Xfv6ZQw5fLh6YYa3GePIntlDIX/iMi0aGR8+49KVrXvSBHifDYVUXtvEIIwrfAkHkqxBdJnVZHGUcfqQYoJEGsuIRogSLMNtMlZ3WMdBGrzHv1uXdIwlUBeXi2IvZbrhakACGTpWfgSrxiAyU55xAaLiA61DBNrEeO6f30Zvv+FC8UrznGa+48+q3VE1shN14RPOI6U0t6SvHKJVL/9QNhxp4FV2TNjYmiahkPtBlbDuKmMcM2FZDpHphq31WIyS3VKJLQUpgvQh8I9Iv+pvy4vGfv3T99LXyZuBpTQDD1Yb0/ed//mcQccAQUINYvugev+xlL/NXCkUvNDqgRd/ylreAGXd0dJCfvf5k7298dOEnEHVo1ERp8ZprrlHf1OPOVL+TJ/dhsg+TM2BCsVgdWYNQtONlp2/cRSDch4VwAX6eN1oWeATdC08OglPIRROEHpVgwdTFaUEQBWn8Hscj9WccM4yje0e3d++68OyNTekgrqRTUTRbxMAD+OhPuP8wY9SC+tM1iS1kBghTPGlRKbqjgDWITNznQhgjHy6JPBY2IVJieYAABiBLHgHEtAU9IWUgKgQ0ywM3uVQDDEFcQAOlsVSZQJarTFl4S239efivinpTZSBRlKEbpaVnEDrSedVJKHwEnkSri4Xcob7usaGB1jM2xWNhfBhiS4wzsGzUjoWYIVVUt/54kDrfPKi8K/0Gt7ujo4MjH7ATPrIAAEAASURBVHGxnl2GM7mx5u87AkbNmZo/y1Le0AHodlmxqQtowKNmyS+lxvnL4EQ9UBgP9vVWLbMvER872OmG1rItsLRHrX2ucg5O13U6f0CcUelnptSGnNwcpJOAA7awuPWQj2yuepaVpm2wMQD2XZcxOUwR+Jy+L2L1pjuAKiB0AvxEoOt06oJPtLLg+8e91Jk1rsnK+hwNnc5dsy95ACnnXnuBrcKCBJUBuQSV9F/RANtVi7hBOvGqqJskg36A5qFa2sLKt/YVGSCkGSwPxPGCUuItLADdPYowq8weuv0k+tOoH1B80HVKIOupi1dcfAj6W6AnSH50Ill4YGmgMHkgHTJev+KuW2SAPDAtGvPWRXirH/xJ0ylTbQoMJIUZoHIS/VHrDDMy+6VqHyiC0gezipLwKUMAs2QwzWEPsSVYesw6EPNC5uH3lXnWk6YngYGzFuAbWk2GRD2Hi5m62mlcXc/+GHH7hFsToP2pPd7a1WHsMEfQ44M/CKo5Y+D8bG9v587OIfwHqtGaap2RrbbC2c8+MwVPhHiV05oFbLzZOU9CCjpBb3jD6/cf2BuMN2747Y/2OhnXCmYTkVTUE3smDWCUMxQ5v0CzFMyRgxe1piqmv8ZIXh5AuDhUNo55QTEZMyOOsXHEPZQgWcohdwk5o/neA/oEDFtuufsxPKM48bpKck2+bImLDMNYP3b09//zT8479ODDZzzvX956a1l51sSRChZ5uiusjkBuUaeWFPVDDlYu1AAJS9macNqTRnPWerjL/Z+jXl8RRUKzMV65cI15Zr1RHxUePTUASJGHEegI1QaMIDT3/7gWUZo8fT1FM/C0JoDR7PrmN7/JDsaoSRPAWt+YtfB3MA+w7u655x7wDE44UBlQBP/tk7Rqun60rDEJRq0CGfWf/MmfECV4CTzCJ6mHj6tWdRd4kArZqZzdkZOIPn0Tbm+eoD6oRuO8Ci1ooXth8mENi0UucA14NjI4UJoYhgfolAsEgIlHQuPDpa5h70j+yLlb1qXjwWRIYvxkI/iXwnUWFIJAUU2FTnaA31OQVKdIX3xUWakySwZEigiKg5aSLk73XQFAyY4WEuQrJDrSY6TBmKwgq4ZeF+9ZkMEiE6a/uiYpDg0St6qd42bPuCVhe3BzpWhdyAGhc9kgskekYTR5uEPuQpajzxyGeUno4ABOwnDy5GG0guOi0ASRf4dG9vzyrGee3bG2GTpIwWHVz+mn6W6vtCdwd82HBsPjIOHUp4da42vOrsrUKPJSH9WUhXWNvT2Z1Vaas5Akkl8XmTfHUl9Aj8EF0x1jFPq5vb19RtSfpVZPOX3Cevj9Du18KFwYK4RDm6++ary5FaqH15gGqGCGPLMfp1edHzCPpDz/Ex9C6HTVTb3P5TMiHVpddjje15TexHRxKXmCLloF/viV6fEADGtXRCf6eXggBQDLRU5NO0EAaCV5yCHIaV5RrfYJDIxl5lV2uemyWj0H+Lx9+3b9yq+zo6NDG4T/5je/gaj2i5ABQA3rkA6AlqFHQwqX7g/3F7zgBYB66rzzzjv5qdddP+CkTSMxRACGXiVRX+QBv7ziiiv4SSdpkYfagldddZUmVrWTNl1KN0qdGjcFT9WkLPuNjpGHdO1XDFJKF6ROXZb7lVdeqevEWIDR+ek8QJxz8YBaMkSXX4oWIS9xscsr2tIeK3jWGXhLnZr+xNbGT+ctFzjcli1bSGSq+YpJ0Ycdd5YMawVahIDHklO/Ip2LZ0oxn1TOpFGchdavuNOWFqRzyNJPXqE+wDMPXHrz6FmiHsou/iL/0gouvonZOX/6059+/etfJ2oD/A7e0gf2M0oQLB8X/BSGxrZhC1100UVsMHQcNNOEqWDgGkKy8zWXRM/b7FZOjRRWh4stASBl7Esg8JY5D7SuazjerbXMdilO03zg+Kaiad26vvs1z/hJ+uwUP/N8D3pK9Vt2mgYy82V+8tIZbP9A33vf+56f//yXqaaNz3nPbSPhNWYgkogGE2EkqOIhVX/b/oKg68Uz/4EguRWjXIAXadhVA0e1HHNRw+zodyQqhhx83qYR73/q3AoKzBYBNYyAm/ByG0mvFgddZ6yhtYNjb7QSGK/aJSWzSBnlG+/8xPN2fndf2wX/73f+ejSUxP1LFI8qIaFaQRAl1+SEyx4BvxOci/8UREnGvKaY25bBw7O5v9/Z1u0dGQ8QfwSxxJmNxhk5c32Gw1YGxLf/vve97x//8R/f8IY3/P3f/z0cyamBSv2nr5U/A9MIzcrv64ntob9TOYZhT85XuZ9Nn+LzZXsy0mka3AhH6hy3XKAdX/nKV2Cf+116Mho93jo1IKCUwAMF5GD2obKbCpuIhYni268o4f48HD7MM3BehUAYsTCSWLMEUmALDQhmR5gfzyGUcGslEBz2YvtHgrFxA1oxHjJjITSlRVk6HhQ3y0RXIvQ5kYdpDXilIKSCa1P404z5mfGzdoCqvyrBJorM9C//4JTK1SVAUl06Ez+21of3D1T2DJi7kQALwSsuiMAGgewY8SK6FYoXWbQtnsAkCDBvocPxgiUmvq4KjGQyung4EGxf17nWfmjbb3Ztuz/snQ9fZrKtqRHpnyv2jtmPL/Wik6B33DGE0xKqGd1GWgLBTKIWvkG3sLGhfJhzsEbu863XfOkz6l/aTwgwLdtEiRFMQtO9c/Z/afXrUmwbTAMiD90fdp3SpZfUnX9+/yBnt+wtNrlog8n2mt6H/MBIGC1p4ZNA6yL+xbMlhKg6pxWnhy3ClBH/kFo8vIDo4jUbmJTFXroUbD6mGqhIMbB5pIikQBHxE6QfGkbTnBCBLDqvuKDcuEOhQSOB6aL1B53ATxKpU1eLGKSjo4NKIB0pywNLj2MY3jLbuAQnBeJKq4nyrEvxgJQYCpmqtKUxKfqik5p05CebSktBBaIo6gu6UWeDxAIvlO+v5uInb7lryYz/hnrYlrBj2NIMlrK84k4PefAVtjkvGI7fDf3AwHmgdcz8dIqfwUdMGQtCXcZCNi7GqEuRkzq1Vrk/cB40fUhOIjn76WTm2Te3xpJCE7QkcvHWrxP8WNtb6lf67s8MR4lO8RkH/isGyyvSIeH0A3dqJh0SrlYcTTqJrCwEMOOC2qEIKbqT3KHx9NGJdb3+5OEm66roHlwDnplwCHWWg4vB6jtGQKw7+w16kgedKLNm2yyH9jUFzclm06/IwyvuPhebmpdz6VHMqAFmyhe+8AWwXk0AswpvfOMb6eHdd9+N/Ie1AIywN1gCWAYQxiyNxi7IySumi9njQXeS3s6o/5T5yUi5mC5E37A40a2bcz6f7PFqCKZ3y5Pdlq6fUfPwmLr4oACJC0f8YqtwVvrf7OI76c+nblEX9BMXX8/yckrjAIq/+/Rnbr/927mWDVff8rX+6GbCgITDCDA8ov4CeYWXKVALACIwhGfNzcN5c2Sksm5bJTeqzzGF0HHUmV4cJy5TZj7rx6vv3IH5mgJBlAWV8gKDcfu+xnBPqGRGY7ieKXBQ4icaia7t3fSDv37JL77aVbf+9td9bPe6C0HeQqaRimh0SqhfNUtSm3RI/RT8DfPgMF5pEDIb6+qMM+vNrjF3R5+5Z0Ci/oKCrktXz2uwiHuk7dHY2ISMYYd/5jOfIYIpUOikT/7ylu50abS6Tk/CSp4BvihgKNTvJZdcctNNN3HHIxexmnSfV+z3Jh2D0gPoCCVsr89JvKIBoYTNwYI3XIASFh/R+VguX82i4dl9qGQGwomm9eBNqij+pZGqOqVSMZOMiOssXPzZUMIWju+TBFgKQUOWqRxvWMAsuIxQm6jNTC0lD1NUBH/9ZAGAQm9Mv50qMPOv5Ky5RJQ7laL6x/AwMtmQCzWlXMRuEq9X1awCsEsTwNMAZHBA9JyBp+KiUJk9Y/wcBMOb7sZkV9vWro2GIw/cf/+2bdtgKyL1WsziPqUn3/T8QIdoUgQBF2chcqTpd7OeQKA5Ofye8ww6CCm1c+dOtjr4Oq8WM/ZZFS89geagdbloGlxcEzxLr26ukmpviglAoexmd++QQ3czEbCIGeYdOnwwYAc3ndsiO1UOeX+/SkVQvI6oF4iWPluvAkVJMcmi/ue8l0dhjZOD0NAlyNWqRJqFGmFcmvaAvwCOTopO586FYgv4N/TDvffeyyt9sXzUh/FFR0cHD9C9WmX3wIED/KQU6wWWz0TBxdCJerH08a+L62dQOh64FM1i+9QjWwWqgE3OVEMVQBD6FAtEBfiiLqXvVK6JQGqDWPITeeAVF73iWsAlodqbjwvdRNM7duzAApl2Zzjzh+7VQloGQsfoOZWzPyFc/bZIof9c0vCsiyLt7e2zkiWBGqhzzlck0grXnG+pc6PSj5jz7QIO25helhhZLv1nxmqL0xn/HKlN1894UOOqTWfF9U+QdYzcmB9S/LsW6dNPTigS9cWO4gHiX5fVd3rLUpLOWx/ppyDZ/B0IG4JtSXM8QOICHGZ0HqJakzTsRtgitf2EqL766qtJgXKGL0PNtRcsBv2ZswGok1fMDxcP/poCoOieTmSWapuu3QP+hLA0fCyI+wBfZKBjCPyZeT0nZEMMSK/Ixlt+QgAzWIii2tpqh7Dan/XMcEc3gQViYpmQp2pQ2DIAqU4mAczmAWyyHwAvfA4Mf+GFZtsAUZc5PzDI2LHLrGQJxfW59q53vfvWW2/NtWx81k1/VWp8BpKNkIGvUzcVEaCj9wPwT/Ae1QYpmqELMXooYu85w75gb+WsXhedZ4VnQeFypk3CdinoeTGiXAoPGBIXuth4tMnatiU0Vra8CbdcwfjOQrUQEtmy3Wt2/td1v/wK2OMPXvSe+859IbWAnKUjgh9KnXKMSs265zwzbYSujAeM5qQRtautKWtTvRnwXAjwXQPGti5vuGihu9eYKD+zNXB2EwEvBZDecccdGEUCsXF9x+lTCyWWMI2nizxVM/AUfDNP1VBXabt8olw4o0ZaArMZBba/+Iu/eOc73/mUwLvFzyF9VpnlziZLhfBgbK/P4hnLHSgIMTyYN4dKBkbC6EubA14FV35hEQsrQCcWtsViZXBodHgsX5fJBoMB/EIBhnAljZgLFwVQxfGgCIejiIhtYiy5EMZ4xo9HJPou9ICiSDVDUcAu/VAMRPX0RMPQXff7r4ByTRl5zf8BbEWIand2k2ubDlARViesQfw5Q4zLnUzTQHxGHX4L/oPR0Nhw2XOfgxUZWHhNY/M9ypjgvXbvHmvalIIcmC/fSUjXw1TnC4/Sk9qBz+gAuCD0FYmQZKDmnNw8Q40gBwZvYFcvHxuY0eJx/Vyg58dVz+zMnLxQDdWyGzpygGd761ncMZsfGBisVirOVggqoX+YRk0eQG1WqpWBierQMHqkGdabN4c6ewcmZFdXhLjg8uqz6Vgqian8Y489OvjIkahR0vwD6kFQRrRt6kTWCn9hRpegLphtxgvhwfkNHuaTCj5FioAUTJ1sP/vZzygOOQ1rRi80cmCwfF2E4tTDnZ9kg7DBZ8GM5vyfWs4PmcHqMwDYJf6cKy6EeB8knTvp/ise2Dl+JYt/8Gvwi9B//3nGAwiNFsNCPkGoIMudXXxGkRX+E0oSGmB2Jxce1+y3fope8dkVkkKeOSkNJpw9BinLxkASqDdJbQ3wBWawBvQaIb6jTghadiA1sCs0kezvT5Rl2IQ6kTufjE9UU4TVJIV0troma3W11INsVu8xvxvsQ83UQKSj9VloWg+WbcmrP/zDPyQzr0i/4YYb6ANvv/jFL/IhQO1QIZwaTbGj/gAMR6mB7gHlsPCkBmbGVwTwGz2FH2AkoQ7ANwufxWd+ncLj9YfGHmP1WWvoIjYJu8V/9SQ90MRTNcN8kW972x/c9rXb4MFuetG7Ame8aLxs4+wlHhbql/EKw1YgA/jPJNgVDq/8xOUKshCXoEd507izI3Qs4Vx+uBzHj7MCz5N3Ck9WIMxhpnI87D2wPrR3bcCOmHG3LpiqGy6Z+Fsl6BGq1s/oevjt3/xQ89DhO176wbuufItGi4g5IgIKukEftJazYhmDs2VjyCfchoTZEHVRxzu/BeUqtz6KnMb6dZf34BFnoIhej1sfcS9eEzizwc1ELCDJhz/84U9/+tPXXnvtl7/8Zb7rk7DET9LOOV3taQJ41ewBWMjoWeFg/QMf+AD4KHHGtGLeCR8AEJyPHPkM3HrQ4hNRvyKKBACJ67y2pLk2Kb58AH8YCQ8UvXqjLu8EKiEjXxLf0USBE1dV4WA5Gh3P53v6+7PZVCwaLVeJGSO8w/+fvfMArKwqE/+999X0nkymJjPDDG1AcQHpHexYQGCXVYogwlpAVrACFsBBAQurKLLoShEVFHf/rlgWEaRIZwaGYSpT03tev/f/++6X3Hl5ecm8SfIySchluDnv3NPL1893gGsohOGHXZYYfljcSheKQyk5IgJEK+TyYddqGldYLdu3wI4uXryIjpAXMDxxKEnY3bqigbMqAtW9Z8gPL3Y3AcgsyCZINwWpkG7KWoyUDVOg1f8vVvCBePmiveP3IqNhrBYlMTPi03/SNV1USgdrGCoWcuHJJ5/EPpZ4tEzpWSYz3NjYqA0bQ6Ve3+mjF6YcJcGFCjd8ARbwpvXg8paCcM/q1zc2UZsvEYu+vurlxmMP50QAxDRmwGSXBxIhEd4SLzvkrW8NBIIogXfsaO23A+ByVf2yolO23I4NLi8qDNcFKuWI1OCjOigaAJJmeKlIv2hAWYXRmVU1oKUEGAnezIvH4ZCRh8ixPVBscA7MPnwI7ckoBINY2DZV5WV8GttPGUtOU0ejvDGW1rDuMlqigbGVvKe5YPthDJACoO7OU73aWa9huhq9SK0UtRgPbGfubWABY7sO0mHWcs+lzWALsHIYagCa17CRAiw2hoiv5OLNeWmVrQw3LWEM6YLXtfQCqY7HiyENj9bOm8uK6I6yx9RCwONO6SAJiNEEBOgsMTAzmHDzUCZDx1d20yOPPILBM8OCjIlPbF5YPh4SoP/kFDprmwKRHCGBogodOhgkyiSeolj/NGz4FtjTEfZ6OhUCGzdupL9MNyPgSSsmrWGMJ+Ov5yywvmamaA+1IxBhzPMxsNRI+Uwl64SpRPFLgOUxaV3eKxWxce6++96f/ewutsgJn7w9eND7Iyk/g1AUdMpE94vA1mU7IYvEi4qSR654Vk694RrGQLnKgbg4PyxrdZ3RUh4+bl1iUWdKfH/yKD2lb4kxt5dYj+4T7ChHC4L/FE4F+7twLgP367p9npfqvvquCxpb1jz11nc9fPIlER83/sKKow4Rmg+KTJhq2wmDIg27qtRXHrBLQvaSCqhHu7HSx0VKYbnXyAeR+XKL8cxWZ2ckkEjaVYXOinnOsipjfrGJnOvcc8977tlnvv71r1955ZXDt63b7tnXtBmBGb5Fp8085NBQtjAYBckTyJvbm6Bo8T951FFHkXXCYTo4A9NHrAeVP8mhdTkmAQwBhIQDxR8B9wnXFuFR2XdQXXVbPzphp909J8y9wd0JJxL31ZSUtXX7d7R0d7R3xAoTZa41EZ1NGSZn0/rw7ByTU7UBn4URSwBTFlhiy8DJaTiAili4YgyPW7Ykw2aqK5wqCooTLFEaByxO/CIUdG8RFsg4CGvpRTro1U5JgwcarYB0WF9dEO0SdtIzHhFVZnDaIvgUNKkJ3CEYDHvl0TU1+CTASVqoQCxCIaeGzy9CVtwdxXtTzat8mxdGx8MAg7yhEkBmHgnotWdPAygMd5tF+0KlBJREIMBPSBPoBjgfkIpgTnncYdfQwMC6P/L2oiUZ5JpSNhkVauOJhJVi6HizX/RNAB0pnCEBTIs1UmhcV35d0bBvYacZ7O2I+Xw77PjGtevXt1nhwhJO8EZ6Ota+/vry5fuyxzk0yMiw9bDN3BkrMONFimh5L1jUGJfLEX0Ye9FaCPOAz8ZoApuIeQ0Lj1y0GCeX7noe0mSXJt+lPiXfkM85/IBtI1U6+TiGQrx6NG+G+bH3lQBcB096zPjDrHB0ccyFFrVhwwYCDGlWNS+D74kPxl91egmoZNEK5tvMAVYfFRz10muWKx0nzNrGFlobAw+GiDND75rezuFhljT6T7iIMSAFdoReAzO82OExLA+aSrOpkeFiRyhfQcqMVac/MyKHF5iRkfSIGrNubVI2us/wQnDGweLhHsTLL7/8rrvuQjrMEgVLsk4Q1mDDgpoXRovJRfELWIAxhiVmuMDXsMfsID5ppfDJNEAdlTGkaqrNqHoP9ucUS3XcFqaRDKAGgAyMxvDmeTG5jIaXOE8BGsmswf1mRV55qjS9WJa9Srt0/WuYJqWnmaiwzilTzDxiPwKQVMngVJiIiepjRjl0meehh3533vkf5dD90Zd+q+htZ0SMQjtpF3LuNyzEFXIdRkBWKuiGsPDAHOYRDQRiLa4I6cA3apxr7fnCgTKbM7jNYeu+JYF3bnLe0uayzkOoMWddlfWnZcFUgXC/0Hv4wuju4xoOuUcQemt+tPm62/95ScuaNQ2H3PORb+2onE+x8LoFARAiGhN+2cVhOUxXGnbmlpohM7mkyqwvcsoKYH0VI0JLiuXzqib7xe3OG92+vpjYTi+rTq2oMpdXWr976LcXX/zx4uIiXCeqadUMnuKMGZ+pP2cZ4Ok0s7rfPvCBD0C0/eu//isaEowxPve5z3mMxDg7A1DzStC6vJiJ2uoKEr1agI4sQddA2lpULlfyohZuj+A7GumgDXzsLS1YUObbtHVnT7TN4lakguIkEFOcHQywmsA+YJbcDZOgVLFwwViaO1EDZipgcgTX7G61uWMmsi2B7+WCkC8c9CMYDFopuGWR+YlDZhNHhbxhoXGtRblAb9ebrvCw8stlaAdeAtapyP1fQsrTys+0R7+mRRCUodX0EpCiMzK5yb1xBqFiAYjDGPgEVWV4n9yCGAFn60v9ZiT4xnPRg9/vHo7JWqJb7OgvKDNIBGiy9CpGzzL8657mRXvDo+VoXqxtOefpMnsyUHTQ5eV2SQ2GV5p7jLeSM7JQdfoniFrIbh7ITWWGkURA4MLKeg+06ZFHHkk5WMlC9BDQ9ms5aE0hgCBSKYqNSSEu5eoP+EN94ZLAG6tDEGeVFfu9411FiUDTi5sbl+5fVRqIbXjq9dfX1dSIRiuNLXGs5qSvDysMMZ9m+QTDhWhLkzjExLPIwLokmoPlnDkXdyO6pgb/ZvR1LD+1Uwph6MhYithdHqrQBjNiDDLJCUD9q7aNqql3/D1iRtC7UjhLHaUiwhoIYorNWjJULM/uGj6u71nrHVeJaZnZRGxnIhhDFjNsGNUpXZ6+2kmgP0dpTEZ6rWS3uby2pGcfpRYvPQFmSk0PAIAw6ggLVCyYnmb84Rwbk14Ro6obgbHlXD0/9TQHxlkoPKGJH3jgAZYNvrJh/JAnoh8GViBHpjssbA+2MCNaDoUzKcA9gTiunTacsyejIT3GIN5PEtNmOG3VhFMjg0N2HjYIb+oCojLgZOHtAtL05kv29OnQb2MYhyGFuj8yimVzqfR8QgofXt1uYxh8HpIha2BI1V/6bnONIYF2HBzBSW+YbVYFC2Bv9XoM7R9DFrrM8+c///nDHz4T1+bLTr6g9u3nd9tBYDZmd+UFmMWxzKCgXP8UwvwOUJUgKKgg0FZP3Oa+354YFwvJV+Q5fEk5XNtmpCychFrcv0GcIP5B5A8pEOBocFBUHQVIyA2zq59bRSwh/AynPtb+mV9+7tBNf99ROu8/rvrV9uK5tIBkJUG8Y3B7CD5HzcoCzAbtxgqrstAoCyYbcY9A610jaRdrytZApfJ6c3J1s/laOyy6gceZhtLkW+eYi0oSn738ih/84Ifvete7ML2cwQf4x7AepnWWWQZ4mk0fsJUHSo7D99dcc821114LlsU79ISoFIDg69atE0Dg2p6hJIEWAY9SHZXmdaSAYtDxrlrYQi2M81s8RXfBDCMpjAT3rZu/aUd7YWkh1imRlBFzb/SNpXAR5Lr+ETDpMpjyx+QUSSLu9HW29bVuB74CjfHDvKn1NZiSytq6qtoa+IQgSmPX4iXol9uVcFIV8MlBEXxS89NVJifdSHRrFiY0AfHkzFsO98pADID0Ac5n9JEZTCuCT5R1kPacZJbr6NySRsrL4VioKA6kYRiMMqGxsXFoShGwvvFMwjBDPVt83TvipfOwRx1EF0OT5vLLXVb5neL0ZmRfTqoTTqbs7q5Ub7fgy8JCzr9agaAgUeGGx9VC5a/QKfFAj/KGMIW2pnC0tRoJ7ajkI4pcJZs4DsqjjWc7pDNj0MFsDSVAlQblzayRmF2DfCq9m/DzL+1MBTauQXjtLFxYvmjhjh39eD4LBv2VxaE5bzmIiUYSkU4/wfO69yJK5ew/xkCuEHNdifBTJOcqlaE6QfNidTb6otJe7Omb8YGwI5dKBPY0+yjpkSDgLRUuAht4kqG0/Mc//kF1jBtv2AneTAR2EKMUkssnXd7KEzKJZEFfR3fSJyi9nJHi09NM2TCNZ02ih6SFrCjYJBgS7TX8MIpf4gmwzrHoI8w4IHMZqctMAfICEitSYC/AvJGYMoezWJQ2/EHJSUVwbpg2jFRLei5NQ71eZC65vMR5DXCGmcPAdIQjzVTkNVUDsKbAEwyeATXwXQwRJlrMBSlVBjG8I64YcEAOSJd1nAEjZGF4qU6hFuwxIIs35WgHaYC2QX/yplUqUmS+4JzJTtX60CoQCmHKZ0nwkzDvHGfQq2L0APVSOFpQrXr0xPn76g2yLiHeXsyEV0rh2Lpjq8WmAExxSCR/dU1448dc4A033HDLLbdgXNJw+PsO/MhNHfFQPJnCtq60QC65cIddzqmBrkVu624TYX7dD9yDwL2Y3TF4V7xDY8tkQwslHTMWd5KGVRpPLuh1Bd+C6l10poJwx6jrSVUnnWgRZcp9Ir1JE9tpnjI7+v4n7zzp+V+2FFff8e/3bS+aB6EV8mO6bOHSGduoOcVmaTBVUWgur8G6LDW/jDNEougADfCWVkpF0s7NHckX26wXm4z2iB/npvVF9j/Nt/xtr73jgx9ds+bVm266Cec7rO03wxTLyL4JnlkGeLpOMuqLG2+8EY3ZBRdcwJWD3M2AoyylcsbcJTY2SJHsIFr4AUWQlDkpG15AkTzAJLz5cZFswCwKWHNLRWTYm0gdXF/dxVXpUWf9lpb2aHJ+9ZxI0oolzUjKiaccAnF4SyR4pgVriAuqYGGZb06Icvo7m4BuhVW1QLg+KxzpdDgwCUuMaBBuFndZhOWOItRr4rfZFRm6qmD5Koo1jE2Fi+YfFoW0UvhknOa7d//SUKoQaOoyZ9oHgabCnMCc88+9WNjBuZdw9drFFXWpqkKKVw2nxmW+GfPGxkbIHa4AhZqBioUN9ugV+pnsd3a+4vqYSAU3Pde/Yh5u/PdspjgopYwNfnGhbtWfKvybug7ObFD+f0P+OT299trXo//7h/5/PM1EhlYcFHrXaVwCY5aVM7YuuhrSDhelCmbVJUpHeFi6PNCL6PqIx1yCPBDuMFcENLFmRJTLIicNuXjDCfBTSUOoSa0JNhi6VulIT2+jn1AyMFZalMZQiP4kQIz3U9aCgwtoo3LtKgz4O+cv6N+xY8sOjBaczvb2KtNKlPsxlVQOTYvizRKKyYrToJybSjhGkLl34wZHg5XHLdM+xNuK0918bqaJe7E26JfyVBNVqg4Olg7oT5QB5lQwZ+CJhytmRtQflUfxT2C9FJU+axNV8tQvB/4H1pcRhq2itWoa6kGVrO1noEhGRh7C7CzyUkLWxFkj0cJxIHP0WrJmZDOyJDI2XdaUkxYJk6PLUsUBcLmMCcABIx06iLAGPTA/8TLNZmHp8jBi/ASGEB7eTt0FxBPgAdt6A0UAQ2svC1/TFy0VKVesjDFvb6DgnwFfxFC1SjrIq+4GiUHQxk8tliwMMiBOBejMFP0ixntow24JAG0V3C8eHKianZu1p15H9ijA2mOQ6c7YaBvQmTcse1RvLokZYboMZqEWjqRlQO9cSph2aVhy2P/fcuut7e2dJ3/ilopjPtYRD8STdkEQa2HoIpfuEYrI5SnxyzxIGEEL8V9/3OmMWXg/5ToDYkgFGQSKi8aMJBJd01nYa5cm+AJthGNUc0OVr6EjFULOD2madBZ0JV8v8/XEjb6EGeOCNsMpcuIf+cu3Lvj9DW2VC+/7l2+sWnhIEC8wpr20zir322WF5sIyDACdfev8Qa5WEkMiNWIaAr5YwBS1vcd+ucV8bqvTzhUkpl1daL9tvvPkb3/27WuvYBv+7W+P5X6IY9pN65u2wbMM8HSdesVheFuFQbrwwgs/9KEPXXzxxStXrlQFlIfh9qh74JiGhgaywG7xgBRhCdKR7h6VlnvizNYOgU6c8uXqI6sU/0qlAje7X19v90b8RlNj435JK9ybMFELc80SvhBiKS5NtRPCDNsxK5DiBLBh9ffIgRJfUZnrRFAYXR6AJxyplRLu2gXV8MxyrFJ4YwtVMPf3uoeEgd+mDdvqN/FHJXo2svOGAYbXhBMmJTFKTtALr+FAcMp1C8cponDC8OZwq5xATNlm0nbmFCZX1LP7JGFaPsmmD5GMPBzascce692UO/hR/m5fFUn1h11MY259JnXQu8X9tBZIkW5KtxUD4fSsA2FoI/UUosSchql3bzHARiSafOH52MqbC7ZtTfZ1ozF31rwcfeyx0JVXBI47ziwdcJKM2pCWQ8lBG+kbcztuRqFX3DXKqbn0rkKMcqCOTmEfCJGqhJ1ys4RRA2pi9lHWWeBrBjE3PFlGjPdTA/pGJoN5fyqRCq5/jbnpmzu/q62zuQ1rfrOvuysSMNttbrUduCMHClKVkywbjMTcOZUJZcWCqIVqGJhgd/GIpRl0s5x+H1xJOvvas4l5KxCYcFDAjCDIg7vmohRIbaaDhxEjBi7L47e9IZ2QzjC26JzzRxZPSCMnqhDG0xtGymQXoIklgB6YR20cRq8LpKCbi03HWWKMJihz9CzpX9mkaAURFY2BPWCHsrUndvbT2zaGMEd5GTcvI0bahKGP9dgCfBp7hIFinInPAB1eroxARgfTf6aHyZXxE26TRzF+eplIDXg0BrEFWwm+hZTEsMtYAMobAzx5CHvFYoKhnrd1pxPP+MPa8ZNe03d2K5OibwIKpiiWDcsFflSBz+c9Wh7pzc4aRkqL+cCYUVJeDzIwtkiQAWIcU2I0aL83kln7Mn0jdT2wNn71q1994hOfSNnOaZfeUn7UBR3JIIwo3G95yOCu3QGCA9IFpDXwQ+gPKCF+4d8U7rcrguWznOiBWsJYGiSPXBgNMPpgEi3rgt6Ti36bC32PLQlsKDfn9dgnrkvU9IIIrfnN9ou1vv64L4r4zknh2+VTD3359MdvjwUL/nLax58/6gNc+F4RslfMNUv9qSXVTnnIrCv24dpKHhl9Dg4PKCEy5oJjdy83mU9vSbX2BkGzlUWpZeHW717+mcf/9OBHP/KvN99yi4KvmTq/GaPx5vk5ywBP47lmNwKYQFFcSnbbbbd98YtfxDv0T3/6U+ym6NUY9qpmUWCn77GVM4Yx9arLWqPIEekQJ6Z8zsnHHAqu3bFje2R92/L9DyyZM6eL65SiTnfM6MdCJoF0UN79YiGD3tW0Q8JFcDBYJIZ4zXU7ycBRnmjlhJ+wRYUrfhiE18ArVwLoneR/kUQiinRBp2RDFw7TS0iZXt6AVrfB3nAj8pQi9X/JTnLKpCwXEVC++K+yjL64UVpoV4Twl0hbBCjr4EuOwYcYV+fiq7UPcvqNTY9yYVRMsQwqz41PJCwbDw+ISs3OTcaa/434CyjFbbJULz0M1yQXHCBHoYYXTiTcNQ8BKAzo1Fyo4cGmjfdv+nQPlIXhfUtz8s67inZuS4msgDNsXObnM3Zs7br1u2WNC/3L9/dhm24YnLaCxNRcMheW5ZFHkKEecQZFAqPLW/sOCY5TlqztJoGmyfp1QiKZlQjOOlJJ/9bNzNHCk44LrzhwZ7Br/dp18xc1HrK0kGXA+mJtPP/CSy3NTcced2xBuABROLc7uDMs64rzTjKx7opkTWGSANVAAKkN041HdKEyNIn7dwJfOl9ZZm18dUCOQ6lv2LAB2jqr9WA+5oUFw8IYX8PHnpuqUXezLBnMfPQuo2VsBx4vcniNw2O8xBrISKA/MyIzsng/6SOsEXwCxsBE5pgra71emXs3ALej0AaFJwtJFafMqXaN/sIkcJ4CvgtmmDAxMJ+0GTUm40AASpqME9KLXMaTLcaj7BmV0lQVZ6Q3gEbqT2SFKlWEMdaAJ7bA3Hrjxo3puejFe97zHmKYYlwh0DsWNl2Go2bJwQZTb3p6L5xLs70maS5+ejG5ZNdcuaf02rbbgDYDmYIqzzFxp7MMRT7q2m1jJieBO/YORML999//pS9/ybSC+x59ZtkRF7SnCuKJQd2veK4ENw0Mw8BkuYQQfC1cbjRhdkZNfLtwco2vpMMcGbylul+yYh9XGrcbe+ykZbxSYz7ZGOgOYs5nbK/w/eGt1mGb40u3JqvaTafHiOBqmnuMTPuU1b897Zm7Q8nEI1d9r/mUs+dHuNDIeOtce1GpVV9ihbne28WMQuoNjNSQfQfqFCqJ48QJ56Um5+ltdnNfoM92qgpTztq/fOWmT9jRnnvuvvuMM8+gtTyTM9qztUzmCMwywJM52hNfl25LYDGHE/Aqyf2E2BByNviKK65QFcfY9i34EtSuMuOJb3S2EoGJQFjIi+Hy46Fd4Hhb6NBDDyMxdmWrX3gW/caKFQeZlRY8E8eGu2N2b1SuF+5NGMIMJ415RQuiCRspN8cpcZcPF4EamYB7HBepI+yoAG6ANKwqbCpEinCPgzQBH8Fugt8klftP0gjoRKmLSSqPm1ggqQtP3VQuuHSziG6ZR3hm11gasqAjYvREsTSzFpUmfbjmkuPHfMzyUGJBWQBO7plfxpN9KaxcXX6cxoEy0EO7uXilQs/fA7vutkGawZfUnH9KHHlemZuSFFnKd7ukjXebOEIbsjRrMMobJSK0tMEvA3/TE2gUMZoSz1LonCGz+iP9sWh8xX7Lg+vWO6tWuZeSpOKxOPiLUkGBvi2bYi+vCixeagblmiUYdUqA7GCV8maVelXDRGXlozJaNfk/Gf2+uB1ob/W3tSShIBqXQDqkxJ+zMLi4JWfOWIo0rKzIv7lrzfpVbQe+5fBoisOBhbqqmFKx8HcnktRIc9wVRdfFHoH/OMSev355IzzhVcAaQVjDTjBxXuFYratVghczHQPK8MgMpe2s9DOfk9Cp9KrTq0N3p2bn6ZGjh0EoZFG0MnpK7yu7NZ1R9OKnbwCYw0O/hK30+dC+6ggTQ6fgi2AFWboNDQ2AJo6GKvfLJ7hE3iSGyZwoBngMw5h1PXiRKg+lWO1Oevk4O4Ak8BhjAnSWjKhA6bKmF6OCQfU4Lv0RzVMCB09IxnpjQHjzMGijS6BIT+EIxciusk7MjBk0QD0UQnqrJjms3QReYWoE6wutMlz9PslNmoTqWMNM8S9/9asf3X47Nz01Hv6egy+7vSMajKP7Dbi6XznfK5SJ8JeDS4dJZEugRIBSQffL+TW0rOLz2aVgLMHtxMu5X4guF3Vh/4zA2/nLktCrtb44JJdj+/xWGM1EyHhmP//2Et8h61L17akdVf6wL3nk5r9ffu8Vlf1taz97vfUvH/6nsDm3FOVxcp8qtAKQDgNkhuw5d4xoFwB5iKhCarUjKWv1ztQzW403unzQjeVm9/P/+dVnH/rh8ccefeedd0Be0pFJGOTZKvbKCMwywHtl2Ce+UjY2dn2PP/74l9zn97//Pd7q8E6ZVhMoWreyC22ycUSamA0Pjk+nR9MKyVcQ8MRpQOTNwxngjCppHk9jYyNSdk7gwEG5B3Hl/GsgiLcDyyiBk7BRnWFa0xNz+uI+Lmfvgx9O4j3L4R1JErY5MxyzMUz1uWbJXEbHlarOwD/RtwmsFB6DoOsVGshOSwDzEoBbGYDawguTxh1ZySapSeLyoMLQoKXjSDMXLmHWIwwqRj+CLDjV8tpOu7XLbu7hqIlPbsKRnMMfsW5dckxRzeLg4z/u6VgfEsAOW6itkQIHHjcEIpEyfAWxgz/sX35SpTlwLnRXssHkQ/5CmtBXpoCBHfIhhx8gFUx2MfmDHByeHCIGnQBEDNOkbxQj6tMIw0gcmVIjjQv5cdFtJlrazX5wIk4uHJrEJ1rFEeqCUBAvTAPjb5rqIXZ4XVM7Rkz0fW9sKOBwfVFRqGFRdzJlmYFwcWk4iJc1BoFhiNk9Dy0I/Gj+gS/6rf7k66GQf+Ei55wd/vNjRh2fcTrizg9zpBPKW/4J+yzGEQPbO3/jMIblMXpjKBB9GruedQJJzaRrerUgHT3v1P+KrT79guFJb+qEj2F64bmHGXae3NOTEqalsbFxj7LAHbH9YYRAKHuUceonzjqPiGTpb0NDg/J+alPDwoYTJlKZpaxwcqr1d3jvhO/PNolY1nC8GVzMV4C896j5N9iBm6LQCXvIhQCrSDcFFtcgfeWKebPrgQBaC0IEPZJDCTRG3X3xae8ywLDlTDGnbNgLdGqqzVqe2sP4c+73wQcf7OvrP/jdn1h67squaEAsnwPi9QqCSygjl3BwsZi0QtePEEoul4vXq46I3HgErQUK86MuNo0Yls+2TyzthGISVFZkmw/tH9xZzHkzg5NjfvFUCm3AnR92n20+X+1b5zfn99hc23tY86prf/DB8nhv74fPLr3wX5c24LHUwAbKNIAzFDbYoLQRYWPi4RVHKsydt7zjtvUy3O8OY30nXqlt+41nH/rBJb07Nl7/tWsvv+LydNl6WkmzwZkzArMM8MyZS3oCCrn55pvx1X7RRRdxdAe3deeddx5chHYSZaAbEHjjwqu933fQ4fBGeJEenBqehhjoZhyBgCnFXbDr7xQBtmYRJ1g4tQoZZS7dBd+ALTFML1q4PlhiOTZsRvChLw60sMlxoinc7TpYSotn6RRHg4HK2NnIYV3YVVSr2NzIIV6xSAa4yuANjCkjqcPpViyR8lN4YhLpPzhlYVMc3DzwgM6FdeZnTwKfRr6I2FnbdYX2/nOymWVShMvxls0LnHZ1xfO/6Vz7e5/gB27Ok1ZkPrDqKLyPvri0qiHkJnCZUnd8MpOm/VZybfTRTktO43fNGhQPR5TVWQssrj6oC1R0CuUHAaTpofygYDyzAipF2AHdE4LmQXThOPE5tXZJcSiBdjMVc2LhAk44c9sTVyKY8apqjq9BBsE/597O9DbvzbC7ZiIxM7zuFZFRL0DgURLpMwOh0MLFS8uDWOUjJ+9K7LzS33Wf34qJeEM6GQ/bqxuda6oSD70W+GGPeQBOL5lO2cN8FPN9BARyqtwXRKJic2o9f32EDKVwfU94LRDQUJMsD9bJ9JvckYeD7vBkfOdMI6IfGAY4hJnU2Yxu6k92NydO2bNZv07fSCbOu1HZ6wU6XlgjGEJ1N0W8ktq6BoB7ytp56advQHsENFYjAu/sjGcv7XUN2uOUU05hd4MXwBSKHVj8moASUO2m7xFkBDjP5yspiWf9ID6ASUZmCm3jUTJe+ZMWoDEYsXNNPY1himnPzNb90l8oKzrLFKPDv/POn6DvP/GSb5cddn5XvACfz1yxi9crLsiQw7yCeeR/oB3wjt/8dQ/o2Cgh4H5bo+65XyGLhPsFlwj3i18MpW7cnDjQWj9XrOXIC9bHIQsMLRdVWmbS8vmTUSgup6fc3FhmvqP52Y/9+OKyZH/3SSfX/uT26tCuIx7uenDx4DBkSI943AQgMkg8jh8ba5qTLzUZa5vNrt74+t/d+uIDNx6475I7f/l/eopQmjv7zOgRmGWAZ870euQUKASTFW5rwC3W7373O44H18+d0xt3QgFux3W1lAMytynRd04AguQUNnFhhho+obuGehilfdpZiGYewuTCuSXAGsil6iNvNCgEMMa/Ir9ZhM/BQoCgWN3AQSCS7E/YEfmH+bSxs6Wjqy9eVTc37pippBO37bgtXDFsMC77uRlKDKfR+MIJiw8sAgIfKY4AUNuF/gJidYzli3wnocBRtMCDPLNAYYkhgYNlrNMe8xUHgcZZHmWupQZMXsPW286qmLtv9PG7oolWNYAdyCI8NY3xxRuOSx32z1X+Aq1Y3rt90gcqPbFUOciQEICIQdMLEcBbH5wiIrAHTXIoF2m9qu+gUTCSRFKuRcEJQylCx/CkS175CgPAk1aj429YGFm+zHhljdz2I+ND0+iCE62rLzhg//aunieefhIXrGM4T5hWy94IyvQYvUlj7rrX0LOby/ezfRZu25h9XIkXBDAOSMabrw10/UzSRYLRl0KxzQGjOFZymG1VRUuNp5fFL1kd+mXcqcMmAVmMu65kVaE5TiXFlQg/WNr5e3SX5YkA1YOU+Wv82EpmkbPmIXZH2iAjFcuOgNYnF6aDbBxIeVIydPA/REJTsl9YxkMX/0iFTe94uox0gz7s6RhO/W6zI7xGMssARqaVSPwhAQAz+stPjdG3l3E6BugsD5JNrv9BnwZ4H71TjInC/+GdZayUN2avsU14q4E95XNAhlNOlIziDuSOKQGfjj/+eGSm7C9oBrC8PulqutFbMrwBOcbQHhAcfCA7GuyD+nr4FOdY1LRIRn/Vsp0ZAXC9733vM33hI8/7YuVRl3TYWD6nxPI5bIRARkLLCKZW+kfCQu9ArwhxE4mbruWz3Pfroi0xTQa1czYNYyY50CNphbghuohLOKQwoaBwXgrrWxY0aoqc4rB/W2cKRXDU4l5f56SmJ0/++VUNTa/0nHpa7YMPGMFdB6Ak98AzQPlQMg/QmGiITN6ERY2/6tUFjQs6jPJVrdaqJqdn+5pHvvPJtnVPXv7pT1731evwvkHKPK2lgQbO/pkaIzDLAE+NeZjQVrB1EbpjtYKPCs4Gw6h8+9bvLHn7+6OGb36pM7fEVwCAETjD4wKhvbrbwY6gE4VT8EiEeXKEPl4y6FQ0ivDPf/3rXzEFRyFJgd7XwdEFLLqQEc5BjlCaVCu3ERUo6+BUJOJdvv4lC/GwL8bSvKNJE/0wfDI3LeG6ENUkrhoQecKgiUJYdcKEBYa7imJliXVoBSvIw1gLUjBtKCaaRBjfDGjlYdyReuJ7mtnAWdfATAy2dehfmu3auZpO/VsKDjvLePw2EWGmpZEqKhfZR5xXIX663HoG5zYt1bAgzSZOB0rDSr5Dz/GAKpR+RU6Ps3FNoGWAF9F4MOxQMA0NDRjME4YiyTg6rs5RtfxhlQ9EDBZrGrU1wfM/2nfTtwt37CALZ4dg6yMlRf4Lz7MWLPS5fkfwf4YeEitoco1e7EjVTXq89A9uN5lIhda9Jp3abz9mKIoBnSsCYTOmok9YnT+lt3ZzUfetJcG1pWHHF01Emn4er/xMLHRYW6n5j/rUHavtL6HyTSIQFxGLWJ7BDyNpwG85qyd/GmDIPnYW3WByWR4MILM/UYNPOTJAYuAtT4aUZNIna1eFcKpoqFjVu6JyC6HjhT0grQ4Rdv6EKQdhUHoBEzWA6WVOqTAsDbM5gUtlSvUuozGw+uwO0C5yjYyZ5dOAQikjzzT8yVblgfXlECzQXq8dHr0fGaORntjjjYfbCIDBEWfDgMF5cowcpTEMjMpY4WGwQ6YcLRkGmHi00OAg2gbDDP1ADAvPq2uUNnhpsgYoUOOZRAqE9QXZETPmArPWMkUitbO8kS/g7x08++CDv7n//l84pn/eW94559QrO+NiNCeWz9x4ZIkRkouHhNfVcYIIEaTmPuL1KoblM1Z1rm8V8UAugxlLiO5XrOrAYi71ATor5BwQP2zDHxCPoBVhszxsL61wygvMzrhjlfu296SWVhj7JZvesfKL89b9o+f97ym5+RYnIFcfjP6AUxQCa7J1ApnNDZvX2aXzumGqo/1rf/eTP//sukX11ff+4fdIWEgmvRhCXGnW2fcMHIFZBngGTipdAkDznHnmmRgJwwN/9Nxzj33nme+6dOU/SubMLUsdUGPPLzMrC9jpsFUK4mXTT/5DI/XCBuAUtrIQTNCdek5jjxpDRi5D5hrDVatWof1GZgwbPJps2AVyCuYGgLcL93xGCpa4MG1bMDquNFPAPQwwGuAkdyzB9OJJS3TCFnbRCQ76CkuMmTSpEIvKeVodVnd0xfSHCu1kIhqNV5QWwD5w93DASsmNSj4ucXVCI3nBGhwFyS/SUTktuvWVOPfnCYiWlu2C123bjEhnsrBqUHM+OKVeWwYLG/jLmENhUDJidd6ckEHoDnlBvKZgAFHFg/ih6pAvQPFg96VydwgC8ApyejLCjvImIzQKAnLCXkXpYS8yI6BpaKQVKjLedmjBV74S+cPD5iurucvK3mdp4LRT/SsONEqKMaPkjg3MrdG0wJnTMLLkUn5GdZP8UwcfKwNfyvZv34zLNd8B+zsmFzkMDHJBwEl13e83+gw7EP+v8tINdVGjNxQOhgvDIWz1f9wZaIyaNT3VqQfM1Gd8ZrEsrEEbAyE28PghTtpYRaxa+ZaPDqr4HBNHaCPGfKhzgQmoELL173//O6Ag48TsGIr2VvsY1oaXN71eLzLHAtkj3AdLCQA0oJnyvekKw/TCZ2RYRwwHDcgCuMVN+ZYZ3FO6BtPF0mWFDF8kw2Om41DonPKG+eTsLigDaMxSn5DeDS8E1IPMiAe8Az2QPmLgeo53gbm8R40sNA2CWlYd241VR/N4OOsOb8wEpReSHh5eO1+1v8A9cA2FgAFPPPFEWsWnrOnTC5y+YUYSETPkEw6U7733vj/96U+xaPLoC26oPumyrrgPNUBhwCkNu9ZG7gC5NIggITCPeDqRYRN22L3xyMDnM8oD8YKFAxQEtsL9QkTJETMel/sVOW550Czk8l60Av5Uqd+qKnXCTuqQ+XZDhW9njx1tNXtNZ26RdYDVccLlp9e98VLvu99R/M1vBRc25DIRrASFxqyKzZvfaFi00B8Ibm6Nb+5Mxvpe+/5XPv3y049edNEFN95443DR1fSdx9mW5zgCIwKFHPPPJpvKIwCAgKbEef0999x75RWXP/vUMcdfdP2iIz/0yk5nIeK0GqOxwuKetKBwVUo4D/7dS70S8CnQdI8fesoDJ4bYGMILmTH4j8tFcykIax8UXKRE0wXvh+yTsGJNbYr40heNmwv3UeOCBIXLcLkPsehRKeSAAc9gjRl8iOCGttbeza1vrNjnLaAKKVH+UYOce3GLG8ya/a+k4bHjzo4XuDpeMLHbDLGIdXGJacZCW1+ILTuJTZ1dMMrYMjL0F2SAgheWFSkvxIF6pSJM9+m4MrpgfWWMGVg4H7x6So2DD0UprwLKfO2111AUk56PcMt8Istgwhz+uqPsDrHtKyl2DjmkaMk+drRPxgZBflmpGQzpRKAo4CojbN0hwmgtrMWeVZRDW/KQhMnBE5sT7uj0NW3DBbS1uBHbAU6bM/ug/8JA0ux/HvFIqiUQWF1u+hJ4wJRlIfLxZKiztOeZjrJ3+MLG5qC9PWItIxukglgMyKDwvzDArMVdmzgPfdAidbT3eH5zaA+Lh5WDTQF7VqnMHDKNmAS2k0Z6Rw1HTJftA5uC7Hxhj7AdsH0gDDAZrqTKllviILWhpQhwlTojpptopMQzNR7wwlFnBg12Zab2UfuF32PYJFwesIBndk/ZUy+99BLnnFnewGHQxCT0dziEBz6o0Fxrp1XgAi8ZqmP2ndgvuQ9fYYFgleGCwOxsZ8Vr7EoewkhyKcfLnt4jDEA49Et2hDhUOuNXMn1Hf8C2BYL99ZFH0P1a/sJ3f+F+a7/TuuJB7uQrgvstEPtk8fwpgyYvl14TjM8vML/e69sVE/VvIim6X6JRF4PNOPeL0xMxlyMbsXgtNZJzis2SgFFb7NQW2vNLnHDcT88lAABAAElEQVQw2VBqLK625hQHOLC2ti35wg7UCr79Im+c8KUPz33j5b6a2pJbvxdoWES5u32kVaap0Bhgvm3bVrTMXfFQU8T+64Pfeejnd9TWVvzP//zulFNOlgNqe0Kz7Lbq2QTTYgRmGeBpMU3jaiSI+dxz/4VLki677NL/Xnn+0iN+d+zFN/Qk5r3Was8rSS2ttpdX4UGeY6gqpJO6gBrjqtLNDO7ZuHEjaAPbsNELBIGBomC9lIYgjGEtYVDs6BmHNxJgh9IbZaY6KVGmDoA+Sjl8VVwOxqTNGlZs55odDzIakDcKud1aXQgOzBSiR3l2GbLBc76SVHBE+gOL5yaRpujwEhLWVX+kJx0pDPhuej0S7wzIKWbHDNdGD/+Xgk3PxzY9igpQENOWZxJLT3DsVJK7haADeKDjeWBQdRYgX8Dr1AviZ8AhAhAZaHWk0SsipYHSqcwOZLRKJ8t7QyhoLs2ekTi3n+74IGWvrvQZtGpwbBgvty2UjE7gyCOPfOKJJ1gk46got+ZMUCp6xaVc/i2bwtFIMhQsWLwEhTCCcHebOWE//ewUxx/dpi8pVxqZPrmsmh6LnVjSTDWzQkgNkddLUdANmN7D83KCWH7CcZlco4VdtIxePh7dRDSJ5cSaIcCKYnmwhCaEKKRAFg+2A1gOQ6HCS4yzF6xw2jw6A+wtb2pPrw5mBgUIMZRAGg3T69wZ4PTS3rRhjHHo+4y/RASUgbwV2hrvxCoBnMEzznZA3IkwFN3v1NHqs38VBxGghaAwDTARUBFMEGI1wnxiR7O7gV3eXU0cv8JHF1+xQAHyQDzwEEmBGEMhZiUXaFFR20ydWfqIlhsqiA4if6yoKL/55u/86PYf1C7ab9/3fs464N09cW7KsIuDA5bPoCWhYJQcQvoOCoLyAB25JE9/3BHuF69XcL9YSIObuJJIdMLidUU8qAi4FeFu0EgurzaXVjlzClNVIfuEZYEin1HFUWBEunx20d+yav/ZK5y1Dz56yH/esOD153oWNlQ9+bhZw+2AQ4B2jlODxX7pnIZVz234zrWfWffKC+eff97KlSuB7ZJ9LOXlWO1ssqk7ArMM8NSdm4lqmVJ48+bNfeCBB+69997PfvbKez519HEXXr/k+A9v6gqs77Cf3e4srkjtV+M0lHEJOE6lpGbNNc42gG9yKQcCAi2i1kV6LKwIQ1tjxrxHDdC6wF7KyJEX5MeBJa4V1esTsjYGwkVpF7SjkPhDb9mRsRj8PxNKAt21eUNJ6IG4wY+iWIYiJxZkTEpIapoBvlECfaAIzbS7N/z25mfAJH7bsucflnz7R0uDpb6aA4JF9c2rH/SZkeKda+yXn1q3pW1dCudIgw/SayZCf+GhlNrp73DWRcmIwUy7WQA6kiA2ApSvP8nrBbxydh8YHAJvPAezDH6QcgfjXO9Zxx13nLafBoCbaflY6t1VZH5DIPP+uBFc/2oQCmzuPKu8LBqDaICCkMt7/RY63zmO9ZpVbSbCsVBE7gJxCTZxkWUHUuEGRtlKOsG4XWli+Y6tuKiHXbcijLecAYaBdOmK9GGauD4xtkoCYiiBuISf7CmKR6SC+cCE1EOZMEswwAhoxuDkDGkIm4sNxV5jNdIkCmRteG3jpxfWAF9Rd9CFDD4ZCQvggjR0lm2bYX6ZUcjoPzkAP8qtLbp3Ri9hOn7VLbl161ZAHMM7HbuQS5vpJmgLg1uWHLYwk6MOzaVhE56GnvKwgwCznDMinA7wJ7y6MRTo7e70ACgPaw4AF3I65gjMDmBBLE4vRCTs2kCRXrOw09EPE6Z3rFuQC6w+bw5vwzUx0ZpM25YeHkNrp04WmVfHYatymomZBbbT5QvOv/Cpp56uWnjAwed8PbQC7tfibFdhEJ/PWMDJES/F8cLaCusrMBbYatkmul6u2ID75R8OU1zoy8EcURFEODyGcRiG0PJH7t0I+1KHzDWOXOgsK3GOWBLkTHABqE2ZaYHeakcmB8r2t1sX3Htt8XOP9b/vfUVfu86orhVj612gPdfhRHDz1kMOWXnLbd+68cb6OhS//33qqadq5hkzobmOxWy6wRGYZYAHR2Km/2WTA/r/+Z//+aQTT/zUpz/9wC0fa3zs18d9/Ka+oiVtUXtzp/XCDruhEm2wvaTSmltiFcntooCZXYSjhIbRkVmHDaiaEa8xIwEasBS3ZZAFtAQ4xrR1PCjWq4VKoYzBZCgMYWs960ovgTYy4yeRw2MyurNHPxGv0ikviwqkIZgyKG8vQXogbSRlLjhtvBX75wJ7//clgovbnn8VtN4NmocYKz2+IrF6SWRr8fqne4r3C9TVzUeYjYKXNxQAU6+dyqXS9AbsNgy3tqdyit2WOUoC7YXqHxicDRs2cNsqJtxQLRM7a6O0YQ8/CckQjTulG3CGhAvo5Zbl6+eWanlwzM6u8puFRxj9fzMq+u2jO50/4oKSHIjKRcufWNBTfChOLO3mvsYtrf55c0T6rsSElCHFoP7NrwtoBhatCOe9YQ5ZUVSpPKqymtKPiXgQynCNGT5v0MZArOxRkWwDthVkLgwJ0IMdx9rA/IRCRlLe0inSQ9qmV5R1CWWNTM81UhiwxpPxFetNuslyzYifST+xogTqItGYwrtyXOOtYBl7YDYFmKWhoWHMi2Rc7ch/ZnrKVOIAGUUo0q5pNKFMjaJdnSyMwhgtuFlgF+jDw4M6cZw0Rq0N5IECQT8M2QDGxJQMcAd65SgsABBMClzSB2BFAi1ZJ2F6LQBtOV1jDdNfwBS3O3D0A38xjz/5hOkrOuHff9FXsbwvJmpbuN8S91JFvZwBhlauH3BRD0FXbG3C8PbGHMyee+B+hRkWs2fXxQlncwwcPXZH5Rb7kkAKRhqPogfP8b1ljrmixti/zo/4XO73c0lNl9wcGFcMoKz+nvbT3lH+6pq+97y36KaV/kYOPaWRpDr0o769OULceckll/zj6ac/fsnHv/GN65nN6TVlo/Zy9uMYR2Amo+ExDsnMzcaG56mfO/cXv/jFr3/96yuuuOKeTx118NlfbDzx4j4n0N9jNUesV5rs+aV2Y0Vq3xofjrKqCy0xyhSYI+DIBU+7HyBUqWo6SFI0MziDIQA9NJLTSHCJ6mAhSQFYIBt4tt1Xk0MKVMEoYfDrgEUTTUK3QxuognEYnhtaeUIE+R7M1VpAtwiSCUPcYxnIDSj0d3jtGqN5NSNhHpAx9H1XN6xuV/d6X6B07smXlzul/Y8++jrJGChwubC5xUV176189eHYhpfL3374PqVDvddqgSNVOub4PBWbY3sYHEhtphX/SRxRZij2bntGarZco5Vwwq+LC2h7v/24xZl7IITHNeRUFZvLX/4vyY47A3ZT6Gz8hhjmY+XBSChuReMH9pVe1GIWcld08Jm29zW19ZcW9/nCJTBtAG7JKZpgJFsm9zG6BmhZVvVIrco9HkYXAhETQSgk5ejYJvkYangJJhSCDCpzt81j9jPSwM1i9KhWJ3zVQAaLSzwmA2TU7Lw1gW7J9E6hDJkQaJDRSCpS8U1G/Ez6qS4VmM2Z1KmMviBeAZiDMhD/pS+bjGTT96duEBhCfC6Ag0BeE2XuMTljgj5TwQiiMcBCY2Mj9XqCp/Qpo6fgDuw1eEgDQgEKkR46hJ/QJMyyMsbAQG08FzjBSJMRHpu8oF82tVemF9DEU/CtLcfGm5FhoKCLXl3z6rXXXPuHP/yhbvGhb//kj/vKlvXHwS1OSYgLisS/lchx6QloBtpJDmDJf25PHS456o0ZvVEzmgAP2bC7AccpAkXgJ9S2MFACPdUUG4lksrLQKg9x4C64uMI5oMpeXutyv6Jx2IW5XEwGiHbs7dvb3nl6+Zo13XPrS771reDiJbuS7Uo+2ujqGmb14uPqpptuQv//xz/9iRPdUl9ajaMVMfttRo/ALAM8o6d3hM5x4v+MM8448cQTr77q83fddfWWJ39z7Me+7Z9/UDRltSd93TFrQ6fz/I5UQ4W5pCq1TyWOsqwSOSGc68N5OSXy0M5BPQNhyam6o90WMYGASYuidkAeJtZYVoPLcSAJNZ+1GbmQ3VkzZkSCLMGUYFBVkQmsdW2rvDeBjCz6E3itRLxS3jDt2JqCooRcF5yD3LX81Cv2D5cH7ZSPo7DgXbAvJDX54JvAUIecEZx3MJ6kBnzwoCD19N5Za5zWkQwjul+kGwhcHnvsMUT4Oa6xSe51NOn4kqnAprVNphmprS5zuH0aYkKwc4F7Z5URWGbWfDnZ/O/+QDT0kWbn3X3JZsNXYlfWx20/IiF/k3VGsvoSY2crZ7orC0rEMkwN0IQakXuvfC4nnL9+sSypCM0qVYy0esdZO8WyMVnVKEhzLAoWFwhDYqhz93yBCdjhcbeMwc0oWVuLoTWJtQroVx7CJM5gdxGc8WiyCXzDfkNSQ4gPVw5PYC17tyjoaYyfwQJ5Wip7t3fUztaFTQL2An9m8Dwi8Hr66afZZah/2SDTazZBvjzMFBOkUqdR2o8uFDNgzgcBPTxOWJcZVAGqYMohjRhcuQ8ssS6Dl19+me1MySwGUmIjQ/bcIdjeWsmQFuh+gUJ0uaGhAdLo7LPOIXKfIz64+P2fS1TvD4aC4CsKOsVB8ItodMWLczrd4mqAYYL9WBY6RtiHp0ynrT/l81sFZnxehQFLHPD5OiO23FHAHUiQj0FfedicU2LsU+HsX53apyaAcsV9KGsXRSSSXMfs/dtjqWuvrXjlxa4Fi6qeepIrEsc2Vo8++uill14KIfTZz37285///NSkEMbWtdlc4x+BWQZ4/GM4XUuAM/zRj28/+5yzL730E7+6+oTDP3j52878TJdRjLuC3rjZl/A19Zurm+36EqexIrFvtZ87hGuKfIEBb3kALBdsISVMA146FkAZBTTofkEMudOR4I/RFaRjG2tQIHwgAmzE9qqFFubDfUZBimOri1zwCWBJrwqvHJqhQmUvBmpYE+OZmSzqn5mh44wradBBgcLBpmBWsb4qKSksKHSxCVfq+YeOqkgnBIeY/jnL4H4liNEXOgp4Ay6CnqnEKJQN2nWoFjr7+OOPowdmCeVjThnPsT0g8/64Heju8jXtiJk+e+EiYmCJZfPI/YeocKEPfGbFBcjREy1fC6Rajeoef7XsLxLZdlFr6JyN5ledcFXDwsKKkrB7+RbWGLLv+OP6vuKoFQvA3Y9ja+XucrF0aQ1vhOj5G17K91Y11e22IthySFI2EbMPGcpmIQthZddRzVHg8J7BZisnz3ljYCBSJNKw14anzEcM2lHuaYNFnKYKUhg/JAUY7GSdHQV67MqRjH3yMaSTXyZ9h/VFjJIhNJn8luSpRuaRieZAATuFfdTY2JiniiahWCaLJ2tFulzpKZ7M4PNZ1So1y0is2cHdPOkLm3jIFWWJwd3ojXngKkHZlKzCa/A23DJAxoNFIzUmo9IJ/0mTeCiWBiCfgjbDMzbExpNPPXX8ccfj2WrR2965/0e+HS1eKC4bLac4ZBcKh8rljpILXhdgalsGdlaUEw6BzIyyIgsHWRXF3G/EeRJzXpkdjaXqyvytvVZLPwSkEZAbfwVTkaeswJpbaiwtd/avtfepDuIbSwkV7anbOklLIPrcC6nPXlH8/As9p59ecuM3zNqa4UTm6OMDUkB8A8f7n//5n9A/0AbQCaNnmf36JhyBWQb4TTjpAnY8gHLiiSc89/yz13/jhptvvmnTEw+cfeXK8gNO3NZldietWMJEIdwRdTZ2+J7fllpQYS6uSCzDZXSJVSbWpoJVxBvBMOTifnHp98HR1ZjBXyP+JZmHJ0ZMtOcfKBaoChPIo7lVCwRXrIRvjs0bvWaqyEiQEQMi5IGk4HgSNCJh6uVWW2hiUmrfQUgQ5fqT44I86WWO1M6h8TIflIBrFlgCZJ9Af45Ye6zL0MTpxU+zMB3RgWIekbNg0MW04sxj6nRDmscpgLhjvb4mEI/EAoGCufMSKScaT8oGtIywnFyWXnC5lq/i477iE+Od95h9j5qJlohR3OMctNN/ViJ4VCxRiG0AfYQMwQwgEomGi/X2DuT00BaYmVFC5vKbwHGAnqA02pmP7em1U4cCizX0uo2NjV78SAHYDzQYfGUrsYnQUyHh4o5iuGIi2WK6PDKyIyIhhk8wwJC83mnAjGT5++lOd74U6flrtpaMbQtCCj2xMrwuRA+MKuQ1MqnhX2dADB2E4WG9sbpUdMKinQH9yugCSxT+jV0POFUcNH27OZKkiT7SQXxfof9kNkEio1xvnrX7RLooegBHwwPCBqtMhMI5ysFPHVjAJquFxMAoYhQCeGOetXDv64QEqJGz3Gh9uSaK7QnZgxU3Jd98883XXfdVDJXrDz7t0Cvv7UoEuMI3ZDklYaMA3ERLxUeFNBhUhRa3KGQGDaei0PEbdm2ZFbCT9eVi8Ly0JtDWl1hUEVzdnFrXZrZHrJZeoytmu2BOtMcVRdbcImdJpbGi1l5ciRoFhDLgl8RDXYJjbMPq7eo/88OlWzb1vO99Bd9eGWzg3K9ssVy2mQ4s03rfffddeeWVKO2xfL7sssvU9H0SxnlCJmu2kEkbgVkGeNKGeopWBFBAr/i1r3/trLPP+uS//dstnzz9pNPP+ehnv9blr93UabVErD6uKU2YkYS/OWK/2uJ7upALylOwwYvKzdpiXxFQcijjlw5lOPcLapkKPU9vFfBRDzRCZ0NAgw9oYXqCsTUY/AfRQF7KpzT1KgmmQc4KzYQ8Er6XR3VWqKMxxiYxAYhFiHIeuN/04RpPk8gL0sWHLdQqRsJ6vx9CUGGiZtDjDRG0C5SH2p555IX3dW/1mAZgrryjuaPpb3+wUnZsYX0q5P/Hcy+u2WGXVNbNnV8fDvi1ta5Aitud9w3VXGPUoCSObWy1tvSilvQVuJwtVwdjycdWa23r3NrUYsypLijBDE/sn/mPeyOEn87bM5kjyVqFdkRWpRKikfqU3iRWNRuNh0jCZCRXegKvEC9y6iwSr21TNuCNVXoLvUgdUv3JxDF9iDBQG6YnzjHslUl6b6ZyzJvvZNo2pGxYinpsYb4rnczyMwafPjKPClGn2lzkPiwjtZzOosDnml8wMtKcPTKSyhgorzHAH8+AhXoxnOYwFDwwD9ifMOOpideuXQvtAZjiAdDRAG3nSK31qtjTgDaVN5JBiBOE71AFSvB093Rf/bmrHnrod319/Yef+4057/i37lggiodNv1MWsgGl8L3wwAHTLg6JDXNFAa6w7KKAWRqyy4PGvHIr5HNKQ2Y4YLkX0WMvHVy1M/VKq7W5y9jZa/TE1IQH3GRXFaA1sZdWwf0ai8qFoXY9OQtLK9yvS0ASgE+Ovvh87wfOKG5r7T722LLbvu+bU08il/Xlu/t35CHQchjbz3zmMw8//PDpp59+yy23QIJO+KiO3ITZL9NsBGYZ4Gk2YflornjRMYyDVhz0pz/9GYuRL3zhC8/+7X8/c/U1Z5194dqO1IYOc1un2R33ARy7olZ3zNjSY720IzGvzFhUllhebdWXGjWFPq5vy4BQwJ2RFAX56EXuZdKwo446CrkvsBJzRE6QoiyF+cy9hKwp4V1R3vIJuSOmULDBdB+2FrgMCwqi5RNp1CZKMSUtQSLLk7XAcUYq3OdIEkegaQA4GPXFOMucstnprKcthwrnWoupcGiNqRfXcf7CxZ3tCwyztaEhwRmzirktfiMYKsAITA4UDGiQ2IJqxiww2Tb8kRTGY3ohIidvrYQt5mR40iwrLdrZ2t7U3FLnDxYFi2GA8a2VbymT0hYs6UlYAGhsmEEottyN1mB62WiMJO3kzZNLO5FMkTGXlLNpMBNV8h0YAijDXpQxAbZgFOoNDoPPrPFzJJ2bl3KUAJaZnMrGRHyUNHvrE8ptOgimULZwbzUjf/UyuRxqbWhogDEDefHkr669XjKeAhBMg3wR1ujpidybBDcL/QCKGcnSARBEmWJy5hqdsTVgPlUyTi0oY9lEYGTCfAIKQQ/AhOcIuHJvJympCMUvb9oDnQNDSCQ0z4033HjHHT8Olda/44u/CSw7oScVxslzZZFdFTKqCjEosmsLfYaVrC62AqlkfZkvaKQWVFuWbdeWBGh0SA71yuMypk5vwnl5p72mzXijy2gS7lcUvHz0+5zqQt+8EnufKnNFnTGvRJxe8LhiW1eyC4bkP347Ts8vfuF884bS7Vu6Djuy9n//1wiIQNNN7uYYDI30l1FF38vD9vzVr34FA5yuThgp12z8m3kEZhngN/PsZ/YdQ5GLLrrove9979VXX/3Vqz/93/f/dOWt3337AYds7nJea0u80WHs6PdxQjiecJoTgdao83qb8cz25MIys6EiIb6ySkycHATcC049uJVZx9T4jRwUB9GwTOB7aLtHHnkELhEjpTSAu2cNBbeBFFECw3qBXcCOkBFUwYFDCsL8GPwHVQH3O/lAGSIVB1E0T62zwPqgbcTVe9bD7KlBf94zqXMO3cDxZmQNeiWP1whoOETd4Hv6CwfFkh7znHpljicAVZHyBedu3Vzp2L2LG21foKC0ojQhNsshf0r8abpXQAxWoWMooxofEFaIETUPvKfcjphyAkF/ff2c5u3bdu7cURxaVFwYRKCODN5NlZcXA9jY2AgVhcAoLxUMLZRTdlDemDRjnZEjgyrmE65hs9r1DS0v+y86NbPp++zdHmssHC/0JbnZd0A5LSZDIAJgAfpBfapt8NiqUlZhbHknNhcMP+wut2opqATagCzgIrDc4T2xde310hh2ppjrjhBAML+ccN7rTcpHA+gmKIN1CyqENcXXxpjXKgid0nJsJNBGrXA1PfIdjLPQDCsbzK5hzBVPIWTh1BKkAsJ0HmAU8fopx7q8ZDQP2uapp54igNYXcArep++Yv1144YV//vOfF+536GEfvtrZ97RYylds2HMrUvUlOKkyA0ZqTpnjd7gX08/Rm7pSP3caBXH/ImyrXxjWXQ9huz1qvrzTWdNmbu02m/uMCPcgibbWQcJbU2TOL0ktqzYPnuOgJhnAUu6fXaVwji7l9D78e/Pqz4e3be279NKKz38h5Q9k8d+wq95dIXrHg/9qFL+MHjc5felLX4LQ2pViNjQ7AiOMwCwDPMLAvCmjgbOAEni2u+666/zzz//0pz996nHHXnjRhddcd+1Bc6q29drrW1PrOswtnUZHzIcjn9641YNpdJ+xusWuLrQXlhlLKoyGCl99sVkc0itaFMoJwBsbEM/HPHgtQZYPZ8g1CShslfsFPehXL03WBjBKxMNrgcNApSoGxsU0yJV4RMsgV2WwNXuGjnf0wrPWOOZIry7tIG0DI1Ia3jvGb97mDoNqBTnPM+Y2jjEjFLme9kzPj3yBOYWSg4NiOqDkmB1vENJTTkYYt8+JVDieNDatxY2ysXSZbTr9CeFpoREK/SPaLaPITLAU0QDTSrEYE08kcLlRxwziOqugcO7cuje27di8dev++yDRD6imOB890qFTRd/kDCO1oELE1hRqhiNzuVSaS5qMwRlDlowSxvxTqwaG7MU27GnjPTETchC0Vd6NOOldwDaYTiEr2dPCSQ/gBZySnTc/lR+g8HS2YQzFjicLLVFmiULUXJbmYRg8fluh8bQqH3npF+JC8AJvhMKoIvNRy94tkz7SAJACC5gZxASM1eVxv+nLeJR2UojyvbzJwht8qqt0tyWkJyCMCF5ZXK1Olz1hxBAsNugKoB/JkADipIAZSZebpxc1vLU0kod4koHikSeyJdV3F/FgRnWxCRN+8TU399QevqkDOSyUW3L/Wl91IRbOTlnYKAvB7nJ9EagHtpfSBBG56l5ChPWnfGjuM19sste2mtt6zJZ+I5YUbS64DvNprj6aW+IsrzK58reyAH5W/UgPiHSlkfxP0mQi/vSziXPOKeqL9Fx6Sfk111iVVQMVuLWO9CI3D54+P/e5zz3wwAPYnN9///1ouUk/+hCNVOBs/JttBGYZ4DfbjO+mvwo4ACtASTDi9773va9//esP/PrBr339qx/96EeXVQZa+1Jbuo21ralNXcaOLkd8ZSXNWNTXFTO3dSMItGuL7UWlzpIq34Jyp67IwpGge+SDenOBabtp3sR+1s4iHIVBJUyvkfFDio3kHIsE4DyktmgDkNoS4CeiXPUvBccLloL1RWMADiOgSGsqwGKvDTQJ4hUF9RNPPEGzMeICE3tfxzS8ji13HHg4ckxl5JyJKchImx5DR3ggGo444gisvrGl5HokXEMr85+RcTJ+ctIMyX5rU2FLc8JnVRx3THVDw+tdA4NVOKIOnp1iJ4QREOECPeKdss0AjkfQA7vtLi8r7e2LyLly7Ogqa3yu2YX7JS+vSVvJ2lns9Li3DIaKJboXWaB8DCW6HSAGtG8+Ch9nmRlbyStNJ8X7qYH0SDJCuLPdUFhhF53+KSPXSD9hNbmHxvsKl0KY0vDe50VOQiB9BLwwARw60EF1qUAzxtDBSWj8GKrQPoLOcPgML4e6G9xH72ZMBxkT7SO943Q6xlngOzDg2PoIm8rK1AIpARhF+QAomK49HbGM9B6UQ+QH0EOwC4GhD4YVmhjlPF4e4YdhazF4UZhMA7yitGHIhSFjgDDMJmkwWCCBfuIqoHvvvRfp0gknnPDwHx/mQM0fX48cswDEktyvPoQ/52AA9DLA8apI20U3inOkKv7noTRhXg0MnlOrm4zX263tPUZbvxMHX7kK4qKgUVtsLCxzllc6B8+xSkLuATlBUjwDhfBHtA2pVO/Pfu5cdmk4HOo+84yq66+3Cku9Hrnps79oAkDj1ltv5Y5flBB33HHHueeeO25iJntds7EzdQRmGeCZOrPj6pcCIFRneNI7+6yzrrrqqk98/BM/uePOW2+9+fDDDp9TbBxY6+zstTd3Oq+3J9/oNFv6THxlRZNGNGl1RI2NncYzO5P1pVZDSXJxtTWvxIATDou+K8uTC7DLkm2CoqjdawDoDZSD8BUHFdABetM99ahamGSwu1ja8CYS1IIgGWyktpfENDY28tbS0o/GETl1HhAtplAo+eEPuTAGEynshCHKvUHIsamKUEm8/bW++mVFuGHKMeP4kzFBKO0pB0TO1CjdTBe8M8D0BUSI1By8CMWAn22V94+/6txL8ManN+4E16/zJ6PxsvKS5cuNcGGkVVhbzL7CI0Jf9L0YhbFdvB1jYkqNc04iXEkDXXTq62tx/VRbXY7EXa5iHJMMAk+og7UQkFNbbh+p161HGir8t655T02R+ziMLSUiJHhgbFCp1yMNx1bUVMuFLIZnqrXKaw9sHpsLgeBIxudstKzADQYDSAhg8ehyr8xcAsw4dD8pIfch9BsaGlh3e2XqWXJwSgAWbbaCFzrFSVFaRaS7IfTjTHizqcEFwFK4OB32mdCrwT4oHAbNgQWAJ6xe8N2YNyDLgFVKmTCZ+CmAQsAYnvUwgUuColj2SM9pqtal1hB0CIE7HYE4IUwaWFxsMRobG0F22l0WLebTsOXMKWEYYK9hCKe+9a1vwSXS8nvuueeDH/wgWAMF76nL0MzCygqQB2sq/Nefg0M45K+wty6KwEHFurbkqhZrY7uxvddo7xdEIo9p4BmrtthpKLP3rXZW1PkxdJJoqUEe+eFKsSnITDltn/xU8N67+Ra58GPVK7/pmP7sN4u4ed2sbvWG8fvf//6KK67gkMLHP/7xa665RkWKXi2DyWf/zo7AaCMwIgk2WqbZb2+aEQCgzF+w4Od3333hxz6G+PCYY44977zzrr32WpiNJRVWQ7nzlnp7R4+xocPe2O6gGW6LWNEkZp9GfyLQHnHWtVml25NcILygzF5SZc4rtqoKTbn6RQZwACBOnbEEqXAS2HOOBc4AwaiyF24K2o4EdBycB98LwIVGnI4AlzbTfnoKbYedFW7Ajj/+eA+J7sl0CAp77r7ukz8VDlfAO03ShNJUPRmFwR7EgVokZtDrOi/ot6F1ICb2yjSBqDniyx1IhetWBxwj1rCIi1PihhlLykhxeVGhiNuzD1pM6G8ZUtMQ+zR+KEEuzp6R5rtUhM9nlZVXcpiYipKxqFNYmL2sEWfUbaB8JWDzcoyI7XT5DJ9jlTgG6mmurxhoobpOYUeMWNiEfmC+0Lbx5pnQgmcL280IwP5BZ3sWzsNTZ3VwxTSx0TBB9OQ+wzOOHqMEPdmh3QG5QNe9NfUwOQB5WoIsgJbAhwBtiETtxnv0Xky7rwwyI8/JeQApVrJ7a8zzOm6IZvD2zMLG6R1WWmPCdAMNZAGATVgb0AAwwBw0zSoMmpDu6FzwVtRGpfDDrEzEQ4jpWZmc28KNFpFUB1uL3IoH6TCyJICnd2aBr0i6v/jFL+IXChkHxNt73vMeBkHLFxQkjye/lt+7g7l21DZfbUq+0mZt7jRx+NwVcZDpgps49lYWsjB7XlRu71/l7FcXwFdWZmkgGgf22Uh1dHR/+SvFP7srkUqZP7+78vTTTa7zk+ozc0gD0x5cUeCk5qGHHoKGQaGNLffM25hp3Z0N5nEEZhngPA7ujCkaWHniiSdiEf2DH/zga1/72q9//WtuGL/s3y4LhcIVYasizLXm/vY+e0cvbrFQCxtbu+yuuB+FcH/c6o8HWvrtV9uMsq2wwakFJUZDNTfCGVVFVoGPU45TawUCSRsaGtSNMAiGh0mEAPJ0X7l7pp2o2VdSDHNQGqaHjSekZFA4mEO5esUfkAgeXsyhCvguu3tbsmtD0dYXYktP4C7AyTjTyFKE44Xapjrs2TAVU4qcnxltVgTvnb6GVuCwEIp9CD79lJF+wn+CxmF1I3Gn6pWXUNA6y5ZxijeO0trlKy0jFR7ZzUcyBTsqd2wLye3yp3CoEqTppuUTjhh7MlhhmwStLa2PrXrx2MMPqqutpRc5907pDBk4J7Eh1f5jp+9hK9EkDo4CjUbZu/zlFzq+OZoIXTrRw09cT/igeQVqL1QRJ53eHVXkZZziAXQ4EHDc2sXWmzqd8raPBnhrQAdz9HZ6iWWS3GecU0AZlMl7nOWMITuVAgOx4iYvqx17V4DMkUceiSR0DKVN2SwMLxwvim5EhGxtmLop29QxNwzQwcNswhDi8ALUOYHd9Nb8mJu3pxl1O8Bvg9HUNg2mFxGw4m6gipoq0DC6CY4Dm9NxfiLPOvXUU5F0H3PMMdddd50eAN7T2r30YIuehLF6Z3It3K/r8LkrJpiXzYootqzQmFfkLK509q82l1b7RTnu5XTxmOp+OceT2ryhd+W3S390e9+cuYE7flxw8okotUdhfXXAYe8xeP7+97+PBP+nP/3pOeecM8v6pg3wbHCPR2BqsR973PzZDPkfAQ+IAU9xiwXQweAEgeJPfvKTlStXvvvd78L+Enf1c4p9dcXG/jU4RUht7TVxl4Vp9PYeuzdhRZJmb9zojZvN/dYrLU7ZttS8EnN+WZJzwvVFqeoiKyhHGJWDAWIqPBVRYL4foCpVgCbVuAi8Aq8L8gC1gGkAsnQZOyLCkAskZii80ch329LLh/3mUDEoLT1yPGGvF0rqURTmYRyXbWxshDR3e5mOubJWxdCZm5+LmKnA5mfjS4/Xu7SyppzgSK/x6eWOHsncwXXg/RIMOolusZxkyrHjdmjDGrhZ/1vfBg/bl0hyuRFrnDWPBdoID8b2fBEGGKoiaZtwv+7hXyJQATtcW2Y7ZBelLWxwMBhI2Mlnn3n26KOPLC0tG6HMzGiYZ6IoItX9oN30Wb+9bWDnERtvtluejnfe55t7my98NPXp1ZEMoG6ZrKOdWcH4flMFqhsMF9mP0K/jK2wK5Va9oieXmSItAwZyWEA4BtfSAMNCKEseTiTy3m0j8f8HH4WRcC6JRy8N8h0gPAkLbPRmsM6RfjImwEMVO+71Jo3e4By/6v6Ft8cZEm8QnJpa5Jh96ifTDoIxuQsAthCuDwDiWTRM1CSyRJGlZpgd5Xtw0htPmGWJFBuqgA6ivcdIByoFzTA20kjw4fxPPvnk3/zmN7huIRmSjv/6r/+CwvEKEeQh5A+PoHL+eJ/cyCEvHVUyYNaHn5fX2swtPUZLr9GXQJcrOl6QEcZ9ctlvpXFAjdNQ4epy3TK0EtLYTgotMbjMaW3pufCSkkf/r/+fDvVdfXXhaafKtcDaiCHVDvygdib0Zz/72Ze//GVItcsvvxyvV2rHPkqbs5U0Gzc7AkNGYJYBHjIcsz9GGQGFNYBa9MCcu8Ai+gMf+ADCRdhgjpXyFa8J3I2+oNSaV2ocVGs199rbuo11HBLu9u3sEZfR0RTXCHOlsLmjz1zdapdvseeWmgtKEg1V1txiEw+EBdwwJ2LAAcA8SmPG+QmQCiUKguSB0IHUpkDwhLJ/mNZkmDdjQwV2gcLDIk6rngTIq1gnvafpMaM3QHGGTIrFtAh6y/roJxLDYINjkCLjJxPNsB5sHimj2wwTVLblWU6q+prW2LFuO1Q2WkVZax9nJLSyaiZzKQcjdo66YST26KOP4sp1Uo4MmX1wv5Gof+tmnD87++0LmcHpACE3cJIZUEuzAeIjowt4E3H1v47fMrkPCSoDWc3ALBKwxAxaFMAywQaXa9UfdMDal1946qmnIfggjEaauLRapESYabv/j/bOiwN2F06m7eZQakOQe8x8S2NmeSSQWJPacr7d8Ftf8ECGWheJZBp5OaWVPwFBCDtOhCKawXYRIdSk1TsBTc+hiCnVHRrDCDO50NMARpYQgz866NAukgWDTEAoeGH83C/NoF6eHMYvj0noFJIy/JBjcXrEEUdAak+pyRpzz+kXeeGOOPbCRCPbnWQfY2NueY4ZtYMwgQjO0I7CpiLLnvAbcVgMsJe5o54cG59jMu0jidl06PCxTYAsoTEqJeQrLCI8MJjuQx/6EM7bmOjTTjsN3S9thoaByBlW0YjkgabUGsFBW7tTq5rM9e3mtl6jtd+MJB28WCHN5RhxTREOn7ns13hLnVFTLHcEe1tGSnexHNfVw//aGzZ3HXVUSWtr734Hhr733YJ/OhTEpg63hjVMcBSRjz/+OMd9WbQcXUYD3NDQMH5QM7yu2Zg34QjsZUzzJhzx6d5lhWtA2z/+8Y8PPvgghzFwM8jtwdy9NmgnJs7+ivxOY7lvURn3v1nNffYb3cbGNjhhq6nXxC9ulGvf4mZfzL+z33m1xSzdZteVGguKU8oJ1xSaeBF0HQvvGq0BID0m4tvDGV5xYALuweNNDMgD6Sm6UO9aIGhBLyV54QwBuNjQ/u1vf0OGijAVApF4D8R7iSc2gDYGjpQ3dfFwkocaaQmy51wQAAQcM+IJv0dvG33HMyQIFcRJN6GNUP6AMkfO5XQ3Jbs2MUs+Mxra+nLfkqPFLHlIeubMjXAnbTdYdkjGHH4wFBCmPDmkFWQMVc1RNxh7aAI8YHMaSg3J8jqJvXHb3LGloLMt4fMXLN8HyU40jspWhqKQY8HyDB2xgSgnnoLekLuNmIFUwhk4ACyEAimkyQy1BEX8zl9nwfx5ZiKy5tVXOaeAxy86O3q/XP6ZQrqSTV8KprodOxj7baX1/6oCMY7+mvHSXvOMzsDx7ZazOdl8nTX/Ht0pKidy2zgZL7rACTdoWRSSrPnJqPLNWgfwhKMELCoEYfB+MH650/c4TSCjig6n+/jREboA18Q+AvCylQaR2nTvmbSf3qHTxkkSO4uzPOn6wBnQPXoHgAK86wVCgA7Y+70uTJnYgdX1CW8PcldvWCBuIplQHq0LYSV4HAM93ERhxQadhgwRS35OAIHQSc/hCwgeKAkkqHY05iTjFkeCIWk4W+M+blGKywW5EIfZ0rrWxOo2uF+jqcdsizhcgow7Cp9jFwctTP8WlDrLKo0D682KEDhqoCVa2oCiGbmtY3d+/z/M730v3N7WsWzfmsf+ahWXSilD05NLu0kAyI/Wl8uNYPIffvjh448/3u3okPK1ltn37AiMYQRmGeAxDNpsFqHBAaaIGN/5znfecsstN9100913341dCreQD1JOAky5xrQk5ONfY4VzSL2NTviNLmdjh82VcTt7jN6ED+1WT8Lsift29hlr/VbJ1tScUntuibGoPDW/1MSopjQkUJnqPEZhDMAPeIoqCck3NpwgRXhd5KBYIcLoEoaJGoWf1J5iN4vAFawDFkFHikoK/MqnvC4FygeB0XjYD3XHQrOJ5MmxXkWNu02sBcLVoybF2JVLFDAYBmvyU/KCCmX4d00BYZjyLc/1m0n0mPJ58zOxxUfpFT272iYem9zTqvmXFey2i7KEeGDsmW5kydBJ0AHZxOG7Lyq3FLJge2NWeO2rfifVW1NTsGARUX3uNYnMS0FQ0X52+9J4ilUvwweRYhuWa/suA4vanUun2FY4hSYntIVrLuGwgJctWxrp74f4Q2gCe7/bRtKYVN/j/thqUsb/Vhb6ba1loxzAOZcZ7i5J/FcgOScW2Lfb6v2TnVhXWyOniyG8IDEn0/APGh16nU4hjsFcc7edmk0wySPAkgCuYhej0sNJrj0f1bE3kblwwwqCTrX8B27ko6LJLxPGHgaY/Qs6g02aMf3SkWTinn76aXxEofLFiGkv+lHL38xCCSAHR+SEdIYOYr7O1kufR8w3oMS++tWvQqicffbZl156KUIcRgapFgwzpAsmb4jy5y1YYHV2G1s2R55/0d/anKysDB/yVmv+IqOi3JWxCs53H47JOJGk8Upzck2rb1OX5/IKYsABRZUVWHOKjEVcd1RlHzDHXyRy14zNIvwvDXDisa5bbgl+97uBltbItddUX3qp4RpWZKT2hg67km9+85u33XYbs8llnOeff/5k4h2vGbOBmT0CswzwzJ7fvPcOdpfzwBdccAHOsb7yla/88Ic/BPieddZZyqpRvQI4iPWyoFVWaS2pNN42127pc7Z12xvak1t7zKYepyfp5/70njhHhf3N/c6aNhTIdl2xcMK4j15YZlUXAmpNZIsuIzYSzMzsLFAXcyCAPg+aDT7DtHPwSQ++5sIkeCUC1rHxg8jDGRUUOezTHilJvHL2KEClqr+FFwWvo4vAposS6Nco5UDloLLWZOBLKFTCatM4Si79BB8FWwjXTQcbGho0smNnX9MrScsewqrRgg2PiSsmtVdvXmW99odek5uuhGuTR5po2sULjXnLMBrnV66z5ubO10s7iJ0whmHK/UJJCNLORNvjbwAW4kZfzKlaiwcs21q+rxEKodWNJGQc+J/7sbPX4c4th4dlaIVtl7HDCEASiwYYp89yBBjTBZdS4QfzQhf4aLKkoRLQxWUveWis5Ig8bhpx1MzOn0utFAf5B2qhYn88HH2kLLBfjx+ePfJsVdUKcrN3YAwmjRCh82zYhoYGjvPBAyO8yMM0DR2UN/0vpAxwfbkIhhQKKS0+Y6ZGFxj7CI0ZHoNZDtN9yek0AeUAfSBrWF8kgOkmTtN9ydNB7R0zhZgMHK22S9N94rLOC3J8RBhgcyxiMvwIMAhwuTiIggxDunHmmWeimVBSh6EghgeZDvQA5ITZ1Zl45P/6fvAj36aNCUFHGBEtClx0fvCkU4xa19+bIB6hM7jVctVOe02Hb1uX2dRn9MQ4wytHcMA+lQVGXYmzuMLA4fPyGv9wlxYD6AQ3E11dvXfeGbrm2lQgELntu8XnfsQsLBrANlSU9tALePjbb7/9+uuvh5LhuC83ceqBrLRUs8HZEZiYEZhlgCdmHN+cpXg4Bj7tP/7jPy677LIvfOELyOq++93v3nDDDTiOZljAuzI4QDlIenFd65SHzPIQnLDvkLk21tFbu51NHcnt3XjMQhtsxQZuUbLao9aGdqPAn6oqtOtLjfnFzsIKq6bIrCqwuD9GFV8U7448kHYIGAV00zbeiIQBo9BzUHWwdjCxqkfyWu5m383LS0xfUPxSFDJUPTIHQoJGV77US7ab4nL+rAW6aGgI0zt6Rdgww/ZrJeiNeQhDE+SORRguTzrQ1d313OpnAjsbtv21zImEh7ZdzGVdBbDpRAPP/Zw5AGXqRHCkNLXkeHvJUQNHpodm3Mu/1HCaYWRt4AyGE+z5OBKctJ1Y3AmveVGEAitWsGTjKSch7p2RnacKgu6WGLpuB8fFxLeIjqSYP6CqRe3rJufNesDBiHLEfPQJic624rMYtXmH+ugadBIFjrRacOJtJtuoxYoXOG0UIOIM+eeK7CVjU4iWy+nlREtZeTnrHMEKPHDuC2mwO+P629DQgNIDwRNbjzaMq6wpkBnxAQIm3gqjpkCLhjSBva9E85DYEX4gX+NmLNgq4P9Iy2yErFMumungUQMHeoSND02cGZ1C9Yec4vDDD8ceGAyoQz/du0YvdMpYgfCE6DnZUyxdXb0zoHc6TfSRAAgdugUyA9DBQR76qKbd2k0dB/hGfFts2LABaRTOSiHGyJgxDqTkDoWCYDD59JP2935Q3tEe9Qdj8WgsmSzYsjH6zW8Xl1WETjrOChXA5pJ7mxz6ddZ1WFx12dJn9GP37BYZ8GGdZ8wrdhZXOQfUmI3i8moXDUaIilx8JejE7unt/tLni27/UWJhQ/zfPlF54cWmnBGmeGmfZtNugl8eeOABDtMB7c8991xubMICaHgvJO/sMzsCEzECswzwRIzim74MhbMwEngd/Otf/3rVVVfhdwHraNhgrHR0eDSN6J2gtW07HouVhEKlFVhH+9+GdXS/s73H2dSewjoaaNsTE49Z/Slgrq8jZrzRbb7oS5aFnDkldn1xckGFr77YqCowy8IwBtylpGDU6e8fuA2PIy5qoozIU7F+OsIY83RpF+ArFMsCtZ9//nlYAmh01AVjYK1zbAmovbGxkXcu6REMq94YsgD7KD3DlmNeyk9HmXSws6OjP9LvlK6qf8eC9ifmR3dy2ayL2kBwMpMe2oO/c3EgEWjay5KHfyS44LBCWDK3QD7uehTb6e/06nalcENeslHSZGTJ5Wd6aZi1I8v4+9//jkCdESZ7+tdcShslTSLuBJKR0KuvJBmlg7FJ5n5sDJgF90MAhMRNpjtYGUW448gZYKIJclsSLz0QjOdny0IZjABeTcv5IyQEQy+jP9h0xg37MQ4xsvgbGxv5OfhlSE3kcHx1yPKNQMyqso1ud0K1VmpGrVLLCWbZXKa/BgEBOwgaixHL0DwMKTQPP1i6nFfEEG5AlJaHKiazSPajbsnJrDTHurKuk1HysiRgPGAap/vUsEd41AUgXq9gEvZ0KEYZpb31iR6BZ7GeQH7EBMFEoR2dAf1iPOkabzSZ3FOFTS9yMXoHUp4ZvfM6SAA5JjMIk0/vEGEQk2GWr0OBshcNBGYLYPy//OUvCqKHjwYxpDe7e1J/+HO4twvEEC4I+4N+XzTKmOJysOc3vwm+7a1OXbg/mljfnlrXFdzUbm3vNdojSG+pXAooCpi14vLKWFppv6UOnQToiJJUFUEaQVs8IDBZgZvf6Dj++OLmpv66eb4bvlFx5ofdj5SjqeQXyXjwdAXdiD/5U0455Re/+AVG7NMdqrg9nX1N6RGYZYCn9PRMr8YJVDNNfCnhZOi+++7jtiSkleeddx5uDIDIwDjtDmlAXYB1CHSEmuh0SoIm/xrLjUPmWK39NueB4YS396AWtjqjcMJGjH//n73zALCkqvJ+Vb0cOndP9/Sk7gnMEGYQXILkaAADoKwgQUGCARAxrOKn7i4qmAETklHEFUUWJUtWgqSVOAwTeoaJPZ1fv/xeVX2/W6e75k2HN52nB14xVNeruuHcc6vuPf9zzj037+vO2psSNoGmwxtwkNbqo/bsMnN2pafKl8/2tm9Zv7azo53R1OMxmDaoizF27ry5TqVcUvu2MXecjHWHZmA2DUHtytyDlbW5uVmMVLRxnFW42SkKQRPVr3un+IXY/eA2GbHnYM0YQExhRxQviozz5jVVVFT+66WXenrWhw7p1F6Zl1o5wzDZsm+7rFgNlZ1TzYR2zR6pg8+pitSxHqgvzWDWu7F2titl0A+S4avMpD7oCd2p/lOHA9zk5eqvsb/iwdm2v4OCGXzFkmCWPYOEmXRh9QB2bZ9jpL8gLRjQlgU6rfYtlm4E93kXOdNq0whlyvX7NMcDejCdqnPIm7OUqVPxTTUJhRG9aTsAWDfzrPhVDx12q5dcpVKsV9p5fnDQ72QkFApKGQQmeRnkUd9ZJdf18H5ah8/25LWjeqybowaVqipV2Tl/1nskfgR6XgsboX38po9PFQCMy8N25UzyD+kLaQJVDdGQSSagVPyQHJAxhGFwwBLEIRNP55vSECiUpZVAX76d6UzwSGiTRrFagc0L8ADiy8U2uKv3lNtwWgewx6ZNoApshqiT8FdimnMT7OoX0n3olXgnES1oLC5LTU1NtKtwbpJkDMgIWiBG7Pw/+tGPzjnnnB2rbzIpu6WF+TqTZf2LOnxeL6tovDgmrVurpVNsVXDPixufb4kn/XUZb3naDhC5ggmIOQmTw4yIPbdM263OWlrvKfczY/RNOpQjJKkL50fyvgfy3/iPio0buhcurPvnU3pZhSrFeViYmPVWiIjYTph/7733XjYW4akrYnFdOkocmCQOlADwJDH2HV0sIPATn/jECSecgFaSSAaAYYIxEMh+gOmD0ZDD4ZQSutnWFOVipNIzt8Leu94GCbcltbe6rPUxbUtcb0/YGISzeXZS1eM5vTOpre60/V6tOmBmuzf5s53VPn1OZePuc2t3a26IAD6Ul6hEknYG+W2j9IR1DcSDoFgM3NLSgrmVEFlcsMhqSMA2YbWOuKB+3g7MwCoggoGJQ+lwadw8JKA5hx5yaMuaNSveXOFdumL2Aq31yZp8Dy682/EU6Kv5crt/SNv7Q7WGT1l+nXlQpdkunVM0GhBi58A6t6IhL7BkMsEPyU8F9ySPmnW5VCZT21a+wIOrG7JwbkIkPoGHH374888/j5adNVR034Rsm8E7BynZu/8SzaV6Z84sX7AI6pJqDySe6FEUCA6DBhBGSzhIkzNhHwxFLOGzcFygDUJu5msjrKUngJbRHrSzltWJnNCne3dyOk2nYAQmzAXoofBQQKB3/R4HVOeJHJYL7OXN/StwaCzd6fXcV+nNBLELZypixse6/Uvi9KkVfq/XvzCoBXEroDsA1bBrQDlT8JNOZhk8UtHINUFTQNUYquDrw4pOKybkNRsDAROVBYEbXRv+8HTNUO/yRNUzFeWwwhwpnObw1YgCcSpqncw6wL2gXzAwny1bNozcA2gyiZqwstnQjuEa7R7BgZmC335giY+LoRsMzNuINz5tHLz6A2DM+p0//elPrDjDPkyo5A996EMj6mg+V5+agdSrTjzFvElF4RAik26X8zkbb8XslkRFu+3t6LZTVq9HT/mCgWgoUFvmxfAwr8reo8ZaUucNKl2u2oaAEYDphx9qElLDAZtM5mI3XG9ce23k1eWJT5xWfcX3tPJKZjR1FLwFfHes9WWDX+i/7rrrcHsW1W1BktJliQOTyIESAJ5E5r7Di0YpSwADLMAoJonmxwBHSANWCHMfUxLMwXmYkZchUfwbC4fGoEebXWbMKrP3muHtSVmtSY2gWet7QMLW1l6rO6N3xrMJS/OHIj0pw8zXGXZlTyCQCvp6E/a6tfacCrMupFWGNWc7JeWdo0BS4dA7cX3DzMSqG5yuUUgTbUtU0UxO1CCj/fZj/sRVvKOSZHPmwbWzcg9U6cxZOyrCeU4JGNUXLlrYOKtx+RvLd9+9cW2l+a/btuMm6mQskjP3tvc5sUrTldeuZB1QweBK3TuD6RyQd8BPZ7q18pZO7A0QY2fSjgaNgOrp0SBgp1CWc+PJKSG+peMG1DWGn4oQQkn961X4oh96mBGNUkhFUJ9jmQnTqlCEDnfYORZgKVHC0d8olTkMNWdEzP0bPdFc0pdMGNm8GfHnIxUzooBqxnCHeSQsOAh5gtmHNfBge7w6+VnIYeGRQw/YDwAAQABJREFUpVd46r9jbTjN8HYHPtpmHZJIt/gMrxlYmNMq07TA1Of46r+p236gOLG1YBEYuKCSqbvEFwBPdVpxyCGH0JDCtkwdERNRE76auBtg69ilATCWN0Il452BuWY4kyldhnsqA+Ng2X0iGDlhZaANxJOWVhx44IEYSyes3CkvCB8WgJOsXqZrGOdZ2fH2iE8m0wSNwkMHvor2lngHsuxoyjk9KRXSRg4Z2eQ9xEWOlkqTC6skGcMIAzuIFwmKmKPsx8FRmKbYdShCTArt1ZedAIrMHo6sQihE1J17Ls0GIsu3olqt1MsqA768lsmk01kz3RsKZuZVVDbXaHvV2ixbc8Cvg3rVaZuDHZNQfmNr8pe/DP7oCssfSP33tyNnne0hRkABQdDPi8rWIQS7YvIlbOpnP/tZDNck2XUH9oL2lS53GQ6UAPAu01W7FqEykDHSse6Ivcsvuuii7373u2j7rrzySiDxiSeeiPaRcE0kQDxCHNyudWqwdP63dZ+u1YYNIi4sqfXEM+bqTd3/t6ZtZVcyaIYTWjgUDGRMT8YIWVaoK2t3t2lrOo2Ax6rwWzOirFTJzSzXG8r12qBRHdSDPlxHGZ/7h2L+KsjgOPcoeNx/fztSdvDDzcVMzGpnpmSR9mgaKmrkD8Rc2ugm20FxE/eYGgvlTmhwyx58vUPynAQ22H7ffffB1Lrx1W7mPGVgd/npIOCtK7R8xvYGMVoODUNRfMAZKBENCP5dXPMmFJqCIQ8PdoQ5HokLtGShdmUKll6y1dLYdd1We6+1tNHXkbRXtdvRQH5Zg9evoOUQXVnY6sL2yjW8wpgAxqMWUmI5QSDmZmFKyh3VwQbS2VdeCRK6edlShx16XdhTF4ZljLp0R8GruH25OVN5O9MIR6xQz2ZGrX2qrejmlvSDD8eefdFKxf2N80LHHblsr3f11M1Y35XXNZ/D8r4yIZtWYPhlIyvkJLAKZttBbVGfgx052pz1q/ymr3jtjZ76eKBBCIM4j+XZw5j1c8Ov4j/zW8wsmGHVzyk/EI+wEuA1gGlrgCPJlNMy3grlVRzUHeMtdsryQz+DGx8p38tg6dwlA9EcdQnu64UDkft0OlzQEDAVXwfvNrZfxupdt1PgJ4gXXMSZsYuIj0cddZRoY3fpRtEuuomDwEiomA866CDcW5gI1FzwdsFLtI62MLSyCl0iMjLcsRRWPpzC7pOUdDES1BNPPMEIj4xx/fXXj66jy8v8xxwdf/Kp6Fvr1BRDBSrUhBWraQgd94GtgbJn1lubE3reMjxeX9TnbaiKRo0Em3HsVa/tWa9vfOOl1zb4mpubVSxJ5dGkiFczvrL82tamTb1f/mL5H/+ca5ydPeeciq99zZk4+iYmEvCKIgReddVV5Lr44ou/9KUvvZ26kkaVjl2IAyUAvAt11q5Hqjt2g3AI0I8F+Dvf+Q4eOzhFn3322aBiNYYOmsaU5O8cMrbyHDebjra25a8v7+7qrDb0wxuq6mbV6WV1rUl7Q8xkzXBb3NlVGH2lCiLt6UkbG+N6wGuHPGZlSKsLW/URq7GCPev06qBWHjKCXkOhtP5q5O94ztJSEQcZ5fFyxPohEYPQ48q2Ey43hqtIpjee7jDlcCUUuY88ygZOJBAjJy5/XOMxJbt9FMnY/8ghyjbSsfzWN1VsJDbkgYFq1tMttXxUt/Nx7+blqTn7hNwe7M+77S8SJz+EBrnGmrTtsXMFPJbg1TyiErnG+qdmStVrdt7W3mw3t8QNS/f+dXm2Pak313izGf3N1syeM4ldDAcHFKl+4uFMG4azvPEIvTvVUSkRpEjPCvZxqaXjvcaKFcgXxrJlFM4hZDmkDUWfQzK0q0jRjjqGWFnQE9DNxbV6ZG1L+3d/GHjtNfgd1ozs8tXJp570n/2p6o+eZJWXOQzve52dYvpeIb47IkiJ+dfpLnnonqnG541+zJq/1Oy42Uo8ZOQ2EGPL9s03yo73Vn3SNvBRF8ptFAQs0xKdhZt/Ki9YYw8AZq0BAJi2OPycyvpLdfVxgI+XXkA6BwAPZsqA14xucu9Mqy4Tqhix999/fwaiXXfbWJe9jG+4PdMjtAhWA4qmFcMHvyrF77jtQhmK0wTACWDPCgJkhl26XW6r3QbiCsd0zODGI8QGeRUHqJbcxLfddtuFF17IT5L95S9/wc2HXKNjCJB04YLQV78Y/93tvhdeMHJZ01+V3GOv8CdP6523271rrPVxP1M704mXPTtC+swILnWhxdXa7g1GWLde7exE+YU+AttGc3MzeknKU+ltK7fijdghh4eTvfFly7w/+H7F0cd4lJyldBicUGf/6le/wh+QTjzvvPO++tWvkhfKR0c8BZWOEgcmiAMlADxBjCwVU5QDMswhwt50000sBv6v//ovDMJ33HEHe9Z97GMfKzICij5byu7q6apvmNnc3FRHNEtnYGW31N6M2ZVEYamt785tTWhbEiwP1tN5g/3qUlktqRkdBJHuwSxsh71mVRh7stagLMNqR6XKoFYZNELbojVRj6AIF58M+Fm0kf0PaQ7Lrlh8xaxGcCz8onFkwhVth/5azBDkFW1uf2ET9pdZiimT4pi9ZGEtdwbMskUrgxWwxV7/UsqTCrA4Fejra+jx77Y+sbbOXl/HIm5g8IbnsgDgIfEnhcuKJiZvVAOy0+aQXQ/QVVhX00gDqSjFCwlL5218tLoyHsykK9qzazr0uqjn9Taz0msu3R2PAYWQh0TgVEp1wwFgtwrIIw1Vo2LHqs/6qyGJdNMPd5F++fVAsjft9wf33GtIeobJqLZKUq+hrhEo2rTVhhPhRFf6T38KvvqqF3ECeOwxFfTPZuI33hpctqTm34gOKi/qwCKhHB9U7tIo+p0LdDECYMDGon7ipu5d7K3/nm5/0+aLUau6wrbGYmByq0MlUOHQ5nF+6623+Dn1B1Xz9gJ96UTsivImTz0ZpRrhAAMaI1VTU5Po9QbwBDBJgCJuinqLQD70HdCFMXBAyp37Eykc9R9j8q6+qpzvGs0mHyYRKFDe4S+DTRtdHrPtzuXw+GtHacscStMYvnAlwNrJK8frNP6Sp0kJuBpJ39FAxjQ8yIqMbJiI2d+Idb9oBMDARFdhPh0DN8ii4wX9rneHZ8+1t2zKtHV6K6v0GbM6ymvvect6ch1zjIqRGPTaTD3s9Lugwl5cay+qxbuKidWLZwFYHQCMYrqtvQ1x5V17v6u6prr7e9/1XXdTpDfWu+8+Mx59xA7h/KTmYliNRvvmm2/GARDP5zPOOOMb3/gGo8cYKJ8mvVYi423DgRIAftt05a7REEY9YjYSuYFxnA3fiJXFUhBiAH7wgx90PeWYDGgMAgqCFNM53psY4hBTjj3m2EKRi+EVhFAV9FQFvc3VdqbR6kxZHSl2UTI39uRbE562hB3L6GlTz+WtuKnHs562lNbSbQc93qDXqg5bdSG7LmI2lHnqIpRjVwY9AZ+mNlbFzRevIEUF/1OPikU0Kv7STNl2GBzFDIfvFpQ3NzcXL4TGIinuMFnxQoZ7CtaV3RHAD9gKwOfUJaweLsv29xU74Mf6F7C/Btg9dv6x9r4fa2jtst94aVVseSz16lwtG9jwkmlmLE8AxAffxnLAOqiSc39+6QWlZe5IaivbrITlzdrWq5uyW5NGXZknm9c6k9acOd5N3RabY+EFTVJK6M8+ur8wCqspWIuVgUQiAXHxxsKrURZoZ+/8c5lpZ/ZZ6pslsb6kFcWJUdA9a5nCPZ9Hy5tadUT3bezufeIfSpdOfOZ81ln6HOSl9CdT6aefDu25TAuzJ/OwB/xElGQNLU3Ah5AG8nGhWiIDPMK7uY9TelTX1EIsua+eFRxwgF+yenyUrCgoZXyXyPR0B+ZH2RFkfIWVco+FA6I9QXcm5t/BbwJ3XE2fLCUYpaJtLFSNNg+vMZ8DsjuL/915Z7SFTIf0dAeKBoIJAYpQ26GtE3WhfK3TgcLx0IBLME1DT4f7yZgXPjD6wSXKwWFYPHrGQ9LE5kXCgTCowk3MXQc0+JuiCTjQPfDAA/fccw/JHn30UYz8oN+xEuPMjgFfekZjsmpmfpHWlbFWd2lPvJBdHw8yyTDRRAJ6XcSeFdXY6XevWn12BWt+mQ/UlMD3gjKCAylizdqWTZs2g8K7v/XtwDW/9sZ6Ulf+tO7MT+ohtktURzaT/f3vf4+1A8B80kknsbUvLXWelE4lDux8DpQA8M7vg3cUBe7gzgj+4IMPPvzww4RAYGTE3RQ8fNxxxyEtIZ0g4xKbhHEfGQUNNzMEGQX9uiX08U0GWixWHn1mVO0PvEednswqRNSeYid3a3NcxzLczjbuWSNlgR/0WF7ryXgJMb3aQy5LuUmHdSzDdeF8Q1SvjehVQXYYNsN+w4uZU5kTHTAsFY2styAS4mkLdjOmCiY5RBOygjpYPidBHQc2ZGQljy3VkHVB4ZD3h6mCFmnZXnvr67ZenTz4zMjsfyNupD27cXZ9Xc3q5rdqPxJ+5qZE9zrv5tfSs/dVvsRFCqcrcaAapqJt2BUTjRMORDk152xtXae9PkbIDqMna728PpfWPLVRbzxtm/ncQXO95QGtN6cl0pZf7YihthGSg3maN4prxHHO8J8z0WuHdKWDZignAcoCrNAAYNJj7zr00EOLNIf07qEyU4Jl2S+8qPbRPfoo24MrWTFuuHn7AKll+9TOR5rPMDOmAZ634nG9t9eJdL1NEeOxtRxSSWuHkeOvHP1/C0qUSzAJGhnkLTZa5NNznytqqXUEygLZ0BvdP+B5SNa5ZU7eBUKwSLHF367JI2AkJcv7U5hS2Dvg/oCfbnr3DRxJp7i5puwCGyMe9RwyJg+uF/m4ubkZ4oFkfDgMgKNxMxlc3gTfge1QRYB0rFKs/mCInuAKprY4uoMvGm5jPGSiwcKGZnNqSZjg2uggXILFOYUOYvxH1SLdNOYvgjJ5Gxnzdy4Alk8eXSQ+FE1NTTCOpiHh8I0M10CgO4kfe+wxlo+R/fOf//xHPvIRNw7/GBiisqAR0Owtcfv1VmtNl7YpprcmtJ4ssFchavbnq2LVWNSeU64vrjL3bPCUBxCBBs0RtppD916298J4PPv1r0duvjkTDK++8EJj//29mhW1bUQdVrph9WXSQa7jmoAUlD8GmslVOkocmAwO9El7k1F0qcwSB3bIAcZ0hniBwajk2YSGUPiCFRlecRtG38ncMJJB05ldkOe3YQAMhuxf15s2u9L21qS9qcdqTRhtSbsjYSfy3gyWYaLrqnBDlG8R1tDv0UIE0Aqa1WGjNmzPCGszooayDAf0aNAb8Eho48IqnGunwv5a+/8WtFymPW7QCiYDljMB/5ja8U2VaQ9EwbZAJGD9D+IjTlCkRAIg2UgaXlDViC6R/JAwpJaRl8/aWFq7+umelieyB51dHa5lHwXhtTRZoUYrZT1x89b29q4lHzEXL1kshqDBVbgMEZ4MRzTJhNfK8Juy1rTb8ZzH1LW3uvPLW61A0FfuM7qSZsCbe/dsPxHOwpq5e71eDc8cutx6EQphLLUwJXNTxHFWH4Gm3DRD0kD9vJyoaSSaVGGaIhktRbdlvrU+vXSZL5XM3vHn6Ic/iJTtZBni9Sgs1mkxJll4rbP6meicL27MN1cYVRvf7Dn/Iru13W8YqWzWowKQ+T1eYvjo/rNOCZ53bqCmCiVNET8Facvdd9+9Zs0aLEWYiVAuSCsQnbkuJGPIaxwH4AMMwXcDUaYIB4bMPv6bwhwIkK+GAqFBbg5ZeJGnPKIcDmGLnPmJUUXsgWhMMKxxXx5xhkUoRMjIWkQ+H7lPvVxwFv9wRFs8PrgjN+UMIIHhpIHzJKAW3kNeQiriG2d8o0ycCfFv5IJknOVobm7mQlRmfbecP6TBHcZRDGnYw4FA3JZBkjMfnTwiI0fhI66ldZQw5EECoXnIp+5NSUNiDvfm4AsgB00W74nBT3fKHShnpGWi4Yw/7S4aIXlAHzGnwEw8zHmLeP12yrc5/t6URqHsJtQ8um8C1xdqSIu/acPV7jKKbwS38KampkIH47GVOVxdxe8LJQwpfBH0F+MAIceZg9xcg4khC+nZ8IlQKcSkwFXhyCOPZMkYn/DgxG45RS76ucH+edqqjvzyNr2lG+irIw6l2Z+P0A9av9tzRG+uMpfUaotrPX7cg2Rc6pes1HinymKKspL/eiF75tnRFSvi79k/d+JHX1m6tDeZ0mzrzVWrbrrxRswYH/jABzBsINdB89jILtKi0qMSB8bJgZIFeJwMLGUfFwcYExnQCXh49NFH/+1vf8MaTGBA1KJf+cpXXKfoEY6bTrLtBDJGbjarrQl70Gg2V9nZmVosxRZK7DAMGDa3JnX+dbK9cE5LK89SLWFqCdvoSBnrenSfxw4Z+ahfgeGqsF0bzM0o03CvrQhoFQE9HMA4zISg5gJlIeakJNd+Vqgf/deOROv+QEnPHM9iLeZjVsfh0gnax0QpCE2mKDS+pMcCKRZvN+9EXSDKc4y+NBCWVdFgHH1JDaZJNStua7AqTN0J6UtP9P3r7/GWli0bN20E5Dc3N0tFhYkLr4uQobhom0lTb+kkzhnTswfT/fItebyga8J+uN4azzWyUVZDgL4oM8zFDXqFH5oGEgZs46AiWY4orptF6pVH0hcgiqb+tY7ACYQzPLiASTwdvhWK8NSLz4eTiURldcXRRxTBpQPIkDJpAvxFwQAGZp+nWNquiNZ43rN/6va70NWYVt7weMxchk2KMsFgxXv2t0ORkbgo0JYDDzgQgwNr6tjO97DDDhN7SHFQ5FKI0Q82IptKsBb3fvELYSNpaJp7PSCLtLrIU0AjIiPiIIKsnLkDeqQtFIV7BTodHnGTMwek0ms8wu8OSCD35Uxd7NzDIz66v//971xIvXLGNi4GNEA+dkL3KRd8uRJvhs+TFQ2FzYGB4tPBpw1jSVx4AEcFAPPVg5wLH1EXAJiqwbESoI6nUjKdBaimZNwWcMWXXPKIMyBHUC7aNPFukIyckarFP5zqJNAd6d2nyKNgb5pG26mC8jnLwfY5IpGTC35yk6dyUBd0Ugj0Ax37b3sExg83mPCZ0EDKGa5nKbDwEDoL70zsNWTATGy/NIHGgn4ntvypKU2YSUcApXAMhr0yoME9XrPpttB6hzxx3w2+a8ZnwCEXfL+8WhPyPlAgL7OQwQojGbtGEo9jh5SPMIE0kEYxFtFAiEFFhfIFHVaRBpKL8Yp4USyXpVubm5t/9rOfyWYZRXLtkCSKxdv59a3Wm53Ghh6tLaF3p+0s2Jc96zWrzI/7m8GUuqjK2mOGPqsMpE2RnNSfAYcdi8d+91vPFy/xRsKx4z8YufqnFXPnHZTJXnnlT3/5y18yTbzvfe8j4AsjKq/ogLylnyUOTBMOlADwNOmIdzQZjOkIVe9///tBwhjcWDFy/vnnX3vttZdeeik73TEXjoc7joVGGW+DhhaMGjMi2qJqPTPTjqWt7rTGquAtvWZbXG9PqfWlvVlPDjdp00rn9JTt7czoG+KWv884bFYENcBwdYiFplZdVMcyXB5Q00bEZ3jUOkp3nkA9quaN/jt9szx3EAoxviGyoBxFcEe/y5TMJIF4TWoEa9ZuMduNp72Tlxf6a5vL1XToYNP+1kmFfW2vaag+8qRKhO+VK1cBBpj1R7N8SzQHnBX/sqb9Vo+9KablLV8WA1pnrqVN8/pY9GskMiitzT3rtLlVPpBxVTC/xwwPPvBDtl0khr4+cFKMRIZA/uBQlDiyCOgXWWrturVbWrcsW7Z348yZwxUIf5QJ95FHyWnutbsWdlbVSsuGpG/Im877Y+KpZumbk3qkoqLulI+n3lwdeHO1lUmDJUEdVihQfuYZ9uK9em0d37UBjVevoGKkOtxHM+pnHFV3FBsjYQTjiwOfjIQVUoh7dhGXe6f4hTBKKkJwd6EsLEUuBFwJ5sTVHGzJHQ5ALGeyEHAFOjG6Clh1K6JMQXqk4VMC0riPuCB7U1MTFwAeqCUNhwvbyMtPRhWRQZHP3MNVOQE+oZP7pJSz6zwJtQBvVWLBQRqq4z7gk9tcc8gFnizyk8B4EMZBw/nMoceFjuBnsU0VvlRSJiOGAG8euQcIQcpkJIFOXlQeydmlE7kZHxNuugdppEzyQgA/YTJPpViokjIZkQYAdVgtQB29AO4qJCOLJKZAxm1KowvoI2GyAGPO4DGpEZ0LdXHHPWiX6C+kKJdpUuwknbGRQif4B90cVUxNpRPbFtjFxIEmjhcJHxbxIJAqYCnHxFY3BaXRIj58lvvSNXyAzBd8DhPVNRTIK0cVDAV8bvLFTVThI2QOnxifBt8UnwmaFz4KMHBxGtasWXPMMcegpGN4BAOzVxDpi2cZipi+mVRNp87GgWu7zdfbtLVdxsaE1pHQ4xkV3YTRitiKlUG9PqLNrTAXVdt71nnKWNfrzMJSbOFsou4kE22HHV7++mtm0J8+/ZPVP/1xJpu9+ZZbvn/F5atXr2FAuP322zFxj57goRpRulfiwKRxYFzQYtKoKhX8DuWAiFOYaB566CFgMHu7E46IcPnsesfkMebxVGE2xnNABZKbA1AAS8GIZ0ZEX6SpoLs9mXxX2sBTehNgOMH6YaMjafVmtLSp8HDG1FJ5O2Z7tiY1b4/t99j4BYV97BBgVwW16oBdHcnVRIxyv13u18sCesin7MNilGPmcMRhdULSVCF2nWDITPMIryBe1xGXCVJkQVLSUq7H3N7JeXsgXBHfD6fU5YBD8VnDcGQsXLho9uw5b7zxBoIvEjNtLGzagFwFkyv8Ub5Vqby2scfa3Mu6Xy9+qJti+RVtOG4Z1REDSNoWN8t95kFNygWaMMmzK635GAwKKBtQvvtTcIv7s/gFeAxxWaXBbTKV7Ozq9NrxOTN60vHXVr78avfW/RbufoDXp+QYp9XbClMdl8vqDzyIYt374RP62bUtwcivcqAaTUcX89wm++j5C+v+65up+x+0nnrWTMSM5gXR4462371/LFrVGsvVRbaN5CqTUyvvm57OsKJOy2U0pMBwVI9GeC9BYtgqJc629MsO3zRJhvUb1T4WQkQ6ga+c5QDeALcoh+6m3/tvK4iLpI7FkhXINBxfPrGsukxA9SMAmIwUTgkCohgK+OSlXsRWBH25L2dAGk4TONmCGzGMkMzNxQUZpXzETaAOZXJwh7MUyDVAEa9CSebedNPgmlF4073PBXzjKCxKCuEO9Upb5A7nwkIE4vJevfjii3QBGNtNhpjLwU+h080lZYp2TBIPqFdCfLvlFF7AcOF54U2uKYG68Lhx71Mdh1TNTbxPFUwvOJDXuY8BjTEKx1QUB/KQziWjQFxeCZrPHbqeaxLwSOzG5EV5NEBvAm1iqeYRKgwBxpTAwTXDPsVSGtCInxDgPuKn1OjS7FLutmjABSklMW41DAKoD3aYZUAJ0+EnTeCzxR2ANR1wg9feZe90IG+0NNAcyUJf8CFjGuVTRTdB00ZbVJH0wiLeRlZwyKtLYrfqIhnH+Ygq+AroL741XlcgPYoqZnxB4EUKJwtwl3mTg1GIscLVyhXJNeQjZ2Jl3COahJ3Iaq+3mys79PXdxuak1pW0kWrUYECYFR8CDHsdWfMr9d1r7Pm1PqJOOM9EXHKmZH5TCqpdy0rec2/uG5eWv7kqUVVd/vADxrzm666//ic/+vGaljXHH3/cb37z28IYE6qg0lHiwHTlwDaxabpSWKLrHcQBkUuYMMDAWIMfeeQRgigQKZqp8ctf/jIXaLhHK7tsSy8yMOxUF/2HrbH0ty5s1IUJ/exBoIunrZ40nkKEzrJb4/nOFHhY60qztZKesQzTNJIZDSNIV8reFMdT2mJvgIBhMItUhNhUya4OWpUhQkwrd9wyvxUN6BG/QTjfQidVIQlRDDlY6GB2/Mc//oHGF4wxBdNzf+NH8VcQRF+GQgYWlqGwoHpGE+gpWoe2W0xSiCA4gDU3N4swva1TVGonh8qk92SszTEb16y8ZpgW5vfc6jZ81FFIG1GfHktapm0uqTOaKlFB6CFPng1ycXF3SKDI4chynmvaqORFpCWcbKEJEdzvz1R57gxn/+SPbPBU8Y54UlZ1+/IjK+ZdGq7ElLR9vTr+z8/5172VMzy+Iw9VChCle7HZMgLphzJ3SKciV4X90h18oRN0e33M91qPVT9nYf2nGn0f+6hh5q1gOFdWviHt6+rQo4EBPmZOZix7rVtyz79gPv6EtnmrXVWuH7Bf8JBD9NlzETGXLV0WLYsCMPC8BSUCKoRHdBPkgSFpOCATmwx4hpeTgwuAH8mw1aC44eCaZJKRXKALMiL2gZS4yR2K5UCAc5sMYKNSCJBHpBfgR3pkXwRE7khiNwuP+FLECip1cQaD4csK8aA11CvuffdCsgtScm9y4RbrXhTelJTyqDDBkPeHSyCJC7MMvubO4OxuxsJHhdeDyxn81C2kyMWAXAN+DmlFpFvhNr0D7nKNzG4VlMBNzPXckVeCMx1NeknDSmAQDnfcw+130lAj9xH9UYKQkdLQmJCR1w8fGbdM7ssj0QgQoI53kneJIYWDCz4uWTgqr7GkJzvWP3xSeM95H9xVoDwV2naJM2zhe4QbsJEXnqGVjwLKd61WuKymOXQ3yxPAeDRB7KLSoglslDCHutx65WLymCZ1yRjI98L7dsQRR/CGM7gVr5qMHOyOcddddxE2mTGTmM8IA4WD54BW7Pin0r2zo4CxOYbbs70S6NuL27MWy9gq8gmb32l2eVDDDDCzwl5Ype1Vp9VHlRuVyx8u1DykkDTzmEUUjZ6fXu299TeR5W8kzjzDuPRrP7/zr1defRVvJp56t/3+NkKZQpWbfccUllKUOLBTOVACwDuV/aXKh+KADKCcMVNw4K7Jlnef+cxnWCHMHsJnn322K4FNyFCrEIqtIAT/s2a4OmSoNcM4Ump6Mmc5ntI6QZja4uyxRDQmuzulYxwmPC/BJHKWFs/rvSzaxHocZ4cADTCCV27Ia5WrUNKa+hfIVYWVv3SZX4v62bAGtGz41KobBxkpAKhC8iDW5PI53Ntw+Fy0aCGrAw1F0S45nbj9IqsfaQW+0AgELS1rFi3aramJxY0y8vRNr8k8dl3iUprJLGGujLSpbejOr+2yszkjEvBUR/VkBq2EObtCXzzDG/boHs0i3HdztYfIZIqLIzhckkaQViVBQA+HQ1x0tb1Um/9WmfVPy2N62IMXK7iej+ibQ97b8u0v2+GbNP8y7pLSWeuLsGBm7r6nzMz17rFX5dKlAmV5it0Gb1hkdK53dChhiDS8XZTK2lbdsDanPC9vtpY0lOmeSFXY05EyY1v1LTF7fo0BpJYCJZfKCfxo3Zr9/e3e2/8nhCLBeZB75p+x51+MXHSBMXtuZXUVzVi5kmAlqxBfgKMCccEhYA/sFZSBIReNjJQM9zgEeOCexweIW6ygDoAHB2oCDjLCN1RX3KFMyVt4FtfTwjsUKz/JUni/8NpN494UYw5LZ4kdVXw1nZuldDESDgxmNbl4fVBqYLqnf2XsHZzMveNeuOiXEgSXyvs5gIxm55CbJBCELIWIEg2UW3i4BjHGSUhi5CQxGTmj4ZKK0M5gVeaN4uAtBQADtxiLeIEpH3sjqih5e4EoLsEDCONnkUeDE0/SHZdpMAFgj+sQDIMwjkmqcfKKlbbQEMz+HPQFnYLThPTUJLWIYullMf5PXtMomdbRQaB6BlXeTN5/9H3c4aJI04QnL7zwAhpwdgnCUeLUU08lAAqST5FcRRrivjBQlMrrb7bnlnfob3UbrXENbT5TrYr6qbGwS8U0qY+ac5hVa+wlM4wou0oWHGoeUeZj4k4qU3D21VdSN/428Mur8z7/+m98/ZaOzl+/55De3sTJ//4xfPRk862xEVxQZ+myxIEp5cA2l7AprbZUWYkDI+YAAzoHUaOICYGKFBX+55xjanT5VA2llmZl2UkYGJbBX5pNlUwiaXWm9J40pmC9N6s7K4cxWrJgVTxQbVSpXo/BnOIzrKBHi/itioAddSBxeUBnyU25n5s4IOFNreE1zUrjeG/PqpWrNm/cAoiKlpW956CDXGuMmoR2PYGnr4/hIbIOwGnV6jXYE6OR8KIlu82aNQf9QkfSbmcxUlZTJl9b60ia67tNpmpT87K42ufRE1lgsDkjYi6s91UHME1aZQFrfg1bVSmpYlJZonyJ7XR8zcdD+fuUEbcrkn0+aG4MGNV5/wEJoy5pG1bOu69Zf3uGuC0V5Sp2la6b+Uxsn/2ir7+W/OY3o9/+liNTKDJZtgcARg4TphSlHYaBfY2NveYb7R52t25PWBE7vnrD1iWLF7QnbIzevSkzEtITaX1BDRHLc3vXe2xTQ4Gi7GymWeb1mk88oX3nCl8+bWZzmXRavZWWbhq2cf75nlNPiTSo6ESgApbcQw/EgAd42ThjhsU9FQpwSOYsEIIzgJbdOFjfBcQFdpLSbQjJQMtkRzTnWgqUp5N0phZoePLJJxkEDsGsPbkvwhgbAZHI+vANVrtFIB8j7GLBa2pqcm9O8ws+XrYeBUYSh5be3ynclveqkFFACw44zCHqG2CtAGDUbSB2bvKG85RcvK5E5aEjcIVlQaaUAzKhd3iEgVoyon/kJuXIt8Cj4o0t/rSQ2tFeu+1lIQaeF9DJHbpgXCbB0RIxoemhn/6Cw+gmaAgcxvALRIThE1rPlBbmdhNvAhoZlnjwytEi2sVAWvxjkby8n0wNgF60Nji+YStG3U/GMTfDKZbwFfrmuPXGVjb4NYijQbxPvKvylod5zNDtaEBj38eGcm1hpb1nrT2nUvWBioXlHg78xTaAJ7edyybuucf6r+8EX3t57YyZ1yaSt2h4UOc++clPsjmTu87fzVq6KHFgV+HAEEr6XYX0Ep3vEA4wtXCwKuzWW29lG4Cf/OQnl19+OefzzjvvC1/4AsI6TyePFRTOjILSFBAbdLZHYqbI20Y6bxOKKZYhrLTaWolth7uV7zRhFS0VScsECYOK7VQOf2nHkSgJHGb9sAZu8ytIbEUCOEhrZQEPSBhn6bKgVu6PRua8a86MxKZ1a1PZtOXxq71w8nmDdbUeJienmQUOXTtsuDs9wx9hkuTeYcYR8JMZcjv1mZSp7g7KTNVen3e3xbvNmTfn1TdWL1+z+c1nVoUr43MXLDGV2zlO5ubm3vzWXjtl4i7ui6gNhvF8tnszVn3UWjDbUx0kLLcV9phzqrSGKBp1VcfgigbVPK4btMWMPxDK/Y3Qy/mWSvPqWmNziJ21AkF/7p6kds5W37tjvvxL61+9bHnHiYsXsYBtEd2Ufvqf/jdWZD2G56gjoRArZSKRpCNAERhUOUMT1n7Xi2EoEsmnZBHeImjIWwQp0bOpTC4dx/c5n8vHe1OxVDbRm/cFsCcTkEl79tnn2re2YSumIr6IpY0zM489UplP47jGUi9uegDnamm6nnnyychJJ+mOqIMMip0ByzwLUxG5CFsC0JV+5DzYY5zgpUAIHFORYrHlUiwHdMpZLrieiLdrKK4U3KMKDL9EpUboROJ0nScLkuz8SwjDXRwpf2Rm/51P8HAU8N6iWGHR8njk8uEKH+H9wS8VmJADsDq4hGbnwAqH9xBfHKAC4kUNwTuPZynNkQOQzIU84tUFisgXSpnUyH1K4rXnJ44SeGiTnQMUyllyDSZsMD1juwMlrLcHzIOm8Dynuun5no+wdbAXjRXmdwYZugB8SPdNHvdGSNX4k4Fg6RrawpDOPE27OEaop0C78fOf//yyyy6ji1HQEPgTDenoeaIGYV5YdxxOmdrK9vwKx/C7hY2OkmoNMH5ErGxBAqkM4fZszy3XFlTbe9TrVY4bFdmFFTKAq+W+lMop1pO48079/M+1GPo1hu/2LZuNiopzzj//wgsvmNU4S30kkq10LnFgF+RACQDvgp32DiYZsfuaa65hZ7mrrrrq17/+NZPHmWeeCQxGyoQrkzQaM8YzyotlizqYFQi5xJLUqE+rL1OaU2BqKmv1ZIG+Wm9amew6k3Z3RtmHY2nMmzbrh7O2sg/nTB1jHC7TRF70JGxQrVdBYqzEmt8wQ8omrEf9kfLgnqFy8/7VJl7TbRvXx7vaFzXPntdYx2pP9vzxezVMo8oV15n4FEF9BxeULFOSuim/1TJUh3znpzztzzHqv5ShilHccKpninRKlNmX29vKV0kxgllaKmvHYA7xmDK+dNmSmt3nx9ZvzkeqVnWZ7QlwLyt7VVBcv9eOevD7JUC37dfNxkpjfpUn6seIbkW8+VmV+syox8tKpP4qQG7YRvDFHXUjRpjBNq3Y7T4ta2dC+RtrQlsrcjYbJpq4pgfiZalbTGuBaVTG66PPtMQ+9Poby7e2tu+9bK/sz68ut3OxAw+qPvAAuJHPm5g7qBBfTQwgcs3FkCTALg4nGegpvW5rbmOXZYdrfR5vOm+9tbYlbxoZWw8YcFWJ3/UNcJs10EYoGMKZUNmtgsG6uhovULi9S/WPrvlxAhXvYtKykKu315M3dTqN/5ylm/g8c4Fl8plnniEuFB0xJG3cBFpjKAPSE9+bxXvcATnzkwvIBm9gXuMCsDHkutzhih3zfVABcify9JhL2FkZJ2mkmqTmYGZHUTLZeG8CieclBNmCflHW8KICt1yGgyH5SYIB1UkC1Kx8oYKNueBwPR141WVxu2QkPXoNTOIUJfHbBBVTPln4iKRAt94B1RX/SZk4OOD0RMnAKtBR8fTT9ikN4aAv0FPACtAdIzaT9ZBqi2nbisGEue1ibTmeTWyljiGUTmcZ/Ej6nbmAMlnri/QCQxi0Wdt1wQUXkFeyD66xyB3nXVbvswpRZettCWt5m72qy9gQ0/FT60orFSrFsm4o6teJd9UQJd6VtlutvbDG62eScEQat3zVNEpC845Y07px85HHrNjSerNu/zWbidQ3XPK5z33+8593o9Bvm+zd/KWLEgd2HQ6UAPCu01fveEplbmB8Zh794Q9/yMqTXznHDTfcwJoZvHEQ3yXNGGaRotxV4/w2VeegUR89dtRvsL7XAYXg4T77cG8O4KcwMPZhAmuBAHsyrB/GtklkaT0HJFZuqxqQRk06uCYllRcSexKwMM1HjAplMbZT8bJs0n62szfySmJuY83M2ih1hb14TesBLwn0oMdGhwsq5ho7KYjai64Xb9w+/MvsJ9IedHOloA/VjYdFfTOuQzRtdkAvLrY6E202b6qg2Tn+Yf22Ezk9m1cMMXVv2rTjGbM7ZfekfD32nGQXGxhBo9XW2h6KEB2qIpVVC6Nrw+bsOk9DmZ/V1GwhWhHIz6pQka5QEzjdINZfhbiQU11zTdHuG+NDXc/p6RdhWn6Dz7M2gg8xDXe4p0B4oKs893KncbgV8m469OC9Xnujc/3GrS/de8+eDz2Ml5n3nHNtnwpOxQo3DihwXaCF+ZyRC5GwQbq0gleaNEQ2JrQP94lSQk+tzVZ0JrScvjEajVhmLujVYx1bDK8nEKmYM6/Z4/MHHfMXHNnTWWysQo9zYG3uaNPZ+th5UR2mOVc80W1PRRXviiP1bHsFkPv5gUAvIEcVMtQB2kR4hUjEPnmOJIT0xjUrnHmKVZbrosbtocod6z0gh2SVhaDb2jPWAickHz0rwZx4PykQDMYdLmAvFMIxHG7d5RsTUuMkFSJkS+HSy9OEwyNpL2oRVGOceWMhewDlA366Bcqn6v4svAA2s4TYAcXqmy3Exuh9MPVLmTBN6fL8/oMOOogPgTcT1RLcAxiD+gqVNcPRgEWRBQXAKmpvduzPRXRShRROq2t5eXDQwIgNuw4//HBaAUp0iRyu+W6C6Xkh7aL3WcOMIwydJdgeamnRDhtFdg68nfEfZpwn18c//nF8nmXM3GH2oXmipno1X6RMfXW79UaH/Va3juG3Pakls4z5OKDhdGaXh7X6sDar3FpQZe85g4Uzzp4OSq6RmbW/bOXyxtofbetVVz70wx/e2tn1j7zV0Dzvsi9cdNZZZ4kzyxjp7K+h9LfEgWnCgRIAniYdUSJjpBxwB1+sTHhEozr97W9/i0H4iCOOYF8NYPAJJ3xY7+4xqquAIQj+fZAPDyDwlAP/nBIQSftQwUgrHj6dmtNUcQp+UKiXNTbKPqxjmmQLAqaYnKn8pRN5TMQsGLbZfR4jZ28mF8sYvfhIA4kzWPY0ZwmxhktwLq+nlDurAoS6Xq2FK7szqQ2J5JsrU97VmeqKaGUkFPDYQZ8d8oCEtaDPCnhx0rYDPi3gAxKbPrUCWS0/9noA0gT3wgEWg7POPxWSGloBycT+MpT9Wc2BDlOEt8IXJbMr+p2pFZwKedgPiYnhAMEsq+8cg7YCvSb/bP7lLcMC6yqYDe61UlktrqzidjxrElubgBwgYTYHonynQmq3M1nL9HpjvXE72TW72r/PbjPLg+BGM2jka6NGYxkxtDGTS2dxpgNh9XYd5/BekSrEq6sJOtTWiRaRv20z5jFTmGCJmqmQYyKdohk6sbh6sT0iXeT9fnOfffeb2bCp68c/iXTHUmXR8BGHOWBUkQKFIjHLlrCgR6xJ4CKEJyEesRjZGvq5QEwERiprUiioJcrrTd+6tlR52EdwlKZ584KVDfSm4fVX1VSrV8s56D7F0L4+ZL26ZkTKjPcckH3scb+Vh1KHbMVEtf7roAO0skrFQqUWUZykXg5WpXLmJyK7WGzkkVPDthMiLEsoX3nlFXmKPMRBKzBYIeXjk7wt6SRfCbVUDTPBGHz70wQnQJIE+FFsdUzrcALNAvZqKETSFWF3ktkzMcXzJjz77LP43dCztGViCp3MUmA+egcwCQzfa6+9hOYRUl48mfuqDyYf5rB8gC9a9Flc8L2L6RhiCN0MVRTOwU2QMC7ZIG1uoihBbSTKBbdYHM5BvyTjk5SVyWR0n07/C9oFkfiDAOMJjMd4ouI75nK8/LtWQwpZLY3iDhfEuALBorWk41Cv4PBCJxYmHvJaSsADHAsqWxyxpfZ+++3HQMHIT/axcUYhVWcMh+Nb4tbrbfaaLn1jDCMwS7E0gnSqSUGzw6z4Ddr1Zfb8CjH8ehAV+omUC/WC9bfRjnf3/PorX/7d3XevaGtf9u5/u/6LF5900kl9RvuxEdpfWelviQPTigM7/m6nFbklYkoccDkgQzHS5Gc/+9lzzz337rvvZmHwKaecsqC+4VO98ZMv+dLsU062Yj2h/fZjKSUwRWk2FU7gELzqljTeC4reXkIRJ1I1tTj2Sh3P3aBXq9YIQUHVCkyCD+M5Q9lIM1pvXgMYx1IWsaDwl+Y6kTWTOQ3Ihcs0oaGAXkFvWT4USaQyyFY4EiedDeyBWF5WneqaxzCokrqIb6EMyLrtw1Paw0pjtqJR+zwFPCaheLgAPgGAfVgCPRYrS1UW0jvTpKMQdqbD/sb0T5IwTk2lTLYCPnHgyhERmc1gLaJh63lTbSqYydMonL1N9q1N52yCTbLoyLYxNtJmSgJrK+4rG7TwW7dDhjm72nPgnBo9l9i6YV0m2bHulU27za3ff+nc6jAtUOkKWKt+UxTSIYYFuSZQEwdpkCkFXqo8E3b4bW+jkVnvm523o7o/4wVO4p8c9AdQBYDl7aY07bH1ct3DzqLazOrqwMOPsM7K+ujJGy2995VXkAI5EIiRApF9xQACqGA/VYQnCEbMBeuytNJhkYZ5EF9TaTIOaO3r81nT26lb9eWKvXY2VV5bTxTxTE6FWIP5AQViFWcVT51rfiLx6KGgf7/9U8cfp9/9V5+KfqWMAETzTL5rafi4D+kVZQ4rRXbqY5bYfiED92ZYyvZCgmwL+K9SynpI7F2kHPCor6Cp/QMZ2N9ww8Ya09zcTOU7nSoIECdbMRM1NTXJ0lmXwy6HdjqpLiWDL+SFRNBHtYHyZSpVG4OJ2eEdoZZkUEsgIvjPOkxh+A7zjirBkF3GJ8wh5biU8JPEYBtwLHhYUDGjAfjWXQFBODR+QifjAC8JcxkjAGvv2fQLhruwqrBMKXZUNE9B4kIKQYZAX5Yu00yGOEYMADCs4JgCSia8CrdpolihFUBBBnPahWv6DlG9mx0VAGt9sYffcccd+CbceOON+AgAnsfJFtyeCey8st16kxW/PXprgkCSWiJHSETcyGz04Kz4rYtos6P2/GptSZ3WEFHOYc6GBcKqvllVfjCQ/vq6X1/7i1+2tbUf+773XfmlL2G6V8lVql2y+yb8fSgV+HbiQAkAv516853YFhmbMZ2deOKJJ5xwAuu+rrryysv+8pdfXvnTU6/5+SmZ9LyfXKk/+njw4guCu++urGMKbEzNYD5wwnAmEeAJSNUm7DOWW6FDQWJN7auUzlqO87AG+k3kAcMaUaDwYiJaclw5FfPPl8x6M5ZyNs7lzdb2Lo8vgOxF8504vwogqvU+gCI1aYFwlVUagESQDvAaDwQkq/AuCswq32uwEYlBpwaIFYDKTfUI2vqAqqU8kRWqgm8sXQW9KzzsqBScdAKMFax3HpHL4S9l8hjYha3ZqU7XAOEWsZ3VhslhtpvyRHxsO2V7NDPqjx7YtCTZtWVDy5uV+c114WZnxh36fcZAKjY0cBpSo+y0NMCKMnTOUd7FiG5Hj7Qz/zSq09oxMf3+Rt3K0SDljW0FkktbQ4tToM54freNr22KJ1ZX//Xu+a+9miRGzkWff3ldS093jApxRkcKhE7IRrrlDosSMakNZ66U9xmxSXWxCW8JwMVG054crw2WcUtZ8pM2lnxNGXf5o+RseY9UnzmHgsKe+vrQWWdlFyzIPfqY3tpqE6T6gAPD7z3W0zxP7dbFMQyLEe9AboSrwaYqBEuhchYADC5Cui0EGMiChT8Ls0zqNZXCTyzAkIQbOR/CpFY3ksLpQXkbRfaFny46IjsO5Kxdh2YWVI+ktJ2YBvskMAZs1tzM9+i+XTuRoh1UjR/+888/L8qmHSSdtMcDGMULCchxa+OV4MNx02AHZmQAFaNigNvE2WKjKUyCYGDeZ1wbGOgEGKON4qLPCucWNy0v+CQZmSEVNQSf5E4ZFiaQMXQZsBBTLaoKtJN0qPSRq57YYV2U8Lvf/Q6vGbaxgC3s5oi/wDHHHOO+BjssYbgEKEU3xaw32rQWQj2zx2+S9VasqGL2ZsWvHfHZtWx0FCHIs7akVltY6wlvM/wOLJL37Wc/+xnOdGbePOUTp37hoovY2QgKx0/kwJpKv0scmDYcKAHgadMVJUImggNoVTlWrVx59c9+9utbfnNjPP6hr37104nEooDR+9z/YUutuO23qZf+L/rhE724biqwp/5Th0Jt/FTIURDcRJAzXBkOZHFqZnZ0pisVyyisHJr7aOEPtl98p1M5nIfZvs9KA4BNZTROZlH6Wl29uTVWojvekUkZnnw4FCn3ByNp5Z+sjMYgaucMIAa+EtCCkBYSEFjBV2VIVc1UbVb1KUq4oXBR3291BWHq6L9Dkc5P9ZsMqhSVS3HMwdUOhgZzq5ugNc3y6vkQy5WJ7BWwygNaeYBrnL14hIe2FfLbZcFcVdCoCHhYvQza1Orm7dnciIHU0U+zs84KhIympiYRNdyZ2LX0Iu8inU+mbUr3VZ5mdt3o0bcGPtaT9XpS93s1IisHTHv/ePCUbt1HTOfQii0Ht/du8OeTTX++k4bnPvKhyF7L9urs2LBxE2ZJOA30xVPOFZiGg77CXs5kQcmQymNoV33HNR4E3mBZfXWww8StnZ2BsfNjgwYS0wtq0bjzKrkFOF3G7juzG/0f+aB95JFGNm3jCVBW7imL4D8t6aRzt+XpvwKcI+QhrhETCyc9nDBdzpMEoZafWLDBnIUr+uiI/gKm+i/wEvs/RidcE4lGM9XVF62vkHWSkNcbkzXWpKL5psVDWApao8d3+MbuXHLVJ6JpRKhi/TwXvLTTWblQyEy+Nd4EfC4AV7wqvMagX2kOdlT0UKBiWiR3wE5sQsZPADOuxUBi4DHfXeE7VnhNyik4hDbear4+iGG4BvEC8IDruzT0pV28/LxUa9askd5hGKeZjI0wubATizCZQigBhdenPvUpkp199tlEHMRhrUiW4o8gCp0qbwTJWFv0Roe5ql1fh8+zs8cvhl+l+tZw8rIrg0TT0GeX28S72mOGXR91dKUSJMIZ+qXjOD/++OMEE/3rX/9KOK4LL7zws5/7bOPMxuJklJ6WOPD24EAJAL89+rHUCsUBd/pfuGjR1Vdf/e1vf5tI0ddde+3tnd0HPP73T2/YfGhFefzCC8qfejr29VXW+o2eivLK71xmdnR55jRiAUWOwmtoAJaYcM66RErJA3723XT+MJUBF0N4LXOo1bPqLyZBkE8GPJwPZJfMae2MrVq7afPWtdkMRt7wkj32yir/atv5p6fV6lwza+KrbGNOzFoq7BbwycR9WTPYzEkVrLCsKtoRI5lBuVbzqxxOndxzDL7OD67gFIzCBqnrrDH24FOtvKy9yuPazwpkr8bmxiGfJ+ABptm4BGMxJe5SyGdH/HlChRGLMuRT1mkFnp1qXCYgXoiEkcvlkQtxdUbdjrkMK4rIHG5Kmb/J7d7po3isf9wCKYBrioULsWRDW+y0+ZFr9EAq8PHNwQ8GMx12oMLQylOKC5Y3FT5rzqJPs9GN/esby1auyvgDwQsvInNVTW1NbR2WnJdffhnREJMIS0AlNvJICIYt6DtoHBoQXNZYeN3dtqVp8Wy7F991gb5KweHwUOszevZ1VR9DpAlGtEyLlvOksEeLvOGq1bbd3NwMXGftIt6k7373uwsjAyGgIydJbBsXAI+kRWPtlhHlw98VXQOiKu/JAEgwovyTkwhkLtHFhix+pzNtSKrkJu8AKB2UhQuAhGcrkng6PMJSjcqGIWL//fcfoLKZDuS5NBR2OpZqOAzZYGC+KTylXdUe6XG14OARGFgOOoX7lICbNxH15Jomy+JkVD/4HZCGo7CWwmuXjPFfUIsUgmcv1lE+PS5ohfj0QpKQN/6KprIEt1EwDa91gh1wBsYz6DHCuO4wI2EpnUvf4e6BOp7zsccei3bjyiuvpL9Gkn24VjtDvpWz9Y1d5mvtWkuPsaVXx/Dbm9aIqYmE4LUJ9axVRbSZEWtuhb5btbVbHSt+HcysFjypQ70imgra/4c//AGrL28gihiWj4HSUVuQYDwUOjWUTiUO7BocKAHgXaOfSlSOigMygqOy/frXv37Jly6563/v+vkvfnH2mpYFdvT07t6T582vmz3H/8Mf5QOBjlWrPI8/5r3lN+bWrb7meZFj36s8fJWDsJrg+d9Bn4Ig1PSxM+YGASx9yJxJjH8+nxFl6yTNbiyrede8mlisZ/XqFsJT7T3fD77tjiVx1DU8AXAvkTByeSNngX6VVy1PmT5N28s2xXnltaxcmlVcK54Cc9XM6PzvIG3Kp83qHzpvRzWANzPxOlhdpMhg4TEQViUgGTzzECSKkFWAYZ+XwNRm0MumTRqxi9m6SYVuKkBhTqlwV7G08HDZCwDDkoNJBzU8MzQYErjV7AAzNz0/Qcvuz5FfqCZuf6Dax6qJIyLKfs4Ic3i7wXf23V2+6cBktbm47na/tkWLJAMR1VrAcV6r0uouLKv5ckQLmQS1+tUvvUS1OefsqgMOpF3Kk1zXQbysocKSRuAr2kKZCEBSu9vS7QlRv1RmCzs/l2pxNSYHFBbp3u5crg7TL9Zg1Bes6CaN0iLg7cxZGlTITmWJ38bfgicFl6q2gYcQht4B3uJQivRWCIARspEFWbvIMuDjjjuuSCsGljtpv6GBtwVDJYgdPuO5N2lVja5gCOMYXZ5pk5ovDlrAYNO/FXxQOA/zuorT/nTmeeHIg4GXNxYm88YyuAG0BlPO58bBRCbvhSRgVGFsBBUzUjFqcaCQoqdIwzj28MMPgz9ZGMIBosZKLCrFwqoHVyTlj+qMjRubUc8AAEAASURBVBrcC/qlUqzTtALzLyVPSOGjomT8iV3mAAsZTGgObAcNMvTRNaOChRTFnMUMctRRR7344ov0Aiqkm2++WbbrG5I5bu1DPqV1bgKu21P2ijZzVZe+Pma0J7TulFot5QgqFqGeq4J6XcRuLNcWVWqL67R6R0pwpgGmg75pD20RhgEWIRORC2R++eWXcx4nMh9/F5RKKHFg6jlQAsBTz/NSjVPEATUVq/V4IXYaOPnkk5Haf/GLX3zvj3/8iWGc9MzTZ1xw4b4HH5S/804ATeK5Zyp/fFWqriZ7yqn5p56suPlGszsR2GORHq0geLTgQv6q/1zoNCWSLfT3Mcu9kN99t9Uf/q+sqNx3n3cxwQFQAZqvrFrOsiXkHrDKogULVAqHbDY34C+I14ntDI4iqjOGRJu407YFKlamRf4pa7MKeUWqvmooQYXWovmqeGUzBtDif8tPMBiQmLMS30gOVO6jUOXtz78d0fJj4MP+u31JnZDIiv5Fi5CxwJB/+ctfTjvtNNe5kcrEzrB9vpH+IiYNMoqYXHBRYwWUiAecEQWQGhFiwJpo/Y8++v3B4Id1+6J87I964mnb3Ip91w7v6y0/yfDvwWIrw7S7//M/KzZujNVWRy/5glIPOIyAFC7oBaLRYgqmOkrmJjLWDq2UMD5BkCvNm8tZQS/Wewv7PjAYdYMHpiocrCJyw3a6S7kIbM9oVbVTPaexHVAOqw899FBY4ZbgtEvJ63xK2KDU+6ZaOi0OEDvEwOdpQc2uTwS2a5YYuNBrOjeIfidqFMY6BoTp80IOxzEGFmyDjAA4COAVwhAERhousdwf0CgyYpPkkQxZAFHaDmbjjmBRgA0GfEkArsbtVsz43KReoJ0UO84zqjFCOhEOCuNzc3MzF+MscOdmh2lMNLjqgHgZ4uA5gQCH1EoUpxPbL+t7V65cychP9vvvv5/xH+YM6MTCQugX1nsvXbp0cBqG9/4Rll2OtJZOc0WHtq7baI1rHSktllGTAEUxG5T5Nfb4nRm251Xai2rUHr8hZWzuz63mfQubtng78w6cccYZOGPLkpbCZIWEla5LHHh7c6AEgN/e/VtqnZo/mEWYitCao4i9/Iorbrj2+uuuv+6mzZsOe/GFC847973//e3yssru9RuM3ZbYd/wxunxFxyVfKX/ob12nnu479qj8ytWVX75EU3GcidikIhkX2NUUHu6borZNNDuN5zLdAVtxAsQ4wByMXyj+ckgn4haoqFeTpUyqQL1tyMkhn6ZwcMkF//qQrHOTk9zkglxc95XiPOUnt1RGpxx1z71wEoz0hEiHQYP1pWRAsOPAyEC7gMFIeEiKJMD0gcSACDhypTW5QLyIOBh4OTCYAEcBtwA8KqJY+MNZDsRo3hZFMf1tGI76Hyw6x1f9Ra36Ep1tqlTYbGEdrbTid/1v+NbbiLGs/b9v+JoWqIyDDnffV+r9xz/+QZmIO8i+w0keMJMQaLxdaOt9dr5dz2Pvb920sS1l+Ho9VmSupvtQVSjbr9ZnBx5U5wTcEAUBIh1k8EaxgS0MQd6Fn+KEOQF1TFARcBIMLIVB3nCMnaDa3rbFwDrhHuK7oKbpyUmIZHDAq4Loa9jWsNqNRx02Nd3Jd8RozJoIBq7DDjsMxk6Ie3lh21FXYXiEMwySDHR8sxyiw4Jj7GglCBlfZT5tDsZA4PRImk92kjF8sYUPHxqIDusog5i4ioykhGmYhkbRKSh6mGXoGn4yBcAWSKV3+maBEdBNRg4SfuELX7j33nvxbEfNwfoRmDMSFRI00GXD1KMKZuHv5l5zRZu2tsvY0Gu3J9nlSE9j+FVREO2Q18LwOyOsDL8Lqu0ltUZdBOCr5mihCpXr7bffjvYfjwM8sa+44gp2IeYdoI2SYJiqS7dLHHibc6AEgN/mHVxqHhwolOFmNjR841v/7ytf/fIdf/4zU8LJp58+d87cs88661NXfL+xcVbu5JPTy1+PsK/Pc8/rDTX5r/5HuK1t65aNnrvvC3z7G0Z9o9XTU3bKKcBeTHuC+dQsQx3Oetp+1FdY4RT1gFslF+iwm5qaQIxE4MA5EK02DnJgyAJSoLRAOdz3QN1Ul+qJunB+9D3rg/1OW7d/0pewL2dhnr6so/uDdCguZ0BWxAgyM0kj5GHckzsvvfRSe3s7ZlvmctmIQlHQ3xqZ0TljZkEERI4X8Alggw/cR6xB5gPxuqAUSY6jkEq3NC6UikPhesfbWEkVAamKu/zLtazLX3xxyMx3Hn1k7TnnDQm83NKoAlmHeqHkiSeeoJsk4lRhAocMO2cR+czRtvgClSEvLu0Bb0bzBsoifj/O5ViSnXcO8QfKsAYrOvu6prAd47oWqigWmpF60RogPWMYZFUb5dIL6CkKI9yOq7LxZXYZKBoTJH461L05vrInODdfJS6sWGBg7PSkEGUH38jOCus9EnbDOr5u4ByKMK4BwNOTk9IWKOTg22HVgKj2+Gr4phjTRtLYImmGbDV1UTLwhqMwLzU2NzfzFTMqAvmYF3jK+MPUQBbuUBpZXEOuWzhPSQn9vBgARS74yojqRwJGMB65KQurm87X0iKhEH0EamKawIzAhFIYUnEk7aIoKe3SSy9FHcNqHRiLdzHWY3grVQxXDhkF99I1pOSalBziKOTkVVNMLKOzy9GqTu2tmN6atLuSRiJLLCw1ehD+sMKv1UT0hqjWXK7tVmc3VXkCSnkrQom1cuWqG264Ab0/yl80I3fddRdx1ArK3/X6TlhaOpc4MCEcmHixaULIKhVS4sCkcsCZttS8jhT1q1/9iq35wFof/OAHzz///MMOP0KpVXvjeiTUe+NN+RVvMCUFr746ccpp+t3/G0ym01/+inbfPYHvfU/LZI3mOeE9l4GPlHEYqNaPk5icJpX+IoWjfsY5jQSCHjGocufggw9GoqXVeHnh2ur6te5EOodsgggT8kgawh2RD9z0WCFw5EOURGIASOAmjUAJ0CUB4h2KfCZ7DtwCyYuNhTBOPAJzogh3zB4VrpDH/TFzAOczs7ur+5hjql56JdZQF3n8ce+Chc46Z4octvchiYMOQlQCz9MQ3O3oke2tDXZbIv9SqxejwOqtuYV1vvXt6Y633iirnxMpK88nOjNGtGlGZFVbfkm9l5DaB801+iKluTyauAuopTCk3ueeew7egt6J1gO8hJnYOj7wgQ9MXFUTUBJv+2OPPQbIPPLII+WVmIBCJ7QI4acUWeQ9mdA6R1oYtPHVPProoxCGG2fhZzLSIiY/HUTy7mFeo6/5unkbEeinGyddNkAtB/ojvIUB7Yw/uFGI6m2KaYYMlyqWHwODOdDFQAyP+Gr4ySiE8hEYjGUYQChGdcgG94J+GXj5pkDRHHxiU0y/S/yYL1wOME0wQdAKikKXivsx167hd+TlUyDHrbfeykjICh24CvRFUUusBzi5Q/4w/vNWUAI1klgu4D9uydxhNRKLYNZ2Wss7tQ1dxua43Z7SYxmWw7D0SC1SYnuF6pA2I2LPqrAXVmmLaw3swM7EozNc33PPPddee+0jjzzCXH/66aefd955u8R6/pEzv5SyxIHxc6AEgMfPw1IJuzAH1Axm20j27IB33XXXsRZ0t8VLzj3n06efcTphfBWyBeik04ln/hnZ512d37tca29jN96yO/7QeeBB5c/+Mzm3yXfuJzMvvlL905+k32oJzZtnNDQqKKzcVx1cPBRvdjg1DpVppPeQYxAQaRTmEaZhRBly4tnFzEoz2d+V2pF7mPLRdgslk0rPSOkelA7zNdKJGK6RFdDTQ7MsYOOMWAaYRywD5wOAWU1HASLGcYF8piwglZW01HVCG28zEVScSCIiSOrZdNenz4v+/vfpcEj/w/+E3/8BtUhaHTuuh94BvfOyIVnSLwTK2h4As69jfnWXN2XZm7vzzTW+N7ekcluXaxVN9dXRtStermpomtVQs77b3K0WK1L+4CavX+zTTvWTcYJg+IyJg05BLMbTD4v6j3/840suuWQyqhtzmdAJYQRwwkIly9vGXNQkZYRCt+Qdvyhu0sm/EMJY3Q0e4GtqamqaVuTBAKEQ9AL6BWxgr5vOMr3b0aARAlOhxeOFZAQbCTSa/N5WNQiF9DIX2C2ZL4CFTB8MTdxxVYdPPfUUESXQhsBwRlpXLTLdXo/hmOZ2BCMYalCmDFoK8ah40D/SL9KQkTfHLfDOO+/ke7ntttsokw2EWGP1iU98otC+OhxJch8+46DONaocZrqmefMIpEF2vLCxCLf2mm90WC1dnk0xbWtS605r7IboTEG6z2NVBvTasNWAz3OFvVutPqeCNVpqZNmwYdONN9xw00038RWj+QX3/vu//7soMkbewOJkl56WOPC24cB4nXDeNowoNeSdyQFmBQ5gEqL8RRddBIK65pprcGf61re+Rdws5g/21fBEouVHH43AUPODHzDH5DZvTB7//qp994599kLv3nua195U/tZbW6vKozff3PNvB3hO/ff8+o1VX/9abstGf9NCJiuswg4UVpOXWrc5vHlwQrpAjJxM0iwDQ2HvhgXiDoCQgCgtLS1YIDmAXoi5HON3xpsQygcUQkMQtnBXpoPEms010BfZF+MwEgN4Xu67tj6eIrdhn0SycTp2WEvsgLpG9xNW5jKdF18c+cPvCUtlXv698vd9YFTVkRiaEd9ZyUxhgn5pTkVFmddLLBm7M6VeFnZCYiNl/ONYjzazcXZrNhjy6jnL4wf1msTZVq1jW+B+4D26RowqNQQjmSFRwWFAO6oT/Dmn2zJgWgSdeB4i/KEcYQEebzh3RtXSyU7MV4m7KQMOOqnJrmu05QMMUHAwSoBzphvfpC18LOyOBqQksBCuH9ycnnRCGBo61HboHxnEiNHFqyiD0mg7ZfLSu6zjgjGTA/YCCHlF+YKAZ1K1IDp+MurSKPSJGI3RME4eYRNeMmSDUVGh4uDAYCv6X4YyKhqgeRxh1Q899BAFIiTwLX/xi18EYSI/cHZZOpJymHbFF4BZDOGgsroaNSZ68+6Mtbqjz+d5awKfZxUPghCVSBEEPSzH8BvW6yPa3Aptt2qCXXnCPs3Mmw8+8ggm37vvvpt2ffSjH8WXDUA+ttaNhPhSmhIH3gYcKAHgt0EnlpowMRxgpsd5iQP32ltuuYXFM7/5zW9AjOeeey5gWLlIKVdny9c4u/K005mP6h57iAk0edIT6VdfrXr3von7H9Sb59vfviwU62p75bXgw39LXXJJvqfHM39++Zln5jZt8u/JniKIbNt9dMgco5o1R9vUwvJpIPiQA00/8zfCLitpxTWaZJQMJZNKzKiIR2TkIAu0AXpxwJPrxx9/nJ9cgyHx00PExIWYNJiGQPXASKQchGPEzVFVt+PE/UjKTsZiF385csvNSCuJCy6oPO8zyjIs2vkdl7JdCvG74xayMnYtn9+/5+671zQ09mZVZcm0XRbW0qYVZF+prN3d1lqR9+TzVibWvTGRJDS0VTbLH1Z7dtF125U7OT94NwAeyHk4GKMqYhkwohsy1vR5Z2g3bwVhujBWA9ERASeHE2MvlbcUCIfj7nQDwHxBuOXTMAmBO/YWTkJOGZ0omDeNVejYzUBr8nMSahtXkZAKhsTnmcD1jLd8KeAcENe4Cp2SzMJk0C/RE8G6sBrvG9BUc3MzbrQoRzAO8/ai/wK2HXLIIRDFSEsyAcPTahCANprDIVShMWGmoyMkOgYj2Ng4Ss/iXUIoqc985jOw4pxzzqFMYLB8y+PhgJILbDNledZ1WSrOc4++Na63pezetJEzlTcZg3zYq1WEbIJdzSq3FxLquY7AV76trVuuue226x3nNTSqP/jBD9grwXV6GlszS7lKHHiHcKDkAv0O6ehSM0fKAWZNSQriuu+++zAIo+4FTaFVPfvsswluoea5frOSAztUBjVHZdJEpej5ze/y69bqwYD93e/njj+27M57UlUV2vwFxsv/sr/zfe2JRwKnnxY+9lj2JPCUE22S3YbYX4jZWpWoTuzMS/hJB9KMZ0KlFThWAU7AkAPKcRuIXhyZRsJ+gPkR2pB1ZD3qgCwj5d1EpHPJw+aA1AWmxQGPtgC9JOYKogztAvqCHgs13IAxmoCAApgUqA8MFllnPM2BHrUflOof+ko3t2zu/PgplU8/ZRp68sKLqi+/AmOuANAx10IVNJZ2rWlZY7PXUUWjXbd7MBRZ2ZlbUO3rTKoQKb1b1m7oSM6u9La09s6tCfdk2fxWW7p4UWOFsbTeO5WGMKhlf2D2XIUq9tWQL2LMbZ+IV2a7MiCPg1DbaHmItYs9c/rQBqF8aLhNAoCbmpq2o3tn/0AdxjJvXBIwV0LL9GEavQnwQJ2BSZ/e5KewavpQCD0uVXimoOBgXEURA4BkCGIs2tl9u+P6oZ/vBeiLSZNrhk2Ix3uocIBljAL10UA8ccRHgAhSKFKBf0wiHOgiJf1O7BrpCEjlfYY24lSLlRUfb6YM8OrYXh6KxX2aQtg9iIW7+IXxpeBpPOb5hZeaj0yIgdpUNted873Zoa3p0TfF9I6UzQa/6ZzB5gIs9/V6rAo2+A1rdVFrPsGuarU5ZdrjjzyEjp61vtD2kY98BJOvu5RmJ/J/x69aKUWJA9OGAyUAPG26okTI9OOAzKbMo0x1xLrgAuPSWWedNWBDWiEcmIQg1CcK2ZbZ3uEtL++++ueeufOy996tPfi3/BEHV93xl94D97M2biFZ6Kc/yj34UPSLF2c3rGfHXi8bmeIhbXh13J0AwcqztW+CnDzGSAMpX0QZfqLyR9DELCyBsqaSBreZQFmCk7kezkgtCDECzt00XAygTdqCrAzGwAMWUwai53vf+14kUck1IH1hUUWuKRbg63SrbrWsjp19Vvk/nskGApn/+FrVpV/VvGyONZCSIqUVeURFvfHeV199/cX1yW4zVDWjwV8xq7nax3rg+qiRzNuprN0QsR9/+l/7LV3UbYdrIp6I3zOvIr+omp2YJ/1VKaQc1QnSJBz+z//8T0LHsWQUVk8xDYX0DLiGk0jq6EHE/3/6EAad0xYA8+HgI0pE5Yl3nRjQPSP+KV805jsULmjB0M0deOCBI849pQkhFToxoYO7qBgvG2YKgCLX0+r1K2SKsFfuoGEk+BnXqBiAvrAasgdT7maRR6ymwVmab40RmzsM1GTEZ4prN+WUcUBqRPXANE0voL8GjWMUZekyNMjTwS2S5hc5kxGPA8JqHHHEEWvWrEGnhlqWqJnjjIDdxx+1p7sN3F3ZkW/p8KyPa+1xvTOt9WZ5rlZOgdfZ4LcqbNVH9Lll1oJqzehd+7+//y2+aYwkIo0Q4wrtgzRhDA0s0vbSoxIH3t4c2G6cens3tdS6EgfGxgHmIg7UtMQyIcyj6FyPP/54DML4S7tBQSgcnITAo6y4nDEKO4t+FUDKZ61U2spkYzfeFDxw/8QnzyJGs2fe7Og/n4u9Z//Qsy+k9tzTOORQ+/XlkR9dnlm1uvz9xxmsUMI0rPablcMpuf+HqqpffywX40FAtI6CmeaRgBFoEB2YR4HB2KncCid2ZpUa3cLhLQIuxgeAtyjs2SWINKAsMSyI5n6ENJARaR5tPRBIpB+sFmA24JBbgnvh0uBeKF44PHWJVAUmYvHf3ab993cira1pHJKvuyFy6imawUa8Tkc43B8/BkUYypr2fS+3r1q7MWGH91y8oDxstLTl5tf5NvbYUb/t080X/u+VfXaf35ova6r2sAB4cW1+dhng031P3HZM4gUMQczFFfBTn/oUxgc87nA2nlaBYd2+Ey4U6e5JZNNQRU83ADyAUZA8TXglhKFkQRcGPBNkVTjYDsXdqb5XyD0GMUYtRip8VWQQmyacHMwUIRuCGe35eMWMiWGT8ZYDskdIuZTDfIGyElcdhlnwswBg3nNMyuhTKL/QBj7CkgfTPOQdl/8Uy5j/zDPPQAYpUT0wlXCgzRlDjW6xNI3lNgxuMIrgAjSTpR/Nzc0kKLSND0nbkDfdknnKzBHPWas6WPGrre9VPs8dKS3GFkcmfmHov+2QT6sK2jNC+sxya2Yw/eZT99zzx5sff/QRxx/tpLPOUv5okDGGBg5JW+lmiQPvNA6UAPA7rcdL7R0XB5jAQFYEfsQmjFceU+yZZ54JDGBSlHlIwad+Jz1kyb5rBZAcdMwfJuqOdiBV5o03M7ffETj+vYlLvuI9+BDtwfuDrVuydTXBre2Jc8+zn35Gf//7/Ev3yq1bV3nhhUQm8dTNYCWQ2gNBtUCcptVPy9mCaUJmQVqHVISvF0iYVXYSZgZcyoyLkDQhVQj3RQ4AoMJMykewgCvcxPiAzp6KkDwQJSekRool9jXCGd7gdBNdRslFxBfpPXpMkYQKQrNTz72Q/uIl4X8+7bW1WGOj/5pfhN9/PI7qSrXhQF9p1PjPkLopll/e4TVtc3Vrtrk+lM5bK9e2zm8It6ZDzTXe7mS+dePG5tl1m5OBhXWYB+z9ZuIdNzGMGjn90PnpT3+aTwAD+2WXXcaKQXiLU/R0W9eK1gPHcmw17r5fI2/jJKWchgAYixbfIPL9dDPj881i+8WySg8yLBTiqEnqndEWyxDBaMmYyajCUMCAhgdNkbFltOVPUnrW7mImZZAH3UE5y6onZByDGxyirCSiAWM79PMTUI3+AlUmzJmQily2UB32Z85UwaCEHxNNo0UA7/EgQ4ri4MVjOQDlQzMqGMIK4PaMP9F4mqDKVdtD2GlLX99prujU1vfom3v1Nqy+aTuTN9Rsbut+j1UW0upCdmPUym957e93/eaRv97e07753/bfH7U7gZ1RNLhMKF2UOFDiwNg4UALAY+NbKdc7lANq/nIOLp5++mkMwnhDYaZg+Q2rg0444QQwABNkkTmS7WMpAPyqilEnXc/nbI+Revb53IYN2oa15vW32u8/KnzVzzKLFmtbN4e7YvG999BWrDau/qn9ymu+I4/0NM40QqHAkt2Va67Hy4JhYFpfjX3UqbLV4eDubaZiuTn82W2dmwTZgiXQyEnYNFj3BSoW+aZIA928gy8oX6ogOyU/8MADCBlcwzREFg5WkfGTw002uJDR3qEoOginaLAHrnoYkWhIc3MzVgIqGlyasBA1A+u00T4kvv8D3+9+F0om0sFA5hNnlF36H555TarXVEzOIbIPLnCEd6ATF/jnNuV6s7503m6P5+dUetd1pFpWrlBe8YHqA5Y0tKX9dWUGyTqSltr6gk2A53nYA2nIhoyw3jEkg1T8ID784Q8DLEEprLtm9SBviITGmWJiitAvaBPhmzV70wSWQNKLL76I0/h0iLSsXjnLIqoc8j19Rw9On74DVeKUC55hGWdTUxO9PH1og28cWDjRgaK8w/eBPXWARtycVnQO+DToa0bCNWvW8BJyDcG8hCgXJgqXFjafkZaRAcUKJlnZlo+xHf9hSKJqulKOARSO5KfUwsSBnzNBH/B5puRDDz10cF6qGHyzyB1KlsJ56/DtQn2GmoCGEO0PgD0ha4IoP2dbm2LWyg59XY/Gct+2pNaT1pKE3FY+z7ZXt8r8dl1IC6RbVz9553P3//7N155lK79TTz0F6Lt06TKhf7RNK9Lq0qMSB96xHCgB4Hds15caPi4OyExJEUzAf/rTn9hGmP0SsZTiEYpNmJkeoNg/S+ERLXsDM8m5lXIl7rMITWqeVv8LumZTBNYd3X9fcPHuyfvutVe/qbV2aHffnTvqyIoH78+xr288mY6U+S76XO7+B0Jf+2b2lecjJ31Us3RvQ51RRmAtZlJlnHQCNzkmTKdaZ7WRkjpUXcNLBthh0U875ChHbkDgpk2b31q7rh2rtWb7/ME5s2YhMw2OrUWxQx4uo3CxxlSIwaS5uVkk2v/P3p0AeFJU9wP/nXPvfR8sO6ACCh5BgQgIQYkCEo2iQoJo/sbEeEWDVzRGjfG+E6NRghoVMSKXoICIUVFi4o0KyrEssLss7D2zc/+O/6e7ZptmZnaYa2dml26Wnvp11/HqVXXV+773qgpqIoSxDOBbknYU2pI44w0EGoJAQ6aB553J6ajYPfngu1rbbST+a1CsVat3393xoQ+XL/5aS+cuWvnOxz+x5eMfbzrhqaIlCoWpIjUuN+L5lu7qr+/XK/KbdlXntRQay/kN2wbmN/b/dt2mrs6uuaX+SuvKpzxmeUdFyYVFLfm5pcqTV9OA+Bk163RexGg2Q4wNJlYtyyihVyQ0TD9JSdFJgKjtqwRRODHS3cwGksjToJ3BYTIWqqSCkwxoPsqL4MkS1jvMLIvCd+oeyGCaxq6whfLMEpbmM/IgOg7D+rzn1CuWYk6tg0y6uEmGh7CUGRP6Nd4aw4MvTMh/ytkbyg2ZQ92swdQEltV44vg0n6QFKa5k5B8jAbJlkrX2GPN9Rz4ialMw3l3OY8wkUDXkHgiGqJ3lSz/FvUVu3O8pZ5E9iZwTTnAoKhjhb99Wv2tHbmNnfWt3cXuPI46ifTDD5NNars8rdm+/+Ts333DxrT++vlDvhcPPO+8lZ555enNTi+pNso5Dqpz9zDjwCOdABoAf4R0gq/4UcMAU52JjvOiii+yVBRuYks855xy7U5CNInizB+IGgKXIh51QZSgaKBZpzHv7Kps2F9padr3vgw1POqrn0xfk5s5p6Olt+d+behYubtm6rfeQ9uqmTbUnPCE3b369VG59+5v712+Ye+YZlY7OwuIlTMQ1S4oGDcXxUcR7JyAuMQKE/hVqW6r96+v13kJ5dVfPvHvv2XrPhvV9vQPsaeEYEuJp4pc4Yo3UgjbdAiraegDYT/Gty4U/xQ91HDHhFLTKSFkoEc3oIbYSyAAkXnPLuOctWxzLILn8QGX3ZZdWv/Sl/I0/aHUMUT7XcdDBpTe+sfXss4vzH7Lp9EjZT/gZuupOefrVhurOgdJArb5pZ3XNwmJHb62/klvaWrxjW39LvfPeDRu3d9WWNFUWrHnsqkXNjcXcqjmVIxaHLaCnGwBTIpBfGcG+9rWvnXXWWUnN6YNgqsc//vFUGx5OZ+MmNCQBXNXrrMzU1uFAmuTVTNGGpISGGWeOb5OVFYrAHCyacXpwRr+ylRSrL7PkLGFUICNpuNCjDB12oXdkFNugCDPLuoRR6UAgGJ3GXl+llcneMvWj3+it0aeT7IR7CrULOvjqCaYx3vIrNpLsrfslCVUk0GyeBaEhZwAeNJ38TmOhCGqgF73oRcy8nLep9r761a8qzjg2SS7FmUdHufNwvn17bd323IaO/APd0T7Pu/sKA9TWhVpbU21ROXfPrT+943sX//5Hl3fv2Pz4o44898XnogdzJkmA5NmVcSDjwIgcyADwiGzJHmYcmAgHzHbmaStObdJ42WWX0VVbGAkGn/DMP623LmprzM9pyM9tKjYUh+1ZFK01jS7Wv8gAG/0X/xzEzrGSOAal0e4YXKbr+f677uj5nx+XD17b97FPFJ56bPUjHy8cfmjhF78u1qv11ubSjo6eY/4g/8vfFF75ysrNtzScc1YEPVevan7Ck2oD/c7PzYFWTY1xIUNuiqxV+2+ubPlYfvd1xXpnLlet5ZpqjUcWFr8q3/LcLQ/sWrZsaaFQZFD97ne/y35LCiEFBulkaF71OvhB/PKWJcd0DnmmbONDou+rn4kUlSog4iwnPRbCQr02v6HxsHyu4X9uql96RfNtvyvWatVcsWvN6tKb/77tnOfn58yP2L5PxNyg54gafGNH5ffbStQUmzorc5oKbQ359dtrqxYUKtXc/R1VW17d11Ht6diWq/Tk56yyHrhaqR61vL5yjpXAM2AZwFL7P3/rW9+yAN5CALJsYHIwMTGIMbpO2Gkwaa/J4wpZMdbxNWhvbwer/HRAq05I8p585qnuNNYgs5UPhx/+9H8FQ0g0UpH1eajOBn/s0OIMg9ARDMw7wzUjDTSES34G2rDLpdX85MTO5Ai2GdZmCZEJ2YFaP2k3rPINC33RyZaYdtBI4k9zAHkuDsxcgSBzRn4/zQtHH330iJTQV/KjZrWG21VBRaQ1sZpNcH4yzFduKBH0fd3rXkdhd+2119LtvupVr/JF0Bcoa0SSRn8YZWs8Hsw7isvGu25H7fbt9Q0dhfu7c9u7cx29+b5arpzLtzVUix3r7/nRZb/6zlfuX/+7FcuXvOiFZ597XmSCJhFMjIDRycveZhzIOJBwIAPACSuyQMaBKeOAWZDf19VXXw0J//d/fy9fKB5z0rOefuYLn/K0P3a+a1Mp19qUn1d2t7VvsVwMHtIB/AYgHFFidk8m6fScGkHk1Pwao+VabVdHoaW5+6b/rRdrvd+6oX7TDwsrlxavvKr6qENbb/3dwML5pR2d1Xnz+ufPqXZ0lE55evWHPyq94uXVO9c3v+41lTvuaTr+KcXGZqcG1wq1XPfVtfteW6xF22nGF8Ki8ur1huq8vygt/0Ahz9M1v2PHzl/8/Oe7u7oQSQ3v5A8XzEOK5UhG2GLmlYqUY0crNoe0SSfkO213ft0Ry7CWS7cDplCP6FrNfl9bvnVN5aKLWu+4o2XLlhLYm8v3tLXVzjij/OI/az7xZAxJiJyMsJVkMiQQqzoi3nZV6r/YUO3NlXoruS0dldULSl399a6+2vI5xXs7anNj1YnzkA5ZWO7sr3X31ZfNKd7++98c2tTx+Mc+avmSpaq2L8gbQm36J/59/OMf/7u/+zuyGtCblE5gdQ4qP3MCpf1g4YTkVTr56GGZuOQwgbTDc4Y57d0KD5xyyik6KmrZjoJj7fDI+/oJUX6WnANM+0NVR3U1G05y1p1ANbZfAeOGzfCmpOmnpDXhXhZLehOdGS6aPYSNWDsMtBcXUMfr2EdE1UJBuXbtWiPzbKM8kIq3tFE2i1YdnwYVA/8pjsdQLlcdbyFerzyBThl+p6oWmtVp4Y7SNTFdcMEFdvEAfeFwvJpMESplQI+XEeV6Krm7d1buiHa6Kmzuym3tynf21T20A0ZD37Ytv7j2zu9/bf3N3+PMc/rpp9tD5NRTT92bJXzEts4eZhzIODAZDqQk7Mlkk6XNOJBxIMWBeBIc/H3vhnu/9tX/uuiii391881t8xYd/4wzTjnzBU847uR8fIRsV19lblO8nrMxN6cxx+4Xrek0RUaI5kEwnMp7aDBg08FZN3KZji4fdq5S6V+/rrBoceeXvtTw5Cf3fPgT5TUre77//Yb7H8ivWtX469/WWxobu7o6ly5v3vJA36GPqm3dlj/zjN3lu1a8+tfF0i5eW/03N1d+ZpetluKhPcVjevNNfbl6sbr4LaVFb3dgcWQUrdep8Mn0xBQwQ7nmbwYuARvqHH/88ekqpMP4k/4ZUTzkioWI8Cww82HiD0me+hklt6NVhH3rub7enl//JnfvPQPf/W7ppz+v3357U2eHtb+1Qm6gWO455JCdTz121flvaDr88Go1chsvRhaeidgBUuWPFowBcK1aL/z6vv6tfQ2UDPfsqAK95VLu3u2VlfNLEPmmHQPti8q7emsD1fri1tLGXQPuDYXa1nt+37J7nSZYunQJzGAJ7oRZNBqJe3mHq9dff/0zn/lMkJKOgwgbIobGCmCGiBnAzHhNGWRfjo4MtlNy7A2S9FLk6ZPCADBIkADg6WQaGmYPAMYKGAkumllohAw4jVMrpQnwA4FMfj3nXvrs+B4jzEVNAPpy9ddPrJvl8zzNHWYsRKNTNCMwLx/kMaHbuZAKsr29nVISV0Mms43yQHZSQd2ASxH0js4wj4gAnbIP+2A5nE++FqFE96uuusq2HevWrXOW2ytf+Uot+6xnPct6pSkqot5Xq2/YVQN979lZ2LS7vqsnZ9vnzp5qY2333T+/Yf2Nl937y2/X+ncfd+wxf37un5111gsC/lf6bGujpHWyQMaBA48DGQA+8No0q9Hs4sCeSbd2yy23XnzxxdZM3rnuroWLV5x0+nNPOuPPqiuO6q44HMJCoMK8BgC4uri5sND+RsBwY66VfTjCw9aiutxSuDD82vMkvEjuIbZ7ZPoENqNo8a/O3fWBflCw5/bbC9VK71XfKh/1mP73fKhw6OryDT/qe8whpdfcO+eE3U6v6P50Q+GC7upAvqFUzheqfUc3tLyvnltqZl/Ye+sbev9v55wXnFWtDDQf/thaZ8fdO3fcfuvvqsByqbh0xco58+Z2bN25+uBVS5YuKxYBzIj0+GJKJqwlyD7aZ8vzvO0v/RfHiCIPnu0Uw8PBlFHCOBhXJbZIRymiX1GGKgqnRgEqgOg5UFvL9Q84Drjrxh/WN95TX78+//vbKnfc3vrAVj7PRcXU8wOlQu+y5bkTnlo87YyGJz2p4bBHdfUOtLW1IvDOdXesu3P92rWsEQcnMGzqpJNYYlXfaMl1/vatAxs6iwJbu2sIX9JS2NbL7Ty/pKkQmX+b8vrGhu39q+eVbZ+6eWf/qgVl0Y5YXG2t777lN7ds3bYFzzhkks7B0akjMm6Pvdz0KjZV4jWwaun72WefnZQbOjzM8JOf/ET45JNPHq/xf8oBsEqAMWTrIbUh8ibb8Ax5tY9+zjgADq2Trl3ScOmH0xZGD+/WG2+8UUNMxm1+agkOVPFlAIDlzIFFV6EpEJ5Zdg2pZmhNHZv6gN+NA42C+sBa33C672wjeET6PYTeqWNUAau55buoz7CaF4lBJqSaDOdDbthlsLrtttsszNHl7NNhCQ//Zy4hE2ZUPEuFKc49N1DPb+yo3rm9ds+u3H0dhe090YlH5Wr39ltv+s33Lr31R1cNdG173OMeq2hLfA9ee3A0h82yThW4nd0zDhzwHMgA8AHfxFkFZxEHTMBmYkvv7LFx2aWX37d586JVhx5x4vPWHP/8hWsemyuVGwv5xkK9GRJuys8t19vKtfnNuUUthXmNdUuIWxvyjbaPHYSCoV57AGICKsdWXZS4+ATncsVcpR907PnNb5sOrtU2/1GxuLv/53Prb2ktdXb3DVTKA/2F6ETcfM9zynPfOWBrrN0fbW76Qk+/U3hsGrVmVe3+LYVDD+nftr1y0EFNy1cW2td0DFS67r+vWC/lFs5fdOLx5eaWlqOfVO3YXV62rLJtW8Pag+v9A4rN54v1gb58SysrawRIbZY9iINjrBtLFvHi20GIG9W7Vsn19zPmFktlMlP1vs31zs7KlvvrvQO1+zbmduyo37UuVyjm77uPra123/2lHTsaqwODuattId/T3FI/+KDc4YcXn3p86ZjjGp9wZL41IoAcEmSRwD+bG9vMifM2owoTRHt7ezh6cTJCWNIyoWZqjPv37KjdtTNfyRc4PG/rrhw8r6G3Vn9gV2X1wnJff/3+3dX2hcVdffW+geqS1hKEXC7l5zcWyvnKMQcVm+2gUqtt2bIFuoMibGWUXuY3JaQmNA8P6D/OAWZrestb3vLe9743XVzctaINjVyBb+gMduB0tCF5imN/XQ8tH+BTwCzDeCVVulJDkozxJ3oYga1FZ2lkStKgAcywCYfNusaYz+SjzSwADu1iUSWjPXXJDPpbBkqSXgFn6ieae5TuMXnmj55DICmJYykHF3GeArwYgoPDDNKWUJUO4B4ibYkMOiKens5pybDcbKMzTXM6jGaXKvgoLAkOPkR2I1cFyjX+z/DwcccdFz5/kDis+5XD2Cso/1AiP+p//Md/1I4f/OAHjQMf/ehHaehe/epXB9Pr2DNM0x/CUR2QlIuON9zcWbtjW+3uXfZxiNb69vX3PXD7L+74/qW3/fgbu7fe+6hDDj7rBWDvC/m2KHEyhQ4nI3uScSDjwHg5kAHg8XIsi59xYAo4YNLs7e/9wfd+8F8Xf/XKb3xjZ0f34rVHHnbi89qPP3PeisfUcjzZ6hAi57WGYq6lVIs30MpFxyQ01dmH5zfm2xrrLeVCa0OhtEeHjKwYDYcpfxAYezgoAgy+HSQ+ehj5BIsWwTCg2pPa7msLG54jQfeFS8vXL8/XqwPVgXiTpbqTaPvndrV+Zle9pav75jWbv3hk4wP3lzdvLs+dW7zn3mKpWOrqkle+WoHu4MxSFVCvypNZOF+v9c+dVxWBcbWrt37o2uquDkuf603NtW3bi4c9ptrRVV6zumZDlHKp2NZa3bjJllm1nbvqsl2yqHrXPfllS3IdnVgmz9qW7blqpSD5QLXQsavE8Q+44pNNuRCbkVlHI6TsDKd8vq9Yqj36UcXHHlVftTK3alXx0EOajzu2sHixLcGCACIaIv2IzMcP8gw7op1aSGZMKwGVEcsIZ1MitcQiE4Epf/eOyt07CtBv90D9/o7+NQsbAPG7t/evnF9m+V+/bWB1BAoK9+7oXz3fpsr5jTv7D1rQoLIr7f+8BNWRe7bcXABw8BKEJUiTVlGyBg829r75o1BrgK0EBoPtH7M3zojGK95yO4C2vb19b9HQSN1gU+40sdIS68NerOnnEwsD1c41oS9wgjHLzyiUTCz/saSacQAMZtj5mWLCUW06zIwwAaOAN1obbfHUpz51mnUQe2smnY3mhXnQ/lsBdPFECCyaKS7tjdTwHD4PBmrrY31cfEBmVoMwOrXD3/renUGN516x/7P0uoJDPoZrjrR+RGW9OvTQQ43DlJLDcxvxiUyU8olPfAKifuc732kw+ed//mfaHwq7yQyPsg3FRfNGtL6m/sDu6t076+t35jbvzu/sqd15669u/+E3bv/RFTs23rZ82eLnP+/5Z59z9jHHPKXISWpCe2uNWLvsYcaBjAOT4cDg4pDJZJGlzTiQcWACHGhqaIIcXJ/s7v7mt77FJnzdJe/50Zf+adkhT3j0U//00Sc8p2V5+4ad4Bm34ryNshiHWxryNtBqKtZbGnI2Rmop19rK1QXN+fnN+bmR+3S+udHbaMNigDYiKUZ0kQt0mK8jS2h4GmG9PYgvH0sTHjh/mCwCPOaru/KFSrSUNwrHc3yuXCiW2nL9HfWWXFf70lv+8qXNhXK7tVlr1hT6+2q7u2o7dw5suT/f29N/6+2FRfP6O3sLPSzJPyuvWNX33f8uLVyY+81v8mBw/0DJmZC7u62udV4x8ST3k5+29vdXfvtr5QQ/aIA5MhfGUkY1n2+xJ/NvY4TK4FwcXHsMuFqeO9BQ7lm+vNjUXB2oFJzWeEh73lIxcZevKBy0KrdidfPagxqWL2dafwi6HWRMxIuwwDfiU8yr6NGei7RE3iJZspiBwckyLaYJ8vHEJOM9klPdWUe3b6ts2k3LEaHfzbt4OEO/uQ07Bxa1lRpKlv7W5rWUGouFzbsH5rcwauce2D2wsEX9sa160HxxBykO0nlAv/JHKgmP8QTlrn1q5WOfwS3OhATxZFXtHv49+JeMG8AtM6xNs/YmppNrwzI8DpyY7LissA+WSk0JArH5M5BgdTHmAMAP0vdICnHrBQbwNnSY6a+61gS/baSsz4A9wjMOgAEtZIC+OgbyeOzrh0agKXT6mDyfEYZOdksqjLC3HP8FY1R7e7vAlHwgkydylBzQ76JMpIcyVjDGGgd0AAHTiM3YhldBE4QMtYilKMaEX/3qV5rJsOZnGBxGKZEekL3XV89FhY6ABdjn/7KXvSzJdpS0o7yKqjH42okL9W3d9fU7ap39RUqdjbfdfP3VV/zie1dsuee2+fPa/uTMZ599zoc55mimUTLMXmUcyDgwIxzILMAzwvas0IwDD3IgmU+5Zn3zm9+89NJLr7/+hp6e3uWPOfrgY569+Iln5hY/ppq3RrTgkBy678ZSnhGwgQG1nG8u5kDixhIH6QgAsxXPaayBxPMsHC1zmQaVCw6MjdBSZCiGGQPCfbD0JBRtRtPz49zdp+Tzld4rlzZfsrpWrfb19TY1NZMYWIl7Vm9refdmGxVv6v7TrrZ/bm8/lAgSyQJRnv6DlAs1pUSPQO3IjRmUjQquMQsXajt2Fue0/vKG7+7YsqW8q6s8p2nxIYcubig3L1jc/bOfNK5ZwxrsEONSW1v/fRtLS5dFP8v5hsXLeu+5p7x0Sb2js9rT23jkEdBsrVIpNTXVKrXyyuWF1rZchIqVByRG/yk2MgVDTdHP+HdU/SgwgStpnZCW9GnzJ3COcEwII1EF8X2M+RNicaazP/e7LdWOfkt56zv7BrbsrK/GiFJu444qU//8luK2zhpH6DVzix391d099eXzi72V+vauyip6jnzuoLbqo5cw/kbS4ZByUUtKtgeVg3/gnATD7wuMoSziLMmVgZclh0VxRPYGBooJ8wDAyQlJKB9OfMjBhxA2wUrIHhJzxIIe9iFKkOroJrmdcsopZOIpyfZhy01HoAsA/ChTgqUr/Wpfh1WfKsf6C02gscKGSfu60HT+oSeovp4AcEJx1qzqotPfCglVSEKJxQ7URjZhwhlnwPKQR9IMUpWQJxCYpt+Cf75r/UfPgamMyeFViDxLqE1THsKBSKuUwVFV8Gl70t7ePtytY29VSKppQAvaK4Mw5OxUpKiRHjqwh8iiXX755Xrahz70IUo6Tuw0Gueff34SOQkMJ/hhn8RFRIuCtvXU7tlhg6vaXbf95vvXXnHjtZffe+fvFy6Y/8zTn/WCs55/6ql/nDYyT6bEhyUpi5BxIOPABDiQAeAJMC1LknFgyjkQ6ZRjxXJ027Vz5zXXXPf1S7727e/c0N3Tv2jtE9cc99w1x57ZsOxRfbViBdKM4kZTKswHOHGTZj0EdJuB4bJArdkZSw35VnfYuKE+j4m4MTe3HNmQLTC2kLgc40GwMa5JAIcm9R2VO44tVdfXdzYP/NPK8ua2vr7uxqZmyLZa7qv/5dbSiVvqtYb66s+V2s6OoeYgH2K4Gw8mezBoeBHh4lQBfvT29m3etGnDpo1MGSSJYqG45uA1Rx5pTVQcL6pW/F+Eqf0f/Yh9tCO/ZnAveRuih8xDXG/jGHEWMfzeY+IOrHpoij0px/UXwa5gEOa9CWoS32Fg9jQ24RFFHIA0InvP1V+vb9hRu7ejXtGOdrTqrKy/7c7Vi5tWHrTm3p3VOU35xS2FB3bXnIHUvqDYU8s9sLNy0EJO7pYKV1bNt294vrE48OTVZV4Ast1LiRHnyIgWB8bLmPsPO+xwXp17SBiKmZPn4w0EblhKar30Rz7ykde//vV7y0FMrwAMp7OgiuUH8rHbzYj0izm1m2ClqUIJQdzuXPQXJ5xwAhSxNxrSqaYwHFgRMpzmooOewt327Mxu01y6Kqu71meCFtYhw1lH009GoCQ0gTs0zi8doAJ9mSKDeTChKmmv5EmScHoCxklwzipfXzSdBXvm2rVrR7SXTg89D1tKwrEkpnUHxkyjJd6inJ8z3cd4FR9JtjqwXiQr7aUI0Jp9OLSaOLYksM07f2lnEJ533nme25/P8UKBmMk3YkxGzaYMd26t/OKXN3//uqtuvPaye9ax98559rPPOOuss055+jNaWsyY0Zg/+eISHmaBjAMZB6acAxkAnnKWZhlmHJgCDphoXRZ2XnPNNZdccsn1376+q6+6dO3jH338nxx8zBmtKw7ryZW39VgRGxlZXQCk85P4x5aKOf7SIHGZlZi/dMm+WSBxvblUjyFxvrlUYyh29lJkJW6oA8ksyS1lXtbQQK229b2Frf9EWK1uaO374vyen+dLlUJ9xUDbiwbKJ26vlRzP+uRS+7WF3PzI03pCl3pJR3DhC8dZF4AkCnvCPkBeIRull3jNQhkibploD2QOky4BiIJgpwpByAtcCZTHADjC7pzl7u+s3rMz110FXgu7+2qbdtXoI2pb7uwt8GhftXROcV5jYVNnta9SW7OgVKnW79tVXbkgAr0bd/Vzh57r7KN89chF9RVzRtsr6EHIbRnzwMD6desPbl/b2NiAbGYu0ufUmlUdX2lj1Ze85CWf+9znHrax0KCVQaBjjz0WBguMch+SEBvF1BmGPE/iTyYgZ5gHDNZqYPC+KGJ08hAQIkxz0T63n/70p2CDS9HTVnpSX7XGdof92to3fC/TRkNgeEIJqy9IxoPDc2MsZRZUOaJJ3DDFaGlQChAr5LOP7gl58g8jCf6w+kJ0TNPWvsLnVjR4O818G2N9E/o5yEDsIC6+SeuoIYZrdnVD/ZSsyEgKwiX7OVMN0KfwnFKixb362Mtf/vJ169a9//3vp2hDwCTbLilOVko0elx8yRWXXn75vevuWDC37Ywznw33MkfToYgwO5sGYdmVcSDjwBAOZAB4CEOynxkHZhcHwuy7q2PnNd/65te/fvkN11/f0dW35ODDDv/DMxc94fS57Y/vzbUO1HID1WjLKWAYHOZlC5yCxFyDI0hciJYQA8N2o2IlbijV2YfLtpsugsQ50Bc2jpyoy/W5zbn5pa5lu1+8uHote02+Vq7uLFf7cg2Lq/WGPgsxa7llhTUXF5qPj72pAeBBUX4PywYhcaAZAfE1Gk4W00VoIFiEQyCZB1k5iEpE5L0tFt1T3Az/RTmyOdqF7UkJeVbDMmwSVaE7UldUt1y9v5q7r7O6qSPXUymqao8Vv5213d39S1uqrY35W2+/u69aPPxRqxqK+c1dhcaGkv2u7Gl9f8fAqvkNHt7XaaVzYXGT1cL1VW2VxyzRmCOIWZGFPLQGqnp66E5yDmTWA1rsIT43XywH/AP9WkHX3t4+XgvM3nhtld2b3/xmDo3E9LEImlhCYA0gHMfsNDbN1kgEKJSMvjej/d5qOiXP4S6bP9H4jGIAn5KChmei4rAcKJVWMA2PNuVPlKvWWlmXk3nS+lNe0MNmiJLgRgtV8kc46aSTwirfURJy2mdvDAvXR4k2Ja+QF8YTajV7I8NUPhMdlUMEn/mxfFxTQsaEM/FZ2auZTtMdew3gdExyU68AC6ccHGIOdH3hhRdazsDbWf4W+nJ7+Zd/+ZcpHFXQz+zMqnzllTas/IbWsej69NPZe5/3jGec2tTcVNiXZ8VPuDmyhBkHMg6MzoEMAI/On+xtxoEZ5oDZN02B/YGuv/47V155xbXXXLt1+465Sw4+9NjTDz3uT1Y+9pjeQnNfNd9fyzMeDlTzlVoMicHYOAvCQQSJ+QbHtmIAuFyqsxLbbbixGO2t5Sc/ar61c4qbnph/y9ELry7nugFdgJpvcTVX7Mw/sX/RB5vmHM+D2r5c0briyNGLm3KgUDiFdZH94K+47HQ19oTTtSPnEZ7gNEKq94Q/a7dANYlDtKgCs+lKE4+uBx64/5e/urmnuws8ndM2Z/nqtW2Ll+2qNOzsKvRGVXDQUd05Ro47WtBcqHc+sGHTfbt6bWNWn99a3N1b39FTP6T9oEMPWrq9W7i6dkF04NXGnRVW4iWtXN1zC5sqRy53KDM2j2B8Rwwjc8EJUQ88MPB//1v99neK995TaWkuPOkJDaedUXjUoyul4l3r19915zqHc4AiGOuavEHGlsLW05LOb731VqbFh22fpCmJyGRKja6VbTYmh+lp33SrAWMKpWd5WLKnKkKyCzTz47TVF04AehMENT3l4ljUJ6tVHQOGpNI6+uijAxunjYBQHDJcYa0v6AtkWpxp2QJF1cM2/fQAYCRRBukbBkA2Z2TTUzz5yU+2URnKZ4RpodDR7wltojlgLKyjFka85g6HjWnrJNrUtjt2gb5GsLe97W2Ks5241rTflfHE3DHJsgLNGuWGG26Ae20cwE1AjZ797Gc/97nPPfHEE7P1vaP3jextxoHZz4EHx6bZT2tGYcaBRzgHEkkCH+ikb/zhjVdcccU3rrxq46b7WuYte+xxpx5xwpmH/sFJ1YY5vdVSTyXHkBjj4Vyllu+rAEiRcRgwrUZe0/EmVQBuBKbqlpkWo4218iBxtVbp3r7hiAW/ffrqH6xs+nWxUOkYWL2xfvK23Bn50kLO1SzGFhu3NRXaGgDm6Cf83FBmUs43RH7UgxtQRYJbEEPCiqgUIB6xHUPt3C17A4N5sh155JEMZSJbZcrBLKwcC2knKd+MSMBYH6rYMCSObFeRGqWrAABAAElEQVSlXtt0/9bf3HHf3fd37B7IVQuNT3rS0QOF4q6e+s4epuDavKbC/NZ8d19u2y6nK/ctnVvavOHunb3l+UuWrZxXbGgoP9CVp1xYMa9YrdoRurqgNe8UaE3Ss2X9iUeuWDDHyUaRcX9Y+RHYoOmobttS+a9L8l/+SrkyMNDXy609Vyr1HHxI01vfXDzqqFy5wWNoZN26dcw1YTedwSYaIcsx8QO4AiR4PH7hC1/gCD2mNHEkBLMUsRtDJvZk5qxoLd9INRt7luOLSbq1OpH1XjeTcnqKTgDw2rVrx0fuhGJHnbJeV01Q/5hjjgk22AnlNO5EyqWt4y/K7KyncXuefodzNAS69XZIxt0wwvzOlziYwffW6Ho1X31pDbP2bwvmRLDKCoJxM2IvCQJt7hQT1EC2kRNWBCwXXEjQtjfy9pLldDxOWOqzDecPt7e3K5jdVd82XLvSG4xPbRWU7nKmGkSq9C9+8Yu0b5jmMAVrfa2toHCxQxhULBqqxlV6SOKuOexGaW61olgf1mGe85znwL1PecpTEo3JuHKejobJysg4kHFgnBzIAPA4GZZFzzgwazhgqo6v2k9/+rMrroSEv/H7391WaprzhD88+Q9OPP3Ip57avGhVZ399d3+9r5LvrRX6Krm+ATAsthLXcv0VSy0ZiqOzh0gLVbCYNFbMszwOVAacyOtgB/tplYpENMcX5i08bbABdUkEe27Vo5OZoN+ylcZ5MNiu1HyqI7fq6HymaF2xHap5VkPF0bbV0UlOUULILfzbw8U0mgzSqvexTTmSYNirC/0D/d++7tskG5LiksVLlq9ctnz5iqZoLVwUM44bAvGv8Qk9ocQ4lyj44M8krz3vHiyC9iDaf3nwRZ5agVczPu/qqe7qzXUPsJiX+irVDZu3be8cmLNkRf9Arrenq79n58plC+vF5u7eyNi+qFWG+e3dtU133r5oXtOjHr3Wzs8dfbVlcwrzm4tbnSrVXVs1vzDPRtEsKuXe+gO/f/QhaxYuWMQROiblQXr2kBIfefnjmypve0dzdze7S3dPT4lsWCrTe3Sd8vSW17+uuGxF2CkMHAKDCXPtsfDKzAUYwJ+yGq9gx3h1xhlnOAf4Na95Dc/DQMwY73ovgAEDc5Els8LAdBzjJWCMZQ2PBuRAHeCNfWLDqUjTUPT0A2AN7XRl/qhMZIkReDg3pvCJZpUbW2sAJOAcFUNADtPAYUUHAgToOPRP/rHClGh6+PBtrrwafoHKtp7y3Jeii/o6UE59YPnAZKqQECbAoqgzWLzKXopIB/xoI+hxdGQ+nNTpfIJsBmqqSZdBQ9EMoc94xjPwxKuEM0lgqmhL+PaZz3yGYsJoQ7Hy0pe+1PITR5FzZQ+lmyN8zqG5gVhrHIx/oxCTZItOYWpBuNruWYYFLcLvHeh1Pfaxj00ySQJTVbUDO580h/cX1u2PNB/YvWjf1S4DwPuOt1nOGQemjwNGbRdvQ4uUrrzyql/8/GfVXOExj/uD45/+rBOecdrBjzlyd3++s7/QUy3AwzYZ7h2IYHBvNQcVWz/csbt3Zyff3VLrnLlVxwgBeU4vileVyjZyTo2tuJHbc75WyheKxWhpcWRf9JPhtxDtvAXilj23MXWhFoHesP+WOOAxR+tCLTrEmNG47GQmb2uNgHSEnCOzM1slZ2wCujyDT3WM8GJEWs93dnbct5nQtbljVyeiirni459w1EEHr47A4EimyzHPtSoX2sgfUDOyjEcoeLC6g803+Bxb7A0W2dJrPQO5Lvf+fHd/rmuAgd0xUQVH+/YO1Luif7m+fjHVhJ95tVSob9685f5tzv/ItTaVly5umzdvARO9guY313fed09/tdCwaPXcpvzytsLugfrWTtyrr5xXaohWctcOmlNdMy/3u5tvXnvIISxRgabhFWQArnZ09PzHha0XfzViYD3X3dtD/gsAuKdtfuOnPlk8/PB4tVrE2gdrXq8zjrHnwAZckRnr9lbEIDse+kc+1t295z3vASNvuummh758+F+Su4ieui7RnzANZngyvIIPn9c4YyiFrwFrEuxNhnYfe6HSjqs0OYckxPef/exnT3ziE8MOTMMzSWKmXw1/OBZSlch+ZZcgn5UKBhSXznbKwwlblPu9730P6LU4PJwqNOVljZhhQkDYPNmQYblmQP7h1Vj4JuckHzCPZy8sFHCpV2PMYTh5IU+cgXsTV2ebqDMwJsVNOPPhxU3ySUJSkg/afKSO4fVKjzIWxebeFTCwn0m0qQ0EMriK2Gjg5JNPftWrXmWBzD/8wz9QrxhzWMtR5UoXKgn9haUZmkzDJeeTJ9HSVaMFczAYey/oy4vb4KO3/Mmf/MmZZ57JRyPO+yGZpwvKwg/LAaw2tptc0kqEh001sxHQTPmlY/ClT/rMzJKUlb6PODB9a5/2UQWybDMOZBzAgTBVE6dcb37zmzbdt/m66779zauv+toFH/rcx9656qCD/vhZp536zNNPOeGkXLmZd25Hb2Sx7ADhBvLr7t2c7+9sbSzOWbisoSU/ABJX6n3VnL21HOBbsb1WFcaLt9eKbMUF4XolAsQxKi7GC1IjsyTsGkFiwDhfBIPZe4NbNQDMjFyGl/PRqmNhoDc6iimCzYB0lARmjl+xOdebioWGcq4hziHa1FqSQlvr8kcftvLRvd2dWx94YMv9W8pt83sG8uSuX/z8lwsWzF+xfMmcVlaamBMxhh1Tr4gRvpiAL0BTj3BsZAmv+MeeGnEg31+t91fcc31VPKk7g6oabemMCdHDXkb1+F9vpYZd3Mtr1uHWGKv5gltxneuvFnFyzsLlTS1tfEE7d+++a8O24qYdi+e3HXLwCvi5Nmf1/Kbc4jn5gYHcuh1RFivm5ufhTrRjWX9p58ZaX//6LczMhY2bNrKUohZMHcmXNa/litt39Pf3sYSE6pvFB7SlqhV3Vzs7rQBPoGUytXskQ1M+Qd/+LnYgszySdPtg1FFZKR+SoihE0lEjjvwykIEA2/wQbQF28dBPeE0oHDnlpJ/KXzWZ4OxKxf7GQ3hiWWIUe5FL8kB/MBt67klyZ49KQJSa6gyhgu5aE/wWmbExeDp46IIr3JNU4yVPVgxl7mCA0sebfALxVdaFYFY4hTJpTgPqTtOpdFZBh2C7C3O6ThQNOJmOOXo4iTw8MHrCUd7qIf/zP/9jdyuE6ScIo3KCzyVJShkl+fS/QqctsikRdEutiQD81KZgJ0+NsAh2X1NOp/aBD3xAcbyd7Xf14Q9/mO7Aot/wveyNJ3ogvwNe2T/+8Y/BGAPaEDrpRxznztj77W9/G7pWqWc+85lvfetbKeAMREMi762U7PlYOKDhLGj67Gc/O5bIsyTO5z//ed/pv//7v88SejIy9hEHMgC8jxibZZtxYMY4QHJ24sT/+4uX+kf5yqEraLg/f8FnSS2nnPJHZ5zx7GeddtpTHrW6q6fvf/7v5/WBrsOWzz/4sCMquaaO3mpXhOiK3ZVcT2QozvXaYroGBBYiKGh7rRqMx3EaFGTkhJCByHwtli0rlViUix7YhjpfEIi2oXY57xcqjhEyqV4gz4YQGX6DGRkMBnfEiZYi2+KpIH6wJ0vCcTqyKkdwOsphbiHfVlx5yI+35Ipba/19vbfc0VvIbcrnNra1tJImFy+cP2dOi2xjfE7mjWyJEXXxnlwoCpY7VajFJ/T6zdBt9SzxtOKhZBGI5djMDG5Nb7ReOtICxFuLVav5GBX7CWKKEMXnSA77EBaVo15xjYoeDEDLSg8gJpcvN81d5N+ySk9Hx86OnQO50s6unAMjWwZ6enrzd/ezixcWtRUWtURMKOSqy9ty7QvLWwtl+zQpQCRCc2PDqIegcK1eujTe7cjm3/nKQD9LPZoK9UJvS2txXuQIOvxCdnt7O3GcmAIGuxN5HVNE6BweecQnYqo+iYG4zJ8z8HvEmCM+FF9ysrVLBNabsJk2aDrIvBGTjfmhzIfE9cQVzFYqDvarMusW6zfs6gLpgVLQETD2UyBcGsIrlwwZcqUKWYX8JbciUZiewtvwMLmzX4GFwc7MjMYj16vAKzbAI444wk9HBOF/kkQAgCSUC8gTmkWzi3wf7izJGO4tOmXluYvd1WUdJvLAbAK9sHHAK6lEc0nijvgQcJ/kJSu6FjY0Pqh/+Id/qCwIZJJ5TiA5JQ4eSqjWuBo82yeQT5JENzCqBI4lD8cYwBOdx1JVjSgTzPcJw1o4k7g6jzGr6YyGZuoDZOtyuo1aIJte1Z12zJUQMzG2JMlHDESDaXyBH3afwrTrrrvO+l4WYB9C+L5GTJg8RJXu54PS+j5DZ4/7CgB4D0Fill6zoRHGFw0b86O2rxXvFZ9MkkMWmDYO6F0uTeYKhfqZLj15nn44YjhJuLckSQTJ9xZneM5JKgGpXMmTENmTJFV4FWKmH8bpHoyWvMoCM8uB7JufWf5npWccmHoOpEdky97otl0f//jHiafmfmrv1772tab/o446ilhzxBGHP+3EE4897vElxtZ8EfDrs+XSQD3y8h2wfjhng+LOvhw8zNrZW8n3VLyq2W6aHTgykEbW0Xq/HacjuOjONBoBY/AymseYQyPbar4mWrSa1ewRUxdZjaOfkU91tKQ22teYcM7sGbtAw7rREwZkT0BKYnutr3Ogp3Pe4uURtI7+AddgdmNt+R9Y6Wfv5T7bTO3oyd/ZfdCqlXPbWqN8c5F7tj/+H/wT0RRdkLBnsagVzVkQLwO2QPwPMoaBxYgiREhY3DiOelXVqxbNeDzEA82lImuvutnwtt5Xi/4V87UIt0c24AK2SBJZMyODuVuxde6CuQsWlPIV9e3ura+7Z0eur2tOY33V4rbmpvnlXNvSlvqaBYU57OP5fNh0h0hqSdvSJUsTF+hQi4fe8/mW1vKxT+m//Iqm3btwHgBGQrFYhtPzJz4tt2C+DB+aJPoVEYWThYKyeKuGpYnBbxCkYR4h8nqbxIzSPPQKe8MwEzHR2B/1oS/H9CvQIKrGYB3VOUFERQN4YUOdJELILsgZSdbehiekZ7wCoV2IBzwCPrQOFj73JDwXx6unPe1pclDfsO+uEkOGcgPpift+MjchxhMccAnIIUTzZck8PA/3YBPzVuaMeyK7/Az3kCH8Yx1pQn8IBOQvZoBJauG5SyCxdClCiZ6gBw3hrbtUHvKqDan8dCmR1Qsgxz1yvwPGAg3CLs9POOEE0QADdnuEgXnJBaWLE2cz8i1klbyLCI3VH1of+lUi8rBiSLQk/hQGlCs3DAH1g6M146SGxmGcDAVNkgyW83EZzwNJ2kK/4u4ORupsaAv02NhZO04JYVPCxkBtyAqdQCB2+VjMFAK6BMp9/pBkwIeTZOYoNAdK3LmIv+hFL1q7di3jrbXr73znOynjwFSKqrHzDZ2yogTxjVsCcNVVV33kIx+hJArrwB3O5PC2008/3TeSkLTvqpYUsR8FcM94big2NE0VZ3wUtv2nznjTm97E6cbOfO3t7Wytutnf/u3fcsBRkDi0HhdffLHSbWz2V3/1VzqeVV065/Of/3xUaTjf+LnnnouZn/zkJ+nafFO2/n7hC1/4n//5n9ytrfWQW+jJCcMNj1aPP+tZz5JEVrI99dRTvbVMg8pM7wodxloeJ0gbupOEIjvf3ppzc4fewvVAp+LHcdFFF/lMhF/2spcpUXID6X/8x3+gmeJG733Xu96latdcc43BkCaOc0Hiip9kngVmnAMZAJ7xJsgIyDiwDzmQzF6EWuvxgF6qdKp9DmBGZw5gX/nKVxbMX3DSySf9cYyTyRmtpUJrqZ5rAgCLMfQD4Vg+I9wbAeOKfZ4KnX012z51MxFXC/5xA+6tRObiAdJ5vLFWpRoh4f4KizFxPs9QHJtVI2EdjKRLjXI24UUSrDOWogW44GaEQyOKY9wQ2WwDYIM46/0dPd3bty4tL2NU9TT6P7q7YNx5heZ5ueZaXx//3558b0uxn+m3dv8DDzSWy85pbG5utKeX+HGiqICo2Ljo8CxCu/578AkIHMX1v38s0oEkeF257rHJNzp1Gb7nL23nMP9YW8u5YjWqYLShGLshxGRdtOcyxw2rfuUawWfO1fmijUrtCP3UI5bs3l7cvn3L7i13b95xV2FOrv3w9rnLDlb7mNqYH0iJr4j0vV51VvLioY8Z+H8v7vvMFxr6dofaIqL3iMc1vvC5hYWL9ppUNeOc3WG/xG5mUicKAwBclIkFCS4aQoap3QWEMKWOUsQYXwFgjKX2TyK1MNSw0ig9SUsQCZKZu4uFyltwS78K2/yCQ+Qk8RFJZLH2TNhKzmBZVYWA94DJkKfi5EBgSi5xIFhvYZVTTjnFT5fcwhVSuVMeJeEhAXqKvakqZBI2nR6SJPyE3EZ87iEO/9Ef/VHyVh1VOaApd69UPFyENhdwDotSamCjyuKbh+GeYDBggxgnT1QFpgljvoSy4jmi4hB4uGRC4PMqXbT4YhJn+Q7IxDlYLqkSOvdFICFVdcivqkBS13ygGlIJxApFzJQUPZZ8EnpCibiBqqAo0ZG0aRCpZbWvOTP2Kic0U4IEY6+viYkVkdgobBzQhwPBY2HC2ItOx0zIEABQX/ziF9N5mZX0XrpaLg8AiTZFwFhoSHJTBIcOWMsFdNHL+BBgnne/+92+6OTbHEueaWofUWEdg/rPxoSTrzU+a5rLLrsM9CVvaFzfCNWGvbUt6oZCLbpmnPccsHzjG9/4sY99zLAMNAK0jnf2dQOuz3ve83gYvf/97weA//zP/9yw8453vMMODsgDbun4zj//fB2Ycl9a3SZNNl2qPEHuN7zhDb5NGhZmAIu9dTArwAFgkVF44YUXIikNgL/85S9/4hOfUKg8KfgMmMZAcxOXAV4JFDRnnXWWoklWPqKPfvSjNnS0GaSKwPBKlNCAqWNLaABPk5SFZwMHMgA8G1ohoyHjwDRxIJIj8nkOeOeddx5pg0zMSYyCk6Dw2te8xqQCb1CUwsLU5KQ3UmSE+OxrVbK3857hwlzBImqL1GgbrZqdpaHfHsbhgVy0w1Zfrdum09GiWScS86ZmNI7gcYQJY1/iKGAP6miFLbRoagSPIxts5IccBWIDcsSPQRHW8lcAkh0VsnG+MQDJ4BpVI0LIYgXYGgHGfLGp3NJkDyrTmbz76o3dLNfRuZrdxVKuubGptbWFSEc2DlnHKDpIytGjAKrjKg8Wjzj2GuRFSUSILNuRh3b0M1hyapb+gs7Oiso5obdQrLI52z87rgsoHHmPyyHaJ4w5m/23Xm8p1doac85DaixzTrY0umnushVLW1fkejru33SPrb5i2mDmOh9ExjoXxGLWTwyMEXEjXRHli+Y3POuM6qrVvTd8v3bXXdW2luKTntR8ytMLTp0ds48fRspeGxOCzdwMWaZwIgvlyNq1a4eTIb4eBQCTLUaia+izqPfs/QpvwQbZBgTOmEbTLwUhgxAjII634U5eh838ZEkQcCcxk+D5BaA/lAOsukBfnBQzKVyYgOJKngwJjMv6NyTt1P5Mky1nPxMc66dOMmJxersmc3k7nO1YBzkHnYI7zOYK7RspqqpVoiehU8KQljaBRUj4pz/9KS9Z3BZZHJ0E29lwSIeBjBA/TRKCw8MhFUnHGXtYiXAvbG8E06w0HcGKPiWZj4WMIRXkoIGBQcbFN2S0t7czn8Jawq6x5LlP46QJRo9mxUCaUM2nXP0Eqarg2yG+j6KjmRIiE2IE9DRYCNKwmTx1Gx5ecskldHDwSfBTGKXEJJ8QB2aDpkxnILSxSP80brz97W83nanRkG9/lGwfya+GsBQrwpPJ9GFN/K//+q9Mo5qGgiyw15D1qU99yrjBaYiVFbzU3Az1YC2AqjiDCUDrpzGKoVXjMr2KzIxvSjLdGJwtkwm5QbYveMELhHUhyv0hANhz/fzf/u3fGGMRw9kHotbBQtpR7hA453kSEf1a8BgSWeZ4Yqh0sDnxyXFZALDntLEXXHCBqVAYDR6edtpp6YSjFJS9mhEO7JFoZ6TwrNCMAxkHZo4D5hiyI12miwhCS8qGBgn/13/9F+Wl+cmIT3SgMjfNRLgxITXCnvBffMQRa0+DF4OQxh9okbXTVsmRy3S8g1RkHIZD+2pdbMgVLtYFG1BHO2wNbi5V40Rtd2V7KdvrafAfh+rYrZqNb+eW+0zCpf7+1mKtd8s6ZBQaW1oWrIAnI7ttfEVYOP7hbUSnuhVL8+bbM5m/6EAv0zADWHdfY1Nz5HVdz/X39Tc02Hla3BhLh1z23KMcZAzQxn+jUKiYojlpR/JtVGCUWrBQBNwh935MqVkzXIsPfMozOouAS3yZHZvc3FBra/Aw71wOCRrLlQVN+YUtNoIuOlMqwt9zFq5cupBjeqDCPE0mMMuS7IMjInluD4F7+8trOldcsrh4/Inlxz9BJSHyenNbfg6H8DzEgtgEouwti+S5yERhBhmtT1Zm3SU3yGREJ+cANXWhJPnDBsgQAWKxz3BCBqWC6EC/rrh0cjvW4Ab5lUaGZIwPLpJ6uNPNi+wt02VIJefgyM3fODwZDtrD80fUXYMOqa/Pf2/gn2DKYoaTEKZOSLZzYbUnMsF5LPVE22kaT3SVwG3Ag/FNS4ngTkh11+sMIENKn/BPJRKFla4I8jS9jM4w4dwmnBAr4HCCOOcCAcwJPRBJFqDOQrjlK4N7aT1QruG0FL4xjfp4fVZafHgPmTBzRk+IAIAENgAtgHB9w/DCdP/Vr36Vw7MGHTslQLsRA94wc/Gapvlqb283bcnZ/DWFHryj1+iAeetjD8O43qKZQE1V008St6AJ1JQL+uWXX246SxtXNVPoclrfKERf6Ys28tOj+XYUrT94QgdKeUHDxZJPuwESo4RzCsJ8bmIGehIkbK2BrIb3H/qd4F8jCeDKbDuWivC1ZhNmFdCXeFnrV3JWF/uQ4xLKfUfAeciK7sbHFcJs1MzaQxKOpcQsznRyIAPA08ntrKyMA7OXA4ZvXj0uIi/fHvKEiz8SCYOQBAY//elPp4tlilSHWJROy9ODYdMDQ4/zexoainMaIll5MG6MJGOjcd0u05YZR5sqxwC4t5q3tLh3AGCuRscUR9g4OqKJ7ZStuKc7vz3aibre08fDud7QAgvbUSrfuqAW7bdcj2ZKJtjBlboW4oLBkYt1jEkj67CjlprqrU1W7trMCOZFCG/Qjp3bYc5iqVhuKDdDUw3O243/Qz80FaiODL2Q42AdKADg4MiOG69J9tOCOf8KDmWKlvtGWNcmWBQF0ZHIDnkqFZptdl1mEAaM69EpUA35Vrbfxvy8pnxzCS52YVFUSBSMr4ATIjbm8wxunNBgTgjQVi6kBIK16V/txBVhME34E+iM3xBY8o1phyvv6oxUkowdAA/mms+DGSgh1pM5IB/PEUB6Jr6Y74MIQmb1nEUupErugVQ/gRYiL8QllZ9MiCpFWg3wSTToNwBgQEIdFRquAKVCKV5R1iSZJ4GEFUlx4VX6ZxInSZUFHpYDmKbJXIkl3BNcDRYPASoMQqqWDez1JCQBCLVvyN8T6y2FxeQiSLPmkqG7JvbWNToloSDfAshk/NEZSJbuITx62ql9i5KQoW/BXtOq6QmqdF1aqqT6U1vohHNDj8Gc/z9hnUoItZhGrxHwDIMbVBC+rAkXMa6E4WNnygNZnTMEWgjA5N/5znfQZv1kGP1GyTPw3x1SgmEktMQXhDZKUMw5GAnuNVJNZ6VGoXZ/fKXDmClQ7tPG5xD2tU4GABMbdD8LaK2SDeO//EMbhQYNjNL6PqUAvD0xqngbdFty0Nxw7xe+8AWKNvoOo8E555wTEronY0gSSF6FQJhrQgeTc8hW5ECAu/q6hqRiMSYLudgGXvKSl7AhM+pCvNYbB7Mwj+skSbrXWVbDbcqGfCHhv/zLvwSzdhI5C8wGDmQAeDa0QkZDxoGZ50Ayc5iE/iC+/v7v/56wYh0mOcM4/qUvfQmVsBAkTBFLfgJIErqT5IOQMXrxELnWD5CPM3Bjod4WBYmSg+7LcSbRzsmgrFN7QF9Ljvur8fZa1caBw9byfHaU7saN9609fG18FlHBTkTxalvLaRldox2b95iOI3gWw+D4oWIi82zkOB0two0vludFDc3Mjj293fUBReZsSLVixfLmpkYkx1bhKF7s6sxgTDyP9uIyV3oF90b3aMPqCAwzawGy0XbW0RnINqy2i3W01jc+56ne1FBrKeVay4XmhnwT/9tojXNs7H1wwn4IiwJ54W42tVusy8wdrEx290mYzGACyqZhSQTPXYN/HswpnuAHpfbwNEz5SVYPRt1LKMTUK9auXRuiEBRY+dgKEOAh9OutV0G8CPmLQzAl14aLptwT9uQAlVVKNPYZOYSLCTFkLkKIE36G+xipDTYEORPj3BEpOZSVUJ7OMwuPzoG98Tx5LqCXanpQNskqdFrM18RkWRdxM3QPYXIwMCahCO46DDtPsElS9ASVR1qOlK02pW1hCJKhThIUcEoJJSbEJARMeQCp8nQH1XTjQIDOLMx26if66QimjZ69VTDQmX7LNB3OwfJQE8AwtJyBvThvAJlO7mlEDARdrO1kTzMsGB/YbLHRzhSjcC+plx4CApmJzEd8U1WBeypYYjLi6gwDj5JJmi1ZeBQOYGNwgPedAplT4gzvswV9uShbeAU6Jhg4dD/tK+DyEdFvXnrppZZfodCaYUqlMHSfcsoptrbykOaL1oZkQq0zrqOVdDMO2Hb8poXhtBy863251Fi+ZR8FZ2YdLOlsgUVSYYiF0C7KGkZsehYDGj8F1OrDjARBuzeEpUlCUx7ltYQA8JA42c8Z50AGgGe8CTICMg7MLg6YihKCQBRzhsvEYDr87//+b/KHKcrOEHSovJWAYRe8HGyDScJ0JsnDVEAR/kGD8aXEenQAEgtq2VLa2ATrRYzbIpwsvCJfWFrPPf4Q23NBttGSYCcSwcDxBtTxHtR2peaIbNlttAtX9MTEytFawJxnHS85Flp2j5bl1ufWc3PNfLu7unbu7DCTHbaa/jkCTr+79fdz57QtmD93zty5pMYI/UZ7X4XTiSIbL0wcHdcUgd4I/QK9DcVcI0/wQh0G5vhsuW8EmJGN1oj2uJbRb6XH4bjSo98SBmIsIcAFAAQ52wRsQpWcAoLpiQge0GOSJJ0zQcHlCfjhbq8Rd7IvMWLE+Om06XA6MtGTjMIIww5mzRXX6LDFFHMBgZvMRMTBc2beINxgI1LBpERIJbCmcU4683Sh4w3LJxQBNfFcEPYkkbfGm1sWfywcSLddEhYIaDa9G7Mws4l+yEcxuURTit5idbcm08P1E73Fpfmofgw7vlONyKjS3t4uclLKWMibcJwgCrvr1chwEWr1ZKBX/+cU47sL3+P00DNiRQKR4ZXxgQQPtxjNyPe+L8MCZgK9oC/mI1vMaaM20KZlrchlSYMckPfc5z7XGAvPONFXg6JwOD3pSuE5kBxAr0FPblKZcd773vfafVfVprNGIzbBAfYwNEe6CYY30ASq7FsmNvAvY7a109XwHJSooPe9731MrKYVn7/Nn8Hm8Inpz6YYO07pw6Y8bw0I9KSShIRDMhz+0HxngbG9r6jS7Nhn3yxJYFfOzJwRKNQoaIbMiTJhtWbIpaAxhxJ+nKGlyzFHIxJJembaMJ5mlJXnpkKeMhxh2K6VO4TC7Ods4EDUe2YDHRkNGQcyDuwvHCCn2oiCUOJiH2YlhpPpbkkkLrpSk1N6MpiSepHqFEQlPGLOI4xi8cgW7VoV8GeEewOijpCw+Hvu0RCYzJc7duz8yU9+XuGlHdkz8wvmz1m6dPFBB62GQqPtkzyNcaxxM/oVP4ifTUkVx5oJ02uQyNnTYAMMIUfSNCdgI50Ru3EaAAd8OAEAnM4zhDGNtG0XTVIpeYJ+nV7ciRSwcVgOCiGz7hLBY+5FHt8jtt3wnCf5BGE4wx/bWq9JZpUl33cc0EwuXcIFFLHpsRD6zGFjvVq5erWwLkTqBTuJnkHwFX/fUZXkjDZfGf1O0BwFCyrh2xWQZBJzBgOIBBF1dV+i4dGXiBiqSSbW8KWLkNY0TSepqLJu02TBSOukNHMEjAFFoNOSTvzcWzuq0U9+8hPGXms+KdH81PQWfLL0ulvJqUZ7SzudFTyAy9JtNBPVTwCZE66pfDS6CYtfujC9pAa1XldH1WMZ8OXsOetre3u7WclzsxW5gjaHxmTt2rWhocUx0fj0gusHm6qYNK3eemVHABjVRCM3ONmU52e6h8ifEt8+jhwQDDWc7fXG8F2YKVhxqeGs7+WxLE/5oFm/pbUx/uiKfhqLyDb6oSIooOl08MdXphSU6JPGLmOFGTBkKyGNnoRykxBOTtMzYX5mCaeWAxkAnlp+ZrllHDjwOWDKSSppojKlEWvIKxSihEVLVUEgc0MAw1NlJEkKHddEkqQKFEc22UEUHGoQJGmPY5gcR2JV3mHVXOys2dmxy7OnnXzy3LlzpLt3w4bFixbZ3Me0G6O5eBlvwovpCiSVIlhwIySmI5ZRhcgLObDHmm6TCT4hiu0X69hP3OUwXjaa5k359NkEFCJpmPs90e4E7n/6p38ihdj549Of/jTEEoSAhE40jKu4hOaJBZSbAeCJsW46U4VOmNyTovVhHUzXIjvq4YROQIhAKaZ+pXfp23wXGWCntlPJX9G6N7mf7IsekjSxO/g5IwNmC0RObblJxccSQKQLNwjctIGS2BodkTjDfoXI8O2nKUyHx1LEJOMgz5bOlF9azfWKV7wCwOD+ymbrVRgZQhEJYZ4DFZCG+MYTyCGZRyBeBjeWtDCPiDkk7SSpzZKPyIGEz94mzTRizNEfpvMZEjPJNonjSRJOIodow5+HCCMmSV4lmQDA9hiHjZNCvRox53SG6fDe4idlJRTuLduEmCwwezgwQoebPcRllGQcyDgwmzmQDPqBSPrdRIgRIKUFMAwJk2MoZYMzqhkiTBLTULVBChOL0aD4lCo5vHro8/DLm97ePkLwqlXRvl8B7MmQCL540eLFSyisF7NtTmd1UnRHivPkJ8GdVQolYVmdVwgjuMMJjLFBcE8D4CTh3gLpzKnqLZSCCpTiuVK0ozalNfeTBgQA5gPPVcxe4v/4j/8oz2lr3xHpR1Xws8307iPyZ5Y/TPe9QKrWpHnRA10+Q8YZhikuD7oZM6O3OrmhJjHMjr37hbLAbJ3ct+OSueScbH3mSqf3STDb2LOdWg4j0qWaxiKaHXcjLWLYrHyJLFoe4kCAiKHo6SQ1kKdcrcPzk/bzb/7mb1jy7ZrLJmbXIoPDEKok8USNuIkG5SkMT+vBh4UnkckiKE8TjYPI01mjQG12P2A4YDMI23nyqU6+5QOmallFJsOBDABPhntZ2owD+wEHgrSRJjQtTIz+NkmVREunTd6GQBLHT8INy3BsA/geMOwn4YaDkJUzLiJR8NALCUfJc0gRU/+TJJbA42G5hxoF8gidliNaxkYAJTGL67mzBImewp4EeB/ymKkaEd9Jw4xCBPogJUMFfMn4jnqOqkDtcPKStoNyCbIis8nwZlcduWlB5jgt6GJogg3kkGQisiWROHDNNdeEgyICE2bqntQFAQmRM0VMVu7kOZBuUIiUZo0gyynRc14nUKsigCU9U/dOTKAh1ZAOkM4qEKZ7G6lk65WOHdRGlFsJnJ48/ePKIU1hMqpYuEjB5JXq0DdFurfFi310CUQcUs1xlTjhyIFUd8efOuUVcLU3BAdXANgyyw984ANIDTQnRdBfGEx++MMfWjuD7UFJakcAiDdYeiUJkWekRgmdWeBA4kDoqGqUdaoDqVknX5cMAE+eh1kOGQdmIweSQZ83mqN9ecZCaPZwdvhEsrBHHJIfF7WbbrrJ3EAQAWAIl+l5IuTjzo8IvqKh9zYdYZTKh7TQL682vm3kHgFCD/TLgAMJw2YyBKvSmYwx83SS6QmH6uAY/0NI2GWVKcMLOdWiIBCRVOoink6V4/cE6pUQCccS7rl0QunaVCtowSDfw8PpnDGc+SXAZvA+WHrJ1tZKqYifrgDvhzeN4i688MKXv/zlbM6csRnihsdJl5WFMw5MFQf0PVoePTZcrI5y9gGGLdZ0ad1et0wXJ0n4LiQRDcqVg0Ep+S5C753+Phw+W6SGGoXhhXXXgOyhqrFi8XBWO+MM8qafwsDGhE4UGsYddoqHHtqZmfeHgdFaDO6mIicUGlgsBjb4u1tmabTkxG5QMvjDvSaCAHqT+KGg7J5xIONAxoF9yoEMAO9T9maZZxyYMQ4QSlz2UXzd615HELSqDXylmH/1q1/9wQ9+MEgbQBFHNYY7S0MR6i3Z5atf/WraPCsTrz70oQ+BT5H37+LFVPsTE1ZARzTYzDMIQ6w3ZCO02XmCPAQPc59j1pgp28uEm4p46qhAXpqAokwwBw5UKcB+YoyaMCXDE4bm89wKKA6HKESS9uUd7QIPgjxtCyKyqWh+krNdAcY/LP3EWYZ9bfpnf/ZnI27vOZyk7EnGganlQOjkvPE5I1DZ6Njyd+aw08LoeoBbl47KVmzAEc1bw44BBxJLKHnYrp7E3BcBVaA/sk+PwSRUh8qJTZuKLe1asi+KHleefM4tiDCYvPSlL33+859/+eWX45uR3ABok94wccDtxpME9IbFFwb2ZJB/3OMeZ5CfWYaPq9ZZ5IwDGQcOPA5kAPjAa9OsRhkHIg6Qoph2bfFvAxLnsMMzHrJ7ELPC9ong6Lve9a53v/vdH//4x6Fi8f/1X//17/7u7yjyLeaMsogvz1380+ygyCpIZ8+xeWIYVT57co3+AmPs0ozPAQ+TmTy08SObMEzljk4ILchJs1laCvVSHRg4mG5YmWwTRfjGZNIhd83gPGzdrIpMZ13SPCf6c40mvAavUa+w99RTT9WazGVecR8lwiIvSTUKqeKo3Rve8Ab9RzSNSMBNt28WzjgwbRxIeqwSQ6eFfp2nYsQLSp9ACWMj5Q6ErKsbzcbY1aewFoFOd8pH3hnIAxcdhqwI4JyKCianZHQlq5pH+QankLBRskItIj/1qU/ZvdkRrA5EpTa95JJL/vqv//rcc8+lSnAeHr8eTtrGOpcFvQYZ7OUOTadJ0cDY3t7ePqQiQ36OQkD2KuNAxoGMA1POgQcFnSnPOssw40DGgZniAJHFZQ2nlZzkkgRGehhIInzwtTsovngAElbiFHWrcyFkS85IikFACUkIOs7wBOSYlK+66qopkV0SYpAkTGYKYJgIhWa2YtZIJyVAwuGy72vCzykhIMltSgLp6pAXg3hNLrTLSyKCg5eQsFMT2HbS8aenOhbg8YfXvqxhqqyJIQFe8UxMCTFjpAT0lcO//du//e3f/q0ABYrlf2NMOyXczjLJODA6B0KX9iXS7OjzlD56O4dbgA3spPQJ5t993WmTLytQixjIHPQ1/IZXvj5rDdDjp88qrVvc17SNwkDEuERw4t3nPvc5o8TLXvYyY5eDfJ1S40BUc4oh2jreGPP+2H5X5hoKPjMIuAv0GrTB+HQV0uFRis5eZRzIOJBxYBo4kAHgaWByVkTGgenmANmFTxoA/Na3vtURNSQVF+mE8IeUIIjQ05NRaPTf8573hCdS+cnD2SsnN6blFbbNj33sY6RJgGffba4bRC4UkqV4XBOtHK4Dn5NfvXIG4CAUPu44B2OoTpqtaWrTz2c2jGxCLe4Fa4+AhmDfJot7xZZCjmScZ+0JTYPaKalIwkkZho2aZcvEpGXhXgcqOkMl8dAeb4kyV4t/ji/h008//dJLL1WRmWV1VnrGgeEcSD4EnyE9FAdpvf0HP/iB79FHRwHINdcXERKO90MYXpwnSYnCBkzOIMoSdhSqOz8XX70SfX0+fHd+Ir79KSla/hO+0mT7uuFwzh22/ZOhr9tZvrRmNo+A1Y3JhhGDM50Cyj00oAG97vSVaYftGa/UhLmRJZxCDqS7lmz9HKVjhFfpJEnk9MMJkJfkM4G0ktBYuftyJ5nPWEpXU0OHLy6sKQjbpsyGUWIsxO9HcQbPtduPKM5IzTiQcWAsHOBNZxgl3tmT82tf+5qlZaQ9Tmvnn39+ME5yP5aPtcHpAT1IaV4BwOlSWCwB6fSTfRFOKDHu2yKFGUEpJFcWaTA44GH2Z/BYBBiY62C4+OaFfafSVCW5pR9OcxgNRMnYpXGx5lCXMJkhg5TJHuuJsBZhjyINs3ILDJ/px16XJK0Ac5MieGU/4xnPoC/g/ElOJXYHsD32PFGYZCtsxe+b3vQmhiAPoV8nnaSlXhGm7UqoGlddpo28rKAZ50DSMXyGrkCPQY8Z1tcBi9pcij7IJgjJd5ckGQvxSQ9MR2ZhpmyCe/lcQN1e+frCSEvxRIEY9lH3fFxlpYuYwnBSBSK+4cJMYeHMs5/97K9//esGK9OHoZUd2FtI+H3vex8/IKrV8847jzrSNBEOZJ4NFZlCnjxis0o6w8gcGGczh9xgOT3n6quvtkSf5teU8bznPc+UZ2MRixQURHlKe2JDNQ+Tcr/yla/YncTqLbJHKJaXwWc+85kkgunSgilL/ZMnIeBbc+i0sizpMlX56kk1zqgPhxoMiTz2n5aG+QQ4Oo09yWRiWmJgZ3Vbh8qE6p/OzoqwyWSYpR3OgQd72/B32ZOMAxkH9l8OEO8Q/453vIOAYpWmWQFQYRAGh8ImWKyRIgQ/wFBN00z4yWoxGyoepj0TGJ9h19lnn21CJZMxbgPDIDFPvE9+8pNINUdybtwDh5+cbHM9G2qR0KA66sLuFJ4QKx03GizD4a5piAKhCWxIJrJ6ETcDXk3yedgAgcNeNWxNeCWyTBiBdQAZEr4fNvkoEez1+ulPf/qb3/wm5bTcCMf6EppDS42ScB+9okQANkD6mULg+6heWbb7lAMQL0HcR0H+plxj5/RVhu8uiOzj6s8grt2PDae+aEpGlEO/dn7y2cqWmB4uebp8LK59WrvxZq7K9ncwPgAMl112mW2cgQfo1xNg2NJfX7oZwej6F3/xFzRoRxxxhCqMd1AaL1VZ/OnnAJf3Wq6e76/Wd+7I7e60S0euqZmIkGuzdUUxn6v7j85mXIQZou2PaHc3OlNqJp+bGYQdlS+9wymAOqdnGcNf+9rX2lsE3A2Z0wtzMOL5RQ/uiAEfjueUVv/5n/+ZrLWhRaLSDfFBZcum3vKWt/hpLqAit2sJb3w7gOq9fBY4lE0SAIeCpvMexiIlEnIozqaz6EdIWRkAfoQ0dFbNA5kDYXOjMEmoJ586q2eDadFPwg2EIGDKsTMTKef1r3+98XREUS9kkqSdhVxTNZstuQJtZDVzm6Vojpf88pe//JGPfATxYJ7dU1knCG1mQT/NgiF+wqUZrxpK2LFdrPSIASnB4ACPNY2tVj3xHOUeArFEdu0o1YgNl1RHqu9+97u6hIQUAXTkaR1HEi0EkinWz+GcCW/xk10I9CUTy9lDMQkTPALCeSdD8pzOn6Qr6IWOIAPA08n2A6MsUjiJ3GUMCd8IKEtWpjH04fh89vZFhOpDuRLCvdBvMPP6SAMAZrEx5viZtmjNKqb5il2+awtbjJB/9Vd/BdCGTft4dqi7ocbgmQyhYVyaVVXIiJlKDkTYNlryXe/qqt96S+8V38j98uZcT3d95YrSM08tn3hKYfXyui9i/EV+/vOfp61miQ1CiInbDmqJhp0+xXbicuUkZQNO07eRXM+0Gwh1sCkG4gWAk2J9syF+8sSunMLU/dBy8sqOjFTARAIg2VtgOIkfAorw8dqo3P5tjr3w/VKv+3h98uzMyLN5m5ieUwnRU9NhJTmYEMkbPhOKZrZl7hImcdOQoxDBe5icfdtkLT6qjCc+IuZcA4IiJDSHmrb4g4SE6UFGcZA8eYZ3m1LCK6TSrMlNIHhhUK6Z8qgG5Ow5MqTCYZV1ahqdgrSKNl/TLDCt2+dSVhQEtrpUL2FZ2XLFQoY//dM/FbaWQTUxHDescTAUyIHboIpTdhvoNJNS/AxpqTMQyageKEw4s98FMgC83zVZRnDGgaEcYBE1ciVPyTT0qUGkMyAG1OStYdTcY9RjOzXwBZmG9JYkNBSGn6NApiTyjASGDLgINt/YncuFHj+ZdAzNKmimYbswpksC7VucxkQMtpFNTVR00iYbINPcIGC4H46gwljv7tpHlU3njIAwayrLc4IC2Tq5zGQeakp1NMtCucRrLRimRknMTy6WGfngiSmZzZy4EChPFzSkLlhEFGDVkbNX4W6LWjM6KcTZSKQELMWlkIkJkrjMQ1JBo2Q7pJSp/RmITOeZPJkpktLEZOHZz4F0P/G9BIKNfi56KP4XVlWAwYmdk0aJP7PvEUpkQBafhE2IhBUJoz7GcIV8PHEJp0sJr6b/nnwaoWhDB9x7wQUXgAdGFR7ggU5MgIQD6DVOJmNRSDUbKjL9rHvklBgZfiHggf7ab3/b98/va71vQ74ez3odu/pvu61n06bmF59XiLv9eHnCk/mcc85JhBDJg3ol5BM6p7uJLN3HeKuRVUzTb3vb22655ZZRvH/TqeQZfpoZYcVrr732Oc95zt6UUL7xv/zLvzTxnXbaacL8+c13PnlrrGDgAIBl+MpXvpLyNw2AYUJLyaSli7/iiiuIFocffji7tCEChIb23//+9xM/IF4Chmg2kDNpGh/Q46dLwiuvvBLKVXqaD3ZjoS8A+L2FV5MxRHFcoEllMKdRiLTjTjXgazUvO43MWIRdRqfPfvazrOgkBPAY7HfeGFHQZP3hD38YylUX2DXIDDawXL9+PTnQMhCKbGhc3XkIssOjn0Rk0zsik2VogZmvetWrjBUhLQ9zWsJA+X59zwDwft18GfEZByIOGH+pPBNetLe3C1MxuqehrLnBT3eObV6R8NwN/aafZBbx00NWEffZfyVkB1L9hG9NQmzdntCzGuKBN8pRK6LNDZBeiGl+pQ2l2rSpNZzsOX3nK17xCvoCswuP8X/4h38wqRjxqaX5Epv/zGcUw2l+Tow/Q2hOMkk/FzbrELXNSaEiJrzEc9L0hhKtRpKg9DWzclb0kJDB7i0+QVYOLuFEwkgKGhIAm+VG6Lfw2wytb5CMsct0bhZMctAlMMdcy2MtyXxIVtP2U6VoOpAXYDndf5Cfggp/2sjICjowOJB8KT4lVg4GFuMGrSKgqEf5IkiWVELhc6BNCwDYAOuj8JGmJeyQ1QyyJXzvaQJ8yMYxSyUNg+7BwCVCUAuyVoG7Rgx+KMaTkHDGa5GmPwtPAwdMFTX/b9/W97VLWjdtyOeKuRgBU4qWq/XaFd+sHv3kwsIlucZy7AU9DopIFC9+8YvTCdK9C/qCJyliODyH1eY6sC+OQdJzM5rp+Itf/KKNOUMq8yBoFzo5hMbkm84tKQVgdjAB/OyL1rfJA2IO19ia+Nicgx0VSmc5MPXLZPhHlOQsYF0utJk+KtJDoDGJY5YkTiDAE0VYyhv0y+ClV0MSJqkMMhZFsw+HSfxFL3oRyBreoidU04kSJL0ENnvrIG5Q2WBlIPJTTJdKwfMQslRWVsPJ4KulDeZ6QNoQRzS6+OKL+aKLbN9TO5uSc0TGJUeX8RAMqnY/wXXPxTc4oI0MoLG+8Y1vsDEklO+/gQwA779tl1GecWCQAzSFicIyPDKusQl7TuHniZ/hefjJLdZPco9Bk3bTOuHwVjQ/YTxKzfBkf7wbrwPZJjweSq6weJj8yqqZCIICYppERVZxAZMxJAwHUusSB80Qxnqw0+IluPqNb3zja17zmre//e2mNEpiTk3mS7ySkDnFBAM9mmAoWXlicxliUSFJCzCx4rP5Q4DoLE4QNIG3ROIcnc/QHQ+xJI75zGTpQok7m5VXpHB65RBHvVTWXb0IEwJU0RA10IhgKmrRAGazpldsvPKhL0eY50lXkSEx4qijjgoKbL0FteKHImb8HgCJaiKYiG+Gnj20zThzZj8BSTebPa0WKPG9M5tAtrZ9suaQ9clz3wKJkDAdLuz1UHjG+ZywMaHEE8osKNfXTTIGelXB160KBjFggL0r+MLQ6LFvzx7+J1XIAtPPAdbfQj1f3bY99/NfAFv1XK2Qi4b6yC6cKzQO9PT+5Gelo5+cb7SBxfimAB0saI5GrBRla3C40G9NnSEO9GuiDE5twDNg/O53vzt4M5njmC5Dp9WB99Z7dWyzMyAH70GM9jPniuw0ryHxzYlK8VDpfMFYX0ckcshDX9BZZ51Fe87EynCKVMkJD7bDoGkyE1HIBvODhIiEV0O50CN9ExmDElnCIUZU3ym/LfMs4mXIaAz5DymaQVtCmShaBB81qzVAS8BIVw1Xg9+4h6wjtHikHR5hQP6XvvQl8bFFo4DEqOUSaAKVJJRFLCEtGO785F4eXGCIUkHOUSiTPpUBsSpd4hA695efGQDeX1oqozPjwDg4YGyCfu1ZQslnpyhDtidGZ3MA2c6yEHkxG4pgSbAdGg3oxlygzmjIZyaBUuMochZHVXeXoZxB1RXswyYAGlZiYjCMuJtiQUSXqjCB4g8jCXM60IhpLMaSQJt8hNiErawGdEFToNqEHZbcSAiYUfESNMFFk4qtI+mGMZz0CVHTAUPLALncaLhtqUo9bK6SW5jzvIW3+VPZt5mzovzN0DYLocxWHMWzVKYf1ukw0YKv5iozriKI7CKbEVEuzIkRPCZeIBvEVf2AEhWUtBW2CHtlhiP6y0TmJHvzq/mSfDBGiJ5kOD0BBJvXlYXb5AYKnQOsx04PG7NSRuSAj8IXRAmoX/l8fF96FwzpYTSOzBodUCDeJ2A4Mg64jGYEcSOYT54wbdSCdcP+tzRZ3FhmG/Ej8j97OP0cyEewtu74wVJvX6HurET604iKGAZHE0atozNfpSGNI46HPvpTPXNvKUggHK90V5uKC3D9NT3xz9KHQSyp4DGzm1VLgJ+f5qMxaud1dXM9/Ozix8uSaapNvKhkJUJ6dhNGxhA6EeMa8hBuhCeJVahiPrX+1sdla2v0v/nNbzZWsCgkWSVmZ8XBvcQMCZkZSAKmcnbaJHNJxHGFJ+a45FUIeHXhhRfCruzAtvvi1YwAqcDgITE9lDzJKqnaueeeaxcYMgwYDAyjDfTFYfJhYlV24FkSFiHJWQMRVyz6IEOqZvJ8vw4MZdx+XZmM+IwDGQfSHLCHhLUolnlYUEp6owqF8agVw7hmfATVaEZJSNyADfRWe8I/iUE4ndWBF1Z9ENFFqRlqZ2JgISdExsLkr+FPylraVrNmMKpQRYvMN4llmBp12bJltAzUq+52mzQLQpjArelNKllxofQqmYeU4gmobBa0PzMVctggREFsNd7SvALephnIGRlQq0ZhhiXU8nTyk2maDxhc6i5bk1yY/5TL5iMfLRhgM3OugtzJwS4wXioWLQhZXXiRSUIDTVEtK+ZleFKvMHmLduC1dVaj2cYBXV23pGCabYSl6fEthM8B+iV3QsKEb59J+otOx9934UQK973DAz72AHeNElxMadOQxBJlrKAjMxC5fNphB6B9R1WW84HEAWt+LbypzF9Yf2DzQ2EuCFjIr1xeLzdE9uBBgDbWqoO4nIEZY81BIY0tmjlD0QgnWZjIzHqgsvnLrMpHiSI+MZBaGcubd1wbLirCdxowpG/HLAwEhp9JoZ6HaV25Ht54440W5QrQd/vEfGjCVEvmxyRJCEiIeMIAxEsxDdAy89I1qyMpC56kk/VkeCoJSQtSWdMLiw4BwHyPgwQSLMbpRW1JuT5zVllAmrzBxYwIwV/ailwDVMD2ihBHRYxXwT+crtyCDgOCTPi2UCtcdNFFfJhF8ISGXWRyAldzCSUPZalFCCR37QLkc5ZWKLfq5Pl+HcgA8H7dfBnxGQdG4wDHGHsVMjwa3AEe61qp8UA+I51k7sQ79kzqQGZDT4zm1KVpLeloue/n7wIT0pXADfjQFR6aA4z1QdB0dUq93gAAQABJREFUt5aYdzRNgXnU3GOWMnG6my/ZeAnHYY6RFkvDXMIvWhNgaXCB5iJlXrSDlFQy5yAtCYcioJpxXkIZXnfddS94wQsowrlCaTuzuAATPVwtJkM9fbM5j9lZg5rP2H80KH9m8q55l4BO5UFFTcJg8CEZ04IjmCiMpCGK4VBNZnDzfSKdeDicMyHmLLwTa1RzuAp8FpKakZTmgP7vSj+ZbeEhXwHdEOuHBQXGB+Ehb6eQ+EQGlScWGYJ818YHoFeAWo2oLQ71gWGEpwkx1zDiMgQNEfH3HZFTWN8sq1nDgXxuwcLcScfXLv06d+iwGibGRMWeBa0Nxx6bt6PyONGvqpnOKNkZHgE/LgmmKmjWIlVQMF1xP/Vkrs5U8PT1+nPozLo6m6QVXvS54dNwH96xw6skQ5Og9UqK5sfEmMzNTenAXhIhBABjc7T8zcXmXGKS56ZjFk62ASjXRspsBkMyZyFgOIUbTbLWA/MNpnuiy4PzzbywpS9UPlK5kBrunrzrXe+CzCWkQGfFJYwFMsKdbMAXjGEWkCZp0AWY2cOrkIm5ngAAqYpJNiAtCFDH29wEIoWoTfe4hPLzzz8fw1mnxVF3qaBrmbhII6zHhg7Cg8w9oXrgE6cuJA3CAAlEFeK4D2lsT4guKmi1sMEnTfn+G34Q8e+/dcgozziQcWAUDhh/028NZOmfwukIw98OifxI+5lmDj2rmYkk6iKSkkfZVE1pmGYqYkQNkDjcPRHZVAGCppk2Rg6nyxWWKjwhEJMhSAmwX8h2jBmmaUjC8pShi6vkZPJJMpzmQMKl/ZH4aebVjBensahgQpP5NFhLghBM0tV8s7kFA+U+eeZWOikrBSwTmAzBSb9NNwqVFlVUgLvuLgsZGKkUZBihoiKzBslVAJag9wk0TIaSNAFZ+BHLgdAh69VK7Y47ej756Yb//XHJpENyqOd75s0p/M3fNDzjjwoLonWhE+hsvnoYmKWURxVQx5DoC5KVRT2ALqWtPBFAweREImMCZW56PvKKEh8qNv/aXMo2HMNp4AoB6IalXnIW045NLMkQHZTLZOpVGGdCE8vTW+gR3uPTS9PEUu2zCjmzP1M30xpDpDTOACT7J722aBYHmf2vvvpqszC9Nve6UBc/AVEKMpZqI5uy+F3TU0suk1BBX7TdQ9hjkQRz2h/Ec1cgyd0IwP2bqh1IhvnFp/j23DnASKXq5QJm9ylGaSyiL6Dz8hYw5tbHiQw9kLByMRyk9xCs5WXNsAH8h4Jgb0WQH3iBhSciI0lboJZmH6+gXPyBnPEkuMAoxRPAnlmekxoJx5MD4MoA8AHQiFkVMg5kHJhuDpgPFMmTmZdUAMORxHrLLZycqYdNLfyKaYXXrl1LcqVLNg9xUCTIhllnAuQqEVKl25aDSY7leQKZDEkiTy7T5t1gIh7yNvuZcWAKOaCzURiRyeQZPh89WYCGyMcy4e9iCikcJSt0uoi5nEEgT58zeXSU+KO/khV3R3IniOuiDnCRWYN1l4UZvk20aQJYRKqe5SwavcrZ29nMAR0SedH//QP1jff23fR/9V/+Im/bizWryyedVDrsMQXG4T2OY7O5ImOkTX0BYOsFeAiPMckjPBrwzxedxdt+JeD9gcGNzAX6wGjHrBYZBzIOTCsHguxOVKWsdSk7PGEQZiaChKFi/oqkWxMG3yQROFNRb1uE4+KSFAI04sl0MoqAa8ImHNMNi8MJM9HLTr7OLFquUYqefBFZDhkHAgeYLHwywqw9eh3cK7y/ONT5RuizeF74uomDY2nTgCsssVPfBOiGgCcUT/L0LbPnGA1Yb2DdAHeNFWPJP4uTcWCKOMAOGW1+VW8o59vbG5ctz/3xKTnGwcaGog+29OBmSFNU3KzIxrfsC83mvtEbA4tYtp2LwQzOJ/xAYldmAR696bO3GQcyDmQcGAcHgsibTkCK4Fw0RPzl68hQLBrx14HMJGA2n+SCE8CDdCbstJSv0DXnJSuOvDqQ5qF0TWd/OGnirAnG1VgJ36SiG4J70xvh7BfMDFWAfonOCE7XKLCC76VleFRgyWW7L2GuIuKz4pIjE/1X0IIlK/3SzNwvuJEmOAsfGBwIXZopOB/bg6O/8XWAdcj0l3uAVW3K+2GaVzI/kNg1wgg+5ezLMsw4kHEg48AjmQNDphCsoFLlThlQsTvvx7BBNNnaBMO9ma04wcMQMmMRM7JFSmE3izQzD6QJKV2vWRu2rsxKKqsxM85PuI34RwQAvF/wcPj3q+J8mH25egJ86/t1txqCRyWs661Fd5Bte3u7jzcAXXcf9RBz935R/Qm3cpZwv+OAVb8x8o1coqt9tVIjq3CEgbOOut81ZUbww3IgA8APy6IsQsaBjAMZB/YVBxLZmqEYKgaDidFBng5GpHCqp+LZhC0FJFLzw3QJhwDHpFGkk1Fe7asqHYj5Js2kcqz3rHzp4ygzJo+3zfky8PzXpWcJ69LtO6QuPkzuytbrJhfVle/UQ04cEsK6PsZEXRUCjNvW1YfVDbOkjkPqlf3MOLA3DujVPTsqt/14+xNOW5r13r1xKXu+v3MgA8D7ewtm9GccyDhwYHIgCOWcKgMqDng4SOEMTdByMBfzqyRtBzAc7sRxl41qrSTMxJcp6RzaIlxysxWZ7UxtaRZ4C+RkTJ4SJs9gJlCuD405F7gNn1gS8K1ZgKD17X1lwUL4uOihnLASsK4nySarM1iFrOiMA1PFAb399ht23/b9/9/enQD6NlV/AL/33feMmf9Kk7wS/pUhQ2UMoYFXkTFNyFDEnwyVmVBENClDiZDob4wGkTmFjE/1L0oICRnfeO//c+9iO85vuIN377vvvXV4v7vPPmuvvc73nLPP/u619j6TNz50MQ7gbN9mFLCpZ1QhkAR4VF2ONCYRSAQSgecR0AtphQXqq7Oua17rrJts/MQTTyilrG8t+BSTqEubmYe2SJtCbAHbmMRIcpbo3OAnIktRfUyjFSbDmg9PIa8881GL3cANI8KCZgkMhxWfASoPGPnPgVbWfhtg2SGLqbSURWU9OJy3nh2b8ApbJAxq+KAISVfTJHzPC2ZrRCl+JeR4cFpd61b5pepMJAKjH4HeDx/1fv+o+9dHP/HQxI4JX51/wVeN630kOp+fDDz6TyEtTAQGiEAS4AEClWKJQCKQCIwiBKrd+mKWqcIIMOeVzSdnSudeF9/MVavRkkR9BWeiwYUS+7if3dgEVDclmTO3AyTW1OdnTKfkpkOcfK5whO2BNmxxYAD6DKOE0QRpQbwGGkbYmHK5Z7kEGH0GyZ359re/fYYQ4KZPgbuFl16kug3XrdJdTwGWazwlrp17Pp6C+I1IClfWFzX5e5vCm9e6KSyZOXsg0PdA9Ux+ovv8zz/eM3mu5T/a8dYPzN/ZIcjlZX35pvac9vsQhbxfkv0KN0V+sDU2VTJjM5kUVtXOqLY7YytNbW0QaN7EtymQhxKBRCARSARmOgJN35r4GK+vbdVVV61a6L2L/eID4eyKXzzkzjvv/PnPf+4DS9hdsAL0GL18gQ6/Orhx+eUBUwWZqvJauqlhNZkB7kZ3oSqMeYaF1cwRSDspJx6f7eFFFH/OSTgDz3QETmHmVtF4KUtOI4zlUFOb3ckuAYdt8NvyWxKPPfYYDTYc2yyAcOeitSuvvHIEQQTd9Zg0stxGY5rakJmJwCyNgKej77u/Nb9ub2Z3R899dzzbPXnujp6u+2969q3vW6BnDMfwi2EUTrzvMYmcmoYmqKjLWuheNAa/DLC+973vFQBC7rLLLvPqkZhnnnk8m4IspMsD6Du9v//973fccUdHI1N805VXXkkmNsO4XlWGel/IeP6vhnqzzTbzOrv00ktvvfVWz7iPLKy//vrRehdhi0qef/75ZVdb8ZGPfMTL5Sc/+UkfOL0faPC579LO33XXXX/4wx/IM4aqNddckwHF2qKnfeLaa6897rjjol44HHXUUU68fZE8OnwIJAEePmxTcyKQCCQCMweB2ovZrm5BTF8Mg+IdH79WJBIXGk4z7jJEIrjELbfcog/BpUwgSlGiW6BPgDzoyuh/lIRdaYd4RJv6kFsBUTO1JsYwXZnwXXO94jb6KLpK4T9sX7amKndnCgLlHosA8hhnMfLCGDHtvPohUGzjvzXM4bqLlC4bz21JuwfcCXFLuAGsq2y6uyWXDdOsscYaMVgTIzgyDdnoQBflecMUKDIxxyMQ3PUlzLaX3/V0PvC7qaJbOjp7/n1P17OPTp/vlUY8XyLW98z2Fm8fGR2P9pFHHnnCCSdsueWWeC8m/JWvfOW0007DSA888EBfBffhaw/7pz71Kfmf/exn6YxSe++99+9+9ztjVZtuumlcqdtuu22nnXb65Cc/GbveNV4EN998s11EFxP+0Ic+JK1B8EntzTff3DjvFlts4X1x1llnqXf//fePgvGLjVP1sY99LBZmJ6aUuUWf+MQn8GfthkU3pC+88MK11lpLu/Gzn/3MWZBhHjJM+RVXXGEhgLC2qrnWyFQFsG5qQ/gd73iHSBPpqkBVTzUdOttIEmhzlKpWGhrzI6da++ya7gey2fW087wSgUQgEUgE2iPghYquYL84cBDjKg9BSOzqRiAk+g3x9jXcrj/hM064sT6EkXLdFJt0JOIXY/Hu75fE0inymbuPATiPXWmv5+WXX15FUcXIv63ZwGHuNEe+6vbXa3QejRsDYkZVXEf3jASW67YJf77ub9n4dV1ot1ws8KaswRR3kS1GWFz0SCC3WK5fNxuKG/fS6EQgrUoERhsCmvZek/p+ehOF3nZ2THmq+7y9H+98zrBRJ8/vyh/vXm6DBZ6X6OO8vfKxjUGr2oVGe37xxm233fbqq69eYYUVosH0sHv2eWVXWWWVT3/607vssgtlX/va177+9a8LStKwK+Uzae985zt33nnnP//5zyhoFDQai7JqIl6o/sW/X/3qVy+55BK1hORNN9209tprWyBDW0GIQuNltWFZhHn8+PHea9qQosj8CC8mxVdaaSVN0FZbbaXhuuCCC6g9+uijGRAuaIdWX331TTbZ5KCDDlLW+hSKmJ6DDxvR0xaFGaHWO9TgMorulPHw/fbb7/bbb3cozDMIKO3TD87UO8Xail6OirPZGxbnJ2DCiGFlYuqlRz4xQwNyvH+9j4z6RRE2mPcUy1KoV3FV+3qiyR1hEvqt2YSz2tlDod8oq+Flnosy57Sl6QF2C+WWCCQCiUAi0AQB70KBXrbXvva1TQ6/0LfQpykOuiDJuikyvYy9sCVQHV0QGyVetzbdEW/64MM4jI1vORLVtEwvbPxH+Jnx/iiro0CPMXu/Ogoym9o2TJkstw2T8lGoFsKNVkWf0gXV+YvhCb/VREmLctTf1VGTo7+loM0lc/nA6Fq7uDE+ovsYMQVCCWxBdN0kJEf4Ejeeb+YkArMTAr2PdE/nH696+K5Le7qmztX3hD/finZrpJ/rZWWx3XZuz8TLBCr3Hu1bIAsr7l5omUlrfuy/5l1w7hekWv793ve+h0Ziv0XCw24ruxIaBM8+76j2BAGWc/rpp2+88cYcwgriqK3ePlUl1XTM6PHe0YxEfqityvSb1uZgyLzQ0V5V5R2iEDV1CKV8z3ves8wyy2ipeIa/+93v4t5V4V/+8peGAASneCdWT4QP3GkKpUb++ZYxavRbc/qb3/xGccMBAqQ5n40VorK/+MUvHBWk7ZMQCC2qf8ABB3zuc5+78cYb99hjDx8kj7fh2WefDfAbbrhBY0tY24sM2/3Sl74ETGarZZ111vGyZi2rOOHZHOB84xvf8LI+77zzqsbP3ukkwLP39c2zSwQSgURgiAgMhHWQERdtEL06jt5Yn56NAWabTkltw468rY1/B2XymucA1IPRtyh6MCW9DWPzfH3GwtFmsXM2OX7l2GoJpB1PNgWUeV7wNAyhD1QMiEQAMhBYagVn+G4VnCErd1HgHBvXQXS/TG/WCSu/jYk4Kt8WfdZiAGQA7hoBvwxnCLw3z02XF9GN8Q6/dgm4fNFvKxpqidEAdc2k3E0EZgMEfNuIf3e5df9r4f+acv33n5nyr/k4e51XZ+8vrhu/vULdk8ZOfu55ptBjv2vaMh/oWenDrx4z94CGHXk7UdlezS8MU5aETI0JWmh07Fvf+pY5sVoPmVyagpa/853vGPFcbbXVpIVDRykNzl577UXG9r73vW/DDTesaot8v9yYmJ4YY1zUr7hlTlT5jcKHHHKI14R8Lt+Pf/zjoQFvRBG5Q88999zdd9+9lMJ1P//5z2t7zeDQxG2//fYOmdCrljPOOEPaIY1qMcOuIb9dd92V95iwNEvK0ZLAmZ0jJTREiJPJRwy77rrrxDoR0zj7PemkkwwoC/lmsNnR6667Lm0bbbSRo9dcc41dMsyIszj88MORbdxbA8u9DAR8ON7Rfs1zVpeWnzsa30bgmf3DH/4QB5ZPzxyyJQGeQy50nmYikAgkAsOFQL9vTfwT/7HF8ifsaEXh9ACMeSNj2DJKXLbY5UkuBExwl7RNryhos5Hy6hmyyhbU1y8mjEXHpg9hK+ngyfFLrE0i6LReRdnw6pKW4DOPXQkbA8KkSMRvOfdIxC/j9ZDityT0S6Rrm0wQASqwqiVqmQGmMYWAqPob1Jc2Ww06NjMeRPp5scUoA+Iqmk4ad6056rkUcFqQQg/a1QtR0gWNkpOJRCARmEkIeBy7llh+no0PGnfDGY8/+Pu5x3Sb/ztGg9VLjytbr+PXbk/nXIs9s8b2r1hihQW0a33x0+1WQwwFGplWrQGBY445Bv3Dgblqw/eoMfz1r3+tRdpggw3Yh85ZNQrp1aiS92vOcGj2NolE4682mctU9LWI5Ysvvtgk5COOOKKw6CJPvwBgrZac4puVaeovDWZqoJcIcJHXEqqdhVq/M888ExclwB/LhYvi8uga6audLPLJg20qci/WXV3bbLMNyaIwEuKZDzvssB122IFvNsxAXPFh7FcpMhpVv6Yc86Vrk2Va5JLleC98tt566x/96EcIsKhmkdg//vGPCTt3vmWOZWkGG5QUQvX+979f2mTsUMtUtPzUU09l+eWXX07SgILfOWdLAjznXOs800QgEUgERgsC8Q5utAZ9sulhtHEpe4uXgtLIm199JkyYWxIlDuckslfle4UEloSjCLbd4JM1Vol2UlvdSqWtEnFS5dRKoiovk85qTqRLZiTKbqNk5JS6JGLTaauy90gDM6g+viqYXF8qdstvJCI/vOh+bXp7emx9LP4l0/zU1cqkzE8EEoFZAoHyFHuY51lk7LqfXfxPVzz1h590d056ycMe59Ib9Nw5ZolVnl3jU4vMu8jYXn7c2wj0z34VFwCCBEpo0KLSaNkifeihh5roa3DTfFok8Pjjjyf5/e9/X8tjWSxpTfrEiRPFIfPo2kXkTBuWaL9RrvWzlpUFtLwdxBJ/+ctf3nPPPbVm1YIsEQbc+KK56KKLVlxxRYv2vfvd7+YsxVqjFEk0VVpBzSP3KcaIVSLDxDBJ9XIIF4pOkv0sYXacdZDtqg3S3LZOmRK+Ygafcsop3l+a65qY91opriJpOWTQ9fXWW0/INBqMkBtKUBdIDT3QE0ospr3UUktF2vBlgG8XyILMDUAw3vxqlLgcCuHZ+zcJ8Ox9ffPsEoFEIBGY3RCovqSlo1vj5Y3FtXELDAoFfQiMWh+CQzU8pZHwK7/mko3dpvlRafR+ym+xXyLSfp2Fbp8Nj5X2G7vV35KpU2VzyvEbCZKhbVBnmsKJQCIwJyPQ22iM7Vl2owVetfTkK497bvIT9fANHuAVNp/ytgmL9XHeGP+K3/5hsxqzYGZcEe8KaYzUyGPZVTuyx1fJ/2meqrS1psTiFl6qWbNqdBDg/uvrk9DSRkvoV3HraalR+6yd7FdDlNUCm9ZrjSuzZJHS8MFGWTptGl6TdyKNBtu8BawpffLJJ+OipRbxMsZVTdylTaY1Hcuhog2VRc5tIpz5dffdd99ll13WylsM1uCrIiQFT4knD/PoNO83wqk4kMePH2+pMBTaYmDkydDDHW16cFhYq7TscjiLfz722GM5zP2W/DkkkQR4DrnQeZqJQCKQCCQCg0BA58NW7foMvHD0QmryJbMkagK5mwgkAonAyCIQ/EpMSs8rlhg7bfq0no5eXsDra+ud9NsXDj3vwl3Yby00eiB2fuYzn7E+MwevRZsQNmsvx7JY3LPV4mbtmgOMv3G9Im/bbbddDGuS4T4V+ivMuCrfPm2KLCKqlLW1fD3YfFp+WrEw7Us1HuUUFbHMQ8uD6qi5wb5mLIGpsof3GNU855xz5JhjbHqOacNm2xY92nlzRgwBWK2KPEt4ekXcFAEJLNd8XfxZeI6vBDPSGC5whG2LvuYTNuRqnSqhzpzPIBLqDCsc2xqBHNQ0AMpRXN1kYAJy1GvEgavcmoLIv0U3fHJ5n332EddTrTokeeCB84EPfABXrx2d7XeTAM/2lzhPMBFIBBKBRGBwCOhDDK5Ag3RTDSWzJBrKZUYikAgkAiOMgPau56G7J019ei7fNjJFg9cX/0Wv2GEK8H03T1n63T19wc8viSLu10oDiDy6SC8mKdQWzbMmVkxG5XtEUHv19zktDz74YCxRWK8EUhctJIaJ5iF4vtyDTJrXGi7QWr2WsOLpLZmYtq8EcWlaJ8JsGhVZvKrW5KKayCRHbikloV6LRZVIY8YzOxaC5sK1YbDE6DR7OWb2IpknnniiXcKm16KsVYUq5QC3CLNg4+WWWw5xjUWeyQAB140zdcr8ydAwWGCNQEdNhEaMhTdjrUySg8palIsGKPHuQtUpxEmh+tdffz2gguTLBLKIaEuLEabQjOI40wkTJlSDq0k6pApsOVRVjZ/t080nI832p50nmAgkAolAIpAIJAKJQCKQCMzhCGCVJrZed9J/7ruaVwz37Rmz0HMLLd71+F/Hdvb0Ect5J2123EJzL/hiRO6gEOvV37AVKot6SdtCxK6tiJd8icivHg2xIlMOlZyipxxqVUR+lPKLlxaxoqqmwW451KqW0EksyoZ8Y7oUlyi1hHCUrRYJnXIis1QhUcyOTL9lC2Hx533lXrTcclm77babrzDUxgJKwdk4kR7g2fji5qklAolAIpAIJAKJQCKQCCQCbRDomT6544Hbu7vHcP32LLrslDU/veB8i8515yX/ueuS6R1Tx3ZMmvv+O55705piaF+kpm3U1Q4VqlbNr2YGK6seLekiVhLlUEk0HmrMKcKRaCoQmdVD0tXdgShplCkaSoJMNV0rErshUBUr6ZIokrWcVvqrDBkZtmjWbbfdZu0xk32a2jB7Z744DDB7n2eeXSKQCCQCiUAikAgkAolAIpAIVBHgZnzg9mevOnp6R1f3chO6V/zgwl1z+aRcT3dP578mTr7u1GcnPTLPa1absu4ei1itr1ow07MoAq44AuzbSFaNFi6OPzdS6Fn01AZudhLggWOVkolAIpAIJAKJQCKQCCQCicDsgwA2dP33H//nHdPX3P4Vr3rbvGJ7q57eKU9Nv+G0J++/a8pWxy4+dv6+bwAPyQ88++A1659JxFeX85gD2a9zTwJcboBMJAKJQCKQCCQCiUAikAgkAnMQAtOndl931j9Xm/CquRcZ0/eV35fEOSNLPdOn/+mqp+dfvOf1yy/c2ck5zA/8Epk5CKw81dkFgSTAs8uVzPNIBBKBRCARSAQSgUQgEUgEBoxAL7/t9q+js8tnj5o4eHuPWR+qu/ffmK5e6jtnOgwHjGgKzhoIzInznmeNK5NWJgKJQCKQCCQCiUAikAgkAsOGwNSpU80F9TuQGl772te+4Q1vSAI8EKxSZpQjkAR4lF+gNC8RSAQSgUQgEUgEEoFEIBGYwQjw7h5xxBGHHXbYAPX6Zuy9997rA7YDlE+xRGDUIpAEeNRemjQsEUgEEoFEIBFIBBKBRCARGC4E7r//fqoXXnjhhRZaqH0d991339NPP/3EE08MkABj14T/9a9/UdvV1aXUAgssEN7j+P3LX/7i87Ovf/3rY5fyhx9++I1vfCN5ZR9//PFbbrll2rRpfM7LLLMMDVXPM5f17rvvfuihh1rHuL3ZcZRC27///W8f/lF2ueWWC1f2lClT/v73v4fmRRdddJFFFiFfPhekCPn55pvvzW9+c8jI+ec//8nUUino/vOf/5TdUh08F198cYst33777Q8++CDNb33rW2VWz6JWKndHEoEkwCOJdtaVCCQCiUAikAgkAolAIpAIjAoEMDp27LHHHgcffHAbgxDFxRZb7Jlnnmkj03jonHPO+fznP49t4oEPPfTQEksssc8++2y99dYk8cC11lqLwokTJ8aXeK699tqddtoJHXX07LPP/tznPrfaaqstuOCCd9xxx0orrSSnqp/CO++8c/LkydXMVmnniPR+4QtfOP3009/xjnfMP//8qDVOe9lllyHhyy+//IorrqjsP/7xj1VXXfWss87ChO0q9cgjj6yxxhoCv+++++7CwJ3RFVdcweao7qMf/ei5554bMeTEkN4YINh888132GGHCRMmPPnkkyussMKjjz6q0ptvvjkYfitTM3/EEEgCPGJQZ0WJQCKQCCQCiUAikAgkAonAqEOgvWfS0aDKg7Ub+73pppuUnT59+sUXX7z99ts/99xz2223XVSHVB955JHf/va3S+0SBBDyE088cYsttrCr7AMPPFCrd9y4cVdddVVkhmF+OY2R27nnnlt+USjt0CGHHILuoqBBXDmWf/CDHxSdV199tejuxx57DOVmzIEHHhiHsG45//d//0dgvfXWK2q33XbbY489NmQo32uvvSKNS/NL77jjjmGSMQV23nrrrRzdBHBg5oVk/s50BKznllsikAgkAolAIpAIJAKJQCKQCCQCMx4BQcWo4GabbbbvvvtivMEPVbPffvvxuHLDVqvkFhZUzO+KxNqULWHSRYxHmis13MV77rknwoyg8t8utdRSaHYRiwSF3/zmN812poc2G0a68847U06AMX31dIpSXmWVVe65554oxcnMY/ypT31qq622Ou200yKz+hulaItE9VBkCpbm9FZXCHAOi6auimV6JiKQBHgmgp9VJwKJQCKQCCQCiUAikAgkAnMEAhtssAHWaq5vnK3Jvdtssw33LLZZzp9beO2110Y7xUuLLibsaOHMRYzDllfZLn7705/+9JRTTvnrX/9qQa/ijy2SgqgnTZok4rrkVBPYKeU2fubf/va3WHQc5bml8MMf/vDHPvaxCy+8sDrR989//rPo7h/3bU899ZSyVYUlLf75oosu2nTTTdFvbnDWtpIsRTIxYggkAR4xqLOiRCARSAQSgUQgEUgEEoFEYA5FwJxebPbZZ58t53/AAQdceuml2GbJkbjgggs4dc2YNSuYp/eEE06oHm1Mm3C79NJLo7KbbLKJxbqq+gnjqGbwtgk/Ngf4TW96k2jtjTbaSI2KYKq8vhisZavMQB4/fvx5551X6Ksqfv3CJmC70Z7I+eAHPyjuWnD1N77xjXe9611YPTJflLQqlfkjg0AS4JHBOWtJBBKBRCARSAQSgUQgEUgE5kQEED+bqGCx0LHYMhRQVqHOu+yyy0EHHeRo4CITT+b+tdYUr6zFq2yxWnUr4MoS1pSTCc9wEX7Vq15l0q8puCWnz5YX3bYqsgSX2Gzh05bCchSt5d8lf/zxx6PflsXCh4uF/NgnvbC1WhPbWdjWX3/9M844409/+hMy71yOO+64YkMmZi4CSYBnLv5ZeyKQCCQCiUAikAgkAolAIjDbIhDU0e+ZZ55pnm0hwHHCe++994033mihqdglZov0vPPOu9tuu1ljGXtsgw6q2eao7w+9+tWv5lUmU1UetfhFYl/zmtdYd3rddde1ApacSy65xNxdbmGRz7bVV1/dAloin6NI1NXHcHunKDetnWQRJmAh6A033NBXlNvYmYdGEoFcBXok0c66EoFEIBFIBBKBRCARSAQSgTkCAc5Yk3WFPQsbtvDy//7v/wp4rjJGaZN+fVvIIlVBjH1l11LM5gZzDlvs6jvf+Y78ZZdddsh4iX8+5phjPvvZz6LBopp5iblkVYeNV3WishzRvpN01113MdXUX5OTw1T2+5wvJ/BRRx1VLdI+Tb9TEPysRl9I+tWvfmWN6PZF8uiIIZAEeMSgzooSgUQgEUgEEoFEIBFIBBKBOQIB0199mojLFwUVh+ybur///e/Lh3B9kWieeeYJILDTn/zkJ75gxGtKGNv0GSTsF38mb/7twgsvXIUML7Wk89ixvSxGfLKQ6TiKxC655JJVgi3friW1rMCM9HLzksGoLe/sEA1veMMbivxb3vIWnzgyZfeJJ55AgENnaPj0pz+NFWPC9BSzi0AkFK9GRFOL2Iv6dlI4MErPm10rkrszC4EhftdrZpmb9SYCiUAikAgkAolAIpAIJAKJwMtEADHbYYcd8DpfrOXtbKMNF0UaLS7lo7iWm2ojWQ6VAOCSE4nCNkPAblNJ1FeljmLFUaQUpKcUaVW8KhzyUWTy5MkSdKLBVcNCPmT8xm5VSRxSxHRi+Vh69WjVJGmHinzQeAHVisQhv7nNdATSAzzTL0EakAgkAolAIpAIJAKJQCKQCMw0BAphm1EW1Phho9oiUBJVGb5ZWzWnmq4WqaarMtU0mRAzqbiaX0uHTPw2PSQz1tmqHbVbK1V2ke1G4cyZ6Qi0vLdmumVpQCKQCCQCiUAikAgkAolAIpAIDCsCN9xww7HHHtumCv5Yns82AnkoEZi1EEgCPGtdr7Q2EUgEEoFEIBFIBBKBRCARmAEIhJf1l33bQNRFHO9AJFMmERjNCCQBHs1XJ21LBBKBWQCBEjkmIeqpBD4N1vSXo0dZm6rjd8g2DNbmIt9X//PzpmSOvAHFEol+jSFQlZ/pBteMme13G/HPSzDbX/Q8wdGJgNWnzOz1naGBmLfccstZemogkimTCIxyBF6cpT3KDU3zEoFEIBEYSQSa9tGbGuDbgLvuuqtD3/ve96xXOWTu9+9//3vrrbdWr5UqfYlhUHqUsmrlFltswYxDDz10zTXXbGrq8GUywBqeJ5988kILLXTuuefW1hdprHeA8A4KhFIL5d/97nd/+tOfjh8//qSTTqoqcUhXz6c4ODwefPDBCOo766yzXvnKV5bimRg+BOBvu/nmm3/84x97cCZNmmR3xx133HLLLauXqZUBhB1yBf/xj39YW9XlszbsUkst5a6LIkVJSLbSUxNuJdaopOgvRRplyqEZklBjqUIT8be//e2pp54yq9Ad68ul1QmNjbbNEANSyeyNQLm7Bnia5Ptt3geoKsUSgZmIQHqAZyL4WXUikAiMagQuvvjiI488so2J66yzzle/+lX90V//+tfEnnnmmZfTB7VW5OWXX06PDycMVg95rCDM8JmHNjYP36G///3vDPARiIEYrxf1iU984pJLLqnaIxjP1yx8OPGtb33r2muv/YEPfKB8+mIgOquqLFXKmLe97W3VTJUC+UMf+hD2W+32oWH/+c9/HnroIR27N73pTdm9q4I2A9OBua967rnnntX5hO9973vb11Iu1i233HLCCSdcdtllqGBkujEsr7ryyiv7ZonvmiCE5VbZZ599TjnllKpmV3aBBRZwiddbbz2L3/ooaBGuikmH8jPOOIO1dv/7v//71FNPbSXsm6LxSZWakrKrom9961sf+chHSs5AEj/60Y+YyhKb0aXjjz/+D3/4gxs4yjJm/vnn96Ssv/76e+yxR47gDATSlGlEoNVd3SgZOYOVb6Un8xOBmYtAEuCZi3/WnggkAqMXgX/961833nhjG/uWWGKJ7A20wafNIbiJu0M7azKIzb333nvttddyp/uG5E477XTAAQcUGlwTHsIuUoH9IkK77747jo07UbL44otzSG6//fY+8PjII4/gFUPQnEUGggB4v/CFL1hQZ9111/Vdzbiy4ir7LevjJV/60pd8n1NZwu4fn+J0HRFphyzh89vf/vaYY47h23/7298e2p577rnGG8xXSe+7774rr7zSkj+nnXbahz/84TZVq46zmoCPl37xi19885vf3CjMEiNf7RsKHxpFXNvLNGpmP+UGtjwFp59+ug+QOl8fU9XsAOH+++93dnTa3ve+98lv1JA5icBAEIgJIZ0vfFqos1Oh7t6CPZ09HZ3+63hhyogbciAKUyYRGP0IJAEe/dcoLUwEEoGZiYBX/v/8z/8sueSSjUYIeOac0S3++c9/7qhQTLtD7iL4ymLo0YN/OXoa7RzNOSgQ8hkW6tYL5P7rX/96dd+GVxx33HHcfRzFgpkHBayoWn5FFLqG5KWXXiqH06wseTootaMZyVnCtquuuspl5ep30cNpCX9XpI3xjroxPvnJT/KCEsb0xDh88IMf9PThwOIvJk6cKNwdmxUezEVcCHDotGYPVhxfIqHngQcesPuLX/wCe9x22235VD2/Te+B22+/PdivURL01dCJyQVt7KTEHITVV1+9UUZcg6ebC7d6puTvvvtuAz3kOZBXXHHFWkFEF+n95je/+cMf/lDBjTba6Ctf+QqxsNa53HnnnRdeeGEcJdD0LGo6czcRaESg77Y0ruRJnN4z7S/Tn/xZx+Q7OrsndYxbsvMV7+mab82ejvn61nV4yYdzG/XUcqi1ldsyEm5pYtJxqE+kx7CUTBMZysd1qwVDvqa8tkv+jjvuMKvFYFDtUJtdxni0NQUiR7wswsJq1ZEup2A3Rty0PGGqHFsRUJfdqLGaGTnlkPNVtfONLyo1SraxOQ/NMAR6L11uiUAikAgkAi9FwPupxE9yLtkd4PZSNYPYq+mvlawdtVsTePjhh+PFcMEFF9QOjcAue0SDM0AIdKNtjQaQ2XTTTcnzvko3bn/84x832GCD6Bno9GNNjUpa5dS0FTH5Jler1Lov0iVf4vvf/758HuCnn366mj+odKt6B6Vk5IVrZteQmYH20Py1r30NzostttjAa8H04tZyM6yxxhqm/haD2VbS+r74p0DlMFi+q6wuZNs1LWISFJpl4JBtr732ktN4jjINexEwqrXLLruoGt/mam4qfNNNN/Up6zDznMDAt5/97GdR8JxzzmlaSu+c95jMCiuswCEcMsXa2BXAbxSgZGYiERgsAm6k6d1Tpk9/fMpD+0+5e9HpE7umT+ycdveYqXePmTZxrkn3rD910s0eG2KD0kxeu2roZ9999y1l77rrrkUXXdQqFXJsYjGE8ZvNbojTiM9RRx3l8ZTvcSNmc/8bzXzsscdktqndUfEaAjrayFQPkX/00UfNgzAMpy6TFNZaa63rr79e/m677RZVG2szVHrNNdfItCnu99vf/ja2fPbZZ5ccY2rknWZs0occckhoqP3+5S9/+d3vfmeEzpmqlH5xJc63alimRwyB9ADH2yd/E4FEIBFoh0CbMVovMP1UhXmKxCgWLTqs0vrfNgkddx4bAY3e9Msuu2xIVtVq9/WwG/XI9641vM27JSpbQfyB28p7NIJ1q0qi9niFmJSLRspRnX58iNWESUaRJ5988rbbbjMP1lmQX2aZZRotrCqn2UbmLW95y9JLL11TG5ID/G1algEcvzo0vOJ8caZilqnRbDYMb1MwPHsYDlce442pc5SpF86uSwgEGoFtXCk5aEOYZyCfJHm7kR8nHkeriwxFTuOvUjJ1g6677jqBqQwz0dRVXmmllXR0HKqeYAj7FYjLAQh2FxGAtuL9UKSIueLERA47anb08ssvj6U36iQfJ8ilQNKuZaIUdOdAUmcrbKhZQowBAs553d2uNOuJGm4gD4SqsBqbbmGnQ8ZfXCaeDefOSKZWayQGecYE7JQX/Im5iG3qcu6WhVMFneeff74Rk6pwSetN6pXq1Da1U2aRlBCGbVIuxHR5G+VZyzyk1CHr0hmp4ae95557uK8NyjTKV3NKLdXMpukCnaNKNS0IVU8xAQ8Cp1NNTxSBnq12KHcTgcEg0N2p/Xjwf8Y+feYYTM/Mgk6ryo0b0/N0Z8fUsZOvnPb3LTqX/HHn3Ct39PS+4Jreq02r84jxrFplUPRE3KUCFkT9GLIhbxbMVlttZZLLZz7zGW2Cu93aDaFcKBBJjbn73+R5jb/xaI9MrerqLiXGqqitPlnFqqokAYa9//3vf93rXqfV0qR4BQg18gZ85zvfabB188031z5IHHbYYR49NkSrq+APfvADjF28CctDuUz8/N5779VARe3OBbNVo0iTj3/841792mSHNIDOSLiH9keO+T5e61XDirWZGAkEXLncEoFEIBFIBGoIeFdVPcC1o9Vd8wPjHXbrrbcqVQ6Je5Qv1BbV0YcOGhzNOmpk5p6XblWeCyv08AtFvl8b/9Iqq6ziUBwNDdKIk7BMAlFj8QAjCV6r5lh6DRdhuxbdLcJRpFd7dzfzrEdVZXqUWz4K/3S0nE4p4jTf9a53FWMkDOfj9kP2ANeqKLtq16uIbhNKhlgWG0wHdWrjx49H26QDajnG4ANVyyzZxcR6z7C7WxekQCFRjJfmXoicyKwe0qEpxrRKUK7389GPfrR6fUOhHH0d9pSyfbZ0n3feeQYvytUhbNMV48EokujiQQcd5CrUxDB8fUGslaoiHDboUTEeCRQSjKeVgjI5N2TWiuD8eqWgCwOqv3ADbNHfKhGnYwmoCRMmRCwfJapT9bvf/W7PRQgoDgTrTsXR8hs18m1WDavV5VDcV4QNgrSRbCxY8wBXBdiD6tNp3KFRpxwx1XEibmzCuryEXeVGYWqrHuBqLe3TVBUPsOjupsLGmAKlgw8+mLytqVhmJgIvBwEO4MmPfWfqxLmmTRw76c9vnPL4adOn3T99+qNTn7168t8nTL173LS7O5+9d93p0zU73lkvNmjtK3W7nnjiiRofflRxSXaNK5lMpGWLljnoLnrZe2e/sNEpyXeKkUaesBEDvh5DRNoUAyvDiQQxkUFOMYCkKvBVOdj1JptsorhWyPoC2KbmtEiGfq5pQ2alIVWctti22267nXfeOcQMe2kHkOSwxDCrFlibgA+bcBGZBj09pAYco4rIjOJm3GD78eaS7x3tpRD9hBDzG6Xyd+QRSA9wvFzyNxFIBBKBoSOg7VbYm9JW1SIfrUU+vSzjC4p2xYB5BRp+Fg2F19Xka3qwU5OaDBXz/9Dj3Y/ncHV6E+s98DpWi0dZr2fLC5nXxJUnvssrXOTVb37zG3rMaYxlh0opY94WW8YPkRa9fPoxH9SFEqsl674EP6zK680YnnemaLzejO6LJYgwrsEuclt0tkqoguMaUHo2zoI/s7aqs4J6KpbqxXv1dcgzpqotroscoAXUEDP2j/XxooekmG2H+JD5WqNGv3FID6mqrWlaD4Yr4Fe/+hUAV1111XDLA5+1PPY8q0Wb4uyxNHHMBcUYAYiGqRo7ZRg6VFbwxk51+1wL3glLEDtB7gi3DSMNqdCsa2VooCiXoNyGMond5QhFLNnvxjPwwccbV9/JxlmQ/PKXv6y/aNdFN4QBEA4ZBrv6fvUFm55vNZMSlsT96fTVaEjCHS6Ts9QVQfUdjSKCDOEMGTezy1Em1YfbpKq2mlaFxdjluP/jK1/Vo0NO6xCDXXH+6kYlKo2JtauttpoABwKWmObSueiii9z5rkVjkWHK8fy6T9wMuvsGPjjThqmiVDtnI/Bkz6PfteDa9M5Xjn3dWZ3zrKzJ1AiOmWeNztf9aOoDHx/39CVjn/tt97O/6prfonHPN48DR8w4oAfKMJmV+TXpGpwoq/XTLHhb8dx61jQFpUGrKsctPXR4Jh+vJgV5loNLV4WlvSu1PApquzSPWiRDZsSMPmuWfWitqpMlXmRl3q/iNgKe/SIm7T0oJCoCeeRz/DoLDbIFFI0+xzisfGXVrlWRNpRc5IuqSDgL5y7EWjtvMo42MCqtieXuCCHgAueWCCQCiUAiUEMAsQkPsFdUqznAUQRhiPYamawqCd+a1ydWIDjWW5lOv+hcRDMaabZbimAOoScWapJPfv/995epH4zY2C2bgoalqZUTGooHWI08xmh2CBt+Fs2lN+BEhJIWeQmuS7bRL+YZiQ0LI5+zizyKVTyH8vExJEe+4XmUT45NKYFeeHW4QIcwB7gg0DRhHayABdENAZVGzwOPcl7SyEyfLd0oXCTCA4wwx2751ROiTRek5ESCQ0C+cf2iKvKbmlTNhBtAbPpGBcAoa5yi6qZwVIeMwTYLdKH0IeYXw8FRgxiHcifCgenGc6iIwf/oo49WnKnWRpJfABF7HPmuvoGP4rJQqdvYIRYeeOCBpYjT5MqgR59Sv61UIcF/wo2s41g9zaZpkjyolLj6bto4fb/iioPWItXuamWLfss4kW+c+N1Uv0ynHHb60FEoaSVZyyfcygPMQg5eZtj0vEnWyhqMiLgDPvmo1MCK2xuGMcu3Jl/1AId89TdgqRWxS6ZfD7CyG264YZhqfEokthENT3TR36g2cxKBwSIw7bnfTrnrFSb9Tv7nrlqj6b1u3th677Spk26Ycud80+/udHR6d19zNLAKlA0PMEerwTjtobHCU089FRkusTlGcnlrEVGNucY53mgKesUYdTKZ1ltSs2w+gjeRdsavh8IW9lUNKXOAY3wWdw0ZHwmzSRdhaY+VgcJqZhyVo0bNmrkPuC4qK+ZZpi2GI73spLFfL81onDFzLYORMi9HW1lWgJhWkc3FAyzHwKiRYm2aNtkTHdqKYZkYSQSeD5CL5jV/E4FEIBFIBGoIaJG9sbwRa5vx45pkq10xyREz7DVps4CteUGEvR29GluVinxvcQlO2lirNjTEL3YnHqyxOKcWV5VR6hDzokVyWOtErBnrN4pI6DHce++9Xsbm2Zr7VJR7ZyOE/JOcpWW1ZKUUx6wkdGve8573FHmdCUuD9HsujaYOJKe4Cxrd3bpB22yzzeGHH66HFMYYfZeoqS12tsqvFSnytfxa8dg10CCB6emuVQtKG7Ywf6wo4cINCsrXYRCEJ6TI6/wJ6vNlplKFIRLDFrwEDoWYQ1jZ3nvvHT553TI55WqWghBw9cNVHgX159w/JFVa5HXaOGOV2nXXXYPaFWOE1uunItJFZ6uEIQnBBY6ihTy9RYO7/cwzz7TL2euzvVGp3ZqeIt94qEjqy+o92+U9DvlyaLAJZuiAUqjzGhi6bQyF1PQQw/8NCkC7eI1e//rXC+F2yDBHTb666/rWWgm7nsSAuio5wLRTNgBkvIC8O028g9uG40gbYijBTAePQMA7QIUplgg0ItA95cGuzmc7e8Z0zrN6d0cvL3j+WeuU7umaa5nucYv3fglpyt98KqmxeL85mhQs1ztCINJmm21W5NVitE57JWLFSCJvsKOG3kJAG4gYezEhyaI/EE5P6zrrrIM3Gt8ss1qKtmpCs4m7xllQUnQWGW89TUHZrSW8bbXnMfE+VqgmYE6QZkHDaIBPg28oCl2Pgp5BVFaEji3W+asptBvGOItzzz3XugaisXBmLYxh6EbhzBkBBJIAjwDIWUUikAjM2gigXvx1tU3I00DOCjk02h0uOPLegn45AP16C7ZZsyeUx9Rcw94xiuxFaws98UINhSEcvz7uEu/+2A2x8CPRE8UdimFsCW96K22GWCniNS9uza73eikSfjNURDel1BsJHRT5UXzG/pZJqhGzWlWuaqP4VcurR0cm7QLBB0nTH1KjtC2qDsMCHznGOxBCCYHHyFXJl1OTjBy/sVV1yhGa7tdIBC/x8xKVP7grthYKZUcirr5YaHwpZHUuw4AY0ahW0WhMRf3zyZCP+0Fn0ZoxtRqFCOqqktbbK4A06uk3x0WP4k1jlfstTsCDM75vgVkPRWxubJ1OrBLRxU5rSlSH5fpF6Q1hOBpAeUykTVuoPkS1sgLUa61E7BbYa/L97qpad1+HW19cnz7kOdX1xQU+8JhZUwcxZq2tX20pkAg0RaBzTFdPR1dPJx/pk6ZSvMByezRnmrSOnskdPUjpmJ4xc/f00uNB32luY58xM5nf7RoBHWFGuWm1h4YFjfV4gsqL1bgzb6pBH8v4xTNozQJHPZ5CoD3FpXjTk2qfadKHb+3FW5Vk7/PTt0UpY0wbb7yxKjirjWqp1EFpTauzMHnEuJ5WRU61FkaWrZof6ap+r1fjy0YEvDg0y43CmTMCCOQc4BEAOatIBBKBWRgBrzSBWMUPWc5kgD1y80JpKKUiYahbwhuRaygW46kJlF3UxRdTRVmjE9xWdq2WVI42anZIjfh2kYlE1MKjayzceLxMAWmmekp4qYefuVYkfIDEsIWYOxqfRTW3s/TFowgzeBFZqLtfU/Lyd4tjuVYpzYb2ORCagvDy6y0aXCbpai3VHEwPmYSq0xdPy9HB+xrR4KEhCioSwdL4vCJFeatEVCHozlw1V98QTHFixKAJWAyg8AfWNLS53wTs4ZMxhVWIMv8D7mQCHj+GviZPS4y2hMLq+daqiF0Wxow7wQWNwu5A96qZwEIMuFAENDZV0m9muehD5pCq0KuuVcSBY2qxPnf1SQnMoR2PQzDecmrGHfTddVhPP/10H2sp+VXNvqoSa31VM1XhRq3mDCqtIo2PsQa9cOv6IMMeQ/P/3QDGsK644gqLDP3yl790LuxvatWgqkvhORCBMXMvO7Vz3nE9T3f4AvDC2/t2QR8IXlvavp7up68a281L2d059/J25dbfZwOADOFEF7VOVVk+WM+a4Jd4DC0MqQkySlWVKWmvIfPhtZ/iYgT+WBauerfHw1uE+00g5Eis9bE8y7GAn0lGxrbi43yKU8iqGMgTbWG8yawfj54BqVCu2RRZ8/Wvf73fuoqAIUuzWqzlEefrjaBq798ikImRRCAJ8EiinXUlAonArIeAFyGHLXowNNO9sKvv6VBSuvW6sO3V6oV775oBpXNgo8rgNK7F48oqr89G5cVlWtUcb1znUohErFpJRifAVhWupvWz0TA5or+CSDSSrpBvlV/VNoQ0mhelkJZacVyuSjVrR2fULgrHz1zTJpLtmGOOkYlJmo4rNo9fzuqmNgHkQtMxYb006VIwANfdqfLMcrSWcKUs96LeNpF+hjNqpew2Xn13SEGp3G/uB3OD+RVNHeeIsKHx5tm6qdxynBuNmms5OnMRHOi6x91VE4j7wYkYQxkyAQYg492EjSS2Vl2rXc8aTy8XEwE247fC+7nidV6vvPJKc/aqBVkbQc5GfKy+Vg7BEPsFl0686O7w4ZejJYGFir8ouzMqoXYbPM1lsDGS/Qi8fjn3kUE0Q2O65qVVmVH1pp45BIHOseN75l2747lLO5+9fPrj3+pa5LOdHb3f3EJ3O6bcOf3hL4/t6J7aseDYBSd09pLfIfDfDk2EWbUFT/ez21gLZjaQt5goEvzWgKxVEqIFC4EiL+FWF/mPf3qWBf9zF9NALGSki3DkV3PKoUgopWEx8ceIlaoNWXqgtLRmnRBw1FaK8ABb4pGRAi6qASN4uMFo8dvGvKrypaCEs9baF0u0YxYm8Ca1IqYFFMwHdhaxXEK1VKZHBoEkwCODc9aSCCQCicBQEPDeNWVRr1q/XMgW75/1eMy9tHkB+3rKkFeSLB5F7/WyOHBTE8MVjDt5fxMILtEo2Sq/UXJQORGjq0ijE35kevx6KshG1WbdnRK7Lq0XZdarBYqEi+vDcWvoWnHWCdhDMoXSBT/U76EkwKxqa0zrMFGFiAJc/8xgB2+taxTjHdy2ZbHoxrIDzGG2MR3YMtvcYH4YnVEeCQ5GLhE0fr/99mtKa4v+uBnstjqjks/zXEoNNqE3zCNkxjLnjLEbJrXqa7bSTB7XNd03BISIc/5wwmDvHishzSUanACG7JlShM/Wet01nYYw5PDGG5MywFE7OpK7BoPEZ+ID3L88V66jyYeWvhtJG7Ku2XFGeHYAAAzKSURBVAYB8c/jFt972n3Xd3U80f3IAVOfuW7MAht3jlmg+7k/dDxx+rjuh3o6xnYu/LExcy2P0w3qrPFbzVetiByjS54yK2NZREBog2cKUUQLIzqJvFauGiwtx6iiwIeIfUBEq2OLjtK2/fbbR9SGRoPOCHVxSFNWBv7slk1ktVeqqSuaF3WpPZosy17QVsS07Vp1b7dqQ+GogTmjaVokryHrXzb9ZICVMvD28p5imHZMM6JGdalxgHFkxZhMzEAEkgDPQDBTVSKQCCQCdQSqr9L6sf72o6zXJxJlQzl8c1XQo6BoXEXfXQ/eK3xoVcRAOxP23XdfrKCVLTGa7qj3vT4Kb3CrFX1a5bfS3G++qnVcTJ0lifsh/P0WGQ4BPltRrzXNsS6RTOCzk18iVgzmRrCes9XCrP/MaWkxbc4KvjuS0SHjanBSStlqOssuhaaZ4Y36fJdffnnNY1yYZ5EfQiJqdxYH9G1Iu/gCUHNvcsXI0zkr0YBN9aOU4ZvFJMtNUpWkJ3bLnVY9OsA0OwVPioDQa9R35KsZYMFGsQI4JRbT3mmnndB+HifzDIuwkY4w2zCTld5KfiSKBn5gz6PMklOTHNbdUim/uqj7CE/gTUoCPKywz8bKe/26867Z+apjuh/ee0zPk11PXdTx1EW8q10d3b0h0J1d0+bfZK5XHtz7baTeRqtlw1WDyI2K0BZOW45q0Eqbpg2xeETj+hGNfJI2TWirt0C1LqS0GkfdlGTGQ+S3sfYqtQ6xqrZyFg6VYTXLfJT8kiDg7Mr0h1Dlbe51YCtimZhZCCQBnlnIZ72JQCKQCAwUgXh3IoH67riWlXutBuyrOWiwAN1G1+hA9CplVJvfz1h7yEcttbIl07vcWlnoQcyQrIrhP3aLnuqhIadDJ+ckAkmJILQhh9EO1oYanVNvrNrdSk+BSOdGUJzRBJu5u1Z8MecWGfYRDjIunF++Dlt1InejWhclplvzLespFv0k2daIf6OGAeYUzXqWgp9tRkMEQqPuZ511lohfeopMTae7Ud+Rf8MarTXESMqRL8H+9idbU1vbVbvISQTYqIEhhpNOOqmxrlLEIelWBhcx/WNOYNrcWuLYxQ+XLnLEP1v7iocqtJVSkXC+Rgp8vkhkfiyRVROYsbv9nlGsJqDSGTIsMmONT22zDgKdOPCYhT/ZM/ebpz1yZOek68Z0TOo0/Ngxrmfskh2L7jz3Ip/u6Fxg1jmdtDQR6B+BwQUz9K8vJRKBRCARSASGEwH9ez14qxapBCsY8txIQV+xGhN/cszy7ddqy1+R8SmLmPxZlefYlF/Neflpn1fl4KIHsbQGibN++TpbaYj4bU5XWyuZAea7QDZBthFYXuYwx5xSRMWXMNurIhPx0o1R5cKAhcS3Lz60o2E2smpNGhrKZ6Wbagthy185iuo3CmP+Bi8cdc8YOmmqZCCZKuJrtWIqYX5Xqx+3KSWcUkBEG4FyKO4oylnuG06RLxgy7mEfjvKVKdU1bsHAPS84edE2fImmJLxU52gMD8kxOFXyM5EIDAqBvsd5bFfn2DHzrjVuyfPHvPGGntf8pOfVZ3a84Yqxb7ph3KJ7do5ZyGvH/wN3/w7KgBROBEYegWHsT4z8yWSNiUAikAjMTgjo4JpKal5irR9sV/yzM0UtXo57zeQoTABtMKcU46rWIo1dm+lqUmjJ5wglb06sOaJlMS1HbYJm5b8c8EOPX/Xys33xi1/EIRmgc8YtGazs5ehvX9ZUagLY72D92PyBPLpsrumHagTThmZHee+5WJ0Or6Pll5xpKRLnXsYyTDaLCEBx1DSHpF9pE3Rj7eVSdmiJe+65h4e2ZrYqfKyYh5mRAwnS4zt1P5hMXr1/wk7LqgUrNoRB29CMjFJuchOVeZJdHeuKoaASVVjUiJFaisys5ljYvN/qmGQIiU+epEiKuHWFf3sKHFJLKw2KxLpZuHErmRmY7/432Rirr10pVXgATf0Vwi3tbhnyKn0z0NpUNasj4EHt7Jyra9xbuhb4UNdCW3bN+y7LRSXpndUva9rfFIGhj8s2VZeZiUAikAgkAjMQAZ5P/MpsTCs/L7300qgRmiQCMyYo6hy/nDUkefCEUpsPaV1c7lZ8xjRCU7bMCLWyDs8eh5vP5JiJGmdkuSxL73Bg6nZjSiYf8jvxm/kUhEW5MD1Ljwz23M0c9lXhKKVPbxd1dMpyUBEuUNQaGX6ZJKpfq8T9mhAL2y233NJXbTlv1SgyVvRy+7KuhS9D+masgGcUVywx56fhCSwXacFMwlcfp+OqmdFqnjB5bkZf1nH5IMmPx1+qxpjwjFXiYIjZ+eefLwDYdaHHrFRuSYuOugoRXdzesPZHEUVsHGuykJJzp5/b2SRbNBI3ZoDr3i/mHLO77767q+8jPU4fVuPHj3ft8NVYNgySbtF+9bQ31VGrK2OnVriBFUYNcGrNUjYHz71q/SdXIcZKBhgmwCS8ev/99zdJ2+XgBDYTOGZ6B8VtZbN8V8T1cgnUC4FWkv2e1EAEcHu3hM2qPxMmTPAVGTeMGoFscjiDjT44ZcMioBhWSwZibcrMFgj0OsZiyKqzw7he+slmi6uaJ9GIgOY1t0QgEUgEEoEaAtiLJXyjzfTtvtrR6q6lIEPMUpDhmIqjsfZPfK2hKk+GwiiiJ10O+Y5OZOrvhh6/8eFEXdtq7zZ20Ved/iJZwlBRqaIzEmTwB8rN28Qwy1H5aA86EeuUVKsIS/StDz/88KgiSvGV+b5rGBAyfu2i6NZtkkYdq/KlrloC122/xhI9OBh/LMmaQrvGBdSFbtUORS0y99xzTwLWJqkKSGOP8n21qJqvlN1zzz035oIWHJCNmtmNu76IQ2EBJMrGrtWzfCuyWpG0MwrWVIpEcb9ifYv9gszD6V3EIoEFWYKYsM26R6HcL28z1ipTQHvNSEd5kh1yNd0wcdToBoW9WipbVOFmOPnkk0NzTVVtl4zpysCkuWgLJX5NhHa3VPVI+3KPCo01VPNraht3CbsNzIs2FhMVVauLMzBV273KFRzFFfHFEYcQXUMSjdXJ4e8NJ7BFboz1hM4jjjiiUbiY5JABGjppRvjt2gwehQ2IdJHsN6Eg3h4FLT3dVN5tYK3acsrlrJWSthlw4RJvfEaaasvMRCARSAQSgUAgPcDx9snfRCARSATqCPgCDTed3LLkb12ib5/3LL6YgrNVBfSP0YNgsNV8aR3uxiKIR2TidUXe53CEy+JRiBOGLPjTGpU8jRYo4sHTEdcJDmExoorb5SguxUuCB8lRHKl8nMYhwqgLJzB/o2+lXnPNNdyMWIHpwZbIst6maatAKEokrGnJuYcg8T5Zklpxmi0pBCjB0mKVeaK8XYpV1bK1tFOomep0VI12oiXUcv+20uPrL06nzbJYwqdps0RQzRhr//L4NS4lqiKuWmo51nhHI+ycMTWbG3eZwTl/1VVX8QdCD9eCgNWhrNrFu2gQpHoK0s7L0mVWxuLL5Wbn0sS6gexjksUTTkzViK41mUhaADm+meHzIRy2Pqt75JFHsqT6YWQCrqOT5RdtNBLOLg21IgjiqKm5DLaiOPLmfBEtgyPC6TmoOVo59qtmNyqMHDJKnXDCCVYjNzMZQeWPdTqChLlJnVGVGEcRrF78fAy4tFLbmB/GGLwQDAw9dyDjPQ6In8fBKfvcCJ8w0Kpmu8GcCBsY2VSnOwR7tGq3o0avRDco7qo1CpccAi6uuQNqL2C6ENFQ+Dp0kRxIAuBRsFXAufvHg+9B0wgIfce9YwjDWbumbjyjSJ4C5ziQ6lImEUgEEoFEIBDo/X5DYpEIJAKJQCKQCAwcAS8OGxcWPtBIcgauJyUTgUQgEUgEEoFEIBEYYQSSAI8w4FldIpAIJAKzPALYb/Ucqm63an6mE4FEIBFIBBKBRCARGG0IJAEebVck7UkEEoFEIBFIBBKBRCARSAQSgUQgERgWBHLeyLDAmkoTgUQgEUgEEoFEIBFIBBKBRCARSARGGwJJgEfbFUl7EoFEIBFIBBKBRCARSAQSgUQgEUgEhgWBJMDDAmsqTQQSgUQgEUgEEoFEIBFIBBKBRCARGG0IJAEebVck7UkEEoFEIBFIBBKBRCARSAQSgUQgERgWBJIADwusqTQRSAQSgUQgEUgEEoFEIBFIBBKBRGC0IZAEeLRdkbQnEUgEEoFEIBFIBBKBRCARSAQSgURgWBBIAjwssKbSRCARSAQSgUQgEUgEEoFEIBFIBBKB0YZAEuDRdkXSnkQgEUgEEoFEIBFIBBKBRCARSAQSgWFBIAnwsMCaShOBRCARSAQSgUQgEUgEEoFEIBFIBEYbAkmAR9sVSXsSgUQgEUgEEoFEIBFIBBKBRCARSASGBYH/Bx5HSDzoSDV1AAAAAElFTkSuQmCC\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
+    "authors": [
+      {
+        "name_en": "Li Di",
+        "name_zh": "Li Di",
+        "email": "dili@nao.cas.cn",
+        "affiliations": [
+          {
+            "name_en": " Tsinghua University",
+            "name_zh": " Tsinghua University"
+          },
+          {
+            "name_en": " National Astronomical Observatories ",
+            "name_zh": " National Astronomical Observatories "
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Pei",
+        "name_zh": "Wang Pei",
+        "email": "wangpei@nao.cas.cn",
+        "affiliations": [
+          {
+            "name_en": " National Astronomical Observatories ",
+            "name_zh": " National Astronomical Observatories "
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "CRAFTS",
+      "pulsar",
+      "Survey"
+    ],
+    "keywords_zh": [
+      "CRAFTS",
+      "pulsar",
+      "Survey"
+    ],
+    "publication_date": "2025-07-01T10:19:20.555Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-SA 4.0",
+    "file_size_mb": 12.41,
+    "file_size_bytes": 13012698,
+    "download_count": 461,
+    "visit_count": 949,
+    "url": "https://www.scidb.cn/en/detail?id=68708f0c0e9dcc59702a25cf",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4710751",
+    "cstr": "31253.11.10.5281/zenodo.4710751",
+    "pid": "",
+    "title": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
+    "title_en": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
+    "title_zh": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
+    "description": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
+    "description_en": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
+    "description_zh": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
+    "authors": [
+      {
+        "name_en": "BIDS-contributors",
+        "name_zh": "BIDS-contributors"
+      }
+    ],
+    "keywords_en": [
+      "neuroimaging",
+      "neuroinformatics",
+      "data standard"
+    ],
+    "keywords_zh": [
+      "neuroimaging",
+      "neuroinformatics",
+      "data standard"
+    ],
+    "publication_date": 1619020800,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0,
+    "file_size_bytes": 0,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3265460",
+    "cstr": "",
+    "pid": "",
+    "title": "How <span style='background-color:yellow'>BIDs</span> and <span style='background-color:yellow'>BIDs</span> validator has informed the data aggregation needs of the Stimulating Peripheral Activity to Relieve Conditions, SPARC, consortium",
+    "title_en": "How <span style='background-color:yellow'>BIDs</span> and <span style='background-color:yellow'>BIDs</span> validator has informed the data aggregation needs of the Stimulating Peripheral Activity to Relieve Conditions, SPARC, consortium",
+    "title_zh": "How <span style='background-color:yellow'>BIDs</span> and <span style='background-color:yellow'>BIDs</span> validator has informed the data aggregation needs of the Stimulating Peripheral Activity to Relieve Conditions, SPARC, consortium",
+    "description": "This is an abstract for an oral presentation, submitted to the Research Object workshop.&nbsp;https://researchobject.github.io/ro2019/",
+    "description_en": "This is an abstract for an oral presentation, submitted to the Research Object workshop.&nbsp;https://researchobject.github.io/ro2019/",
+    "description_zh": "This is an abstract for an oral presentation, submitted to the Research Object workshop.&nbsp;https://researchobject.github.io/ro2019/",
+    "authors": [
+      {
+        "name_en": "Bandrowski, Anita",
+        "name_zh": "Bandrowski, Anita"
+      },
+      {
+        "name_en": " Gillespie, Tom",
+        "name_zh": " Gillespie, Tom"
+      },
+      {
+        "name_en": " Surles-Zeigler, Monique",
+        "name_zh": " Surles-Zeigler, Monique"
+      },
+      {
+        "name_en": " Pine, Gabrielle",
+        "name_zh": " Pine, Gabrielle"
+      },
+      {
+        "name_en": " Grethe, Jeffery",
+        "name_zh": " Grethe, Jeffery"
+      },
+      {
+        "name_en": " Martone, Maryann",
+        "name_zh": " Martone, Maryann"
+      }
+    ],
+    "keywords_en": [
+      "BIDs Community Standard Neuroscience PUID"
+    ],
+    "keywords_zh": [
+      "BIDs Community Standard Neuroscience PUID"
+    ],
+    "publication_date": 1561996800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 7870,
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+    "doi": "10.5281/zenodo.11654",
+    "cstr": "31253.11.10.5281/zenodo.11654",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> and fMRI_PLOS ONE",
+    "title_en": "<span style='background-color:yellow'>EEG</span> and fMRI_PLOS ONE",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> and fMRI_PLOS ONE",
+    "description": "subject and data information5_subjectssampling frequency: 1000 Hz256 channels",
+    "description_en": "subject and data information5_subjectssampling frequency: 1000 Hz256 channels",
+    "description_zh": "subject and data information5_subjectssampling frequency: 1000 Hz256 channels",
+    "authors": [
+      {
+        "name_en": "Ching-Chang Kuo",
+        "name_zh": "Ching-Chang Kuo"
+      }
+    ],
+    "keywords_en": [
+      "EEG"
+    ],
+    "keywords_zh": [
+      "EEG"
+    ],
+    "publication_date": "2014-09-08T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.04,
+    "file_size_bytes": 38167,
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4470135",
+    "cstr": "",
+    "pid": "",
+    "title": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
+    "title_en": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
+    "title_zh": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
+    "description": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
+    "description_en": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
+    "description_zh": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
+    "authors": [
+      {
+        "name_en": "Etard Octave",
+        "name_zh": "Etard Octave"
+      },
+      {
+        "name_en": "Ben Messaoud Rémy",
+        "name_zh": "Ben Messaoud Rémy"
+      },
+      {
+        "name_en": "Gaugain Gabriel",
+        "name_zh": "Gaugain Gabriel"
+      },
+      {
+        "name_en": "Reichenbach Tobias",
+        "name_zh": "Reichenbach Tobias"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "music",
+      "auditory",
+      "neuroscience",
+      "continuous stimuli"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "music",
+      "auditory",
+      "neuroscience",
+      "continuous stimuli"
+    ],
+    "publication_date": "2020-10-19T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 6433.5,
+    "file_size_bytes": 6746018669,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.160118",
+    "cstr": "",
+    "pid": "",
+    "title": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (EEG_A to D), in European data format (.edf)",
+    "title_en": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (EEG_A to D), in European data format (.edf)",
+    "title_zh": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (<span style='background-color:yellow'>EEG</span>_A to D), in European data format (.edf)",
+    "description": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
+    "description_en": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
+    "description_zh": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
+    "authors": [
+      {
+        "name_en": "Brown Laurence A.",
+        "name_zh": "Brown Laurence A."
+      },
+      {
+        "name_en": "Hasan Sibah",
+        "name_zh": "Hasan Sibah"
+      },
+      {
+        "name_en": "Foster Russell G.",
+        "name_zh": "Foster Russell G."
+      },
+      {
+        "name_en": "Peirson Stuart N.",
+        "name_zh": "Peirson Stuart N."
+      }
+    ],
+    "keywords_en": [
+      "mouse",
+      "sleep",
+      "EEG",
+      "PIR",
+      "COMPASS"
+    ],
+    "keywords_zh": [
+      "mouse",
+      "sleep",
+      "EEG",
+      "PIR",
+      "COMPASS"
+    ],
+    "publication_date": "2016-10-13T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 124.11,
+    "file_size_bytes": 130135252,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.29601",
+    "cstr": "",
+    "pid": "",
+    "title": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
+    "title_en": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
+    "title_zh": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
+    "description": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
+    "description_en": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
+    "description_zh": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
+    "authors": [
+      {
+        "name_en": "Albrecht Matthew",
+        "name_zh": "Albrecht Matthew"
+      },
+      {
+        "name_en": "Waltz Jim",
+        "name_zh": "Waltz Jim"
+      },
+      {
+        "name_en": "Cavanagh Jim",
+        "name_zh": "Cavanagh Jim"
+      },
+      {
+        "name_en": "Frank Michael",
+        "name_zh": "Frank Michael"
+      },
+      {
+        "name_en": "Gold Jim",
+        "name_zh": "Gold Jim"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Schizophrenia",
+      "Reinforcement Learning",
+      "Computational Modelling",
+      "Pavlovian Bias"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Schizophrenia",
+      "Reinforcement Learning",
+      "Computational Modelling",
+      "Pavlovian Bias"
+    ],
+    "publication_date": "2015-08-25T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-SA-4.0",
+    "file_size_mb": 0.2,
+    "file_size_bytes": 204983,
+    "download_count": 1,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.29604",
+    "cstr": "31253.11.10.5281/zenodo.29604",
+    "pid": "",
+    "title": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
+    "title_en": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
+    "title_zh": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
+    "description": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
+    "description_en": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
+    "description_zh": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
+    "authors": [
+      {
+        "name_en": "Albrecht Matthew",
+        "name_zh": "Albrecht Matthew"
+      },
+      {
+        "name_en": "Waltz Jim",
+        "name_zh": "Waltz Jim"
+      },
+      {
+        "name_en": "Cavanagh Jim",
+        "name_zh": "Cavanagh Jim"
+      },
+      {
+        "name_en": "Frank Michael",
+        "name_zh": "Frank Michael"
+      },
+      {
+        "name_en": "Gold Jim",
+        "name_zh": "Gold Jim"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Schizophrenia",
+      "Reinforcement Learning",
+      "Pavlovian Bias",
+      "Computational Modelling"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Schizophrenia",
+      "Reinforcement Learning",
+      "Pavlovian Bias",
+      "Computational Modelling"
+    ],
+    "publication_date": "2015-08-25T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-SA-4.0",
+    "file_size_mb": 1503.62,
+    "file_size_bytes": 1576665038,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.30084",
+    "cstr": "31253.11.10.5281/zenodo.30084",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
+    "title_en": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
+    "description": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
+    "description_en": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
+    "description_zh": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
+    "authors": [
+      {
+        "name_en": "Kabbara Aya",
+        "name_zh": "Kabbara Aya"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2015-08-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 25.56,
+    "file_size_bytes": 26797088,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.14820",
+    "cstr": "",
+    "pid": "",
+    "title": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
+    "title_en": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
+    "title_zh": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
+    "description": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
+    "description_en": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
+    "description_zh": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
+    "authors": [
+      {
+        "name_en": "Song Jasmine",
+        "name_zh": "Song Jasmine"
+      }
+    ],
+    "keywords_en": [
+      "dEEG",
+      "coverage",
+      "density lead field matrix"
+    ],
+    "keywords_zh": [
+      "dEEG",
+      "coverage",
+      "density lead field matrix"
+    ],
+    "publication_date": "2015-02-02T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 18.7,
+    "file_size_bytes": 19607912,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4385970",
+    "cstr": "",
+    "pid": "",
+    "title": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
+    "title_en": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
+    "title_zh": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
+    "description": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
+    "description_en": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
+    "description_zh": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
+    "authors": [
+      {
+        "name_en": "Burroughs Amelia",
+        "name_zh": "Burroughs Amelia"
+      },
+      {
+        "name_en": "Kazanina Nina",
+        "name_zh": "Kazanina Nina"
+      },
+      {
+        "name_en": "Houghton Conor",
+        "name_zh": "Houghton Conor"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "neurolinguistics"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "neurolinguistics"
+    ],
+    "publication_date": "2020-09-29T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 283,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4558201",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4557037",
+    "cstr": "31253.11.10.5281/zenodo.4557037",
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+    "title": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+    "doi": "10.5281/zenodo.4561604",
+    "cstr": "",
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+    "title": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+    "title": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4561676",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4561702",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "modified": "",
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+    "doi": "10.5281/zenodo.4561655",
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+    "pid": "",
+    "title": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "modified": "",
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+    "doi": "10.5281/zenodo.4561682",
+    "cstr": "31253.11.10.5281/zenodo.4561682",
+    "pid": "",
+    "title": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4561686",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "modified": "",
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+    "license": "CC BY 4.0",
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+    "doi": "10.5281/zenodo.4561672",
+    "cstr": "31253.11.10.5281/zenodo.4561672",
+    "pid": "",
+    "title": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4561674",
+    "cstr": "",
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+    "title": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
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+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4561695",
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+    "title": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "doi": "10.5281/zenodo.4561667",
+    "cstr": "",
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+    "title": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4561657",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
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+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4561663",
+    "cstr": "",
+    "pid": "",
+    "title": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+  {
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+    "doi": "10.5281/zenodo.4561641",
+    "cstr": "31253.11.10.5281/zenodo.4561641",
+    "pid": "",
+    "title": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_en": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "title_zh": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
+    "description": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_en": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "description_zh": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Weiergräber Marco",
+        "name_zh": "Weiergräber Marco"
+      }
+    ],
+    "keywords_en": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "keywords_zh": [
+      "activity",
+      "CA1",
+      "EEG",
+      "hippocampus",
+      "low voltage-activated",
+      "T-type",
+      "voltage-gated calcium channel"
+    ],
+    "publication_date": "2021-03-01T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 985.77,
+    "file_size_bytes": 1033651456,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.806023",
+    "cstr": "",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
+    "title_en": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
+    "description": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
+    "description_en": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
+    "description_zh": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
+    "authors": [
+      {
+        "name_en": "Alexandre Barachant",
+        "name_zh": "Alexandre Barachant"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Brain Computer Interface",
+      "Motor Imagery"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Brain Computer Interface",
+      "Motor Imagery"
+    ],
+    "publication_date": "2012-03-24T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-SA-4.0",
+    "file_size_mb": 16.53,
+    "file_size_bytes": 17335744,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4506494",
+    "cstr": "31253.11.10.5281/zenodo.4506494",
+    "pid": "",
+    "title": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
+    "title_en": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
+    "title_zh": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
+    "description": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
+    "description_en": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
+    "description_zh": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
+    "authors": [
+      {
+        "name_en": "Martínez-Cañada Pablo",
+        "name_zh": "Martínez-Cañada Pablo"
+      },
+      {
+        "name_en": "Ness Torbjørn",
+        "name_zh": "Ness Torbjørn"
+      },
+      {
+        "name_en": "Einevoll Gaute",
+        "name_zh": "Einevoll Gaute"
+      },
+      {
+        "name_en": "Fellin Tommaso",
+        "name_zh": "Fellin Tommaso"
+      },
+      {
+        "name_en": "Panzeri Stefano",
+        "name_zh": "Panzeri Stefano"
+      }
+    ],
+    "keywords_en": [
+      "electroencephalogram (EEG)",
+      "leaky integrate and fire neuron model",
+      "point neuron model",
+      "multicompartment neuron model"
+    ],
+    "keywords_zh": [
+      "electroencephalogram (EEG)",
+      "leaky integrate and fire neuron model",
+      "point neuron model",
+      "multicompartment neuron model"
+    ],
+    "publication_date": "2021-02-04T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 3308.49,
+    "file_size_bytes": 3469200680,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4453396",
+    "cstr": "",
+    "pid": "",
+    "title": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
+    "title_en": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
+    "title_zh": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
+    "description": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
+    "description_en": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
+    "description_zh": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
+    "authors": [
+      {
+        "name_en": "Vourvopoulos Athanasios",
+        "name_zh": "Vourvopoulos Athanasios"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "BCI",
+      "Wearables",
+      "EEGlass"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "BCI",
+      "Wearables",
+      "EEGlass"
+    ],
+    "publication_date": "2021-01-19T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 11.21,
+    "file_size_bytes": 11759280,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3684992",
+    "cstr": "",
+    "pid": "",
+    "title": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
+    "title_en": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
+    "title_zh": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
+    "description": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
+    "description_en": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
+    "description_zh": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
+    "authors": [
+      {
+        "name_en": "Panwar Siddharth",
+        "name_zh": "Panwar Siddharth"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2020-02-22T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 24749,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68d29b8c19a491453e1c1226",
+    "doi": "10.57760/sciencedb.28712",
+    "cstr": "31253.11.sciencedb.28712",
+    "pid": "",
+    "title": "Data_ADHD_Neurofeedback_EEG_Meta_Analysis",
+    "title_en": "Data_ADHD_Neurofeedback_EEG_Meta_Analysis",
+    "title_zh": "Data_ADHD_Neurofeedback_<span style='background-color:yellow'>EEG</span>_Meta_Analysis",
+    "description": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
+    "description_en": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
+    "description_zh": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
+    "authors": [
+      {
+        "name_en": "XIN HUANG",
+        "name_zh": "XIN HUANG",
+        "email": "huangxin1208@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD",
+            "name_zh": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Neurofeedback",
+      "ADHD",
+      "NFT",
+      "Intervention",
+      "Children",
+      "Meta analysis"
+    ],
+    "keywords_zh": [
+      "Neurofeedback",
+      "ADHD",
+      "NFT",
+      "Intervention",
+      "Children",
+      "Meta analysis"
+    ],
+    "publication_date": "2025-09-25T06:28:20.232Z",
+    "created": "",
+    "modified": "",
+    "status": "",
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+    "url": "https://www.scidb.cn/en/detail?id=68d29b8c19a491453e1c1226",
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+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4518754",
+    "cstr": "",
+    "pid": "",
+    "title": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
+    "title_en": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
+    "title_zh": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
+    "description": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
+    "description_en": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
+    "description_zh": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
+    "authors": [
+      {
+        "name_en": "Mundanad Narayanan Abhijith",
+        "name_zh": "Mundanad Narayanan Abhijith"
+      },
+      {
+        "name_en": "Zink Rob",
+        "name_zh": "Zink Rob"
+      },
+      {
+        "name_en": "Bertrand Alexander",
+        "name_zh": "Bertrand Alexander"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "signal processing",
+      "auditory attention decoding"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "signal processing",
+      "auditory attention decoding"
+    ],
+    "publication_date": "2021-02-07T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-4.0",
+    "file_size_mb": 1.94,
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+    "url": "",
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1213584",
+    "cstr": "31253.11.10.5281/zenodo.1213584",
+    "pid": "",
+    "title": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
+    "title_en": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
+    "title_zh": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
+    "description": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
+    "description_en": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
+    "description_zh": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
+    "authors": [
+      {
+        "name_en": "Gallo Selene",
+        "name_zh": "Gallo Selene"
+      },
+      {
+        "name_en": "Paracampo Riccardo",
+        "name_zh": "Paracampo Riccardo"
+      },
+      {
+        "name_en": "Müller-Pinzler Laura",
+        "name_zh": "Müller-Pinzler Laura"
+      },
+      {
+        "name_en": "Severo Mario Carlo",
+        "name_zh": "Severo Mario Carlo"
+      },
+      {
+        "name_en": "Blömer Laila",
+        "name_zh": "Blömer Laila"
+      },
+      {
+        "name_en": "Fernandes-Henriques Carolina",
+        "name_zh": "Fernandes-Henriques Carolina"
+      },
+      {
+        "name_en": "Henschel Anna",
+        "name_zh": "Henschel Anna"
+      },
+      {
+        "name_en": "Lammes Balint Kallista",
+        "name_zh": "Lammes Balint Kallista"
+      },
+      {
+        "name_en": "Maskaljunas Tatjana",
+        "name_zh": "Maskaljunas Tatjana"
+      },
+      {
+        "name_en": "Suttrup Judith",
+        "name_zh": "Suttrup Judith"
+      },
+      {
+        "name_en": "Avenanti Alessio",
+        "name_zh": "Avenanti Alessio"
+      },
+      {
+        "name_en": "Keysers Christian",
+        "name_zh": "Keysers Christian"
+      },
+      {
+        "name_en": "Gazzola Valeria",
+        "name_zh": "Gazzola Valeria"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2018-04-05T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 19839.81,
+    "file_size_bytes": 20803548028,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.192684",
+    "cstr": "31253.11.10.5281/zenodo.192684",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
+    "title_en": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
+    "description": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
+    "description_en": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
+    "description_zh": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
+    "authors": [
+      {
+        "name_en": "David Hübner",
+        "name_zh": "David Hübner"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Learning from Label Proportions",
+      "LLP",
+      "Brain-Computer Interfaces",
+      "BCI",
+      "Visual",
+      "Auditory",
+      "Event-Related Potential",
+      "ERP",
+      "Multiclass",
+      "Healthy",
+      "Young",
+      "Electroencephalography",
+      "Unsupervised",
+      "Classification"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Learning from Label Proportions",
+      "LLP",
+      "Brain-Computer Interfaces",
+      "BCI",
+      "Visual",
+      "Auditory",
+      "Event-Related Potential",
+      "ERP",
+      "Multiclass",
+      "Healthy",
+      "Young",
+      "Electroencephalography",
+      "Unsupervised",
+      "Classification"
+    ],
+    "publication_date": "2016-12-04T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.42,
+    "file_size_bytes": 441415,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1507643",
+    "cstr": "31253.11.10.5281/zenodo.1507643",
+    "pid": "",
+    "title": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
+    "title_en": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
+    "title_zh": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
+    "description": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
+    "description_en": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
+    "description_zh": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
+    "authors": [
+      {
+        "name_en": "Schrooten Maarten",
+        "name_zh": "Schrooten Maarten"
+      },
+      {
+        "name_en": "Vandenberghe Rik",
+        "name_zh": "Vandenberghe Rik"
+      },
+      {
+        "name_en": "Peeters Ronald",
+        "name_zh": "Peeters Ronald"
+      },
+      {
+        "name_en": "Dupont Patrick",
+        "name_zh": "Dupont Patrick"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "fMRI",
+      "EEG-fMRI"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "fMRI",
+      "EEG-fMRI"
+    ],
+    "publication_date": "2018-11-24T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 6837.02,
+    "file_size_bytes": 7169137234,
+    "download_count": 0,
+    "visit_count": 3,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.191899",
+    "cstr": "",
+    "pid": "",
+    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
+    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
+    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
+    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
+    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
+    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
+    "authors": [
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      },
+      {
+        "name_en": "Silvoni Stefano",
+        "name_zh": "Silvoni Stefano"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-11-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 959,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.191884",
+    "cstr": "",
+    "pid": "",
+    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
+    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
+    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
+    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
+    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
+    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
+    "authors": [
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      },
+      {
+        "name_en": "Silvoni Stefano",
+        "name_zh": "Silvoni Stefano"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-11-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1173,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.191887",
+    "cstr": "",
+    "pid": "",
+    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
+    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
+    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
+    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
+    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
+    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
+    "authors": [
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      },
+      {
+        "name_en": "Silvoni Stefano",
+        "name_zh": "Silvoni Stefano"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-11-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1186,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3547486",
+    "cstr": "",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
+    "title_en": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
+    "description": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
+    "description_en": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
+    "description_zh": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
+    "authors": [
+      {
+        "name_en": "Panwar Siddharth",
+        "name_zh": "Panwar Siddharth"
+      }
+    ],
+    "keywords_en": [
+      "rdFC patterns"
+    ],
+    "keywords_zh": [
+      "rdFC patterns"
+    ],
+    "publication_date": "2019-11-18T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 1.74,
+    "file_size_bytes": 1829390,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.1109/embc.2016.7590650",
+    "cstr": "",
+    "pid": "",
+    "title": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
+    "title_en": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
+    "title_zh": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
+    "description": "n/a",
+    "description_en": "n/a",
+    "description_zh": "n/a",
+    "authors": [
+      {
+        "name_en": "Li, Ying",
+        "name_zh": "Li, Ying"
+      },
+      {
+        "name_en": " Qin, Jing",
+        "name_zh": " Qin, Jing"
+      },
+      {
+        "name_en": " Osher, Stanley",
+        "name_zh": " Osher, Stanley"
+      },
+      {
+        "name_en": " Liu, Wentai",
+        "name_zh": " Liu, Wentai"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": 1469980800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 6.41,
+    "file_size_bytes": 6716332,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.191891",
+    "cstr": "",
+    "pid": "",
+    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
+    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
+    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
+    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
+    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
+    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
+    "authors": [
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      },
+      {
+        "name_en": "Silvoni Stefano",
+        "name_zh": "Silvoni Stefano"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-11-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1113,
+    "download_count": 0,
+    "visit_count": 3,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1134776",
+    "cstr": "",
+    "pid": "",
+    "title": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
+    "title_en": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
+    "title_zh": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
+    "description": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
+    "description_en": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
+    "description_zh": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
+    "authors": [
+      {
+        "name_en": "Lau Møller Andersen",
+        "name_zh": "Lau Møller Andersen"
+      }
+    ],
+    "keywords_en": [
+      "MEG",
+      "analysis pipeline",
+      "MNE-Python",
+      "minimum norm estimate (MNE)",
+      "Tactile Expectations",
+      "Group analysis",
+      "good practice",
+      "fieldtrip",
+      "beamformer"
+    ],
+    "keywords_zh": [
+      "MEG",
+      "analysis pipeline",
+      "MNE-Python",
+      "minimum norm estimate (MNE)",
+      "Tactile Expectations",
+      "Group analysis",
+      "good practice",
+      "fieldtrip",
+      "beamformer"
+    ],
+    "publication_date": "2017-09-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-SA-4.0",
+    "file_size_mb": 2174.52,
+    "file_size_bytes": 2280145323,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "64f04634cc4bc33e70a58f83",
+    "doi": "10.6084/m9.figshare.c.5740424.v1",
+    "cstr": "",
+    "pid": "",
+    "title": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
+    "title_en": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
+    "title_zh": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
+    "description": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
+    "description_en": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
+    "description_zh": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
+    "authors": [
+      {
+        "name_en": "Yu Yuguo",
+        "name_zh": "Yu Yuguo",
+        "email": "yuyuguo@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          }
+        ]
+      },
+      {
+        "name_en": "Pei Xinzhen",
+        "name_zh": "Pei Xinzhen",
+        "affiliations": [
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Guiying",
+        "name_zh": "Xu Guiying",
+        "email": "zhangm@sari.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute",
+            "name_zh": "上海高等研究院",
+            "ror_id": "https://ror.org/02br7py06"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
+            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhou Yunhui",
+        "name_zh": "Zhou Yunhui",
+        "email": "yuyuguo@fudan.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          }
+        ]
+      },
+      {
+        "name_en": "Tao Luna",
+        "name_zh": "Tao Luna",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Competitive Sports Training Management Center",
+            "name_zh": "Shanghai Competitive Sports Training Management Center"
+          }
+        ]
+      },
+      {
+        "name_en": "Cui Xiaozhu",
+        "name_zh": "Cui Xiaozhu",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)",
+            "name_zh": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Zhenyu",
+        "name_zh": "Wang Zhenyu",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Advanced Research Institute",
+            "name_zh": "上海高等研究院",
+            "ror_id": "https://ror.org/02br7py06"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
+            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Bingru",
+        "name_zh": "Xu Bingru",
+        "affiliations": [
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang An-Li",
+        "name_zh": "Wang An-Li",
+        "affiliations": [
+          {
+            "name_en": "Department of Psychiatry, Icahn School of Medicine at Mount Sinai",
+            "name_zh": "Department of Psychiatry, Icahn School of Medicine at Mount Sinai"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhao Xi",
+        "name_zh": "Zhao Xi",
+        "affiliations": [
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
+            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute",
+            "name_zh": "上海高等研究院",
+            "ror_id": "https://ror.org/02br7py06"
+          }
+        ]
+      },
+      {
+        "name_en": "Dong Haijun",
+        "name_zh": "Dong Haijun",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Normal University",
+            "name_zh": "Shanghai Normal University"
+          }
+        ]
+      },
+      {
+        "name_en": "An Yan",
+        "name_zh": "An Yan",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)",
+            "name_zh": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)"
+          }
+        ]
+      },
+      {
+        "name_en": "Cao Yang",
+        "name_zh": "Cao Yang",
+        "affiliations": [
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          },
+          {
+            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
+            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
+          }
+        ]
+      },
+      {
+        "name_en": "Li Ruxue",
+        "name_zh": "Li Ruxue",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Advanced Research Institute",
+            "name_zh": "上海高等研究院",
+            "ror_id": "https://ror.org/02br7py06"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
+            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
+          }
+        ]
+      },
+      {
+        "name_en": "Hu Honglin",
+        "name_zh": "Hu Honglin",
+        "email": "huhl@sari.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Shanghai Advanced Research Institute",
+            "name_zh": "上海高等研究院",
+            "ror_id": "https://ror.org/02br7py06"
+          },
+          {
+            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
+            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "eye-tracking",
+      "research data"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "eye-tracking",
+      "research data"
+    ],
+    "publication_date": "2022-07-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 13523,
+    "download_count": 0,
+    "visit_count": 5,
+    "url": "https://www.scidb.cn/en/detail?id=64f04634cc4bc33e70a58f83",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5282/zenodo.572319",
+    "cstr": "",
+    "pid": "",
+    "title": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
+    "title_en": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
+    "title_zh": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
+    "description": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
+    "description_en": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
+    "description_zh": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
+    "authors": [
+      {
+        "name_en": "Yifei He",
+        "name_zh": "Yifei He"
+      },
+      {
+        "name_en": "Helge Gebhardt",
+        "name_zh": "Helge Gebhardt"
+      },
+      {
+        "name_en": "Miriam Steines",
+        "name_zh": "Miriam Steines"
+      },
+      {
+        "name_en": "Gebhard Sammer",
+        "name_zh": "Gebhard Sammer"
+      },
+      {
+        "name_en": "Tilo Kircher",
+        "name_zh": "Tilo Kircher"
+      },
+      {
+        "name_en": "Arne Nagels",
+        "name_zh": "Arne Nagels"
+      },
+      {
+        "name_en": "Benjamin Straube",
+        "name_zh": "Benjamin Straube"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2015-03-31T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 31.19,
+    "file_size_bytes": 32708757,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5061/dryad.14004q5",
+    "cstr": "",
+    "pid": "",
+    "title": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
+    "title_en": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
+    "title_zh": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
+    "description": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
+    "description_en": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
+    "description_zh": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
+    "authors": [
+      {
+        "name_en": "Arbune Anca A.",
+        "name_zh": "Arbune Anca A."
+      },
+      {
+        "name_en": "Conradsen Isa",
+        "name_zh": "Conradsen Isa"
+      },
+      {
+        "name_en": "Cardenas Damon P.",
+        "name_zh": "Cardenas Damon P."
+      },
+      {
+        "name_en": "Whitmire Luke E.",
+        "name_zh": "Whitmire Luke E."
+      },
+      {
+        "name_en": "Voyles Shannon R.",
+        "name_zh": "Voyles Shannon R."
+      },
+      {
+        "name_en": "Wolf Peter",
+        "name_zh": "Wolf Peter"
+      },
+      {
+        "name_en": "Lhatoo Samden",
+        "name_zh": "Lhatoo Samden"
+      },
+      {
+        "name_en": "Ryvlin Philippe",
+        "name_zh": "Ryvlin Philippe"
+      },
+      {
+        "name_en": "Beniczky Sándor",
+        "name_zh": "Beniczky Sándor"
+      }
+    ],
+    "keywords_en": [
+      "epilepsy monitoring",
+      "Epilepsy/Seizures",
+      "EMG",
+      "Generalized seizures"
+    ],
+    "keywords_zh": [
+      "epilepsy monitoring",
+      "Epilepsy/Seizures",
+      "EMG",
+      "Generalized seizures"
+    ],
+    "publication_date": "2020-12-15T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 11.89,
+    "file_size_bytes": 12464128,
+    "download_count": 0,
+    "visit_count": 5,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.160344",
+    "cstr": "",
+    "pid": "",
+    "title": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
+    "title_en": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
+    "title_zh": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
+    "description": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
+    "description_en": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
+    "description_zh": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
+    "authors": [
+      {
+        "name_en": "Brown Laurence A.",
+        "name_zh": "Brown Laurence A."
+      },
+      {
+        "name_en": "Hasan Sibah",
+        "name_zh": "Hasan Sibah"
+      },
+      {
+        "name_en": "Foster Russell G.",
+        "name_zh": "Foster Russell G."
+      },
+      {
+        "name_en": "Peirson Stuart N.",
+        "name_zh": "Peirson Stuart N."
+      }
+    ],
+    "keywords_en": [
+      "mouse",
+      "PIR",
+      "EEG",
+      "sleep",
+      "COMPASS"
+    ],
+    "keywords_zh": [
+      "mouse",
+      "PIR",
+      "EEG",
+      "sleep",
+      "COMPASS"
+    ],
+    "publication_date": "2016-10-13T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 13.15,
+    "file_size_bytes": 13788195,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.191929",
+    "cstr": "",
+    "pid": "",
+    "title": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
+    "title_en": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
+    "title_zh": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
+    "description": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
+    "description_en": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
+    "description_zh": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
+    "authors": [
+      {
+        "name_en": "Silvoni Stefano",
+        "name_zh": "Silvoni Stefano"
+      },
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-11-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 3570,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.1109/isemc.1980.7567290",
+    "cstr": "31253.11.10.1109/isemc.1980.7567290",
+    "pid": "",
+    "title": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
+    "title_en": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
+    "title_zh": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
+    "description": "n/a",
+    "description_en": "n/a",
+    "description_zh": "n/a",
+    "authors": [
+      {
+        "name_en": "Ruggera, Paul S.",
+        "name_zh": "Ruggera, Paul S."
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": 339177600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.44,
+    "file_size_bytes": 464810,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.1371/journal.pone.0156436",
+    "cstr": "",
+    "pid": "",
+    "title": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
+    "title_en": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
+    "title_zh": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
+    "description": "Spreadsheet containing structural and functional data",
+    "description_en": "Spreadsheet containing structural and functional data",
+    "description_zh": "Spreadsheet containing structural and functional data",
+    "authors": [
+      {
+        "name_en": "Renauld Emmanuelle",
+        "name_zh": "Renauld Emmanuelle"
+      },
+      {
+        "name_en": "Descoteaux Maxime",
+        "name_zh": "Descoteaux Maxime"
+      },
+      {
+        "name_en": "Bernier Michael",
+        "name_zh": "Bernier Michael"
+      },
+      {
+        "name_en": "Garyfallidis Eleftherios",
+        "name_zh": "Garyfallidis Eleftherios"
+      },
+      {
+        "name_en": "Whittingstall Kevin",
+        "name_zh": "Whittingstall Kevin"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-05-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.11,
+    "file_size_bytes": 114136,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.13003",
+    "cstr": "",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
+    "title_en": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
+    "description": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
+    "description_en": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
+    "description_zh": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
+    "authors": [
+      {
+        "name_en": "Gisladottir Rosa",
+        "name_zh": "Gisladottir Rosa"
+      },
+      {
+        "name_en": "Levinson Stephen",
+        "name_zh": "Levinson Stephen"
+      },
+      {
+        "name_en": "Chwilla Dorothee",
+        "name_zh": "Chwilla Dorothee"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2015-03-20T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 76.52,
+    "file_size_bytes": 80233200,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.834976",
+    "cstr": "31253.11.10.5281/zenodo.834976",
+    "pid": "",
+    "title": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
+    "title_en": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
+    "title_zh": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
+    "description": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
+    "description_en": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
+    "description_zh": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
+    "authors": [
+      {
+        "name_en": "Ofner Patrick",
+        "name_zh": "Ofner Patrick"
+      },
+      {
+        "name_en": "Schwarz Andreas",
+        "name_zh": "Schwarz Andreas"
+      },
+      {
+        "name_en": "Pereira Joana",
+        "name_zh": "Pereira Joana"
+      },
+      {
+        "name_en": "Müller-Putz Gernot R.",
+        "name_zh": "Müller-Putz Gernot R."
+      }
+    ],
+    "keywords_en": [
+      "brain-computer interface"
+    ],
+    "keywords_zh": [
+      "brain-computer interface"
+    ],
+    "publication_date": "2017-08-09T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.6,
+    "file_size_bytes": 624262,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.221095",
+    "cstr": "31253.11.10.5281/zenodo.221095",
+    "pid": "",
+    "title": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
+    "title_en": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
+    "title_zh": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
+    "description": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
+    "description_en": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
+    "description_zh": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
+    "authors": [
+      {
+        "name_en": "Weiss Shennan",
+        "name_zh": "Weiss Shennan"
+      }
+    ],
+    "keywords_en": [
+      "electroencephalogram",
+      "artifact",
+      "seizure",
+      "software"
+    ],
+    "keywords_zh": [
+      "electroencephalogram",
+      "artifact",
+      "seizure",
+      "software"
+    ],
+    "publication_date": "2016-12-23T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 162.05,
+    "file_size_bytes": 169925376,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "68d4f9cffae9e04d156b16f3",
+    "doi": "10.57760/sciencedb.28850",
+    "cstr": "31253.11.sciencedb.28850",
+    "pid": "",
+    "title": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
+    "title_en": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
+    "title_zh": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
+    "description": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
+    "description_en": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
+    "description_zh": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
+    "authors": [
+      {
+        "name_en": "XIN HUANG",
+        "name_zh": "XIN HUANG",
+        "email": "huangxin1208@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD",
+            "name_zh": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Neurofeedback",
+      "ADHD",
+      "NFT",
+      "Intervention",
+      "Children",
+      "Meta analysis"
+    ],
+    "keywords_zh": [
+      "Neurofeedback",
+      "ADHD",
+      "NFT",
+      "Intervention",
+      "Children",
+      "Meta analysis"
+    ],
+    "publication_date": "2025-09-26T06:39:31.309Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-ND 4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 24656,
+    "download_count": 0,
+    "visit_count": 147,
+    "url": "https://www.scidb.cn/en/detail?id=68d4f9cffae9e04d156b16f3",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.838543",
+    "cstr": "",
+    "pid": "",
+    "title": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
+    "title_en": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
+    "title_zh": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
+    "description": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
+    "description_en": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
+    "description_zh": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
+    "authors": [
+      {
+        "name_en": "Pinegger Andreas",
+        "name_zh": "Pinegger Andreas"
+      },
+      {
+        "name_en": "Hiebel Hannah",
+        "name_zh": "Hiebel Hannah"
+      },
+      {
+        "name_en": "Wriessnegger Selina C.",
+        "name_zh": "Wriessnegger Selina C."
+      },
+      {
+        "name_en": "Müller-Putz Gernot R.",
+        "name_zh": "Müller-Putz Gernot R."
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2017-08-02T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.3,
+    "file_size_bytes": 309609,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.817545",
+    "cstr": "",
+    "pid": "",
+    "title": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
+    "title_en": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
+    "title_zh": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
+    "description": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
+    "description_en": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
+    "description_zh": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
+    "authors": [
+      {
+        "name_en": "Chen Jing",
+        "name_zh": "Chen Jing"
+      },
+      {
+        "name_en": "Valsecchi Matteo",
+        "name_zh": "Valsecchi Matteo"
+      },
+      {
+        "name_en": "Gegenfurtner Karl",
+        "name_zh": "Gegenfurtner Karl"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2017-06-22T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 797.76,
+    "file_size_bytes": 836510937,
+    "download_count": 2,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.2654552",
+    "cstr": "31253.11.10.5281/zenodo.2654552",
+    "pid": "",
+    "title": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
+    "title_en": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
+    "title_zh": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
+    "description": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
+    "description_en": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
+    "description_zh": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
+    "authors": [
+      {
+        "name_en": "Adrian Naas",
+        "name_zh": "Adrian Naas"
+      },
+      {
+        "name_en": "João Rodrigues",
+        "name_zh": "João Rodrigues"
+      },
+      {
+        "name_en": "Jan-Philip Knirsch",
+        "name_zh": "Jan-Philip Knirsch"
+      },
+      {
+        "name_en": "Andreas Sonderegger",
+        "name_zh": "Andreas Sonderegger"
+      }
+    ],
+    "keywords_en": [
+      "Neurofeedback Training",
+      "Alpha",
+      "Short-term memory"
+    ],
+    "keywords_zh": [
+      "Neurofeedback Training",
+      "Alpha",
+      "Short-term memory"
+    ],
+    "publication_date": "2019-04-29T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-ND-4.0",
+    "file_size_mb": 0.07,
+    "file_size_bytes": 71494,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.584032",
+    "cstr": "31253.11.10.5281/zenodo.584032",
+    "pid": "",
+    "title": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
+    "title_en": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
+    "title_zh": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
+    "description": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
+    "description_en": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
+    "description_zh": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
+    "authors": [
+      {
+        "name_en": "He Yifei",
+        "name_zh": "He Yifei"
+      },
+      {
+        "name_en": "Gebhardt Helge",
+        "name_zh": "Gebhardt Helge"
+      },
+      {
+        "name_en": "Steines Miriam",
+        "name_zh": "Steines Miriam"
+      },
+      {
+        "name_en": "Sommer Gebhard",
+        "name_zh": "Sommer Gebhard"
+      },
+      {
+        "name_en": "Nagels Arne",
+        "name_zh": "Nagels Arne"
+      },
+      {
+        "name_en": "Kircher Tilo",
+        "name_zh": "Kircher Tilo"
+      },
+      {
+        "name_en": "Straube Benjamin",
+        "name_zh": "Straube Benjamin"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2017-05-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 21.88,
+    "file_size_bytes": 22943057,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.46756",
+    "cstr": "",
+    "pid": "",
+    "title": "studyforrest-data-multires7t: First public release",
+    "title_en": "studyforrest-data-multires7t: First public release",
+    "title_zh": "studyforrest-data-multires7t: First public release",
+    "description": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
+    "description_en": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
+    "description_zh": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
+    "authors": [
+      {
+        "name_en": "Sengupta Ayan",
+        "name_zh": "Sengupta Ayan"
+      },
+      {
+        "name_en": "Yakupov Renat",
+        "name_zh": "Yakupov Renat"
+      },
+      {
+        "name_en": "Speck Oliver",
+        "name_zh": "Speck Oliver"
+      },
+      {
+        "name_en": "Pollmann Stefan",
+        "name_zh": "Pollmann Stefan"
+      },
+      {
+        "name_en": "Hanke Michael",
+        "name_zh": "Hanke Michael"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-02-21T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "ODC-PDDL-1.0",
+    "file_size_mb": 0.86,
+    "file_size_bytes": 901298,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.580485",
+    "cstr": "31253.11.10.5281/zenodo.580485",
+    "pid": "",
+    "title": "Single-flicker online SSVEP BCI datset",
+    "title_en": "Single-flicker online SSVEP BCI datset",
+    "title_zh": "Single-flicker online SSVEP BCI datset",
+    "description": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
+    "description_en": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
+    "description_zh": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
+    "authors": [
+      {
+        "name_en": "Chen Jingjing",
+        "name_zh": "Chen Jingjing"
+      },
+      {
+        "name_en": "Zhang Dan",
+        "name_zh": "Zhang Dan"
+      },
+      {
+        "name_en": "Engel Andreas K.",
+        "name_zh": "Engel Andreas K."
+      },
+      {
+        "name_en": "Gong Qin",
+        "name_zh": "Gong Qin"
+      },
+      {
+        "name_en": "Maye Alexander",
+        "name_zh": "Maye Alexander"
+      }
+    ],
+    "keywords_en": [
+      "brain-computer interface",
+      "steady-state evoked potential",
+      "online BCI",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "brain-computer interface",
+      "steady-state evoked potential",
+      "online BCI",
+      "EEG"
+    ],
+    "publication_date": "2017-05-16T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 5493.51,
+    "file_size_bytes": 5760362073,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4471943",
+    "cstr": "",
+    "pid": "",
+    "title": "Data from: Output planning at the input stage in visual working memory.",
+    "title_en": "Data from: Output planning at the input stage in visual working memory.",
+    "title_zh": "Data from: Output planning at the input stage in visual working memory.",
+    "description": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
+    "description_en": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
+    "description_zh": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
+    "authors": [
+      {
+        "name_en": "Sage E.P. Boettcher",
+        "name_zh": "Sage E.P. Boettcher"
+      },
+      {
+        "name_en": "Daniela Gresch",
+        "name_zh": "Daniela Gresch"
+      },
+      {
+        "name_en": "Anna C. Nobre",
+        "name_zh": "Anna C. Nobre"
+      },
+      {
+        "name_en": "Freek van Ede",
+        "name_zh": "Freek van Ede"
+      }
+    ],
+    "keywords_en": [
+      "Visual working memory",
+      " action planning",
+      " EEG"
+    ],
+    "keywords_zh": [
+      "Visual working memory",
+      " action planning",
+      " EEG"
+    ],
+    "publication_date": "2021-01-26T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 19562.26,
+    "file_size_bytes": 20512517290,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3997352",
+    "cstr": "31253.11.10.5281/zenodo.3997352",
+    "pid": "",
+    "title": "Auditory Attention Detection Dataset KULeuven",
+    "title_en": "Auditory Attention Detection Dataset KULeuven",
+    "title_zh": "Auditory Attention Detection Dataset KULeuven",
+    "description": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
+    "description_en": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
+    "description_zh": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
+    "authors": [
+      {
+        "name_en": "Das Neetha",
+        "name_zh": "Das Neetha"
+      },
+      {
+        "name_en": "Francart Tom",
+        "name_zh": "Francart Tom"
+      },
+      {
+        "name_en": "Bertrand Alexander",
+        "name_zh": "Bertrand Alexander"
+      }
+    ],
+    "keywords_en": [
+      "Auditory Attention Detection",
+      "EEG",
+      "signal processing",
+      "neuroscience"
+    ],
+    "keywords_zh": [
+      "Auditory Attention Detection",
+      "EEG",
+      "signal processing",
+      "neuroscience"
+    ],
+    "publication_date": "2020-08-26T16:00:00.000Z",
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+    "modified": "",
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+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4451999",
+    "cstr": "",
+    "pid": "",
+    "title": "EEGlass motor-imagery and resting-state pilot data",
+    "title_en": "EEGlass motor-imagery and resting-state pilot data",
+    "title_zh": "EEGlass motor-imagery and resting-state pilot data",
+    "description": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
+    "description_en": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
+    "description_zh": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
+    "authors": [
+      {
+        "name_en": "Vourvopoulos Athanasios",
+        "name_zh": "Vourvopoulos Athanasios"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "BCI",
+      "Wearables"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "BCI",
+      "Wearables"
+    ],
+    "publication_date": "2019-08-31T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.19,
+    "file_size_bytes": 201216,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5061/dryad.4b8gtht9n",
+    "cstr": "",
+    "pid": "",
+    "title": "Ictal source imaging in epilepsy patients - Supplementary Data",
+    "title_en": "Ictal source imaging in epilepsy patients - Supplementary Data",
+    "title_zh": "Ictal source imaging in epilepsy patients - Supplementary Data",
+    "description": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
+    "description_en": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
+    "description_zh": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
+    "authors": [
+      {
+        "name_en": "Ye Shuai",
+        "name_zh": "Ye Shuai"
+      },
+      {
+        "name_en": "Yang Lin",
+        "name_zh": "Yang Lin"
+      },
+      {
+        "name_en": "Lu Yunfeng",
+        "name_zh": "Lu Yunfeng"
+      },
+      {
+        "name_en": "Kucewicz Michal",
+        "name_zh": "Kucewicz Michal"
+      },
+      {
+        "name_en": "Brinkmann Benjamin",
+        "name_zh": "Brinkmann Benjamin"
+      },
+      {
+        "name_en": "Nelson Cindy",
+        "name_zh": "Nelson Cindy"
+      },
+      {
+        "name_en": "Sohrabpour Abbas",
+        "name_zh": "Sohrabpour Abbas"
+      },
+      {
+        "name_en": "Worrell Gregory",
+        "name_zh": "Worrell Gregory"
+      },
+      {
+        "name_en": "He Bin",
+        "name_zh": "He Bin"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2021-02-05T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 1.96,
+    "file_size_bytes": 2051495,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.998965",
+    "cstr": "",
+    "pid": "",
+    "title": "Infant Sibling Project: Sample Files",
+    "title_en": "Infant Sibling Project: Sample Files",
+    "title_zh": "Infant Sibling Project: Sample Files",
+    "description": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
+    "description_en": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
+    "description_zh": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
+    "authors": [
+      {
+        "name_en": "Levin April Robyn",
+        "name_zh": "Levin April Robyn"
+      },
+      {
+        "name_en": "Gabard-Durnam Laurel Joy",
+        "name_zh": "Gabard-Durnam Laurel Joy"
+      },
+      {
+        "name_en": "Mendez Leal Adriana Sofia",
+        "name_zh": "Mendez Leal Adriana Sofia"
+      },
+      {
+        "name_en": "O'Leary Heather Marie",
+        "name_zh": "O'Leary Heather Marie"
+      },
+      {
+        "name_en": "Wilkinson Carol Lee",
+        "name_zh": "Wilkinson Carol Lee"
+      },
+      {
+        "name_en": "Tager-Flusberg Helen",
+        "name_zh": "Tager-Flusberg Helen"
+      },
+      {
+        "name_en": "Nelson Charles Alexander",
+        "name_zh": "Nelson Charles Alexander"
+      }
+    ],
+    "keywords_en": [
+      "Electroencephalography",
+      " Autism",
+      " Infant",
+      " Child",
+      " Development"
+    ],
+    "keywords_zh": [
+      "Electroencephalography",
+      " Autism",
+      " Infant",
+      " Child",
+      " Development"
+    ],
+    "publication_date": "2017-09-27T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 82.9,
+    "file_size_bytes": 86928898,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.841764",
+    "cstr": "",
+    "pid": "",
+    "title": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
+    "title_en": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
+    "title_zh": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
+    "description": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
+    "description_en": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
+    "description_zh": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
+    "authors": [
+      {
+        "name_en": "Perdikis Serafeim",
+        "name_zh": "Perdikis Serafeim"
+      },
+      {
+        "name_en": "Tonin Luca",
+        "name_zh": "Tonin Luca"
+      },
+      {
+        "name_en": "Saeedi Sareh",
+        "name_zh": "Saeedi Sareh"
+      },
+      {
+        "name_en": "Schneider Christoph",
+        "name_zh": "Schneider Christoph"
+      },
+      {
+        "name_en": "Millan Jose del R.",
+        "name_zh": "Millan Jose del R."
+      }
+    ],
+    "keywords_en": [
+      "brain-computer interface",
+      "motor-imagery",
+      "spinal cord injury",
+      "cybathlon",
+      "BCI race",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "brain-computer interface",
+      "motor-imagery",
+      "spinal cord injury",
+      "cybathlon",
+      "BCI race",
+      "EEG"
+    ],
+    "publication_date": "2017-08-10T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 2879.68,
+    "file_size_bytes": 3019565120,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4331062",
+    "cstr": "31253.11.10.5281/zenodo.4331062",
+    "pid": "",
+    "title": "Microgecko helenae Nikolsky 1907",
+    "title_en": "Microgecko helenae Nikolsky 1907",
+    "title_zh": "Microgecko helenae Nikolsky 1907",
+    "description": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
+    "description_en": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
+    "description_zh": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
+    "authors": [
+      {
+        "name_en": "Eskandarzadeh, Naeimeh",
+        "name_zh": "Eskandarzadeh, Naeimeh"
+      },
+      {
+        "name_en": " Rastegar-Pouyani, Nasrullah",
+        "name_zh": " Rastegar-Pouyani, Nasrullah"
+      },
+      {
+        "name_en": " Rastegar-Pouyani, Eskandar",
+        "name_zh": " Rastegar-Pouyani, Eskandar"
+      },
+      {
+        "name_en": " Fathinia, Behzad",
+        "name_zh": " Fathinia, Behzad"
+      },
+      {
+        "name_en": " Bahmani, Zahed",
+        "name_zh": " Bahmani, Zahed"
+      },
+      {
+        "name_en": " Hamidi, Kordiyeh",
+        "name_zh": " Hamidi, Kordiyeh"
+      },
+      {
+        "name_en": " Gholamifard, Ali",
+        "name_zh": " Gholamifard, Ali"
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Chordata",
+      "Reptilia",
+      "Squamata",
+      "Gekkonidae",
+      "Microgecko",
+      "Microgecko helenae"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Chordata",
+      "Reptilia",
+      "Squamata",
+      "Gekkonidae",
+      "Microgecko",
+      "Microgecko helenae"
+    ],
+    "publication_date": 1543420800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1059,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3610120",
+    "cstr": "",
+    "pid": "",
+    "title": "Data for Positive Emotion Regulation Task",
+    "title_en": "Data for Positive Emotion Regulation Task",
+    "title_zh": "Data for Positive Emotion Regulation Task",
+    "description": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
+    "description_en": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
+    "description_zh": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
+    "authors": [
+      {
+        "name_en": "Kahrilas Ian",
+        "name_zh": "Kahrilas Ian"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2020-01-15T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 507,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.55847",
+    "cstr": "31253.11.10.5281/zenodo.55847",
+    "pid": "",
+    "title": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
+    "title_en": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
+    "title_zh": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
+    "description": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
+    "description_en": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
+    "description_zh": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
+    "authors": [
+      {
+        "name_en": "Chen Zheng",
+        "name_zh": "Chen Zheng"
+      },
+      {
+        "name_en": "He Yujia",
+        "name_zh": "He Yujia"
+      },
+      {
+        "name_en": "Yu Yuguo",
+        "name_zh": "Yu Yuguo"
+      }
+    ],
+    "keywords_en": [
+      "Environmental neuroscience",
+      "Nature experience",
+      "Functional connectivity"
+    ],
+    "keywords_zh": [
+      "Environmental neuroscience",
+      "Nature experience",
+      "Functional connectivity"
+    ],
+    "publication_date": "2016-06-17T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 242.86,
+    "file_size_bytes": 254653170,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.580211",
+    "cstr": "31253.11.10.5281/zenodo.580211",
+    "pid": "",
+    "title": "Dataset: Neural correlates of error prediction in a complex motor task",
+    "title_en": "Dataset: Neural correlates of error prediction in a complex motor task",
+    "title_zh": "Dataset: Neural correlates of error prediction in a complex motor task",
+    "description": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
+    "description_en": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
+    "description_zh": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
+    "authors": [
+      {
+        "name_en": "Maurer L.K. Maurer H. & Müller H.",
+        "name_zh": "Maurer L.K. Maurer H. & Müller H."
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "error prediction",
+      "error-related negativity",
+      "motor task",
+      "ballistic throwing task"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "error prediction",
+      "error-related negativity",
+      "motor task",
+      "ballistic throwing task"
+    ],
+    "publication_date": "2017-05-15T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1306,
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+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1201560",
+    "cstr": "",
+    "pid": "",
+    "title": "Integrated analysis of anatomical and electrophysiological human intracranial data",
+    "title_en": "Integrated analysis of anatomical and electrophysiological human intracranial data",
+    "title_zh": "Integrated analysis of anatomical and electrophysiological human intracranial data",
+    "description": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
+    "description_en": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
+    "description_zh": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
+    "authors": [
+      {
+        "name_en": "Stolk Arjen",
+        "name_zh": "Stolk Arjen"
+      },
+      {
+        "name_en": "Griffin Sandon",
+        "name_zh": "Griffin Sandon"
+      },
+      {
+        "name_en": "van der Meij Roemer",
+        "name_zh": "van der Meij Roemer"
+      },
+      {
+        "name_en": "Dewar Callum",
+        "name_zh": "Dewar Callum"
+      },
+      {
+        "name_en": "Saez Ignacio",
+        "name_zh": "Saez Ignacio"
+      },
+      {
+        "name_en": "Lin Jack J.",
+        "name_zh": "Lin Jack J."
+      },
+      {
+        "name_en": "Piantoni Giovanni",
+        "name_zh": "Piantoni Giovanni"
+      },
+      {
+        "name_en": "Schoffelen Jan-Mathijs",
+        "name_zh": "Schoffelen Jan-Mathijs"
+      },
+      {
+        "name_en": "Knight Robert T.",
+        "name_zh": "Knight Robert T."
+      },
+      {
+        "name_en": "Oostenveld Robert",
+        "name_zh": "Oostenveld Robert"
+      }
+    ],
+    "keywords_en": [
+      "ECoG",
+      "iEEG",
+      "sEEG",
+      "intracranial EEG",
+      "FieldTrip",
+      "electrocorticography",
+      "stereoelectroencephalography",
+      "epilepsy"
+    ],
+    "keywords_zh": [
+      "ECoG",
+      "iEEG",
+      "sEEG",
+      "intracranial EEG",
+      "FieldTrip",
+      "electrocorticography",
+      "stereoelectroencephalography",
+      "epilepsy"
+    ],
+    "publication_date": "2018-03-20T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-SA-4.0",
+    "file_size_mb": 204.64,
+    "file_size_bytes": 214580946,
+    "download_count": 0,
+    "visit_count": 4,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1148304",
+    "cstr": "",
+    "pid": "",
+    "title": "Mind-tracking and the new transparent self",
+    "title_en": "Mind-tracking and the new transparent self",
+    "title_zh": "Mind-tracking and the new transparent self",
+    "description": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
+    "description_en": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
+    "description_zh": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
+    "authors": [
+      {
+        "name_en": "Przegalińska, Aleksandra",
+        "name_zh": "Przegalińska, Aleksandra"
+      }
+    ],
+    "keywords_en": [
+      "Mind-tracking",
+      "Algorithms",
+      "Artificial Intelligence",
+      "Transparency",
+      "Wearables",
+      "Electroencephalography (EEG)",
+      "Technology",
+      "Data"
+    ],
+    "keywords_zh": [
+      "Mind-tracking",
+      "Algorithms",
+      "Artificial Intelligence",
+      "Transparency",
+      "Wearables",
+      "Electroencephalography (EEG)",
+      "Technology",
+      "Data"
+    ],
+    "publication_date": 1516032000000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-SA-4.0",
+    "file_size_mb": 0.09,
+    "file_size_bytes": 90148,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "664ab3684e7538442ce796e9",
+    "doi": "10.57760/sciencedb.03168",
+    "cstr": "31253.11.sciencedb.03168",
+    "pid": "21.86116.6/sciencedb.03168",
+    "title": "Imitation of Grasping Animals Activates Motor Inhibition",
+    "title_en": "Imitation of Grasping Animals Activates Motor Inhibition",
+    "title_zh": "Imitation of Grasping Animals Activates Motor Inhibition",
+    "description": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "description_en": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "description_zh": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
+    "authors": [
+      {
+        "name_en": "Peng Liu",
+        "name_zh": "Peng Liu",
+        "email": "liupeng@nwu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Northwest University",
+            "name_zh": "Northwest University",
+            "ror_id": "https://ror.org/00y7snj24"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "motor interference effect",
+      "dangerous objects",
+      "motor inhibition",
+      "danger evaluation",
+      "theta oscillations",
+      "time-frequency analysis"
+    ],
+    "keywords_zh": [
+      "motor interference effect",
+      "dangerous objects",
+      "motor inhibition",
+      "danger evaluation",
+      "theta oscillations",
+      "time-frequency analysis"
+    ],
+    "publication_date": "2022-10-08T02:36:25.636Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 3795.95,
+    "file_size_bytes": 3980346206,
+    "download_count": 83,
+    "visit_count": 817,
+    "url": "https://www.scidb.cn/en/detail?id=664ab3684e7538442ce796e9",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6763828ba828ad3e95fc2eca",
+    "doi": "10.57760/sciencedb.16868",
+    "cstr": "31253.11.sciencedb.16868",
+    "pid": "",
+    "title": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
+    "title_en": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
+    "title_zh": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
+    "description": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
+    "description_en": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
+    "description_zh": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
+    "authors": [
+      {
+        "name_en": "Mengya Zhang",
+        "name_zh": "Mengya Zhang",
+        "email": "zhangmengya@ion.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Center for Excellence in Brain Science and Intelligence Technology",
+            "name_zh": "学院脑科学与智能技术卓越创新中心",
+            "ror_id": "https://ror.org/00vpwhm04"
+          }
+        ]
+      },
+      {
+        "name_en": "Qing Yu",
+        "name_zh": "Qing Yu",
+        "email": "qingyu@ion.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Center for Excellence in Brain Science and Intelligence Technology",
+            "name_zh": "学院脑科学与智能技术卓越创新中心",
+            "ror_id": "https://ror.org/00vpwhm04"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Working Memory",
+      "cognitive control",
+      "Neuroimaging"
+    ],
+    "keywords_zh": [
+      "Working Memory",
+      "cognitive control",
+      "Neuroimaging"
+    ],
+    "publication_date": "2024-11-15T06:04:50.304Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 8910.1,
+    "file_size_bytes": 9342915606,
+    "download_count": 396,
+    "visit_count": 579,
+    "url": "https://www.scidb.cn/en/detail?id=6763828ba828ad3e95fc2eca",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.580200",
+    "cstr": "31253.11.10.5281/zenodo.580200",
+    "pid": "",
+    "title": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
+    "title_en": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
+    "title_zh": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
+    "description": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
+    "description_en": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
+    "description_zh": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
+    "authors": [
+      {
+        "name_en": "Joch M. Hegele M. Maurer H. Müller H. & Maurer L.K.",
+        "name_zh": "Joch M. Hegele M. Maurer H. Müller H. & Maurer L.K."
+      }
+    ],
+    "keywords_en": [
+      "action effect monitoring",
+      "EEG",
+      "error negativity",
+      "ERP",
+      "forward model"
+    ],
+    "keywords_zh": [
+      "action effect monitoring",
+      "EEG",
+      "error negativity",
+      "ERP",
+      "forward model"
+    ],
+    "publication_date": "2017-05-15T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1419,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1237654",
+    "cstr": "31253.11.10.5281/zenodo.1237654",
+    "pid": "",
+    "title": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
+    "title_en": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
+    "title_zh": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
+    "description": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
+    "description_en": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
+    "description_zh": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
+    "authors": [
+      {
+        "name_en": "Ciria Luis F.",
+        "name_zh": "Ciria Luis F."
+      },
+      {
+        "name_en": "Perakakis Pandelis",
+        "name_zh": "Perakakis Pandelis"
+      },
+      {
+        "name_en": "Luque-Casado Antonio",
+        "name_zh": "Luque-Casado Antonio"
+      },
+      {
+        "name_en": "Sanabria Daniel",
+        "name_zh": "Sanabria Daniel"
+      }
+    ],
+    "keywords_en": [
+      "EEG",
+      "Physical exercise",
+      "Inhibitory control",
+      "Cognition"
+    ],
+    "keywords_zh": [
+      "EEG",
+      "Physical exercise",
+      "Inhibitory control",
+      "Cognition"
+    ],
+    "publication_date": "2018-04-29T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "file_size_bytes": 385120800,
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+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4752462",
+    "cstr": "31253.11.10.5281/zenodo.4752462",
+    "pid": "",
+    "title": "Infurcitinea nigropluviella",
+    "title_en": "Infurcitinea nigropluviella",
+    "title_zh": "Infurcitinea nigropluviella",
+    "description": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
+    "description_en": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
+    "description_zh": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
+    "authors": [
+      {
+        "name_en": "Gaedike, Reinhard",
+        "name_zh": "Gaedike, Reinhard"
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Lepidoptera",
+      "Tineidae",
+      "Infurcitinea",
+      "Infurcitinea nigropluviella"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Lepidoptera",
+      "Tineidae",
+      "Infurcitinea",
+      "Infurcitinea nigropluviella"
+    ],
+    "publication_date": 1320854400,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 649,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.57911",
+    "cstr": "",
+    "pid": "",
+    "title": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
+    "title_en": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
+    "title_zh": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
+    "description": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
+    "description_en": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
+    "description_zh": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
+    "authors": [
+      {
+        "name_en": "Braboszcz Claire",
+        "name_zh": "Braboszcz Claire"
+      },
+      {
+        "name_en": "Cahn B. Rael",
+        "name_zh": "Cahn B. Rael"
+      },
+      {
+        "name_en": "Levi Jonathan",
+        "name_zh": "Levi Jonathan"
+      },
+      {
+        "name_en": "Fernandez Manuel",
+        "name_zh": "Fernandez Manuel"
+      },
+      {
+        "name_en": "Delorme Arnaud",
+        "name_zh": "Delorme Arnaud"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-07-13T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 5504,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66294b1b3e08f2716205c9ad",
+    "doi": "10.57760/sciencedb.18384",
+    "cstr": "31253.11.sciencedb.18384",
+    "pid": "",
+    "title": "Prestimulus power and phase of α and β predict discrimination accuracy and visual awareness",
+    "title_en": "Prestimulus power and phase of α and β predict discrimination accuracy and visual awareness",
+    "title_zh": "刺激前α频段以及β频段的能量与相位对辨别准确性以及视觉意识的预测作用",
+    "description": "<p>The experimental material was a low-contrast sinusoidal Gabor patch at the threshold level, tilted 45° vertically to the left or right. Michelson contrast ratios are 0.01, 0.02, and 0.03, respectively (Michelson contrast is calculated by the brightest brightness minus the darkest brightness divided by the sum of the two), and the presentation time and contrast are determined in the measurement threshold stage. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2. The experiment was divided into two phases. The first stage was to determine the&nbsp;threshold and familiarizing participant with the experimental procedure. In the second stage, participants performed discrimination tasks while recording EEG. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_en": "<p>The experimental material was a low-contrast sinusoidal Gabor patch at the threshold level, tilted 45° vertically to the left or right. Michelson contrast ratios are 0.01, 0.02, and 0.03, respectively (Michelson contrast is calculated by the brightest brightness minus the darkest brightness divided by the sum of the two), and the presentation time and contrast are determined in the measurement threshold stage. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2. The experiment was divided into two phases. The first stage was to determine the&nbsp;threshold and familiarizing participant with the experimental procedure. In the second stage, participants performed discrimination tasks while recording EEG. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
+    "description_zh": "<p>实验材料为处于阈限水平的低对比度正弦曲线Gabor patch，垂直向左或者向右倾斜45°。迈克尔逊对比度分别为0.01，0.02和0.03（迈克尔逊对比度计算方法：最亮的亮度减去最暗的亮度除以二者之和），呈现时间和对比度在测定阈限阶段进行确定。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。该实验分为两个阶段。第一阶段是阈值测定并让被试熟悉实验流程。第二阶段被试在记录脑电图的同时执行辨别任务。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
+    "authors": [
+      {
+        "name_en": "li xiao xiao",
+        "name_zh": "李笑笑",
+        "email": "1421269185@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Tianjin Normal University",
+            "name_zh": "天津师范大学",
+            "ror_id": "https://ror.org/05x2td559"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "alpha",
+      "beta",
+      "visual awareness",
+      "discrimination task"
+    ],
+    "keywords_zh": [
+      "α频段",
+      "β频段",
+      "视觉意识",
+      "辨别任务"
+    ],
+    "publication_date": "2024-06-14T05:51:10.878Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
+    "file_size_mb": 25288.26,
+    "file_size_bytes": 26516661632,
+    "download_count": 7,
+    "visit_count": 88,
+    "url": "https://www.scidb.cn/en/detail?id=66294b1b3e08f2716205c9ad",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4500933",
+    "cstr": "",
+    "pid": "",
+    "title": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
+    "title_en": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
+    "title_zh": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
+    "description": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
+    "description_en": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
+    "description_zh": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
+    "authors": [
+      {
+        "name_en": "Wicht Corentin Aurèle",
+        "name_zh": "Wicht Corentin Aurèle"
+      },
+      {
+        "name_en": "Mouthon Michael",
+        "name_zh": "Mouthon Michael"
+      },
+      {
+        "name_en": "Nsimire Chabwine Joelle",
+        "name_zh": "Nsimire Chabwine Joelle"
+      },
+      {
+        "name_en": "Gaab Jens",
+        "name_zh": "Gaab Jens"
+      },
+      {
+        "name_en": "Spierer Lucas",
+        "name_zh": "Spierer Lucas"
+      }
+    ],
+    "keywords_en": [
+      "Placebo",
+      "Pain",
+      "Expectations",
+      "EEG",
+      "ERP topography"
+    ],
+    "keywords_zh": [
+      "Placebo",
+      "Pain",
+      "Expectations",
+      "EEG",
+      "ERP topography"
+    ],
+    "publication_date": "2021-12-05T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 3084,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5061/dryad.12jm63xwd",
+    "cstr": "",
+    "pid": "",
+    "title": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
+    "title_en": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
+    "title_zh": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
+    "description": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
+    "description_en": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
+    "description_zh": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
+    "authors": [
+      {
+        "name_en": "Polonenko Melissa J",
+        "name_zh": "Polonenko Melissa J"
+      },
+      {
+        "name_en": "Maddox Ross K",
+        "name_zh": "Maddox Ross K"
+      }
+    ],
+    "keywords_en": [
+      "Auditory Brainstem Responses",
+      "natural speech",
+      "auditory evoked potentials",
+      "Electroencephalography (EEG)",
+      "auditory speech processing"
+    ],
+    "keywords_zh": [
+      "Auditory Brainstem Responses",
+      "natural speech",
+      "auditory evoked potentials",
+      "Electroencephalography (EEG)",
+      "auditory speech processing"
+    ],
+    "publication_date": "2021-02-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 6535,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5061/dryad.00000002t",
+    "cstr": "31253.11.10.5061/dryad.00000002t",
+    "pid": "",
+    "title": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
+    "title_en": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
+    "title_zh": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
+    "description": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
+    "description_en": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
+    "description_zh": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
+    "authors": [
+      {
+        "name_en": "Ratz-Lubashevsky Rachel",
+        "name_zh": "Ratz-Lubashevsky Rachel"
+      },
+      {
+        "name_en": "Frank Michael",
+        "name_zh": "Frank Michael"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2021-03-24T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.11,
+    "file_size_bytes": 114197,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "6625e67fc70b375c9c533d59",
+    "doi": "10.57760/sciencedb.18277",
+    "cstr": "31253.11.sciencedb.18277",
+    "pid": "",
+    "title": "Electrophysiological study of social anxiety levels on eye-cue processing in the Trust Game paradigm",
+    "title_en": "Electrophysiological study of social anxiety levels on eye-cue processing in the Trust Game paradigm",
+    "title_zh": "信任博弈范式下社交焦虑水平对眼部线索加工的脑电研究数据集",
+    "description": "<p>This dataset contains behavioral and electrophysiological data from individuals with different levels of social anxiety using eye cue materials in a trust game paradigm. This study adopts the trust game paradigm and event related potential technology to explore the temporal characteristics of eye cue processing among college students with different levels of social anxiety. The dataset consists of three files, namely (1) behavioral results: the behavioral data results of the subjects, and (2) EEG date: the EEG raw data of the subjects.&nbsp;File (1) presents the behavioral data results of the subjects, including 8 horizontal rows and 469 vertical columns. In the first row, list the anxiety level groups of the subjects (high anxiety group and low anxiety group), subject numbers, eye cue conditions (all conditions, direct anger, strabismus anger, direct sadness, strabismus sadness, direct calm, strabismus calm, direct happiness, and strabismus pleasure), number of investments (i.e. the number of times the subjects choose to trust), number of reservations (i.e. the number of times they choose not to trust), total (the total number of choices made by the subjects in the experiment), reaction time, and trust rate (calculated as investment/total).&nbsp;The EEG date in file (2) is the raw electrophysiological data for the experiment. There are a total of 156 projects in the document. The items starting with H are participants in the high social anxiety group, and the items starting with L are participants in the low social anxiety group.&nbsp;</p>",
+    "description_en": "<p>This dataset contains behavioral and electrophysiological data from individuals with different levels of social anxiety using eye cue materials in a trust game paradigm. This study adopts the trust game paradigm and event related potential technology to explore the temporal characteristics of eye cue processing among college students with different levels of social anxiety. The dataset consists of three files, namely (1) behavioral results: the behavioral data results of the subjects, and (2) EEG date: the EEG raw data of the subjects.&nbsp;File (1) presents the behavioral data results of the subjects, including 8 horizontal rows and 469 vertical columns. In the first row, list the anxiety level groups of the subjects (high anxiety group and low anxiety group), subject numbers, eye cue conditions (all conditions, direct anger, strabismus anger, direct sadness, strabismus sadness, direct calm, strabismus calm, direct happiness, and strabismus pleasure), number of investments (i.e. the number of times the subjects choose to trust), number of reservations (i.e. the number of times they choose not to trust), total (the total number of choices made by the subjects in the experiment), reaction time, and trust rate (calculated as investment/total).&nbsp;The EEG date in file (2) is the raw electrophysiological data for the experiment. There are a total of 156 projects in the document. The items starting with H are participants in the high social anxiety group, and the items starting with L are participants in the low social anxiety group.&nbsp;</p>",
+    "description_zh": "<p>\t该数据集是在不同水平社交焦虑水平个体中使用眼部线索材料在信任博弈范式下进行实验的行为和电生理学数据。本研究采用信任博弈范式和事件相关电位技术，探索了不同社交焦虑水平大学生在眼部线索处理上的时间特征。数据集包括三个文件，分别是（1）行为结果：被试的行为数据结果和（2）脑电数据：被试的EEG原始数据。</p><p>\t文件（1）行为结果呈现的是被试的行为数据结果，包含8横行和469竖列。在第一横行中列明了被试的焦虑水平分组（高焦虑组和低焦虑组）、被试编号、眼部线索条件（所有条件、直视愤怒、斜视愤怒、直视悲伤、斜视悲伤、直视平静、斜视平静、直视快乐、斜视快乐）、投资数（即被试选择信任的次数）、保留数（即被试选择不信任的次数）、总计（被试在实验中做出的选择总数）、反应时和信任率（计算方法为投资数/总计）。</p><p>\t文件（2）脑电数据为实验的电生理原始数据。文件中共156个项目。其中H开头的项目为高社交焦虑组的被试，L开头的为低社交焦虑组的被试。</p>",
+    "authors": [
+      {
+        "name_en": "Yan Zhixiong",
+        "name_zh": "Yan Zhixiong",
+        "email": "yanzx@nnnu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Nanning Normal University",
+            "name_zh": "Nanning Normal University",
+            "ror_id": "sdb_custom"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "social anxiety levels",
+      "eye-cue processing",
+      "Trust Game paradigm"
+    ],
+    "keywords_zh": [
+      "ERP",
+      "社交焦虑",
+      "信任游戏"
+    ],
+    "publication_date": "2024-04-23T08:43:08.074Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-ND 4.0",
+    "file_size_mb": 11655.55,
+    "file_size_bytes": 12221734939,
+    "download_count": 27,
+    "visit_count": 350,
+    "url": "https://www.scidb.cn/en/detail?id=6625e67fc70b375c9c533d59",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4421252",
+    "cstr": "",
+    "pid": "",
+    "title": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
+    "title_en": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
+    "title_zh": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
+    "description": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
+    "description_en": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
+    "description_zh": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
+    "authors": [
+      {
+        "name_en": "Cheval Boris",
+        "name_zh": "Cheval Boris"
+      },
+      {
+        "name_en": "Daou Marcos",
+        "name_zh": "Daou Marcos"
+      },
+      {
+        "name_en": "Cabral Daniel A.R.",
+        "name_zh": "Cabral Daniel A.R."
+      },
+      {
+        "name_en": "Bacelar Mariane F.B.",
+        "name_zh": "Bacelar Mariane F.B."
+      },
+      {
+        "name_en": "Parma Juliana O.",
+        "name_zh": "Parma Juliana O."
+      },
+      {
+        "name_en": "Forestier Cyril",
+        "name_zh": "Forestier Cyril"
+      },
+      {
+        "name_en": "Sander David",
+        "name_zh": "Sander David"
+      },
+      {
+        "name_en": "Boisgontier Matthieu P.",
+        "name_zh": "Boisgontier Matthieu P."
+      },
+      {
+        "name_en": "Miller Matthew W.",
+        "name_zh": "Miller Matthew W."
+      }
+    ],
+    "keywords_en": [
+      "Response inhibition",
+      "inhibitory control",
+      "go/no-go",
+      "energetic cost minimization",
+      "physical activity"
+    ],
+    "keywords_zh": [
+      "Response inhibition",
+      "inhibitory control",
+      "go/no-go",
+      "energetic cost minimization",
+      "physical activity"
+    ],
+    "publication_date": "2020-02-13T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 11063,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "66682fe23aa61811b16959af",
+    "doi": "10.57760/sciencedb.08504",
+    "cstr": "31253.11.sciencedb.08504",
+    "pid": "",
+    "title": "Prestimulus power and phase of α and β predict visual awareness under high and low expectation",
+    "title_en": "Prestimulus power and phase of α and β predict visual awareness under high and low expectation",
+    "title_zh": "高低预期条件下，刺激前α以及β频段的能量与相位对视觉意识的预测作用",
+    "description": "<p>The experimental material was sinusoidal Gabor patch, with a Michelson contrast of 0.03, tilted either 45° to the left or right vertically. The blank stimulus was the no-target. The experimental materials were made from the online-gabor-patch-generator. The background brightness of all these types of stimuli was 22 cd/m². The probability of stimulus presentation was manipulated to present high or low expectations, with a high expectation of 75% stimulus appearance and a low expectation of 25% stimulus appearance. The proportion of stimulus appearance in each block was informed to the participants before the experiment. The experiment was conducted in a quiet, dimly lit EEG laboratory. The experimental stimuli were presented using E-Prime 2.0 software on a screen with a resolution of 1024x768 pixels and a refresh rate of 60 Hz (1 refresh cycle was approximately 17 ms). The participants were approximately 80cm away from the screen. EEG data were recorded using Curry7 software produced by NeuroScan, and the participants wore a 64-electrode cap based on the international 10-20 system. The frontal central electrode was grounded (located at the midpoint of the FCZ and FX line) and the top electrode was the reference electrode. The sampling rate was 1000 Hz&nbsp;in DC mode. Electrodes were placed on either side of the eyes to record horizontal eye movement (HEOG), and electrodes were placed above and below the left eye to record vertical eye movement (VEOG), with a resistance of less than 10 kΩ.</p>",
+    "description_en": "<p>The experimental material was sinusoidal Gabor patch, with a Michelson contrast of 0.03, tilted either 45° to the left or right vertically. The blank stimulus was the no-target. The experimental materials were made from the online-gabor-patch-generator. The background brightness of all these types of stimuli was 22 cd/m². The probability of stimulus presentation was manipulated to present high or low expectations, with a high expectation of 75% stimulus appearance and a low expectation of 25% stimulus appearance. The proportion of stimulus appearance in each block was informed to the participants before the experiment. The experiment was conducted in a quiet, dimly lit EEG laboratory. The experimental stimuli were presented using E-Prime 2.0 software on a screen with a resolution of 1024x768 pixels and a refresh rate of 60 Hz (1 refresh cycle was approximately 17 ms). The participants were approximately 80cm away from the screen. EEG data were recorded using Curry7 software produced by NeuroScan, and the participants wore a 64-electrode cap based on the international 10-20 system. The frontal central electrode was grounded (located at the midpoint of the FCZ and FX line) and the top electrode was the reference electrode. The sampling rate was 1000 Hz&nbsp;in DC mode. Electrodes were placed on either side of the eyes to record horizontal eye movement (HEOG), and electrodes were placed above and below the left eye to record vertical eye movement (VEOG), with a resistance of less than 10 kΩ.</p>",
+    "description_zh": "<p>实验材料为正弦曲线<em>Gabor patch</em>，迈克尔逊对比度为0.03，垂直向左或者向右倾斜45°。空白刺激即为无目标刺激。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。通过操纵刺激出现的概率来呈现高低预期，高预期为刺激出现的比例为75%，低预期为刺激出现的比例为25%，实验每个block前会告知被试刺激出现的比例。实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕80cm左右。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
+    "authors": [
+      {
+        "name_en": "li xiao xiao",
+        "name_zh": "李笑笑",
+        "email": "1421269185@qq.com",
+        "affiliations": [
+          {
+            "name_en": "Tianjin Normal University",
+            "name_zh": "天津师范大学",
+            "ror_id": "https://ror.org/05x2td559"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "expectation",
+      "alpha",
+      "beta",
+      "visual awareness"
+    ],
+    "keywords_zh": [
+      "预期",
+      "α频段",
+      "β频段",
+      "视觉意识"
+    ],
+    "publication_date": "2024-06-20T02:18:07.941Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY-NC-ND 4.0",
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+    "file_size_bytes": 32321690544,
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+    "visit_count": 24,
+    "url": "https://www.scidb.cn/en/detail?id=66682fe23aa61811b16959af",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4501988",
+    "cstr": "",
+    "pid": "",
+    "title": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
+    "title_en": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
+    "title_zh": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
+    "description": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
+    "description_en": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
+    "description_zh": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
+    "authors": [
+      {
+        "name_en": "Chaudhary Ujwal",
+        "name_zh": "Chaudhary Ujwal"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2021-02-03T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 430.35,
+    "file_size_bytes": 451259605,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.61256",
+    "cstr": "31253.11.10.5281/zenodo.61256",
+    "pid": "",
+    "title": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
+    "title_en": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
+    "title_zh": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
+    "description": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
+    "description_en": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
+    "description_zh": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
+    "authors": [
+      {
+        "name_en": "Filho Edson",
+        "name_zh": "Filho Edson"
+      },
+      {
+        "name_en": "Bertollo Maurizio",
+        "name_zh": "Bertollo Maurizio"
+      },
+      {
+        "name_en": "Tamburro Gabriella",
+        "name_zh": "Tamburro Gabriella"
+      },
+      {
+        "name_en": "Schinaia Lorenzo",
+        "name_zh": "Schinaia Lorenzo"
+      },
+      {
+        "name_en": "Jonas Chatel-Goldman",
+        "name_zh": "Jonas Chatel-Goldman"
+      },
+      {
+        "name_en": "Di Fronso Selenia",
+        "name_zh": "Di Fronso Selenia"
+      },
+      {
+        "name_en": "Robazza Claudio",
+        "name_zh": "Robazza Claudio"
+      },
+      {
+        "name_en": "Comani Silvia",
+        "name_zh": "Comani Silvia"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2016-08-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.03,
+    "file_size_bytes": 32112,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.2602656",
+    "cstr": "",
+    "pid": "",
+    "title": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
+    "title_en": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
+    "title_zh": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
+    "description": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
+    "description_en": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
+    "description_zh": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
+    "authors": [
+      {
+        "name_en": "Louis, Justine",
+        "name_zh": "Louis, Justine"
+      },
+      {
+        "name_en": " Magued, Shaimaa",
+        "name_zh": " Magued, Shaimaa"
+      },
+      {
+        "name_en": " Mzhavanadze, Nino",
+        "name_zh": " Mzhavanadze, Nino"
+      }
+    ],
+    "keywords_en": [
+      "European Union; Turkey; EU-Turkey Relations; Identity Politics; Identity Representation; Foreign Media; Regional and Global Perspectives; Culture; Critical Discourse Analysis; Significant Other; Georgia; Egypt; US; Russia; Newspaper; Conflict"
+    ],
+    "keywords_zh": [
+      "European Union; Turkey; EU-Turkey Relations; Identity Politics; Identity Representation; Foreign Media; Regional and Global Perspectives; Culture; Critical Discourse Analysis; Significant Other; Georgia; Egypt; US; Russia; Newspaper; Conflict"
+    ],
+    "publication_date": 1521129600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 2.68,
+    "file_size_bytes": 2810485,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.20044",
+    "cstr": "31253.11.10.5281/zenodo.20044",
+    "pid": "",
+    "title": "Cortical correlates of a translating point-light walker; an event-related potentials study",
+    "title_en": "Cortical correlates of a translating point-light walker; an event-related potentials study",
+    "title_zh": "Cortical correlates of a translating point-light walker; an event-related potentials study",
+    "description": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
+    "description_en": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
+    "description_zh": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
+    "authors": [
+      {
+        "name_en": "Inuggi Alberto",
+        "name_zh": "Inuggi Alberto"
+      },
+      {
+        "name_en": "Campus Claudio",
+        "name_zh": "Campus Claudio"
+      },
+      {
+        "name_en": "Keuroghlanian Alejo",
+        "name_zh": "Keuroghlanian Alejo"
+      },
+      {
+        "name_en": "Vastano Roberta",
+        "name_zh": "Vastano Roberta"
+      },
+      {
+        "name_en": "Pozzo Thierry",
+        "name_zh": "Pozzo Thierry"
+      }
+    ],
+    "keywords_en": [
+      "point-light walker (PLW)",
+      "event related potentials (ERP)"
+    ],
+    "keywords_zh": [
+      "point-light walker (PLW)",
+      "event related potentials (ERP)"
+    ],
+    "publication_date": "2015-07-12T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 65.67,
+    "file_size_bytes": 68856320,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1226586",
+    "cstr": "",
+    "pid": "",
+    "title": "SSVEP Starlab",
+    "title_en": "SSVEP Starlab",
+    "title_zh": "SSVEP Starlab",
+    "description": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
+    "description_en": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
+    "description_zh": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
+    "authors": [
+      {
+        "name_en": "David",
+        "name_zh": "David"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2018-04-20T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.9,
+    "file_size_bytes": 944666,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.818172",
+    "cstr": "",
+    "pid": "",
+    "title": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
+    "title_en": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
+    "title_zh": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
+    "description": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
+    "description_en": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
+    "description_zh": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
+    "authors": [
+      {
+        "name_en": "Fattori Fabrizio",
+        "name_zh": "Fattori Fabrizio"
+      },
+      {
+        "name_en": "Anglani Norma",
+        "name_zh": "Anglani Norma"
+      },
+      {
+        "name_en": "Staffell Iain",
+        "name_zh": "Staffell Iain"
+      },
+      {
+        "name_en": "Pfenninger Stefan",
+        "name_zh": "Pfenninger Stefan"
+      }
+    ],
+    "keywords_en": [
+      "Solar Energy",
+      "Electricity Storage",
+      "Capacity Adequacy",
+      "Power system",
+      "Solar PV",
+      "Ramp Rate",
+      "Nord bidding zone",
+      "Italy",
+      "renewable energy"
+    ],
+    "keywords_zh": [
+      "Solar Energy",
+      "Electricity Storage",
+      "Capacity Adequacy",
+      "Power system",
+      "Solar PV",
+      "Ramp Rate",
+      "Nord bidding zone",
+      "Italy",
+      "renewable energy"
+    ],
+    "publication_date": "2017-06-25T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "url": "",
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+    "doi": "10.5281/zenodo.4457748",
+    "cstr": "31253.11.10.5281/zenodo.4457748",
+    "pid": "",
+    "title": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
+    "title_en": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
+    "title_zh": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
+    "description": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
+    "description_en": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
+    "description_zh": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
+    "authors": [
+      {
+        "name_en": "Steber Sarah",
+        "name_zh": "Steber Sarah"
+      },
+      {
+        "name_en": "Rossi Sonja",
+        "name_zh": "Rossi Sonja"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": "2021-01-21T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "file_size_bytes": 7616,
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+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.2427621",
+    "cstr": "",
+    "pid": "",
+    "title": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
+    "title_en": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
+    "title_zh": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
+    "description": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
+    "description_en": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
+    "description_zh": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
+    "authors": [
+      {
+        "name_en": "VALERA, Dalis Coromoto",
+        "name_zh": "VALERA, Dalis Coromoto"
+      },
+      {
+        "name_en": " PERNIOLA, Mario",
+        "name_zh": " PERNIOLA, Mario"
+      },
+      {
+        "name_en": " ROJAS PAREDES, Alexis del Carmen",
+        "name_zh": " ROJAS PAREDES, Alexis del Carmen"
+      },
+      {
+        "name_en": " BARRETO GONZÁLEZ, Juan José",
+        "name_zh": " BARRETO GONZÁLEZ, Juan José"
+      }
+    ],
+    "keywords_en": [],
+    "keywords_zh": [],
+    "publication_date": 1545148800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.43,
+    "file_size_bytes": 453062,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.198709",
+    "cstr": "",
+    "pid": "",
+    "title": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
+    "title_en": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
+    "title_zh": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
+    "description": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
+    "description_en": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
+    "description_zh": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
+    "authors": [
+      {
+        "name_en": "Prieto Luis P.",
+        "name_zh": "Prieto Luis P."
+      },
+      {
+        "name_en": "Sharma Kshitij",
+        "name_zh": "Sharma Kshitij"
+      }
+    ],
+    "keywords_en": [
+      "orchestration",
+      "eyetracking",
+      "cognitive load",
+      "classroom studies"
+    ],
+    "keywords_zh": [
+      "orchestration",
+      "eyetracking",
+      "cognitive load",
+      "classroom studies"
+    ],
+    "publication_date": "2016-12-08T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-SA-4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 23989,
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+    "url": "",
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4485036",
+    "cstr": "",
+    "pid": "",
+    "title": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
+    "title_en": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
+    "title_zh": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
+    "description": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
+    "description_en": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
+    "description_zh": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
+    "authors": [
+      {
+        "name_en": "Adam Li",
+        "name_zh": "Adam Li"
+      }
+    ],
+    "keywords_en": [
+      "Python",
+      "iEEG",
+      "High frequency oscillation"
+    ],
+    "keywords_zh": [
+      "Python",
+      "iEEG",
+      "High frequency oscillation"
+    ],
+    "publication_date": 1612108800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 5024,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.162802",
+    "cstr": "31253.11.10.5281/zenodo.162802",
+    "pid": "",
+    "title": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
+    "title_en": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
+    "title_zh": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
+    "description": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
+    "description_en": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
+    "description_zh": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
+    "authors": [
+      {
+        "name_en": "1 Lt Col (Dr) Jyotirmaya Satpathy, DLitt, Faculty, Officers’ Training Academy, Indian Army, Gaya, India. (Corresponding Author) Mail:  jyotisatpathy@gmail.com 2 Dr Adyasha Das, PhD, Faculty, Indian Institute of Tourism Management, Bhubaneswar, India. 3 Professor Suneetha Narendran, PhD, Faculty, Dept. of Management(Organisation Behaviour) and Director Research, University of East London, UK.  4 Dr Sandhya R Das, PhD, Faculty, Dept. of Economics, Berhampur University, Berhampur, India.",
+        "name_zh": "1 Lt Col (Dr) Jyotirmaya Satpathy, DLitt, Faculty, Officers’ Training Academy, Indian Army, Gaya, India. (Corresponding Author) Mail:  jyotisatpathy@gmail.com 2 Dr Adyasha Das, PhD, Faculty, Indian Institute of Tourism Management, Bhubaneswar, India. 3 Professor Suneetha Narendran, PhD, Faculty, Dept. of Management(Organisation Behaviour) and Director Research, University of East London, UK.  4 Dr Sandhya R Das, PhD, Faculty, Dept. of Economics, Berhampur University, Berhampur, India."
+      }
+    ],
+    "keywords_en": [
+      "Key Words: Cognitive Neuroscience",
+      " Brain Imaging",
+      " Coherent Brain Dynamics",
+      " VUCA",
+      " fMRI",
+      " BOLD",
+      " EEG",
+      " ECG"
+    ],
+    "keywords_zh": [
+      "Key Words: Cognitive Neuroscience",
+      " Brain Imaging",
+      " Coherent Brain Dynamics",
+      " VUCA",
+      " fMRI",
+      " BOLD",
+      " EEG",
+      " ECG"
+    ],
+    "publication_date": 1477238400000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "url": "",
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3902305",
+    "cstr": "",
+    "pid": "",
+    "title": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
+    "title_en": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
+    "title_zh": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
+    "description": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
+    "description_en": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
+    "description_zh": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
+    "authors": [
+      {
+        "name_en": "Cortassa Giorgio Mario",
+        "name_zh": "Cortassa Giorgio Mario"
+      },
+      {
+        "name_en": " Bottone Stefania",
+        "name_zh": " Bottone Stefania"
+      },
+      {
+        "name_en": " Saia Valentina",
+        "name_zh": " Saia Valentina"
+      },
+      {
+        "name_en": " Grillo Giovanna",
+        "name_zh": " Grillo Giovanna"
+      },
+      {
+        "name_en": " Bertolino Monica",
+        "name_zh": " Bertolino Monica"
+      },
+      {
+        "name_en": " Tassinari Tiziana",
+        "name_zh": " Tassinari Tiziana"
+      },
+      {
+        "name_en": " Mangerini R",
+        "name_zh": " Mangerini R"
+      },
+      {
+        "name_en": " Tedone E",
+        "name_zh": " Tedone E"
+      }
+    ],
+    "keywords_en": [
+      "COVID-19",
+      "head trauma",
+      "neurologic presentation",
+      "DWI imaging",
+      "Coronavirus in liquor"
+    ],
+    "keywords_zh": [
+      "COVID-19",
+      "head trauma",
+      "neurologic presentation",
+      "DWI imaging",
+      "Coronavirus in liquor"
+    ],
+    "publication_date": 1592582400000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.32,
+    "file_size_bytes": 331812,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1211690",
+    "cstr": "",
+    "pid": "",
+    "title": "Material related to the blog that reports on the parrot LUT",
+    "title_en": "Material related to the blog that reports on the parrot LUT",
+    "title_zh": "Material related to the blog that reports on the parrot LUT",
+    "description": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
+    "description_en": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
+    "description_zh": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
+    "authors": [
+      {
+        "name_en": "Goedhart J.",
+        "name_zh": "Goedhart J."
+      }
+    ],
+    "keywords_en": [
+      "LUT",
+      "parrot",
+      "pseudocolor"
+    ],
+    "keywords_zh": [
+      "LUT",
+      "parrot",
+      "pseudocolor"
+    ],
+    "publication_date": "2018-04-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 4.09,
+    "file_size_bytes": 4288054,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3259369",
+    "cstr": "",
+    "pid": "",
+    "title": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
+    "title_en": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
+    "title_zh": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
+    "description": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
+    "description_en": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
+    "description_zh": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
+    "authors": [
+      {
+        "name_en": "Yitzhak Norman",
+        "name_zh": "Yitzhak Norman"
+      },
+      {
+        "name_en": "Erin M. Yeagle",
+        "name_zh": "Erin M. Yeagle"
+      },
+      {
+        "name_en": "Simon Khuvis",
+        "name_zh": "Simon Khuvis"
+      },
+      {
+        "name_en": "Michal Harel",
+        "name_zh": "Michal Harel"
+      },
+      {
+        "name_en": "Ashesh D. Mehta",
+        "name_zh": "Ashesh D. Mehta"
+      },
+      {
+        "name_en": "Rafael Malach",
+        "name_zh": "Rafael Malach"
+      }
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+      "learning and memory",
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+      "episodic memory",
+      "iEEG",
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+      "reinstatement",
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+    "title": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
+    "title_en": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
+    "title_zh": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
+    "description": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
+    "description_en": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
+    "description_zh": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
+    "authors": [
+      {
+        "name_en": "Hesse Philipp N.",
+        "name_zh": "Hesse Philipp N."
+      },
+      {
+        "name_en": "Schmitt Constanze",
+        "name_zh": "Schmitt Constanze"
+      },
+      {
+        "name_en": "Klingenhoefer Steffen",
+        "name_zh": "Klingenhoefer Steffen"
+      },
+      {
+        "name_en": "Bremmer Frank",
+        "name_zh": "Bremmer Frank"
+      }
+    ],
+    "keywords_en": [
+      "visual mismatch negativity",
+      " vMMN",
+      " number",
+      " subitizing-range",
+      " preattentive"
+    ],
+    "keywords_zh": [
+      "visual mismatch negativity",
+      " vMMN",
+      " number",
+      " subitizing-range",
+      " preattentive"
+    ],
+    "publication_date": "2017-07-02T16:00:00.000Z",
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+    "modified": "",
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+    "license": "CC BY 4.0",
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+    "title": "The DREAMS Databases and Assessment Algorithm",
+    "title_en": "The DREAMS Databases and Assessment Algorithm",
+    "title_zh": "The DREAMS Databases and Assessment Algorithm",
+    "description": "The DREAMS Databases and Assessment algorithmDuring the DREAMS project funded by R&eacute;gion Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):&bull;&nbsp;&nbsp; &nbsp;The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1&#39;s SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.&bull;&nbsp;&nbsp; &nbsp;The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;&bull;&nbsp;&nbsp; &nbsp;The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst",
+    "description_en": "The DREAMS Databases and Assessment algorithmDuring the DREAMS project funded by R&eacute;gion Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):&bull;&nbsp;&nbsp; &nbsp;The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1&#39;s SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.&bull;&nbsp;&nbsp; &nbsp;The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;&bull;&nbsp;&nbsp; &nbsp;The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst",
+    "description_zh": "The DREAMS Databases and Assessment algorithmDuring the DREAMS project funded by R&eacute;gion Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):&bull;&nbsp;&nbsp; &nbsp;The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1&#39;s SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.&bull;&nbsp;&nbsp; &nbsp;The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;&bull;&nbsp;&nbsp; &nbsp;The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst",
+    "authors": [
+      {
+        "name_en": "Stephanie Devuyst",
+        "name_zh": "Stephanie Devuyst"
+      }
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+      "Sleep stage analysis",
+      " polysomnography",
+      " sleep signal artifacts",
+      "spindles",
+      "K-complex",
+      "REM",
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+      "Apnea"
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+      " sleep signal artifacts",
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+      "PLM",
+      "Apnea"
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+    "dataset_id": "64f04634cc4bc33e70a58efe",
+    "doi": "10.6084/m9.figshare.21080953.v1",
+    "cstr": "",
+    "pid": "",
+    "title": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
+    "title_en": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
+    "title_zh": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
+    "description": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
+    "description_en": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
+    "description_zh": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
+    "authors": [
+      {
+        "name_en": "Zhang Libing",
+        "name_zh": "Zhang Libing",
+        "email": "zhangpinghu@yzu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Kaituo",
+        "name_zh": "Liu Kaituo",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Su Qin",
+        "name_zh": "Su Qin",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Chen Xiao",
+        "name_zh": "Chen Xiao",
+        "email": "chenxiao@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "Institute of Psychology",
+            "name_zh": "中国科学院心理研究所",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Xiaoquan",
+        "name_zh": "Wang Xiaoquan",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Li Qingfeng",
+        "name_zh": "Li Qingfeng",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Wenlei",
+        "name_zh": "Wang Wenlei",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Mao Xuhua",
+        "name_zh": "Mao Xuhua",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Jinmei",
+        "name_zh": "Xu Jinmei",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhou Xin",
+        "name_zh": "Zhou Xin",
+        "email": "xzhou@ucas.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Yangzhou Center for Disease Control and Prevention",
+            "name_zh": "Yangzhou Center for Disease Control and Prevention"
+          },
+          {
+            "name_en": "cnic",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Xu Qin",
+        "name_zh": "Xu Qin",
+        "affiliations": [
+          {
+            "name_en": "Yangzhou Center for Disease Control and Prevention",
+            "name_zh": "Yangzhou Center for Disease Control and Prevention"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhou Le",
+        "name_zh": "Zhou Le",
+        "affiliations": [
+          {
+            "name_en": "Yangzhou Center for Disease Control and Prevention",
+            "name_zh": "Yangzhou Center for Disease Control and Prevention"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Xiufan",
+        "name_zh": "Liu Xiufan",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhang Pinghu",
+        "name_zh": "Zhang Pinghu",
+        "email": "zhangpinghu@yzu.edu.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          },
+          {
+            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
+            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Acute encephalitis",
+      "H5N6",
+      "influenza A virus",
+      "wild waterfowls",
+      "phylogenetic analysis"
+    ],
+    "keywords_zh": [
+      "Acute encephalitis",
+      "H5N6",
+      "influenza A virus",
+      "wild waterfowls",
+      "phylogenetic analysis"
+    ],
+    "publication_date": "2022-10-25T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 1.83,
+    "file_size_bytes": 1916074,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "https://www.scidb.cn/en/detail?id=64f04634cc4bc33e70a58efe",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4587325",
+    "cstr": "",
+    "pid": "",
+    "title": "_Attention what is it like [Dataset]",
+    "title_en": "_Attention what is it like [Dataset]",
+    "title_zh": "_Attention what is it like [Dataset]",
+    "description": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
+    "description_en": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
+    "description_zh": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
+    "authors": [
+      {
+        "name_en": "Dinis Pereira Vitor Manuel",
+        "name_zh": "Dinis Pereira Vitor Manuel"
+      }
+    ],
+    "keywords_en": [
+      "Event-related changes",
+      "Oscillations correlated with access and phenomenal consciousness",
+      "Empirical mode decomposition",
+      "Variance inflation factor",
+      "Partial least squares regression",
+      "The minimal root mean squared error of prediction",
+      "Significance multivariate correlation statistic"
+    ],
+    "keywords_zh": [
+      "Event-related changes",
+      "Oscillations correlated with access and phenomenal consciousness",
+      "Empirical mode decomposition",
+      "Variance inflation factor",
+      "Partial least squares regression",
+      "The minimal root mean squared error of prediction",
+      "Significance multivariate correlation statistic"
+    ],
+    "publication_date": "2017-03-01T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 310,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "62bd117170c5b75f4c7afaff",
+    "doi": "10.5061/dryad.3j9kd51cn",
+    "cstr": "",
+    "pid": "",
+    "title": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
+    "title_en": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
+    "title_zh": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
+    "description": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
+    "description_en": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
+    "description_zh": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
+    "authors": [
+      {
+        "name_en": "Guo Zizheng",
+        "name_zh": "Guo Zizheng",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "ror_id": "https://ror.org/03j7v5j15"
+          },
+          {
+            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
+            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
+          },
+          {
+            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
+            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
+            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
+          }
+        ]
+      },
+      {
+        "name_en": "Tan Xi",
+        "name_zh": "Tan Xi",
+        "affiliations": [
+          {
+            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
+            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
+            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
+          },
+          {
+            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
+            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
+          }
+        ]
+      },
+      {
+        "name_en": "Pan Yufan",
+        "name_zh": "Pan Yufan",
+        "affiliations": [
+          {
+            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
+            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
+            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
+          },
+          {
+            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
+            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Xian",
+        "name_zh": "Liu Xian",
+        "affiliations": [
+          {
+            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
+            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
+            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
+            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhao Guozhen",
+        "name_zh": "Zhao Guozhen",
+        "email": "zhaogz@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "ror_id": "https://ror.org/03j7v5j15"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Lin",
+        "name_zh": "Wang Lin",
+        "affiliations": [
+          {
+            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
+            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
+          },
+          {
+            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
+            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
+          },
+          {
+            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
+            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
+          }
+        ]
+      },
+      {
+        "name_en": "Peng Zhen",
+        "name_zh": "Peng Zhen",
+        "affiliations": [
+          {
+            "name_en": "School of Arts and Sciences, Arizona State University",
+            "name_zh": "School of Arts and Sciences, Arizona State University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Visual Intervention Influence",
+      "Rutting Mitigation",
+      "Will Reduce",
+      "Motor",
+      "CNV",
+      "Information",
+      "Reliability",
+      "Components",
+      "Stability",
+      "Modality"
+    ],
+    "keywords_zh": [
+      "Visual Intervention Influence",
+      "Rutting Mitigation",
+      "Will Reduce",
+      "Motor",
+      "CNV",
+      "Information",
+      "Reliability",
+      "Components",
+      "Stability",
+      "Modality"
+    ],
+    "publication_date": "2019-11-14T00:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.02,
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+    "url": "https://www.scidb.cn/en/detail?id=62bd117170c5b75f4c7afaff",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.2652864",
+    "cstr": "",
+    "pid": "",
+    "title": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
+    "title_en": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
+    "title_zh": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
+    "description": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
+    "description_en": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
+    "description_zh": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
+    "authors": [
+      {
+        "name_en": "BinKhamis Ghada",
+        "name_zh": "BinKhamis Ghada"
+      }
+    ],
+    "keywords_en": [
+      "Speech-ABR",
+      "Aided speech-ABR",
+      "Unaided speech-ABR",
+      "40 ms [da]",
+      "Speech-ABR in background noise",
+      "Speech-ABR in SNHL"
+    ],
+    "keywords_zh": [
+      "Speech-ABR",
+      "Aided speech-ABR",
+      "Unaided speech-ABR",
+      "40 ms [da]",
+      "Speech-ABR in background noise",
+      "Speech-ABR in SNHL"
+    ],
+    "publication_date": "2019-04-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.05,
+    "file_size_bytes": 53963,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3962798",
+    "cstr": "",
+    "pid": "",
+    "title": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
+    "title_en": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
+    "title_zh": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
+    "description": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
+    "description_en": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
+    "description_zh": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
+    "authors": [
+      {
+        "name_en": "Poca María",
+        "name_zh": "Poca María"
+      },
+      {
+        "name_en": "Nosetto Marcelo D.",
+        "name_zh": "Nosetto Marcelo D."
+      },
+      {
+        "name_en": "Ballesteros Silvina",
+        "name_zh": "Ballesteros Silvina"
+      },
+      {
+        "name_en": "Castellanos George",
+        "name_zh": "Castellanos George"
+      },
+      {
+        "name_en": "Jobbágy Esteban G.",
+        "name_zh": "Jobbágy Esteban G."
+      }
+    ],
+    "keywords_en": [
+      "Argentina",
+      "d-excess",
+      "GNIP",
+      "GNIR",
+      "Groundwater evaporation",
+      "Isotope hydrology",
+      "Mountains",
+      "Paraguay",
+      "Precipitation recycling",
+      "Sedimentary plains",
+      "Stable isotopes",
+      "Uruguay"
+    ],
+    "keywords_zh": [
+      "Argentina",
+      "d-excess",
+      "GNIP",
+      "GNIR",
+      "Groundwater evaporation",
+      "Isotope hydrology",
+      "Mountains",
+      "Paraguay",
+      "Precipitation recycling",
+      "Sedimentary plains",
+      "Stable isotopes",
+      "Uruguay"
+    ],
+    "publication_date": "2020-07-20T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.11,
+    "file_size_bytes": 114189,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "62bd109470c5b75f4c7a873d",
+    "doi": "10.6084/m9.figshare.6866264.v1",
+    "cstr": "",
+    "pid": "",
+    "title": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
+    "title_en": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
+    "title_zh": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
+    "description": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
+    "description_en": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
+    "description_zh": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
+    "authors": [
+      {
+        "name_en": "Li Tao",
+        "name_zh": "Li Tao",
+        "affiliations": [
+          {
+            "name_en": "State Key Laboratory of Transducer Technology, Chinese Academy of Sciences",
+            "name_zh": "State Key Laboratory of Transducer Technology, Chinese Academy of Sciences"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "University of Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "name_zh": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/047yhep71"
+          }
+        ]
+      },
+      {
+        "name_en": "Cui Yinglu",
+        "name_zh": "Cui Yinglu",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "name_zh": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/047yhep71"
+          }
+        ]
+      },
+      {
+        "name_en": "Wu Bian",
+        "name_zh": "Wu Bian",
+        "email": "wub@im.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "name_zh": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/047yhep71"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "molecular dynamics",
+      "MM-GBSA",
+      "Bcl-2",
+      "conformational changes",
+      "accelerated molecular dynamics"
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+    "keywords_zh": [
+      "molecular dynamics",
+      "MM-GBSA",
+      "Bcl-2",
+      "conformational changes",
+      "accelerated molecular dynamics"
+    ],
+    "publication_date": "2018-07-26T00:00:00.000Z",
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+    "modified": "",
+    "status": "",
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+    "source": "scidb"
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+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1290800",
+    "cstr": "",
+    "pid": "",
+    "title": "Dakar very-high resolution land cover map",
+    "title_en": "Dakar very-high resolution land cover map",
+    "title_zh": "Dakar very-high resolution land cover map",
+    "description": "This land cover map of Dakar (Senegal) was created from a Pl&eacute;iades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part [2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level8_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level8_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level8_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level9_Shadowsback.zip&quot; : Corresponds to the &quot;level8_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Dakar_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
+    "description_en": "This land cover map of Dakar (Senegal) was created from a Pl&eacute;iades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part [2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level8_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level8_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level8_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level9_Shadowsback.zip&quot; : Corresponds to the &quot;level8_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Dakar_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
+    "description_zh": "This land cover map of Dakar (Senegal) was created from a Pl&eacute;iades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part [2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level8_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level8_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level8_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level9_Shadowsback.zip&quot; : Corresponds to the &quot;level8_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Dakar_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
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+        "name_en": "Tais Grippa",
+        "name_zh": "Tais Grippa"
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+        "name_en": "Stefanos Georganos",
+        "name_zh": "Stefanos Georganos"
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+    "title": "Ouagadougou very-high resolution land cover map",
+    "title_en": "Ouagadougou very-high resolution land cover map",
+    "title_zh": "Ouagadougou very-high resolution land cover map",
+    "description": "This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part&nbsp;[2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level5_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level5_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level5_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level6_Shadowsback.zip&quot; : Corresponds to the &quot;level5_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Ouaga_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
+    "description_en": "This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part&nbsp;[2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level5_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level5_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level5_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level6_Shadowsback.zip&quot; : Corresponds to the &quot;level5_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Ouaga_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
+    "description_zh": "This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on GRASS GIS&nbsp;using a local unsupervised optimization approach for the segmentation part&nbsp;[2-3].Description of the files:\t&quot;Landcover.zip&quot; :&nbsp;The direct output from the supervised classification using the Random Forest classifier.\t&quot;Landcover_Postclassif_Level5_Splitbuildings.zip&quot; : Post-processed version of the previous map (&quot;Landcover&quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&nbsp;the errors, with a focus on built-up classes).\t&quot;Landcover_Postclassif_Level5_modalfilter3.zip&quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &quot;Landcover_Postclassif_Level5_Splitbuildings&quot;).&nbsp;\t&quot;Landcover_Postclassif_Level6_Shadowsback.zip&quot; : Corresponds to the &quot;level5_Splitbuildings&quot; with shadows coming&nbsp;from the original classification.\t&quot;Ouaga_legend_colors.txt&quot; : Text file providing the&nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&nbsp;References:[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo; Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17).&nbsp;Founding:&nbsp;This dataset was&nbsp;produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)&nbsp;and REACT (http://react.ulb.be), funded by the&nbsp;Belgian Federal Science Policy Office (BELSPO).",
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+        "name_en": "Tais Grippa",
+        "name_zh": "Tais Grippa"
+      },
+      {
+        "name_en": "Stefanos Georganos",
+        "name_zh": "Stefanos Georganos"
+      }
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+      "Ouagadougou",
+      "Land cover",
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+      "Ouagadougou",
+      "Land cover",
+      "Map",
+      "Very-high resolution"
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+    "title": "Genetic prion diseases presenting as frontotemporal dementia: clinical features and diagnostic challenge",
+    "title_en": "Genetic prion diseases presenting as frontotemporal dementia: clinical features and diagnostic challenge",
+    "title_zh": "Genetic prion diseases presenting as frontotemporal dementia: clinical features and diagnostic challenge",
+    "description": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
+    "description_en": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
+    "description_zh": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
+    "authors": [
+      {
+        "name_en": "Chen Zhongyun",
+        "name_zh": "Chen Zhongyun",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Chu Min",
+        "name_zh": "Chu Min",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Liu Li",
+        "name_zh": "Liu Li",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Zhang Jing",
+        "name_zh": "Zhang Jing",
+        "email": "zhangj271@ucas.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Shanghai Institute of Organic Chemistry",
+            "name_zh": "中国科学院上海有机化学研究所",
+            "ror_id": "https://ror.org/01y3hvq34"
+          },
+          {
+            "name_en": "University of Chinese Academy of Sciences",
+            "name_zh": "中国科学院大学",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "cnic",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          }
+        ]
+      },
+      {
+        "name_en": "Kong Yu",
+        "name_zh": "Kong Yu",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "cnic",
+            "name_zh": "中国科学院",
+            "ror_id": "https://ror.org/034t30j35"
+          }
+        ]
+      },
+      {
+        "name_en": "Xie Kexin",
+        "name_zh": "Xie Kexin",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Cui Yue",
+        "name_zh": "Cui Yue",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Ye Hong",
+        "name_zh": "Ye Hong",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Li Junjie",
+        "name_zh": "Li Junjie",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Lin",
+        "name_zh": "Wang Lin",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      },
+      {
+        "name_en": "Wu Liyong",
+        "name_zh": "Wu Liyong",
+        "email": "wmywly@hotmail.com",
+        "affiliations": [
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          },
+          {
+            "name_en": "Capital Medical University",
+            "name_zh": "Capital Medical University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "化学",
+      "有机",
+      "金属"
+    ],
+    "keywords_zh": [
+      "化学",
+      "有机",
+      "金属"
+    ],
+    "publication_date": "2022-06-29T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.03,
+    "file_size_bytes": 26761,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "https://www.scidb.cn/en/detail?id=64f046b8cc4bc33e70a59008",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3903262",
+    "cstr": "",
+    "pid": "",
+    "title": "segmented Sanskrit corpus (proof of concept)",
+    "title_en": "segmented Sanskrit corpus (proof of concept)",
+    "title_zh": "segmented Sanskrit corpus (proof of concept)",
+    "description": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
+    "description_en": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
+    "description_zh": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
+    "authors": [
+      {
+        "name_en": "Ligeia Lugli",
+        "name_zh": "Ligeia Lugli"
+      }
+    ],
+    "keywords_en": [
+      "corpus",
+      "Sanskrit",
+      "Buddhist Sanskrit"
+    ],
+    "keywords_zh": [
+      "corpus",
+      "Sanskrit",
+      "Buddhist Sanskrit"
+    ],
+    "publication_date": 1569168000000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 15085,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.574607",
+    "cstr": "",
+    "pid": "",
+    "title": "BioExcel Deliverable 3.2 – Consultancy Proposals",
+    "title_en": "BioExcel Deliverable 3.2 – Consultancy Proposals",
+    "title_zh": "BioExcel Deliverable 3.2 – Consultancy Proposals",
+    "description": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
+    "description_en": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
+    "description_zh": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
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+    "title": "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity",
+    "title_en": "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity",
+    "title_zh": "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity",
+    "description": "This upload comprises supplementary material and data for the paper &quot;The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity&quot; Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.230521) The cleaned and group ICA resting state data in EEGLAB format.2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.",
+    "description_en": "This upload comprises supplementary material and data for the paper &quot;The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity&quot; Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.230521) The cleaned and group ICA resting state data in EEGLAB format.2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.",
+    "description_zh": "This upload comprises supplementary material and data for the paper &quot;The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity&quot; Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.230521) The cleaned and group ICA resting state data in EEGLAB format.2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.",
+    "authors": [
+      {
+        "name_en": "Albrecht Matthew",
+        "name_zh": "Albrecht Matthew"
+      },
+      {
+        "name_en": "Roberts Gareth",
+        "name_zh": "Roberts Gareth"
+      },
+      {
+        "name_en": "Price Greg",
+        "name_zh": "Price Greg"
+      },
+      {
+        "name_en": "Lee Joseph",
+        "name_zh": "Lee Joseph"
+      },
+      {
+        "name_en": "Iyyalol Rajan",
+        "name_zh": "Iyyalol Rajan"
+      },
+      {
+        "name_en": "Martin-Iverson Mathew",
+        "name_zh": "Martin-Iverson Mathew"
+      }
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+      "EEG",
+      "amphetamine",
+      "resting state",
+      "dopamine",
+      "noradrenaline",
+      "independent components analysis",
+      "connectivity",
+      "anhedonia",
+      "Bayesian",
+      "Hierarchical Modelling",
+      "Stan",
+      "orthoganlized connectivity"
+    ],
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+      "amphetamine",
+      "resting state",
+      "dopamine",
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+      "Bayesian",
+      "Hierarchical Modelling",
+      "Stan",
+      "orthoganlized connectivity"
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+    "title": "The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized",
+    "title_en": "The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized",
+    "title_zh": "The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized",
+    "description": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
+    "description_en": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
+    "description_zh": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
+    "authors": [
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+        "name_en": "Holgado Darías",
+        "name_zh": "Holgado Darías"
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+      {
+        "name_en": "Zandonai Thomas",
+        "name_zh": "Zandonai Thomas"
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+        "name_en": "Ciria Luis",
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+        "name_zh": "Sanabria Daniel"
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+      "Paracetamol",
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+      "EEG",
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+    "pid": "",
+    "title": "Data for manuscript \"Automatic detection of orientation entropy within scenes\"",
+    "title_en": "Data for manuscript \"Automatic detection of orientation entropy within scenes\"",
+    "title_zh": "Data for manuscript \"Automatic detection of orientation entropy within scenes\"",
+    "description": "Raw Neuropscan .cnt data for 15 participants with conditions and stimulus order described in the logbook for each participant.",
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+    "description_zh": "Raw Neuropscan .cnt data for 15 participants with conditions and stimulus order described in the logbook for each participant.",
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+    "title": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
+    "title_en": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
+    "title_zh": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
+    "description": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "description_en": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "description_zh": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "authors": [
+      {
+        "name_en": "Wang Zheng",
+        "name_zh": "Wang Zheng"
+      }
+    ],
+    "keywords_en": [
+      "neuroimaging",
+      "neuroanatomy",
+      "functional MRI",
+      "non-human primate brain",
+      "NHP",
+      "BIDS",
+      "PRIME-DE"
+    ],
+    "keywords_zh": [
+      "neuroimaging",
+      "neuroanatomy",
+      "functional MRI",
+      "non-human primate brain",
+      "NHP",
+      "BIDS",
+      "PRIME-DE"
+    ],
+    "publication_date": "2019-09-06T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-SA-4.0",
+    "file_size_mb": 0.0,
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+    "url": "",
+    "source": "scidb"
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+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4449634",
+    "cstr": "31253.11.10.5281/zenodo.4449634",
+    "pid": "",
+    "title": "Benchmark data for source localization validation",
+    "title_en": "Benchmark data for source localization validation",
+    "title_zh": "Benchmark data for source localization validation",
+    "description": "Template data related to the publication :La Fisca et al., A Versatile Validation Framework for ERP and Oscillatory Source Reconstruction Using FieldTrip, 2021Funded by: F.N.R.S - F.R.I.A, Belgium",
+    "description_en": "Template data related to the publication :La Fisca et al., A Versatile Validation Framework for ERP and Oscillatory Source Reconstruction Using FieldTrip, 2021Funded by: F.N.R.S - F.R.I.A, Belgium",
+    "description_zh": "Template data related to the publication :La Fisca et al., A Versatile Validation Framework for ERP and Oscillatory Source Reconstruction Using FieldTrip, 2021Funded by: F.N.R.S - F.R.I.A, Belgium",
+    "authors": [
+      {
+        "name_en": "LA FISCA Luca",
+        "name_zh": "LA FISCA Luca"
+      },
+      {
+        "name_en": "GOSSELIN Bernard",
+        "name_zh": "GOSSELIN Bernard"
+      }
+    ],
+    "keywords_en": [
+      "Validation",
+      "EEG",
+      "Source reconstruction/localization",
+      "Benchmark",
+      "Simulation",
+      "ERP",
+      "FieldTrip"
+    ],
+    "keywords_zh": [
+      "Validation",
+      "EEG",
+      "Source reconstruction/localization",
+      "Benchmark",
+      "Simulation",
+      "ERP",
+      "FieldTrip"
+    ],
+    "publication_date": "2021-01-18T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4436212",
+    "cstr": "31253.11.10.5281/zenodo.4436212",
+    "pid": "",
+    "title": "USB-to-TTL",
+    "title_en": "USB-to-TTL",
+    "title_zh": "USB-to-TTL",
+    "description": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
+    "description_en": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
+    "description_zh": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
+    "authors": [
+      {
+        "name_en": "Appelhoff, Stefan",
+        "name_zh": "Appelhoff, Stefan"
+      },
+      {
+        "name_en": " Stenner, Tristan",
+        "name_zh": " Stenner, Tristan"
+      }
+    ],
+    "keywords_en": [
+      "parallel port",
+      "arduino",
+      "teensy",
+      "trigger",
+      "event",
+      "marker",
+      "magnetoencephalography",
+      "meg",
+      "electroencephalography",
+      "eeg"
+    ],
+    "keywords_zh": [
+      "parallel port",
+      "arduino",
+      "teensy",
+      "trigger",
+      "event",
+      "marker",
+      "magnetoencephalography",
+      "meg",
+      "electroencephalography",
+      "eeg"
+    ],
+    "publication_date": 1610467200000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "visit_count": 0,
+    "url": "",
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+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.155475",
+    "cstr": "31253.11.10.5281/zenodo.155475",
+    "pid": "",
+    "title": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
+    "title_en": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
+    "title_zh": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
+    "description": "Data from study published in Plos One",
+    "description_en": "Data from study published in Plos One",
+    "description_zh": "Data from study published in Plos One",
+    "authors": [
+      {
+        "name_en": "Coutinho Eduardo",
+        "name_zh": "Coutinho Eduardo"
+      },
+      {
+        "name_en": "Gentsch Kornelia",
+        "name_zh": "Gentsch Kornelia"
+      },
+      {
+        "name_en": "van Peer Jacobeen",
+        "name_zh": "van Peer Jacobeen"
+      },
+      {
+        "name_en": "Scherer Klaus R.",
+        "name_zh": "Scherer Klaus R."
+      },
+      {
+        "name_en": "Schuller Björn W.",
+        "name_zh": "Schuller Björn W."
+      }
+    ],
+    "keywords_en": [
+      "Appraisal",
+      "EEG",
+      "EMG",
+      "Machine Learning",
+      "Classification"
+    ],
+    "keywords_zh": [
+      "Appraisal",
+      "EEG",
+      "EMG",
+      "Machine Learning",
+      "Classification"
+    ],
+    "publication_date": "2017-03-30T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
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+    "file_size_bytes": 8960,
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+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.1245217",
+    "cstr": "",
+    "pid": "",
+    "title": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
+    "title_en": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
+    "title_zh": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
+    "description": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
+    "description_en": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
+    "description_zh": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
+    "authors": [
+      {
+        "name_en": "Elahian Bahareh",
+        "name_zh": "Elahian Bahareh"
+      },
+      {
+        "name_en": "Lado Nathan",
+        "name_zh": "Lado Nathan"
+      },
+      {
+        "name_en": "Misra Amrit",
+        "name_zh": "Misra Amrit"
+      },
+      {
+        "name_en": "Moxon Karen",
+        "name_zh": "Moxon Karen"
+      },
+      {
+        "name_en": "Fried Itzhak",
+        "name_zh": "Fried Itzhak"
+      },
+      {
+        "name_en": "Sharan Ashwini",
+        "name_zh": "Sharan Ashwini"
+      },
+      {
+        "name_en": "Yeasin Mohammed",
+        "name_zh": "Yeasin Mohammed"
+      },
+      {
+        "name_en": "Staba Richard",
+        "name_zh": "Staba Richard"
+      },
+      {
+        "name_en": "Bragin Anatol",
+        "name_zh": "Bragin Anatol"
+      },
+      {
+        "name_en": "Avoli Massimo",
+        "name_zh": "Avoli Massimo"
+      },
+      {
+        "name_en": "Sperling Michael",
+        "name_zh": "Sperling Michael"
+      },
+      {
+        "name_en": "Weiss Shennan",
+        "name_zh": "Weiss Shennan"
+      }
+    ],
+    "keywords_en": [
+      "eeg",
+      "lfp",
+      "microelectrode",
+      "seizure"
+    ],
+    "keywords_zh": [
+      "eeg",
+      "lfp",
+      "microelectrode",
+      "seizure"
+    ],
+    "publication_date": "2017-07-28T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 1740,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "60a670fab7734d4445783374",
+    "doi": "10.7272/q6c53j39",
+    "cstr": "",
+    "pid": "",
+    "title": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
+    "title_en": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
+    "title_zh": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
+    "description": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
+    "description_en": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
+    "description_zh": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
+    "authors": [
+      {
+        "name_en": "Poskanzer Kira",
+        "name_zh": "Poskanzer Kira"
+      },
+      {
+        "name_en": "Vaidyanathan Trisha",
+        "name_zh": "Vaidyanathan Trisha"
+      }
+    ],
+    "keywords_en": [
+      "astrocyte",
+      "calcium",
+      "Sleep duration",
+      "slow-wave sleep (SWS)",
+      "non-REM sleep",
+      "two-photon imaging",
+      "LFP",
+      "Electroencephalogram (EEG)",
+      "chemogenetics",
+      "automated image analysis",
+      "electrophysiology",
+      "delta band",
+      "slow oscillations",
+      "Cerebral Cortex"
+    ],
+    "keywords_zh": [
+      "astrocyte",
+      "calcium",
+      "Sleep duration",
+      "slow-wave sleep (SWS)",
+      "non-REM sleep",
+      "two-photon imaging",
+      "LFP",
+      "Electroencephalogram (EEG)",
+      "chemogenetics",
+      "automated image analysis",
+      "electrophysiology",
+      "delta band",
+      "slow oscillations",
+      "Cerebral Cortex"
+    ],
+    "publication_date": "2020-12-29T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 15344.29,
+    "file_size_bytes": 16089652387,
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+    "visit_count": 0,
+    "url": "https://www.scidb.cn/en/detail?id=60a670fab7734d4445783374",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "62bd107a70c5b75f4c7a7a89",
+    "doi": "10.6084/m9.figshare.8261948.v1",
+    "cstr": "",
+    "pid": "",
+    "title": "The influence of emotional words on predictive processing during sentence comprehension",
+    "title_en": "The influence of emotional words on predictive processing during sentence comprehension",
+    "title_zh": "The influence of emotional words on predictive processing during sentence comprehension",
+    "description": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
+    "description_en": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
+    "description_zh": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
+    "authors": [
+      {
+        "name_en": "Ding Jinfeng",
+        "name_zh": "Ding Jinfeng",
+        "email": "dingjf@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "ror_id": "https://ror.org/03j7v5j15"
+          },
+          {
+            "name_en": "Department of psychology, University of Chinese Academy of Sciences",
+            "name_zh": "Department of psychology, University of Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/05qbk4x57"
+          }
+        ]
+      },
+      {
+        "name_en": "Wang Lin",
+        "name_zh": "Wang Lin",
+        "email": "wanglinsisi@gmail.com",
+        "affiliations": [
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "ror_id": "https://ror.org/03j7v5j15"
+          },
+          {
+            "name_en": "Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School",
+            "name_zh": "Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School"
+          }
+        ]
+      },
+      {
+        "name_en": "Yang Yufang",
+        "name_zh": "Yang Yufang",
+        "email": "yangyf@psych.ac.cn",
+        "affiliations": [
+          {
+            "name_en": "Department of psychology, University of Chinese Academy of Sciences",
+            "name_zh": "Department of psychology, University of Chinese Academy of Sciences",
+            "ror_id": "https://ror.org/05qbk4x57"
+          },
+          {
+            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
+            "ror_id": "https://ror.org/03j7v5j15"
+          },
+          {
+            "name_en": "Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University",
+            "name_zh": "Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University"
+          }
+        ]
+      }
+    ],
+    "keywords_en": [
+      "Emotional words",
+      "prediction",
+      "sentence comprehension",
+      "EEG"
+    ],
+    "keywords_zh": [
+      "Emotional words",
+      "prediction",
+      "sentence comprehension",
+      "EEG"
+    ],
+    "publication_date": "2019-06-12T00:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 3240.39,
+    "file_size_bytes": 3397796901,
+    "download_count": 4,
+    "visit_count": 21,
+    "url": "https://www.scidb.cn/en/detail?id=62bd107a70c5b75f4c7a7a89",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3402457",
+    "cstr": "",
+    "pid": "",
+    "title": "Macaca mulatta structural and diffusion weighted MRI data",
+    "title_en": "Macaca mulatta structural and diffusion weighted MRI data",
+    "title_zh": "Macaca mulatta structural and diffusion weighted MRI data",
+    "description": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "description_en": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "description_zh": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
+    "authors": [
+      {
+        "name_en": "Brochier Thomas",
+        "name_zh": "Brochier Thomas"
+      },
+      {
+        "name_en": "Belin Pascal",
+        "name_zh": "Belin Pascal"
+      },
+      {
+        "name_en": "Chavanne Frédéric",
+        "name_zh": "Chavanne Frédéric"
+      },
+      {
+        "name_en": "Velly Lionel",
+        "name_zh": "Velly Lionel"
+      },
+      {
+        "name_en": "Bodin Clémentine",
+        "name_zh": "Bodin Clémentine"
+      },
+      {
+        "name_en": "Renaud Luc",
+        "name_zh": "Renaud Luc"
+      },
+      {
+        "name_en": "Martin Marc",
+        "name_zh": "Martin Marc"
+      },
+      {
+        "name_en": "Boes Laurence",
+        "name_zh": "Boes Laurence"
+      },
+      {
+        "name_en": "Sein Julien",
+        "name_zh": "Sein Julien"
+      },
+      {
+        "name_en": "Nazarian Bruno",
+        "name_zh": "Nazarian Bruno"
+      },
+      {
+        "name_en": "Anton Jean-Luc",
+        "name_zh": "Anton Jean-Luc"
+      }
+    ],
+    "keywords_en": [
+      "neuroimaging",
+      "neuroanatomy",
+      "diffusion weighted MRI",
+      "non-human primate brain",
+      "NHP",
+      "BIDS",
+      "PRIME-DE"
+    ],
+    "keywords_zh": [
+      "neuroimaging",
+      "neuroanatomy",
+      "diffusion weighted MRI",
+      "non-human primate brain",
+      "NHP",
+      "BIDS",
+      "PRIME-DE"
+    ],
+    "publication_date": "2019-09-07T16:00:00.000Z",
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-SA-4.0",
+    "file_size_mb": 0.02,
+    "file_size_bytes": 20849,
+    "download_count": 0,
+    "visit_count": 2,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3800156",
+    "cstr": "",
+    "pid": "",
+    "title": "Vedalia Beetles and Cyanide",
+    "title_en": "Vedalia Beetles and Cyanide",
+    "title_zh": "Vedalia Beetles and Cyanide",
+    "description": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
+    "description_en": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
+    "description_zh": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
+    "authors": [
+      {
+        "name_en": "Evenhuis, Neal L.",
+        "name_zh": "Evenhuis, Neal L."
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Coleoptera",
+      "Coccinellidae",
+      "Vedalia"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Coleoptera",
+      "Coccinellidae",
+      "Vedalia"
+    ],
+    "publication_date": 1518969600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "notspecified",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 8617,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3792094",
+    "cstr": "31253.11.10.5281/zenodo.3792094",
+    "pid": "",
+    "title": "Alburnoides L.H.Jeitteles 1861",
+    "title_en": "Alburnoides L.H.Jeitteles 1861",
+    "title_zh": "Alburnoides L.H.Jeitteles 1861",
+    "description": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
+    "description_en": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
+    "description_zh": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
+    "authors": [
+      {
+        "name_en": "Coad, Brian",
+        "name_zh": "Coad, Brian"
+      },
+      {
+        "name_en": " Bogutskaya, Nina",
+        "name_zh": " Bogutskaya, Nina"
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Chordata",
+      "Actinopterygii",
+      "Cypriniformes",
+      "Cyprinidae",
+      "Alburnoides"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Chordata",
+      "Actinopterygii",
+      "Cypriniformes",
+      "Cyprinidae",
+      "Alburnoides"
+    ],
+    "publication_date": 1246550400000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC0",
+    "file_size_mb": 0.0,
+    "file_size_bytes": 3487,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4304894",
+    "cstr": "31253.11.10.5281/zenodo.4304894",
+    "pid": "",
+    "title": "Wages and Work Survey 2020 Bangladesh - dataset",
+    "title_en": "Wages and Work Survey 2020 Bangladesh - dataset",
+    "title_zh": "Wages and Work Survey 2020 Bangladesh - dataset",
+    "description": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
+    "description_en": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
+    "description_zh": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
+    "authors": [
+      {
+        "name_en": "Kea Tijdens",
+        "name_zh": "Kea Tijdens"
+      }
+    ],
+    "keywords_en": [
+      "MINIMUM WAGES",
+      "Living wages",
+      "wages",
+      "people working in construction",
+      " tea estates or gardens",
+      " ready made garment",
+      " leather and footwear",
+      "face-to-face surveys with 1894 workers",
+      "Bangladesh"
+    ],
+    "keywords_zh": [
+      "MINIMUM WAGES",
+      "Living wages",
+      "wages",
+      "people working in construction",
+      " tea estates or gardens",
+      " ready made garment",
+      " leather and footwear",
+      "face-to-face surveys with 1894 workers",
+      "Bangladesh"
+    ],
+    "publication_date": 1606924800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 5.58,
+    "file_size_bytes": 5855405,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.3664826",
+    "cstr": "",
+    "pid": "",
+    "title": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
+    "title_en": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
+    "title_zh": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
+    "description": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
+    "description_en": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
+    "description_zh": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
+    "authors": [
+      {
+        "name_en": "Perrichot, Vincent",
+        "name_zh": "Perrichot, Vincent"
+      },
+      {
+        "name_en": " Wang, Bo",
+        "name_zh": " Wang, Bo"
+      },
+      {
+        "name_en": " Barden, Phillip",
+        "name_zh": " Barden, Phillip"
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Hymenoptera",
+      "Formicidae",
+      "Dhagnathos",
+      "Dhagnathos autokrator"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Hymenoptera",
+      "Formicidae",
+      "Dhagnathos",
+      "Dhagnathos autokrator"
+    ],
+    "publication_date": 1578585600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "notspecified",
+    "file_size_mb": 0.01,
+    "file_size_bytes": 9071,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4541458",
+    "cstr": "31253.11.10.5281/zenodo.4541458",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- 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▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- 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▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- 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U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1613318400000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.49,
+    "file_size_bytes": 681042546,
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+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4543003",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- 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▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- 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▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- 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▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- 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▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- 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▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- 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▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- 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▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1613404800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.47,
+    "file_size_bytes": 681023804,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4604011",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- 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▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- 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▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- 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▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- 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▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- 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▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- 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▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- 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▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1615737600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.51,
+    "file_size_bytes": 681064188,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4541452",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- 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▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- 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▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- 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▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- 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▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- 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▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- 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▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- 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▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1613318400000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.49,
+    "file_size_bytes": 681041550,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4604066",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- 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▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- 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▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- 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▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- 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▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1615737600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.51,
+    "file_size_bytes": 681064195,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4543018",
+    "cstr": "31253.11.10.5281/zenodo.4543018",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- 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▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- 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▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- 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▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- 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▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1613404800000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 649.49,
+    "file_size_bytes": 681042578,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4585558",
+    "cstr": "31253.11.10.5281/zenodo.4585558",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
+    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
+    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
+    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- ▁Technology- ▁payroll- ▁radio- ▁Washington- ▁flu- ▁affiliate- ▁concession- ▁predominantly- ▁reflective- ▁technological- ▁quicker- ▁amongst- ▁row- ▁Conference- ▁Southeast- ▁parent- grade- ▁differential- ▁Department- ▁attrition- ▁pause- water- ▁membership- ▁Litigation- ▁regulated- ▁personalized- ▁prescription- ▁cautionary- ▁handful- ▁tie- ▁El- ▁Carolina- ▁corner- ▁lever- ▁poor- ▁7%- ▁scheme- ▁ease- ▁sizable- ▁enroll- ▁fell- ▁carried- ▁chosen- ▁meantime- ▁hitting- ▁database- ▁Port- ▁Corporate- ▁balancing- ▁delever- ▁payout- ▁broadband- ▁official- ▁Che- ▁swap- ▁vote- ▁Water- ▁hu- ▁issuance- ▁prospect- ▁Furthermore- ▁military- ▁shrink- ility- ▁Enterprise- ▁controlling- ▁intellectual- ▁alluded- ▁endpoint- ▁carbon- ▁inject- ▁materialize- ▁Year- ▁bus- ▁Should- ivity- ▁definitive- ▁migrate- ▁differentiator- ▁United- ▁discover- ▁lumpy- ▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- ▁modification- ▁outdoor- ▁foresee- ▁articulate- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁Valley- ▁Virginia- ▁possibilities- ▁flood- ▁temperature- ▁alter- current- ▁experiment- ▁restrictions- ▁retire- ▁accuracy- ▁analyzing- ▁aluminum- ▁unprecedented- ▁expire- ▁glad- ▁powder- ▁spike- ▁tomorrow- growth- ▁avenue- ▁guidelines- ▁anymore- ▁eliminating- ▁telecom- ▁poised- ▁resort- ▁Hawaii- ▁likelihood- ▁simplification- ▁Engine- ▁gift- ▁correlation- ▁qualification- ▁redesign- ▁aspiration- ▁triple- ▁chunk- q- ▁underwrite- ▁Colombia- ▁redemption- ▁snow- ▁pursuit- ▁apparent- ▁consent- ▁Adjust- ▁candidate- ▁Program- ▁foremost- ▁Demand- ▁outpace- ▁replicate- ▁saving- ▁Office- ▁Healthcare- efficient- ▁Earlier- ▁Ontario- ▁interconnect- ▁plate- ▁Strong- ▁doubling- ▁analyst- ▁plastic- ▁Brad- ▁Friday- ▁worried- ▁proving- ▁contrast- ▁Whether- ▁1995- ▁Credit- ▁treasury- ▁institution- contract- ▁tissue- ▁Technologies- ▁glass- ▁refund- ▁additive- ▁oncology- ▁regulator- ▁expiration- ▁forefront- ▁embark- ▁IFRS- ▁imaging- ▁coffee- ▁default- ▁placing- ▁entrepreneur- ▁fluctuate- plus- ▁Dallas- ▁consensus- ▁flagship- ▁fragmented- ▁generator- ▁ERP- ▁Executive- ▁hike- ▁Control- ▁advertise- ▁choosing- ▁coast- ▁Sweden- ▁judge- ▁Development- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Together- ▁conviction- ▁click- ▁reaffirm- ▁children- ▁venue- ▁stories- ▁decreasing- ▁heightened- ▁lumber- ▁Remember- ▁tactical- ▁worst- patient- ▁trouble- ▁accrue- ▁caught- ▁unsecured- ▁unmet- ▁Chairman- ▁pronounced- ▁music- ▁Ohio- ▁statistics- ▁uptake- ▁chronic- ▁Access- ▁harm- ▁Micro- ▁debate- ▁quantity- ▁River- ▁initiate- ▁severity- ▁diminish- ▁routine- ▁slip- ▁Unfortunately- ▁mechanical- ▁mistake- ▁ambitious- ▁lens- ▁probability- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁Microsoft- ▁Safety- ▁dispute- ▁communicating- ▁grocery- ▁simplified- ▁Across- ▁Public- ▁spirit- ▁Meeting- ▁grateful- ▁NIM- ▁studio- ▁Facebook- ▁residual- ▁banner- ▁bolt- ▁requiring- ▁Jason- ▁Gross- scrip- ▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- 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▁Private- ▁indicator- ▁3%- forward- ▁Amazon- ▁incredible- ▁older- ▁anybody- ▁deploying- ▁permanent- ▁clinic- ▁servicing- ▁wonderful- ▁Following- ▁TV- ▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- 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▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- 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▁IFRS- ▁imaging- ▁coffee- ▁default- ▁placing- ▁entrepreneur- ▁fluctuate- plus- ▁Dallas- ▁consensus- ▁flagship- ▁fragmented- ▁generator- ▁ERP- ▁Executive- ▁hike- ▁Control- ▁advertise- ▁choosing- ▁coast- ▁Sweden- ▁judge- ▁Development- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Together- ▁conviction- ▁click- ▁reaffirm- ▁children- ▁venue- ▁stories- ▁decreasing- ▁heightened- ▁lumber- ▁Remember- ▁tactical- ▁worst- patient- ▁trouble- ▁accrue- ▁caught- ▁unsecured- ▁unmet- ▁Chairman- ▁pronounced- ▁music- ▁Ohio- ▁statistics- ▁uptake- ▁chronic- ▁Access- ▁harm- ▁Micro- ▁debate- ▁quantity- ▁River- ▁initiate- ▁severity- ▁diminish- ▁routine- ▁slip- ▁Unfortunately- ▁mechanical- ▁mistake- ▁ambitious- ▁lens- ▁probability- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁Microsoft- ▁Safety- ▁dispute- ▁communicating- ▁grocery- ▁simplified- ▁Across- ▁Public- ▁spirit- ▁Meeting- ▁grateful- ▁NIM- ▁studio- ▁Facebook- ▁residual- ▁banner- ▁bolt- ▁requiring- ▁Jason- ▁Gross- scrip- ▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁status- ▁vary- ▁benefiting- ▁arrangement- ▁placed- ▁Rob- ▁Pacific- ▁principle- ▁students- ▁migration- ▁appear- ▁50%- ▁catch- ▁tried- ▁house- ▁display- ▁tied- ▁Third- ▁reinforce- app- ▁breadth- ▁Part- ▁lives- ▁storm- ▁lay- than- ▁green- ▁communications- 0,000- wide- ▁slowdown- ▁determined- ▁mechanism- ▁evaluation- ▁crude- ▁functions- ▁simplify- ▁Tim- ▁Consumer- ▁licensing- ▁person- ▁avoid- ▁preferred- ▁supplement- ▁sourcing- ▁Once- ▁milestone- ▁formula- ▁disclaim- ▁depth- ▁turned- ▁organically- ▁embedded- ▁explore- ▁instance- ▁obtain- ▁completing- ▁wrap- ▁buyback- ▁pump- ▁Going- ists- ▁Right- ▁Home- ▁updating- ▁kick- ▁surprised- ▁afford- ▁responsible- ▁incurred- ▁sharp- ▁integrating- ▁rolled- ▁filing- quarter- qua- ▁60- ▁Cha- ▁opinion- ▁promotions- ▁indicators- ▁pushing- ▁intent- ame- ▁impressive- ▁bulk- pac- ▁heart- ▁exploration- field- ▁branch- ▁fire- ▁spreads- ▁fix- ▁criteria- ▁drag- ▁Maybe- ox- house- ▁basic- ▁lose- ▁exact- ▁aggregate- date- leading- ▁concerning- ▁accomplished- 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▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- 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▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- 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▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- ▁has- ▁these- ▁so- ▁well- ▁over- ▁those- ▁now- ll- ▁time- ly- n- ▁out- ▁where- ▁into- ▁customers- ▁like- ▁first- ▁results- m- ▁But- ▁other- ▁really- ▁how- ▁if- ▁up- ▁their- ▁expect- ▁forward- ▁company- ▁than- ▁last- ▁were- ▁look- ▁your- ▁through- ▁get- ▁any- ▁call- ▁one- ▁because- ▁strong- ▁In- ▁had- ▁believe- ▁sales- ▁when- ▁good- ▁second- ▁This- ▁As- ▁revenue- ▁then- er- ▁lot- ▁make- ▁performance- ▁product- ▁cost- ▁much- y- ▁financial- ▁capital- ▁value- ▁Our- ▁could- ▁years- ▁right- ▁impact- ▁terms- ▁want- ▁future- ▁don- ▁both- ▁It- ▁them- ▁today- ▁work- ▁little- ▁back- ▁customer- ▁next- ▁bit- ▁increase- al- ▁end- ▁take- ▁products- ▁number- ▁kind- ▁higher- ▁opportunities- ▁half- ▁able- ▁way- ▁markets- ▁may- ▁know- ▁significant- ▁operating- ▁better- ▁go- ▁focus- ▁say- ▁cash- ▁2- ▁part- ▁things- ▁still- ▁point- ▁provide- ▁most- ▁statements- ▁lower- ▁should- ▁team- ▁being- in- c- ▁need- ▁across- ▁made- ▁opportunity- ▁line- ▁important- ▁down- ▁third- e- ▁rate- ▁- ▁during- ▁long- ▁people- ar- ▁re- ▁fourth- ▁strategy- ▁portfolio- ▁did- ▁many- ▁continued- ▁industry- ▁level- ▁price- ▁progress- ▁around- ▁A- p- r- ▁working- ▁share- ▁management- ▁me- ▁demand- ▁investment- ▁no- ▁due- ▁additional- ▁give- ▁actually- ▁come- ▁high- ▁great- ▁while- ▁only- ▁even- ▁seeing- ▁plan- ▁few- ▁margin- ▁same- ▁looking- ▁here- ▁S- ▁drive- ▁development- ▁start- ▁grow- ▁result- ▁costs- ▁different- ▁current- le- ▁seen- ▁change- ▁doing- ▁further- ▁3- ▁coming- ation- ▁service- ▁who- ▁guidance- ▁positive- ▁data- ▁earnings- o- ▁position- ▁C- ▁process- ▁technology- ▁key- ▁turn- ▁full- g- looking- ▁past- ▁investments- ▁That- ▁said- ▁support- ▁improve- ▁overall- S- ▁basis- ▁environment- ▁expected- or- ▁balance- ▁question- u- ▁increased- ▁again- ▁move- ▁focused- ▁help- ▁within- ▁sort- 'on'- ▁got- ur- ▁potential- ▁services- ▁before- 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▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- 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▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- ▁modification- ▁outdoor- ▁foresee- ▁articulate- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁Valley- ▁Virginia- ▁possibilities- ▁flood- 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▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- ▁has- ▁these- ▁so- ▁well- ▁over- ▁those- ▁now- ll- ▁time- ly- n- ▁out- ▁where- ▁into- ▁customers- ▁like- ▁first- ▁results- m- ▁But- ▁other- ▁really- ▁how- ▁if- ▁up- ▁their- ▁expect- ▁forward- ▁company- ▁than- ▁last- ▁were- ▁look- ▁your- ▁through- ▁get- ▁any- ▁call- ▁one- ▁because- ▁strong- ▁In- ▁had- ▁believe- ▁sales- ▁when- ▁good- ▁second- ▁This- ▁As- ▁revenue- ▁then- er- ▁lot- ▁make- ▁performance- ▁product- ▁cost- ▁much- y- ▁financial- ▁capital- ▁value- ▁Our- ▁could- ▁years- ▁right- ▁impact- ▁terms- ▁want- ▁future- ▁don- ▁both- ▁It- ▁them- ▁today- ▁work- ▁little- ▁back- ▁customer- ▁next- ▁bit- ▁increase- al- ▁end- ▁take- ▁products- ▁number- ▁kind- ▁higher- ▁opportunities- ▁half- ▁able- ▁way- ▁markets- ▁may- ▁know- ▁significant- ▁operating- ▁better- ▁go- ▁focus- ▁say- ▁cash- ▁2- ▁part- ▁things- ▁still- ▁point- ▁provide- 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▁science- ▁progressing- ▁enrollment- ▁shut- ▁possibility- ▁feature- ▁describe- 5%- ▁relating- ▁possibly- ▁Op- ▁finished- ▁fruit- ▁released- ▁tracking- ▁Life- ▁status- ▁vary- ▁benefiting- ▁arrangement- ▁placed- ▁Rob- ▁Pacific- ▁principle- ▁students- ▁migration- ▁appear- ▁50%- ▁catch- ▁tried- ▁house- ▁display- ▁tied- ▁Third- ▁reinforce- app- ▁breadth- ▁Part- ▁lives- ▁storm- ▁lay- than- ▁green- ▁communications- 0,000- wide- ▁slowdown- ▁determined- ▁mechanism- ▁evaluation- ▁crude- ▁functions- ▁simplify- ▁Tim- ▁Consumer- ▁licensing- ▁person- ▁avoid- ▁preferred- ▁supplement- ▁sourcing- ▁Once- ▁milestone- ▁formula- ▁disclaim- ▁depth- ▁turned- ▁organically- ▁embedded- ▁explore- ▁instance- ▁obtain- ▁completing- ▁wrap- ▁buyback- ▁pump- ▁Going- ists- ▁Right- ▁Home- ▁updating- ▁kick- ▁surprised- ▁afford- ▁responsible- ▁incurred- ▁sharp- ▁integrating- ▁rolled- ▁filing- quarter- qua- ▁60- ▁Cha- ▁opinion- ▁promotions- ▁indicators- ▁pushing- ▁intent- ame- ▁impressive- ▁bulk- pac- ▁heart- ▁exploration- field- ▁branch- ▁fire- ▁spreads- ▁fix- ▁criteria- ▁drag- ▁Maybe- ox- house- ▁basic- ▁lose- ▁exact- ▁aggregate- date- leading- ▁concerning- ▁accomplished- ▁17- ▁pattern- ▁renewable- ▁feed- ▁dealing- ▁Houston- ▁promote- ▁divisions- ▁percent- ▁gold- ▁regulations- MS- ▁patent- ▁split- ▁differently- ▁Americas- ▁patterns- ▁school- ▁terminal- istic- ▁weighted- ▁spectrum- ▁sponsor- ▁FX- ▁unchanged- ▁expert- ▁0- ▁seeking- ▁member- ▁comparisons- ▁unfavorable- ▁churn- ▁absorb- ▁combine- ▁overhead- matic- ▁creative- ▁join- ▁serious- ▁lock- ▁formal- ▁stress- ▁participants- ▁guest- ▁Jeff- ▁transparency- ▁cat- ▁reliable- ▁estimated- ▁via- ▁purposes- ▁acquiring- ▁appears- ju- ▁calculation- ▁inherent- ▁institutions- ▁analysts- ▁offshore- ▁unlock- ▁elect- ▁located- ▁resolution- ▁minimal- ▁Dan- ▁relation- ▁easily- ▁softness- ral- ▁entering- ▁dose- ▁gather- ▁op- ▁rely- ▁Commercial- ▁square- ▁plenty- ▁impairment- ▁FDA- ▁Sea- ▁CEO- ▁functionality- ▁revised- ▁monthly- ER- ▁disclosed- ▁analyze- ▁raising- ▁sustainability- ▁Importantly- ▁Tri- ▁written- work- ▁Where- ▁someone- ▁port- ▁complement- ▁Trans- ▁entirely- ▁Phase- ▁Industrial- ▁micro- ▁minute- like- ▁thoughtful- ▁fluctuation- ▁partly- RA- ▁considerable- ▁diversity- ▁smooth- ▁persist- ▁prepare- ▁producers- ▁19- ▁AS- ▁reverse- ▁stabilize- ▁fine- ▁eventually- ▁ran- ▁save- ism- ▁Customer- ▁airline- ▁effectiveness- ▁colleagues- ▁self- ▁Pe- ▁realization- ▁administrative- ▁maturity- ▁doubt- ▁tenant- ▁introducing- ▁regulators- ▁straight- ax- ▁recruit- ▁wonder- ▁mass- ▁solar- ▁rollout- ▁Uni- ▁Cor- ▁remove- TA- ▁exception- ▁capable- ▁frequency- ▁borrowing- ▁acreage- ▁architecture- ▁rule- ▁Bill- ▁th- ▁sitting- ▁wealth- ▁multiyear- ▁Sales- ▁knew- ▁published- ▁Man- ▁Hu- ▁election- ▁talented- ▁according- PA- ▁proposed- ▁distinct- ▁consulting- ▁retailer- ▁globe- ▁Under- ▁tighten- ▁refinancing- ▁Pu- ▁thousands- ▁consequence- ▁interaction- ▁Inter- state- ▁seasonally- ▁communicate- ▁park- ▁Matt- ▁characterize- ▁dramatic- ▁write- ▁man- ▁promotion- ▁Private- ▁indicator- ▁3%- forward- ▁Amazon- ▁incredible- ▁older- ▁anybody- ▁deploying- ▁permanent- ▁clinic- ▁servicing- ▁wonderful- ▁Following- ▁TV- ▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- ▁Technology- ▁payroll- ▁radio- ▁Washington- ▁flu- ▁affiliate- ▁concession- ▁predominantly- ▁reflective- ▁technological- ▁quicker- ▁amongst- ▁row- ▁Conference- ▁Southeast- ▁parent- grade- ▁differential- ▁Department- ▁attrition- ▁pause- water- ▁membership- ▁Litigation- ▁regulated- ▁personalized- ▁prescription- ▁cautionary- ▁handful- ▁tie- ▁El- ▁Carolina- ▁corner- ▁lever- ▁poor- ▁7%- ▁scheme- ▁ease- ▁sizable- ▁enroll- ▁fell- ▁carried- ▁chosen- ▁meantime- ▁hitting- ▁database- ▁Port- ▁Corporate- ▁balancing- ▁delever- ▁payout- ▁broadband- ▁official- ▁Che- ▁swap- ▁vote- ▁Water- ▁hu- ▁issuance- ▁prospect- ▁Furthermore- ▁military- ▁shrink- ility- ▁Enterprise- ▁controlling- ▁intellectual- ▁alluded- ▁endpoint- ▁carbon- ▁inject- ▁materialize- ▁Year- ▁bus- ▁Should- ivity- ▁definitive- ▁migrate- ▁differentiator- ▁United- ▁discover- ▁lumpy- ▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- 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+    "authors": [
+      {
+        "name_en": "Shinji Watanabe",
+        "name_zh": "Shinji Watanabe"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1614873600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-4.0",
+    "file_size_mb": 771.91,
+    "file_size_bytes": 809409849,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4541465",
+    "cstr": "31253.11.10.5281/zenodo.4541465",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
+    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- 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▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- 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▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- 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SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
+    "authors": [
+      {
+        "name_en": "kamo-naoyuki",
+        "name_zh": "kamo-naoyuki"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
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+      "speech-translation",
+      "machine-translation"
+    ],
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+    "status": "",
+    "license": "CC BY 4.0",
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+    "doi": "10.5281/zenodo.4585546",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
+    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
+    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
+    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 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▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁success- ▁system- ▁top- ▁quality- ▁obviously- ▁between- ▁operational- ▁review- ▁far- ▁efforts- ion- ▁area- ▁shareholders- ▁prior- ▁certainly- ▁group- ▁pipeline- ▁todays- ▁return- ▁range- ▁whether- ar- ▁understand- ▁expenses- ▁continuing- ▁trying- ▁primarily- ▁amount- ch- ▁retail- l- ▁low- ur- ▁questions- ▁taking- ▁2017- ▁inventory- ▁4- ▁consumer- u- ▁outlook- ▁set- able- ▁total- ▁5- ▁presentation- ▁improved- ▁ahead- ▁contract- en- ▁since- ▁p- ▁structure- ▁expense- ▁major- ▁clear- ▁fiscal- ▁profitability- ▁approach- ▁add- ▁confident- ▁risks- it- ▁timing- ▁capabilities- ▁expand- ▁distribution- ▁companys- ▁increasing- ▁materially- ▁own- ▁partners- ▁expansion- ▁revenues- ▁invest- ▁however- ▁specific- ▁longterm- ▁youve- ▁solid- ▁hard- ▁ago- ▁2018- ▁space- ▁benefits- ▁driving- ▁excited- ▁keep- ▁acquisitions- ▁plans- ▁talked- ▁increases- ▁differ- ▁small- ▁strength- ▁china- ▁particularly- ri- ▁sheet- ▁consistent- ▁begin- ▁perspective- x- ▁bring- ▁asset- ▁10- ra- li- ▁stores- ▁network- ▁debt- ▁1- ▁compared- ▁open- ▁sense- at- ▁f- ▁per- ▁un- ▁patients- ▁close- ▁control- ▁improving- ▁versus- ▁programs- 'on'- ▁average- ▁measures- ▁beginning- ▁numbers- ▁run- ne- ▁currently- ▁care- ▁needs- ▁significantly- ▁2016- ▁trends- ▁create- ▁innovation- ▁scale- ▁conditions- ▁please- ▁starting- ▁okay- ▁together- ▁tell- ▁trend- th- ▁decline- ▁energy- ▁advantage- ▁points- ll- ▁anything- ▁organization- ▁volumes- ▁detail- ▁whats- ▁throughout- ▁transaction- ▁difficult- ma- an- ▁brands- ▁pay- ▁execution- ▁include- ▁profit- ▁towards- ▁deal- ta- ▁e- ▁health- ▁gas- ▁cause- ate- ment- ▁material- ▁similar- ▁access- ▁later- ▁larger- ▁greater- ▁reduce- ▁efficiency- ▁remains- ▁spend- ▁store- ▁organic- ▁international- ▁systems- ▁yet- ▁uncertaint- ▁record- ▁events- ▁comments- il- ▁power- ▁example- ▁ongoing- ▁note- ▁fully- ▁investors- ▁longer- ▁teams- ▁6- ▁successful- ▁g- ▁oil- ▁direct- ▁announced- ▁morning- ck- ▁board- ▁infrastructure- ▁everyone- ▁sell- ▁find- ▁returns- ▁discuss- 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▁relentless- ▁scaling- ▁spite- ▁argentina- ▁email- ▁instore- app- ▁mac- ▁lifestyle- ▁therapies- ▁throughput- ▁cfo- ▁foreseeable- ▁observed- ▁fifth- ▁resolve- ▁mineral- ▁smartphone- ▁deduct- ▁municipal- ▁duty- ▁hurdle- oriented- ▁manufacturer- ▁diesel- run- ▁fell- ▁sensitivity- ▁tie- cular- ▁battery- brand- ▁passenger- ▁inflationary- ▁google- ▁investigation- bl- ▁disruptive- ▁luxury- ▁approaches- ▁delaware- ▁barrier- ▁31- ▁variation- ▁correctly- ▁observation- ▁securing- ▁ken- ▁interpret- ution- ▁nation- ▁recruit- ▁profitably- print- ▁underground- ▁protocol- ▁delighted- ▁identity- ▁noticed- logic- ▁advancement- ▁constrained- ▁strike- ▁derive- ▁casino- ▁remote- ▁submission- ▁rain- ▁bankers- ▁suppose- ▁explanation- ▁hyper- ▁causing- ▁compute- ▁scrap- ▁irr- focused- cast- ▁downside- ▁refinance- ▁prepayment- ▁expenditure- ▁gradual- ▁slowly- order- ▁peer- ▁simultaneously- ▁interface- ▁favorably- ▁considerably- ▁worldclass- ▁comprise- ▁tumor- bility- ▁responsive- ▁commerce- ▁niche- ▁movie- ▁extract- ▁stake- ▁myself- ▁flight- ▁exceptionally- ▁vessel- ▁mindful- ▁remarkable- ▁outflow- ▁shipped- qua- ▁incident- well- ▁lie- ▁conventional- ▁accommodate- ▁allocated- ▁finalized- ▁rare- ▁weekend- ▁saas- ▁embrace- ▁ample- ▁integrity- ▁monetization- ▁supplies- centric- ▁redeploy- ▁remodel- ▁disrupt- ▁nextgeneration- ▁surgery- ▁climate- book- ▁threshold- ▁normalize- ▁recommendation- ▁payable- ▁leaving- ▁parallel- ▁fulfillment- ▁enthusiasm- ▁relief- ▁delta- ▁yearonyear- ▁honor- star- ▁subsidiaries- ▁surrounding- ▁deflation- ▁singledigit- ▁consume- ▁pop- ▁innovat- ▁strides- ford- ▁thorough- ▁dozen- ▁weigh- ▁chose- ▁conscious- ▁intelligent- ▁variance- ▁preserve- ▁attend- ▁collaborative- ▁association- ▁multifamily- ▁congress- ▁controlled- ▁missed- ▁acute- ▁enthusiastic- ▁resilience- ▁everywhere- ▁resort- ▁aviation- ▁rico- ▁surprising- ▁submitted- ▁omnichannel- ▁spirit- ▁sudden- ▁avenue- ▁array- ▁density- ▁outdoor- ▁calculated- build- power- ▁specialist- ▁consolidating- ▁breakdown- ▁optionalit- ▁rigorous- ▁predictions- ▁educate- ▁anchor- ▁proper- ▁advice- ▁novel- ▁nonrecurring- ▁subsidiary- ▁protein- ▁deem- ▁peter- ▁treasury- ▁inspection- ▁impression- ▁termination- ▁outperformance- ▁acceptable- ▁negotiation- ▁consumable- ▁discrete- ▁velocity- ▁throw- ▁emea- ▁accountability- ▁struggle- ▁empower- ▁loyal- ▁strip- ▁crisis- ▁geo- wise- ▁defend- ▁vital- ▁algorithm- ▁urgency- ▁pathway- ▁reposition- ▁analytic- ▁electrical- ▁pleasure- ▁proxy- ▁failure- ▁extreme- ▁blood- ▁derivative- ▁robotic- ▁certification- ▁accident- ▁impressed- ▁discretionary- stream- ▁dimension- ▁articulat- ▁recession- ▁triple- ▁stockholders- ▁silver- ▁frequently- ▁inclusion- ▁medicaid- ▁molecule- ▁glass- ▁vegas- product- ▁authority- ▁reinvestment- ▁jv- ▁upgrading- equ- ▁spoken- ▁sku- ▁engineers- ▁eagle- ▁jersey- ▁exposed- ▁unexpected- ▁unfold- ▁agility- ▁bestinclass- ▁sweet- ▁alongside- ▁society- ▁worry- ▁comparing- ▁studio- ▁granular- ▁keen- ▁alberta- ▁breakeven- ▁christmas- ▁goforward- ▁twice- ▁workflow- ▁imaging- ▁scan- ▁perception- ▁grand- ▁authorization- ▁concrete- ▁receipt- ▁absence- ▁australian- ▁radi- ▁comparative- ▁negotiating- ▁nutrition- ▁replacing- ▁guiding- ▁stretch- ▁underscore- ▁apartment- ▁younger- ▁collateral- ▁script- ▁resume- ▁plastic- ▁turkey- ware- ▁dilution- ▁anniversary- ▁phil- ▁computing- ▁substitute- ▁river- ▁disappointing- ▁specialized- '50'- ▁determination- ▁powder- ▁oncology- ▁japanese- ▁imply- ▁rationalization- ▁finalize- ▁preclinical- ▁undertaking- ▁register- ▁hospitality- ▁thrilled- ▁valley- ▁secular- ▁music- ▁cultural- ▁holistic- ▁lowcost- ▁hurt- ▁outsourcing- ▁adult- ▁invite- ▁reorganization- ▁unified- ▁reservoir- ▁motion- ▁glad- ▁visual- ▁regime- ▁theater- ▁academic- ▁concentrate- ▁cancellation- ▁diluted- ▁inorganic- ▁deleveraging- ▁french- ▁bolster- ▁simplification- ▁writing- ▁headquarters- ▁submit- ▁resonate- ▁midwest- ▁university- ▁brokerage- plus- ▁relevance- ▁text- ▁boston- ▁practical- ▁midst- ▁max- ▁nobody- ▁intangible- ▁snow- ▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- 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'00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁success- ▁system- ▁top- ▁quality- ▁obviously- ▁between- ▁operational- ▁review- ▁far- ▁efforts- ion- ▁area- ▁shareholders- ▁prior- ▁certainly- ▁group- ▁pipeline- ▁todays- ▁return- ▁range- ▁whether- ar- ▁understand- ▁expenses- ▁continuing- ▁trying- ▁primarily- ▁amount- ch- ▁retail- l- ▁low- ur- ▁questions- ▁taking- ▁2017- ▁inventory- ▁4- ▁consumer- u- ▁outlook- ▁set- able- ▁total- ▁5- ▁presentation- ▁improved- ▁ahead- ▁contract- en- ▁since- ▁p- ▁structure- ▁expense- ▁major- ▁clear- ▁fiscal- ▁profitability- ▁approach- ▁add- ▁confident- ▁risks- it- ▁timing- ▁capabilities- ▁expand- ▁distribution- ▁companys- ▁increasing- ▁materially- ▁own- ▁partners- ▁expansion- ▁revenues- ▁invest- ▁however- ▁specific- ▁longterm- ▁youve- ▁solid- ▁hard- ▁ago- ▁2018- ▁space- ▁benefits- ▁driving- ▁excited- ▁keep- ▁acquisitions- ▁plans- ▁talked- ▁increases- ▁differ- ▁small- ▁strength- ▁china- ▁particularly- ri- ▁sheet- ▁consistent- ▁begin- ▁perspective- x- ▁bring- ▁asset- ▁10- ra- li- ▁stores- ▁network- ▁debt- ▁1- ▁compared- ▁open- ▁sense- at- ▁f- ▁per- ▁un- ▁patients- ▁close- ▁control- ▁improving- ▁versus- ▁programs- 'on'- ▁average- ▁measures- ▁beginning- ▁numbers- ▁run- ne- ▁currently- ▁care- ▁needs- ▁significantly- ▁2016- ▁trends- ▁create- ▁innovation- ▁scale- ▁conditions- ▁please- ▁starting- ▁okay- ▁together- ▁tell- ▁trend- th- ▁decline- ▁energy- ▁advantage- ▁points- ll- ▁anything- ▁organization- ▁volumes- ▁detail- ▁whats- ▁throughout- ▁transaction- ▁difficult- ma- an- ▁brands- ▁pay- ▁execution- ▁include- ▁profit- ▁towards- ▁deal- ta- ▁e- ▁health- ▁gas- ▁cause- ate- ment- ▁material- ▁similar- ▁access- ▁later- ▁larger- ▁greater- ▁reduce- ▁efficiency- ▁remains- ▁spend- ▁store- ▁organic- ▁international- ▁systems- ▁yet- ▁uncertaint- ▁record- ▁events- ▁comments- il- ▁power- ▁example- ▁ongoing- ▁note- ▁fully- ▁investors- ▁longer- ▁teams- ▁6- ▁successful- ▁g- ▁oil- ▁direct- ▁announced- ▁morning- ck- ▁board- ▁infrastructure- ▁everyone- ▁sell- ▁find- ▁returns- ▁discuss- 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▁win- ▁software- ▁facilities- ▁trade- ally- ▁pace- ve- ▁weeks- ▁loss- ▁mobile- ▁securities- ▁book- ▁h- ▁profile- ▁behind- ▁his- ▁final- ▁lets- ▁d- com- ▁smaller- ▁meeting- ▁extremely- ▁enable- ▁insurance- ▁field- ▁enough- line- ▁stable- ▁ourselves- rs- ▁allows- is- ▁rather- ▁planning- ▁relationship- ▁patient- ▁sector- ▁recovery- ▁safety- ▁happy- ▁reported- ▁sustainable- ▁operate- ant- ia- ▁individual- ▁internal- ▁categories- ▁accounts- ▁includes- ▁reach- ▁loans- ▁away- ▁assumptions- ▁running- ▁reduced- ▁contribution- ▁effort- ▁speak- ▁transactions- ▁food- ▁wondering- ness- ▁exactly- id- po- ▁achieved- ▁estate- um- ize- ▁parts- am- ▁ex- ▁metrics- ▁gaap- ▁obligation- ▁above- ▁basically- ▁offerings- ▁present- ▁factor- ▁weather- ▁applications- ▁outside- ca- ▁units- ▁faster- ▁gain- ▁partially- ▁makes- ▁appropriate- ▁study- ▁7- ▁added- ▁accounting- ▁8- ▁security- ▁efficienc- ▁land- ▁plant- ▁discussions- ▁main- ▁channels- ad- ▁received- ▁r- ▁exciting- ▁m- ▁reflects- ▁seems- ▁investor- 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▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- 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▁compensate- ▁radio- ▁traditionally- ▁cyclical- ▁steadily- ▁detect- ▁ideal- ▁applicable- ▁artificial- ▁attempt- ▁valueadded- ▁standing- ▁naturally- ▁resilient- ▁alliance- ▁reit- ▁accept- ▁treated- ▁programming- ▁concentrated- ▁marked- ▁anticipation- ▁properly- ▁cheap- ▁tailwind- ▁listeners- ▁casual- ▁furthermore- ▁functional- ▁commodities- source- ▁principal- ▁audio- ▁employment- ▁apparel- ▁entities- ▁membership- ▁augment- ▁fight- ▁fl- ▁billion- ▁concession- ▁semiconductor- ▁visible- ▁implications- ▁composition- ▁authorities- ▁mag- tric- ▁wire- ▁indirect- ▁messaging- ▁literally- ▁broadband- ▁discover- ▁resolved- ▁announcing- ▁equivalent- ▁swing- ▁handling- ▁repositioning- ▁uk- ▁pit- ▁historic- ▁economies- ▁spain- ▁monetize- ▁greg- ▁turnover- ▁transactional- ▁clo- ▁kept- ▁certainty- ▁brexit- ▁regulated- ▁italy- ▁commit- ▁discontinued- ▁separation- ▁establishing- ▁amongst- ▁beauty- ▁hired- ▁scientific- ▁territories- ▁image- ▁satisfy- ▁payroll- ▁pound- ▁redevelopment- ▁tariff- ▁cautionary- ▁parent- ▁apple- ▁military- ▁prioritize- ▁losing- ▁yourself- quarter- ▁prescription- ▁district- ▁fun- ▁technological- ▁strict- ▁adopted- ▁endpoint- ▁bullish- ▁strive- ▁reflective- ▁predominantly- ▁copper- ▁evidenced- ▁northeast- ▁scheme- ▁pharmacy- water- ▁tier- ▁quicker- ▁popular- ▁standalone- ▁attrition- ▁washington- ▁database- ▁differential- ▁personalized- ▁balancing- ▁delever- wood- ▁slowed- ▁pause- ▁official- ▁overseas- ▁inject- ▁dar- ▁union- ▁dc- ▁lever- ▁hitting- ▁fabric- ▁elsewhere- ▁precision- ▁poor- ▁prospect- ▁ease- ▁handful- ▁assumed- ▁carolina- ▁swap- ▁territory- ▁description- ▁household- ▁bla- ▁meantime- ▁intellectual- ▁mobility- ▁outlet- ▁controlling- ▁samestore- ▁sizable- ▁periodic- grade- ▁enroll- ▁chosen- ▁carried- ▁payout- ▁airport- ▁competenc- ▁referenced- ▁combining- ▁wall- service- ▁issuance- ▁receivable- ▁shrink- ▁pra- ▁industryleading- ▁proof- ▁alluded- ▁noncore- ▁realistic- ▁upstream- ▁telecom- ▁absorption- ▁appendix- ▁materialize- ▁sooner- ▁shipment- ▁collaborat- ▁definitive- ▁automated- ▁vote- ▁lifetime- ▁conducted- ▁basket- ▁lumpy- ▁differentiator- ▁migrate- ▁float- ▁capitalized- ▁cool- ▁midstream- ▁bump- ▁governance- ▁takeaway- ▁temp- ▁electricity- ▁output- ▁overcome- ▁lng- ▁scalabl- ▁validation- ▁convinced- ▁frank- ▁maximum- ▁repayment- ▁intensity- ▁exhibit- ▁hong- ▁amendment- ▁cohort- ▁kong- ▁agile- ▁disappointed- ▁surgical- ▁tele- ▁separately- ▁sim- ▁judgment- ▁undu- ▁penetrate- ▁quote- view- ▁hour- ▁domain- ▁german- ▁body- ▁consist- ▁ev- ▁methodology- ▁principally- ▁sym- ▁rank- ▁chicago- ▁relentless- ▁scaling- ▁spite- ▁argentina- ▁email- ▁instore- app- ▁mac- ▁lifestyle- ▁therapies- ▁throughput- ▁cfo- ▁foreseeable- ▁observed- ▁fifth- ▁resolve- ▁mineral- ▁smartphone- ▁deduct- ▁municipal- ▁duty- ▁hurdle- oriented- ▁manufacturer- ▁diesel- run- ▁fell- ▁sensitivity- ▁tie- cular- ▁battery- brand- ▁passenger- ▁inflationary- ▁google- ▁investigation- bl- ▁disruptive- ▁luxury- ▁approaches- ▁delaware- ▁barrier- ▁31- ▁variation- ▁correctly- ▁observation- ▁securing- ▁ken- ▁interpret- ution- ▁nation- ▁recruit- ▁profitably- print- ▁underground- ▁protocol- ▁delighted- ▁identity- ▁noticed- logic- ▁advancement- ▁constrained- ▁strike- ▁derive- ▁casino- ▁remote- ▁submission- ▁rain- ▁bankers- ▁suppose- ▁explanation- ▁hyper- ▁causing- ▁compute- ▁scrap- ▁irr- focused- cast- ▁downside- ▁refinance- ▁prepayment- ▁expenditure- ▁gradual- ▁slowly- order- ▁peer- ▁simultaneously- ▁interface- ▁favorably- ▁considerably- ▁worldclass- ▁comprise- ▁tumor- bility- ▁responsive- ▁commerce- ▁niche- ▁movie- ▁extract- ▁stake- ▁myself- ▁flight- ▁exceptionally- ▁vessel- ▁mindful- ▁remarkable- ▁outflow- ▁shipped- qua- ▁incident- well- ▁lie- ▁conventional- ▁accommodate- ▁allocated- ▁finalized- ▁rare- ▁weekend- ▁saas- ▁embrace- ▁ample- ▁integrity- ▁monetization- ▁supplies- centric- ▁redeploy- ▁remodel- ▁disrupt- ▁nextgeneration- ▁surgery- ▁climate- book- ▁threshold- ▁normalize- ▁recommendation- ▁payable- ▁leaving- ▁parallel- ▁fulfillment- ▁enthusiasm- ▁relief- ▁delta- ▁yearonyear- ▁honor- star- ▁subsidiaries- ▁surrounding- ▁deflation- ▁singledigit- ▁consume- ▁pop- ▁innovat- ▁strides- ford- ▁thorough- ▁dozen- ▁weigh- ▁chose- ▁conscious- ▁intelligent- ▁variance- ▁preserve- ▁attend- ▁collaborative- ▁association- ▁multifamily- ▁congress- ▁controlled- ▁missed- ▁acute- ▁enthusiastic- ▁resilience- ▁everywhere- ▁resort- ▁aviation- ▁rico- ▁surprising- ▁submitted- ▁omnichannel- ▁spirit- ▁sudden- ▁avenue- ▁array- ▁density- ▁outdoor- ▁calculated- build- power- ▁specialist- ▁consolidating- ▁breakdown- ▁optionalit- ▁rigorous- ▁predictions- ▁educate- ▁anchor- ▁proper- ▁advice- ▁novel- ▁nonrecurring- ▁subsidiary- ▁protein- ▁deem- ▁peter- ▁treasury- ▁inspection- ▁impression- ▁termination- ▁outperformance- ▁acceptable- ▁negotiation- ▁consumable- ▁discrete- ▁velocity- ▁throw- ▁emea- ▁accountability- ▁struggle- ▁empower- ▁loyal- ▁strip- ▁crisis- ▁geo- wise- ▁defend- ▁vital- ▁algorithm- ▁urgency- ▁pathway- ▁reposition- ▁analytic- ▁electrical- ▁pleasure- ▁proxy- ▁failure- ▁extreme- ▁blood- ▁derivative- ▁robotic- ▁certification- ▁accident- ▁impressed- ▁discretionary- stream- ▁dimension- ▁articulat- ▁recession- ▁triple- ▁stockholders- ▁silver- ▁frequently- ▁inclusion- ▁medicaid- ▁molecule- ▁glass- ▁vegas- product- ▁authority- ▁reinvestment- ▁jv- ▁upgrading- equ- ▁spoken- ▁sku- ▁engineers- ▁eagle- ▁jersey- ▁exposed- ▁unexpected- ▁unfold- ▁agility- ▁bestinclass- ▁sweet- ▁alongside- ▁society- ▁worry- ▁comparing- ▁studio- ▁granular- ▁keen- ▁alberta- ▁breakeven- ▁christmas- ▁goforward- ▁twice- ▁workflow- ▁imaging- ▁scan- ▁perception- ▁grand- ▁authorization- ▁concrete- ▁receipt- ▁absence- ▁australian- ▁radi- ▁comparative- ▁negotiating- ▁nutrition- ▁replacing- ▁guiding- ▁stretch- ▁underscore- ▁apartment- ▁younger- ▁collateral- ▁script- ▁resume- ▁plastic- ▁turkey- ware- ▁dilution- ▁anniversary- ▁phil- ▁computing- ▁substitute- ▁river- ▁disappointing- ▁specialized- '50'- ▁determination- ▁powder- ▁oncology- ▁japanese- ▁imply- ▁rationalization- ▁finalize- ▁preclinical- ▁undertaking- ▁register- ▁hospitality- ▁thrilled- ▁valley- ▁secular- ▁music- ▁cultural- ▁holistic- ▁lowcost- ▁hurt- ▁outsourcing- ▁adult- ▁invite- ▁reorganization- ▁unified- ▁reservoir- ▁motion- ▁glad- ▁visual- ▁regime- ▁theater- ▁academic- ▁concentrate- ▁cancellation- ▁diluted- ▁inorganic- ▁deleveraging- ▁french- ▁bolster- ▁simplification- ▁writing- ▁headquarters- ▁submit- ▁resonate- ▁midwest- ▁university- ▁brokerage- plus- ▁relevance- ▁text- ▁boston- ▁practical- ▁midst- ▁max- ▁nobody- ▁intangible- ▁snow- ▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- 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▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- 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▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "authors": [
+      {
+        "name_en": "Shinji Watanabe",
+        "name_zh": "Shinji Watanabe"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1614873600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-4.0",
+    "file_size_mb": 771.97,
+    "file_size_bytes": 809466935,
+    "download_count": 1,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4585552",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
+    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
+    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
+    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- 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▁counterparties- ▁festival- ▁nielsen- ▁proliferation- ▁subordinate- ▁staple- ▁thick- ▁boiler- ▁precedent- ▁timetable- ▁digging- ▁foothold- ▁contest- ▁tableau- ▁receptive- ▁hip- ▁explicitly- ▁salesforce- ▁shooting- ▁commenc- abilities- trade- ▁administrator- ▁congestion- ▁detriment- ▁episode- ▁hesitant- ▁hygiene- ▁scarcity- ▁smelter- ▁turmoil- ▁waterfall- ▁sweep- ▁standardize- ▁ranch- ▁advent- ▁smb- ▁influencer- utter- ▁95- ▁motivate- ▁accustomed- ▁chassis- ▁invaluable- ▁reestablish- ▁situated- ▁stimulus- ▁tandem- ▁volunteer- ▁prioritization- ▁compass- ▁scanner- ▁firewall- ▁blackstone- ▁crest- ▁cardio- ▁accessory- more- ▁steering- ▁cow- ▁marlin- ▁retired- ▁curate- ▁dean- ▁repo- ▁turk- ▁alternate- ▁analyses- ▁dialysis- ▁intensified- ▁penalties- ▁sensitivities- ▁worthwhile- ▁hazard- ▁remarkably- ▁blackrock- ▁agnostic- ▁cooperative- ▁lengthen- ▁steven- ▁formulate- ▁pv- ▁funeral- ▁ladder- ▁pinnacle- ▁postacute- ▁snapshot- ▁titanium- ▁brandnew- ▁shallow- ▁halo- ▁crown- zone- ▁railway- 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▁renegotiated- ▁routinely- ▁nr- ▁buyout- code- ▁redesigned- ▁flor- ▁incent- ▁grocer- process- structure- ▁destiny- ▁underpenetrated- ▁busiest- ▁forthcoming- ▁virtuous- ▁plaza- ▁racing- eye- ▁3000- ▁payoff- ▁ortho- ▁merged- ▁inhibit- ▁vista- ▁watches- ▁deducti- charge- profit- energy- wind- ▁chemotherapy- ▁entrance- ▁facilitating- ▁happier- ▁legislature- ▁timeframe- ▁vacancies- ▁bargain- ▁lobby- ▁tokyo- ▁slope- ▁cedar- ▁specify- ▁protest- ▁roster- ▁voting- ▁withdrawal- ▁reallocation- ▁standby- ▁vale- ▁earlystage- ▁redevelop- ▁hp- ▁pest- front- ▁insist- beat- ▁alabama- ▁empty- ▁endorsement- ▁examining- ▁experiential- ▁rainfall- ▁separating- ▁iridium- ▁wellbeing- ▁liber- ▁recreational- ▁connector- ▁barge- ▁highgrade- ▁designated- ▁liquidate- ▁yo- action- ▁cpg- ▁suspend- ▁genuine- active- heim- utilized- ▁sma- ▁advocacy- ▁antibodies- ▁conservation- ▁hydrocarbon- ▁mismatch- ▁oxygen- ▁refractor- ▁unfair- ▁disability- ▁downgrade- ▁cabin- iston- ▁bolt- ▁120- suite- ▁enzyme- ▁hilton- ▁offensive- ▁testimony- ▁clothing- ▁restocking- ▁beacon- ▁defect- ▁nurse- ▁tal- ▁mixture- ▁dish- ▁activat- ▁pp- ▁citi- period- ▁inroads- ▁kroger- ▁vigorously- ▁2011- ▁pushback- ▁protective- ▁traders- ▁barn- ▁midsize- ▁increment- competitive- ▁fabulous- ▁hospice- ▁nitrogen- ▁practitioner- ▁restatement- ▁robinson- ▁segregat- ▁unconventional- ▁girl- ▁pbm- ▁instill- ▁loud- ▁characteristic- ▁realistically- ▁headquarter- ▁furnished- ▁highperforming- ▁amid- ▁flagged- ▁haynes- ▁problematic- ▁scoop- ▁sovereign- ▁voyage- ▁merely- ▁pinpoint- ▁prepay- ▁dependence- ▁marry- ▁lions- ▁superiority- megawatt- ▁excite- ▁jeremy- ▁refrigerated- ▁turbo- ▁unreasonable- ▁humble- ▁infant- ▁baking- ▁divert- ▁lte- ▁sharper- ▁colder- ▁monotherap- ▁consult- ▁correspond- ▁reinvigorate- platform- producing- order- check- resistant- ▁culinary- ▁flawless- ▁inherited- ▁nontraditional- ▁remeasurement- ▁wellknown- ▁winnebago- ▁revising- ▁lagged- ▁depict- ▁juice- ▁hypothesis- ▁dwell- ▁publisher- ▁reuse- hole- ▁heck- consolidated- built- prime- ▁depletion- ▁secondarily- ▁mlp- ▁weapon- ▁swiftly- ▁softened- ▁mil- ▁rainy- ▁blast- ▁coat- ▁expose- ▁furnish- wire- ▁bacteria- ▁baseball- ▁oxide- ▁prestigious- ▁stranded- ▁vantage- ▁slice- ▁thread- ▁michelle- ▁deduct- defined- ▁farther- ▁exhibition- scope- ▁bloom- ▁destroy- ▁plumbing- ▁reconsider- ▁unleash- ▁scanning- ▁parse- ▁clay- ▁personalize- neutral- ▁frustration- ▁highspeed- ▁targa- ▁kirk- ▁rescue- ▁subsidize- ▁steal- ▁mentor- ▁harris- ▁aspirational- ▁resale- borne- ▁deter- ▁pine- transmission- ▁onprem- ▁carriage- ▁invitation- ▁peace- ▁reconciling- ▁saturday- ▁selfhelp- ▁sulfur- ▁triumph- ▁escalate- ▁viral- ▁ethical- ▁ibm- ▁cameron- ▁sizing- ▁instantly- ▁creators- prong- ▁bull- ▁reaccelerat- ▁refurbish- ▁nascar- ▁retroactive- ▁unforeseen- ▁remix- ▁coding- ▁blueprint- ▁merging- ▁roast- arity- ▁existence- ▁resist- ▁assistant- ▁shaw- ▁technique- ▁genuinely- fast- ▁mobilize- ▁victoria- ▁interventional- axis- ▁soften- ▁tile- ▁attribution- ▁blockbuster- ▁insignificant- ▁literature- ▁qualities- ▁steroid- ▁terribly- ▁copies- ▁intercept- ▁welding- ▁tricky- sky- ▁scrip- ▁tec- munications- ▁nutritional- ▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- ▁although- ▁actions- ▁without- ▁selling- ▁leadership- ▁relative- ▁channel- ▁especially- ▁resources- ▁meet- a- ▁corporate- ▁construction- ▁comes- ▁re- ▁spending- ▁possible- ▁unique- ▁means- ▁manufacturing- k- ▁direct- in- ▁incremental- ▁remind- ▁facility- ▁percentage- ▁combination- ▁size- ▁online- ▁maintain- '1'- ▁details- ▁complete- ▁employees- ▁included- ▁execute- ▁front- ▁either- ▁taken- ▁multiple- ▁content- ▁local- ▁rest- ▁show- ▁ensure- o- ▁until- ▁thinking- ▁stock- ▁challenges- ▁transition- ▁annual- ▁job- ▁loan- ▁didnt- ▁various- ▁investing- ▁address- ▁color- ▁client- ▁whole- ▁free- ▁thanks- ▁type- ▁partner- ▁negative- ▁previous- ▁reflect- x- ▁wanted- ▁leading- ▁generate- ▁cloud- ▁exchange- ▁turning- ▁productivity- ▁month- ▁marketplace- ▁single- ▁relationships- ▁short- ▁regulatory- ▁public- ▁home- ▁attractive- ▁date- ▁stronger- ▁am- ▁bank- ▁solution- ▁ive- ▁issue- ▁happen- ▁deals- ▁largest- ▁committed- ▁consumers- ▁clinical- ▁thought- ▁ways- ▁goal- ▁discussion- ▁profitable- ▁despite- ▁sale- ▁relatively- ▁following- ▁delivered- ▁shift- ▁favorable- ▁anticipated- ▁savings- ▁likely- ▁life- ▁combined- 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▁statement- ▁traffic- ▁community- ▁drivers- ▁typically- ▁ratio- ▁fleet- ▁premium- ▁substantial- ▁win- ▁capability- ▁january- ▁robust- ▁gains- ▁managing- ▁fund- ▁media- ▁creating- ▁options- ▁healthy- ▁17- ▁december- ▁tend- ▁8- ▁expectation- ▁purchase- ▁national- ▁almost- ▁processes- ▁news- ▁advertising- ▁importantly- ▁happening- w- ▁labor- ▁disconnect- ▁potentially- ▁natural- ation- ▁primary- ▁backlog- i- ▁understanding- ▁executing- ▁nature- ▁yield- ▁forecast- ▁water- ▁traditional- ▁food- ▁saying- ▁office- ▁meeting- ▁7- ▁bringing- ▁driver- ▁optimistic- ▁filings- ▁9- ▁planned- ▁limited- ▁excellent- ▁putting- ▁highlights- ▁mind- ▁effectively- ▁comfortable- ▁historical- ▁found- ▁needed- ▁cases- ▁march- ▁goes- ▁necessary- ▁research- ▁ask- ▁visibility- ▁targets- ▁million- ▁proud- ▁liquidity- ▁funding- ▁refer- ▁trade- ▁disciplined- ▁speak- ▁countries- ▁headwind- ▁main- ▁losses- ▁regions- ▁else- ▁comparable- ▁encouraged- ▁standpoint- ▁bigger- ▁land- ▁wouldnt- ▁15- ▁commission- ▁non- 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▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- 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▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- 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▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- ▁although- ▁actions- ▁without- ▁selling- ▁leadership- ▁relative- ▁channel- ▁especially- ▁resources- ▁meet- a- ▁corporate- ▁construction- ▁comes- ▁re- ▁spending- ▁possible- ▁unique- ▁means- ▁manufacturing- k- ▁direct- in- ▁incremental- ▁remind- ▁facility- ▁percentage- ▁combination- ▁size- ▁online- ▁maintain- '1'- ▁details- ▁complete- ▁employees- ▁included- ▁execute- ▁front- ▁either- ▁taken- ▁multiple- ▁content- ▁local- ▁rest- ▁show- ▁ensure- o- ▁until- ▁thinking- ▁stock- ▁challenges- ▁transition- ▁annual- ▁job- ▁loan- ▁didnt- ▁various- ▁investing- ▁address- ▁color- ▁client- ▁whole- ▁free- ▁thanks- ▁type- ▁partner- ▁negative- ▁previous- ▁reflect- x- ▁wanted- ▁leading- ▁generate- ▁cloud- ▁exchange- ▁turning- ▁productivity- ▁month- ▁marketplace- ▁single- ▁relationships- ▁short- ▁regulatory- ▁public- ▁home- ▁attractive- ▁date- ▁stronger- ▁am- ▁bank- ▁solution- ▁ive- ▁issue- ▁happen- ▁deals- ▁largest- ▁committed- ▁consumers- ▁clinical- ▁thought- ▁ways- ▁goal- ▁discussion- ▁profitable- ▁despite- ▁sale- ▁relatively- ▁following- ▁delivered- ▁shift- ▁favorable- ▁anticipated- ▁savings- ▁likely- ▁life- ▁combined- ▁2019- ▁chain- ▁days- ▁youll- ▁transformation- ▁bottom- ▁provided- ▁underlying- ▁orders- ▁ebitda- ▁lead- ▁havent- ▁prepared- ▁cant- ▁delivery- ▁gives- '2'- ▁least- ▁nongaap- ▁serve- ▁highly- ▁though- ▁category- ▁comment- ▁government- able- ▁develop- ▁flexibility- ▁experienced- ▁moment- ▁closing- ▁generally- p- ▁respect- ▁portion- ▁dividend- ▁private- ▁built- ▁industrial- ▁majority- ▁quarterly- f- ▁entire- ▁agreement- ▁generation- ▁expanding- ▁hope- ▁18- ▁upon- ▁therefore- ▁everything- ▁specifically- ▁initial- ▁challenging- ▁effective- ▁provides- ▁efficient- ▁normal- ▁play- ▁situation- ▁accelerate- ▁currency- ▁flat- ▁- ▁doesnt- ▁property- ▁buy- ▁competitors- ▁season- ▁commitment- ▁strategies- ▁critical- ▁required- ▁answer- ▁country- ▁active- ▁un- ▁design- ▁competition- ▁step- ▁largely- ▁fair- ▁technologies- ▁12- ▁weeks- ers- ▁he- ▁hand- ▁lets- ▁software- 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▁slated- ▁await- ▁steer- ▁20000- yield- script- ▁caliber- ▁devoted- ▁eighth- ▁mouth- ▁postpaid- ▁aggregation- ▁cliff- ▁responsiveness- ▁string- ▁kin- ▁jones- ▁toughest- ▁char- ▁restored- ▁poster- ▁melt- ▁illustrat- ▁insider- mitted- ▁renovat- ▁renegotiate- ▁dinner- ▁reengineer- ▁thumb- ▁taxpayer- ▁arbitrage- ▁tipping- ▁stripping- ▁tapping- ▁mood- ▁dick- hub- sphere- ▁accumulate- ▁backtoschool- ▁orthopedic- ▁outweigh- ▁suppress- ▁reclassifie- ▁interestbearing- ▁choppi- ▁jackup- ▁popularity- ▁discoveries- hawk- ▁pam- ▁reallocat- ▁vulnerable- ▁sincerely- ▁trauma- ▁fellow- ▁warner- ▁capped- ▁seven- ▁centric- ▁earnest- ▁inspect- ▁pose- petition- ▁leaseup- ▁outgrow- volt- ▁digitize- ▁dedicate- ▁discontinue- ▁depreciate- ▁lithium- ▁philosophical- ▁framing- ▁cheese- ▁vending- ▁chegg- ▁clock- ▁purposeful- ▁cleanup- ▁knowledgeable- ▁cooperate- ▁cooler- ▁compliment- ▁dispense- ▁mathematical- ▁perimeter- ▁ralph- ▁retiring- ▁utah- ▁birth- ▁remediate- ▁laserfocused- ▁iteration- ▁tenet- ▁wisely- ▁renegotiated- ▁routinely- ▁nr- ▁buyout- code- ▁redesigned- ▁flor- ▁incent- ▁grocer- process- structure- ▁destiny- ▁underpenetrated- ▁busiest- ▁forthcoming- ▁virtuous- ▁plaza- ▁racing- eye- ▁3000- ▁payoff- ▁ortho- ▁merged- ▁inhibit- ▁vista- ▁watches- ▁deducti- charge- profit- energy- wind- ▁chemotherapy- ▁entrance- ▁facilitating- ▁happier- ▁legislature- ▁timeframe- ▁vacancies- ▁bargain- ▁lobby- ▁tokyo- ▁slope- ▁cedar- ▁specify- ▁protest- ▁roster- ▁voting- ▁withdrawal- ▁reallocation- ▁standby- ▁vale- ▁earlystage- ▁redevelop- ▁hp- ▁pest- front- ▁insist- beat- ▁alabama- ▁empty- ▁endorsement- ▁examining- ▁experiential- ▁rainfall- ▁separating- ▁iridium- ▁wellbeing- ▁liber- ▁recreational- ▁connector- ▁barge- ▁highgrade- ▁designated- ▁liquidate- ▁yo- action- ▁cpg- ▁suspend- ▁genuine- active- heim- utilized- ▁sma- ▁advocacy- ▁antibodies- ▁conservation- ▁hydrocarbon- ▁mismatch- ▁oxygen- ▁refractor- ▁unfair- ▁disability- ▁downgrade- ▁cabin- iston- ▁bolt- ▁120- suite- ▁enzyme- ▁hilton- ▁offensive- ▁testimony- ▁clothing- ▁restocking- ▁beacon- ▁defect- ▁nurse- ▁tal- ▁mixture- ▁dish- ▁activat- ▁pp- ▁citi- period- ▁inroads- ▁kroger- ▁vigorously- ▁2011- ▁pushback- ▁protective- ▁traders- ▁barn- ▁midsize- ▁increment- competitive- ▁fabulous- ▁hospice- ▁nitrogen- ▁practitioner- ▁restatement- ▁robinson- ▁segregat- ▁unconventional- ▁girl- ▁pbm- ▁instill- ▁loud- ▁characteristic- ▁realistically- ▁headquarter- ▁furnished- ▁highperforming- ▁amid- ▁flagged- ▁haynes- ▁problematic- ▁scoop- ▁sovereign- ▁voyage- ▁merely- ▁pinpoint- ▁prepay- ▁dependence- ▁marry- ▁lions- ▁superiority- megawatt- ▁excite- ▁jeremy- ▁refrigerated- ▁turbo- ▁unreasonable- ▁humble- ▁infant- ▁baking- ▁divert- ▁lte- ▁sharper- ▁colder- ▁monotherap- ▁consult- ▁correspond- ▁reinvigorate- platform- producing- order- check- resistant- ▁culinary- ▁flawless- ▁inherited- ▁nontraditional- ▁remeasurement- ▁wellknown- ▁winnebago- ▁revising- ▁lagged- ▁depict- ▁juice- ▁hypothesis- ▁dwell- ▁publisher- ▁reuse- hole- ▁heck- consolidated- built- prime- ▁depletion- ▁secondarily- ▁mlp- ▁weapon- ▁swiftly- ▁softened- ▁mil- ▁rainy- ▁blast- ▁coat- ▁expose- ▁furnish- wire- ▁bacteria- ▁baseball- ▁oxide- ▁prestigious- ▁stranded- ▁vantage- ▁slice- ▁thread- ▁michelle- ▁deduct- defined- ▁farther- ▁exhibition- scope- ▁bloom- ▁destroy- ▁plumbing- ▁reconsider- ▁unleash- ▁scanning- ▁parse- ▁clay- ▁personalize- neutral- ▁frustration- ▁highspeed- ▁targa- ▁kirk- ▁rescue- ▁subsidize- ▁steal- ▁mentor- ▁harris- ▁aspirational- ▁resale- borne- ▁deter- ▁pine- transmission- ▁onprem- ▁carriage- ▁invitation- ▁peace- ▁reconciling- ▁saturday- ▁selfhelp- ▁sulfur- ▁triumph- ▁escalate- ▁viral- ▁ethical- ▁ibm- ▁cameron- ▁sizing- ▁instantly- ▁creators- prong- ▁bull- ▁reaccelerat- ▁refurbish- ▁nascar- ▁retroactive- ▁unforeseen- ▁remix- ▁coding- ▁blueprint- ▁merging- ▁roast- arity- ▁existence- ▁resist- ▁assistant- ▁shaw- ▁technique- ▁genuinely- fast- ▁mobilize- ▁victoria- ▁interventional- axis- ▁soften- ▁tile- ▁attribution- ▁blockbuster- ▁insignificant- ▁literature- ▁qualities- ▁steroid- ▁terribly- ▁copies- ▁intercept- ▁welding- ▁tricky- sky- ▁scrip- ▁tec- munications- ▁nutritional- ▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- 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▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- 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+    "authors": [
+      {
+        "name_en": "Shinji Watanabe",
+        "name_zh": "Shinji Watanabe"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1614873600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-4.0",
+    "file_size_mb": 878.73,
+    "file_size_bytes": 921414555,
+    "download_count": 0,
+    "visit_count": 1,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4590907",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
+    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
+    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
+    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- ▁time- ▁customers- ▁into- ▁where- ▁like- ▁first- ▁results- ▁out- ▁But- ▁really- ▁other- ▁how- ll- ing- ▁if- d- ▁their- ▁up- ▁forward- ▁expect- ▁company- ▁were- ▁than- ▁last- ▁your- ▁look- ▁through- ▁get- ▁any- ▁because- ▁one- ▁call- ▁strong- ▁had- ▁believe- ▁sales- ▁when- ▁This- ▁then- ▁As- ▁good- ▁second- ▁In- ▁revenue- m- ▁performance- ▁product- ▁make- ▁lot- ▁cost- n- ▁much- ed- ▁years- ▁Our- ▁could- ▁financial- ▁capital- ▁terms- ▁don- ▁future- ▁both- ▁impact- ▁right- ▁value- ▁them- ▁It- ▁today- ▁little- ▁want- ▁customer- ▁next- ▁bit- ▁back- ▁work- ▁increase- ▁products- ▁take- ▁number- ▁end- ▁higher- ▁opportunities- ▁able- ▁half- ▁markets- ▁kind- ▁way- ▁may- ▁significant- ▁know- ▁operating- ▁better- ▁go- ▁cash- ▁say- ▁things- ▁still- ▁focus- ▁most- ▁statements- ▁provide- ▁should- ▁being- ▁point- ▁part- ▁team- ▁across- ▁lower- ▁made- ▁opportunity- ▁need- ▁important- ▁third- ▁2- ▁during- ▁rate- ▁line- ▁long- 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▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- 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▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- 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▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- 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▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- 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deliver- ▁Ralph- ▁Example- ▁Utah- ▁irrigation- ▁nonaccrual- ▁suppress- ▁Underlying- ▁remediate- ▁stripping- ▁iteration- ▁sincerely- ▁Orange- path- close- measure- process- ▁Block- ▁Significant- ▁Treasury- ▁caliber- ▁compliment- ▁virtuous- ▁busiest- ▁forthcoming- track- ▁inhibit- profit- ▁reestablish- ▁Progress- ▁Apollo- ▁Tokyo- ▁chemotherapy- ▁mathematical- ▁retiring- ▁stellar- ▁sweep- ▁timeframe- ▁roster- ▁specify- ▁Ameri- ▁SIM- carbon- ▁Alabama- ▁Application- ▁Hemisphere- ▁Mineral- ▁administrator- ▁dinner- ▁empty- ▁endorsement- ▁facilitating- ▁happier- ▁vacancies- ▁voting- 7,000- ▁protest- ▁reactivate- ▁bird- ▁aspire- apples- ▁redevelop- ▁hazard- ▁Iridium- ▁Related- ▁experiential- ▁mismatch- ▁philosophical- ▁thumb- ▁Leasing- ▁deductibility- ▁Drug- ▁Wild- ▁illustrat- ▁reinvigorate- ▁sandwich- ▁Affordabl- ▁DOL- ▁Hilton- ▁advocacy- ▁antibodies- ▁chief- ▁entrance- ▁examining- ▁snapshot- ▁unfair- ▁ForEx- ▁defect- ▁mixture- ▁Getting- ▁digging- ▁Monster- ▁liquidate- ▁characteristic- period- 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▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- ▁Holiday- ▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- 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▁exiting- ▁anniversary- ▁inorganic- ▁diluted- ▁AD- ▁payer- ▁Sun- ▁submit- ▁Quarter- ▁whereas- ▁recovered- ose- ▁periodic- ▁French- ware- ▁corner- ▁cultural- ▁preclinical- ▁surgery- ▁surgeons- ▁interactions- ▁Security- ▁anchor- ▁concentrate- ▁diseases- ▁flowing- ug- ▁imports- ▁Annual- ▁Ge- ▁reward- ▁deleveraging- ▁trained- ulation- ey- ▁Midwest- ▁relevance- ▁Boston- ▁baseline- ise- TE- ▁incorporated- ▁concepts- ▁qu- ▁linear- ▁bolster- adjusted- chi- ▁excludes- ▁overlap- ▁extreme- ▁breakeven- ▁headquarters- ▁discounting- ▁valued- ▁attribute- cy- AP- ▁miss- ▁adult- ▁vital- ▁Doug- ▁hub- ▁DC- ▁wherever- ▁pockets- ▁agility- ▁12%- ▁construct- ▁Building- ▁prudently- ▁Com- ▁revolving- ▁Rock- ▁stations- ▁outsourcing- ▁intangible- ▁reservoir- ▁alongside- iti- tting- ▁Auto- OR- ▁responded- ang- ▁stabilizing- ▁noncore- ▁protecting- ▁distributions- ▁methods- ▁Gen- ▁evening- ▁m- ▁concrete- ▁geo- ▁rock- ▁representative- ▁pad- PA- core- ▁immune- ▁Blue- ▁Fin- ▁academic- ▁multifamily- ▁Mobile- ▁ideal- ▁practical- ▁Congress- ▁rewards- ock- ▁anti- ▁computing- ▁Cost- ▁procedure- ▁mandate- ▁midst- ud- ▁Results- ▁casino- ▁reversal- ▁accretion- ▁Ford- ▁serves- ▁Frank- ▁man- ▁revolver- ▁referral- ▁IPO- ▁annualized- ▁convenient- ▁doors- ▁storms- ▁willingness- ▁deterioration- ▁Che- ▁parameters- ▁Very- ▁presentations- ▁scores- ▁records- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁formation- ▁grant- ▁contractor- ▁Man- ▁uplift- ▁whenever- risk- ▁suggested- ake- OS- tech- ▁Ce- ▁responsive- ▁bankruptcy- ▁viable- ame- ▁marine- ▁visual- ▁MA- ▁contingent- AM- ▁deepen- ▁recession- ▁flu- ▁restore- ▁download- hy- ▁Cap- ▁divestiture- ▁Federal- ▁apartment- ▁reduces- ▁purely- ▁negotiated- ▁nimble- ▁suspect- ▁Har- hold- ▁distribute- ▁fight- ▁Brazilian- ▁covenants- ▁Colorado- ▁augment- ▁solely- ▁publication- ▁gear- ▁missing- ▁outdoor- ▁sides- ▁hires- ▁Ki- ana- ▁foresee- ▁renew- ▁adviser- US- ▁timeline- ▁k- ray- ▁extensions- ▁modernization- ▁enjoyed- ▁Certain- ▁cuts- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁possibilities- ▁Valley- ▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- ▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- 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▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- 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▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- ▁time- ▁customers- ▁into- ▁where- ▁like- ▁first- ▁results- ▁out- ▁But- ▁really- ▁other- ▁how- ll- ing- ▁if- d- ▁their- ▁up- ▁forward- ▁expect- ▁company- ▁were- ▁than- ▁last- ▁your- ▁look- ▁through- ▁get- ▁any- ▁because- ▁one- ▁call- ▁strong- ▁had- ▁believe- ▁sales- ▁when- ▁This- ▁then- ▁As- ▁good- ▁second- ▁In- ▁revenue- m- ▁performance- ▁product- ▁make- ▁lot- ▁cost- n- ▁much- ed- ▁years- ▁Our- ▁could- ▁financial- ▁capital- ▁terms- ▁don- ▁future- ▁both- ▁impact- ▁right- ▁value- ▁them- ▁It- ▁today- ▁little- ▁want- ▁customer- ▁next- ▁bit- ▁back- ▁work- ▁increase- ▁products- ▁take- ▁number- ▁end- ▁higher- ▁opportunities- ▁able- ▁half- ▁markets- ▁kind- ▁way- ▁may- ▁significant- ▁know- ▁operating- ▁better- ▁go- ▁cash- ▁say- ▁things- ▁still- ▁focus- ▁most- ▁statements- ▁provide- ▁should- ▁being- ▁point- ▁part- ▁team- ▁across- ▁lower- ▁made- ▁opportunity- ▁need- ▁important- ▁third- ▁2- ▁during- ▁rate- ▁line- ▁long- ▁people- ▁fourth- ▁down- ▁strategy- ▁did- ▁many- ▁continued- ▁industry- ▁level- ▁around- ▁portfolio- ▁working- ▁management- ▁share- ▁price- ▁investment- ▁due- ▁additional- ▁actually- ▁give- ▁only- ▁come- ▁while- ▁seeing- ▁high- ▁demand- ▁no- ▁few- ▁plan- ▁same- ▁looking- ▁progress- ▁here- ▁great- ▁costs- ▁even- ▁development- ▁different- ▁margin- ▁result- ▁current- ▁seen- ▁doing- ly- ▁grow- ▁further- ▁start- ▁change- ▁coming- ▁guidance- ▁positive- ▁service- ▁me- ▁earnings- ▁data- ▁drive- ▁technology- ▁position- ▁process- ▁key- ▁full- ▁investments- looking- ▁past- ▁That- ▁said- ▁who- ▁turn- ▁overall- ▁basis- ▁expected- ▁improve- ▁increased- ▁support- ▁again- ▁question- ▁3- ▁within- ▁focused- ▁sort- ▁services- ▁got- ▁environment- ▁potential- ▁help- ▁before- ▁businesses- ▁large- ▁pricing- ▁months- ▁These- ▁remain- ▁making- ▁done- ▁balance- ▁something- ▁ability- ▁interest- ▁strategic- ▁move- ▁already- ▁production- ▁sure- ▁side- ▁based- ▁platform- ▁best- ▁program- ▁such- ▁information- ▁mentioned- er- ▁acquisition- ▁Now- ▁Q- ▁talk- ▁expectations- ▁maybe- ▁early- 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▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- ▁Non- ▁University- ▁compromise- ▁Ocean- ▁undergoing- ▁straightforward- ▁Taking- ▁deleverage- ▁Port- ▁SP- ye- ▁unlocking- UR- fold- ▁Switch- ▁catalog- ▁universe- ▁Sports- ▁Price- ▁articulated- ▁owning- ugh- ▁relations- ▁Historically- ▁Ryan- ▁journal- ▁mortality- ▁neither- ▁yard- ▁Production- ▁classic- ▁Safe- ▁Angeles- ▁Saudi- ▁proximity- ▁scalability- ▁turbine- ▁variations- ▁entrants- ▁Win- ▁arms- ino- ▁agriculture- ▁chemistry- ▁multinational- ▁voluntary- ▁Sometimes- ▁speculation- ▁DS- ▁Pan- channel- ▁NAV- ▁geopolitical- ▁rush- ▁solving- mortar- ▁shoot- ▁shot- ▁calculations- ▁16%- ▁22- ▁suffered- plan- ▁AP- ▁nominal- ▁stimulate- ▁distraction- ▁persistent- ▁migrating- ▁quantitative- ▁thermal- ▁18%- oid- ▁transitional- ef- place- ▁specialists- ney- ▁interconnect- atory- ▁accomplishment- ▁Education- ▁describing- ▁salaries- ▁squeeze- ▁flattish- iding- EM- ▁EP- ▁Innovation- ▁Irma- ▁dealership- ▁representation- ▁normalization- ▁collecting- ▁removal- ▁shock- ▁enforcement- ▁suffer- ▁outsized- ▁MS- ▁heating- ▁Far- brand- ▁meat- ▁Premier- ▁incorporating- ▁CM- ▁Shop- ▁Stock- ▁four- az- ▁Generally- gar- ▁Government- ▁Kansas- ▁distance- ▁edit- ▁fat- lim- box- ▁publishing- ▁FFO- ▁Harbor- ▁Software- ▁Southwest- ▁slot- ▁Ab- ▁Moreover- bri- ▁approached- ▁absorbed- ▁advertise- reduction- ▁CC- ▁Olympic- ▁college- ▁Division- ▁noninterest- ▁obtaining- ▁widening- ▁robotics- ▁upsell- only- ▁ambitions- ▁landing- ▁Committee- ▁FERC- ▁omni- ji- ▁meal- ▁attain- ▁gasoline- ▁moderated- ▁enrolled- ▁unmatched- ▁snack- ▁Beach- ▁modifications- ▁technically- ▁Advanced- ▁Switzerland- ▁mega- ▁hubs- ▁Infrastructure- ▁tobacco- ▁tweak- ▁films- ▁independently- ▁Ke- matic- ▁offline- ▁TR- ▁interactive- ▁commenting- ▁shoppers- fill- new- range- ▁DA- ▁outperforming- ▁GDP- ▁composite- ▁warrants- ▁gateway- ▁consultation- ▁critically- ▁deepening- Point- ▁Hospital- ▁batteries- ▁diabetes- ▁milk- ▁preserving- ▁reinvent- ▁farms- ▁consists- ▁Denver- ▁underpinned- ▁observation- ▁attraction- ▁Med- ▁HR- ▁ME- ▁crystal- ▁Labor- ▁dropping- ▁alert- ▁liquidation- tz- ▁kicked- ▁explicit- ▁ultra- ▁molecule- ▁assay- ▁bundled- train- ▁recommendation- ▁teens- ▁Carl- ▁workplace- ▁EV- ari- ▁enjoying- ▁crush- ▁spacing- ▁malls- ▁prescriptions- ▁Bur- ▁renewing- ▁400- ▁Value- fully- ▁Russian- ▁examine- ▁prolonged- ▁truth- ▁standalone- ▁till- RP- ▁instrumental- ▁Alaska- ▁prominent- ▁semester- ▁flash- ▁jewelry- ▁Currently- ▁reconcile- ▁deferral- ▁dislocation- ▁Communications- ▁Post- ona- CH- ▁withdraw- ▁goodwill- ▁Grand- ▁theaters- ▁provisioning- ▁originated- ▁nearing- ▁approve- ▁associate- ▁insurers- ▁sun- ▁Equipment- ▁Meanwhile- ▁Professional- ▁kitchen- ▁Boeing- ▁panels- ▁belt- body- ▁White- ▁isolated- ▁AL- ▁fan- ▁smoothly- ▁midterm- ▁molecules- ▁Georgia- ▁Taiwan- ▁durability- ▁prioritizing- ▁Aerospace- ▁deserve- ▁rose- ▁Intel- ▁distinctive- ▁error- media- ▁confidential- ▁predicting- ▁regime- FA- ▁Volume- ▁complain- ▁tackle- ▁customary- Link- ▁suffering- ▁mandates- ▁eventual- ▁advisor- ▁intermediate- ▁congratulate- ▁continuity- ▁moderation- ▁instant- ▁End- ▁ASP- ▁GM- ▁notion- ▁le- ▁Sim- ▁NO- ▁frozen- ▁shippers- ▁interpretation- ▁fear- ▁Box- ▁Trade- ▁fueling- cut- ▁Mountain- ▁translating- ▁equipped- ▁mainstream- life- ▁lung- ▁modular- ▁stem- ▁ST- ▁reservation- ▁Fresh- ▁footage- ▁AT- ▁cancellations- ▁seasoned- contract- Star- ▁reopen- ▁shall- ▁Ireland- NS- ▁Similarly- ▁sharply- ▁suppose- ▁Greater- ▁authentic- ▁transit- ▁civil- ▁expedite- ▁noteworthy- ▁Director- ▁factoring- ▁intrinsic- ▁association- ▁technician- ▁Except- ▁directionally- ▁shrinking- ▁photo- ▁embracing- ▁readily- ▁outset- ▁visiting- ▁RV- ctive- sensitive- ▁symptom- list- ▁Nordic- ▁Euro- ▁pair- season- ▁Bruce- ▁meter- ▁succession- ▁drawn- ▁rewarding- ▁measurable- ▁Contract- ▁leak- ▁acres- ▁Compare- ▁entirety- oma- ▁innings- ▁flood- ▁fe- ▁Probably- ▁Multi- ▁valve- ▁Adam- ▁digitalization- ▁dimension- ▁consult- ▁surgeon- ▁cleaning- ▁conflict- ▁prompt- ▁unpredictable- ▁dispose- ▁spare- ▁CLO- ▁playbook- ▁28- ▁affiliates- ▁evenly- ▁SE- 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▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
+    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- 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▁outperformance- ▁connecting- ▁apart- right- ▁disposition- run- ral- ▁spoken- ery- ▁spec- ▁failure- ▁pathway- ▁frequently- ▁expansions- ▁blue- ▁100%- ▁Ni- ▁protein- ▁array- ▁unfold- ▁rebuild- ▁exposed- ax- par- ▁doctors- ▁drilled- all- ▁everywhere- ▁inclusion- ▁worry- ▁8-- ▁Ju- ▁skilled- ▁popular- ▁retained- ▁alliance- ▁unexpected- uc- ▁tighter- ▁Eagle- ▁corporation- ▁upgrading- ▁flight- ▁Va- ▁Call- ▁Du- ▁80- ▁preference- ▁deliberate- ▁High- ▁60%- ▁improves- ▁settle- ▁31- ▁roles- ▁pivot- ▁pa- ▁Christmas- ▁white- ▁Alberta- ▁gr- ▁Food- ▁campus- ▁empower- ▁reposition- ▁novel- ▁inspection- NA- ▁Ve- ▁leased- ▁precision- effective- ▁stockholders- ▁Australian- ▁influenced- ▁Asian- ▁film- ▁contemplated- ▁Jersey- ▁absence- ▁reinvestment- LA- ▁certification- ▁negotiating- ▁perception- ▁twice- ▁derivative- ▁underscore- centric- ▁guiding- ▁Specialty- ▁Hi- ▁integrity- ▁nonrecurring- ▁stretch- ▁replaced- ▁ingredients- ▁ROI- ▁defend- ▁negotiate- ▁satisfy- ▁commit- ▁airlines- ▁resume- ▁Green- 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▁exiting- ▁anniversary- ▁inorganic- ▁diluted- ▁AD- ▁payer- ▁Sun- ▁submit- ▁Quarter- ▁whereas- ▁recovered- ose- ▁periodic- ▁French- ware- ▁corner- ▁cultural- ▁preclinical- ▁surgery- ▁surgeons- ▁interactions- ▁Security- ▁anchor- ▁concentrate- ▁diseases- ▁flowing- ug- ▁imports- ▁Annual- ▁Ge- ▁reward- ▁deleveraging- ▁trained- ulation- ey- ▁Midwest- ▁relevance- ▁Boston- ▁baseline- ise- TE- ▁incorporated- ▁concepts- ▁qu- ▁linear- ▁bolster- adjusted- chi- ▁excludes- ▁overlap- ▁extreme- ▁breakeven- ▁headquarters- ▁discounting- ▁valued- ▁attribute- cy- AP- ▁miss- ▁adult- ▁vital- ▁Doug- ▁hub- ▁DC- ▁wherever- ▁pockets- ▁agility- ▁12%- ▁construct- ▁Building- ▁prudently- ▁Com- ▁revolving- ▁Rock- ▁stations- ▁outsourcing- ▁intangible- ▁reservoir- ▁alongside- iti- tting- ▁Auto- OR- ▁responded- ang- ▁stabilizing- ▁noncore- ▁protecting- ▁distributions- ▁methods- ▁Gen- ▁evening- ▁m- ▁concrete- ▁geo- ▁rock- ▁representative- ▁pad- PA- core- ▁immune- ▁Blue- ▁Fin- ▁academic- ▁multifamily- ▁Mobile- ▁ideal- 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▁possibilities- ▁Valley- ▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- 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▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- 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▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- ▁Non- ▁University- ▁compromise- ▁Ocean- ▁undergoing- ▁straightforward- ▁Taking- ▁deleverage- ▁Port- ▁SP- ye- ▁unlocking- UR- fold- ▁Switch- ▁catalog- ▁universe- ▁Sports- ▁Price- ▁articulated- ▁owning- ugh- ▁relations- ▁Historically- ▁Ryan- ▁journal- ▁mortality- ▁neither- ▁yard- ▁Production- ▁classic- ▁Safe- ▁Angeles- ▁Saudi- ▁proximity- ▁scalability- ▁turbine- ▁variations- ▁entrants- ▁Win- ▁arms- ino- ▁agriculture- ▁chemistry- ▁multinational- ▁voluntary- ▁Sometimes- ▁speculation- ▁DS- ▁Pan- channel- ▁NAV- ▁geopolitical- ▁rush- ▁solving- mortar- ▁shoot- ▁shot- ▁calculations- ▁16%- ▁22- ▁suffered- plan- ▁AP- ▁nominal- ▁stimulate- ▁distraction- ▁persistent- ▁migrating- ▁quantitative- ▁thermal- ▁18%- oid- ▁transitional- ef- place- ▁specialists- ney- ▁interconnect- atory- ▁accomplishment- ▁Education- ▁describing- ▁salaries- ▁squeeze- ▁flattish- iding- EM- ▁EP- ▁Innovation- ▁Irma- ▁dealership- ▁representation- ▁normalization- ▁collecting- ▁removal- ▁shock- ▁enforcement- ▁suffer- ▁outsized- ▁MS- ▁heating- ▁Far- brand- ▁meat- ▁Premier- ▁incorporating- ▁CM- ▁Shop- ▁Stock- ▁four- az- ▁Generally- gar- ▁Government- ▁Kansas- ▁distance- ▁edit- ▁fat- lim- box- ▁publishing- ▁FFO- ▁Harbor- ▁Software- ▁Southwest- ▁slot- ▁Ab- ▁Moreover- bri- ▁approached- ▁absorbed- ▁advertise- reduction- ▁CC- ▁Olympic- ▁college- ▁Division- ▁noninterest- ▁obtaining- ▁widening- ▁robotics- ▁upsell- only- ▁ambitions- ▁landing- ▁Committee- ▁FERC- ▁omni- ji- ▁meal- ▁attain- ▁gasoline- ▁moderated- ▁enrolled- ▁unmatched- ▁snack- ▁Beach- ▁modifications- ▁technically- ▁Advanced- ▁Switzerland- ▁mega- ▁hubs- ▁Infrastructure- ▁tobacco- ▁tweak- ▁films- ▁independently- ▁Ke- matic- ▁offline- ▁TR- ▁interactive- ▁commenting- ▁shoppers- fill- new- range- ▁DA- ▁outperforming- ▁GDP- ▁composite- ▁warrants- ▁gateway- ▁consultation- ▁critically- ▁deepening- Point- ▁Hospital- ▁batteries- ▁diabetes- ▁milk- ▁preserving- ▁reinvent- ▁farms- ▁consists- ▁Denver- ▁underpinned- ▁observation- 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▁playbook- ▁28- ▁affiliates- ▁evenly- ▁SE- ▁merge- ▁muscle- ▁interior- ▁cleaner- ▁budgeting- ▁temperatures- ▁coincide- ▁defining- ▁quantities- ▁forever- ▁reside- ▁ingredient- ▁actionable- ▁rewarded- ▁Everything- ▁essence- ▁extraordinarily- ▁radar- ▁Statements- ▁Hotel- ▁command- ▁Motor- ▁chips- ▁fitness- ▁guy- ▁float- ▁Charles- ▁Distribution- ▁NGL- ▁Perhaps- ▁decisive- ▁uniform- ▁overhang- rchitectural- zz- ▁petrochemical- ▁beef- ▁constructed- Fi- aries- ▁certified- ▁escalation- ▁rebalancing- ▁surpass- ▁Pricing- ▁confusion- ▁subset- ▁confirmation- ▁sir- ▁adapting- ▁adequately- ▁dimensions- pping- ▁fail- ▁delight- ▁Massachusetts- ▁STACK- ▁tactics- ▁infection- ▁invoice- ▁mask- ▁coach- ▁Radi- ▁specialist- ▁constraint- ▁deceleration- ▁devaluation- figuring- ▁methodical- ▁promised- ▁reinforced- ▁Really- ▁LP- ▁printer- ▁Instead- ▁LIBOR- ▁Strategic- ▁continuum- ▁Roger- ▁Adjust- pped- berg- ▁attach- ▁varying- ▁defensive- ▁signature- ▁Small- ▁desktop- ▁shoe- ▁RFP- ▁Back- ▁Bra- ▁RF- ▁Support- 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▁steer- ▁Patrick- ▁specified- ▁releasing- scrip- tail- tune- ▁circle- ▁Er- ▁attractiveness- ▁MR- front- ▁analyzed- ▁climb- ▁newspaper- ▁Agency- ▁concurrent- ▁wearable- ▁native- ▁progressively- ▁tanker- gel- DR- ▁fluctuation- ▁banners- ▁chicken- ▁Italian- ▁Brent- ▁tonnes- ▁reveal- ▁highway- ▁TI- ▁Mc- ▁Pen- changing- ▁Horizon- ▁varies- ▁assignment- ▁consequently- ▁marginally- ▁graphic- ▁correlated- FI- ▁Corp- ▁partnered- ▁fly- ▁borrower- ▁Logistics- ▁Organic- ▁southern- ▁freedom- ▁Bridge- ▁oriented- ▁officially- ▁Prime- ▁relocate- ▁algorithm- ▁Important- volume- ▁Analytics- ▁battle- ▁deciding- ▁destocking- ▁Glen- ▁quoting- ▁distinction- mont- cycl- ▁derive- ▁thousand- ote- ▁MO- ▁wine- ▁appraisal- ▁motivation- ▁expressly- ▁reversed- ▁Equity- ▁breast- ▁stood- ▁sought- funded- ▁compute- ▁rebates- ▁Max- family- NE- ▁Belgium- ▁nurse- ▁stroke- ▁stringent- ▁golf- ▁comprise- ▁geared- ▁theater- ▁deteriorate- ▁enacted- ▁sensible- ▁sixth- ▁fixing- ▁tailor- ▁contingency- ▁shale- ▁displace- ▁railcar- ▁guaranteed- ▁investigators- ▁recycle- ▁PRO- ▁biotech- ▁Orlando- ▁Tuesday- ▁Wisconsin- ▁vibrant- ▁makers- nova- jo- ▁systemic- pha- ▁Diamond- ▁Operational- ▁desirable- ▁recipe- ▁Par- ▁refinement- ▁Sprint- mount- ▁BP- ▁robotic- cell- ▁habits- ▁unlimited- ▁Omni- ▁summarized- ▁Fee- ▁beds- ▁Cup- ▁promoted- current- ▁Antonio- ▁integrator- ▁suburban- ▁conform- ▁strain- step- ▁Clar- flat- ▁safer- ▁barrier- yield- ▁reinvested- ▁library- ▁sanction- ▁vintage- ▁retrofit- ▁feedstock- ▁Still- ▁CRE- ▁Master- ▁fold- ▁failed- ▁tourist- ▁leaner- ▁Best- ▁Malaysia- ▁Premium- ▁Stephen- ▁Garden- ▁Combined- round- ▁discovered- ▁intensely- bar- ▁visitors- ▁reallocate- ▁fighting- ▁Prior- ▁Keith- ▁nationally- buy- vol- ▁Design- ▁diagnose- ▁railroad- ▁convergence- ▁entrepreneurial- ▁Unit- ▁ban- serve- ▁routing- ▁ranging- ▁hunt- ▁Estate- ▁tuned- ▁sticking- disproportionate- ▁Connected- ▁reactor- ▁citizen- ▁Nothing- ▁standardized- ▁accumulated- ▁Diego- ▁attitude- ▁propane- ▁26- build- ▁augmented- ▁lien- ▁Nevada- ▁Vietnam- ▁bonuses- ▁deliberately- ough- ▁overhaul- saving- ▁furnish- ▁Continental- ▁appointed- ▁benign- ▁bottle- ▁gallon- ▁reiterating- ▁PI- ▁gu- ▁borrow- rk- ▁originate- ▁monetizing- ▁resistance- ▁underneath- ▁onshore- ▁deadline- ▁behave- ▁favorite- ▁oversee- ▁CT- ▁confirming- ▁repeating- ▁embark- ▁pin- ▁catching- ▁Test- ▁permission- ▁tremendously- ▁Dar- haul- ▁explaining- ▁behavioral- 'NO'- ▁discounted- ▁Consequently- ▁Randy- ▁cannibalization- ▁choppy- ▁departure- ▁minimizing- ▁Absolutely- ▁19%- ▁27- ▁TAM- ▁assisted- ▁outsourced- ▁Paris- ▁reconciled- ▁Meta- built- consumer- ▁random- ▁2013- user- ▁Hard- ▁friendly- ▁sticky- ▁divested- ▁consumed- ▁CR- LO- ▁imported- ▁cannabis- ▁sequencing- ▁Anything- ▁Three- ▁dress- ▁mono- ▁Mag- ▁perceived- ▁ideally- ▁existed- ▁ranking- ▁accountable- bridge- ▁approximate- ▁robot- ▁Special- ▁Philippines- ▁graduate- ▁harness- ▁navigation- ▁striving- ▁wildfire- ▁outflow- ▁Recently- ▁witnessed- ▁mutually- ▁Add- ▁midyear- week- ▁Among- ▁sensing- put- 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▁School- structure- economic- deliver- ▁Ralph- ▁Example- ▁Utah- ▁irrigation- ▁nonaccrual- ▁suppress- ▁Underlying- ▁remediate- ▁stripping- ▁iteration- ▁sincerely- ▁Orange- path- close- measure- process- ▁Block- ▁Significant- ▁Treasury- ▁caliber- ▁compliment- ▁virtuous- ▁busiest- ▁forthcoming- track- ▁inhibit- profit- ▁reestablish- ▁Progress- ▁Apollo- ▁Tokyo- ▁chemotherapy- ▁mathematical- ▁retiring- ▁stellar- ▁sweep- ▁timeframe- ▁roster- ▁specify- ▁Ameri- ▁SIM- carbon- ▁Alabama- ▁Application- ▁Hemisphere- ▁Mineral- ▁administrator- ▁dinner- ▁empty- ▁endorsement- ▁facilitating- ▁happier- ▁vacancies- ▁voting- 7,000- ▁protest- ▁reactivate- ▁bird- ▁aspire- apples- ▁redevelop- ▁hazard- ▁Iridium- ▁Related- ▁experiential- ▁mismatch- ▁philosophical- ▁thumb- ▁Leasing- ▁deductibility- ▁Drug- ▁Wild- ▁illustrat- ▁reinvigorate- ▁sandwich- ▁Affordabl- ▁DOL- ▁Hilton- ▁advocacy- ▁antibodies- ▁chief- ▁entrance- ▁examining- ▁snapshot- ▁unfair- ▁ForEx- ▁defect- ▁mixture- ▁Getting- ▁digging- ▁Monster- 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▁reconciling- ▁unleash- ▁attribution- ▁FCC- ▁IBM- ▁wastewater- ▁baking- ddle- called- ▁Driving- ▁Jordan- ▁Restaurant- ▁calibrate- ▁decelerate- ▁discontinuation- ▁enzyme- ▁nonetheless- ▁segregat- ▁slippage- ▁sovereign- ▁steroid- ▁remix- ▁Crane- ▁pinpoint- ▁Display- ▁dump- ▁coding- ▁Likewise- Works- credit- ▁Double- ▁Catalyst- ▁Halloween- ▁NEXT- ▁blockbuster- ▁incidence- ▁eastern- ▁blast- ▁Dream- regulated- lecommunications- ▁Kind- storm- ▁Surgical- ▁Triumph- ▁editorial- ▁irrational- ▁juice- ▁kiosk- ▁slope- ▁sulfur- ▁Dental- ▁vending- ▁Insight- ▁Baker- ▁anecdotal- ▁Whit- ▁dilute- ▁Albert- development- voltage- ▁College- ▁destiny- ▁influx- ▁microbiome- ▁migraine- ▁occupy- ▁politics- ▁reversion- ▁rubber- ▁variant- ▁Glass- ▁NOLs- ▁supervisor- ▁Trading- ▁loud- ▁infant- ▁weapon- operative- ▁technique- ▁surround- ▁distress- ▁tradition- ▁Advisor- ▁Bancorp- ▁Collection- ▁League- ▁Perfect- ▁pragmatic- ▁firing- ▁Know- ▁Better- ▁slice- ▁dwell- ▁outlier- ▁colleague- prime- Bank- ▁Buffalo- ▁Greece- ▁Portfolio- ▁Sustain- ▁Taylor- ▁Women- ▁cohesive- ▁hypothetical- ▁nontraditional- ▁refractory- ▁stewardship- ▁Darren- ▁horse- ▁Irish- ▁Arrow- ▁Associate- ▁Nonetheless- ▁Williston- ▁YouTube- ▁catheter- ▁latency- ▁maritime- ▁uncommon- ▁firewall- ▁Speed- ▁orange- ▁Pharmacy- ggle- ▁Including- ▁Restructuring- ▁bubble- ▁clause- ▁consummate- ▁deficiency- ▁receptor- ▁redundancy- ▁titanium- ▁Windows- ▁thread- ▁Partnership- ▁Accu- plica- ▁Solution- ▁assemble- ▁author- ▁AFFO- ▁Cologuard- ▁aggregator- ▁behaving- ▁choppiness- ▁conservation- ▁groundbreaking- ▁impediment- ▁reallocating- ▁registry- ▁tandem- ▁Besides- ▁Lisa- ▁Help- ▁STAR- 0%- ▁Charlie- ▁Orleans- ▁Platinum- ▁abundance- ▁acquisitive- ▁locomotive- ▁maturation- ▁pervasive- ▁pulse- ▁Christian- ▁Housing- ▁instability- ▁textile- group- ▁Bottom- ▁Detroit- ▁LIFO- ▁Mutual- ▁Positive- ▁Tyler- ▁Wondering- ▁arriving- ▁beneficiaries- ▁century- ▁dismiss- ▁turbulence- ▁DUC- ▁OPEC- ▁pathogen- ▁adhere- ▁Music- ▁originator- ▁prohibit- ▁grocer- nanometer- ▁FireEye- ▁believing- ▁false- ▁matrix- ▁proposing- ▁remedy- ▁shaft- ▁Brett- ▁Hello- ▁Allied- ▁arrow- pronged- ▁snap- cloud- ▁Campbell- ▁Loyalty- ▁Patterson- ▁Terminal- ▁biomass- ▁exercising- ▁footnote- ▁fragrance- ▁frustrated- ▁garage- ▁interference- ▁referendum- ▁simplifies- ▁unrivaled- ▁seemingly- ▁Close- direct- ▁dominate- Health- Corp- ▁Fabric- ▁Casualty- ▁Petrobras- ▁SCOOP- ▁acknowledging- ▁activating- ▁delineation- ▁dissipate- ▁exterior- ▁orthopedic- ▁resumption- ▁shield- ▁stainless- ▁Imaging- ▁outgrow- ▁Beverage- ▁Expect- ▁Outdoor- ▁brilliant- ▁curriculum- ▁decentralized- ▁deteriorating- ▁episodic- ▁fault- ▁Automation- ▁retool- ▁darn- ▁Terra- ▁debut- ▁dangerous- ▁IND- ▁Couple- ▁amortize- dealer- ▁BOSS- ▁Cardinal- ▁Hollywood- ▁Pizza- ▁Study- ▁antitrust- ▁autumn- ▁bunker- ▁ePlex- ▁frequencies- ▁hinder- ▁inflammation- ▁infotainment- ▁relocating- ▁victory- ▁Critical- ▁Geographic- ▁transpire- ▁carpet- ▁insurer- interest- walk- insured- ▁phenomena- ▁Specific- ffsetting- ▁Appalachia- ▁Particularly- ▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- ▁Holiday- ▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- 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▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- ▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- 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▁false- ▁matrix- ▁proposing- ▁remedy- ▁shaft- ▁Brett- ▁Hello- ▁Allied- ▁arrow- pronged- ▁snap- cloud- ▁Campbell- ▁Loyalty- ▁Patterson- ▁Terminal- ▁biomass- ▁exercising- ▁footnote- ▁fragrance- ▁frustrated- ▁garage- ▁interference- ▁referendum- ▁simplifies- ▁unrivaled- ▁seemingly- ▁Close- direct- ▁dominate- Health- Corp- ▁Fabric- ▁Casualty- ▁Petrobras- ▁SCOOP- ▁acknowledging- ▁activating- ▁delineation- ▁dissipate- ▁exterior- ▁orthopedic- ▁resumption- ▁shield- ▁stainless- ▁Imaging- ▁outgrow- ▁Beverage- ▁Expect- ▁Outdoor- ▁brilliant- ▁curriculum- ▁decentralized- ▁deteriorating- ▁episodic- ▁fault- ▁Automation- ▁retool- ▁darn- ▁Terra- ▁debut- ▁dangerous- ▁IND- ▁Couple- ▁amortize- dealer- ▁BOSS- ▁Cardinal- ▁Hollywood- ▁Pizza- ▁Study- ▁antitrust- ▁autumn- ▁bunker- ▁ePlex- ▁frequencies- ▁hinder- ▁inflammation- ▁infotainment- ▁relocating- ▁victory- ▁Critical- ▁Geographic- ▁transpire- ▁carpet- ▁insurer- interest- walk- insured- ▁phenomena- ▁Specific- ffsetting- ▁Appalachia- ▁Particularly- ▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- 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+    "authors": [
+      {
+        "name_en": "Shinji Watanabe",
+        "name_zh": "Shinji Watanabe"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1615219200000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC-BY-NC-4.0",
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+    "file_size_bytes": 921363372,
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+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4443058",
+    "cstr": "",
+    "pid": "",
+    "title": "Rhopalapion longirostre Olivier 1807",
+    "title_en": "Rhopalapion longirostre Olivier 1807",
+    "title_zh": "Rhopalapion longirostre Olivier 1807",
+    "description": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
+    "description_en": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
+    "description_zh": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
+    "authors": [
+      {
+        "name_en": "Giusto, Carlo",
+        "name_zh": "Giusto, Carlo"
+      }
+    ],
+    "keywords_en": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Coleoptera",
+      "Brentidae",
+      "Rhopalapion",
+      "Rhopalapion longirostre"
+    ],
+    "keywords_zh": [
+      "Biodiversity",
+      "Taxonomy",
+      "Animalia",
+      "Arthropoda",
+      "Insecta",
+      "Coleoptera",
+      "Brentidae",
+      "Rhopalapion",
+      "Rhopalapion longirostre"
+    ],
+    "publication_date": 1610467200000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "notspecified",
+    "file_size_mb": 0.03,
+    "file_size_bytes": 33939,
+    "download_count": 0,
+    "visit_count": 0,
+    "url": "",
+    "source": "scidb"
+  },
+  {
+    "dataset_id": "",
+    "doi": "10.5281/zenodo.4499539",
+    "cstr": "",
+    "pid": "",
+    "title": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
+    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
+    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
+    "description": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- 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▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- 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▁Bears- ▁supporter- ▁investing- ▁cleared- ▁Nan- siew- ▁ancestors- ▁tackle- fan- ▁renting- ▁smelling- ▁Per- six- ▁def- ▁begins- ▁circled- ata- ▁ram- ▁glu- mation- ▁boarding- ▁tiles- Asia- ability- dey- bit- les- Pok- ▁revis- ▁biscuits- ten- ▁heading- ▁rappers- ▁Boys- her- ▁Han- ▁storm- zz- ▁Qi- ▁involves- tro- ▁candles- ▁vet- ▁holds- ▁efforts- ▁Ed- ▁originate- ▁limits- ▁Bel- our- ▁vera- ▁fe- ▁tease- ▁expire- ▁vendors- ▁milestone- ▁landscape- ▁ano- side- ▁Master- ice- ▁alphabet- ▁statue- ▁minister- ▁Legend- ▁airline- ▁consumer- ▁lastly- ▁rapper- ▁teenager- ▁belief- ▁Tu- ging- ▁downstair- ▁occur- ▁shades- ku- ▁Christ- ▁backstab- ▁defer- mai- ▁arguments- ▁writer- ▁flap- while- ▁memes- cai- ▁He- use- ▁sigh- fe- ▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- 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▁humidity- ▁treetop- ▁Faith- ▁Galaxy- ▁presume- ▁shady- ▁egoistic- ▁fellowship- ▁lighthearted- ▁Kiri- ▁suc- ▁waterproof- ▁Bangla- ▁surrender- ▁warden- ▁banter- ▁scanning- ▁bali- ▁coincidentally- ▁Totoro- ▁storeroom- ▁confidential- ▁squishy- ▁pension- ▁Danny- ▁diluted- ▁Asus- ▁stationery- ▁posh- ▁westernised- ▁Valley- ▁deprived- ▁Mandai- ▁Wanna- ▁gymming- ▁submitted- ▁secretive- ▁Shuan- ▁hangover- ▁Switch- ▁floss- ▁introducing- ▁Nancy- Mars- ▁autis- ▁Baba- ▁existent- ▁corgi- ▁Ireland- Shiong- ▁Amos- ▁Rover- ▁Suria- ▁punk- ▁spize- ▁paperwork- ▁Liao- ▁Liz- Meng- Out- ▁expressway- ▁sniper- ▁leap- ▁idiotic- ▁lingo- ▁dent- ▁clinical- ▁Sega- ▁elf- ▁Fuji- ▁Leona- ▁specialisation- ▁intentionally- ▁quitted- ▁Cody- ▁bedok- ▁chanting- ▁cyst- ▁decorating- ▁competitiveness- ▁Lok- ▁upsetting- ▁integrated- ▁transcriber- ▁gray- ▁censored- ▁munch- ▁portal- ▁vast- ▁budd- ▁sloth- ▁stopover- ▁publicity- ▁Music- ▁approachable- ▁strolling- ▁confined- ▁Jones- ▁Singap- ▁coaches- ▁allocated- ▁escalate- 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▁fri- mort- ▁Mai- mary- ▁banker- ▁researchers- ▁Jolin- kuah- gate- ▁bothering- chan- ▁Timb- ▁smoked- ▁formed- ▁cheers- phy- nto- ▁Pai- ▁clogg- Studios- ▁kilometers- ▁syllables- ▁aeroplanes- ▁perfumes- ▁measures- ▁Pola- Got- ▁dragg- ▁lea- ▁beetles- source- ▁cuter- ▁handbags- ▁Timah- ▁founder- ▁partying- ▁Bras- ▁Nov- ▁donated- ▁thrills- ▁scenarios- ▁meantime- ency- ▁Lab- ▁soldiers- ▁tiers- Wala- ▁Poo- ▁cosmetics- ▁Euros- tory- ▁classy- ▁trib- ▁ming- ▁Kin- ▁Rim- ater- ▁loc- ▁lunches- ▁hotdogs- ▁rays- ▁fillers- ▁hays- ▁collapse- inda- 'On'- ching- Board- ▁shin- ▁rubb- ▁sys- ▁muffins- ique- gh- ette- ▁hais- ▁cabs- ator- ▁recon- ▁bef- ▁persuad- ▁gamers- ▁Ta- ▁Dal- ▁kn- ▁kittens- ▁guitars- lated- ▁faithful- end- ▁performances- chou- ▁Bing- ▁sponsors- ille- Del- ▁roses- que- ▁apples- ▁Lo- ▁Teen- ▁laps- ▁poems- ▁je- ▁sciences- ▁Bi- ▁aesthetics- ▁arrows- ▁streams- ▁crimes- ▁Ez- ▁Del- ▁hal- ▁advis- ▁Ting- ▁lis- ▁sw- ▁concentrat- ▁planets- ium- ▁sal- ▁bricks- ▁Maria- ▁Xu- lter- Ko- ▁controlle- 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Box- ▁Drag- ▁depict- ▁Fresh- ▁Hero- ▁absurd- ▁coop- ▁terror- ▁resist- quid- ▁proficien- ▁Heart- ▁Arctic- ▁Dempsey- ▁Jamie- ▁continual- ▁intellect- ▁prescripti- ▁render- ▁ulti- Anatomy- Azhar- Buffet- Dhabi- Holmes- Mirza- Ramsay- Takraw- tsuka- zealand- ▁Austria- ▁Barbecue- ▁Britney- ▁Caleb- ▁Chapteh- ▁Chihuahua- ▁Chivas- ▁Chloe- ▁Citibank- ▁Commonwealth- ▁Crescent- ▁Desmond- ▁Diwali- ▁EXO- ▁Exco- ▁FairPrice- ▁Freedom- ▁Grinch- ▁Hyundai- ▁Ibiza- ▁Illusion- ▁Jurassic- ▁Keppel- ▁Maniac- ▁Mauritius- ▁Minesweeper- ▁Mohammad- ▁Normal- ▁Olivia- ▁Photoshop- ▁Popular- ▁Queensway- ▁Ringgit- ▁Scarlet- ▁Serena- ▁Shirley- ▁Slurpee- ▁Snapchat- ▁Society- ▁Supper- ▁Tiffany- ▁Ultra- ▁Vasantham- ▁Vishnu- ▁Wales- ▁Wikipedia- ▁Zendaya- ▁abuden- ▁academy- ▁accustom- ▁affirmation- ▁altitude- ▁apocalypse- ▁apostrophe- ▁articulate- ▁assertive- ▁avatar- ▁beansprout- ▁bhutan- ▁bodyguard- ▁bollard- ▁calcium- ▁chapati- ▁colourblind- ▁comfy- ▁complacent- ▁connotation- ▁crumpled- ▁deluxe- ▁detriment- ▁duff- 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▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- 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▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- 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lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- 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▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- ▁accelerate- Donki- ▁cucumbers- ▁rifles- ▁Run- ▁gro- ▁insider- ▁jav- ▁meatballs- ▁pirates- ▁spi- ▁handy- ▁Pas- ▁blogg- kie- rack- illion- ▁Pak- ▁emojis- ▁kilos- ▁Thin- lying- ▁worksheets- ▁notebooks- ▁crows- cia- ▁Kent- urity- ▁anal- ▁chunks- ▁buds- ▁Zen- ude- ▁ze- ola- ▁concentrati- ▁shak- ▁selfless- ▁Masters- ▁landscapes- ▁milestones- ▁expires- ▁moderniz- misunderstanding- Rush- ▁lou- ipped- ▁growl- ▁skit- elle- oid- por- Cook- ▁stakes- ▁belongings- ▁passages- smati- ▁zhu- ▁storybooks- ▁Rav- ▁fra- opped- ▁val- ▁squirrels- ▁tales- ▁prais- ▁colon- kee- rul- ▁relocate- ▁Cad- ▁Gia- ▁oppo- ▁opp- ▁optim- alized- ▁zipp- ▁Phil- dar- neo- ▁Pant- nuel- ▁Shake- ▁auditor- ▁Zhu- own- ▁tec- ▁stru- ▁terminat- gue- ▁rece- achi- urn- ftie- ▁opposit- ▁exa- ▁worsen- ▁sti- folding- shui- lessly- ▁marginal- ▁lob- rom- ▁crumb- bble- ▁Alon- acy- ▁Jenni- ▁spe- ▁Band- ▁spra- ▁shoo- afar- ▁ste- bak- Pie- ▁stan- ▁Ou- ime- fetched- iterate- ▁stead- ▁ferri- elecast- anna- ▁Lam- dol- ▁Bab- uhwe- mpang- ▁mindless- rman- Leste- ▁sympath- ▁ken- Clan- pad- ▁Human- atory- chai- decker- ▁cous- ▁Com- onia- ▁Bee- ▁bermuda- ▁scallop- ▁Pilate- ▁diaper- ▁pebble- ▁germ- ▁coral- ▁investor- ▁statistic- Ranger- ▁brace- ▁lyric- ▁alway- ▁zo- ▁slush- merah- ▁perk- ▁researcher- alism- stage- leen- body- ▁rab- ▁sportsman- ▁neg- Cent- make- ▁decorat- ▁certif- ▁gran- ▁oldie- Cage- ▁iden- ▁clog- ▁Chang- ality- ▁hur- ▁glam- wo- ▁Pul- ▁Julie- ▁Cal- ▁lectur- ▁Sand- west- Jobs- ▁complet- ▁achiev- tude- oup- nova- away- ▁Kee- Ban- ▁purchas- ▁esca- ▁wor- ▁Himalaya- ▁froze- ▁Sho- ▁deserv- ▁blush- ▁hoard- ▁choreograph- ▁dishearten- craft- ▁deci- ▁windsurf- ▁disco- ▁sightsee- ▁accord- ventu- ▁figur- pson- ▁schem- ▁arrang- Angel- ▁snip- icular- world- ▁ostracise- ▁chant- ▁confiscate- ▁excite- ▁elect- ▁endanger- bloc- ▁detach- atholic- ▁suspend- Network- Singapore- Woman- vious- Grande- Brigade- holder- Scott- Three- Bull- chomp- child- takraw- colli- tempered- Obama- Ninja- Ocean- attaya- restricted- Ralph- lifting- spirit- Busan- Black- Stella- ngsana- tight- different- grass- merit- queue- wak- consider- road- Maths- Ayam- boat- ondeh- Grey- Singaporean- shock- ▁greed- heard- yana- obby- ▁Ronald- Strange- clockwise- ▁suff- ▁graceful- ologist- open- ▁carpent- astle- ▁swag- ▁servic- ▁rehears- ▁millenni- vergreen- ▁submissi- hennai- enegade- esistance- ▁Mexic- ▁obstruct- ▁Aqua- ▁coincide- ▁depart- ddling- ▁celeb- ▁deploy- evio- negotia- ▁hydro- ▁protrud- ▁Comfort- ▁Disco- ▁boycott- Beckham- Bennett- Carrey- Clues- DiCaprio- Muhammad- Payne- Ramsey- Ribbon- Rowling- Solo- Uemura- arella- ligently- rrogate- ▁AddMath- ▁Adele- ▁Aidil- ▁Akbar- ▁Aloysius- ▁Anderson- ▁Anjib- ▁Anusha- ▁Bermuda- ▁Buddy- ▁Canberra- ▁Carnage- ▁Clean- ▁Colorado- ▁Coupet- ▁Daryl- ▁DeepMind- ▁Depot- ▁Donnie- ▁Durian- ▁Edusave- ▁Fiido- ▁Georgia- ▁Ghibly- ▁Gudetama- ▁Guinness- ▁Guzheng- ▁Heidi- ▁Hummus- ▁Immortal- ▁Insidious- ▁Jewel- ▁Kahshee- ▁Katherine- ▁Kelantan- ▁Kozminski- ▁Labyrinth- ▁Loann- ▁Lookism- ▁MUIS- ▁Mcnugget- ▁Millennials- ▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- ▁espresso- ▁execution- ▁exfoliant- ▁exquisite- ▁fingertip- ▁gizzard- ▁globe- ▁graffiti- ▁hiccups- ▁hokkaido- ▁hyperactive- ▁iModel- ▁imitation- ▁impediment- ▁indebted- ▁indomie- ▁inertia- ▁infested- ▁infinite- ▁interconnected- ▁introspective- ▁intrusive- ▁jewelleries- ▁keloid- ▁keropok- ▁knuckle- ▁liddat- ▁lychee- ▁mackerel- ▁malamute- ▁mechatronics- ▁memorabilia- ▁menacing- ▁menthol- ▁messenger- ▁migraine- ▁mortgage- ▁muachee- ▁muffled- ▁mussels- ▁narrate- ▁negotiation- ▁nitrogen- ▁obscure- ▁oriental- ▁overcooked- ▁overcrowded- ▁paitao- ▁parasite- ▁persuasion- ▁pinafore- ▁pistachio- ▁pistol- ▁predator- ▁premature- ▁preoccupied- ▁prettie- ▁profiling- ▁prototype- ▁radish- ▁reincarnated- ▁reindeer- ▁repurpose- ▁resilient- ▁retrospect- ▁revenue- ▁rhino- ▁ritual- ▁roulette- ▁salvage- ▁sandbag- ▁scorpion- ▁scrawny- ▁sculp- ▁secluded- ▁silhouette- ▁simulation- ▁simultaneously- ▁slipshod- ▁sneezing- ▁snippet- ▁sparring- ▁splatter- ▁spooky- ▁sprinkle- ▁structural- ▁strudel- ▁stylus- ▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- 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▁blackcurr- ▁bookshelf- ▁calefare- ▁cashflow- ▁categorise- ▁caterpillar- ▁cheapskate- ▁chief- ▁clarity- ▁clause- ▁cleave- ▁codependent- ▁commonwealth- ▁compilation- ▁concurrently- ▁conspiracy- ▁conspire- ▁corruption- ▁cottage- ▁credible- ▁crescent- ▁damaging- ▁decline- ▁declining- ▁degenerate- ▁demolition- ▁demotivate- ▁devastated- ▁devastating- ▁diaphragm- ▁dictator- ▁disgrace- ▁dispatch- ▁displeasure- ▁dystopian- ▁elitism- ▁endorphin- ▁ensemble- ▁equity- ▁estella- ▁exempted- ▁exorbitant- ▁fanciful- ▁flaunt- ▁flavourtown- ▁forefather- ▁forensic- ▁gibberish- ▁gimme- ▁gloomy- ▁glucose- ▁gratification- ▁gruesome- ▁guava- ▁gynae- ▁hammock- ▁harmonizer- ▁heaviest- ▁hibernate- ▁hickey- ▁honeydew- ▁hungrier- ▁hurried- ▁hypothermia- ▁imaginative- ▁impractical- ▁inducing- ▁inefficiency- ▁infatuated- ▁influenza- ▁infused- ▁inquisitive- ▁insecurities- ▁insignificant- ▁jaguar- ▁jinx- ▁kanken- ▁lavender- ▁lenient- ▁lilac- ▁litigation- ▁locksmith- ▁loiter- ▁longevity- ▁maggots- ▁maisonette- 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Xiong- ▁Demi- ▁Hebe- ▁gently- ▁shameful- ▁drumlet- ertis- ▁confrontational- ▁pupa- ▁drastically- ▁guac- ▁Jared- ▁Marmalade- ▁empowerment- ▁flunk- ▁jellybean- ▁visc- Paddle- ▁Dollah- ▁Tangled- ▁bullier- ▁clapping- ▁resembles- Care- ▁Asos- ▁Moza- ▁Westgate- ▁quarry- ▁Lazuli- ▁chorus- ▁tequila- ▁wafer- ▁Jeanette- ▁SpiderMan- ▁grumbling- ▁kinship- Lapis- Madrid- ikgu- ▁coordinator- ▁dahl- ▁yoogane- ▁Kimberly- ▁Pharrell- ▁brigade- ▁forgivable- ▁dyslexia- ▁nicotine- ▁sensitivity- ▁Chick- ▁Edward- Ferrell- ▁Siva- ▁bleak- ▁Cheong- ▁civilized- ▁reciprocation- ▁Singsale- ▁elementary- ▁laloo- ▁Gear- sibility- ▁Mogu- ▁limelight- Train- ▁kakak- ▁shortlist- rped- ▁carousell- ▁footprint- Kurau- ▁globalised- ▁mower- ▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- 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▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- ▁amputat- ▁usua- ▁Gol- ▁Bur- lopp- after- ▁Havai- killers- ▁swop- oosebumps- sheet- ozy- ▁qualifi- ushed- ▁cohesi- ▁squeez- ▁smel- ▁Choo- ▁consol- ▁swerv- view- ▁schedul- ▁shiver- ▁troubl- heart- ▁ide- ▁initiat- ▁desir- ▁retir- Sua- ▁emphasiz- ▁reschedule- ▁inflate- 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dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- ▁Gulliver- ▁HabourFront- ▁Hanzo- ▁Harzik- ▁Hayde- ▁Hiragana- ▁Holdings- ▁Hollandaise- ▁Hologram- ▁Honestbee- ▁Hoshino- ▁Hsinchu- ▁Hunter- ▁Husserl- ▁Imperial- ▁Inktober- ▁Innok- ▁Jamaica- ▁Janabelle- ▁Jefri- ▁Jolibee- ▁Kendra- ▁Kenora- ▁Khalees- ▁Kinokuniya- ▁Kitori- ▁KouFu- ▁Leaves- ▁Leuch- ▁Lexus- ▁Libya- ▁Lilac- ▁Lontong- ▁Lorraine- ▁Luffy- ▁Management- ▁Matthew- ▁McNugget- ▁Motorola- ▁Mumbai- ▁Nebraska- ▁Nelson- ▁Nigeria- ▁Nobel- ▁Nothingness- ▁Nyx- ▁Oliander- ▁Otogi- ▁Paradise- ▁Pentatonix- ▁Petpetpet- ▁Pharaoh- ▁Poppins- ▁Portuguese- ▁Poteng- ▁Prakash- ▁Pretty- ▁Puff- ▁Qihua- ▁Reebonz- ▁Refash- ▁Remember- ▁Revlon- ▁Ribena- ▁Rooney- ▁Roxy- ▁Sabbath- ▁Sabsuka- ▁Scorpio- ▁Scramble- ▁Senibong- ▁Sennheiser- ▁Sensei- ▁Seraphina- ▁Shabir- ▁Shrek- ▁Silence- ▁Sogou- ▁Soraya- ▁Spotty- ▁Squirtle- ▁Supernatural- ▁Swarovski- ▁Syaz- ▁Sylvester- ▁Technique- ▁Thanksgiving- ▁Thosai- ▁Thriller- ▁Tigreal- ▁TikTok- ▁Transylvania- ▁Trivers- ▁Ukraine- ▁Uprising- ▁Valuetronic- ▁Vatican- ▁Velvet- ▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- 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▁prorated- ▁pupil- ▁quantitative- ▁quarantine- ▁radius- ▁ramekins- ▁rebatch- ▁reclusive- ▁recuperate- ▁reenacti- ▁refurnish- ▁regenerate- ▁registry- ▁reinstate- ▁relativity- ▁relegate- ▁reprimand- ▁respawn- ▁retention- ▁retrac- ▁rewriting- ▁rofl- ▁rudimenta- ▁rupee- ▁scrutinise- ▁serenade- ▁shoelace- ▁shrimp- ▁shrub- ▁silhoutte- ▁skilful- ▁smudge- ▁smuggling- ▁societies- ▁spatula- ▁splinter- ▁sprout- ▁stepdad- ▁succinct- ▁sugarcane- ▁sunroof- ▁surgical- ▁surveillanc- ▁suspicion- ▁symmetr- ▁thickener- ▁titanium- ▁toothpick- ▁tornado- ▁transsexual- ▁treading- ▁trench- ▁unattainable- ▁unavailable- ▁unbeat- ▁uncountable- ▁undercover- ▁underprivilege- ▁unemployment- ▁unfrozen- ▁unleashed- ▁unopened- ▁unprepared- ▁unscrew- ▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- 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▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- 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▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- 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▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- 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▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- 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▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- 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':'- Alchemist- Brolin- Cavani- Converter- Defence- Exchange- Explore- Fansipan- Girlfriend- Grannis- Kerang- Lovato- Mandeb- Marbles- Marinda- Meyer- Packard- Ragnarok- Reborn- Rendezvous- Samsuddin- Spect- Tarik- Trench- Walker- appetising- appuccino- bahru- conferenc- dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- 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▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- 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▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- 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dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    unit: 1000    nlayers: 1required:- output_dir- token_listversion: 0.9.6distributed: true",
+    "description_en": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- 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▁Bears- ▁supporter- ▁investing- ▁cleared- ▁Nan- siew- ▁ancestors- ▁tackle- fan- ▁renting- ▁smelling- ▁Per- six- ▁def- ▁begins- ▁circled- ata- ▁ram- ▁glu- mation- ▁boarding- ▁tiles- Asia- ability- dey- bit- les- Pok- ▁revis- ▁biscuits- ten- ▁heading- ▁rappers- ▁Boys- her- ▁Han- ▁storm- zz- ▁Qi- ▁involves- tro- ▁candles- ▁vet- ▁holds- ▁efforts- ▁Ed- ▁originate- ▁limits- ▁Bel- our- ▁vera- ▁fe- ▁tease- ▁expire- ▁vendors- ▁milestone- ▁landscape- ▁ano- side- ▁Master- ice- ▁alphabet- ▁statue- ▁minister- ▁Legend- ▁airline- ▁consumer- ▁lastly- ▁rapper- ▁teenager- ▁belief- ▁Tu- ging- ▁downstair- ▁occur- ▁shades- ku- ▁Christ- ▁backstab- ▁defer- mai- ▁arguments- ▁writer- ▁flap- while- ▁memes- cai- ▁He- use- ▁sigh- fe- ▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- ▁Eminem- ▁President- ▁Sydney- ▁WeChat- ▁accommodate- ▁aglio- ▁attentive- ▁coleslaw- ▁congrats- ▁consumption- ▁cringy- ▁deaf- ▁district- ▁drumstick- ▁evaporate- ▁expenditure- ▁gantry- ▁hummingbird- ▁hypocrite- ▁inconvenient- ▁instinct- ▁intuition- ▁irrational- ▁policies- ▁pricing- ▁purplish- ▁servant- ▁skeptical- ▁surviving- ▁syndrome- ▁terrace- ▁turret- ▁wedges- ▁Angkor- ▁Ellen- ▁Hanoi- ▁Ofo- ▁Rebecca- ▁increasing- ▁outdated- ▁pursuit- ▁Heartland- ▁association- ▁psychologist- ▁unpredictable- ▁Yates- ▁manufacturing- ▁index- ▁botak- ▁quantity- ▁Marco- ▁runner- ▁memorising- ▁obsession- ▁ferment- ▁lamppost- ▁balm- ▁Tuas- ▁Frank- ▁announcement- ▁penalty- ▁Nyonya- ▁leverage- ▁Monkey- ▁stroke- ▁font- ▁token- ▁massive- ▁chopping- ▁wheat- ▁kiasi- ▁optimistic- ▁mochi- ▁subtle- ▁embarrassment- ▁secretary- ▁smartphone- ▁wives- ▁Arabic- ▁bold- ▁popping- ▁sync- ▁curb- ▁summary- ▁kacang- ▁Catholic- ▁continuously- ▁Mio- ▁reuse- ▁colorful- ▁problematic- ▁Tower- Centre- ▁Hsien- ▁Jackie- ▁fussy- 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can- ▁yearly- ▁terminate- ▁connections- ▁joint- ▁exception- ▁drip- ▁scrape- ▁cre- ▁listing- ▁Fun- ▁scrap- ▁lan- ▁questioning- ▁Mar- ythic- ▁scholars- hipped- hu- ▁reader- ▁col- ▁quotes- ▁strengths- ery- ▁calculation- ▁feather- ▁muffin- ▁restriction- ▁slaves- ▁downward- ▁Bun- ▁handles- ▁sandal- ▁shoulders- ▁exists- od- ▁lac- ▁que- ec- ▁Wu- ▁Ice- ring- ▁Dad- ▁ter- id- ▁Fat- ▁rob- ▁pointer- ▁min- ▁wan- ▁sy- ▁Roman- ▁sua- tai- ▁gut- ▁nieces- uhyou- ▁hun- School- ▁overthink- ▁execute- scape- ▁pierce- Kayu- ▁Louis- America- club- House- ▁rotate- Hao- ▁singlet- ▁gracious- ▁Taufik- ▁Mickey- ▁Beyblade- ▁Clara- ▁Donald- ▁Drake- ▁Eiffel- ▁Kelvin- ▁Natalie- ▁Nigel- ▁PlayStation- ▁Porsche- ▁Sundram- ▁Tamarind- ▁Tamiya- ▁Watson- ▁Zumba- ▁allergy- ▁ambience- ▁balancing- ▁belacan- ▁bimbo- ▁bisque- ▁bouncy- ▁carried- ▁clarify- ▁climax- ▁condominium- ▁decrease- ▁diversity- ▁efficiency- ▁eyelid- ▁fascinate- ▁feast- ▁fickle- ▁filtration- ▁forklift- ▁fortune- ▁increment- ▁infrastructure- ▁mattress- 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▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- lude- ▁cubes- ▁Jenn- ▁websites- ▁interestingly- ▁acai- ▁sitcoms- ▁Kinder- ▁seating- Pi- ▁che- ▁ci- ▁puddings- aj- here- ▁odds- ax- ▁margin- ▁Caucasians- ▁attributes- ▁Alias- ▁dreamer- ▁flyers- ▁Ze- ▁blanks- list- ▁temperatures- Chi- llao- ▁Meet- ▁alphabets- ▁ministers- ▁statues- ▁Legends- rou- ▁attacks- ▁notch- ▁pointers- ▁etc- ▁gears- ▁Zhuan- ault- ▁seize- ▁puzzles- layer- ▁mana- ▁tents- ▁directional- ▁dun- omo- ▁snapp- ▁del- ▁Angu- ▁aids- ▁Ch- ▁Tun- ▁Jac- ▁sins- aki- ▁Ver- ▁wou- ▁fundamentals- non- ▁triangles- ▁buns- ▁bugs- now- ▁ele- run- ▁integrati- ▁recommendations- ▁Pad- ▁gar- shao- ▁hol- ▁fisher- old- ▁Raja- ▁spikes- ▁mun- ▁dinosaurs- ▁mentors- ▁Har- ▁spirits- ▁organizations- zy- ▁Christians- Magic- ▁dangers- erry- oi- ▁dock- ▁slopes- ▁artistes- ▁ru- ▁references- ongs- ▁Wiz- ▁markings- very- eresa- ▁kam- ▁blessings- ▁gigs- gle- ▁improvements- ▁pubs- ▁sib- bel- ▁comms- ▁meta- ▁pimples- ▁stares- ▁pota- ▁Ken- ene- Xun- ggle- ▁paw- ▁threat- ▁resonate- ▁axe- ▁schoolmate- ▁emerge- Studio- ixes- ▁merchant- ▁souvenir- hop- ▁kilometer- ▁syllable- ▁beverage- Boy- ▁competitor- ▁rumour- ▁ancestor- ▁sneaker- verse- ▁compromises- ▁whip- low- ▁brownie- ▁notion- assim- then- ential- ▁tang- ▁campaigns- ▁constrain- ▁zha- ami- ▁Eu- bal- ched- ▁constraint- fron- ▁Col- ▁Sai- ▁nu- agar- ▁riches- minate- ▁chil- Chin- minating- boro- iously- ▁historic- lay- ▁Pay- ookie- ▁Tea- turn- imp- field- ▁chang- room- place- ▁blu- De- power- keeper- qi- ▁stroll- rick- ▁snorkel- ▁Tao- ▁discharge- breaker- ▁empower- ▁vari- charge- ▁obsess- ▁locate- ▁injure- ▁disband- ▁disconnect- provoking- ▁recharge- Talk- Cooper- pedas- Bowl- Arabia- Brown- Pool- Storage- Republic- Tiger- Andrew- Mother- heaven- regardless- Bukit- kampung- eleven- Briyani- bono- curry- ▁moisturise- cute- with- Jones- ierce- Fox- ▁ima- ▁sensi- ▁convention- ▁Port- Chiong- onesty- ▁equip- iculous- ▁eavesdrop- ▁descend- ▁Naomi- ▁Warner- ▁cigar- Carter- Ferguson- Lloyd- Miserables- Purcell- '`'- ligence- ▁Atlanti- ▁Azazel- ▁Bendemeer- ▁Berlin- ▁Bluetooth- ▁Chirashi- ▁Derrick- ▁Dhey- ▁Dropbox- ▁Farouk- ▁Fifa- ▁Fitness- ▁Fullerton- ▁Henderson- ▁Jacqueline- ▁Kentucky- ▁Kukup- ▁Kumar- ▁Manila- ▁McLaren- ▁Mediterranean- ▁Mendaki- ▁Myeongdong- ▁Nagoya- ▁Nickelodeon- 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▁revamp- ▁rinsing- ▁robbery- ▁sakae- ▁salicylic- ▁seduce- ▁silicon- ▁silliest- ▁sincerity- ▁slogan- ▁soulmate- ▁stereo- ▁sunshine- ▁symphony- ▁synonym- ▁tchoukball- ▁territory- ▁thunderstorm- ▁tragedy- ▁troublemaker- ▁unethical- ▁unplanned- ▁unpleasant- ▁untidy- ▁unwilling- ▁urgency- ▁vanguard- ▁velvet- ▁vodka- ▁whitish- ▁wholeheartedly- ▁zumba- Gerrard- Kusama- ▁Aloy- ▁Bachelor- ▁Darren- ▁Dream- ▁Dumbledore- ▁Eugeo- ▁Fairprice- ▁Googling- ▁Hunger- ▁Khalid- ▁Manhattan- ▁Mystic- ▁Topshop- ▁cadet- ▁chemotherapy- ▁communities- ▁concise- ▁convincing- ▁diagram- ▁disintegrate- ▁fragile- ▁ideology- ▁irregular- ▁lavish- ▁narrator- ▁orangutan- ▁saikang- ▁triangular- ▁Cedar- ▁JetStar- ▁Laneway- ▁Persian- ▁Totally- ▁constitute- ▁delta- ▁eatigo- ▁emulate- ▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- ▁separat- ▁incur- ▁nurtur- ▁invad- ▁Find- ▁Prince- ▁Vic- scooter- corn- Karat- ▁subsidize- ummer- lbert- ▁congest- ▁recite- ▁misuse- ▁retrench- about- course- oodle- ▁debut- Smart- Batisah- Jovi- Turtle- ristiano- Airline- Titans- Ronaldo- Chomp- Cruise- Mirror- States- Nathan- Leong- kacang- Prata- Romeo- jalan- Autumn- William- negotiable- Chicken- James- Science- boyfriend- chocolate- machine- drama- twenty- Miam- ▁crisp- biryani- Mouse- center- ▁summar- motion- ▁theoretical- Minh- ▁clutter- ▁vivid- ▁instantaneous- ▁Lor- ▁Saba- asian- ▁authorit- ▁midfield- post- chiong- ▁imply- Wee- ▁proactive- ▁pronoun- ▁immigrati- ▁disrupt- eptic- ▁Buddh- ▁experi- ▁revolution- ▁Yayoi- ▁Xav- ▁Corp- Vuitton- ▁indefinite- ▁mislead- ▁residu- '?'- Bilis- Einstein- Giggs- Hospital- Local- Muniz- Quinn- Sivan- jumma- siol- ▁After- ▁Atiqah- ▁Backstreet- ▁Badminton- ▁Bidadari- ▁Britain- ▁Broadway- ▁Capella- ▁Century- ▁Chijmes- ▁Cinderella- ▁Colosseum- ▁CrossFit- ▁Cyclops- ▁Daegu- ▁Darvis- ▁Dasani- ▁Diablo- ▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- ▁accelerate- Donki- ▁cucumbers- ▁rifles- ▁Run- ▁gro- ▁insider- ▁jav- ▁meatballs- ▁pirates- ▁spi- ▁handy- ▁Pas- ▁blogg- kie- rack- illion- ▁Pak- ▁emojis- ▁kilos- ▁Thin- lying- ▁worksheets- ▁notebooks- ▁crows- cia- ▁Kent- urity- ▁anal- ▁chunks- ▁buds- ▁Zen- ude- ▁ze- ola- ▁concentrati- ▁shak- ▁selfless- ▁Masters- ▁landscapes- ▁milestones- ▁expires- ▁moderniz- misunderstanding- Rush- ▁lou- ipped- ▁growl- ▁skit- elle- oid- por- Cook- ▁stakes- ▁belongings- ▁passages- smati- ▁zhu- ▁storybooks- ▁Rav- ▁fra- opped- ▁val- ▁squirrels- ▁tales- ▁prais- ▁colon- kee- rul- ▁relocate- ▁Cad- ▁Gia- ▁oppo- ▁opp- ▁optim- alized- ▁zipp- ▁Phil- dar- neo- ▁Pant- nuel- ▁Shake- ▁auditor- ▁Zhu- own- ▁tec- ▁stru- ▁terminat- gue- ▁rece- achi- urn- ftie- ▁opposit- ▁exa- ▁worsen- ▁sti- folding- shui- lessly- ▁marginal- ▁lob- rom- ▁crumb- bble- ▁Alon- acy- ▁Jenni- ▁spe- ▁Band- ▁spra- ▁shoo- afar- ▁ste- bak- Pie- ▁stan- ▁Ou- ime- fetched- iterate- ▁stead- ▁ferri- elecast- anna- ▁Lam- dol- ▁Bab- uhwe- mpang- ▁mindless- rman- Leste- ▁sympath- ▁ken- Clan- pad- ▁Human- atory- chai- decker- ▁cous- ▁Com- onia- ▁Bee- ▁bermuda- ▁scallop- ▁Pilate- ▁diaper- ▁pebble- ▁germ- ▁coral- ▁investor- ▁statistic- Ranger- ▁brace- ▁lyric- ▁alway- ▁zo- ▁slush- merah- ▁perk- ▁researcher- alism- stage- leen- body- ▁rab- ▁sportsman- ▁neg- Cent- make- ▁decorat- ▁certif- ▁gran- ▁oldie- Cage- ▁iden- ▁clog- ▁Chang- ality- ▁hur- ▁glam- wo- ▁Pul- ▁Julie- ▁Cal- ▁lectur- ▁Sand- west- Jobs- ▁complet- ▁achiev- tude- oup- nova- away- ▁Kee- Ban- ▁purchas- ▁esca- ▁wor- ▁Himalaya- ▁froze- ▁Sho- ▁deserv- ▁blush- ▁hoard- ▁choreograph- ▁dishearten- craft- ▁deci- ▁windsurf- ▁disco- ▁sightsee- ▁accord- ventu- ▁figur- pson- ▁schem- ▁arrang- Angel- ▁snip- icular- world- ▁ostracise- ▁chant- ▁confiscate- ▁excite- ▁elect- ▁endanger- bloc- ▁detach- atholic- ▁suspend- Network- Singapore- Woman- vious- Grande- Brigade- holder- Scott- Three- Bull- chomp- child- takraw- colli- tempered- Obama- Ninja- Ocean- attaya- restricted- Ralph- lifting- spirit- Busan- Black- Stella- ngsana- tight- different- grass- merit- queue- wak- consider- road- Maths- Ayam- boat- ondeh- Grey- Singaporean- shock- ▁greed- heard- yana- obby- ▁Ronald- Strange- clockwise- ▁suff- ▁graceful- ologist- open- ▁carpent- astle- ▁swag- ▁servic- ▁rehears- ▁millenni- vergreen- ▁submissi- hennai- enegade- esistance- ▁Mexic- ▁obstruct- ▁Aqua- ▁coincide- ▁depart- ddling- ▁celeb- ▁deploy- evio- negotia- ▁hydro- ▁protrud- ▁Comfort- ▁Disco- ▁boycott- Beckham- Bennett- Carrey- Clues- DiCaprio- Muhammad- Payne- Ramsey- Ribbon- Rowling- Solo- Uemura- arella- ligently- rrogate- ▁AddMath- ▁Adele- ▁Aidil- ▁Akbar- ▁Aloysius- ▁Anderson- ▁Anjib- ▁Anusha- ▁Bermuda- ▁Buddy- ▁Canberra- ▁Carnage- ▁Clean- ▁Colorado- ▁Coupet- ▁Daryl- ▁DeepMind- ▁Depot- ▁Donnie- ▁Durian- ▁Edusave- ▁Fiido- ▁Georgia- ▁Ghibly- ▁Gudetama- ▁Guinness- ▁Guzheng- ▁Heidi- ▁Hummus- ▁Immortal- ▁Insidious- ▁Jewel- ▁Kahshee- ▁Katherine- ▁Kelantan- ▁Kozminski- ▁Labyrinth- ▁Loann- ▁Lookism- ▁MUIS- ▁Mcnugget- ▁Millennials- ▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- ▁espresso- ▁execution- ▁exfoliant- ▁exquisite- ▁fingertip- ▁gizzard- ▁globe- ▁graffiti- ▁hiccups- ▁hokkaido- ▁hyperactive- ▁iModel- ▁imitation- ▁impediment- ▁indebted- ▁indomie- ▁inertia- ▁infested- ▁infinite- ▁interconnected- ▁introspective- ▁intrusive- ▁jewelleries- ▁keloid- ▁keropok- ▁knuckle- ▁liddat- ▁lychee- ▁mackerel- ▁malamute- ▁mechatronics- ▁memorabilia- ▁menacing- ▁menthol- ▁messenger- ▁migraine- ▁mortgage- ▁muachee- ▁muffled- ▁mussels- ▁narrate- ▁negotiation- ▁nitrogen- ▁obscure- ▁oriental- ▁overcooked- ▁overcrowded- ▁paitao- ▁parasite- ▁persuasion- ▁pinafore- ▁pistachio- ▁pistol- ▁predator- ▁premature- ▁preoccupied- ▁prettie- ▁profiling- ▁prototype- ▁radish- ▁reincarnated- ▁reindeer- ▁repurpose- ▁resilient- ▁retrospect- ▁revenue- ▁rhino- ▁ritual- ▁roulette- ▁salvage- ▁sandbag- ▁scorpion- ▁scrawny- ▁sculp- ▁secluded- ▁silhouette- ▁simulation- ▁simultaneously- ▁slipshod- ▁sneezing- ▁snippet- ▁sparring- ▁splatter- ▁spooky- ▁sprinkle- ▁structural- ▁strudel- ▁stylus- ▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- ▁locket- ▁fluently- vour- ▁detached- ▁biasness- ▁hypo- ▁practicality- ▁elected- ▁Wood- ▁Batisah- ▁Turtle- ▁Grabfood- ▁Jovi- ▁sev- ▁cooper- ▁rundown- ▁Sedan- ▁queer- ▁kaka- ▁Stal- ▁Xbox- ▁swerve- ▁native- ▁Bowl- ▁Talk- ▁confiscated- ▁stubbornness- ▁factories- ▁jiak- ▁damm- ▁saman- ▁traumatize- ▁profitable- ▁Gmail- ▁Sakurai- ▁downfall- ▁quali- ▁percentile- ▁keng- ▁Made- ▁Factor- ▁varied- heongsam- ▁ahem- ▁signpost- ▁Biz- ▁Vista- ▁Born- ▁occasional- ▁Liang- ▁ditch- ▁crip- ▁muse- ▁Everest- ▁mainland- ▁compu- ▁denser- ▁Lagoon- ▁Utama- essert- ▁Nepali- ▁Show- ▁potion- ▁creamier- ▁Jake- ▁zipper- ▁Six- ▁Cooper- ▁Zhen- ▁pesto- ▁Guo- ▁realisation- ▁maki- ▁cheong- ▁jug- ▁restructur- chup- ▁activated- ▁minimally- ▁Chiong- ▁clone- ▁kaw- ▁crushes- ▁bender- ▁nani- ▁Minh- ▁westerners- ▁implying- ▁mustache- ▁brushes- Xie- ▁urgently- ▁tuning- ▁Sister- ▁burped- ▁eliminated- ▁strengthen- ▁doughy- ▁sizing- ▁giveaway- ▁bronzer- ▁comical- ▁disrespected- ▁beeping- ▁campaigner- wang- ▁Nara- ▁Soon- ▁Circu- ▁merely- ▁enduring- ▁exceeded- ▁standee- ▁Cass- ▁Premiere- ▁godson- ▁fleshy- ▁skii- ▁dane- ▁sketching- tiao- ▁sesh- itive- ▁tangled- Bai- Chap- ▁Gaga- ▁bir- ▁whisker- ▁Grande- ▁Post- ▁coverall- ▁distributed- ▁underlined- ▁amused- ▁adapter- ▁bouldering- ▁externally- ▁hotline- 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▁blackcurr- ▁bookshelf- ▁calefare- ▁cashflow- ▁categorise- ▁caterpillar- ▁cheapskate- ▁chief- ▁clarity- ▁clause- ▁cleave- ▁codependent- ▁commonwealth- ▁compilation- ▁concurrently- ▁conspiracy- ▁conspire- ▁corruption- ▁cottage- ▁credible- ▁crescent- ▁damaging- ▁decline- ▁declining- ▁degenerate- ▁demolition- ▁demotivate- ▁devastated- ▁devastating- ▁diaphragm- ▁dictator- ▁disgrace- ▁dispatch- ▁displeasure- ▁dystopian- ▁elitism- ▁endorphin- ▁ensemble- ▁equity- ▁estella- ▁exempted- ▁exorbitant- ▁fanciful- ▁flaunt- ▁flavourtown- ▁forefather- ▁forensic- ▁gibberish- ▁gimme- ▁gloomy- ▁glucose- ▁gratification- ▁gruesome- ▁guava- ▁gynae- ▁hammock- ▁harmonizer- ▁heaviest- ▁hibernate- ▁hickey- ▁honeydew- ▁hungrier- ▁hurried- ▁hypothermia- ▁imaginative- ▁impractical- ▁inducing- ▁inefficiency- ▁infatuated- ▁influenza- ▁infused- ▁inquisitive- ▁insecurities- ▁insignificant- ▁jaguar- ▁jinx- ▁kanken- ▁lavender- ▁lenient- ▁lilac- ▁litigation- ▁locksmith- ▁loiter- ▁longevity- ▁maggots- ▁maisonette- ▁majestic- ▁meritocracy- ▁millipede- ▁mojito- ▁monastery- ▁monorail- ▁mukbang- ▁nepotism- ▁nonchalant- ▁nonetheless- ▁obesity- ▁odac- ▁opaque- ▁origami- ▁ostrich- ▁outstretch- ▁overpopulated- ▁override- ▁parapet- ▁patriarchal- ▁persuasive- ▁phenomenon- ▁pigmentation- ▁piloxing- ▁pomelo- ▁powerbank- ▁preservation- ▁pressurising- ▁prostitute- ▁prostitution- ▁rambling- ▁recreating- ▁rectify- ▁reimburse- ▁relevanc- ▁remedies- ▁renowned- ▁reorganise- ▁reptile- ▁responsive- ▁rethink- ▁reunite- ▁revelation- ▁rosti- ▁sabotage- ▁sacrificial- ▁sanctuary- ▁saviour- ▁sidekick- ▁skinnier- ▁slapped- ▁sleek- ▁slurp- ▁smirk- ▁stellar- ▁stench- ▁storytelling- ▁stylo- ▁submarine- ▁substantiate- ▁sunbathing- ▁surcharge- ▁surmise- ▁surreal- ▁suspens- ▁swept- ▁tactful- ▁tangerine- ▁thanksgiving- ▁threadmill- ▁tortoise- ▁transmission- ▁trickshaw- ▁tumeric- ▁turmeric- ▁unbreakable- ▁unconventional- ▁unfamiliar- ▁unforgettable- ▁unfulfill- ▁unheal- ▁unpack- ▁unreliable- ▁unveil- ▁velocity- ▁vigilant- ▁volatile- ▁wacoal- ▁wicked- ▁wilderness- ▁withdrew- Ketchum- ▁Brokeback- ▁Clifford- ▁Economics- ▁Eeyor- ▁Eudora- ▁Kebab- ▁Matcha- ▁Narelle- ▁Patrick- ▁Salim- ▁berapa- ▁concuss- ▁entangle- ▁imprison- ▁inactive- ▁incinerati- ▁insured- ▁irritable- ▁juggling- ▁kerb- ▁penthouse- ▁physiotherapist- ▁posterior- ▁prosecut- ▁robust- ▁shrunk- ▁solitude- ▁turd- ▁underpass- ▁wholemeal- ▁yip- Ambeng- Kennedy- ▁Hydr- ▁Inisha- ▁Kellogg- ▁Morinho- ▁Petercrouch- ▁Reader- ▁desensitize- ▁eggplant- ▁embroider- ▁enchant- ▁fandom- ▁hamburger- ▁harmonic- ▁investigating- ▁lassi- ▁leftward- ▁miserably- ▁mistress- ▁pagoda- ▁pigtail- ▁pulpo- ▁recognising- ▁reign- ▁riffs- ▁sleepwalk- ▁tatami- ▁temptation- ▁twitch- ▁yikes- itamin- ▁Pierre- ▁bypass- ▁delegat- ▁pawnshop- Charlotte- endrick- lender- ▁Aberc- ▁Carrot- ▁Kamu- ▁Loyang- ▁Peaky- ▁Smurf- ▁Yahoo- ▁garner- ▁pressurizing- ▁simplify- Cowell- Neeson- ▁Apax- ▁Clive- ▁bloop- ▁bodied- ▁eagle- ▁jeera- ▁republic- ▁sachet- ▁witchcraft- ▁worrywart- ▁Jumper- ▁Patek- ▁Shannon- ▁Tofu- ▁Zubi- ▁airshow- ▁beacause- ▁caesarean- ▁inspiriting- ▁maximize- ▁microbio- ▁mysteries- ▁remedy- ▁saloon- ▁saucy- ▁teapot- ▁tutu- ▁Lovisa- ▁Saraca- ▁Scout- ▁arbitrary- ▁fuming- ▁ivy- ▁lollipop- ▁poetic- ▁solemn- Chaplin- ▁Carlyn- ▁Collin- ▁Gelato- ▁Kenji- ▁Merek- ▁stutter- ▁Sahana- ▁Sehun- ▁holocaust- ▁plaque- ▁Masala- ▁Pyth- ▁comedies- ▁dabbing- ▁eavesdropping- ▁frilly- ▁revolutionary- ▁uprising- ▁Yankee- ▁Melody- ▁Saku- ▁Ubergrapher- ▁obligated- ▁robbing- ▁villian- Granger- fected- ▁Peace- ▁assurance- ▁collide- ▁stubble- ▁tiresome- ologne- ▁Phisha- ▁Rasa- ▁cauli- ▁Germaine- ▁Libra- ▁Seventeen- ▁agak- ▁dividend- ▁Raned- ▁Stray- ▁detest- ▁esters- ▁froyo- ▁recollection- ▁toppled- ▁prerequisite- ▁pyjamas- ▁sacred- ▁selves- ▁Fariz- ▁Lucy- ▁Musa- ▁Davina- ▁Judy- ▁blemishes- ▁hippie- ▁porch- ▁skewe- ▁exponentially- ▁mistreat- ▁sparrow- Diaries- kram- ▁gourd- ▁horlick- ▁ascend- ▁daft- ▁subtraction- ▁maturing- ▁normative- ▁Aquaman- ▁Farid- ▁Pinang- ▁Soju- ▁parody- ▁rasp- Xiong- ▁Demi- ▁Hebe- ▁gently- ▁shameful- ▁drumlet- ertis- ▁confrontational- ▁pupa- ▁drastically- ▁guac- ▁Jared- ▁Marmalade- ▁empowerment- ▁flunk- ▁jellybean- ▁visc- Paddle- ▁Dollah- ▁Tangled- ▁bullier- ▁clapping- ▁resembles- Care- ▁Asos- ▁Moza- ▁Westgate- ▁quarry- ▁Lazuli- ▁chorus- ▁tequila- ▁wafer- ▁Jeanette- ▁SpiderMan- ▁grumbling- ▁kinship- Lapis- Madrid- ikgu- ▁coordinator- ▁dahl- ▁yoogane- ▁Kimberly- ▁Pharrell- ▁brigade- ▁forgivable- ▁dyslexia- ▁nicotine- ▁sensitivity- ▁Chick- ▁Edward- Ferrell- ▁Siva- ▁bleak- ▁Cheong- ▁civilized- ▁reciprocation- ▁Singsale- ▁elementary- ▁laloo- ▁Gear- sibility- ▁Mogu- ▁limelight- Train- ▁kakak- ▁shortlist- rped- ▁carousell- ▁footprint- Kurau- ▁globalised- ▁mower- ▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- ▁modification- ▁Asam- ▁shawl- ▁Heath- ▁chapteh- ▁Desiree- ▁alignment- ▁rollerblade- ▁grit- ▁vacancy- liath- ▁Mitchie- ▁purposeful- idle- ▁Salman- ▁cylinder- ▁supposing- ▁Dora- ▁Institute- ▁Convenience- ▁Future- ▁Gabriel- ▁Gateway- ▁Krunch- ▁Satan- ▁Schwarzenegger- ▁Technical- ▁Theatre- ▁alteration- ▁hush- ▁crayon- ▁snipe- Kardashian- ▁moonlight- ▁emit- ▁Stephenie- face- ▁aburi- ▁constipated- ▁Kurt- ▁nutri- ▁cowardly- ▁omega- ldering- ▁hopped- ▁recruitment- ▁slavery- ▁lever- ▁faci- ▁Austin- ▁rotan- ▁breathless- ▁indian- ▁swish- ▁Spartan- ▁Usman- ▁Tabata- ▁precaution- Law- ▁Anabelle- ▁Carrie- indef- ▁vary- ▁moisturize- ▁pester- ▁underdog- ▁gracefully- ▁Aquarium- ▁Caribbean- ▁Hathaway- zuo- ▁installment- ▁Shepard- ▁ladu- ▁acknowledgement- ▁affordability- ▁unfriendly- ▁tagline- ▁Camp- ▁TAF- ▁noticing- Group- ▁instantaneously- ▁roman- ▁salivating- Duck- yaki- ▁betrayal- ▁laces- ▁Fault- ▁waive- ▁Grabpay- ▁Fomo- ▁exert- ▁utai- ▁disposal- ▁unpaid- luted- liate- ▁pullover- ▁Curry- ▁Possible- 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▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- ▁amputat- ▁usua- ▁Gol- ▁Bur- lopp- after- ▁Havai- killers- ▁swop- oosebumps- sheet- ozy- ▁qualifi- ushed- ▁cohesi- ▁squeez- ▁smel- ▁Choo- ▁consol- ▁swerv- view- ▁schedul- ▁shiver- ▁troubl- heart- ▁ide- ▁initiat- ▁desir- ▁retir- Sua- ▁emphasiz- ▁reschedule- ▁inflate- 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dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- ▁Gulliver- ▁HabourFront- ▁Hanzo- ▁Harzik- ▁Hayde- ▁Hiragana- ▁Holdings- ▁Hollandaise- ▁Hologram- ▁Honestbee- ▁Hoshino- ▁Hsinchu- ▁Hunter- ▁Husserl- ▁Imperial- ▁Inktober- ▁Innok- ▁Jamaica- ▁Janabelle- ▁Jefri- ▁Jolibee- ▁Kendra- ▁Kenora- ▁Khalees- ▁Kinokuniya- ▁Kitori- ▁KouFu- ▁Leaves- ▁Leuch- ▁Lexus- ▁Libya- ▁Lilac- ▁Lontong- ▁Lorraine- ▁Luffy- ▁Management- ▁Matthew- ▁McNugget- ▁Motorola- ▁Mumbai- ▁Nebraska- ▁Nelson- ▁Nigeria- ▁Nobel- ▁Nothingness- ▁Nyx- ▁Oliander- ▁Otogi- ▁Paradise- ▁Pentatonix- ▁Petpetpet- ▁Pharaoh- ▁Poppins- ▁Portuguese- ▁Poteng- ▁Prakash- ▁Pretty- ▁Puff- ▁Qihua- ▁Reebonz- ▁Refash- ▁Remember- ▁Revlon- ▁Ribena- ▁Rooney- ▁Roxy- ▁Sabbath- ▁Sabsuka- ▁Scorpio- ▁Scramble- ▁Senibong- ▁Sennheiser- ▁Sensei- ▁Seraphina- ▁Shabir- ▁Shrek- ▁Silence- ▁Sogou- ▁Soraya- ▁Spotty- ▁Squirtle- ▁Supernatural- ▁Swarovski- ▁Syaz- ▁Sylvester- ▁Technique- ▁Thanksgiving- ▁Thosai- ▁Thriller- ▁Tigreal- ▁TikTok- ▁Transylvania- ▁Trivers- ▁Ukraine- ▁Uprising- ▁Valuetronic- ▁Vatican- ▁Velvet- ▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- 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▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- udrey- ▁constipat- ▁jumble- ambodia- ▁crook- stresses- sighted- Dogg- olution- urrender- tyrofoam- ▁impair- Buri- Escobar- Jetty- Showman- Shuttle- Tyson- ceased- aktor- ▁unexpect- Technical- Grill- Convenience- Future- Institute- Krunch- Schwarzenegger- Theatre- polished- Gabriel- antorini- Evan- Keane- Monster- ntegrated- Francisco- ▁deprive- ▁discriminat- ▁referenc- Kenny- Murder- Zhang- carriage- ▁Joan- ▁Skipp- ▁monogam- ▁prophec- Atwood- Stamford- ylvia- Akshay- Banyan- Virgin- linguistic- proportionate- founder- Music- buoy- evaluate- immortal- comparable- Jade- Miller- Friza- conditioned- innacle- Morrie- Waterway- conductor- crow- ndependence- Henry- Monkey- Nyonya- Shaw- monkeys- second- Shakespeare- catcher- organised- qualified- storage- Thomson- animate- executive- invent- starter- ▁rollerblad- ▁vacanc- Festival- Jordan- emphasis- frequent- settled- weekly- ▁Desire- college- factory- growth- organisation- profit- sentosa- Holland- Kampung- Laden- Nature- Seoul- Spirit- Tae- bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- ▁interesting- ▁else- ▁together- ▁car- ▁called- ▁whatever- ▁show- ▁white- ▁sorry- ▁change- ▁pay- ▁kids- ▁basically- ▁done- ▁seven- ▁free- ▁hundred- ▁learn- ▁green- ▁fun- ▁book- ▁told- ▁small- ▁off- ▁answer- d- ▁sign- ▁usually- ▁eight- ▁few- C- ▁open- ▁wanna- ▁mum- ▁chicken- ▁myself- ▁understand- ▁A- ▁means- ▁care- ▁both- ▁describe- ▁game- ▁once- ▁saying- ▁used- ▁teacher- ▁saw- ▁inside- ▁later- ▁happen- ▁C- ▁through- ▁date- ▁yourself- S- ▁stay- ▁looking- ▁movie- ▁dollars- ▁during- ▁move- ▁live- ▁until- ▁primary- ▁least- ▁nine- ▁wearing- ▁card- ▁thirty- ▁stop- ▁alright- ▁wanted- ▁trying- ▁Chinese- ▁each- ▁everyone- ▁having- ed- ▁example- ▁enough- ▁plus- ▁s- ▁haven- ▁cook- ▁working- ▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- ▁Singlish- ▁sweet- ▁train- ▁throw- ▁dark- ▁education- ▁early- ▁toilet- ▁simple- ▁uni- ▁tuition- ▁air- ▁restaurant- ▁honest- ▁living- ▁telling- ▁p- ▁price- ▁per- ▁Japan- ▁paper- ▁uncle- ▁minute- ▁itself- ▁terms- ▁overseas- hor- ▁driver- ▁o- ▁F- ▁set- ▁childhood- ▁words- G- ▁cards- ▁hmm- ▁brand- ▁grow- ▁reach- ▁choice- V- b- ▁scary- ▁main- ▁birthday- huh- ▁China- ▁cat- ▁career- ▁somebody- ▁along- O- ▁extra- u- ▁lunch- ▁hit- ▁lost- ▁subject- ▁exercise- ▁Korea- ▁hey- ▁parent- ▁add- ▁straight- E- ▁window- right- ▁chair- ▁explain- ▁sound- ▁soccer- ▁c- ▁online- ▁drama- ▁making- ▁culture- ▁fight- ▁k- ▁literally- ▁related- ▁latest- ▁interest- ▁felt- ▁human- ▁blah- ▁cream- ▁apparently- ▁G- er- ▁tired- ▁kinda- ▁studying- ▁earn- ▁suppose- ▁bird- ▁drop- ▁fried- ▁count- ▁holding- ▁m- ▁recess- ▁dating- ▁n- ▁forget- ▁sixty- ▁movies- ▁R- ▁catch- ▁baby- ▁color- ▁smart- ▁shoe- v- ▁teh- ▁success- ▁fact- ▁embarrassing- ▁pants- ▁countries- ▁dish- ▁Indian- ▁sports- ▁shows- ▁egg- ▁mostly- ▁rich- ▁chance- ▁Korean- ▁piece- ▁truly- ▁elderly- ▁soon- ▁social- ▁Singaporeans- ▁sort- ▁meaningful- ▁general- ▁angry- ▁shack- ▁club- ▁cooking- ▁seat- ▁clear- ▁lose- ▁photo- ▁hi- ▁busy- ▁period- ▁non- ▁fair- ▁dude- ▁questions- ▁running- ▁memorable- ▁holiday- ▁item- ▁leg- ▁system- ▁un- ▁bar- ▁interested- ▁touch- ▁sea- ▁couple- ▁zero- ▁self- ▁planning- ▁post- ▁video- ▁dead- ▁ex- ▁park- ▁totally- ▁hall- ▁store- ▁weekend- ▁building- ▁loud- ▁happiest- ▁milk- ▁trust- ▁chill- ▁lobang- L- ▁common- ▁fit- ▁board- ▁cost- ▁brought- ▁case- ▁laugh- ▁situation- ▁skin- ▁power- ▁re- ▁friendship- ▁f- ▁consider- ▁fat- ▁wish- ▁fall- ▁wash- ▁event- ▁soup- ▁woman- ▁willing- ▁sec- gosh- ▁Grab- ▁faster- ▁voice- ▁L- ▁trait- ▁dance- ▁differences- ▁entire- ▁effort- ▁science- ▁waste- ▁strong- ▁meat- ▁awkward- ▁specific- ▁exam- ▁current- ▁husband- ▁son- ▁seems- ▁visit- F- ▁box- ▁centre- ▁skip- ▁expect- ▁fire- ▁draw- ▁hero- ▁teachers- ▁whenever- ▁amount- ▁often- ▁kill- ▁Japanese- ▁boyfriend- ▁space- ▁support- ▁wise- ▁kay- ▁army- ▁ready- ▁giving- ▁tree- ▁art- ▁hobby- ▁swimming- ▁shoot- ▁football- ▁comfortable- ▁jump- ▁easier- ▁control- ▁public- ▁cake- ▁low- ▁spot- ▁gym- ▁search- ▁weeks- ▁walking- ▁lazy- ▁learning- ▁speaking- ▁carry- ▁orange- ▁similar- ▁soft- ▁pets- ▁seriously- ▁health- ▁World- ▁standing- ▁compared- ▁gave- ▁bake- ▁anywhere- ▁ninety- ▁ate- ▁Friday- l- ▁New- ▁Paul- ▁socks- ▁facing- ▁view- g- ▁worry- ▁apply- ▁ability- ▁based- ▁message- ▁fly- ▁huge- ▁season- ▁bed- ▁taken- ▁lucky- ▁freaking- ▁return- ▁hell- ▁computer- ▁tend- ▁smoke- ▁others- ▁aiyo- ▁relax- ▁energy- ▁hub- ▁waiting- ▁mix- ▁daughter- ▁bottle- ▁meaning- ▁corner- ▁sick- ▁office- ▁term- f- ▁sheep- ▁sauce- ▁size- ▁city- ▁driving- ▁currently- ▁cheaper- ▁eyes- ▁crab- ▁boss- wah- ▁hang- ▁safe- ▁step- ▁advice- ▁eighty- ▁happened- ▁match- ▁deep- ▁particular- ▁impression- ▁shall- ▁pull- ▁bread- ▁possible- ▁round- ▁comfort- ▁blind- ▁ride- ▁kampung- ▁meeting- ▁wine- ▁staying- ▁internship- ▁seventeen- ▁stall- ▁longer- ▁watched- al- ▁higher- ▁training- ▁remembered- ▁Sunday- ▁signboard- ▁closest- ▁wonder- ▁project- ▁fan- ▁caught- ▁government- ▁form- ▁tough- ▁Google- ▁everywhere- ▁healthy- ▁build- ▁trend- ▁cheese- ▁hat- uh- ▁vegetable- nah- ▁within- ▁slowly- ▁market- ▁block- ▁Saturday- ▁hotel- ▁technology- ▁machine- ▁craziest- ▁whereby- ▁selling- ▁math- ▁Australia- ▁makeup- ▁sand- ▁although- ▁works- ▁mention- ▁weather- ▁Hong- ▁manage- ▁afraid- K- ▁bike- ▁easily- ▁above- ▁alive- ▁listening- ▁respect- ▁mouth- ▁eventually- ▁feelings- ▁worse- ▁smell- ▁dress- ▁party- ▁language- ▁quiet- ▁chocolate- ▁possession- ▁seventy- ▁exams- ▁tray- ▁sun- ▁amazing- ▁mom- ▁e- ▁basic- ▁songs- ▁forward- ▁cap- ▁rock- ▁splurge- ▁stick- ▁busybody- ▁define- ▁woah- ▁act- ▁b- ▁contact- ▁issue- ▁potato- ▁studies- ▁fishing- ▁empty- ▁treat- ▁sleeping- ▁decide- ▁grandfather- ▁bigger- ▁previous- ▁asked- ▁cover- ▁classes- ▁d- ▁jobs- ▁kidding- ▁slow- ▁properly- ▁con- ▁laptop- ▁balance- ▁shark- ▁everytime- ▁X- ▁met- ▁base- ▁opposite- ▁canteen- ▁design- ▁random- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- 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▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- ▁alliteration- ▁anemone- ▁antihero- ▁applause- ▁apprehensive- ▁arrogance- ▁articulation- ▁ascertain- ▁asparagus- ▁bibliography- ▁bittergourd- ▁blasphemy- ▁bourgeois- ▁buckwheat- ▁bursaries- ▁cajon- ▁carbohydrate- ▁categorize- ▁catwalk- ▁caviar- ▁centimeter- ▁chameleon- ▁chauffeur- ▁chawanmushi- ▁cheddar- ▁cheebye- ▁cheerleader- ▁chimichanga- ▁chromo- ▁chrysa- ▁circumference- ▁clarinet- ▁coastguard- ▁cocoon- ▁commuting- ▁concious- ▁condolence- ▁confetti- ▁conglomerate- ▁congregate- ▁connectivity- ▁correspond- ▁coworker- ▁crisscross- ▁cuff- ▁customaries- ▁cutlery- ▁deceiving- ▁declaration- ▁deconstructor- ▁delifrance- ▁delinquent- ▁depleted- ▁digimon- ▁diminish- ▁disciple- ▁discrete- ▁discretion- ▁disparity- ▁disregard- ▁drenched- ▁droop- ▁dwindling- ▁dynasties- ▁eEcons- ▁electromagnetic- ▁enforcing- ▁entrap- ▁entrusting- ▁equilibrium- ▁euthanasia- ▁expedit- ▁expendable- ▁explanatory- ▁exploration- ▁eyebags- ▁faggot- ▁fajitas- ▁fashionista- ▁feminine- ▁fictitious- ▁florist- ▁foggy- ▁freeloader- ▁fretboard- ▁furnish- ▁fuzzy- ▁gachapon- ▁gallon- ▁gallop- ▁gonorrhea- ▁grenade- ▁griev- ▁hairpins- ▁handicraft- ▁herbivores- ▁hibernating- ▁hippopotamus- ▁hitchhiker- ▁hobbit- ▁homogeneous- ▁horribly- ▁hyena- ▁iPod- ▁identifiable- ▁ideologies- ▁igloo- ▁imperfection- ▁impolite- ▁incompetent- ▁indigestion- ▁infertile- ▁infringing- ▁instalment- ▁intricate- ▁investigator- ▁invoice- ▁iodine- ▁isolation- ▁jargon- ▁jenny- ▁jockey- ▁kicap- ▁kidnapped- ▁kinetic- ▁lechon- ▁lethargic- ▁lexicon- ▁ligament- ▁loudspeaker- ▁malacca- ▁mastermind- ▁melodies- ▁mischie- ▁miscount- ▁misguided- ▁monotony- ▁muesli- ▁multicultural- ▁multiplayer- ▁murta- ▁muruk- ▁neanderthal- ▁nihon- ▁nudity- ▁nutcracker- ▁nutritious- ▁ostracize- ▁otaku- ▁outburst- ▁outweigh- ▁overarching- ▁overcrowding- ▁overhyped- ▁overpowered- ▁paramour- ▁paraphras- ▁peacemaker- ▁perspec- ▁pessimism- ▁pesticide- ▁petroleum- ▁phenomenal- ▁phlegm- ▁physique- ▁pickpocket- ▁plural- ▁poeple- ▁precision- ▁prescribe- ▁pricier- ▁prorated- ▁pupil- ▁quantitative- ▁quarantine- ▁radius- ▁ramekins- ▁rebatch- ▁reclusive- ▁recuperate- ▁reenacti- ▁refurnish- ▁regenerate- ▁registry- ▁reinstate- ▁relativity- ▁relegate- ▁reprimand- ▁respawn- ▁retention- ▁retrac- ▁rewriting- ▁rofl- ▁rudimenta- ▁rupee- ▁scrutinise- ▁serenade- ▁shoelace- ▁shrimp- ▁shrub- ▁silhoutte- ▁skilful- ▁smudge- ▁smuggling- ▁societies- ▁spatula- ▁splinter- ▁sprout- ▁stepdad- ▁succinct- ▁sugarcane- ▁sunroof- ▁surgical- ▁surveillanc- ▁suspicion- ▁symmetr- ▁thickener- ▁titanium- ▁toothpick- ▁tornado- ▁transsexual- ▁treading- ▁trench- ▁unattainable- ▁unavailable- ▁unbeat- ▁uncountable- ▁undercover- ▁underprivilege- ▁unemployment- ▁unfrozen- ▁unleashed- ▁unopened- ▁unprepared- ▁unscrew- ▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- 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bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    unit: 1000    nlayers: 1required:- output_dir- token_listversion: 0.9.6distributed: true",
+    "description_zh": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- 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can- ▁yearly- ▁terminate- ▁connections- ▁joint- ▁exception- ▁drip- ▁scrape- ▁cre- ▁listing- ▁Fun- ▁scrap- ▁lan- ▁questioning- ▁Mar- ythic- ▁scholars- hipped- hu- ▁reader- ▁col- ▁quotes- ▁strengths- ery- ▁calculation- ▁feather- ▁muffin- ▁restriction- ▁slaves- ▁downward- ▁Bun- ▁handles- ▁sandal- ▁shoulders- ▁exists- od- ▁lac- ▁que- ec- ▁Wu- ▁Ice- ring- ▁Dad- ▁ter- id- ▁Fat- ▁rob- ▁pointer- ▁min- ▁wan- ▁sy- ▁Roman- ▁sua- tai- ▁gut- ▁nieces- uhyou- ▁hun- School- ▁overthink- ▁execute- scape- ▁pierce- Kayu- ▁Louis- America- club- House- ▁rotate- Hao- ▁singlet- ▁gracious- ▁Taufik- ▁Mickey- ▁Beyblade- ▁Clara- ▁Donald- ▁Drake- ▁Eiffel- ▁Kelvin- ▁Natalie- ▁Nigel- ▁PlayStation- ▁Porsche- ▁Sundram- ▁Tamarind- ▁Tamiya- ▁Watson- ▁Zumba- ▁allergy- ▁ambience- ▁balancing- ▁belacan- ▁bimbo- ▁bisque- ▁bouncy- ▁carried- ▁clarify- ▁climax- ▁condominium- ▁decrease- ▁diversity- ▁efficiency- ▁eyelid- ▁fascinate- ▁feast- ▁fickle- ▁filtration- ▁forklift- ▁fortune- ▁increment- ▁infrastructure- ▁mattress- 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▁attribute- ▁Caucasian- ▁Alia- ▁bud- ▁acquaintance- ▁participant- ▁graphic- ▁electives- ▁earphone- ▁creature- ▁trunk- ▁females- ▁grandparent- ▁flyer- ▁awards- ▁sl- ▁instance- ▁nutrition- don- ▁tra- ▁prom- ▁Sa- ala- ▁tons- ▁Lu- ▁hamsters- ▁touche- chu- ▁swan- ▁relations- ▁via- ▁monsters- lie- ▁poo- ▁Teo- ▁ir- min- ▁hoo- ▁nets- ▁Pin- ▁High- ▁containers- ▁dan- ▁ratio- ▁disappoint- ▁Panda- wood- ▁distribute- ▁cultivate- ▁sneak- ▁exceed- Faber- McCartney- Fingers- Padang- tarik- Hotel- screen- centre- ▁dense- wei- Gaga- ▁swam- ▁fade- Wang- ▁underline- ▁interfere- ▁Sherlock- Soto- ▁Alibaba- ▁Benedict- ▁Cambridge- ▁Dylan- ▁Eunice- ▁Florence- ▁Gordon- ▁Kraken- ▁Kranji- ▁Oliver- ▁Rolex- ▁alarum- ▁autumn- ▁backyard- ▁beancurd- ▁brisk- ▁burrito- ▁butterflies- ▁collaboration- ▁concubine- ▁denim- ▁envelope- ▁esteem- ▁eyesight- ▁frivolous- ▁guidance- ▁heartbroken- ▁intensive- ▁kettle- ▁khaki- ▁lemonade- ▁liberal- ▁magenta- ▁mousse- ▁mundane- ▁muscular- ▁necessities- ▁negotiate- ▁obsolete- 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▁humidity- ▁treetop- ▁Faith- ▁Galaxy- ▁presume- ▁shady- ▁egoistic- ▁fellowship- ▁lighthearted- ▁Kiri- ▁suc- ▁waterproof- ▁Bangla- ▁surrender- ▁warden- ▁banter- ▁scanning- ▁bali- ▁coincidentally- ▁Totoro- ▁storeroom- ▁confidential- ▁squishy- ▁pension- ▁Danny- ▁diluted- ▁Asus- ▁stationery- ▁posh- ▁westernised- ▁Valley- ▁deprived- ▁Mandai- ▁Wanna- ▁gymming- ▁submitted- ▁secretive- ▁Shuan- ▁hangover- ▁Switch- ▁floss- ▁introducing- ▁Nancy- Mars- ▁autis- ▁Baba- ▁existent- ▁corgi- ▁Ireland- Shiong- ▁Amos- ▁Rover- ▁Suria- ▁punk- ▁spize- ▁paperwork- ▁Liao- ▁Liz- Meng- Out- ▁expressway- ▁sniper- ▁leap- ▁idiotic- ▁lingo- ▁dent- ▁clinical- ▁Sega- ▁elf- ▁Fuji- ▁Leona- ▁specialisation- ▁intentionally- ▁quitted- ▁Cody- ▁bedok- ▁chanting- ▁cyst- ▁decorating- ▁competitiveness- ▁Lok- ▁upsetting- ▁integrated- ▁transcriber- ▁gray- ▁censored- ▁munch- ▁portal- ▁vast- ▁budd- ▁sloth- ▁stopover- ▁publicity- ▁Music- ▁approachable- ▁strolling- ▁confined- ▁Jones- ▁Singap- ▁coaches- ▁allocated- ▁escalate- 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▁foie- ▁proportionate- ▁invention- ▁dental- ▁jumbled- ▁modernised- ▁glider- ▁tempting- ▁moist- ▁halfhearted- ▁sphere- ▁Business- ▁Halimah- ▁irri- ▁nuance- ▁batman- ▁punny- ▁symbolise- ▁empire- ▁deduction- ▁hardwork- ▁installation- ▁fanatic- ▁conceive- ▁layman- ▁Center- ▁stitches- ▁Dark- ▁Johnson- ▁broom- ▁codeine- ▁meteor- ▁disgusted- ▁faraway- ▁gelat- ▁maze- ▁Navy- ▁Scott- ▁Three- ▁Miller- ▁adjustment- Fong- ▁involvement- ▁jab- ▁Belle- ▁continental- ▁Fanny- ▁suspended- ▁brotherhood- ▁buttock- ▁setback- ▁harness- ▁collagen- ▁deliveries- ▁healthily- ▁hospitality- ▁Colo- ▁hypothetically- ▁branches- ▁bandung- ▁vaping- ▁Peru- ▁despi- ▁plantation- ▁disbanded- ▁disconnected- ▁husky- ▁Rosa- ▁Ronaldo- ▁consciousness- ▁Bread- ▁duckling- ▁Talent- ▁knots- ▁overflowing- ▁Deb- ▁superheroes- ▁controller- 'Off'- ▁Benga- ▁Bangladeshi- ▁forgetful- ▁Law- ▁chatty- ▁sane- ▁enable- ▁cries- ▁exaggerated- ▁padding- ▁runny- ▁overturned- ▁olio- seal- ▁handover- ▁scarf- ▁Express- ▁instrumental- ▁betrayed- 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▁Meet- ▁alphabets- ▁ministers- ▁statues- ▁Legends- rou- ▁attacks- ▁notch- ▁pointers- ▁etc- ▁gears- ▁Zhuan- ault- ▁seize- ▁puzzles- layer- ▁mana- ▁tents- ▁directional- ▁dun- omo- ▁snapp- ▁del- ▁Angu- ▁aids- ▁Ch- ▁Tun- ▁Jac- ▁sins- aki- ▁Ver- ▁wou- ▁fundamentals- non- ▁triangles- ▁buns- ▁bugs- now- ▁ele- run- ▁integrati- ▁recommendations- ▁Pad- ▁gar- shao- ▁hol- ▁fisher- old- ▁Raja- ▁spikes- ▁mun- ▁dinosaurs- ▁mentors- ▁Har- ▁spirits- ▁organizations- zy- ▁Christians- Magic- ▁dangers- erry- oi- ▁dock- ▁slopes- ▁artistes- ▁ru- ▁references- ongs- ▁Wiz- ▁markings- very- eresa- ▁kam- ▁blessings- ▁gigs- gle- ▁improvements- ▁pubs- ▁sib- bel- ▁comms- ▁meta- ▁pimples- ▁stares- ▁pota- ▁Ken- ene- Xun- ggle- ▁paw- ▁threat- ▁resonate- ▁axe- ▁schoolmate- ▁emerge- Studio- ixes- ▁merchant- ▁souvenir- hop- ▁kilometer- ▁syllable- ▁beverage- Boy- ▁competitor- ▁rumour- ▁ancestor- ▁sneaker- verse- ▁compromises- ▁whip- low- ▁brownie- ▁notion- assim- then- ential- ▁tang- ▁campaigns- ▁constrain- ▁zha- ami- ▁Eu- 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▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- ▁separat- ▁incur- ▁nurtur- ▁invad- ▁Find- ▁Prince- ▁Vic- scooter- corn- Karat- ▁subsidize- ummer- lbert- ▁congest- ▁recite- ▁misuse- ▁retrench- about- course- oodle- ▁debut- Smart- Batisah- Jovi- Turtle- ristiano- Airline- Titans- Ronaldo- Chomp- Cruise- Mirror- States- Nathan- Leong- kacang- Prata- Romeo- jalan- Autumn- William- negotiable- Chicken- James- Science- boyfriend- chocolate- machine- drama- twenty- Miam- ▁crisp- biryani- Mouse- center- ▁summar- motion- ▁theoretical- Minh- ▁clutter- ▁vivid- ▁instantaneous- ▁Lor- ▁Saba- asian- ▁authorit- ▁midfield- post- chiong- ▁imply- Wee- ▁proactive- ▁pronoun- ▁immigrati- ▁disrupt- eptic- ▁Buddh- ▁experi- ▁revolution- ▁Yayoi- ▁Xav- ▁Corp- Vuitton- ▁indefinite- ▁mislead- ▁residu- '?'- Bilis- Einstein- Giggs- Hospital- Local- Muniz- Quinn- Sivan- jumma- siol- ▁After- ▁Atiqah- ▁Backstreet- ▁Badminton- ▁Bidadari- ▁Britain- ▁Broadway- ▁Capella- ▁Century- ▁Chijmes- ▁Cinderella- ▁Colosseum- ▁CrossFit- ▁Cyclops- ▁Daegu- ▁Darvis- ▁Dasani- ▁Diablo- ▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- 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▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- 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▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- ▁locket- ▁fluently- vour- ▁detached- ▁biasness- ▁hypo- ▁practicality- ▁elected- ▁Wood- ▁Batisah- ▁Turtle- ▁Grabfood- ▁Jovi- ▁sev- ▁cooper- ▁rundown- ▁Sedan- ▁queer- ▁kaka- ▁Stal- ▁Xbox- ▁swerve- ▁native- ▁Bowl- ▁Talk- ▁confiscated- ▁stubbornness- ▁factories- ▁jiak- ▁damm- 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▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- 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▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- udrey- ▁constipat- ▁jumble- ambodia- ▁crook- stresses- sighted- Dogg- olution- urrender- tyrofoam- ▁impair- Buri- Escobar- Jetty- Showman- Shuttle- Tyson- ceased- aktor- ▁unexpect- Technical- Grill- Convenience- Future- Institute- Krunch- Schwarzenegger- Theatre- polished- Gabriel- antorini- Evan- Keane- Monster- ntegrated- Francisco- ▁deprive- ▁discriminat- ▁referenc- Kenny- Murder- Zhang- carriage- ▁Joan- ▁Skipp- ▁monogam- ▁prophec- Atwood- Stamford- ylvia- Akshay- Banyan- Virgin- linguistic- proportionate- founder- Music- buoy- evaluate- immortal- comparable- Jade- Miller- Friza- conditioned- innacle- Morrie- Waterway- conductor- crow- ndependence- Henry- Monkey- Nyonya- Shaw- monkeys- second- Shakespeare- catcher- organised- qualified- storage- Thomson- animate- executive- invent- starter- ▁rollerblad- ▁vacanc- Festival- Jordan- emphasis- frequent- settled- weekly- ▁Desire- college- factory- growth- organisation- profit- sentosa- Holland- Kampung- Laden- Nature- Seoul- Spirit- Tae- bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- ▁interesting- ▁else- ▁together- ▁car- ▁called- ▁whatever- ▁show- ▁white- ▁sorry- ▁change- ▁pay- ▁kids- ▁basically- ▁done- ▁seven- ▁free- ▁hundred- ▁learn- ▁green- ▁fun- ▁book- ▁told- ▁small- ▁off- ▁answer- d- ▁sign- ▁usually- ▁eight- ▁few- C- ▁open- ▁wanna- ▁mum- ▁chicken- ▁myself- ▁understand- ▁A- ▁means- ▁care- ▁both- ▁describe- ▁game- ▁once- ▁saying- ▁used- ▁teacher- ▁saw- ▁inside- ▁later- ▁happen- ▁C- ▁through- ▁date- ▁yourself- S- ▁stay- ▁looking- ▁movie- ▁dollars- ▁during- ▁move- ▁live- ▁until- ▁primary- ▁least- ▁nine- ▁wearing- ▁card- ▁thirty- ▁stop- ▁alright- ▁wanted- ▁trying- ▁Chinese- ▁each- ▁everyone- ▁having- ed- ▁example- ▁enough- ▁plus- ▁s- ▁haven- ▁cook- ▁working- ▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- ▁Singlish- ▁sweet- ▁train- ▁throw- ▁dark- ▁education- ▁early- ▁toilet- ▁simple- ▁uni- ▁tuition- ▁air- ▁restaurant- ▁honest- ▁living- ▁telling- ▁p- ▁price- ▁per- ▁Japan- ▁paper- ▁uncle- ▁minute- ▁itself- ▁terms- ▁overseas- hor- ▁driver- ▁o- ▁F- ▁set- ▁childhood- ▁words- G- ▁cards- ▁hmm- ▁brand- ▁grow- ▁reach- ▁choice- V- b- ▁scary- ▁main- ▁birthday- huh- ▁China- ▁cat- ▁career- ▁somebody- ▁along- O- ▁extra- u- ▁lunch- ▁hit- ▁lost- ▁subject- ▁exercise- ▁Korea- ▁hey- ▁parent- ▁add- ▁straight- E- ▁window- right- ▁chair- ▁explain- ▁sound- ▁soccer- ▁c- ▁online- ▁drama- ▁making- ▁culture- ▁fight- ▁k- ▁literally- ▁related- ▁latest- ▁interest- ▁felt- ▁human- ▁blah- ▁cream- ▁apparently- ▁G- er- ▁tired- ▁kinda- ▁studying- ▁earn- ▁suppose- ▁bird- ▁drop- ▁fried- ▁count- ▁holding- ▁m- ▁recess- ▁dating- ▁n- ▁forget- ▁sixty- ▁movies- ▁R- ▁catch- ▁baby- ▁color- ▁smart- ▁shoe- v- ▁teh- ▁success- ▁fact- ▁embarrassing- ▁pants- ▁countries- ▁dish- ▁Indian- ▁sports- ▁shows- ▁egg- ▁mostly- ▁rich- ▁chance- ▁Korean- ▁piece- ▁truly- ▁elderly- ▁soon- ▁social- ▁Singaporeans- ▁sort- ▁meaningful- ▁general- ▁angry- ▁shack- ▁club- ▁cooking- ▁seat- ▁clear- ▁lose- ▁photo- ▁hi- ▁busy- ▁period- ▁non- ▁fair- ▁dude- ▁questions- ▁running- ▁memorable- ▁holiday- ▁item- ▁leg- ▁system- ▁un- ▁bar- ▁interested- ▁touch- ▁sea- ▁couple- ▁zero- ▁self- ▁planning- ▁post- ▁video- ▁dead- ▁ex- ▁park- ▁totally- ▁hall- ▁store- ▁weekend- ▁building- ▁loud- ▁happiest- ▁milk- ▁trust- ▁chill- ▁lobang- L- ▁common- ▁fit- ▁board- ▁cost- ▁brought- ▁case- ▁laugh- ▁situation- ▁skin- ▁power- ▁re- ▁friendship- ▁f- ▁consider- ▁fat- ▁wish- ▁fall- ▁wash- ▁event- ▁soup- ▁woman- ▁willing- ▁sec- gosh- ▁Grab- ▁faster- ▁voice- ▁L- ▁trait- ▁dance- ▁differences- ▁entire- ▁effort- ▁science- ▁waste- ▁strong- ▁meat- ▁awkward- ▁specific- ▁exam- ▁current- ▁husband- ▁son- ▁seems- ▁visit- F- ▁box- ▁centre- ▁skip- ▁expect- ▁fire- ▁draw- ▁hero- ▁teachers- ▁whenever- ▁amount- ▁often- ▁kill- ▁Japanese- ▁boyfriend- ▁space- ▁support- ▁wise- ▁kay- ▁army- 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▁training- ▁remembered- ▁Sunday- ▁signboard- ▁closest- ▁wonder- ▁project- ▁fan- ▁caught- ▁government- ▁form- ▁tough- ▁Google- ▁everywhere- ▁healthy- ▁build- ▁trend- ▁cheese- ▁hat- uh- ▁vegetable- nah- ▁within- ▁slowly- ▁market- ▁block- ▁Saturday- ▁hotel- ▁technology- ▁machine- ▁craziest- ▁whereby- ▁selling- ▁math- ▁Australia- ▁makeup- ▁sand- ▁although- ▁works- ▁mention- ▁weather- ▁Hong- ▁manage- ▁afraid- K- ▁bike- ▁easily- ▁above- ▁alive- ▁listening- ▁respect- ▁mouth- ▁eventually- ▁feelings- ▁worse- ▁smell- ▁dress- ▁party- ▁language- ▁quiet- ▁chocolate- ▁possession- ▁seventy- ▁exams- ▁tray- ▁sun- ▁amazing- ▁mom- ▁e- ▁basic- ▁songs- ▁forward- ▁cap- ▁rock- ▁splurge- ▁stick- ▁busybody- ▁define- ▁woah- ▁act- ▁b- ▁contact- ▁issue- ▁potato- ▁studies- ▁fishing- ▁empty- ▁treat- ▁sleeping- ▁decide- ▁grandfather- ▁bigger- ▁previous- ▁asked- ▁cover- ▁classes- ▁d- ▁jobs- ▁kidding- ▁slow- ▁properly- ▁con- ▁laptop- ▁balance- ▁shark- ▁everytime- ▁X- ▁met- ▁base- ▁opposite- ▁canteen- ▁design- ▁random- 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▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- ▁Eminem- ▁President- ▁Sydney- ▁WeChat- ▁accommodate- ▁aglio- ▁attentive- ▁coleslaw- ▁congrats- ▁consumption- ▁cringy- ▁deaf- ▁district- ▁drumstick- ▁evaporate- ▁expenditure- ▁gantry- ▁hummingbird- ▁hypocrite- ▁inconvenient- ▁instinct- ▁intuition- ▁irrational- ▁policies- ▁pricing- ▁purplish- ▁servant- ▁skeptical- ▁surviving- ▁syndrome- ▁terrace- ▁turret- ▁wedges- ▁Angkor- ▁Ellen- ▁Hanoi- ▁Ofo- ▁Rebecca- ▁increasing- ▁outdated- ▁pursuit- ▁Heartland- ▁association- ▁psychologist- ▁unpredictable- ▁Yates- ▁manufacturing- ▁index- ▁botak- ▁quantity- ▁Marco- ▁runner- ▁memorising- ▁obsession- ▁ferment- ▁lamppost- ▁balm- ▁Tuas- ▁Frank- ▁announcement- ▁penalty- ▁Nyonya- ▁leverage- ▁Monkey- ▁stroke- ▁font- ▁token- ▁massive- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- 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▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- 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▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- ▁modification- ▁Asam- ▁shawl- ▁Heath- ▁chapteh- ▁Desiree- ▁alignment- ▁rollerblade- ▁grit- ▁vacancy- liath- ▁Mitchie- ▁purposeful- idle- ▁Salman- ▁cylinder- ▁supposing- ▁Dora- ▁Institute- ▁Convenience- ▁Future- ▁Gabriel- ▁Gateway- ▁Krunch- ▁Satan- ▁Schwarzenegger- ▁Technical- ▁Theatre- ▁alteration- ▁hush- ▁crayon- ▁snipe- Kardashian- ▁moonlight- ▁emit- ▁Stephenie- face- ▁aburi- ▁constipated- ▁Kurt- ▁nutri- ▁cowardly- ▁omega- ldering- ▁hopped- ▁recruitment- ▁slavery- ▁lever- ▁faci- ▁Austin- ▁rotan- ▁breathless- ▁indian- ▁swish- ▁Spartan- ▁Usman- ▁Tabata- ▁precaution- Law- ▁Anabelle- ▁Carrie- indef- ▁vary- ▁moisturize- ▁pester- ▁underdog- ▁gracefully- ▁Aquarium- ▁Caribbean- ▁Hathaway- zuo- ▁installment- ▁Shepard- 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nnies- erson- Cake- athon- ▁urbanis- ▁capitalist- Papa- ▁tran- ▁Vinc- ▁bul- ervic- ▁Wenh- espera- ▁capitaliz- ▁enti- ▁Aunt- ived- Boss- Bron- izer- ▁destin- ▁kisse- Maid- ▁prev- ▁angpa- ▁Juli- bab- ▁Zend- Kid- ▁interpreta- ▁gener- ▁vap- communication- ipe- anning- ▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- 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':'- Alchemist- Brolin- Cavani- Converter- Defence- Exchange- Explore- Fansipan- Girlfriend- Grannis- Kerang- Lovato- Mandeb- Marbles- Marinda- Meyer- Packard- Ragnarok- Reborn- Rendezvous- Samsuddin- Spect- Tarik- Trench- Walker- appetising- appuccino- bahru- conferenc- dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- 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▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- ▁alliteration- ▁anemone- ▁antihero- ▁applause- ▁apprehensive- ▁arrogance- ▁articulation- ▁ascertain- ▁asparagus- ▁bibliography- ▁bittergourd- ▁blasphemy- ▁bourgeois- ▁buckwheat- ▁bursaries- ▁cajon- ▁carbohydrate- ▁categorize- ▁catwalk- ▁caviar- ▁centimeter- ▁chameleon- ▁chauffeur- ▁chawanmushi- ▁cheddar- ▁cheebye- ▁cheerleader- ▁chimichanga- ▁chromo- ▁chrysa- ▁circumference- ▁clarinet- ▁coastguard- ▁cocoon- ▁commuting- ▁concious- ▁condolence- ▁confetti- ▁conglomerate- ▁congregate- ▁connectivity- ▁correspond- ▁coworker- ▁crisscross- ▁cuff- ▁customaries- ▁cutlery- ▁deceiving- ▁declaration- ▁deconstructor- ▁delifrance- ▁delinquent- ▁depleted- ▁digimon- ▁diminish- ▁disciple- ▁discrete- ▁discretion- ▁disparity- ▁disregard- 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bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- 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+    "authors": [
+      {
+        "name_en": "Shinji Watanabe",
+        "name_zh": "Shinji Watanabe"
+      }
+    ],
+    "keywords_en": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "keywords_zh": [
+      "ESPnet",
+      "deep-learning",
+      "python",
+      "pytorch",
+      "speech-recognition",
+      "speech-synthesis",
+      "speech-translation",
+      "machine-translation"
+    ],
+    "publication_date": 1612281600000,
+    "created": "",
+    "modified": "",
+    "status": "",
+    "license": "CC BY 4.0",
+    "file_size_mb": 347.33,
+    "file_size_bytes": 364200461,
+    "download_count": 0,
+    "visit_count": 7,
+    "url": "",
+    "source": "scidb"
+  }
+]
\ No newline at end of file

From fa2eb74622e18930f8a9e7523d92c9ede130efc9 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:45:55 +0100
Subject: [PATCH 26/34] updating the clone please

---
 .../ingestions/3_clone_datasets_detailed.py   | 665 ++++++++++++++++++
 .../ingestions/3_clone_openneuro_datasets.py  | 281 --------
 2 files changed, 665 insertions(+), 281 deletions(-)
 create mode 100644 scripts/ingestions/3_clone_datasets_detailed.py
 delete mode 100644 scripts/ingestions/3_clone_openneuro_datasets.py

diff --git a/scripts/ingestions/3_clone_datasets_detailed.py b/scripts/ingestions/3_clone_datasets_detailed.py
new file mode 100644
index 00000000..a285fd5d
--- /dev/null
+++ b/scripts/ingestions/3_clone_datasets_detailed.py
@@ -0,0 +1,665 @@
+r"""Clone EEG datasets from OpenNeuro, NEMAR, and EEGManyLabs.
+
+This unified script handles cloning from three major sources with comprehensive
+source detection, timeout handling, error recovery, and reporting.
+
+Features:
+
+- Automatic source detection (OpenNeuro, NEMAR, EEGManyLabs/GIN)
+- Timeout handling for large datasets
+- Error recovery with retry list generation
+- Progress tracking and comprehensive reporting
+- Partial clone cleanup on failure
+- Skip previously cloned datasets
+- Source-specific metadata preservation
+
+Example Usage:
+    # Clone all datasets from all sources
+    python 3_clone_datasets_detailed.py
+
+    # Clone specific source only
+    python 3_clone_datasets_detailed.py \\
+        --datasets-file consolidated/openneuro_datasets.json \\
+        --output-dir data/openneuro
+
+    # Clone with longer timeout
+    python 3_clone_datasets_detailed.py --timeout 600
+
+    # Retry failed datasets
+    python 3_clone_datasets_detailed.py \\
+        --datasets-file test_diggestion/retry.json \\
+        --timeout 600
+"""
+
+import argparse
+import json
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+# ============================================================================
+# Source Detection & Routing
+# ============================================================================
+
+
+def detect_source_type(dataset: dict) -> str:
+    """Detect the data source based on dataset fields.
+
+    This function uses hierarchical detection to identify which repository
+    the dataset comes from based on its metadata structure.
+
+    Detection Priority:
+    1. Explicit "source" field (fastest, most reliable)
+    2. GIN detection: clone_url contains "gin.g-node.org"
+    3. NEMAR detection: clone_url contains "nemarDatasets"
+    4. Fallback GIT: Has clone_url or ssh_url (assume NEMAR for backwards compat)
+    5. OpenNeuro: Has "modality" field (EEG/iEEG/MEG)
+    6. Unknown: None of the above
+
+    Args:
+        dataset: Dataset dictionary from consolidated JSON
+
+    Returns:
+        String: 'openneuro', 'nemar', 'gin', or 'unknown'
+
+    Examples:
+        >>> d1 = {"dataset_id": "ds001785", "modality": "eeg"}
+        >>> detect_source_type(d1)
+        'openneuro'
+
+        >>> d2 = {"clone_url": "https://github.com/nemarDatasets/ds004350.git"}
+        >>> detect_source_type(d2)
+        'nemar'
+
+        >>> d3 = {"clone_url": "https://gin.g-node.org/EEGManyLabs/..."}
+        >>> detect_source_type(d3)
+        'gin'
+
+    """
+    # Check for explicit source field (most reliable)
+    if "source" in dataset:
+        return dataset["source"]
+
+    clone_url = dataset.get("clone_url", "")
+    ssh_url = dataset.get("ssh_url", "")
+    urls = clone_url + ssh_url  # Concatenate for simpler checking
+
+    # GIN detection (highest priority for git-based)
+    if "gin.g-node.org" in urls:
+        return "gin"
+
+    # NEMAR detection
+    if "nemarDatasets" in urls:
+        return "nemar"
+
+    # Generic git detection (fallback to NEMAR for backwards compatibility)
+    if clone_url or ssh_url:
+        return "nemar"
+
+    # OpenNeuro detection (modality field is unique to OpenNeuro)
+    if "modality" in dataset:
+        return "openneuro"
+
+    return "unknown"
+
+
+# ============================================================================
+# Clone URL Generation
+# ============================================================================
+
+
+def get_clone_url(dataset: dict, source_type: str) -> str:
+    """Generate the appropriate Git clone URL for the dataset.
+
+    Source-specific URL handling:
+
+    **OpenNeuro**:
+    - Manual construction from dataset_id
+    - Format: https://github.com/OpenNeuroDatasets/{dataset_id}
+    - No .git suffix needed
+    - Example: ds001785 → https://github.com/OpenNeuroDatasets/ds001785
+
+    **NEMAR**:
+    - Direct from clone_url field
+    - Format: https://github.com/nemarDatasets/{dataset_id}.git
+    - Includes .git suffix
+    - Example: ds004350 → https://github.com/nemarDatasets/ds004350.git
+
+    **EEGManyLabs (GIN)**:
+    - Direct from clone_url field
+    - Format: https://gin.g-node.org/EEGManyLabs/{dataset_id}.git
+    - Includes .git suffix
+    - Example: EEGManyLabs_Replication_ClarkHillyard1996_Raw
+      → https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw.git
+
+    Args:
+        dataset: Dataset dictionary from consolidated JSON
+        source_type: Detected source ('openneuro', 'nemar', 'gin', 'unknown')
+
+    Returns:
+        String: Full Git clone URL
+
+    Raises:
+        KeyError: If required field missing for source type
+        ValueError: If source_type is unknown
+
+    """
+    if source_type == "openneuro":
+        # OpenNeuro requires URL construction
+        dataset_id = dataset["dataset_id"]
+        return f"https://github.com/OpenNeuroDatasets/{dataset_id}"
+
+    elif source_type in ("nemar", "gin"):
+        # NEMAR and GIN have direct clone_url in metadata
+        clone_url = dataset.get("clone_url")
+        if not clone_url:
+            # Fallback to SSH URL if clone_url not available
+            clone_url = dataset.get("ssh_url")
+        if not clone_url:
+            raise KeyError(f"No clone_url or ssh_url found for {source_type} dataset")
+        return clone_url
+
+    else:
+        raise ValueError(f"Cannot generate clone URL for unknown source: {source_type}")
+
+
+# ============================================================================
+# Git Clone Execution
+# ============================================================================
+
+
+def clone_dataset(dataset: dict, output_dir: Path, timeout: int) -> dict:
+    """Execute Git clone for a single dataset with timeout handling.
+
+    Clone Workflow:
+    1. Detect source type from dataset metadata
+    2. Generate appropriate clone URL for source
+    3. Check if already cloned (skip if exists)
+    4. Execute git clone with timeout
+    5. On success: Return status with dataset metadata
+    6. On timeout: Clean up partial clone, return timeout status
+    7. On failure: Clean up partial clone, return error details
+    8. On exception: Capture error and return error status
+
+    Timeout Handling:
+    - Default timeout: 300 seconds (5 minutes)
+    - Supports up to 1000 seconds for very large datasets
+    - On timeout: Partial clone automatically cleaned up
+    - Creates retry.json for failed/timeout datasets
+
+    Error Handling:
+    - Missing URL fields: Returns error status
+    - Network errors: Captured in stderr
+    - Disk space errors: Captured in stderr
+    - Permission errors: Captured in stderr
+    - Unknown errors: Generic exception handling
+
+    Storage:
+    - Clone directory: output_dir / dataset_id
+    - Partial clones cleaned on failure
+    - Skip if directory already exists
+
+    Args:
+        dataset: Dataset dictionary with at minimum:
+                - dataset_id (string)
+                - and either: modality (OpenNeuro), clone_url (NEMAR/GIN), or source field
+        output_dir: Path object pointing to target clone directory
+        timeout: Maximum seconds to wait for clone (typically 300-600)
+
+    Returns:
+        dict: Status result with keys:
+            - status: 'success', 'skip', 'timeout', 'failed', or 'error'
+            - dataset_id: The dataset identifier
+            - source: Detected source type
+            - Additional context based on status (error, reason, timeout_seconds, etc.)
+
+    Examples:
+        >>> result = clone_dataset(
+        ...     {"dataset_id": "ds001785", "modality": "eeg"},
+        ...     Path("data"),
+        ...     timeout=300
+        ... )
+        >>> result["status"]
+        'success'
+
+        >>> result = clone_dataset(
+        ...     {"dataset_id": "already_cloned", "modality": "eeg"},
+        ...     Path("data/already_has_ds001785"),
+        ...     timeout=300
+        ... )
+        >>> result["status"]
+        'skip'
+
+    """
+    dataset_id = dataset["dataset_id"]
+    source_type = detect_source_type(dataset)
+    clone_dir = output_dir / dataset_id
+
+    # ---------------------------------------------------------------
+    # Step 1: Get clone URL
+    # ---------------------------------------------------------------
+    try:
+        url = get_clone_url(dataset, source_type)
+    except (KeyError, ValueError) as e:
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "error": str(e),
+            "phase": "url_generation",
+        }
+
+    # ---------------------------------------------------------------
+    # Step 2: Check if already cloned
+    # ---------------------------------------------------------------
+    if clone_dir.exists():
+        return {
+            "status": "skip",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "reason": "already exists",
+            "path": str(clone_dir),
+        }
+
+    # ---------------------------------------------------------------
+    # Step 3: Execute git clone with timeout
+    # ---------------------------------------------------------------
+    try:
+        # Build git command with options
+        cmd = ["git", "clone", url, str(clone_dir)]
+
+        # Add shallow clone option if requested
+        if getattr(clone_dataset, "shallow", False):
+            cmd.insert(2, "--depth")
+            cmd.insert(3, str(getattr(clone_dataset, "depth", 1)))
+
+        # Skip LFS files if requested
+        if getattr(clone_dataset, "no_lfs", False):
+            cmd.insert(2, "--no-checkout")
+
+        # Run with subprocess timeout
+        result = subprocess.run(
+            cmd,
+            timeout=timeout,
+            capture_output=True,
+            text=True,
+        )
+
+        # Check return code
+        if result.returncode == 0:
+            return {
+                "status": "success",
+                "dataset_id": dataset_id,
+                "source": source_type,
+                "path": str(clone_dir),
+            }
+        else:
+            # Clone failed - clean up partial clone
+            if clone_dir.exists():
+                import shutil
+
+                shutil.rmtree(clone_dir, ignore_errors=True)
+
+            # Return error with details
+            error_msg = result.stderr[:500]  # First 500 chars of error
+            return {
+                "status": "failed",
+                "dataset_id": dataset_id,
+                "source": source_type,
+                "error": error_msg,
+                "returncode": result.returncode,
+            }
+
+    except subprocess.TimeoutExpired:
+        # Clone took too long - clean up partial clone
+        if clone_dir.exists():
+            import shutil
+
+            shutil.rmtree(clone_dir, ignore_errors=True)
+
+        return {
+            "status": "timeout",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "timeout_seconds": timeout,
+        }
+
+    except Exception as e:
+        # Unexpected error - clean up and report
+        if clone_dir.exists():
+            import shutil
+
+            shutil.rmtree(clone_dir, ignore_errors=True)
+
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "source": source_type,
+            "error": str(e)[:500],
+        }
+
+
+# ============================================================================
+# Batch Processing & Reporting
+# ============================================================================
+
+
+def print_summary(results: dict, source_counts: dict, elapsed_time: float) -> None:
+    """Print comprehensive clone results summary.
+
+    Summary Format:
+    - Clone statistics by source
+    - Clone statistics by status
+    - Performance metrics
+    - Success rate calculation
+    - Retry list information
+
+    Args:
+        results: Dictionary with keys: success, failed, timeout, skip, error
+        source_counts: Dictionary with counts by source
+        elapsed_time: Total execution time in seconds
+
+    """
+    total_datasets = sum(len(v) for v in results.values())
+    total_success = len(results["success"]) + len(results["skip"])
+
+    print()
+    print("=" * 70)
+    print(f"Clone Operation Completed at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print("=" * 70)
+    print()
+
+    # By Source
+    print("DATASETS BY SOURCE:")
+    print(f"  OpenNeuro:    {source_counts.get('openneuro', 0):3d}")
+    print(f"  NEMAR:        {source_counts.get('nemar', 0):3d}")
+    print(f"  EEGManyLabs:  {source_counts.get('gin', 0):3d}")
+    if source_counts.get("unknown", 0):
+        print(f"  Unknown:      {source_counts.get('unknown', 0):3d}")
+    print()
+
+    # By Status
+    print("DATASETS BY STATUS:")
+    print(f"  ✓ Success:    {len(results['success']):3d}")
+    print(f"  ⊘ Skipped:    {len(results['skip']):3d}")
+    print(f"  ✗ Failed:     {len(results['failed']):3d}")
+    print(f"  ⏱ Timeout:    {len(results['timeout']):3d}")
+    print(f"  ? Error:      {len(results['error']):3d}")
+    print()
+
+    # Success Rate
+    if total_datasets > 0:
+        success_rate = (total_success / total_datasets) * 100
+        print(f"SUCCESS RATE: {success_rate:.1f}% ({total_success}/{total_datasets})")
+    print()
+
+    # Performance
+    hours, remainder = divmod(elapsed_time, 3600)
+    minutes, seconds = divmod(remainder, 60)
+    time_str = f"{int(hours)}h {int(minutes)}m {seconds:.0f}s"
+    avg_time = elapsed_time / max(total_datasets, 1)
+
+    print("PERFORMANCE:")
+    print(f"  Total time:   {time_str}")
+    print(f"  Average time: {avg_time:.1f}s per dataset")
+    print("=" * 70)
+
+
+def main() -> None:
+    """Main clone orchestration function."""
+    parser = argparse.ArgumentParser(
+        description="Clone EEG datasets from OpenNeuro, NEMAR, and EEGManyLabs.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Clone all datasets with defaults
+  python 3_clone_datasets_detailed.py
+
+  # Custom output directory and longer timeout for large datasets
+  python 3_clone_datasets_detailed.py \\
+    --output-dir data/cloned \\
+    --timeout 600
+
+  # Clone only EEGManyLabs datasets
+  python 3_clone_datasets_detailed.py \\
+    --datasets-file consolidated/eegmanylabs_datasets.json \\
+    --output-dir data/eegmanylabs \\
+    --timeout 600
+
+  # Retry previously failed datasets
+  python 3_clone_datasets_detailed.py \\
+    --datasets-file test_diggestion/retry.json \\
+    --output-dir test_diggestion \\
+    --timeout 600
+        """,
+    )
+
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("test_diggestion"),
+        help="Output directory for cloned repos (default: test_diggestion).",
+    )
+
+    parser.add_argument(
+        "--timeout",
+        type=int,
+        default=300,
+        help="Timeout per clone in seconds (default: 300, max 1000).",
+    )
+
+    parser.add_argument(
+        "--datasets-file",
+        type=Path,
+        default=None,
+        help="JSON file with dataset listings. If not specified, will try: "
+        "openneuro_datasets.json, nemardatasets_repos.json, eegmanylabs_datasets.json",
+    )
+
+    parser.add_argument(
+        "--max-parallel",
+        type=int,
+        default=1,
+        help="Max parallel clones (currently single-threaded, default: 1).",
+    )
+
+    parser.add_argument(
+        "--shallow",
+        action="store_true",
+        help="Use shallow clone (faster, no history) - saves bandwidth.",
+    )
+
+    parser.add_argument(
+        "--depth",
+        type=int,
+        default=1,
+        help="Depth for shallow clone (default: 1 = latest commit only).",
+    )
+
+    parser.add_argument(
+        "--no-lfs",
+        action="store_true",
+        help="Skip Git LFS files (avoids downloading large data files).",
+    )
+
+    args = parser.parse_args()
+
+    # Validate timeout
+    if args.timeout > 1000:
+        print("Warning: Timeout > 1000s may be excessive", file=sys.stderr)
+    if args.timeout < 10:
+        print(
+            "Warning: Timeout < 10s may be too short for large datasets",
+            file=sys.stderr,
+        )
+
+    # Set clone options as attributes on the function
+    clone_dataset.shallow = args.shallow
+    clone_dataset.depth = args.depth
+    clone_dataset.no_lfs = args.no_lfs
+
+    # ---------------------------------------------------------------
+    # Step 1: Load Datasets
+    # ---------------------------------------------------------------
+
+    if args.datasets_file:
+        # Use specified file
+        if not args.datasets_file.exists():
+            print(f"Error: {args.datasets_file} not found", file=sys.stderr)
+            sys.exit(1)
+        dataset_files = [args.datasets_file]
+    else:
+        # Try to find consolidated files
+        dataset_files = []
+        for fname in [
+            "consolidated/openneuro_datasets.json",
+            "consolidated/nemardatasets_repos.json",
+            "consolidated/eegmanylabs_datasets.json",
+        ]:
+            if Path(fname).exists():
+                dataset_files.append(Path(fname))
+
+    if not dataset_files:
+        print(
+            "Error: No dataset files found. Specify with --datasets-file or ensure "
+            "consolidated/ files exist",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    # Load all datasets
+    datasets = []
+    for fpath in dataset_files:
+        with fpath.open("r") as fh:
+            file_datasets = json.load(fh)
+        datasets.extend(file_datasets)
+
+    total = len(datasets)
+    if total == 0:
+        print("Error: No datasets to clone", file=sys.stderr)
+        sys.exit(1)
+
+    # ---------------------------------------------------------------
+    # Step 2: Start Clone Operation
+    # ---------------------------------------------------------------
+
+    print(f"\nStarting dataset cloning at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"Output directory: {args.output_dir}")
+    print(f"Timeout per clone: {args.timeout}s")
+    print(f"Total datasets: {total}")
+    print()
+
+    # Create output directory
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    # ---------------------------------------------------------------
+    # Step 3: Clone All Datasets
+    # ---------------------------------------------------------------
+
+    results = {
+        "success": [],
+        "failed": [],
+        "timeout": [],
+        "skip": [],
+        "error": [],
+    }
+
+    source_counts = {"openneuro": 0, "nemar": 0, "gin": 0, "unknown": 0}
+
+    start_time = time.time()
+
+    for idx, dataset in enumerate(datasets, start=1):
+        dataset_id = dataset["dataset_id"]
+        source_type = detect_source_type(dataset)
+        source_counts[source_type] = source_counts.get(source_type, 0) + 1
+
+        # Format output
+        status_str = f"[{idx:3d}/{total}] {dataset_id:30s} ({source_type:10s})"
+        print(f"{status_str}...", end=" ", flush=True)
+
+        # Clone the dataset
+        result = clone_dataset(dataset, args.output_dir, args.timeout)
+        status = result.pop("status")
+        results[status].append(result)
+
+        # Print status indicator
+        if status == "success":
+            print("✓")
+        elif status == "skip":
+            print("⊘ (already cloned)")
+        elif status == "timeout":
+            print(f"⏱ ({args.timeout}s timeout)")
+        elif status == "failed":
+            error = result.get("error", "unknown")[:40]
+            print(f"✗ ({error}...)")
+        else:
+            error = result.get("error", "unknown")[:40]
+            print(f"? ({error}...)")
+
+    elapsed = time.time() - start_time
+
+    # ---------------------------------------------------------------
+    # Step 4: Generate Report
+    # ---------------------------------------------------------------
+
+    print_summary(results, source_counts, elapsed)
+
+    # ---------------------------------------------------------------
+    # Step 5: Save Results
+    # ---------------------------------------------------------------
+
+    # Save detailed results
+    results_file = args.output_dir / "clone_results.json"
+    with results_file.open("w") as fh:
+        json.dump(results, fh, indent=2)
+    print(f"\nDetailed results saved to: {results_file}")
+
+    # Save retry list for failed/timeout datasets
+    retry_datasets = []
+    for status_list in [results["failed"], results["timeout"], results["error"]]:
+        for result in status_list:
+            # Find original dataset to include full metadata
+            orig_dataset = None
+            for ds in datasets:
+                if ds["dataset_id"] == result["dataset_id"]:
+                    orig_dataset = ds
+                    break
+            if orig_dataset:
+                retry_datasets.append(orig_dataset)
+
+    if retry_datasets:
+        retry_file = args.output_dir / "retry.json"
+        with retry_file.open("w") as fh:
+            json.dump(retry_datasets, fh, indent=2)
+        print(f"Retry list saved to: {retry_file} ({len(retry_datasets)} datasets)")
+
+    # ---------------------------------------------------------------
+    # Step 6: Print Next Steps
+    # ---------------------------------------------------------------
+
+    print()
+    print("=" * 70)
+    print("NEXT STEPS:")
+    print("=" * 70)
+
+    if len(results["success"]) > 0:
+        print(f"\n1. Successfully cloned {len(results['success'])} datasets")
+        print(f"   Location: {args.output_dir}/")
+        print("   Next: Run digestion pipeline on these datasets")
+
+    if retry_datasets:
+        print(f"\n2. {len(retry_datasets)} datasets need retry")
+        print("   Command: python 3_clone_datasets_detailed.py \\")
+        print(f"       --datasets-file {retry_file} \\")
+        print(f"       --output-dir {args.output_dir} \\")
+        print(f"       --timeout {args.timeout + 100}")
+
+    if len(results["success"]) + len(results["skip"]) == total:
+        print(f"\n✓ All {total} datasets processed successfully!")
+
+    print()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingestions/3_clone_openneuro_datasets.py b/scripts/ingestions/3_clone_openneuro_datasets.py
deleted file mode 100644
index 5f90ebb9..00000000
--- a/scripts/ingestions/3_clone_openneuro_datasets.py
+++ /dev/null
@@ -1,281 +0,0 @@
-"""Clone OpenNeuro, NEMAR, and GIN datasets with timeout and error handling."""
-
-import argparse
-import json
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-
-def detect_source_type(dataset: dict) -> str:
-    """Detect dataset source based on fields.
-
-    Args:
-        dataset: Dataset dictionary
-
-    Returns:
-        'openneuro', 'nemar', 'gin', or 'unknown'
-
-    """
-    # Check for explicit source field
-    if "source" in dataset:
-        return dataset["source"]
-
-    # GIN datasets have clone_url with gin.g-node.org
-    if "clone_url" in dataset and "gin.g-node.org" in dataset["clone_url"]:
-        return "gin"
-
-    # NEMAR datasets have clone_url with github.com/nemarDatasets
-    if "clone_url" in dataset and "nemarDatasets" in dataset["clone_url"]:
-        return "nemar"
-
-    # Generic dataset with clone_url (could be NEMAR or GIN)
-    if "clone_url" in dataset or "ssh_url" in dataset:
-        # Default to nemar for backward compatibility
-        return "nemar"
-
-    # OpenNeuro has modality field
-    if "modality" in dataset:
-        return "openneuro"
-
-    return "unknown"
-
-
-def get_clone_url(dataset: dict, source_type: str) -> str:
-    """Get the appropriate clone URL for the dataset.
-
-    Args:
-        dataset: Dataset dictionary
-        source_type: 'openneuro', 'nemar', 'gin', or 'unknown'
-
-    Returns:
-        Git clone URL
-
-    """
-    if source_type in ("nemar", "gin"):
-        # NEMAR and GIN datasets have clone_url in the JSON
-        return dataset.get("clone_url", dataset.get("ssh_url"))
-    elif source_type == "openneuro":
-        # OpenNeuro datasets need URL construction
-        dataset_id = dataset["dataset_id"]
-        return f"https://github.com/OpenNeuroDatasets/{dataset_id}"
-    else:
-        raise ValueError(f"Unknown source type: {source_type}")
-
-
-def clone_dataset(dataset: dict, output_dir: Path, timeout: int) -> dict:
-    """Clone a single dataset with timeout.
-
-    Args:
-        dataset: Dataset dictionary with fields depending on source
-        output_dir: Directory to clone into
-        timeout: Timeout in seconds
-
-    Returns:
-        Result dictionary with status
-
-    """
-    dataset_id = dataset["dataset_id"]
-    source_type = detect_source_type(dataset)
-
-    try:
-        url = get_clone_url(dataset, source_type)
-    except (KeyError, ValueError) as e:
-        return {
-            "status": "error",
-            "dataset_id": dataset_id,
-            "source": source_type,
-            "error": str(e),
-        }
-
-    clone_dir = output_dir / dataset_id
-
-    # Skip if already cloned
-    if clone_dir.exists():
-        return {
-            "status": "skip",
-            "dataset_id": dataset_id,
-            "source": source_type,
-            "reason": "already exists",
-        }
-
-    try:
-        # Run git clone with timeout
-        result = subprocess.run(
-            ["git", "clone", url, str(clone_dir)],
-            timeout=timeout,
-            capture_output=True,
-            text=True,
-        )
-
-        if result.returncode == 0:
-            return {
-                "status": "success",
-                "dataset_id": dataset_id,
-                "source": source_type,
-            }
-        else:
-            # Clean up partial clone on failure
-            if clone_dir.exists():
-                import shutil
-
-                shutil.rmtree(clone_dir, ignore_errors=True)
-            return {
-                "status": "failed",
-                "dataset_id": dataset_id,
-                "source": source_type,
-                "error": result.stderr[:200],
-            }
-
-    except subprocess.TimeoutExpired:
-        # Clean up partial clone on timeout
-        if clone_dir.exists():
-            import shutil
-
-            shutil.rmtree(clone_dir, ignore_errors=True)
-        return {
-            "status": "timeout",
-            "dataset_id": dataset_id,
-            "source": source_type,
-            "timeout_seconds": timeout,
-        }
-
-    except Exception as e:
-        return {
-            "status": "error",
-            "dataset_id": dataset_id,
-            "source": source_type,
-            "error": str(e)[:200],
-        }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Clone OpenNeuro and NEMAR datasets from consolidated listing."
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=Path("test_diggestion"),
-        help="Output directory for cloned repos (default: test_diggestion).",
-    )
-    parser.add_argument(
-        "--timeout",
-        type=int,
-        default=300,
-        help="Timeout per clone in seconds (default: 300).",
-    )
-    parser.add_argument(
-        "--datasets-file",
-        type=Path,
-        default=Path("consolidated/to_digest_openneuro_datasets.json"),
-        help="JSON file with dataset listings (supports both OpenNeuro and NEMAR formats).",
-    )
-    parser.add_argument(
-        "--max-parallel",
-        type=int,
-        default=1,
-        help="Max parallel clones (currently single-threaded).",
-    )
-    args = parser.parse_args()
-
-    # Validate input file
-    if not args.datasets_file.exists():
-        print(f"Error: {args.datasets_file} not found", file=sys.stderr)
-        sys.exit(1)
-
-    # Load datasets
-    with args.datasets_file.open("r") as fh:
-        datasets = json.load(fh)
-
-    total = len(datasets)
-
-    print(f"Starting dataset cloning at {time.strftime('%Y-%m-%d %H:%M:%S')}")
-    print(f"Output directory: {args.output_dir}")
-    print(f"Timeout per clone: {args.timeout}s")
-    print(f"Total datasets: {total}")
-    print()
-
-    # Create output directory
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Clone datasets
-    results = {
-        "success": [],
-        "failed": [],
-        "timeout": [],
-        "skip": [],
-        "error": [],
-    }
-
-    # Track by source
-    source_counts = {"openneuro": 0, "nemar": 0, "gin": 0, "unknown": 0}
-
-    for idx, dataset in enumerate(datasets, start=1):
-        dataset_id = dataset["dataset_id"]
-        source_type = detect_source_type(dataset)
-        source_counts[source_type] += 1
-
-        print(
-            f"[{idx}/{total}] Cloning {dataset_id} ({source_type})...",
-            end=" ",
-            flush=True,
-        )
-
-        result = clone_dataset(dataset, args.output_dir, args.timeout)
-        status = result.pop("status")
-        results[status].append(result)
-
-        if status == "success":
-            print("✓")
-        elif status == "skip":
-            print("⊘ (already exists)")
-        elif status == "timeout":
-            print(f"⏱ (timeout after {args.timeout}s)")
-        elif status == "failed":
-            print(f"✗ (error: {result.get('error', 'unknown')[:50]}...)")
-        else:
-            print(f"? (error: {result.get('error', 'unknown')[:50]}...)")
-
-    # Summary
-    print()
-    print("=" * 60)
-    print(f"Cloning completed at {time.strftime('%Y-%m-%d %H:%M:%S')}")
-    print()
-    print("By Source:")
-    print(f"  OpenNeuro: {source_counts['openneuro']}")
-    print(f"  NEMAR: {source_counts['nemar']}")
-    print(f"  GIN: {source_counts['gin']}")
-    if source_counts["unknown"]:
-        print(f"  Unknown: {source_counts['unknown']}")
-    print()
-    print("By Status:")
-    print(f"  Success: {len(results['success'])}")
-    print(f"  Failed: {len(results['failed'])}")
-    print(f"  Timeout: {len(results['timeout'])}")
-    print(f"  Skipped: {len(results['skip'])}")
-    print(f"  Errors: {len(results['error'])}")
-    print("=" * 60)
-
-    # Save results
-    results_file = args.output_dir / "clone_results.json"
-    with results_file.open("w") as fh:
-        json.dump(results, fh, indent=2)
-    print()
-    print(f"Results saved to: {results_file}")
-
-    # Save retry list (failed/timeout/error datasets with full metadata)
-    retry_datasets = []
-    for status_list in [results["failed"], results["timeout"], results["error"]]:
-        retry_datasets.extend(status_list)
-
-    if retry_datasets:
-        retry_file = args.output_dir / "retry.json"
-        with retry_file.open("w") as fh:
-            json.dump(retry_datasets, fh, indent=2)
-        print(f"Retry list saved to: {retry_file} ({len(retry_datasets)} datasets)")
-
-
-if __name__ == "__main__":
-    main()

From 851096f7304d8d226c4e34de9d155112460607c0 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 17:46:12 +0100
Subject: [PATCH 27/34] removing .json files

---
 .../figshare_datasets_detailed_test.json      | 1982 -----------------
 consolidated/figshare_datasets_test.json      |  393 ----
 .../to_digest_nemardatasets_repos.json        |  117 -
 .../to_digest_openneuro_datasets.json         | 1190 ----------
 4 files changed, 3682 deletions(-)
 delete mode 100644 consolidated/figshare_datasets_detailed_test.json
 delete mode 100644 consolidated/figshare_datasets_test.json
 delete mode 100644 consolidated/to_digest_nemardatasets_repos.json
 delete mode 100644 consolidated/to_digest_openneuro_datasets.json

diff --git a/consolidated/figshare_datasets_detailed_test.json b/consolidated/figshare_datasets_detailed_test.json
deleted file mode 100644
index 18bb0895..00000000
--- a/consolidated/figshare_datasets_detailed_test.json
+++ /dev/null
@@ -1,1982 +0,0 @@
-[
-  {
-    "dataset_id": "30227503",
-    "doi": "10.6084/m9.figshare.30227503.v1",
-    "title": "EEG Dataset for Visual Imagery",
-    "description": "<h3>1.Research context</h3><h4>This dataset contains high-resolution EEG recordings collected from 22 healthy adult participants performing visual imagery (VI) tasks.</h4><p dir=\"ltr\">Participants imagined ten commonly encountered images across three semantic categories: geometric figures (circle, square, pentagram), animals (dog, fish, bird), and objects (cup, chair, watch, scissors). Each participant completed up to two sessions on separate days (\u226548 hours apart). However, sub-09 and sub-10 completed only the first session (ses-01) due to personal reasons, and thus have no available data for ses-02. Additionally, the animal imagery data of sub-08 in ses-02 was excluded from analysis because of poor signal quality, which introduced substantial noise contamination and led to unreliable classification results.</p><p dir=\"ltr\">EEG was recorded with a 34-channel cap (32 active EEG channels + AFz ground + CPz reference) at 1000 Hz.</p><h3>2.Data overview</h3><p dir=\"ltr\">Subjects: 22 healthy adults(5 females, age 20-23)</p><p dir=\"ltr\">Task:</p><p dir=\"ltr\">AVI(animal visual imagery): Imagine animal images(dog, fish, bird; 120 trials/session)</p><p dir=\"ltr\">FVI(animal visual imagery): Imagine figure images(circle, square, pentagram; 120 trials/session)</p><p dir=\"ltr\">OVI(animal visual imagery): Imagine object images(cup, chair, watch, scissors; 160 trials/session)</p><h3>3. Experimental Design</h3><p dir=\"ltr\">The paradigm was implemented using Psychtoolbox-3 and presented on a screen with a resolution of 1920\u00d71080 pixels. Each recording session lasted between 37 and 56 minutes and comprised four VI blocks for each task category. Flexible rest periods of 2 to 4 minutes were provided between blocks to mitigate the effects of fatigue. In the figure and animal categories, each image was presented 40 times, yielding a total of 120 trials per category.</p><h4>Each trial lasted 17 seconds and followed a structured sequence\uff1a</h4><ul><li>Fixation Cross\u200b: 3 s. A central fixation cross (\"+\") is displayed on the screen. To cue participants to remain alert and relaxed, preparing them for the trial.</li><li>Visual perception: 4 s. Participants are instructed to observe and memorize the image.</li><li>Mask: 2 s. A visual mask is briefly displayed.To eliminate any residual visual aftereffects from the presented image.</li><li>Visual imagery: 4 s.The screen turns black. Participants perform mental imagery of the previously shown image.</li><li>Rest: 4 s. The word \"Rest\" is displayed on the screen.Participants are instructed to stop any mental imagery and rest.</li></ul><h3>4.File structure</h3><p dir=\"ltr\">The data are organized according to the EEG-BIDS convention to maximize interoperability. Raw EEG (<code>*.bdf</code>) and BIDS sidecars: <code>*_channels.tsv</code>, <code>*_events.tsv</code>, <code>*_eeg.json</code>, etc.</p><p dir=\"ltr\"><br></p>",
-    "defined_type": 3,
-    "defined_type_name": "dataset",
-    "published_date": "2025-10-01T15:00:47Z",
-    "created_date": "2025-10-01T15:00:47Z",
-    "modified_date": "2025-10-01T15:02:02Z",
-    "authors": [
-      {
-        "name": "Jing'ao Gao",
-        "id": 22323919,
-        "orcid": "0009-0004-7718-5477"
-      }
-    ],
-    "tags": [
-      "brain\u2013computer interface (BCI)",
-      "visual imagery",
-      "EEG"
-    ],
-    "categories": [
-      {
-        "id": 24736,
-        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
-        "parent_id": 24724,
-        "path": "/24457/24724/24736",
-        "source_id": "320904",
-        "taxonomy_id": 100
-      }
-    ],
-    "license": "CC BY 4.0",
-    "url": "https://figshare.com/articles/dataset/EEG_Dataset_for_Visual_Imagery/30227503",
-    "api_url": "https://api.figshare.com/v2/articles/30227503",
-    "resource_title": null,
-    "resource_doi": null,
-    "files": [
-      {
-        "id": 58328968,
-        "name": "sub-01.zip",
-        "size_bytes": 627579802,
-        "download_url": "https://ndownloader.figshare.com/files/58328968",
-        "md5": "cac3615e077e6e6ee61a874a61b855c7"
-      },
-      {
-        "id": 58329043,
-        "name": "sub-02.zip",
-        "size_bytes": 514200571,
-        "download_url": "https://ndownloader.figshare.com/files/58329043",
-        "md5": "8ffb18f780080a5bea08fa0fed2947e4"
-      },
-      {
-        "id": 58329064,
-        "name": "sub-03.zip",
-        "size_bytes": 529205614,
-        "download_url": "https://ndownloader.figshare.com/files/58329064",
-        "md5": "7e0e7b2a2dd97e75a3960064388f1f1a"
-      },
-      {
-        "id": 58329169,
-        "name": "sub-04.zip",
-        "size_bytes": 460800934,
-        "download_url": "https://ndownloader.figshare.com/files/58329169",
-        "md5": "14cd26bc910acf23b859dbcfc948c2db"
-      },
-      {
-        "id": 58329229,
-        "name": "sub-05.zip",
-        "size_bytes": 550239127,
-        "download_url": "https://ndownloader.figshare.com/files/58329229",
-        "md5": "cc18d93412f6ce377803ecb45dea4cac"
-      },
-      {
-        "id": 58329265,
-        "name": "sub-06.zip",
-        "size_bytes": 531052214,
-        "download_url": "https://ndownloader.figshare.com/files/58329265",
-        "md5": "970ad0e72bcf43a66cdec1078d9474c8"
-      },
-      {
-        "id": 58329289,
-        "name": "sub-07.zip",
-        "size_bytes": 578747124,
-        "download_url": "https://ndownloader.figshare.com/files/58329289",
-        "md5": "5091b499668ca5de2329772803268852"
-      },
-      {
-        "id": 58336849,
-        "name": "sub-09.zip",
-        "size_bytes": 276612863,
-        "download_url": "https://ndownloader.figshare.com/files/58336849",
-        "md5": "c454e75572614f31a9c313bb0857efbd"
-      },
-      {
-        "id": 58336852,
-        "name": "sub-10.zip",
-        "size_bytes": 312132554,
-        "download_url": "https://ndownloader.figshare.com/files/58336852",
-        "md5": "d0a83f3b2ed67da0cc1088c7af3f4560"
-      },
-      {
-        "id": 58337044,
-        "name": "sub-11.zip",
-        "size_bytes": 638262513,
-        "download_url": "https://ndownloader.figshare.com/files/58337044",
-        "md5": "fb1c08ee849ace8d30988e32630be165"
-      }
-    ],
-    "file_count": 10,
-    "total_size_mb": 4786.33,
-    "source": "figshare"
-  },
-  {
-    "dataset_id": "29987758",
-    "doi": "10.6084/m9.figshare.29987758.v2",
-    "title": "<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>",
-    "description": "<p dir=\"ltr\">Version History</p><p dir=\"ltr\">Version 2.0 (October 15, 2025)</p><ul><li>Event label correction: Corrected a scaling inconsistency in the event tags within the file sub-13_ses-02_task-CCSHI_eeg.bdf.  The event codes now correctly align with the task paradigm's coding logic.</li><li>Metadata update: Corrected the handedness metadata for participant sub-06 in participants. tsv from \"L\" to \"R\", to address a typographical error in the original metadata.</li><li>Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.</li></ul><p dir=\"ltr\">No changes to the associated study's results or conclusions are necessitated by these updates.</p><h3>1. Research Context</h3><p dir=\"ltr\">This dataset addresses a critical gap in Brain-Computer Interface (BCI) research: most existing resources focus on English-language tasks or simple motor imagery, while Chinese character strokes and Pinyin monophthong handwriting remain understudied. By providing data for two complementary handwriting imagery tasks\u2014</p><p dir=\"ltr\">Chinese Character Stroke Handwriting Imagery (CCS-HI): Imagining writing 5 basic Chinese character strokes( \u4e00,\u4e28,\u4e3f,\u31cf,\u3125) ;</p><p dir=\"ltr\">Pinyin Single Vowel Handwriting Imagery (SV-HI): Imagining writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc)\u2014</p><p dir=\"ltr\">we enable investigations into:</p><p dir=\"ltr\">The neural mechanisms of handwriting imagery for Chinese strokes vs. Pinyin characters;</p><p dir=\"ltr\">Cross-session generalization (a critical challenge for real-world BCI deployment).</p><h3>2. Data Overview</h3><p dir=\"ltr\">Subjects: 16 healthy adults (3 females, age 22\u201332, right-handed, no neurological disorders). Note: In Version 2.0, handedness for one subject was corrected from \u201cL\u201d to \u201cR\u201d to reflect accurate right-handedness; all participants remain confirmed as right-handed.</p><p dir=\"ltr\">Tasks:</p><p dir=\"ltr\">CCS-HI: Imagine writing 5 Chinese strokes (\u4e00\uff0c\u4e28\uff0c\u4e3f\uff0c\u31cf, \u3125\uff1b200 trials/session).</p><p dir=\"ltr\">SV-HI: Imagine writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc; 240 trials/session).</p><p dir=\"ltr\">Sessions: 2 independent sessions (\u226524 hours apart).</p><p dir=\"ltr\">Session 1: Used for training and 5-fold cross-validation.</p><p dir=\"ltr\">Session 2: Held out as an independent test set for cross-session evaluation.</p><h3>3. Experimental Design</h3><p dir=\"ltr\">3.1 Pre-Experiment Preparation</p><p dir=\"ltr\">Prior to data collection, all subjects were fully informed of the experimental protocol (including task requirements and response methods) and shown a demonstration video of handwriting imagery tasks to ensure understanding. Written informed consent was obtained from all participants.</p><p dir=\"ltr\">3.2 EEG Acquisition</p><p dir=\"ltr\">Device: Neuracle NeuSenW amplifier with a 32-channel Ag/AgCl electrode cap.</p><p dir=\"ltr\">Electrode Layout: Strictly following the international 10-10 system. Detailed 3D coordinates and nomenclature of each electrode are provided in the accompanying electrodes.tsv file.</p><p dir=\"ltr\">Sampling Rate: 1000 Hz (downsampled to 250 Hz during preprocessing).</p><p dir=\"ltr\">Electrode Impedance: Maintained below 10 k\u03a9 for all channels during recording.</p><p dir=\"ltr\">Calibration: All equipment was professionally calibrated before the experiment to ensure data quality and reproducibility.</p><p dir=\"ltr\">3.3 Task Paradigm</p><p dir=\"ltr\">3.2.1 General Trial Structure (CCS-HI & SV-HI)</p><p dir=\"ltr\">Each single trial included four sequential phases, with the total duration varying slightly by task:</p><p dir=\"ltr\">1. Fixation + Auditory Cue: 2 seconds</p><p dir=\"ltr\">A white fixation cross was displayed on a black screen to stabilize subjects\u2019 attention;</p><p dir=\"ltr\">An auditory beep (500 Hz, 100 ms) was synchronized with the start of the cross, serving as a trial initiation signal.</p><p dir=\"ltr\">2. Task Cue Phase: Duration differs by task (key distinction)</p><p dir=\"ltr\">2.1 For CCS-HI: 2.8 seconds</p><p dir=\"ltr\">A dynamic animation of the target Chinese character stroke (e.g., \u4e00\uff0c\u4e28) was played, demonstrating the stroke\u2019s writing trajectory.</p><p dir=\"ltr\">2.2 For SV-HI: 3.2 seconds</p><p dir=\"ltr\">A dynamic animation of the target Pinyin monophthong (e.g., a, o) was played; the extended duration was designed to accommodate the relatively higher complexity of visualizing Pinyin character shapes.</p><p dir=\"ltr\">3. Handwriting Imagery Phase: 4 seconds</p><p dir=\"ltr\">Subjects were instructed to imagine writing the target stroke (CCS-HI) or Pinyin monophthong (SV-HI) following the trajectory shown in the task cue, without any actual limb movement.</p><p dir=\"ltr\">4. Rest Interval: 3 seconds</p><p dir=\"ltr\">A blank black screen was presented to allow subjects to recover and reset attention for the next trial.</p><p dir=\"ltr\">3.2.2 Trial Duration Summary</p><ul><li>Total duration per trial (CCS-HI): 2 s (fixation) + 2.8 s (cue) + 4 s (imagery) + 3 s (rest) = 11.8 seconds.</li><li>Total duration per trial (SV-HI): 2 s (fixation) + 3.2 s (cue) + 4 s (imagery) + 3 s (rest) = 12.2 seconds.</li></ul><h3>4. Preprocessing & Trial Integrity</h3><p dir=\"ltr\">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1\u201340 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0\u20134 s imagery period (for both tasks). Trial integrity checks confirmed most sessions met standards (200 trials/session for CCS-HI, 240 for SV-HI), with two exceptions: Subject 06 (SV-HI, Ses-2: 225 trials, trigger error) and Subject 13 (CCS-HI, Ses-2: 162 trials, fatigue). All retained trials passed quality checks, and missing trials were evenly distributed across categories (max deviation \u22642 trials, <5% threshold), so no class balancing was needed.</p><h3><br>5. File Structure (BIDS-Compliant)</h3><p dir=\"ltr\">Following corrections to event labels and participant metadata in Version 2.0, the dataset underwent full BIDS compliance re-verification to ensure data structure and metadata consistency.</p><p dir=\"ltr\">The dataset follows the BIDS standard: the root directory stores core metadata (e.g., <code>dataset_description.json</code> for dataset info, <code>participants.tsv</code> for demographics, <code>electrodes.tsv</code> for 32-channel 10-10 system coordinates, task-specific <code>_events.json</code>), 16 subject folders (e.g., <code>sub-01</code>) each contain <code>ses-01/ses-02</code> with <code>eeg/</code> subfolders (by CCS-HI/SV-HI tasks) holding <code>_channels.tsv</code> (bad channels), <code>_eeg.bdf</code> (raw Neuracle EEG), <code>_eeg.json</code> (acquisition params), <code>_events.tsv</code> (task alignment); supplementary folders include <code>stimuli</code> (visual cues) and <code>code</code> (preprocessing/model scripts with docs).</p><h3>6. Usage & Citation</h3><p dir=\"ltr\">6.1 Data Access & Extraction</p><p dir=\"ltr\">The HI-EEG dataset (CCS-HI/SV-HI tasks) is available via Figshare: https://doi.org/10.6084/m9.figshare.29987758(raw data access). To ensure successful decompression of the split-volume compressed files, follow these guidelines:</p><ul><li>File Composition: The dataset is split into 4 interlinked files (all required for full extraction):</li></ul><p dir=\"ltr\">1. Main archive: `CCS-SV-HI-EEG_BIDS.zip`</p><p dir=\"ltr\">2. Split volumes: `CCS-SV-HI-EEG_BIDS.z01`, `CCS-SV-HI-EEG_BIDS.z02`, `CCS-SV-HI-EEG_BIDS.z03`</p><ul><li>Pre-Extraction Requirement: Download all 4 files and place them in the **same directory** (do not rename files or move them to subfolders).</li><li>Tool & Operation:</li></ul><p dir=\"ltr\">1. Use split-volume compatible software (RECOMMENDED: 7-Zip, open-source & free: https://www.7-zip.org/; avoid default system extractors like Windows Explorer/macOS Archive Utility).</p><p dir=\"ltr\">2. Double-click the main `.zip` file (`CCS-SV-HI-EEG_BIDS.zip`) \u2014 the software will automatically merge `.z01`\u2013`.z03` into the full BIDS folder (matching the structure described in Section 5).</p><ul><li>Critical Note: Extraction will fail if any split file is missing; the decompressed folder retains the BIDS structure (no additional organization needed).</li></ul><p><br></p><p dir=\"ltr\">6.2 Trial Integrity & Usage Details</p><p dir=\"ltr\">Note two trial integrity details: sub-06 (SV-HI, ses-02) retains 225 trials (15 missing, trigger sync error) and sub-13 (CCS-HI, ses-02) retains 162 trials (38 missing, subject fatigue)\u2014missing trials are evenly distributed across categories (max deviation \u22642 trials, <5% imbalance, no class balancing needed).</p><p><br></p><p dir=\"ltr\">For data loading/analysis, use EEGLAB (MATLAB) or MNE-Python 1.7.0 (Python); for decoding, use scikit-learn 1.6.1 or PyTorch 2.0.0+cu117 (all verified versions). Related code is in the repository\u2019s code directory; refer to the README in code for environment configuration and script workflow.</p><p><br></p><p dir=\"ltr\">6.3 Citation</p><p dir=\"ltr\">Cite the associated publication (DOI to be added) when using this dataset.</p>",
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-    "title": "Example manifest for the BIDS Siena Scalp EEG Database for evaluation with the SzCORE framework.",
-    "description": "<p dir=\"ltr\">This is a manifest that describe which patients and runs are used across train/dev/test sets when using SzCORE evaluation (Dan et al., 2024) with this dataset.</p><p dir=\"ltr\">References:</p><p dir=\"ltr\">Dan, J., Pale, U., Amirshahi, A., Cappelletti, W., Ingolfsson, T. M., Wang, X., Cossettini, A., Bernini, A., Benini, L., Beniczky, S., Atienza, D., & Ryvlin, P. (2024). SzCORE: Seizure Community Open\u2010Source Research Evaluation framework for the validation of electroencephalography \u2010based automated seizure detection algorithms. <i>Epilepsia</i>, epi.18113. <a href=\"https://doi.org/10.1111/epi.18113\" target=\"_blank\">https://doi.org/10.1111/epi.18113</a></p>",
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-    "title": "An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment",
-    "description": "This work introduces a bimodal dataset designed to explore electrophysiological biomarkers for assessing assistive technologies in neurorehabilitation. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks assisted by an upper-limb exoskeleton. To standardize and simulate natural upper-limb movements relevant to daily activities, a custom-designed touch panel was used. High-density EEG (hd-EEG) and surface EMG (sEMG) were recorded to capture neuromechanical responses.\nThe dataset adheres to Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. We provide subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency-domain decomposition for EMG.\nBeyond evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.",
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-    "description": "<p dir=\"ltr\">This dataset consists of BIDS-formatted fMRI and M/EEG data from <a href=\"https://doi.org/10.1038/sdata.2015.1\" rel=\"noreferrer\" target=\"_blank\">Wakeman & Henson (2015)</a> that have been processed for DCM analysis. Multimodal data from fMRI, EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in <a href=\"https://doi.org/10.3389/fnins.2019.00300\" rel=\"noreferrer\" target=\"_blank\">Henson et al (2019)</a>. This dataset was prepared for demonstration of group-level estimation and inference of DCMs from fMRI and M/EEG data. The raw data can be found here: <a href=\"https://openneuro.org/datasets/ds000117\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds000117</a></p><p dir=\"ltr\">fMRI data is provided in two formats:</p><ol><li><i>fMRI_ProcessedData_Individual_Runs.tar.xz</i> contains processed data per run per participant, about 8.9GB total. </li><li><i>fMRI_DCMreadyData_VOI_TimeCourses.tar</i> contains VOI time courses for 3 regions - bilateral EVC, and left and right FFA, obtained after reparametrizing and concatenating processed data per run. This totals up to about 440MB.</li></ol><p dir=\"ltr\">M/EEG data consists of averaged evoked sensor data for two conditions - faces and scrambled faces. Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset, which is provided in two formats:</p><ol><li>MEEG_DCMreadyData_with_GainMatrix.tar.xz consists of sensor data and leadfields - sufficient for inverting MEG, but not EEG data since forward models for the latter need BEM surfaces. This is compact, about 390MB.</li><li>MEEG_DCMreadyData_with_GainMatrix_BEM.tar.xz consists of sensor data, leadfields and BEM surfaces - can be used for inverting either MEG or EEG data. This is considerably larger in size, about 2.5GB.</li></ol><p dir=\"ltr\">All four data packages listed above have corresponding file lists for inspection, as well as SHA256 and MD5 checksums for verification of data integrity. Note that the data are highly compressed and will expand to about twice their size on decompression. Please use <a href=\"https://7-zip.org/\" rel=\"noreferrer\" target=\"_blank\">7zip</a> on Windows or <a href=\"https://linuxize.com/post/how-to-extract-unzip-tar-xz-file/\" rel=\"noreferrer\" target=\"_blank\">`tar -xvf filename`</a> on Linux to extract. Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: <a href=\"https://github.com/pranaysy/MultimodalDCM\" rel=\"noreferrer\" target=\"_blank\">https://github.com/pranaysy/MultimodalDCM</a>.</p><p dir=\"ltr\">For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.</p>",
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-    "dataset_id": "25037045",
-    "doi": "10.6084/m9.figshare.25037045.v4",
-    "title": "Movie annotations for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients",
-    "description": "<p dir=\"ltr\">Movie annotations for the manuscript: \"Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients\"</p><p dir=\"ltr\">Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli Rutishauser</p><p dir=\"ltr\">Abstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video \"Bang! You're Dead\" and performed a recognition memory test for movie content. 3\u2009T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.</p><p dir=\"ltr\">This dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). https://doi.org/10.1038/s41597-024-03029-1</p><p dir=\"ltr\">Related code: <a href=\"https://github.com/rutishauserlab/bmovie-release-NWB-BIDS\" target=\"_blank\">https://github.com/rutishauserlab/bmovie-release-NWB-BIDS</a></p><p dir=\"ltr\">Intracranial recording data: <a href=\"https://dandiarchive.org/dandiset/000623\" target=\"_blank\">https://dandiarchive.org/dandiset/000623</a></p><p dir=\"ltr\">fMRI data: <a href=\"https://openneuro.org/datasets/ds004798/\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds004798/</a></p><p><br></p>",
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-    "published_date": "2024-01-30T21:17:01Z",
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-        "name": "Umit Keles",
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-      {
-        "name": "Julien Dubois",
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-        "name": "Kevin J. M. Le",
-        "id": 17821725,
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-        "name": "David A. Kahn",
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-        "name": "Chrystal M. Reed",
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-        "name": "Jeffrey M. Chung",
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-        "name": "Ralph Adolphs",
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-        "name": "Ueli Rutishauser",
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-    "dataset_id": "22260892",
-    "doi": "10.3389/fnbeh.2023.1147140.s001",
-    "title": "Data_Sheet_1_Neural mechanisms of expert persuasion on willingness to pay for sugar.pdf",
-    "description": "<p>Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.</p><p>Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.</p><p>Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant\u2019s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.</p><p>Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.</p>",
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-        "name": "Vasily Klucharev",
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-      "sugar",
-      "EEG",
-      "healthy eating",
-      "machine learning",
-      "intersubject correlation",
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-    "description": "GENERAL INFORMATION<br><br><br>1. Title of Dataset:<br>Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences.<br><br><br>2. Author Information<br>    A. Principal Investigator Contact Information<br>            Name: Stefan Wiens<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/swiens-1.184142<br>            Email: sws@psychology.su.se<br><br><br>    B. Associate or Co-investigator Contact Information<br>            Name: Rasmus Eklund<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/raek2031-1.223133<br>            Email: rasmus.eklund@psychology.su.se<br>    <br>    C. Associate or Co-investigator Contact Information<br>            Name: Billy Gerdfeldter<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/bige1544-1.403208<br>            Email: billy.gerdfeldter@psychology.su.se<br><br><br>3. Date of data collection: <br>Subjects (N = 28) were tested between 2020-03-04 and 2020-09-18.<br><br><br>4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden<br><br><br>5. Information about funding sources that supported the collection of the data:<br>Marianne and Marcus Wallenberg (Grant 2019-0102)<br><br><br>SHARING/ACCESS INFORMATION<br><br><br>1. Licenses/restrictions placed on the data: CC BY 4.0<br><br><br>2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B.,  & Wiens S. (2021). The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Neuropsychologia. https://doi.org/<br><br><br>The study was preregistered:<br>https://doi.org/10.17605/OSF.IO/PSRJF<br><br>3. Links to other publicly accessible locations of the data: N/A<br><br><br>4. Links/relationships to ancillary data sets: N/A<br><br><br>5. Was data derived from another source? No<br><br><br>6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.13067018<br><br><br>DATA & FILE OVERVIEW<br><br><br>File List:<br>The files contain the downsampled data in bids format, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.<br><br><br>AAN_LRclick_experiment_scripts.zip: contains the Python files to run the experiment<br><br><br>AAN_LRclick_bids_EEG.zip: contains EEG data files for each subject in .eeg format.<br><br><br>AAN_LRclick_behavior_log.zip: contains log files of the EEG session (generated by Python)<br><br><br>AAN_LRclick_EEG_scripts.zip: Python-MNE scripts to process and to analyze the EEG data<br><br><br>AAN_LRclick_results.zip: contains summary data files, figures, and tables that are created by Python-MNE.<br><br><br>METHODOLOGICAL INFORMATION<br><br><br>1. Description of methods used for collection/generation of data:<br>The auditory stimuli were 4-ms clicks.<br>The experiment was programmed in Python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn <br>The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and converted to .eeg format.<br>For more information, see linked publication.<br><br><br>2. Methods for processing the data:<br>We computed event-related potentials. See linked publication<br><br><br>3. Instrument- or software-specific information needed to interpret the data:<br>MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# <br><br><br>4. Standards and calibration information, if appropriate:<br>For information, see linked publication.<br><br><br>5. Environmental/experimental conditions:<br>For information, see linked publication.<br><br><br>6. 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For example, we added notes to explain what different variables mean.<br><br><br>The folder structure needs to be as follows:<br>AAN_LRclick (main folder)<br>--->data<br>--->--->bids (AAN_LRclick_bids_EEG)<br>--->--->log (AAN_LRclick_behavior_log)<br>--->MNE (AAN_LRclick_EEG_scripts)<br>--->results (AAN_LRclick_results)<br><br><br>To run the MNE-Python scripts:<br>Anaconda was used with MNE-Python 0.22 (see installation at https://mne.tools/stable/index.html# ).<br>For preprocess.py and analysis.py, the complete scripts should be run (from anaconda prompt).<br>",
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\ No newline at end of file
diff --git a/consolidated/to_digest_openneuro_datasets.json b/consolidated/to_digest_openneuro_datasets.json
deleted file mode 100644
index 2c10fb12..00000000
--- a/consolidated/to_digest_openneuro_datasets.json
+++ /dev/null
@@ -1,1190 +0,0 @@
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-    "created": "2023-11-01T15:54:14.672Z",
-    "modified": "2025-05-27T15:41:36.000Z"
-  },
-  {
-    "dataset_id": "ds004998",
-    "modality": "meg",
-    "created": "2024-03-04T17:45:27.695Z",
-    "modified": "2024-09-09T08:26:28.000Z"
-  },
-  {
-    "dataset_id": "ds005065",
-    "modality": "meg",
-    "created": "2024-04-07T02:26:32.589Z",
-    "modified": "2024-04-09T01:21:46.000Z"
-  },
-  {
-    "dataset_id": "ds005107",
-    "modality": "meg",
-    "created": "2024-04-24T06:49:36.871Z",
-    "modified": "2025-06-30T23:44:09.000Z"
-  },
-  {
-    "dataset_id": "ds005241",
-    "modality": "meg",
-    "created": "2024-06-12T15:20:44.636Z",
-    "modified": "2024-06-14T14:53:48.000Z"
-  },
-  {
-    "dataset_id": "ds005261",
-    "modality": "meg",
-    "created": "2024-06-17T13:06:26.783Z",
-    "modified": "2025-05-12T07:26:44.000Z"
-  },
-  {
-    "dataset_id": "ds005279",
-    "modality": "meg",
-    "created": "2024-06-24T16:17:02.918Z",
-    "modified": "2024-07-03T20:27:47.000Z"
-  },
-  {
-    "dataset_id": "ds005346",
-    "modality": "meg",
-    "created": "2024-07-16T14:15:32.746Z",
-    "modified": "2025-08-06T02:29:27.000Z"
-  },
-  {
-    "dataset_id": "ds005356",
-    "modality": "meg",
-    "created": "2024-07-17T17:45:08.266Z",
-    "modified": "2025-03-06T16:10:24.000Z"
-  },
-  {
-    "dataset_id": "ds005752",
-    "modality": "meg",
-    "created": "2024-12-20T18:45:44.865Z",
-    "modified": "2025-02-18T16:59:09.000Z"
-  },
-  {
-    "dataset_id": "ds005810",
-    "modality": "meg",
-    "created": "2025-01-10T11:07:26.509Z",
-    "modified": "2025-08-30T14:20:38.000Z"
-  },
-  {
-    "dataset_id": "ds006012",
-    "modality": "meg",
-    "created": "2025-03-13T19:13:44.963Z",
-    "modified": "2025-03-14T17:05:14.000Z"
-  },
-  {
-    "dataset_id": "ds006035",
-    "modality": "meg",
-    "created": "2025-03-20T05:08:27.231Z",
-    "modified": "2025-03-21T18:44:26.000Z"
-  },
-  {
-    "dataset_id": "ds006334",
-    "modality": "meg",
-    "created": "2025-06-10T16:14:43.829Z",
-    "modified": "2025-06-11T09:26:21.000Z"
-  },
-  {
-    "dataset_id": "ds006468",
-    "modality": "meg",
-    "created": "2025-07-14T17:00:44.163Z",
-    "modified": "2025-10-20T16:32:42.000Z"
-  },
-  {
-    "dataset_id": "ds006502",
-    "modality": "meg",
-    "created": "2025-07-28T19:06:15.608Z",
-    "modified": "2025-08-12T04:55:40.000Z"
-  },
-  {
-    "dataset_id": "ds006629",
-    "modality": "meg",
-    "created": "2025-09-04T11:55:02.705Z",
-    "modified": "2025-09-29T06:11:47.000Z"
-  }
-]
\ No newline at end of file

From 44c7e3fb9dff1bf8a599b7c70e3d6a8a1cd277f0 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:16:44 +0100
Subject: [PATCH 28/34] testing diggestion

---
 eegdash/dataset/bids_dataset.py               | 161 ++++--
 .../ingestions/5_digest_datasets_minimal.py   | 473 ++++++++++++++++++
 2 files changed, 592 insertions(+), 42 deletions(-)
 create mode 100644 scripts/ingestions/5_digest_datasets_minimal.py

diff --git a/eegdash/dataset/bids_dataset.py b/eegdash/dataset/bids_dataset.py
index 92aec03d..f8c9f50a 100644
--- a/eegdash/dataset/bids_dataset.py
+++ b/eegdash/dataset/bids_dataset.py
@@ -16,6 +16,14 @@
 
 import pandas as pd
 from mne_bids import BIDSPath, find_matching_paths
+from mne_bids.config import ALLOWED_DATATYPE_EXTENSIONS, reader
+
+# Known companion/sidecar files for specific formats (BIDS spec requirement)
+# These files must be downloaded together with the primary file
+_COMPANION_FILES = {
+    ".set": [".fdt"],  # EEGLAB: data file
+    ".vhdr": [".eeg", ".vmrk"],  # BrainVision: data + marker files
+}
 
 
 class EEGBIDSDataset:
@@ -38,15 +46,20 @@ class EEGBIDSDataset:
 
     """
 
-    ALLOWED_FILE_FORMAT = ["eeglab", "brainvision", "biosemi", "european"]
+    # Dynamically build from MNE-BIDS constants (mne_bids.config.reader)
+    # reader dict maps file extensions to MNE read functions
+    # This ensures compatibility with the latest BIDS specification
+
+    # Primary extension + companions = files that must be downloaded together
     RAW_EXTENSIONS = {
-        ".set": [".set", ".fdt"],  # eeglab
-        ".edf": [".edf"],  # european
-        ".vhdr": [".eeg", ".vhdr", ".vmrk", ".dat", ".raw"],  # brainvision
-        ".bdf": [".bdf"],  # biosemi
+        ext: [ext] + _COMPANION_FILES.get(ext, []) for ext in reader.keys()
     }
+
+    # BIDS metadata file extensions (modality-specific + common)
     METADATA_FILE_EXTENSIONS = [
         "eeg.json",
+        "meg.json",
+        "ieeg.json",
         "channels.tsv",
         "electrodes.tsv",
         "events.tsv",
@@ -58,6 +71,7 @@ def __init__(
         data_dir=None,  # location of bids dataset
         dataset="",  # dataset name
         allow_symlinks=False,  # allow broken symlinks for digestion
+        modalities=None,  # list of modalities to search for (e.g., ["eeg", "meg", "ieeg"])
     ):
         if data_dir is None or not os.path.exists(data_dir):
             raise ValueError("data_dir must be specified and must exist")
@@ -67,6 +81,14 @@ def __init__(
         self.data_dir = data_dir
         self.allow_symlinks = allow_symlinks
 
+        # Set modalities to search for (default: eeg, meg, ieeg for backward compatibility)
+        if modalities is None:
+            self.modalities = ["eeg", "meg", "ieeg"]
+        else:
+            self.modalities = (
+                modalities if isinstance(modalities, list) else [modalities]
+            )
+
         # Accept exact dataset folder or a variant with informative suffixes
         # (e.g., dsXXXXX-bdf, dsXXXXX-bdf-mini) to avoid collisions.
         dir_name = self.bidsdir.name
@@ -80,9 +102,12 @@ def __init__(
 
         # get all recording files in the bids directory
         assert len(self.files) > 0, ValueError(
-            "Unable to construct EEG dataset. No EEG recordings found."
+            f"Unable to construct dataset. No recordings found for modalities: {self.modalities}"
         )
-        assert self.check_eeg_dataset(), ValueError("Dataset is not an EEG dataset.")
+        # Store the detected modality for later use
+        self.detected_modality = self.get_bids_file_attribute(
+            "modality", self.files[0]
+        ).lower()
 
     def check_eeg_dataset(self) -> bool:
         """Check if the BIDS dataset contains EEG data.
@@ -93,7 +118,7 @@ def check_eeg_dataset(self) -> bool:
             True if the dataset's modality is EEG, False otherwise.
 
         """
-        return self.get_bids_file_attribute("modality", self.files[0]).lower() == "eeg"
+        return self.detected_modality == "eeg"
 
     def _init_bids_paths(self) -> None:
         """Initialize BIDS file paths using mne_bids for fast discovery.
@@ -103,18 +128,27 @@ def _init_bids_paths(self) -> None:
 
         When allow_symlinks=True, includes broken symlinks (e.g., git-annex)
         for metadata extraction without requiring actual data files.
+
+        Searches across multiple modalities (eeg, meg, ieeg) based on self.modalities.
         """
         # Initialize cache for BIDSPath objects
         self._bids_path_cache = {}
 
-        # Find all EEG recordings
+        # Find all recordings across specified modalities
+        # Use MNE-BIDS constants to get valid extensions per modality
         self.files = []
-        for ext in self.RAW_EXTENSIONS.keys():
-            found_files = _find_eeg_files(
-                self.bidsdir, ext, allow_symlinks=self.allow_symlinks
-            )
-            if found_files:
-                self.files = found_files
+        for modality in self.modalities:
+            for ext in ALLOWED_DATATYPE_EXTENSIONS.get(modality, []):
+                found_files = _find_bids_files(
+                    self.bidsdir,
+                    ext,
+                    modalities=[modality],
+                    allow_symlinks=self.allow_symlinks,
+                )
+                if found_files:
+                    self.files = found_files
+                    break
+            if self.files:
                 break
 
     def _get_bids_path_from_file(self, data_filepath: str):
@@ -136,8 +170,17 @@ def _get_bids_path_from_file(self, data_filepath: str):
             filepath = Path(data_filepath)
             filename = filepath.name
 
+            # Detect modality from the directory path
+            # BIDS structure: .../sub-XX/[ses-YY/]<modality>/sub-XX_...
+            path_parts = filepath.parts
+            modality = "eeg"  # default
+            for part in path_parts:
+                if part in ["eeg", "meg", "ieeg", "emg"]:
+                    modality = part
+                    break
+
             # Extract entities from filename using BIDS pattern
-            # Expected format: sub-<label>[_ses-<label>][_task-<label>][_run-<label>]_eeg.<ext>
+            # Expected format: sub-<label>[_ses-<label>][_task-<label>][_run-<label>]_<modality>.<ext>
             subject = re.search(r"sub-([^_]*)", filename)
             session = re.search(r"ses-([^_]*)", filename)
             task = re.search(r"task-([^_]*)", filename)
@@ -148,7 +191,7 @@ def _get_bids_path_from_file(self, data_filepath: str):
                 session=session.group(1) if session else None,
                 task=task.group(1) if task else None,
                 run=int(run.group(1)) if run else None,
-                datatype="eeg",
+                datatype=modality,
                 extension=filepath.suffix,
                 root=self.bidsdir,
             )
@@ -321,12 +364,18 @@ def get_bids_file_attribute(self, attribute: str, data_filepath: str) -> Any:
         if attribute in direct_attrs:
             return direct_attrs[attribute]
 
-        # For JSON-based attributes, read and cache eeg.json
-        eeg_json = self._get_json_with_inheritance(data_filepath, "eeg.json")
+        # For JSON-based attributes, read the modality-specific JSON file
+        # (eeg.json for EEG, meg.json for MEG, ieeg.json for iEEG)
+        modality = bids_path.datatype or "eeg"
+        json_filename = f"{modality}.json"
+        modality_json = self._get_json_with_inheritance(data_filepath, json_filename)
+
         json_attrs = {
-            "sfreq": eeg_json.get("SamplingFrequency"),
-            "ntimes": eeg_json.get("RecordingDuration"),
-            "nchans": eeg_json.get("EEGChannelCount"),
+            "sfreq": modality_json.get("SamplingFrequency"),
+            "ntimes": modality_json.get("RecordingDuration"),
+            "nchans": modality_json.get("EEGChannelCount")
+            or modality_json.get("MEGChannelCount")
+            or modality_json.get("iEEGChannelCount"),
         }
 
         return json_attrs.get(attribute)
@@ -492,17 +541,23 @@ def _is_valid_eeg_file(filepath: Path, allow_symlinks: bool = False) -> bool:
     return False
 
 
-def _find_eeg_files(
-    bidsdir: Path, extension: str, allow_symlinks: bool = False
+def _find_bids_files(
+    bidsdir: Path,
+    extension: str,
+    modalities: list[str] = None,
+    allow_symlinks: bool = False,
 ) -> list[str]:
-    """Find EEG files in a BIDS directory.
+    """Find BIDS files in a BIDS directory across multiple modalities.
 
     Parameters
     ----------
     bidsdir : Path
         The BIDS dataset root directory.
     extension : str
-        File extension to search for (e.g., '.set', '.bdf').
+        File extension to search for (e.g., '.set', '.bdf', '.fif').
+    modalities : list of str, optional
+        List of modalities to search (e.g., ["eeg", "meg", "ieeg"]).
+        If None, defaults to ["eeg", "meg", "ieeg"].
     allow_symlinks : bool, default False
         If True, include broken symlinks in results (for metadata extraction).
         If False, only return files that can be read (for data loading).
@@ -513,22 +568,44 @@ def _find_eeg_files(
         List of file paths found.
 
     """
-    # First try mne_bids (fast, but skips broken symlinks)
-    if not allow_symlinks:
-        paths = find_matching_paths(bidsdir, datatypes="eeg", extensions=extension)
-        if paths:
-            return [str(p.fpath) for p in paths]
-
-    # Fallback: manual glob search (finds symlinks too)
-    pattern = f"**/eeg/*{extension}"
-    found = list(bidsdir.glob(pattern))
-
-    # Filter based on validation mode
-    valid_files = [
-        str(f) for f in found if _is_valid_eeg_file(f, allow_symlinks=allow_symlinks)
-    ]
-
-    return valid_files
+    if modalities is None:
+        modalities = ["eeg", "meg", "ieeg"]
+
+    all_files = []
+
+    for modality in modalities:
+        # First try mne_bids (fast, but skips broken symlinks)
+        if not allow_symlinks:
+            try:
+                paths = find_matching_paths(
+                    bidsdir, datatypes=modality, extensions=extension
+                )
+                if paths:
+                    all_files.extend([str(p.fpath) for p in paths])
+            except Exception:
+                pass  # Continue to fallback search
+
+        # Fallback: manual glob search (finds symlinks too)
+        pattern = f"**/{modality}/*{extension}"
+        found = list(bidsdir.glob(pattern))
+
+        # Filter based on validation mode
+        valid_files = [
+            str(f)
+            for f in found
+            if _is_valid_eeg_file(f, allow_symlinks=allow_symlinks)
+        ]
+        all_files.extend(valid_files)
+
+    # Remove duplicates while preserving order
+    seen = set()
+    unique_files = []
+    for f in all_files:
+        if f not in seen:
+            seen.add(f)
+            unique_files.append(f)
+
+    return unique_files
 
 
 __all__ = ["EEGBIDSDataset"]
diff --git a/scripts/ingestions/5_digest_datasets_minimal.py b/scripts/ingestions/5_digest_datasets_minimal.py
new file mode 100644
index 00000000..57deee77
--- /dev/null
+++ b/scripts/ingestions/5_digest_datasets_minimal.py
@@ -0,0 +1,473 @@
+r"""Digest cloned BIDS datasets and generate minimal MongoDB records.
+
+This script processes locally cloned BIDS datasets to extract MINIMAL metadata
+needed for the MongoDB database. It focuses on the core attributes required
+for the EEGDash API without loading actual EEG data or extracting unnecessary metadata.
+
+Architecture:
+    Cloned BIDS datasets -> This script -> Minimal JSON records -> MongoDB (via API)
+
+Key Differences from Full Digestion:
+- Only extracts CORE attributes (10 fields defined in eegdash.const)
+- No participant metadata extraction (participant_tsv, eeg_json)
+- No BIDS dependencies tracking
+- Faster processing (metadata-only, no data loading)
+- Smaller JSON files (minimal records)
+
+Core Attributes Extracted (from eegdash.const.config["attributes"]):
+1. data_name - Unique identifier (dataset_filename)
+2. dataset - Dataset ID (e.g., ds002718)
+3. bidspath - S3 path for download
+4. subject - Subject identifier
+5. task - Task name
+6. session - Session identifier (optional)
+7. run - Run number (optional)
+8. modality - Data modality (eeg, meg, ieeg)
+9. sampling_frequency - Sampling rate in Hz
+10. nchans - Number of channels
+11. ntimes - Number of time points
+# Note: NEMAR datasets (nm*) automatically use s3://nemar/ regardless of --s3-base
+
+Usage:
+    # Process all cloned datasets (OpenNeuro + NEMAR)
+    python 5_digest_datasets_minimal.py \\
+        --cloned-dir data/cloned_all \\
+        --output-dir digestion_output \\
+        --s3-base s3://openneuro.org
+
+    # Process specific datasets
+    python 5_digest_datasets_minimal.py \\
+        --cloned-dir data/cloned_all \\
+        --output-dir digestion_output \\
+        --datasets ds002718 ds005506
+
+    # Process with parallel processing
+    python 5_digest_datasets_minimal.py \\
+        --cloned-dir data/cloned_all \\
+        --output-dir digestion_output \\
+        --workers 4
+
+Output Structure:
+    digestion_output/
+    ├── ds002718/
+    │   ├── ds002718_minimal.json        # Minimal records for MongoDB
+    │   └── ds002718_summary.json        # Processing summary
+    ├── ds005506/
+    │   ├── ds005506_minimal.json
+    │   └── ds005506_summary.json
+    └── batch_summary.json               # Overall batch results
+
+Upload to MongoDB:
+    # Upload single dataset
+    curl -X POST https://data.eegdash.org/admin/eegdashstaging/records/bulk \\
+         -H "Authorization: Bearer AdminWrite2025SecureToken" \\
+         -H "Content-Type: application/json" \\
+         -d @digestion_output/ds002718/ds002718_minimal.json
+
+    # Bulk upload all datasets (Python)
+    python scripts/upload_to_mongodb.py \\
+        --input-dir digestion_output \\
+        --database eegdashstaging
+"""
+
+import argparse
+import json
+import sys
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+
+def extract_minimal_metadata(
+    dataset_id: str, dataset_dir: Path, s3_base: str
+) -> tuple[list[dict], list[dict]]:
+    """Extract minimal metadata from a BIDS dataset.
+
+    Only extracts the core 11 fields required for MongoDB without loading
+    participant info, BIDS dependencies, or actual EEG data.
+
+    Parameters
+    ----------
+    dataset_id : str
+        Dataset identifier (e.g., "ds002718")
+    dataset_dir : Path
+        Path to the local BIDS dataset directory
+    s3_base : str
+        S3 base URL (e.g., "s3://openneuro.org")
+
+    Returns
+    -------
+    records : list of dict
+        List of minimal metadata records
+    errors : list of dict
+        List of errors encountered during extraction
+
+    """
+    from eegdash.dataset.bids_dataset import EEGBIDSDataset
+
+    try:
+        # Use allow_symlinks=True for metadata extraction without loading raw data
+        bids_dataset = EEGBIDSDataset(
+            data_dir=str(dataset_dir),
+            dataset=dataset_id,
+            allow_symlinks=True,  # Enable metadata extraction from git-annex symlinks
+        )
+    except Exception as exc:
+        return [], [
+            {"dataset": dataset_id, "error": f"Failed to load BIDS dataset: {exc}"}
+        ]
+
+    records = []
+    errors = []
+
+    files = bids_dataset.get_files()
+
+    for bids_file in files:
+        _ = Path(bids_file).name
+
+        try:
+            # Extract minimal attributes directly without loading full metadata
+            record = extract_minimal_record(
+                bids_dataset, bids_file, dataset_id, s3_base
+            )
+            records.append(record)
+
+        except Exception as exc:
+            errors.append(
+                {"dataset": dataset_id, "file": str(bids_file), "error": str(exc)}
+            )
+
+    return records, errors
+
+
+def extract_minimal_record(
+    bids_dataset, bids_file: str, dataset_id: str, s3_base: str
+) -> dict[str, Any]:
+    """Extract minimal metadata for a single BIDS file.
+
+    Only extracts the 11 core attributes defined in eegdash.const.config["attributes"].
+
+    Parameters
+    ----------
+    bids_dataset : EEGBIDSDataset
+        The BIDS dataset object
+    bids_file : str
+        Path to the BIDS file
+    dataset_id : str
+        Dataset identifier
+    s3_base : str
+        S3 base URL
+
+    Returns
+    -------
+    dict
+        Minimal metadata record with only core attributes
+
+    """
+    file_name = Path(bids_file).name
+
+    # Construct S3 path based on dataset type
+    # NEMAR datasets (nm*) use s3://nemar/nm000XXX/...
+    # OpenNeuro datasets (ds*) use s3://openneuro.org/ds00XXXX/...
+    openneuro_path = dataset_id + bids_file.split(dataset_id)[1]
+
+    if dataset_id.startswith("nm"):
+        # NEMAR datasets use s3://nemar/{dataset_id}/...
+        s3_path = f"s3://nemar/{openneuro_path}"
+    else:
+        # OpenNeuro datasets use the provided s3_base
+        s3_path = f"{s3_base.rstrip('/')}/{openneuro_path}"
+
+    # Extract only the 11 core attributes
+    record = {
+        # Required fields
+        "data_name": f"{dataset_id}_{file_name}",
+        "dataset": dataset_id,
+        "bidspath": s3_path,
+        # BIDS entity fields
+        "subject": bids_dataset.get_bids_file_attribute("subject", bids_file),
+        "task": bids_dataset.get_bids_file_attribute("task", bids_file),
+        "session": bids_dataset.get_bids_file_attribute("session", bids_file),
+        "run": bids_dataset.get_bids_file_attribute("run", bids_file),
+        "modality": bids_dataset.get_bids_file_attribute("modality", bids_file),
+        # Technical metadata
+        "sampling_frequency": bids_dataset.get_bids_file_attribute("sfreq", bids_file),
+        "nchans": bids_dataset.get_bids_file_attribute("nchans", bids_file),
+        "ntimes": bids_dataset.get_bids_file_attribute("ntimes", bids_file),
+    }
+
+    return record
+
+
+def digest_single_dataset(
+    dataset_id: str, cloned_dir: Path, output_dir: Path, s3_base: str
+) -> dict[str, Any]:
+    """Process a single dataset and generate minimal JSON.
+
+    Parameters
+    ----------
+    dataset_id : str
+        Dataset identifier
+    cloned_dir : Path
+        Directory containing cloned datasets
+    output_dir : Path
+        Directory for output JSON files
+    s3_base : str
+        S3 base URL
+
+    Returns
+    -------
+    dict
+        Summary of the digestion process
+
+    """
+    dataset_dir = cloned_dir / dataset_id
+
+    if not dataset_dir.exists():
+        return {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "error": f"Dataset directory not found: {dataset_dir}",
+            "record_count": 0,
+            "error_count": 0,
+        }
+
+    print(f"[{dataset_id}] Processing...")
+
+    try:
+        # Extract minimal metadata
+        records, errors = extract_minimal_metadata(dataset_id, dataset_dir, s3_base)
+
+        # Create output directory
+        dataset_output_dir = output_dir / dataset_id
+        dataset_output_dir.mkdir(parents=True, exist_ok=True)
+
+        # Save minimal JSON for MongoDB
+        minimal_json = {
+            "dataset": dataset_id,
+            "record_count": len(records),
+            "records": records,
+        }
+
+        minimal_path = dataset_output_dir / f"{dataset_id}_minimal.json"
+        with minimal_path.open("w", encoding="utf-8") as f:
+            json.dump(minimal_json, f, indent=2, default=_json_serializer)
+
+        # Save summary
+        summary = {
+            "status": "success",
+            "dataset_id": dataset_id,
+            "record_count": len(records),
+            "error_count": len(errors),
+            "errors": errors if errors else None,
+            "output_file": str(minimal_path),
+            "processed_at": datetime.now().isoformat(),
+        }
+
+        summary_path = dataset_output_dir / f"{dataset_id}_summary.json"
+        with summary_path.open("w", encoding="utf-8") as f:
+            json.dump(summary, f, indent=2)
+
+        print(f"[{dataset_id}] ✓ {len(records)} records, {len(errors)} errors")
+
+        return summary
+
+    except Exception as exc:
+        error_summary = {
+            "status": "error",
+            "dataset_id": dataset_id,
+            "error": str(exc),
+            "record_count": 0,
+            "error_count": 1,
+            "processed_at": datetime.now().isoformat(),
+        }
+
+        print(f"[{dataset_id}] ✗ {str(exc)}")
+
+        return error_summary
+
+
+def _json_serializer(obj):
+    """Handle non-serializable objects for JSON export."""
+    from pathlib import Path
+
+    import numpy as np
+    import pandas as pd
+
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, Path):
+        return str(obj)
+    elif isinstance(obj, set):
+        return sorted(list(obj))
+    elif pd.isna(obj):
+        return None
+    elif isinstance(obj, (pd.Timestamp, pd.Timedelta)):
+        return obj.isoformat()
+
+    raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Digest cloned BIDS datasets to extract minimal MongoDB metadata.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+
+    parser.add_argument(
+        "--cloned-dir",
+        type=Path,
+        required=True,
+        help="Directory containing cloned BIDS datasets",
+    )
+
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("digestion_output"),
+        help="Directory for output JSON files (default: digestion_output)",
+    )
+
+    parser.add_argument(
+        "--s3-base",
+        type=str,
+        default="s3://openneuro.org",
+        help="S3 base URL (default: s3://openneuro.org)",
+    )
+
+    parser.add_argument(
+        "--datasets",
+        nargs="+",
+        help="Specific dataset IDs to process (default: all in cloned-dir)",
+    )
+
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=1,
+        help="Number of parallel workers (default: 1, sequential)",
+    )
+
+    args = parser.parse_args()
+
+    # Determine which datasets to process
+    if args.datasets:
+        datasets_to_process = args.datasets
+    else:
+        # Auto-detect from cloned directory
+        datasets_to_process = [
+            d.name
+            for d in args.cloned_dir.iterdir()
+            if d.is_dir() and (d.name.startswith("ds") or d.name.startswith("nm"))
+        ]
+
+    print("=" * 80)
+    print("MINIMAL DATASET DIGESTION")
+    print("=" * 80)
+    print(f"Cloned directory: {args.cloned_dir}")
+    print(f"Output directory: {args.output_dir}")
+    print(f"S3 base URL: {args.s3_base}")
+    print(f"Datasets to process: {len(datasets_to_process)}")
+    print(f"Parallel workers: {args.workers}")
+    print("=" * 80)
+    print()
+
+    # Create output directory
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Process datasets
+    start_time = datetime.now()
+    summaries = []
+
+    if args.workers == 1:
+        # Sequential processing
+        for dataset_id in datasets_to_process:
+            summary = digest_single_dataset(
+                dataset_id, args.cloned_dir, args.output_dir, args.s3_base
+            )
+            summaries.append(summary)
+    else:
+        # Parallel processing
+        with ProcessPoolExecutor(max_workers=args.workers) as executor:
+            futures = {
+                executor.submit(
+                    digest_single_dataset,
+                    dataset_id,
+                    args.cloned_dir,
+                    args.output_dir,
+                    args.s3_base,
+                ): dataset_id
+                for dataset_id in datasets_to_process
+            }
+
+            for future in as_completed(futures):
+                summary = future.result()
+                summaries.append(summary)
+
+    end_time = datetime.now()
+    duration = (end_time - start_time).total_seconds()
+
+    # Calculate statistics
+    successful = [s for s in summaries if s["status"] == "success"]
+    failed = [s for s in summaries if s["status"] == "error"]
+    total_records = sum(s["record_count"] for s in successful)
+    total_errors = sum(s["error_count"] for s in summaries)
+
+    # Save batch summary
+    batch_summary = {
+        "processed_at": end_time.isoformat(),
+        "duration_seconds": duration,
+        "total_datasets": len(datasets_to_process),
+        "successful_datasets": len(successful),
+        "failed_datasets": len(failed),
+        "total_records": total_records,
+        "total_errors": total_errors,
+        "datasets": summaries,
+    }
+
+    batch_summary_path = args.output_dir / "batch_summary.json"
+    with batch_summary_path.open("w", encoding="utf-8") as f:
+        json.dump(batch_summary, f, indent=2)
+
+    # Print results
+    print()
+    print("=" * 80)
+    print("DIGESTION COMPLETE")
+    print("=" * 80)
+    print(f"Duration: {duration:.1f} seconds")
+    print(f"Datasets processed: {len(datasets_to_process)}")
+    print(f"  ✓ Successful: {len(successful)}")
+    print(f"  ✗ Failed: {len(failed)}")
+    print(f"Total records extracted: {total_records}")
+    print(f"Total errors: {total_errors}")
+    print(f"\nBatch summary: {batch_summary_path}")
+    print("=" * 80)
+
+    if failed:
+        print("\nFailed datasets:")
+        for s in failed:
+            print(f"  ✗ {s['dataset_id']}: {s.get('error', 'Unknown error')}")
+
+    print("\n" + "=" * 80)
+    print("NEXT STEPS: Upload to MongoDB")
+    print("=" * 80)
+    print("\nOption 1: Upload single dataset")
+    print(
+        "  curl -X POST http://137.110.244.65:3000/admin/eegdashstaging/records/bulk \\"
+    )
+    print("       -H 'Authorization: Bearer AdminWrite2025SecureToken' \\")
+    print("       -H 'Content-Type: application/json' \\")
+    print("       -d @digestion_output/ds002718/ds002718_minimal.json")
+    print("\nOption 2: Bulk upload all datasets (requires Python script)")
+    print("  See: scripts/upload_to_mongodb.py")
+    print("=" * 80)
+
+    return 0 if len(failed) == 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

From fa0a05b08dd4224d03a52b66e000d7f2561da8f5 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:19:07 +0100
Subject: [PATCH 29/34] 1, to allow downloading

---
 tests/test_correctness.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_correctness.py b/tests/test_correctness.py
index 750d9614..eb6584c1 100644
--- a/tests/test_correctness.py
+++ b/tests/test_correctness.py
@@ -96,7 +96,7 @@ def preprocess_instance(eeg_dash_dataset, cache_dir: Path):
         ]
 
         eeg_dash_dataset = preprocess(
-            eeg_dash_dataset, preprocessors, n_jobs=-1, save_dir=pre_processed_dir
+            eeg_dash_dataset, preprocessors, n_jobs=1, save_dir=pre_processed_dir
         )
 
         return eeg_dash_dataset

From 4a40623e70576c3b571d02e9c73a60fd719f5e77 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:30:58 +0100
Subject: [PATCH 30/34] using more constant

---
 eegdash/dataset/bids_dataset.py | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/eegdash/dataset/bids_dataset.py b/eegdash/dataset/bids_dataset.py
index f8c9f50a..cff7d3d0 100644
--- a/eegdash/dataset/bids_dataset.py
+++ b/eegdash/dataset/bids_dataset.py
@@ -16,7 +16,7 @@
 
 import pandas as pd
 from mne_bids import BIDSPath, find_matching_paths
-from mne_bids.config import ALLOWED_DATATYPE_EXTENSIONS, reader
+from mne_bids.config import ALLOWED_DATATYPE_EXTENSIONS, EPHY_ALLOWED_DATATYPES, reader
 
 # Known companion/sidecar files for specific formats (BIDS spec requirement)
 # These files must be downloaded together with the primary file
@@ -55,17 +55,6 @@ class EEGBIDSDataset:
         ext: [ext] + _COMPANION_FILES.get(ext, []) for ext in reader.keys()
     }
 
-    # BIDS metadata file extensions (modality-specific + common)
-    METADATA_FILE_EXTENSIONS = [
-        "eeg.json",
-        "meg.json",
-        "ieeg.json",
-        "channels.tsv",
-        "electrodes.tsv",
-        "events.tsv",
-        "events.json",
-    ]
-
     def __init__(
         self,
         data_dir=None,  # location of bids dataset
@@ -81,9 +70,9 @@ def __init__(
         self.data_dir = data_dir
         self.allow_symlinks = allow_symlinks
 
-        # Set modalities to search for (default: eeg, meg, ieeg for backward compatibility)
+        # Set modalities to search for (default: all electrophysiology modalities from MNE-BIDS)
         if modalities is None:
-            self.modalities = ["eeg", "meg", "ieeg"]
+            self.modalities = EPHY_ALLOWED_DATATYPES  # ['meg', 'eeg', 'ieeg', 'nirs']
         else:
             self.modalities = (
                 modalities if isinstance(modalities, list) else [modalities]
@@ -557,7 +546,7 @@ def _find_bids_files(
         File extension to search for (e.g., '.set', '.bdf', '.fif').
     modalities : list of str, optional
         List of modalities to search (e.g., ["eeg", "meg", "ieeg"]).
-        If None, defaults to ["eeg", "meg", "ieeg"].
+        If None, defaults to EPHY_ALLOWED_DATATYPES from mne_bids.config.
     allow_symlinks : bool, default False
         If True, include broken symlinks in results (for metadata extraction).
         If False, only return files that can be read (for data loading).

From d292a7e94515db374dc0a17dbd071c7b6016c18d Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:33:19 +0100
Subject: [PATCH 31/34] updating the documentation

---
 eegdash/dataset/bids_dataset.py | 45 +++++++++++++++++++++++++++++++--
 1 file changed, 43 insertions(+), 2 deletions(-)

diff --git a/eegdash/dataset/bids_dataset.py b/eegdash/dataset/bids_dataset.py
index cff7d3d0..25d02ac5 100644
--- a/eegdash/dataset/bids_dataset.py
+++ b/eegdash/dataset/bids_dataset.py
@@ -27,11 +27,15 @@
 
 
 class EEGBIDSDataset:
-    """An interface to a local BIDS dataset containing EEG recordings.
+    """An interface to a local BIDS dataset containing electrophysiology recordings.
 
     This class centralizes interactions with a BIDS dataset on the local
     filesystem, providing methods to parse metadata, find files, and
-    retrieve BIDS-related information.
+    retrieve BIDS-related information. Supports multiple modalities including
+    EEG, MEG, iEEG, and NIRS.
+
+    The class uses MNE-BIDS constants to stay synchronized with the BIDS
+    specification and automatically supports all file formats recognized by MNE.
 
     Parameters
     ----------
@@ -43,6 +47,43 @@ class EEGBIDSDataset:
         If True, accept broken symlinks (e.g., git-annex) for metadata extraction.
         If False, require actual readable files for data loading.
         Set to True when doing metadata digestion without loading raw data.
+    modalities : list of str or None, default None
+        List of modalities to search for (e.g., ["eeg", "meg"]).
+        If None, defaults to all electrophysiology modalities from MNE-BIDS:
+        ['meg', 'eeg', 'ieeg', 'nirs'].
+
+    Attributes
+    ----------
+    RAW_EXTENSIONS : dict
+        Mapping of file extensions to their companion files, dynamically
+        built from mne_bids.config.reader.
+    files : list of str
+        List of all recording file paths found in the dataset.
+    detected_modality : str
+        The modality of the first file found (e.g., 'eeg', 'meg').
+
+    Examples
+    --------
+    >>> # Load EEG-only dataset
+    >>> dataset = EEGBIDSDataset(
+    ...     data_dir="/path/to/ds002718",
+    ...     dataset="ds002718",
+    ...     modalities=["eeg"]
+    ... )
+
+    >>> # Load dataset with multiple modalities
+    >>> dataset = EEGBIDSDataset(
+    ...     data_dir="/path/to/ds005810",
+    ...     dataset="ds005810",
+    ...     modalities=["meg", "eeg"]
+    ... )
+
+    >>> # Metadata extraction from git-annex (symlinks)
+    >>> dataset = EEGBIDSDataset(
+    ...     data_dir="/path/to/dataset",
+    ...     dataset="ds000001",
+    ...     allow_symlinks=True
+    ... )
 
     """
 

From 39e7f5cee6872cbea29a0bdc46d874f58eaa1b74 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:44:06 +0100
Subject: [PATCH 32/34] updating

---
 eegdash/dataset/bids_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eegdash/dataset/bids_dataset.py b/eegdash/dataset/bids_dataset.py
index 25d02ac5..7e7c1685 100644
--- a/eegdash/dataset/bids_dataset.py
+++ b/eegdash/dataset/bids_dataset.py
@@ -599,7 +599,7 @@ def _find_bids_files(
 
     """
     if modalities is None:
-        modalities = ["eeg", "meg", "ieeg"]
+        modalities = EPHY_ALLOWED_DATATYPES
 
     all_files = []
 

From f350a3456473024e64866b070f4371e7580a8db1 Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:46:10 +0100
Subject: [PATCH 33/34] test correctness

---
 tests/test_correctness.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_correctness.py b/tests/test_correctness.py
index eb6584c1..a9d2ceae 100644
--- a/tests/test_correctness.py
+++ b/tests/test_correctness.py
@@ -96,7 +96,7 @@ def preprocess_instance(eeg_dash_dataset, cache_dir: Path):
         ]
 
         eeg_dash_dataset = preprocess(
-            eeg_dash_dataset, preprocessors, n_jobs=1, save_dir=pre_processed_dir
+            eeg_dash_dataset, preprocessors, n_jobs=20, save_dir=pre_processed_dir
         )
 
         return eeg_dash_dataset

From 5b1e54173c53775549771a0842a785d80e83e86f Mon Sep 17 00:00:00 2001
From: bruAristimunha <b.aristimunha@gmail.com>
Date: Tue, 11 Nov 2025 21:48:22 +0100
Subject: [PATCH 34/34] remove the json to move to another place

---
 consolidated/eegmanylabs_datasets.json |    54 -
 consolidated/figshare_datasets.json    |  1982 ----
 consolidated/nemardatasets_repos.json  |   136 -
 consolidated/openneuro_datasets.json   |  2798 -----
 consolidated/scidb_datasets.json       | 13095 -----------------------
 5 files changed, 18065 deletions(-)
 delete mode 100644 consolidated/eegmanylabs_datasets.json
 delete mode 100644 consolidated/figshare_datasets.json
 delete mode 100644 consolidated/nemardatasets_repos.json
 delete mode 100644 consolidated/openneuro_datasets.json
 delete mode 100644 consolidated/scidb_datasets.json

diff --git a/consolidated/eegmanylabs_datasets.json b/consolidated/eegmanylabs_datasets.json
deleted file mode 100644
index c88157cb..00000000
--- a/consolidated/eegmanylabs_datasets.json
+++ /dev/null
@@ -1,54 +0,0 @@
-[
-  {
-    "dataset_id": "EEGManyLabs_Replication_ClarkHillyard1996_Raw",
-    "full_name": "EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw",
-    "description": "",
-    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw",
-    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw.git",
-    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_ClarkHillyard1996_Raw.git",
-    "stars": 0,
-    "forks": 0,
-    "updated": "",
-    "source": "gin",
-    "organization": "EEGManyLabs"
-  },
-  {
-    "dataset_id": "EEGManyLabs_Replication_Eimer1996_Processed",
-    "full_name": "EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed",
-    "description": "",
-    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed",
-    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed.git",
-    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Processed.git",
-    "stars": 0,
-    "forks": 0,
-    "updated": "",
-    "source": "gin",
-    "organization": "EEGManyLabs"
-  },
-  {
-    "dataset_id": "EEGManyLabs_Replication_HajcakHolroyd2005",
-    "full_name": "EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005",
-    "description": "",
-    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005",
-    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005.git",
-    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_HajcakHolroyd2005.git",
-    "stars": 0,
-    "forks": 0,
-    "updated": "",
-    "source": "gin",
-    "organization": "EEGManyLabs"
-  },
-  {
-    "dataset_id": "EEGManyLabs_Replication_Eimer1996_Raw",
-    "full_name": "EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw",
-    "description": "",
-    "url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw",
-    "clone_url": "https://gin.g-node.org/EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw.git",
-    "ssh_url": "git@gin.g-node.org:EEGManyLabs/EEGManyLabs_Replication_Eimer1996_Raw.git",
-    "stars": 0,
-    "forks": 0,
-    "updated": "",
-    "source": "gin",
-    "organization": "EEGManyLabs"
-  }
-]
\ No newline at end of file
diff --git a/consolidated/figshare_datasets.json b/consolidated/figshare_datasets.json
deleted file mode 100644
index 18bb0895..00000000
--- a/consolidated/figshare_datasets.json
+++ /dev/null
@@ -1,1982 +0,0 @@
-[
-  {
-    "dataset_id": "30227503",
-    "doi": "10.6084/m9.figshare.30227503.v1",
-    "title": "EEG Dataset for Visual Imagery",
-    "description": "<h3>1.Research context</h3><h4>This dataset contains high-resolution EEG recordings collected from 22 healthy adult participants performing visual imagery (VI) tasks.</h4><p dir=\"ltr\">Participants imagined ten commonly encountered images across three semantic categories: geometric figures (circle, square, pentagram), animals (dog, fish, bird), and objects (cup, chair, watch, scissors). Each participant completed up to two sessions on separate days (\u226548 hours apart). However, sub-09 and sub-10 completed only the first session (ses-01) due to personal reasons, and thus have no available data for ses-02. Additionally, the animal imagery data of sub-08 in ses-02 was excluded from analysis because of poor signal quality, which introduced substantial noise contamination and led to unreliable classification results.</p><p dir=\"ltr\">EEG was recorded with a 34-channel cap (32 active EEG channels + AFz ground + CPz reference) at 1000 Hz.</p><h3>2.Data overview</h3><p dir=\"ltr\">Subjects: 22 healthy adults(5 females, age 20-23)</p><p dir=\"ltr\">Task:</p><p dir=\"ltr\">AVI(animal visual imagery): Imagine animal images(dog, fish, bird; 120 trials/session)</p><p dir=\"ltr\">FVI(animal visual imagery): Imagine figure images(circle, square, pentagram; 120 trials/session)</p><p dir=\"ltr\">OVI(animal visual imagery): Imagine object images(cup, chair, watch, scissors; 160 trials/session)</p><h3>3. Experimental Design</h3><p dir=\"ltr\">The paradigm was implemented using Psychtoolbox-3 and presented on a screen with a resolution of 1920\u00d71080 pixels. Each recording session lasted between 37 and 56 minutes and comprised four VI blocks for each task category. Flexible rest periods of 2 to 4 minutes were provided between blocks to mitigate the effects of fatigue. In the figure and animal categories, each image was presented 40 times, yielding a total of 120 trials per category.</p><h4>Each trial lasted 17 seconds and followed a structured sequence\uff1a</h4><ul><li>Fixation Cross\u200b: 3 s. A central fixation cross (\"+\") is displayed on the screen. To cue participants to remain alert and relaxed, preparing them for the trial.</li><li>Visual perception: 4 s. Participants are instructed to observe and memorize the image.</li><li>Mask: 2 s. A visual mask is briefly displayed.To eliminate any residual visual aftereffects from the presented image.</li><li>Visual imagery: 4 s.The screen turns black. Participants perform mental imagery of the previously shown image.</li><li>Rest: 4 s. The word \"Rest\" is displayed on the screen.Participants are instructed to stop any mental imagery and rest.</li></ul><h3>4.File structure</h3><p dir=\"ltr\">The data are organized according to the EEG-BIDS convention to maximize interoperability. Raw EEG (<code>*.bdf</code>) and BIDS sidecars: <code>*_channels.tsv</code>, <code>*_events.tsv</code>, <code>*_eeg.json</code>, etc.</p><p dir=\"ltr\"><br></p>",
-    "defined_type": 3,
-    "defined_type_name": "dataset",
-    "published_date": "2025-10-01T15:00:47Z",
-    "created_date": "2025-10-01T15:00:47Z",
-    "modified_date": "2025-10-01T15:02:02Z",
-    "authors": [
-      {
-        "name": "Jing'ao Gao",
-        "id": 22323919,
-        "orcid": "0009-0004-7718-5477"
-      }
-    ],
-    "tags": [
-      "brain\u2013computer interface (BCI)",
-      "visual imagery",
-      "EEG"
-    ],
-    "categories": [
-      {
-        "id": 24736,
-        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
-        "parent_id": 24724,
-        "path": "/24457/24724/24736",
-        "source_id": "320904",
-        "taxonomy_id": 100
-      }
-    ],
-    "license": "CC BY 4.0",
-    "url": "https://figshare.com/articles/dataset/EEG_Dataset_for_Visual_Imagery/30227503",
-    "api_url": "https://api.figshare.com/v2/articles/30227503",
-    "resource_title": null,
-    "resource_doi": null,
-    "files": [
-      {
-        "id": 58328968,
-        "name": "sub-01.zip",
-        "size_bytes": 627579802,
-        "download_url": "https://ndownloader.figshare.com/files/58328968",
-        "md5": "cac3615e077e6e6ee61a874a61b855c7"
-      },
-      {
-        "id": 58329043,
-        "name": "sub-02.zip",
-        "size_bytes": 514200571,
-        "download_url": "https://ndownloader.figshare.com/files/58329043",
-        "md5": "8ffb18f780080a5bea08fa0fed2947e4"
-      },
-      {
-        "id": 58329064,
-        "name": "sub-03.zip",
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-    "dataset_id": "29987758",
-    "doi": "10.6084/m9.figshare.29987758.v2",
-    "title": "<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>",
-    "description": "<p dir=\"ltr\">Version History</p><p dir=\"ltr\">Version 2.0 (October 15, 2025)</p><ul><li>Event label correction: Corrected a scaling inconsistency in the event tags within the file sub-13_ses-02_task-CCSHI_eeg.bdf.  The event codes now correctly align with the task paradigm's coding logic.</li><li>Metadata update: Corrected the handedness metadata for participant sub-06 in participants. tsv from \"L\" to \"R\", to address a typographical error in the original metadata.</li><li>Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.</li></ul><p dir=\"ltr\">No changes to the associated study's results or conclusions are necessitated by these updates.</p><h3>1. Research Context</h3><p dir=\"ltr\">This dataset addresses a critical gap in Brain-Computer Interface (BCI) research: most existing resources focus on English-language tasks or simple motor imagery, while Chinese character strokes and Pinyin monophthong handwriting remain understudied. By providing data for two complementary handwriting imagery tasks\u2014</p><p dir=\"ltr\">Chinese Character Stroke Handwriting Imagery (CCS-HI): Imagining writing 5 basic Chinese character strokes( \u4e00,\u4e28,\u4e3f,\u31cf,\u3125) ;</p><p dir=\"ltr\">Pinyin Single Vowel Handwriting Imagery (SV-HI): Imagining writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc)\u2014</p><p dir=\"ltr\">we enable investigations into:</p><p dir=\"ltr\">The neural mechanisms of handwriting imagery for Chinese strokes vs. Pinyin characters;</p><p dir=\"ltr\">Cross-session generalization (a critical challenge for real-world BCI deployment).</p><h3>2. Data Overview</h3><p dir=\"ltr\">Subjects: 16 healthy adults (3 females, age 22\u201332, right-handed, no neurological disorders). Note: In Version 2.0, handedness for one subject was corrected from \u201cL\u201d to \u201cR\u201d to reflect accurate right-handedness; all participants remain confirmed as right-handed.</p><p dir=\"ltr\">Tasks:</p><p dir=\"ltr\">CCS-HI: Imagine writing 5 Chinese strokes (\u4e00\uff0c\u4e28\uff0c\u4e3f\uff0c\u31cf, \u3125\uff1b200 trials/session).</p><p dir=\"ltr\">SV-HI: Imagine writing 6 Pinyin monophthongs (a, o, e, i, u, \u00fc; 240 trials/session).</p><p dir=\"ltr\">Sessions: 2 independent sessions (\u226524 hours apart).</p><p dir=\"ltr\">Session 1: Used for training and 5-fold cross-validation.</p><p dir=\"ltr\">Session 2: Held out as an independent test set for cross-session evaluation.</p><h3>3. Experimental Design</h3><p dir=\"ltr\">3.1 Pre-Experiment Preparation</p><p dir=\"ltr\">Prior to data collection, all subjects were fully informed of the experimental protocol (including task requirements and response methods) and shown a demonstration video of handwriting imagery tasks to ensure understanding. Written informed consent was obtained from all participants.</p><p dir=\"ltr\">3.2 EEG Acquisition</p><p dir=\"ltr\">Device: Neuracle NeuSenW amplifier with a 32-channel Ag/AgCl electrode cap.</p><p dir=\"ltr\">Electrode Layout: Strictly following the international 10-10 system. Detailed 3D coordinates and nomenclature of each electrode are provided in the accompanying electrodes.tsv file.</p><p dir=\"ltr\">Sampling Rate: 1000 Hz (downsampled to 250 Hz during preprocessing).</p><p dir=\"ltr\">Electrode Impedance: Maintained below 10 k\u03a9 for all channels during recording.</p><p dir=\"ltr\">Calibration: All equipment was professionally calibrated before the experiment to ensure data quality and reproducibility.</p><p dir=\"ltr\">3.3 Task Paradigm</p><p dir=\"ltr\">3.2.1 General Trial Structure (CCS-HI & SV-HI)</p><p dir=\"ltr\">Each single trial included four sequential phases, with the total duration varying slightly by task:</p><p dir=\"ltr\">1. Fixation + Auditory Cue: 2 seconds</p><p dir=\"ltr\">A white fixation cross was displayed on a black screen to stabilize subjects\u2019 attention;</p><p dir=\"ltr\">An auditory beep (500 Hz, 100 ms) was synchronized with the start of the cross, serving as a trial initiation signal.</p><p dir=\"ltr\">2. Task Cue Phase: Duration differs by task (key distinction)</p><p dir=\"ltr\">2.1 For CCS-HI: 2.8 seconds</p><p dir=\"ltr\">A dynamic animation of the target Chinese character stroke (e.g., \u4e00\uff0c\u4e28) was played, demonstrating the stroke\u2019s writing trajectory.</p><p dir=\"ltr\">2.2 For SV-HI: 3.2 seconds</p><p dir=\"ltr\">A dynamic animation of the target Pinyin monophthong (e.g., a, o) was played; the extended duration was designed to accommodate the relatively higher complexity of visualizing Pinyin character shapes.</p><p dir=\"ltr\">3. Handwriting Imagery Phase: 4 seconds</p><p dir=\"ltr\">Subjects were instructed to imagine writing the target stroke (CCS-HI) or Pinyin monophthong (SV-HI) following the trajectory shown in the task cue, without any actual limb movement.</p><p dir=\"ltr\">4. Rest Interval: 3 seconds</p><p dir=\"ltr\">A blank black screen was presented to allow subjects to recover and reset attention for the next trial.</p><p dir=\"ltr\">3.2.2 Trial Duration Summary</p><ul><li>Total duration per trial (CCS-HI): 2 s (fixation) + 2.8 s (cue) + 4 s (imagery) + 3 s (rest) = 11.8 seconds.</li><li>Total duration per trial (SV-HI): 2 s (fixation) + 3.2 s (cue) + 4 s (imagery) + 3 s (rest) = 12.2 seconds.</li></ul><h3>4. Preprocessing & Trial Integrity</h3><p dir=\"ltr\">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1\u201340 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0\u20134 s imagery period (for both tasks). Trial integrity checks confirmed most sessions met standards (200 trials/session for CCS-HI, 240 for SV-HI), with two exceptions: Subject 06 (SV-HI, Ses-2: 225 trials, trigger error) and Subject 13 (CCS-HI, Ses-2: 162 trials, fatigue). All retained trials passed quality checks, and missing trials were evenly distributed across categories (max deviation \u22642 trials, <5% threshold), so no class balancing was needed.</p><h3><br>5. File Structure (BIDS-Compliant)</h3><p dir=\"ltr\">Following corrections to event labels and participant metadata in Version 2.0, the dataset underwent full BIDS compliance re-verification to ensure data structure and metadata consistency.</p><p dir=\"ltr\">The dataset follows the BIDS standard: the root directory stores core metadata (e.g., <code>dataset_description.json</code> for dataset info, <code>participants.tsv</code> for demographics, <code>electrodes.tsv</code> for 32-channel 10-10 system coordinates, task-specific <code>_events.json</code>), 16 subject folders (e.g., <code>sub-01</code>) each contain <code>ses-01/ses-02</code> with <code>eeg/</code> subfolders (by CCS-HI/SV-HI tasks) holding <code>_channels.tsv</code> (bad channels), <code>_eeg.bdf</code> (raw Neuracle EEG), <code>_eeg.json</code> (acquisition params), <code>_events.tsv</code> (task alignment); supplementary folders include <code>stimuli</code> (visual cues) and <code>code</code> (preprocessing/model scripts with docs).</p><h3>6. Usage & Citation</h3><p dir=\"ltr\">6.1 Data Access & Extraction</p><p dir=\"ltr\">The HI-EEG dataset (CCS-HI/SV-HI tasks) is available via Figshare: https://doi.org/10.6084/m9.figshare.29987758(raw data access). To ensure successful decompression of the split-volume compressed files, follow these guidelines:</p><ul><li>File Composition: The dataset is split into 4 interlinked files (all required for full extraction):</li></ul><p dir=\"ltr\">1. Main archive: `CCS-SV-HI-EEG_BIDS.zip`</p><p dir=\"ltr\">2. Split volumes: `CCS-SV-HI-EEG_BIDS.z01`, `CCS-SV-HI-EEG_BIDS.z02`, `CCS-SV-HI-EEG_BIDS.z03`</p><ul><li>Pre-Extraction Requirement: Download all 4 files and place them in the **same directory** (do not rename files or move them to subfolders).</li><li>Tool & Operation:</li></ul><p dir=\"ltr\">1. Use split-volume compatible software (RECOMMENDED: 7-Zip, open-source & free: https://www.7-zip.org/; avoid default system extractors like Windows Explorer/macOS Archive Utility).</p><p dir=\"ltr\">2. Double-click the main `.zip` file (`CCS-SV-HI-EEG_BIDS.zip`) \u2014 the software will automatically merge `.z01`\u2013`.z03` into the full BIDS folder (matching the structure described in Section 5).</p><ul><li>Critical Note: Extraction will fail if any split file is missing; the decompressed folder retains the BIDS structure (no additional organization needed).</li></ul><p><br></p><p dir=\"ltr\">6.2 Trial Integrity & Usage Details</p><p dir=\"ltr\">Note two trial integrity details: sub-06 (SV-HI, ses-02) retains 225 trials (15 missing, trigger sync error) and sub-13 (CCS-HI, ses-02) retains 162 trials (38 missing, subject fatigue)\u2014missing trials are evenly distributed across categories (max deviation \u22642 trials, <5% imbalance, no class balancing needed).</p><p><br></p><p dir=\"ltr\">For data loading/analysis, use EEGLAB (MATLAB) or MNE-Python 1.7.0 (Python); for decoding, use scikit-learn 1.6.1 or PyTorch 2.0.0+cu117 (all verified versions). Related code is in the repository\u2019s code directory; refer to the README in code for environment configuration and script workflow.</p><p><br></p><p dir=\"ltr\">6.3 Citation</p><p dir=\"ltr\">Cite the associated publication (DOI to be added) when using this dataset.</p>",
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-    "created_date": "2025-09-19T15:59:06Z",
-    "modified_date": "2025-09-19T15:59:06Z",
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-        "name": "Fan Wang",
-        "id": 22125886,
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-    "dataset_id": "28740260",
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-    "title": "Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients",
-    "description": "<p dir=\"ltr\">We present the first large-scale, standardized EEG dataset (30 patients, 150 sessions, 15,000 trials) specifically designed for lower-limb motor imagery (MI) in knee pain patients, addressing a critical gap in clinical BCI research. Chronic knee pain alters cortical plasticity, yet our data demonstrate preserved MI capability in patients\u2014a finding with direct implications for rehabilitation BCI development. Our proposed Optimal Time-Frequency Window Riemannian Geometric Distance (OTFWRGD) algorithm achieves <b>86.41% classification accuracy</b>, significantly outperforming traditional methods (CSP+LDA: 51.43%; FBCSP+SVM: 55.71%; EEGNet: 76.21%). The dataset adheres to EEG-BIDS standards and is fully accessible via Figshare, including raw/preprocessed EEG, stimuli, and analysis code.</p>",
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-      {
-        "name": "Chongwen Zuo",
-        "id": 21546541,
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-    ],
-    "tags": [
-      "Brain Computer Interface in Rehabilitation",
-      "motor imagery EEG signals",
-      "Electroencephalogram wavelet analysis",
-      "Machine learning algorighms"
-    ],
-    "categories": [
-      {
-        "id": 29161,
-        "title": "Deep learning",
-        "parent_id": 29152,
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-    "title": "Example manifest for the BIDS Siena Scalp EEG Database for evaluation with the SzCORE framework.",
-    "description": "<p dir=\"ltr\">This is a manifest that describe which patients and runs are used across train/dev/test sets when using SzCORE evaluation (Dan et al., 2024) with this dataset.</p><p dir=\"ltr\">References:</p><p dir=\"ltr\">Dan, J., Pale, U., Amirshahi, A., Cappelletti, W., Ingolfsson, T. M., Wang, X., Cossettini, A., Bernini, A., Benini, L., Beniczky, S., Atienza, D., & Ryvlin, P. (2024). SzCORE: Seizure Community Open\u2010Source Research Evaluation framework for the validation of electroencephalography \u2010based automated seizure detection algorithms. <i>Epilepsia</i>, epi.18113. <a href=\"https://doi.org/10.1111/epi.18113\" target=\"_blank\">https://doi.org/10.1111/epi.18113</a></p>",
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-    "title": "An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment",
-    "description": "This work introduces a bimodal dataset designed to explore electrophysiological biomarkers for assessing assistive technologies in neurorehabilitation. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks assisted by an upper-limb exoskeleton. To standardize and simulate natural upper-limb movements relevant to daily activities, a custom-designed touch panel was used. High-density EEG (hd-EEG) and surface EMG (sEMG) were recorded to capture neuromechanical responses.\nThe dataset adheres to Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. We provide subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency-domain decomposition for EMG.\nBeyond evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.",
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-        "name": "Florencia Garro",
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-        "name": "Elena Fenoglio",
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-        "name": "Indya Ceroni",
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-        "name": "Inna Forsiuk",
-        "id": 19947239,
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-        "name": "Michele Canepa",
-        "id": 19958176,
-        "orcid": "0000-0002-6168-0332"
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-      {
-        "name": "Michael Mozzon",
-        "id": 19947248,
-        "orcid": ""
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-        "name": "Agnese Bruschi",
-        "id": 19947278,
-        "orcid": ""
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-        "name": "Francesco Zippo",
-        "id": 19947340,
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-        "name": "Matteo Laffranchi",
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-        "name": "Marianna Semprini",
-        "id": 19947527,
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-        "id": 536,
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-  {
-    "dataset_id": "25192793",
-    "doi": "10.6084/m9.figshare.25192793.v1",
-    "title": "Dynamic Causal Modelling of Face Processing with fMRI and M/EEG",
-    "description": "<p dir=\"ltr\">This dataset consists of BIDS-formatted fMRI and M/EEG data from <a href=\"https://doi.org/10.1038/sdata.2015.1\" rel=\"noreferrer\" target=\"_blank\">Wakeman & Henson (2015)</a> that have been processed for DCM analysis. Multimodal data from fMRI, EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in <a href=\"https://doi.org/10.3389/fnins.2019.00300\" rel=\"noreferrer\" target=\"_blank\">Henson et al (2019)</a>. This dataset was prepared for demonstration of group-level estimation and inference of DCMs from fMRI and M/EEG data. The raw data can be found here: <a href=\"https://openneuro.org/datasets/ds000117\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds000117</a></p><p dir=\"ltr\">fMRI data is provided in two formats:</p><ol><li><i>fMRI_ProcessedData_Individual_Runs.tar.xz</i> contains processed data per run per participant, about 8.9GB total. </li><li><i>fMRI_DCMreadyData_VOI_TimeCourses.tar</i> contains VOI time courses for 3 regions - bilateral EVC, and left and right FFA, obtained after reparametrizing and concatenating processed data per run. This totals up to about 440MB.</li></ol><p dir=\"ltr\">M/EEG data consists of averaged evoked sensor data for two conditions - faces and scrambled faces. Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset, which is provided in two formats:</p><ol><li>MEEG_DCMreadyData_with_GainMatrix.tar.xz consists of sensor data and leadfields - sufficient for inverting MEG, but not EEG data since forward models for the latter need BEM surfaces. This is compact, about 390MB.</li><li>MEEG_DCMreadyData_with_GainMatrix_BEM.tar.xz consists of sensor data, leadfields and BEM surfaces - can be used for inverting either MEG or EEG data. This is considerably larger in size, about 2.5GB.</li></ol><p dir=\"ltr\">All four data packages listed above have corresponding file lists for inspection, as well as SHA256 and MD5 checksums for verification of data integrity. Note that the data are highly compressed and will expand to about twice their size on decompression. Please use <a href=\"https://7-zip.org/\" rel=\"noreferrer\" target=\"_blank\">7zip</a> on Windows or <a href=\"https://linuxize.com/post/how-to-extract-unzip-tar-xz-file/\" rel=\"noreferrer\" target=\"_blank\">`tar -xvf filename`</a> on Linux to extract. Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: <a href=\"https://github.com/pranaysy/MultimodalDCM\" rel=\"noreferrer\" target=\"_blank\">https://github.com/pranaysy/MultimodalDCM</a>.</p><p dir=\"ltr\">For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.</p>",
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-        "name": "Pranay Yadav",
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-      {
-        "name": "Rik Henson",
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-      "dynamic causal modeling (DCM)",
-      "Dynamic Causal Model",
-      "Face Processing",
-      "MEG Data",
-      "Inverse Modelling",
-      "EEG Data",
-      "SPM12",
-      "Tutorial",
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-      "Neuroscience",
-      "fMRI Data",
-      "BOLD imaging",
-      "Open Science",
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-      "Jansen-Rit",
-      "neurophysiology and the brain"
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-        "id": 24736,
-        "title": "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)",
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-  {
-    "dataset_id": "25037045",
-    "doi": "10.6084/m9.figshare.25037045.v4",
-    "title": "Movie annotations for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients",
-    "description": "<p dir=\"ltr\">Movie annotations for the manuscript: \"Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients\"</p><p dir=\"ltr\">Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli Rutishauser</p><p dir=\"ltr\">Abstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video \"Bang! You're Dead\" and performed a recognition memory test for movie content. 3\u2009T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.</p><p dir=\"ltr\">This dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). https://doi.org/10.1038/s41597-024-03029-1</p><p dir=\"ltr\">Related code: <a href=\"https://github.com/rutishauserlab/bmovie-release-NWB-BIDS\" target=\"_blank\">https://github.com/rutishauserlab/bmovie-release-NWB-BIDS</a></p><p dir=\"ltr\">Intracranial recording data: <a href=\"https://dandiarchive.org/dandiset/000623\" target=\"_blank\">https://dandiarchive.org/dandiset/000623</a></p><p dir=\"ltr\">fMRI data: <a href=\"https://openneuro.org/datasets/ds004798/\" rel=\"noreferrer\" target=\"_blank\">https://openneuro.org/datasets/ds004798/</a></p><p><br></p>",
-    "defined_type": 3,
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-    "published_date": "2024-01-30T21:17:01Z",
-    "created_date": "2024-01-30T21:17:01Z",
-    "modified_date": "2024-02-27T20:41:49Z",
-    "authors": [
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-        "name": "Umit Keles",
-        "id": 17821706,
-        "orcid": ""
-      },
-      {
-        "name": "Julien Dubois",
-        "id": 351876,
-        "orcid": ""
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-      {
-        "name": "Kevin J. M. Le",
-        "id": 17821725,
-        "orcid": ""
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-      {
-        "name": "J. Michael Tyszka",
-        "id": 4523851,
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-      {
-        "name": "David A. Kahn",
-        "id": 11534227,
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-      {
-        "name": "Chrystal M. Reed",
-        "id": 5011316,
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-      {
-        "name": "Jeffrey M. Chung",
-        "id": 5011319,
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-      {
-        "name": "Adam N. Mamelak",
-        "id": 281705,
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-      {
-        "name": "Ralph Adolphs",
-        "id": 217684,
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-      {
-        "name": "Ueli Rutishauser",
-        "id": 686011,
-        "orcid": ""
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-    "tags": [
-      "movie watching"
-    ],
-    "categories": [
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-        "id": 30334,
-        "title": "Cognitive neuroscience",
-        "parent_id": 30325,
-        "path": "/30292/30325/30334",
-        "source_id": "520203",
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-      }
-    ],
-    "license": "CC BY 4.0",
-    "url": "https://figshare.com/articles/dataset/Movie_annotations_for_the_manuscript_Multimodal_brain_responses_during_movie_watching_single-neuron_intracranial_EEG_and_fMRI_in_human_patients_/25037045",
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-  {
-    "dataset_id": "22260892",
-    "doi": "10.3389/fnbeh.2023.1147140.s001",
-    "title": "Data_Sheet_1_Neural mechanisms of expert persuasion on willingness to pay for sugar.pdf",
-    "description": "<p>Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.</p><p>Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.</p><p>Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant\u2019s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.</p><p>Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.</p>",
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-    "published_date": "2023-03-13T04:21:28Z",
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-        "name": "Ioannis Ntoumanis",
-        "id": 11904629,
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-      {
-        "name": "Alina Davydova",
-        "id": 14781514,
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-      {
-        "name": "Julia Sheronova",
-        "id": 14781517,
-        "orcid": ""
-      },
-      {
-        "name": "Ksenia Panidi",
-        "id": 5991186,
-        "orcid": ""
-      },
-      {
-        "name": "Vladimir Kosonogov",
-        "id": 4415392,
-        "orcid": ""
-      },
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-    "description": "GENERAL INFORMATION<br><br><br>1. Title of Dataset:<br>Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences.<br><br><br>2. Author Information<br>    A. Principal Investigator Contact Information<br>            Name: Stefan Wiens<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/swiens-1.184142<br>            Email: sws@psychology.su.se<br><br><br>    B. Associate or Co-investigator Contact Information<br>            Name: Rasmus Eklund<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/raek2031-1.223133<br>            Email: rasmus.eklund@psychology.su.se<br>    <br>    C. Associate or Co-investigator Contact Information<br>            Name: Billy Gerdfeldter<br>            Institution: Department of Psychology, Stockholm University, Sweden<br>            Internet: https://www.su.se/profiles/bige1544-1.403208<br>            Email: billy.gerdfeldter@psychology.su.se<br><br><br>3. Date of data collection: <br>Subjects (N = 28) were tested between 2020-03-04 and 2020-09-18.<br><br><br>4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden<br><br><br>5. Information about funding sources that supported the collection of the data:<br>Marianne and Marcus Wallenberg (Grant 2019-0102)<br><br><br>SHARING/ACCESS INFORMATION<br><br><br>1. Licenses/restrictions placed on the data: CC BY 4.0<br><br><br>2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B.,  & Wiens S. (2021). The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Neuropsychologia. https://doi.org/<br><br><br>The study was preregistered:<br>https://doi.org/10.17605/OSF.IO/PSRJF<br><br>3. Links to other publicly accessible locations of the data: N/A<br><br><br>4. Links/relationships to ancillary data sets: N/A<br><br><br>5. Was data derived from another source? No<br><br><br>6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: The early but not the late neural correlate of auditory awareness re\ufb02ects lateralized experiences. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.13067018<br><br><br>DATA & FILE OVERVIEW<br><br><br>File List:<br>The files contain the downsampled data in bids format, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.<br><br><br>AAN_LRclick_experiment_scripts.zip: contains the Python files to run the experiment<br><br><br>AAN_LRclick_bids_EEG.zip: contains EEG data files for each subject in .eeg format.<br><br><br>AAN_LRclick_behavior_log.zip: contains log files of the EEG session (generated by Python)<br><br><br>AAN_LRclick_EEG_scripts.zip: Python-MNE scripts to process and to analyze the EEG data<br><br><br>AAN_LRclick_results.zip: contains summary data files, figures, and tables that are created by Python-MNE.<br><br><br>METHODOLOGICAL INFORMATION<br><br><br>1. Description of methods used for collection/generation of data:<br>The auditory stimuli were 4-ms clicks.<br>The experiment was programmed in Python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn <br>The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and converted to .eeg format.<br>For more information, see linked publication.<br><br><br>2. Methods for processing the data:<br>We computed event-related potentials. See linked publication<br><br><br>3. Instrument- or software-specific information needed to interpret the data:<br>MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# <br><br><br>4. Standards and calibration information, if appropriate:<br>For information, see linked publication.<br><br><br>5. Environmental/experimental conditions:<br>For information, see linked publication.<br><br><br>6. 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For example, we added notes to explain what different variables mean.<br><br><br>The folder structure needs to be as follows:<br>AAN_LRclick (main folder)<br>--->data<br>--->--->bids (AAN_LRclick_bids_EEG)<br>--->--->log (AAN_LRclick_behavior_log)<br>--->MNE (AAN_LRclick_EEG_scripts)<br>--->results (AAN_LRclick_results)<br><br><br>To run the MNE-Python scripts:<br>Anaconda was used with MNE-Python 0.22 (see installation at https://mne.tools/stable/index.html# ).<br>For preprocess.py and analysis.py, the complete scripts should be run (from anaconda prompt).<br>",
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-    "created": "2022-03-20T08:43:39.462Z",
-    "modified": "2023-10-16T02:05:05.000Z"
-  },
-  {
-    "dataset_id": "ds004107",
-    "modality": "meg",
-    "created": "2022-04-22T16:06:47.154Z",
-    "modified": "2022-04-22T19:50:04.000Z"
-  },
-  {
-    "dataset_id": "ds004212",
-    "modality": "meg",
-    "created": "2022-07-14T15:47:41.319Z",
-    "modified": "2025-05-29T20:15:29.000Z"
-  },
-  {
-    "dataset_id": "ds004229",
-    "modality": "meg",
-    "created": "2022-08-01T18:25:31.485Z",
-    "modified": "2023-11-03T18:30:43.000Z"
-  },
-  {
-    "dataset_id": "ds004276",
-    "modality": "meg",
-    "created": "2022-09-23T14:41:43.515Z",
-    "modified": "2022-09-23T20:02:38.000Z"
-  },
-  {
-    "dataset_id": "ds004278",
-    "modality": "meg",
-    "created": "2022-09-23T17:53:03.124Z",
-    "modified": "2022-10-11T18:18:40.000Z"
-  },
-  {
-    "dataset_id": "ds004330",
-    "modality": "meg",
-    "created": "2022-11-07T14:41:49.699Z",
-    "modified": "2022-11-08T10:16:44.000Z"
-  },
-  {
-    "dataset_id": "ds004346",
-    "modality": "meg",
-    "created": "2022-11-28T16:33:17.402Z",
-    "modified": "2024-06-07T09:19:58.000Z"
-  },
-  {
-    "dataset_id": "ds004398",
-    "modality": "meg",
-    "created": "2023-01-11T14:18:04.348Z",
-    "modified": "2023-01-25T04:25:53.000Z"
-  },
-  {
-    "dataset_id": "ds004483",
-    "modality": "meg",
-    "created": "2023-02-08T23:22:49.117Z",
-    "modified": "2023-02-13T14:53:45.000Z"
-  },
-  {
-    "dataset_id": "ds004738",
-    "modality": "meg",
-    "created": "2023-09-03T09:15:15.944Z",
-    "modified": "2023-09-03T10:42:30.000Z"
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-    "dataset_id": "ds004837",
-    "modality": "meg",
-    "created": "2023-11-01T15:54:14.672Z",
-    "modified": "2025-05-27T15:41:36.000Z"
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-    "dataset_id": "ds004998",
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-    "created": "2024-03-04T17:45:27.695Z",
-    "modified": "2024-09-09T08:26:28.000Z"
-  },
-  {
-    "dataset_id": "ds005065",
-    "modality": "meg",
-    "created": "2024-04-07T02:26:32.589Z",
-    "modified": "2024-04-09T01:21:46.000Z"
-  },
-  {
-    "dataset_id": "ds005107",
-    "modality": "meg",
-    "created": "2024-04-24T06:49:36.871Z",
-    "modified": "2025-06-30T23:44:09.000Z"
-  },
-  {
-    "dataset_id": "ds005241",
-    "modality": "meg",
-    "created": "2024-06-12T15:20:44.636Z",
-    "modified": "2024-06-14T14:53:48.000Z"
-  },
-  {
-    "dataset_id": "ds005261",
-    "modality": "meg",
-    "created": "2024-06-17T13:06:26.783Z",
-    "modified": "2025-05-12T07:26:44.000Z"
-  },
-  {
-    "dataset_id": "ds005279",
-    "modality": "meg",
-    "created": "2024-06-24T16:17:02.918Z",
-    "modified": "2024-07-03T20:27:47.000Z"
-  },
-  {
-    "dataset_id": "ds005346",
-    "modality": "meg",
-    "created": "2024-07-16T14:15:32.746Z",
-    "modified": "2025-08-06T02:29:27.000Z"
-  },
-  {
-    "dataset_id": "ds005356",
-    "modality": "meg",
-    "created": "2024-07-17T17:45:08.266Z",
-    "modified": "2025-03-06T16:10:24.000Z"
-  },
-  {
-    "dataset_id": "ds005752",
-    "modality": "meg",
-    "created": "2024-12-20T18:45:44.865Z",
-    "modified": "2025-02-18T16:59:09.000Z"
-  },
-  {
-    "dataset_id": "ds005810",
-    "modality": "meg",
-    "created": "2025-01-10T11:07:26.509Z",
-    "modified": "2025-08-30T14:20:38.000Z"
-  },
-  {
-    "dataset_id": "ds006012",
-    "modality": "meg",
-    "created": "2025-03-13T19:13:44.963Z",
-    "modified": "2025-03-14T17:05:14.000Z"
-  },
-  {
-    "dataset_id": "ds006035",
-    "modality": "meg",
-    "created": "2025-03-20T05:08:27.231Z",
-    "modified": "2025-03-21T18:44:26.000Z"
-  },
-  {
-    "dataset_id": "ds006334",
-    "modality": "meg",
-    "created": "2025-06-10T16:14:43.829Z",
-    "modified": "2025-06-11T09:26:21.000Z"
-  },
-  {
-    "dataset_id": "ds006468",
-    "modality": "meg",
-    "created": "2025-07-14T17:00:44.163Z",
-    "modified": "2025-10-20T16:32:42.000Z"
-  },
-  {
-    "dataset_id": "ds006502",
-    "modality": "meg",
-    "created": "2025-07-28T19:06:15.608Z",
-    "modified": "2025-08-12T04:55:40.000Z"
-  },
-  {
-    "dataset_id": "ds006629",
-    "modality": "meg",
-    "created": "2025-09-04T11:55:02.705Z",
-    "modified": "2025-09-29T06:11:47.000Z"
-  }
-]
\ No newline at end of file
diff --git a/consolidated/scidb_datasets.json b/consolidated/scidb_datasets.json
deleted file mode 100644
index d818645a..00000000
--- a/consolidated/scidb_datasets.json
+++ /dev/null
@@ -1,13095 +0,0 @@
-[
-  {
-    "dataset_id": "6380e06e830f751bb2bc035b",
-    "doi": "10.57760/sciencedb.06664",
-    "cstr": "31253.11.sciencedb.06664",
-    "pid": "21.86116.6/sciencedb.06664",
-    "title": "RAW <span style='background-color:yellow'>EEG</span> Data",
-    "title_en": "RAW <span style='background-color:yellow'>EEG</span> Data",
-    "title_zh": "RAW <span style='background-color:yellow'>EEG</span> Data",
-    "description": "<p>Raw EEG data after preprocessing，There are three folders in the compressed package, corresponding to three experiments: resting state, STB, Raven.</p>",
-    "description_en": "<p>Raw EEG data after preprocessing，There are three folders in the compressed package, corresponding to three experiments: resting state, STB, Raven.</p>",
-    "description_zh": "<p>Raw EEG data after preprocessing，There are three folders in the compressed package, corresponding to three experiments: resting state, STB, Raven.</p>",
-    "authors": [
-      {
-        "name_en": "Zhiwei Xu",
-        "name_zh": "许志炜",
-        "email": "zwxu@whu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Hubei University",
-            "name_zh": "湖北大学",
-            "ror_id": "https://ror.org/03a60m280"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "power spectral density",
-      "functional connection",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "power spectral density",
-      "functional connection",
-      "EEG"
-    ],
-    "publication_date": "2022-11-28T01:41:35.595Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 897.21,
-    "file_size_bytes": 940790688,
-    "download_count": 399,
-    "visit_count": 1245,
-    "url": "https://www.scidb.cn/en/detail?id=6380e06e830f751bb2bc035b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66695230b1275d34828fb391",
-    "doi": "10.57760/sciencedb.08558",
-    "cstr": "31253.11.sciencedb.08558",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> and HRV in cognitive load",
-    "title_en": "<span style='background-color:yellow'>EEG</span> and HRV in cognitive load",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> and HRV in cognitive load",
-    "description": "<p>EEG and HRV in cognitive load</p>",
-    "description_en": "<p>EEG and HRV in cognitive load</p>",
-    "description_zh": "<p>EEG and HRV in cognitive load</p>",
-    "authors": [
-      {
-        "name_en": "Yanxin Chen",
-        "name_zh": "Yanxin Chen",
-        "email": "yanxin.chen.coder@foxmail.com",
-        "affiliations": [
-          {
-            "name_en": "Jiangxi Science and Technology Normal University",
-            "name_zh": "Jiangxi Science and Technology Normal University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "HRV",
-      "cognitive load"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "HRV",
-      "cognitive load"
-    ],
-    "publication_date": "2024-06-12T07:43:38.249Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "GNU GPL",
-    "file_size_mb": 0.3,
-    "file_size_bytes": 313950,
-    "download_count": 183,
-    "visit_count": 843,
-    "url": "https://www.scidb.cn/en/detail?id=66695230b1275d34828fb391",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "610a8e8f44b32f0f67587553",
-    "doi": "10.11922/sciencedb.01034",
-    "cstr": "31253.11.sciencedb.01034",
-    "pid": "21.86116.6/sciencedb.01034",
-    "title": "Visual Colour <span style='background-color:yellow'>EEG</span> Dataset",
-    "title_en": "Visual Colour <span style='background-color:yellow'>EEG</span> Dataset",
-    "title_zh": "Visual Colour <span style='background-color:yellow'>EEG</span> Dataset",
-    "description": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
-    "description_en": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
-    "description_zh": "<p>\tAlthough there is more research about BCI Visual, but little about human’s visual colour. We provide a new EEG dataset of BCI Visual Colour. There is no such type dateset up-to-today. We hope this dataset can help and reuse for other researchers with there basic works in Neural Science and Computer Science. </p><p>\tThese dataset consist of 14 healthy subjects. Our purpose of establishing this dataset is to create a standard small sample of visual colour EEG to provide scholars in related fields for research.</p><p>\tThe standard small sample dataset includes the recognition of the basic colour RGB that humans perceive. Each subject in the dataset contains three sets of data: RG, RB, and GB. In each set of data, 3 rounds of visual stimulation experiments were included. Each stimulus experiment consists of 35 random stimulus interactions in two different colours. Therefore, there are a total of 630 stimuli in each subject's data.</p><p>\tNote that when calling data, a data packet usually consists of two .bdf files. One is the main record data file named data.bdf, and there is a label file named event.bdf. When calling data, it is necessary to call two files at the same time. The event.bdf file is the main reference basis for time series analysis of all data, and it is as important as the data.bdf file. The code for calling the .bdf file has been written in the other two classifier codes.</p><p>\tWe hope that this data can provide researchers in the field of neuroscience with a certain effect on the basic research of visual EEG signals and visual nerves in the field of colour perception. At the same time, we also hope that this dataset can be provided to researchers in the field of computer science to bring some research on the application of classifiers. We selected support vector machines and FNN neural networks on the classifier to analyze these dataset and achieved good results. But we not only hope that the analysis of this dataset will only stay on the above two analysis methods. We expect that more colleagues can use more other classifiers and data analysis methods to further process and use our dataset, so that our dataset can get better reusability.</p><p>\tThe ultimate goal of our release of this set of dataset is to hope that this set of dataset can become a standard sample for decoding human visual colour perception. At the same time, we hope that more machine learning algorithms can be obtained on the basis of repeated use of dataset, so as to give the field of neuroscience and computers The scientific field brings further research.</p>",
-    "authors": [
-      {
-        "name_en": "Wu Yi Jia",
-        "name_zh": "Wu Yi Jia",
-        "email": "wuyijia@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Fudan University",
-            "name_zh": "Fudan University",
-            "ror_id": "https://ror.org/013q1eq08"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "visual",
-      "colour",
-      "neuroscience"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "visual",
-      "colour",
-      "neuroscience"
-    ],
-    "publication_date": "2021-08-04T12:53:30.415Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 3487.74,
-    "file_size_bytes": 3657158170,
-    "download_count": 981,
-    "visit_count": 1454,
-    "url": "https://www.scidb.cn/en/detail?id=610a8e8f44b32f0f67587553",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "60b9c3adae98d4289fceed43",
-    "doi": "10.11922/sciencedb.00820",
-    "cstr": "31253.11.sciencedb.00820",
-    "pid": "21.86116.6/sciencedb.00820",
-    "title": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
-    "title_en": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
-    "title_zh": "EOG and <span style='background-color:yellow'>EEG</span> feature for train",
-    "description": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
-    "description_en": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
-    "description_zh": "<p>After the patient arrived in the operating room, standard anesthetic monitoring and preoperative management were carried out. Patients were studied under three conditions: an awake condition (0.0% end-tidal concentration sevoflurane [Etsevo]), a “light” sedation condition (1.0±0.1% Etsevo, approximately minimum alveolar concentration [MAC]awake[3, 16]), and a “deep” sedation condition (3.0 ±0.1 % Etsevo, exceedingly 1 MAC[17]). EEG and EOG signals were recorded by ANT Neuro EEG equipment, including 3&nbsp;EEG and 1 EOG channel in the 10-20 international system: Fp1, Fp2, Fpz, EOG. For recording EOG, two electrodes were placed above and below the right eye, and their&nbsp;difference was the vertical EOG (upper minus lower).&nbsp;EOG (PERA) and EEG (βratio and SampE) feature during three conditions for train.</p>",
-    "authors": [
-      {
-        "name_en": "Guozheng Wang",
-        "name_zh": "Guozheng Wang",
-        "affiliations": [
-          {
-            "name_en": "Zhejiang University",
-            "name_zh": "Zhejiang University",
-            "ror_id": "https://ror.org/00a2xv884"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Anesthetic depth monitoring",
-      " auditory stimulation",
-      " consciousness"
-    ],
-    "keywords_zh": [
-      "Anesthetic depth monitoring",
-      " auditory stimulation",
-      " consciousness"
-    ],
-    "publication_date": "2021-05-24T00:32:53.272Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.02,
-    "file_size_bytes": 15848,
-    "download_count": 7,
-    "visit_count": 772,
-    "url": "https://www.scidb.cn/en/detail?id=60b9c3adae98d4289fceed43",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67ee412a02cc303f2020136f",
-    "doi": "10.57760/sciencedb.23155",
-    "cstr": "31253.11.sciencedb.23155",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
-    "title_en": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> Dataset of Exploring Navigation in Virtual Reality",
-    "description": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
-    "description_en": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
-    "description_zh": "<p>Social exploration and spatial navigation involve shared and different human cognitive processes. While spatial navigation focuses on how people understand and interact with their physical surroundings, social exploration concentrates on how individuals perceive and engage in social spaces, particularly interpersonal interactions. Research in these two areas of psychology and neuroscience can help us better comprehend the cognitive exploration process and the underlying mechanisms of social and emotional interactions.</p><p><br></p><p>In this study, we aimed to examine the rewards and punishments that individuals with different levels of anxiety encounter during spatial exploration, as well as the impact of social interaction on their navigation ability and desire to explore. To achieve this, we designed an EEG task where participants had to search for landmarks in virtual reality while receiving token rewards or punishments and facial feedback when interact with strangers.</p><p><br></p><p>This dataset includes EEG recordings from 60 participants during a cognitive task. Additionally, it contains questionnaire responses, behavioral data, and trajectory data collected during the experiment.</p>",
-    "authors": [
-      {
-        "name_en": "HaiyanWu",
-        "name_zh": "HaiyanWu",
-        "email": "haiyanwu@um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Xuanji Chen",
-        "name_zh": "Xuanji Chen",
-        "email": "mc46540@um.edu.mo",
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-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Junyuan Zheng",
-        "name_zh": "Junyuan Zheng",
-        "email": "mc25802@umac.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "VR",
-      "EEG",
-      "Navigation",
-      "Cognitive psychology"
-    ],
-    "keywords_zh": [
-      "VR",
-      "EEG",
-      "Navigation",
-      "Cognitive psychology"
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-    "publication_date": "2025-04-08T09:07:37.432Z",
-    "created": "",
-    "modified": "",
-    "status": "",
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-    "url": "https://www.scidb.cn/en/detail?id=67ee412a02cc303f2020136f",
-    "source": "scidb"
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-    "dataset_id": "66b0ab6495ad961c18ee103f",
-    "doi": "10.57760/sciencedb.ai.00009",
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-    "pid": "",
-    "title": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
-    "title_en": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
-    "title_zh": "Two-Class Target Retrieval <span style='background-color:yellow'>EEG</span> Dataset",
-    "description": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
-    "description_en": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
-    "description_zh": "<p>Two-Class Target Retrieval EEG Dataset includes EEG data of Rapid Serial Visual Presentation (RSVP)-based multi-class target image retrieval tasks from 30 subjects.</p>",
-    "authors": [
-      {
-        "name_en": "Wei Wei",
-        "name_zh": "Wei Wei",
-        "email": "weiwei2018@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
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-      },
-      {
-        "name_en": "Li Xujin",
-        "name_zh": "Li Xujin",
-        "email": "lixujin2021@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
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-        "name_en": "Qiu Shuang",
-        "name_zh": "Qiu Shuang",
-        "email": "shuang.qiu@ia.ac.cn",
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-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
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-      {
-        "name_en": "He Huiguang",
-        "name_zh": "He Huiguang",
-        "email": "huiguang.he@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "target image retrieval ",
-      "P300"
-    ],
-    "keywords_zh": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "target image retrieval ",
-      "P300"
-    ],
-    "publication_date": "2023-12-21T09:06:16.172Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=66b0ab6495ad961c18ee103f",
-    "source": "scidb"
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-  {
-    "dataset_id": "670c8882129fd9344a03229d",
-    "doi": "10.57760/sciencedb.14812",
-    "cstr": "31253.11.sciencedb.14812",
-    "pid": "",
-    "title": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
-    "title_en": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
-    "title_zh": "NeuBCI Target Retrieval RSVP-<span style='background-color:yellow'>EEG</span> Dataset",
-    "description": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
-    "description_en": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
-    "description_zh": "<p>The NeuBCI Target Retrieval RSVP-EEG Dataset comprises electroencephalogram (EEG) data and corresponding stimulus images from Rapid Serial Visual Presentation (RSVP)-based target image retrieval tasks. The dataset encompasses three distinct tasks and includes data from 71 participants.</p>",
-    "authors": [
-      {
-        "name_en": "Xujin Li",
-        "name_zh": "Xujin Li",
-        "email": "lixujin2021@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "Institute of Automation",
-            "ror_id": "https://ror.org/056qj1t15"
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-          {
-            "name_en": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)",
-            "name_zh": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)"
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-        "name_en": "Wei Wei",
-        "name_zh": "Wei Wei",
-        "email": "weiwei2018@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "Institute of Automation",
-            "ror_id": "https://ror.org/00qdtba35"
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-      {
-        "name_en": "shuang Qiu",
-        "name_zh": "shuang Qiu",
-        "email": "shuang.qiu@ia.ac.cn",
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-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "Institute of Automation",
-            "ror_id": "https://ror.org/00qdtba35"
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-      {
-        "name_en": "Xinyi Zhang",
-        "name_zh": "Xinyi Zhang",
-        "email": "zhangxinyi2020@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "Institute of Automation",
-            "ror_id": "https://ror.org/00qdtba35"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Fu Li",
-        "name_zh": "Fu Li",
-        "email": "fuli@mail.xidian.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University",
-            "name_zh": "The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University"
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-        ]
-      },
-      {
-        "name_en": "Huiguang He",
-        "name_zh": "Huiguang He",
-        "email": "huiguang.he@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "Institute of Automation",
-            "ror_id": "https://ror.org/00qdtba35"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "Target image retrieval",
-      "P300",
-      "Event-Related Potential (ERP)"
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-    "keywords_zh": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "Target image retrieval",
-      "P300",
-      "Event-Related Potential (ERP)"
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-    "publication_date": "2024-10-17T07:34:31.468Z",
-    "created": "",
-    "modified": "",
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-    "license": "CC BY-NC-SA 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=670c8882129fd9344a03229d",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "651d974489be2410e0a6cdc3",
-    "doi": "10.57760/sciencedb.11875",
-    "cstr": "31253.11.sciencedb.11875",
-    "pid": "",
-    "title": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
-    "title_en": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
-    "title_zh": "Predicting Consumer Behavior in Online Shopping Environments: An <span style='background-color:yellow'>EEG</span>-Based Machine Learning Approach - Raw <span style='background-color:yellow'>EEG</span> Data, Matlab Scripts, and Results\"",
-    "description": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
-    "description_en": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
-    "description_zh": "<p>This dataset contains raw Electroencephalogram (EEG) recordings, Matlab programming scripts, and machine learning results associated with the research paper titled \"Predicting Consumer Behavior in Online Shopping Environments: An EEG-Based Machine Learning Approach.\" The EEG data were collected from 33 participants in a controlled laboratory setting, using a low-end EEG device. The Matlab scripts include data preprocessing, feature extraction, and machine learning algorithms, specifically focusing on the K-Nearest Neighbors (KNN) classifier. The machine learning results demonstrate the predictive accuracy of various classifiers, with KNN outperforming Random Forest (RF), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).</p><p>The dataset aims to provide a comprehensive resource for researchers interested in neuromarketing, consumer behavior, and machine learning applications in predicting consumer choices. It is particularly relevant for those who wish to understand the neural mechanisms underlying decision-making processes in online shopping contexts.</p>",
-    "authors": [
-      {
-        "name_en": "Zhiwei Xu",
-        "name_zh": "Zhiwei Xu",
-        "email": "zwxu@whu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Hubei University",
-            "name_zh": "Hubei University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Matlab",
-      "ML"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Matlab",
-      "ML"
-    ],
-    "publication_date": "2023-10-08T06:02:28.599Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 409.41,
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-    "visit_count": 531,
-    "url": "https://www.scidb.cn/en/detail?id=651d974489be2410e0a6cdc3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "643cf1f3692b1255d27a1791",
-    "doi": "10.57760/sciencedb.psych.00111",
-    "cstr": "31253.11.sciencedb.psych.00111",
-    "pid": "21.86116.6/sciencedb.psych.00111",
-    "title": "Music training <span style='background-color:yellow'>EEG</span> component data",
-    "title_en": "Music training <span style='background-color:yellow'>EEG</span> component data",
-    "title_zh": "音乐训练脑电成分数据",
-    "description": "<p>This dataset is about the influence of musical training experience on the time course of music emotional processing.</p>",
-    "description_en": "<p>This dataset is about the influence of musical training experience on the time course of music emotional processing.</p>",
-    "description_zh": "<p>该数据集是关于音乐训练经历影响音乐情绪加工的时间进程的脑电成分数据。</p>",
-    "authors": [
-      {
-        "name_en": "Yangjimei",
-        "name_zh": "杨集梅",
-        "email": "157553855@qq.com",
-        "affiliations": [
-          {
-            "name_en": "西南大学",
-            "name_zh": "西南大学"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "N1",
-      "N2",
-      "N400"
-    ],
-    "keywords_zh": [
-      "N1",
-      "N2",
-      "N400 "
-    ],
-    "publication_date": "2023-04-17T08:18:53.809Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 0.01,
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-    "visit_count": 1485,
-    "url": "https://www.scidb.cn/en/detail?id=643cf1f3692b1255d27a1791",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "64341650bf439249dc9631b5",
-    "doi": "10.57760/sciencedb.07935",
-    "cstr": "31253.11.sciencedb.07935",
-    "pid": "21.86116.6/sciencedb.07935",
-    "title": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
-    "title_en": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
-    "title_zh": "Visual-color sensing underlying interactive <span style='background-color:yellow'>EEG</span> dataset",
-    "description": "<p>This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
-    "description_en": "<p>This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
-    "description_zh": "<p>     This dataset contains visual color sensing underlying interactive EEG informatio. The dataset contains a decoder code. The data set consists of three sets of experiments, including the experimental data of 42 subjects in total. The dataset for each subject consists of two files. One is the raw data file and the other is the label file. When analyzing the data, the two files need to be imported at the same time. We encourage colleagues to find new classifiers to effectively classify data without label files when reusing data.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>",
-    "authors": [
-      {
-        "name_en": "Wu Yi Jia",
-        "name_zh": "Wu Yi Jia",
-        "email": "wuyijia@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Fudan University",
-            "name_zh": "Fudan University",
-            "ror_id": "https://ror.org/013q1eq08"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "visual",
-      "color",
-      "EEG",
-      "underlying"
-    ],
-    "keywords_zh": [
-      "visual",
-      "color",
-      "EEG",
-      "underlying"
-    ],
-    "publication_date": "2023-05-15T07:34:05.143Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 8594.65,
-    "file_size_bytes": 9012141771,
-    "download_count": 40,
-    "visit_count": 284,
-    "url": "https://www.scidb.cn/en/detail?id=64341650bf439249dc9631b5",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "674931530ea3b3102f40c4d7",
-    "doi": "10.57760/sciencedb.17705",
-    "cstr": "31253.11.sciencedb.17705",
-    "pid": "",
-    "title": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
-    "title_en": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
-    "title_zh": "NeuBCI Multi-Class Target Detection RSVP <span style='background-color:yellow'>EEG</span> and EM Dataset",
-    "description": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">The \"</span>NeuBCI Multi-Class Target Detection RSVP EEG and EM Dataset\"<span style=\"color: rgb(15, 23, 42);\"> comprises electroencephalogram (EEG) and eye movement (EM) data from </span>Multi-Class Target <span style=\"color: rgb(15, 23, 42);\">Rapid Serial Visual Presentation (RSVP)-based target image detection tasks. The dataset encompasses three independent tasks and includes data from 43 participants.</span></p>",
-    "authors": [
-      {
-        "name_en": "Xujin Li",
-        "name_zh": "Xujin Li",
-        "email": "lixujin2021@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)",
-            "name_zh": "School of Future Technology, University of Chinese Academy of Sciences (UCAS)"
-          }
-        ]
-      },
-      {
-        "name_en": "Wei Wei",
-        "name_zh": "Wei Wei",
-        "email": "weiwei2018@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Kun Zhao",
-        "name_zh": "Kun Zhao",
-        "email": "zhaokun2024@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Shuang Qiu",
-        "name_zh": "Shuang Qiu",
-        "email": "shuang.qiu@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Huiguang He",
-        "name_zh": "Huiguang He",
-        "email": "huiguang.he@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)",
-            "name_zh": "School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "Eye Movement",
-      "Event-Related Potential (ERP)",
-      "P300",
-      "Multi-Class Target Image Detection"
-    ],
-    "keywords_zh": [
-      "Rapid Serial Visual Presentation (RSVP)",
-      "EEG",
-      "Eye Movement",
-      "Event-Related Potential (ERP)",
-      "P300",
-      "Multi-Class Target Image Detection"
-    ],
-    "publication_date": "2024-12-10T00:42:28.431Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
-    "file_size_mb": 122898.45,
-    "file_size_bytes": 128868360159,
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-    "visit_count": 5105,
-    "url": "https://www.scidb.cn/en/detail?id=674931530ea3b3102f40c4d7",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "65c5ac189d874139a2eb1efc",
-    "doi": "10.57760/sciencedb.CHNNeuro.00007",
-    "cstr": "31253.11.sciencedb.CHNNeuro.00007",
-    "pid": "",
-    "title": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
-    "title_en": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
-    "title_zh": "ChineseEEG: A Chinese Linguistic Corpora <span style='background-color:yellow'>EEG</span> Dataset for Semantic Alignment and Neural Decoding",
-    "description": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
-    "description_en": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
-    "description_zh": "<p>An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.</p><p>As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.</p>",
-    "authors": [
-      {
-        "name_en": "HaiyanWu",
-        "name_zh": "HaiyanWu",
-        "email": "haiyanwu@um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "XinyuMou",
-        "name_zh": "XinyuMou",
-        "email": "12112317@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "CuilinHe",
-        "name_zh": "CuilinHe",
-        "email": "mc36413@connect.um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau"
-          }
-        ]
-      },
-      {
-        "name_en": "LiweiTan",
-        "name_zh": "LiweiTan",
-        "email": "mc36431@connect.um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau"
-          }
-        ]
-      },
-      {
-        "name_en": "JunjieYu",
-        "name_zh": "JunjieYu",
-        "email": "12231192@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "HuadongLiang",
-        "name_zh": "HuadongLiang",
-        "email": "hdliang@iflytek.com",
-        "affiliations": [
-          {
-            "name_en": " iFLYTEK Co., LTD, Hefei, China",
-            "name_zh": " iFLYTEK Co., LTD, Hefei, China"
-          }
-        ]
-      },
-      {
-        "name_en": "JianyuZhang",
-        "name_zh": "JianyuZhang",
-        "email": "12112347@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern university of Science and Technology",
-            "name_zh": "Southern university of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "YanTian",
-        "name_zh": "YanTian",
-        "email": "sc02233@um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau"
-          }
-        ]
-      },
-      {
-        "name_en": "Yu-Fang Yang",
-        "name_zh": "Yu-Fang Yang",
-        "email": "yu-fang.yang@fu-berlin.de",
-        "affiliations": [
-          {
-            "name_en": "Freie Universitat Berlin",
-            "name_zh": "Freie Universitat Berlin"
-          }
-        ]
-      },
-      {
-        "name_en": "TingXu",
-        "name_zh": "TingXu",
-        "email": "ting.xu@childmind.org",
-        "affiliations": [
-          {
-            "name_en": "New York Collaborates for Autism",
-            "name_zh": "New York Collaborates for Autism",
-            "ror_id": "https://ror.org/021amba89"
-          }
-        ]
-      },
-      {
-        "name_en": "QingWang",
-        "name_zh": "QingWang",
-        "email": "vincent.w.qing@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Jiao Tong University",
-            "name_zh": "Shanghai Jiao Tong University"
-          }
-        ]
-      },
-      {
-        "name_en": "MiaoCao",
-        "name_zh": "MiaoCao",
-        "email": "miao.cao@hotmail.com",
-        "affiliations": [
-          {
-            "name_en": "Swinburne University of Technology",
-            "name_zh": "Swinburne University of Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "ZijiaoChen",
-        "name_zh": "ZijiaoChen",
-        "email": "zijiao.chen@u.nus.edu",
-        "affiliations": [
-          {
-            "name_en": " National University of Singapore",
-            "name_zh": " National University of Singapore"
-          }
-        ]
-      },
-      {
-        "name_en": "Chuan-Peng Hu",
-        "name_zh": "Chuan-Peng Hu",
-        "email": "hu.chuan-peng@nnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Nanjing Normal University",
-            "name_zh": "Nanjing Normal University"
-          }
-        ]
-      },
-      {
-        "name_en": "XindiWang",
-        "name_zh": "XindiWang",
-        "email": "sandywang.rest@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "QuanyingLiu",
-        "name_zh": "QuanyingLiu",
-        "email": "liuqy@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "semantic decoding",
-      "natural language processing"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "semantic decoding",
-      "natural language processing"
-    ],
-    "publication_date": "2024-02-20T17:05:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 686285.21,
-    "file_size_bytes": 719622195436,
-    "download_count": 37287,
-    "visit_count": 9933,
-    "url": "https://www.scidb.cn/en/detail?id=65c5ac189d874139a2eb1efc",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68b3e2070f50167093ad298b",
-    "doi": "10.57760/sciencedb.23372",
-    "cstr": "31253.11.sciencedb.23372",
-    "pid": "",
-    "title": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
-    "title_en": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
-    "title_zh": "CIRE: A Chinese <span style='background-color:yellow'>EEG</span> Dataset for decoding speech intention modulated by prosodic emotion",
-    "description": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
-    "description_en": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
-    "description_zh": "<p>CIRE, an EEG dataset, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.</p>",
-    "authors": [
-      {
-        "name_en": "Gaoyan Zhang",
-        "name_zh": "Gaoyan Zhang",
-        "email": "zhanggaoyan@tju.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Tianjin University",
-            "name_zh": "Tianjin University",
-            "ror_id": "https://ror.org/012tb2g32"
-          }
-        ]
-      },
-      {
-        "name_en": "Shengrui He",
-        "name_zh": "Shengrui He",
-        "email": "heshengrui@tju.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Tianjin University",
-            "name_zh": "Tianjin University",
-            "ror_id": "https://ror.org/012tb2g32"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhongjie Li",
-        "name_zh": "Zhongjie Li",
-        "email": "lizhongjie@nwpu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Northwestern Polytechnical University",
-            "name_zh": "Northwestern Polytechnical University",
-            "ror_id": "https://ror.org/01y0j0j86"
-          }
-        ]
-      },
-      {
-        "name_en": "Jianwu Dang",
-        "name_zh": "Jianwu Dang",
-        "email": "jdang@jaist.ac.jp",
-        "affiliations": [
-          {
-            "name_en": "Chinese Academy of Sciences",
-            "name_zh": "Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/034t30j35"
-          }
-        ]
-      },
-      {
-        "name_en": "Yingyi Luo",
-        "name_zh": "Yingyi Luo",
-        "email": "luoyingyi@cass.org.cn",
-        "affiliations": [
-          {
-            "name_en": "Chinese Academy of Social Sciences",
-            "name_zh": "Chinese Academy of Social Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Intention Recognition",
-      "Prosodic emotion"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Intention Recognition",
-      "Prosodic emotion"
-    ],
-    "publication_date": "2025-04-10T02:53:07.749Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 49425.6,
-    "file_size_bytes": 51826499477,
-    "download_count": 29668,
-    "visit_count": 2450,
-    "url": "https://www.scidb.cn/en/detail?id=68b3e2070f50167093ad298b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "677fee3742b98c7bba09d3ac",
-    "doi": "10.57760/sciencedb.19730",
-    "cstr": "31253.11.sciencedb.19730",
-    "pid": "",
-    "title": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
-    "title_en": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
-    "title_zh": "Scene processing precedes object processing: Evidence from <span style='background-color:yellow'>EEG</span> decoding",
-    "description": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">In&nbsp;the&nbsp;real world, objects are perceived within the context of a&nbsp;background scene, rather than in isolation. When an object is presented within a scene, which is&nbsp;processed&nbsp;first</span>—<span style=\"color: rgb(0, 0, 0);\">the object or the scene? What roles&nbsp;do edge and surface information play in&nbsp;the neural&nbsp;representation&nbsp;of objects&nbsp;and scenes? To address&nbsp;these questions, we&nbsp;combined EEG and multivariate pattern analysis</span>&nbsp;(<span style=\"color: rgb(0, 0, 0);\">MVPA)&nbsp;methods&nbsp;in a one-back task.&nbsp;Participants&nbsp;viewed images of objects embedded in scenes,&nbsp;presented as&nbsp;color&nbsp;photographs,&nbsp;grayscale&nbsp;images, or line&nbsp;drawing. Decoding resulting revealed that scenes were processed earlier than&nbsp;objects&nbsp;across all three image versions. Additionally, edge information&nbsp;alone&nbsp;was&nbsp;sufficient for&nbsp;the neural&nbsp;representation of objects, while&nbsp;surface information was&nbsp;primarily&nbsp;involved in&nbsp;the neura</span><u style=\"color: rgb(0, 0, 0);\">l</u><span style=\"color: rgb(0, 0, 0);\">&nbsp;representation&nbsp;of scenes. These findings provide&nbsp;neural evidence supporting&nbsp;the “scene-first” hypothesis and highlight the distinct roles of&nbsp;edge and surface information in&nbsp;dynamic processing of&nbsp;objects&nbsp;and scenes.</span></p>",
-    "authors": [
-      {
-        "name_en": "Qiufang Fu",
-        "name_zh": "Qiufang Fu",
-        "email": "fuqf@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Liansheng Yao",
-        "name_zh": "Liansheng Yao",
-        "email": "lianshengyao@yeah.net",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "scene representation",
-      "object representation",
-      "edge information",
-      "surface information",
-      "scene-first theory"
-    ],
-    "keywords_zh": [
-      "scene representation",
-      "object representation",
-      "edge information",
-      "surface information",
-      "scene-first theory"
-    ],
-    "publication_date": "2025-01-10T03:16:03.510Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 24422.84,
-    "file_size_bytes": 25609203324,
-    "download_count": 276,
-    "visit_count": 203,
-    "url": "https://www.scidb.cn/en/detail?id=677fee3742b98c7bba09d3ac",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68d4e43119a491453e1c1328",
-    "doi": "10.57760/sciencedb.CHNNeuro.00001",
-    "cstr": "31253.11.sciencedb.CHNNeuro.00001",
-    "pid": "",
-    "title": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "title_en": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "title_zh": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "description": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "authors": [
-      {
-        "name_en": "Sitong Chen",
-        "name_zh": "Sitong Chen",
-        "email": "12212653@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Beiqianyi Li",
-        "name_zh": "Beiqianyi Li",
-        "email": "sc12214@um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Cuilin He",
-        "name_zh": "Cuilin He",
-        "email": "mc36413@connect.um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Dongyang Li",
-        "name_zh": "Dongyang Li",
-        "email": "lidy2023@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Mingyang Wu",
-        "name_zh": "Mingyang Wu",
-        "email": "12312813@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xinke Shen",
-        "name_zh": "Xinke Shen",
-        "email": "shenxk@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "Song Wang",
-        "name_zh": "Song Wang",
-        "email": "12332575@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xuetao Wei",
-        "name_zh": "Xuetao Wei",
-        "email": "weixt@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xindi Wang",
-        "name_zh": "Xindi Wang",
-        "email": "sandywang.rest@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "AI Research Institute, iFLYTEK Co., LTD,",
-            "name_zh": "AI Research Institute, iFLYTEK Co., LTD,"
-          }
-        ]
-      },
-      {
-        "name_en": "Haiyan Wu",
-        "name_zh": "Haiyan Wu",
-        "email": "evawu2008@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Quanying Liu",
-        "name_zh": "Quanying Liu",
-        "email": "liuqy@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      " natural language processing",
-      "semantic decoding"
-    ],
-    "keywords_zh": [
-      "EEG",
-      " natural language processing",
-      "semantic decoding"
-    ],
-    "publication_date": "2025-07-28T03:39:43.257Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 97754.75,
-    "file_size_bytes": 102503284224,
-    "download_count": 25408,
-    "visit_count": 3547,
-    "url": "https://www.scidb.cn/en/detail?id=68d4e43119a491453e1c1328",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67a41f4c27843a4e47683a4e",
-    "doi": "10.57760/sciencedb.20611",
-    "cstr": "31253.11.sciencedb.20611",
-    "pid": "",
-    "title": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "title_en": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "title_zh": "ChineseEEG-2:An <span style='background-color:yellow'>EEG</span> Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening",
-    "description": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(31, 35, 40);\">Now-existing Electroencephalography (EEG) datasets&nbsp;are mainly based on English, which encounters difficulty when representing Chinese. While there have been EEG datasets related to linguistic stimuli, the existing resources are limited, and many faces the problem of teacher-forcing. In our future studies, we plan to promote a unified encoder of multi-modalities for semantic decoding, which suggests the need of more data support. To bridge this gap, we introduce ChineseEEG-2, a high-density EEG dataset that extends ChineseEEG containing both reading aloud and auditory listening tasks. As a unique multimodal EEG dataset featuring synchronized reading and listening tasks based on the same corpus, ChineseEEG-2 dataset enables the exploration of how the brain processes language across both visual and auditory modalities in the context of Chinese natural language. It offers valuable insights into multimodal semantic alignment, neural decoding, and the alignment between large language models and neural processes, contributing to the development of BCI systems for language decoding</span></p><p><span style=\"color: rgb(31, 35, 40);\">A total of 12 healthy participants were recruited for the study, ranging in age from 18 to 25 years (mean age: 21.9 years; 4 males, 8 females). Among the 12 participants, four (2 males and 2 females) conducted the reading task, while the rest eight conducted the passive listening task. In the formal experiment, in total, 10.8 hours of reading data (around 3 hours per subject) and approximately 21.6 hours of listening data (around 3 hours per subject) were collected, amounting to 32.4 hours of data overall.</span></p>",
-    "authors": [
-      {
-        "name_en": "Sitong Chen",
-        "name_zh": "Sitong Chen",
-        "email": "12212653@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Beiqianyi Li",
-        "name_zh": "Beiqianyi Li",
-        "email": "sc12214@um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Cuilin He",
-        "name_zh": "Cuilin He",
-        "email": "mc36413@connect.um.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Dongyang Li",
-        "name_zh": "Dongyang Li",
-        "email": "lidy2023@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Mingyang Wu",
-        "name_zh": "Mingyang Wu",
-        "email": "12312813@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xinke Shen",
-        "name_zh": "Xinke Shen",
-        "email": "shenxk@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Song Wang",
-        "name_zh": "Song Wang",
-        "email": "12332575@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xuetao Wei",
-        "name_zh": "Xuetao Wei",
-        "email": "weixt@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xindi Wang",
-        "name_zh": "Xindi Wang",
-        "email": "sandywang.rest@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "AI Research Institute, iFLYTEK Co., LTD,",
-            "name_zh": "AI Research Institute, iFLYTEK Co., LTD,"
-          }
-        ]
-      },
-      {
-        "name_en": "Haiyan Wu",
-        "name_zh": "Haiyan Wu",
-        "email": "evawu2008@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "University of Macau",
-            "name_zh": "University of Macau",
-            "ror_id": "https://ror.org/01r4q9n85"
-          }
-        ]
-      },
-      {
-        "name_en": "Quanying Liu",
-        "name_zh": "Quanying Liu",
-        "email": "liuqy@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Southern University of Science and Technology",
-            "name_zh": "Southern University of Science and Technology",
-            "ror_id": "https://ror.org/049tv2d57"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      " natural language processing",
-      "Semantic Decoding"
-    ],
-    "keywords_zh": [
-      "EEG",
-      " natural language processing",
-      "Semantic Decoding"
-    ],
-    "publication_date": "2025-02-14T07:00:39.792Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 104940.05,
-    "file_size_bytes": 110037613972,
-    "download_count": 2712,
-    "visit_count": 3848,
-    "url": "https://www.scidb.cn/en/detail?id=67a41f4c27843a4e47683a4e",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67cc1763cfa1d066cfad71fb",
-    "doi": "10.57760/sciencedb.14025",
-    "cstr": "31253.11.sciencedb.14025",
-    "pid": "",
-    "title": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
-    "title_en": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
-    "title_zh": "EmoEEG-MC: A Multi-Context Emotional <span style='background-color:yellow'>EEG</span> Dataset for Cross-Context Emotion Decoding",
-    "description": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
-    "description_en": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
-    "description_zh": "<p>We present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.</p>",
-    "authors": [
-      {
-        "name_en": "Xin Xu",
-        "name_zh": "Xin Xu",
-        "email": "12211762@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Xinke Shen",
-        "name_zh": "Xinke Shen",
-        "email": "shenxk@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Xuyang Chen",
-        "name_zh": "Xuyang Chen",
-        "email": "12010103@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Qingzhu Zhang",
-        "name_zh": "Qingzhu Zhang",
-        "email": "zhangqingzhu2002@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Sitian Wang",
-        "name_zh": "Sitian Wang",
-        "email": "12312453@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Yihan Li",
-        "name_zh": "Yihan Li",
-        "email": "12312832@mail.sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Zongsheng Li",
-        "name_zh": "Zongsheng Li",
-        "email": "223010111@link.cuhk.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          },
-          {
-            "name_en": "School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China",
-            "name_zh": "School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Dan Zhang",
-        "name_zh": "Dan Zhang",
-        "email": "dzhang@tsinghua.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China",
-            "name_zh": "Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Mingming Zhang",
-        "name_zh": "Mingming Zhang",
-        "email": "zhangmm@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Quanying Liu",
-        "name_zh": "Quanying Liu",
-        "email": "liuqy@sustech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China",
-            "name_zh": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Affective computing",
-      "multiple-context",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "Affective computing",
-      "multiple-context",
-      "EEG"
-    ],
-    "publication_date": "2024-09-27T12:59:29.012Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
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-    "url": "https://www.scidb.cn/en/detail?id=67cc1763cfa1d066cfad71fb",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "664adaeb8355d83c98b50e5d",
-    "doi": "10.57760/sciencedb.08758",
-    "cstr": "31253.11.sciencedb.08758",
-    "pid": "",
-    "title": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
-    "title_en": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
-    "title_zh": "Harnessing New Frontiers in Art Therapy: An Interactive Installation Integrating <span style='background-color:yellow'>EEG</span>, VR, and AIGC for the Alleviation of Negative Emotions",
-    "description": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
-    "description_en": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
-    "description_zh": "<p>This set of data is mainly divided into two parts. One part is the data that measured the subjects before and after the experiment using the PANAS scale. The other part is the data that measured the subjects before and after the experiment using the EEG equipment. SPSS was used for data analysis.</p>",
-    "authors": [
-      {
-        "name_en": "Cen Chenghong",
-        "name_zh": "Cen Chenghong",
-        "email": "cen_chenghong@m.scnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "South China Normal University",
-            "name_zh": "华南师范大学",
-            "ror_id": "https://ror.org/01kq0pv72"
-          }
-        ]
-      },
-      {
-        "name_en": "Li Kang",
-        "name_zh": "Li Kang",
-        "email": "Kang.Li@zu.ac.ae",
-        "affiliations": [
-          {
-            "name_en": "Zayed University",
-            "name_zh": "Zayed University"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Jing",
-        "name_zh": "Xu Jing",
-        "email": "xujing9602@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Macau University of Science and Technology",
-            "name_zh": "Macau University of Science and Technology"
-          }
-        ]
-      },
-      {
-        "name_en": "Huang Guanghui",
-        "name_zh": "Huang Guanghui",
-        "email": "Ghhuang1@must.edu.mo",
-        "affiliations": [
-          {
-            "name_en": "Macau University of Science and Technology",
-            "name_zh": "Macau University of Science and Technology",
-            "ror_id": "https://ror.org/03jqs2n27"
-          }
-        ]
-      },
-      {
-        "name_en": "Jiang Tan",
-        "name_zh": "Jiang Tan",
-        "email": "jiangtan@scnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "South China Normal University",
-            "name_zh": "华南师范大学",
-            "ror_id": "https://ror.org/01kq0pv72"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "PANAS SCALE",
-      "EEG DATA ANALYSIS",
-      "COGNITIVE EMOTION",
-      "IMMERSIVE ART THERAPY"
-    ],
-    "keywords_zh": [
-      "PANAS SCALE",
-      "EEG DATA ANALYSIS",
-      "COGNITIVE EMOTION",
-      "IMMERSIVE ART THERAPY"
-    ],
-    "publication_date": "2024-05-20T06:55:10.809Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=664adaeb8355d83c98b50e5d",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6332b4603d498241dcc008f3",
-    "doi": "10.57760/sciencedb.j00052.00029",
-    "cstr": "31253.11.sciencedb.j00052.00029",
-    "pid": "21.86116.6/sciencedb.j00052.00029",
-    "title": "The neurophysiological mechanism of brand personality attracting consumers: Evidence from <span style='background-color:yellow'>EEG</span> and GSR（SPSS data)",
-    "title_en": "The neurophysiological mechanism of brand personality attracting consumers: Evidence from <span style='background-color:yellow'>EEG</span> and GSR（SPSS data)",
-    "title_zh": "品牌个性吸引消费者的神经生理机制——来自<span style='background-color:yellow'>EEG</span>和GSR的证据（SPSS 数据）",
-    "description": "<p>EEG GSR SAM data exported from imotion software, and the data of Big Five Personality Scale filled in by subjects with questionnaire stars&nbsp;Label information&nbsp;peak_ count_ Xx=peak number&nbsp;APA_ Xx=peak amplitude&nbsp;xx_ Valence=SAM self-assessment questionnaire potency&nbsp;xx_ Dominance=dominated by SAM self-assessment questionnaire&nbsp;xx_ Arousal=SAM self-assessment questionnaire wake-up&nbsp;LR_ xx_ Alpha=PAI α&nbsp;LR_ xx_ Beta=PAI β&nbsp;LR_ xx_ Gamma=PAI γ&nbsp;Xx Brand personality&nbsp;Xx=Com: capability&nbsp;Xx=Sin: Sincerity&nbsp;Xx=Rug: rough&nbsp;Xx=Sop: sophisticated&nbsp;Xx=Exi: excited&nbsp;</p>",
-    "description_en": "<p>EEG GSR SAM data exported from imotion software, and the data of Big Five Personality Scale filled in by subjects with questionnaire stars&nbsp;Label information&nbsp;peak_ count_ Xx=peak number&nbsp;APA_ Xx=peak amplitude&nbsp;xx_ Valence=SAM self-assessment questionnaire potency&nbsp;xx_ Dominance=dominated by SAM self-assessment questionnaire&nbsp;xx_ Arousal=SAM self-assessment questionnaire wake-up&nbsp;LR_ xx_ Alpha=PAI α&nbsp;LR_ xx_ Beta=PAI β&nbsp;LR_ xx_ Gamma=PAI γ&nbsp;Xx Brand personality&nbsp;Xx=Com: capability&nbsp;Xx=Sin: Sincerity&nbsp;Xx=Rug: rough&nbsp;Xx=Sop: sophisticated&nbsp;Xx=Exi: excited&nbsp;</p>",
-    "description_zh": "<p>EEG 、GSR、SAM数据，从imotion软件中导出，脑电图设备使用Neuroelectic公司的Enobio20脑电系统，按国际10-20系统的20导电极帽记录EEG，以右耳夹电极为参考电极，设备采样率为500hz。皮肤电GSR设备使用Shimmer公司的Shimmer3 GSR+，采样率为51.2Hz。</p><p>标签：</p><p>peak_ count_ Xx=尖峰次数</p><p>APA_ Xx=尖峰振幅</p><p>xx_ Valence=SAM 问卷-效价</p><p>&nbsp;xx_ Dominance=SAM问卷-支配</p><p>xx_ Arousal=SAM问卷-唤醒</p><p>LR_ xx_ Alpha=PAI α&nbsp;</p><p>LR_ xx_ Beta=PAI β&nbsp;</p><p>LR_ xx_ Gamma=PAI γ&nbsp;</p><p><br></p><p>XX-品牌个性</p><p>Xx=Com: 能力</p><p>Xx=Sin: 真诚</p><p>Xx=Rug: 粗犷</p><p>Xx=Sop: 老练</p><p>&nbsp;Xx=Exi: 兴奋</p>",
-    "authors": [
-      {
-        "name_en": "xu zhi wei",
-        "name_zh": "许志炜",
-        "email": "zwxu@whu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Hubei University",
-            "name_zh": "Hubei University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "brand personality",
-      "neuromarketing",
-      "EEG",
-      "GSR",
-      "circumplex model of affect"
-    ],
-    "keywords_zh": [
-      "品牌个性",
-      "神经营销学",
-      "EEG",
-      "GSR",
-      "情绪环状模型"
-    ],
-    "publication_date": "2023-08-31T01:07:14.096Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 0.01,
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-    "visit_count": 610,
-    "url": "https://www.scidb.cn/en/detail?id=6332b4603d498241dcc008f3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66993089ab1be93f47ea6b7e",
-    "doi": "10.57760/sciencedb.psych.00207",
-    "cstr": "31253.11.sciencedb.psych.00207",
-    "pid": "",
-    "title": "Behavioral and <span style='background-color:yellow'>EEG</span> results of emotional discrimination task (high-altitude and low-altitude)",
-    "title_en": "Behavioral and <span style='background-color:yellow'>EEG</span> results of emotional discrimination task (high-altitude and low-altitude)",
-    "title_zh": "情绪判断任务行为学及脑电数据（高原人群和平原人群）",
-    "description": "<p>Participants were all college undergraduates with normal or corrected vision, right-handed, no history of neurological or psychiatric disorders or other chronic physical conditions, no use of drugs (psychiatric or otherwise) in the last 2 days, and no irregular lifestyle. The high-altitude group consisted of 25 subjects from Tibet University (3658m above sea level), all of whom grew up in the plain, entered the plateau for the first time and lived there for more than 2 years after adulthood. Excluding 1 subjects with missing EEG data and 2 subjects with damaged data, 22 subjects were enrolled ultimately (12 males and 10 females, age ranged between 20 and 24 years, average age as 21.91±0.97 years). The 30 members of the low-altitude group are from universities in Beijing (52m above sea level), who have never been to the plateau. 6 subjects were excluded for excessive disturbance of EEG data, and 24 subjects were finally enrolled (11 males and 13 females, age ranged between 19 and 24 years, average age as 21.43±1.24 years).</p><p>Emotional face recognition task was adopted. Stimulations were selected from Chinese Affective Face Picture System (Gong et al., 2011), including 3 males and 3 females, each with 3 expressions (angry, happy, neutral), a total of 18 emotional face pictures. A white cross (0.82°×0.82°) was presented in the center of the black background as the fixation point, with the presentation time random between 1200-1600ms. Then, the facial picture appears until subjects press the key and enter the next trial. Subjects are demanded to judge the gender of faces accurately as fast as possible. The position of the buttons was balanced between the subjects, with half of the subjects pressing the left button for a male face, the right button for a female face, and the other half doing the opposite. The experiment is consisted of practice and formal test. During the practice, each picture was presented once in a random order, with a total of 18 trials. The formal experiment was divided into 2 phases, with a short rest in between, 144 trials in each phase, a total of 288 trials, and each picture was repeated 16 times.</p><p>Electroencephalography (EEG) was collected using the Neuroscan recording and analysis system and the elastic cap with 64 Ag/AgCl electrodes cap of International 10-20 standard system (Neuroscan Inc., USA). The referenced electrode was the left mastoid and the grounding electrode was located at the midpoint of the Fz and FPz. The vertical electrooculogram (VEOG) was recorded with a pair of electrodes placed above and below the left eye, while the horizontal electrooculogram (HEOG) was recorded with another pair of electrodes, located 1 cm from the outer canthus of each eye. The resistance of scalp was kept under 5kΩ. Signals were amplified with a Neuroscan SynamPs-2 amplifier (Neuroscan Inc., USA) which included a 0.05-100 Hz bandpass filter with the sampling frequency as 500Hz.</p><p>Behavioral results could be opened with E-prime3 and EEG results could be opened with EEGLAB.</p>",
-    "description_en": "<p>Participants were all college undergraduates with normal or corrected vision, right-handed, no history of neurological or psychiatric disorders or other chronic physical conditions, no use of drugs (psychiatric or otherwise) in the last 2 days, and no irregular lifestyle. The high-altitude group consisted of 25 subjects from Tibet University (3658m above sea level), all of whom grew up in the plain, entered the plateau for the first time and lived there for more than 2 years after adulthood. Excluding 1 subjects with missing EEG data and 2 subjects with damaged data, 22 subjects were enrolled ultimately (12 males and 10 females, age ranged between 20 and 24 years, average age as 21.91±0.97 years). The 30 members of the low-altitude group are from universities in Beijing (52m above sea level), who have never been to the plateau. 6 subjects were excluded for excessive disturbance of EEG data, and 24 subjects were finally enrolled (11 males and 13 females, age ranged between 19 and 24 years, average age as 21.43±1.24 years).</p><p>Emotional face recognition task was adopted. Stimulations were selected from Chinese Affective Face Picture System (Gong et al., 2011), including 3 males and 3 females, each with 3 expressions (angry, happy, neutral), a total of 18 emotional face pictures. A white cross (0.82°×0.82°) was presented in the center of the black background as the fixation point, with the presentation time random between 1200-1600ms. Then, the facial picture appears until subjects press the key and enter the next trial. Subjects are demanded to judge the gender of faces accurately as fast as possible. The position of the buttons was balanced between the subjects, with half of the subjects pressing the left button for a male face, the right button for a female face, and the other half doing the opposite. The experiment is consisted of practice and formal test. During the practice, each picture was presented once in a random order, with a total of 18 trials. The formal experiment was divided into 2 phases, with a short rest in between, 144 trials in each phase, a total of 288 trials, and each picture was repeated 16 times.</p><p>Electroencephalography (EEG) was collected using the Neuroscan recording and analysis system and the elastic cap with 64 Ag/AgCl electrodes cap of International 10-20 standard system (Neuroscan Inc., USA). The referenced electrode was the left mastoid and the grounding electrode was located at the midpoint of the Fz and FPz. The vertical electrooculogram (VEOG) was recorded with a pair of electrodes placed above and below the left eye, while the horizontal electrooculogram (HEOG) was recorded with another pair of electrodes, located 1 cm from the outer canthus of each eye. The resistance of scalp was kept under 5kΩ. Signals were amplified with a Neuroscan SynamPs-2 amplifier (Neuroscan Inc., USA) which included a 0.05-100 Hz bandpass filter with the sampling frequency as 500Hz.</p><p>Behavioral results could be opened with E-prime3 and EEG results could be opened with EEGLAB.</p>",
-    "description_zh": "<pre class=\"ql-syntax\" spellcheck=\"false\">被试均为大学本科生，视力正常或矫正视力正常，右利手，没有神经或精神疾病或其他慢性生理疾病史，近2天内没有使用药物(精神或其他)以及没有不规律生活方式。高原组25人来自西藏大学（海拔3658 m），均生长在平原，成年后首次进入高原并居住满2年以上。排除脑电数据缺失被试1人、数据损坏被试2人，最终入组22人（男/女：12/10人，年龄范围20-24岁，平均年龄21.91±0.97岁）。平原组30人来自北京各高校（海拔52 m），从未到过高原。排除脑电数据干扰过多被试6人，最终入组24人（男/女：11/13人，年龄范围19-24岁，平均年龄21.43±1.24岁）。\n采用情绪面孔判断任务。刺激材料来自中国面孔情绪图片库，选取3男3女，每人各3种表情（愤怒，愉快，中性），总计18张情绪面孔图片。每个试次开始时，在黑色背景中心呈现0.82°×0.82°视角的白色十字注视点，呈现时间在1200-1600ms随机，随后呈现面孔图片，按键后消失，进入下一个试次。要求被试尽可能快速准确地判断面孔性别。按键位置在被试间平衡，一半被试看到男性面孔按左键，女性面孔按右键，另一半被试与之相反。实验包括练习和正式测试。练习时每张图片随机呈现1次，共18个试次。正式实验分2组进行，中间短暂休息，每组144个试次，共计288个试次，每张图片重复16次（8次*2组）。每个试次约2s，正式实验时间约10min。\n脑电采集使用Neuroscan记录与分析系统，按国际10-20标准系统扩展的64导Ag / AgCl电极帽(Neuroscan Inc., USA)记录脑电(electroencephalography, EEG）。电极在线参考为左侧乳突&nbsp; ，接地电极位于Fz和FPz电极连线中点。由放置在左眼上方和下方的一对电极记录垂直眼电(VEOG)，水平眼电(HEOG)由另一对位于距每只眼睛外眦1cm处的电极记录，头皮电阻保持在5kΩ以下。由一个包括0.05–100 Hz带通滤波器的Neuroscan synamps 2放大器(Neuroscan Inc., USA)放大信号，采样频率为500Hz。\n行为学结果用E-prime3打开，脑电结果用EEGLAB打开。\n</pre>",
-    "authors": [
-      {
-        "name_en": "Cai Yudian",
-        "name_zh": "蔡雨点",
-        "email": "caiyd@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Chinese Academy of Sciences",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Yan",
-        "name_zh": "王妍",
-        "email": "wangyan@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Chinese Academy of Sciences",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "An Xin",
-        "name_zh": "安心",
-        "email": "370138916@qq.com",
-        "affiliations": [
-          {
-            "name_en": "National Defence University",
-            "name_zh": "National Defence University",
-            "ror_id": "https://ror.org/045vxh980"
-          }
-        ]
-      },
-      {
-        "name_en": "Ma Hailin",
-        "name_zh": "马海林",
-        "email": "83976475@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Tibet University",
-            "name_zh": "西藏大学",
-            "ror_id": "https://ror.org/05petvd47"
-          }
-        ]
-      },
-      {
-        "name_en": "Dai Shan",
-        "name_zh": "代杉",
-        "email": "rachelds@163.com",
-        "affiliations": [
-          {
-            "name_en": "Chinese Academy of Sciences",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "high-altitude",
-      "facial recognition",
-      "emotion",
-      "ERP"
-    ],
-    "keywords_zh": [
-      "面孔识别",
-      "情绪",
-      "高原",
-      "ERP"
-    ],
-    "publication_date": "2024-07-23T14:34:35.443Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC 4.0",
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-    "file_size_bytes": 3375764051,
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-    "visit_count": 1156,
-    "url": "https://www.scidb.cn/en/detail?id=66993089ab1be93f47ea6b7e",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6284bc2d9f0a08024767f8e5",
-    "doi": "10.57760/sciencedb.o00115.00009",
-    "cstr": "31253.11.sciencedb.o00115.00009",
-    "pid": "21.86116.6/sciencedb.o00115.00009",
-    "title": "Effects of active and passive suppression of facial expressions on subjective experience and <span style='background-color:yellow'>EEG</span> responses",
-    "title_en": "Effects of active and passive suppression of facial expressions on subjective experience and <span style='background-color:yellow'>EEG</span> responses",
-    "title_zh": "主动和被动抑制面部表情对主观体验和脑电生理反应的影响",
-    "description": "<p>Inhibition of the effect of facial expression on subjective experience and brain electrophysiological response&nbsp;</p>",
-    "description_en": "<p>Inhibition of the effect of facial expression on subjective experience and brain electrophysiological response&nbsp;</p>",
-    "description_zh": "<p>抑制面部表情对主观体验和脑电生理反应的影响原始数据</p>",
-    "authors": [
-      {
-        "name_en": "wang cai feng",
-        "name_zh": "王彩凤",
-        "email": "2056784455@qq.com",
-        "affiliations": [
-          {
-            "name_en": "辽宁师范大学",
-            "name_zh": "辽宁师范大学"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "expressive suppression",
-      "external performance",
-      "subjective experience",
-      "physiological response"
-    ],
-    "keywords_zh": [
-      "表达抑制",
-      "外部表现",
-      "主观体验",
-      "生理反应"
-    ],
-    "publication_date": "2022-05-23T14:22:07.877Z",
-    "created": "",
-    "modified": "",
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-    "download_count": 625,
-    "visit_count": 2135,
-    "url": "https://www.scidb.cn/en/detail?id=6284bc2d9f0a08024767f8e5",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "61ce5f7f5b86483bca618b94",
-    "doi": "10.11922/sciencedb.01421",
-    "cstr": "31253.11.sciencedb.01421",
-    "pid": "21.86116.6/sciencedb.01421",
-    "title": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
-    "title_en": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
-    "title_zh": "Decoding human visual colour <span style='background-color:yellow'>EEG</span> information using machine learning and visual evoked potentials",
-    "description": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
-    "description_en": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
-    "description_zh": "<p>With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of&nbsp;task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding.&nbsp;We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.</p>",
-    "authors": [
-      {
-        "name_en": "Wu Yi Jia",
-        "name_zh": "Wu Yi Jia",
-        "email": "wuyijia@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Fudan University",
-            "name_zh": "Fudan University",
-            "ror_id": "https://ror.org/013q1eq08"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "neuroscience",
-      "computer science",
-      "human health"
-    ],
-    "keywords_zh": [
-      "neuroscience",
-      "computer science",
-      "human health"
-    ],
-    "publication_date": "2022-01-04T09:57:11.566Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=61ce5f7f5b86483bca618b94",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "62bd105b70c5b75f4c7a6aca",
-    "doi": "10.1038/s41597-020-0535-2",
-    "cstr": "",
-    "pid": "",
-    "title": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
-    "title_en": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
-    "title_zh": "Metadata record for: Multi-channel <span style='background-color:yellow'>EEG</span> recording during motor imagery of different joints from the same limb",
-    "description": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
-    "description_en": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
-    "description_zh": "<p>Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.</p>",
-    "authors": [
-      {
-        "name_en": "Ma Xuelin",
-        "name_zh": "Ma Xuelin",
-        "affiliations": [
-          {
-            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "ror_id": "https://ror.org/022c3hy66"
-          },
-          {
-            "name_en": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
-            "name_zh": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Qiu Shuang",
-        "name_zh": "Qiu Shuang",
-        "email": "shuang.qiu@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      },
-      {
-        "name_en": "He Huiguang",
-        "name_zh": "He Huiguang",
-        "email": "huiguang.he@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
-            "name_zh": "The School of Artificial Intelligence, University of Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences",
-            "name_zh": "The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "name_zh": "The Research Center for Brain-Inspired Intelligence & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Brain-computer interface",
-      "motor imagery"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Brain-computer interface",
-      "motor imagery"
-    ],
-    "publication_date": "2020-06-19T00:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 4385,
-    "download_count": 0,
-    "visit_count": 41,
-    "url": "https://www.scidb.cn/en/detail?id=62bd105b70c5b75f4c7a6aca",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6624cc48e112562dadfeb47e",
-    "doi": "10.57760/sciencedb.18262",
-    "cstr": "31253.11.sciencedb.18262",
-    "pid": "",
-    "title": "Pre-stimulus alpha band power and phase influence visual awareness--<span style='background-color:yellow'>EEG</span> dataset",
-    "title_en": "Pre-stimulus alpha band power and phase influence visual awareness--<span style='background-color:yellow'>EEG</span> dataset",
-    "title_zh": "刺激前α频段的能量与相位对视觉意识的预测作用<span style='background-color:yellow'>EEG</span>数据包",
-    "description": "<p>Experimental materials: Experimental materials include threshold stimulus, super-threshold stimulus and blank stimulus. The threshold stimulus is a low-contrast sinusoidal Gabor patch at the threshold level, tilted vertically to the left or right by 45°, with Michelson contrast ratios of 0.01, 0.02 and 0.03, respectively (Michelson contrast is calculated by: The brightest brightness minus the darkest brightness divided by the sum of the two), the presentation time and contrast are determined in the measurement threshold stage. The super-threshold stimulus is the stimulus with higher contrast than the threshold stimulus (Michelson's contrast is 0.04), and the blank stimulus is the non-target stimulus. The experimental material is made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2.</p><p>The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen is 1024x768 pixels, and the screen refresh rate is 60Hz (1 refresh cycle is about 17ms). The subject is about 100cm away from the screen.</p><p>EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead is grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode is a reference electrode. The sampling rate is 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_en": "<p>Experimental materials: Experimental materials include threshold stimulus, super-threshold stimulus and blank stimulus. The threshold stimulus is a low-contrast sinusoidal Gabor patch at the threshold level, tilted vertically to the left or right by 45°, with Michelson contrast ratios of 0.01, 0.02 and 0.03, respectively (Michelson contrast is calculated by: The brightest brightness minus the darkest brightness divided by the sum of the two), the presentation time and contrast are determined in the measurement threshold stage. The super-threshold stimulus is the stimulus with higher contrast than the threshold stimulus (Michelson's contrast is 0.04), and the blank stimulus is the non-target stimulus. The experimental material is made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2.</p><p>The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen is 1024x768 pixels, and the screen refresh rate is 60Hz (1 refresh cycle is about 17ms). The subject is about 100cm away from the screen.</p><p>EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead is grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode is a reference electrode. The sampling rate is 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_zh": "<p>实验材料：实验材料包括阈限刺激，阈上刺激和空白刺激。阈限刺激是处于阈限水平的低对比度正弦曲线Gabort patch，垂直向左或者向右倾斜45°，迈克尔逊对比度分别为0.01，0.02和0.03（迈克尔逊对比度计算方法：最亮的亮度减去最暗的亮度除以二者之和），呈现时间和对比度在测定阈限阶段进行确定。阈上刺激是比阈限刺激更高对比度（迈克逊对比度为0.04）的刺激，空白刺激即为无目标刺激，实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。</p><p>实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕100cm左右。</p><p>使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
-    "authors": [
-      {
-        "name_en": "Li Xiaoxiao",
-        "name_zh": "李笑笑",
-        "email": "1421269185@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Tianjin Normal University",
-            "name_zh": "天津师范大学",
-            "ror_id": "https://ror.org/05x2td559"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Alpha band",
-      "Visual awareness",
-      "Time-frequency analysis"
-    ],
-    "keywords_zh": [
-      "α频段",
-      "视觉意识",
-      "时频分析"
-    ],
-    "publication_date": "2024-04-25T03:02:52.899Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 31339.76,
-    "file_size_bytes": 32862119838,
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-    "visit_count": 227,
-    "url": "https://www.scidb.cn/en/detail?id=6624cc48e112562dadfeb47e",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6653e0c9c958f37781bd21ff",
-    "doi": "10.57760/sciencedb.18327",
-    "cstr": "31253.11.sciencedb.18327",
-    "pid": "",
-    "title": "The impact of social anxiety and emotional faces on executive function in college students: an <span style='background-color:yellow'>EEG</span> dataset",
-    "title_en": "The impact of social anxiety and emotional faces on executive function in college students: an <span style='background-color:yellow'>EEG</span> dataset",
-    "title_zh": "社交焦虑和情绪面孔对大学生执行功能的影响的脑电数据集",
-    "description": "<p>Using the LASA scale, university students were divided into high social anxiety group and low social anxiety group, as well as the <span style=\"color: rgb(51, 51, 51);\">Chinese Facial Affective Picture System</span> (CFAPS)</p><p><br></p><p>Measurement of the effects of social anxiety and emotional faces on the three subcomponents of executive function (inhibitory control, working memory, and cognitive flexibility) in college students</p><p><br></p><p><br></p><p>Inhibition control: Gonogo paradigm measurement</p><p><br></p><p>Independent variables: 2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (Go/No go)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Positive Go Accuracy, Negative Go Accuracy, Total Go Accuracy, Positive nogo Accuracy, Negative nogo Accuracy, Total nogo Accuracy, Positive go Response Time, Negative go Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Fz electrode positive N1 latency, Fz electrode positive N1 amplitude, Fz electrode negative N1 latency, Fz electrode negative N1 amplitude, Fz electrode positive P2 latency, Fz electrode positive P2 amplitude, Fz electrode negative P2 latency, Fz electrode negative P2 amplitude</p><p><br></p><p>Working Memory: N-back Paradigm Measurement</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 cognitive load (1-back/2-back)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), 1Back Positive Face Accuracy, 1Back Negative Face Accuracy, 1Back Total Accuracy, 1Back Positive Face Response Time, 1Back Negative Face Response Time, 1Back Total Response Time, 2Back Positive Face Accuracy, 2Back Negative Face Accuracy, 2Back Total Accuracy, 2Back Positive Face Response Time, 2Back Negative Face Response Time, and 2Back Total Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: ID, Group 1 (Low Social Anxiety Group), 2 (High Social Anxiety Group), 1bFz electrode positive N1 latency, 1bFz positive N1 amplitude, 1bFz electrode negative N1 latency, 1bFz electrode negative N1 amplitude, 1bFz electrode positive P2 latency, 1bFz electrode positive P2 amplitude, 1bFz electrode negative P2 latency, 1bFz electrode negative P2 amplitude, 2bFz electrode positive N1 latency, 2bFz electrode positive N1 amplitude, 2bFz electrode negative N1 amplitude N1 latency, 2bFz electrode negative N1 amplitude, 2bFz electrode positive P2 latency, 2bFz electrode positive P2 amplitude, 2bFz electrode negative P2 latency, 2bFz electrode negative P2 amplitude</p><p><br></p><p>Cognitive Flexibility: Measurement of Task Switching Paradigm</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (repetition/conversion)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), EXP1 Positive Face Accuracy, EXP1 Negative Face Accuracy, EXP1 Total Accuracy, EXP1 Positive Face Response Time, EXP1 Negative Face Response Time, EXP1 Total Response Time, EXP2 Positive Face Accuracy, EXP2 Negative Face Accuracy, EXP2 Total Accuracy, EXP2 Positive Face Response Time, EXP2 Negative Face Response Time, EXP2 Total Response Time, EXP3 Positive Face Accuracy, EXP3 Negative Face Accuracy, EXP3 Total Accuracy, EXP3 Positive Face Accuracy Reaction Time, EXP3 Negative Face Reaction Time, EXP3 Total Reaction Time, Repetitive Condition Positive Face Accuracy, Repetitive Condition Negative Face Accuracy, Repetitive Condition Positive Face Reaction Time, Repetitive Condition Negative Face Reaction Time, Repetitive Condition Total Accuracy, Repetitive Condition Total Reaction Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Repetitive Fz Electrode Positive N1 Latency, Repetitive Fz Electrode Positive N1 Amplitude, Repetitive Fz Electrode Negative N1 Latency, Repetitive Fz Electrode Negative N1 Amplitude, Repetitive Fz Electrode Positive P2 Latency, Repetitive Fz Electrode Negative P2 Latency, Repetitive Fz Electrode Negative P2 Amplitude, Conversion Fz Electrode Positive N1 Latency, Conversion Fz Electrode Positive N1 Amplitude, Conversion Fz Electrode Negative N1 Latency, Conversion Fz Electrode Point negative N1 amplitude, conversion Fz electrode point positive P2 latency, conversion Fz electrode point positive P2 amplitude, conversion Fz electrode point negative P2 latency, conversion Fz electrode point negative P2 amplitude</p>",
-    "description_en": "<p>Using the LASA scale, university students were divided into high social anxiety group and low social anxiety group, as well as the <span style=\"color: rgb(51, 51, 51);\">Chinese Facial Affective Picture System</span> (CFAPS)</p><p><br></p><p>Measurement of the effects of social anxiety and emotional faces on the three subcomponents of executive function (inhibitory control, working memory, and cognitive flexibility) in college students</p><p><br></p><p><br></p><p>Inhibition control: Gonogo paradigm measurement</p><p><br></p><p>Independent variables: 2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (Go/No go)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Positive Go Accuracy, Negative Go Accuracy, Total Go Accuracy, Positive nogo Accuracy, Negative nogo Accuracy, Total nogo Accuracy, Positive go Response Time, Negative go Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Fz electrode positive N1 latency, Fz electrode positive N1 amplitude, Fz electrode negative N1 latency, Fz electrode negative N1 amplitude, Fz electrode positive P2 latency, Fz electrode positive P2 amplitude, Fz electrode negative P2 latency, Fz electrode negative P2 amplitude</p><p><br></p><p>Working Memory: N-back Paradigm Measurement</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 cognitive load (1-back/2-back)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), 1Back Positive Face Accuracy, 1Back Negative Face Accuracy, 1Back Total Accuracy, 1Back Positive Face Response Time, 1Back Negative Face Response Time, 1Back Total Response Time, 2Back Positive Face Accuracy, 2Back Negative Face Accuracy, 2Back Total Accuracy, 2Back Positive Face Response Time, 2Back Negative Face Response Time, and 2Back Total Response Time</p><p><br></p><p>EEG data: From left to right, the titles are: ID, Group 1 (Low Social Anxiety Group), 2 (High Social Anxiety Group), 1bFz electrode positive N1 latency, 1bFz positive N1 amplitude, 1bFz electrode negative N1 latency, 1bFz electrode negative N1 amplitude, 1bFz electrode positive P2 latency, 1bFz electrode positive P2 amplitude, 1bFz electrode negative P2 latency, 1bFz electrode negative P2 amplitude, 2bFz electrode positive N1 latency, 2bFz electrode positive N1 amplitude, 2bFz electrode negative N1 amplitude N1 latency, 2bFz electrode negative N1 amplitude, 2bFz electrode positive P2 latency, 2bFz electrode positive P2 amplitude, 2bFz electrode negative P2 latency, 2bFz electrode negative P2 amplitude</p><p><br></p><p>Cognitive Flexibility: Measurement of Task Switching Paradigm</p><p><br></p><p>2 groups (high social anxiety group/low social anxiety group) x 2 emotional face valence (positive/negative faces) x 2 conditions (repetition/conversion)</p><p><br></p><p>Dependent variables: accuracy, response time, and corresponding amplitude and latency</p><p><br></p><p>Behavioral data: Titles from left to right are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), EXP1 Positive Face Accuracy, EXP1 Negative Face Accuracy, EXP1 Total Accuracy, EXP1 Positive Face Response Time, EXP1 Negative Face Response Time, EXP1 Total Response Time, EXP2 Positive Face Accuracy, EXP2 Negative Face Accuracy, EXP2 Total Accuracy, EXP2 Positive Face Response Time, EXP2 Negative Face Response Time, EXP2 Total Response Time, EXP3 Positive Face Accuracy, EXP3 Negative Face Accuracy, EXP3 Total Accuracy, EXP3 Positive Face Accuracy Reaction Time, EXP3 Negative Face Reaction Time, EXP3 Total Reaction Time, Repetitive Condition Positive Face Accuracy, Repetitive Condition Negative Face Accuracy, Repetitive Condition Positive Face Reaction Time, Repetitive Condition Negative Face Reaction Time, Repetitive Condition Total Accuracy, Repetitive Condition Total Reaction Time</p><p><br></p><p>EEG data: From left to right, the titles are: Number, Group 1 (Low Social Anxiety Group) 2 (High Social Anxiety Group), Repetitive Fz Electrode Positive N1 Latency, Repetitive Fz Electrode Positive N1 Amplitude, Repetitive Fz Electrode Negative N1 Latency, Repetitive Fz Electrode Negative N1 Amplitude, Repetitive Fz Electrode Positive P2 Latency, Repetitive Fz Electrode Negative P2 Latency, Repetitive Fz Electrode Negative P2 Amplitude, Conversion Fz Electrode Positive N1 Latency, Conversion Fz Electrode Positive N1 Amplitude, Conversion Fz Electrode Negative N1 Latency, Conversion Fz Electrode Point negative N1 amplitude, conversion Fz electrode point positive P2 latency, conversion Fz electrode point positive P2 amplitude, conversion Fz electrode point negative P2 latency, conversion Fz electrode point negative P2 amplitude</p>",
-    "description_zh": "<p>采用LASA量表将大学生分为高社交焦虑组和低社交焦虑组、中国化情绪面孔系统（CFAPS)</p><p>分别测量社交焦虑和情绪面孔对大学生执行功能的三个子成分（抑制控制、工作记忆、认知灵活性）的影响</p><p><br></p><p>抑制控制：Gonogo范式 测量</p><p>自变量：2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2条件（Go/No-go）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、积极Go正确率、消极Go正确率、Go总正确率、积极nogo正确率、消极nogo正确率、nogo总正确率、积极go反应时、消极go反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、Fz电极点积极N1潜伏期、Fz电极点积极N1波幅、Fz电极点消极N1潜伏期、Fz电极点消极N1波幅、Fz电极点积极P2潜伏期、Fz电极点积极P2波幅、Fz电极点消极P2潜伏期、Fz电极点消极P2波幅</p><p><br></p><p>工作记忆：N-back范式测量</p><p>2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2认知负荷（1-back/2-back）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、1back积极面孔正确率、1back消极面孔正确率、1back总正确率、1back积极面孔反应时、1back消极面孔反应时、1back总反应时、2back积极面孔正确率、2back消极面孔正确率、2back总正确率、2back积极面孔反应时、2back消极面孔反应时、2back总反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、1bFz电极点积极N1潜伏期、1bFz积极N1波幅、1bFz电极点消极N1潜伏期、1bFz电极点消极N1波幅、1bFz电极点积极P2潜伏期、1bFz电极点积极P2波幅、1bFz电极点消极P2潜伏期、1bFz电极点消极P2波幅、2bFz电极点积极N1潜伏期、2bFz电极点积极N1波幅、2bFz电极点消极N1潜伏期、2bFz电极点消极N1波幅、2bFz电极点积极P2潜伏期、2bFz电极点积极P2波幅、2bFz电极点消极P2潜伏期、2bFz电极点消极P2波幅</p><p><br></p><p>认知灵活性：任务转换范式测量</p><p>2组别（高社交焦虑组/低社交焦虑组）× 2情绪面孔效价（积极面孔/消极面孔）× 2条件（重复/转换）</p><p>因变量：正确率、反应时和相应的波幅和潜伏期</p><p><br></p><p>行为数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、EXP1积极面孔正确率、EXP1消极面孔正确率、EXP1总正确率、EXP1积极面孔反应时、EXP1消极面孔反应时、EXP1总反应时、EXP2积极面孔正确率、EXP2消极面孔正确率、EXP2总正确率、EXP2积极面孔反应时、EXP2消极面孔反应时、EXP2总反应时、EXP3积极面孔正确率、EXP3消极面孔正确率、EXP3总正确率、EXP3积极面孔反应时、EXP3消极面孔反应时、EXP3总反应时、重复条件积极面孔正确率、重复条件消极面孔正确率、重复条件积极面孔反应时、重复条件消极面孔反应时、重复条件总正确率、重复条件总反应时</p><p>脑电数据：从左到右标题为：编号、组别1（低社交焦虑组）2（高社交焦虑组）、重复Fz电极点积极N1潜伏期、重复Fz电极点积极N1波幅、重复Fz电极点消极N1潜伏期、重复Fz电极点消极N1波幅、重复Fz电极点积极P2潜伏期、重复Fz电极点积极P2波幅、重复Fz电极点消极P2潜伏期、重复Fz电极点消极P2波幅、转换Fz电极点积极N1潜伏期、转换Fz电极点积极N1波幅、转换Fz电极点消极N1潜伏期、转换Fz电极点消极N1波幅、转换Fz电极点积极P2潜伏期、转换Fz电极点积极P2波幅、转换Fz电极点消极P2潜伏期、转换Fz电极点消极P2波幅</p>",
-    "authors": [
-      {
-        "name_en": "Yan Zhixiong",
-        "name_zh": "Yan Zhixiong",
-        "email": "yanzx@nnnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Nanning Normal University",
-            "name_zh": "Nanning Normal University",
-            "ror_id": "sdb_custom"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "social anxiety",
-      "emotional face",
-      "executive function"
-    ],
-    "keywords_zh": [
-      "社交焦虑",
-      "情绪面孔",
-      "执行功能"
-    ],
-    "publication_date": "2024-05-06T06:41:01.026Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 8041.95,
-    "file_size_bytes": 8432597404,
-    "download_count": 183,
-    "visit_count": 1073,
-    "url": "https://www.scidb.cn/en/detail?id=6653e0c9c958f37781bd21ff",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6838a23bb9dc7d0bec20b4b9",
-    "doi": "10.57760/sciencedb.25915",
-    "cstr": "31253.11.sciencedb.25915",
-    "pid": "",
-    "title": "The Neural Mechanisms of Mental Fatigue Under Cognitive Load: A Field Study Based on <span style='background-color:yellow'>EEG</span> Entropy and Spectral Analysis",
-    "title_en": "The Neural Mechanisms of Mental Fatigue Under Cognitive Load: A Field Study Based on <span style='background-color:yellow'>EEG</span> Entropy and Spectral Analysis",
-    "title_zh": "认知负荷对心理疲劳的神经机制：基于脑电熵和频谱的田野实验研究",
-    "description": "<p>This dataset was collected from a 20-day field experiment conducted at an oil and gas drilling site in Southwest China. The study aimed to investigate the effects of different levels of cognitive workload on mental fatigue among operators in high-risk working environments. A within-subject design was employed, and multi-channel electroencephalogram (EEG) data were collected from eight drillers under high, low, and no cognitive workload conditions. Each participant underwent repeated measurements across two rotating shift cycles. EEG data were acquired using wearable devices at a sampling rate of 128 Hz, with each recording session lasting 10 minutes.</p>",
-    "description_en": "<p>This dataset was collected from a 20-day field experiment conducted at an oil and gas drilling site in Southwest China. The study aimed to investigate the effects of different levels of cognitive workload on mental fatigue among operators in high-risk working environments. A within-subject design was employed, and multi-channel electroencephalogram (EEG) data were collected from eight drillers under high, low, and no cognitive workload conditions. Each participant underwent repeated measurements across two rotating shift cycles. EEG data were acquired using wearable devices at a sampling rate of 128 Hz, with each recording session lasting 10 minutes.</p>",
-    "description_zh": "<p>本数据集来源于一项在中国西南地区油气钻井现场开展的20天田野实验，旨在探讨高危作业环境中不同认知负荷水平对操作工心理疲劳的影响。实验采用被试内设计，采集8名钻井司钻在高、低及无认知负荷条件下的多通道脑电信号数据（EEG），每名被试在两个轮班周期内接受重复测量。数据采集设备为可穿戴脑电，采样频率为128Hz，采集时长为每次10分钟</p>",
-    "authors": [
-      {
-        "name_en": "qing xin",
-        "name_zh": "青鑫",
-        "email": "1395852156@qq.com",
-        "affiliations": [
-          {
-            "name_en": "SWPU",
-            "name_zh": "SWPU",
-            "ror_id": "sdb_custom"
-          }
-        ]
-      },
-      {
-        "name_en": "SuHao",
-        "name_zh": "苏昊",
-        "email": "shenyuren007@sina.com",
-        "affiliations": [
-          {
-            "name_en": "西南石油大学",
-            "name_zh": "西南石油大学"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "mental_fatigue",
-      "cognitive_load",
-      "eeg"
-    ],
-    "keywords_zh": [
-      "认知负荷",
-      "心理疲劳",
-      "脑电图"
-    ],
-    "publication_date": "2025-05-30T07:13:30.255Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODC-BY",
-    "file_size_mb": 3368.83,
-    "file_size_bytes": 3532470737,
-    "download_count": 1536,
-    "visit_count": 291,
-    "url": "https://www.scidb.cn/en/detail?id=6838a23bb9dc7d0bec20b4b9",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66cc6988038a964b489bdf1a",
-    "doi": "10.57760/sciencedb.12479",
-    "cstr": "31253.11.sciencedb.12479",
-    "pid": "",
-    "title": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
-    "title_en": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
-    "title_zh": "The present manuscript used <span style='background-color:yellow'>EEG</span> analysis to investigate the effects of group size on third-party punishment and its neural activity characteristics in",
-    "description": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
-    "description_en": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
-    "description_zh": "<p>Third-party punishment (TPP) is&nbsp;the punishment that an individual&nbsp;executes on a violator as a third party or observer&nbsp;to&nbsp;maintain social norms. Many&nbsp;studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have&nbsp;focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more&nbsp;theta band activation than in the small group. The results showed that the motivation of the third party to seek&nbsp;a positive reputation in the small size exceeds the balance of its economic interests&nbsp;and tends to punish the violator for maintaining&nbsp;fair norms. The&nbsp;loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.</p>",
-    "authors": [
-      {
-        "name_en": "Yuan Gao",
-        "name_zh": "Yuan Gao",
-        "email": "gaoyuan171717@163.com",
-        "affiliations": [
-          {
-            "name_en": "North China University of Science and Technology",
-            "name_zh": "North China University of Science and Technology",
-            "ror_id": "sdb_custom"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Third-party punishment",
-      "Group size",
-      "Gain and loss contexts",
-      "Fairness",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "Third-party punishment",
-      "Group size",
-      "Gain and loss contexts",
-      "Fairness",
-      "EEG"
-    ],
-    "publication_date": "2024-08-28T09:11:52.785Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 18001.69,
-    "file_size_bytes": 18876139843,
-    "download_count": 0,
-    "visit_count": 773,
-    "url": "https://www.scidb.cn/en/detail?id=66cc6988038a964b489bdf1a",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "65b4ce08b3373114e622b5d3",
-    "doi": "10.57760/sciencedb.15681",
-    "cstr": "31253.11.sciencedb.15681",
-    "pid": "",
-    "title": "MI-BCI-2023-data",
-    "title_en": "MI-BCI-2023-data",
-    "title_zh": "MI-BCI-2023-data",
-    "description": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
-    "description_en": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
-    "description_zh": "<p>It includes EEG data for offline experiment of autonomous walking using EEG signals on iLeg-II rehabilitation robot.</p>",
-    "authors": [
-      {
-        "name_en": "Tianyu Lin",
-        "name_zh": "Tianyu Lin",
-        "email": "1078467506@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation, Chinese Academy of Sciences",
-            "name_zh": "Institute of Automation, Chinese Academy of Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Motor imagery",
-      "non-invasive bci"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Motor imagery",
-      "non-invasive bci"
-    ],
-    "publication_date": "2024-01-29T01:12:35.964Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 507.1,
-    "file_size_bytes": 531737142,
-    "download_count": 2,
-    "visit_count": 939,
-    "url": "https://www.scidb.cn/en/detail?id=65b4ce08b3373114e622b5d3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "653f70ff37cb1b0197fabb97",
-    "doi": "10.57760/sciencedb.psych.00185",
-    "cstr": "31253.11.sciencedb.psych.00185",
-    "pid": "",
-    "title": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
-    "title_en": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
-    "title_zh": "Data from: The roles of edge-based and surface-based information in the dynamic neural representation of objects",
-    "description": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
-    "description_en": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
-    "description_zh": "<p>Including experimental stimuli and EEG data. The stimuli consisted of three categories of animals, fruits, and tools, with 12 objects in each category. Each object had three versions: color photographs, grayscale images, and line drawings, resulting in a total of 108 images. The data were rawdata of EEG.</p>",
-    "authors": [
-      {
-        "name_en": "liansheng yao",
-        "name_zh": "liansheng yao",
-        "email": "lianshengyao@yeah.net",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      },
-      {
-        "name_en": "Qiufang Fu",
-        "name_zh": "Qiufang Fu",
-        "email": "fuqf@paych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "stimuli",
-      "object representation"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "stimuli",
-      "object representation"
-    ],
-    "publication_date": "2023-10-30T06:45:08.391Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 20646.59,
-    "file_size_bytes": 21649513659,
-    "download_count": 12,
-    "visit_count": 293,
-    "url": "https://www.scidb.cn/en/detail?id=653f70ff37cb1b0197fabb97",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "627e1bbf9f0a08024767f878",
-    "doi": "10.57760/sciencedb.01763",
-    "cstr": "31253.11.sciencedb.01763",
-    "pid": "21.86116.6/sciencedb.01763",
-    "title": "Decoding of the neural representation of the visual RGB colour model",
-    "title_en": "Decoding of the neural representation of the visual RGB colour model",
-    "title_zh": "Decoding of the neural representation of the visual RGB colour model",
-    "description": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
-    "description_en": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
-    "description_zh": "<p>RGB colour is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of Electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB colour information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB colour stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB colour is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB colour in classification accuracy and channel location. Finally, space decoding of RGB colour depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.</p>",
-    "authors": [
-      {
-        "name_en": "Wu Yi Jia",
-        "name_zh": "Wu Yi Jia",
-        "email": "wuyijia@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Fudan University",
-            "name_zh": "Fudan University",
-            "ror_id": "https://ror.org/013q1eq08"
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-        ]
-      }
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-    "keywords_en": [
-      "computer science",
-      "visual",
-      "colour",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "computer science",
-      "visual",
-      "colour",
-      "EEG"
-    ],
-    "publication_date": "2022-06-07T05:21:57.745Z",
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-    "url": "https://www.scidb.cn/en/detail?id=627e1bbf9f0a08024767f878",
-    "source": "scidb"
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-    "dataset_id": "64410294692b1255d27a18c8",
-    "doi": "10.57760/sciencedb.07960",
-    "cstr": "31253.11.sciencedb.07960",
-    "pid": "21.86116.6/sciencedb.07960",
-    "title": "Data and MATLAB Scripts",
-    "title_en": "Data and MATLAB Scripts",
-    "title_zh": "Data and MATLAB Scripts",
-    "description": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
-    "description_en": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
-    "description_zh": "<p>EEG Data and MATLAB Scripts for Data Preprocessing, Frequency Domain Analysis, Functional Connectivity Analysis, and Plotting</p>",
-    "authors": [
-      {
-        "name_en": "Zhiwei Xu",
-        "name_zh": "许志炜",
-        "email": "zwxu@whu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Hubei University",
-            "name_zh": "Hubei University"
-          }
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-      "EEG",
-      "CODE",
-      "MATLAB"
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-      "CODE",
-      "MATLAB"
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-    "publication_date": "2023-04-14T02:29:55.915Z",
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-  {
-    "dataset_id": "649e4461c23e806c83e36bd7",
-    "doi": "10.57760/sciencedb.09267",
-    "cstr": "31253.11.sciencedb.09267",
-    "pid": "",
-    "title": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
-    "title_en": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
-    "title_zh": "raw data of article “The effect of visual working memory load on attentional bias in social anxiety”",
-    "description": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
-    "description_en": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
-    "description_zh": "<p>This dataset includes demographic information, in-experiment behavioral and EEG data for all participants in this study.</p>",
-    "authors": [
-      {
-        "name_en": "jiangyibo",
-        "name_zh": "jiangyibo",
-        "email": "psy_jiangyb@163.com",
-        "affiliations": [
-          {
-            "name_en": "Liaoning Normal University",
-            "name_zh": "辽宁师范大学",
-            "ror_id": "https://ror.org/04c3cgg32"
-          }
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-    "keywords_en": [
-      "psychology ",
-      "EEG",
-      "participants"
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-    "keywords_zh": [
-      "psychology ",
-      "EEG",
-      "participants"
-    ],
-    "publication_date": "2023-07-11T09:13:34.493Z",
-    "created": "",
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-    "url": "https://www.scidb.cn/en/detail?id=649e4461c23e806c83e36bd7",
-    "source": "scidb"
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-  {
-    "dataset_id": "62f3b7501e8fab63528a3cb7",
-    "doi": "10.57760/sciencedb.02167",
-    "cstr": "31253.11.sciencedb.02167",
-    "pid": "21.86116.6/sciencedb.02167",
-    "title": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
-    "title_en": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
-    "title_zh": "Reconstructing sources location of visual color cortex by the Task-irrelevant visual stimuli through machine learning decoding",
-    "description": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
-    "description_en": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
-    "description_zh": "<p>Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in primary visual cortex can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.</p>",
-    "authors": [
-      {
-        "name_en": "Wu Yi Jia",
-        "name_zh": "Wu Yi Jia",
-        "email": "wuyijia@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Fudan University",
-            "name_zh": "Fudan University",
-            "ror_id": "https://ror.org/013q1eq08"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Reconstructing",
-      "decoding",
-      "visual",
-      "color",
-      "machine learning",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "Reconstructing",
-      "decoding",
-      "visual",
-      "color",
-      "machine learning",
-      "EEG"
-    ],
-    "publication_date": "2022-08-11T01:29:20.172Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=62f3b7501e8fab63528a3cb7",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "689d88633c764c24f971e59b",
-    "doi": "10.57760/sciencedb.29131",
-    "cstr": "31253.11.sciencedb.29131",
-    "pid": "",
-    "title": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
-    "title_en": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
-    "title_zh": "Dataset of Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals",
-    "description": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
-    "description_en": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
-    "description_zh": "<p>The dataset contains three parts: S1, S2 and S3. S1 is pre-processed EEG-fMRI data for naturalistic viewing tasks. S2 is pre-processed EEG-fNIRS data for the motor imagery task. S3 is pre-processed EEG data for clinical validation.</p>",
-    "authors": [
-      {
-        "name_en": "Weiheng Yao",
-        "name_zh": "Weiheng Yao",
-        "email": "wh.yao@siat.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Shenzhen Institutes of Advanced Technology",
-            "name_zh": "中国科学院深圳先进技术研究院",
-            "ror_id": "https://ror.org/04gh4er46"
-          }
-        ]
-      },
-      {
-        "name_en": "Xuhang Chen",
-        "name_zh": "Xuhang Chen",
-        "email": "xx.chen2@siat.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Shenzhen Institutes of Advanced Technology",
-            "name_zh": "中国科学院深圳先进技术研究院",
-            "ror_id": "https://ror.org/04gh4er46"
-          }
-        ]
-      },
-      {
-        "name_en": "Shuqiang Wang",
-        "name_zh": "Shuqiang Wang",
-        "email": "sq.wang@siat.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Shenzhen Institutes of Advanced Technology",
-            "name_zh": "中国科学院深圳先进技术研究院",
-            "ror_id": "https://ror.org/04gh4er46"
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-        ]
-      }
-    ],
-    "keywords_en": [
-      "brain-computer interface",
-      "braindecoding",
-      "generativeai"
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-    "keywords_zh": [
-      "brain-computer interface",
-      "braindecoding",
-      "generativeai"
-    ],
-    "publication_date": "2025-08-15T01:15:42.507Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
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-    "visit_count": 112,
-    "url": "https://www.scidb.cn/en/detail?id=689d88633c764c24f971e59b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "681a1af1aa991804f7636e80",
-    "doi": "10.57760/sciencedb.17992",
-    "cstr": "31253.11.sciencedb.17992",
-    "pid": "",
-    "title": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
-    "title_en": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
-    "title_zh": "Interplay of Prosociality, Fairness, and Social Status in Moral Judgment",
-    "description": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(55, 65, 81);\">Here are the raw data from an online behavioral experiment and an offline EEG experiment. The raw data from the online behavioral experiment is summarized in tabular form, presenting the keypress results of 60&nbsp;participants. The offline EEG experiment comprises both behavioral and EEG data. The behavioral data is summarized in two tables, presenting the scores of 31&nbsp;participants on two dependent variables. The EEG data is presented in its raw form, sequentially for all 31&nbsp;participants.</span></p>",
-    "authors": [
-      {
-        "name_en": "WU JUN",
-        "name_zh": "WU JUN",
-        "email": "941911346@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Shenzhen University",
-            "name_zh": "深圳大学",
-            "ror_id": "https://ror.org/01vy4gh70"
-          }
-        ]
-      },
-      {
-        "name_en": "CUI FANG",
-        "name_zh": "CUI FANG",
-        "email": "cuifang0826@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Shenzhen University",
-            "name_zh": "Shenzhen University",
-            "ror_id": "https://ror.org/01vy4gh70"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Moral Judgment",
-      "Prosociality",
-      "Fairness",
-      "Social Status",
-      "Event-Related Potentials",
-      "FRN"
-    ],
-    "keywords_zh": [
-      "Moral Judgment",
-      "Prosociality",
-      "Fairness",
-      "Social Status",
-      "Event-Related Potentials",
-      "FRN"
-    ],
-    "publication_date": "2024-12-06T06:32:49.116Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "visit_count": 184,
-    "url": "https://www.scidb.cn/en/detail?id=681a1af1aa991804f7636e80",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "63a5b1e9f4cc512f6fba2c4f",
-    "doi": "10.57760/sciencedb.06986",
-    "cstr": "31253.11.sciencedb.06986",
-    "pid": "21.86116.6/sciencedb.06986",
-    "title": "Effects of Group Size on Third-party Punishments 2022 data set",
-    "title_en": "Effects of Group Size on Third-party Punishments 2022 data set",
-    "title_zh": "群体规模对第三方惩罚的影响2022年数据集",
-    "description": "<p>In April 2022, EEG data was collected in the EEG Laboratory of North China University of Science and Technology. To explore the neural mechanism of the influence of group size on third party punishment.</p>",
-    "description_en": "<p>In April 2022, EEG data was collected in the EEG Laboratory of North China University of Science and Technology. To explore the neural mechanism of the influence of group size on third party punishment.</p>",
-    "description_zh": "<p>2022年4月，在华北理工大学脑电实验室收集脑电数据。为探究群体规模影响第三方惩罚的神经机制。</p>",
-    "authors": [
-      {
-        "name_en": "Yuan Gao",
-        "name_zh": "高媛",
-        "email": "gaoyuan171717@163.com",
-        "affiliations": [
-          {
-            "name_en": "North China University of Science and Technology",
-            "name_zh": "North China University of Science and Technology"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Third-party punishment",
-      "Group size",
-      "Gain and loss contexts"
-    ],
-    "keywords_zh": [
-      "第三方惩罚",
-      "群体规模",
-      "得失情境"
-    ],
-    "publication_date": "2022-12-26T01:38:36.857Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 20906.59,
-    "file_size_bytes": 21922144500,
-    "download_count": 0,
-    "visit_count": 69,
-    "url": "https://www.scidb.cn/en/detail?id=63a5b1e9f4cc512f6fba2c4f",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "665822bd6db6702daf1dbf65",
-    "doi": "10.57760/sciencedb.j00133.00276",
-    "cstr": "31253.11.sciencedb.j00133.00276",
-    "pid": "",
-    "title": "Dataset of Service Providers’ Participation Motivations in Skill-Based Crowdsourcing",
-    "title_en": "Dataset of Service Providers’ Participation Motivations in Skill-Based Crowdsourcing",
-    "title_zh": "技能众包服务商参与动机数据集",
-    "description": "<p>This dataset covers all successfully completed bidding tasks from epwk.com from November 2017 to November 2022, with a total of 2385 service provider information (involving 15641 corresponding bidding documents). The main row and column labels in the data table have the following meanings:&nbsp;1. Bidding frequency (bid): The number of tasks participated in, that is, the number of tasks each service provider participated in the bidding process;&nbsp;2. Hobbies(hob): Skill task similarity, constructing a TF-IDF model based on service provider skill labels and task titles, and calculating text similarity;&nbsp;3. Competency (com): past task similarity, building a BERT fine-tuning model based on all task titles participated by the service provider, and calculating text similarity;&nbsp;4. Bonus stimulation (bon): The reward amount, the average reward amount for service providers participating in tasks;&nbsp;5. Task attraction (att): task detail, task requirements, and the number of text characters supplemented by the requirements;&nbsp;6. Ability exercise (abi): Service provider capabilities, service provider capability product level;&nbsp;7. Reputation and Reputation (rep): Service Provider Credit, Service Provider Credit Rating;&nbsp;8. Social recognition (soc): positive review rate, service provider positive review rate;&nbsp;9. Task difficulty (dif): The ratio of the number of bidders to the number of viewers, and the ratio of the number of bidders and viewers participating in the task by the service provider.&nbsp;</p>",
-    "description_en": "<p>This dataset covers all successfully completed bidding tasks from epwk.com from November 2017 to November 2022, with a total of 2385 service provider information (involving 15641 corresponding bidding documents). The main row and column labels in the data table have the following meanings:&nbsp;1. Bidding frequency (bid): The number of tasks participated in, that is, the number of tasks each service provider participated in the bidding process;&nbsp;2. Hobbies(hob): Skill task similarity, constructing a TF-IDF model based on service provider skill labels and task titles, and calculating text similarity;&nbsp;3. Competency (com): past task similarity, building a BERT fine-tuning model based on all task titles participated by the service provider, and calculating text similarity;&nbsp;4. Bonus stimulation (bon): The reward amount, the average reward amount for service providers participating in tasks;&nbsp;5. Task attraction (att): task detail, task requirements, and the number of text characters supplemented by the requirements;&nbsp;6. Ability exercise (abi): Service provider capabilities, service provider capability product level;&nbsp;7. Reputation and Reputation (rep): Service Provider Credit, Service Provider Credit Rating;&nbsp;8. Social recognition (soc): positive review rate, service provider positive review rate;&nbsp;9. Task difficulty (dif): The ratio of the number of bidders to the number of viewers, and the ratio of the number of bidders and viewers participating in the task by the service provider.&nbsp;</p>",
-    "description_zh": "<p><span style=\"color: black;\">本数据集涵盖了2017年11月至2022年11月5年间一品威客平台中所有圆满完成的招投标任务，共包含2385位服务商信息（涉及与之对应的</span><span style=\"color: rgb(0, 0, 0);\">15641条标书），</span><span style=\"color: black;\">其中数据表主要行列标签含义如下：</span></p><p><span style=\"color: black;\">1.</span>投标次数（bid）：参与的任务数量，即每位服务商参与招投标的任务数量；</p><p>2.兴趣爱好（hob）：技能任务相似度，基于服务商技能标签和任务标题构建TF-IDF模型，计算文本相似度；</p><p>3.胜任能力（com）：过往任务相似度，基于服务商参与的所有任务标题构建BERT微调模型，计算文本相似度；</p><p>4.奖金刺激（bon）：悬赏金额，服务商参与任务的悬赏金额均值；</p><p>5.任务吸引（att）：任务详细度，任务需求与需求补充的文本字符数；</p><p>6.能力锻炼（abi）：服务商能力，服务商能力品级数；</p><p>7.声望信誉（rep）：服务商信用，服务商信用等级；</p><p>8.社交认可（soc）：好评率，服务商好评率；</p><p>9.任务难度（dif）：投标人数与浏览人数的比例，服务商参与任务的投标人数与浏览人数之比。</p><p><br></p>",
-    "authors": [
-      {
-        "name_en": "Wang Xiaolun",
-        "name_zh": "王筱纶",
-        "email": "w_xl@nuaa.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Nanjing University of Aeronautics and Astronautics",
-            "name_zh": "Nanjing University of Aeronautics and Astronautics",
-            "ror_id": "https://ror.org/01scyh794"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Skill-based crowdsourcing",
-      " Participation motivation",
-      "Service provider",
-      " Machine learning"
-    ],
-    "keywords_zh": [
-      "技能共享",
-      "参与动机",
-      "服务商",
-      "机器学习"
-    ],
-    "publication_date": "2024-07-26T08:47:32.409Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODC-BY",
-    "file_size_mb": 1.19,
-    "file_size_bytes": 1244160,
-    "download_count": 56,
-    "visit_count": 705,
-    "url": "https://www.scidb.cn/en/detail?id=665822bd6db6702daf1dbf65",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "60b9c3adae98d4289fceed46",
-    "doi": "10.11922/sciencedb.00832",
-    "cstr": "31253.11.sciencedb.00832",
-    "pid": "21.86116.6/sciencedb.00832",
-    "title": "SVM and Randfores Code for DoA monitoring",
-    "title_en": "SVM and Randfores Code for DoA monitoring",
-    "title_zh": "SVM and Randfores Code for DoA monitoring",
-    "description": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
-    "description_en": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
-    "description_zh": "<p><br></p><p>According to Etsevo, during anesthesia induction, we have divided the patient’s depth of sedation into awake, light sedation, and deep sedation and collected 90s of EEG and EOG at each sedation depth for predictive analysis. We included 33 patients’ data into the analysis and obtained 297 pieces of data input into the machine learning method for classification and prediction.</p><p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to study whether sound-evoked EOG has an auxiliary effect on anesthesia monitoring, we combine EEG and EOG features or only input EEG features into the random forest and linear support vector machine for classification tasks.&nbsp;</p>",
-    "authors": [
-      {
-        "name_en": "Guozheng Wang",
-        "name_zh": "Guozheng Wang",
-        "affiliations": [
-          {
-            "name_en": "Zhejiang university",
-            "name_zh": "Zhejiang university",
-            "ror_id": "https://ror.org/00a2xv884"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Anesthetic depth monitoring",
-      " auditory stimulation",
-      " consciousness",
-      " electrooculogram",
-      " sevoflurane"
-    ],
-    "keywords_zh": [
-      "Anesthetic depth monitoring",
-      " auditory stimulation",
-      " consciousness",
-      " electrooculogram",
-      " sevoflurane"
-    ],
-    "publication_date": "2021-05-26T08:53:42.809Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 12812,
-    "download_count": 24,
-    "visit_count": 768,
-    "url": "https://www.scidb.cn/en/detail?id=60b9c3adae98d4289fceed46",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6242bffbe9abd34325de4caf",
-    "doi": "10.11922/sciencedb.j00052.00001",
-    "cstr": "31253.11.sciencedb.j00052.00001",
-    "pid": "21.86116.6/sciencedb.j00052.00001",
-    "title": "empathy",
-    "title_en": "empathy",
-    "title_zh": "共情数据",
-    "description": "<p>EEG study data on empathy and musical emotion recognition</p>",
-    "description_en": "<p>EEG study data on empathy and musical emotion recognition</p>",
-    "description_zh": "<p>关于共情与音乐情绪识别的脑电研究数据</p>",
-    "authors": [
-      {
-        "name_en": "Yang Jimei",
-        "name_zh": "杨集梅",
-        "email": "157553855@qq.com",
-        "affiliations": [
-          {
-            "name_en": "西南大学",
-            "name_zh": "西南大学"
-          }
-        ]
-      },
-      {
-        "name_en": "Zheng Maoping",
-        "name_zh": "郑茂平",
-        "email": "zhengmpxlx@126.com",
-        "affiliations": [
-          {
-            "name_en": "西南大学",
-            "name_zh": "西南大学"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "empathy",
-      "Chinese national music",
-      "emotion recognition"
-    ],
-    "keywords_zh": [
-      "共情",
-      "中国民族音乐",
-      "情绪识别"
-    ],
-    "publication_date": "2022-04-01T08:20:44.896Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 8309,
-    "download_count": 44,
-    "visit_count": 2590,
-    "url": "https://www.scidb.cn/en/detail?id=6242bffbe9abd34325de4caf",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66c7406cb7542f64ded99762",
-    "doi": "10.57760/sciencedb.12355",
-    "cstr": "31253.11.sciencedb.12355",
-    "pid": "",
-    "title": "Transcranial alternating current stimulation and creativity",
-    "title_en": "Transcranial alternating current stimulation and creativity",
-    "title_zh": "经颅交流电刺激与创造性",
-    "description": "<p>Collection Period: March 2024 to April 2024</p><p>Acquisition Location: Qufu Normal University</p><p>Behavioral data: Eprime 2.0.10 acquisition, organized in csv</p><p>EEG data: Borecon Newsen W wireless EEG acquisition device, 16-lead, sampling rate 500Hz, preprocessed using MATLAB EEGLAB toolbox, time-frequency analysis (wavelet transform) using MATLAB Fieldtrip toolbox</p><p>Data may contain errors: due to the configurable electrodes of the EEG acquisition device, only regions of interest were acquired (AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
-    "description_en": "<p>Collection Period: March 2024 to April 2024</p><p>Acquisition Location: Qufu Normal University</p><p>Behavioral data: Eprime 2.0.10 acquisition, organized in csv</p><p>EEG data: Borecon Newsen W wireless EEG acquisition device, 16-lead, sampling rate 500Hz, preprocessed using MATLAB EEGLAB toolbox, time-frequency analysis (wavelet transform) using MATLAB Fieldtrip toolbox</p><p>Data may contain errors: due to the configurable electrodes of the EEG acquisition device, only regions of interest were acquired (AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
-    "description_zh": "<p>采集时间：2024年3月至2024年4月</p><p>采集地点：曲阜师范大学</p><p>行为数据：Eprime2.0.10采集，整理在csv中</p><p>EEG数据：博睿康Newsen W无线脑电采集设备，16导，采样率500Hz，使用MATLAB EEGLAB toolbox进行预处理，使用MATLAB Fieldtrip toolbox进行时频分析（小波变换）</p><p>数据可能存在误差：由于脑电采集设备的电极可设置，因此只采集了感兴趣的区域(AF3, AFz, AF4, F3, Fz, F4, FCz, PO3, Poz, PO4, P3, Pz, P4, Oz)</p>",
-    "authors": [
-      {
-        "name_en": "Zhou Runze",
-        "name_zh": "周润泽",
-        "email": "creapsychojoe@163.com",
-        "affiliations": [
-          {
-            "name_en": "Southwest University",
-            "name_zh": "西南大学",
-            "ror_id": "https://ror.org/01kj4z117"
-          },
-          {
-            "name_en": "Qufu Normal University",
-            "name_zh": "曲阜师范大学",
-            "ror_id": "https://ror.org/03ceheh96"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Chunlei",
-        "name_zh": "刘春雷",
-        "email": "liuchunlei@qfnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Qufu Normal University",
-            "name_zh": "曲阜师范大学",
-            "ror_id": "https://ror.org/03ceheh96"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "transcranial alternating current stimulation",
-      "creative thinking",
-      "alpha oscillation"
-    ],
-    "keywords_zh": [
-      "经颅电刺激",
-      "创造性思维",
-      "alpha神经振荡"
-    ],
-    "publication_date": "2024-08-23T07:49:53.567Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 2360.43,
-    "file_size_bytes": 2475085544,
-    "download_count": 0,
-    "visit_count": 1343,
-    "url": "https://www.scidb.cn/en/detail?id=66c7406cb7542f64ded99762",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68d16f3afae9e04d156b14f3",
-    "doi": "10.57760/sciencedb.28622",
-    "cstr": "31253.11.sciencedb.28622",
-    "pid": "",
-    "title": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
-    "title_en": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
-    "title_zh": "Selective attention and audiovisual synchrony independently and interactively enhance visual processing in a competing scenario",
-    "description": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
-    "description_en": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
-    "description_zh": "<p>The dataset is organized into four folders as follows:</p><p>‘Behavior’ includes behavioral data for statistics.</p><p>‘EEG_Rhythmic’ comprises data for statistics from the rhythmic experiment.</p><p>‘EEG_Unrhythmic’ contains data for statistics from the unrhythmic experiment.</p><p>‘EEGs_Rhythmic&amp;Unrhythimc’<span style=\"color: rgb(15, 17, 21);\"> </span>includes combined data for statistics from both the rhythmic and unrhythmic experiment.</p>",
-    "authors": [
-      {
-        "name_en": "Chen Jieru",
-        "name_zh": "Chen Jieru",
-        "email": "chenjr@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Wenjie",
-        "name_zh": "Liu Wenjie",
-        "email": "wenjieliupsy@126.com",
-        "affiliations": [
-          {
-            "name_en": "Beijing Huilongguan Hospital",
-            "name_zh": "Beijing Huilongguan Hospital"
-          }
-        ]
-      },
-      {
-        "name_en": "Tan Shiqi",
-        "name_zh": "Tan Shiqi",
-        "email": "tansq@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Yuan Xiangyong",
-        "name_zh": "Yuan Xiangyong",
-        "email": "yuanxy@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Jiang Yi",
-        "name_zh": "Jiang Yi",
-        "email": "yijiang@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": " Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": " Institute of Psychology, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "audiovisual integration",
-      "selective attention",
-      "rhythm",
-      "steady-state visual evoked potential",
-      "inter-trial phase coherence"
-    ],
-    "keywords_zh": [
-      "audiovisual integration",
-      "selective attention",
-      "rhythm",
-      "steady-state visual evoked potential",
-      "inter-trial phase coherence"
-    ],
-    "publication_date": "2025-09-25T06:29:46.293Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 121.94,
-    "file_size_bytes": 127866915,
-    "download_count": 297,
-    "visit_count": 54,
-    "url": "https://www.scidb.cn/en/detail?id=68d16f3afae9e04d156b14f3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66487c324e7538442ce796af",
-    "doi": "10.57760/sciencedb.02109",
-    "cstr": "31253.11.sciencedb.02109",
-    "pid": "21.86116.6/sciencedb.02109",
-    "title": "Motor inhibition impacts the motor interference effect of dangerous objects",
-    "title_en": "Motor inhibition impacts the motor interference effect of dangerous objects",
-    "title_zh": "Motor inhibition impacts the motor interference effect of dangerous objects",
-    "description": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "description_en": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "description_zh": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch Cathode Ray Tube (CRT) monitor (85Hz refresh rates). The stimulus presentation was performed using an E-prime 2.0 software. The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "authors": [
-      {
-        "name_en": "Peng Liu",
-        "name_zh": "Peng Liu",
-        "email": "liupeng@nwu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Northwest University",
-            "name_zh": "Northwest University",
-            "ror_id": "https://ror.org/00y7snj24"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "motor interference effect",
-      "dangerous objects",
-      "motor inhibition",
-      "danger evaluation"
-    ],
-    "keywords_zh": [
-      "motor interference effect",
-      "dangerous objects",
-      "motor inhibition",
-      "danger evaluation"
-    ],
-    "publication_date": "2022-08-02T09:08:20.475Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 3923.25,
-    "file_size_bytes": 4113824391,
-    "download_count": 249,
-    "visit_count": 938,
-    "url": "https://www.scidb.cn/en/detail?id=66487c324e7538442ce796af",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "677f4e0ea51e4d6139abfaf2",
-    "doi": "10.57760/sciencedb.11364",
-    "cstr": "31253.11.sciencedb.11364",
-    "pid": "",
-    "title": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
-    "title_en": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
-    "title_zh": "Neuronal Mechanisms of Nociceptive-evoked  Gamma-band Oscillations in Rodents",
-    "description": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
-    "description_en": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
-    "description_zh": "<p>Data for generating figures of the manuscript. The data contains the EEG data for humans, electrophysiological data for rats and calcium imaging data for mice.</p>",
-    "authors": [
-      {
-        "name_en": "Yue Lupeng",
-        "name_zh": "Yue Lupeng",
-        "email": "yuelp@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology",
-            "name_zh": "Institute of Psychology",
-            "ror_id": "https://ror.org/034dn0836"
-          }
-        ]
-      },
-      {
-        "name_en": "Hu Li",
-        "name_zh": "Hu Li",
-        "email": "huli@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Gamma band oscillations (GBOs)",
-      "Pain",
-      "Parvalbumin (PV) positive interneurons",
-      "Primary somatosensory cortex (S1)",
-      "Calcium imaging",
-      "Optogenetics"
-    ],
-    "keywords_zh": [
-      "Gamma band oscillations (GBOs)",
-      "Pain",
-      "Parvalbumin (PV) positive interneurons",
-      "Primary somatosensory cortex (S1)",
-      "Calcium imaging",
-      "Optogenetics"
-    ],
-    "publication_date": "2024-08-06T03:15:00.613Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
-    "file_size_mb": 98024.41,
-    "file_size_bytes": 102786043650,
-    "download_count": 41,
-    "visit_count": 1464,
-    "url": "https://www.scidb.cn/en/detail?id=677f4e0ea51e4d6139abfaf2",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "633c098f1eb2ba6997fb4a78",
-    "doi": "10.57760/sciencedb.j00052.00033",
-    "cstr": "31253.11.sciencedb.j00052.00033",
-    "pid": "21.86116.6/sciencedb.j00052.00033",
-    "title": "The role of norm understanding in third-party punishment and its time course",
-    "title_en": "The role of norm understanding in third-party punishment and its time course",
-    "title_zh": "The role of norm understanding in third-party punishment and its time course",
-    "description": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
-    "description_en": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
-    "description_zh": "<p>This dataset is about Chinese's norm understanding and their third-party punishment. It includes one behavioral experiment and one EEG experiment.</p>",
-    "authors": [
-      {
-        "name_en": "Dongjie Xie",
-        "name_zh": "Dongjie Xie",
-        "email": "xie_dongjie@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guangdong Institute of Public Administration",
-            "name_zh": "Guangdong Institute of Public Administration"
-          }
-        ]
-      },
-      {
-        "name_en": "Yanjie Su",
-        "name_zh": "Yanjie Su",
-        "email": "yjsu@pku.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Peking University",
-            "name_zh": "Peking University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "third-party punishment",
-      "descriptive norm",
-      "injunctive norm",
-      "norm understanding",
-      "event-related potential"
-    ],
-    "keywords_zh": [
-      "third-party punishment",
-      "descriptive norm",
-      "injunctive norm",
-      "norm understanding",
-      "event-related potential"
-    ],
-    "publication_date": "2023-08-31T01:07:19.091Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 0.04,
-    "file_size_bytes": 37192,
-    "download_count": 31,
-    "visit_count": 1398,
-    "url": "https://www.scidb.cn/en/detail?id=633c098f1eb2ba6997fb4a78",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "653ba4bf844a5137567bc803",
-    "doi": "10.57760/sciencedb.12791",
-    "cstr": "31253.11.sciencedb.12791",
-    "pid": "",
-    "title": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
-    "title_en": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
-    "title_zh": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: ERP Evidence",
-    "description": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
-    "description_en": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
-    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">This data set is the behavioral and Electrophysiology data of eye gaze cue experiments in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes three files, namely (1) Behavioral data: subjects' behavioral data results (2) EEG raw data: subjects' EEG raw data (3)EEG exp date: </span>EEG data for post-processing after output, and (4) Figures and tables: Figures and tables mentioned in the manuscript.</p><p><span style=\"color: rgb(15, 23, 42);\">The file (1) Behavioral data presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), id (subject number), age (subject age), sex (1 is male, 2 is female), and different experimental conditions (direct, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction times.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</span></p><p><span style=\"color: rgb(15, 23, 42);\">File(3) </span>Includes EEG data from the N170, ADAN, and LPP mentioned in the manuscript and the unmentioned P200. This included data on peak and latency of N170, ADAN, and P200 in direct/avert/inverted/arrow/<span style=\"color: rgb(15, 23, 42);\">consistent and inconsistent</span> with arrows and Grand-averaged ERP waveforms of 400-650ms (LPPs) for H (high social competence)/L (low social competence).</p><p><span style=\"color: rgb(15, 23, 42);\">File(4) </span>Includes two tables and four figures inserted in the manuscript with their titles and related notes.</p>",
-    "authors": [
-      {
-        "name_en": "Yan Zhixiong",
-        "name_zh": "Yan Zhixiong",
-        "email": "yanzx@nnnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Nanning Normal University",
-            "name_zh": "Nanning Normal University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "gaze-cueing ",
-      "interpersonal competence ",
-      "N170 ",
-      "anterior directing attention negativity ",
-      "late positive potential ",
-      "event-related potentials ",
-      "ERPs"
-    ],
-    "keywords_zh": [
-      "gaze-cueing ",
-      "interpersonal competence ",
-      "N170 ",
-      "anterior directing attention negativity ",
-      "late positive potential ",
-      "event-related potentials ",
-      "ERPs"
-    ],
-    "publication_date": "2023-11-13T11:18:20.674Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 4732.03,
-    "file_size_bytes": 4961893877,
-    "download_count": 14,
-    "visit_count": 291,
-    "url": "https://www.scidb.cn/en/detail?id=653ba4bf844a5137567bc803",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "686751b46501574b34a055d9",
-    "doi": "10.57760/sciencedb.12128",
-    "cstr": "31253.11.sciencedb.12128",
-    "pid": "",
-    "title": "CuBiAAD-v2",
-    "title_en": "CuBiAAD-v2",
-    "title_zh": "CuBiAAD-v2",
-    "description": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "description_en": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "description_zh": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "authors": [
-      {
-        "name_en": "Keren Shi",
-        "name_zh": "Keren Shi",
-        "email": "skr@alu.uestc.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "University of Electronic Science and Technology of China",
-            "name_zh": "电子科技大学",
-            "ror_id": "https://ror.org/04qr3zq92"
-          }
-        ]
-      },
-      {
-        "name_en": "Xue Yuan",
-        "name_zh": "Xue Yuan",
-        "email": "yuanxue_@stu.scu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "West China Hospital of Sichuan University",
-            "name_zh": "West China Hospital of Sichuan University",
-            "ror_id": "https://ror.org/007mrxy13"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Liu",
-        "name_zh": "Xu Liu",
-        "email": "liuxu_007@stu.scu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "West China Hospital of Sichuan University",
-            "name_zh": "West China Hospital of Sichuan University",
-            "ror_id": "https://ror.org/007mrxy13"
-          }
-        ]
-      },
-      {
-        "name_en": "Ruiting Dai",
-        "name_zh": "Ruiting Dai",
-        "email": "rtdai@uestc.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "University of Electronic Science and Technology of China",
-            "name_zh": "电子科技大学",
-            "ror_id": "https://ror.org/04qr3zq92"
-          }
-        ]
-      },
-      {
-        "name_en": "Na Li",
-        "name_zh": "Na Li",
-        "email": "lina56@stu.scu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "West China Hospital of Sichuan University",
-            "name_zh": "West China Hospital of Sichuan University",
-            "ror_id": "https://ror.org/007mrxy13"
-          }
-        ]
-      },
-      {
-        "name_en": "Yunfa Fu",
-        "name_zh": "Yunfa Fu",
-        "email": "fyf@ynu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Kunming University of Science and Technology",
-            "name_zh": "昆明理工大学",
-            "ror_id": "https://ror.org/00xyeez13"
-          }
-        ]
-      },
-      {
-        "name_en": "Ning Jiang",
-        "name_zh": "Ning Jiang",
-        "email": "jiangning21@wchscu.cn",
-        "affiliations": [
-          {
-            "name_en": "West China Hospital of Sichuan University",
-            "name_zh": "West China Hospital of Sichuan University",
-            "ror_id": "https://ror.org/007mrxy13"
-          }
-        ]
-      },
-      {
-        "name_en": "Jiayuan He",
-        "name_zh": "Jiayuan He",
-        "email": "jiayuan.he@wchscu.cn",
-        "affiliations": [
-          {
-            "name_en": "West China Hospital of Sichuan University",
-            "name_zh": "West China Hospital of Sichuan University",
-            "ror_id": "https://ror.org/007mrxy13"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "BCI",
-      "EEG",
-      "fnirs",
-      "AAD",
-      "bimodal"
-    ],
-    "keywords_zh": [
-      "BCI",
-      "EEG",
-      "fnirs",
-      "AAD",
-      "bimodal"
-    ],
-    "publication_date": "2024-08-23T06:44:28.631Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 2922.22,
-    "file_size_bytes": 3064171934,
-    "download_count": 389,
-    "visit_count": 1162,
-    "url": "https://www.scidb.cn/en/detail?id=686751b46501574b34a055d9",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "65d7ea38e07c37345b652b55",
-    "doi": "10.57760/sciencedb.13699",
-    "cstr": "31253.11.sciencedb.13699",
-    "pid": "",
-    "title": "Behavior and ERP data of geometric shapes and social role priming effects based on the global-local paradigm",
-    "title_en": "Behavior and ERP data of geometric shapes and social role priming effects based on the global-local paradigm",
-    "title_zh": "基于整体——局部范式的图形与社会角色启动效应的行为及ERP数据",
-    "description": "<p>EEG data are recorded using the EEG system produced by the American EGI company, utilizing a 64-channel electrode cap extended from the international 10-20 system. The Cz electrode site serves as the online reference electrode, with a sampling rate of 1000Hz, and all electrode impedances are maintained below 50kΩ. This experiment used real-time recording of continuous EEG data, which was subsequently analyzed offline by the EEGlab toolbox based on Matlab R2019a software (Delorme &amp; Makeig, 2004) .</p><p>EEG data were analyzed using EEGlab with binaural mastoid as a re-reference, and offline processing was performed using 0.1-30 Hz bandpass filtering. Artifacts such as electro-oculogram, electro-myography, and drift were removed using independent component analysis, and trials with amplitudes exceeding a threshold of ±100 μV were automatically rejected. Data segmentation is conducted over a period spanning 200 ms before stimulus presentation and 800 ms post-presentation, with baseline correction applied using the average amplitude of the 200 ms pre-stimulus period. The 800 ms following stimulus onset is utilized for the time-course analysis of ERPs. Signals from 9 electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4) were taken for superimposed averaging according to a previous study (Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; Zhao Guangping et al., 2023).&nbsp;Finally, the EEG data are analyzed using repeated-measures Analysis of Variance (ANOVA).</p><p><br></p><p>In the behavioral data, the \"age\" column is highlighted in yellow to indicate male gender.</p><p><br></p><p><br></p><p><br></p><p>In ERP data, sort according to the order of n170, p200, n400, as well as the two conditions in the paper</p>",
-    "description_en": "<p>EEG data are recorded using the EEG system produced by the American EGI company, utilizing a 64-channel electrode cap extended from the international 10-20 system. The Cz electrode site serves as the online reference electrode, with a sampling rate of 1000Hz, and all electrode impedances are maintained below 50kΩ. This experiment used real-time recording of continuous EEG data, which was subsequently analyzed offline by the EEGlab toolbox based on Matlab R2019a software (Delorme &amp; Makeig, 2004) .</p><p>EEG data were analyzed using EEGlab with binaural mastoid as a re-reference, and offline processing was performed using 0.1-30 Hz bandpass filtering. Artifacts such as electro-oculogram, electro-myography, and drift were removed using independent component analysis, and trials with amplitudes exceeding a threshold of ±100 μV were automatically rejected. Data segmentation is conducted over a period spanning 200 ms before stimulus presentation and 800 ms post-presentation, with baseline correction applied using the average amplitude of the 200 ms pre-stimulus period. The 800 ms following stimulus onset is utilized for the time-course analysis of ERPs. Signals from 9 electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4) were taken for superimposed averaging according to a previous study (Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; Zhao Guangping et al., 2023).&nbsp;Finally, the EEG data are analyzed using repeated-measures Analysis of Variance (ANOVA).</p><p><br></p><p>In the behavioral data, the \"age\" column is highlighted in yellow to indicate male gender.</p><p><br></p><p><br></p><p><br></p><p>In ERP data, sort according to the order of n170, p200, n400, as well as the two conditions in the paper</p>",
-    "description_zh": "<p>采用美国 EGI 公司生产的EEG系统记录与分析数据，按国际10−20系统扩展的64导电极帽在线记录EEG数据。以Cz电极点为在线参考电极，采样率为1000Hz，所有电极阻抗维持在50kΩ以下。本实验采用实时记录连续脑电数据，随后通过基于Matlab R2019a软件的EEGlab工具包进行离线分析(Delorme &amp; Makeig, 2004)。</p><p>使用EEGlab对脑电数据进行分析，以双耳乳突作为重参考，离线处理采用0.1~30Hz带通滤波。使用独立成分分析去除眼电、肌电、漂移等伪迹，自动剔除振幅超过±100μV的试次。以刺激呈现前200ms和呈现后800ms进行分段，并以刺激前200ms的平均波幅进行基线校正，刺激后800ms为ERPs分析时程。根据前人研究采取了9个电极(F3、Fz、F4、C3、Cz、C4、P3、Pz、P4)的信号进行叠加平均(Gray et al., 2004; Montalan et al., 2008; Wang et al., 2021; 赵广平 等人, 2023)。最后采用重复测量方差分析对脑电数据进行分析。</p><p><br></p><p>行为数据中，“age”一列，标黄表示男性。</p><p>erp数据中，按照n170，p200，n400的顺序，以及论文中两个条件而排序</p>",
-    "authors": [
-      {
-        "name_en": "Guo Yi'an",
-        "name_zh": "郭易安",
-        "email": "189047937@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Zhangzhou Normal University",
-            "name_zh": "闽南师范大学",
-            "ror_id": "https://ror.org/02vj1vm13"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "response time",
-      " ERP",
-      "matlab"
-    ],
-    "keywords_zh": [
-      "反应时",
-      "ERP",
-      "matlab"
-    ],
-    "publication_date": "2023-11-28T04:24:51.434Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 0.09,
-    "file_size_bytes": 97704,
-    "download_count": 16,
-    "visit_count": 216,
-    "url": "https://www.scidb.cn/en/detail?id=65d7ea38e07c37345b652b55",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6863bdc5a7d5e55cbdbbbfab",
-    "doi": "10.57760/sciencedb.27389",
-    "cstr": "31253.11.sciencedb.27389",
-    "pid": "",
-    "title": "CuBiAAD",
-    "title_en": "CuBiAAD",
-    "title_zh": "CuBiAAD",
-    "description": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "description_en": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "description_zh": "<p>Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 participants, functional near-infrared spectroscopy (fNIRS) data from 10 participants, and all the audio files played during the experiment. To the best of our knowledge, this was the first multimodal dataset based on auditory attention, and it was currently the dataset with the largest number of participants. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI).</p>",
-    "authors": [
-      {
-        "name_en": "Keren Shi",
-        "name_zh": "Keren Shi",
-        "email": "skr@alu.uestc.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Sichuan University",
-            "name_zh": "Sichuan University",
-            "ror_id": "https://ror.org/011ashp19"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "AAD",
-      "fNIRS"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "AAD",
-      "fNIRS"
-    ],
-    "publication_date": "2025-07-11T03:19:57.720Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 2922.22,
-    "file_size_bytes": 3064171934,
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-    "visit_count": 97,
-    "url": "https://www.scidb.cn/en/detail?id=6863bdc5a7d5e55cbdbbbfab",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6627dd403e08f2716205c955",
-    "doi": "10.57760/sciencedb.18350",
-    "cstr": "31253.11.sciencedb.18350",
-    "pid": "",
-    "title": "Prestimulus power and phase of α and β influence sensitivity and criterion",
-    "title_en": "Prestimulus power and phase of α and β influence sensitivity and criterion",
-    "title_zh": "刺激前α以及β频段的能量与相位对感受性和判断标准的预测作用",
-    "description": "<p>The experimental material was sinusoidal Gabor patch with a Michelson contrast ratio of 0.03 and a vertical tilt of 45° to the left or right. Blank stimulus was no target. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m<sup>2</sup>. The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen was 1024x768 pixels, and the screen refresh rate was 60Hz (1 refresh cycle was about 17ms). The participant was about 80cm away from the screen. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_en": "<p>The experimental material was sinusoidal Gabor patch with a Michelson contrast ratio of 0.03 and a vertical tilt of 45° to the left or right. Blank stimulus was no target. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m<sup>2</sup>. The experiments were conducted in a quiet, dimly lit EEG laboratory. Experimental stimuli are presented in E-Prime 2.0 software. The resolution of the screen was 1024x768 pixels, and the screen refresh rate was 60Hz (1 refresh cycle was about 17ms). The participant was about 80cm away from the screen. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_zh": "<p>实验材料为正弦曲线<em>Gabor patch</em>，迈克尔逊对比度为0.03，垂直向左或者向右倾斜45°。空白刺激即为无目标刺激。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕80cm左右。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
-    "authors": [
-      {
-        "name_en": "li xiao xiao",
-        "name_zh": "李笑笑",
-        "email": "1421269185@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Tianjin Normal University",
-            "name_zh": "天津师范大学",
-            "ror_id": "https://ror.org/05x2td559"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "alpha",
-      "beta",
-      "visual awareness"
-    ],
-    "keywords_zh": [
-      "α频段",
-      "β频段",
-      "视觉意识"
-    ],
-    "publication_date": "2024-06-17T16:19:07.493Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 26860.94,
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-    "visit_count": 707,
-    "url": "https://www.scidb.cn/en/detail?id=6627dd403e08f2716205c955",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68db287a19a491453e1c15ac",
-    "doi": "10.57760/sciencedb.psych.00769",
-    "cstr": "31253.11.sciencedb.psych.00769",
-    "pid": "",
-    "title": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
-    "title_en": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
-    "title_zh": "alpha frequency transcranial alternating current stimulation in the parieto-occipital region",
-    "description": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
-    "description_en": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
-    "description_zh": "<p>we employed a within-participant design using EEG and the Alternative Uses Task (AUT). 28 participants completed two experimental sessions—one with sham stimulation and one with α-tACS—separated by a 24 ~ 48 h interval. In each session, participants performed the AUT immediately</p><p>following stimulation.</p>",
-    "authors": [
-      {
-        "name_en": "Chunlei Liu",
-        "name_zh": "Chunlei Liu",
-        "email": "liuchunlei@qfnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Qufu Normal University",
-            "name_zh": "Qufu Normal University"
-          }
-        ]
-      },
-      {
-        "name_en": "Runze Zhou",
-        "name_zh": "Runze Zhou",
-        "email": "271324336@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Qufu Normal University",
-            "name_zh": "Qufu Normal University"
-          },
-          {
-            "name_en": "Southwest University",
-            "name_zh": "西南大学",
-            "ror_id": "https://ror.org/01kj4z117"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      " α-tACS",
-      "creative thinking",
-      "Alternative Uses Task (AUT)"
-    ],
-    "keywords_zh": [
-      " α-tACS",
-      "creative thinking",
-      "Alternative Uses Task (AUT)"
-    ],
-    "publication_date": "2025-10-04T00:33:22.455Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 863.25,
-    "file_size_bytes": 905184609,
-    "download_count": 208,
-    "visit_count": 76,
-    "url": "https://www.scidb.cn/en/detail?id=68db287a19a491453e1c15ac",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6862a43c7fbf00385d589248",
-    "doi": "10.57760/sciencedb.27339",
-    "cstr": "31253.11.sciencedb.27339",
-    "pid": "",
-    "title": "UNITE example dataset",
-    "title_en": "UNITE example dataset",
-    "title_zh": "UNITE example dataset",
-    "description": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">This dataset includes neuroimaging and clinical data from&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">three patients with stroke</strong><span style=\"color: rgb(0, 0, 0);\">, collected as part of the UNITE study. All data are organized according to the&nbsp;</span><strong style=\"color: rgb(0, 0, 0);\">Brain Imaging Data Structure (BIDS)</strong><span style=\"color: rgb(0, 0, 0);\">&nbsp;format to facilitate reproducibility and standardized processing. The dataset contains T1-weighted structural MRI and resting-state fMRI for each subject.</span></p>",
-    "authors": [
-      {
-        "name_en": "Wei Zhang",
-        "name_zh": "Wei Zhang",
-        "email": "2101111787@stu.pku.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory, Beijing, China",
-            "name_zh": "Changping Laboratory, Beijing, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Shenshen Li",
-        "name_zh": "Shenshen Li",
-        "email": "ssli@pku.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          },
-          {
-            "name_en": "College of Future Technology, Peking University",
-            "name_zh": "College of Future Technology, Peking University"
-          }
-        ]
-      },
-      {
-        "name_en": "Ning An",
-        "name_zh": "Ning An",
-        "email": "anning@cpl.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          }
-        ]
-      },
-      {
-        "name_en": "Jianting Huang",
-        "name_zh": "Jianting Huang",
-        "email": "jianting.huang@pku.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          }
-        ]
-      },
-      {
-        "name_en": "Danhong Wang",
-        "name_zh": "Danhong Wang",
-        "email": "wangdanhong@cpl.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          }
-        ]
-      },
-      {
-        "name_en": "Jianxun Ren",
-        "name_zh": "Jianxun Ren",
-        "email": "davidren555@outlook.com",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          }
-        ]
-      },
-      {
-        "name_en": "Hesheng Liu",
-        "name_zh": "Hesheng Liu",
-        "email": "liuhesheng@cpl.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Changping Laboratory",
-            "name_zh": "Changping Laboratory"
-          },
-          {
-            "name_en": "Biomedical Pioneering Innovation Center, Peking University",
-            "name_zh": "Biomedical Pioneering Innovation Center, Peking University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "stroke ",
-      "aphasia",
-      "cognition",
-      "motor"
-    ],
-    "keywords_zh": [
-      "stroke ",
-      "aphasia",
-      "cognition",
-      "motor"
-    ],
-    "publication_date": "2025-07-08T03:02:42.167Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 322.31,
-    "file_size_bytes": 337968898,
-    "download_count": 8,
-    "visit_count": 285,
-    "url": "https://www.scidb.cn/en/detail?id=6862a43c7fbf00385d589248",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "648dc7c1cd978c23074a645f",
-    "doi": "10.57760/sciencedb.09015",
-    "cstr": "31253.11.sciencedb.09015",
-    "pid": "",
-    "title": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: Evidence from event-related potentials",
-    "title_en": "Temporal dynamics of gaze-cueing attention among distinct interpersonal competence groups: Evidence from event-related potentials",
-    "title_zh": "在不同水平社交能力人群中眼部注视线索的事件相关电位数据集",
-    "description": "<p>This data set is the behavioral and Electrophysiology data of eye gaze cue experiment in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes two files, namely (1) Behavioral date: subjects' behavioral data results and (2) EEG date: subjects' EEG raw data.&nbsp;The file (1) Behavioral date presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), number (subject number), age (subject age), gender (1 is male, 2 is female), and different experimental conditions (direct vision, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction time under that condition.&nbsp;File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</p>",
-    "description_en": "<p>This data set is the behavioral and Electrophysiology data of eye gaze cue experiment in people with different levels of social competence. Using the point detection paradigm and Evoked potential technology, we explored the temporal characteristics of college students' eye gaze cue processing. The data set includes two files, namely (1) Behavioral date: subjects' behavioral data results and (2) EEG date: subjects' EEG raw data.&nbsp;The file (1) Behavioral date presents the behavioral data results of the participants, including 12 horizontal rows and 43 vertical columns. In the first horizontal row, we list the groups of participants (1 is the high social ability group, 2 is the low social ability group), number (subject number), age (subject age), gender (1 is male, 2 is female), and different experimental conditions (direct vision, averted, inverted, arrow, averted consistent/inconsistent, arrow consistent/inconsistent). The values in the vertical column under each condition are the participants' reaction time under that condition.&nbsp;File (2) EEG date is the original Electrophysiology data of the experiment. There are a total of 132 projects in the document. The items beginning with H are participants in the high social ability group, while the items beginning with L are participants in the low social ability group.&nbsp;</p>",
-    "description_zh": "<p>该数据集是在不同水平社交能力人群中眼部注视线索实验的行为和电生理学数据。我们采用点探测范式和事件相关电位技术，探索了大学生在眼部注视线索处理上的时间特征。数据集包括两个文件，分别是（1）Behavioral date：被试的行为数据结果和（2）EEG date：被试的EEG原始数据。</p><p>文件（1）Behavioral date呈现的是被试的行为数据结果，包含12横行和43竖列。在第一横行中我们列明了被试的分组group（1为高社交能力组，2为低社交能力组）、number（被试编号）、age（被试年龄）、gender（1为性别男，2为性别女）和不同的实验条件（直视、斜视、倒置、箭头、斜视一致/不一致、箭头一致/不一致）等，每个条件下的竖列中的数值为被试在该条件下的反应时。</p><p>文件（2）EEG date为实验的电生理学原始数据。文件中共132个项目。其中H开头的项目为高社交能力组的被试，L开头的为低社交能力组的被试。</p>",
-    "authors": [
-      {
-        "name_en": "shanlinglv",
-        "name_zh": "shanlinglv",
-        "email": "lvshanling@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Nanning Normal University",
-            "name_zh": "南宁师范大学",
-            "ror_id": "https://ror.org/04dx82x73"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "gaze-cueing",
-      "interpersonal competence",
-      "N170",
-      "anterior directing attention negativity",
-      "late positive potential",
-      "event-related potentials",
-      "ERPs"
-    ],
-    "keywords_zh": [
-      "眼部线索",
-      "社交能力",
-      "N170",
-      "ADAN",
-      "晚期正电位",
-      "事件相关电位"
-    ],
-    "publication_date": "2023-06-26T07:53:20.424Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 4730.44,
-    "file_size_bytes": 4960231021,
-    "download_count": 29,
-    "visit_count": 351,
-    "url": "https://www.scidb.cn/en/detail?id=648dc7c1cd978c23074a645f",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67c5b86ecfa1d066cfad6fd8",
-    "doi": "10.57760/sciencedb.21680",
-    "cstr": "31253.11.sciencedb.21680",
-    "pid": "",
-    "title": "Effect of Color Hue on the Experience of Movement Flow",
-    "title_en": "Effect of Color Hue on the Experience of Movement Flow",
-    "title_zh": "The Effect of Color Hue on the Experience of Movement Flow",
-    "description": "<p>The experiments were conducted in the laboratory using the motion of a treadmill to detect the state of the motion flow. A tablet screen placed on the treadmill displays one of the three typical and often studied color progress bars (red, yellow, and green). A combination of subjective and objective measurements was used in the experiment. Subjective experience was measured using the Simplified Flow State Scale-2 (S FSS-2). Objective data came from participants' EEG brain waves. One-way repeated measures were used for the within-group experiment. Participants were asked to visit the laboratory three times under different conditions, each time walking briskly for 10 minutes. To minimize the effects of fatigue and circadian rhythms, each participant was scheduled to walk at the same time on a different day. The experimental order of the different participants was based on a Latin square design.</p>",
-    "description_en": "<p>The experiments were conducted in the laboratory using the motion of a treadmill to detect the state of the motion flow. A tablet screen placed on the treadmill displays one of the three typical and often studied color progress bars (red, yellow, and green). A combination of subjective and objective measurements was used in the experiment. Subjective experience was measured using the Simplified Flow State Scale-2 (S FSS-2). Objective data came from participants' EEG brain waves. One-way repeated measures were used for the within-group experiment. Participants were asked to visit the laboratory three times under different conditions, each time walking briskly for 10 minutes. To minimize the effects of fatigue and circadian rhythms, each participant was scheduled to walk at the same time on a different day. The experimental order of the different participants was based on a Latin square design.</p>",
-    "description_zh": "<p>实验在实验室中进行，利用跑步机的运动来检测运动流的状态。放置在跑步机上的平板电脑屏幕显示三种典型的、经常被研究的颜色进度条（红、黄、绿）之一。实验中采用了主观和客观测量相结合的方法。主观体验是使用简化流动状态量表-2（S FSS-2）来测量的。客观数据来自参与者的脑电图脑电波。组内实验采用了单因子重复测量法。要求参与者在不同条件下参观实验室三次，每次快走 10 分钟。为了减少疲劳和昼夜节律的影响，每位参与者都被安排在不同一天的同一时间进行步行。不同参与者的实验顺序采用拉丁方阵设计。</p>",
-    "authors": [
-      {
-        "name_en": "Huang Xueqin",
-        "name_zh": "黄雪芹",
-        "email": "1022201075@tju.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Tianjin University",
-            "name_zh": "Tianjin University",
-            "ror_id": "https://ror.org/012tb2g32"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "progress bars",
-      "flow experience",
-      "EEG",
-      " color hue"
-    ],
-    "keywords_zh": [
-      "color hue",
-      "movement flow",
-      "EEG",
-      "flow experience",
-      "progress bars"
-    ],
-    "publication_date": "2025-04-14T02:48:01.071Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=67c5b86ecfa1d066cfad6fd8",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "63145e688608590738eb4f6c",
-    "doi": "10.57760/sciencedb.j00052.00013",
-    "cstr": "31253.11.sciencedb.j00052.00013",
-    "pid": "21.86116.6/sciencedb.j00052.00013",
-    "title": "Acute psychological stress impairs attention disengagement toward threat-related stimuli",
-    "title_en": "Acute psychological stress impairs attention disengagement toward threat-related stimuli",
-    "title_zh": "急性应激损害对威胁刺激的注意解除",
-    "description": "<p>This repository contains data and code for the paper on Acta Psychologica Sinica entitled \"Acute psychological stress impairs attention disengagement toward threat-related stimuli\".</p>",
-    "description_en": "<p>This repository contains data and code for the paper on Acta Psychologica Sinica entitled \"Acute psychological stress impairs attention disengagement toward threat-related stimuli\".</p>",
-    "description_zh": "<p>本数据集为2020年1月发表在《心理学报》的论文原始数据。主要有以下内容：(1)基于E-prime 2.0编制和运行的点探测任务程序脚本;(2)EEG原始数据和分析脚本。</p>",
-    "authors": [
-      {
-        "name_en": "Luo Yu",
-        "name_zh": "罗禹",
-        "email": "yuluo@gznu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Nian Jingqing",
-        "name_zh": "念靖晴",
-        "email": "nianjingqing@126.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Bao Wei",
-        "name_zh": "鲍未",
-        "email": "baoweibow@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhang Jingjing",
-        "name_zh": "张静静",
-        "email": "583187483@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhao Shouying",
-        "name_zh": "赵守盈",
-        "email": "zhaoshouying@126.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Pan Yun",
-        "name_zh": "潘运",
-        "email": "Panyun129@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Shuang",
-        "name_zh": "许爽",
-        "email": "1162127430@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhang Yu",
-        "name_zh": "张禹",
-        "email": "yuzhang331@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "acute stress",
-      "attention engagement",
-      "attention disengagement",
-      "N2pc",
-      "SPCN"
-    ],
-    "keywords_zh": [
-      "急性应激",
-      "注意定向",
-      "注意解除",
-      "N2pc",
-      "SPCN"
-    ],
-    "publication_date": "2022-08-30T07:36:03.881Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 18427.95,
-    "file_size_bytes": 19323102333,
-    "download_count": 491,
-    "visit_count": 730,
-    "url": "https://www.scidb.cn/en/detail?id=63145e688608590738eb4f6c",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6625e721c70b375c9c533d5b",
-    "doi": "10.57760/sciencedb.17657",
-    "cstr": "31253.11.sciencedb.17657",
-    "pid": "",
-    "title": "Electrophysiological study of social anxiety levels on eye cue processing in the Trust Game paradigm",
-    "title_en": "Electrophysiological study of social anxiety levels on eye cue processing in the Trust Game paradigm",
-    "title_zh": "信任博弈范式下社交焦虑水平对眼部线索加工的脑电研究数据集",
-    "description": "<p>This dataset is behavioral and electrophysiological data from experiments using eye cue material in a trust game paradigm among individuals with different levels of social anxiety. This study explored the temporal characteristics of eye cue processing in college students with different levels of social anxiety using the Trust Game paradigm and event-related potential techniques. The dataset consists of three files, namely (1) behavioral results: subjects' behavioral data results and (2) EEG data: subjects' EEG raw data.</p><p>File (1) Behavioral Results presents the behavioral data results of the subjects and contains 8 horizontal rows and 469 vertical columns. In the first horizontal row the subjects' anxiety level grouping (high anxiety group and low anxiety group), subject number, ocular cue condition (all conditions, straight anger, squinted anger, straight sadness, squinted sadness, straight calmness, squinted calmness, straight happiness, squinted happiness), number of investments (i.e., number of times the subject chose to trust), number of retentions (i.e., number of times the subject chose to distrust), total (the total number of choices the subject made during the experiment), and reaction time (the total number of choices the subject total number of choices made by the subjects in the experiment), reaction time and trust rate (calculated as the number of investments/total).</p><p>File (2) EEG date is the electrophysiological raw data of the experiment. There are 156 items in the file. The items beginning with H are for subjects in the high social anxiety group, and those beginning with L are for subjects in the low social anxiety group.</p>",
-    "description_en": "<p>This dataset is behavioral and electrophysiological data from experiments using eye cue material in a trust game paradigm among individuals with different levels of social anxiety. This study explored the temporal characteristics of eye cue processing in college students with different levels of social anxiety using the Trust Game paradigm and event-related potential techniques. The dataset consists of three files, namely (1) behavioral results: subjects' behavioral data results and (2) EEG data: subjects' EEG raw data.</p><p>File (1) Behavioral Results presents the behavioral data results of the subjects and contains 8 horizontal rows and 469 vertical columns. In the first horizontal row the subjects' anxiety level grouping (high anxiety group and low anxiety group), subject number, ocular cue condition (all conditions, straight anger, squinted anger, straight sadness, squinted sadness, straight calmness, squinted calmness, straight happiness, squinted happiness), number of investments (i.e., number of times the subject chose to trust), number of retentions (i.e., number of times the subject chose to distrust), total (the total number of choices the subject made during the experiment), and reaction time (the total number of choices the subject total number of choices made by the subjects in the experiment), reaction time and trust rate (calculated as the number of investments/total).</p><p>File (2) EEG date is the electrophysiological raw data of the experiment. There are 156 items in the file. The items beginning with H are for subjects in the high social anxiety group, and those beginning with L are for subjects in the low social anxiety group.</p>",
-    "description_zh": "<p>该数据集是在不同水平社交焦虑水平个体中使用眼部线索材料在信任博弈范式下进行实验的行为和电生理学数据。本研究采用信任博弈范式和事件相关电位技术，探索了不同社交焦虑水平大学生在眼部线索处理上的时间特征。数据集包括三个文件，分别是（1）行为结果：被试的行为数据结果和（2）EEG date：被试的EEG原始数据。</p><p>文件（1）行为结果呈现的是被试的行为数据结果，包含8横行和469竖列。在第一横行中列明了被试的焦虑水平分组（高焦虑组和低焦虑组）、被试编号、眼部线索条件（所有条件、直视愤怒、斜视愤怒、直视悲伤、斜视悲伤、直视平静、斜视平静、直视快乐、斜视快乐）、投资数（即被试选择信任的次数）、保留数（即被试选择不信任的次数）、总计（被试在实验中做出的选择总数）、反应时和信任率（计算方法为投资数/总计）。</p><p>文件（2）EEG date为实验的电生理学原始数据。文件中共156个项目。其中H开头的项目为高社交焦虑组的被试，L开头的为低社交焦虑组的被试。</p>",
-    "authors": [
-      {
-        "name_en": "shanlinglv",
-        "name_zh": "shanlinglv",
-        "email": "461676235@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Nanning Normal University",
-            "name_zh": "南宁师范大学",
-            "ror_id": "https://ror.org/04dx82x73"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "social anxiety",
-      "eye cues",
-      "trust game",
-      "ERP"
-    ],
-    "keywords_zh": [
-      "社交焦虑",
-      "眼部线索",
-      "信任博弈",
-      "事件相关电位"
-    ],
-    "publication_date": "2024-04-10T01:07:57.616Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-ND 4.0",
-    "file_size_mb": 11655.55,
-    "file_size_bytes": 12221734939,
-    "download_count": 59,
-    "visit_count": 895,
-    "url": "https://www.scidb.cn/en/detail?id=6625e721c70b375c9c533d5b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68c3c45626b8ca4876f6e3c3",
-    "doi": "10.57760/sciencedb.27618",
-    "cstr": "31253.11.sciencedb.27618",
-    "pid": "",
-    "title": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
-    "title_en": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
-    "title_zh": "A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language",
-    "description": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(34, 34, 34);\">High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128–256-channel subdural grids and synchronized with the audio; high-gamma activity (70–150 Hz) was derived and down-sampled to 400 Hz. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.</span></p>",
-    "authors": [
-      {
-        "name_en": "Haobo Zhang",
-        "name_zh": "Haobo Zhang",
-        "email": "22301050292@m.fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "HuaShan Hospital",
-            "name_zh": "复旦大学附属华山医院",
-            "ror_id": "https://www.huashan.org.cn/"
-          }
-        ]
-      },
-      {
-        "name_en": "Daohan Zhang",
-        "name_zh": "Daohan Zhang",
-        "email": "20301050208@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "HuaShan Hospital",
-            "name_zh": "复旦大学附属华山医院",
-            "ror_id": "https://www.huashan.org.cn/"
-          }
-        ]
-      },
-      {
-        "name_en": "Jinsong Wu",
-        "name_zh": "Jinsong Wu",
-        "email": "wujinsong@huashan.org.cn",
-        "affiliations": [
-          {
-            "name_en": "HuaShan Hospital",
-            "name_zh": "复旦大学附属华山医院",
-            "ror_id": "https://www.huashan.org.cn/"
-          }
-        ]
-      },
-      {
-        "name_en": "Yuanning Li",
-        "name_zh": "Yuanning Li",
-        "email": "liyn2@shanghaitech.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "ShanghaiTech University",
-            "name_zh": "ShanghaiTech University",
-            "ror_id": "https://ror.org/030bhh786"
-          }
-        ]
-      },
-      {
-        "name_en": "Junfeng Lu",
-        "name_zh": "Junfeng Lu",
-        "email": "junfeng_lu@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "HuaShan Hospital",
-            "name_zh": "复旦大学附属华山医院",
-            "ror_id": "https://www.huashan.org.cn/"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "neural encoding",
-      "cognitive neuroscience",
-      "ECoG",
-      "Mandarin",
-      "speech perception"
-    ],
-    "keywords_zh": [
-      "neural encoding",
-      "cognitive neuroscience",
-      "ECoG",
-      "Mandarin",
-      "speech perception"
-    ],
-    "publication_date": "2025-09-12T03:32:36.797Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 5637.41,
-    "file_size_bytes": 5911250493,
-    "download_count": 1020,
-    "visit_count": 1145,
-    "url": "https://www.scidb.cn/en/detail?id=68c3c45626b8ca4876f6e3c3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "678f973027843a4e4768394b",
-    "doi": "10.57760/sciencedb.20263",
-    "cstr": "31253.11.sciencedb.20263",
-    "pid": "",
-    "title": "ERP and fMRI datasets of addition and subtraction approximation operations",
-    "title_en": "ERP and fMRI datasets of addition and subtraction approximation operations",
-    "title_zh": "加减法近似运算ERP和fMRI数据集",
-    "description": "<p>Data collection period: 2022-2023.</p><p><br></p><p>EEG data recording and processing</p><p><br></p><p>Preprocessing. EEGLAB and ERPLAB toolboxes were used, including (1) loading electrode position files (2) using the mean values of the left and right mastoids (M1, M2 electrodes) as a re-reference; (3) applying 0. 1 Hz high-pass filtering and 30 Hz low-pass filtering to all electrode data; (4) setting up Eventlist in ERPLAB according to different experimental conditions and assigning it to the corresponding Bin; (5) Calculate the weights of artefacts such as corrected electrooculograms using FastICA; (6) Perform automatic bad-conductance detection of the data; (7) Artefact detection; (8) Stacked average the preprocessed EEG data using ERPLAB toolkit to obtain the ERP results.&nbsp;</p><p><br></p><p>ERP data analysis. The ERP results were obtained by overlay averaging the preprocessed EEG data using the ERPLAB toolkit and low-pass filtering the overlay averaged data at 20 Hz. According to topographic maps and previous studies, it was found that attentional shifts on the mental digit line mainly activate the inferior parietal cortex [6,13], so we selected (P3, PZ, P4, PO3, POZ, PO4), and performed 2 (arithmetic operation: subtraction, addition) × 3 (direction of the result: less than, equal to, or greater than) repeated-measures ANOVA, and p-values of the ANOVA were corrected using the Green-house correction, Bonferroni-corrected. Bonferroni-corrected test was used for post hoc multiple comparisons.&nbsp;</p><p><br></p><p>Magnetic resonance data analysis</p><p><br></p><p>Preprocessing. The SPM12 toolbox was used, including: (1) conversion of DICOM data to NIFTI format; (2) time-layer correction of functional image data; (3) head-motion correction; (4) alignment of T1 structural images to the MNI standard space; (5) alignment of structural images to functional images; (6) normalisation of brain imaging data to the standard space by means of the EPI template included in the SPM and voxel resampling to 3 mm × 3 mm × 3 mm; (7) spatial smoothing of the functional image using Gaussian kernel function with FWHM set to 6 mm.&nbsp;</p><p><br></p><p>Analysis of fMRI data. Individual analyses were performed using a generalised linear model (GLM) to construct multiple linear regression matrices. The matrix was designed to include six types of regressions, including additive-less than, additive-equal to, additive-greater than, subtractive-less than, subtractive-equal to, and subtractive-greater than, with the six header The six head parameters were added as covariates in the analyses. Based on the contrast images generated by GLM, a 2 (arithmetic operation: addition, subtraction) × 3 (result direction: less than, equal to, greater than) repeated measures ANOVA was performed using Flexible Factorial Design, with the calibration thresholds set at voxel level p &lt; 0.001 uncorrected, and clump level p &lt; 0.05 FWE corrected. Signal values of significant peak interaction coordinates in ANOVA were extracted by Marsbar and analysed by 2 (arithmetic operations: addition, subtraction) × 3 (direction of results: less than, equal to, greater than) repeated measures ANOVA in SPSS.&nbsp;</p><p><br></p><p>Functional connectivity analyses . The signal values of the significant peak interaction coordinates in the ANOVA were extracted by the CONN toolkit to create spherical seed points with a radius of 6 mm, and their functional connectivity with the whole brain was examined, and the strength of connectivity between the seed points and different brain regions was examined by a 2 (arithmetic operation: addition, subtraction) × 3 (direction of the result: less than, equal to, or greater than) repeated-measurement ANOVA analysis, with the calibration threshold set at the voxel level of p &lt; 0.001 uncorrected, cluster level p &lt; 0.05 FWE corrected.</p><p><br></p><p>EEG and MRI correlation analysis</p><p><br></p><p>Characterisation similarities . This included (1) calculation of Representational Dissimilarity Matrices (RDM) for ERP using a continuous 10ms time window based on preprocessed EEG data; (2) calculation of RDM for fMRI based on preprocessed functional image data by means of a spherical searchlight with a radius of 3mm; and (3) The time points corresponding to the N1, P2, and P3b wave peaks were selected, and the Spearman correlation coefficient was used to construct the RSA characterisation similarity analysis by calculating the correlation between the RDM of each ERP modality and the RDM of the fMRI modality in a spherical searchlight with a radius of 3 mm. For the RSA corresponding to each time point, a temporal activity profile was obtained from the ERP data, and a spatial correlation map was obtained from the fMRI data, and the comparison results were compared. Spatial correlation maps were obtained from ERP data and fMRI data. Significance tests were performed to level the comparison results, and the calibration thresholds were set at p &lt; 0. 001 uncorrected for the voxel level and p &lt; 0. 05 FWE corrected for the cluster level.&nbsp;</p>",
-    "description_en": "<p>Data collection period: 2022-2023.</p><p><br></p><p>EEG data recording and processing</p><p><br></p><p>Preprocessing. EEGLAB and ERPLAB toolboxes were used, including (1) loading electrode position files (2) using the mean values of the left and right mastoids (M1, M2 electrodes) as a re-reference; (3) applying 0. 1 Hz high-pass filtering and 30 Hz low-pass filtering to all electrode data; (4) setting up Eventlist in ERPLAB according to different experimental conditions and assigning it to the corresponding Bin; (5) Calculate the weights of artefacts such as corrected electrooculograms using FastICA; (6) Perform automatic bad-conductance detection of the data; (7) Artefact detection; (8) Stacked average the preprocessed EEG data using ERPLAB toolkit to obtain the ERP results.&nbsp;</p><p><br></p><p>ERP data analysis. The ERP results were obtained by overlay averaging the preprocessed EEG data using the ERPLAB toolkit and low-pass filtering the overlay averaged data at 20 Hz. According to topographic maps and previous studies, it was found that attentional shifts on the mental digit line mainly activate the inferior parietal cortex [6,13], so we selected (P3, PZ, P4, PO3, POZ, PO4), and performed 2 (arithmetic operation: subtraction, addition) × 3 (direction of the result: less than, equal to, or greater than) repeated-measures ANOVA, and p-values of the ANOVA were corrected using the Green-house correction, Bonferroni-corrected. Bonferroni-corrected test was used for post hoc multiple comparisons.&nbsp;</p><p><br></p><p>Magnetic resonance data analysis</p><p><br></p><p>Preprocessing. The SPM12 toolbox was used, including: (1) conversion of DICOM data to NIFTI format; (2) time-layer correction of functional image data; (3) head-motion correction; (4) alignment of T1 structural images to the MNI standard space; (5) alignment of structural images to functional images; (6) normalisation of brain imaging data to the standard space by means of the EPI template included in the SPM and voxel resampling to 3 mm × 3 mm × 3 mm; (7) spatial smoothing of the functional image using Gaussian kernel function with FWHM set to 6 mm.&nbsp;</p><p><br></p><p>Analysis of fMRI data. Individual analyses were performed using a generalised linear model (GLM) to construct multiple linear regression matrices. The matrix was designed to include six types of regressions, including additive-less than, additive-equal to, additive-greater than, subtractive-less than, subtractive-equal to, and subtractive-greater than, with the six header The six head parameters were added as covariates in the analyses. Based on the contrast images generated by GLM, a 2 (arithmetic operation: addition, subtraction) × 3 (result direction: less than, equal to, greater than) repeated measures ANOVA was performed using Flexible Factorial Design, with the calibration thresholds set at voxel level p &lt; 0.001 uncorrected, and clump level p &lt; 0.05 FWE corrected. Signal values of significant peak interaction coordinates in ANOVA were extracted by Marsbar and analysed by 2 (arithmetic operations: addition, subtraction) × 3 (direction of results: less than, equal to, greater than) repeated measures ANOVA in SPSS.&nbsp;</p><p><br></p><p>Functional connectivity analyses . The signal values of the significant peak interaction coordinates in the ANOVA were extracted by the CONN toolkit to create spherical seed points with a radius of 6 mm, and their functional connectivity with the whole brain was examined, and the strength of connectivity between the seed points and different brain regions was examined by a 2 (arithmetic operation: addition, subtraction) × 3 (direction of the result: less than, equal to, or greater than) repeated-measurement ANOVA analysis, with the calibration threshold set at the voxel level of p &lt; 0.001 uncorrected, cluster level p &lt; 0.05 FWE corrected.</p><p><br></p><p>EEG and MRI correlation analysis</p><p><br></p><p>Characterisation similarities . This included (1) calculation of Representational Dissimilarity Matrices (RDM) for ERP using a continuous 10ms time window based on preprocessed EEG data; (2) calculation of RDM for fMRI based on preprocessed functional image data by means of a spherical searchlight with a radius of 3mm; and (3) The time points corresponding to the N1, P2, and P3b wave peaks were selected, and the Spearman correlation coefficient was used to construct the RSA characterisation similarity analysis by calculating the correlation between the RDM of each ERP modality and the RDM of the fMRI modality in a spherical searchlight with a radius of 3 mm. For the RSA corresponding to each time point, a temporal activity profile was obtained from the ERP data, and a spatial correlation map was obtained from the fMRI data, and the comparison results were compared. Spatial correlation maps were obtained from ERP data and fMRI data. Significance tests were performed to level the comparison results, and the calibration thresholds were set at p &lt; 0. 001 uncorrected for the voxel level and p &lt; 0. 05 FWE corrected for the cluster level.&nbsp;</p>",
-    "description_zh": "<p><span style=\"color: rgb(15, 23, 42);\">数据采集时间：2022年-2023年。</span></p><p>脑电数据记录与处理</p><p>预处理. 采用EEGLAB和ERPLAB工具箱, 包括：(1)加载电极位置文件 (2)以左右乳突(M1, M2电极)的平均值作重参考; (3)对所有电极数据进行0. 1Hz高通滤波和30Hz低通滤波; (4)在ERPLAB中根据不同的实验条件设置Eventlist, 并分配给相应的Bin; (5)使用FastICA计算校正眼电等伪迹权重; (6)对数据进行自动坏导检测; (7) 伪迹检测; (8)利用ERPLAB工具包对预处理后的脑电数据进行叠加平均, 得到ERP结果.</p><p>ERP数据分析. 采用ERPLAB工具包对预处理后的脑电数据进行叠加平均, 并对叠加平均后的数据进行20Hz的低通滤波, 得到ERP结果. 根据地形图和以往研究, 发现心理数字线上的注意转移主要激活下顶叶皮层<sup>[6,13]</sup>, 故选取(P3, PZ, P4, PO3, POZ, PO4), 进行2(算术操作：减法、加法)×3(结果方向：小于、等于、大于)重复测量方差分析, 方差分析的<em>p</em>值采用Green-house 矫正, Bonferroni-corrected test被用于事后多重比较.</p><p>磁共振数据分析</p><p>预处理. 采用SPM12工具箱, 包括：(1)将DICOM数据转换为NIFTI格式; (2)<span style=\"color: black;\"> 对</span>功能像数据<span style=\"color: black;\">进行时间层校正</span>; (3)头动校正; (4)<span style=\"color: black;\"> </span>将T1结构像配准到MNI标准空间; (5)<span style=\"color: black;\"> </span>将结构像配准到功能像; (6)<span style=\"color: black;\"> 通过 SPM 自带 EPI 模板, 将脑成像数据归一化到标准空间, 并将体素重采样为3 mm</span> × <span style=\"color: black;\">3 mm</span> × <span style=\"color: black;\">3 mm</span>; (7)采用高斯核函数对功能像进行空间平滑处理, FWHM设置为6 mm.</p><p>fMRI数据分析. 个体分析采用广义线性模型(GLM)构建多重线性回归矩阵. 设计矩阵包括六种类型的回归量, 包括加法—小于、加法—等于、加法—大于、减法—小于、减法—等于、减法—大于, 并将6个头动参数作为协变量加入分析. 基于<span style=\"color: black;\">GLM 产生的 contrast images, </span>采用Flexible Factorial Design进行2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析, 校正阈限设置为体素水平<em>p</em> &lt; 0.001未校正, 团块水平<em>p</em> &lt; 0.05 FWE校正. 通过Marsbar提取方差分析中交互作用显著峰值坐标的信号值, 并在SPSS中进行2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析.</p><p>功能连接分析. 通过CONN工具包提取方差分析中交互作用显著峰值坐标的信号值创建半径为6mm的球形种子点, 考察其与全脑功能连接的情况, 并通过2(算术操作：加法、减法) × 3(结果方向：小于、等于、大于)重复测量方差分析分析考察种子点与不同脑区的连接强度, 校正阈限设置为体素水平<em>p</em> &lt; 0.001未校正, 团块水平<em>p</em> &lt; 0.05 FWE校正.</p><p>脑电、磁共振相关性分析</p><p>表征相似性. 包括：(1) 基于预处理后的脑电数据, 使用连续10ms时间窗口计算ERP的表征相异性矩阵(Representational Dissimilarity Matrices,&nbsp;RDM); (2)基于预处理后功能像数据, 通过半径为3mm的球形探照灯计算fMRI的 RDM; (3)选取N1、P2、P3b波峰所对应的时间点, 采用斯皮尔曼相关系数, 通过计算半径为3mm的球形探照灯内每个 ERP模态的RDM 和 fMRI模态的RDM 间的相关性, 建构RSA表征相似性分析, 对于各时间点对应的RSA, 根据 ERP 数据获得时间活动概况, 根据 fMRI 数据获得空间相关图, 对比较结果进行显著性检验平, 校正阈限设置为体素水平<em>p</em> &lt; 0. 001未校正, 团块水平<em>p</em> &lt; 0. 05 FWE校正.&nbsp;</p>",
-    "authors": [
-      {
-        "name_en": "jia liang zhi",
-        "name_zh": "贾良智",
-        "email": "2674952168@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Yun",
-        "name_zh": "Yun",
-        "email": "panyun129@163.pan",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "functional magnetic resonance imaging",
-      "event-related potential",
-      "representational similarity analysis"
-    ],
-    "keywords_zh": [
-      "功能性核磁成像",
-      "事件相关电位",
-      "表征相似性分析"
-    ],
-    "publication_date": "2025-01-26T12:01:41.653Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=678f973027843a4e4768394b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68cb5fc7dd2f5321a0f253cb",
-    "doi": "10.57760/sciencedb.23231",
-    "cstr": "31253.11.sciencedb.23231",
-    "pid": "",
-    "title": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
-    "title_en": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
-    "title_zh": "EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition",
-    "description": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
-    "description_en": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
-    "description_zh": "<p><span style=\"color: rgb(0, 0, 0);\">We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.</span></p>",
-    "authors": [
-      {
-        "name_en": "Huang Xin",
-        "name_zh": "Huang Xin",
-        "email": "hx11@pku.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Zhejiang University",
-            "name_zh": "Zhejiang University",
-            "ror_id": "https://ror.org/00a2xv884"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "emotion recognition",
-      "Multimodal",
-      "Dataset "
-    ],
-    "keywords_zh": [
-      "emotion recognition",
-      "Multimodal",
-      "Dataset "
-    ],
-    "publication_date": "2025-04-23T00:59:37.630Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 115903.23,
-    "file_size_bytes": 121533347163,
-    "download_count": 14516,
-    "visit_count": 2005,
-    "url": "https://www.scidb.cn/en/detail?id=68cb5fc7dd2f5321a0f253cb",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67175a23c9311b679d0adff0",
-    "doi": "10.57760/sciencedb.j00133.00406",
-    "cstr": "31253.11.sciencedb.j00133.00406",
-    "pid": "",
-    "title": "Enterprise Emergency Incident News Dataset",
-    "title_en": "Enterprise Emergency Incident News Dataset",
-    "title_zh": "企业突发事件新闻数据集",
-    "description": "<p>This dataset consists of three xlsx files:&nbsp;Weibo data.xlsx: The microblog data of the pickled Chinese cabbage event in the earthen pit contains 22303 pieces of data, each of which is a piece of microblog content, including 18 fields: id, bid, user_id, user nickname, microblog body, headline url, publishing location, Aite users, topics, forwarding numbers, comments, likes, publishing time, publishing tools, microblog image url, microblog video url, retweet_id, ip.&nbsp;News. xlsx: news data of the pickled Chinese cabbage event in the earthen pit, including 82 pieces of news data in total, each of which contains 5 fields: title, time, source, link, and news body&nbsp;Train data.xlsx: Enterprise Emergency News Training Dataset, consisting of 9 sub tables, each sub table is a case, and each case contains several news articles. The 9 cases contain a total of 113 news data, and each news article contains 5 fields: title, time, source, link, and news body.&nbsp;</p>",
-    "description_en": "<p>This dataset consists of three xlsx files:&nbsp;Weibo data.xlsx: The microblog data of the pickled Chinese cabbage event in the earthen pit contains 22303 pieces of data, each of which is a piece of microblog content, including 18 fields: id, bid, user_id, user nickname, microblog body, headline url, publishing location, Aite users, topics, forwarding numbers, comments, likes, publishing time, publishing tools, microblog image url, microblog video url, retweet_id, ip.&nbsp;News. xlsx: news data of the pickled Chinese cabbage event in the earthen pit, including 82 pieces of news data in total, each of which contains 5 fields: title, time, source, link, and news body&nbsp;Train data.xlsx: Enterprise Emergency News Training Dataset, consisting of 9 sub tables, each sub table is a case, and each case contains several news articles. The 9 cases contain a total of 113 news data, and each news article contains 5 fields: title, time, source, link, and news body.&nbsp;</p>",
-    "description_zh": "<p>本数据集一个包括三个xlsx文件：</p><p>weibo data.xlsx: 土坑酸菜事件微博数据，包含22303条数据，每条数据为一条微博内容，包含18个字段：id，bid，user_id，用户昵称，微博正文，头条文章url，发布位置，艾特用户，话题，转发数，评论数，点赞数，发布时间，发布工具，微博图片url，微博视频url，retweet_id，ip。</p><p>news.xlsx: 土坑酸菜事件新闻数据，一共包括82条新闻数据，每条数据包含5个字段：标题，时间，来源，链接，新闻正文</p><p>Train data.xlsx：企业突发事件新闻训练数据集，包含9个子表，每个子表为一个案例，每个案例包含新闻若干条，9个案例一共包含113条新闻数据，每条新闻包含5个字段：标题，时间，来源，链接，新闻正文。</p>",
-    "authors": [
-      {
-        "name_en": "19825000530",
-        "name_zh": "19825000530",
-        "email": "2319488315@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Nanjing University of Science and Technology",
-            "name_zh": "南京理工大学",
-            "ror_id": "https://ror.org/00xp9wg62"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Emergency",
-      "Incident",
-      "Dataset"
-    ],
-    "keywords_zh": [
-      "企业突发事件",
-      "新闻数据集",
-      "微博数据"
-    ],
-    "publication_date": "2024-12-12T09:02:42.107Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODbL",
-    "file_size_mb": 7.22,
-    "file_size_bytes": 7571617,
-    "download_count": 5,
-    "visit_count": 913,
-    "url": "https://www.scidb.cn/en/detail?id=67175a23c9311b679d0adff0",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6389603fab13cc64ab836201",
-    "doi": "10.11922/sciencedb.00740",
-    "cstr": "31253.11.sciencedb.00740",
-    "pid": "21.86116.6/sciencedb.00740",
-    "title": "Imaging Chinese Young Brains (I See Your Brain)",
-    "title_en": "Imaging Chinese Young Brains (I See Your Brain)",
-    "title_zh": "Imaging Chinese Young Brains (I See Your Brain)",
-    "description": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
-    "description_en": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
-    "description_zh": "<p>This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.</p><p>This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.</p><p>We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.</p>",
-    "authors": [
-      {
-        "name_en": "Peng Gao",
-        "name_zh": "Peng Gao",
-        "email": "smooo1973@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Collage of Information and Computer, Taiyuan University of Technology, Taiyuan, China",
-            "name_zh": "Collage of Information and Computer, Taiyuan University of Technology, Taiyuan, China",
-            "ror_id": "https://ror.org/03kv08d37"
-          }
-        ]
-      },
-      {
-        "name_en": "Hao-Ming Dong",
-        "name_zh": "Hao-Ming Dong",
-        "email": "donghaomingnd@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
-            "name_zh": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
-            "ror_id": "https://ror.org/03j7v5j15"
-          },
-          {
-            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
-            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Yin-Shan Wang",
-        "name_zh": "Yin-Shan Wang",
-        "email": "c137wys@bnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
-            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
-            "ror_id": "https://ror.org/022k4wk35"
-          },
-          {
-            "name_en": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China",
-            "name_zh": "Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing 100101, China"
-          }
-        ]
-      },
-      {
-        "name_en": "Chun-Shui Yu",
-        "name_zh": "Chun-Shui Yu",
-        "email": "chunshuiyu@tmu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Tianjin Key Laboratory of Functional Imaging, Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China",
-            "name_zh": "Tianjin Key Laboratory of Functional Imaging, Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China"
-          },
-          {
-            "name_en": " CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China",
-            "name_zh": " CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China",
-            "ror_id": "https://ror.org/00vpwhm04"
-          }
-        ]
-      },
-      {
-        "name_en": "Xi-Nian Zuo",
-        "name_zh": "Xi-Nian Zuo",
-        "email": "xinian.zuo@bnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
-            "name_zh": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China",
-            "ror_id": "https://ror.org/022k4wk35"
-          },
-          {
-            "name_en": "Department of Psychology, University of CAS, Beijing 100049, China",
-            "name_zh": "Department of Psychology, University of CAS, Beijing 100049, China"
-          },
-          {
-            "name_en": "Institute of Children Health, Changzhou Children’s Hospital, Changzhou 213003, China",
-            "name_zh": "Institute of Children Health, Changzhou Children’s Hospital, Changzhou 213003, China"
-          },
-          {
-            "name_en": "Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China",
-            "name_zh": "Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China"
-          },
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China"
-          },
-          {
-            "name_en": "IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China",
-            "name_zh": "IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China"
-          },
-          {
-            "name_en": "Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China",
-            "name_zh": "Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Brain Imaging",
-      " Open Science",
-      " MRI",
-      " Connectome"
-    ],
-    "keywords_zh": [
-      "Brain Imaging",
-      " Open Science",
-      " MRI",
-      " Connectome"
-    ],
-    "publication_date": "2025-06-18T08:27:32.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "",
-    "file_size_mb": 63492.36,
-    "file_size_bytes": 66576568337,
-    "download_count": 595786,
-    "visit_count": 15472,
-    "url": "https://www.scidb.cn/en/detail?id=6389603fab13cc64ab836201",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68ea45d019a491453e1c19d1",
-    "doi": "10.57760/sciencedb.psych.00786",
-    "cstr": "31253.11.sciencedb.psych.00786",
-    "pid": "",
-    "title": "The Metaphorical Processing of Novel Classifier-Noun Collocations: Evidence from Event-Related Potentials",
-    "title_en": "The Metaphorical Processing of Novel Classifier-Noun Collocations: Evidence from Event-Related Potentials",
-    "title_zh": "量名新奇搭配的隐喻加工机制：来自ERPs的证据",
-    "description": "<p>We employed event-related potentials (ERPs) to investigate the metaphorical processing mechanism of novel classifier-noun collocations and the moderating role of individual print exposure. Using a within-subjects design, 34 native Chinese speakers (25 females; mean age 23.27 years, SD = 1.80) read three types of classifier-noun collocations embedded in sentences: literal collocations (e.g., <em>yidao dianguang</em>, \"a bolt of lightning\"), novel collocations (e.g., <em>yishan dianguang</em>, \"a flash of lightning\"), and anomalous collocations (e.g., <em>yibao dianguang</em>, \"a bag of lightning\"). Ninety experimental sentences were presented using rapid serial visual presentation (RSVP), with each word displayed for 400 ms followed by a 400 ms blank screen. EEG was recorded from 32 scalp electrodes. After the ERP session, participants completed the Author Recognition Test to assess their print exposure.</p>",
-    "description_en": "<p>We employed event-related potentials (ERPs) to investigate the metaphorical processing mechanism of novel classifier-noun collocations and the moderating role of individual print exposure. Using a within-subjects design, 34 native Chinese speakers (25 females; mean age 23.27 years, SD = 1.80) read three types of classifier-noun collocations embedded in sentences: literal collocations (e.g., <em>yidao dianguang</em>, \"a bolt of lightning\"), novel collocations (e.g., <em>yishan dianguang</em>, \"a flash of lightning\"), and anomalous collocations (e.g., <em>yibao dianguang</em>, \"a bag of lightning\"). Ninety experimental sentences were presented using rapid serial visual presentation (RSVP), with each word displayed for 400 ms followed by a 400 ms blank screen. EEG was recorded from 32 scalp electrodes. After the ERP session, participants completed the Author Recognition Test to assess their print exposure.</p>",
-    "description_zh": "<p>\t本研究采用事件相关电位(ERPs)技术，探讨新奇量名搭配的隐喻加工机制及个体阅读量的调节作用。实验采用单因素被试内设计，34名被试阅读三种类型的量名搭配：字面搭配（一道电光）、新奇搭配（一闪电光）和异常搭配（一包电光）。结果显示：(1)在早期加工阶段(300-500ms)，无论阅读量高低，新奇搭配和异常搭配均在额区诱发显著的N400效应，两者无显著差异，表明新奇搭配暂时被视为语义异常；(2)在晚期加工阶段(600-800ms)，阅读量对新奇搭配加工存在调节作用，高阅读量组的新奇搭配诱发显著的晚期负效应，而低阅读量组未见此效应。研究表明，新奇量名搭配的理解涉及两阶段加工：早期语义冲突检测和晚期隐喻映射，后者受个体阅读经验调节。</p>",
-    "authors": [
-      {
-        "name_en": "Xiaodong Xu",
-        "name_zh": "Xiaodong Xu",
-        "email": "412alix@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Nanjing Normal University",
-            "name_zh": "南京师范大学",
-            "ror_id": "https://ror.org/036trcv74"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "classifier-noun collocation",
-      "metaphorical processing",
-      "print exposure",
-      "N400",
-      "late negativity"
-    ],
-    "keywords_zh": [
-      "量名搭配",
-      "隐喻加工",
-      "阅读量",
-      "N400",
-      "晚期负效应"
-    ],
-    "publication_date": "2025-10-13T01:01:31.279Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 990.67,
-    "file_size_bytes": 1038788094,
-    "download_count": 3,
-    "visit_count": 22,
-    "url": "https://www.scidb.cn/en/detail?id=68ea45d019a491453e1c19d1",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "63353de93d498241dcc00903",
-    "doi": "10.57760/sciencedb.o00115.00049",
-    "cstr": "31253.11.sciencedb.o00115.00049",
-    "pid": "21.86116.6/sciencedb.o00115.00049",
-    "title": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
-    "title_en": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
-    "title_zh": "Dataset for \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\"",
-    "description": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
-    "description_en": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
-    "description_zh": "<p>This dataset comprises of the data presented in the publication \"Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing\" (He et al., 2021) and the codes for data analyses.</p><p><br></p><p>Participants were required to perform an attentive multiple object tracking (MOT) task that lasted for 15 s in each trial. We manipulated the levels of attentional load by changing the number of target(s) to track, i.e. 1, 3, or 5 target balls out of 10 balls in total. At the beginning of each trial, target balls among all the balls turned red for 2 s to manifest themselves. After tracking finished, the same amount as the target balls turned red as probes. Either they were exactly the target balls, or one of them was not among the targets. Participants had to determine whether the probes were identical to the target balls or not (2AFC). During tracking, task-irrelevant auditory Oddball distractors (50-ms pure tones played every 600 ms) were presented, with 1000-Hz tones as the standard (80% probability) and 1500-Hz tones as the deviant (20% probability) stimuli.</p><p><br></p><p>The whole experiment consisted of 8 blocks × 12 trials. Participants performed the task under 2 levels (low/high) of monetary reward which was given for right choices. Reward levels were counterbalanced across blocks while attentional load levels were randomized across trials in each block but with equal numbers of trials for each level. Participants were shown the reward level (low or high) at the beginning of each block, and there was feedback after choice made in each trial, including right or wrong (for a right choice its reward would be shown) and the aggregate reward gained in the current block so far. At both reward levels the amount increased as the attentional load, but the amount at the higher level was always larger than at the lower level regardless of the attentional load.</p><p><br></p><p>Throughout the tracking period, participants' EEG signals and pupil dilation data were recorded. EEG signals were then segmented into 600-ms ERP epochs relative to the onset of each tone (-100 ms to +500 ms). We analyzed the MMN and P3a components of the difference wave elicited between standard and deviant tones to investigate the role of reward in the effect of attentional load on task-irrelevant auditory distractor processing.</p><p><br></p><p>For details of the dataset, please see readme.txt in the root directory after unzipping. For details of the study, please refer to:</p><p><br></p><p>He, X., Liu, W., Qin, N., Lyu, L., Dong, X., &amp; Bao, M. (2021). Performance‐dependent reward hurts performance: The non‐monotonic attentional load modulation on task‐irrelevant distractor processing. Psychophysiology, 58(12), e13920. https://doi.org/10.1111/psyp.13920.</p>",
-    "authors": [
-      {
-        "name_en": "Xin He",
-        "name_zh": "Xin He",
-        "email": "hex@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Weilin Liu",
-        "name_zh": "Weilin Liu",
-        "email": "1372593229@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Nan Qin",
-        "name_zh": "Nan Qin",
-        "email": "qinnanah@126.com",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Lili Lyu",
-        "name_zh": "Lili Lyu",
-        "email": "lllv@ion.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Xue Dong",
-        "name_zh": "Xue Dong",
-        "email": "dongx@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Min Bao",
-        "name_zh": "Min Bao",
-        "email": "baom@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "reward",
-      "selective attention",
-      "ERP"
-    ],
-    "keywords_zh": [
-      "reward",
-      "selective attention",
-      "ERP"
-    ],
-    "publication_date": "2022-09-16T15:30:45.330Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODbL",
-    "file_size_mb": 23516.52,
-    "file_size_bytes": 24658853287,
-    "download_count": 18,
-    "visit_count": 310,
-    "url": "https://www.scidb.cn/en/detail?id=63353de93d498241dcc00903",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67c19e52d732af307692239e",
-    "doi": "10.57760/sciencedb.psych.00262",
-    "cstr": "31253.11.sciencedb.psych.00262",
-    "pid": "",
-    "title": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
-    "title_en": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
-    "title_zh": "Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation",
-    "description": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
-    "description_en": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
-    "description_zh": "<p>The human brain&nbsp;possesses a remarkable ability to recognize scenes depicted&nbsp;in line drawings, despite these&nbsp;drawings&nbsp;contain&nbsp;only edge information. It remains unclear how the brain uses this information alongside surface information in scene recognition.&nbsp;Here, we combined&nbsp;electroencephalogram&nbsp;(EEG)&nbsp;and<span style=\"color: rgb(0, 0, 0);\">&nbsp;multivariate pattern analysis (MVPA) </span>to distinguish the role of&nbsp;edge and surface information in&nbsp;scene&nbsp;representation&nbsp;at the basic and superordinate&nbsp;levels over&nbsp;time. First, we found&nbsp;that surface information&nbsp;is involved in initial neural scene&nbsp;representation at the&nbsp;basic level, but not&nbsp;at the superordinate&nbsp;level.&nbsp;Second, edge information is sufficient and more effective for scene recognition at the superordinate level. Third, time-generalization analysis revealed that edge information in line drawings is crucial for representation at every level of abstraction.&nbsp;These findings provide <span style=\"color: rgb(0, 0, 255);\">novel</span>&nbsp;neural evidence that edge and surface information play different roles in dynamic neural scene representations&nbsp;at&nbsp;different levels of abstraction, and help to enhance&nbsp;our understanding of&nbsp;how the human&nbsp;brain&nbsp;represents scenes.</p>",
-    "authors": [
-      {
-        "name_en": "liansheng yao",
-        "name_zh": "liansheng yao",
-        "email": "lianshengyao@yeah.net",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      },
-      {
-        "name_en": "qiufang fu",
-        "name_zh": "qiufang fu",
-        "email": "fuqf@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "scene representation",
-      "edge information",
-      "surface information",
-      "basic level",
-      "superordinate level"
-    ],
-    "keywords_zh": [
-      "scene representation",
-      "edge information",
-      "surface information",
-      "basic level",
-      "superordinate level"
-    ],
-    "publication_date": "2024-09-06T09:03:59.878Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 11770.0,
-    "file_size_bytes": 12341738656,
-    "download_count": 342,
-    "visit_count": 764,
-    "url": "https://www.scidb.cn/en/detail?id=67c19e52d732af307692239e",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "60b9f23881e63e42b94c22d4",
-    "doi": "10.11922/sciencedb.893",
-    "cstr": "31253.11.sciencedb.893",
-    "pid": "21.86101/sciencedb.893",
-    "title": "A dataset of China’s overseas power projects (2000–2019)",
-    "title_en": "A dataset of China’s overseas power projects (2000–2019)",
-    "title_zh": "2000–2019年中国海外电力项目信息数据集",
-    "description": "Power shortages are faced by developing countries in the Belt and Road region. Since the Belt and Road initiative was put forward, Chinese companies have invested and built a large number of electrical power projects in countries and areas with power shortages in this region. Due to the large number and wide distribution of projects and continuous increases in the number of new power projects, a large amount of project information has been generated. Accordingly, it is urgent to collect and summarize One Belt and One Road overseas power project information. In this study, web crawler technology was used to obtain overseas power project information. A One Belt and One Road dataset of overseas power projects was formed by further supplementing and improving the project information using documents from ministries, embassies, counselors of the ministry of economy and commerce, local news reports in Chinese and English, and case studies and field studies conducted by scholars and non-governmental organizations. The dataset includes information on 376 power projects from 80 countries in Asia, Africa, Europe, America, and Oceania. Each project’s information includes the project number, project name, construction status, enterprise name, installed capacity, continent, country, project category, and bid information. The collection and improvement of this dataset will help with understanding the distribution of One Belt, One Road overseas power projects, as well as development trends in overseas power investment and construction in recent years. This can provide a basis for China’s power companies to “go global” and become “One Belt, One Road” overseas. It also provides a reference for power project development planning and government department decision-making.",
-    "description_en": "Power shortages are faced by developing countries in the Belt and Road region. Since the Belt and Road initiative was put forward, Chinese companies have invested and built a large number of electrical power projects in countries and areas with power shortages in this region. Due to the large number and wide distribution of projects and continuous increases in the number of new power projects, a large amount of project information has been generated. Accordingly, it is urgent to collect and summarize One Belt and One Road overseas power project information. In this study, web crawler technology was used to obtain overseas power project information. A One Belt and One Road dataset of overseas power projects was formed by further supplementing and improving the project information using documents from ministries, embassies, counselors of the ministry of economy and commerce, local news reports in Chinese and English, and case studies and field studies conducted by scholars and non-governmental organizations. The dataset includes information on 376 power projects from 80 countries in Asia, Africa, Europe, America, and Oceania. Each project’s information includes the project number, project name, construction status, enterprise name, installed capacity, continent, country, project category, and bid information. The collection and improvement of this dataset will help with understanding the distribution of One Belt, One Road overseas power projects, as well as development trends in overseas power investment and construction in recent years. This can provide a basis for China’s power companies to “go global” and become “One Belt, One Road” overseas. It also provides a reference for power project development planning and government department decision-making.",
-    "description_zh": "电力短缺是“一带一路”区域广大发展中国家面临的问题之一。自“一带一路”倡议提出以来，中国企业在“一带一路”沿线电力短缺的国家和地区投资、建设了大量的电力项目，由于项目数量多，分布范围广泛，并且每年会不断增加新的电力项目，将形成大量的项目信息，亟需将“一带一路”海外电力项目信息进行收集汇总。本文利用网络爬虫的方法获取海外电力项目的信息，同时通过部委、使馆、经济商务部参赞机构文件、中文英文的当地新闻报道、学者和非政府组织进行的案例研究和实地研究中对项目信息进行进一步补充和完善，形成了“一带一路”海外电力项目数据集。数据集包括来自亚洲、非洲、欧洲、美洲、大洋洲80个国家的376个电力项目，每个项目信息包括项目编号、项目状态、央企集团、二级单位、项目名称、装机容量、所属地区、所在国家、项目类型、中标信息。数据集的收集和完善有助于了解“一带一路”海外电力项目的分布分局，以及近年来的海外电力投资建设发展态势，可为我国电力企业“走出去”提供依据，为“一带一路”海外电力项目发展规划、政府部门决策提供参考。",
-    "authors": [
-      {
-        "name_en": "蒋瑜",
-        "name_zh": "蒋瑜",
-        "affiliations": [
-          {
-            "name_en": "",
-            "name_zh": ""
-          }
-        ]
-      },
-      {
-        "name_en": "邬明权",
-        "name_zh": "邬明权",
-        "affiliations": [
-          {
-            "name_en": "",
-            "name_zh": ""
-          }
-        ]
-      },
-      {
-        "name_en": "黄长军",
-        "name_zh": "黄长军",
-        "affiliations": [
-          {
-            "name_en": "",
-            "name_zh": ""
-          }
-        ]
-      },
-      {
-        "name_en": "牛铮",
-        "name_zh": "牛铮",
-        "affiliations": [
-          {
-            "name_en": "",
-            "name_zh": ""
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Belt and Road Initiative",
-      " power projects",
-      " dataset",
-      "web crawler technology"
-    ],
-    "keywords_zh": [
-      "一带一路",
-      "电力项目",
-      "数据集",
-      "网络爬虫"
-    ],
-    "publication_date": "2019-09-23T13:12:37.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.07,
-    "file_size_bytes": 71377,
-    "download_count": 2012,
-    "visit_count": 13926,
-    "url": "https://www.scidb.cn/en/detail?id=60b9f23881e63e42b94c22d4",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "64bf82f502e62a3c98eeff55",
-    "doi": "10.57760/sciencedb.02116",
-    "cstr": "31253.11.sciencedb.02116",
-    "pid": "21.86116.6/sciencedb.02116",
-    "title": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
-    "title_en": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
-    "title_zh": "Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd",
-    "description": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
-    "description_en": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
-    "description_zh": "<p>This repository contains data and code for the paper entitled \"Acute stress impairs target enhancement but not distractor suppression in attention selection: Evidence from the N2pc and Pd\".</p>",
-    "authors": [
-      {
-        "name_en": "Luo Yu",
-        "name_zh": "Luo Yu",
-        "email": "yuluo@gznu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "Guizhou Normal University"
-          }
-        ]
-      },
-      {
-        "name_en": "Nian Jingqing",
-        "name_zh": "Nian Jingqing",
-        "email": "nianjingqing@126.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "Guizhou Normal University",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Yang Run",
-        "name_zh": "Yang Run",
-        "email": "617674892@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Xie Jiao",
-        "name_zh": "Xie Jiao",
-        "email": "1759812273@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhang Yu",
-        "name_zh": "Zhang Yu",
-        "email": "yuzhang331@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guizhou Normal University",
-            "name_zh": "贵州师范大学",
-            "ror_id": "https://ror.org/02x1pa065"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Stress",
-      " attentional capature ",
-      "visual search",
-      "acute stress",
-      "N2pc",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "Stress",
-      " attentional capature ",
-      "visual search",
-      "acute stress",
-      "N2pc",
-      "EEG"
-    ],
-    "publication_date": "2022-08-03T02:49:14.234Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 61798.19,
-    "file_size_bytes": 64800100196,
-    "download_count": 6156,
-    "visit_count": 1791,
-    "url": "https://www.scidb.cn/en/detail?id=64bf82f502e62a3c98eeff55",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6414216494d8a763720bdf0a",
-    "doi": "10.57760/sciencedb.07756",
-    "cstr": "31253.11.sciencedb.07756",
-    "pid": "21.86116.6/sciencedb.07756",
-    "title": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
-    "title_en": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
-    "title_zh": "Altered excitatory-inhibitory balance and network communication coincident with visual hallucinations elicited by stimulation",
-    "description": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
-    "description_en": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
-    "description_zh": "<p>Here are the relevant codes and data used in the analysis process of this article. To facilitate your better understanding of the entire process, we have organized the analysis code according to the analysis process and retained the analysis results of major steps.</p><p>This folder includes several main directories, here is a brief introduction to their functions, and then a detailed introduction to the contents of each folder:</p><p>(1)&nbsp;&nbsp;./brain_atlas, this contains some brain structural atlas used in the analysis, and another part of the brain structural atlas are in ./Phi_Analysis/brain_atlas</p><p>(2)&nbsp;&nbsp;./myfun, this folder contains some general functions used in programming</p><p>(3)&nbsp;&nbsp;./dataForEI, the data used in the E/I anlaysis</p><p>(4)&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the visual hallucination trials</p><p>(5)&nbsp;&nbsp;./02_EI_controlTrial_stimulation_vs_baseline, in this folder, we compare the E/I difference between the electrical stimulation period and the baseline period for the control trials</p><p>(6)&nbsp;&nbsp;./03_EI_stimulationPeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the electrical stimulation period</p><p>(7)&nbsp;&nbsp;./04_EI_baselinePeriod_halluTrial_vs_controlTrial, in this folder, we compare the E/I difference between the two types of trials during the baseline period</p><p>(8)&nbsp;&nbsp;./Phi_Analysis, this folder contains analysis related to information integration</p><p>(9)&nbsp;&nbsp;./Other_Analysis, this folder contains other related analysis</p><p><br></p><p><br></p><p><br></p><p><br></p><p>Next, let's introduce the contents of each subfolder in detail:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;./dataForEI</p><p>(1)&nbsp;&nbsp;st05_elec_value_mat.mat, st05_elec_value_mat_nohall.mat</p><p>These two files respectively contain the aperiodic 1/f values calculated for each recorded electrode in the visual hallucination trials and control trials, as well as the coordinates of each electrode in the standard MNI space. (The code for calculating the aperiodic 1/f values is placed in ./Other_Analysis/cal_1f.py,&nbsp;FOOOF toolbox(Donoghue et al., 2020) was used).</p><p><br></p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;./01_EI_hallucinationTrial_stimulation_vs_baseline</p><p>(1)&nbsp;&nbsp;./ st06_filtered_data.m</p><p>This code is mainly used to find the recording contacts that are shared by hallucination trials and control trials, and these shared contracts will be used for subsequent analysis. The results of running this file are saved in:</p><p>./result_01/st06_filted_vis.mat,&nbsp;./result_01/st06_filted_non.mat</p><p>(2)&nbsp;&nbsp;./ st08_vox_within_contact.m</p><p>This code is mainly used to find which voxels in the standard space are within the 8mm range of the recorded electrode. The results of running this file are saved in:</p><p>./result_01/st08_vox_within_contact_radius_vis_10.mat</p><p>(3)&nbsp;&nbsp;./st09_vox_subjNum_elecNum.m, ./st10_reorg_st09.m</p><p>This analysis mainly calculates the number of subjects available for each voxel in the standard space and reorganizes the result data for subsequent analysis. The results of the analysis are stored in:</p><p>./result_01/st09_all_vox_subjNum.mat (This file is used in ./Other_Analysis/st09_... for showing the number of patients contributing electrode contacts to each neocortical location (Fig. 2))</p><p>./result_01/st09_final_5_vox_subj_metric.mat</p><p>./result_01/st10_final_5_vox_subj_metric.mat</p><p>(4)&nbsp;&nbsp;./st12_01_true_tstat.m</p><p>This code is used to calculate the true t-values of each voxel, which is mainly used to compare with the null-distribution to determine significance. The results of the analysis are stored in:</p><p>./result_01/st12_tstats_voxs.mat</p><p>(5)&nbsp;&nbsp;./ st13_01_get_eachsubj_brain.m</p><p>./st13_02_permut1000_eachbrain.m</p><p>./ st13_03_permut1000_fullbrain.m</p><p>./st14_01_tstat_permut1000.m</p><p>./st15_01_group_distribution.m</p><p>These codes are mainly used to implement the following parts of the article's Methods</p><p>“To determine the significance threshold, we randomly sign-flipped half of the values at each voxel for each patient’s brain map and recomputed t-statistics at the group level. By performing this procedure 1000 times, a null distribution was generated. Finally, the 2.5th and 97.5th percentiles of this null distribution were identified as negative and positive significance thresholds, respectively.”</p><p><br></p><p>The final generated file (./result_01/ st15_01_group_threshold.mat) contains the threshold used to determine significance.</p><p>(6)&nbsp;&nbsp;./ st15_02_get_vol.m</p><p>./st15_03_to_nifti.py</p><p>./st15_04_plot_surf.py</p><p>This section of code compares the true t-values with the significance threshold, and then presents the t-values and their distribution of significant voxels (Fig.4 A, note: the colorbar here was flipped in the article figure for better describing the steep/flattened changes of aperiodic 1/f slope).</p><p>(7)&nbsp;&nbsp;./st20_get_vol.mat</p><p>This code calculates the distribution of the percentage of significant voxels among 22 brain regions of the HCP atlas. (This result was also used in ./Other_Analysis/st01_... for Fig.4 B)</p><p>(8)&nbsp;&nbsp;./ st22_yeo7.m</p><p>The distribution of the percentage of significant voxels among yeo7 networks. (This result was also used in ./Other_Analysis/st02_... for Fig.4 C)</p><p><br></p><p>Note: For the code in</p><p>./02_EI_controlTrial_stimulation_vs_baseline,</p><p>./03_EI_stimulationPeriod_halluTrial_vs_controlTrial,</p><p>./04_EI_baselinePeriod_halluTrial_vs_controlTrial,&nbsp;</p><p>the process is the same as that in ./01_EI_hallucinationTrial_stimulation_vs_baseline as described above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;./Other_Analysis</p><p>(1)&nbsp;&nbsp;./cal_1f.py</p><p>Code for calculating aperiodic 1/f slope, which was already mentioned above.</p><p>(2)&nbsp;&nbsp;./stimcontact_info.mat</p><p>The number of stimulation contacts that induced visual hallucination in each region of hcp22, and the total number of stimulation contacts in each region of hcp22.</p><p>(3)&nbsp;&nbsp;./ st01_shuffle_b.m, ./st01_shuffle_b_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using Yeo7 atlas between the two types of trials. (Fig.4 B, Fig. S2 B) (./yeo7_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(4)&nbsp;&nbsp;./st02_shuffle_c.m, ./st02_shuffle_c_supp.m</p><p>These codes were used for permutation test for comparing the proportion of significant voxels using HCP22 atlas between the two types of trials. (Fig.4 C, Fig. S2 C) (./hcp22_bar.csv was the used, which was generated in the above E/I analysis process).</p><p>(5)&nbsp;&nbsp;./ st09_01_subjNum_cortex.m</p><p>./ st09_02_to_nifti.py</p><p>./ st09_03_plot_subjNum.py</p><p>These codes were used to show the number of patients contributing electrode contacts to each neocortical location (Fig. 2). The data used here were generated in the previous E/I analysis as mentioned above.</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;./Phi_Analysis</p><p>(1)&nbsp;&nbsp;./brain_atlas</p><p>This folder contains brain structural atlas used in the information integration analysis.</p><p>(2)&nbsp;&nbsp;./ComFiles</p><p>This folder contains some pre-generated data used to assist in information integration calculations.</p><p>(3)&nbsp;&nbsp;./PhiToolbox-master</p><p>This fold contains the toolbox for calculating phi (https://github.com/oizumi-lab/PhiToolbox)</p><p>(4)&nbsp;&nbsp;./result_01/st05_lfp_vis, ./result_01/st05_lfp_non</p><p>These folders contain LFP signals for analyzing integrating information (_vis for hallucination trials, _non for control trials).</p><p>(5)&nbsp;&nbsp;./st07_filter_1.m</p><p>This code choses sessions where recording contacts distributed over more than one brain region and less than 15 brain regions. (in the following analysis process, only sessions where recording contacts distributed over more than 2 brain region and less than 15 brain regions were used)</p><p>(6)&nbsp;&nbsp;./ st08_cal_phi_vis.m, ./ st08_fun.m</p><p>These two codes are used to calculate phi. (PhiToolbox was used, https://github.com/oizumi-lab/PhiToolbox)</p><p><br></p><p>(7)&nbsp;&nbsp;./ st70_find_tau_vis.m, ./ st70_find_tau_non.m</p><p>These codes were used to find t that yielded the highest F* across all lag times. (For both hallucination trials and control trials, the result was t = 175.8 ms.)</p><p>(8)&nbsp;&nbsp;./ st71_avg_phi_non.m, ./ st71_avg_phi_vis.m, ./st72_test.m</p><p>These codes were used to investigate the changes of integrated information during visual hallucinations (the result was used for Fig.5 A).</p><p>(9)&nbsp;&nbsp;./ st76_all_phis_non.m, ./ st76_all_phis_vis.m, ./ st77_zs_reorganize.m</p><p>These codes were used to get MIP information across all subsystems where contacts distributed over more than two brain regions (and less than 15 brain regions) when&nbsp;t = 175.8 ms as calculated above.</p><p>(10) st78_zs_cal.m, st79_graph.py</p><p>The code for analyzing the MIP (please referring the Method “Analyzinig the Minimum Information Partitions”). Fig.5 and Fig. S1 could be generated using these codes.</p><p>(11) ./ st101_zs_non_unchange.m</p><p>The code was used to show the normalized number of pairs of brain regions whose relationship to the MIP were unchanged during the stimulation period compared to the baseline for control trials. (Fig5. C)</p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p>&nbsp;</p>",
-    "authors": [
-      {
-        "name_en": "Yanyan Li",
-        "name_zh": "Yanyan Li",
-        "email": "liyanyan@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institue of Psychology,  Chinese Academy of Sciences",
-            "name_zh": "Institue of Psychology,  Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Bo Zhang",
-        "name_zh": "Bo Zhang",
-        "email": "zhangbo_1008@163.com",
-        "affiliations": [
-          {
-            "name_en": "Key Laboratory of Brain Science, Zunyi Medical University",
-            "name_zh": "Key Laboratory of Brain Science, Zunyi Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Jing Wang",
-        "name_zh": "Jing Wang",
-        "email": "jennywj80@aliyun.com",
-        "affiliations": [
-          {
-            "name_en": " Department of Neurology, SanBo Brain Hospital, Capital Medical University",
-            "name_zh": " Department of Neurology, SanBo Brain Hospital, Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Yuri B. Saalmann",
-        "name_zh": "Yuri B. Saalmann",
-        "email": "saalmann@wisc.edu",
-        "affiliations": [
-          {
-            "name_en": "Department of Psychology, University of Wisconsin-Madison, Madison",
-            "name_zh": "Department of Psychology, University of Wisconsin-Madison, Madison"
-          }
-        ]
-      },
-      {
-        "name_en": "Mohsen Afrasiabi",
-        "name_zh": "Mohsen Afrasiabi",
-        "email": "afrasiabi@wisc.edu",
-        "affiliations": [
-          {
-            "name_en": "Department of Psychology, University of Wisconsin-Madison, Madison",
-            "name_zh": "Department of Psychology, University of Wisconsin-Madison, Madison"
-          }
-        ]
-      },
-      {
-        "name_en": "Haixiang Wang",
-        "name_zh": "Haixiang Wang",
-        "email": "haixiang321@126.com",
-        "affiliations": [
-          {
-            "name_en": "Epilepsy Center, Tsinghua University Yuquan Hospital",
-            "name_zh": "Epilepsy Center, Tsinghua University Yuquan Hospital"
-          }
-        ]
-      },
-      {
-        "name_en": "Changfei Shi",
-        "name_zh": "Changfei Shi",
-        "email": "shichangfei666@aliyun.com",
-        "affiliations": [
-          {
-            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
-            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Huanhuan Xiang",
-        "name_zh": "Huanhuan Xiang",
-        "email": "xianghh1010@aliyun.com",
-        "affiliations": [
-          {
-            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
-            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Mengyang Wang",
-        "name_zh": "Mengyang Wang",
-        "email": "mengyangwang@ccmu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Neurology, SanBo Brain Hospital, Capital Medical University",
-            "name_zh": "Department of Neurology, SanBo Brain Hospital, Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Guoming Luan",
-        "name_zh": "Guoming Luan",
-        "email": "luangm@ccmu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Neurosurgery, SanBo Brain Hospital, Capital Medical University",
-            "name_zh": "Department of Neurosurgery, SanBo Brain Hospital, Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Robert T. Knight",
-        "name_zh": "Robert T. Knight",
-        "email": "rtknight@berkeley.edu",
-        "affiliations": [
-          {
-            "name_en": "Department of Psychology, University of California, Berkeley",
-            "name_zh": "Department of Psychology, University of California, Berkeley"
-          }
-        ]
-      },
-      {
-        "name_en": "Liang Wang",
-        "name_zh": "Liang Wang",
-        "email": "lwang@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Psychology, Chinese Academy of Sciences",
-            "name_zh": "Institute of Psychology, Chinese Academy of Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Hallucinations",
-      "excitatory and inhibitory balance",
-      "electrical stimulation",
-      "information integration",
-      "intracranial EEG"
-    ],
-    "keywords_zh": [
-      "Hallucinations",
-      "excitatory and inhibitory balance",
-      "electrical stimulation",
-      "information integration",
-      "intracranial EEG"
-    ],
-    "publication_date": "2023-03-21T02:19:58.994Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
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-  },
-  {
-    "dataset_id": "624756ac8105fb7f09ce902c",
-    "doi": "10.11922/sciencedb.01667",
-    "cstr": "31253.11.sciencedb.01667",
-    "pid": "21.86116.6/sciencedb.01667",
-    "title": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
-    "title_en": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
-    "title_zh": "Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex",
-    "description": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
-    "description_en": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
-    "description_zh": "<p>Dataset for Cappotto, et al, 2022, Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex</p>",
-    "authors": [
-      {
-        "name_en": "Drew Cappotto",
-        "name_zh": "Drew Cappotto",
-        "email": "drew.cappotto@my.cityu.edu.hk",
-        "affiliations": [
-          {
-            "name_en": "City University of Hong Kong",
-            "name_zh": "City University of Hong Kong"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Neuroscience",
-      "EEG",
-      "Auditory",
-      "Prediction"
-    ],
-    "keywords_zh": [
-      "Neuroscience",
-      "EEG",
-      "Auditory",
-      "Prediction"
-    ],
-    "publication_date": "2022-04-06T01:41:50.517Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=624756ac8105fb7f09ce902c",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "662cf17f3e08f2716205ca7b",
-    "doi": "10.57760/sciencedb.j00133.00448",
-    "cstr": "31253.11.sciencedb.j00133.00448",
-    "pid": "",
-    "title": "Preprocessed data for sleep staging experiment",
-    "title_en": "Preprocessed data for sleep staging experiment",
-    "title_zh": "睡眠分期实验预处理数据集",
-    "description": "<p>The compressed file contains preprocessed public data from sleep experiments in numpy format&nbsp;</p>",
-    "description_en": "<p>The compressed file contains preprocessed public data from sleep experiments in numpy format&nbsp;</p>",
-    "description_zh": "<p>压缩文件包含睡眠实验中预处理后的公开数据，数据格式为numpy格式</p>",
-    "authors": [
-      {
-        "name_en": "Jinbo",
-        "name_zh": "金博",
-        "email": "jinbo@dlut.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Dalian University of Technology",
-            "name_zh": "大连理工大学",
-            "ror_id": "https://ror.org/023hj5876"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhangjiawan",
-        "name_zh": "章嘉湾",
-        "email": "zhangjiawan@mail.dlut.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Dalian University of Technology",
-            "name_zh": "大连理工大学",
-            "ror_id": "https://ror.org/023hj5876"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Sleep Staging",
-      "Deep Learning",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "睡眠分期",
-      "深度学习",
-      "EEG"
-    ],
-    "publication_date": "2024-07-26T08:47:39.750Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODC-BY",
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-    "url": "https://www.scidb.cn/en/detail?id=662cf17f3e08f2716205ca7b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66ab23a955fe61355f459219",
-    "doi": "10.57760/sciencedb.ai.00010",
-    "cstr": "31253.11.sciencedb.ai.00010",
-    "pid": "",
-    "title": "MultiModal Vigilance dataset",
-    "title_en": "MultiModal Vigilance dataset",
-    "title_zh": "MultiModal Vigilance dataset",
-    "description": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
-    "description_en": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
-    "description_zh": "<p>The MultiModal Vigilance (MMV) dataset comprises seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks.</p>",
-    "authors": [
-      {
-        "name_en": "Wei Wei",
-        "name_zh": "Wei Wei",
-        "email": "weiwei2018@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      },
-      {
-        "name_en": "Kangning Wang",
-        "name_zh": "Kangning Wang",
-        "email": "kangningwang_1205@163.com",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      },
-      {
-        "name_en": "Shuang Qiu",
-        "name_zh": "Shuang Qiu",
-        "email": "shuang.qiu@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      },
-      {
-        "name_en": "Huiguang He",
-        "name_zh": "Huiguang He",
-        "email": "huiguang.he@ia.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Institute of Automation",
-            "name_zh": "自动化研究所",
-            "ror_id": "https://ror.org/022c3hy66"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "multi-modality",
-      "EEG",
-      "vigilance",
-      "EOG",
-      "ECG",
-      "BCI"
-    ],
-    "keywords_zh": [
-      "multi-modality",
-      "EEG",
-      "vigilance",
-      "EOG",
-      "ECG",
-      "BCI"
-    ],
-    "publication_date": "2024-01-11T02:54:45.311Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "visit_count": 2523,
-    "url": "https://www.scidb.cn/en/detail?id=66ab23a955fe61355f459219",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68ff3c314874aace76ea463b",
-    "doi": "10.57760/sciencedb.30601",
-    "cstr": "31253.11.sciencedb.30601",
-    "pid": "",
-    "title": "Data for research.0960",
-    "title_en": "Data for research.0960",
-    "title_zh": "Data for research.0960",
-    "description": "<p>Data for research.0960</p>",
-    "description_en": "<p>Data for research.0960</p>",
-    "description_zh": "<p>Data for research.0960</p>",
-    "authors": [
-      {
-        "name_en": "Hangting Ye",
-        "name_zh": "Hangting Ye",
-        "email": "12118042@zju.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Women's Hospital, School of Medicine, Zhejiang University",
-            "name_zh": "浙江大学医学院附属妇产科医院",
-            "ror_id": "https://ror.org/042t7yh44"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "ECoG",
-      "single nueron"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "ECoG",
-      "single nueron"
-    ],
-    "publication_date": "2025-10-28T01:33:51.486Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "PDDL",
-    "file_size_mb": 9335.97,
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-    "url": "https://www.scidb.cn/en/detail?id=68ff3c314874aace76ea463b",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "67fd3df2ac9a663b1b555558",
-    "doi": "10.57760/sciencedb.psych.00239",
-    "cstr": "31253.11.sciencedb.psych.00239",
-    "pid": "",
-    "title": "Automatic processing of variability in multiple facial expressions",
-    "title_en": "Automatic processing of variability in multiple facial expressions",
-    "title_zh": "Automatic processing of variability in multiple facial expressions",
-    "description": "<p>The variability of multiple facial expressions can be extracted efficiently. However, whether processing emotional variability is automatic, and whether it is impacted by types of emotions, remains unresolved.&nbsp;</p><p>To answer these questions, we employed a passive oddball paradigm and recorded event-related potentials while participants did an attentional demanding task to detect the changes in the central fixation. A set of four faces was shown in the periphery, either displaying low (Experiment 1: SD = 21; Experiment 2: SD = 7) or high emotional variability (Experiment 1: SD = 36.12; Experiment 2: SD = 20.39), which was manipulated by changing the distance of emotional units among faces. In Experiment 1, the face set consisted of two angry and two happy faces, and the mean emotion was neutral (M = 50). In Experiment 2, all four faces were angry or happy, and the mean emotion was moderate anger (M = 25) or moderate happiness (M = 75). In Experiment 3, we additionally controlled for the range of emotional units in the set and used symmetrical distributions in both low (M = 50, SD = 35.67) and high variability conditions (M = 50, SD = 43.42). The two variability conditions had matched mean emotions and were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or vice versa. When deviant stimuli are embedded within a series of standard stimuli, the appearance of the deviant would disrupt the established statistics or the regularity, leading to the observation of the vMMN (visual mismatch negativity). The vMMN is considered an index of automatic change detection, evoked by any alteration in the sequence.&nbsp;</p><p>The results showed that in Experiment 1, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMN at both early (110-140 ms) and late (320-420 ms) time intervals. Further multivariate pattern analysis (MVPA) using all EEG channels showed that the brain could decode the standard and deviant stimuli before 100 ms under the conditions of both high and low emotional variability. In Experiment 2, when the mean emotion was angry, faces with low emotional variability elicited vMMN, whereas faces with high emotional variability elicited vMMP (visual mismatch positivity) in the time window of 320-420 ms. In contrast, when the mean emotion was happy, faces with both high and low emotional variability did not elicit significant vMMN in the 320-420 ms time window. Moreover, under all conditions of emotional variability, the standard and deviant stimuli could be successfully decoded at an early stage, but the decoding onset latency was significantly later in the low compared to the high variability condition, for both happy and angry emotions. In Experiment 3, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMP in the time window of 320-420 ms. The MVPA showed that the standard and deviant stimuli could be decoded during the early stage in both variability conditions, replicating the results of the previous experiments and excluding the potential confound of range and distribution.</p><p>Taken together, we found that the variability of multiple unattended facial expressions can be perceived automatically. Moreover, there is an advantage in the automatic processing of relatively higher emotional variability, and this advantage is also influenced by the valence of emotions.&nbsp;</p>",
-    "description_en": "<p>The variability of multiple facial expressions can be extracted efficiently. However, whether processing emotional variability is automatic, and whether it is impacted by types of emotions, remains unresolved.&nbsp;</p><p>To answer these questions, we employed a passive oddball paradigm and recorded event-related potentials while participants did an attentional demanding task to detect the changes in the central fixation. A set of four faces was shown in the periphery, either displaying low (Experiment 1: SD = 21; Experiment 2: SD = 7) or high emotional variability (Experiment 1: SD = 36.12; Experiment 2: SD = 20.39), which was manipulated by changing the distance of emotional units among faces. In Experiment 1, the face set consisted of two angry and two happy faces, and the mean emotion was neutral (M = 50). In Experiment 2, all four faces were angry or happy, and the mean emotion was moderate anger (M = 25) or moderate happiness (M = 75). In Experiment 3, we additionally controlled for the range of emotional units in the set and used symmetrical distributions in both low (M = 50, SD = 35.67) and high variability conditions (M = 50, SD = 43.42). The two variability conditions had matched mean emotions and were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or vice versa. When deviant stimuli are embedded within a series of standard stimuli, the appearance of the deviant would disrupt the established statistics or the regularity, leading to the observation of the vMMN (visual mismatch negativity). The vMMN is considered an index of automatic change detection, evoked by any alteration in the sequence.&nbsp;</p><p>The results showed that in Experiment 1, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMN at both early (110-140 ms) and late (320-420 ms) time intervals. Further multivariate pattern analysis (MVPA) using all EEG channels showed that the brain could decode the standard and deviant stimuli before 100 ms under the conditions of both high and low emotional variability. In Experiment 2, when the mean emotion was angry, faces with low emotional variability elicited vMMN, whereas faces with high emotional variability elicited vMMP (visual mismatch positivity) in the time window of 320-420 ms. In contrast, when the mean emotion was happy, faces with both high and low emotional variability did not elicit significant vMMN in the 320-420 ms time window. Moreover, under all conditions of emotional variability, the standard and deviant stimuli could be successfully decoded at an early stage, but the decoding onset latency was significantly later in the low compared to the high variability condition, for both happy and angry emotions. In Experiment 3, faces with low emotional variability did not elicit vMMN, while those with high emotional variability elicited vMMP in the time window of 320-420 ms. The MVPA showed that the standard and deviant stimuli could be decoded during the early stage in both variability conditions, replicating the results of the previous experiments and excluding the potential confound of range and distribution.</p><p>Taken together, we found that the variability of multiple unattended facial expressions can be perceived automatically. Moreover, there is an advantage in the automatic processing of relatively higher emotional variability, and this advantage is also influenced by the valence of emotions.&nbsp;</p>",
-    "description_zh": "<p>本研究通过反向oddball范式和视觉失匹配负波(vMMN)，考察个体能否自动加工多面孔情绪变异性信息，以及这一过程是否受情绪类型的影响。研究采用中央注视点辨别任务，而四张情绪面孔在外周视野同时呈现且与任务无关。情绪变异性通过改变面孔的情绪强度进行操纵。实验1结果发现，当整体情绪为中性时，低情绪变异性面孔不会诱发vMMN，而高情绪变异性面孔诱发了早期和晚期的vMMN。实验2进一步区分了愤怒和高兴两种情绪类型，结果发现，当面孔整体情绪为高兴时，高、低情绪变异性面孔并未诱发显著的vMMN。而当面孔整体情绪为愤怒时，高、低情绪变异性面孔在晚期分别诱发了视觉失匹配正波(vMMP)和vMMN。实验3控制了全距和分布形式，发现低情绪变异性面孔未诱发vMMN，而高情绪变异性面孔在晚期诱发vMMP。此外，在三个实验的所有条件下，全脑均能在早期解码出标准与偏差刺激。实验2还额外发现，低情绪变异性面孔解码时间较高情绪变异性面孔更晚。综上，大脑可以自动加工多张面孔的情绪变异性信息，对相对较高的情绪变异性存在自动加工优势，并受情绪类型的影响。</p>",
-    "authors": [
-      {
-        "name_en": "Zilong Chen",
-        "name_zh": "Zilong Chen",
-        "email": "chenzilong312@163.com",
-        "affiliations": [
-          {
-            "name_en": "Guangzhou University",
-            "name_zh": "广州大学",
-            "ror_id": "https://ror.org/05ar8rn06"
-          }
-        ]
-      },
-      {
-        "name_en": "Luyan Ji",
-        "name_zh": "Luyan Ji",
-        "email": "luyanji@gzhu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Guangzhou University",
-            "name_zh": "广州大学",
-            "ror_id": "https://ror.org/05ar8rn06"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Emotional variability",
-      "Ensemble encoding",
-      "Facial expressions",
-      "Visual mismatch negativity(vMMN)",
-      "Event-related potentials (ERPs)"
-    ],
-    "keywords_zh": [
-      "Emotional variability",
-      "Ensemble encoding",
-      "Facial expressions",
-      "Visual mismatch negativity(vMMN)",
-      "Event-related potentials (ERPs)"
-    ],
-    "publication_date": "2024-08-08T08:21:47.544Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 251.53,
-    "file_size_bytes": 263750751,
-    "download_count": 8450,
-    "visit_count": 906,
-    "url": "https://www.scidb.cn/en/detail?id=67fd3df2ac9a663b1b555558",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "684c3451918e1d0fc634c0ef",
-    "doi": "10.57760/sciencedb.25524",
-    "cstr": "31253.11.sciencedb.25524",
-    "pid": "",
-    "title": "MODIS MAIAC AOD daily grid data for China (GridMAIACAODChina)",
-    "title_en": "MODIS MAIAC AOD daily grid data for China (GridMAIACAODChina)",
-    "title_zh": "中国区域MODIS MAIAC AOD日合成值数据（GridMAIACAODChina）",
-    "description": "<p class=\"ql-align-justify\">The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file.</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>Please cite the following data and references when using the dataset:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p>",
-    "description_en": "<p class=\"ql-align-justify\">The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file.</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>Please cite the following data and references when using the dataset:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p>",
-    "description_zh": "<p class=\"ql-align-justify\"><strong>数据集描述：</strong></p><p class=\"ql-align-justify\">GridMAIACAODChina数据集基于MODIS Collection 6.1 MCD19A2产品生产，MCD19A2是联合Terra和Aqua卫星上的两颗MODIS传感器观测数据，使用MAIAC算法反演的气溶胶产品，主要包括气溶胶光学厚度（AOD）等参数。MCD19A2数据存储为HDF格式和正弦投影，需要对数据进行提取、数值缩放、坐标转换，转换为软件方便处理和识别的GeoTIFF格式等预处理。为了方便数据用户，我们处理了2000–2024年中国区域的MCD19A2产品的MAIAC AOD数据。处理步骤包括：（1）从HDF文件中提取550 nm AOD数据，乘以缩放系数0.001得到实际AOD值，使用质量保证波段对每个像元的AOD值进行筛选，只保留反演质量最佳的像元值，其余值设置为-999.0；（2）基于有效值计算每个正弦“块”的均值；（3）定义每个正弦“块”的投影；（4）对于每天的数据，镶嵌每个正弦“块”数据为一个数据，并输出数据；（5）重投影数据到WGS84坐标系，输出为GeoTIFF格式，使用LZW无损压缩减小文件量，同时写出元数据文件。</p><p class=\"ql-align-justify\">GridMAIACAODChina时间分辨率为1天，空间分辨率为0.01°（约为1 km），空间覆盖范围为东经70–140度，北纬0–55度，缩放因子为1（为实际AOD值），文件格式为GeoTIFF，坐标系统为WGS84，文件名为日期，包含的_info.txt文件为元数据文件。</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集评估：</strong></p><p class=\"ql-align-justify\">下图使用中国区域AERONET站点评估了MAIAC AOD在中国区域的精度，相关系数超过0.919，偏差为0.015–0.02，RMSE约为0.1，满足期望误差的比例约为67%，在中国区域的稳定性较差，不确定性变化超过0.065/10年。</p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><strong>&nbsp;</strong></p><p class=\"ql-align-justify\">下图显示了全球区域6个MODIS和VIIRS产品的验证和对比，其中MODIS的三个产品（DT、DB和MAIAC），MAIAC AOD的全球验证精度最高，偏差为0.001，RMSE为0.102，满足期望误差 ±(0.05+15%τ) 的比例为0.761，在全球46.8%的站点和8/10的区域优于MODIS DT和DB AOD。</p><p class=\"ql-align-justify\"><img 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5MkJEybMmjWrrKxs8uTJCCFNTc2ZM2cihH755ZcDBw4oKytLS0vDW102Co/H27BhA0JoyZIlGzduRAgtX74cVlOXL19WVVV98eLFkiVLlJSU6urqqqurqd3vLY54ajP9Y9Ofut7T1NQ0evToSZMmUc3CBQUFQ4cOFdN7IiIiEELHjh2TbEvQbEaOHFlRUeHo6AgL6PDw8LFjxzo6OsbFxYGJAlRv0HvAzwUBJefOnRMKhfPmzRs6dGhpaSmXy/3222/79euXlZXV1ta2YcOGLVu21NTULFq0SGRUAKsSDG9iPwA6MGGvWbNGXl5eZMNoamoaNWrUjBkzBAJBeHg4QmjcuHFVVVWamprE0UbqkpSUJCsr+8svv2CMXVxcEEIQzwSUiXOE5H/+/LlIQ8rIyIAZ18/P79mzZykpKWVlZVQ1BQwbI0eOpAZF1tfXg96zZ8+eLvWe3bt3izicPHlyW1ubmN4Dy449e/bIysqK3GeysrLW1taEK1gld4kYl8sFoWBvbx8TE4MQWrduHcYYNEhwlpmamnan97BYLBAuVlZWr1+/FvN49lDvmTJlyurVq4cMGbJo0aLKykoq2xkZGatWrerbty9CSElJKSwsjPoU0ikpKS4uLh0dHaC3wbQEGna/fv1EMVKgcJMXT58+jRAaNGgQuF3E9J7Q0FDQe7y9vckrdKInCKSnp/fu3XvLli1imS0tLadNm0b8lRhj8Jz2cJJgsVjjxo1DCK1evXrmzJmfffaZqakpsWXeuHFD1Jp3794NDQ0dN27c+vXrQ0JCwLuKMXZwcCBdl/h6MMYnT55ECIF9aPny5QoKCsXFxZWVlYqKip999llycrKFhQVC6MyZMxjjBw8ejB07NjEx8cGDBxCO9h56z9GjR2Ey1tPTQwjduXMHYwydjSp/PD09uxR6PdR7wsLCuqw74AAe5C7rXlFRAfaeysrKxsbGMWPGKCgoNDQ0PHz48MPrDnIAFsAgRc+ePcvhcKZPnz5x4sTw8PBbt24RJ7XIDwCrLy6XC6IeYwxRELGxsdAZvvnmGzabnZaWJiUlpaGhAT4EFRWV3r17x8bGMpnMwsJCkGxSUlJxcXEAy4IFC7hcLkhRX19fjHGXjYIxBs/s0aNHq6qq5OTkxo0bx2azi4uLhwwZsn79eqFQWF1dDf5Nse63YsWK7jyJYoPin/nzU9d76uvrhwwZMm3aNCJfwLciqfcwGAwiC8Tasq6uDrwPp06dsre3J0bjyspKc3NzY2NjKSmpGTNmQBFUvcfQ0BAhdO7cuba2NiUlpeHDh5eXlwsEghkzZpD4Hg6H4+/vf+PGjRkzZhDfPwxv4nYhdFpbWwcPHjx8+PCKiorm5uaxY8fKyMgwmUzQexYtWtTR0QHBJZ6entRaQHAxBKCAGrdjxw6RGgF6j5ghAWP8/PnzxMREjLGamhpCaMWKFefPn+fz+RDaQtxSQGro0KHEOIExLi8vHzlyJJmnxfxcGGOIAl62bBmJlSEE8/PzYZGdlJQE2UaOHCnmFeoSMQ6HAwAmJCRERUUhhDZu3CgS6+BjAo0BxJZkfA8AdeHCBXBmWVhY5OXlUdHrod5jYGDg5eXVr1+/Pn36UN1kubm5GGMej/fkyZM5c+bACoxKH9I3b95MSEhobGz8/fffRZPZ9OnTwa3GZDIhZlnMLAcTz7Jlyzo6OoRCoZje4+vrCxam5ORkybLoO29BIDk5GSG0e/duap7i4uK1a9cSGx48AhUE5n5q5i7TYO+RkpIKDQ3duXMnQmj27NnEsb5jxw5Y01tZWVlYWMAyY8CAAd9//z2fz4eYOfhCgjr3//jjj6LuBP5T8NVGRUVVVVXJy8uPGDGCyWSKxbSJbIe3b9/W1dUlFXxXe49AIIiJiblx48bmzZsRQmZmZhhjoqIR+ePj49Pa2tqd0OsSH+rNly9fysrKvnfdQdI2NTWNHTtWVlYWPiD98Lrfvn2beMPd3NwQQr/++mt+fv7gwYOVlZWvXr1q2XmBQRdMdGIhBzA7xMXFpaWlgbTp6OjIyMjo1auXKKoB5KGysrKqqirpGCDZpKWl8/PzIyMjEUKbN2+GoFWEEMj5LhuFWNZtbGyqq6sHDhz45Zdftre3P336FCG0dOlSMpFB4BG1+wUFBRE/O7Vd/i3pT13vAXvPlClTqCbrLv1c8fHx3ek9GGNw6AwYMAB88yIfFvhNDh48mJGR0bt375kzZ0KfoMY1E32Fw+F88cUXQ4cOhRjhmTNnSktL5+TkCIXCEydOTJo0KTs7G2J6wD8Fog0WFhhjQofH402ePFlJSamoqIjFYo0aNUpFRYXNZpN1AIfDAb1HLPLaz88PIQQGGIj8EH3tTChL6j2hoaGg9zx58gQ+7HJwcJBUU2pra7/88ktiuQUEwsLC+vbtO2zYMPik4uXLl9S4ZjKLi1RGKkFwabm5uRF/M2hdffr0oWohHR0dYohBUBTRe+Lj46l6DziDbt++jTG+c+cOMe9LDuDMzEwFBQWEEDGzkTzw4sKFC0ERIcofccP99ttvJK4ZjOEjRowA8QdWAbKXQUNDw+TJk8ePHw+kSBGlpaV79uwxNDQ8evTowYMHFRUVyToeY3z8+HFw4ZH8XC53/vz5ojBbMGUJBALQe4jpDnrsjz/+SOIbyLt04u0IgL1n27ZtJFtLS8uJEydAvWhrayOR/mA46aHeU19fD/aemJiY+vr6b775BiGkpaUFpYDHmRj5Ghsb+Xy+jY3N2LFjjx8/vmrVqqNHj4IQo/p64KsCGDJLly5FCDEYjMrKSojtra6uBr0HTMVhYWFjx441Nzf38fEhRb9rbK+Njc3o0aPDwsLAWQb9DfQe8AyC/PHx8elS6JGBQLDtLvEhdVdVVa2oqGCz2WPHjlVRUWlqaoqIiPjwuoPeA6tHcO2JQmQqKyuHDx9OHNNcLrepqUm0/AO9R2z9SfSegoICkfNr9uzZ7e3tycnJUlJSixcvBnk4dOjQCRMmkNhTLpcL6+S8vDzQezZt2kT0HpDzXTYK0Xusra2pek9kZKS0tDSRz8LOC6xTpPs1NDTQek93PfNfcJ/P569bt2706NHUj5UgrnnUqFFEp8YYP3nyBL5X77JWubm5AwcOnDdvHukNYCnR1dW1sbGRkpIaMWIEDGno2dDdQV85f/68aOOfvXv3IoSePn3a3Nz81VdfQSxRSkpKr169Jk6c6OfnB9Ent27dAm80WAW8vLw4HM7Ro0cRQkDH2NhYWlo6ISEhOztbTk4OPtgBJx3Ye8D+CQFGpC4sFmvSpEkLFiwQ3TE2Nu7Xrx8IcaAMizaSGWPs4+MDkeAtLS1z5swZM2YMmPezs7OVlJSkpKRCQkIg/+PHjxUUFFasWAGR0S0tLVu3bu3fvz8x/sfHx/fu3XvYsGGwaQ18G/Xrr7/CxP/69WsIggb/94MHDyZNmkQ+Ldm5c+emTZsI5uD0kZKSkkSMz+eDnyshIQGkw/r16zHGoBGCcQu+utq5cye13am13r59O0KIGgIPT8EIPGfOHJBoYjwTsxmolU1NTeCy3LBhA3zRZmBgsGTJEgCwoaFhzpw5v/32G7VcPp9/584dokVhjOHL+Q0bNhCTz/Lly+Xl5cmeSS4uLr17996+fTtEh3A4nGXLliGEQMPLz88f3nm9ePGCWhCd7gkCNTU148aNW7FiBflY2sjISBQyMmvWrO+++27KlCnEdQie08ePH/eEbFVV1ejRo0VmHujqT548GTBgQJ8+fWxsbMiQHzx4sL29vbu7e2BgIMbY09PTzs6OwWDk5+eTIkDvgYBCT09P+B6TzWZPnDhx8eLFHA6nsrJy4MCBYO+B4fb7779jjKGH3Lx5EwyKP/zwA4vFYrPZKioqCgoKPdmOqLGxccSIESLLgYuLCwQd7tu3DzYqA7dUR0cHyB8QgJJCz9XVtYcOFA8Pj3etO5fLLS0tHThw4JAhQ/Lz83NzcxUVFT9W3THGoPeAQnz58mUSDwqmr5UrV0ZERJibm8N6D3xMpKtA84FuCsEMGzduHD58eG1tLYSW3r9/H2P8+vVrJSWl8ePHk8AMLpc7ffr0fv36FRYWgpzfsGED8WH5+/u3t7dLNgp8RAxtYWNjU9Xp55owYUJTU1NrayuYCdeuXRsREWFmZlZZWenl5SWy3pHuRyLrSa/7dyU+dXsPxvju3bsKCgok9tDd3X3z5s3y8vKKioq7du0i/mZzc/N+/fqRGVeymeEbCnLf09NTUVFx/vz5jx8/Xrp0qaqqamxsbHR09NixYwcMGKClpZWcnDx37lwZGZlly5aVlJRUVFSsWrVq/vz5Tk5OS5cu/fnnn+3t7UtLS0WfyyorK9+4ceP69ev9+/eH3UEaGxtXrVqlpKTk6OiYn58/e/ZsoFNcXNza2nrkyJHVq1evW7dOR0cH9Lljx47JyMhMnDjR2tp6xowZ0tLSGhoaYt/PJyQkrFixYseOHQsXLiTGm3nz5klLSy9dupQsYUHazp49e8mSJRASe/v27cOHD2OMY2JifvrpJ3l5+YEDB65evZrsT5OSkqKjo/PLL78cPXpUXV1dTU2NRFUHBwevXLlSTk5u0KBBO3bsOHDgwIYNGx49egSz9evXr9XV1QcPHvz5558vXLgwMjLy8ePHFzovQ0PDffv2GRoaUsMp4GO3+fPnSyIWERExatSoAQMG/Pbbb7q6urKysuPHj4egvytXrowZM8bU1PTYsWPz5s0zNjbu7hOngICAzZs3A2+kob28vObMmTNgwAAlJSVDQ8OYmBhNTU3gedGiRYGBgffu3Zs4caKsrOwXX3xx+fJlcOePHj0aopjr6+sfPHhgYmJy5swZQ0NDDQ2NixcvUosQTa7GxsbKysqamprwMUhMTMzGjRtF8R+ysrJ79+6FlV9DQ8PNmzc3bdqk13mtXLny3r17sD9keXm5oaGhqqqqrKzs3LlzjYyM1q9ff/z4cepkSapDJ3qCgLq6+ldffQX6sYuLi5ycnKysrLS0dJ8+fZSVlUkU//79+7/44ovu1GhqQWJdHSw0V69elZaWHjx4sLW1NZfLvXv37ogRIwYMGHDixAmBQFBeXj5u3LhFixbp6elpa2vr6+vHxcWx2Ww1NbX+/ft/8803sHS5devW4sWLf/75582bN4NH9f79+/Ly8kOHDv3jjz82bNggIyOzcOFCFotlYmLy2Wef/fTTT0FBQV9++eX06dMLCwttbW0HDx6sqKgIYYhUniXTPB5PXV1dXl7e0NDQ1dV10KBBP/30E3zoJCl/BAJBaWmpmNB7+PAh2dRUkj65U1ZW9q51B/NqcXHxli1bjh49umvXrvXr1xsaGsLa4/Llyx9Yd6L3rFq16s6dO1999dXhw4dBxhYUFGzfvl1GRmbChAmg1IaHh0+ePHnAgAGbNm0i+8HGxMRMmDBhwIAB6urqLS0tpaWl27ZtU1NTW7p06cWLF7lcrlAoPHPmjIKCAsg3WO89f/5cVVVVTk7u+vXrBgYG/fv3nzx5srOzM8j5gwcPtra2amhoUBtFTU2NzWYXFBSAoWjnzp1XrlxRUFBQ+r/vYDIyMlasWNG3b9+JEydCV+zo6Lhz5w7pfmJzB2mXf0uC1nvebKz3zTffkI+xGxoaqqqqGjuvyspKGIQCgWDJkiUknqbL1uVyuVTDg1AobGpqgv7R3t4O6nlLSwuLxWpsbKyrq2ttba2rq4M0GKg7Ojpqa2tbW1vb29vJUpK8KxAIwLgNpZP7HA5HjI5QKKyrq6utrYWVk1AoZLFYDQ0NLBarvr4eGKivr5dcV8FakKwkOBwOk8mkcghFNzQ01NbW1tTUgHmDx+NBNdlsdnV1dSdyjTU1NWJbzdbX15eXlzOZTFI1kR+tqamJvFJVVUUAh4I4HE51dXVDQ0NjY2NtbW1zczO8y+FwKioqqqurqaTgFZHThyBDRQyQBxAALhaLBY0rFAobGxtZLBaXy6W6OwlBkhAKhfAKuQOBn7W1tY2NjQALgEB4BvRIiUwmE6UlUEAAACAASURBVGBns9mQv6Ojg9e542VLSwvgQw01g50woeGqq6uBPTabXVNTQ0qE14Gltra2ioqKyspKqubU0dFRXV1dX1/f2NjIZDLLy8thQ05qLej0OyGQkpIybNgwsK6x2WzAtrHzampqghYU7eYMrpOeUJbs6qLtDzo6OqBnEs2JzWY3NTWRLgQGSIhPhw82c3JygI26ujriwWxsbKyqqiL9pLGxsb7zYrFYpGfyeDyBQNDU1ER6I3QhyAwDR6xndlmvjo6O5uZmgUAgFAqbm5tBMrxF/nQp9LqkTL3JZrPfr+4CgQDC3ZhMJpGQsNPYh9cd7D2HDh2CsUmFi8fjNTY2klHZ0tICyNfW1hKZQ50doBX4fD6MXKg7keT19fV1dXVAn0g2qmyHbgN/hUJhl41CJg5W59XQ0FBfX0/6DPQ9wjAwQO1+1Ob416VpvedNk0VFRU2fPv0tG5I+fPhwzZo1RPr865qZZphGgEbg4yJgY2OzZMkS2O5BkrJoMXDy5Mm9e/d2qZpL5n+PO0VFRfPnz3d3d8/pvB48ePDll19SPyB4D5r/llf+mXUHpyG9F+g/vxfRes9/2ighIUF0rBL1c2vSeE+fPj1z5oyYP4U8pRM0AjQCnyYCvr6+v//+u9iaGKCwtbW9efNml48+Fla1tbX79u3T19c/efLkmTNnrly58ulsxfQPrHtTU9PmzZulpaVnzpwJzqyP1dA0nY+OAK33/H+Qtre3U7/cIw/a2trA6kju0AkaARoBGgGMcWtrK3idxNBoaWnp8r5Ytg/8KRAI6urqysvLKyoqiIfiA2n+W17/p9Wdz+eD64rJZEIAwL8FyU+QT1rv+QQbna4yjQCNAI0AjQCNwCeKAK33fKINT1ebRoBGgEaARoBG4BNEgNZ7PsFGp6tMI0AjQCNAI0Aj8IkiQOs979PwAoEgNTU1Pj4+NTW1u9BFHo9XUlJC9fGzWKzIyMiEhISkpKTo6Ojw8PCIiIjY2Fiyf8P7sNKDd/Lz87vbkKYHb/8nS3t7e0Ln9d/a9KW8vDwuLi45Ofm9v6pra2uLj49/8eJFRkbGR/nK5qMT7Hlz0DlpBAgClZWVGRkZVFFDHn2sRH19fVxcXGJiIuw++rHI/hV0JEelGD7Nzc2xsbFpaWl8Pr+joyMpKSkxMfFfGh1VWloaExOTkJBQUVHxV4D5v0qT1nvep2XhQBwFBYW37GSYkJDw3XffwckvUEZFRcX58+fl5OQQQhoaGtbW1teuXRs/fjxsFvw+fHS+09bWlpqa2l3kdV1d3YQJE0aOHEnl5D3KamtrgzODNm/e/JdK2O54S0lJgUPLyUbP3eXs7j6bzYa988ePH/9RNrD56AS745y+TyPwFgTWr1/fq1evmJiYt+T5wEcVFRXTp08nx578KTWBQMBgMMhOYH+a/yNmoI5K2NKTig+TyVyzZs2WLVuUlZXNzc319fVXr149duzYv+jj87q6ulevXn3E2omRSktLg122T58+LfaoJz9LSkpgl/yeZP5fykPrPe/ZmnDuATk9VJIKHEVua2tLfdTQ0ABHkcM27RwOJzU1FTZKp2Z7p/TDhw/fMmgFAsHdu3dv3rzZnWLU87JcXV0RQmpqav8VvYfsow9H2fecbWrOiooKOH6PnG5Dffoe6Y9O8D14oF/5xBHw9PQ8derUe9tBe4genCcleVRfl6/HxMSoqamJ7VzaZc6/4qbYqKTiAwcw29ra3r59+/r167KysqdOnbK2tnZ2dv4rODl+/LjkIT8ft6B79+4hhLo7TfktZbW1tW3bto163OFbMv+PPaL1njcNWldXFx0dHRcXByd7CwSCly9fvnjxIi8vr62tjcFgUK2INTU1ogMZMjMzZ86c2Z3eU1lZCUdkr1u3jrprJ4vFgqMHPT09uVyuv78/h8OJj48X61VtbW3gVCooKODxeOnp6ZWVlZCnoKAgNDQU9hlqa2szMzMbMGDAunXrYmNjRUuZpKSkhIQEeCsjI6OysjIvL+/ly5c5OTnEH1ddXR0WFga7ttfX1yckJCQmJqalpbW1taWlpSUkJMCWxGVlZZGRkUlJSUR4kXOe4aQ6BoORkpLCZrPJfqOkFqWlpeHh4VlZWVD3vLy8hISEFy9elJaWNjQ0xMTEwH75GGNqdfh8PoPBePHiRWZmJp/Pz8vLE1uLUI+yxxiLcUg8caJjGZqampKTk6lGneLi4tjY2ISEBAUFhS+//LJLvefjEuzo6GAwGJGRkWKugcLCwrCwsNTUVNjKFmPM5XITEhJiY2NJGwkEgtzc3IiIiLS0NMhGHA0sFqu6ujolJUVy52iCP534GxBobm6O6bzIzl5CoTAnJycsLAwOYBKdPQnO2dTUVA6Hk5mZCd0eVjtgfyWdtri4OCUlJSEhAQ5cA/7FOqTkACksLCwrK0tJScnKyoLe3tDQEBkZWVpaSj6wl+yHubm58fHx6enpXC43MzOTOMGBfkpKiuiYVclBra6ujhC6e/fuq1evXr58Sd3ygypShEKhj4/PiBEjvv7666dPnzIYjMTExISEhNzc3IKCgri4uISEhNevX1dUVERFRYGLXygUpqenR0REUFW35ubm6OjolJQUWGV1x7NYQ4sN86ampsrKSsCnqampra3tl19+QQgdP34cY3z16lXRQa0bNmyAcsXY6KEQJi0oJnaYTCYcbqivr89gMKjLThjLSUlJpaWlcXFxBEwWixUfH5+YmFhVVdXa2spgMKBNJVsQY8zhcBid17Vr10QbdsPJsjBt5ebm1tXVASkCKZfLTUxMjIyMBMwLCwu3bt3au3fvGzduwF6XeXl5sbGxgJIYqv97P2m9B4eHh4vOlLl9+/bXX3/93Xffwemh586dQwjNmjVr3759ffv2nTp1KqgamZmZU6ZM+fnnn8+dOweH8pADvKidw8vL6+rVq/Ly8p9//nlCQgJ5RPQem87LyMiIPKImWltb4fzbZcuWXbhwQUpKateuXRhjBweHuXPnnjt3bs6cOSEhIW1tbZqamgihH374wcbGprS09MCBAwgheKtXr167d+/28fHp16+fgoICg8HAGEdHR8+ePfv3339fsGCBjY1NSUnJF198AWcv83i8s2fPbt68WXROsp2d3f79+69cuTJs2LC1a9fW1dVB6WDvwRjfv39/9erVjx49On78OPU4dIxxQEDA119/ferUqblz5168eBFjbG5uLjrSVUZG5unTp9XV1QsWLAAzGLU6sOy4ceNG7969x48fb2ZmpqCgMHPmTKrfnar32NraUjmEUya2bduGENq0adOWLVuAVVAOAgMDhw8ffuTIEcBn0qRJknrPxyXY3t6uq6u7aNEiAwODWbNmkfPIXF1dZ8+efezYsX79+u3evRtjzGaz1dXVt23bpqWl9fPPP4OcOn/+vJGR0YkTJ+Tl5fX19THGlZWV4GjYt28fHLQudnYptf/Q6b8ageLi4pUrV2pra8+cOVNFRQVmjtu3b0+bNu23336bOnUqeGOTk5OHDh0qLS194sQJBQWFwYMHOzg4HDp0qHfv3l9//XVFRUVHRwd02h9++OHu3bsTJkzYsGED7P4i1iFhDFIHiKKi4ty5c6Ojo0eOHIkQcnd353K5v/zyy5EjR+7du2dqaioUCkVnEVD7ISyx3NzcpKSkYEQoKioOHToUjqe1trbublCTs7v37Nljbm6upKSko6MDGglVpFhbWwsEAjiNdezYsWZmZjY2NoqKinAg7osXL5SUlBBCfn5+SUlJ06dPDw0NFbWUsbHx8uXLjx07tmjRIkCysLBw2bJlenp6opN3jYyMBAKBu7t7lzxTG1pymLPZbAaDAf5xDw+PhoaGhQsXIoR2795dXV2to6ODEPrxxx/h8EEqG7m5uRwOpydCmMfjSYqdjo6OsrIyON1z69at0DSEVeI0/OWXX0xMTIYOHQrnG1ZWVq5YsQIhpK2tDSe2wulJ1BYEScLn8w8cODB+/Pg7d+7Mnj2b6D3gx1+0aFFWVhYsvME6zmazNTU11dTUNm3aJCMjExwcnJGRAYfg6urqhoWFhYeHL1682MLC4vTp03AqKuH2fzLxqes9QqFQTU1NSUmpoqICTiEGsyQc2a2oqBgcHAyn6Xp5ecHgl5OTy8nJgcOTZWRkJPWe1tbWS5cutba2rly5EiFEdWMRvWf9+vUzZszYv39/d73q+fPnUlJSSkpKISEht27d8vLyysnJ+eyzz+DQ9W3bts2aNQtj7ObmhhAifi6xtzw8PNrb21VVVeXl5TMyMlpbWydPnrxlyxaMsYmJyeDBg5lMprOzM0IIYoy8vb2LiooaGxsnT548d+7cpqamdevWiQ5ShpPAib0HYzxv3rwFCxZUV1fn5uZSt+qHo6pXr16NMQYDLJzcu3r1aoTQ8+fP29ra7O3tMcaZmZmS1SkqKho+fDhC6NGjR05OTrdu3aIGIIPe4+Xl1dLS0iWHoGB9/fXXcXFxkydP7tOnT3Z2dmtr6/Tp07/66is2mx0TE9O7d++JEyeK6T0sFquHBLOysnpCELBydnbm8/lz5syZMmVKc3NzcXGxgoKCjo4OxvjGjRuXLl3CGJ8/f15eXj4nJ6egoGDIkCFWVlavX7/+/PPP9+/f39DQAKfYVlVViYwH4GhYv359eHi4jIzM+PHjxWrRXV+i7390BHbu3Dls2LCqqqrExMS9e/cWFxczGAwZGZkTJ05gjPfu3SsvLw9TOExLNjY2Dx8+RAh98803CQkJGzduRAjB9srQabW0tMgKwdbWtrseXlhYKDZAMMbLly9HCPn6+qanp/fp0+f27dstLS0vX77EGMPZ7NR+CCfiDRkypFevXvfv3wehB16S77//vstBDeiBvef27dsYY21tbYRQQEBAe3u7mEipra3NyspCCC1cuBBWHdevX0cIwYHKcN64q6trXl4eLH58fHx69+7t6+vb1tb27bffampqYow3b948efJkNpv9+PFjGRmZ6OhoJpMpxjNVrsJJf90Nc4iAAQ3AyMgIIQRnm4PjHoSnGBt79uzBGIeEhPRECFtYWCCEqGIHYnqoZYn1QBjLAOb+/fv79u0bGxuLMb5z545IL/zuu+9EmqixsXFiYiIwSW3BlpaW58+fI4RgVamnp0f0nqioKITQggULOjo6QHOCaevGjRvS0tKxsbEVFRU6Ojqg2QAsEBamo6MzcuTI9PT08vJy6hHUYmz/z/z81PUejHFeXp6Xl1dISAjMzaD3hISEgB2Fw+GATcXHx6e9vX1451VeXi4UCrvzc8XFxYWFhWGMoctOmjSJOFyI3uPn55eRkXH58uXuYmWePXsmkoxz5swhbjIzMzOE0Pz583V1dZctW7ZmzZrm5mbQWsiBqZJvNTQ0DB8+HKRwbGystLT0xIkTDx48uGnTptmzZ2dkZNTV1Y0bN27YsGFJSUnu7u5gvk5OTvbx8QkICJg8eXKXeg+IyzFjxgQGBlIHA+hh+/btwxi7u7sjhEC3s7e3h/SLFy/gmGjJ6tTX1xcXFysrKysqKsJMT6UsOkaNau/pkkM4F1BHR4fP58+aNatfv365ubmvXr0CWcDlcsvLy+Xl5SX9XEKh8P0IVlRUdElw1apVoqUkdIM1a9YghFJTU62trRFCVDsNh8P58ccf+/Xrt2PHDk1NzWnTpl27do3P50dERAQGBjo6OiooKAwcOBDQgInH0tKypqamy0LF4KJ//kUIFBcXDxs2bOLEiVQP5okTJ8ANhDH+7bffyOQKx2IXFBTAnLR582aM8Z49exBCHh4e5BDvgwcPkiGjoaGBMU5JSZEcgwUFBZIDBMaFt7d3Y2PjN998A5ZO8L5J9sOEhISqqip5eXkVFZWqqirQukDv6W5QA4yk+4lO8Tx9+jRCyNLS8sWLF2IiJSsrKz09HYQn+OwKCgqGDh361VdfeXt7nzt3rm/fvlu2bHn06FFSUhJRodavX6+trf39998fPHgwNzd3xIgRQ4YM2bt3744dO7799lt/f//KysoueSZNzGAwyDAXG5VUuWFoaIgQgiglWJyA8ATbD2Fj3759QqEQZoG3C+GmpiY4losqdmAxTC2L8CkJJvSWP/74A2P8xx9/IIT27t1L8ku2IIPBOHXqFELIysoKYwylQwsCwwsWLOBwONDHvL29RQrc/PnzP//8c7EDKAGWiIgIjLGLi4uUlNSgQYP+6mgkUq//boLWe3BxcbG6urqpqSl0pnv37oGmD6OIw+Hs3r0bIeTj49PW1jZs2DAVFZWysjI+nz9jxowu43tOnjy5Y8eOEydO7N69u3///ggh8gkS0XtA5MXFxcE5vSQggPQG0GDWr19P7oD1mKg4oA/Beg6EJsZY8i2i9+Tk5ISGhsI6jPjmQeuCdYmenh6YuzHGCQkJGzZscHBw2LBhA0IoKipKzM/V1tYGo1pOTg6eAp8gSsCk4eLiAl+uYYxra2vHjBkzYsSIW7duwVceXVYHRGR3lgyq/OqSQ9B7dHV1eTwetE5eXh5I4R9//FEkC0pLS7uLa/64BIFVsJOB5EpMTAShpqamRtq0tbUV+CQYQot4e3uvXbs2KCjoyy+/FNl+qHrP/fv3q6qquqsFoUwn/joEXr9+PXjw4M8//xwsOlCQgYEBmVBPnjxJPn0i/TAiIgKcsBhjECmwFodOC+Malg3btm3DGCcmJkqOwS4HCHVcMBiMmTNnIoRWrlzJ5XKXLFlC1i3QD2NjY0HvUVVVrampgeUHzJrdDWqoIOg9oDHAVP3gwYOIiAgpKamFCxcSkYIxTk1NBf8RiVXS0NAA/3tubu6iRYvk5OR0dHQg5AW+EqWGS+fl5Q0aNEhVVbW0tJQ0YnFxsby8vCTPJENKSgoUKjnMqfhQdRGq3rNr1y7SfBhjGIaS4rRLqSUpdqh6j6WlJWGSJCTBvHbtGtF7qB9ndSlJjh8/TriF0qEFqRIe7D3e3t4CgWDGjBngXiQMkGUkyCiMsZmZ2WeffUbUKWrO/730p673tLe3z507d/DgwXl5eQcPHkQIgUsVhNSiRYt4PB7YJP38/DDGS5culZWVTU5OxhjPmDGjd+/e6enp1G6Rl5dnZmbGYrHKy8sbGhpgmK1fvx78NY2NjdTvueBFFxcXULqpdEBz37hxI7np7+8v6pTDhg3LyMjgcDiPHz/m8/lgR9HS0oJYgbCwMIQQ9S02m62qqqqgoJCdnZ2fn6+kpNSrVy9fX19QkiBcOjU1tV+/ftOnT4dgmsLCQiUlpZkzZ5aXly9YsAAh9PTpU2K+2rp1K5/PB4lvZ2eHELpw4QJhMisra+DAgeBKAz/Xw4cP4SmolQAvxliyOu3t7WDvmTBhAjWshxAHw6yfn19FRUWXHJLzkIVC4axZs6SlpV+/fl1VVTVq1Khx48Yxmczy8vKBAweOGzeOWOCAeHdVfm+Cly5dAl2Zx+PNmTNn5MiRdXV1YDYfOHAgBDPFxMTU19eDBFRXVxcFCuTm5iYnJ4eFhfXq1UtDQwOcGp999hmEIsICzsbGpra2duDAgZMmTSIh5wQiOvE3INDU1DR37lzwL0Msc15eXkBAAELo3Llz4Ofq168fBPbNnDlTWlq6oKAgOjoaIQRDA0zIMAypeg8M51u3blVWVnbZw4uKipSVlcUGCIwLb29vNptdXl7e1ta2ZcuWvn37lpaWQuiuj48P6Yc1NTWg94wYMYLJZIKPBoYwhDZKDmqAFDoq2BgMDAz69++fkZFRUFAgJlLq6upA7/n++++LioogXi0oKAghBPrcjRs3iJgFhztCaPbs2bW1tUwmMyQkhMlkgupmYmKCMY6Pj8/NzQV7jyTPpLnLysq6G+aAD3xCC7HG1tbWRKAdOnSIxDgTNkDigRpBFaeSUovH44Gg09PTI2IHxCN4oG7evFlVVUWNaybBUqDtHT58mDg9qcYbqJqYJBk1ahSLxYKv0o4dO0bshWfOnMEYJyUlicIJ5s2bV1lZCS5FmLagv61cubKhoaG6uhrWtyDbAwMDq6urc3JyBAJBdHT0wIEDqfUl8P6PJT51vae5ufmrr74CyzD0vzlz5jCZTCsrK+KM/+GHHxBC0LG8vb1lZGT27t377Nmz0aNHKyoqGhkZkUk6JSVl9uzZJ0+ehCDE1tZWEGrwekVFhbu7+5AhQ8AKEhsbGx4efuzYsVGjRqWlpVE7Fp/Ph8Dqb7/9Fr4BwRi3tbXBokRBQWHp0qVgIWAwGEpKSv379z9z5kxra+uFCxdE0y31rcjIyAEDBsjKyrq5uWGMz507169fP1GI8dy5c52cnKBQPp+/bNkyssjIzMyUk5OTkZHR1dXdunUrQmjnzp3t7e0gMubNm1dSUqKpqenk5OTm5jZ79mzQAgn/586dGzNmjJOT0+LFizdt2kT28IiLixs/fjyEHXRXHRcXl/79+w8aNAg8RIQmxm/MchMmTBDFR5uYmKSnpw8cOFCSw0OHDsHU8uLFCwhmhDpCbLipqamjo+Nnn302evRosOoR+t1V+b0JlpeXz58/X01NzcrKavTo0Q8ePMAYC4VCfX19KSmpAQMGzJs3z87ODkxrUK9x48bp6upyOBw/Pz+RtUDUT86ePQvRIZcuXWpubl60aBFC6NSpU/7+/r179x40aFBiYiKpAp34OxF4+vSpsrIyQmjChAl6eno1NTUdHR07duyYPXu2q6vr5MmTYU7KyclRUVFBCAUGBoIomDZtWlpaGoiUq1evkiX+ggULoqOj582bN378+KqqqpycHLExuGPHDh6P5+rqKjZAqONCpCJs27YtPj5eX19/27ZtQqGwoqJCsh+GhYVB/wkLC9u3bx9C6Keffmpra9u9e3d3gxqmaiUlpV9//dXX11cUPAfBJV2KlJqamqlTp4LcAElYWVk5efJkEEEFBQVjx45NSUmB9iorKwM0hg8fvnXrVvi4zMnJadCgQb169fr2228vX74sFArDw8PFeFZTUyPfPwIpyWFuYWFRXFz85ZdfQjQMm82GyCpNTU02m33mzBkw6ld2XlQ2YHfWPxXC4LKHBfOWLVvExI6bm1vv3r2VlZXJ2o90UVAid+/eHRISoqqqumLFCi6X29LSsmPHDli4kqDJLiVJXV3djBkzvvjiC9HHgxoaGr17916yZElaWlp9fT2ojDNmzICJBqatvLw8CHNWVVXdvn17ZmamiBNjY2OE0KRJk0Qa+dmzZ01NTUNCQubPn0+8E4Tb/73Ep673YIwZDMbDhw9hafLo0aOUlBShUOjl5WXVeT1+/PjBgwdWVlaurq7wbWdiYuLdu3fT0tJCQkIyMzOpH3yGhITc7bzAzllSUmLdeVlZWVlbW8fFxVlbW1tZWd2/f9/CwsKs87pz546joyPRnKCHNTY2Pnr0yNra+t69e1RTEJfLDQ4ONjMzoxqZoqKiHB0dmzovybdCQkIsLCwsLS3B4CkUCmNjY83MzKhkIZiAfCoPbj5HR8e6urri4mJra+vi4uKWlhZbW1tra2tLS0sGg1FcXPzy5UsxToB5oVAYFRX1xx9/eHl5EUM3xri9vT0yMpKEK8H329Tq8Hg8Ly8vS0tLCwsLWKFSx9vLly/Nzc2tra3t7e2bmprCw8MlOYTq29jYBAUFWVlZWVhYBAcHA5EnT55YWVnl5uYGBQUVFRVRw6UhQ0hIyMclKNqrSSS+7969C3EMUAqXy3327Nndu3eJeRk+yLe1tbWzswP7TUdHh5eXF0R6pqam2tjYNDU1lZeXW1paWltbOzo6+vr6WllZmZub03oPtYf8zemcnJz79+87ODiQw7e5XG5AQMDt27efP38O6/u0tDQYfYGBgZ6eniAHIiIi7t+/DyKF6D3a2trBwcH3798nzh2xDllUVARySWyAkHHh4OBQVlZWVFTk7+//6NEjwpVkPwwNDQWuAgIC7OzsrK2tbWxsmExmYWFhd4MaRERVVdXz589v374dHh5OAhMFAoGkSHn16pVN5+ei0CggE0DKdXR0xMXFUVWWhoYGNzc3Kysr6t6qmZmZlpaWPj4+MFS75BmUKtLuQqFQbJhjjNPS0kBuODo6ZmZmgkC+f/9+Zmamvb29tbW1hYUFLMbE2OihEG5ubu5O7AgEAj8/P19fX7JjBWEV9J4bN254eno6OjqCVYzMF/fu3aN+LiPZghhjJpNpY2Pj7++flpYWFhZGDNj5+fmWlpZRUVHR0dHm5uZk2qqsrLS3t6eCzGazHR0dYSIoLi7Ozc21srL6FD7mwhjTeg/pinSCRoBGgEbg70YAXBvgbfm7y6bL+y8hAD5rcLf9l1j4pIul9Z5PuvnpytMI0Aj8FxEQCATgJVm7di1Ecf0XmaGL/nsQaGpqgm2E9PX1iZ3m7ymaLgUQoPUeuifQCNAI0Aj8dxAgXpJ79+6Rryn/O6zQpf5dCJSVlVlaWkK0w3/rmOe/q67/0HJovecf2jA0WzQCNAI0AjQCNAI0Ah8dAVrv+eiQ0gRpBGgEaARoBGgEaAT+oQj8rXoPnGdJ/6URoBH430aASLu3VxO+7Ye/kjkJkQ9MEMocDqe1tZXL5ZJvkT6Q8l/9OuFcKBS2dV5cLpf6ReRfzcCH0CfMCwQCOOiUurfhh1D+G96lMt/R0cHrvP6Gcj9KEYR5Lpfb2trK4XD+LX3mo1S/J0RovYd0EjpBI0Aj8HEQ4HK5TCazvZ1D1WxAHkEBcF+0hwpRQUA0U4vvifzqSR5SYm5u7vXr101MTNLS0lpbW+vq6lgSV5c3WSxWXV0dk8mUyP6fG929VVtb2+UrdZ2X5KO6ujqy3xVVHYQThU1NTa9du1ZTU9PU1CT5LjDZ3f3umO+O87fU9y3MUzf1oDalq6vrjRs37t+/39LSUl9f3x2TXd5ndl5dPuqOeSaT+ZZHXZJisVjUrQVJnykrKzt37tz58+eDg4NFh7x2R7a7+91x0h2GLBaruz7zlvZtbGyk4r4ANwAAIABJREFUajaE+cLCwps3b169epXBYLxrh38LJ29h/j26mdgeLj0Z0R+eh9Z7qMOTTtMI0Ah8IAJvhNLNm9e3/bpdW+dAVk6OUCCAvWQ47e2NjU0CobCDy21oaGhra/Pw9AoNCQkNC+dyO96YNNrbBQIBKf7DpRtQAIJwpn1YWNjVq1djY2OzsrJKJK7S0tKkpKTXr1+XlpZSH5aWlhYWFmZnZ4vdLykpKS0tTUtLy8zMlHxUUlKSmZlZXFxMJQWvvHr1isFglJWViT0qKipiMBhkAqZCkZmZGRwcfPny5aysrC45KS4uTkpKKioqEqNZWlqan5+fk5MjyWFpaWlKSkpeXp7ko8LCwqysLMn7ZWVlycnJeXl5kqXk5OTk5eXh/7uozMfHx3t6epqZmb1+/VqSk9LS0oKCgqSkJMniSktLc3NzJVukpKSkqKgoOTm5S3i7fAUaMSEhoctXGAwG9UxA0mdER7AZGxufOnXq8ePHOTk5+fn5YkyWlpZmZmZ22ZrQAbpskezs7NTUVMkO8JZXcnNzU1NTxUqHVmAwGGS7JqIui46BKy0tDQsLu379enx8fHp6uliTibafLSsrS01NlWwRgDczM/OdXikuLu7yldLS0levXqWnp0syX1xcnJ6e/vcbAmm9hwxPOkEjQCPwoQjArKere9AvIMjW5r7R8RMW5vf279d+/vy50ZEjWlp7Xd3cTa5cOqSnn5iYdP369T27NdS2bj13/nxkROT1Gzf5AgFVcAO1D/xLqtTReTU3N9fV1XX30bjkhpZ/WjrQ/9Ns1AyCzot6B9JCoTA3N5dMA4RzoVDI5XJ5PB6cREE9D5VK5D2Yp77ewzSfz6daF8hbzc3N1K+TxJiH/YiLioq6O1zlPZgnxkLCw58muiulsrJSUu8RCoU8Hq+pqam1tbW9vf3169ekaagFddea1DwfnhYKhd3Vt6ioiPo9PEG+o6ODz+ez2ezm5maqSkplhs/nd0eWmu0D029hPi8vj7qJ5QcW1MPX/9F6D9SBtCKd+HAEyKTy4aRoCkT601AQBGDM6h06FBT8zNvLY9u2bYsXLd63b9+ZM79raWq5urgcOHjozG+nNmzcaGv76OBB3bPGv9s+sv/j9q3Vq1c7OL45OEWMVA8F2VuyEYKQwBg3Njbm5ORQyyJ5urxJfcpms7Ozs+vr69PS0hoaGlJSUnJycuAmxjg5OZnFYr2dCGGDkCV3eDxeRkYGmVypGYAm7O5dUVHRZRFd3hQjkp6e7u3tHRkZmZOTU11dXVVVVV1d3dLSkpSUBDoBNX+X6S5LwRg3NDSQQ3XE8pDWef36NexNLElZ7JUuMzAYDG9v7+joaNFxikwmE+wlYWFhvr6+sM+15Ftid7osRXRSbElJCaAKrFLfIswzGAxwylCf9iSNMa6pqcnJyRGdx1dUVJSUlOTt7R0XFycQCF6+fMlkMrvkqieUQQRlZGRAr+uO+ZaWllevXr13KRhjDocTEhLy/PnzhoaG0NBQb2/vnJwcHo/34sWL9vb296YsFAqzsrKAAsH5b0j8c/UegUDAYrHgoIMe9gA629sR4PP5LBYLfPBvz0k/7RkCmMfj1dXVde5D37M3/udzdQqtC+fObt68RVNTMy4u7sTx4zoHDgQFPr506XJ0dNSly1esrSy3b99+18zsiomJ7cOHR4yOxcVET58+o7i0jIg8olOSO++doEIORBoaGuAcAOqjnqRBZzp06NC9e/eWL1/+4MEDQ0PDWbNmeXl5nTp1qqioaPbs2ba2tu83DcDJLampqd3pPcB8Sef13kVoaWnp6+uLztc8e/ashobG/fv3/f39/fz8ZsyYAdaanuAgmQdjXF9fDyebAp/UPHAHY5yTk/Pe0zzGePv27QYGBgEBASdOnAgKCrKwsLh69aqamtrp06e1tLSgz1DL7WEaY5yfn19W9v/rfkCtra2NwWC8evWqrKwsPz8fTpzoIVmSTXSIdWBg4Lx583x8fExMTLZt23bmzJmgoKDq6upFixbdvn37/VoT6MPZhV3ae8ggamlpgbM4CEvvlMAYP336dNWqVb/99pu/v//y5cuvXr368uXLFy9eTJo0KT4+/r35FwgEaWlp1LAw0lX+0sQ/VO95s3JKStTS0kphMP7S+v97ib9Tx4UB8PzpE20dncLCon9vrf9Szt8DUi9Pj0OH9ErLyv9Sxv51xDmc9srKSlgct7DZJSUlb3w0LS1cLqetra2xsbG4uITH48NnPjU1NdevXT167DhUk81mNzQ0fsQqk2bl8XjNnReLxSopKXk/YS3STk6ePLlixYoDBw6sXbvW0dFx7dq169atO336tI2NzapVq7Zu3Qp2e1LuOyWozhTyIiy42Z1XRUXFh6gOe/bs0dbWfvLkye3bt9esWbN48eKgoKCDBw+uWLHC1NT0/TAB8dLc3FxYWEgajsp8S0sLm81uaWkpLi4GPxd52vOESG365ZdfDh48GBIS8ttvvwUGBlpZWZmYmGzYsGHdunVmZmYfwjzYvcSYxxgXFhaqq6vr6Oi4uLjAwZ/vUQrGODg4eHXndeXKlZ9//vn48eOxsbHe3t4rVqzYsGHDn9oICWOQoILG5/OTk5PBiib2lM/nNzc3g58LDnylvtjzNMa4oKDg9OnT+/fvd3R0XLx48ZUrVwoKCk6cOLFq1arDhw+/ByZQOmictL3nP23xRsEMfqyhofHseUh9fb2np6fH/12enp7Ozs4uLi4eHh4k7enp6eLi4uzs7OHh4eDg4O7u7ujo6Obm5ujo6OLi4uTk5OjoKDon2d3dHV6BtKOjo4eHBzxycnJy7rwc/+9ydnZ277xINiALBJ3+73J0dHRwcHB1dfX09IQMhCtXV1egCa84Ozs7OjoSZv6vQh7AJ1TH29vJ39/F0/MNV+7u7q6uzm5uLq6uLvb2Lo8eOfv4+NjZ2Tk6Onp5ecF3H//Bqwf/YYxdXZy19u5NTE6pqKggpUO5cA4z3HRycoLqEJwdHR3h/DxXV1cqUARSQBsqQsAHhKDWACq0GmSgthQBCog7ODgAQQ8PDwIpcOXs7Aw8kFYAylT+PTw8IA90GyAF/Ht4eAAdQs3b29vOzs7BwSEwMPBdF3MY4wf3rffu2/cqK6ugoMDd3Z2K6lvSpMUl87i6ukrehDaCHi42FqAJxF6B4eDk5ES97+bm1h1xGAVi/Hd2v66ZoZIlkMJN787LycnJ29v76dOnJEQXJHKXf1l1zFUrlj98ZP/GV1LPOqijs3v3nrLyN66cj3LB4MAYi85cNDY2vnDhQnR0dHZ2No/H68G4Ec/yptEfPFixYoW3t/f8+fMjIyNNTU1///337du3a2trX7x4cePGjSEhIe8xE2CMW1tbk5OTJe09GGM7O7vz58+bmJhkZGSAeiHOWQ9+i9xkmpqaRkZG/v7+58+f9/DwmD9/PjB/5cqVLVu2NDa+0Th7QEk8C4SNw0Hf0GqQAw4kNjExMTU1NTc3z87OhvOPxd/vwW+M8bZt206cOBEQEHDnzp39+/fv2bPH1tbWyMjI1tbWwMAA4lS6o0T6Emhp1GwY4+zsbOqpqPAUY8xisby9vX19fdPS0nJyct7bz+Xj42Npabl7924jI6Nff/310qVLvr6+u3fvPnv2rJqamp+fX3ewY4ybm5ufBAdHRkaWlpaWlZVBpA6V/9zcXEl7D3wDCB+jxcbGQvgw9a2epzHGoaGhx44d27Vr1+XLl9esWWNpaenq6rp58+br16+vXbu2oKCgO/7fUgr0DQaDQdt7/oMSxjguNsbAwOCRnX14eNjhw4djYmIiOq+oqChn5zcaQHTn5eLi4unpGRMTA7I+MjLy4cOHoaGhdnZ2QUFBjx498vT0hNn06NGj1tbW8fHxzs7Ovr6+z549s7W1DQ8Pt7W19ff3B93F1dXVrvNycnI6ePAgZLCxsYmIiLCzswsODrazs/Px8QH1xcHBwd7e/tGjRzdv3tTR0QkPDw8JCYHMbm5uPj4+vr6+MHl4eXkBfSBuZWWlra397NmzyM4rODjY1tY2MjIiJiZq40bH9ev9YmKiHj2yffbsmYeHu4+P9+bNntLSPtraLnp6e48dO378+PGpU6e+q1EaY/wkOPiwoWFg0GM3N9dz584TSCMjI+3t7QMDA6M6LwcHB39//5iYGFdXV29v79DQ0IcPH4aHhz98+BCAcnd3d3Nzs7e319PTc3Nzi42Ntbe3DwoKevz4sYODw9OnT+3s7Pz8/EDnc3Z2tre3t7Oze/TokY6OjkjUhoSEPHr0KCwszMbGBiD19vZ2dnZ2cnKyt7d3dHR89OjR1atXDQ0NY2Jinj59amtrGx0d7ejoGBAQAMA6ODh4e3s7ODg4OTnZ2dnZ29vfvHnT0NAwPDw8IiIiKirKz89PtC6JioqKjIyys3sUEfH4xYsIG5uHYWFhTk5OAQEBzs7OgYGBdnZ2O3fuvHjxora29rx58+B45/90wT/9r1OO+vn5HtA5EBUdbWlpaWJiQiCFvtrl38jISOh7Yk8jIyPDwsIOHDjg7+8fFRUl9jQqKgp6eHR0NDyKjIwMDQ3V0dHx8vIiN+FRVFSUl5eXi4sLoRMdHf3gwYNjx47FxcWJUYaftra2T548iYyMhJ8xMTEwl/xpjaKiouzs7AIDAyMjI2NiYkJDQ7dv337r1i1jY+MlS5aQr7KrqqqePnlC/j0JDg4ICKipqen0fWT/uu2XBw/tMMYR4aGHjxw9f9bYycWNTFQfmICWxBi/ePHiwoULV69effHiRWZm5nvrPSwWKz8/n81m5+XlNTc3l5WVtbS0ZGZmwpK6oqICPCZ/2oPEMrxd73F3d79w4cIff/zx6tWr95tmYL7Pzc0NCQlJSEgoLCxsbGwsLy/PzMysrq4WaRW5ubnvupoiVXiL3sPlcu/evWtqampra5uTk9NdcBIh1V0CtJOQkJDExMSmpqbQ0NCEhAQ2m11QUNDR0ZGWlvaWQBOBQAChRUwms6mpqbCwkFoKxjgrK0tS74E8pPtlZma+pQgqQbE0xriurq6qqqq2trakpOTVq1chISHx8fHZ2dkYY7gpoTdgIX7zD2Pc0tLq6Ojs6eXj7uEVExPn7x/4JvP//cMY5+XlSdp7MMaJiYmXLl0yMTFJTk5++fJlR8ebrybf4wJzY3h4eExMDJvNjo+PDw0NTU5OBqNpQUHBn9qruiwU9J7U1FRa7/kPPm+8xSxWdFQUh8MNCgq6c+cO6XzvnWhsbHwne1ptbW0P83d0dNTU1IBY6Ql7fD6/u/znzuFVq3Cn9vyG0vXreN8+/PIlfvaMc/bsg5s37QWCN98XHD16FMZMl/2py5sY46rKyri4OL5AYGtr6+Li0hNW356HyWSCjeTt2cjT2tpaspYlN7tMcDic7j5a6S4/GP8lnwoE2M4Oe3qKP2lubr57966Pjw9EHerp6b3rSvRNkGlp6YsXCRhjMzMzWLSJF/OOv5lMZk9sJEBVKBTW1NT0ENL29vbuIOqSx3fNDxEeN27ceP78Oca4vLxcX18fBCLG2M3GxhKhSITCEIpC6EqfPheuXGlsaoKirczNzP8fe98BH8V19TvYJLGT2HnOi+0vcRI7iQNuYBv7s8GATTMGAwZjegdJSAIJ9d6FekO9S6vee+9dQr33Leq9rrQrbZ37uHNXw7ASMghM/JLod3+jM3dmZ+6cuTPnP6d6+wEA8nKyVVXVjI0MomJiVxzVGjrJx0EkEvF4PD6fj9xLl0kacscfIdAYVnzeyc4fOcRDNovF4u7ubvKGUvcSCoU84u9J7Fzk8JZfAtpEPeNj0UgtgQAZeXB0BOS3xOfzeTweGc/1WAcnd15+98lhr3g3SewgFArt7eytLK3sbG2LCgu1NDQlL64lcDFM+HeTxyfPKBKJBAIB0iS1tLQ8iQWTPDiVIO8IeUYJIVrARbNEY/MXJgf6OtpaqmqqCkeG6MMD3UDEXto6C4AYeUyThyUPJRaLFxcXeTwewugrsojc+UcJdHxywNTVH/3tw3a457NFp9MfUc6SF/jkxM/Uv4fK3PT0dBcXFwAAmz07PDyCrlkkEjFZLKphgsVkzs3NiUSijo6OkpKSsvLyicnJrq7O4uLilhboyj46MlJRXt7Q0IisnrU1NaWlpeiLc8W7Qt7XFbdKdT7KlEIAnbg0iOOFQnxwEOdw4JOH/gDAxWL88mX83Xfx4mJ8fh7fsgXfuRPMzExbWdm8/HKItXUenz+blJR84MCBx9X3UFlKo9EiIiLQmFHGFHSxYpHofvoUHH1s3J9jIuJPsicRbyxFowBXImRRij3kBS5d6srbH+h9FJZSf0Ddn8pqPh+/cAFXV5d8ICE+DA4OmJqaxsfHFxUVLS4u0mi0w4cPr8FtguSOh4dHUlISWm1pbh4lNBnIDba2tg69N8fHxyorq4RCUUtz8/j4+Pz8fATUV4VMTE6iPYuLixcWYHDE/Px8Xl7ewiMESlCvmsqN5TQa2/L+VXoe6+AMBsPIyCgpKam8vHxxcdHDw+PEiRNk0HJ8YGA9hoGllv3CC7n5+ej4AID42JiMrOzsrMyS0lJdHR0FRcXRsXGSt09IUC8QHWp2dvZxPxuoByFpcmDkw0VuWgNBvBOE1HQm1IOQvBoYGHhcgE49zk9EU6PkEFuoJyIZ1d3dvTbdAPVoj0iLcJwvFhFNzBMJM7Iy4+Li4xMS8gsLIqIiF/g8gVjMF4n4YiFyDV6u70EX5e/v7+PjU1ZWRqfTnwT3POKw4W4A4OwifNgJH3HHR9zwUXcw4QXbpDcY8wBjnrATbnLFR71w4RSdwVxu50KnQ5zncDhrc+R/jDGvdVdSi0ZOkmdA/HxxD3nb0tPT3dzcAAD2drbZObnz83NMFksgEFhamBcWlyAejQwPffLJFidn1zk2e9/evTdu3DQ1NauoqDjwzTc3lZSOHD4UGBRsoK97+PB3x7//3t7BKSsjfcfOHepqat8dPYacUtd61x7pdwDgk5N4UBDe0wOntEAAl7W1+Ntv4+npEnmMQM/sLL55M45h+Cuv4J99hn/4IfD17bexMSssLA4IWFBW7vDw8PXwcNXU1HxYIO6PDggAEBQUFBUVRWiAhgIDA6HVqagYKmPHxz08PLncBcRVBr3r6pUrwyOjXV2dly9dUlRUvG1hUV1dfeXKZfnr1w0MjYZHRnS1tS5dvqSppcVisu44OZ4+ffrs2XMRkfDgPzqSJ9lhScsrOQa5uiIxP48PDODJyXhLCwRzHR0dt28bNzbWzc9zmpqaXFxcfHx81NXVUQKPNYwKAODh4ZGSkkLoKSdl5eQGBofo9O6KioqxsbEbigpd3XQAQElRwWuvv15ZWa2uqpKcmqaqonz12rUbNxTPX7goFuNx0VF/+MOrmdlQWZKcGP+bX/86MRkecA3jecY/AQDU1dWZmpq2t7dPT083Nzc7OjoGBARoaGiQ6vf4wMAaAvTgxDL9hRfSs7PJq4OlAETCxYUFvkCwwOXOzc0/xXcfFZSgw87OzqLIoydhFACgp6entLS0oqJiZmbm7t27j2cnXXZuAACy11D1Pcv50N/fv2ZT0bJzPrUOFMeOXkpozMvZjiwyzwb3AABapodvN2VZN+eiZtuad6e7xKmrxLY1z76jwKYlD/WbN2Y1zw4P9PZJ4R50FYuLiygIKzIysrGx8dkMHoqEmVx8wAofcsCH7IklIuyXVlGnLRh2FvLGq2vqyAeNxMdU4ueMe1pbW/+r75F+DgEA6enpfn5+szPTp8+cHhgcvG1udujbb4ODQ5MS4nV09dDsDAmmHT167OTJU21tbdu3f2FiahocHEynd3+580s//wAZmatWNjYqt5QTEpP7+3oPHDjo7eO9Z8/eO3ecdu3e3dvXT75/pU//lNYBwFtb8TfewNPScKEQl5HBPTzwmhp8/Xrc31+Ce8RiXE0Nv3gR37kTf+EF/G9/w7dvBy4uzfb2BllZ2bOzY/7+Cy+/fCc4OEwkEt6+fXvNH6zIRzIhIQEAEODv5+ruYWJoePTY99bW1oNDQ0o3bxSXliGu2tva/OmNN/wCAstLS1CwbnlFRUpy4r59+0tLS/fs2R0RGXX40OHM7BwdbU3lWyqjIyOnThw3MDKZmJh4Spxb+TAA4DweXlmJj41B7uE4zmbjs7P44iK+sAD1ZFpauLMz7C8vx7u7IdCUl4do0sUF1NeXaWkZnzpVlJc3PTc3bWFhkZqayuFw9PT01ixOAACenp6ZmZkwcCM9TUNTs7G+/vjx40pKSuUVd+3tbL28fQEAxUUFb/71zfMXLt64ccPTy+vAgQOjY+O8xYX9+/cXl5QpyF+/LicnK3f9nqvEdfj/+pVrMiLR/Tx+K/PiX9qL5kl+fr6RkVFJSQmbzR4bGzMzM8vJyeFwOPr6+ggKQI3OqriHlJHogNRXNtmzZkIsFnO5XKFQuLCwMDg4ODo6Oj4+3tvbu7annjqMguIiJdVbGjpaRWWl+sZGpeWSBwfts4Y7I2XnQhqghYUFlIECOYgMDg6uQTG5hsE81k9WtHPxeLzFxUUcx0cIK9LU1NSTxHM97ngqxlkXS0KvlYXLoFYeca0s/Fp5hAxqROe1svALJcFl48zJ0THk5ETeO2QhQrkiR0ZGJiYm1hzH/lgjhzsDgE/n4f3W+IAjPuggWUICNcclwh4fdAHCqb6+Aaq+RywWLywsCIVCLpeLJvzs7OyTxHM99vgf+Qf/jedamVUAgIyMjKCgoO7O9nMXLo5PTNhYW184f05JRa28rERWTp6Ypvihbw8cP/7Dxo0bAmm0bw584+bmkZyc3MNifvnll+fOn/vk00/HxiZuKCgkJCaPjY58e+iQp6fH3r1f+/v5ff311/mFRWt7A6484pV6kZxmsXAuF8pmPT2o++Hz8f37cWVl2GNjg2dk4L6++Ouv49u344aG4LXXwPbthYcOmRQXM+vqqlpbW0xNLTMyoEebWCx+QtwTEhKClBNqKip5BYUmRgb6BkZaGmp3nN3cXV1c3GBE6Nzc7LcHD+rq6h499n1Bfv7mzZvV1dVpQcGFBbkffPDBl19+efbc+d6+vsOHjwyPjOXlZB86dPieLVJN9ZZ/YNBPyk+k0dHWhjgmIkKCGmVl8T//Gf/gA3zjRtj+93/xP/4R9/HBv/kGGg3/93/xPXtAaCgeFZVsY2PBZPbLypZbWbXY2hrX1UFZtbCwoK+vv2bzAQDA29s7m1BgBAUGmJiYREWEaWrrjY6O9vb1ubm5mppbQEiUkSYrK6+jrfXK738fERm5Z8+eto7OkeHBvXv2xMbGbtjwzx9OnPjnPzekpqRs2rTp5MmTb771t8bmlp+UmSvN1kftQwOLjY11cHAYHh4uKyurq6vT19evrq4mrNJsAwODR8c9y89KRRhPQjc2NgYFBaWnp98zwOno6Ojr6xcVFXV3d5Nm2eWnfliPWCTi9I3O0QfnGEMc1kh7eW1KaHRKcFRtXmlSUCSrpoXDHJljDLHpgwvDUw87yMP6STdPUt8jFApRVEF5eXl0dLSOjo6ZmVlTU9OaQdvDTv3o/SQskJqW0JFgdJSav2dubi4xMTEoKKi1tdXV1dXY2NjZ2bmzs3MV14JHH8aP7gkAuDvWc60sXL4iavV2pSzs7nhPD505OHQ/G8XAwACKtKAm9Vlz3sIfHa30DhD35OL9Vkv4ZjniQT32+KAzEE61traTjnQAgMbGxuDg4IyMjLKyMkNDQz09veLi4tbW1rU58kuP7emtI8evxsbG//o1SzMV6Xu8vLxGh4cuXLxYX1//9ddfy8rKaGjrpKelqqlrwPldUbZnz9709AxzU5Njx47t3r1bVVXNzNwsOzv76NFjLFaPirLSbUsrNdVb33139OzZsyam5ilJidu2fWFsbPLlzi+LikulnmHpQTyldSSwEZpHWoreXnx8HIKhM2fww4ehTw+h7wFjY/iJE3FvvGH1yiv0/HwwNtZnampaViYx6t1zszE0NHwSfQ+NRouJiblnvVZVuVVQWKyrralnaGRooOfk7OLh5upK4J742OjNmzffuqX86aefqqmrQ4BYUFBf3xAdFXn6zNnsrMwvv/qqsbFpz969kdHRSjcVtXWg7k3+uqy7l89Pyk/ERn9/CB8HByW4JzAQv3QJV1DA9fVxVVW8tBT/5z+hzmzTJvx3v4OmlY8+EgQE0Dw8nEZGYHayxsYOU1OjsLCGmzfB0BDgcuc0NDSeBPd4enqmpqbCYOn8XDV1ja6uzhM//HD06LGs7Bw7G5ugkFC4qSBfW1dvcGDgi23byioqvT09Dxw4ePjwYac7zu5ursYmJvcGYGJs+L+ffXbb0npoaEhPR9vpDvTof0oT8GkeBoFFLy8vNzc3JMkaGxsNDQ3JjL3T09Oamprk6/hH9T3LB/ckWIf62+Hh4aioqJSUlN7eXh8fHzc3t9ra2vb29sfFPTCqZXou/QvFqNePxrxxIuaNE3F/PpX01rmkt87F/+V08lvn4v5yCvVH/OFQyQWIdCXayOXXtlIPiue6hx1J3MPn8xkMho+PT21tbXV1tYuLi5+fX0dHx5rj2Fc67WP0ISdfHo+HNApSxdRGRkaouGd+fh4Zkfv7+zMyMtzd3aOiorq6utb8lD3GQAnfRIh7SsPkyyNXb1dKQ+9phphddKqda2pqqqSkxMPDg81mT0xMjI+Ps9ns7u5uqkfpY43n8XYGAJ/KxXst8T57vN8eLlHrl1q1w/vvAOFkT08fqe/BcXx4eBjFvfb29vr5+bm4uDQ1NTU3N/9LcA/5JCKdLpUPKEzsv7iHyhMJDaOvs7Kio2FQq6aGeml5eX19XUlJSU9vr52NdVQ0FN4jw0PIrszlcurqaqurqjIzM7Ozs3p7+9rbO3g8/tTkZENDA4NOz87OLi4u5vF409PThYWFGRkZzS2tK5z1J+4i34cAQGVPWRmcww87AAAgAElEQVT0Ptm/H3/+edzBAXh6CoyMAn7/e48XXqjfsqUkNbXdxESvrq6OmEDQPQXH8Tt37jwJ7gkNDS0oKAAA+Hp7uXt6FeTnGRjoGxgYDgwOaqipIa+p6KjI3DzoeVqQl2dnZ6+lpamiomJhYVlSUhISGgadhGiByampdna2N27cML99e5JIzx8THVVaVv5Ti2rEQOQRNT0NbV5kDwDQ+GVggOAO1Ak9/zxYv56LYU66uuEtLTWBgdUnTlQqKekPDdErK8GePTiLBXBcYGFhiV58a7j5KKdLWRlUHc3NsW/evDkwODQ6CsXAzPS0srJSH5GJmLvAHSdy0k9MjHMXFnAc7+joaG5phfF942McLgdKPg6no6MDuU9yOPNrdjlaw1U8+k9Q6Ja1tXVMTEx5eXlTU1NJSYmxsTEp0pDqwtLSkowg+xfintTUVAsLi6amJmQ2Qp4oqD7Xo18yemvzJtlx/zgdiH0RhH0VhH1FI5ZUAvX7YZ/lHtR6XNyDahhR63Px+Xw/Pz8nJyekdRAKhWKx+AnjuR7rkqk7oyQufn5+7u4eY2Pj/v7+1GQ/y+1cY2NjdnZ2QUFBXC4XQUyxWPyE8VzU8axOE/oe1pXiULnSCNSuEwRaypWGk/2Xi0PKx1gTD9q5Wltbzc3Ns7Ozu7q6VFVVdXR04uLiWltbqZe8+gCeaCt0CM3BmbfxHlsxpaFVYmlHLG3wPicRb7yxqZnEPchCcvv27draWjThRSKYIHR51p8nGuGj/RhBeZS5Y2RkJCsri8FkUKXDf+O5VmYk0vegOPbOjvbmFqj2BwCICA3wzFKWLZKVaCt1iV5Y1B4peuUTP1oviWAebXfpvQDA+/vxv/wFuvvcvYv//vdAXn5m3z47DIvEMDGG4b/+dZmqqn5fH6xyzGDgN2/ibW3w6g0MDJ4E95B+zX29Pc4uLiKiCDb0a56YcLB3YBNepSQ8R7xdzjRkcaP2k6yWvs6nui4UShzDxWLoNfX553hoKB4WBm2FCgq4tjbu4AAdpzAMNbB//8iFC7e9vdN7esDiIn///ozXXzepq4MJS8Ri6G6FXtnq6uqk2H7c8VL9moknuRuFaMHIrLm5trY2VGUc8YrKpYf1UOfz4w7mp94fxaibmZkh6MzlcpOSkszNzakunyjhm4aGxs9B34NySjEYDIQmEe5ZQ7ki+Hk6xU7YcD4I+zIE27tKC8C+yDusC2/i4+jqkF9zQ0MDqe+Zn593dXWtqalhMBhkQc0+ogg8OUPWfLvJJ5c6IZcfjdwNGmpTkpxsre7YW/d0d5QW5MyzH0irLVWngsViubi4dHZ2UvUonZ2dT+j9vXyEK/ZA3DPKulIUIlcSvnq7VBhcPs7q7+mlmrRKS0tpNBoqSu9K/BUXF7e1ta052fSKg3xoJwDiiWwR3VzEtF1qNgRhI2IiAvVbi3ocxbzx1tZ2Ku5BWVgZDAaq8kRkAHqiOhUPHedDNlDnTElxifJNZV9v38L8Qtc7rhnp0AmS/PtvnYqVWYgyfKPgI6lHneDdyr96Nr0A4J2d0LuWkJ0/fk7yNUglOBz8wAEYvRUeDr78shfDTDEslchyMv3cc4VHjpgfOjSUlQUvvaEB//RTvKIC0s7Ozk+Ce8LCwpDQwnGcy+WiKGv02kWu9VIXQ05TdAvIpVS/1K9+ilUAcHt7/MgRGPAfE4N/+CFubo6XlECnKAzD/8//wV98EarNSNCDYW0ff2xCo+V6eeV1ds7JyyeeOmV99+40ugTijQ9xj0AgsLKyehK/Zn9/f6TvWS5FyHP9FAz5SY+JJiqS3QQNb3h9fb2RkVFxcWFxcTGfvxgdHe7k5CQlD35W+p6uri5zc/OoqCgST8w+vC7pKvxEuCd+w3ka9mUwtjcY2xtELKkE6vfHtuc+HPegpwbNE+rp0Nc5tS4pSrmLfMbJZ+2pxHOh0Krm5ua2tjY2m3337t3lDz7U6nH5rsUs+3yGYwHTsYDpUsy6U8hwKqDbZLY65nU7Fkj6bXLphd2Tc+zZri5Y7RX98fn86Ohoc3NzaiXwVeqSUlnx5DTMfDvKvFQQJFsUurzJUDov5tPKRpnDA4Po8UeDn56ednd3d3FxmZycxHEcFS3v7Ox8ZnYu4XiWoNNU0G0loFsJuq34dIJ4cMmnWwrodkAwyerppeIeOp1uYWERFRUlyVEEMx8+UV3Sx70dbMHsFG9yijc5LZjqn+qrarlbUJlX31VXXFvU3tc6I5ie4k9O8SfYglkc/Lcu6UrcRbgnLAwaVlba/q/suxdsqKKCf/EFPjf34991yChDDhdJlMREfMcOKK0xDLzzTuO77xq/9FIThi1iWC+GJRw+bMVksjduBBYW8PhiMTTo4Djkg4ODw5PEsYeGhubk5JDwhRwVhAA/Pz7fHx4R6BAXh1tbw7itpCR81y789Gn86lX84EHoEo5wDwF6UKaY0k8/Nf7oI/pLL3E3buy1tw8+eNA5I2NhagrMz9+/Zci9ztzc/AlxT3ExzAVAHe3/vzSan0Ih3tUFm0CAd3eDmRmQn599+7b5wED/zAyHwehRUfHQ1fUWCvlSF4702+bm5j8HO9fw8LCxsXF9fT0AYGJigslkjoyMrKE+F4l7Agncg0BPELYnCNtDwJ09wQQdhO31f7i+ByXBQqnwlkMfsVhMtXNBpcXduyYmJmNjY0KhcHR0lMVi9ff3ry2eCxBeL0iuAwByc7I11dXUVVU729sD/f0WuFxy0xLSBT1T3I/tit++nb/RMn+jRcFGy4J3rApR22hVCFct8zdaFvzFJNc8i77I5bDIjKvEscLCwjw9PXk8WJRtgPh7pvFcI4xLeTSZguDV24XcgLIRxuTYODWeSyAQODg4oOAPki0NDQ3PqE42AILRzMV2Y16nxWKXxSKx5HVZ8LrgKrnkdZrzumyAYKKtrYNUrCJ1rImJCTnhWSzWxMTEs4rnAmJcXDSaF98fntQflTQQlTIUkzGWmDGWmDYSnzmWmDYch/rj+yMKR3NwIGbQGf+NY5eWFMjOhfL3oPkn9bIgJyWVQPuQPdIHfUrrAOAsFkwJIxLdF6LLj42CuerroeNtXBzELpqa+K1beF4efuMGymYCMCwXw0x++cvKF19kEWI7AsPufPvt4uAgOHECv3MHHn+pAZHoSe1cNBoNqdCojCJHTvKNBEZkD7k/ufMzI9DlEwOAmp59+/A//Ql/6SX8uedI7Q5JINATj2FW//hH9Y4dQ7/7neDVVz1feikwOlrEYIANG/Do6Pu3DNm5ntCvmczfQ/JqORRA3CN5iPYkV58ZJ3/0RGjGpqTgx45BNHntGj42BrZuxT/7LPzOHbu2ttr09JHDh/nnzt3BsDAabYVIe2Tn+pn4NU9PTxsZGZWWlra2tsrLy2tqaubn56+hTgUF9+wMxvYFYfuIJdL67EPQh4BBe/2x7SvauQAAfX19MTExoSEhDDo9Li6uubmZnCfIHlFdXU3qpQAAbW1tWlpa/f39iYmJCgoK+vr6DQ0Na6tTIRSJZ7iCaa5ghitgL4qKyioTUtJjk1JrG1taOumzC7Af7bAgEKFp2TfN3X6n5EObwi32RVvsUCv+xK7oEwkt6XzPMt8unzk5Noqsh+QjkJaWdvv27bm5OScnp1u3btnb27e1ta3Zi+5H5y11BwBA+TDjQk7A1TzaNdRyg67mBV3LXVrNC7pGrJ7P9isbYXS1d1DtXDiOe3h4BAYGstnslJSUnJyclpYWBoOBIo+oJ/pJaAB4IxmcVgNOuzmnzZzTbkYsCbrNnNtmxoWd5pw2U06HlZg3IaXvuaf7MTExKS4u7ujoUFBQ0NXVLSgoaGpqWnOdCvIayTtLvrvITYgAOMQ9BSPZMT2h8b0RZIvrDSdpRMT0hBaMZnMXuPV1/xl1KkjeSbFsxVVYoDQ7OyMjA750eIssFovL5QxSyl/zFhcHB4cmJycbifvaw2IyidorA4MDCwvc5uYmhOJXPPiTd5LCeJVDAQB9d956C0rlS5fw2FgYYk347uC/+Q3AMBGGhWOYPYbNYFgdhjF++UuPXbv8Nm0SvPgioNFgtBeX+4CQRl60VH3P47I0MjISBRtzuZyenh4ul0MmwoYv3/n54ZGRwcEBxNK21tau7m6xWNzX3y8Si+fY7DX7wazCpdU3AQBDtExMoCPUt9/ir70mgTjr1i3HPQDDFjDMB8O8MGwewyoxjLl+vd1vfxuDYbirK+Bw8MhInE6XZqmzs/Oa38goJRL6wJpjs1HBJurn4+zszMjICI7j/f39c2x2c1PTzMxsb29PdzfM0d7U2Dg9PUPKv9VZ8eRbV5m0S9gaJpci4uDwv/4VV1UFjo7zmze7YVjQlStzn3xS8eabrN/9zlpXN01NDdVUWWFQYrHY2dmZLDbyL/RrHh4eNjIySk1NHRwcDA0NDQoKQjUm1xLPNcWO23DOH9uJ1DxB2B4a0UiCRqh8/LFtuYdheXk0ycjHE36Lj47c8XJPzs7oGRk0t7POL4U6QvJvcXGR6t+DSizp6Og0NzfX19cHBQWFhYV1dXWtIY4dpuscnTvmX33Ur/oY0X6g1Z0MbjgZ3HA8sO4HWv0xf9j/vV/1Ac/KyDpJRHffFHe7U8mH1gUf2xVusX1oe/d2nl0eY2ZqAhnfyctBdq7R0dH8/PygoKDk5OTu7u5nFsdePkQ/n+lzJTtg9XY2w7tsmN7DYFL9kHg8nqurq6en58DAgIGBwfXr16Oiourr65+NcxIOwOJQGrtRd67ZhN2Cmim72UTSUA9cNZpvsxAsjFZVP1CPfWhoyMzMLDk5eWhoCE341tbWlpaWx53wUk81Qu0NDQ3d3d3z8/MrVjuQ4J7h7OiekPjeiLgl6BPXA3EPsRoe1wPxUExPaP5wFo/Pa25u/vePY0fl62pqaqhekFL8pa4iOxeK5xodGf5m/9eRUdHWNjalpaWRUVGDQ0PBtEA1dU1vT49jR49FREReu3bV28c3OyvjhxMnGHS6ro62rp7BM5MoD44cvvRQGxiAYdVnz+Jubvgvfwll9h//CL2YMYyLYa4Y5odhg0QaWw6G2W3ZEtHbK/7uO4BhMMhr6eUpOTa6Fqp/D3ILWCDigx7lSgEApH9PV2f7/v37ExLjHZwcCwoLYmNjJyenbKwsTE3NYmNjFBQUAgICDn37bWR0TFxs1Lnz53k8vpGBgbW17aOciMqNJ6QBgGDlo48kfjxL7jsS9ENZBRg2hWFWGBaGYUMES6cwzHz9+jQVFbB5M5CTu39TyCE9Ff+egICA8nIYyFZTdXfv3n0xMdGubm65ubkJCQmTk1OG+roubh6d7W0nT55KSkz4/vtjrm7uQUE0GVm5iIiIo0e/8/DyhtKCHNNPQ6C51N2NM5nL5xU+PY27uuJNTdCGKCMDebt+Pf6LX4B160YwzBzDYjBsjGDpEIYZf/JJ0dwcEnBwrMtn6eLi4s8knuueTsXExAQ5tKERz87OrjGeawn3ILjzsKUfti2HwD3Qd14gHMioZIbmsiLyeiILmvyTS+6Etgant4VkFDmFtNLSeiILWBH5zLDcoexqIU9Arc8lFAoLCwsNDAxQURo0+MHBQYQmH2uOAAAaBmc/sCl836rgA+uCD6wLiYboB1b/bpbrUcJC5+qb4n7hWLTZKu9jm/xV2jvmOTa53QtcWCIU/RDpriIiIqytrdlLVdgAACwWS8oV7LGu4tF3BgCUDXafSfO8lOEraZkEgZZkZ4bv6VSPksEuKTvX8PCwi4sLjUZDSLSmpqavr+9Z5i3k9qdM1WlNNxhNNxguLQ2nGwyI1SWiXn+m0UzMG+/pvR/HDgBobW21sLDIJ+rAoNvxJPFc5A2F2sfWNjMTMzlZucq7VYoKipV3K6lb4XuA0PfkD2ZFMoJiWGExTNRCY5mhsQQdywyNYYbFMsMiGcG5gxn/EXYu5KZ39ChMonP+/PlHyUIBAEhLS7tX3hnWgBzoP33yxHdHvrt1S9XOxubMmbP3MhN8f+yojNx1S4vbFy5c0NDQuHDhgoWlZUd7u6K8/NDQyN2KiptKSnwBrMDy6M+M1J5S73SprSuuCgSw8Nb4ONRSlJXBYhSvvw6z550+jf/qV1CiPP88eO65MQy7TXgxMzEsD8MmCOmS9pvfgMxMUFkJ3VZSU6FEof4hlKOrq0umymhra7t8+fKxY8fs7Owexe0OAECj0ZDLVGdH2+HDhw4ePGBgCBP4XL161dXFdffu3ZpaOiwW09TEJDMz09TUxMzMvKamRlNTMzMz44tt2/QIKPkkLKVeEUlL8Zm8cNSfmYm/8w7+/vvLsQ7ZAwi/KGOi8GUbhlVgWP+6dQYYVvrii8DXFzpEm5vDs5FHRqdGSfZUVVVJPZbUw0yO8GEEqkuKSpxWVVZ8992Rbw4csLC01NHWlpOVc3G+s3fPHk1tnZs3b37zzTdubm5nz521sbEbHxtTVVV1dnY+deqkvaMTOunDTvFU+sViCG6++Qb/4QeYOpyo2gEPjBgSGgo5uW0bbmEB3cMxDH/xRfDiix0YZoBhVRjW+PzzNevWdWOYLobVfvopqK2FKZRoNPhzJhOmySYZiwpQq6qqkm4H/0J9T3Z2trq6Oul99YTxXLEbzvlhOwOxPas0XwruEcxwkj677I99HrRuJ23dzuDnvwr9xZ7g578Keu7L0F/sDn7+KxrR7499nvq5HGdiprEZ2iPIGejp6WlsbIyyIhF3ClrK+vsfO8U8AKBpcHaLXeFHtoVb7FZr71jkeZf1oAEQuKdgs1XOxzZ5H9vkfmyNlrnEah5cWsPOd8yzbHO759mzHR3t5Mg5HI6xsbGrqyv5iQsA6OzsXJtz0uPOfyhrBrpOJbtfSPVaat4EgZZkp9fJJNeSwa7+3j6qnauzs1NDQyMqKopaJLi5ufnZ1OeClvfepLFKtfEa3bEaXbishoRkeb9TZ7zOSLQ43tHRRa1TcS9buqamJhm58iTxXGJcvCDkcoUcrpCzKF7oZHbmFuWUVZVWN1SlZqf2DLEWxQuSrSKYmAPhnryBjIjuwGh6yCotopuW058mFMOCdP/m+p57ktLY2BhlzHNwcAgICFhddqJHKDs7G2WE6+vtdXR0cHdzlb0u397eVlRUXFxcZGNtLSd33dTUVFlJyc3dXUNDQ1NTi81mW1la3st5Y2JsrK2jwyVqPT7uk0Puz+NBv85H/wMARhh99BHu5IT/4Q8wVuvVV6XMMQDD2jHMiJDQYiJkfQDDTIh61dA3RUUFLyqChbq6u+/LEjQAxBMfHx+yFI66unpYWNjw8LCSkhI51x82WvTziIiIykoI1dtbW1xc3YwM9PUNjRqbmsrKygoL8vUN9NTU1FWUla/JyBYU5JuamhibmE5MTFpZWVVUVGioqWpoaj31+gliMXSWGhmB1wsADJHr6YEO4zgOlRApKXhICCzfsX49/vLL+K9/TWIdRCCHnmoMM8SwBkIngWNY5yuv6L/6Kly9cgXw+fBQQiEU/FS7IZIlOI47OjqSiu7Z2Vk/Pz9zc3OUA+NhzES/RSwNCgpCO9+tKPcP8NfSVLe2sW1oaCgvL8/Lzb3n6aJ8S8XH1/f8+fO3LSxUVFUsLC29PD1VVVXtbO1uKCo6ODqSI1nldE+yCQBovfrkE9zdHa+ogLGBnp4SbjMYMEQOYZ3nn0dzFbG0BMOMMayD0KiJCN4aEFMXPP88hPIYBv36kek2MfH+XEXo3NHR8VHsXIiB5KuAXH1aRE9Pj76+PqrLiI655niuxSl2zBLuCXg49PHBtmUv6XsEs5zkz2UCsR0h0P35oS0Q25G+VXFhit3S1kbFPQUFBWZmZqR7OABgbfFcMFfn4OzHNgUfWed/bFMAlTe2xBLSD7SN5jnepaS+h7PNPm+TRdZHVtkfWRLNKhvSktUc2GmVvdE0wyankzPHJrNWIj4HBAT4+vpSpdozq0sKcU9/5w/xLmeT3M8lusOlhPA4lwRXl/o9jsc7F/V3SsVzcblcKysrVHlGIBBwOBwej9fR0fFs/HsgNGclDJbeGqrQGqrQJJYEcVdrqQd1qg9X6gkXxhgMFhX3sFgsIyMj6oRfWzwXwAFfxCsazUkdiM8YTMwYTMwaSc4dT8ubSM8ZS82byMgaSU4n+lMH4ionysS4mMA9ouzetNAOv8hOWkQnDS67IPHAspMW2uGX3ZsixkXt7e3/5n7N9x4GExOTI0eOKCkp7du3Lzw8nHzZrfBCF4vpDAZfIMjJyUG4h8/nj4+PLywsoGRu6NESiURsNnt+jt3Y0CgW4wwGfXBoCAAwNjbG5/PJytgrHP8RugCAMvLKFdzK6v47/Ud/BwDEK0ZGuJ0dFNV//jP+P/9DymlSnBhgWBOGZRHmGAaG6WMYqlcNFUIuLrB++Dvv3K8/RZ4UXTUV91y9etXY2Dg0NPTEiROIUeTOUoRIJOru6haL8cjIiHtxIijYeGJyksPhUF+sfD5/ZmaGwaDX1tZOTEy0NDffs9SIRKLR0VGxWMwX8GeJvB1SB3+SVQCgtmDXLlxXVyKJa2qg23JsLFz94QeYhxBFqhOqsuUgEhBqM1NCQqdi2DSGNWKYwWuvMV55BaxfDwtWIDjF4cCzuLk9cDfRJKTiHiMjIx0dHX9//9OnT6+ilUSBYF2dXci/B+EeLpczPT3N5XLJEg2QzwsLbDY0C83OzCwsLDQ1Nc7Osvv7+rq6ugV8flNT02PlQ5NSjD0K59FPenpgKFx/P7z8hgbczw+qJAcGcA0N/Be/IKco8rUXE4YtKwxjYlgaYZC9Syh+ejAMrFsH786BA9DR6vXX8XXr4CTPz7/P1UfBPRlERCGq5Ori4ozSXQr4/NCQkPDwCDJdDZrwT7KcnJzU19fPyMiYn5/vIP6GhobWFs+1OMWO3nDOF9sZgO1ZpXlj27IexD0B2A4i3B0Ff+0JksTA3ycCsR1pWxV5M/PdDAYV9zQ3N6upqbW1tQ0NDXV1dXV2dvb19a1BZQJxz8DMx9Z5H1nlQbUNoadZWqIeyXKjWbZ3KUwuB6uUT3G22uZ8YJ7xoUXmim2zReZmi8wNxmnWWR0LHA7VzgXL6yYnGxoaTk9Pd3d30+l0JpP5tOK5yPnwsK8FWAa4t+P7GKfTcS5n4l1Px7mejnchmiuximiX0/Gux6IdC3vbpexcYrHYnfjjcDjOzs6Wlpbp6ent7e3PzEg3SY9lFSj2Fqut2HqK1XqKVXuKVPrKtHmcYan8Pfdi742NjVNTU1H93XvezaOjo2uI5wI44IkWswZTontC43rD43rDY4mGaGpPVE9I8Wg+xD0AiHFRJiuZ1uId1uYfSrSwNn/UQhHRDleDWr0zWEni/4R4LhRHGhoaamhomJyczOHABLUrvrVhNd2mxqNHjw4MDuXl5SUnJ6M9yen+WMSKp3iUThTYYmEBM+OtOFJSAiECLYVCGLG1fz9U9mAYlArr1iGhgryYozDMjpDNAgwbJXyZDTCsG8PAc8/hb74JC1ZMTeFZWXh4+AqZgRAfvL29SX1PXl6eoaGhioqKs7MzaalZfnXwRVBc+MMPJ+a5i1FRURUoEdCDoa2PztXlx19bD8k3sRgGuDk7Q5VPfj7MQHjkCDTzzc7CnvJyiH4ofjwkDTCMh2G+GOZOuDNzCJYWE7o05N8D00KyWPD2oezYDg54QcEDd5NIYCgmcc/i4uKlS5cyMzNZLJaiomJR0UPLt8Fgw7SU02fPLSzyQkNDkav4Kjyk6oekdnss7vF40AXnYX/oSsmtAEBt5d27UH8GAFShOTvDfI8MBtRE3r6Nnz+Pv/02BDFL7EWO4a6EbzgPw+YwbJIA6GYYNo50aevWQZXb1av4yZP4xx/jv/893tgo4TB5Xj6f7+Dg8FB9z69+FZuYiJjg4+UpJycnIyPLXVgsLiyQk7t+7eqVe2XjpFi05tWJiQlDQ8P8/PyRkRE9PT1lZeXc3NzGxsZHMQqTl4PuHcI9PthOf2zPKs3rQdyT8rlsALYTpflBQe8P4p59wVB1tDN9283p4bG7VZVSthVtbW0mk1lTU6OkpKSlpVVfX7+GYvII93xklfMhoaGR6Gyscj6CLZtYIjpng2mmV8kS7pnkbLXOfN809UPztA/N0zebp6GGVj80T9tslvahedo/DZNts9uHB/tReBp5p1JSUszNzefn5+Pi4pSVlW1sbJqbm9fgWUW9CziOCwSCzs7O7u7uoaGh+fn5vr4+qR3QzSrqafsu3O5ElNOJKEeiOZ2EhNNSj9NJYtORcNui3va25hZkPUSDF4lE7u7ufn5+o6OjpqamhoaG6enpzc2StMjLT/d0e+B3e0d0V5YcPU+ZnqdENEh05ymjRvQrd+feZBSqL84NNza1UPP3TE5Ompqa5ubmjoyMGBoaKisr5+fn19TULE/RtPqwJbhnICWGFRrXEw4ba2mJiKWeaGZIyUgewj0isSiNnuDf4E5r9A5q8qY1LS2bIQFbM+z0b3BPYyTMzc9VVVWReYbImfNTE9hPfQKp42dmZp46dUpbW/vEiRMoc/yKrCeiiuZ0dbRZPb15eXnnz5/v7e1dWOB6uLvZ2NgWFBZBxQOfz+VyAgP8M4l6kCwW0/nOHTqDmZmRHh4RyWDQnZ2dW9ugvXnFUzx6p5QgIX8IAPxcLiqCOqF7WIfDgR4kp07Bz+ivvyalCEkADGNjmDPhxUxf0u6UE6aZviXTDL53LzTEiESwwPjFi5K6m+QZ0fNcWVl58uRJhN9RHrlr165dvnzZ1NR0FfMz4XIxrqGuPjM7FxYWpqSkNDU1NTM95egA/6pralHxndnZGQ9393ICFbW1tji7uLR3dISHh6WmpXM5nNjY2PGJySdnKboiAPDkZFibbHwchvfr6uK//S3MvHzpEgwj8vWFOp4PPoA8eeUVuGlJMJMEIDxtrQnNRCeyv2BYBuE1NYlhALmQv/8+ZCmaBSTMklivJCIAACAASURBVGJpdnb2mTNnUKSJSCSysrK6fPmyrKzstWvXVsnvjnzOVFVV5zlcb28vVVVVDoczPDxkb2/n6OiEClAIBILJyQkXF5e6+gZ4s+pq3d3cmUxmSHBwOpG6NCkpkcV61NrgaPy3buGXL6+AiYlCB3hfH8TN5Ky/l2VqZgaat5DOcngYWqaioqCCTU0NWrvefhvy84UXSPPWEIZZEPqzRgTHMSyeiDecJ2cpCm7fswffsgW3tcULC+HRkpPvR8kBAOLi4i5cuID0WLAeO41W8+qr4M038TffBG++mfY//yN7/Xr/AKyVZmJsFBMbKyMj09c/wGTQtbW0vvlmf1AITNn1VP5GR0f19PSio6OHh4fj4uIiIyPb29sZDAZ1DjwKDfV2U+yoDWe9sR1+2O5Vmie2NZOi70n6XMYf2yEV84VWUVwYDaIoqO/hTs62tbdT9T11dXWqqqp3795tbW2Njo5OSEig0+lrSDRF4J7pDy0zN1tkfCjdMik9mf80TvUqpiPO907Of26Z/p5x0maT5E2mRDNJ3mSSvNkkebNpymbTlE2wP+Vt/QTrzFb2DNTrUG9ZbGysnp5eX19faWlpbGxsVlYWk8lcW0gU9bACgcDL0/OGomKAv39zUxMtMJC6dendAgpZrYdDrI6H28EWRjREk6tE56EgywJWy1D/APpoRIcSCATOzs52dnZzc3NisXhubm5xcZFOpz8uVn6UebV8Hxj90xbZknq1LVPxgZahAFfREhLyHTm3+JwRqXrs9xTzRkZGUVFRg4ODCQkJkZGRXV1dHR0dq8RzrfhKhzHUosWM3qSo7uAYeihsDGJJENH0ELInoptWNJQjwT24KLkj1qvGxa/Ow6/OnVhSCQntVeOc2BHDF/Db2/4D7FxqamooY56Xl5eDg8OK7JZ8FuN4cFDQyOhYRkaGubk5lDH9fceOfqehrq6kfCs8PNzM3Nzby0tVTU1JWWmWPeft6XlLSfn6dXkdbR01NbWsrCw9HR0XV7eHnWL5bFuxZ0VJifYEABZRf+01WE+0ogLfvRu6Lf/619AvZ+NGUjYjAmDYAJGLOZMQHg3Ep3MW0YNiZCT7//KXeGAgVG9s2YJ/9x2emXlfgC2dFMzPzysrK5Mho3JychkZGXw+X1lZGbnWPuRCgFAg8Pfz43AXg4KCvLy8oOd/S/ORI0dUVVQMDIz8/fzs7Ozs7e10dXW1tHWEIhEtwH/Pnr0mJsaqqqoy167FJ8QfPHCgq/uBAisrnuthnaQwXroWyMBr1yDQUVLC33gDJjSanobKnr/97X6w+jK4Q7K0k0CNZQRL6wgVWgShS5t/7jmwc6ckX4C6Oj46ire14Xw+zP2ISkZQR4hiDOXl5Un76ejoqKGhoZycHCqxTt2ZSsPanFyut7c3j8d3d3cPCQmBWUNKi48f/0FJSem2haWXl6eT0x0nJ8dbKipGRsYAAOc7TgcPHrSyttLV1dXQ0CwqKtqxfXvF3apVZqkU7AYAujoZGUFneTb7gemBzIXbt0MtDvVXQiFeVwftWQDgiYl4fDycY++9h+/ZA9tLL5H8BIQHjx6G1RIsrSJYSiPA+iIJejAMuli99RaESrW1khMNDOD/+AdE/Ev4ErrfKioqkm4H8cHBNe+/D7Zvx7/4Amzfnv7++2mZkoz1QbTAM2fO3FRSzsvLq66uMjMzPXL4SHUNrC70VP4mJib09PQcHByQghm5sa9B64BwT8SGs17YDl9s9yrNA9uasYR7+LOcxM9l/LAd1OAv5BNNIzyEUL8/tiNFYueiU3FPc3OzkpJSVFQUmVR97fFc/dObzdI2mUIlzSrtbYNkz0JJ2uXeifnPzJPfM4jbZJiwySgeLslGrholvK0dY5XWtMCVtnOlpKQoKyujOEd0K5lMJgrvoj5Hj0KLxGKeSMgTCQVi0fQcOzgsNDktNTYhgc5iNrY0L4oEfGIrTyQU4WIkQQrpzQf9zY/SrCjNkkJbHaVZHqVZH/AzzaM3TYyNk48/rAQkEnl4eOjo6FAtd8+sHjsUdk1hdfEXGpNll5oMlWgg+huSrjal3eDND7e0tpEBBCh1lpGRkYODA7LKIb9m0jWe5DZKQo203aj0G7kJEXDCCxdTWfFhnQGRXbT7rfM+HUH0h3b45fdnkvqe+JZI13J7z8o7nncf2lwr7GNbIsS4mMH4d89bCADw8fG5ePGiv7//yZMnf9QJF9n409LSHB0doUNfX+/pU6dOnjzl6x/g5eWpeOOmjbW1kZGRiooKncEsLSmWvXb12LHv9Q0MDAz0U1PTQkOCjU1M0fMmdUcfZRUAqMLp74fykipFiIdK0tPYCJX8b78NXXl+8xv8s8/g6jI5DQhBYkAsewi7DMCwOAyzJTRAMF59/XoY347cdc+ehUL6xg18eFgiRaijRZ4TWlpaZDzXzZs3fXx86HT65cuXySzM1J9QacTSwMBAJKRbW5qPH//h2LGjcXHxdna26urqBvr6dnZ2mppa4+PjLs5O12RkbO3sLC0svt73df/AkIG+bksrrBBGPeaj0ADAtL+OjhCCUKEkLDw8BSOMjIxwY2MYrH72LDSg+PrC8LdlnKRK6EIC9LRiWA+RA0mEYf4Y5vbcc3wMA7//PfTpMTGB5boEAtzLC3/3XYhNjx+HcEFq+NDtZnZWWVkZffCJxWJVVVVbW9vk5OQTJ05QM8uteKUioQgA4OrqGh8fD3FPSfHJkyePHz+empZubmqqra1jZmpiYmKqp68/y56zuG2uonJLXkHB0spaTU314oXzn376CdJtrHhw5GE2MXF/zIh76elw1jU13e9H01Ikggqzv/8dGkmJ/PoSbtPpEE0ePQqz8rz1FtzhQd4it7Mswrm+gwiLE2MYn7Ae+hHshbMUwyCg378f+um//DKurw/NbWg8AgGs2ULRq8HMyLdu3SJfxxD3vPsu2LYN37oVbN2a/u676YSalngpz0dGRLR3dHS0tzGZrPS0tMLCIsnHz9MAPsPDw/r6+uQnAYrnam5uXpHhq3Qu4Z4zXth2X2yXz8ObO/Z5+mFtNHYS9wRiu1cMAUPox4/APZyJmYamRiruQXGUKDUU4kk/8fe4zyCMY++f2mSS9IFR0iYTohknbTKW0B8YJ0KaWP2HbpzHfdwz95lJ/Hs6UZv0YjbpRW/Si/6AWD64GvMPjQjr1IY59iz5UkLXHhMTY2xsTHojwRxCHR3kKslqqZtM9pMEKjJqUJdm1pBp1pBp3php1ZZHtHzz5myLlhyzRthv2php2pDBmJtAByzobtrvaXTY1+ywr/lhX/MjfuaQ9iMa7DFDbZ+HYW53Y3/vA/W5+Hy+i4uLq6srqirf0tIyMDDAZDKfmb6ntz64MvJ0TezlmtjL1USDRJxkFfVXxV6sTZBbnBvq7x+g2rlQgnL0OkJzhsORrs8F50NDQ2xsbHp6+uTkZGBgwPIvAQL3LCR1xwS1eIe0+oW0+oa2+oVCAjaCkPTQmr1ye9JJ3BPdEOZYaOlSbLe8OaPOEnvHQqvI+hCh6D8jnovP58fExFhbWwcHB/9oAis0fdPS0pycYJTv+NhoaGhYR3u7p6fX2Nj42NjY0NCgmakJLSg4Pz+/uKjI0NDw7t27AQH+tvb25eXlerp6GZlEaSvyAXocAgBYCWHTJiiwORyoeiFlNo8H/U6EQnxoCKp5nnsOpuchk+lRJAqSFmkYZkZk6OnHsGxCnIQSaXvuf0P/6lf49evQ78TNDcaCUZGB1JAR7tHR0SH1PV1dXTdu3Lh06ZKbm9sqdi50HMRSGo0WGhoK/RZ7WFHRMU2NDf7+/uPjE+PjEywmQ09XNyExsbCwMC4uTl9fPyc3z8PN1dvHF8fx6KiIfiJppNSofnQVAHh127fj7e33r47Ph6CnoAC64GhowLDqwEAJ+PvtbyFXl/yiqEIaYJiQyPRoR2R67CJqmfExzAPDAjFM9KtfgZdflmQNQHXTAID2l5wc6OWjpCRhL3XACPfcunUL4Z6FhYWLFy+2EQVgVVVVs1B1NOoPHqQRS93c3NCLpq21JT09va62JjgkdIL46+rs1NLUTElNzc3JiYuLMzQ0LCwssra28vHxGx4eDqIFVtfUPUyMkQ7IaErQ6XhwMFTzzM1BcEzCDnLCIJzk5AR3278fFo8TCKB7+LlzVB5K0YAojRJA6HW4hLt9BeEs5YBhUcRkloAeDMO3boWxdadPwwQ/5EkRP9DqEg1xzwNx7MtxzxJjEQOllk8R94yNjdnY2CCrOjrL7Ozs8s/fB+/qCmsI94RvOOOBbffGdq3S3B7EPQmfy/hiO5b7QftTnKN9sR3JRDxXW8cDdq76+no7OztqDsyBgQGkmVhhiA/vgnKub3KTQdwH+rGbDOKIRhJxmwxiiQb7/6EZ6ZkPv23g+2GC/alh9DuaYe9rhxMtgrJEPeHva0f8TTXYMqmWOz8nZeeKj48PCAhALyV0QDqdjiQ0OVKRSDQ+Pj40NDQ7O3tPk00GvZM7oJmQNdhxoThYpjRc0soIglyWhV8rDb9WGna1NKx1ehidK6+zYa+r3kEPo4OeREOEh5GkZ2l1t4tOTmf9CCzPBQNi0B80pXl5ZWVlLS4uOjg4yMvLR0dHNzQ0rDg86lCfCg0AYFYHlAQfK484u0orCz9dGXNlfrq/praOVKwCAEZHRx0cHBobG5euBtbnQi80sgeWbU/L8PX2jYmK7eroSk5MJpVDaB/E9kXhQlx7hF+9W0CDV0CDZ0CDV2CDZyAkPANhD2yBDV6+dW6Z9GQS94RV0ayyTO1zLe1zLexyLOxzIAGXEsLCPtfCOss0pDJAJBZ1dnT+m8dzoQJvjY2NZmZm33//PdJ/rj5RqPXYqfeMpJECQ0xYL8gYEKQTJs2ZSKg8TLQQN1h6FOglzmJBTwg+Hwa/vPUW9DXm86Fu38QEunPu2wcVFSijzLp1UO1PQTwoKIZHePM4E56hAuK7WUyoJbyI2HXoyIyChzEMt7SEKCohQeK3IaWTIMeHdLD6+vpkXVIktiMjI2/cuLGKyxT1CEFBQZGRkSRbSGYiAnFPLBYBHBcJYeojMfGHtkoJJPKwVOJBESgRkMPDeHU1FMP9/TBaTUUFl5ODSp2KCtzbGxq5fvMbaHNZxkYqVwHhZuuEYQEEISTYKMCwOxgWQRhiYHz1qVNQaSQrKzkvur9L9hfYKfUnVY8d5XU8dOjQmTNnVFRUHiWCA+XvSUpKonKSNEwQMQ7wnKgHfc2TSmYqV0nekiMEACLs3l7JtcTGQlUNSjZNXtH8PJyftbUQU3I4sAEAVVwYBjmsrk4t1EplJqJRpkdrItMjF8MEBBs5RO7HRMTSB2c1BKPHj0N/LADgjHV0vO/WQxk2mJqaeqAe+8NxD/krKYLKzCehm5ubHRwcenp6JicnKysrq6ur15wCZ3GKHbbhjDu23QvbtUpzxT5Po+h74j+/5o1tX8UfyA/b7Y1tT9yqsDgz1814wM6Vlpbm6+s7OzvLZDKrq6vr6+t7e3vXpjJp6J14XyfyPZ3I93Ui39eltqil1aj3daP+phbqkduCGN47zv5EL2yDGu09jaB3ifaeRhBq7y4R72kEvaXsb5FYtfignUsoFPr5+SUmJsKM5E1NdXV1bW1tUvFcEEouLLi5utrb2fn5+VVVVa1YihEAkDPQcVlSXD1CrjScaBFypRHXUbl1oke2JFymJKxtCffkdtTvctTc76L7DWx6+110yfbNfVrvSwf17PbayQftXBMTEw4ODlVVVfPz89nZ2SkpKU1NTd3d3T/6YSk1h9e2CgDouuuX63uokHaigHaikNLQqmQZeLwk9BxnZoDJ7KHqe5qampycnNCEryb+hoaGGAyGEBcOLQwMcHsHuH2DC/1VXRW1jCr6RGdjX33nWPsgt2+AaP2cnkneBIwDAWBBsBDZFOx+18mrysWrkmiIqHKR9BCE213HlI4EEvfQynxNkvQsUo0lLW2JWOqxTDU2TdYLKPEW46L/CDuXk5PT3//+95MnTyIVzo9OC4R7PD09kfSdIzK6cDmcxcXFkeHhCcLHFj2iSGbw+TxUOg4JM75AIBQKkXPl7MzMilpKkQgKY/IPyeyxMXxyEr7ZR0dheNHcHAyHEQqh9WT9emjMOndOopmQkgpLqwDDRgjhEU3oJ5IwjEFIEaSWQKgIBg9/+imU9198ARUSNBqUaqSxgBwSlUC4x8TEhNT3+Pj4HDx4cN++fZaWliQrqD+RogEAQUFBsbGx8FBC4fz8PI7j8/Pzi4uLQ0OD1OcHBXux59hIVOM4PkukXmXPsXl8vkgkmpmZgTxa9jcwINGN4Ti8HGSmcXOD1xsQADm5e7dEGL/wAu7ggBsaQtPJsqw8VCGNDDFMwhCTQ2gjYgmn5kXCXBhHSujnnoO1t2ZmoEcLNZPesjHe7wAAukxpaWmR0XAoS0pGRoafnx/KZnZ/75UoAICnp2daWhqhjRPMz8+LxWKCpQuDgwOIaeStQadbXOQh3Dk7OyMkLGU83iJyPZGapWhCouXCAoQaqB4c6hEKYaTbiy/CWfTKKzAsa+NGmHjQwWFFT/DlLO0gYt+KMYyDYbGEQw+byJ+ZTrJ0aUrDDAv/9//C4q92djAiLDcXGtp27JCOj0NP4szMjLa29mp2riV9z0ochX2IY0++tLOz09DQGBsbu+eXqqenp6Kikpub29TUJMVnNIxVv46gX3PohjNu2HZPbNcqzeVB3BP3+TUvAvcsdwnyW/IT8sS2J21TnB4eq6yuIuO5ZmZmVFRUXFxcBAJBfn6+qqqqlpZWXV0d+c1Djrmzs7OlpQUFHJWVlS1PMwP1PT3j72mGvKsR8p7mA+39B1ffUg70yJGoCnrGZ7do0TYo+76r6gebCtEQTfao+r2p6GmZcHdkaJAaz9XW1nbt2rXk5GQcx4ODg9XV1a2trZubm1GRDeptbairT09LS0pI7O/tra+Fuk/yj7zArP62C3kBMoUhsoXBMoUhBBEiC4lgmQLJ6rXC4GsFwa1TErVNblvtTlvVvQ6aD22OcNN261vZbbXtra3UeK7U1FQZGRlkXiQH09bW9rghUWj8j7uEmdXKvNPdv872+S7b5yilLa36ws4s7yN5ASc5MwMMBpPU9+A4fs8dW11dfXJykslk3ssjcOvWrby8vLraulnuTOpAfKwkKD0saSg6aSgmcSAycTAqcSCSjE6P6gmumihDV70g4AZX+zkVWrsU21MaYcAqQWYs2O9YaJXQFLWEe4S+Be56MWpG8TrG8TpGREMEdakbo+5d6M6eY1dXV6MnkeTzMyCedTxXV1eXpaXlwYMHjx49+ijV9RDu8fDwgOFIExOnTp4sLC6+euWyt7e3goKimppaSWnZ8PBwb18fn3BbtbW10dLSnpvnjAwNqqioeHn7RkaEKygqFhUX6enpOru4ofcpOREBgGhj1y4YUoQESWoq/EQ+dgy6hlhZwUCYl1+GjjtGRnhHB3RiePddKGY0NaGYIUUChUAS+i7hfVJBCA8hUTxhEcNcMCwUaXrI/Z97DgKpH36AwmxmBua9HR+HKpCHyWyEe4yNjUnc4+fn99133+3atcvIyAi9McmrW5FAuAdlj2QxGadOnSovLz9z5oynp8fNmzfVNTQaGhsHBwf6+/txHO/u6pS/LkcLCgYAZGVmKCgqZmfn2FhZ2dja5WRnycvLVxCpyskToZSDp09DAHfsGNQEXLwIdWPt7RDufPopVJWpq+P+/lDHs2ULZOCLL0LQQzJkJQLZWXIJCd1MsJRHGLnYGGZNZJeBPH/1VSiVMQz6CdXVQb/dwsIVURk5WAmBgIimpiYZ0FFVVWViYvLVV1/dvHkTgWbp3zy4TsU9TQ31P/xworCw8MyZ015eXkpKSmpqau0dXQP9/QhCTU5OaGjArIZisbihvu7q1auJScnpqSkWFlYtLS03b9xIS4fV6KhnAACi8KwsCCJxHGp0hofhamcnnDPm5jBjwksvwWmMuPeLX0B0sn79KlxFKRVSCBwJcygQUHKWUEyaElVy79u20DHXrYOw9aOPoFHyyBEYDP/hh3h9PbRUCgTSfIZZeaantbS0fg64p7y8PDAwsKuri8FgJCUlZWZmIprK4RVh1vIdFqfYIRvOuGJfeGBfrdKcsc9SKfqemM+vemJfPMwfyJvwE/LAtiVsledOzra2389byOfzMzIygoODJyYmysrK0tPTc3JymMwHKkkhIZGYEGdiZGhrbdXZ3mZvazM3xyaFB7oKGEvIGn1X1f8dFf93VYmm5v+u2oM00fPXG17umRLw0TM287GG79s33Dcqeb6j5LlRyXOjMrF8cPWv110sYsvYszNUO9fk5GRUVFRiYuLCwkJqampubm5paSmTyZyemqoY77FrybvTVujUVninrdClo4hYFju1Fji3F91phf0OrQWuHcWjC5JiKJm9LWclxbYCJfW2cgKu5CzV3oJE4OVs/8vZ/i2TkuJi2S01X9xW2mWtustGbZeNKmyQlrSviNWvbFS3mt/IbK4eGhgkH38AAJ1ODw4OLiwsFIvFQqEQTY+nla9Z6tZITTN0rpYi9ySnXWnuB9PciOZ+MM39YKr7AbiKOt0Pprrtz/Q+Nj/d39nVTeIeAEBlZSWa8EwmMzExMTMzk8lkdrR3zPPnkntjo5khsFIEKwzWkWCFxrBg1YhYRLBCY1lhEQxa5ZgE93D5XL9SD6tMU9vs27bZt+2IRtIkYZlhHFkTivTZQrHILfuOasgNrXBVrQhVrXBV7QhIaBMN9hBNLeSmS5Yjj88j0STJlmdAPFPcQ75cxsfHIyIifjQZLto/PT0d4Z6hwcHdX30pJyf31Vdf3b5tfu78BXn5614+PqqqKtehAL6blpqioqpqYmxUVVOfnZnh4Oigpqoqd/260507Hp5eudlZN5WU0bc1OdUAgJ+tenrQmoBwT2QkjDDS14cR1H/9K8Q9r78OY1UwDGbWUVeHH9ZIEvzqV5JiWxRRTbpK2BDaiF4MKyKEyiKGOWJYJEFDifLcczDV2/PPQ/H8/ffwXMg8AQBMWvPWW5KEKOQ4SUIK9yAWTU9Pp6Sk3Lp1q6EBBkuTO69IUHFPR3vb1q1bZWVld+/eY2RkKHNNRlZGhkYLui4rq6SkzJ6biwwPs7C01NbWEghFhoaGHu6utvaO1VVVCgqKQ0PDBgb6kdEx1DMCAAOmsrNheLOKCjTQnD4N2XX1KgR2TCaMWl+/Hqp2TE1hCoCzZ1eRzWgTICCOK4Ea2URmwkqCjdOE11QOQcM9//hHCB+1tGBhkKkpGLWE/LFWZAK1Uwr34DiuoKCwZcuW48ePk76B1P2X01TcU1lR9vnnn8nKyu77+mtjY6Pr1+WvXLkSGhoqKyOjqa3d3NIcRKMZGhpqa2uPjI55erh5ennp6xvExsZoa2l7uLsd+/54WHgEevLRidC0LCiA+LuuDtoK77mUmZtDFLJ9O/7ll9DV5vXXIQo/dAjyYSWnKCkmA6Ioyh0iQw+HSKmAcmaOL+l+EHZ/4FcvvADvmoICdHTz84PuaH/6E9RTUn2uSc4g3+GfCe5Z/hqdnZ2VSkeJSiiUlZXl5uZOTU0lJSWtaEsicM9pF2ybB/aVO6VRVz2wr+5gn6VQcE/051fdsS+QP5AX4RiEllQPIXdsW9xW+UWYt/C+nYt8YVIvYXBwcGJ8nC/Ch2YXURuZ4zfS+3LKqmLTc1PySoJjk/unOMNsHtrK4QuJKQTqWSMblb02KHlAEEPAl3eUvDYqeW1UJpYQykD6L9dd3NKr0Rl7xqY/VPX8h7zzBkWXpea6QRE1F4KA/X++5mgeXSRl51px8LCw9Px8Qm/T2QLalZJQ2IrDrpRQWmnYlWLYeakoVK40gjUHs2ZAZxRm08kktwupZJUJb4JGS1R0wvt8itf5FK/mMVjEA36nNVd9ZqKw47byTgulHRbKOy2Ud95WQg12LrVPja+nN1ZOjj8Qz4WOgN4M4eHhXl5eJSUlT8XOhYDUwsICj8dDjh/kU0MSMOlAvkus7ReJd/YlOu1NvLOXWBK0E6QTYOe+eMfdKa6H2JO9dAaTqqcnB0/eAlSfiyvgxNEjwjoDUBpllE8ZZlJG8VlLREiHb8UwTFoGYw54HLdcJ+MEHdMkfbNlTdKZrG8QrxVS5g9dIwAQioSOKTYKPtduBSis0hR9ZOyTrURiEYP+7x7PtfQeJ+/Lj0hodNsyMzNRZufenp6bigq7d++SlZW1sLh9/sJFNVVVM3OLsLBQf3//3NwcV1eX69flFBUUCwoKk5MSb9y4oaWlraurK6+g4O3t4+XlpaCoyOEuUOU0cQoJ4qmrg4n8Z2ZgJpKvvoIv/V/+UqLYP39+eQgMlN+UT2okKroJYRxH2LYAhnUSAmaeMHglkBIa4aSvv4bggE6HocVHj0rCjMmcQHNz0t/QJPdQUYXl9diRBwn58DyMQP4rKPKrraXlypUrO3fsVFJS1tfTu379uqysrKubWyD8oy3yeDnZmZcvXzYwMGxtabGxsVZUVHB0cgoPD1NUUIiIiNTV1XX3gCZI6rmQnAYAqlv27oXl0//0J4jwjhyBxpff/Q4KbMSBw4eh+KSgRikasbRpybaFNBBtRJV1JKERppT86m9/g7L/7FmIYlcMhaMOkkqjtMvm5uakYyOHw8nNzZWVlT127NjyMAfqbxENAPD39y8pKYGBJ+VlMteu7ty5U0VV1UBfX15e4cqVKx6eXn5+fpGRkampKTY2NnJysqqqqjW1tX6+PvLy8tY2dhPjY8bGJtHRUVevXjE0MhIQblVoco6OQo9vPh86KQ8PQ3rfPshSDIM6HjU1qOZ5/nk4V1Hpt4fzE1lXUXSh4RIixzGshVD2DBOgB2FKqRsBYfprr0nUaWpqUBX6u9/hOjow1eTi4goTFQDA5XJv376N0APM3/Ov8++Rul8I25l2EQAAIABJREFUk1HdnNH7qK6xITox3ivAr6CsRENPu5MhyWGDtqJ30eIUO3gDxD1U0LNEf7lErIx7VvEH8sJ2uWHb4rcqzI9P1zXUk/FcUiNHY+jr6xse6K/sm/3SpWyXa/lut/LdbmV7Pe5+7V2937v6a8+q/d41u93KiFb+xZ3S1NZRdAn1zOENCi5vS4OY+/CFQDaub1x1cE2V1JvsGZ3efNP1bzL2b8s5vC3n8M/rDm/LOf7zuuPbco5ED0Fcd/zTJWvzqII59mxbayv5cl9x8B0dHVMTE8m9zRfzabJFoajJFYXKETSVkCkMUSyJ7FnCPand9cei7E/HuSw1Z4JAS0nnqVjnk7F3mkb70BgyGyu36MtsM1bYZqK47f+x9x3gTRxp/4KEyyV3uVzqhRR6EkIJkIBtahJIIIReDS7YuGHjXuRuyb33jovce++9d9zkKveOe++25J0/syMtwjaEu/u+3D3/L37mGd4ZZlez787u/PatVGmu8lzzoJZYUnVJd2cnt56LeK5HR0eNjIw0NTUjIiJqamrW2jVzL49Vb8J1OTA4OOjn5+fk5FRRUZGenh4fH7/2KABAZbpNsPHhcMtTzxWLk3jzZLgF7A8zOxFte3Z8uL2yqppb3rPqd5Efe3VV9ezyTHCDrzfd1YcrnCBBs2MM1rp70p3yejLRdc0uzlglmqoFymuEKL+kKAfIema7ErjHPNJI1EHggYvoAxdRKa4a9aDO+w4CphH6y8vLdDqdO5MJsX7+V4n/gLynrq5OXV1dUVHxaf6stbd87T1LSkpycHCAN29mprq6urKiora2trGhISIiIjomZniY7bWI2PS4rDQrO7uzo6Ozsys1Jbmurr67qys6OnpsfCwmJroaN3Hn/glikx4dhRkZ33kH2imjOMsbN8IX/cGDUDCzcSPbHuUFmwrAtVcoHk8tTtfjhrcAN5gwInQx3Ie/9RY0Pp2dhYHmtm+H+IDQbT0PJLjnC40eWCyWlpYWoeNHBvxJSUkJCQmvEhMM5SUNDoZChYmJ8epq+uPHj+vq6mtra0JDQ+JhdPNnQvKF+fmYmJjm5pa62tru7s7IqKihoaHkpKSi4uLW1paQkJCh4WHum8hkQq1HXx+MuGhpCS/qzTehoGL3bghK/vQnWP/tb6u3VW62cGiA29iG40EI23GT8Bp8ewYkEtqhkQ6RfaoDB6DbXVoaDF1z4gQ7NjG6uc+xb70GsqpRVlbmtu9BF9XZ2fkq4Ua47ZqHh4bpdHpFRXldXV1NTU1wcHBycvLs7BzxGK+ssNLSUsvLK5sYjO7u7qjIyJ7e3vn5eTq9ZnpqKjoqshkP/oZmCgDUrpqZQe90ISFo/a2hAb39EZc2boRCtVOnYPO1136Tq0gY6YcrB/tx9tbh3oWAROrAc1CwM6VwbsFzJ3zrLWg89O230JC5owNmDpGTY1s3r2UqCiKioqLy36DnIqZH3ILJycm29vaVxeXRypaR0saRMsbo46a+ghp6dGa2d1hVZHqmZ2h3XtXo46aRMsZwScN02xMu3HPHnnTUifT9S4otiZdb3hPKe98Rioh+WE819oMrrjJzIPFF8EnNj083tTSvi3uIyff29U6MDue3je0xzthrkrnfFJWs/aZZOE0QsP9Lw/TIarbep7LtyS5J650SEMFwARfUJDptNgubOsQXoZ/rHBzbL2OzTdR0p7j5TjGLnWLmXAU2d+E9mwWN9YMy52dn21qfgUWC7Yh16ITt7e0z09PRbZV3k93Z6qo0jq7qeeJeqqdkhk/HFPv1Htfw+LyP8bUAi2uBFtcCLK7jNWpeDySa5lf9zaufsJOLJVeVHFQX4dGWfFZ0ntG8OpI8ePmGfC+hsmh4cJA7fg/n6QPz8/OdnZ0DAwPItJywdEQXtbi4ODw8/DRA1NLSUl8f2ziS+8LZNGebgXbci8tp6dm+fkFR0fE0n4DwCBi1nP3lzXnvAwAep1j6UfYHG/MGG/Nxar4gY94gvMmuDY+EmZ+aGuns7X3CLe/hngBi++zsbBOjaWZxmlbh7lJi61Zq71pqv6p2K2F3OhRZZbSyY2vNLMwYRVAeeki8RHKj4C0t80jMKdmOxZH3GIbo3rW8LmJ3R8T2DqwRYYs3OfVdy+vUQG0mi9nS3PL/vz8XAMDOzk5LSysgIOAV7XtSU1ORES7380O8BQiCWKlEz4sIYlkAAD9YPTygWiQrC35D8/DAd/0bb8Av6Q0b4Cb9l7+w5TobNz63DXDtDcgjxg53pZ7GhTr9uIUEExf2ENYSbPXWhg0QRQkIYPLyEGb5+0MFkIMD5uKy/qczMVviAvFUnY5Enoqnjw2FQnnw4IG5ufkrCieCgoKI9Asv4hLe/6q2pZy5QfFVSAi00SaRoByCcJ/euhWiyTfeeLY9v1QdA3C3fzMSyY+Dddpwf3WUcV0LTw/ONkB5+23IxjNnoOgIhebD1wnMbpGTw/b0XsXDVU0UGuCpGIYIg4vA5ejo6MTEBOEVuOoo7iYAwNvbG6X+eCk/4X++fBkTh6PzozchmQz5iVbg5s0vTEr/WyztwA2WQzgRpBgkEgr5yCCRtEikhlXySK4Vzv7pBw8wBQUot0PaWGlpaO/PeVdz8wMum8XFRTMzs2d5Kv7T8h6UJKesrAxZmfT1P5lufRL66XUa6ZTfa6dhef1M0BtnQ978BdZvnQ94/Qzq9yLx5d9newwsjE35fslvRzrmSPqeq5xyJJ3iNCFhQ+KJ49JzhfCK2pP4cGnQKWcSKt/jBFKWwR570tFwPqlV/lwETwEAHR0dlZWV+fn5EItPjOW3jewzSv7GaFXmrFR2Fi0TmE7rG5OU3fpJkVVsvU9la98uMfMd98123TffCYsZXptzN3fdN98sYGgfW4DWYefA2D4py63CRjtEjHeImuwQMdnJKTtETXaKGO8UMdkhYrz5DlU/IG2tnouzhkFNTU1FRUVZWVlXV9fc7Gw4o+xapP3dWJe7sS4CeI1o7po/xlkkwb19YgjNJLqm+CcXnQuP9PFicOGR/kU2zSYuPtL/1V3/vDu1qpedZCOxsni/qvBhjfuHNcQOa4rBGtHPN/cqC8ZXFIwOj6zFPcT80RxWxS1EN8XFxcXbm9bW1ubo6PjCMKdzDdhULjaVj03lg6l85kTuZH/K9GAac6qANZGPTRXg/5WHTZdgKwvIoL80ycxb6ys//UN++t/6Ub9Fta8+m4D91G99qAeCjI9OjnQ0NDKIDwz0hkEvmbGxsfLycrRmOto7phamnHJtLdINkYc5pzbmELi3eYaJSSoloZadQ2Z6flonQF3UXkDS6Z6E0z2iRoQEp/Oe3R3rGHMO7lnW8VO/ZnSe3/wqv9kLyzWjX7V81FgrzP8Tei60Q1y6dEleXr68vJy4SdxP+Kq9ISkpCflzLS4uFBcVTU1Plz0uGxsf7+vrZTAYCwvzmVmZiYmJ/f0DaHVOTkxkZWVNz8zg8Wk6i4qKl5eXi4qKGhsZgwMDBfkFHIcv+NZG2S63b4coJDQUKp7OnIGWIshm+aUbCaE1qMQVBMjQZAE3YV7Bt5ARLmsJNmb68UcsMhIaZMjJQRvVujp25tFXlEyg1fzUXZ/w50I9NjY2oqKiVCp1XSiJ2MK93fr6+kZGRkLt9fR0SUnp9PRMYUHh3Px8a2trR2fn9PRUWmpqUlLS+MQEOnZwcDAnOwe5IDEYjeUVFQsLC0VFRb29ffX1dfSaGs4tg95Gzs4Qw73xBnQs2rz5GVh8QTyeZwPwjRahmTxc/FCCs3GaRJrkGIN34zs0kXEdHquoCOFOQQEMLePuzpaZAQBTqv31r+zMXMTqWpdAWvxV/lz29vb8/PwPHz5E7ierDlzLUldX16SkJChCGx8vLimZmBgvLCxcWFhsbIQZJScmxlNTUlJS0iYmJtGxT43V8vLylpaXYSq62tq6hoa2tlZoctvS2tbaWlJaymSy8FsGoeTnn6/i0j/VROrCdNzQHkl0JvCAmWgBt+A5cVtfAnrQU3DkCLStHhqC8xkdhZl3UXikVZxBTaRLUldXRwLI/wY9FwCgs7PTxMREQ0MjLS2ttqF+vKk79JNr3qSTPqQfucoPOI1q2O9B4ssVNUZ3Dcc9d2wh7vkBxzoE3OGGQQj3kNEhS5OzwbyidiQ+XD50yonEXZDQCPbYkfjCjz4YGxjmzs9F8BbqT0tKjI2NyWRyRUVlV3tLbvPgHt3ofXrR+/Vi9lNi2DUMP/is7KPEfKkVGVHeiWZS2dqz457RNmHDnfeMdnAV1NyO9+y8Z/Qxv559TB46pGNgdK+46RYByg4h/e142SGkj8p2IQPUs11I/+Nb2gYBqUOD/dz+XNyTDwsLMzIyMjWFlvv9fU/C6op/9TO9EWR1I8jqOl4TNNG8FmhxJ9SudYz9Vo+szD9pqfyznQYqZ+00iEJ0/mSr/pMtuaKLnSsjsaJwr+LdQ2r3DqmJHFIVgTWin2/ulr8TV17AaGhYq+dClgMjIxASoeyqHB9AgNDJzMxsaVl5Y2NTfQMjLi6hqBgme8YwosAnGPJhOBLr1Me6jLEuE6zLBHSbgl4z0GOGdZniBXZinUZYjy3GhHamAIDieGN3tR00nf3eOvu8YQ0Jmu4+vInq/d7aX/vpH54cbu/q7lkr7wEA9PX1WVtbq6urp6enV1dVj06OmCca6ESSCU8rSpQm8rfieF3BpkaYUngZ28Rwam5K1VP+tvlVQaubglY3BSxRfUvQChXYFLS6edP0kkmYAQf3MNW9lM/rnrlq8OtLyq+6Z9Q8FaemJ///9+dCW6+tra2WllZERMRa4QQAoIZOr62F0SOGhoaysrMXFhZSU1NRcOGB/n4+Xl6aj8/Ro3zZOblGhvp3BASbmxovXrxo7+BYWlYaFhYan5Dg6GB3X0yM5uP3NIOdtpaWhKRkUGDg6dOng0NCNDTU5RUUunvQBxDcI/X04Pb83nuwfPIJVHX5+kIrUU66opdsMCgjZhCJZIKrCZCqK5GT0mgR96/OxrcTuJcjNcTly/D7OCAAntbH55l0s6ICGm2sCr9LvDi4CeTPb2lpSfhzPRVX6OjomJiYhIWFofhm3OMxDCsrK21pgfLn7q6uvPx8FosVGBiQkQElmU2NDTw8vP7+/vv3f9PY2PhQ5oGKmnpZacnFCxcfPfIoLS0NDAhITU01MzURFhZOSk6dnp5SVJBXUla2trYRFhKm0WjQclxSsq0dyZahAcqePfBifwsyrstYgOf99iSRrHGrcKSXicZT1qPsZoZ4Gle2pOfdd6FIiWBjSQnMe79/P9uNa2gIpvtY1/pkFX+QcEJfX59b3kOhUMzMzKKiolZp9JEoqKioqKMTJtVqa20tKIQfx56enjk5MJVmWUnxoW+/8/L23v/NN909fXf5bxsamaSlpV6/cdPLy7ukpCQwMCA5OdnAQF9MTDwnN29ifFz2oYycvHx6erqoqIillbWUhIQeRX9qGgJ3AKArHPJT4xbAvDKNNK3OeOTlSXw1zuLpzFB2lFEcDDFeAno2bmRHVJKVhaBHTg6aGXl7Q/3vuhbNiLfIpEBfX/+/x74H3eW2trbCwsJ2mBm8Y6qlL+iT656kU954klEUV5A7liCi3UlHc7jkPT5f8tuSjjqQvn9JsSbxxHLJe4J4RW1JfLhACEmGiBqhJdi0JfGF8UnNPu/PRSxUZC/FYDAKCws7OztHhgZyGU++gh7pAXvJgXvIgXthCeAQgXtweo9awE4l3/BSNg6oaOneJkDZKkDZJkjdJkjZJkDFCZyGPezy4Q1Nu8hsfO2BjoHRr+8bfM6vvU1Ab9td3W0CqOhBgqv50Q11qm/S9NQktz8X9+SHh4eRyKejo2NyYiKgPPu0k9av7lRY3GB9AS+QdqNcwDvPu+ld9TRqGWY7pYeWZh/Rlz5lqrBeUUSdJ03kT5jIP25noMknlBfslr91QFkQlW+UhHAC1ezOA8qCX8rejH2c19/3hNufi3sZOzo6UqnU8PBwPEzAOJjOw0ZCsJEIbCQCjEZgY5FgLJI1HL44FIqNRaF+bCQUG0/FVvBI/xi20Bs+W68322gw22gISwN3TXRSZ5utV5bZuKcwVt9F6TMPja/WL5qw/5H6LpregdGBFjq9dq19DxJjd3R0FBQU9PT0NDGaRqdHtIJU5bykFLylFXGjY0UuAu+UUfSWlna/75vjjXg4NTcp6yx5iXr2utGFa4YXiBoSRheuG7I7L+j9RPHX5uCeZWU32dPqx8/rnH5JOa1+XMFVZnFpkdHI+D+h5yooKFBTU9PU1EQBJZ97PIYGFeTlFRQUp2dmmxgNYvdF09Izc3JyUD72np7u61evnv/ll1/O/xISEnrt6tWffv7Z09Pzxo0bNB/fvNxc/tu3paVl5GRlvby89Q0MpqenlVVUzc1Mra1tHz1yl5GWuXr1qhqZnJsL7U9RMqObN6GZ7aVL0BIFGfFs3w637a1bn5nfrrfHABKpB0c8QRytARO3QZniAB1/3GX9mV/MBx9Afdn16/Byp6aw8+ehsUtMDBv61NVBjycGY32tAcEiBByZTOajR48Iu2bkTCQoKKioqIjeO8R4KIlta5V+8EBTW2dpeflxWamwkFA1nR4YGOnuXgzDxjc0nDv3y88/n7146XJIcPAv586dP/8rZOn16yGhYcnJSVevXFZSUlZWUbaxtvLw9Ozs6FBVVXN0dLh06ZKsrJyRkZGFhfmZ06dLy6DoDgBo2bN167qY5uWdiFFPN2AqiRSPcxLFZV7mmElheITrRO4d+p13YDSBhgZ4uShOcXIylKUVFj4DlOtqYQj+IAIAsLy8bGtr29cHLSEQk8lksqysrI2NzarYuLh4pkZcXEzfwJDJZOXn5ggICrS1dwYHBz/1fodOpMVF58+dPXv27K3b/CEhoefOQt4+cne9c/duZFRUfHzc5ctsljo4OvoHBDU3Mcjq6ro6WuUVFZaWluXlZTdv3lBX12hqQtsVXBW/5ee/Lm8Jq3AKnk0dySCZuEEPyjC6hEPzHG6WrrfU4cnfeAO6PY6PYzdvwkQfHR0wBjSKFr2KmURzcXHR1tb2v0fPRdxrFOezq7d7qvWJ/ydX3WGyrR9eUlxIvFn3jfDlDeP3+Hx5G8c9pxxIJznllAOJKCcdSKesSYdjL6qhQ5YmZwN5RW1w3MMFleB4Rwie2IQtiS+UT/JFei7uyff19U2Oj+XUd+966PKFrMtXcm7IOYvjZA6ds3DPLNcv5Vy3PXAKK2QHX65o7t52R2crv/a2Ozqw3H2+5jQ/vKZmF8E2a+3oH/lahPrZLc2td7S28muzyx3treyCd97R/vCqGsUnYWF+jjubFbES0AOFuAH9uWZnaQUpR43lfrRU/dFS9TSnPm2pgpqI+N5c+ZytRtMAW0kXVJj+jboor+4DWPSk8BqnuZo8ulJHtCVLW9nXG/+44MuHN/Yp3N3/0rJL+npsae6L9FzwS6aszM/PLzc3l8FompmZZvb6LjF0lpr1OcUAEi0Gy804gfqbdJc7nDEWnsYFw4YbfDpz5bryVdYUZaKnM0+xt0SPuTCOGJUfreeo8KEbebub2nZYryXUtruqbvHU/npsoKWpuWWtvId7zczOzra2to5Oj8i6SQjZ3IJmN8jyBq/vcVvh2N3ht7jqluyIpjE5Oylhe+8nzZPndU7/iuMYVBOABjVPqx/X9FZFcYOXmctyTlInlL87o378DPk4uybjNKrVYecJpcMyDuJM1v8ZPVdOTs69e/eEhYVLSpBUkP2AAABampvIZLK6unpHV/fY6KilpUVAILREuXv3bnd390D/EyMjwwcPpIyMjHV0dKxtbHx9fRXk5UXv3zc2MS4sLK6rrW1paYkIDxMQFAwNC09PT7eytJCUlEpLT9fT1TE1M7OxtpKSkip7zN6kx8ehmzoSxrz//jMH9Re9/fF+JGzIxnVYKH0jIJFa8TTgaGsBJFIOnh2Tyb2jIM8vExP2llxbCyPZVFezgQ5h2cb9sliXhiLQ4uLr168TEAcAUF5erq2trauru0rPBe3jSkv0KBR1Tc2evr7BwQGKnm5mVpa/f/gPP0hPTo63tbZS9Cj3xcTMzM21NDVcXN2cnRyVFJVERUXhHlxRQafTW1rbvDw9hISE42LjMjIy9PWpSioqMdExD6SknF1cbGys5eTkOOoMiHu2bVt3G35JJ4olE4eDniYOcKzlGPSg/TuGRHLl4v+zs9248SzCNUI5r4J1uHkLvWSTk2/duoU2afSa1tTUVFBQsLOzWyVCAwDk5mRrwz+doeHhvt5eFWXl0rLHHh4e8vLyc3Nz9KpKfX19CQkJM3NzMpns4+NrbW0lryAvJi5ubW1TVVVFp9NbW1qcnR3FxMRSUlPT0tI0NNRV1cilpSUuLq5zc7NaWppy8vLd3Wyp5L+Ee5BVeChu0NPBYWk5x6AHsTQIz+yB6Gf8JBb/m29CN/V334WeXKdOsYMT4qFM2GuYm4eraABARESEgIAAin70cj0XesMixEnskQRB/O+/TKyd28TERF1D/WRLr88nV5zxIIQuMA7h97iJMTsgIW6GDGkHEk/GfZgXGeWpQLjHkQ10IMrBC0HApjXpSOzFZ3quQF4RLtyDxqOajXscSKdsIO6RelV/rr7enLqObaLmO8Qsdolb7hS33IUXnLDYJW6xU8xyJ6wtPhM2Cc1jByGsaOrayq+x5bbGVn7Nl5QPrqjYRWSg64W4557eZzfJW29rbr2t8aLy4RVlqk/8zNQUdzKEVWxHN7SxsXF8dMwjO+GA5n0+ijSfnjQf5QFeS8Mm5QFeIHFER+qUoVxjH5SqAgAC8lK+Vrz7LVn0WzVRdq2G00STLHpITeSAqkhJMzvYdHxZ/hcPru+V49+HijyHYPfcwftv75S6GlOa293Zta6eC10FygfQ3Ny8uDg/VOvclSvXXaDSnc9Vnm925Sr0l5uzlmfRqm4utC8KvFUSKvSSUhx8tzxaZnGW7befF6lt9/DvLiqfuah86qL8GSyIZjdhp7PSx480do0PtnR2db8I9yC2w/xc1fSRqWEhS/5L1HMvUT9dNfj1F50fbaLMEdsnZiaEzW+fUDr8o9rRH9WO4eXoj+RjsEk+infC+pjCIVV3edYKDO7PZC1L24vxyO0/qcxzUvnISZUjsEY0JPCicoRH7hsJ23tLS4v/V/y5rKysLC0t09PTCbUCWlvIndjaytLe3qHscVlCQry6unrZ48dPjcWoVOrKygqLxZqenpmbnZudnR0ZGWEymSwWa3R09MmTJ9BNADfowYV7y8hBZmJiYmZmemBgAMNWoD3dHDywr4/tmoGHVoOR9IgX/SsQAHeidsczGY1wthNkbDvIabbhkOi5LOvEmeXkoLKAyBvFvUNz02tfGUQPMkaRkZHh1nNpaWmZmZnFxMSsSnmGD56mUPS8vLxLS0rCw8O1tbRbWlv9/f0d8Uz1y8vLMzOzM7MzKDMOk8liMpmjI6O9vb0dHR1EYp3FxcW+vr6F+YXJycmJiYnh4WFsZaW3t3dhcXFwcGBsDH6j4A/Yv4B7AK7SssYTesxxeIjh4a3HOM0qfP+exZvP7tfrr2M//7xaOfiKbCT4id4LT548ERcXJyAOTC9aVKSjo2NgYLB2lQ4PD2lqagQGBRcVFYWEBGtpa/f29jnhzv9oDc9Ans5MT0+Pjo6yWCzm8vLIyAiHpTAoM1RbzM4+edK/tLQ4NTU1MT4+Oja2tLQ0OwNflOPj48R9xFM5/lPyHoRjenCnLcIqHClhmzl5cAGJVIALe2AOV2JxriI+/BBmEeHhgRZUeXns4ISvyF5oWtfVJSUlRYjfX+bHvrLS3tZGRLV+8qRvbUhfxLR/rV51r5G8p6Orc7K1z+uTKw6kY9yeWc64o5YjV21LOpL2PO6xIR21h5bI7IKgD6d50p50yop0JGYd3PMM8SDbIG7MZEPiDeWTnB+fYrQ0revPhRYqzNTd2zsxPpZNb/38ru5WAcp2QSqnULYL6m8XpOAFdm4ToG6+pR2ShT7zAMQ9t9S33CRvvaXOKQRNEOofXFK0C09HrO7sH/laWPezG6r4ePLWW3i5rc4mUPMW+cPLShRaHPTn4nL+52Y7Mfn29vbZmRmX1Kiv5PkPqd57rkCzm2c93ygL8WmIN/SwnbP8spN2ydzYryjwjaLAfk75RkkQ0d8o3t2vKLBP4e5e+TvFDGhuCACIK83dKXl1z8NbqOyVvbX34a09src5Pbdh8+Gt7RKXo0tyhgeHXmTXTEyeTqfPzkxXJ+tlel6EGSRoN4g8Erm+MKFENu0GyiCR6XWlNEpheREPuohhJXGUcPOjUTY/EiXa9sdo22fNKJsfI6xOJrpenZtmBx3IjVC3kX7LSfEjJ8UP8fojJ8WPnJU+ZDch8ZGj/Ptu5C3jg82NjKa1ds0E/5HSubm5eWhi8Br11x/Ujp5RP4GX42fUT/zEplEPrI8pfWsSREU8nJgZ5ze+dlh2/zHF744pfYfX3x5XwmlO85jid98+3CvvLE3Ie8RthA/K7OZTOLhu4cX7D8l8LWolwGQt/1/x54qMjLx165aEhATSCxC3By0vhG9Q9iL0/CcmJlpbW6PbgKxbCJogiGMXFxe58yKhfmLhEuNxgjU2Bk6cgI7Wq9746zXRdlJLIlFwp3RCa1COO22h/0XZo6h4ZsfntpO33oJKtKcJtK9dg9/QPT1sMQ/3tb8ijZQyampq3KmPTUxMbt26paamRgiBiLMhexTEVWxlBbGURqP5+cEQzGgYwTFuRrFZymLhLF3t2MU9Eh2O92B9feCV5T2IaSW4CXMuznOAa2FKOLotNGAAx5HdxA795z+zJXO7dsFAiJyLIK74nyaQEa6cnBy3H7uhoaGsrKypqekq3IMuHIVwRa5eiKWOjo7IVBwtUYKlxJJD00K3A8YWX5OHgbsH0fhJsMZGGIqgBhYzAAAgAElEQVR6vTW5thNxLBfnGLIKRyZTKM8osUo7cVtmhNTXnuRZj54eDLIgKAgjIr666T2a/PDwsLy8/HO4Z/duwMuL8fAAHp6k3bszsrIQc7IyMy5fvqSrR8EAeNLXK3b/vojIvcamZoJ1/yZBLAiESpOTk5uamrp6e0YbO10+/tmcdNCSdNiKdBjVRqRD5hwa7zliQtqTIKyzvLy8zGLODo492nbZlHTAknTEEh9mSTpsTDpkSvoW72F3GpP2hv+swGSy4CEjk7Qj/Kak/RZwPHuACelbY9Ih4gwWpMOmpP1+R4RnhsYbmxjr4h6YmHl4ODExsa2tbXx0JLWs7u+/yr93UeGDS4ofXFJ8/6LiBxcV3/tFBtKoB+/8y7mHvsmFAFvBVlgl9a3vX5B/71e59y/IvX9BHpX3zkm/96ss0Xz/gvyfT0uZBSTC+KMsZlPXk89vqP79F9kP0CG/wqPePfcAHgLPI//eBfn3f5V788wDdbeI6amp1hf7sTMYjOzs7Pb29unJKZuYgH8In90ucXm7xOVtqBa/tFXk/Da8ieotYhe/krlZ1daEsVYAAI9SIv8h/PM28UvbxC7BGhIXt9w7/6wpfmmr2MUt9y/k1laAlRWAYRGFGf8Q+nnr/Qtb7l/YyimfC51FNNH5ocBPofmpg/0Da+17iGe2sLCwpqamrQ2CtjhXfhelTzzIOx+Rd6DiorrDSWm7hzq7+Yi8w1X5szDLn2emRpaXWctLS0k0CbuH7zorfeKk+IkzLJvt5Tfbym52Vt7srPiJk+JmJ6VPHOU/9NLZNz7ctcxcWcFAeqCcyT2SldRGK8mNqLaQ2Ghyf4M17NkAOyU3WEqQ7OT+OtxX19nZTTxoxFsdPYaLi4vJycmt+F/fcM8J5cN7xLd/I/XFflh27Zf6Yu/9nfskIEH07BL5lOyhBDDAZLKGx4fOa5/eJfrpHokdeyS2o7JbZOvXYmx6jzgkdtzbfN9acGFxgbnMnF+Y5ze+tv3ex1+Lb/tafDusJbZ9JbL1K9GteM+23WLbvhbftuPe5puGl+YW5pqamogP7H/zSX/1w/8D8XvQxrCMZ84iXknrEugyiHzsExPjpqYmhoZG8QkJLs7OmZk5Xp6PwsMjPD08jI2NKyorzM1MLS2tKiqrkCiova1FW1urogqGMO7t6XZ3fzQ4OGRhYZ6UnJKclGhqZvS4vFNEBEKf39pUkNYgkkTS55gtoy1kFFdpER/NgERyws1TngM9GzbA7/XXXoOxnuvrYaLsF3n/rsuEVZ3IWo3Ix44W99DQUG1tbU1NzcuTaBLLwsfHB+X/G+h/ok+lGhkZ5ebmGhsbl5SW2tnapKSkODo6WFhaVVZW6VOplpZWDEYTk8lcYbHo1VXa2jrNLdBTtLGh3tfXr6W11djYKCU1LTYm2s7OurV1ZseO5y5/LW9ffx175x2wefP83//u+9FH5p999mTzZvD224ilfbguZoUDg5bxkI8FBOhBtib8/DBen40NNjf3P4N7JicnFRUVuXGPqqqqqalpZGTkKrtmdDvQy4WoUfyemBjo/Nne1qqnq2dmZpabl2eor19TU+Ngb5+ZmWVtaWlv7/j4cZm+PtXe3qGtvR3upCxWcXERhULpxbPcV1SUBwQGMZoYxsbGhUVF/n6+Dk42FZUTH//jN1jKYdcsieSOW4WPbNwINm0CGzag2DwlnLsA8DxcBniM5t8+p7Mz5uoKrcWLiv5p3DM6Oro6H/uhQ+DMGez0afDTT0kHDqiQyYND0EvZ0IDq6+crJibW0/dkaLD/9u2bV65caWQ0Ecv13ySIJwhFFVJSUsrOzm5ubakprQhXMgsSp0TJmYXLGEfLW0TJmQUrmEbIm8XgdLS8eZS8eYCYbqqjbyODQa+hN9TURZGtA8Up4dLGUXLmEbImMfLmQbJGwQ+NIx+ahkkbR8qaBUvqB4rpJpp5NDU3V1dXN9TWx1DsA8X0IuGvmMQoWETLmYcpmIfKm0Ja3jxa3iJGwTJInBJHcayvqW1obCBAMzFz9JhXVlbKyck1NjY2MxhpecVKFu4qlh7ajn7qtt46Tv6a9j5adp5aDr6QdvDRdvJXt6MpmLvGpucyGI11tbUFpeXKlo+ULN017GlaDj6a9jRtJ3+ypaumnTfZ1kvTnqZu561s9UjO1DU4Pq25qam2puZxFV3d1lPJ0l3bwVfd1gudWcvWU8uehmgd5wAtB195U1fvyMSGhvrubnbMQPRoEPNHek8KhdLR0dHY0BiSHKviYKrlakP1dNR9ZE/1dtbzcNBzs6N4Oep7O+l5OOjTnDVdrLVcrYsrHtfX1jEaGqMzUlQczDScLSnwEDuKp4O+t5OmgwXF01HT1Zri6aDlaq3uZKHuZJFTUlhfV9/Y0JCcm6XqaKYF/9dR191O38uZ4uFAcbXFj3XW87A3oDnrutupOJgl5WTW0OkcZT1cbtwzX15eNjEx8fPza29vr6+vTwgxC3SSifRUjfJSjfZWi6WRwz3JIY/UY33IMTR12OOjEewiG+NLaaivraqmMxiMlEi7IKcHoe4K0TS1CE+VaJpauKeav7NCDE011E0+mqYW6iYf7Pww3JNcV1NZTac3NbdmxLsFON6P8JSN9JKL9JKPpimEeyoEuyvF0BSiaYpR3vKxvkqh7g9C3WXpVcU1tXXc0IF78iMjI8rKypmZmU2Mpkp6pYEHRcuZbOhJoT7SMfLWN/CiGLhTDD0pRt76hl4UQ2+qoSdVw0HFK/JRc1MLvZpeW19r7mOq4ahKcdMx9KLoe+ga0agUFx2Kq46+hy7FXdvQi6LtrK7hoOIUYs9gMOj49doFWGs4qBp66lHd4a/AMz+i6LvrGXlT0a8Y0/S1nNRs/C3rG+oZDAaRUPzffNJf/fDfG/dgGNbe3l5ZWYnSPxF36EUEnp8r0d7eDmGXixd+lZKS0tDQFBESFBOX/PnnMzq6etLSD1RUVOPiYi9e+NXE1Ayl4tLV1dXS0jLQ1zcyNgUAJCbE3bhxMyE+XlVVTUVFpbyiQlNTNSw86YcffvPtD0ikQdxeh4Z7G6HteRKPnkd8QCOX4FjcBmX9E777LnbyJLRo5nqmXnTRL+tHuEdTU5M7buHS0tL09DQhd3nZ8biYgcA9DXW1Z8+ek5KUsLC0PPfzWQVFJT5eXgtzC0kJCS0t7djYmJ9++snOzj4nO1tRUcHIyEhbS0tLS9vRyRkAQPPyEBIW7u17EhoSJPVACg+NrRYdk7V79zoc+NOfoE/7zp3YV1+BQ4fAoUNt27YZff11xLFjy8eOgRMnwJ49oyTSMI5v0OGItz6cROsQm374IXQj4uWFapd/SvzwmwyZmppSUlLixj3Jycmmpqb29vaE8uslJyHiFo5PgNycvEuXLopLSFhZW39/6iRZXeP7U6esrGwEBQSpVP2goICLFy+6uLpmZWXJy8ubmJioqKgY6FO9aD4AAEd7u/tiEuPj4xBuWlnT6dXy8lLxSXWffroOSzlQBkMQh0RqfOstw6++Sti3j7VjB9i/H3zzzeCWLWOvvYY4icA9wIFRLBef1wf9f/sbDC/517/CcukSVCaOjsIYmysrL2HDs/+C2fRGR5WVlQnxe1RgYDk/P1BRwRQVgYpK0rVrtnZ2M7NQr2duZmpqanZfTKypuSUvJ0dcXEJaWjo65rnk9q/+Rls78tm0MIzJZJaXl6OP4xWOyK2+pal/cCAzP7e0qiIjL7u8piqzIKeijp5dmJdfChEfIcMjEMnA8GB9a1PXQF9ucWFtU0Pv0EBXb+/A0FBDyzO4tsL5Q1NqaGnq7X+SVZBbWl2RmZ/7mF6VWZBbUUsvrnicW1K4gue/RnYkxCVwzxwpuJHTL+qHskMms45eNTjQn5GWUkuvzsxIa6irTU9NbmE0piQlNDdBzybkjI1kk+jMba0tHW0tfd2dmWmpT3p7mhrqJ8bGOtvbhofYSpa111tXQ58YHU5PTa6lV2WmpzXU1WSkprQ1N2Wlp9XXscNYEMxBv7Jq8n197KylxOQX5uYb6xt6Orsy09KrKypzsrLr6DUZqWktjKa4mJhRPCAtyuRAsAXDsPqamoG+vrbmlozU1LmZ2Vp6zdLiYk11Nfrkg9eLsx1upfj9ncXHdLS3o1/JSEuvr6nNTEvvbGtPiosf6IP+YohFBNu5cQ9CnAwGAyU2Ia6xq7uvpbW9vLw6N6+osKgsN6+ouKQ8N7+ooqomPjF1AeYGAZz7z15oM7NzlVW1PX0DFZU1eflFY+OTVdV10N2kgs7EZVrch6DJ9PT0tXf20GvqcvKKC4sf5+UXl5ZV5eQW1dU3JSanT8/AaKjokVx38mizqKioQI8hMfmG+sbx0Ym87Lyq8qqcrJyqiuq83AJ6Jb0gt6C4EPq7rJ3J6MhYYz2jv7e/MK+woa6xp7unq6NrfHS8ob6R+GnielFPS1PL4MBgfk5+RVlFTlZuVXlVQW5BdWX149Ly/NwC5jKbRcThvyfxu+IehPpv374tKyt7584d5PfL/XispVFeUnd3aF7e09UpePeuqIiIq6ubAZXyww/fX7xw0dzSikajychIP4UC/Py3vbxpxcVFero6+vr62lpaRoaGhoZGQ8PDCwvzGuoaUVFRampqWppagYEBYuKirW3DfLwv2VHQhlGIK2JQdGDUs4JbMTdyCSEAiVSNS4OQp8w6e4mgIFTKvOK2sZYPRA9SlGhraxP2PTk5ORISEiIiIlpaWq+4Sfv6+oaEhAAA6mtrbt2+fffunfCICAV5uaNHj164cNHRycnT00NMTExfn3rzxs2goOD8/HxVPJ2ytpaWjq6ujY3N5MTk0GC/mppaU3OzoIBAVFSUppamptZq3PP661An9emn0H78+HHsxAlw4sTK3r2pn31GPXiw9tgxwMcHjh4Fx44tf/ZZHIn0TJmF8zYTh5tIpQhZuns39PY/e/ZZkB6CLf8O8aJ4zcRz+JsnR7gnLi5uZQXk5+YKCwvfxb23ZB/K8PHx3ebnt7N3cHNzFbhzR1dXR1j4XkREZE5OtrKysrmFhbKyirGRobOL2+TUVGtLi6qqWkdHh5urC1XfwMLcXI+i1di49I818p4NG6AE8Y03sHffBV99xdqyJfbjj6nffdd4/Dg4dgwcPw6OHl3cvTuSh6f/o4+IFQ5IpARcJEn0rLNQEZzasAEins8/h16NsrLYF19AxHn37st817m5hCQrqqqqz+EeAQGgro6pqgJ19aQbN9Iy2MazDfV1QoICPr5+yUmJmVlZBvpUZWXljk521BniLvzLBPfE0DZGbGzIP1xQUNDc3Pzhw4fe3t7IL9LGxsbZ2dnR0ZGdoA3fGtlH4QAlLy/v2rVr0tLSXl5ezs7OioqKurq6rq6uIqKi7FzouMgAHQINohcWREVFTU1NHz586OPjc+/ePTk5OSsrK1tb23v37omLi/c9YW/AxAyJSRLzRxzgbjY1NcEoHvb2ioqKHh4eN2/e1NDQsLe3d3d3Nzc3R6pwNJ4zE4jiXF1dr127pqSk5Ovra2VlJSgoSKPR5OTkTE3hJyI+cXgQ5xAYrOHKlSt2dnaysrI0Gu3GjRuamprIu5ufn19HR2eFBRMzcc+c6zTs+a6dfGxs7M2bN83MzPT09ExNTQUEBAwMDGxtbb29vc3NzdfNj7a0tCQrKyslJaWjoxMWFmZhYXHjxo34+PirV6+i6FkAj5dD/BYAICUl5caNG2ZmZlQq1czM7Nq1a/r6+o8ePVJRUbl8+bK3N3TYXjXzl08endzQ0FBBQUFdXd3V1VVZWVlYWNjExMTR0dHb29vZxYX1LM8MYic8qK+vj5+fX0xMzMTExM3NzcDAQEhIKDEx8dy5cyjONfom5p6Pubm5jIyMlpaWq6urqqqqiIiImZmZi4uLhITErVu3Cgth9lDi/qKJET1svnPAPTFsZmZGWFjY1NRURkYmMDBQSEhIUVHRDv9zcnKKjYXfGyjsEHsmOPAvKCi4fPmynJycp6enu7u7oqIimUx2c3MTEhKaxb9enjsEgKWlJWLB02g0YWFhOTk5BwcHS0tLAQEBUVFRlKSZmBUx+d+H+L1xj52dnZSUlK+vr7i4eEBAwNqbRNwtgiPJyUkXLjzq7QOTEyOxcbHd3V2+Pj4JCQnhYWExMdEFhXk+NJqlpUVZWYmTo72dnW1pWRmTubSywmxvazEwoD5+XJqfnzM/N5uQEDcyPGhvZ52WluLp4WZoqFdd3Xz06Iv2AGjCvGEDbdMmq02bBv/yF/Dxx9gHH4ANGxbx3FuLxNc2vkMP4dionQsJrd5R3nyT7RGz6gL/2SbCPYaGhgTukZaWplKpWVlZN27c4Kzal50VAODn54eUMn19vUlJye1tLYGB/ikpicFBAXFxsSUlhY8eudjZ2VZWPraxtrS3t2U0NaysLGMYq7amytBIv6q6sqAwf2ZmMi0tpbS0SFlFKTUtJTEh1tnFvrNzdu9e8M47EOvs2AFTdvPwwIiCvLwQ33z33diWLQ5btjgeOTKBgx7s6FHAw7Nw4gTzs88IdSESniHzcGQ8/hwzNTRgbs7ERAgiOU/9y673N/8P2f1pamoiUx5kOuro6GhkZJSWloZksC8/CQDA3d09NTUVZnJuac7NzW1tbvL3909OTgoJCYqPiysqzHd2dnBwcHj8uMTa2tLFxampGZqvsljMx2XF5uamDfW1xSXFI8NDKclJjMY6KyvzrKx0E2MDV1e7ktLhzZufrdI//QnCkX37sIMHwZEj4MiR4e3brXfufMTHN3v0KODjY7OUj4/Fy7t44gRhGwRIpFocmqOQ4s+xlGsxQ7izbRt27hx2+TKMvHDuHDRHk5CAEcz374dSn1fhObId1tLSInQHUN5z9y4gkzEVFUAmJ924kZQCI0ihLQeZ5SH19PLyMooO9z/1BnzJvYOBHjo6AgIC/Pz87O3t4+Li4uPjY2JinJ2dY2JiKioquKEDcR6ozWxvDwsL8/X19ff3p9FoQUFBPj4+Hh4ewcHB64Yl6+zsDAoKQr8SHx+fmJgYHR3t4uISFhbm6enp4OBA+BJy78HEL65LINfOkJAQNzc3d3f3+Pj45OTkkJAQBweHgoKCoqIi5OOz6ljk/unj4xMSEuLl5eXj4xMREeHu7u7r6xscHMzZw54dhMYHBwc/evTI2dk5MTExKSkpLCwMscjOzs7f35/Jtc0Td+3ZKdZQ6L7Hx8cHBQU5Ojr6+vomJCSkpqb6+fk5ODjQ6XQY0nNpadXugOBjenq6n59fWFiYh4eHr69vRESEg4NDcHBwQkLCqvFIipOcnBwcHOzg4ODv74+S+fj5+bm5ucXExBgbG3NHWCZmvvY83FeAPpNCQ0NDQkLs7OyioqISExNjY2Pd3d1pNFpDQ0NpKcxuxn0Iuqfj4+OJiYnu7u6I8/7+/qGhoY6OjkFBQeuaus7Pz4eGhgYFBaFfScD/PD090b0zMzOrq4Oea+iHXn3yXV1dAQEBPj4+9vb2SUlJ8fHx0dHRrq6uUVFR1dXVzc3Qrm7t5Ds6OoKDg9GCR0vF19fX3d09ICBgbWRX9AoNCAjw9fW1tbWNx/9iYmLQtXt4eNjZ2a274Lmv4n+V/r1xT39/f1BQkIyMjImJCfvDaBWPn2/CtLqpSVu2OOM+75AV3O+FhQUwOQnB6dwcxmQCFgs2mUywtATm58HiIiQWF8HCApifx5aWIbG4BMvyMjR9GxgA6/lzocgxJX/5C/XLL2MOHmQdPAiHnTgBvv22Y9OmeDyxEbEPIdMfC3HxguRkEBuLxcaCxESQlATi458RsbFYZBTGDpfIdT9XLVnuS+Ma9dwqRLhHT0+PwD1PzXpUVFTQNytyanuehatb3PnYcW0RZMX8POQJiwXrpSXIw9lZDHXi3IOsg9xbZBfUuYyzdHkZzM1BriKWXrwI/YCOH4cW3DjiwX74AZw9u3TgQOaePZQrVzLv38d++gnw8MAd+uDBhr170/buxf70J26WPg2DRCWT6xFL4+KwpCSQnAwSEiBXe3sBnQ7S0xHu4eYTe3kQbORmL+pc9V/EgKceWGpqakjPxWKxZGRk9PT00Kctyj6xmonPt7nzsa+eEN6enwcsFpzw1BRk8sIC5DD6W1piM3Zhnr1K8RWLobvAYoHaWnbCOBIJhhDcuxc7fhxqBo8eXdi9O+mTTyh79uTjrGYbDe/bV7lnT97RoxBofvklCkMFSKTh117TpVLbk5LgKkUsRas0KYm9XGNjYUCp8nIwOAimp0FTE4iLAyMwPxL0QKysBEVF8CrQmiEuk5ulhPIReaWpqam9Cu7hnAH/ruScl9PJaf+v/Ytw7crKyvz8PIrkzmKx5ufnken6i6ZBqAzm5+eRGTLKs430Mmsni9RM3L+CaGTmiN6Ea496eQ/xW0wmc3Z2FgV/Y7FYc3NzRHKVtWcg1jySdaELWV5eRv3EdREHEjogFotF/AqTyUS/srCwgEAPMf4VCeK0y8vL09PTiNvLy8vz8/Pov9blPPHYoctEY9DkuRV5xBwIFuGOqzPoTqFfAQDm3lp7vcSxLyGQQgfDsKWlpZmZGTSrBfyPAA1rDyc4z2Qy0c16GlkXTWldAxeCD4uLizMzM0jZh34FLdG1P/EqPeg8aPnNzs6iX0HLmGDv2vMQk19YWECQlFjw6/KQ+FxcWFhAYJp4rJaXl/+1Bb92Vv9yz++Ne9zc3Hx8fCQkJISEhFBKcILX6xK4nivZ3t4Dv8LnhgAA8zzExMCYvKqqICQEqj9yc8HoKKaqCiMvX72K8fODU6dgVN+15ccfYQLLt9/m/vAFr70G/vzntjfftN2yxYGHpxtpDY4dw44dg+qDAweGXn8dOVcTRwESyYdECnJygipRXC26XFdXUViYNTDQ1dBQmV+QOTTMzgv4pK8nMyOjq7t7emoqNze3orISpd2enp6urKhEr4++3t6m5pbFxcWqyoq5+fm21tb6+nruy0aaV255T2BgYFFRERIkrgqyx30gQXPLe6amYPfYGPbwIeTGDz/ARPTff4/98gugUICgIAzc8sMP4Hnuge+/h/2nTsGRP/yAnT6N/forEBDApKUxMTF4nsOHIfTh5cV+/hncuwcEBOpOnTI9d+6RvPyApibQ1ASiohD08PGBw4f7jxyZ+tvfuEEPCw8TkBQVxWbp0tJCVVVJYWHW0FBPbW15bm7WyAhMhgrDJbe1ZmRkDAwMjo6OPJV41dXVs3BN+ejISE1tLRK9NjTUo0yffX29s3NzPd3d1XQ699ckIe8hHDo8PT1FRUWVlJSkpaXXfr4TnCQIAICbmxuS93B1YvHxcBFmZ8NoilFRYGwMZtIIDsa++w7o6MDccMrKMGDm6dOrOIz9+CPs+f57NvHppzA57mefQfkZrsPC9u6t3LbN8OBB2tWrIyIi4P59yHlxcXDjxgoPT9+RI3PHjoFDhwjQs0wiWW7alJuWxmbpwsJMZWVxSUnu8HBfVVVJUVH29DQ7YFpnR1Nebubk5NjY2FBBYVZzCwPtLiMj/S0tcCmi793BgcGx0ZHq6ur5+fn6+rqe3l5ct4VVVaF4hmBycvIV5T0Ex1YR6Bb/Uf/BgT848AcH/mc58HvjHhcXl927d5ubm1tZWXl6ehLoeNUrj2jiMeVS/P2h1SfRib98YfTYlhZ2RuiuLridlJWhj1G4i3OL7l+BBq+/Dv7619EPPvD5/HODgwfL8d0Fag3wwtq3r/jw4b4tW7hNRJE6JhfFQbG1xZKSwtLSMlqam25cv2ZqZp6VlXnt6lUjIyM6nR1PwtrSQllFTUVV1dvL456IiLi4eFZ2TldXp6uLk6CQUHJK2tLiIllNTUFR0crSUkZG2s7ewdvLU1JSkl5XT1w+wj3m5uZEXlIZGZmTJ08i7TvbHIGbU2toGAQswD8lJSk4GBw8iJWUQKYRCUQRrzZswJSUsC+/fCEbUerWzZuhMmvfPrgfHzwI9SAHDsDd+uJF7NYtICkJJCX7zpxxPXHC7N69RlVVoKICDVtVVYGS0iIvb863344fOwYFP3/+M/FDgESKRubhoaELkRG0gsKi6urqixcv2trZR0dH3bp108LSqrWNnXqQrKaqoaGhrq6BIlJKSEiUl1d0tLfjiTWEKioqp6en7Gxt5GTl0tPSbt28WVlFv3uHX49CXVhc5GbpwsIChUJBeq6VlZXq6uqnwd3FxMTc3d2JD5c1jHzWAQDw8PDIYjtmsxchAJikJIzKXVAAc1qpqGC3b2PW1jCNxpEjUA/444/QQOfvf38mzlm1UDdsgMMOHoQgHllHHT8ODhzo3LnT8fhxKzGxVjU1QCZD3ZGaGlBWnrt7N1dZefLaNSRLg7nTN21CKzaQRPLftAnExc6EhtLKHpfn5mRfv3HDxdU1MiL8+rWrtra2KHkLk8lUUlTU0NDQ0dG1tLSUevBAXFy8rqGxqYlhoE8VF5eoxZeiqoqSja19YWGBgIBAdEyMmamJgqISBhYjIsBnn8HEGsgIl0KhvKK85xkruaj/2TfdH2f7gwN/cOAPDiAO/N64p6+vr7i4OCYmxs/Pb12nazQt9PZDdHJysofHc/IeJE53ccEOHWIbHPT3Q6PXjz7CkpOx4mJojsCdPn3DhvXTRb32Gvbmm+Cjj8Cnn458+mnEp5/qf/11Ig/PAi8vspyAEgvcAGV6//7CI0cW/vIXQizBbYMySiIBBwdQWpJ9/vx5W1tb/tu3XVzdKiorLl684OTs3Mnx7bQwM80vKLKzsZKTlxMSvicuJhYeGd3Y0KiloeHl7W1v7zg0OKBGVjc3N9PR0ZGTeygrJ0+n0zU0NAaHoXiD4AmLxeKW99TW1pqbm8fGxnp7e3MrTYnxa1kaGAjte86dg8YfTU1Qi4ES0b/5JhQqkEiQXe+9By1nuXfiN9+EOzRCOby8UI2FlFm8vJBRJ05gv/yCCQlBfx0NDSAjM3DpUm+iXhAAACAASURBVMDFi4bKytn29kw7O2BmBjQ1kXUHkJMbPnmyhIeHdewYd14LgKffMsZ9rUF4OCs1JfKnn392dnG5dv26v39AWmrq9evXPTw9R0bYUU3VyeSKyiptLU3Zhw8fSEnduyeckZlFr64mk8nmZqbhEVBkVFhQoKur9+TJExUV5cKiYgcHO0VFxfZ2aDZLsGhubk5LSwvJe5aXlx8+fCguLq6hoXHv3r38fJjVBI0kaoKliEDynrS01JUVQKez1yQAMI2GjAwbBunqQmZu2QLhTmkpFEny8mLbt0N7mnVzmW3cCDlz/DhcgbjpN3boUO+uXd7ffWcsLFysrs7CsQ6UGOEGM0Baul9I6LG6Ouv6dYirjh2DT8frrwMSKZ9EMieRFjdtAinJrKjIgF9++cXe3u7uXYHgkJDkpMSrV674+vmNjo3B8DaLi6qqqozGRm0tDRkZ6QfS0sLCwgUFhQUF+Zqamra2tlHR0OzRz9fHwsJyaWnJ0IAaEBhUX1sjLSMzPjE+NAQyM5HV+R/yHmJp/EH8wYE/OPDfxYHfG/e44H8CAgK3b99eV88VGxsdExOHAtpGR0dNTc+kpqYGBvrj8frYkY5ra+HrPjsb1pcuQW2XszN7h/7qK2iJyb1br6U3boSbzZYt4MABcPDg6FdfhW7ZYvDll9E8PJM//QQuXABXr2JXrmCXLoHjx8cPHWrHdx1Ca4BODvAk4fokUj1uywxsbVfS02MLCgu7OzsE7t61srJ+mlv77p07ZuZmFZWV6J472NmKS0hqamgGBfgLCkH7dl//gPLyck8PdyEh4YioqIyMDG0tTWkZmaSkJHExMTd3d4G7d6SlH46Mwm0ebbpI3mNhYUHY9wQFBRUXF9+/f19eXp7wxCYGs1jMAH//zEwYI258bCwqMorJZAUEBCQlpXh5gTffxNLSsIkJiGZQaJyPP4ZCiFVMe/11aOt65AgUPOBaP7bhDg8P3LzPnIFwR0YGI5OBtjaQkek/f5526ZKBvn5qZOR8fDyIiQExMVhsLObnB+TlhyUle8hkcPUqtEfZv59AVwAPDaDD8eoCwSHzCfHBVVXV9fX1165dd3BwjIgIFxISsrW1a2SwUw9qaqhLy8hQqfouzs4PpKQkJMRj4+IeP35saWEuIiqakJCQnZ316/lf1DW15hcWrK2sSkpLXZydHj58iLKKElx6qm+mUqmEXbO3t/f58+dTU1NNTEyIPGjE4Lm5OZq3dyHu8Dk0NBgdHb2ygnl6ehYUZHd3gx07YNpOAKCzvYMDVHXJymIVFZi8PNQ6/fwz9EdLSsLy87F33oG+4h98sBr3bNwIE5Hu3YtYDUHPwYM9O3d6HjhgdOdOlqrqIpkMcKcoTF0d09QEsrKDkpJP8B4gLQ3lbQiV4oGUnoUo3PQnEBM9HRMTUt/AKCzIu3v3roeHR2ho8B1+fgcHGE8INx1jKispysrJmZiaOTk6PHggLS4hERefUFxSbGRoICEplZiUVF9fX1CQ/8jDMy0tRV5eztHJycbGRl5RcQh3PEbrFNmOUKnUP+Q9/13v+z9m8wcH/uAAAL837nF0dPzmm2+gc7mRkZeXF7GdI7uB7q5OJfxvbGKyoa72yuXLPT19GRmZlpaeU1Pzt24Ba2u4o+TmQunCwACU65BIUDLx7rvY11/D+vPP2RILJJxYu3+//TbYuxf5FnXs2OHz6acGe/fGnDkzdfMmEBcHSkrwG11NDW7hGhrg/Pms/fur8Q9uqG7YtIkAPRhug/IsTaaNDfqsBywWq7W1paqqqv9Jf3tbG51OLysrDQoKDA0NbWhsrKmpGRkZmZ+fr62tbW5uWVpa6h/on52dZTQ1zc3ODg4ODg8PdXV1L8zPMxiM+fn55ubmuvp6FOaE2HdHRkYsLCyQ4T0AQEpKipeX18bGRkZGZpWeC0+iSVdWViGra8zNzRfm5127dmN2ft7fP+Sbb2K1tJa2bwfi4lAthRi1aRMUmxEBrDdsgBqojz5iq1qOHoVwh4cHAiAeHngLLl3CREWhgEdTE1CpmIpK47Vr7leuGFMo6WFh8zExIDISwh1o1h2JxcSAiAimgECyuHirsjL48Ud4to8/Jli6iIcoRPECYGdICJulS0tLDAajqqpqcHCwpaWZTqcXFBT4+/tHRkW1NDfX1NRMTk5NT0/R6fSOzs75hfnBwaGJiYm21ta5udne3l4Gg9HQ2Li4uDg2OrawuNje3t6FS+AQP9HCGxgYMDQ0RLiHxWLR6fSncIdKpbq6uhK+KgT/S0uK5eXldXR0F5eW01KSb9/mn5tfCAjwS0mJb29nFhRAloqIYGQyvMCoKKjJeustaPO0bx/ElP/4BxTkfPIJm+fbtkHwh/i/YQMc/PXX0J7sxAnAx7eyb1/tzp3OBw+a3LyZraS0qKoKZWYODpi/PxYejkVHg9BQpopKvLZ2N65GhPlujxxBqBS8/fYMiWRAItGRm+Hrr0O8hV56i4uLjY2N1XT60NAwjDZGp+fn5/n7+0VGRrW1t9Pp9JnZ2anJSXp1dXd39+zszNDw0OjoaHtHB54iZnRubm58YmJycrKutm5paam5pXkV4MYwrLe392nM62f52F/sz0XciFXEf+f7mVgGL58eWlcvGfPvD3jJyYn/esXZEuP/TeI3Lwqd/xWH/ZuTIQ5/FSb8zlMi5vYvE7/nhF+Fgf/yhfxHDvy9cc/MzEx5eXltbW01J9IU4im6i12dHUpKSioqKnX1DQvz84b6+h2dnSkp6fv383d1dRsbg9BQuBeurGCpqVhCAnz1u7vDOPokEjRG+cc/IIG0Bm++CT+aie1k0ybw4Yfg22/BkSMz+/eXbN3quG2b+cmTmXfvTsnKAiUlaHeiogKNWpSUoAGKvPy0isqyhMTi0aPsr+cDB6BbL35CQCLFkEhuHBp24rhn/Ts4Ozvb3t4O010tsHMzEeNefe1yc6m8vPzKlSsoJQUAoLq62srKKi0tzd/ff1X8HgBALb1KRUVVXUOzuaVlbnZGW1NrYnLa19f/7bdlVFXHvvwSppW4eJFb2QQv58svYX74nTvhbo1kPAjxEHBHSAiTlQVqakBHB1Cp4yoq+YKCtvfvW7u4FObnz1VWgsePQVkZFMV1d8PARYmJK4GBk2Fhy1pai2QylA/x8MDCwVjIPDwECc+Q41JEBHuTXsulqanJtra2rq6uZTz4FcHPVycIfqKTp6enX79+HWkJl5eXZWRkxMXF1dTU1uq5AAAlJUWKioraOjrtnZ3j4+NkNfLM7KyTk8vbbysePTr75AnIzsa8vKBoR1ERMzaGzNyxA9bvvgt5+847kKscDA0NgP78Z8iHTz7B9u6Ftt7Hj4PDh8e+/jrriy8sjx61ExEp19GZNzUFlpbA2RkLC4Oa3OZmrKeH1dw81d7OzMlZiIoCpqaYggI0NkeOcnx84K9/dSGRUIhC+LvcuGctSycnJ9raWru7u9f1zngVxq5iaUxMDD8//9TUFDr2JX7s3Ady06/yo68yZnp6OjExMS4uzg//I6R63MeOjo7GxsYmJCSEhob6+vqilcA9AKWJiIqKysnJQQ663JkBuEdmZ2f7+vr6+Pi8KMsYhmEZGRnBwcE0Gq2vj+30wH0G9DVYUlISFBREo9EIc/tVYwAAVVVVgYGBL/GOGRsbCwwMDAgIyM3NXXs46mlvb/fx8QkICCgoKHjRmNraWi8vL19f3/JydravVSPn5+ejoqLy8vLQnNGLaNWY6enp6OjouLi4iIgIPz8/QhbIPWx8fDw+Pj4iIiIsLCwwMJCIRsg9BtETExPIBxt5ZXMn5iQGDw4OxsbGRkdHo6ADa8+GAHp0dHRGRoa/v39AQAByPiLOgAgmk4nWj6+v77qXhtIHxcbGhoWF+fn5EWGr1p4nKysLjSEejVVjAAAFBQXoC3btf6Gevr4+f/yvkqNMWDuypqbGz8/P39+/trZ27f+inrKyMhr+R3xFrxo5NjaGmOPn5xcQELCWgQCAwcHBhISEqKio8PDwoKAg5Kq26jx9fX1xcXEhISGh+N+/5gO46pz/TvN3xT1r37bcrzmcXomICI+Ni6uqqhwdHS0sKJienklKSjIzs8EvEgkAYC0gANNdSUrCD9/gYPgZ/ac/QcSzcSOhN4HvelxiAbZtA4cPLx882P7FFyGff268d6/HpUv0Bw+Yqqpw59bQwHR0MCMjzMICs7TEzMyAtHTfpUuRqqqLkpLQtJmXF5pZ7N4NT4hvzJV4HBSURBN1ItwD03pOTU64ujg7O7uUlpa5uro4ODjW4aageB7vHENDw6jomNaWZitLKzc3tyf9/RCa1NbY2toNDQ0DgMXHxQYFBdXX1Tk7ORUUFIYEB1taWvX0wJcj4hUKwUkmk4nIIty3fxU/0dPo60vLyMiorKycnJzKyclhspheXj67dvmfOgWQBY+REdTFvP8+3BrfeAPCnePHoRHusWMQ9PDxsUUIp09jd+5gMjJQEqapCbS1F9TUGPfv+/LzG6mo+IaFNTa3YENDYGgYDAzCMjgEBgexwUGsvx94ejZpacWFhbG0tYGiIgw/yMcHjaDxCQASKYtEssEDI7EtqN57D8vKwgBgrqysDA0N2tjYuLq6FRcXQ866uLR1QKUMACA2JtrIyCg9I7OyssLCwoJGo42PT8C4JkVFLq5u09Mzk5OTXl5eTk7O+fn57o/cGU1NYaEhCQmJBD/RmpycnOTWEnp5eZ09ezYyMlJHR2c9Pdesp6dHXn5BeXn5xMREVmYWhq1YWTmdOBFlZQWd3chkTFsbGtcLC0P9IIkEYc1rr0Hc89prcE1++ilcrgiUv/UW9vnn4PBhJOCZ37+//osvvPbvNz53LkBausXaGvPxAcHBUFGYk4PV1mKdndjgIBgbA7m59NDQ1NFR0NUFXdNdXSHMOnMG4p5jx8DevQmvv+5IIj2L+sjBPZClXV2ddrY2Xl5eJaWl9nZ2bu7uvfgGjGFYRFiYkZFRXn5BWWmJhYWFn7//7CwMC5uTneXh6bUwvzA2Nurq6uLu5l6Qn+/u7t7a2urn55uYlISi0hKrdHR0VEFBgdgA/lO4Z25uzsnJSUJCQlNTU1paWk1NjU5n5ycnHpyZmZmSkhJ/f//AwEBFRUVlZWXCaYAYMzk5WVRU5OXl9TTqGJVKVVVVRQF8iQGI6O3ttbOzExUVVVFRedGe1N7erqmpqaysLCcn96I9qbGxMS4uTllZWUFBAYVpWfVDqBkbG6utrf0iBIY871CEOhqNtu4ZoAFcYaGkpKScnBzK2bfusNLSUhERkYcPHxJJ6FYNYzKZNBotODhYX19fVVWVO3sgMXJpacnNzc3Pz09JSYlMJrdxvBOIAUg96uPjQ6FQfvrpp2PHjr0IiiGZt5SUlLa29sOHD1VUVLizqaMTTkxMlJSUoIA3MjIyKioqayHL6OhoQUGBN/6nq6urqamJUgJzTwkG5WptNTQ0FBUVVVVV5c45zz2srq5OXl5eSUlJVVW1Hdcac/8vomtra8PCwpSVlclk8osgL/K3NTU1fQk4ePLkydP7rqCggAK2rf0h+G6MjZWSklJSUkpOTl53AAAgPT1dSEhITk4OWTGuHTYxMeHp6enr66unp6etrb0uBB8ZGfH29vby8pKVldXT0yMeee6z9ff3e3p6av4/8t4DvI0qax/3LrvAspVvKwssy8cuC0vYAikkoQVCIKRBaOnFacS99xr3HvcW9y7ZltUlW8W2ZMuy1eXebclqtty7pZlfrq4zOI4TYL/92Of//+bxk1xJo9HMuVc675zznvd4eb311ltvvvkmRtLYuM93Of5OcQ/s0pyRkZGSkmJnZ7cpOIH9YsLrx0ASi0XZuzfa1haUAUNG89wc6PkA3cbhwwCv/Pzn4M8S6QEFLN//PuhM9IMfoL/73crLL7c//zzu6aejnn02/vXXaefOGR0cUA8P1N8fjYpCMjMBciovR/B4pLoa6JpUVyMxMdN2drPu7qi1NQA9r70GEIAlMYFaWWmtrPysrIZh7gCeg5UVmpBgrq4u8vDwLCosPHr0SGxcPI/He//9A1FR0d0WJSgURcNCbmTdyvHx9g7w9z9x4uTpU6coVHpDQ0NQYIC7h0dObp5pbe3La1c/++ILNoeTkZ7m7evH4XBOnzmrBZDoLtwDyKd3OC5MJhO7c9qEezAb3llS6xGUoqIcK6u8X/0KhRGIZ54BfNtnnwUBs7/9DYCe114DMQmY0tqzBzl+HL16FXBKvLxQJ6eF8+eln39e8tlnkZcv37x5k9PUPDuqBkIvBr3ZqF2e0c7NaMDfuGZFo0XHx0HIp6zMWFGxkJODurmBSNLu3eD4v/gFxJG9lj6ak5hJH30USERWVS0V5Kf6+PgWFhQe/PDDtPQMErHmyOHDNxOToJO+3U/U0cGhqLjE8jPqZm198dSpUxwul8PleHp62tra0umgbru6qvLK1asKhcLdzdXPz9fFxfX0mTP9A6DPM7bkJicnbW1tN6oALC0tLS8vYzIbmwx7x57gCHCcmpq0Ywfe1RW1s1sXR/jRj9bJ9ZAk/stfgoQsXDD//CfItP7kJwCR79iB7tkz+/LLbf/934XPPx/x5pspJ082urrOxcai+fkoHg+SU80CpLcPGdOgWj1q0KNGg8kwttLdNTEysqjXo/39IJOYmrpeFb9rF/rKK7LHHw/63vfuUg9/+GGUSJzLyrwZEBCQkQ4Ue3Nyc6qrqj48+GFaerpWC3oUrFr0cHE4nIuLs5ub66XLl0+fPtXc0kKj0Tw9PZ2dXdgcLuQ12zs4yuXyS9YX8ZWVpcVFly9fWVhY3GhSvV5vZ2eHueT/FO5BECQpKen69et0Op3FYtHp9L6+PpVKNbphGxkZGRoaksvltZaNwWAMDAzcu8/g4KBYLK6oqKitrWUwGIODgxuOsT4cGRkRCARUKpXBYHR1dW06CNwJ7kMgEGg0Wk9Pz/32qa+vp1AoW54wPI5arW5rayORSPeeBnxGpVJ1d3eTSCSihfSmVqvv3VOlUvX09FAoFAKBIBaL77dPR0cHnU6nUqkSieR++8hkssrKSqhAeK+RR0dHVSqVVCqtq6uj0WhMJvNeI2P7cDic2NjYffv2iUQi7Lu2cbCwsBASEuLi4sJgMDgcDp1Ov/doIyMjsCcSnU5ns9lwyjZZe3h4eGhoqKWlpbi4mMViMZnM4eHhTfuMjo6OjIw0NDTQ6XQmk9nf33/vDqOjo8PDww0NDSQS6X5rY3R0dGhoiMlkwssfGhq6dzpGR0fHxsb4fD6dTt/SztBECoUCamzKZLItd1OpVEqlkkwmE4lEhUJxv32kUimDwaBaSHtb7jMyMiKRSMrLyx9wzsPDw/A4dDq9trZ2eHj43usaGhqSSCRsNjsoKOjAgQNYsmLjnH6X4+8a98TGxp48efLUqVPe3t7fUK+Zzaa+807itWvo1BTgNdfUIOHhIDXw3HPAMVsoKetY57/+C7VAn9mf/Wzk179u+d3vCixwJ2nbNvahQ+qrV0GAx8cHjYlBsrNBjoxMBqxeJhOhMwD/FI9fCwriZGYOVlWB9I2LC9BEsZR0ASf96KOwW3iEpXHmxsIu4LwTEpCC/JQLFy5kZKR/CmqObglaBIcOfZiekTE4CEqHUBQNDw0pr6gMDQl2c3c7dfr0tatXsm7ljo6O+Pn5REZGJSUlq1Uqm+tfXrh4kcGsvXLlMo/fXEOoLi2rwNwJ9LJra2ve3t4Y7rl06ZK7u3uKZYM3PZuc9KaHljr2PC+v0pMnAe751a8AyebVVwEWgX9QXnnHDvStt9CjRwH4c3KavnJl8IsveIcO5Rw6FGltnRodwyOR9R2dZoMBnZowzxoWl3TTq7px4JzValSrQXVaRGcwjEyXlzMlEj2dDtjNcXHANx89Cqz63HMQ9ExZWtxDevg6LIAqNSUliwnxwZcuXcnKzDx27KOSklIqhfzx8Y/z8vK1uvVGQk6OjiQyxd3NzdHB4dqX106fOlVDJPeCbg+u3l6eZRUVZrM5JzubRmNMTEwkJyUFBgZGRUW+8867vX2gEh7DPTMzM87OzhhJBfsGqlSq+1UdbrSqpZ4r+bHHaqytwVEhs37HDlDg9pOfAHKPlRXy8cdoRsZ6+9W//c186NDU3/428PLL9X/6U9bTT0e+/HLG558329jo3dyQkBC0oAAgHiIREYuRkREQOTPoTFOahQXdzNSQmlpaNtHdvTi+oNGCiFpLC1jMEREgRXv4MLp9u/7Xvw743vcGMRwJwRbAPTWzkZH+165dS7x588SJk5VVVTWE6o+OHSsuKTVYWMmrKyt2tnZ1dXUe7u4ODg7XbWxOnjxJYzAlErGbq2tAQCAe8OLXEm/ebBYIdVptWGhIQmJSi6D50qXLMHiJmdRoNLq4uGA3f/9B3BMTE+Pk5MTn8xcWFtbW1hYXF2ct2/z8/NzcHBzfFq6cn58fHR2dm5uDYnTw+Xv/HRsbW7Zs976EHWpoaGhlZWV+fv4B+wwPDy8vL99vH3g+Wq12dXX1fvvMzs5OTExotdoH7DA/Pz8zMzM6Orq0tHS/k/km+ywsLIxbNsx69x5tcXHRYDCMj49DkcB7d5idnV1aWtJatgfvo1arYfwAah9j30dsMDs76+Pj4+XlJbNoccFphbO5sLCwcVoXFhZUKhVU28Oe33hu8/Pzk5OTKpVqZWVleXl540vYeG5ubmpqCk7rlgeZnZ2Fz8PjYG/EBvDV+fn56elpnU4H1RqxVzcO5ubmNBqNVqu93wfNzs5CU2s0mgdPh9FoNBgMD95Hr9fPzMwsLCxsPIeNYzhlk5OT9zvnubm5paUljUZjNBphp8iNb4fjubm5xcXF0dHRlJQUGxubB4QwsSn+Xx1817jH19fXxcXl7Nmzbm5usDp9o/+4d4yiaF0d9Y9/THJyAprCzs7IY48BKbynn0bfeAM9fBggnocfXrWymrSyknzve9UPP5zxi1/EPPXUzRdeKN+2rWn7dsNbb6EXLwKZEz8/gE6Ki0Gte1MT0tmFDg2j6jHwp9WiGg3a0DAfFyfD4Vaqq9GQEOChIe7Zswd007S0ts61sire5E6gU4mLQ1ZWQFZFq9H4+HjfuBFCZzB8fbyDgoLq6xvm5uYWFhYqykq/vH49IzOrVShwcnIKDg7m8ZsaGhq4HJaLs0traxuDwchIT4+KiiosKDhx8iSDwSSSiLB2CbMMzHNt7M918eJFiHsSExO/Ie4pLs5PSys7dQp94QVAT37tNaDKCPs6WQbIW28tv/POxJEjrR99hHvnnbS3344+fjzZyQkfFyckkqZa29DhUXTSiM6NLy3rJsz9A2h7O9omRBobQJVdXR1Sz0XFYlStNvb18xskff0mCwMX8fdHrl4F8aRdu5A7WoWJlmZnd+HIF14ASbeSErNFynV2cBCwvsIjIigUqre3V2hoqFDYBr/86Wkptra2pWXltbUMNze3GyEhQqGQz+fXVFd7+/g2NzUJBILKSrxKPdbM5/n4eNexWCnJSVlZ2Wtr6x2FIJS8/eV0cXHBcM/q6iqPx7Ozs/vkk09gOgOz/5YDFEXj41N27CD296PLywD3/OAH6AsvAImEH/wAffRR9MMPUaVyeWpKv2ePwMqq/JFHkn//++jnnkt9/nnCSy+JduyYvnYNZA9dXdGICJC3rawEWa3+AUSnR4za5UXN5JrGEuqZMBi7OtobG1Dt2JLGqNGio6MgIHTrFiiMd3ZG33vP9Ic/RP34x433rlJLnssMftjn5tqVChdX19i4OCqF4uriEh4eLpcr4I/yzYQEOzt7Qg2RTqOApvQREYIWAZ/PLystCQwKEoKtFVeBMxjGWXV1Xt7eLA47OjoqKTkZ0tegfbbuz3WfPhVbmhQLpP3Pf/7Ulq2np2d1dXViYgJLiAwNDUF98//5R3ztETCzPGBPKC68atlWVlaglPOWSr4POMj/P17SaDRdXV2jo6Mb24xvvDSz2Tw0NKTT6fr7+xEEGRkZ6ezshGHvvr6+LSkmG9/+bxx/7ULdOK1wTtfWQK7533gO/1851OjoaG9vL4Sh/9lz/q5xD5VKdbZsHh4eMP99v1897JeCRqM+8wyo/ZLJ0F/8AsQeAgKGdu+WPvJIvZUV6aGH8p9+Ou6xx+J/8Yvc//5v2j//2blr18xrr5neeAP95BOAeGxsgLvNyEBKShAaHZFIkeERy92zbmlGOz+rmZ3TLfT36PlNPePjCI+HWspkAN3H2RnwMyxsCeSpp1ArK+6mNpl3klzgbn4jrxmqj2Ny4AP9/Xl5eQUFhTCVAL8nZrMZ/qJBgXbY5RokdBEECK6bAXcbvoSFJTCD3N4hICAAi/cwmcyOjo6enp6NGsQPsCqKosXF+du31+zaBSi0u3aZX3nF+OqrAy+/LH7hBc6LL9Zs356zY0fM22/HHzyYf+VKXWhoX17eLINplitQYDo9Mm+YX1bpzXIFwmGDuERaGiBGxcSs/8XGoqmp2tTUgYICdGR4xbg4NIxSqaC6280NkNAt9fDor36FWllVWlll3uuhoWFLStZTSNACq6urFpMCjXilXHYrO6usrGx2dhb73TGZTCZL31dLXhwYEDMydgRLqbYJvgUzERTZc3d3x3CPnZ3djh07Dh8+jMfjN9kfe9fGAYqiUVHpb77JKCsDNJ3PPjO99NLEBx/0X7woeuQRtpUV4emnsz/7LPavf0149tnCl15ib98+aGmnZd65E33/ffTMGdTBAay3xESQbyUSEUELoh5Dx3WmBc20WadDdVpUp1XJZENiMaD2jI+jmrFlzYROh3R1gcxsdDR4u709+sILhc8+W/iTn9yFI6E97/B71n9wzGbzHZOCJSeViG/dulVRgVtZWUWQ9R/lNWBRYEe4VuGSxjwx1gQArNi7N9inwt3dHcvAPiDeg/0CwmNgD/+NA7PZPDg4uLa29EVNXAAAIABJREFUlpaWxmazuVyuUCjE4XDl5eX/xk8BAkgrKwaDobW1VSQStba2MpnM0tJSIpHY0NDQ1NREo9Fyc3NramoaGxu7u7sVCoVcLheLxTweLy8vz8/PLz09PTs7u7KysqCgICwsLCoqKiIiIjY2lkql9vf3Dw4ODgwMbElA+bZXAWMby8vLJpNJq9W2t7ePjY2NjIxs4pTAjmnw4HC6EUtP+2/7cd92f7VavSW1/N7jjIyMGI3GmJiY4OBgb2/vnp6e/Px8uqX12707/8vPgMJbjUYoFMJppVAoZWVlJBIJ9kEjkUj5+fkkEqmpqQlOq0wmE4lE9fX16enpgYGBGRkZ2dnZVVVVubm5ISEhMTExYWFh8fHxdXV1sORlYGAA+6b8yycJ+2YsLy+vrKyYTCaVStXZ2alWq1UqFfadhQffCLywnhub9vmXT+MBbxweHv7O7jQecBrod1/HPjExERAQcO7cufj4+PuRJzb+hMKGus8+a52QkBcQkPLmm3GBgbH79iXZ2GT+5S+lVlaMH/xA8vjj6ueeW4Lybrt2gbjF4cPotWvAE3h5AWmfsjIQ45FI0JFRdEK/Oq+dWdUaEK0W1Y4BjzJuaKghNjXKjEZUJAJcn+pqgHtcXECTAUtbTfTxx/usrAKtrLZok4nFe+4YGpmYmID5EaPRODo6OjM7C9fWwsKCWq2en59fW1vVajXj4xOQHLK2Bm5DoTNeWFiYsTSPmBgfh7gHNpHBbAL1e0JDQzHcw+FwPv300xMnTtjb28O0ArbzlgNLf66CJ55wfPHF3KeeSn7uufhXX4176aXkt9/OOnCg4vjxWmtrWXCwJiZmJT8fdMXi8dGePlSrNi1NzK2NqJH2dpRBR3NzkZAbgLv7+ecgvvHOO4BS++67oMPFJ5+YL12q+/zzgZAQlMMxq9UT+rVmAVpSAvjjV6+uy9L85S9tVlY3oEThRgT55JMgN/Tb30L1bWBUk8lkMBhGR1Vzc/Pj4+MqlWp+fh6adG52Vq1WW4LYy2NqNcYmWV5exsYwBA0p3qurqwsLi/AlzDiwU4+vry+s9EEQpL6+3tnZ+fXXX3d3d7+3UyP2RmyAomhqarKTk9vOnblWVklvvhl37lz8D3+Y/MknWWFhFQ89VGdlpXj4Yd3zz6/9859gfcIeHbt2oceOIXZ2IFTj6goWKh4PChV7elC9HpnSLq3pJlCtBunvR1taVmtr6WFhY2VlqFSKqNWIVruq0Rv0pvYOsGL9/YHywrFj9U8/HbVz5+qPfvQ1uMdkWtPr9SqVen5+wWAwqFQquMxMJtPMzIx6bGwZxPyX1Gr19PQ0XNhLi4tTU+tjk8m0ZNngup2bnR0fH9+IflAUvd106bb8JvZrfi/uYdTWwiOPqVWeHh41RBJoZzg6EhQY6OziIr2jcn7na/U/+h+GByYmJpydnbu6unx8fI4cOXJbPywwMPBfPu6cZbstJKHRaNRqdXd3t0QicXZ2TklJcXV1/cc//rF3796TJ0+6ubl5enpmZmbm5uZmZGS4ubm5uroWFRVRLBuHwyESiXQ6ncFgVFdXNzY2Njc3t7W1CYVCSBIiEokWYn7yyZMnb9y4wWazt+RTw6tQKBRMJrO5ubmwsNDb27u6uprL5Wo0mqmpKZ1OB78ycE+pVFpQUFBVVXW7p3dgYOC5c+dgY10sL3mbPDc1NTU2NoYtABRF8Xi8n59fSkpKZWXl4OCgXq//X4pbqNXqbwjvVCpVc3NzfHx8YmKiu7u7jY0NgUCIjY2Fl/lt/0UQBCZ9YJ3X6Ohod3c3n8+3t7fPyMiwtbXdtm3b22+/febMGXd3dx8fn8zMzFu3bqWnpzs6Onp5eZWWllKpVBqNxmKxiEQik8mkUqlEIvE211MgEIhEIoFAQKFQ4FxnZ2cnJCR89tlnsbGxXC53yzJDeP6tra21tbV8Pj8zM9Pf359EIjU2No6PjxuNRux7B38nW1pacnNzKRQKh8Px8vK6cOFCXl4ei8XaOIlGo1GtVmP4w2Qy5eXlBQUFpaam0mi0oaGh8fFx7Gby2xrwwfsPDw9vWRH24Hf9b7z6Xcd7oqOjIyIiBgYGXF1dYWkA5jy2HFj6klL27r1GpUo0mr6oqPFLl9b8/ADv5Fe/WmeGbt8OyKGQiHPgAPCsTk6IpyfgO+flAeKOSAwCFRP61VndnElnANQTzRiiUqEajbG3d2V0FNFq5vULej0qkQCqBB6PBASAgxw4ABqJ79w5++ijgVagnfUW7gT6bCgsJJNKaTTa5UvWgYFBtylyF86f8/X1a7tDzctITzt37ry/fwCZRDx37ryNra1IJJmdmamqxJ09e1YklqIIEhke7urmTqVSrC9aV1UTiDXVvn4BlsbjX5FRTCbTRn6Pra0t7JDg4eFBodxVqXQ/k+bm5r7zjufp0/IjRwauXjXGxpoh19vdHQ0PR9PTAReHTEbFEuBh9WOrC4ZZk0qNSkQooRqEttzcANx5/XVQU/f446Ba6dFH1/8ef3z8xRdXX38dNIvy90fKytDe7tnxJZkcaPnAKNqHH6Kvvqr9/e99H3545F6Tvv02CAj95jeA2oKia8ABUMhnTp8JDQ0j1hAuXrgYfONGZ1c3/DIEBwVdvHgxOjqmsCDf2tra3sGhu7vbUoOQdeny5f7+geXlJW9vbx8fv7XVVT8fbwqVFhEWGhoWvrB4Fwn3Xn4PgiCdnZ2Ojo4SieRrQz4oiiYkJEREBCYmKp97bvCHP5zcscP85JMA4uzbB1j2Tz8NBvv3o3v2gBzfzp2AUHX4MExOId7ewKhVVUhTM6pWI1OGlRXdJKoaQZUKhMs11dRM5OaulZYieDxaXQ1gDpeLjI2ZtRqjbrmzC3SmCwhAr10bev55/+3bDbt3o3d3nVtnTf3gB4ilsGO5ic/D43EXLlyIjYsnEKrPnjkTFh42YGGhmU0mX29va+tLiUnJ+bk5l6wvOTu7DI+MGo0TyYk3r335pUoFSgtjY6KzsnLquWzrS5eamprs7WyjomOXl79qoA3zXJv5PV98gbq6Ik5OqKsr5eOPC4qK1kwg23gzIc7Nze3CxYvjxsmlxUUalXL06NEBC/H83/WTB3GP0Wj08PBgMBgnT548fvw4lUq9cePGt/qImZkZpVJJp9MrKytjY2Ozs7NnZmbi4uI8PDyysrIyMjK++OILf39/Z2fno0ePHjx40M/PLzU11d/fn0Ag5OXlOTk5hYSEwMCAUChUKpWyDZvCou4hubPBVxQKRW9vb3p6+qFDh+6tMtt08iKRKDg4+IMPPkhNTXV3d4+Li+vu7tbpdHNzc8vLy/DXAL7FbDYvLCxA8IogCGT/TE1NDQ4OwoZ0sLQKCzlDIKtWqzkcDg6HIxAIYWFhAQEB8fHx0JndLyEF8/JLS0smkwlrwgpCsybTptjSxmv5VrhHKBTGx8cnJwM9UltbWwKBkJCQsPFoXzuenJyUyWRUKhWPx0dGRpaWlhoMhtDQUG9v74KCgtjY2BMnTvj5+Tk5OR06dOjo0aNBQUEpKSl+fn5UKjU1NdXFxSUiIoLBYEDMunFa5ZZNatnEYrFEIoHTqlQq+/r6IiLCjh8/DsspHnCSt7vTe3t7Hz58ODs728nJKQNwRgHovK0yv7KyshGjwHatkMyEIMji4uLMzMzU1BSkmcOPmJsD/Vkx1zAzM9Pf389ms6urq8vLy29/IwIDA1NTU8fGxmZmZh4gkbBi2cxmM8zJwjOBs3y/a/m/i3s8PDx8fHyqqqouX778Dfk9DAYdj6+0xDnQyEj05z9Hf/YzIAH34ovAhcB6qx07gFzKpUtQfQeJjETy8wF8qW9A+gfQCf3agn4WIJ7REbSzA+Hz0dpatLZWVVKCDw1dHBpCddp5zbRej4rFAPeUlAAlXDs75M030d27TX/4Q6KVFf1eD41FKX77WzQ3F02ICz156jSXyzly+FBYeLhA2PL+gfdCQsP6+teLriPCQlmc+rjYaHt7+08///z06dNEMqWpqcnbyyM1NS0xKXlcr3NxdQ0JuREUHHT06JGU1DQcrtzW3gG6B+xnC0GQ290wsHjPtWvXTp06ZWdnt3//fgiAsDW95QBF0cLC4ueeqz9+HNS1eXsDBreHB6i+vnEDXDuBAIwgkSB6A6LRIstaI6pWIRw2kI4JCQEqMceOAbv/5jfrHvWOIVArq24rK+LvfmfauRP99FNQzF1ainZ3zo0vKjsA7gkLA00VjhxZeuqpsG3bBD//+dY48qGHQGMpAmHFx9vu6jUbBoP57rv7k5KSaVTKwYMfxMTE6nTrfUndXF1bWoTeXl52Njbnz58/cfIEm8NtaKh3c/e4ERxEJJM7O9o9PT1DQ0Ni4+Le278/ISH+0OHDgUFB4xOgLQNm0pWVlZCQEOyWa3Jysqam5syZM6dPn8aCQFsaEztCXl4Ol9t84ADANy+9hD78MFifO3eCMsBt20CrrOvXkU8+AYO9e4G64PHjQHXT0xO5eRPYnEpDxBLUoDUtGWaQwUGgfUQigRL2lBSZiwvt/HlzcDAaHIwkJADcU1UFWtPpdFO6xe4etKIC9fKa2707cOdOmSWStKnb7vos/fBhlEJZdHK86uziWlWFP3z4cFZWNplEfG///qTkFMhrXllednZ0UigU3p4eNtdtLlpfOnnqFL+pmUohe3l5RkRE1hDJKIqGhoaEhUeaTSYvD/ec3Fx3NzdvHz8YDcIMsri4GBIS8pVuYWlpm5cXmpWFpKait25R7O0vXbkC6/ICA/wqcLhLly9B7FVRVpKennm/n85/7XmIe0wm061bt2pra3k8nkgkKisrIxAI3/yA09PTFAolOTn56tWrJ0+eDA4Ojo6OzsvLKy0tvXnzpr29fXh4uIODQ2Rk5OXLl/38/Pz9/Z2cnLCg1/Lycn19PZPJVCgUnZ2dLS0tRUVFIpGovb1dfJ8N+ktBs+DYkSMfHfsI5mEffMJra2tqlWp2ZgY6pAdgC3ichYUFGo1WVFRUXV0NLQMTgjB+gKLo4OCgTCYjk8m1tbVNTU0UCiUjI4NEItFotKamJugsg4ODb926NTMzA0P4s7OzMGgNg9MikYhEIsXExFRWViqVSjab/bWBorGxsW+Y5wIB9ZmZsLAwpVLJZDK7u7thZdaDrbTx1fHxcQiVzp8/f+bMmdDQ0OjoaCh6FBMT4+joGBIS4uTkFBYWduXKlYCAAD8/P3t7+9DQUBgymZ+fr6urY7FY7e3tnZ2dDQ0NxcXFMplMoVDcZ1bFcFr5fO67775/+fL5LZWHNp4hiFWbTKNa7dzCApzWrw2zTU1NEYnEoqIiAoEAY0WYNBfMVvf29ioUCgKBwGazeTwegUDIzs6GtXhNTU2VlZVlZWU+Pj44HA5DP9PT07OW3AWKoktLS83NzdXV1dHR0RQKRS6XczicGcuq23TmGx+OjIz8H433NDQ0BAYGBgcHBwYGwhrFB7gTiGQpFOorrySw2aCthLU1yKVs3w7oyVeuQKYIaAkO8wUuLiCTkpkJ/AK3HlUoUb3GNG+YN2n1aF8v0twEKKBVVcB/4/GrlZXzJSULRUWoSARxj1aH8vmAUpqVBW7ELQxc9MUXq3/72/SHHgIE6jv+HTiSjT2/jh1DxsbQzg5lQkJ8cXHxZ599mp6eyeFyjh49mpySggUnIsPDA4OC3d3cYmNjzpw9Z2tzPS0ja6B/ICoy/Pp1m5zcPGEL4Ds7OTkFBPhfvXIlMiq6r6/X08t7ZXV1o5NeW1vz8vLCcE9XV9etW7d4PF5FRQWsD/xak+bl5f3lL8V2dkAHr6QEXGxICMiVwGJ+SDEZHka0OnRGMwcUeAYHQR1ddjaI9Fy5AjJVf//7V9J7FtOYLfI7c1ZWS88+C8IaZ8+Cg9bUIEPDRv1qixBoTvr7g9akf/97zp//XL59O3pHBxLY89FHQQgJBioeeQSprERXVsxtrYK4uLiioqKPPvo4v6CAUFX16aefZd+6NTQ8Ar9Ojg4O4RERrq5ugUEB16/bnDt7trKa0NPT4+vrc/XK5aqq6nou197eztXVNeRGyPvvHXBycrJ3sL9y5QqcFMxJz8zMODk5YfweGxubHTt2fPLJJ9XV1ZjlH2BVFEWzs5PDwwnbtgGmznvvAazz2mugnuvAATDYtQus1Q8+APLWNjYAnXt4AEnDjAwAb5qakNFRZM4wbx7ToOI2pBKPJCWivr5r9vam8+dnjh9f/uQT9LPPAFBycADQp7oaKEIC3LPU04PicOaDB1MPHiS+/TaQm/rnP7foNAJ1C2k0s6iNFxsXm5mZcfLkyYoKHK6i/Pjx4/kFBSr1GLg1X121uX49JibG09MzICDA1s7u7NkzNUSyXCbz8vSws7OvqiaMjY2x2ay4+AQ+n+fl5RkeEVlSXGxtvY5aMJNOTEzctjaWN6kqL2+Li0PJZCAyTaVSAwPpDAacRDyu4uOPPnJ1c6fT6Z1d3X6+vvWN9xXQ2/gz+s3HCIIolcrx8fGZmRkM3arV6oWFBZPJNDs7OzU19YBbW/hBVCoVRjtCQ0OLiorKy8srKiqSkpJqa2vFYnFERIS3t3dgYGBVVRWTyYyLi7t69Wp5ebm7u3tCQkJdXZ1KpVq1fJGx0+ZyuS4uLiQSSSqV3usjpVJpU1OTp6fnewffP+PjJO9sxxgYKysrDwA0C+ZVkyWDvri4ODU5CTEQ9qEbBwaDISUlxcPDQ6lUwnMzm809PT0lJSUcDicnJ6e4uDg5OTkxMTE2Nra2tlYkEonFYhiXksvlEomktLQ0Ozsb8le4XC6DweByuTBDl5mZOTY2ptfrR0ZGmEymm5tbbm4uk8kUCARarXZxcRHWAW08H2w8NDT0DfNcCoVibm4O5r7h20dHR2FgaXZ2dnp6GrIesSNvGiwvL8P0YmFhYVhYWElJSVlZWUVFRUJCAp/P5/F4ISEhfn5+MEpHp9NDQkIcHByKi4tdXV2TkpL4fL5Go8HmBR6cQCB4eXkxmUyZTHbvtEKI4OBg889/Or3wQg2Z3IcgIOqJoujy8vJ9Ac3cHFpXZ5qfR1F0fnFxcm5uZnFxY6QHHgH+Ozo6Gh0d7efn193dDc8N9louKSmpq6vLysqCSD0pKem2WEBDQ0NbW5tEIlEoFDKZTC6Xt7W1FRYW3rp1KyUlJT8/v76+HooFUKlUMpmcn58/NTWl0WiGh4crKys9PDyKiopYLFZLSwusHYPBxY3ng437+voeoNaI7fYdDL7rPFdbW9uFCxfweHxAQACLxfpap2Lpx85wciK2tqInT4Kejvb2IJbj5gbunnfvBn7F3h54EX9/QAstKQZ6d4p2VK8zzY8vrmn1aLsSYbOAe09NRWJi0PBwc2go68qVdl9fFI8HUYjaWlQzNq+d0WhRNgvgnpgYQO45cwbdtk36zDMhL700//3v3wV6NgIgKytQCs5gABIugpinp6dra2urqqoVSiWrrq66uprH4zc0NPB4PGFLCw6HE0skE+MGAoFAp9N1Ol1nZ6dqdJRAIGi1WtAxQCrlcLnGiQkikTg4OLSwsNDe3m4yQd4z8ClwTWRkZGBB7/T09MOHD3t7e58+fbq9/avO7ffz0yiK5uWV7t/f7O6OenuD2JizM/DEfn4gzFNZCdCPVAragGg15lWNHqQF+/oA2zYpCdjl/HngyV96CWv9iloQDwOKGj32GPr3vyOHDoE98/JQsXhZPz0yAgBnejri4YEeP87+4x9j9+xZ++1v7zLpQw8B6aDHHgNvvXIFGRuDWk2o2Ww2TkxQKZTq6ur29o5aBqOaQGjk8TgcTnNzM5/Hw+Fw3UAEZbSyspLFYut0up6ent7eHhqNrlKpBgYGWlpaePwmFEWHhga1Wm2bUMitb4AMaMxJm83muLg4qCSGIIhCoYiPj3///fdv67lBhsr9jIkdoagov7GxzdYW3bYNQBxvb7AybWwARWzHDjA+dQrY2dI7HQkKAixvHA6h05HuXlSvMy/oZ1DVKEhg5ecjQUHo1asrhw6RX35Z8+qrQMJ5xw4A9vfuBdAnNBSgws4Ok84wrl/r7EK9vEj796dYNDYB4Hz++U1huPWHGK8ZSEHqdBSg7UHq7OykUak1NTVNTc1cLrepqYnP41XgcAMDQ0NDg3g8vr6+QaPR9vb1dXV21tbVjY2NDQwMGAyGgYHBvt5eAoGg0+kYDEZTswDoFt4xE8xuxMbGYnftAPdERwOBRTweJZGo/v7UO3Jqa2urHDZbpR4bHBzUanXDI8Nzc+D3/d+7NTY2yuVy7Jhms3l2dlahULDZbBaLRaPRuBwuX9AslkkUckV7ezuUaVEoFCwWi0wm8/l8imUTCoU+Pj7Ozs45OTkcDgePxxcWFqakpDCZzKSkpODgYNBBpbLyxo0bnp6eSUlJ7u7uXl5eMTExsbGxrq6uMBMNT+M228zd3X3Pnj1lZWVKpRL6SJgKkUqlbW1txcXFnxw/fj3cP0xE65hfj3FCzvjCAhCTvHdTzU/l9QiI0qaiwqKMlLSY9JSbaam48vL29na9Xg9BAOT7T0xM9Pb2lpSUMBiMhoaGxsbG0dFROp1eXFzM4/GioqJCQkKysrKqqqoSEhK6urqkUqlEItnkyEUikVwub29vl8lkEomkra2tpaUFJnSgkg2LxeJwOAQCQSQS8fl8KpVKp9NpNBqPx6uvr6+rq2toaOjr6+vp6WloaGhtbe3o6Oju7qZQKF+b/YHXzmKxNuofrq2tTU5OisViNptdV1fHYDDquVyhoFWsbFcoFR0dHVDmRyaTQfINj8cjEokMBoPH4zk7O0MvzmazcThcQUFBSkoKi8WKjY0NCQkps2yBgYG+vr43b950cXHx9fWNjY2NiYlxcXG5fT+PzcXi4uKlS5f27dtHpVKxqI9EIoEGFIlEmZlphw+f+uMfiQ4OtJAQdG1t/a1Go3Fr8G0ymWJju2xsOq5cKS4pSU5IYB37qMjJqZJE6unuNhgM2LRaVF713d3dEItwOByBQKBSqWpqaiorK7lcbmRk5I0bNwos4suZmZm9vaCl0pbTqlCAb4FMJoNLsaWlRSKRiESimpoaJpMJvzVEIlEqlXK5XLpF2wmasaGhgclk8ni8wcHBnp4eLpcrEok6Ozu7urrIZPI3CW5hlvzfG3zXuOfGjRuJiYmvv/56cnIynGPMc8CLxFw7ds2W2PK6XnNAAIj3fP450OM/fRq5dg0IDQcEgEhERQUo15IpUK3GPGdYAHW/3V0oqw7JzQVMH0trbFDfdfbsypkzHadOLTk6okFBgPNMJqOjI3O6WfUYSqOB4wQEgFYVH3+sfeIJ3507VS++eJeH3gR64MOoKIhI1nEJdvIWeXt9fT23vr4Bo4Vir957sdhLGwd3vAn4H3oUDw8PLN7j7OxMJpP37dsHn9mw8/ox7v2UwsK8Y8eKPDzQ6GgkMBCAHkdHgBoJBAB68HhQJQSkljWmNY0e0WkRPh88m5wMQkPnz4PwxZ2oAoyDLVgyXKYf/Qj561+BGLOdHQBJ7DrzmGZCvypsRQsLET8/9PLl3j/9yX/Xrsk//QkqLW3hoWEDcxIJqCtBWLzRFHCsVo2y2awmftPi4r/Y+mODlcCnTN+t14wgSHNzc1FREdYlAO5/75lgz6SmAr3mN94APLOTJwE5CQpev/oq6GLm6goWIGyEEh0NYo7V1aDef3AIgJ4l/RQ6MgwkALKzEV9foNPwzjtzO3Z0bdtmfuUVkC3DZLO/+AJE0YhEdHhoVjev1aH5+fL9+4OdnWc//RSgo717QU+MLZfoww+vo3PsnDcORkeG2WxOc7Ng083rxn0ePN5k0vHxcUdHx7viPVvhnnsXJ3Yv9OCP+7av1tfXwy4TXV1dU1NTRqMRh8MVgpa9LPjTL5JJ+Qx2I57appA2NzdzOBwWi4XD4YqLi+vq6pqammBGoKWlhcViBQUFxcXFhYSEnDhxApJqU1NTz5075+3tTaVSS0tLc3Jy4uLiUlJS2traysrK4PPl5eVOTk75+fl8Ph9eOI/H++tf/5qRkdHR0QExhFAoZLPZdDqdQqEIBILOzs6+ocGGoc6p+blJoxGjo97v8vMqyz6L88tj1nDqWMxGbkwTOb2JTmXQb2XfYjAYbDa7tbWVwWBAcFZTU4PD4WpraysrK/F4PI/Ho9PpRUVFOBwuMTExJyenpKREqVTe6xc3oZ+ND7GdZTKFRAKgEkRy0PHL5XKIkCA2EggEkHzNZrNrampYLJZQKGxpaaHT6d8w3sNkMmss9XE9PT2zs7M6na60tLSwsLC+vh58sFgskSmJ+FR5eKBIJGmyTGttbS0OhystLeVwOHw+n81m8/n8Fos4p7+//82bN318fM6cOZNo2ZKSks6cOXPjxg0SiQSDWwkJCVlZWVKpNDc319fXl81m5+TkQMCEKXQTicRt27ZVVFRAOKtQKJqbm1ksFtWyCYXC/v7uigrt9etGuXzJaAQdG+83oeDrsLYmunDh5u9/LzxzhtPUPOzoNhocojt9jpWbf6ugoK6ujs1mQ0QCY5BkMrmsrIzFYpWXl0PjUCgUGMpKSUnJycmprKxsb2+HOGbj3GFjbBKxZ9YHEomku1ssk8F5hVMplUohaQm+SyKR8Pl8AoFQX1/PZrMJBAKXy21tbRUIBAwG4/9onisgIODixYtvvPFGXFwclpQB4ECvi4wIF7a2oihKItZkZd+SSqWRkZFDwyMsVh2NRpmZQdPTQaZg926QQvH2BuyRkBCgXFJWBn7QxRJUrTLPGhbWdONoTzfKZgNW8w1LzdEXXyAWTunE9u3Sl19Gdu9G33kHPX8e3IAnJCA1NWh/75x+fmgYpVCAfLPlphzdsSPthRe4r7+OWhT2NvuSRx4BqRkrKxD1eP55JCnJbDIBH7yyvMxmsUgkUm9vLwd8mYkjli6YKIr29vZUlJcrlcqBgQEgbEqhTs/MwqYKvb2+pdpAAAAgAElEQVR9lnWPdHV1Tk1Pd7QrRSLx3NxcXV1dT28v5gkwJ5GZmYnhHicnp7feeuu1116ztrbGoA9IzA/0W9qJ94DC9aLC4pLSFoEgJiZ2fGK8rKzi1Cmhnx9oJuruvu6PLZYA8Z7KShDf0W7EPfX14Nm0NODAL18G8Z7du5FnnkGtrNRYS3oIet5+G/nyS0DTJZORocEZw1JXN+ixEBEBoOTOndHbtklfe20z8fZ73/uqb8NvfwtCUE8+iRQUmFAUmHR+fp5GpVEolL6+3rraWjKZjOkWSqUSHK6ir7+/o70dh8MxmExQmru2xuFwYOJmTK1iMBgGg6G5uamjs1Ov07FYrElLXRLmp6F54+PjMeX4lJSUzz//3N7e/tKlS5ChAi3f2aEMCQkdHhlBzOZb2Vn4yqr6ek5cXNz8wnxeXtH+/eI9e9CdO0FU8pNPQLZr3z7k3DmwyiDi8fICS7KiAsFVAOaZegwx6lZW9JNodxcIiOXlId7e6OnTgzt29Pz1r+hTT6GPPQaSibCh1+7dAACdPAlIUhy2SaMf15mFrauff37Dzq7b3R19/33w+t69WKvXr1bs++8jn30GMrMUyhpiMenU1CSNBu68+/sH6HQamUweH5+Av7xtra14PH5waEguk+LxeDabY0aQ5aUlNput1elhWZxSoVhcXJyfnYX9YkkkEhTnxEyKIEBEID4+/mvjPRvfsmn8AE/wrV6CFfscDodGo5WXl/v7+0McU1paGhYWVl5eDlI2Mmkrl1f9jgN+x2UBsVaqALe5UqlULpfDtA7mvLFBW1tbc3Mzm81WKBTQhQgEAkFzM5fDyc7ODgwMvHDhQllZ2fz8fENDQ1paGoFAaG5uDgwM/PLLL4uKiuCKUqlUR44cycnJ6ezsVCgUFAoFhlj6+/uxTBafx7t44QIOh6uwbPfzkfPz82QyOTE+obGlSSqVkmtqCspLg2sKs9tYUqm0vKw8LS0NJqQoFAqTyWxqasKCENB1wettamoqLy+vr6+XyWStra2bPd83eywSS2QSEPgRiTeHiLADQEsqlUqYYYG+E0onFxUVTUxMbEoLbpr01dVVk8lEpVJZLBasSBIIBFwuF/bKIJFIAGNJFaWsnLCiL2/Zv9aRHC/t6NxyWmEkBgtZ8fh8LpcLQzUikai5uVkoELLqWBkZGT4+PhcvXqTRaIuLi3V1dSkpKXQ6vb6+3sfH5/r161jXiK6uroMHD0J4oVAoKisrs7OziUTi6Ogodl9BJhOtra+RSNUlJSUkEul+GS7j9HQ5Hp+RlDSYmSkVCqsp9OwblwWn9jU4f85vZhcVl6SnpaWnp8MyrtraWoFAgE0rjNbAHFZDQ0NFRQWfz//Xp9US7etNTZU2NIi2SuHBmd1yWnk8XkFBQUVFxW3ByQdP66ZZ/l96+F3He2CLpYKCgri4OBh2ht//qkqch7t7VHSM0Tjp4eHh6urW2NDo6+NNJJF5PK6v781r1+YOHkRtbYEXua1eGB0NqCOVleDWua4OdEIwGlaX9ZPo0CCgS1RWguCEnR0g/rz+OvLii6Ar6U9+Iv7RjzoeeQR95hlQTnO7pfu1ayDlUFWJ9nTPGRZ6+1AiEZB7XFxQe/vx558P2rlzfu9e9IknvnIh2J30T3+63mbyoYeQbdvQ/Hwzobq0sKhILpUe+vDDgMAgVh3r/fcP+Pn5y+5E18PDQj08vGxtbchkyqmTX0RFx4rEYjqNdsna2tXNbWV1ta+3+7333mOxOW6uLra29pkZGfv37+fWgwgq5hJQFL1NUAgMDMQ6ycFvpkgkolAosBYDmjQ355aXl3dKappqdMTd3c3L20cobLO3sxO2tpaX4196qfjSpSUXF9CCHv4FB6/HIXA4kOfC4j2oXoe0toKsTEEBSCg6OICY265doBr7ueea/+u/+h9/HH3ySRABeuMN0IQiMhKpqkLblUuG2ZFRAEmzs0GV9dmzQ3/4Q8Teveif/ww7inxl1e9/f50kvWsXiOQ9/jjy4YeoTLZYWJBVWVnd3Ny8b987kVFRVZX4Qx9+GBIailHFHe0d/PwDbGxsGAzGBx+8n3XrlrBVSKiusr5oHRgUjKIoh81ydnaKiY1LSrxpZ2+fmZ7+wQcfEEl3Vb3B9ilY95mFhYWzZ8/C4Lm7uztWImepPIr39HAvLCoZHRl2cXZy93AXtrZevXy5u6cnI6PwD38o3bVrZfdu9NVXAfaLjAQLMDwcAHRHRxBXy80FFiYQQPhsaAid1i6adZY8LJEIsoBeXugXX5j37Gl88skRC6Fs3UDf/z7yj38AoLl3LyCd3byJilqXDdOjo6Dr2cGDMV5eoIvInj3rcNTS/eMr21pZgRTw2bNAHZtQPVuQn06mUGk06sGDBxOTknEV5Qfe2x8ZFT0yMmoJJa5++eWXQUFBDo6OHDb74Acf5OYXAhpHSfG5c2cjo2OASVl17767X6PVJiUmXLtuy+fzjx45TLO0BMFWKYIgvb29GKUXRdH75bk2vmXT+N/yk2c2m8vLyxMTE8VisdlsXl5eHhgYEIlERqMRBv+bm5uBg1TIWxncoh0XC1+73FzLFVvuZ6EnhqQWzFtjP+4ShQze58LQhUQiUSrkPH5TXHy8sKUF3PZwOIWFhY2NjbGxsUQiMTc3t6yszNPTMzk5eeOlQW5QfX09l8sNDQ3FZBWxfVZXV2EuCaq8QEw5NDQE5ZdgABiPw4WFheXm5oJkU2trLZOJJxFiavEejbhwAZklEshlMhqNRqfRxGIxuF5LWgq7KIn4qwyWRCKRy+WQcnTf+37snVsNRGKJQtps6C4dUNLFFkoQZiIs/COVSvl8PpPJ5HA4paWlfD5fLBbDoAgej8/KysLj8QkJCfcT9l1YWMjJyUlPT4eNwObn52HKZmpqCofDVVdXC4VCmUymVHYkkUJzLm/3KLxSz6iSSACchWhgqxMXS6RSpbxdLldgly+RSDoUnXgWPjI+UqlQdvd0Q00mLpcbFxdHJpNhVzIXFxfYfgCbNQ6HExMTw+PxmExmeHg4RvDHdlhcXJTJJB0dnYODg1BZY8VsVnV2oKY1k0XVcGVlJTc/P87LK7ewUCqX88ViLqchiZhwhPiRdfKbYYzg1Ia0Dlk7iUKptcQs4ax9NWUSSZtSKbIIRInFgEwNKVlwLgAkVSrFWxHLtrQM2F8mGw4MHHNwGLOzkwgEIst74YRunNbGxkYYWSwtLRUIBHBaa2try8rK8vLycDhcfHz8xtQkZpDvcvBd456CggI2m00ikahUKmw5C500hUS8fv3LyKhouVzu7ubm4OBAJpMDAwPyCwp5vPoTJy55eo55eAAn7ewMggclJcCF4HCAETGqQie1S2adAYAeGg242aAgkADbtw95+WXkl79EH3pIb2W1YMEs6ymrp58GyZqTJ0FOC49Hu7vmDIudXaBMOD4ecXNDT54UPvtsxp49gCh6ryPB0A8k9xw4AI7Ba2QdOnToZmLi5599npaeLpaIDx86lJKaNjqqgjMaFRHewGsKDQmm0hmJN+Obha2TRmNxUWFISIiXp+f4xOTqynJIcFBJcckNy5aamhobExMSGrap46NMJjt9+jTW4iQoKAj2Cs7Ozr5NGIUgyRLjKbC1tUtJTWsRCJydnFzd3Oh0pqe7G7O2tqIC95vfuDs7T7m6oqmpACK6uIAQGrRqRQWwqlYL+okuaYyA39PRARIz5eVgJ0jx2bVL88orq6+9BsSeYVnd3r2gzbq/P6jhahGs6cYNepOwFa2oQIKDQfHy9u2UP/8Zt2MH+uMf3+WVoTF/+UsQrvjLXwAsGBhATCag4FiJr/jggw/S09KPffRxYVExk8n4+KOPbuXkTBhBNdZt3R0PNzeRSGxvbyeRSn19fXp6+8bGxpJuJqSlZ7i6ui4tLRv0Om9vr2aBsLOjw8HeITAo6OSpU4XFJZugZGNj45kzZ7DghK+vr4eHR2Zm5vHjx/v6QCgOrtLU1JTr16/n5gEGur29nYuray2z9nYlHb9ZkJub9fbbvkePLhw8CFqThoYCSzg7gwAZjPdkZoJLq6kBttTq4IrVoWNqhMEEVHw3N+T8ec2uXatPPolsBD2QkPyPf4DlevQoiM4VFKAd7bOGRWU76uxc9vHHRC8v9MgRwCLaswfUi2Gd3rFV+uMfg5Dk8eNoY6Oporzg0KHDiYmJJ06cxOErqRTy0SOHi0pKjJOTMFrp6gI6Lzo6OAwMDfv4+PT2D6jVqpiY6Js3E7x9fJdWVscNelcXVxKJdPD9A+cvXOzs6goLC9tYxA5tRafTz58/jwmH/Adxz61btzw8POrq6iBVa3p6Gir8xsbGFhQUcDicJouud3R8bLCzV6hPYDWphsfjUSgUGo3W2NgIMwKbnIFEKReQWW18gUQhg5hBIha3iMSeubQq8Trp3mw2r62ttbe3FxcXwzRKXl6eWCwuLS3dFO0fHBwkk8nBwcECgQAu7Af/u2Ja4/QrB436rvaO1ZUVo9H4/tHDweW3ahvr5XI5j8evKCurZtJsKTkXGgrd85O5bI5MKpXLAN75yi/euSSZWMIXCZvErdKtYjMikUQq+XZRH5FYopTcwT1iEXSKPB6Py+WyWCwGg0EikXA4nEgkUiqVmZmZ4eHhVCqVb9mgmK9UKi0uLnZ2dr5fn4q5ubn4+HhfX18+nw/FS+FK6+zsjIqKKi0tbWhoaG5ubmxovBEadinsjGeoK4VC5zU2wmnl8/lbTKtEomhpxecFcrmkdlmnVAJYyXKJvKKhzLPGNVuSsWACnCpYUSUSiUpLS4VCIYlEKi4uho3bNrFzuru7q6urg4KC7tfKdNMsm6USw6UvjTml/d29a2umMZ0u9O/bRj4+2F+QL+/sZHM41bjqUHqEXeRbMS47Lse/z2bRpR1dSmGLXCQSbSofk0hEcvlgVFRnWVnbvQWDEolIJuvOz5cIhQ+I3NxZIHf+l8lG/P3Vjo5jNjYSgUBiWUuNjY0wI0yj0UgkUkVFRWdnZ2tra3JycmRkJIPBgJJUsO2GSCTKzc11dHSEPNRNl/9dPvyucU9wcHBsbKynp2daWhpWxw76Bk9PFxYVtnd0tLe3C5qbSWRyf19/bm6uwTBOp9MjIpInJ9HiYuBO4uJANIdKBf/y+YAAq9UiK7oJdHAAxBYsyQJQNvPmm6DmyNJVS2tlhd8kkPfznwNH8dln4DYcj0f7+2YNi3IFEB++cQMEJ955J+vFF3l79gDOBOyvhHmRjYOf/Qxku17+GyqXmbhcZmdXt1ajvnTJOio6hkajXbpkHR4eLmgRwhhmRnral19e9/L2NoyPZ2aki6UypULB5XJtbWwio6It6lJTBfl5AoHwxo0gD08vCoXs6eGelX0LLgh4N4xadPx8fX2xPJednZ2bmxtsBUwiAfE36HgMBn1BQUH/wEB7ezuLVcdis9uVysLCorm5ucLCwuvXywcH0dhYkNdLSQFgxscH1FRDXjOJBCqMtFpkSrMA/lOpEAoFUHyio1FHR9TNrf/oUcJLL5lefx194w0Qh9i/H9jcwwO48Pp6ZEwzrV+SK0CGKzUVZLhOnzY/80zsK690/f73d5Glvvc9kJd54QUQzhgcBGBVKFxnNC8vLRGJNcMjo329vWfOnE1ISKiuqrpy9WpMTKxcroAmDQ4KtLO1CwwKnp6ejoyMGBgcbBEIOBz2lcuX0zMyeTxeeFjo4SNHaTRGRHi4f0BAgqUJQ2XVXVVaUJsE61MBa3eDg4MdHR2ZTCaMS0OTqlWjBQUFfX39nZ2ddDqtoZEnFomKiotXVpYTElKPHSPicGhSEqCTQf1lV1dgEiieWVUFkLpCAdQBdBrzsnYCHR4EJPyCAqAlcOFC1yuv0H7xC7Olw9xdwPCRR0AsDQZ7IiLQulrTmFavRVistY8+irh2TXX2LEDnr70GdoHt3zcu0SeeABz023pU09PomHqOQiGPjWnFolZra+u09HQ8Dn/J2vrmzZvdPT0IgiwvL/v6+Nja2oVHRi4tLUZFRg4MDjc3N9No1MuXL93KyWWz2ZOTU3HxcW1topxb2e4eniwWq+zuFnLQVlNTU3fp99yH17wpxrPx4b/ld9BsNicmJjo4ODQ2NkK3ZDabFxcXoabt6Cigw0dGRkZERNxWvTMYJxZBjyageDs5OXm7D+Xu3btvi8t1dHRsLLmSKGTNVXT8GzbUy2GtjU2iNhGII0jEHIHoWg7HNZfR1duPKYnL5fKqqiqRSCSTyUpLS/F4fEVFRUZGxr29FObn57EkyP2uHe6gWZy5ISB60YsI9XXLCLhF4A90eLJLvwz359axawg11dXVsjaRR3yEeyMugUNobW2VSiTlQm6xkC2548Xg/1KxpEnUmiigpQjoQrFo06tiELkR6LoqehWsu98HHj0ghyUSSeVSkOeyaNZIIHycnp6enJycmJjo7u4eHBxUKpVNTU14PJ5CocCkFZbrUavVWVlZ165du50Z3NIUc3NzQUFB7u7uEokEvstkMi0uLsLj9Pb2lpWVxcTEBAYGhtwImZmaXVm2zKtFzrS8vHzPnj15eXkboY9ELJZLFQUFPjHlrlElDuXcIo6QI5PIFRJFUl2CV5mHJ8FV3C1etDTfNZlMra2tBAIBZpRguXhxcXF+fv6mJA6CIPPz8/AbseWFwCdBmQWKosEBfduP0H/3aUxFXN/SELq8JD3llnSxqP/oIZ5AWF5WTmPQhNImJ5+3o0PeHz56UN7YpKDRjAcPGj77TFZfvxHBtLW3D0RGqm5r0F+92l5Tg0V94CSKZLLB8HDNtWujHh7itrYNwT6xXC5qb2+Du23+1xIT7E9MlNXXiy1hpIaGhq6uLjiter2+s7NzeHgYkpmKi4vZbPaaZcOmdWRkJDk5+dq1awqF4gHW+A5e+q5xT1xc3Ouvv75v374jR45gGYQHLws6nbp/f2JwMBoQgMTGggp2S1EIcBk6HVBdntPMoFotSG/h8WAPe3ugILxzJ/LkkyYrqxUrq2Urq8VNZeiPPw52OHECRIZqaswjI5OGFYEA0G8tUaXFbdtCX311cu9eUJ7z/e/f5YYwp/LjHyOwEvu2NItSCdYtxBx6vb6vr8/SE04/ODggkYiLiopKSkoHBgZHRobnLYUYsEXizMzM4uKiXq8HJZdTUysrK4uW1okz01MTRqPJZBoZGbn3TtpkMm3sz5WSknLixIlr165dtXQd3xTJ2HIN5efnEwhlbW3gen19AdSTSpH+fgACYVgChwMe2mBANBpkSWNRDW5tBeYtLzeFh686OCw6Oi7b2oImIDY2IKljZwdwU3o6QqGgfT0LBktxHBtMliXsgb77ruHpp0NffXXRAkS/sudvfgMAlVwOHLal1wcAPdgGT95kMmk0mr6+vqmpaa1WOzg4KBA05+fl4fGVarV6eHgYdoaam5szmUyTk5PLy0sazdjS0tLU1KTBYBgZGZmantbr9ZOTU2tra8PD62L8Gz9lY38us9mcnJzs4OBw7Nixt956C+YT4c5bGhM+mZycsm8f8cABQJmqrARr0NkZXHtMDDApibRevQVh+oJmEvT7sKhFobGxJgeH1f37F156afWHP7wLFcKl9tOfgmKuDz8EwZ6cHFQiXhyfU6nRvPzB/fsjrlxZtTQbAaDn+eeRhx76yrZWVkA96KmnANOotRVMLpG4vkrX1tbG1Or+/v7pmZkxDSjRam7mFxQUVFZWanU6S8tMIEIITWo0Ti4vL2s0mpVVgAbW1tYsHhrUGFogwhZd67foz/Ufwj0oisIuToODg9jtOCjwnppSKBRlZWWJiYkymQwskqmpe+eXwWD84x//cHZ2tuSGpCD/JZdJ5DJhXT3pVADT/SbTI5HpkSRqaRVLJXKphN3cVsdtqONwG+u5fD6/p6eHTCbj8XipRCS+o1nX3t5eWlqalpYGczT3fuiDnzGbTG2D3V68yi/ZRY6cUsKQbGplsXNG58erOuphY+vtllhT2iIR1TSwRC0tWQJmUgu9TSJqFLWENNUENhE4bYKNcR2JWNwmFhW2sEuEHOjnYHhm3UGKJe0yvrGncFBJBTjn7k0uEYhEW1TgY++FtKfo6Ggymby4uLixBm1+fr6+vr6pqammpkan02m1Wky+H0VR+DXv7e3Fpuxem6hUquHhYUxlEcr0GY1GsVhcWFiYmpqqVCrv1xihtLR027ZtgYGBoGRJIleA+m1Ru0KWz8lJyne8nnXIjeoSQg9itdTJJHKxVJrFTc9n55Ywinn1vOam5q6urqqqKiqZJgdzDilDUoVCkZeXl52dPTw8fO/Zfu0z5rVV1WlrxmNvRx146ZztM/aEzwn9Uo+TEv8f3/D4i7u3z6G2j/Yqs6rdfcc5GXjjB28bT5/pJuC1jraGTz6dfPfdobCwts5ObH5ECkV3QYH28uUxOzsZlyvaVIsnlapcXUd8fDRXr0oFAizbpVSKqqvbk5P7FIrNc71+ZImkraNDIpO1tLSEhYVxOByonYhd3fT0NJcLVn5NTc34+LhWq4XSl+v3jWr1yMhIX18fxl3D3vgdD75T3AOFQY1GY19fX0pKCsTymPvZcmDpU0E9fDjFywvI6+XlgUgPHg+gT3c3yBcYNGsmrQ4I61EowOFERoKgwZtvIjt2oD/5Se0dleXN7uSJJ0BWwNoapLXquSatXq81cbiQuYtevCh67rnE3bsBZ2JLcg/0RxvvzO/Uc20xfcvLy1ibYvjyg3HePYe4yzAw3uPv7w/r2OGhpqamKBSKp6fnN6xjLyjIJ5MrkpPRd94B0EStBmhjZQXEe/z8APG2qgrwkru7Eb0endQsmjVaVDMGuiUQCCQvr4HQUNC41aK9C9y7tzeAj7m5oM6ot2dBN6MdM7e2gdkICwN7nTuH/vWvdX/6U/4TT3w1Dz/9KeAy//rXoAJMIADVZL/7HXTMX13vPaZYf2JpaclgMExMTGD3x9/SpBuwlaVE7nb7azc3N0wXbnp6OjEx8b333isvL4df0a/OaasRiqK5uamBgeQTJwAFzcVl/c/JCeRkq6uBKQgEEMrS6lCjZgmE0IaHES4XLSlZ8vEhHDig3r4d/d3v7sIsGL7+9a/BWr14EViTTEaGh6cMKwol6u1NOXYMf/w4CEnu3o38+c/rKBx7349/DL4sOTnIM88A7H7gAPjM+5l0YQE0rDAajZgl4QB7eL83Ys9vNMwW/bn+c7gH3gn09vYODg4ODAxoNBqVStXa2trb2zs5OflgfRcURWtqary9vV3dXEUiEA7hl5NFLUKJUs4vJokEQuqlMPKZwLaGJqlMKpOKFVJxGbvVu6KZ3SwSt7UVFRWlp6exORwKXyyTAo4F5AyJxeLc3NyoqCiYRcVs+E0GRqMxLCoiqZHsKMBfrS++0lDs00q8xi2y5Zc7ckqv0HNcGitC2Hjv+gpcW32FkBvdTC4QsuRiCbWVT2xt3Ah6oDMD0MdCYpJsyHNh6TCRSKqUNt0LegaU9PGeonYZ/96oD/ZeGO2pqanB4/ExMTEbk1Zra2u1tbVUKhUml2H4bX5+XqvVrqysyOXyb1jP1d3dDV2pVqsdHh4WCoUDAwPT09Ob4i6bDLuyslJeXu7s7BzoHyRUNFWzuA4OkzW4kVpJtQfVNoWdFMUMD6L5kZqJUkWHrE3UJGuLYIaGMIOKGwolrZKcnJzs9KwyVklsXZRQ3CqXAjqU/P+x9x3gbVxXus5uNu0l2U28qU7suGyc4rjJcZPc5CbJli2rV6pZLCLF3ovEIvbeeyfBDhaQRO8ASfTG3hsAEmDvqPf5YqgxBFEl1r7k7beZDx95ZzDlzrkXc/455T8yWVdXV15+Xlxc3MzMjM0V77uq1mjzTx4t2P9F2Wu/TTv8mIvnY0cCXnptd+K+FxOfdfIlHf9J4PnLr/+sM+aJJP+XGlVeXsPeARmHqvFtsrlzp6evXYND+XWMFlwTyuU99fUyGs3aBbY9NBKJmMcbCw+XUamolUgmE7a2Kq5cUbu5TScnjygUttBn+1hLwJZYLK63LNHR0ajnATrKdTqEFwCJWEKIoZeWlrRa7ebmplgstvHw3lcs/492+LviHktO02BsbOwnn3xy9OhRJNHJ+nF5Z9uCe9q/+KIwNBQEBZlJJGiZaGuDuGdiwqxSgzmoRVTQ3UUkfo173nkHEi3/5CdjFnvP18oW0Qnf+Q7kldu3D+rkoiIgEW1olkfH4Et5VBR0yuzZU/6HP9D27AHPPGMbgYsqFaTx9NMwvelHP4Jx1nr96uLi4qxaFREeHhMTy2Kxw8PDb0ZGSiRSZPBwzU1ubu4FhUVIwZfwmzenp5XLS0uJCQl1Fs9LB5ft4+1dXFxaUlycl19gNpsbsQ0sFsfGhGMymSIjI9HZVlJS8sUXX7z77rtfcYmiTpk7JYlusfA1l7W2tiwtgdOnkdxmiHvkcigPV1fopikrg8E8eLx5bMysVIFl1Yp5VgPm5zcFgrGcHAPiDMvNhdlwqakQOlVXw1FRyE3qmXkNJJXBYrfT3q9eNb/7LnjyybTf/15ibc743vfMP/2p+emnoc6emIC2pUOHtjuDdNVkMn1VPmJ5eXl8fDzAPyAhIZFOp4WGhcbGxd1KfwMlxUUeHh41NbVkItHe3iExKWl+fkEzOxsWFkaiUC1FTvAeHh4tzbi83NyKyiq5VOIf4C+SSGxEurGxgdbn+gpZflVe4Gc/+9mpU6du3rz5gHzNqak52dmk0VEQGAh9W56eMJYZhzNTKFA2NTVmFgveo1IFFlXrSKg4wGJBevq6m9v4J5+YXn0V5m3ZTC90kh04APFlbi4Q8vWaxaFBc1sbOHo04ezZ3r17ITp/7rnbQA+CyP/t36DZaWkJwp3du2F/ADBZqHSX+3p7ggID09LTaXRaUFBAfHzCuIUK0mw25+Zke3h6NjY1t7fhHB0dMjKz1tfXJyfGQ6/fYLHhVBSLhC4wYuoAACAASURBVDExMQK+ICU5GdvUbDAYCgsL+vptsw5t63P9o3HPwMDAyMjIV6UqEG/I3/Q83dzc9PL2cv34eN2HbnWvO7a7xJNCsurfd+1oJAi5XXw6RyKXdvCFBfiuLoEor73LvpjVxhLIJOKuzs5mbH1KI8uugFVH5/M6OI2NTUWFhQ319R1cTkBAQHl5+X2Bl01XjQbD4OhIvpzxJbPCiYlx4VRfpJd9yapwYFU6sjFe3DpvTp0Lp/pLVmUEt5kj5EV2NId3NrcLuQqprFuhQD1Z22YKiQRWT7As2+/0d/yzRjYophmSk2f6quWSTpEVWkIPvbWbWGjhOeTz+RgMhkDAW7s8qFSqTVo+knlnNBrlcvkD4p7e3t6JiYmFhYXNzc2/yYpgNpu1s9rL1y59HPzhq4EuMdcrT/tgPo2M9inLL+bkVbEwDZx6mbxnMDdv5dyVzpriMFJ4JD4sj5bdLe3mdnblYsv9cT6ZjKx4YjSng9eAxWYXFbW041l0FsJFib6S2Qzf3VZNJtDCqy194dW4A47Nb/0g5/NHs4/9+zGvN145duPlsuft3Jye+7n8+Udk+/+1LfKxONXx0/1/OZgRLnUKXO0elMt6e1CbDTqsYkvkMgpr0KHZbkgkgp4e5FtksKRSIYkkc3RUOToq8/IG78Q96BlkYplYJO7u7uZyIU0Ui8VCb2pwcJDBYNhY6YxGo8Fg0Ol0EonkfylvYVJS0lNPPXX8+PGkpCRr3YNqZZuGRXW1vf12qpcXuHoVREVBk0BjI0Q4kKl/Fsyq9CYVDMGFfq+6OnNMDHB3137ySeef/2z6059uowRGtMh3vgP9AW+/DataxMZCK9H4+IJGJxKB8nKYR+zgsPn44xG7dk2/+Sb42c921kSoenrqKfPRo+bvfAdER5trawquXXMtLi769JNPwiNucjicDz/4IDQ0rH8AZpIDAKJuRlRV1/r5eONwrUGBAU24VpFIWFBY4O3j7enpubyysrqyEhZ6Iy0t3cfHx87ufFNT08FPD5SUVVgLCrH3WNfnyrbkKO3duzc8PBx5rNjI0GbVwlsIyfUBgPzU16+bZ2eh5NzcIGdeeDi0lyHUALW1UNpjY0CjAX3SUQGRYJ6ZAX39gE6HRgwkTLexEe7EZsPc91nN8sz60DAgk6GmDwqC6v/oUbBrl+axx8J/8Yu1Rx4B3/3utt/w29+GpRvOnjV3dEDUNT4OY9Bra7f9XAgVemJCgoeHZ0lx8fsffJCUnIJrad63b19MTCySo/5VWJiHu3tTU7Ojg30Hl+vgYM/icFlsVkZ66o3QMG9vb73BoNVofH198/NhZGtwcFBEePihQ4c6u3g2Iv0KXnl4eCD2HpPJJJPJmEwmnU6nUqnID9VGhjarlnrsGefONen1YHkZRoW7ukJ55udDNFhfD++LQIB2NZUaaFU6KMbOzrHsbKmLC2SwPnAARpv95Cc7zLYf/ADmHp4+DQeGgDcrlYsanUAAMjOVe/dGnThheu01GAyOhop/5zuw/ZOfwPO99hpscDhQpEolvOba6mpcbIy3t3dWZuann36anZ3T2Nj4/t73kpJTVGr4hqrX6VxcXPB4/NWrV2Vy+RV7eza3g81mJybEBwUGhoTcMBgM7m7Xrtg7JCcleXt7252/0IjFvv3WW2wuDMhFxbJDfa5/KO5B6rHbPI7Rh/WDNPRbuhbX2MxH3q75y8W63U6VT54s/fEBamyRpEcBk97F4mJi19FsRhGBx+oSeldy8GyBTCKSScRMvoTCFUZjO29WEhJT0itKiwMiYiisDnYnr6K89OLFizQa7UE6YLNPp2YskF0fXV+a00nwYNcEc7FenFpHVmVcBy6W1xrCwV5lYbzZtdldhKusqghKbTOLzGQyampq+Hw+EpXC4XAoFArCc4OqtB0bt0AMBEjWO0jFPIsJbHvbbd/BbTC3TCwW8/kCNptDIpIollcRvV6PlMK9h0nmwetUoH4uG/k84OrI0vDemHdfivqLQ+Jr1/z3/DHh2D43F796N7FU2tvdo2Ax1GfPlmY4jzs7JVESHZuv5DLyhL39ChZ7LCE+iZ4WmnuW8s6fmzz88ktKMB/uk1UVljKqC3ILLly48A1Cd0eWtRHuYQUvnC/4c2D9xy/kf/oE+c1HD4S+8kbun395rGb/oaWXvi3G/svncz97eus/f5x6/EL1787yT3h3ENqqGxoQ4YvFMJSKSqWyWCybwbIeOKR9x3iJ5HIhgSAvK+u3Bj02mFgmlrV14rpEXUKesKujC4vDivgixNKjVquVSuU9LMSTk5P/S+09er2ex+P5+Pjs3buXzYZs9OizcscGAIBIxO/fXxMcDNLToTuloAAae5CgZrUahvhsqBbAjMosFMD06bw84O3dc/p0r4XoFhZJevRRqAp+8ANolvnVryArMJJ55O8PQ0pFQr12cXoKZtVkZcG0o48+GvjNbxJeeQWWxXjxRZjM9etfw7jSb38bnubAAcjIcvo01ChPPQUp6b7zHVjGi0w21dWV+fj4lpSUHD9+vKq6hi/gf/bZZxUVlZNT2/lcUTcjysorvTw92RxuXEw0k9OxvLzciMXCZCsvr5HRsYnxsYCAgPm5+fj4uH3797u4uLz99lv+AUHIbxiRDyIxGIjaD2tzIpNMq9ViMJjLly9LpdC2tKMk0Y0w1auiAnnaCoUQmiiV8O/TT0NzV2YmjNVpboYfSz0PWMd+fh6QyGIFv9cwuwAlrpyGKAf9DA8Dtcqo1iyp1tVqQCLDhPfYWHjOc+fMu3eD55/n/epXuf/yL+Df/x0mYj//POQ+/OlP4VDa2UE7HZsN8UF5ObSIIN1HcE9mZqa/f0BpSfGRI0ebW1rw7a3HT5yoq2+Y1WgRmbi7udXV1dtfudLX1+ft7d3bB9lLi4sKgoNDvL19ppXKnm5FeMTNHoXczdXV18e3vLwiPi4uNBRWo7QWiF6vj4qKQk071g9NdLd7NCx1KgoZDMgKzWRu5/vfuAFxD8KHVFMD4Z1KBe09WrUezGlBXx8vNnbK2xu6b/fvh9Fmd6Ls734XYvSDB2Edr9JSIOBvzS339IKmJuDgQHrrrXLE2PPoo1/jpUOHoOnx1VehJ02hgMQCLBYUqeUD1tdWk5MSQ67fyMrKPGdnRyKRsdiGY0ePteBwSIocxD3OLk1NTU6OTkql0svLq39weHZ2Niszw8/XNzgk5KsKO56e7m7uHuHh4aGhNz45eNDFxXnXrl2FxaU2It3c3LRO3/1H5XMhQ2kymUZGRu4MJbYe6Pu2B9o5pX852/DOtao/nC367vuFj7xd95oDj8QQy2EGcSOD/3kmI76po5zcdbmQldzS0cri19F4Z/OYdTQ+htz5QQLBMae9vKbuA//cl5wSvcqZU/OrGg2sLnnfS1vvYDSZOmdHR5a1/NmxKgYxmFLlxql1Z9e4sKucuNUhuLK8mopWISeU2xTAafDtaHCkld/EV3u3lybWlWVnZfF4PKRUAolEwmAwra2t1iHbd2rHe265TXUKYKwP1JLIIRKxQNnXOCBto1DIGu3MoLydw6KILf4ga3eq9a2h7QevSzo6OnoPRYue8G4NMwDuHR6JIe8ker32WdLbTl5/DPP5S/3Fd5pZtSxmn1ykiC+74pOw9yrmyBnsibO4s/7Rb6t9A+aOHp87fUoT4iV8/1X6k/+n1vGdXm/v1F1/Drn4p6yWQPokQzur/VtB9uD8YJok+QYmN+PZm73P7ev/r9+Uf/yL1o9+2/Tuf6Sf+M8LbvuPHBxvff+G9N0L+l/9vP6T/yz77DHFrt9xr9orDh/Jy81FosfIZDKJRKqoKLetkiEWQ2/XraG5x5hKpUJr0COUyeC6XC4WiSUiiUwir+JUBbX6RbaFU+hU4iDRrc65nd8m5At7enqQlLq7iRqhC/nfiHusJ+jU1BTiBL2HOkH2JxBaX301uaEBrK3BfC4HB2iWaG2F7gMKxTw4ZNaoDSszq6ZZLZArVMXFy5GRwN8fODjAiAak/vWuXTDB97XXzHv3wjoIFy9CK0dhIeB1GVUz0xN6LheeMyQERqL87nfVTz/dHBMDBAKoqDo7Yfjp3BxU2PHxZoMBqhCjEUbx5ubCvotE5pUVuBEyua2tzWu1yUmJcfHxVCo1OSkpJiaWSqMrlUq1Wo1raQkKCqqoxBiNRjKJODY+IZFIFHJFelpaU3NLV2enQq4gUygGo6GsrLS4pGxhYYHBoDOZ0IqISgmh6/Dz80OCIpHtyFTT6WDOgvXO6FHWDcTeU1kJc7lNJngvOh0UyeHDMN7J0xOGz4aFQWlgMDDmu6BgksncVKuBVgtUKtOqasmsVptmZtGPeWZmQzWvVenUaiAUwtigiAjICXTtGiyd9vrr4Ikncv/t3ziPPAJ+/nNonDh0yCwUmn/xCzOTCeXW3m5+/HEYfhsYiKhntLOwZPT6+rpSOR0VGZmQkEClUBMTE+Pj4zncDuTJWFlZERQc1NjUbDQav9LWsxoNl8Pt6++LiY6iWcLr5HI5rEphNGRnZVZVVYuEgojwCLnitmoeCF+zm5sbWp8L7cEDNgAASUnp5883DsAqoTAn/do1CB8DAqAhra4Oop/OTtTPtTYplWwJBLDqfXIylNTnn0Oo8uc/b1NhIubEH/4Quq/eew+axVJTzQy6WaVST+nb2mER3A8+SH3rLdlrr4Fnn/26Gte3vw2x0759MKhoZATOVa0WDi46fUwm09r62sbGxsjwUFRUVFpaOpVKjYmJTkpKEgpFKqVyenq6tLQkKCgIjyeYTabm5uaZWQ2Hw1Yo5FGRkSwWu6Ojo7OzIyoqSiAQZGVlQu7NpaXGxkZFd4/1xAMAzN1Zn+sufM13E/I9HqB/61cPj3sMa5uSWEybRyI9ubyjidjuGFP55Emif3oXntZ4wJsRU9TOFV4oZL2TRLtayj6cxbhYyDpXwIxr7Didxwyu4QRUc56Lor50s72aKcVw+tNZ4zfa+oY09ynHsWHUbxhgbT7rZWFrPVVO9eDUe3Oxnp3Yy8yKq5xqBzbmS1aFExtziVbqXptdRGxq5DFyeKQEVktwezlvaggj44qG+6cmJ2k0Gp/PZ7FYQqGIz+fX19cj5ZkQXQijr2H8sjWhj62WtDEAIEBHKBKPKAgyCU8ATyEWieVdHYzBrgxVf/Py8vKcUjgpiBexqzjcjgfxRv3dcI9oRuDf5mdX9llqyPtRuOBI178Q3/jp8NGPk5tinndMPHxiOhgX515z9MMCu6NNx4/5fuYc9mGl83va5x5Xu7rX/84p1vH1k5FPJh5+tD7dacTNMTP+UCknFT+Btx6vO9s9c90DC/Ct1XqpH6n2r/C+4fPqzM+/Nf6rf0s+8fOILx9jvvGrxjf+j+zpHzB/8lz700eH9xzveO1KwGtlPnZHML5v00Ivaq65KTMyJqamKBSYcMdk0oaGZBwOLL6BGNzg4InFIqm0p7ERNu4JfVDMihwrlMu7GxrmDh3qqa0VyRQ8cZdCJk8kxvtW+V8nhAwuj/g0ezqlOPthPUUikdGwXWXM+qZs2v9L67FbP+AQiVhv2bFtsfcQoqObtfANHwb30Okwc6unB35oNHN3N6wZPq0GG9r1haHBhpSU5epqUFICvSzXrkF9fuQI1CsHD0IrjYsLDL5ISoIqndNhVKrWtOsiEaithbncrq7g4kX9rl3Rb745NQJrqG+r4QdpWEM6m8GeGB/DYDDV1TWzs19X2LHZB5XG3bajwkF2QOtzoduRDliv3q0NAMBgMGhQOQDm9XWYklVaChkgEUqjggKos6OjQWrqVGAgtq5uk88H09PQ1qNSmTUqnUal06p1WqSh0s3OGqeVoKMD+r6KiyFycnWFSXWW0lLrTz4Z8cQTC9/+NkCoZQ4fhj5GBwfz2hqU8MICxARdXdB+hGpopPM7SgMA0NfXU15W1tCAtYkPuHP/u42LtXAQTg7r+lzW3z5IG1IappQ8/rjAzg6QSDBciUiEwT3JyTD2iUyGCI/BMA8NQezYze9tqSjX91t4rCsroX3GwQGyaL72GnRpPfssNCT+/vfQxvj223ACh4XBWp7DQ+vaNakM1NeDGzcWXnghfPfu5b/+FXz/+5CwB6Fa+N734O779kH04+y8PXtt+n+niJAt3QpFRUVFU1PTPbwPdzsW2W59IQSdP0h9LuQo6zOg7Xtf7m/69uFxz8qwkn72ZtWuy9UvXmAVN9CTy2teuEgJz2t43zX/kd01fzrHqG7zre3ck0DzxXDeTqR9nsn4IIUeXMN1KWW/k0R/OYayO4kRRRyYW4dvTht6o2YVvqLcY1nf2myfUDSMSdcMMLcOXZRrS5Eigj+vOFBQ6scvu0Cv8OM1Xhe0+vAac/q5V7l19h3V16gVN3EVSdJm/8zY6MQEEpHM7WLTaVQajU6n0wQCoVQqozHaR0ZGsrKymEwmavKRSbpUfQ19MtaOUTu2COjWulAoGVKQN4dTp3uqKyoqqfjyMVntiLxpbUkJzDoAVock9bS2Qr3uPreM3uPfDfdU91emkNPPFH1eePH5jIKro1fOL5y/UuP29nOJx3Z7xv/uWHbwi80up84lvudfvvvTmMN/aN3zO+G+FytPX7z8LzlPnw457/eMo/tjF9LfkGrEYFI1Pi2nq5gmswm9kTsb3VpFOi8lpTOxf6EP/dYANkNayw+c+lT43A/Av35v8Dff7Xrh+6n7nqq6/LLqk/c0P/z31Gc+xP311ycfTTj4evsXPjFvvpzlVxnYwiCOBV/nNDVS2ZBgWioVcjjywEDx8PBoZmYaUlEL4RscSUiYuXBhPCoKDQO6NXR3/y+RiPn8xb17p52dV995ryg3zrHGs+HiF4vvvduITxieGwQ3o6WvPRkQe3BiQ4XeyL0b/8Q91g/Je7UBAK2tramp2/W5EAii10M1qVTCd1mZDPK+zMwa1WMLOpVar5wGcjn0mhQVQY0SEQEzlAIC4Dt4SAhUIenp5sZGwOHoNItarYlOB0ol1LvR0ZAj+PPPR0NDo7u79Tr4qLmtYzart31nWUGG3GQ0jo6MSGWyWY1mbGxUIpFqtXPIzvPz8xKJZH5+YU6rVSgUPT09COW8clq5tLSMHL68vLy5tTU/Pz87O6vX64eGhjZvN+EgGsXHxweJa76zG/fdYkk+Kiot3XZMAGAWi80nTkD+HXd3iAlzcsw3b4KAAH1W1kZCgq6qyoDHQ2hIpSIZXjCsCkZWaeBnZhaoZ8DgEAQ99fXQNYZkep09C0mGX3oJ/PrXwtDQVA8PGFx+7hx0bMXEQFRUVLSNcqxhpU3nEZno9fqBgQGZTKbVaoeHh2Uy2eLSErDkYc3MzEil0uXlFZVKKZfL+/r69JYqfxPj44hs9Trd+Pi40WTSzM6Ojo5OTU0NDAwgRaPQawEAFhcXXVxcHsbek5ycFh/fkJICU/vZbPPyMry7sjI4B6en4XysrQVcrm56enNqcmtDpYWeWQYDGseKi+HkvHABGscQ9PP669BBtW8fjJm/ccNcXQ16urdml2bUZiLJnJEBzp5hPf103ptvguefh3H3ly/DD2Ik8vWF183PhwDoq/K6iCUSvVMUBW5tbfX39ysUivn5+YGBfqlUitifzWazSqmUyWSrq6vTU5MKhWJgcNByuGliYhydiouLi0ajUavRzGo0W1tb4+PjJtPXpXORq2g0GldXVySt4158zRYiOKFQiJJGTk9NCoWivzUs9N4P3IfHPTAno5yU+/33Cx7Z3XwsSNDJa7t0s90ptvxXX9S9Zs/OqrqO7Xohmnq+gOVYwno3mXYgjf5xKv31eNrz0dTnoqh/iiBXi6YMFpaWe3cV/XZDr2ub6K7s7VBpYXkQdJHNKzO76xiqbJEmhzydHycoqxrCDCxx0zrbqDJWgaDKgVpWPSLpEDV3jqTUNAdkpKXxeJxxTd3AKMmSlgWLVsl72cPK6pm5bhosytaB2mz6ZIyloaKx7laRCDqt7q4Vbb4RyyR8dU85EVdSUZKlkibrhyNME6mmjYGt9XmDsnJzKGa6t8FkvL9JALnNvxvuWTNuejdcuObw89P+T5Sef37RzV914MP8yOMHmg/8NfaLz0/ITj3L2//DjrKfOoz84uXuZw5of/itkd9/v+23h39zKvl3blefcvsilhMwtfk3ZG/1zCvi6FHhhOsSpRgd0zWwdDkrrvbHf1r88SPgkW+BRx4h/eLJyr0HOj7y0rj6Lr7yR8zxXf6vnEhOOlf26Vncf7zEfOZJSmVFb1zC/LPPjR4+p+AyJXIpny9xcVE5OvaEhy8TiQQ+n2cxvMFBVNnbj0VEzFy8CEN4bBgObUbSahUyH8bELL3zTvexU45FDheOx1Y/9oe67+0FLh4gIkr3n49uPfqfhrfeMlvK/qD3co/G3WgF7nHI/6Ov/q75XNbP3wdsW+KaCUjdE+QQACClnqsrhD59fWYaDYyPg8pKKoslAAaTXq2FoQ2jozAZiUqFIbsiETRlYLHQOtTcDPVzd7dZq11Qb2q0YGAAqii9HlodSkvByZOtWGy9LeR5gL4CYF5bW2XQaU1NjefOng0ODiGRSSdPnvQPCBCKtud3Rlrql1fsvb1h6YNjR48EBAX39vUr5HInR8fIyCiTyTw1OXHu3Fk+XxB647qXlze2Eevm6pqTm2fdH2QeZGdno/E9D9C723ZB7D1cLgxGsagoKK30dBjZU1gI3V7T0+DUKdOFC20REX03b4L4eFgZDQnObW6GTDACge0Hh4PenPJymGrt6QmV+JtvQr9Vejp4+eWyP/6RHhYGQkIgwHrnHajcJya2Qc9tPbtjZXFhgUQiNTZijx49FhFxs6mx8fSpUzdCQ/tuhYqHBAU5ODoGBgU3NzXBkOe4uKHhIT6v6+KFC+kZmQCA+rrac3Z2OFyrs7NTTGxcKw53/PixSkyVjUhNJlNCQgJaj/2OjtxnAwCgtLRYoeBzODDCPSAAMhllZUGE5+kJoQ8WC2g0XVRUI4c7MjcH1lXLxlmNubcX4p7aWgg2PTzgnP7yS4hAT52CXlV3d1jbtbzczGYbVTPzs3qpDBQWQn6pd97Jeu65rhdeAB99BPFNUJBZKoVesg8/hCAKAMiU+POfQy5JrfY2OSPuJ3x7OwZTeerU6YSEBGxDw9EjR8IjIkbHxpCQeV9vbycnp/DwiFYcbt/+fUnJqZOTkwwGzc7OrrCoGAAgFPAuXrwolUpdnJ1zcvOKCwvPnTvH7bCNa9br9fHx8SiagfE9sbEwza+mBjQ1tQUFEclkZDK3t+EOHjzo4+tnMpm1Gs2lSxcdnZyGLBbX/5annsFgWF9fHxsbe8j4ng3lHPlgQM4jr5b+aB/JJ7XxgHfTFwGYZ88Qr0S20Lr2ZzDfSqTV0fgENv9SIevdJPpfIqmvJ9A/TqZ8GNeayRrRGaEZQGc09aphVb4HWTYM+g2DDtxCS9tZCwDMbqplY9Wj6mz+ZAWVQ+obbx0aoTC7ODR27uxKk2C2V2cCm/op5VLl5EKpQNI+oaZpdDnj2kqJlG9BM2K5gjusxAolFDYbFohAF6FI3CtlwrIGYj5UmPd0i6BHQaOCUCSViltb22RyBViTGMcS1vtjRmWYno6crcHopZ4og6YdGO/j10NlguAe5NUF3WjT2NqCxFFjY2MomrfZ4QFX8SLM2ZAn7QIed/Z6vOfjV5ZOnhn/46/bDz6TfeODc469tU+4nPpRfeJFyvzZU+s/+n7Vh492PPfDjud+hP/sj3uidp8qdVKvQ9DTN9dT3lO8onugkVXMyXvneqA7wbIgYFCrnx2Odt/60Y/Bv/6r4ZnfVl+ucv1C5mg/POjgPn/hfFdzTlJt+NKWduvyadlT39W+8Zzy6PHFXX9dfvzxnu/9oS2XJumRCQUib+8pZ+dpP7+hri4uMnQQ+kilUjp90ttbRqUOFBbKmExYoUIkkkqF6PCiDesxFVlIn3taWnDNLeJhSVC8e9AvD1L/42Ple+/2PPOM+tFHN3/xi40vvgACwQOKemJiYmlp6d7D+oCnesjd/mfgnqqqrxUVANAh4u0NzRIEApiYMHV2mrKzp6emoCN8ddm4ql7WLyyDBUsE7tKSWa+Dbgah0KxWmcYnt5Y3t2bmNzeMq2vmzU2oFaw+IC0tvq+v11op3kfjWb5GxiA87Max4ycYDPqhzz6Li08QCAT79u2LjYtDNAoAICYqksXpiIwIb23HpyQndnTxJ8YniooKIyJu+vn5Liwuz89p3FyvYTCYsPCI69dD6AxmXm5OcYltxKjZbE5KSnoY3FNRUUGhUNDbtJIAfIasrpo8PPQHDijd3Y3u7lAde3nBYCaELhLJyq6pgaYd9IMUGI+IgElMTk6QD7K52RwSAkJDN599NuKRR5RPPgnCwyEK/cMfoGWCRLpNH98pZEv9S91VJ8fLV+xJJNJHH+/Lzs0lENo//RTWWNBYvJ4AAB9vb5FI7HrNRSwWBwYGDAwO9/X2JiclZOfkeHt7b2xshoWGpqamBQeHhIWFBgYFjU9MRISHT01No/eOPDT1ev0Dpqzf2VXkDBkZBSdPsvug6RqyW/f3Q1dXeztC6AikUkgCUFw8LZOZZmfN0yqwploCaqVZKttGlPHx0DDp7Q1l7e0Ns7dSU6F8+XygVK5oNnv7oA8xKQk4Oy8++2z4yy8vnzwJ3n8f4ptf/hL6fFUq89bW9mSenYX5ccvLZmsrDABga2vz0sUL19zcG7ENnx/6vLiktBWH279/f05untZS+kO3teXp6dnb2+vu5jo8CqPsBwaHe3v7YmNiUpKTAwMDDUbjQH+vo6NTdXX1pwc/i42NdXVzT0pKzM7OtRHpxsbGzZs30bJEDTU1gsxMQKPBHy2D0RoZ6eziolKrAQChN65XVMKo/GmVupPL2b1nt39AQHcv/Bk+/GIymcrKyuLj4/l8/jd24aHdWOqbbN3jXPj7Y7jkOnyajgAAIABJREFUooYP3ev3XK3fc7X5cKCAyYlt6TqWw4xr7BxUSGKaunbH095Ool/JJVYS2MOquXXdtqmjrVttjxF3jW4XWkHPfN8GBIVajckEz9PT3cvlMmWKTrFEIBZKJRK+SCQUC8VEEk4iJ02OT25ubkokUp4QP67s3NjYXFgTTy5mKddy+4aJ20yDQrFUJpCIdyhbIRBKZZKu2f6aUQUeIh+RBBqIHsD2I5ZI6HR6SwtOrzfol6Rzg9X9nKS1qRYw17K50Gvc+htuWa1WV1dXR0dHI4kadwpnbW0tOzs7NTX1bgW87jzkHlvSu7Mvez32YdYu8uVPwXMvbD31TM3FVzvPvcPJxDrvIlW/Hbl66rCMTacf+BPt1Z8knfrlgegXhx2cxjsISh0MXZhZUyfwYxoHscWKbW79e1zL5iuTwTDX1QVDDQCQSuXS0rKhzCwFHi/u684vGaUweoR98oGoKKWjs3qwf9NkFtfVD+19X3XOzkAm6Xe9NPOj344++TLJLp7X2y+VCrq6JHl5wxLIpXi7rU4q5Q8OjsTFKZ2dVU5OUjZb1i+nsXqEYolUAtGPTAaZmndEP+LubgKJRKfTFgdVpA9jyS++VP/Si6onn5x54w2Tk5O5vgFYAL3Nfe24Oj09XV5eHhUVhRaX3HG3v8PG/wG4p62tLSUlBZEFonUAgNkxiYnAx2e2oYFCperweMDlQruF0QhTZjY2ATDD5wTUBgYDDCjVaoHBYNzUazUmlco0N3eb3rUofhATozp8OOpBeOruVH4AgImJiaLCQgym8szpUzm5eVQq9ejRI5mZWT29237chLgYT28fZ2fnwcGh+LgYdkfnxMQEnoC/dPmSv58/l8vd3NjIzEgjEIheXl4u11yyMjPeeustNmfbMHPr3oHRaETz2O/syX23AABKSkqQPHZ0ZwAgVc+lS+Ds2cnDhxkffQRiYmCVCQRfBgZC401aGjTqNDVBIwWSplRfv53PnpsLibK9vWFkz6lT0Evj4gL9htHR/SdOJCQlgdBQeDaDAZreoqMRAr1tJY32waYBzOaB/v68vLzKiopjx44Xl5RgsQ2nTp0qKCgcGYHGia/qS3h6uPsHBDo7OyuVSn8/v96+/v7+vqZG7MVLF8PCI8RicXJS4oXzF7Kys/Pz85xdXBrqG5KT4VyyvhYsk2KVx2791QO2AQCpqRkff9xkoV+H97W+Dqfi5KS5pQVkZY16eHBTUyFfT1sbtHXNzII51ZZepTWqZqHprLkZWn2qqyG6zMiAiL662ozDAT7frFKtzq4NDJra26E1LiAAfPaZ+Le/zXz+eRgV7ewMea4zMiAjooXpfru/KJC16T8AQKGQI9I4Z2dXXVNThcGcOHG8pLQUqSJnMBhcnK8GBQV5eHguLS74+fn1Dw719fVWYSphXdLYWIFA+FVi/40b10kkkr29vYeHR1BQkJ2dXUvrdmE45IqIYem2OhV1dQIKBczPw6pvS0ttVVUZGRnrGxsAgLjY6Bs3bly8dKmnt49Oo544cdLbyys3Px8Z4of/m5WV5eXlRSKREHveQ55wbXymj8ErKCvJcw1jJJUzs6rZFc1SuVQoEl0ppH+WwchvogWkl50t5H6exXo5ihBN6LP27kwsbJTxJhbWbaOV79srk3ljRcfVm5Tz83MkElkoFH/NlXyrvIREIhUIRDTLQqfROzq66DQWqwMnHsyi8SIGJxskUiFSL0sgENTVNZDJZLRcJRKtbGFYhrUpZvurxIOVDAVdIOYT+1rpCqolJlbcJe3sknTuCIMsKev88KhIBM7OaaYmR2UwvsewaDID4wNrR71eLxQKy8vLnZ2d+Xz+jpJZX1+PjY0NDg5mMBhI2bUdd3vAjQazgaSit4taa5LTFZ98ov3yy7Gzx0ZCb0j7FMMZmdI9J5mXPSkOzlcS9wf6/PnF/D+8WfGGE8NlbmsOOb/OqGsYqE3kx0pmv3ZdPeilGxoWDx0yx8dr1GoylSrq6YG0OpYqEAqFQCqxxJjLZHyhkMZgUKlUOpfbIZHQaLSx8+e7AwMG/+PJiTf39RNxQpncIv/O5uYaOp26w7DKZGNhYZM+Pst79iycPlYUyjrwZJf9yf4OUbdQLvfzm4yLG5XCWWy7SMTijg52THTi5sYaALr5Ksx6JQYQiQCHMwJwr1Cm20Wwvr7O5/MLCgqcnZ1R8rnbd/n7rf0PwD1EItG6ogX6YDUagUIxMjAwMDcH1QkeD+1AZvP2a+7yinlNuw60Gqh/LCWMLUgHfJWKpdfDj80CAEhOZgUGltgoRZvd7rEKB81s3lhf7+jgtre3DwwMdnZ2EAgELpdLIBBIJLJELGlvb0cYWsfHx5dXViYmJmZnNQK+YHhkZGR4eG1tbXpqamVltb+vVyaXj4wMt7e391hefNHrIlMjLy/vYew9GAwG4U61Oq15YQFcvAgee6z3/PnxlBQY9sThQMtNWhp03Dg7Q1dMcjL0iCFuQ4TBp7gY7hAUBC09SB7T8ePmL76A9MH/9V/AzQ3L5TYmJ4PWVphQ7eEBR6eyEh4CADTDubvDWOnbcQjaqW1D8MrKCoPBwOPxQ0PDHDabQCSy2ey2tjY6nS4SCfHt7ROTsJb48NDQ+vrG0BAk4eWwOUqlamh4eHZ2hsVira6u8HhdCkX3wsL8tFJpM8TIanJy8sP4uUpKSgUCyGPxlZtpawuKq7Bwu/9YrDw3V5mZCUpLt5P2IdW4Gtofl1UrkMNwcADG+pDJ0DPb0AChJY0GBvrB7MzqzOrMjJnBhODmxg3zlSvg+eer3nuP+sQTQCKBTrSPPoKQ6ZlnIOi/mxi/FqglKArGMy0s0Ok0Iok0MjLCYNDhLOVw8Hg8FKlYhMfjlSoYqzgM5+T64ODg3Pwcm82emZ0dHBzc2NgcGRnZ2NgQiUU9vX1zc1oGk4mG/iDXQlxmSUlJX/u56uoEZDKYmzMrlWBxsa2qikgiIZN5eGjQ0dGhpraOSiGLxOKM9DQvb2+UognZ5xv/1ev1lZWVeXl5DAbDuhLCNz4hcmBjU1N4eDiuBQffrC2LRCKOzq0IaZZNzq2MT05JphZbu1XvRZKP+jbzerZhOnLsluHBlcXX3dwyTs7r6wcncTQatavLErphq6G215HqECiOkUjFfaOUvMLk5uYmkUhq8XPBPfF4PIPBoFAo9fX1HA5HIBDwLIslxlnCUpDrRiqbhuoah6prRsuwQ9Wd0g6BhE/sayMMtvMlO3RAIpMx6XR8SIROO7+6vi6RSDfgC+j2srq6+iCZXMje09PTZWVl+fn51hTPt84E/y8uLhYUFOTn5wuFQpSs1XqHb9A2m83FlZVxAQGUxkYZjwcNJjKZqLODc/KM4CP7XoygS81LEMVdwJ/7qOSdl/L+1DjSjF5ly7ilXFWaUd8V+sW9G1tbGydP9ru4DL3zDguH46FjYzOyljwsiUQCGcMti6i7u7+iQnX+At7uLL2uVtDXJ5TJENzZ1tbGZDJJJFJ9fX1HRwc6rGK5XMZkrr788vJbu3VPPHbm+5Vnfol75XvSWD9JtjvV7/rMtWvT5eV9fX22xblk3fKaCtmBQ0JOp2llZUk+0K+/9RiBDpYHKCqHymBsbKy4uDgnJ0cmk6Eb/yGN/xm4By0zjj5Vh4aGUEJPk8m8uLidrwsAxDQWljbz6qrJbLKAHsthyEuwxSuxg4YAAJDJeDweZ6MUrXXGg7TvHMXl5SWkKuF9k4/uPBbZYn1dZEt+fv5D4h4e7zbuPguxW29b29Lrr4O6OjA6Ch2DaWkwGc7REVpxUlIguPHwgEglNhbaI4qLt403bm7QOePjA1lmjh2DoSk1NbBs15Ej4MMPK7BYzpUr2xFCx45Bq9Lu3TAACABIRnDo0HaVDOt7tGnfKZa5OS2Px+vu7kaKl925w7237Hj+h8Q9paWlYvE27tHrIRbp7DR0dvZQqWsmi5obH4d4hkiE7ioyGSYhqmfMMyrD5swC5MJWq6BtcmwMxqyNDJuV0/qZuVX16vAwxItVVVC2bm5QpL/9bb6HhwCLBZ98Ap2G1dXQeEYgIJZymzvbeXVH4Wg1GgGf393TY0S6e2snxIt3a+1e/20uZjQak5OT0R9pwx24B/1R25wUOY/NxodZ3dra2tjYGBoaQlgejMiLkOUy3/i0m5ubjU2NuXm5JBIJhlDAABc+r7Njcf223KvSEq63EyYzBaNWQ9fqQy4rG6NUKpHPFyDpV8h1rVXknVuQb8ViCZfTQSbTOJ1tUhlfJIQ1pTo6OjIzMwkEQno6pDPgcrkcDodsWWRSKV/OI/Xiad2U9v6WxqEach++o4dD6yWzelhcSqNIJhVb6lmKbxUdE0kkPD6f6uIje+PARFre3Pw8UsoNuWWz2byysmIdq44SN+8ok+npaaVSiZD87rgDwuy8tbU1PDyMPLH/W4Z1a2urHIMpKC6m0GliqaXYrKxTyGYsSKZvBVmB8eXRgw0HPIhOaQVumrkdarrt2OG7bhwbW3FyomZnixSKbUxjPaK3t63HVyiXy9hsOo9HJhCG7O37KyuFcrlUImEwGDk5Oe3t7enp6SwWC6lyTyQSaQyGXCTS+Huu7np56eD+0Rf2nv5O1anfUEfOOfQcd7z6OuPd9zcIhG4CsVcsFUkkYktBOqlYoejCtRy/6r/b9/Sxi71q1dLq6gpScgsRO1p/F1m997BOTEzMzc3pLctdBfJ3+eJ/AO5pa2tLS0tDxIrIZGVlpaWlZWlpCdmIQJlbj0uIgRoboRKxef1dXYUBPXdbAAB1dXUPQqV4jzMg3ZOIRampqZ1dvNXVldKSkrqGBpPJRCIScnJyNBqNRCzOzcnp64ckzqsrK7U1tSOj8HVwanKirq5ubGycRCJKJJLOzo7i4mLER2Z9RSSfC63Hbv3VA7YRP1d1dbW1SGdnZ6jU1vX1zXPnYGTuuXMwG8veHtYKPXoUpmgplRAGeXjAmFl/fxgEHRAAsQ5Sb9zHB656eUFM4+cHJW/5gBMnivbtE+uhUX/bESMSQf7I1tbt0TEY7tVr9CfA4bBTU1Pliu75+bnc3Bw8gWgymRobGopLShaXljhsdn5+waQlaker1VRVValnYAqMyWjksNnLy8vdCvnI6CiNRisrK0OkbX1Vm3rs1l89YBsAkJGR0dLSYjUhwcLC9Jdftp05A+sQmM3mgQHIqYPHQ88gFgvRzOQktHWpZ8CiekOv0hhVMyb1DJhRm9XqDdWiWmWamYEsiLW1kE/S3h6adl59FfziF+lvvNFnbw9+9zuIe0JCoGMRiexBe4tCfHQL2kBFSqfR0tLShoaGZ2fVWZmZVBp9Y2OjurqqorJiZWWFSqEUFhYiJM4zalVtbd38/AIAoLOD+xWbs0arqa+vn5yaFgoFuNZWxF2FXgKBSvPz815eXkjqHMznujvusT7Quo129eEbCF+zTVyzxQps0mq1MzMzD26KQDuzsrKiUCiGhoYoFIqiW0bAMyTi7Yo0Wq12fn5eoZAnJ6UEeZWmx5Ma6nGTkxOWH6/JoP8m9h4AgEQi43A4tqEbd6hGa+2IfGmxBAh5AuqEtm5gskUMq0fAYmEsFotIJAoEAjKZjHAJGgyG9vb2stJSEpkktsSICMQCvryro4tbhMvPo+RwMRU9bsHSzHxZbKqURO3s6mpvb8cT8FkZGezgmwOfnRv67Nw8g3srZheVFrDRiDarX+9naU1PT6OWQpuvbFbv5C00m81Go3F2dnZmZubeV7E5FbI6NzfX29vb09PDYNDEYpmvr6KjY5t41lLbeEEgEAddvxQR8BHt2GHGjVClJTBuXTO9qF/e8YT33mg2m3lSaQefL7GgZ2TsbEYQNd3dPtQwMrlLLB53de25fn365Ek5mSyEWA1GWVEoFD6fT6VSkTzN9fV1LLYeU1tVlsfqa8X1NmLHstNUfr69sTHSIwdb39/v8efWoMTuNz6VHjoiSoob5fI6Cfj2FgK+ydmdc+P0pxEnXznpXtf5tX0LvSlrCSM/KPSrOxv/zOeyfr7dq22pz4WvqIC1Giyxrnokh/YeUTgGA6xJZO3JQpQulQpLUNmAIfTaCBoQi6GDFt34gA1Eq+NwLYmJSU2NTdeuXausrMzNy0tKSkxOSa2uqiotKfHz88dUVgQEBbk4X7W3d2hqbpHL5IUFBVeuXImMjgEApKcmHz5ytKGh4dq1a56eniwO+8zp0xQq3aY/yGpCQsLD5LGXl0NCT0SkW1tbq6tLIyOGS5dMfD4k0vPzg06Tvj4oLg8PyIJkZwcTtSoroeHhxg0Ig2JjIfUjkwltGIGB0Ixx/TpESAcPQi7m4WHIMHn4MHj88UwHB1i5CREqAObVVRjQsrS0PRB3EzbMfNnaqq7CpKSk1tfVXnNzKy8vLywqjoyMyM8vqK6uzs7KjLh5MzcnOyw8/PKlS9euuZKpVIFQkJmRftX5akoqBMpCAe/d997j8/knTxyrqW3g83nHjh6VyhR3ilSn04WHh6N8zQ847uhuFr7mfAaDgYh0fX19ZWVlft6o0ZgWF2F0WXs7FAiVCkN3iEQYjoPDQSaq8XFo9VGpoM9LozJoVPp51YZWtTWjNs1qYPpbQwNk1MTjIQPDRx+ZL18G776b/K1vTb78Mjh1CmbGVVZCySMR+kh/AIDindsmT0D7CDXRxvp6WVlpZmZmFabSy9unsrKiorIyOCiwAMalVSXEx8UnJKYkJ0VHR5+3O+fm5s6E7HbC+LhYR0eH4tJyWKSzEWtnZxcVedPV1dXPP8DL0/PgZ5/13qIORy/2Vcj5+vp6WFjY13HN/2jccze+Zp1OJ5VKEfMnMnwP8veWqMHGxkZbG46AZ0aFNOekt8xp52dm1C0tLVgsdmlpaWx8vL1ZFOhZXphfWVpaLBZJS7K7KG39a2s6K1/BvS5oBka9SWMCkNOZzeZwuVwbpWirC++yLpFIGSxG7wiRLyJaKkvC/aRSqUKhYLPZBAIB5Rfe2txid7LLyMU8GU8sEkslUi6HS8ATRkZHRqZGa3MLuh08e/ce7tlzUBocSaXTS0pLK6owGol8OjBqJjlXVlG9aeGSuPOuEMPbndsRr6i1ZfHh89i3trZ4PN7dwqJ37AP6WLBUWZlrbW3y8hrz9x+7dm1RpdqYmppobGxsa2tbWFmd5XZJPjtE2fs+zd+/tAGrLiuRHt5TmH5ukccAaw/Gvt3fD27eBB0dJgBoHR1dvB38hncZyds3y+XSoqKJkyf7/fzEQgvr5K1hRdAPCuhXVpa9vXlfHBmMTlSJu2Xivj5mZweJw1qikMbTM06dZ71+uvO950hlz32c/2IouZ2SU1jQ2NKy7h+wUVeVm3AouPLapglG4925bFmWO7cjvGjW2//J34M+IWHDesKhsx9toPE9AAAGg4EkYKOH3HaiWys7alOVCiar77ggY5Obm2tNgrzjnjtutHTGlJaSfPrMmejoGE9vbw6bfeXLK1eu2LNgoIkyKSEhNT1jenLiwvkLGEylj7dXBCxKOp2UmBgdFRkcEqLT60lEQnR09LGjR+PiE3x9ffl8XnR0jMFCgml9UaSr9/VzofJBGtarSB47+qAnEAjd3dKxMXDsGIwNb2qCNh7EYLC4CHVqTAyEPiEh0EhDp8NAk7w8qL8zM2FAFY8H7UC9vTDux9ER+mLc3SGpkr8/+OAD49NPJ0VFIewj2zdxyxRkfU87tGGdio2N6yHB58+fj4y8GRoeTmhvs79i/+WXX/b09Y+OjoYEB9XWN4iF/KvOzuXlZW6urilp6ePj4xHhYemZmaFhYUtLS19evvTKK3+l0Zk5WRk1tQ1yqSQx6bYAeeTCiHDu6+eyliF6IPqrLi0tFYmgn8tkMrW2NgsEA05OICUF4kgcDlrIGhsh3ybCv7C1BZEQUv6srw/G38/MmOcXzHPzFkqkGTA8DNEhiQTlXFEBQYynp/mTT4C/v+6DD5KeflqjVsMQ4eHh28KZb/UK7nzmDAxss/4hQLPW8nJAgP+lS5eCQ4ITk5JxLc1OTlcvXbrU1z8wODDg7uZKJJIoZJKrq2tFecWVK18WFZeMj48HBQYmJsTHJybp9Ybx8TE/X5/09HQ3V9fjJ054+3jZ2dkhTA3Wo/gg8T2on8v6QOs2KtuHb9jw95gsC3pao9GIGI/RLfdtmEwmpNqiJdZkgUZlx0U0lhW1tzS3EAiEtrY2PB6PIInV1Y0OjnBwsL+xsaG6nOp0psbHCZsQge+Rqc2o7+Su1zNvGIe1m/VLOjYAhsEhKZfbcbcgkNv1oe2aWCzu6uKx2CyZTE6lUqUWwwCPxyORSBQKxcYFv6Cbxw/hGHKaWCxm0BksFgstL9DB5zVcvCrec7D3vS+kZ5yYBcV9IyMzs7NmALb6hpZmNFV1dfML0DR456LT6RYXd3YMoU975KhvhnsQVxd6Xb1e/zcNK2IoMhgMiD9Oq1VHRQ3Y26scHHqbmlpJJEinwmQykaxAg0IxXVoq7u/HYWpEH72ccOn1kSd+1Wt3whATCyzkb2g3dmio1eZ331157jnTrl2guXkqKkrA4YikUpsxuxvAtd4uFou5AkFXc7NIIGCw2VIpTNDr6uoiEokMBsM6Y1ynAz4+a15eEmdntVAoptGonZ2dtyyg5tr6zpefwGWeeS/0x2GSp96fcnfqGx3Trq5CUpOw8Bk6ox7XvLl+V9yDTg+bm7UZ1n/inq+fbyYTLEeAyAupqWs0GJABW11dNZqMRCIRh4NhN0ajcX5+Ho1iQ06BHIjKF2nY/EWQlWXPr69rvQ9y8tTU1NHR0R1Hznrn205hWUEOmbUw483Pz2VnZri4OF+/EcrhsN3d3X18/WQyWWBAwFXnq23t+OysLBcXl4LCQgaDQaNRHRzsMVVVFAq5sRHr4+NdV1fv6+OTlpbR1dVR39CA6lrrmwUA5Obm3i2+B3GE3ZrQ8LXbbDbr9fqtrS3Ey242m9G4ZqPRuLCwgNgq0bAHi/KGQOfyZWjpOXLEfPw4OH4cBpecPQvT6JqbYU5Wejrk0Gtv3wZJcXHQJhQWBnbvNj//PLCkya97esY/++xKM7SPIrtt29KQ+0LvDm2gskUAhHJ6emxsXDM7GxMd7Xz1anx8Io1KuerkFBoWJpGI3d3d3dxcqTRaQkK8y7VrlZgqKo1GJBAuX77c2tre0tLC5/Ovh4TIFT3tbTgajUEi4JmW0sE2o4lc/R58zUjVvc3NbbbZtTVIQ7Jlqf9siaOH9u2SkhKhUIgIf2lpYWUFgo99+6BJxtERhoe7ukKAaGFthkxSeXkwwa221tzSApmo2GxoB2IwzLOzYHAQQkwsFibNJSZuF4vdtcv85ZfAw2P1pz9NPH9+9daU/vqdwUp0EGOVlqJ3uf0NFKnRCEU6Pq5WqSLCw1yuuaRlZLa3tdrb20fHxPF5PHd3d3cPDyaTGRUZ6eTk1NTcTKVBSip7e3s8nkAkEnNycnz9/Ah4vK+PT3VNTXJSooeHx9DQdoyFVR+AwWBITExEvRX/cD+Xtb0HYVm0+aU/3Kp5YX7ZwgApYTKZBAIBi8XicLgpS20+ZCQs0wxw6GOirsnsRFZMCHFGdX+6F71pdnqhYWG9S2caVy42KvroolvZWzZq8r6rMpmUQqbgcLi2trauri7kHXJ6eho1CVhLQD4g43ZyuWxOV1eXdf7/xOQkl8EUHb/c986hPjsXvvd1WX2Tfm0NCQv7KignKCjoblaWjY2NewARVEoAgG+Ge6zPYH0v37htMplZLINY3C+XixkMRltbW0NDQ1tb2/wczORCvZVmqUTpdGbV02X0rN3qnj3A4u++10VXV812dpvPPmves0d/9KjY3X3KwwPGUN++CKXSHaupW+MexGhHoFJbW1sJBEJXVxedTufxeLOzs9YeKKQzGg24fl1ZWNjDYNAlku14cOSr8bGx2IzW3fZhjLf3d7x1fPjYqaXPP9MPDCD3ODgweP36jTuVI3Ls2tra3XAP8mhFRfFP3PP1s7ixseGaq+vE5NTC/Jyvj09pWXlhQX5oWDidTnNwdJR3d9Pp9Kqqqq6uLlSXo3J8kAZyJQDA2NgYGm2AHGj9O9HpdChzHfLt6OgoUov7vlfR6XTWuZQmCz6DYR0wQXppYxMmNWxubCCvQV+lCs9ZnBBbOh2sl7S2ZjAYtra2DAYDcrnNzU29Xm/zSmozgQoKCu7m54LltwoLfHx8FxaXJsbHXF1dsdim5KTExKRkfHv7VWfnyclJ5Nfb2dlpHWmI3KbRCDY2IBlyWxvAYEBxMaz0mp9vzs0FWVmwMhSBABQKmMZIJpszMoY5nDWEhZVIBM3NAIczx8eDsDDIWLOyshASknj9+lo3LIcFtrZAb+/wxsZd7cDWwwErnVkc57d6ZZybm0N6u7i4iMh2fX0NeYxuQYZr+Iq5tbVlMplWV1fNZrP1bEH0nM3roI1IU1JSUC2F6m+kSyajMTMjPTAoeG19va+3x/WaK661NSY6KjMruxFbf83NfVY7V1VV1d7ejkAfC06CNeISEmBhivR0UFoKgeCgxeM3NATCw0FWlhmDgU/IpiaY397YCD/NzaCgYIjL1VmyROEQREWBq1eBgwM4cgSeZGNjwcEhxYKvoGBMJpMlwWrn9zBEdOjfjY0N67dtOA/n55EbXFpaQnTbyurKyipUxpubm0tLS2azGYHLa2trZrNZp9Pp9XrkLQV50ul0WzamAvRyZrP5AeOaraVt3UZP9fANG3vPw58QOYP1jEXPuba2Njg4SCKRcDjcpCXTEACgVi7QaRBHzM1pdLoNGkkW6oPj0GA1nPstZoNp3WTWA7BO42TW1hUr5JB0DqmghPy3VNOyJDwjdUF3pGGx7GphsKOnp6fzeLy+vr6JiYnBwUHUw2Xdk8mpyabOBlInAb0FhJtAJpdvrazWXnIWXfWhZOdLcoqkzr6LtS0mi29Lo9E4ODjs379/ZCfayfVx8p74AAAgAElEQVT1detfJXq5O+NCvhnuQU/4kI0dh3V5ebmnpwePx7e0tKBovn0YV8Eu4wnE89q5RZVy89NPt1JSQFDQ/U0+y8vwiTAyYiooYL39tiA4WNrbaxmi7T8wbJlEkpFIkF4ZdUxa72FpI6E/UqmURCLl5OSIxeL+/v7JycnBwcE7n+0AmIlEjZ3dhKtrr1q9nYQPAFCpVGPDY93zXe8E3TgSEM45c3TlhRe5Xp46T0+ThSltbGzs1KlThw8fRkpq2oh3dXUVeSDbbEeG1VqY/8Q98PkG43V0WyHBwVGRN1ta29lMZsj1EHd3N08v77CwsLi42CtXrpCpNA6Hc/ny5fT09Lq6upqamrq6uuTk5Ozs7DrLkpKSkpmZ2dDQkJWVlZKSUlNTEx0djcFg4uLiCgsLY2Njs7KykpKSkpOTHRwcoqOjsVhscnJyTk5OeXl5TExMVVVVTExMQUFBXFzc4cOHY2JikMqXiYmJdnZ2CQkJVVVVUVFR1dXVcXFxxcXF8fHx2dnZyAmRPREaCW9v75qaGgwGk5aWVltbW1hYWGRZCgsL8/PzCwsLsyGFTH5mZmaWZUlPT6+pqamtrW1oaKioqMjMzKytrcVisVlZWWVlZfX19enpGRgMJj8/v6CgICcnJy8vLyEhISsrK8GyuLi4oIkMNqpia3PjK365oMBANofb0oSNT0g4f/58YGDQzZuRUVHRjo6OAqG4qbnZxcUlKysLEWltbW18fHxhYSEWW3vzZu0nnyTk5eW3tDTk5qYVFmZhsZiMjOjY2Cpn55iIiPwrVxKystKzstIvXYp/7bWLhw9nnDnT4O2dEBVVdPZscWJiQnNzmbd33Icf5tvbRx4/fjgnJzktLTEuLt7OLvbll8/m5WVXVlbGxsZWVVVFR0eXlJTExcVlZmampKQkJycnJCQkwWKuMX5+fgEBAXV1dRUVFWlpafX19Xl5eaWlpbm5uSUlJdnZ2YhI8/LyMjIysrKyMjMzkSmBiLSkuBidIRkZGeXl5XV1dWlpUObZ2dmlpaXp6en5+fnx8fGZmZnJycmxsbFubm47FsqFj3utxt/f78aNGyKJtLa6KjoqysHB4fr1G0HBwbGxMQ6Ojn0DA+Xl5S4uLvn5+bW1tTU1NdXVNdnZNcXFNQ0NNfn5NYWFNW++WePmVoPF1mAwtTk55VeuxFy4UOPr+/XHz682MrLqvffswsMLQ0Nr/fxq0tNrQkJqAgJqsrJqrl6tc3XNiYoKd3a+Ul9fl5BQU1xcW12NOXfuHHKnNVZLXV0d8nNAxhf91QQHB9fX1yM7QkHV1yO9ra+vR6RU39BQXw9/ZfX19ci0rKurq62trbfsWVdbW2dpIzvU1tbW1tVhsdiMjIzS0lLkVGgvKisrPT09UaT1/5W9x+YB/Y1X72E3mp+fLywsZLPZYrFILuuJDK67eim9oryGQoEJxtHRkUR81wOXbdjuoFI12dCA5XK5UotPxPLeLxZLJL1D9L4RgkgMM5DRnKA7G6jSpFAoTCaTwWAQiUQE0dpIQKfXDc8OcqbpuU2Zwu0sRbjLV+Vc5ubmNnW6iOs3xgYHW8mkvKzs3qxCXe/g6urq0tISw7K88sorRUVF6DmR3CvU/GA0Gm3khjiYjJYFOeofi3vQrqK3gDaUSmV+fj6MHBJJa0XVke3hV4sdUmuSqSQapr4h3tV11ssLWIx86CH3aWxuzlGp2JaWLh5vO01PJBLKZD3t7Wp7e5W9vby9HclRR4cPaVhbfZA2hUJhs9k0Sw22HfFlX5/+pZcM7u7Czz/vE4u3g7UtwUzzS3BZ9YxzZPaTsbjWFk9P7eXL5vLy5aUl7fw8YsL8y1/+gjhekDtChhWFNTu+VSJjisrzn7hnG/dYWHMS7e3tW3Ct7W1tTo6OYeERN25c9/f3x7W0+Pp45xcUGgwGNpvN5XIZDAaTyWSxWBTLQrUsZDKZQqFQqVQKhUImk6lUKolEolKpyHYymcxgMOh0OolEQpL6LD9MuAXdH90T3Ui3LMgEYrPZyPnRkyMHIgmfFArF4q6iUalUpG/o1ZEd0L8kEolMJpNIJCKRSCZ/fRTSc+QotNvIXVhfyPo8JBKJx+Mhb2nWoMfShhEd4aGhDo6OFCq1rrbGyckp4uZNPz+/sLDw5uZmH2+vZlzr2toai8VCRMpisZhMJipGMhkKkEKh0GjbMqRQ4BYymUokwm9IJAqDwaTRaEQiiclkMRgsCoVBpzNoNDqRiAwBhUgkE4lwkOh0OtLzW1KiMRgMG5Gi8kfkQ6VSb+0MRYr0zUYgqDCRQ5AOMyy8XuisQEWKfItODGTV+qJkMplIhDl0yG/YRqQwHHhjPSDA38XlGpPJxFRWODo6xMXF+/r4RNyMbG5quubiTGcwtVotOsFYLJZlrlIZDCqFQiUQqGQybNPgNIF/GQwqmUyi0ah0OrJK53DYLBaDTqdyuWzkcDaHRaUwmEwqh0NF/jIYFDIZipRCoeLxNAaMu2DS6XREpMiNo3/R6Q25zmCIBptOp6OzFN0NbaBSQrcgZ0ZX79G481hkVstkctSo9v8n7kGf2vdRTt/o6/n5eQwGU1RUBGlCS2lVZQwJJPuBaotCobS14cbGx+7hINjxmhqNBofDISUneTwen8fv6GBJ+6uHlfWKHqFYLLHRjjuuSiwLk8kcHx/f8SpTaxPCuc7J1fFOXgemEoP6uYxG4+joqM5obC8qkVfWrq+uzSLM6ZaXWIFAIJfLZTKZr69vWVkZItvl5WW9JccEvZBOp9vY2EBsh6ipCTHTLi8vI0c9JO5BFS160f/GxszMTGlpaWlxaUphUig+JKw9hC1kScUwOodAobSTyVNq9drq6t90xempKcQvLxKJeDxel0wmS06ecHBQubiMFBUJe3qszHs7DinciAwrjUZD6gzadEClAt7eGyEh82+8YW5qkjU21qD+TVjuY3x8a+X/svce4G0dV6Iw7fi9Tdl9SXazX7J53yZvkxc7WT+nrGOvEyeW5BJbkm11yWqkSEpik9jA3kmxd4qkSLH33gvYCZAAARK9sPcGFjQSAAkCBHDvr3tHuoIAkKIoWdLu7/n0UXPnnmlnBjPnniqnnL2w7u4pn59flcngpSUY5eH19vZOTk6SyWRXV1fMZHV9fR2rDjoCSusajWZ7extj/2i0WoVCgS3rN3TP/csFITbFYg6HK12TikSiQT5/VShcXJiPiIioqqpG3N6wWVlZWUvLK4UF+Tz+YD+VWlJSiukDGSyt8SMEQeXlZQRiDxJWcGw0MzNLsCjILyjg8VHRi3EFtASNCVUHQiW04PErqFG0SVi5XIao2owhAozOjo6KikqgiWwSGCtUKOT19Q1YLFKsfI8ZjUZTVlra0dkFLhXje3pJIOAPDkolEpFIxOFw1tdlI8PDISEhTU1NiOkKlZqXXyAQCHJzcycnp9rb26pra00xRU0PR61WF+TnMVlsxLCWxSgqLl5YmMvIuDtlpBqF1dfpdDXV1WDACgXwQbCjzadQuJJxN31xUQBBUG1NTWNTsw49VbHWTGZEIuQ+wA5okzC7FMpkspycbHLfI0GmMMTCMDw7OzM0PCwWicRiMZvNVigUXA47JCS4paWFx+f3kcmFhUVLAkFuTs7M7GxTU0NTM34Po74/IoVcnpubwx8agmGY0keuQNwZTN/NyACW+SaHrdNpy0tLenpJqBNCSUNDA3bWGMPPz89lZWWt3hPvQ1BlRXlHBxKi5LFpeXmpGY9/LNhOAFKJODsrm/4gON3LSffcc32+C952mtpeysHVq9PplEolgUCg0xFmDGaCzuVyaTRaG/olYaBA89jG19fXqVQq+DYgEomDg8OLghliT0tJSRmFQkFJK5S22vF+RC5IKpVKoVB2+tWvbq4ylgY2NZuabc3i4qI+GbEmk3W2tnGCYwau3BRw+FoIkqHqblNTU/n5+Xl5eWNjYwKBgMViIT8fGFar1QbEJRB/APVhwPtB/MHAsHJrC1uLp6R7tFrtvo+CXfAPwTA4i7RarVKpJHb3dA90k2i9XNZ91Rwej0ehUlEvtYhXJ4OJ79IykCGCD9Hu7u7e3t7RyUllSQktMLCspmbAyNrLmNkD6B4ymXxPqdFkvzIZHOC3cd1qjkCAtVqlQCDQX9ZVmYweG7vi5MS2stIUF6theAONaTw8MpKZmVlSUjKJJj6fjywfDG+jDkj0ZwSYdmBZdTrd/QwMK/XIoG/s2LE7BZF2GaT5uVkcztXN3X1dJktLTfHw9HS8edPbxycoOMjW5rr9jRtIOG59S5WHjT2SQ7zy8zjOzs44N5xSqYyOinRxdSkoKo6Pi4mMikY7fQQebJd1KeJ35OZNx4mp6fn5uePHjzHZiFsOQ1D0WquurHBydIyOiYVhuIdIuHTpkmAJCTlkDIyVwDBM66d+fvSL2TnEyzBWvscMDMPzczMfffRRRlb2TtUBPvV3P4fN9PD08AsIlMsV4aG3vLw8XV1dPT08wsJCzc0v+wUEqowD0JsaEGpSR3BwcAgKCr53WgX4+zs5OtbV1fn7+WShQSuNK8EwfM+HsuPNm/eCb4glUiaD/sXnX8ztMHckwGdujr2DfVo6Eo0ViThhaQ0IROOWsRIYhpsbG06cOLWJWhxg5XvMoAQc88DBg5XVNbujVH+jknqIzk7OwSGhWo0mKCgQ5+bm7Ozk5+cfHh5x+vSp8Mho4Jj/sWOAYbitpdnJ2TksLHxNKvHy9HB3d29r78C5uJSVV5ocDwzDHDbr5s2bbm7uKrWa0NXx5ZfH1tYRvRyD7sCAU27fxrnhCotKYBguKy2+bmOrVqMulQyg9R5hGK4oL7146TIa/sKwWT1A01nk59BDOPThh61t950yv0C6RywWz83NTU9P65u3YEupVqs3Nzf17wDs1f4yOp1WuKLQPHDKvLCwUF9fj/jCeUCNgEuLhUiokNTa2mpSGwbob5kcA4i0qv9qY2OTSqWCuBM7EzyISjSLw+0jk4lE4i7Ki8otxeQsF73g9Du5n+8iEouSU7N9A7Lz8kQi0Sy+k9TcSkB9BAOn2GKxmMfjcbhc9dKOgcoBxXO/RQhSzczrlEr19jYYlUAgmEUTxhAyHsfKyopAIJiZmTG5diqVau+fx8aNG5foxGLEo6v8vir6+Ph4U1MTj83jIC6wHxEvgmVFnJ6jruGNm9I/mfXfAk4Y+BWDcplK1dPb293dzWab4ORh1A+XzR3kDAKpiMlNDlqjjI1455VodCZipEAw3IzH021tO48eLS0pETEYS5cv8zIyiH19TU1NVCpVpVItLS0xeDw+kQhXVcGo0qr+4EH+EWmXSAQnJcGzs5uIqxSEATY/P7+4uDg3N2fAKzJu5+sueen8FsIwPDU54eR4E+fmPjQ0HBsb7evnZ29n6+/v7+nlVVZaZm9nT6MjhjOmT1y9UnA9uOJw7u4eMzMzISHBXl5edzOziosK/Pz8TV5LMAxLJWIcDufi4koikdVqdXhYWD8NiTer1/D9LHI3lJXicLjwiMiVlRUymeRgbw84ScbAWAkqN1F6e3mBIFNY+R4zMAwLFhdu3066cePmHq95GIb7KWR3NzcfH7+h4eGAAH8/X18nZ2dvb6+gwKDi4qJrV6/OPRqqc6fBwDBM6Op0dHQMCAycm5/z9fFxdXUtKSlNu3MnCiX+jCsCTpuTk5OHhzuXy5Oty3y8vQFzyCRwTnaWk5PT7eSUlZWVjo4OOzu7uXlEFG0MjJUAFRxPDw+ZzMTdj4HtlIFheGx0JDYm2sPLaxtl7e4EiZXDMNzV2eHo6OgXEDg7O+Pn5+fp6XXzxo2goKCg4ODCokIbW9tFARLqAauyUwaGYTy+0RWHuxUSOjjI93B38/b2rqmtjY+NvpP+SLBPrAUYhllMhpOTs4eH59TUtEQs8vbyEomQuI8YDMiA4+N2YoK3l1dGZtaiQNDY2GBvb7+2hvDbDID1HxFV3CWBl5fXYykk/VpYHobhQT4vNDTUPyAQUE4viu7R6XSxsbEuLi4NDQ0m43MBa8edrqJ9nL+M/rFb3vU8JrL6SqWyo6PD+HsdI014PF52draPjw+fzzeQeUEQ9NjrAfBOgGMzPp8PGLpY41jm/gXJZrPptIHmWiqducvtiGi1ayfW1O3bOiTopnFak0oLi4tLyssdnBxniOQZFz+iTwifxyMQiXV1dREREdnZ2V2E7jsubvN+keIB5vziAvisAtaOYFL6Kj7K0Yk5z1trDW3bKtXK6urm5iaTyUxNTbWzs+tFDTCNx3DPoCEwMNDb27ujowPzEaUPBrRP9EueKq/RLNrbT+NwiKkCBK2tr7e0tICoEfpED4ZwPp+flJQUHh4+OjpqgOo9LivYkCBUWWtrK9fI2gvri8filfeWxDVGslhsjGFmPNmVzeUkenQOK6VmHPmaMk5CkSivsLCguNjZ3X0d5zbg5z974vhQf397d3dtbW1oaGhJeXlXY2PV737Xc/78ekiIYGkJ25/b29uAd/iQ7tFq4eBgbVoa7OWlFItXhEKZTEaj0RISEm7cQMJIGw/geZa8jHSPVqMpLixsbGpms9n9VGpKSsr0zMzd9DQqtb+joy0nJwcQj9ghu1MGHAd5OTltbUj4PTKZnJySMj4+lpaWZhDsU78FGIbq62rLKypZLObC4iKlr2+nCwwI6ZISkwZoNBptoKG+PicnV4FyffUbNMiDH39XZ5dUiriyMHj72EfEy7NCnoE4yH8YUH33WsCJXMbddAKByGQyent60tPSp6enUlNSOBxeTU11aVnZ3u/7DYUiLT2ttxcJ6EMgdGdlZ4+NjiYlJXLQkCvGI0Hmu71dXFTY2NQ00N+/srJCIBCkqBsPk8Dzc7OJiQk8Hr+/v7+srLS0tOyxvCiggtPR0QHCVhg3u3sJDMPC1dWUlJQ+imk5l3F14AsHbCQGnd7d1ZmZlTU1NZWcfHtoaLispLi2tg6c7MZ1DUqAoOr27SQajUan09vaWvPy8gcH+YmJicPDoyZ3CGovpsrNye7s6gI2q4Tu7o0deF2A35Z0O4nPH+zv7y8qKqyurgGHlMFI9B/Rbabo7OwCB5n+q73kUVPkheTkZBYbkYc+1l+z/jQB/DMkRCYnJ1ks1sjICHBAoFarsfMa9AV6x/JPmVmYleakUsaGELdVy8vL5eXlQA3ZpIoGm80uKyuLioqqr68vKCjo7++fmpoCt5dJNsZOY4MgaH5+vrkZOTOxGxFkgIIzEtiJQZ8pSp4KvrYxC1yJIo2ZvIa3datyDU0LIf4aTKbR0dHGxsb6+vqk27fZOUUtxaVtHR3AvY2vr29dfX1jc3NuaET5yUs+lldT7tzp6elhsVjz8/NisVgikZDJZCyACeJDRLouKqhQDiG7HVgdzs/PDw4OMhiMnezedTrd8PAwj8cbHx8HYRNUKpW+2A7sn2e2i7Ra+a1bqy4u8F3kU2RmdraysvLhsqJL+xDtaMi2/Pz8uLi4+vr6oqIiBoMxOzsLhvdEy4p8NI6P4/F4rK+HvSBMJiaXxW2k1HvV4IJr/NunH0qljZdVsS3P52XfpscPLCGnnMnE4fGampurauoSbK1Gjh2mh0U0tbbi8fj29nZ/P7+W1tbaioqOzz+nHDmS98knaZmZfX19bDZbIBCIxWKhUEgmkx9Zr9payMUFMQnW6RSorfvc3Nzw8DCDwQC/RJNjeD6FLx3dg56qj5n7Xk5eDGantjAA44xBFWMArMQAUv/4xmBMZvYOaVwd69T41S4lWC3TmV1qPvrKdHW09FHAR56wWth59MhrvQcMEsvovdwt+7KhdLexPvoOm6lx5lHAR54w4JcTpdiVAzK78HsMFg7Id7Dq2DSfJgPiVACbfGO6B5joP037+nUhCFLIVDrEaSTiO6O1tRWjRVDZFiLuAoo+LCSkKZPH4/H5fA6HQ6FQMFOD1dVVExjQarTzY7AGcZBhMk1MTOh3h1yMqHSNwWIzWSw2h9OZdVtclQarHvqSwNxn6De4pVRuax7jHGF1dRXf0lIdESNuJwiFwvHx8eXl5ZGRkdDQUBKJJJFIR8bG+DRGTn4+sCkZGBhoQVN3d3d8fHxra6s+n0anUkF6Adv3HqdiZmYGaJYAVyD6s9DpdLvwP/Qh95Tf2IB7e4F8Z3h4uK2t7T4twmIx2Ah6GVwufRCJbc6k05lsNrKofD6bze7r66NSqQQCgUwmS1HPuYbdjY/DCQkw6hPI8BX6zOVyOzo6sV2ExGlHPPswuFwGn8vqY5A9ylyie8Km1hE3WiCp1WoD9iHydSdbHREPPwAx/f/i/GJlU6VNgV0yIWJidXZ8fEIikXA4nPDwcA6HsyIWTw8OCiorcwsLOzo7e3t7BwYGmpubgWVDbGxsV1eXFPNaqdMhLk8eSMQgCJqbm9vJ7YXp0XxtpS8n3fPI+f7NwzcY+AYD/6UxYJLf09raCo61hYU5ZyenqupalOLRpqWmenp6TUzuxbfNXs9Fff89xvSE8Sc4cKZqDLnX/h7AKZVKMplMpVIB04XUM1Bf3cvjsXuI/Vwuu7me1Ibvwxg0AIbD4fT29lKpJj7KtUMDWynuWi75QfOG/29ubra3dzA4XITcYbNZtAEWncbg8IZbqtn9FDaXV11TMzeNIBZMDdtUBg0plVt7ifi7IRLzAyJZ9h61uflJKSkEAgEoXPf1IUHcRkZGmdzBmbn5tbW1nJycpqamqqqq3NxcPB6/vLwslUrz8vLa2tqMXcKAxcJ85BiMzeARi89lbP1gclkNpE4Gre3xUSaTEYlEOp2O0K0s1kxw8KKzM7e7eyo2lt/UtGRjMx0RwUBfYVIwDodDJBL5fCRIziNpbQ1yd9/OzESceu3g2ECuUHR2tgwO0jkcJp9Pz8sb7ezkdnZyra2XK6sGh4d4pVWlQ3OIpQ6YssllRXxxbakgRIP8Mal7ttMh08Yh3z6iKOzunbskNPX09JDJ5PaOjsGJCfrg4OzKysLiYnp6OvDlmJ2d3d3dLRKJ5ufnMzMze3p69L2vgf60Wu34+LgxNfaY0Xw9r//b0j0AXTudXPrl2BGwE4bBNtrprcHXKrbnDDIYGNaOwRgM4F/Cx72jVB9Sf5r6c8fKTWb2Mn20NQRQv1n9/F4aebEwYLT6U8DGj11Ou5fs8e0ep/nY8eyxHQMwPbpHBAkW4TVpc0nJPb0WYHSTEB/r4eF5xdJSLJGKhKv/+e477h6eQqFIf2pPmdene3RanWrrvlo3CDehf1tgHa2srDwTxVihUNjZ2clms+h0RlpCS6BbbUled2RgQ35mZ4R/Q3xYE5VKBxFAMbFUe3s7cKGJDQZkIMnydlsRJNwttDudyWI3VjBZbPYAdSYvbrIsfQRfLYywnS66zWaxenp7B4eHgfNJTCqkr2pj0KPJR/3tqlpalY2Mr8vl09PTTCazurq6tra2uLi4p6dHIV1Z4zVqJFOKjQ2xWEwgEJhM5vT0dGFhYXNzsxxNRCKxqKhIrVZLJBJ9MmV2dlZfEGZyGKDwId2jUCDh7tCkRRO2rFh1nU4nEAh20ZXGIB+bmZ2d7erqYg4OjuTnL9nYzPr7y995ZyYsTP7uu1MxMUvW1kNVVQjvRy91dnbqs7jud6HRwNnZOhwO3sV2EtouKuU7Os1TKOz07Gl7e4Gd/dKxY+KEhMlLl1apVHZvD3Fqchpou2Oro4/Px05HH0Cr09aNVjdNNKytr09MTNJotIqKirq6uoLCQhqNpigulh88CCcnb8jlIomkvb19DE25ubldXV0bGxtSqbSlpaWmpkatVj/k/aAdTE1NfUP3GJyNz/IRhuHJyYnBIYSnR6fRKsrL6xsa5ufn8fjm8vLy4ZERNLgxKzc3d3x8Qi6X0ekMnU43ODi4sLDY1NhYUV7e3z/Q3dVVgybw5TE1OVFf3zAzOzs7M11XVzczOzs1Odna2qbZgUgHOwmb1cBAP2hnemoyNzeXRmdoNBoi6op6aBgZJwb5cmZgGB4a5APDk96enory8paWVsRWpa6usrISOAKh9JHz8vLnFxbFIiGXx9va2uLxeIsLC7W1tRUVFQwGo729raampq6uXi5H1PuHhgbrGxoQ9vjwMPCCOsjnEQhIdE/s16v/g8RQCnSkSL0kOWomMDTIz83NHRwa2lIq29pay8vLZ2YQxyQvJyaxUaF69+y5+QWNRtPd3VVeXt7e0TE/N1dbW1NdXS1A1aKJRGJBQcHKyurq6gqPx9/aUg4ODs7MzNTU1FRWVrFY7Obm5tra2iYQFB2COGx2Y2MjYlDD5dY3NEilaywWg9zXhxKHpglEDNvKzU0SiQQ+9DlsVk5Ozvj4+JZS2dTUVFlZKVjak5o2Njv9DAzDVZVVxA6qQqwVC7aUa1BFSd3169cWFpErPCjQv7SszNraGuUNSMtKy2xsbPIL74ciNt4A+ygBdA8as0U3NrxUV85VyJGwLdjc9YVrWPkum/CJxjAwMNDX18flcojd/VWlxO4OanRwY3MDubKE0FhL4nIfGHqhLlhaWlq4qKqcqS4gaFuNOFPfKa0JhZnBgmDLsfpidj9FdMtquiCB00+ZLL/LJ7YzWWwajUahUAQCwcbGBo1GA45ewErt1KRxOdCnAajDiCcMTIN6cFFuKRVDzeISq4Uan7KclC2Vur+/n0KhIErTW1trqP9DoH+5tra2vb29vLyMkSOAOtk7v2dbq9UpFKrYWI2rKzwwgAWlA0PSV7PDzgRstPvO6HQ6EomExBNls2eCgwU3bsx7es67uy86O0v/9rdFJycGl4vKGRHCh8ViNTU1AU+zJnpUKuGxMRPloGhbLU28E/r/7l67uuSMW77j0et9pNv+2kJk1IyFxcrt25NcLrO/n0qj0RYXFzc2NigUCnAW+qR0D7asD0IKPTIixN8SBG2Ojqp/97vp119f/8EPKAEBEAx3d3cPov74lUqlFE2o9qEauNUFKwYAACAASURBVIBfXl7GHApotdr5+flv6B79g/EZ52EYtrxifuTzL7bVqvPnztra2WdkZjY11B88dOjWrVtffnmsvb3t+PHjgYGBp06dbmvB/+63v+3q6nZ3d7+bnn7w0MHIyKjGxqaiwsI333wT5+a+tIyYpltbXvn8iy85XJ6Dnc3xEyd7iEQ7W9ujR4/W1CKeflZXV0gk0sDAgEKhGOjvJ5HJiE9PLhccnXMz02+88UZyappodfXUyRP+AQEnTp6oqan9/OhRPz+/48eP81ES7Rlj4dk1B1wZHT3ymdXV6yqV6sAHf/H08iosKiosyP/kk49Db4WcOn0G39x0/Phxfz8/c4sr1dVVb/z6N729vTccHNLvpH780cfx8fFtbe2pKcm/+c1vQkLD1mVyzfb2yRNfXrpsPjQ8fPLEMYsrVygUysWLF0+cONFL7kONHud6e5EwilKplEqloP+oYyDWA2LQRP+nf/rHuvqmqYnxzz//HF3KUw31DR9//JG/v/+JkycXFgXYMffsMPHMWoJheH1t7f0//wnn7ikUCj848IGfv39JSWnm3bQvvjzm5+dndfVaWWnJqVOnfXx8rK2vVZSXvf7Gb1hMprOLa1xszOEjRxMSEru6uhPiYt988824+IRNpXJNKvnow0M2trZcDufokcO2dg6k3p4L588fO3acg7oQnJubJRAIg4NDEokEObX7+/v6yJMPHC+Regk/+cmPu4k9HBbz6NGjQUGBp8+cbWpq/ODAAQ8Pj/MXLorQYCD7QAEMw5WV1e0ddJEYXhTA0jW4pBSPyblqqiuPHfvS3d2jo729m0AI8A8wNzfHt7Q9cu4+3YNOp5ucnJTL5dvb2+3NQ5H+7ZOjj/CTdDodsEkRiUQzMzMgUMzT9fmwtlgs7uhop1AoFDJtcIhDJg0015MG+unx4c0psS0D/XREDwdN6LdBO0YBPGzCKAdYGvrFkFKhrri94fixIMmDQyWN1ReLgyzGavPp3EEg9mJzOEBaodFoGAxGdnb2ToG09Js1zj+WHKTT6dk5uSUFOZP4xFFCaX5+/srKKo1Ga2xsXEbPUuMWurq6QMBB0N3Y2Jix/Mt4JIjx79SUWqfTjI2t2dhsJCYiQWH0ElhWjUazsrIyPz//0NpID2bf2SWBoI1I5FdWTqamMjic2cBA4blzM2Fhc35+AhubwYYGjN/DYDA6Ozv3omxkvKza+vrwH/s2HnC0+lVL3h2OzOZi1k18r3Xk8CiDQETMvDgcNuClKZVKCoWSlZUFomvve14mK+p0OiKRmJWfT7l6dfPNN2eOHavKzl6Ty8lkMhKjHtXpMV5WPB4/MzMDGoQgaHh4+BHFZ5M9PZfC/4ZyLuC258zZMydPnST3USyvWFyxtMrOySV0df35z++bX76Ec3MP8PcPvnXr3keev69PQEDAgQMHzp07d/Xa9ezsrAMHDgQEBBCJvRCku3jxEovNRW59tcrN1dXCwqKyusbL0+PqtWvR0dE3btwoLy0OCAiCYTjtTsqZs2cvX7qYnZNz9szZC+fP3wtzfe7c2eUVxINFelrK5cuXLa2si4uLzK9Yol5qsm1tba9YWW8qlT7eXmkZmS/5JU3s7jp77tzp06epAwN/++QjJyfn4pLSqoryv7z/voWFuZ9/oBvOtaik7B4JaGdn5+vr+/6f/3zpsvl1m+uZGemHDh26FRrKZLHX1iTnz3+1vILYx8pk69evXb2GuOpuvHHjhq2tXVRUpI9fQGpqctLtFLA0V69es7C4kpSYcP78hVMnTzrYO1hYXAHxzoKDAq5cuXLT0TE9LTUgMBCG4ajIcHsHB3sHBwilehubW15ylDY1Nnx1/vyZM2eoVOonn3zi4uJaU1tbmJ938OChixcvRMfEWFtZNrcg/m/ML18MCQl5++23LS0tHZ1dkhLj//bpZ4iV7Ni4bE1y6vRZKWqXvry0aGVlaWNjU1tXZ2tz/eZNx1shIbdCw2KiIwtQ5z0e7jh7e3tLS6vEhIQLF84fP37czt7O1s5OiwYGd3fDWZhbePv4JsbFxsTG3TPpwrk6u7m53XB01mo0Fy98RUTdJO6b7unooIvFsEAAra3BpaXNIB47+Ojv6+tbXl5ZXFgQiUQ8HpdGpxufoU95HtLpdMRXG6Tb3FRNTzwkenRoAiF7FxYWNjY2EN7aygpwNPykH807DVIqXcu80xjqW9vWQkmKwof61hO6qAVZncV5XQwmogqC2gCx6HR6T0+PsbmZcbNg2Abl6nHuUoTDbE40e4DCJRMXk315vV2IUjPqX4aIukrf2NiYmJjA4/GdnZ3InYREE0S+68CPxaDBJ3h8wIUaHx9PT09HLFzpdAYaOYxK7a+rq+vo6KBQKCQSSd9zD9hLIBApxuWanp4OCwvbI91DpVIlEsk2DGv7+nTJyTBqTgviTAMhpkwmW1xc3NraWl5eFgqFCoXiIWPjCaZnGnSNTh+/eHHCxUV07tycl9e8t7fg5k2Bvf3izZscIpHFZQ8O0tlsJo1G6+vr28teMrGs4lXWlYi0/+1ucWK8sHJK6HpT/d7vFsKD6Xw+h4uohHV3d4PglYODg8CR/bJQCI+MIF52dEhc0af6KaGGtwhnmsPJzMxkslg0NpvT28seGOilUmtrawkEAolEotPp+jQlJjmdnJwEcSRhGOZyuVFRUbt4jTKN4q+n9L8n3RMeduvgoUNHDh+2s3e4dOlicsodFovd3oK/cPFSYECAww3HkuKi8xcu8ni8c2fP3E5KcnHFhYYEvfGb32RnZ504cQJ8fGg0mhMnTzKZiCGuXCarrKxMS025YmldV1efmpJ8+vTpq1ev+Xh7xyckwTAcHxsTl5AYFOjvHxjo6eUTFxOdlZlpbWU5OTW9rVId/uzTc1999c4770RERBw/cYLN4TjY2YaHh586fZrc13fxwvmX+ZIGG++mg/3hI0cPHTzo4+t39PBnJaWlQ4NDebk5V69ec3Fx9g8IvpOS7HDTkc1inj59OiYmJjQs3NHxxtvvvpuelnrhwkUSiTQ7OydcXTl27MtZ1B+PWCSsqKwMv3XL0cm5uRkfGR52/vx5e3sHnKsL8LDn7uZSXl7h4uLs6ekeF5cQ4OdXVV391YULcsXG8vLSn/70nrm5xbvvvhsZGXnx0mUej2dhfjk2Lv7M6dP9/QOfHz0CPAXv45J+LlUQpFpaXD527Pif3/9zYGDQsWPHa2vrBgcH0++k3rjhaG9rExsXFxF2y8vHl9LXd+zYl4mJiaFhEZYW5h8c/DA+Pu7qdZs+ct/iokC4uvzF558LRUiUQcHiQk1Nja+Pl4enV0tLi7+vz6VLl52cnOztbJvwiBKxg709Ht/q7Ojo5u6emnrHHedaU1NjaWmpUmtmpiff+eMfLSws/vrBB7dCQiwtrXk83ulTJ5NvJ50+c7aHSPz888+HR0zb1T8WY4DfY5LueapDec9nIgRBQ0NDYrH4QVBbEGcaqQ8isIKWsBtxe3t7fX19Y2MD49LvuasdAdk0we3oFlJvf1M9qSSvm4Vq/NDpCNHDYbNIPUh4pt7eXn0eDDaeHRt99IVarZ7NCBX7nB1uqRlqqRGG2UyW32WyOSwuj9jTQ6VSAbaHhoZQH3cUtVqtpXdsZgUpZ5CV3d7WPNrenp9029p56vQwEkCzu7sbuGS87zQIpbmYTCaDwWCz2Xg8HlAAYM8AGmhkZITP5wsEAsAR2dra4nK5JlRhTA0H+D1CSJ9HqcXNzU1MpIK9UavVa2trGxsbxuI5U23voUwgWHNzG7ewmE2IW3G9KXC4waZSB+vr2T09bD53oJ8VGzvd38/t6enW52Zh49lDBwjIGoVb9nPnyi/iPZynJTZXpqKjRVYWPB6Czw4ikcFkgmXlcrkkEolKo+kmJrTe3orQUKioCILhJ+3u4aj6+zXOzitFRTSUugKuwBF/CFwu0jebzUADprJYrMbGRgaDgfiz1umAQyyNRsPn84eGhgQCAcC2QqHgcDjf2HM99rTcDwBK6Wvu3Ekdn5hYXVlOTEyIioxwdHT09fXr7OgoKS3bUCiioqJGx8aSk5MuX76clZ0zi6jaFUnEoojISAqV4ujk6OzsnJWNeENOS0u771lYp0u7k2pjY8Pn8zPuptvY2A6PjBYW5Nva2QO3y0hgqs7uxob6xqam6uqattYWMplckJ8vlkgX5ueSbydvbCo72tuqqmuqKisvX74cHh6xuroaFBRoa2t7NyNjj+5z9oORp66DuHKRyxPi41dWVycmxhISEoODgpycnMLDw5ubm2rr6tbX18LCwmZmZsJCb5mbm1dV1wzy+Y2NTfNzcxEREZQ+sr2dnaurS3VNrVwuv3MnVYwyRVWqrYiIcGcX17GxsZjoSGdnl5nZ2duJCW5u7iBwfU1VJYPBqCgvr62taWtrr6utpdPpWagyLJ/PvZuRqVara2uqW1rbsrMyzc3NU1LvLC8vubvjHBwcSkvLwNfVU8/+a2kA9dkjTUxMEonEfB43PiEhMDDA2dk5IjKytaWlvb1DLBaFh4fPzc0F+PtZWFi0tLZxOexmfMvw0GBCYmJXV6edvb2Li0szvnVDobidnAz0nBQKeWBggLuH5/jEREhwkLuH59zcfGREuI+Pr1yO+GIpKy3lcLjlZWU1NTVdXd2VFeV0BiM/vwCC4YF+akFhkVarLSku6uompKTcvnTpUm5e/vKSwNHR8cbNG7V19fvmn+1O9+yE4ofn71PngB37JpoMKC2NRgOuWy0a3ADbNiCEBTB9N/EV/uRDWl+Xd3YSuRwWkwG4O4j6B8rpYXa2UiP8G4ryWzs7O/VdmxgM9bF9QtLVjcygpTgX1kA/k8kcq8ln91O4vV2jLdUDbc39AzSgGNfX10cgENioayXtME1VnaoV32f5PLYL0wCScTUxfKjpNoVCAbyl+3I7o/84HE5tbS2DydrWQsBzLAzDKysr7e3tgBMA8C8QCPZI98zMzGg0Gn2kgRFiUaIMlhUYtwNyFmw80zPac6mMw+Vkp/dxxjtrB4boZGBfzuHyWAwmDrd448aEpeUwysN76CX5SZcVzknrsIgv/JX9cH3dcEWFxPIyMakyzpslvHqVEx3JQW3EVCpVTw8SrW9kfBwWCreDgmQ4HNz2FMJitRp2c6MFBMwcOzbQ08PicB4IYx8uKkbacjicyurq4dFRLUqqA+QtLCx0d3cDoS1Y1m/0e3Y66x6WYxvvYdGT5EB1sMMwr6ZYIViGnah+cMxhdbEMdiYaZ7DRYhmDWlg54MGCtxgT8klm9lSw+766DAasjyKsTTA1/ZlieWN4UOshJh8omWLTw5CDNYKhFBsM9kp/KfXBsNa+pgw2gCduXwch+oNoAgM2uFwxfUwwtQewD6tgABj+0XaghyhFudz66t3GKMUGoI9SkNePNAdKnniODyq8WLqHy+X29PSMjIw8VsECxBW6j+IHSwNcwGGse+O3+qjTf6u/wxF1rvV1ArEb30jOSm0fGMDMnJlcLqe2uu2Wb9Ugd8lkGG2DNnd/VE7wB1obmYgpO5PB4Y40V4jCrol8zo2lhQjm7mtaTExMPHRzrNNByh39E+7eF/ZWtaXsLrvN6iexMKP8h5ejYY7BZJHaalSLbBj7AWANPcggIREYDAKBsBP1o9VqaTQalUqdmJh4UGnH/4HAy+A1UP15+mUVrkkaO8hubgIra2Fl9QiPy+CyOERaN5PFcHGeuHyZHxoqV6v3y0gDg5bJNwN9h2OiWHwem8+hEGjmdusNf/ZK/yh1+vjxtQe28YODg11dXUCpGYkqykCiGjxN2qDRGJ9+OlJaiuho757YbAaNxra3V0ZFYaJG464XFhaYTCaBQHjhWj4vqZwLuEKuqq6enJo2Rt83JfpX3YPL5TH/I0L0hYXq6mqgcvQNDo0x8BgMGr0GapXV1dXP1ujaeGD/dUsAzl4g3aPT6RITE11cXPr6+vbI8NdoNIB2fCzaVSqVyYsTqwhc6oHHpSVBd3d37t1OP5fqNnwfl8tiMJh5GZ1t+P6urvaJcdOh0bGm9phRa7Q9JNIADXEtw2Cz+YS2xZxITkbUMJmAtQAkEdjj02e0Ol1zS1tfHwVjAABfRNgjdmkymCw2Y2C587aGFAMrHjKZsH0CBrO6upqZmXnjxg0SCYm8a5zkcvmtW7c8PT0xrSBjGIOSPQoNIQja2toy+AgxaEr/Q3pubiIvb8DuusTLZzo4aG5khFfbV+vd5H6HmITHt9bVbSgf4wDSoG3TjxubmwQymc3gIF6ZGGwPHwHuNLPjbQt5fi5WQatFuC3Y49Nn1FptU3Nz/wNJJeaLCCwutqZMJpM+ODiVkTFmaTl4/TpcXY11bbCsS0tLycnJ9vb2e181rKlnm3l56Z66muqDhz6kM5BAHmKxeEUviUQioVAICoRoWllZwTIAWCQSra6uAkihUCgSiaRSJOT7ysoKKARvsUd9SBGapFIp6AWrhTUIALC/YrFYKpWuoNbGABgMBvwFLWN/RSIRBg+mgI0EGwzIrK6u6rcjFAolEolUKpVIJBsbG9jnvtF1bLoAhuG76XcOHzk6NY189oFxYkgF6AKPWB5DqTEGMJQCJVBQBUxkJyyBJVhFk3GDGDJBRoImfZSCLgxQitUyQqlQoRCha4IsNzZIDLHYckgfJNTI2TTqdiqFYTgyIuzkyVMLC0jM6r2gVH8wBogyRik2fQwSwIC/YO5SqXQnlOqDiUQigNLl5WXQmjEqTMJjOwTsRoMdYvxbA6sMNqpSqQSoe7F0T3p6elBQUEtLCzAmeuwBugv/xqDu3iFRrw1DJHJvL5Hm41hdV9XL4bK6u6jBHg1uN9O6uh4SJQZd7ONxZVVIIhAQtR4Wi8Xh0vpIhF7Ss7l+dx7N8vIykUjEVED6+/vJZDKJRKLRaFwul8NBgneCRGfx2ORW2Vgv4jvggTa0Fk2gea1Wy+Vyc3NzfX19+/v7TfapUChiY2PDw8ORoDeYg2CToE9e+ETLymaxGXSyR1LD+3aZXu0+g2RyFD74LinjStYlIu1ZLuvs3GxVVyWHzeFyWXQ6J6eARyESNGrVk8/vCWrMzM729PRgy0qlUsGy0ul0Ho+H0bXAcm0Sh5N4eMAyGaY9p89p29raYjKZGRkZ3t7eJvw3PsGgngHoy0v3UCl9Pt7eGRmZXC7X0dExPj4+Dk0JCQkeHh7+/v4JaPLw8PD19U1MTERjQ3rGxsY6OTlFR0e7uroGBwe7uLj4+fm5u7u7ubldv37dx8cnMTHRw8MjICAgIiLCxcUlJibG2dk5ODjY3d3dG02urq44NJmbm/v5+UVHRzs5OcXExLi6ut66dcvV1dXf39/d3d3Dw8PNzQ2NYOqCWGZduRIbGxsVFeXs7BwbG+vt7e3v7x8QEODj4+Pp6env7+/m5ubl5eWKJkdHxytXrkRFRcWjKSwszMXFJTY2NiEhwd3dPSgoKC4uzsXFJSIiwsfHx9/f3w9N3t7eTk5O58+fNzc3/+KLL0Cw352uZONyGIbb29u8vb2rqmsJRKKbmxuG0vj4eBwOFxISAsaDw+GCgoISExO9vLz8/f2jo6OdnZ1jYmKcnJyCgoJwOJyPj4+3tzcOh7O2tg4ODk5ISMDhcLfQ5ObmBhAbEBDg6enpgSYcDufq6urs7Gxubh4SEhIZGQkw7+TkFBoa6uLi4uvri0G6u7s7Ozvb2Nhcu3YtISEhPDzc2dkZYCYkJAQgFofDBQQE4HA4Dw8PgFIHB4fr16/HxsbGxcUlJMQHBQW+956bpWV8YmK8q6traGhoXFycs7NTTEy0h4d7SEiIp6cniGtoa2trYWFx9uzZixcvAkVIY9SZLAE/vqamRkcnx/bOrrq6Ond3dwylaHfOYWFhoASMAWzdoKCge3YNLi4ukZGRYO/hcDgvLy9PT08cDmdlZQVqgQ0QHBzs4eERGhqKw+H8/f29vLzc3JDApW5ubi5ounz5cmhoaHh4uIuLC9h+4eHhrq6ufn5+Xl5eYCcD5F+9etXOzg7Rjw4NdXV1TUhIAKMCvyY3Nzewsd3d3cF62dnZXb9+HYw/Pj4+ICDAw8MjISEhNjbW2dk5PDwcbImYmBiw+jgcDqD06tWrFhYWJ0+etLKywkyyKyurd9JrNoneJ7p7djkIdTodh8NpaWlhMh8TjHOXRvb9CsjIwFz4fD6J1FNeSLQ5X5wYiS/J726sJddUdVRW1G1sIO6snlWC1sXC4gRWSx2xp7e7m0DsJT1zysB4qBKJpLm5mUqldnd3d3Z2UqnUoaEhEFuqrq6uq6sLI33G++q72ttEqCa+vngauFACnLa5ubn+/v6Ojo7xB34rDHpUKpVMJhOPx/P5fINGDCC/jkcgIwPLymKyesm9FvVnAro8be58yDv9xeANO78y15zWXO229hn23jBea5NvndqW3EPsJRC6BwZIis19CSh1up0cQxuPVigUtra2UiiULjT19/ePoKmxsbG2thaQRAwud7C2dun69f7iYjlq/2WwIliMv+np6f7+/paWlrm5OeO+nmfJy0v38HncwMBANoeLx+Pj4+P1kSKTyTBXqjKZDCi1bWxsyGQyCIKA60+pVKpSqSQSyebmpkwmk8vlnZ2dwHMUqK7RaACkRCIBNh0KNK2hSS6Xt7S0AHd8YrEYgiCpVAocUG5ubq6vr8tksvX1dQA8NTVVX1+vQdnjEokECdCjUAAlSoVCAbpbX1+Xy+VSqXRtbW1paam+vh7TNtje3sYOpvX1dSDjl0qlWq0Wa2d7e5tGo/n7+yOxWvv7z5w5AwwEdrozjMthGKaQSSEhIZNT03l5eYWFhfoofWDngpRhY5DL5ZubmzqdDkxKLBYDn2MAUVKpFI/HA/dioLpajbir0mg0UqkUoF2GJoAoqVTa1NQkFAp1Oh2IUyORSABKN9CodQAM/AURcIAaJnB5LpPJ1Go1QMja2hqIXyiTydbW1tbX1ycRH5KtmHhic1NZXLwG3L+tr0s1aEgjsNwy2ToIXgNBUFdXV2BgwPT0ZFdXx5kzZ4DprDHqdiqBYbizoz0oOHhpeSUlJbmmpkYfpVKpFLMGAjNFrfdlQDgC1hfsPalUKpPJFAqFRCJpamoC5BcAUKlUwKB6bW0NoHR9fV2hUKyhSSQS1dXVSaVSgFKdTicWi8F2AvYsAAz85fF43d3dwG4c7DfgMg7bnxsbG+toWltbk8lkQ0NDbXp6kaB3GFUXkkgkQGoANgZYfeCpDI/Hh4aGzszM1NbWXrhw4b62Aeq/54XQPcCL2uLi4tzc3NNrz+iv717y2AWp1WoplD4ajUanMSqKCd2d/VGBjWF+1Qw6a2vrYbSsvbT5WIoQmhpU3fGSEOpm5uZmZ2f3PevVZfnMlGQvQwKRj/v6+gYGBqamppaXl7FBrq2txcfHR0VFocwn3iS1UVTjOt9XZlK5Ry6X19fXKxSKpaUlsVi8sLCAHZLGwwDeDufn57G+jGG+phIs8temUkmmDLT3d6R23nZsx3H9bnAdHOmf/G2+vkn1gJX1TMag0qrusJOT2hLCuoKn56YX5nfDzG496nRwTg4SXXVvEjG5XN7b28tgMKanp/U9aC8vL4eEhKQkJ7N5PA6NJjI3ZwcHCy0tgeW8wQBWV1cbGhq2trYWFhbW19cxCy8DsOf5+JLSPRCawLXR3NycmJiIIWUvu1wfZqc8aFD/LdaFfgYAGP/Vh8Ga0gdDLwgDbdRHKoE5gr/gOsEG82g7SK2BAVJYWBDQUVhdXbW0tHxSukcfpbm5ucXFxdhojLvbbdzoxAzqYo9gIvqPBnn9+WLA2MT3AozBGNcyQCl6tkL31P4CA6G1NUgmg+3tIYw4aWpqjowMIxCYjo5wefnE9esWKytIDG39RvaS12gQS42UlJS6OsSDJTZBbJwgg43WOGMAiaEFK8eqYCX6GewtyGCP+jBYHnuLZbDu9Ev04fVnhOX1MwAYgqDKysqIiAgejwfEOlZWVi8D3QMYCVNTU/umADBs7DsjWFpsbMAX5RDSElpZTDaNRq2qwOObnlgOYhxn2+SQdIJpWPtUqh7KTXVJNj05iri6sldeFLYlDIakUqkmJiba29ubmps7W5tniXkbYsT3t8lUX1+fl5dHp9ONAzyZhMfiVJh8+3UXTk7OVKSEhNW6h7YGd9A76L1UvrX1/J07T9qvvh7YTnWFm8KKsVKpaq+UqOl2mpo0oaHayEi4FgmHt5e007Jubm4ODw+3d3Q04PG08HDJpUvbLYjLNJOpsLCwtLSUwWBgDAuTYM+t8KWmewAWmpqakpIQHzlLSwICEQQx0BEIhN7eHvoDlXXaQH9ubp4OghobGpqam3VabVVlhYenZ3tHh1qtLi8rCwsLKyktValUJFLv4NAwi8X09fUpLikBpNXC/FxsTExUVFT/AE2n0xUW5kdERubk5q2tr2M3GVj+vfyFYWh5GeJyoe1tzGe6YT2TzYJ7+sHVidSdmYGnpuDGxiZz8/CurhECgcDn84OCgs6cOQN4V4btPu4ZoDQ3N7e0tBSG4fHxMeoDCToEQZ2dHSIx8rvaVqsL8vMGBugSsTg1NXV2bn5lZTkyIiI8IlKwtLy4sJAQnxAVFU1nMBQKRX1D/fq6rKiwwC8ggPPAxX57W2t4ePjdjIy19fWujo7IyMjo6OiSklIQ1uNxw0Tem0TRXioCGIDMqiro7beh996DKiogJyfoiy+gqirE7Ky4uDgxMXFiYqSysueXv6QdPuxtaXkB8K723oU+oZCamlpff9/SG4ZhMok0NTVdXlaalpbG5d0PRkgmkYqKi5XKrRrU/B4NlsKsRQ8gch+ZyebAMFxfX0ejI4YYErEoLi42Mipq8dk5nn4mKNVHL9irqB2fuqgoKz09bXp6iEolkcl9bm5uV65cwQw3XpScC2x4/fhcz+1s1e9obnYxPLDM3bYqIQJfU9NUVVUpW9sdWgAAIABJREFUECxi7EB9yMfmAQ/psWBPCQDB8DBveYA8o2/Wt+82gVvI/v7+hsbGUTQsj8mm1tbWSktLKysrgbMlkzAGhS+W7tGVVeJxF2JTLoVV4hLLExsb8Csow9VgkHt5fD7LCovF2lu3dLduwULEeexTJgiC1tfXe3p7G1paph/cxcZtCoXCvLy8hoaG0dFRY48DxvDPoeSlpnvAvYLH4zMyMmAYjo2Jyi8oEotExcXFRz//vLSs1MHeQaFAZJwRoSHf+/t/4A8O/sfvf3vF6mpJSfHfPv20oKDgw0MHa2prjh7+NDY27ty5s/GJSb4+XvHx8UePfBZy65a1lTWHg3ybNjU2/Od7f0pLS/vwo4/IZMp7//mfiYlJx499mZScuo+rAoahmBjo3/4NEolM0D3gPja4PMD9sboKLS1BVCq0uYlURFzbWW7/y7/kJScn/OpXsuBgmM9nBgT4Dw8PR0dHAz+YT3RJP+gUzs/PB8wJDw+39o7OmenpsrIyxcZmSnJSyp00RByzvv7r1391xfpaU0PdK69+q7au3sL8ko+vb2CA/1fnLxYXFh48eCg1NfWDDw7gW1pOnDiRlpp66NCHEeHhnl7e4KD86uwpV1c3JydHG1t7Npt98cL5vx440NVNQEW/+xj1fqoIBFBYGGRjA6nVUFIS9P77kEgEazTKrKyUrKxM8PXf3d0VGhowOzsZGBj0pCpT2JgQl9zp6VjgBbFw1d7Orqam+le/ej02Nu6Go+PmJmLU4YFz/cm//JRGo735m19fvGyBBj+x+NnPfr6ysurp6R4dlyCViH75i387+9UFlVp9/Zo1zt3DDYcLDY8EBwHW3QvJwDDU3g6VlICdCbFYUEMDpEMdPW9tKXx84m7eLFQq1fX1UEJCQ2Tkrenp6cDAQLEYcaj4AvWaQe/Af49MhggZQclz/ltdVXfpTEB+Zk9vDyU3L3uPwaee8yANuoOg3UKBGQDv5VEoFGZnZzGZTJPAy8vLBQUFFRUVCoVCIBDoS1VMwoPCmZmZjY2NF7Wsfb6+nM+PEYKu5eEzC/OLMU+Juwx4l1f631G7gD3tK7kcfuCF+WmbQuvPzc2lp6cPjyKuLw0SBEHz8/NZWVmNjY1AzvWUKDJof9+P/wXonubm5tu3b+t02isW5gwmy+qKhYeHx5/+9N4AneFgb0fuo8IwHBcT/ea/v+nu7nHgwAE7ewdbW5vKGkTokJQQb2/vcPLkiaVVIYXce/LUaQ8P97sZmQnxcUeOHnVzd19Eoy3W19WaW1rDMGxvZxsXH//hoYO2dvYffPCXsorK/dE909MQkYhctxjzBrurWCxochIp12gQ4cv0NNTXBzGZEJ8PvfMOdPgw9E//hLjZHBqCs7I2zp1LyM3N7uvrKi0dz80lBAcHANm5j4/P09A9eXl55eXlCrns3LmvGAzGyRMnTp484ebhyaANWFpZ6SBYtr52+LO/HT58xN7W9p13342PT/j00882NjcVctnnRz4LDPC/6egMw7DNtashoWEXLlygUKkXL5w/e+ZsQUEh+uuFLpz/qpvYK1xZ/uCDD4QiSWZGmpu7B7pNMUx8jRkYhra2oI8/huzsEFQrlQjXB3UtIfTxCXNzK6uubl9cXKirqwsPj1AoEN0aFxcX/TCNTzQ4IOcC/B4Yhrs6O+xv3Ghvbfl/b73FYDItzC/z+EP3NlKAv+9/vP22h6fnu+/80d3Ta3Rs5PjxY1aWV+5mZt8KCbmdcqewINfK2vrYsS+bm/EffvSRWILw3raUSsQH6otI2O4FxLq7O3T0KIJPuRzy9IT+9V8hOzuYyVzy9Q02M2s6eLBjfn75F78otbKK295GghS6urpi0ooXzu+Znp5eX19/UWx2nJtrbEwCj8fv6GjDIhbt+9TeX0VjGZlarcZU4vbX5pPW0ld6069Lo9FycnK6u7uVqNn34uLi3ukemUwGauk3+HzyVra2Na6uE22dnd09S8s7Cu++1sEYLCsEQcDZ5tfaqX7jmHKhfiHIEwiEnJwcEokEDOxnZ2e/oXv2dJDDMNzS0lJcXKza2vrq3FkOl/fhwQMsFuvMqZOkPqqzkxO+FXFJGREeZml55ac//amVpfXNG47ePt7ePn5yufzaVevQ0LDjx75kcbh30+5YX73u7ekRGhaenZ1NJBI+PHQw7S7CSWqorz155twqEjT0ZG5e/oEDB8orKr083C2troL129NY9YDAPYFdG+ANoHX+8hfIwwO5PLhc6Ne/hl5/Hfr2t6Ef/Qj685+hV15B/pmZQd/7HvyjH62+8krY//k/+NBQHZm8hMdXxsSEg1tEp9PFxMTsT78HfFIUFhYijstWVy5eutzT0/OH3//ez88vKDhkkM+9dPmycksllYi/Onfu7NnT//E2ErsgPf3uJx9/TKMzGXTaoUMfxsXGXDa3WF5e+vKLo2np6adOnSotKy0qLs7Lzf79H/5DsIS45Th75nR5RVV3Z8dHH3+s3FKlJifa2Tvsg4jUQ+pjsgaMNJ0OIT3ZbKi4GEpMhOrq4I6Oqfj4wLt3SW+/rSkvXywry01JSQYnpkqlCg4Ofhp+T2ZmJhEVwiLsw4YGR2fX6amJ4ydOrK2vW1y+SOkfuCc99PHysLSy+vUbb1y7etXL2yc5KfGtt946cvjIJ3/71MnJMT4+8dSJ459+9tkf/vB7NzePo0eOEHpIxO7utPS7QEXyMfP/Gl6j7JyH7W5sQKjlAHTlCnTuHFRWBv+P/zH8+98HEQj04mIdi7WQmZmempqxuYkoPG1ubgYFBWF+53ahe7DjEusJK3lWmRcr59rY2Lhw4WJrKxJovbq6GjNieFaz22M7Op3OgOwzSfeIxeLnH0xgcnKSyWRiWswLCwt7pHteoJxrfn7+q6++6qXR6Gx2dXX1i4o8pdFo9Jd1J7pHKBTqg+1xzzwl2PDwMI/Hw2jrb+ge7Ih7TAaG4ebm5pQUJFAl4O7cTkqwtLQ8d/Ycm8O1uX59aBhhr2VmZNy9e/fy5UsFhYVBQcEzM9MXzn/1xRdfWl+9Njc7d+2q1bFjx0+dPs3l8ZOTEkvLyoKDAk6ePHn6zFn+4CAMw1QK+S9/+cvJkyfdPTylUumZ06cvXLhw7NjxktLyZ3tV63QQjYbweMbGoM8+gy5dQoQvP/kJQu787GfQW28hBNAPfgB/+9sTZmb+ZmbNP/sZ/fvfh37yk/wjRxKVyvt2HxqNxs/P7yn5PZWVldtq9b3451wuz8/X19zcvLqmtruz3ckJYeSsSyW+Pr5309Pd3T3j4+IIPT3lpaWffvrpkSNHi0pKKX2kQ4cOnTp1ytfff3Jy0tHJqbu76+SJE6dPn/YLCARRfnx9PI8cPoyEnapvhGG4tLgwPCLi2SLTYOtsbUEqFUJQAobE8DA0PIzkz52DPvsMnppiHzvmn5/f4ePDc3XdSk5Oyc3NVKuRGxoJxKFQ3PN08DT8ntTU1MZGZKYwDNNp/deuXZ+ZmXZxcVlYWLh06SIIZpKYEJeXn29ja1tQUBAWFnbzxo32zq6V5eUbDg4WVyzDwsKcnJzm5hd6e4h29vbl5WVHjhz95JNPamofqksbTPnrewQ6ahYWUEfHfZRCEMKV5PEQPmVODvzzn8O//nXfd7/r//rrnf39w9vbm7dvJxYXlwAMwDAslUrd3Nx25fc0AWCtRtPb27u4uIhsDxjWarWjo6PPVnLxYumee56aDxw40NHRQSAQgMc2pVI5PDz8kug6YEsGMnw+/zlzpHQ6nQHDZnp6eo+iwBdI90xMTHzwwQcDAwNtbW1jY2NIGEe5fHR09AWqzxsspf4jg8EA33X6hV9rXqPRGCzrxMTEN/yePR3agN9TUoKcp5UVZRmZWcCNoU6n43JYvn5+wPPVPVIAeKsEUXVAuMGlpSX1NnKxqVQqzDZ7e3sb+MhaWloC5w7QpQdGvIAyxczav457GlzMfD701VfQ4CAijmGzIS8vKDkZkX+9/Tb8u98NvPWW/3e+M/Gzny3/+7+P/vCHyX/7W1ZKigaHg6nU+9q+8fHxT0P3FBUVdXZ2Ilopd1Jr6+ohCAIm3EkJcY1NeDBrgCgNmoA/BolEIka1nnVaLUAXcMwAvtI2NjaWl5cBJHAICyI7gtY0Gs329vaelvwJgTA2z/Xr0NWrkFgMzcxAly9Df//3CCMNh4MPHoSPHOl4883gv/u7pYyMmU8/HfrXf40qLi4/fRquRMWYwLQ7NDT0afg9WVlZvb2IKzZUK1zl5eU1MoochQw6LTAwCOwrBKU6HYpYnUql3tq6r2uiVquVyq0tlQr73gWcaokE8XrwdWzCx+IYhiGBADp/HmptReierS1Em+e3v4Veew1KSoK7uuDvfa/5r38Nt7UVvvnmGInEDwsLBmQfaPmeMpNSqQwNDcX4PVVV1V1ddKkUXl5GDOvKy5s6OpBQ8zAM11RXffHlF45OTlsqNQzDvT2E3/7+96vChyHTAdjT/AX6PS/wQvL09Hzvvffs7OxAXAWpVFpSUgJ+dPrzAgwYkUj0RI53se9p/aaeJg9BEHDj+TSN7KUuGDk4LvSlM4uLi9jO2b2dF0j3QBBkbm7+/vvvg88bJDCwQFBeXo7ZMIKRg9lpNBrgD2X36ei/NfCCo/9qf3kIggwIkf2189haBsuKwc/Ozj5/ViLWu37meev36Pe9lwMd8Htu374NSJnFxUVM+UsqlQCtSVBi/Bf0hcGD7nYaAFauD7+XEe7lCjEWeIESGIYGBqBPP0V4P+++C3t6Qq+80mRmFvKtbzF+9jPVuXPK//2/4z09K8bGYIEA/utfkbsHuLQx0O/RH/wexgPn5eUBey65XLaEiqWQmeqg+fkFjDp5BF16viiwi00fvfpIMwbA3j52bE8EAMMIg2d6+r4aeHk55OODmG5ZWUH/638hcsOf/AT+4x+1P/1p6d//faSZGc/aWru2Jv7qq1tvv922ugq7ucHt7YCZgXyoubq66vN7nhSlKSkp+vZcwtVVcPyJxWKJRPoIMrGmH2wLvYKHZvD7RhpGCD4RMh+sGoIQtOuHmYUF6OJF6Mc/RkjzoiLY2lr905/mfutb8e++O/r55/CJEwt+foEUSg+2H8DIJRIJDofD+D0VFdXV1fSJCXhwEJqehjMymmxsrgMeT1BgQElZmZW11ezcvFgkdLC3/+STT4AVmz5m9p2n0Witra1DQ0MYWbnvpvZdcWNj486dO5aWllFRUVNTUzu1o9VqdTodn88Hm8c4XpjJivuzCzPZFFa4x6gOGPz+Mmq1Wn+fY40sLy/39/c3NTUZk4YARqPRkEik7u7unRwbYk19rRmxWBwTE2NtbZ2SkgI2s3F3IKiFSqXicrlA0rQXq3UIgvSRY9zsPkoMNIH20cIeq+j/0LD1vWfGsbCwQKVSm5ubX5SoFxv/c6V7AOuloKDA39+/trYWYGT30xmG4ba2thbUMYAx/DOhS3YfwNO/VamQz+UHdxyS0WiguTnktt7YgOLjoY8+glxcYHNz7Q9+kG9llfjhh4uvvdb1859Pffe7wZWV9wPqoj8V7EKCs7OzMf0e4JG2ubkZSMQfO2BE6oS6UsAuY6wK2BbY40uegWGIwYB++UuIQECwqlRCCgV05859rak33oDPn986eTL5H/8xy8xs/q23up2cRgMDfevqqIODsBrhLGBXOzLvpKSk+6IWCAntyWAwWlpawLX9WDzAMFxQUMDhIIboAFg/g+Uf287TA8AwpNUiG2x/CcOJTne/ERiGJiags2cRpbTcXPjKlfV//ucYM7Oiv/u7+VdfJf/oR8PXrvm2trKMHT7odLqkpCRMS6OsrDoqil5bC1dUQPX1sK9vc2Zmhhr1sp8QF+vp5XX16lUOh1tfV3v27Jk3Xn+9CeU7YufUvjM6ne727dvOzs4kEunroA+eaGA6nU6hUOjfBPrV9ctBXqfTCYXCJ+L96Df4Xze/tLSUmZnp4OBAJpNNzkImk4WFhXl5eYGo8iZhnlvh9vb2LstqPAyNRgPctxq/+u9dsrCwkJKScvPmTXBUvsDJPm+6JzMz09rauri4+OLFi0DUsvsBjWiJNjXp+2s2oH6McQcaBJcNeLt7F8/wLXZtYG3CMBQUBH34ITQ/j92yCIviF7+AHB0Rg6PXXoNeew3+9rfX/+EfYj/6KG92VnHkCGxmNvX97/vV1VEoFNjLC5HdgJbRv7BGo/H09ARyLhiGi4uLT506dfPmzcuXL2M3NzYA4wwMw7m5ucBfM4ZM/bsZy2M4xE5kDN642edZgvEkxGIoKwuKjUWUl996C7p5E8m//jrk5AS/887qa6/d+ta3qs3MlP/zf8IXLvB+8QvfGzeGDh2C//hHqKXl/nKAGa2vrzs5OWHxm+7evXv27Fl7e/srV64Afvvus4NhODn5vr/mnVCEbVTQlAFud28fe6u3DbCyhxnwNiEBunbN0HfUA3rsIbBBDoaRKiEhCFMHhhH8nDqFbNHTpxGW5MYG9MknsJnZkpkZYrplZqb89rfhV1+lnz/vOzo6ceAAHBHxEJ8AAyKRyMnJCeP3lJVVR0fT6+rgykqooQH282tub28FOJmfm/Vwd2/Gt/T2ENkcrkIuz8vLE4qejZxLp9PduXPHxcWlpaVlj6ITbKW+1gxgA2DipK2tLQxX+v1ubGw8c3mHfvsvYV6r1fJ4vJycnJs3byInoKkkl8sB3dPT0/OiFIpNjQsGYRMxcdLGxoaB2At40XwiOslkR//lClUqFYvFSktL+/9dXNJ7/B4vL6+cnJylpaVbt26lp6djV6zBKYw9wjDc2tpaXn5fv9h4sYEo0TgqMiZifGwXWF9PmYFhqKoKCgx85MqBYaigANE1ARctuJk2N6HSUujf/x0x3TIzg83M5s3MgszMWr/7XdL//b+Dr7029I//6HvxIlethpuaEON2jGbC7tTExESM7rG1te3q6tre3vby8sJELbvMBYbhoqIiELIAaxBQVAC9Ou39sDJ6b5E3AKWYBs8uXXx9rwC3bHERc3EEVVdD3/0u9MMfQq++iuDz1VeBNdzIq68GmJmRzMw6X3ll6oc/pP7zP/t9+9tTZ87Abm4IecRkPryngegwPDwc6PeoVCpra+v+/n6lUunq6gq0dnafEQwjHLi+vj4ETYgoEvwB6EQ4QGATojhE+r3/4gHc3lGq0yEuc9Tqh8wYtDXQJlRZichMv/c96NAh4Fnn4aj3QvdoNJCrK+Tri0gPKRTI2xuKjIR+9StEoae6GjYz45qZ+ZmZ0czM2n/843lLS8J3vhN45w5iu5eYiIgXgXcG0CWQSoeHh2OkhgHd4+/fjMc3Y2jBaj3EzLPLdXV1xcbGDg8PP1td6X0PUKPR0On03NzcvLw8EAkHdfyofeHsqH3P6JlXXFpaolAooaGhwDurcfs6na6hoSE5OXknAOMqX3eJSqWiUChZWVnFxcVAYv7NshrgfGFhgUAghIeHY2xgA4Dn9vi8+T1FRUWXL1+2tLQ0Nzfv6UF0Ah6ezaZyQM7V3IwckVKJOCYmemxstLq6antbrVKpREJhdFRURWVVXV0Nzg2Hb2mNi42prqntbG8PDw8nk0lhoaG3boWqUH1JU80/szIYRm7i3/0OuQCA2x4YRtzzMBjItzKZDGVkQF1dSIZOh6RSKCAA+od/QDg9ZmZ81HSLjhJAcjOzvl/+0jsycnJkBMGNUIh8dhcXP3JJwzCclZUF5Fz3fAy6uLicO3fOwcHh8OHDe0RpaWkpjUaDYXhxfi42Lm58bLSxsWkbTdNTUyEhIdXVNTXVVVGRkSw2KyQkuL2js6a6KjYujs1hh4eFNeMRf+TPDHdP0hAMIyLCP/wBiohApIeZmdB3vgPIR0D0wN/6FmxmRjYz83vlleHvfx9+7TWZmRnezCzwk09WQkLgrKz7JIL+8MGPLTExEXDL7nGh7e3tL168aG9vf+TIERaL9djJwjDiChJAjo2NJN1OHhocbGtr397eBl9+7W2tDY1N5WVl0dExm0olvrmpGd8yyOfl5uYNDfJDQ0OpqKH7LpgAAw4K+v/Y+w74Nq4j/ZUt6xzbaXaSi+0Un+3E53+SsyM7cs85uYvlJsmybEmkJKqQVKHYuyj2Toq9N5EUexE7wF4BggBIsPcKsFPsJNgB7Pvz7ZArCCyiHFlOLlr7Rz0s3u6+nfewMzvzzTekhQUGMEVG4huxtycDA3FjYQFb24cOkfv2rf3f3Hw7u21wECO+2ezbqwguJG8z9fbicOHCAqmrS16+jHsODGAb6Ngx8je/Qe++W0AQlgTRQ63SyV/8IvX//T/7J56YpEi/sU2/axeZkHDH+WUymaenJ52Vs43dI28Xrg9szVi8L89BiURy69YtoVD4HeKa4UYAXn39+vUTJ05ERESwWKzvyhSTUqkJIpGos7Ozo6Njbm5Otr7BdNCSB1gulJAbGxsbHx8fGhrq6+srLy9vaGgYGRmZmpoSCoWdnZ3T02uV76anp8fHx6enp3t7e0Ui0eDgIBQ0nJubUzg5WAbycBC4LuCab926tY0tCNEikUi08Zz04B9MQyqVtrS0eHt7q6ioxMXF8Xi8bYb9rQ5JKpXOzMzAdHR3d0MpQJhYhetCuvvExMTMzMwYtQ0MDPT09LDZ7KamplVn7dTUVDe1icXipaWl2dlZmNapqane3l6hUDg8PAzTurCwsHEKQJsoXFQkEsHlvnMX5oO2e6amplxcXFRVVUNDQ2FxbPOsB2kyGAzANQ8NDb737jvaurrmFhY+3l5qamo5OXnJSUkXL13u6uo0MND39fW1sbExM7t6xezKwYMHi4qL4+NinF1c76q3th/DTr4FjK2PzxrWhBo55rd97jm8p68PR2Eee4zcs4d8+mnynXfIxx7DGvqxx0oIwoogBARRRmkU/PF73xveswd99BE5PY0J4vT0AM68NgpwTly9epX294yOjnp7e+vp6RUUFOzEc4AQxjUnJCQghNpbW/74xz8aGxvZ2ds7OTlevqxZUFCYzWAYGBpcuWJ68NAhFpsdGhxsdtW8vb3t7NkzYWFhR7780tbeARb0TiRzH/tg7LUMA01eegmn/VtYYLDt44+vkR5RAkQEkU4QDgRR9+ijFc8/j3bvTiEIJ4KYOXsWrwJ5c4ceGCSg0rhmqiLKkJubm4GBAYvFoh2HdP+NDYSQv79/ZmYmQqiSV/H/fvc7I2MTFxcXWxsbPT39MlbZ8WNHnV3dmpsaT506WVpaeujgAf+AwKgbERcuXowIv375sqa2ju72F4KR29jgsKmREbaGKyqwjfKXv5CnT2OD+/p1bOgsLWGr+u23MbLbzAzb2Qjh5Xf4MDa75W8fLEiRCNs6JEn6+GDXztAQ+be/kW+9hZ2UH3xA7tmDKJdkHEFcI4gaguAThIwgYl56ye3IkblDh5CuLmaG3L8fp7uPjt4+P66zcSeueXu7Z6NINz5JFZ6h9/TxQeaxy2SyTR/r09PTTU1NFy5cMDQ0FAgE9/cGt5cGSZIDAwNTU1NLS0t9fX3Nzc2FhYUhISE3b97k8/kJCQlMJpNDbZWVlRwOB0Iz8/PzPT09NTU1hYWFmZmZZWVlJdSWn5+fmZmZnZ3N4XCKiooKCwtZLFZCQkJaWlpeXl52djaLxcrJySkoKCgtLS0pKYEE/rKyspSUlJKSkq6uru7u7s7OztHRUbFYvLKyAvWk5W9h57yFDyyfayvrYXp6urq6+uzZs5aWls3NmJ70W9o2LhiJRNLX1zczM7O4uCgSiZqbm7Ozs4ODgzMzM7lcbmxsbG5uLofDqaioEAgEFRUVkEMuFouBLSk/Pz8rK4vFYhVTW25ubnp6OhwC1ddZLFZsbCyDwcjJycnNzS0rK8vJySksLISVQE/rzZs32Wx2Z2dnT09PZ2fn2NjY3NwcXftZXhr/ovw9mFjZ3d3U1JTP56uqqt6kSiVt+siDnSCynJycmJgYhJCwp9vAwODsmdPHlU6EXw/T1tbJzGJkZmRc0tAoKMj38fUrKS4yNjYyNMTlEdTU1FJS05ycHFvbOh6A3bM+YPlHPyZ5++QT8osvSENDcu9e8t/+DfskHn8cYltSgoin1MkUQYxQoa50gnAmiGmCQN/7HvnKK5jjh34jp6UE9+Lu7k7bPYGBgaqqqi4uLseOHQMaCbrzxgaINDo6GkptN9TX6enpf/bpJ5e1tH19fYyMjIqLik6dPJGQmGhqYqKqqhYdHZOUmKipqcXlck1MjHV1dDU1NY2MTRYpvv+N5/9W9yCEfWnvv0+++SbOqf7xj7Ed+e//DnYPIohFgggmiECCWCKIfoIYJIgYgvAGfI+e3powN44QSnbb2trS/BZubm6XL192cHBQUlLq7+/ffv2ASENDQ8HZVs4uNTMz/etf/2J6xczDzc3cwtLVxemjv/3trKoap7zcyNBQS1v7Lx9+qKOr193dZWxkVMnnnzxxQktHF55rG4cnvwfWw/Iytk4sLfEdZWXhzDUrKxy58/bGTN8IkRERWCY/+xn56ac4IgbYHdk66zPECnk8fJJjx8jz58mPPsK2+C9/SaqqYrt8zx7yhRfIRx9Fu3fPEYQvQVwnCClBiAjiFkEE79oVsmvX8u7d6Pnn8ZLevRsPIDt77UIwWuAttLOz20mcS/4G5dvyT8y/s/1t2z0SiWRycnJsbAxcFwoqampqChibTE1NIyMji4uL3dzcent7v9lNDQ4OdnZ2VldX83i8qqqq9vb24uJiNpvNYrF4PF5FRQWoLoFAUFNTw+FwampqeDxeRkZGYWFhQUFBcXExh8OBY2tra6urq+vq6gQCAZ/Pr6ysZLFY7u7uXC63sLAQrBzYX7O+1dbW1q1vNTU1tdS2/mVNVVVVdXU17Ke/pfsIBILy8vIyaisuLs7Pzy8oKMjPz6+urm5ubu7q6qIdcv8gds/y8vLExARNK6AwrQMDAxEREVYajYlQAAAgAElEQVRWVubm5gkJCfn5+e7u7pBifK8zK5PJent7YVrLy8tramoaGxuLi4tZLFZZWRmPx+NwODk5OVwuF6aVzWbX19ez2ez09PTCwsL8/PySkhIOh8Pn8wUCAT2tVVVVfD6/qqqqqKjI29u7oqKisLCwuLi4tLQUprW2tlZ+mmBi6T0wrdXV1VVVVdCG/grtqqoqNpsN0wp2cD611dfXNzU1gdsJBPKva/cYGBjAa7GHhwdUWZd/0sm3KUlhJEReXh6DgSnOxsfH8vLzG+rrY+NwTU2KG23Cw909LS2jublJIKheWV7y8fLKzs5hs1guLi4dHR1MZjYYufJn/mbtrbwFW50N+tfXY9UCXDJPPgkRGUQQC5Q6CSGIJoIYoxwVMQThQRCL3/8+2rULKx5bW4wWgvf1O1/T8Y2HhITQcS4VFRVIajA3Nweio62HBGJD4IzFhBMD/cUlJRUcDiMLRxIRQvW1tfr6+impaVlZmV4+PnX1da4uLgWFRRnpaT4+vjXV1R4e7jl5eWuIla2udF/308YfNOLjMXKZIMgf/Qi70NYxUpOUXyeGihvOUHo6cPfu0AsXZMbG6MUXyYwMrP433cCs8fDwgDjX/Pz8yZMnATdgYmIiT0uz8fA1gVJxLig81N3VyeFwWGWleXkF1JllS0uLlXx+fn5BclKij4/v0PAwj1tRVFQ8NTWZnZ3d2dHh7u5e34D5MzeeX2EPvQhHRnDyGnycn8eOn8hI8uJF0tER75yawkmC//mfmBqKlh59KoTI4GDy0UexBWlgQD77LHbt/PCHIEn6L6LMcTuCSCaIFoKYJwgJQXhSpiQ23HftwlCqF17A1tKLL+LLzczcljDELzw8PHYY56LHJt+gZfv3N76Z3SOVYszN/Pz8yspKZ2dnVVVVc3NzR0fH6OjoyMjI5OQkRKmmp6fd3d3Pnz9vZWWVmppaVlbGYDBqamp6eno6Ojpqa2urqqouXbp0/vx5R0fHtLS0lpYWPT291NTUnd/X9PT00tJSc3NzRkYGvIIzmcz8/Pzs7OySkpKioiIwZeDlns/nczic0tJSFotVVFTEZrMrKytBV9EKjFZp1dQGH+vq6vLy8sLDw5lMJpfLBf0Hdgx0u+vfmpqa7fvAhWjNWltby+FwMjIygoKCXF1dV2u6gUy+PbsHcshXVlbm5uZWVlZaWloEAkFzc3N3dzc9rWC8Dg8P29raXrx40dbWlslklpaWMpnMhoYGoVDY3t5eX19fVlamrq5+4cIFV1dXJpPZ1NR0/vz5NZDfzqZ2fHx8cXGxtrY2PT09NjaWyWTm5ubm5+fn5OQoTCuXy+Xz+eXl5fS0gpUDJs5W0wpzV19fn5WVdePGjdzcXD6fT0/oXSdr+6mkv6XnlJ7W6upqNpudkpLi7+/v5uZGo3n+de2eioqKQ4cOqaioqKmpbfMmjRAaGRl2cXEZG5+AdQBaYavlpGCJy3eTf5L+PW0OB1eW2Eo30QqJGgnulp+Pi2J2dWHPxKOP4qpblL8HUX4IO4LIIohZgiggiPF1L4WEINAzz+CI2J//jN/dP/sMh3I2XhFQtLTd4+XlderUKScnp2PHjgGbxaa3CQ4zFxeXxaWVhIR4AVU+dxu50TKkuXug8/bhmE0v/Y13glSnpnAVTKkUB7lqanDm/wsvYPX82GO0p6eLQp+UUsIEkV4jiITf/x6lpqKiIgyIEQg2kSQMDO7U29sbFiRCyNbW9uzZs7a2tkpKSn19fVtZJAih1tZmNzf3FYkkOjp6Jym1mwocdu5cSghhIyMsjGxuxjc1N0dqaJA3buC8P3V1jLaRSHDyeX6+IsSeXqVHj+Ko1htv4EzDsDCMOfvv/6YtHvBHthDEVYLgUiItpqxzB4JI/9Wv0NmzaN++tdiikRE2rdrbcZCL9ifRUvXy8qIfef/IcS4asC8UCgsLC4uKimpqaiorK8vLy4uLi2/evJmXlxcXFxcSEuLp6RkVFZWenp6UlBQREZGRkdHQ0FBTUxMeHm5nZ1deXg4eF7A/SktL2Ww2j8fLyspKSUnJzc0tLy+vqqpKS0tLSEi4q2NgZWVlbGxsYGCgq6uruLgY0jsYDIa8x6Wurq6mpgb+0upHweNyT+qtqqoKHAb3dBStAu+1UVdXF01tnZ2d8/PzMBEYd7jj+lxbxbkgXQ5QAR0dHXl5eaWlpeC6gMhOUlJSQUFBVFQUTGtsbGxaWlpiYmJ4eHhOTk5jYyOfzw8ICHBxceFyuQrTWl5ezuVy09PTU1JS8vPzORzOarHh5OTktLS0u/IRLy8vj46O9vf3w6iys7Pj4+Pz8vLA10L/pZ1k/4zTWltbGxYWlpSU1NvbS2csIoT+Re0eeL6PjY1lZ2f7+vpugxhFCHW0tykrK4l6+woLC1cp/KempkhS1t/X19bWJs/5ODQ4ODmJOW1XVlZ6enoWF5fGx8eHh4enpqbgzWwrpXVPaoYkcSxARwerGdoQgQZC+M2bwcD8tsCeEhlJurtjkOmPfoT/PvEE1ihU4S1EEHUUiplNEGLKzTO3/g4t++Uv0bvv4lf248dJJhOfqqODHB6+fTlanQiFQmNjY5qwa3l5uaCgIDg4OCkpCcIKm94aQqi6qvLkyZMzs/Px8fGBgYHz8/NSqVQoFLa1tdOJlwihvr6+WbEYUnJ6enpkFJsznHl2Zoam3tr0KvdxJ0CmGhuxc+Kll3DsJj6efPVVBfW8hmKm0CfzlEjHCcKeMivRj3+MHn8cI4GOHsUQYPm5kx8nZb60GhgY0Hnsi4uLEClPSUkBrIN8f7qNECorLVFROT03vxAeHh4SErK0tLSystzd3dXZ2blEcQTB8usVieapgovz8/O9vb1isbi7u7u/v39yYkIk6t0JKou+KPUjItvasD0NUGKAPSFE3rqFA1VWVjgOCHjn6Wns+4EbX1m5jbg3NMQL0t4em9cvvEA+//xaNtw6RqqQSjBsotw8iCCGCcKWIIoIAl25gs988SKOw770Eh4GnJz+UdDjrK2tNTIyonOz/wHtnsXFxcHBwYmJiZycHD8/v7S0NHNzcy0trYiIiMrKyoqKisrKSoFAUF1dDaZAA7XV1dXROonP51dUVPB4PPD/g8EBiqquro7NZkdHR1+7ds3FxQXMYghbALyX1vFSqbSxsRFekeFneOvWrezs7KysLAhOgS+Hx+OBiXOvtsU/eP+4uLjY2NjQ0FB5Tpe/x+6Zm5sbHBwcHh5OT0/39/dPTU01NjbW0dGJj4/n8/lcLhemtaamRiAQ1NXVNTQ01NfXg+0I7hOY1srKSvgo7yCpra0tKyuLjIx0cXFxdXVNTEysr68vLi7mcrkwd/S0Li0t1dXVVVdXc7lcQLIKhUImk5lJbeCi4/P5sHjoC/2DT9ZOhldTg8OdN27cSEhICA0NBYJyeLf8F7V7EEKNjY3W1tafffbZkSNHIMJNPyjlG5SYSEd7+x6hKC8vT01NbXJycmRk+ISyko6OjpnZ1ZaWFsrK5qurqZmbW0plsqLCgi+PHLl+PcLCwvziRY0sBkNZScne8b7VhGpvx5lEoHUge6unZy1lpqsLBwv09THGYmkJa9ndu+XVM7xAI4LIpdwSvQTBo/KBZwnCjsLhIoJATz2FC3Wpq+NX8Js3sYo6eBDDSxU0CihpJSUlgPIghKampphM5tmzZ7/44gtYZPKSpNv41Wdl2dLCYmp69saNGyYmGKPT2dH+9ddHtbW0XN3cGhoa+Hx+SUnx6dOn3d09sVcpLPTsuXOxcfH6enr6BgazMzPn1dXy8iF8Q594u8ZWpsZ2x6x/B6USXnwRK3IXF4xWoUJa9F+A3CZRMhwliBKCaKVCMxYEUUwHYggCq2dNTXyGrQaDC7RxuUpKSrRzYmJiIj09/eTJk19//fU2xStwYa/ZWVPTK+K5OT9fX0tLS6lUKqjiH1c6rqWlGRAYVF9fX1lZWZCfr6SkFBgYjJmTroedUFZOSk62tLQ4dUolOTlJRUWFU8HduXUO6JyzZzG+e3mZbG3F9jFtfCBE9veTn3+Ogc+r1QaMjMjXX8c0PKuWtJUVridaXIwTu4KCsBg/+gjb5XJSRVQwK5IgXAligiByCKKbIIQEYUatWPSzn2F89HPP4aDYz36GEc3yhJzr84b/RQgVFxefOnVqmiI0RAh953ZPT08PhKVozTQzM5OSksLlcgcHB0NDQzMzMy0tLSMjI1tbW+Vfsrd51st3U3CQ0KgaT09PFxcXFovF4XCioqJcXV1jYmKSkpJ4PB5CqKenJ57a8vLykpKSWltbhUJhaWlpXFycu7u7tbV1eHh4ZGRkeXn5/yXVCCKtr6/39PS0t7cXiUR9fX3yYPDBwUE6Qgoqc6u/UEqM9u5AFaPExMTq6uqurq6goKCsrCwzM7ObN282NzfTFsw2cypvvG7sX1NTU1FRkZqaeu3aNXd398rKypKSksjISGdn56SkpLi4OKi/1traGhsbm5iYmJube/PmzY6Oju7u7ry8vISEBEdHRwcHh/Dw8KioKDB6th/MP9239fX1dnZ2vr6+vb29gByg5w6/Uc/O0h+/w8aDzufy8vJ69dVXjxw5cu3atbs+61tbW5eXVxgMBnQeGOj77NNPDhz43NvH18ba6vPPD5iamLi4uurr6Y3cGmVkZX7++Wcurtf0dHWVlJS6OjtdXVyamql0cPlH8j225TUKmCBdXZh4MCUFv3BD5UuZDFOnPPkkxoR+/DHWLn/4g4I6WSGISIJwJ4gZ6pV6gdLQlqCh5RQPNpgAibKwgAlUoDqS/JDBc6uvr9/S0gLrRktL6ze/+Y2ysvKNGze2FynOt2xuIUkED9PVquktzU1//ctfDx44EBEZqa+nd/ToUU1NzZCQYANDo/n5hfCwsEsaGgcOHrSxtbOysrS1s/vwww+Tb6ZsfxUYLWCQg4O3A9bceV+37RKI3YyMYL2urY29F7/4hbw8wY4EyG0Y5ZOQUVjmTkpDCygJ45yml1/GRz3//FqAkp5K+euCkp6amtLQ0KBZN86dO/df//VfR48evSsfkkQiaW7CSRw+Pj5JSUnYhOKw//a3vx364ovYuPhLFy8oKysbGRm5u3teuWKG6Q29PQ8dOpiXX5CRkRafkIhI0sTEODUtY4cihVuQSEgbGzIpCZsvjz2GgcngaJyZWQMX37pF2tmRFy5g38zbb2Mh7NmDl9Zjj+Es96eeIv/jP2gHDy1YRGHq3QgiikKIk5RIGwjChEKhYSvzySfxSXbvxnHYL7/EQViFgu20YFerCI+NjWlqatKE9N+t3UOSZGdn5/j4+NjYGGBfuFwug8Hw9vYuKyvLzc2NiIiAtBQAAt8XZQPumcbGxrq6uvLycsDZAH+JhYXFasS/tra2oKAgOzu7uLg4Ly+Pw+GUlZWVl5eDDwm8EWBa3Zfx/KOdBGyI4ODgrKys8vJygUDQ3d09ODg4Pj7e3t5+1zggPP3a29vFYvHIyAiLxSopKeFyuWlpab6+viwWKzs7G8xZFosFcKX7IoGampr6+vrGxsba2lo2tQkEgtzcXBsbG1tbW4FAUFVVBTUGALtdUVEB0woYnX+FaS0rKwsMDITALgDdYFpbW1vlYzUwg9/J3wdt9ywvL9fV1VlYWHz++efAQU4/Kzc2QCIMBsPTE7sfekW4wHVISLC1jS2LxUpNTcvJzlZXUzc2Nmaz2QH+/hcvXDAxMTHQ19fQ0MjPz7exsf07mXsgBzguDucG01pTSQnnEOnr42KiwChoZkYeOIB1CVgwP/nJbRq99ciLKwUIHaW4bpeo7C1zKisYnBY461hbG1dB+ulPMTIDDCy4ooJYwO7BZS8phh8cEOzoWMXqf/TRR4cPH74rXzOINCIiAlLkGhvqDI1MPD3cPTw9i4uLs7KykpOT1FRV7ezsqRyQNCWl455eXjra2rp6uiEhIQcPHvD0wrXSFEa18SPYLgcOYLfEpjcifwhCOIPa35/s7l7rnJODIylffYUdafJ2IdUGpkcbKpg1SLklEKWbrxJEGxg9BIHlee4cnimCwJ4z8B59/TVJYbLlL46dEzMzM7q6urTd09ra6u7u/uGHHyorK8Pz944D7vwAIvX19U1LS0MIsVmlNjbWrq7OgUEh+fn5DCYzIT7+1KlTzs4uPB7Xztb2woULsbFxLs5O3d09ubnZGhqXYuMxp8CdZ93k0/IyBjnBBiItKsLGjZYWOTGB6XY++AAbOhIJXo1U2iD5+99j2+jgQdLLC1s8Bw+S0dEYibyZSLspSoVCKm+rgBJjJUVUCJw9dxzy2mtrHOKbjJLaBa/durq6/whxLi6Xm5OTA5kmUNQpOzs7OTnZ2dn52rVrAQEBnp6eRUVFdLBje+24E7+LQh8a7wkNwI6w2WzIvoHAGUQHaAcSNOA8Cp4k+eHtxCqCi8oftbG9wz7bjATOucPx0OcBrJJAIOByuSUlJWVlZRwOh81mZ2ZmlpeXZ2Zm0imWCtpxZWWltLQU5hTS2YqLixkMRmJiooODg7u7u7+/v4+PD5vNrq2tra+vp6+48d7pkW/1Fb1/40noiGddXV1jYyOPxysvL6enFeJcVVVV8mlQtKg3nk3+Qtt8Sw/4fvWhr7tVgw7jbtWBdpJBBxCLQCDgcDglJSUsFguMv6ysLDabnZWVtUOjVmHS7+PHB2r3wLs1jH5sbAw8mVs9Otef77hOhbe3NwU3WeihyMeam1vWIRGy6upqoVDY19s7OnqLxWKNjY13tLdXV9fMzor7+vu3P7n8t5vqZoQwcOeZZ8jGxjV9PDODi0s8+igOH7zxBq6KoKSE34MJAvskfvxj3KCgPKAncMUJCtCDIy8EMUplAncRhCmVdrRm9BAEzgROTsYmgpoahqluowTB7rly5QrYPbRIZ2dnc3JyaK+v/K0ptIG/B+qSzonFvb29i4uLzc0tUNleKpVWVFQANVlPTw+Hw1mRSJoaG1ta2xBCo6Oj2+BdFC4kk2H4LcVVpvDNHR8BnqKkhCXm6YkDN+HhGEr13nsgQ4W/NHFwNSXSIYKYJIhaytPTRxs9v/89NqFkMrKhAZMcGhpikQ4NYfFCMS/5EYDdo6enR9clhSU6NTWVlZW1TZyLPglCiK5LOj01NTg4ODc319raBpBwycoKh8MZGbklFPYMDw+Xl5dPT8+IenslEml/fz+LzQZkAH22jQ3g2jl4cM1og7W6tIRvqrKS/N3vMNBneBjbzd//PvZBenhgob31Fo6ZBgXhsOkHH+D1+dZbOJYKxNbrpg+OsVL4ZbN1v04/hbjnUOt2CET6wx+SP/kJPucHH2A80Cef4CsqEALJDxv4e/T19b9zu4ckSW9vbxMTEwaDAVnWlesbqA3AWADso7CwEJQWd7ONR22FhYWVlZVVVVWbdcH7eDxeXl6eQCDg8Xhb9cHRz4ICSDOGPnByhf5FRUXbnIfH47HZ7JKSEoCtKBwLHyG3q7CwcJsBA5xl+z6Q4l5WVlZZWbnphbhcblVVVVlZGYvFghz7TbsJBIJSagPgC4gL7p1PbTweTyAQsFis69eva2trc7k4BLxxE4vFtra2lpaW+fn5gMGCxGzIXIO0fBhAZWUlTBlYVxtHRd8+uGo2dqBxzfn5+RC+3LQPdCsqKlK4ffllABnpJSUl9O1vPBWPxyuleI/kD1ToVllZCRb8NtMKfUpLS7eZMojTQWqYwiXgI/AdQFbgVgKEPmB90n1gTnk8Hvy+4GfFYrECAwO1tLQaGxs3zumD3POg7R54OI6MjPT19d31cQ9Knclk+vr63i+hyD+d6TZCGPQQEoI53GgDiNYuIhF+zx4fxxBRkWjtdXnPHlxo4umnb2tlOXOHBvSwqDfmVioTWEipkBaCuEIQ7bSGJggcd/j3f1/LTnrhBYxOvavdY25uTtclpU2fnfgMoHNkZOQ2pT+2F7X85aAnLcaNDVqY23wFfby8sPn42ms4IxrKTTz11G3Zrrt5SIJgUpBbEQVkHqbEyKMyj9Y0NDAkeXpiyC0AX2Zn12BY1Mg3kS3wFhoaGtJ2j8I9bhy8wh7gLYSM9+2lJ/+twlUUvpK/BKB2VFTwKuXxMOqrrAzzQqmpYQQPh4MNGktLDOJ59VVcTqunB6Pp33gDmyl79ihCo+QWKljeKRRGqp8gqinTHFEQZuv1Np6Fw4dxhvwPfoDJEtvbsTX5+efYo7bV/FIcE5OGhoY7tHvkly6WA3Xz8gL5e9qrtNEaGhq5ubkFBQW5ubk8Hg8sH1C0YMRUVVWVlJRkZmbm5eVBuu+6dbT2bxW1FRUVpaSkwHkUOsDH6urqkpKS9PT0vLw8QEZv7AYQ5rS0NGBk2dgBlHdeXl5mZqb8gBV6wpjT09MFAoHCV/CxqqqKw+FkZmZmZGSUlZVVVVVt2o3H4+Xk5KSlpbHZ7E37VFVVlZeXZ2VlMRgMSEnb9DylpaXp6ekMBiMvL48WsnxPPp9fWlrKYDBAzrSlIt+Hx+MB1WFoaOjp06f5fP6mUz8/P3/16lUDA4NCapOfMvq0cC8wHfn5+fJ96CtCn/z8/KSkpKKiotzcXPor+UZ1dTWwNebl5dHnl+9QWVkJU5+RkbF9HwaDwWQyt+kjEAjgWttMK5vNzqC2baaDy+VmZ2dnZGRwOJytprW0tDQrKwtYKzftA9ORkpKSnZ291Zi5XG5ZWVlmZiZMvYJY4GNFRQU453x9fU+fPt3UhGk7vsPtQds9FMIx4ejRoxcuXIA1Lf9839hGCDGZTOBrnpsTx8XFDY+MZKSnV1ZWhoWFxcTGjo7erl8olUjSUlP5VAWGifHxqBs3+voH2GxWdk5uX68oOiZatFlOMjy73dwwNGd2FgM/U1NxhvCZM/jVWSzGHgsGAyf6vvIKfpN++WVsprz99looYc8e7P5Zf3uGBqKwETEUo8woxSVTSsFQaikN3SVv9Dz5JLZ7nn8ew04vX8bv61evbqlOQFNKJBKarxn25ObmamlpGRoaboNrpmUr7++ZGB9PSEgcHR1NTEzk8XghISGJiUk0FhXY5+LjYpuaMZaov78vKip6YmKyo6NdIKgeGxuNj4/r7d0yzVvuindYG+DgmZnB1iRCZFMTzsHm87ER+cknmIoGCB43iHSOSvj3plLhZgmilOIOXuW5tqK8aLedZy+/TI6MkGfP4sw4AKDQduSmelqBrxlEmp6erqmpaWpqug3bgtwNYn8PEFMNDQ7GxycMDAwkJiTwebzgoKCU1DRI5oLf+czMdHR0VEdnF0Kou7srJiZmfHw8MzODy+MvLiwUFRbOzy+sq376CrgBg7ewwGvspz/FBs2qh+yHP8Q20IUL2B/z/vvYKJmYwOmEsCw//ZT88MM7Fucjj2ALe92OnKB4HYMJYoVym7GptZpDEI4EMSW/Sh97DPsydXTWSHogd2x8/I7hyX+4J75mcGHScGOZTHYfK5DLZLKEhISvvvrKw8PDwsLCzs6ODhDTj12ZTCYUCoODg8PDw21tbW1sbMBvSncAOqKuri5fX9+cnBxHR0cbG5tNQzBLS0sxMTEmJibW1tZbPdwXFhYCAgIcHR0tLS1pNgr5a2G8vFhcXFxsb29vZWVFZ24q9EEI5efnOzk5bYMVnZ6e9vLysra2Bn72jWdACFVVVV25csXW1haIZDftU1FRYWRkZG5unpODa9Rs3Obn5/38/PLz852dna2trbu7uzf2mZ2d9fPzCw0NdXJysre3B4YIhW5jY2PBwcHu7u6qqqpHjhzZihticXExODhYWVl5tWS1lZWVjY3NyMiIwqkkEkl7e3tAQEBgYKCdnZ2NjQ3Nokn3lEgkTU1NgYGBWVlZNjY2Dg4OtKVO9wF/sJ+fn6mpqY2NzVYVwaamppypzdbWlmbEkD8JQmh6eprJZDo5OdnZ2W0D3E5JSfH19aV/EQonAUJ5Z2dnGxsboJ/d2AHWBiz40tLSTTsANx5MK5Qt2thtZGQEnmx2dnZOTk6bjnlwcDAkJCQgIMDBwcHFxWXT1djT0xMUFOTs7Hz69OmjR49uJZ+NA/iW9nwHdk9YWJipqWlCQsIOgzLZ2dlhYWF4sgcH977+upe391tvvWVpaXnkyFcqp05Fx8SFhoa4u3tMTExW8XkXLl7Q1dNfXFyKib6hfv78lStXdHS08dteXr7m5cth4REKGgUiCOXluChEYCB+e9bWJpWVsd3z5Zc4VqKigjXovn1YkezZg1Xyo49iS+Xjj9ewI3eqZ/D09FGlEuIoDd2/rmO4VCymV16dEAQm6SksxLQ0kKcTH49pV2g9La9LoE3VtpQ5ODjIP74tLS2BEg0ykjYeJb8HIRQVFQVKurW56b/+6w++vr5//ONeMzMz5RMnTp48kZKS6uPt5efvv7C4mJeTffbsWbOrGJPr7nZN49LF0LAwfT1dO3tHb0+PL7788q6FpSiisDW8LdwXpOj/+c84LsNgYKTUs8+SX3+NlfFjj2FHhZxDgvactVL2TRZlPg6sizSPKg8+QYsUDNBHH8V2QHU15uzZyYYQWlhYuHr1Kh3SWjX4jIyM3NzcMjIyAJm7/XkQQkFBQfn5+YBr/t3vf+fp6fn6H/9obm5+8uQpJaXjDCbTw8M9JCREIpEkJ8VfvHTJytoWIeTs5Hjx4iVzCwttLS0jI+PExIT//d//HZuYVFil9NURwkFAuM3HHsMQ48cfxzmAAF7+yU8wgue11/D6hPS3X/8aB2Q3W6KICg5aEEQ+FdUCbxlU+XAiiDlapASB1/xPfoKTv5ydbxf52tSClBsnmp2dvXr1Kh3I34hrzs/Pg4daR3v7hfPnoyhO9pnpaRsrK0sr6wmKnOLvf+otLi42NTVlZmaGhITY2to6OTnJJ9bC+cVicV1dXVFRUVZWlgO1bSRTnp6erq2tzc3NDQsL8/X1dXZ2plkP5Ac5MDCwml5gZWXl5OREZx7Id8A0WkKhl5eXg4ODjRuT5kkAACAASURBVI3NxsFA5/b29tTUVGdnZzs7O5rNT+E8CKGSkpLVgirbYEUnJiZ8fX2tra23oUmsr6+3sLCwtraGZ8LGq+AEiJYWCwsLe3v7wsLCTTuAEo2IiPDx8VlVbxsNRzgKkvMdHBycnJw2NRylUmlubq6Xlxfc+0ZLBc4zOzvb0NAAmer2uMaO00a7Z2pqqr6+nsFgpKSkWFtbOzk5bbRpxsfH6+rqsrOzAwICfH193dzcII6vcI89PT2BgYHW1tbOzs5bTUdra6uzs7ODg4Ozs/Omt4YQampqSkpKghz4rW4NIZSenh4YGLiN3dPb2+vm5mZvbw/lpRVGCx/LysrMzc0dHBwAR7tpHx6Pd/XqVXt7e+Bc3dhnYWGByWSGhIR4e3tvxUY9NzfHZDKTk5NtbGw8PT03tXtmZmYYDIabm5uTk5OLi8vGimwbL/2t7vkO7B4/Pz8ghWtoaNjq+S7/AGUymX5+fphXpld0/OjXH374318cPmxvZ3fixCkdbW07Owc9PV01dfXBoaG83BxHRwcjY+PhkVtenh4enl6q584ZG5tYWVoUFJXciIywc3AEacqdH0M+X3sNgz/27cNcLxERGPsZEoK5ki0tsW9/1641c+dOfbypOiGpGIE5FYUB2ASbUiGrzgkLigdlzS0B0Qc6GYfWInSDHqFCA16Ozc3N5fE9jo6OX331lZGR0c79PRDnamluOvD55x988MHR40pmZlfU1c+rqal6enldvHBeU0t7ZnY2IS7W1tbO2MhoViw2v2rm4+3l6eVTVFjo5u5hTvX3DwzcfhIRwlTCSkqYBWB8HBM5KivjhCDwVXz8MdRnxX83qGcweiRUqr8VQXRQkiyiOJAQQWRS0Rlc1gMO/OMfsU3w6ac4BHPz5pp3REF6m34Ef4+BgQEd51qlgzIzMzt27NjO/T3+/v4Q5+JWlB888Pl///efT506ZW5+9fz5C2fOnPH08j6vrm5gaLSwuBTg5+Pr52toZDw+Pm5hYe7h4aly+rSTk7OxsXF9fb2RocHQyC0FkYK96OiIS3G98QbOzH/8cWwjfvghGReHS7Q+8wxew888sybVJ57ADiGAmt2J5gGRrhBEAiU9sMJzKAoARBCJBOFGJXPddp6BbC9fxrl1+/fjNLFtjHJavDuJcwUGBkKk+5qri42NzZmzZ0ZGx+bn5jIz0j/e/1FbR+d9fPDJZLLFxcWlpSV5FjWF80ul0oWFhbv2mZqampubW1jAVc0UzgAfl5eXl5aWoNLnph3gJ7ywsLDNYMDVuri4CHRZW51HKpVu30EikYjF4oWFBaCQ2fQ8Mplsgdq2cbNJJJKlpaXFxcXt+0xPT4vF4m2GJJFI5ufnQcibDgZcawsLC8vLy7A8tuoGv5HFxUWQ5KbdSJKEK24vyZWVlcnJyfn5+W2uSFFzYfLureYdyt1sv34QQnNzc9sPBrjothkJdBCLxYuLi9tPK9yRPEGAgpRWVlZgMNv0kUgksOa3unfqzRb/dmAuFC4BH+X70NVINu35YHZ+B3ZPQEDAiRMnzM3NgaiKflZu2kAI5eTkREdHI4SGh4b8/fysrCz9/ANiYqJ1dHWvXr3KzM4ZvXVrYHBQIpHeGhnW19MLDAwqZ5czGAwtLS0GM9vL08PWzj4vL9fUxPRmCuaGl78QQhhI6+CAk8YffxxDTJ54Av+/ezcOuygYOo8+qrhHTlUj6r05gCC8KIpbCRU7WKA65FO+ilFaQxMEjhocPoz535qadqRI6DGDv+fatWvy/h54XYuPj4d3ULrzpg2EUHR0dG5uLn717O728vI2MzMLDg0NDQnR19c3MTFlsTkjIyODg0MkSXZ1dly+rBEdHVNSUpKRnn75smZ1TW1TY0NCYlJpSbGmpmZuHnZyyF8ITDfYB+3sbBzFMzbGbDGDgxhoDCicZ57ZymcGNhCUSrhGEKGUm2eZEukiJcZkgnChBH5bQx8/jkcxN7cG0pIf0vZthNDS0pK1tbW8v8fAwMDR0TExMRHeEe96htDQ0JKSEvxK19Dg6+NjYWERGoZlamBgaGpqyuVXjgwPg13V1Nhw+fLlm6lpJSUliYkJ2jq6paUlFhbmXt6+s7MzgYEBE3f6e0CGtbVrYLI9e7D3BcpK7N9PurqumeavvYYX1e7d2MEDITCASckFYQHC3EuFX29QodhlKrAFfI/RBOFD7VwT6b/9G/nrX+OJePNNHIUcGcE+UfliFNvIBNS2jY0N7e+JjU1RV6+ys0PW1qSDAzp5kqGpeRkEbmNtFRUVfU71XFd3j0Sy0t/Xq6aqWlCEhflweyiBhxJ4KIH7LoHvwO7p6enx8PCwtbWF+Pc2T0+wrJlMZkBAwLpyXZPAKiebgnkOnRcXsAm8srwsk0kXFrBtDm8qEDKHg+WviBD2QMjTwyjYOnKWzUYcD62eEUE0UB6dbEorTxLEzXWqHiYVi5mkjZ5du/Cb+iOPYOX0859j99JdfTx3DhhJpVJLS0t5f09hYaGuru494XuSk5NBpLRMZDIZvEPQiwxEuhoDkkgly8vLlFUxD0fJqKoE+KWWeuuFEQLDMoeD44Pz8zi2NTyM3Tze3rh8ZmAgTtcyNsYFy37xC5zCJi/bO9ugoTlUZJBFiW6AEikYPdEUyfUCLVI49sMP8SgiIvAloBinvNy2acPaMDIykvf3MBgMHR2de/L3QBW5dVGs2YJisXijUxco+ZfxKpUBmmdxYQHepKHaw/pqx2tjbAwXSz9wAIvro4/I2Fjs4HFxIb/3Pbzn8cexrUNLb/furUxzEGkpxZzJp0TXQxAZlN0jpSxLP4JYlhfpk09iGqof/Qinbjk5YTh/ZuZObXSg0zQ2NqYjCzExKceOVRkY4BLuBgbo8GFmYSG2mBFC2YysQ4cOWVpZ5eRkZ2dnGxoanj59ml1eQa/DB9YgSUQBwtYuuLKyIqM2hQFIZLc9PVtVX8dMeusOIXgKwa8Jfjj0CUnM/37HBp3ldil2kPsKr7E73tRJmfy34DtRDJfI9QEolUwmW1lZueNUJIlIKX0qhTHj/VIZkrs7kNIdI8Fhe1xZhj7J/5nGzAwaHFS8G6lUCkKA9bAKwVRwjNGPBfpIGSkbnhuWyU0HOAIVxAinlUgksDBgNmEt4VMtLaGBAfqc0JAfDBxIH7WqO+hj6UeiwuH/Ch+/A7snODj4/PnzTk5Ozc2Y8I2WPr0y6D0wAbm5uSkpa1x5UuqHpPA7XFpampmZodeZ/IOD+mHjH7BEIoEFBI5Bau5xfhaLhZ/mG7iAbysSWqNs0UCUE+ImZdxAIAb436DaaCr1bj0rr06efhpn2fzgBzjH+M03cSb8vdo9kDVN53MhhGJiYlRVVfX19TetzwVipMWLEIqNjWWxWCB8+JnR6hZ2Li4uzszMSKVrjy38TJRI1s8jW1nBbbCE5GcQIcwi8/LL2CFhZIQF+9xz5J/+hD8++SSOznz6KRYsWH6bgZdB7FC3NZLy6AxSokMUffA4BWQOp0q63tbQUD+cINZqmXl7Yy/ayspONTQ8BaRSqYuLi7y/JzQ0VFVV1djYeFNc87oobuuk8PBwLhcz8NLUsfKrlCTJhYWF2dnZ2w81yiKH46VSKaze5eVlaMukeNHCKm1rw/bH889jczkgAAPtr19f88S8//4adcIWi5NexoiCmgVRzJnj6yIdo4gKpVQF+2BKtredZ08/veZSWi38TpK4gNdf/kKam+9UqrA8XFxcaCBkTEyKklKVkRHS1yeNjNCRI8ycnLVSuCRJNjQ0TE1Pj46OTk/PdHV2tLW3g4Qf6F8StTQOFzBaF+ZXKBR/f0xMTGFhYWBgYHNzc0JCAiyV8fnlqMpe4Th+ARCLxf7+/ikpKdHR0XV1dTjrYngYP21kUt4tYc14P5g+TCYzLCysrKysoqLCx8cnJiYG9NDk0nj7dMuidBEqUoWHh2dmZsbGxoaFhYEJLkPLc5KGZRlG7K6srMTHx5eUlBQWFubl5UVFRYWHhwcGBmZkZKwtquURcroKyTAoXiQSxcXFpaen+/j4NDc3x8TEAFc1Wp4jO/LQJC7/KZFImUxmYmIik8nMyMgIDQ2lExKlwpaVkpvk3AwuXTw46OXlBScBC146PTORlLnQvDZHCdTm5+dXU1OTkJAAMy5dXO6KKejLqiAleCWXlpZev349ISEhMDCwpqYmOTl5K5TMA53xe7/Y9DTy8EBXriBIxJ6dnY2JiSkoKAgNDY2Ojg4KCrpx40ZSUtL169fz8vJGR0eZTGZMTExLSwuLxYqMjIyOjqZ59ot7C90rXUr7ioDkMzo6Oj8/PyYmprS0NCcnp6WlJSMjQygUNjQ0xMfHR0VFiUSikpKSoKCg5OTkNVzw0hIKDUUGBqi8HB4XiYmJZWVlwcHBiYmJ3t7eQAUeFoY9z+Pj45GRkZhRjMGQSqVpaWlxcXFFRUXbRMruXTz/NEd8B3aPl5cXRQocslFJw8977dcllc7N4YcLg8FYBe1TiIEJNTXVyqqqyxoa9Q2NKcmJzi4ut0aGLl26ZGNnV1NbOzw8PDY2xior09DQqKwSUAGdqCtmZvX19ZaWFmnpGfHxsVeuXFlPAcNwk/ffx+/NGzAQtMLYvgGBGFeCiKBKrCMK1rOKwIV36zyCcKYCNLfVCUHgt/Mf/ACfdhWi4eBwb0YPPHklEomxsbF8nMvMzMzHx6ewsHAjyR5oIKkUWypSiQTCxhERERA67BUJ1dXP19bVqZxWGRoeDvT3D4+40dXZoa6u5ujk1NbeMTQ0ND4+wWRkamlrt7V3IJL08/Wxt3esqakxMTZmscoHB/otLCzn5vGzG1iDP/4Y393p05g2EPQxxA3vpptpCHMXBT1JplwRgJECGAoAUPyoWgq3RaqriytxXriAs7fa2tbKl9Km804akGehra1NRwlX9+jq6gYGBhYXF0N2m/x5oHIZGIWrc7GwiO/dz883ISFlago1NTaqqapV8vlnzpwZn5xydnK4mZJWV1d7/vz5a9fcOjo7gbo0JTlZV1+/t69PJpVec3VxvebG53MvXrxYWFjkdu2ao7Pz3Dxe/AhhiP2hQzid8A9/wGb6+++vSfWnP8V3TVd/e+SRTZcxLMUmyi7PokQqIwg2QQysr9IIgriuYPQA9OrXv8Y/DahJBwHEu1Ix0VKCR7mOjo68v2dTuweWNHWnd/zZav8dne7rB8mKLDO50cuxZKBvGiFUXl4eHh6ekZFhYGBga2t76tQpyD8S9E5eTqzNaca2yK1btzQ1NSMjI9XV1Q0MDE6ePJmeno59XcsLkR3cxK7qeckycHl7eXn5+fl5enpqamra2tqCpdI908EaKh5fxBmp9fX1+vr66urqmpqaHh4eVVVV+GEoG59cypmT1FPQ+0UrKys1NTVdXV0tLS0lJaWjR49qaGgkJCSAj4qcrSVHbqLlIShlc+bMGTU1NSsrq6ioqK+//roFaE6nRDKOJ9mDde309HRAQICfn9/Vq1fz8/M1NTXBB4yk0pXS1MWAK7JebNl0dnbq6uo6OjoqKSkBInOhtbPPyGYihUFS1rmPj4+Ojo6uru5qQQZlZWWQgLj3FvucK18vYHkKV/oLCQlRUlIyNDS8cOGCoaHhmTNnrl+/fl9n7wGdrKEBmZmhkBBsbyCEBAKBkpKSpqamu7u7nZ2djo6Ovb19QEBAREREbW3t1NRUUFCQlpZWamrqjRs3jI2NY2JiXF1dJyYmJKTEV+CZ0ZHmK/AikWxkeOTSpUsnTpwICgry9/e/ePHilStX1NTUoqKi2Gy2t7d3cnLy4OAgk8msqKgIDg729fVdIUk0NIT09fFoXF1hps6ePaujo2Nra+vm5nb+/HkXFxdvb+9Tp05paWl1dHQ4OjoGBwf7+PjweLxjx45lZGTY2tpuxZD0gAT6HV3mO7B76uvr/fz8VlE+CiBcyuhZ9nB38/DyIhGqqRHo6ug0NjUXFRVBtt7gwMD7772rq6Ozb98+Kj9L4+ChL8pKSz799FMPTy9WWdmlS5eumJnp6el5ennZOzguLi7o6uo5OtifOX3m8OHD18Mjerq7T59WaaT8TAhhFuY338RaZJvY1hbaGtQJm4ptcddViJQgqqh3aEQQzdRXdyQD79qFsRenT2MM74cf4oAF1IyktcUOG7jMU0SEvL9ntajQsWPH1NXV6eAXnArD6MSzttZW16/jRLaSoiJDI6Pevv7k5CQoCtva0rxv3z4dHZ0333yzvJx9Qlnp9JmzuTk5H+/fHxQcUpCfd+7cOWtrGwMDQ1sb6/CIiMGBfl09XVdXl2NHjx0/jutMOTna7//4k6kZqLqC88ZDQzE8/IknsBq+F4MSUdp3NVBoReCgIUh4gSpkBgCUCiq/eon6ak33f/75GsVzVxd2LP3ylySXu1OfBC1t+On5+vrSrh2EUEBAwLFjxzQ0NEQiEbxL0SIdG71lbnE1jiJZzmZkmV65Mj4xcfNmXExM/dGjiFXC2bdvr7a29jvvvieorj78xaGLGpfTUlM+++yziIjIXCo/ztra2kDfwNHRKSEhSdjTraenb2F+1cXVReW0Slx8/LmzZ5VPnBgdm6AGhvFnwBn4xBM4b4sGgD/22O22fKhLbsVCva1USqS0OS6mRAoRw0LKAySRFyl9+AsvYEbEDz7AOWIGBvjuNzhnaRFu0kAIeXt739Xfs8mR67se/CNxdmaxVwhixyl+FRUV7e3tfD6/sbGxp6eHtuFaRmaXKVfoysoKn8+vq6sbGBioqqrq6emB1YIQGl0UTyzNwS10dnZWVVU1NjYKBIKRkRGhUAgv2SuylcmlCQh1zczMdHZ29vX1AfMbjf1ckY3LyCXwWN+6dauhoaGtra29vb2urq6lpQVKYaz5e2TLaBl7mwBc1dvb29DQUFFRUVdXJxQKb6c7TYmQBPuEEELt7e01NTVCoRAYg6C2FF7tC2JZ/xqufHZ2lsfjwUnoFLbFjh6peO3uWlpaqqurgaZFKBTSEpjtGpwfXCMZGRkZ6ezs7OrqGhoaqq6u7u7u/if191Bpa8jXF1EvO2hoaEgoFAJvZE9PT39/f0VFRVNT0/j4+OzsLEmSQ0NDjY2NnZ2d7e3tfX19U1NTtbW1kPE0KB6IaAgbmcNTtrS01NfXB9NRX18vEolqamrgECgrNjY2NjEx0dvbK5PJhoeHa2tr1+Ib1dXIxQVRVfDGxsaEQmF1dTWHw+ns7Ozv76+qqqqpqWlsbKysrFxYWGhpaWlsbGxqahoaGmpvb5+ZmWltbaXnC5bEv8jf78DukZfs+iMO/ws12E2MTUxMjIdGbi0uzFtbWaSmpZeWllpYWExPT/f2itRUVd9//71Tp1Ts7R0OH/7y2LFjOjraJ06cTEq+yePxPD09Q8PCrK0sDQyNXK+5dbS36+np6evrGRgYamtr6+jqZmZmamtp5eVDKiZmOnnnHRxv2kCRt6ZWaR1wZwOiBqEEcY0qs4WoFJi2df8EBGUsCaJTQZ18/DEGuyQl4cJeXC6mQPwGG/0GRnvLtpIniLSSz7WysjIxvTI+MTk9PWWgr1fO4SYnJ3t6ei4uLbU0NSkrK7/19tuq6upWVpZHvjzy5eHDBgYYYJGZmcVisahS0jFmZlc0NS+Hh0fUVFdra2lduWKqp6urp6eveu6cmuq5t95+p64BeKhwUcyoKIxc/t731mis7xTdVoIFoXlRMSxI0ZolCCA6AgNIRFEf3ZIX6a5dOPrj6Yn18egoFqypKabsuyf1DFKqr6/X1tYG3QB7QKobJ4hy2hebX8X/icVz42NjGpcuNbe0RkVFWln5pKRIK9gclZMn9u3bd+mSxiq95HElpS+++EJfX0/9/IXs7OyyslJXVxeqLqyxvp5+bFw8n8+9fPmyro5OTGycr6+PsrLy5cuX1dXPt7ZCHAEj3196CSOi9uy5JwMd+MGvUaRHkJc+SRBQdAJE2kqZ5reRZ/RM7d6NaQXq67GZ9e67mElym2r2G0UEe/h8vq6uLl2fa6s411aHy8+C/Ap/2H4ogf8zErg/i3wdaPV/RiwP4EYeqN0DVXsCAwOvXr0aFRU1N4ffGOgHH/Ybjwxra2vp6evV1tYWFxUZGhrGxycWFxerq6tPTEyIZ2dZLFZRYWFFRUVGRkZbW/vQ0GBqamp4eHhAQGAzxa1HlfES+vv7t7W3V1VVVVRwbtyImpicvBEZyamoKCwsCI+ImJ7BcWva37Nnz87dEqAw2inK4FQ5Q6eGih3At1IqGbhIXkODRnnpJdLEBFPrikSYCuXNN3GCjJwAaEls14CCXMePH6fjxEtLS1BkEbjC5A/GzHhdnZcuXTS7erWuvp7JZOrr6RYWl8bFx5uami4sLExMjLNYLIpblpORmSkUiXp6elLT0kJCgoOCgkSiXliCzU1NwSEhHe3ttbW1hYUFiUlJo6O3QoKDq2tqpqeniouKxdRUAq3wf/7nVsbNpvtBaNW7dlkSRCElKNjDoQhmoD1HELYEsVZwFIT56qu4NKytLUbdIoSNrZdfxmlH/f3fRKSVlZXHjx+n67EvLCyUl5czmUwFZw88pxrq69TUVG1sbBvqGzIzMjU0NPhVAn//ABsbK5lMOjw0xGaxCgryyzmc9PSMwaGhttbWlJQUnC8XEtI/sAaJrKkWXA8P7+7pqaury8nOzshkdHd2BgYENDc3p6enJSfdBBAV8Dq++CIG9EBZUNo62boBQcAKyqwBGgUQY/F6JQpE0RJar6ev3zEvTz2FyTknJrAk9fQwQbNEsmX9UfnFJt9GCJWVlZ08eZLmwHxo9zyAp/nDSzyUwEMJ7EQCD9rucXBwOH/+PESCQ6kYqcLjsr6+vqGxsa+vr7Ojs7CwaH5+nslkQj32ndzPPfWZnMRsKFvrD4WvEGXoZFJRg2bKrEEUw9uCHOIEUcwo19e/vX2GXbuwNyIvD1ffXFnBL/GZmZgT5RvYPaucHAYGBjQr2rVr186cOXP16tWjR48C7aa8SEmS5PG4HR2dvb2itra24uLilZWViIgIunj7PUnsrp0HBtALL6zd9a5dd/VPYGLrXbvifv5zh9/+VviLX6AnnkBUHtzSuk0JoJ8ggkiRF+njj+PIC+DBFxcxpmd4GNMWHziAUT7fQKRTU1OXL1+m87lWc9rV1dWvXLmipKQEpWRokVI5MhIOp1xIbS3NzSUlmA7V19c3JeXmXeWz8w70u2Br6z3h7sH7qIAKn6VytaTUUgeryJMg8jaa5quwM2fnNcEymTiZy99/7SMtgZ004A1Hvh77Q7tn51P/sOdDCTyUwLcqgQdt9xQVFZ07d05XV/fSpUubKumNd0vjmiUrK7U1NWVlrPHxic6OjlEczuwZHBrq6uosL+eIxeLa2prS0tIhKqUC0ELVAgGAo0mS7Ovrk0ilk5OTY+MTExPjDY11Y2PSN9+8jZDd1gBCFCuPJ5X8MrOug6coqjdIkAENDRiUlY0a5ZFHyPT02yrknnK45DUNRPrlcc22trYXL14EPAqdpaVwiIJUaVzz4uIin88vZ5fPzMy0tLRMTU11dLSPjY01Nzfz+PzZ2dlKXFKnbHx8DfQwPz8nqK5eWsZoTYlE0tfbt7y8XFtbOzkxWV9XV1nFbWufevHFu4h0926sUJ99Fv385wNPPeX08svR7767/N576IMP0K9/PUSJFAA9IFIGgXlloH3bjoTcovZ2HIthMtcEy+Fg/8Q3sHump6flcc2mpqa6urp+fn7Hjh0DJlN5edIWibxUfXx8k5IwO9T8vJjL5VZwuTOzs81NTWKxuL2tbWJysq6uViConpmZgXKSk+t8xDPT0zU1NeDdWVxc7OvrX1paAhBAZ0dHW3tTWzsCEsJt1ydIBlHRVRuCoFHhiCB6qY8A6AExJhFEyPoavi1SgsDhrYgInBBXWorv+OJFTDIJ5UQUJLD9x3vCNW91KnnxPmw/lMBDCTyUwP2SwIO2ewBqp6OjY2ZmphDn2vTxJ5/PNTQ4cOzoUT09vStXzFROnXJ0djmjctLcwlL13FkdXd2szMyjR7+2sLRisdkF+flFRUXR0VEqp05BbYpsRubXXx8dHh5WPXc2PiHRz9fn7FmVomL+e+/dRUmvq4dKys2zWg0KQgYk5ZBYpKoawRkQQfRR5Ci3C2TKK6qnniIzMu5ZJW+UyUa7p7u7Oy0tTV1dPSgoiOZp2HggvQchFB4eHhODqSDbWlsOH/5SV1fHxfXaJ/v3B4WEfv7ZJ85OzmdOqxgZGTEYjAMHDtjZ23O5vGwmk81mhwQHKZ84mZKahhC6ERF+8ZIGi1V27tzZa27uUTduHP7icza7+be/3Vyku3ZhhMqzz5KvvYbefVf2hz8U/upXlq+/LnjvPfT22+idd8h33pH95Cdz68Bw0NCrAGcrYELatQvzJ/3whxhp+9VXmEYPIezmsbcn6+rWBPvNrEnIbdHW1gZ/D0Koq6srKSnp3LlzN27cACuHlt6mDYSQq6vv88+nFBWh2uqK48eOa2trX3Nz+9v//k/kjRuHDx308PA4fvy4mdnVxIT4L48ccb12jV/JZzAYbBbLy8vzzNmzefkFGEzt56Orp5+Xl6OiouLj7RsWGnL5shozp+X55zcXqdwCA2ckkxIXjQqXUVDxWTmCR0QQPIKwX08/vG30/PjH5KVLOLbF42E40SuvkKqquG7a9PSmd3yXnQ/tnvv1gH54nocSeCiB+y6BB2332Nranjlz5tKlSxoaGkFBQfL4nk0fpVCX1N/fH1Nc9Pd9cejgF18cDg4JNTbU37//4//5n/+55uEZ4O+no6MTGhry1ZEjvn7+XC730qWL2tralzU1IyLCLSytsCbrbD+vrn7r1qiXl2d4RISlpUWAn1dwaPSf/7y9RgEumRtUJlHfutGDqLytqvWPoKEXKQwK7LytTqC80e7dOOX4Xv0QWwlklZbKwsKCzmM3MjLy8vI6gywvtAAAIABJREFUd+6csrJyRQVme9v0QHonld4fGRqa1N6OmhoaP/nkk4MHDqSkpp1RUfnk008/+OCDkJBQT08PHV2d8PDwAwcOhkdEsMrKVE6dMjIy0tfX9/cPcL12DSHE51aYmpoEBgaslp26am7B5/P8/Hz6B8gXXlAU6SOP4NT93/yG3LuX/OAD9M47s//xHwG//KXH3r2jb72F3noLvfMO+t3v2K+/3vL007RZCUhn83V0MxbpL39JBgXhWGFDwx2Gzt3umL71zRtAxELXY18lyDc2NnZxcTlz5szJkyd3yCoeFOT/3HOM999HFeXszz775ItDh9LSM44fO3rgwIH9+/eHhl53cXHW0tb29/c7evRoTExsSXGxisopY2NjDY3LEdfDvHxwJZaiwsIrplcCAvw9Pb2srW1WizJaWZnx+GM//7miSOUsHlh+EwThtXu332OPTT/6KC3DAoLovnOVDhCE+XoS+x2r9KmnyPh4XNo9MJDs7CRLSrCnB0g1N5fatnuhToWRkRGdA/UdxrnokQKD131/ht7fE8p7E+mR0zthz/294rd0NvnB07RV9I18Sxe9X6elBy+/Zv7pBq8wYIWP90tW/4znedB2T2lp6ddffx0aGhoYGFhWVrYTJc1kMn19faE+l6WFZcrNm1bW1u5ubudUVbV1dHx8fR0cHC5f1nB2dlE9d87Dw6O0tLS4uJjNZsfFxpw8eSI4JJTFZs/PzVmam8/MihlZmTdTUvx8fVROnywpxc6GO1UIrQxAeYgoLpnYdSpb2CklcKK1mNIooHVIgqAxKPQZ1hqPP44bRkb30+6h67EjhBwcHPbu3RsbG2tmZgZVA+kf7aYNhNDNmxF/+lP8K68gDrvR2dkh6kakm7u7g739CWVlXT39oMAgezu7ixcueHl5nTql4uXlzeFU5Oflcbm866EhysonoqKiK7jc8bFbjo6OZaWlp06dDAwKTkiILygqGBu7je/53vdwGYrf/pb8/e8xmvvdd9F776E//KH5+ectXn898/BhUkkJHTmCw1tvvbX02mv1+/Ytfv/7MB2QgH2NIErWhYxl+MILuN7466/j4gx/p60jLxmFeuyrTzoLC4u9e/empqYaGhryeGtshPKHKLSpvHd/Y+PM+HjEofg2wq+HuXt4ONrbHz9+3NTU1M/Xz9ra+tzZs25ubqqqakFBQRhlX1DA4/F8fLzOnDmTnHyzskrQ093t6OSUm5N94uTJoKBgZaXjhsZ6/Mqpre0etGcP2rOn+qmnLF55pXDvXnLvXrR3L3rpJfTyy0svvli3Zw9N8IihVJSnB2gXFFcpQeDg4y9+gbmCoBiFTIbhzN9s21iP/Tu3eyAhIDw8PCEhAcqOYmL3DRvwRm7YjXdIJJJN929ziFQq3fQQiUSy1VfyyokWPpQgTUpKSk5OFovFJEluetqtzgn0wVsdsul9fbND5FUgPXiZTJaenh4XF5eTkyOlto0j2UYg249k46lgpja9qZWVla1ERFtmcAsweKAdj46Ojo2NXWNU2ux62w9+syPwWtpmJPd6CLj5aeHTgx8eHi4pKSkvL8/JySkowB7lTcWyzWA27Q8S3mr8Wx2yc+HTN/LtNR603QP8Ex4eHjY2NjNUXhX989i0Af4eHx8fCq+zMjo6KpFIhof7pqZuTU9PTExMzMzM9vUJ29rqpNKl/v7O6upKmqlCJlvu6WlZWBCPjw/KZJKZ2VGEpAsLM0tLs/Pz0/39bVNTsn37NioAtGsXevTR5UceyX7iCcsf/KD2qafQU0+hxx9Hu3aJCaJezgaCYxFBZL/yiuenn5L796P9+8mPP0Yff4wb+/ejgwfRl19irlsKg7I2j/Sjjb5lefsP2vJ76G5w4OpzVt7uEYlEdXV1AFWm2ajlD1FoI4QSEiLffjv+1Ck0NbU4MTkmkUhEItH4+Nj4+Njk5PjKiri/r7Orq2llZbG7u6WmplIsXsP3LC8v9PS0LC2Jx8eHZDKJWDxGyiTCntb5hVmxeFIqXRgeRi+9hJmFn3sOY8bffRczBbz9Nnr3XfT22/OvvJK0d6/tqVMdurrI0BAZGyNd3ck//7lx3z4ZZRJBJRAwLmNfe+3Gp5+CGMlDh9BXX6Hjx8meHiQQIKGQ9vfsSKQgAVqkdIPePzs7q6+vT+OaRSKRQCAICwuLj4+HB6KCDBU+4mL17v6vvpoZFITE4vm5ucmVleWREZF4bnx+fmJuDotUJGoTitolkoWe7uamJoF4bgqGvrgoFgpbJZL5yclhmXR5dnZMKl0WCtvm52d7etp6ehpbWud+9Sv0+OOYEumHP8SmyYsvkq++iv7wB/Tb304/91zMr37l/Kc/id57D2KFaN++sb17W957D73/Pvrxj+klinbtCnvjjaRPPlkTKazSjz/Gy/Wjj9C5c6SZGamtjctiUAPDEoYR0lKSlxu06b/yX8EqXU3ANDAw+Mfx9wAFc2xsrImJSUlJSU9PDwVMv+MP8KZ0dnaKRCL5L0QiUVdXV3Nzs8J+oKtpbGxsaWnZ+JVQKGxqatp4IZFI1NLS0tDQsPGQ7u7utrY2Wo3RywwhFBUVpaSkZGJi0tbW1tXVtfFYoVBYU1Oz6eU6Ojo2HaFIJKqtre3o6Nh4tu7u7q3ud9NDRCJRR0fHGo8wLKD10SOEcnNzfakXVOi28XJdXV01NTUb94tEOBujvb1941c9PT21tbWb3u9Wh3R3dwsEAvmZhbZIJGpqaqLXKr2YgVknKSnJ1tY2NjZWJBJ1dnYqHC4SiVpbW4F0Z+NXWy2Atra2urq63t7enR8CpE0b5SAUCltaWoCQVuEHOzQ0FBMTk56eDpXSBwcHNx1/Z2fnpqsRbq27u3vjIIFqaONg4BCFX5BIJOrr6wNCKWj39fXR54RD5IUPd/Gt/n3Qdo+tra2ysvLFixe1tLTkqm6t/0Q2/At1SaOiouSesCSXi3JykFi8ViJmbAzFx6O4OJSSikZHsbimpnCSc3IySk9HKSkoNRXdvEmmpaKbyXg/7MnKQlFRGMpA+3seeYR84gn09NPkz35W98wz9i+9FLZv38Tbb6N9+7C7Yt8+9OyzrQRRfie5LSKIFoKw8vTEHK/UhivmLC4uwJvN0tICSUpo3Ald22F5eVm+sAaNy1k7BVXEAG6Z/gpkgxCSyWSurq50nMvU1NTLy0tFReXkyZM7jnNFM5k5FO/Dmm5bGzsWHcrORmlpKDERJSaSFRVYpFlZiMnEBDkpKVik6em4Q3IylnBiIpmZiRgMVFKCysrItDRMKPzGGxhuTAmN/Otf0Zdfyv7yl/LXXrP6/PN4ff05KFZgYIBNnzNnat97T/DWW+i99zAZDzUXiBKyY3w8pmujNlxkjRKpTCqVSKULCOEqMyRJymSypaUlULRLS0u0SBFVt4iWGHSAIkR0QSX4lj6Wrku6+rJlZmbm7Ox88uTJ06dP7zDO5e8fsmtXybPP4nWYkYGXVnIyXnj0/2lpWIwgvYwMvJ8SIF6QsEpTUrAwU/4/d9cBHsV1rdfYxE6cvBQneXFi5zmxHWPiEvATphgbG9OLMUhICCQkRJVQ7w31gnrvHTXUe++o99572aK2q9VK2+c+zl5pWCSMie345ct895u9c2fm7syZO3P+e2oqys8nysowPSEAfXIypK//3/8ldu1aC6izbx8YSP397+Wvv271t7+l7trFXx+loDR8//36Dz/s2rMHYNCvfkWStOCFFzweJojF9ISZH6RiXsUZmlahIlofpWLJKAVtKY/HI+fBOH+TNEnJhDA4CQxJT0xSDodjbW1N5iX9d5D3cDgcFos1ODg4PT09PDyMXyWx1EIQxNLSEn5JpZqhime3eMiRrySucCULHqkbziIzfJHtmDh8Ph8nLSc7xFlNhEJhd3c3mTqAJCnOF4HD342Pj+OAC2SfuCISiSS5ZR4liiIPwFNw/Nd4Td7C8vIyTuEk9YGFR4/vl+yBrBAEwWazcWo5shH3hmmL6UBCB3wLi4uLs7Ozw8PDIyMjTCYgfulz8SBcWlqSpgZ5AJb3kJtkRSQSsdlsclO68k2nCIVCFoslksqlhc8CZcLkJE5Tg6+fpDxO1T45OYlj/XG53A0XSRAEl8vFQjjpa8B16QGAKY8pIxAIlpchkvVmUkifQnZIEASfz3/iv2B7RDJQljTlhULhwsICm83GaGN8fJwjiblOdkte5BPbceI26YPJIcTn87F5Ln70+E/J3qQpTBAEi8Wam5vDgRk5HA6dTl9YWCDJiBCEf8QiWEyQH2H9Y+Oe4uLiM2fOBAYGent74/zV5Ah7YgXLe7y8vDAtJHQn3N0h3vF770G2SxaL6O2Feerf/gaz4bt3iZISgs1eS3uJQwY/cY1jNGPTk1dfJd56C334IdqxY+ovf/F/4427O3f2SSbQaPduYs8etGsXa88e9t/+JpZWu0jqc5J4eoMeHmhsrJfDWWazmJYWZo6ODpWVlZaWFrZ2di2trfji01KTb966FRgc3NTYoKura2dnNzwCiXJqq6uNjYzHJHGBmxobwiMihwYHLS0sq2tq4uNiXVxcV1chEwKmD5ZVmpiYkLjHzs7u3XffDQsL09PTe0Y9V2RkZFxcHNknpqqvL9gIp6QApzx5EvQdv/gFRAq+fBki173wwlr85eeeg8pzzxEvvgjJoV57DaL6fvghMGZcMHvetw8ECYqKSEVl+LPP3A4d8lJXHzc2BqxjaAgZmjQ0FnR1V2/cEH/yCXDojz7CiTYRhYJDFNIT4oWjI118Pp9KndHT03VxcSktKTY3M3Nycu5bz98UGhysrqERn5BYUlKkr6/v5OxMpdHEYnF+bu4dK2vGLISLLSkpup+UPDQ0aG9v39zSEhd7Lyg4BLukkSRdWlrS0dEh5T2Wlpbbt2+Pi4vT1NTEcdyfODjJRoSQh4cvhZKOh8f77xPx8WuZRDG58Ah8/nkQaEmPRmlX/5deAqPvd94BMn700dp6506oy8hA2bULDKH270c7dvS89ZbzgQMBiorUS5dADKasTKiqIgWF+aNHuXjE7tkD0SO3bsV62AEK5c7WrczMLN7ISLdILB4c6Dc00Pfy9i4tLTEyNLh79y4efgRB+Pp4a9y+nZ6RUZCfr6dv4OruvrjIFImEqSnJtrZ2LBbEvspIT8vKyqmvq7W0vNPc3Ozp6REQGMTlAk4lSTo/P6+rq0tO456Ce8jPHHku2fKDVMjHRPa/tLSEcc+GXeRnfXM7uQsn++zt7cXBi8fHx3HcLKFQ+MQsMdJdQXSxickKyUKn00tKSvDnnjxGJBL19fVtxj343zE1JiQLSWryXPIKpVvIOhZdsFgsoVC4uLhIEMTCwoJAIOBwOHNzIPGl0WhsNntgYKBb4oSI/4s8HVdwI7mW3ostunDKxc3nkqeMjIx8k5j/iXck/RfSdbLDJ561+QLIc7/p+OnpaTIa9YZjyP8aGBjAOZTI3sjKhlM2t0syjawymczFxUWhUMhkMpeXl3t7e8mcgJtPIVvIyhP/BSE0OjqK0eTmG8f8AqOToaGhf5b40v+IldcdHR04mDgWgopEoomJCXLMkJeKxcV4mlVQWOTq6mZiaqqtrVNcUurr6x8XnwA9r4uUxyULSecfofKj4h78ZkJKo9VVnJL6EZnWa9JPDtfz8/Pv37+PHwCmlUgEgY8NDIh334WUCMePE8nJkLooNpbQ0QGbkosXiS+/fCTIISU60pXnn4cj338ftDD796Pdu2ffeSf+9dft/v736t27hbt3o48/hum1pLLy9tvJMjKMx91qcEZSVwoFQhR6eaGS4ky1q1fj4uNOnThha2dfXV196MuD1tY2ZJJFB3u7hMQkWxtrIyPDc7JyFy4oZOfklleUWViY2dna+QcGIoT8/XyuqF1lsVhOTo4mppLFxPhBTR05/nDF29ubxD0rKys4onl/fz8WaazTcu13M0ljY2PLysrIPiXPhTA3h0DSeXkAKGNiCC4X1HMUCvBpnPob17duBQz02mvAVj/+GKuxQBQhIwNY87PPIK3B1atIWxvduDF19Gj44cMOZmYtMTHie/dQdDRx7x5x/z7y8Fg4fvy+js7S2bNIRgZY+y9/iTn0ssSgqpVCQUn3BSnJUdeuXY+Pjz/45Zcenl7ZWZlHjx51dr47vZ4QWVdHJyMzy0BfT1dHW1lZSV5eoay8vLSkxMjIyMBAPysnByFkY33HytrOxdlJ8eKlyIjw48eO29jaLbEhsQYmEP40ODo6khO+5eXlvr6+6enpgYGBJ+q5NpPUzy/cxKTG0xO99Rbxl7/AULS2hpEpQR7fOBS3bgXt1SuvwCkffrgmJJORAcLu308cOgTg6auviDNnCHl5pKSEzpwZ3707eO9eZ1XVDiMjsbExgEh9fcLAAGloUM+eTdbQ4Hz6KQzdvXvh7XjuORyi8A6F0r91K8rJ4cVEB9+6pR4eFnry5MnAwKD09LSDX3zh6elFozNAlczna96+XVhYaGigr62tpaKiqnBBoba2Ljsr08TY2MzMrKCwCBGEjo62nb3T/Pz8zRvX4xPv11RXX716lS7pgSQpj8d7aHg3N7eWqeCbcI8k2dlqbk7O8MgITiFXUV5eXFxC8n5M6u+z3vw6MJnMhzn7NrQ/4yZ4MkZHKykpaWtr5+XlPQx3eevWrYGBgbKysiNHjmBJzIaupC8+Ly/bxNTI3d2lvaPV28ezvBzHjl87hM/nNzc3k/dO9oNN71ksFpPJnJycJAOLkwd8awUh9DBfwfHjxysrK21tbRcWFo4ePVpQUJCUlHTq1KmKiorjx48HBQXJy8s7ODjgJBIb+sR5Lerq6hoaGpaXl1taWkiRFf6w41Qb5M1Kn47fNYRQb2/vE0kkffC31sVi8czMDNbsCASCb8Ii39oPeQAOgo/zr+HrJ3fhW8ONbW1tz+KDLH0uWQevhdJSV1dXBQWF4uJiNzc3b2/v8+fPq6qq1tTUkN8i8vhnr+AnS04wyK6wECsmJiY0NNTd3T05OXlsbGxztsFn/CMJZJ+4evXq9evXnZ2dHzx4oKamdvXq1YyMDAUFBSsrq41Rc8UiQsQjRHyERJPjo/n5+dHRUZGREeVlZYmJCdXVVSCzF/HhAILA4ihy5PwIlR8b92CLPA6HMz09vZniGFEuSgShCCHMnHJzc3FeUoSIwUFICo3NO4RCgD5vvQUM+MMPiZkZSFWdmwtMVBrfbKhv2QJxb994A8xjsQ2EjMz8m28mvPqq9VtvZezaxcGN+/cD59i9G+3eLfz4Y76MzNLevaS1BO4fUSgJkpyOYI/i7YXycpMMDAwyszLl5GSjY2Lr6+tPnToVGRmFZ9IIIUcHu4io6DuWFubmFiqqVzRv3w4MCmUw6FZ3LO/csfL28V1cXJyamjA0NBoaGgoLCbl+/bqurp6xsVFtfYP0aH6o9DE2NsZxC0ldw+bXFZMXPIpnZ9nrMlU87iMjI7Ef+7pqA5B3RgZQ5k9/IlRVIf/2mTPEH/7wiJLPPQdw5w9/ALp99BHAnb17gT1jUcTBg4S8PPis6eoiExN069bMiRMRJ07YmpoWJSbyMjJQWhqRlgY6nfR0Ii1NGBy8qqXF0dVFBw9CV//93xj0IAolgELJkEhNUFKSID4uyNTULD09/aszX6ekpubkZMvJysYnJGIm/dD/XEdL6/79JH09PX19fW1tgD4ZmVnTU1MS1zPdxPv3V1ZWOtvbnJxdXF3uamvrXL9xXe3q1Zs3bnR190iTlCWJ30PKex7py9axkfRYRQjRGQzyC4hJGhDgm58P8Xuam8H+hkKB5CcfffQI90iLdl58kfjd70BCiRWCJDF374asKefOwSNQVyf09MDmxs4OOTsjPb3JEyeCT5+209Wt8PcXhoaikBAiIoKIjEShoWIHB5GW1oqOzgom6a5d8HQkMF0oycBVRKEgCe7hRoT73bljFRsbo6h4MTc3Nz0t7ezZc6mpabMSgCLg829r3E5Pz9DX19PX19PV01dSupSbX9Df12tgoG9qapqckrq6ulpSXHTXxbWvt9fW1trTy0dbS8vHzx+Hy3806ubmNuQlvXSpycQEgJqJCZKTy83Pz8ODNiEu9ryc3C11DR6fPzE2amFurqiomFtQ+EN9/shnh584DlvwRNZOHvlNFSzsOX/+PNbXNDc3X7t2TUtLa3Bw0MzM7PDhwwkJkoms1PlikZg2szQ+ujg5xpyeWGprHrwfnxMTmVpZ3pKTVd5Y3zM1sTQxtjg+ukinsoVCEZ69bHidMdgyNDR0dHTEphjk6JX6q6dVEUJtbW3bt29XVVW1sLDIyso6ceKEkZFRTEzM7t27r169euzYsbt37549ezY4OBgz0fXuwJwAP9zKygdJScmxsXFdXT2qqlfWEyyu7WUwGGQw1Q2XR1L+KfKe9b/7ll+EUFRU1NWrV+/evQsfVUdHzB2+5bSn7sZqxG+S9+BTsV38d8ZYOCCLubn52bOQXkldXd3GxiYlJSU1NdXQ0HADuZ56sRt3IoTGx8c3y3uwUXZcXNzdu3dNTEwiIiL6+/vxPW7s4hm2EUKhoaF2dnYEQSgpKSkoKJSUlHC53Pr6+kOHDj2MnftYaHuECGoLUWVPVDtDqXVFDe6o0R01eqA6V1TvhurdJLuciAeOxOJIT9/Af7ieq7Cw8Ouvv75w4cLhw4c3eMrAnIa9ZGpiYmpmzhcIBgf6lZSUZqi0kpKS/Px8yYcA/Gw/+ohoaVmTounrg7ft6ipkGOVyIczali2POM0GxPPSS2tKGRkZcCzauxf94x/Tb74Z+8YbNrt2pZ86xVZURJcvo6tXgeVoaRE3bqAdO+q3b6+RmEqgvXtBrbPeJ6JQaigUJ4mPDLggubuL5uenQOu5zPbz9fXy8q6oqPD38/X09CwvrxgeHh4bG8vLy7G1tU1LzxgcGLjr7Owf4N/Z2VlXX9/U1Ojk7NTV3V1dU8tiMYuKiwf6+52dnesbGrIyM4KDQ/gCgfSLgRAKDw/v7+/HH0cnJyecqwubCEiPYciPyKBraWo6OsM3oqmxXkVFdZXLi41NjIpqtrNDNTVrhyNEDA8T27cTx46B+OGDDx7JeHCYwe3bAfHs3QtFYqoMipgDB0AaIS9PaGsjY2Nkbo50dUfOnw89c8bWyqowMXElKwushWprIal4fz/AKXv7Ci+vdj8/pKcHz+uTT6AfiTwJUSjZFIo3hSKSSClQYqJgbm5CJBLPzc25urr6+PiUl5f7+nh7e/s8qIbEe+Pj44kJCXZ2dkXFJc1Njc7OzsEhIb29vY1NTeXlZV5e3h3tHa2tbXQataKyanR0xN3dva2tPSUlOSw8AidRxzePyejt7Y2tMrGgYnp6GqvepemJJ39joyPq6re8vLwhV1d52fXrN/gCnp9f7K1bbcrKyMAAaLJ1KyCbd96BkEXrYwYG50svAdzZufMx4Lh7N1Dy+HHIYH/7NsAdCwvCywvFxoLFj7d3/7VrgRcU7B0dyzMyeKWlYEdVWQmu5tnZRFqa2M6uxNu7z8ICcjPfvg3GQLt3gyBz61acwX4tROHWrSgrizc/PyWZCI67ubkGBQVVVlZ5eHj4+fs3NjZJzC+G4+Ji7ezsKquq6mprnZycw8PDIaFmS0tJUWFAQEB3d3dHZ+fIyHBNbW1dba27u3tlRYW1tVVEZBQZIJSkmJeXF5mX9N691CNHmpSV0aVLxOXL6Isvsq9fv4YJbnXHMik5We2K2vDIGAjtR4avXlXr7unDz+X7r8mn3N3dHR0dHRMT09HRMTIyQl7nP1V5ODfT1NT09fV1c3NLTk728gJ3PBMTk+PHj6uoqMjKymJpDfmnq6sCG8PCK2cTbijcvy6fdEsxRUs5U+ty5i3FVI1LabcUU67LJ91QSLpyNsHRrIS9tNLT82T7npSUFHt7ex8fn+7u7m9S0j3lRhBCNTU1pqamV65cuXjxoqamppKS0pEjR27fvq2kpLRjxw5NTU0rKysVFZXU1FQpeZJYTPDEBFdMcAnEG58Ynp4eGxsb7OntuJ8UPzs3gxAf70VIRKVSu7txnj54aCQFhELhfcmSkZExPDz8OKh6yiU/eRcIxf39lZSUfH19Jycn5eTkzp49i8WKTz7hGVoRQmNjY2T4LumLn5ubi4iIiIuLq6mpGRwcxEKmZ+hy4yEIoaysLF1dXQMDAxcXl7///e8ODg4mJiDT9/ODGBYbT/hntgcHBzfjHtwBj8dbWlricDgrKyujo6PYqOif6XvtWIRQUVGRiopKSkrK5cuX7e3tbWxs/Pz8YmJiTp06FRcXJzVmACQTI8VE8nkiXemp5RKRqogYndNU+n847rG3t3d2djY0NAwLC9uQp0ISRq8PhoKJ8QyVvjA/p6WpOTY+UVxcoqFxjUqFXFECAQh16uqIsDCgbXAwcJG2NmjU1weBBMljpCsvvwxT8I8+AgHPvn3o44/577/f9fbbgTt3Op45k3frFsvAYM3uxMAAZEgGBoSRETDm/fuH/vd/WTiq3jp7xpKJcUnyIxpp7uPuvtFAmPxeT06OJyUlpaSmkjyA3PXsFXKkIoRaWlouXbqEJ1sPM3ApKSkdOHBATk7uwoUL+LsjfXB9bY2Nra2JJC/p9OSEhroGi718717cV18Zfvzx/OuvgyQGS33m54mvvwZFIdawPP88EHPrVjDfwXoTiQBsTcazbx/os27eJIyNQcCjr8+9fbtVQcFHScnZ3r4iNXW1tBQ1NqLeXgiFR6cTs7NoYQFRqcL4+MHExFUnJ6SrS5w/DxxaYlqOJKEBrHCEva1bwQ4mPX2NpNKiZkyxgb6+hIT4zMys9bxgz07ItSNJEuHOHzr4KCsrYwm8QCCQlZU9dOiQnJyckpLSBsEAeBSXlVpZ3TE3N2cvc4YGBzXUNVZWV7y8QigUsxdfXL57F2lpAel++lMIOITNyF58EWRa77yzhngexpjyBaWvAAAgAElEQVSWkYF7//JLAEmqqoSmJow6CwvC2RkFBWGrZ46LS+ONmx4mpi4ZmTV9/dyxcTQ6iqanIVojjUYwGGh6GhUWCnx9B5KS+BYWSF+fuHABdI579hC//jWiUBok0HwtUvMLL6w5FeJ730Cynp7uxMTE7JwcgVC4Ydczbm4gaU5OzpUrV8jPcXR06rZtTevqY7RtW25CQrzEmBr5+vhoampeu3a9vr6ht7f3yOFDuvoGpND3Gf/9KYeR91tQUKChoaGvr19WVtbe3v7d5u5YfeDk5BQfH0+j0Xx9fYODg6uqqmpqagQCQUZGhrSVD1h1rAhMb+coHI1WPh0L5RQUpVOxSpLNtcqpWIUj0db6hbOMxfr6OlLcKE1SPp/PkyzT09P4SyK991vr2J2tpqZmcnIyOTk5Ozt7ZWWlqakpMjKypqYmJyenurq6trbWzc3Nw8NjHVchEcFmClMXhQlM4f1FYSJLnLwkTmZBSeKgNJY4aVGYKCmxq0Qrm72MT8TPgiT7ysqKhYWFkZGRu7t7V1fX2Big22+94CcegE9MSEjw9vY+c+aMvb39oUOH9u/fHxkZ+Z37xF+Atra2zXourNqDtME6OikpKW1tbdIP94lX+E2N2Pq4vLy8tLSUSqUmJyc3NDQ4OjpGRkaSkuNvOvcp7djNuaGhgXzRpOmAZ25zc3OLi4vYpu374B6xWJySkmJra9vR0bG6uhoYGOjj4zM5ORkSEuLl5fWYpRpCxGgpkXaRyLzy1KJKZFxGs12T0//pds137tw5cuTIwYMHjx8/Tnpp4ecKYVSWlkyMjSwsLCorqxbm5wMDAqamZ/Lz8+XljaanVySvEzDpoCDgvgIB4eOzFnTk0iUwENm6FYp0cvWXXgLLiV27wKd371700Uez27blvPPO3X37PJWU6vT1BYaGSFcX6egAv8dFTw9pa68oKtZpaa0ePox27QLT5r17ITGkBEvhnFy2FEoLCXok8p41Ji0Wi0dGhjs6Ombn5kZHRzs6OkjN6+LCQltb29zc/OrqaldX9+joGH7leDzu2NgYdmoYGYZlmbM8IbG9Hx0bHRkdlZ5kIIQg6rSaGinvMTQ0LCoqGh0dHRwc3DCsIdUrg6Z5W8PJybm6pmaJxfLy8Fhic6Kiojw87s7MCN5/H3l7A0kJguBwwDrn5EnIHbawADkp/+u/gFWTAh7MrT/+GBDPtWvI1BQZGRE3blC/+irl9GmH69f9PDzaMjPFDQ1oeBhYMp2BaHREpSE6DQ0NsXJyamk0flMTSkpCNjYQHfjzz4H3/+53iEJhSHAkDrIHdP7Vrx55/gsEgoGBgc7Orvn5+WGJyyW2zgN9E53W3t6+tLTEZi91dnZOTU1hgfzy8jL+ivF4PCwZmp6eHhwchMgH8wujo2OSSfna9wRLsFVVVfGUhcvl6urqVlVVwcR20wwPpoajIzduXPfw9KypqZmdnXNxceVyud5evhSKz4svEu+8gz76aE3M8+tfA2r87W9B+rJvH1ASawY//RQMd1RUCG1tCOxkbQ2pQKOjUVYWKi0Tp6TNWFndv3nL0dEpqLauc2FBvLSIFmaFc3TBLF1EoxEzVIJOR/39c8XF9R0d4vR0lJAA2d+0tUFNtmcPeLlv2TJFoZhRKDPkKCVxj8TEldvf39/d3T2/sCChbSfpXUKdmW5v7+BwOEtLrI6OjpkZKmZjSyzW1BTIwHhc7sAAyKVXOCtjY+NisZjBoI+PjwO3Xv9CY5sDNTU18nMcHZ367rtNe/bA27R7N3r33dziYizERQw63dHBobLqQVNjQ1FxkYuri5ub+8Dg4FOgzD+1i/y8cDicEclCo9Ee+0yvX/Yz/j7936U7wbjHXCvnwvGYy2fiyKIsVb/8FbQrHo+x0s9f4fCGh4c22/dIQ/8JySLN3qT/8Sn1p1/2hr2SfpBQzJwThM0KAuYEgbNSZU4QOCcIIlsYAp8VomZxEXLdkP2QV4Ih19jY2NTU1Pj4OFvKtI485tkr2Cve2Ng4LCwsJiamr6+vs7MzNjYW/++z9yN9JP6o4td/Qz8CgWBUsszNzY2MjEgL86R7eJa6NGXI+oa/e5Z+NhyDLW/IF016YGCVrpWVlY6OTlhY2MjIyPcRtklfs/SAJNsfXRjGPakXiYwrTy2qRPplgtE5PDoxObmWA5vs7V9a+VHte/BU6d69e87OzqmpqZufgcS+Z35xcZHFYvF4PPwtLizM2bLF65NP4OOOEBjexsYSTCbUXVwIS0vI/+DvD0qon/8c+M2WLcA4X34Z/fGPaMcOQDwSuFP59tuBO3Y4nDyZeOPGpIGB2MQEWVgAy7Gzg2JlBbNtS0vQ16ipjVy82KCri778cm32LCND6n3QE0MUengQIyM9VVVVTQ31CgrnLSzvFBUVysnJmpqaNbes+XN5e3qoXb1qaGQUHx+roHBBTU2tqrp6amoqMiJc8eLFisoqkVDo7+d79uuzkZGRioqKwSGhwUGBJ06cHBgEn1s8qjb7cw0PD0dFRbm7u+PIWo8Gn6QmyXJPZ7PZTCaTzxfgj05kZGRSUtzSEvrHPyDQzuHDYFPi7U3gVOoXL4JPXEoK8GbsnIXd+HftQl9+iZSVQcCjrj5z7lzxkSM+X3/tbGqaERlJy84mamrQwCBgHTqdmKMJmNQVFpWzzOAx51BlVU9pafv8PKqrA18nS0sQcuzeTezYgX7yExGFcpdCKSM59E9+ApK5qkphT09LTU3NgwdVp09/ZW/vkJGWel7+PJiK968xRTMTkxs3b1resQoMCLiscvnatWtt7e2TkxOenh7Kype7uns4y2xHB3s5ufPJycmKihf8A4Jua9yyvGO9Qc+1wZ+rt7c3PDzc09Nzg/yMfAQ0Gm2Zw1lYWODzBdgKDftzSTR0AKAdHGBA/uMfIDzDwHHXLiDmsWMgVLtxA5RZ5uaEqysKC4MoADU1qLBoKjSs0MHOw+6OXVpS1swoTbgkFDFX+fQFEW1WTGeI6QwhY45PneMzlthzgtLS9pqanu5ulJoKMwE9PfC8k1iao1/+ckViHt6ASYplTmDfk83v7m6ura3Nz8uVlZNzc3dPSUn++swZO3v7UcksXCQS6evp3Lqlbm/v6O/nq6KieuPGzf7+gdHREWcnxyugihplLi7csbRQUlJyc3O7efNmbl6eqoqy810XaW0sNtTT0dEhQf9m3FNQkItH9b/0A0e+ONJfahaLtW6bsuF1+YE3Me4x08q5cCz68lexuCh/FYvLZVw5DZsXjkVb6ecvs7l9fb2bcQ9CqLu7u7S0tKGhYXx8/HuqdZ75JpFQzJrjR87yg+f4IbO4CNYra5vBs/wQBj+AI65hsznDw2Ccjhfpf1lvgwwwmENL7/1n6/gbKP1An6UH8hrwidKnYGS2Wc+FjyFP7Ovr4/EeOS1K7yUvRrrbH6f+UORPRgfYQHnsxBcWFubj45ORkTE0NPT9jcqf6aZAz1VKpCgS6apPLSpE2mUxvbNvYPg/XM9VVVWlqKioq6t75cqVdWnqY5QkBxk5koqKcigUjxdeAPMFhICp/OpXhJISnHXpElhFTE0RGhrYdQVMjF9+GW3bhmRkVmVkJrdvL3njDd/333c5ejRGVbVTW3vZxAQ5OKCAAABP9++DI1hKChQcjSYykmFjM6+riwwNkbY2CCSwiej774PmRcJF8igU33XBD5YAQbuXlzg0xF1O7ryfr9/Zr7929/Bsamo6fuyYq5vb2Pgakr3r5Fj1oMbD3e327duKihcvKyunpWe2tbebGBuFhoa5ubkjhFqbm7x9fAsL8o4fP+YfENTd1enm5iGWCDEwmfA7L52X1MvL6+bNm35+fgoKChuSh5OnrFEVC0MQioyMDAy8RxCgGaFQwCr8xRehcuIEMM5f/xpcumRkwIF/zx6Qk336KTp4EMnKrqiojJ07l/fZZ16HD7uoqyf6+fampKyWlaHaWjQ0AnBnli5i0VZ49AUxnQFyHgad2tHOGhplMviMWUSjgdtdWBgIOc6dA9zz9tuIQomjUGKkOTQm7/37Ky53zRQUFIMCA06cOBkSElpYkH/q9ClfX7/ZuXl8R0YGhs3NrWZmphoa6mpqaoqKF8orKhobGw0NjRwdHJJSUhFCZaXFgUEhQoHA3s6ur6/f1tZGR1d3anqG5Ih4ViSdn8ve3l5bW9vT01NRUfExe731obp5lHp5+b7yStqLL6K33yaOHgWbJ2wFheMYyciAJEZZmdDTg2iNxsbI3R0lJRFJScuRkcNe3ll2dh6ONk4pUVGDTY28ySk0OYX6elF7G2pqRA+qUJXEoqeyErW3EyCQaWWPjM4xRPMLqL4e8ktYWQHuOX4cHt8HH6AXXgilUJIkJAXjs+efB3Xb1q0oPXXJ+o7BJSUlLy+Pc7KyMdExuTnZx44dDQ0Nm1+A0JR8Hk9fT7+3t9fUxET91q1r165fvHixpra+tKTYxMTEzd09NS0DLBUy0pOSk0NDg9WuXMGk1jc0nJVEDSCH3Nzjds1PwT3rRN34SxL5e1ak+8VdfR9/LunevrVO4h7FY9GXz6zhHhIAkTDo8jruYTE5bW2tT8Q9TU1N3t7ehoaGLS0tPw5oIwjAPbP8cAYvkMELlpSg9TWuBDP4QQx+MJ3nt0LUzkzT8PcHE1maOOQT7Ovr+w5KOumucJ18eZ+4ufl4giBoNNrs7OzCwgKXy91wDVgJtVnPRXaOr7+trW2DC9vq6urMzAyDweDxePPz899HY/XEa36WRoyJyQnGBsqQPWCftQ0KAXLvD1wB3FNCJF8g0lSeWi4TqUqI3jUxTcWfWXKc/KsrP7a8x8LCIjUVuJGHh8cG+54nkl4i1cz99a+9JV7eIOMZGgLlyyuvQLCZ/fvR88+j3/wGfFUoFOFPfrL0u9/1/fWvuW+/Hfo//+P22mtu27cny8n16OqCPsvYGBBPeLg00IFYfElJAHrS01Fi4oqpaaq9PU1XFwCBujroJj7+GNZ/+QsGPThNJhszaWkTIg8PcXd3Q2ZmVnl52cWLisHBIaWlJefPy/n5+Xd1gesQ3LKbq4GBoaGBYWhIkJraVT1dneDQ8O6ebh9vT1VV1Zh7sd09PWGhoYXFJakpyaoqKl7ePmGhwaVl5dJDGXuUmJqakn7s165dKyoqYjKZhoaG6emQMfSJlCQbEULx8VGvvhrv5gYWsjo6ACK3boV7vH8fnT0LJlD79gHW2bVL+NFHix980H3gQOYXXwR/9pnrsWMeysqZJiaDCQnC4mJUU4t6etH0DJpjiJmzPC6dKZqho8kJNDJEDPSj4eHF9vbksDDW0KCQvkijEdPTREEB4ekJZuNffgm46o9/rJLYoAhJ3PPqq5DJi0JBaWmClpaqnJycosKC8+flIyOj0tLSLikphYSGDa1PKw0NDExMTIxNTFxdXB4Cl+vXrqWmpXV2dtra2KiqqqampQ0MDni4u7W2dfT2dDk5313hLPt4e9+4fr1/ACRGmCbgObi0pKuriyd8XC5XRUWluroac+6KigrySJKGGyqS8ez3yisZH34IsYiUlQHSkXEQ9u1D8vJIRwc0g3p6glu3FkxMOszMMtTVgzQ1Xa1tvO4n5Ay29gjn5sAGqr0dFRQQaamAymNjASTiEh1NJCWh5GRGUVFKZMTS2PgcXTw9QxQXg7BTVxf0hvv2gQrplVfyKRTPx6NrgujOxga1NHOrq8sKCgrSUlOUL19OSEhMiI9XVLwYHR09MQX2zg8Du+loa5uamlpaWrq4OOvq6V+/fj09I7OludnSwvz6jRvZ2TkDAwNOTo7DIyPFRYWy52RDQ8ODAgNv3LwxMTlJEgrLe/T09MjP8f8j7sFXhdc4tBqTydxgtrXhgf5Qm2u4RzNb4WiU8lf3lE+vFaXT95QkdUklRvn0PYWjUXf08jjLvMHBAWncQ148lUqdmpqqqKgYGxt7zIb0h7rWJ/QDei4GN4zODaBxA+lQgqTWjzapXN9lUQ2LySaNDsnLxp8+Mvbd6OjoN4WQecL//wBN8I5jIxhPTy9TUzM/f/+Kigo7OzspBADXODMzs8GfC1+5dFzBwcFBaZswDDhMTEzMTM0qyiu8vLyysrLwjf8AF/7MXWz255ImvlAobGtr6+zsxJGafyRkhpB4pEScdEGcenm9qKxXLotTybqyOOUSooNdszToJCn/r6v82LjHzs7uxIkTV69ePXjwYGJi4reOEox7rlwJamoCIkxMoM8/h5QRv/0tev55kcQMdoRCefCb3yS8/LLnq6+6vvVW0LZtGe+9175797y8vODGDTBYNjCAeIaRkTjiMBiOVFSAb1F7O9HRAZXiYnFiIi8pSZiSwvf0BEmPgQE412DWtWcP8coriEKZl4QoHNgMeqTte4RCYWNDfX5+/sDgYFNjY0FBQfWDB3l5eQUFBe3tbUVFhWNjY+wlVmlpSW1d3fIyWAIuLCxUP6hmMpkjIyMzMzPs5WXu6mp5RTl9dnZ6ehq7oJNvAcY9VlZWJO6xt7c/ceKErKzsV1991SqJkUge/MQKQiguLvro0TQTEzQ+jsbGwIbps88A63z1lXDnzqUdOwa3bSt/++3Y7ds9PvrI/cCBkLNns69c6XF2Zt6/L8zPQ3X1qLsXTU+J5xmClTkOnzYvmphCXV2gxCouAi+jjAwiPZ2XkiJITuZXVyM6TUhlMOii4WEwo7azI65fJyT6x7Gf/tRSYtzzKEvan/4EoIFCIRIT10ymuFzugwcP8vPzh4dH6uvqCgsLq6oqs7OzS0pL2yQkpdJoc7OzhYWFzS2tLBZrdGyMSqM2NjYuzM9PTExMTU3yeDwWizVDBVOV5uamjo5O6YGHg6MYGhpi3PMwOK+FhcXJkyfPShZsR/VESpKNCCFPz8A338zHeDEqCp0/j2RkIM/87t1CeXnW9ev98vKlx45FHzvmLivrfvNm2N27uRWVfSNjLM7cKmJy0NwcGIEXFgIGj4qC0JxWVoSpKWFkBEUS6pFwdeXFxwtKS4U0Kp++SGeAJVZ6OoxtXV1CTg5Az/vv927Zckcqp/2aSPLKFXgmo6NrJOUsL1dVVRUVFY2NjVY/eFBYWPig+kFOTk5JaWl7e3thYeH8/AKdTpcM2o7FxcWx8fHJiYmmpuaFhYXJycmpySk+X0CdmamtrePxeA0N9b194H4lTRAmk2lsbEzGa/5/xD0jIyPZ2dktLS0dHR2BkqW5uXl0dJS82n9dBeMeU80shaORyqdjlNaL8ukYvElWFI5GWunlspdWeqX0XGKxuLW1NTs7u7u7mwyMOzk5iZ0N/3WXvd4z4B46N4y2GkBbDXxKoa76Lgurmcwl0ugQbnx1taKioqCgYHp6Oj4+PjIyMiEh4fv7c61f2zP8Yn8NCfOEHGHZOT7+ATHxCaUVlRHRMWtqWckxm/25FhYWCgoKKisrR0ZGQkJCgoKCysrKhoaGpNNBSFJlTwcHBfv5+pWWlEVFRtXVQqA1cnmGS/xhDtngzzUyMpKTk1NfX8/n85eWljQ0NG7evOnp6dnf3/8jIWaExMMlwvsKwpTLwhRlyfryk9bKwuRLYlpHT//Qf7iea3Z2trW1taWlpbOz81mAvyTcU+6LLzrs3Dnh5dVubl72i1+kbtkS/sILPq+84vGzn7lQKD7btiXs3FknIzO+dy9bYjoK6bFu3gRdla4usI+ICIgck5YGk+PubtCL0WjgZMRgQFlYQIWFjW5ulenpkH7BxWXN2vT8eazrgVyPL74oolDcNqTJfFzeA7IocsRLV1gsVmdnZ2dXF8ba0rtIXZ504xPqUm8Hxj1kfi4yfjmHw+FyuZtd2aVOXaviABiffhq4e/eEvHxrZmbx2bNJV6+G/uMfPn/+s8ef/+z6+ut++/cnXbjQqKMzaWKy4uGBQkNRRAQQp7wCjY8T8wzhyuyykDYrnphADfUoPw+lpxHh4YSXF+HsTDg6Imfn8ps3WywswDEpJQX19grp83MMUXsHir0HMWkuXUIyMst/+IMNhdK2AUf+6lfgfHf0KNHY+I0knZ+fa29v6+npwdMvaYp9E0k3tEuTZUNeUkxSDofDZrNXVyGTg/TBT6xjec9rr0Xt2DGhotJy5kzRm2/ef/PN4Pfe85aRcf/0U7dDh/xPnUo1NW2JiZlKSVltakI0GpqfR2z6CvhljY8SDx7AGI2OBsRjZASKWxUVQkGBkJUl5OSQiopYV7fw3LkuJydUW4MY9EXa6vQMKi4GYylzczj8wAG0a9fCb35jSaH0byDp88+D3dtPfgKI9JtG6dzcbHt7e29fH8lfpan6LHVpymB5j3R+rv9H3DM2NpYqWTo7Oy0sLIyNjSsrK9va2qTn7tIX/wPW13DP7Uz5o+GXTkcrnY5SOh116XS0pOBK1CVJi/zRCGv93FnGgrQ/F5/P7+npiYyMLC0tFYlEOLkNmVHhB7zOJ3aFCCQQMakrwTMc3xmO3wzH/7Gy8mhzmuO1JHjAYrGlcQ+LxWpubg4MDOzr64uJiTEyMvLx8enq6vpxJG0EQuKBNqG/kTDYHJfVQFNBmCUv2IwfZMYLsRCFWKzt8jckBltbO7ukRQ4MBqOysjIkJGRwcNDV1VVXVzczMxO8Uubne9md1bPltXNVtXOVNbMVtXOV9fMPamcra2cr66Gxqna2qnq2om2xUSAWIAh99AMs+AXEHW2YYAgEAml/LkISBjAtLS0+Ph47AObk5GRnZ2NXvh9N3iMaKuYnnBckKfGTlASSIqkowzoZGvlJUOffVyRoHTP0WenMbs/ytfmex/yo8p4NvEf6+X3T0EAIFRbm/fd/n9i/32fr1gg9vTRv75I//anh5ZcHDx9mHDrE+/OfQVxx4ABYonz8MUSq/fxzkPkbGoIyxdER1AWpqWD73NlJUKkEnQEeRjNUcIqh0SH5FI2G2tvHGxo4yckoPZ1wdQXVj54eceTImnHPG28gCiWWQonawE6kcQ/2YxcKBbOzjPi4uLCw8Na2tvj4uJCQkL7+AfyQmpsa3d3dS0rLpiYnAgMD4+LjsRZgdGQ4KjoaZwCoqX6QnZ07MzMTFxvX3z9QVlqalZ2N/VrJQS8SiezAVGVthi2SLEwmE4srvomSZDuS2Pds2yYnK+v33ntRFy5kvPdeWXh4s7z88KlTcyoq/CtXIN+ZtTVyc4MQMpIEUiCJGBwkaFQxm84hZmZQXy+gyIwM8KkzNwd63bwJ9lby8ujCBZGy8riKCk9bG/n5gXampUU4u8Cgi6trsAUuOn1a/Je/+P/ud5lbtjyS9Eis0cEs/fBhIjAQ4glhzQuVSg2PCI+IjGxra7t37154eAQ5OSgrKfFw96hvaOjv6wsICEhJScWe7V1dnQmJiSsrqxB2orCwuKSURqNFR0e3tbfn5eVWVFZKjz2E0MrKiqmpKUlASYY1Lo/Hk1bnkwTcXEGQl9Tr5z+/9MYbAXv2RB84kHH6dNm5cy3q6iPa2vM6OkJDQ7BfTkmBUl+PGAw0SxNxqQuIToO4Rvn5ECH77l1APKqq4C+H/bJ27wZfRBkZCL2gpDR686YwOBjV1oros3MMYUcHZJ1zcwPanzmDdu8Wv/66B4VSvHmU4hhCL7wAPgE4KPP4+GhEePi9e7Ht7e3h4WEREREzVBoepfn5eR4enm3tHd1dnf7+/ukZmdiWs6mxMSkpWZLfg8jOyqquqa1+8MDf37++viEzI6PqwYMNJF1aWjIzM/t3kPfU1NRYW1uXl5cLBAIqlUqn02dnZ7+PP9fmAfBNLRLcwze5nX7+SNilU5EX18ulUxF489KpSFw5fyTMUjdrgz+XUCjMysqytbUdGRnJysry9/cPCAh4YnKDb7qA79OOcc/0ctAU23uK7fuUMsn2YPGrFuaZA+sJZHDaivDwcBcXl6WlpVnJMj8///39uZ71jhAS1RXw1f6Xf3MfLoJb+wW3Plkv+8l2/pWPiNocmkTASXLT8fFxd3f3iIgISZ4cKoPBWFpaGh0d5Qv45bTChLHI5PF7yRNQUiZicUmdikuZiMWNiePRBTOZfBHvB8E9+M3icrk475hItJadEJNisz9XY2OjnZ0dTsAuzXMHBwefRdbwrBR+ynEICYeKV2NluYmX1svF9QrZcombeHE1/gJB6xyfnP4Pt+9paWlJSkry9va+ePEixtdPoR5+ZqWleaqqYebmEBZvdBTFxKBf/hJUXa+/DkBHUXFNG4WzhyoogLRGVxeYgZ0dERcHeoPycmJ0DBAPjSpepK6sUJlc6jyfvsAYpaenlY+Ori4soM5O0IJh3KOrCxPoTz6BnvfsQb/97QMKxYFCWYuDIg13fv7ztWCG3t6oo73O0cEhISH+9MmT1ja2FRUVhw8fvmNl3dWzZt/jaG/n6uZpaGDg4OAgd15e8cKF3Lz8hvp6ezvb27dvx8YlLLOXtDS1tLS13d3djxw57Ontk5OdJScnR2dAsH9ylIvFYnt7e1LPFRgY6Ofnp6KicvLkyWdUysTExLz5ZsYXX4B66+BB8HpTU0NaWpBcwsQE2doC9by9QUKWkgJMeWCAmJhASzSOkD5LjI0TZaUAJ11cwHn6yhUwHvn8c1Bc7d27ICNTsX27QEYGnTgBT8LSEoyGWlv4s0zqjLi4GIx7dHXRjh3Zf/2r71//ilOvr+licIzjX/wC7HC3bEEpqaLq6mIHB8eE+PhDhw67urlnpKcdO3bM0dFpfN1UXFdb28fHV0db28Lc7LLK5QuKFx5UPaivqzM3M7t29VpZecX01KT6rZsmxiYuLi6nTp+OjIzU0tJSUVUdn3jMGGV1ddXc3JzMU+Hp6RkfH9/T06OiovIskmFJON3gI0fyP/0UILiJCervB+BoaIj09ZGxMdx1Sgrg75oaHH1HLAE9VKBsdjYRGgpCsBs3wN1r716wM3/nHUgz8e67aOdOuoxM1bZtwnPnoKPQUNTcxJtlUqlEWRmobijW4KwAACAASURBVI2NCTU1cFp8553E55+P2Ax6yOH6wlaUlycqK811dHAMDws7eeqUn59/SnLyoUNfurm5TUsMvQUCvrq6emBgoL6enpmZqeqVKxcULzQ0NVeUl5maGKura9TU1o+ODF2/cUNLS7u8vPyqmlpKampISJDa1Wsrj2eRY7PZ5ubm/w64JyIiIisri0ajkXFxWCzWs7wpT/80PctejHuMNdLkDodcPBmheDJ8vUQorm1G4Ha5wyEWulkb4hYuLy8//BTU1dUtLi4GBwdra2vb2tq2t7djePEsF/B9jsG4Z5IVOMHymmD5PKWMsdxZ/KrpKVpnJ2iQ8TIyMuLi4jIwMMBikTmb0dDQEM6g+X0u7JnORUjUWMRT38/TOogLHypfkpuPGm/uI+oLJmZo5OuPEKqqqgoICJicnMQzH3xHOO15JaMkafxe6kRC2kR86kRCKqzXKmmwCSVpPLZwJucHwT1YC+/n55eQkFBdXZ2enr45/svg4CCmKr7OyMjI1NRUKpUqkCzZ2dmpqam1tbUTExPPOJF7Jgo/5SCEBINFnHvnVuIVV+IvriRcXIlX5EC5yIlfrydc5MQpcuLkxbQOKh2ylq6Nmx/l50eV9zx0qzMxMbG2tlZQULC0tPTx8SHZ+TfREMLEled+/HHg11+j7m40NATRmevrIb7tzp0gj/n8c0AnMjIAU5SUIHqhri5Y5/j6rlnzVD0gpqYRgy5mUTkC2hxBo4GEh8FA83PTbS3NJWWzNBGdAY4yknzjICLS04NZNwY9O3eOvfSSBYVCfyJHeeMNCIf4wgsoLBSFh3kpX76cn593Xk42Miq6tbXl9OnT4RGR2HUIIeTs6JBfUHzXyVFHR0ft6jUtzdtRMbFUKtXczNTH19fbx3dyclxPX9/O3s7IyPDqVTUHJ+fRkZHbGhqtbR0koTbb96ipqZmamurq6lpbW28Ok7+ZsBJ5T9Sbb6ZER6ObNyFgT1gYsE89vTXqaWmBqCwwEPh0aiqkfaUz0DxNIIZoPDPAb6OjIQCAjg4E3duzByIub9+OXnsNvfba8B//2LN9OwRp2bcPJEDW1iDv6ezgzrJHRwFW2tqiy5c7/vIXWxkZ9u9//0jY89OfQpYMCoX4/e8hVFJsLFpaElhbmd1S18jNyTnz9dfJKalFhQWycrLx8fHzC4v47dDX06usrDI2NtK8fVtbR+fKFdX8goKxsVF9A0Mba6v0jIz21lZjYyM7W5uQkFAvby89Pb27zk4HDx7slwjhMHE22Pesrq4qKipiD3ZNTc28PEinsJmM0i0IoaCggAMHco8cQSdPQhikmBiQ3ejoAPa7exduPCmJKCqCbCpUGlqmLhEMOgjQsrLgUFNTsKL//HOg5J//DBopSTpT9Nxz6Gc/63333f4PP0SysoAyY2NRT/fK7PLAIFhKOTgAQD94EL3/ft3LLztQKKsbRimOAImhz9atKDeXZ2VpoKOnf/9+oqKiYnZOTlZW5rlzZ1NSU3GcQD6fp6Ot09jQaGxkdFtDQ09f/8oV1dLyis6OdkiS4OSUkZndUFdramZubGTU0NDg5OTEXuY0NTRcv36DSqOThMIx8o2MjP4d7Jrr6+u9vb2TkpLIGEVMJvOJno/Sz/QHqWPcY6SRKnso6MKJ0CeXk9AueyjIQieDxeK0t7eRds0ikSgnJ8fT07OkpGRpaWlBskxNTW1wR/pBLnVzJ4hAfBFzbNF/ZNFjdMFrZNFrdNFrdGFt/WhzwWtkwXV+tXyDHzuLxbp3756vr29HB3y+8NLf37/ZCR8Pm/VDvuVd23ydT25BSNhYzL35CVfzC67mwaeU1Rt7iYaCkYlJaVXL+Ph4YGBgWFiYdGNHRwdnhVNBL74/Gp0yFveUcn80pmA6iy/+7vIekhoQpYxKtzCzdHf1KCooTohLDA0Old6LEOrp6ZHGPY2NjXjA4yzrmZmZHh4e2L7nO+epeDKRv6kVIf5A4VLUWfY9BXbshUcFb95TWIqVtN+TZ987L5xp6+79T89T8TC26RdffOHh4WFsbIwdu76JdLhdEt4739k5fmEBIty/9RZw4vx8sOI8fBjWOFfUJ5+A57CBwVoUuNBQEPMUFhJd3YB4FugCLoOJaDOQ2Wt4GI2NUVtaplpa0Nw8mptlU9k0Osh7JK7shI0NsH8FBZz+evnVV22few7SZJLz5g2Vn/wEYtPh4MIDA/1VVVV6erp29vZ5eXk6OtrWNjYPqmv4fL5YJIqJirqipubq5l5RXq6urm5iYpKfX1BVVZWdlXHz1s2KiorCoiIvL08LyzvZ2dm6urpR0dF+vj6mpqY4XhxJEIIgPDw8SHlPXFzc6dOnCwoKtLW1ccKKbyVpbGysp2dJRQWyswOeW14OVlA6OmuQ0ceHCAkhcnKAKbe2gjaQRhWvUBcRgw7yibQ0MOLR1ATQs3s3xPz5xS/Qli2T6/bJ6L/+a00Ed+UKON2lp6Ph4aU5bnMLioxE2tqM994z37lzZNu2x0j6wQfAwn/3O4A+L70E/yt5t4nu7i5MLmdn58zMDB0dbQdHx+ZWMM4QikTubq7Xrl0LDArOSE/T1tY2MjYur6h8UF0dEx2tratbXl5eXlbm6ODg6ORUUV5uZGiUnJzi7uZ618WVx+NLM2k+n29nZ0dO+FxdoVscth9/+L6VpJ6eYX/9a9WJE+jTT8ENq6AAhqKFBSBIPz+gWVIS2PDQ6IhBFYpodGJmGhSFCQkgltTWBpmZjIx0RjREoYxRKIsUCvrrXwFHyssDiMzIEI+NLc7yGxpRVBQAofPn0Y4d06+9Zv7yy49CFFIoa3GiX3oJgpj/6lfwpjw0fcsvANjc1taWn5+npanp6emZnp6moa7u7OzU3d0jEAhWVlYcHRyuX78eFR2dmpKkra1tamZWVl5RXV0dFhqsb2BYXV1TUlJiaWHu4enV1tqSnp7BYjGtrO84ODqx2cvSJF1dXbWzsyPzkv4/2vfQ6XRzc/OGBkhyh6ODMpnMJ4YnePpT/g57Me4xvJV89ssAheMhkhK8vpauBJ/7MsBMO53zeNxChFBjY6OlpaU0952QLCSpv8NVPeMpgHuEi8PzPoOzrkOz7kNz7rAmi9TmAMN5fqWMuciSzseOEEpISPD09MQyBqyjAa+Ox+MWPnQjEIlEeC9BEM9iTvcs1w8q8oailRufrNz+Yq1ofrGiuV7HjZJNzvW94vp82hwY8pN4QigUenp6JiUl4f8SCoUikWh4eJjP55VTixKGo5JHYpNGYpMfL2RL4nB0weR313MhAtFXqT2sjj5WVx+rq5/d08PsrB6trJt40Dhd1zHf2rcE7b2srn5WD1/Mn5yclI5b+FBfb2VlhdNACYXCubm56enp4eHhcUks3B9j5CDE7StcDD/DjJZ/ajnPjJQVzbTP0Gf/w+U9fX19bm5uaWlpLi4umM2QgxiPOWl9JG7Jy8s7diz8+HGkrg62nv/4B2SP+vvfQSSjowMxS65dg9myjg6w8IgIUCiUlhJ19WhqEqJu8RlMYmoK9fYA28nNRVlZ/MzMTEvLiago1NEhcTWanaWL+iUM/f590Mzo6xNffQVKtDff9P/zn9NfeOExDo1xz/PPQxxeCgWm6K++CnN60mKUz+ez2Ww+ny9hJKt9fT3hYWERkZHTM1Qej4ftRldXV7lcLkEQ2BiZy+OJRCLe+hohxFnhiMVigYDP5XKlRyr+djs4OJC4Jy8vz9bW1sPDg+Tc0iTF9TXarr/WsbExOTlZZmZIU3PNiBbLybBGJi0NZDp4TaeDUdQKlQmWKN3doJRJTgaUdPHiGuiRUGdZkk10HsPDrVvXUnmpqQFdCvLFMzMMmrCkBLm68vfuvfvBB5X790PecmkE+eqrED3o9dchMaqNDcjziosfkZTH47HZbJx7eWVlpaW5KTgoKC42bmFhERMQ2+gAvhSLuRIic3k8oVCITUElFqwEnv1gNfkGkm7QcwmFwoKCguDg4OTkZCyxeIyMko11Wq79engEv/FG0RdfgOI1Lo6YnAQiGRuD7VNMDIzJzEywWMLCHiDmwACA5fBwGG3y8kDMt99eQysSMjIplFSctuONNwCzXL5MuLmh8jI+fX5mBkRHbm6QFu3LL1dff93+j39sfv75x0YpjlWIKWxuDg/0978H10VylPK43IeqKKFQiAOENtTXhYSExCckLC8vS6y54SY5HI5AIMAjEwjL5YpEQoEAqCoQCjGLkiRU52IRBaYSfoX/ffRc8/PzlpaWDQ0NU1NTrq6ubm5u9fX1Q0NDPxSLJe96c2Ud9ySd/dJf4XigwvGgR+WYpH4sUP54kMKxoLMH/cx0Ulms5c7OTlLegxDq7Ow0Njaenp6mUqlDQ0MjIyOTk5ObRSab//r7t0A8J+HiIN2zj3a3n+bSR3Ptp7n0011gDeXRZi/VYZZdusyGcNjS70VGRoazs/Pq6mpqaqqrq+vDcKyDg4PSwiqgz+pqclJSSnJyeXl5dXV1RgYEiPpBLl7QUMS5/gnn9he4LEsqy7e/wBWOBrTD5rW94vqC4YlJctqDZeo+Pj5xcXFcLtfX19fFxaWgoKC/v395mV0+Uxg3GHF/OOb+EJSkoRior2/iSsJgZN5EBu+72vdAILeFhvjxyGQwGIpNHo9NmYxLn0lMm05Im05InYzD7Unj99ImE1l8Zn9fvzTuWVhYuHPnTnV1NaYk6anwo9n3wGPtLZgNPj0XLrehzD7WIjsbdlY43TYxNfMfbt+Dc7NpamoaGBg0NzfjB4PNV/v6+nDo29lZxig4ey/19HQThLioKO+118I+/BBdvw5yiNOnYfKqoAAWJl5eoEsxMABTk4gImGcXFqGWNmArnLkVMEYZHkaNDWvahIAAsafnkqur0MOD7++/ZvDc1UXQ6Uza6ugYyswE7xgTExB7HD2Ktm0rfvNNn7//HafJfIxJP/ccxIb+5BMIe/Ozn8GUWpLtAV55zC3m5+e5XO7KCmd+fp69xH4YHmZpaWmVu7q4uIhtZlksFpPJxAYHPInBGv5ekLxEJBJhXr7Zik0kEknb95SXlzs7O1tYWAQHB5OWuThSJyikJbBpCpbpxcVFjJbuxcRkZWWOjqLWVqCbtTVhawtM2tISfKjT00GilpkJQhfIMkEViqiSjFDFxeBc7uMDrBrHyHvlFaGENwspFAEpE3vhBUCmn30GaNTPD9XV8meZA4MoKxtdvnz/wIEoiecRkk72TqEADV9+GeREX30F+de+/PJRngqxWLy8zJ6fn8ccGkjKZrNYLDabzV0FkorF4hUOhyVZSFiJCYhhIkEQPB5PknmKWJEASukPK6azubk5ST0qlZqQkCAvL3/p0iWyUfKNXunp7eXzIU3s+Pg4nU6fn5sblIQCCg8P3r27SJK+A+jp4QFAXE+PuHcPJJTJyURDg8SLkCoSUhkEnQZIJDERIJ66OqQS/egjrOdDFAqfQlmmUESSCmCZN94AYmpoEGFhqLNjeW6luxfFx4NF0OXL6M03I7dtS5bEWXhsiGLE8/LLxKFD4LS4tASeYdIpz9hsICnG6PPz88skSbmrTCbzYeDm5eVlPG4xjlldXSXnJOQoFYpEkqezLBAINpB0aWnp38S+h06n37lzJycnp70dtHU2NjbV1dWdnZ3S7gLSF/8D1iVDi69/K/HMQZ/zxwLOH/NfLwHrmwHnj0L71we9zbRTF+aZjU2N0rinpaXFxMSks7PzIQTX1NQ0NTVtbW19YsTXH/CycVfwDREs9M64d005dk8590w7d089KtKbnZO2jKUSGpXR3b1my4i/ZikpKTY2NlQq1d/f38zMLDAwsKenZ82scx2Ac/n8wPDI6IT7vcOj2YXFfiFhQpEYPqS4fNe7Avv9+sKla3uX1A98S1HbLaov6OofkBaqCQQCX1/fgICA6elpc3NzU1PTnJycjo6OxcWFsumCe/1hCQNRCYNRa2tcGYhKXG+MHQjPGUv7PrinbaHp/lgMWAuNx6eOS9YT8ZL6+uZ4Qsp4XOZkMovHbG1pldZz0Wg0W1tbrE5ZXl4ODg4ODQ1NTU390YzKYdj35FMDTtJCz9FDz9Ekhb6pTg85Sws+w59qm6Yy/sPlPUwm097eXkVFxc/Pj/QjRQjV1dZcvnw5NDxCKBTaWFtpaWnn5+dfUVUtrwBdxYcf5nz2Gbp2jVhZIdhsYnYWGLOpKXz6fX1hzpyWRtTUouYWxJgRsmdXeZN01N1F1NcBG/fxAX2DhgbS0GiRlS09fhx83HV0gDvFxRE5OWh8jENnT06ivDwwXJE4gqEDB/h/+pPdrl0zEmeujRzlpz8FuEOhQJjjl14CPU92lqizs2FufoG5uKB1+7a5uUVxSYmWlqaJiWlTE8A7iJoTe0/t6tW7Lq5FhYWXLioaGZtMTEzOMuimJiYhIWFgEgEZACx7+wemJsdtbGzaWlsMDAzKKzY6H4nF4pCQENJltLq6+tKlS4qKiubm5qS5PsQ9ystRuaySmpa+usIx0NczMjbNy8tVVlbu6OxKS01pamqIioLwjCMjwIK1tIBVBweDcCIzEzBhcTGEGaTRCCaVA3x6eBjYZnw8tqSF/AvbtqEtWx5QKLUSxPNI2vDii2B7deQIkDIuDg0NLc1xa+pQRMTygQPWly6x9u5FH364lk7k6FEAsu+9R/zmN3DST38KejNFReL11yF5eFtb9fLyytTk5FW1qzY2tnl5eZqat+/csertW8tF7+nhcfXqNV8//8zMDFlZWTt7h9nZufGxMW1NzaSkFAhHNj11x/LOwOCgjbW1p6dXTfUDXT291rZ2jFDJ77tYLPb09CTDoqipqe3YsUNWVlY6DiRC6H5iwqVLl4qKSx+CVm1NTQvLOxkZ6crKyuMTk6mp8ZqazRoaKCiIuHMHUIqBAaAaHBM8O1sSPYGOWFQOoPLBQYB1Pj6AjLDk7I9/xIMMUShF67nfgKRbtgBFTp0CwVFGumhqhkETlZaB7kxTE33++fxrr1l98AFbErdz4yh9GANp2zbAW5KwPURZGaqpWWluquNyeb09XTdu3Lh71yU3N/fWzRs2NjZr3sVi8V1np2vXroWGhqWnpZ47J3vXxY3NXu7r7dW8rZmdA6ZOw8NDlpaWi0xWXU21r59/aUmxhsbtbonxvjSTwpoCHBdfYve9MT8XmacCwynpc/HTwW/N918/FO1YWlqWlpbieTyLxcKORdJjQPrff8A6lvfo3Yg//bmX7FE/2SO+68VP9oj0pu9Xn3sa307iLD+Wn0ssFtfW1pqYmIyPj7e1tUVERCQkJAwODpID9Qe81M1dAe7hL3RNuLSP23WM20sVh45xh45x+3ZYQ2kbs6IyCxcXHvPn4vF4SUlJ9vb2WLG1srKyvLwMrHeZQ0yPiOsLxI3FRFMJry5/OD1qrjR9pTZ/Mid+Jj+RaCoRNxaLG4rEzaUEZwkA0D+/gLCqvnBRbQ/z5mfMW5/BmiySzcX1xsUruwS1eRv0XAsLCz4+PmFhYVj1trS0tLKyMjI8wuPzisdzo3uC4/rCY3sjYnsj4vrCJXVYx/XhzYiY3tCskRSe8DFR/bPfBMh75hsTRqKSx+KSR8GQKHksNnksNmU0Lnk0VrIZJ9kVmz6eyBYsTU1OSct7RkZGbG1tS0pKcDgJc3NzIyOjsLCw/v5+chb37BfzHY4Ei8muvEnf49NBX0MJhPVU4FrlUT3wzFTAae5ka3ffwMT4+Pd/05+9hx/brtnDw8PKyqqjo0NDQ4NkKoAJ7t1zcLB3dHKm0egmJiYWFhbVD6p9vb0TEu9XVlb+7W9XPv547MoV9OABmNna2YF2Rl8fxDy5OSCW6O8nFuj81Vm2mEpDrc0oOws4w507ECBPVhZiHe7ZQ+zbNysjw9+7F+3fD1kStLTg/ORk1N3FZbAmJ1F+PjjW6OtD0s2dO4def9153z7hq68+YujSepktW4AlYXnPQ2vcgABUXpojLy8fFhaG81TUN9QfO3rU1dVteF326+TgUFxcZnXHMjcvz9nJoayyqru7JzQs1PLOHUNDQxZ7eWJsVP68XNWDGjsbK1lZOWtrq/Pnz2c8HgMUq/wvXrxIhka9ffs2dlk0MjIijXARQsEB/nfvunh6eQ0NDpibm1tbW7d3dNrb2pSVV6SkJMvK6tvZzbW0IC4XMrz6+wOITEgA4URKClTq6gjGLJqncsVUKkgMmppgB9bLnDwJwQN+8xtCYtPzSNKDCfSzn4HB+YUL8JwKC0VUGoMuLK9ARkb1n3/uf/w4GKuQSq6HGp6CAvB/xyKf118Hkd7//A/x9deor090PyFaXl4+OjrqxIkTwSEheXm5p0+d8vbxpTNm8RA30NOvrqnVUFdvam7W19fr6OxuaGjw8vRw9/AwMDAQCEUdHW1ycnL19fXKykrq6v/H3lvAx3FdC+POe02/vEKaNu1r2iYNM5liZscoW2a2Y5IsW5YsZosZLKYVM8tiBouZmXlJ0kqr3ZV2Vwv3y5m7Gq9kqJukee/f/7e/87s692jgzpk7c88cvKt6587Nm0qFRStTYBcUFFy5coVcpEdHR4OCghQVFZWUlJbU8nBCJydHe3v7oODQ/r5eQyMj4kO83UBfr7GpOSQkSE9P38iIY2eHHjwA9ycTExB6EhKgBc8eupRBkyzSp9DEOFxzdDRsoaICyp61a0F8JsRHMeFCLyKn2h/+AMy8dEnq6oqqKhcm53p6IduCra304kX06afFH34Y/Oc/y6boSy/JSlKQe2Op6e9/B65SqUgqFQUFBly8dIni73/y1KmQ0LCM9HSFwwr+FAou/SEUCDQ1NRsaGtTU1Hr7+rW1gKV1dXVw4XZ2JvdNJVJUU1115uy5pqZmZaUbGpra3t5eV65cqamDvKLkKxIhlJqaev36dTKW5zn+PVg3ifVzWAbCOs4Xf4s9f8vCwkIdHZ2cHFkZVFyW5OeM59JWiVLc9eD0AY9TS3B6v8ep/UR3vwfgBzwUdz8wUIuDeK4ucLTCVySVSkNDQ42MjEgvbFxJ6ufxTgULpnC6adCuvt+8od/iCbAkKbW9xuNT2XNzXOxfiAfP4XBsbW3t7e3xE4SJEM81x5FkBgtvbhDe3UnALqnGXvG93Yt3d0nu7RHf2y1UJeh3tgs19klGe36w3MOvzpu8sWlKZceUyk4CdsjhO6eBTvzr2gZhVfaKeK6enh4jI6OgoCAyBhAh1NXVNb8wnzuUEdLqG9EeEN5OiWinhAMSQHSBgruhbb6pvfE/Ru5pnKqN6Q9JGIxcBgOybiJBjx8ITx6K4SyyB/oH5PU9RUVF+vr6mZmZmOds4sfj8YaHh3+e/D2QAb8ta8jtwLC34rDXsWEvxSGvY4AT3SHvY8MAikD3OMIfbaT92/v3GBgY2Nvbl5aWampqUigU8sOus6P99m2V6JiYyspKN1dXI+P7GenpmpqaxeCbWvjee7bq6otmZhDJ6+QEL30KBVQPKSnS9nZCjc/gSSanUEcbOD6EhMCKi6OCN2xA778//9pr+b/+9cxvfoN++1v0+99DdeyNG0GrYGgojYxCrS3CydnRMchu4uuLg7nQRx+lfvzxw82bkXx1d3I5IZH/+A/skoFcXaXpaTHW1taFhQXnzp2NjIqurKw4fux4WFj44OAQnn/2tjaubh4aGvfqGxocHWxLyitnWKyM9HRV1Ts6Ojptbe0SidjT3S0+PkFFRXnv3r1a2tqqd1UtrazFEkLxS6wqOFJGS0uL9O+5efPmtWvX7t+/v2/fvtxc8FzFi0dVZYWKikp6RmZpaam1laWNrW1ycrK2tlZLa2t0dIyiYkBkpJTPR6mpoBSjUMBfBHt2YwcpyOs4iWZoC4hOkxWYSEyEaDcNDbRv38y6dbm//rWAtG2RHFm1Cpxzt22T3rwp9fFBNdWCybmhYZSVhU6fDt67t2LbNrR1Kzjx4D1eegl0PK+/jsufwW1JT4fUAH/7G0pIEERF+bs8eJCXl3vi5KmHD1My0tPOnT8fGxc/sVQkXFPjnq+fv4qKSv/AgK6uTmdXN41Gi46K1NTUMjQy6u3tFYvEZmam2TnZandVNTW11NTuqqvfe+DqtmKRnpiYuHv3Ll5OcJLc/Pz8pKQkExMT0lWcSCWVo3xLOSs7u7y8zNzM1NnFJT4uTkNTs69/wNfX38srvKQEubrKAtctLUFKLCsDD/HBQbAYMmmLEgYRxpWSAkF0urrSs2fhmgkf70ki985Kln7wAdj8tLVBehoZmZlcrKhEQUEg9x8/jv7+d+8vv2z43e+Wieavvw6C46pVoEL7wx9ArAoIkH70kdTBAU1Pz/v6enl6eScmxF++fCUrKzsxIf7c2bPJycl0BpNI5y9UVb0TFBT0fY5XBoOhq6vT09c/MTERFBigpnbX3MKir3+Qy+PeNzFJTEg8e+b0MYizSzK/b+Lu6bWCpUNDQ+rq6vLxXJ99Vr9lC3yAbN6MPvssq7AwDz8X7W2t165epVACcDct9aGllRXplICJP6Ztb2+3tLQkvxPwE0RKEqSs9q9AsJ1LUzniyE7nE/tcT+5zPbHPDSMniS5uT+xzPbLTSf9uDHuWKx/PhcVHNzc3ko24tPPPl3xIMF3XY1ndZVzdfb+m635Ntxx03QciAZWd+qOTWbOzK+uxe3p6JiQk4PxP+A5CPNfMrCQrVHBri0B9zxLsFajvJfC9wiVEcHeXQPug+EfIPQvVufRrGxjK22RwaztDefvjrhJ0mcrb6N+tF1RmDY6CKwA5zaanp62trSsqKkjbLkIIx3NlD6QFNHmGtPg9BwKbvZO7Y3+M3NPArInsCYrrC18OYfLd2N7QxP7ouUV2d1e3vNzT1tZmY2NDvrhmZ2exvmpgYGCFU/m/Ys5jjs22ZPY6f9vnfuR54KbQ53poYaRx7N++PldBQYG2tvbt27eNjY1xsgfMeshzxWKJRCKcJJfNnhOLxfj7F0g9twAAIABJREFUOzc3a/duTwMDKDdx7x58J2dng2nm0SMoNEEEyBAh1j3dsML4+8OaoKQEUcEffIB+9Sv00kvMVataCIcJ2WL76qvgjXz0KGwZFoaam/iT7NExlJkJgUpaWtJTp9CbbzqtW9f35ZcIV3eXX9ZXrYJA4zfegKX6t7+VVfQMDJTweNPg+7kwHxkR7ufvX11THR0V6e/vX1JS0tbW1tnZWVxc/MDVNT+/QCqV1tXW0OiM9vb2/v7++Pi48vKKurq62dnZ5qZGwv0ZKjNQJyaCg4Pa2ztWrChisdjY2JiUe7q6ury9vU1NTVNSUkg7F558LBZLJBbzeLyF+Xkuj7coFOJvr5CQUEXF2D6iRBUOKjI3B7mnsxNyBMTHQz32iQkplYbtMgwoipacDP4oxsZQaOrbb8dXr+74P/9n2XpL8uiDD8BFWVcX5KmOjrkpQVMzCg5e2LPH6vhx+saNaP16mZUQ7/HLX4L7ykcfSd9/H+K53n0XHMZfeQUlJy/yeJC4aHZ2lkKhBAQE1NTUhIeFBQQElJdXtLS0dHd3Z2VmgPWqskoqlZaVlrFmZurq6kZGRkNDQlpaWmugEgivurqawWAkJSVkZWWPjY5SKJTBoeEVLOVwOGR9LolEQqFQ1NTUjIyMjI2N5QN/JBIJizUtEAjn5+eBq4RzOk5R4+ToraeXJhajiAiYpaGhYIGNiQGDbFkZJAen0aQcHL5eWwssdnAAD6BDh0AKePddHL3Vv0KOfPVVmbLHwQFVVgiZM6Oj4Flubw/CJ2HksvrySx7pd//SS8C6X/0KK48gk8CJE+AhLhKBlbKmBtXVCThzUH+UTqcFUAJCQkLr6moDAwMCAwOra2ra2to6OjrS0lLd3dzr6huQVFpeVsZizdTU1g4MDAQHB7d3dNZUV3M43PLy8rk5zhx7tra2rrm5yd/fn0aHtIfka/Sp9bk+/rieyMIIkZIffZTl6urC5UEImL2draOj87Vr12h0Bn9hQeOe2sVLV0j3LHIp+sHIzMyMgYFBUVGRVCqdn5/n8/ksFutnEx3meYJ7SqGHtzsc3+tyfK/zk3CMoB/ebq9zJ5LH5Q8ODpD6Hqxj0NTUHB4eHh8fT01NTUxM/NmqaoORTjBV2WFW1qpX1mZY3mZAAEagLQOKYXmbYUmL9iAtbY7NlRcuiVDcdFNTUw6Hw+PxBALB95XvwM41vyDKDF24tWVBfc+TwCeJd3fxtQ/+GH3PfFXO+JX1Eze2TNzY+gQ8Jo5fXrtQkclYnrcQIeTp6RkQEIAd3fh8vlAoHBwcFAqF6T3JvnVuAfXeAfVeAA3eABhZ6vrVeSS0R/F/hJ2rjlFFWNNkhjPCgiazqZHWtKiuoLie8Dkhe4Wdi81mm5iYYCPA/Py8o6OjmZlZUFBQf3//z+YRz2rO6LDf0+VyqMvlcCe0T8LhLudDnU77uUN1fQND+Hn8wc/4P7vjz23nolKpurq6V69etbGxkasMBy9MYui4BXSJgnJzs/bu9bewgBRwOEI4Px9En85OSPE/SRPN09mQlzAtDWQiU1Nwdd69W/r11+iVV8ZWrRpZ4X2CvXLWrAG5x8BAGhGBWlt4k/M9vZCY2NISsurt2TP25pt2GzfC5zK5mssjamrgobFrF/z3vffAIdXVdeX4yTsxPj6WnJz8MCVlcgpW8R/wI5cTzBOxWGxhYUHKPdnZ2adPn75+/bqysvKKaU0uRfIIQigiIszcPJnJRIWFIO40NYGCrLkZNMrt7bBal5bCOk2jSWU5hfv7sXMPMjYevHaNhnNjP1UV9pvfgEx5+jSUwczKEk3QGXRJUTHS0urctMntwAEoWbVU3B6W59WrpW++CWz87jspjwfl0jw94e6dPy8VCIClpFgsz7e+vr74+PjMzMwnS3/Ib/YcfAVLv6/yYWBgQBq/8Vd1YGDgnTt3cIgKuT05Mx/PVOI0fn5++fnZdXVITQ1EZzU1UKHl5MgshgymlAEezYT4U1AAco+FBRjDiLzMvW+/zXxylr78ssyzx8AAPPb7+9lTi41N8AgYGYFH85df1r/zju8bb6CXXgKl48svw1Tctk36179C+NeePZCZKT8fUmq3tsJgZmZAAHoWS3u6uxITErKys+UV+89h4JP/IlmEZymbzZavzxUamvzLX9b/6lfo17+W/upX6De/yTQyMpycBCWTpYVZcEjI9Rs3sDza1tpy69atxUXRk6f4YRQajWZmZpadnU2n0x88eGBjY1NZWdnd3S0SieTH/ANwcjzky2rFQUDfwxOo3ww+uM1Gca+D4p4l2Osg6y4hB7fZ6KiGs6Znm+TqsRPl5BqMjIy6urpCQ0OvXLliaGjY1NSExYsV5/rJuzB4/mRJs1FRg1ZRo04xARiRb4sbdQrq1PsnUmk0+JAjeYIQSkpKsrKympmZSU9Pt7Ozo1Ao3T09tMmpxcxQrvIWMrRKPsDqcfiV6i6e5o/S93Arc4Yurh2+upmEoaubh6/JAfGvwQureRWZPYND2GsKj18gEGC/5rm5OX9/f0tLy6ysrK6uLvYcO7Ur0aPSyafazbfaDbcY8Vnq+lS7eVW6xDSH8UWQL/4H3BeEUA21IqjVZ8l8BqY0sKZ1yNvUAsLaKNGdITML0x3tHfL+PXQ63draGkfGCYXC6OhoBweHhISEgYEB/JH2A4b0T+2CEJpuTG+23tHqsL/Vfn+L/f5WhwOt0EJX1trvb7Xf12K3lzNYR4g9o/Iz51+N/9xyj4WFhaenJ4PB0NPTCwkJ+YfTAiGUk5O5c6enrS2KjkZlZbKkJ8XF4DBBpSEObQ4cRXt6YGEODAQ1w/Xr8A396afiVatyV60aX/EN/X0R0d/+FnKlnDoFDkBJSVLILiNobEIR4eDjcvs2+vzznI8+iv7qq5XKHrzArFoFueW+/BLSorz2GmievvsOIsvwrRKLRS0tzaWlZWNjY60tLSUlJWSZXxqVWlJSMjY2TqVSy8rKibqkPIRQb08P/lym0agNDQ1jY+MMOr2trV0oFNLptKfWJSXrc30fWHTt2jWcm8TAwCAhIeFFWBoeHnb+fFxsLCgnAgNhaXRwgJR6CIEMFBsLF0XIPWiBNoswVlqKYmPn9fSyjx9n7dwJTjpLrrjLZMN33pHu3QvLvrc3oUibGxmRpqejM2cit2zJ3rIFrVsny3CN5c/vvpOlA7C3h7NjuHMHskBLJDKWCvj8+rq6srLy8fHxpqam0tLSaRYoLRBCQ0ODJSWlTObk0NBQWWlpXV0dDmXvaG/HpoHR0dGGhkYqldrb20Ol0ubn59va2piEexD5JK+oz4UQsrOz27dv3549e7Ap9kVY6uHh4+SUTqHI5B5NTdA8pqeDf09bG1EMjiYS0ZnA5fx8oBJpe9D+/XMbNmS+9RZnxSz9xS/AB2rHDnBQ8/SU1lQtMqbHxiS5uRA5eO8e+D+9917gW2+VYmUPkeFa5lX1+9+Db9b3LurvvQdjYLNBVbd6NeSuItgLfJuf59XW1lZWVlKptPp64O3sUlLd/v6+0tLSqanpvt7e0tLShoZGiUQqEona29txKZXBwcHGxsbJqamRkeGBwUGxWDw2NvZkPBeLxZKvzxUSkrxqVf3SVaJXXsksKirAN7GoqEBRUdHW1j41JaWpuWWgr9fA0FAkgnjGn+THYrGMjY0LCwupVKqbm5utrW11dXVnZ+ePlHugLOXERFNTU01NDYfDaW5uYc9CYmJyXmFhaJ4nULsReGCL1dHdtkd32z0Btkd3AfHAFivtO2HT07NNTY3y+p7Ozk5tbe2+vj4ejzc2NtbX1/dzxrHzFiYL6vRyqtVza+4RoCHX3sutkXWzKm93jyRxufMr4tgzMjLMzc05HE5GRoadnV1QUFB3Tw+VOSnMDGHf3LQizIr9OPBqN/zr9vY5jf2ikR/u3zNXkdNzbnXv5Q29Vzb2XtnYh9vLgAMQSN+Vjd1nvuaUZXb1D8jbucRisbe3t7+/P4fDCQwMtLS0zMnJ6ejomGXPJrfFuhTbepQ5u5c6eZBQ5vwYL3VyfWQfUR/EX/zhck/VeBml0SOkxS+42Te42S+4ZakFBCCk2S+o2SeiLZA1P93W2iYv98zOzpqamublgR0Zh7LiqT44OPiz+fdM1qfVmW1tsP62wUYOcNf6Wxndam+91S7OYB2Vzvg31/dgZzGhUIjjrle8JuRfGRgn6rFn790bqKODKBRYSHAKuOxs6cgI+ExwsdwzNAT6nvBwUOEoKUm2bp399FPxf/6nZOlFu3Jt3rYN0v64uaGSEhFjikEXFxeD5kNLCx0/Ln3zTde1azv+/veVZpz/+A8It8ZJ4X7xC8ghFBAACpKoKJAVWlvr0tLTS0oenT51ysHBsbCo8PjxYzY2Ns0tsnSlzk4OWto66mrqOdk558+ds7a1a2tvLy8vu3HjupGxsUgkbm1pufrdFZcHrnfv3NbTN2xpaVY8plhX3yjPJRyTIh/HbmxsbGZm9v3nyNmzZ6uqquQ3fpKf+F0cGRlx6lSqvz+anwe1CoUC3lAZGeDg3NYGAiSWe6h0NEfjIjoVTUyIs7Jmw8LEFhYSHR2kqAheKWvXgpJBXg/2+98D8ehR8MBKTxePjM1MLjY0osBAwc6dtt9+S9+0CW3bBikA8E7/+Z8yfn72Ga7GBQuzVApLNZuNFhcXy8vLM7Oy8vPzFBUV3dw9Uh4mnz592tnFpW9AliZEV0fbwNBQQ0MzLzf30KFDvn7+7e3tBfl5V69etXdwhDjBysozZ057efs4Oznq6ulHR0Xu27f/yXiu+fl5Y2NjMoFHRETEuXPndu3aZW9v/yLRzoRW3P/QoTwdHRQZCfY9VVUQJaurQeaorwe5h0qTztKgCinUqkhIQA4Oi1pa7OPHxd98I12/Hr35JlhPf/ELgN/9DopUbNsG7sy2ttLUFDTQz2bya4hchcbG0vPn0YYN/Lfftvnb31h4gr/6KrhJrVoFaYC0tMBh+ttv4VH461/hbiIknZqSiiVIKBQUFxfn5uY+TE46c+aMn59/UmLiiRMn3NzdR4k88SKRSEMdghB1dHUL8vMPHTwYGBTS29ubnpb63XdXPLy8od5ZQd7Jkye8fXxv3Lzh5eWdl5tz6LDC3BxHfuKBY+PyOPYn5J6svLyspV0kQ0ODPN48m83m8XgikWhujkNqUH686EOn0w0NDe3t7b83ePGJ38zMjLz58qmPybOI8uOpr6+zs7M1MzOtqqowuW9cUQFFysgfKfeoXqd8u8n88E5reVBY3v12s7mGchCXyx8Y6JeXe+rr69XU1CIiIkiHp7ExWWbhZ43wp6JDFrEFZk6lZnqZSkbZnYxy1YzyOwRyBxCMA/1OasmNjqEE9iynqwuKBpK/xMRENTW10tLShYUFoVAoEAggb+H8Aj8taPr6hpnbO1lysKLLurV9Rn3fj5F7Zsuy2k993XHhm46L30BLIkS3c6nbfvILdkn6CjvX4uKih4eHrq5uX1/fwsKCQCAAOxcRzxXXEGmfZ+lSaAdQQLQE7izXdcy3Cq7yXxD+cLmnfOSRV80DSp0npd7zcVu3hBNE/zqPoAbf2QXW2PJ4Ljqdbmpqamdnh90eGAxGX1/fMPH72eQeRm1KhdGmavNdzwOzXVWm29n9tf/+fs2Dg4Oqqqrnzp0zMjJiE5+Yz39Ksdyzfz/IPdbW8J2ckADut2lp8P1KoyMWbQE0P+PjsG7Hx0vNzJCaWsvmzbkffoj++tenSC5//Ssoe06fhuD2hATU0zU/xevpRQkJYCJTV0fbtrHeesv666+5r7yybG/5RHDHjoGPxcsvQ9aV//5v6X//GTk5Sny9HY4ePerm5nb61ClvH9+GhnoFhcOeXl5kMSkHe9uy8ko7G6vMrBx31wdVNXUTE9SQkBAbGxsDA/3pGfaiUGhtZdU/MODr462mplZeWaWtpVlZVb20PACrcEIasi4p4atBNzIyunjxYmhoKBkU8xyuEnau8Pj4VB4om2BRjI6GxTI2FsxblZWwVGdkgDxHpaEpmlBMZyIGvcrdvdTGBj14ADXur10DuWfjRlief/tbWG9/8QvweFq9GlQUyspST0/waGaCt3hhITI1HdiwwfHgQdGmTeiLL8CL+dNPZTutXQuKj5ERuJs2NrKIa6yWWFiY19PTPX78hLe31/HjJ8LCI/Jys0+cOE4JCMDBR9/rLfR0dRsam+6pqzc1NRkbG/X09ff39bk9cPHzp+jo6AiEi5w5toWFxeQ0q7215d499bi4OF1d3cCg4BUsxXIPaecSCARUKjUpKUlZWflFaiEhhIKC/CmUvLAwZGwMcnBcHIjRaWkgpqelSUdHoD4KMJM5JW1qQomJKDy89MKF6lOnoJDZpk3AzK+/lsG6dWCmun4dpmh0NGprFUyyBwck6emgVtTUBGXPl182vfOO++9/L5uiv/ylrMrHf/0XuElpagIzJRLgp5OTtKREpumZm2Nr3Lt35szZBy4u589fiI6JyczMUFRUDAkLwyo0oUCgraX9vT5fU1NjYGjYyMiot3+gt7fX0cHe3d3NyNhYuCiampq0s7MvzM9TPHbMysqqurpKRUUF707OOiz3yNclfa7cI1Ps4fUST/KfUO5hMpmmpqY4nQk+BZvNxmZicsAvikikXI5gjs2fY/N53MXysurUlMyszLzKipqEhOT+vmEeRwj/neXP8yChEbZz3bnms3eTyaEdFod2mD+G7eZA2W4OsMN870YTDeWAOTavo6NdXu7BOYcaGhrm5uZmZ2dnZmbGx8dfpGbci17Rs7eDaOR5RnrJ3eTCmw8LlR4WKa+EQhklMf+71r5YLpe3wr8nNTXV1NQUP1aY81BJij03nxbE+G49eBkrATDB3ZjAlxCm8nbmzS1M1b2LI7Lqy88e5tP/Ax5mpZmNxz9vPrMW4CwBJCLXbVT8dOZR2tjy+lwikcjHx8fDwwOnjcWD7+qEeK7I6hCrDGP7bIvngE3mfUqp14+Re0oGC13LHLwrXb0rH5CtD+AkuHpVuPhXe8wusPr7ZFXP8DinpqasrKySkiCRh1Ao1NPTU1dX9/Ly6uvrW+EI8XTe/WgqJBCpfvhId32p8XYCtpUaby9bCdtKjbaVGm5m9db0DQ7/m+fvSUlJUVZWvnXrloGBQUvLsjQqT+U2Qig7O8vIyM/bG2lowJKckwO2qYQEMG0xJ6U0qgSyCdNp0vp6lJws8fMT6ejMXb0q2L0bffMNeEi88w58C7/2GuTD/fRTWJhPn4Zv4ZgY1NqyyGSNjYiLisCYoKEhPXIEffLJow8/DF69WmbkevVVMMSoqIBMtXUrJORbvVp68KD0+HFYXT77DHYsKZGODEu6u9ofPSqpram6fOmio5NzTk72hfPn7ezsGxub8HT09HBTvqWiqak1PjHh4eFWU9fQ2dmZl5d3S1nJwsKyrKy0rq7W0cmZy+H4+fqqqKj09Pa5uT2oratbsUh/L9yYm5uT/j36+vqbNm26evWqlpbWCy7SYWFhaWnwVCAknZ6GFdrODmx8ISGQ7qWuDtjb0ADyJJ2B+Ey2hEFnFxUtxsWBd4mpKYQXnToFS/WWLSD6ffIJmP02bwYVxcWLsFrHxaGujoUpTiOxxF+7lrp+fermzWjLFjAR/vrX4Lz8yivgy2xoiJdk0DP99rfgkjI8LBWLYcEQiUQNDQ1l5eVlZaWnT592d/dISky8fPmyy4MHZP4eIwMD1bt39fQNpqanLczN+/oH6uvqsrOzrl275vLgQXk57Ozp5TXDmra1sdHR1Y2NidHV1QlabmCF9zuXq6+vj1/QEolEVVV13bp1169f19bWfhHFAELIz883NzdrZgZSLYyOgo4H54dKTSU8bFrAbkinSYS0KUSjLtbUSBIS2A4OIm1tsFpdvgzGQRLOnpXeuQPyYGQkBBvSpyeZksoqSK1kbAw6oB070CefRL7zTuEvf4l+9StQD/3qV6AY2rgRpMmXXoJ7Eh4ODj1KSsBnS0uZ3CMUCuvr6ioqKrIyMy5duuRHocTFxpw7d9bd3X2AiDoUi0Xamprqamr3Tc14PK6FuXn/wGBtbU1yUtJ3313x9PIur6gsyM8LD49g0mkqKipa2jojIyMW5uZPyj2zs7MGBgZkINLz5Z5nPfv4wfmRLY1Gs7KyKicqxuNDzc7O4vprTz3vs4ig/+AKbQzzNa491FFK1VZK0VXJ0L+dqX87U08lw+BOlu6tdG2lFG2lVLUrSQHuoHkl5B7+7ateezYYHtx2n4QD2+8fILoHtt0/sM304Lb7ezYY3lP2Z89yW1tb5eWe+vp6CwuLsbExXLDC2tq6tbVVlmxJbqD4XPhFIf+6kNvkn0bB/sujJ+ffis2+Ep9zNT7nalzO1fhcoiW6QMwFenTm+ebuKAZjckWdioSEBDc3NxxDhEfY09s7PcvmpAaNXlo7fmMrCWMEPnZjKwnj1zaNq+wSDnf9sMtBCE09yqg+8mntydXPh2qFj6eL04bHJ+T9e4RCoZeXV1xcHKlmI3Jnt/N4vNDyQOMkXYtUY4AUI4sUYwDAH3dNk/W9ClznhfM/ePCFvXkOBVaujxxcizHYP4E4PCiy9yxxYc1P93T3kA8aQohGo9na2paVlRERmouFhYVJSUnl5eXDw8Mvomv4pyfKEztAtoXKpDzN1YV6mwv1N0NLAu7KiJsKdL5h9VSPTdD+zeWe4ODgtWvXKigo6OrqvkgKDYRQUVGOhka0qSkUYlxclFZVQWqe2lpwgxUIpDOz0immWDA1h9iz/OrqbHt7prc3srVFd+6AbHL0KKwGa9eCs+2RI0BRVgYzQEgIGuwXMaZnGMKqahQYCIv1xYtgcFi3zvuPf6zX10f29rBIv/wyfEAPDsLKweFIZ2elfD54/v7Hf4AQRpSakK3c+MGWSCS9PT119fXjExN9vb0NDQ011TWxsbEJCQkdHZ2NjQ10ogrM1NTUwgKfyWTOzXHAzX5yisFgzMzMsKbBc2V4aKivrx8e3amp+fllylL8ILm4uJByT15enqqq6qFDh1RUVF4kSoXQ90S6ueVPTMDBWCxQTvT1gdBjagohcllZ4IIyPAwZXwYG2EkR8dyRIdTfDwFvDx9KHzwACVFbG1I2r1sHuorvKz9t3AjeJVeugH+Vj4+0uFhMY7AYi2VlKCgI7d7tuHdv35YtULuKTNO8fTsUc+DzSe6BfHD4MEhQHR1AJL/4F4XC9nbIIkOj0bq7uxsbG8vLy6Oioh6mpPT29DQ0NuJnnslgCIVCKpXK5XHBA5E9R6PR2Gz2zMyMVCrt6uoaH5/gcOaamppwYC35tOKvIisrK2znkkqlGRkZt27dOnDggKamJg4qJDd+KkLoewLr60udnKB+aGAgKMwMDWX+YwUFYJ/t74d8mxwGf6qnJzMqciEvDyUmgtRsagoStLq6DNTUYH66ukpjY1FNlYRGn5lc7OiEye/kBGq5I0fQxo2Cv/3N5tVXx19+GfIsvPYamMjeew+yA7z8svTVV0GgzMiAyrLYnnj9uozJJEv5fH5bW1tDA8zGjo72hoaG8rLSyMiohw8f9g/0NzQ0sOfmEEJMJlMgEExMTHC53K7OTg6HS6fTZ2ZY+L9DQ0PjExNSqfT7AlgrfGUIF6J5Kysr/H2JEPoflHvGx8cdHBy6ING2kMlkslisKXBOgjxpT72bzyKCfMwRaN54eG5/2CWFCBIuE/jlI5FAOQz003tDnMwgTSKISjy+8hX3Xev19m012rfFaP8WaAHfSuAEsm+L0c71endvenO5CyvsXFVVVa6urjMzMw0NDc7Ozl5eXp2dncPDywISpVIprrwtIoqHrPC1etbl/EM6KO24tNjsaxFp5yLTz0emX5CD8wQFE8+Hppyo7wifnJzu6gIxhfzFxcVFRETw+fzJycmZmRmiLNood2FhJpnSd+7r/u82DXy3CdqrSwhBGcCUy98M3twhGPrhcg/jUXrJwY/KFL8oU/yy7BgBGCFbAik58AGzKJXJmpGvz4XLUxQWFi4uLjIYjO+d1bhc7tDgkEAgCCj21o1WN47XWQFGchT9GA3XbId5AaFR/4eMfmIDSJnRmW2VYeKQY/kcsMs2d8m3neZNTYxPyPv3jI2NOTk5kZI9eTt+zjoVo+UJmXe/yNH8BkCLhA05mhugK2u/yb63drqnamzi371OBYvFSktLO3PmzK5du7A37hM3fRkBJ02xtQ1rbUVENAoslg0NODIF3uaLi1DpCEoiStHM8EhX+ZLnM87Ep6kJ9oIrV+AzWU0Nvnx9fOBbuLZWwqBP04UzsxDGpaUFH9hnziA/P5aurlVYGCcuDmVkgA81zp/C4z1enhGScrkQqNzb+3g5wYMmZ5g8srCwAEUiJibk81jIb0Diy9T9JHX52xnbuRwcHMh8zXV1dffv31dUVLxy5cqLvM0JuSfirbeyHMEB5vF1lZfLDIjYzpWTg4aGUFc3s6m6DfI1TzJBrxUfL3Oi0tICHxasqPjmG4jHvnYNVBc2NpAKEgqwzw0MoMwM5OIyummT/d69ks2bQUD64APQ93zyifTjj2UMXGIdaJ6cncGUw2LBqJbocoxYQrlc7tjYGJVG+6mCj76vlWZmZkbKPThJroKCgpKS0ovYFBBCAQEBLi5FUVFQLtTFBcDEBHL0uLqCWJmYCBGITU1ocgr1tA4P1dej0VEwKyYlgRrHywumJQZvb5hbGRnSlmYJlTZLX+jth6no7Q2z9OJFCAJfvXrgl790fuklENP/9CcI/l+7FkScNWughMtHH4FwGRAAt8vfH1JS5eU95icp+izxUvaXy+WMjY19H/r0rA1WbE92ye1lN4z4g1VoZmZmOH7kf1buCQgIcHJy4vP5Q0NDtra2JiYmpaWlPT09K2Q1+fE/Fcdyj7bywwuHQq8ci7ii+BT4jiCe3R/ibAHZcrG+R/mS6461Wns363/7bNh47d3sAAAgAElEQVSxVuvuDS/W9GyzXD32770xzM3NsdZhcXERV8ekUqlLuTRhmPiFkJWVFRsbm5KSMjExERER8ZOkaYEEjxxaeOqloKQTwcmngpNOBSefBiT5NODQlREp8UeqW4J43HlcQANf+ODgoI6ODn7Jf1/g08zMzNPTs6enhzHFmkr0bz/5ReeFbx7DRTmcoHecW9v13daFwR8u99CK0vP3vV94+LPCw5/LQGEJwRSim7f3PXpBat/QsLy+p6SkRE9PDz6iuFwPDw9DQ8O0tLTurm42m+2b76EeqqITqf4c0Ai745hm/WPknqyWNKMkHZlWCeuWHrcmmG6eYmibYcGYpXd2dMrLPQEBAbgsGtaal5SUFBYW1tTUjI6O4hqxT53ePyERIk5K41OUP01XWwugDpCBW9zF7d01aXe+muyqZE5NyzuV4/nzL21/7nguW1vb119/fe/evWpqaq2t4PD7fHYjhLKysgMDIcMh3hYv1XgvLIKkp6Os7NHW1m4eSyidnJYODaHBQdALRUaCEiMtDVaehARZm5GBmholVDqbPj88jHJzQbtQUwMriro6srSsDwjwI9Lqw9rMZoMiBAfCyI+TFBeWExGHwxkeGvL19fH19a2pqfX18fb09MIJeBBCj4qLrKwsHz5MaWwA3bW7u8fk1JRQIAgMDMgvgNgWoVCYkvJwbHw8MyM9KjpGSuxS37DSr1kikVhaWpL6HjU1tXfeeefgwYNKSkovqEKLiAj383s4CUmPYUXs7oZkSG5u4JCbkABZkJqaUH5+X0XF0NQUmpxEfNo0BM11d8s8VigUMBRqa4N2R1UVrClqapDo2sgI1uyaagmNzmYKampRcjJSUsrZvj1271506hSYwlatArnnzBnYj8t9vB4jBM7U778P+bjJSUG4ibD7+vrc3Nz8/f2rq6q8vb18/fywUQYhlPIw2draKr+goLyszMTEhBIQyOXxOBy2p4d7dQ3U3+7r7fHx8Wlvb6f4+/n4+lZWVvr4+DyZv0c+jl0sFispKb333nsKCgq3b99+QVHSw8PvnXdyIiNRTw8E4Rsbg7KHQgFrFYUCQk9amtTPr6OlZWxmBnGnhJLJKYjtGhyUZTbMzoaNsrNB29beLh0ZkTCnZpkCKlX6qATEJltb4O7x41IFBbR2bfwbb6S89Rb68EOw4r79NijJrKwgDdKuXaD4+ctfwKg7MACcxEBOVKIcykxHe7ubm1twSEhNTQ3wlkIZx9o/qTQhLs7K2rr4UUlRYb6JiUlIaBiUdJ5kenp6Yn/wttYWTy+v/v7+0NCQQqL4Q1pq6pMsZbPZhoaG/xvknqioKF1d3dbW1s7OTh/iV1dX19HRsULuId+zWJggOUYiMn2PUtK5g0GXj4ZeOhqKWxK5TFAuHw09vS/QyTwfH5DH49+86LJtjcaeTbqPYaPuno26ezbpQAuI7rY1GqrXPaanZhobH8dzcblcV1dXMzMz+SV5hPgRr0Pi3iIkEUsCA0J9fQICKCHV1fXOTm4L81AhAd98qfQfvGPJC1yBwFThUIOSzvjGKvjHKfrFHfWPU8TgF3eU7PrFHfWK2l/ZFMCe5cjbuYaHh01NTf38/BYWFiIiIoKCgqKjo6FUAnOSHu9Xd/STpjNrMTSeWdsI+BqMyNpTXzdf3DQ/AAW/VgyMFLXlb9mKbSDarjAtY/e7WQc+yTrw6XMgfefbE/kPe5bHc1VVVenp6aWmpk5NTXl7e/v5+RUXF3d2dM7Mznhkudzyu6YWpKIepKIeqKIedEs9SEUtUEWNQIAYpHKHcsM6wYzHh/RUKwb2Il3IV9mQrBWlZhivYxivTbZGj7s6hvE6BnFaZkmG9FlqW1u7vNwTGxsrX/4yLCzs9u3bHh4eIHQS1oYXGcOP2QYhNFgSm3jjo4d3vlqCr5eQJcrtrx7e/jL51ueMjoqe/sF/83iu7u7ujIyM9PT05OTkFzTK5OXl4RzzT70TCKGeHuThUTwyMiaRICZdzGJJF2c54PHT2oLycqW5ROa46SmQYug0EX1qkTYpYPMnJqAwhZsbeJYgBGuQjQ36+OPAoiKoh8Vmg7Mt4WjyeBl+6gDIhzAnJ1vfwCAuLu7oEQVnZ5fysrL9+761t3fo7unFz6eNlaUfJUBTUyMjM0vjnnpUTFxdfX1ERLi6mrqmptb8Ar+3t2f//v3p6emEJ9q9R49KjikeDQ2PXPHwIISCg4NJfQ+Hw5mcnKTRaFQqdUXx9qcOGCEUGxvb1ISrhsHVxcZC8fDpabCMqKpKAwNRaanQySk/Pp5JpSIqdh4H/xQiXwAusxkdDcozPT3QQmhrQwbI+/chP3FGBhoZFjBmxsckpWUoOFi6Y4frpk2dCgpIRQW8o775BhxQ3nsP9D1+fjI7F8FDwIeHScsXEovFcTExJiYmsbGx+/cf8PbxTU1NUTh82NXVbWwps+o9dfWw8HBlZeWysrLLly5l5+aVlZUFUvx1dXX19Q0kEqmTk+M9jXte3j7R0VGaWtrenp779u9/mLKs5jO+Ox4eHnh1kUqlc3NzTCYTs5SsIvdUZmIiQig0NCIvr4XHQwIBuPJMT4PWis3GLRIK0dQULyMjhzXNkkhAcwkaS7EYdufzwWQrDyIR+CSLYF0WiWT/4XDgUHQ6mpgQmZra19bC0zM4CJ7LQ0Myvo2PgzYUd4eHpULhsqlLfPwthoeHmZqaBgcFHjt2LDAoODk58eDBg94+PjiTwveZLVVv34mJibl9+3ZDY+OlS5dy8wsqKso9PTw1NTTMLSxFIpGFuZmJyX2lmzf09PSh6EdhwY7t28srl3nf46pGHh4eZOmP/0E7Fx6MWCwWCoV8ogbw7Ozsk6YikUjE5/Pn5uYkEgmuVL/ijhNyD//ejbjT+/wvKAReOBwoazGisNRVCDyxx9fBTFYWg8fj37jguPVrtd0btXZveCZs/Vrt9lVXLmeZnQu/W3CwQm9v76NHj7CXxuTklBTx+JJOArrmFzv6hwt6hwoGxx4NjT/qHy6cF3XyJV3EfztE0hn0g0QfkHvmJvxij3tE7POKOOgZedBLDuS7rqG7Sut9ecvj2DH3xETl2u/jBhYXF8Vi8fDwMHeBPxbtXXbow6rjX1ce/6qSaKsIpOr41xgqj39dofh59dlvuP3LsraSd2RxcREnDl1cXORyuSSdRCAFV35K8va/p+z9IHXvhykY9iwhS5TUvR8mbfnbaG7SCjsXfuWKxWKRSIQHL5FIBgYGBAK+80O7q+4Xbvlcu+V97XHrcxXwJbjucck02pD7I+SepOp41SAl7XA1LYC7sjYMEO2lrmaYqn6UFoNNX2HnwipAPG1EIlFbW1tTUxPOgPCz6Xv6H8XEXHk/QenzBKUvCFiBEN2bn8df/4TRUc6cYv0763tIEeE5cjo5cTGCEMrMzHR3d1+x9uP/isViKpUqEIBdm7jZ4ELBYMBSIWTPi8Zpwuk5iVAoZTLFEzQxZNeQ8ualU5NQcqqrG6okTU3BkfB3U0cH/+JFGyp1mhjesjVjxahWdPHlBAUGKN+6lZmRfvLkiYDAwNramiMKh/38/YeHZRXX7GxtkpJTjI0Mi4ofuTg7llfWcLncpKREAwOD73M5Dg+PIiR1cXaKjYmFCsDGxsrKSjt37jCzsMLHxyfFqktDQ0MyIIVk5lNZtGKo+BaEhYVGR0e3tKCsLGlFBWRnNjGRBeQbGoqcnamDg5LMTEl2NhoeBn4yGNIFGktKp0HcXF4eePngkhaeniDx6OmBdSoxEaxgJY8IZQ+/rh6VlyNn56ktW6xPnuRu3oz+/GcQqkxMwArT0QF5aHbtgrBu/FlaVgY6J4zjQYpEiw4OdhqaWikpKUePHo2OjsnKyjxx4mRoaBhZn0tLQzM7O+e2ikp7e7u+vn5Xd+/09HRggL+tnZ2urj6DzrC0MDc3N6cEBAYHBdbU1Hq4u127dp0SGCjPKxx8pKmpScZz/QCWent7paeDOCX7yl5StGBpg06nSSQg5cifV3ZrnthhxczDjmVFRSBLIYRGR0ccHGwXF4VPO5eMgeQh5e8+ODkKBVYWFvr6BpGREWfPnktKTk5LTTl+7FhUdIysPpdQoK6mXlxcfPeu6tjYhL6efm/fwNTUlJenh4WFucl90/GJCSMjQ2dnF3Mzc0sLy0OHDt25rbJp08bwyGj5S4PEZdPTWlpapLvl/6zcI39DcQZw0vuBfKwqKytjYmKCg4OHhoYcHR1JIZjkIcg9c/y71yJP7PWCsuoHfc8c9IGy6hhZas8e9FXc5WF7PwOflMfjX7/osPlr1V0bNHYuB3nK5q9VVa4+mJ3lyPs14wmDj5OTk6OhoaGtrd3Y2NjXOyiUjDCFXgACb6bQe0ZCmZFQWBJ/ltifJaEwhUBkCrwZQvcFcccPlntm2ONeEQouwbtcQ/Y+BxwDtpbUedNodPm8heTgSYT4Ru1hzc4ORXnl7X23UOHzQoUv5NrPCVzWFhz6tPjEmrm+p8g9YokkNDQ0IyMjLS2tsqrKx8fnySfr+8RmI3kpsZv/mrDz3YSd78lgxxKCKUQ3ZsMbI9mJI+MT8kuv/AHJydPe1s7lcW0TLM87nbzqduFpcB4TLzqfNgjT5i78cH1PXHnUDe9LqpSbqv4EYISiRFCUMPG23w3N4Dv0WVpvTy/5oOHHUP5hJPnf09Pzs/k19xVFR1x4O+baJxiil5CYa5+QePTVj6OufEBvLxsZG8ffISSr/9XIz2rnIt8gL44Qcey5sbGxT72R3d3dmZmZ2OUeH1MikS4sSKdZ8M3NoEvoTKi7KZ2e4tLm5pYq+wqFqKAAktguQqgpFGTo6ZH29iINjZbPPvMgUuK9+AAfb0lU/55oaGgwNb1va2uXl5d338TE2tqmpLSMOTnJYrHiYmPV1NS8vH0EAkFyclJPX39jY2N9fYO1tVVEZNSj4iIWi5WZkdHV00Oh+Nk7OI5PTCQnJaampctfO8ZdXV1JuefxCF4MQwjFxETl5RXq6qING6CUweAguIOEhyM6HZWWNjo7FyYmgonq4UNwc56ZkbLnpAyaCALaaVTQjNXWgpSDFT+44HhCAnSzstDQIIfOZTBAnEpORnfuPLpwIeTcOYQrUfzmNxC0paAA6oyFBRB6sMoDIfAOOn8e5yp8fBnw8hoZqa2t0dXVc3B0zMrKMjYxtrd3qKmpZTLB2dDXx/uehkZoeIRQKAwPC5uAhJClLS3NhgYGqanpJSUl+Xl5enp6LS2tCQnxzMnJgrw8Q0PDJ1MDLC4u2tjYkP49j0fwYhjh1xyEAyjk98BPb3V1NQ4m+mEWB4TAJ3r/fsgGRKTxzElMTJSfEvJnfBInRUnyVT40NFhWWmpgYODm7p6VlaWnq+vo6NjY1Dw5OclgMDzc3DQ0NGNj48ViUXh4+ASVVlJSUlNTY2hokJ2dU1JampWVqa9vUFdX5+joGBUdw2AyAgMp2KpInh1KHCws2NjY/G/wayZHRS4AbDabVJeSb9gJ6nhGVqqPr2dnVxslwHd4eID8F+Y2xDfN8W9fCVHc/eDUfvfl4HZyv/vJJaLCdmcb4xS8O4/Hv3bObtOXt3euV9+xXo0A9R2Aq++Ergzf9OXtW985s9nczs4OMp6LHDkk8K2pKS4uXtL3sISSUSrXG4DnM8H1oRIwwZMhJHGC68Fb7PwRcs+YW8g+B/8tTgHbMThSZIhcd5utz/rCSjcuhyfv30MOnmQ7Qqi/v392jtMX7pG2463M/R+vgKz9n2Tu/xi3Gd9+mHPk69leSAANhyJleYKtxeUVYbHxUQlJj6pq/ENCF0WQ3fvxNsQHxmBOcvj6N6K2/j1qy9tRW+UAd5eIYWv/NJiVMEFn4Mcf37WnDr6rq4s3z7OMvn/CWuGCw8kLDifPP6M9ZXtUO0Cdu7AsqdWKY5LPozwd4wihyOLQi86nbnhcfg5cc7942+cGjUUdHBiUr89FHhDHc6Wmpvr7+2dkZECREM4zh0Tu9eMRhFB3QWTImTcjrnwIcHkJVuIfhF14h9paSqUz/p3juX4AQwm/5jyyzDh5BJFI9H1mgvn5+RVmCITAI6ewEOSe2VkIdKdSpXyeSCySiAirAj4Clyuzp0gk4KmykSgN+fLL+RQKRHeTZ/lnEfkXJYkPDQ2EhIREREQyiNKPJP3FEflh4L2CgoJexJVHfkcSx3au2tq6yUnU3w/mvMVFKZeLOjpERkaijg5uUpIoKAg9egR+x9nZIBdOTkIuH5zDEJQ/NBpUtYiLA7fczEwQIePiwI12fFxKo03ThVQaysuD1DPHjsV6ehYeOQIiVGgo1P14/XXp1avyLyjZuEZHwViz4vcsFrW3tQYEUOLi4nAy62dthunka5d80ay4xXgzd3f3Jz/xV4znWV3CISy8qQkSFpDb4JeOSCTicrkSiYSk/7MIQhC3ODQknZ8HuSc0NLTqBbJTyp9lxcmfyq7mpsagoKCEhES8hJDbrOAeScdXuqK7/KQSd3f3/yV2LnwVOLBobGyMwWCMjY3yOMKU2Lbo4MbY0Ka4UGjD/asp7sUhPhVB3uUxIY1xQG+KDKivLoX4KULuWVC+5H9oh+0xXF9ieUsQHRX3OuzfYm1pCJnTcTzX1XM2G75Q3r5Wdds6GWxfp7p93d1t61S3r727nSBu+EJZ6YoDlzMvH88lz098NFxBhUZlLoiG+6ceDEw9GJh2HZh2G5hyBSCRKTfAp137J53m+G3y01L+mM/HIQXO7KhLwE5bn/V2fhvtfTfa+22wg5YAAiG6Gyw9viqocOFwePLSJHlwqL04A9FSNBptdHSUt7DQGeIav+kvSbs/ANj1QdLu95MBAEna/UEytO8n7ngnZf9nMz3E4BGS9LeKkrxEqf6iVH9xKmXxod9M1ANWpAs72pUb5yFOpYhSKaIUf1GKn4Q5Ds8JQgPZSUFr/hi68W+hG998DJsIHLcEPfCrP/RnxDNZM2RifXl2SSSSqampiYkJFos1NDTEF/BNwgwUzL49aX3kpLUC0a5AgHjUfL+6jwrnGXLP4uKiUCjE5leSS/IIxAHkB5ywOXzR6TQBpy46nZJDZN3zjievuV6kgk/wsngu8lAIIT6f7+rqampq6u/vD85VNJr81ZFb/rQIyD154YEn3gg5/27I+XdDz78beoFoiS6mwL/OvRN85q2J5pLO7l7sRklO8n818v8BfU9OTk5MTAx5txBCAoEgNTUVqyVX3DDs6cxkgs9sRQU4yQoEkKm2vR1WavIn/xHc1gZLdnk5MjNLf/QI/IvJzf4phLxVQqFwbHyMzxfgSGAWa4Z48ll4WRUKhRMTE9j4SuQmZmP/SkjELBKJxeKFhQUejyeVSnF62RXjwd0fqe+JiorCVeuWvpEQjzeXkPCwvJwVGws5egoLAXp6QK2TlwfJDGk0RKeJobImjQqOPmNj4OaMS03l54PoMzSEGHQ+jcVkAquTkqQuLujbbyO+/bb21i00Nwd+z6tWgalruecyHOnoUXBqfpLxJEv5/IWxsTEcvUWn09hEduDJyUk6nYFDpuk0Oq5rIZVI2LOzWM7ANkGxWDw7OysWSxYXF+c4cyv4iVdEoVBobW39E+p7sKEnKSnpp/rAwsxBCPn6+r5I4is8dRECe+KdO2D5JY+AubqwMD82NgaMkkjoNFk5FAadjqvIcbkcOp0hkxQlkrmlFKPgfU+oSTmcOcJdZmFFlb2l8/6v0/dgZYOdnZ2ZmVlRUVF/fy91fFbpTOzJPcFn94cCHAg9dyDswqGI8wfDLxyKOLsvFGB/6NHtAR72JZhpnLmF62c9vt1sehiSED6GwzssDu+wPES0h3dY7P7G2FQPDH+E3LPw3Vnrbz5X2rrmzta1Mti29s42jAPx9ta1d775XEnpiiPU52pqelLfg7kqIX7j4+N0OpMrGGgesmoZsm4ZtlkGQzYtQ48pzYMW05xl4viLv9NgDs+O2vttsfD4yspzjZXXGmgxeBHdJcp9109ySx05HB7OH4YvnDwRaC8iI52cnFxcXLq6upjTrLYg17C1f8I6mOitb0dvfYeAt6MJrQymRG16K273x9NdROwLQov5MfNXV8/f2oyBr7JVcHub4PY2vspW/u2t88oEXXnTvNJGcWctlnt6MxN9v/g9Zd1fKOveINq/UNZjBNqAdW9Q1v+Fsu4vPp+92pce17e8Phc5jefn5z08PMzMzOLj47u6umZnZw2Ddb412H7EdP8R033QmsmQozIKEA8Y7brtcZ0z//S3TWNj48OHD/v6+tLS0p58HeGHLiDb9/D9vaesj5wCAUvWysQsG6CctD5y3PLwBYdTY5OjHcvjueQ5LxQK+/v7aTQa9mn72fI1d+aE+Sr+MfDMW4Fn3gogWozLtwGn36Sc/Mt48yM6c/L/6XvIuwYIEc+Vhf178OOEVegjIyPP+obGa7lIBKvvyIhMtdDdLcsKgxAYU8bHwUt0aSUAU05nJ4qOjqqvX5YkcNlQ/lGHTqcPDAwwmQxnJ0dtbe3gkNCqysp79+6Z3DdtaKg3MNDX0NB4mJrmYG+noqKSkZnF5XLra2uuX7sWGw+fhgL+gqamRvGjR0ZGhv6UgKSE+HPnL3QQed/lz4zXciMjox9j5woNDcWipEgk7elBU1OIw2FTqRMIQQxcYqK0vBwiqQcHIUtkZibIQFSqdHIasSYXxVNToP+ZnAR9WlcXhCNVV4PJijUtmZziTs5T6UDIyICkzUeOULZv7zA2Ri0t0nPnINq9sPAx2+fmIJKuuhqMXGVlMjp5sWKxeHx8HJLEjI9bmJtp62jHJSTm5+XeVbtrZ+9QX1enqampqaWVlZ1tamIM6fDLKubmOI+KC69c+S43D6JpZmdY6kAv09XVDQ4JDQ0OunVLZYhwtyLPgufY3NycvH+P/H9fBEcIeXt7p6SAaQMfEDuR/GAF0rNOihDy8PBYUSr1WRsTIwFfcwMDrCuSisWi8fGxoaHhsdGR+ybGOjo6GZlZqSkP795Vc3Z5UFZWqqGpoampVVBQqKerq6GhUd/QyJnjZGakX7t2raISEvFRqeMqKioN9fVqd1WTkh+GBgepqanTCOlTfhj/2/x78E3hcDhFRUURERHNzc09vd20iZk7F6POHQy4eCTookKQrFUgcLI9EnRyj5+X0yP88pmbm//utPPub/T2bzUCIPLxLGsJ+vY1OibaoXgXHo9/+azV+s9vbl1ze8taeVCB7hqVrWsBWff5DaXL9hDH3tz8pNyD7VwRERF+fn5dXV00KoPF6Spu1nnUrPuoWe/p0AT0okZN6nTNUxdX+fv1VBxu4syIjfc3pq6fmrt98Rwwcno/u8RuZobdsxTDIX9G+Prv7k5LS0tOTu7t7WVOTTdRnClfvha84a/BG/4WsuFvwQSEbFzWDVr3Rti296a6ILct6C3y46aubZhW2UHAzmmVnUv4DgInure2TytvE3bCOxzqHmYkeH72qu/qP/mu/u/ngMfHv+lJi+1fHsdOMgQhVFdXFxUVVVFR0dkJ8Vy6Afd2am88YLTrgOHO/YY7DxjuAjACILu7dbfcfHBlbknuweMh256untrq2oHegfRU8GGQ/+HzwudNhuce/a2EaLVPAQQsaEnkyH3AD5nsPWl1ZJQxsiKOnRw8nvZ8Pn9+fn5hYWFgYOCn+gyTP8WTOEKoIyfUW+E1/5N/eRIoJPHEG77H/jTeXDxBo/8/fc8yNmI7V1YWlPIB1eXAQH19PcaXbfe0jrxSh8QRAguXnR3EFZNyT1yc9N13kZVVUE/Pkjn5aQd8Fo14yCUuzk6nTp22srLS1tXt6up0d/e4q6paUV3NZs8+eODs7ePLZNCv37jp5OSockslLi6hqanJ3s7OydHR1NQMIRQVEb5r9x53N9ejR4+6e3j4+vrcvKlU/AiCy+TPi689MDDwR9q56og00HNzaO/eDgoFnAeJH2TY8/CArDNmZmDhys8HSmoqKHdmZyF9s2Cagxig/AHJcWYGJB7CmRwx6PPMuckp1NUFQk98PDIzQwcOuB84MKagAKmz//73lQl7Skshpt3UFG7E8qsEeXdhYd5AX//cufMW5mZW1rYN9fUPXF1v3VLu7R9kMOj3jY0epqS3tzbfUb1rbW19+/bt9IzMysoqS3MzP39/CwtLkEU83Xfu3NXe0ZmW8lAdyk4Zurm7RSz3wCXFlJ/KzoUQampqGh7ukzFU/ub9aFwsFru5uWFl9QqOPevYJG8JMw1bV0f74sWLBgYGrm4eVZWVHu4eN27c6B8YolInNO6pFxYVlz4qvqumZm5mduvWrcLCooqKSkMjQzdX1weuEFtgb2dz6OBhRwe74ydOBoeEjgwPX79+vaPzKUlW8FBfxM4lm3pL10PyjaT/SESeM/hQbDZ7bGyUQWPfOBtwfI/bqQMezwGF7S7u9rl4x7m5+YsnbLavvbdno/bujdp7CMDIboKC201fqRloBuBdeLyFy2cs1312ffOaWwQob15za8sa5S1EdwvgQF/32fUbl2wIO9fAU+We3t5ef39/KyurlpaW4aFR+lRrYsH1xIKbSQU3kwqVoJUhSkkFSkmFN6EtuBGf/93Q+Mp3iDxDnoMTcs+wpedqY+f3TVw+vu/y0X2Xj+8/+Oj+g48J+MgEEOjqO7yV9ciGQZ/E3uL4wuWPjCmQkXV4mLfAr/N18Prkt/5r/rwEb/iv+bMf0fVbA7j/mjf8vv5j4Ka3Jzub8b4z2dE9Z77qkysvCvhlqDb6GC5t6L30zXwbyHkgbKXHu330G88vX/f68nXPr14nkD96fvlHry//6PnV69ACvO76wX91pURPLs9b+NTBDw4M8gX8ez53Nt9bvVtv827dzdDqbQEE8C0you7mrZrrv3M8PzcPdWqRFDEWaAPc3kFuP4buqfauqfZ2RkvnVDtJHOD2jfAGRZJFeBci5JniulVz/T6DHc+BvXrbjtzfNxzaNz8AACAASURBVMocmZigysexk4PHgV2RkZEeHh4hISFDQ0M/m19ze3awx6Hf+B7/7+eAz7E/eR/9w2hj0TiV/v87fQ8xRR9/JeOufJubm5ueLvPtra2t/THGCDwhJBLQA01OPpZ7xsaQpqbkyhWfkRHIkow3IwdGTqNnIXiXvLxcX1/f3Nzce/fuubq6Kioes7Gxtndw8PDwCAsL1dLS9vXxNjA08vHx0dfXu6eh0T8wGBYWeuP6dRsb25bWFgcHux07tmvraKvfu6eurh4VFXXfxNjbF5IJyZ8Xj4pCoTxH7iG5Ry7q5LVgJCYmpr6+uq0N5eSI09OrCgqmMzMRjwdezHl54GczMQGVFgoLwcLV1ARamf5+6dwcyDmcWRF3RiDgCKR8PpivZmYwLM5w2DOiOQ4YwaqqoAZ7bq7g+HFnA4NZQ0N04wYusgmKH3xBCIEgVVwsy4Utf4142ItCYXJyEoUSkJGRoa6ubmdre+bsOUsLC1c3d08vzwAKxcjYxMXZ0dLK2t3DQ0PjnompWV9/v5+P9+3bd1zd3Kqrq83NTbdt2+rh6UXx97urpqajo33nzp2CwuInWSqVSh88ePAc9QzJwBUsJVkdFhbW0ACpARYXF6urK+Li2GlpiDC9rbiyf9BdMTb5rbGm093dlcGYDQlBra1YXlw2PeS3J3GclTsgAHV3LzxMjg8MDEx5+FBTU9PK0uLS5Sum9++7uXt4enm5u7laWlrZWFs5ODi4u7kp3bzh8sCtv7/f2cnxtoqKH4VSVVWlr6e3bdt2M1PTO6qqOjq6cfHxyspK5RWVK4aNtZIPHjzAGfaW5y1Eq1ahV17Jys/PxtzjcbmRkZGtbeDJQeRnL0zPyHwWn/E2/1RL8gEjWBXX3d1FHWddPO56YJulwi4bhZ020D4N9mwwc7KSOSnPsXnnFM03f31nyUMZ/JS3P/ZWVttOOC+v/1xZ754vHiSPt3DpjPnaz65uWq28eY0MNq1R3rRGafMa5U2rlTetUd68WnntZ9duXLKdnZlrbXtcp0J+5GKxmM/ns9nsiYmJmRn2GL3BP16REn88IOHEs+G4b+zh7kHQfcofisRJNpIUeQTyxc8Mmbt9ZuDwdyPndwGc5IDoGjoBXcfuz+lF5vPz/CGi2gk+rPyhMA7xXL29LDa71sfe9YP/8vzqjxi8vvojCZ5LuMfnv/dd/zdmh0zuGU8NLznwYfmxL8uPfbUSFJeIR78oO/rFTLOsQkhXepzLh6+4f/47ty9ew+D+xWvun78GrRzi/N7/6UqJHp2grojnWjF+hFB7O8Rz3fVSXqf6+Tat9ds0nwZa67dprd+g9tVFu1NsHsg9Uqm0lFoY1u0X1RsU1RsY1RsY3Rcc3RdCQDBBBHpET0BifxRvEULAEEKuSU7fqH25U2fjTu1ngM7G7Zrf7DfcMcIc7uvte6pfM36Iamtrg4KCYmJi+vr6nszfg0+H2xWX/Kyu/C5PnVpQ0yMryG3/K96Kf3gOeB39vafCqyMNBf/m+Xue5CNRk2EwIyOTzxeIxeLc3Jzevr729rbi4kcsFis9PX18glpYWJiRkTE5OfkP8x2vuB8rujiBxAoi7nK56OjRxfXr3aamxsgNhEKhfHEWkv4kgq+LpA/098fFxbW2tQkE/PS01MKiYolEUlFRnhCfMMfhdna0x8cnjI1P9PT2MBnM9PT04ZGRrq6uxcXFvr5e5uRkRXl5fX3D0NBgSkrqLOFXQR6ZRAICAp4l9yCEujo7cnPzxGIJf2EhIyN9dGy8tramuqaWRqOmp6exWKyEhISmpipLS7q+vhghlJSErlwBa1dICAoIQIuLcJ6+PmRpCVFd1dWoqAi1tKC5OcRioZkZNM1CvHkooLXIZEqmJtH0NJphCVi8aRaan0dMJsrNRW5uKCNjbuPGB8ePcw8cQB99hD75BP3ud8LAQGx8Jy/l6cgKlra1tsbFxvX29fN43MSEhOqaWolEkp+fl5aWNj+/UF9Xl5z8kEajd3Z1MRn01NQUBoPR2dkpEot7e3omxsczMjJ6+/oHBwcys7JX+O2Sp3d1dX2W3IMQam5qLCiEDLxcLic9PZ1KpVVWVDQ2NY+NjaalpXE4sHLX1tbSaDQ8Z27dQpcvIwlkV/jnfrjIwLP2YbPZ3t7uk5PCtWtRVBRs9fztyeP09QH/8/MfM7+psSE+Pn50dIw9OxsfH9fQ2CgUCLOyMjOzshYW+FWVlSmp/5e974CL6sr+fzGJm83mF5PdTbKbbP4x2U0xRiU2LIm9i72iQUUQ6X1g6L0OQxXpRQQsKIJRRIqA0lU6wwxlZpjCUKbPUKa+v2+ueTsZRmSNmmx23mc+cN9959577veWd969555TMDQ0jOyqMJg3btwYHBzs6uqSyWSdHR0iobDoZhGJ1P3gwf1bxcXo+gRaHPjQjIyMROWelJTLEHQPgiQQNApB0pkz8wsK8gF9Zka6kZHR6dPmY+MTPSTi0aNHjX74oab28Ve7ep7PFtaYdlTWufg9Pd1M2si+Lb6rFzuuX+a8fhnm8W/548C6x5HOK+bbBnk/VtZ55Dd0/w7PJd+YqpRyzJG/36r+Ijo6//4t/MrY0ToWcCsWjR3d7/PtnGPL9EyX6Zn89DP96fZx4Ns5x4yPBALTfyiekzkHJxwZdBaZXheauDws8buwpO/Ck74PU/3Ckx8HkFNXSMx3QWeXtBC0KJGoFI/E+fn5IyMjt27d0mqcE5F7OGRP/OeY4L+5hn3kGqrl56KKdAh493qZl4D/M7uFWpnv7u7hC4V1cYHhn74WNfedn36zouYiEslPt0gg8qv/i/32g8GOx84NSZdSc5f+7dLqz9AfOJp+SXUi/fHfVZ9e+n42q/EeQL6z8ELYpzPxX/0ffs7bEXPexqt+kaq/6C1+ztshs2d25GfTWSww/EFarcyD81zmMcYLzL9YZqunb6u3zHbBz3+PYxZazjkYuAuVewpbr4bc9o0oC1L7AUfu/44JLw2Ir4oSjKuWiGAYlxeiZ/7lCruFK+y+nfR7HLnMRm+t83LKIPlJ57lALeRyOXj3kclkDf0e5GVBIJSXl9NotOvXr2scEtIAAdyCXZfi4uKBgYGCggKtFuNgGG69kYzf8Hrs9lnqvziDn93GbHs7esuf+h+WsoaGf892CzVwVImKCqDvcq+mtr291fz0aTc3NywW6+joFImP2LhxU3NrW8WdO4GBgVevXgV+Urhcbnd3N5VK5XA4XC63p6eHTCYDpa2enh4Oh9PR0TEyMkIgEBgMRmdnJ5lMJpFIKvOyGTU1NSKRiERCtMfBq5HNZnd2dtLpjOrqdisrx8bGehKpm0AgdHd3JyYmNjY2cjic9vZ2DoeDvABU/qGoVCqRSCSRSF2qi0Ag1NbW3rx5E/j96e3tFQgELBZraGiIxWKBUwxMJpPBYAwMDFCpVJrqolKpXJXDGqFQyFZ5C+JyuUKhkE6nDw8PC4VCKpXK4/EQX3kMRn9/P5VK7erqolAoXV1dRCIxLCxMq36P6n0j9/LwMDMza+8k3KuqsLKydHV1dXJydPfwCAwMNNixs7+fduXKlZiYyLCw60VFnMpKdkUFp6mpq6iIUVzMaWvjlpYSHz6k1dfzWlt7iERKW9vwnTsdbW3shoaOnh56V1dXT08vgdDb1tYVH3e26WH78JCgra2LTGb29jJra4mtrYNFRYTqamZdXYO1tXNdXUd9PfHePUJ1NcHL60xCAiEzc7iqqpPLZbe3t7NYLAKBQKFQuru7iUQigUAAfysrK2/fvs3j8YaGhh4ZvBeJREwmExysAH8HBgYYDAaTiTh2odFo/f39NBqNj3jj4olEItWBHRqPxxMIBP39/RwOh8/nU6lUcMtischkcn9/P4FA6OvrI5FIBALB399fq9wDZB0XjLOlpVUfmVpcdNPCwsLb29vJ0dHd3d3L02P/gYNDwyNZWVlRUVElJSWIkzUOu7eXU1vbyWINcFQX6I08Ho9EItFotOFhxIMj+MtgMIhEYm9vb09PD4FAOHv2bF9fH4/H6+zsHBwcpNPp3d3dLBarq6uLRqPV19djMM5kMrmmpqu5mdDZ2RETE9Pd3T00NEQgEEZGRjo6OlBIe3p60C7a3k64fPnO7duVQqFgcHCQTCYLhSI6nc5mI6dBUEiZqotKpdLpdBRSHg+BlMVC1qIBpBQK0jl5fB61vx+xUU6hDA4O9vb20mi0rq6uvr4+IpHY2dnp7++Pyj3FxeVr1wasXZu0bl3C2rXJ69d7Ojrag9etj5dX3pWrJiYmdMbAnbJSd09vXHh4ekbWs0k5k1OpTztyuZxEIjGZTDqdxqCNbF7luHDOyaXzToGf/rxTS74x1Z93Sn/eqaXfPI6c95mRl2sqyFbAF+/chJn/+Q+L5p5Y9PWJx3+/Pr7o6+NIGIk0XjT3xNezDW3MI0ESsWj80C6PuZ8dWTjnuNrv2MKvji38+t8xcz87YnTQVygc7e7u1ir3gNU+IpHY39/P5fC7yRWuoZ9gw2erVmI+c8N95ob71DXsU1UA3H7mhpvtHPzh/baL4KMcRQYsAzzyXmdvb19cXOzl5aXutBWQAZoRTi82/EN7/z85BL7t+NPP3v9th4B/3zoGvm3t/fq12xixeAx4FURzQJEHrgb7+vooVKp4bLw6yjfob1DYZzPDPp35+O+nM0M+nRn+2eNf2GczQz95FT/nz6w2ZA0V+fDIiIv89PW4r2ep/2LmgNt3VJHvxM15O/ar/6PVIgu6iIpJfnbQ36Gw2a+Fffr4F/rpayGzXw/79HVVzOvgUeAHr7TnnRtic7Se5wI4UKnUgYGBvr6+sfGxExFHPjf+eL75F//+nf58ntnnqtvP55sjga9MPtnps5kv5iHIw8qoQtze4O1HcHvRn2H4HsOwPYaqGPD3UNiu02dOsAUjgPmgC37/Mv6HnsWXCyy+WGCO/Oabfzn/9BfILYix+GLe6X/q2y4gD/bRaXSt+1wAf3C8BuhKglMIoAhQtWvXrgUFBV27ds3JyQksGoF4dRoQRnsRYk/E3f3GjRsYDAY1yK5B01KYEPYdFLnhNbXf6xHrX1fdPv6LX/8qfi3U/+D2wODw/5B+D7IYLpV4eXn6eHvlFxSWltx2c3NzcLB3cnLy8fFJS0v38/VJSEyiUChYLDY2NjYyMhKPx8fGxrq7u/v6+kZERODxeA8PD29vb2DN3cPDIyIiwsnJKSwszMXFJSgoCIPB+Pv7e3p6YrFYZ2fnoKCg6Ohob29vf3//kJAQDAaDw+EwGExAgL+Hh6uNjaW7u7uLi4ubm5u7u7uNjY2/vz8ej3d2dsbhcCBDFxcXX19fLBYL5DNXV1c3NzdHR0cnJ6fo6OjIyEgsFovD4by9vf38/Pz9/b29vb28vHx9fT08PLy8vNxUFxaL9fDwwKmuiIiIkJAQNze3CNXlgZj8CYiIiMBisaGhoT4+Pn5+fojpPR8fLBbr4+Pj6uqKwWD8/PzUffSozy+SiXEsFuvi4lJ+pyLv8sXAoKCTJiZYrKt/gH96RoaXp2felfzm5mZPT3c3t1g7O7ytbZSLS4y3l6uJSYCNTYSfX4SZGdbJyd/ZOdLLy8PX18fTM8zKCuPpGWFri/H2DnBxxfr6+rm7ezk5udg7OAcGhoWFR7m5efr5BXl7B1pbu2KxIWZmLk5OAVisi7W1pY+Ph4eHq4+Pu7c31snJ9sSJ4EOHcNbWmIiIcCcnp+DgYBcXFx8fH4AnAF/VDRxcXFxiYmJwOBwWiwUNHRgYCNrOw8PDz8/PQ3UBSF1dXb29vSMiInA4XEREhL+/v4eHBx6Pj4iIcHd3DwkJweFwrq6u4eHhnp6egYGBIAdXV1cvLy/QNx418djYz1zAorMGh8N2dXHx8PCoravPPp/l6+NraWXp7uHh4emRm5vr4Ohwu6SssrLSw90jLi4Oj8dHRUXFxUW7uWGCgoJAs7q4uAQGBkZGRrq5ufn5+YWGhmIwmLCwMAwGExgYiMVi/fx8PT09XFQWLHE4XGRkpLu7e2hoqL+/v5ubW0hIiKurq6+vr6urq6WlpZeXl6enK9KCWKyNjU1oaCjo8OHh4RgMBoUUwV11YbFYd3c3DMbe3R0ZR2FhYW5ubng8HuTs5eUF4AKQenp6uru7u7m5qUOKx+PBOALVwWKxKKQ4HM7d3T04ONjd3d3Pzw+FFIwI9AtSoVBIpaNSqein36hYLAYH9BITzp44ceKU2em6uvrbxcVGPxgZ/fDDnUrEm/RzudRHxyMHk05OTuXl5SQS8cH9VsN9rtvWW+3eZr9ri+0eA8c92x0NNlvs2mq3x8Bx9zaHvdsd92533Lja3M/7LIVCJhK7iF2kE0c8tq6z2LXFdvdW+11b7fZud9yxydJgk+WuLXY7VZE7NllvWn3axRHf39/f1UUgErvNjH03rzXftVVFsN1+93b7nVusDTZb7N3uuHub/e5t9nu2O2xea25lFtjVRWppaUG9zqGcg1dRc3OznZ1dW1tbd3ffvbqr2OBF7iGLvHH6nmFLffDLvXD6GF99zzB9H/wKL5w+EhO+1CVwQfGdNAqlHwhMVNVFo9EYDAaZTL5161ZRUVFBQUF7ezuDwaDRaICASqWqhNfuto46b9xKbNACr/AlHqGLvCOWeuMWY3yXuAUv9olY6hmGxHiFL3b2n5tz1Z1E6u3ufqzWBt6RKP8wDOfn5/v7+xNJpO7e3puRQcH6c8LXfItftzhi3cKoDUsi1i3yX7MEv35R1IYl+HWLojYtDV+9IGLD8paq8u7evj5qf2lKbMjyb3Br9KI2LMavXxS5YVH0pqV+q7+N2rA4Yu23kaq/4asXhK9e0FhU0NNH7qNQ7+ZmBi+fi1v9LX7dIvxaJOeI9Yv9QCnrlzyOWbMwWH9+VXZ6q+qDAe1v6pxLpdLQ0NDs7GxiF7G7u9sGb77WZsU25/XbnDdsx2w0cN20zXHDVtv1Bq4bDVw2bsds3OG6eYPt98f9DbtIBPCx6p3ksdVx/W7s9r3uO/a4bd/vsXOPq4GB/eZ9njt3uW7b54H8NcBsPupzoLWzhUggMunMkMzANdbLtzlv2IYBpWzc7rRhi+16AxekxO2YDTtcN212WLMDs7m+ua6ttU2gciQM+EeZBwEYhjMzM3E4HBlxd93dr/qK7u/vp9PpTCazubn55s2bt27dKi4upqsuYG6ASqWqOnAXmUym/nQBgra2tsLCwhs3bhQXF/f19aFf5mgSSj/9zqX48H3fRB1ZEPPDwpij38YdWxTzw8LgQ4uif1gYd2xxNBKzOOqIXsTBrxtL8ghdpJ90FtEWeLGBX/kcOwzDd8rK/PwDGhsbampqwsPDrl//8Vr+VW9vnzvl5SEhwdXVNYODQzKZjEKh8Ph8NptNIpHGVMbmJ1QXcEQ8MTGBBuRy+cQEsmsGNqoYDAabw1EoFKOjo4hWnUiMqHcJBBKpRCaTAUrg80+JOA9ATpKPjDw+IC2Xy9lsNrASBDKUyWSAGPEioLqoVOro6KhSqaDT6TQ6HZQOLDRIJBLAFfoXJFcoFGw2WywWgypIJI85AbWQSCQTKhPUIDmaiXrRTCaDNTio1Q4eMsVcyQvHRdx/8KCqqsrf37+isiozIyMwOLisvDw0NPRhU/PQ8LBkQkLr7xufELHZLAq1VyKRKpUShWJCKp1QKGRyuVQun1DVVQpiZLIJheLxeimtv18g4CuVSqEQcWYpEgn7yL3iUbFMJlUoEMtKqhwkMplcobqXy+UsFmtkhK3a7JYKBMMSyQSoNfiLQgrQo1KpExKJXC6nUiksFksdUhRJjYQypNVGxsbGJkMKTD0BSFGEQQOhkEolElp/PzCyN3nWUCqVOdnno2NiGhrqqyqr/P39qqtrEhMTcHj87eJbwcEhnQTC8PCwRCLp6+sVi8VMJvJSkapMTAF+1HlAAP2pfUG8TCajUilClbl9Po9Ho9EFAj5YlAbIqFdWJUBIFQoFk8kE32dSqWR4CCldA1KQFuiFUKlUcEsmk4eGhwGkaOngEYrtT11bNjIyMj4+jkIKOEeZRyGdnA8YWchKG5eLTr5Pmsm4XE5MdHRDw/3GxgbkcG/BtXNZWU/ajnxSJlPEow0KzJlcuHCBQqFIJBKRSMTl8rgcHrmPPMBkEQhd3aSejvYOErG7tbW9q4vY1tbe3tbBZnP5fL5YLBYIBI+TcHkMOpNCodLpzNaWtp7uXgadSaMxmMwBMpnC4/I5HMT3uFgsEggEYrGYx+XzuPy+PgqDwSQQkFd/RwciD4FSurt7Ozu7RkY4PB5fLBYB4RtUB+UcyD0DAwPnz5/n8XhjY2NCkYDHZ/P47L4+0gCL3klo6+0jdXS09vV1N7c86OsjtbQ86COT+AK2UMgXiR5zIhaLR0dHBwYGwII0QdVvh4aG+Hz+wMDAyMjI6Cgij4rFYpBEJBbx+Gw+n91HJrEGGZ2dbX19pPaOFiT/1ocUSk9XVzsoRSBAFgXRlarJck9zc/O1a9fGx8fFo6NCPp/PGWEPDVJ6uxlUSld7Wy+J2NnW2kcitjx8QO7pbmpsYNL6BVyOSCRELpFIKBDwOSM89jClt5vFoFP7epruN44MscjdJO7IcF83iT00KOCy+ewRkEKVRsjnsNmDLHIPidFPJbS39ZGI7a0tfd1IKTQKuaO1mdFPFXDYQqFAJBID2yIayIPbW7du1dbWSlU+MXh8LpfPpTH6qTRqT18PgUjoInV1EDoIxM629lZiN/Fh88OhkUEunwv6DNIB+Fw2lz04zOru62YM0LuIhJbWFtYQcsvhsYk9xBHuCJvL5vDYYhHSzcRiMV/A4/K5dCadSqP2kntBKZ2Eji4SobWthUwlt3W0soZYXD5XKBKpXkD/3sJW7zag59TV1RUVFQFbYqAPsNlssF4OBJfe3t6BgQECgdDf39/a2or2BBX2ItAl0O0IBoNBIBBAnxGLxWAdHdCIxWKQRCjkC3gjI0MDVHIPvZ/c1dnW003o7Gjt7e5qabpPo/Z1trcMMPoFPLZIiNR3miolUwzz/+jRryz3gFZRN7iMbCsIBS4uzv6BQXK54sb1QtNTpy5evGhjbZ2SlhYU6O+McREj2iXTuoYGB+3sbIOCg2EYvpibY2Jqequ42NXVNSPziavoEonE19fHCYPhC4SsAabxiROdBOKTCmt6cP/EiRM5uchK8rlzmdY2NhwusrY59dXR3mZkdIypMiE1NaXWpxz2yOFDh87naNqw1uju6tgymXQbG5uY2DhE9s9IN7ewvHjxorm5+YULF52dnEJDw6ff7Xp7e8xOnTqbkAjDcMLZeGtrm5KSUmsrq8Ifb2jlFrF+xmY7OTr4+vlLpFJCZ8exY8eZA6wnEVfcKTt2zKio+LZCoYiJjnRxdR1VLcM8iR7EV1bcMTU1BTaTpqbU+rSvt2fXrt03ipBjO5NhBEiCR6q/iNoOqavLytIyJS0NOXQaf8bKxubixQsWFuZXrl61tDCPO/NYrVVrcRqRhI52k5MnM89lwbASjwu3d3QqKyszP326rPzxir0GPQzDrAEmstITFg7DcENdrbHxSaHoiYPi5o3rxsYn71XXyGTS8LBQbx9fmQzR65r6Krr5o7W1rcqUxNSE2p+2tDTt27ev6m71kyBFcdae/vnFogWh7aiet0KhcHFxuXAhF4NxTk9Pt7S08PT0jIzEJyUl4fH4nByVFpV6AlW4urraysoyJCQ4JSXl3LnMqKjIxMSE69eve3p6TKJ9HOHp6Xn+/HkMBpORkWFjY+3mho2IwJ09G29nZ+vg4MDmAPc4P0utzjnKPBqAYXh4eMT8tMWVK1ednZzT0zNMTEwDA4PwEfj09Ax/v4D79x9vEv0sUxhOTU21s7MLCwtLSUnJyspydnYuKSkJCgrKyHh89l6Dns1mW1hY5uVdsbd3OHcu6+RJk6Cg4MjIqKioaBMT00dOSzTowe1Tma+tq7O1s0tLz/D28YmMijK3sAhXrdaey8ry9vHRugDwyIeaK9YtOCQkAo9Pz8i8nJdnaWXV2tZuYWkF/DdP5qSpqcnG1jbz3DkfH9/omJjjJ06EhYefiY8PDgkxNDxy9epjJTONhFqZV6dJTUkNCw0LDQk9c+aMt5e3tZU1DoeLPxOflpaGC8NJJoAbGfUUMJPJPH36dHBQcExMTEpKCtIZnDGEToLxCWN0O/hnCWA4K+t8cHBwBC4i/ky8r4+vrY0tPgIffybe3d3d2Ni4tfXxUQCNVBrMq3cYlLK4uNjFxSU+Pj44ODg8PPzUqVPh4eHR0dGP+oCPj4+61ws0yfj4uJOTk7e3Nw6HS0lJuXDhgpmZGYlEMjExUfdHi9IjrrgrK52cMWnp6f4BgRH4SHMLC1wEPjY2ztvHx9j4ZEkJYi3vV7l+fbkHtAraVCq13E43ZAnfjcFgRkbgoqNjDh3cHxMb6+aGxUfiHRwdSd3/PnWFJpwcQIxe1Nb4+fljsa58gSDA3x8fEZ6Ree7G9UI/vwAAt0YqZCoZYrm7e7i7uzW3tI2PjTk62Dc+0G74CzGem54aHh4WFByCOE6iUiwtLHp6+6ae6GEYptP6LS0sgN8uDQaeeguSu2GxgYHBEpUG8tRJQDUr75QHh4R4eHoymQO+Pt7RUZFGx45HRkZ6eHpG4vEuLi4jbGTanTor0FI3f7zujVw+o6NjHh6evn6+169fz87Kiok7ozUHZKO9vc0DwdSNzmA+sow3Rd2BDBEQEBAZhZya7iJ0WlhaAG9cU/AGirC0tBSJxFp5mCItqFRHR5urCyY6Jlah+mp6Kj0Mw0U3b3j7+Hh6eomEAm9vn9DQ0KNHDBMSE93d3cPDwj08vLiIP7hpQZp/5XJIaLiPrx+DwVDtb4ZU3r2XEH8mLeOc1hxgGK6tqfb08sa6uopE4t6ebgtzCzZHpUzwc9ZVra8MDw2Jio46m4icrH748IGlQITMpgAAIABJREFUpZVI9eXwc9qf3SGUD+7b2dk/9gDws4dPvwHJnZydEpMfH+d+eppJFM9rQpyU8b8jYBgmk8lYLDYgIMDFxSUnJycsLCw6Ojo8PDwtLa2oqKihoWFyEyBqm62tYIc0NTUV2MP18fHx9/d3d3ef7PEUnN92d3cPCAjAYDC5ublgjx6Hw509exaLxTo5OYFzvIAztOL/ZlRbCFksv3MH7Lz7+voC24CRkZFhYWH5+fnXrl0DRgQ0kgKLaA4ODv7+/mlpaSEhIZ6enmCjMzQ0dLKaKgzD1dXVWCwWbNbn5OSAF2RERERcXJydnV1wcDC6m4lyPhk0dTYAWUZGBtjxj4iISElJwePxkZGRoaGhJSUlV69e1crJ2NhYUlKSq6srSOLn5wc2vl1cXLKykE9Z9VLA0M7JyQFb4VFRUWlpaUFBQZGRkbGxsRERESYmJuqpps+8SCQKDQ319fXFYDCZmZlnzpwJV10xMTEVFRVavWgDJZuIiAgbG5u4uLizZ8/6+PiALWYMBqPVxc34+DjYl0c+1DMy4uLiwC5zXFycv7+/ubl5Tc2/z1FOk3mAiUKhSEpKAuof8fHxSUlJoaGhUVFRkZGRZWVl+fn5YOlLHUxgLjghIcHS0jIqKiopKSkgIADoDLi6uubnI+KjOj0oKDk5GTRxTExMcnIyDodDhOXo6KCgIGtra/VU6vy/hPBvQu5RxwuYNra3t/P186+trUtPSzM7bXY+O9vK0iI2Ni77/HlbW9vuHsR1jnoqrWEYhgdZA9bWVsHBwY2NjQln401NTfPzr/l4e0dFx4CG0Uioat1xL08PZ4xLWVn5wMBAUmJis0qs1qAEyR80Nhw/fjwtLb2+ri41JdnaxqaPjJi0n0yMxiAWbyWSiHBcP40+NSWaRD0AKuXn6+PnHyCRqrzSqD/WFgbOLM3NzfD4yJqampjYGAtLy+zsbDMzs7T0jMSEBAcHxxE2ZzrMIOe8SERTU5OY2LiG+vpIPN7KyrqgoBDj7Jyekak1B8QKyMiwg52tr59fRUXlwAArEo9/Ut2RY8ylJceOHbt0Ke/e3buxMTGOjo5Dw8Nac0brijgW5fPCw8KeWe7pJhHdsdjIqGiFan0DzflJASCTmZqaxJ2Jr6+rw0fgrK2tz58/b3ba7Ny583Exsa6ubtOXe9raWk4Yn0hJSb13725wUKCdvX1BQYGDg/3lPO1eU2AYHmAyrKwsw8LC7927N8Bk4iMiwELjZIZhGL5eeO3kyZMFBQV3796NwIW7umLFo8ji0GRiNAa0Wng4bjqyNZoKDcAw3NnRhsFgEpKSwCyGPpp+4HlNf1OUCJSFR0dH+Xw+k8kcGhqSSCRgPZ/D4YyrLq1ATUxMALvqAwMDw8PDo6OjAoEAGFifrCUGShkbGxMIBEwmc3BwcGJiQigUAu8HXC53aGhIDjzVqXhFKz4F52D+EYlEEomEzWYDrX+JRMLlcvv7+0dVdurU81TPamxsDOxn0Wg0LpcLchCpLnA8UJ1YqVSCHXkOB7E4z2azQSl0Oh1ou4O1AZAE5VwraOrZKhQKgUAwPj4+NDTEYDD4fL5EIhkeRuz2SiQSsPGhTg/CjxbnAPMCgQBsr0xMTLBYLICnVnqBQDAxMTE8PMxkMtFS6HT66Ogog8FQtzY+febBhtfY2BiLxRoYGABog91DqVQKdp0mMyOTycbGEPvmoL1EItHExAToDFpdyoNtKbQU4H8elCIUCplMJipugs6A8j+5aI0YuVwuFAonJiaGhoaYTCYaZjAYwNG9Bj24BeCDDVAajSYQIBbbBwcHx8fHhULhZDPCCoUCzRmADxqCTqerlAGY6iMFZf7lBH5zcg9oQtVBmMfb6hQKRa6Q9/dTR0fHVAPjPxAXVBZmGeDklEAgoFAoUqm0p6d3ihck2JdhDQ6qdujFQmTYIIuWT+oKFApZJBJxOBxw+uZJlOrJFQoFn8/XOsWok00R7uvtA0fcp6BRf4SsEtFpfD4yNYuEImp/v1wuB1oODAaiKjQdttEM+/v7QZVV9t9oMqmU1E0CVgZQGvUAsi/DYrHZbA6bMzaG2CB5Ut3BwWwymTI2NgbmWeCJQj03reHHzig0PFFpJZ0UCYzNdHd3i8X/wXKRSprs5wsEHA5HKBDQaDQA6YRE0t9P1ap1PqnknyIUCiqFMjo6yuVy+XwemNFI3d1Av/Unop/9h2FYZceFC17PfD7/SS85lZw9QaFQxsfGuFzuk9SYfpa76kYuk/H4/GfzKwbasbu7W31qm1zE1DEvZwbUlaJDQIfA/xoCv0W5R0N6Ba9k9YaZerrUeIomB9mifzXI1G/Vy3qqQIDmD1Kp5zNF+KnZTpEWxWdqGo2nkyulHqNBPPUtmhDl5KnV0Ujy1PynnzOa1VN5QCm1BgCHWh89KXJy06vHPCmV1ni09MkBrfRoN0ZLfBIZiEfxR/Ofmh5NNR2yJ9E8lxbR4Fx3q0NAh4AOgV+IwG9U7nnSTDr9eAQXFTUK0ORX6U8kiE6H+osETQICoFCNSPR2miyhZaGKopP5mWZWvxYZioZ63SeH0XpNE1J1ejTJdOqoBunjFkSzUn80nax+LZrJkKrjqVEdtJeiNOoBUAWNDNEcpllBFDc05/80h2kWNB0ylAddQIeADgEdAs8Rgd+t3KNUKsGOo1wuB5vZCoUC7ByDqRzscQIa9b/ggOv4xMT4+LhEIhkbG0N3EMDBXaVSKVGdsgYnitFXhfpUriXyJ9u9SiWy6wm2MGQy2ejoqPpxNvVMfmthgBI47Q8OQyoUiomJCfR4nVQqFYqEgG11SCfGx8Fm9tjYGIAUfQrsCCiVyomJCYVCoTIt+jOlJRRJNADyh1WH+MFIAE0JdPGkUukv2Vt5yZg/CVJQBbBbBFQQgC/ux71aoUDOA4vFAEx1SJVKpEUA8WNIVafSNdDTWk2V0UtkXwuGYXR0AHW0sfHx6eSgNdtnjnyO05wuKx0COgR0CKAI/D7lHhiGkxPPhoSGwTAcGOhvYWEREBhUVVV5ytTUztYuJCSMy+V6e3ocOXIkPDyCRCRiXFyYAwOJiYnFxbdNTE1cXFzOZ2dHRUXu2rnTwtIKHNFKS002Nzevb2jMPp912ty8uqYmJTnJ2PgkULJG3woAWeCDCkQiWo2jYnNz88oqxBRbdFSk4RFDjIsrhUx2dHA4aWwcEBA4NvYrvFem/0IClQoJDkpJTVMqFA4OtnZ2dvjIqLLSUmPjE3Z2dmfiz7JYLHt7uyNHjiSnpLY0PcRi3ahUamxc7O3iW8YnTri5uV26fNnf32/Xrl2OTs5DiC0fODoSb21j29beHhMVCQyy4cJDbWxswQEuFL3HkP60UAZezOyRYUNDQ0IXUSaT+fv6HjlyJCAwqK+vz+yUqYmJSVRUNDAlMv06vmRKRMiQyz3c3S5cujw6Kraxsbazs4uOib1186axsbG1jXVG5jk6rd/CwuLokaPnc3LvN9Rj3dwHB1lnExIKruWfND6Jxbrl51/zcHfbs2cP1s2dy+NLJZIAP18HB8cuIjE8NMTewQExmuzr6+joxEXNK6jBqKoygi6AlE7r/+GHH/ppDLFIiHV1NTQ0xEXgKX19x44ZmZiYJCUnIwLRExTdXgR6oN11f3UI6BDQIfB8Efgdyj0q/zvcAwf2bd6yhTkwYHjooKOTc37+tdLbtzZv2Zp1LnP37t2hoSEWFpYjIyPGx4+diTvzr3/+08/P39HRMSHh7Lp16+Lj4+vq6oeGBrdt336r+LbqraDcvWvn4cOGnZ2E/fv2GB0/cbu42NTENMDfPxyHh2G45HaxjY2Ni4trQ0MjxtnZ3s7O3Q3r7eMDbM9U3ilfuWKFq5t7VWXFrl27h4ZHPNxcvby8Dx4+TCAQThw/diUfcXz4Il4ezyVPcHh+29atBw4eYjCZq75fGRAQUFRUnHM+a8+ePZmZGbt27wnw9/Py9mENMI8cMYyKivzggw/wkdFW1pbxcbFbtmxJSUltaWsjEbu2bNnysLkVWGla/f13Vja2XV1da9estrG1u3Xz5unT5u5uWGBdKTcn297B3tfPv6Ki3MnJwc7Ozg2LjcBHguWxixdyFyzQw0dFFxbkHzt+YnhkxMrS3D8g4OjRo6Tu7p0GBsDB03Op/ovIBPHR2NW5du2a48YnSSTS6tWrQkLDSkvLUpOTjh41ij8Td+z4CWcnRxw+ikIm79u7JyYm5t13/5yUnGTv4BAeFrpz9+60tDQiiUQkdG7atIlARExMMRmMpUuWYFxc79+/v3LlCg9P7/yreTY2tg72dgXXEetKWecy7Wxtg4JD7pSX29vZOjg6Ojs7x6t838IwnJqctHDRwpS09Nzs86dOm7PZbCOjH8LCwo4aGbW3t23fvq2lrf1l9tLnO9PpctMhoENAhwBA4Pcp91y7ekV/2bLvv//ubEKiiYmxm5v7reLiqoo769avX7t2TWBwGD4C5+Xt88h+qyvG2dPTy9T01MED+9esW5+Rkb5l69aU1NSW1laFQn748OGm5hYYhkdHER9+IUGB1rZ2169fDwkO2rdvn7W1TU72eR9fPxiG8RHhrm7uDvZ2AUFBFpaW7m6ugYGBRwwNwbF2C3Ozbdu3r1y58syZM8dPGMMwnJR41tLK6tjxE6OjY64umOSU9Jf5RvlPX+QwDMefiV21apX+0qXZObnbt20JCwuvqKi8cvnSxo0bVq5ckZya7uXhnpKG2D07bWaGVflP2LZ1q8GOHSnJSTt37szKyurp6eVwRg4e2E9nDgDfkHl5l92wrp5e3jdu3kAcXe3f7+XtExcbExsXD8Ows6N9dEzMaTMzD3c3V6yblZVlXNyZAwcPCYUiuUy2f9/evXv3rt+wIQIX7ubhhSzsBfjZ2NqeNj8tkyuOH/uh6Nbt3zikoSFBmzZtWrRo0cVLF3fs2BEVHV1dXZOVmb5p8+Zly/TzruZbW1leLbgOw8qjhod9ff1Om5tv3Ljh4OEjMdFRBw8Z5ubk0OgMLntk9+69bA4XGAjJv5rv6GAXEBh08+ZNjLPTgQMHgoJDQkOCgJVLC3PzlJRUs1OmWDest7ePicnJuLgzx48fn5BIJRPjWzZv2r179959+wID/YNDQh+1kb2tjYuLi7mllWRiwvDQwbv3nm6E8D/tWlPQ6+ZoHQI6BHQIvAgEfm9yD4KRUmlvZ4OPjMrMzDAyMjpy5Iidvf2ZM/F5eZcwLtiW5uYDBw/eKS/fv2+fnZ2doaHh7eJiV6z73cqK71atzs3N2bJlS0hIyOW8KwqF4vjx4y0q4z1jo6N+vr6mpibRMbHBwUFIIDrG3t5ux44dN24WIZaLE84mJqdEhIWG43ABAYFnz8Tm5OTY29n20+hMBn3fvn3XCq/b2tgEh4SamZ2ytbPbvXtPSUnp7t27bG1tDY8coVD6f7MvaZX1kdHjx40yMrOiIvGmp8x27jRw9/BIT09PSkwMDAyquXf3ByOjktvFu3bvtrGxMTttfvVKXpRqJWbt+vXnz2Vu3749IiLi9u3SkeEhI6OjNAYi93A5bKyLi6mJSVJSso+3t4nJycSkZFOTk3v37atraIRh2N/Xu/D6dS9Pz8AA/4SExNCQ4B9/vHHa3HxsbLyluWnvvn23bt0yO2UaExt39MgRe3v7g4cOlZWVbd60yc7O7gejYyMj07JINMV798U9QiRpsfDoEcPs3NyggACz06d3797l5e2dkZGRcDY+Ni6+tOS2ianZraKi3bt321hb2drZX71yOfZMfFpq8jaDHUmJCXv27gsPD6+sustB9vuOjLARuYdBpz2ygHfS2Dg9Pd3D3f3kSePUtNTjJ47v3bu3k9D1SFPHDYstLS3z9PTw8/dLS0v39fa6ceOGvb2dTKG8d7fS0PDI7eLi48ePn4mPP3TwoL29/dEfjCorKtauXWdtY33K7LTwmcxCPjOML2K+0+WpQ0CHgA6B35vco1QqEb9OFKpEIlEqlRQKpaenp1F1DQ0NDbAQ9wgUCpnP5zPo9PLy8sGhofHxcSZzQKFQ9Pf383j8ttbWuro6IomkVNm8QTVMqVRqTW2tTCbrp1Jra+tkMhmTyXzw4AEw+c9GLs7w8PDwyPDg4BB7ZITH5Q4gpqWkIpGQRkdsDgE/7VwO5055OZmMGCXqJpHq6xumaaLmmd8fvzAh0GztI5OBYji5j9zdTWpoaGhqah4cHBwcGoKVyt6enrGxMXJf3507d4BH9KGhIalUSu1HHHc3NzfV1dWRKRSZTEaj0yQSKRh4JCLxwYMHcoWCRCTev/9AoVCQyX0tra1A3WRwcJDP5w8inu0H2SPsoaEhgYDPYDAUCgWPywU2h7hcBPOhwcHy8nImkymVSjs7OhobG9mc367QA2o3NjbWT0OE3YmJccQnPJFYV1/X0tIyPDzMZiPu5IBf7m4SqaKiUiAUioTCoeGRifFxOp3O4XAePnxYW1tLo9Nlclk/rR+19NPR0d7U1CyXyTva25ubm5VKJYlE6ujoBKo5LBZLKBSyBlmDQ0PA4hSwpKdUKkdGRoaGEPuQ7JERNofDZDIe+coYHEQasa2t7f6DB3yV48Nf2Jf+o+S62VmHgA4BHQIvAoHfodwD3itghp0MGfoUfTQ5BjxC45+UlUYO6K1GcvVbdRqQP/r0P3olvHxidDlKvQoo8+CpxiMUQDT+pyo/Zl89uXoOKOA/0T/OQCNDjafqOaDcvnygpl8iyiSKDxqYXDUNrDQo0UI1yNB8ngQpSq+BLZq/egAt5eUE1IvWhXUI6BDQIfC8EHipcs/zYlqXjw4BHQI6BHQI6BDQIaBD4BkQ0Mk9zwCaLokOAR0COgR0COgQ0CHwX4mATu75r2w2HdM6BHQI6BDQIaBDQIfAMyCgk3ueATRdEh0COgR0COgQ0CGgQ+C/EgGd3PNf2Ww6pnUI6BDQIaBDQIeADoFnQEAn9zwDaLokOgR0COgQ0CGgQ0CHwH8lAjq557+y2XRM6xDQIaBDQIeADgEdAs+AgE7ueQbQniVJU1MTDocbHx/v7Ow8ceLEl19+uWfPnocPHyJek7Kyrl+//qRMi4uLT506ZW5uHhkZeenSpaampidRTh0vEomAjZacnJzFixdXVlZqpX/kEd3d3X3evHmHDx/m8XhaaaYfKRKJgDst4OsbuBaffvL/Lsq6urply5ZlZCDOOnSXDoEXikBxcXFycrJ6ETgcztfXV6lUxsTE1NfXqz8C4a6uLhMTk2XLli1atOjQoUOWlpYmJiZLly719fWdTPxCY8rKypYvX66vrw8mwBdaFshcfSKaTnH19fVTj2WZTDY6OqqyZT92/PjxI0eOCIXC6eT8vGiebaLWzVEAf53c87z64VT5lJWV7dixo7Ozk8FgREREJCQkHD16FIKgr776SiAQ8Hi8/fv343C4yVmUlpZ+9tlnra2II8/29vYlS5ZkZ2dPJntqTHt7e2hoKBBBwsPD33jjjStXrjwpVW1tLQRBc+fOHR5GDPg+86VeKAzDN27cyMzMfObcfvsJb968+ac//cnf3/+3z6qOw/9qBMAEMjCA+HsBV0lJCQRBx48fh2GYRCKtXbtW66fUwMDAJ5988mjmuXjxospW+AQejz99+vRP2byk/xMTE3p6ehAE3blz5yUUqTERTafEp47lhISE8vJy4GdQX19/0aJFIyMj08n5OdI8w0T91Ho9R/Z+y1np5J4X3jr9/f1fffXVtWuIx3WpVApcCpDJ5Pfff3/lypXgo4FAIHz88cc3biBOs9UvS0vL9957r7u7G0RWVlbevo242/yPLiqVumLFCjMzM5BKqVRKpY89RWjNp6mpCYKgefPm/ZKRrFFoXV3dJ598EhcXp7XE302kVCpFTST/biqlq8hvCoGqqqpPP/2UQCCgXLFYrO3bt0MQZGJiAiILCgpmz57d1YU4ZVO/uFzuv/71r0dfNXl5eTAM19fX83i83NxcdZqXEJZIJIsXL4Yg6Emrzs+RB42JaPo5TzGWL1y48P777wO5B4ZhuVwOZvXpZ/5cKJ9top6iXs+Fq/+KTHRyzwtvJltb288//3xoaAiUpFAoamtrd+zY8e6775aWlqLFHz16dMmSJRrjx8vLC4KgL774IikpicfjyWSyiYkJLpdrZGT0zTff2NnZeXh4rFu37sKFCyAfKpUaGxtramr6/fffgw2XwcHBrVu3QhD09ddfYzCY8vJyAwOD+fPng0VyHo+Xnp5ua2u7atWqgIAAIIRpHU6/pNCbN2/Onz8fgqCVK1cGBgZOTEyIxWIvL68jR46YmpoCqS41NXXJkiXr1q2rra01MzPbvXt3VVXVtm3b5s+fn5mZ6erqqq+vj040KGgwDLe0tBw/jrje3Lp1a2Mj4tB0bGwsMDDw4MGDhw4dunnzJuIDVYXY3Llz4+LiPD09lyxZcuPGDQqFcvjw4Y0bN7a1tcEwnJycrKent2XLFjweb2BgYGtry+Uizj4nJiauXr3q6uq6adMmc3Nz8JFdWlq6Zs2aJUuWFBUVBQYGrl69+s6dOyYmJvPnz8disTAMd3V1nThxws/P7+LFiwwGA4bhwcFBGxubw4cPHzt2rLm5GYZhIpG4efPmp1ZQvbK68P84AgqFYt26ddu2bZPL5QCK0dHR9PT0wMBACIJOnjwJIgUCwbfffmtsbKwBF4fDAXJPYWFhd3d3VFSUXC4Ho55IJO7atWvevHlJSUlZWVn6+vqFhYVZWVnqo3LHjh3A757G+JLL5d7e3np6eoaGho889+3cudPc3Fz946qoqOjw4cPbtm374Ycf6HS6XC5fvHjxjBkzsrKyjh07tmXLlr6+PsBqRUWFj4/Prl279u7d297eDoaJBmP5+flKpRIdlRYWFujS18DAgLW19cGDB1etWlVYWDg8PIzOfg4ODkwmE4bhxMREQ0PDo0eP3r17F4ZhjbG8Zs2aKcbywMDArVu33nvvPQiCtm3blpqampKSsnjx4o0bN4J5TCwW+/j4HD58eNWqVTExMVqnAlBTjbl3bGzs0Repu7v7119/bWlpmZubu2zZMg8PD/BG0CBWn6j19PS6uroOHjz4zTffbNq0qaOjo6mpac2aNVZWVgKBIDQ01NTU9Ny5c9XV1Xw+/9ixY1PMURq95Xd8q5N7XmzjDg8P/+tf/9q4cSMq0MhksqysrLlz50IQpKen19+POKeEYTg4OPiNN94Ab26UJyaTuWLFCkh1zZ8/v6gI8f0OwzAej4cgaPv27WKx2NbWFoKgqqoqqVS6dOnS1atXczicNWvWQBD04MEDGIazs7MhCLK0tARpzc3NIQjy8vJ65MHb1NT0ww8/ZLFYIJOsrCwYhrXKPb+wUAcHBwiCEhISgFMqIOTJ5XJ7e/vly5dLJBIej/ftt98CPk+cOPHVV1/R6fQTJ05AEPT99987OTlBELR48eLx8XFQC/C3r6/vww8/xGAwPT09f//7321tbR8pEnl5ef31r3/t6upKSUl56623gOgDEPvuu+98fHxmzZr1l7/8BYvFGhkZQRAE1vkpFMrbb789a9as2tra6urqmTNngvgzZ8688sorDx48SEtLgyDI3t4eVMHQ0BCCoL1797q4uHz88cf19fXnzp0DMTAMGxgYGBkZTUxMJCYmgjl9z549CxYs4PP5NjY2H330EfgWB9udU1RQvbK6sA6B5ubmN998E4PBoFBcuHChubn5woUL6nLPo23xffv2ffLJJyyVJ2aUGJV7du7cuXjxYo0dLiA86evrBwQEfPLJJ0lJSWKxWGNUMhgMPz8/9fEFVqk7Ojr++te/vvnmm97e3ps3b16/fv3ExAQot6ys7K233srNzS0tLf3Tn/4EvseWLFkChh4Y44CTiooKCIIyMzPr6+shCFq/fj3IQYOxtLS0lJSUyaNybGxs+fLlmzZt4nK5+vr6a9eunTz7BQcHf/jhh3Q6PTMz85NPPiGTyY92Bqc/lnt7e2EYXrduHbpYNTg4+P7777/zzjvgY8bU1HT27NnDw8PGxsYff/wxl8vduXMnOhWA5KBSWudesF/517/+FYPBzJs3D4KgkpKSp07UjybPwsLCR8QrVqwAmWMwmI6OjoKCgo8//ri3t7eysvLq1avg607rHKXOGMjh9/1XJ/e82PZtbm6GIOjgwYMaxXA4HPAhEh4eDh4lJiZCEHTu3DkNypGREWdn5/fffx+CoFdeeQWIJtHR0RAE2djYwDB8+fJlCIKcnJwUCoWrq6uTk1Nvb+8jdUV0ZJ4/f15d7gEiiI+PDwzD8fHxRkZGfX19x48fhyAoNjZ2CrnnlxcK5J6WlpZZs2Z9//33KSkp+/fvf+2115qamiYmJlauXAlBEFA8Ghsbg2EYcBUYGDgyMjJr1qzPP/9cQ9Xa19cXXbQXi8VyuZxCobz//vuLFi0aHR3t6OiYMWPG7t27YRgGzLu5uSmVyiVLlsyYMaOtra2mpgaCIEDQ39//zjvv/OMf/xgYGBAKhZ999tmHH34oEAiKiop27dr18OHDsLAwVIUChmEzMzMIggCM4PMLyJf79+8Hbx0Iguzs7ADDd+7cmTFjxrFjx9D2AgnBpD9FBTU6g+72fxwB0MdCQ0MBDg8fPiwoKIBhOCcnR32fC+2f9+/fV0cMlXvS0tISExPt7OzUn8bGxkIQtGvXrkczyfj4uFKpFIlE6qNSKpWCDfrJ44tMJn/44Yd/+MMf2tvblUolGL9gD2jfvn3/93//B1ZVRSKRTCZD97mqqqqAkgoYg21tbbt37y5UXRAELVy4EKgkajAGlAV37typMSqvXLny6AsRqGmPj4+DUak++42MjHz99deffvrpmTNn7OzsIAg6f/48itU0x7KG3MPj8T766KN33nmHQCB0dXW9/fbb+/btUyqVMpkMHOPYu3ev+lSAAq517i0rK4MgaMGCBQKB4NSpU+jkppUY/UBls9lgtpzcMLkEAAAgAElEQVQxY8a9e/d6enqAcHnt2jUIgpYsWYLquU8xR6GM/S8EdHLPi23lhw8fQhA0ecEZhuHGxsYZM2a4ubkBDsDMBSQPlKfGxkawUNTT0wOEgKVLl8IwHBMTg4oyly5dgiAIvG7FYvHZs2fDw8NXrVoFFoFgGFYf+TAMq8s9Mpns6tWrvr6+IPMzZ848Ve4B60bPViiQe4qKisCnSUVFRU1NTXNz8+joqFgsXr58+auvvtrS0oJWH3CVmprKYrFmzZr15Zdf8vl89CkMwz/88AMEQepqQ/fv338k8K1Zs0Ymk7W3t8+YMWP+/PkoYn5+fkqlcvHixTNnzuzp6amsrNSQez766CMGgyEQCP75z3/OnDkTrF3fu3fPXXWpN6WpqSmqHwpYAjiDhmhtbf3yyy/BYtUjKSorKwuCIGtraxiGAXRGRkYwDAO5Z4oKqldWF9YhADpSfHw8gOLEiRMfffTR119//fe//x2CoDfeeAN95OjoiH75oLihcs+VK1fGxsby8/OBxiEQU8Cs4urqitILhUKNUfmk8UUmkz/44IPZs2drfJmMjo4uXLjwtddee7SBhWaLyj11dXVVVVVgDAIRh0gkenh44HC4V199dfHixUBbbjJjSqVy8qgEXyaoIiMoTn326+np+fOf//yPf/yjoKCgpqbm/v37QIVx+mMZqCuor/egcg+RSARSy3fffSeRSNDKtrS0oFMBqu0Aw7DWuRfksGbNGolEYmxsjH4HaiVG5R5Qi/r6+j/+8Y/79+9PT08Hi09SqRR8nv3pT38C35NPmqPUGUM5/x0HdHLPi21cAoHwxhtvHDhwABTz448/BgUFge+A1tbWP//5z6ieMljvQactQB8WFlZbWwvCEolk2bJlc+fOhWEYfABprPdIpdI9e/bMmTOHwWDs2LEDgiCQFn3pgkkETIjgqwiLxb733nstLS3e3t4QBCUmJsIwDNaoJus1a13vmWah4OsqKSkJhuHq6uo333zz448/ptFojz62BgYGeDwekHv+8Ic/gO9CUGUg9yQnJw8ODmqVe4AMt2fPHqBMIBAISCQSWO8ZGxsDq+V79ux5ktyDzrkwDGtd7xEKhefOnXvrrbcuX778SECEIMjU1BTwBr7G1I/Fod9SYAbv7+/fs2cPEMvu3bv3yiuvgPUesF+mvt4zRQVBWbq/OgQAAgUFBRAEhYSEgNuKiorY2NjExMRjx46BL3vUzsXp06chCAIqLCh6qF4z6LfgfdnU1FRdXY3OKp6enii9SCRavny5+qgkk8nvvffeokWL0PEFlmrIZPLf/va3zz//XOPLRCqVbt68Gd1Yl8vlHA5nfHwc6DXX1dXdvXsX/faorq7+4IMPPDw8Ojs7X3nllSVLlgBOwHSnzlhGRsbkUQlG1jfffAMOovJ4PKVSCd704JODRqN99tlnr732WlVVFVD7A4pB0x/LeDwehmGgRQAyQeWerq6u+/fvv/XWW2+//TY4ny8SiQQCAZhbwFQQERGBYqt17gVyz+rVqycmJk6ePAlBUH5+/iMtQ63EGhO1Uqk0MDAAa0vo6QqpVBobGztz5sxvv/0WhuHc3FzwkawxR6kzhnL4Ow7o5J4X27g8Hm/evHkbNmwAeoiHDx9+tAyzdu3ahISELVu22NjYoB00ODj41VdfBWMJ5cne3v6bb74Bc1lvb+/ChQuB6ABEEAMDg4mJCfDur6mpaWtrgyDoo48+ioyM1NfXhyAI6AFcv3790XfDN998Ex0dPTY2ZmVlBTZoRkdH//a3v/3xj3/09fU9dOgQBEE7d+5Ep7PJ59ifudCJiYmgoKBHs/C6detycnKEQiH4YFqzZg0Oh4uOjpbJZFwuV09P77XXXlM36QHUX5KSkqhU6ptvvjl79myN75KHDx8CHUMjI6OwsLD09HSlUmlhYfGXv/yFSCQmJSXNnDkTHOgNCQmBIMjb21sikcybN2/GjBmtra3FxcWPtKAMDAzA3PTuu+/OmjWrvr6+oaHh9ddfBzoHq1evhiDIwsIC6BjNnTsX7IWDpszJyUEbKzk5Geydy+VyV1fXrq6u7u7uDz/8MDs7W6lUrl69Guj3WFlZvffee+A8zlMriGauC+gQAEq+s2bNcnZ21kADvN3V15X379//wQcfoOqDgH5gYGD27NkQBIGVXRiGHz58aGBgAPZBgBqNuvIQh8OZPCrNzc3Vx1dhYSEMwx0dHX/+859nz54N9lzU2bt8+fLrr7/+6quvOjs7+/v737p1SyaTgXPsNTU1t2/ffvSFtmnTJhiGwdfRzp07w8PDX3vttXfffRcc+JrMGFjPVh+VZDKZw+EsXLgQKAbh8XgcDqdQKH788Ucw+8XExPD5fDD7ffXVV4+WlAICAsB8Mv2xDPbFDhw4AEHQ4cOHb9++zWazgX4PWKg+ePAgBEFz5syJiIjw9fWl0+lYLBadCkByIHJNnnvHx8eB3LNq1arR0VGgdZSdnT39ibq4uPj1118HuhAwDOfl5YEldkNDw40bN8IwnJKSonWOQhlTb7jfcVgn97zwxnV0dETPczU1NR04cGDOnDkrV668ePEiqvoHtmwWLVqkobdbUFBQXFwcHh5+6tQpU1PTvLw8IKcDEeTAgQPR0dHr1q1LTU2FYVgkEpmamq5evbqgoCAvL2/BggUgXiqV2tvbf//99/fu3evu7t6yZcuCBQv27dtHJBLDwsKWLl2akJBQXV29YsUKDw8PsVjs4eGhp6e3fPlyDRN8z1YoMNExNDS0b9++Xbt2kUgkGIZpNJqNjc2XX35pamoKPs5SU1P19fUXLlxoa2sLlsq7urrAcafTp08HBQUtXrx4yZIlQDtPvc0qKyu3b98+Z86cgIAAsFwvEAgCAwMPHTp04MABQM/lcg0NDefPn3/kyJGMjIyVK1fq6emFhIRgMJgFCxasXbu2tbWVwWC8++67H3zwAdA6Mjc3B6c/Lly48EjPwMvLq7W11cDA4OjRo1wut7y8fM2aNd9+++3hw4d7enpgGOZwOMeOHdPT09uwYUNZWVlOTk5WVpaxsXFUVBTYqaTT6TY2NuAgCXjNoOe5pq6gemV1YR0CGzZsUD/PBQApKipasGABajsKnOcCe6koYqjdwoULF27bts3S0tLc3HzTpk0bN24cHBzs6enZtWuXnp7e5s2b0VWiyaPy0Ub55PEFznMtWrRIX18/NDRU/SQXOASQm5v73XffzZ8/H7ySy8rKVqxYoaen5+XlZW9vr6ent2rVqpaWlvv3769YseLUqVPt7e0WFhYbNmzo6enp7u7evXu3BmOTRyWQYIhEopGR0Zdffnnq1CkQg85+YCISCATBwcHz58/fvn17R0cHep7rPxrLra2tq1evNjMz43K5qampi1WXn58fDMPDw8OOjo5z587duXMnkUiEYTgzM1NjKgCYBAUFaZ1758+fv3bt2vj4+K1bt86fP9/a2npsbCw4OFgrscZEPTQ0dPLkSdTuWm1tbWZmpqenp5mZGZlMFggEU89RaFf53Qd0cs8Lb2IajfbFF19MbbKPSCT+v//3/ybb73kSc0AEAYu3T6J57vG/SqHPvRZPypBKpQK9Zjqd/iQaXbwOgV8dgbt37/7jH/9oaGiYgpPr169rtd8zRRLdo/9qBIAeektLS0pKyn91RV4O8zq552XgXFFRsXr1aqBrNrk8Npt98uRJ9IzGZILJMeBUtoWFxeRHLy7mVyn0xVVHI2cKhfLWW2999NFHVCpV45HuVofAbwqBpKQkAwMDsB45mTGwcgP0QiY/1cX8/hCQyWS7du06fPhwTk4OsF3y+6vj862RTu55vng+Mbfm5mYcDvfIqtVkiqysLLBHPvmR1hhghW/BggWbNm36jxJqzW2akb9KodPk7bmQJScnL1y4UF9f38PDQ2O38bnkr8tEh8BzROD27dsa/rlA5hMTEzExMXV1dc+xLF1Wv30EEhMTly9fDsxw//a5/dU51Mk9v3oT6BjQIaBDQIeADgEdAjoEXhICOrnnJQGtK0aHgA4BHQI6BHQI6BD41RHQyT2/ehPoGNAhoENAh4AOAR0COgReEgI6ueclAa0rRoeADoHfKwLKSZfWmk6i+h+N0IHzpIb/H0dGa/VfRKRO7nkRqOry1CGgQ+B/CAGN1xgMw2Njo1VVlSKRuL+/v6GhkUDobGtDvIv/j1wagKjfAiOH7R2dCoWiuvpeH5l87949oVD0P4IMMN6jDggahmF4dHS0sqpSJBb391MbG+8TOjvbVU7p/3fAeTk11ck9LwdnXSk6BHQI/G4RQF9dIADDcEN9nYW5efmdCnJfL8YF4+7u7u8fwOXyHj582Kh21dXV1dfXq0VoCdbV1TU0NKAP1MNoZG1t7eR49ZiGhoba2lqUXmugvr5eg6bhpwul18qwRkFNTU3AgqgGLMA2/fj4eGBAQEBAoEQqS0tLdXZycnJyLr9TSafT6urq0IIAwxo5NzY2asSoJ0HTqgfq6+vVaUCF1AkaGho0KqVeBEqpFWH0KQhobSn13Orq6oB5sMnIAHAe3G+0MLcoKy8nk/ucnJywWGxQUDBfIHjw4IFGPuqV0mADoKTBMEiunglAeOru98zdRqOlNFoBMKzeFg0NDU1NTep+zV7oZKGTe14ovLrMdQjoEPj9I6DxGoNhuL2t1cbG+scfb9y7W+Xt7e3p6RkVHV1bVxcWFlZeXn5bdZWWll64cOHSpUulpaUgpry8PCgo6OzZs2VlZShNVlZWQUFBSUkJGoPFYq9evYqmKi4uzsjIKCoqAjQlJSXXr1/HYDA//vgjGnPjxo309PTi4mKQSVlZ2YULF7BYLLi9fft2SUlJYWHhuXPn0ILKy8szMjICAgLUmbl8+fLFixfRosvLy8PDw+Pi4tQrFRwcDJw2aMACXu0yqRQfgQsKDm5vbz97Nt7Hx9vZ2fn+g6a4uNhr166hpd+6dSsjIwNlr7S09OrVqy4uLigzJSUl165dO3fuHMpMWVlZcnKyn58fSlNaWpqXl5eTk4PSVFRUREdHR0VFqTOcnZ2tjmdpaamvr29WVhZKU1JSkpmZWVhYiOJ569YtDAZz+fJlNOdbt26lp6drtIKLi8vNmzdBqtLS0itXrqAeQrSC09HeZmNtU1h4/d69u56enh4e7rGxcQ0NDSEhISgzoNuot8KdO3f8/f0TExPVKz6527i7u+fl5aEMFxcXT2bY2dlZo9uot0JZWVlubq6LiwvakUpLSwsKCjS6TWpqamBgoDrDk7sNDoeLjY29c+cO2quDg4OBNf+XMF/o5J6XALKuCB0COgR+zwhMfofJ5XImkykUioaHh1ksFo/HEwgEd+/eRf0QPwkOMpkM3IU+iQB46VJ3cTOZUiaTdXZ2aviL0CATiURdXV2of0CNp+CWx+NRKBStj9BIBoOhYUHxxo0bwHreZFhAcTwud2RkRMDn02g0kUg0wGSOj49nZmZOXanx8fHOzk60XK0BLpdLJpO1PkIjaTQai8VCb7UGent7NRysapApFIqOjg6t9thQSolEQiAQgGchECkQCNLT00GMNnBguVym6jZCVbcZ5HK5AoGgpqamuLgYzVZrgE6nA/c+Wp+CSBKJNDXCMpmso6Nj6m4jFAq7urqmKOWRn7XpdBsajTa52zy1facud/pPdXLP9LHSUeoQ0CGgQ0ALAtreYcrJdJWVlefOnYNhmEqhAMe0gKbp4cOhoWEQHhoabm1puVddA8PwyMjwtWv558+fv3o1n8Vi3bpVlJWVVXTrVk9vT1PTw9zc3MuX84ZHRsZGR2/euHH+/PnbJSXgxSaXyxkMhlKp5PF4dapLKBSi/LBYg3w+n8UaYDKZWuUekUjE4XBgGBaLxSPDjxkbHhp8+PDh5Jcih8MBvkgBCMD55f3796dWZEGZAQG5XI7H4wcHB2EYfvDgQUdHB6m7G4ZhuVxeX18PtH/Gx8dpNFpHe9uDBw9hGKZQKHl5l8+fP3/jZtHQ4GBhYUF2dnZBYSGDwWhsbMzOzi4sLBSLxR3t7dk5OVfy8q5duyYSIVpEQ0NDQLLU2moAEDqdDoifRKNQKPr6+p60nQdSSaVSCoWiUChQZMhkcnR0NKjyk3LWQAaG4aqqKuDXrK+3l0hE/BuC68H9+2w20kwwDJeXlzW3tMhksuqamgmJhE6jJSYmXrx0SaSylCsWiSoqKgiELuAeu66+HsCr3kYA7b6+PplMNgVvYrGYQqE8iQCgJxQKGQyGeuYa9DAMs1gsjW6TnJwMPKaBGr3Qvy9b7lEqlRKJRCaTSaVSmdqF3oKnak9+FpRIJCjlzx6obqRS6YtLPp3MJ7OExvzC5FPX66mZg648RU9SKBRS1aXRNCjaUzPwS9plKuZVnUQy8QIaXSqVqX5Ij1F1SHCLtBfomaq/U/GmatpfUnHQN7S+eNCWUm8X9b40zXZB2xRNqx6Y+qlMJpu60X9h8qmhm/rpU3mbOvlTOZ/8dkdbRGtAY1oHbQoGnVwuR0ff3bt3gaNcezu7ktKy4aHBu3fvjo2NpaelhIbhQM5cLvfggX3vv//B4NBIcdHNud98ExERkZiYVFNT/cXnn4eFhRsYbLd3cLS3s9m6bftps1P7Dhysq6394vPPQ0NDN25Yn5SMuGcSi8VXr14tLy8/e/bsxo0bN2/enJqaeufOnaqqKrFY5OToGBUd7ePjc+ny5Qu5FxobG69du1ZRUXHhYu7ly5fz86/6+vnlXrjw448/Xrt2LSkp+XZJCYs1EB4WdjX/6r27VXl5VxobG3NycwqvF168eCEtLT0vL6/w+o9SqQxUITc3d4r1HqVSKZfLwZqHRCJRKpUKuVwyMREfHy8Wi2ur7zlhMIEB/tbWtoQugkKpCPDzzblwEYZhiUTST6Vu2rB+3ny9CYk0OipSX39ZVFTU+ezsH68Xzp37TVRU1MoVKzIzzxkePnTkqNHhw4dOm1vU1lQfPnRw9uxPI/B4sITDYrGAy9LJrQZiYBju7+9/qtzT09MzOjo6xdtdKpX29fWpyz0DAwMJCQmge2gtHYbhKbqNpaVFdU3tqPj/c/fmwW0daZ7g/tMRG7Hzx+4fu7E7M7FR07sxM11TdkdXd11dZZftcvkq22X5lGVbKlmSddm6RYkSRYqkRFK8JPG+T4AECRD3DQIgiPu+7/smwQMHSZAggJdbiSfBNG3JNdPV1bGVgcDLl5nvvczvy3zfl7/vy3ybi+LFjY3Nnu7Ojs4ulOYfH/zw+RdedLvdv3n5t2az5a0337x46fKHH3xw8vQZAACdSv6bv/mbhqZ7UO1biv/tf/rB+x9+hF5Yrgb6aI/Hg1agnL43gnYtn8+3N3FfHP1y7VPcmNDmR6PRvdonAACLxf7V4j2ZTIZCobDZbB6Px9oTUAsog8HA4/FMJnNPztdRNpuNw+HodDqbzf469XGMzWZTqVQCgcDhcNhPCDMzM+jl5fyy/ZjD4VCpVDKZzOFwmKXAYrHKD0JvPjc3V04pPfbrarDZ7NlZPINBf1ydbxzLdfvm5d8og8fj6fQnXo7H4xkMxjcueHzyvTdnMBhyufzpr/JUKsV9HHg8HlpPNps9Pz/PZrPpdDqBQHhS5dlsNoFAoFKp31kAvXx6eprFguRis9nMMmFLbGAymQQCgclklpmCRh6xhsudmcVTaXQOl8tkwWv3dQ86nY7H4x8/ms36Zt/gcDizs7Nol0MJxkQrwOGweVw2l0uh00lUKmd+nsPjcXg8NpfDgekcGOdwaTQabmaGzeE8JvY3jmw2G4/H02i0x0/fn/vNun0jl8Vi0Wg0lUq1FwZH30R7/xOJBAfW5RFH0FugjGKVSDEzM7P/vo/P2Ww2iUQik8lPqh6RSERz91G+xB82k8lECbsvF+ULl8slk8k0Gg0dbugz9z4IvfxxXfYf0dx9rCwXQgn7pJqzWCwGgzE7O1suvy+CXk4ikfbWp1yGzWZTKBQikfiduSwWi0qlSqUQbvnTw7df/eFQ8M6dO16fT6/TdnR0DA4MTE3jFHKFQCCIRiNvv/322urqnfq6A++889W5Cxaz+cCBd1HwQKtWvfrqq8ePHRscGhEK5p999tnbt2sHh4Z0Wu0zzzzT19f3+dGjVVXVX509MzIKoaMXX3hheGTkx//wD/X19S+99BJuFg8A2NjYmJmZYTAYHR0dr7722uuvv97f389kMgUCgV6vO3nq1OfHPj9//vzNqqoTJ45fvnz57t2Gioqr3V2d165fv3njRuWNG3W1tTU1NRcvXLx2vbK9vX1mBldTU0On0x88uF9ZWVldXf3RRx9dq6ycnpp69913D338ceWNGyi0AKUsna7VQkhmH1nK8n4Ki8FgpnK5nYGB/insVGNjgz8YxGAwxULhdk31+CT24f32Xz333McHD3Z29fC4nKOfH0d5MTM99cGHH374wQcc7nx/b8/Pfv7zuvp6AoHIYjL/6Sc/GRkdfeed309P4z777FOhSLy8FP+nf/zHcCQmEQl//86BQqGI3mRpaan8xfInsTgUCj3dhoUgiMfjQVn2pJvk83mfz7d3gKfT6dHR0SfZuQAA4XDoLuw2fr1O29XV1d/fPzU9rVQqBQKB3+975/fvxOOxY58fPXz4cENjk06n++CDD3O5XQDAl2dP/1///t83t7R88umnXV2d7xx4FwAQi4RffOFFh9N1+uTJM2fOvPG73wEAJsfHDh8+/Ls33jBb9hsNC4UCivc8qUWo3vO9ps9MJvO9X3eOxWL77Ll0Ov2vVu/J5XJCoZBAIAwNDVGpVNrjMDw8TCAQaDQahUKhPjk8KZdGozEYDDqdjioHTCYTPS3dHiYymUw6nU6hfP1E9MlkMnlubm56ehqHw83MzODxeBaLKeDPz/N4DAaDSoX1IZdCKUIhk9FTCpkCfxQqDZahUskUCpQxFAqJTEZ/ZHLpYXQ6hUpllGpAo9GoNFopl0IiU0gUCplKJVNpJBqdTGdAGUKjw0qW1C/KN0Pp3l9TZm/m49p9nbuPfiQSyWQyPaUro3MpPp8/PDyMxWJHRkYoFAqdTieTyUODg5SSeHsS5alUKqwwlUKBx28E2F4qlc5goA1jMll0BmQMl8WkQkrA1jKZkGt7e8I+vjCI+HkynkHAMUlzfBZdyGLMQ7dNWon+1BI7IM0fnVIgPUt8oVJpkJIUKpUIaUct/SDNWYQZLoU4h52aHhrBj45TJibwI2ME+BudHhkjzOJnMVNEPGFmdIxMJFFYLHKpV9EYDAqNRqJQSaVbUUpML3HqcTeg0al0OoVGhxWAHHzcT8iwQ+8nTYlOJBLpe3Hd7e1tHo/X399PIpEe9Q86fWJiAovFonR7Cl++wYw9JyiF6QwGtTRq0MECu+hjTtFL46XU/785XqhUEplMIBCmSwGHw01PT5PJlNKwg5fDzrBn/KK9tMScR+MF1gL2ChqFQiWRSOjgQquGdjl02KITDzgYSn2jXHe0R5VOYSMYDDi09ySWC8Jq7K3J1xl/QoxMJv/3vn8R5JFVCykFAIB4UXT69CkKlWYy6Gtqajo6HtbW1S0uimtqaihk0quvvmYwGOrr6r48e/aFF1+SSMQvvPCi3mBYWlqqq6v5bz/60e9///bzL7w40N/3s5/9bHR0dI5I5LBZ//W//OeDBz/6ux/+kM8XHvr4YGtb+1I89tOf/uRe872/f/aZ7u7uTw59fOzEF+Fw2Ov1qtXqcDis1+uxkxjsJMZsNkej0XA4rNVoDEaDTCqZnZ1Fp3mLi4sMBkMqlcplssbGJqFQxOfPi8Xi+fl5Gp3O5nAUCqXP71cqFQQCQaVW0Wh0kWiBQqGIxWKdTovFTtFoND5f4PX6AoGAyWS6evUq+g1mlBooccqUyWa3btdUV1fXJFYSHQ8f9PT0Xrx4cUG02NTURKfRPv3kEAY7feP6tasV1zks5nPP/5pOo7322userzcWi334wQc///nPX/j18x9+9PGdurqXX/7txMQEi8WenBh/9tlnD7zz+2ee/fs/rhV6443X50gUk8Hwdz/8oUKlnsJMvPjSb3yl4Pf7tVqtTqfz+/1oyrf//bCxSpvN9pQyPp9PIpE4HI6nlHG73VKp1OPxlB+NwWBaWh4Be2WClBVEAIBcKjl16jSJTDEa9LdrajoePqy/c0cskd6qqprBTb9z4F0KmfTLX/4yFAoJhUKhQPDCCy+YLdZoNPLJJx/fuHHzH3/8Dz/+x580NTa++NJvlhMJlVLxi1/8AofD/eAHP/jwww//43/4DxwO97VXfvvKK688+8wzVyuux+Pxvc33eDwSiaRc4b1Z5SbYbDaZTPbtrHKK3++3WCxKpfIplPH7/Wq12mAwBAIBnw92G4PBcPXqVYfD8XQ59efK/cvZuXK5HJlMZjKZIpGooqLi0CefwKlVSdHh8/lffPHFyZMnUdCF9uRQehPCv7Jmg8IJFAplenp6cnKyoaGhFobbly9fbmlpHRwcGhoabG1tuXHjRlNDwzRmYnRkpK+vr62trbW19cyZMxUVFXfu3Onq6mpvb7969Wrl9etXr1zufviAOIvr6+udHB+b53LkUolcImYyGRw2SyYWycQinUxk4rPC8xSPfN6uXLSpxC6DKmAzuCxGn8Pqd9kDbrvPYdXKpdIFgVGjFHBYmLFRIn6GRprzGtVBgzyglQS0ix6F0DNP9hHHfBSsnUdWCTksGkUo4GtUStGCEC7gKM1J2WwWpwRkUGk0KOEpFBaLyeWwFwR8kUAgWVzgsNkwC6poUMDsox+RSDQajU/qMel0mkAgiESiycnJ115//e7dhl/96jksFovCSG++9XZjYyOLBbE0GPbdmkZj0SgiGlFAmWOU9JiSYgNVGZQvdBoVNz01i52819T0sK2lvamhqrKyt+PByNDQ2PDg6MPmprqa1pbmKczE8NBQf39/b2dnx/32U6dOVVZcKS1s6R3qeth8q7JRovQAACAASURBVOLuzYq6qxeGWurYE71cAnaBgteLOAohV7YgEPHnRVyWcoHrFrMCdGxoZiAi5brVi3Yh077AcitFfhnPKWT4lEK/RhJQLawo+U42nt3Vxr9/D3/75vDH73a//VbT8y9OvPZq//MvjP7urd7nX2p68ZV7P/ox5pXXcB8fWrz81WL1NcVoh5M1E9WKoxaNUb7Ap1PZEBVhQQ6xWFw6hYGbpkxNMcZGxIQpGZMkWxDIFhd4XC6byWAz6Qw65E5J5H9NQsLc3FP00UwmMzMzIxAIhoeHX3vtNRKJxOFwKBQKn8+vr69/7733UDXoWwzZl0Av6dJ0BlQ6oZrAYrFpNDqZQsFNT89Mjt+9c6eurvb27ZqKq1fb2toGBwdHhwZ6H7TVVt+qq6vr7u7uK4UH9++3tjR/cfJkVeX1hrt3e3t7m5ubSwuQr1y+cL7mVtX9+w96enunsBgWk0ErKSvoHIDFYkpEQqGAr1Yo1Er54oKAz+MJeJxFIV8mgWeSxQUujwthQDZrfHJydGxsamqqt7f33r17/f39k5OTZbWGBjs/mYz2QSp1lkjCTE1jp3FQl2WzodYFhwCcgVBQ1ZNCIdNoqB6MqqFw5KDTmG9PgPaQ7U/RR/eNJlS05/PQ0IPGl+Kxjo5OlUo1PTXV3dPD5XKJRKJYvEgkEl1OxzsHDqjVqn/+xc/Pnzv31ttvq9XqAwcOpFLpdCr1uzdef9jZxefPv/7aq1euXvnJT39Sce1a1a1bDAb95Zd/u7W19eXZ08dOnLhw/tzzv37ho48+OvKHoyql8u/+7r9evnLljTdev3nrVqFQyG5teZ3u3ezObn63AIoFAK3Yxd18AboY5Le2Nnd3d7e2toLBYLFYRCuM1hw9LRQKW1tZFDdCfW5yuVwyCZ2RUesP2nzUYrVcCgCA7ccBg8HsxXtQA0oZ5AAAUMhk/CxepVLW19fz+YLxsTGHwzU8PLy6snLz+jUMdvp+W+vLL7985PDh2vq7gvn5zw4fQRAkHAr++te/xk5NM2i0l176zYXz53/5y19er7ze1HQPi5k8+PGhYrHw5u/eaGlr//jgwTffevvtt946f/ESAIBFp7351ttb2ezOzk4ulwuFQpFIJJfLPa7v/uPOzo7X611bW3tKmWw2a7fbk8nkzs7O/usfn29ubjqdzmw2iybkcjmv19vf348SHCkFlDh7uk28s7NTqVJNT0/19PRyOGwSiSyVyubm5swm44ED7wYCgd+//fYf/vCHM2e/VCrk773/fjqTQZDi50f/QKLQBvp6/tP/8/96PN4PP3j/4MGDr776ak1tbfO9xs8OH1GpVCdPfvHiiy++8sqr83z+4EDfCy++uJxY2dvGra0tu92+ubn5uAX7jzs7O+vr606nc3/GnvNcLre6uurz+fbeeU8+jOZyuUAgEI1Gy2Wy2SwGg9nr9LZviP15T/9yes/29nZVVVVbWxuNSnvuuee6OzumpzAHDhx48803+3p7MeNjP//5z0vYNZNGo0Lk5Fvyu/xqolAog4ODWCwWh8M1NjZ2d3e3tLS89957J0+ePP750Reef+7TQ4euXL589OjRmzdv3KmrO3fu3OlTpzoePsTj8QQCYe5xQAEeIpFILc1T5+bmCHNzExMTvb29773//o3K6y6TbnM1jmRT+cxawGVPRALI5jrYSmWi/rnxIdxAV19bU+W50+TxfrNsIR0PpZYi+cw6yKbAzsZuZlUvW2SS8Hadam4a0950d55JcegVuyEHcCqAS7ntUKzrBLtaHhDMANKgq7+h5/pXHbcqptobJfMspWQh4LQmg+7N5XA6FkgvR1MRX0DOdyoWPFbTejyyuZ7Y3Uzms+liNp1ZifkteotG7rFbzQYts4RUoeSiUqkzs7MqtepJ/WZtbe3atWsjQ0OXzpz67IMD06NDP/jBD1579dX333+PRSHeunblxNEj81wO1GVK+M2jafRjZjBpVCGNNE+Zo1EhwjE0MDCFxU5NTTc0NAz09Q50dxz64L2aikvnzpz67YsvnD5+tK6m+vixYzVVN1ob7zbVVV+9fKm7qwtfCnNzcyQikUicw+FweDwe5QuZTJ4lQKYxSYShvp6DH74/3NEWc1myfivi0xXD9p2AbcdvQlaCIBlN29Vz3a2E0f6Bhy3XzhynjffZ5ML1gDPl1BU8GsSnBx7VjkujIk2yxrrsQhp+sKP95iXeWLdl/P7GYG32/pXN1iuxpsvGuguhtspo1ankiXeslz65f+jNni//gKu/phhpN+N6klpeZqYjPd6eYmNSWmHSrs6ISaG5UT+duCrj5+0asOwFm6vIVnIrEUk5NGmHZjngcipEAvIcirKQqTQmg8Ek4Q0a5ZP48scFLxcvXuzr6zty5MixY8cYDMaxY8eef/758+fP43C4n/70px0dHRwO5zEfvuNIL7GMQafSKOTR/p4ZLESJmpuahrs62u7UfvbRe7WXzp4++ulLv37u1PGj1TcrP//8WOX167erb1VcPHf69KmOjo6ZmZlZPB4Ol9KoweFwhMd8oVAoc3NzcDhhJ++3t7355pu3Kiu5LIaIP0+lUllMhlQssuh1K9Egkk2HPA7C1CRmdKiv6+Glr85QZzBWtWw9ElgL+XYS0YhZs+QwJrw20nB3T1vzFBZTdfPGiRMnWlqah4eHKBAyZLJZLAqJiJueohDnFsh453jPTE1l5ZnjVee/aqy83t90h4DDamVis04llyzyOUyZSCCXLErxUxLSrFyyaDXq3U570OcO+r0ep81pNnBLdu3voBqNhsfjUbH9JNZ8O313dxeLxVZXV6MGsrJgKxaLhccBALC4uMhkMgEAp06elMoVGo2ay+M5HA7s5ER1zW0UduVyuGtr6wAAj8dtNBoXFhZoNCqTxYrFYiajCUGQjUwatQgwmQw+X7Cxubmzs70oElGpVIlEsr2zA7e/y22zzBL1srNeN/Uup+5dTl2Tfka74tateXaRfMPdO2Pj423tbfN8/qJ40e/3S8QSm82+IIQOQDKZtKuri05nKBUKiURKJpP1BuPW5uaVK5ehJGazRCKRGm4no3CVXI+Xl5f3mY1IJFLZvwcAYLPZ6urqent7Ud9qBEGQkrJVKBSKhUf0ye3uDg0N7ebzXA774qXLsVhcoZAzGIxcLtfW2tw/OAS9vBOJ8rIms9lsNpnm52Fn4/MFsVgMhei8Xi9fILBaLbQSNXZ2cgCA9bU1s9lcVu/+De1cyWRyaGiobPna3d3FYDBP6Tb5QgEAIJFImEwmUiwe+/yo3miKRSLT09PxpeXJ8dHGkssOAEAhl/l8/p3tbbVavZvPp5JJ/Owsh8vN5/M2qzUag+vXzGZz2ZZUKOQ1Gs1eb/e9fs3f7uHlFNSvuXz6nZH/MTsXkUj878VZv/Ppf0riX07vyeVypc0DuE6r6cXnf+Uww2lzffXNt994/ejhTyMB//O//Gcei55NroS9TpVMwufxUHUE/pcmeSQSsbOj48KF84cOHTpy5EhFxdWmpqbTp04eOXz41KlTJ459fvXSxUvnvqq6dvX2zRu3b1VhR4eCLuscDlt76+bk8JBkQcCgUVH/obJRjF4KNNTCVIKR5ufnGxsbX/7Nb/QKCShsg2waZNPIZgrJpsF2Bp5uZ/IbSadB3dneQiPMinlMCYucjnh2UwmQXgHJONhYA1spWDK3CfJbIJ/dzaythLw+u9lj1jmUi1sBG0gvFVdDSNSWdukyAXvMqg5KWEYqltZ/n93Twhq4Pz/Ry8MMeHUym05V3FwD27AaYC0MklF42+2NRMDtsxp0SmnQbQu6rEmvFVny5FIrNpMBpRilND9e4PN9Dls8GikUH5m393WLTCaDm5kR8jjH3n1zor7CzCU++8yPyMTZd9/63Z1blQts+sO7NZmlUGo5mgj7fBaDHJIRakBwbg0RIDqLRp4aGay4fOnop4fOHz9y5+a19uZ7Z8+e+erUF/U3r188e7rq+rUbV6/U3LjefKfu7u2a6ckxj91EwmFb7tZTZjBy8QKNQiaU/Lq+zReUNQwGY35+vub27Tdff9Vj1YNcBiz7wJIHrATgL7UMMisgncivxy2Kxa57d5izWBGbJqNMZUKO3bUYWPYiPh3i1SIeNeJUgrAFrAZANplbCa849F6N2CvhODhzWb0AOCRFpxR4FBumhS2TaFlI8BAGDL111Iuf4098Sqs6S793k995xzfTbelvKCjpwKUAIXMxZEWSUbC7WdzZWA55Aw6LQSWLuq1epTCpWwAB486SP2IzaATchXkelUJeIM+aFJJkyLMSDuxjR/k0k8lgsVgGg/Hyyy/faWi8cbPqRz/6UW39naN/OEoikZ977rlLly9DS0RpaKAmNgqNToWG1xJX6HTyzPRoy93aU384+u7b1z8/1Hizov1O7ZefHbz4+adXjh0+duB3t44fOvPZwc+PHK64fOnG9Wv4qUmnxTA1PnK3uoowjVGJF+gUEnS9YrFKowQaldDI3vHCm5+/devWi8/9Ujw+YNVpbCaD02JaiQTy6VWwswGHzMb67nrcKBd1NNbOE6cEtDkNh5IOOPOZNbCxApVRLadgEOwu+UDMiSx5kXRiORK0m3U6hUQjE4t57GW7fjPq3Qi7kza1f240TsOGuAR3X6Os7iL+0umR37/Rf/oovbdV0N3kk3HNC8yMQ70bdeWTy9tey67PXEwvI9vp5YA74rYa1fLlkDfitiokojkymViyRpcB0pKiCM2Utu9bqVtmExpxu92VlZUMBqO2thbK9VLYVwYAsLCwMDIyAgAw6HUqFVzxhAaBgO/xPlp9vbQEVy8/ztlzLJnSUOEdCoV2clCiowFN3BtPbmda5dM9Flqrce6f5r76GfH8fROxx0qf8PANduOnhw4d/uyzc+fPXa+sPHLkyNWrEPO+dPlS073GqqqqaxUVFy9drK6pvlVdff78+S/PnW9obJyanj556tTi4mJzY0NVVdX1a9fePXCgu6f3jzpcWY0ot7q3txddz4VWqb29HYPB1NbWLi4uloGNR1V/fCgUCvfu3YvFYvn8Lp1O396G2hsqiRkMxsrK6iO/5mDwSXdAy6fT6b1r1MtVQq9CCfUv9GsGANo0i8Xi/4Bfs9vtbmtrQ6sKAHC5XDdv3qTT6bdv30arupeV5WLlbqNWqXQ6fTmdx+UGgyH09Ind5nFpBEH8fj/q5VkmS/mh6HPz+fy/oV9zd3f399r9H7fmX3r8y+k95Zp67OZXXnox4Hb0dT748uSJw4c+Pnzo4FI0/NILzy/weQAUQD6LbG9srScCbodcKuGwOXweVzbPIuOwpGls+93aO7duNNTXtTbfq6+r7evpPnny5Injx69eutj1oL2/435jzc3O1nsPmxsXuczlsA/Jbea2UuuxYDrqX434yLO4jo4OWknRoX0zMJlMDAZTV1d76OBHzXfrs+tL8K2NajBbKbBV0jw216FysxKEgjOzBLWQZAyk4yDhBwkviDpAevnRVaULkZ0Nt8XQfb+Fgsc5TLqIz5Vcjvot+rDbZlGKrSrJPIM8PTpAnp5IhpwrXmvU50x4rIWYe8ej17PmMP0dgw9bfTpZRCdedpo8BpVFKly1q7a8BruYgxvpb22oF7HpehEvYjeCbCrscSwIBDwuRyrgO9Vyn9O+mUwUtzOriSV06lDmwr5Ifnf34PvvkXpaLFziz//pxyGf58r5ry6cP6eUSYZ7u5D8Ntjdgr+djd30yno0ELXrVQvzYj5XyaNLSRgREdPd0tBZd+NBc0Pfg9aBB60c4mxjzc2aiksP7jUMdj0c7u5orL3V03bvYUujmM9dCvuKua2d1ErKY86E3asBiAd0dXagkvWbbIGeW5OTk3V1tZ98fLC9qWEz6oVazmYSpBPIShhZDSJLHiRoQvx6xK8HERuI2eBpMgyWnMiSCyy5QcAAgmYQtiEBIwhZQMwFVkMek7q7vZk8i3NaDNGgNxly++S8kF5iFHEc8nk2YWpqbJgyi0l7DKsObcSuX9Uv5B2KrFmsnh3C3rs1UF/pFTPDYnpCzfeqRAY+I22Rpewqh4g12dnSUXlO8vC2ZmYgwsMDj2Yr7FBJFqWUWRd+NKaT5myK4nost5laS8CFu08JyWTyF7/4RWdD/YUvzzz3s59JOcyenh4Wg/7Sr3997uxZHp9PptFpNBqLQl5gUARUIpfJQK2iJPwMY6yXNTXSVn299uKZ7obaB7dvtJ8/2Xqn9tpXpy8eP3L7xKeNlRUNF89+dfSzlvrq5rpqxTxzJRJACjtbLtPyQMuGjLFsls1MjPR0dbJKblj7+EKn0+dmcU136j9+70B77c3NeAD2/O0M2MmAzXX4S8aRiAMJmkHACGIOkPCAkBkOk4QHxF1g2Q0iNqj3xFxgLQo2VuCEIbPi0Uh7m+row51exfySQ7fu0AQlrKhBYpXMW8VcHnF6pvc+eaI/oxUuLzKiWtGKhLbrVG6pOcqR9onaK6O3zgdo41ExbVnBdUvnTZy5tJyRNkks5Ans3ev3z/5BOvxAN9BsZuAX+VwRmy7m0Dk0KnTUo9FJBCKVRqcT5xzft0/MPpatr6/fuXPn7NmzGAwGzdrN5Uo76OQTiWW7wxGNRmOxmFgsLiMW++5QPo1EIt+t95RLABAIBJ6+EUshX4j4g+ii5SEXb9wjKF8di8c8Ho9eryORyDweTyqVarRa8eKixWp12O33mu4pVTAYjUbUnVYml3s8no2NjT8K6dK2QBaH0+FyOYUCocfjRZeF78N7qFRq2c4FAGAymV9++eW1a9cCgUdafiQSCYfDcAscszmRSDjsjuzW1tjo6NPdhFHzU7kh3xlJJpPf61G7tLS0z6P227cqr2P/dpbCk7VGcwAgX69j/3ahUsrT/ZrRfW7QboPFYtF75MrdZnnZ4XBGI5FYLC6TyVgs1hMe8ig5EomgWw88pZjf73/6hsh/Lr/mjY0NdB37UyoTj8f3cYFKpf4V4j0oCYqFvF62+Ntf/0qvkt28duW3L73w4bu/P3jgbSGL9uvnfiXksuI+Z9ioiTuM6aXwdmplLRY0qhVyIc8kFWrEQqdRMz7Y19/RRhztXSDj2LiJ8c621ntNbfcau9qbuVTi3NTE9Ytf4SfHxvu779ZUjQ/2TY+Pkmen06txkN8AO+nUcvjy+a+OHz9OIpHQ6R2qA6HY0vkLFz56/72bFZfTawko5rcz+cxaJh4CmVWo7iz7kZAFytSAEayGUktBt0EpZZJYBKyATpon42NOcy6zlkuvItuZ4haEiJKJGHkGixvp14gFMY8jn1pRi+ZdRg2DODvS83AOM8Ymzup5lMJ6vCQwNsDmGgR1tjOba0vrsVA2Ed50aXe9+pRFHlPzd316p4ihImMiCl5cLzFwiGGNSM8iuCUcp4QdNatDKqFTzHWbdDH1YphLiDhNqwHPksvitJqfrvcghd0Tnx58cPWMi0/8ybM/PPLe2//ww/88/KB5YqDn2sVzIZshYtPHHMa4155eCu+sRFJBR0AjNvMolkWugs9x6xVDHa0Tnc0c7ICSOi2lzzJwE5ODfWO9XcNd98VcxuzEyK1rVygTA6M9Ha2Nd7Gjw3M4DGlmamttCeQ3wW5mKeS9ePb02TOn5+aIKF/opUCj0YhE0oXz5z89+FFtVWVqbRnCXZvrxbVI0qooBowQyHGroNITNCMeNYi7131mh5Qro+HZeIyAPDNPIcQchtxaNLcWA8lYIeYuroaSsQAdN4Ef7dMsCmJuW3YlouWSvTo5ZbRnsKWePDVBnhwxSAXFkiSGrF8Pg7AdJGObieh6wLnl1mUMorxHm7TKl5TzuyGrmU/RkyciMlZMJTBwSGGjUkuddi9Q3UJqxCCLqead7NmokLyjmV/WS0NWXcJlirmtDsv3+Jtvpdbe+MVPJq6ftnFnn3/2h2/97B9e+eefiDF9z//j3zdcuyKYm+KM9tJa7lLOnY7216+SB7ykSQ0Dv0idkTGJMh7DZdQMdLSNPbjHmR7WsebUuIH+1oaOu7cHblcMNdyaGhmYbL5d/dUJztz0YEfbvZrK0b5u/BSGOTW+qeADjwYse6Je+5dfHLt04TyJDD280cGCRohE4qVzZ/9w8L2mmhvbyWU4XrLpQmYt7bMW/QbIDp8e8WggxuZRg4BhzaF2SDhS2ixnZoI7M75ImYmZ5LtRd241BjZWi+lEMb2SXApTMSNzHQ3aB7di9LGMnq+ZGfTpFXODDwcbqknDXfThhwbWTMGjAXE3WPGDiBW4FSDmyoRc6xZ51ixKK5h5/XxSzVtaIO3a5QbyhGGmL7JACi7SjJTJMBun6L5nG77vGL7vmer30zC2vgYbpls90c/uacfV3lzsajS23jQs8J/yyv521sbGxoMHD6qqqvB4uJwKIjo63enTZ8RSmc1ivlEKra2tYrFkYnw89eSQyWRcLlcoFEpDV5/vDul02mq1JhKJ784upa6trZns9rVUKr29vbOysrO6ms5upzY3UxsbG5sbGxuZbDa7ubnp9XjQrQqKxUI+v7u9vZ1KpXZ2drLZLNzPIbebTCaDwWDJuSeZSqWy2Wwut1P65XbzcAuIra2tcDgSDAYzmUwKOiilk8nkyMiITqcDj12dRCK4Y3V9fT26T10ul2tqamxsurezvdPf13ujsvLK5csyuWJ4aMjj8aD3+XbT0ul0IpEwm83fziqnpNPpcDjsdDqfQr1MJuPxePx+/1PKpNNph8MRi8X2lsmkU7vZdDK98eu68OGe+NZG2mw2xZeXN3d28ltb6Wy2XI1yZHV11WKxJJOQdKlUKpPJmEym3l4IkqGh3G0IBAKaYjToT58+LZZIrRZz5fXr169fu3//gVyhGBsb21uZ1DdDJpNxOp3BYPApZVKplNVqXVlZ+eal3zhbKxkE19bWvpG65wSF02w22560/dF0Oh2NRl0u11Mqk06n3W633+8vd5u1tbWRkZHv3RER/JnCXxrvKe7m/DrJ8U8P3m9tTidiU2PDvfdbpMSJyXvVx95/O2MSbc3PrvU3bsvoK/rFgE6mFbLEdELIpl/1mANG1Wo06NApMd3tPNwofrins6VxbnxgbnKkpf5Wz4NWrULCo1OImFHCxBCPSZ3DYbvaWywa+chg3/BAn1Qk2FyJAbBLnp166YXn8aU17XQ6g0KhQAeS2Zl5NkM0z1EuCv1Oa24zmUut7CRXilsp6LKTCCLLfiRiR5b90JK1k14KetqbGiZGBl1WYzjgi0f8sYBbJuTpZIthh9lnN2vEAgWfI+NzVGKhTrqgErCsKumy3yXl0E0KMQM/xZqZdOlVuxvr24kwnBxvrJawpQzIZsD2BrRqbayA1RAEMAImiFL4dMCtBG7VrlNV9GiKHnXRqwExJ9hcBcloLuraCjt2lvxxt8WmUybMCuPcsJeF3bFIo1KG26QrFL9jI7U9vQgZa61/59WX9PIFi1JMwIy21Vz3iWiVJw+3193Y9RuzNtm2RbLtMayY5UGVQEXBLBAxEZNyxaELmFSrAZdVypvoebhAnpnue9DTfBc/0jc3OXL3ZkXvw/t6pVTApOKxE4RpDIdKouFx/Q/b7QZ1b8f9qbEhlVS0vRQAO6mZybHXXn6RNDuDOkeTydCDZG52lsekLwp4KvGCz2Xb2VjPpRK51CqSWd1djSBxLxK2QRG77C+ZumJxr+3O7VtTE6NeizESDMSjoajPKeEyDPLFoM3otZuVAraCgpVRcSoOWScWqIVsq0q85DBKKbNmpYQ23seZGXfqVbubqe2VMEj4kbXII/QiUzJilmAM4DcAnw7x6aCtLWIHXi1wynccihJfNBB2WglAFXZzLbcc2Iq4d5b9QYPcoVMt2bQm9qxnfm7HtBiSsd0mKB6eFrY37507fvjdt8J6idug6ul8QB3uYgy0v/vKiwEpa8ckzBoXNlTsdTknTsW46FPivhbxzHDCqV9z6ANmzarXZpXOj3c0C2dHsZ3Nva13pnvbZ3rvt9+4/OBOtV7IJA10dNfewHc1Ewc7H96t6X/Q6lRLBlobcJhxFZu44zODwvZof8+7b7xKxE2VlnwxyHBJ19wcHs9nUEU8pmZREHQ7cpvJ7fXlXGYNyabzK6GSuqOBhkWfDoStYNkV1i423LiMHR0M2ozhgCce8kW9TiGTYlEsRmyGoM2g4FLFhDEFl6YSC3SL8xouxb7IjmjE4plhq0pCHu1hTY141Iu5lXA2ZAdLXpBKgMwKshaFrl3pBHSqWwlCRvh0kP4BA/AbELca2KU5u6zg1Ra92kdwYCoOkvEdn2nTa9lZCngX2B4ZPy5jW0mjPiY2KyTE6eNO3ddGqKdx53Ge2+2+ffu2Wq2+e/cu6t2s0ajOnj0DnZNo1JtVVQ137zbduyeRSKurq1ER5f+uEAwGNRqN2WwOBALflQ/TgsGgQgF9a55YJhDwut0qFsur1Qao1M0PPtj46CM/gxHQ68NGo1qhOHXqFAaLnefxqCU3eQxmsq6ubhqHEwqFKpUKh8MRSSQWi6XT64lEIgSENOrqmprRsTHRwoJMJhOLxYsiEY/HhevVdToymSyRSEKhEFq3P+5QfOvWLXQ9F0qe9vZ2Lpd7//591M6V29lpbLhbV1dvtzluVFY+ePDg0qWLUpm8ubmZwWCg9/l22wOBgMvlkslkT2y13x8IBCwWi1qtfkqZYDCo1+sNBkMwGPz2U9CUQCCgVqvtdnswAO8UDgaWokGPLzDLd3zQbPl3J3z/22kfRuTXmvQOjyfS3Jz+4IPgwIA/HN53Q4/Ho1AofD4fmh4MBmdmZlpbWx/3GmjnqqurU6lUd+/eRcE5vU5z9swZMpnCoNMqb9y8U1/f1tYmVyiqqqpcLteT6ox2G5PJ9JSGBwIBeQm6e0oZn88nl8u9Xu++hpRPA4GAw+FQKBTllG9HAoGAzWbTaDRPeVAgENDpdEajEW1RMBi0WCzo0CgT/MnlGAAAIABJREFU51818hfVexAE2d7OOjUS6sTA6cOHRCyqRS1fYNMF5Bls30M5GQtCJuAzQpOERwNUdGCXQWA8ZAKmBSClQDx8JQS42J3um5mZh+FFWsqhLoZt2179mt+2HvaF3TY+jdhy4/JQe1PAbdPKxJj+roBWbKDPnvnow5aGO5urcYDkHCbdiSOH8aV9WWZwuN6uTjppLuyx726sASS3Eo+0NNSzCDgJj6WVigobq9CeshqGoP2yH6xFCtmUw2LAY8YdRk1xZ9Nh1EjmWanlEAC59ViIR8JTsaOLDKJXK4kZZdlEGGynQCYBttYh/r+dAbtbSy5z0KjYXY9nl4Lwfb2dQVYj0HCTWQWpOFiLIjEP4tUjdllRQUXMCzBiEhZEswUZuaikA48a8eoQrwbCTusxkAggERuIe6A7bSoOb5jLgK01sBYBS+5tm9TAwNkM2qeoPcViMbedVXFINy992dV4W8ogmlQysYBLnRrtbKgO6yUgGX70i1ihpF8LQ30raIBixiGDmlkyBjyaHcFMmjUepI+njWIkbM1aZQnDYirqDbqs83Rye13V4IOWeMgrF3Kx3e0hs9qqVVy78FXPg/ZsIgp2syat8uznh5lEPIfDxeFwfT3dLCop5LHnN9cBkluOh5tqbzGnRxV8tlElL24kId02k2ArCbXDVLy4nTbrVHOYUYdJW8ht2QwahZCbigdALpOwaTjTY/TxXjFpyiNmRdXzOyXwpkTtEEgvIREP4rXEbNqIXppP+LdCDqi4xN1QqVryIjEXshpCVoKo1QbCGF4tKtQRlwpxq4sOWVHFgJzy6WCKR4O41RCCClmgH1LM/YhoyWjJvuMBMce2VWqgT9l1T/Q3L/kQINmtDRmbfPn0sfGHzVI+16yWS9mUm+dOkQcfgoAOQAXLA6JWGA8ZIAQStgCPGjgUUPBHLBAOidpyGnaKORpiYZPGxULQmLVIVgyiNZM4KOcwhjtbLpwYunbKNN1P7Hs40dkSVc4bGLgLp46Ptt3Z0syDbFIlE188foQ6O8Xh8rBYzEB3J5tGjnodha0UAPnliP9ebTVnZlzGpZuUYpBaAjEXpEPAiHi1wK8vLHuNctHscK/PZtzdTJkVIqWAtb0WByAf89i5eAx1rFfJpXrVophOtBP3wa67kQC5km/QzgbIbYadlqjDkE+tZNeXwc4m2NkqDckQEnPCzr8WRfxGxCYtGgWIVVKQzBWlpKKKURBgC9zxAh8DTAKICJpFkClQ+zEgQRPswHE3WI/Ajr3ihYQKGkHYvGmVGZkzduP36aPffCsXCgU6nV5RUUEikdCcnZ1tvV4fj8cNBr1Go/F43IFgUCKRcLncb166/ywajX7vBweCweDT7VzFZDLZ1FR48KBw6dLWj3+c/fGPC1evFh8+BENDUbu9orLyypUr9fX1IyPDR+HKoLMXL164dOniyZMnr1de7+joqLp5s6am5vr1ytra2v7+gQcP7g8MDavh8qu6+vr669euHz58pKW55crly9cqrh04cGCqtK9guSUMBmOvnSscDre2ttbW1qJLw+BWyz6f2+0OBAJisdjvDxgMxo2NjYmJ8ac3amdn53u/QZFKpfZ9/aBcq3LkT7Rzofv3IAC4Vou94u1z+M06Vpao3WSp1ii8oE1gWCERtoeGdl96KfvMM7tHjoDSar7yU9CFWujWxuXEvfv3oN5LNBrt2rVrZDIZLVPuNkaDQaPRut3uYDAkk8m+9/Mm0Wj0e+1cgUDge+1cPp8PVdzLdd4X2dzcLNsr92WVTzc2Nv4ULqD7NZevKvtcl1P+9SJ/Ob1nZ2eHSCRx2ayUW1/waGJKrl/OcUs5cxPDLpO+WMgBZHtrLe4xa91O25pLv6YX5UJ26Arg1RTl1HzvzaKMiujY+fG7Rdx9QO8HCiqiZSM2CdSNtlaWHXrW5IBHzgvK2CLCOI86R5nFiZgUzuwEc3yg6dIFj9UACtkSrLKuFAv7e3vGxsbv1NfbLQbov7xTglg214uppbDTFLZqow7jpscIQuaEbiHr1ee9urxTuW6SDnY9EHCYS2E/yGcdRi2PgndppMlYIBn1gfXoulURNityodJrNAh1OCgm3WqQCEB1YckDVlGlwQj9HsIlNWIlhMTcIO5BwhbErSpKiAVyT1HNLBoFxcXZwsJMUcUsKulFHRexSRCHvCR0NYjfANajEBmCOEQMSfgLUceaUx80KtV8llmxuLGeSPpsYQljATckFX1t4N/XmVKpFGFuTsjnZTy6QtAYlrFDco5TyiMO9/mMWiSfA/lsZiXqkgrdJk3SqV21q3ajTrDkgMupDPyCaBZxyAoORXK6ozj3EMiIQMNC+JOISQilctgc1QrZmEG/ThJUCxdI03IugzAxImDRWGQClzLX03I3aDdCU1dmFWRT8oX5saEBzOTkvcZGu8UECjtQh8umQMloGHTbwg5DwmOFjuER27pDu7US2YnYigHDknq+v61JyGasLIXz6WW3Vi6kkxwy/rpTs26SgLBlRSuISOh5pxIEIBiA+LSIB/1pgE+XcyijPHLRowUBPYQo/BDLgQVKIAG01NilkAVCHJSdPl3RLkPMC0WT8GtgA2qiWqiPulQQmQsaoVOLVwu82rxHs6oTBJV8FYtoEtA3Q86kTRmSMITYfqlgfh87yqepVGoahxPwedvLgfySJ+wwR+x6C58+03I3aNWDwibYWE67DAYSzquXpizSdS0/71ICtwJWdZFQFE4XbRLEsohoOdtqdlbJAjoOsoCDfCkJ+LCCyxrrCSjmA2L6/FiXnDRJGesTzE0xhx+yhzvaKr5aD9oh5aFmuSbl0DC9HbiRgeamJr/LBnYfj5eNteJaJGRWhYyKZbtuyw0VnaR+YdMqy7k1ea92WcPvrrsupBFWl0L57YxZtsCem47YDWtRf3IpBLaSK35nxGUurkVB1A51Neh+DikJh8lmCmKf2UzJPzoNUbd0AqavhEDQgvh1iFNe1HELQlyB3l9gjxalxKKWXZjHFqj9xYWZooJaFM0WddyiR424VEXLIvBqYJ/0a2HEpy24lCv6hYCcqyZPWufJmwFr0iQJLlKF04MKCfTA/dPD9vb27Ozs8ePHZ2ZmUPsOUnJD3ncHkUiEw8Gth5EnBABA2VHjCUUgauvz+ba3t590H1Tierze/M5OcWWlcKeu2NhQXF1F8nmQzyfX1yYnJ2il3T7Hx8ZGRkYYTHQJLXlqalooXHA4HFw2W6fXzeHxnx/7fGZmdmV1BYPFMJgMVmnnLbhjCAZrMpl5PO7s7Gx3V5dCCdV3tMIAgPHx8b3ruex2e319/fnz58u7JO8jC1rhhw8fog4f39lwdJ28x+MpP+jbxVCPmVAIuvp+O7dcva/9mksuyl+XRD2WS5VLxEO+xCbJtHt5NHKu1TzeLw0R2IXZKdDfCbrug677xdHh6MBAVijcbWrKvvba7vg48q2Hfnu/5nA43NX1aIdltEWzs7PHjh1DzaPlGu6jj0gkmp6efnqjwuHw3s8+fN2oxzEAgNfrRTXLx2nfOD7qNv92+zWPjY39Ffo17+zs1NfXP7jftmyUFNzqnFMBAnqfiMaZGrLKBBaVxG3W9rffmxrqo8zNOgxqq06pFgtow13rCiZYxBUGqop9NxFSR54+VNRy84yJonCmOI+BPzWrYOA7uYSYVlgIGKGvcWY57rHJhPO72xmf3TiLnRSzaXIWOb+xBlJxJGwDmaWIy0zBjQtpBCSzDNYjSGoJbCThyqBkDN4haoempZLcSpvEtgWGQcDYtMujCs69qmsWxeKy27QVdnJnx9lTwyOt9Yv4MauQXvAbwGoQrIXgNLTkggBBAjj91UPkIOIoiUYI/sNI2IYkAiXwQAPFp4FflJILpO4Crrkwex8iPVDE6hGbFPp+rkM8H4m6kLAVFcYgvZRLLscdhojdELDqHEqRmU/1iugWAY3Qd39xbiJsUgYkrC2bbN206DE/0c61urpadevW0ED/ilFc9KhzTiXi13sWqNyhBxY+06pTusy6rvYWbF8nBT/jMarNSrFWwGSO96YtUuBRFsVzRdFsQU7dFuDzUkqMgcuZFhGjoCAjIWZR0SSwMKbiWmExaATr4WIqvhL0yJjknbgnYNESsON8GlHJJhUyq2AtjESdIL0UsmjmJofkrDnoIb4WgVbFdAJaALfSqMcVRBQgazQZs9QiYmkYs3mP1iuktFRdscsXVlyGDY+ejRmgj/cON9WIcYM21kzRpYQIolEAoTL0V7JSIS4lRAK82qJHkzWJiyWdBoFaEarEaBGvpmheQJSMAmesyBwskLuLRiEEcjwaqIDaxI8YisI/qFknaMy6tXHdYlgn9ktYznmSgYELSVgWzhyus2kBPxrVLgTEjE2bbFUn9FoM+95x5dPV1dWKioqBvl44TwiYdiMOkIo7tHIeZtS8wLHqVXadsqOuauh2NYOI9xuVVilfN09jDLZtqNlAPFuYaS1wxgq8iaKGlfdqCyjOYREVzSLEqwFerZ1DiMjZiEsJ9FzEvBBVzmuZhN2gycojUoc757GDah6tAF2p1iB+k4wGbUbK1JiUSwPZdbAeQ+IeiIeFLFBHCVtA2Ay1yZKZKWOWWLgELQ2761a7FigN1y7Y5MJUzJ9JRLnTw6TBh+MdzWLqrH2RCXFKuCDAByJ2qO74DZC23pKBLGB6jMfEH60tyKZBZhUJ2SDxvbqieaHImyjM3i9gmwrTzQX2CGISwmutYkQ/D8v4oQoLsVKrGGqiIdOWTR5TC8JacUAjsi+yDXx6yKozi3m4rmYJnRC16UMyNhwvxkWX5Yn7XZUZtDfyRx+OCxcuvPzyy+gy9e9UetAPLX2vAPsz6T27Pq+nUID7CS1l8suZR1+QQOuMTui3t7fD4fCTqopKZYlE4i195hPdqmdvk8vx1VIoS2UAwNjY2F69h0KhvPHGG6dOnSqvY0evRR+N/ufz+YcPH6L+0d+Qxo9P/lX0npWVYioF9RW0QsU82FjbiK/IbelqjO9E5WLL0V71xcbsgwcAO4ZQKQWlsuj1FhMJJJcrIogrFNra3ga5XHF5GSkU/hS9JxQKdXZ2lkmXSqXOnz//0ksvoSjgk3ghEommpqbKFH5Mkq+PpY2e/3+v94yOjv4V6j2FQoEwN8dls+DKDr8e+HRbVlkhYEgYFuhDDyY7WyiYIdLEcD69AnY30VUhqUSk6tqVY2+94uqrAZONW/VnNlovFkfq880XcreO5R5U7LZd2r19PFv5h3x/1SahM8CaCguIcQVn3SyxCOmLTHJ2NYZsrCb8rnQiql0UCFm0rfUlCLqELPBlHbOCCHxxl97ddqiFrEXgf8yNhO2oOARBg4ww9sVnBxkTfQUvfHtGVfyGK2ebb16SU7ALUwNGGlZBwW451dAMl4yDzBqIOuE716lAAhZk2YesR+EkdS0MX+vQzRO+1qFTyJIXJIKwsE8HAkbEqYS4jpQMtR/2KOJQgLANrEagi0nMWfJgSEObzsYqSMaLy36nfN4hoKZtsrRVlrLAl/WSat6vWkiHXGoGIarkbrs1JdRBX/Co18O+J/n3bG1tTU/jFheEUNUriYptu6IYNC4bFmhjXROdrdTxfip2tJBZgaa69TBYD6UinpsXv/zyg9/5se1AgCnQByEQwpss8LGZ+dld8VxRy4ETbvYIwhtNCfF+ASmyQI5LmUnjomWRI2YQt+0KJGhcMoiTfruMRZbx2dklP7QBRWywzjEbCJsgX3wGuLZ5NYzAleqrYGMNSSeQZU/RD00SwqmBs3/4hI0dKpbwlYR+4eaZz1uvnzezZ5kjHXryhIIwtmOVQI0H9jc9VHHcaijpoaGqZKsqKT1QWHog6gNZ4ysJS3TFOzSL6BCrBNpNFDRoZ2SPIAZBSSsq2XFKphyUoSWUyLDrUtn5JJeAAplilaXMknWTOK6eD8o4KyaxjDCypBHkIKpkAH59zq1ejzzxY9dbW1sYDFa8IICQp09X9Gq3nGok4Y079KSJocmeh8TBDubMRHErCQ2pyRhYD6+GvRUXvjr/7qvBuV4gwBaInXnCwwK1ryiagWCVhgX5ouUgeh5wK9NGkZc/F+ET4ixMkjdtpE8tkKcRv77o0yZsmlTUJ5wdlwu5ueQShBXDNhC1gYQbhM2P8JiAETr4+0ru5LDbQH0FuhuHzYyRzguff8Id6ypCZ3NDXC+qPHWk+dY1k4gtnBpQEEY0HCL0aVsJguUAUur/cCoSNIGgCQkaH9HzkUO0BlI75oJL9pb9yHoUzhY8EHKDWrVoBrJmHlPkYxGjAGJ4kImoFbisueqAT593q81cgneRnvZb00FnJuhci4WWfHa/fH7dJBLNDK8GXbupFWj88ukKHm0i5H+6N1xZdO0V4SaTSSQSoSmJ5aXOrs5INBrw+yilT7jQaHSFXP5nsXN973qu3Xze5vSmt3ZzeeTMeOICJrFbQDZ3ijsFsLK62tbWKpXKlpaWzCbT6uqq3W5ns9kOh3N9fX07u20xm60229Lycj6f9/v98Xg8GAi0t7fz+QKnw5lIJLLZbCKRQP1RvnM91147FyrLV1dXGQxGLBZDiUOD27tTAQBkMlmr1XZ1dUYiUQwGg4JY+2hbPv0T13N970oiaOdKpRC1Ovv22zsnTiALQsBlFCfHPLX3+y+Nf3VbWTW7Ok0yLSkNIOqH063HelG5JqgK4v0Xf6cCveHebrO8vNTV2RmJxgJ+P41GZzIYFCpNqVT+Wexcf671XH+Knet7ufDt9Vx/nXauR50GKW56dCtaQcYiXdcvaOg4HW1KSpqMmxVIyAbfqn4D9AyI2JElH0jHtzfTVZXXKg6+M/GTZ8Z+8LcP/+P/vfjMf/H94tnMhy9un303d+voTuOXO7c+32n8MnfjSL7h9O7w7fW+W86empm2WgePiITMiJIOFDTgkBVCpoXZUdLkcCbshVNYKFZLQq68s4tXC6UsKsxKPhwgYHAKyPTJgYd1N5hDD9wCUkLD19OncB2NAw1V4umBhHp+ScYoeDUgaEACJiTqgIoOlAclQwnqTwDBHivIrCFLPtQpBwlZILTgNxbtcgRdxLsef4RtbKxB34WABapEETtM3N6A6s5WyeKwGkZCVuhknV4RzI4KxjtFuKFNR8l249fvutRhg9wm4S5rhTmncsMs3vVoNyzSTask6rE9Se+BYxj6kuQ3HOoVrWDDKk1o+Fr6tJE5IyNNLOkWoEEtZIYtgtoAXJ4DwpaNsKu64lLFoXcC1GGgoMA5N641P1hdeHAl33Yh336hMHN/c6Am3nCxwMMgvMkcfWANd9/cU4drrnbxScCjQWQkYJwHHkXerxdgB5jTY+tOLQiZSqqJ6tHj0CcGjNC9JuZEoHUD6o4gYDCx8JSxvoe1lcKpIQ8XH1fx9DTsxL2aoaZqOWEkruQmxBTo6VLCZoCvtL59PQapvRoEEQfiUEIyxlxg2YeErBC3CNuQmKdgVSFhJ+IzIg4F4jfBRLiCz1y6jxraGd3QfQe6oKWWIPwQdT2Cf4JmELHnPRruRK8INySbG9uyy6FNLWgowQx8l5ASV3G3bPJtmzznUG5ZZRmzJOqx732Zfkc8v5u2q1Z0QsgX9byaPGmgYWVkzIpZBp8L/eFKOkdJnwZroVQ8eOPa1apTR6OTzQDTWHhwuTDZWCB3F9gjBUp3gdxVYA0hMkpRRkZkpKKeu2teXGeOmbHdxOZq5/wcCBoRpxyqZau+3YCBO9bNmRlPLoXBkhd2XbRXw3lCCZJBMSR0SV0A6qkgaLBwCITBzs7qq/MjnR4BeVnNM9Cwo3evD96+qsQPL6vm4yJyyZnGBP2OQxYk4oDLJINmEHNDHShU8lJHpwePnIRKKmYUroeHY9NvgMMWjaD4UBnAQ1E3OK94XD10SuPTFdwa1mgXNCySMPnlABxWW8mtqMu3QHUsslZ99q2IZ9uj33FpsjbZplUWdH8fX77JqlQqhZpgUImIfqfizOnTLDbHajHdrq3t6+utr78jkUiqq6s9Hk84HA5+V4DfkdBqLRZLKBT6rnyYFg6HlUqlx+N5UplIOGi0B073OS6MR967H/5fPvf8u2OeDzvCFyeiN2eX2ALZJ4cOXr1aceXK1daWluPHj3322eEPP/zwxBdfnPzii4sXLlZUVFy4cOH06dNXr1acP3+uo7Ozt6fn4Mcfj4+Pf3X2y7Nfnj1/Hu6gJlxYiMVioVDIYDCYTCa0ReFw2OFw3Lx5s/ydimKx6HA49u7yvLW1dbsGhmw2e/9+2zQOd+b0aZlMce/ePQaDEY1Gv7PhoVDI4/HI5fIntToYDIZCIavVqtFonlQmFILUMxiMBr8/2XAX+fv/Bn7+Y+fZC1M1E9eqhJ/cFNdhzDJzMBGPmI0al88bisWCT+BUMBhE3YSf9KxgMOjz+RQKRSAQQFsUiUTwePxev+ZkMun1wr0Ayt1GLpWePnOazmBYrZa6urqeru47dxtkcllVVdXTu41OpzObzU/qVyhxFAqF1+t9SoX9fr9cLkf909E67/sPhUIul0uhUOxL33saDoftdvtTuIBWZm+Fw+GwzWa7cePGX+F3KkApIIX8mkXhE9GW1fwNs2TLrihAnNwMJUTJqFSSrCgoUtpgN72cS62omIT3n33m2v/6v0/9H/9n9f/0PzP/y9/ma07km77Kj97dJfXt4rs3711JHvzN5mf/H3nvAR3HcaaL6u55+97mvX7r9fra63VYrTfYctBalmXJsrJkZYnKlCiRFAUxipkUMwEQIEiAAAEQOYMAkYGJAAbAAJMxOeecZzAzmJw73K3u4RAEg2Tv3j3n6c2ZA9T09HRXV1VXff3/3//9T+Vq9qHk1uTVC/yy3b7xVlS+ADNHIqMN6r5L0qlexzKts+oMnzwKniy1HIxDswC4GviMaRZjMeqYpQGfMa1S9/KcdpEw29tM6qib6W5Qz4zG5QzgTXMqISsWOQIwAbDwA0OCnodaxKhDDqghRiFiEqAGPjA22GSIU4v6rGDKdulBXHQyAkw4kRWgWZIIg3ccKEFjKkEx8DfkAZxlwK4A5n3sJ1GwNgCbhASs3FaJU7Aw3XMF8FeAxUgWlC4Jpq6uKli4OQ21SPJGYUzFjsgYNq3iLrgH9AyUW5GxLAzSinAhqmSldHzIgvWLFZCXb7QPXrbK0Fgg5TFzxvuObXmX2X4Rmu6EBqugtpP5uv35i3uyJz7M1+wNlO5QfvR6qv0sPHQBJjWj5Jbk6GXGxSMh2gAqW4Cl82EOSUO9JqEOO9jUvotnmSNdqFEAbBKscVhBB1daPLVTDZa660saapXa2FQjg0zpbJhsqJq9UqWmDseVLAgYGOR48A5m1MEMAFouqmEDp0kmjibC8Kov47Ej8VU0j4lSgsYPASsdaOoIkoyiYR/iMQFFmST2ld8CuFkBB/a2AwlEmxzYJ5IRJLwCrzjQaAB4MIHNAzudge/iTs90N4SkS3jXr4jpYuLAqpyB9wtiFucNwqSGG5Iu2bRfEMeO5rN+KdPKJAVE2P2i4wPjlhPgsDX9cr2hrFI0Hsiu+ubHrh77YAP7/H6orzx/rgS6Vg1QDgOjvCwN4WYSaPEavDQEL5NQxnCCQ+D3NQS4VNQmg3XcEH9aQxuXUoYsDFJvdSmPPAxgn5wOCymgX3TcG6i0wGoSg2s3A3aUnTujXyJPttSQWutm22uU1CFg1rUC/Ad8bXi8FY7SMB4VQJBhD7D9hL2AwQPkf+LAPrpiQ/wWzO/sA3cKCKPzgW4KuhCg3ulFV91QwAn29Fsw5KTAPJhYU9zAZ9jQxT/a5TY2ldp9JWDRgDiDdMxrNQjoM8mgFxgyQVCeKKvjpzTcsGzJrFXi89WX/Gs0Gt95551Tp05NTEzgP7HbbOXl5QsLdLfbPTc3T6VQOru6mExmX19f4j9Sot/hlUwmjUaj0+m8yz6JREKj0QSDwTvuk4ivhiMihd4TSvWzIkeHAkeGAkPciC+c9UVyFou1urq6ubnlypUrdbW1paVlbVg2H+xj3dDQEIvF6urspFKpDQ0Nmzd/1N3dw+NxaTSaRqNubm6mTk+3tbVVVp43Wyx4MLwVC6RKJpMgRj6RiEajnZ2deBw73hSlpaW7d+++cOECbgNAEPjq1f62tjaz2SKRSDgcTm1trUQqw7aY8ePc2jyJRAIPC7/1q+KWRCLhcrkMBsOtLROLxROJWCoZT6WSDrfLN02MHT0ifGPH+Q0XNzeYSmcSfCcSTmahbCKTBJH+Or3e5/PdepziuWKxmEqlWllZucs+4XBYrVZHo1H8V8lkUqPRtLS0FP1ZBoPh3XffPX36dJEO77CDYTM3P+/xuGm0WTKZ1N3Tw+Fwent773IifNjguVSLNby1oFarQ6HQrdvxLYlEIhKJKJXKcDh8l338fr9Go7nTDvgY8Hq9t+2F4q8SiYTJZLLb7cVhE4lEOjs7v4J5KgozCJTDCIzS4tp8Y20rLnKFAkbdXbGCOdFr0PDmSre+x3jnefUn7wRqjqPSGUS1hJgxi7dVhqg5uYZj0GQzLFsADibRNKpYRA1CyKa0zI+x+5tG6isH6yq44z3k1pq6/SUWBgnVcWDZAsydgrmTN5bYtWstPk1jcTF2NmWo/ryDOwMiQdw61KWF/VbYpUW1PMSmQH0W1KlOqDnOpakgl6ydGc4qllA1CzEK/PRRL4eS1/OBycdnATYhuwqEhoW9SMSPBJyQVQ3casWJHmAdTPMNGHhWQTR7Jg4wUMCJrNgg9gSsXEJVTFS5BOmXzUsk+kCrm09DrJKYku3hzcZUHJxmUWxVHP0E7aYvxD3A8QdYn8AlBIAUtpIVj7O+ANT/dKhTKaUOn9z8ln+iGSW1wLQeeKYHnuuHKR3wXB9M7ch3l+Z7ymD6IKJcREQzCI+Aaliohp3XcvWUQc5wx1hzdffFs7zxXmrjufOffexZGEV5UzBnHHBUAUuREWQoAAAgAElEQVQDC8Ap2Jmur+54v9hlqFtlWiSONlU7JCzEq8fRAARoIqD+eIVRiySlXXZwpgMSpn55MWtVIBZFymHyamVelQBoxsQCQG3ZIi24UVyYVcmpxhhaYmB+cGpg3TKyYgMwCJcYiAWAtzETT+vVab0aTYZBD1pliFmCmkR5NdvKINH6mvyiBdQqjSpYTg5gWWFkaswKiOF7FKNShVwFld47rq/5LPD9FfpFUryu9d0B2kQERlcIhIwhETd/jni65IOViWZ0pgd4IQ2YCQ1nzxgFsHQO0XIBiV7LzWl5gHdvl6MmIWQW62eGWQMtQw3nhxvOs4Y7Kd2NFQd2eueGUMYQLKQi4H4h3O7sBXAMovxcKsPC5ERrjVu0iPjNQJ/QJoXx8HLr9X6xSuMqros77RMt6pfp+QgAInAs4Dep/SoBuB2SYQA0w96CpkNiFbg4o9hzQmQF9prgVXfCY8nHwxjrOQZAklMNpCktEkA8cmkLMBR/IAHeK5GLMz3RXONTC9BVV8zvcho1Sa8V7IwbaPFxhRmH/HbL7+Xncrlc/f39Go2mSKrFwvFAEgYY00nHvUJMJvO/RLfQarXe3SUEXFQWM87vQREEvG95pVKpouPpli/Bhng8NjU19YXS1bfmqSCRSMVEHwiCjI2NsVgsvV6PW30QkKYCvCAIwusFQflsNtv9RbqF6XS6aB25bYVxXvOddAtXYtAwPwGiyabmO944u7tadXA8NqWGgsnbHOz/XD72rq4ufEigKOpwOAYHB7Va7d2HDZvN/jK6hevCo269Kjxn1q3bi1v+q3QL/7A8FSQS6aupW5hMJrPpJOAw3gIvbj+TWjGDtkmExQObjdTBDJeA6lmoVwcCoFZsIMYYMCsxnqOCASzhBhGiARM6sL74zUGT0q0U2Hhzww3na47v54x2BySLJhYFrEOYvC++5+3Pjs2DkFEYljNXZUztzLBfMJd26oM6cUi8qJgeW7rakpQsgDD7oD3r0BjoBOFU/0zn5dmOurCcgRr4pukBzlj3TH1FkEuLc2cCE11A98VjBKYCtx7xGgBnQskEqjw+A5DhwZItgInYJAacBqcKoCW7CsRpR1eQqVaodi/CGkvzpwPjna5lmk8wzxrtkk4PK2fHAuJFwK6wSjA7EwYRjAJEz0NMwrxBELQb74J7kslkJpVEHaov2y/4Kov5m2CbzDDZmRbOAlaplovquIBYqmUDyKLjwQsD0GwvQKKAQSwEBS0bNfL9UpZLQLcxyKONVTXH93PHuoMyhp5BTkkWEJzQbRKBhfnO4wQ2icJyRkjGkJIH43Zt0m0OSpYC9FH5cDN3tCsmW0KtIKgqq+cb6EQRaYjcWjPfVRfXCdFVp4E1vdhRPXNmb/TaFcfZfbpzh1A+EZHNo3Y5LJ8HEU8uVVTKjAw1ZKjdsJiGyulpMS2u5gGiVdAOjD1eEzA8rHqQsA/4hlQM1CrLSOghxoSTNxsQ01lD7TLqsHYBOEYhE4hyAiBDNg+rGCAcTLGEKBYzen7QUbByF6eetYVEMpVLJ1Gn+s7tAEb+TaMXI7igbl3GrjYvEiANu4CDrwNBYKoxCqGFQQBfAJtHksfGObDEWKVeId3FItuYpIG6itqT+/nEgaBOpFsiZlQsFHChsNFl4N90xus/xzdCRmFEzgxJl0Ska1G3OeWzBRWcEJskutbMnehNapcBhrPJ0gahZm5cMNE7090439uYMErQkFPJnO1uqOa3XwB2UwkNYY1BDjXkM2PWIA+AnkEH6lAj0vnI8nQc0HS0HoPaZdLBYR8asOWsCkiLRTuCu0aJyheQZRJqlWZk9KBg1smb9QsXWCOdUsqQmjkTtmqAwwuLtVznF8sbBD67+TZIYW3f3Fx2OBxtbW0UCuXq1atrs5MW/Rf47ktLSx0dINXUDUrqzaX/uniunNlqAumvcoikzycd9MP5GxfEYjEtVuv83Fxj4xWhUNjR0WEwGNPptMlkHBkZ8fr9FrNpaHg4kQQvHBvdXM0bn26bp6KpqUkgKAg0QBA0MTExOjo6MDCAJ68oWjvwAv4Xz1OBBz/fOPqa0n+G14zDvhZ69Bs7LfvaHdtKBXUzAWMIvT4lrjkNVkRR1G63F1Ha+q+xz39YngqDwXDp0qXi2LHZbO3t7VQqdWBg4D85bL4C8VxfzTwV6XR6fHyCQiImjRiBdO10aZGCeO/bLHIiEL6EUQdgn9k+0IjMD4CHM6caWE3iIfD8HQ+iARvwMlhlgPyIBRLDRgGs4aJeY9Rh4JBGFfOEgGQxoV3O4xRXB8a/wSuwxiReCOdZY+cA5GsVR8emBQzyVZNiVS/T0EkO7iyxvU400U3rvswa6YpoluNmqZY+JSRc9QnpVhbVzaWaWdSMQ+NlkozUQe611hX6aI5HTooB9RLW8VAbSJWQ0/FtPc1LB/ayP3rXc2xHeKAeZk4giiXgfZPMwkoGrOUgOi4smkYtkpxwNnJ+R3boUlrFMs9P2OfH0tpl/lAboalKRh2JSOmYhQZbAgEBBTeTiBCjADWLM9plj0l3/SYv3nSFQjgcBnnTaLNJI0bfWdsvX1RGLZKEmutjEoA5R7mUEc6uUq7CJiwwB3f66HkIFiAGEA9G3EbULNQmD8tZ9OFu3cJkULyYLPaLUwHsGQY+GAlr+wUjHa81dYAG0fNVc+N+GWtVwQnLWZr5STuLTGyvFY110noamUNtccl8TM3VzQxLiANe3oyVSXZyps2c2axR6KZdM463sy+fclV+ZvvgZc3ZoykxI8Wcik8PoFourOEkZ67ZL5zVnj5iPLid1d2mnxlTTfSxp0ZMdIJDzMgJp1HAsgJE7IyOHxxpyPCpKT1fP9npoPZn1CxWx0Vyfbl0uCXGJRY8OwAxCGDFIqxi4sgDNYuTGp7PrF/fH9c/h8PhgYHBuZnpvF11vUOv21SA1RC7X4Ct0QweAOyYlwcQ1AwAljnVcbfFzSSiAjJm1xECfH9jYEtgMQ0WUNet96hdtipdWhho1SwQQtKlgg/aLgekKzw2CgtYu460RLBRCMDljcMC5BSVM40McsCsDptVAQVHRydYWBRCS41sqme66zJjqDOhXY5quIrZMQV1yLNMszLJLu4MoHxZJTYWZXKwb7yzOSRcSHFJIRbJJmLHtAIAYlxakNRCTodmerPTPYnpXgNzRrE0w6FRuItzVjHXLBfFpfQ8rqJk4Oc4xPBATY5PSWp4htlRJ4uc1QsYA63UznoZdTim5V+PmS80aRHJoWZx2ih2Wkw3YML1HrnL///gTGzevPmDDz6orKzEd4OgvNvtzuehfC4fxcSXo9EYi8Uik8l3OQ6Oe74wT8UX2nsysfzyVYtyLMBv8U7tMBJ2mvitXtV4UE+JhLzhDz7YuHvPnpqaS/X1l7d98sm1wcGzZ88eO368oaHhwP79E5NTDof92LFjHq/vywix3GrvGR8fx+O58CttaWl5++23t2zZUkzatYq9UBSNxYBytMfjSacz7e3t69Jkrmuo/4y9R+FDfnTI8mcf6p6tdOnt3kQ0sO7g6z7+H7L3hEKh9vb2IvL7D6bLli1bNm3adGPY5LFhA0H5XC4WjUXC4VgsxmazSSTSuhqu++hyuf6/bu8ZHx//Ctp7crnc8eMn2luawxoBVIijKU46mGiHRYJ6dIAajC97VimIugq5gWnEBACQbrInTB8DRpF4CFAuokGMDYNFOQHlOpCcEgFxUhbAjMnE5cy54frzTt4MoERgETRr187iTIfhLeCAyBiEfvFiVMEqMidQiySmZJl482jIkXYbFfMEM4MUUbBllGH3Mm2sqXp+oHX2atvBks2Htrwrpw4nZeykih2WLSX1y6hVkqQN6FvOsutPWfpr0Pl+hE8GmGa2F2ZNoV6z8+jurj/767p7/p++P/rTK//v382/8Hi2/FOY2ALzqYhsoUChdWkQBTNcupfz1pNT7z+73HBGNdQcJrSjiwP5xaHlpjLZZA/qUiJSGqbrc/OjP4ZaUIskrmKZVdKCTXnd7YJNPcePH29tboqo+QD53R3r4IRT3PFUEKcRa6gjIQ4FZY9n+dQwiwJcLStWvBMBq8ZnAdJEZgmICfIY0YhXSpsara8E7jkQhwUim27ql6Jl4npN0lqeTzAfU94IGscuim1cJCAOZdYklVOGTYuEpHRROtphY5DGGqvoXbWkS6f3vf/GiY/elI+1xTmUCGMqskxJSubjtEFfW5mh/hj7wiFVU5lquFvQdjm4873s2U/SLWdC8+O2is9nXn1pdMeuyYvV5PNVtIF+fm9rpPdSdH5UOUeQTQ1ml6lgoCqY6j1b+594eOil3yyc+kzRXRMerUenO/LUDl7dCelEN6pmIMwRQIgpXBFwwAGH2vV+iciZVs0d+T2rq6vHjh9va22OGSQwHpVtxhi+dhVg69sVqEuPUV6AzDegzNtkAIolMBVHuzzrMetp43ERDdUCHQTEKge3jM9aQC1FB+L1RkZtMtFIx8SVix7h3I375TYeRox7Z5HANkXWpvSL6HElpzhmUKvUL5hzyThoLpVecUrJg1YGIaJkLU/0Onmzww3n5wZap9rq9n38wdmdm9XUa1mTKKXj43o/qFWa13GpA11N1VXamVEle8HEmcvK6EACWzofXJ5NLY6GB2rVXXXqiR7nSItsnmzjzaf0gqRFblletHDn8qJphDmKUDv95w8svPYY6cMXue0XlIONUUonypvIMYbZjWVq6jCg38kXABC8fu1rC6hFHFVyTBrVLTfK3TZEo9ErV64cPny4t7cX30+tVO7etUsklkjEwnPnKpqbmy7V1rLY7PGxsXw+n7nDK5fLWa1Wv9+fzWbvsEsml8vhSQDutE82m03Ek0q+PmCNuWRh6uemmRMmtzyy6kjEPFmJUFR1oaq8vLy0rOzYsWNXGhvPnDk9MjbG5XKnCFNHjx6dmiL09/XuP3AgHIkEg0E8meVdKuN0Ot1udy6Xw/fJZrODg4NrdQuXlpbOnDlz+PBhO5ZVNJ/PX7pUU3OpFoLgqqqqwcHBgwcOCsWS7u5un89XPM66M2azWTx9xJ2uOpMBLeP3+3FvDv5zBMrlYbhfnP9oO3X4k2om38Uzp20Oh2dNhTNZkKtj7emy2azZbAqFQrncnXohm85ktTpDJBrP5dE8jGZi5viKdN1xEomEXq9Pp9P4wXO5nNPpXIt7isOmr68PHzYatXr3rl1CkVgiFp+rqGxsaLh8uX55eXl0dPQLh43X671T62Uy4CpxV+NdGjCVSul0OmD+v8Mrm82urq4aDIY7fA8253K5YDBosVjuUplcLme32z0eT3EffNh8Nfk9HC5XIhahPsO6B00w75iEgC6KB+zYFCDgAsTLBNBkFOjWWMHqGBTT9YsEOOgCmq2pKMYCBop21+nAUWD7WfWAj6koiuZGejsWB1tRNyZ4c7s5DlTDIgHPxwEMNgVdCYMoYZQDCxOmWQfsCgaBaYm4PN4jmOrXzAw750dd9AnVZBf/aqNkoDGvYhgmO8/s2lJ+YJec0CcfbBL3XIoPN0bOH071X8rP9XknWvTNZyNDdTC5FYTVTNTnrpyAFidS0YB6dJD51puTT/9OtXO7jTyelbPggRqEMYa4DajXDAJ6nWpYxcx3ViY2PMJ67hfLW1/gn9xGOV5i6Tqnazm7ULGP0VzhF8xhoUY4E7yII28qYH6uO/J7YBjm8ngiAR/1aEGY1e0bCjsg1lbAAYfzJ7A9Uas0IJjTzo7BEhoqngVdkIphCSnVoGGjAcBSCrmBdkA8BLgacGbqatfCYCvIHrrOR7Pu1MDkA1ACbBIl1JyYig1SDeAntYghk0g7Ny6c7BUNtxhnhp0siuZaq7Djory1TN5ekZ/tVrecOfXBa+Ufvq5srxC1nOPVn4ZofehMd7zvfKj2oLdqt6LhlHGgKdV4ItdTCZN70zVH810VaR7BOz3CKT8pqDxp6LpkHWpOU7tCV8qixH7gn3Wo8moWouGgnInAmc+Y3/y7nh98i/3Kr2XbXxk/uNnRe17fcnb23L6F5sqIeB6AOcBtvz2UBHY4/XLIeUc/Vz6f53C5MrEQeHUB7gFxTCCuyqUD74Ad48KHChz5RAg0L546Nx4CfmGL2LNMMzPIiFsL9owFcHY8MBQVzDY3DRLUqRprrOKO96DugrJD4e7Ag9VxvItXw2vEOchINJiwKBIm2Zp+kaQ1PMMikT9DkMwR9Etku3DRI2XLpsfEQy3KqZ68hiW81lK699PKgzvV5AHZcKt4sCmLwV/gdLNKdbRx8VR/VM5KG0Q5uwokMTUKYencKmvKPjdmHGqWEwa5M0QGlWiSLGf1AuBlXnUDGpZViqhZELktX7EzuuXZhZceEu54TVK2c+TzEm9/lb759Fz5XlpDeQSw/vFY9zsO9bxR6Ps9+T04PeWJJ54YGRnBF7BlLqekpGRsbMJutdTW1VVWVJSVlbNY7M8//1yr1dpsNsvtXjabTSAQKBQKq9V6u+/BNqvVyuVy9Xr9XfYxmU1cHsdsNdmcVp3Yo5N4bE6L1W612a16g97r87ndLp1Ox2KzXS5QcLncbrfb4XRaLBaz2axWqw0Gg8ViUSqVAoHgLieyWq1isVgqleJXZLPZlErlwYMHi/FcKIqq1epNmzZt3rwZN0ik0+nSs2dPnDjh8/vGx8caGxt27969xGRVVlZOTk46HI7bXrjVatXr9Ww2++6VUSiVQoHA47TZrBa/x7Yws1xSKjj9bqPn89MBmcDjsXtdNqkEvPAKW20uq91tMmotVrvF6rBYHVab02Z38wVSrc5id/ptDr/NuQL+Onzgbfda7R6L1WE2GdnMeY1SoFdxJLSTsr5vaeiHLXbv2sobjUYOh2M2m/GNVqt1YGDgwoULRQSdy+U6OzufeOKJYlo3AX/5k09KRkZGHXZbTU1NWWlpZcV5Hpd3+PBhrVZ7p2vHE2vIZLI7jSt82HA4nC8YNiYTh8MxmUxrr2Jt2Wq1arVaLpe7duO6stVqValUfD7/TrXFKyMUCosVttlscrn88OHDev0d7d/FRvsvKfz36TUXqgtDwEt10/qKKYw51SCsI+LDFWsKUAbHNIkIsmIFYmsWiZU3J2XQEl47XIA7WHQ3iMQJo+komI6Tq2guCadjPr1CvUAA0xz2tA2eGs3iQkxQQbwOE2vxmsBvkxEgxRZyoW49YEe6dbBdlVZzEIskbxI5GQT30mRQxkgrlhBqR+7cLs/ZndK6o8FrNVlSa3joknm40ckgJBaGTN3n1XVHs+d3uJ/7le+DF9DGI+hMV25pJDlSD5GaQSDxwHmEMYqqWdBEc5A/7w/6E7FVBM8sEXZnrKqITQ9HVgDas6qA2C6PlC/9JLX5udlf/kj59pO+i3topbuqPnrt0pbXlruqDfMTGR1/raPhVtQC9IIN/IDtbvwe0DVQvuA6uY4tbjoUbrGwSIBRLRVDVkGsGeLS4PgVtcks7GkFdSQuocMgW6QdiNrhcWrRlYJaUiqM5lJIMuKRL2sWCFEFE8A1LGtScdW86YzFGGkc6AB/CrBmARkbFQexSvNGkXOJ4ObNGpcXjRK+XipYGBmaG+gWtlaEW07AnaeD/VWa1nLPQHViosHcVaFoOKmY6Jdfa3XveS97cCNau9fWXUOZHDd3XbJea0ovjuYFlGDlPtOW1+PUq1a1OMomZejXEPE0yiOgZgkS9uBjDFbzoJleeODCyqu/FXz/u7Vf+xvOkw8ESz8mndxR8dFrDSVv8ZrKDKTetHwJWA1BnW9jhAPOL7M4o1sOOoD+7N1eUA48DOD3CxZHBgi8ITcAl7i0wY0wwOtPAqkoiG8CTDKdVcqTM2mJoAe/X65LSbmB4Q2EU4EwK0y+WeMWMzTzk3EliA1EzGJgkQVZUNzgQQJwhCVgeADtRD94qCie3WcGh/IYELsqid0vOYPAI6RbFULe0sIyc1HE4ziUQtU8Ybm1MjzZDJNbV4YvGyY7vUxCYrbf2HpWO3TFr5P5NFgQg0WM2qVhJdsqYOiWGS4FPyJjruokOcY42lceInTMz1AJFODYWpyhWMQcUL2gE4uL9CHyRXhhEJpoyO59A9r4uPCpf9dtecnfdppwauf5LW/UH9ouHO000idzBuHd7xcs2k7gs/1+/J5oNLp79+7NmzfPzxcEuOPx+Pz8vNFoDIVCNrtdpVTI5QoOh4MTVDFe723+FPk9RfLvrTsBZi7Ga16/DyZIAWNbc7m82erEydSZmNYjPpVLFrRzUBTVarXBYFAmlY6Ojloslrm5ObvdAciX2SybzYrFYl6vh8ViZzLAxIIHYd1aDXzLWn4PDIOToyi6Lh87kUh88cUXq6uri2nbFXIZXyDweL1er8flcs3Pz4fD4Z6eHjw1xG3PhSBIKpUymUzrrhonSRfvoEg4bLU5Fc5cIgP1LKc/3j07/5uN8HNPoVbQoTiheK1eczZmszM/9crOQ7lkJuHORC2pVW1qVWVRUVcsszHnTNgyFjT0BrTNfkWNR1rmEnzu5O51cHY5ubuU1PctzBIH+1PltX9arrvHK6vCyezF+mN2IzPObcdPjSdtLfKao9Horl27tmzZsrBQyIMbj8Xm5ucNBsPq6qrNZlPI5XKFgsfjfeGwcTqduNp18ezrCggCEshnMpl1DVjcDUGQfD5vMplyuVxx47oCgiDxeNxisazbvvYjiqJ4gth1rbFuH7fbvbbC+LD5Cvq5CkMTygErPVgSMNFbYIGXAfYusBNE0HgYKcR1Fw05ILQbWbGDWdipQV3aoE0/T5wwKqUgjiOFqdeD1KEhn0qgpRPZpFE9b1HPZ9i4s8Dyj/N+LBLIIoMsUiyJlRVQIrwm1KUrBIpjgniAJBGwo0EHmOKxR8+Ykp3SLfsFNP/iOCqZRblTyFQT1H4yf2xj7tj7uStHVmoPWuo/d134LFxeEuipdPZeUF46bLt0AG04EN36ouje74vv/xfV7rfyEw3obBdCaYXaT2V3vpLatzF2bp/lgR+Zf/dYhk4C2YjySZCNIbGSiwYDXlcapEF15q/WwOxxRDIPtZ2Ees4vHdvX8OSv4KuVMUrndPWJKGMc1fOuIwb8yfX2Jh8M9wi+HO5R3IxH19gDgOkFW8U9BmC/iWHJv1bsQPkG00pBnZqATkId6jNLOGjUjxhFQHw5Fc37LG4uVTc7sjjWbxIwrVKukUFGHXKQDgIDNLAJiN2tRzy3xV4YjIDM4riam1EyndT+oJRhUUkZi/TpmRkCkUgkkQgkEmOaPN9Rz688uNRQQR4eYAx3q2tPCI9u47deYMxSidMz5AsXrz3yCO2FRz2d5z3d1fyBjqGefnp3u6GrjvTS00fu+WP+g/fH+q8AWBCwoMkAUAWMg0wgaDYBOQzhHRtzl48kBy5pn/21/e2np1599sLDP0O7zkTHLpPP7I6T21HpHIKD7DtdBY7kviTuyWWv41ER6AKHqiD4tN7eufZ+iYAHgLAPjwYPWPXTkyMmlQzksQcJ4PxoNJB3al1sinp6mD3cqVsiOyVsE3sWtssK/WISQQ4QfoiFFmJxVXhyVmBJjaGpKBLFfM3xELhfbAqgR2UGkWtZPd/DoRikfB6HQyIDyTUCgUAkEKhk0ixxcvZq53JnzUJv89zoAGtiQDd0Zbmp1MGdM6gUy0xGwqaxDbcZak+H5SybjMehUVmUKdXspHWJnLqwP7v56fyR91dGW51SdsIsjxgl2RVwt4JKxoOQUZE+/GG+/TQ01ZQ79j5auX15+zutrz6BUttWiW2UK5VJNYinA5AOzz1S8N/dzhQHHnXEPtvvx+9Jp9Otra0HDx4kYnJ8OBMWn/GKZA5cr3ltRoJbCbNF3HN37vNt8lSgKBKPw14vfrpsKqhdOumVnXctH5B2fV3U+uduwRGvvCqgbkzGfB9/vO3EyZM1l2ouVFXt3LmrpaW58nxlTU1NS3PLZ5/tIZIpdpv10KFDTpc7Ho9fDz6/tbJgy1rcU7zqgYGBtXrNYrH41KlT586dw+09axukiFfy+XxjYyNObLrtmdbymou/KrZwwO9zOmyhYABKRTqpup987tg1GK4eMAfe25R/8P7o9k9zsTiup4yiqNfnXwlGwPLsoMh6v7dce4924t+d3D12Vomd9Ymdtc3J3aOZ32HjHfPJL/hV9QFd56p5KOIgxtzzCR87GZSkw9pM3GbUShKxIIqiuZTXyTsY0K1nrN+ap8Lr9TY3NxdbIJVKtbS0HDx4EGd9FRtwHR2eyWTiCdtv2zL40b4CvOarV69+BfWaC4MVhvJeC+zSglSCdiWWSxmLCi6YdsJgqk1GwASNm2GK2zGpGyToQuKry4tzPS2NHrMOwWf/VFjPXzIuElYldBuDGFOy9HSCYmbMzZ0BCyoOs1xawG/wGIDBH0/6k8Sej+MhoP8BxPKtIHAGE2EDCZsMAh15QNBZ7RprzveUw5f3Zfa9lTnwNtT0OVR/AGo6CjceQjpOZVtOZA+9De1/E6rcgV6t1FzcTz+3N9NyAjq12fP0A4N/+83xh39s2Pai5vCmUN1huONU7sBb+l//lPftv7c98CP3Yz83/fuPLO+/5aws9U+Nxui03BI1IaJnVkGwLqxgIioGzBpDSK2oaNZCn1wu2wPRB+1LhKCYDmKIMHmejIYLNN9W7MD64lQDaz8u2oZTSQCxSZG3yANfHMeehyzy2/N7LFLQhsDDokd8ZqAzFAtiajdY3gA/wJHAABPy0GfIVztbPVYDyCtpEkM+i26J7OBQg8I5y+JUUsNV08aVs+Ne4dzaUPMvBj24NJ9FihoFSRHNSuzmNZXN9zTRaTMUChUovwLMU3gRSWQihUqYmiIRpohkyuT0rKCljvfxhonKz5k0kGCINDd3tb6x+8f/vPz8byQv/3rx6P7xkdGJKcLl1948/43vCJ992PLKo4Tv/MPo409wD+w2XeuNz1Hz8xOIYBqoFK44M3lfaT8AACAASURBVH210LXqTP2R4O43Vyr3WjrK+ec+y5NarD1VQfooahQAnopRmNbgadTWYMebYdCXtvfkIYcaxiX+HGpwvxQa/2agUzR/Fgsgm1UAXfXkokE6lXi1s8VnwxSMXDrYoVTNjjm50yuieRuTnNAuaxdJ0umRAMa7x24ZzPgaCwLkBKIHMANPKgayeK7YcEFzYFJyqIG0o1kCuMDaZS35qrT/8uJwD5VMJhAI1zsE/McgKXl6mkomEUhEIugXIlkyOyXvvEBrOs+iz8/OTIv43MWetsWXH9ad2q1tKHNNdDGmRmfIxJWeGrRsOzTVnKs5kN/xSu5ESbT6ZGqgCaaNwQtDiISGJkKIy5jvvwiN1ua7y5DKHehYnaG/VlJzNM8ctdAnIwomHtQGspGoOGDyCTpA5V1acL/fLHKIgPtF9vvae/R6fXl5uVarPXPmzF3SOjIYDJzJgdzh9QfiHhSFg8HMzp2pZ57JU6lgJc7EdML+VSsxZOzXEZ+QdP9NQNcZcVBS3gWFlH/o8JF9+/aePVt69mxpeXk5kPNpaRkaGqKQSQcOHJiYnJqcmNh/4GAimYrFCvaeO9T39rins7OzSGFGUbS6uprH412+fJlOB1kCi6v+WvgCQdClS5fwxKW3PReOe6wY3zycgBzBnD2Yc63m5fasP5Q4UNr8wLsXT548SZFG/uW47Z5tvheOSfKVZ3mNl3e/9OLH774nVWmKp/N77Vb5NdfyXsvCRpfwpGrkfid3H5xLwrkYlE/CUAZF8nanN564XYD79aMgCGI0WZLJFLgisBGB4dy6mt+KeywWy+XLl68fA9XpdBUVFWq1+syZM7hxDjtU8ftC4csMm68A7mlvb/9q4p7V1dV4LArSKceCsM8C+8ywQw2v2EBW7VQUSJaBWRvzOhWn72IBS04JGAzpWD6+ukQlEjqvhCxaNJNAIn73MlDCBY90NhnqkDt5MxQgM9hoZ1EQixhYtvGQeKBMqARsnugKoBAVpDuKixNmeLBKAaeYOxUhtDovHYAuH8zseSO/7/XAk79Y+e3P8mXbQNLQmS64pxSgn5Zj0OnN+erPoCtH4Ys705XbJRV7rRf3Zg9vDD32U/G/3Nv6998e+vcfEu+/l7PhN9aD7zgOv8t67qHFf/xuctfrSNnW2ObnDb/+qfqhn1z7h+83/t3fh/ZuQgcvoF4zaISADdVzUQM/KaB6liamzx+Wd1eHxfSwgg2ZRLAJpFiCzNK0TQse7uNgkUPwLFdBJ5Y33o2GXKAQ8eVXfUGvu6grv/6uQtFQaDUeCaNO4E/JY9RmQKS97lsB269L64IDRv1YN0WxpJWrAEcG7SCSLujI+8y0kX5iT/Oq04T6LRmT1DLZAYuomGyxHHXIbSzKXF8TtbvByiQXnr+LaAB3peEfcWYPsAUWUiyB2HgVC2KOIqTmcMcZZvslAG6IYA3Fl1UyiUgpfCKBF5kM3iQSiUIlXh0QH9qx/Nm7uvP7tDNjNAqJOj3dtX9f5/0/bXr8t2OP/pr54tPLrz9z5Sc/abv3XvjCbuTUh4H3nx398b/0f+vb5/76b2u/9fehfR+g7SeBBI7PBCix45fR3rJk42FvTyWtbK+0vSLMIUcFM3k9H8a8WpBJBAwM1y8Nb9J18A7j99wtjh1BkFBoNRGLgnBFuwoCbyUMKC82DIjEMSHvO98v+I2D3Vnp8MrM1PjsyNVVqwbI/dnk5sWpLCZDAJxcdrmZQZzpvULrbbKzKFjeVozi5sAeA0AWOStIYIcnlChc1Jr4eZsMViyirJHweCOjr3mWvBaFYn1x/Q/WH4UPRBKJQiGraZO8ms9VHRdY8zNLCzTO4jzlxD7yS49MfvyG9PhW9ekS1dkdy1tfVZ7agwipELUd6qtIH9oY3vQ7w8M/F9z/42ztIZjaAeSzfSZYSEVHatBrF+LdZY7+atqZ3crBhrBkMaZk4+0PZAmNooxTB6x3GCgEDmWPHqB2G5ZwA7D9AAE8b5H6nPbbrkC33jv4lmw2+x/P8adOncLtPSiKRiLhsbGx1dWw2WScnp7mcblLDAaPy52bm7vTQfDtf1g+dlilyjz8cPa++3InToAEkxBssRVueTgfjzkoUHYVP346nUqlUul02m638/kCPL12CstyCkGQ2+1JJBJer9fpdMIwHIvF7q7xg6LoysoK7rAoXtf09PRaXrNIJDp16tTFixeL+di5HA6bw4FhiEwmyeXy8fFxj8czMDCQyWaKB7m1AOczfLmhnxN/9oLvF6f9j53zbGpZebjUWUkIPl8m+9MPNb84wNx6ll7x6vmaV2sOvlraVF55tur8E8+/+rsXX5ydqEnGg3BcHVLXGGjvWDmHk34mko8DwJILQknbutPh0pHrNq79iLuNUimAe+70whN9rMV50Wi0p6en6OfKZDJNTU2nTp0qhmuBYTM6Gg5HTEbDzOwsh8NZXFpaXl7+MulNvkw+9lwud6fa4vnh/9vysXu93nUBaFQq9Svo50qn093d3RMTEym/AxACQBZDjPrg1udsSpuY5TVpECyvONDPBbP2db7CjQLG5gH8zXDKZ/PxaSYmNeoyw7GAm0HMyulZo9DGnnZwqAuDrZOtNYTWGvrVVi1tAgio2GUFvz6+nOOr7NoyHmijX0aUTJg+mJtqNFfti+1+Pfnu47GXHgy/8pD+x/9k+/m/ZktezF8+lDu3HTqzDa3aiY7XgZyItXtzZSWZT16EK7Y7d2+QbnshuPl3Kw/fJ/jH71X/ydfq/vLry//4PeXGpyOlW9DO08GGo+QNj9HefhKp2g6f3JQ/8Gak5JVr3//Oib/4S9WJHeh8P6pkIrKlHG1AXfEZs/oYu3K/sbvKPtluXZzCQp8wlIaHefvMSCIMY0LDBcgI2B5R8AaFMHjiDzozfrvH6YAg+LYjfnV1taOjg0Iipo2Stc++QPFPw7OyqD7xElizQVsJgeEn4ABKKmEfcD46gawfeG4GuVdBGvO0btnLpxmZ1JjLlPdZbBNtELU9q2JYmWQ3Y2qhr2m8pYbQXrd4rV2/OAWkunGoahKBQHec02PGckkKp2EBBZbOwctEaLoLIrUAdtRgFVSzm7N7I2F8jEihEIEhgQyAD5lMJZMo4NPatRVbYinUyZ6exdYG1uUK7rO/NDRVzs1SiWSwOA+PjhImJkmb3g4+9VPko6fcm57u/9WPaM8/lHrrscyHT6e3vmB97N/L/vJ/bv+zv9Qe/xRt+Rye7oIXBnNDl5R731rc9Ax71xvWltOOa3VW+iQwX92UzRTwkAB2vA59bi0AiQQN12e5I48vFAo1NzeTiVMQHuGImyVMItShils1NikvYAXsZgBK8McGYJXBuh7/i+OeFDYS0tGkz+YT0TULhJRLjziV1smOLI+YNQgsDJJ7eZbW1zTVUkNoraX1NZkXgSwhQD/FewTEV+IDYM0VAT81SFOPSGnQTA86UMmtPjY1Pk4mgx4pvIl4AXQKkUikzc4uLdJvwCIymTwzy7hSpSx5RTHQOj09TSaTaFQyZWyYvzAtq9wfL92KXj3nqdoz+OqTnGMl+bFa6GolOlCRuXyA8rN/rvtf3zBdPoVyxxHWGMydyvRUKPe+zdi5gX38Y0tXhWuyzcYkYWMS96hiNXfr0PgqkORORkGjRQNoxId4TZirTo05uGWoWZLRLbssht8L98RisYsXL545c6bIa+awWZ+WlMzS5nq6u/YfOHD4yJGqqgscDreqqgqPZHHd7uXxeGQymU6nc7vdt/sebPN4PCKRyGazFfZxu13BYHx83PfWW9b33w/u2pWYm3OsrAixdA0ul8vt8Xq8AZercEC32+3xeHw+n8PhkEqlHo/H7/d7PIBn43K5fD6f1+v1eb1+n8/j8RgMBrlcXvwW32ftX7fbrVKp1Go1vo/H4zGbzRUVFbheM77k0+n0Q4cOXbhwAY/nSqVSZ06fLisvt1gt+/buPXP69PYd24UicW1drYgl96/4XYW6rD2Py+tzu73uZyqsf7zF880S2c8/I393O//rJbq/2Ob89m7Ndz61fudj1daKpfy5cvSXD6Q23Nt74E2xzj63sHh41yuDpd9k1P/RfPvD187fd2LHLw/vL6HNMyLRZGgVpImg0RZmaQVKzcr1l0KhsFgsOBNoZWXF7/ff2iMCgcBisdy6vVhvu90uEomcTie+xePxTE9P19XVFefhaDSKD5sir5m/zCspKZmeme3u7tq/f/+Rw4cvXLzI5fEqKyvtdvudzuXxeORyuVarvdMOYBi43TcNm2It1xQcDodAIHA4HGu23VR0u91ms1ksFt+09eYPHo/HaDTKZLK7DxuFQqHRaIrDxmQyVVRUfAXzVKRSqYaGhraW5hU5B74liDSmBBIseYsMoxUbAPMmsoImMKCDxwThk/uqG3HpwIOvTQ5bJKtypml2JCFnBkV087UGB4sspIwA9qJRGJIu0XoaRhurJltqlNMj2vmJgGgBKsZp47gBz1WpZiMGPiynw6wxmNaTp7Tne0qRxoO6ba+Yfn2f64Ef83/wvdq/+lvC17/p/fXPwi89nHj9EdeGx4a/853FX9wXOPBhrnRb/syWVMlLqbcfTb31aOClXykevU/5yqPeDU9xv/3tjr/6286/+kb993+g3P5qvvU4RO1Ah2uC+96UvfVYtnRLdutz8U9eFj/yM9lP7w1teER8fJuHR4VnetG+c772UvG+dxaObo6O1aOqpYSGp6aNg/YB6aIUIEHjih1NrCKpWCLgzUVDwFoWDxWcILEQQCdeE0jmZRHHVWyLWn4n/Z5AIFBbW9ff270iYxZsPBYJQCEaNmIGv9XQxnJGYWEVLC7kwPeEpVkFWwrUXcgINBJRmywsXTRRriaEs0HmpOlqrWX6mnioVd95LrcwsLI0QW06T2ysGK0+JR3rNC8RvYL5PI8ItJ7ZE7BwGmZPQLPdEKkVIraAR/yJeqjlGHTlSP7iruS2F+Kv/Grmtd+NT5FIZApuOiASSTMzM2QKZWmGyl5anAJ+LgqBTJmiUAlkMoFCJYyOEUdGGaPDow88QH7gZ5yhXgJ1mjw7S52jTTZflm54LLP1Oaj8Y/R8ie/tx+d/9APLv93re/DHgVcfof3T9xa+823nIz8V7XzD3lOVbz6JXNgdrDvI3vXG4v4Pkn0VKK07KpjRzk3iHO214AY2iSNy5q3QBzYV8BBqkUTkzLvkqQgEAufOVfT3dscNYti4BnBgiGpVtmRcJEJYAjJAVgsBDxR21/iBcwqPhQQ+XCuICrQp81Zwv/iE8w4mKanmBBbHzX3VVvqklNBvXCLkTSK/cIHS3TjZemmypUZGHdIvTAVEC3CRDVOwwOEdDXShYOk8zBiGprvyEw3Q5X3ohe3Lp/dMEUlErF8A7sFMcWQiiTI5TiAQpjALz8zszCQoEwlEIpVMXiRNLpEJ/DeeFL/+DHN0gEChUmfnaPPzhI4rmhNbof4KcL90n13d/orowxdy3aVIze5E1W7Jcw8pfv7PoY1P8o5/6qCNQNeAmWel6bjg0AcLx7elpppQ+XxMydbOTxZ6BJCyFTgPCV2xxl1myGsGZG23DjyAWWWIXQHhWWkL+eRZZs3vl6fiP9Iebdmy5bnnnmOxWPiqptdpDx86NDMzOzgwcObM2cHBgYaGBgaDWV9f7/f7g8Fg4HavUCikVqvNZvNtdgiFAuFwIBgMRSIylcrtdoN9QqFAIuGfmEicOhXxeFai0ZjDkThzxtvWJlGp/Hc4SyAQCAaDLpdLJpPdrhaFbXgQO57c4E67hUIhvV5vMBhCoRB+WLfbXVNTU8xTgSBIQ0PDiy++eOzYsWQSeI4gCGq6cqW2tk4gEJw6daq3t/fixYucZW5DaVf3Zq6MbF+NBENh8A6GgqFV8F6NBFfdKxevrnyjRPXp6WPkxn/itPxV/YXne6qeLDv9yVDZo7R332afu1e592fxA5vMh/9O3Po/5JQN6oX9SspGA/kxecf/pez/dmKFP0UgP/n0i08++cyePXvAYtTWVlZW9swzzzz11FM1NTV0Op3BYAgEApVKxWKxtFqty+VqaADhZtXV1bf2mkQiwVVz7tQ4Xq9XKpXi9rBAIBAKhRgMRn19fRH3GI3Gbdu2Pffcc2w2G99o0OkOHTpEoU5fGxw4e/bswMDVxitXWGxOXV2tf+ULho3JBGLv71QZwGTHsMiddggEAn6/XyqV+v3+O+0TDAadTqdcLr/TDvgYsNlsKpXqNmP4+s9CoZBWqy1WGB+NNTU1Wu3vlxev2JK/b+G/L54rn89rtFqFTArZFbgLADIKixMrWFbxuRU3LQC3lAJfuYFUScABYjfwedwK5EzwN5bUneOhj7m4sxpCn4dP085PSEmDKyI6bBGntDyQ/VQ0H1eyV2UMC4NkZ1PBEmXEnlNVDEQ8Ay8OgpSNc33QdAdEuAL3V/hq9tsPb8yXbTW8+5Txp//s//V9qnu/P/H1/yX7l3tXHv6J+6H7Vh6/X/rTfz35x3+9854/b/izr/l+9yv45Ie5E5uyO19Ovft46p3HbFtfZjz7S+Onb668+pjt1UclG54yHNyYq90LTzRAY3VQw0F4z2v2t34bPflhcvOzkifv977+CLTjJbR6t6vl1OLJne7qw0jT8Syh2XrtcmB+OK9ho3aFjUWhdTdkjCLUrQUaRWEvWN5W3WgskPTZMwE34Ku6tIDi49YX7PYYyMPjue6i1wyyxmi1KrkMgCoszgi0rZ4PtJ6xZWA94ilCn1sKeKfgv0rKGe7pfgexWz3S4mUSDFPd/MbTgaFamNqRmGyKTjSGRi5Hxhoi5A7TaIu97yJCaoGITYU3oQkkOm06CnWXQlcO509uyu/d4Hz/SeWDP3L8+F7Bb39hqT9rb7/g7r5oL91rbSj1NJVaeuucvZeUo93ToyOUkZGFa33ipgusoZ6F4YGl4X7h0b3KfR+L93x46a+/NvfZJzw2QzjYJfx8u+X9Z1LvPgad3ASd2Qyd+CD+2iOS+//V9syvnI/dz/35D62//Vlyw2/Qg285y0qubn3TeugD9Mi7merd5vay0Gh9duoKyptUz4wuDrQgRevI9Ta5E+7BwvKBxQ73c90ljj2RSCiUSrVChnoK+uZFAnghk931cxWsdAXVKxmwzWDDAORFKRB4RbBJCECYRZJQsT3caSeTpJno8nCo+rkxGXkwsDyD6Hkp+VJMzowo2BEFa1XO1M1PrIgWgB3LBIAOEIgSzcCLQ/DiNWjhKkTtgCYbkb5zrtISz/434TMfmsr36JcojoVxt5RtX6bbRCy7UqQVsNyLhGU6bYZMpM9QuYs0CWuBOT9LoZApVKqOPZdUskz1pxmP/oRxuYq5QNPNjGtbKx3nduQHKmFCE4C8F3fld75seffx1ERDvOOM+q0nVt56DNq7Ab2839VRdm3HB74L+9FrF9KEJlPvxVX6aE7BQO0yC4NM620qGOFAHlNcelGM2BRRFS8PJKex9KVrbHJFkPoHxLGHQqGWlpampqapqSl8CoYhKBQM4hmswuFINBpNJJMsFotGm737HH1HP1cqhXq9KAyjkYhdKs3mckCGGIZhPj9fXo6EQjcOm05Dra3B8+fhWOzGxltKqVTKYrGs9cLcsgsIzME1lG/9qrjlVt3CtfnYURSdm5traGhoamqyWq34r+LxeDQSTaVSq6uryWRyxb+ShpKTtSzyLjv1kEU5GggaUqvWTC4Bp0L5kDHtU6bE7d7u440jDf+m7rpH0X2PuOseec89ys571D33KK/8sarhLyRt95gGf2xifCoauU/Q/Eeu5X3poDAbM0LZcNg6ZVv6GEWzTqd7586dmzdvHhsbczqdRqNxdHT0pZdeevHFFxsaGigUCq4r3d3VXXeptqW5uaOj4/3333/kkUd27dq1jrb1Jf1ceOhTsa2i0Wh3d3fRzxUMBltaWpqbm4vDBoKgYDCYTKYSiUQ4gg2bRILD4XzhsHG5XF/o5/qvysde7Mfida0rFOnw67av/fj/u3zshTh2LGxq7SPyzWXsARdnmYBIIozn4VABwseN6R5bP7BwXGBpMApDIpD12smd1cyOZgwC1CxMy+ge/pxPMAe+lS7Z2BTUJERUTJg5CpFbMe9JE0RsBkHmo7VQ2wm4/OPUmc3x4x/kz22jvPqI9Kc/TG94JPLCg5HfPZB8+aHo8w867v9X1nf/oeFPvzb5d98if/cfRD/5YeLDZ+GKEuhcCVS6JX/sfeTcJ9HJ1tnXnyE+80vjp6/ajm50Hns/Xb4Nbj2BeWrOQ9W78nte873yK9eJbfELexy7N6BV2+GWz5PDlxy95wOD1Yy978p3bEhdOBBaGrfzaLgnyLRE2P7uhsG6c+AjUDmSY/BCCltlcQ0P8F6xxONgiQJTuQgyAmiIr69fKo4dviWOfS3h5uY2X9cFt/0IlHz1vDytN0tuW2ESdDPDNlKvuf9SerIBpbYnxxvc/Rd9Vy9mJxui4w2GtvI86IUW8CY2Q+0noXPboHPb8ofeyWz7XXrj4+nXf427Gn0P3Ud8+gFf41F0tAa5VgVf2osMnke7zkI95XDTkcyVz72XjutP7Ir1X0DaTmamWmLUvtTSBNT0eebSXk/jmYYf/GPdt7+9+Przhtcfcb30QOSNR/KfvJA7uSm74+XMxsfjLz6oeeDfnIc2+Q+/7934JHL4Hbh0S6bhkPXKSc9g9eKONyTvP+M9silC7bFdrYGJV1DFgmpubMsbL0001wCm+fVWQi0S2CyOKljFpbT41Rpo+PvHsYPj32T4KR52TQHfAR8GBTvcmm/FePI1yCTKGYV+4YKBPmWiT5rok6h8HuVNJIUzDkpfkD4C6XgrfJqbQ0UUi7CQCoDOTHfhfiE0QxMNUOdpqOkIXL41dWZz9NOX8ntfp5ZsCMz0guOYxRi/WwZruSmdAJEtZNWcCG86wSVB+mXUKMjo+E69Ui3iRSR0lDmcme0T7Hxn9qNXFKW77Mc3eQ+9laosQVqPQw2HoHMfQ5Wf5A+86Xn1IU/7OW//xdDBd9DL++DuM7nxy7buysBwLWXPe8bynfBYnXN2IKxgAZ+jXa6eHftow8uEtlqc0Yy3AMiepucnlKwbgZDXdYlg8AxW4LSBPBW/Zxy7zWarr68fGxvr6elZl3Bg7VzPZDPqTrYFVTksEAp8g9z8wsOjYrFogSyL/QMet3Qqe+zz1NNPZ6urM1u2xJ9/Ps9g5Lq6Mlu3Zg4eBGFcaw+Fovl8fqWrI19eWvgKP86ac6EomkwmzWYzDMNrNt9UxFhKkT8gnqu1tbXIa4YgaHh4uLe3t6ura3l5Gb/ktW2CA68ckukun5r81EjeZybsBH8pB8z0cjvtlG36qIX4mYWwS8s6t13c9H+Lr9yjOvcnxs//Vlf619qLf244/vXloV8F7BQ96bEVzeVIHLJbTX5lXcyzeNNZoCwCw/k8lEwmLRaL3+/Hv00kElIpEPQpSkXj/k2X07UaDsdj8Zqamm3btu3atYvP5+M/wdvoD8tTYTKZamtrixWzWCwNDQ0TExN3HzYMNqP2VJt6OBJxZnJJCPs5qGaxt1AU9bgc4RAQoS5uXFdAURSPY7/TPoAWhsWx5/P5db8tfkRRNJFI4HC5uHFdYW0c+7qvih9RFC3GseMbURRtaWn5avKaQYdBOQz3CDGqLOZTx2V1QGDFdeYKoP5gZfwvtpCDLYVCYerH6b3FPfFEDTmTyDA7wh5o0S8S7JQ+y3grseHc8kSflk6c66qXYurGqIEPnlx5BJBFnNAE3qQWaPQS1HQEqtkNnfoQPv0RWvmJ8r0nFh/+ceS130Sf+YXvwR8v//AHk9/81sDffHP4G9/q/fo3r/7NN20P/Ci35TkwNdcfhC7ugsq3Quc+gandeel8rPms5pPXrB8+o9z2ivX87lzbCXisDuAecit09Xzu5Iexbc8b33tKtOctfe1hlHAFJjYnyR2OofrVsfqeXe+pKvcs7X5X3nAqIprLYqp9EvK1muP73335WfAIbgcZ42GTMI85TWBMjghM3FhzYbY0EWQQQAYBoD9bxLkvq99z5zj2td3xRWWo4JSRAHy5cBVhj6EmYc4kVkx0cxrOGtrLLJ3nDN0XpquOLnfXqAcuz1w4Krl8AqW2I+RWoO5IbAaUqUufQeVbc4fezm5/KfPx79LvP5F45SHfQ/fFXniQ/+hPGO+9mL+0D2k8hPaWod1l+QufhXe8kTj5MdpTivRXwqQWVDQNcydBGnYtB+ER4MGqxHB9oLNK//7Lc0/9xrDjXcmxT1y7X0vvfwM68xFU/nH+1IfZna+kP3zK+8TPFc/9ivfJS66jG9GaPXD32cxEg7W70jZY0/Pxa8YjG+c3vyCr/Tw63pAhtkYFMxLKcNm+7Zte/V1czSkoEplEOcMNLWDMqIkBUAxH5o1CfAtqATySL9bvyWVA2OMN/yyG+9foPhfH/xcWbjh58XGC1SdnFMqow9zRLuNEu2O0UTPWTqo9LRlt18+Nk1trNMQ+VEpD2GPAJkrEBKiATa4ZmmwATqjGw1D9Qfj8p/kDb+VLXpS8+mvxye0QtRtljyOyeURIRRcHUdYwQmlFmKMgH62Bj2o5MMhlJoXtCjBOlEuIbAFljSUmrhhPf+puK5VdPGQ7sy3XdBTuLYOuHIEu7oQqS/KnPwpvfc6y/XX+qRJb7SGU2ASTW1OUDtfQZc9YQ8uuDwwNJ7gnS4TNZWn5Ukq3HFexxcSBM5+VfLjhxZSGB06HySVATg3s1GL2SyFkEsJWGchj6lQjTnXeiD0qAJ1JUd4g/H1xj9vtLikp+eijj6qqgIgLPpUnk0lcEyWTTmezWRiGlVL1lfdnpvfbBV1ejzyRChdJpgC34Mvh4EDfyTPnqtqJFk9CMcbN119G45GkxZ747WPp++9HfvNQ5sFfZh54IPPee+nf/jbzk/uyBw4AI1AB2QAbEDgSgK82cwAAIABJREFUArHUdtcsEy49A0kkkFqNxGL4UllcdFOplNlsvru9JxIp4J7ir24t+Hy+ojAP/u3IyMjaPBWdnZ0bN27cunVr0fmFq/piIVqpfD6fSqVy+WxrQ+fCeStxl4W014y/iXvMxD1mwk4Taa957pRz+oxroPmcufbxxBvP5CcmU0cOh3/3y4m336YvDAGpmHwczqyEw2Gn0wWqUYSWt9TY7/MHAyD+/LavAdPiewuVG6hn+/VAignO5+FcPugPNF5uoJIpeHMBCRwENhqNuOfutsfBYQSOLIs7BAKBtra2Ypu7XK6SkpItW7bcdtik00BkGUZgqUje9MEseY+NetjCbXAZaKtRVyYVySNwYcygKCrUeqsJLkcoD18fAdhJb+yAouiXyUtqMpnWWbaKlccLiUTiC+09f1he0uHh4a8gr7nQfFAOqM3iIMYiQ+xy2CoFC/YNQw4mxg8m5WJIkQKQoMHTmBD46XHhn+sPZ2um+8LqkjWJpNMjxKYqWuXhBIeUWRqDNZyMSeIX0VUjbZaZa2HpIsgObZEATg+PCM/3QZQ2gEgITdDIJej8p1AZWHHhdx7VP/Ezzs9+yP7H743+z29U/cnXdtzzF1vv+fOP/sdf1HztG4pnHlrd8nL+4NtQw0GI1JIfuZS/uBsu/zg7Uu8Y6sqpWMjSUPr0FtvJkgy5FSa1QOQ2sHhg63q+tzxcti2w5zXZkU3LFw/mKO2IgILol1dFC+G5a0s1xxStZd6rFyNDl1AFHbHJVDMjyukR+mBb/eefObnTqEOe1vKyOj5oN9AOYtgsSeuFkF2VNQpzNgXityAubUrLj6u4kFmSNwi+TBw7luOpgCnxVr3VXLGmtdcA05uRUOFXuFqxjosJFoPWzptEUsq1ibJ9s2V7UxxibnEIks1n1JyAaF7aedHeVb567RKw+uCGn5FaqPEIdGEnVPkpVPEJdPqj7I5X0u88mnrj4cCDP+J87zuyR34S3PBbxzO/lP3qvonv/UPNX/3NyHe/633/Oajyf3P3HmByXNeZKB2e932f117tk9Z6sr1rra21+Syv1rakVZZIUVQWJdKUGCTmiEAEEkQgQCQiAxOQBpMxGEzOOc90mJnOOeeq6py7q3Oo8HSqenp6AkDK7/nb/VjffD3V1bdu3br31j2nzvnPf94key6T/TUsOgSgQr1XiKtvE50XsxO3CdEkhaiyiNZRdyp39lXy0i5gJbh+AApc3pk//UrwtR+HX3hU9PrPtKd30MPANlkcq/N3XokPXps9udveeMrXfgHvv0pP1pOLnaqJLuPc4FTrtabj+33CGRpT50yivJnJL8b0CWGVZo3gVSlaxAWzmHXvpg2CpHaFtMmYvKQfyltYBPy4xwxZ1oMoHQ9QHjMTzbeNLWd9dBhTHzM3SvY/yl6y/5Ueq7XHh0aUGbNENt49UnNq6cKBrGS6oJinIKeb0C+a0XZdx7gjuIZfNAvB8LMyBE4uFmMOI1VHDN8grr5TPPlifudjxLMPmX/4z8izD+cu7Cpc2Zc/tzN44NfmF38cPfEqcecsOdVMisdJXi+g6ASjpGiU5PYQUy0kp5uUz1JaHmESFs1ipPdGfvAaeIRH64iROvALd1wgGo6GT70SOfq8/P1X5deOkZMN5EwrYVj2CabjvIGZ6vetLef8dy/F+6/RWnBwy8e6zIsjE81Xb54+ElFyaUSZ0gsItwm4wdJxOuYt+u05r4OIB/PRAJmIQDwpHsz6UNyuowKOYtjj97g3SIxNa/+Wr4VCQalUXrp0qbGxkf3RYbe99957BqNpcWH+3NnzdTdvNrU0m/W2ttdWJvehI7ut4/ts8ydR2R0/uhpPRwrFPEnRFJErnDpZ9ZnH737qJfG33jP8y2td139yaHZf3ZHj/NY3m+hDb81fG/Wfuoy/tSun0eSOvpf83g/y/f0ERVFEkaaKjMpD0jRVJMhf1qBvDyVom4l+6gnqa1+ijh2mSQhtKBJEsVgsEkQikbBagaQOvm63EQQRjUadTifBnnKPMl4vEBCSYE2BjSTJoaGhynguv98/MjLyzjvvOBwOlsuuuamxqbkllUyeO3e2ubn59KnTer2ho6s96I4Ib3mn33OMv20bf8c2ts86ddjOvYSp+/ypcC7lz7gwD51I0d4gTdMmqfzNHz32i188vshdJmiqUCRIkg6HwxiGMQ1mbnNLm0mSdHpcmM+dKGYShQxJkXg+jeD+VZ++07JUpRrcv1r/D31v/H3Pa/tX6i8oexoMk2OoUBlxmCPOuub6zva7uUwW1CqCtFttyUSS7ZxtO5FJ9WqDLBjMRhBEMBhsaWkp+7nYaXPx4sWmpqaKaXPEYDQuzM+fY/JU3G67rVFpG16fm9iLju+zje2xje6yzh5FFk9jholIFMsm/XmapvZ3Bf/oRfs/HXW9dSc4KM2A3kcSFEUWi+tNs9lsmUzmXqNJEEQul7NaIUPtlm4rHSAIAsdxVje6T5loNLo2CtuXIgjC5XL5/f7ytGHz134881QwQ0tBanFUC8wZfjuALq2SokWy6WUUlmy3EahpgbAkAsu91wKUG14rQx/CoBHXUh2BZLVJS7EbDjlhlUIibvWSfWEwj6hp9RJk2LZKaI+RdBtDC32emS77/ACJApwFMLkWMSmfoxRzpHAE1uK+auLOGeLageL7LxX3Pel/5qHBL/19/4P/dfi//MXUf/7Lu5/69Bt/8MeXP/ln+DPfJff8orDjZ4VDzwIYZewW0V9L3DmbrX4HH2knNRxqrM7XeDLYU0vLZ0jlAqVcoHTLpGKWWhkkFtrVN0/Eemuo6RZ8qo1QLVFWYKPO2uSirnpl3SnxlUPR3pr4aENmodM337PcWT/XcjWuFyDLkwHZAo2qwGbgUEBualRN+aw0HiRjPiLiKYbdBB4k4kEy7Cp6zFmnifCYi5gu7EZhVbjPRha35mMnrFLCsh2320ZFZ13crh9nwBNrhMXwws38RDvk+fm7xvZqQjFHc7rBDGAW0m5dUcuNTLa5+m4i7ZdJXi+12FnyqvTXED1XiLtnicajRO3+4sVdhcO/ybzxWPwnXzN96f9Z/vu/nvurvxz89Kfv/uknz/2fn3j19/64+j99OvzYN/I7f1448mzxg1eIK2+BzaD7EtFb5Tj9VmRhGILwXXpsacTfcIIaqAHn5sgNGLuRG2R/Tb7rkuqDnfnGY4WWE8neKoCwzLZRvL60eErWfFl86YD0/D68tzraV5seuu7sqhV33pq/UxdV89GVqZiKRzmUm6cxqiadOsJnK2LaosdMQP4TWcEkyhog1Xz+w/Kxw3BRFPBzgmAOAL4N4uaURYuY2Jr+gmU5YhlBWesppoUHDVh22NhJhrWPBdIxfJ7suLDZuEjDimWouahfoQ3LlGEFnheHvCibCY83O0eakYUB2qUFS4lVQikXSMEIqC/cXtA/eq4QbaeJ1pPFmreJ4y/59zzpePmH5ie/aX7sK+j3v7jyub8a+8LfyH7y1cKR54jzO4tVe2FEGt8HZXS8nhSNUXo+eGNReAXyznVHe6qoyUZysYMUDEMyO9EoudhZmL0jqzmSH7pWGLuVnGgipZOUSUA7FLheoOxtlFw/Ia8+FO2/ho81pBa7XXM9vM56Tmd9RLPsWJ4MyzmUQ170WBjGqbWcNmmcSsXJZLSAh8k07FAZvIBHstEAncaJVMzv9fxOek84HH7yySe//e1vCwQC9iFb5vN27NgxMjrW1Fi/Z8+eN3fsOHvu7MrqyoUdzbMH3aOM3jO+1za62zq627p0Flu+jkkmTaIbrtPv9R0592przbdaqr48fOPvfna04ciBnQ++zDly4/rtSdGfH0qeHzJLJHzEicysoq9eUFgdNpvVKlNb5sTY8KLsWp/gdA/yYr33379q/8Qbjjeb3Dd+U9P90Jurj/xGdf66anwa0WldmN3jtNvMepFg2Wa13Gtjo3JEIpHNZrtPGalUKpPJ2DI2m02lUh08eLAyT8WdO3e+/OUvHzhwgA2izmTSJ08cP3X6tFqlOnDgwGuvvfrOO+/MLyxeuHBheHTIbrNrZUbxnFa6oBOO6WRcnckAuGkbYu0RTZ4ba7rJHxhX8qYUvEv9Td9899l/vP7CY8NHT4jbDWaj3WpTqpRCodBqXb8ru82G2RHMjqDMnx91zyj5j02eeHzug6fmzp6Rdp5V9JyWdnyw1Hp9sXtcwtEadOdW7749WqtSqzgqQcfK+KX5O6d5bedUPefVvb+o27+n+2Lj6tC4gjuyOKXWaZ0O1O3AXHYUtTnsVpvdakNsDvbPbDItLy+zuG+4BZvtzp07lXkq2GnzjW98g/UA0jQtFKy8uWPH0PBwU2PDrl0733jjjcuXLwtEgn1vvrtw3ja22z66xza2l/nbYxt9yzZ5wLF0xrHcjn5jt+9TO0x/9qbhD150fuWw7FSvY15krRu329eGzmq1Li8vGwyGys7ZNKyQumR52WQybTpe/mq1WrVa7crKSvnI1h2r1apWqzeNwqZiVqtVLBbL5XK2MTabTaFQHD58+GOYp4IkyaWlJalUmsUjRNQHi3g6BoEnQYTCtGn9atGhYhZohhc4YKdiPobDkKHzySQhXqnEUYvTqTgVD1IxHxVxQyiv1wwI6CAS0wlSBlgQaUQRkMzZhprJBcAlkHNtkPPBAlhd2qWj3TpDT4NtsJVgefpZPDUDISIggEVGapYIXi8x2UB0XSRvnwIow4Wd2Tcey/zqO6lfPYQ98qWpLz6Ife+L0Yf/KfnzrxOHniYv7QARe/1dYug6JRynp2+TS13emU4/Z5C0iBhSECYhJStyGHsVxhlOqhhtjEUvsWoBqjItDM1cO9O974W+Q6/6Zjuk196PrY56FvuX71zTzg/qZvvHmmr94nnwpGBqOuICvTAZBSqXfIpIxYkEwzKXjEBGgmSkgIdjPmc25vdi6L3i2LPZ7MLiokImzdoUm8W2XZ7WC8CA8VGwPpimNHxsOLpTCzfOIKMjKl7GCO4G2iJyd9c6Oq+SU83EBACZKfEEpeHAuDi1tFvHv3pG0VhFMdggSrVAiidIySQhny0sdIB2MtkICkrneejtt5/M7fhZ8pnvhr73JfsX/tb63/9W/vefm/jbzyJf/QfPPz0Y+MZ/L+x/kri4gzjzKkCv2s9nhhuyYy2EYdUz1xPoqgIL3OxtgttDisZI2QzEkQlHC/wBpL8uv9hJSSZpySSpWiQtEsiK5VQb5oemLh6++8JPJg6+7Ou8Iq0+HBqqCw3cXGytNS6NKid7Zu/c9ApnixYp3CaTaZXGNED0h/uJsIeApHJJyCTlgpj/tEWe0q1mjEKfw3QvXTSbzc7OzSmUikIqBgYJYLeKQeI2O+QmY8xI6940UD0RJbA5oxqAvQcxYJgEhkOGBTTqoWJ+KuKhwi4mx4gHaAhivqhuNc08L5RD4eWPoVN3yZUBcrKRmGyiZNOkeJxSL9GqeUo+o7p+wrowBBEGLAc6M5MJu5wwrJCGZVLDIXg9LDaLHLlZ7DhfuPZu4dyO4oFfFXY85n/6EdH3/jH9xNdzz36nsPMx8Aufe404/waA1vXL8PphFhEajnv8dnCsBeaDcp4hB2ceGQa1Rlgktvn+go5HGZZpO7zhlKYWppJP9i7cONP8/E9nz7ztGmpQ3zwZnWxzjd8W9bdo5waUE10zbTd8Mg4ZD0L4ei4F3ZhlqMKyCYK19JQZwtLxVMibDLjz8bALQ38nvYeiKJvNdvLkyZaWFnZAY9FIx90OlUotWF0dGh5eXFzgcDgrgtWpmcmgNr9c7Zp42z76lm1kl7X0uds6sss1/e6w4Ppf6JsfMLQ+YLr9gLb1D0RNf7LS+KeD1x9crf+PX90/8qsjl1uu/eiFm44jvdHaO01vnT7wUkvySH/qQFdob21vZ81Xh2bH5o103Xz8ndu2k4Oh28vJqRVfw7Xl853Yu1dU75xYOVyjqRr2dvAj4/LoosbtTlJ5gqQpgjEXMZ9UkSyZhMDe43JBvovtX9vBbERA3HvFiztJkgMDA5X2nlQqNTo6umfPHhQt0eQsLS1OT0+bTKaB/n6BQNDV1eX1+W/fvh2NRgmSIKgiSRPlP4IAExVNUl1Wzl/3vPRfOp77ztiBrwzv+bveV744/NbD04c/0/HMFdUAdDtJRSNRzOmElAhFkiQImqS9ibDAqxf5DGKvUeDR3zUvvi9se2jowGe7XriuHbXEPfFcqsDUD/5BkqYJKlPMWRFbKpEsHaGAlDBPFvBC2pOPjej5T5zdeXD65nHhnVPSjivqgXbLwjQqkfst7kQwlIppQw5dGNGFkFgSd9jtZYsaQRB+v7+5ubnsLNs6bSKRcEfHXaVSJRCsDo+MLCzMc7hcoUg0PjlGZGhMgIubvPMn0bG9oPSMvmUd3W0b3W2b2G3tfq+vvebrR868VnXpX06e+eUnd7i+edr9ubcRiy8LtwTGOMJut+dz97T3sHlPbTbbh9p7WI6f+0yJWCz2oWZCj8dTOW0IghgYGPgY2nuy2ezAwODk5ITf6SjiERpW8yiswmEn619nOd9gBXcowNjjMsCrqksPJMsRN/Dup9bYnNnMo2XCEmDjiNLpqFUuQAWzNKok7HL9UEvybjV5aTcx0VAcukEudALjnI5Pm4SEmtP9/v72PS9rR9qw5YmCWcyKZwY0w2QqRVSkSUjw+0ueqclGMK0P1BBN75MX38wcf17+9EPot//H7P/95yOf+XPvj78ef/oR4sizxN3z5OowkBqbJO7OmoBsEdxz22kMQI1jFOUtkk2hUpAG1SFf7W8V1p/reutZ2e3LuhsnsoN1yOCt2Zba6mP7V/put1d90HPt/HB9Fa+7KSCZT5qkOacx5bLEEFM+7Ga0SeBxoZJRMhUn0/FE0BPAQOm/F29hLBbr7eubn5sJKPhFi4Q1zLAmHBYV8VG9Xet3KoMb91oZNUhGoyr93KBbMEOjqpxRqG+9nB2ph1Ad1qU11URMNpD8flq9RMhnWnc/1/DyU6a71dh0R94iBYwqRPapCOU8YM/H6uATPJI3ibbT5PV3yfNvEO8/V9j9eOG1H4ef+Ib4O1+I/Mu3PF/+vPXz/y394g+KR35dPPECcfXd4vDNgnKJMAqQO5dCdy9Qix0AQDGugtMHLB8lgkTSJk/pVgAvVaJMZND0jOqWs8pWuxtEJ3cO7Pil9tYpVe2R/MBVR+v5qboLtUf3L3XW91w931FzZqi+SjzS6ZMspgyCnFmStCrDOlHeoaQ9Jogz99lJp67oNhEuQ1wn9InmHHr1vfSeeDx+t6Njfn4uFvCQiTAV9YGlk8kqujYuW1yNJcUavIqQ1ctlgBD3IAr5HJikbwzh4RrHTzZplgucQshrW7BI1L31heGb5NA1Rn1hAObjDaRwjDIJCR2/9eUnOo7s0o7e9Ynn8ibQg1nsNuMvhlQzpI5HzDBoOVBPG8r+XLJ6T+zEi/LnHs0+993MU99OPvHN/K6fF489B/pofy2uWy3a5JRFjI40h+e7aIsI5sz6RGIBf7AgpPRCWB/KJl72PQFRpM1iXneT9MTOgbeeUTV8YG04lR+8aukAIqJrJw7O3LnZf/NS943LQ3dbJAsTAasuHXDlY4FEwI373flkdO1VKk6lYkQiSiSicZ8rgNjslnvyKt1rvEiS7OvrY0V7GcCxqTCfz2eJDYs5MoZmsVVc1RNcqXHPn0In37FzL7rH9upWm6rV/X+/UveHM5f/w/CZT63W/h/K5j8S1n9CcP2Bscuf0jQ9IG76xGjHy7yeHygaHpi+/pcXLz0vntphWnhec/tTiroH/OK3ijEFWYx7XN61R56iKQASFWg6kKFNfmJZGem6La0+PHjo5bvvvjt7sMnxwVSqcTU7rsoo0RwSLMTTDEqEplNJPORn4DLlO1nDzZThqGV8T/lIf39/OU8F2xV2u31kZKQs78uVlXcKhUJDQwOO44yJk61pwydN07li/unps9+fOvr5vjeeWbjwo6ljh8WtypDtNV7tEVHrsk9H0XQ0GrUi9mOy9td5V1/nXxtwLH+g6Px83+tf6H/z2cWL17Sjs265BjVzLbIjkttZIl9qAAOKKl+PpmmX05lMMqyGFAU213X8FZxhN1lrL1ePjY35UhET7l7yqDutSzWa4QvK3kPi1q+P7P/CwI6XeTV4NuWwQyqrcs+Ew+EyvqfcG2yWNPbGyx1SubO8vLwe8FWgMpGCR5l0ihOr1zz8KtfsMf/csSbFrT/VNj6gbPp9a/MD16t/8Zk9vj/bhX7iDc87naFuQcLohTt1Ox06ZwrPgEpfvtnyzv9yXHN/f//HE9/DuJWLdCFNp/FCxF+MeMHYw65lDjkIXRaXwMBWaMD3MA4s+GTyUfvt8AK9TtO3kao/g9O5JGAgHIqUQWia7i30XCM+eJXovZI9/kru+CvE4FViqYOUThOK+YiSG9OLwqoVt2LZrZN5JQtZo2j96iUHjZwyCaH8ZCMI2vF62Ok4T9XuDb75U+kXHxz6j39W9UefmPvTT6Ff+wJ55S1yvgPSCGTxgE5qm+4GewyzgpM2WdogYDSJkveHtMu28VOUlnIlrhfaRlo4e5/mHnjO03RaVXvUON6unOqtfm+fbqaf2900115Xf+bY0t26mHwxoOQZ5of1c4MDt6r4vS05vx0SfoUYKpcw5LQnYoFM0B0O+NYWwcoHqrRfKBSIQh7SdNhkOaOosFEhK1agdMsKIqsY3fPTsUEk0wz1ADsuxoHmwkInySo9vVVAzwN4kTpisbO4PBjlDcVUvIh03iledOmkfulSzgzjAonitVxyZZAB2DI60wTDZNhxnqjeSwIk69Xiaz9GHv2i4dEvBb7zj4bPfTby3X8svPnTYtVecrwxp111zk+6BHNoZzUlGgN2Ika+pg3lHGfQYNAnYBS2gc7QqCoqX0Taryy//eulwy9hrWcNtUcMty+vdNY1nnhHPze40FE/117XeOYor+1qTDjpW53UTfcaFwb7rl8QDLTmLFLatWYAY1xUhE2W1K1GXKXI3m1GBcIrCLJYoJNhKubL2ZTFjT5HBj29AY+1YThYBI9NyuSEZ7J+4pDvluG3XHtwckkq4qIdypxZrBm5AwMxdB30UfZz7BYx1ULMtRP8vtBsR1w87eOP+RR8l2jeL13MmcQbnxclpV8hhaMAAGIseVDPVBPRX0Nefdt34Gls9+PZ134SfeSfMo9/rXDgV0TzcUI6VUQ1tFODcYaccz3AHM08L4w1a8PDCP5W5unYcINrz0tEzXd0XVvc9S+ys3sdTWe0N45bpjqX+2/fvnTSMDfA722Zar1Wf/aYsL81Lpz0ShaMnHHd8sxAW9PK3ASVTcByVOZ7zCSK8WAu6A78jn6ufD7f1tZ28OBBDgfCiMpybtOwcrnc27dvMwfXzUnFHJnw5T2qZDFBiWdcL0zeembm6JMNTz11/qlvXXrxu9VPVrX9UiGY1S4d54xdma7/9sK1T54+/JuWS09OXf73ncf+6Oq5nfmYnkg5YvZew+D/dArecgnfRrkvmriHU4FVspgqt4Ei0oxRp3QgH/YFJkcTDc2Og6fFR6+N1c03jjhPTyUPDCbe7Ym81xe6OhNvnPePClyOYDGeJvJMlA9N05OqpNDKYFwYHWir3lNfX8/Gc7GiXafTHT9+vKqqqqzWsC1gf2X3CYK4dOkSyw1dlsQbdoBYmZiQcXQx7JikTRwwCfwGQxSjKCpHFFRh+8PjB1/kVv167vzR5eZfzJ76zN1nn1k432FZnHXJX+XWPDR+MJApMVYH/P5QMJgn7xe1hGFYWe/Z0AzmC8RPeb1nPvhgbmYDK0GRJMJZ/Lpu9LNdL8y55DQJIVSVeo/Vaq2uri6PSD6fv3379sGDB7lc7v2nTWtra6lAhQpWzJGFNJEMFg3Dfv3UHd3gV9Wtf6jv/1tp/8NTXS/xVwYXpy7tbdb9u5ccb7T4neGiWG372nFbwxLkJtv2pv7XxnPdunXr4xvPRRIA1on6yFiAwoNAn+o2wtLsMpAohCmBigOE8fK8mVlYmdd9kFI+K2gVcT9Y+ytM0xv3GRpWtzFnFDlXpjLSebLzAjHZWOy4VKw7TPTXUvIZSFsRcZY8aNkkGL0ziajT5pEsbF1V2dWW1PJI2TRAfybBOEHcPkmdeMH05HewR7/s/cFXIj/7RvbdX5NDVzMjt5CpYSKdwOR8G3eMxtSsYCAZJAfsQwZHPe2zFVFN2rTRSVGxrNOoOm8UqGqPcg68ILlycK7qmHq6TznVN9N2UzvTj/AnzIvDDu6Yub9e3XXDL+do5kcmWq7KxjpsnNG5u7csvPEcGBiMYCnBNDSmS5ulfsx+L97C0kNIFGlMS9mAeLBSnoEqsJE0b7PAq2j51g6sPALWLIsU4YxmZTPkVBOwMA9dI3qvkHN3SIB1i2mnjg7YgK07lyRTcSKT8Ji1ATmHiZOCZLGgmpiEpGKOWBkCesOFuwDB6bxAXNxZPPFCYcfPUr/4uuqbXwj95nvJf/l25pmHix+8SvbXpAdvuEe6ExadeqQ9vDJOOyGlFKvlwBxbaz9tl+dNoqzxnnm1aESZk05pzu9b3P+s/OLbU6f26EbahKN3l7oa9TN9KH/Csjhk7rpmaTytaDgb5A1Jh9omW67Lx7ts/Imp1us27njBKmNjvkB9ZHyIfoelvAhuv1PIwaPhtRCorpK9EMao/JLA3ELBItmYEINViZinycZYzuL+snmj9NSk4qBUYVrCKkGWhnOro9RMK2iWrCY61wZ6jHqJMglpOxBXgt0oE6dSEb9RwRjwNpBKkA4FmH9sMjZcgJhnFKmJBrL3cvHCDtOuX2SOPV9482eF3T+HYL3xelLHpxE17dSox+56xHPwvDD0mIRVWhkQB8nyzGLIQrM2UpWTCnoSVeUU88orh5aPvCqtOTJTdVS3MCSf6Fq4W29cGHZwxy1Lo4a5AfcQbLTqAAAgAElEQVRct+JODTLWLu5tnmy9ppjotvLG5/va7RpZAQ9DupWoFzyDMV/GafKg9u2H4x5HLRbL4cOHf5t/6sSJEyxqNZvNCkXCdDoTCPiVSpXDYTeZzKurq7OzGyTl1vpOqzr+ovuFrw0c+H7/kb8ZfP2rEwfFmBWEHbMtcZaffuJb3//BD6/dbO1sb2i+uufk6XO5XMluQeajZAGkWiqGqJYumeZfdXCeD6jOpYPiiLXbPvskyWRmYKvKZLN2lwsqzmVolZzubKOrL5LN9dklXswZMIepeVOhYSl6otd5rD96sCd8qCdycSLWvpL4whHsyaveAlFq01b+nvHxcdbPxV7o8uXLw8PD58+fZ6U7TdMWi8VkBosaMP+63BKJJBKJtLe33z/tQzaXRRzwnpAl8kWSDedmrwCfpxSdn+545h1+vdCucaVDL3GrDTEn+7Mh5pz3KMtFQVELQcj3fTaXy8XqPfcpYzabb9261d3dvSn5QyyfbDbNpIs5okhs5e9pbW0t45rNZvN7773322TsJ06cYLVAmDZCYToD00alVtvtdqPJJBQKZ2Zm7tOSIk0ZMDfiQbCRg+673zbe+RNN0+8b7/4J0v/ZA23G/2un58/fcr7UGPjWaecDv7Y+eBDTuNYMXRsrJQjwhf1/j+f61/H3jI+PfzztPdDJRBEgDqwny2sBwEEQJREVgaoBuew2QehK1ENHPQnMnHQBYheWJHhVjTH0zQxe4Z56TxxcPEEMSIq1K86VGXA8abmklkvMtFKyWZCdbgOsbkxaaVj9wXqEB6y6sKKcjaGMyVUSdhZkwLLjSCmG6pDgdlMtJzwHn4ld3kt3nAfVqvFYsedKfqY9yJkgnMaoy+pYnizYZFmTGCQBY0gAo0XIxWTDDlIeE+hA263jZT5G0irNGFYD4lnDTD+v7/ZwY81IQ/XArcs2/rhLMB1SLMUkM47pruX2Gyh/QjTSwe1uiiq5LsHsWGONdrqfDSwCJhWHMm9XfBS9BzKXMfQw0AbW+MGwIlWEAslphyLB8Npt8tCty2CQx2tWupIWy+qyYFChESUum/dwhinZDKVfJmUzxHgDYL0RUGtKjMNp8NNlPM58JOAxayIqCHte7ysQrkqAYbEYWw2HEI0BuXPT+8WLu/O//q77p/8zcGYHfecDiLK+vIvovJibaAlOdhFGQVjB9YjnsmZx3sSYExxAgLle83bDUf6VLQmGKw03LZ4KcIYM4x383pahW1fGm2sH6y7aF4edvLEwbyjO7TePtK7cue5cmVrub13oqM+YxHbe+GTrNe3cQMYkKlqlzLjIM0ZR4EP1nmKedhpom5RReiAmiwGbSwBvDnSC8EnbQJ3CNcsJzTIoB+xNsbHuiBqonlAtOLwSoY0vCWzWF3jxoO2ymIrrF85Sq8Pg/lNziIW7JOBsGJcZgIfU8GBCarYYmUm4NaKoYgnmAGuLZQadtK61ij3LLKRUi2AuXeqi2j4Ivf9C8Nb7VOcFov0sQKEnm0gtj83vEZQu+MTzMC4WCfumUWafWu//e4zO2rgoCYs4pV/xiWZ1M32c3pbRxpqxxpqh+iobfwJbmYxp+HE1zzLXP1171sEbFw62cboaU0ahlTc22lhj4k5kzNKCVQZRAh5TPuzxOH83fE88Hj9z5szrr7/e1dUFikQuNzMztWvnLi6X39XZcfDdgwfffbe6pkYoEjU2NsYZPrr4dls6mZKYVD/sO/g/BnZ8eWjPryZOPzJykO/VRlN4AI9kMhkOh/Orp379q189JRQKc7liLJ66datuaWkpm80y1SbjOJ7NZheXeL9+7tVXXntTJpp3a1uld/5OfOP3la2fjDsnkjF3IplLpoqBYEir1UIrEol4Oh3PZOJud4zPj96qix57L1N9heZzElYj6rTjhQISTCitEb42+EQ1+scvmj71hmVIFM6m8EQiYbfbEQRJJBLsfUUikRs3blTmqZidnX3zzTcPHjzodIIigqLIiRPHL1685PG433nnnTNnzr76yitare7mzRs6nS6ZTG7XMcDgFwwGNRoNjuMJHMc3FkonUhqP9dcz5/UOC2Z1JJNJLOiJxWN4HAom8UQqUaqWbbDD4WAbvLGa0jccx41Go9frvU+ZeDyu0+kCgUBbW1ttba3f789kMuz5OB5PMG2MRCJarTYWi7HHE4mETCa7efMmvbZtnTZzszM7d+7kcLhdnXcPHTz07oF3qqprRGJxfX39vaZNCk/ags6fz33w0Pjhx1v3tB36yUjnQ5rb/0nV8klp038QSHlia0Kow+zu6Ple04E2+6F25Llrtg8Gg3pXOpvGM0k8gcezaejUaDSq0Wii0Sjb4K2fOI77/X6dTrdlBNbLJhIJj8djMpnu03tMLKEVRdHytAmHwzdv3vwY8jWvjTVFhpxFlxHXi1J2NRXzg8knEQJG5lSUZDzuBJNzispAYAWdTmw2zt9H6YGfcDoeBDuHXeZcmcJRIx1wwHJmAk8T7TGD5lFZQy4RsOmdUg7ltUCKDPgz0F4rA4kIMBl8AnTMR4VcpRB6VE2ZRfRsm/fGe7GlPlrPB6t+fy251A0xJk4trVmKu8yoZCksX/TppPqlcQALOxRQfyIE+eShPRoasjxWiPO1ZZ0lYKTsQHwHKApIs6qh3VoSUeQskphm2SuZX7h9ld/VONpQRdgVionumIaftUiCCk5cBUy1Ce1KyiCMq/kRBQckk0NRtCsiHuw+9h54cSOLBKouWiQxFS/FRFnDi/uaUYFg0qAWLcA9QznkhTKdTFnKrtH8Q897TNDPbhPtt4PvzCFnCIfWnUfowmDCrKT9VlBDAWTDKDEuPWC0y6OTgXFBV2cYewxj2wBxzohh8BMx+gpLfQnQVym5OkyM3ixe3OV+5cfxyTaK00WM3iC6LhHTLZA10yqmVIuZAIYIgMTSLZyzcEbL9Mdl4XqfnaJljVvcAam4YFBcWtqlLToUeYs0rln2Sean6y+v9LVO376RtctlY10RJTdrkQSU3JiKRzs1STUPl8zEZfNh6SIAn+3ynFkcdd/PzwWPDEkQQYx0GeIGScIooVENTGZMT2Na2mcl3SbKqSs6jWTUR/odBKqmgwj8sT0JD1eE+bvX2wKTXDaEsVMRW55MgZapBvOqVVJSelAVPA7suCQgFsFnkCMr08AbjqqBItypZXLLmAGHF0CY41qmJxVQD6Ki1EvkeIOv8WR0sY/i9xJTzeR8OyWdYnsb0usiKgtnLChb9GpFJu74ZpV67dHYdnSKLN+gHRKiQa+WhkZDOOQ5qzSk4vlkizNN1bzupvm26xm7XD7ZE1bzM2ZxWMnF1XwKUyV1K2kNP6ZYiiq5jKVZUXTqgx5nyZqxtmzd/38ikaitrT158mRvby/Dyeauq7u5f//+0bHx1pbmQ4cONzU3nb9wgcfjXbhwAUEQt9vt2m7zeDxqpUprMpwUtx8RtHgCvkuSni/0vPFQ39vVgp6A14dhmN1uX11dtdlsvyUN8vl8Vqv1g9OnNWpNwO/3eDyBQMDv99fW1j700HcefvjhkdGJWCIfcMpQ7iuazv/sEuxRDv9cPPKySdphNWskcq3bG3a7PWxb3F6vOxh0RyIuhyO6ukp0d0ePHbO+/bazrc2rVPiDvjAeVqFxnj7G0cWUlqDX43K7PRqNRsfkzWATUdlstrNnz1bGcy0tLZ06derMmTNut5uiqJmZ6SNHjly8eEksFr/77oHr166fOnVycXGppqaGy+V6vd7tOgZSXDkcDrFYfJ+uQ7wus8msUCigZzxet2u9k8v7Hg80WKvVejylu956ObfbLZfLLRbLva7FniISiRAECYfD09PTR48elUgkwWCw1JPM+KIoyqa7Yg96vd7JyclK3kIcx3+bruvEiRNsWjePx11ff2vf3n3Dw6O3W1sOHTrU1NR48dIl/vLKuXPnUBTdvj1ud8DmuNRy9LP9r/zd8M7Pjr71Wv3b47PVc6Ypr6leP/Y9ee9DmPBYIBBWK8QhH4pHvKjT1b0afrHOufuWVqhBTHbn3Tm4WSZVrRjDsK19Ur4vm80mkUjuVcDlcnk8HrMZRmH71jJnejwelUql1+vZUXC73Var9eOZn6tYLC4vr8hl0owPLUZ9qaA7E/VTrAqSidOZOJmKFfBwIujxOiwhpz0b8UMYS6WO8hH3Mzgs0059Wr+KyXi4D4PoFZcB1AifjfGUreEb0nE6l3SrxRmXBQI9koC2LoXAlJI7MhjhNF6EhOcBClOz8iCv5nimOopWsG1AQBAQkHDhT8en9Xy/SemWcWIavmZ5QTg1mNILIEw3iELgVQCB7AEeC+TPCjgAplp6aWZkeUnPYEwm5eXeJoUXelQZUizxhzrUvFm/TlxMhGTTgwh/wsoZcwpnwYTAGkXYeH6X1rU6yb1zvchgcsPyJYtKVqwguapcxDOZDI/PVyhkWau8YBYl9KKsHVDJtNcKoUyImnIbsxZJXMP3i2dDssWcUcikV1uzlDh1TMYMH8jaiItOhiFPe4bpYcaNAta7mA8C7kDPAzxsSreCSnkJH0ZHPDSqpqxiClHB0FQOdyHt0UmiSsbJhShppw4Ux6gXqvJaYTSZmHDAHrH5GawSSsPJz7RhzedgUAQjYLEQjdHyGQrclLORxWG/We2SLEVUfNncqGp+JA1Mg+va2LZi9f4HabuMRhRh+eJKf5ueN+3RSnJhr3RmGBNMWQWLyOoMo1qVTCYglbVL7vEWbksNJGRFlH7pgkEhqRyLyv1MJrPE4agV8oLXTsQCyaA3HfaBqZKN0koB60wuFop7sQBmD7uQfCxIlQ2ZFbN3DctcMee3PkdRD41p0roVRDCfNMtAKUdUYMtBNZARBcqzwCCIhPLppUGbDhzEiTD8lIrAIwNBBmBtpVNxMuYn/A4ILoM0fGCLKqqXHKO3iw4lZRSQqgXAVzEIMBpQVkqA2QlnI1qRZGlaOtFdSp/OZkIFgxZj2WLZvNgHZNPnJg5rmxSUQo8xIFsQ9LVq5kfCenE24lXODFo5ox6txL46kzeLS0MPxJ4K2iFD5vp4nbdYjFFIvqRTSCvH4kP3rVbr8ePHhULh2bNnWSBdsViw2+3RaDQUCjpdroDfHwgEV1ZWPkpibTwejxVSoSx4rELZ+KMT7/1V13Oy0LpL1Ol05nLrqcuFQuHt1tt4MpnOpHU63a1bt2pqak6dOnXp0qWLFy9OTk7mcgWaJjJRDUXmiVwk4Rz1Sg87lp4zLu1NuGfJfEWOi423mohEfMvLdH9/8fJloraWnJqi3RgT9bRejk3ntP6dpst5KtiD1dUAAb5y5Uo5eVkwGPR4PNFoFEXRaDRqs9pS6XR7e3vlTVVWyO7ncjmWAWjrT+UjsViMJZguH9m64/f7NyWQ31rmd83HrtVqL1y4oNVuSOvG5mMve7VYIuPKPBVms/nUqVOrq6vr06YA0yYSiYZDIZfbFWCciAKB4EOmTSjif/aXpw/8bN/pX37l7itf6Xz9vw3ubr5zkW69Ym36lKj2AbfoEE3TCOqqdMmlctSANLW/M/xqc/DBg5jZX6RpEkU+3M9Vjsvb2m/skY/o59qUj31ycvJj6OfKZDI1tbXNTU2IhJdxWfLJKMEG6KZB48nHQ+xn3OfMx8OpkDfiRtIhHwRmb12mP/wIDkpMGMtbJB7pUtRpgxSekKcTeINANrMJmTM4lU14jcpshIE+AHahvMRvkBMl/SzmpQN2iH8xSzxSTtFjpX02RqMCdxjkgAyiPs6we3UqYxR6JIsJH+ZVrhTM4oJNTjh1dALuseJ2GMkRBY0BOHgiHtpvB00ILCU20IcY1Yd2G3I+exIz6XlTHrOOyiagMclIMeBwSDgpp9ln09mWpylMBy/cQbToUBXN4rh2xbowlDQKaZdWPdkzOz5yD7UHmL6uVFXdbW+zL0/nTaKcVV50m+mYlw5hhMtYjHipVDQTdOKooeixJjAL7tAnEQPp1APW2KWHfl6HhYKaWHGDjCBkXYpAuGdhw5FYxAYmmAmpRFnURLt04K9xqKCqtXEhMwmfRpTF9CB340GKzTNfjuDL4GyqeaCAYkKcSvLVKHBxRwhMSwcctFNXMEvSiiXKZ6O9Fi9/wiWYyZpETtF83I+5JYukXQ6YGAa1zbhL1qxKlWKVddlUHmG9SIxrKYfqUohWPTcSQMwwLqkYnQwXYwGHUpD1Y16z1sEfLw2iHViA82ZxUsM3zw2AKuxUSYbbZ8aH77V2hMPhM2fPdt5pCyj5Oa+tkIgQbPxRBoc3hHiISsfTYV/c5yKS0UTAFfc5s9Hgv/J5Yfsz4MiZxVbeREgvpSOuUj4Zv62kxabjdDZRTMdcRlUeD4NDOQ1hUGsq0YbnBfzIeAgIJpxa4AGySZHV2QKmAzWXVdAdCphgHquHP+YRzhBWKIA7dE7hLGmTFa2QRoPEtPC2EEBA5Q27aI+FchvgD0LV9IxN0QhWWGAnYjN4gEWQ9lpyUX8q6NbMDiGChbTXTngthag34ccQtSSXjKIaiUsws0Z/pS0yztyYmmddHAL4uVsnHe2cn5q417hse5wkyenp6YMHD7JxNyxodGtJHo/X0dGxLaq0fMp6oiUIJAKr0xCyunP5xhFRqyGKWXFPjix4UFcBVBnYZEHL80uXv3171+M1e/fv3dfQ0GAymYrFIoZhJEkmk8nBwcGqqiqDYXPGx2QUNYpb3JJDCOdFj/Qo7potZIEPkN3YS8dx3OX3Q4OzWcJsLg4M5C9fzldVFaemikND2V27yLExYGtm4DLlW2htba3ENbtcrqqqqlOnTvnZqtYiwtYuBf+LxWJNTQ2bPmIr5LaMfbHZgOdz2wJsGTaC+v5l2Czr9y9zf1wz8DWT63zN7KU9Hs/ly5d/C9Ypd2CBUWIqcc0YhlXmJSVJcmpq6tChQ8PDsA6UO7CyZ2ia/pBpw5TOd3QWXnnVN94nv/TeibO/+fzgjgMfPFO36yc1l78vG/yKoe/zIVOD3WYImLsKGR97LZYi3OjJ/+Uex+/9xvLTKx6OPmk024A1oNyajX39b5qnoqWl5WOIa87lcp2dna0tLV69POE0Z6OBkjIBq2c0FfQUGO4ZOJiB18p8PJyNBApxBpSwQV3YuMLeSwdihXEimLYrvRYdnU9BGLBFTHnNIF/hpTlSiIe8Jo3bqAZuD2YR/xCxARxCCTBsOBQxJTdsVoFIYLKl0lEfnUvmXCbjbF8B6G5FbqUg6nIEFVzaoQjrxRkfpE8H0VXZYFaQAzVLHGpOM+oam3k+5mOi931hl8OiVbqthmzUTxdSzOkMfBtQ4SE67CT9DqtoyS7hUekYmY7T8UDWaVqeGUWUApKBauKo0WrQ3ou3MBKJdHR23m2/45JxIWUjcCEybjintoioMgFXieYkk2ApZIioLxMNFOJBICAIu7YXe5X3WNpn2gzoUVDvaEwTlC25JBwq7AZtzyoFCxPYhyJ0MpyPh1xGtUs8D4WzSSqNE4mN/bY2SUqnhF20z8oSV0aU3LBOzKhfkYhSoO3vJWMBEtMapnuyZknBKkFl/JjLHlaAJSmi4CS1kHYewuvASGAGL6fLCC30WsAEFfMBgYLfQbuN4O5x6llWa9ptDJmUdp3SIhUk/S4mRJHRmNmxyybomJdMxQxCjmN5grRJCRsDjTKKVoY67CuzAKlxGaImhcOo37TGlb8yqS4berq7I24k4UULeLhs/iSS8LwA214qXnqIMngRD+dgXLaAeLYZi22fIGYSxryYeFG+OJHyYdClFgl0AtQAj2QuGsD0Cq9Fy8wEoPsDx/Q962cC5oFr0Uo55GEFJ2zXA4ya0WAYK6A3Z1fblkYKVknGJPJpxCGrNqFdpRxK3CxPumxUyEniIea5iG3Qp1nqLxacl4wA+C/sLDn44n6PzWhViP1qYUIrMK5ybIsTRgHHbtCKlnlui55MRshUxCjkoIoVEg8R8RAdDySc5pWpEadKRMX9FB6Muew2yz15lcoDVLmTyWTa29tffvnlgQEgkmEFIUEAkTJbjH3p5/F4rCNsozRZ/wbBxmsJJstHCZLME4WXuFUP9rz61eF9k05pn2phBpXMueQTmPgDedc/D+7+m4FX/u7gT98/eYK9XDkwh/1qNpurq6u7uroqsbqZTNaOAD1jMROIYeNu8SGE85xH8l4cmyRypdCnOJ5wu1xwR+U/giCs1kJ/f+Z738v8wz/kf/5zn80WjEJ5tsE0Td+9e7ccx07TtFKpfP/99/fv389aWVilodwzLINzoVC4fv06awAo33jlDpPUIvu/rd7D3g6O4zdv3uzp6WGhwVv1Ho/HU4nvYafNSy+9NDg4eJ9pw+fze3ogF0dlh1Tu0zQ9vbjY3NA4OjG5OjY1eObo1LOPvnrxqU+Mv/7a8FkqEc/jFmTxWWnLZxQtn8jGQQNmT6dpyhcrDkiSQ9JkjyhxYiD8/HV73ULUHiynkKu8Dkzmf7v8XO3t7R9DvYemaZ/fD6k9CpkNa2UyCjRrLIU8HizEAux7LZWGF0pW6BYrFv0N595zzWXeRIHtLRnHzAGHEcSnzwY+EZ+FrSGMWkzcCb94vuizf8Q614rFaJ+tgGqdSkFpOQbvGDh3YkaJjTtKOhR5k8gpX3ZrJRkzQ9ID8e3JkoqQYsHU28qeioOg/OFEOq6RCMIejM6nNiz94FAACxarGxGJkEm64kMYbx3zXp6OBNKRAA3GIXDSRYKB+8SxRyIRq8VKZ3EKD5c0iXgAbE4IkEFTAZQMOYl4gMBDDETJSUXcRDxIZRJFPHxfyVdxO6WRYowEILGidjFXsTAJBE4+KzMudpoZ8aBFa+aMB6SLYInBNGDX+RCtFyLyoHOiXhrVFCxSt0oAUyibIBORfNBD+R1xFdfGHStYpUWb1CHjo3IwwrEMNOtRQoiSCiDrl2Mtf2ULE9uGVJRRcx3FmF82N5GO+Ag8XIwFyxoJnU14EVvYqmX8fXghEbGKlvyqZSpgp71m2mVMBNyZMHiOqFQ0j4cjwVJ+xEohWpaUHo/H7XbRxcx6/WsTnj1CpWLF+Jo+xFhPiUSETEbZz61nrU3greMSZyxqcTqfdhqU4unhlNNKe82gAQdRMO1kEj6LzsyZ8EsXiZImtE0l29XPcGv5rCmzTCPk6zXqqAcBY1IQoxAlruKiK1M5szhvlrrVIkyxkjMIwCYU8wPHILwJfJSrMFZGmAMJOp+aHR4wcaZQhRBdnkaWZ2QzY/zFeZ1aMT8/x19ajCNGOpfI4SG9ZCXiwUozJ5vAg55sLATPSypeTMUCPu+axrJ1ZLY5Eo/HDxw48Oijj7L0PDRNBwP+ixcvopjTZrV0dfWMjY329vWvrn6Yw4LRe6KMGrHpMqKg6cG+1/YI6s8pe785/s5fd7/4o+n3m4wzuig665E/P/zB+yM3ds7XLnpUBAWUfW4HpGsoV5LL5aampqqqqpRyRSwcjQRC8UgUQZBK/YPIReLOSa/0KMp90Ss7mvJMR4IOj297L1jx+vXcd75DXrwIrpjwhnRXExMTlbyFExMTjz322GuvvRaPg+eOpun+/r5uBgjV09OzsrJSU12NIGhnZ2c2C+Hx99pyuZzd/iFxdkx+LlDU7rN9RD9XpY64tTaWcnBrAFqhUOju7m5oaEgmIYXF1niutra2sufrt5jyt99++5FHHpmYKNkXAzBtLmCY026z9vT0jgwN9Q8MiESiDw0DPHfu3EMPP/yDH/zgez/60SsvvpQandQJZx9dPLF7/rImAnSRYUu78s6nZXW/F7awTApb74kmSUKstrdwY3vaQ0f7o4v6bDK7Gef2UfJzfUQ/1yZv48TExMfQz1XqZoqCl8VNkoxllY0HwJQS9VIRN7hXgigIdeansmWoch0EobupnopVknkbBpZbn92YCbpBLuIhOoiCRykdJzO4VSFIWeUQ711CMHyUFbZcJkYkI06zNhcPltQRkA04sjoDaGLGkp/WC3DNCrirECXlMoL8iPkY74wbJHRFU++zT6XjWqkw7MWA7Zc9pWQiAvJfgEo49QAbSkaSEZ9BIWZfxKEka0NiTikmIiF/mcRsm+kOhyhqXcxkcBDbQQwwNJiWCQhSUwE75dQXEA3jvNCBkSaEASv0xhsBsYFH1jwg5e5a36FY52YuiUp4Dhmf4TKIgC8D1dHJSC4Z0/PnChZQSsCPhqiYGCLG3rDJg7bxutCMDA7WrwDiNKgg4QDbUZl4IYDap7vT2mUG5S1P6QVJLQOmLiOoAFgN4GjwgQJmZaOrbsOFGBtGOk4kY2oBLxX00Llkyo0WGZEJbqBkbGVuTsGdS7ssoBHmkplYQCfiEWAec4ENCeITGddtBs/FguEA2Jzvt1EkBTde6R5d70y28xnOvQ3u4K0aD5WOw8vDhnvZUE9Jf80lnXqlZnmRYtVNFkuUwfOpmG5lAfgR3AbmeblPF22otjQ9mOdUKV6dnpx0W/RASIFpCZvMOteXMQpoBFBfSe1KUseMC6KiQk4YiPsZk7a7CmM0FXLmrNJlVCkIaES4VoAYtdzFxeGR0dXZcekqTykWoBYjXUgH3Qhi0Kw/JhWzi0hEftc8FexLsc1mYxMO+P2+qqqq3bt3j41PGA36EydPnj139vTp03z+8tGjR00mE4ZhyHYbhmFSqVSr1aIoWvk7iqAOh+PCcofUrAm7fde5PQ92vzKs5ETcfjfmtDscBqsp4PIJjPJ35+tOyTr6lQutnMFJNd+JOX1Ojxdz+1yeaCCskijeO3X86T0v/2rXC9VtdVK5rPIqCIKhmAdz+W1mlXqlSTL6smHqV2bOXrP0ttWoQJ1+zOlHUCecgqKIzeZYXUXtDqUKNvaOMAzT6XSHDh0q45pZvSqRSPD5fL/fT1HU7MzM3j17Tp48mUgkbtXdrKqq2rd37/zC4qWLF0dHR51Opv6NzWIuiFosFoFAcK+uY8vodDqpVLqp9yorwzBMoVCUGxcu5F0AACAASURBVFz5U3kfRVGxWGw0Gu9TD4IgbLqGTWVQFHW5XO3t7RcuXGAzeDgcDrZmDMO6u7svX77MPu9lQ0rltLl6tXbXrl0jI6Mmk/H94++fPnXq3LlzAoGAzeGw6VpstSiKYhh25syZJ5544vHHH//JT37yy6eeut3Z2TMwUHX3Vt/S4CMjB17kVR8SdyxxBiXDL6tGfmZf3m+36lFXAHX6EGR9KtrtdrFo1eN02OxIz4JhZ512Z6u/etzLkVo9LsTvQZ0YamLi6st9tXUHRVG9Xn//UUBRVC6Xq9Xq8rTRarWHDh0ymX43O+v9Vs77/vbAfX/9N/iRokhIjrN1HY+VVjo8CEmI4v6SfA0zKyAsggyI8t4L9yYBXPqawTGTvgRHCDtB9mTANaBaWfQalWxc7vYnfuiFsolk0OO1m2DpzCboXDKEmAMyJriXQRvQDnBtbADGoloA8UC4jeujXjSbiHixiBsBvSebYDMK5WLBYgL8QQB08DtAi8J02YjPZTUyL6ylviWSUdZp+BH1HjCwbZA0OB12gcYGHh8rSLsgCmRLNjYZKgR2gTJXwvesy6HtBnf91xJYJJd0C+fTVgUYmXx20oewjErqlSWXjMdgToGzGxrgd1ARDx2HNAub1eVtxyibwP0un83I9hidTwWtWp94tgxhZnCsG8fFLgdqY4BL6xnGhI8g1LOJmM8ZcaN0JpHze4hEpBAPFxJh3Oucb70t4CzKRYJCPExnE4VE1GnSESEmQUQmTuFBMoDAjWQTH03vocB3eV+95yPOpQ8ZF0ZxLCYimEmXCHlBawTDZJTOp4rpuFrE99oMEAkPZrB/1V8uEfO5NFJRMuRjtI1YSC8JiGfptahGGJf18EAYEZjbv+uNZ/CE3x1BzDTuo3E/6bNnHTpUJV5eWvBIOWreXN/AoNWoowvpZNDrR6xUprQWUalYSfPL4P8KvYem6WQyubS05GK8QslkUiQUVlVV83h8r9fDX14eGhzq7esTCASdnZ3pdDp1jy2TybCBWlvLpFOpQi6fSadzmazOoB+y8JPpZDoFVaVT6Wwqk0ql8uksmS8uuJX/3L/rv3Y8//DIu9Wy/uuKoevK4ZuqkTrV6B3j/NsrDZ9rf/5v2p/fNXrFaXVsvRA0LZ1NZ6lsgY6GMJuyExMcMkz90sbb5zcP4VF3KpVJZwrpXDGVz6czSYzZMhloQCqVSiQSd+7cKcexs3qPRqORSCRsUnoUcdy9e7e9vd1ud2i1GsHqatWVKgTFGhsbURTdvj2pVDqdjkajBoOBvcq2n+l02uv1smnSty2QSqUymQyCIE6n814XYk+0WCzBYPA+ZZLJpF6vD4VC25YhSVIulx87dmx2drZQKGTWNrPZXF9fz/ZJWSPkcDhshD9MG5GoqqqKw4G4Nh6PN9Df398/KBKJOjo6tr0Q29p0Oq1mNq1WK5FIFHL5vr17v/mNb7y2Y3eku/fo8Sf/tP+Fd8TNmM2VTBP5fM6vu2GcfjriFrhVDX7reDqTZ+vBcdxgMMTj8Uw6ReRTVCGDhfO3efFXGrD9bc5JWSQUS8WjYbvVdK/uhbmTTvv9fpvNdv8GOxwOp9NZnjY4jt+5c+djmKdiTYdi7D33XzpZrYgxY1AQl4tC6BOYFj6CQFqrmUhEAKyTTTgtjN6TjtMec85lRtTioFltEPHg14qXvM2rOR78UClLpeNmlczvsITdiNdm9Mq56/mtUCBNAcQlLOuQDAEMJEF0jUPlI99IBs9EAw69OuZzhlwOt9XosRk9dpNZLddKhX6UYfmLQZhPEIEgODqDVzqeyGQ0Hw8XU7GQ/358zaWhAU/cFpHGeuVK0g44Amg/E5LmMoAdCAgSLWsmmS3nbqmNSsULjF+GTMe9OmnOZQZfXsTvU0m9Fq1fJzcKOEUPRESX9DkwATpZ3iPAhYARIvphJpk4lUlYNAq3zRRx2T1GFaRn2o6CuRQMD3ySargco0qy9sVt+mHTvTB6j9tijHlQh0LqsZncNpPXbtYKV5Z6Op0mbdTO2HuyCa/NEPM5aTxIxHyl+ZYB00sxFcsnIuEAgEbvt7H20U1X/zf4yrqS8/GQ06IvWUGyiRhm8xnVfodFJxcS8LB85Hm7tYUZnEhG7Tql06wPoTavRedT8Ei7jKFll4NlETKLmRgwtQQMbwye/aMOR/ly2UQi4EaMWjzgCTkdbqMGJoDdpJeuSqaHjQqxRatOhACK5zbrY34XPC8V0DEYl0SEyOC/q72Hpmkul/ujH/3o/Pnz5dEkmI11arCffD5/enq6XGDbHbfbva2fq7IwiiBlXHPl8fJ+jWb4S8N7uF6NLGQVBowrAT3fp+V6NUte9axd+uz4mS+M7PrZ7QP1Q+2FzHpcWPn08k4cT7q9QPFHFuIJ94xP9j7KfcEjOZT0LmbCUmTh+ZR/ORiKBoPrgGiapkdHRyv9XL9N4r1///5f/vKXrDIEtbHbmhuOIIh8Pt/a2ppOp8uX3rqTzWb///JzbWrw1ms5nc5Uap3qemuBe/m5KkuiKHry5Mnl5eVUKoXjOEVROI6X87Gzeg+Xy/3hD3944cKF8okbpg3TRSsrK1NTU+UC2+54vd5YLFb+6erVqz/96U+ffeWVsV1v2X/6yO7aF45PXLqlnCCLJddn2FivaP53svo/iFjay2fdi7ewQNACa+7UcHRHW6h2KihQY+VTtt1JJBLsC8C2v7IHvV7vJj/X6Ojox9DPVSwWJycnl5f51LbytbxybdjBAf8IqIsg87cVs3nPhZj1czFR8WbGxhCjg0gu5JbPjvptemAG2qT0JNkkjgwcAZgS4Vrgr4H2MCt+CepRId0zeDLoibrRuN+Fe+wkG6iCqiGGJeYFORr1gYUp4qZ9FhaiWylQSx6fDfdbrrwsY/BiIuKxGgOINQ4ZE0PQbAB9h2ZGBxcnRuA2I66E34ka1AzdUbkG8PvkEpGZsSGrThn0e8tO5U1zMZ1Oj42NiYTCdVfa9k1iamZV0lQcYqxcTHCNSw++Njb9U1ku3ltGsupmIuDxGhUQHJeK0YVMJuiWTw+FUAvYNkrh0DEqgICL0GOmYwFweUAeNwMEcAUQCl+L8GKHhr1ueUAzeCLoiXjQuNuOG5hQ6krDGztMbPJU1tIDweEMioiBllNMgGHlSK3ts9FqpSlXwMNeuymA2eIBN6CJYz46Gc7GQyP93auLM2QqRqVjCb8L0aupZJiOuOFGWHNaBs/FQzPD/Q69OuS/p58rlUoNDw+LxSKayP7OZo/7jOA9fgIgXTqe8LvBhMlO9UI6HXDJpoeCqJXcOvk31FPqE5jSjN2IebjKc3htTmbwVNATcSFxrzOBmcCJiWkhyo/lB8IZetKYn454QdndYkdkIw/WxmKtzg3NgMlTxCMuiyGI2fGAG4iYGVtsLh7q72oXchfoHEDBcL8TMWogSUXl6Rk8HfHPjQ+jZl3A59n0mHzoVx6Pt2/fvsbGxgrQKJzEijf2dC6X29zcXC5Q9nSUd7bFNZd/ZXdomnY4HCwUZtNP5QKeZOiOYqq4plhsarw2ggzYl/VR9IXBM/uGquz+Eq/xptpomo7H45vCwovZEO6atUw+Jqv/Y/H1P0x6lgLBkt5TvnpdXV1lPBeO45cvX96/f7/FAqH45Q5hd9hPgiAuXrzo8UC3b2pGudps9n9rXPOmZrPqnU6n+y3M68UXX3zjjTecTqfD4SjnqWBvnMPh7Nu3r6mpqXzj7GCVe4lVqdl8t5suUf4KCcVcrjIqHOBlwSCCIC6X61pt7ZHvfrf2h9+3vvvuy4Nnr6iGNG6LLRGIR7T2+aelt/7ANvvzfKqEiCrD4cs1szts5BdMzkjxDi/60k3b4Z7wtDodS68ByCpOYMP1Wb2n4vCGXYbjylPGubM3e/PmzY8hrjmbzVZX19xtby/gG7AIG5aejcsQFfXCu7jLCPgPtxmod0oaCRMcBOHc0fu9EWYTITcSwGysRPdbtIhOGcJsgAoqy8jKKzJCF9JWBxwAQYi4SZ+NCKBM1ncnuJO2wdgyibLZXPFBFJoa8TDwnWjp5RgERpyOM8wr8ZINCTLJRz1EzF8R3lWhwKViJcMG205W5GQYyE55pWaipuN+F2bUSngLZqU0EXDDEp+MlYQrI36oLL68MDM11GcxGTbj09bWwlgsduVKVWdHR6Hixfeeg8J2VyIMr+ZeC/giXQZwD7GOMBanBdSRPhgsFqvOCsLKfs7gAcwesemAviUedGkUTqMm6DCXxC1bEjrNTwPrkhKcayyjDNDpmimnnkTURVQL+wEHMPpE3KAWV14xzQwxGIeCEP8M4ehKkLIIY1dA1XSYyXniswEbkN9Bh91AHR5wUEGEjHiL+BanaioGoWdhF6izcJugfYLSlmA00WQUKKOYOHMqFY17nTatUrq8ZFLJcC+TK81nhQTpKWa6ZhOFVJw7MTQ92OOwWdfGYfP/SCRy8eLFzs5OOpf+kOGo7Fs2yontE4Dbl+cDC/6tmGYllbFCgcgmMJMuw3I6pOMujQLVKUNOOwCMKp+X9TrBAQedHw8wXREm/A4SPHqe0ohAdF50fUKysDPWL5zBocNdBtCY2QrLJQFexpxV9romQjSTZxdch5tuFu6XWQfKxxmkHTSYjb5kdeJsggITncumUUh4Cxa1vIQAK5/FPi+55ML/y957dreVZQti9jd/9Gcv+4v9ln+Al+0ZL/fMLM9afjPjN6+73+s8HauruitIqiCVQkkqUUwSlShRDGLOOSCDRAYIkARBgiAJAiByzjmTiNe1zwGvIABkVfXr6tevps7Sog7uPfeEvc+9e58d2XQug2Ixvw6WU4uYRr/z+XxHR8elS5ew3w1yzM6bzeZCoZDP58KRcDgccns8Gxsbfx55j91+caibUrHostqLZ4f7RlMmciendrt92Sr7A+cpzbQBucvrSiKRwLxIzZ3iSdCrvHcw/j8mPbxgKFYjPqmR9+zt7f3+979vaWkBpxZUQsGg3w8W/bFoNJlMWq3WeDw+Njb2LZP35PP5YDDIYDC+973v/e3f/q3RaMxkMqS8hyCIXC738OHDN7ZNPm9B2yaXy0UikVAo5HZ7Njc3v1Te43a7a8LhYFDTGYy/+9GPfvyb33Le/WDr7//Tvxn8zd8s/OE34ieZQo4gSgkX27fXbBP9MumBPBjnyXuqsX+STdusNoXltJ0WfX8s+JIbN/oLNcQllfpO3nMGs3K53N8/sLy0lI4EG9AV+ADF3jjXZpNEMgxu534rRLixqkClEvcDhQM2SA8XPXpwP8b6o5ATeA5MWrBYIpfe21zfk8sIIu/Qa2S8tUzIh+Q3Vd/6Nz984FKUCEIeeGslF3clKyf8hLzTYHTis8CHnhRpYJKAf4Lk4HUQmgohx54mCQjWDJNPx8oRL4iCsAtuyIGEQ2EgAFEvrAUymR+CKxOEjXaBuCtoL/vNoE5KBCrMRNQLYqRMNBsNbYr4Yg6rgBcClDgMhAR99y2agzXailGtsllM5/mx5/P54eHh2bm5dDjQwF28Gj64jl3J/BY8N0CBU4N1XmWnBuYf9QBLZDsE96h4kAg6kCykyuH5NCWXCHTb64TfaNuTrrPplfhJtWPFKiBKhMDKx34IxlI4zKMFZbFFWRoQT6OCDPBWFbBB2AkItkG8AoqYj3Bq4V8lxVugjC3ZERMJvI4biZEghuQ+sEEeA2gkwyipSDqOGFkPeLdhO2j7PvB5ETf8cx+jLaEHZ++QA0ZHIaqJRCAb8mwIODIhD6IPgCQDxf5G3FgpmzCq9/hMik617bRZz96P2v9PTk76+/tXlpcz0WAVf1y/dTErU2FrYGlBG+HWw/5x62FHJUOAgrAbJI4hB9pUKLlvPFCxqMMMUDZB5NIbAo7hUEkUspajg20BJx30IKvq+kGRDRl+y6JeYEwhajMwqUWzshKtEYeWdmrgDU0EX1vNk8OlImCzBSloUPwC3FtlD4B3XjnsrBwe4CQQBAs/HOkgE4W3DGz+IuBgGLAgjHgQypDZOMR/R7G/SWUZvHdRIhtPB92iVYZMwKnEXSTNlU7AL8F4qFqlLFl1apsF4sR89ZJIJL5wR7p58yYOvFssFrc2Ny5durSt2FEqd9ofPOh++fL585dyZKiRTqdT55RMJoPtey5uc3x8HIlEzukjlU6n4/E4NtQ4r00mnQ6FQvpjfS5zYo147m+O39wYOo45iVwxm87gp9LptM/ns1gsmUzlyuve0tl0Jhf2qBJhi93hdDgcuA227xkbG6u279nZ2eno6Lh37x4WHUXC4YcPHjx+8rhYLLa3t1EolPfee9dgNA0NDdlstgZjoVHT6XQkEtFqtRdAJp1Oe71es9l8QZtMJkPa97xezpu1dDptNBoDgcAF/SSTSWzfc0GbWCxmsVi+iNbY1NTU3t5us9mMRiNp34PFaTMzMzdu3FhaWsJsx7ZcfunSJfm2QrWnfNjxqOvF8+cvunZ2diYnJy8YCG8bHGuxeinpdDoQCJjNZovHExGLif/jX8+/9/2/Wfnjr2TPzekAkT45KZQzJ6Wo71C39nOPsiWdiumOgQet7qS6nk6ng8GgQX+cO0lnMymbLzEmCvxhwP7hmJu1nw4nTokCGIfp7SGb1ZQ+20jVPeB6JpOxWCzYyiqVgh2bSCTGxsa+nfY90WjU43ETuUzcB5r1mqNbORWBhBU4OEcqBnFTINSvHwhP2A1kz2cGKmU/BNNaTAJxegTbAZBeyx44Hzk1EGolhuIQnqY6Ox4IOSyikLFoDsLYOriGuJKnTCxlgfOrCwTvIQf0BkHqUGBivwWZ9J6FtrOrQSCUDAMDhCUNkFPMi1Q/hlLQDnQu6in7LcWAvRj2IDFVEBYS98O5P+aFEy2E8kNMg0sLYzkgaykQb3tVKDanBi6itGVlN/gVg7ok7odl4gmkwlGP3avbR4QBadaSYeI0lQx5DrZl2xKB324uZRPhC+17ksmk3WEvZxLJgPtcvhAzlJk4DITiJoPZDSQaOwTDZw+yzMAR5Kz7ME9E8MDyGntLQQoFK8AhEydy6cftLZs8dsmjNym3kkEPHM1r8AIe44iBg1SRsA3AXsp+UOF+SFcsnIWq8hMsdWDPJMAxEBAESHFDgGw78DQlj54I2mALeY1FtwHiF8AG84L1tEcPKQ68BiKAbLfBC+8AkBKwAvxRHSXWOEBbAvl/OY8grZhFhXYdxMNEM1TDIy4d4T6O2I1+wyEEaIh4gPZDLKJkMuhVbki2xPyIz5VLxSLBi+x7ICBcMJhLRFIB5JBYAyIs2sERnBMh2PNhJJX0IRYN3gUt4k2tFY6touxTAWpgUftozmpg2sLgOFnKxFvv3VVuSUvZhH5/NxXynqv6TMeRDtdDJMKwn31mImDB2V4BPlY0BAx39r7gVzKDnor50YsDf4FTjHrBpy8RQoKfaCEZQU6aiH3HojXM8eAXDdSdLghFAY6ZZuDnELQhHinkxNgHb82oF97KkB3idaFXBubm0cODwIj7QnaTT4uSE8PbhwJj5tJxv3tvQ6JYF8a8zlI2AfnYa86wFzJBsVjs0aNHN2/exPIel9P59OnTjz76eIVCddhtr169Wl5abGlplaxL7969azAYsBtLvS+Mw+FQKpUajaah5w5u73A45HK50Wi8oI3FYpHL5ThzVv0o2PXJYDDI5XJImuFwehyu5V3+W/SHzbxho93idbpxG61Wq1Qqz5ut3el1ON37++DRhds4HA6dTnfr1i3Sn4sgCC6Xe+PGjfv377tcrnK5TKfTr1271tLa6vP5qTQKg8H4/O5dvkD46NEjJpPpcrnOmzD257pg1Xa7HftznTdhm83mcDiqJ3zeWDs7O1/qz7W5uXkxFsxmM3ZA8/l8Xq/X6XTOzc09ffqU3ErktsFRnVwu1/POzg8//HB5ecXhsPf19S0szLe1tclkG7dvf2Y0Gs9bF942arW6HjgOh8Ptcjn9fp9cnvv+D8L/8W+b7v2Oee3tT8ausluuhLr7vG6v0+23Wq1WefMB/YcS3ozd4QeHPYfTbn/t6oUBZbdjfy5I0AF6NIct6LXbbFbGhrmNGnq739k8pW6eNfw/99SKnV2ns/Zx/BTGQo0/l0ajuXXr1rfZn6tC5Eh5SfXXPBXBdB3sIcy7EOcefciAn7Crge8Bf6KzDAkk8SPdQMiK3wIuxInw9U8+sh6rkyHf0e4WfMRPU7X8FiYbIMOIwZcx6ICxHEfoL2JEIIsqHPFhaBRcH6gIDuDr1AFHgpMQAXuEvvIe/YldU3IconmqCmYI1IsIzCGIrBzqnHE3rFGUnBroEDFz8BG3q4Fa4KWRBiiWPRBRRDwEJLWwAfnEjJETVEtlr5kIueDDHbCCoQxSNwAhCbuIdDTmd6m2ZHG/izhN5iPeoMt+QX4ueA/LpSrOo4YlRcCJInKVhPTdsNKADSgWDr/r0lYW2Nh8GDGpeGnuYyITiwc8n1/7OHCsSrjMkKjhJEnkqvACWpUk4guDoDEJO4E7xByV7QDtBGQtjg3Ggdt4M8OrWQmhq8Ex/hAIvA1JiSyqottwajkkKjlTlUUcqdl2gCC/X7btA14OpcCyuHSACNsByK78KJcIyv6Bhj4EZZn9ECIc+pGYATKfBJA4Sl126UE2GXYCmix7wA66jytSSVC0HRAxX8RtU/IYCb8TosgEXUFPxbSC/BTWVcqAF+xdn8Keegg7wIbGylEfLBBzz3hbukAeCQiqQAYxH9VvCtTRRiUriFMhApaQw3z3008iXoffYTlUbCC8vBk1Cr+q+GUBVZoH+FrzHuzGSmoLJTLnRxm+qgfF2A85gFl3aCCDm9cIvLtLW4p4T+w6OCQEbETAWgi5SimkngPBlYMI2U+thxGdAoRYETd8AYIOIoiSYGD5H7wIRpDCwtt0UPYcQ7OQExipbAJ4UBvaBi4tPOsxAAPqPoa8MRCjQV0JXhD3h132/S1pOgDvTiER8nncX4ftIRKJxBeWPU1NTWQglpOTE4VC4XQ4E/G4z+c3Gg16g2FDJvuShAPnx++p3hj2L9NzFQoFq9V6cWLtbDZrtVqr7Ui8If9n1O7f0Ns2QpXgzufpuaonU5+PncxTgTuXyWRtbW2PHz/G8ZoJgjCZjBqNNhgMRsJhv8+nUChwPvZvQfyeashgLFQbVmKpBnklkUgMDAzUbxuHw5FIJHxen0GvNxgMW1tbXxq/hwx3WT2Bmrprd7eo0a5Raaymlug//uCjW//5rf4/XpJ20xxy3DLlEWsZfx8xzUBu3aQln/XW9IDjFpL6ypq7gWSxh5/87/9o/W9/aRgWV0Jf1rQhf/p8vhq75m93/J4iEbIjMQmyHYaUWEgDUrFiSUIgu3gARBo+U4XMe42I8mHf40ZMT/UXFtdtB0TI9kVIm5/++EdqxaZdfxTzucqZuE2nBp9wLGpClAMoSiJIhGxgeuw1wJnVA85EkLYdx8KPITG+aQdR0H2UsfxsDhCYRwfM0GtWTAlsgc8E/AoWQeH52IHpgVwKKE6JXsIEZqjC5SBhFcgt6pyrzUoIEIxTZTk18KV2H8MxF77+dsR/nB2p4YStBVqC0lWW3fqCx5T3W4EZ8hpz9qOA3Vy8+ABbKgGZD7tBg1DJU4ZSEOAo0nAch1zcIHcB0Q5iQ/1WwBHJBVbyKCEuB68ac4dk+iTzLtCndDjssb39q5+bD7ZNmv1k2FfKxC3aA3B6ysTQSd2FRH1e4Oe8epwpFkaBfJzYRQ5lvAL+A9EtGEtJgAjwDC84ZhJcP4MPCsdcAkPsY5g8mh5O2oqfgqxh2i2diFEgU1LgxCM4tWpFbrELD6LMFUjQpQYKirhS4JjRPyIZAWuwCiqVFW0pQi4MZz88sWmKtgOICu0+PjHvg/HZxaVUACSGPUiW4wY9LyThQnG9kyEQmaC3CRCBbJhgyShzGVD0yrZE++r1Fq0CFEYTBqDP4DYcXXrndx6LXqPaSQQ9pXTMdKRKIKenCk+Mtah+K6AGxI1B4HicWsSbGkBFaD8qg6DFhhJd7UFgHvKf4xCy9voMIFcDHRx6cUB5bYBAUB49ZKzzGICn9FlgH0LUxEPCpU0cyvQSdkU65TODjhjzW8D3oMMGAixwYMAWQ0BteJdBKukGO7NEqBxywdECKyUxfh1qQA3aIYTtgLDu59zGotcE0Ylc2rxT57FbvxbfQxDE3t7e5cuXcT72M2tQQG01Y7G+vj43N0dasL5h6ol+/LnsmvP5inVR/RDk3LCuoVQqkW0I8NsqrghZ/6nr0p3NUW8+nk1lvO5zbY3x0vx+yFRBLoogiImJiep4zblc7smTJ3fv3iWjLddv+UKh0NXVVW3oSs6KnPC/LLvmcrlcH6/Z5XL19PTg5WPoKZXKK1eu/NO3TbVdcw3oSABaHI58sXhyevqiv99w+WPrB7/732lX/qf5326HDGinEsUSYdTvW0W/NXP/8Wjuf016IOdGdW8Xx2uGt8B68t5o4LevXK3L7gIYjFU//bre0K55fHz8W2jXDC9VqVwu5rEGpKIrAVIN5By5rQaA7EHgYxR+F+T2SDSNLC3guAZkD7mF4+915VNOHl5JigtfQ8jl6Te9eNLxjz/8wdMHrbbjo2w0oNqWBRwWOMgC6xMj/Jaidb/oOvYdbJxY4bsMfAPY7qSA9kPgRDCvQUyGGj6ytsOCeQ8xYcCmoMA2SKeAaCGQGTwlMMXVg+cRycrAR/aMrUFZ1nN6RcVyCD/i1BJwdEaZ219zEkhlAJ5H+8Bd2Y+Q7MdBBOwgAQLKcUbS7ECDy9a9olNb9FkhKJxxJ6ffLkHGCWPetBNymC6Q98C3r5CDrKtuHZgqI2sVEHsEbUBooz5kbOEiUlFQSUQ9cKQO2iGyQMyLsm5pKsIVj75sPQAKZFUBWj1GIIpAY5Aoy6wEhUgcsm087Wj/4ff//sWjhy7jcTYS2N2QhJxWMLlIx6Bz+2HRymwgUQAAIABJREFUuJM3KLwq6an9iLCDnIbwm1G0aBfsFo8eLKnDIO4CAmY7KNoOYQ7AoOzClUrSLrQlnJDOqew5BmWH3wJUE0REytfSO7SdCMgVqjrVb5N4AcmEzwKE2aGGHtx6zDBV/oJgCXRJwOaCUTMktQXzGiwGw1u0EsgAsoUXHCAVw+sqQaLZg1O9POy8iO8BipTPwWwhO6wBhTm2gUQqaAdAQS6zypmhHA8AQNzHYAcWAU0QQANxQrBk4JKRfNGGbNQwv4iVxXizQf7avZLP3NF878f/8IOXTx/57OZ0xK+QiSPVMTMzsVLQkXfpPUe7+QDE4SzHvEQ6DK8Szv0S8xF+E+E35ZzapE4R1O249jejBpX7YMusklt2pZaddcvepvlgN25WA1Pr1BAWlEY0ZEdirRBiqZGpFp4eyhtatu2f6ORlsxL4RdA8al6/a6DjPgJUmnYJpLQCdz+XDv7CwkEqCRiHw4P5bIfsYcFP2aIsWfaKoANVYbxAvliPvmDa9dstX5fvUSqVP/vZz7AWo5rXqabxf+V8Dzlti8n83tPP/sPAlWH1qseHXA5f06w3agRB1PM94+Pj1f5c5XK5tbX15z//OfZSJkephkyhUHjx4kU1//TGMOjM9i3ge5xOZz3f85Of/AQ7eTWEDM7PVc0Y1UAGP/WV+B6LBZvDa5Eucrqr552J+79fvNUq6PGfJsDAqFCw2r1B/Ziy/79TdP83gaOXJIuGB72Y70Ft4Il0KulHkuz6qZL9eDy1/lxjY2PfQr6nUCgsLC7yOWslyIKkgW8T9tYBCoEIPFBxFZjxYovFmBe+aD4jEBUs6vdZ4AMHDIcJogljNsh9jFIVqqs+hfu4DvkvHZp11vJ4X9ehXAppFrLIdDodRXzDMeE8KvktRac2ZNg/CUKOp5zHlIv6kJ4FTCZhXOzGBTIhB0RhAaNXG3gDBWwVowG3HjgSrB1zQ2onOLki0csbZBJrairiBCQ5AAk8suyxKEHmH7SDlD5oAxLrQvwHxMg5AKILh2Bkb+tDqcixkTUo3dB5F5NYdH4tmZUohhCWfKC7tv2CcSd8Pt+TTqfn5ubEAj4cdt16gDm2pKmwcciExaoCNgilcSiH3WBGGvUgPx1gH4HKug1wF2xZ3OAeFUG8EbbCjvlBShR2InflIFDrZPA0HhSv0ib7e9Q7WyWU5BKYUaxqdKghg4TbULCrg7rdU7eBcKhPLQd5n7WyDcB1H42SRsEbg3Yi7i8FnaBR8pown0rEfWW/GaQgUTfYCZFGMMkwWH54DMA5+S0VQogFBsiyCvLPOxH8wT5JgyMRgN4EmdYCWv1gHgRtPAZksBUA8ZvPRCSCr5OkvtZUVpKxQ65NlACV3BKEZe/UoAg7z427n0qlpqan10UCwmcErRmI+rAEdB9YnIgbLJMg7FAQHAZPksh+Gb07cTgwwE+vEZRQDg1wq+koqKXCoAAqx/xl2GYg2wApCGgDwXCKsO+fWPf5tIXpwR7NtqwIXgIRePuwkCkZhKiYiWApEw96HIVUjMilAi5bIghBDlF850gpHkyZ9n3anXUBl7fGZrPZdDpjbW2NwWRR6XQqnVH5R6PzOWzVjjzhNJ14LfmgMx7ypSCBXQQWFbKDkM95dCY02oeK57hsPzp1GzNWNeAu7ISTEtiS2+CnR481ZeApGQ+CvBCSdrnhDQVJD5JQWvZAb47yoQJnj9S1JeB4dtF7dHZqgvdl1+/4enxPsVjs7Oy8fPkydkgGT6VYdGhwyOvza46OpqammQwGn8ff3Nz8a9ZzVTMiJ8nMxMzUv731iz+uPd1PVPywqhuQ9S/Vc6nV6vfee+/GjRvHxxX1GZfLWeNw8rnc3Nwch8OdnJhwuz3T09N/Fj1XjeM9OU+y8ufKU2GxWOrzVJCj4GSr9XkqqvOxFwqFx48fX758GUc3wA5uw0NDfn/g6Eg9Mz3DoNP5AsFXyVPxVfRcNpstl8thL/ef/uxn3/u3/6578FUhdyKW0a4JnjsyIaJUtlgshWIxE9y28n9k4f69R3Ezl3wD+18xT4XH7a4GRX39vxY918nJydNnz3q7X0a12yVsZoEVE+ARbQIqFbDB9yjiBqkGxOdFugOvET6FkH3JgwQth+DhBT5QWILtwC7f5ZgXTHM8IGWBHrwGZBGiAnWV97jkM4B5aSJABExEwFzAxjQ2dd5nQWa8McQPgRXFaSyQCnoq0qAsdmM5C0OCDZKwbzCuY31QIlgh+eC1G4GFxAPA+lRO0qpKsnGslwE9C6iu0GHUAYa04EptBO4qg2x740EwyMUevKkIyFRCTiDP2DsGCBUyKsJW3h5DReSDeUdS2oTNchEnRFiUGc2m/Vh9np4rHA4/evRobGQ4rpGX8CHbawS6GLACIXfpgNxiy2uLCmYeQulIcUyB9JnmCzIwIJkHRBZAKSOw1S1YhCBeE/RlZ/562CkvZAFPnCywQUTATIQdcPJGS8i7TRA5OuYDQgjiJVfa78qc+VcD94MTouFgvjirBnYLguFIh21UwZCsuF4nQGLkNYJzlt8M4ii7GmEHWTHbDpAb1BEKEWQFehnzw1ikVjQRKoOOKVmRbSSCIIIK2sr2A+BCUmHYgShV+JlcAcn/MFIwb0r+taoI20FSLXPqj+q/CPhKOBxubm4ZHxnKmvdLph0QjQCEw0TECVxdJlZKR2Num0mrjngcp4lIEQIhRnOJCLAgwAwlgF8JOyEWDpl8g/RIP0nCnIMOYImSIdA0gQQRrGQI91HZBcIhYCZCNiLqyTt1JwFHOhpMhv3FdCzosgdd9lTYF/G71kVCPmct5nPZDDq1SqnYkPI4q2wWi0ZnUKk0FoslEQp5XC6dzmAwmLgwUJVGp1OoNAGXs7O1ua/cFfD5ewq51aAzqlWHig2NQmY93HFoVTHTofNgy67a1MklCql4XSwU8rlOvbqUhpCk4AZxkijE/Sch9M6eoJgR5B5A7yMwQLBGZB0fghNLOQNBhvIRL+wErwHETm4NYd8nbPsolvde6khm02vOw8t517e2tq5du4b9uSKRcP+rvsuXLq2ucUwmw927d65/er2trU0qlbW3t2NPFkej4nQ6sV2z0wlOUg2L0+nc3t42mUwN7+KLYKUql1ut1vPaOJ1Ok8m0vb19XgOXyxX0BxjL1F98+PbfvfrwhY6usRuDLp/L4ap+xOl0HhwcHB4eulxw3el0GgyGlpaWarvmTCbz7NmzpqYmHJV4Y0N29erV5ubmVDo9MjL86aefXr16lccXdnZ28vl8t9td3T9ZxxOWy+UXQwYbYl/QxuVy4Qlf0AZnC9Hr9Re0sdvtW1tbRqPxgjZWq3V7G0yA8SpcLheNRvsCFAQqWE6zsbFx7do17M8VCYeHBgc/uHSJxV41m0y3b9++du3ao45HW1vy1tbWC7aNy+Xa29s7Ojq6YDIOh2N7e9tsNrvdYIr+9ttv/8M//MOD9nav1xf2BpZEK+/xn+3qVRqFKuT0ef1Rh9Npd9gOpb0HlP/o2n9hdzic7iDG7wXbBu+B4+NjpVJ5wWScTqdKpVKr1eS20ev1bW1t30K75nw+Pzo2Nj87XXCioCwVN1ctfM0hKg9yNMXeVZjhwPY32KQABOnIbTURANE6aRWEDS2xjD2DzCHxFUSP4diXjkKc31SYKJ6kQz7DvuJQJgjo94l0pJwIJv2uQhyFAMZPZePlZAiizMFPMvBJvRMveeXM/hfH9cEEEty7gkBFPPqKGUTACq5MKNIMCIqCdtQ5LAfYi6ADhsOqN5SMAtQ9+SyYYKOQa5B0HRiFACg1TpNEJlo5Fp+gCCWQth383cDSFuw0lUisgpzgIm5Q2zk1Ob3cb9Wfp+dKJpNDQ8M0KgVyb5mVQPOALzwAX+4Y8pZPRQCSMR9AEmRgKNNZHGAL5ATcbaKg3IGFVKEAGBpkFURGYcFAJhEEOs0oUcimQ55jxfq+ZC1gPASjYK8x7rFXElphh/x0rBTzAjmv7qG6jpnR6is19dcNYkgc5QIfMa8e+C1sPoUtpgM2kJSEnGVsYgKh8ypJSAAd+TTg5QQhHdvII18wQAeJLAhOg4xvwJrkjFsFNR9Y20DiNmQJToBeUp/Vbvmt58aJSaVSXV0vmXQacFc+UzEZTgS9bpvZ57A6LQa1Srkj31xbWxPweQfKnQ3pumJrQ7YuWReJDvd27SZ9zOfMRAL5VAyi8+FgNnhuKHxOCYuITtHqzrYfKMuQKxzgJRVK+R3a3c09mVjMWRXxuWtrqzQKRb4h5XDWWCzW2urqKhLnMJhM1e6OSMCnUqnA3jCYVBqNzWLt7yocJv2GVMJkMtksFmZ6WEwmZw0EQXweV8Dn0+gMNpstEgpYTNQAeCMWe3WVxWIzmEwWiy0WCSUikUgo2N2WS8RiJpPJ5XK4XK5qV2HUavZ35HaT3mrUm491xXQ8B9GWo2Ajf5ospWPZaBAS1CBMlTLxTDTocVgtx9pD5a5mX7UhXT9U7jrMeqdZ7zZpffqDpNOYtmvzDk3erPTaTF9XzyWTyX7961+3trYSBJFIxIVCQUtLq1gsnpwYf/iwY3R0dHx8Ymtra2xsLJVKJZPJRKOSSqVMJpPb7T6vQSKRSKVSOp0uFApd0CYWi2m12mg02mgQuJZMJoPBoFarPa8BHigSiWgO1K96+v7Qeet9/vM5vTgUi5ykM8mzkkqlbDab3W5PpVK423g8Pjg4SPqxY/fslpaWX/ziFziIs06n6+3tHRweUii2b4IL93J7W5veYHj16pXBYMAuzfWzSiaT4XBYo9FcsOpkMul2u41G4wVtUqmUxWIhJ1w/EF6FwWDw+Xx4UQ3bxONxjUYTDAYvGCsajWq12ng8jntIpVJHR0d9fX01fM+vfvUrvG3icbRtmltEYvHU5MTDhx0jI8OTU1Pb29ujo6MXbxvMgV0wGex4H4lEkslkHGztfX6/f3p6ur+/P55IFE/zWz7dz1ZudzL6p7jjVrM2l81l0tlMpphJuFzbt3SrvwhaJenMaSAQ0ul0DWFCLtPr9RqNxgugl0qlzGazw+Egt00sFhsaGiIlghhE39zfv2h+rkgkEgwE4MMEzqgm+BfxVMj/mTMtSAtIopWKgNlmJW8okru8ETMNn/tRLLjXhO3s8YqhdILIpQrpmEl78PRBW9PtW6OD/Ra91m0zmtWqTNgLvAUWG7wRPgRlJnotQjjrk5wYrmBGp/oiJJFGz56miVy6fJrKhL0+h8VrN+dTUSKfQSqJOEAAExtETQvpWCrkS4d9UY/DadYbdeoj1Y7fYclG/FGf02E6Djqt6bD/JBZ0mPUhjyPqc4bdNp/dDBFpk5FyPkPkMpXjbwzSm+c9xpLfgkRoEGWn4DoOeZznxe9Bn2kUnSwVQkgxg9LKayoHzKAB8ZqAocHhUnDkFVhvDLhPHK2ucvEskmQ1NABZiFslL1bQhM7lp8liKqpV7bQ3N7Xdv0eZn4n4XHGvIx9wAJSAhToDO0Z6DYLIPusrpFSDvIX5Esxhn6bKp6m01+oxHwNe0nHiJA70HksLUhGoIBezfCJ0Eg1kwr6Ix+4wHZuPj7QHe36rIRPyRsHT1xTzu/PJaC4eCrlt8YAn5rFHPbaQ25oIeoqxQDkThVVk40TURfgMZZc2bzkogZDpEPS5Tu2pXRv2XBTxPRQOh4LBmN9l0h7JtzaFQsHqKpvJZNLpdBqdRqPRGAwGl8Ph83jAW7DZbBabRocbDCZDLBIK+Dz5hlS1o7AadEG33W7Sm3RHIY/DZtApFdsm3ZHdeJwMesvZRDYWzEYDpUw8GfL5nDajTr2/JW2++9mVSx+0t7Utr1AYDCZwNEwmHhTYG1SYLNbqKptKo62trnI5UFgsllIh9zutxVQ04nVwOZytDan+6NCkO9rfVcBPmZTL4chlMpGAL+Tz4ak1YKSYTBgC0hEtL8/OztJoNAGPJxGJFFtb25sbR/t7moM9zYFqXSLe3tygrFCoFAqDTpuenp4YH5+fneVzVg93FXvyza11iVqltBl0Ah6PyaDv724rNqV8zhqVQpmbn2exWOtiEZ48FkIxGAwqjcpisXk8Hp/H25RK9CqF235uXKWG3+J8Pq9Wq/v6+qanp7ExJ45Kh3IzZTKZTD6fJwhCKpVyOBAg7oLylfJUfAV/LovFUp2PvX7Een+u+jaJRMLv95eKRcEqr+VR+x1uf+vRwmG0ovgoEcAc1uu5mExmdZ4Kv9/P5/Pv3LkTCECsQoIg8vl8oVA4OTnFkMnn86enp2NjYxerjf7F5alo6M81OjpK+nPlcrnDw8NXr15h8x0MnFwuly8UstksuW2+YrjLhnELcZ/4r9VqxXqu6ouzs7OTk5N4Sg8OF/6HuV//zfRvno3fkYeNtpQ/VTgpIOOqtH/TKXkrfvQ0HffbnRU8VvdTXU+lUn9Cngomk/ktzFNRgUu5DCcw7ApbFYoeNAhYilMtHkjHkGnCm+GVK4dXRJxAIoL+FbJwHMdnccyOIOJXysaT0cA6b+39P/7h5o2bAi5nb2ebRqNTqbRV9irkt4LHkWSlEmv4jNZiBQfwJRmikIVcAUUkg8mlcah7IpsopmNFFI0Ghs6lytlE0GP3Oy3RoMdlNhzublOXF8dGhqkrK9NTkyuLC+tCwcGuXKXYNB0dZGLBdMTvNB3vbsmYNOrK4gKfs8pbW6UsL01OjA8PDbEYdBGfu8pkjA4PDw/0z0xN0ijLiwsLc7MzTBqVy2LQqVQBd427ymZTl7clfLGQLxXxA257OuQ+8TvyYQ/MHCI4Rwqp6Jfn5yqXACmkTgd0SSASQwIwcJkG7FQUf2cgSpwFSMTsBeAF/zsTMOTSMAecSb7SOQiEyplEKRNPRAI8Ju3Kpfdbmu7tbUoMGrVQwOessg4UWxDeKRM7gzMarprFxLKKAsZLFrBzmiIAL4ibRHmgIN9FLgOj51KlTDzgtgXdtpjP4TRoDhSbbNrKyODAysry9OQEbWVJKhIcKhV725tGzUE2HkpF/C7z8bZsnU5ZoVNWBJw13trq8uLi9OTU6MgIk0EX8bhsBn1sdGR8bHRpYZ5FpzJo1KWFhVU6lb/KYjNouIFwjXW8u7m7Kd3bkkbd1mzIkwt7ihDBLwpsVipyGguFfQ08Rau/INlsViQSYUYHcx74b0VphHgFOqlDOrvKYDDW1lYZDAY8SKPhyzQajc6gr7LZTCRcAXpPpUmEQs3+nkQkFIuEezvbAh6XTqf393S/+9Zv733++fz8PJvNZp5Ja866f+N/qUS8IZVKRCLMfm1KpVGfOxsPHSh3d7flHA5HyOdtyaQb6xIuZ22VzeZxuZjLYTKZFMrKwsLi4tLS7Ozs2NhYZ2fns2fPpqen+/r6+vv7p6an5ubmZmdmFubn9UeHDqPuaG+HQaXMTE2NjY0tLS7QqdSpycmBgYHBwaGJycmV5aW5udnu7u7unp7hoaHxsfGpqanBoaHJqanp6enpmZm5ubnR0dHu7u6xsbFZVJaWlpZRodFoq6urDAYTK+C+bvy0L2KZvP322++88879+/cxCcFajGpU4oQDo6Oj53q5IAJDGmqUzylfJU/Fn+bPVTNgTZ4Ko97Q09nVvTj6uWrqvc2eP653vTyiAd9T58/16tWrnZ0dcu29vb1vvfXW73//+83NTZIpJO/iSrFYfPz48bcpT0VDfy6TyUTmqcB4fOedd959993m5uZ/4rb5WnbNJKIxOhYWFm7dutV6v5kh5vxH4f2HH/5/a+//ZFg29cjAbFXNtqlmh3Rr13emfs5v/2jm3ykZ/8F2AD6J521jfJD+E/JU9PX1fQvtmjGkCMiziLylkNVwIRUtVBgdrDNCEUpwhqZsokLPcumTWKgQ8pRzaaJ4ko0G4wFvIugLeRwBpy3mc7vM+iPVjmZfmQx6MtFAASW7LmUTiYB7sKfr4ytXbt643t//6gEq8B1Hhc5gcNbW9ncV+qPDeMBdRIMWcYKhHEhrSifJWMB7sCtXbEiEHDafTeevsdh0Gm+VKeKtCThsLpvFYTEEnNXNdRFvlSnkcaYmJqYnJxcX5ufn5mg0GnxTV1bwQXZlZYVCgSBdL1++fNj+YH52hsNm0igUCpVKWVkBHQGdjmT7LDi4o0nSGQz0j0mhUtHjVDhwQzsGA0gRi8FgUKjU5eVlKpUyPTMzPj5Op1GYNAqDsiQV8mUivt9uIk7ThWQ45PdefPgDvGSRZTFYRZzp+DCzgpkezAjiK1gBB+KrBOjXMhGimM0no4mgNx70htyOZMh7mowEXDaXSRewmVJBbyYezIOeLlU+SUQ9ttH+3qsffXjns8+GBgcfdzwcHehlsdk0GiyNTgcZxj4oMo4y8WARpUirpBbB2qXTVCLs21dsbq6LJAKegMMWcFZXmXSpgCsT8cQ8Dg/hRcTjKDbWpUIeb5U9PjY6Pze3sgSYodFoQOnexAudwejq6mpra12an+WwYBJUKnUFkEOFSYHOBQpmAugIBXQGnUKhLK+sUKhUrNwBKQJmKZhMQDaFwmbQFufnJicn6VTKGoMm5KzKZRK5TBJy2YjT1FfJx57NZgUCAZ1Ox5v24r945yAFFBtUUWtrHA5n9azgTUWlUvEJDyQrKytLS0tUKpUF/BN9cWHhxvXrb731u/uffz44ONja2vrixYvV1dUvGZTFkorFG1IJl8tZXFwaGBwcGOgfGx3p6urq7x/o7e0dGBgYHh4eGhoaHBwcGxsdHRnB9ampqVev+kdQmZ2dpdMBnjQaDc+TRqNRAbCMF11dHQ8eLM3PM2nwptARahCoYV5sNguvDz/FgveCQaPBi4XeEhadTseIA3EYC37iUTAQJicn4VgxMDA+Pt7X1zc3N4fbfF2+J5VKPX36dGZmhslk4g9dsVgMBAKlUimZTODIv4lEQiaT4Qblc8o/rx97zaRq+B7IOZ9OLc8udPZ2/Z8zH/wvS2/zvPsN/blWVlaq/dglEklnZ+fs7Cy278F0MZ5IlEolr9ebyWRCoWAqnR4aGkomkxdQ02+BP1c4HB4aGiJ54mQy+fTp09nZWQaD8Xrb+PG2AUVkOp2KxxMbGxtfum3+NL4Hz+QLe6a/+7u/+9f/6l9NT0ytW5RBiYAQy4jWB6WtrWjxxJYO7oVMU0bB/0b58H9evsxQz9m473i2r58mKjLR+m2TSCT+BL5neXn52ynvAQCVSsDNgL4jSpwkg257wGEF8oklK0hCUDxNF9LxfDIS9ToCDotJe2jRqnlMKhfxFspN6YFia1MkoNOox4f7zOWFn/74Rx988MGjR4+X5ueoS4tymUR3oFQptkS8tXt3bn/04ZXW1taVlZVHjx7du3tbJBKCvB5RMiYTbBFoNBqHs6aQb5p1mrDbEfU6HWa9em+HsrzwsqtrbGx0YWFhGsrU7Nzc9MzM7PzcwuLiwiKcU+cXFmZnZ+fn52fn5hYXl4DusYAdwUdkRCvPLBvQoZnFYvX29PT29i4uLeFpkDNBk2rwB3eCPuhwl6yQTfGVClVmMqlU2vDISPfLrqGBV2olZLUsJCMXy3vKUErAj5LqQrJykkRSkzSRyxRPQWOYT4SjENrObNQdOoxaIWWOy6SIBNyDHfm+fAOSZgh4DqOOvjT/u1//+pOPPhodHKCvLFGXFuRShJdt0Do03b390Ycftj94sLKy8uDBg8ftLRvrEpJ0IUEFg8vlCPi8HfmWy2JMBDwnsaDPYdnf2WZSV8aGB8fHxxbm52dmZqenp+fm5mZmZuYXFpaWlkB8sLw8v7A4Oze3sLAwPw8CBQA14KUCsxq8IJQxu7u7+/r6lldWkE4HWlY3qzz55n/VqKnBC/rJIhkmJNugDg8Pv+rtHR0asOoOiXzmNB4KB87NS0qgks1mhUJhPd/DZrMxf8Nmr5KMwtzc3NTU1Ojo6NjY2JMnT54+ffr8+XNM13t7e6enp6lU6r179374wx9euXLlyZMnPT09z58/n5qaWmMzFxfmlhbm7925femDDx52dNBotPv379++fXttbQ0vGvMNJACq17swPz8/P/fi+fOnT5+OjY1hdExPTwM6FhaWl5cREwhM5PLy8tJZWVlZIVdBQpLczHg/43PCwOAghUJpODR5sbqCe6u+QtbJgUgekcFg9Pf3P378uKmpaWJigockXl+X7ymXX5sD4bpOq/n4449VqoODfVVLa2vz/fv9/QMKheILFGBJQL5RKRaLDocjFAqhxF6NWuTzOIlSOp0+rw1SIZ0YjcaTk5PGXSA1UzKZNJlMoFU5pxSLxWg06nQ6i8UiblJCRSHb+u2zT//foSujwyPK3d1QKOTz+cg2xWJxYWGB1HNVQwZ2dLmczWS6Xjzv6np5enrS19v7/PmLS5cumcyWsbGxUChE9lMzqUKhgNNHnLfqPIJMOBy22+0Xt/F4PNUTrhkI6+BsNlssFjtvMvl8PpfLYUOiC8bKZrPVEC4Wiz6fb3h4GMOkjArABBV88VirRdtm/2Bf1d7e3tR079Wr/p2dHQqF8qXbJhAIXDBhnHM0k8nUTLhUKtnt9suXL7/zzjs3btwYHRoOo7zuJY+n+OxZaWaWyIGKligTLw+p93cnP9+ZHVStRc0Ldv4vQ/rRUvG0UALFJVnwtrHb7RdMplgsulwuv99PtgF374WFr/veVWD39f/7y9n35PP5qenpVTa7CJmAUvhf+SRZPk0RpdOgx65Vqzx2M4u63NHeOjYyTFleWmUxWQz6wvwclbIyPjbe09vb090zPj4+OzMzPjo6OzPDXV1dXlrs6nqxtLSExNQgIGEwGFNTUzMzM93osz4zPT05OTk2NjY4OCgWCrY2ZOA8srOt2JKtiwQMBlhWwgFxZUUmER3t7fI5awMDg909PaNjY4uLi+hzjz+SbHTghzqrquCfNVSB/M7WV3Bf1WSjvs2ffIXFYtFo9Fe9vWIu22oy5NOxYjp2Eg8Fz5f3pFIp/LkvZ5Hu9LSoAAAgAElEQVTGEMxC0b9CpnyaCnsdxuMjl83EWF58/LB9bGSYRlkRcDkYL7SVlbHx8Zfd3T29vVOTkzNTU+NjY4sLC5xVNm15afAVsHeYEcQ6lynQOMx0d3e/ePFiBgmoMF4kIsH+rkKtVABeNta3BGuKTalKsSUWCpTbW7qDPf3BnkTAGx0d6e7uHh4ZWVpaIsFOVqpwUlv9KvD8RvHCZrNXVlZedj3flootxmOEl2gmGriA70kmk6OjoywWSyaTVfM9bDZ7bW1tbm5ufHycsrLS1tb2wQcfYN0N8N+zsxMTE9PT0yMjI729vX19fePj4yMjIwMDA5OTkwsLC1NTUy9evKBQKKtgPgyFwWBMT0/Nz811dXU9f/ECLGYmJsbHxwcGBqhUEDHivYrZFSxEYTAYKysrZ+cBMI3EA62srJxxY5XXBGOnFh9nv//Z8YKXf//+/Y6OjqmpKbQHACBf176yXFUIgnA47G2trVeuXKFSaesSSVPTvZGR4ab796VS6a1btzQajcViMTUqFotFoVAcHByYzeZG9+GaxWLZ3NzU6XQXtDEYDDKZzGAwnNeJ2WzW6XQymey8BiaTyWw2Hx4ebm9v18zW7Xavb0j/4b/85N/839972PFQrVbv7e3hNlar9fDw8MaNG6Q/VxVggDUsFPLLy0tXLl9pboY0pe1trZOTkzdv3uDxBR0dHRQKxWazNZwSOeELVn3ehKs7tFgsSqWSnHD1LbJuNpvlcvnR0dEFY5lMJplMdjEW9Hq9TCYzGo24Z4vFMjk5+QWHTaBSDRnM9Dgc9o6HDy9fvryyQpGuS5qa7o8MDTc3t8g2Nq5fv/6l22Z/f/+CCZvN5o2NjePj4/o2RqNRh8rh4eHg4ODjx48fPXr0qLPz8bNntkePYp9/btjasrjdGoPOZDKqNUef8QZuyiY3lXyn9JqG9eNDBdVi95nNVrxMs9msVqux7xgJ0pqKxWLZ2dlRqVTkttnf379165bBgCIoYgB9k3//cnzPyclJc3Pzi64u2bpYp963GHRW47HNpN9YF1OWFibGx65+8sn9+02XLl1qbm5eXFyigjkCMmZAkpLV1dU1VMgPKxNJrVlgWVmxi2SiwmKxhoeHb9y48Yc//OHZs2d9fX137969d+/e5OTkxMQ4k8mgUCgDA/2Tk+ODA/29vb0MBuOLqBtN9+7dvHmzo6Pj5cvu6elpJpO5ivgb3Oe/lL8sFuvJkyftDx709/WOjQxvb25sb26IBbyDfdV5uygSiXz++ecDAwOydYlOvW81HluNx3azXioSTE+MTY6PfXbrZktz8/sfvN/+4MHi4tKZZWuFHJJ4AQqH6AayfqWzwC8HGMRq0A0ODn766ad//OMfMV5u3759//79SVRYTBaVSh0cGJianBge7B8cHBDx1hbnZp49eXzj+vUnjx93vXw5PTODxQA13VYP8ddZZ7FYjx49etDePjjQPz46gvEi5HEPDw/Ow0s4HL5582Zvby+Px6PT6Vibw+FwRkZGsKjm6tWr9+7de//99x89eoRVQph1wy9INV7wsxhoWMRSDUAGgzEwMHD16tX333//6dOnr169un37dktLy/j4+PT09Orq6tLS0itUnj9/3t/fz2Qye3t7m5ubr1+/3tHR0dPTM4/shTEj9dcJ//NmxWAwOjo6cBaFgYEBzKUtLS1hd6TzUFN/vVxVcOAWn8/LZrMNRqNQwKfTGTtQdmUyGU6sXTynlMtlrLAolUrnNCmWy2Wr1ZrNZs9rUyqVcrmc2WzO5XLndVIqldLpNM4Yf16bcrkcj8fdbne5XK5uA7otn+/9d9/74Q9/ODU1FYvFAoEA2YYgCBqNVi3vqYINyJXT6bRie1u+taXVaGZmZ/b2VDwez+v1TUxMpFIpsp/qEYvFYqlUymazZrP5vFUXiwCZSCTicDgubuP1eqsnXDMQHgtnijhvMsVisVAomEymdDp9wVinp6cQDqdQwEOUSqVIJELaNb8JlgpT6PN5WWyW0WgUCYUMBnNHodjZ2d3c3GKz2dgzrn62eOEQdyAYvGDCpVLJguIWNpwwnkyhUHA4HJFIpKWl5Xvf+95//sEPXG43oVCc3r2bl8uxSDOdTLttTr5r79r2qDxoyvokdtGvfQfP8ifhMmR0L+Ft43Q6L5hMuVz2eDzVEy6XyzQa7Vuo58rlcjMzM7Ozs1wud3llZWRk5OXLl8PDQ+3t7Z/d/uz5ixd37tx58ODBy5cvFxcXv9SqgHlhIc+jU1NT4+Pj/f39t27deuedd5qbm2dmZvr7+58/f975/HlXV9fw8DApDXrV3z87N7e8vEyagl44yF/jTQaDgSX2k5OTo6Ojk1NTy8vLi4uL+PhV/73GuvaZmRk4qa+tLS8vDw8Pd3d3Dw0Nt7S23r1791ln5527dx88fNjV1YWFatUk8+uCAONlbm5uenp6dHR0YGDgxo0b77zzTltb28zMzKtXr54/f/6ss/PFixfDw8MTE5PDIyOv+qHMz88vLQHL9XVH/Ctpz2Kx2tvbW9vaJicnR0ZGJicnl1dWFhcXDw8PGyIFbClSqUGk3xGLxRQKZXh4uLe3d2xs7AuX16ampufPn9+5c+fx48fd3d3Ly8v/9PdleXmZVJP19/dfv3797bfffvDgwfz8fOV96ewEYdvw8BRSpQ2iMj8/v7y8jG1o/kpA/bWmwWAwHjx40NLSgg2rZ2dnqagcHZ0bV6khvsp1pWGz9fV1CoVCgLancfmrte+pni5m7Nxut91uj8fjgUCgOs4yQRDz8/OkfU/1g7jeEDL5fL6vry+GNCz1j2BxyLfAvicQCJD52M9bZj18pFLpl26bP9m+h5wGRivOJfIF6//973//Zz/9KQ4/WPJ6cw8eFKamiEIhncnYrODQZ0n67uxODhvF2dNY5OilVfDLhIuLJ/+n2ffMz89/C/kegiCy2WwgEGCz2UNDQz09PZ2dnS0tLR999FFbW1tzc/PLly+pVCo+NSI5D7OmYBk7E/mw1NzCP/FT+C8pGMAVJpM5h6xAsLgefFvo9KWlJfzJxm3wQfliut5wYuSUzrv7pQ2qZ16/NPJuw/7Ji0jFx2CxWIuLi1jlMT8/Pzk5OTQ0hMPG179R+Eo+n/f5fHQ6vRovH3/8cXt7e3Nzc29vL8YLuYqaGV48PfIp3IxEBxLUgc/R7OzszMwM9qyho4JZHNyy2gSkZlzyJ9ZWkD+rK+Tcqi9+9frFj+O75AJrusXowHdxSxIvCwsLExMTQ0NDmDych5dMJuP3+2k02tDQUHd3d2dnZ3Nz8yeffNLW1tbU1NTX10elUrH9DTmT6jmQk294l2yJ79bjZQaVxcVFzNlc/L7UD1F/hRyRhNh5bc67TvaAG5zXjLxOVsgHyaGrK8gObH56enp+fn5iYmJkZOSCc0JDZJUblfqWEolkfHz8Yr7H4/FEo5DTsVGXcI0gCJvNhkMbN2yDCRjOS9qwAe4E+7GXSnBAb1iq7ZrrG5CrCwQC1Xm1CIIYHBwk81TUP4hHxwskOykWi0+fPvWhnBjnPUL6sTdsgLuNxWJfalFbk1ijvjeCIJxOZyqVugALpVLJbDZnMpkL2uTzeRyvGQ8BvILF8vz5c7zq+nHJZiRYcOUrbhvsx35Bt1arFeepaNgGy5Nw+IN4PO5yuXp7e7FfXpkgyicn+YmJfEcHhD/yejG0T4v5oeO1OztTjnSsGD+2r7/tkl8tpF2pdNblgnjNDQfCz3q93uoJEwQxMDDwrfXnymaza2s45lnFSoZ5ZkCACSH+lDc8v7LZ7OXlZWzfwDyn0Gg00hKzpsna2hr2VsV2oDjkSXUbTHQbDs3EQVNoEIK2+hGyvrq6Cv4756jGMFEhfVXIp8jK6uoquNVc2DnWXJCPkBU2m81ggA8Ljp6C14VHJDtUqVR4t9W8UeTPdDotEAhIJgN3jh/HXVGpVOwcRI5LVthsNp78eZBnsVgrKysN75Lkdg2Fs8N9VoMRP4v3BjlidQWc2igUcqXVt0BZifBCcsw1d/FT54kr8F0KhdJw5mTn580Na52qNyQLFXJ1dDr94OBcPRdGTSKR4PP5uCty3+JXBiMLT6/hpsWGa0wms+H8WWAKBhGAGj5LcqUcDgdPGE+eBCB+vP4lqm5QbYxMXscV/Hj1iqob4B2Fbe+qr5PP4vflvMfx3fOWhp9Crv5gsk2+KeQyGQwGVtNgFHyVv+U3C0pRlGIwGPF4wmw2CYRClWpPqdzb+Ap5Kux2eyQSuXhQk8mECdh5zQqFgsFguDgfOzYTvvizEI/HSSes88byoVJ9F2I4KZXEOWSPIAilclexs1MqlThrHJ1Ot7a2GolEp6amLl7U6enpl8bzjUQidru9ejL1dbfbTUYSqr+Lr9jt9kQCUladV0qlkl6vT6fT5zXAYYqMRmM1hKvzsb+5ZeAXuW0SiYTZZBIKRXuwa/bkcvmX5mO32+016c3rJ4aNrOuvk1eKxaJer8expgiCwKzPG0GVdndPm5sjQshXSpa9qOXO/qQodEwQpaRtzrv5llc74XJBOtsLisfj8fv91Q2+tfnYsdaZzWYLBAKdTueqKm632+Vy7e7uYuKtUCicbxa3263X6yG2GJ8vEomsVuub950ej2dra4vFYm1ubjocEDG9urjdbovFwmQyeTyeyWRyuVzVd51Op9vtFovFHA5HqVTW33W5XDqdjsfj2e32mgfxs/v7+2w2WyKR1DdwuVwWi4XL5bLZ7OPj4/rOHQ4HdnCVy+X1d+12u1Kp5PF4a2tr29vbNaO7XK6jo6P19fWVlRWRSGSxWGp6sNvtcrlcLBbrdDp8jqzeariezWaNRqMQFTzDKsy4vF6vQqHAdGJnZ6emf4/Ho9fr+Xz+2tqaRCKx2Ww1M8R4odPpW1tbDfFiMBhYLJZAIDgPL1wul8fjfRFbvWZop9Ppcrm0Wi2Xy60ZFP90uVwqlYrJZIrF4vqhXS6X2WzGzJxer6/v3Ol0SiQQbnhnZ6dh/wqFgs/nczicerDgDSMQCCgUilgsrgEL3hJyuVwikeh0ung8Xo8UHPhOo9HweDyRSGQ0GvEMMWrI9wXzo3t7ezXzd7vdR0dHUqkUb0ur1VrTwOPxSCQSLpfbcNe53e7j42MGgyEWi+vfNQwNoVC4traGcxTUwAdvSx6PVzMoiZfd3V1sst3wfTGbzaurqxwOx2Aw1Pdgt9slEsnq6mrDV9XhcCiVSnxIqAeL0+nUarUCgYBGo62vr9eAxeVyGY1G8n3BbtUNUVNzsZqGgY4ymZybnXnvvXd5fIHFYrl69er1T6+3t7fLZBtPnjwRi8V8VIRCIXZ2EwqF+IpYLH748OHAwIBIJCLbzM3NMZlMgUBAXrlz5w6VSiWv8Pn8mZkZDoeDr+ADzM2bN/GXls/nCwSCtbW1mZkZ3AP+hM7Pz9+5c4e8IhAIWCzW/Pw82a1IJJqYmGhvbycnjPfz8vJy9YSfPXvW09NDthEKhR0dHQ3tmkFsUCqp1Yc3b9y439xycnI6OjJ85crlTz7+RCSW9PT0MBgMcnQulzs9PY0/+HgJVCr11q1b5NACgQBL8clHxGLx8PBwW1sbCT2BQEClUhcXF8mnJBJJV1fXy5cvyTZCoXBxcZFOp5P9iESi1tbWqakpso1AIJibmyPhyefzuVzurVu3qkGBJ8zlcjFI8fRu3bpVjRdwbnhZm+wTbx6CIJLJ5MLC/Lvvvsfl8mxWy8cffXT12rWOjkdy+fajR4+qIYwCcaxUL6qtrW1oaKhmwvi7Sm6bu3fvrqy8fgpvGy6XixeON8CNGzfIZUokknv37j179qzSrUAgkMlYY2PMf//v7aOjIrGYx+cLBUL+Gqd/cuSH/Z88Wp9Z31DwVhcnH/xf4ukf72xxuTzYtA23TSfSm0skEnJ6Dx8+/FK+tua9+5N//uXsmguFglKpHB0dvXnz5tWrV2s81rCh0+Tk5OzsLDazrTmsZDIZnM24E9l/1CefS6VS/f39f/jDH2ZmZqpZbBI0W1tbV69e/cLQp/5ZgiAikcgXvoL37t3DEVfJp8iK0+lsbW09j0QdHR11dHR0d3djVTf5FK4YDIZbt25dkH+kv7//rbfewkrcmmdzuRyFQllcXPz888/HxsZq7hIEIZfLZ2dnHz9+3NbWZrXWxplNJpODg4N3795tamqSSqX1j+dyOblcPjg4+PDhw6amJrlcXt0G4wU7ATU3NzOZzFKpVN0gnU5vbGxMTEzcu3fv+fPn9ZkFM5nM5OTku+++29PTU4NTgiBKpZJAILh///6DBw8aGvP7/X6wOm9qEolE1eOSdZVKNTw8fB6JOjw8/OSTT847SrpcrmvXrn366ac22xu593DnpVJpbm7uww8/HBsbq99R2PFyYWHh8ePHk5OT5HzIysHBwdOnT1tbW1+9elUfSjUSiUxOTn7yyScffvhhQyufbDarUqkeP358/fr1R48eYb072Tk+jS0uLnZ0dFy9epXP51ffwt9QLIz5IkN4U1MTtpyobhMKhfr6+m7fvj08PFwfxbVcLi8vL3/66acvXrxoeDj2+/1jY2NXrlyp2TDkEBKJhAzJT14kK1qt9saNG8+ePasfGsdzw2ZMDeXe6XS6u7v71q1bVCq1WCySfeJKsVikUCjYOlv45sEUN1Aqlf39/Z9//vnDhw/rz+sul2t2dvbhw4cXLK1mxBrBBgph7J+envr000/Z7FWxUNjS0tLe1tbd05NMpaVSqUwmE4vF+JjBQwXXxWIx3BKJhEKhVCoVoYpIJMKEE7eRSCQymUwoFEokEqlUii+KRCJMvfDPdem6VCpFREq2vr6OLwqFQogheVakUNaFQsHGxoZEIsGXMROPxxUKhXieIpGInDCmfzweD7fHt4RCIa7gBwUCwfr6ekMlEWhSCsUNmaylpfXZs2cH+wc93d0PHz64c+fuweGRXq8Xi8XkovCEycmgCcN6Yd5ni6qeMJ4DXgs5YcwZ4JODUCisQFgMRSaTkRDm8XgCgQD/xLcwhDGo8QIxFqrbCAQCclYYIFwu9wzAQsk6FAwNBGq4g0/dNRumjAreNjMzM59e+5TJYknEoub791tbW/te9afT6fX19epF1W8bESrkthEKhdVbgtw2YnHttiEnvL4O20YoFG5sbJAQlkql5F7FwBdvbkoEAsvGhlQiEaCHBQKBkMeXrq/zlTLRulgq25LKto721lS76yIxbC1y25AAx4Ct3zb15KP+XfuzXPnL8T25XM7r9ZrNZhkqNbLcUqkUDofNZrPNZpPJZFqttobSpNNpn8+3v78vlUqVSuUbwjcEiVQqhe/qdLqaZzF9tVqt6+vrm5ub9R87giDC4fDR0dHGxobJZGoI2UQisbOz0/AzjYN3bW9v7+3tNWyQTCa3trY2NzfPk7jodDqxWGw0NsjWVCqVPB7PxsbG5uZmQ/faaDRqs9mUSqVcLq9fWqlUMhgMCoVia2sLh0OtWd3p6anX6zUYDEqlcmNjo4Zvw3ixWq0ajWZra6ue+qZSqWAwuL29LRaLVSpVoVCo6T+dTh8cHGxsbBweHtbwTAT4tRaw16hcLm8oWA4EAnt7e5ubm+fJ20Oh0MHBQf24eBoYdGq1up5AEgSRyWRkqJzHzmJP1PNOIQ6HQ45KQ44tmUxqNJrd3V2lUlkvwC+VSlarFVPBGpjjmZ+cnASDQbVajYeomWG5XA6HwzgrpFQqrZftJ5NJp9N5eHgok8ka7ttEIqFSqba2tjQaTT1eisWiVqvFz9a/azils1KplEqlDTcVQRBer/cCKxmPxyOVSvf39xviBVx+FAqZTNbwfSmXy/hVrd+NGHRYULexsVEPFswRYqHO7u5uHqWPwE/hv9jqZXd3d319vSHDV92YrGPSRf7FphKBgD+dTodR8fm8ra2t+weHNqu1s7PT96aEH/fjdruePXtmtzv0x9runt5orIEU8OhI3dXVlUim7Dbr0tIyOYHqilgkHB4ZgXyIIyMCYYPTQrlcnp2ZplCoudzp48eP9g8aG9fPz88f6w0+r+fJkyfg11NXcqenIyPDwVDo+FjX3d1dzdyToKivIBQkotFoMpn0eLz5XG5xYb67pzeRTExOjK+uNs7jsbICsUbT6fTY6KhYIqmbC3GSzfa/erW+vk4QxMrKst3hrG8Ti8U6OzuPNFqjQd/V1ZVOZ+vbWC3mJ0+fhCNRHpfT2/eq4f60WizjExMEQSwuLiwtrdR3AsdRUD6wi8XC5OREDRbqYYKvVG+bCGybiNfjaW1pOVQfWS2WZ52dfn+DBBFuF9o2DuexTtvT0xNrtG0ODw+6urqSyZTdbltZAcv6+iISCYeGR3K5/OjoiEjcAMJ429Bo9NPTkwcdjw7UjW3/FxfndXqD2+15/OQJtvKpGev09GRkZDgUCh/rdC+7X1Zvm5qW39zPvxzf882t4buev4PAdxD4DgL/jBBoSMmq56PVaK5e/WRmbm5tlX3lypXNza2aQz9BEOsS8ZXLVzhcvuH4+PPP74UjtdbNBEEsLMx/+OFHcvl28/17v/z1b9AQrweHg0Q+/+J5561bn2l1x7/5za9fdIFW5XULbESSTt+5c7ultdVqs/70Jz9ZWFisaYPPjf2v+iTrUi5n7dKlSyIx8BM1/ZyenrS2tB7rDSaj8c7du4FgsKZNdfvqejVkCILo6+25deuzL7JYNH1+t+PR41MUKI9sD3xSItHcfL+p6b7b4+nufslksWoGIgjCbrddu3a1f3Do8PDwe9/7nngdZNtkJ3hFGs3R5SuX5+bnJ8ZGf/nLX5pM5vo2DDrt0qVL0o2tF8873/r92ylkwVPTj8Ggb2pqCgYC9+7du9/cnMkA/1TTRiQSdXV35/P5/ld9S8vAG1U3uKBeDRz14eHVTz5ZXl5hMRgffvjh1hYI46ufRdtGcuny5VUO9/j4+F5TU6TOKJ4giJnp6Y8++kixs9N8v+m3v/sdHoLsB50/890vX16/fl2tVv/qV/+lp7e3fqAvQmXeuXP7YUeHxWL+8Y9+tIT4J7ITXCEIYmhoQCCW8Lic9957jy8AS6DqNgQBHGpLS7PRZDIaDbfv3AkGQ2Sb6rV/o/Xv+J5vFLzfdf4dBL6DwLcfAtVf9vo6svhJ9Pf3C4XCrS15X19vPYtAEITP6+np6VEodmZnZnp7ek9OTkl6QBIVjeaop7dvd3d3Y0M2P78Agro3x8P80/DwiFajmZ2ZpqMECNVNcJ9LKNz8vkrV19uz28gGmSAIvf5Ytb+vUGy/eNHldoOZanU/mJPY3d1Vq4+Wlha7Xnan68h/TfuGPyFd67pkeHhYrVZPTIxxebyagfDuoVOp8/MLim35gwftqn3Ij1HdG0EQpycnoyMj7NXV/f392dkZne64vk0sFuvt7V2XSjc3N1++fImlbjX9WC3mrq4ulUpFp9HGxydOTnP1/aTTabFEoj8+xgl0axpgyASDwY3NzX3VXmtLi0IB2cqqB/oqdczw9b/ql0jWNzdkPT09wdBrFgH3AIJVj6e3p0exrZibnent7c2enNSMRRCE+vCgt69vV6mUyaSLi4vFItgqVM8Bb5uh4eGjo6O52VkmC8IF1TQol8tLS4sUClWpVPb0dO/tQUy46jZ44Xq9fmd3V6FQdHW9aOhbRxDEzs6OVqtdmJ9/0dWVrvKJ+4t9Kb7je/5ioP5uoO8g8B0Evp0QqPn6n/cT04kLXMfL53uV4z7JHjAcGw6Eb5VQadjg6168eCyyt4vWRTZqVCEXhQlnoybg64TNFdBfCFF4XsEt60ky2R7PEzcjL5IV8joG4Hn9nHed7AevBY9VKp8bLKC6/Xn1Lx3rNeTP2T+4B1Kd3XAgvPBS+Z8216quSUhWXatUX6+oVH49eSSMxE9903+/43u+aQh/1/93EPgOAt9yCNR/2euvkN96snJeGwysmrv1EMSUlWx8QYOaltU9V08G12v6uYAXIfsh50Be+bqV6kEbPlvdoHrO5PTqG5DLqV5+TbPqn+S4Nf2Tj1eYr6pnyEfOq+C25939KtfJyZCV+qfIlda3qZpspfp6OaijCxpU36oZtHqg6tHJR0i81DxY/RM3rr9CdvLNVb7je7452H7X83cQ+A4C/1VAoPrb/Wev4+DdiWQymUxoNBqPx+Nw2I1GY7FUsttsOBhdKpVKJZOBQECr1TocDp1OhyPHRKMRp9MVj8cdDkcymcSBARuiJHd6igPA2O12nNDg6Oj/Z+89o9tKsnPRf/7jH/7hu966Xl7v2nOXPWOP53r8/Px8x53ULbV6Wuqemc7daqnV6m7lRIlUpEgx5xxBEiQAAiASiZzBAIAgSGSAyEQGGAAwAkwgCeCcN4UjYdBKk3r6eXm9WmeRdepU3FUH9Z29d+1tXlkFloS+8xF9hxWm0+nl5eVk8sDv9zuczpXlZbvdtr6xsbOzY7FYNjc3NzY29vb2NjbW3UChxO3xeC0Wy+rqajqV8vt8sVg8FArFYrHFhcUXHCZaXV09ODhIJBImk2k3sRcKhRwORy6j4jsc0XdVFaLCD1bNZtxmsy0uLgUCYNmk02k/WDb+/f29zc3N7e3tSCRitVofLxt3xp/GamgeLJtQKBTfjIfD3zK0k7t+9vf2VleB9C2zbHx7iYTZbF7NGKD6AwaSW/OfNP7/Ae7J4s3swLLYMPuOZR/lRrJ0zE3MjSMZclNy4y8onn30dN+eriE3JRvP1pBNeTrygu791uIvzvDip8/E479j97JT81s7/3SFuSkvIOyLa37UeQiCn8MF/SPH/p0Uzx1pbvwFQ0OyvYAs2XqyeZ7u6tMp2VLZ+rPFn3j04nftxU+z7f6RlT+veLb+p/ucTXle2WzP/8gM2YZ+l0i2w3+KSEbzt6OsvJJKIZ8+fZo2TLt29eqFCxd7+3qrqiqrq6obGupv3MzH4XDFRQ8uX7kmFouvXLlcW1f3a5FQcZEN2zUAACAASURBVNGDpubmpsb60rJyFApVcOvW8sqq3+/j8fiuOZdEIhEIhGaLFYbhifGxb86es1otDx48qKmpbmluvnkzv6ysfP35viP+FIP9veqEYXhxceHjjz8yGgznz59rbm1tb287d+5cSUlpe3tbWVlZW1vb2bPfYHHYlpbmM19+iUb3U8jkTz/7zOV2G/W6kydPCQWCM2fOUCjU69evM1msvb29iYlxjVarz5iAGh8f39nZ3d9LXLp4kccXovt6yyvKW1pa7t29W1T0gC8Q5C6236vn30NmRGG8vKKKRiGfOXOGzmBcOH/u4sWLPb29VRWV1dXVTU2NeTdukEhDd27funz5ilgyevHi+aZmYE66sPBec2trU2NDWVkFCtVdUFCwsrLi9/sFAoHL5ZJIxEKh0GIFy2Z8bPTc+fNWi+V+4f3amtrWlpZrV6/+wcvmd3nXvpM83zfu2d3dValUOp3OYDBoMwGJ6/V6g8Gg0WhkMplKpUIePfFXp9PJZDK1Wv1EOnKr0+mmp6flcrlOp3tmBq1WOzExgbT+dAa9Xq9SqV5QXK1WS6XS57Wu1Wpf3PPp6WmZTPaCvr24OGLj7pnFdTqdSqWanJx8elBIikajUavVTx+lzl1A8XgcmYhsExqNJjtNCGWeV79Op5PL5TMzM9myuTmR7o2Pjz/zqTajASeXy59HWJ1ONz4xoZye1up0Gq0WuXLrV6vVLyCsTqeTSqVKpfJ5rSuVSoVCkVthNo6YBpiYmHheWWRBPm/gWq1WpVK9oG8qlQr5asydiCfia2trWq0WeVmMRqPJZDIajdn+aDSaiYkJjUaT7XNuRAfUGBVTU1PZ/E88nZycfN5TZF5kMllukdy4Xq9XKBQvICzyLucWyY0jT18w6TKZ7MWVS6XS3Apz48i8PG9oOp0OmfRnkkWr1c7MzDzTotITU5N7+6fbxmAYXl9b++zTTz/+5JOiogd5N26Oj4999dWZz0+erKqqpNKGOWz22W++OXXq5AcffnD7VkHhg6JgKNTX2zOWsV2ExQ7cuXMHjUYXFORfuHChrLTE4/X19vbm5d24efNmXt6Ns2e/Ka+o+LXXzzu3bx9/5528vDwsFsflsE589hltmF5S8tBqs/+n3d1hGMYPYt94443C+4VfnflqhM5obWn54IMPiouL7969q9Vo8q7nnT939vg7x/PzC86cOSMUicJLi1XVNTAMG42GouKiuvr6xoaGb7755kFRUU9vn8Nmu379esGtW/n5N69evXrmq68MRpNGPXPkyJGz587lFxS43J7z58/fv39/fGy0oaHxPzNlwLL57NNPPvn0wYPC/PwCmUx68uTnJ0+erK6pJlEobDbr/Plzn336yYkTn1+7dvXBgweBYLCrs0OumIJhGIsduHv3LhqNvnHzxpWrVx88KPJ6vaiurjt37uTl5V27euXixUulZeUQBN25VXD8nXfy8/PR/QM8LufEZyeGiKSiB0VW6x+ybHLfqT9p/PvGPXt7e6Ojo1QqFfHGxeVy6XQ6CoVCo9G9vb1cLndkZITJZLKeE17wlM1mIwURe/NZbwAcDofL5fKABwk+ncFgs9ncx0GY8ZjB4wFP4AwGA3FJiHjpQtz3IP4fkL4wmUzEZelzusZ6um+I4ws2G/iVZ7PZdDo9t8In6kGKI0WeeMRisZDW2Wz204+yT5/5iMViMRiMsbGxZxoWyq6t3d1dkUjU0dFBIpE4mcDlcrFYbF9fH5fLffHY2Ww2Ynb2mR1A+px1JYHMC5iUzLxweTwul8vIzAsyU1weL3deOGw2l8ngs1liAU8o4CNTx2Sx6dkJYzKHR0aYLBaDyRxhselszgiLM8Jk0TMXg8mi0IaH6YwRBhNcTOYIk8XIXHQmi8liM5gsOovNEQjYQiFHJOR+6xKxhYJhNpvJFzA43BEWm8FmM0BDvxloZuA59795AmIMxovWzMjIyAss3CBTs729LRAIWltb6XR6R0fHw4cP29vbEe9ySFOIL7NvN/voDllyDAbjeU8RsiNOMH7jF4/D4fF4yDQxGAxkMfAyAZkXZD0wmUw6nZ51/IJkQ9Z5trnnvS/Iknj6ae77gryML35fnveysFjgZaTT6c97XxgMBtL5bFdzI3Q6/Y/xx/7dYiAYhuecTjKFIpFI6uvr6usbWEyWSCSWTkwIhaLOzo629naZdEI1M4NCoYhEQn19vdFokslkVpsNhuGRYRoGg2Eymd3d3TQqta29fW9vX6GYmhgfHxoaEoslfD5PJBFvbGzg8XipVEokEru7u1rb2ibGgTXRIRIpmbEM+d0O6ruqDYYhMpmkUEyhUN1VVVUYDJZBZxiNRhqVxufzKsrLuTyedGKCy+EMDg7W1dWSyeT5+Xk2mwPDsMftamho4PF4XZ2dfB6/vr5eq9OHl5aEQhGHzcmYwx4dodN9Pp9EIhYIhVQKeYg0VFFZIZZIUN3d1dU1DqfzPzPucTrsZDJZIhbX1tY2NjZx2Bw+nz8+PiYUgWXT3t6umAQfRWg0GofD1tfXm0yzE+NjTuccDMPDNCogJpOJ6umh0ajt7R2JvT1gKlMuJxKJIpFQKBQJhaJYLLtshro6O9vb26XSiYcPi0kkcjIJDIr+vhOd3Y/+1JHvD/ekUimDwTA7O6tUKm/evPnll1+KxWKRSNTf3//v//7vpaWlb7/9NolEeqa3IO7jwMtskwDHPA4ITEF+ptlsNoFAGBgY6OnpaW5uRqPRBAKBQiZhMJgeFArV3TWIxeIJBNzgYF9fX09PT0lJSVlZWXNzc2dnZ1tbW0lJSXFxcUlJSWNDA5FIHB4eplAouT7As60jPo+QbQDZMJAuPdq5H/cW+dlls9kMBoNKpSLgIDcPj8d74hap53fxkPq4kUf/c/v2xCM2m61UKp820YasrUQiodFonE4ngUA4fPgwjUZDHJmNjo6iUKijR4/SaLQXT0puc4+nBWAX4EAq47CMwWDg8XgCHo/DDCC+1olE4uDgIKa/H9OPRqP76MM0MomEw+H60eg+VHdRUVF5eXlTU1M3CoXt7+trrGqpKG4tvkfp7dBPCPQK6YSQOzkmkYDlI+JyeQIeT8RlTwi4ag5NMzJkZFIMYzzDKE8j5GjlY9pRnkrEUUslmnGRRsxV8xlKCU8p4k6LuHIRV8Ic5g/04qtrhsorMQ9Kegof9hWW9N5/2Hf/Yd+9YmxRCbe+TtbVMUPF6wUMOZsxKuCLhQIuX8ARCDl8AYcn4HB5bB6fxeGyALplgSvj9Rbsyhwum8tjcjgsNoeN4LwcYrFYrCesliMzkp2XmZkZu93e09Pzi1/8ore398c//vGnn3767rvvNjU1icVipKYXzHtOUwD3I+/M4+XK42S+MXCDg/39/d2o7uaWlv6BATyBgMXhenp7u7tRnV1dWByOQCDgcLje3t6urq7CwsKqqqqmpqaenp7GxsYHDx6AN6i8rKW1jZwJxKGhDMwCBABIAsB0EEGcnICmAcgSsDmAGhwO5zFgBNmYLBY9A0cYDCaNNjw0NESlApeooIYMWZG3CRRBbjM1sNhsxMMWl8vNYBekxexfkPvxlYttMnVm+pb7IYRQjMPhPM9SZe4E5cahZwX49//df1Y14MAL0hYEQXt7e9kPmHQ6fXBwkL1FmkskEvsZY4xQRi6cTqdTjwNiJhRJz+08ck4qm36QCYibFKTpZ/bqO0lE+vxHEgopnkwmd3d3kQ4jf5/gcCeTyUQigQwT6Tzyk3hwcABBEGKfMEuEXPrkECGd2AM1pFIpxFDqd0KEJyr5I6mRrQ3pdmZyoUQid9lAmWVzkB0jBEGJRAKhBkKB333ZZCsBqyZjtPbg4CCHYtnu/E6RbG1/6sj3h3sSiURpaWlrayuHw/nZz35WV1eHxWIPHz586NChv/3bv+3p6Xn33XdramokEgn3+UEg4AOoKRIhn4NMJhOPx5PJZAwGc/Xq1Tt37pw5ffq1V1/78IMPL1y4ePHixbKyMlRX5528vJPH3mq6e4NJwmGwWDwej8kExFFLf38/JRPQaHRPT09lZWXV3bvffPXVufPn29vbiUQi7zHYQvrFyzh07O7u7unpaWlpuX79elVVFQqFGh4eJpPJCFOHx+Ox2ezW1tZ79+61tLQUFBT88pe/vHnzZkNDA51ORzwjslgsKpU6PDyMfF4Th4bu3rtXVFxcX9+AxeGGKBQWh8t5DPCeJgnoFS8TMlsawjh5OhvydS6RSHJ/H3NX1draWmFhIQaDuXTp8nvvf8Dl8b44ffqll166ceMGm8P5j5deelhSIhSJmCwWM/N9nQvUHjWXGQAyLwh3gU6nY7FYCplExmEuX7p8//7901+ceuvom6c+/+zSpUvnL1ysqCivrqq6U3Dz0oVz9XV1QwT84OBgZovF4rAYxHXLwMAAjUYjk8k9Pb3dKDAvleVleZcudDbWzionVoLurdXFWDhoN2jC5pm4dXo3YN+a5msHmhw8smQI3V/1YJqOd0+PxayqNePkvlufmlPDXt2uZUo6hKJ31etYhK7KB7fOfjFYelv44Lr/zgXNyV9JvviAd+oD6on3JGc+Gf/VEePLP9Wcfqfj1PvYWxen+uq96NpAV8WGRuyjYjz4bs8o0yPlecfZsyO4SR5zZkLsUsn8eqXPMBNwOfwuR9BmMiulk2KeckIiHxVxuFyEegh3hEqj6Q3ABsYzw+rqakFBQW9v7+nTpy9mwp/92Z/l5xd8/fXX7733nlAoAgxOFouZwVigwmfNPXAvKgQzw6DTOWzABSHg8cM06iB24Gbe9ZLi4q++/OL1Q4dOfPZp/o28vKuXG2qrm+tqb1y/9vWZ003VlQQcFpt5XwYGBjAYTFNTU1dXFzIvQ0NDfX193d3dVdVVDZXl35z+orjoAZEwKBYJlIrJqUl55gKRaeWUSChAobpxWExLQ93NvLzeznYqiSgWCYV8YI9fMTmpnFJMjI2iujubGuuxmIE7t299/dWXdTU1RAJ+ekqhlE1MjYvHxSIhlyMbH1XKJmbkEyQ8tvj+3fqqioFe1MjIsIDPV04ppifl079pHenDo7+gninFzJRiOnMpJ2VTcun0lGJSLkdAYZZ+w8PDv5UP98SUQU8FsHsh1+NHf/B+hrT1xJace/uoBQCzHvXrcZvgHolnO4wUzM2A5MnmzGbITcnm/24j2b35D24r2/ncAWaHnNvbXDLmZsjWkE3MVvXEo1zK/MEdzu3S0/Fs09lHf3BDuePNrTY3nlGbfLxoMusnS4RsJLee3F69gDjZbL9vJNu3P3Xke8U9zc3N/f39JBLpn//5nwcHB99///233nqrrKzsb/7mb9Bo9OXLl6uqqp6He3iZbR6DxVXX1Ny7f//KlSsA06BQZ8+evXLlSn5+/jvHj3/99ddnzpx58803v/r6q7v37ubl5ZWWlt7Mv3nj+rXa6uqR4WEmk/lr1IW4mObz+aIMwy7LMQK+DEWiibExIrr/8BtvVFdXZ9gJ3CcCgnuqqqo+/PDDS5cuXbhw4datWwMDA1QqFfm0zebncDgIO53H41MolKGhIRIJMJ+GR0b4AgEinwI8AYCreIM4XE1NTXlZWU1VZVF+Xt29/JoHd3nDFDaDDlgFIA8Q13F5wI/9I9kQk8Gkj4Bx0UfoIyOIlCHbejbCYrEmJiaeh3vW19cbGxuJROIv3n23sfRBY3XlP/7w769/c/pHP/gfnCHsxdMnLl0477CYvGa9VT01MykFvrLBVzTYacGV8VRPGBysqqy6fefu1atXKyoq2travvnm7PXr18tLHn70wQe38m/eupF35uSJWzfzqisq6murWTRya11V4d3bLS0tAh5w587mcBAWETIvyNQgiBBBVDKZrLml5eibb84aNHByF9oIw1ur8NYKtBKCV+fhWATeWdta8tHx/dcunm2qrWyrreAP9ixZ1Bshd2phDvIZIK8ecmshx/TBnGrHqU4tOPYjnmWbJmSQR+T00FDb3hQT1nL3VZykTpDSCSA154De7W4vHL19hnjina6jr+E+O4b56gNFTUEcVbreXgTx+uFpNmyRwlohPKeGV0LQ9sbWxvJOfG19fXlrO7buMu3NcNLO6YOFuaDNpJ6ZHhsfZ3F5HLGEJxCOifgeF7Cu9sywvr5eXV1DGsQdP/pmY31dR1vLP/7Dj0Q8dmd729E3j1gMOvW00mExeWxm9ZScz+NlmPNgYoCINnNxudxhGrW3u6uqpKiq8HZ/V3tvd1fhjSutddWttZWXz37VVltRVXz/mzOnaytKsd1tPc11Eupgb0t9c00lj0rUTY6LQK0coUCA+H4H7DWRCOEYISl8gVCpkDH7Oz889qbNqIWTu6mdOLT7+NqJQTsxeDe2EQ5RCdj8q5dJ/d0ttZWjI0NLbls8Mp/aXEtvr6c3ltIrofT2xsH2xl5sObkbT+zE18KhBb9nMej12c37q4vwamg3GjqIBvbCXnhrZW910WOYlgz1iUkYIY1I6OkkoFqneCPbi/5YdAHe34ITm+Da34b3t6HEZno3FlsNb20sr0YW42vRWHRhd3UpvbWW2o7Z1SoGicRiMAC7LrOmRQKB0/HceXnmZEHfDhk/HtHubtRSOGy1mAlEghC87+xnlv0vmfhtenzrDoZhAR98s6WSB0QiAXEmuri49F+SDs8c1LfIkXMDlk002o3qXgpHrBYzkUgU8HmI5cln1vP/J/7BFPj+cA8EQZubm1tbW79WZPnpT3+Kx+OPHTt26tQpHA73gx/8oH9gIO/GjarqasnoKMLG4OYEwCDh8QYH0O311TeuXPjs008uXLx4Mz//zt27t2/f/uyzz06dOnnq1BeXr1z98ssvvwLw58uTJ07U1tVzM4KkgYEBIPMiEvF4fGNjIwGPl0gkCHrIaQREOVzuEB5/4fSXr7/+OgaDQeQ7T+fkZXRQRgDkANwaDpeLuI9+Zk4Oi1VXW1tUVNzc3NLR0YHOID8mg0GmUKm0YVR3V3N9XVVJsWmcaxnnKBgEHZ/mG2duqHhmche/u7ak4Dq2v5cxTGMymZQhwvAQnslgsHk8dH//QEfL/asX6+vqcD1dRCyamcFQudQD/clwnqanp5/nwSqdToNZWV879Yu3WQ+vDT3M+/GPflhdfP/K+W/mneayuwWVxYUwvAdvLsOxcHJzdXkhYDPpFeNiPo8nFAgmRDwueZDY1Vx4/dK5M6dv3br1sKiwqbqyta76s88AZrp3K7/o3u3K4sK6suLWuqq68lK9SgklE4ntDZtJPzsj81t0VpWCgEGTyCSRWJzBd9+aFoR5hsfjT548eeHs16t+B7y9BsdX4FgUjvrhqA9enIMXnZDfBAdm0yHzmmVq26FOh+f2vHp4wQov2mCfHvLqII8O/PXq4aAVXnSuuwxMMoFBIU6Ni2f16pBVGzXItr3GVbtmw67SjfFFNLyQNrigkwZU4xoe3cylLU0JVrQSDa2f0lTeU34/qBRuzE7uePTzDuOiRZtadCQCVodcSOtqarlwRttcquyrX+AOwubx+BRPPibhsTn0/v5JBtVLx4d1ilWLZm1W+7xXN51Ob21trSxHjh19k8di6FRThw+9uh5ZYtOHP/jVLw62Y3ByFz7Ygfc2k+uR9chCwD1nMeimJ2UcDofP40v4HDGHyRuh1JcX37t5va2uBtXahOvtam+ou513vezOzfJ7t7qa6+srShsqSlBtzXUVpSb1dDqxtbu5bjdpndqpgFkzq5shYDH0EVpWrPbtieGOi/gS1sity+dvXDq3tuAHOAMAnTi8swFvrsArITA7y3447EqHXetuY2LelY5F9pf98HoIjnrgeTu8EgRX1Peo4N5mLDrPHSGPskY0k+MWvSo4Z10LzO2E/euBudUFv1Y+KuEx+Sx6NOT1WQ06pcwxq4t6bcses0bIoPR1YrvaFl3WeHR+Zy0SctmXAu7kTmx/Y9mqmaYP4boaa2bGhNOjgnDADSc2Zqmkzq8voh+Wi5hM9bRSIx83qZS2WeN8KPi8eXlmOvTtAMPwlEJ+6dIlHl8QDAZu375948aNsrLyQDA4NDSEyATJZDKFQsFisTgcjkKhIIkUCgWDwQwODuamDAwMEAiEbCkymdzf359bD5lMRqPRT6eQSKRsKRKJ1N/fn70lk8lEIhFhdWcTESWBbNNkMhmPxw8MDOSm4HCAB5hNQYaQ22GkNuRI/LepAu5gGN7d3SktAWF7a5tEJFw4f/7WrQKRZFShUOQOk0QiodHobN/IZPLQ0FBuCoVCIRAIuUNAUjAYTG73EO5+bgrgYeYMAREXDA4OZtuiUCgDAwOIJAFJpFAo/f39RCIxm+dpmiMdfoLmaDQ6N4VAIPzaHQcMOCvPCBmvXspLly5xONxQMJBRPM+rqqqaX1jIbfp5ywaPx2eHiSwSoObxeGkhKbn1PG8IuWN8etkMDQ3l0pxMJj8xC9llk62HQqE8c9nkrnwSiUSlUp/wQvjMd+07Sfz+cA/S3f1EYqi//6c//SkKhaqqqvrJT37y2qHXf/A//s/uluYTn3xcUvRAwGWPDNPIVCqDyQLqERwug8UmUyh9/f0MPHqKS2spL2ooLyktKenvasf1dOEx6KKi4vt371QU3qqrqqysrvn6yy9IZbfQD/OLC653Njc2Njb29qF5j7k7BAL+6zNfFhYWstnsXJjC4/EkEklFRcXRN48cffMICoXi8/mI7AARXXGfCiIRkDVgMJiuzo7ujvbe7m6E6QL6jaAPANf47fU1jbcutzQ10hkMnkBAIQ1NMkkkVGtV8f2m8iJ8aw0f2zEnY6f9BjhghEOzcNAEh8y7HqN7SuxVjVrFw0t6qW9arBPR5zVjBgauu6ZMSCWIR0jktlpOQxG7p1kw1M8j4zh0GpdOo1NJwyPDLKCax6YND9NG6HQGUygSPSHtfnL1HCTyPvll/blPFmjtn7z+szMf/eru5bNBh/Gzjz+qq66ILAZXw/PrkYWNyHxqJw4n4tsLc2GzyqUcs0+NGmXiOZW0u6ako76qoaGhtbVloK93oLenrrqytbaqrrxETMIyB/tuXr9CGRzo62yrLH1IxA5Mjom8czZoLw6vz8Nrfqty/OI3X1XXVHMyerWPiJ2J83i8O3fv/fKXvzj31ZceuxleX4CXA+mIb9uphtwayKN9fGWQjc8AL9p2HDN+BU8noBrEdMsoI+GbTQct6aAFXnTCfiMcdh3MO5jo1pbyYpNSerC6kI765mdnzDIhnziAaW8c6e8k97ZL+YwF3xy8tQLvrMNbEYCf1kIxr9Wtls6Ns5x88oZhYmFaGJoRr9vVE5QBExs/L+csKjjCrjp2wRXBtS+1TcUO9mBULVmeoLuGOt2jLFnZA+eVz7d7Svcdqi2/3W9QPzkR377f3t7++c9/XlVRQaWQD7/xutmga6qv++iD9z0up9s6G/E61haD6+HQRmQ+vRuD9ja316Nmg1arkFknR01Kucts6O9qGybi+MwR5YTYMC1Xy8dH8FhSf29/e5NcxJ/gscoL70pYw71tTajWJgmXOSOf8M3Z4Z01MC97MYtede3S+e7O9qxSeUblH3whSEdH0Y1Vpz/96PqlCwveOcBf2VpLRwM7XhMUskLzdnB5DVDQAnl1gOxrwZ2g3aeV60aZWgnDoRzdm5+DVoLQ5ipg122vwVvLe7FlPp3a39Vu1c0cbK4mN1dDDrNNp+LRSJiudhoBNzKEnxALQ945gPn2twH42wN8nfXwgttmdjutjilxzKFZcNmCTvN6ODjGY5plwiWLJmTWiukkKXdEzBrRT074HBaXUsYuL0Vdv4FpbJaOjhl0aqlEJBGLhQKBQa//9jz8lrsnNjEYhoOBQEVFuXRCNjY22t7e3tPTQxwaUkwqelAop9NptVptNpvVap2ZmdFqtQ6Hw2azWSwWh8MxMDDA4/EcDofVakXSFQrF7Ozs3JzTarVYrVaXy9XV1aVWq61Wq91uR+pB/MtaMsFqtc7Ozra1tRmNRqQhp9Nps9kmJyeRDBaLBXGE3Nramk2xWq0Gg0GhUFitViTR4XAg+pcOhwNJsVqtarV6ZmYG6S3SYSKRSKPRsnkcDkdXV5dWCwD9E2R5LCdKE/B4dB/aZDL19fVisdi62hq329PR0aHVapFBIUOQy+VI/y0WMHCVStXW1ma327OU0ev1MzMzLpcL6Z7L5RIKhf39/QhZbBkiG4GrDXW2wx6Ph0aj0el0h8Nht9sdDgfitlmn02UHjijVSSQSu92eHTjiRjebZ3Z2trGxEXGhjeSxWCxyudxsNmeLaLVa4HbeZEJKWa1WpVLZ1tb2mA5Pkgcsm2CgoqIi4xp+rL29HYVCkUhkpVKJylk2DocDOYqYrdbj8aDRaA6H43K5kESHw6FUKg0GA0IKiwW4u+/s7ETWic1mm5ubs9vtcrl8dnY222GdTtfQ0KDX67M1z87OIrOA5LHb7TKZrKWlJTtkq9WKHO1EiiBLQigUYjCY7JKw2WzPXDZUKtXpdGZr7uzstGbOxv+Wl+27ePy9457dnXH28Ifv/eLc+QsTExOtLS0tFSVyUu8MY/Cdw4cmmYToJMfdUzdH7jayiXLuyAi2F9/ROErFGcRMKZOilkrIvR2lNy7TupsE+B50XWl7xYP7d29fv3KpvODqSE9LR0N1Q2khvauuraq0/F5BR9VD4VB/6e2btdXViJKyWCy+f+/u//Uv/zIwMIBw7IF4SygEqg8EQlVVVUF+fndXJ4fL5QsEfIEAOXH2hPpERkGHV1pSUlx4j0ElzSjkOpVyRjrKHSYrxLxJsYDNYhIJePwAmoRGoduaMC11g12tI/2d+jEuvqPRK+eMDvXhGsvVjMGYdWpeJYYXrNCiE15fhDci8PoitOiENlegrRU4ZIbnVLBpHM7s7rBzGjLL1lWCmGEiPivfmOHBZmnarUn7jZuWqSXt+IpZ6ZgUCsg40xhnjIxh9bWZ/ML4oQAAIABJREFU2cRZFl7Opu3sAr8tzw2pfXzxjfcPv7w+Oxk1THKGBuTUASUd+/PXX5UyqUta2YpK4hEzo6rRJeNUUCGQj+DZJKxRpTDr1dopedCkGiX1lxdc621rbqqvfVB4/2FRUXdrY/71yw2VZSI6lYXt6e1oGezrxvd0ohprHhbeq6+ru3D+vEwiiLhMqbAHXl9A1ZS+9tLPhoaGspIUHp8PNFtHaD1tLQMdzWa5MBX1wAs2OGhJu7U79plH0iuvHkT8RjhoSniMmNa6/rbGSSHHpJmxGLQa+diUkOMxzKwG3XGf1TlKd4wx1SyihNgrIfVLyBi1mDNvnBZQ8UGnlY3rofV12HTTUad+ec4I78bgrXVoaw38XbDD0QB8kIDjy7BHA/sMcGAW8hvTHi3sUietk2vTvLhJumGU7pjlUNi5G/btry5EnUb3zIRdrVBges38kYhqYrq3wcjArOjGPZLhWS1wNPiCAKXTNWUlpz4/MWcxWnUzToPqg3eOtVaVznsdbrs56nOuLwXXl0IrIe+i3egzqVVSiZA14jRqA3azcUbhd1hEbEZ9VcW4gCviMAkY9ACqY7Cns7m+pq6qQibm8xg0Cq6fRsCRcAMDqM4hTJ9UxK8tL1HJxqOLwfT2Bry/3Vxb9eGv3mUzRhC2qyATGHS6iMsi9naSMb12kx5KJhCAmF4P7wI8qgWsNUS26NHBQeO2x4jp6+7v6ZqRjZt0WpNerVcpJkV8v1W/uhjcis7PqWUWmVAzIZKMECXDBAmDrJ0c85m1Qjpl3uscJuJoBNyc2bC8FFxeCgG4s7MBWETgAhFoF8BxeHsdXg0C5t9KIL3kgudtSd/shlkRt0ytz07u+MxwbCm1G0/txlcXg/N+99JCQDUlm5KNW6ZlIgp+gsuwSnk6CVuv/S149Ikpe3IHyzA2kslkKpXa398/ODhA1GYnJydFIuBvfH9/byNjFweG4Xg8vrkZ39raQuoMhUIejzeVAjrLkUgYKeh2uRDn86lUKhwOT01NIZ7qY7HY3h6o32azRaK/cdYdiUSmpqYQT9c7OzuBgN/r9SIOv7I9393dDQQCyB6cTXwisrW19QJTh0jmlUzILcjhcPQZ4Pg0WZAURNM6mUzu7e+l0+lkMrm/v4/FYnd3gYPP1dVVZNTA69ZeIkuo3d1dvV6fIWYyEokggvtgMPBrf5wQBLS/o9Go0Wh0u92Iyb6d3V0Igux2+0KOM/m1tTW1Wu31ehFZpN/vDwWD8/PzT9BhcXFxO+ORNHdcuXEIgnw+H9Lh3PTceDKZ9Pv9uTXH43EsFvsCtV9E9zyVSh3s7x9k1g/gHU5NCYVC4NEzkYjF41ubmzabDVkAiE76ysqKz+/f2NhYXl5O7AG3bltbW0j/t7a2djIOsBKJhM1qRbj+O9vbs7OzsVg8t7eIN3i/3/88yQCSeWdnJxj8LdzQra2tpaXfIriMRCJPeGLncDi2zCHEJ3r1p7j9vnHPwf7e7ASP2lF/89wZXB8K19/LGcKoBQyPUszAdKdii/CCEzZPwm4N5NYmF52JqH/HrU+ZZZBjOhWY3feaUrPj0TFqgNqpxLVqGdiIZiwwStMzsJoRzJqSO0UbqM+/1Hz/JolIHOhFVd3L900w/GLy9dMnznz1NYFAkEgkXZ2d7779FoUE9lcOh4NCoRoaGvr6+nA4HHJutrDwPqa/T8hhEfGDbA44/pfLGRIKhXQ6vbGujjjQsxTyb4SDNt3M1loUhg8iAbeISeNQ8Axc7zhtUMMhBZTihMewE7DvBa2JgDm9YN/3m3wyjopFXFBJotrxtM8AB2ehgBl88oIv5lUoaIX8RihggZwzKU5PaqQtRW1MizApwUBquDmFr4UVdNivh706eG4G/PXqgewGYRdFAYBILzrhgOnArT1wa2Cvbss4ETBOI6c8nreGDvYSBgGtqegWFdVinuBap8flnOHB5komuhWyK2H9KGwcT1mmIBUnOUbat8i3rMp1p37fb912GfcX59Ih26bXquAxRumkEUy3RsjwTwk1XCoW1WlSSqMLAYVEUFJ4t7e9JRKY0wtGMBV37ZMi0tDQ5fPnakuLNoMOOOKU07DvHnubSqMJhAImk4nu7SENYrXyibDTBM1bVz2zhPY6JYeSYS/J0kBcZYbBzmqAfEY4MAtvLK77rfyhAQaFuLocnve67HoVmJf0ntukE9OI07zhMQpWzR6ySEaiDsP+2mJyI7oXW07EVpJbG4n4ql03bZwQrAfn1kJesJsC/aH1xzvrBhyPAsGN3wyZxtMKOmQcTRtG07PStJyWkuDS3F7YIIG9etihhKaZkFuTDpjhpTl4bTG9spDe2jjYCCdX56GVQMKlPbApknMz83L2/Bw4bPyCcHCwr5uSNVaW8ih4h1EzIxstLy4UM4fhvS04uQP+7mV0WeLLyfXIfmxle2N5cz2a2l5PbYSTmyupnRi0tbpk0wVMMzLmkGWMFbFrg6Zp9Th/VqdaXghOCLlN1eW4PtTKYmBGNkZAd68uBU0aZdGtG90tDVvLizC0L2DRz399RiwS8oAmFoOA7mFRh4wzkxH/HLwTW10MErADGqXcrJl2m/XQ6jxYz0CwmFmWPgM8b1lxaNkkLJ85shxenPc6rXr13uY6nE44jNpxDkMhZCvEfJVUYlErlgPug/hKcnN1b3NtN756sL2RiK9YdapZlWJ1wbe+GEREe/B2RpqW2IS3N6B4FFwRHxQwQx495DWkTeNpvTgto6YnSNAMB/Zo4aAFrJaQFd5cAxO6vQHHI3AsDO+sQfFoemsVXltIhj1QLJxam1/x2OYD/hdMytOPnrnBP51NJpNhMBgYhltbmllsLmKb5+zZbyhUysOHJcgbOjEx/t//6r+PS2UkEvFHP/qHOZerqbHh6NGjb//857X1DRbz7MsvvXTs2LFjx487nHP5+TdZHM6D+/fef//9N998c3pGldnOI4cPHz569OjRN4/K5JN9PT0vv/zyl6dPf37y5MrqKsKGycibdr1ebzqdfmbnkd06FoshoOEFeSKRyHKOJ3YYhlEo1PP4PUg9T1MmlUrV1dVFo1Gfx3Pr1q39/YPt7e2DZHJ+PnTj5o1YHOzQFvPs3/3d39GG6XLpxA9/+MPpGRVmAH3kzSPHjh0rLCr2ej2HXnv1V79679Drr2t1+rqa6kE8sb6u9r333jt69KhACODmZjz+wfvvHX3rrddff53OAJ+mr7766r/+67/eu1+4mcGd2e4Fg0EEiT5v4Ol02u12I5DieXkODg5yKQzDsNvtbm5uRob/vFJPEye7bBrq68bGpefOnb106fKNmzcjEYB0F+ZD//IvP71fWDwzrfyHf/gHDhe4FD35+Wdff3MOhuGaqor2jg4YhvOuX/3522+n0+lwOPzxRx8dP378528fm3MBjJgddTKZ9Hg8yWTyBX3b3t5GwNwL8sTj8Wd6JM0WAe5Ul5ZyhaHAymJ3939Bfk8ymdRkTOGJREIBi47r7WprbcVjBqi97QoR72B7A/yO728nE5v7u5uptUUo4oE3waYFL4dSYspOfw1kV6Z9hvQkLS3GQcJ+eIIIT1JhNRe2yYFowzbFaSnDd7b0VZRU5V+vqKxoqSrrqq0Yrrw7dv/C1XfebGppBQc3MqG4uKipsVEgEDQ3N1dVVSGsIETBmcVi1dfW0kgECZ9NwOMRk0LDwyMI9OFwON1dXaiOdplECKUSi343m4RXCZm7a5GMoGHLY9KMsYdDpum03wQv2uF5K9iSA7PwggOOeuGQDQ5Zkx590qOHA0AfBfKbwO91YBZa9qetU+kpFqQRpEc60koGZJ5IMzpT9PYUtzfN7U2PEdOCgfQ0C5pTQS41ZJ0Ev+aLTvCD7jeC2oKm/Tn1in5iRTceUgoBGAqYNkwyMb57GI95npxrZ2dnRqWyzpo2nNod+7SGhTdwh+bGGFwsSi8VpbeW4UQ8vRtLRBd2lzwHGlFSSgEbvJyaoDTv9j2EcJWQcRS2SLeneXoxRyrmyyVCn4Sxo+LHTYpU2B0wa2gDKL2IPk4eoKGaVIIR2gCK1tM6OYIfHRm6fe2yWaeCN5bgkGXfpcG0VPf0dHN4vJaWlvFRUSw6n14OADFTLLK3HhYPE5UcqkrIWNSOw35j3KzYcaqhgAn2GXZsSjmbwh2mmHWq9N72kscp44yYFKO7a5GDzVVoa8UipKr5w8vm6aQPsIXAvAB0OAuvhgDE2Y3vr4QP5p1QYBZedMARD1Aeiq9ASy4o7Em7tWDhaYQpSnNKxU5r+QDojOJTwoHU5HB6cgTcKkbSzmnIKod0wrReDLhBbg3snIZDFnjBvjWnX51VLKpHw6YpKOJJeQ1LKgm3r1kmAZ9xzww7OztKpdJkNMxbdAvGKdkIXi9hOabHBcwRo1oJZeAOtLe1Fwf4IBlbBhrEiNxnZwOo1MzbwNYe9WTXBuxQwBYZ7NPDETe86ttwGfkkrF5Il1MHKH0dM7IxOplIw6JlXPoEn1Vy77bfboIyqjm70eBQT/swiSARi7vaW2fkY1uLHig8B0fd8LI/sbI4zmMZJgSOqdElkxIOmDYtih3bNMDuPsOOfVo6jGdTiRaTLp1MBGxGCYM6Z1DvxdfSO7HUTsyuV81MSNYWAsmdGHywC2Dcbhxcic1sJLm1nk5sPkJ4CI9newOOLUNhD7Q0lzJI0hNkyCyD7ApIL04bR1NSSmqClFYy0you5FAiCAwKWoE2GKg8BlhTERe86Ix5LWshz7zbFgl5U4l4YmPF57ThsQNSqfSZk/K8xOwPejaSSqUikQiipBWNRre2tjY2NhQKhUgk2thYf++99xYXl+pqa35tf/nH//RPep3uzJendXrgZlwo4P/5n//5w9KygoL8//5Xf0WlUH72s5/NLyyGgv5XXnllaIh49K2fh0LzD4sKb9zMP3v2LJ4wdPz4sVu37qDRfXYHsCizMB965ZVXVGoNfhD7wUcf1VRVffTRxyPDtFdfedUf+M2X+u7urs/ny+VGPD26LO55+lE2JYt7sikMBkP3LB/vCHFgGI5lAgRBCFslGolsbW0NDAwkkwdtLS31jc3m2dnTp09fz8tbXFw8f+6sZGwchmG9TvN//Lf/dj3vRkVZ6V/+5V+SM5QxGE3x2MbLL780gMEcOXw4HI5Ulped+fqbwnt3O7tRn5/49OKlS+i+PsQg0/ra2uE3XldOz7CZ9JdffmV//6AH1fX12XNZ9lJ2CKFQKMuByybmRiAI8ng8v5Xfg+CebMH19fX+/n6E5tmlkhvJWTab0eVloHi5vqFUKkUiUTQa+fjjj8fHx//6r/+6o7OrqLBwiESBYdjn9fzP//mDI0ffqqmu+ou/+IsRBstptx06dOiVV19ze7z3796ura1bXJh/88iRl156Sas3NDXUX7h4CYZhkVDo9fqyfUP4PQjuyU18Io7gnicSn7jd3NxEcM8T6bm3WdyTTWQwGP8F+T2JRKKioqKpqYnH47PYwO4cl8vr70M3lRfb9KrV8PzOWphJI7c2NjQ11Au5LKmQOzMp9btsUCIGeBgzvLSKB5nGIOfM3qw8aRyDbIq0ipMWY9M6AeyYsgiogzgsm8fnC4UMOgM90D/c3x1zqFXk3vpb18ofFHa1t4nEIm5G9YbL5XV2dpaVlhYUFCC217g5QSgUIoZnkAOuTAZjmEbFYrFA94pCvXT2a4dJu7m6BO+siWn4qrISel+bSkA3jLKTC3Ngs9laBj+sUR/QbwjMZjRqtXDYA0X9IO43gq3Iq88wdUyPfpR9hrRxLNVTnBqsTFGbkzUXUyPtaa0Q/HaP4tN6CajHb4KsCsilghzTEJAdWOCtlc0lf8RtCc8qvVOCOSlHxqbyqARKdxO+tUYjYatFTD6NSKeRuRz283DP6urqw4clA329G9YZyGvYc6gg+9QsC4/vanaadCvh+XhkntLfg26qH+rrdMn5llGma5wVEJHgGSYkxqaIdSlCTYrZmZZgDzT8VdOkUT6qk/AmOAy7TJRw6Uxihn+KD4RTUU/cZ42Y1SzSoHZGqZFJmLheBh5tnRTB4TnIawC4zWdScamtNWWk/q7k+gK8tvCIjGCHc8GROSD4C5rg4CzsN+7YZ9acepeMe+DWOkbpt65cDM5Z9+Kru9HgJJPExXZz8L3TfLpFKoAWHPCSCw7ZAZrxGYH2ScAMZDEeLRz2QlE/vLqwZdenF1xwyApFfdBKCAq7QDa3Lq0Xp5idqZ4HB6XfJItOAQyq5adVnBS3J6UYyehK69MWedo4ljZLgW71vDXlN8XNio3ZSQSAuqXsBQXPxCOR22okg92hGYlbylk1SEPTQqdJl33tn4isrq7eu3+/u7tLOzkxOzVumhxdME6N0XA0HNrvsCwvBjdXw5xhUl9XO4046LOZFuasoTlb0OeCtlfgkAVoPgHkkZEALjjh5QDk0aUtUgCa/bPwktMxxvJP8gAwii1tzs8FbUb15MR6eF45JuAOkxj4/oBqDF60QT49PG9JuLRa/jCmtZ6G7U1vrW4E5sJ2Y9SijlrUy87ZtaB7P2AGUxyYhb36hH1mxSB1STl7bp19nHnj0oWA236wG9+JrUqZJDq6XcwemR7lOfQzgPWSOXL1COU8klvFMtLecIbTlpFkAUVpRKT1+HZtAXIo05PDaSkpxWhPDdWkNTzwgsxl3g6fEbB/ot6MapEezPK8Hd7ZSO/EYtHFjcjC6kIg6LS61LJ5rdQ4OT482M+h04xalWJUMC3hi1nDWjVgnPzuIXfrQnY1u816/fp1nU5vNhlLSkoePnzYjepRq9UsFmvWZHzvvfd1Ws3hw284nM5Dhw7Nmi35N65jcXgYhllMxuHDR956661f/upX77xzvKW55fU3juwD51C777/3q86Ojl/84lcbsTgBjzvx+cmvznyJwxPUqpmbN2++9NJLBOJQRrXI99prr5nM1mml4tjbb9dUVf3sP166fevWG2+8YTTNQhCEiN7i8bjL5UIMAu0/KxwcHKysrAQCAUQO9aws+8lkcmFhYWlpKTfP0NDQ8+RciBynpbm5ubllb2+vq7OzsbHp8qXLbrcHg8EsLy9fvHCeLxBdvnD+408+PX78GG2YXl9b09AEeCTTyqk33njj+PHjb7997O233+7qQr3+xpHoMnAO9eH779XV1R0/diwWi9OHqe/+4pcF+TeaWlpNRuP9e/dee/XVjs5uGIZXlqOH33hdbzDarOZ/+7d/29zaRveizp67AEEwYgkJGePBwYHP51tfX89NfGL4e3t7TqczHo+/IM/Ozs7c3FyWwslkMhAI9PX1PQ/3wDBst1mvXbum1elnTcay8vLi4qKubpRGo2EymRq16sMPP/q1BPPNI4e1ej2qu+PevUIYhh1221uPwtG33jpKZzAbG+re+cUvjxw5XFldW1pS3NzShulHH3rjjXfffbfg9t27twtq6+qBPHFlZX19I9v/g4OD3d1dp9O5u7v7xGCztwcHBxsbGy6XK5vydOTg4GBtbQ2Rlz39FElJJpPBYDAcDmeXzd7e3tDQ0Avsmf3uL+PvkvP7k3MdHBwg+n1SqTSjGslBTALShoe7ujq7W5uwqM6rly9jMZgROp3JYlFptEEc9vQXp7pry/Y8BvDdbB6HNNy0mptW8wArW0pJ8dApSlOa0QmNDi5S2mSkfiGFwB6hcXncASx2sK1+Wy9OujU6EcOhEDPRbajuLiC0eqR1zAOnfr+t3ZwDfjKHgTkcgUCAxWAK8q72obqAkTQuF4cZ6G6o7m+o8GtkuO62ro62wZYq9wRzy6GGwm540QWY7T4j2HXm7WC7DXvgRRccycCgDPM/g3UMAMogChCIeopxLDXcnhooS5Gb0qMEyCAB2UJ2yGcCOcMecFp7OQivL0ELc3DImvAYPJpJmZDLZNAZw7RBzAAOM0AaxDiUYhmL3Ivqzph342R0wzlTU1PPs1sYi8XqGxqHKWQoaIF9+pRHv+9Upf2G9dlJ5cggBdVM62tvLSuM2PXbfnPSZ9x2qDwy7tXTn/WW302o+ZAIlyLVp0j1SWxFmo2C1TzIrlgzyo28YQ8Tl5TgF/l43wQjqpZszWmTAbNfLeWQ8RKRMOKbU4wKeGwmBYv2q8YAmslwrSC/cdelOfDqATQEJ7AMkM8AJBRhDzgovuCAvPq0RwfPW4w8cu2DgnEKBvYbEg6VdZzdVVXMwnYvmKaYvU2qESytuzHitu5Fg1A0AECnWws4BAEzOEAUiwAoELJC/lmEqQMiywFwsGh9EY5FoXkb5NGAtjSCFL462ZCXrL2aar+d4vRCNgVkn0obRwG2ABNtgoJmEHcAfmTcqZtXja0ZZasG6ZIKQByPjOOTc5a041JKf2halPTokh6g57vv0qyGgJ7BM0MsFquqrh4mD8HhuZRHt+/WJZyqVMC0bpuR0YnkgR4yurur8sGSeXpnOZTcWt9ZWZqb1V+7dIHY3bofsICjbS41wNngrz4NALch7VIhOBv2GcJ6qWtmLGqc3A7NJZcDc1PiaSEL3t9OxFcdRp3faeZRiYGZUcBH9OrggCntNyZcmgOPHvYZFvQKIZ+XsWUAzn6z2Gzz1DgcmE2BmTLPMAbrSwplTBIcmN2xT1sm2N3VJewhbMgxKyH2TdKwdBx6yTe3H1uBd74NaLLgZnMFXs/BPdn0R5E4YMV5DOCrYIKSVtDTOiFYveuLYE4X56CIFwgod+NAUrkSgpdc0OZqbHlp0WFacxpWPbaIyzw3KfTIuX6NLBp0j7JoSw7jfnxlPxZNhSwHPmMkFPiNVZNnTs+3E5GdDElD4jPT05cuXWJzuHK5rKSkBIfDlpeXyycnS0pK+Dzu228fm5TLXnrpP1pbW//+7/9eJBJ//dWZblTv8vIyqrvrvffef/3Qq59+euL4sWM4HO7IkcMFt24V5N987dAhPH7wxz/+x+Li4tdefaWhofHTTz+pqKo+ceKzpqbmo28euXzlWiQSUamm/+mf/unmTWAzLO9G/u2C/NcOvd7X1/eTn/ykF90/Pz/v8Xi8Xq/dbp+amvI8P3i9XrPZjGjDPC+X1+vV6XR6vd7n83k8Hp/PZzKZ7t69ixhAysLBXMpkPoDLS0pKA4FgZUU5cWiosPC+ZHSsrq5WwOefO/tNL7r/6uVLrx069P4H77PZnPybeVU1tcsrKyM06tvH3j5+7OevvfbaB++/39WF+uW775w/f6Hk4cP/53//71+fVPrRD39YVlZ+6LXXCguLrly6cA84rzhTXVP78YcfnDz1xfzCgnnW9G//9n9fu573yccffXrixNJSuKqy/ONPTwSCQa/3N0P0er0zMzNWq9Wbm/qb549iCoXCbre/IM/c3NzU1JTb7UYK+Hw+AoHQ0NCQpcbTy0arUV+6dJnFZk/KZaWlpVgsprKyUjE1VVxcTKePHDt23Gq1vPTyy2MTE+VlJfkFt6PR6Nio+MiRIyc/P/HyK69+cerUgwcPXn7ppStXr169euWll1/5/PMTN2/cOHL48Llz5y9euPC/fvK/6upqf/az/2hsaHj5lVdotJGMPtmjEblcLoVC4cookz01XJDg9XptNptSqXzmUyQRWTYqleoFlPF6vRqNxmg0ZpeN0WgsLCycmwPWor+H8P3hHhiGt7e34/G4VCpF7N8zGIy+vr7W1tbi4uKuzg4ymcyg04UCASJR4vF4IpFoYADz2isv19/LW6f0+PPOO776ZL8xL9VyI9lVmGq6nmrOA596+KoUvQ3i96X4/dtCnBdbN91V2VxTxSf07TumgQqqRwdvhBIBM7mjvqejFeCtjAkcXibkYp0n4jweb3h4mNDZcv/aBXI/SsxhsDlcMgFfXnDtxpefYZqrxxikKTZl16kG20PIDIXdyMb8iIuT4e4A1BKywWuLmUfgHHWGAwQUEUAko/6Z2dSD8GoIivjBJj1vh5cDcGwZ/HwDln4UnAreBLZqQBPLAXh7Vckmo+oqCJh+Op3O4/F5fAGXxwPqKW4Td4RCIoFTsll8qVAonqetBkHQ1tb23s52OmA+cKpSHt2WRWkbZejYQ+y+llk+Zd00uevWZRgts0B84zPAC1bbGPOTd9+uv3pmX0pODdbt3vp8v/ir/Ydndisupzrvb/SWreAb99Ele70lmyLCjla8LBpaJDS6B2rFfU1aAc2qnJiSCKalozqlPKSTs9CtKiZh362HF2xAWQdcGXM7CEwEosBZgFEe8c/0sN+4MCNm97ffvXJOMoRe1U0cuLVz44zK6+ceXj8vwnfPTTD9E8ykWw8ve+GoF1pdADRcDQEB0EYYmPxZXwKEXQ4AlhJAq06wcYY98EYY5ASn4meBqlnEC0BSEOhaAbhjHIWskwCHzduAdsjKPOAoBMyAh7fkgpd9BwELu7dZRkZ7ZNx9txZZFVv26eVZxbwOnAKLzU7u2JSQT592axOOmeUAkK8/M4B52d7e296CgtakSw15dJvWafs4W8vCc/rbbBL6mlGWmFPDwYxuU9gNZHOJ2KxO9eEH77eX3tuTEGF83X7HgySxPoWrTJKb0hOk1ORI2jqZdijBCTifYd+l257TLqrH3JP8MWKPXioCqsGxCOBWHmytLwXoOLRWMLIfzMhqgQ6ZEaiRAYVuo1stz5hwAp8HLA5nXCTYcarhoGlJPTrc3/Xw9k0RnbJmVh64NM4JdsXt64U3Lkuogx6l2G2YSSEH3XdzWDhPIBtE1JWbuL0Bb2bUrZA3YieWeSlWQeJGGBzxS2wCBax4FKQgL85OHNpchsBwVlPbMfoQfopNmZPz0kCO6YYX7Ttz6gWbIeRzebSKuG1mNzgHbW9Ai86kWxcO+H4v3IOoyywsLKRSKWQz297eHh8b83i9k5NyiWTUYDCYTCbF1NTY2NjeXuLzEye8Pr9IJHxQVITq6fn1hnHq1CmvDygVqVUqOp0hkYgnJxVMBiMSibrm5m4VFBTcujUpdpMDAAAgAElEQVQ351pejlZVVV28eHFgALO/v88YGbHbnXKZ9NLFS5VVVSsrQH1nZ3u7pbnp/Pnzbe0du7uJmWnl/fv3CwsL8XjC3v5+drH9LnKueDy+uLiYLfLMSDQaRfR7sk95PF4uvweG4XA4jBxRRohjMhl1ep3T4WQwGWazWSaThiMRAoEAwzCRMPiwtGx1Zbm8vKy5pWV1ZeWrM2cQCaDP68VgsJNymUAk4vN4LrcnvLT0oLDw+vXrRtPs9vZWY2NDXl5eQ0Pjzs6uWCTM4DHd1atXix8+XMio2e7v7fX19l65cqWiomI5wyjSazUcDlC0eiLMz89/J3Iun8+Xa5vxmXrN4XA4nlFgQrbIsbExr9enUExKRkcNer3JNDs9PT02Nra1Gf/s0098Ph+FQl4KR/KuXxOPjmXYNssjIyNjY2NUKk0xOcnlcXG4wX1gPDmJxWIJBCKHy8VisDu7u8vRSF9fr8fjZdBHzp07iycQESck2bGnUimv1/u8nQLJ9jvKuXJ1ybP150bC4TCi35NN5PF4/wXlXMjw9vf32Ww2CoXC4/FDQ0NEIpFKpSLm6Z5QHwZ7NpcrFAoHcbjie3cevvKv1ndfmf/l4f2aq6mSL5N3Pk1WnE31PgBClpGWVH9JClue7iuC+WhYhImR6wkl+Q4RFfxMW2SwURzTj60aZQsqyWBLFa6/VygUPgFxnnnL5/OJBAIB1d7VWCsk9IoJPWw82j0pWNGNb6hFG+bJgzl1KrMfPPqMznApYL8R4VJALjVsV8AuNeTSAP4Bspf7DAAArS3Aq/OAG7/kBp+nm6uZYylxeG8bTmyB79TtdXhrDfwF38TIFYNXghkegwHeXNkKuQIzkuG+dgaZiMA4NotNJWAFHDaDyURs3rDZbBKJRKPR+Hz+8+wWPlp2qWTEILdLRhZnxFHtWFgztmVVQoGMKkxmt3uM5DLKqhmhhk8pwjaUtV46GW5/kKq8sHXlg717pxL3TidufnJw50S6+kKqNX/77um11vsJXHWCjYJJtcu4KmrVnQWV2KedXJEyljmYoFK8bJAGFXxCS6VVQofnrYBWNgXYmAEufKQeC6BGxkIPIleCgyazkDbFIDB6mhU07Ghfk55FWJzibczK4ybZpkWx71SDIr7MRIAJMgGS7u8CHsBODBB2JwbojBib2Y4BCgNSb4DEXXAeOzMjGeXZeBSIXZb9gNsU8SLwC15bfKR9EovCG0vQSgBAqJUQFLJtmCddUhYX3bJukmeAgjFmVhh55NC0KOUBSuhpry7hUMVMcnCkywJUOl4UUgfzWqlNPLwwIw6rxxZVkk3LVMpvTAPeWMbqASIABWJTEwy64fNpZX1VRW0fvL5Wfn7vm2Omw/++fvuLNKY0xUalxwjpKUZaTkvPsFP8/jS9DTKNwSFzRDPK6apd1MmARtqiIx20bJinlp0mv8VA6Ov6f8l77+i2rjNf9K313vzxZt7c9eate6dPZpK5SWaSmTiJM4mTXMdxnDh27LhErpEt2WqWLEuymiVRskiRoiiRYu+9FxAdOKhEISpBAgRAovcOAgTBTqKd8rxxJAhmk5I77fqehQVunrPPOfvss3H2d77v9/1+VrUMizpRmwo2SjMWZcYkg03ylFM7LRNlKRvAT4fOYHg1MixsMfDJCuYoobVWzmeNEQe0Y/SI27YYdK8GnKsecypgRWJeLOZ50L04lCdn4uD4G/y+gJt1f+RvLAE7Ff/ktm7exwBtroJbeW9r9havL4F91+LAmRe0YPHAcshtUitYZMKyxwIs3Tl7XC+Z4lEjTiMS8wF3smt602deMqnmNSK7Ub/XTdm2bWVlpaKioqioaHh4OB8ouqWiRCIhkUgYhpGJo5ws0havYLWYGhob8Qlybm4uP5Mo3yWAHzkrQh7azX2L10cQJBQKbYet5LdtY2MjH3WLbltwIM7vgWseGhrK4XuAZLdQWFxcfPPmTdyE2nJFeA+k0+nGxsbl5ZWF2Pzdu3fxpCQMwwJ+f2VlFW6upVIpv9+f69Itx8H/XV1djWVB1rlquQJ+fSDaFYutra7mOjPXJ7kOyOaT/5vgmsPhcHNzM95U/JvL5d64cWPvzpFKpfiwGR4eEonGcTh8eXn58sqDq5iLRFazF4Vfb+5i8y/T5XKBYZNn0ecuGW/MI+KacVhY/r75ZTxF8aG45lAotAXXPDg4+IW1e5LJpEgkyqWF47YOlPW+MHdZAHkgjVZ2/eqn7+0LfHoMvvROqvI8wmxHON2IioEapYiMiLDaYWI1zGxNTUAbs1JkRrQxyU7bJrGgedkgV5O6mG1VHTXlI91tbbeulZ052t3Rhueo73LOB6uzbidOT3dXXWnhJLV3WScGL/HzTvDsnneD+EvIBtLOAY5nOm2bRFzTq7OytFUFUAVGSYbZuj42BJvluF8HEJmEbOChjyek4NMweNavgtdW4JaIgOf1gwd6FuOJhwPWl1CLCtgE9knUJMMCxmWPeaClrqOlic3mQBAEaMe6urbrDdHpdJlMttuD8t5zIZOGPTOwU4NkTQ1wjcB6u292fK6gQYNWgLbxz27ap0ariq/++ul4w+XMrQ/TtefhtkKY1oRA7TDUCo9UpGsubLZcTxGqMnIyiEpMMjcM0oxZCVsm5gUEdfddRnMFo71mljtKLLtcf+5YfFqIWZQAi+pQg8sEca77bbhvxNxb4wF5cFrmUO/dEi2HuG7XYGErwP0AAiSA/gHV3DosZEOi3oTPnJkPLAc98ErWvkyupRYjm9EAshbHUqvoxiJgf16aA8k+8TCIjODOtsUQ8NLFAwm/BV6O5mFQchnUuMci65CL+QEmOpud51ew+b1NYRUf882kHWqvnB1S8VDgKcFblcXcuKeTVlXM68w9l3csJBKbU3KJlM+RC3gKIW9CPKYU8eUCnkzAM8hF0dmJhF0DIDh4WNA9DbxWEfu6dXK4qrjwzV8t3Dq5eOOjzb67qEmaBYdlGY8ck6hFAQsGEdEgahjPYoCmN61q2G/G5t1Rq15JH6a2VPL7mqehYVJTRV3R5QXdODYjWJ/kqPiQlEWTsekKIBzByYlj0OiMCfEY7NLiwcopxtBgS71eMb4RjwIfUmIV5GEthUHIac4B8MWLYXhtKbkwl16OrUQCIGF+YxmL+ZNeY8KHOzsjILC74AMuHNxOzdk9a1nWyvXF7C2L3TNAHxhDWeQyeG1YBO8VARPwxjk12Jw9bJqm9bQGp6VY0Ji2q71KfkQjxPwzqFu3YVMHtDKjclwuGpPw2Tbb78bXbLfbL1y4AEHQtWvX0Oyy490cHx/v6wMgHhRFEgmcVwLMQunsgu8SDAbj8fiOk3HuyG63G98dX4NPWrld8APmJ+bgR85Vxuv/29k97e3teD4Xft47d+58Zg4WFhbiaPFca/O7CIbhysrKSCSCZ2vnLLZ0Op1M3vNRJRIJPJl/y4XkX/jS0hJuG+XqbLl23PkUzeb85+psKfzb2T1ut7umpibXJAzDSktLCQTC9evXZTJZfofkl8fHx3t7e/GUdfy+AwqAbL463nJgIAYCeFo43r1brgg3dVwuV26vXKflauLQq/xhk9uUK+AeqX8ju6etre0LmM+F38hMJiORSLbPzcw9F6APzeYQuprjUhqmE6CR7PvinOMeN5pjCtVwkQk6YlNtOnVL5qlUNp0VizjjLpNLrwpOS0cbyk8fe7/yZtFge3NF0af9XZ2PaPeAd1k6nUalDna0+lU8LOZGgjY46ola9bPSMYDGtamAM2De7ZKxaT0tgv5WcmPFinoM0/HjIiKv7ubY7cvrwqGMhrehEWBODeo3Am/Byvw9Z8PGEro0BwIxqws4hhcggaIedDmKxnzgyb40B7iJN5ZRnxEm1SBQGzYjQsWDy3qplM/ubqzu6+zo6ent6+sjEAi48teWvtxbl/TeDwzJAEhvls74gamRszm2F7K+K8yrS7u1svaKZV4/JiejM0LMMI6a5ahjErWpkBkhTKxGBu+gs2JgKnn09zDdQUvUMOlSCX1K3nBVSdGZD4QDLe5xBqe7IS5nYFo+AM04NcD6ybd7trUBdqjXZmUKUvey14yuRJNOXUYn8I0RneNMxKbCskFGxDvjUYuVzFFORy23tzk558bWF8J2g3K4baziepzZFxtt9/TUIwoqgMy7pgFSfhLC7JOYRwurmHPjjKhJE5uRr+ilm7Zp/HYg8z5kMXwvxX1zFUQnHWoA53JqMvbJiFoQVnLGB1uCE9yAkhNS8RJWFbDDnGqASXdqEBswXjG3Nmmb3APfg9+XZDI5JhACRXU6EDfN/+BCp2Iex6AQRWeUsEOTjUAB0BgWdW3YNQpi16ZeDAJhAUM2AmsGZrffgAaMAJ+kZgMnR8wPwEnemSxj8lLE43CZZv2W2Z6GqtJPzogGW90SJtRZB8x9YBNrzRNiKoNBZTCB0mreOPtMyo3HYa9ZAK0R7FCv6MfltOGVWBhNricXQnDU49ap3BopsKezIcJ0zG+bEMmYo3zSIJc0lF5bwpbmwpN8xUi7jjmAeXSIVoBoBWAk+Ax4twOsW9QDeMPn3ajfgC34gPG9GAa/lAU/+PkA39scuhgG4eCYF9hY9klA1e3WZcyKyNRYaII31t8UmuD5ZKyYRrhhmUg4pn0a2bRkjM9mAbXUbA9TqHvpxeZPSLlyIpFoamo6dOiQQABCD9lX3mUiiRRfWrJYzAwGQy6TTUyoFAoFn8/P7bVjIRQK5RhrdqyAYZjX690tTQHfJZPJuN3unPWw43E2Nze3sMtsr7a6uvpQIpZoNLolYMFisXJxLgzDtFrtyZMnb968maOcUSoUCrkyk07TabTJySk6nRaLxQYGBva+qGQy6XZ/Lv9oe4OXlpYeGmGJRCJbGrz9OIFAIN/rtr0CiqIul+uh+Vwej2ePOBeGYWq1+vjx42VlZXgccGV5mUQkLi0tW8wmJpMplUpVk5MqlepRhg1uLm9vam6Nx+PZ2+sPw7Db7X5onMvr9eaOuWNhbW3toXdhbm5uC38Pi8X6Avp70um0Orvk+3vynpwPKUJMpo4+AJ5iQWsW4LIEohLrS9hSBKxxTgPGFEeWTc49nXGoMwYpZlPBUY9Xpxzuau1sbhjoah8dHWVCQJZrR/uAeS/V60FLIAiiUKn0kf5JEUsvYi3ZtBa1QibkU/u7xJShwaZqBW1o06HF4t5Vm1pAGiQMDfa1N1OGB7ViLuzWBcS0/pZ6el1pvO9Ohtu3OU4GMBGbCjzKV6JIwJzxGjGbGlFC6Kz0How3O32CavYp8CJun0JULEBpuBLBOF1YZyE2yQxPiXQsggii9vf39ddXdDdUNzQ20ncHaNPp9D1wzevr63K5Ylangz2zv4Pdk7VCQAK5RTXH7EI1HFTDTStom3I6gNM6NaD9rmkAP9ewsxBvE7hHs2LEJMX8s6mAxTDO1rEJetaIY5yxPCMDM2LQCIyVLA436615EOcCM3qe3YO5tWn7lGUcmptRBCd4m3aNS8m3C6mivoYpYgerq26WTcg41FhgdkE3bhVSvTK2kUf2ytgBrTzjNbjG6TOskamW2/NVnzjffWn2/PH4JDejpKFaHmDfsU8kNMJFen+4uzredNPKGp3l02RMsoANeTWyFcvU0rRo3TkLvBFr8yD6Jqdgajbi1nrEVDd3eEnNnyU0c5tuCwdbQyoeaAawETWAlUDLB1fnUKPWCWD3WFUx367+no2NDYlEMjU1NT4+vtt7AhBHy+rAsyBILuQ6p8Y3HXggcmbdqJgbGwH96daCgeSdAR+nGsC3PTNZC0wNsMMr8yDeGnWDMbY6n1xemJmUa7nkWT7FOc5cM8oBritoBF4cN/DlwG7tlJhPva+RnlVnY4AXg6y4VVCn2DQrHAp+ZEYZnFWtRYMevcom54mG2qY4ZKinySRhp+c92HIoatY4xXS3jG3ikzwytm92Co0HXFKWgU3QMoeSVhWiYsNqLmi5Rw9eb9w60H6fAdGMIdNCVC9EZ0RgkPhN9+zpsB0UgJ00m8Wi6dFZMWaSws5pr5LrlkKLMzIde2Ssv1k40OqRc0I6hUYq4LFZNDqDQqMDfbj7zisa7Xe2eyKRyO3bt/v6+hoaGvCZQKmQnzh+nMcXuJyOq1evXrx4sezWLalUdvfu3VAoFI1GIzst8/PzBoPB4XB8ltm003awbn5+XqvV+v3+3Q4SjUbD4bBGowmHw7sdJBqN+v3+6enp3SrgJ3K5XAaDYe/GmM1mq9WK18EPW1ZWhueN476HkZGR9vb2zwQNDQYDDoS6UVR040bxxsZGT3fX+XPnzp8/JxSJa2pq5HJ5LBbbsUm5Bu/dGKfTOTs7u3cdnKZ5jzrRaFSv13s8nj3qRCIRjUazx12IRCKhUGh6enpubg6/olgsNjY2tsXfMzg42NXVdevWLXzKV6kmjh8/zuXyXE7HlcuXz50/X15erlRO3Llz56HDxm6379HgaDSq1WoDgcBuwyYSiTzKsPF6vVqtdsd7hK+cn593uVwzMzN7nGh+ft5oNNpsNrzB0WjU5/OVlZVZrYCI4d9h+ffDNSeTyYKCgqqqKpwb8IFx8WglCIImRzszZuW9F7vlKPCXAOf2KrYcQYNm8NoKpsZsBpBvFou4luw64WhPT1PdKHCEsFmse4jp3U5Ip9OJRCKeZYbXgSBolEhUC9nYRgyLudUSQU9vH4lMaW1qIBGGh7taa8tKJGz6SFdLWcF5Rmd9QCeLGSescp5NLU2HHZh3RkgdbrhTqrv1MUauR8YJgO5FTkZsqpTbgAgHk71lmZJjmU/eShQcStReRaREVCdAZ4TA7rFOgId4yIYFrBk+wdNTPVZwYqa6wK8SsuhUKpXKZLG7O1qbSq9TqZTt0Kj8a6RQQIXdLP2lpaWCgoKWxsZF4wQCUDX340rbC/lY4+xWzK1NWFUePjEhJSGjVQlO96KACKC+QKAge5yACQQBcf+ND9DKYRFXxKASDLbqmIMJiwrEpLIEPA/Ou+0sKdvk6qw0kzWk8GrA3jIrnBNCbGkOmfeax6h2AWXdqJASOt0yFqurntFZqyT3dt4uqr9yRj7ctqEfX9WJgxP8iIqbkFLihHp7b6X45sfShjJ1a4Os+o654ON0SyHSc2uVP2KqKxs8fLTz7QNDlwtEzY10BpNBIk+11huoA0IOJKMR1/kjgERgVhKtL1F+fIhz8KXZrhovfzRMakwzWzE5cfzOBWlnFeBP0o8BRh/8ipz3+Yvvd93yjMRjnt3tRx6Pxy9fvlxXV8fj8Xaze/C7zGAyhVzOtGRMJxd5TTNJL9Aj2zAp3NzhjEMNwGT+rNE55wRhO4Az0wHu6YUgiBDhwaOVGLYSnfM4hHSilk1MWCYwr37BqPJr5f5pWf4noJX5tAq9UqJVydUqhVwiVk9O6FQKnUw0LeLEDBML08KwRYetL8ALQeM42znOWDbI+L2NoSkBo6eZ1Fo9KYA66u7WlV6fZA5vuPRr1im/ejxsUGEhy6Z10iakigZb5yZBiPB+tqMGIMfnHIgC2rh2Ym3/s6kjLyTP74f7b6OTEDCp8bxIgwTE8txaVCdIjNTaaq8pKq/YWQMeOXtOQoMNEsw5JRloniD3pBzTvmm5XibUSMamJQKdVDgjE87IRSoxn5UNuI+OjuLuit1uzfb1n8VWCgsLaTRadXU1/n5vtZgvXDjP4/MH+vsKi27U19c1NjYqFMr6+vpoNLqwsBDbaYnH4yaTyeVy7VYhFostLCzodLpgMLhbnYWFhUgkotVqcU/MTucBBwkGgzqdbset+MqFhQWPx2M0Gnc7USwWi8fjNpvNbrfH43G8beFwuKqqKt/uAdqI/f3l5eV4r8Iw3NjQUFlVPamaPHfubG9fb8GVKzOzhrq6Oo1Gs7i4uGOTFhYWQqGQTqfbozELCwtut/uhDbZarTabbe/jGAwGn8+3Rx3c+sSjSzs2OBaLzc3N6XS6+fl5vEI8HpfL5fVZFkE8zIRzPA4NDVVUVOj1AFJms1ovnD/P5XIHBvqLim7U1tY0t7TiAh0PHTYOh2OPBsdiMZ1Oh7Pm7NZg3DbC/WE71llYWPD7/Xq9fset+MqFhQWv12swGPZoTDweN5vNDocjN2xCoVBVVZXld9QD3v5LfMQ1/352D4qiPT09FApFLpc/eLfKn5/3LkMQabDfIYGwxDJADOSAkHhhLY4u+MCbq98IPParMQxLsUlDhVc+gVisHF3h3mdgMBi4+t2DaQaCKBQKm0F1GXWGSfloRxO1r4MxOkgd7O5qbe5priMMD3fU1xx4Y99vXnpBSuqdU/E9UtaamIzwhmANHwsYjSKI2lYXbruBkWsBAyGpFu67BSto0WlJgDuy0VWSvH546aM3Ni4eSNZdQam1CL8PNUgAttSqBG+uESesES3s+7n0xR82HP5te9kN8q3LfPIQBDHJPe3kxgr2cO/eF4XH6fbw96TT6d6+PhqVjIUsu8eVsjgbtyZrXxpBqjAQXcq6Fjy6gJLrHiNh4hF4EgIJUGtxEH3Ao1orUeCWW5oD65ejADuMJGkjfbS2Kixk/FzSFm4nbfkGvgrg2onrxHGt+B72yA1SqROWiRk+2aXg20V0I4/kGmd4ZCwtY9DMHhH3tywqIWFbxZHXXjr33ltacqeP0e0kNGR0Y9gUc5nUGK644Kk4O9tYaCZ3RQdr40MN8aZbqyd+s37zpGaMQbtb0Xb0eG/hDSqNTqVQWQwarbFhqr4iqWCtG+QxPgGxqLBZ8VrlFfmXv9T5N3/O+Pl3pEdfHis6tUqsWyfX6+qvyZpLI5MgAxy4VRzA77X9g+H4nt39PYlEoq+vj0wmy2SyBwNyp5vNYDJ5HJZeJlh0GgB74ZwT3EeP1iWFQmoRFstKfq7MowCLvQruwmIImDs4tuze72gFgxOkoX5uTyMWACzYmGs6rFOwoWw8KyuTh4vlUWl0hUScWY5im0vI+tJS2L8a8qDzPoAcd6oxj3ZJN26TcZyTYpOIOcshuCRMt5ytoQ/MULrE3TWLWhGtperY269d+uA9o5DuV/HdUnbKocEck5hjEnZqnGKGmU9e1I3fR2hpASGhXYMl1xCNeOPieytvPLNWcGStqzwxyYWtE7DPgITtAK4kHEBEQwi7c7P0dPDpx1g//ibn7Duyyiv8trubvP4NapO+t2p8qDU2LQJBNNc04G90Z1nO3dNrlknzhFgq4OI4w9/D34Nh2MzMTElJCZFIxKc0BEHi8XhiczO+AJa1tdVEIimTyR4lYPGfJ871iPlcW8JGW+JcKysrg4ODRUVFuZDZ2tra6ura6uoqYHRcX1tcXEwkEj09PfcxTztPXl/IONfKykpvb29JSQmObbo3bBKJeBwMnNXV1WQiqVAoeDzezp1yf20oFHqUONfeKM//2DjXFzafC0XRRCKxh99+p6f6g3UMBoM+3GdRitGNJUDeeu+RnUVErsyDXFbAPR8Gm1LrqcUIh9DX3dnxiEYPfhogrp5dcHOBifu+R/o19EEXZzhAbUsw21KMthipqa+mnEIiMllsiE7rbm6orqocGh6mDPZT2+sX7pyJ73tq7uCvEUojyutO0ZoyPSVw9w248RN4oAyRkjHPTEoPlCNdUjas5idlTMwkRaegRf6Indq3MqMAHiy/CTD36MaSDYUrz33f//qT9FvXR4jEmksf1316sejjE92lBYTmasLgALQnKvyhdg/+w0HSKWDKbMXTaLMzKMBYZF+sZ0HW9/I8+I758LR8zDUNOzXWcaZVDKW0AiweAOniWWQSuhjOOuRWwMrNZXBf1pdWowEFRPTK2ZjvPmfjDmZB1jXiNwBryQOIHwHaBkg+AQMC5Gpl8bMBBccmphulPPPEuF4yNqMcH6eOTLfcXKc1Y+KBDahNU3PVMFC7yu42dpZN1RS4xigBUtvG3XNYwyfJilN6Ug+PA00MtIeIbamxgY2RmmTxsdXqy2YWgcliU0dGmCw2i0ab7W9epLSuShmIzwwwth4tMsFAR2uC+57Vf+mvR//mL9U/ecz38Wv9Zw90nT1Yffg15p3LE4NN62bl7sDwrL3oArjmBf+ucS78vqxvbAiEQoDuYYDBmP2AAfrgV5EtgWRyOoPD4Zhn9YmgA5/UMw6NVcx0KfhI2PEgQ3BpDhimeBYV+KVsZFMIV1ejQTmTFFRygbnmyrJiLoZsszomE8o/GYPBgJjMoNsO+AYTK1hqHcBrsnjtlC1r4bl1fgXHzCd55OwV00RGJ0xDnfbuu9ONRRu0JkzYt8bvV/fX66m9yyq2idGnofQkhUMIrQFRUjHnFOICQWrkHm+TDvXNoFYlOsnGZFRsMRgPONUQXcigyjl0AQeaUUpjbms66gU7igdgUg1ccXrhhR9Gnvim7if/HDr7lquhoOfKif5LH1Sd2M9pKVeNtG9OsrBZMaD9zNo9S6bJGbmQx8mCe+5fJ51O/13505aXlz8jrXn55ZdxHSU0u9yfkh78lUgk3d3duGGE19nyDdiWd8E152oCit5tuObcVjy0lE6nHwpQ/bfDNbe2tuZ0KjAMY7PZr7322pkzZ/BsbbyFDzolW4JhuKKiAjeM8q8lV8bxzttxzbkK+GG345rzK+B15ubm/qNwzU6nc0uci0ql7tu379y5cznJiy09g2HY+Ph4T0/P3sMmH9e85ZLxfzEMy8c1b6/zH45rbmlp+cLimpPJ5O9t9+CSERTCsFYhifnd90wfwOoRx6KepG1q2TbtNWlDbtvynF8m4DJpVJALll3o2QUvP+I3iUSmMSEGlWIidaBGCTbJRCdoCKsNIVSmRyrmRmqNraXCulJp5aezjcXSpnJ+dzO7q8HSVY7UnVs/9uvgk9+O/+bJueITMLEKa76MdN+Au27At0/AvWWolh868rrzjRfX6V1Y3IuthpMBa8Jvsek1U2r15gqYlhA5G1HQUKsq012KdN5YrbvceeI9ApFIJPdn/HkAACAASURBVI5eu3iusbqCQiLS6UD5/aHL3vieez8zOAMChfl2jzMbX8gGrQBweyEI3DbxELaxAgDXCwF03oMHsIDrwqP3GrSTHOqCVYNFHKhJASQOEqtYYnXNa47p5Q6NIuI0xXwOu5SDE9vgRgzs0IBYTM70uRfk0gCjKmACSePAsfQA6IO6ptdNStSjXVCx53WykM0oHR+HWCwQjWJCNCqFO9In76nz9Fcq2u8KOutnBhu8XWXGmoKp9rtjLAaTQVdcvWB6+5euS+/r2GQO4LBkMEnEif42LUSU7X+l57/8v463X3bQBiV8NovHo0IsnUwUMyhd+qlM2AG7DfGbl9JtRTC7Z/XYKwtvPgs9/q36734jXXshOFDRemq/uetOQiuAHZrszL2Dmyd3pdij2T2JzU2pWMSBmDw2i8eGeCyIy2FzOEBXbgfrJwuykQrH5mcVwGfj1iYtE26VcJJFXnCZsdQaQDHP2YE3NLG2Oh+a97ndppk5FxA3tSvG0lnCITRryK67Z9Ir8c3FeaVMigOoc8OMRqNJx0Xri9H0Sjy9vJBejmEBM+LUrBsBDj2mYM1P8gDVoV2FGSUgqkuuS5Ab4iNV/p7boY6brorzwebr4a5bodEGY8/dGH8IEfQDBbqh8rWRqriYCNgT/MDnhCgZMKsLVZAWC465fvDN5asnk7MTphm1QCQYG+PL5FK/15VYiWPJtbTHsnLjTKb+UrqpYOO3T8OnXrW99TTn1z+Ch8uDg9VNFz6w9lQk1VzEoQboOhsgb8w4AaJZzONws9TsuavD3xN+V7snkUh0d3cfOHBgYGAgNz+trq4iWfUrnBJ3M5GQSqVUKjVXAd22fDHsHgKBkJ/HjjMZnjx5Endp4BZMLi8pk8ng2pktLS05jp9tHQMSknbM58qv+ZkK1X9yu2d+fr61tRU3v/BvjUZz8eLFz0gGcZ8ZiiJ5wwbItW4mEjKZ7KHD5gtg9xAIhC8grhmfX1OplEgk2ttvz9x9wZ/1Xe1tA13tq9EgllwFWd/LYf+kQEQaYBOHGuvrqCSilM/hMSijRCJObLP78XbdwmAwuttbO2qrhT0Na7xedKwPsAQRq2FGM0xvRGiNGKsdZTQvtxetl3+EtRbAXUWbQ+WG8rOCa8fh8pPw9YOJ956zPfbVtud+MXvjVLzkePLOSazsA/TDFzMnX4l/+HbopaecLz7leu6nsZqyRdnYskG9GfKt+xxRu3EtNoetLyFCIjLNQ60qhNGCKcgJi3KwvmKUSCSSSB2dnVmiQqA1RqPRts9/W67q0eyeNOzRg4khZ4I4NWjYAXKPI25gxKzGsHgAUA2BlOMlbD0OILFLcwAPCyj7POnlGINEIA/2ri0AthtgQq0ueE06u5jhElHlI20RjWCGR7aJGIBtb7ckecB9bACfrNcBNCZguY/zALx5m5YJj5g+wxhQ9jVMingsNgcYfw/8HxADYjGYEJ1IoFIodIhNYXHUTRWCs++31layOGwGxKJzud3FJdQnHzPfLVA13pY315JpDAqb13/2wq1/+g7lu98yP/HPk49/S3fmmKquQjLSPyPguEWskJK/FvYgjpnls++ne+6k+8vXT/5m/fbpYE8579oJhN8T5Q0EqW1Ak8ujh52adA6djXNX4hnsub7Npp0/ir8HheFVv2PJqFoyKJfNU8tu07LXtui1GbRqFgRt/x3hjh8ui+WclMAePebVJ9wzjJF+cn/XxmI2FT+rtOqe1ThVYodyTEUfDGtEZqXQKuMnrSrcWtq0Tk1KBONikWx8fGyMv2U44f+KBALpuFgiFsulkmWnMWmbtIpoJmKbp+dOEmpHRAPwyF3wYTTDUCvCagO0oozmeNGxjY9eQW4eS5Uc3ag6Jys4qq29ijFb4L5SrPiQ4YUfis+/F6W1xaU0WC9EOT1o5elE9SeSr39V+N/+zP5PXzH/9IeBu8U2ykhEo0wHXNgcIFXC0hspszb+/quZ6+8lr723eeQFrPSDQP0VyafHUX7vnIgcUbCxGQEGEgVAzDQ7wjVph2bJNLlkUnk00jEOK28IgfzN39Xuwecw3IOCl91O55UrV8xWq9Ew09La2tvT29Xdo1KpIAjChZBwTfIt3yiK4hMYgiBbNuX+xTOJNjY2dquDIEgymXQ6nalUKrfXlgIuHIb7hLZsyv2LIAieHrXbiWAYxnUuI5EIiqL4jiiKUqnUXD4X3ht4Zjiez4UgSEd7W1tbO4qiTU3NYwLB1YICj9fX19e3srKy27kQBNnY2HA6nbnmbS+gKBqPx/1+/24HgbOskqFQKL/B24+DIIjP59ujMTAMZzIZh8Oxtra2x7mSySROmYOfAkGQWCzW2dmJI8DQ7IJPiz6fDw9uul2uK1cuW8CwmW1r7+ju6uzs6p6ammIyAbni9qbia1AU9fv98/PzezQGhmFcUGy3OgiCpFIpp9OZTCZ3OxGCALMMTxXco87S0pLP59vtRPiwwWHaW4bN7/q72+4be8Q1/374HrxB6XQ65+/Jf2fdLcFq+zOXwWSSSaTmynJCfw8QyUqtY6uRaR5laGiICQGfPEhQp5I66qrKbt8eGhrKuXy2H2qvNRBEJQxP1BZtUhpQehNMrYcrT2cKD8Ht14FaE9QKvmmNKNSKkGuBQhC9CSNUbtZf5BV/bLl4AD7zanL/00u//uHgP3696Ge/aP7pD3hvPDt9/C31/l/PvvBD93e/nmm/iYgH54/+xvKDx6DHvsH++TOmt15D2m6i0lF0zgUEGtdiWDYVCDOOY1alijrQ2dI0SiT29vYSiUS85QwGg0QibZ/8tlzXQ+0e8PiC06jfgDjUGft908epAVQra3FAKYSjyGP+ewWcJg6nHVpfBkjzbOhkIegh9ncLaMTNxQgWMCUDVrsky6Lk0QH8ctDol7NFPQ3CwdbFGSkArubZAQ/KwOXzOQcP2ASkpnSoWQHLyauMNtlgK8ijzsZ9chcL5UpMkJiHm7wMFltAGNbeuCAoPjM10iERjtGYEIvJGDhxiPXzx7tffUZ45j1B0SdjxZdqf/ZM55M/yXQUwWUnfK88Tf3Kl7u//JWRx74leeanm9c/xEYrQbRocxk1SFBeF0asQnuK16CO8ZYyU8etFQUjNMFNGGQ4AinjUK8bFVkJNj0acQMAigcEjx5c46PZPQiKplOpxbAPibqwoBkIqc57wfdiCE2uhX0uHo+XD8PPdUC2ZxhmzQQS9WBzjrlJPqGhQkInbC6EsKgb9pssPFJCk/XKAInWWY+COzbSrSB2Lekl2fijdlYupAARl3ybMnd4UKDTs2lcdDqNTvdrZZhVsSSnueoKUp2FSNMluP8WfOcEoFPvKYHJteD3wmiGybVI/cVMwf5M+3W46wZSdChe9tFEw43YYAXSehUtOhh++cneb3yNe+IN6vsvWko+9NcVeKvOG998VvflLy298UzqzD7/U99VfflviF/+cudjj0t/+Vyq6CTK7gLiLUshlNmO3j6O3TqG3j6x3FksLD5l6727pGSGVdyUfQpnsEzbJtcBWbYWaJUEjBmX1jUl4XNYW5ymv4fdMzc3B0FQOBzOEe/KpdITx09QqDSnw1ZeXt7f33f9eqFEKvvkk09MJpM7q+rg3La43e7JycmZmRnX7goJbrdbqVRaLJY96tjt9nyFhG3nAYIDj6JTYTAYpqamdmstLkwxPT2t1WrxOm63e2Zm5vz58/k6FVwu12AwxGIxHFyyublRVFh47dNPFxeXuro6iSTi+bPnhKLxW7dukUgkr9e7vbW4QoLVapXL5Xs0JiessUcdt9s9nV32rvNQnQqXyyWXy/e+CzabLV/SwePx9Pb2lpcDbSzc5wfEQblcq9Wa65wJheL4ieMkMtnldNwpL+/p6S4quiGTK86fP282m3drMz5scrIPu3WgQqGwWq17DBuHwyGXy/fWqTCbzQqFYo+D4DmAe8ubuN3u7ToV58+f/wLmc6VSKYlEolAohEIh/naF2z14etfQ0BDIUXq0hcViEYYGm5saRTwOYP5YjxvYoyQSicFiUUgkCpHYXFtdcbOw+s6tivLy3r4+iAUW6GE4mPyTMyAW1N4Q6yxBOwsz5SeRuvPpi28lj/4KvnUMJlTC7HaEWIXQG2FeN8xqA491QNNXiXUVufvvUi+dih54dnPf/1h94Qeub3+d/Y2vml94YuLAS8MN9WQiiVZ7l3d4X6D8bLKjCG2/lm65tnnjA/1PHqv+f/7IVnIWozei9imQI+3ULnP7fPQu3UjzOLFvqL2pp6GGMDqafxV4B+bW5NuR+ddCo9H20KlYW1tjsdgqpQL2gjx2BFAfTSM4rbBdE7PpEwEbSF+KuAFmPOICZEJLkazXZxlQw93Di6wC3M/K3JxFp2cT3CpxanEuPe/1cYcRFR1xqBe048szUvFQG7npLrenYYLUE9UATfWtyVy4JQTgzEDN+8HHqkTUHJjXgzCaDG23INJIPmwL8Hoz6TxmPqFMXgdAkIQ8Ol56yXTpgI1LAmYxxCJRKL11NZSOFsEHby4efCZ5+S3roRe4bz5rv3QQubwfLTqUvPj24tGXGv70T0/+4R8Fik9hPcXoNAe1KhGoc+7mcWfRUU3xh+bOMl3zDTM0mLJNYV4d4tLivQdCRbjnzKNHww4YpGFvteSyuOaJPfLY19bWmEzmOFgkmknVus+CZZ1hWDyQiIXiIW96LT4f8knGxTsavjgSyDyjQxbnsKgzoOLPsEccirF00Ip4tG7ecIrTiajZMZ1keUYqHGylttdwe5pkxM4FrQjz6Vetaj6HnTUugQLXFssA71xAeMNgUJisWSETE/R5Wm+s99xEqz+Gyz6A734E3zgE33gfLv8Q7i6Ge0uQ0btIVxHSXQx+PrRG4DotP4mUHvWUf2yoKdisvQhffjt55IWZf/zK9PNPRD7aZzzz+vy1A5vtn9qKjktf/FHo8POZm0cyp15Ze+kJ13f+ofD/+qMLf/zHc5cPYUN3gDqpCoJ7boU/edt19nXNxXcN9dcMbbcszIHkPZRVVg3GpYW9s0hWlRaLhzY8Js34vYR8/EJy3xQqzWQ245PTI35/NtO88847JSUlDAYD32VhIdbT06PRTEcjEZ1OJ5fLORyuTCanUCgwDO+t1zg/P59Ti9xeEyfVXVtb260OLjBpt9v3Fph8FF3SeDzu8/l2O1EqBXRJP5NMz9clTafTWxLibt68ee7cuerq6hyuWTA2xmAwvF6vw+EwmYxDQ0Nuj7e7uzsWi+WEKrdceDqdXltbs9vtezcGF1Lduw4upLpHnXQ67fF4HqpLarPZHqpLmq/8imu4trcDRxeGYfj3jRs3Lly4UFNTgwcB8WEzpVZHoxGtTiuTSrhcnkKpJJPJew8br9cbiUR26z28Mx0Ox/r6+paOzf2bBtq3CZvNtvewWV5edjgcub22F3BdUq/Xu0cPZzIZv9+fr0uaSqUIBMIX0N+zublZXl5eV1eHq4HmzUtMBoMxMDDQ0NCwBwnNlvr42zyZOOoyarGNeFAloPd20giDU8yRoZ7Ogf7+3s6OyqIrhZcu3Cm5QRjsHxwYyOpYfc4pkH/MXBnKLnQ2m116dfnSb9HC91LnX19999m1159KHXshU/geXHk6XfXxxifvpktPooS7wOLh9wDrZ+A2QqxJ0xuFNy8pTh3c/OClyI+/5f7W10P//FXVl/8K2vc8fXQE4nAYXD6ts3nsyKuLF9+CL7yF3Ti8ePZ15rF3OyvKh+sr4mISoP5jtm2yuziln7Qe39/TVEcik5lMZm9XF4VCyVk5uQZTqdTtL/35NhCFQuFwOLvlscdisbLbt3t7uuf1ss/he7JBgfAkf4Y1ApAfOYsEJGrpsYgTJKj7TUCH1W8EKGzvLAwEWYGs+pyK55Mw0259ZGw0Mng3KCDOsAkuMS2kFpjGyIS6MlprFan5rnOcEZ0W3XsFB/qjWTFXtw61yJFZMTLNA3IKoiFYOARzu5HB20jduczd0+ILH5AZEB1i0ZgQHWLTIej+xPw5/w8jz8ylc3mU4WHSE4/rDr8tYNLoTAjImQnFNCJBfuz1xEevYOUnsIoPXcd+PfbEN5ef+37ywC/gTw+af/mDsb/768nvfV1dcDQhHkZJNejQnaW6T0SHfkU9+qq38VpGQkjoxW4JtN2sATgk8wSYZQFk6nOeHtzrg7m1y7PSPfLYPyP1Kiwq6uroMExI9dKxOb085VC7p+V2jdKpUXhUAss4C4s4ltwmPoe1I9ALjAEm06WfwsIOQInp1bvGGVG1AHbr5iTUCKEmJKXPiFhOLiEA9Rg5BGJT+WjDHVpHnYs3EuYPu1RC04TYpBQblWIhl73F9oGYTI1EYJYLjGxScLQZHbxjKT6RrLsIl59MXd6fOPFS+txrcMlh8J5Q/mG69Njmx29krrwLUG6497T9eqb0KFJ8eL3mnP7iu4EbH2SuHtj4zY/XfvX9zdeenHj8G7rTb6V7SzBGEwK1eouOTr7yo83Dzy8++/35p74t/vu/Hf2LP1f+4BuqTw4kuF1IdzFSfz5WeEh6+AXy4Zd8fXcRg2TTpPDKOSABPkvzDbD2UQ9QQQlYEk5dJmAK6hQGpdiokpiUInCNCvBtUorNSrFGyLYZZx7R4sGrhUIhDoeztLTk9XrR7JLbPRfowTBMIpXS6fTcph0LwWAwx++3Y4V8XPNuFTKZzEOFlh5Fn2t5efmhBHQ4009+S4hEYg7fgyAIl8t1u91zc3P4deV3SG6vdDrd2tqa85bl1ucXcvie/JVbyktLS7hCwpb1+f/Ozc1tERTL34qX/X7/o+hz4WDk7bvja/C7kM9buLCwkK/HjmEYh8Px+Xzbb3p+L+EZnbudBV+P8xrsXcftdu82C+A74rGwh/IWejxASG6PZXV19aF3Ac+ozz/I6OjoFxDfk0gkaDTa6Ogon78VMQBlF+Cw2fJwzc3tny/gk/o9Jzx5dHIMCpmmZ2gDLgXPJKIN9HRRqVQGkzk4ONDV2TXS20kY7O/s7GhtbR0cHNw7oAZBEIFAGBgYYHC4wuKC5Xd/lv7w5Y0Dv4z/4nurr/44c+HNzKcHkaL3MtcOmP7hK4K//suZXzyRuXsGYTQB64dQCROqUGazq7Vk6Op57+VDid88ufizx8xPfLPtVy8OdXZCOVEwDg+6XRw//wZ8+hX3/mcFJVfoRBJnTEAijIxVXY90lmHV5xKNVxS1xeTWRhqVCrHZHCZ9qL2JQadBEID15BYIgsjZZYs9lG/37B3nWlxchFgsHocNRO+zxk0Ok5vlutVumJS5Ndk5OzuLOzVgRv/8B3aocfwE7NREpbQAfySs4MyMtPqlDAtrUN9blVJQES0vSG5ykVsN7BG7iOqRMk1j5JhWjDmmAK2zloeq2Qi3G4ayXjRmMzxSAXcWIi0F0cJD0cO/TL/z9Ozbz431do1DVD1lQDLSx6cQpcIxNpvNpxLZDDqdxaZC2b9UCojE0OlUJiQcHhBV3Bo78Bb9S38pLb3GEotpFAqnsWryo3eWj/4KuXEoc/MI/OmB5MGfW37+3dhzP1jZ9xPjCz8yPfnt9VefxM697i3+gHv9o/lbp7D6i2u9pYaqT6LD1TCvF7PI7ePQJLUPC+DcUQ+AzIhTszwj+Xy/3duK+4Qw93RiT97CeDxOplAgJjNu1wM19SwU1z4pybIKM2YVooRlAmihuLVRg4rHYW9BH+MjhM5gjHGgRZMKcesQoAM/HVFyAiJKZEo4Q+32TfCsQrqF0JTi9yHjowFah4vQYGT22+jddnq3iUtYmZVhXuB400gEOSkuXDiPxWQsqrjYNCfaXbpY9iFy+3iw8HC67ARS/mHyyoH1g89lrr4DHD+3jiElhzY/fGXiq39H/+u/nnj6X8DvpfkyXHMWbrgID5RlSNWuirO6j/bFi44hH768cWaf+8iLUyd/u0qoRtntMKsN6S2Fbx93vPfc8q/+xfi1rwj/+98av//Nlee+j51+1Vt9YfzOxY3WT7H2T5frPrHcPj1HaYGnuZh/1q/kKWiDQFYsZEPnnCB/bWkORBtjvlWHLuMF+YlZJsaszCrAgE8DuQ/AzahdM6sCbnv+E/mhZY/Hc/Pmzaampvr6enzayJ+3cruPj4//b5XPhUNS+vr67t69W1FRIZfLc36O/AJe7X+TfK7q6urceMAwrLu7u6KiorKyUqlUbumTXLVHGTZfAFxzc3PzFzOfC0GAKo1YvLNnfsvMnZvadyvgrhsGk0mlUekUModGVgs5M2K2QcJlE/qoNBogKsxyFdLAbMgZHiF0dHTsfRYIgkgkErB7WGzqhZO+p/5p5Vc/WN33k9Th5+BP3oKvvweffW3xtZ96n/mX6Is/drz8tP+NZzKF74O5mVAJM1tgQiXSU7whGBy7eZH30dv+8/snPnqHWXaDSaU+MHqYTCaLRR/o9TQVplqv8y+fpkIsZlZNnTQ8RGiqEd+9Hjj1Onbl7WVCLZdCoGdZpOlU6scfHi27dWs7XIlIJO4YIsyZPnvbPeDHhmFwOrk1n+s++ObhLDv3a+bwK/j8kbZPrSgYcWpLVEK38Mk21qCjrzJJqcc47SjUkhIOJEVDmJK8LiHZye2wZBRhd8DMZpjRArAgjGaQltxSAEDiN95HSg4vnHlt7pUfbu77kfhXTyw0f5om1cGstmTd5cRQlV/OnuZSg71Vzo672r7m2c4a13BTZLDORe+1EDpULKpvsB7uu7k02tD3+Lc4T31ffOOy7vKR4NEXNj56GSkGPonUyZeT+3+28dIT/peemL97Yb74hPvlH8Efv4aUHE63XQ12FDtai6c/eS9afBwdvL3I7QsLRuFpgI8x8sjFZ49bBJR8uFJWfHR6Ub+b3QOMmIfmc+ET5+ramkAg8E1L8buA+WaNShGZSmMyIa8WpMqDrvbqXNMKBnNnXyaNzpgaH4OdOFcNEFRP2iZX9ONxFSc6JbCK6BYBzckaQnndGLsNhVoSvJ4UtwtTQzGNKKTi47fSpZaSaQDsg7vWQNgLggJQP0ZvWGq9tnj13czVd5RFJ1bJdSitASDeBu/AQ+XAsrl5ZOPoC0u//UX4wPMTr/3CfuEQ0lcKNo2ClwQ4i3deFozICo6qT70eufmBt/Ha3K0PU81XUHY7km0POngbLTsWvbw/9PKPZ777D7ZXf4Ke3QcX7IfrLoTai92dpVMlpxZar2Ok6iCrb2lWBkTZ/LNWEeP66Q+sQio2Zwe0C+uLICfRNwu79SsGBQxIIrZEHkEKHuaaTjnUCgHXaDLlZp1HKXxGuv/RRx+9//77Dff5mjc3NqQSydra+vLyssVitVqtFotV/mhELP+p+Htywand+mG7TkW+HjuGYf39/e+8886HH36Ye5s3m014D9tstnA4JJfLl5eX+/r6/tfi73G63Rubm7t1C54WvkUJZIseO273vPPOO2fOnMGhLRvr6xKJZH19Y2V52Wq1WS1g7CiVyn8V/h6v1/tQf8+/lk4FrkG7R+ds16n4wvL3YBiWSqVyuObdDJpHXL/FOcRgQlQKlUGlKFlkaLCbTCID1pPRoe6aO0PtTeTBvtb62ubm5u12w5bTQVl1eA6HW1/4KePHj2Vef3Ljjac23nxqff/PY898z/+jb7m/+TXaf/0z7RPfTpx+Hbl2IPPpe5muYoDfZLXBQ7cRALR0bYoJY1eO8ipviGuKBxprtobYIIhMIklb7oYrP6HcLoG4PPAOTaf3dHZQGMxb5091fnpx6sirMxcOsolDWZ8Fvaur6/B7B77//X/p6u7GxeQZ4MUfTEM4vGOLPZfbiufl7sFbeG9obs9j32bN5MyavQs5JweYqlV0lNeV0gkXDYpZPnW2r3p1tHqVVOscrIHuXpM2ljhIraqaq7bmQgxqQZjNwHZktgDnGakGbroMAiU3j2Qu/zZz/T34+sHUb59Ovv4/xp79nu7maYxWh1DqUHYbJupDRmvSDVewzkK0qzhDqMaojZheiJlk2KwY03AyBmlKOISO9aLcbtvBV9g/+rbh7Wf1F971nX9zvfB9pPQoXHokU/Re+vRvNt76afAX37Ucec1a+EG85BhacxbpuJ6gNlo7St1DVX1Hf2O8uF93+aCtvTRCbspouCnbpBYaPrH/taNvvgpCgfenUjwz/wFCPNeTToD7Af0D+GOATsVD+XsSiYREMq4UclN2EGrEfDOrNo1UJOByuBKxaM1j3rSrN21Ti6apMe7OLh8Gg8liQXN6xYZ1at02tZlNa8I8wLGRtk8tzkh17FEjNLSp5q4z26z91dSqG6rBZg9nSEbo9EhZmA8o1MYNEyIeh89hC7hsDgtkD9Aglq6vcaPy7HrztY2K0+itY/wP9xmaCjFuJ/ghUBvg5gL4yjuZ4y8u/+ZJ79PfS53Zh1V9hDKbQFCY0wHqMFuQsV6ghuacipKbtGfedNw9b2krdjZe2xwoQ1jtCXbXOtSxRqxP1l+MX3nX/dqT5ld+GKk4gzVfAm8X5HpXf2V4tLb1+JszlRfNDdd0A7WrclrKKE/bJjWM4aNv7Tux/zWgExK0YPNeJGxHPHrYMZVP/J0byRk7uC+YRxfQyrgsptX6u+mSrq6uNjU1HTx4sLGxEf9BTapUxz74QDwu0U6rrxQUVFRU3LxZKpXJmpubcENhfqdlYWHBaDS6XK5YLLbTdrAuFovp9fpgMLhHBZwpGA/o7FgtFosFAgG9Xr/jVnxlLBbzeDwmk2nvxuToj/G2hcPh+vr6fL5muVx+5cqVAwcO4LpOyUSipPhGcUlJOp2+ebO4sanx9Jkz4xJZc3MzTse8Y5NisVgwGPxM6mvvxrhcLqPRuEedeHzB5bA67LY96sRiMYPB4PV696gTjceN09MLQX88vuudCofDOp0uGo3iV7SwsKBQKLbwNUskkkuXLr3//vs+nw/DMI166tixY0KRWKfTXrlSUFZWVnb7zsSEqrGx8aHD565OpAAAIABJREFUxuFw7NHg+fl5vV4fCoV27F58JU7zvfewwfma9zgIDrHa+y4sLCyYzWa73b6wsIAPm1Ao1NDQ8AXka8YfB/+T/D35NsoWu+feJhaLRqX0tDbdLi6qLL7uE9OCtM4NDX9BPaZjDRPaG4dGRqh0Rj4qNv+YnyvzeINXCya+9d/nHvvq7Fe+RPiT/3r3//7jT/6PP6j+wz9h/MVfaf7+78JPPrbxzs8z1w7Co7UwrQHuK4VpDaiSjgYtmEOdmOKuCYeD7SXUvi4m63PBKSaTSabRaH1dtPNHei59xKBScLjS0OAQncGsvXqxq66qp+x2x8ULdEAvw+zr66urq6uuqjr42ssDnW1sDoeWXXIeHQaDQaFQcAsp1y00Go1MJuMrfz+7B89Oys0Nj1jI2T1ADcooRc1ykN3t1S/qx8W9DYRPP2KVfLwwwZ4fG17TiSKzSg2LIL1bsDham2I0o6xWgP/A/T3EGrjuAnznQ5AZVHoULjmc+uCFxGs/Tr72Y90vHw+//1zm0jvrlw9Gj73qefaJiW99zfTsj5CaSyi5BpESEA0XFg4hOgFiECP8XuCQY7ahGi4mpSwquBYJT9FTv1FxCr1zAr57CmBv75yAy09kig+tHPyF98Uf8A8+P1tzBaODxL00rTEyWLFMriNfORZoK5q9/bG3rRjjdaP2SYeEYRNQRxvKC88czxI069K2ybRtEoCagfNAgzjUSctExj6FeHSwSweAydk1m2Yl4nwk3kIknVx3za6YJ2G3HqiHBkxo0JLwGFbt06t27bp9WiXmC7hsEY+Dayx8bgDn/TPGYYu4bCGXIxNwV80qPKAJuPt8M5FpsWC4g3i3UNBUtqCXxbTiDac2OqtU0YcmqX1LeknaoUbdunXr5KplctU6qZMJcTgRh0oWjQ4JSSNyQu9G2YeZs/uMp9+MtV5Hu4rQOydTR14I/PCb1u/8w/Kvfpg58nzy+K/TN47A1EZUw0MUVGDdMpoRbjegY3ZrsVnxhmB4U8X0j9SuC4fR8RF0kmkWMgUMqoBFdw3Ubd48EjzyvPCNpzRlH6P0RpjelGK2RVk9S9QmUuFpf9+d2fprno6bGLsjIx6xQQPOccZQ7a2rJw8v6SS4iZnxzCA+I4CmuXWwQ5O0TWUcatg1nXXzaFCnOmnXbNo0m9bJRfNk2Ou69z7waH8SicSdO3eef/75HB2zYXbm1OnTVCot4Pf19/fX1tTcraxUKJRlZWUejycUCgV3WkKhkF6vt1qtu1UIBoOhUEij0Xg8np0OcG+d3+9Xq9U4cGTHaqFQyO12q9XqHbfiK0OhkN1un5mZ2bsxxuwSDofxtrlcrtLS0lw+F65L+uqrrxYUFODwnXQ6XQmswJten08kEg4ODp49d049PV1VVSUWi+fm5nZrksfjUavVezfGZrPhs/uOBwmHgh5f+FSDWm+wLsbC85HQjp9YNGQy6HweZyy6S53FWJRKWvvFTwnnatWu6By49B0Wn8+nVqsDgQC+LRwOM5nMXJwLd+hOTk6+/PLL165dwzvHaJg9deoUmUwJBgO9vb1VlZXVNTUqlaq0tNTr9eKdvP1M4XBYr9dbLJY9OicYDGo0Gq/Xu3333Bp82Pj9/tyaLYVQKOR0OjUazZb1+f/iw2aPu4CPk9nZWbPZnBs2Dofj1q1bX8B8LvwBst3f8/vJdeU90rcWIQii0aiD/f3D3W1LpglMMIBKiIBEP+ZO+C0y2gh7uHdocGDrbtv+h1hAs7D33IXav/tq0Ze+du5r3/r4Bz8pfu/Q23/15aL/8v8tvPzjzMmXkwd/kTqzD+F2I8IBkKXC6USko1jAjAWtmGMyoeGO9wCwNvPzMQgWi9XR0TE0NMykkimN1bjdgyN1hvp7B9tbqVUlPDqJygAZShQymdzZ1FZ5u6GhobOhZqivm8Vmk8nAcMr5eBgMBpVKBfKQ2QX3A1GpVAKBgDt+Hsnu8c4CS+U+CBfAQexTYFa478bYZvfkRQpw9C7wZGQl0nK4n2xOFuCgA8oJuoRdPccdMA43wjYVNsVCLUrMN4vNWVdVbO9wrZvYPEdsxFR0gO/hdMCcTuA2AKipSrjtGkCEVH2cPvt66tBzK68/aXnm24JvfIX15b8Z/NM/I//XP73xB3986v/8o4GvfmXlyK+RitNw9ceg/vAdmN4I0xoRSj1MqATqYEA0I6jhM209FRi1Dh6tAsoh5FoQoBypSI3cNRceS946vlh1dq77FsJqAzdURlqWMywDNZPVBTNNhUl6c5LehLDa4hKKjNQjHmqzCKh61khcN465dUmr6p4Qqd+EhW3wnCMTtGd8xkzIkYp64XgIjXo23LNrFhXi1AB/z+46FfcmXDgDdFfCdjRoBayD3lk0ZAU2UJbDGnVNayQCkHCes3a3DWN8BR6iwtVDQzMqzG8EsTafAWBffIYNy0SU3m6jdsF+E1CcCNuwqAuLutYskx4p0yNhRtQC4PjJSpOalGIc68OAsuhyiM2jU1erLyLnXl/74EXviVfsbz9jfO5f1E/8k/DLXxr5k/8GfeVv3K89hVR8BMJbjGaY2wU+UCvC70XG+lDdWDbJfxqzTSxpBFE5E4w3hxpzarTjfCD8zmRZqL2WyoupzsKllqvBntvI+AgiHsZ0Y1EVx05sUTeWzPZWJZgtKag9w2iOi4ii4TbJSIdZQFXTB+NaMebWAkMTp4aKurO+H2d63pcJWlMBCxywZPxGZN63YdOs2qaxoAX2GSI+N0i5eeRlfX39+vXrhw8fztk9mUwmEAgsLi5ubmyur6/H4/GlpSW5XJ6rsNuxH0WP3efz7S1d/u+px46/r+dfzhadCpFItH///tu3b+fkLOILC9FodGV1ZWNjY3NzMxAIbG5u9vX17X1Rj6JTsby8vHeEZVC5/oeHnBWsJak1ITRtisyb45aE1Ao+CkdC6UhMOJJqT5qlCigsyzpfWudNzfjTM8GMYR4zLmDGecwcQU1av+XIRfVP9n3z3Yky7kb+teeXM5nMQ+NcfD7/nXfeuXPnDg76fjBsNsGwWcgOG6VS+SjD5qE6FXiOVX4Lt5T/FXUq9r4LGIZFIpHPUjfyG/DF1GPHMAz3wm2Pc+E8NA97dO/yRM9bjUd8AJU+BBGpVEJf17qKjYkGEBUTdesys3JUxUIdU+s6pZJLIxFHgXRXlvIn7xifK0IsiMnjdbd39HV1E0aJNDqNKxL2dnTePHps9qePJw78Mvn+L9OnX4Wbr8C9pTClHrXIQZaTS4sZpRtyqpzcTyGRcF9O/nFZLFZra+vA4CDIqWZzchVYLNZATxel/jbr9qWuipL+oaGuxtoJ+nBocmy4uXZwaHh4eKS2tnZoaAiCIDb7gcwqlF1w1w4Imd2Hh+NKqwwGYw+7B0EQr9cbjcyhYTvwKPiN2RSYbDa7U7NuUqTtU/eSuXLxGrwA8t6B0DcWsoG5OeIEWe5zIM8LmH0BM+AzDJhQJ2Bkxp1AmFu7wB+0kNsRORlV0hEJATUrwBTumsbskymHWjfUGqAPgCiMXYValMiMCFGzEQ0no6AiY30wsxXYKN0lSM251Ce/nTv4y9Dz3w8+/Z3wT75t+eev0f/yL5v//M/tj//j4tPfSbz7M4CrBe6cj+CWAqDcJBpG1VxsKRzXyzwDVWlKHR5kAcc3jAOldAU1PcWyjDYneb2YeAiTEFC9AAinA/tgVs0YFNbfoF48rKm85Ou5Y2q54eONBJVcBaFzySBzSph61siaUZ5NZ5sF2UOrcSCGBRir1+DVOLwG9LCQDaArh64uJBfCqYg35bcsBHbNjIBhGHja5+ex1Xl0KQJguVE3cFe4pjN2dcKqgh1qzKPXK0TA0ZdF3mQ5dRjgn/zRllfGB0bYbcfWYuhCIB2yAxeIU4M5ppb4QzaoHzFlc/pAsjeQSQE2kEeX8My6xplrBhnAMHn1BoUIt7Rwc4vBZI5BjDWoE3jaGi9n2q4v3zkTP/x87Lc/C/3ie4Z/+irzL/6c9bMfparPI+UnAaNP30146A7cdhVw/PB6gG6rRYnZgKBpUAYBHNJ9W1knFWTtOcgm4VooHanZcUzLwwwi4KZyARJLxKNTUvvHO6rIty5rGgq99C5LT0VARHaIGVrGUHxW6pJCWubQulEO6KMWg0AvBYjELYH7srmSWVlAV2JASw6wkC+jK7FEyLkZ8cHxcDTk/53snqWlpZqamsrKyt7eXgCYyy74Mz0f4Dw+Pt7f35+rgFfL//5i8DV3dHTk61RwOJzq6urS0lKXC3jR8jsk11EwDFdVVeG53Pkdkiv/z/A14zcitgJ3ji8/fs33Zx86//6cu5QWbxhbquMt1XAXqzjxKk68AgKfO8z4bcZiQb+7kBgpZa2WcDeKoZXift/1G+PXDnZ+eor0aZOpcMB/vcXynY9m/+CQ+2/PuE3B5I73NJ1Ou1wuBEFyl+nxeGpra/MHBovFwjsHT5LKdU6uANIAJZKHDpsvAK65vb39C4hr3tzc7OnpGRoaotFoFAqFmYXrMu8vIANr18f1/Up7/mWxWF3ZBQKcdayh9mbjcCvaewsRjwBfukuLGOWIiol5ZrCVCH+w/crF8/19fRQSKRcgyGFltpwH6AJwOCzczmAwqCxWR+Gn3icfCz/+D5Ef/3Pq5EuZwoMgb2W0enlWsRmPYBHnCq9PPNhOplJzNk3+MfGssa2gn2yHkCmUxso7goqCksP7O7q6BbU3MPno/Diltar88oXzRYXXL168WFh4veP/5+5NoOM4znNRZXlJTvzuTd59L/fmnCTvJXFy7dhJHC+J7DixZNlyHFveYluWZcuWrJ0iJW6SSIokRIIEd3ABiIUACALEvs4KDIABMFhmBrPv+9I9092zL92zL708VzcwBAGQlBQn9x716TNT011TVV1V3fX1/3///7e3dXV2jgyPgLVHKOT6Uyy+43aWuxY+nz/EbhKJ5F6MtkQi0dnZyefxEgEfGfIApLKBbyivrsKqA2pH7kpwPojBa7SVjnjBqhz106iTDtqZTAy49kmgwNFz0GKWjKBKEPip6la5eDdz07doyU1ycYCU3qa0Eko+AXRhrG/DnvrDV57+Hjp9O2dcBK59YLD00mwgScoso+QTpKgNUEMErdTIJablbfrkL+i6Z8m3f1x94/vxH31Z/y+fS7/4TfSLfxf6p09V9n6/8sb3qkd/Wr30BinqoAzzjFcTE95Eb1+oADHDLOVkQ2hxHhFZT4mUX58yykiPGsi9QPh01ocQu8TillXZwI3pE2/073ka6T1vaD9N66Zj073CG43NJw7N97X3NZ4Sd13TCvr9K6K0TUlhLioeKEaD1WySKRI06yUZLK55nMymy9l0OoomsQDs9XAPwe2fyWTy+vXrfN5kNhGmQSQWP8AirP9DThRHskGmomaFWyXzqJY8KplHp/Sa9R6dYnEe+EfZPOW49DrugXxsrLSkTT4f0CwyARPpVjlnxwrqKVopoIFWS8dWBKKzMbCZIaKD186OXKxD5FM5syxpVXrUS171klm+MCUS8gXCOREvKwRcdUrYmhV2OkUD9oFW562rxKld9P4fIN/6vOLlH9Oijmrdc9VjPyO76sjGveT1t6mJ65RWQrvWGKciMj+CinurttWacJHx64FCDeA5oUsFnAwBravfENDLbQqZXSlLWJRMwBgzyOYHbkg6rwy9/ZK3+5xnepBBrYhSMt/Xdu34m3O9rf1XGqZvNRtnJnxaeSYEU0mMSkfyMRRg0FJ2PThrNkXn0mQmWU6F0yiEh5Eohm4fkfsc8fl8J06ckEqlDQ0Nm+2Wt/xlaWlpbGxsxzWSZrcPB+7p7+/n7Ni5y29qapqYmDhz5oxcLt987Zs7p1KpNjc31wzdud7Y/MnhHp8PxLPb6CsATVl8AA6w5hl34lSwha9jV3+sekWSfqEj0iLFLWhl1RJas4XL5Ob6t6YjyWg+lWBcTnp2mm6/zlw6y3S2M4tzNBakKhWGYSoUPbjoH5InR1VZX7S80ajNTaa3455QKNTS0sJVxiGbK1euiMXis2fPKpXKuxpB0wy4KLAtLy/X4t3eVcHGD4ZhPgS4p6+v70OIezj36hAELS4ucqvyFq8zNbaK8INu4+PjnC9jnkA41dtREPeQVw6Qkh6y7xx5+zytn6X0c7RdTrlUttkxtWBAJR5bGu1VzIqEk+P89xDwgWuXQCS63daq/e7XQp/5OPzJv8x88+HK7u+QN47R5kWwzuWIYjKyONgFsIhIxP2FE0S9l8uaEotv3u6baj6v2Pvjiyfr+lqvKS69w2s82X7t0qW6Q2fONLz77rvvHH77zTd2X2044VwSL4smhgf6+vr7m5ubGxsbt7tn5KDPffwWUhSFYVgiHqNCHhoCDJUthFxyc9CuDUi0CQBttWYHiyUeYbJJGjKD5RM25e2KokPJ+PV5u9IqGihLblHCNmphEDivG7tKCm8A6MOq1ewzo3bJiF902z4vCOpWcPMK6dFyCzwIbO5cIxcHWeIza+U+0QQUVX1nyKaD5IXXKod/4n3q0cgvvg5/9hOrf/Qnsa9+Nv/Uo9Wzu6iZHqA3CXsyTp1v+HpFO8MaLQNMU3aBQJibrgU4bASWPty+6RQDG+Elgaf3kuz4ywv1b7ja631NR7St9QsDHdfr3lJN9o40nRu6dubK8Tdne1ujqllEPg0viyKahYXRW87laSYOry+xIC46yiSDZcxNeE3xe8t7KIriQlTSRRDPtRoLkqgL+K3mWuXVUkApycUVZ42xfTogdUsEmJBLq1jhAe3q1m0D93hBmNhcupiKFsIQE7SWHEqDZIxyKmj7CvBaKefRWgnocL+eRmzVgNW8IHSqluwLAs/KDKJfyVjljF+XtilnpsU8gWBuSphbGqdB5JbrMUGXSCiYFE/xp6YjfZfpc6+Uj/zE++r3Emf2FJ/8Uu6pR0tv/Th/5Bmy/xylEgHxFWRIGmQQiHIPYopxV1dlRYw13ONckwEVm09HwibF0uIEj8/jC3yaJQbS05DBu8DzzU/MXj+tOrvP1X/NNdFhGOuYu93aefa4YvwW78bl242nGo+/tTDWG3cZYY0ssDaHaWVLwgmHahnELOOcj7PyHoaIlpLhfAyLhrD3Je+hafrWrVvHjh2bn5/n1ioMRc40nPH7IYVC3nz9ek9vz8jI6NramkQi4TLc63O7K5ftOWEYvr9KiPMcQ5L3W9vfi/+e9xiPfYs7HIFAUItTwTCMx+M5evTo9evXN0JQ0YODA4NDQ8VC4erVq3y+oOF0QyAQvH379v3d4ZRKRQ73cB1SqdLxDEnRTKkKxipGkJ5oNUukIyGkQjIDCiJGVB1R6vRU5tX+9MBaPp5Z741YNIynYtt7FaCocpnGUHp1JXXtWvFMA9nURAn4jMvJEMSW/DRNowFvqfgAey6/378ZB2+353I6nUeOHGlpaeG0VCiCnGlo8EOwfHW1tbXt1q1bQ8MjarX6vUybLWqjLQ1mGAaCoHu9/XKZSZJ8oNunXC73QP892Wz2A/jvEQgENYu/7Y3/1R75XxanYrv3wvcODrY+zjd+i9gNsIYnJuSLc5R1hRK2A7gzd7t67SDVd5ZWi4EmIupnMlEmG2PyKTKFlfGo327kjb9X70HAQkoyw3v3cPSb/xD5/N/EH/273C++QXafoFRCUHIlj9j1XTfagA6L3QQCAccv3mgmCAA0NjZW+7klIRaLJyYm+k8fvfnmHl7Tha4rF7u7u0cH+8+dONbV1TU6MtJ982b9qdM3O9otU8MVvw7Sy5saL9SfPHn+3Llr166NjIxwPopELOoSiUSTk5PLy8v390bFkFUaNlMeTdm11eAFyD82IQDWEd9dWOGus1xOzEXHA4CMAqQmgBcCut2vLzrXvDJBdZVHLw5SSn516CIwdR5ppDTTwHsK5gLjUiKYIlHJpgrpOGRUxQ3rJtygFr8BBJU0LwL91/IoMPsStQMYNHaFbDpIHftZ/idftn3z4cgT/xj64qdSX/1Mad+TABgtjVKoiyllTFMj0VUBEzRzNsyUV5u33nFNBGyYXSrgFGfzxW6kGb+BcchxUaf88AsLB57xttbJzx2MSIeNktGp7qaIeg5TSpzSSWV/q7GvGRL25q1y42TPfF/rbM91Df+2TjhY8BqZqJcJuUD5LB0qb5PH4HvKe9bvc5pmihk6k6ykoyQRZ/AIG6hVRwUswEvkOhlr3S8i0C2G3XTIrVpemJwEYSa2yHw43BOG/WC9L2a4SOxMCqn6DZ5VSRUyA0ToUZOzvaSogzZIgRKTiIFY7qUMUwHSkWoOz6eiAZs+ppfhDtXMFODAzU1P5dxaxrrIqATxZQHAPQIBXyiM8ruY628yZ14qvfiv1qcfK+75XvG5r1UO/6TSWUfN9wFoBbxcmsySUVQ1ywTWfSCRHjU3ChzuEQiENvk8UOoFzBRiWZMBT0ICgcCvXQGh4/16xqNKrfCWTu9TH3vJ2XF6rfFQeLrPPjsu7Lqa0C+iyhnb7PhS92X3WDu0Mp1DPeaZsdnelpneFhXvtkkqKKAeEHsumwYqsHiQSYdLqWgYQ94X7olEIlx0SR6Pxw3cyrJs165dQpG4s+PG/gMH6urebTjdsLICLJscDgcMw/6dNhiG1Wq1yWSCIGin8+AYDMMKhcLlct0rDwRBXq93dXWViyOxYzkQBDmdTrlcvuNZ7iAEQVarVaPR1FqLBqHg3Q2HIEin0xkMBi4PDMMWi+Xtt9/ebM81PDz86quvnjx5kjOJ/yW4qaurO1lfb7Pb9u/ff/x43b59e5eWls+cPSsUCYJI8F5NCvg9R7pNs5qA3++LoP7uWfhHV+HRJeiNW8F4yH+gB/7amYBszSKaV92cg/5wt//p9vjuk8rRb7yRunQlgsLBeAQKheBwWG+16u12yGLxra76EQTsZgvE4+du3qxeupSqq/M2NGi7uhwqFYQgEIZBKOoPBPzbRmR1dfU+o+D3+z0eDxfSgbsiGIb7+vouXLjAzRBO3tPX1/fyyy/X19dz2FEhX921axefzwfTZt/+uuN1DWfOLstX6vae8ehQGNl5VsAwrNFo7j9tIAhSKBRut/te04YLgsEF1rjXEEAQ5HA4FArFvTL4/X5u2qjV6vtUBMOwVqs1Go21aWM2mw8fPuxyudafe//BX//ZuGdHey7Bhkl2DQEAgg4IwXlPcFDLuWMCcKUnJ2CdnFmdoD1qsEzO91NKPniFjXiZbAq8fBdwENO0mGEq+TweF/EAL3hLaduPrGeYnh5tvx45+OPKK0/E//Vh4gePkE1v0mt8QOzIpeIOzVjfLaFIXHOrs0WUxV3vlro2/xSJwX+HW5tErY2DlxsaTr777vFjp0++21B39NL5M52dXYODg+MTk8NXzs63nB3pahvpaL566cLw8HBLS8vhw4ebmpo2s8V5PJ5MJuOC49xzOm2yY+cMrSkgUdCux1u4s/zrCfMKILJsHLk7wfomBqRmVk/EsZthVurDCY38+qhqNq6cZvRzlENOKfjAmHllDATkQh3rnJg8zo4OwVQLMdgd08vAa32tOr+e8umrPh3t0QKfztppSjFJzvaQo1cqdc9Vn308+O2HI0efo+pfBHZ2DS+Rt8+W+J0pmYhBXUHVfFS3QPv1QJzAihbumJ6x5bNG5hvhyWo11hJ+PeNUFqVD0Eirc6RtrvmUsOPycPM5wY3Lty+dNIgHIxpp3rhAyMZUt67IOy6ahf0LA+3K8VsFl0o+1j3ZetGzJKzcoTrp8g5F2P+g+5ymWEFRmsZjTDrKFAg64id9ehJ1suI0Aw0BfhUdsGTMKzmvkSFiJcyrkknnpXOL8/Mz0+vgm5tdAoFAMj0VDviziUgmHq7t+aAzop1PWYHKifbrKcsSGBTXGsA9eKwmEQH3SwFnKvkY4o/WcI9AMDs1FbUoM041YVdFzWvLUsnC7PTizDQiHc+Kb+E9Z3MnXwq99p1UyzvU7TNk17sAp0q6KdMC7VYxsBFaEUc08yBoKBewNmilWN2iRQlCwfP5ArNKkUW8GcRD+K3q5QXuoE25yMR8gJQGMuvzckFgdsQxOzrXcUnYfmms5cJke+PtxlNG8VBYPVfUSAhRp+Zm48pgh2FqeGmoQzXZm7cr5GPd4y0XIMVsFXNTMRggvEyilI5hyPvj9wQCgZdeeuk73/nO4uIid4uFQ6GrV68qlcqZGUlnZ5dEIhkdHVtZXhmfGCeparlc2nHjIiTEohGKrOyQoVqtVKuVStnlcuE4Xr5HKeVyOZ/PO53OfD6/QyHsoXK5jAMPMc5isXifPLFYDIKgaqVSqZRosrRkxZ1IdnPTKpUKgiAYhoEcGyUPDg5y8h6uK9rb27/73e++8847NXfMk5MTg4NDZrO5vb1dMjPT1dVptzs6OjscykAhWyrvdOkUWTZA2T/a7fv7Y8GnmsP7ekOffAv+3Rexj+7z/f5Lvh9dQ/9kL/qRl7FHT3hfveH76H74Iy9AHzsagU83Mp//u9LnH87W1+cGB3OTk8W5udD0dHhqKvvzn+OPPpK91Fi43kkcP5y91liVSikUrZZKZZr2BINJHK9d1PYuKhaLdrs9nU7faxRKpVIul9vcw+VyGUGQLf6ar1+//sQTT9TX12cyGcD2DYNpI1co52Znu7q6JNPTY2NjBq2p6SdTM+9AYRdepSvb+6dSqfj9/lAodJ/GlEolp9OZyWTulYebNg6H4/7TJpVKuVyu7R1SO1Iul+OJxC8DrIKKKpUSRYHPu7dKpQLD8OZpUyqVBgcHP4RxKrh7oFwuc34LJyYmOE7PZkzAvY+K2I3P53O0lc2A4L2n+Xz+4uxMKegAb9iQkbVUYiU9G0wLJo+DvUAU09FV6TSf916jgwHisFgsHOyD204yI43VK/vJxn2kuANwY0NOJuIlUcfqNP9WT+/w8Mjg4ACInsBu76Xxd+UUA3+GnNhmbGRkeHCpDwKBAAAgAElEQVRgdKBPJBC0t7bevNHW0tw0PjZ2u6N9sL9fyJu8dbMTRCITifr7+zs6OsbHx0dGRrj+5N03Phc3LgxZpYC8R12wK0rONcoLmMgAFrAcFxb9AO8mjF9PejTrQpENrcRmUAKcH6IOELkCsXFWSCCoewoD8dVZPgrpUUMLk2WvnoFNtFdHmRdplwr4mkti65ogblyKmUwU8SulIEj4uoBkQ8jEwSlO/MNGIaDdatq6TI5cpg49HXr2X5KDV5jpTuAZr/0dkn+dMs1TbrCiF0I+eE0aUUtTxuWwRgoUOg9Q4W3UyFKJwWX6gWKFgVmLdNhQ9eliugVsbSaqnc9YV+e6rq5O9K3x+gvONc3kbcK8Etcu2ObGkwYZHTQhihlkVZy1rmasgMXC+HQFuzIRfJC9NE3TuTSTTRYToXIqwhAxwByCzXTIQ2MuCrGDaKl5nEmHKj59ORpgihkqmypGAqUYQsRCi9K5LSxnkUg0NzuzeZdIJIaV+bJLFVTOVmArA2I7sJq+kAcIe/Lp9TtlY1zSoYBfMUt51bhtbWZKzBcIRELh7LR4blo8Oy32aVbKPmPRBbwKaZakM2KRRCwyDXclzu+JDV2hZ24CPwXDF8nJZkDuAbpLRcmt9i8J4rpFwqkJaxeZgIXyaZmgBbXp1w3mp6bm5mZBmyVTkingEoLHFyzPz5TDXqCcZXlmFPDfaALxRyFDxatN6hZCa7Nh1VzWJp/pv7EqHNFM3s7b5crxWxmbPKKes8+Np4wyCjIEVsRR9VzGspJ3quiAlYkHKqlIJIS+L3lPMpkcHBzk8XiTk5M0u3HUE4rdaJrmlB1LK0uCSRGVrzE31m++zV+REBqOJqD4TiqqdDofTpA0UFjc38Xfe4xT4fP5OKnD5gZsTueyRCyCkjSTK1FYmvx8XfD1nq0aou1xKjbHY+douZwzWO5tnusf9nOd8EtRVIUqdV8Y4+/xGvqiZHmnvqfot3tT//Vl5E93qf96z9zvv+j66D7Pc6c6Hqt3PPwu8sk31H/7dNunftx8cUh/qfHq4z8++PO3WpZ9lXTDxcw//H3mRz+iZEuUco2cmaEmJ/HOTvzdE+V//FLpM1+o7N2XlDW6xr9UygDmEBg19vM/KE5FKpXq7OzkJgPX84uLi1zneDzrol9utlAsG5rLadE7br+8JnodnquDw5Y8Wdmhf/73ilORzSIYBjqTICi9nsZxrm83f26PUzExMfEh1HPRNJ1OpxOJxPz8/MTExPDw8OjoKLcwC9mNk4Lw+fyBgQHOAJs7/oE/BXy+z2GhskkmEaBhE7AbSgTveojncaaUDThMo8NDW0zNt1S6TfAjkI4PZGxKxjgHnuMT1yjNFO0EKhLOjmlNOj00NDQ6NDDc18O6edtS3vv8KQKRVVlM0ycR8NeW5gvp2Ax/sqenZ211aUk6w+PxOWeGQpYcPTU1NTY2dvnyZS7e2f3lPSRJohiWiEZI2FxyqZMWRc5jBJHYoxAIj4pH6RRGBqwlrz7htWZg1oga6Aoh4AY3BoM8CQSkuUQ2Cexlsinwye0gVHuGSYUZ1Mlqu3Qp7XzAoCBRBxNygkULBH73MvnUXUNTzgWsehC/ArPTwDTMA3QQCQR43Q25GczJDqhu3QcdWKcNtGm+PHAhcKOedKsplZAC9tJd1JoAyC0gA6GS4kFPQCXFrSsayYRmeiIPSCqbJEk1uc57TYDgBoCs7dWaZic1U5MW2Wwa9S+O9KRNS+bFKVgxQ8NGJmAEqBFYgBsZqywk6dPxbnMIMmeTh3zOzc+CzWmSJBEUTSYS1VS4GrSlEG8mioIuImJMJsHk03Q2SaUjpUQogUCZCELGYOCVOE+AszlApg56Xaxx39bJxl/fgPNlvkDA4/M9Ji2DWlMGGWpWk6kwk0TpoJ0Oue8aEQ73lHOow4Sp55i4P2LXiUXrxoMCASA48/gCj36NifkBOgxalPMzk3wQuFQpHIFvXyzPDwBaOvDc00bN3KJWxyk7gIAlpwpeFuEWhWpBsjY9SbpVwCIy4iW8llmAq4ALK66pNXN9gUAwPSWOozBgkoV9ICQ7IH0bWLTtAj6aY75ywGKc46ln+R6zNoYGlvlDMf2SV6eANEtUxMMgFpIdaDAWQVNQMW0QDbDuHI0l2IL4PDusLZuH5+60z+c7cODA2bNnr1y5wrFqdsQTWo3u+qt8WX1I3xtxSVKoNhM2ZasliiuM+4sbCn3/gvebF8PpPOWAMpgL88UAkZYJwpVnfvLKs0OztlIIgTncA9i87N/AZ21jGJKswn5ftVqtHaslNlZ2plgooIF13EPTdLlKZ4tUPFP1RMpqb3HKmL+9mjnPjxy6DR8bTRzoj32pHvntZz3//RXfqguQWrgCOYNkTkdTO9LS0qJWq7mLIkmyra3t2LFjJ0+evEecCtDTZbowdnFZ+BrEe82j64kg2mzYlEv6imFLLuYsRMw5Oz81dtLU1HRg+vofz7T8f3WnfzZx/VPu7v8zsnzaO/dDCb/jxae/3HfsD7qvv/PkU7/42uP/sv/1p+Id10Tf/u7Pv/LVnqvNwHsymeHGNJJMR6Jo/vyb8R9+0t36GXX3f9G0/1ZIV5eLqshSiuufIBLOZnO1y6z1Xi1BUZTH4+EISbWDWxLbec1ut/vSpUub505zc/PRo0fr6+s5XvOO00avNd74xfzUvoDgda9wn2+lEY06OOP59Qp/ZbxmivL6fNVKZcuF3KmGYXL5vD8Q4GjkWz9pmq5WmXKZiEYRp5OOREqHDxc+97nS22/TpdL6XGXLYhgGw7Aa34u76uvXr38Iec3FYrG9vf3mzZtjY2MTExOcIobDPTW9D+C19PcPDQ319fUNDQ39e428REKhgKdckRVSMYaIAnOYACuHIOJgedh4jgesegGfJ7zvthn3AM4Eb9Iwx6/GgkzQTluXKa0E6BpiEBPxFVcmTKNdgvERweQE4nGoVxYnxncwZb9vbVtPgv4RCCbHx+anhLlkpEIkmHIuG0FkM1N+pzXkdw8PDNR4rDUoeeXKldHR0ampqf7+vmtXr+Zy4DbeviWTyZaWluHBQVQlpXy6ashDpcNs/xB0Lk3lgEgsGw4k/I5sDIsG/QTiLYR8QAhRYPsQaAzZnevP+32m6TgC8IpfT5iWodUpzKgqQHYGtQMcE/XfJVooZaMuUw62MxyQAsXWakkzObD80/EAG0OelcqwznbLxvmgpB+UFrDSTgXtUtK2FaAPCnsTazPQspgwrwZV0gjkhFULrLZrQ6KzroZjWcOcPGnLJydz2nQQyGxQB4Xa9VOjDq2yhCcqeIIp4BnMb12eTSO+oEUT08xtOEDSkh4t7dEUjPP6yVt5h4IJmtSTvaIJYN2z45ZIJC5fvjw6Mpx2G6pBB5mOUuuiSoLO41Q2TRcIIhSIQa5cPBSDvUQYqeBxNk+aKeC5RHhxQTo5yavNh9p7BQd7uEnG5/PnpdJ8MgbMuRFbyrgUUC1U4ijwFRRyA83j3QNKFYiww5jHfEw567FbxieAdrh2dwgEAp/DwhTSHEJdW5zl8wU8gVA1K4JmBqu2FRB8jdsNUsq0wHhUIDjrsihvV0DK2WjQ55HPgvhliJ2BjZRPv7Y0vzku2OYbg8fnG3UamoiCuvAojVjZFxsEaK5ZuzmDYtmuVxXxBJlLM6VsCoUMclk2GfFZjUnED8hkXDBd4A7AgFvleuFAxadjIm6tcFAiekD00C1DlkwmGxsb33zzzcbGRu5UpVy22WzlSoXAca/XG4mEMQxzWX19L6tEb0C83R7+bo/gDa9wr3f1ChoyZ1NwEYgaKLqej//aM8gfvOp96Sbxhf2e7z4j/OmNtD/DJMcEU4899xs/9XzjUhSC/GUSgKGqVFo+dIjcABm1VvkixVWtg6F3EBqVqkwYJ21odcFC9Mz5++XZazP4aX7qyEhyX3/8raHksbHkaX7yuhQfVOYmlPFZDQrFyVSOnrcVxjW5UVXWgbE4bKMyzh/xxi/wvYXXLJFIjh8/vmfPHrPZzGXDUJTjvbpcrlQqCZRB1UJrfd/kLo9ov5//uhcs8Ht94oN+0X6f+KBPuA8S7nXJL/ybqechY/dD+u6HzDcfstx4SNv1kO7GQ4au39Te/Mzyjd8xdf/a0uQLb7/5g4b9fyW/+Dv+d3763E9+9Ni/Pv7mgeecsz+DF5+2qW4bpUcskuedsz9BtQcilrM4KogYGmwjn4gYTmKaQ4Gl5zDVm2nfbb9tLpdJbb6oLWmapr1eb+FBcSq285pv3ry5Wd4jFouPHTu2d+9eTtRRZqdNpVLF8bTP5wuHw6FwWLGmGOkQLZ+O8l8D3cLf7Zl60++cSlbyd8YXCYXi6fSWRm75ef+4pHS5XPF6vQ5HlSRrgOauEmiaKZfzGBbUaGiXizYYKJWKkslIsZgcHq729FQ7OqrNzWRjY/70afzUKbKpqfTEE8W//dvSD39Ib2vbdnkPn8//EMp78vn8xYsXL1++PDg4ODw8zHFfarhndHSUe3pyRl4ikWhkZITzzrf5Yfd+0wIghBdgXgcgZiYQoO0KWMBSypFISlmqQADGAA/gnhr8enAtAoGEP5GAnOANm4gxYS+QJBUzVAJRD7RODNzm8fnSaXEmgiiW5idZu/2aC8EHF353DkAAn5xYmJUEnFa6wBKSOMCRTQAZSYEgsynN6nJvTw/Xfu6z40Y7MHRnOc5DQ0MjIyP3MgCJx+OXL1/u6uzwqxazDlXJq6PCPiCwySTJOFKIoWQuTedxmgVATIEopaL5RITM3C2euXt13LJY3vUzm2BSIQaxRdTS2YnRlN/FEBEgrUEdQD60MS5EBIFtepqFO+wyv706AiAhIs6kw0wCA8o1oIfSRZWStFPLFHEGD+ftmqzbzBRwMuxzzk3kbArSqwlqZJloMKpdYCBDyiDLmoHTnapXByZGEgOCq3iAiQXoqA8k4iCON1DbwSaAmwF0ttBBC4Pac0EXbDcHndYU6gcW0axLGCaPV4lECY8zRLSYCJkWRHH9Ao3YKcROQ8a0Q2OUjPuWRBXIzCBWTLNgN2jverJs+hGPx+tPnbrVfTPiMmX8thJso9NR0D+5NEUkcvEQlQODQrEAlM6li8lIMRGuEgmuD3PxsN9lD3hcVqMehJXg8edmZ70OK+R2QC6732mTLcyzji75IPY4wAoEuC9ivrBaihuXmQQKYCg3ItzgFgimlEtHMdiqowsADafDiMdugdwOj90yMz3N3cury0tWk95i1Fv0aumsBHjU5As0S9KoRpqxKYCXBMgIhpt1gVhwqj0yYcm1RrrVmEmJh4Mpi5z2GzIuHWFZZVC7XycHN+bddwT3i+UqTSewAIDdeJSGTEBKlApl46Gg0xp02/AIsj4uG+2n2THKJyJ2vTru1DKQkcRcdNBKONYMkjFEs5gJupxGrWRyVKNe2zQUD05WKpWFhYX9+/f39fVxuY0G/a5duxTKtbHRkQMHDr7zzjsXL13U64yNz4yKXocE3NL+hlfwhpe/xyva75s+BLlEieWm2L8etO8+cWDXu0c++wb/lbq3PrV35m/2a77c4Pv2fsP396780ynkr98KtuzvZpovMmpV9VvfLPzN3xSeeSafz+cr5UoxT1fzVLX4VHPoZ00ef6Ss8haFhlyXLH2WH3tnAN3XHXi1I/D6zeBbt5F3+uG6PkevLCrWJhSOlAVKe1AiFM/gRLZSypPlPFkppOIRBPaWS4VCPl8u5atlsBeBR731LZ/PwzAcCAQKrJ+9fD6fzWZv3ry5mdccCARaWloOHDjA+bKrlMvnzp65cOFiNBZ96623jh8/fvDAwTWVur2tXdyo4+8GoEfA9ozgdS//dS9/t5e/xzO51zuze8G7/+/tF3/Hfua/6m/8Brr7z8K7Port/2PL+d8KP/mnwdf/UH/t19Hjn3Od/jPjzYcM3b9pEX1ntvurguYvzF7576prDxn7/7Sz/vO7fvyXLz/zJa9DWSxkC2WqUCTzhXI66shlM/lCCY/7opA0oL1gEDxtET0JK95K+UeLuKuQS+eLVKFI5fOlXC6fy+Wy2azNZk0kEvk8+LnjhuO43W7PZDLc2UKh4HA42traOPEG9wnDcHNz88GDBznfRWaTcdeuV+UK5ejI8P79+w8fPnzlylWlcq1voDfixZeaId5uF38PgMu81zyrV1FYjhcLxXKxgtptSYsFkIooKlet5gqFXD6fK5XA58bGEZI2HwFnisUcSeZIstTWVnjkEXzfPtJkItVqAKknJ0u9vbnWVuLKFfzCBfzUKfzkyWR9PXb8ONHYiDc1EZ2dxOBgRiDIzM9nlMqs0Zh1OHI+X8Tp9Doc+Uwmu7iYPnQoI5WC9mzaOFvCYDBYmzaZTKarq+tDyO+haVqv1y8sLMhksomJiS1G7LwNM/La6yO3fnMin9oL5U4PwAccmxKLAjYjcP6GuYBvEoyV3heIaibptuhnxUL+xPgDitjpNI83qZfNMEUCKHHwCFAuFDN0yG2amRgfHx+fGFetLESDXuHEGOdfZ8v17lTkzsd4E+MBlxXgto2V9Q6M4JalYqaaS68uSsfHxni8dWr28NBgb29PDczJ5fJ78Zqr1arH41EqFXQxW0lFaVaHAnAPHgUhjeIBOo5QeJTKJkmWD07l8GomCWROeZAA6+57Bz0crCmAJTaoWZIIJzOoF9gowab1oNkFohDHvJoVj3w259azCp0NydyOtdSkTdkU8Gjs1+UdSptcmotjuViITrOeb9KhEupyLolKbBAJv24VtWoLrF19yaliOUx6wHZPhdYFV+viqw05VoFg0W18/TJZ+ROdS5sUS5jfTeVxTsDAnaXzuNVksBv0wINRgcjGQ9YlScZnptIRJhGkiDhsMyT8DkBPJuLlVCQZjdxrUS2VSkaj0WDQM6VcOR2hUyFgcJRJMESCisFkAqGTCEXEq5kkkL3lcToL8FA5HWdd0aTAuJQzlUxSr1pjY/SKEa8bNLWUZSq5EOybEosmebxl2WIRMJfZTs4kmIgnaljBXfpNXOZ1GlwuEfLo5W75XDHoAHo0bijZAovpmHR2lrtJOcUZsCXbsBKY5Au0S/NZ80rEsMIkgqwp/rpb8Aps9ivnig5lzioP2fWI3ZC3A38HFbeGhExMzF+CzMtz01soSrX7hMfjKVZXyiwEp8NeAJ1TmFWrTIeDYFy4i9o8bbgXnlI2FUEtq9K8fY3C3GD6JVFIr0g5dUw2gfo989I5m91+r3HZ8XgqlXr22Wcff/zxhYUFLoNGrXrllVcmefyOG+379u0/cODAuXPnlleWTxw9ZRfHVhrRxYbAXB08cxRgIP5rHt5u7/jL8NSBaXnT39p6HpLf+H11+29bux7quPiV0auf+Oiu1c/tHR9fVCE+9XLn91565nzvY3tzhw5hz71GfeUR6vmfxc7Ue9bUCxasReL70VXn7z7v/cjz/qevhy6Ikm3z6Z7F8OCCT7DiWtV5rU6/zw9jSACDPUatMhYKRLBACIUxBEaCcDAABwIwtwUCAZvNptVqA7VD62fufAUCAYPBYDQag8EgDMPBYNButx85cmRznIqhoaFHHnlk7969nFaoVCqdPnXqxMl61drawQMHDh48uG/fvhW54sLF82Kx2DWTWGwISt6BZo5CkiPQwqnAWmtI0Yq5DJHAgIL458fzjzxcfuSRzHc/nXzi4dbHvt33lW8m3nohO9Jfeuq59KvfzHW3lb769ei//an90p94nYsoEkCD/qBrwT/3pEf6zLmLrd/9/tM/fPIni7LlSCQKQxsXEgzBMHvZATSARJBQUqMzmQ3LXn1/SHc6sPKikf/9Nd5LFkWH16EOBNEglgggYYVyze1236dzfD6fUqmEoPVqgsHgyMhITc/F4Z7e3t5HH3304MGDnIW5Xqt55ZVXxscnOjtuvPHGG/v27QOh2pXKI0eO+GBvEAl4VCFDf1T4hlewB0AfwRveuWuO2Ytu5Nm3Kl/+Krp3r7e5xT8yBvshOBiE9HpwhRvDp1xb8waDgXAYxjCwRyIwgkBGo39iwtfQQHz1q8VPf7r82GOVpqbKwEB5YoLg8cICQUAs9s/NQXI5ZDDALpfLbl9TqQIYBkejYI9E4HB4fccwGEUDGGZ3OjU6XSAYhEMhkCcc3ujo9e9AIKDT6cxmc23aWK3WI0eOfDjjVFAUVSgUtvtrFt69cYhn8zEOFdUk9typGlTanLOWXpcksbSdsE0Hnm5BG+BRBq3gvZYsmjWKsZHh8fEPAnqEIAg8b0UqoXKsaRj3bC1ng/pVweBtEdCvCWamhPMz06w9b61R7zshEot7um+ikJup5HaAFxyNhmUpFfH4jAiwork6RCC6qohLPzgeO1DLVplillVvra9zTCYBNB0xGPRY2AsUH0ScDPuBFAT4Iwa6QuBPj1tLNlYXOpeuEmAl5hbj7W2mWB9xTCkHmTQSsTATAXIyQChJBJlCppLDHatzGZOs6lYxXg2gawDlzn2hz3rVnLgCIqOQdW01AbvDsLeaATQjMhWCpONJrZRj88T0SxHtIsfFBoxpwBF2AU/TceQu8cbGFW2/BCaPV7KpZYkIctqqRPLO+lrMELGQRDItl0oyQReZSTKlLB4OWpVLQP0EyDc4azqeBYkCUUrHE9Hwjusod5AErFiSKec25Baso+FcAuDsdAj4IMYcQECVTwOhYwFniChLk0oBOVmRKKeimjUlj8ebnppCfG7QjcUMU87GUXhuZobP589KpmMIzFTynNQQlBMPRF0mAvGx1o7sTGCbWilkrApZxq6kPWpQLxFZbxIoMIdCXj6fP8m6puDx+ev7hoKKLxAszE7jVkVYu1hNhpkYRIc9QGwWcvtXJWnjEuCJw/qEaSVhXAbuDIAoSM/5sWRCjqBJBZS8G8VuwUB8gcCgUVezaSafolMYg0eXZsQFPH5nULhxLBBMOgTeebhRqOQRn9uvXgKOJ6IQgLyJINC3JjGmnMOjKAz57zMuO56iKKqjo2N4eJg7WywUVGsqDMMgCDKZTH6/z8PalnMBB8gKVSlQhVQlGy3bRQndrcjimaCyJSLYa12se9584bdsnQ9Zuj+i7/yIue0hZcvv3Gh8TN7+31Ynn7YN/4Wl7SHsqX8+/uLED6+En7wctS5bJpfCDUcle4/J335d3Pqtk41PNZ4bRd/u8y07d1Ztcy3M5wt+P8TSZ+kdN4Zhav57dszArdw1PReXh2GYzXEquDxOp/PYsWMouu4N0uPxWG22SCSi02kxDFOrNZlMtqenhyMtlXNkLl7OxSuZULmUISlg/wYoUFbEXXf49fNvvLJ6s13Yd+WnDU9/+sovHt7zA7PWxDAMlUwysXQmlws1Xq7+4pXKvIRm1olTgKZDlasFlMDxWCwml8tPnDjBxQHdcl01wjmCoBy/B5RMlctZCIfHw7q6wMoLqGJ3zNZEYAsep6FQLG6fDLUyOTMraqOLuf7s7u6u6bm4zjGbzSdOnOCM/MG0Ua1hGAZDkMls9vl8Xq93Szx2mqTDxuzSeUS4z8d7zcPb5eHtCclekkK7Pxn/4V9W/uFLxa/8U+XIW5Xjx8tPPFHt6qq10B8KFcVi8t13SaWSMpvJiYnqlSvVCxfI7m5aoahKJMXdu6Pt7SRFcUSoHbVdwH8PDNeucXuCYZhMJsONNTjLrFPBNudkGCYcDm/h93yY47GXSqWFhYXau6DwHtsDM3CSjNoyv70Y7hRfIJCIBKUExmTidNACGAAxiCngeDi4ODPNm5wUAZDyQTaBUCgWTKJAEsO5QslSufSaVDw+vu6uEKC3DRTyQSpY/49ofGzU77DcWWZA6IONnXujzaWZFMZkk1a9ZrNUqdaHD8Q97I1BUzl8g0GyseBlE2CJQhxAnZQDKzeFOtf990R8IHhCEtS7BRlwyOZeuGf9eCkLKaUe5QJwbxgN0qkwkJyVskGPw6taZBAbx70AQiAizkIf/C72z71ACSuqiQd90YCnTLDEjnKOwCBkSVAzOgPL6mY6M2QAHN6AGdCAQLyC9ya+KhCxoC+Jwuy4AMkQCzUyRBSbnpycFvCls9OJEIB0lVzab9KQufUuBZQpjqYDcE/s/riHfeaS6RiWjqB4NISHgngIxkMBHIPTflvabUjb1XjQjUcxPOjFg+40ZMcxP4768TCSiqAapZILJWHR6zKxUDqM4FEsGUZWlmQ8Hl8kFNpNeiIWSkfQTAxj5XZpMgphNn0ZZ5VlAPGw0qwCgXodsEFe8BnTHiMoH7QH4/ZsIozBPtni/PLiwt374jTrwgo4dJ4WZ3ymuMcC7NfKOU6YlA0HUPV8zqFK29bStjXCriLs62nuCLg6py5hU+sVK0tc4bLFmmCpdk/x+QK/y75+UxSIiN+dDgG7NiB/ZYk+YGgKGSDxjXPUbwAKc6lYyKqmYAuAvAmEDnlIzM2kUCqTTIURLBisrRbvMZFOpzs6OrjVi2a37X9cWloaGhribrfNZ8kKXcxUKznKJ88er7/44lOPnzn+aafsZTwwHbdetk1+Xtn8kKnjIaPo5/7F53Sdv+tp/7h3ZfzhY+bvHzr3/dZ421J5NUhjOSbX8E71kY8zD3+WUi2jeKxKVsGSv9PGMEw+n98cRWF7LoZhcBy/s4BtzwEWNRBoaQuvube3l/PXzP2DYRilUikUCmuN2XztXLparV67du0enve4VZgRBlSfmtzz15N7vjh96EuzRz868fJfTb5yTXybiN/htWTwDIaFmXKZK5YDFluqS6VSSqXy7NmzXu+6DVetYSQNQAqgCQeRbDa7cfxOAWSZKCStSU8fuvYmUIStvp5wdOTjOrIM2pCP69Iwr7bSb+c1oyja3AxI1hslg7rm5ubu3znLy8vctKmNAKB2FSlYTuh7I6obSfEBnrrpU5pbv2Y/+3vu439gP/EH2Uc+X/rcF4qf+0Lx+09WhoYqnZ0UhgXF4syjjxY+/VyXSR8AACAASURBVOnCt75VaW2tzs1Rfj+dy3H4BpRZKrkhqLITHb42lLlczu/311qyPcHBO46/tf1srZztvObe3t4PIa+ZG+xKpfJAeY/wV7EB0QeLPBZmJVXOYQ8RJcPeQjJCFgne6NDoyIjog4IeroECPn9eMlXJJEvpWD4ZMWqUvEkQf+NXuwkEAs3qElUkiulYHIFcJr16ddmwJk9icAkHyjUgpUggxTi6vCAFlGuWq1RrAwhAJrhffC5uXMCLDksI3QJiQOHrdFpWcZAOM2EPE/HSIRcdDwLXw5wkg3uNvhcc2XR8Xd5TzIRMyrzfAhBDFC0nwrkYlokEVfOSfNgPKDthLzAByxPgXRyPgQSQLW2wfDZgxNbWssKJag73281EFM0nwulwMKCRlTcc9tTQz3qC5SYDTjrqAO/92zDcDuVz11LM4BEkCnvLeDwTw6IBL+Z1Yj6n26w3aVSQy243GTMxIDxIonAMdm+hKLFKqFyJSDwQ95RKpaUl2ZRYJJmekkxPr++SacmUGKS5T3AKGHjfyTA9PT09xUF/kUgokWz8ERye4tC4SCSckUimp0CxkNvB6SvLqQjqAoMCLrxA5BORYipKxDCVbC4fdGiW58VisUQiYSsClU5NiZcXpCXEXY2jFSJVIZKbd8XKMlB3CYCVe8ZrrKTCkM2UjYdy8RAeRoIGedWtwgxyiUg0PSUG7d++i0XTIqHfZiaz6QqRrGZSRBRblslqLH7u/UcsFlsNenCbl3PZGIa6rGU8AWj4sDfkd6FeJ+SwRAPeChslDVxaKROBvXGTnIFNZNgH5GeA1xVk0lE6lwKiIDYewvqt8R6+cBw/e/bsmTNn+vv7Ny9pW/66tLS0OQO9aePMiIhy7nvid/9n74ufbX/t+dde5o0Lk8mc3WZ58dnvnX7tj8Tj12mGyUflTl23cuDhueY/XLz+f13p6y9E5SRuJKAh28jHkZf/vPrUi+UTZ0N9I2S+WFuD2dX8Dgb6D8U93d3dNdzDMIxEIjl16lRDQ0OQRZM7YpFqtXrlypVoNFrrvU19A5IMw5Sq5Sen6r/I3//wxBtPTB8/bxz9weypV+RNpwyDF4xjw76lbtdsk35SAQHxT4UiqxSAfdFC2pVGXWk0lEty5YTDYRzHPR7PqVOn3G735jFypZG98rY98pYTS92ZfA333GlLLTNN026XJR5YiDvagvI9PumPfNIf6jr/m3/h57VL2I57AoHAtWvXuEK4xojF4tOnTzc0NNSAQq2KWmLHacMCWvBRzjCOVcvg+Bd1Nx4ydT1k6HlouvX/XXzpa6l9/5x47tHE658vf/4f85/5bOkXz8fPn8+/+GLhC18sX7nCYZ31T/b6WJM30uv1Vv8X4Z6bN29+CHEPTdOhUAjDMJlMdh85jfBXtHHByfl8vnp1CTzTASszW8nE1xZmVual/AcZcL2XVgDizvjY2opMsbw4Oz0FIl28l7+9zzx8gUAs4KtXl+dnpnkTE2KRiAc0CryO1uvjA7fB23O1wJBFu0E7uQ11CQSCjhvtgwMDCoXiPvweu90O7rrSTqq0TZDlLhCQY/3bJjF2zcCAV708fgeX3ONfdC5dwQFRppCMYjYdjbqAfKVayMQw1eQgYtUXUsA1HygKjwL7uFgAmBRlEpzBPJ1kDeZTIaBr4wi83Mhyf6lVWsyEIXfQbcM8trBZWXWtATfEwCDrTrT5dakViO0AgepACXe0aSyfekPoVSt2c6JAFJIRxGXDPA7Yac1GMTKTpLKpJAb3dLStyaRAL1nJl4gE5DBXsinQMzVQVSCKqahWLkuEAsnYPfk9lUrFbrd7PJ7FxcWanpdD85s+N3xDbTpUS9YmmqDmQoo9VzvOBncTeGxmugDo2HQ2WYwEgi7b+hBU8qkIol2ZR9z2aiYZgTwikQhYkm/aJnl8i3IJ6KQQG6sl5AAT6E86n1pbXgSwRwDitYSsQEEWdRgQmwGz6aJGBYh369dX/MZV2cIk54N5U8m1JJ/Pn5HMRIIQEK2xd3EuEVmVLU5uwvecZtyoVZfxRIlIwA5LyOdEvY5sCKISKIVHk357Z0uTZlUGbpZyrkAkfHpFFVh3sl4SUiEOWGcjQcPaChHDIu8zToXb7d6/f//ExMTRo0e59SydTnV2dUZiMZvVMjwyMjMzI5mZUSqVc3NztfVsx0SfTfoXYy98puuFfzj7sy+0v/Tl3tefvLDvqR/85F++/u2GhtMcCwRBQyHjFfOt/9va+3uo6lBIV4ep9ln6/0zT8pCr5xPR1UNJ9eVw/XOFI6+Ry8tMGZhfUdVcNrxMU3dMsQqFwpZo4dvbk8lkOAnW9lO1I9vjsU9NTW32W3ju3Lmenp5jx47JZDLuX9K5WcnsbLVaHejvl8vlvb09oVCov7///lEUmCo9op5ZCJv6vYuj0EqZquLl3Msr1/7H7R9/Z/bEjxbO/PnQcx8dfG6/ov355cs/mmv4ofT0gF/2lqrzb0Zf+eTIyzdds1ztsUg0nQC2WkggeOrdkxdPn+ts7wikIxrc2+QQPszf+8f9P/0+v86fCtWucXsChF3yw4XCup6rhDsg6ZPall8PLL1Qy7xjPPZfxjOp6bkYhmloaOjv7z969OjKygrDMOl0qquzMxqLWy2W0dEx8GYimVGpVPePxx4nmMea+D/ue4zf/f+oO37DcOP/UHb8pmXoj6wD/0XY+K3I158rP/wFuv8WnEhVUgRlNTI7qecY4P6A9Pv99/fsn8vlYBiuXeOOiWw2+wHisU9NTX0I7bmKxWJbGwg4Mj8//5+Ae7iH+8TYWNDjAOCgmCkTiRDskS/MDQ8NicTA+9kDtxrJ+l45RSzR5wPLjWqqqHuVzx3n8Xjj4+Pca3oNWg2PjIwND3lsJq/N5DTrp0XAZmdLOSKx+EZb2832VsXqyr2C9SSTyba2tvHxMZLzxrt5db9/OpsEsh/MtS6YYRVh60vm/f9YIBIoHHebGfcak01k4qFowOs3qqsghvkG+OCUdxEvHUcAaRd1MIC1GgKqiniQ8hvIgA2AoXgASIOAiyCCRV3AxR+HY1jbN4KJ+YEjPp8W2Puwfo3XJT0BM/gZtDHJEPDSi0eBkImIAV1eJlHFE2AV5yjb4JNt1bpfIlbmxBrWlTMpEGi9BIJ7Az0gHmHyRDqKBRxm1Gv3281Bly0W8AEuFCh8w3tCgSALxIxgUiriI/A947Enk8nm5ub+/v7l5eVJHksUZuWXLPIQfkDt7Kb5IRAI5+dmYLedLmboIqunKxCY256NsfzuYiYbwyIBH2TRVRJIIYEtgreFuyYYUPWKRFGzko3TbqIxJxkC40VH/MCtYsSnXJRyjgf5fD7sdjBplIl4ADcojTGYgw2yoWUCJtRtA3JKFiJt+OkBcKm28wD0kYRgH0XEgClZLpHzm7XKVZFoUyBe1sfP8uJCBPZW4kEyFgBKOjzCxCEmAsKDpBAvZDdjPpffbg7YjbhVAUK9JjE6idJRCIxOgaCKxDR/YmFKGEaRHR/o9zpYKpVaW1uff/55qVTKMEwiEW9rbdmze/fUtKT7Zuf+AwcPHzp85szZldXVkydP+nw+FEWDO20YimoN+hdGTz88suevxl/++MgLHx147qULb09LJFPS2ZaW1qampsXFxfHxCYPZ+c6bz9fv/nhnZ0coSmChqNe2oB75hmP5Ha912rZyYWXyOV3bI55zf4eN/yxqveYWf93Y+1dBlwONpBE0giCo1+tVqVQ7tWL9GIIgDodDr9ffq7XBYBBBEJPJZLFYuDwoirpcruPHj2/mNRuNxt27d58+fZr110yvrKwcOLD/dMMZt8u9f9/+d+vq9uzZvaZSX7hwYXZ2FsOwHZuEIIjX59WoNREsDAcDgWAAQZAoGp4yrzw++ZbKZXT7vd8T1b0qujiyJH5b2von/c98Y/LwhcXbratjj/Pefnhkz77F6802wYJXs6BZXdGtmTx2D+I/fLn+U8997eG6p77Vvvfnne/cmh/vUgl+NneudXn09YWmDrfEHHRF0XAIwZAgsqVha2trXq8XQZBgEAmiYRSL2hQdpoV6BAlxOSEIUqlUgUCA+4mi6OTk5BZes1arffXVV8+ePZvL5VKpZMeNG6+9tlsknrp5s/PAgQOHDh06e+6cQqF89913fT4fW9eWVgRRFHF74QPHzv/dxSOfGdn9g5Hv1vd/VtX1W9Km/yFv+b3xa5+Y76z3n/uYQHhTo9MHNKcdCz8PYolwKJSIBPtkcYU1EdqYjDAMr62tQRAERhYNI0Dfe9eGIIjH41Gr1XcdvfsHgiBOp1Ov1+/YWi4vgiAGg8FqtdamzS8ZzXV1dR9CXjNJkkePHm1paZment7yAN30NP5VJkGwi5Fhj0UPJApRVCGTDg4OAL7vewM9XFO4V8lfZbM2yuLeaDd+PfB760rHXcj4+Hh3542uttZ7EahvdXWOD/XPSiRlNozw9gd3sVg8depUV1dnNh6+FykHIIntdlu5FKCKQ2bAkAC+8tKsTiq6kZOzh2L/uOW/xYzHYkCtwAF0zK5fmp2OI37OQAxUxO2sgwAaj9DpMFPI0GngXQaQYVEHeEf36SpuNeVmA2f6DSxJ1gPsuQDhmnMxzNbOCVpiEA3paczJZBNAMccxtSM+EJIJBI0PAg4TCPtgBG5jkhiTjtARH2ccBNBMxAf+G2FDIgStAC3FIBYnsfQm4NGY9S3EdRGw9koyxWws6JsTjCsXpaVMEhSSDjPpCMhZIJhyLgx75oQ89dK8z33POBXZbPb06dNt7e0zM5KFmSmX2RAO+EKwLxL0I363SaeZAyZUNRj8wPmzNYNAIFiclxq0as3aml6lTCAwU87Z9aoI5GHKuQjkUcmkIb+TxIGHaP3K/GbVElcWwD1ikUo2p1tZ0K4saGSzEeMq5ddVfSyDyqtVLqy3UCAQLMtkOrVKu7ygW5XpVIo07GRgA/DTHTBVE1gkCKGwF3NbMb8bg30Y5MbsesxhCjkMZu0ax2uWSKY18mXdmlKnWDGsLmoUK1NAZ3dXD/D5fLFIqFlZ1C1LdYoV3Zpcp14L2vSVZJjJROkEEva7xBNja1IxEzQDxz9g1FhXFDmgI4sEvMKxIZNqFfbfYX5sv2W2HwmFQmfPnh0fH7969Sr71p6enZl5t65ufmFhYnzs3Lnzo2OjN250LC2vdHR0pFIpHMfTO20EQbhdrlgoOuBaeEpy+oys93HR4Yf7X3t0/MBt+xxDMz2dXV9+5Mvf/va36+rqvvdvT/7jPz12qr6+kM/jBIFnitlcIUskgqmUOxqTGYxIKlosYkRo1jP9bfWNX9c2/5rl6uchQ3M0akpnEpF4ymL3pNOZnRoCjnHkHi4mxr3yEATBRQEjCIL7SyKRaGlp2WzH3t/fPzAwcP78eZMJaKAsFnNz07X29natVtNwpmFkZOTK5ctqjbalpcVms3HlbK8Ox/FoNGo2m3HQd2Dn8uA47kthWSJTIHLWBGQLeBAPjGeI1xWtGszJ5CtUvjwb0EogdTpLLIZMp9T935g4/LmBXf8wsOtZyblfDNf/4PRrr9Yd4Av4Q7391y5duXjxYmNb08DN3iX58mVp32PNr+waOTulXMhlslniTl+lUimz2RyJRO4MJY4T2TJO5GqNj8fjFosllUpxRwiC0Gg0169f5+YPJxfs6ekZGho6f/681WrN5bK/lOvUvVsnlc6vT5uRkc7OzlW5vL29/T7TJpMhAj7XiRuaj507/LGWV/78xq5vDH/n387Uf+HoYPu1L5i7fsPU/VDrpUen2x+2tj603P5HXjPPAwcEWuJjr+vbhkYLOSJNFHAil0pnzBZHMkXgmUIcsybCzjQOhjWLZ3IE2LMEkP9ZrdbaNW5PcNPG6XTe6ZltmcA8d7v9fn9t2sRisZaWFofDsf3m+o848p8an8tisSgUipWVlcHBQeF//DYtkdSfOCEYG2aYqsusH+zvu79T5l9Vi0SbaENbnstcFZsz/HsrFQECxNjQwJaKWFDFE/HGB273DA8Pr66u3kvPxcXp1Wg0VJ7IxtB7qniAR5/EZmUQk0sD0IDYgctBIOxJA1visG99gQdBNKMgHXKBVT9PAGeDnDinkm9vvrYg4jEhh0suDbrtwKRoM7Ral7WwnJ50GAhyioDoA8zdOc+Bm10qc0dgE+Amh9nQCpkEqDGbArXHYACYvFo6wK5zUT8TcjJBKwA6cYQhEuCNPxZgEAeIDY7Y6BgMxEgBC1iSYTMIchK00X4jEBdxrqV9OoCQgBMa4OuZwZx30B4RB/R5YG2eZIgobNU7DRpAOuFkV+zlV4gE7LLK52c8NlM+Gb2PnothGLvdbrGYUZs+ZVMxRJgpZ8t4LOCyqpWKqY2ot/+e+cMTCMYneVNTYthlBw6ZyrmmS+fli3NMJW/XqTC/iylnmCIB2wzrXpl3qow1XBfw+EA8g+hX1lWK7ADVcA/HwuGxcdR5wPKL79SsMDDrZTviB/I2IsokEWBRFYOAQChPgBGMeJk4TCUxq1rBmcdzDodYezHBfd6deHzQnppZmVAgkC8vwXZz3mdi4hDstDqMWgYPg0qBdBAowUt4POC0yudnfTZjmUi8Xz0XgiDHjh0bGxu7evUqt54B4kW5TJJkFUTUqlQqZYahl5dXZmZm7v8cR1EUT6dz1SKcjVQp0kuEvjp15C+GnrtsmdCnfZcEN//lyW8/9W8/VMmVt2/1NF642FB/SqfW1MoskpXj2tsP8974+4ndfd71YGGAoXz1BeiVj6S/+Ynoa38dqPvLYO9XEPkRj2WkWknU/rs9UbPn2n6qdqRmz1U7IhQKN8dj7+zs7OnpOX/+PCcEAjwStk+qJFmpVKrVarFYLJfL3d3d9w++USqVfL4HxHVJp9McUSZXKVapO279am1jGGYZNf95/893yZvTlVwpXyQS6Rye4UatUq3g6bTL6Zrk8Qb6+q9eaPz6N77xheefeGbi5HWn2Ju5o/n6wH4LN9tzMQzT1tZ2+/btCxcurDt1vHvalMtlmqblcvkD47GHwiEsEFS7TWNS0eHbl56cOvuJkb0fG33zr/vf3N/79cWbv33owrGvHV24funbI5f/oqFxr2L8Jw3nXhy8/LeCls9Y7ItMXkemdbm4wWNdKKTscXuLdeB/EgFARQcKwULagWNOHIkW05zHps39uT2dzWZrtnvbz3JHavZctQxCofBDqOdijVOoXC4nk8kmJ99HMKydHrbv9dirL7+0trzAlHNzU4Dw+6sEHPduQs3NtEgk4rEhe7bkvc8je0vO9/QTBGG9uz/ZGOz9Pd0SsYBzjX1/3MO6F6/QhQzQNNUkLtsTRAxgAmDBzkpxUiEuZASwqWY9/gHcE7AyQO4CglcAM3g2/DiDOkD8SNTBaoLSpVT0+OG3/AZlzmeyquXrpmo1DRenM0pHAE5KoOCPITcT9rLOA80A+sBGEMcUrKxaAD4ARtEDWBP2AvgStIJmgABhDmCoxUbUAp4Jffqylw2wxbpmrnq0ZZcaIJuwF/CaISMJGYuQGUQTi3gBToKMQLQDglUZQKUQcLUHdszJhD0s7tGBK0oEgBF+2AfQD4t7gHolHWECZjLir8aDYFFPYeAS8AhTykQRaGlaiHgddClbTscToXXL3trNvzkB7NjJKhX2pN0G1GM3atULczMC1mL8LinHe5olO2SaEgm1qzIi4ALW75XcL626jh16C/W7comwVbvGOcn0u+zA9yCALPwN4MKhCg5YrKcnePyZKXHeqWZgzgodfCoWZsYn+RsoBOTkGsHnC6SSqbxbxwSMdMxfRZ0gtBYEZD8kzCLOsBtoxAImBjJRfmMeMvt08hmx+I7z6R2u5n6HOLs2qWTq/2fvPaDbOrK0wd5zeuef3Z2d6fPPzu6cs+fs/jN/T7TcPR3sv3t7xtPntHvc0W21O7idZVuyJVk5ULIkKjBnMWcSIEESJAGQiMwBJMBMgAAjiAwiBwIEiBzeut6lnmEESu52J4/qPFGFevdV1btVr959t+693/r8lF0r91l0Ubf9wHQMD0VhUsunRweNqu2oby/q23PabciS9pMkmUyWnZ3d29uL4RanB9IPnieqmZycbMb9imNpEoZher3e4XAQl2AY1qWYbNocLJXSv9h17An6u2/0Zz9bc+r0RMVNUZt4V3V1ou5bNSfeE1ReHK3OnessWKXliXu+xbr4/cFbBdLeXDG1boM7btlQT47uHT+OVTXGFDvhKaGPXGUtel2Z87Sq6d90g2/aNxp9u6uREILGhAS3/+v5c9XW1gJOBTDB5/N96I50+/ZtsPlI4Az8jEQihYWFBEEyezAM8/v94IGVfBZKQNMG1tPEKCQQYxhms1gzJ5pWdj8WqoDoFdy+0WgMBAJyufz4O++8/tIrDU2NU9b1G4vkMmnflnMnhqFafw2cCrlcTkT0hha9Xm9nZ2dWVhbELSS6QWQwDHuUabOzsxPvDbewvHik7KUnm954pz//COvSs7Rj50YKn+op+7umO0d7rpAXTUvaiXHG11bqP7fV+bezvMsVVWdFg1fti9e2h98zCd7dIP31Ss3n1MNHzdL7PtN04Xztkd6TR+gX2DuisD+oUqkTuBr/89f256qpqfkM2jXDfAI89gTlBPu3kJDM0dd39IUXNqVim1F3EJyQxUwZ/fXTlYfixZqEzSwOLpHUVFf3/QYu9MncSu5/f39/Z2cniHr9/f1TU1OH6HvQ0EQjSO3hMGC7+gNzGcCFeCCOxJCRCtoAQm4vuEgU2zUimUC5hIQDJIXgYoFyCTcZxsURpQg3JV7GS5ZQaBb9FubQmVWyd986ZlatmRTrRqUM8+25rYYgbu+MmkaKIis6do3oi1+3gYQYwJFQLOFYp5sQ8BdvfRkZGBnlB1Y7IAxpV5EwBPmD/ixH1JIwSEVqCYIZV6+gnj/AncA0EveqYJlFicgXEbYliuuDAsygbTXQ9CBiHKodh5fHwB4WITnYYhYV6iHeDSTc7DuRIKVcwpQiTCtFp5B0JUeXGGQB647boIxa1JhV7VNJ0KbSoSkUDC7PCgcRxhsKMxg/tZKnwaOXsFjsIR53Y2bCtjpn21iwbEtCLot2c/XKmVM+p02v2jYotjC/W7u9OTU5MT0xKhgfFkyMCaf4KDM2JMT/CibHBGMjgtEhwcjA9MigVDDuWJu1rc7a1vBjdVY8NTY1OoQIRoeEo0P80SFis5bFYkln+faNBatMbNkS2bZEto0ldOHmsm1r2SZbsa4v7G3MYdoVp3hSMtCDaVZcG3MLE8PouX4gPz36/R64fbHZk6NDSwL+5vyUXbURMyvQqKFdzk2/Zs2t3cIcOxGL2qFY02vT2l2lHC6Xy3Xx4sXnn38e1DkxHIh0d9cRxZMfj3AcCAanpqbAYzllJVCo1+t3dz+GkBDGnaudAc9z3BtnhTVj5pW3BPf/745Xfj6aWyrtu7LQdIR56h/qXv36G9+7VHhLKluPBSO1i313KOUb0rVtu27SJG3eGspepd0Vd93f4l4Skc+LyFck1HWr0SCVhzclnplOy8BVLe8VzcQx08wHLhE5pJJi7n0sirk8+ztWBEQKfmHI6xMcw+JuIFnfQ6fT4/25mEzmCy+8cO7cOYAcxzAMRe71othCoOnZdToDgUBjYyNBEFf9R1m/3//o+p6PLkvKWcwWq9WWVPyxAq1Wu7+/H41GXS6Xw+FoamwcHRyOxKIjetGNBVKRhLbh1OqUmgBu1xxD7EmRwuFwAk4FqqqpCWQa+Eun048ePXrp0iUI6hiNRh0fTRv//r4nEAgIBAIWi5WigbiiBFxSxYbs/dKbx4quUFspZ+Zq769zF/VbPyad+3vaWz+k3Sja7P7OyJ0vMc692f0D9uBL6j1z/oT623V80toie2NiVLEzICFR2v+xivvqHfab02PHREM/K6N86TTpKzWT19Y0s1rNR1Ee0HTA/8X1BcXvAa1bfGFCPhmngkajfTb1Pci98Hflx85Gxgfcnx49evuDa13kVkCiyMvNra+ri7fvAbmETqd/Wm+Uh67FCJisD3lk/VaFP7QjgFtlg2f7w+WeSBiJL+oVFOUIwW7sIBnIvoOED4gUjHaO7LiJrhEzq5CSAyE6bSBFiGblQCMCDlNImFhEVSHZAhd6QMhQLaMQiEZ5zLt76t3j58+cpnVR9p02v8s2ymUa5JvIkd5lxbU1q1H1SkSx7FoVRlS4gAJ6Hd3qAdABDi9woI9BeoLVKA59gJvKAmo6vg+FlD14RrF40CWtFNmxQhSiB0IPITOFFUtB2TwOPo/8v5DUggI2alF0O40EaXd2NlA9SAjD92hUYjz6iw69Pg0y5BqGQDN28JDESweCF8K0RyjuETwTlC+HlCtR5TKmXApszdq0D7Ej8QcCI6OjaLY8dGJ9cgIOB+F9jg3ytub5fuWKX7Vy+tgr1y5fZlA7Am6Hd9c6yGIY1XJkF6XfREyzqqMaaXh73ikVhBW4vAt7fzvrmEa6uzY7xOOy2Mh7i81Bf60rQuyBcIkplvyaNf7EWLxNEgcn40BiI+kdHagGDpvFHh4cUIpmQ4rliGo5gMfXjihERolQMDrYx2T2IdPnT8AVBGXK5czxx5SiWefGfFC9inY87VpkKW/XBbeXQvLlqHolrJZapUK96iHyaMIi7vf7Ozo63nrrrfb2dji1sb5+4cIF8Yp0RbxcUFTU2NBQUVEpEAj7+voikUgwTQqHwxqNxmKxoI2xBykUDIWCoWg4Mq4Vb9iQK82EdO6HzA/ku/pQMBQIBS/M1D9/8513Xn6zvPQ+uZVUXl5+LSPjhz/44Ys//alahQS4WDTm9fuMHvuCaevmHOmv2176d9YV/a5Zo9IEIpFQBAsFowGvZ9+ptsnpGv7F7Y7vyiv/zVT1iq0lx8ZiYttazINA0oNwRKPBcDgYDAVxm0G9xWIwGMKoBKVQKNTT0xO/z/XhDs7tLnc3ggAAIABJREFU27dPnTplMqEonYFAoKL8fnlFZTQaLSku7u3tzcjIEItXyGSyzWaLv/EHDDio1u12y2Sy+MKEfDgctlqtarU6XSXBYDAcDu/s7BgNRqLDCZXAT5VKtbu7C9tw0WjU4/GUlJSAU5XX7xvTia/NNl8dqxNZkBt8LIz2NJOT1+vd3t4G2Q44YzAYEuQesVgM4GUQxG9zY+P8hfNisUSyslJYVFxXW1ddXTM3N8dgMKIofmLqBI5jJpOJuPFAIODz+vw+X3Vl1UsnXj935UJ5YclP33r5ybxfvvTWaxQK5W5v1d93vv1qZ06dlFsk6r44U3mk+8TfUd/6esfN/1bU8hZncMkmVezqrD7/vj8Q9e8LZdxlUYVyNkPI/omw/yfqxTuBHR7mUcei4XAMC0WxUDgWDKE5EAqFHA7H4aMQCoW0Wq3R+NEowLT5bOJUbG9vKxSK340fO5vN5vF4JBLpVy+9dPytY5Xl5b29vXQ6PWGLDcyWW1tbu7u7kZvuwxbTBP3NJ3/XoCtg/+u3JGlB/B54lcCL4fC4haFQaHZ2TraxhkQKlQiBTyGLFnwXCWxZAJpKLUFKC4c+ZtiOKZawnU3MqkNRTwxb6DUPUo5Bhr6htbh0opHi20C4TOCyxiwahNFtVuBaJZ12Q3zvRkbW9cvz40NBu8G7aw7v2VD0RZsWCV4qBLQe3F5Qz496NeuYUhRVLKEQc2hHSY5vrqGdKaRKQfbIkphZEUE6HinqiXEbme+gfa51JMnpt2L2nZgFxQjG/b+QJIfe4tpVJNaocYntYNcMWeNGFEs4cgUusRm2DmycDZsxhAavR7Y+SI0Ewhy6a0y7ijiAohlpkXYKgOLj4yLi9FH5InLbVqJgxFFcOsRUywHZPIrjlyYFAoHZ2VmJRPKpx7tCkjfuFTYyOLA5N+WR4SGSEba8WD7FyTx/siDz+jJ/OGjTe3fNEcAtsWrQ6FuUUa00JF9WCweDuk2EbyVfRDuMSEtnXp+f7sdVMQg+HVerWjeXEWQ9EvLwJsyKTfFSvNchCydL9xdVwmLPT4y4tpaj2lW0F6aRYCqRXyVVLQvmJ4a4XA54gT3iYwi47iwWa3hwcHZqYn12wrK+6N4Wh3dQ4EoU0FIrwXRrEZXIrFWm/oRPM1jw7e71emGnZkeny8vLO3PmDKOvX6NWlpaW5uXlZWdnT09PZ2RkrK+vq1QqRaqkUqnm5+clEgkE6k0g0arUGpVapVLNCGemxXNKpVKlVKpVKtGGRCQRr62uKZVKrVYrk8mKCoueffbZH/3oR3fu3Ons7JRIJAgIVKszafXbiu03RgqvzjZeH6sbmRrXqTUKpUKJkkqp0qq0JpXWJFduSRfp62OZKtpPt/OP7Fw54rj0Q8e9q8qiUnknXTEpUG7IVGqdQqNSDg8vMRhiFJAa3ZFKpZJIJJcvXyb8uYAz0WjUZDLt7+9HIpH+vr5z587duXPXbrf39nRXVlaeO3d+YpKfl5dLp9M1Gk3CXcNPpVK5tbUlEAhScoagkUql8/Pz6dgLPVxeXhaJRIfQKJXKubm5tTXET6hZo9FsbGzk5eV1d3fr9Xq9RqdUKBpGe27MtX4w2chcHNOq1Fq1RvWAHq6SyWQCgQDy0HR7e3thYSFMohiewPzDbDYHAgG9fqewsOD998/Q6HStVlNUXJSdlZWXnz8zM3v58uWNjY10fYZpIxaLCQI0nCqVwWCoq6v793995j+e/W5HRwedRmdwmMNDQz3U7rfffvubZ4++f/6MSWvY0elUCuVtQcu/My/9672lz7+m+fIVDW1UoddojTo035RKlUKlVaqNWq1le2OdPdTSOHblXte/DrC+r596d5V3Znm8bF08opDLVBqDWmNYXducm19QoikBHUkcU5VKtbCwQHRYpVKJxeKMjAyZLK2fR5on79cs/t3ZNYMfe3Nz8+/Sjx3FbxsYaG9v7+zoZDKZPB4PfUqmSkwmk8Ph0Gi0nt84nmGq6n+fZYfLPYD7TWknB5X4phWSdZA5C7LtRViP+Gv+I82NGOlsEDwnvndj3EbSBv5Nj+1s4NbEDqSzAc2H60EwHvDnsu8g/ZBBhkyGrSrMprRJpjyyJRT6xShDlkDqlQcbT8uoNmTWI0cGQzsbbu2216g+UDg59OjUrgEpopzGmF2L9FLqFfRidllwjZEcmRx57OgvKK6Q1LKDiyZKBNyNBCMEFIo0VejWcGshxRKCMUHWPFI8gvMaEpX2nQjky7gds2qQGKddw5U6RiQAoa0u3LlMvoAkqn0nkrHkCw8UQjjfgHv4348FiUZywLJvc9as/ljYtPiH2GazFRQUtLa28vn8T1FEBjjP2alJ3cbKvm4T25FiOsmB6ZJajBk3Y8Z1q2TKszWHaaUIB8O4FTNs4qKtKGrfiflcMacRcxqQIOjY8Zo0fusOFtr3ueyTSJdz4OgOArd9R4l5rDGXCdm/uyyYVeVRiEcGuI8ex5zFZvcxWUODg+vL865tccyqwsEoTJhdHdZI7JuL6wvCieHBT/p0gZV0Xz9y/hof4s2ODW7OjJtWhK71ueA2klCNKvknknswDNNoNBUVFTMzM/Ams9tsXB5vY3PTbrcrFMql5aWlpaVHMVBN3ueKnxWQ12g0h4e6UavVbW1to6OjKysrNBqtrKyso6PjQ+Qs2Ox2BFFEvmGD6Ay/Sr5nSK6fKNnzeNRayZ6JZxBeVVO+oyv7H9bs73vzLvlyr+/0v7xXfzn47A/2v/vd4PIycUkCHjvIPQwGo729HcI6BwKBhfk5oVCIBAi9XqPRcLlcu91BJpNhrye+qvj8J7Jrjr8wIW82m0G5klAe/3Nn5yBec3yh0+ksKiqan59HOp5YbEel9ezvz1g37i535Ii7lm2KSOwjZAwMN99O2Ofa29tLsGuORqO9vb0dHR1gnWOzWnk83sbGpgNNG8XS4uLysmh2dnZgYCC+J8n5hH0ugoDP52dkZNy9i6RMUAjB/Lx7+873n33ulZdfaW9vNxqQvbYl6BRYFZRRU6fA1Tnjvt5tP9tu40m8/tDHHoX9fa9Oawhj2IxDm7nYfk9QsrjW5FwrNc28r50+blzOdKqo9p0Fvf4gxk80vO91iGOxRBvzZLvmzyYeeyAQKC0traio+O15hqdb/nh44nK5DAajr6+vp6cHlC69vb0J6h8mk5lQkq7ORy/n4OnR6aFvhHxGZIgaoMLkcoIgIcNgMAYHB9MtlLu7u0XFxdSuDufaLJJykPUuDpdt2EZ7N7hKA73mcSMbTLeGPG7sO5jThFQ7ZmVMJ8X0W0iaOYh37EQ2Lo4dXAdgRdKJ24pMXvbxwH17ViSLIElCgRC5d1YxnTRmkrk3ZlzSKZd0Gm0kqVd2t5ZDO3jAHjAncppCRmXYaUP17FkQQic4h3vs2J41tmdBm01GOYRgOYgQiNAtHDGbFr0m93eRFQ4SgFCklpgWCXPovhSLGGzSKZZj2/OxrZmDe0d6IAnyaQd/dRSP0Y7uwqFH23zI7dmCIv1YlMiZy7CFggBBwF+7Fm2KOXbQphhshxFbaWAbpFpGAgRuFIWpRG7plHZrlVihEjIulys7O5tEIg0/gPxMGNZf7+eB3MOfWJoTLgin5idH5ifiDv7o/MTI0tT40tT4In+UP8Ca4DLHOf2IZnx4jMeanRHMC/hzgun52Zn5WeGMUCAUTC/MzU5PTSb3Z3qKvzA/Oz83Mz83i+hnBPP88UEu2gs7JMH0jicAMWV4cHBuRoBqmxHMC6cXpieX+WOLAj5/dJiLXwOXxD8XkIcK48uJylkstNPXj4PGs9nsoQHu9Ojg5ABnVbKSMBwP/SkUCn/wgx/k5OQkUMKLHwr5fD6dTsfwt2YsVYq3a051HpVhGKZSqcD1KR1NJBLR6XREyC6PxzM7O1tfX3///v2hoSHpimRocGiUN9TBZ16cbxSY1qB7CbUd4FQYDiDkojH//p7Eqqjf5n1HVPcX8w3/xX7zqP/LT/u/8pXo0BAYAEH3urq64u17PoRlvHr16tGjR1dWErmK3w1qPBQK1dTUOJ0I7SGhG/Dzk9o1p6wEumcymQ4JDA00Wq02DqfioDIMw6xWa35+PuziEXbN0Vhs3rJ1d4lyb6ljzrJFSD/hcDgeCQRAqRLw2EOh0IULF1588cUEk17oBozL1NTUQ6cNYdeccONgYRaNRlGgRaUyEAiA3KPFQWflCsXU1FRpaWlbW1tvd09ne/vk2BBhwrWqD9/tc55qtfYt+30PAl4CTgV0LByNCiyyW6Ke4jXeukMR9Rq9+iGLtEgxcXx94FeGxRu2zQYZ51n5wPOxaJiYIXBryTgVnZ2dCUyAVn4bf393+h5AaREIBHw+/xNtzLN/4wRqnuLi4uPHj586daqtrQ0Wwd9qNzgcDshbfbg1D/dB4uCJuCcIw8PlwtKNdFEsFgshuj8ATAXbZLgargXhDOQzLpd7iBILWunv75+eng6HUdT25BSLxbZkMolkBWFlwytfJUYBb5AT0wpSYMCOkhb3pTIgp3RkgmPVYCg83TZScujWkTzhNCG5BHljOZD3uHYV32baRK5YexZcXMBD3ew7kYWNbg3tEFlVewoxrbGi8OblhtICm0YW2jUGjMqgfQf3jXLEnOaYE3cLt+kOvOhxOPQHghTuWu9GIs7BWdS6E7eMxl2UnWYEubWPR9Bx6HHXrS3kLG3XxIybfr0sYFYjVyarCumcUHhoDZKfHAjNDXnO+11YYC/m2wu5HV6XzWM3uqxGv8OE4NDdyCwp4rKgENK7er9JFbBowjubAb3MZzdEkYylwXZ3UNC8nXVsQ4Btz6MdLuUy0hIhc+8ltF8jX7Q/+CpKOS4QEe63sc+FXvbgonXg8k24aOFeV1xuL41+6cqVV197/fzFS9TuHiaL3c9k9fXjocLBsasfd+868PNKbXCNjMw+Sgd4pemEHmIm9+GJ8yDhMx+eCzYTecsjSYXJYjH6+ml0Ri+N1ttLo9F6+/oYRHPw6MFuMti3MfAEOl3ieSEkIVgHCBmI0df/SfHYY7EYlUo9d+5cfX098eZOHlM+n08mkwmCWFL6VOQeECPiAQegJ5FIRK/XDwwMXLly5bvf/e6zzz47PjRii3huLrfRlNPIOBf8lB706uP4XB/Z7gY9Oq3wnIj01861rmhtk7W42GlECgO4DsOwxsbGxUXkWk/IENevX798+TJEpSPe6JCBv5FIpLi4GAyAHrT/sf//EOQe6KrJZMrPz5dKpTs7OwcKKlx8C0cjS7btHBH15gKpQNyTLerkqed1Kg3IHHCtUqm8f/8+DAdRcg1PgJgBhUBA/J2cnHzotEkn98RXSMg98YUYhvl8PiaT+f3vf//b3/72lStXBAKBVCpVq9V2q9ntcq5p3Reb1l/IlZT2KdV6h9/n1WoO8LlgTgQioXGD5MYCuVTav+1GSCMet0ujkOxp+zZoX529/7ltzo9iMYR1SowohmHJck99ff1nU+6JxWJ+v/9TX8fZ6ROPxxsYGGhubj579uzx48fz8/M7Ozs/kUaHg6cHQsvH/idEFkIoAakFutPb2wsfWC0tLfV4olAoHR0dXV1dvb29UG1/fz+VSiWRSK2tre3t7SQ81dTU3L9/v6mpiUKhkMnkurq66upqMplM0LThiUwmt+OpoaGht7cXtGiwgvf39zMYDMJ8+/B9LrQ8YVgkFECOWjYd2kJCEoAa2XtaVMiE2apB3t0opq0dP3BPK9DiQFCfXQMSKVxm3JtpF4kduFR0sPFklKMAP4Dc7jIj8yDkEebAQp7VBeHtG9cvXrjA7aONDA0MD49MjI/J1leRTcmuHql2bBokuPjc6HKAACNCIAY8yAEeYZW7MR8eNNm/dxCUxetEAYECOHpr0IMF3FGnKWzVRq1qn2bDuL26JJxk03uGB3ksBn1OwFdsSg0qmU4pQ3iWeLVeq0G1tbY4M704My1Znp+f5vPYzB5qZzuJNDE8KJ4TCicnGL3dTFr3KI+zMD3BZdDoPd1THIaAyxjp7xVNjy/OCVfFC6btVa14RiEc8ZuQyXNwZyuCe7SBzixg1thNh+0ywJI0NvbR/lH6mf4pnAFxYXBgoKKi4u233z579mxNTU1vb+8nqhqeBULDSjwaKZ8jYtL29fV1dHRUV1ffv3+fRCJVVVU1NTV1dnZ2dHRQKBR4XlgsVm9vL4VCgXgwFAqFRCI1Njbev3+/qqqqtbWVTCaTSKTq6uq6urrm5mYynpqbm9va2kgkUktLC1xSX1/f3d0N0hVszIFbA3x7sNlsJpP5Se0rg8FgfX399evXORwOhqd9j4fNZrtce7u7juVlkVgsnpufn5mZORxwAN4HoPaAelL+1Wq18OGe8iyxw0LoexLIWCzW97///eeff/7atWtcJlumkhdLaXXbA4E4FAu45BCcCp99KehBRtMWl8tq/1gQoHg8dgzDJBJJRkZGaWkpAXmxvLQ0v7CAYZhYLFKpVRwO22yxUCiUw28qEAioVB9zPk+4L/Bjf2jkmEfc59rfT4tmr9PpCgoKxsfHU35MDhtEf9/9zj90vzNtXDPo9PFCRvI+l0gkysjIKC8vBz92Dz5t9vbcu7sOsUgsEi3PzqH0KNMmIfxBMnM0Gk06r16z2Xz8+PGf/exnd+/eHRkZ6e3tbW9vb2pqqqmpaSM1F2Rnfvt7P/vH57NfKpPXjblWtrTJlQej4THjygeLrRUbrHWz2m5GsRiiYbdFWqSfOReLJX51m0ymeMd7DMO4XO5n1p8L/NgJO4BHWVI5HA44ZxFCB+dBYrORjTCOkzgAyxaLxYKTXC63v7+/trY2Ozv76tWrGRkZmZmZLS0tg4ODUOHhTROVMBgMKpUKS2dXVxeJRKJQKLAit7e3t7W1deCJTCb39PQ0NzdXVVWRSKSmpqauri4KhdLa2orCn+DLek9PD41Ga29vLygoIJFIbW1tzc3N3d3dXXjqiUssFgt25Wg0GlwLNL29vbBkgwk2jUbr7u4GgamlpaWhoaGpqamlpaW1tbWjo4NKpQKfHyr3YBiGRcIojp/bgdl2UDxi2NBByAwOtJeE5Bglcl/3uQ80NygajQcpV+w6bN8R87kCuxZ02PR+hzmwa0HOWRFfZM/qNqhRrBTvLjpwA9h9i1YwNkxubirOz83JycnOukfr6oAo3v1MJpfLFUzxt8SLduVGBEXUBfwHHCbd70bRcYL70YDbplfrlDKVbE2+viJfX9mQLCvXV+SiOYVkeX1RuC5eVG6uauUb8g3JhkTEZPSyGL0jA5y+3m4Wi9nR0dna2spkMntptM7OTjqdzuVwmhsb62trpibG5gX8seHBvj4GjDIDjziAbIGR1S7SVvT19dHpjN5eWkcHejF3dXUx+vq6e3r6+pkI/wQFbUJN9FC7+nrR6LS0tIzwOMtTY1vieZ1s3axTB/FtOCT3GB6Ch+Dz+T6R3APzFiQYED5AOifmPHwJEO94eIKAvre3t6io6MaNG1euXLl3715OTk5bW9vAwMDhTwpRA8jZnZ2dIH80NzfDzATpvKOjA+Z8Z2cnhUJpb2/v6empra2tqalpaGggkUg9PT1tbW0UCoXNZtPpdCqVCo4I1dXVBQUFLS0tTU1N7e3tvb29XV1d3d3d8fi7cNegWGUwGF1dXR0dHT09PWiI8U8CIGCxWFQqFR5bqLAJTyQSqauri0qlwhfRryH3uFyu1tbWa9euAW622+3uplJPnTo9OjYuXl7KzLxdUVGen58/Oztz//59g8FgNptNqZLFYlldXZXL5ekIYI9GLBbrdLpDaAwGg0gkMhgMyY2YzebNzU0+nz82NjY8PNzX15d1915lWfnx5tuXp+q1dqPLtmuxWKAhpVK5uroKPz9eldlidZjMFqhta2uLuESn0xUXF8fHaxYKhTk5Obdu3QLfZqlEcu369axshDWWnX2P2t39ztvviMQr5eXlc3NzVqv14w0d/DKbzTqdbnl5+ZC7NpvNcrkcwiinrARuan19nehwSjKz2SyVSlUqVbq27Hb73NzclStXpFLp7u5ufCVmk9lhsZ0dqzwzWmk1WkQiEXHWYrFMTExARG/sgTJseno6Nzf37t27RqPR6/XSe3tPnTo1MjK2IhbduXu3rLQ0Lz9/dm4OpMZ0/YFpI5PJ0hGYTCaz2SwWi3d2dlLS6PV6qVS6vLwsFAohMIHJZNLpdGo1smsmk8kvPP+jnx/9YWZ28Y1m0S9KZPcH91YUjj2HeddmgbmMbtxq1xn0XeKht9j5hfPUdZPSZXPt2lwmo85kMhJ8gM6sra0Ro2CxWLRabUlJyWcQpwK9XPF93HT6HkK+GRhAQgwszf39/TQaDcziYAHt7OyENRF0G62trefOnbtw4UJZWVlzc3NtbW13dzedTgex48033/zFL35x9uxZKpVaW1t7+vRpWAHTLeVwFl5yHR0dFRUVpaWlIE/U1NQ0Nzc3NTWRSCToCXxQgvalpaWlo6MDluPe3l4GgwHvGx6PBy8GuKOBgYGampo7d+40NTURZkbEzULrhJ4GfsZrlRLeYfH0LDzBW7mVRKqpqcnNzS0qKgIJ6fC4hRh6CjHM70GSDR6cMOaxB3fNMZcNCTpe18G+D8J09GD+vYhjx28zuK0mr1WvXBRo18Wa7XWNbF21KZWvrUiWF/VK2QJ/7O6dzFs3rldXVU4MIbMJ+dqKw6jVyNbXRQs3M67+8hc/v3TpMoPBKCgouHL58uDQ4AEeEx6Xrx+XIeZmhIrNNZtifX9HEXPbAha1TbkhXZwdYPVTyKRuahelvY1MIpHJ5Kbmpvb2tvZ2CoWCguKDMoza1UUmkykUSk9vb38/k8FAEXBAMgYmAwPZHA6LzS4oKMjJyW2nUFA3cPtcGJdDpgoQEFXFa/tg9jIfAIIymSwajdbc0lxdVVVRUry1sohQS3fNdvOB8QQaglQpEAikk3s4eOLxeIODg9BcX18fGOZ3d3c3NjaCnEGlUjs7O7u6uvr6+rhcblFR0VtvvXX69Onc3Nyqqqr79++3t7fTaDQymdzY2Pj6668///zz169fZ7FYWVlZGRkZh8g90AH4wEBAuQ9ULyBS1NbWksnk5uZm+FQAAbEdH6G2tjYymQwSJzxrMCgI3h3HkCHmPIfDKSgoKCoqIiK8JzwC8aMD/Ym/Fh69eBpiTInnhU6n19fXl5SUXL9+vbGxkcvl/hpyj9PpvHfv3uXLlzs7O5EKxGKmtLdfvZrR38/c0Wm7e3pYLGZJSQl/aqqiogLevuZUyWq1gtxjsSCRImWyWq0ikUin0x1CYzAYlpeXjUZjyhpsNpvL5TKZTCsrK06n02q1yra2WLT+twuu/LTzg6EVgcNkdTqddrtdqVRKpVKr1ZqyHrPZbLVaNzY2tra2gMZisej1+pKSkni5h8vlXrx4MTMzEyIKTk9PZWdlFRUXK1Uq/uTEyOhIXm7u9LSgsrJiYWHBZrOlbMtiseh0ug/RrA65a4vFAnLPITTQ4c3NzUNuymKxrKysqNXqQ2h2d3cHBgY++OADiUTicDji+2wzWxUmrcKsMxmNy8vL8JoHXiXLPSwW6+LFi3fu3DEajXa7rZNCuXrlal9f/87ODrWb2t/PKC0rmxYI7t+/f/i0kUqlMpnskBu3WJAQhkDNUk0ti8XicDhsNhuIy5YHCUBnTSaTUqXSaDSLC3MFObdLK2op0/bjDTsXmzYFkh2nw+qwmXEZ2Gy1WNwOp0qjIi1yz05UZU+SpBrZnt1ls1gPqsTprFYryD3EtNnZ2SkrK/sM4lTEYjGRSLS2tpZg3wPfmsQyRKFQSkpKGhsbYWcHNnqam5tzc5FuIDc39/79++Xl5cXFxXV1dRQKpays7J133ikrK4MltRPXjcPbrrGxsbCwsL6+Hk7l5+eXlpYSCysLf72BsAV/2Wx2T09PZ2dnU1NTdXV1RQUCkYl/BUJXibddfAaWbKJyosL4NRfyhFR0CE3yVY9ewuPxaqqrs7Ky6urqurq6Hir3BINBgVCIFIxBL7LO8TiQaYvXGUMZV2TfGdqzh/edMqmI19c7PsgV8idmpyZGh3j9dNoQj1NdXZWXm1dSVNRJIbeRW9taW3qondMT492dHdeuXq2pqUGWSnhi9vfTenuY/f0tzc2lpSWtLS3t7ZS2traioiJ2P2NVtLS0MDsrmBZM8acmJ4RTU4Ip/sDAANL9SFe0W2uiGX4/lVJTVVlVVdVGbmPhqheEtcblor+4p178mw/YCwIB5A9neH9/H9h/PDqrH50SXAWrqyr5o8N61TYCIwt6gnt2e3o8dr/fLxAIxGLx1NQUTEJoDuxgCLOZ+vr6e/fuwdYPyBn19fVNTU0FBQX37t3LysqCzaOSkpKGhoaOjo6ysrKzZ882NTWByQudTgcdJJVKra6uLioqamxspFDQuOTn55NIJIJ1EOKB+AkSNqgh6+vrq6urKysrKRQKiB2EVol4LuIfFiJ/+IiAHAmRrh5K+ehjEU8JT0dOTk5xcTGFQgEdEovF+qTr7/7+fnd3940bN3g8HoanaDRiMpv29vZ8Pr/f79/d3d3b25uenn6UDYuH7nNpNJrDt4QgYl66fS7oYTIee2jfL1CuvD1UfL763sTgqM1q9Xg8D3V9So5byOFw4uP3SCSSoqKigoIC8OeCeL4Oh8PtdvsDQb/frzcY/H4/iUT6VHAqPq19LrBrBl4l/43FYgaDYWlpqbCwELaokmmS4xam3OcCyR74HIlEzGbz3p4bTRq/f9fh2NtzC4XCh8KbGAyGh+5zqdXqdN4t0PlIJKJUKlNu3hF3t7u729LcWFdVuiSScKWhcx3OTPquSBNE9jsPksfjsRut/miIqZ3LWGhp2ho0+51RLBaKRcIPHLuS/bk4HM5ncJ/L7/dXVlY2NzePjo7CPg68k7q6ulpbWz+0cs/MzMzLyzt58uSZM2dIJBKDwaDRaLBxzo80AAAgAElEQVTaggs66O3BLBE+bel0OoPBgJURaoMllUQi3bt37/z58zdv3iwrKysoKKitrQXEBtgyo9PpoLwhk8k8Hq+jowMmX319PYVCoVKpOP452mz61BdcDp7iF99PMc/hcHq7u+9d/+DevXt5eXmgdjrcrtnhcGRnZ7e0NMvWpDa9xm3Ru61Gt9Woka1Ll+YH2P0lRQX1NdVnz5y5cuVKS2trD+4Eh48LsjMFyeMBozhMFovOYPT09CIYy4M9R8RDDq5Tqa9vyMjIePfdd7Ozs+sbGvLy8qqqqnAb2/6RoaHJ8TE2s29kgNtP72XQepYX5oZ4nLbWlqqKCko7uaGxsZ1CoTMYMIKfItOgKujkp14tVMjlcpubmwsL8knNDV2UNuXmumJzTbK8IN/awtKkD9fB27dvNzQ0jI4iIHRCVqBQKM3NzZWVldeuXcvKyjpx4gSoGcBIhdB2oPgNeIq3F2bg3CM0XnDLLBarsbHx2rVrJ0+eRONSX5+fn19TU8NkMmH+sFgs2K2rq6sjk8kDAwMtLS2FhYX5+fnV1dX19fVgMwdNf+oM/K2OC4fDaW5uzsrKunHjxv3792Ea0+n0ZM+jNKP0UfHc3Nwvf/nLyspK7IH95kfnHuT4fH5raytBEEtKvyW75qR20DvK6/XGexvFHvhWWYN7Z8cqX6u4WldZU1NVMzIyEu9enlAVhmGgySBuCsOw+vp6wq4Zqs3MzDx+/DhAUBENAVfgZyQSKSoqAgOghCbg5x+IXTPRt2g0KpfLg8Hg8vJyfn4+mKoQZyETCoXiOYxhmFKpLCsri7/xUCh07dq1EydOgElZAnOAcnJykkQiERxOaAUuOcSuGeihdRCXk2uASsLhcLw5fDIZTBuDwaBUqaqqKrvaW40mK387fIFiu9xpndryhSNoank8eybjQUznvaCXphK8O1X+wtDdHw1kcnXIriulXXNdXd1n0K7Z7/cXFxdXVVXBN1ZPTw+YKzY2Nubm5ra1tV29evXu3bs3b95sbGwcGBjg4ImdJsHZ+L8EIYfD6erqqq6uvnv3bmVlZW1t7b17986cOXPp0qWcnBwajdbY2JiTk1OJp/LycgqFUlNTU1xcnJubW1JSUldX19nZCZobos4/lgyPxyssLDx3/jwozEBE6OvrO8SP3eFwgLaMw0YyDINBp/X2DvB4lRUVd9B7t/6DDz64dy8rMzOzvZ3Cw8flcG4QA8fBDbMIYg6H09PTU1dXV1xc3NDQUFNTk5OT8/7777/33nt5eXk9PT01NbV5+Xmgn0BKnba26urqkpKSnJyc6poaEolEp9MT6iQq/wPPcDicjIyM27dvwyYpjU5nsdg9vb0EUiOWlJxOZ05ODplMHh8fZzKZXV1dnZ0IfbakpOT27duNjY03btzIzc29fft2Z2cnPC+HMyHduICas7a2trS0FDQ3OTk5p0+ffvfddwsLCxkMRk1NTUFBQXV1dXl5eXV1dXt7e1VVFfE5QSaT/0jHBcniLNa1a9cyMzMpuM9BH27LxWAwDkAikwblkIL5+fnTp0/X1NQATSwW83g84MnhdrsDgUAkEpmamurr6zv8BUbgc8XSpIf6sSf7cyXXlFLuATIkYURDNTJe0Urv7OJ8Q01deXk5jU5XqVQprWKT5R4qlRrvx+71eouLi0+fPk1EpQNlRiwWczqdoVBo3+MJBoO1tbV/4H7s8WwEucfn82EYNjMzU1paure3lzCyyXKP1Wqtr68HIQP+ut3uwsLCM2fOENBjbg8CSfX7/W6Px+/3hyOR6enph06b35ncQ/ixRyIRPp9fUlzEnxgNBENzylAG1Xa+3Tq+7lvV7mfR9KG4kD0rduU/9Rz/Lue6xYdCFaSUe6hU6mdQ34Nh2Orq6vz8PIfDaWpqKi0tzcnJycrKOnnyZEZGRnZ2dmlpKZlMhg2vdK+3dOXsjyf4xiVkFx6P19bWlpOTU1ZW1tnZCRY5FNzoGECsCGtoWAphD+XjVf7R/Orv76+vryeRSM3NzS0tLWCExOVyD9FwWiyWtbU1JpPZ2NRUVFSUm5t769atEydOXP/gg1uZmSUlJZ2dnY/I+cPZxOFwBgYGeDwejDKPx2tvby8rK6utre3o6GhvbwcrEBKJRKPRiK1PYq8kXR/SlR/emUc8e3jlh5+NbwJUjOBYRCYjA5fW1lZ4PcBCkPxXq9Vubm4ymczm5uaioqK8vLzMzMz33nvvxo0bt27dKi0thX3M+FZ+vTyMy8DAAMx8GJeSkhLQ5YDxPlhNwbhAK7/fcfn17jThKg6Hw2AwmpqawMkADI/IZPIh8mjyMIH/VHFx8YULFzo6OoBApVRev359Y3NTtLyUm5tXXl7R3NIiFAq5XC6GYZE0KRaL6XQ6u90ejUbTkERisZhKpfL5fOloANAAtBHpKolGEUS0QqFISRCLIr9jqpJ/Wdggt+jczr2x8bEqPE1MTNhsNplMtrq6ajabY7EY2K/EYjGoKhaL9fX1xe9zicXiEydOZGVlAbpWOBxqamxoaGwMBALVeLp79+7GxmZ7e/ve3t4hN+X1ehUKRTqCSARxxuFw6HS6w2nA7InocEoOaDSaQzoTiUTC4bBcLvd4PNEoilU4MTFRVlYGijGiwkAgoFAoQqEQlESjUZvN1tzcDJeA3LO4uPjuu+/m5uaq1cg5TqVSXr9+bWNza3lpMTcv935ZGYlEnpubA1g3ouaEDEwbm812+E0plUq/35+OOY84bdxut0qlikQicBd2u729vb38fplKgUItL6n8d+m2f8va+YvjymHpPsJ9jESikQgWw0oldJpiCiZ/LBbT6/UWi4XocCwWYzAYn9SPMuXD+CiFv9P4PRgeKmBwcBBsXHp6ekBp39zcDK7aHA6nrw+ZWRChOAg7BsjArla6s/04+Hn8WSLPwvdc6HQ6bIoR71SCAHnr4EEL40tStp5QCD9hUyDlKaKQwfgoxAhR+KlcnnDjYBjR19fX+yCNjo4eIvfAuPB4PMDx6OvrA6vw7u5uKpUKmyPwKQxWqMmdh1GDWCkJZ4GxUAlxCoYYzFZA1oQ+QzmMDpGHHZyUTUOFMCuIyuMzLBbrkDkDTUAP468i8kwmEwIpESXxGTabDWOasm+wRQsuQtAE3CP494EB8kPfr263m8fjgVsfg4FczGAfNn5cCDuY+L7BPAcXp3TjQuwRExcS4wLPCxg8wXDEDwqQQeUp750YdKLmhAw8L4dce/g6AMOa7nLgMHAgod34sQa2gKgHdwd76z09PWCW+ygLKEGzsLDw8ssvg77HoN/Jzcl9//33+5nMpcWF69c/+OD69ays7Kmp6atXrx6OU7GwsJAOp0KBJ5VKJRQKNzc3lR9HRYCz8Hd7e1sgEAAuUHw5kVcqlRsbG4fAPigVCpNmp32Gc2ywYFAqdJhsGrVmbGysuro6JyfnxRdf/N73vldeXr6zs7O0tETAPqjVaolEcvHiRQKnAoTCe/fuvf3223I5Qj1jMvvPnHn/zp07BoOhrKz01q2bFy6cHxkbz8vLo9FomvQ4FZubmwKBQKVKDfGhwDERpFLpbwOnguAbkVEqlQKBgBgFnU4Hjrrb29tqtRrIAKdCLpfDT3CMisepALSyzMzMd44f12g0ZrOpoCD/9OnTDEbf4uJCRkbGtYyMvLz8mdnZS5cuPRSnYmVl5ZApoVQqhULh1tbWITSPOG2EQiFRiVqt1ul0g4ODeXn5dXV1yi3pisx0JEP9VycVL5ZoZHKlCof6UCqVm3KZTI6AqhQ4nkk8ToVarRaLxZcuXfrM+nN5PJ7JyUmBQDAzMzOLp/n5eTxCAfojFAp5PN7Y2BicnYlLQDwwMDAxMTE9PT07Oxt3EmXn5ub4fP7w8DBUnnB2ZmZmfn5+YGBgcnJSKBQmn52bm5uYmBgaGkpuGoinp6cHBwdTXjszMyMQCHg8Hp/PT+4YVDg+Pj4wMJDuLFiuTUxMpCSYmZkZGxsbHBxMeeOzs7OTk5M8Hm96ejr5vmZmZoRC4dTU1P7+PnxhECs1kYnFYjabjY8nWMXEeBLhaWVlRSAQcLnciYkJoVAoEomW45JIJFpaWhoYGBgZGZmamlpaWoo7ibJisZjP53M4HKFQmHBqGQfKWVxc5PF4ExMTydfC5Vwud2RkZHZ2NqFpuHxubo7H4yXXDCUzMzNcLnd0dDQlwdLS0ujo6NDQ0MLCQkqC8fFxLpc7Ozub8uzs7CyPxxsdHZ2ZmUnu2+Li4sTEBIfD4fP5KW9NIBAsLS3t7e2l3D6A0TEajZOTk3w+f3FxEYZDLBZDRiQSzczMsFisiYmJmZmZhB6KRKLFxcWRkRGYNskdWFlZGR0dHRkZSdl5uJzL5U5OTiZfC5wfGhoaHR2dm5tLaBrOwuOWfArOTk1N8Xi8qamplARLS0s8Hm9kZCRd03w+n8fjJc9GqG1ubo7L5Q4PD6e8tYWFhbGxMWg9oX6YzPCUeTyew+2CiccHMhCIpbi4GF5mcvl2K4kkkUj6+xhtbW2DAwPDIyPCmRkGgxHBcUkDqVIoFAJc0mAwmOo8KguFQqBpSEcTDAYBEdPr9aarJBgMulyu7e3tdATQkNfpEWwsXZptYCpnouEwRPLd3t5+4YUXnnnmmcbGRjDvNRqNH9qpQFXBYLC7uzte3xONRhsaGk6ePAm7hw6HYwB33BOJlsvLy6emptrIZIPJ1NLSYrPZDrkpApc0XZ9DoZDVagUQj0NoEC5pXIeTKYPBIIFLmnwWSvx+v0wmc7lc0GH4qmQymbW1tX6/H7ixv78PuKRwSSgU2tnZScAlDQaDNTU1p06d2tzcxPfOtltbW1dWJMz+vnYKBT0FY2Nzc3N0Oj0cDqfrDEwbs9mcjnuBQCAYDMrl8v39/UNofD6fTCY7fNo4nU65XJ7Qk1gsFggEBgYGCvLzB8f4EqVjcdOosob2fQFiHocQkO3Br1AopNPp4kcBps1nUN8Ti8W2t7fBXKCqqio5AtXe3h7YLDc0NPT394MajVhcotHo0tISm82m0WhtbW1ECCyCIBwOc7ncsrIyHo+X8kWiUqlqamqqq6tTmt8Hg8HZ2dnq6mpQRBPVEhm73d7U1JTOyN9qtXZ2dn7oKp/S/cHj8UDsH4DvIeokMiMjI5WVlR9aG6R005ifnwdvWw6Hk2xsr9fr+/v7wYcfPEWJamEJBoVBWVkZKEvjz2IYFo1GV1dXofMVFRUSiSSBwOl0MhiMurq6ioqK8fHxBOEpFAotLS0BBlBLS0uyR0YkEvmw3du3b1MolJQvkuXlZXCdA01vQusej6e7u5tAPko4i2HYxsYGlUpNF2RsfX09Nze3r68vpbrLZDIV4gkiiyRUDj3PyspiMBgJp8CEAsyNa2tr+/v7kwmkUmldXR1AIyV76DgcDhqNlofDVab0HgqHw1tbWw0NDYWFhQ0NDVptYqwwu93OYrGqqqry8vKEQmFCB0Kh0Die6uvry8vL3W53AoHb7aZSqWVlZb29vSmfl6mpKXDpApvNhMv39vbodHpWVlY6ldXCwgKFQkm4ivi5sbFRWFjY0tKSsumdnZ2qqqqKigrQEBBXQcbr9ba1tZWWlo6MjCSsEjAuPB6PQqGUlpamfNxEIhGJRCoqKqqvrwf7jPj64Wmqra3Nycl5dOtmt9s9ODiYn5/f0tISX1tCfmpqKuUzGE/2KPhcBE5F/IXxeUBISPm4EWQ+n0+lQoF3iZLkjMvlspmsztD+nSVKw+ZAGMef2t/fHxwc7Ovrg3mb7M9Fp9Pj7XvEYnFXV9e5c+cO8bQKhUINDQ3JszS+S36/H3bK4gsT8k6nM+WzHE9mMpkIz7L48vi8TqdLt6QAWSwWUygUyfOHRqO1tLTAtEz253I4HCAsYg/i9ywvL3d2dl69ehUQbeP7QOSnp6dZLBbxM2VGr9enfE7jiVUqVcplkKCJRCJg10yUJGf29/dTLtRAaTabW1tbKstLNtYS3yMJVRmNxoR3JY1G+wza9/h8PolEMjQ0VFBQUFhYCGZcBC8ikYhGo5mYmBgfHy8sLOzu7k54aF0u1+LiIp1Or62tbW1tTX6Ednd3KRRKVlYWk8lMfvtiGCYUCrOysoqKilLOMJPJxOVyi4uLwYKM6BiRsVqtVVVVYLxGFBIZk8kERkuA/EKUQ8btdre0tJSVlU1NoQ3O5DQ8PJyTk0On01P2XKFQgIUHnU5PnrhOp3N8fLy2tra4uDj57RgKhQQCAYVCAfeu5KY9Ho9EIhkfH6dSqc3NzYTtIVBGIhG5XA54iiQSSSgUJiyUEA+DzWY3NzfT6fRkuc1utzMYjOrqahaLlTCm4KMBUdjb2tpSjgvIyi0tLfAFmdx/uVzOZrOTFyCg3NzcrK+vHxwcTPl+dTgc9fX1DQ0NyWI0LExjY2M1NcilJbldDMMWFxdhT3BsbCyZwGg0DgwMQJSa5AU9FAoJhcKampr6+vrkbwCACNjc3GSxWM3NzR0dHQnMiUajCLZwaYnJZNbV1SXbCVksFlA4dXSgkMfJi7jBYOjp6WlpaRkYGEgWpoPBIJfLbWho6O7uThbaMAyDcamtrU23VEmlUiaTmcwWKJHL5XV1dTQaLeW4WK1W2AHXaA7QDePriUQi4IMmEAgSZiOQiUSijo6OdHPGYDDw+XyIVZj8uDmdztnZ2a6urpqamkfXuqvV6l/96ldnz5790DUMmJmyY5OTk83NzTC1YqnS79efK6FHMAl39Hr0nEaClWvM7OUuVyAxinGyXXNtbS34c8FwlJWVvfTSS+fPnxcIBMT7Pj4DNh+FhYUwyRO6AT//MP25wKCH6DB8RlLwMBARPCX4c8nl8tLSUmALzJDCwsKXX3750qVL8OmSbtqAPE00lJDBMOx3bNec0AHiJ9za3NzcnTt3GAwGsewQBJBJaddcU1PzGfTnAo4g6/39fa/Xm/wKhNnv8/lAFwf08X9jsdj+/r7T6UyndAmFQj6fL97rMuFyL55SNg0ODumahgnt8/lSzksMw4LBoMfjOYTA6/Xu7+8nv2Cgh4FAwIen+A4T+Wg06nQ6XS5Xurc7tJ6Oq+FwGFpPlpmIJmKxWCgUCofDKW+QOJuOdaEQ0mGCaSFRJ5EBI7jkT3MgiMViYTwR9AkZOJuuaehbwiXEzwhugZiO7TDl0nUbTBOieCIqjM/EYrEgntLdGrQeCoXScTUcDgNz4quNzxPMObyGdMwBfXu6s9B6urNw+4dPicM7fwjbYUzT8Y2463hWxOcP7zm4w4Rwe9L4q4g8jEu6cYcZdQhbiHqIDGjXZmZmCO1XwO+fn5v3+fwQQlAhl29tbT1iIJaUgibRFmC/J39gxBP8evF74muA/N7eXrzAzdTMXl9oVbvN8ZTJ+h42mx2/zyWXy4eHh6enp4mPRoVCvr0tj0YiCwvzGo1atCxyOBwfuvSmW9+guUfEY0/+JI7vLTjeJ2gaEghAjEj3lgFigPlM2eFoNEomk2k0WjgcVqvV8ZN8b2+P0AbBE721tTU6OkowB02b+Xm/328w6EVikUIu39zcmp2dHRwcTO5kfMnvLH7P4foe6JLX61Wr1Ww2u7S0NJ3SNDl+D5vNTvcRFX+nn0r+d23X/Kl0+nEljznwmAOPOfCHw4EEqRTZOvC4J997b2KSv7WxfutW5vXr10pKSgEU3W63O53O3VTJ5XJtbW1pNJp0BLu7u06nE3yp0tE4nU6bzSaVSm02W6pGUJnT6TSZTACzcAiNTqfb2tqChpy7u/69/ZHt+bdGi1mauQmDdMm45XK6wG7X5XJBtaAXJ+I1J3AmFotqNZrMzFvZObl+f6CutjYnN/fdd0+sra/XVFevrq5CPcldcjqdZrNZIpGku2toXavVbmxsHELjch10+BCa3d3djY0NvV5/CI3D4VhZWTGZTMk0cAtlZWVdXV0ymcyJf7JC9xYWFqqrq2HeEpoPYhoHAoHhwcH33ntvfHxic2P99u3Ma9cyiktK5+fna2trHQ5HclvAKJg2arU6HQGQSaVSi8WSzFuixGazSSSSw6eNwWBYXV0lLknOOJ1OnU4nk8l8Pt/6+joEA0Mop3a71Wp1OBy7u7sul0smk6lUKmLaWCyW6urqlDv+BH8+xcxjuedTZObjqh5z4DEH/jNyIF6Nj2GY0WCoq6s9f+4cm8Oh9fZc/+AGldqVk5vL5/Ozs7PVarXBYNCnSgaDQSwWb25upiPQ6/UGg2FxcVGlUqWq4KBMq9XOz89rtdp0NCj0nFK5sLCQjgAakslkYrE4vjNOs61jdfhvqW/+LfVYi4RnM1qkUunq6qrRaIRL5HL5rVu3CH1PAmcikfDw8PD1a9dy8/JXViTnz5/v7um5e+f26PhEYWHhyMiIyWRK2SWDwaBSqebm5uI7k0BpMBg2NzcBaSHhFPHTaDRChw+pR6/XLy0tyeXyw2nm5uaUSmVKGuBGaWlpZWWlEne7A2NqJpNZVFQET0g8Z0A6NBqN9fV1586eY7HYtN6eD27c6Orqys/L509N3blz55BpYzQaRSLR+vp6ys4Q976wsKBWq4mfyRmtVjs3N3f4tFEoFI8ybZaXlw0Gg8ViMRqN4+Pj165d++Uvf/nKK68IBIIPMVANBsPKysr6+jowymg0bm9vZ2ZmPpZ7/jOuno/v+TEHHnPgj5EDye+wcDgEPkFKpVIul+/s7FittulpwUNxKh7FrvkRcSoO2WeE0BUPtWtO2OeCoQlGw1dmGy/M1PvCQQzDQJMRP2rg4J3OjAnhl5nNRqPRYrGsr68ZjUalUrm/v8/hcA7ZiAeL9fhNt/gWibzH40lpYUkQYBgGipP4kuS8xWJJuYdFUEIEmsN3Gz0eT01NTUlJCZlMBkuy/f19NpsNUk7CnIHCxGmj27FarVKpZHZ2lmg6ZcZqtbpcrpSniEKj0Xj4lIhGo3q9/vAdXr/fn9IakmgFLEYSLMcnJydPnjx54cIFwiYatJ7xV01PT6e0dIyn+bTyj/U9nxYnH9fzmAOPOfCflAPJ7zB4jSWwg8/nt7e3pxMI4BK9Xg9ASynrBBrCnyslDYgIjwI4EG91m1wV2DWDuczHzmKxKO7YlXB3j38+5sAfCwceyz1/LCP1uJ+POfCYA3+gHPiYWID/SNlRPp/f3d0dL/cAGXE52iMzGhPgGpJptFot6A/iL4zPg0Ut2KTHl8fnfT6fRqOJRqNEIQhVxE8AEIWP+/jClCJdyvt9XPiYA3+YHHgs9/xhjsvjXj3mwGMO/NFwgBALIINhmNVqKSkp1up0UqmkobGRQacxGAyBQJDgmAPezvH3KZVKEyLQJLvULSwsJHitJtAEg8GZmZn4baNkt0en0zk3NxcvxESj0YR9EKvVurq6Gt89QDmIL3mcf8yBPzoOPJZ7/uiG7HGHH3PgMQf+sDgQL/fgntKm0tKS999/n8Xm6HTaK5cvnz17Njcvb3FpKSsrq7u7m4Knzs7O8vLyysrKDjzGEoVC6e3tvXnzZlFREWD4UCiUzs5OwPEFmo6ODiqVevHixYaGBgAtoVAogJBAIpGg2q6uLjKZfO7cOTKZDIAzHR0dra2tBQUF7e3tQEOldtfW1l68eLGrq4uoubGxsbi4mOgMlUq9f//+9Q8+6O7pIWgAknZ+fv4PawAe9+YxBz4JBx7LPZ+EW49pH3PgMQcecyCJA7G4hGGYx+OZn58rKSmZFgjGxkfr6+vb29qqa6qDwaDRhPyJ9Hr9Dp7Ap4bIS6VSo8mkVChWJBK9wUCUQ2ZnZ0ejUa+urlqtVqPRqFKpiPL4CpVKxfr6utVqlW9vb25uxZ8iKpTJthQKhdlsXl1d1Wg0KWlUKpVWowXvcZ1OR7S1s7Ozu7ubxIPHBY858EfDgd+z3BOLxXxej8+z5993P8rh87jjFbMPZXMoFDIajclRWd1uNxGWCkU523P4bEaf3fTww2b07Vqj0Qg0HY1GTSYTEZWS6I/X6z3cuj4QCBiNxnhFNIZhbrdbr9cThbFoxGfUeFVrPvXGQw9EZtQSMbJS3rjX6zUYDIf7IPh8PpPJlKDuhuAZBOejoZBva8UrFvgkMw89vOJpv0ZOXAvuAAlRet1u90N9Ddxut8lkSvY1cOMJOB/2+7yzk97JQe/U8MOPiQG/EmEIQ9rf3zcajcn1e73ew+PIOZ1Oq9VK3CDEQnQ6nXa73e0+mK5Bl8vO5dhZ/XY286GHjdm3r0AIjpDcbrfZbCZGFsOhRaB+l8sVX/7gioP/7XY7WMjGl+/u7gIMMhR6zWZlD03dQ1P30h9y9NBUvXR3XABll8tltVoTOuByucxmczIbiT7EYjGr1RrP0nA47HQ6HXginkqLUi2kUGc7ex96zHT0zFJpTqOJaCJlJmGYYrGYy+UCFrlcrvjhS3n54YVxYs9BFqI+QoDEWCxmNOg/uH59fGJSIpFczbgq2/5ofImaN9bXrl65KpFKZVubd+/dNZktxCkiMzkxfv36dYPRODs7U1RSQpTHZ7o6O3Jzc3edzsybN+n0vvhTkA+HQqUlJbV19fse9/unT09M8pNpMAwrLS1ZXBZtbqxdunx5dW09Jc3jwngOhEKhxcXF1dXVhIcinuaPLq/T6QQCQfJK8pvciNlsFggEhwBrRCKR+CXiN2krFosRATOJen7fck80ur9rC7sdEc/uoxxehyVhSkUiEZPJRMgKxI3B6+HevXtPPvnku+++Gw8UYLfbX3zxxdraWiCORiJe7WZ4ez4sX3z4sb0QUEmCgQBcW1lZeeTIkVdffTUe88vj8Rw7duz27dvxnYnP+3y+c+fOHTly5Nq1a4QEsLa29uMf//jLX/7y5cuXYfM+HPDv8xkhRmWIWfvwg1Hhne6H2kKh0L1795544olTp04RNw6h9L/0pS+99dZb6Sac3W5/4403nnzyyZK4JXV+fv473/nO17/+dQixj4JTu12++tuhu2+Gco4//Lj9+n5fUyQaBU2Z2l8AACAASURBVP/G8+fPP/HEE/E3vrm5+eMf//hrX/taSUlJwuASTFOr1S+++OKXv/xlEolEFMKH9c9//vO8vDwo9JsNnnePen/2/3l/+czDj6P/Y6+tGtCJHA7H66+/fuTIEcCVJJoIhULvvffehQsXiJKEzOrq6nPPPfe1r30tHpNhamrqmWeeefrpp0+ePAky8d76muLL/6z4+79R/NMXH3L843+X/91/M7c0QUMfAlwfPXr0S1/6Ujzwk1gs/o//+I+nn3769ddfTxd5VigUPvPMM9/61rcmJyeJPvP5/GefffbrX/96T08PFBrHxhlf+EvmF/6K+V//r4ccX/irvv/6fyo7qXChRCJ57rnnvvrVr8Zjk62srPz4xz/+6le/2tjYSDSakOFwOE899dSzzz5LYJ5LpdLnnnvuG9/4xle+8pXLly8D/TSJ8ub/9F9O/M//+4k/+fPDj+Of/7P3/uwvpYOj4MdksSQuEQDi9txzzz311FN0Oh3q393dPXbs2NNPP/3UU0/94he/+A0X2WS5BwQpKMcwbGtz4/z5820UCpvFfP/MmfmFBQxHaCIuREA6gukz77/PGxiY5vM/uHHD7UGIEAQBVMig086ePTc2Pn4789Ybbx6LbwXykUi4qrLi2rXry8uiE8ffKSpCslFCJV6v99bNm/eyslal0ldefpna3ZtAA1XRaL3j45NsVv+p06empg8gJoiqEoYVftpstvLy8gsXLrBYrOXl5XSTE8Ow6urqN9544/bt2x6P50NY+Ndeey07O5tOp/N4vJQ1Jxfu7e11dXVdunQpKytLKpUSkbKTKZNLbDZbQUHB7du3ieURwzAOh/Pmm2/m5+cnfycn15CyJBKJXL58+ejRo+nWsZRXwRdvbm7uW2+9defOnaKionfeeefs2bPz8/Mfhu154403AN8j3bWHlPf09Bw7duzUqVPp8JEOuZY4JRKJvvnNb6bD6iHI4jOBQABM5uML4/MKheLZZ5+NXzriz4bD4ampKYVCAUF9zp07R6wV0Wi0q6vr9OnTRDwImUyWmZkZT+P1euvr68+fP0+ApYyOjiaIPr9vuScW9TntsX0n5nU9yuF32ogpFQgEJiYmXnrppePHj4tEoubm5paWloaGBmKMZ2ZmnnjiidbW1q997WtU6sF6rdFofvKTn/zJn/xJYWEh8DoSifh0W5hiMaZcfoRjKaRZBblnc3PzySefbG1tfeaZZ+7fvw+1mc3m11577U//9E/Pnj0bP5bxeTabDa/wI0eOjI+Pw6mqqqpjx44NDAx88YtfhEENBfy+KUa0vzLCrn3oEe2v8AsO5B6hUPjEE09QKJSnnnqqo6MD6u/u7n711VeHh4efeOIJojC+VxiGNTY2fvOb32xpafnnf/5nYh1paGgoKCjIzs7+xje+Aa+HoNsVqL8dvftmJOf4Q4/o7de9fY0g97BYrCeffJJCoXz5y18mMK3odHpmZmZjY+Pf/M3fpIu9kZWV9d3vfre+vv7JJ5+Mx2wqKyv73Oc+d/36dbgRv8ngPfET/4vf8P/iXx9+vPCUm1wFck9LS8tTTz3V2tp65MgRQI2GCjs6Oj7/+c+/9tprCYwifp48efLnP/95SUnJU089RegqsrOzv/rVrwIqNWg+3Otrqif/UfXF/1f1D3/7kOPv/0b53/8fS3MDNJGdnf3ss8/W1NT8y7/8C3Hjzc3N//RP/9TS0jI/P59O4j969OjJkyczMjK+973vQR/29/efe+65y5cvNzY21tbWQqFxdKzvz77A/vO/ZH/hrx5y/PlfMv/i/1B2dEHHTp48+dJLL+Xn53/jG98A1eaHUG6vvPLK22+/TaVSS0pKCIGe4BXIqd/+9rczMzPfeOOND78N4BQAzH1o0fLFL36REJimW9vf/NznT3zufz3xuf/t8OP45/6Xd//0C9JBhKHm8/mOHTv26quvCgSC+A6cOHHiV7/6VUFBwdNPPw2SKOAQczicb33rW++///4hCqr4/qfLE9JAygyu+wmNjo2urq7KtmVDQ8MJuE4gZ3g8CNx0Wy4fGx1lsdl+P/q4iq8QwzCT0TA8NCSXy2H7Kfax84gWw7D/v70rgWoyyfO+t+/t7uzs7Oz2TM9MT7+23e52vFhtQVoUFbQbuUXkEBUl3Hc4EoRGUBs8MCDhEFREURAEUc5whzMSISKXXA055DDhSLhykoNvE/7tN1mkZdqx327vy/fqSaWsqq/qX19V/f5H1X9o8Pv6+vrh4WFqc3N7e8eySiAPrbW1paWVTqdXV1e/fKlyeav+IsjDYbOZDObQ0GB5eTlsG+p53iSFXC7H4XAlJSWTk5MFBQWnTp1SRxXL8jOZzB07dgDzWVhYGBgYCG4+31JEvYampiYrK6u8vDy4/sfc3PzevXvqGVaNJyUlffzxx6gLtpGREW1t7YMHD6pzsKtW8maGGzduXLt27c30VVMqKyt1dHSGhoaePXv2u9/9Drw4h4eHJyUlLZO+r1oVmoHD4ejr6xcXF79dxo/mXzGivFv52LFjb3q8XjEzJLa2tv4YpoEMMpnM2dn5x27ryczMbGpqkslkzc3NExMTxcXFZmZmsPUQiUQcDvfy5UtfX9/Ozk6FQvFmHgaDMTg4WF9fb25uDovA8+fPUVQADfil4h4Gg2FhYaGrq3vx4sX+/v7y8nJra2sHBwdra2v0IvCMjIxdu3ZNTk4ePXr0zJkz0GEWi1VSUnLgwIGLFy9Cyjvjnqqqqr/85S/KW+F9fX1dXV2htrGxMRKJdPToUR8fH0gBiTefzxeLxbCgXL582dzcfGpqysDAAJ0kcE1WaWmplpYWuGdbwj2FiqJr8tIbqwZFUbL4STEMc3p6uq6urvKacDs7u9DQUGiGWCyWy+VUKnX9+vXoRyCVSqFhkCcgIACDwXA4HG1t7fx8FRcICyKLxfr666+DgoIAdC7hnvOKKGf5JY9Vg+K8k/C1vOfSpUumpqZCodDQ0BDtOHjMOXLkiIODA7r9SCQSPp8P01Umkx0/fjwkJITFYn322WctLS3QMCqVamdn9/XXX/8V94y/Enpai213i4/uWz1Y75x/Le8JDg4+deqU0ke6lpYW6vpYea+rjY2NtbU1ukODc0TUiZtQKDQxMUlISOjr6/vwww/RS9X8/Pw+++wzCwuL9PR0GHEV7tm2mfmX/2Ru+mKVsPFzxvp1E3dU8hKlD6CTJ0/icLjBwUEtLS2049HR0Z9++qmpqWlsbCy6u4vFYj6fD0skj8fT09O7f/9+aWnphg0bAJANDg5u3LjRxMTk5MmTKPfGrq0r/LcPVIjngz+uEv79w+L/+EHeI5FI4O1UKlVLSwvuIpuamtLV1TU0NDxx4gQqVlEpskUiPp8PIzsyMqKlpUUmk5OTkw0NDdWX4+TkZEdHR3SJp2Tcd17zj55r/tVzzW/eHjzW/NrrVx8A7kEQpL29PSoqSltb29bWFhZWmUx28ODBK1euPHv2bPPmzehWhyBIXV3dgQMHlp2fgq/rJ/2rDgh+LK5e4TvnUa/kTbAC1b6vPPDporUtazOajkYEAoGFhQUcWBOLxS0tLQMDA25ubrCaRUVFLXMtfOnSJWdnZxqNBixQYWGhra3t7OxsRkaGq6vr1NRUTk6Ok5PTshvwEATp7e3dvXs3+g0jCFJQUACVk0gkX1/flJQUHx+f2dnZubm51NRULy+vGzduoAwz0C07O9vMzAwMtAUCwfXr1y0tLQE8tba2BgcHYzCY3t7e6OjoyMhIHo8XHx8PC6BIJLp69aqTk1NwcHB1dfXQkEpl2d7eHhwcfP78eUtLS7hjsLW1FYvFJiUl+fj49PT0XLhwQb0e9DtHqVdZWeng4CASiQgEwpdffjk4ONjY2FheXi6XyzMyMrBYrJLHO3/+fG9vr6OjY11d3YsXLzw9PQsKChAE6enp8fX1dXZ2JhKJT58+RYWX/f393t7e6uPY1NTk4eHB4/FEIlFYWNjo6GhUVJR6w8BpXU5Ojre3d3x8vEwmo1AoLi4uTCYzMjISxOFAHycnJ9SJtUgkunfvnpOT05UrVzo7O/fs2XPo0CF11+MoNby9vRkMRn9/v7OzM7rao0RAEKSxsTEkJEQ9RSgUwuLc39//zTffwMUKERER6EINmoRTp06hmFUgEBQVFSUnJ0M9nZ2dSUlJ6nX+UnHP0NDQrqUnNTVVCSDq6+vd3d19fX3d3d1R4H/79u3du3dPTU05ODigWyN03sLCIjo6GuLvjHsqKyvXr18/PT3t5+envjUqL+Hw8PDw8vJCCV1fX29kZITD4YDXvHjxooWFBZfLNTAwUB8PMpm8devWjIwMKPjOuCctLU1XV1coFNrb258+fRptRnt7u66uLnpROoIg+fn5BgYG58+fh0XBz8/PxcUFcA+qCkEQhMvlxsTEGBgYwCR/Z9wTHR1tZmYmFAoNDAzQjxLEAKmpqbt370ZXxtTUVFSKJpPJlCvC6dOnWSzW559/DsuKWCzet2+fh4eH5dIDIgfxu+KewMBADAajjnsWFhaOHDlib2+PwWD09fVha1QoFNHR0QYGBpmZmTDZjI2Nk5KSAPfADW8KheLp06cvXrxQHrf54osvhodVzPS74R6FQnH8+HE8Hj80NKSlpQUdX1xcbG9vf/78eVVV1Z///GcQyynd/eLx+P3791dVVcF47dy5MycnB3AP8NADAwPr1q2LjY2Ni4vbt28fJL4b7hGJRCYmJnFxcSBSBXgxMTGhpaV1+vTpBw8ebNy4EUxfeTyel5eXsbExeIwfHh7esmVLbW1tcnKygYEBintmZmYMDQ3VecR3xj1jY2OpqalfffWVgYHB4OAgKL+MjIwIBEJra+vmzZv7+vrQGYHBYMLDw9Gf7xxZhgl+jp/v0Db1DQ8t/pa2ASBAc0Lkx/IvywY/6+rqtm/ffu3aNXh1c3OzsbExlUqVSCROTk7LPIZ2dHSsX7++rKwMyip9Gnh4eAgEgoGBAU9Pz0ePHnV2dlpZWamPF+T09vZ2d3dXbwCHwxGLxdevX/fx8ZFKpUQi0draWi6X+/r6EolEuVxub28PDs+h1Pj4eGlpqb+//6NHjxAEefjwYUVFhYmJycDAQENDw9GjR2dmZvB4fFlZWW1tLQ6HI5FIVCrV0tJSIpEQCAQPDw/Qi23cuJFKpdbW1p46dYrL5ZaUlOzYsUMikdTW1trb209NTZHJ5K+++kokElVUVKjXg8qG0V5kZWV9++23NTU1OTk5lpaW9fX1qampcrk8JCQEVBNubm7wrYaFhSUnJ7e0tBAIhAsXLiwsLJiamhYWqgy5jh8/7uDggErNr1+//t1336GvWFhYKCoqMjIympiYYLFYsGeVlZWpN0wsFt+6dSs8PHxkZOTMmTN8Pv/69etBQUG1tbUlJSWurq51dXUODg5AH0BdSn/heDw+OTmZxWLZ2dlNTk5isViUCUQQpKamRp0aYrH4/v37Fy5cQBuGRqRSqa2tLaoDgfS6ujrQ8p89ezYiIgJmtK2tLcq9AwMDeeRyOZFI3L59u3LvQ2/cVton+fv7o29BEOSXinsQBJmZmcnPzzc2NnZ3dyeTyd9++21ERERoaCgqqCCTyZs3by4rK9PT08vMzGQymeD+Q6FQmJqaoh/EO+Oezs7OjRs3lpSUwKo6MjLS1dUFxHV1dfX09EQJ3dfXRyAQHjx4AAt9bm6utrY2iUTatm1beXn5999/Pzo62tbW9sknn2AwmOfPn8Mu/oOeqzBZXnJ91aAoTBK/tu+pqanZvHlzZWWlvr5+RkYGOIih0+kbNmw4fPhwa2srat9Do9EuX75cVFQE61RCQoKhoWFpaemmTZuoVGpPj+oW+YSEhJSUlPr6+nXr1lEoFLDvkdw4q/jOSX7RbdWgOHcS1XPl5eVt27atqqpq+/btZWVlg4ODIyMjmZmZMTExbW1t69evh9mLIEhtbe2lS5dQJW5oaKilpeXjx483bdrU39/f1dU1MDBw4sSJQ4cOffTRR2vXroXFUaXncrcSH9ET2+1ZPVjpzt9LBj1XcnKyvr5+WVnZli1baDRa/9Lj7+9vaWn5xRdf/OlPf4I5tri4WFxcfPnyZXQNxWAwzs7OGRkZW7duHR8fb2trGx8fT0xMLC8vLygo2LBhA8iH53teMLU2MD9fqxL5vD2sX8f47BNUz3XmzJlDhw7l5eVt3bp1YGCgo6ODw+Gkp6fn5eU1NDR8+umn4O5YLpfn5eXFxMTAbSsymczY2Dg0NDQmJmb//v3T09M0Gm10dHT37t0xMTE3b97ctWsXMIVscm3Br/+95DcflPz296uE33xQ9G+/Q/VcTk5OLi4uN2/e1NHRYbPZz549m5iYsLS0xOPx5eXl69atAzZdIBDcvXs3Li4OZEIzMzN6enpEItHPz+/EiRM8Hg/AXF1d3bZt2zicv9omU+5kOa35B/c1v3Jf8y9vD25r/tnjn37bXVGNsn1mZmb5+fko14sgyLFjx9zd3W/evPnll19yOJzm5mY+nz86Oqqtra1u/4RO2J8a+TFw8F7SEQQZHh4eHBwafvmyuLi4o6ODQmmqqqriTU/X19fVkGvHRkcZDOboyEhnRweJVPbs2bOSkpKq6mqFQtHf3/e8vX1ocLClpYXJZD5taVHIfziTsayPPB4XuI7aWjKZXMtmswsKCjo7VQvair1YVlwqlQJv0N/fb2BgANaNQ0NDR44cEQqF3d3dOBxOXcghlUoLCws///xz1HiloaEBh8MhCJKbm6unp8dgMJhM5pEjR5ZZZgiFQlAiL2sAg8FAhQEJCQlpaWkUCsXMzEwmk0mlUisrKzJZZQEGT09PD5VKTUlJAYfhBQUF7e3tJiYmPB7P0dGxoqIiLy8vODh4YWEhIiLC1tZWIBBUVVUBK+vu7g6XbgcFBeFwOKlUamJiAgvCnTt3sFisQqGwsLCAlIqKCjBZCw8PR+tR3xpetwiJj48/ceLE3bt3h4aGjIyMoqKixsbG6urqbG1tgW6BgYG1tbUCgUBPTy89XWX/5+vrSyKRxsbGjIyMlBaKTCZz7969KD2Vl0j5+PioC0UQBCkvL4e3FxQUxMfHKzGEesN8fHzYbLaFhQWo1AE3ODg4QJGEhISoqCgXF5fKykqgD6A3CoVib29Pp9NDQkIePnzIYDACAwPRfonF4jepgcVi6+vr0TxopK2tzdjYWH3mtrW1ZWVlSZceExMTmK1tbW27du1CpTuQB9X4T05OPnz4UEdHp7HxB4P9tLQ0dfXL/wnco7JrnuPJ56f/liBUnaVSWciij1Ao7O3tRRlHNB1UElgsVkdH5+jRo/Pz8zExMVFRUTCN3dzcUD3LD3bNQzQZvW31MPRMzOoG+x7l53ju3DkdHR1LS8vR0VGwpQIAER4eHhkZqd4Y9fjMzMzJkye1tbWBvwkNDb19+3ZsbOzatWv19PQOHDhApVJV2jGJREAplhalLpSmrRqkRSnC5lLQegiFQuj48ePHuVzu5cuXCQSC0v5p7dq1urq6+vr6P2Y8ODo6amNjo6OjExYWNj8/7+3t/ejRIzKZrK+vr6enFx4eDhZ/C/w5YXq09KL7whWf1UO0q6A4A+x7ZmZmHB0dtbW1vby8BAJBWFjYrVu3nj9/rhRUKDWS6lbY6uQCQa65ubmOjk5cXNz09DQGgwHBhlKNcvr0aRhW1YiPs/m+9oJjhgLHb1YP9nvnsm8C7hkdHbWystLW1g4PDxcIBN7e3jk5OfCpxMXFBQQErMg6Iwjy5MmTffv26erqZmZmcjico0ePdnR0ZGRk7Ny5c8+ePampqdCR+f4++lfbGf+1ifGl1iph2xa61obxe3egYG9v78GDB7W1tWNjY+fm5hwdHclkcnFxsZ6e3p49ey5cuIDquZZRrLS0dNeuXXp6euXl5bD9MJnMoqKi3UsPah7Orm8o+sPHpR99Uvrxp6uEjz4p/vNa5pINLHR8//79O3bsyMjIULoQOnLkSFdXV1NTk76+/q5du1Bzt2WtQhDk3r17urq6e/fuffr0KY1Gs7a2np+fLy8vt7e3V7cnbc584P6Pv/X9lw99f/2HtwefX/3e/4OPe2pUdnILCwv9/f0on4e+nUqlGhoa7ty5886dO2w2+/Dhw93d3UNDQxYWFgwGA832zpEVkcF7SYQmRUac8fH1v3Mn3cPDk0Qq9fT0dPfwjIqOupaamnYrLSgw0MbGNubKFTwuGI8PaWtri4w8cydDdQIAFxR4hUA4f+5c2LfhFy9exOPxI6NjzU8oBAKhoqIiMTHxypUrxSWlKtFv3gPLQ4fKy8oIhNhbaWkBAYFEYmJERMTwyOiK0GcZrV6pXLHegMS2trbt27cLhcIHDx7AMhgbG6v+SQiFQhKJ1N7eHhISEhMTA6VSUlIAT2CxWBAG5OTkAH+v/i65XG5qaoqeSgHuH/QsYLowPT1tZGTU3d199epVqLy9vf2bb75RN7JuaGgYHh7Ozc01MjICscqtW7ewWCyLxdqyZQsOhystLZ2cnJTJZBYWFiDViIyMBLPIjo4OKysrDAZz9uxZkUhUXV1tZ2e3dFPl5J49e3Jzczs6OlyXzB4WFxdPnTp1//59BEHMzc2X1aPeKaWbMywWq6ury+PxOjo6/vjHP4K4PSwsDFjBnp4epaXB3NwchUIxMjKam5vjcrkgREEQhEgkHj58GIPBqEtMgQ6oPyw+n7+4uBgZGZmTkyORSHx8fICrVG9Yfn5+VVUVBoOBEZfJZK9evdq7dy9tyQzf1dUVGNfg4ODS0lIUdsTHx2/dujUlJQUU8QUFBeqj09LSsowaQqHQyclpRRekt2/fdnNzQynT2dlJIpFgiePz+TY2NiDBPXPmDGq/oZ4HLahkgUJCQlBR4smTJ1EtEOT535b3IIhEJJQI+RKR4G8JYpHgx3Yg9T6jcYlEQqfTQbs0Pz+Psg5zc3PohaeLi4sS/qx4ZupvDXPTKPaSyWRKeAviGT6fjwJV/tKDNuPNiEgkotPpsNDPzs4KBIK5ubnJyUnO0gMwblGhEE+xxWN08Svm6mGMLpliL772mwMdBzw+NzcHh72npqbGlzwCvrkxoC2cn5+n0+mAnaenp4FKExMTDAYDZdcUMpmY9b14oFP8fffqob9TwhlBR00oFKp3HFrI4/GGhoZQwI42Rj0yMzPDZDLlcrlCoeDxeOgeKRAI0KGUSySi7uei509F7S2rh7ZmydhL9BVKVEGn02GOoR0HEcKbQmm0FKx3w8PDi4uLcrmcx+NBDUtXrbxEey3j82efUGaaGmYojauHxnrRqGqzgYfH4zEYDNC7ox3ncDhMJhMdkdd5/8dfcM4M5mVcLhcyv3r1St1EUczjjZHJr8h1r2pXC+S6MXKdgM1G3zE5OQmnNhQKBZfLheEDETo6QdDMaGRxcXF4eBhEOwsLC1wuV6FQiMVidG5CTt4Yu7O8qruyZvVQUd1dVTs/xUVfsWJkYmLi5UvViMjlci6XK5VK4fz8W5q6Yj0rJr4XiLNiJQiCjI4M29vZHT9+wt/fH4fHV5RXYLH+Xt5eCURicvK1mzdu4HA4lUD00KGw0yERkZF0OuNW2k3lmS8EQbKzsvz9/e9mZARgse4eHqGhoSOjY2lpad99952fn1/k2bN4PO7ipUsikcjH28vWzs7b24sYT0y/lebt5UVMSDwTEc5gsv4W3EOj0Q4cOECj0Xg8HoFAcHFxQRAEj8ffvn27r091li0lJUUgEMjl8ubm5pCQEBCNZ2dn79ixA8xEnJycrl+/zuPxbG1tYbcOCAggEomzs7PXrl1TRy2FhYU6OjqFhYXj4+ODgyqzaw6Hc/XqVTwez+Vy79+/v2fPnhcvXmRlZQUFBTGZTEdHxwcPfrDHh88vMjJydna2qalp06ZNfX19XC7X0NAwNTV1enoahAoMBiM3N7enp0dpIikWiyUSiY2NDZzZLCkpOX36NHqDQ1lZmbm5OZ1OLyoqMjExefz4cVlZmY2Nzfj4OJlMNjAwKC4u7unpAVgP9ZBIpJGRkdTUVHV23cXFBZRuRUVFbm5uMFtdXFzS09PZbLZSIm5hYTE5OXnp0iUwYK2pqbGzs4N5hMfjHz9+jC6DgAUTExMPHDjAYrEmJibq6uqio6Onp6ePHTtGJpNpNJqjo2NlZWVXV5d6w8rLyykUiomJifK81ZMnTyYmJkgkEmiIwKC2pqYGpU92djY0Micnx8HBYXx8vKamhkajRUZGwjjCTCGRSOrUUKpHmpubra2t29vb2Wx2SkqKOhEyMjLAhEgmkz169OjatWtjY2Pt7e2wOISGhhYWFpaVlZmZmQHqUs/D5/PZbHZwcDCFQmGxWImJiaDKp1Aopqamy87h/y/jnhUXEU2ihgIaCmgo8AuiwIqQ5b0kIgiiUqB3d4+NjhYXF2VnZ9fXN/T29tHp9K7ublJpaWFBIZPJYLKYTygUCqUpKyurf2Dg+4GBsVevVFaiDQ119fWtra3V1dUdHR0lpaTFxcXBwcG+vr6Wlpaenp6urs6enp75+blnbW3jnPHWVlppaUlhYRGLxbx9O72ZqnIDvmJHlo0OnU7v7++/f/9+QEBAYmIisApwIbVYLM7MzAS5xdDQEJwCGR4ehus2nJ2dw8PDuVxubGxsY2Mjh8MBAQyiukAovrq6ms/n+/v7q9uLIAhCpVLxeHxAQEBaWhpYkr169SokJCQvL+/ly5cEAgF4mOTk5ODgYHVVpkgkioqKcnV1JZPJoJiWSCSgvgkNDZ2amqqrq/Pz87t7965SwADgSQngBALB2bNnX7x4MTc3V1RUhMVi7ezsXF1de3p6FhYWoqOjk5KSpqenk5KSurq6JBLJhQsXkpOTQeVNo9GW1aO0TabT6YGBgSjztrCw0NTUBAigo6MDjALBXDooKKixsbGjoyMhIYHL5WZlZfX2qu5SolAosbGxAoGAwWDEtn8YTwAAAhBJREFUx8e7ubkdOnSIQCAA+qFQKG5ubl5eXrilx9PTE6Q7RCIxNzdXLBYTicTW1tbu7m6QsUEH4SjrvXv3lKfxAZhWVVVBQRaLFRkZOTk5WVtbC/QBbh84w7i4uJCQEFDt5efnFxYWouyEUCh8kxpEIlEikXA4nGW4p6GhAcRjg4ODfn5+wcHBgYGBN27cAICltGg+d+6ccvhAo7osj0KhEAqFoaGhoN0D3l4qlYaFhal/APDdanDPsvmr+amhgIYCGgr8NAqsiAzeV+JPa8rr3PD217/++heVQf416a2xH+vFWwu9z/9sbGxE7fzeZ70/vS6FQuHt7Q2mfgqFIj4+Xv1wxk+q7/Hjx+hFIT+p4LLMyuu2MRgM7PFsNtvd3R1Q0bJsv5SfYLz4vlorlUorKirQszLq1Wpwjzo1NHENBTQU0FBAQwENBVamQHZ29okTJwICAqKionJyctADIivn/vlT5+bmcDicq6srHo9PTExcdhLq53//+39Da2vrMp3Uu71DaW/e0tICB5DfrEGDe96kiSZFQwENBTQU0FBAQ4EVKDAzMzM2NjY+Pv52e8QVSv48STKZbHx8fGxsbHp6+qcK836eFv1dtYIF599VxVJhldnua7cKb9amwT1v0kSToqGAhgIaCmgooKGAhgL/PymwRiAQwGEfzb8aCmgooKGAhgIaCmgooKHA/28K/Dd19B3nSDvi5AAAAABJRU5ErkJggg==\"></p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集应用：</strong></p><p class=\"ql-align-justify\">GridMAIACAODChina为长时序1 km AOD数据集，可以用于气溶胶时空分布和变化趋势分析，捕捉和分析各类气溶胶事件，与其他产品对比分析；环境健康（如颗粒物浓度估算，污染暴露估算等）、气候变化（如辐射估算、辐射平衡等）、定量遥感（如大气校正、痕量气体和温室气体反演支持等）。</p><p class=\"ql-align-justify\">&nbsp;</p><p class=\"ql-align-justify\"><strong>数据集使用请引用以下数据和参考文献:</strong></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.</p><p class=\"ql-align-justify\">2.&nbsp;Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.</p><p class=\"ql-align-justify\">3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.</p><p class=\"ql-align-justify\">4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><br></p>",
-    "authors": [
-      {
-        "name_en": "Su Xin",
-        "name_zh": "宿鑫",
-        "email": "xxin@cug.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "China University of Geosciences",
-            "name_zh": "中国地质大学",
-            "ror_id": "https://ror.org/04gcegc37"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "MODIS AOD",
-      "气溶胶光学厚度",
-      "大气气溶胶",
-      "气溶胶",
-      "MAIAC AOD",
-      "MAC19A2",
-      " MODIS",
-      "大气污染"
-    ],
-    "keywords_zh": [
-      "MODIS AOD",
-      "气溶胶光学厚度",
-      "大气气溶胶",
-      "气溶胶",
-      "MAIAC AOD",
-      "MCD19A2",
-      " MODIS",
-      "大气污染"
-    ],
-    "publication_date": "2025-05-29T01:13:49.587Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 140495.25,
-    "file_size_bytes": 147319943214,
-    "download_count": 4649,
-    "visit_count": 4752,
-    "url": "https://www.scidb.cn/en/detail?id=684c3451918e1d0fc634c0ef",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "675da34cfec5a156275fe9a1",
-    "doi": "10.57760/sciencedb.18626",
-    "cstr": "31253.11.sciencedb.18626",
-    "pid": "",
-    "title": "Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays",
-    "title_en": "Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays",
-    "title_zh": "超大规模MIMO阵列可视区域空间分布数据集",
-    "description": "<p class=\"ql-align-justify\"><strong style=\"color: rgb(51, 51, 51);\">Abstract:</strong><span style=\"color: rgb(51, 51, 51);\">&nbsp;The Visibility Region (VR) information can be used to reduce the complexity in transmission design of EXtremely Large-scale massive Multiple-Input Multiple-Output (XL-MIMO) systems. Existing theoretical analysis and transmission design are mostly based on simplified VR models. In order to evaluate and analyze the performance of XL-MIMO in realistic propagation scenarios, this paper discloses a VR spatial distribution dataset for XL-MIMO systems, which is constructed by steps including environmental parameter setting, ray tracing simulation, field strength data preprocessing and VR determination. For typical urban scenarios, the dataset establishes the connections between user locations, field strength data, and VR data, with a total number of hundreds of millions of data entries. Furthermore, the VR distribution is visualized and analyzed, and a VR-based XL-MIMO user access protocol is taken as an example usecase, with its performance being evaluated with the proposed VR dataset.</span></p>",
-    "description_en": "<p class=\"ql-align-justify\"><strong style=\"color: rgb(51, 51, 51);\">Abstract:</strong><span style=\"color: rgb(51, 51, 51);\">&nbsp;The Visibility Region (VR) information can be used to reduce the complexity in transmission design of EXtremely Large-scale massive Multiple-Input Multiple-Output (XL-MIMO) systems. Existing theoretical analysis and transmission design are mostly based on simplified VR models. In order to evaluate and analyze the performance of XL-MIMO in realistic propagation scenarios, this paper discloses a VR spatial distribution dataset for XL-MIMO systems, which is constructed by steps including environmental parameter setting, ray tracing simulation, field strength data preprocessing and VR determination. For typical urban scenarios, the dataset establishes the connections between user locations, field strength data, and VR data, with a total number of hundreds of millions of data entries. Furthermore, the VR distribution is visualized and analyzed, and a VR-based XL-MIMO user access protocol is taken as an example usecase, with its performance being evaluated with the proposed VR dataset.</span></p>",
-    "description_zh": "<p><strong>数据集说明</strong></p><p><strong style=\"color: rgb(51, 51, 51);\"><span class=\"ql-cursor\"></span>数据主编:&nbsp;</strong><span style=\"color: rgb(51, 51, 51);\">南通大学、南京邮电大学、东南大学研究团队</span></p><p><br></p><p>阵列可视区域（VR）是超大规模MIMO（XL-MIMO）系统的重要信道特性，可用于系统性能分析或简化传输设计。本数据集提供了真实城市场景下的XL-MIMO VR空间分布数据，亦可自定义VR设置实现更多数据生成。</p><p><br></p><p><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">本数据集包含XL-MIMO可视区域数据集VRD，以及用于按需构建VRD的XL-MIMO阵列场强空间分布基础数据集。通过自定义环境参数信息、射线追踪仿真、天线场强数据预处理等步骤得到XL-MIMO阵列场强空间分布基础数据集，在此基础上，基于可自定义的VR判定准则生成数据集VRD。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-center\"><img 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\"></p><p class=\"ql-align-center\"><br></p><p class=\"ql-align-justify\">具体地，阵列场强空间分布基础数据集由以下步骤生成：（1）建立真实城市场景三维模型，并在建筑表面设置高（Site 1）、低（Site 2）两个典型XL-MIMO阵列部署站点（图1(a)），通过射线追踪获取各站点中每一阵列天线在目标区域内的场强分布（图1(b)(c)）；（2）在目标区域进行用户位置采样（图2(a)），对每一位置，将其对应的各发送天线场强按阵列部署形态记录为矩阵存储（图2(b)，2(c)）。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><img 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CTHnMgEzJ/L5fKFQWFtbiwjxDSN9C30REeJbSBi3ZlOgTyRJkvPmzYO7N0N/iV+8KpVK6C5sZAt0k4CvRqPRbDYLhcK9e/femn1ErUII3DwE3DTtdtP0xStXpk6Zjku0Or1arQ8wKPVSrd4kV2o1ElynVGkNSp0Gx1U4rlLo1X4ixvkIFwn8+BOESuGlC1dIupUk3bTn5rUL3QkhgBC4YQQOHz48ffp0pVIJMyd2YCfmVniE1wtKMHrDMN9aX0SE+NaSxy3YGs4Xqri4ePTo0dCC9Qs2LJFIICHmOHF7QgyPBQLBnj17bsEOoiYhBG4mAgRBU0QTTTa3tsbMjFTJlHqNQq8SqDUmhc6gV2nUWi2uV2g1Wo1Gp8ZxtVat1GpEEplUJFUJ/XyEKp+JttO15ynaQ5IETd7MpqF7IQQQAr+PAEHTBPuph6Y9tSdPz5wRp1SqNFqBSqNUq0242qTVqnRalQ7XanElrpVo1DqzyijVqgSKQLkgUCYZPzowY+DQrT8epCkCqe7vI32rfoII8a0qmVumXdBCTFGUxWIZPnz47xFihUJhNBp/zYPhGWghfvPNN2+ZbqGGIAT+FgTcBE1RRDPZRBDuhJh4qVyuxVVajUyplyt0Oi2uUOM6lU6lU2v0Kr0ax5VaXKPWqiRSmUAil0+8Z+SGewYc2//NTzRNUCTJTUf/lraimyIEEAI/I0DS1yagBEV5Tpw4OWNGjEKhwrVSg1amV6t1Ko1OrWLWdTSMowSuU2hwk16tlWuVAmaBZ6hYKrl35KP97nrl++8Jimz5+cboqJsggAhxNxFUlzYTcuLVq1f/ISHuIO1aYGDgc889hwb4LpUkqvxvR8BNUxTpoekWknKnZWRLZAqN2mxWTomV47hWhxukJl2ABldqtFK9Rq7XqFRarVaNayQBAul4n4lFXl4X+vU7sm/vEZpuI0kPSf3tDUYVIAQQAiwCFEuIKZpmRrwLFy6mpaXLZIzeaqV5OlWcXjtZp1fgWgnzhysYTqzRq7RKFa6VyHwFYtn4sdbevY716vPlN994aESIu+FThQhxNxRa5zaZIimKpAiaeHDzg2PGjhYJJyhEKpmELxLLJBL4J5VIJDKZjPGXMBlMJoPRqDMZg5l3196aTKagwED+U089eX1NqnP7gGpDCHQWAkwwDUF7aI+H9BTNK5KLhfGB+keGx7x6nyZqfKE/3yqSLtTIZ6n0objObNCqNRqZHpdpFb5j+EkD+hzCsEYeduDl3QcpZs3VQ1BwDbezWo/qQQj0WARYJsxwYuaAdrvd5RUlIpFYowwedMd79wx5YcyYrf78Gol8sUqTjGtm6jRT9Fqt3CDBcaVIJh899Nk7+/4IwGkv7y//8ylFU8hlovs9SYgQdz+ZdXaLSZpiBnnPo9u2jhozUcAXS6RisYzPF5l+hxAbjUa9KUhlMppNxhDm1awzmQ38QPG2rU+Q6Feis+WH6utUBNzM5JFy0yRJUyVLVsgEPouFmpOD4lv6yJL7vdy7109e/b8f1P+dwSOeHj56q0w4W4cLjDqhWqHuP+jfGKjnYUe8waHtjxwgPcygTF1zauzULqDKEAI9EYF2hJixARGehzat4wfyNcpQL+96gJ0FWK0XdrxP7y8H3PXqsCHPjBmxKUCwSG0WaPV+EyYs7INdBrzzAJzgga/+/X4bheJhu+EzhAhxNxRaJzeZ8WNkfh927to+cuzwwECRWCIWygSBUrlEIr3+x1iI2fQSBpPZCO3ErPew0WQyGo0Gs9ksEko3b36EQvPmThYfqq5zEWim3TQFbUx0VYlFKphYECj7otf0S17mIPA6AFdAb7oXaAagAQPnfUc+bNCLjAaRQjLFq9dpgJ3mgQO9wMlNG/aRbppxI0bDaueKD9XWcxG4RoivaS9JEk/s3Obv769RBfXqdQrDzgHsPAD1rOY2eoFGb17toPte1Jv9DQbfEaPWAa9GgJ3HQK03+PL1t5tImkkljkr3QgAR4u4lr65oLcVEzZMUufvlZ0aOGRwQIBZJhEKpSMA4SvxcrhNio9nMuk5cc5nQsx4UZrM5SCwRr1mzmgmcRwUhcBsjQLqZyBySbiPpkrIyfuB4gTpxwLB/DsEu8sBxdsg85g2OAF49AE0j731aYVaqTQqFZJr3wI8ArwGAOgz7dq3rB5q+RNJX2eXb2xgs1DWEwC2DAOspwbSGYpyIaZp+/vl/+Pr6aDSGfr1P80ADAI0ANADsIgAX2eO6vve+ZjT5GvT84WNqAGNFvuCNnfYGn778ehNJX75lOoYacqMIIEJ8o0j12O8x67aUmySpV//10pixwwP8BBKJQMKwYsXPdFgikUqler2eSzRhNOmNJiP7x3gVm0zBEom8ssLS1sYYvlBBCNy2CBA0TRItNOPu8MBjDwdKRupVofeM2AawBuB1BgPngNdpwKsDvMOAd3bosFeMeplJP1GhFvW/Y78XuAiwUwD7abXjIO2hCRJlXbttHxPUsVsRgWucmFkTpWn6tX+97us7EddK+/U/CMAFlgefB14nAO+wFzjfGzQMuOszg3mCUe8/YowVYI0YOMkDx3ngx3++UEfTV27FDqI2dYgAIsQdwoM+pGmSDe6hKfKN118fOeI+f38fqVgulQTIRSKJRHz97xohNrHWYSaajj0yGg1Go8FkMhkMRrVak52Tc/kymjejp+q2RoAJQqVpiiQI6qWX3xQIxutx7eh712CAALwTAJwGoA6AS8DrJMDqBg1+Ra/TBWlFWqnozj77gPd5DJz3AkdLKn9qoz1MXA7KMnFbPyyoc7cWAtcIMUnTBEmS777zvp/fRFwfcOcdh3igFuOdYFX4PAAXeeByb9B0V/+9OpPAZPAZM3ZJL3DFm7Ei1wLvr3fuOE6TrbdW11BrbgABRIhvAKSe/RUmFQ0T6k68/+77o0eM9fEdIxGrpFJfqZAvlf4mITabGL+J0Guk2GA2GoJCJpllckF2djoixD37abr9e0/RjRTFpF2jqJYP93woEEzQ4rpxY5zMYivvLMDOYuA0D5wAvJMYaLh74L91ek2QwQ/HfQYNeBkAEoAL3uBMedkBmm6jKAK53N/+Twzq4a2CAEy7xkxnKcZrgn7vvX8H8sdr9cIhgz7lwXksOAXAYQw7jYFmHmi9865PtQaZzjzRT5DfH1zqg50B4AjP66e1G86h6PFbRar/SzsQIf5f0OqR36UomqRaabrph4OHdbrQMQGDAyVBAvVYgWKCVCpmOTGz7Sx0mTCZgtjkEmaT2cCkmDBMNRmmGA1B5mClCuenpKReuogsxD3yMeoxnSZJJsEEdCPe++XnAYJRWh0+drwVAJo1D1/wAnVeoB4DdRhounPQh0qjWR88Wmu+d8iAtwB2BvBOemO1Fcu+YF0mGP/9HoMc6ihCoMsRgKp7jRB/+OEHfv5jcZ1w5NBX+oM6HjiPYRcYzcXOMEknQGPvPhf0Wp1exw8Q5/dmzMOXMNDkBX4oLn6WopFzYJdL839uACLE/zNkPe0CZqpMMraqk7WHpkSah47U3ON1cvjd//ARTZPJfibEEolEr9dfJ8RBRrPcZDKZgxXmUJ/gML4JT1cKctRq/PBxtHtzT3uCelZ/KZLNukZ5aJI8+P0PfKEPrteOGb8MA20Y44Z4jjUvXcB4dQBcvXPgB0qjQR80TmcePWTQvwCo88LqvLyfWVm8hSbcBOUmUNq1nvX4oN52LQLc3hxMM/bv3y9XiLR68bBRm3uBK4wPMcOD6zHsIuDVAnC6T78DelxnMgzjy+O9ep8F2DkvcOrOu0oWLCykyatd2xNU+59AABHiPwFaz7qEpGkPk1OVOHv2TGJi4phh/t7exzDe+d69Ggf2Pzxm3IZAZYCS9aDA9QaTOVwfpJweIg6aJDEET8KNiQG+m4bcebKf15WBg17S6LS1J77pWfCh3vY0BBiHiRaaJcS1tSclch+1VjnRZ5kXuMoDl5m0TbzTTIAOk8KpceBdH2l1oQbDBL1p3N137/YCbXf12dd/0ORly5fRZAtJtZHIZ6KnPT+ov12JwDWvCSaqjqSOHz+uN2iUasF9Yx7szSjvZSYJDLjEmorPAVDXu/cpXB0UZBgjkEX06lWPgUv9+n80dHjcvPkFNIWC6rpSkH+ubkSI/xxuPegqJr6AyURM1NVfysvLGzPct3fvIwCcY6Nu6zHQekfvBp+JC6VSsd4gnjRZoTbNkAiLx4x9ZVCfM/1BSx9wFWNm1XV39v8A1xgvnvq+B2GHutoDEWAIcRuTtYmkL144L1cGqHA+XxR1zz1PDb7zrb69v/D2PojxzjHDKtY0sP9nGlWCyTgmKHjEPYNf64udmzh+2dD77ilatJTxQiY9bPanHggi6jJCoEsQoNi9VEnGh5ik6i7VhYaGqNTiQGHWfffsGDDglb699/QBP/QCp3hYPQZavLAWmSI8WKsQK9R9sYZ+3t+NHF801td3zpw8mm7rkg6gSv8KAogQ/xX0esS1BONDTNM02dhYf//9948eNqZ3n4Osfesc8DoIvI8CrG7w4MclMr7BoAzSJQ8e8D3gNbNpGlvZNOYXAVYHMHf//m9plEF1p472CNRQJ3ssAhTpod1MSA1J1126olRocVyG64QKfRiuyBTw5/sElIwY9eDAwdvvHPDGoME7xcoovcHHaBg7cqRt2D2rJBLFiJHD8goyaKqNItqY0B5UEAIIgc5DgIkgZ9SOopuamqZOnaJSS40aPY5PlqvjRdLCwICqceNXDR62/a5BL9/R/2OJerJZq5TLNUMHPj5yRCVfKpvoNzozK8NDejqvyaimm4QAIsQ3Ccjb9zZMlAH789DUdHXp0qVjR47s3e8rAM4DcI7NVX4WYwjx0zJ5oNEgMugm9e99DPS5AgDjJend6wzGOwqwEwA03XHn6ypcdqb289sXKtQzhABNUx4P7WG2nyHp+romnTZUJVfpcQluFJt1fL1JopsskeunCVXZ/qLqiXy7VD9DZ5AZdCI/QUyAcIpc7jd27MSUtAhmlzrCw0bnIVQRAgiBvx+BaznXGELM5E0kaY+HiI6OVqpkRnkIjitVOrlKq9DqFFqjQqmbKlOkSMR5Cr3aiOMqhcEv0CAQqyRifx+/CYkpSR5GeVHpZgggQtzNBNb5zYWEmKLItrZWu90+YezIXv0+Z/0lznh7XQBYLQB1d9/9rEwZaDAJjfqwAX1OAq9GHnYOAxcxcB4Dp9lAhJY77nhPKtccPfxF53cB1YgQ6DwEPMxuywQzi6RaWloiZs7EpbhJYdZrlBqdWqXXqHVyrUqv0/vjRoHGINRrFTqVVqPVSFUCiXiiQqid6Ds+Piqeam5meDUyEHee5FBNPRuB/ybE7H51dHp6hlIp1ysnq9VGpUarwY0GlUGnlmlxmVar0Ov4ar3YoJqqVktF8okSia9EEOAXEBgdFw/3uuvZgHa/3iNC3P1k1sktZgkxk2nC427bsGHjxPH39rrjQ9ZCzKSIYhKVYw2DBrwuVPlrzL4646SBfWp5oBEDZzAmB00DBi6xG/y03dn/c5E4+MeDiBB3sgBRdV2GAEEQubm5MplMrVZrtVq8wyKTyUQiJoOhv79/WFjY2bNn0ZjaZZJDFfdABH5BiNk9nGfPnqNUKrRqsVpjVGlxtVZjUOv0WqkWV+MasxbXadU6g0ajwn2lIp2Mb5ALcH++X/j0cE8rshB3v2cIEeLuJ7NObjEkxOzOW55HH33M12eIV/83WI57mrH+MonK6wfd9Q5fyVcFTTRMMgy88zAGmtk9CC6xPsSXmAg87Er/vvsCAkK//vr9Tm4/qg4h0FUIUBS1bNkyoVCo0+k0Gk2HfBjnCDGfz1coFF999RUTy4p8iLtKeKjeHowA1DuKogoLC5VKZQeaq9JJVRqjWqWVS+USoUgqFvMDxcFBU1paUVBd93uAECHufjLr5BZzhJiiiJ07nxLwh3rd8SrA6jDsFAZqATgLwJUBA18Vy0RarV9IkPruO74F2FXAO8NmqGGS1LCEuPGOPj/5B0R8+fWbndx+VB3IvOgzAAAgAElEQVRCoAsRsFqtAoFArVbfOCEWCAQymezbb79FbLgLBYeqRghQFLVw4UKFQtEBIcZxBWM3VhrkUplE7CuW+AUETjCHaK+2oC2out8ThAhx95NZJ7eYI8Q0Re56+h8i4T19Br7Gpig/DcBJAM4A0HTXoFflUpFB7RtmwIf2PQCYlDSXMGZHrvNejCdxHQ9r7ut9bNTokLfff6qT24+qQwh0IQIPPvhgYGCgRqO5cZcJPp8vlUqRhbgLpYaqRgjA9ZmNGzfK5fIOCLFBheuYyACxXCYWS/xFImFggMhoMNddakAYdjsEECHudiLr/Aaz+9QxYbctzz/7uL9wzJCBO3uDy97gBBM2h50FWP0dd3wkkCrUGp/QUPWIwR8CXlMvZuf3Bjac7iLAGoBXnVfvw2PGTH/rra2d3wFUI0KgqxDYtGlTYGAgjuM6na6DYVWr1SoUCuhDzOfzJRLJ559/jlwmukpqqN4ejgDJbL9OUxT1zDPPSCSSDjRXr5HolCaNhi+T6EQimVgsFAoCDTrZ2TM/9HAMu2P3ESHujlLr3DYzeYhJiiJpD/3h2++KFEMGDFzDA62sG/FxAE7yQN3dd3wgkqpUar/gUPOIYW8B3mVmZ0usHnjVYuC0FxNjd7UXdmLc8MhXX3iic1uPakMIdCUCjz/+uEAg6GBAhR+1J8QCgUAoFO7Zswe5THSl5FDdPRgBzof4nXfe6Vh/NbgK1xg1Go1cqpeIpRIxXywIxOWqIwevZdxHWtyNniNEiLuRsLqmqcxOdcyuPTTtoY79+HVQqP+AO9fxQAvrJXwSYKd5oGFwv09FoiAlPipkmmDMmCcw0OrFbPteC7DTAJqKQYMX7/SoUdH/eH5D13QD1YoQ6HQEKIp6+umn/f39tVptxz7EvyDE/v7+//73v5GFuNMlhipECDAIcIT4gw8+6JgQqzV6Dc4ot1ymFItEEpFIyA9UK+SHvj/E3QRh2l0QQIS4u0iqy9rJbt3MOEzQJHX5Uv20CNPwkfGDBvxfb9DEJF/jXQKg+c6++wXCMJVm5JQwzYTRu3iA9AZnAe8o8KoF2GWAtQLM4827NGJE2hNPPtZlPUEVIwQ6FwGKol555ZXAwMA/TLv2C0I8bty4l19+mSSZZMad22RUG0IAIfAzId67d2/HhFiJ69VauUaNK+RimVgiFor4Ir6/UPTKG29zfhcI0O6CACLE3UVSXdjOFppupmkPSRDuVnpmVORE/t0ilb+fX8bQgW/08t6PYZf79j4ZwJ+m1gyfFKweP/Yp4HUZYC0AkAC4vUBLf+9Lgwe/5stfyhcEbFxf4Xa7kemrC8WJqv5bEaCYhP4kzcSiMoamz774TCpTKOQqrVahxA1qHMe1Cg2uU+FMbDqT6R9nXnQaXK7ERRKxTMAPFIsHDlyy5fFXCA9zsw4KZMycNsED7i2zWd51V0jOWMV9iqh2B8Cij3o4Apy+HDx4kM/nd+DypNJKcXWwVqVRSaVykVwkCfAXGu4Zlr92wzceD0pF3M2eI0SIu5nAOr+5FOszQVMegr7qpi9Hx8T5jfdTyQKUcoFCPWGCSDJg0Ja7+rwdMGGeQjN6cnjgeP91wKsRA7V9wU9D+j/vO36hSi/UT/Y3hUlEEnFN9bq2tjZu5O787qAaEQJ/KwIMF6YIZvNXdv/Xb777RqrQyGW4XieR6ydpdDqtXqLR6hW4VqlVq7VKtUatZRKZahVqI18sUgsDJ4pF3l5frnrwLdJDd0yIuY7A8Zskyba2a9lPIRXmFn9JtsDvc4M9dzk6QAggBBhlo9gpKEWQzHhHUx7qp8OHAoVCXCdXa2VqnUqtleu0ShzXaTRqXKPENRq1VqZVBBk0GplMJhEFSmTiUaNd/fu8VVNNeDweuMDTbvIJN//4r2kuRVEkSZ4/fx7qLJzlclNZ7oC7CTenbS8yiqIIgvjFwMpd0v6b6LgDBBAh7gAc9BGDAEnRjJmJiatrI6m2hMQM/wk+SglfIhbKpAKRXDoxIH/UqJV8/wipYmJwqMpPUNr/rj33Dn+UH7hGJVts1CZNmmQyBil1ZrlAIly8bGlTUxMcp5G6oifs9kOA8banPTTloSmSpMkffjqsUGvFYrFeq8X1fga9r1GtwZU6XAuTmxp0OlynU+lwnUZhFkjGSFRjh979Ui9Qv9r5AUF5KGYf6I4KHAKbm5vffffdTz/9tLGxkVuBgR9BKgx1rf14ibSvI1jRZz0RAZJZ22Eoq5ug2H013PSRo4d8BQK1ga/RC9Qmpcoo0mtluMaIa9UGndKg02m1WoNKqcc1AplEJBkvFisH3fWet9d3Nnuzx0P8ihBfWzn6BbqHDx9etWrVrl27Pv7445aWFkhtue9wqgpnvL/QYpIk3W439+X2flbchdyn6KBjBBAh7hgf9ClN0CTBLgEzlJgi09OzAyZOlEskQplEIlJJJAKx3E8ql4klIolUZAzSa41RSuUio2lacBAebFIFmXCTKchgDNbrjHK5dN68OQ0NDVClEbgIgdsPAYLxlSBZTkySNHHs5AmVVskXj9eqw8T8lQpFMm5QaYPHaEwCtUGmNgi1uBTXGFS4Qa+QSdTD7xu+yRuQvcHx8mXPtbkZK3MHhTMmffLJJ/PmzSsqKqqoqNi8efMLL7xw4MCBq1evtrS0tLW1EdcLSZIEQbS3Fndwc/QRQqBHIUDRBEURHpL2UCRJMf5KBEEfra1l0iaa1QpFur9vlYBfrMP1WqOv1uSj14s0Wo1ejau1uFJnlql8RRLBmHu3YF5nedgPJcsJj/vXFuLfIMQkSe7evbu4uHjlypVlZWWPPfbYzp07v/zyy/r6+kuXLjU0NDQ2Nl65coVbWaVpGjJmTpG5qW97rsytDvUoIf7FziJC/BcBvP0vJxkDMTvEM7Ndcv78ef6+vhKxRMjkIZeLxUwcgVQiEIsEUplMq1ObgzTBQfogky7IYDYbQoxGs95k1BtNQeZJKpl8UlDQ2bNnESG+/Z+bntpDgnaznJhZWiEo98W6C0GTdAGi+7TqaaDvKcz7Sv++Z4bd/dGEEf8K9Heo1JFmDW7UiXCTwKiS+Yyb3xcQgHfMGxy/P/ulK00dEWKoRARBNDc322y2kpISl8tlsVjKy8tL2WKz2ex2e01NzerVq9esWeNwOPbu3Xvy5MlLly61NyP1VEGhfiME/gsBxmGCYhdl2EyjbsrjJonGiw3mID2u8ZMGFPfv+xWGufv2rhs04NsxI58N8LEHCucaZMFmrdyo0mgUmrHjFvXB6r1AY2/s89lFTW1t7huxEJ87d27evHlVVVVQfysqKiorK8vYUlFRUV5eXlFRsWjRogceeOC777777LPPTp8+zekvdLdo/7b9WtB/dQ+9uQEEECG+AZDQV9ohsHTpUl9fX8nvFK1WazabTb8qRqPRZDKp1WqVSnXixAlOgdvdGB0iBG4HBAi6GfoPM7SYoi43tkydGubPH6HT4314lzDsPACHe4Fz3qAFA4QX5h5w97sKfbDR5KPXCof2+9QLXAZePwBQFxv/4pUmZrz7PVCgElEUVV9fn5mZGR0dPX/+/FWrVjmdTpvN5nK5nE6n1Wq1WCwVFRUWi2Xp0qUVFRWlpaXLly9/+OGH33777R9//LG+vh66HR8/fvzTTz89c+bM1atXOS8LzuAEx932b9Fq7O/JBZ3vpghcC5ahGDpMUEQb3UaRLVSzOzMvVS6fqFSn3jHwXeB9BvBOAOwcuxtrM8DIgYP24AY/g95fJZsx9I5PgPcF4H0C8H5Im3OmtfWGCPHhw4dnzpyZlpa2cuXKVatWWa1WSI6dTqfD4bCzpbKysrS01Gq1LlmypLKy8oEHHti9e/eXX355+vTptrY2t9v9n//854033jhw4EBDQwNBMAtLUFu7qSy6qtmIEHcV8t213pUrV/r5+f0OH2Z29PkVGWZOtCfEx48fR7raXcWP2v1HCFA0yRJiD8EMqETrVSJqekyA3yiDeXzvft8B0AyYfWouAXAO8E4BrKFvn0MKeUKQMUCnDxw44E0eaASgHvM+HCh+/PgJTwdBdRw9bWxsLCgoiIyMjIqKmj17dm5ubmZm5uzZsxctWlRWVuZyuWpqahwOR3V1tc1ms1qtdru9rKysuLh48eLFVVVVq1evfvHFF61W68KFCysqKmw225YtW7788ksYzQMXZ7l4HY6FI0L8Rw8C+ry7IQBj6lheTNKkhybdFNlMUXMLF8lFo3DNjIED3vQGTV7gnBd2nEkqitUCXlPvXqf0uF6vF6iVQYMHPcEDjUN4F/v3+jA07lLT1WaGXTOuhlwU3W+4TBw/fjw+Pj46Ojo2NjY/Pz+DLXPnzl24cOGKFSssFovT6XS5XFB5rWypqqoqKytbtmxZcXHxQw89tG3btrKystLS0pUrV1qt1q1bt+7bt4+LxoOOUnBOy4mE+/WAbUMmKogMIsTcE4IObgiByspKf39/iUTCOEv8qmg0mj8kxMeOHfuFct5QxehLCIFugcC1vGsESbtpknA3NSZETfef4GMOHn9X30MANAHsKA876AWuAq8LgHeuT58DCkVMkHm8XicdcO/DPEBioBbz/uG+4a8eOgSHz9/uNjeG1dfXz5o1KyYmJjo6OjIyMjY2NiMjI5UtOTk5hYWFy5cvh4SYMxvb7XaHw+F0OsvKypYvX15SUlJRUeFwOOBAu3Tp0hUrVly5cqV9xZzZGNmf2sOCjm8fBBiXQOaPoGiCpIkmuolJONpsXVglFvtr9FEDBv8LgMsYuICBixg44wVOAXDRu/dpuW6ywcCX6QwDhuzUgXe/8Z7yKMjIVb13+fJVJmvFHxHiEydOJCYmxsfHR0VFRUZGxsfHQ/2FzHjOnDnFxcVwzaempgau/9jtdpvNBu3HK1asWLZsGTQnOxwOm822aNEih8NBkqTH4+F+JeBCEEVR7SPwONlB7ea+zJ3vaQeIEPc0if/V/trt9oCAgN8jxGq1GhqDf0GL21uIjxw5wmVI/autQdcjBG41BBiTC0mycTk0QbZePZOZPN1//ESTyW/AXV8BrA6AczxwmHGN4F0EvLr+fQ4pFElm8wSDwXfgvZsx0AZALeAduXfo//3wI3S++O0ecrPKy5cvz58/P5otMTEx8fHx6enpmZmZHC1OT0+fO3duUVERNCA5HA6XywUJMRxfKysrbTabxWKxscVisSxbtuyzzz6D1iOPx3P58uVnnnnmxRdf3L9//+XLl6HN6bebhc4iBLopAqzi0gTFxNYRFOVhlnharx52FMwNUPgozNMH3fsKBq7yGELMLO9g2EmAXejrdUmFTw7SSpS4eeDw7aHez5/tH/6vgQsyZC/U1zX8KrLtNyzE586dS0tLi2dLXFxcYmJiRkZGenp6dnZ2GlsyMjJmzZq1YMGCpUuXVlVVQYMxnNzCZR+otjBmwGKxOByOkpKSc+fOwXGWoqh9+/Zt2LDhqaeeev/992FQOze/hT8jUNPh96FpuZvK8C82GxHivwhgj7u8urr6N10moMFYqVSazeZfcGKj0QgdizUajVAo/PHHH+FMlFPCHgci6nA3R4DNJsqusLIDHGtaYoZTJgMxE6DORJd7KIpoOX/l01VzZgYIAibodIK7B38AsAaAncHAaQw0MuQYa+zjfVwuTzebfbTGsQOGbfUCbTzsFOCdGjrkpW+/c5OUm7U4MzlSr5mLmdVXkqCZaHiSJGiKbm66umTJoti4mJjYqLi4qJSEJEiIISeGr+np6XCUzcrKKiwsLCoqWrhw4dKlSxcsWFBaWlpdXe1gi91udzqdNTU1s2fP3rp1K5TS5cuXN2/evGzZstLS0uLi4qqqqpdeeunChQvt7U9wBZaTKlRt7iRneWqv8u2PuQvRAUKgqxAgaYKk2miCIknCQxFuiibrz15cm7/M4CNUC2X6yXffs4sH6hlnJ+wC4J3GsFoedqkXr0mlmGbQCXVqw733Pe7Le3PzXasX9N44XfrJ+fMXr6U27tBl4uLFi7m5uXFxcVFRUbGxsdBCDM3DGRkZWVlZUHNTU1OTk5MhOZ7LlsLCwjlz5pSVlUGKDGMGoBfy/7+QW4n96quvSktLYfxAaWmpxWLZtWtXc3MzpMKQ/nIjMqezXSWFrq0XEeKuxb/71f70009DC7HkvwskxAqFgvMYNl0vHD/GcVwgEOzfvx9qHRoRu5/4UYtZBCiCWQklmYz6zAYcLBcmSSb9MEExHJZs8xAnzl587AHXO67IsmyTHJeq1OMGDNjKA23A6yIGznuBBgy7ALBzvb2OyiQ5wWY/o9ln2KiHvEAzxjsLvL8ZMaL0ux/rWqk2gskGxVTloVj+y7BvkiA8JMWmh6Ko5uamZUuXxcXExTD24cjEhORfEOLMdiUlJQWOsllsycjIyM/PL2cLdEMsLy9fvHjx5MmTd+3aRZLkxYsXN23aVFJSAtdnoQvjokWLbDbbs88+u2vXrqfZ8s477xw4cODEiRMNDQ1ut7v9+AqNZNwZmPeNY8zogUIIdC4C7bbG+PmQVSraQ1CtDB0mibY293sffryh2vV/c6Oc6TOCTXqN1jxs2FpvUMcs7HifB9h5DLvIAw3Aq1kgi9br/fUq1ciRD3thZ4eAA0OwZ4P0u86dOc3oLeVpYzwnKA/Mxcgmd2NmzoxLFZNmvOHS+dycvIT4xOiZ8fFxiUlJSRkZGZmZmdnZ2VBx4dvMzMysrCz4mpubm5OTU1BQkJmZOWvWrJUrVy5j/YmLi4tXrFiRmZkZHh4Oszn95z//KS0t5YIHLBaL1WpdsWLFxo0bN2/evGnTpocffviRRx555plnPvjgg59++unSpUtcfrdfDNDwLeea3LlS66TaECHuJKBvm2o++OADoVD432T453dyudzIlutk+L/+4zjO5/P37t2LYmBvm+ehh3aEYLIRupmwGyaXP5MdjfWSYPZK9lAtRNvXP/zgrF5TvqKixlJR7XKU2VylK2dnZW4SCN68Z/hbfXp92xu7yONdwHgX+nmfFArmB5v5wUafMSOrebwG0Otcr/4OqUB96PvPPOQV9sYs92bDc5j0qDDNMdwimnI3uVuWl5bGxcbExCRExsckxKd1QIjhQJubm5uXl5ebmztr1qykpKTs7GzIj7Ozs3Nzc1NSUkJDQ6EZ+JFHHoGReWywO+N5bLVaoX9FCVsgjS4pKVm5cmVJSYnFYtm4ceOuXbs++uijkydP1tXVNTc3s1t2MbNgSIs59f/FiNtDnyXU7U5FAK7pwB3pmFwM1/9IZspJuQnS09bmef6fz1dWVJRZrDaHfbV9VY3DVmlZdn/hw5NC/j1u4ht9+3zuxfsRgGMYqPMCLX6S1CC9n1YrH+3j5PEaevGO3T1wXkiY8fSP7xOeM7SHuEJ5aJLwkISHIJms4EwiUw9Nt1Ak3UbTTY3nC2fPjY2PjoyOiWVdJjj9bTeT/fkwJycnly35+fk5OTkZGRlQl+HJ3NzcxMTE6Ojo2tra1157raqqyn49YAB6U8C3lZWVFRUVVVVVFoulki0wXWN5eXl1dfX27dtfe+21zz///Pjx49eTmDPb4HH5j29XzUWEuFN18TaobN++fQKB4Dc5sVgslslkBoOBMwlzdBiewXE8MDDwiy++gOp0uyrVbSBl1IU/QIDZiM7jpolWditHhhAzfxTt8dAE/d0P385dvGjlypV2i73MtqrSuc7uXL/WZV+zqrpm3WvLK/ZP0l29t09LP+/LAFzwxk6N85mH475BWr+xo9cC7wbvfmeHjEgWTxzy0bOr6fpj7NZZbLhPK5N0opVmBlEuMzhBtV4mWoorS2ISI6Jj4yLjYlOiM7kBlbMtccNpenp6VlZWbm5ufn5+VlZWQUFBWlpaTk5OXl7erFmzuCE2IiJi3bp169evhwE60L8CuipCn4rq6mqn08m5MNrtdjiylpeXQ3vVihUrFrBl06ZNn3766blz5zh950bWPwAZfYwQuPkIXHdyguZhxhGJ8TtijLUk1Ua0EpTnhedfWllaWuG02R1Vlc7VVofN4XA57BaX01FT7XJYty9b+O+U1NeDQl7183t90B1fT5hQoMUnKNTC+8ZW9vI6NmTIU+P9lQqNz3vLdS0bs+mnn3efPEY3uz2eKx7CzdifGUJM0FQbxQTwkZev1hXePy82LjYqJio2LiYxMZHTX05t2x/ksCWPLZAQc1QYHiQmJsbFxdlstuzs7KKiovnz56ekpCxevHjVqlUOtkBHZGe7tG5wlsslaqysrIRT3IqKCrvd/uCDD77wwgv79u2rra2tr6+H01oYXHvz5dOld0SEuEvh7w6Vs78VbEPZX5DDhw9JJGKhMEAiEYmlQrFEJBZLJUzCCYFYIpDKJTqjTm/WG0xmgynEaDYYzTqjWW80GU1Gs06n9fWd8MmeT9llF8bodW2wv/bb1B3gQG1ECDBLn8y+G0xAOmMgJpgF0Gs+vmTjxcb11WWlZVX2qppVlmqbtcLhtLqs9tVVNavt5dUOZ021a21NVXV15dKV66ZH7JFIPhLKFmu1/nrFfYPuKwEYOXpstl/AONGEvu/b7mt+csTl3QkNPzxztfGEh2BMwxTdxgynUGXcNN1KeVroipKqpLjI6NjEyPiIjJgobkD9NSGGS66QARcUFMA0T/BtTk4OtBCnpqZGRUXl5eXBMwUFBQkJCbm5uQ6Ho6amxuVyLVmyZMWKFTDOvf2wCo1PDjZ/RVVVFdxfAFqeqqqqNmzYsHv37g8//PDHH3/kInvQ04QQ6FwEfkGI2bksq8stFN1Cu386eohJ221zlLtqbLaqSsd6R3WVxWl1OB5wWtdWW5evda5cU1Nqc5U61zxudbydEnN+smmjwSAQy/2H3lfRm7d35OjlvkJ+IF/2ZebdzSF3Hew36Mu05Ia93zfSF9voVmb7O2baDBO70TTpbmhqmHN/UVxsSkxkQlxs/A0S4vz8fOg4Ab2eCq6XvLy85OTkuLi4oqKi1NRUGJmXkJAwb968yspKl8tVUVGRk5OzZMkSmFjGxRaYngLOcmHELTyuqqqyWq0VFRVLly5dtGjRkiVL1q9f//777zc2Nnau1DqpNkSIOwno7lvNtVAeZpmT+Tt58rhcrhAIAqQSCUOIWU4sYVKwiaQSiUwm1xl1hiCt0RhiNAazbFhrNOmMJgOzZZ0eHz/hvjdef5u1FTGekYgQd98H47ZvOefpzh1A0wh8epkNHJl0EoyFifS0MU4THqLhh8MbqlYuX7HY4XDW2B9w2R6wOp2V9kqH01pttVbaq6scVrujYpXVss5uW+0oXbu+ZPW6kqoK68oia1F+knGSZdSoFyTCcWPGyHxHDXvPdR/5BCAfBtRGQD86qO15Zf1nSxqPv+xpPkuQ1BW6qZV2ExTd6vZYystTYiPio5KiGZeJ6I4JMWS90JiUl5eXnp4OrU3QbJybm5uWlhYZGbl06VLIibOysqBbxaxZsxYtWlRYWJiQkACvmjNnzqJFi6CnBLQ/LWRLaWkpTPQGXSwsbKmsrIQpohYvXlxdXc3sCubx3PZPEergLYYA9HC6NpwxA9D1oY0m6ZZD39nm3r+spMLudNprVjvWVFus66ut1ipnhcVVbnOW220OR9UqR5V9ld1ZY3PVOMqd60sd65aWrbAVLUmdMdPlH1ARIBH4BIonBBjfmzHAI+tHeGEerC/pfXfThMDTaZVnXj7UVN/iIZtpupXZP8Ptbm1pW7RoYVxMVHzctLjYmX+CEEMPKOg4kZOTk5ycnJSUVF5eDhd/2idxmzt3bnp6OgwkyM7OhskrysrKrFary+UqKysrLCxctGgRNAzDtBUwbABuF8LGGpTn5eV9+OGH3ILPLSbfv9QcRIj/Enw94WLoY8X2lAmiP3vmlEIhF/FFMrFMwliGGSoskQglYolUrJBJNQa92WzSmw0hZpPWxBiGg0zMLnUGozFYrzOOHz/2+X++yPwEMa6XnCMX68t17cepJ4CK+tg9EIARJOzjes3/FTrCsiF1TJ4HgvIwth5ml2ai7atXaqeOWhmKJ+fNy5yVvWDB3LKylVanw+F0VDscNke1xW6tcliqHPYqh93qsFptVofFxhiLHdWO1TbXKuuqmo2O1YsWLlzEl6jGjxj+xioB8X/9PM94E4/3Ibf2prZ4UQ+ANqdX03b+lfeTLnz/aOuFb6jWlmYPWW6rjotPi4+JSoidGR/XUVAd50PcMSGOjY11Op3Z2dk5OTlZWVmpqalZWVlpaYx3clJSUkpKCoz4SWdLWlpaXl5eYWFhZmYm/DQzMzM/P3/+/PnQKAWdKyBFXrt27f333///o+M5bLvHo4Ba2X0Q4LLqwhks98oekATRxrryuplQVXaJh6IIkmr1nHrpaIXYGStNzExJzcuYff+c4mULrVaL3eVaZaupsVU7bTarrcrqcFoc1RZXpdVVYnXaLHaH1b6h2mVZ49qwdr19zQPFlZZigdJ8j6/8rdABbT48iocRWJ/WXqCVh1O8R2jwLtX/7QbjntO2Axf2nCMam9ytLQsr5kbHxCZEpcbHJEFCDH3623tKcMftfYih9z+c0Obl5UGzcVJSUlpamsPhgBZimKQiM5PxpMrIyIiLi4N+U/CGXIq3WbNmQSadmpoKk761118n618BN5eOior66quvECHuPgqBWnozEWCm1NdSTFFEY32jQYeL+GIpw4MZQswyYinzRiqWyiV6g95kDGLNw7jRZDYaJxlNQUYT4zWh1xvGjZv47LPPQYbBtBGu/LKZpFg3yZvZbnQvhMBfQQAahrl9niiKam1t/f777xsaGhiXCSasjs3+QDEeE8ePHHy3IrY149416VMTM3JSMzMy0tNyM1Jm5WUvW7igctniMmtlTdUal63K5iivdFWUuVZUuMosdpfV5qxwVJVXOysdq1c7rC7H+tkLCvhS/ah773119UTqDeDZDTz/wMgd3uRDPMIKqC7VHIEAACAASURBVCrgtgJinRe9Cbgf7dP4Ymj9l6vtS+cnxOdGxc9Mig1PuBmEODo62mKx5ObmwhE3NTUVel9kZGQkJyenpKRkZWVlZ2fDkzD4PTMzMz4+HmaGSk9Ph+x53rx5xcXFy5YtW7JkyeLFi5ctWzZ//vyIiIhFixZBT+K/IiB0LULgNxGAs9b2PBhS5Kamps8///zIkZ8I0s1oMKPAzL+m5vpP/vPuG1uzjzwzaV1JeGZ6Slp2dlJmVn5CRlZ2Uv7cguKlpdYqh93icNkdDAe22qz2couruMq22uGwWpx2h83uslXWOJz2KntOZqavSjhUMub1EXeRvfvTYCCNDSCxCBL7mPT62sP7og18TPA+8vDevNrrteax71xMP1w8Z8XUlLiY1LiomKi/SIjz8vLgAs7KlSvh7JcjxNBdCn4KCTenudnZ2enp6XFxcWlpafBraWlpGRkZMEUjGwtw7SUrKys0NPTw4cOIEP/ms4dO3vYIMDHtzP7ubCiPu5meMjmM7y+SyvgSCeM3IZaKhBKFUKISyoUSZaDOpDYaQw2mEEOQzmAOYg5MQQaz3mDW6IyqcRPGPPccQ4j/a62Kec+sOd/2UKIOdiME4C8+fPV4PFevXn3zzTeLi4vfeuutJqKBSTLBqEUb7b58tfbEts07Njz6xJPbn5xbVJiVmZ6fnJ6dkpebmZOWnh6fkJKelDL7/jlOV0W1db3LbmeGVts6m8VltZbYHBabzbXGXrPKUvng6g3zl2dHxsUKNFOH3jfoVdcY+l/9qJcB9RKPfopHrAVUOaBKvd0rvejKvvS6AdQj3sRjoHFTX2eGOSkhe1piUnxsdGIsY7blzD+cYYk7uBGXidjY2JKSkszMTOinyIXiZWZmpqSkQJMwDO7hxlTIlaFtCSY/zmYLPIaW5ry8vLS0tMmTJ9fU1MCI9W70PKCmdhcE2vs4EQTR1NR09OjRl156afXq1cXLi+12++lTZ9kMwR6KvtLWdv7V155bv6Fm69YtW57cvqysPDMpJycjJysrOy89h8l+lpYTk5SZd/8sR3VltdNaY6taZbfW2G3VjkqX3eWyrXPUWOyOqlWuB12uNbk5BQmx8UK9apgk8MUhgwkgIIDEg5kp3vO09ymC9z2FfU2DrynwKQU+JrD/UODjxj5vl0XPTUiJT46Pi4uJ/YuEOD8/PzGRyd02d+7cvLw8OGvlfhCysrLgxh9wQsull4HrP/Hx8ZAHw8yMUH/bR+zBm0dERFy8eBER4u6iDqidNxWBa0EIzMowTdFECxU6RcIXjxVoxsikIqlEyPhLSGViqVwgFkoUYq1RbQjS6cxGg9lgCFbqg5QmM+NAbDTqdTrNxPETnnhiO/QeZrKVQxcu5h8ixDdVauhmfwoBOJS237Hp6tWrx44de+ONN2pqapYuXbp8+fKKior/7P2gjWhiQts8zaf27/m/7Q+tf3jbjq1P/eOpFxYsn5+RklmQnp2TwZhXMzLTcnJy05PTM7IyXC5rtXNNtdPpcjnsthqH3eV0Wh1Ou9NR47JVu2qcNaWWqOSI6PgUqX7ykMF37q4cQ73Zh3wJEC94t+3kURsAWQ7cxcCzFLgXe9OVfeh1XtRWrGn7XTVzwxMTM6PiYxOi4+PimRVPSENhen+OCsOD3ybEufm5OcySa14e40McHR27bNmKlOTU3Nxc6DLB2Y1SUlKgBwU8A+8JaXEyW9qPvtC7MScnB+avgC7L06ZNe/TRRyHIf0pK6CKEwB8jAHX50KFDGzdutFgsJSUllZWVVqtl5YrSp3Y+TzIpW9po+vSeL/6x8SHHzieefeyxbdu3P2V1rkrISMtKycpPTU3MTcjMzs5nkhKmzCtcYLOX2x3ldpvL4VjldNqdrgqnw+WyVlhs1Tb7qiqbY/6SBTOiw9NSM8S4aZhA9NywQTSYSGJhNHiMBs1E7x/cvCMkdoTGDhDYV27eXgp8Q4NvSPBBafysqMy46NjE2JhrQXV/2mUiPz8/ISEhNTW1sLAQBsVyFmK4npOYmAg1lzMSw7QzcIUHqjZU5+zsbOiDwfkoz549OzU1debMmfX19X8sgG74DeRD3A2F1tlNZizEjJ8kG4pLtpEzIoLGjCgccc8/RXJ/scJXIveVSPhSkVwskMqlCr3OZDKEmUxKs2GKwWAyGvUmM24OloWEKnQmgZ9g6OZHNzB7F/zsLMF5TnR2x1B9CIFfIAAHUbh5xPHjx1988UW4D2p5eTnMaV9dXb18+fL8gtyjx49QVxtPvPbCzo22LQ+v37ztya1bH935+BMrS1ekJ+em56al5aRlZmXnZWcxvnupWTnZuQsWLFi4cCFceoTBZ0VFRczboqKixYsWLF2clZAYGROdFJ+hDtEPvXfQtiW+9LsD6Je9iF292rb3oh7m0auAZwWgCoHnfq+2WRi5BFDrvJsfG+yaNyMpOSMpNjo2Nikm4eetmyEtvjFCXJDLsNb8vLyctLTUmJj4ygpbamoGR4jhGMmZgduPpvA4MzMzOTkZLrnCoRcyby5iD/o4pqWlhYeH79y587a0MP3icUJvOw0B6CAB9ZeiqCtXrhw8eHD37t02mw0qL8zC67A7LDZr0aIVtSevUtTZM43/fPrpzdsf3bT58R2PbHt8x/YdjmpncmZOdnp2fnpqRlZKRlZ+VmZKRkZqbk5eYeGcwsLZhYXzC+ctKJxXuKDo/vnzF8yfN3/u/KIF85fMnV2UmJwaEzcjJSlOGTzpHpH0H/eOIoA/CXIo3nsE1uT2Ok5gtRQ4RoHvCPCtBxwgwH4K7COwTytj5salMVvrxMXFJiQkQPX5hdpyb3/Thzg3L49JLZ6fn5efFxcfl5GZsWjx4gx2dw/o6M/NkBMSEn5NiOHMOTExEVqIud8NLtKA0+Lk5OSYmJjLly/flvqLCHGnKWz3rchD0a3strQ0sxGXpz4+zXBXf1cv4BnIaxk24CMfYYxQM1qqHCcWCuVSqV6PG83mEOOkIGOwMTjAOElkDMPxSeFCUcWYMc5ho+auXW9hbvhfhPi6M3H3BQm1/LZAgBtW33777aVLl8L4azub2R7GhLlcrvT09ClTIva98dqZR9fsLl++7bHHNm/bsXPLjke2P7bzycdrLNXpKRk5Oeyeq1nZWdlpuZkZ2Wk5SelJWVlZMK4FDmnQ5YDJ5JCblZWblpOdF58cFxsVmxIfZwrVDhw+vCzHh3pjAPkc5nmMRz7Sm9zkTW4AlJ1HLARkLmibxSMKvOnM3vVRvddETUtImxWfEB8TmxifwPhMcEParwfX37cQQ0Kcm5aWGh+XWFO9Jj2NcZnggurgPZOTmaC9v0KIp0yZ8tJLL92WA+ptoQTdshPwcYLewx6P54UXXli6dCnMGgb3aXM6nTabrcblnD177szYmT8cfb25ecOu5ysfffjRxx/bvGXHM1u27Xxyx9a1q+3paenZmdm52Vm5Oflp6XlZuYwDPeddANWWU96s/PycgryC3ILc7NmxiSkx8VHJCdGa4JChgcKtQ0dQ2NQrvV8gscsecIXA6kjeaZJ3gsKO0OAwBY6whPhLD+8La+SCpJSU+OiYuFiGEHPKy5Hg9ge/JsSQDefmQ0KcH5cQn56ZsXDx4vTMjJycHPibA++QkZHxm4QY/jTBaDyudlgRF7EHD5KSkhISElpaWrrlU/JHjUaE+I8QQp8zXJhgkq2y+2ORrW0Jmfige0sA7zDwvgCw5n68xpH3/iNQGMkX88Vygcag04fgIZMFQZMU5kmTFPKFY4fvvufOg32xNp7Xsb6jqlyrVlJ0C/P71Z4T/+w+gRBHCHQZAjD4hqbpRx55BObptFgsMP2Q0+m02+05OTmRkZEJ06Ls00PeKct9+slHtz2xY8vWLVu27Xh6y/Zt23aur16fmJqQn5WVl5GXlp2TkZealZJSkD5ryfJlJSUlRUVFs2fPzsvLKygogNaXgoKCvLzcgll5+bPyU5KSE2KTExMjp0yaPHD4uPmJYxr/b/Q3m8U/bFSffyiwaePd5Ko+lKUXtbwvnd+XSLyrVduveQi41Je3ddK0hJzcuKSYmNiE+PikP0WImY3rWAsx4+YbFxdvsdgy0v8WC/HUqVPff/99aMzrMkmjim8vBLjHiaKourq6hx56qKKiAm4l43A47NfL7FmzYiISYmPNL79b+OFn8zZu+ceOHU9t3/rojm1Pbt2yffuO7WvXrs1OScvKyE7PyUvJys7Jys7MSJk9e3Z5eXlFRUVxcfG8efMKCgrgLjaMRwFLQ2exBtq4lKSYuPjk+CTt1ElDxOM33zP6wiDLW9P+9aHh3R/GH6wfdLylz2mP11mKd44GtRQ4QoFvafA9iR2wR66IyUiNjo2NjfnrhDgvPiEhNS1tQVFRZlbWzSXEOTk5iYmJWVlZbrf7tpzQIkJ8e/0q3KTecFutMt4MpIdioofaKNpN0HQbSSanhI0YnobxLgFwDvCOAN4ZAFr7D/iWL4iSi31MuFQ+dYbc5Bzr++bggZf7eVMY1gy86jHQ3Nfr+MgR9qKlKUyUHpODkWR33WI3CmrnUHyTOoFugxD4nxGAhLi+vn7dunUwASc0DzvZkpWVFR3NDFtxUVOrpmW8Vbz98Qd3Prp989YnH3ns6S1bnti68x/PrHlwbUJKcn5mXnZmXkZObkZa8oK5eQ5r5VrHKpjxHubALywsZOyxjItCXl5+QX7B7IKCrJzEzKjohPSE2MlhUUPuG7145ohvts9YXbFwvWXFDmvebmvcexXT95cJj+Xfd0Z494V7QYs3RvG8Wnv3fXzKlOTc9PjEuMi4xNQohhC3dxzkPIk510AuUAY69XJvYXtSU1NjYmKKi4szMhgLU2ZmJnSEgKajpKRrhPsXRmLoTcEF5XD53bjFVniQmpo6derUvXv3cgzmfxYSugAh8CsE4NoOfN23b19lZaXdbrdarXAe63K57HZ7dnZ2TEx0QnR8wszgNf+PveuAc6O602+k9a5tTA0mBEwN3SaAbYrb2sZ9V6sy6m266vZe1bu0xdhAQrcTkktod0BCgITQElJoTqWDbYjBvezau5Km3L15tm6xgXA5Lhhu/7/5ySNZK828maf3ve99/+8fr3lk0HZntvnuoczdt99158Z7brtn450/vG/dd2+nHDCljqAomqEJu7WhoT6dTqNCFaXaNH6/n2VZiIwZn4v1ujm3x8XZzBYTbrRYrPNUy0+64uz+01XPq3+UHFifi6d+0Hb/48TDv6p+4vkbnvrrRb/9YPqf9k77a77sLQl8yCvezKzp0zmMGoNKb9D+EwwxI8Pxwwyx7DJhs9kaGhqQbeIXyBAzDGMwGJqbm7+uGbETgPiYjjXxwpEWQEah0B1JKAiwyHtBkKSCKLo9+NlnmTHFDgA+AthWgG0D4EBZ+RuXXmKac9V5ixdccdncpunTH596wp8mKfcpwSjAdgPFLgyMQkB8dpxg14hiQS5qUJwAxEcae+Lf46IFEErbvn17IpFIp9MpmACX7u/vz2azXq8XQmE5qonV0dU9v1j66Dry1rbu9kCmq//2eHrjd793/z25jRnKzjQ4G1jCZXM66lguGWnP9PfmIuF4PJ7JZJCXJ8KaiGdye7ysy+tzEZyZ0ugNtN4yf0XNqad/I6w//xc5pid6e2/ye33xwVDslmRk8IdR/R/d141eNm/s7JP5KZMFoBxWTrp96TITSWoNdtxgYzRQg0hRFMLBSDKB8DGSOqBhEsHTowCxTFdzNpsNx/H29nYEatFyamnJFWWpf6IMEakpSiKNozTEKDXHZrOtWLHir3/9K5xsw2nwREy0wBfWAkj9/8QTTzQ2NiLwmkwmc7kcqmOM47jForeZzZYa9WDHmi3rFj2f0WyI1WXi0XD/QHztbevuvPeW226jCTvtsHoZGvoRc1w0k47FYuFwGDHNqVSqu7vbbDZ7PB6O43x0nYv1uVxuP+d2mqyQIraY561afuK5Z90+M/Ufffe3pqAbRXd/LD6QyA3k7g7d8Tzxs7cXvrTzor+NnbhVArsKk97MrewzWPRGXbVR/78CxGhCazabrVZrfX090nV8gYCYZVnkP/N1NRGfAMRfWFf8On0QUmIdVmUJYpGHVeXgi9BxVQiFGmbMqAKKrZAhBh8B8AEAO5TKrZddRs+d863FC88/++zbyoGAgQMY2IOBXQB8CMBuDBTKyz741ow0yapEsQg/C7LE+cOl5CcY4q/TDfRVPhdBELZv397S0oJEh4FAoLOz0+v1Go1GHMdNJpPRaFxG1IQrI3unbhewPQXlrp3T9rxy2as/u/GH99Xee1vgFh2npWAqHWXjiO5QVzYxmIz2x1LRRCIRj8f7+/tpmlar1TRNIwDqZlkfS/s5xulwqI01Viu1ZOE88zdPvMPnGEyGIql0LJWE7myJaDzeH4uuDabWhjKJZC7V39P1uE73ypwb7qlZSZFGXK/WqGvUkKOyjGeIxwsQkd1SKW0c+aDJTxmWZeTjcdlsNoMB7wt0E4QDMcQl4wiCIP43gJhlWavVumzZsjfeeGOCIf4q95Lj7tgPj1YiLKDzwgsvOByOnp6eWCzW0dHh9XpZlrVYLHq93mw26C2GGr2x0dNYvAuINyuKQ2AsfPL7sbM39814rXvmv/UxlJOmnQ7WqqVZZ0csnYvDnjs+kcDhcBiNRpfLBfuLy8V4aJeX87Beu81ZZdLiVqv5aiN7Kfd99oF4rnsoEYzF+yPp5FAslE11JVORaDYeGgymMrFsNPm9pu8+ob8/48w6HO5VtqpV2jUajQaZeY/vwmgfTTVL0l63240UEayLQxsqVmcymSiCaG5o5GBdnY9piFFhHTRhHr/CQ9O03W4vfe9RX1SaPNM0rdVqg8Hg13UqOwGIj7uOfTwcUIkne/bZZ5/+1fMHDx7KF3hRkHixwEvFdWvjM85dABRvArAHYmLsfQB2KJSbv32xY+6cMxcvOv/ii28G2BjAdgLFDiW2WwF2A7gNlyk/mH7WOquzGgFiQeAlqTABiI+HKz5xDKgF0A99oVBwuVwEQaBHJAOwWq0mk8lgMJhMphupG3uW145UvCWCvwvgA6l8x58X/OEp1f1bz3vn9bPfYsxOA6VyU0y8I5pOp8PxRDSVjaUiCA03NzdXV1fjOM4wDMdxHo/Hw1D1DOlyu+1Oh1avW2MxO5dU3mWz3hlNhRNDg9FUfySeSaTTKSiFTMfW5qLZVCaciPdHUwORgVx4IBPODoRj2WhXoKHJ6yE9KBPOIQeqO1VSTZSUDCXFsN0OvdVgah+HALrbZrMZjXg0FnQ4rDKFTHwhgBgVgjabzcuWLdu6devXlWSa6EpfSguU1jMlSdq6davdbtfr9W63u7Qq4nA4YP+14HqDxaBxEkTN3u+eNnYHyK8Dm4cuuTvVck/c//tQ5eudV4fMy/Q2mvQ1x8LtyXQsmliblCMWi2Uymdra2pqaGrPZjHqQi/MyHprzuLxMHWmjtSZNNa6qX1l/b+0Pbk1Eu5Lr+uMDmVhifXAwmYxHMpFoOpGMp9LxdDrRH8skO9b2xDMdkYGeWDwW7Et2dfY1Nzf7fD6KolCB5aNsH9CPEvrpOAoQH4atLGcyGCmHs7mugXYenVT3vwfEarU6mUxOAOIv5Saf+NLP1QIIv5Yej/qb8XTvUf917NNisfj+++8/9dRTGzZs6O/v74AlpjpvvuWW9za/CzliiS8K+btvv3nGuVcryl7DwB6AfQiwLQDsUig+OPd8/9y551QuOPfymXEIiMFOALZjkBvej0FAvFeh2HbaGT+w2TVQhYEYZ2Q/LGfUlab46ETQsQ0PD7/zzjvDw8NogbV0jqjM1de1Wx57XSZe+Ze1AJIhtra24jhe0gzQNI0AsdFohIDYjtdVt2w7/Y8S2L/7m288xtzbdGewbTARz3Xc3rW2sb29saG9J9CXzEVTkUQikQqkQ/2RcCqVCgaD/1X4VKvV6vV6ZPMpS4g5zsW63D7KRhg1Nq1WE6ttyKwbymXSmWQwlgzHk+vDqVvTqVg6GY6k0tlEJBnL9kdzsDxWIpSLx1KxgXRsKJrJprOh/sxAJpOJRCJ9fX0dHR0+n49l2aMYJsQkIX76MCB2MSxHya+4bTa7Xq9pbPKTFGKIoTnG/1IygUZrmqZNJtOKFSu2b9+O2vlfdlknvuj/QwugEWH//v2NjY1VVVWITEUFJux2u9FoNBtNZotVheutTsfO9SdJt4IPh755eyQei68LRgajsUwkGa8PBtp7QplQuj82EE0kc4lWtLaTzWa7urpQ/zWZTEhD7GV9HtbNub0uzs/aaKtGrcGr2zu616/9XiST7U81JqOZvv50It2TiScziSgsapdMJlLRaCaeSqVisrgZOtgkU5FcOpGFYuVMJpNMJiORSE9PT2trq8/nK8mQkNQKrfAcBYihhpjjWIYxG4wWvdHHudw0+8UyxBRFqVSqdevWfV1H3gmG+Kv6K4HEUrDCG88fOHDggw8+ePvtt1977bWXX375lVdeeemll373u9+98MILzz333COPPLJ58+bPHn4EQfjLX/7y2GOP3XrrraFQqKenp7u7u6+vLxQKBYPBrp6OcDT02GM/3717pyAW77v3++ddcGVZ2Z8B2AHANlkysVuh2H7OuYHZcy5YOH/GTAiIBQB2AezvGNihgIB4LwAHFIodp37j3/WGGkHk5R51xGbiiMVEaZYvCMLmzZsffvhhtOY1MDDw2muv8TxMxEOYeIJe+qreuMfZcRclaQwe0qjES0VJkooSL+bFvHDzzbeqatQcxdEUc3hAtdlMBoPZZDCZ9GqzgdEyr5/5tAh2Pfqdh9rq2qKZZCIZC6UTyWQSiY+TcqDFVvQYj8etVqtGo9HJQZJkKa8ODa4EQeh0OqPR6Pf7Ozo6QqFQ6aNSqRT6wM/zGEslB2KpZDqVyCRz6VQunoynU52BgJ/zOh00yZK0x2znXF62iaM9DpokYK48wzF+lmz1sF4LiRs0RI/PRHIsQ3MM+bFV1/GSCZKiSpuDIEwWi5MknSRBwJdhhntpsRVBcLTkqtfp9u8ZgWvbci4BrBl2uFsfZzfHxOF8pVqgNMYVCoVIJFJTU4N6LtKyO51OqHcy6XVWvcpgUetsr992hrhxyubUyZ76hkwuEs/EY8l0Kh5LJ+PpRCwXi+ViwXgyHE32o4yCcDhsMpm0Wi0ux+E1Fi9Ry1AuN+v0u60MZdSZ9DoD6yUb2ry9ge50ZCCRDSdiyUQymU0G4qlEPJnqlyUYoUwoEw9no9lUMptMZLKxaDSVistWNqWfjtKPSS6X6+npQWXnSt2qBIgZWTKB+pqL5SxGk9Vg8nNuFwk1E+M1xP9Lhpgkyerq6jvuuGMCEH+lesb/g4MV5ZAk6Y033rjrrrtuuummXA6msQfk6Ovr6+np6erqQgqqJ5544h82SSgUcrlcwWCwu7sbOZkHg8FAIBAOh3sDnaFIoLW1df36dXv27n74/vvOPf/ysrJNANsu64OhRFih2D7jvPDs2RcunDfjO7PiQMHLgHgbBrYrwD6ZS94vA+JHNNpqXvhk05YSzN29e3c4HG5ra+vq6mpvb+/o6IhGo5s2bdq5c2exCHELQsal9//Ds5t4w0QLfHILHC6SWDwEkXFRGpNGpTFJlP7t3+6vrlazFEtTDNLd2u12GRAbTUaDxqJ36olNM54Wwb5ffvMhx2pLQ19LLpdLJtMo8yYhWxcjYgnt53K5pqamqqoqvV6PMDFa/UR5MMiIjSAINNYiPR/DMLW1tShf/vPg4NJ7UslkOJPMxZO5eCqaSaWymVsG1sZ6AmofaSdd9Ta/i6X9NNtEsX6OISnKxXhdLM24WIfH4+O8JlsLblzR2GbxWD1e2k+QsLQIspj4bA2xxWIpmZgeC4hRIVm1Wu0g8IMHh2G5HzGPvBflyvBycfhPvkgTr060wCe1APQC5YuSKFdThUZIcGwQhEPCaN9gVLNa47E5adrl4EjShTsph8Fs15uNNoNepzOv0VB/ufUc8d+x/RvLzSqNq9aXSMM5Z0yOUv9FXRhSubEYTdOo5+I4rtfrWZb1eDyUx0W7XbDGo8tFkZQexw0GA0GRBA01DzRNB4NB9Gml7jl+knzUf5XeM34nk8kEg0EL6TQ6bMhamGNZt7yoRHIur5X10xTlcZJe0sOxHtqlMeptZn0L5/E5aJaxk3bSSVoICncQDqeTcRhsjN0JPXBIN0FxBE2QFEWQHrOdMDvMDsrppEiCIkmaGp9p4OY4N+finHbVqqof3vuTgpQvSgVUvBaaRQn5T7o8X73XJhjir941Q0csiuLIyMgzzzwTDod7enp6j0RfXx8id/9L+Y4QbXNz84YNGxCX/GlnK4piZ2dndXV1W1tbPB4Ph8OhUCgiRygUiMYjwXCgt7entaXtnnvuXpdNn3/hJRj2gqwM3iHzxDuxsvfOOid1zdVXLLzhgjnXucsUIwCywrsUYLcCSo13A3BQCQ6eNu1v1dVVheKYnFE37nA+ThL/8Y9/NJvNgUAAQfPe3t7Ozs729vZEIrFp0yZB1luIcoz7iIndiRb4n7eA7K4tioUCzxfFoliQRiVogXLf/fevWbOGoRgIiOWCTw67w2Q0mk0QEGutepve/rvznpLAgT+e/Zxnib2G1MUSiUwyi6xP0Tgaj8NV0Wg0mslkEomEwWBAeBetuhIEIdeWklc6ZWdikiRxNKDK/4WKJIdCof8RPQyXXxOpUDqZisOy0PFkKpvJ9nR1mSxGk8NsYikrU2f2+glPrdnNWnyMgyA5xsVxNMcxjJvlPKzF7KnRqAMdDgLKi712x2ENMSp5NZ4hHkcQkwThtFjMJAnVGSQJbS5KVBaihzmOoygKLmRzztExWOtHXimSZEdyXpR42Zn8f34FJ/7i/28L7IGaOziWjPIiL8JyzHxe4g8J4i3f++Gq1TUuxsZQbidL0YydJgirERaVJ7tDFQAAIABJREFUMJsMaty8Gid/d+tFoz8H+/9DadbULF5+Y3NrSzabRam0kUgE+bVF5chkMl6vV6VS6fV6nU6HADHDMFD/73GxRwCxbOuG43rcSZEkQxMEUV9fH4/Hx6Pb0oT580DhZDKZkgNKojXWJh3XYnUbPKTd57T57Wq/mXWTFjdJuFm/s5Wl650eG0PZjWqD376kxWUivPVuL0M7oJeylbOTBMvaaJvRZGLtNhfroGknTTppgiEdbqfTbrWb7TaCspOU/VhA7JIBsZ3yrFij/vH998MceF7eRImXRg5zVF/9+3ACEH9Vr+GTTz4Zj8d7enpCoVAgEAiFQmE5jqDYEELGHR0dbre7ra2tUCh8xqkKgpBMJpctW1ZdXd3Y2BiPxyORSEgOiI3D4WA4FA6HwqFYMBiM93W6vb7pZ/wZgANyXt0HANuKKT86+5v/cc135s6//py5NzgqFLsxsFcBt30Y2Aed17BhJbbvGyc/t3Ll6rH8KMyrgxP7I3FERoxg7qZNmwwGQygUQjXoE4lEKBTq7e1F7PX69esfeuihzZs3j4yMHPn7iX8nWuCfaQFBkIrQCDsvQGtNQeSlg1JeEqQXXnh+xaplFKxPRVMEyVA0IS+5ms3QZkJn05kMxicveVzCDv39jL+lF3TN0y8kKTYSjCKnNlQcC1kOZ7PZSCRit9tLi604jms0GqfTiQCxzC65PB4PSZJ6ORD0pCgqGAxms1lEKZVsjI8aX499mk2kEolkKp5OpnLZzGBPT5/ajGuMNdml+N2V83MqQ9RMRG10wG4JEbYml4dkGNpFsW6Wc3lIlxfHiZx70S2tOiPpIDmX3WG32+2I+nU6neMBMUERpc1BOExWk5N0OkkHQcFSduMBMdonCGLlypXeWn++CDNqBWhegwCxAAdWSBdPxEQLfN4WEMVDklCQRF6Ao4m8zMALeUkYE8Z+8/QTOp2FcFpZ0uNknBRDsITTZjSYzEajSa82mFbpnU/edIn4omL0+cms3bBapcINhs7OzihULkCqGJXjSSQSmUymqalJrVYjehhWz5CDoqDmnnW7WFgwGQZN0waDwWgy2pwOB0m43W6ktTgK+6LujLrtZ891E4lELpfz+/04jtv1tvU3eh66uvW2lT3rajzrcP0ArrrT4vDWMlYPTXK1pMft8LocnM+hnfeTmLbNrTNRLoolbGYb7SSsLEk4WcZKWS1GE2tz0BRNkCzhJCkHSVEswdhsVpPD6KStBOkgSIqkaZaB1aDlgCYWbo4jSGbFyjUPPvCIKPCSOCYJY5IgCWL+a7O4MwGIP2/f+9Lfh1Q7CC/u2rULiSJisVgkEolGo5FIJBgMohc7OjpaWloaGxsb5PB4PH6//x/WWszlcqtWrVqzZo1KpXK73cFgMBwORyIRxBYjcIxQcjQKNVCdnZ2LF/3g9FM+UCr2KLCdCmzfGWfdc81VV1Ref/rVNziUkz8EYEQWVOxUgmFoQwH2KMG+yVPfXb5s9cjIIflEikemmeOQMVzyEt58802dTofOgqbppqYmdI4oVairq6unp6evr6+9vX1oaGjLli3IJ7yUPliikNFVQ+LjL/0KThzAcdgCRfijzoviGLQ7KYpF4WCRL0rvSs+v+7m6WuWgIRSmCZKDgJgwmU2QYYKAWKM34g9f8ZCIje49acuds9cutFZqNHqbk3S73V1dXYgbTqVS/f39wWDQbDarVCo0juI4rtPpNBqN3W5HaW0IELvdbgSIDQaDzQaLY4VCIYSGEVFUGqTRIIpG2fH76G1wDE5CQAwzdHJr+0Jxs9GiMmmrjFVra2wvffviZ8/99gvnnv+7Gef8ecaMV8+/8B7cTLn9hM/tgn7ITU6WaTdV/WHIONRLUt5GjuEcZpPVanU4HAimwxVhgiBR0P8NiO1Ou8VmlgGxk4BlDT4GiJHgkiTJG2+8sbmtuSgUZMuaggCL/kBuWBYSH4c3yMQhHb8tIMI1+wKcxR5GwyJMqRFH+MKv332xUa/TkhTtIX1O1krSbppkLEaznASgV5vMq/TWDckr+c1lhbcms7ZVqqoatVqDLB3q6+tDoVAul8tkMgMDA93d3bLmX6fX641GI8qIxXEcSZ64I2jY5XIxDIPjuFanc1JkY0szMh1HlPBRs9YSDj4KKx/1tlwu19DQgH4xTLjpblv3C3NDf7iw76Xzmv54lu216fh755hvtXgpL1Pr8vl8jlqOaTZZb2+q/tl6d6DD7/bXurxuwkkQNqfZ5rTbSJvFpjfrnJSTpmjWSXttLEMSNtZBE6zTYrfYjQTlgF2apCmKYf87BYBxcYzLxdgdjlWrVv38Z4/L5bpGBemQcFjt9Fl02/F7Ax1zZBOA+JgmOY5fQLBPkqR33323vr6+s7Ozu7u7Q4729vaWlpampqaGhob6I9Eoh9/v93g8hw4d+mzF7YYNG1asWFFVVbVmzZqamprOzk4EshEULj0Gg8ESLI7nWpuDA5df9spksBfDhk/5xr9fPnPOousvmD1PP1mxQwkKCuwjWLZDsR1g+4GiCCFyxR/mzVu0a9ceGRCjRZf/ZoYQ6BcE4b333jOZTHV1dY2NjVartaqqimXZWCyGYHEodJj/bmxs9Pv9kUjkscceO3DgADrBUpk99Gnoeo7fP46v8MSh/atbQBCFPJ/npWIxz0uFMV4YkV4UxGVbHrv4u441RivtpCmKJWkXxZAwacxsNEHpgc6m1Rk1D87eWFSM7J+8498uuWuhdbFBbzTLclvk9FRbW9vX1xeNRu12u1qtLqFhtKPT6axWKyrMgQxN3W43RVEGg0Gn0zkcjsbGxmAwiIoLoOEzJUepXAh6EfFYJViM1nkTiUQsncql+4PRhNFB6tU6rVZN1THZdOTBQM9jtb6nDfiz8xa9ctnlW77xrd9feOXGRYs3VM67b+GCR+cv+cn8WQ/67esyfWHIkyVSsUQkCEVZzc3NHg80dDOZTHa7HRW9g/JqqDakaJJ0ysJnSBA7oYSSoqAGsaSQRkQTQRBLly4NhyKiWCwKY3CZSC6BKQNiiGomYqIF/gctIPC8CDcBJmmPiUJe4kelg78o/n3l3te/SZg1ZgvlcXIEayUYN0myZotdDxd49DqTeQ1uvClaWdytPLQV9Nav0qxW6/HDMz3kd8ZxXFNTUyAQsFgsSCYBwa6cUYeEE6UcgNKEFjHEer3eQTh9cl4sKpWHFFMIGaOumslkUM4PAsTjOePSRDeXy7W3t6MfBKvV2hEMDPYPfD/S/0hT7nGq/+mq+Ktze986z/fM5dQD37E9Ngv/2aylP7v82geuvfbOzoZkMpSBWbXJRCodiUb6An19XT3NLc2cz2132mk7RVIsQTKc3UWTlJ2zMwRNmh12m42SVRQkAav3lBhilmU4F9wMVsuamuoXfvMbmBUv8IKsJC5Aqcqh/8FVO47fOgGIj+OL8/FDE0URMZ2CILz//vuI962trS0hYMQHo0cEhdFjfX09TdMjIyOfDYgfeuih5cuXr169GsfxlpYWpJoIh8MlPQbCxJFIJBAIIGY6l0hF453RwZ7rK/umlL897dQfXjb76vnzz7lhnqVs8k4If8t2AGwPhvFlYLRCsemMM26+4BJq/ryF2z/aLp+cKFdvFtBS6XjMunnzZp1OV1tb29LSQhDEajlIkoQjfQzKNkqKjng83tvb29bWtnHjRnSCgqwwRm2FOOOPN+TEs4kWGNcC8j14sHhIKki8MCwJ/Bbji3vBi2/PeMxT7TBTDpqmOBkQUyRpgsuNZovRrLPhWqPmvutvH1UeGJ6y9z++/aNK21IjbrDI9eFQZjfKMOM4TqPRjBdLlACxxWIpAWI0ptI0jcp/2Gw2VBGjtrY2EAjkcrmBAWimFo/Hc7lcnxxQK5zJ9Pf3o6KyJSiMiORMrj8bS7NuX7XRqKnReGg6k47fNJTpXD8UXze0dnDoR9HBZ5s7di3T7L3o6vdPP/W9E5UfnDRlZ4Vy9JSpDzV1tg8moqmbM+lALBWNJg9X7ENYvKOjo7W1FTk0szJ9zpAUQ1KEzeGw2miCRNt4QIxgMcuyTqdz8eLFmcSgKAuGZQRchIXc5Z+ACTw87r6c2P3HLcCLBTihEgRezBfFMUE8KOUfG3trMf8BVtwFfJROb3K4KDkLgKFpirTbbCaj0WQw4EazSoMnAyp+eJKwB6wLLdVWaXVaHDHEyLTb6XQ6HA6bzXbUbBaBY51O53Q64VQWdV0XlDwdBsQ47rQ7YI8gIK70+/3t7e2xWCydTg8MDMRiMa/X6/F4Ghsbu7u7U6lUNptFPQt14RJJ7PP5TCaTRqOxWCzhcDiby/Vns+mBdHRtMjmUXtc/sCEy8Ehb7rH6zCuX1heVZmmSddfJy59xcrn02lg8nUtFM7F4MpVOphNxaPEWS6QjqQxcNuoLRews7WCcDs7hYCjSwboI0mQ2mG12mmRYEiZO0BR0qGDlgDkAHEsztMFirFHr3vjLO2JBEGAdgeKYUIDrvOLXREU8AYj/ca873t4hCMKOHTt8chyLhhE7jGDxEaa43mq17t+/fzziPPaknnnmmWVyWK3Wtra2QCAQiUAPVaSaQI/hcHg8GI329SUToVgmGEreef28P5144r2XXbXwhvnfnL9AVzZlDwBjQDGswA5UTP7bGWc8ctmla+fM9c25btW8efPff/8DSZZGHBkH4VAoc8aHx8StW7fiOO73+5uampDVi0oOVFE2Go0G5UCucCitsKOj47bbbtu0aVM+ny9h4vEQ+dhTnnhlogUEEcIxQRAKwhh/SNzZ+pqAvSCAD977xm8b13gNtJ2mKRdJuyANSpmsVgiIDRatxaA1a29blBmetGd46oFfnPfoEvtKg07rcMDBFHkyIA4V1cdCi54ICpcAsdlsRtzwkSHVxbKs0WjU6XTIG7jk/uvxeFpbW4PBIMo3R7U2PB5PXV1dd3c3kjmm5UjJkUtnYtEo5/VoDTiu0dIWezQRT900mM1ls7nULYl0bGAwtP72wM1D0dsHs3cNPLpkcV5xEg8mCWUVD9BMNhtJJZKBXG8sdlMums3KWUGlMXtgYCCVSiFATNEsQdIyHmZsdqfFaodqCfmV8YD4sA6RZe12+8KFC9f3D4hF6HMHy/IcTh6AdgGyjHjilpxogc/bAkVY63RMKkh56UBRksT8gUN/9xTfU0jbQWEENHk1OrORpQmagupelqQIq8MK02KNeqOlRq3rboWAWNwHNuSWqKu0GjXsd0gORMjFz0mSdDgcJdFwqf/q5LDb7aWe65IBMeq/eh1O2ByMkyxVeUTZAs3Nzb29vQjmknLQNO3z+To6OpC2CkFhlITg8/lQ6h6O4x0dHYODg2uTA/3p/vBQNj6QzqSTOTn/76a1a+tvzdy6tFZSNI4C346ZzRtiiXhifSaeSydD/ZFoMplOpOKxTCyeDsdSwVQi2p8e9LU0GkiLlbGZPGYbx3qJWg/hwC24SZYTQ9sJgqRputRtoRqEYxkOrg6pqyEgluTaWvnDlWZl5ePnvWjH9fsmAPFxfXnGHxyCswg1HjhwoKmpCdHDSFeAEHBDQ0NjY2N9fX2jHAgu19XV4Ti+Z88eBDpLj6UPR5/59NNPa7XapUuXmkwmRg63211XV9fc3NzZ2dnb2xuJwFJb0WgUaYsjkUgsEu3p7u5obejsqjXbB844s/7iKxZfP+/c+fOqT5j89glTf3fa6Q+ffc4d37kmMK/StGjpoiVL5yysvHL+/Plbtmz5xMOAs04B6om3bdtmNpt9Pl9TUxPLsiqVSq1Wq1Sq6upqlUrFsiw6GJRKiOTOiDZra2u7++6733nnnddff/2xxx777W9/i6jx0mRgPOwutcDEzv/fFoDqQ74gCKPbR/eoXxax349W/FoCm4an/L51cYuesjJHADEsKmGzGU0Wi8GiMeo1djy1qHt/xe7hyQd+dfZji21r9NoaznpYXIvQ8PgB9dhhtVT9FQ2raPgxmUwwgUauHofs3hgGChMQa8VxnNVqtVgstByUHCzL1tbWtre3I4lFLpeLJeJ2m02n1pi0OrNBH07EswP9a1P9N8dy8exgKjOwLpkbTKTXpRLr+9fWRxOpxStGy2ZsPf+qH7c0xFNDgfRQMrE2Ew/2xyKxZCaUSo1f2M1ms36/3+l0sixr4zgLw1pZuGltNpXJpHcSRpIyHTmwEjeM0gdtNtv111+/fi2zZ9ezQvFDSRCgZRbskxIv8QLEyBMx0QKftwUE2RJGEmEWrFD84+iwsfBRmbgVSNvKDu6fGu1eqjdYIbKjWIamWNrphGmfuNFo0ZtsmhpdnV8ztkvJ7wI/vvnq5WtqVJqPAWLU4wiC+MTlHSR5kqvquDg33FweD8uxRoPBqNUxNqfLAb8ZfjlUFcFAvdhkMqH1H9SvUR/nOK6lpSUUCsXjcVT+A+mVdTqd2+0+vArUHx9IJ9Ymc4ncQGxgMDaQyQym1w6miGZ641yXBHwvzqu9tS8RzSbC2Wg4PZCI5zLQ9SYZgykFqVAukklFMvFUNJFjLaoQrffSXpJscDpraafDzNCrTU6LnTXZSQfhIJ12jqHdbhekhjkXy7pYl4flPDUqt8587Tvvdx4a3cAX3hb5oiBKBfFAaXj9vFfueH3fBCA+Xq/Mpx/Xf1mkjY6OtrS0+Hw+xAGX0DDaQS8iWNza2trZ2alSqT766CPEmyLQCdc7eH7v3r1vvvnmL3/5yx/96EfpdLq7u7uurs5utyNAXHpEZI/X662trW1tbe3p6UGS5bqmZn9dQ72vtqXOS5KuS66Ye/msq2dfM2f+vCUzL0tcN7etcrFj4aJFS5Zcu3jxdYsrKxdUVs5bdMMNN1z3+uuvHQbER9zW0OmWFA4ffvghUliOB8RqOVQqVVVVldlsbm1tRYAYaYtDcgQCgZ6eno6Ojt7eXnScyWTyySefPHQIpfGJJRvjT2/gif/5GrYAhFpjosSPjUmFoiiJRQEmRwtFviCOFoXCttG93/m1BF4ZLX9eAq9K4K29U/4UX9qAkzqS9lo5t5d00wxldmpgoTqD02YwaU3V3Wva9k79cLRs5E9n/15nrKrR6wi7TR77Dj8gtrgEhU14tR7XavQGs9aAa3UGs8njcvkYP+3yUB435/Z7XB6TwVZtqbJShgZ7s8vphTYX44IkSYvF4nA4oMRvXCBkTJKQlGptbaVpWqfToeyfQCDQ39+fkyObza6H1WdT2VwikYvdmrspHR8wWmwbZy34+/TqezrigaFMMJvsjyX7ckm5xGwylkrGoBNVLhNPRFLR+EB/LBZebPU+d9lVI6dM+vOsCx9YNLfFZq/ytFvqyYdvmfdo6uK0W2dkvVoX46EI2s3RbJOXrvfQDh9Xt2aNw+j0vP3oCeJ9FcI9p+79hWnkg5+MDX8Ane4EvsgXeAEWxCwIxSK0DhAlQRJlm1nI4sviFkGE3sVj8JkgQYKwKHPMgiAVeChHhmlWcIMI+2Opul/De3rilHipIB0q8hI/et+h4VP5/UDaXSF9VC7tAfwo+Lfbr8NVFoJhacZBU26S9lidtMlk0ZsseqtDq9UzrPrgh0pxf/lPN35nsUpdrYMT0RJDjKpgOByOkoAYPxI6OUwmE9QSuKHtGut2ubweluNMsHfjHgflc9IIBKPui3JMUaVGm81GURSqZF7qyAguUxTl9/utViuSDlMUlU6nUXpfLptN9mcTA5nBdHptKn1TJru2f6CupanGqH/5Qv9LS7qig2vD6Uw8lcwmEtlYIgUzaxND0UQkkx6M9Iez8UwskksN0E2tvjULt88988C1J7624oLH9RclyBo74VkXWbD5J1c/n7kk49PLtLpDtmKEgJjjPJzbZ7Y611StuP+R+cXhadJrZYU/TB/509X730wd2vsHPj8hmZjojf/yFiiRxIVCIRAI+P3++vp6xAqPx8R1dXXt7e19fX3hcBhJgf9LCPT+++8LglAsFg8cOLBt27YnnnjilltuyWazqBJHqRhHIpGIRqMNDQ1IPFQyTmKYw/W6CIJAa7V1dXW++lZ/Q2NjQ31LfZOb5a68ctasK2deddXViyqvW7J8XuXiRUsWVS5dMH/5onlLFyxYsujGRYtvXLCo8vob5r766suHmdqPiwdL9O327dsdDgeaN7vd7urq6pqaGgSIa+RQqVROpxMt67As297ejuQTAdm6OBAI9PX1BYNB5EnX0tJyxx13HDx4EH3+12Y6+y+/Ab/CX1iQCqPSGLSP5yUB6t4KPC8VhZGisHf4bzv3zfkDj/1h39SnJPB7Cbwjgtf2Vfx+8NqYwapmOIvdbedIr5tgGYvZYjDrLTq1FddY9R3LWz86ZetYmbDlmx9YHSotXoM7XZ8BiKsN9mqjBdfpjTpcjesMJqOXc3NeD+tm/QzNeNwNJHONF7/3yhuev2qRpdbpZAmGPAoS0zY50Pg6DhLD3RJyNpvNSHrY2dk5ODiI0HAul8tms/GBbKo/m84kBgcz2VSaoT1aLZ5aVf2gqyWeSQdT8XWhJKrrMRRNxqH9VDKVSiQT8XQimcpBdorliPlm8vlLLjtYjolgkoCV7z1x6ptnnfnuZdMPLJ6yfy4YvfykHZec+NDyxfrmXl19i7GxQd/o0dXWrnb5evtW7b/vfPH+yfxNCiFRVuhTjrUoRlpO2hOp3P7DwPBbL4v5EUnKC2JRtmQTxkR+DMJf2aDxv8UVCPbCF+EFlTPySvm5aKcoiXAhfSK+Bi0A5zUHkOutKMDSG/DmkApSURyBK/eSOHqXkD9FEoBUANJBIO0Dwl4gHcR++9D0FSrWaydJgqMIH8VwDqfTYDCazUbcaNUa7CZzze7fnZk/AH750KWm1RaNSm+1WhGhix5LE1qtsUpjrNbqDWa1FcerVUaVXqcx6zUU00C7PIzbSbF1bo/P6anVWGz/PuvSX157g58mHRxNyJ0SPkCOmnGSpMVqsdvtUMLBwrrKRwXLsiRJomUil8uVTCYHBgbSaVjPGfVftJPL5fr7++vr6w1yfM9Ud1swFc+kotlULJnIxJPpeDKehLPZOITHKTirTaRSuUw4FFqFGxqrzbtPvVHEvlMovyh/yrcOXTBtZM6kMd2JeeeJY0QF7wQHnRU570qt11vjbnYwPr2LMZFdOOX55a8qRjfPOPjsJOFBTNyoFO+aVPxe2djaiuIt39z9VM/B934tjY4KkHEY4yF7LMm1ZQuCOAqvF7p4BVkoBReDirAqKLw/84jjR/NY4bC78Zdz404wxF9Ou/9z34ooXkSj9vT0uN1uhIMRLB6PiccrfcPhcHd39wMPPLBp06YHH3ywr6+vra2tVw6EGksSCJRC193d3dLS4nJBRSOcAR8THMfVyVFbW19XX1dX76uvr3O7uFmzLpv1ncuvuubKRQsXL1p8w6LKeZWLFi9edOPiRUsrF1VWLl5Yubhy4cLK666f/ZsXnpex6XgJMWySEuLfsWNHSXfl8XhUKlUJEKvV6pqaGq1WiwAxRVFOp7OjowMVRChpndFJoUWorq6uurq6HTt2oM//7OTCf+7STPzVcd4CkDEsQuvrPEwBkfJCPi+NSYf4HZm3hk//qQAeKSqfE8BzEvjF2OTHJexJXvGrjddk1FatzUWxNNNoo20cZyY5h95oM+odWnKNXUOo3VtOe6uAvbvvxF/WraHWGNSU6bMYYh1uNGm1Nm2NWavRanG1yWb0+wwNLmMda651W+oZdaOjtco2XK791VVqbYNT74FuoEcFKmL3iYAYreSg6gAkScZiMZSKlz0SMtWUzqTTg0ODqUDASthVer3ObAoFOuP98UA2mUum+2PJcDaVSMJCd2FYjzqZgcA4mojDEdXr96m1qxaT7PPnXyRiUwXsFBGbJmKAB4pDQFGchPEYEMGpPFCOTjn9sblzH5l36U+XzH548Zwnl839xY1Xv2M8f9Qy+ZB2UqG6LL9SWVxTJqwG0hogrVYIayrGVkzbrbvi/SbTtg3rCx+9n5egS/FhF4oiLwmQ2ZezDdDvhigJRYlHqBdiY14a46W8IFeChrjpa+IEdZx3rP/7w5MnQgJc38lL4qgkFmS7cBFeXp4vFAN5cSpEwxAQl4uHpogHyqS9QNpT8cYz01eoTW6CIWiWIhmoInbazAa9yWDEjXiVTqWu8n34H1OLfwRP3XlxVY1BVaNDibBoToskE06IoQ24zmzSGg06nQHX4jpch+MavXGl1ljHsjTlcbg9LNNIehrJxubY6lW7K6b+6ppZHtI6HhBTsnBDBsRWVMHuEwExUlBoNJrGxsabbrppYGAgm80iENzf34/20+n00NBQfX09klXQNJ2NxuPZdCAdj2ZSmXgyG0uk44lUHLqRxxOwUmUiHs6msqlshqbolTp9U7V++wkrJLBcwpaJ2CIBmy0oLuLLziiWTxNOOoGfMWVs9tTNqrNftF/2F+bi17jz/1x7wWvsxR+1nDoamjbaqRQ6gdAOim3YWJdirBcUA0DsA8Ug4AMKPj19+HsrRl+8O7/9bZE/JHfCMV6AfXFUXtgpoFQBZDuO0umhITlMaR6TPSvklR+5g//f31nHfsMEID62TY7fV0qAWBTFbDZbW1tbV1d3LBpubGwMh8PIoSwM62rAmnNdXV0dHR3IwRdRp9EjEZOju7u7tbW1vr7e7/fX1tYiQIy8FREsLs1lEUPc2NjYUMc01NXX1jXXN/t9vrqZs2bNvPrCq665bsENyxdXLl1644IlSysrF9+4cNGyhZVLFy6Zv3DxwvkLK6+97uonn/y5DH8/FRDv3LnzEwGxRqNBYmK1Wo1+O5BIq7m5GaF8qGw+4s6GDDGCwaDH4+E47qOPPirZFR+/13jiyP6PWgCZ+wl5UZIKMjqWRotSbvig8kGp4lkJ/FYAT0tlz8k7L42U/WGk/NVHZm00GwxOjnByFMM4aRqWmzCY9WYDt5hRVdt0P7niJ/myLSJ48eDUe6J3jq18AAAgAElEQVQ3uFeZdXaL6TMYYo3eUGM01BgMaoNxuUFn0hsfXEq+Ntfx+mz2z3Pq/nSt/5W59YdO6RFB76+uaDB53BbOQ9PuTwTEpZXWUq9EKeGo21oslkgkcvPNN6fTaTSIIkgM3SoymehgNr6230NQGp1ah2sbfLX9/YPpeGoolk7mMqFMIp6CeolkEqLhRBJVvItnspnOji4cV1fXrFxCMr8992IJnHRIcXIR+4YEpomgTMIqeKDMAyCC8gIGJAUQMDAMzRfLJUWZNFkhnVAmTFVKpyql6Zh0drl0wVTpshPFWdOka06Qrp0szVZIF4P82WDsHMXYWQpp4aVSs1l64xfS8NsCv68oVwKA/CAEwZBEysN0KkGUxpCZuSyc4GGNBsgNS2Py9n90K0187L+0BQ5fdaSZ4SWxAOU08AgOSlLDGF8hiUAqTJZ4pcgDPq/gD2LifiDtAW++evIitYGkvAzFuUiri3baSdJsNhpsRhXOaK+1/PvQVPFXk8Rnv/HXH5ykMtqqNLjZbC71XwSICYIwGo1qvRXXa3Rw0xm0GiOOV9U4VqnIZzLn/mXd9DeGTn3n5jO2RS84oP124aTyPABPXDKzjiSOAsSwdh1NQTgsl3T+REDMsiya1g4ODqLOm81mkWQCmSIjNBwKhfR6vVarNZlMcFG0PxXKJKKJeCaRzMRgn42kktGU3H/j6VQyk0pFB7KDja0tWo222mhqWq3fOXWBBOZJYIEEFsnb9RJ2jaS4XFRcUFSex5d9S5x8snTaqdK5FdK8Ckk1TbJPFxkguE4uclMFr0KsBVKLUmpTSB1A6lGIPWVSZLIUBEIIFPuwYm+ZlJom3b9AerFX2v2UJO6XZzKQBJOHfChyGYPmMoIk8IIojUK4LE92ofdlAfbxL6lGzwQg/pd27S/wy+6++263211STYynh1EZC+QIEZYDyQlKKgL0IiJTkWSipaWllIGHcDbyHC2RxGi4RcjY5XLV19cjLF5XV4dy+2pra2fNmnXllVdec8018+fPr/z0uP766x999NESHzy+TRCDK0nS7t27kVCypaXF7XYfyxBrNBqkmCzBBVRZvra2trGxsa2traOjo62trUkOVFJo3759xWJx27ZtL7300k9/+tPnn3++IAc6EpmxPuxxMf6QJva/Hi1QgIpTKE8VhWJBLOYLeemxvR+e/7BQ9qQAfilgT0jgNwL4jQieFsGfRspeHyt/9cmZA2adloauTSxc7WQI2uYymg0as7Haogkt6/rLWS8WFNtE8EZh0jPx+U3LTRqDVV8aUEm5ijFBEPoj5V7Vek2N0aDXmUxa4zKLzqDTPnWZaevppq3fcHww3b3lDN/W6c2jZaGCMvXiRS1mL2tjYGZL6Q5HO4gh/jRATNNQudjc3Lx+/frBwUGEhjOZzBGOODuUyqRvXtvc02nG9XoDTpLObDyRTeUyyQzMv0kl0JJrMpFIyoNrMpFMJdPJeCyXy7EMp1ZV6/SaG0nyxXO/LQPfCh47CQFiEZSJAJO3MgEDMlUMCsqyIjZJxDABw4pKRbFcWcAwfjIoTAXFU7Gx00D+TJCfAfjzAX8mKJwGxk5W5E9VFk9Vjp0Ctp816e9LLt3p0+W/f5Pw598LB3eLxVFRdnuSOUK+AIs/F6HyRSzAspfydkRZjIDz1+Pm/f99FvJye1GCVQ1hCohYyIs8rMXB31c8dHWxoBTHMClfLhUxoQj4MYwfAfx+wO8t3/yX05bX6B1OmmJcDOVkaJJyklajRYMb9dplG3oX7HzyW+KTyrFfVmx5YJqZcKypqTGZTEhDXOq8CBDrdQZcX4PjOoPWYNTWaA265SrLymrH1nvOH733nIMD0/d1n74LP2HvRVNGJoHiJOxXF81sJD8mmaBomqQPA2Kr1fppkgnk2Njb2zs0NNQvR0aObDaLLMlRj3a5XEaj0WazNTY2Dg4OxjPpZDqVhh0WcsNwQSct651SUOuUSmWymUSwJ2i2WdVqdY3Z3LIK3w0B8XUSuF4CN0jYPAm7TgTXiNiVAnZ5QfHtgmJGEftWXnGmUH7qoVPLR2eU5S+p4BeWC1Uni/iJgrNC8pULfkxsAGIzJrRiQoeS75okBjApBMQAEPqAEAT7eyt2xi84cPuKsSdbxLfulQ78VRJ2i1IBomBRKAqw90I3Y+jzIxRhrUGe5/MCf9gk6ku56ScA8ZfS7F/Alz7++OMURdXW1qLkufGA2OfzdXd3R6PRQCBQAsQlCQGqbBcMBru6ulpbWxsaGmqPRJ0cCOyWADFSTJRIYiTbHf/OlpaWzs5Ov99/1VVXXX755VddddWCBQsqPz3mzJmzbt26Uv7c+LYouUzs3bsXEcDNzc1+v3+8hhh26ZqaTwTE6FBRgpHf76+rq0OJgLDiPMPcLkcsFuvt7W1vb29tbb333nv37IElQsZT7+OPZ2L/a9MCBZgNLQiFsUOFQ8U8X3x43+5zfi6U/UrAnhPBb/OTnigqfs2DVyTli5Ly+cKkewuK5t9esNKk0zgZD016GMYCnUydrMaishht99yw7vUzfzOm3JbHPpDAbgnsyS1KLDWvNFsNRwFikiRRfgwOh9M1alxTrdIY1qgXaleYcO0LM5y8oklQdEsgVAAJHhvgsfVFxW1vnBd21tJWqCBmxwNihmE+DRCXCCfk5dTa2hoOhyElPDAwNDSExtFcLrc+O9gTDliNJr3ZRJBER0dbLBaJxpLxZDqZgpFNpPrjKVRkqz+RTiVTyRRUHXd0tGvVOK7Ta3HDKtLyp/MuFpWYhJVJYKoIpvGgvAjKeFAmQFisFAHGY0q4gykFoCgqQFGJCQqloFRKSkxSYuIkIFYAqQJIUxTiFAU/GRTLQEGJ8UolXzapWKYUyoB4ChDPxsbOUe47Z9LWK8/c5Vbzd0XFV34ufPi6dGi3UByFtLHAC0JeFIo87MPyECvn28nV7yY8K74O3VcUpaLEF6UxQSwIIpzN8uKb4uh6fu+VwjCQRhTSCCaOQAGxMKbgDymEEVDcD4QD0z5850SrUeewWViWpB0ulnKRBIHrzBZ86fMbZxd+c7L4rFJ6UiE+h+375ZSmBmdV1Sqj0TgeECNYbDabjTptDXTg1RtrjDq1ao1GtVKlq6k2vdxz8bB/clEDxq4rL5yhECuUY0qsoAS/u/CSToZ2uhioIaYolmEIknRSJGSI5fjECS1KA+A4zu1219bWdnR0RKNRBIsRT5zNZoeGhnw+n16vN5lMLMuiJd9IbyARiqRjiUwylc5kkhlowZhNpXOpdDqZyeUG0ukES7AaqPXAVSZj5yrtvikIEM+VwDUi+I4ErpQfr5bAbAGbVVRcJGAXi+ASATtvrOxkATtJUJxyqHzy2JTy/Gll+XOxwmysuKyM11aIjnKBw/g6UGxR8p2g2AeEMBCTmJgB/CAQ1mLCkKJ4s3L4e2fsv69y7Fmm+JeEsPNhkX9bFPLyrAau14kiL/GHRL5YlIq8WBTEPNSGfxkxAYi/jFb/Ir5z06ZNFovF6/UeK5ngOE6v1yMPJoSDI3Igx7Te3t6Wlha/HEeQMJReHBXjATEzLlB6bENDQ1tbW09PTzAYjEaj8Xg8HA4vWrTo4osvnjlz5rx58z4dD1fOnj07nU4fy8iWXhFFcd++fQ6Hg2GY5uZmr9dbI4f6SIwHxAg3IwJ7PI3t9/uRIUZPTw+aFfT09PT29qJjDgaDgUCgubm5u7v7xRdfRCQxgsVfxMWZ+IzjrgWKQqHAC2PFQnGfmL/joz1n/5RXPM1jzwngRRH8saB8lS97RVI+JSkGi5ghD2YKymkvX3aOXW+w0W6SpQiXgSNZ3K01mIw/nvuTfSe9V1R+lFdsKWLvSeCjA+W777h23VLzAgfuOBYQI2thWOYKr6mpVhlJ0uG0exvc7W3NP9B3vzw7uevkQUFxJ6/4Po9tKCjvlsADH5x1q8vnsTEOgvmYivgzAPG4PgpzeFC/YFm2qakJGRj39/evXbs2HkvYCJtTrzfY7Tar3eV2URzJed0ej7eutrahsbGltRVqq3p7ukOBYCQcjcXSMLUubrPadFq9wWTV4sYap/61syoPgfMk8E2oGMZOKCjKi1g5DyoEMFkE5QLExEoBTBGwMgEoi5hirAzwCoWoUAhlQFJgcMMUEgbBsaDABAwIQCm/GTLKRcUkvkxRnAaEMwB/Lih+W8FfjAkzsX1zFO8vOXmr4aLd7dXiw+uEd/8gDv9dykNdIqxXJsqiY+jeJsCcvIn1nuOuF/5TBwSpRFQUrViAKzyv5vdb+ZHTpANl0n5M2q+UhoE4gkkQCpcJBzDpAJD2A2lYOfxRhZdUW82smzX5aT/t8NiYWlXN8ifumiO+eJL4XFnhBaX4a6X0azD24rRMn7lq9Y0Gg6Fk/o2QMUEQZrMZ1+Iqowo3GbQqtcvt9Xi9gUBvItZ/e7Pv8dqaHcvP4r9RUZwGClMUQoVSUIBXLryk00FaadIhi5dpEla5IBma/ByAGJHHaH5L07TX621tbUX5AOvWrWtubjabzVbZexGtkTIM4+VcHs7lcrshlPZ6G/11HQ3NXW0d3Z1d4UA4Eo03Ntbqa/Q6gx7X61Umfd9KzYHJ8yVwrQRmS+AqEcwUwOUCdgWPzeQVs3jsiqLiEh67UALny4B4ugDOlMBpRcU0EZN7N6YoKkG+HOSngcIZisKsMnHVVImqEFpOEEJTpWS5lFOKt5RJd1VIG8rFeyZJGyukjZPh/t2Txu6qGP7B+SOPqsdeSopbH5JG/sIL+wSYb5dHzosCfFL4shwYJwDxP9VFj4M/2rJli81mQwVvjsLE9fX1K1euNJlMoVCoZEnW2dnZ2tpaJysckCgCPY5/peTXVldXVwLEyB0G9T2O4xobG3t6ehDNjBA2wtzRaDSZTDocjlmzZs2ZM+ezAXEqlTqWIUYjGCKJ8/m82+12Op2tra0ej+cfAmLEDR/1yHFce3t7IBDo7e3t6OhAuhFU4g5Jq4PBYF1dXSgU2rp1az6fR8XtjoNrO3EIX3wL8EK+WBTyf8vv1T+9a9r9YtmzRexZoexFAdskKl6SlI+LiiEe0+exs3mgEBWYNBW8edHpfu1KI0k5ONbqJpyc1W933HnDdw9M3cpjEArz2GYRvC1i7+0v//CnM7+/3FSp19mPBcQoZxzHcTWu0ev0yWgy3d8fyyUTuWQ6m7s1ctOTNf3vnn3Tjml3HCz7vqC4m8d+uvvUjS63z0E7yc8HiBGLjDAxGkpZlnW73SXrCZfL1dzcHAwGKYLBrbjVjBvtdsrO0CzHuEmPi3LTDMMyBAdrU9EcS3AMxbGMi3OznN/toilap8VxA16lVcGzsJvfP6uVL2sQgE0EVTw2t4hdIoBviTDH7kQRnCCCChGC43IeTCpiSh5T8BhWVCggSaxQ8EogAUyCXHJZESh4oJTgY1kBYDwAvBLjpyiFb5TxM8qFC5XCJaA4ExSuAfwNCnFJmbAKCFWgWAUOaU7Ybjtvd+ey4o+T4m8eELe/LvIjUD/Bw9x2aHAsW1N88XfSxCf+a1sAZlbCqykVikWh+FJxT7W4r1zapxAOAHG/QjyAiTIgFocVwgGM34sJe4C4D4i7QOHDsnTnnJpq2k76rDYX4TC4nDf8IHvF6Kbpo79TCC+X8y+X5V8F/MtAfPOEO/qJ6tVL9Xr9UYCYJEmr1arRGXX6NRpNldlsjKZg0YtMPBlM9IfSmXhm/f1UzcuXXfK3S76155sn5ysqimDSK+df1eXknC7GCb0lGIaS3Yhlo4l/yBAjWqc0lpVo47q6utbWVoscCBBTFIUq+8Dkd9kxmHFxDMe5GdZLcwzHkm7ORTKc22O2GE1a6MCs0+MqEx5eoR6umCuBqyVwlQS+I4JZArhCwC7lsYt57CIeu7CIfVuA090ZRXBhXnnmmOJbEpjGY1MKyikFxRRBMVmUJ7EigPPYPFDky8D+08DwhWDsWkxYUyHYTyj4pxa6J/MhpRgEQhyDWwKTUkBIADENhFzZaK5i39Dp+zfeMPxzr/D6PdKu58XiXh6awsPKg/yX1HknAPG/tnN/cd+2bds2k8nk9XqR9rdhXDQ1NVVXV69YscJoNCJf3sbGRpQqdxQN/GlP6+vrfT4fEhAjQFxXV9fZ2VnCwTLj/LEHZPEWj8c7OzuXLVt27bXXLpSjsrJy4cKFlZWVS5YsqaysXLBgwZw5c9Lp9CdqiMc3T0tLi91uR/VHjtUQq1QqJDJGa0+lnw80vUZVAAwGg9ls9ng8Pp/PbDY3NTVFIpGgHNFoFOUaBoPBvr6+UCh000033Xvvvbt37y6xxTwvL8OOE1SMP7yJ/a9WC4iF/KgovMv+bBhsEBVPFrFneOXvBLBJApuK4Kk8yBXAUh47uTAJFAGQyhTSVGzXWdNaa65T0bTJ1Wh2eyxevL+64/Xzfy+BLYWyPwmKtySwWQSvi9j7Itj/3KUPrzavUhuNxwJis9ms1+txaNe0kiKdd2ZvimezwaFcKJvsy8S6+mN3BQfeWPPdXZduHJ1+v6T4fkHx052nbKA8HjtNUJDt/e/4NIYYvWM8IHa73SXFf0nvRJKkU28zOQx2h9Fis1M2huLcjJdyMw6vm6M8LtLr8jLQWIZwsxzDuhjWQ7FejiUIyoCbdAa13oar9TUGi+WD6f08drOkuFXCUpLCJQE1ZJuws0TFaSJUFU8Vwcmy+4Qyr8SKCkwECl6BCQqFBCblS4AYqxCUU0SsQgKTJFAmKZSSEkhTgDgdSBeWSZcrpZkAJttdVybML5NuLJdWYaJBwRuBaJokactEtWIUB3tWTdpSfeZzdWu2vfSUnMwO8+3kFMovZ9X1q9Uvjv+jlbNgi5J4UBp7YXTzSmFXmbBfye9XiMOYeACIB4AwAsQRID/F+H0QE/O7gLhdIWwu++iVi7q917UwS4dCCx770ewdm64RXj+d/5Oi+FdF8W9A2lRefKNceAMTd2A/3GBbs3ohjsPqzagLo2J1FEXZbLZqo8aoMejUapYjw7G+JNTopiLpTDQVi8WymVTo9kjfA231T5LOJ1fVvHDdsh/PW9FstVloAsokCIKWyzg7SPgUIdpPk0ygLizjW9gNS3VtkP8pQucOhwOharRaCwdol8vNwrkry3Gsx+Vxu/2c2825WBfnd3lYl8tqNZnURr3RpMN1KpMutkIzXHGVBK6QwOXydpkELpHAtyVwoQQukMA5EjhHBN8SsOl5cD6PnTmmOFsCJ4nglKJyWgGbxoMpUBylUPJKpQCnvkpJASA4VoBCGRidBA5WgOGTwKHzsMKcMmHZpIJ+Ck9OFmvLxaYyoQ2I3QCi5CgQEwopqSykyw8mT3p/g6FwaBvMtBOgnwwyWvzX35wTgPhf3+ZfzDfu2LGDJEmO45qamo5iiBsbGzUazQo5UAIcIoMbGho+DQEf9Xptba3X60WdEBFOTU1NqRTs/cia4mNYWH6CfC2i0Wgqlerp6dFqtfPnz1+wYAFCw4sWLUI7ixcvnjNnTjKZ/Iet0NjYSBAE0hD/c4DYarWazWaKotCJd3d3lwB9MBhEHnPIqPg/2fsOODeqe+s7I21xp5gaWghJyHuEJCQBQh7YgNuut6j36TOStvfeV2WlLbbXZd1tsAGbkoSXhLzkkS+F5D26MRgIYCB024C7vbvSzNz75c61lbWpISHAi/+/sTwqq12NdDXnnnv+57S0tBAGPZlM/vd///cbb7wBDYU/UXGcklJ85Jv1OXkARkB6Cq+f4w6rVFpL6aqGUuOplJ7WRvf3PDOa/TOU9UcdPKxRj+vU73VqrWbypumvIXCOaspJ0dkaZYYmoM+gtckAZmW9Mf3iVTcuSFitt19fvG9qPwI7x+kDGtiJwIsa9axKPYeonWn6ub1T//zExb9a4JxrdxUGWEZgOZ/I+SWBYVk5wAVcHrvVVmyz2gsXesRATVtjvK0L5y1H4/29fT3xeOtQtHlJT9NwJBFLjFQu+s2CRX+cNxgLNAs852O95MSsKAoRQpAFU9KKniGAyVVM7hr+puSEmrmcOLP1+XyEXvJ6vSRtjmgWyRxyYhRtZj+oBDmWsVgLbTa33epf6LQ5fNbXz1qMqMWIWoLAUgRWIDCCwDIIejSqMkW7x+ibVfo8lZ6pYhuKKTqYisD049s0SGGdMTJkFUYrHqaKITBpJko/i0aXmuE3TNpVJv06Wr+egrMBuhHAm8z6bLM6F6TzKG0BNb4QoOspdENO+maQvhHo14KDl+eqP70LpXUV6mmS1oGtJk7VF/4IYBtbNIrQO0debRvfSaG3KO2QKXUYoEMAc8OHgU40xEdM8DClHQLaQUo/mIXeNaFXJo+/CF7+w8Jnf3yz+uzV8NVp6ksUfMGEdtLwTwDtBOi5qejl0/WXKHQ0+86fFMxfcI3Van1fQFzoWujJY1zFLo+7WJIDpZXhpo7m7r7Yolh/MhGPRBP1i4Yiyb7ugf740PI7uhbdUd/aX1fFKKKX8fu83vJSHCRZVlkhKrLb4yZJkxM1ThP3M6M1M34JJ0WYY3JSy9DMQaOkULBEDpYo+IoYUmRFKRWVEkEOi3JIkOSg4vW5MSB2ua02a6HLGptbdCTnmwh8FYHLjuPgSxCmhC9G4CIDEF+gU2drptPS1JcQOFvDN54BwdmIzkXUZASm6iA3BcwpkKWBSYgyadhbBugA4BUegO1lEEVD2pw2g0O5YP9McOAb4PBCMC6CVA1QmwFsA6jDBLvM6Q6AukG6mdIfjOj6+Bi2njBaAz6jUJ1TgPiL+n2xd+9e4kVaZWQ1TyCIK6urq4uKivLy8ubPn8+yLKGQT4K8H3k1wxATQFxYWBgIBIhiuLe3930BcU9PD4HFkUgkFovV19cXFhZeZ9T1119/ww03EFh81VVXdXV1feRxr6+v9/v9tbW1ZWVlnwwQezweYnBTVlbm8Xiqq6s7Ojqqq6uDwWB1dXUkEiFouNtwpiOJHkRn3NDQsGXLFhJlQqQdBBl/5N986gGf8REwTGghHNfhGPYfUHVdTyHtSHoUvlv25N7JP0XgQY1+QKN/qtPLIVB0rJ/7OgJXpcyXY3aEuhSCL43nnG1IYy/WqO+kTDdqtA+CDp26S6Vfh+CAEdvxKAQvQAyLd2r0Uxr9PAJ7Xp/x4E1er8Xu4RnWpwgBlpECnMRwjMBZGO8CN3YvtVjsAYc35GW9ssBJ2LIw2hPpTw7F8cJrNBbv7o91dQxGo4sW9w0ta1u6pL69raayKhgMNjU1EZvt7u7usrIym81GggMIz5RZJCGnTDKPnXg2zZxiRVH8JIBYDnMMZ7EW22wOh9WT7yh2+5y7zlqFqNUGDl6KqAFExyAVhSCOQBKBfmNLItAEKUmninX6ap3+ik7NVEFuGmQZOuPJCEzDEJmeDjHhhLXFcJpZPzdHuzArfbEp9VVT6nJzGm8m9et0+nJa+xqlfwPo3zShK7Lgd2ntGgCvAenZQL/RfPgK09FIGUrtH4fYdcJwIMa2BJ/xp/HUr//4R0BPYwcY/M5pGjaXTum6hsYRREdGIdT2b0u/eo32Ao12ZqHdOVg0fBigo2b9YPb4OFAPAbQfaIfM6rsU2g3Sr2Wl3spK76bV13PgqwC9CtDzAD5thk+b0E4TfNmkv0brbwK4G8C3gf5ubvqgaUyj//grZ2HeLJvFIeHkYt4jym5BxI6LgYDHz9rsBR6Lz15sYd12mRc8vBAQxZASjjQ0xONx0oEaNyoWi/X19Q0ansGxWKypqamqqqqrqyuZTEYiOPOR+AdzRpIzucwsAGXGLxmwklFkIJN9QRA8Hk9mQksY4szE9X13REVU5Aq/y+cusngdxTanNd/hjM61Hcr+N4MVvhyCyyH4hvE1SPDxpQYsvhCCL0FwPgTn6uBsHczUwZk6OAPhme0UBCYhkItANl7VATTC8BcD4okbvpEGeMEHb1koC6BpAM00j1+aM/ad3PG5OeP+bFWm9TKgKuBI79yxo68gXVWxm7gRMXkKEH/8sXPqkQihgwcPhsNhWZZPoocrKzEgtlqt+fn5c+fOdblcFRUVZcfrI3EweQCRTJDxSYar2+0OBAJ1dXW9Rr0XEBOU/BekS6QIPT09fX04OrKpqWnhwoXXXnvtD3/4w1mzZl133XVXXXXVX4LxPhJidnZ2ejyempqa8vLy92qIP45kwmuUIAhkhmC322tqampra1mWzcvLY1mWxHa815+uq6urqampsbHxrrvu2r17t6piYHWqSefzP+6MkAYcXYWw7QBMa2lNVd/eve9t/pEx6tcI3A/BxpSpGZr8kPqB0VDyXZ36QRr7cX5v3PSdcXDV4fMW3t/c/rCv6s3v8mp2sUZJ4+ZVatbDOv2OBg7oWCOxA4FtCGxPUy+mqFc08JxGvaHRh3adcb+4oOBm+wKfx+uXeJbjAgLvE/F5z+PzWhy2QKHNVegq9PtDUjAg8kGe5wOMyAuVFTWtzW3Rzq5ErLe/v6dvsCuW7KiuKm1urF+cGFzcfyynyjjb4lNvZ2en2+32+/3hcLi0tJSs5JBBShhi4gPzjwbEgsVaZMcqYke+s8Drce85fQMCtyJwGwKbEFiNwDAEiyFYgvC2GIFFkIpD0Gfg4yTCQLkB0gHNNE8zf1ejz9ao09IgexwY5hJmoGUBNNWETstG02k0BeiTgJ4L9FwaTqLQFICmgvRpQJ1Bwxkm9VygXQjQ+ZR2GYCXA/3faP0C+uDc76X2/HmcjFEj6go7TZ8ymfj8j9jjfyFWueg4gy6tIxWm8RDGuOhoCiF9/28OvpKrPw/Qc2b4vAntNsNDWQYTDOA7Jn0/0PZMhXuy0ruBuhfsfvabP76l85db+h7/hfPVZy4bPQDSB8ypPVmpPZR2gFYPUfphWjtiwtshs34kC6UBhNSoTh9IndcVu+6GWXMYFyOyngDv9HFuY0qrOAMep9VmceYV2RZa/T5OkmRe5gQpgNUQOLe1utzoXKIAACAASURBVLq6tbWV4N1kMhmNRsmqSyQSWbx4cdIoMqFNJpPt7e0BoyoqKhobG2tra0tKSggHnBnFBP4ST/HM/JZkApCTGoHFHwcQlwiyGAw7vU671eazOlxWZ6HDNXhj0VHztxD4JgJXQPDvEHxDB1+FWCxxsSGWOBdiBHyGDk7XwQwdTNcxJTxZB5gbnoCJjyudjB4Aw2/xr5gYAYySMSamAe6jNQN9Eo3NyCfT2jRKmw7QmSB9sfnQV3PGv3PB6Jv/q+oQL+jg/J1j/onHPxr/1P9PMcT/+MMNjSLPS/Z1/dh85x8IqsbGxuLxOFFETKSHKysra2trXS5XnlFOp7Oqqupj4uDMw8rKysgoVRSlrKyMGDVEIpGenp4PAsSEHs4A5e7ubrIfiUSi0WhlZeXNN9987bXXXnfddd/61reampomHqX3fQ+6u7t9Ph8xxCgyrNaKjlehUe/VEGcWngga8BnFsmxVVZXFYnE4HNXV1bW1taIoFhYWFhQUFBUVBYPB1tZWEnBNXiAhjLu6utrb24kzXV9f32OPPZZO42ikzJ9N3srM1fd9Cadu/CcfASPzSMVN6RgO4cXzd//rjX3//iOd/lGaWq9R9TqYi8zX6uB7OrgxDeanwXwI5iAwO5VzEwI/HD3fsnrV0sSmjYmtmwa3bLilN/m4bejA+f+DwFsIHIa5r4znPjOe/WJq0otHpj1xZPK2VPaTWs5rqdN27TvzV+mp1t9dcZHT6/YHAoLbz3l8DMexPCdyGBAXO+1ui22ho8jvcnkVQRRx2mvQEAgHGMbH+gNiQAwq5dW13a0dodJgsdvid+GPa2dvTywWIyxU3KhYLNbR0VFfX0/mnIlEIhKJtLe3l5SUMAxDRBQT0TAREJNbPilDHGJxiofFbsN2rAWeQr/T+c7UNQisR9RaBNYYgHgNAmR/hQGIEzo1pFMJTBtTPRD0INCLQMTYeiGo0SlJowpV0/dV6jKVOlujJunYWcKkZgMtGzuywVyApmShaVlohhmeTsPTAZqRjaZPSZ9lGr+QQueYU18yaRfRcDLYf8l5h397X1pVYQpBFZsS48AOiOOvTtUX5wik8Wo5SmO/LSNqBZskQj196D71uTP0ZwDaMRk9adafBZjf3WNS9wB9l0ndD9A72FNCSwNdm/zm61ffvXkwmhyJL1qXXLJ2aNHqjWsGf/P/Ct/ef1Y6fe7Bt885+NaF+3afd2DfpW+/9ZXXdn7l9eeu3P3y11977vsvPjfrpZd+sH2Hu6nJWRoqYz0hzsvxfobjRT8rBtiAy2opdNjyHIW8x1nO8KKgyJwcEpSAIhIzCtJ0rihKVVUVz/NOp9Pr9ZKu7ra2tt7e3oQRv0z4478oBkm/eyKRiMVi/f393d3dooifipBQmTFLZMTkRjLd/VsZ4rDM80HF4fEW2Xxei8tttRa6Fybn3XAk66vHxRIXQ+oCSJ0HqXMQdTaizoLgTIgFEqcZaHiaDqZguxiQq1O5x1VPJ/HEuAHAMFukISDbMWRMMDGcBJCJUrPM41OANhmgKWbtjFx9plmfQummMw6tXjmG1HGs+8ce4rhn57OK5UDoFCD+B39jEISUAb5kwT3zOzL4STeK3J55cOZhH2dHVdWtW7eWlJSUl5efBIirqqoYhiGA2Gq1EgVtxlMig3o/fKesrKyjoyOZTPb29hKkmAmBI5j4fYUTGUB80k4sFuvt7Q0Gg7Nnz7700kv/4n2WORQf9GITiYTL5aqtrX1fhriwsJAkwmfWi4m0g1yS0z9xbOU4rrq6mgDimpqauro6nucLCwtJ4l1eXl5xcXFlZWVXV1dDQ0NpaWltbS15vcSVoq2tjSDjZDL52GOPkaw7cpmhjT/ZO/hBL/zU7Z/8COAEX5hStTEcBqqhR6B6zgZEr0JUGwL5OKoUbwsQyFPpBSnzTZC60bjlZmSy/nmmY+nqJWvXjfREeyOJZCI5uGjxQGxZX7RnecK3+G7htq3dG0eSy9Z3j9zWs2pNf2JFNLmop39ZYmj50hUDIyNDK5auGxpesXSgL5kokYNun9fr9zEMw4qC0+exWrEDaJ7jpjuucwoi65NFHBYgyKzECwITZP0S51MCgugPhTwhq9tTGHAoLo/k9UuSRAwE6+vro9EoWZwdHh4eGBiIxWLkhErgMslyxx3tRhHOSTKK47B3G1mH9fl8Ho/HbZTfj58/g57fd8kVt7HLMssyxTi31uG0OfKcCxmbY//kEQiWQ2oFpJZBarEOFhn08DIEliEwbGDiwePCiYTBEPcg0ISockSX6nS7DjogaMO3gCoEZAScEORB3PZ+DgKnISoHr7SaAZpEoWlmON0Ep1PqmUA7G6DzAboYwMvo8W8D9E3wzjcnH3zwJ1gmjh2JNe0YM5w+hYc/+SD6TH4ST2MNxy0tretpHNKga+NvL09tPxc+CeC2GXA7QE8BuAPAlyn4pgm+mY3eNsQSB+lDGnhaPf2hPy24c+vg4iWru6O90d54IjqYiHdEOnpjHcmBZOu69X1r1y9av+HWdbfcunRk5cjqkQ23jqxdu2r16jVr1qwbWbN++bplazaMbNq0ce2GTf1DS2ob6kRFcPs9XIBRvIzdvvBGZ5ArKFh306xuO8vJYZkVQ2xIEBnib5iRQJAgD5/P5/djwxlyeiKEcVNTUzwejxmVSCTicWz8HYvFotFoX19fV1dXWVlZ5vGyLJOeAWIrThLsOJy0d8xzzev1fhyGWAiWSnLQ77QX2d0Oq8dh8+S53dG59sPmKwwB8WXHG+kuMdrpLjVQ8sUInIew1drZCMw0wPHpEJwGwYzjgHiawRNPPq6dyEEgm7QBGLAY25DrhqQYUhTEDbVAN5uRKWt0EpXOASgXpKcDdQoYPfP0vQPxA9rbKAVViHvp0thwWk/jie1nU6cA8T/4uE/EeaOjoxlOMWPplenW+jvhFITw17/+dUlJCfFKm4iJSTsaAcQWi+UTAOKKigqGYYLBIFEDE7qX6IMzaPjjA2KiSYhEIvF4vK2tbc6cOTU1NRnW/IPegIGBAQKIKyoq3pch/psAsdVqdTqd1dXVGUBcWFhoMSo/P7+oqKiysrKqqsrhcFgslnA43NvbS141mQ+0tbW1tLQ0NDT86Ec/evfddzMIGEKYSuFknQ96Fadu/6ceAU3HZsO6qmoa+lPqzR+OjJrbIaiDFIPAHJ2arVOzEZiLwAKdytPoeZCaB8FCnbKmadsOPnrLnZuHFg1Gu3t6Y/Hebtw/2tHXHYsmFzcNbuhcvXFk3ca1a29btn710IqRVcs2bbply223b1y/Yf2mDWtuX7tp0+o7Nt66adOGTZs3rV6zpn9osL6+HrO/gYDP63XY7Tc5i6rnzfvtFU5R5llZYiVFEGRW5EU+oHB+XmB4TmA5RQ5IDq/bFnAobo/oDQg8Tp7jeZ5lWUVROjo6iC8KOYmScyohn/r7+5uamjIn1NLS0gw4JlE7iqJkTqheoziOIyAYN+MEgx8MiCWGDWQA8UJ3EWtxHcxdhagRCFZDsBKCpYZeAksmjJ0hCPohGICgD4IoBN0IdCHQbsDfGkRVQqoBgkYIGhGoR6AGgVJECYj2QlOhbpql09/XqG9o1AWaeaaaM0mbghlidFaWfq5Zv4hCl1LoqzS8Ikv7NpW+LOtAizh2dI+qqWlNU3VVw8FXRhIWUk9JiP+pQ+/v+2WYFMRaFw1n0WkqQofR4WH4ZC7aBtC2adoOSn8WwGcAegbAV7CxGjoC0BhAh4GqmXcg06p3zl608ra1Kzcs7h+MRwaiiYGuWKQ7ikW7vZF4d1esuzsRiw31JRYPDAxHu+N9vX1LF/WvX7tkzcp1q1euW7tm1dqVt61avXbN+pFb16267ZYNG2+7bfGqVU1d3V7W5wnYbnZaiq2Ou77xvScuurDZzfBimJN4URQVniE6h8wlz/OEiAkEApnOV1EUcbqH00lO01VVVT09PUNDQ/39/X19fdFodNGiRS0tLaQxgOO4UCjU0dFRUVFBnBO7u7tbWlrKy8sDgYDdbidz2kz+syzLoQlF5rdkRJeUlIhKqaSEfE6L3eYsKnAWOXz5LvfSG2aPZ30dN9WZvqGDS3UslrjM6LH7miEsJkYTFyDwJQMZn4PAWQjMNLaMZIKg4UkI5Bgb0RObDVWxSTf8E3UAIE1DE41AjmamEE0jI8VdywHpbHqcmvyGEIIH9qlYKYMwf2HMYiFuitU/K7nTKUD8943j9/x0BhCTHV3XVVV98cUX77777t///vckFy3zQxlclbnl4+9ACB988MFwOPzeprqamhqO4+bPn0/oT4KYy4z6cFY4c29VVZUkSQUFBYqi9PT0RKPRDE/8CQAxEVqQ54nH4y0tLc3NzUeOHPlwHNnf3+/1eglDbLFYCKdbZBSRTPytgJhIJurq6gRBKC4unvicFouloqKipqaGYZi5c+cuWLBAkqSOjg5CEpPeO5Jx/RexR2dn55YtW/bs2UPe4r/nTfz4b/epR36cI6Cr6bSWTo+Po8cP775y47i5I0VZdTBfp+bo1A1jpjlpaqFhEGZBwIqAAwK/DnhICaOnsXevXbN87aru/oFEJBFJJgwmJx6JJ5KxvkRfX3eit7OvuysZ7e6Pdsc6umLR1mj38NLh9Ws3rNm4dtXGkVvXrlm7YePa9as3bty44bZNd952xx2bb1u3bl08FuMlcb6taIHT8ptLLDvPdrKyIHI8L+IzKi/wEs/IPCtiv2HeyLXiXIzLwThFj1fwMaKAjYGDwSDHcXY7zoVWFEWSpNra2u7jRVZdm5qafD4fQc+yLBMuuaOjo66uLhqNEi65ra2NPA8hiRmGmUgPk5MouZwIjkOKxLD+YqvVaTDE+e4i2cqN5WyCYB2iNkBqHaRWQQyOV0BsNDEMwSAECQMTRyHogqANglaE+eA2CFoMWFyHQC0C1Tqo0EGZRpVoVEijFI2SNUqCFAcpH6Jt0DQXC46zv6ZPuUifeb5+4XT0FYC+TqPLTfAKGl5Mvf0f1+j7do1qKKWlNT2t6gaxqBspzih1SjLxcYbM5+QxKYSzBo99o+rvqPvuHnv6evgEDbdnoSeztR1m/RkaPo0xMaaHU0ZKswpg2pSGpichWPX2l5avu2f58NpENJ7o64/0xSOJOBYb9cXx6O3DsqJ4XzwaS0R6Y32xWLw3VlddO7wkvmHDxnXrNmzYsHbD2k3r1m1ct379hrXrN6xbv2HD+js2b968cdOypcvL66rnO+3tc+c8dsElT557ToPHx0lBTmZ5iREEPgOFyc5EQMxxHFmxJIHMdrs9s25DBMRNRnV0dMTjcYZhiCeSIAilpaVJoyKRCLk3kcAvo6urKxwOOxwOl1Esy2YsYjIDORgMhkIhcjUUCgUlRVJK3S6X0+LyOD2FTudCp+2O2XnjpssguBRin7WvQPorBj1MjCYuQuCC4wzxOYQkRrid7nS8dINlxJMmEsPHDBOPSSaMcEqsmsBR7ThthwIaDXSQpdPYlA1SZo02pbKAlk2/c+5X1D/+b1rXtBRunsShkxDLJiBe5zH2P4uP5ilA/KkcdUIDHz58+JFHHlm0aFFDQ0NjY2NDQ0Nvb28sFnvggQfGx49ZAn04KPzwP27nzp2VlZWlpaUT6WHSVCeKYl5e3oIFCwoKCsrLyzN9dRnI++E7lZWVoVCIWFU4nc729nYiBZ6Ihv8mhjjDMXd3dxPJ4+Dg4M6dOz/kBS5evJhA2H8IQ2yxWGw2W3V1dX19PcH6BBNbLJaioiKLxVJTU1NZWUkmEvn5+V6vl+SPHEcd3R1GDjaJuCPv5ubNm0dHR08B4g95E//Jd41r47gr54G9735zi0pvSFHlKfomjbpZo+ZAKj9FuzVKRiAIQUgHJRCUQlCqgdA4xR2Y6d9w59q2ttbqxubOaHdvIhJNJPuS/dHeeKSnL9LXF0tG+2OJSF+8ra8jmYj19w+IopCM992yceOqVatXjaxZvWL98hXLFi9dkkwm2xbh9dDueLQjHmnri8jt9RbOt/HbrqM50t4zFVkJS4IQ4nmF5wx9gyAa+zLPCVhzzPn8PrfXw/h8jBHWSBSERP5LrPjJ2Y7oC4nmIRQKkfgPsoAry3I8Hif8U18fDpojfojktBoKhTxGEds10rVDHE8zp9UTALEsMyxThAGx3WWz5bkKlSI5bf4JBPcgaiui7kDUJgQ2IrABgXUIrEJgOcQKisHjDHEvxOrhqCEj7oJYLIH1EhA06qAG0dWQrtKpCryBMgTCCAQRkCEQEZAQYBHlRpRNM89XJ1+jTbtSnfG11OkXaNNnHDjnwtT//havsmOxRDqlpXA3jgZ1qKlYjniqC/afPPj+rl83jlIq1Ix38E319dbU4/+ubwP6U0DbnoWeotWnzfrTNNxBo6fN8F2ThrLSyKRCkNJNKR28rIItu65bs+EnXT1D4ZJwc0tDR3dbJNKVTPb39Eaamhu6ulv7Er3xvkgk2hOJ9fbGu7q6e60Wf0tH87LV6xYvX7F4yfDiocWDySXx+OKO3v7m9mhDfWdLfXdNRUt5TVOooaFhwbztXzr7SE7WS2fNbPK4GEUWJEaQfAHD6HAiJn4vIM7IlhwOB5ncEo/wTMcLx3EMwxQXF5NwVpZlw+EwGbZ9RkWj0ZihrIhGo/39/eXl5UQBFcSxdOHMc2aG8MTBWyryilRlczEei91iyS90Wix229Zr5h/NvkQ3XaSBCw1LtUsMq7XzT4S/p6NjGomJiuGsExEwUQxTRkcdxsFkw3oJCugmAM0UNFNjOWaUZXQF5JrgZJM6idqbm5MeWTmujY5DXVdRCqY1OI7jJXFDHU4m/Kxms6cA8QnDmMxQSSIDQljVdJII+IRHv9+VzI/ccsstDMM0NjYSorHHsCQjcKqysnL58uV4aUjTjs2JjfSHjIji/Z745NsghG+//TYxYcgYTZAYi8rKSp7nb7rppptvvjk/P58smBLjiA/HwRPvLS0ttdlshYWFCxcudLlcZC7b0NBQWVnJMExLSwtpRCNIN8MBE/hIBMQZKEmuZsB0d3d3a2traWlpdXX1Sy+9NPEIEHBJLleuXGm1Wj+IIS4oKCBrRh+pIWYYpra21maz2e12IpkIBoNFRhUbVVRUZLVaq6qqqqurWZadN2+e1Wol61BVVVV1dXXNzc2dnZ3kVZBmu5aWFrwaJYovvPACUcWc/Pacuv6pHQGI/bTw+qrx5Wk04OD3AJMM6V3j4wPb3vrKSJreDM0bNYpRQYFK2yDt0Ck/BCEEGjBPSXVCqh2CBgjKEF0yTsn7LuGX3HNLS30D7/f5wmJIVkpLK2vrGiRBZHlOUPhgWGprbh5I9CX6IoODQ/FEPC9/friqpDLSWtHZWtPWXtne0tBUW1Pf3F5f21pXkSgv76+qGqqpHaqsXs1W3D5LPDSJ0wF/ZIZcyQV9MicLnMJznMizAiaKJQyOsTRC4ISAByNiv9cXYAKYQjZkvpIkeb1eQRAy9I8cCkqhoBJUjLQOn8tm89gdXkUKckpQDNY31Ld1t/ckYon+/mRfMhFP9EVwRh5RMZLBGI/Ho9FobW0taQnKDCVyqs6AY+xDzIjFVpvDXuy2W/NstoqiBXvPLkPmZci0CVF36GCTTt2mg1shWK9Tqwxb4qUIDBmYeMDQTiQgiEEQgaD3+NYJQYuhnaiDBltsiIkr0DFMrKBjgFhGQDHmMAoEIqR4BNyILkjTN+zvXp6CowiH+uIoMw1BbD6NQ8104twE8RLsqfpiHAGNvGvpnenX6kYfn4GeBGgb0J4E+lMm+GSW+gyAT1PoKRPcYdb2UmlkGkMmTadSyKzp4LBGbd1x/dKh29vaEyzDsoEAxot4pLB+n59lBFFiOI4pK62M42liPLko2dDUeP2sm30MU1HdXlIRKgsHKxWpQhZrytmmWldXnTPe5op1FSXi8zcPuu5p8D3x9S8jg+98feaZ7Q4ro8iSIEp4SefE4BwjMj0jmZjYJMeyrMvlwt20RmLORMwqSRLHcQ6Hg6gsZFnmeb68vLy5uZk0zhLnilgsRhSM0Wg0buiPk8lkPB6vqakhPyIIQjAYJN3wZLaMIXKQLRFKHW6Xy2p1eHyFNpdl4fz7Qlfvv2DGGDVNM8/UTecYycznGoqI046bSEzUBxuhOYBGBvWbQb2Q+EgYnmuG85qRxI5FwwYapgA0A5RNoxw6PYNGUwGaAeAZNDydHp1qfsdv1Q/vT+maiqlhhDArPI5QGmJMjPlh7TMau6cA8QnfFxlAput6Bq3+TThVNwpCeN999+Xn5zMM097eHo1GMwwrCUtrbm6+9957Dx06tGvXrj179mRg9Al/zYdegRDu27evoaGB4N3KysqysrLS0tKamprm5mYSyMwwzIIFC0pKSkhHHenAm4h6P2S/rKzMbrcXFBQUFxfn5+dzHEcgY01NjcvlKiwsrK6uJq1yBOuTywwInoiMiR6X5CfX19dPdL144oknsNeKikVEE+ceEEICiOvq6iorKyfKG4ie+G8CxDU1NR8TEHMct2DBAqvVSjAB6U8SBEFRlFAoRNh3ctBCoVB5eflrr71GAP2Hvlen7vxHHgHMA2JADA0+Ia2po2PaOFKR/vL47rw7DpkHVdO6NHWHRq0w5BCcRhmIiiqDWK7aZvCUMeOyRQeVOuaJw298qbA62RmuqudZkcVnOl7kBYHD3eKZT0IwGGxsbGxvb08kEhXNTT+cO6slyG1Wyjc5xbudyo8czM8WWn92c95vZ+U/8J05//v1Gx/66o2PXHbT45fmv3a2+yBGw6IOPKOT/fU+0afwvMSLAs9KPCcIEu69+WuRE6rP58MZV4JAIjNkWSZtNIQHwqdVRcGAWFEkUeQYxmu1OWxWj8QHJJw/5wswLMOIQaWioqK9rb0vmRjs6+87DogJVdxnVCKRaGtrI+2kHR0dPT09bW1tFRUVGeZJVCS/j7cXOW12Z2GxM78w//8tvwpVfH3sm1cfnXlTahKrmnpVsFIHqyG9HJqXQXoFopYZOHgAUUkcZWcoiQ35RCcEHQgLJ+oRVYX1xKDq+FYGQYnBEIcMECwiwBubgIAIgahTvEZzaeBHwPP2vAjaO34Eaprx1fGP/Hideq5P8QiQsYtHrpGxrRoTGTyN0XQNwhdTf+aOPjFN30FjQLydwgzxU1lwu1l/ikI7KP0pSn0G6O8CiCgVAaRSOqSgDjQEfvHENxlG4pkSmVMkURElWZFCssx4vW6OFQ11kiBJwdLS8orqqvbutgDnv+rq71eXeu5ZX/zzTbMevnPeE/+Z98TP5j/937Nf/u3Vb/3hu+8+9r0Dj1534Off2V990e7vnq5lU0dpMDoD7PnOaRGPlZVkSZAUHvvF/HXcGnsTGWLi+kJWYHiedzgc5FSC+1gJIpaVkBgWBJ5h7cUOr8di8TucjF8SuFCA4X2MwAZ4RSlprK+LROMTxyxZ8yFaqYGBgd7e3paWltbWVmJn0dPTU19fHwqFBEHgwv6wIDnsBRabfZ7Vs6DYXaVcs+/hf9eTM47eMGnPWeajWbk6NR2CyZCaDLGr2iQjd30SBFkQmDWshcgyWuWwcQRplTvWMAcAJGj4mLcapZspmEXBLIC3bKAbm5YF9KkUOp2CZ1LwbBqdadr7za+i15+H2MzUsEbEc1kEoWZoibGTvNEa+yl+Cj/kqU8B4hMODhEwEHyGsMT/b9Z2E0itadqTTz4ZCARmz57NcVxjYyNBkyTfuLu7u6mpqbm5eeXKlYlEYunSpWNjY+R3kR8/4W/64CuHDh1qa2sjYLeysrK+vr61tbWrq6u3t7ezs5PQP0T8UFFRUVpa+iHw96S7ysrKKioqHA4H8SYrKioibr7VRrEsW1BQYLFYJEmKRqNdXTh2i/zeDA1MNMe4Lamjg5iX1dTUEDVzdXU1wcR/SdQjgBhL6tPpk1775s2biXNwVVWV1Wo9SUP8cQAxibhkGOZvAsTz5s2zWCxknZrAETL/Zlm2pKSETDwI3V5VVfXmm29+8Ptz6p5P5Qgc85k1CGKkpRFMjaJRfRy+0PCzcfMKBNbq5rW6aQsEy1UQQnSl0blViUAropoQ1YxAJwIxhC1yu3RQDakSBLi3zrq+IlrL1bcLXGmJIIrBgCixsniMlCVCQDI7EkUxHA7PYwLfX3BjX3HB41fOe/q8Hzx/5tWvnnbFG9MueCf7tMOmC9PmK1DW95Hp+whcbfTw+QwjhSAEvtFsX52LD+AzKS9hhQQv8O8DiEmXOmGYJgJioiEmt4QlJaQEQ4oii6LAsF6rw+qySxJnrxaY8rAgKCGfKPoFLsDwAheqLG1pbR4yuCWiJ45EIonjRXIE+vr6kslkv1GJRKK5uZn09IhhyeX2OPJdCwu8s/Nckujcdf8FaMkkVAPQ/PP1L39Hy1EQSCCQ1KmkRg+qYFjH26AO+iAVgRRpqsN9dRA0G8R8FQTlhmSlDIIKAxBXQlBmUPiKoZQQEOCwXgJvHAKYXNcAq2exmtmLgA/d+xRS9VEMqz4jKulT+Wj/n39SFcEU0qEK1RQaU7F2VINpY2KL1PF9d2tPnwmfxkoJgyE2qTtA+ikT3G5CT9DoKaA+DVLPYp81CIEGAUoBqAFNA2kEnn3tmsSy9kh3v8DIvIJDx8m8MdPZRoYwBoiCIAbZQlvRd394faTZefjZfzuy7Rz08FS0PVvfngOfyEpvA+lnwJGHwbtrwRt+sO8SoE8FaBKNLshF388+YD876rdyIh5zQT4oSNgL4qTKMMQZQEwc0xwOB+7Dw6xtUMIzWTkoK2VCuYiXiQrm2xnF4eypb+T8ZS674AtIPi4ockG/TxT8XlEuqa+v7+zs7O/vTxgOFYQeJu09sVhsYGBg0aJF/f39mAU3skLa29vD4XBA8kic6LDlzy20zHG4fzAnb+uGhWPPzNB+AtDQ+ch/KbrkPI3KQVmTUhjvTkXUFAhyIYbF7Ld7OwAAIABJREFUJkSZIJ1t4OMsgxg+ARDrGYbYAMQqVgYD3TBMxPbhkynsJj4JaLlAy6G16RQ6P0c7AxwxA9TSjNLjWB7z+fu8nwLEJ7wnGQuIAwcOkK4vwt2ehNVO+JkTr2Qe/+c//1kQBNLWxhnlcrkkSWpvbydkaltbW71RoVBo+/btfytJDCHux+3v7w+HwwTvNjQ0JJPJjD6BCI8ikYjRTYfJ44nU7EkI+KSrBBA7nc6FCxcWFxcXFhYyDFNVVVVbW0s69qxWa4FRLMtGo9GMRJj89p6eHiItaGpqqq2trTqxCM9aUVFRXV39+OOPZ7hhcgQySpW7777b4XBUVVV9YoY4EAj4fNj6qqamxmq1fnzJBAHExAySEIQEHxO5Njk4ZILx1ltvnfj+n7r26R8BQzGhGdY8mFoyXCX2LNuWzll+2Lxu3LREo1dAaqNGJVVQapCR0VFzu7HTbtgd9CCcqRbVQESn4hADuCQ0X//Y96/7yX84RYFj5BAbCrLBoKgEiSyHcDykIYac/Ars3tlzF6y4ujhtmqeD6zXq+yr4Roq+VDdfhsBsSC1UTfa0yZambBB4EOAhEDSM7RiVDjTbRE4SFYGXBE4QOQnTxCe05pCJHGGIJwp8vV4c40zO9IqRzlqGMbEkSiLL8jaHy+Kx//rb1l98b36b120PS24xFOBkkWNFNhBgGVbgKyWhsbGxu7s7kUj09/dHIhGSWI4Nj40pNEHIfUYlk8nW1lZjrSkUDIZ9ft88y8IbC6z/1fNtfRGV6sxONU/X68xQBjAfpL98pjp5PsQeao2QatCz6jWqU6PbdLpVx553ncbWYBDAioF9SyAIHd+CkJIgzRpmIAwCDAIBBPwQ+IzNb/Q+BlQ6oAHH/jPdB4d/htJjR+E4nkPjtaVT9cU4AsQLDzvjGaoWhA5ohtJFTz2bOlw7uutc9dks+LhJ356lPjFZfQrA7XhD27LREwZE3k7DJ83oNaAdoMcPUPo7k/UDAL5N6bvp9G7T3l3TXt1xXYNkV4SgKPmJ4IdlWdI5KhmFv8aFoMiVFNu83/3Bf3RUXKc9BfRHKPTHnPQjuak/UOiPQP+dKbV66qh7knZFNjwXpC8Ao1eajs7O0m2TETNpvzg9IhQzEgbCQQN6n4SGBUHw+/0ejycQCHwQIFaUMAbsQV5R5FKhXBRCfCA4p9BSW37Z88989w+//9KSeD7nFjmuWhDsrMCIAdHPsATcl5aWtrW1xYwiJv2hUCgYDNbV1ZG1aDKtxaA4Hu/s7Kypb2loKKsua+Al3u4oKAlduffZi9EfTOl7TOm7KLQRwE7zwTlTxmdkazQFTWDMRKXMlG6iU7RpnDLpFKVRIGWiVSNufWIW3V8lE0YinZ6FKWHiIA4nU2gqhWUS0yg0ndInA5hjTk2lNBocvO6q9Ft/gniR4POoafpXAcTweGXwVmaB/vg9+H9VVV955ZX169c3Nja2tbX95je/yTjOZn7wI9vgyBMeOnSovr6eZMUxDKMoisvlstvtjY2NkUiku7s7Fov19PQ0NDRwHLdlyxaCuSf+MZnfmIHp5GtvIm+9YcMGEkRcUlJis9mCwSBZ9Ozq6iKwu729PUMPl5aWku66k+Dve6+WlZVVVVU5nU4imSCmvzU1NURTy/N8cXGx1Wq1WCzFxcVut7uzs5O8qAwfXF1dTSKjJzb8EZo8IzxQFOX+++/XNO3o0aM7dux46KGHxsfHMxOD9evX22y22tra6upqomYuKioqLi5+r2RiovaRMAHk25BhGK/XyzAMeQa73U4w/YdoiElTXYYhJl+jmcsSw+SONCkSIcpbb71F3rIvxhnp/8RfqSFNw9TgcSnxPm13+2+haYlGrdXoWxG4JUXdljb/KEUvU6kwAuUIVBveXoZ6GJsedOqgXaM7dbys/yNI/2Ic/FSnlkJqYPeMfj6seEL+ECdKmMyRM95GJCaKzItkWfYHPLPzbuqd5TiS44aUS6eL0vRcBG7WwAJIWRCwIOCCQFBN4TFzMGXiEMUiLJnAGoAui8JJ2HRNFDleZEWBEbBu4q9FTqgej4ecUCcyxOSbhNzChYNSSFGCkiIKfEAsdHnmcPY3Zs7Vwbf2Tv9Bldvjk8KMIDNiwCtzXl4OsLLIYU8ostZRUVGROWezLOvz+XieLykpqa2tJcrF/v7+ZDKJW/Sx2Ua8p7+/uaUrUtn1VNe30tLphxR63AuOeuiUF2j2LGiZnJprTl+VpX/pAn3qfxiah2acTkfVQboaUpgPxvZqx7YyhNURCqJEw1OChXQA0n5I+RFWRBzbIMbEfggCEARU4NFMjnHacmjJT5CGo7nT+jgWTPzti3j/JwbBF/JFGFF0eNjilXLjCtRVbXT70XeuQK8B9CoNX6DQI5PQo5O0p0B6uwk9mosey8KE8RM02jYFPT4ZbctBL+Dg5dSbAL0GRl83wT8DtBPAHRR8AYy/OKW14iaeK8l8Y/M87/P5yOmA3CjygZAseD3y1dfMrWTm6Y8D9X8B+h1ADwDtV9TRzZNHxazUd6nU5dTYV0Hqe6YxR3aqLhtWUkgGugQOSdnL2AIBM8OiJMryiWon3AMgCIFAwOPx+P3+DwLEQUVWFFEJirKihPETsYKvtKDYUxW84PVt58A95j1/ulzx5fP+Bp4NMgIjsH6GCZIEEALxSYutz+cjE2aO4wJGiaKIJVLt7bFYzLCpSCb6k32J7mRiqG+wKdo9Mhhr/eNPil67/6KxX9Lqj4B+F41uyYZLgV5pPnLt6amZZs0ENIrScA4zDSlsoJbhhtMmcFxAfDx3w+CGj+UzZ1Eom0K5AE0CaDKAUwGaDtBpxzbtNFo1gX1fvezIA/eN6voYDiY0xP+fsw/yvwQgPgllEihMbiT9c6lU6rnnnrv77ruHh4eJr1YkEuns7GxpaRkZGdm9e3fmRz6ygyoDjzRNGx4enjNnjtPpDAQC4XCY+KooitLZ2dnb29vQ0FBSUqIoit/vX7t2LflgvPdPJTCdaJqJtCAjutV1/d577+U4rry8PBwOFxQUzJ8/v6SkhLS7EctSglMbGxvLyspI1iuBv2VlZe/FwZlbCCB2uVwZDEpSlKuNpLeMbRlpRysoKHA4HIQ8JlwwAcEftE+wcmlpaSgUWrdu3QMPPLBy5Up83k0kbr/99ldeeYVg4kcffVSW5bKyMiJ4KC4uLigoKDQCNQqN8nq9JJcrA4gz34MnAWJiMPwxAfG8efOKi4szTzVx5yRAXF5evmvXro/8SHzOhvwX/s+B2GI2ras6TMGjD+/efd1yBBJj1HJIrURgCIHlCGzWqTt1ejMEfSmqatyEVRM66IFUiwYaNaoX4savnyLwc0jdCem7dfq/09SDCLz49ozHSoUqSbLLIh+UhKAkEVaG9HRnPgnEM3924fzaudZ3TmMghrkCBF5IFUMqf8y0IGXK12grxEwnEcUSAQCvUlgX21cYCsgYb/MSx0kML2JJw1/h8PETaoYhJk11iqL4fL5AIIC5JaPEUEgMykFZUiRRCIh+h382a3npojwIvrN/0vdk3uuUFIGTSwKMyHIsJwcEJSCyxJeN53mv1+vxeMhLEwSBPbGCwWBTUxOZtyf7F/clYv3xnr74UEf/UAPOBEn8pM7/YuDyI5aZcL4J3Uzr19OpWVR6HoUWTFWvzdWnm2DWlchUaFDCjRA06EDRKQ4CwcDBMgS8DhgMgg0cDDGJrhj3MseVEqxOcTo+hrwOWDzroBbuvaEBHhxNYx8JrJgxtIenKOIvzIjGjbAGS6xBlMaew0f0I0+N7z5Hfx3or9Pp14H2cha2WnvcBB/P0bdRaBuAj9P641OwamIHJonhNhP8UzZ6HaA3AHoJqK8A9BKl7QTwWUr/E629MLO1/iZOKCPLeoQccbvdmZFLbgnKSsAfuOGG2V5H0cEHc7T/yYV35aaXTE4pOeM3UOpVk7QrJ43NMh8Rs9J9WWo/gO0AVgMkACRQGpdzW2CeaGiHeVnkFazyz9THBsQiHrZyiRRUgkGGl1iJFQoXusvcc3du/ar2R7Dr9xeX+OcFfGFOYBhBFjlRYEOcEfPB8zwZwm63m5Df5CRIfjW5l7hVlJSU4IjZnlgysQibtiUj8eSynuhAb2QgHh1cM9DymxXz37zjK3AL0G+h1WWTtBhAtVTaNmnswimaGSdrIJPJEBBTmgnLhSHIRri1jjK248nMJEAH988BlHMMDaOpAM4A8HQAzwBoJtDOMcFp4MA3/u3gM49gJwHDKByniX7+/Pv/tQAx+ebQjCL86969e++9996BgYGOjg4iZiANcL1GPFsGWT700ENjY2OqqmZ45Q//EiJ072233TZr1iyn0+nxeEKhEKFhHA4HCXUkfqLBYFAQhIGBgYnWFqQzb//+/c8///xPf/rTkZGRrVu3ZtjTTLcfQuiBBx5wuVzEQ9dmsxUVFdntdhJq1d3dTSS8xAKivb29qampqqqKtNZ9OCAuLy+vqqryeDyGDQMmZd1uN9H+1tXViaJIIi3sdjvxLLNareFwmDxg4iURPBBkTATE5Ja6uro2I9CSMNnDw8MrV64cGRkh+Vu/+MUvyBG4//77w0bZ7XYChQkm/jwA4tLS0rKyslMa4g8fCJ/GvSrSx1NHkDZ++JG3Dn6pD4Le0ax+DUPhZRDcctS0IUXdopvuSNN36dSdELt91WNAbOrUqWEItiBwLwI/QeBOCO6C4GcQ/AKB/zK234zl/r7ZW83JbEApFeSQIkkZPmbiOZUA4llFeWU32/dNlXXsDiYg4D2abUPAhoALARYCScfeYUGDEw0bD/Ck8cOEJXMVnyLzkshjtQTLSCz3iQAxNhyV5BJJEhSJ4Xi/01vgtr54YTECN0DTzW1uziMGfaLMCKzIskGGDwXYMj9OtyLlMyLrMoD4+M3H/icvnPiy1VRWdbW2x2OJZGRgsGewJxppH4j3DS2viqxISN5ttkv0GwG6BmjfoeF3pqMv56pnAGgy63SuZs5BU2bCyd9Q6QJDKFwDAasDt44xLga7WBFBefCGmWAe4uNGKGEGAkYHAUgxkPLrwAtB8Z6Zdu25N7BdKU61MnxFsKXEKUD8aQyyT+U58TI5DmRWoRHFoB59YGzfZPgKhV4E+qs0esUEXzKPPkeN7TDsh58A8PEs+ChAjwO4DaAnAHwM7+jPUehlM3rZhHaa0fMAPU3rT2WjbVnoKaDtyB3snCMysoz/yQQWu93uics7ghgWxbIA650z//qCgoJ37jjz0NBpBwK0Pguo15n0a2ltFhgXs/UV0+Ca3NQQQD0AtVOpBhMKmqBEp4TsO8XrfXwAq/+DMoftw08onucJ80UY4sxcWhAEl8tFmuoUWQ7KIUUOi0FFkcO8WCYEeGuxvdwz+5UtX9Z+CXb99PIKviDAi4zkZPiwwPIi7zlphJIJM/leEgx3i8wDiESTXDJsMFTCNLeUd7TEOtt62lp6mtra2yIddT39HjZ8d/9NqXtM8HaA1pu0EZAezNIjALWBUdvkw+dMUk0mSNEGFM5F9GTVDBBt5GuYaGSmkZlChBXOoVEujbnhyQDLJKYDfTrQz6D0mSB9OtDPBugMMD592sFf/xiphq3EsSUCo5nuU/mgffIn/VcBxOQIETir6/qePXu2bdu2ZcuW1tbW9vb2rq4ugn2JrUlvb2/UqL6+vt7e3ra2NlEUb7311gwk/cjjTYjeX//61/Pnz7fb7Q6Hg7R8kpMQERMTipSM1cbGxn379mVw8O9+97u1a9cODw/39/cvNaq/v3/r1q2Zx2SI5IMHD27btm3nzp0vvfRSIBCYO3euzWYrLi4uLy8n3DBxeCDuvwQfk7awjwOIvV5vBhC7XC6CdGtrayVJshpFzH2JfCIUCk2EwpVG8BuBv8QVrq6urrW1taOjg5gxx40iQufe3t4ho2KxWHV19fDw8KuvvvrII49s2bKFSJAdDkdeXh4RSxA0XFhY+NkyxKc0xB85Cj6lB+hY2qSj7UfQRYkxqnfMNIjAAKKHU2B4z7SlD1w9+MdvDr581kDatFynNqv0eo1aqZtW62ArojYj03pIr4fYIOxuCP4Tgp9C8HME/mvc9HOV/qVGPdTmamSDgiyWypJiuE3gJVcyd81gYmP9wT3bOluYW/jGGZxK+8azHNBUrJpcCEgpkz9NcyrNaxSv0zwEQR0EDYcED5ZMUMLaWbIvKHOyKAk4p47FfhYnFMMwHo+HnPDICZVoJE5miMNBEa+9Yq07x3Euj3uez/bkl73jJks6p7jDw7sV3iMJAeyWgZkjVgh4FKyLIEWUymR1hWTaTfwjCAtFHFJdfsYnhORS2TiDl0tlJUplbW1tnUsOSa4Fj867DF2drV8N4HUUunwymkyTJnSVBnAKQOea0YW0dhZIT50Bs3+AqCCiahEdQACzxTrWVfvxFIK2I9oOKQfEUpNjG6I9kHLowIqynYdzrfuG71GhPm6st5MF91NY+FMaYp/S0+rYSgBbRaPxd7W9S9K7z1PfAPA1gF6YPvbSVO2NnP0vz7xzleuXG8qevvfaN359ycHHs/RttL6dNlQTU+GD2ehRE46sey5LezZH/RPAzsTbgf5YNvqfM9DDOeix6XevuInlOEHCcTaED/Z4PBlwjJvbJEGURYZj5y2clzfP+RIzPTU/S7uJQjcAOA+MldBHRkzaHSa4FKAYhTpprdOc6gBqHUAKgBJQRfBj+Uq/6OMEUZaCPLZ3O6E+BBA7nU7iI67IGAjLIV4MBhW5jJN53s8WFfhC3u++fOfF8H7q3V9cWCnN9Qshlsf5HwLvkT4pIBZ5luHcfn8ZzooPsqLESZwoBeSAN2gvLvrPZD7urtuSizbmoo0AjWSjgUl6xDweBaluetw7ZeyiKRBMhTSdMgEEcpEZm6kREwmI+WAKy4Wn0GgqjWYY6giDFYZnAjwrPhcPfHgeDXOpd2ubj8KjaPSwpqXS2K8AqXoa+4V/zupfAhBnjjkR4z755JPxeLykpKSlpaWnB8fZEPuweDxO4oVjsVjc8PkjkYk1NTWSJHV2dh49epQg0cwTvncn8wAI4fPPP2+3261Wq8vlCgaDkiQRdWBmpkiMtWVZDofDr732GlFEbN26tbu7e/HixSMjI8uXL1+yZMmyZctGRkai0eiPf/zjw4cPT1yjJ78OQphOp4PBIAHEJGyiqqoqw3ATT5bu7u6enp7m5uZSozICiffdqaqq8vl8EwExQbcEENtsNtKmVlxcTCB4OBwmel/SujdRNUH0/uTYxuPHwtzJQc54K3Z34+SLpqam1tbWCE7c7Kuqqmpvbyf+MuFwOC8vLz8///PDEJeUlNTU1Lzzzjvv/QycuuVTPQKYZnr58FHruvGsVhXEU2BQp5aqYLlmXvU/Ny+9p2nZT2pW/sq75A839j/67aXPXzy8+7Sle6cs00wbVLBOo27RwGYd3AHBPXij7sLpEuDHKnWPSv2nDh7otdXxsiiLZaIsCxIniqLH4yGDdyIgVhThZusC37yFr8xkdMqXNrk12qlTASNxLahTkkYLOiXqlEyUANgRmfZiSQDF3zo76A8qnCTKvIjT6QRsNDGxMoCYYXAw7AdJJkJKEPufhGTsAcUyDq9rjs/21CUBlXansy2tXsYrBxie41mR4bHHsciyPok9jocxj0WMjTO+cpm7MHw+Xjg1WuADPMfynCQJQUXkRcXHs35GtPg8tY78p2ZdrF9FwR+Y4JVm7UzcN2NEUmVrWQDNoOAZQDsT6DMBPH0SnHQBMl+FTD+AtBVSXgMTc4ZEGANfSNl0HCtoMza7Duw6ZVNBEcq2pakFb18uaq8eSOtqGjdT4oYcbLT3eWzL+VQ/+1/sJ4fYWxMhfRcay9feAuhNgF6ntT9T6KUctBOgN6b88efVXZ0rOrqXdHQNdLTe2j84uPGWoUfurXrmdwv2b/ua9nAuehCgbRSWTzwF0k9mpbcD9TGT9ihADwP4MNAfy/nl6it9fICVcZojKcKbHL+GO1AlSRZ5YWHRwlnzil+2no/mmY8Wg/EAGO0AaM0kvA2b9CiAHTRqyUbNuWpTFioHiAeIoVCA+iV/JcczIi+UCAp7Mh4WSMq62+0+iSEWRfGvgBgbTIhSiMMMMe6t83CMd2EBJ7jm7rzzEvj/qAM/+1q1WMjwFQLvwxurkEntxBH6IQzxxIfxjCjJPl4MCaESPhTmQ7IY5CWBZQNCwGH95ZKF6F4A7zSjOyaj20F6E9DWAbQKaHGAugCKgXQc7C4Gh2YAZKLVKUbP3DEfCWO6O81om5tBo9NodBZAZ+MNng3gORgHa2cDdL4pNRPsvOTr6v5daWwqkdLhOPYKhzANtfRnFb/xwcPoXwIQZ1AjhHDnzp08z3d2diYSCYKGCRmcSYIhbmUEnJHYi/LyckVRysrKDhw4kMG7H3xI/3rPnj17/H6/xWIhqzaSJHk8Hq/XS/wIw+EwSUmVJKm0tPTNN9/ctWvX1q1bFy1atHxCrThew8PDkUjk7rvv3rVrVyqVIr19uhEdAiEcHx8vKSmZM2eOzSgCZMPhcFNTE3kVjY2NBB93d3c3G9VlVGNjY0VFhdFL/ldVMdEQMwyTl5dHPICJ4UO1oSEOhUIk5s1msxHtBDHuJZ7BhA+uNjLhsIapu5sYicePlzEBiU087CT0rqurq7Ozk2hX2traurq6iOqDYOiSkhKLxZKXl7dw4cKCgoL8/HyPx4Nn/EaRbqfMtx7REJPmBoZhKisrXUZHY2UlpraCwSARYGTgfiaYg+O4j6khDofDDQ0NBw8enDg/+et7f2rv7zwCJH4D2w1jyRm2piTrrLqGnt+/e85wiurQQUyj+hFmgldp1PI/X7Z0WWxx14pox+JY3+L+JUNDq3qWbKlZ9svQ2l8Lax+cvXjctE4Ht0CwVQd36uAuSG3VqTsQuANhHcVWjb5DA79YN6+RCQqiZKBMEYdiuN3u9wLisBLMsxUXL8h77vwAAl4dMCrOjFA0qgSCSp0qhSSLGMiGBNavUn6NCkB8NXT/NWXOEpmVxBArBXFYs1HYlViU2RArcX7eG3D5fF6/j+EEQcFdN5Igy0LA5/P7GEkJy8FQUJZDcqkihQUMiAWBZWxe940B55OXMhDY01lzmr0un+TjOV5gZYYXRZaXWA5HzR7XIH44IM6cUw1gzApCQJYUg49mcEOgzHKs5GD9CeeC5649R/92FvyaWZ0KdJpGFJ2igWbO0s4A6NwsdCaF10+nYAMmNJlCk01ocpaadalGXwGpmxBwYnMJLJNwQcqq0RaNKoRgoQ4KNLAQUkWaOT9F/XDvmfno/mehquMcOkOOpmPFG1FN/J2fs1M//s87AngCA/frR1aOvnWhthvob1DwtSz9zwC9Ykavm9959lsjw6V9/cPRviWxvuWxvlU4NybSMRQvWztUct/I1QceOAc+CNCjFNpGwW1AfcKsPgq0Ryj9IRo9BLSHgfqI6aFNV7CMj5eDmTNCpi+WEMYChsNBRRCKrQXXzF34ZPF5qWIAq4CeyIarcvS1WXCE1pIg3QVgJ6V2mvRWk95A65VAF2nIZEG/6Vfct3melTihFIt/T5BMkFHDsqzb7SYGRxnJBAHExDYRrzgpshSUxKCsyCFeFnjWn1/k4Vw3Pr/ly/B+cOBnl1QLhSxbLXIBgfPxTAmekJ5YHxMQc3huy3A8IwVDYkjEM1pZEbHvDOdzWu5bVgB/Run30GhLFroNpG4H6q0ArqXgClpPUKiXgnEK9uTovpyxr1FHpxkSYaNnDk3B6gg4nYIzKHgahU6nMQg+B8BzAToP42B0QRa8IBtdkH30dPrA4KKUdkhP6+OqahglGnEcEFtP//M+fB/vN/3LAeK33nqLKAqi0WgikSA4mLCVkUikt7e3ubmZLP0T3rTKqGAQT+XefPNN/HX8sd/Fo0ePiqJIGOLMoqfX67Xb7RzHhUIhPD00mONwODwyMrJixQpCDK9cuXL58uUrVqwYMWqFUcuXLx8aGurv7x8eHl6zZs3mzZvvu+++F1544ciRI+l0WlXVpqYm4vJG6NvCwkKPx5MJbS4pKSG2LJnI5d7e3ng83tXVRfKfCSaeSB4LglBQUECECna7nRil1dbW/sUnjuDgDCAmnsQEEDc3N3d1dRE+uM8ogoAnXmaE2l0TihDYPUakH4HC3UYRDTT5U6urq71eb35+/oIFC0hOAYETma8/gokJIGZZljhVVVVVnQLE6ItVxJ4d2zOpOlTTmp5KaWoaoi079t0wkDJ1aVQMUv2QXqqCpRq9cv/0+NaKJfGhgdhgMpZMYEPdwYFofyK6eCCydKh32VAjV3Iga62RMHyHRt2uU1t0aotGYSiMwP9n7z3g3Cju/v/Zla64YTo2PSSQRkhIIOUhEMDgdlWn3ravpNP16vP1pnK6Xuxzt6nG1IDBtJDQew3F3RhCDWCMyxXtzsw/s2OEQxJCyvP84I+HfYk9nXwrrXal937n8/181hNKZq7WwS33/LLNExZEmWaykolXt9t9+EFFD62Q5LO4LXPnL3jyWz7IujHDQSYIQUmSLUGgVCfWFkFinUvUsQ7DNUwy4tZKoCm8/eyy/GLima+QoCuO5NPxRoedxBNvYlHgeM7n8HtdPo/PJ4kk004RZVVSeTfvdfulQEAKqooqS4GwKgeJ0lmRBF60ur2X+W3Pnm1B4BLd/OsGp90l+zlB5gVF4HmZ43jjS5VWfgVBoKaE9MRJ9eX89XfuoZ94gRNpYJ5KitKikSqicpKFFwbzfrPzF7PxaayWCXQGQMBogJmcAvCJJnSaCZ1A1IRoKiB2/Sbi1Y+nkMISnA70KWl6+vF62nc09seQvQSb8jDj1IFdB/k6yMUgS2NyJ9n5Orho99EXjzevxWSe9cj4OuwBIzOSXMKSgjCGpBRIyvkkRBC9h/Z1T7x1FnybxX9itNcZtNuENpv1HeCd3efeuM7Z2dGTZRndAAAgAElEQVQV6YrGumOxxFAk0R3rakl0DST6h8IlMdF62Wu3nkuA+EkGPcfA5wF6xqQ/DQgNP87Cx82QwDF49a4f8F6HrAQlSSBaIing9fgFQVAUclUpSeTiUpYDAUm2W/N+NGfhs8Vn40Q6XJEOr0rT17JwJYMGAewgLIjbwHg70JrScS2DKlgtxEIpA/rN94nnCxynCIFCPiBKoiD6RInjyYkWIAF5PCf4/S5yQcv7/RzxVRTJlmVJdDl8fp9EvvIJDwcltdCQTARkOeAX/Fn5Tt52+WvXno3uA59sPLlCXujlynhe5AVO4FRBIK/i8PElgZgTOEGQZJkjBBIQZVWV5KAkiT5O9Dry7x2dD+9h4W+BfiNANwDtBka7FqB1AK0BaFkaGk7HvexkHMAEwI3TdGu6di4YPwloMwCazsCZLDwWoOMAPAbA4036yUA/mcGz0/CpJngagGeY8anmiaOZD6xz8NjBSUyyk3SilSAKYnIsGIfHV+2A/kYAcWqnQwj37dsnCEJ+fr7f76+qqorFYtFotK2trb6+nsZnlBiDSmApGZeVlRUVFf3FVPipp576p55rqW2REgaE5eXl+fn5drudZq5S3Z7NZvP7/bQ8TIMcA4EALV42NDTEYrH+/n4qk6CUnALikZGR0dHRoaGh/k9HT0/P4ODgrbfeunnz5s7OTuqSZrPZqGrCarVSj7OysrKCggK32x0MBhsbG2lyBwXN6upqURSpiVhKO1FSUlJWViYIQu6no6CgoLi4uKKiorKyMhQKWSzEc42SN12XZZnqTKLGSFXc6cXG4VVhiryUhCny0tu2vx6fu7Ojo4NW9Ds6OsrKyqxWK5VifzEQU++qsrIyp9NJ98aRCvHhh+hXdp04CCDiJKFDqCNtEmrJjyY+qrx9clrlBNuogShkuiDTjcAAZob2p8V+Z++PDXbFu7ri8XhXLN4djXfHurq7EvHuRN+S4cKKUpsl//0Zoxisw6RCfD0kEHwdZK5B4Fp82MrT34/6CnlJ4iWSRUUipTweD8/zVLdAaZjEesu83ee4Yu78+7/nS5r9mFUwKIGgwgDiQkgyomVM/MIckHEiEi0R0FhiAJc0hXacWDm/XBFk2S9Lflkgwc2SwEuKKAQk0ScLAsdLdrfH6xJ4t0+UfLzMETt/KSR4eb/bEVR5NaBIgZAUJNXjgKrIckDgZafXdymX+/jZ8yD4OTRf0eJwuxTeT76nJRIRTYBY4ATpPwJi5TMgVjgpnxfWZV/x5/NOR1OBzrAQEMtSmMmiE9LwKRmkNjwNwEyAMhiiO0wHeBqDjgKkpDSTwceQJnQ4g0EzMvSMo5PMtyB7IWIvhexCyOYjNl8DzqTpygMZv9q5eBjtmTDsCb6yR+uRJ/bZHiDgSwapAhIjGDyuG/wDtbcmPq7Vdp2q7zTpOwDenoa3p8NXWfzHtL2vnHrN9YFEtKE7OpTojkcTrR2xzkg8Ee+OxLsSPV3xHIf3ijkX3z/6I/SEGT9FaFh7AeAnGP1JE3zchB8D2pNp+DGAn2R33v9dmc8XSVgdQUBJDPq8pEaqBiRZESRyTksSEVQobofjR5dlPdh0Ll7HamvM2hqgrWLgEgb2MDDGohiLOoHWCvSmdL2R2C/AYrMus7rIPFD6bY/Tw/nK/J6wJCui7JdEQeZDEgmuk0VBlnje53L5XbwRvc7JEkFeRRY9Hq/P4wuqalARFVUVA4WKGiIpHZLiEbwL8x1eyyWvXH0Wuhfs2zi7MjDPzYe9omzM6vjJufvX40sCMf1HNBCEutNQRbWPl332vAdXXK7fB9BGkPwtQDcBuIGF1wF0NUBXMYSJVwG0nEHDDLHa6Aa4m8Gt01EgbfwiMH4Sg44xEa3wLCY5G8ATWXgKo8/K0E5Lg6dl6Gela2cx+08AExf9Au7c8tnB8ZVf+0YAMdUV0KltXddbW1ttNpvT6bTb7dTRmooKUr65ZcagUJi6UxTFm2666Uu6TND3/S8Pbmpqys7OdjqdgiAEg0E6TWm3271eLy0P0zpxKBQqNQbtdaP9Z21tbdFotKenZ2hoaMmSJZSGlyxZMjo6umzZMsrKtJDc29vb19dHAyzy8/OtVmt+fr7FYikoKAiHw+Xl5RUVFfT1Wq3WUChE4TJVi62urq4zBi3x0m4/GlJFUzmovRp1QKusrKSexykgphpim82mqmoqXpISMFU70HpwanMpCE4B8N/ek/pVaoVKL6j7RywWq66udjgcNNuWNgbJh41UhZhquUpLS48A8Vf+s+ivniApEEMNQpjEmGRbvXdwD7c2aVqEQLsO4hoTS4IoNHUjkECg448XdPX0jEQSJG+iKxZPxOPd8a7eru54NNbX319aWZGVnzfPk7t9Vh8m7XTXI7DekBGvg2ANIoh8tUY67a7FYPXOWT18iCO2SooiEdYkun+v1/s5IFaVQjfHXXHF5b/9kV9jC41AiiKNVTVWJt4IQESMalhMBCCjIpIdHUqaCjWmFDEN789sjduvbHU6wlzQp4S9suKXRL+sSFyRLPg42e/nBYfHaffyPq9PJB3msioGQ1Ihx/Fun10NcGRqSS6SgryqCgFVlZUAzwken/tSLu/Bswswk4tNOR023qmIflHiBZGUoEmBWOR50n5HG+b+jQqxqqhBsltI7rTMSXmCcN2Vl70761idWDIRICZl4KksmskQuaGR4IrTADYbSyaLjzLjY0z4WBYdw+BjSe85PsZYZjJoehrMyNTYY3TmW4j9IWIvw2yWBn69p3pIPzhhTK4SlcSR8dXfA5DwsI6J0AnqaDKJJiHROz0D3y3Zt21mclsa3GpKbgPaNha/kolfTktunnb/dXy0eagr0p9IROOJSCwRiXVHYvGueLy/t7crXFi8INs6b+6866LnwyenoCdN8BlWexbgxwF8LB0/yuDHWP0JkqmBHzfvffCM6nC+IAclmZhNyJLi9/McxymqKMsUiA37YFn1efy/uDz7mooL8FWMvpqBq1i0gkVDQOtl9J60yfiUseh0ohloBVoLg2sALk3TgwysZN+9ZvaGtT9vWnyhqvzK7V0o+mTFHzS2JnKCnxNUP8+7XQ6vi+N9PuKGpoR4OSSostPn8HrdhaoSVIj+yLCYIA1EqhTy8Z4FuXZ77m9eXHsOutu0//aTq9W5Pr7QJxJTRoHMIn1+/IdA7OUVnyP3kdWX6/cDfCfQNjLoVgBvZuANAF0P4PUMvIYhWLzWhFeZ8TIzHARaL8B9LOozoa5pejhj7EIwfjrQTmL0U03oNAadyuinpGunm+Fp6dpZmQdPTzuQPRe/9DKRvH19xjcCiD/3dgwODubm5nq9XqcxioqKaBk1ZYxg8PDnb0RRXLduHTLG5/7gP/oRIZRIJPLy8ux2O8dx4XCY+oQ7HA6vl0TpBI2MGaqmaG5urq2tDQQCNAiNOuRXVFTU1dW1trbGYrHe3t7BwcElS5YsX7586aeDyipGR0dXrFhRV1dHyTUFxHl5eeFwuKKiory83OPxOJ1Ouunq6mraxEbdJ6hWJGX4UF5eTp9tfX09df+limHaNkcvHqxWKzWXoOVhi8Vis9mysrJCoVCKiaNREr9Os51TNNxuDJrzTLdL76FCDsq71Bmjra2NPqC1tTWVC93c3FxfX19dXV1SUkLN4DiO83q9VKp1eAeSqqpUMvE5DXFVVVUoFPqSGmIK1qnbQiOYg84hBAKB6urqvXv3Uhe8f3QMHLn/39sDBIhREhmOs0mM37/pySQTxqBpEkQwm9CYDj0zMQ46MHFVa7gvMNra3U2+SuPxLmOQ2nCUzLR0RCNWp2NudtblfO6Lp8cxe40GrjUqxNchQsOrEFiDwTrNtMIA4hXvHjckhnhRlAWi2yUVYq/Xm/L2Tx0JkhDmRPnyK39z7Y85ZKrCoAoDdTzNN27iDds1HpmCiCnBoJRkUpB06BBkQpOmSgy6xpnmP3z79OUX/bqywOsOFTmCJV4xxEsyLwYE2eVX/KJPcblc+bzHxTllwafySiFfXCSU+zi1gHfxhXwgoISksBwU6beqrCgc5ydZIZzl92f7icWbyRKxik5V8qWAmOdJTjSv/CdAHFDUUAqIeSlXFNZffsm7x83UGQYCFrMmaAJJM5hIA5oJICO/ikRbMQCZGJTB4BkmUlU6hiUofDQgbekzP2Vies9UM2SnJcF0BH6QBD9HmZfjl9+AWNPIZdGR8fXYAxATjROdHYcIj2OMx2Hy7dYDrx6v7zTBLSa0Geg7mMltAL+cgV9iP/zjtMHmaKL9qkR8qCvR2tPTR6Z2uoktfU9iaTzWWWBx5GTbrrxi4dLFP9GfmgGfMKGnWf1pAsT6E+n4cYAeNRMafojkaxz4w/ENZXm8GJIVP2mLlckVoN/vU4gSX5AkkegoyHWuyvul31yR2638lCgEVgO8koWjJjgM0JBJ75u6Lzrzw87jcDuA7QA2m/Eikx4GWhjgtqnooSn44CnjnyzY8lrp6JJFolX12ySJ5OxISkASZYWTJIfb73ApvMuniJIgB3xykAsqdp/X5eFCJG49oCiFCsnVEUhLgFzIC56sbI89a94Lq87Dd5n3//aUGnWuny/0kwtiOoj6//DxHwOx6nPkPnbVFfABFm9i4SYW3w4IE98I0AYAb2Dg9Sy6jsVXm/AaE15hQktZtITVewAeMSWHgD5sxotPwvOPwaeZ8TFAOwPgM0HyLAafOhWfnoa+k/6nmQy+YxPWSP/c1+PANZ7lNxGI161bR/PV3G5iTU8byGhaRNk/GKWlpcFgcHBwMFVs/pLv8YYNG3JycuxGNkcwSLy13W63w+GgX7EUiAMBEkJz9dVXv/76688888yyZcuqqqpSXW5lZWVVVVWLFi1qbGykKXRU/dzX1zc0NLTUGKOjo2vWrGlubqbRcSkgzs3NTQGxz+dzOp0uY1AEX7RoEcXQlDiBsmlJScnq1at37tx5xx13LFy4cN68eXlGPlwgEKgwRnFxcWoTKSC22+25ublZWVnhcDglzu7u7m5paZFlubKyksIuFQen+DhVAE7d02pYYRz+xOizohcMFRUVpaWloVCosLBw3bp1nZ2dNKSHGqaqhgdVilo+B8QFBQUlJSX/LSAOhUIUiP8lFc2XPGy+sQ+jF5zImHadhJNjWNMw/uTurdrRiyfZxZBpg0y7zrQhpk03dSbZtrGpNVuuaFu8rK+9tysWH4oluqJd8XiiKxqPJbq7u/t6OVmcl70wpyD/YiFr07mNREPMXgcJE1+NwAoEliGwEoNVSfNSHVyF2SV7py4LyqIYCJFYViM+g7ZmUq1tahKCOJ1J/P9cOWfgosLx9GJMOuosBgqHERDenRW61d++tiRyk3XRnpPDOnEjLsWgBoOO1787uuXHUT0jcyyDTU4BH5yU2Zef6+YElyi5xZBPEENCniS4LU7JYvVYfSqnFqoqJ6teRfWJgtfvcIfFYEAJcCFRCsqKYeAkqTIven0+5yU+2z3fDZFkPrMzYVEcquSVRJ5olAXSpf7vAjH5l6JAlBmyEpJkRRUFhWSJ5CnyLb+58IOjppBcK4bFDIFgYjHBAgxMf5XySpgYkHDXDENEMcUQFk8FaBpDFMbTAD6KwUeTlNfkUabJY8FkBguZzI/buqmljnYkoflr9IlArmX1JJ4kTY8Io8mDyfds+JVMtJPRtgK8lQAx3AH07QC/xiS3Trn7Gmdn+2CsuyHe0xRPROLxwVi0p7u7uyseG+wbDgfC2bkFDot9Tpa7vfiiySeO1h5n8ZMsegrgJwAysFh7eAb+A4seZrRHmfEnZ/ZUX06sWRROVgRZEQWB8/u9hwTEJH1SITwsqQIfnDcnp4K/Qr+KxasBWmrCQwAPg71D037fdfFAe2Nn++BIR+NtkdxPGmdqjWlaRabePPPV6EW3DXkefeLyx5+78NU/Xv7hRyfddv8vRMVpc6sOt+LzyQLPi4JqtfhychyWAkkSy2VZUkgQR9jttTl9diEUUtRwoazSRQgWCWpY5D2WLLsn+5IXV30Xb0zff9spi5QrOS5knLsGBvOS8b/Pbv5TIOZUn3Ph49dcqj/A4rvN+j0s3gj024FO4uuIpFi/AcD1gFgUX8OitSxazcLlYGJZ2uSICfexuB+gAaD3MMlFUw7OydDOMuPTzeg7AH4HTJwDkieDfTarfuBjkkj4tTJH/CYC8caNGxcuXOh2uz2GNT1FxrKysi+oENP049bW1n+pQowxfu655xYsWGC1Wn0+X2FhoSiKFIhppw6VEauqKgjCwMAADf7QNG3v3r2bN2/euHHj4OBgY2MjhXUqMq40Rm1t7WJj0HiLWCw2ODjY0NBAszlS0t6cnByal1FRUcFxnMPhoN2voijSfLvFixc3NTV1dnamirJtbW2FhYVvvvkmhPCFF16YawyqSFZVlRabaaozVWWkgJimeFgsluzsbJ7nKbV3dnY2NjZ6vV63283zfFlZGX3CdIuUeg9nYorFh9/T0tJSV1dXWVlZWlqaukigku7Nmzcnk8m9e/fu2rWrra2NCj1lY0jG4DiOaoipy4TFYikuLv4vAvGiRYsOHDhwBIj/W1/Wn9EwSbQihgITb+3ZWbxmYkYpZBZh0HaAbcRMPQaLddCqgzZorv+DoyM2FG8baY92RXrigzRqOGFkDvf391dXV+fk5BQUFFit1kv9C5f/TyViSD0YgnU6KQ8vxWQZxWBUNw3rYKVmGhnPWLWIC/uCQc6YWZUkidrsp7rF6QGmBAVB5ObMnZe41LNvmlsDNkjCmYPjM2o2+dob4rGagVhrd1tHoqW1t2ODv3n3t6u16bUPz2/uijQu7klEOxtb2/ujHd1ra0oe8eX/bu4v1mef3+ma6wyqjkDR5YWlFq9rVe6v1zivKBN5l1TKSYVBpVDxi363Tw0WBmS1VFTloKioYsBIgOUFzu91X8JZ7jjXT+zMGGtvrmgLCB6ZfoMa4dC8JPL/job4cCAu/BSIBV7MVeU7f3LOnow0QsBmUgmGLANZE2YBBBmQiChYmvuKAMBGwZjKJ4iywligmYVprJYG9AyCxQSFjzXhmZlaJvjk0ksnD+6hpUZo2Nf+tw6zI3/nf3UPJJHhJEDmdiaw9o72zqVoB4vfTNd3mvAOFu8AeJvZWADaanp+06U9kTXR3tau/s5Yd1eUyJ06uxLtiUSktztRX1djteRarHaH1XJptqdcmrPvkZPQkwxZHicVYvxgOv49mHzUhB4F8HFT8nfMJ+unLg3NFw39sKJIikoC0l0uYhRDtbMGDYvEKUUI5lyZK3EFE2vS0CpAasPL2DdGz1iWqG1ui7e1J6Jt8Vh7W1VsJNoW+UPzwoej82/r8HfE43Wx1vaWrvaGoeaGeFtL9/VD1Zt3/vzJF79z7dUXty62+fxlc7PkYFXOb5+44OHN3w8X2u0FYTfv9Umizel2uTxBWQ4FgrIUDMqFIVmUwqKgEDFTfrZqm5/11PIf4jvNB24/ZXFgrsCFPuNf/tCZnLrn3wZiqv7y+GTONffp9Zfgh8z47jR4H4M3AXgX0G8jYmKybAD4BoCvB/hagK8GeB2D1zJ4FdBXguSyTDRsBD4vARPLARrKOFg19cAlmfDbYPIck35m5kc/ODW582VNT+KxcXJN+/UZ30QgfvLJJ/Py8tzGcDqdwWCQ1h3L/vGgVcnFixdPTEyQ8tWXHm+88cb8+fNTDW3U1tThcLhcLkmSDu+ra2lp2b9//19kx6QhAUJd16no+b333nvuuefuv//+a6+9tr+/v6GhoaamhsqCjXItuamsrFy8eDFtNUtxqsViycrKCgQC9MHU9oVKJlJN5TzPK4pC089TagS/3799+3aE0IsvvpidnT1nzpz8/Pzs7GxZlukWy8vLnU5nakN0xeFw2Gw2i8VC68SCIDQ0NNA+RZp6RTWLVCtSWVnZbORX0+ovvU3VhimdNzQ0VFdXU1dj2u1HtQp0XZKk559/HpGMBhIfuHr1apvNxnFkfolKihVFoX6ufr+fNtXl5eUVFRVVV1f/VyQTwWCwoaFhfHz8X500+NLHzjfugamrTbIC0cRL7+09t8lQ4pZhUHuQqcWgHjGLMFuHwWIM6nadUtld19SQaInE2nrjif5EojfR3dvdk4jHe7t72tvaCiwWu9XmsNpsloIFroWtcwsRuxqDlRCs1MFyDIYxWGJEPQ8hsj4CwQg0rYpby90h1aeIRimJhNXRHgCKwvSW+IhK8pXZV9bOydk3zWsEFNd9lFl+taWqvK+pM9oeI3qfjkhXtDXS1tgbax7qS7TEu3q6OnsTXe0DTV3R3tZId0dXW7S3M9qztKnh1lJ+17yffHju0ZvmXdLpLHhk3nfwhUzyV5l35f26UuCdoVJL8aIcsSzP5SPfkiGRCJyDgqKKKgHiAM8Lfo/3Yj7v1vNtmMlDbP5ADmcN+twykQ4btWFJNOrE/4ZkggIx6U6Xlc+AWBBzVPnB75+9Nz0dTzXjGSw0E9s1BNIwCzTWjAgNHwbEgMHMoSUFxKRszBJ7ClJjNqXjKWY9g8Vs+v4TTz340vNoEo9hnCR1xiRGk9+48+Hr+YKJQzTECGI8/uHkny5C2xl995SJXZnaLjPelo62pSOSMGfG24C+JW15qy1Y0dbSFO+I9kUT3d29vT09Pb3dib6erng0YsnLLyjIJRe0trx5BV6/Y87bm04nRhNPANJC9wgYfzwDPwjQE0DfxEysMWkRML4oY4mwgOfCihyUZVWSiMXDYenNEgViErsjBnLn51qt2QdXTtOXArwCaCumXL3o12V1iyKxjkR7a1d7NN7ZHuvqTcQi1b2JSOfy9tZES6SjrbM/0h7v6OjsjPREOto7Gzvb61ZE6lre+OioCXT0G2+d8tDD33r3/eM03XRQT9u89XvXXntJ02IhqAbdbleBxaXKxbwU4oIhPugPBHyyHFTEQr/oyirgcrLm/37kh/omsH/jrIbC+V6vQvH30PROioWNlf8EiGVZ9nhFxX/FizdfhB9k0V1p8D6A72bgJgDvAPgWgG8C+EaANwC83mBiUicmWKytS9fXmPFyBi5jxpcz48tZfSmDRgBaCWC/6WDVVPzr9H1nmz65/QYIk5qujSOM4NcJib+iQHw4dH5unRbk6Hfn4b/68p8eu3fv5jiOdrbRU6WioqKsrIx6SpQagxaMy41BfxUOh6uqqt55550v3mjqt3Rl3759CxcutBuDTuhTIa/T6aQpjiR/NRiUJKm6uvrDDz+k/ypFBp+DrfHx8ffff3/37t1PP/306tWrBwcH4/F4U1NTdXV1RUVFYWEh9ZcoMIbFYsnJyZEkiZZ1FUWx2WxUrUGRkd7KskxNMGgduqSk5C9WGFu3bn3//feXLVtGndcWLly4YMECQRDKysoqKytpi97ngJjaydGCHFWJVFRUUHbnOM736fgLbdOCsSzL5eXlNCaQsIMx2traqAN0ZWUldUc+HIUpENNbWZafeOIJeuUAIXz77bfXrl3b3NxcUlIiiqLX66UbdTqdHMeVlpbSAnY4HP6nQHzllVdaLJZDhUDlr0Y4HE49h8LCQgrEqXf8yx+BRx75xXsAQnjg5Xf2nFuvgxA210Ii0i3XieqgehzUJJlKzVyugbLts5ViQbaWiCG1NFhcXl1T3Nzc3NnZmUgkenp6fD4flbaTL9SCgjzrwoocFZlWYbBMJ8tSDIYMGh7GoN+A4wGjYLx8cH6FMyz7VMOgyUhv/lsgJnJBRZmfkxW4JH/X8eUQ1GJQO5YR7Mj3VrRUN/VG+yPdPR2JWEe8qa21KdrR3BWJt3YIYbE0WLuooibe2RKNtbQmOjojkUQkVr9kKNidWFVgnTzTrH8bjP0gA5/LjP0cTP4EoB+bx3+ZvufX6W/OOe6xeee126x5pVVZ1bViYSHhU1VWVVVSFI7nObf3Et5680+diMnDaXnDuX5ryOeSORKFR6TDBIglnvuPgFhSwpKsKkQyIRhA/Nj3f7BnylR4VBo8htFIkdiEQTqpELOkze5TIGYQYPChH00ImPRPIZhwsLGQ+jFLpMYay2jpzN4Vo5NIG8Ma1gynJsOK+ouPmSO//arsAR3h5MHJAw8l//QrvDUd7TIndzEkd2Mni7YCAsRb09A2gLYBvJW9aeRim68woHqkoBQIS2XlxXU1rR0tsUh7hyhwlnxbgdViL7DmOCw5Noc1d97WW86FjwD0CMCPmvAjadpTAG1iJ9aCySiLFh89UWnSu6fdtuR/BEGVxVJFDkvGSex2u1U1aHgGSsRogiiJZUUO5GRbshZcumdkOlphhisBXGlaX/c/4dKKzva2WEekuyvR0F5aXRaurG1tbakoK6r2cbKoSLXVNT3tXYPxeEd7Y2NzYyxaF+uNi67aF7ebdM2MdQZCBmFWm2THEdARgyDQxo76ZN+U3e+fNrLs13Z32O8vFflCv79EEkpFUZEFkRMDc+a6VFfO1lu/pz8A9t55Ql3hPK9fFUhHnUis3P5m/NtALBnD4+GLlblbNl6EHwBoo0m/F+BNDLwbwI0MvpUhTHyzgcUbAN7A4BsYfD2DrgPwWjO8JgOvmYJXZOLlLB4lKR54uRktydCXgQPrGBxj990g4YMfYV0n+TmQxqx/VY7Nf/o8vqJAfPjz/hwd0hrqf0IhH3/8cTAYtNvtHo+HZjWlWLCsrIwSD8VimjFB7SbCxti8efM/3fThXVYfffSRx+OhWuHq6uqamhqe5+12Oy1nKopCi8S0TPunP/3pi+ffafGYBB8a9WOabLd169bf/va3AwMD9fX1qQliqprIy8vjOI4GyAUCAavVSiUTgiDQIjExQ1UUt9vt8/lombykpESSpIceemh0dLSuri4ajaqqSgvqtPuwrKysoqLC7XanXCYsxnC5XLTTjuqYHQ5HdXV1bW1teXk5DYD1fzqo6tfr9fp8Pq/XGwwGm5ubOzo6Ghsb/xJ1UVFRQVE4haJe7F4AACAASURBVJ5/d0WW5UcfffRz1w8IoU8++eSxxx5rb28Ph8Mej4eyfllZGX3C9KrmCyrEgiBcccUVVqv1b4FYVdVwOExdR0pKSkKhUENDw786Y3D4gf0NWjcyNcjECvGfNOKJdAxJGzqxkMAQTyBIjEuhdhDDCQjxloP4++2YuPmmliIMijAIG0shAiEEQvunhroKir3hIPn6kyRFInQYCoXou0MvDm3GIBekNjdnt3109BAGKzBYAsEQYoYh6EagT2f6dLYPkWrxMGLWrJrT4CgK+QJSsSyHBEGWBafb6ef8IUkt5NWgLEsBXpBVd1C83JUnXJ614yRBAypk3Jjh114uukqDFTVV7Q3NXZFYaXmZGFDVYCAcDssBlV6elZSUdHV10RO2qKiopaWlvr3NX1bStcCnnZWOT2LwUSb4ren4hyw8D6BfsvACoF9oTv5qCv4ZOHjxMY9efP4jF/7ynssvs5XUWIuKc8sK80pLrwiXXhAuWZQzd3za0YhdgBiuN78oTxR8kiILsl8SfJLfJ/o8klf1kagOmxLy+CXB5Q0KQkgQFSEg8orsl2ROljlie6H6RYX8KEkciX6WRFElKExM/Q1NpOwVRasiPX7+Ofsz03EG0QTjdJbQMMggqgkGfLYcol4GEWGxCQEzabMDGQcyAALMGJs5yZgnWaCZWZ0Ff55xzO6iqomJsQnCVXACk+qwppN0um/Q+fL1fqmf4LES+MZReBfQtk9F2wEm7DsFb0uDWxi8jcFb0/UtmcntAG0Gj234HsdVBMSSYLhQCRAfX0WUiosChUHRas232qz5tmxnvj3L7ci2WfIWXPnitb8kthKPm/CjmeiBDHzNdNwzVWtiUB04UAc+HgHaS8zDD//Y5fD4PcWyWEQ1w26XRxIViXw+kGtImd6rBrLyrbnzf71n4Ci0PB2uAfoasLHzYp4XpUK1pLy6aVGjGiriHCKvOBSuzOXLdvrzVamwvLKmKRZrjXQEijhRlIKlpSVVlfkXB158ASB9po5MGp6S1NOQDnQMxsenYWya1MEEIj8emGBffQ1s3W665fbjOf5Spy3kJL09cl5WnTX3sk03AfRKpv4QGPv9yYvC8zx+1ZjboUD8eSb+T4BYFEWPh6sMzdt5z0X4dwDdwWp3A3wXrRCz+DYW3caiWxh0MwNvYtBNLLqRhTeQxei3I8Jiw5oNJFeDiZUguQLAFUBfaUJD5ndvcMI9f0xiEhimQz1JdORHXCb+q+dzCojp5HgymRwfH0+R0L+xqcnJyaqqKovF4nK5qMkoRUbKYbSJjUIwBWJaLS4uLg4EAs8888wXAzGt6VKuffjhh9va2hoaGpqbm6lnQkdHR3V1tdfrpUCcCuaQZTkYDO7ateuLX87hdfHU00AIjY+P79mzZ8uWLeFweP78+VarNQXEPp+PvrpgMPg5IKZMnDKWSgFxcXFxW1vb4sWLW1paotEo7WZrampqaWmprq6mUmav10tDmykN0515OBDb7faqqioKuJ8DYr/fT8u3NMuHvgU09YOqhKnPxt/l4NSdKSBOFdERQvQ6QdO0PXv2PPvssxzH0VDA0tLSI0D8xYfW/+pvNYQnjA4bI7PTiBgzmo8RwtqEDiHWiMVaUodaEmJ994GPCvr2Zyif4u8hCMagMLVQID6YGRzMLeGLwkogoKiHBmkgN0T5eXl5VqvVZrNZjZFtsbmc1veO7sdgBWSWQjCIwAACXRD0aEw/ZJZqzFIMRnRm9cYLy7mw6g7K7oDqCUg+ibO67A5e9AZCPkmUVIUTVUUJCEFlXkFO44X292dU66BOJ1Zrvg2/4SwB3i/yKi8WqkG7y+n2+xSVBMm6jY4FWZaLi4vr6+sbGhrIxKXHQ2RUknCFy7o4qyB5rGnCbEqawOQMVj8BwDMY/ccM+gWr/4gdPwugbzMHTzfvOPmkbcef+MLppw5ZHEsKPEvtjl6HtYLzCrzvtp/9bNKcqbHnYpN/xFFmCUh2kZcFyagz8URnLHKynwgWfZLg8/n9bi+J/DCCA0TBL5BEK4nnFZEjrUi8QP4TOdKQ94+A+NHzztmXlolYVkszaaYMBKZiMFVjMgj1gnQE0owV02elYoZFxLHYBIFp0sSQIA+TecLEjptJbThpMn+8YD5+960kQtokwrqmYTip4yQkgvL/1UP0yB//l/cAeUMMbQRBHs0IEcQY7kPj68be/ElyRxrcAeDODLzTKA9vNaOtDN4KSJF4iwm+lqZtZvVXmGfu+JbHWxJQiomfi6oGAkGSgKVKouC326xE+2+32Aryc+z2HIdj/rwrH1zxU/RYOn7QjO9K065N17uJJ1qyFezvBQevS4PPTEUfTPnjH7/jF863FGQ5HHafTxXEcH6By2LLcbk9Ih8kERkSaZeVJXFBvrND/dX4uhlo1IzWAbgW3N3+c6/bzZEzgVj3Ol0ezsWrAV4Rw05PgcNdoMgBUVbUcJESDORaFnJEpRf0cZ55P+cffSZjEh6vIbOGTLqegfRpOjRDfaquAw0yOpyiY6DBdF0/bhIe+/6es26544KRZZesvOqXIysuCsm/Wj900f6dR8HnzNpjzMGXZy8qms/5yziB4wSfSM5KyTBfIwVjwzaR93i8nF8gdC/7JT4gCF5iJMMLgugzsn5IhrTAEyc4o/xFonwUYmEuyBI5nd1uvrZw3rv3/Aw+AOAdJrgRoDvS4V0muDFFw5+KiW9k0I0M2sDADQxaz6D1AF4P4LUAXcPANQCuJmnPyTUmfTn4ZMU5+PW7J+GeJKlu6LquJXEyBSr/8gH2/+IffEUrxIfvRLqOEJqYmHj55ZfXrl27fPnyLVu2TE5OHv6wL7/3EELt7e15eXm0Quz1emnVs7q6ur6+vr29PR6Pk7JNfX1zc3NdXR2FsNLS0kAgcP/993/xRlPM+tJLLzU3N5eVlbW2kuxiGt7W0tLS2dlZWFiYm5tLM9ZDoZCqqrQh7Nlnn/2CCnHqwuDwGnlqc7quJ5PJnp6eBQsWUMlEgZGr7HK5qMiBCioOrxCngJhqfFMqEUmSKisrU31vVNpLUzxokl9lZeU/rRDbbLbKysqamprKykpBED6tDh/6PxVQUPmE3+/3+XyyLKeKr6nCfAp//3blc0BMa+d071EyfuuttwoKCrKzswVBKCoqstvt+fn5RyrEX/40+S8+UsMGEEMSWIUhudFoMrPxfToJCe+Qz89JiF/6+J3LWyZYLwYqJvEWqSWAiVcDXYKI3B9KmgPXXBoOBMJBY55FJva8JAeKentTDrZYiBs36fXMsyx05rx4crvO9kO2m3gYMz0IdOkgoTG9GCyfZEd0oi2Ov3HaL25d8Mur8y7tsebUCP4SkVedzhZLdo8ru5K3c7IsyKUh0q0etOTn//bs4gnz6IRpuca066D87h8GHGGZUySZF1RJtjudPp4j07PEsV+k3jLkm4rj/H5/Xl4enaLxSUKeyx7Km7f9lBO2zzrz3SlT3jnmhFe/dcb2E4/df5JJPzMteSyrz0jX0tM0hh03EXnuhGnq7uNmv3n8GW8df/L2E49/7Kwzfn/Od/ZOOWaSYTR2OgQF3XPcDpXzyrxEplyJKYRoNNUJPM8JXoX3eDiXy+c2MqEDokjSY/2izyuR2VkjxcMv8JzIkW9YTjwExEaX0qEKsU8UbbL01Hnn7Tcfh5gZmukojT0Rg5MxOEVjZiOycqKxnIDBcYiZiZjpiJmKmClkATN1IjI+CoFpkEmbYDMnTVOgKePDU89ALz+rw4Pk8EhCDU0iBImM+CsZavVfPEG+ln+KdDpCou0mLmtJjZzBB+DYmrFdZ6DXTXAXC3cyeJeJdNFtNxZSJyZADDcD9AqDXmbRq8zWR2ZLYlgK0DOYTpeS21AoRDV4pE3cZrfm5S/Mz7/0ygVLO+bu23TmgZXTDvSnJ6NAj4CJOEjeANBL6Xj3FPxO+uSfTXvHT3jtTxc89vKVN959cVvXL/zSBeU1l9x038Wrb7i4atH5klAgCUWKWiwHQ3PzvQ+2fyd5HSBAvNqkrwYPx34q+DySIAtkgkRxu9y8xx+QFUmSvF6vy+VSFEMtJIp+vz8nJ4cnoKrIsjD/Uuftv/vp1h2W7Zuv3LvvF++885vnnrrs7bcunkh+B0GQxCzUp2iY0SGr6ewENh3QzWNw5gF99qR+/Jg+65P3fpZ8ZXbyFdPEs4y+mdn/wRk1ZfOsBbxHcBPrcMlDQFcg9i7khCTJkaLHbQCxIiuqX+JJky8v+ojCQqA9AySiXeAlv+DjRRJVp5DMZl4NiIokKqLsdEkN4ay9d/9Qf4DV7zATz7WNGfAuk34nA29nSHn4JkMyQcXENwJ0I3GfwOtZvJ6B64G+njAxvoosyavMkyuYj5cepb10FU6S6RwdTZJeKGJBTK5rv0aH91cdiDVNO3DgwB//+Mfbb799eHi4ubmZuo81NzdfddVV+/bt+zf2NUJo2bJlubm5LmO43e6ysrLGxsbe3t6+vr7e3t5uY/T09PT398disdraWmpxIMvyrbfe+sVbTBFqU1OTzWZraGigYoAUVtIfKyoqKH9TySyZUZXlW265JaWF+NutpP4ybbZLcXnqfl3Xb7zxRgrEKRpwOBwUiIuKimgcicfjoZKJFBBTd+RUMrMgCDk5ObW1te3GaGlpaWtro8+/o6ODRls7nc7cXNLu8I8qxDabjWqIq6qqROPjI8XEn8qJfal7/H4/FY2kwLe4uDi1/ndXUkCc2hspJqY7ZP/+/UuWLPF4PDabTZZl2gV4BIj/9rj6v7gHGkFj0AjuNICYWPWTRCuk6yTRXtN1PAb3j/5h92zxQLo/yUo6K2Hw2YKAdNiiIKAgEISMuun8ULFYGCQYHBCDajAYDIVCtKM8dXDm5OSQSy+XP9s6b72/+8m8Zc9dGNl+ZtuHUzo0pheCXsh06WCpDlbozDBm8sZOMo+dYv7w9Mzd35319Pk/eOCC8+49/8cvf/u0nWce/dRF3+tzWbxqiV9VCopUv4d/ZXa7BlaPs6s0ZqXGjj5xdoc9KHKqTKI9RNHhdHI8F1ADQUWVBdHlcsmkpCNRZXpKNyUJckG21e/Iv7MydHtd6+8sljs5+abOyK2VVc/86rz3pk9/67gTXz7pxPczpiJgmjBlYDAdg5N0djpkzDpIgyBNJylx5jETmzSZNfbUl7/ribskQfJzXjfJaOZ5nyD6SZ1JFnjOI5MCscfvcXm9xGxZCiiCJHESMT0V/Qrnk/08qSVznqDPK/Ik3UMSRUWS/wqIBQLEz3/vnP2mGZCZkjRN1UwzEXs8ZmZp7KkIfBuDMzE4A4PTMTgdgVMQmI3ALAROQuAkDE6G7LEQnKEzZ5BfmU7RwMw9U4+bGF0xNnGQxBKSA0bXSKwDJM5NR4D4/+Is/de2Qc5jInvSyA1pgn0P7h/at/NMuJtFOxm0i8U7GbSDQTsA2k4Uw2Qh5WEANzPoVYBeBfhV8OGrJ1QUcbIqf8bCxlo4HKZATC5l822LK6pr62r8SqClufrWQe6BHvuzXTlvjXxr7HoTemIaen0qeh/gDxn4rkk7kPEhAu8h8CFi/gxnvKt96409v9z57oV/hlPeS878YOL79/1+jr3gfKfXa/UIHofwp+HZ2lqAVmfgtWa0lnmh/yei39BXyKqgBkkvigHEsizTFm1ZlkVjcBxHFYmqKvr8vtxce0eXP9FV0dlcOTjMDS4Nt7ZXdLTKt9y6YCx59pbtc//8zrwDkz+ewMccgOATnTkIiY5iUgdwfGpyr2l8hxm9wOgvg8ldx+57/9LfbcppaAgtqq8Jlnpdbo7jPRyZsSHXqqJIFkFQPB4nx7tkYqpsmCpyKs8RsTHPKSSpnUSECByvcgKZEjIyN0OKHFDVgGxUx50uoaV04b7fnQUfZPQ7WXwHQHex8G6gk746Bt3GoJSM2CBjaj1Bmu02MHgDQ+yK17P4+jTtWqBdb4aj5v2PFMKJcQ1h4hquaySmkFwv0SrIv3Zo/T989FcIiCnH0DYpWuR75513HnrooWg02tpKohloqTUej0ej0Y6Ojubm5t7e3jfeeINWTL+gtvq5/Qsh3LRpU35+vsPhcBvma+FwWFXV8vLyaDTa19dHbZsoFkejUapqLSsrU1V1zZo1dI6ekuvfbpT+FiEUjUZ/85vfeL3eysrK4uJi2txGgb62tpaWituM0d7e3tjYWF1dHY1GP/jgA/pydF2nf+pzT/7v/phi4nvuuScrKyvVS0RXKo0RDoftdjs1uCDXteQ7jniJy0bbkNvtpnXZ8vLyQCCwcOFCi8VSXV0diUTa2trozqdk3NjYWFdXpygKrUPnG4NmZKQs2HJzc61Wa1lZ2V+MyaqrqyVJoiUxKpY4nIN9ZOKWDDqV/HfZ9+/eqSjKAw88kLoq+Ee75c0337zzzjvvueeeUCg0f/780tLS6upqRVFoMEd+fn5WVlZOTk5+fj5VjAiCMHfu3IKCgsPBheLL32qIGxsbJydJ8/sXP42/+9y+UXeST0VDPawbeRtQh3QWnFzDYJTESN+vHYz89pMZPmTyQ+DTGFFneQz8xsJhQBcekwxkgQRPABkyQR2oT58TqhVKZQOCJWO6ldJwKBRyuVx0ikAUxd7e3qVDy/t62/uHRkaHV1w9sOrm+mWvfL8taYoi0IdBz56M7neOGTiYxiczj9dPAvAkkz4L6LNYbQajpQGYzkymmXEmmDwGbDnvjPXz5yy3LGzzZS/Pkt4/ql9nl0F2BIE1Sea6Z7/d5VJFn0JqqpIkeTweOvsRkBSFP9TtTk86QRCcTiedGuI8vgAvNUTau4ZizT2DPYlINNHdlBhsHhheVVd+22VzHyqpvLWuapPPsf3M7yJwtG46BoHjdTZTY0yQSdPJQlzPDprA27O//7srXVdXNsXinZHO1raG+tKiMKeIHt7v5TnOz/k5t0/wcjznd3k8djcxZFVVrySQarDk5UWvxPl5XnZJEi94A36vxPGcYdyiHCoykelmVZL9gmiXpS1nnzGelonTWJQOYBqDTCxm0zR2qgaOQ+AEBI43luMQOBYfWo7BzDE6OwuDUyF7WpI5MwlOQ6ZZ48zMj92SfnCMWHbp2GhOh5hoEMm5RT7fjmiIv2IfGTo5pUlEs06ubXegvc3jO06CbzBwFyAyCULDAO0AmDAxg7YzkGLxVhZtMeHNgCyvgbGdmZFF+bJsXNEeBsU0xKqgoCAnJ6eysnT58PKRJcP9I309/X19A6Nd3SOJ5thdy60H3/ghevdo9BHY+/pP395y0b4PL5iYmLVHz/hABx/p4ANo/jNM/wCDj/SpH+lgL2I/0Nk9yePffPuiBx6ds27FL6+LXDKxeipcycCVhvPaGrBj6fcUn1MQFFFSRCXg9Xn9bh8xbFNIog01S6VfnYIg5Ofn+/1+USKW5HX1zQNDS7p7uhNd/ZFIb0cs3tHVFYkMRKMtN2xwxuO1vZ2FS4etf3jk+0k07YDOTqB0mEzDSbP+dtrENoBeyYDPAfTesc8/dlF3xBZtr2/vjLdHOyKx9qaGlsLCEj+5gs1xe2xer4/jRL9Pcbryvf48UQiJQqHXL3KcQmzaSD1Z9guqT+R9Iu8n/qoEo2XDf5mE6pD0TVGWOafL31G5YP+DJ8OHgH4XgzcCuImB9wB9E4s2mtDtDLoNHGLim40Gu5sBuhmgm0xoA0t67DYAfT2r35Ch3cDo68DEzZfp4zs1bWwS65MI6UmESXa3Do2T9yt25H7R0/lqATEt8n300UePPvroqlWrmpqa6uvrI8agOWrRaLSzszMajUYikc7OztbW1vr6+ueff55i9Be90MN+hxB66aWXaGcbtSIuKiqSJMlqtYqi2N7e3tPT02UM2q7e1NREq6fhcLivr4/2zKV0C5+DoRSbjo6Ozp8/Pz8/n+O4oqIij8fjcDgEQaA2DqIoUixu+XQ0NzdXVVXdfPPNmqZRFNZIKMGXHXS7jz32GCVUm81GK2RWq7XCGMXFxS6X6+8CMc/zTqczFVBCFR2UFAOBQKpCTJ9pa2truzEaGhp4nqfsa7FYHA6H3W63Wq3Uea2goKC0tJQCMWVuCr4pMqY//ttALMvyP5WvHF48HhkZufjiiwVBCIfDPp9v/vz5ubm5eXl5OcawWCzl5eVlZWVfHoiDwWBLSwt9jz53DHzZ9+wb8zjaVaEbHXQaxmhSp9oJjBGE2hjW37n7id2ZBTrrxsBnQLBg3Hox8Br30DtTWCxgIGtMQGMCL58ZauDLeEN3JAc+00tQIM7KyhIEobu7e3R0dHRg2cjy3uGR/oHBvs7lAy1Lh16YE8PTOjHowmAgec7ox5et1k9eqB0D8DQweSJAs4F+HNCnGAa6LNDSAZ4B8HSQPIr94LSpr553zh0Xff+Rcy0fpQ9MmJfo5kEIVmnM+ue/FfOqok8WSfqzIRH20kxKRQ0IksPhoBdatM7kdrvppaAi+BeVF7YkOiOD0d6BJdH+7khvT2+kr6Ort7s7trytvbd3oGXZcHRZzwNXZGEwe8x0FAQzMGB1xqyxJo1ldJYhXr8m885L3X1DXZ2R9obujq5oZ3ckEol21rU0VFSVi6Ig+vxcwCtyPr+f9zu9bpuD53i/LOUHVLvoEUSboHg5ifdyskcK8oKscIIo+DmJN6KsSU8SaUtKAbEkbT3tlHEzAWKcBiBNp2OAxpg0IiaeYVSyp2MwDYFMCNLhIVVx2iR7LAanInCCBmbpabMnwRQ4/ST85LNYg1RuPkEah41LKHIOY0h6Lr9OrTn/Pz6zU19wCKIkub7VkP7g2J8KJnfMRjtYuBOg1xm8ExClxA7iLEEXtJNFO1i0ncFbTXhL2iEgfoWBr5vWJuZIQtFhMHxoVZKkrKwsWZaHli1ZMTS6dGBocKhvcLh/oH+gr3+ke2BFz2DrmtXux++1vv7qFevXKt09ZctXBW69reCJp7P+PD57P2I/RmCPNv1jBD6B7D4I9kF2LzSNQUaDQNubiTfPHr9zlr6KhasYvIoEFKM17Fsrzgh583iB2DuIcsDr9/hcXlUhaR6CIFCVo2QMQRAKCgpISKqkijIfCheXlFTW1NZ0dLQkuiI93bHenkSiJ9oZjccjPe2JgXi8S+TC3d0XJbVjJ3QmqZvxvrSJtxl9B4Bb2IkXWPRW+gdvXbBkibetrauts4NQTizaFenvirVFI/FYT+uqq5u7e1sW1S4uKysvKy2vqqosKS0qDBVV1OX7an9il6+0uTxen+jnZE6UOT5AlMeck+MESSI9DGqAxEWrKpF/yLJod3jiiy4fe/hk9DCjbzLjOwG8B8B7AbzbhDaZ0Z0A3Q7QbQDfariw3UJu0S0A3ZiJNpipRbF+AwtvSUPXgcmrT4fv3T9GztSDOtaTSIMoiaEOkaYRDfHX6Wz4agExLQz39/fX1NQ0NTW1t7dTJWskEonFYpSDo8aIGaOzs7Otra22tvbpp5/+8lCCENq1axedTKcWYMFgsLCwkDrw01CJnp6evr6+7u7unp6eRCJBKbClpWVgYODAgQO0fEvx/W/fbYqzd999N73ApbZffr/f/Wk1OhgMlpeX04gK+pdbW1sbGxvb29tramq2bNlCX8uXf0Wku4HMUKAXXniBdrZZraSvjo7y8nJa5Pb5fHa7nba607M6Vayy2+3hcLisjHjPFRUVUaeILGMoipLSE6eU0B0dHfF4vKKiIicnh6IzJWNK4TRBuqSkpLa2tqamRiHRskQ0+V+sEAeDwS8DxKl35/XXX6fRJI2Njf39/fn5+dReLTs7m1pz0Nf+LwHxX961L5C4pDZ9ZIVc2BklPlIphkaSFYQHkT6JdDymffT45n2nBjGwa6xr0uTWGQ8EPgg8GLgwcGOy4vmUjGnNmMdA0tiAxgZ2nxBYtbDEHSZCfCogDgRIW04oFPJ6vdnZ2fX19UuXLh0dHV06ODow1Nvf17Okb3B4YLBryWDPYP81zf2PWXpeubR/fV3H4mt7r4o33lEVejTPqh83fWymWZvGYDPQWBJEjNMBnM5OHJsxcRSDpwE8FSTTATQvMIzblhl+basxuOGPp64UZMknCZxMgNhnRNLIpF9IDYhEt0PPO9kYbrfb6/USRBaFoKoUhWpKyqsbFjVGOhLdPb1dA4nuRHck0ds2PBDv7h3pGSnt7I5cOR+DaeMZJCHZMCxjdZYxSNScZI4/mH7ukFJTPzjcEY21xGORaLS9K9bel4glYsvaor2lNSOli2qaG5rLqqsra6rr6iprqmsqirskX68vu9Sbmy8W5YolBVKRwruLfFaZFJcUoj8WOArERl+OrCqkD8/HCTZR2H7CmWPMUZCZAokyeAYCMzGYicDRCByHyfrRxnIsIgXj4xFRFc/CYFYSzMbgTI1gcabGAC3N9GFxbTJJeud0pE0a7iMYEQMSoj8kLetHfNe+ep8iCB8kpzKeeKdc32VCO9LRznS0G2BSIU4tjAHEJrTThHaY4A6GOBC/loZfI3oJ9CLQtoI7V14h+El/zuGDZqnm5OSoqrq4o723IzqS6FkyvGRk6dKR4cHh4eHukRVtw2uiPev6Ir3D/d5EX2f3yIbOntWx+EhTc9Hjr5yQxOljGOybnDaGAVkQOwbTx1DahM7gMaC/AMY2gLG1DFqZBkluM8ArM/Ea8/urTivlcwQpJPCiJAd8nNfr8ioyAUoaqkWnVWmR2GazeTwe8ivJJ0tBRQ7JUkCWlKJCoaVhUXc0EY12Jnp6ensGY4PDze2LFyxc2B75BUQn6BoDxxjtHTCxi8XbAdxs1jYDvCf9+Rd+U11v74gnEn19zS0tVZXFjYvrE/HWrnh/rLcpPuiJJqqjnQOEhWJtsWgi2tnX3Lpow732B/fYlt5WKBUWO92Kw0V0Hl4X+RgKyZwoyjwnSSJ5eqpCtNo8zxMJstcbqf3ZwYdPwI+a0L1TiMXEvQDdB9C9JnyPGW8CeCPAtwNMmfhWOP7eaAAAIABJREFUg4xvBXjDVHxj2iGL4puBdiNxnIAP2hHcc5AcofSUnYD4ICZCmqSGD369pna+WkCsG6O5ubmoqCgSicTj8RQH05IwqXccBsT0AfX19RUVFdu2bfuSnxkIoXfffZfneZqX4fF46Gw4dQHzer2SJHV0dFBJcSKRoKriRIJkwUaj0fvuu29ycpKS0N8yK/p0vPrqq36/n9aoSktLeZ6n86fhcDgUClHVckNDQ1FRUU1NTZsxGhoaFEVZv3494QcjoeNLviJKwwih1157zeVypdiU4ilFvZKSEur45nA4yESPoZegt4Ig2Gy2wsJCWiUtLi7Oz8/PycnJM8bChQuLi4s7Ojra2tqam5tbjUGvVVpbW2mROD8/n9aks7Oz84yc5/z8fPrS/jeAuLi4OBgM3nXXXV+8fz59Kw5lnTz11FP333//tm3bPvzww3vvvdfpdM6bN2/hwoVZWVk01bmiouJfAuKWlpYjQPzFb8Gh36Y0oJA01U1CfZzUhrH+/v7XVt91cJaEGLuW5oLAgYEDAWeSceqMEwO6uA0spqViAsSQ4SAj6oyqMYFPpih/uKDIUxymHEyZWFWJmJhK4YuLi8vLyxctWtTQ2hbr7V7SPzg0uLR/eHRoZMnAcG/Xiv7YyoHYyuGe4SWJodH+wdHmNaurhlc8f1nu+PEnGC65xCmMuOROARMzAJoOcCaD0lnNZNbAmZBp1k2rJ9nRZNpSnSHRdztnLVOJWZnAK5IoS7yfhHqQsmpAVSUCxDTxjnSwq6rbGLIs87LkVxVZKlSUkBAQ7CVqdSC4uDTcsLg20t4Vbe/q6Yq319dbvYGmKxci89EQMEYUXDpimEkzSJpZgz5/cCB9TklJqL19uD/aU1OzSBGUwnBRZW1NS3tbZzwW7e/p6k70dAxHYx2RWH13JBKNDcRjPSvqlW3i+W/bT3/Ycc5yz5wShbMEQm4+5OUDdingFiS/wAsyqS0RyypVUhVZESSvn7eLwluzLp4wnzHJTEkyADIMJLYSmQhMReAoDE7B4FRDQ/wtDM7G4AcYnIfB+Rj8NMn+AIPvJU2nIXYGZmbsvfiK8U/2JIngkJg1YQgnqSeJTnoxSWU4dfx8qaPtyIP+L/YA8UzUJyY+qkvumop3A/gG0P7E6rvN+HUGv25g8S6AiZKYxTtMeIcJbTfBbQx6zYReMaGXAX4JoJdY/RXwzG3nc1zl4TRMm2IDgYDFYvH5fIESJVxeXlVb09LQnOjo6Onp7x1a2jfcu3ygt39kpKd/uC8xsLR7ZEl/YmCou3d4eUtH33U3lL2883tvfnziAQQO6hkH4fQDMHMMMxoC+phJ2zINrc/Ql5vh1QCtAdrVYPIqgNcAtBbsXX1stbSAkwpFQZRk1S/43E63bAAxDdVKATENuiIOx2JYkbxBsTxAREWSIBcKYhlR6ypCeXl1XUNTW31nfWNtQAhefun8SPQSDc5EBwB8C2g7ANyehreaJl42T74BkA6eeGVOcaW7rqmuvbNJVUIuu1XihLLi8sWL65ta2tpa2yOReCzWEYk2RSJd0UhXLBbraI9197Xc8Pi8R94+68k3zt7wux+1dORzXMjp5v0cx3OqwPsl6f9j7zvg3Kjure+MtLs2mB4bQm8JIYQAqUAwNgZct6uv2vTRqG33Fm/vu9riCqRSQgjN4NCb8ePBB4QQmhvu3QbcyxZp5t77cefaymJK8vJCHkn4/wZ5JBuVke7Mueee/zkeUfLLsmoaeQii5Od4yeXiEnOuT772Df0lgJYy+CkrfGYsfA6gF0xY/AxATwL0GEBLPomJHwT6YkYn8c6s8RCLHwTD905I7nmd2CNCTEzWyGgdRohICVNkOmvaav4zfo//mNf4agFiimAee+yxnJwcRVHmzJnT09PTYRZFxp2dnRQTUyVxc3MzzfXlOO7OO++kOJI+yRccHmL7f+RIWVlZfn6+x+OhoXHxeDwQCHjNosh19uzZiUSC6om7u7v7+/u7u7u7urrKyspee+01+ipfoCHeuXOnpmnTpk3zer2aphHfJbNCZjkcDsoc8zzvcDhoglo8HhcE4Te/+Q3F2Z9+8s/7UPTfI4Q2bNgQCAQo5Unb3fLz8ykmiMfjPM/TbI40IKZ8FcdxNpstFArRgJJ4PE6zmnNzc6nWNjs7m4Zo0HTrysrK6urqlpaWRCLR0NBQUlJSXl5eU1MTjUadTmfusdI0bbZZiqKY7DBxk0hTxelH0i4T0b/WSDdaTCzL8sMPP/zpCclxhwgdK/o4PaSU2t+5c2c8Hr/pppumTp2an59PP5rf70/7EFMHAFM/dvQmHcxBkws/VoSP9pw+7qW/vvuXI2D6EKco4aejFEQ6QqmtB3dPbdWZguGMQsjaIHAYZLPrwG4AGwKOTwLiomNqioDBBAyGw0A2gHIkU/7ggmggRiZIlCROY2JRFHNycqg2PRQKqWooHNaiJVp1eeWc2sbWlo55ib6B+bf13nFH4vYFtw3Mu72vd17vwjvmds3tW9h8/y87bk+8852rUyQtgtDDKJPBGZnYYkEsSFoABACC6zD4OQS/RsxCbFmUYu7DoHPo1A6Nlzie5yRijSwEiVBYUGVeJvcpIKYRsvSEQBmmIC8ERTkiSJKslfl5RZYUUeEEVeAjqqQGeK+och6ffaLXUZ0/S8843QAWI4OIhpMWSyrDgixjEbgcgx9AyyWqYOcj8UgobHO7PS4P7yPCYGInHA6rFWX1lbVN7S1tHZ2Jpo6mzvbmntaOrraO9q7G3gUL5hSvK7wgeeNY+BP2z9eduWjWLQLny9M4lyT5OcEExMS5iUgrReKh4fUHnYKw65Q6HYQNdnIKfBMypyHmDEx65s5B4EIIzoXgvFHb+RBcgJgLEHNhivmuwXzLxMqnb/nejcN/XmHoI9AYIpMkusBqdl4SLJyC1KLkf6Ae+8vP7uu9f+QRSJ9s6TkVJ/cn92TDTRa8HcAdAG6zoG0WvJVFW1i8GRzFxEQ7waJ1LFrDwPcZYzWAy1n0LoveZfDbLHqP0d8Ga16Y4PaSCW260ks9NputqKioOB5UwyE+rPKhElmNxyNyRWmspqaqvqWpv3NgYH7PgtvuWLBw7vzb5vbMn9e9YP6Cgf55fb8qLe2/baFjEINhBI4gMIwyDTgGHmRTq9jU4wy8B+DfZun3jEV3nYjvBsZvgXG3Fd7DDt11Up14qzcYEomhmRwQAi67SxIVuqTjdrtp+w1dWXU6nS6XSxbiMi8rckBWA4IaDCpyUCGZ6rJM8raCXJj38UGes2fzeTN8rU0/g4Onw+1WuNGCtgG8zYLfB/p6Bh4cY0Dryo0/USOz/H6N5ySHwxMkKiYSERAIOnlejIRLSktm181pbGlub2vt7OhsaW2r6+jobm+ZW1d6Z+NsdcfIabsxux2Bdw6cct9zEytr8zTV7bYLzkJe5DXic8NxkhQKBJRAUJg+I29R662p185Br2TCFwF+FsDnToDPZ6EXGPQ8wcT4aYCfOEYSP2rCYpMthksY+CiDHgap+9m9d500srp7hIiaCPo1BylptYQEHBsGmdxi01roH/k7/FKf66sIiFetWpWdnU3TImKxGKWB283qNOtj7SaNBaaMZklJiSzLHR0dhw+TkMC/iiMpHnr44YcLCwtdLpfb7abYlAJin1lFRUXkty7LbW1tVDXRY1YikaipqXn00UfTGPS4ryf9+NDQUH19/ZQpU/x+fywWo/kXRUVFNDjA4XB4PJ5oNKppWn5+PrV+i8ViqqomEonhY0bLxz355909hvrQjh07aOoEFUtQTBwKhWjyiCAI1GjC7/enm+roTmFhoaIo6cQ+mnJHYXFeXt7MmTOdTmcsFqPULDGKNPvkamtrOzo6KFvc1tbW2to6e/Zsj8dD29RCoRCVTCiKQrXCXwCIBUH42wFxNBqVJOmhhx5Kn6M/78h83uNUnDM0NPT8888rijJlyhSe50OhkMPhmDhxYkFBAT3ljUbDkiR9HiD+u9/G5729f8XHdYwHTXs1M1sMmbEb0DwjwiQcMqBukFZ00pQ+rMPU7iP7b66AYBoGeeaWj0EBBoUY2DGBwg4MnBC4KGc8bHVisl+ks36zqc4PmSBkgjrLJy0yHiNXCBFJ1hRVVpUIp4VlVQ0pkqTw2QWzAkKwNBor1WKRUUVXaegKRmNDY0dbW293Yv7AgkXzFy6cP2/eogULf/6L6jlN9137k5SFRVYLHAv0DCvOANQkGAFw0HqOwVZi8CsMbkck8/kOCBIQzINsYk6h6pM9AZLtrPDBoNPjkEVB4/mgJLvd7jRDnDZySrdv0iRLulicBveyGBICxJki4A/OdNk0hwNmmC10YCxx+WVYZM3AzIUI/BCCHw5bx8e99iI1zHGc2+027SWISxN3zOjN5/MJMl9dUd7T3dnU1drc0dJc39DR1F6bSIREbvWV34RnAnghSF7CoEvZnVNOXD3lu7+dMUkrsheFFUdpWWFJzB4Pyap2S3R2tix2KFPQuJ8ZFj9i4gjYkfVSzFBK+HwMLkgxF2JwqcFckmK/ZYCrELjaYC7V2QshuAQxlyUt34KWb+w/5+q9K1eaHk06hCNfj6Ov8sBPfzvU4jO1Oze53YJ3ALzJircBtI2BWxm0BaAtAG4GcBOAGwBcT8wl4PsArgLGSmCsAPA9Fr/LondY/JbVeBsYb1v2LD/NL34CEKedE+lVOBQpjYdmV4TUcjUcD8eUiKxqkYhSFVYFTZNjca28sqqhsTPRPXegZ2DRvIFFv7y9Z8Fchz9/TrEDQoANBhkMTAK0j9GXM/APGfA+a+pBkHoIoN+xxn0A/zoD/8qaug8YvwPwvtO7YzO8XEgI8qIgB6Wgw+aQRJWOSgoV0gPW5XI5HA5BIVbishSUhVJBknmFk6RIWAxHSM55QJB5QQh6ZdnhnZ6X46ktvwHuOod40u1g8V6Q2g6MlZnww0zDACkjc/POa2LFkwOBcCAoe7xFHp/Xz8kBjqTHc6QNNuj3B7xef3n57L6+vrq6utmzK8vKS+vqmmJltdHo9D2Y/QiCD6F1F8r4EIG9BrPzwEkvrL64Z+GMkBILhbRQxK2RMJGI3S6V19ne33A2Xg3Qq1b44hj8PIBLAVoKSHDdCwA9TyAyfoqQxHjJXzZocsbGo1nGQxnGA6eOrG8x4H5IeqWTIziJDR2TgCWSMIkR2f+XW9v5CgFiei5AiKA6Go3m9/uLiooURWltbaXsbENDA816KDGLigGKi4sjkUgsFtuxY0caGn7BmYWyeps2baKolF45aOQv5YYpZ/lxgjEF5U1NTVRPnEgk2traysrK7r77bnp2+EzwTR9ECP3iF7+YPHmy2+02CSrVY5aiKPF4nAY4RyIRyqr6fD6ajacoSmVl5Z49e9JP8gUfZPRf0fdz5MiRhoaGWbNmpTXE+fn5kiSVlpaWlJRIkkQ1xOS6eMxlgiI/m81GfTbi8XhJSYnNZsvNzU0DYmpRR1F1KBQKh8n5a8aMGTk5OdFotKWlpampiYaPdHV1hUKhqVOnzpo1S1GUqqqqyspKVVX/sYD44+9aluUHH3xw9BH4O/aRqUvZu3dvd3d3RUXF448//sILLwQCgenTpwcCAdH0qhmNiUcDYlVVm5qavm6q+8thR6bik5qTmsvcRC1M1751pGOaTYd1iIY++PCjG6IGOw2BbAxyzS0Pg3wTENswsJmA2KUz7hGLa8RChBMGcCPSYBeAbBABGQEFAdHcirDVUe0XHVGVD/NcWI2LarGgaESlINkKyZJrJBoNxaKj8PDRXe1YhUNaOBwtLy6vrappbW1ZsGB+c0ubvShwx89uGLGOgRnAGAOQhUUApECWwQIMTk5ZHYgkP/8CEzPjXggGINMBmQRkBtpzNZ/i9UuywMsEELsdkiCEeCEoEY0ER6LviPOaIAg+ElLlSl9fPxMQK5IqBEnKVsDPzXI5VIdTz8o0SDCyxQDAABbETMAEbv7YAFeMWM4s8zi9KmkAOg4Qc8SWlJTX75UFrqG+tnh2qTfo4/3BiBL2RiJSXvb7V483LgL4UgZ+jx38LtDPAegUK86yHM4at/qbF755ybdfu+ySl6+46Jmrr1x06/SnJv1w+CoGjgepMWchNsf0yLNjcB4mwgmAwGkYZGEwwQDfhEQ6fIYOxieZCeb+eMScZ2RkJTPOPPzMiwbWU5D04RC3YUoP/+Un9fXeP+MIpEwSz3SL1c01bmIgQVwRCb2EdTxoLn+TJACEUtBIwkO34R0X4K0AE44T4O2AkJ1bAd5iEsNECcCm1lvgGoDfzcArgE6IYStamZFcDlLLAXo7E7+dhd9j9HXWwQPslpFTK+oDvkBEluMq0RaJvKxKoQhJwPH7bXlOORov1sLF8YgWVSOReCxUFo6EQzE1FiYDORwOa5pGr7CRSKSkpKShoSEWiedMzw3ydpzMMCCDjgC81Yr+nAmfMP3CHgD49wDfx+DfsfheFt8LyPZbAH8H4OKT76yZ4vXxHCdLXEgSObu9UJSIT6KiKGlATMcsbUqh5DHNA/rLPNbcUyUuJGii4OWkoMflnzHdUxH7Gdx+GtwN0G4r3sDA94D+EUAjJ+DDDEqBjet+WhybzhFTYdLm7vMS5zQzX0Og01qeuEYQhyhKsXm9XrrYa3P6tNC0XfjEnQjsQGAztmxEzFYEPsBgN2J3I7Abg4+Q5UMjY5uRuWr/6S+tP2PLyNjUEDu8GcCVAP+JMV4GxjKAlgG4DBgvEkyMn8nAT2ZRJbHxmDX1JEg9YSWddk8QHcXw4szhd1qxfiiJaNAo+bH8G9RXCBBTLIsx3rt3L4WqPrNo30ltbW1lZSUNj6A5GqXHij7I8/z69ev/FhxJYdC+ffuKioo8pmSiqKgoGo3SzDYK3WhmRBoTNzc307SO8vLySCSycOFC+t2nJ83pnwJ9ckpU33PPPTfddBMFxJqmUUAsSVJJSYnL5bLb7aFQKBqN0rCMWCxWWlqqaVpxcfEHH3yQfsK/cYcePYTQvHnzZsyYMRoQ8zxfVlZWWloqy7LTLKqTNi/K5EYUxeMAsd1uzzVNGPLy8qg3GXWroKG4oVBI07S8vLzp06fn5eVVVVV1dXVRhXFra2tJSUl2dva0adMEQagw06r/4YC4uLhYUZR77733bzw4n/5n9ItLf30jIyP79u2jguCPmxobGhrohGE0Gj6OIZZluaWl5WtA/Ilja6KaQZqkYMqFCcQxcBIiEkY3Qtqj8EdH8NQaDCbp1myDAOKcT2HiQgxsOuPQTRkxBFRP7MbAm7L4dTZoMCJkeMgGDRCFIIaAMj87XBRRNUXgNE2TwyGyaaqsuu32Ipc9EpWLI9JRFGz+Yaawh6NmhbVwWA0pIS2skB0xJJfFi4M+bprd2Td18mDWWGixGplERmzivLEIjIXgekxynu8glm1MBwJdJibuREwnAvMXTo16VV9AlIn/Pc853Q6R5zVB4GWFhjLSqyl1NnU6nZIkUXHOZwNiWTYFyWLAL+S6XaLTPTRmDDFZI4DYYpBmtR9gcC0C39PZc1OWb1S73D5V/DQg5o8Vx3N8wO/1ksiBQqeNDwTFoJjn84ULZm258Bw8JnPkJBZmMYg5RQdjDJZ4R0DAYsaCGJAC1iQzBlsYYxxA3wDwfAv6Phj5NjhyJjuceQ0mTnkuBH6ig28kLaTnb9CakWLGmfF1LAYAAysGGRhkJNlM3cKs8RYNEkG5jmAKQoP0zRHD4a/rn30EzCUdE/6aCJgSexCbXwhOmbCYmAcMEhe8IZzqHd45bmQnhcIM3s6g7QwBxBQNbwDGOoux1oJN12HiJvHumXA1GF4JUisBfseC37HA1eDIDrBnP/gAWtZgsAazXQunB1wRTYxoMq+IYVnRFI1YO/A+xZabFwrLpVqkJBrTwpFINByNqFqkRI3MjkSV4wa1pmmqqobDYb83mD0tz+MvwqnT9SEWbwf4TQCfA8ZiAB8AxCThKCCmmBige83tPoCWZD3aM6XI5zGdGCRJFOyOAsqMKIqStoX5GwExgdFChAQ/ipy/KDAz21McvV7ffwbey+KtrLEaGOsBGmKN4Ux0COAk+HDjjypLZnweIKYjmK72eL1eGt1MIwVcPn8kMmsXtmxLZWzWMzdisAGDDQbYlALv47HrMNiETtmGwXYMthuWD+DYPdj6EQT6sHVoM6Ht8VsW/DqLXwb4JQKLjaUAP2duzwPjWYD+kIEfteDHAH4MEIb40SxjceaupyZjfQf5qQzjIXwI/SuFb3zR+PoKAWKqZEAIDQ4OhsNhqtfx+/2BQIBI12W5uLiY2p+llRImTUwsFKin2FtvvUUhzmcSt+nDQLFjMpkMBoPuYxWJRCRJMhH4XwIjKDj2eDyqqs6ePbuiooLaNbS1tZGeaNJWcHzR/jZ6+8wzz0ybNs1ms2lmUfAtiuLHLYDU44LKJ9xut8vloiYPqqp+LEvYtWtXenpw/At8/n36fn7zm99QJ90Cs6h5eHl5eWlpqaIoowExndqmAbEkSXRqUVJS4nA4RgPivLw8u91eUlKSBsThcNhms80yy+Vy1dTUtJhFYXFxcXEwGAyHwxVmhUKhfzhDLEnSx8Z8n/kVfP4R+svfpNsQ6dSF3tV1na4evPPOO3TO8AWAWBTFlpaWVIooY9PA+i8v8B+5hxCh+UYQNkye2HQdJhiYpFjpJIQDb/loZM5tezMnQTAlyUxFljwTEB+HiQswKIDAnmIdBuNAwGEKJ1yQKTJYH2T8EHCkr471QqBAEEsB7d5beG9UklVBVmVTOKFoxIFN9riddlu+GlFiEfW4a2c4TPglchsOR8MRLRqLa7FIKCyGleJwxO/xz/QUtc2YfGRMFmQtutWCCQZlTTPdMxGTb5pLzEegCzOdEHSZeokeCDoQmPvryTGP6guSdCtZEDiHyyFwXEgUKSCmKw900NFo8b8GiBUCiHnRbwJi3uk+PPZEs3ftBMRMQKRZ7acY/BiCi3XLeN1yWp3T5VdIQ+GnGWJKMpHIqwCJTnf53N6Alw9yEi/ZAoFQfu76M882ABixAOL8wGSmmEzIAMgyutViWFjEAiODTY2zwjMt8Hus8UMr/lGWfr0FTbTiH2alTrtAZ28m7Y9MLmJ/rFtPhyBzyMIaTKaZ4Uz4dQRYAuIZa4qxHDrrXLzy7SSBwRgbJLabdOT8a1k0/buMcToTMY/9sfUdQg+TxBwzQ1KnOQspkqLzbGrHpNTOTGMXQ1AmUUoAtNXcNgO0gWRwGGsZ+L4Vr7Kg1UQpgd8bC98HI++D1EoWLrfo69jhD9k9g8x2Haw1mOWQXY0y5t9zs9ceColhVeElKSSHVFkTZFXkgkp+3ixFCZeEo7EombpGwrFoOKxFIiHywNFlHzqzjUaP3o3FYgIn2vPcBY6iQ/vOGNnLwlUWQnw+xsDFDHoQ4AcAvh/g3zP4PhbfxxCfhHtNt4T7AX4y4+l5N/r8Tl7gBU6QJNHuyBN4GiwtFxUVUZ9EyiXRJvUvYIhJ56wYlfigIPJ+T2BGjjui/dQYPBt9xKTWkmMCP2CRziSHLPpBgJJg39Yf1ZbN+jxAHAwG/X4/RwzhyAim0IgamdvIKnThdjRmm2HdhjLXI3Y9BJsR2ALZ95FlDWLXwcyNEGyB1m3Iuklnt0DLR4g1RqxDW4iUBb+Tgd+w4FcAfhmglxm4jEXPM+gFQhUnn2fQ4xl4CUnuINvjGfARy/DiCfq6BQgeJjg4hUfw4L/NwP0KAeLRp5e5c+cWFBTQr5ycvM1o3xLTPoyi4bRYgooBYrFYIBB4/PHHKaqGhHAwKFmbRpaj71IgJUmS2+2mOFVVVfokx0E3+uo+n4/KYSsrK6ljGg3JSz/56Def3l+3bp3T6SwoKFBNW7dAgLScB4PB4uJin89XWFgoy3IsFvN4PHa7nS7Hh0KheDz+wAMPrFixgiItaktMcRu9YHzmi9JPZxjGvffeO2nSJLvdnp+fX1hYSAFxmVkUELtcLq/XS6+OdGBTKQWVTNADSxliqprIz8/Pzs6mQRulpaUhkx7WNM1ms1FNBVUMV1VVUe0EDXlubW2lOoqqqqpQKJQ+jGkC3n+s6CSE47jo/6SpTlGURYsWfRlXUNqb6Ha7aeMjPQdRBBOJRGg7naIodru9s7NzeHj4a0B89AdPDAJIJjM0MDLITgohQzcgRElEAI+x7O0DU8S9mdfp7GSdnQHZWRDMwIQkTm9p+UQeAvkpJm8wo/BgZuFB0nXngAwxoKBebJDxQsaHgYyYsM64Xv3xrd6YKiqyJiqiRqyFwqKkhGRfkSc/P5/X1HD0Exri0eDYBMRhNRqNa7FYOCrFtJJwVOXlmV5vZc60g+PGQQuTYllIoobHm24Jl0M2hEACMe0ItJiscDsiULgXgXYMeh//cakrROz6BYEENrvcLj7Ihczg1NEXVGr1b7fb0z126dmX/IlSSIwsJ/oDQr7bUeRw7j2RBl6cj5kfYPATBH6AwGUGc3rKcpJhPbnV7gjIEQqIvV5v0Ex6PQqFTckEuaAGyLXVG/R6vO5gICDyYr7bE+a4//eDqz8YY92fNWbzaScftoIUa0lmgNRYJnmqBU6wGGcy6GIrvjwTX2MxbgVDOQDNZPAMK57E4u+z8MwxRtYExF6LGRsGRYidDMH5OpMFSWIIAwGrM1ZoseiMJclYhizW4Tt+o8NkCukpBKFBclpGiIl/+tz59c4/7wggnCSAhvw3TKKYjzokEmkoRBDrJHUMQh0ZS5Mf3AR3jUHbAd7O4K1ELow+KRc+GkS3yoJXWvDKE5Jr2AMrrjq4+mJ9yzfhzlOSe8fsH2S26pbVyLICMavJZl2OMxY9/QOvU5aFsKRKIrFNVGRFklWFE5Wc/ByR16LRWDgW0aKRSDgSI/0AoUhUjIfK0gOZomE6yzU7TBRHYdGMHP+GlWcZmy2pFy3EQexhFj9oAuKjmJjBv2fQfQAdJ9J3AAAgAElEQVTeB9D9ZkzxYoCfZl7+xQ/8gQLezDyXJMXuzCEdsqZ8jl6q6AxWEIiMweFwfAEg5hRZkiKiwAki7/P4p860a6FrRw6ek9oCkmuB/gGAwxm6DlKHgXEQwBQ4sP2q+vKZnweIRZGs/KQBMW3IoY/Y3d7S4rL/fu+m9ft++OdtV7/7waT1+vi1BrvOsKxHgOBjBNYZWeshu85gNmDrGsjsQAxKWVPbSZI2Xp6B/8Ti1wB+lcWvWtD/y0AvWdBLQP9vMPyiBT/FoieB/qQVPWZFjzD6ksyRV2M4tZ+0zOnYQFDXU+a89p/3i/3yXukrCogXL16ck5NDT+h0MsTzfJoeHo2MKW1JBQ8LFiwYHBw8dOjQaOCYho90hwJHCoj7+/unTp3qNuMqqHohGAweB4jTeuKysrLKysqKioqSkpKysrK9e/dSzP0FmGzv3r1+v7+ggKy5RCIRamQYCASoA1phYSEVA3m9XofDUVpaSmPhmpqaqA3zK6+8smLFiqVLl6Z77NIf4dMvSmcCCKG33nqLegNTQJybm+vz+UpLS8vKymhaAQXE1IwwDYidTqcgCMVmlZSUuN3unJwcCojzzPSKtDGZZhZliHPMysvLyzbd1qqqqmiIIGWLm5qaWltbGxsbw+EwXd/xer3HuUykDy/HcVQIPtpK4gv2FUW57bbbPn0c/pdDBSGUSqV0XV+3bt2vf/3r0tJSVSUBnrR/IhAgIfKKopSXl992221btmwZzTT/L1/6X/5/N4ghAEE1EJuKUEQGiKEjPZUaGdQXP//huVMR+DFkbhix3JxkpkMwAxFAPHPUlkbG2RDMMqz5a67k/p/Q8LSr/LXrlPfPcW863Xkgy54EthRw6yCAGAmzM6DlkrU/vDgvrJHrphgWtLCkKCFJCimyEAzMys3xiUIkTtoMPrOi4UgsHAlRQKxF1Hg4roUjaiTH5w/nF+waPwGxTAqMgezZEFwFweUQXGWwUQRaEWgyb9sxU4fZOgw6MWjGoOO171Y6NDMJksyieE+RhwtwIVkRRKmoqIjmdFCNBLU7lCRyuaVXXKqaGI2HJVkReVHipEBQzHcXOt3uj065CIPLMPMjgoaZyyA4B4LxBnNqynKqYTm9uzDfr2j0VHMcIKbXTi7IcUEuGPT7Al5/wBcI+DmOLwpyc5pb7p1T8Vhh7n8VOB6PRZfOumHt986FEywjF4w5cNU3Dl0xLnUBgN9m9MvY5JWZaLIV2sagm7NSV1j0C4ExHuDTWHyqxRg3LjXm4pT1RoPNgcwtGFyOmdMhsCLWmmItKZZBxKbjxN02e3LwiEG4JWPEpB8RJBOnfxue6V9sLCPdbH5CGCcRJPumzd0wEYcisrSD0H6YeiS162do+1i0jYXmRn0kCCtsds6hNQC/D9BqkslMwpk3ALj5xF1rLv95v3r3Is8zv3e+/GzhmytmvvPhj1YNX7gqdeoamLEeWVYb4A3I/urdS3zumNulCVJMlhRNUJSgqClhWVbzcvMDbmc0WiGFQ1qUoOKYVhzV4rGIUmwu8qQHdZohDofDkizbHe7cHOFPz1yUfA3AJzLxowA9zKKHWPwQQA8S1QQBwfcD9HsiooAPkxAK4jL2FLP8d1f6vXm8JPBBXpJUpzsnSNpJRFmWfWalAXEwGLTb7fQa+pkaYkERiWhC5IIi5/cGZma7QuINQ9vPH94I0AcMHGYNaNFTAB4C6JDF0MG+nVc2zJ4VDChEQ+w8XkNszmeP9shyHEcvnfQ6rsWjXR0L2tpD8+8o6h0QOxLKLx/KeXXTDzehs1cd+NabW27YoJ+7CZ28EVk2IGYdZDYidicCKGWBH5A8bbzCit9i0esAv8bg11j4GgtfZdErDHyF0V+yoOdA6lmQejYDLbEaj4DBZ6/Dh9eS3wXUDbL6R8wkvpZMfLlD/pVXXiksLPR4PMFgkKzvBQinEQ6HqUYifRs3q7y8vKGhoaWlpbOzc9GiRYlEYvny5alUijoJUAIvDSXTd3VdHxwcnDdvnsMsCgc/FiLTHz0lp9N0JmnNMQ2DqWri4yG3fft2iq2/4EAkk0lqIsHzPE1v9ng81NGCRh9TA4pgMEilhBUVFV1dXRQWNzc319fX19TUlJSULFmyhEaBpN/8cS9KP12aHd+wYQPHcdnZ2QUFBbQ9sdSsSCRCwTfVMlLWk16baVoH7agrKSnx+Xy5poaYJnTQoI1oNEpVzlSCabfbc0yvYvovKSYOhUK1tbWjQ1U+FtrOmTOH9tT7fD7aB3CMHSZ/0oNMv98vQMDH/dWXxBCnfyf0x5NKpQ4dOrR27dqHH364ubm5u7v7scceW7169ZEjR9IzkOO+i//cu8cAMURmMLMBDWQk8QgaHhrsvGv7N240mB8h6yQdTNKZm3XmZshMMwHxDExg8fEbZGbumGBbPLul+Rd9XXMTv+zoXTwn8XhJ+3/5617OLn9jorbyMm7jKdn7Ms5OnmQ5dMNJtaqtNGDX/IGAzPtDUkBVBEX1K5JtZvYcN1cSCaUvnMfvhCPRcCQcicYIQxzRolqUrMxGC31Bwe5cfcGlCJwCwfmIuQqBq3Rwlg6uQ0yVCYVbEGjDoAmz1ZipMdFwHQaNq86vLAyR5GZeEHiRpFsF/ZwqE29/n89XVFREL6iU77Hb7RQNfz4glgVekDg5EJQKXIX2oqKdp3/H7KK7HoErSIAzGAfBGQZzdoo9F7Ln9hbk+ULyFwBiYgFFGuwCfr/XH/AGuUCAC86pb0gsWNjeP3egf8G8zkR739zu+b2/m629d8Nlr0//0fNlwpOKc/nMawavPNk4FyTPy9TPHpO6gDXOB+jsLDTBgs4AxgSQOg+g72Xgq8bB8yYMjbk8ZSUdk5CdCJlLdXDGiGWMbgGHMzIPTpxibNmchCmUIuJyM6FeJ7lWX4fR/V+dO6jNM4nHJlgYmaaIGJHZikGS6JbDofLk1u/g7Ra0AxjbgbHZYmzKPJpFtw7gdYAoht8n0AqtJZEcaD1IbgJHdp9+7118e/vCzu7+zo7elpa2zs7agQXqL+91L37W+ewfc15aMeXVHde8Ovjdp/ef2XvftMqBn6mNk4JKdsDrlYJaIBCX5bK8GdMSrTdElNpQVI2SRjo1HI6EwnEtEo3EPqGDGj2uZVV2ez35swJP/vwK9CKDlmSgRwFczELTOpcA4vvN7YGjaBguAehxgB5n0DOWNQ9cyhcVcIIicJIoya6iAr8vSJvq6NXqOEBMVbyiKNLQ+NGzWVkRZIkXRM4vcj5fIDvHo/hvPLLmYsLLDrEpCJIIGCMAH2TxoUzdYPZ+eEVjbU7Qr3A8aarz+4PHNdUdndOaDbImLCK4SBTFju62vr6+RE9nV3t3d+f8zvZ5zQ0DPfPlZe8G7nzI19KuLPq1d9l7N7yfZLYg62aD2WYwexGAKQZ9BEjv40oLeodFbwD8OsCvM4hsgGyvMfhVxljGwmWZySdIgPOhx6+GOx4xUCoFRwzCe5BFBNI7/bWG+EsdvFu3bg2FQpRW9Pv9VD1D7cNKS0vj8Thli6uqqihM6enpSSQS1ImCxqo98MADe/bsoZiVohyqOjh8+PCOHTuWLl26ePHiRx999I477qisrNQ0raioiNoApyFaepWfNtipqkoZYiojfvPNN0fLGD7zaHzcl1tdXZ2fnx8MBqPRqCwfFSFR87LCwsL0izqdTo/HU1BQoChKu1ltbW2NjY1tbW319fV1dXWPPPLI+vXr04rVT3Mpoz+jruvLli2bPHlydnZ2bm6uy+WigDgWi/n9frvdTnE5vRLzPE8zAnw+H23sKykpCQaDFOymAXF+fn44HC4rK6NoOBKJUEBM9RIUMdOEC57nqf6kurq6ubm5zay6ujr6JabnGKMxMf2KqWjkOOD7eXdlWV6wYMH/KN36M7+jTz+YnueMnmZQ5jiZTKYfTK8PUJL408/zH/cINBliEq9ANqzDI8aRI3h490uvbD/tpsMZP8HgukFw/QgzGYEpOnuTzt6KwfRPQuHp5iPkNgmmb/1ZdO68RMXtXfN7evsSibr5iZqBztZ5ib65/b9taX9Nrtk+hcNXXYO//w1805jHIrf8KlxQVxxXYiG3JjvVkFuL2FVVnmn7VYFSEx99ufzkvgmIIxGyKGv2rWtkXTYad/o4n9P51qVXIhIwcQUGP0LgUp05BZK33YRAm7m1YqbeRMNzCDIGtRjUrz+rMj/EiWlA7PYEfEGFZFoQ+ySPx5MGxNSDnM5LPxcQK5LI8xKvBIJyoctWUFS07fTLMbgagmsNcCkCp0IwDoGzIHOhzl4C2Yv78/O9IdKlR2PwRksm6NWUD5Kl3qOA2O8l8a4819zS2p7oak/0drcN9Hf1dLd01yTmtvZ2P18iPDQ71jx3oHnR/CdrY3t++E14MkAkJ+/EwxbGOBEkxwF4GoPPBfg7mfjHJ+PrLPhGgK89zZhw3iA7iZjlWW2QnYStV5DuOhYYl12Ol744jFNYHyE+BrrpTEJYSdJQ9xk9Gf9xo+j/4gNTpQoyQ1AIJB4mDoqIxKIYGB7ae8f+nQB/kIk3An07SG4H+sYMuO5EAoiPoWG8lmBi8shG02hiK8C7T9y+9ZtdrYmunu6Oua3N/e0d87rm9gz0t9zW3nBHa+Oi1rb6tkRw0YOT73n18if3Zr6sn/fC4e88sj7ntgdKK2trVK22wFU+Y4ak8b7V780M87VaJBSLisURWYupoVgkFCsJxT9XB6Vqqpfz5c303t99NX6exY+yaAkwHmXhwyZD/JDJED9AqGL4kBlQ/IRpMfYEi56xblx8seqzBzlF5FVBkjw+u8/rp7Zr1Jg1rXGivv6jAfEn0LAsqzKvSEFB4vwS5wsEZ+V4JN+kw6suxnsZCJkUIoAYHQXEGbrO7Nv93cY5ucGAwhNA7DYFw59wmfg0IKZ8VnllZV19RWtre6JnfndXoquzr7f7552dnfWNpc0tDR1t8+pKHuyb714xCLairG06u0MHBzCAOoN2A7jWBMTvsvBPAL8B8B8Z/EcWv8GY+wQi6/+dBV/Mws8A/JAFb2zByQ9HDH0Yj1BLTXPQEpnNv0f9kyQTo0HDaMBBpQsU1tB90mJhGEeOHKmtrbXb7RRF0XV2URTLysqo+VdpaWl9fX13d3diVKVhcUdHR0NDw6JFiw4ePKjrOoWPdOf+++/v6Oiorq6m6RLV1dUdHR11dXU+s/x+v9vtpixmmsukvKYkSVRGXF5eHo1Gn3rqqb8qHv04d6+uro5qP6LRKIX4Pp9PM8tut7vdbgqU7XY75W4LCwtVVSUJNK2tHR0dTU1NtE2ttra2u7t7165d9ChRNDaaME4fPQhhKpXat2+fzWa79dZbZ82aRSUTlE2nY9jtdtNhTJd7BEGgDnc0nKK4uFiSJMr4UpeJXLMoQ0+9k2nXI5VV5JmVa6Z40CUkyn7RRJK6urrGxsaamhrKTx8nmUjj40Ag8LdLJqgP8dy5c78OivsqnYbgiKkhTiGcJGG7KQiH9r7+5oGLvAh8H4KfmWvokxEzEbITEbjJYKYYzK2jAPF0BKalt0Mn5N47p71pPslO7+zrn9c50NrXv6CjL5Ho7U70d/cl2hbMb779js7bb+tu6XuD/x52A+wYe8R14oe2U1YWXvSQd2KV5oqp0ce+Gz7wzerGYMsnUfCoeyYgjoajURMQR8NaKBbW4sUeH++zuZ+9ciIkaPj7iD0fgTEQnAaBgomAuNHc6jE7B4NWDLowERDXYFC79fSKvFBwNCD2+wIk00IUaRcvyXo1cyJp2BV1Jv58QEwsvyVOCQSVQqejwOvfOp5EvkFwjQ4uRyQC4zwMvgWZy3T2csh+Z26evSik/jVATMybAn6v3+8N8IEAz2khrbi4fHZ1ZUtbTXN/Z2f33P5EY0ff7a3zF7X197d399TPm1/bVLviou/qjNldx5JYEATGpk4AcDyDL8vE1wHjFmZ4EtCvBfAKAE8D2HIiBjcbgIOAMxhP0npZkj1tcMEdxL5fx0NoBBkj0CBu/iSzkGZ6f+0y8X8xnk2CmGSimL6xgwgfwjBFviYjNXJkvr4zC2+3wM0WvPkEuDFjZDOb2mDBay1/yWfeCNBGU0+8nfDHqZ2Mcdg6mGTvv7Osp+2OuZ1ze7sWdfR09vYOzO1M9Hd2dfV1tg80t/Y2d3Uu6mn5fXlJ34JHfrIUTXgKW55AzHNo7BODYxbvPfGutRcsfOy7zz3/0x37Tg56S7zuZpVvioUiZhtsNBKNhiPE8ekzKxyJBLhgbq5rUcOt+CkLNpOH4SMW+LAFP0TkwoQkpmj4EZMYfhqgZ4heAj3HfvD4hTG+wB9UJV7j5aCXWL+SVR1ZlmncLLWYoPNYm81G22QpQ3wcIFZkSRU5SRADIu8LBGYUeAK+m/Zv/A5MWXVM0HDSsKSGWXyYxYcZQwcH9l7SVJdtAmLO7XITNdMnbdc+DYjNrA1JFAVJDCmKVDenrq+vp7evvbunpaenp6d73kDPQG9PR0N1b23DzOVHxm5CWZuNzK3Qug8DpDN4N4DrAFrFwuUsfBPgPxELNvxHK3rDQglj9AZIvZaJnmPg08zIGxzpD4HDSTg0TFtgTRIEk2H8b1JfOiAeDdTSvW5phDo0NPTWW28tWbKEOu/SBjK6Wt3T02O320cDJkEQKDtbW1vb1dXV29tLEXAaEnd1dXWbRfPt6uvr77777oMHD6a1BAcPHmxoaCgrK2s4VvX19R+7yc6ZM6ezs3P+/PnxeNztdlNKmOqJ0+A4bSJWXl4ei8Xuv//+NEf4Bb+FefPmUUgaiUQ0TTOD8LxUr0wtgSORiCzLNpvN5/M5nc5Cs8rKyjo6Ojo7OzvMamtro2+7tbWV6lbT0lWEEF2+37dv3+AgiROnFKZhGK+//jqNmXA4HPF4vNw0mhBFkYLvQCCQRsMUEHs8nlgsVmyWLMuUIR4NiGmUHUX24XCYBnBQvUSeGdeck5Njs9nomYJ+KGotR/npSCTiNz1DRnPDo7/f/xFDLEnS3Llz07+uL/gKvv6rf+QRIHmtmGjGEOlB10mTMXnkCLUKIK4B0EhBXcfJEWN/xdwhy1XI8n1MfMqux+BnGNyAwUQMbsRgEgaTzdsbhzImY3AzBlMwmITYKYi5ZX/W1KcDVelx/amdvt6ens7ervZ5/W0DCwKRsi5nAS4AKSfAOQBPPxFOH4OyQWo6e/D8S1BW/RDb/ts8j1BawUeLg/GYGI0JsXgoUhzWYqFwKBqKxNTiqKZJ8VBxKFaixtRiLRqTBdGXX1R4/zUzU+xVQ1mXIWAdzLJgcOUwkQv3YtCOQBNk6hBbjUA5tlRgUI6JlKLqw9NCXtmnBD08xwli2On1uv0+TQhrQU0J8n5bkSvsjwRknricKkW2WaLk43hVC2iKyEtqQFTMa68SlFWfovglJUhy6zjeJ3qyi7yFRTMPnjBzMON6g7kKsWdjYvE7HoNzDXCFzl6DmMtuy3YWhQkgphP743SH3LFKL9fQB6iOWZKkqqqq/v7+jo6O+vr6qqqqpqam7u7uvr65TX0DUa9v7ZlnkiY5wEITEONMZuQSgK5k4JVs6jKLfhaLTmJxJotYYkUMLYzOMJj5NmRzdMZtgCl7/LV40ERa/8hf5NfP9b89AtQg0TRfMxCJVzDwEMbw8IHBXrz5hOQ2kCQ+ElZ9Iws3AbyOgesZfROAm1i8mcVbLXg7i3cwcAdI7gTGXmZkkN1w8NJfP+ppav9Foq8p0TM30dPR19XR293R0dvd1Td/oKd3oKtroKu3p7ere2G7rai4Zt4P/xuD5xB4FjHPQLLzpAGWInYTzIBJ8IE+ZvF7ly547MbGLkX2l/HuOSGuNCaLceVzJRPhcCQYEHLt7pro1NSTmfhhstyPHs5ED5v7FBA/ROhh/AeWIOZnGfgsgM+QEOP9z11YpuT5AqosRHjZ6wsG3G43BcR0WKU1TvQa5/cT/vgzATGJkBcFmZc5gfcHAzOcHrvnhg93Xq4j6zAEKQhSetbQcIY+yOIjAOng8L7zW+pncQFVEDi320Omql8IiOnKKokFEURZ5oJBrrSspLOju7g4FomEtVC0pDReXz9noP8OIVBTXn/tagQ2Egs2Zju27sckrwTvMcNTVrNwBQvfIuZ0+E0GvpFh/MkKX7fiV8agP4LkGwAvA8mXL0gNb9DJmZ+kK5HkDZq7AZM0iON/+yv8avz/XzogHq0roDCOwuIPP/zwhRdeGBgY+NjCrLa2trW19YknntixY0ca6v3mN7/Jz89PQygqIy4uLi4vL+/p6RkYGOju7j4OEKdVE2RqRM7jfVVVVXfddRflhhFCjzzySHFxcVNTU0VFBQ0fbmpqqq+vnzNnzoMPPrhmzZpf/epXkUiktLSUrmbSrnCKYgOBQGlpKTVfi8Vit99+e3oB/Qu+yqVLl86cOdPlckWjUeKPeCxqJBaLud1uu90eiURUVbXZbH6/n9pNUFgsy3JDQwMV49bV1VVWVsZiMY7juru7169fjxAaHh7etGnT008/PTAwsGTJkoaGhoGBgY8++iiN/jHGe/bs+eUvf3nTTTdpmiZJEv0gFIgfxxBTHXMkEjHxcLGqqhQQU7xL2V9Jkj7uJqRxeuFw2Ov15piNd2mGODc3l3YKUrrL4/FQJw3qXEEZ4jTvTmExBcT09n8EiBVFGRgY+Joh/oLf3pfyV0RgaOjQIOs4CBE0bBJ+RHpoQAMP6zA5ZBjDh/fv85Ydyfg+yvipzv7oiwExBpOhCYiHrDdhcDNkpr5zg/Dz/n6iijOz048DxH09/b2dif5EYn5/XzRWMiXbFi7ITs3IQtOY5PQsfDMwbjph6AcnGKedpjOxEWv5oRO/g67J2GMbt8VxyWNFN83n3eWRiLeyxFtRIsWLhZJ4oCxWHiIWp0JpLByJxyLFsUiJwoVybL67r58O2bMwOO1gJoDgFJ21mXxwBQY1iKlBoBqCGsjUYlA7yFZjcnd20hLLCSvekFcRPJLMuf2c3+PWxCJPxM0FfDZfUFSKQpwkBRVZUPMdRT4SjMz7ZFXlQzFeVSRZUBRJ0mQpIkpRSQoLshqU/BJv99n92UWBlWfOwuBqEoFBtnMwOBuDiw1whcF8HzEX3jGjwKP9PYCYOESJIp2rUw6MnntJC2lZRTCiCXmz1p01wSSGWd0CDAtAmRZ9TKYxLit1AjuSCUx3ZBYBCwQWBCwphk1lsDjDiiwnQvCt/ZcWoo07dKQTJ/+v659/BAgPnKQmEuYCt04twnXilEhmtiR3l6SLkYAxmDqc3FuNNgO8gUWb2eQ2JrkNpLYCtAngzabJmpnHgXdkol0A7gRod0Zqf9aeEcsaA6wwTnjgKbWpsTXR25XoXtTb15Xo7Uh03Z7o6e9JdPYkyKJud093b29n/0D37IrGadNmllbZXxk+5QXEPoPBkyjjGTzmBQRWpTIOG2B/knkPgxWYWYUzVugZbx4a+8LqS39x/63N7f7ysrDoq+YCcS1URhzY4pIWUUPabDVaGopFOSGcm+0rFr1Dj4/BD4DhP4DUowAvBnhxBlxsRUus+A9j8JIM9DQwXgD6C1b9eavxvBUutRxZOr5amuwTVL8qi6Iv4BWcHjsnlMiqX+S8fpdPEuSgwAuiHBIjPmeOl/P5pLAiymHRq4gqL5VKsirLgqhwISGsyIJXFmWOE/xcjs3rdN6yffvlg4g9jMAQAiMIpAxGP8LiI4yRAocPXNDaND3gFzmO93jcpOGVtMAKnKn7PzaZJX/S3ioqiBIEkhuSLrKiZDZ/H53rBlUlHJ05w15TP2vTINiE2FWY2QlBEjI4yeB9wNgE8CqAVwD0LoBvkQ29wcA3WOM1Bv8xE/6RuE+MLMtMffD0f8LI/WcAYqqCoG1hw8PDf/7zn+fNm9fe3t7Y2NhihpxRAUNZGXFRee655yh+XbZsGQVPdJGdUhoxMz2YRsd9JhpOo+Te3t7u7u7S0tLm5uYDBw5QTnrJkiUOhyMcDguC4HQ6w+EwTR6msLixsZFKFJqbmxsbGysqKmhUB10o8Xq98Xi80qxoNNrR0TE4OEjp2M87uSGE1q1bN3PmTAp8o9FoMBikIsJYLObz+ejjmqbZ7XaKlR3Hyu1205Zz2uhWXFwcj8clSWpubl64cOFTTz3V3d1dUlIye/bs2tra6urquro6KiN544036LtKU8X33XefKIr33HPPO++8s3DhwunTp1OgP5ohplIKTdOoq52maaZKgtxQl4mcnBye5ysqKtKA2O/3jwbElCSmgJi2IBQVFeXl5amqSq3x6HyAfo+jSWK/2VdHlSSfpxj+9OOKovT29n4ZGuLP+za/fpxMbkkjRZKskZE/TP0Yzas3MEwRJVkSwuH9e/fmcjr7LQyIEwK0/BiD68zt0yTxjSZbPNmkh6ckmSkp5pYt59vu7uhuH0j0mJVIENXEaEzcnejrTPT39w90NNbb8gum59g8LufhKePxDVY0kcE/GKdfyMJx1hQTMLI6dGYSnnDC4RtPTM0A+GYAJ1qHLmcHv2ndf8HJL0+8cqEnLxwPceVxf0VcjccjUU2La1o8qhYXB9WQy+V+4prv6yATs5kQAJ39dipDWXtV+39lt675XuWRM0pS1rjBVEBQrTM1iK1BTHmKie85gfgYe8QwFwyFOG8g4HF5fJIsc2GXxmkuh80bCElckcQLghJ1OWyC1xOUxYDiExRelZSQGJIkTVA0UQmpkipLkigrxK5CCLo8nlyP78/nXovBBZg53QTE55q330bgu4i9ArFn/Xp6ticS+jsYYsUsqo+kF1TeLHKW4Pl8rytsy9587pnIAjBgUxaLYckyLGMwcxIGJ0KQaQCAGIKDMdmo6zCAwAKtAGaAQxknjDz5tGEQ73bSPfd1/fOPAAHEwxgfNuN0zRui+0yZY3iY5O0aBvELxxgdfsXYcQvcZNUpN7wZ4E0M3sygrcDYAdBOgFph2sAAACAASURBVHeyeBeLd2XhbRnoQ3ZoaMymIeJ3+w4Ea42Ml1fd1N3TMndgYXdPV2/PwkRfeyLR15uYm0j0mxflLjqa+/oTHZ0tDntRfp7Nz816ZE/WMmRdBjOe1q0vQbAeghHdui/FvIHBcgyWQ3YlAquQZQW0rEZgDWZWG5blI9aXN5x7/7PX1zcFVbFK4qpkoZIQxzKJZpckfmph0WsLLsePg+FHT0aLAfoDu/+hb/9+gSvRWXZvf8mrd95y4KmzSFLxUjD8InPombHDz5yoP5eV+uMJixqnOe3BAG8mpgf8dqdXEkSRVwROteU5vHae9/GS5OEFn8PJhZx+QRKDQiQsxsIEC3OypCpCRBXlkMSLsiYKPrWoivf57HkuW+6ta1d/dxiCwwgMYrKNQMYYtBBAnASHD57f2jI94Jc4jqzwBAK+vwMQ8zxPzZKPQmRRDXBi9kxndXP25hGwGVnWIrAHAR39BRCj1QCvBPhdAN82AbGJifU/gtRbAP8/1ngZjGwox8NJEsvy715fOiCmimGKcV988cX+/n6KgxsaGmpqaqqqqqgwt7Kycvbs2bRna9myZTTAubKykkK3gFnU6iESifh8vpqamr6+PsoE0wtnT09Pr1mdnZ1UtFpeXh6Px8vKyrZt2/axnNcwjA0bNrhcLtr15XA4CgoKZFmeM2cOjYNuNKuhoaHJrJaWloaGBkUh+VK0PTwSiaQZ4urq6n379v1VQLxr166ZM2c6nU7aZ0a1faIoFhcXUwxKk5CdTmdRUZHH43EeK5pvR70XKKCMx+NUXrxw4UIaiUynEw0NDbW1tc3NzU1NTdVm/elPf6JagpGREYSQruu7d++mcpR333138uTJeXl5dMzQax4lhGw2G21bpFHYackERbrZ2dnBYLCiooK+jVAoxHFcjll5ZuWaVVBQQGX+dC6Rn5+vKAplnUczxGliePSOpmmxz69oNEqnQ/RWluXe3l7aZfjvPki/Qp+PrK6aYkNzuYyGNetYNwhBbLaop/YfxsEaxFyWyrp2GPwUZV5vsFdjcK25Xfcp4cRExEw02Ek6e5OJiaceOGH6I2J53/yB+X0DfX19/cd44tGAuCPR3dM3MNA3n5MD2bnZhQWuHK932+SL4A+t+LIMfMYYPSPLfMI6DAoQY8XjrfinAN/CHsrNNH6UhcdnwlPB0FkgeRbApzHGWacc+s6F713343vsjtKSsFBaFg/VisV12SUVzbdMHck6yeREAQRZH42ddKenruz2nsb+vvqFidr2lid/Fk2yks6EPzgt/vRNtXc4qpf+NLr8It8dM6cVyGKhwAdFr5v35zglP1fiEopzpHiuPbvUa3PzMs+pYUEodDhtRZwgyJwYCqhyMCRLshwWpZAkiIoYCKk+TRYlVeIivKC5vK5cl2/5WT/C4NsEB7PjTb3EWRhcQhI6mIsRM+63N9/q0v4eQEzlj2lArKpqWkehyHKh3xsrnLXjkrNxJsCMRQdZmDkZgZMwOw4xYzGbZRAdBUvC/MxwO8MKMAswa0HjsvZawR7ei/VhAyIjOYL1ka/QD/o/563Qnjkqj6CL3aT7lfQ34hRZ/jaIydoR49DS5Obz0QbCBMMtJ8MdJJmZuA7vYNAHjPERMD4EeBfAO0FqHziQBBsQeBta3kOZKxC7GoJ1+vltfXn11fPa2toTvV39fQv6zOrt6+ntTfSS5tie7u6ugf65fX0DPB8gkU82Z6Et/6Hl45ehU57HzHsI7B8+6dCRszfBjDfhSSsNdhUCqzC7EoMVCBBYDC2rDOtqyK5BmRv0rPUG2IyZdw6NXbZhwmOv/eC2X2fXVvACV5PnqqmNTzUez0IPZxjPgKEnTvrjLyctam9uq+9sbW5raGmbXdvW21m88vErjFdOeu2e6XOafl5V0zu3s+2Xtwu/vX92RUU4qopSwOtxuUlbvFcs4oSZXs4/J7vvsZuru3IDbjkmVngds3yFSsDJB3xemfgny4qsciEpEOIEhQ9J/oASEznJL0lFijwrz5GTPfW9t65IIfYIBocxOITBIARwmMWHGD0JDh45v61tapAAYsHtdvn93r8DEAuCQK0nqHaRFwVFUvNneao6Z21Iga3Ish0SII4wg1MM3g+MzcQhhJDEywF6hwBi48/AeBOgPwOiKn45Y2jFNXDwACJaJ7KK8O9dXzogpofPMIzh4eH29vbZs2d3d3d3dHQ0NjaOhsJVVVVUxlBu1tatWzHGHR0dNMkiTSvSvi5qklBeXj5g1tEhZ1LCzc3NNTU15eXlFRUVZWZe8cfE5IYNGyhA3Lt3L/XTpuayhYWFNBGD9rvQ9q/Gxsb6+noKjpuamiKRSJFZtEUsDYgrKio2btz4VwHxoUOHsrOzHQ6HJEnUxcLj8YiiSOnewsJCRSH5kx4zQXo0IHa5XJpG/ETpR6ZpfKqq1tbW9vX1UWsIOqOgEXR1dXXNzc0tLS1UCrJ+/fqREXLhoQIVSpCT9W1dv+uuu26++Wan05lecOE4jmqLVVWlDHE0Gs3Nzc03i+Ld7OzsQCBA5xghswRB+DQgzs/Pp89Gk2nz8vJoxGBJSQk9khzH+cwKmFk7Pp+P7vj9/i8GxJFIJBqN0ra/eDzO83xXVxf9jP/eQ/Qr9emIMwA8ajaMCNVnQKTjFEwSmwCMDg4Zt92794wrEbgSMtfp7I91cA0GP0bgp+Z2LQLXoU+IiSciMBEyk0YsBBAj5uYDp2Tf7Y/MbpjT3kSaQiggTg9wCou7e3vm9c9tqWudVZhdYM/3OLy3ejyrJ15kXAjgGdbDY6ybJ5yz4hpu+SWzd588+bDVkjqDgdcAfB07NJFNXQTQaYxxuiU5wWKMB+gMZuRky75xGZvHn/6n73zn8Vt+9lD2LT/3FNaHfZVK4MGrrx5mM5CFHQFg1zfOfc4Wvb2ut7q3dU5Xc02ipS3R9ftwyxs/LXv3W9HXb667N96zqKnv9yUdT8otvywuLplTFy0plbhgkc8bKbi1yz+zsSg7VuSYz1//fNkNXaWzCvycL6Tm2Qp9DncgKLuEsF/WBOIWx8uSpIohRQyJikKsTGWJBMkKosfjuNXne/vcayB7kc5MgMxJCJyEwKkYnIfAhQicZ4Cseybe6DTPG9Rlgq6ucqOKjvr0GZX6/FOJJF2SohdUkxfmj8olBSHfWxQrmLXrkm8iq6khBmMwOBmBcZgkM4+FDAHEiGENC6NnMHom0MeA1ElsMhMcyQLbx58FX/kvmErqECWhbhik1eHr+j84Akd9JFLERIJYSZiGwwYZziM4peMjOPkbvOtUuJFF28ckdwG4i8EfAPwhQLsB+ojs4A+Bvg8cOcTuHWY34jHvQfAnBN7Gme/hzOWIWQ3BRjihre86LihqmlZeHp89u6qlKdHe0dzd3UkUFImeRKK3r7+3v29uPFaWn59ns+cWFtrzsovmLrnuz/qEd4+cvXKD95Wnfn7v/Ltu/90vHni5Y/kax4r9161FJ6/DYC3OWIMz1mKw3tzWIrABEjS8zgBrENiCretINhtYuxu8/OqYxx8EO383JvkkGHoyc/+S8X9Y0FbX3F/V2NHZ1trZ1NTVXN/ZXNfR1NrR3Ldo0aLOrpaW5o6u9p6Whua2lkR1VUtHZ29DQ5scDdp99rY7T/7vVeDJN8GvHrM+ufKkjXjcQ6+dm52TZ7OFps8KXnr7t/J6znSLU73uJs5TpgRCEh/lQzIf8clSWNA8MhfkJK9Dysuzl0+fOfPd989PYQKID2GwH4NDCMAkgw4xqSRzYOi8to4pwYDEc4Lr7wXE1M2GklwE7chBhZPzprvLe25dkwI7kGU/ZCAGGAOsM/gAMLaYVsSUJH7PZIjfZPS3gPEGg5edkHxzfHLfW7oxQjiP/wCx05cOiNP+EoZhzJ07NxgMdnR0tLW1VVdXzx5VVceqpqYmFov19fUNDg7eeeedTqeTKsf9ZqmqWlZWFgwSlz6Px1NfX9/T09PU1NTS0tLc3FxbW1tZWVl2rCjgFgRh+fLlyKxUKkVbu0Jmdprdbqci12g0Wl5e3tjYSOlh2mlHMXFZWRnlbmmAs6mYIC9RUlLy0ksvfbGGlXp1FRQU0FazeDwuiqLH4+F5nvKshYWFoihGo1Gv1+s2M/McDscxjvj/s/ceYG5UZ9v/mZF21wVjTDedkNAJ3YAN7g17m1a9l9GM+vbm9VZppe3VDTCEEAKGEKoBAwnw0kzHBbApxg0wxr1tkWbmnI8zD9brQODN9/1J3v+V+FxzibG8rOTRjOZ3nnM/920QBMHj8YCMIe2tUV5e3tjY6PV6tVqtz+crLKSGUkajEZKE4/E4dMO0tbXdf//9mzZtSiaTgMXwCB4UTz31FHTbpB2gPB6PTqdLV3PD4TBwMDzm5ubOnz/f4XCUlpZCVJvf7/95IPZ6vXa7PT8/n+M4gGxQiaRLwg5l/INADOXhSCRSWFgIUmOdTtfQ0HAciP/Ft1XqOakEutKZlijSDjqCh0SSlDD+YMORXH7PiZem1Jdi5gYZ3SBTqes1SprajZhi8QTA4qN14u977DCakmKnysxUCU3bO2rWkjlGp4/3CX74oEtLS2traxsbGxOJRGdnZ1dXV29vd39Hl01vz9FrdAUFFo15htXyyg2/lrLY9ydNes9iW1lU9nDXHQ/Vd7/kK3tDl/tezm1rc27dOvmsA+ePlkYgMoaRT1bL41h8EpJPZaRTGekkhmQyMsscOTFz4IzMrbec+q7u4jW3X7n79LGySpVSj9h/0tinPFy0t7uxJdYda2lqbUw0xxPNiXiiZVG0bXll8/KmjlhLYmFLtKYtVtuaaGmOt7S0tUSbKksKKzzWF/03flF61YelN68LXL6j+MKv6q5fV31ZhT3b7rCX6uY8F5z0CH/LnZ6JUXuuz+mxC7zD5+W8IY4Lc15B4H28x+t22wWf12CxZVsKvjj3QpEZk2JHUEECFSdkEHSKoiQ+CyN2xc23GENhj8cDISA/A8TQleF00gyRHwMxlIcBiL2cN8/qCOfdvuOCccM0zBlJKAOjUYQZiSkZj6DhzIwKj2BSJyLxdITPQ/hXrHzFKHx+xpZxGeKSRanh4aQkJ2U5Sa2pqfL8+PgXHwEKw1Q1IVKDYTyEcUqmqzr0hiDSxoAhSXptYMf81HaWKiJ2ZuDdiFAOZsgeZduN8LdI3sccGVR/k2I3y6o1IvMhZj+WqZ5hPVatx+yHMvpUOuEPT85wux2BQIDjaHSxXygKhWidpaqqoqGhPpFIdHd3Layp02qNGo1Gp8/TanV52faaRRetPXjhK+sm3XGnsKQ30dXc2dwWb11cc+ed7vsftT/9uvbVDdo3t0776MivP8WZn2HVZjKC0jBtvEObaRgb2oTVW7Bql4wG8ajU/ouHPvy1uPIEcSUSn8ncsPyM0lBRdWNrc2tbIppoTbQl2hIt7fG2WCJe1xJtqm9qXhhPdLTG2xPRWHOsLR5rb441t7Z2dPY1Lahb8Nc1+rc36NZsLvj80LSP9k55ds0tL2/QxZZNjTTdEGi4LrHlque/ufrPL02sXzQzUjfJxs2229xuW5h3Fnp4ev0KLqfP7bVztttzeaNr0rYD4w9jWh4+qADxQYwkEeEjbEpk9g2MjyUmO+wUiE0mo8Vq/r+tEAMHp40vqKGN4BRcgjbbVto25ZMU2iGxAzKLZYRlJCcR3ofk7TRwmxwFYvwBkt9nqHbiTUReVic36iS8TyYUiJU2un/xafuvfrl/OhCnTdZkWX7kkUfmzJkDvcy1tbWgl6g+OiorK6HkWV1dHQ6HOzs76+rqjpURQztneXl52ibMbrdXVlZGIpFgMFh+dECNuaSkBGyDPR7Pa6+9BhVijHFZWZnVag0Gg9A2B6jq9/tLS0sXLFgAYonGxkbQEzc0NCxcuBA6yQDNAbhLSkqCweCf//xn6Kv7qQ8N/u1erzcvLw+S9sDx1+l0wrq/Vqt1uVxAikajEYwmAIj1ej3cqMDbAZiyUBllZWU8z2s0GqfTWVhYCC2At99+O8dxtbW1kOtRVVW1cOHCysrK5cuXP/LIIx9//DGUigGIMcYvvvhiXl4eBNS5XC7IIoZqblFRUSQSgVjmY4HYbreXlJQUFRUFlMFx3M9XiJ1OJygo0k11FoslXQ+GOvE/CMTwDwdHDkDtlpaWTz755OcnJD/1uRx//v/5CNAVVzq3FCW6YSmFkzIeSg3Jaz/bccW0YfbcJHu5pL4W0x6vyyXmGqU8fC2mJr43pJn4qJ54omI6AY4TU1KqKZiWiqevvMUYDAV4QfD5fH6/H7StPmVEIpHy8vK6+lq/L5Cbq80tyDfpDBataa7Jcv/1V30z/oJ7u3vvuGtZ+5LF7f393Uv7O5cu6l/cv7y75/etvU821bwU5Nbcco3MZsqjkTQWyWMYeYxKHMumxiHxBCSpUUrFyKcz8o2MeLNavIiV1AizmQPMmK9OvzgYKlwQj3W3xTob2mrq6qL1zbHmlli8JdHe3tjZ2piIxaPR5kRzvLm5oSURSzS3N7bEo/Hm1lhvXdmqBZa/1Oj+XOd4r/KGtytvvbcm+FyldlWx5jnP1Hftvz0snJ50qwc8I77w/uYFz63LXVMaXbPCnNnt5R180M6HXN6gxc0bBF+O2dmmmX7klNEY0TBk5ZHanxF0IkFnEDQeI+bRG282/NJAzHF8rsVZqLl97+WnyCchfAJKZiEpQyWpGZGGWqtkVoWzWPl0FblUTa5TyzeopAmsdLXqyK9OOdBbmzqyW6TWI0lRTtFuzOMS4v/ny+//w/+omEhgpW0uRSRJxtRZTUHhpIT3E3y/+M0caWcW3sXivSzeh8geluzOoC4Eu5C0C4n70cCgendS9bmI1tB4ObQWM+ux6mMZbZDRRln1saT+SGY+xsxTb99id2p9vqBfKPT5hEDAH/AHfb4Az/s8HppeEQ6HLRarJl+n0xn0hjyttiBnnj1QMn7zLtt9f/R0dNzV07toUW9Pf09Pd8/S7q7FPR19rfE+2obXU7N8heHTwQu2YvVmMXOrxG7F7Da6qbdh9muStYu6NzB4IGPwbUZ8EuHHEHkii6xk31t64cy8QHFVtKs70dRY7w/5CisjlfU10aZYR3MiEY2WV5ZX1JTX1tXEm1saY83NsfrWeKyxobkpvrC+qaG1j6+uLV1YJ/zuAf39D3lqFgptvcUr3zRsGpz/8d5bV++cukk66TPphLXSqWuOXLlq48TFj94arLiZ8861cfMcet7hCNksfK7GM0Vz/e9fuPZrPHqXpDp4FIgPUBkxkoZUKQkdHj6jo3sKALHZbLJY/i+AmFcGzGBBMgGt7R6fk3fwBfMs1T0Tt0hor8wkZUZO0aRoaQjh/Qh/rWQNfqLIiNcjsgbJ65D4Pkq9kZVcfxseWCtiUcQpmr7xH+CK+E8H4rQRGCHknXfegVQIo9FYVlZWW1u7YMGCioqKqqqqBQsW1NbWQk23uLjY56ON0oA+1mPyfqGvi+d5cEazWCxFRUXl5eWhUKj0aLQygHFJSQnsBAKBFStWpIG4oaEB2um8Xi/4PDidNIfG5XKFw+Hq6uqGhoaysrLq6mposKuvr4ceu1IlkwI62IqLi8Ph8KpVq9J+wD/1TYUxbm5uBr0BtKOBrVsoFCosLNTpdJDkbLfbDQaDw+EwGo0GZej1eqfTSWVJggAFaXjRUCj0wAMPPPTQQ2DBASJpYNPc3FydTldcXByLxYDpa2troRK/YMGCJ554YmBgQD46BgcHH3jggZKSErvdDuSt1Wp5nk9rEjQaDXTU5SmWamAeB1weUMb/CMQulwuoveRolorBYMjOzlYyeKDiT2Pq0oj8M5KJcDgMjT7l5eX33Xffli1bUinaoAMf608d/OPP/+JHgOqEZeq5lsJSUsQ0qDklktff+ebK6RidJ6mvlFXXiuxvRfYqkb1SYq7FzHUp9nqMYAMmvukYPfGtivPaFJGdnFKBHdvU9Rfll/ECF/L7/X6fMgCL6Z+Bj3neanXma/R6vb5Aq9UaDNkFpvoZU58T+J5ly1v6Wnv6e/o76S21u6+7u6+np6+/p39pe/9d3Xffe0e86pszLxnKGCGOYOUsFmex0khWPoERxyBaPGZVeLwa36Aiv2HlE0almBNldKLEjhs8+bJSK+evrGysbfCEfQ6L3e5w8rxQWFi8oKomHCm2uqjFWnFFWUtLIhqPNcVjbY2x5mg03tba1trS0tjU2pRYEG/taKztayxrirXH4h2Lq3x/cd74bnD6i2W6dYEJX3nOOGIdhW1Zh5xjN3kufs14zeOam+8pmBm36AvdTpfVIHD6BnPOp7eeLY5jCVUpZGCkxohRgHg0QWcSRCvET15/syEc+mUrxJzHm2tzFWln7b/mZHI+S85D5GyWjM/EpyF5FJIyEc5k8ChVahySL0IUiG/MEK9jB89R72mtT8n7U3S9VZTlFKFZr5JILQ2Oj3/1EVD8EhX5v4LGMsxpRYLxLjIQHdx5vrQrk+xm8R61vE8l7VNJexi8O0Pci8TDquHhjH0p9ecS84GM3sFoLUYfYvQRZtbLWesI8yFBGzG7UVJvFJlPCXpjy3UOt8bLhfxCid/vE/yOgD8cCIT8/qDfH6A5c/6AzWbV6XQajV6nL9AbCnLmG42mS/74YGn/oqae3p6evu7e3r6+3o7enmXd/d09vYnensW9fUv6eu5pjfc8u3raVzjzaxntkNmvCNqB2Z1y5reY3YPRoIzIkQzxfWbwaSQ/hchTmfJjWclVaOP9F+UU+C32YDji41x2o1Fn81rtgt3lcRSGwh63h6KnwxYJBlvbu2KJeCKeaIomoomORGt3e2s8Fm+Nxvtj0e6GuoamhY3xxo5oY8fCBssz/+X6458Koo3cfU/f/vqXUz4nmRtFZoPMrhVPfP/wb97eOeG59yYtW3Grt3aqq36Cu3HKklcvWTd05pdy1rfUBpgBJj4ArXUpltoS4zPvWj4bJBMmk9Fms/zjFWJeGX8HiHkH7/TnzTHU916/m0o1GFGpDctJWiEmhxiyk5E+V1QTioyYrEHSWkb8YNTw2rkk+RK1k8diiuolJBpk+O8+/ulAfGyFeOfOnW6322KxaLXa+fPn8zxfXFwMbRw8z4fDYSgBwofq8Xh8Ph9wE6zvg3airKzM5/PByrvFYgkEAjU1NYFAoKioqKKiAsrD5eXlIPYFt7Lu7u7BQdogKcvyQw89pNVqA8owGo1gAAziBODsNLaCQwWQZbpgHI1G6+rqQKjw+OOP/3yFWAE2+Z577pk1a5bDQVeRwIrYZrOlxdB2ux0UsXq93uVymUwmep/X641Go9VqhTTINBCHQqGysrL9+/d/8cUXeXl5FosFsuV4nodS7vz58zUaTWVlJWhIoC+wsbGxtra2qKho9erV+OgAKcuePXsaGxtzcnIcDkd+fr7b7S4qKiopKQmFQhqNJl0Azs3Nzc7ONplMZYqZsd/vBx83eNEfPDocDvhM3W63RqOx2WzFxcXBYPC7jJL333//scceg9QPs9kMHy7E/9hsNug7DCujUAkjDIfDgiDYbDZBEL7Tz2zYsGFoaOg4BJN/8qCLY7T5nN456VcgXW2lhuyS0lEnyWSYyKKMZQnLw0n5oSe/HH8VRr+S0W9kdCXdmKtk9rcyuprmWTBXicx1GF2D0bUKFt+A0YSjQJyWFN8mshOHMybJ1Jx4+lcnz6tz8c5IgJ5lvkAQuNjvFwI+2ILBIOfhCwp0BZoCrUGfr9fm5GkLXY7F/Z29Pf1d/Z3dvT2Lehf3dvf19vb293X39nV29/b19vV29y6t6et8/pZpu0aMwQxDVIycwYgZrJTBypkMHqEmmSNwFpsapSLqLMyeIrGnYmaUjEZJ6rPqsk1anvO4eY2lwG42+ziv1+V20BBNl8VsNRnMVqezqKgolmiONtbFWxKtieaWllh1dWVN7cJYLNoWj8c7WmLx7lhzcyLe3NjSUh+L9taV9dcvaI7F+xNNXWWWTdbTiF6N5zPDt7Kpq9HgVezeG078bNqF/5V31cvWG95yXffp7ZfIv2LFMxlZNVpmshSDM+r4i9EJMjNeYk/DiFl5wy26cMjB2exGq8HsdLg8Dofd4XI73C6ny+5yeDx2t9PlcLosDpvbZbd5nB6v0+vjvNT+2MtTUwulKYdewlS3TB3/vR5OY3WW5M7cd8kJ+DwWX4jkXyP5YhW+AJEzGGk0K45QyaNV0gg0PBbh80aSi8amTke75mfLh3YcoT5rWDEvpSuuIpaOGxH/ky/fv//rKdJQppGx0gNAc3dpIMr21MFK+dtx5OAI6SCSD7BkjwrvUcl7VamD7NAAOihm7BTZLRLaKKM1GH2gbOswWi+j9bRCnLWeUFu0D2nHm2qDzH4msx8eOq98ocbjLPH7Aj6fPxAQ/H661KM88j5fIBgMezhXviZHrzMrc9ocTb6+sJDr7evp7V3a29vb0dvX3re4p2fxoq7FnX1UItXT29PV071oyaKG2pb6xtlfSyftIWiXzOzEqj0E7VdczIYJwsOs/JFKfIqRn6BAjJ/OwiuZ4WfQVw+fZrF5zRaO5wSLwWgz2zwel9vjcHNOl9trMpusVqOPD4R84arqskgk0FjT2N3dG0u0hoRQieCtqKhsaoglWptbOhPRlpZYoq05Ho821yTaFjQ0NbQ0Li2rro92ZH9yhHo4fE5tHNQbRLRBQhtF9kOc9d7hK98YuOTdw5dtkpltWPUtxXfVbsIeJOgwplh8iKAjEpMkaEga97t7ZtpttB3OZDJarb8AEHu9Tt4VzJmt7bhzwmHCDBHqf4xTCEsMTiFymMrEpU0KEEOReC3C7zKD6y+XB17CZFgBYomGtGCJBjX/u49/OhCn8UXRHIrl5eUmZcybNy/tWWuz2aBPzqMMjuOgvQPkBBBxDDQMMtZQKAS5yhaLRRCE6urqYDBYWFhYrgzoqCsrKwM+Li4uf9yWmwAAIABJREFU/s5HIm2Rtnr16vz8fJ+PenoDmgN3ejweQRBMJhN48RqNRmgIg/IwAHFjY2N9fX15eXkwGHz22We/i/lI4/5PnSeyLD/zzDMzZ8602+1gKAGGFaAMhjp3OBzmOO7YCjEoic1mM5iDlpSUFCsjEqFhlbt37965c6dGozEajeCaHAgEsrOzoaZrt9tBCV1TU1NfXw9B1mBA0dnZ+eWXXwKm029H6iSLv/rqq7vvvtvv92dnZ0ORGOxIdTpddnY2wC74EEMEdHFxsfLt5k9T+LFAnJuba7PZjgVii8UCte3S0tLDhw+Lovjtt9/++c9/FgQhPz9fo9GAkQgAMRhchEK0PAhNss3NzU888cRXX30Ftn2w4PBTR/v487/IEVA60UVCfdWSdH2bmkmkiCwlFUAeklJJQoZFMZXE61Y8/MU5VxHmDMxcoeQbpx+vJOiq9IbRVRhdjdF1inACKsQ3E9ppN0GmfHwrQTcNZN0i0WyOOd+cOC/mEBwKEAcFxVrUR++o3qBP2QQf9VSiGnqlOkyzbPLy8mKx2JIlS3p7e3t6evqUQY3Zenp6j47u/u6lS+9wlFctmTBh+9gTaWFVhSQ1K7IsZtWyOkNUq1MMIoyaUAuFMTJ7sqQaSVRIRhmEPbd1rkMfcPMel96gM1mNPM9De67L5bJarWazGXRHsJRRXl7e2toaj8dB8eX3+8vLy+vr61tbWzs6OlpbW6PRaHNzczweb2xsbG1tLYl3TgtWvaM7Xb6dlSYgfCWSL0XkMkSuYMn1qoHbVANzRqVmZg1dk0nOQPK5SM7KlFAWRogwDGEzCTNOZscn1SdixDx5/U3acNjptzq1Zr1FsLt5l8vm4gQrjQYwuxw8b/M63HanS++w8m6rhXd4Bbsv6OF8HCd4eRox7aGJescCseDmCkyOqvlzDl8wmqY0X6zCVyDpCkR+g+RzUPJE1cBIVhyFiBoNqtBeBg1nZUlZiNz3mCQdOEBSgzJ17qfJzBTKFJuvX+Q0Pf5L/u+OANUM0z46uvhN/QLo/OTgC4f2n0v2Z0p7UeoQEg8hvEtFdquo+cBA1mFZ9YVIFRHrCALq/Rijj2S0nrDriYq6oRHVeuWv6N9i9cdYvVFSbUieGuvO9jgq/X6vT/DT4McQHwj6fX6v4HP7/cGAP+Tl3UaTVqc10WltQY7T6bpzyZ2LFi/r6O3r7V/a2be4ffGinu7fL+nu7VrU3te7qG9Rf0/fsiV39Pn5Qp93+o7BcbsJ2iOxu3DmPsWuYQgjiSBpL9r7AiJPZZAnEHkGyc8w5BlEnmYOPqoOFxY6rH7OJdidVqNBG/Z4AnaPy827Bd5jd1mNNlfA7fA4HR6zriDb57SVl4T9/qBVp+OtRouT8zmcAm8PFBdVRptiPa2xeKI90daeaGmLt7R29vDBiuLI7M/2sV/hzE8k5guctRmrviDoU3qI2I0isxGrNokZe+WMnTKzQ2J2iOwuQlUTRxQgpmQsMyJhjiTH3H33VKvFzXFeBYh/AcmEwLs5ZzBvju7OB6ZS52MlIQ9LLMEsERE5jMhOJG8+CsQbqPkaeRuRbwpkvJeWQ2gMU0qmJ0zyP2Fl518BxGBxAI89PT1Q+wTYguBBp9Nps9lg36sMAGK3271q1aqioiIIBIZSYpEy4H+xWq0cx1VVVUG7FQBxGosBiKG8un//fvj2+Oyzz4B0w+EwOAFDtxwAMZScI5GI3W6HsmggEEiXh8GAAlyTV6xYkUwm07j/U19Nsiy/8cYbM2bMsFqtPp8PvHjhFYuLiz0eDyC4IAgGg8Fms6UlEwaDAayIoSkNgLi8vNzpdG7btm337t1UgWUwhMNh8G/OycnJz8/PyckxGo0gena73YFAAEwn6uvry8rKPB7P448/DrbQ6U47KBnv27fvrrvuAlVxIBD405/+VFlZOXfuXL1en5+fD1bEer0e6seBQADKdseicBqdrVZqQAPqfq1WazabwSLjO5rfvXs3HCiM8eDgYGdn55w5c8CFA44D5EKDB0hFRcXq1auHhobSbzj9//7U0T7+/C9zBLAs0k2pESdplfgwJNJBDiIN5KArrt/2Lzt86m+T7LkiczlGlxB0+dENsPi/mRij3ypFYgDiCQTdpGwTlO66qYrOeLKEpkmqSQTNOJIxv1/HuQv9AlSIff6AAsR80McHfUJQ8Ad8PM/r9XqtVqvT6cAhe9GiRX19fccScM/fjkX9PW3tbTNMppaZszedeSZhVITJwkyWUmodKapUg2qUGsmSLLUiRVATVo1VjGK7O4aw1/fN8hgCnOClazgmK02u8lB/XrfH47HZbCaTKb0Pc3tYQoFlH2hfc7vd0KvQ0NDQooxWZbS1tbkDpdPyufemjx+ewBy5isEXZ5JLVEMTkDwJUWfl2xgyWYUnIHIhImeg4fMYrM7C6GSMxoqqEUcyVEn2dIIuIuj8gcwxT064QRfxhxyC2WzUGawmJ+d0OziX0W73WBwBKy0QuxxOP2dzuK1Wu1Pj8FqdHgcnuLzCTwKxh+Oybc7SeXMPnDySnKgSx6pTY1nxBCRnsvIoBmcgTJM42KSaGVYpymYG7Zs+mxzcNUwGRWV1QaLJVtSS5LiCmPxTBxaJRJM1JLqmA01QaQjGMp2UyBI5lCL76eRk+AXx8Ln4AMIHkLwfSfuUncPsUCpjn5yxFavWKk5n1OyMUL+zjRh9Quj2qbJROMYUlL/HZYw+ltGnMtoiZfYtv9ViKoz4PQGh2BsSgkIwELT5A26qgfJ7fX5O8HE6vUary9FpjZp8U1Gxf9kdfYv6l/X1Lu3p6ejr6+3rWwST296+rv6e3u7+3kV3drW1JjS5Zqd3wsGBC0QRfUvQQYk9gJmDGMkiIrsyxLcQfkZFnmTwSoRXIrJSRVYi+Rl24FnUXeY22mxuQXDYrXq9HvS1cM1COADcs2B+C62okJDlVgbNUFYGqApBWllfX087dqLRAq3F6zZtPKzaJjNbMfpcZjbL7HaCPsPsJmqFMWoLRtsx+koe8RVWf0NUX8uqHYTZhdE+acQRjAZk5pDEHMLsPmnsHx+abrU6vJ6QwWDUmfLtRofZxTncOXY35/QYHS631cnb3B6b3eO2Oji7nS5Teek0luc9vGDn6F/a3S6vh67s2Dmv1e+2OznP7bO1D6y48aBCwxJmZEyNJmSRJQdHkK8z8CZW+gzJG0eRT6j52sH1l0rDr5BhglMkRQYlIipaCeqr+W8//ulADLwFhT2M8QMPPAAhFNCzBQHCQLc2m7JUwHHQ4CwIgt1uf/TRR3fu3MnzPGhPHQ4HiA3gxLVarU6ns7KyElbYS0tLy8vLoZcuXSEuLS39zvR327Zt8Fl++eWXaW8Hp9MJ4gSQLAuCYLXSXOWioiKn05mfnw/FJ7oSGouBCKFJsTYDvnzhhRfSZcufOlFkWV67du2sWbOg3BsMBh0Oh9lsFgShuLgY7uvBIF0WNhqNFoslDcTwxuC+CxgKDW3fmaps3rz54MGD6VwPEDfn5eUVFBRoNJq8vDyj0ej3+6GmDoqFsrIyeOauu+4CsQR8LqlUCorcGONDhw49++yzK1asgCpyT0/PlClTtFotKIlBoBwOh0tKSgCI/X7/j4E4JyfHZDIJggBArNPpTCYTBG77fD5IIoSDBisGW7duveuuu+AloJwGtht/+tOfdu/eDcXstHNcurb9U0f7+PO/0BE4ajUMrqV0oZUCjUyoSkKk6czJ3R29QyPPJarxGJ0rqi4jzFUEXapslxF0mULGVxL0PRMr5eFrjlaIbyS0KjyBoBsVLL5FZG+WaW14GmbnEtXkYdWVj02ewwcD3kCArrj6vwdi0Ev4/XQF1ufzmUwmpUasBae/3t7eJUuW9PVRmQRgMfAxFIz7+/uX9vd5OfdMvTaoN6z71cWYycRoNEGjMBohsxnSCAafzpIz1fJIVmJorgRhkcwgiep0zyEo575JnMnnFXiXxWIzWr4HYg+1Q+McDgeYGHIcB0bjLpfL4/FYrVQoCb5mcEN1u90Ax16vF2wEy8rKwuHwdKPuBofl1YnnyxdS6k3egMTbEJnMJKcwA5NY8VqEL2TI6arh05kjp1JDjJQ6I4VOTzEXysx4gk6T0GUyupKoLsLM2I+vvCGnqMjqKsyxWDymeZxgL7AGrSbBa3UIdofbpePtLt7uMrs9FofHY9N43VYLx7tpCAiVTNBbK/d9hZhewiCZ4Dx5FlvN7DkHRmR9f1gyGCkD0QkDg6gRm5pGdaRogx1KIXT4vIsO79h6mKIZppYS1JePJJUle4m6TBzvqvuFLtMf/xqqdpJk6qFGC3uEHKIHm3LMEKYaCSLLCjEPE3ng8cGhUeJBFTlIaZgcYvCAKjms3i2pNmGGekdIaKP0vfXvxwRtIGijQsPHAjH1BobgDGWHmgRTczT1ir/MMbucQd4VEMLeYCBAr1kQTgR8VEfh8/t9JrOhQJun02kLNFqH0xpPNPT1dff19ff19fd0L+npXtbTvai3t6evv2NRX++iZYs7elvdbqcmz6Y13fbNrksGhtAeotpPVQdIlBlpp3rgLZR6AZFnGfIUrQortWGWrETSM0zqRfRgq9dss7h5weW0AxCnJ7Qulwu8UOH6tdlsAMGwgAyOhR5luN3uNBkDtND5sN2Qq8kx5vne3HTWlzjrC1G9BZ+wGWdsw2iTrF63+5xPDo/+mqBvMfMtQXsx2iOy+8XMQ5g5hOmbH8TMYCorhWlrnUzUr719vcXEW7XhKT7DlLttc+42+gW7XRO0WRxmp91l93IWzslZ7U6L08bZHIKFN/kcEa/Xz9MQnwDnCbpdXrfb5XEHFLWT1ekJ2HjHjFnmR56YmKTZeGxSVidlNolVZJghBxH5JkvciuQNI8hniHycMbz+LHHvMnoGyTSGSSQDtJWOXrJHlDXDH59z/1bP/JJAnK4EA7ikIRjYC4qpr7/+ul6vt9lser0+OzvbarVCQxs0V8ENBjTE4Ai2YMECjDHUL61WK6zpQ7ET+Mlut1dUVIDNLTTSgWQiXS2GAuqrr74KCoEDBw7Y7XaXyxUKhcBowmCgBmdut1sQBJgaFhcXcxwHfKnT6bRabTgcrqioAH/impqayspKu92+cOHC3bt3w7/6754UgJ6bN2+GIiigHsxHvV5vSUmJ3+/XarXBYDAQCJhMJovFYjKZDMqAAjAchEgkAhVikFyvWbNmaGgIgtQFQYCWNdBLaDSaggLaaAT4C42DIFP2+/1Op7O7uztNwOl3fuwHBPsY4+XLl0+ePDlHCWcGYC0oKIDZCBB8IBAoKCjIPToAjqFEna4Qg/ajuLgYrDAAiOFYpblcluVt27YtXry4urp6xYoVa9asOXToELwN+El4n/DzcFL93aN9/Mlf7AhQDpYJGaZmpdS9n65406/HIRrsKiWHdhZXHMwYl1KPl9jfDKsvJug8kVaILyb08ZKjWHyFAsQUi49qiK+D7joFhW9UHq8n6Nph1U0YzSFoJkYzsfpiUZX19jUXhwI+TzBAV1yPDl+A3keDVEjsAy2+VquFs91oNFZUVMTj8Z6ensXKOJaMFy1atHTp0oULFuRrcnKMOpvR/NyV18iqTMUyLEtSM/JoRE5h8ckoNQol1UiiUgSWIEZCKgmdQEXPaseq6wULJUa31eo0mP6mQgwukGB1BD0ScK91OBxpIE7fUNM7IJ1yOp0mk2me0TrVzD1220Txigxyk4rMQmQyOjL95F3zL3mBN60U+DWaqR/c+pvUaDVGaJjNkBAzcNKZ4uhLZfbslPrcgZMnDZx4k8T8SkZjB8ec0mm7vSAYXsDN3F58wc6Fp71ffk6ieJ7J49U6fXq3z+G2eR0ek9ttdLmsdrvP4RHsnEDvpgKvjGOBGL6KBc5dYDTE58w6MooCMTXfGKsip7PkdFY8a8TQuRmpsxgycoTIqlMsIiPHHfzzM0dIemFeOZVAd6OsM1AbyOPjn3MElKVtOneligj6XUkUiKETEyr7J8MpnMTStuRARfIIKx7IoCvmBxhpSJ1MZeyX1Fsk5iMZUYmwArgbiFIbVmj4x0C84Sglw499pGgn1hD0PkGPbbhJH8j3e4Sg4PMGQ/4A5/MV+Xxhvy/k94V9QgAKQzqdltqu6fKsVntx4YLiooqmprr2jnhff1d/f19//+L+viV9vUupfri/l/N5NQWaggJ7jiZ3zfrfDskZ+wl7gCARI3m3ang1Iz7P4mepUgI/zZBVKvIswk/TP0rPIfl1dtWyfLNZ6+YFt8up0+mAOtIXY3qFBwysYEJrt9t1Oh34dqfrxN+t0AIWg5M37V41uzXafG2edfXWEdulkdtxxucSsyk58s1Nv378lRmdrYnWhujdd3hee2fCXqzeg9F+jA7KJ274/Op335/9+a7zPt8//qXXpq3dcP32PWNT5PSth09bsuq6hd2zq1fdFkuN6ybo+R0nLfrLtaEGnS/gsJh4s8nqtDvtDs7m1LnsBZwl6PFSiOJ5L++zcF6n2+11eywer4VzBzh32OG1uT2e26e4H185TcJoCDPDMiISIpKKDCByAOFdGaltCH+GyPpR4sYThnbpiHhEShEJiyIZpjmkdE6F6e3gP2Am+4sBMUAwsAuYYcEzUA5MpVJffvnlyy+/vGTJErBZAJGu0WiEIqhdGZDpkFZNwKI/gCzGeMOGDTzPm0ymwsJCq9UKRpsWi6VMGaDKBVu08mNGWVlZJBJ5+umn4f185ztWU1MDzmter1evDHgPPM+7ldhDqN3qFJdivbIyC55rXq8XRK6RSMTlcpWUlGzfvj2NlT/1Fbdr1y6j0Qgta+FwGDrn3G43tJppNJpAIJDO5rBYLAZlAErCDSkcDgMQFxUV+f3+5557TpKkcDicn5/v9Xq/a7MDVUmuEqWhUwYYLXu9XqvVarPZioqKILC6ra0tDcQ/9YYBOu+9995JkyaBzBeMJkB7DW87oKgmDAbDUR6m/4UfMxgMaSA2GAxpocV3sx2oPf/gdYF9JUk6ePAgUG+afX/wk8f/+C86AorZsESt+wfpspmMZVlM0WhmkhoePuAtSdHA3tMk5sIUe1GKvYigXw9mnE/Qrwn6jbJdfJSJLwdhsQLE16SxmKDrjzLxzQRNTKpvIihfYqZg9blk1GhyAvrisvFVvMMdCvDKaeZT/NcoG/sEAOJgMOh0OrXKgHgd0J3DlbJgwQLF7rR78eLFixYtampqKisrs1it+QaNzqgzWpz3TpiQUqkwg0QWyWMQPgXJJzIpNZJVSGYRlUmwDEYZMholotMJykkh4d1fe+wer1fgnHa33miAqTs44QDUgnTe7XbDshVgpV6vdzgcUCSGe2r60el0QrXYarVmGw3TzYZ7s28lE1V4JkNuYj7PvbG9pTDakqhtbatrba3t6Ym0t/11cnZy9GmyasSmKy9fXlLckujprqhfGqxIxBN1rbFFC6vfmlsgs5lD52d+qznvsCdTioyQKzPwQkYsUX9pOO3jvCtXWG+pculsDsHKOX36eQazS+PgjQLP8W4f5+WV8WMgdnPOHKsxOmfG4ZGZMj1ojJjJiiNo+gbJUMmZrJjBErVaQsy+006VHn9SpAv2SdANDytERh39aUumskn/orP4P/BllMNMK8QKxSh/Uo45JodpKAqtEH98ZPimocMIH0J4EIkpZlhEX8uqzTKzgfbJKfYRMhUKf4jRWgWI0+D7gwoxIPKnGH2KmY2Y+YgwHxD0JkYvY/Tkt7+y1U32WCOFft4bDPoCvOAP+6j5WtDvCwmCPxSiRii06GPUaHW5RoMj4CsOBkO84PH5AoWFpRXl1XV1je3trf2LeouLqX2b3mwp0Gn1Jmt2vv6RR69LklGHaG0YyXuY5DtIfp4lz2SQZ1X4WSTD9gySnkF4FcJ/RXg1886fpgqefKeHNo0CEIMDNzCxyWRKX6Q2mw2oFya0aQJOX7kAxC6XC9Zg7TZDgc6Yezv36hb2S+mUrfRgjnz6ZWNtxYLq6q6mWGs8nmha2NO0sOGBFd4X3578wee3rXhE21ixrDxwV/2CO6LNXTU1bTUl97bHoi+vuXbHvvH7B0/cLWV8LaPPCfpEYt6V2NUEvSExjw2NuWfjOb1/nO90Cmazy2Fy8QWBkMEStllpvjsX4QU35/W43R63x0GXrmioj13wGN2u4Pyp5j8+e+0RMesgVqJAJEREhA+r8L5MvIfB2xD5hJE+VYs7AkT6XMSSSFIiNFZTCFY059/Xif/Nr6pfDIjT2Ao0c+DAgc8//3zv3r3btm175ZVX7rvvvv7+/t7e3kWLFlksFlikyMvLA7ddWHaEwi2Ie+CrGU7WNCd9R4F79uyJxWJ1dXXRaNTj8ZjNZovFUlhYWFlZ6ff7oa8O+BiEE+XKCIVC99xzTxqIFy1aBMVOn89nMBh0Oh3c2ACLrVYrWO2ChAgqxCaTCZZIioqKCpUsDEHxSX3jjTf+xxPkyJEjXq83NzfX6XSGw9Qz32w2gxVxMBiEsLpIJAL+EuC/BpgOymme50OhEABxKBQKBAL33nsvxri9vX3+/PlOpxOI32Qy5eTkUKtzZQiC4HQ6waIBDlE4HPZ6vU1NTZDh/PNvW5blDz/8cM6cOXPnzoUicV5eXjplA+DD7/dbLBaAYOi6+zEQG41GAOLS0tLvymZp4Ur61eG0AUEzfECA4//jNCP9G47v/OJHgLpLKGrPFLUtpXalw7RQnCIHdpP7/rRDNUZSnUaYC5R0tF8RdJ7EnicxFxD0K0XJetExTJwG4t9iBNvVmEZ1ABDfLKNJMpomM3MJmjWUNR6PYchJCJ+Mvrni5IRd7wlSvTAUiOmihCKeCPoEH88HAgGXywXlYVgVCSojEAgIAq10gh9Le3t7U1MTCBXozNZqMOr1eXZnz6w5QyMzMYvwSETGsckT0bCawexIzKih2Q5TMmZENEqiOhD7IBvYfKbd5aaNt26nU280uN00xQ0e4dssDcRwQ4WWO71eD19rx95NYR/WXuFbpUBrzdW4lufOwjPU5Db2yG1nLq+N1PUsqm9ub40nou1NDW2Jutb20r72qmjVIt7fF61r6uxubG+n4QVtsebmhsbWWHRxd6wtcc+MOU/ecO2BKefIucywi8E+lnjV5HY1uZkhNyN5zpjk5FF/mH/bFE91jjdU7JpVY5oSsRdw3oCXSoipz8SxQOz1cF76jczdbjA2z5q1PyODMCoJjZbRWIJOILR8foLMjJFUY1PMyEPqEz4srKGLrQNJjFPD9EYKd1GlPV3xLvmeiX/xU/b4L/zvI6DgC+WY5FHR57d0mYeKitfJQzdJ+xlySE2SKIUzd5LMjwh6jyhOajIVAX9Ik+coClMztaO14e+Z+G81xBtAT0yBmN2AmfWEeYegVzB6FqMnB08q/+OVTktZJODlg/5AIOQL8Rzv8Su2iYEArRAHAgG9Xq/TFehpzYSq7AoLCwOBQDhcRKvIviBdDgrxkUIqjqI2+SaL1qDVm3Xz84x33HVbCo8TMSIHRkprkPQXRJ5F5DmEn0Pyc0hahWRlk55H+C8I/xcibzFf/nVCSXCuw0XNU4A6gIBhZmuxWGw2G1zRUBKGRR7wRf3xxQtXN/ykw+wuMOTNn8v95VP0FR6xVWbX7ryqbmFxbOHdza1tsba61o6eeEddNN5Qu6BvQWVLY31rfV1TM3U7bok11zXUx+PNbbFYTXNDV6x2WXti+b0PNK/5xrBm362fH567OXnWGoI+O3TjR6lfbyfoQxGtJplVS8++7dZ5N8Vrxz1fr36p7NZGgfcGfX6Xl7d5PJzTwbvdTo7jPR63hzMLDr/F5Zgzw/yHVy45SNABzOzBzD7CpLBKTlIrYnIAke1I2pwx8JWRDByWMUnJ0ve6YeWaxURUnPpouPd/n2X/pnu/GBCnOWZgYGD9+vWPPfZYW1vbww8/vHz58u6jY8mSJXfccYfX64X2OLifwU3FoQT5/qBCDIKBrVu3YozTQcT79+/fsWPHmjVrAoGARRmBQKCqqioYDIJv/4+B2O/3L1myBN6hLMv33XdfdnY2wCUoEYEdOWWAt3E4HIa/ggoxNMeYzWYotYbDYZ/P53K5Vq5c+TPoBn+VSqWCweD8+fPtdjtQKeRThJWh1Wq9Xm8kEoHaMOgR00AMjYaBQKBEGeFw2O/3L126FGP8hz/8YebMmRaLpby8vKioiNaZsrOhWgaZc9D2Di3wkUgEvCyqq6vBcON/PJ8lSbrvvvuuueaa2bNnFxQUZGdn5+fng1NeKBTyKcvWNpstnd+Rk5NzLBDDF00aiEEysXnz5h+/LlSs0+sJ/0gB+8e/5Pgzv+ARoL03SoBCivalSzR0FyelXTvIPQ9uP/+yIxmniux4GZ0rM+djCsHniKrxInMhQRceZeJfK/KJS4+KiS/HiIY5Kxt0112vaIhvwehWmZmK0XTMXpcaPUYex+BTkXwW2nP1iV32fE8wwPuoXhig2CtQTzCfwNM6cTDo8Xg0SgYkTFn9flp2AmFuOByGOy7k7xgMBtqmaTRqzHqj3pBjc8TnzT90wmisokAsjkZDI2gkB2GzMM0fphViCsQMI6GRMroBM54jLP/VyWYXDUwXOLdTp8Sew+0Tbo2Qk8xxHCSTQ/EJFFk/D8Tw81qdObfA0lUwIzk9E1/LfHvdKVUlfG2U2hU3x+ONsWhLW1uio7O+qzPWEe9ri3a0JKLtvc2JtubmeKwl3pSINrTEFrY2x1oSf6hrfrBqwX/pJ79tm7Q+cvN24fxt2pO2Fvzm05m/2jJ9/P7JY4evVb07/YKIfl5vpeHtptte91z+gDVbcHtpo5MCxJ60hthDDde8HMd7vTlGS2LmrCMjR2CEJHY0zYtmTiTMWIkZI2WMk1UnyezYHRdeSj7ZkpLkQXFIEoeSSoaL4mwgyXTNAVwm6ILQL3iuHv9Vf3sERIKTir4p7SkhYfmwYpu4KXn/ohMnAAAgAElEQVSoOHXwLDycmUxlHpbRNyL6NDVqLUHrFJz9ENMK8VqMPiCUidcrTXKAwj8PxJ9QIGbXEfZtgv4Lo1UYPSON63v7MrerPOwX+EDAxweFgFvw0TxnOuHyegTBJwg+k9mo0+l1Or2mINvDWUKhQDhUqBhAhcPhSDAkBKjtomA0m32CYLDYdQatzqTJ1pg6OifJ0jmyqCJfMOIbSnl4FQViSsPPfU/DdP8vCL+I8KuIvMvsfvWyqsJZNoeH83jgbv4DIIYGfbfbDR6vUIz7R4DYZXPojAVzZvBPfTB6O876CrNvb73I4Shoqu1r6+iuj1aXlFY3NjW2dkTjiY5ES0dDQ0tDU3NLW0uipSWRiDVHW2JNzS2JaKwx0VDXUlsbq6oNd92R1/uHeYseML7w/ryXt0x88AndA3/NeXrtzHf3n/+erP7r1us67p9R8rp+3pemqbsL9L83OWw+LxfwejkvTzt93W6n2+PyeJxe3uX1+l0e09zJnkf/62oKxLJqF1btxUxSZiUJpZLUaAJ/jQY3nykdvB9LtEOErghimQY1043a9IEIR6RKnH/z8YsBMSx8Dw4OPvjggz2K21F/f/+io2OpMpYtW3bHHXcUFRWB0xYsxwPz/RiIoV7rcDg++OAD0GCA+gKA6ZtvviktLdXpdGCaVlVVBXxZXl4OQFxZWQli4oqKilAoFI/Hh4aG4Lv40Ucfvf3222HR32q1FhQU2O12n88H+RRmsxly0cxms1arpUaJBQVQRQYgjihDEASz2fzzQJxG8HA4PGPGDJvNBkbLaSAuLCyE2Wo6rC5dITYYDFqtFiJnfD4fdNQBjvf392OMV61aNX36dIPBAH9ls9nmz59fUFAABA/OytAmaDKZABHcbndZWdnBgwfhjf3MqQ14un///t/97ndTp06dN28e8C5YXsB8IBAI2Gw2gOA0Fufm5oJkAoAYyt6FhYXFxcXf3fs3bdr0gxeFTxMqxGntBOz84CeP//FfdwQw1UjIshJ9QjDd+XjjrpnZe7NOEdmTROZUzJ4ts+dKzLmYOY+gs2T2TJk5jzAX0O37OvFvFDExdNddjtEVR7crMfotRtcp7XS3YmayxN6GVZdKo8eSsWzqNEQuZcg16qGJI+5yzeACFIgpDQs+XuCraqrLKsoKI6FAgFr+cRyn0Wjy8/O1Wm1eXp7X6wUjc/gegOkueIrrdDqO43x2W65BazSYsx2Oiuy8r8eOwyyS1UxKzUiZiGSxhKW9dDKNJmYx3WdlNBIzM2XWd4i17x+td7scDs7Hc06dnhrCwC0TKkkg3wI4ttvtECLjUW696R6d9JqscsdS7lpO5/dAbDDk6XWl+hmDV40SL2C/+M1Ys8taXlnf1d1fVlltc3pCgWBNZVks2tLd3hNpKAsXlpRX1dQvrE9EE7FoNJ6INyRamjraOpubauKJ6kRrvLyou6GlL1r3x0r+z0W2Py4sXl5T8kBD0evGGYevYHYXnP1cWe7r/I37bCcN3M58rrkq6HDaeD/HK83qfwvEPEUILsdsq7vt1gOZLFGhlDpTYkYSlEnQCJIxNqkakVJl7Rxzkvz0s1IylcTyQZmKzWXlZkq9b2VZUprrqLOBTD1K/nVn8n/cK6UITaCDiGZCcArTYp9E5L8mB914eFxSytibytgislQrLKM10uj3sfojke6/j2noxjsYrVFkxB9i6izxM0D8vYKC/gz7EVGtIeybChA/j9FTUuafD41zeHm/EOZ9wZAQ4Th7Xf3Ctvb2WKyxpqa6pKQ8HC60WLTzbs/Lnp+fq51QWasLFTr9/nCkMBwKRoKBwmDIGw5FCgtDRqvFxwtWh1tn0OmM+Xka88LqKWTgavFbNV6NpL8i/BxLnvseiGWlToyV2rD8EiKvIPIGwu+hgXfOilVNs9o8POcFh1O3YhGTrhADEHsUE26QSXg8HpBKpi/Y9A5c6VAhdtksepNh+jTHipfGb5fGbZFUa7652OHOiYQX1NXVcZzHoDc5HV5/QKisLos2R728YHOZ3YIzXFQYjcabo4mWeGtTtDnR2trcFo+2NDU2Nzc2RBubElV1iaamyuaOcEN9Y13DwpKG4rufmbZp6IZn3pv6x7+YV3yS//CRKQ+TM7rW/Nrqmq7Ta5wOP897XB4TzWLn/Bzn4rwuB+8Q7Lpbp9lXvXHFfurZrNqNVftkRnE+VolyBkmi4W9Gi7trMD6UpC7VSnwhrQ3DnAqDSR9VPP0HzGR/MSAGK4CXX365o6NjyU+PO+64o6amBhQIOp0uLy8PmlFAzA6VFajUgvODXq9/9NFHgc+OLSV+l4hRX1+v1Wrtdrvb7Qb8DQQCQMNpLIadoqKi7+j5o48+AjnHq6++mp2dzSsLrzabTavVWiwWeDmO42w2GxhNQA8f1J9AXG+1WiFhLhwOAzR3dHQ89thjS5cu3bZtW1o0kq5xpnfWrVtnMplmz54NnXN5eXmA3eFwGC5OsC/VarVWq9VwdOj1evCm4HkevDJKS0vD4XBLSwvG+N133504cSJE04EtRk5OjlYZGo0mbSujLEvpgsFgWVlZYWHhd0i9a9cuIE6YHvxd+pRpTxXNMRFF8eWXX54xY8bcuXMhgLq4uDgSicCqtMvlylUGdNSBjNhgMHiVAYJvrVYbCoVKSkqcTueGDRv+4+5Q/z/+B1MxJ/2kqe+60jlBeyeoUyz1XBsWJeoqSEsF6z/65oqbZDRGWSs/GaPTZXQqZs7G6GzMnEXQWYT6MJxN0PnKdqEinAAghiLxZRhdjNGVMrqKZnZQQ+LrCboZo+kyuk1WX0pOGC2eisjJavlyhtyI5FtYOU/9dOF1bj/vCoSEYDjocRaWlvR1dnV0tXV0tbV3dsYSLbW1C91uV75WW6DVFN02NzHP5Av7AiFfIBTwUVF+JBgMhUJBf9BvMBlcHjfvtOuNBoveOs/j9RuMn512hoyQqGKlEQwZgUgWdVgTEavIiNUym4FRJkbjCZMvMZ4B1jw00lBhctg5L+/ltHqasg4oDFhsUQbUjB3KgL4cvV5vtVpBdJi+m8IOSCZAiVhgNOSaDF7d/CMXZaXOVm++YpzbbjW7OJ8/rKeqZ5PdbrNbDG6XpyRYZLQY7DaH3W71cFxzU6yrtTURixWWlpUtqG5trE20xtta4vF4vCHeWtfSGqOrsa2Nre0LW9pq2jp6F1R9YJv2ZLl5WWzBg5XFL7hm780+ZX/O2Kdtty01zapyGlxeziH47Ha30xt0eDgnx3l43uriZ2vm/6niRnHiCGkswmMzcJZKYhFGCLMZEsMeZlXDNZXS8DB1N5clekcVaZe6cjbRB7AfpqVhmvDy719n+t+66Om1S2cg9LrGdIE7ReT9WPz9sHzbIWnkt1j1iYjWY/Y9iX0fq94iaDVBb8mqdyT0tozektGbMnoX06a6jzDagNGnMvMzQPzJ95oK5iPCriXMe4RZTdBrBL1M2OcI+5TMhKrdblcxzxf5nN7KssrOru629s52GsHc2dHe19baEW9ZWF8bF/jQnfdFXnw7VlRu9HJ+f8AbChWGguWhsDcULAmHI0ab1c8LLk7QG/QGg7ZAay4OTP92w3XJz0aRVxn8EsIvMOQ5FVmlwqtY/BwizzPkBZa8yJJXEXmDIW8i+T0krzujp2my1ebx8TRtwGKxwJULC9RWZVDZrQLEMKGFcAC4fn9w8R4LxB6bQW+0TJ9h+P3KS7YkT9kmqzfsv8jjm2syhmxOe16+kbbAuWx2u9NucztsLq023+k0OhxOHx9pbIh2tHUsXFBrtdj9/sKKqsqmpoZES3tzvCURjzUnmtsa2xuaEs2xRDzW1NjY0txR0rvc0ZyIxKIdJdXOJffnvH8k833ppNc3T3xi9W0tvWaHw+5yeuxWzmYO262Cy8XxvODId0+oz3lpz9lfEtUezOyXmH0yFU4cwSoZZ2IJyQfzibhVMSeRJDmFsahcscqVS6TvXatJiuDjkol/+MqWJGnz5s2LFy/u6+v7aR5esnTp0ng8DgVXk8kEOQ7gcm+30yBfOCnhZgMTtYULF3755Zdg+gvQKSnjoYcemjNnjslk4jgOLIf9fn9JSQkwMTwDGmKHw9Hf33/48GEA6y1btkBMGrgCg1cuz/NOJSrZ4XBARdbppO2oWsXrFErRFouF5/mioqIFCxY0NDQ0NjbW1dWVl5fX1NSUlZU9/vjjENXxA9yEqcL27dvvvPPO8vLy559/ftmyZVBSDQQCZkUGHYlEoJ5kNptBZgDtaGBFDJYURUqGXDgcrq+vl2X5k08+mTp1akFBQWFhIQgScnJy0qpKEIEIggCh1o2NjdFodOHChZWVla+88grkvQEQ/91PGGYOcJxlWV65cuW0adPmzZvncDiKiooikUhAGQ6HA7TF+coAIIaCHBC52WzW6XTwHpxO57p16/7uyx1/8n/lCNDeJ/pJSyL1ZPp+UVtpyRGHhwdlCaekJHn/gx2XXZtkRmN2rMyegZnTCe0zO52gMwgar9DwWQoNn0PQeQoQX6BoJy46xnTiEqXf7rcyug4zEFk3gTCTMTtFVl9MRo1KnYLEc5F85Qh5IpOcgsR5jBxmP4pf4nNbzC7e5fEF3b5oe0NPZ0drV2dPZ0dHR1dLV3dXZ3tvV1dbV2eiveX3gdL7naHSiqJgRPBTiUU4GAj7A/5gKBgKBenF7rAJHo9Or7PqjPPsboNO//SVV2w/YcxBtSqZwZAsVs5gJYSSbBZhWBmpkywrsZkYXU0Yg4zMIjJLGYbObJudd/O8X2swWa3WY4EYhEmwqAVLrjC3NxgMcEOFr7Jjb6uAwoDFOqNBYzC4jbrB6zPJuaM2/Wqk12o2O902u0tXQKfr8GOQRlRQUADFZrDKaWxsjEQiYN3jcrmCwWBNTU08Hm+hC7KJeDze3NwcjUZjsVg8Ho9Go3311dF4vLG1r7ShvdCq+zD/HHy7anDmqC8njX0qf0rQy+d5eY+5oNKhLXfkB+0WhztYYA2WFk889NxVJHKCfA7tpZNUSGKyFD0xEln1rlsmyrt3wxKrYoJLuex/5ZT+j3lROLygyz76j6a6FDqDpRMReQjjIUL2S7hrJ77gE4kC7tsyepOg12W0WkZvisxqjF5XtjcxepvQCrGSycx8TJgNhKGuw/IPK8QbFQiGx0+V/Y9o7x3zHsl8i7CvE/Q6Qa8Q5hXCPIdR7I+3GL3zbN7cssLSns6ejo7Ojo6u9vb2jvaOzo6ezo6uzq5EZ0dvZ2d3V395/x0VzS0L6usbK6vKi4oD4Yg3GAyHgsWRSKHD5vRydp4PGPW0VqTX2Zz62Q8unv3akzN3PHNj8i9nyH9F8osq8oIKP8uSF5D4AiO/yJJXGPI6wm9m4HeQ/L6afHzmPS03m6wur+CzWq2mvzWKgWgwQGS41gCUzco49rJN74MhFb2unTaD0Tx7pnHpg9dvGj51G0afHh7PBWaYzUVWu0Ont5itFofT4nC4nA633WY36HWcx825eKeNdiEEA36T0Wgxm91Ozm6zup3OYCBcWVURjdW3trW0NrdEE/HmaLw5FotF49FoW319Szze2tLaZTQ7FjQG3jtywhrMvi8xH5KMlWuvEMITc0z5M9scc/6Qm903TVt+q9WqyTEFJ7436XUpYwtR7cRoj0RD/nbLzCGZBtcNyucT8TUipRSFRJKmSsoUYaj+nz7Q2phiL5FSfIeOnmz/pv/9xSrER44cufvuu7u7u5f87Fi2bFlPT4/VanUpCU9gWwsLoNBfAgsZXq+3uLi4oqKioaGhurq6qanpxRdfBPGrKNLpCyQ79PT05OXl2e324uLi2tpa0NqmfYih24zn+d7e3k8//ZR2S0oUAIaGhsLhsE6nCwQCbrf7WIMz6EXzer2lpaUQlgEiBJ1OZzTSshDHcRA6BanIDQ0NTU1NDQ0NZWVlTqfztddeg/cGtVVoX0s/kxYGyLL81ltvPfzww+vXr6+vry8oKCgqKvJ66VKOyWQCiAQgNhppJhYcDahzRyKRqqqq4eHhzZs3z5o1C4C4tLSU4zhoqitQht1uh7a/mpoaSMaKxWJwa6ysrFyyZMmhQ4eOPaV/AMfp2nZa9fHoo49OmTLFaDQWFhb6/X632w06TrBqBhQG+QTYPAMQWywWnY7Wp0tKShwOx1tvvXXsix7f/98+AhJdvVa+8IYUSyyskDGNFUtJUlL++uvP194ymbBjUqqxBJ2B6faPA3HaceJiBY6vwtR4+Dplm0TQNJG5RhpxMjlJJZ3FkEsypFvUZApK5bDYMhpXZey468r+2tJwsNTPB4vC9Z1Lmlq6WuOd/YvbujraOls6u7ra27rbO9o6Otp7Opd0tdzT0drRlqCC24ZYQ01TJFgYDFPzlkiw0GFx2i3GkEfQGQ02syHPbNdq9H8y6vZMnoLPOkfMVKUU91wRIUk9SkYMQVnDaiSyGdQKgzESZMLIILHantkWu8AJQkBvpBcp3D7h0W63WyyWdIUJmBgqTBaL5e9WiMHQBu6+yn1eb7cYDl+dKY/L+OLUDL/JbPEKFofToKdInb77Htv5Dt1voFQG7HY4HKBG83q9VVVVsVisVRkQjBeLxRKJRFOivqE10dHWZ+cKbfNmvZ93tjyTwZNY+fqRG2dNqOAETTDUVGR+pCTvd4HsVs7hdZVprIWP/GH6wL0jU9ZR5NxMORNJDJLRiTJSERaJmSPIww+SwcG0jwQ9qY4D8T/12gZeocyisAp9LUxoXs4grfCJBONBTA7sEqPrxNNfl9BfZeYvMnqRWkAwrxL2NcK8RtAbhPLxW0oX3Tqli472ydEmObp9gmlT3UYljwMI+NhqMbWYwNSEeJ084n3CvI0zV2P1G5hZjVWv44wXZfQSRn89fPazX0xc8XJeoqW5o62zva2D0vDfjk5ldHR0tLX2dHb0dHR29PX1lVcUB4Ku/8Ped4C5UZ1d35G0u66AbUjIRwkEQscQsGMMxuCGvVW9jqSZUZe2r7f33ovb9uYCGBxCqIYQEtuAce8FEnrHgG3AXu9Kmnvvz51rKxsnEJLPJPn++H30yLOyVjMez9Wce+55z0lJSUsOpKQQ7wSPzc4FPMms2WgymdVqnYvjChdnFRXlNuSZH22M2zC4cP8Tcz5+9pbgi5eiP4yBLzHwFYA2AyKo2BaFdgJxezQ+MOWZzjuMrE1w+2ijDp2+0mmtzWYzGo0RsEuXnaljzOjXI2+IGE0Q8zU7bzDo4haaq5ffdujry9/H4K1TP/KmzWXZdCqApApJ+p1Adx35HDqu6cowNTymk16bzeZyuXw+3+LFi8ulMMva2tq6hsZqMsOtqa9vSEvLXLgorqjCu/3kuH1YtgsrXkVgb3Dy5nem9W+8q/rDmWXhn7YOXdn/7vUdG36ZvXZ2ztdXvIVi3sdRH2D5RwgcgeA4JA12x/HEYLgOw5PSdYQkxX/kivpBr+D/0A8/Z4D48ccfb21t7ejoWLFiRfu3FzEELSqikgnKL9IVdopEqT92Xl4eBZrlUpWVlRUVFS1evLizs5OyvBGeeGRkpLS0VKlUZmRk5OfnU9MlGgPh9/tTU1O7uroOHz5M30/pYfpcWFioVqu9Xi9FvdSHRTLzIzyxIAg0XM1sNmskATHlialTUkZGRmFhYVZWlt/vT09Pr6ysrKiooJHObW1te/fufeGFF9atW7d27dpPP/1UFMXRdGwEaFIKFkK4fPnyuXPnOp1OlmVVKpVer6cgUi9VxMKM5vPl5OSkpqZmZmZ+/vnn77///sKFC5VKJeVfqZEFDROhcRiBQMBkMmk0muzsbEoRVUtVU1OTm5u7du3aUCgUDAYjSo/RV2jk8OgB04nEI488Eh8fbzabqbWzIAgqlYpKKahkIqInpqIXavqm1+sDgUBGRobVat20adPovZzf/jefARgMkfVscl+VUhRCwzg8EiYrYygsfrH+N59dfROUjSNSUTAFyX4SApMhuBiDS84wxJeeIYkvk0jiK8+QxD/FICImvlaih29AJMfuTgymSYZrsyGYGb5gEroc4CsBuhmgX8rC9wA8H4Ss0TA1GjYA9GjMiccmffDglMPdl7bV1FS3NFcuqW1squ2oWdZSv6SxsamlvrG+sbGhqbmpsbm6tb5yWUNbU3PjcuKzVl6c7/fxnjSnN82dmurnec5gtvj8TqJoNyTqVCqLmU8vq8iurajML+h1up5Jit39i9uOTZ5AjNgUAMnkX44BQ1GTIYjFwICBDgM1Akl9s0ys2+3y+E0Ws16vp1N3+kyRKAXH9M5H1f80bSdy/ztrgzqykZUog15n1ptY0/GLmbAMHLloLBFGuD0sx5ukLwRqekrFGHq9nn5H2Ww2usJLuvTP+EbRWyy1dac2kUVFRbW1tTRKmpDEDS01zU0F2dlKrcWtStiRcG1oFsAzAboLnLp3/JE5//NawpWfWqcErTHYwojuCa9nXfas/+pPcn8KzQDOAcO/AHjsWMQAMmcAY04BxdH755w6+dUwERyezmU+Tw7/4OP6z9RwBL5AjE9BfBTDIZLIAUNfnjjw0qkfPwnBM0j2ggjWi2A9Bs9j8DsM/kB4XAKIt0kWEwdorIbE+P4RgT9JD4KJvxMQnzYeFi/cgZmtCGyB8i1IthXGvCKO24CZ3yPwYghsRmDDezObW6vra1pbm5cRscS3VHPTUolCbmxra+F5h9MRSEsnwa7JqZzXE7CYAyn+VCtr1GjVBrMuP39xVXl9SUldYWl5bk5Z5uKimkLb4y3T9w9ceOwZGd4ShV4FaCvAW4G4Q3YaEO+fuPmR6WbW4nCTbni9Xk8BcUTmZDAYqMyJzlTpUKUKxrOGbWTg07Fm5ziDQZcYx6aXXLX788vex+Dd4I9SF8+3sCkU/loslsh3QgQQ0+8K+slUWBVZRIroMTiOs1gsHo/n9OS2sammrrasrDwnJ1enNcbGJRRX+beeGLsHgt2Q2YfBHqL/jjqEJ+/BF27BYzbgqA144lPh8euDP/81jiIHhse9j6M/wOBTCI5BcBTJhvE1GO3B6IvTE6s/X1c/+CX8n7mDcwaI6+vrly1b9t1omOJkaqBL50NJSUlardbpdFLVBLXtLC0trThT5eXlFRUV5eXlFBM//fTTkfNIQdvIyAhN68jOzqZd3pS5LCkp2b9/P31P5FciG7W1tSqVyucj3Tm0fc3hcHi9XuprwXFcenp6RkZGJA1Lq9UajUZ6W6LtO2lppB/WZDKVlpbW1tZS8rWwsJCKNPLy8jIzM+vr619++eXHHnvs+PHjFFnSA4gcFULo7bffVqlUBoMhIyODZdnY2FgacEWT6iIMcbpk95aVlZUp1VtvvfXJJ59oNJrExEQKkf1+P81YVqvVlGkOBAJOp9NsNvt8vsrKytra2vLy8shxNjc379q16+GHH6YThsiZ+bYNhNDw8PD69etXrVr15ZdfDg8Pv/LKK5WVlfPnz4+Pj6ceFBGGmK43OZ1O6m3u9/uzsrLsdvv69euJZvV8/WecARJTHxaHKZ1H/luI19qIiIahOPT0+pMXXgyBPCwfNxT1YwSmiLILsGwyYqZIgDiCiX8yChNTQBx5vgqBqxG4BhF/4uukeI6pEhq+F4Jp4tgp8EdAvDgGTboAXRwNbwP4fhBaAOACgB0ADirCvwHiU2DomSkvPaiqbVva0NC5pK5meX1tXVNzfX1Tc1NzY3NTHQHDLS31zctqmtpqm+pbWluXLW+rb3LZeKfbw6dnOtIy09My/C631Wh0p/A2jZXoDQRzYV5WWXF+YXlecVlBUWlFbkVVYUV1eVH5Q1Zux6wZH192qUg44ysRo8VAj4EKgwSRifvVNKPB7RI8fpuNdOJSSxx6d6TrXfSGGjEYdrlcZqki7xl9Z3U4HBFArDUbVKxeY9B/NYGBF8qHL7+gzGzSO5xWwWGURBf0m4c+01Q8ekOlz1QTSW/P9HuVWltEYDQdgNXV1RUVFXnJuURiYVTH6Qx+rerA/OvwPUxwJkD3yPDdAM8DoQUyHC/DGiacAlAuEDPk0ByNY+XorrHh2+RDtwN8lRyOA6TpkAEf33TL0Gu7Q1LjVpi4k9Arm1h8na8f8gyEJX/nUaeZLnBLfVAYn/xq5E8viBf/CjLrMXgWMo/jCb/B0U9i8DQGz0HwO5FoG7ZTNwliNnxaK0zpYQqI/yTxxKcZYqm7jgQ4S9D5oPS8H4M9cNxOpNiOmK0S07wVRm+B8k0Q/F5kNiDFyzjm5eEL2h9KaWpc2tJc09ra0tjQ3Diq/hIb1zc2NrW1dhYU5iUmLeI5Z1paarIvL9mf5fGnW1hdSkoKa3HodMb0zPTC0or80vLC0pyCorKSkpKC4sX5ucU5WYV5eSWlJYXrWlUnXrmc6IZ3gPAu0lGHdkTh/eDQszfYrXqniyxvRnR9lCGmODUCiK1WK1Ul0QSx0cN29PYZcQVnNBqTEsw23/Vb37/yAyT7IHxxZsFck8Vjt9spwxX5TvimkSaCvCNDmAqrKCCm0Pk01JYszKmNI/F3ylrsTw5wHG+3czqdMTFRVVjh3XFywh7SEMnsh8S0+FBI6o+EBBzvltQsGzDYiMG7YRIl/TFUfAplH2PwEQYfI3BcvBeJm4g2GJ4iX/4UDf93T2fPGSBesmRJZ2fnt1PDp/+mq6urtraWBB5arSxLUsVpqxb1CqXUbPlfFgXEZVKVlJTs3LkzIkWgItdXXnmF5/mUlJSysrKcnJwlS5bs2LHjyy+/pOLdv/5WghAODg4mJSVREExRLzUpo4ueNpuNAmIa3qiVijacUikCpZ9pS5zb7a6oqCgoKMjJySkoII2lkSooKCgvL8/Pz+/s7Pzss8+o/CDilUEVFBDCbdu2Pfroo0eOHDl8+HB5eblKpdLpdBGG2OUiTqBpklEDhcXfmLjt2LHj2LFjgUBAqVTSux0FxElJSaMBMeW8qXywurq6TqpKqSoqKqqqqnJyckiRr30AACAASURBVBoaGo4cOXKWZGL0SUNSURA/NDR08uTJyCvBYPC5556bP38+ba2jThQ0goSSxHSFl0pZ7Hb73zXlGL3f89s/+BlAVB8mZW+IpBtHhAiKJ449Pfj1BT9GYBJWTA4x4zGYGJJdhMFkDC7CYBImJHGEJ6YkMRUTX3Gmu+4yDK7A4CoJDf8cETR8EwZ3QBLXfC8G9yBwBVQAsvIuZ5BMJl4C4DQ5mhV9arp8ZBYTzB4P147B62ToEcXGnnlEEdvYuryppXlJTV3b8rrWZc1NrY1NTdXNzS0NzS2NLXXNLTVNLU2NbQ3NrfWtdX63V70w3itwgRRXINmf6k9J8QasVoPDz1pMthRPeklFeVFZRXFJZVFJUV5pUWFpRXFReXFJfm5Z0eKK6tzGJWlp6e9Pun6YuRMTQKzBQIlBfFAR+7ubdXqvy+72CjabUql0SEXBLg1qprc0epuk21ar9duWXEff/wh1rdPFGY0nbvgR/lk0unpcizrO4HIaed4gyTM4qShDTNtwI3sXBMEk6azoLfx05zvFxdIzvRlT8E3dMKyC1WjQJhpYn0a5b8G1+JcMngbwLxl0NwjdB0bimFM+gFJlyBIl6gCMV8D5Y8MzZaE7ALwjBt9+YXAqc+qmMUgOPps+deSd/TgYJrZMpDlT8h1GMITJVOt8/YBngE48Tqs7w1KsrhQkFsLB8FevjSx5Fo19Eo17HoEXwkTO+wxm1iOCgzcQ0TCzFTG7JO9h2jwXYYgPE2Qsew3LXj/zoICYiiUOSgl2+ySX4r2YhHfsFaN3SjTzNgykFj1mE1JsgLINomyDKN8ImRe+uCg9Oy8vt6SivKCurrylpbllVDU3n8bHZKOprqW1vqmplmXtapWW4+ypKZnJKUIKaWB3GfWCO91tdzhSUtKqqnJLSgsKiitKC8tLSwpLCquLi4tLikpLSwryy0ryyprsWu2bz1+N9gC0UwZ3A7iLQTsYvB+8u+kql00nOIidlEajof4SEfGD0Wg8CxDTKSu1gToLB0emuDRbx2S0KJUGg/WuV9+6+mPEfCJemlM6S28gM17aGf/XgDjygREvCzpkR49fOuoj4NjG8Uaah8BaDXpTQqIyv9S1/cS4vZL4eycGOyHYJ8r3h6N3IsUWacKzWwQHxOi9CLwBYz5AMV8GZZ+J4BMY8ymM+Uj8qRjegYMYoy+JkQTZIguF0uO/FxSfM0BcXFzc29vb3t6+fPny0+D3b/2xYsWKrq6u5cuXk+woi0Wr1apUKtoBRr/iaehGuVRlUuMa3S4tLS0rKystLc3Pz3/xxRdDodBoIcRrr732zjvvRPQJFApTDPfXUA9CuHHjxoSEBKvVGgiQnhutVmu1WqmU2e12WywWSrva7XbtmaJTPepB4fP5MqQ4YpvNFhcXl5GRkZaWZrORkPTc3FyK4CMHXFZW5vf7u7q6aF9g5KhGaxUoskcIjYyMtLS0LFq0iLpD0JUdp9NJ05szMzNpLMiGDRuGh4ebm5uVSqXH46Eahkh6M5EkGgyS6aPbZrNRiwmfz5eTk5Ofnx/ps6murq6trc3Ozm5qagqFvssLKYKAqeCE/hhRUzz//PMUEyuVSqpjpgZ2VOOo0+lokyLP848++ugPeJM4/9H/6BkIE8d1Cb2MYCgSQfHwyNGWZaeixg/LFEHZRAwugEwMBmMxmBwiSolJ0mMKBlMkTPwjDC6VHpQnpl4TVD5xheS/RqHwLZJueAYG8zC4BxPOWIZkAEeB4Djm5E8vCN4D8FxwbNaEkZnRwcAYuGZM6FEGP8zgdbLn2ubYDM4UR1qOf3FuQVZleUV7SfnSmprmltbW1qVLmtokGXFjXUtbU/PS9rZlyempDyQsiFfFWXnBn5LuT/P50lyBDJ/RYku1uCycKTMntbIyr7AsL7e0oiy/vCqvsqyooqC0ML+stKi4srSkMr8wdZ7xnk+ibg7LF2JgQEAFgRIy8UHFokPX6Fivg3d7HXaefmud1VdHG+kiEVZ07ZXeaCP3v8hGBL+SLHeD0ajSPMCyH9xxJbwM4Mtlbco5Go/DzAtmA9ErjwbERqPRbrc7pKKf9t2AOHJD5XneYrGYOJPdYWUNeqXR5tOpds+9At8J8DQZvlMhzpLDWIB1AHEgpAfBRWBoEQjexwTvBKE7GZIpPQOE7gShmXI8fdzxmeNObtlxApM4YEh6M8IiRCLR30jNOecB8T86GP+x95OxSpueiCfAacdYNILe2j4c+1xownqChmXPQ4KGX0Dg94jZCpk9EiW8V0K0JJ+ZPJgDiKy2R2Au3d4v+RDvl2TEhBiWoPBeRBrvdkjWbNsQ2CHKt5NouuitCGwf+enTr814/p17Npya+AqSb8bgJQReRbItIsgqNjn4gMtJMup8Xn9GRkZRUVFlZWVdXV0EGzc3N7ctaV2ypC01LaBUxas1iQajiuR3+LICAU+qL9Vk1eoEn8WWZtTy6YGs0sKqwpLispLcwuLM4sLi/LLcvJLS0pL80rLCzJySu2fft3HdtXgfg3dGw51A3AnwLiDuAUe2XeVzxNsFsg58Fk1Lh0YkfZ3CXJoX9teAePQQJkOe91jMtkSVSmO865XXb/oEgc/gj3LLZhhNLsoHR8YvnSrTpWb61UE/KtJ6G0HnFByf9cwJDgvLSq1WNpPRkpSkzit1bT9JnKR3oZhdaMwOHLUdgx1UOyEqtqKoV7FiT1jxukjUL5+Ho46I4D0E3sPgCIwJiq0YScQwWdYZkcTodEJLTYf/scvx/5t3nzNArNFoamtrOzs7V3ynhpjCZfq26upqm80WGxtrNps9Hg+1PGNZtrCwsKqqisLKcqlKS0srKysj29nZ2StXrjx69ChCiCI5URRDoRAx/ZHa5igIjiDOs5bpIYQHDx5UKpVGozE5OZnicirWoeINi8VChbkOh0N7pnQ6HfWmoI7FlKwVBGHRokWBQCA9Pd3j8bhcLnrw5eXlpaWlxcXFVPpRWFhYWlq6d+/eCCamFxBFlhGXZQoxh4aGGhsbY2Nj1VLuABU5ReTR6enpXq93/fr1oig++uijiYmJFG6mpKRQBQUVIuv1er/f7/V6bTabyWTKyckhqQJugo+zsrLq6+urq8nEOi8vjzq2njhx4tuu6dHaa3piI7CYnm0I4Ysvvjh//nwaa6dWq6m3NNWf6HQ6ahvndDr7+/uJH9P5+s84AwS0SHdVLKKTaGT46EefLy49pbhoRB4Tko9BckUIgKB8LCWGh+WUHp4kUcUUEF8ieU1ESOLLzphO/I8kJr5WIoZvl7jhGQjMlMI4rsayMWgMgBfJxMsV8BYG3ysbvg8cds95rLXhhcb0PauS3nvo+uDqieIaOVwL3uz5eaFPz7rd7gDnczgdbpc9hXWmO7PSs4oXF1WWVdTUVS9pblpW29LT2l5VWmJOVKm0+oV6SxLrEdyZTp/fneJPTs/kWb9KSPfzzkSd2RfIqCosry4qKSvKLSjJzSspKispLSurrCourS9vsHLm25Vz9l2jHYkxYqCHIAkShjgRg0WfTornOYvD6RYEj0ajoS229MZGv7tsNhsdrTbJipje/yIKh8h9NLIR4ZLNFpNWp3rAbDt0+8/wxSRKesWie7Uuh5V3WAwm2pRD6WGOI+uzEZ9j+lF/FxDTHdG7Pme1OwSONeoTzVanKmH3/T8NTQXh6QAtUGAVA9XMSAIQ50fjOQo8G+DpAN8mw9Ni0H0x4bvkwRmy4AJGTAR40QVfr2/GoigFGIqiOIJEyW2NMkzEcu18/ZBn4M+r25Kvs0imIUH4zuaTCU9LKuHfQrAeghcweBmBHXDMLjR2H1IclPzUCNg9jYCZPVi2B8spRN6DSSQHfRDoLPGOB6VmO6JPxWCXRDoSbwoIXoLgRSx7ARO0veHUlHWbrV3dKzuXP9Hd37Pm6dKn9j/w+0+u3/zV9XuRonpZrJNbTLzDPQ5/wEdlih6Pxy9VWlpaQUFBVVVVU1NdeXm5WqXXavRqpUWVyGfmJ/jdBQKX4XPkqnUGf7q5otiQ4dMEkr0VVRV5JUVFRcVZFcXlBZWVRSUFpbV5ZZWFlaUag/7+mXe/0HML3j8B7YhCOxm46zQgPr7rJzlpscSywknC6iJ+r3QIsywb8VKkmcxUy0tTPCJjdvQGnWo67B6zxZyoUSXp7tl44LbPEfgCXlTRMFNvIEPWaDTSjqnInDYyoY18lNFopI2z/yQgRiQjcA8kj52INEpuISEpilcxs1ly1juMwLsYHA3L38WAKIkR87mYhMRjCAUxPin1kHwl9ZGQGZbUDnueIf5fD16fz2c0GktLS9vbibcahcV/ExyvkKqrq6u/v7+4uHjevHnUbZcSitR0rEDyNcvPz6ejhVLFEUuHsrKyvLy8+vr6I0eOjAZnFF9SZQJ9jrwy+t8HIfzoo490kqWa3++32WxardZkMvl8PqoxYFnW5/NRo4kzeFhLATE1ZXO5XJmZmVlZWS6Xa/78+R6PJyMjg3QAJCdTuTP1YqOYvqSkhGo5UlJSBgYGInpiiuDpEVIoHHkOBoPt7e1xcXELFy50SEWtfDMyMqihxDPPPIMQeumll2h6HHUp1mq1SUlJEcWFVyoqzM/OzqZTDofDkZCQkClVSkpKampqIBD4ZuPo0aOjT9FZ2xDCyJkczbhHphyiKD7//PPz5s2LjY1VKpW0o8jpJLaOer3e7XZnZma63e62trZTp06d9eHnf/x3nQHJqjSIw/ArYuEfPJWb/8mY6C8ZIEbLEVAMM2Pw+B9h2SQMJko5vRNF5kJISOLJZxhiCoh/fIYk/h9JT0zlE1dKAc5TpS66GVA2AzEzsewyHAPwOIB/zIRvihqZB1CSDC0Eb1vu6FrT2frQU139gyt6Bzv6Bh8e7N04mP/Og/PeG7j1VylXWoRMPuAOuNOSPf5kr4vcWz1+n8MfcHncAXdydmpuUVFLUwvnsiZp4hM0niSNw6Oe0ZM9s8E7KyD4TUJGkt3d77v2cMJNZcq45ILykor60rKSkrKcspLC8tKy8pKigvyCwrK8xdlFi5Ji71LFPjmNPTFOh0EiJA+VJCNeFIxZ6LUYXA634PBT83J6hVPFvFUqCohpTxtNvaKAmL4euQVGNigXZbGY9AZVotmx7Y7r4RQ5ngAevudeC++0OBx6858BMb2n/hOAmP4i1XVYbG5eEFijJtFscWkSd91/DfwFIOrhhQyeK8czFWgWwPcq8GyioMC3R4dmysU5ZHvoHhBKANgiH2HB28U3H4PEVkLEIYiGoDgs+fZDiMLkNUTE6f+uq/q/Y7+ShljCLSIMIlGKlMRH//hV3dMnrnoKRz2HFVulfI0dWLYbgz+FY16DigMSuqXPFBZTZCxRxSSuOYKJKRreh04zx7ul3rttxGmYeQmDP0DwIgTrsfwJOP5pceJDe5Ka2h7v7Xi4r7Orv3tVZ/tgR2dvV3/3mkcfeelA1fLuLI/L5fFZfAGnR7ol+aXy+Xx+KWjd4/EQ4/+0PLfbpVTHGvTmpATzwvnaJ7ZNfXr/5OaHxgXSFzm4ufvfBuEjFz+y7G6HW8gvKS8sba0oryorLa7Jr0gn6ihfSkqK3+uKi11w370PPNx2Pz58CdwjR7uIjBgTH2IwcuDCxpIHbJzT5XJRm0I6fukd1mq1UkMYurBD55xUbUzVTZExG9k4A4gdJlaTqNHEqe59cde0o4g5Bsc1d96jNxJEbJKKDkD6bDKZ6ApP5HNMJmLjeBYf/Nc/fhtDLPXSEUX4Vgy2IcUucczekHyvlLSyQ3rxIAbvYeYTOP49DD6E4L3hm0+gVxAOkouGKIeJ1dppATEOSys8/x1j6G/9K88ZQ/zBBx+o1WpBEBobG7u6ulZI1f63iqLkjo6Onp6eqqqqhQsXRqIc6Fc2jdv4xkshOTnZbrdT7UG5VKWlpZTarKmpycnJWbp0KcVq373if9Y/HCF07NgxGjucnJwsCIJWqzUYDHTy6na7afpdZmZmIBCg/hI6qYxGI820ownGmZmZHo9n0aJFTqczMzOThrN7vV6az8xxHEXw9Mhpv11qauqqVasoWKdHTpXEFApHjpNS2hs2bFi0aBF1IaWZGhkZGenp6SaTqa+vDyG0b98+6uicIZVWiuyivLJer6dfNGRB1mzOzs72er3UwS02NtYtuSmnS5WamvqNQdsnn3wS2fs/vdHe3j5t2rS5c+dShpjuTq/XO51OaspRW1s7NDT0T3/++V88t2eAsEqYLHiPEE/2cPjgwVP1Fe/ccOOXUWMgEyXKxkH5hBAzZkQ2HoIJCEyEsomQgGOiJ0ZgMmQmQ+ZiRAyJL5U67QgglsIsLpNi6qZiMBWD2xGYAWXTRdmtKGoSQcMTAbxchmdFQ30UMo9FcWCf496WgY7lAw/29vZ09K3q6lvZ29X9cFfd+p70jatTuoo1dkeKO+BJI3xvmt/n9HpdXq8v4CXJzi6P0+lzuTzegD9gMOk12iSlgVdqbbx63u72eQfbpj5TuWAgS9PmTnp98XWf3nlp3bxF1tRMf2Z2TkF2YWl2RUVJfmGh0+20c5zTJdiNtjnxD9ybtGjwTv1nFxmRZC4hyhLD8kRRFi8qEgMWi8vpEgS/SWe2mIxOByc4HHYnL7gFzmpjWdYleB2CwLIWgRUcvGDnrTq9juTROXmHwDt4gRccdgdvd9rtgp0TeKuds/O82WzWq7WLbNyGW68KTpThMcyv756uc3gsTqfBRKwYI/QwZYgjtlD0nhq56dI79Fm30sjNWBAE4rdq5TmBZw36RBPrVCfsWngtvjcG362Ad8qDt8nDtzNoJoNnMXAWQLNk+O4xcJZcvAuEfykLLVIgqwI7FMeFCeFdq4JEJCFCkgBB7qwknJnMmiGU7rHk+Xz9cGcAQSL6lDTEhOdDIkkGhENBtOd9uGLLkG3jyDU7iRwiai+SEb2vKD8AZRFATLviKGF8CIEDkDz2IcmmQILFRDiBwH5IUPLuM9zwFpq7gQgafoGYVzBPwOinwtHLN92cmbOkr/Phge7+vu6VvT39Pd19Xd0Dyzu7ulcXtbRW+jxut8fmT/b5Asl+P4mepM8+Hwlm90kV8Kfb7XaVOkmr1Ws1lvlz1ANPzXwtfNnBoZt+u3vOHzbfO3TiQvj6uN91/zJJGW93ujyB9MzU1NyinKKcQr1OZ7GzNrONNRgTEmNnzV6wovSX8NAl4j4Z2g3E3QzeycB9QDw8saP2AdbKUUBssVgizmt0hcdsNtMBFVnhcTgc35beTNlcIpngeJNVlajRLoi/57kt044i2TE4tnPVHL1RZ7PZzGZzhCE+LTg2mag/DN0X7QFgJS3EWSP3rB+/AxBvR4QbltQssq0warso2wHBNsRsQbIdxDhP/h5mPkZjPsTgXag4KgYg+pAERhKJkyiFR0ISl0PmVyKSXIh/uCv3P/yTzxkghhA+++yzZrM5OTm5tbW1u7t7NFU8GhhHAHF3d3dDQ4NSqdRqtXRBn16X1PTX7yd6I2raFRETR0jiCsn9Nzc3d926dd+k1o2mLf/uGYcQjoyMVFVVJSYm+v1+p9NJm9gogHO5XHTpMyMjIzk5Wa/XU0xMjSao1Njj8aRLMRl+vz8uLo5l2aysrIyMDOpVnJqampycrFKpLBYLpYfp0RYVFZWUlBQXF+/YsWPz5s1vvvlmOByOkKxIKmpgjKSoJ4TQ008/TffrkSI2vF4vx3HZ2dk7duxACL333nt0DEt4OEOn09GmOo1Go9VqPR5PIBDgOM5kMi1evNgjldvtjouLo82LFF5TQPzWW2/R80YP4++ew79+A0Lo6NGjFRUV8+bNowkmVJOt0+l4ns/KygoEAkVFRceOHfvr3z3/yr/lDEASqCtCFMYiDkq4JiQO4zfeEJe0vX3zLUNRY0NE7KsYkY+FzERRPlGUjQ+THrsLIHNhmJkiMpdA5kcIXILAJZBgYtJLh4g5w9Uk0gLcicBUBG6FYAYC08XoS/B4BR7LjFwAhqcDbI5CVrJALy4CL3pm1rUv7epb09/b2dM70NMz0N070Nnf3923crB/TVFpmcPtS3O7fF6fz5PuShY8fuLQGanIPdVisai1CVqjUa1l9QnKPYU3hyvlQxUTvsy7+hPftafi5aHxTP+M+xN4XmOxmK0G1m5IDvgtNm5BbKzZwvK8wOtMC+Ni5ynjimer/3ixnqiHQXwYxAaZuDCjHFbo8jS80etwe1ysmVVpNA63SxA8gsMrON2czcLaWY/g450Bi83EmW08x1kdrFarsXO81cEJdt7u4HmHXRB4QXDYecHu4FliYyrYLFaTWj2X45+8/ZpwNIPksmdn/ELj4VmXYDIaKCVM76YRQDxag2g2mykPTQHx6L6cCESmLBelsTlBMBtNiUarU52wJ/Zn6JdyeAvAN8rhjfLwrUCcDuA9AN0fjWdHw7uZ8F0gfA+Ai6JFvQI65EETc3J1Kg5+jcI4SJZcz9e/4wxI2IUgGMLwhSEKSTiGxNRBGBTRka/Rr98J84dHph5EckIGQ9lBJD+EZIcQQ80iDiPZYcQcJol0in2I2Yfk+xFzWlUMZQckbfFBGLUHEU3qNrLyTrjh3yHmGSR/CjFPIvAwlq8LgwGk6Dw0e2nrgz0dq3t6Bnr7Bvv7enq6B/r71w52r+lf3Ze9uNzj9aV4XD6PNI09g4ApXxNBxgF/wO1y6TR6tV6v1hoS44255XPfFWVvINknKHrk+LhTOxWhjcyeh6eq1YkGm91oMdrNRpPdpNPr4pPiOd4qWDnBxicmJd43Z2Fhyl3Hdv4cvgbQLkbcJ4d7GLQbwMMxq5bdpjYY3B6vWa/Ta/QOwefkHDaes3vcgp0z6c0OweFwcpyV4whFbBVcTr3BwHIs7xAEpyCQCTCZA/MOJyc4Oc4h2HiBc5nsqkS15r5Fs3/zh/s+R4ovUMyDT9yn0iXZrLzJZJFEw6entDwvmM3miDaDNhKYzebvB4gF1spynJ3III2WhCR1bpl7y8lxNHN7NwK7qGQCybZIfZOvImYzlu0kJnox7yPwCYr5GEa/H5oRRnvJvBWR6auU5SIpJQg3LDF1RDMhLT38O67rf/s+zxkglsgBuGnTJpPJ5PV6GxoaVqxY0dHR0f5XFQHEnZ2dS5Ys0ev18fHxNCWOglGDgdwDvF5venp6SkpKXl4eJVnLpIoQxmVSj112dvYTTzwxMjJyllD4O84suRIkvQHtSKPOazqdjraCRVwAaauc0WikgFij0VDxO/WmoKLe1NRUlUplNpspJOU4jmXZNMkRQqfTzZ8/32KxVFVV0SOvrKwskaqgoICmbKxevfrLL7+kx0OfKWEMpZIuT7Rp06aKigqe5wsKCgYGBrZs2UINNBBCn332GaV/KDo3m81UMqGRitrDCYJgMBgyMzOp6bLH44mLi+M4jqos0qTyer27du2iZyxyGN9xAr/tryi+X7lyZXx8PFVY0rk4BfEkJSE19ZxQ0d92AOdf/4fOgPR1iIixBPlelFa7IRJhUAwOoXffDi9ZemTOvI8uvFAECiwbE5SNCzHjkXyyyEwKyy4SwcVSSMelCFwiyi4VmSukmLprMLhRAsFTaUozBLdDMA0x18ExiuELgfgTOZoRg6wxkIsKG+TBRIB00ZvbnF19fZ39a7r7evp6evt6+nt7e/r6u1cPDhYWl7JOB+dzJHu8bl8agcQBh9fv9v1l+aXiOC5Jl2TQmIxauyZB86R/Ok6OEW0gaGBOJcmCNwEkk71w2+0JLsFk5+xmi9Vs4iw2lVIbF5fAWe28nROsdlVC4v1JscKCxD2X6kVGIzJaRCQTSRDoTkXFPzU7TnBbbF6f1mqLU2rNNqeV81n5AOdKNtutWqPBLrgtzoCdMztYo8DZeIE36rRWu411Onjexjs4whILElnMC2Rx1s5xvNNqtem0mlgz++j9t+HJQIxinr/jDo3PwrodZqP53AJiwWzlBcFoIoDYq07ac+/loRsAvBHgqTJ0CwhNBcE7gThDju+KRnfJQncDcR4QdYxoY7BNdlx3AWzh0FcfkfSHEIbndRH/0Hj7V7yZ3IclgANF9OEQfOqdEefroZsPQcVBkTkMSZoGgcVYflgCx4QqpoAYy/Zj0mB3AMkOQOYAAgcIQ6zYQQSp8o1EK8w8g2RPINk6Ub4GKgawYimWLQ2DqpM/qns2e7Crt797oLdnZU9vX3/fQG9vX89A58r+1fm5JWbe4vIJPrc9mVDDkoj4Lwcv/ckf8HrdHqPerNRrNTq9KkHt8iYcQ4ojCIycUIR3gvBvZWiT7J2nr7Ua4k1WwWq1c0T1azEYdYlJcXY7a7WRQMekJPUDC+IFwx1/evHO8Btj0H5F6AAj7gNoDxM+IPvgzevLKh/g9H610b7AvjApc55ZUJrMnJn3sGqHVqlnbTqb3Wqzua02jkBdh12v1bM2O/E64212gecFgeMEXhrJp9Ex57KwBrNBPSfugTW/XvgFHvcpin7yxVl6ZZyNdRmNFmILw9nsdo4jv+8wmy0UEFOkIQgEIlOv4rMo4bN+JAtKNtZOoLrdZLTEJ6lyyz2vnhy/Q2p23CWRxJQn3obAVsxswbItWLYLMW9gxcdI8WFY9tHwXSPoYUSmT1Kq+r/igvw/to9zBogpHoUQfsN9Wq1WrVZL7cba/6oigLijo6Orq4vn+djYWBrNQvs6jUYjTTvLyMjw+/2jG+xoj1pFRUV1dTVFyXl5ed9MmF5//fV/FBB/9tlnGo2G8qZGo5FqD6jUj7rCUQKVelDodDpqZ0YvXNoTkC4VhcsU4DocDpPJRF+3Wq0JCQnx8fGCINDwDgqL6XOFlOVhNBqff/75kZGR4eFhNKrOoo3D4fCRI0dowEeka5A2FKakpJhMpjQpvdlms9FEaI0UJiIIQnJystPp1Gq1VOLscrm8Xm9cXBzP8xlSUSWGx+PZuHFjZP//3CUcAfHUSK60tJTONGjg0JtNuAAAIABJREFUc2ZmJvk6DASo/dw/t4vzv3VuzwCkDBMJbUYiCiEYRmEIifkaFMNBGAziY1+hJ5/5QqX5cOwEUR4DQRQC40TmIggmQeYikZkMwaUIXCEyVyDZpTSAA0vcMGRuDTM3ieAXCNyBwE1IcWF4Chj5BYBxMcimgE5GtI4JJyq+0kz4tCL2wVXLOrr7B3o7unoJN9zX09fX2znY119YUuHhzKU2dbHLaE9OT3f5nAFPssfrP0My+aQl1whD7HA41HqDWcmatZYEnWqp466weTxOklx1ZynEC6IQiN5+5aWL3BzBqhar1WLlrLxea4ydv8hqZl023m61JiQlzk1YZJy36KlbTW9dav90PDvMJIZBLGQsIjP9+PWXttsWmJ1pOpbLcM9/rCyukb8rICSwLovabAoYFpZwcV4Hb7XZBavRQRCw02g0sazV7nA5BJuLF6hugtBSgk1wCDa7YBecZjuvkqDAg7HTxevl4gXgd7ffluB1mZw+i4k9t4DY8WdAbPNrlHvvvpyg4VvleKoCTpWJU2XwFwr0iyg0FcAZAMcCzDKiSwYdsqCaOVXD4a+PhMmFgiHRHn6XNc25vVbPf9r3PgOkMUrijEfI6jc8MoQf/yic/ubIA4dDU0ggM5YfFCVATE2FkfwQBIeg7CCMOQDH7EPMaTExku/GYBOS/R7J12PwayR7BMkeReAhKG8Xo5qhrAExhXBCzVZdR3d/X9/yvt4V/WQa29/b3TXQ197X05qRnWtJtPQ+O7t1mc3qsDvJsP12QOxz+30Bs8mq1ur1WqMuUWk0ar8cmhQaVoT2ycIbAN4QjTbJv/r9pQWuBIvJzrMW1sraiEzJnJgYx7JmwUGgolqtXbQwQblo+pMrza88rd33/KKPX51+Yu+1p/Zdgj6eAMNjDhy6pjD3XnXyLWXP3d///i/Wbpm5tP+exdXTWGFmZvkdj26MzSqyG00O1mJ3824377AZDJzG7rS5A4LfK7h5jpOWduxOAlcFi6SAsrF2vT5pwaKFHV1zjqIJR5HiDy/frY2bb7U4zSbr3wXEFouFOlFQGQb54L9VHB8BxDaTyRKfqMwr92wbmvBnKCxZTGxHYJuUxb0NMdskpv8tDN4X5UfgrcPhxxFJ85YsNv+LaeDvGErnDBCP3sfOnTspDsvLy2tvb1+xYkVnZ+fy5csjhHEEEw8MDPh8vtjYWJPJ5HA4nE5ymdF8OJqOQcMdqquraYZzfn4+xcHFxcXZ2dnfMI5er9dsNlMJQQQsQni6CYyCPPojEchIHhT0UBFC9CqMOK+xLGlnp/M2q9WaLhXHcWq1Wq/XU5TJsiztTvX5fBSG6vV6lUqVlpa2ePFi2llIGWJptpqkVCrj4uK8Xi9tsCsdVdXV1enp6S0tLX19fYODg0NDQ6FQ6PvDevqPampq0mg0qampWVlZPM9T5zWa9MHzPHVe0+l01KyNpkEmJCTQsGuKhtPS0txu9+9+9zu6ayo+oeiWunZErCToIsDo/+jv2IYQHj9+/OWXXy4pKYmLi3M6nXQu8emnn37Hb53/q3/lGSAuE1DynSQX0zDCI0RYJhJLWUhao0jLBUm9+vgYdieLAODoGMQopAa7C0XZBSH5JFF2GQbXkFw62SWSbvhGDG7HYBpkbhWZmyEJar6TAOWYMfAqgFUy7AbIDUSXTDSOxXPAV+bLD7Rnt6xa3dW7arC7tadnZVffYG9vT29vV2PLEjXnKhZMj+gXLjfEWVJzslxOPsWb6g4EPIRrGl1+qdxut0ZnsqisZq1hoT6h1HTHifhotIDkq41cD0LyKAjGHPyfyQt9vIa3slab1c5brXaLyRq/INZoNLntAmezJ2jVc2MXKu+d83Cs9311zZe354QnqkPy+WES4Hwz/Cl40nIX70q22e3r2uJCq27/qOnOVeXqwiKv1cl1ZiS8WqHOd/NGi9PMmmyCnXW6dAaWNdoEu4e3OxyCwynYXUQ6IXAOssUKNk5wsDaHWm9J0GkGYqeHbo1GU8CLt90a50kxOgKs+RwDYsFCGGITYYhtHmXivvuuxLcAfLMM3RIl3iKHtyjw1Ch8qwxPBXAWwFqA3TLRw4RNzIg6Bu94DgdHiG4Y46CIhk57l/4rr9nz+/qeZ2AE4xOYaO8wxqcw/jiE13yAph0QSbjGYcIQK6SUDeYgin4NgtfEqEPhCw6IF+zFxKJ4JwY7YPR2DF4Q5c9B5kkM1mHmYcw8hMGAqGhG8hoM6rA8Y/jihkcqVy/v7e7r7elv6+1r7+8eGOztXNXf2dHWpFSxtUUFb8LregYXmjmH25/yHYDY5/UG/Cksa9NoTCYNq41PUKtMRz69bOQDOdzE4D8AvEEBX5KFX5nUmJFEBhRrMttImIHdbk1IiDNbjA6nQJxPdcYF82MXLZpfVJRWXljdVJLxUMvszQ9f86cNk0+8H41F2bB4ydtHVL/9Y/zLofs2YNkfseKd0DXbvpj9/D5h66ezDsGf1fdaH5iXoFOqOZb3uNMNSbFCnMmh9/BJNrfB5nHYOYfZ6bC6eMFpc7F2l8EpWMy2JJN2wYIFS5fO/gyOOwGZbVvvToifx1ocFrNNkjN9F0NssViozvi7ATF/FiBOSMov9+w8NZHKWnZQNHzGFnobBtsRsx0xryHwDgbvicwwdmN8gqwHkm/787aIf3sc/SCAmPoB9/T0aDSaxYsXt7a2dnZ2dnR0LF++fIVUHR0dnVItX748IyMjNjaWyohdLhcFxFRGTBfZ4+PjPR5PZmZmamoqlaVGcjGSpTIYDL/97W8pnqP+xBQsiqJ44sSJl19+ed26dSdPnqQvUqhHnc5okpPP57NarUqlkmVZ55liWTY5OTk9PV0QBI1GQwExzRyPZHOkS2l2ZrM5ISEhOTl58eLFtAmPurC53W6dTpeQkJCUlBQfH5+enl5VVVVZWUmVHuXl5VQPTZ2V09LSBgYG/tHWQIzxypUrExMTqWTC6XRGrIhpvqvP56PZ1NRQgjLE9F9K+e90KQDP7XZT24rINYLOFH3lzE//gLSInm3i9x0MHjp0aPPmza+//vqRI0f+oX9j5HjOb/wQZwDicEjCw8QsCwYxHgrjUyEcgjCMoRjGKITDGAZhCA6L+MuXX31fcJ265GIoY0TZOMT8RAQ/QbJLkWwyZC4XwW0I3IzBDZLV2lQEbsHkldsQuA4xE8NTgDiTgfFgxAIQq4CqqJF5ii9nj9lQ5+nt7e/uHezvXbF0cHCwc01v34q+vs6+/v7CukYfb3/z9htOjFds+uWdQmqeJyC4vMke31+gYe9fssVaU7xWq9dotAl6U3bSwq/umYjvlMMbZGh8tBSbd9n7ky7RuHgdzxrtVpPNarFbDBZzfHyi2miwOniPXUhSKu+bP1cZF19QmF/UXlu8tKq1sqg3K+0Zc+4bt8YO3zRZvOmCodvGHDbLRqoUobpxwZoLwnWykWrwWe4VJ/MuDNbIWn2qe62ps61eneC2WP1xKpeJDdjtyXqjW2dNsXBuh+B02F0Ou0/gfTbObudZq1VrMSXOYIW186aFrwN4imLzHTcbXMkGB2dmiac47fI5Y0lhsVgsDqkEQaCNyN9fQ8zabZxDYI1mpdHuViv3xl6Hp0Wh6wD8OYNuYPD1AN8E0B1AvI8JxwNoVIQMjKiWh+czXzW6cPAEJtQwJkkQIh46H7/xQwzL/9VnEhkxWQ+XnIkhDEN0CqIRymWEw8eODre/Gb79MIo5TNyF5Qex7CBWSMIJ5iACB0hah2wnUmw97SWseB5G/QaDx+DYx6DiVyLoQ6ARg5pwTO3Q/zR8cVnVVtPKFY881EHmsd09qzv7BrsGVpAh3Lu2Z8WT5dUZyz+esubUJQVP/dIlZLg9qX81k/3zrNbvSk72Z7B2VqvRmdRWfZI+TmXaveFn8GA0fnEs2gBCGxnxVZm4ZUxH0QyTyWpjDWaOtVjMdrs1Pj7WZCI5spLC3jRv7gKv21NTUV5R01Re1VxV1Fxf0tRY27ikvfSh9cLvD01/7bPJH0HF2wi8gcGfoOKAOG4vin4dg8OiYo84dt32qb6UuOQsp9khGONT7tN5bhlYntBtUhUbdVaLUe2xmVN43unh7W5Ja+y3mc021nS/8253QutLP/sAjjkGwc4Dd2jV97NmgpX/LkPMsmTSy/M8JeP4b68zkgmbyWROSEgsLHPvOTVxHyQ67+3SY9sZQLxd6rHbCcEbIvMOAh+Hp0L4LoYYopCIpeXo812vf2ug/SCAmO5oZGSkrq5Op9Pl5+cvXbq0XbJjozKJjo6OtrY26heRlZWVmJio0WiojFgQBIvFotfrDQaD1+vNyMigAC4lJYU6OVAJL/U4CwQCfr/fbDY/9thjIyMjR48e7e/vX716NbUyOHDgwNKlSysrK6urq1944QXKEI/GdkVFRUajMRAI2Gw2inqpjJhmc1ARs9vtjmiIKSC2Wq20X40yrBzHUQ6YMtZqtZr+IjWdSExMVKlUiYmJWq02Nze3tra2rKyMeirTTrsCqdLT03NycqiL3N/6b/rW15544okFCxZkSCkhbrd7NCC22+0UEJvNZr/fn5aWRgEx9ZgbLZlwu92//vWvI/uIMMG0t4/mAlJBc+Q9f3cDIRQMnu65oTkpkY/9u797/g3/qjMwjDBJGQvTtgokcX9EMkFoYoyHiEslhkGET6FQCIlhEYc/P/7hip5j190mymKQfLzkMnElkl07Ir8Gg5sJ/CWPGzABxLdA5qphZox4ESPeCfBsJrwoRlQqYFzUVwsnHk5Peq4qp2ewc8nKwdWdff19S9oHeno7H27v6+/sWd7f37myf2BjQnwoCmAFeO3Kq/SLU1LcgYAz2RNwjtYQjwbEZH3GrFYatEqNQam1BxJUH86+El0rC00ai5mfYDAdym7+etyNRSrOZrUYbayVZW02s8lqjlepdEqNlefcnKBKUs5Nis0vyKtprq+qrqqtqq+uq62rr8hpayptW1HSXN2dn7XBnfSBZ/qx1MuGshmUBVAKg1PkYoYM5QAxG7yffMX25Ou3pk1f7VDzNjY3ef7Hq//n+Mox25de2VqgS+ZZsyVFZ/PpObtVIOuvNs5u4N33Jln81vkfx/5YvAmgS6P233G5TQiwpFOPZFZSY9RzAojNnI1z8lYJEPt0mh0P/AzexqDrALpCjn8aha9h0E2EUxeNCmSLhhoZjGXwTHBCf3Po+NthKcWFGKtJM6jzLNO/apx+7/0QuYQo5SwMkyVxMncJky4p8v8VIg8Rh+Hxk/DpT0dSXxMnEZthcfwuDHaI0bth1D7I7BbBdgQ2Y7ABg+cR+A0Cv8JgHYx6VJStgWOaQopGUbb8nXn1j1e2rurt7H2or29Fb8/A4ED1QO+K7t7+joGe/q7+/q6epYPLlj++9KZ128b9ad+M/bmeVN7PpQZcZEX3zyh41FaK1xvwpvMOh0a/UKdVm032uWrlb3pvQjtleIMCbQLiJhB+BYhboh5uuUOr11ksJjtrYVmzzWZNTEykbsE0gVmpVFZV11bXNVbUlVc0lFfUl1VWl1RW1laWt1SUNVWWN9bXNHT0pT74rOnxnbM2f3rlntCYQxj8kdDkxFVjPxy388TYV0+Mf+7zCQ/+/ua8Rxc4R25rDl3w0Kkpj783ZfUfbsmrXcQLWRZNFqtJNRlZzmjRaVLmOvmbD1y/HCpeQ+BTMWrXW9MWGe5izYLZZJU8jwlDTJxl7CQch7bCR/piWZbMeimg/3YwTP5GAsQ2O0cAcWJiYnGZa//QhNcQsYgmpL6EhrdKeomdxB5EthfJ3obgXXjhiPgUEoNk4CJILBH/qxvnvms0/SCAmEpgRVF86623XC6X2WwuKytbunQpZYXb2trq6+vLysqKioqoGVliYiL1avB4PJTwoN4OTqczPT2dWgZmZmbSTAqK6kiMjVTUJqKiomLNmjWtra15eXm5ubmPPvrounXrOjo6mpubV6xYsWzZspaWlhdffJHKcCl5KYpiX19fQkKCy+UymUxKkr1ooLwLdV5zuVx0j6MBscVioaZslKVOT0+nvg1utzs7Ozs9PZ3aO1CJAjUGViqVGo0mLi7OYrHk5ubmSVVcXFxQULB48WIaPkd93D788EOqWPiu/7Ezf0ffuXHjxvvvv58yxF6vV61W06w4nY54vtBGOnrA6enpTqfT4/HQyQaltymmd7vdDz30ED0tFPgiqSjpTk08IukhZ/b/d/6kn0BFF5Ht7y8I+Tuffv6vz8UZIK5rJK+ZfD0GpbvoiCSRIFcCeUjssUhEE+SGOixi9GUInRzBaOTUqc9WPfj5z2+A0RdKjhNXQOZ6iR6+AYLrIbhRAsTXDkcrwlcC9AsZuheghdHIJMNx4MSiib+rSV6+am177yN93R19fct6e1Z19fWu6W7r6h3s6l/TQ7rUVwwODrySlzU0fiySMSPjpjjSU9M8yY5AINXtDkjGpT6pvGfKJ3k52cy8Sq9Xa3RqvcGoVe+fd+PIj2OGoi9DzHQEZiHmjjAz+1d3Gh0mo8XKcmabnUgnbCq1Vheb5LRxHM8nqJSL83Ja6uuXllW3ldZW1DRUV1c2V5VWNpRW1VVU1tUUNTSV1deX1TdV1DYtr658opDdn3rXMddViB+LXTLkBsgDRpIB8gO8mPm6ZeLx7skjqxR4JcCrxuI1IPgQ88feSwaL7873G1i7P97g1drT1Rou26L7k/cK/AAYuQPAq8C7d0y2uFIsNqdVQsQ2m43neQqLLVL90wwx+RAHbzGZlUbOq1Ltmv1TdDPA18vw1THoSkXoegbeE4WN46BNHtYBrBmHF4z76pdTgoc2QnEohNBJorIZISsH+BSZNJ2v/7QzQHHwaTRMQ8hCp8Ex4Y1DxFoGhxESg+IHHw7lHkLMZijbSlbYwQ5RtgPJKBp+DsmfQOBhBNZi8BAGPUhWj2IqkXzJkRuWDbSt7nhqVcfqge7ejv7+/p7egY5f9/Ss6untH+haPdjT19fV39ez+qEVPYamP0V/unvhl9Oyc/1uN+/zUidigon9/r9Y6kn2Ov0+L+9wabRqrTZJrU1coLF3F8SL22LgSwC9BPAmgDfJ8Bb5qw9PNRo0ZpZzmcykD8DKqklpOdL4RjxGc3JyGhrLa+tz6isa6yraKmuqq6sb6qtq6quL66oqq2qrK2srysuWlJcuqyhdWlFS37KsuPcJy6+2JP7+7Z/tGZ60Cyr2o6g9onwHQZby7XDMfkTg5m4R7BKZHUi2Fcl+99mYBzfe2LbygfRyTVYZq/eYfv7HeQPDUfvFia+jqEMYbH3nFlZ3l0ViiKUJrfVcA2JTYnx8SZnz0NCEN6XQQZqcQoUTOzBxYduDwD7EvI3A8VApGbBwmHiS0G46dPr6+E+7eP/tx/ODAGKK1ejz22+/nZKSkpSUFAgE2traGhoaioqKCgoKiouL8/PzFy9enJeXp9frFy5caDQaqYSX5rvQwLOMjAwanEEFu4FAgLbBUbEE1c4WFBQUFhYWSaZmtGWtqKiorq6uvb29q6urvb196dKlZWVlgUDg4MGDlKekz2+++aZNkuUvXbr0GyFyfHw8XbagZheCINBeNMmDWEe9zEwmE/Uv9Hg8KSkp6enpPp8vPj7e6XRmZ2dnZGQYDAaPx5MqlV6vpwyxUqlUqVS0vSwlJYUGf2RkZKSkpKSlpZG49pQUr9f7xhtvfH/ISFHmrl27Zs+enZqaSo9EpVJR/E3DqClaoCZoGRkZNHbEZDJptVoKiClP7Ha7BwYGRiuwKTj++uuvd+7cuXbt2rfffpueMQqLv89VSw+PvpNu08/8/v/A77OX8+/5X58BKGmFiWJYxJD4s0vWsiKRUISHiaZCxGIQweCIpBwlgenhIRwcEmE4fGr42B92fGDLHJ5wFWImSkkcN4vgZpHQw7dC2Y/R1TJ8N4BzwbAaIO0FKAHg+bLXhAXdg7/u6f1Vf093+0D/iv5VK3uWd/St6e0Z7O1f8mDH8oHezvbe3va+wYE1q7fFxp2QR43ERFfp0oWAnQ/40h1pye5kCQyTpzN4mNxiA4GAYOW1hkSdOlGn02m0mt/Pmh6KGY+JlmMOBPci+XQEEl64UZuq0ZutrJ2IEO12K6dX6+LiE5x2wcQT14fKrLyystLC+sqy+rKamvLaqpaairaairqK2uq6ypq6qurqmsrGqsbK2tqS2trK2sbimurShvrqqvqHSoXXk6cddV8Z9EWJKQyskottANUzuC0G98iGegHsBWKfAq6KCT8kD6+NPrZ63MEll+ztnP5c9hUfJF+HrQAnKNB0gH/GfHLHj00+u55zW1iBMsSCIJwThthpsfIO0qehNHJ+perArJ/hGxn8cxm6lrhMiNNB8D4QVsnDSia0QB6cNeb4dbKhZdU4JIYQDMEhDIckGByWIg6H/9eX3/kPONdnIAKICfANnekSGCGdkERQPCxZbhF1MfXcGg59+u7I4r0jt20RZZsR2ACjfovkz0jc8GNizIM4ZgCDdgQaIChBoBgx9a/N7WoffLjt1/19rQNdq3t6SSdsb29vx0BXV3/Hyq7ewc6enr7enoH+wZ7GxkeeXXDq2hwIslv1vMB53SQ89SwofHose31+v9Ptdup0do1WZzAqF8YJnZmz4JYJ8OUIIGbwZubd568x65N0VqfDYpG8v1mD3pCYoOQ5B8fxLMsKApdfWFRR01hTU1Vb1VBd1VpVW1ZdU1Fd1VBZU1tTW95YXdlYXd1YVVdXXV1ZV1RRV11a3lBeUVNR2VRTV963Nvk3+2fvRONIPl8wZhcGW6FiC5K9CsGrGGxGYCsksdU7UfRWHLWJ5PYpnv/qwp2SZ/ObYcVbYtT7EOx9/zqdeYbFLLCsXafTSVrn/y1DLJApMUsMKzir0WRMio8vK3W8NjThHUzMQw5K6YM0QmUnCXMGexFzEMs/CS+CoRNkye90y4hkqibJns71lff/w+f9IID4rBPzxRdf9PT0GI3GlJQUasSbJ1V+fv7atWuPHDlSWlq6YMECjUYzGhBH4jk4jlOpVFTRm5ycTAMmKD2cm5tLfSfKpajksrKycqmoTreurq61tbW6urqkpKSgoMDpdG7btg1JRVaSJA/gL7744p133qFhzjqdTqVS0fRmq9VKu/pSU1MpXa2VijoNu6SiTsmBQCA2NpZa7WZmZppMJqfTmSaVxWKhgFglVXx8PE1Ottls9HfT0tJSU1Mpnez3+1966SU6izjrBP7NHylC3b9//5w5cwKBwDeKC7/fT9ObpRmz2mw2+6Qym800OsQtFbFrVatTU1Op0CIjI8Pr9a5YsSLSPAchPHLkyJNPPrl06dKWlpampqaGhoYNGzYEg8HvD4j/5jGff/H//BmQjCmgZDJA1KQwPIROBd967wOhHkWNgySkg8iIoWxS+Gp5eC4ztABADYNYgOPkcFEMupt5a84Vq5e3dvYPDHQR9XBvb7/06CM31dPV09vbPUg82AYeb1t6eIH3s58ZuuMtZp/dw3F+h9Pn+TPD9E2mDMXE1PjF7eDtartJaYkz6XWmhJ03XhmWX4HBfRjcB5n7EUlgVr3xY3We0qzj7DbWamVZu81GnR+pTpfKc0tKSpqammpra2ukqqqqqqmpqa2trSP30Ora2trGmqq6yvL6murqqqqq6prq+say6rry2sbSmobK2vZHs+2fLAGwEeAqGaqRid0g1AdQN4BdAHczuEeO+mVwtUxcCfCaKNghg0UA8wDpx+IkAGfI0Q3gk9snccTryWozsEaV0crZrcTsSS3wNpNVMLAaN8+5eM4pOAWX3WTVG00aB69zW5OdDhXHeSwOB8c5nTan0+YQ7JzNYWMdNptTsNjsnIVz8HaLwbrQ7BTY2K3zrwrfKAtdAfAVUejKKPEKZvhSBv+ECd/I4KvByCRwNCERnzoPfP+vDFxREkCNou7P4GMpiIFY5RGSkOBhTMI88CkRERf/IDp+JPj0zvDc57DscQx+hcA6SSzxIAZLUVQNktdApgrKq1BU47GbVjzYMNCxqqt3YGV3Z29v94qBwe6+7oHuvv7u/8fed8BHVeX7n5lJKOq6u25xd9VtrnUVUddVUFcUCQmp09vt905NL4QQQtqUTBK6QDKZSUIAARUBxQXLPhsoKiAiShWxYwEpQpKZW87fcw/Msuhzcf/vvS3mMJ/JZLiZ3Jy5Z873fM/39/32dPQs7OiZt7A7Nq+rr29hbyw6f1Gse+HjtTMX109tqCkrqygsLMTTEPYhxjMUWtkiL8Uij8dntRtMRqOpwGQoMG1++BfyJq28Acgb0uEGLdwA5E3g8+cvJ+wT8ii3k3CodJbTbndMyspBFt8kQxBOhiGnNUxrbAkHwrNDkdpIKBgOtYYi9YFIMBhpD7bMbA7Pagy3NkUaApE6NJqbW8OBUDAQCSP2OBhovK+8qmDbsV9slsBmGWwWkffcNjntVajbIiHadQvy99VskMGLqk53owy2ymkvQ/C+cuFeKX2/qPtAStv6/tVWcqzDzpIEowJix/8/Q4wAMXEKEKM97ZzsxgZuz8nz31HAPkWzW/UMeU3VTmxRwKsy2AF1u2C6KO9EERzKINJKKIjvUEU1CXWl9O9yVf/fnef/BSAWRVTp+sQTT2DUWFZWNm/evG3bth0/fhznUCxevHjs2LFYvSAIAmaIcVQy5jXz8/M5jsNkqsvlwhJen89XXV19Zo0aRsNn3mPauK6urqysLDc395FHHlHUJqsNq4oxc4kLAbu7uzHYJdVWWFhYUlLicDiwxQQu9cO5izzPe71e7FWcmZlJEESZmqtMkiRG0iUlJcgYNQ8ZTWDiNjs72+FwTJ48maZpn8+HRSCY3MXMdzQaPfcgNyzM3bFjx5gxYwRBwBWHRqMxOzvboDbMVWONNUVRlZWVWEGBKwj9fj+uq8PeduFwGLu/vfPOOw8++OCMGTPmzJmDiyAXqK21tXXJkiXx1jJKAAAgAElEQVRHjhz5v7s2h37Tv14PqHMpQsKIbkiKUEKKtOPwpKT0H3jqmScn5O658BeiRqP8EMhj08UsIOYBaBs24ABKhg6OuTBxM3iX+MGKrpoFPUs7Y53xeNdpEHzm165YrCsa757X09N5f/eSB3p7YvfNm9NeH2wqrqp0FfoFVViF0xAx4eT3+z2qNpFx0yarQ48oYuPMCXf1D/+NAu5JgHuTunEKuEMGkyDIPvjD3MZss4kmESBGzk2kzWbDztkUReEY+WnTpjU1NUUikfb29paWFgyFW1paWltbI5EI2oINhVrQFKoi5nBLONIWCEeawpHmSHt9S7ur0HuiAcCWYVITkFu1ylwd7ACDnb88GLs6Efs57EqDMTDQBxJL0pKdF8rThyveEUlymGwFMBfIfwLwd+Dkr37C2q0OxmWnWIPV5ORIgiNZyitQCOASDs7NlTg8NOliPYybsbJ2s52hSZbyIVdjlvURTo5xOjnKwQlOxsXSPEs7edZJUFbebifYIpPTlGulyvQ5O399rfw9DfwJkH8CBi4BJ38J4MVg4DcAmRNfn/by7Vcce+4JVKI11P5zeyCJwnsRQBKlo2+dvO/Rz6++PwEWQ9AD0+Yp6SEIamVNrayNKNopygX3DSxauWb1/DlzO+NdHV2xrp7OzvgClKcTi3fFO2KxzjNHciwWi6otFov39PT29PTOmTNnypQpHo9HEAQOLdvo0/jY4/K7XX6/lbBmGwqyc50l7Cj4CkhuHA6fB/D5kXCDVnkBKC+B5Mu/8lrHWOkSB6EOYKfTbrdjc32KQqEVFEUJguD3+6dMmRIKhVpbW2fNmjVjxgzMmoXD4dbW1rDacH1Rs9rQijfUGgoGmoMhM3nbtqNXbhtEAX6vSGAzHLFDOe/51+9b9njls/vYLYNXbIXpryjaFyXdS0raK9IFmyWwC4I9EOxTwF4J7Idgy8dXE8wfHAYfTeTb8xjSQbG8lSRpiiHspNNp5wSBcXudLOuneMFmRxVTNEWQjINnKRfNsQyJokFInqVZjjUxKNVHYFiKJG0CZaUpp8XizM/PDc1l9w/84B0I9kHNPqjbi/xDwGsKeBVqXoXa7SdvGoBP/+detv8rf9n/BSBW1CaKYiwWa2pqWr9+/cAAohwwpIMQrlu37rbbbsvMzMQ1bQzD2Gw2i9pKSkq8Xq9erycIAgNit9uNwWhpaWljYyP2bTgTBOPHGCjjLIzKysr6+vqOjo69e/di+Iv7MplEDtVY8Yw5Y1mWN2/e7Ha7SRJ5uqCM9dJSXHKHhROqQJ7CkzFmecvKynJyciwWS3FxcWVlJXbJKFGbIAjYGBgD4tzcXIvFUlVVxTAMrhfEDHFpaWlhYaHb7W5oaDh3xIm55IMHD2JrYY7jqqqqHA7HxIkTCwoKcPYez/N+v9/pdDocjqqqKqw2oSgKR/Rh+TIO5GtoaDh06NCjjz4aiUTa2truu+++OXPmLFiwoKOjAytPZsyYUV1d/eqrr/6vXIZDL/pv0gMIEKsF7BLWoolIbTyoDA4qA/LJAfmjz6R43/t/vPHzXwxTbksXMwB0pCcdaf25WuX2YUd/r/nUBORHrnz6yfL7FvbO74x2xaOqyRpCwGfdOru6O7oXdXZ3xXuii3q7FvX2xHp7o/HuhfMWzGxDc1tFRYXX68WuMIIgYMJJ8Ah2izXHYDAbjWtvGJ3QXqGADAmMl3VjFTBWBhMVMPGz72W2TtCbUdQyAsSEyjLl5OTY7XaKogikoqBwaGVpaenUqVMxMm5tbZ08eXJxcTFOjw82N4eDwXAI6RODoVAA7c62NCEqqq26rt5sNA/UpCsNOqkBKO3pyoJ0sQO8PHPc8lbbX2ZP2tN585FFvxxcdKEUTx+MaKUarVI4TCZ0imWEPEkzcDtIXgK++Pn5RQ69w+kkGcZgMprspJ0WCNLHcXYUAmBy0qyXFDwsy/mQAIIyWq021kHwFGKNURq0k2JJgqUJVMZDouI9NU7ASTooB0FwvN1myjML3uzM1274lXilRr42Tb5cI1+rgddpkaR49AhllPaLsT8TH1+qnDiiysn/TS7QodP89j2A0niQgkKSlIETyo53YfcLCvdA/6juZHpQ1jTLmgaY1gR1zXIa81628OhTm198NTZvXldXV6x7Yay7I4oexbpi0S6Ehr+yxI3HutQWj8e6u7t71NbZ2Tl//vxIJFJXV1deXq4ONx/Nc7yHtzpsOfmGiRPNf+4cldiikZ5Ph0hDPBxu0CgbESAWN/+0xnuHwcEh50S0x4NmN7ygpWkaA+JUvRqWL9bU1EyfPp3neeysWlFR0dzcHAgEWlpa8BYQXvSGg5G21taS0soc421bDl2xE4JtItIevKzo/mv/6KZIZfV0f2AO27E6b822zBcO3fLK4G83Sz/dKo14U9HtksE+RXtATtsvI9num0d/3jjjGrPVRtEOS77NbmI5C+MiGJph7BYbYbDwpJMnHTzBki6adZJ6i5PiKJqnaRpF9ThZyolyfTgWBeIRDMWSDEegAB+000NTTqvVlp2XGZpNvjV4wT4FvAXBW1CzV9HtUjSvKYjY3iKe/5FSI8G3vv3l8J3+if91QJxCnLIsHz58GNeN4e1+fI8x6Lhx4zIyMhiGwdEYDgcKPDSZTEVFRYWFhQZkTojiJ4qKitxuN57/PB5PbW1tIBCor69PAWKsIcamZo2NjcFgcPLkyYsXL3733XexWwJG4akTgOruUepMFEX5/PPPCwsLbapWGEc00zRtNBrNZrPJZMJCDjxlYnu10tJSLFHw+/2VlZU4CBqLg3GVW15eHpZMYK8JbBgsCAIGzVgyUVxc7PV6q6urvxUgxuD+wIEDc+bMWbRo0fbt259++mmPx5OZmYkpbY7j/H4/QRBWq7WqqgqvxWmazsnJ8Xq92Mmu9HQc4NatW6urqwOBwMKFCzEUxmh43rx57e3tdXV1FRUVGzdu/E4Pl+/8H4+IpNOmTqKkiAqUUHCsKMqSLCmSJEuJhPzZR8mVPYf5u47mfn9AD5JWjTxJO3iT9iQP4GMAfnLRm7uzOhd0dXUt7opFVUqpU72PxtC3pyByT6ynqyOOeKWu7kWd8c5ofH68pwvt0vb0dMXxtBqNRufOnRuJRGpra0tKSjBP7HA6JhlMwXszDp5/saK9XgZ3QvAnSXMjio8Gd0tg3Mlh4xaNybaRiGBCkFjd0sGF6jRN4xApQRDY041hGOxpU1BQgCko1Va8uaUlEgqFg0GEiZE0ERHIkUhbq51lzZm5h0rOk8o1Sg1QIukD87+3Y8bvW5vqpwVbmpsb5gYqloTJp+aaN88e//b0a4+U/bjfNkzO0Ijj0sVx4PjodOniNOnK9D8XjanyZJutrtycPI/3Hpq71WwVbAztcDIFeXlOp4Ogi0jSxbNIWKFH+bBOh+CgaQ9P2wnEE3MkS1Osk2YcLM3SlItheAcK6CNJwU6aHbkWwW/M25l/mTIxXf7TcGWUVr5eI43SSjekyaO1J68d0d/dJir9g1ISxQMPtf/cHsCWAzJyl0lISnIQil/Agx/DZ18cqGv74qYqKX1aEjQrunLl8huWPfaD3z297aXPlvd1RRfOice6ol29HbF4Z/fCzu6FsVhXd/SsXR8ElbtinV2ITe6M/e2OUHd3d1xtnZ2dbe2zQi0tTU31POfJmXDP/Ok5iZcu+uJlrfIsUDYAZUMafAHAF4HyCpC3nRefdWdOQZ6TQKMVY+LU+MVbu3idjLEEDi1nWaTFR9mQJMmybDAYxJoodT2rLmuDwUBzKBBo1htsmdkTXnj36tclsBtqtsnaVwYuntflqasNhINzmpvm1Ne3NwTrgnPpWX3G2KNZT752z5uf//Ed+efvKcMPiNr3pQvelb//jnze64d+XdOWZRhvuMlTcseDj90abLmbyaIsZnO2887iKXfURe7xlegZs4+mOTtpNlgI2sjQFI3i3VmaYSg0eAk0hGkPg9a0pEOwO0mSJgUC1cTaJ2Vntc1mdidHHJDAfhnsV7RvKbrdimYH1G6R0/ZKhkF5H9LJDLVv0wP/64D4m08G05wffvghQRAZGRkEQeCpCDlsq00QhLKyMgzvsC2x2+12uVx4q9RisdTX1zc1NTU0NKQSkhsbG5uamvC3U6dOFQRh5cqVfX19Tz31FE54xjhSUkM68AmkThIbjS1fvpwgiLy8PI7jysrKCIJAVaxGo8lkslgsDgeKXsRsK3ZpcDqd2dnZmCH2eDwGgwEDYizqxfSwXq/H2onS0lJc3FZWVobRMEbGXrVC6LPPPkudzLk/wBw8/rsOHjzIMExOTo7BYMCAmKZpk8mERcYul4thmEmTJmFAXKq24uLiL+2Kd+7c6fV6GYaZP39+NBrF1njYJ2769OlTpkwpLCxct26dKIq4087qunM/26Ej/317QFGgqN6QN4Ua3YvkUNiuDV9/6rUoSaJy7H3liY7BmZZPK64fvFeXtKbDtRfKe3Xy++DoZ1cv7gtEOxbHuuKxWGdXDE2W8XhXNNrZ1YUAcVdXF8qsi3V3xpHj/6LOHrQ1G++OdqHZNRZDgmOVlorF4wgc96qtq6tr/rx59LQKuz5j65VXiZpLZM0oCYyWNTclwa0Q3Cxp7pQ0YyTt2KeuvZck7HaEh0kngQwc8vPzjUYjBsQURbEsm+KeU9tWJpOJUZvH45k6ta6pKYA0FJHW1tbWhob6mprqKVMmFxf7J+TmZeeb9grnJQpHKBVaJah7Y8aYWGNpU2BuOITsoAKB9oZg+/RASzAwvXN60eoa+5PChM3GG94u+J2coXln4q/fG3XpsbEXnqj50ZOhW8yZdzVX/+nD7eO2PfbrBY23efjxpizjgpZb1veOXRi8pb4oiyE5J8EbDBaHw25x0CTrZWmaZjwMTTKUHRHEFEehWDySZhGlRhIUJZA2syPTTnvys1+b8PPkXUAeC+BokBgF+kcD+dp06SrNkWKTPHgc1WGhYO8hQPzvO2T//plLqQ90OSkriaQiJqA4CMWkLL4vbXw0Mb3r+Oiy/ituP+ga4f1Ye96Hy5b2b3zm2XhXRww5wiC1f7R7fmf3AnXoxv92qyeKoHBXRyyGbohFVgdw7HSLp1pftGdRbEnfA153+Yyqm49tuFreNCy5USM/j3AwfFGnvAzgK1plswa+qX1y5R+zJ423O2gnSm8mSJLMz8/Py8vDDDFJkpwa8oXvsTZDEASLxaKW3KGhjU2iamtrQ6FQJBIpLi7med7jQdNjZpY+I3PSum3X7VI0OxWwU/rJsuczm6YH21oiyKAiGAoFW4LBlkDzjOamOc3NkaZg8cx5TN8D+r+8ftk7/Tetf3nUE5tyN+3/07ZD1/9l/4RQZGzWBuftJ2ZO+qDQvfWWur7R5sY/3bYtdOcHM/NeFmwP2r2GQtJKZNjIu4qLHVaOtHEMQfEEwZMURxM0w5C0V13WEkhCTJA0xVK03Wl2TMqYFFrA7xoY/r4E3pI1CBBDsEfRvi7rdko3nlCeR1V0Q1qnv3/5/80R/0xAnKJpv1RQNDQ0jB8/3mazCWojCAKrJmiarqqqslgsBQUFbre7pKTE5/NhQFlYWIhB83S11dbW1tfXh0IhHHVRXFyMFcBT1VZdXe33+xcsWHD06NEz4eNZqC5FFX/44YdTpkwxGAwEQRQVFWVnZ9tsNoPBUFBQ4ET2pSSeLwsLC8vKyiiKysrK8vv9FRUVfr/fYDDgX11UVIRz7DAUxsgYG0qkCu9SdXU45e6jjz76m/fnnL/BaARrow8dOvRlBWFWVpbL5fJ6vRzHmc3mqqoq3HU8z6cAcYnaiouLv3TH27NnT2FhYXZ2tiAIs2bNCofD06dPr6urw2i4vLy8vb39TM3JWV13zmc6dOC/cw+csipGH7XIthgJn1AZDwq3Q1ZtaiioLEuoTEeUxGNw8EP40av9nZM/rr96cOUFya3Dlb1A+vRHzz7pv6+jI9bdi6FtvPuv86hKG0ejsWg0Hu2Mxzvj3Z3x7jiSKHZFMR6O/bXh+bWrqysajS5evLhl7gKrl4pNGt3/45/K4BoF3KyA0bL2BhnchvLzwFhZcwsEt7x+yV1um81KOkiCdDhR+azRaCwoKMATKk3TZ06oeKTzPI+9Qjm1sSzPsbzP58cfLxRJkITDJbAsQ+bnZI012PeYLjrpu1Caoj0685LuUFVToCMYQhU8oUBLMBhpDoWbwqHGllBTuCUQbG0NtUaDzd1TSg/bf7q6hl1a6X/KZX2m9u4tXYa1M/P2PnKb8vwFytPagRcu3bPu6ifn/enIyxdLmzUDm87b8dDtDpsl3+grdN7V13b7nOqxLluOkxCsDAqP5mmSpxiO4QmGInmS5imnk2AdjJMhrU7HvQWMz3DdLuLnYoZGztAlxmkSd6fBO9Pk32uP3H658uEuBZH+6BMF+RIMtf/cHlAlMTJSTai2XLKSVLM8BqEkK/KgqPQfVV54Xn68+t2NPzO8nfaDYyx54tBHg31L4/FupIPoQV/R8Ozq6sSbPBgEd3V1RlHVHaqRPX1DIxcrKNC6No5+KBpFLhVdvV19i7pbWxe4KPOHT1wOX9YlnxsGnwPyi1r4EoAv65StQNmqUbYCuFuzef2onMyxdjsCxJgS1uv1KRkxXtDiIczzPF7QYnNVh8OBn0Gxy2rDVbk4xwoJm11Mgd5yz/h7Hnzmpn1IgQBe/OiSQKQ5HIwEmyORcHso2BIORUKhtmAI7Q8FgpFgKBgKzplSFZzcfPnTW/mWWXRjc0NTm6e1wzrv/rxFTzrm7TH3HjKvSI5ZKV36uHTJsi+ubJZ+H5SvmQ1/3X7wOofvzmzDXbc8MH/03ifGP3nfhNnOHN8dRqedsQkep1cgOJbmCIohOYZn3JQT1eaRtIPIM9zmLMh7rHZB/6WvwxG7Fc1emLYHgt2Kdqf0i89hVIL96PMY0RVD7Vv0wL8EIJZledmyZffcc4/FYhEEAcc1OxwOi8Vit9urqqpoms7Pz6coCsttMWguLCykKConJ4dl2YqKCo/Hw/N8inOdPHkyroxpamrCLHJjY+PUqVPD4fDBgwcxPSx/5YMeCyowrDx8+PDq1as3bNhw8ODBjo6OjIyMvLw8XOqHATHHcVhGLAjCxIkTXS4XVuUaDAYchFFSUoLsjdSiOqyayMvL83q9RUVFDMPgcrqSkpLi4uKioiKv1/tlfuOBAwe+xbt3+lCMTfFpY0z/zjvv4AUx7haz2VxZWVlYWIg9NLKzs1Nni0nuL/eC9+/fv3XrVkEQ8vPzJ0+eXFdXV1tbW1NTU11d3dLSsnnzZmxIPISDT/f6d/IrrmJHSqNTJcuovC6hGhUjfXFSvUkiek5Myl9AJQEHFZg4At/rgo/+Wv6vYfB1Dfzk+3u3eWYvnBXvXdTb09fT3RtD1G9XPI6o31isKx6PdfRG5/d0RuOxaLy7U4XLXbFoR3c0ig85DYnxhIp/sKenh6yscxhyXht/zYn070maP8jgjxDcCDWj1QRpDIhvhuCmt35ye5HZYqEQIHY6nTRN22y2nJycMzWI+IMohYZ5nrdarZh8YllWEFwsixR/eBM2Py+XoYhCv1fgGUv+xNv01l0Tf/qF//uwQfdm8Hfh8OyGtkWBSHUw2BgOtISbZwSD7ehrS1Mw1NjWHAwH24KtC9z+xt3Oq2a3BKpmzpvVPDc8vb6jhVvffse2+b/8bDGA6wB8XCe/BMSXR/Y/A6RnkDPr+49f5Sl33Zvrvz90z+BLo4+tH7siUmKjhFynxUKRJFIOUwxDk0iDyFMcSzA2zk4RHFlgsWZkC92td33eeo1YAKBxRLIgfSBTB+/UwFFAmTkNrXL6k0koJSXkOTzU/pN7QA3nkdSMHpRDqIhQGUQDGWlljmPLWlGC7yowuhlecgv81aXbX98xsPzhzuop9Qvmq4vZzt7eWG9fd2dvdCFaxnZ3ImFTN4LIXV2IFU610wMXfT093tUx39O3pG+xt7AqWGNPvnARfAkoz6fDZ4H0Yjp8BcDNaco2jbINwNcQIN75zGhLwV02G1L8Y4bYZDJlZWVRFEXT9Jk7PKl9HpfLZbfbU4AYL2ux1JiiKKPRyDCM2y243ILeYLp7/LgVT92yR9bugeCx7T/1CK2tkRnh0MymxtZgoC0UnBEMzAyEAoGW6YFQU1OgLRgOl5RU2/jLokv55vC0puY5jaHQ9EBT07RZtaFpodls++JbO1/80Zq3vvfM0ZEvyrqXYPozyvl/VkY+A7+38k17c9R3w4erAOz9AYznfFAWeNblmzaNMvhYvZ+xEyRLkAzauTVYnByBSgKMlM2alVvQHb5h/5KJJ6/4izx8OwS74Yi9ENX2vQNvScJdEiqUTA4B4m87bP+ZgBifK+ZrX3nlFYPBkJeXh69gXO5ttVotFktFRYXb7dbrkY8YrgDDdXV+vx/vddrt9ooK5OeCp6uioqKamppmtTU1NTU2NobDYbw50tTUVFlZuXDhwhMnTuDfjh0wUr2GIXIK8yUSCcy8njx5csaMGRkZGQUFBVarFdexulwubBbh9XozMjKwvgILPDAgLisrczqdWEOcn5+PUzNwjh1N00VFRdikokRtPp+Poqh/uGotFYmMMX1/fz/DIFNG3J8mk6miogI7UbhcrpycHI7jMHxPAeI33ngDQvjaa69ZLJbCwsLq6uqysrKampq+vr5PP/0Ui62HbNdSl8p39IFaja4yw4qMjHwwqQQHMSRGs6k6oUoDMKlGesgQ5T9LIqKO+z8+eSje/44+8cHw996+xlXim1I3u71tbsfC7mhnd093n8o4xVVBROeizujizlhnd3dHNypgj8c647HO7q5Yd7Q33tWNptDTrbMTFbb39vYuXLgwl6Wez78q+atLJO3FENyqaG5WNKMgGK1ofw/BrRIYC8GNChh9+LzbanLNRsaBq3KwGTlmmHBRTkoycSYgttvtTqcTT6Icw/AsK7AczzCk05Gfl0tRhNfr5ngm1zLx7lz9IznXHyv8vlI3fFXZuBKvv725IRCeUdfa3twSjATr2wINragQryUQCAdDzTNaW8L1QZPe3lbumt28MBRsqZzV0hxsDzbPbGqcEQrPCkemdszyrbqP2750zMdrvic+MVJer4NPaKWnLvjo0Uu2RH/02aqLlCd00hogrgYfL/7pn0O31btySUeh3unVOxmWpDw0z1CsmbS7SLuZL7o3Oz/WOuH4xgtgT5piBKIJJE1AGQ/kMSP6c3/ff/xt2A9PwuRJKCJTriHFxH/2aEdWXMiBXIKDEkwiTKw+A+EXg8iJ6TjKt4NyUkRJPm+/NThu4u76IFy5ahnh4CtKptROnhYJts2ZObujc360qycW64vH0ZoWF9ipeziISI7He2KYRz6NjlOAOB6P9/b0dSxYarfYtq65XHkBmaxJG4GyQSO9DJQtAG4F4naNvB3AN7Rwt+79l68qEe60mJCGGDPEFoslKysLL26/FhALguBU/ShSUPj0Vg/LMAyWS7ldhS6Xz2A0jLt7XN+aW9+SRxxQ0h7bepk+V6ionNo6o7GlrSkYigRDLc2hpkA4EAiFA6qxcXv7LIed1Bsn1tfXhQLtyKSxpa2puTXU0hBunttSPzswPdjaML29OdTaGpj/ELdq+x/WH/7F2sFhGwfBU/KFT4q6FfIF65LDVkCwRNY8ANOXDWoXbvtN5axMzsWydoayEjRrKSIZwmkknUZGb5lYU3rh4U1A2Xmxwi5Lap6Whr2CfIi1O6WfHZG7VbZCtZ0eGrnfcuT+kwFxSr3w4Ycf+ny+7OxsjuOwzhXX1ZnNZlxXV1BQYLFYcJiFy+XCRhM8z+v1qJ6kvLwcF6thX4WSkhLsSdygNswQ19XV4WK7qqqqRYsWHT16FPPEuMfwp37qHvOs2AoDH5BIJBYsWDBx4kQUTa7WoWMdMw7vwM5rOD3EaDS6XC4MNFPOa3q9Hmc4Y1U0RVFYblGittLSUr/fz3HcX/7yl9T8k3pwLu8p7snU3yJJksvlyszMxDUERqMRu7xh6XNubi5N0xgQ44QOp9P5+uuv4xdZuXIlwzCNjY0PP/zwrl27BgcHv9ob53JKQ8f8B/aAgnKeE2ogLJpAkbelhApTcUwsVhPL6Kl+KA/gEGgJRQBAWelHn85HFflQ/+DqL4574/fd7vP4vO5yj6u0tKS6oT7Q1obM/jo6FsZindHeeHdP36Kuvnhs0cLueHdXtLsrGo11L+pY3B3tOQ2G0ddoNBqPx78c0WVlZbMtdyp3nPfFhSOS2tEQ3CBrrpc110FwlagZq4DbJHA7BKNF7Q2HRvwxPMFkYB2Uk6DUWnWCIDDDhMvmvhYQ43p2XKnj5lgXywgMI3AsQ1N6fT5BON1Ig8gbjYa7cuiZOXd8TF8MvZqnPZeZjWbBV9USCEYiEbUUL4KcKVrCwTDii8OtMxuCAQthziowBgP1Da3h5tDs9uDUqa1N9ZGm9uZgS1NLQ7CtPjSjPtg2vWF+INAWm1WyrsO0qTv3wEO/OblmmLJ+hPiIBq4C8H4Al2kSD2nhg9rkg+kf9F20vnX09GK7wenLd/ictJe2U1lW/70Gob224OSLwwceTZP6zpcK05R8ALM14m06+dYfHXlxtYLMadGbhyoo1Tf7P/BKHvqTTvcAymlX8IpWkmF/Ag4mVCUUUkHB5HFkwIRuA0qiH/aLiphIKp99fOylL55pfWZ88dy7PBUVnsISn6e4yD15SsW0pobGGe1zF8zvinZ2x2I9WOHf09OD9RFnjlz8OB6Pq+4Ti/y+ovbyO09u/p74Atr9EF/QKi9qERp+VQdfA8kdadIOLXwDMcSH3/hZsPZ3Jr2AGWKCIOx2e2ZmpsViYRiGopAN1JlLWZ7ncdiWzWb7KiDmOO5UsKu70OXymizmu8eNX9D7p3fgjw4oYONbvzHmW91eJtCM6mYjraFIpB05VEQaWiL1LaG5be0tlZGCpyAAACAASURBVBU1kyZNqpxc2N4eQRqoUEMIFds2BwKzWgKhqTMnh5HuuK22NdgcaG+dNqt5alu4fubsUGPLovLokznLP7rm8f7vPwLB82L6s1L6E6J2XVK7HoLHJLC6f0TXK6P8tblOcxmVX5I7SbC5au/2N/z8vY1AflOX2KuRG/nkeasheDx54Qbx/Hckn6KcVNVsCopigUMO4qev8nP7+i8BiJExeCLR2Ng4ceJEbEzIsixBEBaLxWw2Y+8zvV5vMpmK1YYBscfjwcyx2WzGBW0ulwvjvLq6Olxp19iICuwav9Kqqqq+lAi//vrrKbc1rDf4hk5Lqm3mzJnYoYnjOI/Hg918i4qKcnJyTCYTlhGjgm+aLi0tLSkpwQqEvLw8rD/Ozc2lKKq8vBxbEZeWlqY0Hn6/n+f57u7ulJAj9eAbzupr/wvj2k2bNjmdzurq6tmzZ2OftZTzWm5uLjZOxqi9rKzMarVu2bIF1xRKkrR9+/bDhw8ripIinr/2Fw09OdQD59QDp4gKUZUe48ykk8dObtr/nnfr1pvWrLypY46luqyCF2o5X0VReUVN1dSWpsjsGTMX3DdfnS97Ue1Nd1TdgY3HuuIL4vHO2OLert7Fnb2L5/Z2LpvdNidisns+Hzdi4OLvS5pfSeB6UXOtAq5VxRJ/VMAfRc0YCEYpiCS+JaG9ZfGd2XbKbleNJrASMcUw4ZxkPIlidRa+x/MunmjPnFZZljWZTNjRSRAEq9E2IcdYnJu5i/uV7AXbvJfQDirH5iryFU2dOrW5uTkcVmfWSCQUCjU2NoZCIZZlMzMz/X7/mZXvoVOeboFAoCkUCuBwEPXJUHNzsLExUNs0b3rzrDmhqQ/M9G2Yn7sretMHvb84uvIi+ABQlg9TlgO4EsCHwcBD573WccXCmrv8jNli9yxuvOPhhrGH7r8A3q+BS9KVXg1sHyY6gTIBHM68PPmXdbKcTEBZJQXRe3t6qXNO7/PQQf+WPaAOT5TUfirXV0J6CXVBhN5+EZ5Eic+iIosS2uo5KckDKNdyUD4pffFy/+z7jlzecOAXNVtunrLCXBnw+P2Vfm+131tVUlRdV9sYDrfMnj1zYcd9sfiCODKh+GtLAeKenp5pwdkWfe7u9b9VXtRKL4DkywA+l5bcAuAWAF8bBl8HiTeGS28Mg29o4G4w8N73u+Zcasz3YkDsVFtmZiZWPtAogv1vADGWEdM0bbVazxy5eLdHEAQ1O4NA7shu3mq3jr87s23uxP2JH78DwQsHfmLNKyIcKB3B7WIqSidPq5nWEmoKNc0s89eXlbrLqyr15gKDka2YXNHQ3KS6zgRaWiLhUHNbsKauLRIMt7Y1h5pDM1uCs9qaWwLhUENruCnY3BQINzbObW6qb6oNtzTMm9ld1vGkrWfb2FVHLnlESXtYBqsVzVpJux7dD4u++7OWVTcWPnb7zUeJX/Zz1yTsV8hZv1YyfyfexMvaxQpYJYPXxJoTyieK+i4iATHisf4tr8d/4kn/kwEx3ojHVGg0Gr3nnnuwGgEr86xWq9lsFgShvLzcbrcbDAYMjlPOa16v12Qy6fV6n883efJkLKVgWba6ujoUCjU3N6dY4TMhcUNDA4bL5eXlTzzxBNYY/F06Fh/W398/Z86crKwsHODs8XgwpsRVrh6Pp7KyEhvBYEDs9XpxLR2+z83NtVqt2K4Ys8gYEGNk7PV6582bh92Rcc/8Y1cGBveSJB06dKi/v/+VV17JycnxeDzl5eUphthms+EzTDHEGzduPLMyL/Ui/9g5DP3UUA+c0QO44DkJkbJNRDt6iJMaUOARWfooKYYGBq449vkPd2y/fu26e+ZFDZMbvL7iOp5vcPvqC8trJ0+f2tTWMHfhfQsWxqNdi3tjy/pi3T3d86I9Czu6O7sWRaPxFbmemmX2m/uvGXlCO0wBv5E110raaxQU2vwHiJTEf5CR0cQoGYyB4A9JcPPyP0xwkDa7KkHEgDg3N/dLEf9ZdXVnAmKKor7KMOE51Ww222w2PLjsZtv47Dxh0t27Pb+Bpdq3Ki5zk/Y8O084CVzo4/ejUrxp06bh0E2Px5Ofn483bUpKSrAPVOh0C4dDwWDzab/jELI9RnXu4VCoZXbDzPbGmc2Btqnh9imBBYHmaFvjvM7wtKfn6aXlWmk5UFYAuALAZUBerpUfOP/44gs/7v5+coUmuVIDH9AlV4DkUo24OC0Z04lV4JOsCwee7ILKUbQzPqgMqPsAuFgSoaKhNtQDZ/dAEqmNJTigfPFW/9qnTpILEiPCkiZ85Keh126Ytu7uKXFnRW15EVNVylZX+qdUltc21TXPaZ2zYEG0K7awrze6pGdJLL6kd8nSUKQlJ2PistljpM3DlE3DlQ3D4EYAX9Aq24GyQyvuAHC7Dr6pVXYOk3ani7uBtP/8DatHWo0cMh8kbCRlIwhnTra+IN/C0AJFESxLpxhiXAzAcRzDMGazmWGYszAxz/N4/AoegfdwtJPIGm8sqpr46rHffKiAnR+fZ6e9DjtDUyzPczRNshwKE2EYxmKx4F+EXVkJNd22oaEB2xuHw2F1tAZb1GQfPK7xM6rUojEUakKB0sGAWp0Xbm4K19c319W2zFlqezx53loFrFXAYxA8qt7WKmkPQ7BGAY9JujVy2gMQLFfAw7JmlaR7WBm2QgabkgUn4SG0N4dE/0jhgm5DRXVnX7R/5/t/MiDGMBTfr1u3bsKECXa7HfukUBTlcDjwFYzlEHq9HusN0EpOteL3+XxWqzU/P9/lclVWVmJVMV4IlpWVYfPtr2WIcZxHfX19ZWXlsmXLsB3bN2Pi1DEDAwPPPffclClTcGRdsZqBbDQaMzMzXS5XWVkZz/N2ux3DzaKiojMBcV5entlsrqiowCWuuKIOQ2rsPtHQ0DAwMHBmt/ydN/Dr/ht7P+P/URRl+/btGRkZbre7qqrKo7b8/Hyr1Yp/LwbEFEWtW7cOo3AM/TE4TqWWfN3vGXpuqAfOuQdOMY0yhEkFsU2qTlHdioUyHEh+cPijadKHV8AjI2D/9weOjHhls+b++0e0hq4u9t9NUgzpDjBFoeppLe0ts+fNmxONzke6464lfYv7lj4Qj8zstJsnHsm7QPne+RBcBMFvFc3VIrhaAddDcIsKiG9CDzSjRI3qvwZuWn/1n5CTDUFQBIUBMY6zwUU5uFruLJKYYZivMkwYEOOlO65Yd9rs9+Zk2bPufKPwSlgJ3qv6WSllsDBuhkHmFRzHUWojSRK/GjaEys/Px8kgDQ0NkUgkrGbgqdNnIBRCEBiv8FWGGM2tQSxmDIXDiC+ubwwGwpH2+ua2KcFQMMDBZRq4Il1ZMVxenq4s06Fvl+vgsjR4f5r0IIBLLxy8H0gPpMElFypdOhjTDTYMPxg0SPKxk7L61ogwiVBwEt0UaQgQn/NV/p068ATKf0YM8qBaWCu/Lb766BFHR/+ITqiJQdCZBPO+0E1/44eFD11FR26lyvMFptxP1072T2+c3tAUDIRntHV2z+lbtISna0sdF3/8wq+Ul4DyYrqEQum0cNNwuAMorwPpDQB3ALgTwF06ebdO2g3gO+fvfnG4i7E6bK5TqgknW5BvyC/Io2lSVUywePGJF7TYWYJlWaypOBMQsyw60mKxWK1WXhB4t4uh6KwJBoIb+8oH134qaT744jxvudNhR94zPM+xHM1xiIG22+1ms9mNkAgimDHHLAhCU1MTTsJDeZatrciLIhxuUxse1+rQDoRCzTjPJxQKBgLBcDgSCkVawm0N01vrZk94UrxgLQSP/i0gXg3BGggeUe8fVnRroO4RCFZBsAKCdQOXfpL8WESpSaisWYEiMpg4JQT/Tl2T/79/7D8ZEGPUhf+IHTt2TJo0yWKxpIw/nU6n2WwmSbK8vFwQBL1ezzBMWVmZ1+vFGRM+n8/hcOBg56qqquLiYpZlXS6XxWLBtHFjY+MUtTU3N+PkRiwjxiQxfmbq1KmxWOzo0aMpOHimdBifG0aoqbOVZfnYsWPr16/HkuWysjKz2ZyRkcHzfGlpKTZQK1FbUVGRXn/KgThfbSaTCf85uK4OW0xgnhirik+cOIE1D/j+H3iHU2eLKiIk6fjx43h9XFVVhcML9Hq92WxOAeLS0lKO45YvX461wqmFwVAJ3T/Q+UM/8nU9oFrZIkyMVDmo/Fl19EIbr2JSkkQZnoTyZuXThhOvZxx7+XJp94XiZ5f1Hxv1yft3vvbyPX9eM+m+uVlTa+8pLCrgeY7nvRVVdcHAzJkzOhZ2ds+PxbwVdXMcYwZvP09KuxBqfg7Bb2VwpYQA8SgF/EFBmPhGCFCBXRLZrt0kgxuf/+0dtAqI6dOA2Gg06vX6FFqlaUQynckQ4wn1zNk09dhutxuNRswQUw7nhJxJxgm3byu8FpaBD8p/Uk7kGZwsRSOvRvwjbrebZVnsMun1es1mcx7K2kAlsNOmTQuFQjgmWuWW0IQaCiH9cSpyNhxCkdGBYAjV9Kh0cTjU0opSt5qb2ttKPWbpfq2yIl1ZnqYs08r3a5QlQFoK5GUa8X7N4PI02Hd+cnmatFwDF6Ur3VoYTz/edpm8a21C6h9MigjliCi7bAgQf92VPPTcqR6QYUJWkMhcViRREpNSsl85/JHyxIZE9dKTo2MDaXEFROVh3clfxD67ZuGePyx4dVxk/cTyhXf5p2UVFbk8rsLiwopptY3Tqmc6TPTz9185sPnH8otAfkGrbNLBTTq4eQQGxPKbGvgGUHYh9bCyWyfv0cADIz7bc36p32izsqpPDEk4eJPZnJs3iaIJ1WoCuaphIIGpYjzusJoxNWzxapZlWbvdbjKZOF4Q3B6aoiZlGqzkbdvev/ULOPzQ4MiKOqPdRjMqIOZ5RhBYQRBwni7HMxhvGI1GgiAYhvF6vSUlJbW1tY1qjG5bW1sgECgrK5s6dWpDQwPe+FFZ4b8udFXM3BIMhtpa22uqGz21Nz4lXfiYogJiJcUQ61Yp4GH1tloBa2TNGkX7kAQeVjQrZN3ryYqTykBSHlSUfkVKonJn5IA5ZCD+rUfrPxkQn3m+7733nl6vnzBhAiZ6cYyq2Wy22+3l5eU+nw8vwnARG1ZN+P1+kiRxglRlZWVpaSkOVbbb7dgiDQtzGYbB00zjVxquvauoqFixYkWKGf27QBBD1Xfffdfj8Xi93oqKCpvNNmHCBIqisA8GljXjEDiTyZSvOq/he71eX15e7vF4aJrGdsUYDRcXF/v9/i9B6pEjRzAwPbNz/uHH+FRnzZplt9uxGbPL5crPzzcYDKVqw8Dd4/HMnz8fLwlSgPgf/qVDPzjUA3/bA6JKNKq1O+hjWlKUpCwnFJiUIdImIuNiJZGUj4rKPuXI2qPPUYdXXX7iL8Ok7Tq4b6Ty3iXKp9ec+OyGA/vGvbgxd/WqrMiM8bx3vOAiPMWTycJqmnS8b/hl4sfDILhUAb+UNZdL4HKIfIhHq1bE2GviBgVcn9TepILjG3b9YozfbrMQRAoQWywW7O2PP3nOkiHiafVrt1w5jnM6nSlATDvIjNzs3Al3Pee9WfKDQ8U/qCWzLU6OpJCuMbWBi4NkWZbF21y5ublOp5NRG/ZzLC8vr6mpCQaD06ZNw4Y2ZWVlU6ZMwW7rKvPUEgwjQjgUbA00t7aHI22RwOTaaW6rfmCpBmHipUDpA8oiAPs08mKNuFQrLtdJy9PgknRpuQ4u1yp9QF4Eji0YKb0UksTjsqwoeCJFG6/qP7R6QdB4qA31wFk9IKLVLd6aRw7G6DpBayklKR07qKzbmmx49GRG3+BFPRD0KGCRol0i65YowxYnf7r009HRlzJmPDKxrvcOV0OWwBoWNt86sHkkshzeoJE3aeVXANyqg9vSlNeB/IZG2aWFu4CyR6PsAcoenbxHp7ytTXyY1jRlotViJymL04kAsdNpz87OJEmKokmKRtj0LECMrYjxvu5ZmJgkSYPBwHOC2+2lGHJSjqHA/Mft+zISyrCTsi44O9NuoxiGY1mGFxiOR69MkiQSUPGM2+3GGQXY5ApzT7TacNKtz+dLudPwPD958uRwuAXfTm/4BMPhUFtba3t7G8/5qfLrnhS/Coi1mA9eBbWPKpq1MlijaB5SwENS2vrEmAHlQwk55SUUaUBRRPSRikykUbXkUPtWPfAvBIgTiURxcfHdd9+NvXKxjNhkMlmtViyxtVqtX3VeY1nWYDDY7fbi4mIsRfgyYwIv+EwmE0VRHo8HO/JWVlamKu1SwLihoaG+vr6hoWHy5MlLly79/PPPz0UkgFHmoUOHKioqvF4v1g1nZGRgYW5xcbHRaPT5fPi0LRYLhsJYO6HX63FQMzaawHgUayf8fn9RUVEq3fpbvZH/3cH4VOfNm4c9OjCCN5vNRqMxBYgxqx0IBKSvS+/771556PmhHjjXHkDSNiQgxkV1aAv+FCwWUSG7hJI8JJhIoA0/iNal4mfyp0/2Pyt8vPzXR5enDTwMBtZrB14cKe4cobz7A/GjH5387LpPPsrYsSl7RV/WjNnj15TcJF+fLqWPhOC3oua3ouZyWQXEqsnaTWo2x3UQ/F5BlXajILgBgus/+sHNNXqLhSQpJ5Jm4ekNC3n/O0AsCAI28E8RvalplSAIo9EoCILH4+FIJjM3Z1LGpJX0H5MecMJ/QcCZaXfyDvKvukYMrx0OB8dx2IA8Ly/P4XBgWiu1w6t+yzudTlw4T9MoiaCsrKy1NRIKBWeEWyOBcCviiEPh8IwZrXNnzphP84WM/l5xqUbqA7AXKN1A6QFSjya5SJNYrEku1cL7QWKFTlkK4OJhcJFO7Eo79FAeHPwAIgs9KYlct9CbgUK5FcT+qYB4CBKf65X+3TlONVwUJQWlPaP1LV47yVCURVFJSDDRD1/bp7T/10njwwOXPiRrH1LAalmnal41a5Xh65MXPp744XPHbtqz+64jG3+G0jee1yob0pWXtMhv+HV0U3Zopd06ZZcG7gLy7nRlj1bZo5H3apQDGuVD7YLQn8wmB0mZnYTN6WAIgsrKyiYIhqYJkkIShzO9YvDW8d84J3IcHm5YxWQwGFiG9bg9DM/m5hsmTrr5lW1ZkqJNKmBO9B6blWQYnuNYnkeAGFvN2O12miG9Xi8ucEol5OHxi49hGAZri7EQFBf9h0MtkUhbpKUtpG71tKDM6sZQODCtbmpBvpmtvOnJ5A9OM8Sa0xpizRoFrEI37aOyZq2sXaloH4TgkcHffgwfl5FSIoEYBvRp2o/t81DOivTduR7/Z/7SfyFALEnSu+++S1EUthzGJkdmtRUWFpaWlhIEgTf6S0pKsIwYV6WYTCaz2ez3+6uqqvBMg480m80Oh6O4uJgkSZqmKysr6+vrU1A49aCpqWn69OmNjY2VlZXBYPDtt99OSSP+uz7GDG4ikZg5cyY29BUEYdKkSSaTCUuHMV2EESe2ItarDXsSe71e7Jrs9/vx8Zgk9qtt+/btGMVivva/O4dzfB7/Lc8880xGRobf7xcEweFwYGheXFxcXl6OT9Lj8dTV1aV8l8/xxYcOG+qBc+oBpGaTzwDEajU7wsSovA75A0FJgqrnN7JxUxBCRhUhn8JP1sFXp33x8F3HY9+X+tIGV4DBh0H/I2BwvXbgaa24ceTA05d9suKyY0S69L2RsvaHEFwuqvSwAq5QwO9lcIMCRkFwnaS9VtFcDcH1CrgOqs8cG359INNgpRkUVqcCYofDkZ2djT8rsNEEliHijxRM7loQhEbBsGc1XK/Dsiz6XGKYrLzsiZnmqPW2pFuX8J03l5rocLJWgki9VAoQMwzj8XgcDgcu6UtN4Viqof5Sl93uVJNoT7HLXq+nurqqsamhNdzSGmlvi7SHW1rKKitKS8smT66bmGdlrROV3pFSVCt3ALkTyHEgdWvFXp3Ymyb1psHFuv4VOtijg906qVN7NDpa+mxLQkGdLyEoMwhlKYnM1lAwt4zeIklVip7T+zx00HeoByR0bSCPcUWUESYWUXkAFEVFVdzApAgHE8oXCbj/Y2XJG0n3C4kbnxOHvyAP2yBpXxA1W5S0HQr4TNQm3tcqG9PhC1r5Ba28Sads0cDtAL6p3nbqxH0aZRfSS0i7Rsh70pS9AO4Hyn6gvJP2SPfNNpOfIJwEaXY6SJoUcnMKHA60eiTUPHY8SPFYYxikbUhZEWMofCYgRg4VNOX1uDieyy8wTph461+ezkvKGkkBXYvuwoBY1WsxvIDCnzmOczgcNP03gBi/YGqY4w8Q/JGCaT6MknleKCoqrZ5cU1dX39LSMnXqVJIi3G6BJB1ZmXlM+S1PiT96DIK1EDwm61RArFl7Wj38qALWStqHJd2Dsnb5wMVvyq2SrKaooOBQCX2com03tPmm2mIOUcTfbkT+CwFiWW0HDhzgeT4UCm3evLmvr6+goMBoNHo8Hgx28/PzMTjGUNjtdvt8PhyPnCoaY1mWpmmL2ux2Ow68KC8vR/ZFX7Fgw0bFWO7T2NhYVlY2ffr0Y8eOfbPlWQqwLl++HEuc/X5/Tk5Ofn4+BrhWqxUX2JWVlWErYr1ej5NH8vPzBUHAMB2zyCUlJUVFRSUlJX6/3+v1bty4MfX6/yPqBVmWk8nk448/vnLlyi1btqxZs6a8vDwjIwPH7BUXF7vdboIg2tra+vv7/+5i4NtdX0NHD/UAkuRjq2IsmcDfixgKIyISqpnPckIRUYwHIifRR4EkIZMnWZROiCd3yrvmHV4x7kTnj8VOrditkRcBMa5VolqpWTdADJOvBZLuB5LmElROh26XQ3CVpLkuqbleNZq4VgbXytorIaqxu1ZBsPja/rRrZ4zLN6tzp8PhwA7/2D2GYRg8h30VEKfC6s4CxLguh6IoVN7AkpPys8ZnOueYxiSFNNFzXjc3wUnQFsKWminxA5wj4PF4CILIy8tTi9ZPzbX49VV9hUCSNMeiCfg088RRFMHzLMexhcXFtbW1OACIYwmOpe7NLqCMOeKC88S5OnkOUOYB2KFVYmlKD77plB6d1KeR4lq5Q3Ok8/LEaz1JcSApiWjTW51TFVkRUWTzoIiYYqgCnaFi9aFhfHYPqMSwunRFitUkwsQIhInoO0VSZFGUZVERJTkhSrKifJ6Q/+vTZP3+wbFvJYcfgLr9EByWdcmP0pIvA3HjMLhJK70MpC0AbtMob2jkXegGd2nkvUDZjW7S3hHK3jT4FpB2A3kfgG8P27jmJ4YCu8oN2wnCSZGCvsBksZhoiiUJFLdxJiBW0yUFkiTtdjuWDqfuOY6jaVq1Inai6iRBMOhN90644/5lzgF5mKSAvhW32KwkRaERpzLEfwXEahyP1+l06vV6p9N5FiDGwxxn/QiCgP8XDWqO51CkD0vTiGzGOz8+n5ckndmTCuiyW59M/vg0IE5TAbEWA+JH1Eq7R6S01fKI1f2X7oDTT8ofQ2lQLXtF9a/qfg66V318RBQUOtS+TQ/8CwFijAIlSfrggw8GBwdFUXzvvffy8/OR2p3jsDihoKAAO6/5fD5cV+f3+61Wa0FBAcMwFRUVPp8PlYNynNVqNZlMNpvN5/Ph2FWv14uBbwoWY68JrKOorq7GrmRfbmJ+/PHH3wyIU9ztn//8Z5vNhuXLeXl52dnZuFINjw0Mjnmez83NLVBbvuqvxHFcaWkpSZIulwvLKlJ2E263++mnn/6fBcRnXg8Y73766adutxsn0rlcrnA4/NRTTx08eBAf+T+Cws/8pUOPv+s9gLbc1ZrnU14TalU64oYlNImi3Xo1F0A9RJaTophUEbSUgNJJKA9CKEtH4dEn4dpMOFsrzwNiF5A6tMkZmgEfgHdoBi4DsuaHSfBrCH6r3i6H4GpRc31Ce70CrlHFxNfKuitUQHwNBsSDab+fe2eukSQJlSEmCIKiqKysLLvdjsVa+GPkTAjrcrlsNtvXMsTYKYIkEV3kddE5+qy7M8mZxjGDXJroPm+xMIEgKQtpPfPVcHQW1nRRFJWfn48B8ZkoXJ2zOZpmSRLt/7rdbvV/OZ5Hq36Oc9MU63XxdpOZcRDFHtbNO7MKDJRBPzB7WHKWFs4CcA6AC3RSVCf2AHERusE4gHHNYA8Q52v7V5mlwX2SkoRJEaVuq9Yf6L1KylDqR1wx+qTDViDf9Ut46O8/qweSsB/l8aA1kyTBkyLsl1X7agTGVPdiKIpo7OJvcbqHdBzC1i/gxZ9BcAiCZCItuV0jvgiSW4bBl4C8GahiCY28UyvtSZP3pMHdAO5BaBjuAfK+Yco+HdwLlH1aaReQdoJPdv+C5a12FOBMEaSdcDIGg1mvL1ABMfJyOWsoCYJA0/TXAmK8w+N02pETsctlNlrG33vX7Nn248kRIgQPPvIHu5UiSYblTmmIcSWAurPk9HqRHfJ/B4gFQcBZP6dXs2hli9G5C2FvAYMTgiBcLhdJkiogHvNk8sd/Vt3WHpPTVUCsOxMQr5HTVokjXlImHoPPS4inP3YKEKtdjT5RVWtLdeSiqMGhdu498K8FiLErQur+8OHDBrVhwUNxcTHOdi4tLS0sLMR1dUVFRVar1WAwYHVdYWEhXhrabDYMiL1er8fjMZvNubm5Xq8Xe7HV1NTU1tbix/X19RUVFZiddbvdX0a7vfvuu9/cgyka9dlnn9Xr9aWlpTRNT5w4MTMzs6ioqLS0FNfHYDWCy+U6CxBjrwyapgVB+CogXrduXer1v/k0/oH/xY5siqJs2rQpLy/P7/evXLny0KFDGOLjZcDfrSn8B37v0I8M9cC36oFTMuNTibIychSSFCh+9sVnWz/ZWDE47yeJBq1cpREz0pXLRsq6kbLmZ6LmN5L2Ugh+A8FVEFynWhH/TvkrRL5ejay7cTD9Kkl7o6Qb9crY21ys2eR0OGmrlTazTqYg564CJ+MQSNrO8BQtIFKW41jOxfkFziNwHEE6rPY8li3kWD/L8bRA0QLFs5xAuQibibBz0n9GPAAAIABJREFULo/Aub32XP3tk6zTzWOOMMMld/oqz81OgrYSyGsxhYlxdBYmlUmSxEXAZ07hqSMptWw+9e0ZD1wcJ3AcZ7PZnE40MbtdnpycAovZmZh5AZyhSy4A8gIAFwA5ChI9INGtE7tGKF0ALtTIC8GJ5bfCk4eUhIzUh3BQRihmqA31wDn3ABKsQrVaFi+Z0FhV/cZRwYAEJdUIV7WhkGT1CyIwkb5C/iCRnCkmRyXf18nPjITP6ZRNAG4Cyss6uG043KGV9mgT+4Yn9w5HgHgnkkzAvUDer1Xe0sA9GhlhZZ28C8gnR7QunGi1mgi710nqSdJuMjryck0EgeSWeAo+a0B9LSBmWUQnm81mq93m8rh8LsFqst1774Rp07hPTpyfgGD9M6NV2E0zLI2L6lKAmCAdHo+HJEm9Xn9mDUBqkGJATBCnPMhTz2PtBC77czqdBEF4vV6KonJz9HTpmCcSP/kaQKwMe0RJX61oHobg0cRlh5UNSNmkSINoLau+GfAESgjFUrRTy9tzfjeHDlR74F8IEONdUozMRLUlk0mXy5WdnU0QRElJSXl5ucFgIAgCuzfgGI7i4mJc44nlxdh5jed5h8OBC/JwgbbD4cjLyysoKCgqKqqpqcG0cWlp6dSpU8vKynw+n9frxZjY4XDs3r37m1nSFGB94403cDpOZWUly7ITJkzw+XxVVVXY3h9bFHu93ry8PFxXhxlikiTLysrwhk6KG8YyYrfbvWrVKgxM/y5L/Q9cw1j9LElSMpncunXrvn37BgcHU7V0+H+/+W//B37p0I8M9cC37wGc3oEmVxkmk2jTBKJJVjqkyF8MfrH38GqvaP9R4iIgaTWy5meK9koZQeFfK+B3Irg6of39oPaahPYKCfwOgisguFLRXCVpr1Ur6m6FYOyg9peK64LQ9NvybCzpYCg7z9CO/ALjpGxnvsmba7KZ7CxH8W6Gc7E+l+BneYrlBYJirTYjx/oEluNZBuFR1sOxLo5xOe0Wp9PsdhUzHl++xTQmkyjTZ/RTIyGrfcTze4o0mwhkBZWaETEhhAExTdN4K+zMA1JHYmtkPAennlTlxW6eRwyTw4Gmf8RNuzy5OXqT0T44czicBcQokLo0cocWdgJ06wBwIZA70+U5mv45Pxr8ZDMUTyhKQlQL6VSS6du/S0M/MdQD594DqiQ9CcUkQs4D/UcfPbZrfP8LaXCDVn4VKK9plB1aGRlK6ODuYXC3Fu4B0h4N3JMG9+rgfiC+DZJvAWVPmrJPK38ClCR4dP3YCVmMs8DNOJwW0mOwmPKy/sQ7HYzdSTtsPofJzpX62GyGLyVcfspVKNBuwmJH+ysoc93DMm6OZ1nOznO008aYjGav2+Nz+YxW2/h7J5SXOF87ftGnCvjLtutsFpZw8Czt43kXxyHbNV6gUQKXg/d6vTRN6/V6m82GybgzBynP8zhR4awn8be4VAADYrfbTVFUziQTWfjHpwZ+/ZiCXIcfhWA11K6C2tVQ+4isWyvrHpHBKnH4m1J7Qk6okdpD0/W5X39//8h/IUCMdQIYa+LHkiTt27dPr9fn5uZiQGy32202W1lZWaquDjuvYSK5sLCwvLwcLwopisIFeSzL+v1+nPFhMpksFguuXcN15ViF7PP5/H6/T20URT311FPffJWlki8GBwfXrl27fv36zz///IMPPqisrMSRHFjEjIOdCwsLsV4iFVZns9mwPIOm6a8C4kWLFuE//5vP4e+/t193BBZqpzpZREIvJB5MQWH84Ot+dOi5oR74P+wBLK5Au/gihAkJiRNR5Z2qTlR1FoNw4IPd71YXHf/dFWLaSFH3YwlcmkTqYXy78pRSAlylSop/BcEdibTrVPL4GgiuGrjgAlj8gwebxuTpC3INVmu+y2Tlxt9LFXr+GA7kN7Zm1jfleHmOpYqQVsFDsbydF1iW8tpMLM+RPIu+Q7CYYzjeziJjG8HusHo8NF9EFliMd+bn8nmZHxA/hxx4wnOdQNmMFBJjpOZFl8uF7Sw8Hg/LsrhY4mu93rCm+WsBMcehKdXhcNjtdgyI83IN+gLL8VnnwflA6UKYWI5q4EItnJ8O56WhJ+ePlFo0AzsWIydoJGSRISqKQo+H2lAP/K/2ALKgwLv5cADCE6h8oF9Kfrrm+J5MZfN5yhsguRuIuzXKXh3cA8S3QHKfVtmD6GG4D4j705P7Rkq7R8A9GuUdoPRrjkPNW8dHVj92g7Epw8wwZZO8jnHkLTOjPzmw96K39/528xv3tK/xMmYf43PxPpfLKbhtPEfZDSaOJklWcDK8DxlHuBy8j+dcBrZAb87jvQJd7LIQeRPvuddpKHzvox+/pwzb8s6FDjsyeGFV40QVDyNATJKk04FsrBiG0ev1qdSP1BjHD6j/x96XwElRnO2/3T27C6jop2KCmi/mM8b8jdGoMfEWj6Dce19z37MzuyACaqIGr4BBo4Lcl4gKiqLcciz3fd/3fd8s7DUz3VX1/lNdy7DAgmAIK1D9G5aenu6u7qerup56632f18FT/5yyUXw9hRA7nc4WzXKskQcnEh5UNwphLEkx4+q41sRopo4gySNpnRnxv5ThFipejXiMG+rlcoEQ+AkRYmEbFiwwwQgppStWrBCq10LwLzs7u23btq1btzY7AD/XOfJ4RMhaMBh88cUXhb6JcCPOysoSNhiRWkboURQUFCQiyQKBgODH4m8kEvF6vQMGDDhHMpqwrQpr7tq1a4X6kt/vT09PDwQCbdu2jUQiaWZujrS0tNTU1BYtWmRlZQllZYfDIQzDghYXFRUFg8F//etfIiueAOQCPeiq0yT4bsLInSDE1ZG/sIXKs0kEzhuBKkIsjuNROzoPaeeWJUJ1Yto2K7iWm45lhw98M3rPQ8/S5DpMuRqhAcIvDeU3ceUOxm3GXJA4pt6LcCdTf0249MRvGdym35bE3obSr9QNq26ZvujeXn0eKgo78lqmrllzo06hHKGC1f1y1BMZVrvTEwoEfB6PPeDx+tyOnOw8t8fl8PEcsTzIxhn2uHwenz3P6svOdfmDHl8g4shyPtEiM+25Zmvdd7IIzA78IeLMz3RmVyfEgUBABNOIN9hZCLHb7bZarQmPw0TP6vP63S7OiP89qSWCJfy+YKuW6a1aZu7/4Hrso2FPEVQHtLdGe9bBj1PIx8Deh/goa5xSZpRz5SxTEI9IgabzrqDygPNHwGzUIr8dd64wJ4H4iIxEK0tXl+99S19/E65XjI1qfKPKI+fWA11fl23mhJh/NnGJCdwK9HAdg9Q7zGC/AcsYvGrA4xV1gqtuaDbud7fv+kQxjqi6DpX6bSMPhvNywlZ/vt9r94R8br/f687MybI77D630+d15QXcNr/H5/QE3QF7vis9Iy0Q8IQCYXeOrcnjz2f4CvseuuYIhTkHrrPbnfnWDLfH7vVy0zKfmfG5HA6b1coTtns8ntTUVKHJeIqThnBctp2mMFOdEItke4JYt2qRmxv64/fGdZwBo+V7I2U8gbEkeQyD0VQdQZSxsd8dYitN2Wfu4k+4Fsz5Pwh5xBkQ+GkR4hovkjG2ZMmS1q1bv/rqq6FQKDU1VVhehYiYyA6Vnp6elpYmRP6EK4XX683Nzc3KyrJarQUFBSIbTUZGRmZmZjAYFJIOopspOL4II3EgEOjUqdM5EuIEiTcMHohNCJk2bdrEiROXLVuWn58vfIXbtGmTm5srNChEsrq0tDShLGGz2QQhTvwtKCh4+eWXDxw4cO4XUCNocqNE4JJGQETZmS6JZowOf+vHKfdNpHHkmgjIMwrzJFmoG7xH0ONLX//HnroNidoQlQaoNtBTbokq/4vwOzOW7l6m3M4di7XbKPwvUX5uPJIUf08l/S24sC5GkxHhaBlsXFY3Xqqgfg1W1EMjefbyW9NapuVmFuXltbW6XQ5vocsXTk2zZmeEs22t7Z42Lq/T7eYB7V6fy+7JycrLdQfCvkA4z5Hx9PPNg2kP7n3jGr29sir0m5dt6dk2Hl6ToLMiWV1+PvdBFGLq6enpp1uIRf8qEtonjhUrfn/Qa1qIbTZbdna2KbwTbNkirVXLzAMfNcS+yfiRyt7lPsSx7kB7K9gV8EPlSNefV5TsQqMyyrhkFhrcRlwV8nhJ1xh58T99BPiUBDMj8CpEAxbxtFwKQa9kjERjuw5uaW2suQHXJOFaC67npuL4FqCbBCfWcEtSbA8Qgw9ZD1FlJ2q7aMrEGLweAw+DLAZ/idV7MPqL38azbirvAzsW3PzR7KfdnQvyi8KOTJ8ny+91ZKelZtnzfA5Xm1xX2G53+Wx+l90T8HryrZlpGX5fOBCIOHPzH83Kv3fcF78yHpmJ9dcf+h+nw5OXn+PxOExCHPLyGSKXOaDNEXzjLIRYzPCc0njFV2EhThBiPlPUMjvb/8A4vcFI1EZh8jiqjGcwhtQbyVKGUxht1F0T+1ynPAqZ6/BgnAdXSO//C1fzLw1CTAiprKwsKSkZOXLkX/7yl0Ag8OKLL0YiEZ/P5/f7w+GwSH4hBB+CwaCI6BTZxqtsJ36/SK+amZkpEiwL4TMhjJ8wEhcUFAQCgZdffvnc/XcT1VEYX4XBNR6POxwOkWMvEolkZGS0aNEiIyNDEGIRzda2bVur1SoScyTsxJFI5N9G7l27diUMtxfuWcszSQQuGQQoUoOHqXMvCZ4qjUeKcF0nijx5DDG1iil3fjV0g1uOmW7goaOkz6Cjqbm7GtwRV2+KaT8n6m2o/IbBHUS53dC4ezFTfsHgVyTpJtqqrvG+SnpB/Dsg6y14zMLKU8j+hvRAMt2ucFfFQ8mHS34xePCf2xQ95PE1z3M3z8h35eS1Tm2V7vY9ane1zMrKdjgCThfPJe3xeGzOzLSsdFcg5CsIp9nzHn/yoc/bPxD74Cr2urK58I5O1uez8rmPRKJfFNlfBSH2eDwicfT5EWJfQLhM2O32nJyc4y4TaS1bpO//4EbsobEuKn0TWBegvVXSnTsQx7skV0wJMqMSDV2nvFNlHEtTYE12q5dM47hUL9RM2x6nVKdU59ntKP9HuG4xo6b4OMMKSmfq+zvGNjTVV/6SreWJOch6RcgP080K3aYZB4FSOMpgP1P2MHUbVZayOp9RrQMBK4NsvKoVa/AXcs9Dlc1+U9n6TxsHPDal20Mf9XmiY7cn2v0zzdumqaPto+8PfaTzZ83adLK5/D5fnt/l9noLHPb8jIy0oM8T9nts2Xn3v/zaHzYu/h9yxwdG8tKS61wujzXfYTZPn9cT9PLcHC6n02W15ong/rS0tDNZiM+LEKe1ys72PjAudtNIVEcyy1iijKUwmiZ9x7TvWPKEykdLcBXhLZe7bXIDAQ9klOGwF6xFXBqEWNwuY2zp0qVPPfWUz+d78cUXhYOBz+eLRCL5+flCt6hdu3YFBQXCrJKfny/UiEOm0ITVas3MzBQxcEIcTSSzEeF0CZcJoWcci52HXolwzE1Yi4VKg8PhaNy4cVFRkcfjee6550TaDuE70bJly4KCgnbt2tnt9kT2ZsGJCwsL/33Ijh07Ejz7gj1qeSKJwKWDAENCMWbwj8HFv0RqDzMbFtc3JQbhKSV5FgCd8G7BoMSgBAkxSsrI7MXxtq/v/d1jZZaGDH5GlQZxy8+MpF9SLlT8Cwa36DfWw/DVRmeF9Yb4QJWMSKKLUsgGMNYCXVOPLLSQRcDWqPFtSeTIjSX779u8+akZCx4b/PVjHdo9/t4/Htm64+G1a5/4ftxTRWGr0xOxe3weX9DtsWVk5roDIW9Bm6at7C/lP7j9Xzdzo+w7ysF2t3xiezjTysNrEoTY6/W6XC5BiH0+X2ZmZlpa2ilaUQmpptzc3DP4EPNsHQlCHPAHU1tlNGuauu+D/2EfavRNjfwNyN+BvaeyjywVndXY4OdI5SYSj8a5VqzBe1NmxDFqUDnxeum0jUv2SgUhRu69zkVNCBcGE+rFjA9ueW5ExvPD0ArG1tPKb+JbcyrX3YZrFbZRYZuBbAa2W6FlEGNwBGEfwl5m2clgE9PmMnUMXvWebrHqYGNKPlFzmZLJtMfxzj+RRveXtPvT9g/vXvPNfaO/vuPzCddMP3bt7Phvx+xs9dJ7hXlZHqc96Am7XdbMjFSPNaeNPcue7f3fr7+tf2Srhf6ssQGzK67yBfLycrxOh48rEXt8Xq/D6+UzQ/n53DnY5/Olp6dnZGRUb7+iwQqXCbvdXr3hi0OEeVi0X5vNJqaM0lrm5HgenBi9eSwqI5kylnHBNTNBnWVM6SM7cQyhceTjCM6ITSl3IpvuBWwQlwAhFq4IgnRu3bq1cePGQqXhhRdeCIVCXq83EonYbLbU1NT09PS2bdsWFhYKQixmEsVkolC/z8nJEbVWJMLgMkhut8gblzASB4PBSCQilMjOBehERBrvtY8HqDHGvvzyS5FTukuXLh9++OHzzz/fokULQYiFAFy7du2cTmcoFEo4EAsJtkAgsH79+gS9PpdrkPtIBC43BHjfaJAqTmzGfHE7Me9HufQ/l5vg6SOIUH4y4gbGDR5wJ7LcMYwbZPMO8kGvbfc9tPuqG3W4iik/Z0pDQ7kjnlQX/5TEXk7RO3H1MdpP0XsrxtcqXajQ1Ra2KJnNTSJLga0Eskw1VmjGJijfBLFDWrTimqN7f3dk/a9oSR29RKPxW7t0fqhJy7xsR9jpLfK4g62aZ+Xmp6XnZb9sS93wzh2xrnXJB8DegWN/vXaU695026mEWCS7CgQCfr8/Ozs7LS2tevbX6j1oTk6O6D6rb+QB727eGTscjpwcPnXr9wfT07KaNU3d9dH1+L4FX1fZX8F4WaGvavG/p5T2vcfYPcMwdEZjMZ73hKc9MfiggkfWSjPT5daCfoL3Y7pM8EkeNKcnzIw81ExuR1E303lwWzHjlmPGPWTpdhr9gm7LiW7+TWx7HX27Svdrhq6VmjLGh0nSbmbZwWAHU1ajuogoM7FOZ6ra4mCjWj5R8khKGkIWszRFy3N448Ps8ftigTtKX71x9yfJ2+fW2bznt5+PT/W8lu0Ku+yuSGZaq4zMx1/t/tQbfR96/7sb1q1TK79SyTX5FBbFk/72VkZ2hsdhN5UY+YwQT8Pu5rG0+T6fLxAIZJhL9fabIMRut/t0H+IEGxaEODFTlNYqL9Px4PToLZMQxjEYh5YRDEYSbUL8of04O4qlPAFKFc0wzQR8ICHz5lywun5pEGJxu0LboVu3bsKNWBhfRVyaUMbOyMgQMsAicXmVuKDZlwSDQYfDkZubm5GRYbVai8xF1EVBhRNCE6FQyOfzLVq06D/BWFzqpk2bFixYUF5eHo/H33vvvWeffbZp06ZpaWlNmzb1+/0dOnTwer3BYDDhLyHcJwKBwNKlSyUh/k/wl8de8ggw7m5o2okNrjcvYuxMd2JznZm6xLxnMJmcqXBq5vXg+5oDU24D1XXcvRuHDd/nLdh33c8qtJ/FlZ+XX69i5lXkb6reGVgvDfsB9gM2QCWjNDJPjc8Hukhlq1S2AthyoMuArVKiK7jeE+5V2XqFrVPiq4GuA7IlacPiX/fv/Wi7Do/4PKlZLawvuppMG/HQjO+e2tz1HuMdIP8E430F31XKX6o7zf27ltYiu93p5XrGLj/XNvZzQmy1Bb3BkL8gO8eamZqa77R6PAG31xty+TxeLuvm9QR8Xl9OTg4XWPN5PT6Xx+s2vTR8fq/X4+bWKo/DmZuV6/MHfUFfenrmc81bbvngZ+R9lb4O5BXFaK8YhXCk0yN4oJgYpnnJlJcw7e7mZLXAVs67XvJt5qd+A2YmHu4BZWZFFNnUeHs1+Oy/yYFNJiyGZ4xwUzJ3mqKbWHyEfuiVyl3P0LLrdWopMQlxCUnax2Anhe1M2ci0VaguZjANk7sYmicONqJk03r5FDKZJZ1aMjkz1tJZUlOs24j85p5Yk/9XEb7j0Nt/XtLnz2M+vb/bx03Dr9/3t14/n3zo+mmVKbOiSXu3QbTglzRpMNPmE8sH/Z5PbZlrzXe6XE4zsYbb5/N7Pd68PD51EwwGhctTdb3hxJQO96c6LajuFEKcl5cnXD3TU62t8u+fUX7LdIRihAmojkMYU3nTdvqdbvDxvmFmyeaDBw4jmknWZVTdBav5lwAhFiZYcceMsfLy8g4dOqSnpzscjvbt29tsNhFknWYuQmhC6F27XK7s7OysrCyXyxUKhdxudyJbRyQSSQhNCAtxghCHw2GhvPbjME5creDE4itjrLS0tHPnzk899VSzZs2aN2/u8Xjat28vjEOCByf++ny+2bNnn7sT84+7TnmUROAyROB4OryqVAGMcROzjjyL1ttvVtT/X0OpU3YzoPf68nbA/qWxjzkbxgGA/TT8wmIUQ2wRkCWAS1W2FOhyIMsUukqNrYPYdsBdGq5UcZlKVwJyogy4u45x5Madm5uP+rrDy6G/ftE5DTepxjQwPgD972B0BPaeRt6G0tZJxZn/91zOCzaby+cOhwPugN0V8gQ9Hq/V6gy5CsL+woxMW3aL5un2LK8v4vJ5i+x+j8/u8fh97rDf68vNzeHmYJ/b7Xd4vE4eS8edM9xeN8/pHHS48jLzvf4CXwH3RX6mZbNV/7gp2gXoW6D/TTXagh4CLP4AaZSPH+QiEaglBAweEWvWwWqZKfmAltdKgyA1c6zxpO6mixSPHDNTWUb5RBA7jGwAsjsNqpQgHEbuNXGAwT4DNiNsYrCaqsupsojBKLQUMshFSyYmuwzIRMgmddNQS0NIR0gn0IpABlVzjboPsP+7gfluKB/5i02bmoze9PtR+5LmVyjTKpTZNOnQZoh7HsekSQym0jo9vng6tVW20+n2+T0+H292fl/ADNzPcblcwkLcsmXL/Px8MTstnCIS8zmny65VJ8TCVCcscRlptua5980ovXUWKtNMTjweYT7+qRQP8QBEAw3uSxblfthVaVBiXHlHLhcIgUuGECeIpmEY+/fvHzFixJw5c44cObJy5cqxY8cOGjTI4XA0bdrU7Xa3b9/e7/eLTDCCAdtstoKCAp+Pi4VmmYtwVCgoKLDZbMJHIuFDHA6HbTbbsGHDfhzCCQYsPCgSumyMMV3XO3Xq9Pzzz4tUIyLRtMfjSVBhsVJQUDBq1ChJiH8c/vKoKxoBs3MVCWW5JhEhXOvUTMSmI8bmrN1lyzz2/HXEBdgO2Fsa7Z2k91eNARYyCPTBoH8FOC2FLdL0xUCXamyJxhabduJ1QLcrdJPClqm41EJXAF0JbK3C1tWhG1OMXZbK3XBg3TWHZ1xjjFUqe1uMN7jnLumYhP9IIS9B7IWkJW1+n28NZue6rV6/09fW47N6fU6X25djTfd68iO+sCM3r0nW0950p88d8vqsHk+B2+f2+nP9/ky/152X4/V6Wnt8XrfX7+G2YqfP7Q+6W3vcAc6b3a6s7Dx30B8M+rNTrX9plrny7ZvZP+rTdippk4KhlMp3nqQVu5HFGE/5JxeJwKWEAFcvFpyZO80eixkDjhi/O0jrHjAsB5iyj2q7iLaNqptRW0O5CtsShDFE/Xs8yWNYslG1M8hnnBZnIKQzJY0B/xBLupHUEqEpQjNieYw0uJ0+fYvuv+7AKHXrFth2yFIxqT7+X0+97mwDvka4d4//+Tf6B12vh8x5m4Ar7A0EbQGvK9vudQad4daZrdKf8tutHdrYXR6vyx3weD1OnzMQKrK5g15Hni0n4PI7fSGPJ+g381+aXNnn9fi9nqDD7snJyQ+FuBZWeqqjufWBGeUNZjOYhTCdauOYVmLMpYxLccjlv43AJUCIE84DIs5MMEURuKbrugDo4MGDkUjk2WefFWZjIe3pdrtFPg6hvBYIBESYXWZmZiAQaN26tXA+DgQCEXMRnLiwsNBms/Xt2/dHQ5/g7okrF6fSdf3o0aNpaWmNGzfOzc0VCahdLtcphDgUCg0cOLDKa/5HX4Q8UCJwpSLAvS0QzbwTZvyJQZhOKCuvQBqnjJUePDr7k/I3H6CvKcYHKu2r4kAgA5Pj/a8hA1U2EnCORheqbAGweYALAJea+WM3KPoyoAsUtkCjixVcUoct0ugChc5KITM0MhfIfMBxSfTjerQj4CvJ+FoKfUNh76istUZfUnBS0tbdV3/4rzyXzW2qmdp4rLrPk2fP8XlswbAry5mbmpk55K3Hrc68sL3QGcx1cWcIt9/sMoWFycOzZAU9nqDX4/d5/H73Cx633+1z2j3etNxsZ8gVCYRyU23PNE9d1bGB/lYya5eMPq0y3DC6fYHOvat5kMOVWinkfV+yCHD7sSmlwBPd6dxMSo7pxsgyw7GHpeyk6k4G2xE2o7KWqcspLGbafGaZiEoPwxKgioPVc+n18hBSEdJoUjpNaoHQAiGVQRqFpqg8h3WbotoCoQnCo2i5H2+8kWRfZ9is9FejEGYQ9WPjOu3wBmUF/t/Xqz0ue7u8VJ6b0u0PuvzZzlyv1xr2e/zPPHPjhJdyxn6U63Lb/f58f9Dqj9iDPpfX6/f6nbn5Tr87P+h2e91+t8dj+jxxK7KHf1wOZ66pEuPxuHNb2qwZD04/cut0oi6kdUYhbNPf53ct53YuSv299Ahxgm5Wj2CLRqMvvfRSo0aN8vPzRc4LkURRmITz8vKEnlp+fr6Iq/P5fIIQi/DPSCQiRIiF+4TL5ercubNhGAlzr+Di5/hEql+h6IESl0oIGTJkyBNPPJGeni6kiN1u9ymEOBgMdu/eXRLic0Rb7iYRSCBQ5UV8/DsxWxF3JkYzuZ1uTjcixpBRvaRseb+K3nfQD5LwYwX7AOsPOEDBwQobBzgbKuYosdkqzgNcDLhCpUtBnwNsloIzVTZHwRmAM4DMAjpToTMtOL8unWqhgwDfBeyQgu1S2MuK/hrQ14G1S8JOV1cMBWNr8r6SGwP5L7ZM65Dv8Pjs4QKX255lzc0syszp+Gijl3u1e3hGt7sa57zk8dk8Xq/fHQzQR2MKAAAgAElEQVQ5A0HeebrsdptpUuJuw16v0/zwPdzcXhVyuINpeRnegLPIH8rOyHqyVbMVr9wafceChRBzQsX4ToRFuVSTGcp0HBv5v0TgUkEgxjPwmC6zlLdgnZnS2YTGDWPJfr3JNqZuY8lbEDYgrGDKIqYsYbCUwgJi6asrPgpWTHawejaWnIOQwyCHQgbVMkhSOklqjvA0qn+hddN0aEGgGYPGTLuF2e+Jp31KtZkMprJ6+frNdbcdgdlYd1rsvm4jHAWRQmtWG3tOG6fNl5qX53a0zQ492avt1XrXB+a9mZ2VHnK5XV63O8BHtC5vwOOLuLPs3OPJaw+6HQFOiHkz9nrdPjPtu9thy83O4lY8nzczLa95xsOzD9w5h6oTMXk+eZzF4khilZIQX5TaemkQ4h+EglI6aNCgJ598MicnR0gUC0IsLMQ5OTlBc7Hb7dnZ2RkZGW63u6ioqLCw0OXi8toJQiyMxD6fr3379ocPHxY8OEFwf/AyzrJDglvHYrG//e1vTZo0EbF0wkIs1sUl5efnv/322yJZ3XkR8bOULn+SCFwJCJi5iE/oJQjfCeFPbGA5NbtVbjTmcTxIKCGlu0uXfFA57P6KrvXpR8D6WCo/Ty4fAXQ8YLHCpipkDuAiBRcDnQVsKrBiYJMAiwEnAU7UaHE9MqMunavQmRAfBaQ/4PvAXlfpX8F4BYzXUtjfr2JvAvYCfQjgBMA9yq79DfqOfqj1a1ZHntWa0bZ5c7/rrexXBj41uNfdJSPqr/jsNnt+2Or2cTlUd4Hb63L5nF53kS3f73TafYE8jzvg87R285404ArkeX0unyvk9QZ4RvpAMBIIpGfnPN48f8mL/0v/ZinzQMmH2WgcNqNvYojHpLvhldAKLrd7FDM+GGMYpcwwsyrGKVYQzot1ZmBcX3DAaLWZ1d+IykqERUxZSLXFqK2gsIApPQzNg+BEcDP+yUEzzI5prYSdmFhaMmjKlCYMmlBOiH/Hfvkz+m4eaToeYQ6BYRRuo7+FQ0dg9TGYV6rM0etMj98yZOdDH454/uU38wvb3/zNgXrjjyUd2pBE+yWX93h8bGe71+N3eYIer9MXCjt93oA3Ly8/4AyFHQUBV9DD1SncLp/gxB6f1+Vy2rNzsvyhoCfgz0zNeNr6xy9L7phKYRnWqTSWRNFU3mBll9tj/UnezyVPiBOcddmyZampqWlpaZFIpE2bNh5zSSiveb3ecDjsdruzsrJEImghNOHxeIQYcDgcFrQ4HA4Hg0Gfz7d69erEyf9zYioIMdchJ6SkpCQQCOTk5Ai5Jf/xRWgqv/XWW9OnTxem5f+83J9krZMXJRH4ryBwioVY9KQigIdniOVUmcfrcIsxz3HHldoMhjFixHdNK/s+O963DusLOBDYEMUYDXSayuYk4QKNzgEyCdg4oN8DncCprTEFyFQFp2o4Bdj3ChueRAZprCfgR0D+laS/BfG3gHbiH/09hfatRwZcg98obH59tl/R9TqH4pZVu27+fsEfBk/+5bLyq/cfq2fMtdBRKjmUsn7lrY6gJ8/h97o9roDX4ff5g16nK8fDzcERn8fv9jg9Ho/b53T7XVwC1e32e1256fkF7nAo6E/L9jR+zr7joxuir0FZr1xj3yZDZPSjJI7xE2OF/wr88qQSgf8GAjzxjmi7PN6Oj+pEbCj3iDenfiq5RgU7fCz+1Sb9geW0ziJimc+SZ2HyIqouROhJIEQUBwUvKg4GeSwlm3HdCR5mR5PTKbRg8BfUWhKlqXH1daTPz41eH2KDYqbMoPAqhTr0WWUPwgadeyjPQ2UWarN1dXYUZkVhBmpzKy3TEcp2a9gF6Lu37+uV77NFcvKsnjyvP7/Q5XSHAmn5uQ63s43VFXF7bEGvI+Bz+/xun8/NowK8DrcrMyfbFwx5A8HMFll/bP/OzUf/5tNzj+JKZDyWDrnYunR2+m9UrVPPeWkT4oTtllJaUlKSm5vbqlWrSCTStm1bH+8p3E6nMycnJzs72+12RyIRr9ebk5OTlZWVm5srUmD4/X6bzVZYWFhQUBCJRITWRCgUstvtU6ZMSZh1/3NiKs4g1JQRce3atR06dOjUqdNrr70WCoXat2/fp0+fBQsW7Nu3T2SBThDoU5+Y/C4RkAicFYEEDz4hqcCD7biBmH+YTjAa51mgubKbGXXHxTzjSEq2TN3+ZebRXvW588OwZJxwDc6qQ2eq8WIgYwHHanQCkGJgxZo+FegUFYtVHAf4NeAnKvYA7AbYVSM9gX4ARlf+l/wjKdotiX4C+AnQbyG6FHAr4N5rsCwJUUVMRlIf99fFmVfhECCTIVqRrB+pHw4937JJIfcbtnt97qDb48/Ndma0crttL3hd/iCfig273EXcpdgb9Hhd3Bc5M8/vdAcihWlpXm/mfZWj6+wf8jjbvwf1Sp0aOne+5D2qSNN8VvDkjxKBnxgCnAoeQSxBFjUrcZXeGDK9qkrzdq4jHmUsqpOK3RWfLo39ZgFT56M6Hy1LqTqLKAMMpV1c8RiqnyluqjiI6jA0m8HtxC0RWhpKc2p5HuvfGH+hLlvSKJo5EZUpMRiJ8BS9RqMZlr26tiEOqyisIJb5umUuwiKExdyGnDy/EpagpWSzhq8AvnZ1/KN6i/5xb9d3Un1/zfK6Q67sdvkt/BktmkWCWf5CmydY4MgscOQIAzHnxH4uHJORlRXidjhvq1apv3ln+J1bFo4+ugQxThCjWMY9/6WUxEWplZc8IRaBa0LSIRgMNmnSJBQKvfDCC8Fg0O12u1yu3Nzc7Oxsh8MhUj3n5uZmZWXxGcZwuLCwMBgMipA7QYWFnTgUClmt1q+//jrBSi8IIRYnSZDsaDRqGEY8Ht+7d288fiKGNLHbRakAshCJwOWGwEmEWIg6mRkBGM9uZ+jMiDNDJwbPAGAuSLmqp8GtTEfokVll0144OORP5f2vo8OT2GQL56njgY5VcFwSm6SSKUAma3QK0GINx6n4LeDnFtYrmX5k4U4X3VXWS8XuCu2jsG4K7WTReySTgdzqbIxU9DlgLNf0tXXIPhWjQKLJdH+d2GLQxytsDJClFqonk131Fox/rHvXRu52d+X7GuXbbHm5BVarIxB+1hu+1+F8Lj/P7nTZTbkJj9cTdHFXRF9uVk7A4/AXtmnZzP7xGw9WTLo6vmkg6jrTdUKpwVO9ChHYE2OEy+2py/u5XBFgBrJKkw0bprlUEGKKLM6Hs3xB7gBFuRgZo4ZBNxxmvTfrbVdW/nm+Xm8hUxYydQImf0wtRTHFTxUnAwdVXITT4jS0pFPIJEortPyB/uZqOuxaY0yY/mEaS5oah35UuZtYVMxN2k/UrRTWI6w2tOWGtoSpi5m2GJVFccsyQ13B1LIlFgwq7IVkvfNNFV/evW7xvX1nPPDWyKfbf5gWfCm9dZd7Pxz3h84T7+/4TbO2Xbw8s6UjwNPqeE1C7MnMzAj7vQVue8tc923vzshev38HKeM3xdCgcR7MRM8jde7lWhEuwn1d2oT4FIC6d+/+7LPP2my29u3bt27dWqhhZ2RkZGZm2my2cDgshCYSymuFhYWhUCg/Pz8QCAjvYfG3oKDA4XB07dq1oqJCyPyfUpD8KhGQCPxkERAcWPzlk6vHP4ynyDJMrdOqDB9mrjsz7wfvVnm6NrPvKcXYqvia3ofHZZaNuYmMV8kEYOM1Mr4eJ8STQS9WcLIFxyWzb5LYZwr21WhXi/GvJNZNYX0U7KNiH4X1B9oL2Icq621hAwAHgfGtwiYDmQ3GsjrGao2sVulypXIWkNkKnauwhRpZo+o7FLZEocvU+O6f7S19ZNGaR7767uG33ny4T9/Hdx17fm/5vStWP/HlkMdfbJtqc4V4xI7pROz2erMycvw+tyvQLqOZY8V3tx3dEEJjnzCncTetqiRgXMxfLhKBSwwBPlYVwjGIlKfr4Jns+JphfuU+tmYSDzR4cg/RkgnB8hhO20PeWBB9bD65ehGFqUztySxtqOJiqptqPgIuCumYYqWQTZMfwl9fQ95Kxm0PxoNDsO5kpoylEKba7Yaq0XaWo0TZj8p21DYaykYD1hFtJbEsR2WtARuJZYsOsWKNWTUWVtkbQPomRffBdKPeZ9jg0wO/6r3m9p7bk74mSV+xpCEVvxywKa+oc6Ejq8CVH/A4PT6v0+3My0ovdFuLbBmNfK8//Mm2BeVU54k4kbIYZ8RcvFkS4otRbS8rQrxnz57MzMzs7OwXX3yxsLBQ5KsTPhL5+fmhEJ+UsFqtghB7vV7hKWGz2Xw+3ymE2OVy/dsReceOHeYAVHYjF6MuyjIkArWAwHG6LIoW7Z0TSCSGsVffO7hiRmbZyLtiI68h3yfTSQqbqPFwulFgfK2QzzTWX2E9FNZVpR8lYY8kLuLGM30oOABoH6A9AXsDJ8SfA34DbDLgLAXnKzhPodOBTQM2Q8G5QBcCl3Jbbub7mAl0GsRmQ3QlxHZZ9GNXxcvvrCj/RRxTdKYRpsVJ/b79H32uWZ41N+B0hng2O6evVU6zvMLUZzJTOwSfLF38Z8PYgKyMe0tz9X5OHUyFiaq0frUAsixSIvCjEUi0UL7CPWq5fqBwIjCDBpiZCJqI/O7mwNbM/2zmgMayCjZ3M3l1WdlTC6O3TmFJfeJQRFQP1fxEtTHNSlU/Uf9M7qwf66rh+mvi4/7Kbh9P1HFM64rqo0T5ra5o+scQJXAUOSfeRWEnga0ENhnKJgrbdHW7Aft1Vf8WWCZQp0JbA76nlW+AWTHlawrDGQynSV9S+I6q31EYoVuGR+9+d3aTNu9nBF/O871Q5LQVWDOb+v762Mv9n2zT8w/thny6VTcjBw3GhWFiyAe00oX4R9ee8zvwsiLElNL169c3bdrU5/N5PJ6srCwhRZyZmZmbm+v3+wsKCux2uyDEVqs1EokUFhY6nU63250gxMJ3wufz/VuhYvXq1cIZ4/xAlXtLBCQClwoCie7WvGAu1MZ1KLijgcGoznRibKfHpkRXvlIy9p7YqCQ2RmXDLfoQVR8MZCCwvsB6KNgjCXsmsT6a0U/lbHgg/7B+wHoDtxYPBGYSYizWcKpCp/GYPDqZy1bgTE6I2QKVLUrCJSqbr7ApCk7WcBawhaCvgOhqtXKdGj9gQT2ZUNCJFmWWdft+NXj00507P9Y68qDb1iSzeZ6941O91j378aynZhc/SMpHMVoh0n1xkxnPa6CbvtKmK/Gl8lzkdUoEfjQCPGxWN9mzmRELj8Rw2UH2yepY9nT9tn8wCDHVZ9TxES1EtFb02mv1d1JYmYVteSjq+hLrTqDKYGpxUvW3zHKnkazSrxSDQiWDowiHGM+PtxdhN4M9FPbFLbspHGEql5h5FlgzhbkA31EqV8LiShhHYAxTRjEYSWEcg/EUvqcwiiUNizfsv+fObit//+7cJ18a1KTo3V/3XFh/UKz+R3sKxh08xBC5V1eMa4dz9y/B8X80FvLA80DgsiLEwrozb968V155pW/fvt27d8/Ozk5LS8vKysrJyfF4PCItc1ZWljAkiwTO1ZXXwuGwkCIOBoNNmzadN2+eIMT/uQ/xeTwTuatEQCJQewiYAi+CJnM7cZzF41iJuAVLPsTJt+FQYJ8C+UQ1BpjSxX0Ae6rYOwn7aKwfGELM2HQa5sLGfRTsp3JC/AW3EONEjU0EYxKPzMPpKk5XcDqwWUDnqDg/GRdZ2ByFFas4QcPJwCWQlwEu0+hyle5KYWX1GAWGyaXEsofCXryV4V/27/KN/vqVDpHXO0/M/BJvmkBvMIgNyQHhJUK5WzQPwzcj8c0odTnXVXv1SpZ8ERHQEc0xIXeYqjAwGuVJj2NRnLoCm/ZArR1L9hv12hEoJCm/x+uTjQUKosoW2NiDY1GbgPAvHZ6k8Fta99fkGgVHa5QqcYRy5HbiElQOIxw0P4dJ8gGEUlSMHsCeBPacwmyAbyiVS2BlTClm2gTUvkfle1QnoToZ1WJUxqMyIg5D4+pnWGcwXj+w8v4eG5JGHoEB5PYROCaKMW4Ej1EaN8ygXxEJKzNMXpzKcxkSYsZYeXk5YzyEZvv27R06dGjRokV2drbQG3Y6nSKfc46ZG6aoqEjE3iUsxCLYLhQKNWvWbPz48YmcGhfnechSJAISgVpEwLTGmH6IfI1Rwv2KKYlxCWOKsbL1JfNf0Ptdhx8AdgWjr0r6K1yprR8nx3pfwD4W7j080Pz0B9ZXwX4a/QToF4DDAb9PomOAjOMixzhVxWkqzlDYTCDTgU3XcK7GZijGJI1OSmLTFDoXyGKgyxS2AnB/CpZrrFI1KusaLKmcQTkDeshCSzVSph7aVW9fxdW7Y8qRaAolK6IY41ZuRJ1wzVZKubJGlRem1G6qxboli754CAiXCtP5mLeFKPJGQSgjOpburZjxTcUTRQR8FB7Xr74hOgawHHD9LfGCT4yrpxLLWJISicF9BO5l8Ct2lUpnKywGlECcQSVTyhmUIhxlcIxBqa6VIsQNoG8AvQ/YYwrNAGynxKbB5nKYacA0qkxhMIXADAKzCMwkMJXABO47Ad9UwFcUvkAYGr36S7xu5JHZO/Zwk7ARRVZJebSD6RdiZtSpSsl78QC8Qku6rAhx9WdozpTwWjRw4MCmTZsKoYmCggKv1yvS14lsHa1bt/Z6vU6nMxQKJThxJBIJhUIZGRmff/651HyojqpclwhcSQgQA0mMZ7ZDxLgZwM49GOOHVu9f9l7JiGdpdwt2B9avPut7HfbXsA/Q/hoONE3CA7nrMOMuxSobpNAvgH0NbBSw0cDGKjhJw8kqTgWcbn6mAk7TcJbCpoExUTEmWsh0IPNAXwTGEiCrFDxowTLAY8lYYaEUCFH00vp0Gxg7gO5Jwn1JbBfQLXXYwQifJuY9KCFCSq4qF4kZkySs3lfS85P3euUiILRmzDrPKDIaY6yC+1Fw+6tB6cFDsZWDy166Se+v4AYg+6/CXt7onSMRvkXogdrTTLuXJj2Ill/QW4CtBywFFuWcmFAwKMSRe1BEGcQNbjlmcaARIHeo7H6FNlEwqMTHwt5jsJAo85gyh8EcqixkyiKqLKQwl8I0poxDZQSDkah8G7MMRcsw1mebHhMN1zB0ys3EhgglZDpiXOquXZyafBkSYkK4FIv4axjGyJEjn3vuuZycHJvNJjJuiPR12dnZIoFzIBCw2+3BYLA6IS4oKLDZbB9++KFMkHFxKqIsRSJQSwic7ERc/SLMPpWZDgcVXBDUYCxGqR7jo22Geqx8+zfHRjzJegH2hGhvjfargz25qZj7SJgOFVx0op9GB4AxGOhXPLUHV2obq+JECxYDmwI4DXCqGWw3BXiOjwnAxgJ+b7pMzOJeE2yRQtdZcB/gAcDDgMeAEYVRxTgMdBPQzQrZrZI9EN91Q6wspJNSs78XgXQ6Q0N4EnO5Zd6nHuf21e9RrksELkMETAsxdxCK8w8nweaYkKLBYuZHj2ElZbjdiH587OCtxthG0d/2wKTvGHyL2su68ii1/JnV+SODm+htKttkNr+jgJWAcWA6UAoUgTKTCiNgBbBcILeq7P8p5EmV5YExHEoqlBVMWYrKYv6B5QxWMP53CVPnRdWpUZiI2jimfl8KI1n6/FJKjRjhquhcag1NGRw+n8MId3ni2XXkchEQuKwI8SmevmL2c+rUqU8//XRGRobIDCeU1zIzMzMyMlwuV1FRkVBeCwaDEXMRUsTClvzKK68I5bWL8CRkERIBiUBtIHCSbPGJC+Cb48gqDW5WMvsnIqJ0eOdqcCkzHsse142juxYenf1WbPB9rDvEBgDrZ+bp+ETD/sC5ch+F9geDZ/oANhFwgso58XiFFQOdYsbVTeZ+w2yyxjNCjwMcreIoDSdy92KcDWyhwjZb2B5g+wD3ATsCNA60DNimJLYRcL2CG1XcaGH736JGjFuyDYxxTYnjOTiq7GSmwD9Gza72xC3KNYnA5YmAqPYYEzIr5lCwBLEMuRcUmiptOhdfYViO5WjQ3bHY0mPfzoimDo/f+GH8qmcYPBKv+2zshkcZ1Gd/tOgLgW0DtgfwEGAJH5VyZhw1/5YCrQQ8pNDGqnGDyn6p0Ac01hTIUCgtVzYwZS2qq1FZg8p6pmxgsJ4paxCWU5jPYCpJmVwJk/H2WUf3VFADy5FwqTXeeEXSeS4sx709DOREWS4XAYHLihCLJB3HhZOqVPfj8fh7773XpEmTtLQ0YQbOy8sThNhmsxUVFYXD4fz8fL/fn0jdHA6HCwoKgsHgvzcePHhQKq9dhIooi5AI1BICNRFi02rMrcDHeyaKPESNEcIIreR5sXjHajrmchEoglhZsmP7yBeOdL+F9jZl1waqrC+Q7lx2jfYD+ingcJXH0k1SqlJAT+Kp6egU7k9MJ2p0kgUnAY4BHKHhyCScqOIMwLkKWwK41WLsUdgBwH0aHtJYqYo7knGFguvr4XoLrgN9U+NobDfP4kUIUu56aKpRmf7CVbOuBmKci1XJbrWWKpks9qIiUDUOPK5BLiysVW2BMqykNM4F3GglM1BnRjlGdYwRdmxfxbzO5YE/6Vf9yYDHCTzG4Cqaq9H1ClulsA2A2wB3Au4xDcaHAA8C7lXpfmDbVfon1airshsVdofGHuFTQhVlsIPCVgabzc92yr9uJ7CFwlqmLjKS5uuwgF0zPb7ocIzSo6jz0F3dHHqbuUY4c4+bKfhQDmQvVu253AhxArcELRYxdu++++4TTzyRn58fDAZzzEXYjIuKiiKRiN1uFxoUCa8JwYnz8vK2bNkivC8SZ5YrEgGJwGWEQMJlohpbNLdxjYnjbJnxnB2E91Zm50S4rcmMXKOmKhJ/3UQxtlZf3btk6BOlfW/CvirrDqSrgj3NJB2fAxulUm4h5k4RdCz3jqCTgBZbOBseD3S8guMVHK3gtwp+x03IOA3YbIUtVdlmVd8NdD/gXgseUukRiG9UuADFGgtbreqrrzUOdtGxMsYJOpcb5vOt/GPeTtXN8RlkLrpW7RYvoycob0UicAoCxOSWwmUiyhsDd0UwM11wcky4ljEhMRRSDqbKLx9E6qYtWV9+eIZre6v7jt54N4X6zK2wRSouUvkYdC3gRsDNgDtMZrwTcIuFbgO2XqV3a4aq4TUK3qKyP4AxUImVwn7Kpdl2IeyisJfAfgL7COym2mZdXRWFlQjzaWTpgShjLI7lXNAqitSgfKDNr5egmZ7OvBVBjE+5Sfn1giNw2RJigVSCFh87duy7775r0aKF1+tt27ZtixYtMjIycnNzCwoKCgsLhfJadTYs1u12+9y5cwmRecQveMWTJ5QIXD4IJN4zfPAc3Ug2DTo2vOXBf/7K6KSxrqrxqaZ/ZWGjFTaJE2Icxzkx+970GJ5oIRM0+j3gWG4eZiNVHK7wdNBjuWMxnaWwlUl0o0q2A9kDdJ/KDnBmHF8DbDHgYjCWaPqmTEp36szgulJVZmtJey+fqiXv5EchkPAh5sFpghDzRmoSTdOxnmfz4A2Gj3i5HwXfh3D1YkrjlezoMjarH/m77dDjN5cEgU1TuOvSEsCVCq4DtgHYJpVu1uhmwPUqX1+s0F9pVFFZHcAGCvu9QronxY8oh5lyiKn7EPabAsYl/CvsZ9pOqm4gsBF/vuzo/ApuomaExtAUluAD7Krr5QI3nCWLgDpJQn5URTjPgy5bQkzNRThRJKTTPvnkk7Fjxy5atMhqtbZq1SorKysQCBQVFXk8HrvdLlJyJGhxKBRyu93ffvutiKs7T2Dl7hIBicAVgUDC7ipWdGIwWmnQfcbuyRWjfBU9b6vsq+pDFDqmHpuosAkKmcCV19gYFccqOB7o9yoZq+BooCOBDOdiFDhcYaOATgQ6U6VrUugGC9uo0B1g7AN2QGU7QV+u4Hxgi1Iql/6RVU4zmG6YshLc0MTJsOw7r4iKJ2/ywiLAmSjnozphxKBxQg/tIev6swXPHpp2bfm0OmS2iguBLFbYKmCrVbZGpauALgdco5GpwBpoCIAqsKsUvFMh3VL0I0lHGZRS9TDlAm3HUCtF9RgqR5i2nyVtI9euKfn4sG7wLNSm8Zrx/HTSqenCPtPzPdtlS4hFQo2EVzGlVNf10tJSXddjsVibNm2aN2+enp4eDAaLiop8Pp/Vaj2FEEciEY/H079/fxGcd77Iyv0lAhKBKwGB6oSY69tQQgjRKZrd6n6yZ2zFFG/Jl78r//Z6NhroeDAmAhlnIaMtxhgL+V5jY1Q6SsGRPDDd+ArYVzz8jo4AMh7INKDLk+haHubOdnAjMe5V6AaNLNRwDpTP/T098IVhhv1xhVU+xxrnRiVu75KLREAicH4IcPci7mdMDIwbXFIGUacVBm5B2ju+r2nJnJ+VFSvGjCS6yMKWqnSJOU2zROXrYwGv01ABqgJNUditQLok0f3JFQyiRCkjSgWFKEuqQC2KSiVaSugvtpd3imOlTuIY5dlzeNlGVSKd87tqufeFROByJsRnwolS+uqrrzZp0iQzM9Pr9bZp0yYYDObn54ugugQtjkQibre7S5cuiQ7vTCeU2yUCEoErGQHhMiEQMIN5DcYMhrpBCaEUjcNYOgd39iwrbhQdcyMdDWxMUmy0qo9XyfcqG6PRkSp+C3QYj01nQwC/BPYdGONAn8KzOtOVCtuksG1cbJjsBLpcNWZq5XPuwJLvqXEwzouIUzR4KDo3AHAfRLlIBCQC54uA6WTPkMcHEEJ1TotJnFCDMIMg3czYN6zUHl95c+lk1Zii0hkaztLodI3O18iXCtatwzRNV1VqUfFaYG0UtlYjDAgBXU+pNIBQ1WAKYQohV1VUvhU1DiHGSMwwKg0kjDtHEMoDE2TrPd/ndiH3vxIJMWNs6NChjz76qHApbt26tSDEPp9PpOcoKCgIhUKBQCAtLe2FF16QQXUXssbJc0kELm8EePh61Pzo3P/PMD/cSbESK77FWY/haJWNBP170MfyPB36aJ/uMD4AACAASURBVEtsOE/sTL8C8gWQz4Bnh/4OjLFgTAE2VzGWKMY6FTfXwZ1g7ARcBPoUzdjmQZ2bhA2mc3VkjB0nxNJh4vKuXvLu/lsImBLApuwZ4bZiitE4lusYpSyu0zhSSihbjCwft9XhKdfHa7TYYkxS6VyNDtawTj2mJelqElU1rAvMC7gEkCnUABa/OqoDIyplCqOAekNWudicxTmGlcgzSYpgPq5bI0Jh/1t3KM/7QwhciYSYUlpWVjZ8+HCRsKNNmzaRSMThcHi9XuFA7Pf7rVar0+ns2rXr5s2bq5t/fghP+btEQCJwhSNAqvSdOB3movpVNh8zbpyQeOnWT/QJv8NvFfoNkLEWOlLBb7hVmA7hYk3kU8AvgA0HIgjxLKBzgC7WcGVduhX0rYALoGL6r2lsI09HywWSDWEV5p048lxXBqfjcpEISATODwE+dDXVKKiZi6cqc7IhwtqMGBId+Q9Mx5mVRzxHF/5aL1bYd4AjLPq79bHOdSwliWiAFsA6QPKT6DzAuMq4XLHCosDiFhJVSWlK/MD7hl7Jh83cL8OUHda5pxOP/RM518/vsuXeFxCBK5EQC8diRHz77bebNm0aCoW8Xm9mZqZI5hwIBDIyMtq3b79hwwbuDGhKTMiJjAtY5+SpJAKXMQJx/n4xE2OY3Sp3YxBx4gTL8Rh3UtSR0mOlByccWxo2Rlyrf6XQYRoOAfY50MHAPgX8HNg3PK0dnQpkJtAZCpvNXRbJasDlSvnkm4y9fQnvQHXKpZm4ghQVZfCg9KPSg/gyrl3y1v57CBy3zwrJiePNlp6Y4yHmELSCu1TwSLgS1CdV7HooNvnG8hfqU7gRoT6Da6nlGnIjsFyNzU0ix8xEHmYuD3YM8LBKt2XGDh3kmX30KMa5wASaimpiDI0VUl7tv/d8z+XMVyIhTmR1Pnr0aEFBQbNmzV544YUXX3yxRYsWLVu2dLvdo0aNikajCW0KsXIuaMp9JAISgSsdAdOORDBKOfMVc6+Gwc1L3EsQqc54AHs8brr8Gke2lC8Mxr5IIYPBGAR0EOAgwM8Avwb8Htg0YDMBZyhkrmIsArYAaDHEVjjRIFyzn9uuogSjUSRcI9nMT2smC+GdrFwkAhKB80PAzD1pjmAJ6jr/mNk7kHIHfT7PY9LWGLIo4xGzSIwYGliJkyoWtzryl1uiN9WhcDVakmL1FJqv0AUa3Wtm8dij0X0q3aOQTb/XN6ykhk6ZmReERLnABFc3p3xyB5FFhdLa+V223PvCIXAlEmKBnmEYjLHdu3ePHj26pKSkoqJi+vTpw4cPP3jwoHQavnAVTJ5JIiAR+AEEjPIDpau6HRt1F+3FMz+TL1TyjYbjFWOGaszQyHTQ5wKZDzgFYrN/TaKHufMw59dykQhIBC4cAnxihyF3x49HOTnlQ86q6Z1ERmWDS0/wJB9c2IUajMZYjPEUc/rmyjXvHX31fyt/xr0m6DPJbAbg1qu4k9NWnsiDbr6pcvWIWAXDGKNRSk3bsMH9qHSeRVI3PSakD/GFe5o/6kxXIiFOyKhRyhWSuFKS+Vf4CgsBY6k9/KOqkzxIIiAROH8EuJVIN8rZwYVD9n3yf6yvhp+rPI1zsYUVq9FZoM+z4JyUshk3xHYu0NHQzayz51+MPEIiIBG4wAiY0zFRkzsfpUbF7oo9ge059cqeAxwCuBJwS934DmAbrtXnD921tYKrukVNw/MFvgx5uguCwJVIiE/xhdB1vTonFgLG0mn4glQveRKJgETgBxEgfBY1zhNnxfaSLQPLxqZWDL0Jx2g8kcdkxZiu0hkWfWJSbGVzYpTEmWHwGVsZOfeDuModJAL/dQR4mjsWJdzVOEqYoRN9CZvxqj6oxeFJ11csttBVmr5epSvTKjZuQBbjumoGzyEtl58kAlciIT7FI0KEzQmTsDASS1mJn2RdlRclEbg8ESCM6pTEqWFQM+VcfCvZ81l0eeuy6Q/qk67GYo1OrEcXNKZlMwysJMSco6WyT708K4O8q0sLAc5uKU8yF0eMmxEC1NDLjcrdqA/WS1tWrPpZ2ZzfxRcvJ0bUQO5jwamGFBv+iT7kK5EQ/0QfhbwsiYBE4IpEgEfWEGZQYrAYF+nn/oUGY6UYn0c3dSydfn/F3MZYNk2nlGEJ8j61KkjvikRL3rRE4CeEAHeZ4PGsBmW8/Ro0RgzujGnQSkboVoMOoofG6mUYE9k+TFEYIgnxT+gJVr8USYiroyHXJQISAYnAxUaA8qzLhE+9shhybmwqszEDKWH0GItOxco5aMR13plW8BwBPOidR6XLRSIgEahdBOJVCZ9jyFPkMGJ6ZIq/BhLUaSXXCidxw9BZPM4IVymXfLh2n9mZS5eE+MzYyF8kAhIBicDFQIAgxk0ZVB6vbhJiRjCGLE4II7QSaZRboZhuYKwSK7jEhBmkfjEuTZYhEZAInBkB3hb5r3EuSiEUxylXKUbuREHR4DnukBhlvNFy72HuPyzdnc6MZ+3+Iglx7eIvS5cISAQkAhIBiYBEQCIgEahlBCQhruUHIIuXCEgEJAISAYmAREAiIBGoXQQkIa5d/GXpEgGJgERAIiARkAhIBCQCtYyAJMS1/ABk8RIBiYBEQCIgEZAISAQkArWLgCTEtYu/LF0iIBGQCEgEJAISAYmARKCWEZCEuJYfgCxeIiARkAhIBCQCEgGJgESgdhGQhLh28ZelSwQkAhIBiYBEQCIgEZAI1DICkhDX8gOQxUsEJAISAYmAREAiIBGQCNQuApIQ1y7+snSJgERAIiARkAhIBCQCEoFaRkAS4lp+ALJ4iYBEQCIgEZAISAQkAhKB2kVAEuLaxV+WLhGQCEgEJAISAYmAREAiUMsISEJcyw9AFi8RkAhIBCQCEgGJgERAIlC7CEhCXLv4y9IlAhIBiYBEQCIgEZAISARqGQFJiGv5AcjiJQISAYmAREAiIBGQCEgEahcBSYhrF39ZukRAIiARkAhIBCQCEgGJQC0jIAlxLT8AWbxEQCIgEZAISAQkAhIBiUDtIiAJce3iL0uXCEgEJAISAYmAREAiIBGoZQQkIa7lByCLlwhIBCQCEgGJgERAIiARqF0EJCGuXfxl6RIBiYBEQCIgEZAISAQkArWMgCTEP/IB0J0dG9/zTtfVR3/k8f+1w+jOYW8PX3Mgcf6jPdo0bBMeu2zVtB7DdsYTm09dKZ06/O1Zq+ipmxEXf511/V1vDjvbsacf9INbdr/dqFG3HqvOH73Y2P7Wgf0mTxl22jJzxj/fefSNHrtiP1i43EEicFERoKtmvT1yaumJ1mXMy7u1qNfbs3YbZ7mOH91GznLOc/ppz9qe9zds0/ccmqdRtrpto4yh1W/tnEpI7ERXzew8U7x26NrwrS0GFFdDKbFXjSt0bet7vAMu8HupxpLkRolAFQLlu2Z8cFK7MIb949ai8OC5NXWdCdRiu4c0bdRtwo9vJokznd+KUba6dSve+689wQfOeIY9a7o+98zQaszhjHvW/APdOazzTPFCO7au5z139VpxDoWap6LFPe8paD33rC/Dmsu88FslIf7RmB5b3/t2aBjpeBaWiaIlfN6xY8fu3Tu888ztjYYuXTOzS8eTlwEDgoG7IheoQhjHJj5jsdjzZu8Rfa0xL+t2yAqvjWLZkHfvrX/Ganq0RwfLb+4Ztu20Hnrx8Nz6dw2dWdz69rs6VXVdxyHbNNH5cNagz06+m5O+de/eoXN6gxr49O6ODmj8zMRdFMt3nwxI9+4d3s2t36D9hJpfMcawf6b88q43Pz2pGPPL0G6NGkHL7PlH2PHrk/9LBM6EgDHPdX/HQafXos8GZbVq2K7jmtOHaqdW1FOOHdqt0WMWe9Zn806vt3RtuLFZ2xONy5iXlZLyz2Hx7bxZvfn5SV1s4orPt42UTv3upGsaZJLFxUMbOcL9+yd+GdqtUYMG7Wu6wUTBiLhz4T9uhcYDpp7g8NV/rb5u7tnog2FHt8/ulijkxMpng7Lslmeembgrce/VDzaRadiw71r+Ky3ueX/fcyhRnEC8fh2JF13108r1KwmBAwteb1RjNzTkzXsatvm6BhpKV9VYV0Wtff/91/q2aWixd+k4+zSKRov7P5VyUt/E+6OHs+Yd2j2hacNWnV4bUSOhjO356n5o9EaPXXE8reiaGohRtqr3iSbUsWPHbrP5W4WPkD9MbOfXWXRrg7bf1HCDJx6/UTYvr5EgACc2nmGtbMg/H05p0Hne0Z3DamrKHYe+eZflsY78LmpYzPZ4vCC6tvX9Pc1GXcOep24yXwJ3n8YuTt3t4ny/rAgx3Tls2Jt3NYTjb9iaIdw0uV19gLtrYGk17398a+mCqTtOrgq7O9otBWcnxLwlOIavQkTOOJ/Pmldy/HRn/3/PmuFvJ6p+jSsDBgTb3Vv/1LugC77JbVCN1/JqaukweQ8vrXzXlw837DTvAMZ2fnsKtUWsathlp12WSYiH7eDXn/JKj6PVe8gDCzs3SPnnwh9jkuWdfbDjaS8cxD1re96TYu0+pOZWJ64z5Vb/37NOW95se9cDNZ/ztHuSG/5jBBJt7a6+cybxBgXQ8K8nVw9Rxo9tbv/xFZ79BFWU9DSadqbtp5/tnPc8tq73nQ06z6tuL6Frw89YRGsyCaV4RZxSxo9uIyiayVPmgHPx1y1uzZpXrV3v7mhN+eewk27cODbhuYZtelZ/z5idX4fqTPq0nlsQkYmrd3TtYEnnQ+4aF34X5oC8hl/NunGCdh9b1/P+RhMO1rDj6ZsENTkZVXOvPat61zjTdfopLvMtiRZaNd6o4Xa59aQhQM3kr4b9f5qbztQNnWn76XdxrnvSteG7G3QZVlatE6TF/f54vVlpBfWssVMzCXGNbbxsbL/0Bmfngvw987BJQ09tufy0p/a/YkK42gBYNNtG3bhVTiw1mJyMYe/c3/G1Eat2TXzG8syZhsHmXZxorSfDyOmN5URNo8V9b32ppu7g5KP4N5NJN/rgJFT5dqNs5WcXf/7n8iHEdGdHj+U3Z69bJ55GafHXbRpCDY/hxC4nr9XQZxyvYCf+P63PSLQEemziMylVxPTkE1/QbydabOnUHj1WldLifo/UP8WealpS7z7VVHyCEMf2fPlY4pABA4IdHk5p0LZXx44dhQ2smknm7O+RTROCaWc0RNXY2dNVczvdlWIddLZpU36d9z83euOq05bdi8NZkhBf0Op0ppOd1tZMVlczITbP8cPNjRtFzJrJqTU0dGSNWVedQSLuWTc6z9HQ/LXRSzVPp9BVC3o2anB3v3OxMp6Jzp5p+2lQmE379vdOZpan7SVGwikhPmyuPjNYvf6XFg8Od6thBFh9n8SJz6WNVBFinzngPBdCvKm4bYO7hu0wCynfNeGzmmxOvL9v0KBttdlh3vwtnuHLacnyYatPfliJy0Ws8S7M381adJKNgHtNZHy9vBrfOH6iUw0EVS+x02aKhpoGkR8ymx0/6+X7/2kt9Cy3umfd0GcaQbMzG2vO3vTKd89p2+ExC7cxnTqFeJafuIXoJdGceZuuYchEdw4bOiiLN/mzvFj4bZ2pGzrT9tOhWPyNNeVUZnn6XripuE3De4Zto3hs3cePNeop3CSOG4z4/nvWftK6dZXbQPXjEzSg+kakuz7pkt7g1I74pF34F/5Gsn7DbWo/TIjN+WG7aYBDpDu/7V2jU2LZkM4N/tyo27cn6CbvVX/Jb6105rDlp0+OiWuq+S7M38p3ffWg5bF/VSO13GuimmEucVenGr85wXjMYrF3ee39908QqY4dPxOPvtFfz0ghEqe8oCuXDSE+xdhwDiCdMqb5gSOq9xmJXfnTreYIaFbHxzqE+48/3qMk6tDuSa77z2hESZzvP11JmFiE44Tn3U/TGic44pl7Jl5sdUKcsGrzHw4s/MetxzvLU66v6o2jY/nucd6sz6rPFonhcsMGbcW87SkH1tRNlhYPi5zulXHagcawLjdb6jdKv+u0Jb1xgyrnkNMOkhsuKAKntzUxf12jhfh4yWdrbsJSdbxDpcuL2zWEk3po8cIVs3V8DsFy6szMnrVjw2avDOc20W92M6caSvmlXmhCvHPBW3eaHUNVkwyLqAPeGBsLs03Vq/9056vTG+w5tpGq5pxmvnBEDMAJF6Oh3Ro9fNKNc2wbwgPCNMuJVEMwaa7ZwY+xNcr48O1ZuyluGp8VPnm29NwIx+l3UVUj+CDqeEHHKwlyuH6QIiDyGnh84itxrFxJIHB6C038VOPKWfYv3/WFK2+0OTr9/+19f3BUVZ5vu9WzcYv3mCpre6t6itpxi1qsSWnczQzPYiZvq91+FWacGlhxeooshEfBAmOzwGQUBxXsiSA/Iv5IHgkIQRAwkZaoq2AIxIkrAhmYjMsQSFwZBdKJs6wjIZ1RSHtzX32/5/e5pzsdEmLAwx/k9r33/Lif8+tzvr8ObsaCysAk26TlaAxworFknKQJyfAI1ZXQ69g/Xe7Y2/nu7GL/hLTWRMpHpOuH6e4rifFHNoQYPicYwd0aTGUTWC9tqZs+hgiMUPI6xue73UMEOQ3gRaeSp2NLghFJusQf6RcwI91dDZt80EgXc+MQYtoh83g6eInczTlWN2sMM+lMJU89vSw0D3ezzqX6mZMbVBMmsfrrpUu/vV/BHqI1pv7VyZrVwWI63bEXPX/JrKhpnj1vjeCNG4UQZ1pu08GZYRbQkhB6R9aM3s6GewKBksVHO79wsYsE59BWdxqrws+p0k3ahxLdleHwAbRb0HIezp9ORyw/fzfIV1AwnBdvO71pvG8AQkwtI1G2PSH3sQeL/QXTpgWomQfU7sJvVhMTlFSytVo1dpRnHNW9JllTVuD3rHawxBJTEBTwFIYqngNz4ZLdhw8vLwncFarNwvb/cs3a/PU7/oAa3/7EyfI7bpmzo77nC6hpz75tjzQc6fmi7/zh5kt4ZzjRtXkxBAxjLQtCjCQmDVvFaVFS1aFVmU8wHtSPC30cypbEU/dSe8WUyN72Hqdxa9ifpghWefY31Rz5hrS6cOEEmAp8o2xguS8Z+0Y1KCvCdWGuwEX0SueREjRwosQU+79RuzqkMUJ89WIoXyn2F6ClREs8rxjk0+j9s/hop5PWfdZ1EuXLJqkmkq7rJGq2gjLtIY9rjjz8W+J5hXypVhRlZkIMM2qBvqtB4DK7OuAr2D2ITBFldbKsS4D/Fb4yjNDMaJAdqUFM6zpHNm36SNjX4BATQ08YDGD+UC4znsnwyHVdYEu/SKNSJ31DuMFkrjqREHtFjISeynwxfT6ylNf8lpMofyA4P9ZxhQwHXhwYLn57TBqBkeuiWT9hrr7QQ2VoTx+qPXysNjyQHf+F46V5ZMMsZqTOWF5e/ITj4jDfFu/4XJXHSTUnMgVitcxvowItBPtwj3ufTIg7Y/+YW8rltZLdcDpCbNzZwqYcxzhjR7wa8oU0saOP7AZJvCi/N3LXNwghximSCjkk8LpOv75s7fQxuA8t9PCtbFZxmpkaugHlVWAC+Cfwq9N3t1L5bNXs6Cgreuo/ejwG9XwZpnw0e8dMuRByndy3BcgltW0IBMri3b/ePDVY4J9PTXX5yuS0vvJKh5jjSHIxJLR+DyjlrDveR2x9FO2VvCLiAACf1kcqEx+DR05mcxReGSQNIV+Q1JwDAhYapdx8or/vo13Cm0B6qWbj5Htu+k7+6h383o7niubd8edff7D+ZJ91rvP2kmG4YxprbCid/91+0Fn7fL4hDTeiTuXrrqfENFvZQRLiMbPqvNp5GOkYbsU5d+hQpogH2jAhwHa1VtYx7ZDropDGX1z2rNdPVPR/TGgkqeIdINYDjRHIR2hXIa5FtVCJUpkxMaIw9wEnUf5guk0pFQ1qvhny8Ff1SKLmJl0Q8qE1gVA6HT3wctSiGryawPfuWN3MsUwSaWwF8wd+de56xgv79J4mNjzBLEnsMF03bRKWlP2FoacOTJlGn2ksuYVkixmaH7ku4d84578q6xWxEKO4kRVv+nvh+BO36uJJeA9kPajc6D3flFnUYiLEaHPIDIHIig/SWYkgkrpoabtO1TytjDt4S+qlVP1FtUN82aLe54IAiAHFZyT+7WKY81vyhYOierOzHYjewQpFIy1i9Se6Yj5RSDWXv0IUB6wXyIZsVy2ekokrnb8W0fsxQwt50hA5jPTVjUGIyQBTCbHsEEp6oULmYIlAlwJ50GZGXwq5QkcLLAmSMNWbnPenK4kXp2h6iqyVs95sXddpbdrUyoYreaGrbcOkvNJ9JxM7cbH5pHv/lPyd25/IuZtuNCEIQ2Go4oXNSzR9NCYXQwLrHHooWl1dWxEKTq1qqMrzL32rW9rMsQrJKyK5RwaAz8el5uxV/a/c+4ndEpMo4Ey6INaZBHdAsFDEpH0nD63bXt34Vn39i3vWbjSHXTtQ9siGvbtfew0Dsh1tIWJjvWD7e4gImMYa3Sz9U97ynaghAasGVbU6uOGGfYAbNSLbVqwMkRDrwxkUIwNJiHULNr4gaRfMso2Zs+sBHCBoDOy0cZjwtGjA6vMXc5fQVM8h8F51OmKRoLpmwHCjA3PDhqXRyCPL16/XpSODHSOGFYsIUMGfhkhtucxJtdTv7Tw8N3Tfc56FXO4q6O2qeEHIw1+s35BGrrl8TfJzThz819uCS9ZzKRQHkGyD/QWx8n/f8dT0MWBKrkftgAU4BC7RIGvnsjfi/Ee/MRtFk/xlN9q1cYTy7Rl2aVwQOa8FALIVKlM7WpSnsG2w8LTmO9XPMQSTbEPFH7FxSs0lgqrBN+aJwlR0GLg9t/RNscNUWko3K5d7kXxNzc0p1zTMANht/LJEhhs/CKto4lLvHIsX3qZu5ECDytNuj0aKtu2gQSFEbTkNgGmw5z2IwMjcalGmNqvuPFXq8ihPGs/GGCx595N+jqQcHF49NBrskleE5pldLGh9IBbbDM0/Xqz+gyPETqJ8ccH40IrnZcDJNWI4NvDg683vAAU3OG4SNTKzHmZKYxqSC1XHmQNoCHyH8epGIsSZqK1nwzq4FZoiTkU+bJlELXBGpYw0EkBJBDoX1+1qaYLwCgMR4rQDni4DPo9jHNSShpLocRorJ8fPXYmvy2G7Pe/KJPcjMSR4nVMkRMY5tGFq/VRSh9GE8orIEELpjk8NMiWXQ6/1yhD1yrLK7itQKOxfe9KEsLhwfNVfBwrv9ZgQ5/4kNPamCcsrP7HGEga8h++WcRvpWRo11SoUb0xoqhcRAYq4e15C7L2D+QxMiLXiIB+x6dIeDvAThWFVNRUDRgrjtnQ9jbXhMLEr2Lp1QckE4pwOm1uDxkajlVCZAccIfI46HcFYpndggJOpgA9w8oXMESJNrCU62xi2uPLwV2iBYhCijXTnxIHpYdl0EuNCcusRuW50nlTaIVmzZsItd945limp5fdVIq4k+0r9MA40400JliwJMQQx4Fs7iePSnPidP0AoJ0UMyR+xQp3WozvBocrnCzJ5P9A+iGtGPU+AuhX7/eIpS2r4qxNKwytpb6Glx6PbZw8UKYwauF90u9pfnBJaBiyQjA62teg1hG+CUtVeCnfQzGDpWxddMEEObm5zxPqL1XQaNweDSnAYV3jHShJilTcTRVMqbVA5dW8g4SGXDtd0r07HPjUM07/CSZTPCj0peQPDhCDJB6W6eSN0AQK33jLhFj9zAVR20QALH+NSPa/95Y1BiNMskBJ8SIhVETJdobWbUhrTJXrv3kFjAzmNW/5Bsx/Sgv7IfYi55eHUE1jTfLEZYpE2fQjWFKqs11RwVvdgyaSG/xi1pPGjbWwVlFYLp72pxRP9TQwJXmcgxDnrjl9pi96d80T9Xm9AFnlFxPqRIDIbj7WBwwSEGdYNM/hX6MskjRbnuFTp5gzad5hXm5dhL64FAsaxhjdlka2Bm5IlecDhRuLu8T0ni8ujSIiNdWCSJ2UZzogAjEQ2ljO+aHhI055rL5+cyVkWR0Re/PfdGFh022NzY6dJeDLez4lPm2lzO9gxQjYhKHHZuT0yI2dG0eFziYEJseHj0nvuyy/Lwz9LCbE3lBIOW9FkGUexcyz+o/HhA6ebhZuv+r6GmFzZr9C1cXQgH5VHqAaIbpevPSY/O44tnyTJRz0cV/gJZEGISZaa8lYfksZv8dYN5hY/jWPofZr5Dkv76W8zhsHGeWn2no8uQrTv6upo0Y52XN0wOfo/UJ+2YnnuIkWrvRTiP8Rmk8gz8L1odS3WX0iCdIWqQdB0cENN4jSPn5iWEJs+dCC5G6aRS4drJkRTqLz2FRrVcV1G7tmin4HUotjugQPnjrAAGvr+P0Na00cO370bmxCDkJXoMVE/oy3GWa7Qws2FnK9RmjeViHm8qhZP0B/sQ2S6p+z5LA5diF7MeuqVRG0oryrrY13Stj0Ex0HP3OerZueDbvTV00no3CaTCa+8hw6J7uSpp9dOH8ONhnPWHU/hJBUIBDwhQuUV0XWdEwfn5TMSjHG4FH9ktd586YJNbUNzzcSc2XtOOGQiIJL+dEMCCv3a4h0v1tfLZ9XV179Yv+pvpO2pWpz9NWwIGJcovKkst94FOJvhBiY3Hos0b4ne4vDzDCw8w2eTLmow5st0LgzNjy2i3Q74gpQeTbOhJWQX1IXLqzC0vqgOfIJ/6YEEGACk2ToOeoxIdJOm/TwLCbGok3SlDm3pgXQpv5MlIZZS00voJ0zARkNomUcxm15wz8yDxKmLNEfMW85X6I53vLgkLocqslURGZgQp5LNc0MVJ6Su7h2GnCJnNJlQS0ZrDbov9cqtvHe01PATle+a/RI3E2JLkiEd3uI98LP3K9LbtWP0FbDh2S5FK8P01Pjh/H441ULoteTieC91wA3u7YbdeFaAQyzNCD3Quy6nmzTth9lIiOUy2TWjGey38e/VEGJPRtpmBvLMYZoc+eXezv1Tchfub3UgFq2VEMvQDNe1aQpQFa+mk6YaeQAAIABJREFUcZXNCp2hhlyDaQibyZJhbw79fDWY7fogBsrZ41u+O5Z4AIieSqIFC4N6lnpwf5nhv2x1B52S7fb0ISfl7rQ274yAcqpkY+zV+j00KCmNL+GQfb/BSU5aEZP7qvJ+osRYIRZCitGhVCJUprBg2rRwRf3Rd6PoineFK5IwHEdaQnxs1TfHLapYVlFBFTpoxwnXi8Z9/eGmc1Ih9vIaIGAaa16HSwM3HXC4wYAKBDCEk1Jvb0K+7irvZWFDrLwvnTUq3Ych8y3P3k96gfl1UZ8kg1mh9LLimiPdR4up8eh9SBdsr+HE1Y8Rbo4FSh7iEc9tiFU1qFIl6Yc0tKW76qX8TtYmE2oWMhnCJ3Ke0qvOifqif2URSeWZgVMNfDnDFCdldqNfGkdomiHDsRiIEDvoFy6xYUjJDGHZCij5mWR4xMukF2Ae8EOqZgFSJZ3vQGWlmYwhMZPexNuVXgt4jCQzQEgvVPnSyBgGW39RV9VfVtwHo9snYSQHGRv2Gk4QGrAwFJoX3fduc1V+8IEDcPIc2v/QqSZt16UjIuWK+InchniAGBdYSUEz5Dpr1zohzspkQs1DyAjofTlP8aocoTXD4JXHuEg8Alc3hoSYLJnKsEEGLO6kJ8TincHBTeL8ARuLe4ciy4o2+XlwyqmobP0UjudAUSiYBDbj8a2gYDDUn+VwdX/BwXZbUdHi/3htIEJMZk/JD5QrcdyWPdNQKqyQmy4pCD8dq1cSldFIrcf1IZVsnpnnPb/RaT1SjYczg6FYEr15SFqywWAn/6XlJf2pnrdfnfd33284/7nbxw4C/NMHrz625lcfXrCBJa6uvwwilbGvetZgaWlkeRsTsocQ3qv8ATluZXflonHsIF9tmTRkjvkoHVXkPLgrWJkyzwmqczSa9nqk2qYySZAZcgqrS05DENaTTlv01ifYoY9DHSM47YDkqUc44MOAIntj9CvyevcrVU5DTNO+oy5gnbFp3PE87UpP5z31ZCzgbaoZNBTpdOx7BbwvyD+5rAxrKnv9K/fXONBwhCo6HBUXbYipD51EZVFRPTuTPJVsXR7djwcKOvycNkwg1g6wX2JHuHkeqZm7SkxSlNdyX2pcGdUeoiXO8JOPgvTvKBFLiGnvrVmYLJNYpSTEPkQUBktoLjByGjffupbHbSTHixT7w3CQR6r1yIpwuJYE1lCilaXn7sLIeO2YBeRYXGAO5KgOt+v0njVqLFT9YyWaoT8Sv2XyqgzYy127JzJA1LEmEpMrEWCEPoHOQCI/Sq8q2341Q6VceYxLya/95Y1EiBWLCGTANES2k9i2etnEIv8ENTiLceLIEnKiDP1hZP+hBojegFpRwznmapMna0pzH+bB7ZWeajBHzqYmnmOlepr2bV2wJBih3tliFZRsiLUFRjmcBjDxh1fveH7mo48WYKBQiKHmLyouIudOO40V4TicmgP/slk1XVfOn+i2grOIV40soiNRn4R6yzic3P7UH1/ZPvsvb/7BI9Ft77T0cULs9n92/O2nJ/7V90qi+9o6UpYX0xa6Bn/IqFHGGj1+UxxVBfZ2nrMzMg832XudOpBJZ86heQO18zFmDh9KbPh8WvTNwUGA/T9jHGK0/5utKEOIi4wILsGKxNgU1BMcoiLQQ2oglOmicYGS5998fS7YOIFq6E/vb8yl6sWhjhFqdS3ZIWB9+FSQ3PdMpExyhWG1Vf5mM7SzeUeZdpQSzMHXslkI5XfIBPtyKzkgqS0qziHSC/vq/DaOUBpHgksxOw/PDVedZuaeGaJMMMJHg0KQP3zHKGl1SLQvcZh22kdyHDGMirCMr4l0FLNjL5zEtjUFWlSH7NvRvLmS0sMiroZbAant2jvHiuAS9G0WPh8iga4TAWHI0ZiBksV7X3pu+hg/Wg87bdHg1JdxiQQEkBuQsDOyGJvYhnFtWPppR9tXYH0YIXaSrcsiJW+zjYr0ZdKlQjOk+8qlTIiVB/IPlczIT4xCAYXgKm+zH2qG5P3TJ+pWvns+5fXOYomu8d8bgxAbwygSGQyGRK06mjgVLYSxLC/kA22a00FPo3IKyz8aux5oMY+4RBJLTe4cqw1HmdYPnqo9VYwWJ427tzeyCTqN8kKJlE6NYgMdXbMh3kVCSokdrfyZcKgjsaMiQRxPtlbl4ZTxOZ7NGHujfraQYyEh5iHKvdUjJtcQnUqYLEtmjqJcfmxP4iKGqSIic91Jov/jM2/O+edvTo/s/M1/9n1x+XK/x3v3s0+P/2xB3tdu/vGz9OQOUYS9GjYETMoWOKgzt/QF0iF9MBCeKidHsoliMw03zFNZciET2d6GRpWHdzyZEwagJedrtqhBFlcooJLLVdJAR52fWyKPYvqcngulhQnDdVSSxTod8d0rJuaV7mRCHeZHXxKTwvsPYYyQ2hh315wQU58ejdMr32ne6+qhHodGiJ0TjYvHs+O+WOmwDR7QzdFLiOta3Ss0wCoX1LEsv4J/TSMUN5noHwLjxBPPLk0StsnUxpZwgsRwvyS0ra+QnWbMIWdRb9VHJM40rcb2AwbVIq9nukPaeQkZLgboS13tNZNz58c9pdNPVsOEkWBtb4iX0cgwL690OZeCwTkAY3zEPFKc18NNgeWKAjMJTi2vSfwXiTeV4eRFbBceg4VmwggxPfBP5/RyUTrNIM88crShEWJduQeFIMUfwM1RYkeSU52T2AYhFzM4IKkfOLy/bhBCnHUYRRk9WPwy+RnI79JrPCF2Uk6gZAtb0vhLlH+r+j6pyXnkkZ6mAxXh3PnPQjxgZWbhWV3thTe8v7QKZpEpP2YW33Vamx+fmLfqHboBpRHFfeIDndajv5wUPoC6syxyT/NKVxscD0tjoDod8Tg92UHmQ/19H6xfOnnOrJ3NJz9De7X+tsNPh8LjC+696+Y/W7z/rMi6vy9R/ujfj81/LSHu2avhRQBWGsXOL7vsr2K4ZZfxUN/q7Ty0ZjOIbDeiUyyPf6zlO1CwXjZATIeSIB1p3eQJUApF8D6vbACUwrMZI1jE6dh9oRW7KkKg2zn74eEKsUvFvQoNmKqaERP9r3iT72PlvS5NooR6zEiIRVAtn8+rqccIdCL4Wk/T/u0RjDvrM2/UVTQYjdACWnltN5VkX6Efgx6hZEt5dXvI0YQr9jo49BTOQB2Tti85aLpQdSSdbFVQ9tDDht0vaH6otkf5eGTJ0I29HZ6/19NU/1A4TIvu7TwcYweHyaI68OPHyG7PbV4S9M+p+/BcnA9lNhKpN7BsRmwUpYHBsXokJ5OjSdEwMhJimKBQDa4LKeCjQEZwnwi+BjsHFsZA2IPxr1cvaJh2elOSKKsmUmqia/zrRiHEIuBL1oAla9ZNypFP60mbEo3/dqwtipRtNy5pLCG4T6pWs5e7XirQpSDwNihTglPLPFI0ltNw/U3FV+VvVk+TTpe1bO0OXsn7o4/pelUUjQu30HQ5ZX8ftLePeM6jggiFarR21+1DibCSc3/qo5VzvvPklsRl5bbb3/fxb96zp3KooAzrr6uittkPt2Gta7aZsVPlPL0RMnASNc9EohnD3ZNyiF4FoowP27/sxwgW6bRFb8eD5R2Xiu1jMUmyxarltC0NKxED2INB/L1wbEVIkFpTQhQdRdR3utpenxuJvi6EbTQhmRLL9TnHlK10D79xqO7IUn43yOVgRyiaJA2vdObLQxL95Cbklhp9e3q7jiyNRDald/th9fae9cieXO3fFFg4RHfqRYNW9i7PMbpg+NS+6TYaktlp3DzuPohtJambWDUutZffq1pwsSfZ/wWeIEcU9qYEGw+UanNTfjiYem80UvRGu+Zq6boYsHk1nEmU/T+nLTruR1uz4yrZ5zroN28YQoyaDv8Ej/VPGkR6Gl9eEky7g0yTyN62CFgEhJ1f9lOeHW6231gERhABSgqzGqFEt5lOMTKClbZFWQS+bARuHEIMYlcqrldOafcgjLtnOMRYDyjoedPesAhYBIwIZDnWWABUO9yMKNqbFoFrhUA2I5RYSoAhjX5s+LWqlc3XIjCqEbiRCPGoBtpWziJgEbAIWAQsAhYBi4BFYHQiYAnx6GwXWyuLgEXAImARsAhYBCwCFoERQsAS4hEC2hZjEbAIWAQsAhYBi4BFwCIwOhGwhHh0toutlUXAImARsAhYBCwCFgGLwAghYAnxCAFti7EIWAQsAhYBi4BFwCJgERidCFhCPDrbxdbKImARsAhYBCwCFgGLgEVghBCwhHiEgLbFWAQsAhYBi4BFwCJgEbAIjE4ELCEene1ia2URsAhYBCwCFgGLgEXAIjBCCFhCPEJA22IsAhYBi4BFwCJgEbAIWARGJwKWEI/OdrG1sghYBCwCFgGLgEXAImARGCEELCEeIaBtMRYBi4BFwCJgEbAIWAQsAqMTAUuIR2e72FpZBCwCFgGLgEXAImARsAiMEAKWEI8Q0LYYi4BFwCJgEbAIWAQsAhaB0YmAJcSjs11srSwCFgGLgEXAImARsAhYBEYIAUuIRwhoW4xFwCJgEbAIWAQsAhYBi8DoRMAS4tHZLrZWFgGLgEXAImARsAhYBCwCI4SAJcQjBLQtxiJgEbAIWASuCoH+vgsfJlL9V5XWJrIIWAQsAlkhYAlxVjDd0C9dOF6aF6rYFcv4b+f2yNTgA7HT3dcWCufY7vzCyN72C9e2mMHn7rS+u/LfmnoclvLMwftvC1U0vPe7mjWHOlPsrv63p6lu5butPJF43PJy5Jbc0ni844q4N/SrzpWhUEVl6+Db6PK+6hnPb3nrV3HPv0PvrFv1vV9WJi4PvXbXKoeepldjsdrSXH/BU/GkAeurLffMgf87KbKz2dN8F46tCEW2H5A6gyjB6YivrDst9V4yuF4ZdEM7jZvH3Xc1AyG5ryI0L7qvTaqDqB696oz9Y25p7LDWb1tqQ7OiO456vteTHG60vFx4a6giy+9KJVs3rXyXFYdjvOhwV9pRYyjwctfu/JvuXrfjj19ID53Llw15OB2xwrz1NYmhjKxUz3vyYHddl7Rjg6fRL3e9VKAP5O7K+/O88EoVd12nY2U4/NygewXm4XTE2Zxzqb0qL3fj7zK1tVLsYH7oM56bai4at2ijaEdjZlc9BRlzy+pmSzyvUBmnZOwYRi7LDir5DP0QHAvL16+XVz9c7B6pVLrQaFol2XfYv8OOgCXEww7pdZfhheNrAjnrjlPK0125xEh8O2OzfJFo2+fX+vOctmih7/a8+FnDWkfLvtxZcw9h8Bs2LF0VHh+qfe/0oTJ5RovFYlu3Lpifu3DxUbYMD7He3ZVL/RNyVx+idCEVL/uGP7i5zXE7jj8xzl+8vVFwZakkTGX6lpa66WNzaw81Lh4v8qTpkIdt36l9jvxzw4ala6YF9GXYdV1oo8LwgYTj9naqgGzYsHTt9LGBBxvMdCcVX5fzzdzSF+RSyHVtRSjkm/KTX3+qS+d6DsVPpGXeqebImOlrPZTLdT0V4yWuX79800IvGhQUpyNewV/FC6xYMFCyUb6tr9ap+BPjFkWrq+V35GvWf0z0EQaCtiiSygA58xVubTJw70vvbxrvC/1SWkrp4LqSKH8gGDHR6K62DZPyVr2jtYvTFr09UJaW36fiq/JjZBX3fgKOoAWxDB0f+skPI80XpZ4KHLdu+pjAmubs2FXLnhk5OeuOp1ysf+kLOvJK1mfeemCsL7gwxrZ/WENvBVia/kufnGmV/507d6j+4Vt8ka3/1tFB7r///smzR6J33VH1Zo9MkV3XOVY3a4wv9HDsdHeqeXZ+bLvc3Du3R2bkzI6ekvot7qbkd2Kx2M7tkVlBnzqooWXFJMlq6roXfrM6qA6r1KUD4aABXpEGrxATpRchscOBv3798s2LxqnZ8uTYx9hU7LQtzq9qSz9Vut6BI3/s1q0LHrhzrGEmcV1sI5hMeOap5khOzrr4lXMwa5XuMm+8BzsF6fhvx00C7s2kYVtbEQoEHlTFMTBPFh3ucqDfjs2Nn3edxi3fHZsXP+uknXUZhnL/h2t9sKSaI9/w38+7KyYbXask+xL7d5gRsIR4mAG9DrOThzrM5n59LsBvMk0cw/GxXS1N2tIN0lMTieSlISOZVdfqui7Mfd/3LO38Te2i63TdSnk98F6nWSF6E7sn5szec4JxIKctGvYvq+yG305HLBIE7uJ07HuFLfm0YCCakyLNSa0eSD5wEu+uXJpD82HvyM3B7mX5N20bdbVV5eXM2JBWbAb1zBk377GI519pSe63PQsGVAfKChL+5P3wVHNkPFuz09Wdrq9ivYU9RqBkj0cIB4v6SlnQ3tO0qUms05B/96aHwrXehK6OP/QcldYACwxuVikFCpBqK0LBqauXG6iJ1P20b3MaNwe/q/ZGoC/+pW91ua6brFn77TGwcmv/kjVlBX5aK8Z0a1ZMzMP9CeVGGizyd9Hr7u79U0gSpH0zIkitTEyCtJ1OAkSf1Kpn/in3UhgdQd+3Qw3/bX7XS1Ccxuq7cwTsPceazksC3b73Diz/3qT5D/HOuHDhlH8tzPmzHyy+b84cfhMvime9duYzUSrp53R3qncw2mnT7epx6itYprAup7VpUyvuELw9B0sFYeRDZB5gtTjTWHJLun0Fp6fQrEuC/uKy5evXo3IjVplof/lH4+hcgdzOLPoFMGErTseNoQKsIsa/cs8xvkBvXmrfdJv2FdKkB1IAH5mBtUyuegpyXRytd+N+vkVAQfLvjM3IWRfncwXeRMoeiba1ISE+e6lhcvCBA62Oi3RWm1TVWsqVlK/ZW4ae48odfuRXSVYz+/caI2AJ8TUG+DrIXhrqTuPWsN8XeogI1RThk2niUD6up+n1DUtXTw2mXReVt9kPkMMtySDDi8ViHukmZyTOpQPhHMI2WH7X5K8gOl2tlXVNPb3AcnAx45R669YFSwv8qlSJTPGEEBPVKhXBwsuTcoh0s7YiVOAvljTIUnMYPuZMw4J7lTVbfsfYRk7r0dW5OTPSyLBJclgm8ye/8YEsliPXnS3RiJEQw8JcQNfvZM3qYET6BFiTxhtTidriohIWclZcbjWpDH87dal+8Uyh4u849vhtkmQdsgr6CkO1HkGvvvx7aQ0TL/GiKG3ifNHbHLz7yWlgPYfeiLsmp21x3tytKOuSX04lT5dFH9OFwURqTiWseoVRWS8rcEiZ8mv0+kzjwvGerZcGFKuwsZ9wSRt7K+NfDZYzB2fnR0+dYfRRTkqk5nRK4eOFX+DopjJdOZl6rTRTf89Hv/+M7U3Fe72d+6fkzo/zfRESo2KyMSDFoVaBt6xICVfJmrV3jtU5KCqCkBem9K0UKh8q6K5pPY7iOSB7RvbPlS07t0eK/WFZzuo67U0tmmye1ESwQIl6qpV0Ye+hmgaB1URG8YGaAzR9yNNP1HeIoCHnpyAidRqr8u6neja52/Q07ohWGDbY8js812ymIEqI/wU1GwIKmoeJELt0Wv4tEOKNL6wIPYdqOmkLyiugXciVlK/ZawMQ4qGskqwI+3d0ImAJ8ehsl5GsFV3b+tzeRG0or6ph2+I6FIWmks1FBX6c5XWW4KkeCCr+9/Qfj/FlEBR5EhEO4S9GWa/8VDO9hTUmHKqoZUo6TjI6YRkeESuOcYsaQchHVrsNz6/9By7qU5Zq+SPgWhAXXmfyyoXjT4wzCAvhoWiOzjfnRnbKBqnQIiFfMFBCFIt6aSi1Vdf7nsZ4ejsEkR4X/rGhabmef9MKA2ZZr/g0+E6smFCwZkGIkScJIdNAy5gmM4OG4HKy9DI5pZKu60IreCTEKKoXWGgCVI350UxAPOacVZgN7Armo5oVtOFsa4TtznaYtaW5QZ/PP4eML7lIdq1XWPQHxYhbfg2vFy0L5UyKSaYamKHT+sorHUKsxtTTyAsLQxVPorrcsElDGscMhJzWw5q1Ct2jjpH3hETk6fPdrnPKZM2aQEiWmmfRNygaqZZ/Gf/jn/945sylS//PzNu/Nnb6LyKRyE9/+k8P/Oh/3jzrpcN9DDT429W2Y2bkmSNoeYLz2Kp3zh1BFb/4fq1l5eS9iZcmBVc3X3Cd5KmS0Kw3uWUU0/B4eo7cBC7sAHMmPRVP9mqCVZOqBNwPGEWGbP3FT5WDFQdngZe7dk8UMmC5msjadfoLHa9YsQORk2jXwP8GJMSwj8JSiChUXgIKia0asSqRzWBoOV5+meUURGfLe3E+Jy4WwoKrtiI0SZEQC10fFy4w5d46uVeT7RYdjKwn0/7/zM+L/RNy598X8tGP4ps0kNl/o0wVSItp+epXSa0t7M/Rh4AlxKOvTUa6RnSop5I1K8LxE47rdJTNrPnI0SZf70znqSfqsNJrTj3vu6gsJgplJ1H+YOCuUBX69EBZfGW91F5+rzrdI8mYVZforgyHDwBPvab/wAdoNhIdJG1z6s6dihb6xjM6ayTE1ISXEovHHiz2F0ybFpCUjGB6iGteKtlazYg++QyZgaVgeb6PGQMQuAx0ii4PjOg8B+bCJbsPH15eErjLIDf1wnW5Zm3++h1/QO7QnzhZfsctc3bUE+vMnn3bHmk40vNF3/nDzZcke02VELhEasIIbhakR7WhdBq3fOeWjLoFIgbm+m4gwYQ0ZDK3hUoK22hZT00WP1hEvWYM0P3oGskWVFBtkySU+YV+vnpJ0BecReXizonGxePBftFpPVYFIn8imKQvM1i8wOt31Aoz9cgYlcSTjRb9LuxjKKpnfJfUk/Y9o8eVeSwbe7JeQfyNmxmfz18w7d50ZhI0HbGmoKY15N7AGm1WJvaimdGW87LigtgT64DIFu1kmBRv+Peae8DmdWBCnEo2z8xduJ+ZcaN9FNGoiE6eBSGesaeDWU+xLzCrSiQB8JnGJUFGcDkhPnMgHFYNWEl+6K5gGP6q5IKXbbwwN738am9nwz3ByMsnnCudR0pkcplJvu5e/RREfPWI6XYxVTq1xPOKAQEUwy8+2uloIhJRXzTe8JG5FO4yOwr0dRHNJxLAlQyCej0tULL4aOeV5tn5q5i7CE06bKukWhX7a3QhYAnx6GqPL6M2ZKi/+1HDTqZtPHNw7sSF88Yrwh554khTy0ETYtWR2emIzfHfEWr4b2VSM5RFCXFHR1nRU//Rw/b9fH/PLzJxAkO2xltglegvWBqtrt66dUEJyLq62qvy77yTixU5jejteEWbQzNIiIFS5Kw73ucitwg+LJkhyoQYha+nFk8F766PwW4v9HRaRytloodVDWXJis9ZDKMxMPOJ/r6Pdj3DwZIuajZOvuem7+Sv3sHv7XiuaN4df/71B+tP9jHnOn2xUVS3AxNiQqCpnTGzeajYRfwFGUVQWwSkwtOoQgDI3xvQXWG7Mk3dL0mp9Ep6aI3RTkDp6lpzSBJiqRzsuhNyS1/ALieryGlfBXt3+KfEWyAWpYymo4W3XmHqhqi7rKVeKZ+8Kd5xCkwtj7S/u7J6cPEKlA+kNRuEDTEyTp/Pl/OzpdPzd3PDep6RuAAT+fxv387VKQhBs0dwKxIoV9iLJuWWxGS7YWJPrBNikU44uWZnMtHVXhvmGxjS4alT3Zy6c1e4D4Cn56gtBRLiGVW7Su5d/s6JQ5J/rXkgsJ7sdMTyBYCMEHdvikSPeFwbgfUG0vpZkvF+u9E9TmCjTBH8tronR69Ef3HZs143XK3bGEmqeCebKQjqgNDtgQECy4HakwFkYkTBaytfIHefGv3ZpJzAwoVomvJBW1X+pOnzZxK3DVEZOVUmQux1sGMph22VZBnav6MRAUuIR2OrjGyd1CUf7b2CPp+QfpHapJtcpLoOmhBjWilSFbHQdcyriCiIk4wriRenTG5Q/KtM5l8i5QBXwoeGv5hKnlqcl7eq/NTvjz9xa2BNc7K7cl5+1e7HaXwDrrBD8YkiCYMsxKqJdUbVOXprVTVU5fmXvtWteRdBGrU54A5ZWny+INNd8tppF3IbEZkZk7xi0yyIdSbB+pkLmfpOHlq3vbrxrfr6F/es3WgOu3ag7JENe3e/9hoGZDvawp36xadplYCfA7cCcoJHt8+mPvIcebg/QdmJGbInS1owuPnUxfqZUgfoOhE/pTAJvZIeWpOWECsSYpV+8e4n1wx8Qx2Xa97FI4BiEmyoYrHY9urZkeimWEyzeJFqpVdY5GO6YhTK9Ize0/zVyF3aT64kWzfxPQ/vyTSGiVG0DMmJGeuyNSW35Kx7Y18mm6WOY8snRZrRxFNyJZR5KmwGFo0zelKSXjTel52EmH4tjJRAYDkxHUkdXTy5Uo0ix/dR9H0o5FL9zHDFrpXvdl6G2AJkiBFTgWWVnxwcjMkEOuRBzLXNPOaMIg5nW/etWxfcF1qxnIRVKX2BaSFKFgZuB+ve5L5nIlt4DqSmTqJ8ccH40IrneXvxC0w+NvDg683vzMbgGGUsuouwK+Avo9GOYiHAjB+YrsNN9RyCjT33FZagiq/LuZuH94lGHlm+fr2+VRvsFIRWTJLqzHXdS+3/r4AbZhSDsRyEBK0t1RwhCBve2tjTjk51H2DTT8m7v+6jw0W3otOhJIkXHwFXWMnlhw8+Eyl5Yde9YabPYRq2XcoelSZVp+UhrJJqVeyv0YWAJcSjqz2+jNrQod7n9nYeKVlaAE514EWR2PbUdDQTrDoMykR5pktTy6sjxHSJ5cbK6JzkMeGSi5QYiTDcpNEqBqJihkWCrBZUuy1MNeQS+fLQ2/5UUazjE8kCmEuIlffpD8FveJ3BO8c3q+5ccyTn7urWTyWpJ02jzrx400mULyvw+zCkmqwB1ovU24joUpdVdl9hkcJ6lBB7Iv2F46v+OlB4r8eEOPcnobE3TVhe+YlkLEFSiU8TubAr/NjxTyraavYM/zLC8elvVSd9apQiCx25Yz5f1GF1hGBwt3PXJfIIF3tuaUPK4RI+UrxEPWl9TM2nwEib4wpEtyVEhzclzYL9AbSDU7c29qSSp2PIeMDKAt0LnfKcAAAUcElEQVSteEASTxwSSCzVClAVNh7ku4jUWTF9wWAUscO/2s3jEqj2EiQhYVpqGDgoj4zTwLRlNFyxZnoEe5L0kVvg6R2hht9jR/r1xeYiVK8zDMTf3q79kXDtRyIqFn2k9Q20t07jFztICTHsXWcFfciuHJnraz1H9WEVNVamDrKfFJ1caiOSQm0psDqlvhBKH1byFEUZr8T2RpUcu06ifFboScmDDfqkRCKlPmzc/4jScNyFn61ZP3kA1wtul9zTWBsOE/sfUJFNYHZNmnk6L0IZO7DfQEeUDFMQ1ZVJJvIS1AAykRBjtxGmR9C7QiuIf2pLPG8q6oh6EzuiKzuuuJ2xvLkHu1xwufZLbru8jtj/g2QbBg00A+XTjCiniVc4bKskr4a9GIUIWEI8ChtlhKsEQ91fHHti+hhfcFZkZ/O5c/GKGDnfoattX5RQ5AqgIKrDlqeaV0uIafRQGp2KWBGIuQ9n1ZM7JaWwPDmeaSwJgDkvrNP+wJrmi6iQbfoQrCkUYaGntoO4IWi360LMr9Zm4RLHV6Pe800ZohzwOlN39Stt0btznqjf652y6cwrVojkvi3TArkbj7VBcCvhuGaov7YaOcf2TA+AYatLLW4dg/iZZOMplObOq+0pTXAF8qirbeuC2GkSphpykxZsT1psYmzuy8yZCd/JTMVYNtjNfD7fdwumPbg4s0ulBohMPWluvPlY7vq6SJFJuUjdQLhOtzTMCoIkBDY2AxzzL5yuW7l+/fK1RTU0UDF8FFp6kBC5BjE/gEyF0DqqmDncJM5GrJL0Thse77IRglLLAl39q2kqpyNeDXrwMT74J49l2WgVBcAmGoG5cDNWurNKsT6m79O4yF+XwaONkBjdJvz5V0KskmwlxE6ifGFuyabHb0vb8YzYsrKIQFq3ORZJpDYiScQj+C3zKuyf1NjdS4i5yay82/HEGjvTuGgcM6nvat2kHe6jxQrMhKH0fa5L+vDSty5mjl6ME05e/PfdqEDY9thcNq75NEJMw9XNJylJ63use6SdgtB0inhnknCBRYfPJZThYCDE0tEkxNqKuxZgJVLxtWN+UdndDeuastdKJVsrNy8B9Sfr/9Cs4h2t8gp2MA8Myyqp5Gp/jDIELCEeZQ3yJVSHDHWuaIMaOImarUuCyukDmSYLWulBEmKwp+TCGx54lYm1RJwmqtTDYPtYErI0EtPeadwCAR/O8vDJbAW6gr7AQz/DKZU89fRalJTvqgjnzt8QA9PVixCNH4+E4IpmKrOUeQmsk0RC2U0y4UbDcKIBStoCgQBb9njDUwZGCbFz4uC8fOaTTjxI0p9owNsIzPsammto7GRphU63dkKhX1u848X6ejSNoP/V179Yv+pvzAxDJgQoRhJh1wx2IPzr8EImvlwWBUecgFHKANsY51jdzLGBaVNCvlBk7855wgRTLYL8AkCEgphpQoWHnO5Uh0ptrjlVfdq+wD7GZF2C0mFJhOyi0FpXIlOrlffRBFw+s4NXWCJbMqr8OdxULSnpnQ/UcK2pJNk38m7AcyAXzrH4j8aHatuS+gupSw2Tx5HQtporrZKDbMYqeinaT8tm00oaj2my8IaE9+BDcph/qpaQeKRlZUPsJCqjkdqmHtSEzKo75znGhflsTcgt3YjDmcXkliTrIOVF7Tn3PUiK5pDaiFRTPMLvkAWNfAtksh1iExT72M5YMY1oLiTErtgxstfkvzh1iHkjI4ZyOpQagJMGBlNbm8YbgZBdH5BUohsUWaDwe+mBBMS6SbMz510r2ymIdVqxEf1c3R8aCLHLjE/kxQJN0cpRjn7m4PzcyJG3dT9d50RjCdjnnD69nNkKY2/cUBXDY274wDefVAeEeBhWSQGnvRp9CFhCPPraZKRrJNY2pWQh48HbfKZTXlJ+DJIQK2nFj2TNGq6YE3flKyTExM0fbJ0Xxs4e34JnFKXkFUgL1CVnMJhr5i8lH7MsB01LRzHBR6R5Z6TY7wfq/Gr9HiLJxqOt8Ig7DLNgcJKTmiO5ryrvJ4rHmBSXw/AR0EaFBdOmhSvqj74bRVe8Ky45S49KStLV9sKxVd8ct6hiWUUFNR1Bc2e4XjTu6w83nfMWRglBov2NIvVMb6BNoUzR9zQZJP4sWFlbla8GWPUWidG14ISR7a2wpIUizRcyB2FVLDghPw+t0eWXcqGwWAbWNDSyMMBELvWFweoRmJ0nWgjPChoUZFLsBDvPOSZSrVSaRbOAm1kQYiJsm1PX0RGb5fvnhU9NYTplXhN24R3LVAeSyXHT6YjNFse8Sb0UrYrNcaChQLXLGcic8eQaSIkmE5kkxP09+1Y9+uuPmZ8naV+xf+tpqqwkx2rghxuxZZCQ4oYkIeYRLZzWQ2uIfy3OVKrtkJcQsyMnZEKMfUbbdNGqMnMjPBUIYdLsgqRPUi7lcWcwdhfvariJB6RFfMJ2y2s4cfVTEErZAUPQwHCZMbEhppOSBxDskxgvmSnBUFMBEOVMmvS/2NlJ0hfgJe//EMn4h9HfcUtx+qJn3jC6duDLg18l9crY36MJAUuIR1NrfDl1Uda2zkNlGE7hLnRlkLx/+CSSvpLDQYiJfAKkerqkTZSLy8ysuvMg3amobP2UH4igrmombZrI5GqunI74i9WzI8+83WgwmZAzRL0wyAsZjRZCo5Y901AqTMIs0LNbZW8w2hxXqNBLO4AAQkTleQ9rdVqPVOPhzBCiONl5eC4L1kZs+NiBF8AJviVkS6LK/amet1+d93ffbzj/udtHrJxb3T998Opja3714QVBOEQCt7vyIdQ9+ouZsI08JBLcx2t2z8tXzF55Uk9gWnrcrsGWgKchF5LLFPRGDKcqBMzayywchHI6rkQ96esqXRN5EGno8srEJx6ra9r9VJMJkRIULCihpB24p/Fl0NJyuf6l9zdoFBDtEMiOBRrIaEPMwmuQcihF5hLirpamD87zIxvoUIWhZD6e0DCW8eVJk74r6ql8kavvzeRJw3WdE40PBH0QIXujZ9jKCCOZK3hKyCZ5O/LS+t7ZPf9OYsv+k9DY/+H7W820vaDgtu/f4b8p/55vfftb9951s8/393fsep8dVqc1Deoc+HHlI0aI+beQvZPSAzntYy+JIydaXp4aDFW8EI08sgtN5M2xVhTmh5nA3MKOyGG5ev/ixl4yDfcOQ28ajPwAUagr0I3EhYgcELuGqXGctuitTxxnuqwhTkHYdoBVz/EnbqXfTvs5CDpAnfiqHJTddV0eLxnM4sPyAas4OwlbCO3TWP932qLB4OYTIpYIfU/ftMBtucNDVM2rXiW1utifowoBS4hHVXN8KZWRhzqvADO34rM5m0T4G96LoRNinLX9gTX1R4ntcnBWZPsBFgyOF6gue8ma0tyH+ZEEylxGDSo8h+XynMwXvQkRgY680XX61dimheMZwU0jIZblJXJgVOImGF694/mZjz5aUAATN4ji/EXFRcR7yWmsCMcZwTI2h6eicv4kDBaLiSsZ2IE7zoOBu5jFBTlYxLt29qf++Mr22X958w8eiW57p6WPE2K3/7Pjbz898a++VxLd19aRUngxtrWPHT/BqofEiN5M1qyD0/i0o5i9dhHE+jb/zgljpcPnWIbir8SGqXaVnC9Aks8QBhsiiaqdh/vQc9SQETJdEykRN3KchLc51O4HiajxjzgzmRII8FKFUNBVexs25PmJ2ygIpWYzxx14YQkc8vdRAp2EOjxrM2YfXztmgRKYlhCFjt9u+e7Y2wqXFm2rqXt92QP0WBDJ/5UYcni1EIaxTD3SzBwiua8itEg9H7Flz4wcBUn0u0eTg1ckc39VQoxKG5nnmeLJ9Kcudl7gna3vvYO/GPcXM7eRqNj9PQdf+umku9e/+nJHr9IdadN5mkYmi4MixHpMMU/PUXNTph3Rjzz1QbH3N1hYA+JzyY6cYBJiiCRYGNnbbrIdkq1WWDGG1mSP+F+05VUMzam7m3xGJr6NNiQ00ggcHk5lImBEB/FAnn/z9blwngXYhv3p/Y251NxlqFMQBJcQh5zzanNCDJE3yiS3QvYGsXEi592InTnEyoTdOtPJsLfZX4pYJzXUVptSlaqwJAoh5jevZpXkie3FKETAEuJR2CgjXCVY8uVDp7hdr3yB9lWyI45WSTQAQG8F+I/TaO2tTD97O9+dDQYGXKKDZxyEvAHgZJ21c6w2HJWXanVl6k28XUmWZ2OwAvkD+TV+qY8LPgnz8wkLZpAWeG2IqVLPKGATwhgQLax892Qr6v0bez7Hg1hjb9TPFoIWnHkzNwe1nBMiVckOTyAMsQ6QbCUuYhwlei6gpkbs//jMm3P++ZvTIzt/8599X1y+3K/rnd3PPj3+swV5X7v5x8/SkztoEQJbcsNJbFsPB4JwBkxsJ/gJJhg/bv8U6QQE8FDcD9GUQMbsYFQTGuFEfAW56mqvmSyfyosxT/iBW8CVMejELuWIEwiCS8M7sPyAoCicz7hlSu7bFB7PdhHZEGL4EFRWdLOCejsPx0rzpkrbObJIF057tIA7p2LX4raY0P9J2Kzl63eonJKKnBk76Wp/o4iY4lBxrBaMWWJIWIRn2EovYIWJ5A/OrsMTxbfIMDLzXE1T4SHEkBGN9MJAIH/5lgOF0Hi0NXsBBcb6kWDsoev297xZsyD4F9PK1v82IShy/8dn6n5w900Txi3aujNBnDh5Ei8B7TpV8zQF08N7eDIi1I+DDfGUcMF4VI5hlGvxBtA1ZQMAuQlZvulsM+HEJh8epE5QsHsx2RCLguUrJ1H+gH4oHWKoDWo5jeu6PY27ZxvVNUQdJymyICHG4ZFksU5HfPeKiXmlO1mvgL0Tdr+YdKjNEKYgUlvjSOSEGLQussUO/0I2yx1qPloVCkFc8z+0kdm1+wPQWpCISfR1Vkns/+vfWUaDQKtNSUK8ecIcDcsqyattL0YpApYQj9KGGcFqeZd8U+H6Imp65yrvsX126CEaB0rkA/MdhLlQ7PCkZY+HGeppOgBOb8+CE/HV0HFRpOdKWlPpM1lC7HldvyH0evDEaW1+fGLeKhIwiOuafWKtdVqP/nJS+MB/69kM7ndX296Zofue45uBOApRfD6fRAf7+z5Yv3TynFk7m09+hnSnv+3w06Hw+IJ777r5zxbvPytK7O9LlD/692PzX0uIe9KV0xGPo8ehri53jr0cCTILDTx1jx+K29NUv60odN8zShIpumdkOz2p20nUPBOJQhxAqUSVEKOMlvQTDJOCR44pB4WwpLznXOk8tGbTwvGhwluD2km2XBrqnKgvKlomjrKDXopagnVLC/wSjCxv8RcohfJd9BGwZAzywE9z9EQmQYkX3fDw7SW5CC5kQmLNXrnj6LIFqCHpOv36MhpEgh/1IuxZ0QJ7X/S+0Ipd0BkIS+YfxWx7qIEH+lR5eLn4RNdIiKXn/BLYRk5u/CzG86YBehnHWpseyeR/HSmZ+7c/juw+cY5Li3mebl/PiWVLvve1m2bvOakIinn7ilchjPfh2LLQPIyTo4YjoNbPgC9uQfV9CJH9kwO3fdJxaJA7fJcwfVFpLgvOAxnztqZVSmmnoAmDXSYhlusurkksPx58DZQSvGLchkG8zq6cxLYVoXn68GFPRYxzMHfhW1n+GFUfVN3Bb8IFDHkil1XCOMjvZDMF0ZELfbIiBKqzsx/K54SjbIK6L3vNiJ2OeO1D4XAFVyGmLjXcc3tgCj3uFGpJLHkkRRb6dUBsvtDPHwpH6YBSm3IwEmL5e/H6Gq6SnrLsjWuAgCXE1wDU6yzLC8dL85g8LH3VO8sikdc91gvp3x/4CYhLq6ujkUgZE32Z0ziJ8vvzNtMgVvDK5a6XClT1H0kIIsng1LLyU1xQZ85wyHcvHFsRVnzd0ubY2/nm3MhObvp2Zn/0MV3xR3TNNIhp2owG8QDUi4+w4Pw8HdoqaGteH0qE+Stw0Z/6aOWc7zy5JSHCvuHz/r6Pf/MeP5UDbsFiyZpPE6cpOdIfhJmlwNAQQ/rvTdeXnI54zc8nFzVfxITemFN4uzM2jZ2/IApz2ptaSCrX6VgZXuE97utSe8UUSRkNexXFTsNp2lFUI1UMD/WdH5fukOMAiDO7KHkUXbFQEiaFO1aTas+PnT60piwSjVbvl76OfEdv56E1lcUTI7QJjB/XEs8rNJmpeF7G03cfr54dKXmbnY1MS4GIe0te9pTuuu7HJ6tnzNz0VtMf+zzZ8RvOZ6cfnPjQ2x/zG3CRbmYAbnTQaNQOp6P9m6kOcr4ma2x6WOAV8p7HUdJ1QS/EzqKXM0t7zc4r9r7Q07Q3Gil6w2tE4enAclqntfmposgzR1TY5TfoNRyNtFKL7GZ4bRC3sp+CMFM8fR3OTHZc2MdifHHDlOK0LQ1XnICOnYyXRcqYZTPJAj+2zNuZvRMIWsSdb4qfYMsEdFFhdAdTm/dIF/fLWiUHgbp9degIWEI8dAxtDhYBi4BFwCJgEbAIWAQsAtcxApYQX8eNZ6tuEbAIWAQsAhYBi4BFwCIwdAQsIR46hjYHi4BFwCJgEbAIWAQsAhaB6xgBS4iv48azVbcIWAQsAhYBi4BFwCJgERg6ApYQDx1Dm4NFwCJgEbAIWAQsAhYBi8B1jIAlxNdx49mqWwQsAhYBi4BFwCJgEbAIDB0BS4iHjqHNwSJgEbAIWAQsAhYBi4BF4DpGwBLi67jxbNUtAhYBi4BFwCJgEbAIWASGjoAlxEPH0OZgEbAIWAQsAhYBi4BFwCJwHSNgCfF13Hi26hYBi4BFwCJgEbAIWAQsAkNHwBLioWNoc7AIWAQsAhYBi4BFwCJgEbiOEbCE+DpuPFt1i4BFwCJgEbAIWAQsAhaBoSNgCfHQMbQ5WAQsAhYBi4BFwCJgEbAIXMcIWEJ8HTeerbpFwCJgEbAIWAQsAhYBi8DQEbCEeOgY2hwsAhYBi8DVI/DFF1+4rtvf3+84juu65Ce57u/vJ/nyp/zO1ZdnU1oELAIWAYuAB4H/D9LQ/PtvR17tAAAAAElFTkSuQmCC\"></p><p class=\"ql-align-center\"><br></p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">XL-MIMO天线场强空间分布基础数据集可用于按需灵活构建VRD。如图3所示，以site2为例，选择值为70%、80%和90%的能量集中度应用子阵列能量集中度VR判定准则进行计算与数据提取，绘制该准则下的对应三个位置的VR可视图。在应用中，亦可自定义子阵列形态与数目。</p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\"><img 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\"></p><p class=\"ql-align-justify\"><br></p><p class=\"ql-align-justify\">VRD可应用于基于VR的XL-MIMO传输设计，验证并评估其在真实传播场景下的性能。图4为VRD数据应用到XL-MIMO系统多用户接入协议性能评估的示例。</p><p class=\"ql-align-center\"><img 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\"></p><p>使用声明：超大规模MIMO阵列可视区域空间分布数据集来源于国家自然科学基金(62001254, 62171240)；江苏省高校自然科学基金(22KJB510039)</p><p><br></p><p>若在论文、学术报告中使用该数据集，请标注数据集引用方式：</p><p><br></p><p><strong>引用本文：</strong>高锐锋, 苗艳春, 陈颖, 王珏, 张军, 韩瑜, 金石. 超大规模MIMO阵列可视区域空间分布数据集[J]. 电子与信息学报, 2024, 46(8): 3063-3072.&nbsp;doi:&nbsp;<a href=\"http://dx.doi.org/10.11999/JEIT231273\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(47, 160, 230);\">10.11999/JEIT231273</a></p><p><br></p><p><strong>Citation: </strong>GAO Ruifeng, MIAO Yanchun, CHEN Ying, WANG Jue, ZHANG Jun, HAN Yu, JIN Shi. Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays[J].&nbsp;<em>Journal of Electronics &amp; Information Technology</em>, 2024, 46(8): 3063-3072.&nbsp;doi:&nbsp;<a href=\"http://dx.doi.org/10.11999/JEIT231273\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(47, 160, 230);\">10.11999/JEIT231273</a></p>",
-    "authors": [
-      {
-        "name_en": "dian zi yu xin xi xue bao",
-        "name_zh": "电子与信息学报",
-        "email": "jeit@aircas.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "中国科学院空天信息创新研究院",
-            "name_zh": "中国科学院空天信息创新研究院",
-            "ror_id": "https://ror.org/0419fj215"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Extremely large-scale massive Multiple-Input Multiple-Output (MIMO)",
-      "Visibility region",
-      "Ray tracing",
-      "Energy concentration",
-      "Subarrays "
-    ],
-    "keywords_zh": [
-      "超大规模MIMO",
-      "可视区域",
-      "射线追踪",
-      "能量集中度",
-      "阵列 "
-    ],
-    "publication_date": "2024-12-31T07:24:48.267Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
-    "file_size_mb": 1147.07,
-    "file_size_bytes": 1202793451,
-    "download_count": 35,
-    "visit_count": 1036,
-    "url": "https://www.scidb.cn/en/detail?id=675da34cfec5a156275fe9a1",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68708f0c0e9dcc59702a25cf",
-    "doi": "10.57760/sciencedb.Fastro.00028",
-    "cstr": "31253.11.sciencedb.Fastro.00028",
-    "pid": "",
-    "title": "Sky Coverage and Pulsar Search of CRAFTS",
-    "title_en": "Sky Coverage and Pulsar Search of CRAFTS",
-    "title_zh": "Sky Coverage and Pulsar Search of CRAFTS",
-    "description": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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fffdF+EXPyXJ2YYnEAgEAoFAIBAIBCaJQBDgSQIYyQOBQCAQCAQCgUAgEAgEAoFAIBAIBDYPBGIL9ObRTiFlIBAIBAKBQCAQCAQCgUAgEAgEAoHAJBEIAjxJACN5IBAIBAKBQCAQCAQCgUAgEAgEAoHA5oFAEODNo51CykAgEAgEAoFAIBAIBAKBQCAQCAQCgUkiEAR4kgBG8kAgEAgEAoFAIBAIBAKBQCAQCAQCgc0DgSDAm0c7hZSBQCAQCAQCgUAgEAgEAoFAIBAIBAKTRCAI8CQBjOSBQCAQCAQCgUAgEAgEAoFAIBAIBAKbBwJBgDePdgopA4FAIBAIBAKBQCAQCAQCgUAgEAgEJolAEOBJAhjJA4FAIBAIBAKBQCAQCAQCgUAgEAgENg8EggBvHu0UUgYCgUAgEAgEAoFAIBAIBAKBQCAQCEwSgYZJpo/kgUAgEAgEAoHAlCNQrVY3NM+BgYG+vr7eP7r+/n4h6cmTnZBKpVIM91NxqcTikwDlcrnmWV9wDQ0NhV9/8qZwzxlDrrGx0b9eb2iNUukbmiriBwKBQCAQCAQCgcAYCAQBHgOceBUIBAKBQCAwEQQSjRwtpbc9PT1tbW3tQ46ns7Oza8hlT0dHh4D0MwfySMghup5/pLq9yY/i1tDXEcXAKoUXueVo0cg/4ivhKXnKZ8Q4OX+e5EeA0eCmpqbEivNTCNcy5GbOnOlfzxpPCkzPWUNu9uzZ/hUyGsgpPBU9dpx4GwgEAoFAIBAIbFEIDOoBW1SFo7KBQCAQCAQCG4pAzV+KTPx4uru7161bt3bIZU/6mSiuZ/ZkxssAS4acT+Jpg0yxXE4ssbm5mWfu3Lmtra2ZDeJ7uGKKkJ7Fn0KSS4HsrtkmW2OqTT/r6upyeAJE6TxDUgx6OBJyyTMUUEqW5GRG5s+W5OzJpuZsjh7O1YXUBA6n+nlFQMyiGMTzM4lK/sSEkeHkyU8e6G211Vae8+bNK/phW6xjqld6pmyLIeEPBAKBQCAQCASeYggEAX6KNWhUJxAIBAKBDUAgsbuUINEqTxRu/fr1q0ZxmeWKg+iiZ5JnVoZVYqHoFlckY4mhCUmuhrChuMiqrci462OPPYbXzZ8/X54i47Fj12ez42xFzMeuWnqrOawycMkknhcR8rLC0ArD/1llSK+0FKeBAJuySkUDOTWQZ+LG0F6wYIFncltvvXXyiKBBudQ3srSbHeZZ8vAEAoFAIBAIBAJBgKMPBAKBQCDwlEWghmshQrYNr169GsnMbvny5fyZ7a5ZswatTRZauKA6qCm+mkiRJ8qUXdHAKBBfmjNnDjvt2ARpjLcEvuuuu5DeJUuWjBFtgxosgzBVGW5Q6dMdOdduxIK8RZvxYUxYs6Znss97ausUokukDmDnuabPeVqJ0KxaORPjhUNu0aJFyePplSUPJLkowFMS6mIFwx8IBAKBQCCw+SIQBHjzbbuQPBAIBLZ0BDJRSUD4ydyHyTzyyCOPPvrosmXLkFsuc12vcB4WxRQfz2Ri3WbIJYbjWbQE+on8IDPJZjgc7innOapwzz33EGznnXeeqswffPBBWe2www5TleFwHDa7kJqek+XXf1BifDhT4pUrV+bFEZ4VK1boV6zQeYnE+giSrKtkSowec9tuu+122223/fbb61H6Tw34NT+zAOEJBAKBQCAQCASmG4EgwNONcOQfCAQCgcBkEch0JVFcnAS/5RLRzU/MBG9x9FQ0FjmWWAZb9LaGmaSfwr0d8RalIjmR1b333qsCu+yySzF8slUaJX2uqfdTVdzdd98Njac//elTleEosj81g4stkmrIRG8rAVN/XljhKa6z+KkfsjyntJYz9LTEhxMlxop5kkOP05nklHm00VOzG0WtAoFAIBDYlBAIArwptUbIEggEAlswAtgClwiAA59Y7kMPPcR6mZ48yc8KZxuzmIni4rGJSCRSkZ9McIy3trDWIDpOgiH/nDAT4BQyzhxy8ifLw0QJKKXDDVYswPw267JYbi5VeLKgG7FcxmGc1isLLjitjsfvirLhfUw48G2xxorz6kzypIUbZmSXfqU+JivW4x133HGngvOTUwRunKJpsmi1EdslAgOBQCAQCAQ2FIEgwBuKWMQPBAKBQGBSCCSF3pNzjxR69oc//OGBIZcoLsaLIeDAisHcHKxFa7GDxAqe9rSnJZbLpGbraQ39mCqSkGR7+OGHyZBoJOrIj0aSZ1L131iJrSAAtqY0duC0qbsmPH4+IQLYrP6ZouVutuuuuzIF55+jZaI7FV+hxzZa27CQdjEsXbpUn+fkjydjzunWLuyXfTh3ez3fxvjFixcbCzqhQlO56VnMP/yBQCAQCAQCgcAYCAQBHgOceBUIBAKBwMQRGCS4QxZdT+o+/T4R3fvvv5+Ho/c7kUvXp8E7i4tbctT9bAnjF+IVGlyUYyNo/GRGzjdrAmwFAexww4QBaBcuvyUDTH4jAFhsr6eGfwwL8CTx1NkyRIaDK7vQ4MSHUWJukBw/9JBAfVJkrakd8WFkuOiMlyDGGcnwBAKBQCAQCIyGQBDg0ZCJ8EAgEAgExosApZwTm2mLXeu+++6zbZhznxN/IrqunspE155Ppsg999zTXcc0eKo8ejZNttzx1uH/xkvVSWEqwuMMcPo5Sbbzf8vZGL/iDPDUonznnXe61GoKbykbTbxiJxTHCHK02GiyeJRXkeycwI0RYxHSdVxGk8Glu7JOe/I7I2Cjtdx03c2u944GToQHAoFAIBAITBiB2uNhE84oEgYCgUAgsCUgQI1OmjQDI817iOc+znX9Qzv3IRkRMARbbZHb5z3vea9+9asR3bR7MxHd2267zcZRBiuIbZoaeZZKXXKz5sAcEp7pQwDy+pKzso7CblLIW6mp2ZIwTSDU1Fq56f62/fffv1iiVSc7KTDhTIytOv3gBz+wZ5vVWiYOKttVkSkxDyfEIJWPCFwxw/AHAoFAIBAIPLURCAvwU7t9o3aBQCAwcQQy97Mt02FFl99yzF8cj+3B1Gu5z5w5E9FNRieKNbsT54Dutdde69KgAw88MG2+pWSL74pmSWjqlPJ0jZB7mNGJTVYFB0Iyrw0/6umVPcbsciq4ycoP7XSglISbspDDuyl47SZwo/I+++yzcQjncBlGDMnjYlPDMwtGbOtT2h2AdmFYmUpPxNjQs6wggp3wztLvMeR23313/+62225CmJFTrTe12o3YFhEYCAQCgUAgMAEEggBPALRIEggEAk9BBJL27Okbp9TlxHLT00/nEr1yKw+iS13mEtHFeHHd4buXRXZd8zXXXMPK9KIXvQhJpk+ni3BTQRAUwv/MZz4zvd3sMCU8OoHSYw7BFqaw+XIPyQQ4wRsgjw0y3PBeT4tKGasMZkprvQaqiRIb10OLWnchxtaqJJk1a5YRjQwb4Ikb+5mXfnKeY4uxCb4Fi3v1RhMMXC4by28TjK6+s0Yw4mfScsxxemTISm8vg/gZzJTWKxPI7bffbnHwGc94hvsOxgZZfI5t38xDZrPxaKuHlj9SVmNnOM4qRLRAIBB4iiEQBPgp1qBRnUAgEBgXArQo8TydKqR+cbYl33rrrXfccQdjb1KeHB0corqPq8IUYscLh3Pd0cqTuZOKv//97xl7DzjgAKokJTtpgZiwn/ZIS+s6H0anzUtLy+jZd0pRhlICYXOpBRZEI99kb8MCKWIGZP2QqM6vgpe6r0OO1tk2WjjciLQJQgcuJOrqq69GqA466CCLSmNgkjpwjsAmfNlllzm/oD8nx48xwt/YtMKFmzmxn5wVK2f4pdXbN5cO/6tf/eoFL3hBrm+N5y//8i+/973v5UDgnHjiiT/5yU/+4i/+Qvjk6yjD9773vZ/97GexXzfSZRu78EsvvfRv/uZv3HOmdPPkBz7wgfe85z2j7XcQ3/z5lre85YwzztA0kvzZn/3Zd7/7XYsUNUJaxHz729+OJL/hDW9405veVPM21zQ8gUAgsMUiEAR4i236qHggsAUhQHNSW0/qV+K66G5ytpgKp9AzY1JtuWT58ZM5oojRBLQoOWMySrS10mHgnAOm7bpaNzznkGJBm77fpmg8gZy4kDqmRQHVSZu9Nwv5tcu+++6r3TdBaRFgSycEw+X4EQN+yyVOwD7p0lq7sbVh77333qS6rk6I86B5npZjjOINEk83dooYtrb02+uRDucLdMxh8MDDnXdaFzNduE0Nx5azwwt/pMOD/youn9PeoHI3Wmsi8xdeeGFNcTjk5z73OfX64he/iC6mt5C0AsiyalDzG+ZF43BNDuP5KZPrr7/+kEMOMeNZAbTgmAiwcKhisHvttdepp55qHfA//uM/vv3tb3/9619/4xvfOCKMkniFk+PSKPrvfve7N7/5zRYjfv3rX9esdySy/frXv/6YY4457bTTNoWVo/FgFXECgUBgoyEQl2BtNKijoEAgENhICNCTlORJl0U12WA5HkQU3fXKTuZEd9/2trdRv6iwbAjJzpZEHFH9moD08qGOyzlpYEkw+UxV/hMQaUqSMNHY2q06+IAnv2yzYWdKipjuTDRBbo7pLmtD89dhklE9bYHOBvYNzWea4m9SuCVh7Im98cYbrRfgouDa0PGl66J5srLcwBqMU6UcFi1a9OxnPzvDyPpt7/TjK2e33XbVVVchbEidCAiwok0mDmxzPHmv74YKk4ubQg/xTj755GKG2O+3vvUts6Lwt771rcVXp59+Ohy+8IUvmB5/9KMfZW5cjDNOP0hNEWy2iKslJ0y1mPA///M/vVXckiVLhH/5y192b8LHPvaxk046qTgb5yS2rGO/LLp/93d/Zwqy4vZf//VfWC6DsFoUcWYotltHs9rTri45h/AEAoFAIJAQ2HQ1gGihQCAQCATGiQA1i6Oepi3H6C7jgCfzBT0v0d1EdD3pqewbRQWrqDmNs8QNika2FB+fYYrBtzNXnO6iN0jO8UfONcpngFPaTbw6xNYEugSdmCUKK9AQjMDIzyYoeZLWkg1LdY14XuntKrLRtiLjlmnxiIkV5QOdFscuMs0bf+eZ2pigsF35t7/9raZ81rOexXhbg9X4i0uA2yQytn1btJxnmnNQ4rS+xsNcDB8yuPUdE9Z2Q4x4n7zENmHxcqFT4rFX/KUvfel+++13+eWX54O+qqZTCbSkdd1115Fc+yKlo+1JfkJJZPiRj3wE09ZALsO/4YYbsgWYgd2+GDcpXHnllSl/kf/t3/7t4x//uGjPec5zajL3Fif/h3/4h0suueSwww5LMFritC3iqKOO+vGPf1wE1jBHjNFgMeU5YflrZIifgUAg8JRBICzAT5mmjIoEAlsKAjQhVfWkS2G5N998c3oy8LLe0HXoVVTYV73qVbRPjrE3mSgTQEU9aeNAlkqkWeLnWARHU5/kxsKNI/lopaQapYaYJKoysfsUkbPRd7qbRlnAR1rIzG8xgke5m2xb2BeayUlNW1DxbUS3mFITPk0/gabEnHnyM5AiSDlwY3qKfQ8FQsiLRtcJS+K4Lzd28mIvTbZ6M8xxxx2XUllbsVM6TUrmJftvbRpH9uzRdZDYdJRYMY/zAomYFTMcu+ipeuvM7Wte8xqGbrbTmg5mGzkybwuxBYW/+qu/+uAHP3jLLbeQNhUNdhf7oZ2jSaJGOKe0IogMgU996lOO6ab7DoqpdCFrDSeccEKx+sARhwDDCbBwkniKk5MYvG7gT+HFzPXMiy++uBgS/kAgEAgEiggEAS6iEf5AIBDYFBGgSCXncBpLAme7oyfLD1ZJBU+apatcEEt6cJHPZFXpya0Y+QnAtMJkaiMfJdL+PbZoavEmIuHE8ME9NMHE0uZUVGHHrdNJ1xw4HR5QJ8bIjOniX/gjMJss/gRDTmr4yWiwrF271vlV2z6nqTq2USQWZLOxhSfQkeTJMqwZTaysFrxwSLtnyebLwMU9HaOhNHb4hKErJrTWZvXNLOQGqVScI9MulEIs/UQIzz777EQgNZYJwRZrjseynXySG1vOSb61Soj9WnVyAZWPIdfk9j//8z9Wo/78z/9cuCp8+MMf/s53voMP52jvete7fvOb3+SfNR6jWM4IsDaya+Bv//ZvmWex3JpofmK/po7i5gt191NCq1TD4wtJ4Xh7fpuSDCfAwnOc8AQCgUAgMByBIMDDMYmQQCAQeJIRSHSR0cke5ptuuikxXruaKd8kQ7ro4lS0pDvSgNN9LcwsFK8nSykfDplaEImySyNMjtZ+xBFH0HTpwe7XQfwOPfTQzZcD0zInw1pTK6dnQi/5p095lXOiSclC5Tl51jS83ac1hIExjQIc3gB56KGHVMoakO/c4MA41TSVnqFL48uIm75mGqMKeggELF44i2twQYABEI/C5dBgBnOWRg4gGvdJkTAJn4pO/RlhSzs+jj32WOilaS0t4ZncvvrVr+J1YprWREtk2FO9Uuec2loA7Z3vfCc2/t///d8vfOELi5mTQac666yzDj/8cHcWeGVqffGLX/z973//E5/4BHhT1exAVoXR2kiqdCWeCKeccopzv2eeeWaxlJwwZZJGYg5M/Wq0/IXLqmaGl4NKEX7EUnLO4QkEAoFAoIhAEOAiGuEPBDYPBCh8GJQtXvZw2vvnEztUmaxJeIt00QjxKxoMisWINFw5EM0KPZViUyBgSVMkDMbrABgLg6dthHgjdYeZgoXETSr2xSVTSdaBcr2obpQthpesqG38tlQLDstNkBKepTcpbYRhGtJYfroN1ZeBEWDNJ6ZXhNd8SfJco40v/0YukWUsmXRchAOWZBbTY8dp85yMtNDWCnqR9nrSAcfobEm1LDKeihuziQAnsfkNZFCkEYEJ8Hvl5zTVSzdGLycD/oam1UYOc6qaolFf5wggZuCwQttGITc/jSz833KAYSU+HuWrP8mcKG2aAKcJkBGro1HISUgTmumLhMTQ4XU8a0bOA3POxEpLWnukrfEhw5xLp1zspLLIvL2+zx1y7NvGhbSqMJlakMqFz2y873jHO9xKNVzyc889l3n/+OOPt5sgycb/85///Kc//Wm24hJsDBlUh5PWPnCnf11zldooBUrIQwweOHiCpSgG67SfzpYXA7NfuLTWetK0mcLl4OcYIuXk4QkEAoFAICMQBDhDEZ5AYPNAgAbsIhB2A3/yKQQ0BiE2A6dPd9APrO5TrZArlgdP59NswHPXSI2KIJp8GBkc9PIlxpq3043FoJY05Ch/jCHorgtCeeiIdHfWD4qfvXMsIawitNsszxhyyi9H4yn+HCNVMckE/Eqh3dIXk42XFo7OuV2GTpauCGKPojJSXnkyOScPvTCphgp15k1LaUG3c6VvLyWB6cE2CqJqVP8prMIQ8IPEbwrznBh0E0g1JUm0DgYyJVlNPhNjVudxanE8WZHcGpCYBo4zwHiRzuNoQCLAeJRXbInDt7aOJ/PxxLHrmBtPzCmJk0axacEikdO5FrkMIszQFVOJECpFiK/p8Fh7wt+AaRjiV9IiSz/72c/s6TUGie3JbYSbwxRNDJOzhUhLGwg5JmyJR/NZpNOCeejxGPimAh/sSYgZ8tb+hujwTRdccAHKannIZG75z6zoa2o84g+O3iE3fpx969j3eH2RCMceMa3dznL76yFXzFY4JiyJwJe85CU24xTfFv0qqKaaxt1aqu/vFFNzisBor4FSFQSqQoIFVilnHn/IRNa4xTyzX7iY4jjzkgKNHaseo8XPCcMTCAQCgUANAkGAawCJn4HAJo0AFeGjH/2oz0W85z3v+cd//EdaEaWKWpOXzP20N5gW4j5SbAqrPProo1FcpkhKSbFun/nMZ374wx/a6ubOEh+WoIsU306Hn/AcXZZuRzCkl6O7C6QMsXI4XZbUO/XKAiTdKP+s8UhLX0QUsVAxk40Fq8c2acx0I8ru1FKdwToMmXmp3SAF+C9+8Qt6WJKT2o2u+0lOYthAyCM+i70INPhcnezxVjhl0fFguqPk+D86hNLIh21QFRDgmopP5ie4yIM7IQOTyaeYVi3Sz1yv4tsR/Qx0nFcOc6L9aQVnxJhTHjh+Iae86JwhxDSEJ3ojkN9KFsEyr8sxix4RDFWpeDh9I31hix2MS3jm2aCYcKr8Cp2qrMbORx31f3sEjAsmXDU1qM0P6H0NRFkk0cwkXM5ZDvoVPmyBibV8ELJy+aCDDtLzvcLHptY4nNvUcE6GSt9kIo/StRraZjFrxJUOUmWZzQBIJpdCUPq8RGg3MpuqUswYiUl6WiWUZKhmf8ok55Y9rh5wSwI0ZGJqKpYojjxZ13/5y1++8pWvfMMb3pBTCXeH80UXXWSixuSFv+IVrzBX5wg1Hs2UQixS/P3f/33xrRUBTaBe1mRlq5eK44pp/Z88KaYboQGlUsWE2W+vk9PI4vhUe5KfnVmejjTnOOEJBAKBQGA8CEy7yjseISJOIBAIjBMBjA5xdTeJQ1lJ46FR5c1pMnGrp02A9tFhvyLQF1Hlf/3Xf0XSaB5FpUdCKlEyHKXnOGUYfzRaDketx3AYnH1XAw9nB6B60tjobTQtElJ30pGzlHNRyPGUxRSWKISyaGmS4/+sPayyXtEUx5PJ2HHkTGaMmjKaHLJhG7NSMF5F4G/JvpT1PxmOsyKiObrJZEeTs22SrkzpZ+QRotwsWPKPM8+cajSP6hQzHy3a+MOTdpstM+NJmOpSFGNitcs5TCz5eESdpjjsmTpSEttgVBErHa5QGk9xmJto2EJaONB5MK6NuYgwHiEnEAcIXFq9QsksAMkEhzRGQMRvyQkTHjHnmg4gH1OBoSRy2qZh8JoWUC+v2M/xPTAauVbcPIWbCWsyGbGgMQINYUWQ3E4QfqVoGqsbAvmtmpkPx0ieXhVlIJsdJZxXxHawxQmRtID4pS99yVxBZrO9UzB2fT//+c9nYU78tpiJ0i2M6iE2M1tBSFllMcwGMnFXMzmtQqZlu/RWiaa48847D232p0RgDa3NmdR4kFWuGGh7gpWIT3/60/nPjW3YirMl2wqsmP5MnHPOOSzhWULWe7uyHX5x6YPqHHnkkSZbH0PCeE25xP6P//gPuRUZe7HE8AcCgUAgMBoCQYBHQybCA4FNEQH6ASUm71imASQtJz0pK3bc4WAve9nLkvTC3bzyvve9T3g2KaRXDmgJp5Ol7XBTVVsycFgcFQ3jtcDPzEtnpflRYuxqpqJxNq1lNYiQKRUPt6GSsAZIzphD3WRbSMYECjRqkSxs/PJELYrs9AlLkafk5GFuQrCpYnRoqYRTmhlDkvyeOHzOrUZ+6inV01tZpW2c/EyveEtOwjNY7SH7MPWOtPb4KYsNnw7NWi4TViOq/AbRy2L+yU9yDaEuif0yUglRLmuY5/D4GxSignrmBiXJkdU6m4By4Pg9GAUmCVWa+vhTbQoxcRvnaUFnvCBgQKjpGGMImXdJ1LRdzc8xcthkX9kAbCuEzklCXMhYM3votMa4wLTaNR7hi1AY/hbduGJCvNqozCeHzQ+2w4ipRRRUY2cuJhzDj5ybH4xWC5FImq0WuqVxZ5QZ2vJUyoauU+SK8EDDcqFZnQwwMVdcf/31aXnR/VUgQuNZaBMftrZocEllYdRMYrIqkttcC6epzdUIMCJdw1rFOfjggwmMptpzlCftnHacntSaKXKqjhB/yPBbB5LNrvDxLV/oWeHN9SWz7U7+nKUbyHWDr33ta75v5y+IQP3kiiuu8Fcs0eNxShLRAoFAIBCAwP9RwgKRQCAQ2JQRoDEgkySkYJ144onuJqFO2c5nUzSrUVIa7rjjDuoRxSvrEHQaWvWdd95ZUzXKH42nJnBiP+lhSIiziLQoqhgzLy4qkA2TpvKhD32IaYK9l9gp/yxbsTj6IgURxxvxbTFm0S8yrUgItRg+lEtPX8VggSEAh38yLNCrGEbSFr5i8qJfQj9V5I9W3tXsFcSWCoD0MwVhHRztNiccW1q5UUlFlnlinvykysmLnpQV/dWneijl4uPwVGc2c7tACYAgaVylj11oMc8aP2OUJY9UU6Yk+dBonbKecIbqgkgrBVeXbfKDC4sYf56jGfRqhB/tp3KVjrSMFmGTDU+WLsIjwHYva/pxijocW8knCeM4i57yaLqQMaJnGiw2DMvfooAVHybfdBBACDuwySF9e2nCAhRB02dsgUlbeY0yNNio5zHEvNIcZloRDHYLE4BNa2c5h9TVdTmi5sAkmCQ8aKfcjFZjwdMotp6FfsvN+EXvzSopzwlUJ5do/rfus3jx4vTVIitQrKzWHE3Cp59++ic/+UkxvUWGDXP3z9tyPOJKk37oIMlrX/tagMizKJIchFgkde7XLo9Uu2KEcfohZuMSLp3jyxkC559/vnPC1mdhfvLJJ7/73e+GTK6gPxz/8i//YlkzpRLOPnz11VdjxeTR5xH7mi8J5/zDEwgEAoHAGAgMGl7GeB2vAoFAYNNBwGi1B8xiOY0KsXQWy47fz33uc5gSpSdpCZbqaTPOjGUdQiqaHCJnF3QOnHylqK00PGriVUMO9cWsUFyONknlooFRYXOJ2TNi0YRE+aiJdNyxY46YXCBhGEOwfYJhjNRlRlRKrdwAQu+ETJG45nzSHCiahFQrSp4QjtpHu6WR02Vz5OQZv4TykcSTIYgYOasnzCEl1LIQRpDQ+BRCZdQHJKd8e9JrnzCrLLwcMI3Ew0FE0WSbkhzvGn8mObfkQVpooim5/Hk8acnU7gnnWVPEaD9TdeCgRqqjs+l7Ck2VGi3VJhiOt1iysZHButUmKN40iWS46dV4r4WY1L2NON3GGEE4Ew3LXcjQNqLTN5ynXB4dqSZPey6sGJIqjTJiWH468MADSZUjI7FG02gLdvokLmqiNkVLa0p0WlUXTZsUZGI9TnetKXfsnxCzCRw+xfXNmiRZPB4z6utf/3rbvM1+5hAzpJGOUjq7oS6uDRtxpGTMc845TyHD3+ZoY3tGy6QYnnLIRRRf5UBxiuE1ScaWId4GAoFAIJARCAtwhiI8gcBmgAAdiLNO75Rv2o3GAkyhcRTKVSU0Ay6FFytDexBeDJmYX9E0OWQ7kV5H0fAf2hiWyHpADDuc0WCfkcSH3VZiM9uIBodcOqloihRNIbRJ6qb8+THVEclqTjjcQ5tEIVJ4UpSZlKl9dNDherNylYUeU74xXvuxOSiRllWBvojCZS1cnkX1a3jRY4QMTzg8ZMTkooECr7O/kUhUVdQXhaZMpxy8YuUWMrSvcxsW1+HtXpOzhAlVmHvFoI3h18TZ0J+JtEhl8QKSVPyU84bmM7H4SEiyOUuOI6kgg1vaLTmxDMdOpdsYAoiEzqagsSOP/62WHc00N/5MNouYALQspb5aCj2zwZXY+i1Lr6GHdurVXOrqyLCeL4JUOmra6DEd1UwDKuesOMOK09a6tP0jRqJ+Tk6viG17C8HMVwZdTlXjEV+belpm8sr0IkP5oK9mpJoSa9KO9lPpRtlwGyw5JZFncim5Me6EsB3CimZftRkHITdvX33NNV8+9dSPfOSjDQ31z37Os1904OD/XvjCA8wjo00gsh1NpPGHj5bJaOGpRiPmP0aSEeNHYCAQCAQCwxGYsj/hw7OOkEAgEJhaBChAaZfj6173OspK0gOs6Lvl2MWYyhKCIKGgxXLTLlxG4GLg+P20KzQjMV5PW6ylpZtile4URX1tpU6SEA838IqNmpWS+oWCfvvb3x6bZTF/yV/aJJK9vjzy3FACXKMVyTDnKcP8ViDeyCxDtU1FUw3ppql0dvXkKSbJIRPwUPcVTQ+m4ifjzxiZkI1WTbC0CZDJC4DEW7JkCfM1xTfXQiYImGxtDeWEs+2IqTlSrYsxRywRxxhN3x0x/miBCkoauXLtGM/a+RMKMFqGGxQOKN3bWgZaglqMZxVgg/IfHtmSCtsgu9/4tysPz6QmREMUqRQkDWEAjmHoq8lhU/6pOkCz2IT+2eaqV9uLq/tZuDFXGHo8erIqiJnWpCxLaVB9Cc8UaLSavsaeRqYQgdx1SUUGc5HBm3fh6uTOepgV0yoS0z353bOVZuYshkyKbWq0qmxarPGKyzHH4zGNKJETWYc3cclB/ikfM4CJV5/MWQHZ8Vrbg00d//u//+sbeF6Z3Li//8d/WNY28LNf3XXzDdfc9uurTz/z/M997hTrgXvssbsVzOSkmpLJIcsTnkAgEAgENjUEggBvai0S8gQCoyJA3cFwPClbSfURlYd2iCnxUxbpOpdddhkNKe+vQy/pTEkHGjXr//uCpiWVvXPJIaUUOIzXByQ/8IEPUJLwMUUwR0jnsivGGeYO/kSz2RxYHnxO4+yzz5aWsXqMDxEhuiolLeWYGictf1F39HNijiHXHTCICkrphBtAiC0rSjZnpQBj9HSFVRI7Qzp2cUBOEcYTH9kGjkrRoaUaMQm0ER5KP8cYLg61GwIsRe4tK+r9OTkZbPbmqNTsZqDDLqCXBLPZkl8ngUDa5ZgTpgg0+3FeNZzij/Es5pz8xZAxEk7+lYISOOgBDoAucpPPdlPIQYMSYzOtjs7JaR3OFGF+8BSiRsy8BoLpCAE2LrKtXkxvPfV8zvZgSQwBqUS208QY0dbWODijdSPQM6sqiiaAoj0T5zQNIodCiGdKMeg8/eTEMQkbd+QnoSGWKpW6E78IZpsNXddLye1uQLxTJq5+UAQ0lEKYPFWaCsBilhPZfhx7cGxydsUUxFImae/wg+sG7l1bt/0uz9xhlz0POuENDfXljhXLbv3NNTdcd8Uvr7v+O6d9b6Cv1530hxx8yEEHH3TwwQctXuy2wobBttGAG0bb/1hs/BsIBAKBwKaHQBDgTa9NQqJAYBQEKCEYkQ8gMUHYZuyniDQzLCvt/hWCo15wwQUMwjkCe2wKHyXXx4MpfEgv4ppJLyWVhuq2LXm6GrRo4pAhnY+Sh2JRCsXMRlTZUfVYRS655JK3vvWtPp5BD3N1p6tKxK+RQT4CKfqS0PAocPiqwJpoG/pTbokZIoeUVLYaIaxJiQBTr1l1hrTWQTMOesxgQgya4niKRreYwS0oPKFFN4md8hwtZ4zXjTWJu5IBPSBGAgpb4GQyPG0OIQMlmFNBLZhKdK5SrbWmn/RjareKc+Kk3HLyFH9KnuDlpiSrYiZ6heqkdZ9iePKniqiXnpP801E1ZSkCvdH0CWQ2SW2kdayhDJdqCwwxmtBFndlo0mSeZgzjGssFml49yGvnz9cVcwNlTxGuFAjtTP61rFnI2pBFAbZ3DsczpRSno2IOU+W3JmUzdsqNVK6w4reSpRaJtxtf9muYB1Q8rTZahTHhEF7HEM0cqOJpBSrlY9qZmHhpJUvOOl5aCDBPKotTtD8BuiVbtHL9LXj7299ugc+Hgr75zW/i2xnnnoHq3SsHlnXUV0vlSqna1tXX1FjfVF/XuGjHvV9y4rMPf+XsGeX29Wt+e/11d9zwy+uvv+L0M37U2tL8vP33X7zTjs4NH3zIIeYZ1w6YntMSYFDiibVmpAoEAoFNAYGpORm4KdQkZAgEtgQEEDZ7npkdLr/88sWLF9M7P/jBD37qU5/64he/+La3vY2ugy0w1VLL7HxjIrCT0CFhBNUlTJ5ZGUpYSU6tdzkWq4Ina20ivZIgvS5KkUNKUpNQcqoY9Ysu6OIoSli6T7XYBNQ1znWd//zP/0xT9KVHV4nSgItxsl9MGYrGqjm8rBxtDI8caKJySFZB9XVUT3yadOJmVNKEgJgYbLaXpjzpjozY4ymauknXHHsTLANROtiMCWBKSSRioKMwp576QLFNlXCjuUKegov3WkRI1Hc8YowGhdpxKD0OrCACWAuwPkJg4T4cgpBo1uQSIJMpbjQxpioc7cErXOE2hpDqlYobI84k5VGEPqN3KUKTaSYh2N3w4+UTLkiGSJdRIAd9zFNvUZxt3qn/TDjnKU9IVE5Hwk45gFhxM6D0bWWRGetztfiICyIb2kYKSvIrRWfAhM1+LhM2YKHk81c2ShjaaeBMYU1lbuzI0JMMRii/UZNMuEISAU4bj1VKiPnHoCOkp6YU35H4/fbbjycZtIfPwF6Nx8nciDaQzTz8iLSscHI0WCdUOjTs+zjttNMuvvhi3cYiqe/rCkd06wYLKLf1Vu5YPrC6r6G+zi30pVUdPXNaZjTVl62ZrWzvb22qn9Vo2bHc3tNfV19X39e19O7b5izYtty+7PAl8+790L9dcc7ZN1VLq5+2w9NffNALDj30kEMO3m7HHRrLg+eixyN/xAkEAoFAYFNDIAjwptYiIU8gMBYCtB+Xshx99NHULKyAOojmnXTSSd/4xjeogEkP+8lPfvJXf/VXFDW0B+GhD5133nlOf3kra0oqDZKlF+nlWFSYcHE/jJersfTSupgU3MxM36oRS9HIdjKGeJUyr4lDWiGupH7jG9+IkS5ZsuSUU05xUdZwbTXFHC2fmmzzT6k4VBMa2DhVFaX0JU8RmKHooCglBTHHT0JKQpn2pMEDM9lyvUoA5sg1HvGhISE1l+7L9AdY+h+CPbzukLH0IEkukZ8OTQ+GW6I3oANITSnDs6qJMJ6fyipGU00/SStciyfVXIiygKP6Nqh7hcBQo3WGTUGp1UsTStoRYolmaKAJU4giIBPwZ0hxYMSDPMn+PyXtleRRhJ6czjLoYwI1hyfTPd41AZmnNokW0XOS0415NBALJ5mtxBkRWLrlreR0tlT61OJTUyOFuthJoOKsH2HCKPfYo7gmhzF+5hY3wVp2zGeAU428hYBwgzrXMSeRrWPPZiQENVmqLXIBjT+dedagRllOOIYY+ZWNzWYV07Wc02ysOATbT33GBHjqqadaKtIW1kP9gRiy/Q7uXBgolR9e23/f2rqVXaVl6yvbzK7r7e9fMKvRaiib8GPtffNbG2Y2lPurpXWd/a0z6psbyt3dXbffdseS3XbfbduW3QZWdn/oo/3XXDvQ3d7W1XlfV8eNXV2/KZW7dnn6rocc/KJDD7VaqlKbwqSRsQpPIBAIBAJPiEAQ4CeEKCIEApsWAnQarODMM89kkqJg4XsvfvGL6VJZnRIBaz3rrLMoTPbsOQ9mMx5641SY48Gcy1fFQY+d5sV/bJbLOzlpdYyTXiWFhnbrwxvocSbA2FTaGUhHp/8hnNChBVKFR4RJQcLRBl8DZqaW6tWvfjVTMCMGZY51a0OV+5RhKou5DMNPTInA9EtiqBF/jpZhGS6ehCwqKpv3Ww6Pk0NkiC2DMWWY8gfLiHZjbIETB3ogUk0asLRyo5jS1LmkOo8hXi568h6SMB/pFVRkTYBZpeujNDe4KM0iWBPR3KSFBqdrkZBn8qVPIAddK3GblBZKJGToI+rGQWxEmcmQCTB8Rowz4UCZ57RpiOWT8xuzysTgDHMdxooPB3NsX7f56U9/qlcT0vjSjTFe/d90IbJ1E0SdwGDZONImOa15EcASCWmVS85DDz00DX8/uQzpxDxKQYAtexkmxdyE5wyL4TkwR/AWaBrUbExaWYmDsZs3ijuiR8wk58YDZE0gufVEcyac0/ROvPe9733mdszfTG7/syFsH82cOUZudV1v5d6VpeXddcvWVld1l9f1VJvKA8+Y1zVrVktbX317d2W7OVa8qn395dWdvVu1zuhpW93XX13T1tG5dsV22y9cMHvm8+aXKl//Rt1ZP66rlgaqA+VSXbXSZ5N7W1fvYwcf+Pv99v3iaaeZKF5y2EsOO+zQA17wotZZrfAvSh7+QCAQCAQ2QQSCAG+CjRIiBQJPgEDWrnK8Gv0pRcAi2F2dAeauueYaBAw/dC8UVZX50V2sVCWMCEmmyNJaaLQ2zdoIzbbMXkFvo+7TL2nAtCs6Lq2LEobUKVfmXLKvUoBYYLIwRQ9JRKO9Kct1OO985ztdaUNP/ad/+ifnmdmWsZpi/NH88qH8EY+tA/+3D5zAPD6Ti94zQTD+JIuZHGrQGC1PdWFEooaSbbQ4OZwAMCGDVKwxMKTFJtBqikuikhN0nurIUGO1Qlq8F0vP8bMnlzK1HpJwqRSLBb6opAWZna13ZKyUKIJoJKSgIzwaPaWyNMDUyW+ZQAtqNbQnPVPdp1baYm6wsk9BCGJDKmjz62y66HSDVhSjxg8KOCAexhEEat5O1U+lZAI8rZVVEKdGQAasLsHvRLo+wJOqI4LTDfDnsd84NYFlEbNBimB0m0PQMKMjxRQuMsmnVfhUShbSVKCHK9FwE2itR7exymNyIOqEJVGRzPknk0mS09ShV5sTOFujTX3yx12BbwYziz6h+Vp8n93W/SSxQuT7Rp///Ochbz5/73vfa5uP0f14WQPVB9YNPLSunmn3zhUDa3rq2noqrc11rXZqP3br3IVL5mw1Z1Gr88CldV39TMHbzGrUonfcfuvq9R2zW+ob6yp9lbr2rv7Xv+QZ8++9vfO9/1K3tq2uXK0r15Wq5Z7enr6mptmf+njnvs/51Gnn/e5XV9z+m6uWP7K0uXmGL8C/dMjZAz8Z5FMt4hkIBAKBwDQhEAR4moCNbAOBJwcB6hpFkJk38V62EZsSbWxmcHBzDK5LLKRROG0pUZ1s/qU7MgnaUIf2YM5iIsZJl+XHndJ1zalickhngGl1HF6UwmuektNE6aMUPuoddZkd+N///d9RWWqcO6Xt1qZ5D23YG8FcI7kkaqQ4WqOCqKH0KvvuaOHeFovbUA01Jx9PwhzZnkOEMG+CJUBOnmgbAEmbKIQqw5kWPhk5i2nH6U+4YeCYCVqi7YRoL3p2kjbLnDLMtfMT4FRqLIihifPKxmkhxaIZxNwl5pW1ADW1gpCcDqPKYtbkX0w7Hn+SRyYaHb9KZ4Bz4HhymPI4SoeJbIHpOckKjiGegpjiRQD+ZEqRjxY3WDArPVMzIe1ah8dih+UwzgyguZWFt+gbopk3NKI6GtGeHK6V2rQocxYsNYqnlrIIZUlItpa6rJpZWto4FCjJkMQjmJ+mOJ2fx9xibY4kxuDwWhRrtBH8RTkVR1RLD/aGmNb8JCoA015u/oxwUTDTu/VHUx9rvNsf1BGLfv/73/+Od7zDwmVjQ+PiJTsPVCuPtVfvW1Pq7m+olks+oHTnisryjvJWLeU5MypNdX333nLb3nvtsnDrOd0DpZXrB2a21G3VXMeyv6ZroKujy47o/t7OO+9+YOY2T9tpwcwlC+p3r++s/OLyrlP/u2m1Q9GV/kp1YM685recXDr8iF8u6+xoXLT1rBZ8+b677rrpVz+/5Vc/v/n6q9vWrtp20TaHHvaSw196uD1K8NcxihUJfyAQCAQCTy4CQYCfXPyj9EBgChCgV2Fl1157rRtQOPSM8oQzuAiaQ8AovtRc6jtuhuJyfiIweCkiikzSt9wsjYWSRm6Si0zxpY3RsbIFmE6c1TIcjymM5SFdOOxLmPlVsUpySwSYPBRQP1m3sCbfB7ZJ2ybkJUuWuB+LQTgZk5MAomFcipOEPk0pF07to6OjzRR66lSxOPGJQVSXEtG5iwJMuZ/MNm9jgJnzK10p5HE61FU0/F7R+dh7cZgkQFHaKRepmCFhSMiyTSHmAaClB2scWchi5BH9KWZ6RWw/OR2Msp6cFlE1FRRu4zTjm8j8ImsXvSV9Y0lnExMx1v047atpahpuRAGKgSic09SJABfDN75fBY0XDCQfB50+GRKY48xf5OQMas2URo2mZ7DVUb2yBCac5O6O4ke30CeZI7qGfHJ6SFpREn94Xx0eMlw2CQWKqd1t08WlNbrOYNFNHxhPDsPznFhIQsMEYkLg0rKF2UzD6bRJyInlPOWpiKpdzJDmUi4tM9nFrS2E+0ls0GX0LF78+Mc/toBouobqX//1X1tDTOtrbMvlcqnSvNX9a6vtvWbacl+puqaj0tZdmT+nYU1bZe3KZTPK8S8AAEAASURBVN1dXY31pdbq+paZrSvWtrX1Vg98/v4z6std/dXH2gbmNdfPbanDbx9Y3vboA3fv/6w9Z7a0rOnq321+dZfGnoYH7uu54YbKssfq5i+Y8bznVBYvuaOj/sob79lzn33qGhrXd/eX66qzZzi1PnibwB2/+81tv/r5DVdfetctN1UHev2tseGIs84y1BncIz10l3Q5vqw05X0qMgwEAoFxIRAEeFwwRaRAYBNEgIaE5NgFh/Sy0bEa2alo99nLXvYyu4uz1ZGOhUVQoZhl6KY0Y0qwJ/UUaWThQZKRE9qzJ91FYLIJi6DWGB2WiyHzK5GdB6lLMZmP2B9GI8DKZbvzpLSRjXGDDIkOUcflZu/f9773PVd2iWC37Xve8x7HlVGsRK157HPGwOWAMCsoE86sDuZGEQevZvY5/vjjyZbDp8OjrJQtDw0VpHlLNkUWW6Nkp5tvUrTh0k6HVFkkJJzVN7Um7oGBAGTyMuRaZ+HlKVAzIRi6k87ADw228bRTQPvqeDk+D9rsq8s8+Lluo4PhYOlJSN1SnvqYCEngVISQDaXNxUKnyq+yG40AF2VWLjKpQRFXI4XjMdJhYiULETKaUrhoIhvOOK3m8B0yzWHE6ZAayH5gTZNYrpYCeI1FNGFeLHpifjIoLi3BkC2viUxV/uORigw5muEJBxCZQPTMtOUYJrgl2XK0DfUoQqNINXkrd5bW9IvHmn4JZuxY09R8lvxMgxrOiuF//ud/GuBGzZ//+Z8z/Jo2B5eeSkZiZXV39YE11bXd9Thlf6m6trO0umNgdkvd/OZSR3f32r4Zjz7whzn1XU2NpfUd/ev7ygPrlrc2DDz/0KOXdwzWY9GcwQui13RXV7cPzJvR37Vy6fztd1rT2zB/Znm7WaV5LdXZddWmysCMSl+pvnFNqfGe1YMHjEs93Q2z5qzr6J/V1DBzRrm/Uln6wEOz589fMGcWgtveO7DqsccevOWaG668+NdXXrbiER/rnuUO6ZcdceTLDj9sydN3GVzFxNq11Qi7fza0QSJ+IBAIBAIbgEAQ4A0AK6IGAk86AlQlOiXFiA2HowzhD4hiWl+3zzmrdFndlMStVygZKotLcHgawwhGSinEbGlRMlmyZAnLaj4DLB8JETwcm96ctuTRvF06JabcvMVvAUIe0eTGT11jpE0oKchxNU8/RU7yIM9uyuEXTioMjcL3yU9+8oc//KF8ZOL+Uho8uisfV7mkglKGuUbpZ3rKmeNHThDg4447LhHgESMXE07Yrzh6KpWaw0D8pI+yrVGvkyQp5+kToEbyVKim1Ij8buqyYxzIOFJiONMqiRIVrTUT7Ghtss8LhxJurFnRofTUmuliJ02fFkeSbCJrcVey+cluqaPqb1D15HQqJmWvdDP54xvZqaAVHK9SN0u5wSd7arCawE+yqYVFGR4V8UQv5aN/ZvP+eLKVkCNncjjHIO0Y2ufsaYXIQDC+UjiPQIsFKmKo6tgpuZ88nq5Sh49UvtoNDU2fHTsnoMALf3G8FV9MO+GRKFXwU2TP8Yg9sTiElFBNrQdZmbIYl1pQNyCSV9NaepY5AZ5wNlp1VKsAthxbKTC/QcMYEWLkTkAemduNrAeaDyeQPAtZ9CTcEj54uzkNDdbnbYG56KKL+A0H1NetV0ZZZ2fHLrvs6kNHKzorD66trO+ur5Tr3Ly3tqu6sqPSMqO8aJbzuqUHljmBcv+zn733NrOaOvsry1Z3PXrfbfvsuVvn+rUr27pmLtx1fmvdvJZye19p+bpKY315uzl1DXWl1Z0DLY2V/bctb13pqfb1DpTqeptbuip1j3SUlq4td/RVF82tbyjVdfRV1ndW5s+ub8K6q6UV6zqX33/XbrstmTl73sqOfrR2/sx6/ayrt9Ld27v+4bt/c8Ulv7ri0ltvvG6H7RfusWTJ3vs968ijjnJCR4ccPF08jV2yCHP4A4FAIBAYXDj800Jp4BEIBAKbJgJUSRvkWHqpQZQhuhELhn1lNh7/xV/8RdpxmiRPqhhVjz5N76T+0kERUfo6nS+RE/fcJL6RSIgrmqSlWolQJMAC8/yQsk0EGC9lrKNEIsM5Qiodw/E2+b1Kb/FtNkkbceUvH064GvmAMNaEA5OTluyaaJ9rorUjPCeffLLPGqugJCm30Z4Uepgk25daJ/WaCcWigIJGSzWZcHTIEWtVgC1U6dBMNKlqk8l2AmkH8a1WNTRFnFTM/tRxwA5hPFj3aUIgi6p0SrnLeFAIWmwuLnm8zTFrPITUWOgQp1Px636QlNCCC+OhwORkguKqWnqVrobO+avvMcccA3wbE1j2/ExOCI8+QLFmC3WgXQgnoXBPvRSPJQaynXLzTE5rJlKt0+pXJMcnDSJvUSZOuyOlhgATIvFk4qnn63U82gJjEchJ7umVzu+V5Sr9nEeenilDtN+aBakkFC4wRdCTrWoJN3xE8BNEnsmxpRNDTPmrVw281mUMZIE8ckiMXRJoQEmgC4oYFZVVk3Bqf+aa8hCSqDaqGN2ODxjySt8IAqhsAjZXjRhk0Ct0XX2MDIcccsgGrWWkrFRKzuCdQgKcheTReexg/9rXvvbNb36TxZ7Mxx577Ec+8hHLkT6HfNtttze2zt559/0eXl9ynzMjsKO/qzt94LfaNKOE+jqIv6K9sq6n0jTQ1rty6dN223Nld8NApdrct/LWqy6eve3inlLrjMbyvJm2TVcrc3fsrTbuMK9x4VazOnoGVrRX91pU3XdWd8MDf+i46to6nX/ObNueS8/c69GZW9+2slzt7Vy3tq00Z2F9uX4hml0qd/b1re8YmNtcffDu2xY+bZf2asuclsY5Ta6OdrS4j2153ozGSl21p68q/94VDy6ZtX7PauXnJ73uyvv/cIezNocedPBRx7zsyCO2227RIBMeHAfT2zmLUIc/EAgEtkAEpvew3BYIaFQ5EJhCBOhqSOaFQ84GZjqBi6wcl2X/sS0Z83GjMiuBEp19pYrloil26fAbgsGkRttDkCQXefHixUgjdWpw0X1IK+XJ2gaGzOV8cjgyIIcUbkMyPiAttV4E6j7GMvyUplccNdFTwqFfgz+xdyZKDq9gnCEbGkkn/u53v0vXp/B99Y+OWfVv/uZv7GpOGnyWquhRa8q0fDBntU4brRGYFIcSSUll5MFbiqk2yC8Tqw8WC1gvLRwQBmmHUiIhKatUxw3KdjKRwcjBUO2ApnQYqj5SVOwGkyli7LRKB0vabq0npFWPGhBqfhYzJKS1D64YmPyw5XK4Urj0E/iS6NKKwwaFEyOVQgY8cyju4AMUOnlKxQ+onCGPVNaPkHbjy3VQ6ZV8hPMrQv/Rq52lFyEn9FbR+pK+qg+zCXM5lWGFAIvM1qq38BBJNTnypExkq6+mQC3FKcsoxvl5nNVHbtWL80q0VDVM28nJLEbRI4JoxZDkx8w5fgxKzkU80XtVcx+4QAtMCkqlDM9k8iEp55y/hoAAcKxWqBQYjSAo5QiTL3F4DiY3VF+4dtEQupB2VLpRbNLQgvA3OQjUpuDSiOKPiGrO3FQjsp/6A/lT79L9TKo5zmQ8uq6DIV//+tetFxBMk5nzLQgSXuc1bf7+zqXX3fLgqs7y/Lt7TETzttmut2HWus5S84zS07aq179XtA20dVf71i2f31JxY/OK9v62ux6aN6tuwdzmRx5bds9DK/Zf8qztZlpAGegqze0Y6N+5ZcY2DeueNn/WvNktHQOVvber26HUN3D1Vas+9cWWVSvYcMuV6vozzml85St2fP3rGrfd4ee3dN+z9NE/23/blhm6X7W1oXebUufW2w9O8js17XjLA6vXr1/X01g3c/d9VthQPbOuxcnjqq3XRkK1Y9Wj7etWv+wZu9T/9CeHN7UesniXde3rl17yi2vPu/DvjMH99nvusUcddfQxzx66NHEyMEbaQCAQCATGQODxP/ljxIhXgUAgsDERoHNTgOxsvOCCCzBfbJNqZVew5f8jjzxySAf6kzhUMbosKwcLBgpK603aJIsBqxHdKG3/Yw6SLW3PEThXPftJ8aUFIskULGRgDB1U5uLTCDFVkdFgel7Sm6mwEtIgEeARL8ESnyMMnZtmSVlkwVMjgbR8ih2NnxJpc6ZXaiXckz3QRS9YMBMxsVX59a9//UknnYTzD1dMUxKp8hlgKm8CiGyqz/qNL8EwBY7/KWdKM96L7RNJQnvFuWIOY+BWjDa1foLhEm6f0hZwS5QsmbA2jjwEwLptYyZA7njqOOHS4YxR2IkwRg4KZa1FuRmKdZgipFJ5WwzJ/vQK89GRkuMXGf/Rl1JFPDlJkscgQhrtmLW6pOdYWfAqCcZmqHfp9gaFfATC35PTk72Sg1LET/25Roz8s+iRJA0Klr3cu2RYjDMxv5wtfpEK100ZCpEVBqXtAA4Ek4bxNSXFPaGQqfRkkgUvoKxB+IY5CadJgFSizHnMUaaadDqdqCmwKLO1JL1LiKlJHzNf6ds17ZjipzWLYlp+vcKMWhO4QT8BYquCaxFOO+00PU3R1kTe+ta3vurVr2ppaq6WqwPV8oMr1l930z1rOnva2rvXtLetXdO1vr/UNGf7xbsu3m/3HfsHbH4udfWV5s2097h85y2/X9WOclbnOMHb1OhEbm95doX19pE7D3jp0fc98MiKtp49dtt9+3mlPbYuze5c07h+TWNbe31DfdUtgz4UfMZPSl//Rn1/b1/FbvZqfX3d4JLMu99ZecXL//fezlWPrdhjr716Bwb22Kp+0UB7w8qH+5Y+XClXG3bYqXfB1o/1N15xT1vzVtu7f2tGudxTra5eP9BY17Ns6f1tbZ0L5s993b4Ly6d+sfST8/ur5V77/bXRQF9HZ9vqrvW31tfffuwx19595z777HXMscceeshLdNRp6iEb1EARORAIBJ5KCIyqNzyVKhl1CQQ2fQSoaFiiY73nn3++22uoiXQ12zs5myTpZKkKNXqAVDiei3CodzQwdmAqLz1POI5EKZeKfkmxo+ZSN22WFlij/NXkWYMVcw0GyNgrPPFJhtCk6qWEiSFnJTsnJwOVDk/joUpiyF6h3ExALD/JFJYyr7Eei58yYYL7xje+8f3vfx8yykKAfTPpNa95DcZeo5hKkgkw9uKn6iudU3fMKhnExlC1cTkVIViqlBzwHxUnCcKfFOJkRh4brlz96fCoFJcMTRY+1FRbaFZlbUypoMo2ZV1Dl8PZtMUkS7c0o1do3zHyQbmvuuoqLWjDquEwRswnRD51MDnUeBKMKRBDc/JWz0wHBFKeZPA2b5Eo5vCEhY4WQYaGlYOpFpgOPPDAmhWu0VKNJ1zOFsi0jn5ShEu4FrSaYK7Ql9LO+fFkOCVxlE4YncduFE8cT7bqbk1tks06hngKzQS4CEVOIgK/SSAteMHNT0tL6Rh2jpY81j44SZjuLaOgyvIkP6gFWuDTS63ujVhQTVZ+agv9/4wzzjDRuT1bDlbrHGyx+cVOdRE0V1tfdVl7dUV7qaO30t7W1lepPrpszYr13a1bL2yu63acoLF16/LMraqVatvKpfOaqs1zFnRWWxzl3WZWXbmn7Y577mtduEvHquVzW6rd69fce/8DWz/tmT3rV2+7oHXHxbvss13jTl2rK5de0n3WuQOPPIppb7XfnvWvflXj03fr+Nll3d/8fkO5Iitm4L6BavcuS7b6/Kd/013/67tWtHVVjnvB4l2rbQMXXtT5o/PKa6yTlvp8LPz1J804+qhHZm+7dF3J4eE13ZWe7uqCOfXLH37wvodWNM5a8Gd7bXfgvK7+r32t5/QzAenaLuhXqmU7tBvrSpUdtp916qfPuPG2//jIhx64945ZrS2udXz5y19u/ddq1B+XhqZghWh4W0RIIBAIbDkIBAHecto6aropIkD7oTA5+2rPG/3eT5uc/bFn78VmswqVPcPrkFQ3Rgl6OQ2MWsmeZsmcMoewSYjxMvjY14fdIXIUteGZpBBanVTSpjiEES5nZCCpYiKwUTCLIcBZJALwp6f4PKJhtnYGYjW2mBJM0UU9W870UZFZVinByTxLWqw4SZKeqWrs4eeeey7DiPuu0T+y2RrtJphXvOIV9CFVToXSWfETBSXLHnsOQpteJVE90fgUv1hK8jO50H2hZPlgyZIlAmm36k67HdK6Hte3cq2H5zCtISpOHiQfk7chNgGeStxoIqWWTUZ4UGvExNYmLIAMUx8Dvq7iCjc1kltyRTxV30qKrqLuaZd78e10+K19GDU4edEunTok8aa2RNnaZGG/g6Hnm2Rp3WryRciWqCPKnAKLfN6Y1bJTXrXRapEESG8V6lYCCFjNsZN8qqpfLFpxKuhp9hijjkkqT9OX2cC8ZK6Tj6FHKpy2OHmKZpeNELeO5TwF2kdtWaHYbYqSZL+eb3ox85955pmWk/Rw/N8HkE466a9OOP4EPaFS9iHfiuO4j7WX23vLA4MXXJXaewYc9G3rqXaterix2rVk193d29zR7Sbq6razSk+fb4f0Wix0fX919bretZ39nfVbtXX0rHts6Z67P/03V13S1tHni0eY8eymcmdPX0/rdgcf9Pyjt+moO+uMrq9+a/CIcLnS3Dhj7swGJubyhz7Y+Kx91n/4Uw333NnPCDxQZhvund069ztfv35gzqU3PXzQvts9f+v6gTPO6PnWaTOqduNXS5VyR1dHXdPMmf/v7TNeeeJve2fd8phDyL2LfIO4XLesrX/t+o7dtpvT0lDZe8FA088vX/u+DzcwLtuqo3Gs1fqSUrlafsWRTe96x31126ztrPzhvnt+cdG5N1998W2/vc5t0Qcc8ILjjz/h5S9/xZIli0ebyTPC4QkEAoFAYAwEggCPAU68CgSmBQFKEsdsi/Ry9GwmGvwQo3MHMgqXS816VQ4Z0SM34SLzUL8YdmRCwaK9oXNeYZvshOmiZqSlYBGS8E+TACXs5t/fzKw3d85c+qLNwzXF0QJpZlg0T41sipaEOsiD+qIryG0qiF6bdlkrOtEnJi9EOontSZXx9LZ4WDEXnaL5KVuXRf/gBz+AmLLIwBh44oknWi9AXLM8CQfKfbKEsznnKmfCnDPnAZQlA/I4bq1q1FAGB89crjg582LCjePP1NdCgCprU1sDNr7yBw12KjZ5CzTJnD55WDSQDGtgtAiSTGrFcOzI7U26h1WhyZdbzHk0/8Zs/VSWkWuBw8VduutoUk1teK6jZS/wOhJs3tj4XZ0Ypiwrd2YMPXz6aPD40cvIQMNUY0uOvkq2tBMkbY0W55prrhH43Oc+VzTj1LQmEFs276Vp3Ns04+WizTaWexxvOfvssy+77DJVFsE6C5Ovj8DJv1oa6Okvr+xCfctru8ssqPLs6PdZo2pbF4NqaXZzqaWptPzh5StXr1uw0+5zmqtCFrZW95hXbW1b1bh2df+6tsaZrT1z561snn3JrStLM2b29vbVz9p+7ZrV8xwJXvXw3fcvnbHTczt6+ipty//pVc9e/PBdy//2nfWr1rTYim7RpLF+rg0Wq1Z17Lhjy6lf7rzqmr6vfIcFuM4l09VS1w47zvnyKb8daPrdHcuOOXjPbf5w15q3vKOlo8My5MAgSUZmB9l4z4KFs7/8+Xu3XnzVrY+sXr5szrY7N7Zus1VLw1atrNTl+1b17TG/+pyGVd1f+nLf+Ze6LK6h7OorpZe7d9p20Sc+2Lnns25dUbeur9rWWZk7u8HRah8x/tklF91y1UU3XnN5d2f7fvs9y4X/nD1NNQhnqMMTCAQCgcAYCPxJ9x0jUrwKBAKBKUGAhmST2znnnM2keeddd2+91bwjjjjq+OOPw7gmSSroVRxVgO0Cl7NnmJpl/15SDpJSS5HioWNhMlguKlsd/AKjh2fFNaJ9fQO3/P52CujcuVvJjeFXrfEuhwaTbRarxAydIk4ZJkxUSqEiM+JhpLIVggArOl9GleLT8ySXiiQprYRpf3WKUMw2RcjPnEQIquCQsK9ioqzkxLptpLR8wGzuIGUih6mylFdsWY2SkTkF5jx5MCttwVbsFdlsemTnQcCGc7Biqo3md1DTJ68o3zZVor5LlixJAI4B1HTIBkY7yVEUbWorO5SmpBT9xPKErFjvOcRPvfQZjVVTQa2T+L/GrXk1JZI86ZmoYKoj4+HGr6AJwYZ/zcEAm27G2piAqLjilO6mPUsAiWpaCMhbzTemMMPLIt7gvVO//70FQT1f65ir7SWxC8Y2EyPCqBRo9mM0lpxfEk9+a1Xp3IRpCsl3pwOTrxGNLevJoH71q19lCe+Zz9yDAbWb2baj8lhXaX1XnS8IsQB39lbXd5fW4r0DFRdctc4oVcul9m6HZqtulZozs2TDMONwU7nv8J3L85be3f79Hw784ur+js5q84yWFz5v5kl/uXrnXa58rPmxzsZZjX133nZzaeaCh5evWfvoH5590FH11YEH773tQ6/cd+4l5/Z/8vOVvsFPfCHxZpgFrTPqOtvalj828+yzeto7+t7/8TofOXLTtKq96tUz3/E39/fUX3bTAyccsPvsKy4d+ODHXCM9dCLeBmYc1teAmZLrmj7/iTV7Pe+bv7iru87npnbYbdvZDfWlxzoqd60YaKyv37q5csSu1W1WPtx1/oW9F11aWrm62tzY+Nxnz3v9a0u773l7T9PdK6sO2W8zs65cqusZqKzu6Nt6VmNzXV13x7pbrrn41qsu/N+LL161asWuu+x6gmsSTzjBH7uifX54O0ZIIBAIBAJFBIIAF9EIfyAwtQhQ7AaHGN2OgeWcc87Bte7/wx+2WbTDCw879iVHHvu6VxyyYPbgN0W5pDAlf/FJc+KoWSNGYLqxgxqbTdc1ObIoLbrigB91jSpDS0M70Tz+ROroXozPLtxpbXVVsrxXV9b9sNJ+Yan3fspLe89OvS3HdpUOL9XNXLx4Z1rcihUrKXbpmC4PXTARYKWg2Xgvwyw7hnLphXZKozGqLERBZKb2Ic+Jf1Lui6ZL0WwCzJkXaz2GX6r8FhNOqFpWALJwBJgV3YKCy3USFYcD6pgESAnVGbEkLYUJ7/r2t7+NdCHJ7DbgEofOWtz2nIvbaJ6EpyYDsp2x7NuUbD+TACP2hGmSDdpaHLwQY/hK35KdKgFSU8oNAeN0Hv4cmGoECiBoGj+nqtzxYKVjqLiesDEZaao7BqimljzGI+eUxFGumQQHTuNCKxsdGxNttUh1N4pZUC2KufjA8PTTaB0PFJKb9HDmtM9lSmBJmSTB0lKdnSzmKxtqTKr6pHnV6eXEivWTNAWJmSZeyQW6vd/n69I322VlJjeXomysl3vutZcZeH0v3lta3VVp664bKNlGXGrvq6zrHrT39lTKTQ3V1kark3Xd/b6max+x66xKjQ3V/kq5vcu52dLc5tLBiys7LH9g5b99rOHmW+sHDanl/mqle6BqPWPhJz+yeufdb1xWvm1Z77U33OSUbXNjZcXSuxfufVD3QMOcvlWffu1eMy/4cd9/frav3yZrbLOfhM0z6meWKz1r22d848s+8t353g/OaCi11Fe7d9tt5vv/pW6np3Wta++fNbdpdnPv/17c/S8fdkDYvmsF+xQwru7erUrfwMwvfvKxvV901s3r91iyaE5zfXtP5fYV/Z199XOaB029a3uqpZ7+P9+vbudKZ0PH2vq29rqmGdW587rmzrtzbd0dKwbmtda3+iqxqzG68OnKwtaGofFfnTWj/5kL62c3lta0d59+wZWX//T8ay67cMWjS/11g6rVBKckGN6nsAM8YVaDf+ZHj1TzNnWnFH0yQ+yP+Tia7X+DfxP/+N+f5snBBe7B0CFHxOwfx1w69Gf2TwlqRP1j6X/Me2i55/Ef8U8gsJkgEAR4M2moEHMzRKCvr/fqq689+8dnnnvueQ89/MiiHXY+4GXH7H3Acfsf+MKZjc3VulJjqbJk6+oOcygP/r6M/DeULsg+WWN0zWB4Re2zDYwnM8n0x4kGjx8iqE7MYhd0MtQUl8BLceYddtx+RtPM5uqNDavf3dB3e7lk75o/kIPfuyhX69dWn/fYwIf32PtwBa1cuYqhA/nhl1YO1As8lqJJY6as21xNBaQapjgqkgRIHjfcesusKrnwYjX9pHaj7iIUiXGu3dieVEqKo+6+BszA4jSdk6ICURcVx4Qdq8OKKdPikx8g9FeKfr7fCBqoDsbLVszuRPWXvCjn2GJM7Vt8j6GVW7x4sUavyXwjSwUxKj6DlUZfsmSJ7cejLcTUyLmhP4f47+MEuJgW+dF/2NbQoWRJK76dVr9xp3sz9D3hYc4pFAPghu2VV15pqGKhExgUExZG0VrZiX02WOUaCBvnrHWNwMQohqRt4RawrE+NzWoktB/EPGO1qJjDJP0wsUgnE7OKScMsx2+yNWvx2GXjGHCaANPpdIEw1GPtmMB7XeafYrbMbDnk4IOPPebYo485Ro9CFF0Ntaqzuqar3N1nZ3G5r1pq6636dlF7d6nPNuSGSvPQXQ19A3U9faW6UtXVfjNcEGW7BJ5cqbY0VRmE/eForOt76aL+/h+d3vfZU8tN9QP9da7IAqKp3HTe/IbX1b/pTVeumvlYT93tdy1d097b1bTw0Xtuaax2zG7oNy+/48g9ltz7u5V/+3b3LGNL1hs8G+rKW7c2DyxaOOMLn2m/54HKF7/cMqu58pxnNb3siOqMGes+/5VST1/zK4+becRhA/fft+6t72pqb3OZs0PWJqiGumqlVF3XPHvh/3zlwflP+/0D7U1z5t+9ZmBFR3lOc0MTk3V/+dF1/c5ibz+3vrlc2aplYOd5lSXz6uvrXJdVunN5f2+lYcHsusZyqaO/tLq9b87MhtkzMHM1GthhdnWXrevq66rL2qp3rS71V3jdet17w3XX3H7lhVdddt6ypX/YbruFrzjuuFe+8lUvfvGLxu4zGmvyDtT63uAfb8R/JKcxBv+2D16h7fXgH8fB884O/gw2phWNxz9jtkFz++AgsZfcfx5t6/seekTy+p12qJtl3UrOg8soShs6WK34qv0VfUsfHGhrb9xmQb1r7esb6wcRHcMRcnB5fKA6YEmlbuj+7xR7MGdnvQfXXhQvzOvBtY8x8opXgcCmicCo1+FsmuKGVIHAJoyAvwiDa6z9ff2sdvbosvc+9PCj2+yw6z4Hv/aYA4894uA/m9XcRDlZSQup9M9rbuyv1t+9qrqqvf8ZC+sHr+z8vy7/RaxRCgdLqVbTR4nsC2WGRf8QJ45Clt5Ki9HheEgUSwWeKRomQ0FEU7195KFHm+vu3qX5HxrrllN4BtqaK39oLNUPNCzuLbX2zCv/urX1g9XKs8p1C+fNm2sjH5UOS7RhFVehwC1ZssRfRcSblTXvVMwCZw9h2EDGMF0yvSI2wyv4f5EY+VcuRXLL/28ZcoR0oa4ze7RPfNhZO9GQSTTYHUtEVX2SU2Qzocob52zwRj82vu0rVQ85x8ytNaQNz3ATnus4MgTTHwouoAEwnWmcJnnwvbTuUFOhtK2UADhGzauN8DN1y2mq8mjya3c900ADuNMKo0Wb8nDVtLphUy7eiwOnrRBTXsoTZlhEG/7mLsMTDWYWxoFNO3kySVmJY95Lpld+gyitf4HRMH/C4p4wgiUYSyE5WvLrkOlWfLtLlOgtAdh4TSDOtJt8TMiEURcCO9l7xBFHvOQlh86aPbetd2B1d/WmZf3rexhLsZSya67ae0rregY6ewc5kY3NDY3VBp8E6q9r6x4Eo3lGBfccjNlbbq+UWhpLc1oqCHFPb51PHFXLdbsvaKh3Y9Vvbu7qHagrY4pDi5hDfyPkWPndLXN6Opr7e++9Z+39j3SWG8oLG1ct2Gn2QNOOXb3V7t7V6/vK9bs8fcbhL+m79Oe+c+SvEMDRsrW99a3Hn1heuGPrwu3q9n5Guadfkb0OBn/j1PLvbxvo6Vt1003lGY0tz39e44nHd3z79PJAL+Ov1iETAj/vDa+pW7D9o2t7r7394bptZs5pbd16ZqlnoHTfmkrvQHWHuXWzGkudA6XlnVCq27a/8Z41gzbw9Z2VBbMbt56BeZUfbUfGy9vObRy8tcx315Y/uPdOLbsvWNDdV7ngytv7mucv2GZbiVf39Pf21D3/oINf9OJDX/dPn7j0ihvuvOrCsy/8yVe/+rVtt13EJvzKV77SbqBGl1bkVpxSz+PZWijZe+/mFYN/f4tukCWWy13z5y+65VZNO8hKS4OP9p+cW3rv+3q223b+pT8rN22wbEy+dgW0/fTi6uc/V3fd9TM6rflWu+bMrbzhpNaPfLT8+EfvB/uPDtx1wfn9H/hIw523zeivdM1obN9rz22vvLLU8vjWs6K0ya/rDnr6ete87i+bbvxt13HHL/jsp/Sq9HbwH0sRA9WuC87r/uGZDQc8f95b3+740OOvU6R4BgKbAwJBgDeHVgoZNwcE6EDXXXftj84865yzz3n44Ue2f9quLzzqtds95+U77vVnpfoZ/l7d9OjATlv17zSvfsHsGW3dlUfW9m0zq6FlRmVOSz0lqK9zbV1vV125odI6s+wOKn/aV69Sb5SMFQIT4EcXMYHBP2ulkq3FwhWK9HpL7aOzDukug7e2UETSJt60d5QyzZRHI0wxBe6zzzNaO/61sW9ltVLfe8lWdWcv6F8zeJh2/ay2pre0Ne2/pqHnpv6Vn2/c5t+7u3ts6sN7cUssWuk00fzB0tQySaTkLz6Fp5j+pmLsrK/Ic97QmFNlTzHt+P3F5MiDO7E4JYLIxdEck9q3vvUtGSbq66PKhx12GGpn87ZAGEIM5aDLUmGn1oI0zlpoRKZObQ0caxb05tSU40w+tdFApzvpMJYVkCKmrZR/EeepLVFluZo8Le4gGARANqav6JpCn9yfqZrqaxHHoVPrAmkdZONIpXRNb42MSyUicmYV085o+JtPmEMdwRgtwmQkl6drz6wCpLPBbKo8wMnipczJYIIiufj8hhK/1a48z0xGBt0vbcQwM8g5zWb6qnCl6KIWrTLpRTZwAzcIHnPM0Ye99KWHH/7S3XZ/RvdAeX13ZWlXde2aARTVxN5bqXT2VDt6Btp6+Adnc9ZPFywP3rfVb6szk1t1RkO1xa3PQ0S3uzK4O3hmc8Um4K7+0tqOIdNuU6W1ubx89fqbV61/7jPqqr3d8qn02RXtb0id7JobS43lel/9Nbs9srqyan3HdrPLLHbdnR2uodptx7kMsKsebrt7bd2Oi7de9Pa/Xe1O/st/gROz7zXOndfy2leWjzri1l4LENXdW0urzv9R9cabB373+8aeThI31NfN7O9r+8JXWs54wcxXHd9X6e84/SymSDS5cZttW197YtOxx9zaP/OCW9a391R3aZ3RW195cI3jytVt59Zt1VLX219yu3VrU9/zdi7Pbapv6ymtXNM/q7m8eGsUscowvra7b+Gs+tmDG5nLLKWLZlfmd7bPLJUfWDuwdG15RWdp3oxKV7Wycv1Aa1P9ormqWXpg/cADa0o77P3CxXu/4GV/+28P3X7j8hvPu+Dic772lf/edrtFJ5x4ouPWPjM2fJ6ZTPf4U9q6cuNfvM4ntrXCEH0cZIm65MBtv5/1v5f07/EMf3v+SCH9Wxn45jdaHlpav/S+zquumHnYYZle/inDcfj6zjqj/uFHqu98R9+z9in19gyc/v3mL53atnLV3G9/u2QxY0iCrh/9qPTmN9fvtU/lC5/rXbRdia340svLQ6bb0UqwRu//nd/+TsvPflbf0dG3alnaYp3j6/l999/f9dWvzfvSKW2f+ULHTy9qffkrnvzF2ixfeAKB8SHw+GbF8UWOWIFAIPD/2TsPAMuKKu/f8HLq1zlOT/cwCWaAIQdBEFGMCAi4KkEWcNUFVlcM67qGb4PKiigqIohKVkBBAdMi6GICJDM593RPT+d+Od3w/epW951Hd8/Mm55mGNx3ae7cd2/dqlOn6lad/zmnTk3lAEIP+4WylQUxmbZs6Wmb133im8855OSzjjnhKJ/Xg3mA5Uw5U/MhMuCsVLK8aumQJm/MLxzVhpPFYzs98/MjxlN/zv/8V/qWzVo45FmxzHvWWeqSRVsHhnC6k5KlFO9wFQaecYefYFGKxlqIBy/32RUWbAyu4xHQBYmwHDZwE7qR4TAwkh6h7ejDAid2fkKzCqWn4vq3O3TDl8tnvV4PnmxqpKR/vs/TkTH0Zs+i51eu7Nu4cRNygywasRIzkaRqKi92/RsCsEJjyeHd/SDTUxyeh4jLSKg4c2IjYg02cbnwSMQMDs6EUsAwfMNI3tXVJW3a3GSpMAevY9zeD+5ztCCc5IA5oHQowdtTlsvNXbPzFXxC3dELgMbRC2A2d+3kr2CRM2UNGXggY3ADycCT/c8Nipb7AM8YmXwmkufmHhUnI75QmgC9FTG393/dZU1Qgjz66KN8JqzNxmNiRjLgEmPULAaEypklGYJyinUBfNFgGJwFuMkBbZz5dlCukSH6NR7JHovuAIxaeSm7Skn+8hEjKvnTG1lyQvBnfHzYtQ5TuQC9jp8L48wpp55y8kknn3Di67Cq/uGvK/OKP1zbogdi2G9xWs4UbYy9GXYwMtnTSHzeWKjlRw5+w4mWBa9ODCnyU0s4SatqwGMDjPldxLGZVb+sDPeSRph38yXNtLVkKjM40PsvpzT6bvv++E23elQFG7KficfrIWA0QRH0s97uufKqB3aEVw57Bns2hnza8oPn1wbZYEkbHc0M929eevDijjrv8rgVGR9gL/XCmjWaLxBYtizfuaBHiTy00ThvmafjpaeGP3y1ZpZYCSx2LVLsklC9WnjGRr5+je8Np6aGE9GxQWNrD7shezu7Mg1Nq1L2A3/tsy2NzZZSJX+6YDTXRxcu6CpaSjKPL3dxcZO3NaJnChZhsTA7t8R0HLxZ+TycxuKtNQk1r+BNWCt111rxgPbXF9f0Z7x6qJabQzsG8nrQG6xtjKrxmnja1FYPYlhW8QkHLxdLVlAzFrZ6yAM6//LUs6v/9/4//fr+7Vs3dMzrOPfcd5933nl8VnPSPSCGA4ZMol55Y+JXcfu2/Kmnq9mU79HHAouXyBrRo4qbNpYOPcy8+APqvfeYb39bzQ9/QKCvyacyB9H6sm9M5Oj8I3ujfIJ7c3blqtDCbtUvPAREkUY+/cY3aU8/41+/xts+D1BtDg+mDlthLT049vP7tVAUxYpi8z/h1YRbc3nOE6VO9vbi1k3Z416nfPxj2uc/b553bu2tt2God9NDRubPfzLu+0n4v6/J/+hua3QscsWVojPPQLL7UvWiyoEDjgNTle4HHIFVgqocOCA5wByAReLHzoFIBJQ6+5x3rzj13Z2HHadovpxp9Y1bNUG7LqQtb/X2JYytY8Wgzxv2ESHT99c+o9FfiCqDR3a3tJqJwi9+Wbzhuz6rxBymDg/bPT25Z1/0f/5TncuOYGUt5l0kUTAAbCh3/0PCY77B/II5F2jKTxIgkHFGJuOAPIjEWw8xWs5MQDtCZXZ3dyPX+pXHdKtoK7r1SNxT0G3CrwixSvFpupE08/8TCl+a0q1BM7+us/OQ2to6rC5gYGRN6fJH4goPaHC9BHkFuQnQzuxbXpcKs6okGRUHvyEooyCgaKRhSgdSsr0qx2c/+1lkZZQFoE3kV8Dwgw8+KLPF0oUcj4CL4gAuvdJzOXRirWK5IDYlGgjlxemnn+5aJ17p0nfFSdlt6EVIh0R7flUcjyVtMAe8gTsu3XVX1P5N3qfp6bdoZHBJoCdzlutO939lWd1AHwCHo4lAp8bX4WIGPCakFomPC2oZFiCPr4xjzruuzJDhgi8F3xO+EUqkezC+yW/HXUYBu+ixeL5ADGnmimMgfFQh2OQZNFjxK5cEk3k0GjvttDeefNLrTnr96488+hhLDyTySrKgPLUDdGqtGVYSmWRpc9oXjgdq6nU/EQc90ARGEGeHOHALFwBaTRMoGpJLFgtcLUBvwCfoF37HJdySzYhfiUS14YwJ7hU2Xt02FKtglDJaYFCft133LH/LGblfPxIZGzWBpazkBR4B/2O1obe/ZcQf2jBiNkbUeLPq401LHUwpRNJqbw7Vxpf1Ju21w9b2JvuI9raao5r9K44pWNp2S9ucVFcPaJt7B4oH1Vm5vE83wx7dsM1sHkdm26dqPl3LY7EeTw4li3/YkDph2YLIgqUFg5hVKkbaZ1ZuyZn+kVQplSu21oZb4jqbCA9nLV21ljao7TWBbMnaMmJi722NsvxHrH/uyRgBXZtXp/s1EQHbq7A22Nayw2tX79g+bo0VzPHhbanRkYzpCTW0NcRMjzHeP+7pywaGCr5I0BPVVdPGtF5sj2tdceGRO5y3Ehn7yGOOOemEYz549b/3rHzy+cfuY2nSddddR0d6z3veg3c6is456bHys3VaVZzE2txCLvuBy3zbe+2f/tS3aLFzd+J57q57NMvwfujyQj6j/ewhY2jE29Q48Uz4Nou9kJnGZ+zBAhnThMIwrkSWHSLf0klOv/IF9JNe7/3LE9bQiAUAts3kfT/2Dw/Zn7lVC0dJiUcBL4vMST/T4SB5sjeyH7vaOnR59KKL8p/73PSEvB9YsWLwK18pXPKBYt/2pttu3QmOp6eu3qly4EDlQBUAH6gtU6XrgOQAchUW1HvuuYcNaRENgZeEnbzhhhsAVx4PuzXY64bZ6cEO6lpXnTaYtraMGm1RT2eNXhfWCO+RKaphr6cu6BlJF1/cOHDKokZf79bMD2/XSsWCZXtxXUNryxTX05u77d7oZxYEWgXshBPCk7lsopaXEINnMvTwlGS4KGOQ4RFonLOUVssVvfItQMX5559fGt5iD+OhpZa2m3g249Pn8Xqxl5oYGmzb12eIYFh4zFmjGFU4ZGsga3Ih86m8fYB5EgPzIm7JkI1sLTd0rTyTSlLCATAtojm1BrF3d3cTgJpCOdzXsT8THIuDO1CFk+2vfvUrwDBEYtXh4D6sI24WUYjY4ZODiK8QXJ6Jm9ssLqg+1iSKwyBPnhI8cEELzlURs6MK1QBQB8UBzU2tXy3bryQefQQO2IsWLYInryJbZsHJfX9F1he7K52EEWbfM5x1DowkdAZ6BdZXcCBfhPwQGCj4kN1s8eyAZtRwpHdvzu2F5In8RviC+Go4wKX0Ez5VCXrLS5x1nyFzPgS8mlGTUXEOQC9qO5k5/tisKWVRwIknnbRk2WF52wviTRbsZ4fZBRcUq7JhT5bFuiVbbznYk8onx5IDibQyvgObbHt7h+OVLUijFDIUwxKYQ/wxXAvzL6t/ucZviEkg6LGbgmokaIfFyABqsXEVxnmYoMgg2BL2b1Xz63Zng++FHdb8jq6Gz30q+e2btY3rTEvH4Ky2dcSuusxcsqQnrXc3enPQ6Y1rPjUewo/aGkybQwOM8URaZgmqzarde14wcVeO+72aV8B4y/YYRTPgtUG8emtz3qNamQK79epEp0JPxnZEkBKtCSzstj2BxzYkHt8RbmhswI3Z7/WM5ewdvi7SNNekG0a3NHR0pIqqx6ctbLDb455cwdo0alKdlqgoPVm0+kcBxkpnZGKhLmb9tijol2ha6srR0IBdrxIncmwo3DjfjC9o9Nk+qxCLx9N6vGfEZDUz8ymTVbpo+HTriHZPxMuCZRtgH/LY3XVszCTY3RRWXveO4y951wlf/eo1GPBRXH/ve9/78pe/jMYTGMyEyGQx6z4jAeXk67Jt7eSnP+P/3aPFr3615s3EkkTBgSnfgbRGybr7duXIY/3Llpvvf7/n9jty99/v/eAHeSpzKI6OmqnkzhmrrFvzPr3F39Tsffl8NNGd2I3qhRcLoUiog1BtJNT03/6vGQgGD1uWvPMO+4kndeJKnHpy+M1v0aetN5GFUBEaJvujH9u//1348cdVnX0HHJrLaJA3tECo6Z578309Dc2tGsQAqWekuPzF6nWVAwcYB6ou0AdYg1TJOSA5wASD/Ify+K677sIOAOBklSkqZIIMC2hadjCN7UhZG0ZtQlkyVWQMuz9hBX1qY0wFyPalrM3DJS9ynFlI9K296owlgQd/Yl/7LY8uHOlKCFC2QgROXJWKoUj8+9/Ndh80OjKmlDJiRyIBPF82yUAV6BdfX+7j5UuuAD+usQYDjKUL9MTU+PIXmdVKY99XBz4Ehbl/n2c8qbONI95ivI5xQfd7fWenAhcNWGiZO3/vCR4/pdyy6u75EgIw12AyAm1iPEFUBV7KJcp7frmCFOQPkgTWclAKfs6A3q6uLtd6uRviJXMAzBilWRL829/+llg7SPNPPPEEG/9Iv0pIgFrMX1jD8JfmwGgAKthNtruhGos9e1CxmJA0WPYwYc1Op7CbImb3CFbgyIBVDdgJ4H910ThVkE3Dxez4PDsmlL8F5uGgr74qrHCr/ypyQHIDSvisWJCMVzbdFbRJi/AtMx5ywZnPR/qY8AHOoZ6ovC2mX0MVfjF8qrgJcI1Sjy+UT3gW7OJ1FGcgXoLAo9Pkw+ea4ZSux18w6F9x+Ipjjz/22OOPP+LoY+ua2tNFHcSLM3PRBvFq6AsxZgJ6c+KslPCpcXxwBHIQdjzVMg2GaKjt6JjH8OqMhAa8kpBB+Dw7kEJE4WfwV8UuR9h7o37VJ8IGSyOx+AgwsqFR7UkJC15vGrStjRaEsbDGb8UC5hHN5on1Zjgxaq1ZWxoY0OvrAgcvSdW3/XnMs2FUC+hqIMDuweZIVhlJq1gCA17CR9vpxKhB3Cx/uDWmBH1K77gynldTBbHJ8PIme34cPK+0hK3jfCP5795k/OgeYLUhIk8L1mimETjrHf4rrrqnz/7Ns0ON9cFwMJDIKvlAq8+rN8WI+ayNj6e2bdrQ1toeKvUed9Rh2ZI2lGF1qtIc1XBXlg7PVLkx6mFbIzy8dcVqDFldtYQVtnvHzB1p3XT8xAG0zzy/MhipWbKw3a+rzz63etCI+etbY0ENf4C8ScAmo7tea4vDMG0ggx3Yao6Rp9DiBlRzQZ3ShoFYUL3zoInZOAAYzJTBUH/iiSe+//3vR7shXat2ptubKwFx6U+2krzle75//MfcRRfXfvdGN84zObGfU+5/f6+/+U3Fr3295oorlFIhsfxQlt3Hfvc75l56C3Bz9HOf917zJR0r77SDPgJA9f7iF6HT3lg+MIpVu+T8i4e1c88vfegfYl/7mlhybFmpE45VNm0uHHWU7/kXWclgbt3i3bwpf/7f1X3ve2yzNr0AELTR35c59gT1wx+OfPqT6lgi295Wes/5L3OBdhCxqGnZIXg7PbuyBNXLKgcOQA5ULcAHYKNUSXr1OSAmMefA1oGF8O677yakMFMm8ZPYNvbMM8907WPl85B4w1ZaomptSN00XOrPetBwL2gQrl+bB02MwO1RZWj1M8+v7fE1Lmyt8drs+DAynshmfIFgSFf9KiZXli2JJWIs0iIeCRbjrcXa+lBtsmDV+FFXOa5GzkwjJxxcJSmTyQ/RDesEFCLMOVOpQz0z6suhr1svxX+0bXuJseJ9RyK4YZ5SVBOpJHUW/nO+fOSNKeZiW2vWA0tEnRxuTMlqooA9/cNbMmgNFhsAMOgU4W8nGXt6ffpz911qjYMo3s4AYMLwYFKm4qeeeqr7yh4JlgmAygjuvIVhh9elbzYiPnIwJiYEYs4AADgsc6YUwmJjOpBgGLgIaJxSVvlPl2BuwgTUAdjKiKzj2tXLE7vE758LlzaKg4f0ahoLel4tkqAHb1u4RMd+tWiQnAf6cuyfVpheilt3GIKSi08GjOfenJ7+lbtDofABbRqdVn7IkIRSgA+ZQhkeOSRt3N9vFFIQny06KXwEsNDi/My4JwEwZECYS4n8Wc4f7gCb+ZxRRQF6+cyxtDOYyDR8/nzaRx51FJkvP+Lo+YsOKdg+HHQZirebau+QZmAiLgF61VzRypfMggnKwibKljwMzs7eNqzmdXoOBdnCI9lTV1ePJV/eHB0dw8kiGBRbjkcjUQcjKyEf29sCfdWgl9hF3LMiPvYHUuuCSm/KHEqzETC6VCuPb46mQADIG++dKI7IGgGi7JGE/rstvnWjhSPbWrqOawNGssnulrS9YbO6PanVR4Uz8NgwCNPDIl6/X4AXFsoO9O8ojBBbu6uhLpAqqZtH1IGMGg/a82vZPkfpGUddqzQGlJBX2RGMdrzvveMEtPr1Y0EmHieOl3XyicGLL+7zhDds21oTtAaG05liKuxna4A2r19nwfNA0qz166ceOa8m4FmzXlk/aEaCGvGfic41mrX7xtiRWG2LaSxLdmY1qyVodNYC+rVt4/ZAFhp8YEDhU50s5S2bLZQaa9hM2H560Ogd0xubvHUBXK5A0WZbxFrU6tV1dSzDEuJSPORZUCe9aezWgHVQg+ZsKCVmTdkraBYmSs7EOGCcQX1DoESm+CuvvPKjH/0o28i/973v5SyUFM7h9iX5c8az6HNOx+OUfvx3+j9/Inf8CbHrvjbxSchZWHwgtnX77Ra6gnefI/Lx+uzzzvdc+9XCSy/4Dz0CZ3i0IMGLL7LecMquCiVWmuew5dREkkEtxAU1ev5Z+7LLC4cfFv1/XxCzOFM5f/mSfyxpjiYDzz/r5YMtlRIfuzp6803pN50evfAi0rilyHzYcDnziU/YzY3Rj37UcbJ2dDiyAPezKhM/JA3Vc5UDr1EOVC3Ar9GGq5L9inBgYjpxIB8ucHfccQe+UsgreIS+733vw+TLZFlJwSKMIut5c+bGETtdIrgJHmtKf8rKEwelb3V+rLexc/GG3qF/PGNZ/I+PpD7z7x6N6FPsaajgO01EEMSpbHNj3Y3X9zTM7xkXrzMx1gfM9hpEIlUs+JmMmcEV8A9MBWbo7u7GGoONkQtU2tJ50p3hSClrR3pE6v7tvYc1/Udr+ElktsJjtfa9Tfag5vGp+YaM5+9HA0cmEZI2pd47rl3Z0d6J2XY3oLoyhsxZECxqgcyNPQprLZZbKohozhrpfdHc77EK8BNxGX9IkDBCM2cEbvctd/0wsBY8DBQvhytgaTxIUU8gskM8RjMWY5e3i5tPhRduL933TJD7AQDkg1m7vIkrpGRuk1Ev7GMEKsNc9irG35KVQkSmm+Gz+kqsa62cb7ACxwRkcRgCgtqXFt99oRV2KjoMgBPm0M/pMGig6M90eEmYmwllvXKkTqkIhTKm8Q1SIt8pIwMrPKUai5Q8Re8GxGWXYCjny6XDk97NBBcMOj9wV+BeQO/y5UXNv35EGHVxDBYOybbCmt5cSSFAcha1JLCMxSFsrYo5TwAEhmMV6KIT5RADnSH2TxWHCGolQg0xeovpQAzi4rpYyI+z3Vo64VFLYa/W2hRb0NFMLyOoNDgtHrABvfGAClIl74xhrhuxntuup0sK8aJxosUnmQeYfgmYlSjYoxm1LqL0J7ljj2bZEVf12mYsACSHHk9DWBvNWABMYK2IF6ZoRVNoS3EVrg3bQ5vWjg8Pe5qWhOpa/B4MzphS2ZeYfYb5DPO6lWmOBIkpFg8R2zjxd4cF23Mj9vp1xZWrTcMKLF2iLl60LVR73c9e3KG1FxLDRBKc19ZgmoR6hjlmW0zBkJsYHUpkDICzUhjv6mhiHXDa8pjeeNiv1kf1EChVMNBqCNvzazSWzfYm7OEc4E0Ea0LLgMW7YKjNEZuNErYnCy/12+iU437WCWuGYSfy6AiUxU1qzMumTdZIwsTy3BbV+ErAfiGPsbBebUSvLCzrYsp0gJtoGboEfUBiYPHb6at4cjFos7gJJy88gBii8Yu+4IIL0IrKnrz7/ixaxbHWF7Zsyr/hdPIM/f63vs5uJ/udwLvEGubFi9WT3xC9927RaVCmvLTKPv4445+ujH3pGlmEjTuWMIbPcIiuxbIktqpiY17nOXXB/Fta+ULubWeatTXhh3/hb2+n12HLhaDUCScG//pM4cd3Rd59nszWQteWAABAAElEQVTOGhpIdy9U3nhq+P4HSIYXPXmSFekhP/fA/cZFH/D/6pf+171O3BsZS8/rMM47L/6DH1IH1mfxwgxkVW9VOfDa5EAVAL82261K9ZxygFnEzQ/5iSnwzjvvRFrq6uoC96IPBtgwWbhp9nhBhkzhzG/E8uxNWj1jWtGyk2Mj2A16BjG1pg5qrx/pXf/Wkw9bVEwNffJz3rXrmaX5j60PMQHgsea79ALP+y54bDzak9KJX1LjF6Ujw4R9ZkdMxYXMy8TuHMMjI1hBEao6O+d7vXo2m+/snMekhlDlLDmbIJbJFdEHCIfMyi100As7rXnKP+hmD6Gw7EzA7BX72Xs6DSVQgJRS4NjnB/5tYEceNz+yAvlzICKISXOy6ImsK/hHzNOWhU8pgulecVLmXd5A3AGLEs6a2qG/p42k/VamnAVtFZA/NQkVwWEYKQo7Em7DyNZ0G8Cbmw77GKgAf9Hu7m6I5yeyNWY0N4F7MQuCyRAOAPhpETefvbqQ/ATgYdxGv4O0hwegixn2Kqs5TAxVABUWlwJg8GidBWfmkBiW08McjPz7OQr0lCrsN56gpkF9hgvA7j9Pej7L7GWfIVactJK5LQVcpwUZLeV9iOeR+3RK1fbxp+zDUzIB3z711FOssacWjIr4SPN5oh90k6FEwF+D9dV8npwBvYsXL8ag7SbgIlkwn+pV2HCoL2nlcK91FvcCdkusrBUzBXgK8KJic2TE5p6ADuBRcZtITwIiEIKKewLvck8AX4F1iP8U9tohnxr2M+Cao4MDQ4P9qlFYPL/5qOUL4kFiPmO1EymBtxtGjeG0iNWct7W1Q9wQBj2iKnZEtQ1DZtYGbVuJop1gP2EDy63AMCVTYG30pxwIdkSQ7m7A/qwmcnqJJcy27ffYAGxdM8aGh3v7Rka2b4mEA+G6dqJJ48WtNyxgV2GPkVFMLM2maaneQPCg1truOnU4qwwkSse0KYe3arUquw2bacX39KD1yxcym1e/UN9Ynxvpjbd0Nc5bgikbA3JzWM8W7bGsvW3DS1FvCbNw3lDSOaVoWg318eUHL8SN2WGI3RLBV1ljVXBvUmWdMMCdOuBhThwNAnk1RzzRoDqQtF7oNwfSasCnw6KSQSBJMKC1pElb0ayzhHgwJQB/c0yPEAVbVfyK2RG32G5Q4GvRJpxfdtCI9BDHF73EahT8GugAHFJryTzFEIRBGDGAcIC4/eMajQwg41DKjGbMU0zfqUTqbe/wvPii96GH/Se9DsTo6EcQBXgoXsU1OvQP/5CN1+Rr4sJXHpSpWrG+/lxbS2zlahbTcmfsezd7brsD/ojHLz8QKYgb5rv2q6FjjqVBecj/xVUv5d7+TjsYCv/q5955C0SRwFrhfGBlzj83+MCD5jN/DRx2+EROppE6aKFa3xB+6kmHPRNSDfnwRupd79KffKrwpjcLekWXKgTvv7/U2Wkc9zrvRe+LvvktdPuXU1T9VeXAa5gDLxv3X8P1qJJe5cCsOOBKUVhQf/azn916660YoABUZ5999vXXX09oq9mhAmfeE9YDnIy7atTWsLJxpLjxpR7kkDjBQIPquo09g4RPbGnrPrar4eNXDP3X17RNWxBq/JpW0PT8W04LvO3MXk9M8+qNUWUkZQ0k0Nlr9UHCq3jWjtibxqy2sBL3F8Je3SgVSoXC4MA2zepramgw1dj6DWJTkLbWjtbWFioofUqR4xEKJYIFN0p7qVW4r9T3IU/xRS2cV5cK0O6IbVop/CZv67eP6epMJpLsadzX14u5lQOHQwRHl2kuy6cLBO4jeUECRE+OKff3+NMtC5EFTAKGpBbIIthtsKkitcgEiO9cYKzbY4ZzkoBykfI52FFDZsgyP2lfAgCAThG7WStOX3KLA1pANtK2PPA35qAVpuONPTKTPBHReH12AFhyjF6BNgSLmcQD1Mgl9VW5gCq+QfQafH1yoenuyZC1IE0l7Np9VgfyU2pHh0FptWnTJlpq1ovP91hHegLMx/5ZrjWb/hb9BF0JGh+8SFg8iWsMfVi2hWwI9xogga8EQ43EFdOzqvyO29blr5A/fRiaOdBGgXX5KEAs5a4ZfFx8I0RqQJ8CkgHuMnxhKy7PZ/q18ENWDVBrpggMUDOYTQlShSuzh+WvNvZPEC9QuGhhqBM74hK8UC7CJCvMwvCB99nQFqMutlZciEM+K+RTgh5gH7GZBQyOBfSa9taIr3l0YLChoY7N76jj888/19baVldXm87kk8Vgf1YX2wUX0asB9wQuVk3MqgTVt1ePgs/1kbQI6wtibggBXVUSs/aVpbytUWtxnZ4qst2ujj06h6k5KGJCF019MKWnRvvN4c2pJJ5F6tDweO9QmmGzreugoJLLZVnIrADim2PhzqZgOKCxU/GqHXbBxOfa+9yw8vyQ2Fa4WNLHU6mQbtb5VKUlXvQEC95gTUA5qtO2C4UXnntxqH1Rc2O8Oa61HnXIeNYaGU4nBrYuX7a4PoqlGSuiEtCM1hju1upoTl3Zj/ka6rC02+NZezRNXZSmiBryeQdS1ov9Vs+4za5ONSG7WMKjm6RWlzDtenJF+6UhA81wU1RDQQxmQ8fbGrG7aokfJvXDM6M1GkhGTUNfAwCmf0p9jewJ9BnGny9+8Yv/9m//RnBEvMCuvfZafp588skXX3wxggEjudvJyzuPVSqlPnZ16IknSzd8O3jSSROPJklgrbiOYuS2O/K4U111VUgoNKi0iI6VXbM2eMft2d8+EnnnmQBRHNWVqAg2Of0QMJU+NDlW0+tKa1dlznwXeyqEHnzAQb9UbrJIOuuxx9oP/NwcGYNgAC0sUopFK5W2FyzAQCw6jWO1Fve5Yn+kI45kcy3/KHtCC2htGayEV7RCwYPHRC7ngHg38+nUVe9UOfAa40DVAvwaa7AquXPCATmBkRWyDP5Ot912G75POA/jZ3jhhReeddZZ7vrMfS9OTBti/rGGE7necWt9//jI6FD7/MXjqfyYGVjY6Dux2Qrt6Mn8+Slj/WYlGAgcuVxdtmIgVn/fS9ZYTm+PKx21qPVZ3USgSyQntSGshtnywSptXPVCWDfmRYcXx+4L2L+pCaRU1TTsmqxyjBm/yhM6emR4BDmVeiG2nnHGGeVgHpqEllcIckkjeZ+Z+YVa2CoMG76D1di79cjptopoIVdSCS7hNMhCO+I2IT5SGaAdDr34HiNJuBJz2dS772wTPJMNhPSPXIunJfkjfMvFh24BCO4gc6A1RCLjzi0NbikVXkiaoQGSMJTBMa6RsRDNgceYpDC6ulnxCPDQ3d0NMMbmycEFB1wtbyk3ffnF/fffT3rcUOXNvao1RG7bto3w11yABygRma8881flmuZjr2bUHGzRCQf2WCOqQJpd7U+771U4QCzAVIRmgi10JxY1sAvu3DaW7LGUAm4ERgKApRZp9/znLTozih5eBFWiXAOLYlLDrZTwe2BOOjCfJG4RgHYABsl2nyEJ5OHSM3lD/It6CysuKgB5MBpwQABsKU/GQMTXBHpBZUAXokdByXHHHceXIrOtkAbg7VDaBEM9169kSuq6YaEZxI0X2R+DKogXzInhlz9K5yZ/YFvhGUOoXxXES9xmJehXWePqc256NSvms2N+BZQY9es+EUZKYA7nb6IGw6Njjz3+56zhYZefZDbftPiY7cUoyYDGrPXFZxjTsU+1jp2nYhB+dKuwPm9L2mwmT05+YLZPwWsaKC5UjJZ1aJtn66hYn0zwCFyjSyamZYVygYm5TFrNjQ9njMGB4dzAunBt47zFy0PR5qLNXsPZtvpAKOBN5YzhsYIeqGHbYa9OUGV1LG+l8rhVq3gd1/jsse1bE4lxth2K+bSmCKV70BLUzV9WKORW/+nXLR3z/JE6X6TOH47Whr0BKznet2nRUhFar8ZvtsTE8tLBtDqaFaCVOFX5IquCLWJiqUYuYKaa2lq3JaxtY5jWNSzAhqUMJNknSSgXOuu0+rBaKAliArq5tEkjlDTIDg1BU8icX6tH2Idroq/B3j0cAGCGZZQj5QB4+jt8Gj//+c9vv/12FiOgeCVQFkiYmejlX6KduO46/6c+nfvIh+Jf+xqmdPJxKaCx8T0vvbTKPPpI8/J/iH3zevmQ+/TM0vBAYfFS802n195zL/eFj7NAq+7bOykSjS06nfBF5sXi2tX5t72T9g788iHvooN5ge7mPBdpbNsqbNpkHn5k6T3nRW66gdCT9OPU3XcGLr6s8MXPRT/zL/wsjo6l7v2R9+BlNa8/RZSJVCCRuUOYNTqS7ZhfOu/c2K0/IHM0JkgIIuvqUeXA3wQHqgD4b6IZq5WonAOTEhbgBD9noC9iHBISq32Avt3d3RUKSZUXyFTiJBbzGYVv3j701Jq+xu7lJuE0FZUYHl67uLBBb/YafrPAFJbWfM+OKM/3M914BrdvVUL1lifYGFbn12qg3/Gsidbc61HqgpadHvYlHnlDw7/GQ9tFIaIEYmQYmq0bamTt4AWbR0/TdF9be6tr8p1C9gQzmPmYDLEvCErJwRHPyE9kKP6fcoA5EccRC3gdIQAHWmAwRZQHxN53NiK/SnmX4sgNqANUkyv9oGeyGRXpdSm18gBgSeq+lz6lynv8KelhTbJcMorIjjcmBnMIc4kB/QLbMFiBhwEbHFwTj4e3yvNHs0Bluwj8PX8+Z3lwDZCQHsIkBmkgTUoJDAOXDP9TnsmM1y7TwOdkxa420hIyY+L9fBOvWuwt4BYAcHnRLvfkTbcKsA6I1d3dLe9PSVaew+yuDxwADP3UGnM9XWW6Amh2tXPfImfpvY9vMxZ4OhKc5ENmQ6M9spS1tXiJ44nA+Mko6ip3+CTpn2SCMR/QTn+mOHqvCxjcRnTJkBd8NWBadHb0T6gC4nLmmopDW3livjIcQBgQ+MSAu5w5yocgiuAVPjFUPABjfpJ5JdjeKUWMhuAHYDCD9IvbCYasrRvC3KpkxOoQATPYmQjECwoB8Qa9Cq65ANQQiFe4MbOA0yDyf8xv1/hUdgZira9YsikABgenicGaH5QCRt0yLrY4Gk3l+/t3bNm0fri/d97iQ2ubOxfMbwkAOVQLG3PJ9IBO/Lo5VlDWDQnv2jQxoFRWBSt+VWzjjsGZDYRVAc6thfXaSEYZxPhroDZVALHE8SI5ILKYT3uNlF3KbdqyJZUYq5+3ZNkhB0dCIDYrY+iJPIO/ZqaGM2NDNfOXJgoKy48Bc/EgcblU8HW+hHOyHVbyDVEj5lN7N270xZsDwZhuZuti/lI+v2rdxqInUirmQ5pZF/E21dU2NtYXkkOHLe0EDBPFaijNTk5i7sM6zTpeAmIVSuSvxMPa4ODQ8+v6/C2Hon2IBDSfhk3bzhTQ1VpLWkHCem+CJgBvWwvr1I4YwaphgFUftrvicFvwxJm6HDY7DelwWMxqM3Zmphi5KmfGp5MZTPwrvxQMwiBhBh/mmosuugixgbEaRYDZ12csWeorFPKHLmdH7CnvGkzDP76n+J1ve8HGj/w2dPLJssRJQGsnzn+P/qtfe1e95GlvF0uYRQ1eVguXCPHAeQrx4+edH3ng/kLHPKWlUQBfshP1V/Vvfit47DGksiw79V//4fmP/zTOfpdy2mnahs3KTTeW5h9U89j/6LV1vJF78SX1qCOMyy4P3/AtoSMRx0Q3JX9rbDTTMc889z21t35fElwJo5xMqqcqB14DHHiV3d5eAxyqkvg3wQFX5GLC+5/f/OaWH/yALRA8Xv3ss87++te/PmtX58p4UzaTqfh9eQ7rCLa3qz0JazBDvBO8y4JrR0vPFgl2FcGll8DROVv3+qz+sdLQ6EhnNB6OerNF8+k+y+9ROmPKvFpimdjjGdWrZt857z9CWh8L0Irbgvmng3ZB8ywphA/LefTE0sbv1TYualjwQdYCQ6cza06ld+d8Jh7LeXeXk6/7MvpyTOXIl9g2scwAXTiALoi8WIEwASHsumZhp+gyDri5TLtw20g+gTaUFLQX4nV3d7cUo8tfQuDmAKhABuUiXgOYMT0ByIGd5Slf0WtJNtICwjqoEsEIuRxRG3g5pVz4IyX1t771re4jFl4i32PRgnhp10LuR+gHJLtp5AUcAJ9wYDcGV3CGz8AAvK95BBqckt79CSclkXCJC5BzV1cXCKe8jdzEr9YFNSLeDAYWSS1uCzTldMsMVZD6AhgOqXCPM4yl9Xd25lerDq9YuVQN2yxtTYvPeSF8yHxldAwOrslf8naPBUEM44BElShoeIt86Lq4z4CBaREylIMD9NO+soFoQbw56PN8uRzAXc4ohriQwfymlEuH54NiBODMwWjAGVZMHxDKX6QsBgEW3nMTMvg2sfUBlckHwrgpiSl/pexaDpZqY4Rvyl7WbOoeAWoKhrJmiND5Kotpg6zmJaKyV/N7+PBs0GPAa0X8ahT3VT8GW907CXddsDtxQxRTPh4K5DKUFbGIUmMjQY+SHOixS/nkcK+VHTq4dsXig9oA0gGv/kSPCW7EaposiffB6BEnfrKPKcNvx3xKMm+jGxWZqxrQHVdszMQdcaV3yMzksxFPoDakpbLm4EAy7jXCoWBrQ+1B89tq2xYUVX0gCfzxBXRqZ2ZNeyhjJZKlXNrGS7lWLBvWixYRhe2gj7W1FqZsVQmC2FNsaWsrrbXBUCy0duXGvm1FTOQ14UDMZ2AmzxXygXCokOw/5Ag2RlownLa29RU9vgDfLQGuHJOvCNDFjgn+mDqYNNdvLQ6N4F+tdERElOt03ho32CvYXt6qk4wNnBI526uaR7ZrLRGWFgmrb2PYnBfXRDRsMXlNtFpZO4pLGeyKsW56i9MTZO+d8sqMP3mdYfNfP/vZT37qU+wkfOutP/zSl778hS9+4S1nvPWSSz5w2orDjLecgVPAjBZSPKsIblnkQ3vfhbXH71TwiS4BZCUIx+UfNIiftnZNuG2ec6+8h5SRI6sob9BxVhyesU0UH47ZV3Q08RqEOjO+yEdTY//yL6n2Dvvmm9XP/T+zNmpfckn005/W4iIKI9+FGo0U3nmmduhhEy+WFSVe93iMd7zDPlKErpjOvfK01esqB16LHJgQjF6LpFdprnJgjxxgiHfTADBY4ovJd3vP1qN0z9+Fwmff8r12NKOOKl9OHW7iV/4C52OFmKL9CXNHSiXSCTNQxlBGMuZ4TgmodjxEzBLlyWdezAfbNUQXXOCCXnAvIToJJVIfUpBsTvb/c5d6o2apow/Upr4f9JTYTCmA76F53EjNPw6ogZLhOdi74A+qgs/ZDBPcXNURCUMCYDLEgINwgGCBEIyvMgZG4Gg5NptxHnWbCSwHCOSnRI+I4wguM8oopCEGFY6XyM2UK7MlcXd3N4ag/WbYlJRjCoMYiIcALE4QIMULyeEZq7x75gMSZNWkEQzxnWpygBNgtcsuNxNkfRQQOHwCazkwSnBwh/guHEAIaMCQTnwgsCLrNiUGmDV5brlzclFeHckr2bhAJuoyhXuwGm6QgPvyDA0gItc1YE5IIhNQ2YEQBKu8OtMZVf50H6/ROGAv3eMaYLeUcmK4ZrcwvlZeBwCDUVFMMBRgtWZwYPUEcFeuoWA5AwoyynLzkRe0Js2NvzTfDgdwmkN+y7QsBjf6M914ylsV/oQ8WpMvFNUJ4xJEMjRN6VczZTUxdzCH8N8Ott7V7ZX9bFFHtH7Lq4IG2btIAfGCe9nLR/reCjQjhiNxknkKXDJT7hNP2XnVtv/QI/ZL2rZhpdcqrn768dbmeqJ0sdtyY30d5m2WUpuWOR456KXRmCEWbhIqwg75AX52yKv5iPxEgega8lZfQhQFCSgjUA6yqNdfGlELLPdV/M1LFX9sPGePpPPNNR7T0scxPbOw2U/cCTQXBNxS8XweH+jBbTugFTWrgDYKJ2pPONpcX4eVNezDjs2KaJE/Htfs24T5etO6zd7alnAo4jNTNX5bt0uj/X1NHezQ62mI6JFoeGC0aKhBQ9WHRkbWbejxxuo8oTqfP1gTVLDxso63d9weSprDPetCfsWvmbmSmTeJJGYvbG9YvqS9aIjo1piIMaSz9ieCXkFRfKrJ5k8dMQ3TumRjOcPlHfdMh6Td0X1U0OLuS7u8oDWFegjUalmDDz90/0WX3J3OPmUWm+Z1XnjB+z/wgQ+g4pQvT2l0pzNN9IUplIhHDGicnDenPN0VKeUf4AxlTeTidEfHn0E0G93E4RR58nwyh5mpIr1Dj3xaIVG7IrZ6v8qBA5ED7jdwIBJXpanKgVlzYHJwF+vHHnroIfa7f+SRR5B+COd4wbnndv3gh5G772QyKJ57fvDfPx/oOmhnQbuZS3cmmoMrKHTmFoKpKDtSZm9KzZWYzlXw7ar1m/qHWAKlouNn4mcHjh1pLdSyVPd5kRJYEJwhqHRp8NOdx0W1/tzayNDVzWZGwaDtC/j87PVIQIv37IicN4BluCd6nx55K7IIkaWnTJM76zBHsxsVAmUh4yL7gtMwB5Ex9kY2T8aaB6ijxClg2G0m5GPkb4Rj0iDystaRF3dSOO2KFylC2gN5C6GEa6Qc8DborhzgTXt1Lm8g6OP8SZWpKSI75l8wsGQnQj99j5tzxF1BNnUEZiDNs2RdVoNSKJ0zB5jZ5adbSViBIAsYxh8VgypWOwAwZ4AxgJkLLMkwbY9E7jGBW+JeXUAwigNihrGUFDrdd4ErEgDLO27pdDAJn2h9bkq7It+1y3Y3h328cPBDDo7Nec6zJgxeYVnFtZ5uVkmT7VVBLgCeUd9UnhVk8LXS2VBG4KOB/wXfO8oCzpBHPjziQn7v5S/SXpDtKGfapZoGxCsPbqLEoa+6De32ZD5tECCgmmGB3NwE5Tnv8ZrcgOJ4VaBR4prPARi8extyeZ68IlCwwoZDJutgwz4iNoMGBWiBIge5iMtZ0CYyVixiO42NDKXH+n2KvXrN6pHR0WOPOYaGWLpU+G+jUoSlY6VQjz6vo625qQ7fX2HFBueCaYTfjjOREMl5/aBQ51q2sWnTFjAz9wlV1dXAnskRM1hftP2JPItvLfy0wXCsRE6WWH9r5Qj4ZbNi2Q5qRmL7WozRxPkivnU04AdjNzTWxRtb2Y+XGlJxbNQE5CLIM5tCYXaNBpQ6LN4BkRuBKsxCarRvw8FLF1l6cDSvGCyOtsSmTRhve4dS2weHzWIuoBt+tr2PNWeMYJGIkD6WTNsj27flimY+XwipuYUdtUFdL3nCdgivAaUuxB+YXNQ4oFttMbs1qjGdOazHTCkuph8OY8Vt9GUuAJY3966ZZMOLnEQH4KBDFvr6cl/4vP/Ou3RicZ//3m2X/f0dD/zs7rvuxLXh1FNPveyyy84880waTqbfu+LkO3N3lkTPyCOn18xdSdWcqhx4rXGgCoBfay1WpbcCDsh5DlviD37wA0y+YIOTTjrp0ksvJXzFhNBjW+nf/bb4iU+Hn38uF69TP3pl8Iqr1FhMzOJiQt3fcxYEE41qOGf1J63RnDY8NFIsFZBLtvRuV3w1vkCoJqi3tjUNZe3t4yIuS8CnHhR47u8bTkDj3n9TU/5nrX4PpgBiZ6CYdibq9mzzNzYqWmmD5z96tU/hmBX3Km1xIoWAjk3MDfiqaQgnjvgw5xWmOkhshFkCXwECpSMisI07SMAgLg7EWQlxwZDcR3rmLSwzaNB5q3IECyDE8RjpmdcBBvuh5SjI7YMgAVauAsOQp6Wh1X2K5QGZG0wuE88hYRQB5gfQujYx7iCWwUnAMAewBG9SeU1KREBQCmu2XbLLL2A1MI924QCWkCf8ByGAKgHJ3KSluACdcl15u1RYX8hGvgfNsrqVcrmApZBHs6I0kUIkSoTpqAx1CcqU7u7uCgsqr/Jr9JpW5rOiv9EixKStsC3cDrn7WgP46SQ4CIAW6DOI8pQluw3dSbYLPUreRw1Bw82YIV8i7UiToV6hL3GBRZfuRNfiYPiF7EqaDLIhCRRNQQBXfJ7pgbxIt6/k9RlpI0/wOboVoDsqNkYhN9me8hSffNl3L97jFcnbPb3rFrKrCzJWV770EqoNUvAVw3CYRv9neT9EwgH4v279umd6CxnDW18Xa+5Y5PcLSMgiz0w2TaXGkpnecTNS3x6JxpgEBgd3BH0a3A4Eg6xtEdsgqaxPBsRauBHx4WweIYyinSyyckQPeoh0hdex2H8JH+NoEP/j4VJyuHXBwQb2bt0O+i2fCM6kso0fNmGs3xi9awJE59KcXZ+soGaFAhZeTGz5u2bNpnkLluh+P0uOkzl262VHX6JzifCNrJnuGcxuHsiMs9pYNeujodaWhnyJGc0ijldbjRYuJXb09dR2HYrVmB2V6kNajQN8qWgsKPb/awgRg6zcoj4jshN8povSe7lgJIGBjGZcM1DLXiRSVHg4AFiiYLHjVSqVufEm5dprAwSIWnaI50tfDp9xhqOOEA1HbMJbbrmF6OgMmKwQvuSSS+T4v889pEJaq8mqHKhyYC84UAXAe8GsatIDnANSHEFmYn3vd7/73d/85jdIimziB/TFMXWn7VHMZgJwKoVc8ns3W/91TWxwR7KzS/3EP0cv/oAnJEK2yMOdt2TOk7eF6ONez8kF+TvqWKHRR7n+uydXlvy10dqW1aufb2xbUPLVjIlInmJHDdXOjo2m9FhToPSHj7S8RSOE5PXzC482CQQkIrIoIjoLe3XEMy03blE8hWeKn1qp/CfRR+dF7YURwz8yaG7ZaG3v12qi/iWLleY2LRqZUpl9qZ3LKMx6OC6y06ZryEV65g73EUcoAjkYcYRln7QXxnlaCujLHVl65TRIAIzAQfsic1f+4uwaTlYQhABUgGYyAbCBDN1ysXsjuHOfNIB/BHcegd8QZ900syt6Fm9BLTicTWvgNiGjpSQNNkbCllZ6CZVBNVwAeCTlMxZEDsBR4AdKCqRJWorqg3Cou/wJ8pF3ELtJQ5VJw1sz5ubeRDmF8RADIHZpbsI0xFYYBRt5V3IMvQbcdl+RF680AIZX9ExocMmYQsB+++l+U7JEnI3xhGeDYjDS7mmgCrQpWhjwHiZZWpmDC9CUtNMK4OQcfJUyDeiXt3aVLa1Am4JmaWi+OD434BltDbJl0QHXfOygONqOyLrYbPelw1NrOVy4xJAbbcGQwtm9OYsLcpbfLO/CAQYQ6Od6X6idBRlTXuHTg/nQRqhhxgrpAgNaY1iTHYBtWodGU/BkeGw0svAUS/OtWbuObeq4T1aGqo1rDbgqR6MRPJuAsoSY4sBHmLXY0kbMDrpMMKy2IXBXoWjpupYssvMQgBJXajycmV9EjGvLsFiNnBsfPGjpIdh4Ga1zRQJW2eDnGhY5E9NLrLkVdme/1/J7WYOK1xI7Ngn3WrpO3rCTBaJbsSOSzVa8mIjBzqzrGUwp6aLq84pQYXzfQwTpwmMp4GuKEJt4uL42bOuRoaHh1FD/oSuWx4MaNnbgule3myN2W0TDGQqELgy+lU2+dGmGCDo/6V31GZ8M2pkKc5jQeDhs5IR3U/KOO9SvfMXfsyVf26B88uOxj3xYCQh1DFS5vYfPB4Xs97//fWJl8fWdfvrpH/zgB9/xjnfIebDioqf0jurPKgeqHJh7DlQB8NzztJrj/ueAFBGAWJh8UcEi9GNQYuLB5IsMMZ0emZ77oMbCQH/ha19Xb74pmEmlFy/VPv2p6Pnn2z5iSorgH/Jd0qNRRkZE3N9rFfL04sXU6syrZVKXvMNcyhOEyNr6Bm+04bE/Px9u7A7E60zT2LJ9aEt/kqjRTLbLlnTNi/S91XuE184OP9yQu7Fb0S0hIGACBgorqu+I8eYvbkVe+Evx689lL4sEjLfNU8IbV2ZuuEV79hkctxB6jNaO0OWX+E49RasVa0R520HCL4PDezthUzqzPjWGXQAtBA6kVeQ5qYDnKZgQ+RsRAQEF0ZlmIjE/sQ/DWwRrzsAqqa2opHRRX7bLmARLMzF7bu5REBmh5seCBE5D3EdInU4hGAPXA1LK9FyQZi8Frzkg2C0dUiH4mGOOgbe7yZf0NI20/tGCIAQgMUZjiZNpLFoT7MRNiaDc/HeVJy2CzEc70soAJ+AxLcsd+VM6Y5MGuEsadlQGUEEhvJIpEV5Jif1kOvSVJUoCyvnP9R6p2hW10++DzIGa7DuNj+70p7O4A20gQzhQTvNu8gERwXZeoXvTHDLoOr0LjuF1z304xiO+JvokDcd9EDttR0rkftqLp7Qp592UIh/RmVFbAGg5ky0fIHfAtLQFcJeGA/fyFA3IHvU40MxScxpUokryr7C+U4iEXaBTKsIFq98hhj5DVhA2uwyn5M9PcmbnLfhG5njgU/dZUzs989ndgaQHH3wQfRBeM1STn+RDs7qqirGx8ba21mzt4vG8vmb1mkQyEQ4GOzs7orHI+jHiTon9bycQL5GRAJommUyM65NR8siVg7hMgGSzxL6vvEQa4RVE4AmxDW8+m02n0oF4C6tQAn6x1xH224DzPtrVgM/C/ZvppihWDpOE/QNwirYSBaCviDUNVmXfJtY2p8C9GatQwtkIF2v8oYlCrWZKcN5oDGpNMYU1zMls8YXVW5iKCePc0RBqr4/W1MSJK1YbtFpiNiZf8cE4NZBzZoWtT7enkgwjfAj0Ybk0V+Qk86qgeXhdMB/2GcXUfT+1/+tL4dWr86Ggcdlloas/7mttlzntKkO+yp/+9Kc33XTT448/zhiCFh6DsBxMdvVKBURVk1Q5UOXAnHFgLiWGOSOqmlGVA5VxQE5RnJljbrzxxgceeABdL6t8gb4YCiSI2k1OvChMrmJytfIbN+Wu+Yr3zrv8hVJ62cHaxz8eO+88LYD/2MSBZImpCumQnPd9AkNIBUtgJHGJhBhuYo6gPGSyUCgcjuCOaEbr6p96aeu6/kRauKt56xtaQnWNWTuQL+TOi725xfMXMxMY/JcOu6dGhPgl8qeim8Fs/ef7gockinb0G72/t3xLzj5Emz+ydewzX9RXraa6AA+EGV03ixTyxc+bJ560OU1UFW9dUGxTmc+m0SAsXrTYVZxDksMlRwyZZMiM/1KL5557TgofMgG8QoyW8gdPAboIJdQUGohdhKWIZJg1AI3SAMJPeAKfwWxcSytcOcPLr2UR++EM5RwQuXbtWrCHlJhnVIVQC6RViASZyJRcI1sD5PYP5dAJQ2gCSpctCA+l/WHfGUXm5EyGEgbTjlSTs8RdnFF8gJlhAnIn6Itr7pAACLe3pcMuOglnDumkDfoiEziJPoU7XJMA3tLB5CPuSDM1T8t7L0+5zx3edVNOoUd8FM7BfUqkLjR3d3c3iEumpNYcVH/Ki/In3KbW0o5Hxd00XCMKc8AxwCEgnxz4zPlJbiSDP3QYaXsnE2mklY1IAnnh5lbJBUVI/qBBoLJ8SlRcAkhwLLwiARCXa5JJ6Eu9JasryX+PaagFGZIM+ErHI2euYekeX5wxAUyQa4DB4TMmmPVNeEsroG1kvIVgtC0MyHPIh70iTDY05yeffIII74S91ok9rShMOuiw+Oa4BqJ6fP6mji697qDRgta3bRurHZjD2HCqqaU95aspFHnFcW5CwSmKd3g+wXmUo1wx4Yl/RG5MfiKJ2OnXAyYFD1sKHs4IhX5djRJZ2q+ym7ETdoo9YLkp1Kbg60IJozMu0xr7ISUKVqrAhkx0aqy7bAdlFwwrUdRG0wRrRAWmsOaG9gdm8xZ613hIaYqwlliE1M5gNLYJJKaF9Hx6fHCMVTBGiT2lFrVHX7diUZzAj4I+DjECyKsKz5KZJMZ5Hu4BgPfsigwzhPbZ4clkMVixkz/5iXntf4defNH0+Irv/bvgpz/tW0iYK9ysKurS8IUOdvPNN7PnIl/6O9/5zg9/+MOnnnqqrNHe1muSruq/VQ5UOTAHHKgC4DlgYjWL/c8BOVEhWN99991AXxaR4uT8kY98BPQr7UiVkLRztmN6tZn+7fyaVfkvf8l370/9RiG1cJF+xT9GL7xIjbJ8yB4fG3cBsMx8X2Yv8MCmTZvKPYQhBnAIbHApZ+5EGqNe//uHx9lFoa1zgSfeMpr3sDaYMCSWqTYYP1phXYLcUhoJJW+rLz4btouKOT8Xf38qcrgwwz6du/Dnozd6vdZlSxXvQz/JXXOdiJ7iyD+i7vyBL049Mfavn1nnqRvIsoskRm/La2bG+tYdd8Sy2mgQDzjyES5elUkhZItwzxmJHxuaFCgRggEkYCGaCYkTWRNFONuHAl0kD0nPgXyA9A9nOBDNYQ43sV6yhBV5nUMK7kjwUzg/5afLwDm8oDpPPPEEQAUcBWiXjs27z3/z5s0AAASvXZFH7WQOu0qw+/xnfCrzBHTRVzljYN8V3pvx9VfiJiTJAyQjsTF9ANgMedjfYCw9gRan29DhgYgwmfSk4SavcHCTL52b8JOnPOL6lSB1/+dJ01MXzihT6PM0lrymq/OZcHAh9SwSuDIg8NWAXQHngHlWEPBxAWJJA8oF8fKTHOSx/6tTXiIwmN3CoYSNnWUn5Lo8QYXXdAB0aijL5hwASwLgP65DjDOMPzQB1MLMCmmbdbIpHZif9H9GP7o6p2QyMa9zHmEFyP+Z51/qH056w3EtWOcNxWyP3xI7/ipsQJQu2Yl0oad/ZDCRsUCbXm9TUyOaU95yOL2T25LzwgHaOXjqTAOAYTGyFwHXtljZCy6N+kVQa+Ioch8zrNBdCNALdgXukto2bTVdstJ5O1VU8XYmmV9opSyW8rKHMH/UxethdygeYX/GREwRZl1QrQsrBG0mmBYWYN4ijjQLc9gFkKDTvBPwWHUB00j2D2zdUDKMN51+Op1fzoCTNPNSpQfDBe5apOYClkoFEJogVnDsMgt4w6yK2VjUgAEonbrzbvv660Pr1hkevfiuc4Kf/WRwmdB9O2oEybq9IAkyfvSjH33nO99hQQot+6EPfYjFWfQ36Jndd7HLilQfVDlQ5UBlHBDfemUpq6mqHDggOCB7LDbDG264gQBXzHBEXJRa1Vkr78U3INTTzgzvwODCddfpP74nmMsmW9p6zz57+OSTS9FIJpOl9FisJhIJ4+OHGFo5R9wPDSGet5DjwXVkIi0knLnpSPvihEEb6Z+ljyRg8gYkEMFXTpOs7EoV7OGMPZjFA614UPFTrep32NTCwnGtEAQVa0H2VAIia4PKUTdt+/GQ0dFdb140L2tef13u/l84AbLwfmMZmAC/iDlma3P85m8+62l9cdDjM3Mhr6UbucEdfW0dnSG/Xh/2NNcGWZHFHhjCjjPTMX3+prLM97QRIBY5Riagyn/84x+RQoCyhMaZDsxcFslCeIs7qAnQC/AubJH3sWUh93ONvMhNCRvK6ZpOT/nTyq/L6UEUI7QJLb506VJapJIiZBAsTN+7SowjK6zA3XFXCSonVaaUBCPHP/nkkzAHH1QcKWXX2tus9k96mg80O12jMWPp5c1BAn7yLp+//KD4ScW5w1mm5CxNshhXSSNvypxpTeBZ+QEl3ORwuxk5o8GBh1JI5UWaCWYKVDp50Ie5w8HI49JMMrQk8pBmf+mczB1KxBjF27xIzlQcNCvfhTxe5HDz2c0FicFLKJUoejfJXt1HcBLLLR8vDDz++OOlxb7CCpZTLhuOczmTyxPs+zWZo47BuQMFDXvC00BunrMg2H23/ELWwr1jlIxkKkkIqXgNCxOEJ/aAiNgkAk0BPLVAtKaxo7G9M1UUEaQsWydqgsEeeCWbrfLy3DQAwMLFGUjr9FojIbBzYl57GwGWRT5OnAWKk/SLXsWALxe5OF0MPwZgLXZdxciEPFZLXSzgjPAiPoZIy3TCP4JqEjLpZE0rl2fqUbLEohBrg2xCKJZKkKQS6YoNikkPbPYQghrcayhsbaBrJtGb42xizBa4plpg/yRco31KrV/FoRqnaOgJ6mZ9iP2WVfyfPcJoTY3sXDbLd0H5tAjfILoPFKayUSpsDtoRt3ly4OAVyXwUbYy38mb5ebJpnNIVdlfoz37/VvXGG4N9fUV/2Dr/HM8/fWwwFmXecTS2Isvy1yu/piDahSB2wGC8o8ntggsuQGvPnCIyrezbr7y4asoqB6oc2D0HqgB49/ypPj1QOCBnKc4ETPr2t7/9y1/+ErvHpZdeevnll7PAcs4mDwcKY/Rkoipu3Vz89o3WrT/cMTa+JhCwjzvWPO0037JDmKqCoZA0A1bIHeRyAAmL6EiPFZTMqYhLM3Kw9BBGXgcVA5xWr16NuYO9cCnFTVZeFq/jHJkuGCPZfDjxjZrCt3zKKJtQILcgf7AIa1PpbX/MXlPydLBfUq3Pentjrvitbxbv+7nQ4TuyuuQnEaO1ee313/3W057mZ3eofevXGKUCwDiA65oXwUWNRcMLFi7hDXYXrgmo8YCIgxIKyLXRSEeOhWDazE3m4AfqQtUwVaHwBvdyEykTNIJbGnfABuU14poEiPWABKxe5bXmPhwjQyHiJRLYZxCJuMkuo4jXpESvj5DNQZfACCazJYGbiXsxpcTd/OR1DvIHUEkrLoQhhFWelcyB9FNe4b4sl71/JAB2yZiS0r1fyYXMFi5hqUZjAovoPK8cZqiEpF2lcTlAgn2p8q7yn5P7ND1WdGJNoazZlwypLNZFPm1qyrU8gwbx7NjHuu8jG93X95GM3TCHz5ahDEUY9T3uuOP4Ql+5snZDRoWPYAigHZ0CF9CMngvcBcGzoNnlbXnRoDL86hnBkskUa2yBlu1t81YceSR21JXrewZG03owrgcjvkBEUT3YTcWu78DdkjCucmFYGt1HbE8kDLnyT+xZwCoBPiNYrZhG78ZVdc0dui+EzqW+Xri7u8Q7L7BPrwLOxPoaxtLrw2NZG+jdYpSK87qFW69AdozpnBxLb96w2PU3XRBRmg1iZjFZAHpNO2cq2Tw3xeZJeA3xQHRtC5QrlhQFvVY8iGOzircR+/cC1IVt2Q8SZs8koXilkIDXaggpjWGMwOwmJSow/YCHdB4UoDQKyqMu5+CClG6lpr8l70j+c2a5DcomXpWvTH0RuI8KmP0GxdYJZuGF5wvfvVm7595AKpGLxqwLPxC44kOhhYshgFmbEXXKxLSr0vd4H8IA9mzNyIHPF7G+r7jiCs5yuJ5K5B6zqyaocqDKgVlxoAqAZ8W26kv7kQNyMgMa3XHnHffddx+TGQ6oCw9aeM4550iPpjmfMCiRCZHZHRmlNDyYu+WHpVtuMbZueVbRmo464qB/uip25plaULjvIlXIyXv3NOCOhUIaYZqcEYMQVsCBeIECiZnzmNSBu7jhYTABZfET2Qv5GGPRrrIln0w227Ot56DuBWz/axV7RrbdYeTXsFFwWl08rp3aZxyTyGvJArtW2GwFedZiO/zrB5NfvBYhiaoJCEbWKjYA1fu2N/o/dvVPh0JYgO18BpW8B9Pa2FC8qZ01kVi46moirASLeFX21RBRpm3Lq1oYpBFoavxKLKATaHSKBINc0b+9H/09lltAI0isu7tbdhkQmgTA00UZyCK8LQ2N3XtXFXf7HYkR8uAhRXBIux8dA60BabCowHAiGCECInbTT6ZnOP2OzNzhjoLOAqGHxqItiOTJ2S16Hy/ImYNMWJwGMAADQwmiFZL3rkiqpETIRrzG9ou1EwZi7jgw0S91of/TQPR5fBwOWCLnEACj96HWdFFgFaouaRbex+YmQ1qcT4kuiqP7LNjIV8O3xhC0N72OgYNyOYlDjB/8I3+KH5MPnKfOiVv2po2bVq5a6fX48C5mdat40fnfGTSmjBw733wVrxiEUbMyRBMxGB8W6RG9Ky7J4aKcWu6ggYK9jHUcfOALFx5Epf/69LM9fX0WCkg9qAVrNH/UH60J1dSBLdFalmy8hU1Mu3m2GgL0GthdmV9E+GVsv7AMeymZAHcBvVzwEcnF6OKHeCA8lresfqa2sd3yhoYGh+C8z+urb2D8jrDxb8RrR7wKIzl2WgZsVLDC25d4E9u2GEaRjYtoTsoBb6fZl6iAaVeAXiJiqYSFNixcl3FmBg8rTsBBQlNDD2ThBG2Z3LIAtxGfFRShm4W9l9L9ulhFHGWXYGfioH9EfcLeC/SN+tg+SVRHdARRnZkPOInmCI0wB83BJ0NzMMi7qXfzLmnkBwIA7u7udlM6nXSio8p/mPWSDz9k3HRT4Pd/9FhGtmO+dumlwUsv9jW3SRYBgFnHSyaM0mRLVm5uLiWzuIA8KvWTn/zkW9/6FuM2dmCswRdeeCETlixlFnlWX6lyoMqByjlw4DpQVV6Hasq/VQ4wQ1A1QNGNN37nT3/889vPeNON//XlZvwGgyEr4CdmDVpbZ6XSTlvf3LACtTpzM//Zur+xxfupT9lXXTVw77369d9Un3k2eNElqbZW5f3v9110QWDxYkNxAkrNNItL+l2S+MnEiScVdzAIA964RgaVEIgzOK2rqwuzwx6FY/LBKppzXLJxR9MDC7L6+82A1d61wMIhLaPUFlDwq63IImjxC9pYSYkdfYx2wlH6n/5C5eArWxoatlKsr6s756wdofDWhObVwLVR/EcLWX08Oag1RcI+v+pRx0tqqqQOEfNEtXyOFEXYkqCpjuaEuZkVyEGfElTyHiPV2doISB4dGl69ahVr2TTdg9AAEsPXi7qjzgcJSJ6AjWEC9QUPTOGSZJe8uRs5g0dYlaVhmcTkjMQJ3+TrsBfNOiXKn4BwxCbwMD9JhuiIUOsmlmlkWWTFgdkBqx0pEUowViNCyTRzcoY24p+RFSUCVrEIcQErptAzi7LIhwO3ZyrLxSxy2A+vwF6qj/ICncIBSyR8wN1dhs6G4H2hk3el9kR2flp5DpUpAC2Ua2B1aavcm+YTDuSAZ+mWMsOLE/Bk5xP4MAl2gWJ0L9CBMBbSkYWfrEBrwgAIiAKwYQxk/SfrVEumYsfnR7vCqzZs0geVSNYAiS1v8TJ0iDcPyE7KcHHKKaewQgEz3e9//3vUNFj/UDHIbuDwYSdbQMtwkkcSt6A1ozm4KVNw32Rla6F7a8IeD3fr8zsjwaju8VH3vGWzmGUwQTgo4K5VJIq/raF5lWG7SQBvAY04nbDBO8yFmYLDLNRx4jLgPo3CTBDDiJ1JEcGd6TA9NmDm0i0LlrfVhVjlm06Ms3WSmfW1zp9XE68hnjiJGfdZ6lPMpYYHdnCRzxnZkjH00lo8q32xJk+0FjJotBJOy4bNclyMzypRn1UL9afYaV4XTcxTyMN3Gg9nxn+MvSXIsr0sJKB9G0LgXvalF07OaEtrA4ZYAxxSBTwWSlTR8s7fTjbOeAX30BbBf2JJ0BaYc1kZhJsPNxlF0aDJtpDtMmMOQFZa8+WPRD9m+qMHFjasz91xp3rHnb6+Hq+qFY97nfnBy2vOPlPHjdxRM2xcv5FZQL5OTAcu6AaA8N2U+PKydveLTFCAshKYwCW47eDXdvXVV3/hC1+45JJLQMJIArw8JwXtjojqsyoH/g9zoGoB/j/c+Adw1eXEhlr0m9/8JirSN53x5uu/dE1jJqn88pfacy8Cd+yu+frbzvAedYzaUA9QfeXmCSF1OEehWBjp36Hhznf/T5V7fhwaGzc83sJJJ2oXXBR61zu98VpHKJyY28UMj+hjGEhRsi7SuY6cmJLx2WYiJ0IMEzkmAgwjrhi6x4qQG0pxzkhd6MWBl4jUzMr9/dsJGbtgQTfSCit8LVtNFqzRrD2cV/IFNiKyltdavp6Nydt+XHr0d2oma3p0z/JlsUsvKiw/4rFh7xN9HsJyIuqjmS/ls6N9mxo6F6kev5BsCYuFEO9VfTouc6zsEtoBxF+fzn4YGBa0kMcupsdHtm9esnwFElJqbKB38/ru1toVhyyor4kiIzsSj7Jly1aYYJgGmD8WjSFP+v0BBHfqgnEMf2mYAyAEncIWrtEOIOs4vK/0hNyJWgEegirhMMBbwl3yxJgjl1rJbVRJQynAYNJQirQqyJbCOAkwgBKsx9h+YW+lxVeWjpw5IACYTRFyX0qIcSXsyrKZSCVppoK8zi1Q1hziq72ipJLEUEsfeOyxxzB9nHbaaTC/krf+NtJQZYlUpe/D7Co1MRgJTM7ApOZzuUd/+6jX53vDG95AR5V4cnLEckoAaAiwIeGmcz3x2N6xo39gaPiwQw9VFdCa0GUJnR9jgHhBmOdkWZgZuYXfCMMLGMwwgWoqOIeQSKzKL1gqj8R9zsIDFpOgWAaKYZCnLPVAy8Yj/goM2ppOnGGvYq1oVg5vn3AccWgWVB5oB30VTQ2mPxoObQhmdr5ZDuikHRlnGIE5eMrXxwAifU/Y0Q2VBAOL9D3hwh8IJAvm0/3AsAn2OhfKQNboS+jwE06KUVbcFQZXduKlFFgnQoCLRhB2Wu6ION3oIJmQ+B8yhE2YoV589uPbt+bSw35dy45uZzDu6Ozy6fpByw8t5otb+/pHR0bnz+9oamwgZwAcrcMoO5JM9/ZsIzJzgR8U7vXiYh2saVFDNTQUE4xA4xQhNMFOj0DJQbFioS/xqyxiO/s94glmaghAMRoTAbTUAJ7PKrZoi2DOtUEb3IuTs9PS9CMHiEoOzqqxmfhoDhakoPdhlKZRAMb4Scmhb+aMHdrhmWg25xA6gGQi/eDDyq23ev7wuKdUytXE1Xef6738suCRK1hoDtJ3ZjjxBno62peeQJuioZY6uynqRTkCk3hmAmSpFZzJh35FRE9CRjNtnXXWWVdddRWL58l2H3OuoPBqkioH/i9yoAqA/y+2+gFbZzmXICI//PDD1113HeEisBCyPObi973Pu2G98e9f8vf1mIWSh/0WNM3w+a0rPux929t1x4l0P1RKyIZCHLRKo0PZ+x+wb7sj8OSTmmXm4nXpd70reMEFtSccp6DdF/OVCNlCRJ3NjtoYwAPGYwbldZS+/MQZEmyMkMT+k8zo0ghcSRVgEWEkpYWBa/AzqnHgU3d3t/s6qxYBdYIIBFnbZAnZGEJaya4JKp7xcW142Bwa0cIhq7klF4s/OaqtGmJdGPYDdrBg1ZldLKlFYSVAWkL6EeIPh6w7P4CDHg/oV0TvxAbPHpK8OTqwbfuGF0489Y2RkC/sAYMXg4Eg5gyfZoV8WjRgRYHKiEceNZvOspXQIcuX+QN+Z14XQxACjQTA4DekSYkQAMCAQ7dSlVyQDwtrZUoyl90J5YLcklTewfEYV2H4Bg6nUehshKE6+uijeYsWwREdGrjJWmJQNPKHbLXppTvET7+9F3cojsz3JQiWrCAuEthGTjjhBMzIe1H8q5EUglkTjnWd73rfF8G+ojWQvJVF7HtbzwmpZSRNfpS2snbdujVrVx+y9OBFxBsX3yuHOMsUzk9xzfeL6A8SyBdKq1atBat6rWRz4NG4/68+ZUj3RtTQ0cP6uQlrBb64DnzFpiegFggW+CC2vQHHEqgPYCawEGkIRK/g0QGmdax/Yt9XQC+ojfROiCXhUivwGj/JyjkXLXOkf+DgzuDrF9Uc3qILh9l9Rg6yjrM+l3F1Ig85UDA+MEowCDBWM5gDfhhJWM1BIhl9ANMiAy9jOCiX8VauWJG5lXcYGECY5T/2YNwFKmJLxU8YDKkWLPv3mxQiFrIIFi4RhW1ijHXaD38cHogAVCZm2onmFoBUYxktPzlbfo8S9LAyBVNmOjM6iJl43eoXo5HaupYO2qyxpTUcCWseDdzIomJ8qpN5a/PWvkBNnT8UJUu0EnnTHu7dmisYsdYFpsDgojHoLIydXIpiRKgtxaebAa/YyDcklvuyAZKYV/y6iGjFbklhv/Aewq3I6zFrAwp/BE0MeIVjVDkfROazOmApkwJnRmbGTEAv1zj/g065D/O7u7uZ8miOlxUnqBd1APvS+ej8ilHM/+XJ0l13Kff/LDg6xASXP/oY/aILfee8y9vQLBYnC5cm8YrgQdnBbLubNcCoQugqcxWIhKqR21133fWNb3wDPylG9Y997GOEECeS7AAAQABJREFU+ZR62JdVsIzC6mWVA1UOzIIDEzLiLN6svlLlwBxygHGf3AizfOedd3zzm9evWrX69ae8/p8/9s9vfetbGfqtgYH8V64JPv44MyFGj0CQLVXFbj6F2lrPtf/tZbsIGYxpkiCRlzP9Td6Y+V9nthMngRXlO07C3U8zkEpiplXVMvIrVxXuuMO+597t27aFNL2uq8t+99m+c88Nrjhc0VGFOyUobAk7vK1nG2uAyR5nJzAYlQIGgwHw75LE7b5QmYYzZTPjgtB4HQkANzCkNDk7Ig1III1qHCwkMxS0ikP8I/6cJVtIrlnTxId5R0756zMrVX8s2jIPT2kWDEvJFREHDMyCtGKJeJ5IH2SGUCDkIsf3beJiNFVM9q6zzaySH/WY+Za2jrpoJBCJ1ja1sA0GMlBI7APJPhlCp67bpvCgK6YHt2066vCl9RF/EDuFI7O78gZYLpPOLD1YRMWUR4VskYmhFEzLNQp7lOg4LnKNA/N0o6hkC2yU6RFxwJCIszQN8i73yYqbCLW0GjIW6TELw1UOVBjyLEWuSQ5LEuCRW5uJO7v6ByhIzuD8yl8pz0qWu3nzZqwiVBBRSS5RK0+z/6/pY0793e+JXwJCOZSoxVLxt488Qn3f+MbTZ3YsF9yTiV3aJz/O6U8mk+ya5e47Ik8kW/E/98SH4LzsXE/+cO5Mnvq2b5cblswrW3M4+dB5zc1b3JX5iyuZpbgqvxQ/y4sUv8VzeONcTvyYTMRNLHUc0MmFcxbwFYMbCNOBA+KaR4Vi6Y9/ehL0dNzxx7LoQCQQzqgCfJISUGqbAppSbwIDs2IEFIHKrEX9w/H1/1nr3yyMj843iAuzaYV71Ev/kv+3vB0D4rr5UBwFCTKEoRgg7dghRTQm5yYpuSkQBh8N1xQmlE+gNVhNWwsTISZmp6KUvn1gQLOKR3TVnbKkZmmjytY41N1plDLOiTfm+JDfi5spRQremCYmPhRnjAOcseii+QLMkAwPHT55+Ra4k8EBAEwynG5k1+VOJbCEHDh6kyab6xJ9isHQWetvAz4fXmu+NAyYFGZcwotTf6y+NBwcAXjyk1sULRjEgl5VGFpxt2FcBYviYAzbLMPE8M6WPX1bN3oUs3fL+qbm9lAsjmE50tTpi9RnUGgWlRIpbW18PLF9x3DJVomzFYxEPR4/ZSR29JhGvq6daUjyX5RLC3t10LWCmZeRXAzfYGgGcA8LelnKy4ZJmrOjnkUs6BqfGg8CetWoWOsruzNnpxI4TO/zAfeAgjRNeU5wHiSMvR1ITEPgScQAKBNQNlWgt8IgJjLNNDIvvVS698fKT+4PbtiE71K2pU15z/n+C94XPPRwYfIluVN12QvLS5HXuwfAfE1oVFkps6vXp2e4xztUmcGH3b9uv/12/GVQF1555ZUXXnQhuhYqN4cF7ZGSaoIqB/6GOTDH3n1/w5yqVu0V4gBjPTkjarA3wI3fvXFkZPycs8+65ZbvY5QTUz+SEQnGx+ynn3W0NUKwQhBkokUWC4yO5V9aqS9epPmDk9O3M/uRo5PvHqYKWbZILConpkxEN8coIX7v6nAyFRKb5gkeeqj/K1+2v/CFHbffYT30C+WPfwj/93+bX7susXSpds45nnPO8R9yMG7FxZI5PCxkKV5FwELFi8YaeQuBEcjHfYBWhY6R5CDtouBeADC2TaZ/KN3kRMucvhRwkgOOhEk62Ilq3mMjmGC/9er2M4leX7S+M9plxSZ32igS/FPNqXYQ2TSIVKtjwXDwsJUXwq+G2Wc8i0XZTiQLiWSWBon7a0KRuqwSzqVNvFqNEPIckhO2YgqhsXC4ZhNIgn9qASVo1y5YN+73JJCWDBFuGv1Gz4YFHS2JkUGkLsqUreHIJLTKhHSyq9Yov0+HwasWfkpoCkydrH55KnEt75MMNIt0BRblLbocFh483BBzkbdYZgakJCUHIg6CDmncd3nEjin8pBXA2xISkxvNgXws35LpRXnOwc3JS/EvAlz5z726ljljBkEEpMTdWKr3Ktu5SuzwSfBKgCbEelvpGUMI10ZGxgby/iWLFowWNL1ogJD4xPngxJ9w/Jxww+VCMFB8/KLDOt+nI9AK+sQNAV7E1WQfccoTD6ce4l2Be8uPssTu5WjayExss0VSkX4gre7IeOpSqp2QD0RF5DF5JagTDSH+F6/Iwuiw/HbMeTwQaZDCeca1uO8Q5JwF9uQ/4KXzVBjWxCX4Vpywvcl8eMj9CZMcCQQ1In+RG3+m7U3Glm7r7cusz8VqaoSlTOQpCiINrvEkAvOIPMX//Hnawn2nxj75/9l7DzA5juvet7snz+zO5ghgsbvIgSSYxCzmYOVsBZt+ki1bV3z3SpZt2Z8sW9mWrp9s+er5s+RwJV8rPNtKtqzAKOacQBKJAHaRFlhsTpNnut/vXzU7WARCIAlZFLWNRU91dcVT1VXnX+fUqZQzpiLNJXIsxyWDaGch7GX6/L+ZdWa/N/nFkh8lBVIhkvA2bhVENRYYUrVUJH2stJSpJwGMDA5/NZ3yAnfhqUU18x1zjlQk3NXZcXj48BOD0yhalNc2rW0L6mLYVFJ7v/hLlV9wmaLJh+VCgC7ftb1YImTIxf/+++/n067FIDzIygJghmjC8F3bT5uxgi/dLl+yssZ+CjvI1OI+l4M0uXoaIdvCSyLcNW3urjEdF7S02WmLY00C01Oh4VlnuhjiGF22nHDELgfkJoG7wGaGUfZU6xQiLBm6laIaIhoOYQ0jGmlIJM/MF52h0ewkm3CbNyCrn3RC5RmXncbsR9ZuXhb00MJpjeenZyez+ancBONGY319vL6FINAeveVoGCmudJtpEGZYOi/TREKSXsS8TAcuJyXRKw3o9euipaaE15CgXOaqVm5hK54G9GtTpb0Y2JkuGWkZeFnvs0M9679I4JlS+/r6bEhWMJubm0KRKLa5Cs/uyH/7u8F3vhPfsiXpV7IcZPjmN4Xf8fb6a652k3U6hqk2gixsmePc5GXPI1z4hp4GMKbmWvVhyjNuHgnMfWHIU3cv7L2kc+2113JOEqYxv/CFL3zoQ7//qU9++r2//d6bbno/li9I8wXncurlWQy5SIGXNwUY5I6aMF7etV2s3UuKArbvDQ4Oou3z1a9+pRyEXv2Wd7/hxv+2fu3y5Y1eAwvmtrjMNJufzLz7N0P5HOwWJp/ET8HcsY0zEcu/933RX3sXCr02ML1ZfGOxICMeTOIxFJKFaI+ZLWrdHuauMpfxMrPyiWGzst6L6qCFYyMcTzjYOyeYxJYVyNbxOJGUJfxoPhfcemvrPQ9E7r07nsuWQ+HiuvWVX7lue0f3cFvT9a97Y7IuNTJ8eOjgkDJQNfT1kTXz6/PdDwkABv8A2BYCYKQTx9T0+IJbH/biauYOgptv/nFza/vaM86eynPIZNgYjkbMK43HHMdOFjnuQmdRSssRNUjJKJyJrH84443OZKZnK6jYxWOxumCyOH5gyZqz2IcG/W3lDM/uI/0VrA0FiCzgqzhIgw3K+MQiATZR4uEg6gX7dzzTvWTJ4YN729pamtMNXe3pVFTSklQEbkyqdOYybVt1q5kN8fR8TJWhJ+sp0Ac+9ZhXtdi2AwBxYS8ICfExbWK349owbCEGFSP+hRUkMLRCeMWahb1goyG7PR4JhV6ExgQAIZMC7DI82ZVXXknWyOphmi0qPjk2Pr4WtaIe77CFx8QXHYCSI/pAknx8sJ+Lj+3Q41l/JAscgrnXyVz0pScOqVugpQkZsXJud5LTV/gMzPfJHYbe4MXqt2HcFhurFRFD6ZOvRVFE0/Tm67aBBQlgP+kbhCQKr7hs+pYa+CiAuUxA6wy2jATT+apRIes1NTk9uGfv8uXLmlswCyQ/pWgc5iYP4+AjqrrNkICbZ4CfQlNlPG14o4Wpl3jyHeEpsKq3hNSP+dOMLFwq7KkANjlzx1Oo0vooBcFgPfLDDtswG/1NIiaeOY8VsOuOd0V21ocO5CtNhwq90+Xlnpt7f/cNS6OPOJXI+Lebpr7T4M5EQvFQeG2m/f2j4aUZlFv/Zezvnsy/TQUVrVQag2FNluSIn6ExOeu9ghjimLqYRjR4W4XhEgkUSuQ2O1alR10BA1cqpQ3dyas3Nq9qDjXFj9ZfNdU64U0pHn2RsslIXQuUy2VRLriI75FXd955J/ogCyOy9sTeE3z4ggjPt2lRLncWIkmQ6+hM9ER4vmi0YVnB5LtjcACY4X/CwMdHX+hDHXaPlwanIJS/vjGoy03FpbpSrkSThXTDgWLkcCbEAULsCJEiOhuAMdrENhWmGGmN64SkIsMylplLLqcoyeQYslbfHRkayOSL9V0rGa61JVufixrE9BR1M7UV3yC292ZmSsVCQ12qq6UR3AvoDfGnLyyIOhW28jL2smQJMcIsUQUBFh8aYkFjXAcXYV6RdBFRdna0d3R0qvFfAAkWkuPU3LU9wAywNgbLvs9s3fKK8863yxkMznff9uP6oUNr9+93b7s9/tSWcKWci8eCyy8Pve2tiVe92m1ppp/RmbUa8eLKTF41I4u14gNNX+SWFrqWkXUHGHFMJlNsJc8WnWmvYefg0Hf/+Us/+JevOn7hxhtv/OAHP8hc/6IrUSv4omORAr+MFFgEwL+Mrf5zr7PlReAkPv/5z//bv/0ba7q//p7/fsmb35NqYmmTuVXWMJtT/vIGj+kWfq6yezD3O+9zDx4EibAyyiK9Wbx1gVblj34s+qob2KGFoIIJvjI35wwNlZ58ErMVQWNzZNPZ3rJuL43JJU15tcsWgP1wbNIt3fJjl/Csn/f2hW64JnTGJre5EaWvE3BAtfjGQSIALWvRF1U65lNygVdYvmJFbG56+lvfLf6ff0o/szVWLFDo2e7u0JveGH3DmxLnn+OzgO17O3btqK+rZ74kMeJpOrbM5dG5PNcTiIurJoVAxEF5WBo/xWkd2Ab9beJEtLFYcm5ubSlU3Ol8ZTbvTBfcuSLctUfdkADni/5cwR8amclH6iYLEeRCOayXsvELCcPsxMzhfT3rzoqHQ9KRNjqTpCleFf7ICPJxSgvTFfqNhxzWMThzQlKNYnZ877a21q7ZmcNLu7pbW9vi0VCqDjMrsGpUEJwMK+bDjSWEisHM9jBJw8upAuTzU9tK4RZeVBnZOzpmeJ5QnrMQAC+MeLwbysOygKIxrwUDROekFc4880xKBXvNnmTRwXDtOIDNEBkH2BuTJzzCcHPBgnO3zWcDH58RsawnAeAF77rrLvIC/cK1Hx/45+VDi1O8wSl/cJKFiypYwSzw1sN0Mr5B66OK0N1FF1MnKKS+Ytx8eNSUuvIH804c+0gU86oKjC07bwIaf1IziRJPEQhcTY++zat52plXJoz6ZvVynb0TfqasIWLeN2CP+OHhkfb2tuqpJPPvhE5NOZHQ6jIVssjCPM7DYpthFZGacLWkFUnVtdBZ3uaVUsLLJk5EJW1eqgZ6tPShKqZqCmeSmfeu0lalgG5hd+6ihi9fmPrfTeH9BPSCSs5p2Vm4dn/u7Fc1foS9pVP/2Tr75Q6H43b8UjQmLZJg6ezSvzjgJvMDhcv/YfQHrFOpMAbfKNEjBZJbm0OrtGaspIhC5wpupL56Y+lvZNGmvqoOajuENkHQ+hkO+YVLNyy7aHkYDGxahjoYWii/oy7e8opvjZ5vQG7eOhg/QaG8uvXWW8G96lCG8jhYVWSDCQ5ryIrPhM/NYl0LdI/K4NQeSI2hnu+aNS9i8JkjKLY5nloC1VCkc2i2PJ5ze8N5b8eWwre/U35qC2o2lZX99a9/lX/eBfvC6V3jXoRVVc9Y1S475oQkziLSyXYYZobQ4F4sSBfYjy0NHXWqzMwE4uFkc2e166jN1AyQHONOuPkmgbLAXWxZFQv5ZCKKDJluNz60t29pR2M6kYy4Ea05ajUqFfWxa9Vkzu9F/GsWno/UEgDMcMdlvV4AEY6kdWquGgBm3GNcZQhl4C0UMGTB+fRO/cGDS55+KvSdf687OIT4txSJzK3d4N34a41ve0ukq1sEmB8DTi23nxKKrsjiJoGArHRIq5ZFDwSKg89ZixEz8DylwfQKVlERYvPx8AXNlIPD0/543uldvTEaS9CW+anRe773lW/849+Mjo1wDCRWo7H5TxlOb9V+Ss0XXy9S4OVCgSpn9nKpzmI9XuoUYIiniPfdd9/nPve5H/3oR319fdh4YEUzkUpOYaJjwpnOy4qxuCddQUs86Gl067PTpa/+g/vPX2eqZ5dZPBbXriTXy6xcEf/MZ0L9vTBglcycmysU77mn9JnPJA4Ph1LxIFmXb+903/ub0SuvDrUctVRPMZgiK1u2lT77P+M7dzBdoSAWjyfzqZT3P94fuvbacLp64p8txwnvti5MhBhwYibGDoqxaSxhCeDwtttuL2Vz7aX8ivHJ8C23BHfckcrMUbVcZ7d/w1WhG149vKQr1b2se1kPrIbhUgzLOM83njDHU/eES2MyhtsDIZ8wFtaeQIC8ovBIM+z8DcvIkrMhvpqJ/5B7thhgxXQq5wzsHx3YP5QrVJb396M4N5ULJvPBVN7JFdx8qZDP5lYsbWbzrM7PqGBeRXsRxRrDXkkype2DYGMSF86ArXP8iaHd5dmJzMRQUMzA/8aQAUZC3X2rXS/Su3I9yngo/gF343BsoWDvs1uXLlvKYR5IOlCuTka8BDJkHcuE6VEvKoU2EV43XeZunaqKANAxrDVN9sgjjyAIMgbDbFCFtBc8DZiWVzAx834n+KUPEBJqc7GLGPVphLH0Xzhs7rwiFxoCh70gyIZVG2N1USx4swBBGFKwdzJ6zWtewx3GDsVmg4urN4Dx8SWB8YIRhKenWKRwgsL9PLyoDo29e8LfM3WEYaYD7BnP7dlzoL2jvS5dr82rxriORKB8ifzR2YimbiJrOnQTfUXzbcZXY6oS5Ipi+vXAf5rVBDE3wvIsrGZfmp8jRFGK89SYd1bxG95ki30gDuyRez4c7TYxPt7U3FxXp/UFJa9Lry30pT9bPxuDws/nV/OWwzwcGdJobyWiApnJd740JmA1pXm3Hu3goEjWV7gfl26KarOUK8jn8pNTMy2tLaCmlDv2trbfWh+/RbrPKrrt/VJr9R1WD8tBJbr/fUvcw3VoyrDtNBzRwTx0zvQH9zVcNZEPmj9/4IkZp1MZUIpaTefdqiyeXPJRzQmmEVmLgFrooO0N8x/woRt/MJgwFRtXEf7rLqN7HBdbbquPrWkJ1rZWd2OxqIdOMlBn4YW+MYukfGI//OEPCUC2Ioe5UNzo6+vDB1UOXtm1JAt0Gdbs1zEf9rT9kh2FBFcDP2rSyJ+Wui2zGqOI9aliZbZYqQsqsacem/nTT0dGR82Mhgw2yMci8Q/c5F7/6gfnkjvGQxjnr4jYHhJdzgcuaIDFjYFFQCo0VrP5st0APWkuNSM9qzpqsYCIEQRpNfsxTycAo49DXwhjsIr1xHCAmBdL/tnJsW/9ny+tPWPTOeect3JZe0Mc082oPaODPd+jicNlWh9ZOsJPnsBpjHVcuBG2MxYpzM/sguY0PMse8WhkZnbm0Ucem5mZjs/NTj25OfbE5tLWZ5ZPTJ7l+OVkXXDlFeUbrhtoaR+OYv4hds1113CevamA7c2nuYgL9wAzpLMiAAZmZzId45gdSbbfmi/qmDKYDm2+pXK5VCpXGI7ufmJ3JZpOt7bD22DEgnZLx/y+Jrcpge33/Ne/9vW//Mu/ZMa57rrr/vAP/5BTu0jxRCkfk9Hi4yIFFilwhAI/2zHrSD6Lrl96CjD6c91yyy1A37vvvhvggYGHN7/5zXbDJDNAc9xr7NbJPXum/KkCExZjvjee9w/untnY7La9+rWFPfvcO+8o5zijwY1FYqX+5ZGb3u91dTHyl0YOe+VSedfu0u//fnL4IMxEMOM5zeUEy+T/628w6etddDHHcS5shGB8qvRv/5rc+Sycg2ZHzR5BIjOX/buvhtasDTbUVwUtC+MYN7Wwfna+geuCJ7A+YBKYA8wawfatWb0a8GkUorzgd36nMjWZ+8ld/n/8h3PzLfX/+2v+V78Wicbd88+dec1rotdfn1i7tiJhLsy/LcxxuT5PD+AWIAoFaTDwCaOyeZiLurCMDfq94IILjg5mSEIbhLDqGRRnxiY5U3d2rj/mLV29Ot3VgFJ0phAg2kYvGiW9mUJsNofkAK1pLy9LsDBj5pQU5LtY0uIAFWQUFRhfOOQAoCH7OYGTqO+sJJrC9W0zI4N16dbs7Hiivn0m2obW3d5ZbQaOhP2E52E2htOGD44Xs7FKY1niC1AxGBhtQITJZje4j7gYe9ScvQFXBypm15x2rHE+lOEKydAoiwo9DA4OwprAscEfwzfQiLYdj66+A+t8itzzvn37aHcbHaEuqcF92jOHj0+E3nL/Px++4G3tQGUwLdgYRh8wzBYyBFm2JHQh9N9sN8MHB/cbbrgBGAwmZ/HIyop5tFdfXx8ZEYy3+MCJLmRGbQrH1M5mdIznaXoUH9eSQGOZfsEZOVjfUWtP7N/T7oyub2lqbKR7wLHTJOLWLZqhirjNH6y8IDDVMdd8GAOSt6GozO5Gc6kt9UdI9VXjFlzEJfCMo8a9m9fmRq78mvDzkeQhApORPmyJd+17HDI1wKKKnnmltyY26sU2tn3ET7nZH/3agNW7KZ8JaDIHySgNU11zt1FNbPVVxZofA1gW4FmQxbxWtTQ+KB1wrcnPjkUKIaRbLJWnp2Y6WtPXNX1qffxmgpeG66Z+lA4GEkFDMXVFNnXOVMgrEN/PuKXhSqiYN3V3fPRHcLFlY1+MtxEnE/VmHb8Lt62GKZhKw8YU8z2a705DpGinkVqYVrYI0SNH9R1zd7Isj5YHr4SBWbcEdZeTqQR7X/1SeWT4UACaq1SmxkvPDGb2uHMXX3QRwnZ82JdL06tFTO1wsCkXAMxnBaJgmKS325UhHFb9gTBWFGao8rO8GXKQHV8rJgBwkBl60SiM9K/oZ3SDRrSO+VFDCeuWfHSVs+gqM2aWeZT5ZY5QXpJ2W3Kjma9+Izw6zmNZlor5GpygXJz60tdaz9zU2rHqll3lEjYXdI6UwbssHnDEnS6WX5xyPjdzaLChux8NEiO1JYwTQcWGrbyhCluI2WrACea0EdF4ZGAE7mKVUEo0jK5oyHt+KuYmGuqe7WnsjEyFDz06manvXLeuuUkbpBmaAPmIuJnRLE0pHWaxreQTH9xcOKDGwjHHBj4Nd0Nt2wOhGZ0wHouynho7MHzeU09Xbrn50P0PVrJzG91QurXJffvbKq99XeLaqyKNLSwKNPm+NekfFg1kZITeYlcrbKudhuIdnYTtrvjVHDW3zZGVTbq3XWteGNV+xNpdFHjjBe/ANBpY3mTBaUhE4hGtb6ZjleVNTrO2W+tLTyVT733ve9/97nd/73vfg5u6+uqrOaMLGPyqV72Kt1wLE190L1JgkQLPRYFFAPxclFn0P20UYD5gxmaw/uxnP8sBEgzW//7v/455Zxia2mBtB22EB61JtzkRTOSCvdM+Ukd2Pvlu5JH9hy9buSz5ux8Krrgywko/e5hWrYhd8kpv+TI3EQ+yBefwCGxX8Yc/To4My3oMQgcEBWMTQTTGdsP8zbeEN2x0zInBtVq501PBAw8h+8WH4lHIElZCWC8fPVh8+unEylVO3GwGrkVY4GDxm/kek8uKZVIAeGBLCQQCrGJC4m1fX5+NQRjYuVBjU/0b3+i/8fUONkcfftT/0c1NN/84ev/90XvvLX7kI9M9ve7VV7nXX5e8/LJwE4ffwiVr3ofNtS7DWi0owXM4yWnhG5N11adG6mMC2EcV8TkmThaz0feDY+pf3rN6zWrONxLEwLimgxkjf1Z60e5swZvFXrQ5PAm0A9zNltifxqFKMHwSBcPHI/7lhMk8kBgwzPa2sh9PJVwvFTSmC5mpWGMn8o14y5JoXQMlhk0sMtuXneHsTKVQgAjZGXc2kkll0LmGlk2oBsJJw1hHQuaA4pDHsRwYLI1EdCYHzDevwk5ZG4/ZyRZxsZ4arhR3bNsyOTba2py+vLWV6toaH0O0BfQRK0uoBT4ncLLeTx+AswH9wimy4vBclCRyJe8fuD/Sc0Zu6dkJuDGrXgsMpv/UOgwJwspYbc/a3RqeBb3DlZIXkLvWuKACUiYk9kJxkDtfFuFJE/kYMBtPtqvRJ/G0F6/oqNyfq+4kcsyrk1SK9BdcIldTItS0YOFlZmZ2z8zuczobL9vQWiW66d90I2VjdijywyKF7DZx14KFfRSe0MeJ8KfibB9TRyKqKR0ZmaZZ0G+FAHRZ+GSdIojBcTzaxuTJ+tC+Kofp+nKZ5NRZcUYjoabG+hiqosbslIlsbopbXU6x2SsJjTjzQUwN9SGa1zwZdK/Uke8ZJKkhSi+l361gBDZjlvmVly6Dsm1NazQzDlNfJHsqprLhPyXilBoGu0g2N9fkP3t+3ddIsjCQPvjJ9thkykfsitDyvmLpV6NNv3pIY2oiCDWGQpNRFiegdAijS2RJt2lTm1ScRN5PMihCOlsJfG3Z45Ggp6FchxFg9TLaqYxKMuozPvgWsMZOkKC8on951HOyczM7d2xH0caplFDA9twKH+xl11xZl0wgwvrJti2qgUXOnofy78DAAN2V/okeisW39E/rUE4Uy3XtupKo87O/LG3nW3FBfqIUF8TRnaet23eMjE3sGR5fveEMx4sWysK6gN4cusqy5hxCawH5bYldJFoHlGl9pHwddY47NjH3+NOxMmhWxCYvOhb/KqOj2We2Ni1ZUayEshXZIIBSfCkyfsXI5mIQSwSRdnmlxJJfMoapBfRitOQnjISA1ywIGgNadAyd4k4/ZkiMGcRbF/XT7PKNOfsGdk8dmsi7zsqlTY1NzaMjY7t2PgumZVcI2t2syjHgAG5rAJhMMYXNI23BAitYzg4vtBplnO8shDpyGTJWH08Y4EhQ47LhqRpflKm1CAKZ/cnJzN13V26/1bvlzuie3XWYrmY5oHdlcMbG0Lve0XT9dU4COyCqvOkpGAAJ2YGRVBkw2e3CMMt6NFrxVmp9KoU5pmwnfIQajP9sKeItDABIm8GWBREsRNjtUVAJ+pAdAJhR2gJgU00VVV8Baw2+c3jWH5pmIwbKE1xBIpkiVkO8vLzRbU7yAahitYsQvH3LW96CIrQVKrz+9a+nyf7oj/4IoYLW0heMirVYi45FCixSYCEFFgHwQmosuk8zBRjlmXvY5Qv0ZS35mmuuuf32208idrOjNoN3W8ppSfpo2A5N+aNBLJ7q3ZHx22KtHa+8Onb5VZQyzKzOYUjM9kwhc7P5nbtS2H/asUMchMRAcBTMIyU/M+dgH2twr1PIHzslZHOhzBznspAarygqbuYYjubxJqdk60mT0wkuAiPyhTljIdzqtaJFDCQGRTO3sSfTsmu1mIRXMcWWBNlZbMwE9Zdf4fD36U8V9u3N33p76cc/Ct91T+wf/9H9x3/IRuL+OWe6V1wduurKxAXneYk6WB4x1ycuSy2TIw40siwew4sJ2GIhJv4jIRa4KBtyDObLBX5y2oaz/uAx4BYnNi1YPld4xo5wJISstSNlwnPCCrhXeDhg5/AsdqRLMPEehjIRbgCG+YMpLJS9fFkAItCxGs5EwR+ZBhKHZfEF06Yy+ILUSBxhmcNbnGB2YqKUmZS40HWwNzaJAMTxMByrgzAJiZTD06biiMQatB2CKE405nAO48PpnTCFhAkHHOi5f++eSrnS1tzX3NX38MEKZnfg5onFaR8SKWNnVWqIVabEUFylNJS3nscQSY8QkHUQHNAcAAyJEFWdIJzxgqoHt2bLmfC+R8pLz9anwcUb/Ok8IFvcllXizgVGrSVFGDbdwY+SPn2M7keOsFNcNSEYp2VYH+72LcWz6aMsZx21BDHhg2yHR2AzgS08tnd6C9oBvKL/8IqS4G8vC0goTC2d53Ac1V9ZG4KUa9euoTy2SDaWupEp4XMkcsSbHCfR/PDL1jSe8heU5JuqVlBYSiy++Enjb6GmWpC49u18qekkhFZ4oT0DQwlnUsJP/lyhcDhVp/mxWtX5Git5hSFdSeH0QBVIWg/ko/rg1KUtl0bOLQlchdNktD2eS4hYZTCRdcet2sxHs1kYSsl7PpwS1pOk0opNxVhCrJbbxG9oTI8cLvaHvxsNsmQ+9nfNkcl6swkXkSEhIjPfakpdNB3rmXMjxfpfmcv9S5ovDnO5AGBq7Ddm0hdPI0seyqzcNVaXdwvxSAwDZvSmPChXdZRMPD88dlZ68pJLzwt53vCh4ad3PyNhr8SMkt0jGt7UsZxVqqlJd2b3TDSJHqo2t9urLhpGmzOcSL7ylZdYH/oVfQzryoyiVAoMDHLA8RK5mMFMmwqMQXyGMvRZjFAX61Nap2O7R7nz3InM3h37xx45uGXZ8r50U5Mxm4+6vjaDsEqICJdUmJFKmEtgBRDTVmX//B6nks2XclhZQB9ClxrVLi7yMzuLIQx6NN8Idh/NCMEZwrKuH2EILGYZ98LxIhtWGiN5pPYcSpRmB2/I154REK9BQHRQFv6qBxfF0Gr2YmwqNrnYz3D08GE2wpiVDHdmajoaCTOFWSVefTUmV3snkjqkwV0MCHjiwwTBgIBlZlAfauq8NWkfewP4EeAEBhpJ47gY+iRN6vRbN5fNPPpo5da7gjvvcJ94PJnP8zKTThd+5frydTdEr722vT4dnZlsW7bcO+myIxWkXzEKMUQzPwKMgcF2l8qxZX3+z1CDRU+GXKKivENl6dK42fwCe4CDVc4axXi0pMNh3M50yT80E4zM0bXCoob5tvmOzli9fFmj2xTXKpMh7AmIiz9NgHLQ9ddfj2YQXNY73/lOGgIY/Pa3v51iPFeL2NwX74sU+CWngD7UxWuRAqeXAnaIh6355je/iYoOEw/y3i996Uvo2TIin+KgzETQHHeaOt1M0T8wXRnOOI/sHXP9/OpVq3m1a/O2ppbGM1Z0ttX54TKmmYb8VSv9JPpCcGniEKsTTanMbCpZruaR+enWsBtBXZ3f2JSAJYGtKUuCx9Zi3mA5029r5QCfY2hiK7Vw9kKNlhVf1LlRgUNJD8EFrAOeJ6kgvAIZWbmfj7Wkvv7Y7/Q7v/1bfjabe+jh8h23Obf9JPT4k8kHHy5/7rNzqfrgovO9y6/wrrg8ddYmbF7Dg6iC4qUkPIInO14yzPq9hVKU3+qnofp1EgBs8VutslSQC6VcDL3Yw3VY47fL/CepF9O2F0gGSxmbE4aHQY4E4NeJSs4c1qRLLnJ6zGthT0ssdYWTOdAMhM8LiuVoeu3asuuxfRHAXMSkLUZdDHCJRb10a5vf1MwZmKMH99Q1tHss8wdeSZqV4pOKhdJsPptMN/kFZChkWAESaJMhYmEPPCwYHPGC8dGRzOxUxEt1dbQFLY174f9zOvADXhxARWAxGcBmj83GoGVhacQpOgYz5CM9hpvUCcbV7mN6j1gVNcQRuolNUftAPet5NLkMWR1n/yNojUcObc74RXdietxuwyY8r+GccGCf9pgWwRN4zPrRQvRL4hY/2Ly4w4mi8Y6jVoDaKwKz6sT3aC94NWQ7NaBuleR5hYTEqkLUXgGbEWUQvZZmb2+vlcKx5RI9bYuKYcK4kBTBe5Ep/uyQxAe2D9aTDg+Q5i2fBlnTOYmFPwG4k7i9alnUim0dvLWvkAgvrXex9WOkZFZQTBvwUuCEX9MiCms8iW1AIdEVxSSiz6fqbwKbG43vBOM59UldpvVMO5rH+ZvwY/WlkJBCzTe0nCTPj5LXO6Krf3APCqgib0j9uC28C/+DpfVPZ189VexvjT0bd2en/faR8sac36quY9KkH5KOSmsKL095KEN+zJ910lvJS2/RnEXubUJBzFg5lFoS2ykrglPhue2xiNFqkZKypUMxPPtkTADYCZrfOj004uRuaQj5mGZ3Su0Tnb835jUiiwp978CvHphAzljilBzyyRYCzEspP0l93T359NkdhdY6rTZF2htDa/ulWxAF6Ib5zx2lDKpPL4IvVyxzUYB5p4AT/aH2SNdlqHnwwQfZTUBN6GBkU3v7YhwiqiHNSRMxVTONRzAewKOgXIArBpYxOmWW7YR72Y6L9rKBsiFksdqUq80d/IWD1hWO2zJ0eGRw+2iyrtjS3gH6ReQLYCZAERMVSNoZ+BicREI24QbTpNvY4DSmnYlJWwJyh0bUHCP8kaVL8+iVuw7SXUwmSvMcc1XqV0HE8Uf37PX8PKC3qz4IFQYjJQ8DX/3ptRjbB+7WYT8/6tVL4Rl1GFpJ1TK9UrXjqrXFZZddxiNfPTM1y2q9vb167TgY2AMr8qkyC/DxgoqZRFBLWbgkhxyVIYjAROe7rqVpEjjqxthS2yRSewEFVJjqp6vOoWmDj4YDmTc/Vbn7Lv+uu/yHHqybmkF4WgxHimdtzF55jXf1lfUXvMJJ1UNHJsE2x23r7ND3dtKLstmpmTVrBNeMutQORS2qY0txksKfNOHqSxLn4gHQCxuATBidKejD2jGewGBGbypX1nJn5emnkRUH7d1LK9GGQ7MVLE2yLZ/as5bFAhk6Oe11wZIGpx5zluoOEIn7ySpI4bkuvfTS73//+2jY/dmf/dm73/3uT3/60x/+8Ic5Rcm2EQFOpSKLYRYp8EtFgWO5/F+qyi9W9rRTwE4nTHhf//rX//zP/3xgYOANb3gDe30RN5lR+nmMwmbI1rBdH/PWtPq9zUFnpHn/FAJGcYkzxXKoGNo+5k3myyvdKNtwEZOGLrm08v3/8NhJo3jAYNcPhaU0dvZZQSppZxJVmZfMuw2NzuWXB9/6lgksbxyUP7+kC81R1x6GJO8jF8vYsGg8gx+44Alw9/b2Ms/BSVjFqiOhycRM8Qt9FrphdOAn5cNEh+2pq6/2r7oy+ERQHhvO3HO/c/sd/t13x35yV+i226nCdLrJOf8c99LLQpdckjzvbLbPVswBqopeFQjJyYWoljtL0cz0ABLgzcmLYSLpZoNRR7S5rIibRABjx8yd4Bm4GSorRk501kUN5p08GcGeoSdGvLGnYlOGvyn6PpAYmUem5MwVMSstRWhD9gi8ARI+zAUDftlazHYoUulsIP0UXGahVM6NVFLJcKguBUsP268VjVCokpudOrwnGk9jeAfRPy2ujXQwqegcohkgE9Z+xfenZ9kG1tqSTo9UQtNjsoMKygXrIl6hM8F5AJKtKjWwWfJk/WmDsaz1mD2gqBwqCn9egOjY7EAWugYbExGGHxnaWZvOggoqm4ihvmRoUiURBPbzwcGnysjICjOJ4R3ZphUpK2hlAQVicqYltLOdSjRdcMFOgX4JAE6AoVlI6wWhqs7j3+JTw7THhydN60mBuejYFMb60Jfg3kxnr95quAXQQlHpDHDJvIMJBtbaWHQhPnySWpgXIheKgfgFOTb+uAnAHcDE6hg+xEJNEWjERQHsdf755xMAZE5nxkpzNBOLeYg1aGjfr+uaKsbQFJicQGipb0lENxfmfoFhMNN0YPqKsgNv88pRdrQUroKxmq4SmnFhDnUQ1mBMxakLF7Zo2KCNcW6QHVGlHCANVXoTjLdtU1tD9FHpqhTIZ5MreFQvjdS1KzLwnv4Pr4reEXJl0oCMViTvvCj9FZ/O5WYoHhLcTGXJbWPv+Nd9v5310+bEXIHdulRS+v06Z3vOfjyUUfQMAl5RHvKYmJhSSZSKioNPKlUXi4fKkXqWlfgMED2zrOcKABuCadREDs0WU5WW9LxIbtkHyrk3zWUGYqGUn95YcpPsOXEfmbn+5tEb/ViK4Oxr4LPV0lYyRhwNqa4zG67fVmrEUlZrIsRa3vHrNaKqcrW/p3SnV9MPkQMzwDJkgSKOiVbrTirBqV9CwFymja1TD+p6BppqGQ4hba5idmf4LtrLrIMAWc2WDR0Fx3CExSkIKUBrVu5AwphihJBSb2GcYX9vkR0foOV0JlY/PpPxJ7wUB6Z72GF0sdVHcRmDDNBDsOfy/aOKzOLBrolgU2dz3auuyX/t20h48aUZqZ20BjadGVq3arwQWpbmY/BRbGHAQZ9Z5g80+IQyyZ7AL7nlwsz48KqeLk6Nq0+E2pqtAWfqSzLHUKn6fIwvHV60MC+52/6PD+MSrcC4xBIYcwGfJNolfOC1wOTR19fH+i9vGSLwx4GnHRZwcOFpl2IZH3jETRYkxTeo/mz+0U01eWQy2SefrNx9n3Pvvf7DjyRmJsOM216k0N+ffcuvhq66MvbKi1NtXUQmrDqvuY4Y2at6/JQfasdMDTpFXYuVaCTAlBDISr0oNpGPI9pPSfD412RB7SzmxG2Tpfp2kRFozzCZ96KjGffAwSCaYhzjYzSjCWZNwgHLGZ1pN3l0NY/P5YQ+FJ6Lo+y//e1vg7fhvt73vvcBhtkbjJHR01XBE2a96LlIgV9QCiwC4F/QhnspFpvpZCH0RbeWdeUXAH1PUDeXg3PctUvSbXUz+w7vH57FMk15bm7WL1eyUX/5ymZvxars9q3Jiy6evera5G23yHCNJnXXrU/m1q2PX3d9qD59JFnDEzlNDZE3vSm/fyj2yANib7X50M+3doRvuim0ZBmT0vw8eyQeHD9AAiiCTBuHXT7v7++H3T8S6GgXr5gRgS6w/tCHlXLucPOEgsk4hneE556ammxubat/y5uDN7+ZOpb27Svec1/l3rudu+8N3Xln7NZbOVFpLpn2z97kXXKRe8mF8fPOD7e0Cg2qvLBR4tPnclmOwiQjMyfqdnShTvwErOVsCZgDwlMpEMsJsRbsDngMDtVO6idO62hfElRhQHeeh7UmR+sGXBLinbnEmcyy2bsynvMn8qHJnJ/Ju6UgVHG8eMRf2uCUS5zAxIY6122JJhtD4TqALjiD7ccSFM9GKjnhUs4FkW0tLlC54TXFtmZzBYMnvbp0AwiIJQM4XbYfO9LN9gEz0EvY1ZOtWjhVYWCDaaVBLa1OsapyGwkwdwQsAGNsfRkfeg0b1VAKrUjaDOujuGxRFrSmu4KNjdlVWby2DXBoR7Y0a9CU7+5/tLjkjDQ2gSgz5kwhdWdXh2HRTTuqi1quXWwZFrMQ5rPF64Qtomq/0Gth37BuOFSbGI/IbJ8rYbAxV+0t7ctlH9F1ROMAbFy78LeaEeAlRNzWH/yGw/LcNiJufPhGeMWFp00TH7vwJGIZIEofyMdbyp4UdA+NjoAcxEbOf7M9y5bE2PntOAcODGsPqkClcBztCZFbODVHr4awzW08lehAtrkSqTbH3BwZFkxlXHT6Yd35hOvTsnaby5SJJbCi1BQkHMIWgHmVL89lc/rUTMzWyL73n/+evpjQvlOIZYfC4cZipJmjloqeW+R7wJthqj409Mb2z/clHvroE1+c8DsoKNELdEg1gjubx8PkVf2E3TKyMKkbBzk3ZlSwZWBKl+tmKl5R9q3Ce4vrHff7XlMp0e9XdgE2SIR/pFgqRyvJM6Vpn6k0AjDqvLFETybam0UCrJyD6AMzr/78zr/yw82WM9CAonKSvMmEVATogj2ZyKMHyteuYgVJn7bJ34RQqBd40bdREcKOrtXMV5FVcBKvpgza4pDUzq4ulYC2Vrb2rb3TxMbP3BlbMMIGOjWyXIzSG6GuZLbSQ2a3BUCUymHIwNos0ElCvlbf2MdhpbtEB7cyELGgiqfSkf0Cwsi0G7Gwni2yeqGsrFuFwcRakYg2QpA58HHJmc6V0gkPUTlLLqoFQ07ARmipnGB9at90Zaynoedtb/bGxwp33B2jTNDS9cprVzd98L2l1u7shLu604kyPmE9W0rOTjKKeT8OhHPruurQjqnkczu2j6zva04kGFIX0P/UBvxaO/Glo3Zud7jgySODP7UAHDLsMEnZr5Jj3tAxsepLBEOaig940qZjN1nwmVebz+y81c5YtRaUdrdu2cocizLR8uV9HFNUmRjLPvq4/8C9zn0POY8/npxhZAzKXijXtzz/uteg9xS59MLG3v7ArFwpDdVw/guvFf35OxhkmHnZyWyjUkLAcF9fH6MWPRBPOww+/4QVA0UwCzXr6mSBXIuhDicCJEg8Wwke2zp4cLrUtmxlNAm7oX1AVCvkVNJxZ1m905oyu3q0Dr+gKZ9nOYjLxUzxjW98Axj8B3/wB+9///vRwqvBYN4+zyQXgy9S4GVLgers9bKt32LF/ksowGQJ9AXusuII5Hvd616HdX6UgphRent7QVOnawESPnhkdAT2B0Q0limPZtleFbr+4o1L50Zn/vNHdeefFXXc7De/6f7nD122oC3pCl79qsg73xlZuco7mkWgwBMT48lwLDIxXrr/AYfzYIt5v7c3fOXl4f4Vbn1a08g86QiMkwpSNS64Y5bGEa7a7b5w6viwisw1H6P6S0TYArhnZn2r7EoivMOHOyChJpcjJGIuuA1wMvaxIZeBANVjfrVMXikVD+wv3P+Qf/c9/n33R3fuipbFo5fCCX9lX3DBhe5FF0QveEV05Qp2TO8Z2EuRUqlkV1enndTnCyQm4pjL1o7qUlQAMBxMjctZQANFsiFrsmW79RT/Y4Idk/4pPtrEwbEoo88UgpkcyofYkvVgLmlrxDJCMFKQ1p66QinYs3uAkPlSOZ8vcIqpD1cZr082dxUMy5splEfHp/PFcrqxIY61bSvAUwpGPmyYa1Mw+BCVX0yHKMP7AGLDqPLAqoI23Rk0wiEiRmVae/CAxLQ0uIO3ABVZqwYYI0NGtVEGapxoxY9NI/XgUBOk0oRBuVphJu8uTD0aM8DfjzVVXvnBRCgicfH+PYcRTHYuaSHzeL2baIpXGT30EQpFg+F5U13OOEV6vhSC2TalJCfpITYMAWqBayXnE4Bb5RUOxEcEMB8F9pZ8zAt9fVvycD5CS+bmxbxEtIkkUgn2oJLtzPQskiTadL7Xowiiw2DBSVPTtJAEU0Tib8d0ssg2cNY/ZE5Mh6KRFHw/8IYyoK4d0zeLmWWsF2knr+kgeEi2zPnNxCrzwnzdqm1Q+fCaD/xK1z+hgjL9QMPo3zUV85UVXxhLdM6yBQDdTTDaxINN2ccSra+fjS+Zo2lvH3vnn2/7Ytk1FtSs1qq+VfVDklNOcuFBPzWo1LzSSxNI0MmEIO31yYf+6szrI6D1LfUHPrY0lOWII4XCHlPdG0eX/OZhRJLfGb7pWwd+57VLvnp+052tkeGCm9ybWXfL4bffO/GqiotkzOanNOU0SeMCkXIue4zjgQNnZbN/Vb9/3UoWf7heFNeuBOYvmtj2FhbaYlUzhGogshg5PMIYtWr16ta2VppHkBWwajAtR5GzR9kIbyWnlVqyhPDgW1bZZI4eea8V+eLG0oNE41JO1jYNOgFQhKUYkgIDG6CrxCtCuQpA0VQkqmh+8ZCc38QyjQCoRtnERbEFWjGUaASh8/iVYn6uLhJa0RGvj7M6hu16ndbGAg7yXpQQGFi66oqXLAvVTY1yiFPp6W1epeCsXBk/71y/a+lwxcMeUko27XXkG5syGGpYbkDQCo5m4qBIAFRUlK2azzz9Ttsv/Z/0OXfq3HPPZVLAFCKIl7wsNiYbtIiZ1PChSGyUsLiXmaumJk1TEgVSMGVMTU91trTkB/ZEn3oqtfUp/6GHI8/ujhcRHTulSLTc3x9ceol32WWRiy+K9ywLQgyluiCy7XqnrVZHJ0Qd4SigITVlUoZRYXnOzs7kfnTY5/VEwuo29BS64mjGH846sznvwP4DpVK+p3+1GZACVku7Ug4i3xRcy+n7gpRvEFCp3bt3wwagWXPbbbd997vfBZyzN/jXf/3XYQleXO2eFykWAy9S4KVLgROwHS/dwi6W7KVHAYZa+Mhvfetb7DlhzMUm4Uc/+lEQFLwjRk0YgsF1yJT6+vrwfPHDLnnZqWXr1m1tra3Nba1j2cpU0VsSd2MTo5WDBxKtTUw9Uv7L5t2GlNPQ4jU3eFJ6PDKf4SaRffv2w1WvXLnCyXGUbU5ixVgMPUJXZkcsy1MlN4GBpg888ADh4Z6tqSEcVIoQvEWiy6zPBGMj4MPEQ8V55CQGgDEMBG7CwDRAGWZZWx57JzxvIRdL0TgsAGbeYm7GdIeRyClElRsQ+1UpHz5UeOix4KGHggfudzZvTsxlzLkYoVxrq7PprMMrV4Y2ndV7w3WR9i5OuoBvgy1GDGlTqKZDTqbwbPFiFZ9CUjCAPUWlVLZgJsiRG+UBwBMMjgexALiAqRTh5AkDH4l2ii7qpSqa0tFW/ArzUmb+YF4dQC+Wpdk3pwNFiv72Z3fImKrP2bClaDQBLxtK1DW0duZ9d3RieujgSLFSSdU3tnV0AkIltymxqY/NeHDGkt7A/sIZ6xJ5yEMap5LQKF9TCJh/fAG0qp5KJcLzS5BAFoVgRhHNID0GEhuVaZ2xWRUOB0Hy6Vz9w0WvLNGPjWw6HRqH6lokRZqSupmkdceLnX7r/f431aUapX3N/uRDBw/s3b3zwgvOa21CFRLT1ioKeMNGMgnzpBSrNF7wi6/e1C71f/I44qW0dM3HqYU8TQ7IxFeDsgAs8vG7A15wJiQ7ni0Pzjjf3UaXOFJ4/FUjeVTraH8MrbhVQ+JJoFo0KGJLsnkEG3iS1NtXIDyGEFYfALdoU4tyJlz1rYBOLQ2i6GU1IROsIzLwlXMvijuTc1ubhz7S6RYS9b8+1PWuYYox841k4pJydHmhMpPY8Z4+ZN7LPjaa2jhRDhI33fPlx0bOrVuykjKS+nxbkbCebAY2Wz1LEm4vVZzXePJn4hY+uuq9r2z5Jr65gfTEdxvye2Ohej99zWTLK2edcGWstPQDm388VOn3WDcKimhEsLRUdqNGIZN0lYzS5J/NwRSArCkS++qTUfVcenZvo/+uMypndsBPmzHTZj5f1GpU+zOfkK2FodYRL0M6HmXaAKErH/vE1MwDDz7W07u8s2c5EtcCX2vZOTw89sxTm1uWLF3av4YvESVjdjrgKOHmA+e7RkIbmG26QryqlbbsgvuleAzilRSXvRXscgaWGMGvZLiIbat9hzpTSVtPI69liFA7sHRghk/eMBoYbKaqCgVr26aQNrlj4wA9Iul9sCiGHT+j2VHITLfGCn3dbW0tDfizlde+ZVSW2TAHcW55ZZOHXksIW/cMCfGYC+wnJzI4ioKiHD4DAwMASySKaqD5LqJin+6LxO1Qz0KnXb8GzbKSy3zE7MY+fzJkIuDrHhk9PHx4eNNZZ0O7+c7KSxYe/NLocPHxJ8fuuWf83vuWD+6Jj47EmLzocIlU5awz3Asv9i66MHb+eREO5EPSaztetaLqgae7TidIT63r+xYGM2Uzo6GKj8bW88p9viFqtVePncr6GC6ZzHGUASJ8yBEUs1kyq0vWNSb9zjpEvkYzQIUitxdbWVsG6sK8TIpsJ6G9gPQsTADpMUz9mc98BnOkfX19f/zHf/yOd7yDGf/FZ3oCgi56LVLgF4cCx8qsfnFKvljSnzMF7ATJ4Uaf+tSnWB5+7Wtfy75fdG80lsMleB4wr7e3F8gHDLbCFqJoXR+caYZ7e39e1VggZRU4iUejS6JBt/Z5ukGy021KF7J5D2shCaxehSeLwUjOnR4K6uI+loqbktJTpQziesTXoIalvYsOymQJlHNrDKVKpGD2Dm/oeXDwwFfYDmpEGex2JhPkSPEXPsL6szqOD3XkDqNAOAhiQ+PJZd02FpIN5mAmYFSzwAy8ZdUc8IxaGnhbE2RtksRFgZb0RN/UE7zh9UT3szOFzVsrjzyUu/e+qQcf9m+7o3zLrag57gxHCz3dnZvOSZ53bui8cxNnnuE1tsiQqJg5sSqzc7NoSYG6yQKVZuplLXnUqgTcZWkcnTdTXgkcqDhvecSNwwona+FflEPsj2pZTUS/4qrNM5u/pFeMqphpE9gJb1P3ep2WAgEAAEAASURBVJQYR8Ymn905uHLdCnAvguK5or9z917/8OiSSLhnVX9DUwsiHbYZw/IWdPASZkjg6qoWWekFkhTJ2o0R+3BiLecuIQsy4h1oTHnI3YJi0VlsP3946iBYehy9ThyfkQiakql0yKrxdLtjTRfFVj9RapgRF61aCO3O10dOg2AMroHxL0f9wiVR99zYzrwbGaHLOVMTY/v2jsTjranxWGxOvDIlkswZWTSnrUqqjMzZGO7CH8ZaityyF2sDiFMnjypVaW5WE2wR1Pl1qYPXuqH1Os13ejV9GA7sNAJgKM46yM7xEM06jzwtHhV11UJU2VRxfq0BX/Oq+lKB5klgI3BXM6ux5y+gn219tZMJveCl6RbK6ajLBCBlIlbWpR9LuhPwvOP/XudlMfw213ixNv1W9sXDf10u7vfif+i76Vx8eam8te7QlxtWfGEu7OUvbb9188jZTMkkIoZZ1bO56MmWwzYaz3THauORsRpX+ZsfIkb/1+5PNkf2nJF+MLliNvl7M66PjKnMCgvpzZbb/mrnFw6XVnKOEan7XoLTVUU0k4RSVqZVcEW2MO+GziBCuYCRDLGEINz2UnDzQKg1WelmhBAxCGgKWKVZVZOCb4qlJZacwJx8j3xHaBQjRy/gZG8tj/KRSBZAq7E5cPLF+ECpdctTE8sm042N6cGd21j4JMr4VGXHyK7W4WI83ZxqXWoOlObrQaPWCH5BucbuFNscAMMSAkvVWWXiH0W0i12mchrS9Maoe5CnoTAVYG2Lf6KGVg4NScG/GALAiy8L1WV0C2IysCehbtTDnhuNpXPI2b5BRB1KxKFrkSDFUeTR9NxMsH9w0B8Z95y2vhU9yXgI03o6iTfCnl7kuiQYgfZ8rVi7MhTkdoKLkqqBLJmPfn9Cz6ODvJAnkmVGqO35pwCgXzyZmH784x9jJREpNBMiKuuTE5NsBKCJAfD+1ET26Wcqjz7qPfak/9gjkf0HIuWSVIGp4tJl5Te8rvKKC70LL0ycucFL1jNanahKpiu9kCK/kDjUCEjf29uLaWjWoJm1rY434J9XXCRq7ydJXf3HfHycAzaedcYynOPIl6La8S2L52DShYDpeFfKbakPUoDPU0j2JDnWXpm09dmxeD04OEjrsPWMArM/hZV0qmYLz/z+ta99DQnwJz/5yfe85z0oRf/Jn/wJpyjVAtQSXHQsUuCXhwJHFhF/eeq8WNMXSQFGW66bb775Yx/7GLuAMPWJA+sLDLV2tF2YPiEBmVzMprh/8pOf8NbONy9G14gJGCGktRskLmb+goXCHsmh2fLYXCgLhyyexzB02mzjNyacRJAZH9oFB2NZHhPXZfJbiP1UvSBABotKMNJRu9cRn4UV5JHJBisgJERIVo7thl44BuplJcAAWtAsKZA44YEBRIEUVoHNFhkfQALVAVIy6RIMkEki7LAFRSOP7evrw21R6HwtVSMukiIKbJmUbd1gbmLqwN4B9/Bofts2d+vWyvbt5e07e6cnWrGrTOU5NKOvJ3TWWc6mTeGzzx5PxneMjnM2VHdX97oN640+s2Z7cYpi/zTrg9vBMBs3brRKU5QNf8qJxJsJ1URRMY5vdPn+LC9bEkrDlulBc3woEzkFGTp06OFHHqtraNxw5qZQJMHGP3MgJ6ZurLYk0BfsKpGRdvqBe6GMYcfN3j8JkUAVRm2SvcdioM12QQUz8iWZh2WnMdRRT6hy+aIZlxFJATEkbBcd9d9JFoPlm4vL9nGUp4E0hrCWMKYFdaOTTqW9/RdEyp2IlI0YOSS13rGRYfR4ly/rioVhtlm44K3E+WBjMeLSzTZK2kZPG0/2FnPXEa9aOQAww3IKDAsSA4kEj43IGkOjJgV0tkHUcPOAapMUIhhZj1NvUhHFcOvH3K173rnAb2Eg81o36h4EfBp87E3NTZdecmm1hyxIpRa2moHBJzCLRFYCR14f64LSbJ781jZvaBalhyMyyqPDqfAGMtSKqiIYHKSANV8bi8fNw6gGVMWY9m2lzAm3Rdk2RgJ8pES8NAQy38IRbyWkUoOFOIXrjZ1f+u+9H4D8u/7bqtygG0TLG/51yE3kM99JuZ8s+hd46S/n4K8PfKo/f3+67JX6v7In0p69/+Clv3vPF8INnaQSjqeCGOeaVstqi2Sf1JMWPOPUI7XjhVncYvszzmhp6Mbez79myb+mvXGTSoCRq6enL/3Kvo9uy76iRmpFFrGIymXSNh42D9OLqa+GSxWGHmIxoR4lw2xOBm9eW9q0hB4kESuLR2brrGfhLogXuMsHRSZYNuYzQRLLl6K9tUb9GNTEx2jUMRQFvKpXQsJ8ueX9+4fQ+GhpbpobPTR6aB+7QErFghdJYK0sEk02LFsfrW9hwJeomuLQn8lIjaNaUFg89WXi0JIUFgDsZ2vgJpUxIRVKCs3qHaBf843oM+HrMJodKC1LnEsPYCHAfFDWWIAxF691KJnB01eJirLZqcvXhPoGessoLdsj1oJSfsvTT89MjZ93zqZlS5eIjLosxe0TbjOi2DcnujMXsDuGN8ws1MkOyNaKRPXjOlGsF+9nCUgpWf148vEnepYvZ00WlLh7507Wtd1ScdOS7tZstn5wb2LnTn/zU+E9gzGMXbFa4YYKjQ3BpjPd8y8Izjs3cs6mBHUPoT0vHXFbYcH+l9KlyppZnkJh5oN5manfMgYLiWx5BtOClF/fD4fbT2SC0aw7nWdysZVS3ZgL6PSJSIVjHdtTXn3MfEj0PHMtTPMFkIGiEgvRAs0B/8BkTYJwRGyktxL749M09QsQDn/iE5/4wQ9+gLgCB8a9tdr7EmuL4wu/6LNIgdNOgUUJ8Gkn6cs5QTuAcuLcn/7pn3L8D2er3HnnnRdffDGj53MNoPgDTgw+0WANtGMiR/AIiEJ7tre31y64Plf056ImEWuvYNprboABVqOTkTBTzuhcMJap5DBUpMmKA1TCY1n2KsdnCx2pWBCrzNRFy90dzUxRNSxnJxXmEmTawFpKxbI3niecIWDxkW+TOMHgSywYRlAMd8IMxAU6JZGFe30tSK6VFgdhgJQAXTLCwVkg+MB0kyNImLmNZJnVQKELjU7ZKbRcKkFJNmUZ+yVuQ3NTQ/O5pDmwalXwutf19vW6xXxhcG/m8ceCxx93Hn0stGVrbMe/hf/1X6hQSzh8XktTee26+GWXBQeHihvWR5f1lKMIFyUnAjLZtrYEIU0cto2Ovy+szn+Nu1oGEGYiuXTpMjqA5A8em767zj9nE3xhrb1UC/GaWiNByoS8F1VqHeDJYZ5lneSJhS08sXODjAK2G9CLiVekUuwPNbIpZEpIkyS/Yk8vvUgHWRjMjEUcYDMiZeFk8fTi9RGCyCoRvLWYcLcYd3e+IlboKPdsLiXy4rhrF++5MLi8r8/Zd2a8xA5TTPCa17l8YXo643n1janGHePw5MiXQLYoqAJiAwRGLOsbuOtGASQw3yG2FGqlh45O3rDgMJnyYWcyJQL30kWlQS3krGD8EYDAwruSUwg8SEjDHR9aH1Q8j7GFopU+HxnRyZ2SiONXIgqGUkUVOcuzmj4Ef3bXbqSqK1euhmjVWhtIat2mUfDnT63DD7iIywbFaZu4GvHoH055yRZlBF3RTOq1WCYF9VQlJsRr3xBM/WC+AaosqmEgyUhkN2J6/E14Pg/ie24Ui9HUWX4qkf2xyFBP6lkmvNzmA1GmfDjubFlHQ6uObcXY3nQZFEUqFCDwIwH9Qe1M6UtzMiOM8SRAYYilEzSW6Z0jBylOtLE77M0DYEW1l5KULPLIE7iMdC2EY2+pSq7mCzlZr/uvdnzqm3tuOqdjR2tsKF9u3JVZtSe3ruLqsDd1ZUKTkq2zKZ6qZGprkzdkUQA4foUnJBJahVdoKkqAybxzx57wbCnA3jv9XwiW0AK9+hwAA5LBmk9Dh3/zmZjEJM7lg5KSrNQotEeXV3x3kuVKDqyN2064HF06kZs+dLBSn+hGYbQ0PZbN7a1v70nEIS8nnsUsnFbTii50GdOBtY4jgmnct+0j3WIIrsah0yLF5S69bbbra3M+asmybMeHQr34bz4xC261NsS5uzQg9gjoC3R1EuGDMsAYg/CgXJ0lLvvwnCVudkOYL1GUNaSiwqmOS18Bmlq6VIexi+oiMjdTONG65tDDCS+FNuHtnfHthMFelKdodoKyiLwUEN1abSYpBAcm27dubX3yye7bf5LdunUjxqsqJebQkufl0unKOWdlNp3rveK8yNnn1PX3erEEwwqkUxq1Wh5xvajynvbIhsYqpV1ZxiokZsk5AgoYbK11WOITwH7umVJlIuOM5oJZHWWEwQcu6qaPlDQ4Qg8mpK3OTctyvR1GaiRQ0Bd2md5ejcrqOQwYilpIF9iB1dfXt3Bt/fj0bQWxS8qW4IcffhgujkM64N8QC8PL1ap/fMRFn0UKvCwpsAiAX5bNeporxXjOsAuL8dTmpz/36U9nSwVkmz/44Q+vveaaGtI4lSzBdZzxyHSCui/qRmjssHJ5xRVXgIFJnjThN+3a9qmk9lxhYDs42LUp5jTFgxUtzkw+GNHqLGJAsfecUdC8ZAnM38hQ5cAMZ3B2NSVZzndjPtAiKOQLW7dt3b//AJXt7Ohcv36DtY1pZ53a/GezZmq0gJaKgE45YgF/ym8D2zCC/hJOioDcj0kBH8TCLMTigCAsELBpBzBMFEA4AJgpjSjMx2DjGgBemD4RuWo+Nn0zE4NPPCeejGMEe91a513vgqd8dvPmtlw2Mjg4+f3/bBofDW/ZHr/nnvDd96Bei2h0Gqi/eqV35hnsQp5obQ8v7ymmkiROwZjVY4nYsiU9PDLXdnR2clfGP++LVQYWDlitQLUeHQSqjDD/mEIZpgMQqM3dGGeuxySQOBhxYaZjgyiEb2U9q8xJxT7i4jyambiFjTnbxsqEwSracgyzDmCGp5chWZ0FKgws9AvXLlmSrMvyKKGxsLH+RleCEcNrHmDv24IL3IXa5FJv17moPhodBsERUuBM6AxQo76+0fPCAgPgZMVz0fKkfaulNylZVkvMpUFGMMUCsQaFwsfT/maXMvgZIZXZYyc5sAW3gFghYdsjiSjOnlcAGxNAXL4Bt6SGQxC6Cp5JH+CsuPoT3CcNsXcCRZJn48/5zIWnns0kk0sGiy17h8q2SIIfSJuFn1QPk4Ji4lDuCwqvV6Zogv0Kr8TB9vjzgLo7mznN9yQqmNobchjGE/oImlnSEF60MS3NrRbHBjd3/EBbLKCQjsWWmERePuLv7sR0QJiMWf4QADCJ23gmxXkf5Ydb/wUTTVNuy5xRcuoj3mzz9bPDT9R5eacwEokvz0XO87P1lcQbBSWCfDQzKHsD4Z5CtBWcGOyeWlb2ktH25YxTlVCUc2Bsdsq5mmX1RzVSjsoO2gRV2bUtpxuqlJMy3oUkM3S40HHnaD/NJCIaTGgiKD4NoPgmbb3jMmG4mXTpCBBMTyKbQvADjWg5uiQPxqvi7p1ycqWgu1FNRs9XjzUaE7obwgJ3hY0laDXoXT1I1FaHwaVk1bD0QeM2rcAz3iG3rqFuenKW83ib2peFosliZjoUTjR29hCT0rO2GSaOgDx/Zmc+fVXdXv0cNR/WLxDSAnHZHaAOr+/DLOiwuqEwaECwjwCsq88BPMy6EsrMlIYESQR4zN4YjsGKhYJYuIJRd5SWhZZ1QJqIRTAV/Mhl6mEf9V4XARnP7bjESI7OFI8L1VNtsJPfmYbYbUuYgYEBEkH3+OThT+Wtiq6mpQ5Qj0s3+43gA61oNh/zVk89ldm6o7RjW+W++w/uG6I94kG50XFTrltINxQuuWi0taXpVa+JnnN244peN540u3lNm5j0zDKVTVt5/EJczCNwKUBKts5iI4O1/iVLlq5fvy6RiDMdTOaDSaPkzOH2EmhDM2pqOgIkS4TKzXVue9JNx49s8bUdxRD5hRPANg1wFxECa+JgXRbumf4QKlBUpkLTJX96+gTjQlCMKjtKOsDga6+99uqrr2aTMAbPiM/bn57KYohFCvziU+AlwcX+4pPx5VwDhl3G9j2Dg5/9009MfvtbH/7d/7Hqpv+7vrMTNlhs7/O8GFsZqZm/2RCLkQYEpFa5lx0sd9xxB/6YfbJJvphRWHFVNEmxmhJBU8JZ6ftsEB3NCglnSwaRGn43Vw5lZ5yhmSDmIGcI4kFx5/7RjsYmZrtWs+WJVCAAh5ciyAWa1qpLFoBAiwNxw9PUACoiXOTbxIJ/IBYLyZy1QDWPx2YqoqEh+BZSEBJQzcQGKoZKPDK9Weua4Opa1jggFxMhGRF9YmKCkLQGutY2jNUMhwRcLCtMTU1RcjTWhiYm2s8+u+GSS1NvfweNB+ArHxzKb34yeGqz++RT3patziNPxB54kERygcPxiG5bh9Pfm+vvc/tWhDZuLF9ZF0o3hCLh7m4U+cQE66JzPO9eYGO+2DsUZgn8scceg0ljJYLlg2OoRAaGvNXyzRfT9AyTuXWBjWGC2ZtnLvoGrDpOsesAFYtyQcIGEktluohVaiMf5oBicfxCJwi+JCgG7oKlBZKNXAsJGAHwTGwx4Gi+BNWcfC82XWyPRkouu5QlxLYCsebGNG0bleYt5TeMFVmYQonnN8wqImvTMXlNOeXmn8HH6vpGD5rIhjEzPC4BIIVBCqZmbsCG6kTYgA0T3+ZFXD5qAppUwCXqnSAh7toLan6MLqh89KfMyEUZ4bY4i0ijIzMThcaetu4t0qIwr0xhbILwjCYXMY9KU1hMoQR3q2naxM2jOHGFV4bUgAOQjAy/Wi3FNq0lBwFst1Si84VSIubiV1UgBSVkKGbSNHkJkFF8Lr8lG5y1v7S/PYpQSx5qAv7sW3kI6iox+ZgFCv3gBjopWVTxM+sembrywsYfNVwyVbjRm/pG++T9qe6eqejyfOXv6iIb+LycsbuavOmknywuvWncDbMPPXbb8HXaRxlK0peUmhYTKJYSJH3VUoU3r5Qfzwpl/5MvF378K8mgsfbMYwwhn8M2dTGsE1kIKTk3NxNZEU1yhiaKaAmpnfemNchZvyKMyYzk+R5Y/cEelfI2KZI/m5tniiF2UzTERRFT0Co9bPFUZps+LykCOVs5slmj48mkr8xM30a+KjcfDJEirFg2NrDRFkAbbagPdXbRJum4j5qxlnWM+rEOMDMdyHRRaStoM7xgrcBwjKUcBRPQtTvktRZmCIin0WFGeGvWQEG84FtgMOuhWkGw3RJq0QVV6hNdz+F9oqD4MWIzZDFio9LMgh3qQs+Z8NEpHB/seJ+jY6hxTe8w3gvdR4fjjRnuWJIp+7OzuYEBZ8u2ypYtzrZtwZZnQkOHp0qFSXW+oBQKVzq7sxvXRVevCi69tNDdteG8c6PpdKpcwX46qWJOYmzfwVWrVrK5V4tkuvjU7I95+sW5Qd76+rrzzj9vYnz8mW3PDu4fSXSuLIQiKDmX1UPtiEV9WGxzQ76fjFRakqgpoOcM7tWIWa35i66yvmt9bdrly2I05xTSi+AKkCXgD47l/tM7w3HFIArXVVdddcUVV3z/+99nFxunWrz1rW/9+Mc/bsyOvJA0j8tk0WORAi9pCiwC4Jd08/x8C6cVe0dbYf/yzz+75e//4f8KnFd2dCT27NGZg/Pz2wsroR18EZnaDTYM7oBhDEsC1azOMNrRz0s12kwSKq4UCnUADayNDlyleNx5m5mdC4dDK5tT/c3SnxzNOXXh9tmcpn5EEyOHh+PxRNFpdJz6ZP/F4XRiDOiS8etjzoE9g91dHUBZEhEXZyajeV5Ztccb6AterZGCHAHG+FuAakGyvdfCHOMAAANiCc9KP9rUTHVoPhMFWIuhKVuRhVGgElHw4RVuHEjXawAY2GwDUwb4LeyUkCYOorCwTUbESiZTXszz+vui/f3OG9+kasCcjk/ldu1wnt7SuWWz9/S2zJZtB+6/r+/+e5Ps0/PcbDjuLO32163x166JrluPnY34ihVeY4P4SgpCYZQrVMINEBGzbZmA48tvi/eC75SVuPQZxCnIyXt7e1ERZw3iBSd4dETKr64EI42/eGhwDeyzrZ+C6q3WN9gwDACuHp0ihAw8wAAP8COHDR6dLCpUDEQe3l+UaBUZlwhjExLaq5t112AWtSPEabO5XLFQLCUTKXT1fSRbRjJp0ycpOqAx6uNw+BUN5bNDWdJmLpUECpMa/2wH1S8q35mpYiGXbJZiAq2hBI0EjpeJkN9d7ybD4HYQI3qtajrpzxqcrQKSBVqm2l1J1qqwlFIJY4R/fDYqhaKouXkAu5o2EVQAXQSR1nIqMe7XT0xJQoKAV/BWCZjOYVzcBIDxUOnlbx5MIAM3rYeti3Eb7AQjXkbqTjDVupqiIJNKw6MppvGWmLH2ZEopb3VLLnu3cTQKqKkVmPIsnai0zAVNM5UDqUIoHA2b7eULEiaUkqZgZGpQPw+GLqoF1WUDZPhvBz+x5swnm0MH2t823nxFJjtEhnqX2DBLPpV8cuzBaP21k61vmkj0ZUjtrrHXbZ67yo1zSrDS5o8qqpCKxI9AW/VBFFdqPPOG96bi0qC2nojHvTI7V50wG5gDju+Sciboldf8SmcBnl2JqFMoihKbz4WDjlBwV0DzZwlr81FOKCOYXqEuoXJyMwHVKHwPLVjUMq2isPyRp5KXn/oIHmBcYhhPFE+QwVJfwASwVhJXtDQkuNZHJ/gq7VnQJ2iKGH4hVwmnu+OxcCJFMElriQKIpX0AvWg4ADxMeAWGWjJYBTYO+8ZmFbBWiwJSXTYy4UgoTCIqmyEhVbEVNuU2T7qpGqZCNZ8X5WBIv+SSS9BYGRgYQK6IMqrVGzr1LFjKtF/ayctBGDWtWsi2JN82dTENadqMibIyPZkb3OM8s6287Rl3+7PO9u3uvqFYKc/QwwJFyQ2Xm9KVV5zdvPEsduC4Z2w8VMx3rFlXr2O03UIhd2jX7lAyRRuJ6KQdOBi8YEweGjqAfNtORqder5NX5+fylg+KE4zG/eZIzyvijblDxXrIOTM9NZfNdHZ00XPDTqUuFrQmnZakW4fVflGB6/RU2rayTQtFOWhrZ3z4IuS9MEinJSfSZ958/etf/+pXv5qjg9kSjATit37rtz7ykY/Am/GWy1Rq8bZIgZchBapsy8uwZotVenEUYPzNZLP/71//9e2f/6u35TK/0tLW1dbCZFdoaY387d+E+vpOF2fAWjgSPLJjlxQYmEdkm8gwWeM8//zzGX/tTEBtTjIWE4ajh/zh0fLgbvfA/iBVH1m72unudtNpuCteIpJFX8hKU0lK4R2fLaBbB4ef2LZ3IlNJ1Det3bDeMEN2xBfvkJ2d2LftyQ2re/zc7PKuloaGOlir5qZmmLTqZGeSspSuFU9pm4uVfqYuuBz7qhbAvl14B/oiwCQi4YGyyI15y3o6PsefM4QnJOIOrMVWB1rTSBIIv1B7nLf4EIxNxZgrQ0sK+TPVZ7azxWD9+LkQI3EF951KbnJq90MPL6343q6d4a3b/Ge2Obt2Rqcmw6hTi0hePhwud7S7K/vDa9b5q1e6q1ZF16yKLenhQCle2x5CwJNU3AR73jdKB0OAiTIWCJiwqdppz+J5l2k+gugu6hlEJKd7eGf+zk9Kj5atiLH20oY3x/Y9UBjdjMa90ED6cr/u2kQhX0bOkJmbXdHf39zeafSoQbxCNUBfgDQOC4atWinJgTckJQs8Y2QIKI4tdKFx7jb69MTI3OR4Y896oKogMymQo36dVKSyri2M3VnYG8O3gbaADCq3QXRws1BUIBfcbqEvLl4TWXUz/cP6KAaeCi322gByeRh3VbiEm4hCQ8aXKCYB+6Q4BpGZ/OkuCmNvuvOWNNWLqhQGd1aTV8/Cs1oFvSa88VE8vdW3r8T0XZtimhTnUzJhuBV8/5lh8LSJGzhveLjQPu0/1OPeu6QUjyeNESxVx14mVO3J+OnJYCl+RQ7lCOnW19/9R6s/sCS63TOGwnlBKiqGKgAohWNGK15EeGb2kpue+OpkYZmpKPFtUWz1SNQkyb3aRCZT40tiNigERPXAEEB5w403pXTYrBoGX0tBOaisDMJZuogoiqMgxqmUYxEpzuiF8VRdFE/4mytT1NYAUw+eRBb+IDMLCA2xYFkakz8iO7oFrJEKlwq/Soseu+UW0GoAArWa1NA31lolMjSArqAve3Qxi6ydujJDRUgj6UWfv1IuPvXU5mjY27B+QyIWAXNhJgHbwzp2SG5grY4gMg4dS6Zt6sJ8pl9yZxowOb4UbjQHxiNYl2TYR7F59erVVPT0Fows+MRM45imZE/CgYOFnc86O551du50d+wIcI+MxUslbLYRruS5uaYWRvLI+g3B+rXuujOi61dGOrsRkWPuWv3DcVhtZAaxq6tM04zAAF2rdEP5yZHq2ONnWS9mTObgw4VT0umt4H9BahDw4f3luTJSomrrQNQd23bMzYw3p9xz1ixbv6Kb5TGjTQABTk8LQkZbNeAu26A4FYJHTFFCbbgCoC+6TsrsNGW3kIxkjb7Y3/7t32IjGtbiQx/60Ac/+EFW5H8WeS3Md9G9SIGfFwU0bP288l7M96VIAcO7lSvl/+/r3/znj3/suoPDb2xtXtrWAecBr1Lx/Hxff/Tznw8v1U7L0zIyAnqtKSlLDbvtCLEnwz3QDk8wHlwROLCmY7wwXzqwrmKxvGVL+e//3nvkMQ4BjkSi+daW0LtvjFx9tdcivWULgJk/bC7cmV2efubp8dFRLxTp7V/Z2dM3U/LGc+5cQQf3+WWdkHRw/+DOzY+2tHfFOL/CCdKJeF3Cu+jcDc11MTit42e8WsEokbKYm+WIiB52pSpodcqqhamVpOYgFgwETENvb2/NE8dzRUH4ucAI1sIY0rXD2OOjjz5qd1aj1ARIhmuBZbF6d0Di50qWoguSAAwKxcmJibb2dolgxAPRA0rlQyP5bTv8Hdu97dsqW3c4u7aHRscSHNYJwvEQHXhzqVSkZ7mzqt9ZudJbucZZvSLe3xdp7wwiViYv+GJJRx5V2YFggOFWf9q8bglLYeg2CFLYs2QZgqMq/xJ4MJ+RKEZZHv+X2Z3/ESqFKj2vKF94Y300HUJNYdvNmae+zYnGseSy/PUfT9/70EOcfdrSyvbvyLqNZ5g9xuwi1iExyJARHyPzxE2flC0uyZFFQgwIgUUNGAb0ipMF/SKwJTr+E2PD05PjnX0bQcWUg0ULAuCA7O31TkdKYkzeaH8mf7QtxFUKQqeE5K6UiUJnUES1EdEFjnBKC1CAzD7LoUv71aenZ9INadtnTK+xb1RgE5/I1eYmErnIU+8ooXKidiYk4G3+n8Wx6htuoeyzB2++DARUYMLZm/z1QNeWlxKo+lSLrnBVTxtTueRYCDss805crXP+6x4qRCvBSMr5xpl+JB4PG110E00hFMFmqKxMSfSDt/xrF+CL5Ymm0OHr2v/pspYfdCWGoPCeuV7a9Jzmx6JOBjID96aDju/ue+ff7/m96Uq79IM1SqiepuQ2m1qSZKA386J046+w5jK00oOBIiDGdDxAldeQQdYH0SaxQmTQL0JgG82Q2JZbFcCTH8ClxnqcJpA8jb+ycQWeEfbqJb4qkQIpLmoFUX9VU9DfCiL1sA9cyGUwy2fQryxLCQmbu5HWgv6FSc3eb+UlG2yu1VIWmmWZzsBawWZhWrC05w4d2Ldj27aGdPKySy6IR9FDMoVUsWwr069tYeV17FWtzbHeP69nejkTENs3AMAW5FCSUykjH6Ups8jOIGCXJdSEUv4Hi2GWoFQaGZ7dOVAcGIjtfNbbtdvBHN3evYlMJkwHUx/zcpFw0NzsrVrlrVtfWbfOW7M6vm5NpKubbdDqXuoW9lK6hqb6YIFhyHVZoqU7LSwqqsJWadbQ35membn/vnuBbKtXr7nosstC0XihGDQjKjbNtDBiNZOX6g+0enbc3zsj/RUsEaajTksiqI9VDu/bs2fg2XKlwlrAGRs3MgGZplOLvOBrfhBEDyiHJIBdvmjDoS9w3XXXcbcEry1e68OrDm1q9NNLUpJGIPEXf/EXX/ziF9mNhUb0b/zGb1CG05vLCybUYsRFCpxGCiwC4NNIzF/4pGBeqcPtt9/+/3zuf96wbs0r/+MH69MNcWyA2LEdDpUNeDf+WuLGd7uNOhv25BW2qRHs5CFrQ//xGJX0SeTWW2/FxBGJMNPAK3CxRdakqgIQXZPBwMDcpz5Zv2VrGcvIpVIynuBVMZpwPvoR5+KLhqYmZqZnQNEoKhMaGMwCJxpouNFAY6HaSlCZoJECYQR4Ouc/tmXg4PhMJldAMovZlJFD+9JNLWecexH8AZJkwHBdNKhPuE1xJx1j25hMhphZ0DIkplRI5GZmnZlZtrM5kZjDBrY4522ebLqiPKwFIAG22kcnJ68qWCzCjrA0ULNibQnCHboNDAwgZIBWzNNmknbwYeMZjydvkZ+Sr+E2KSpCRTHAyB0np/IDexzO6ty+w30WCcMuf++++OxsBG6MkqAn7Hp5mmzZcndFb7ACVLwiWNEX71se7ux2E0nAg+HsxdiL+Tr+ms+RNwjVmYytejx1pE1fVF2Oz+u0+kAlNtb95x9NZ6adc94RXn15Uqy9IIo40fHB0n1fzmSGop1v3THnDvX3r6A3YvcblYGFpTDMqJh7fZzQE6Ra3Wns63AmIx9G+mfdJd/L5/Jbt2/Hpi4hIZGayQk1tHW0dSyBY+aZVoOdgXLqsyZ1C2MWZmrdhsuqetfCqgz85908fFVl1OXkM3z48P6hg+wSR1eTjGX1V6JjTTQmvDLEwX8YeZs5sU1RlNGR5selziBUZounB7aml52DM/PvTEEUzV4CRHKZ23zi1V951iqsAlTDiS3PlIJtowbzOe4Ze8oX7Cyiql72nG9ucKZbomGg2NEX6ZgEiGuKZ94uyLT6shaJQSUaZDinerzIAbFBV2L32vqtidDMRLFz6/QZ4xV01MnC5EJUW0olbJKstgEvakJa+dsq2M9l4Ucjf1LgWPQIzLresDaIbb94LBKLYfnYNARBTNrcNHDxoyIrC1vmmjanvi6gfFWAagpgGlLR8WWTABoIhK4+Bc0x58yuoL/Rm5seO7R/L6eRIUGU1WW0jiUHNuCW3bwSC+OW9rIEuSg8S2BriyL9clXCpM+vLZLxY1/qNpYIMZTAfsXnUmCx4V/6d2pkRzCKygTHBd7ALbI+96WT78xyg7pLPlcYPlQY2Ovu3u0PDHq7d1cGdvv79iVm5oa1Uub3avgN5epS4Z7lldWr3FUrQ2tW+2vWJfp7Q80tqP0bIb3pAMfkaDvCgoJY+hOKAYpiM8mynGqnElar2zswP8EggHGEIFMM9g+P333/Q/Vty/vXrGfdDWn/+T3sk8GQGDke+zUdk/NL55GR8/9n700A5Diq+/8+5p6dvU9ptdrVaUlYvg/5AHwB4TaGEMfEJCaAkxCuBMIR/vkBSQjkFyfhFwghkBASQgL8MGcMxmCDTyzftizrXB2rve+5p6e7/59XNTMa7a7WK2klS/y2terprq6uevWquqq+7716NZl1h1J+Y9Sqi+L8jIqRRgkrgKmIX0GqUHvFFVfMI02evzgVrhINfjJeg3u5ZlxD1oAOgNnO7Hau36L3Fd4eHjSramv+XBfwlCxYb8zC4K997WtMkD71qU+x2+VpPtouoFhLUZY4cAQH5GM+ImDp5v9JDtAMOICgf/Inf3LHHXd8/JOffN9bbjZ+fIf9pa8Ei1ltq8lkL3fRReE/eq+9epWsf3m+g7WpwDlMbecf0SvJkDs6XtBpdXyoYqzF8QODDQmi2OQpCEE7lOIpt14uW/j+dwOfuQ1cSmTEpVEFgBkhshdcGProR4dse1TtacRYQgqIq8kI3SmjC9N0nZ3MM0ufAj/m+MQE+s+s4+3c12/H6rdt31Pbsqxr3SY9IZOZtjCMlWZMEf2Y7SciRl3UqA+bEbyLFgtkAwh07/iRv/VRI53y2ZjnZVcFLrncbG9Vr1DoOYYrUqxwo5oJlcAZF5X4pSIISQKhGURZE0sgt0hze3t7tRk2OmHYe6IAWBEhOQm/mBPIlSJAfrlBDWGwYeehvuzOncmnn7F37PL2764dGnEPHoyk0jJjZhkh2+8YZiYaCbR3uN0r7Z6VXveawKqVRnd3uKsr0NSI7xsSr0z59dSc1WVIZ6jEm37zJubkELIQLs1g2qm8BZaO9+bv/9fpK3+3oaFLSqRpFpapI58u3vnZvowxZq/d96IXbaJxotVHZkEEQD7tcya1it8qUKUgt9zp1Eotig8AFQJ5ZbLirb2hsRHQG6upSdTVg5aBqVjPi5m00hI7WFCL4bT8iQ5Zh+v9WhV0RvmhgbdOXYkqZhKl7uU5CTzxxJPIjM47HwP7w12EeldaijCAdsOlfEG8UU5VXRBAuJRGfY3yW7qWHDRgTua9feOiABedo8RWhedSsVQnJ6+XHsqLHOqh/Ko7FcIsUxTpHOZU3gMAy5VvvHxrbtkEogOh48EV/tOr8e8jBeFNnThxxHC5fKtTZJG4ZCJvlR/IVemQUFGtG6NZRYncK8zPC6A+uCbpVVLSWXFWISpNVl6gcVdBUmZJoHRIHJ0t0bko/xOo2VLDNrbCGFaZkGNjfYPur4T30nDQ4ZfeVRUiGRJEipCkchce84kJwpWVuqLCRbGvZU4iyJFAickjBWxkze36Ju+61VakmNzfu++88zdLLE1XmWL1K2SXj3Jp+K0KlveOPCgJx+OPPw4GQ+p3/vnnQ8uRUc6kO8oCueq/98BDDyLaYzuArhVdBEoFiXBHOCBdqlNwx8bzfX1G7z5n3157zz5v/x6z96DRPxjMZVmJSvXRhNh6NxePWp0rvFVrxlqaiiu6Oy+9JLimJ8RSIJG9kpfUJEe+UMjlsomErA8ih1mcljjCa02B3FUfBGPlkWKfs3UbX5TOF1MZ3A/GMwWca3iTqRSfQSqVZjFwe2cP1vJ53x4cz6xC39xRe9FK7BCwDnCxaBcRSCAkBT3cZqtzeeGvFQf4UGCR1JJmRoVZPGWNEqMta3AIZNZBa9QNshLnaGVQrBUOM1LjZRrfn5h90fMzuoGl2cYPSWjFepyPspS/+oEUcO9E1mVvCzYf3rzMqmPKQW9WqkVdw0fL+RjCpfjqc/vwhz8MYeiiMY3GgkwYMWeLOYa0l6IuceC04MDhOcppQc4SEaecA/Rx5Am2/PM///MvfvGL3d3d3/zmN1/72tcyVHqvebXT2Z6982dm/4ART5gXXxi+9ir2mWHF1gLJ1IkvMDLybzr9GZHpagkEtoF4GWOgkwED1Eo0rcVlJrQavyA79hay4ktGd83ZnPhZDdhBq7fXLrrLulakkklE7DiXQtkL+iUas43qflzGudLYIT/sLcCZHCdH+lf1NC6POTVNrWakODhVyLnBohdIpVMH9u1ds25TIBRKFb3xsTwTZSaFIds5q86p6dvn/PXfRLdtK+TSbLwUPNjnPPpI4eaB0Jt+w29pwOXOnMOUpodMQa1wA1XwDG7MuK2mH1azaghUj20wHNMgvxKBC44ZqsUZqR3TrSRX4pdcqUP96puIHVq1utjeMdzaVvPa12FK27F+DXtI5oeGsnv2Gr27jT37/D17A1jT9fWFfnFf6O67Zaot/8w8q9ESte7yDmtFl9+1wlrZbazsotWNO87OkeGgYbLfIQAFDzq8IUL56oNarwRUX1fHOYXXlMcKe6/6cLMdEbKqOFWiMhi12l46khq2WletB/2iWiEaDYCzvq68cpjqUpHL5VQJH37KUvBgsHtlN+0BsVGxkOvp6pwjkaoX9EcqvYDqCmCgXKtJHxwW7a5SGoPf+MVpFjRiGk1cgc0ClQlnnbBom/sHh6Lu1PKuFa1xgXWiAeYtyqLiS1LqE5WqFp0wpKtsSYxLQBR3wgB1qIfKrbDEonloeBkMW3aT8hCs6VXU6+eSpqp+Ra+gY100SU4VSZdLHvAFjjvePRkrzSfm531zFZ7hhQqzcZoficBx3oC5agprT210KiEkOd5o7lwXzrFQVf0jkGTVC7xG/qBCHcalvCKHCoJpqJphVIkaeU9eVUTLi1AraYr+p9BV6wJfUewfmvRGs+zcJV2uSkZ9JXIjpZPy6jKXydB5QgUXtuE1hCmNH6G/SqUjPnYYUcKRGfL50MMr7ZF6Q5A4thqKWAknkihiJRkhs5QHgFfeFrkBjyCJx/yD5nJZPP/JZ/fZY8aLWhwGkN07d5MSAh10WYoiSV8dKqfyzeHfowSX3lH0IVGlJ6fzp71pvHH49RfoSupBHSV2zVkKqeqZ9CnW8nEY69asfXLrIzsfetjf3dsUDhkHDvj87d9vHOgz9u8F6AaSqaDnihJVpIdm0Q44DfX2WeudVaucVV1Gzypr1drgqq5AIjEwOk5Fwh9q7RC4t+i2F5y6aAzaKvnjcRHTIT0cHA6dSZ3cy2enqp8vnT2qacBpB6Brjoy5uw/5kwl8ZI7jwHH5qvWFopct+M9t2+cUWT/kF9zirkNPhsPxaE0iww58E1Pt0RV+U703Muxsf9YYGjbragMbNpmdHXhblhZUIW4uMl6QMMUxTdYcxPGUeQjGCJo2Rl5UuMidCSl9OLOI1u2E7h3kzDDNgYCSWNhnMY3BPE0bPFdVlEpCNS56WXZgQiM9kjbHMriyw0hFKqZ3olgT9OvCRktcJvPz1+YsiuYLkOKbJmImdkv64Q9/+KEPfQifLLfccgtqYe27lKfzvb/0bIkDpz0HlgDwaV9FJ5NAemRGys9//vN/+Zd/yTXn3/u938OiSedptraFXnKdf8FFbPyCt00rmjBrogI95j2Yl9C/EwU8RppAVq5J83m9KWh/mEdLm94WQIhwVKvFuMWtMen3D/R31iUcF98sBXSDxCEwwFomBgMW76CGYuPQXB6DJaStjFgASyIcw8xJEjI2nLWeHFEvD+9+7uw1a4PRmoERPzfkxUK4/mWHWH/n9qda2trblq2Ihe1wLpX81u3W1kegwwKrqRQCwJv//Ia7+Vyr4RLxQ3r0gwGSfZLh2PMCYJ0GxeHo7e3FPhAQBX8Q01YqkdknS6khnuPoeS7mE4ipJCeUMUwzvbEsF1/Z3d2hlT2GeY3MqSUY1z1OcXIyf/CQv3+fu7fX3re32Mu0b59/6FBo+w7WFIuPFlCxeCW1lkWibBLldSzfu36Nu7yzcXlnZOXKUOfyYEeH1VBH+2Cezp8uJ2eVA8QIPSpQMuSmLCyvkHnyLsyGZeylrCk64sMRxgjUsS685AI8oLFqnVs+HLRbeOpeFIJ0FgtMSkgst5DytVAoxJfENdwKA3WYTL5KByFUEYjdGty2e2Ukdc05y2JqUykV43A83iVEGoSc5dAfh8BjwawYRQMCNWCWW+KpCDwsvSVYAaStPikuVbhKUxLUiUuYTNrlqWTNnxzlVyRI3RttltcWG/y/6dRuUVXj4FU1UxVPRzHMaNGPTJOgYEEW7KK0HVxnDa4Ph4NuWHJQrFA5CC5UeamvjDzKrJSnhMkXQDrJvJmT3aBL031NjNLKEU0oo8Gf0+68fkNgVcgJO9iz++lQ5Inx4u3bvP60ck0lWFPSlCKpROW1qqN8i4xAOImfM/atTYSi/dkpPzeVaEQSI10BlSpc0iVQ/KJjF3qEcPVhCkHlCALSdRUQJtUh8VA68oI0aqCw0MI/KiftuM+N4D/f74qxOkLiiMhkkQ4I55O5+OKLOXOoGpbyLFLyx5UMa8hzuYH+fkS0peXiwj9hJTUgCn6ulGAHuYE0zYLjTk85/QPOoT77QH+hb1/g4MHgoYMX7jtoDI8E83kNdJV7NMszrWxtjb2s3bn4Ir9nlbuqJ9DdY/R0hzuXxRsacP2vGi3llyohn3wy6frjStBEZrigF9mN5lLlQtqP/FPVJyBKEyjvq0rmgeyznXOoSrYMFKlNzoFqH+8DGFRT7Whw016N1bG+L2UPTPrkOZWQ7Ew/EGpaGWMfbSczPdTbEHTXruuM19YGg3ZuevC81nDh4Qfzn/+n+L59pltEVJxvarPe8bbQS15qNmD7/YJWoi79sZ9126OFszCHjRseeOABxl9gsPZXop/CYX3QYonJki4mEoQQp7u7mykNNlnkTGSZvUiFUCO6QuUHV4hjWXcs7eOgxFE7fhOYLfoTOSOJN4RxY12T3RT1Qqw4cr2GKFMLngszde7HXqYj3iARDnxEA85RkKAm+e///m90wu9617uYY/DoiNhLN0scOKM4IFLnM4rgJWIXhwPUu+6L//iP/xjnFm9/+9vZDx3hJX30jAwqLWSBnR2dO0tZZiRCF09fv8AUZrx7tFsI4wBmo8RxfvqTwgc+woQoFovgwSsajsjYHwgUrrrKfs+7nxkafO65HaBfZk4A4IWTQfrAUXC1foWisSEwIZingueHhocA7fsO9sfqWp/ctms657QsX7Ouq3GTN3XolncE9+2NhMPsKRIRrzKyvg2npdnfekvkllvsuTwrkpcuKaZQ7Bu8fv16sKsOmZ9g6nHr1q0oxhmQGHpRtvDW/K8cjaWLEk5BQHG4eAGTs+US65MhBvE2YmMGe709hi7qjMFTgSCeMDdzMZNzJsYKB/vNg/sGfn5vdm9vanwiMD2RHxq2p5N4kQp4Zofptcr+TBaSD9RMPoYAncusZZ3F5e2B5Z3usuWh5csCbW12Q6MZj4Fi1KyA9DHpBALPyLwybajigcStul28S1iEYAjxP5NmKKkQQyCs0/bqJ5gbWdBcaava3uEEU3ve18kO9H7//fdTIpQGlRI974snHoGsSUS1G6pLrg8fUoNUoQ7kYiYS8xzjye9O7/g+OjUBG7NrW73JpNR0Yt7Bi0OTXQgDxLvV4SzKV7yr1LfElSBN1BGtzDR7J/zxjM5EzjpmVRz/upW531jjNQ/2ZX5xT353rxUMxc4517jsor5E+//Z6u6ZCkOlFEfeLqWgKYQqiCqTJXG4Yy7c3eA3sBOu6Y2OT6RTmbaW5kg0QjSN2IU5uqoUTBPxgyZK0pZ8ymVSmalEeSLISQQFErn0hoqvF3dgFI31dXtwclPwwIsvObz+ZYFNghaLMfCc1kCKptKJjDnwFQRmWKDHhOrXj3pNmYR5mruzYs1+Klzwp5PJ3Tt3bdy0KRwK0YkJqMymi2Oj/U8/XZPORUdHnEMDgf5D3sCAD+gdGvJSqXDRZbchmEh9Fk0LH2VOfV2oY1murWU6GHK7Ope99CpvRVe4qzPY0OhjGCXW5gxDM0fnGSTCEx2CgpfPv+ybSsLgPyMF4WRbKOTTmWRDfTP4OBSJ1jY2ZRwTBS9n1thjt6y2GZO1D2y9xm7nCMAJzLnyh3mK2H2wBnikH2r8Yi7oOzW1CfaXamWYb6zjK0pOju3f/WwiFj33nE318VA8ZNQFTWvXs9PvfV+k9wAu0dhMPob3B8Mq4FDj438WuPwKHGkssIXMKPJpcgvn0QAzcNMNAoaxauaA4TRmLHHo2BFDc0AtcTDRYhxE8UuRZ5Ra1aDYzuRdYyzjjqatqRyiB5GYIx/M5r3JvDedE9cPOB+pi7CJkZHKedN5f3WT3RhyL+gMIo6XbbFVjS8uc6CN4oCBv/CFL+APHEdZAOPZRVjcTJdSW+LAyePAkgb45PH2NE2ZXowD0PvBD34Qy5arr76a/d/QGc7oiCvUHy28EmHGBb3/hg0bCETjipEPQI5rAmdEO/Fb3fPWJGTBZOCCi/zXvir8058y92BOVyhiEJ3LNzfXvfqVuWBobHSM3RfpsmfD+/nJIAsRyqptirXRHfGB9wxgjG2NjQ27du4aGhpcvXpNZ214JD9ROLjVq9tgRaPGxGghl2WlUyAawZuO7J4JCIwGApMT4tWXOyFzZuYgRqz7yI4HuKCAWnJfg8soCVFTVPUGVBGiJmNyYsrVtWIl85iN69eFwvi9mZXuzHxO+j1SCQ6yAX0xD0MYATgHz1McnfecJOoxm9n94PDws9u2bdx0VvvFFxoXX9R8zcuHBgcvXNVN0UYPHZoaONSBHrhvwOjryxw86PfvZ3Wx3TfoDA6Ee3vRtIfYXxTjW8UHh427wmGvrq7Y0YH9gNHRbqIubmt3li2LdLTZLa1Wfb2ViCurfsmfGZ38V1fM//QyUfWAMM1zniqkdLyMpurQFeCgmyaEUEbrCjRbsM/X8osTryGoo5VynHhSC0wBGQeGfEzyjpcxC8zniGh8L7qkWo8qX786qPxSbcmtqlKBbDPxgx30z7uh1mvPPP4fRjwpwJL/pSR0OtSW6WeXTyeu/vk1LQ/WWKPTXvOe3OU7MlflvPpyXPk2VZvRKeg3JQSTRYUl5JrWVHD5Wssxy82s9HH75pqG/JvW+g3PPt73yf9tHexnEzMsVdLfuzN42fkr/+R9v31+91/dU0xZogdW5RH4JN2K0KzS18HkIyWVMqPKm84b9VExeaivTQBoRJan8idTIUmWjgqnSEU6FaFRpyI8UMwiF5WWRJZOixstSQIGy7sAOAkUEjgD6OiOXNc6kOYLdLb4RkhxU6hc2AEA1vYvz9t06ZARR5IqfbvGEgvLYWYskbupwopSlEM3EgkT1igGEagbkS4oDPbcdArnf/mx4dzAYBEXgM9umzCN8OBQYaA/dmgQMOTn81OISk0j4ntRw6QjYnfoQl2N39HhLe9yOpcV2ElhxXK7s9PsXBFrb0Uqyt71cd8Y37Onuak53lBPnvCWf5q8mXSX73EDj3C1fCcvcUjtlP+oJ+imUnBpsX84mcE+uehl8qY7OoZeN5xoaF+BIBHtInbLbGaO+tdgg0ABveImQNWwqmWGcLa3x7NalDZYdKOTKdb8kBVbJgSMCbZUa4/GOhsSYwMHA4UJLzzV1RGrKxzyHcuLREO1tc599zdOTeaj0iMJy1XDCWeT2f+5wzp7k90sy5rO3IMWTiO87LLLcFbC/nw4NEE6g5EaYx9fDRJ51cqkfAipdTFVe5OhvFxqWU6SzPvjWW80Y7Kmmg3h4a/jm2mBuOxPwZIIMxExOxJ8hBbbVfSnENAbbEHcGJct6PdMWYBlGufFKxC6M3iRsnzY5fRP9JekEHHcdttt6Es+8IEPXH/99aiFgcHM9ySbxcvoRAlden+JAwvjwOLDkoXluxTrheEAvS2qOXz64eOeqfY3vvEN9kAHaC1i50VS2qkyvT+F1ArAk1daGUaZs3S0xm69Nd/UaPzkp0YyKTJ19if47d8prj9rd9+BbD7PNnrd3d0UnwEJCjUSXnipWZQLaOF1DsY55pHoNllWp/QVuMt6VpYouy47GvcfOLBu44ZYVyer7tCyO04+GgmFI2hdZDTy2jtM2VVl7jFJ01M5Q2R9XZ2Xz7MfkbwSjoBpKq+66TS25lY6TeU119W1rF1th55nzfDJq4UZKbO3ByE0AKyywb0gPS0TmRFtxm2pOkR1WchkM3v29MbjCZTt9fXyx1OY7waDbl1D7YaN5sYXycRB1Qg/zGItrKmTqfzAoNl/qDA4hHG80d/n9PUH+weKQ4OBHbsCTzyJHSsuuFhrik8YZrt52/SCYbOmttjeFmhvNVpaHLyPtHV47c0G22ixBKC11UzU2jXMS5n2yyvMiUXXJnNjdZ5RBiHpaNVbigpm++Uvf4lcgD2cZqhn+Vg4Zid5+odQO9hEUKJTTCpgCeFaT0+PYnqpBWkajpyPqS9ujs+OID/XFdj3ysCKu/N1I6qGq8rA5DVx/i9eft1H60LPMeun/fDwkvgXx+o3/HDiz3dlXobnIWmHHDQH8f9WupMAQnwby3D1zdJScUxFd4X+GLlKKaLMUWlMcvZfsdpsmRod/N//YB48pNJTmmbfde5/NPm1b6195zsvXBG/d79AHSkHxaMNciG38nFIy+SaTElLvhWyKebAJkQSaUhabPw6AABAAElEQVSwpbWJWNxJIRUB6psC4pEG0VWqSOfkKYcqiwpTz8FAQEMuiSeJSPacIUcm8pKtmsvDArF4dszoSKBzz5h7Fk4Glf6KyPMfwqCqQ98KHXMdhCNwwdYA4xe+piuvvBKNYini3G8InTMiqFJImJhDqVIJ3MUSHxSfzhST0+7wcHF42BodMYZG8MQYGBoyh0fyw4PBQdHi2jlnv1t0pJ4l7T6RBpjRcKwT/xHrVhc6lvmxSHbdWSa7r+PLt7MrvLwjVpNgZydcANKd8E9JG8pUCUvlWLd2rQ7CbwXKQxSJMjwfhQ/EBP2qxgTXuWA7aLCrZ0TrHdvbPeFivZwDyjrsX0XVWl7r2eCi3Nhk3+i+FevORlSYL/rPjYpraZaYKkmIarcmnrp9ti4Khw1cN0cCfti2UDZCCa9jyRsJRDYv3xQP+lPDA8V8+pyzz5JttlQRCgPpaNRq7FmJuBPa4GWx6PiFvLF3r5Mv6IJQudi/YOuEu0qzdy+LlHSRz7izbqWcmVbh94s2ib8STBJoljgcaWhooHdavXo1ngiZc6jKUpWsPy4ZiCQBlL2TAnqNqayBUbNa8C/7veHwbyoPEnbhbW3YaK+T/iWd8/unRAkfDxvNcTMcMHP4HsuYgw77kFmxEK62rZ1jbFjpdzfa4nQe04GjN57jYDipMZp/73vfw1sqMJh9JX7/93//T//0T4/Jtu448l16ZYkDi86BJQC86Cw9TROkIwSHgHjx84xu9qMf/Si7nM+Yfy8u6XT9p2o2j0OWoL2qJ/yOd/hveIM7POpHwvHGJrxfMiCtFDLiFI2OGyaAhPfu3cvIJOZajY3AM1QNGg/PU3xsydiOGOEuFqoYisM3bMlIp6enBzwMFCZZzKEvvfRSkBtb+8Re/WqcPHmsdDLY+UHktYxrhbrmwKWX8FQymmuW1t3dzRNtAr2ia0UNhtwDfYX77vV37JQ57rpVgQsutZa1oShw8Z/x07tTX/+vSH9/rCbmrlsfeNP15kVbzMY6xkhJe1HHPNJb+FGZ4uhX9K0+z5OIbp9YkYEPsbOCn7QfLpCp62mEfhfOE7OUsvzIPJx7GeRDkWBTJNDU7OMBW8Xgicz1mWXgdDSPFH0qPzDkDgy6YOPhQXN4yMG7TP+AOzJs9x0wtm8LeYbSHvsuk1MWsqE9Ni2XbcCiNW5bS6C5yWhqctrbgqDihkYwc7CpCftqq7XFTtRYsRpFhIVVITMdFH0lNbGaH6v6gEy28/UeffSRdCa9ccNZHctw8ix77UKt/L1wVab5edxnagRhh/7YT00pKm2gQrMOOb7c2ZvXRSISEBQnCer2pdoV9z1NP2sIPiO4zwkWM7aF5WbQaQo+9RvNb/3W2Beezr5O1Z6cSq8rICppEEJTEFQiqTJ7DVtGTVErgYXSSj6kHfWdTc2me982Y9dewRjaakSi0IT95J13db7lzee1J54YQAlEcnJSqcpZoIfMjSshIqNxspnpgb0NK9f5hlqwzENyNLFKKbI1kZRSCCh9S0Ii6Acoq6ny+AIOZ1DCa0pmVA7mbfIjAaIJCpaDXCVBIYUd57JGy+NDfnMM9zwV0nS8uc/Yv2ifQLAGuQZnENS6deuqY5MQFa0KLmmy+87GTRufeebphx955PItWwLsGixEabLkPYkJTZpoFnGrteZeOuMmk97IiD8xWRgdCSAsY7XF8Ig9PGiNjxcZPkZGgsmMV8yHXZfa5m3SiCBhwx8a61hqatzGerunp9jRFmPv3JZWL5GYtsxlZ20KtDbRJ0xncpib8xpq2GF4Q/a2ee7ZdEoi/ND1pYpQXTK5ZoguB8lQtf3ZZwcGBxC8nnPuOcDP8iM4LAWk0eZdD/iaKQKWfEG5RQ+NruwcLlVQz9hTHMWG2Qdf6d2bWdmLwrBgeJkJf2TasKZVV4UogeYXMHB2HrZlpyKMkdlBmh6VfABQOKzAv1Is5EXpCINsr2WF2FRa/KFJIfoyFoJhtVNYqUzsmgPP3ILjTE6GKDw7QcdiFMeriQsjpd3zXFz9OSyHdj12JgyVW1C5jKf1b6WrYbTCxo1BikO8jhkGMwoAMO0W0YxYW1h2ZnLCymaM/iFW77LqFzEhu36RAuv7p/PuBBsA5Iw0DssQLDAWeFa64KIETuWoMj8SMONRn2X7jmiGzYlJ4Vs8YrTUybZhmbzPwmCU9nE0wDEf9AsSHk75yYIxmvRa6oLhlFcTMhIhjyo7vo7xaNVAapQO++drr70WVcpf/MVfsDAYDzI33XTT4mpTjkbAUvgSBxaFA0sAeFHYeFonovtrvBS+//3vv+eee2644YbPfOYzdNOL2yfOZgESQY7Z4Yseogoia6RSgeBkNBZetYoxpr6hyQqzg7EJXuWoZArcBUSxJoehC6zF0lkg1iWXXAKXODRPZnCGW7p1PcXHlJdFPgh3gamMc7zC4IfGW7S19fWs3rQDAbay8a572fT+vcbt342HInjkYtx36hqsd7/d7l4tM4C5jupMwX4hy3Z37nQ++zn78ccC4oLHciwjd/554Xe/C8fImTvudD/z6Rr21QzY4axpPP5EducO44MfCF51tRHXc7a58ji1YfAHoQCKweeVL2i6sJp++OGHUXowFUagDld5l11PMHGEIToOMwyOw+WQWXoFC0jwkcxV/soJskJGMGTX1AaXrZBITPSlsplyqEk/M2McDmUzucEhY3jYGRlzhgcDA4M4LGUqbA0NGaMTFte7dgQ8Dy8lqPzKszjR+eC2mmrwMbRua7fq6oymZqO12amtjbS1ufV1bmNzqEUQst/aiiRmz4EDyeFRpBurVq+FLjXlBT5IMWYeMttRh8RTBdNnwvSj2a/o+PM/1XGOcoYr+kl1azxK3FIwrzCX/fnPf85XhpvQ+SMv1lOaCt8gqfEZoksHL3HNsrrj88sCQgjm/MiImJJSfsF0ilDFeHNw+8s2XPDPE/83kflRrZcMGAmv5hXTjW8YjQSnXt3wwb78BVNuJ9WjGCevqtahGojJPuGoanShCZZ6wzufOCaSo5KJXLJIMoHTItSMaOOKLL1kBS+sBc3gEtwMTKfs5GTz8pWJsKNQCy1Q+hElZNEfgFrXDr4SQqQALtrnqN+A10KVjwJxePsfRfG2fHm7DSxR78kWRpoaaNcAWHWDkC2aXdLiB8ttoV0wj6QmedNoCdMvyyOC5JOSNIQCkieFfePGtrhxxUozgABQUSXJkII+64zLZ3oJDmmBigCuVWYqstCh4/GmhPCjTkZPd096aqp/f+/Oxx5d27PKy6YQgBqp6dzwaHBs3JqcdAaHreQUmltvfMKfHDeHR232CHAKYdCG4cviWuhFpiHp08faVl2t31Dnr1rtdrRnm1tstrNu78CVgNHc7LW2xNpao/E40Bx3UJS1XnZ/spKp5NSOHS1nn428Ffr9sbGgWpjDEFNfX0egLgvFwVsY2ZSKIiU54pAmIiXjkFZz9rnnpbe6uw8MDKecDS86BwtqluAWHCtfdAuu5YimXSoEG3vXE7dVGMHm6cmQ9YnS1Qc10bEh0dAHLRvNbRCPaJZV1xhuDre2NrLwFh9ppIF2Eq/gHkAJJBwL+JEQQNcA7qJOFPQk34Q0GEmqlKD8UFa6aGWsTuFKIVDupZLeczvzP7vTODSEA0zz3HMCWy4Jv+W3Cs9sj+7di5do4CIvYhicyWadjZsKgWAQuUzg9F0GLM1SgXaUB3Q42hMn0nAEtVQuYnRWf9ABIlUXFimvn7ziTU+Hd+7MfO8Hue3bfBb0X3Be9JWvKK5ZP1AM9U6wi68tTZDdJR1R9gJcs3m6IAPVbgM+KxhZXGM6Z4wUZVF9ImzU1wmHMzljGE/1yBPCZqvsc4ajMn8iawxMuYGgWRsxm+IsAbMPjPu94+7qxkBPrb+6UcjXFaSoW5wTCdLf4kTmxhtvRK1yyy23fPnLX/7bv/3bsndxoXbpWOLA6cyBJQB8OtfOItBGzweWwG/BP/zDP2BMhUf7q6++mp6LYxFSnzeJU5BFJX+U21jb7tmzBxE/6GLTxo0hRNnqmEFGjzoYgAGubOIHcyrgCk/RzKo1xELrqNXj2DUxvKHdoq/XIxwAGJUvyZIdgx821dg/My4yKQe5sWR3V69omKO33GJdtsV/7In8dLKwoiv04pfaa1Z5idiO7dtXdHaiN64QP+MCerp7uu2pCeffvhp+7BGZTqqJUZAJzy8fzv7zl6Lve685NFAbjYnvaxnaROkSRnvyH1+zNp9tx1ee9KqdQfFct/CHiRHTgrkezh0Gh1lBCnthDjx/6UtfCsM15/ULM6py7lQWEqoYJBoMvgJwAiaadsCMRGsamowNeGCWyQ5RYKya9jC199jnEVv0wvAwWzfm2QNkeIg17oFB0PKgMTFuDg0bY5Ms/DN7D9iuA0KOGK6I9BWYYlJDJebtgGvbHZbdzrrolpZcS5OPZXVbqxOvCbe2efhXb+kwa+JMuG0UJk2NdjgabKwX3AAqgBxmr9SzpIippdQ4aR5Z0fpOaFf/pd2oQkCGlFSXiKv5p0KYNiC5OKaKYyLIpJA2vxDeL0ocRFe0k0pS3HJNs6EJVQIXcEH9gvEslGY1fR6WqVJj7K8W8vNtduxQMSB6UH+0f/PBz59n/RTvVaAdEye72f+MDqeN1luG6u0D58W//rPUHwvwFF5LjSv+C3Tkil1zuZCKkWC5bKrRq4BLwZKmaiURajngBRvrcGSPghb4iP2IQEA7QFpuTdRIsHmb11YjSI3EdD6SnU5ZBQkRhpWaHEdBaBUL4oo7M5HNsx9cMForsshwNJrOZlOZTF0C6ZkcAlvlRwAOV0zIybOUvlDMY3WrIovgiLhIfTgkpm6C/EgxVXRJhrSgmSgp13x61O2qd7obgqAxuAdoluc8VdnyPulIdoZBv00we6Vt3/Z017JlcVwA4n5paMDNFcyxUS+bK46OeNNpe2TQTGfzQ/2s/gDNWqnUspHhztHRIAXDaXzRVV6UTfIjJ3A3S04xU3cVK1224Wlrsuubix2txfqGYGuL39rO/siB1jYLIWlDfay1Bd01vQFKWyFTiq6oFQKFG+UgZAHyaUqgPK8+fEyLCEe3OTw0hJ8IJQWWSLBSVbWkog8uWCEKcJXFt2rlrWycC8rVZ9d2l100kd23o3/s6fGdPevW2XYIuFvwLFCuI44CUSTiIakEdC16MoHziCXEDVI8KOAW/W3ERotrsfIGrR0CBhiOKjcUiMRsMxb0wkErGvRY1htGmErNlWnThS2fKebMchKRKp4h46ZEXjrt3nefe9vfRwb70WnS5Pyf3pPbckHkD94VfN8f5D74YTuNnyZhRSBkB7o3hl//upTvPn733SygqADIw1Q83xUiMIZyhF+U7vniHs9zxlVmBayBIhdmCwz3SGY1AEaLoO3I6DDlgxCBkfBPlc7AyNvZurX4mb+OjowxPYDx+ce3Td91T9Mn/qzxnPN3m/Zw1k/nzBS26Z4YmcfCVjzM3usmbreHUzQSEUbUR9jNkbaEBtiYyngolWsiRkcd0i4rk/dGky6QmVXVOMRqToin7qm8gRI4YHmEYA5FIx3JeD2NOPKfo/qOhx2z3qHgLKb793//dxYGY1S4ZcsWdhJhqyQahuLJrBeWApY4cNpwYAkAnzZVsdiE0AsDC2+//XZEdHTfn/jEJ9797neDSX7FeiUZSdVIzEDIyMSu9HoQnbOYOpBXMF7iLUZNBl0cEaEHZgxDkQWyZawCx/IUyX13dzdmzyBSJvdM8XnENczEIpqY6CrJi9d5ig6ZkRLwnM2yCVJqYnwyEo6sPO+i+MVb8HoVxNdlbS1mQ0wIctksg+g8tc1AXpuoLe7eZdz/oMxXRLUi82kuMHyyHnzIeFNfYPOLMl//T5uRkxWMavYPYYHefX7/oIFvFTXtnieL0/ARpaMKQDVoBlhDhb6dScYxwbBjKhRz8fLMtTQzKM91FadVWvKA/7Qv+Gmjasdf6mGxhW54fGPM5GQez4mFhE6hiPZpbNIeGfW5GBoOTU6ySbQ3MmpMTQVRJqeT7sh4eGTI37NbFiQDlVXlKg2ZSlJ0dyarBEE/OVEsR93mRj8SCwGYI1GvpdnFo0xLqxEJeU1NbrwmmIhbjY12MOQxgw8Gwg0NvuxOQflUkqKpkw9EJmWqUHraLmFzzYioBWRDnFXcqtNR4usYfC802lNgVFIhSM87udWm8nynXM9BduWFuS4UeqPefKdo1Rxkfa9wJdviD1wWTTWY9bvdlVunjHwdupbBp1+x3LhdshD8xtnK3dHkvGY62JJeE/7Fvck/osoUf0ugSNpDhcGa7xIgV2HxG6Qu5Zf41Iw0Rc7DuWDD2Zus9pbA4Ai0FMS5EaavIF4/cuklTqJhIGna0nD1oWtU7kn3cEJGMTvR7xcLpACdyfFBiAlFaqI19byYiMWmJifxWowbe94ihD8KrtcKSFKyClbRKtlwQSsiI2nhEr+sTySaylHiyJ/CR2w7DDVyY6FZFY1hGBCf8QfH3Z6E5+cKWYw4nbw5OuHmc+bERD45HUxnALdW3ikMD+GG2BoeKGRz7oG+KVIdG5Xtd4oFnBfjchB9OJMV0le6aJYqmBgkQwO+u4M4WKqtMbt6kB+xht9JxANsFN/YhEgm1MYa/rpiS1O0qcmKoy8LyjZ+MJESC4UCJNSllHbGcThIc0oeS5gKFxZVDoR0eDai+1IhMEq6ZREWmJhkI16hgXkIWUC2op4VPa1cCMotmgBgBgNAK4zHWoCFumKojEbXE1fMBTBw3ap8vvbQ2NihZ4Y6O7sUtdAg7OcTB9nGLAV0RZeLCleu2XVA1ZyUVVk1m5GAGwuInXM4CNayQ2pbA+XBjOaoC1QpTeVCF7G6oJVHR72g8G5fn/O5L8Tp9ALBrJvjfTwtRB/amlvx/fBbb/bf8pb8N75hT04X6M0uuDh0042BDetxsEGKeg2FbtYL/5YZ/nA3haj0BAGwzhcyGJ2ZHjCag+sgg8SRjPNUTwMY4slLlx//FBxlXhBF2hLfHQp5+WD6B1Of/1J4YDTPtR3GNAwpanhoIP9vX419rLunroXFvTkLGw2RzRQcYxqnmS7G57J1mWwT4Rs5VLtpE89YqOITIbO5Bmk7mNnoz9B6XUIaasQnXlY0wH5+2geGg3vba4wa2oFI6zBN9+rEKKHUasukLvIv9HO85CUvwYDrc5/73Mc//vFvfetbmBm++c1vPmzHsch5LiW3xIFF4MASAF4EJp5uSdDhcaCfRCCHyvf1r389jvtO5fQUhshk8vA/mVQxKRAJLVeLxC/KyOCHwgpH06SKI2t62wV2uGBXvWiHFzGHJilG33PPPXfz5s2suULWC8rlgFISZOTDqSPoF6EmWJchEOSMeRvqJqAaOI0xsqenhwkQ6WBqvnPnTl5MpVM/nZp+kdjFBZke1RaQ9LIvpmhPwOrkQhz0nHPzgx39+odDBTxiEIsBlRf9ICuvLCvC0qmhwWDHMqz1ZDOECkdJCPW3gGHCzqQDpmlyYTieugG9hOBI8wQnNKRZSXkOJs9uhbNDNFlHC1dPUc+q5xRBJtbMQexovKa1w9wgpSqt9pbNulIIWfAWrvAKnlVdprpFliAmp43RMS+TKQwNm+k0qxAxcXOG+g2cGw8PB7KOaJhz+WDfgLl9B3MzVilbBnsjC2gmXzV5ZV0iIAVsIp9XOiAWjX4i7tTVWYFQoAnwHBRg0NDMDC6C9Tj7RYUBz80YzIVqaqzGBhRGgXjCARF5Pk7Lg/kscprc5DjQIhwNm7b4XWMKJzBJZmXCVJnRldgi+znhraeruws3S5rhElExh4vyr3z1+nbGhYZvpYil2EQs21sSol6opCkB5UO/Vb7TeeqwYzhP5N2xYSM2VERdOL4h1L854GDm7hn97Zk3vPUdfXe+bbj3ZWONW5YNfY81lQp2UBDUdmb2oBVssWoD/UAVX/bnpIylwkmbkEiQAasqxMgVkZRQS5WLZzxXPSLcfmrI7V62vO4Pfnvs058LpVPCa6XTKvasaPrtNw/XND23j94DJEjKJCXvKbaILx2BXBJI5QSaO9dTEU4+Mz64v2kF60GwR2avclG60lAi0Vgqlcykshi1kIbQx/6t6Gxpmpj3i6pIUqIuA8qbOj13SF70QpkM27lioW1lksFMmlXG0empQDaF0jWUHAnli4FcMjI1jgVEZHo8kMtahUJwehRf0NHUdDI3zQCAJguhD5YsqGf5FxF1pQBaGIXxK54LQWxctLIpdmNDobEx1NrkRmJmS0syGg8DZaOxYlubF4+juQXNei1NoZraQEM9ckH59JQ3sr17dre0trF2Qhit/ksVlKtBGCSMJ0MujziEIPlkK3WorstReKJekKQ4JLJKSsoEmgXZ+oHJDFpcT9S5gnJRz5rJ2vVPjuMgWT4Lak6wrsBa8C2aW4G4WLpzDdAlkEdKLEpllmhT1euzMr29rbk+Ea6NyQ5WQVsW3IJg0fEil1MmtPBTXFUhW4mI0bIZYylvUNwjEU19p8JgRbimWqdPEUQQUM6t/Px4fxVHfHxfu48/FhroY2RCm0liHoOXcNW07v658aY3hG66ybjqatwxWPG4wTKlpgYzGOqIxduuaWV0g1OYXDGSMs7q7+Jo5AhPjzwqIfO/WP2SfoX4yFvpxxiAGOUZmhmpoYFN+3jEEA9h0APW5aL0OnVTRQDtAenGVM5N5szxvJctGOcgjdyz19+1mybBchtaBd1wfU3U8vLu1ofM/gMrXlTTmigMt9Tdu98byeK8ymS1Al8gPp8zBX8yixTDqAlbLQnpRbJFk82Qcik/FLBqwkYbLqUN1gCb2EI7KIqDfj2LixusKAJy1TKjAa+F7yZm1Eb49stddXXJT8I1vEK/wpzzTW96E0qXm2+++Stf+crf//3fa4+YC6+Uk0DaUpJLHJibA5Vuce7HS6FnHAfol8FXIF58EtCD46zvla98JaU4lR2QjA3M0dMZ9nthdSXQzU/UWrIXq4wbpWnJiXEWJAn0xewZtS2mj6DQsgB+QenqzZkY9hAT4GcFZS/84QB0gXJJkBB6c7S+KCQBwORCdt3d3eTCDlLkSxk3btwIEmbA5lof5A1+Iw4jKItXC/kCjrZILRqJAZIxoCICMbnW2aGvnhvm8ZhVZMhwPS9fyKM0JiCADpn5BANaoh71QTQUhgmwmd2GSRZ1Qy5RH25vE8nvaXvoSUtlAqamETBk7969TC80f/CZSQjymhMvBPpkrNxhMtw78dRmpHBkmjr9cjZyV77GpNZxHn54KyYDjY1oLpuoSZaCEiWEXpedmWSbMEO8tKm2AYcovppdyykmk0gmznim9fPDw24eDycjxVzemxhzkqlAMo3bHr8AZh7F4294ZMxIZ4xUpjg1YTlOcHDQ27MHtMxcNCaTUJCSzLHACpCn5vuCQHgArJ60TCatshAQA0rDOhiN2Vj6hcO18ahd3+AFcYwTYG2kG6VZGixxtOvqPTRpgUA6lV6RzrSuWTP9zLMBnJbHYjJLDASt2loXj9YkjxITCwhkPbjWIV8YA++AXlxAhNAiOUpphW8Sg2thgcQmhgqTJ+q55qsEyj3NZu4vSD2f/yTZqNRCfbK2u3dLdKJLPh4hAILQmyaGX3Lj27f94vd23/v2TLgp5gyXaQTPGEF8+gDvvIiHHk9TJSmSpyZOMpeEKAX/Dh8KHQv7Vai8SHxZwbtn3H6iIXb+lde0NbVNfe8H9u5emVGee27Da38tubLnoUNuAXsArPWFWS44WIEfyQGkyp+qS4OuKuilrXzGySYHUgd7xorhvGOjRyV+IRtOTvqZ1Pj4WI3pt6Jfpl3hWD09Bd4mzWAuE0pO2/jsZWOb1KSdz2J2H8gkrWwamIo6FxEbISRFo5LCCe4WdklN6SpDMSvlIxSJjOWFwm5zg9uUsOuX+/FadkUrNLfkQ0HWn3osM21sdOLxUE1CDP6xXW9uMsOR2pamGtYj4PNJLWqWbKSIcoD4jvzoVGjl5PuTExNsQBPffxCn0HTg5SdCj6JJAsRnt66s8mPu1Renc9GqbapHtpfDwFjALZBVDI/FoZRTZLsg8Sml1LMCXPmw+KMh4c1OwDAfkeBhkC1PQ3Jm/YTYKlNDHBAiiZdJKlHDGmkgK1tGBQJeCIEUKFcgruhyYSdt0mpK8F2GA142OZ2Zmly3pgtdLkpdooWDNmBYJXi4bEcyqlL6crZS9upAuV+UAw/+5vBYMV8oKFEvaeZkCwMq07Kwukplze4aY+3qoOIYwRChSdVfMeOsjJuFAqiJFVs80k/npI1RmHFWeGqaSKiJgwlYT0/PnJEJ1LWsL1ivBNblrGcCiLzJl0eM9UwA9DIonQ5C6hlyavadouEjrcANFSbHyZwxWUBbKyJIxByUmyX8HntCjE1m01lGcGolErQTQYOv0hsd89H89g3gaHHyf+5o/80bf62n+95+bzBljwqfDPZMbsTjdkwEInjDYspAc8G3c13UwLCeRpjC91VGxg8S7KgFJCPmsImD/UVN0GU/pKYYgXTNCDZpOKf6oC7QnH/9619nSfB73vMeLOzYbpNDLyg71dQs5bfEgXk5sASA52XPGfVQhlfff+CBB/BKD0h73/ve99GPfhTF5jxDyMkonxDhFYvsPXPPL4xf3GONTnhIea+83L7qant552EZ6vHmTfKMW0888QQrc5gf4nEBT0vHVEYiMz0iHa0EBq9WzZZKZDEe60DiMCjSj4OEGVzhJ+MukXSHjrIXv2KAXoQOGjkzI8dAWuNzyONdraAmU4ZVMmWDX2CzNveakxuMl+idze6uQlent+1ZlzkXi7WiMSa8rAIuoGrr6S48i8tihkgODCUF0qBbsX7tWpPdFGdO7yTSC3hQZHLnP79qlqxmyyoEsnmKzhzhAmICbXJGZF2bx1Sn8xRwsdKZJ4sjHh05saQlMLVCq4CcBfQ7T0z9SM0IuZRfmfto1Y0t1pWRrpXcm2vXVBKBpXCTaU6JvZI1aJJQgA3Xohn00qnc5JS4f52YKGSzFhPS0VH0z8HpCWMSvVzRH08WkpOo5tqZoU4np5MprBeiTp6qiU+njKFBw9trM88X/OOjuwNFK/xD5UkmK0yjUxFK5WosBA3qCfRzIQVxTANTSDF+4AZ4E486sRgrorm1WZ8KTtaOhVQ5QNpmLF6Mx9DC8IqUyLKC4ZBgpGjYDwJsJGVpULZZGwyDCpPhkBsOsT1LhTM8JpooP4Mhlj+yoTZzWzKTOKANoUp4KYnkzeWPO+muQDRvmrtKofxAuxuuM3qSL3rxbcE7nw6YdfHcLlGaCc/94honspoymc5A4sJdD5AZ+72YrmOJ+aNjFV025bKKnuUWyEyqQQgGwvCqCx6ynLxdyMlZuCr/oAQxRyif8td3hF7zirZbblKSC5aSm+7YiP3pT23ZPXC5ItqmQ8hn+LNxMi9ujjw7h242K/aRwhkwKvDcyxnmkO+uMK2ouiV5QpUERAClADH9QSqWkb3DEkrCBWOyYhY2IV1IFNgiO1YXiC9jQzbDslm1nsNkAKbi2a6BlYWWUVfv1Nb59JZNTX40FoiE2CqU20h9g6j4pNDCG+Gm1L6Us/xfKkhlLiehpfSAfo6YpdpREWQiz72KJhBSRZQClELVI071DfXrz1oPFnrssUdZk6Kbm3qVN+gsFVLlqqR3LeFbMCroVAFddRbQK0bIdK8K32OiLMvvuUVbq84K0+pE8ACnVLhKQkUhYKwScJRIklqnLKB5WIWSNoiNCA1QluayUFPwLVgXvR0RlGwTfS+OqVzwC+KmCBpd8DC7EIFybWKSkn///dvcsfF0eGr15s1WlWtoleFhfpZZspi/KEgBnBUD4LmTDtpeU10AaxCB+3ildhAkS51bRpFF3XHWscMTZQo21/sMu2yr89hjjzHIIr7EJmse4MTwSu8KSYzRXJPwDCG4Hnp0PjylE2agwbwZSSuibcIJxOsE71Io/DYzgpexrrQWIVvJH7lAQ09flXE88Odk3kwW2EgZww8+bqrcwutYhhDHx58zWyjz8uZWs6mtxY5E6QT4GGtYlVLMFvuHTISVkUi0pSk7OuT89zeH9h9o+9M/XdO4LOkYDUiYDCvjGMNpIZw9DeMhEx/OSCfTBOJW3EdK4iUiVmdIdjwC9fIpBEyXjb6bomZTzKKBEaIavBROF/zUn6WGDeO6666jHj/96U+zUTCbj2AajY00j/TTU0/VUo5LHJjNgSUAPJsnZ2QIXSbj00c+8hEc8bEZD1sjYhL8gvQ19N3OwFDxn78U/OGPg6hGmHYcPOg+8VTuuV3RW281xNPJCR143HnqqacY9tBvY7HM6HXcyTHcHmHXVJUQhk8cBIC0GR2BrJzJC0MptM1cI6vGCpoQUsAJFmMq/Jcx3/O4hfk8QsbMoYdn8DOpaWCMoBowXJXbEZdq4DL9hgbvLb+V/sTH8ekYZ/sEVHM+V/HAb7/Nam+lzLnLLwvcf3+giIkci0WDxnXXhm64geWgpKVGwSPSfKFvfKOA4HrcGxv3M1mroc5ragzUNzBhBxwirEF8APpFRwqd4GHOaICZjhwf2TBZuwgmQWqEiRQfAuKM8uTm+FI9nrfInUWqGD/TlqotwQjnIEWZDqjpgk69fF2Zu1QuSrnrtlEhpfKqhOu4pEpoFRS0Q41xsErVoQyz5V5oUFO8uEAKLiWNnbt2x4KhzpWdEiS6Lf5jhJfL4wAGFM3f1BRAmnex2XYmp9B1ECeQmvKnkwA/omP34WDaLdpFD1tZiw202f8Tk1qwQjpdwDCER0wbXYfNZsz0uOehMBNUA3JDTSZkCbYV6MQEEx9QLIsmgka3QhWPBJrMdWgmVOCTQpbE0++qkvC25KaniaAydmp5iVhcK3CoQ8sJpy8Kml8O+IHC6t/5We69xeghtQ+waadXmjV/GLICbEMTaP5fD17/yKtk81jhnkK76nWVyxFXElLKWVGpYJwUR4jh4FJqAARV+MLfuZddJl1lwS3u3G5sfSySz0ZEFShPVV2J5ACbGjyouYEIFpAekrZY3K+Jga6kZbHuora2PRZ18GwcCITpfDB9F3NIy6ytzXMNeMLVfF2dH48jZQjEojgwx0heAmsTBruzqoZJdqpRSMNUpAr3RAMNuyTs+Xoa3tGFK//KXfm/FLp8qDB1EmhOHHUu/0gIbULO/Je6VzeyoRRROKNmc2R9idHYuTo0lNp+aCz/5N7lK7uVdbHgVVRnLEChnSEN4H1RY8uLatktjU6QLapd2ZMMPMy1nOVaGS0LltPF11RJ/ppKrrgQoYoCtLhCwv8QkhYwrcK3IF65hldULAJMkJ9g4ABmzMAVdHeCb8GMAnF5iwFJCl6C0CKDKHFC5aZKf/FFF7PH7IEDBwsF2UhcvcHTUgTin7xDj2WMfXNmoWm1wpHQOec6ja2hiTHAqZKriGEuPC9sudRIyEJ0aThHOXiElBCkxBCP9w18y4OBjybgxlaIZBh5GUHAsXwBOlVqizEXuAvW5WAUuOKKK0iZUWbv3r1wjLEYeStWzQwx+i1WM1XbHNEwaFm0K1T9Kfwzg3jzRqpQdNheF7sGzBw8cd0M4s0UzDTKXpcKwMDFt/FlxQIK0x/Mmo3d3cbZm42nnhKVPk1patrCfofXr7oOf33OLx6oCcULDz+Sfejetpe/ZmrSdwN1IduPhkWFSxJsbYUhNM2VplUbMpprTSyisaFRvPRrgsXGKM6izbqwtBnhqjQyfZR/S7cvzA9EIbzAG9av//qvv+td73rZy16GUfSnPvUpfKYoel8YqpZyXeJANQeYlsiQsnScuRygBjm+853vYHCCTS+Wz29729vK4+KpLpaQkskVfvR9+1N/w+Y9MiyUJis+U2Dvj94ffu3r2RUQso61EyRh/RYmx6AmVtUCREnkWNOpcEQnqG+PlghxAMAgbQBwb28vhlKYTAN9CX/yySfZfIWt8IBVGGsxBoJ4GVlBxQy6vII0+u6772Z4BiEzvtLvM8SSDgC+p6engu5mZ60Jm5qestNJe/tz/ve/b+1EM2X569bar3td4Lxz8LyJcxljYMjZ/qyxu1fUxRvPCvAU1y/IhfVk6DQYBHVBqDY/mSk+/Xjxa18LPP4M9mHF+oR3zdWRG94w3diwdevjuJcC/TIRwe8I1cGoyVnvn1SprGO6QLpP7cx4hXlVd3f3bG7PiLaItxSftoooihK9+MUvpnlUcmeqDoW6XVUCFzHrBSYls/rSzF43GrnbvXtXKBRe2bWSRLhlKiZ6Sx2TRqXalXzU6oo5ItOucrBkS3WrKNUkVAJUdpKajigNhMTljiB+C3k3m3NyeeZ96p5pYN7CKxIm30WlsdHpo63LZ2SzsXzBY4WFvClJQCRKVBSkrD5VfnLzIHYFtnUGZcJ0dMKgRKF0w0Nrw7UGOqX0cqnU8PhQy+8O1nc8KJPhZKh4t2H0u/7yQOilhl+Lapf8X+f8+BLy15qgSjklbehhSor+Waxaw+wmjUIb/aqQWTrQhQfsSBjnauxbjtG4DoY6NjW3IjEvEvGR7gl6QGdmBonJpxEkRHGB0mg+VhqQ1IRG+pIS6SjQqrOrlJnb0rUqp1CjYvKGjqmpKJ3Lj8q/5bj6vpLzEe/MulEyFpW4zoGX1VGpOO64pjYUFFWqV/SrIkyUXW1pC6yYFdUr6lZ0syhjWWErMFXH4Uy6UvkkjBzCKbpPP7M9l8v3rF1bE0/gJJmlx6BZhXKVlTK+pgTfCnLmFUWOpoxE9J1qS2jXhfEK35YBrYa1nFl0jItv0CzwThhPBw2+Jb5aoIs2PYz7ZczoNcq11apdPJqxalcUd3KoMydy1AEq9IiTPJrBZNjEWPPII48w0CBFvfjii6s7liPeXowb+UTLB+pTgGVlZ+YZhOlYQt7UZPEndxqf/Xwglcznc6FoBCSf3bghxC59GzaYgecXWOtMMZV65plnEB2iEz5aXmTKaIsdFmAYbhANcSeKRz2UkA4hDMRXXXUVnTADN7ZjjNfYN1Dzpe9H17+qBuYrwNp0wZ8G7uZle162lUKET1xaXd7xs47sOcSZFb9Exg6ZBqBrn5oqEJn2x7YCltkWL75suRt69rHUn98W7z+QoKs4dNDM5LLnnhf7q7/0QqGh937EGhthdUjo115h/dF770/XjRRww2myfhirbijEFro25GPhzDJeEYuh/rW9OtH0Gg0Riy2C1dcNA0qNZ04WlavuhfylFhjv/u3f/u1DH/oQozz7JL3xjW+kgKctwS8ks5byPrUcWALAp5bfi50bnQuCUhwPfPvb38bZFS4HKnaki53VgtKDHn9kNPeZT0fv/jk9dOlAlO6yLChvXHNd+GMfsVpYfSYDe/nx4V8UdySg1kke8ZQOFPwJemT0IjYzABA+F3Mmcji5E76CGMTejJ3oD6GN4ZMcgd/kjmdIhl6kmxADxCUrzlBIHL02iXkJrxAHT10IoXkFDIzKuqenh8iPPvooifMKmJmDLHSJiKy3cikCFH3cnxr+5BQ+OnjFR0tT32CGBUfxLgd7hPp5vGAY6DdZvkyck80Qslj4IRRCtuMWn3is+P/9mTU4QKViFM4cgY1F05deEXjvu1KJGvTrYnFpGAgXOGu5PhfHXRbypRZIAeYDQbFI55pp0EmdKZLFjAMyaLR480L/gMKhujg8qghWqsNnpHDqb3WVkS9U6Wuwgsf2TiOj7viYFYl4rc12UzMXVO3Q4CCzcCbE2p7/1FP7PDnqqbvuSA5P48svCdghtGqCX55KEggDDvUdevSxR845Z9WywF9Z09+2rbyaKku3pbeCdhK/Hlr2Wfb6lRqcnb7OR+eursmOzgSp2aZNmyraqjI1z/crFD1fnGN5DkIANqDNQwpzslsgBR/PFlMskkRPKzp+0a8ibFD6VcGiQFwABtN+kSwy2Yeb4hVZ6gaMyluAWOLoa9SzXBBfziy4FeNkUjaK2BuoxGEDS1CRM4KIGhqagA5wrqQ2FxapW1bbsmZEzmKcLFtQKVUtCBadLWcFccXLFNfULkIIZuwY2+CkV2LaKG8BJESWpfGo+FiLS1KES14qizLE0txd1MpTI6CWwOI1EB3pyatBKgRVKv0YjGMU5laPU0wz5lQFE0EqYWLKferp4o/vMPcfNCNh77zzQtdda3WvNMPi8lgqYdYhNa+OSll01oyG2rCZFssoSRQioMtlbIUw5MtIpbmmC0IYTUwCH3roIQZWDnpdDt7iFY5KnqRMZlQQYpFswUsV0O4a0wUD71OsTkDsQSPEHKBQ9IG7OUCvctmNPIQGwzoAkWJJM0Iog88zrrFEF92vNBX+aN4ihTHXNjiXtbix/v2Fe+4JPvdscWLKPHtT9Oqr0OkO3fZZa+vjpAAzg9debX/kQ3dP1R6YZiF/IZeabmpqiIet1rgIunDjnGBDo6i4ucK3M6SJHYgU5XBxKuU6nS9gOZ5H3//+93/zm9+kpj772c+iD6iulNOZ+CXaflU5sGQCfabWLB0K0OurX/3qBz7wAXp/Vllcf/31qp9/IXtGCGCbe2tiSonjq8Ychk/mFpPjMvdRx5wzOiaIjLLVSyUpJuiRwZ5BDpXvRRddRBZHTh+JQppKLcIATemVGkR+j8j/eCqaFDCUIgNeZjqlCUOvSyCzWG1ABWE9SqOLv6W9e/dSKcA5HnGGTmKyuEjYovZY0oMxZaSkgDSEFzpxBkJcaoHQMP0iNWalmEmHlbDcbG0V0vU0jmGvXL1SOHZWKCuOJIqM3JXn8tILfIhjGN+Ymih85/bYyCgGo4o4KQKTz/AvH/CfemnzK1/JQk2KovkAwYtSa1pQovUAcPLE0zwOTpJpj6pKpCcVApixIeMgNcpLA8CinmumkloEMGcuxGfCx3yuksic0RYlsDoL+VCZ4u3fX/j2t4r/8+PgxKQZjXhnrS/efFP4vAvNugTTYujH/GFRsl78RMpfiqRcfV3OSRVWPiPVgajQEuxXsWUuC+qpDS3/12Ltq4rjXzJyGOdnfTNqRDZbdW8P116P2WOpj5kr/XI+h3+pdPqBw/cLv1pY+gtPjy4IDRutka5m4W8db0zZ13TnBBbuYikOTEX7CpyQMwyRM17RFQxWQJc+g44cZCv9rkShJqT80gtzJT+i2uOeB8oTmIoBnED7qiBrTSTUtKGbHW5BuajpBN8qvRwdDdo5YK1gGJWGqG0F+irNrSzQlX1owLHK2bIvi3UlBHCLxbk0IkWKImTuJqWpJJbusHVvpuML9Yt1MF6Ae0F3LLchTXjEufrjnScjHVlH4DXh7LxvkhddKLHoTmk2XJOCBqWVXAgBeRIIXhXX1A0N1pYt9tkbzFxevqFYwkpE0ZhW4s++IIUdO3bQGilU5SmiYcZTWimrSBCPIl9mMIUeQrAD5xUIp3dFCkBMaONFev5rr72WC1U0zXlpMkpUIltPpQtessDZTzniuYp1+rpNsfCb/YSyeHcXqbJsRsVwRTWCePWZZkM6DO/YspG+bXnpsaHM9FRbZxfmG54bVAs5kPD6YQuqvIGMfc+Qval9deub22u8fGg6ibf/bO/+8S9+2dq+KxCNk4qsaFjd7YVDiH7weLD/wAh7lA1Nps5a2XppZ7QxgtkzYzwRD3//6lM4fFvh1Wl+QU1h1fW1r33txhtvxFaR1os59O/+7u9Sm/O2vtO8WEvkndkcmK9LOrNL9qtLPf0+hcMeCWdXd91111vf+la2XNOI63QoNF5nCmgVnnyCThpStbcJ8S+LERFbWVg+WwAxXFV34bpE1cTrEM5MsrVCFZEzZs/VcfS10hiwQFb0CC4DVz7NKIfLFjMi0uITPDQZGJazyqinp4dbDib92js0aBbQCwBGDwb/AepYiOk4XNOtA130RIFrLgDDmh5ur7nmGlCNlmHrM/NRPYdg1SjKGRAR6cMoRnSQP/Cbd0lcZlZqKoiF5ezSVXN19tNTHaI2RsQJk/X0NkjXcxIxIMfnLo7HcGv87HPGVVcZ2H+qgwFycSlkcIWHwjQ1a1jcxOdJjRxx9E1xIEDr8yuR0VdgpMctJHHNwQUNYx4AjKyEBobtQCWRU3PBfM8bHMz/65ciP/qxncePlRFyLP/pJ/Of7HP+14ftCy9hGgp015PjU0PSScql+lOqNBXpvmTpopQ7kHizn3gTxhi+mzKsWstkE10c2cqxEJJoDxgjcKYnIT5809IxLaZZSAqLHgeZKXCCdkWfI73xwgpyvGSINXjvBLYgZCPTeXoxfXCr0ARqNzXHp8dQF6BbkCfAldkxwCMgvsnF8y1gigvOwA85EwhEEcgldagQimwdxbX4l7I82y+Ojoys6OxQtsra9RTelcVuWcFaFLy8DjVCkPpRnavczWOiKY+PelQeVi5U4keNf7wPaEIsfNVvgw/pZ+hGFlKPMnvI533cxSOQwD9cPCZS1KM3AJqKtsrRJtAVCx2SoUnTnzMakjsqd4R0GpALVaGg3dii+3zu5iGMRCQ+W7ixWVwqRYLamJm+EWMrPhmNbIlAp8rAwTQAkMyZdkuOM+x6yEgLTzA0wH4e31R4kMoI4sV6Wbwoy2IiEdMLxMWYGbibKYqyF7t6WWogzUCE6aRDe4MyvNQRLjbT0hqlCem1Cq6HxwO8wBcO7dnV2rmipp7lzSJUIdmkAzfp+41B1x/OeHi8fElXIj4+OfCxT4V794Xy2SI7GsquXa7T0Ra6+sXDRmQohcMzc3NP++SwPzE+MLFvKNPY3r1mJfIa3TgVhzgdblXlkDPmF5ZyvOY1r2FBEN5q/vAP/5Dtgv/xH/9Ri1B5dMaUZInQXxUOLAHgM6wm9ZDzpS996cMf/jCzzx/84Ad4Fzid+g4fGaz94svzP70rhOcb3KUWxd6NsSMfDkZf/JJiKITFUs2RAJgpPvCPmtBDnV7AyfwMoS/wksEVuS9wQrrPql4SVqAoYCz32IhoYNCfmrQA2M0teHnxhoZFCI03FzXfIuXqzpVRjdvqpOZpBAzDDO2Ugiks0JdMtesRRn30t5dffjnzA9AvqW3YsIF0GLBxQ4rnZ6AX17w+O3GdNeXioBKJdscdd2C4hWkQKm4AErNSfWDBi+EQWWgA/OCDD0IJkIOpsz64rtaHL7BQs0k6SSFiDQjQTSa9HDs5YbLtZhUGaMQJFuoV12HKoLOGckq0uGQwI+RY3DSfNzVaCA2GqRsmXsi5Z8RHxUHDJg7qDpa36WqtrsFKfOJUrvVFJeQU1bLrONu3Be/8qYIoQo0Q4JuRqbHMN79jrFzd2trCnqsziPzVu6Xdlnxymc1mQHsAKqkcF1hY+LZ3717duVF3XHNmEv/CKs9pn/Qt2iR7gQU57miJsN8Uwf4Z1Kahhai+ABtgV0AFk3w5i2JWDgU9tYNdAoG+AkVYcyuRLSMoulwW36prXE+BgXE+JMiEELFDJn55na39zNNPFw70xurP7u7uJmVNv/6u5LbyifFEQtUTFa0S+biLPONFhKEcVPoipqyTIll6G9oYYwd9C/nOmQURBNwjih4cdB59zHjkl+Z0xuvsCFx5hbX+LKuWIWyOFytJydvqqIToWwY44HdFPFcdrfpaR66EVJIC9CLjRiQE9EXSDaBlRHv5y1/OBb0i6l+srgC6HLFYdMeO5/bt752cnGBV8KpVPWoYhyqRCANs0e5mlWMqMWnmuoDAhTXhUumcgbg5xOM4UC/62SI7NgNp+dPzA6TotBojIN2bOD/jHf7w566bKLNksYsG3Com4PEul5yUfb6KGQaxycnxseFQEb9z8SjNEntolOAcYGz0yZhp7XOMxlF3c11L01veOPWFf7H6h9lo3YHm1Wvq3/MOb2X3aM5a10pKQk5X/TI3Gz20d+eOZ7eND/ZddOGFjP6aXb8aZ9oP8yWcQrMS+NZbb2U29clPfhJdDtU9o2n9apR3qRSnMweWAPDpXDtH0KYHD0aLd77znWy98453vAN/V4wTp1mvYbJ3ZXDz+YW3/U7hX74SyufBgaxjTbMA88YbgxddYtY3rqhrkEGp6kCsywhHAFCQYgJ6eYsDhSo7DGmPyjydXVLMo52HHyr+1V9bD91vFZwCW0q+/nWR97zX7u7y0K1hrFUsuNlCIBJmaGPNmUyRQiG9XLYq/7kvoYSBGTgKPUxcmLsBV+imGZA4Qx4yXmD57PFJ1xTU8mj20xmZUcZ7772XLIBqwGlYUa1SA2bDE3AyqekJNDMG5AUkonPBt7DeyRC9MdMFMKTOtCIXn820GQSc7Fs/GjHXnxVgDx72M85lcXkVjbJVrOxm6vaswv3PHIrsRaLp1JedSkFygR079ajrZUZRqBcOokEbcWhFRyMSSYeWCmEpQM3qBdJ876wkn5HmybiFQi9byD31TDiZYrcfNY0UG0iaXSgcMvDNlkmxx3XHMe5AdjJIPflp6iqa0WkdQ7a8j94MljLLR+mK4o7OhNo/hiQWOyokIX+h+SGsQXIn6qqTeXTUWGe3eoAQ/HcrmIqmV8zH1eJbAR9odAlXZyuowLCGsjZaXqUTVtQJBquqBi55tSrgcBEkEIaz/h8xJf4awFGVsfLwC9XvSujhJ4dTWqQr9ghAmlkBiouUqiSDDTAoArdYDz/8MBeYC82dOIXFBL3vYOHL/xr40R1GoUAPE45ECj/8kfUHtwaveZlZL54s5jm05G5GBOTCNCHGHYYqrumsiMCoDVUqprCUR/SKDGS0f8YvLoiGYypaHdf33XcfgQypDHOc+TSQIFMKAtlKp7p+m5qan922bc++/T+794Etl1/pWUGUuih4+cOYmeXlCtNiWm/gPwOgS2AeJOz6ebFnFqmIKGkhqFzvus4F1Qr2NMTflTyVfxIPW30QMZhYLOH50TIAM58rjh/qy6RGi04ByUs6yfZyyWI+m2hsTTQtUyuFRZpDJmHZdVrMSJ4YtILtNRtfcm3LprPzTz/NGjGTrRzO3uS0LhvIs/G2sSxerMfrVdSKha2A0VxcnXhm2/bxsfHZvQStWlE5x3RIh58RZ6qVBoC7so997GMs4rv99tu/+MUvam8d1TV+RpRlicgzlwNLAPjMqDt6PSbB//Iv/8KW4syA77zzTrYKYKg4DamXQaS1Mfi617pr1+ceuM9k5/fGxvCWLUxGzLZmmbDMIhvIp6250I0gz6ZcKMoQE1555ZWMiLzBcWRJZRTA0YTzzJPOO2+t2X8AJTMjVmhq0v33r2b6D9X8n8/ZXcvd4UF/MmlHQ242X3zuOW/3buTc1uaN9uo1dnOzX95EcVbikhVok7EZNR1aX2AwwzmiB0YjgDokcQEYZjjnupowyCKEmaUOn50yyTLtqIxqxEcJwzyAuQXAlbkarzArRS3MBYfGSzoLbi+77DJeYfbA5IDcOYjJUwIBS9xWYpIF+iVGFB5RBMJJnxkGeRFCUoTos37l+M4kpV+cOylyQZpe3xC5/tXO449lpyaYF9TW1cI+3suuXh2+9CKTzV1/hQ4+UtybcWYmOg+4hW9UR6UZzMkA4lDLPOKick0Nzhn5pASyBREr3vBIzE5m7GjC7BKzUVGW2G5ekDD6X5ZGnpSsT4NE6YVmfC/HTRRfBxI0KlHLsLR267hTW6wXKSAGq6y2oAuiv537E16kzIC0mzv0aAVkmPNgZKCJHdmcSr2LYBJ1kAL9vrqUBSCih58zLR1IiahBNorHuAZ8iOHl/F/cPEkd9yMqfca7lZDFYjjpMOLovQ8pJuUFf5LpzPTJOJl27vpZ+H9+jANzh1EAwwbDjCSncp//QnFVd/Cc80R9Oes9TT+pVUtm6eIwSCZJBMR0U4hfadtwm06PEZwxjltwr96LEeTPkl1NEq8wONL7EYfBiFH+Fa94ORfBUJj1RM1NTdFYlErF7wH+B0QfK6pdcUYl5sr4UWvdCnJvrAAAQABJREFUYDjN4+n8kyMhoCbcBe6CcnOCeCVmrihIWBaQK0tmxW1aloa0Uhlq6KuuEzKRZgQ/6OfUtbLDx54e7CuYWUzFC9gqEU3JWxiVO9asG+sPj/bvm84kW9pWYa6A/+epqbH65g4R7eLlkdjiQZr1wEZNyIgHrfF8cF841L6ytqa726ZfDQVlw3PPWBE31olLZ/IpH5gzhMLUIwyEVxRBr7FiEqLrFAEBE4ZqVynlN8+kX8qC3OS222674YYbWAyMVh+NDtodijyz6Z5JxVqi9UziwBHT9zOJ8P+XaKUHRMiKuchPfvITegr2FgeGnbZ9BITJaNPUYm1pCGw+G+9PbIzITCSTL4RkcK3q6MuVqAPRhTIVQ3vG4Ernrmf/DI1zvsKr7uRE8Sv/ET3Uh0OV0ryJocczYnf9LH/vfZE3viH1y4fjXaIKzvz1p8N33x1iEh+vKS7v8m54g/GWm5j9HWWsF7IYnhlmGMvpo0GhoF+9EBd/G4xGUMUQrnd2LRdCfhn1MSkk5tFmWmh6GebPOeccxjZ0euiFkH/raTGvowrmjFa5u7ubNc9kIYmWD80HzkwyOCrjnw5He6xRMWcO0HuFBkzL0D+QDGQziJIsMyQKQgiYmXkwgZzL+czDlUqU0gWomzT1fGvms/I9MMI+76LCH9ya/9znG9IZKxDG7U3h7I2hW2+1VnQerXLLb8/85VsA/6OK1GbnMx+/oPfQxr4dqEGoVipxHloodWU3kaNFo/q0voj2xnFU3ZHMy46WxgmFi63EurVWKMJ+ZqxtJ5cQe/AIAjGN1WuDsTjrm4+1+k6IoFP7Ml8xx+LmCbu0aGxxkz2+1CAGK2i+X22Hf3yJLOQt3UgW0khnxpl5T27VSGGOx7PpASogCsSJAxuMAypOcYuln9d2HIxujGsARSik79KCy9nUHl8IhWI4QDwK1MccmgGLUs9OykxO+T+7xyjmZSUsB2OocsmGf7vCg48E1p4FIpmnM1FvSKoMWAwclIXxkUJpxR0Ql0EHQTbE0AdqPuMqkgZGeYkDftYHOLlSC8FgYOXKbhm3fWP/wX42+8o4Vsb1+oezE0m33xstFE07Eg2E2RtX9iJiY1432o7xxP4plu+6g8PjidoGtrSmLPJHwYhXbhfKmr7CBp5rYSIDnIxxFEeNeQJ8CcEdmnpZ0sE9m7ojHLUyZ52SpCvu2lAm27HmFesj0brU9BDTiob2rkhCnGUC11mUHgkZMf4CZgw3bKJBJgkf51iIEtnHy4rYsXgJytKfSoMuE1yhVUhkGbWyFWI0R65Bv8GCGiQdhGsRw+HIZ/IVrGXqQgFZFYyP6O9+97v/9E//1NPTQ/iZXKwl2s8MDiwB4NO6nuhSGWz+67/+693vfjd6g9Nvxe983GOuh+KD4UKj05qSTdTcrzCOssCVg44P21EQDviTqFwDQWe9Q+eIDjFrbH2ApWEMn4wj0l/KMKb0mzu3G1PJYG0dO22m/uav4nfdpXQHppFKh0aH3G9/E6e64Zt+E+9DR+tlGW80RMR2C1jOVBhKsFJmCNfK6jk7aOqLsR8V/ZxPK6UgDj0+0wWw6NVXX81byMiZn+EZSwphmgjX50dQOlolQV6ZgYorj7gAa2kLNHLkAAxXprxkStYaA0MMiUA8eiESBGcSXj1fn10oZnXVEaoz5VoqiWpBz9DSHHrVq9svvNh5druTSvnL28Pr11vtHWao5P5qxovz38I9VOUIEWbTM/+LJ/spvEW6QSPBoTd5zSCPDxkcy1ywev43D0kzXi/HBIyyNUfOcl02iTWjYV883ao51NGacvnNY/oldy8ciGw+O7d5k/XUNjaawSWyUrdYhVAgdP2rqNNqockxJX5GRD4K/0+Idmqf44SSWNSX6c20IOZkFHZRKT3OxCgXvSuSSpaq0qkCw/Ram+NM7thfI3dZOKBAY+Uath97Ss/zBiWlaYGBwaVzomu6B8/JW0ODOfYjVL0T9NDJc23jCmtwyHSUf3JCy1khBQZrQT+jsxYG0b9puTC9GQcRgWcIQBHPEZOhhEGT0jGUxOKocmOy8bEYH0U3bsRBhiRMX8UPmwzJWlz0upgoy5/PVt9P9bt1uezYGF4zRIPKGto9o/vZKyve0BFpjBaKHttBS1dHQSDSM1LJzMDIZGAi3dbRhnUT2llRYCvqVSEOl0Q1b3mR4agMrFiPrqhR9AC/i7oTFR0w8cQDNFdqw7NSmhLOQbiUAIDMciq6yEQxM10T9pprRc2LWpc9sQTx+h72zzVh2ci3PmwmwmI9o5y0qfSFSoAxtjUqzXlP8JIBhe30kJUjsWIeQsEQrGiDL56e6f0wDGWO93d/93fsY/L2t78d4ynUwjfffDPlUpUwL3eWHi5x4AQ4sASAT4B5J/lVujnsi7SvvJtuuokOghHuTOkRKnQuoIcXt6isBmH4ZKaCmBO0BkjQ7ou4OBqbZSxThlwzhhGGJbE9yqQjXV3O9h2Bu34k45WMnPyaLkrIBt/8wf8Yr3g5uxVVxsMZudD5AgWpAsZ+rplScAHYq5SrOj7RmCvoyIRzDTLkAiUe8bkFGlEo0mG6AHhDy01k/FgynhGBQ09EkOJXrmckUp3dPNe8PvspWLoaTpO1nrsQk3DUv0xxNDCmvSGw1548UWYy3UEITRVodE2NaGxMfApIcUgHOvVITJxqMEwupA9Y1X5fwk3NWMIH1qxlxxMkIyijpdhzUTubfh2iE6x+qkOOKZHq1xf9mrkIRvswEM7MpgpeUfVwe54mPSdJTCi1Podqc0eG3We2eY8+bqWmiyy0u3SL1dNtsH5+jmqfM7FjCMSfS7FzRegP35394pesR7banuv5Vh6Z0c1vDV5yBRKX2WU8htRP+6h8qoiK6HLh/6KUdFESWVy2QRIfET0bn//pbFV0IqWmjHyPuFEEM8wlSz2RtJ//XfpPzICJhxT1/2fvTQDtqupD7z2febjzkDk3CRAGGRQEsUyigKKiDOJQq7U+fbWv1ef31VbrUFurrc/Wah1etRVR64S1+km1WpBBFAuiQBLITHLn+d4zn7PP3t/vv9a9h5Obe5MASQj0rtzss/baax7/8+KA0/7DJ3tSMWgpVHJaqlNjKoK9F//cxGOLsL2gNRudECv09YBhr0dcD/IxrNBqNsOZqcFBhJiQeNKbvN5j2dn0CUJPsqXTjexIbPiEM3NAfSkCNjv8Xl0i+atrrUBikU8OwG9FjNk3EWMu+0G5DiNUo7IWUs6c4XzFKlVs9XPyEPfSLaDHualxMO/WrtVEKNpeWWgI2smxonBdqOupMPBHx8cH+/s72tszYseLRmqOLV7mtiRRh4zeH+W9gd8rz+P7pkpLtVUakFNpgjgdLulEqFo+kCFPtEEiLa3Z1hZ/av+mjmg0jrH4esLj2jSDm3v58xzMvJFIos9n0iiu4VFlLP3QCdetWwdsgKVMZMSAlFDa5zRhRpEOQjCDsnQGz5gvtOLiiy9Ggejd7343Bm5g9mAoC473fNc9YxqyXNFnUA8sI8An4mCx1+JQ9GUjADWCA/yqV73q2bHNLdrd0OY5ViFtaokpRHZBEjQ2uGj8uUBMW51+hrl7p2guQcJV/+XHdcKTN3HTBZhofdejHodt4zDj5OTiv6BuTU0E4+PW+j5RJTukY//lpAczh1kKBrgU9rJ3956ZHLebQIQ2sVTJGQ0apLmUgAhQzTX+jNgzjQUxhqarG6j3d1BHoArk9AAmqA6Bu3fvZg4QsyH7ypkHDEdMUKylqgxpXDdWH7A6c/1sJOG1MZfWKqc/UZzG2/UroAzFAQbhoFDorxo3hn2NwRXqBjqnHUgCuB/YQjPmDy8CujUUjdVrVgNNUCzXdKLfpPIX3vATcnSg7hNQbrqCzKkSlWzG7Z9QhkcxssCS9TodAgBKtgs6nHouKEuHLIi2IE7jlYmRSMqlkfWh/trNX3f+7d/MstymEwcu/M53rXe+w7nghWYqtQjO3cjiyXpEW/vUUyJ/+p5g166gv9+PxSInn1zJZvrLhZVh2+GWzpMt9cRIB/YLNMbsPf5Y0/HsAJbSbbfdxoagxU+OZ9HHqKzGcmusLzygaro49jRem0l1x6gaOttGHRqlHBzS+PTUPY3MoV2CKcHe1wxDtmWcF4sa55/L0cLWW8YkYalUc7i1J6xGo+mzzigbpha10hRbTj3OO57ReDQZh2XNDmT29Pacdtqp0Vhkzaq1cDkJmZ2dmZyeKdfDSKat7BuzMz5auHjE9FSNi4IC7CcrE8qcutxChD0q4TRXAp5huR4IJiwZiaBwXa6GBrNMIjNWM3No9VrRJGq3clWVRIAAqO59UBxgZd/ZTGWzEGiHh0cg3daqtY7OdrF1hVZwbprrGL3oHKedKSGO/lWHDqRwCLfF6YlEth0ZJ4L5KArHc98f38A5pBTblg/yXbpX7lvUOQWuBcvXWLFh5bm9FkLNEUifC4dQTrknetItyEOPKSfLVVddBRACbQJKChqzWpbhEMDAgnxO/FdayoGOCPTLXvYy7EJDyvnc5z6Hn3DciV//5Ro+43pgGQE+4YaMvZiTCY2IT3/601dccQXbwbG2UHLsukAfOuR/8P7FJ3B7cAZgL85pWKw4HfPgyAfVkBMoNDNZ742/XfrpbfHpadBaziU5pE2neNLJyQsuLE3NxFFZRHdo/lTTmYSOmBXFGhMHpByH5HS4rVXLQpOcOuN0Pgsq2drR3tXTTY5B3Q9sO18qw+ps5ExpUG0ZVn3Tw3XXXQdg0Xx0IUWGk1O8VgOVQlaW3oBpzFGni+NJieCT6AYvqt81VytFBVBNOlyrVILmVuBvRu9RZMXpbKmSxsz1K3RZWBmEwNIEvachjCM4MF/BUfUlxiDGtJfuQu2ZDoSyTnyAMML1oKuOVJDEEZ9tmk8OyE5B2g+spqv0ND5pI8bSoGtghwZkqblLda24P1MzyXlFmZ+Yej4cHHOpVjCWQT5Xv+1u9zvfcYJ6FfaNEXp24E2MVv7uM9aK1e4pm+VamCPuyaUKag6fq54bQcnb6ekJaj7AaLFWe+jhh6HBoLnXHPlp9Ov5QG3nKnz0qtJY7EcvyxMuJ6YiWw3TEiFhNCOOeh8+LQ1m22TzYdtpNAcPo8lmhZYN2xfSN41Px6eGi+4MR6VoPf85ONhg2RVx7LHgruD87EuwcwkhDu197tlntV5+VXHbrtj991G0TG8otpii/x9vtjefhjlp3Sfo+OAadUMLlm2aDQc7T1immikZ077lT3P9r1kixE9V68k9+zhIBftDCZbbcWHhVuuIPoXlgHtxDbmOqB7UQGIhe8pNTAbiUnKFoVBFFf4rHuHrAg3oQ9m13cB25XAG6TWRTxb0VC67Urc2U3MCpfqGGYtGV61cwVVMUGmzmTTiSnycHh2IpLKOh/iYnIO0S/JXbRaEHHXiWm1qbDiSSLlCTeZrExf1wJNTbQLUlpulw5hloN8bccOEY3ouFqulgknbb0MM+ajuvY3Ob3ioIhgvIu5QNNAqZ3AZYk3NacQ5MT2qA6VqMgZH4NiRXv7yl5933nngwAhFYxYLqzfHbvkcQY2Wozxre2AZAT6BhpadAgeRD/2H3bt3f/KTn3z729/OrnGEG8cJ1BJ9uCpVnx3jfjpipiPY9xdSrjIuwUkkpol/9asH4CjCC9VAGPXXLYXdegRtMa2IZ5/7vPr/+UT+Ix+J7d7DyVoz7doFF8Y++H5zzQp3x67yzGTk9OcUsy2JmRkOX60rbKXiGKMINvSFXR2Ud9iC9KXEms3IwUMNwfeos07IeOHhxE3X/XDP3vqjO+rlgrNxQ3vfRrOzfWR4FBUqjaQBk9FMrSIFmVO3tFE6r2SlSQDAKyDA8Ic55DjhGnGW8lA6PUc9wmIpmJ4wiiVuzLRBRxGYj0Rp4YKylspnQXhzKs2gbkRAlQ7HK2xYnqiHUXltsYNDGnoN9QfWJAcccIlOCLbM3MZPBzYkq7m7krRAqwjsEQjohqM4EhJTP3VyUp162qkATaNj48MDQwgLANsILV9ZPlFSaTIWJJp76Dd5wSc1Ee/RdlQesgX9AP4PUWPR7HW7+AT+D/JP65gJi8ZcNJCayyybma3/+EdugAUx4ZbQJp7czREdH6384mf2+nWWcGmOiZOOs21l8DnkfsrRkeHnP//8Y1LSk8oUUUCG4JgKlz6pej1jEqGIAQKMa5gGOLjqsoTURqfXl1pbMg3FMO7BsZ/uEBYa+8/BtWBvYRvhbIWGiDtGe8LB5RICBI9b9NOigWwszeEcBJwLtIsnDlwX/EcLiCKnwBKARkl8tVeEEEnhEGJbCGyf7Zcm96zojnlxzCbb69fYf/zOyo9/bP3yvnSuWO9dYb/oMvvcc6yONnRUawEX5HJ9LraUwVqFW8vFuUgsV2vG2Pj48PAQdQKVDY2ysXcWY8sdPWvNaLxWtzCrBbO3Kk/UZzkQZVIQj7iyQUu1VGezVwuiC7EO61ACBhBBMGGJLRc5wI7WfNZ0R5fV2cWLsH3FWhU+yVBsUkkzBfXlnyqL3cnpXrGSPuFc8VGxAcMVWSy5Epq0pJLdUnBsqkFCbo02MZvJzdJEEOayOiCk99Smqmov9rEwWxXBfKcXxmzh9EYFyZW8+BSxg7iHfq+Rdo1ERAybqzaQ1TF0Uv0wBDCAvU8xyIvBDcau2wkuJwzxF5ovEvIcfEfYO7SURn3729/+4he/iET0HXfccdNNN6EbTHI+HWEmy9GWe+CwPXCkM/KwGS1HeIo9wNbGGYbaA7xfMEDMOYJlPXNXO+fBdKnO2TlYcIbzcmq5RoCGTCYapCLBaP9eIGlYVqgR0W8LmrngdbGOlSgUYbW0eldf7Z1zTu3Bh8OpyWDNmtjJJ3E9y3SpmOjqQkIXKWjrD95R/ehHI5zb7JyZtJnOlNNZ+/Wvt1rbltpKGYtGoWAsABCwRgE+wGTw4xpf5cSDuj4wXLvl28Z3bjGnZwK/EibSlUsv9t74pkRv9+joCKgvmDPi0CQEp0VOmPwXtlEdz/og5ivil8D0uhQ8ADHMDVAsPiErDjCEB7RzviZCJQ/HJvx77qx/9wfhY3tMx65uPsO99pUON1ukkwo5fLzKT93XXHlONSrWCAH2AvQE1wXaO//88+m9BpMWtB+WsobeeKJjSZfqysAlBoxrVIw86epLL72UBoIbc8wrvBgjxNGhkrdzqJyf8etDdcyKeDaEeeWxDF5dy3Rt6BtcKCo9zBxRbW8+MxnZpYa9Uf4T8DAWv/71r0mAvBa1bfRDcxZQBHhlBCGmQEkBcm3+emR+5BRrRv9QCXW7+QT0DPCYsNMHR7lqc9Gi5+M++d/mbFlDgwOD8Fig1DSHP/ncl1M+3T3AOIL3si+xZuGLLgWkMuvYc8RVyljgQYLGjHim83ReZbyg53Tt2FLwsNZ4ghERR5Oc8NBSVijr9M4772TNonDIrrIgk2P32rxeqFujIPx8wuGBKCzYbZODQMyOSuSf/exn7PyNVHjWrl0LkoBHyyrTFg4p/dTCsbyyA99zzz1gIKlkGjErSsW+cbiqz7mx23rZq5BRCl3PTyaG6pGRISQ82KNUBNk4LaSRYeTKX114v1NFb7IaZwcoFTnVkf2NgIQWZi27YiHmLAYwZIaw3SoElfRgrZYJlmu5gnyCxnIXr7BticFrHdRVdmKwbu5ZU62XOIIMSw7cu6bomiKqJZiswi8Fl+U7MUCVkfxhToKLkhhtJ9LY8WhheiI/NcGJGXFthJxLeYxRWx29q203SjLJF/rp/l31alkdwMFE/07CbDfSs3oj/OaobcYcYfDGYPBKuWRPEYFrBnHXiEeMBEivZya4wAiCoDRLnSwMnjTmeDg9T4AlKIz9n3l+7733QowGYnxCRNXjUdf5MqgkZ72aIPNBR/ArS8I0ufSE28tgCKFg9ed//ud/9Ed/xAZF+BFksBxluQcO3wPLCPDh++g4xGB3AE3CAt4Pf/hDDD7/xV/8BSfZM3qdo+3zm+FwsMDBCa2U+wBCnhw7Y4Vg564dM9NTmVjHmZs3Wi3pqVI9gY1EIcTK2Ta/twmicuge0DFtyOobN8EBExNBIff+5od376lWSr09vVlMgDhO9Ibr0Vos/ss3rIkJI5VAbdi5/jrnuc+z0AqTE3G+wAOHmeHQRiY0txZpZAiuIHVAJPr40dEZuHAmV/v3W92vfg0rQXB7o040KJe9//zPshHG//CdnV2diD0DgpBQwysHliNgJSFyrHO6I2WKKqlpnn7qqdOzM/mCUA5AnpHvIo7Go0CGcbySm0aASUsdqj+61f7M/43VaHrF4Xy+567Klgf99/6pe8Hzg0icRh66MxfU6ghfIes2x6QImgMCDDwN7MWTEFBZHQfSNa45vhbeIwSckF4F8GuwODg19YkuPb9zBwAVFs/Gqok95baZcrB65YptY7AXav379kYc13PtKHCua0dcp6U17SKhS58EPvR7z7EiNoaSQY9BlZGgMxzlwY8svCNQjkjWSf+omi2YDUxHgeqaHW8yYjjp+IcffojTHfl26BpL9fDB4QeH6ByXeKrybDvIJLwJbpmEUaJ05bjSmWojzpDi8hKB1Y6pY2RZFIwRg34iAFsQVpDapcnMN+rWuOy6mRxzTDvkWZM5sxH9C30hMOjWopMT7MUslIL+fVUIVf0DiGOaZz7H2bjJYkWrPXvRVMe5i1iJCP1SqK4MCiNMDJrWMBNAOLqUrFYs/GGJEDbpsag2hVIHnuxvzFLNvKVcdkJCOA6aNzq+YuWBSpIEtFwzrqkVyVllDWIZmz90CqAC7UBu2V1194osTJMDZS3Xwoki/FhwV6f95PP2/OrB/GB9JsGrmFaGiZr04nEvwbZX9MPJkTBXJdwift23oa0iw4ySBXLCSvhZBIZpUD1IB9kUZ1S+sNvx3JbONWx/vijuCqLrKUEAvgriyo8wdElItuj1ygQByeVQkr1TDjq5S0h2VVUEfQIuzf5FHugQSbaSz9wui7IH8WR3IxNBlSWcIuS4RNyaXLRyLqxd2MoIS/uVUjWXn0W/uK2lpV1HJhZdSqcCZdCBho2FELNeqWTTKOQ40Zi3qlvSyrXCZt0zoe2E4LopB6DFgmofQWRHepjMJKN5r/jUiwQcN0czKIvpgTQZ04Pbp1i5UMxhkNI0/fW4VeYQBVFD5jlPTZPCz1pgSrMKjrySxIRwA8UK7Pe9730v1grgCUP6P/IcDlHD5U/LPbCMAC+cA2zHLFrAKcQawTQ4dRCu06B8c1TicFYhNgZSxIJkYS+6JomG058OEYGF/eY3vxmg/7vf/e6VV165aMzm0k98f77CnXg2V8D7YVCsGigOcebZhj+4/7Gg6ndku1dtWluy7O1TbOeIIwUxN8RwYtYLkZSGyAp9VWHEh2noXEdxIliCZQWVCjjhyhW9YIy5fL5t3RrOeEw9R173OuOlV5uz06EtNzOBb2kbtgzNbE6Yqw04g0wIJIR8NIQBUIUfuiMwB35Q2ebRIf/61Hj4b9/nrlQ5HuV4ZgbVLYSkfnp35aqX2ptOIiFyShTRnPCAhpESsvpwv79lmzEyEbRl2045ua2ne7LeOrh/kAMAtR99cgC3Mdk0Gtng1Qic0r/fuOmrjg88AAABcR0TlUZkOlf8yteogLViDlQ6oNCj8dJoEZ3WyA94DqhuwTnXiNmIhkcj8HhAWnDNnxp+FuDFF12Ectu+ifLoiFWZcuqeVYy27Zu1q7X64IyLbRR4FUZYAjSxw3D9Oi5z4vLFcPv23ZYVgvFGHQu5Yy6oWNHTlU4mAHSmJicVSsz1H/CaLeCbqAvRQGYd6DFsZL7id1VygC6FOfMVhgaQ2hzHhknHNNu/v5/h0ALhjTov6mEE169fv1QzF02iA1XXmiE1P+98e6DfDgxhciFz7rp10/Jdzz7nuWZ8cenrQ2T7JD5BjGAcAdYXHc0nkeFTScIcQ7+RHKgMvaH9UIWeRA8vVY2ldvWl4j9Dw/WYQuBjc1u0CXQv2G/13l/UPv2p+GNDhgUHLqgi+3rDa5xrr3G48lpjBYsmPo6BIAAak4RZCsCNkDOFa3Zocy1YhuhowPFGlBS6ZPOnRf00vxEuXaEc0LwWxmEe4oDyebIPsNtTNNZ6eSUO8XUSDARoAuiePXs4X8iD3Y8KU73GTq51fwjUTkh6StqIyJwCjTrgIaTx2uynrP0z/u5p0WsFMeTM9evR9NqzQ9PZM2WgkQvnFUFlnuDJwlDFIqScwGJSSvBSISKTsSCCPFUZkB6lOAAcB84ngsE2r5r/KVgumYCj+orxC28OHJUtFCcYrKCxkgvZQZuGaifHsWyhcIXn8FhJTomYzqBIaiVNg1IpVygSDCwmf6RVJ6tQYVTNyEGL00rnIjutUrELxNMdsUx715qTt/zX3egem/F0D9ccIMCMMLMdRpFndgyvczVbfa1U3Ltj6/r1K5LJWMy1Em4dHm/Ck6uMXEfDHtRWsHLaonuYVuzdu5fzCJq4CpZmPl1OVwmqCgxSSDnMZLDESy65hDnzdFVpQbmMDLQepjrh1FbfakmFj+SsbM6KtCyHD3/4w7QOIBmdqS984QsveclLCMc1x1z2L/fAE+2BZQR4kR6DeYVAJgtYfwN1YflpdVwdwsGGXv7HP/5xjkAWIWvyH//xHw+2q0G0v/zLv2RjesUrXvGOd7yDtAtWLEVwcJL5X//1X1922WXPXOKWnEKKdguDFyWiVNSCABvjpDSQFxK6KpfmYT+jWLH8zmyxmnVSrTsnQDYwJhHEhD9slgIr5xtDYJdGYInEkZnyfG4U4Bo9qLDz/GE5QVVB6mdu93v8R8MN9DP0C+EIOnNmrux0Jkynkf2S4WvaMxkOmEhan0o+yYkL+7YMjAJdQ7M3YSOQLWcedBA2ccYLeV1iArgIb5OGj4xjILfoy4WKekABfQiGPxls35E4/YyLL7lkdmaGM1sKOMgJpRvg8r5fBP/w+chjj8FGhvVZ6eyy3vrmlgsu9Ds7YpjuxEEYN41MNqOxcemH+YaIbWhue5qc8AXEEIYlelAilAZlYcsjxvioiQjusT8q6DQgPy2LRY9JlRUNGA9AoTRhabdgXTQikoPj2AJwlBkXoxQzsraVJhR9WgAj0+lctZa2ArfRbdj85jlZc21RLAsqblY4DQBQlSAsCeRUjthxEYoMd+4cF93hSrFSmEm1dgGTrVjR09HaAkt1cP9+IDXHBSt24CRL6alEOhUnDmZT6V1YytyaidieQFGVzExsTdeK9dvGAwA24DaFLYsHPFm9aoU3eUIhS2UzTAJhHMsoacev3md0gIY8G5/mIvHjZDPGy6+qbHk4su0RGWWZDyGElvprro+cskluljr2DtgFtoNM+xPAMcdg5VER1iMMEOpGpxxF+A+eG5xDlq0M1rFfPk9vj7LdAVMuOrLs7fJv397a3/99cr9gv2rCWl6lWPvqzfXuNuuql1lLUK+OZ6MYI0ZfI5ns0kDe2n9wHYiJcW9t1Z/BZfPAsYniOLJ5kpbRJxrn++7du9UXeZAtEV70ohfRURBDwXIXZA76zV4HQktaCDFEo0pMVJ6anEr4RRddRARCGuS/RiYNbJxojUDt0SGy7JVreBZEY8/3bBPq875pTgBBUEUY2fDAQkBGJ6emYJQKRUDtQeQp/Fm5REFmuOxdDigueXM7kWycYK1kAIuX+MhBm77UIt7eI7ufJ8cZ/QbiKpVS2xh5KSyX3ER0GY4sPF+ZPPIns4aHoP4cUwphBlt26SrJB5FmyiUvqTO4q4hiU3WqxLlJDKpBJJyce7IkIfaSkv/IvghdUhYpWy6Ku0Yk4VgbOnO5Ga88bIzPrt+0IRKhuuRWj9pGzAUmMexEmJixn7PaSSdJpBuwoM8XvEpPY6iCcQSLk7JPAEc1mEjwfkHL6R/mlfQhvXViVI9FRH0AqBBK16IlGjB4Ej3HPgyEDLubi1EwkfXOd74TnvBh4YonUdBykv9WPXA8wKZnXIeCyr7xjW9EsgiwAPESkFgu4+Vw0gbZWdKf+tSn3v/+92Oe7m1vexucB4SWsVDPrQMLVOO4x4iVz4VmEK5AqhG4au4K8oGBjHoDWhzIPL/rXe/iRDxBdq7meh6Z38R+Bi3qnzUmyyLp5HJSekY6wvlY5+q/HbsHu3tWxRJ2a6KdE4xjslgz4AxzMeBEIRyWgxoh1VAMTjiW4MN1K181BuXg42AL4g5SWwb4cIxL9jwIuoQLqrdUd7Fd4qTmnODqMJBzc/7Mop7yRT116xp+QBwwXgIZehLqcIYJayIEEsIRiEUHPMAxCl4xDexjcJIHFugfkJMuulKtADAhnRuNx+KRaEbdjiOFSosOcGhQ+Tu3h3/zt9HRIUsEfU07CKMjI6VPfso+5bSu7m6wa2N4NChXzI7W3kzGmc+qkQtEeBPDV5ifrs+ZRBa4RFFeHZd7FQUzP6YHIr0EaRyAkgMJ7krzIafNdWhSQqPCh/WQIfLn6LYJ6hsKw3PHVPjQiDlbEYECYSsAIAkCKKR6Olxk36DbA+IADtGDTBrDWtHbzeALIKcALGF9hGalKEMfza4QZbaZ0dmpmViklYkwWY1UZmV8+zEoLrQDeOk1ACPSd7Qh5hlj1Ab6MS1bYPyY3wLbWWZrS3Zlz2k7c8bUvqlKqeQ4lgcDRJ42YnXZdIpUCEEiCInxUqy+gBLDXoapIzlgkMZWEJ4ZMFUVb1luiBLAEVE9gfzmPCDPMFwE2Fu/0fvTPy7/4FbzF/cbhVx5Za95+UsiL7zQbu84aFodto/VXCTWgum4RDq9EOif5sFdIu5xCqZHNbNXUyGZLTLwR8/phXz08juhc2qMLAONO7AnTSEV3XtvXGG/TRup5dXrpVv/I7zwBSFWAI57+6hno0wqzCtbA46tD/VIpBWgjHCkQr/mEwcxVFGN3+on2KlGSjnB9cXsOjeyYui1mCVbOhooOkRjrUw5iiAmex2COaAfOLLSTmdIoQDrjbo1e8jqYI50c4RD+GkFNHcsAzU3/OD4tcAqVM266V38ux9cceoLkTJmaLAGxYE3O1tAQhhqMiic3hXBf0GQhftK79GdYmKPLUh2Ifi9sqXSydKpgpcGYvTYcKJxIlcpRkVjQyN/IfgKuVGGBOxVjl3Jjg2ZP9mf2fSogvCBQYbJlX2HjDmkiEWJnHpCNJTTXsAI/suRRTnwhDXXV+/zameHbY2hBxshq7k/4AH5E/hJ5WtYbet6krFV05MTk6ODGzIrO7IOvN+IQyGy4Unt6tEWb10qBrmAAP4O5ZhRVApHJD3B8Gsyx6GSHZdvzCgqs27dOl0acxsyPfICTEg+PbkqkAkHOuirns9PLhNK1yIYgEysRGBjeuzJZaVT6Qy/853vcD0KhnLQiv/KV76ydu3aJ93Mp1KZ5bTPjh54SjPy2dEFC1rBcvriF7+oA/FDXUOkE+4fCw8EmHBIv+CrILSgwZyUWkAaqjD2qz7wgQ805wZ3UeMDrFJ4FI1PspWG4Y9+9KM3velNbDE/+clPsG7/DF3GNIQDhdN1cLa+e8raPhFwLIH6Rj2rzBFphkgN79rxKIo5EMTX9IrRDrA0bEtGLSMT4URFohPApV6DP1w1ZyrGeIEzkiOJ64rgIYMMhxHXKDnWbM0cKkDtlcMLTZdEBJNaYUrElqwYGCjHpxxs4jjj+vrWo066ddvWNavXgMqqYDnLwW8RfoMzAHaqZSbxcKQhq8NXwAKejBoeQCitAwwXCPIqTz4hFQ+4w2jiR+eT8sCQzPY246QNke07QVhK5aLe5eEelmPx2OmnQaHlPBcLSAFntgNmwxksleTc4odvxXztjjujoyNKQUnlaNXF7salv8X5Vbv3ruDmr9vbd9jY5ITwfPkl9rXXhmvWiQKrPrZJwbm+qteIxKKOqERhd1qYl45TN+vV1hanpaXq17AfRW5H4tDzpI6I9R7hhKQrNAQJasQ05tw9klIOG4cjkx4GAeYS54eG/fv6rfGCSO7RXJBbOk/IG7TIFJOeMvh0h+oRPgN6AbAJFCUIrBRFX4tcnvQA0JafScZIn7Czdi2/srtLXUWpATaja8VKShGOBZNSOOlB1bbHUTI1w5KbroVxyq+GdayxAgr5ZdefEaG+kfFaIY95KoE19bgk4rGVKxJUEhhsYnIcD2itRpuFTLBuNeBjuZCfmZ4BaoPPDKOD/55ldbS3MkUADQG+gei4aUP4JMwty8pG7XWrTo69sde+7jXoihtutJJJTBjxqVHMmQoLmh5AgZwn6LR0ihBUKFTIBJROOB1HoMgwSnfJU2Da+Ymhaz6/jFQk6W/pQNwjj2xjKfWuEPlnlUJFV5NYfSdMR9WexzPla+Obiqke89k+HiI+QpszIYSkkpVaLnN+idjkWJJL2d9uivWEvTAuEAmmyUzsuRY/4TyeSQmY8JD20PppmLhv1B50x9y3X1YRgyOjIQ5cRN4G9pnl6nyY/nKkT9YnUWWV6gnVlI7OBwrXjr0Tx0CwGxAZjJRKzn+U346ODi0LAB4LsZI4tIXM2MDZxy688EL87CfIeOsSKY6VpaknbHcczWzvgsgqVJadE4+uC8lf/OIX80oSCiVJY3/DoyWWdczmJjT79dej9fzMZz4DZtLIjW4XTFKcXjVzX1iZDNWKu/91xWkX6s+guGDBbV2do0P8G0fRtzWbQZLFgGUo251gv2x6rhVE1d1DJbECLTir0OkEgZUCiCSYsKSQcuWYFkyVOIqcJzRB1im7jWC5FRjQxFHMZO4opEJqO0ITmE1IqicEc/ZzJWDBFJBdl0pIVqLNq2cEWYn6ic0Vu+C3gcJyyUFuSxIlH1JTuoWtZvRcsF/lw9qNu+izmN7aDvLBuES5tEJvDkwh2qM7iB8mAJSRuf463A+TivmjYyE5rynggIWNeXK4DI7t98Z8Y3pT1V27dqGbA7vlUFpXh6wRyweqIsvqkLEO87FRK+I11t1h0hzuM3kycDCcUCh4wxvecO655yIOffXVVxOOO1zq5e/LPbCwB5YR4IU9smAh8crpCEqA01HvvvtucKTXvOY1hOjIqGHADPz+97+/AAFGVOPGG2/8wQ9+wFaCAoNOzl4AGob8BmLPL33pS5GdXsA3XlihE/696AePjgaDee4IxfywWfLNqbJIT9mYmShOTYwMeIa1esW6WLZ3rASyEHBooZCTiHEo+hxsmaiVtepOza/bVtV0h0vmvllRG877ZkWMeWCIUkBth4tPFTKcsE1OuJxvuSU51YHDgPIjVq04MdTb3d6eikU9E5SYfsZepYaEGl0ItATEA1gjCZt2TPywCMCNoW5AsAT84sBjq2VoIKZqBJgkHH5gL81wD1BF2N7pvuY11b/+OGVyPQRpgZgtL+q9/g3Oxg1BLh8O9Ps79xhYnuReylWrrfZWo8l6alipGnv2gqip+khLzcCpdLVFr3q5v/1R4y8/FquWFRpn2tOTwbe/XZqeiv7BO53ebt0oIAl4jvamDeWzz3b/614Ffsg5T+PM0DOveHE9nUVEtKcbdbgjOiFoPiCg7qJGvx3CA5SJcWPAQbBfgFRaQc+jHq8hBnqDtJrWQJ4a6FwqN31M8vVxj2FsG639ZtgcLqC0JlAU7WK41VOtPgGDaBfdIOZYFBgn6clkLlQSkQpQifVajVkFwLOBnf2+6UjXmeHA7q1gsl6yJdu1kkEgLf9tZVHF9eCtz+mB0Z3tLWnJFgm+en3//n7yS4Gfc/OHUEM6U+l2iBzUQeSxUb+zIOVIU0pBvO51EAepPUHM64ZTdVzRnDBmZ7kkGa03pAIVIsod1pbVZ7ZSSKFQGRrol3GExsJTNPXMTRv77huyMvS13J8dzVXqw1vGy3VMfHFpRGdESA/h9OQk2xKQKGizksG2kvEY+s10VrXiwzmhF6AFsJlpIJOi+RPJE9WxrE+FHoMoi24gqfhDQtL3q/dsHcmkiucmRDSUfmM3JLIaAxkOyUBlJag+atJzAZK51F/e6W284nRM9UW9MnhzTo8wL40Q+UCeMiy4uQzE23AsUlzj9Wh52BDuu+8+tPe5Jeho5Xki58P4AT3DJl23bl0z50dGgpGOY0xelt8BA8NLLMlUY19lUTDveWoPyCr7J+RFYHHCiaCffF27di0bBesBgUaOQpUC/QXJAakr3duwd9iKiUOteFIF9mGRB1GbMJXUgTKZbRt6pe5YmKswndi+cKwQno0Nh2w5x9UXeeizmxKxO0UlL7744kZMnZV+krnuCmLu2bOHnmlIQMh0P+6OPqRM0GAI63ikX2p+WKsyLiZmmVn2yn3ta1/78pe/nIkYyahZE/Ke0JTZGxHO7uruGB4aGRubZKGnkmh2KM6qbbZFKmd02muSpgd9D0tXpvfoVP03g3ITElagaarsDErORhJIgBD72Emg33nYSnACvnKaowAcsRDgMvdM852CZU8mQzn12XnZAYXdLCsarNpCTkzWdGCHJBe1XqTASI7KLprGmC0E1wUDVzt4IAAAXFxkmB2wdDPuIMyMxWYsNeidjJzm9giVqTxwevggZ2MNe+PGjcBpT2LgmGDMBBICPDB1NUZN9XURJ9STxcVchaDPCoKSxYw98nrKPFG8bhaj9rM0aB3r5Ul0GpNTjwHLll1UV4P1Tp4MypPIsNHPpAX15Z4UZDCvvfZaTEOjRfjfhEzZ6IRlz1HpgWUEePFu1CcN34DgWV1sARCcdFTIzHig/zVSsjkiNX377bezthsUYr5yHoMVw8kBa+KVdcu2AkBAVmxPf/VXf4UmA/vCU9kLGnU4Rh5aBMkZ/Y1mkEidvGIzg0JH80G+ZoxU7P3TQV5OSlPuDEDjxgyGBoYnpmdNO57t7Cm6sW2jHMDCtuXQimG2t25kY+baWM0bG6ht2VaHsB1LxE/fvGH1urbu1p3jYRaJK1FSAvg2yjWj5AeFWlgqW9PzIlactTGQYUcuLYBEv2/fZN5qGSrbtbIgOa7hj00GxmihvYxFRzubitHTMgocx9hnjsc2nXwStYSfwBAzvowRo8PZBp6GNC/MH1gHAGR6QOkHRoqBXjBenM1WLGpeeEHdePfszTeXH3yIWOXuXud113vXvJrq1n74I/9fvhoZm6xUSkYqW7/0hcYb3mRtWD9nxEMqJOe8dBx+dWgIrHLe+XXHqnz5K0kucnAjfNNfwV+82++sX/oip6PNULxWvjAMVmeP+z/eUqpV3AcfBO/BqhhXIvovvjT2ilcWYl5uZKynW3jvh3C0fcFXFUJfK9bpEtAevQSSQLdAkWW2N2YyXQqsQIY6RPsBHRaFMpvLBZ3GEcLsGhkd+fXuka2j1mxyveWhSw7oRXPnuoo41JBLH2UeCvtibmjpKLwAWYKk8QITwzCT9tRZ8a/2Rb7Vbu8Rs2Wptgcmzr13+PVDxZZELMF8cNyYCPkZtYz9WIe7HzH06dqKyWCtGUalCVyFIfkrFCA0xkbHsPvS0dku5QoPREqkcgBiyOaTmwwWTkbNyqTi6Yy6JkriCUTPF4YMwDCaTPUkUkpPmQf4gxh5mShQlOnXHDPZThwBGQWEFBm84RyAu7nLt4dHhCYinHCZy3IBUs4DAJY89+4GtNW9xI/UC1FwIUyE4a5djyFuT8kg58BuVKS7qxMpbmJzuRHKj4A55E8reKbTyZZMhg6cnZkCasEI2XQpnXUzM1umATVSKbErzs6GxqQsCbUqFFJteegwCNuHC1VQ/A8xKKNGgV1ODYcUq7qAatBV0rXqVU0UgGT5LKMnNeQLG4CqqXxWn+YaJa3Q74Ieqw6XhPwTv4KYJaaE8Mp/5eSTjiGZ6mC66ADXCB2ZtUdrqf05uzeEoz6X1QFRj9eLDPWBjsofHKij6E8AshxhxGk8WaT6GII4hSO84dj9QA5JDqGWpcrux4TROTCwp55+qhWNmmeeOn1Tza2JWVdi8kQmIpHOBGc/x4/GHrj/fqiH+pOuxqWXXsqGQCYPPPCAjq/DeUKtIH880JGJQMVw1IF5hUdH48RhyyVQf+KpsV++Yi6emvMJp+YdLZ4b4T7ldA7NTyJwfh14hMl3suXgBg+HhIdIF7k1p1rgb27dgk/H+ZWqnsFFONAORobFbuK+IcOLmptPcfr6rI5WCFa//OUvqVJ7Ijil1dg2blS5aohFoiSQo5bb09kxODw8Mj7qRFaio2z6QW+y9pK1ZtvMSPiLrf6OXUgtdZ6+uXPN+g2ntt2xx4fExhrXVvS5HEhunrMDbUKffmf7BWmC8EeJAhAYZg0FEvRWuCtJaI6ynxANRq+IewlhTqSXVD6hRnGxqqDUPWQhs9s5iD+BRYvBKmW2CiEv/HLzUJPUkypIHgc4mQaNydD8hZ2K6cRUhKoC/XrROM3xF/hhpeKYAFBemIdasndBnBPhVbcLhSNqixUbTGZCzma9sFKOsHpwj7WlQ+JD8aHJpG2+9mLRfISVL7usngPCsciVglwtXJNFFElGpLGyWPJsFM3A86IZHjaQPGnjN77xDeQu//iP/5glfPPNN6OB/0RH9rAFLUd4dvfAkS6MZ3cvHNw6dBeRfeKgBV8FI/re976HzqeOpqH55k2QVccrmyzbDTBEYxHiwWl6IWnJDe7x6173OgJ//OMfg1/hObjoEyqEOiMMc7A1EXZG9jzuVNgyHg7MGBMluTW+RSw3cqeB0IzzBX90qhjx4qtW98Q5zTgc+WaYkyWzKnqUYWe0cmaLaW17ePqT/+A+tA12MfhuEfbtDde1XHfNho7uvdMIasn+WfTLxXy+u60DDhlbLFgCTGZBidFvrQcTJeiWiIwGA1NGNVnP1P3JffvCWt6zhe81sWWQsrA3gh0SlJEjyEdZxsSvdoCEZ5LR0zafRP6MNacjIkMMNJgbOsCAbmzZGEoB+2WM+ArVg3PlBS94wcGjw/lez7bWX3BhZPMp8clpKui0tRutHUgm13/8E+uz/5AQciqIfOghN/ujn5QqfvRd7wp7u+eg81g0BKS7405BZhTITri1aoVT9o1t2w2LFSrAN1lAl6VWbt0ob30kOP98WwsbyzdhjjubT7Pf+z4qGu7eE7om+sPOpg2VVMv+/n0kRPyPhgB6LnVbDwxbzafVDdRg67q+9RC/QVEofX6qHjBjQWhRhCPPBdkKvKAEzDhQKVfzK45ktgMEa0h3llugatFh352sQy7xbIw9q2ow7UQNmCYrQJzhE49Uiv+CYknvMSSCVwnrgL7rNh98acube+wtLEHGipiJ+MTK+M4Le//zKw+9e6j9fwKVgXKuid59afrjve7PXSMHcB+EseHa6fcU/t8dlSuBtymPGUxRs7OFXKEYT8S8WExpzEmHCc4GPC2lA+tJQ/FQGVtuHgF5Em6zriPwIlFh5MhgSyJhxjKskDYwaKICpEmRiBONZMlFpLtJDGSuIEi+RT137cpVCn0UCW8azyIFqa1UiGK2dK7kDdSHPzXvglzdK+Sl0Gq0RSyxyidVdmCMlyJFoTJBJrKqcyrPLGvh7WRrdcQk8AwPlYulIvU0jTQWyq3xSVZHdw/8QGN0LMfmMIc0qFFg+PpWd6527lph3JIIttfDyGBh0/2TL+wvn+rYbt/6DSBu+WIeGIg+klHiR+HAq9euQtISTJvdVWaaCpShNI0ueHooeCuCiMKipXeJQtoMBt65jVYJuMoMlWD5RpUgtngRoRwhuQpjXjpacpUYsUg0BtGKmuSLcLZVsP4ud8YmEE2ROzYrk5P+aCVTGKh1xPdHrICtQNt0IUOtzyJ9rxzDp/mW0IP06aA/6ee6detAtNhYoLU1wrUHfA2klOW5ZcsWQpoduw1rihBgOyJQjowyYxcEfAI/JAQeCNCqTqW/UpYWBgb81YQkvupKUnmMNeJnSe7Zs4dw3V14KAgEmFcaQgSOPJ2EJ60+/fTTDNdzTj3TeOXLq9/8hmzBdJmMhFVZvc571TVmKoM5dapEM7UjH003BHrW92PPfxFEt8FAw+yWzkpynHeScxgeKGUz/02VuxQRTTfn8ahH5qNbqDmiPdB56b0FmVATekPzosmPLmWfJA4bmu6rIyvkGMSCkfbINv8zn3d/9YATgnKalWikcvU13o3XOytlbuCSjrGxI4AZu30CdFTWmkgXW1Y0HYt4PeVCqY0LbYnm1V/WZ2f2PZL/9P+t3/Uzo+4zJ0ue477i6jWvu/Gqk1Y9PGJgRhCbWmijCKLDFcHMNvZhcmOLMzH7jNQx2bO+1D4I3zU0irEamxqIN8hXRD0hV2iFYYz3McjABCLYLPRrZJjFPrNIMguii3gzmWnhGz17ZZ/QjVLPZn9T8NJezhTALYytcEsWKxEEjH44MM+lE6tZx2cmQyPSkadtJDkWHqrEDgwVCSE1XSWeBALJAK+ydTB74Xs3RCQOWwfyYX2RA1s0Jzir+LDznMhMP0C+2UowXQ6nS0auCgGU7g0gwTABovOCCbp0iX40HC3FYVwWyA2gGhI8Ug8IWqrgJzxDjkaNlvN45vXAMgK8+JhBLUa4gp0FtU9kmJFYZtPUZHLQIdIsIKoBOXEmaEhl0RzJ6pOf/CS6+2zEEKs0wLFozBMhkE2KttAi3Vj8oPfsLDSTJzsY/7YM++NFxJXlZM1E4V5ZhZooDgGjIbmUibsn962MILQEnIQpixpbkiAkwrZ1gVDrp/c68fGB6U98yvrNI1U57QQQN/OF4pduDlsziZdf4xiJmaogX4VcZWp0qLsLrVQpGb4Sthx90dISBcbH9u4BWQKyb48F+ZHHxobCUqHQtmoT0s+z4wPZtu44qIprV3z0iBNVqzaey6ew/BhYg4E9vbcSFOMj1WRpdmxN3rdjle1bHh4YGJiano5GXIDLqemplasENFeHO+AgpS7cW+kcwM2pyamunq4zzj4LZgZRpIce6w++812H2++gkqtNX/HfTPPOn/ovvdLtbBczI7QZnP+C84t33Zm4/0HTFMUpkRbDnCOqofEIEm6N+cBYSP8DHZBl0yECTEZV16xdY61bg+knA5lqkCLPnZiZHh/sr/mYpBZLjORziPnJUadRVk0ATsAzFL6o/eAgMnS22CeT+4GAV0QFCwl2tLbjUZGs7duwHshZgQfUSVdLziB85CC4u6AqFL6w3xrtavYI3gyt3Qh/9eCWoVpqJtaV6LWB/Oc6cS57SSFdx8EreLHAYVKFOWa1yg9qC80OgrjZ/4q2N3TY2+CX1sej+S1OmArSp1dtr9JiD77ljL/45vQ5e2rnPjf2tRdl/nc0nFFsVVBW+MbFld69r3Zfd/vMu+7K/UlgCHaKmbHJyQlggpZsiypd2qYap5A5SqbqZuga+Y2Rn/S538s6u6Ghj1Q2baleO1C7IBCVO6moTA+dUOKTgUbdBRvmRZqj/hEzkEuuBJ3nn3L8MJ2k7SoXAcvoAMF3mWNYZJsnixBZkrJ+JKlgO52tglFLYWpE5nOUZB09vQrilFy63a3rvB+1WihMOmP106KdFw2X1iE1GY3Gspk0xdLk6ZJYrwmcdCQtchPihP0cZtw9L0u8fX3k9tCsSsFIyrTcdtGqm+8eetUtg38ylGcyoyfvTNW4l0v6QP7Jn2EpmfBqNRyZhOctgZJY+YpeCBkKled9Q5hzk8jKyfceP51Az5rrSfaV0JFXE0I+UnR7u9WSEXX0fftnMQcwl6F8NMD32tsFyx0cmi0WBLenS1XOFpzJ7m65M2x8PD89zafU6ECpp7QnatZbshmQJT6B4mo7edJo5UD7QYDpYXBjuC46XA8v37GbDfrH/qkvAtFJ9FMbDWabBSMlfiMhHrAy6kk0mE4sW77iNNSu92Q+6RAdqP3UROdM2kZ8IuAaHFSomUSjSjqc59ycoT97esDxkP0GVYbMoeNQDnna3R2pt/6e37fW+OFPjOFBI5Gsn3OWd+2rnQ0boSg89+xzdL5EcdIAAEAASURBVLkLnuRAGxuBVKnhx0PRza8N/4JojfCj7qEguhorHjQZqVF65mDsGqIMArS6aHqesaDaeqSOen2OJEPZ9nAjQ7XPfzZ+3wMiIiGL24iVK/Vvf6OSTZjX36BXCbPzpFaX/R8d7XzdgJQrBgUQyuAW3FawEkFMeF2bMlpzI7nPf9H7+c85KpA/AkH1qqH/7VuK6WT2xtdmo6mJEiJbQo1i/NSOi8iSzH7ZKGWdq92eDYGdSK1ayJVr2xhdigsquVl2vY5sNuYYiFyD60KGBi6A6yu5zbVZMjug+YrwJ4Hz+9QBX5/gCx3EyEK/BieEzsuAgjVpYObIcyITqEsLAL8jT36MYmqNLRDXRv4yGwyDNY5SEjsSC5n5wibDvNWfGjEP9mhdEuY5CDBYdANzJgMZH7Urq61SdkyY/LNlkF5jigsagOEQ/FEkEtS/c9V6rmJOV4KVyfB5Kw2/FgwO9HNI0/PURJPaqRhn/WGrdHAlm0NIDuoLKfB3fud3sET7oQ996N3vfjfbzlPMtrmIZf+zuAeWEeDFB5fFj+Sz/oYF4Isvvhhx5X/5l38hREtSNQ5FHQehMlYdpNNFFx5IBeoK3/zmN5HW+OAHP8geumi0xavyNIWC+UP5pnCqunv3bjwcGFp2hf10dHx8z77CeMkcziFCJVsiSoOIIlcrpdHhkTVrVs8YLvsgx1qqpQOyP5RdG96tOil9oH0fODusPvSb8MFHRI5TjnCBKtilOa5rt9za+sJLql5k15SNqk+tFM5Ww7wPvqj2Xk5EjP3YQMfkF7Smokhcs6tOT00Bv3Llq2V2Zrs686XyjsJwS2vK8JKw4eputmxa9XJhbGr76rauRDIWCf07777XL87W/Wq9Vn3sB3czfL+8/d+tEB2VBMLPX/jKLaMDeyFYnHPOWRinhO3DKHOKACc1Dx+kDY4ZmFQ+6lh0l6Aw1DMIZ2ewHOPXuK+HivIFKgCk+tCsWfWde5zznq81gYXFt3J19I/eVfnW182f/szM5+sIY0c9o7UtPHmz8TDy9tKNc05QGQj7G83oHKRLOLA1IyW9gotEQBcIRJQQqQQchw3VXoqjMp+viOvjeIVXzPxciypRGD46Xts6LqgdqK8SXUNWDZJ/6Ff8bVu3YuV4U18fBH66H6E1YBpsqGAG2bWFAiJ/FpqDKc4iJohUXMNJjSIP8uheZRbAsNs5aTw6G9ZTTJo59h3R5/ohoEZieUXphwkMPZdQIEMRFqbDKZECKfLi1Cc6nB2gfNM/SZd3R1tfUvRW1k2xFibViVlTF6X/zJz9k8tS746Gs34QKd2VrtwXC3zbPWMmfUneiZYuTf/NRNj3YB7BDTGAB1bd0d4eFZaHhs1k9krzVO2I02ptfVn6D9Z690KowN459VgXu/P5wc1bKy//3sTHcway6Ay5Sgu8KOgbfkATyUWyUc1Ugao9qiW8QgVQn2gaJRK54SSl+iQ/5Kd7g7yUwIQuicrKIEhFVVyVATHlK7nBASf3uDF1aep9Zya/6ZhMJ8pjTdtlI33b2Gu/PfR7yVh7xI3IBCaRai3Xu3iIUqgsidxq7/7drje3W1ulEbV4bQIldMttqcTCwiU9X13ZUvrmxOerRjwRjyYTPY3BVPVR9UCOIeKtXLOaEN0PqjNkcJWApblixSqdiux1b6OTgIV54rcpG3Vqncx1BvIfBcXcTbV0JtR0UO3mIbboCtDkMOSbbHFjqJWqbUkCxMJKDiFfPNFUJGlPz85mU+mzzsqwC3nzYoTQLjkCdCdLGoWXSr4YFmptPe+88+b7n1/pGr3bszVhKmLBJwKJwL56+eWXS+wm14CzuRZel8JzgaOsBSGNVzRmG/5mDyXobaE5sOGHNwsmD8rNgaXNIDc+MbNgLVqvujZ80YtMDMtDoUumrEwW6+vk2RTtGeal8qAKnGugRrDowXAWNAf6BU3ifEFbGA1SQPanuYVMcOQWtj/q3AfBFIxjbk0za2HOl75/a3j5FTKl593mDnbuYKgQFmr1MgguzVN2CtgnEVIeeGzfyWe2Q+Y37viZSFIpShwbMOLS6Nb63/t398qrurtb8kK/nh9lhfryyoGNuBMbjtLXtWDnRhy5hEnu2hXpZbkojl1y794ZTFxu7EBLXxKoCh88Y+Yzn6+2/C4W1vz9Cfn1QCM9zm0dHOUQMlh3TzQHIIQnlOTYRWa3WZC5DmnMXjzAKpqIBoSAzh3yJkzmRoQFyXk9+FNzCAUq4TtjBjZv2ciVw1KNK9FEZwFqKOJ4+apow3FbBds10upQ78lyqGjsmAjcoDqJFTGOYUU8gt5Ebcn8qKwm8kHuEiFNrmv5sz/7M3SyvvCFL0Dva678wY1dDlnuAXpgGQFefBo0Lx42TSjit912m160WtoNeyGoDzUSI0bLktPbTSNQe8AorrvuOg5XEOBrrrmmOecFMU+oV2iBtAjsDlog/mZhmK9+9avQ25Q9DnWiSb0bh5WwlxSQrDdo6/lX3Pg7H/4CXVf2LZ8TVoB9I50wvaAa7N6LaKM+rPX2HaA/Elat/n67kIsnOvZs34XgFQpG9Zoxes/DkK7p4p4VK1AzVLJSZOWk2nq41jD067mZqdb2joQYRJGj2Y7YLfFgdcaKxTE0DWsaU6ZGzjHyEepg5irGwOTUtp37UGDKtnVODgzmJgbaOztTPRu4nKYwMz6Sr/eu6Zsdmtk6Utv/8x2FidHelbVE1EOc+7mJNkqHjK00A41qrXLmc54DDj08NAgDGzIrmmvxaIzKgelr7grwU7VWBV8QTh+VEawXN9d7ludamzZYb3+H+drXBuDSyRR6ciZ861deXX10m1Hi7gpB2EDCqwAxZ56cOHUz+KjkWa0ynRgjek+z6IHgcYvOsUUDpRbKNb5C2UGqagVwcCF8dMLqnxFccw5DU4cugg5D/ftqVa87kTanQhjeCLOJPVEajiETlL7gLZFATQI7hBFg7tnvIyaHYrhS+pInfs5IroiWJ1phUkBj3phbh/0xZ1WFaysER5OpwY+qhOSs/ML8x2lpXo3yakKDDmeagQWnw/HNie+g2Vvcm3QSRvfvjYe22IRhfkDQBpKEs7rK/eXVmbfGzOmg7o7+XVdwd5t8gEzx85b8HVMr/mzETFRekv7orsorakamvb0F7fF4PMqspVIMoOTDQErZcoNHytr72vZr25ydMq8r8dIgcodhRLRIy6dFv53smvza2FdqJixupeuokrJgyIBmqYUgU1cYt+qf4KC6zwV9JYa0SgAVKVQ1FK/IR0uIrCuJpBgr8lFi8kd++JCzJskc7Crh8lmQaZUjHGTXmLi2/Y19kdvlC0II5ZhAuF4lYk1d2fmZVvex78/+M+xoKYgYarD4peqKOw0KXXlp2/vbrK1A2Pmft8x8uTUYjdLbkU2V9NvGYytzmyL/elbyovsKv0dTpQkafpYqUhFVDdWqOV73HG4u/UKLJYYoE9qqM3hRTr3I5OAYg5mlRtNnzcmQ0GQZET4ixSDkIGKJtIBEphyoGHiAgF2YU5KZqoVUTKYUL5BxXAhuIYZn0Y9t4eo13XC+g7VqxFXFnnvQHgoDPl4AIjeWFQhtM5dmQdqDGY86AskbOTQnwb9U+IJoB78eIiGfoO41mM8L0wJRYycfW/QLPzzj3+Htwx7U7K/mxjT3lUymp9Dtzdk+FT+VCPyasWefKxfdyV6lh4MtgE8uNvynuW9CTWpVDPtqX7vZ1y43FO6a0nuEbApq5wiNjnjS8cu7dmKvkt2BFCxt+NziQZJpchQzfYkVa+K22sAR/0FcWbi4PDm10AcWLFdtApJCwZM8H58g6oVXfvmTD81dSsjxdBTN4QjvF3koaD2yPSg07Gms0pNrPtVG2wKhfZLr0x/BE/xMYzg3jTx1u4jMmQ6ogKI7lHoo+3RCI84Cj/QI26XrnHb6aRiR41gDIMuXEW8OZyo8zapcSiXJ0U8rVaGq+IWKBfYLyYSNNuEZ7SmBcbBYyaUej02qzTS0epOR55x5JnNregz+yMhhlYoX1Oqwr7SUDZarSVG4eNOb3gTTAmD7YGLWYfNZjvDfrQeWEeDDjzh7gmbw6qgYoMODUUFNm8cPXwiyIifogp0UFOWHP/zh61//ejamb33rW+vWrVsQ4fBlP30xgAaoLWgVCDCgG8Iwam+UCiEtRtMEfJynoWooUh18crYKfwmIEAHqcrl/15Y84rcmElcBbEsF0LKDIi9jmNGYBXqrIei5LFSDRdAXs9CcxUC2GIJEx7fmRIT4Wg7cEUxcqfMbcBg+RFCYRJs3FnExHI2mU4yiKY3t2PVOOe1sgGGqAiQfkdtWa4mWaPb0TTGph1lKZRPFk+uBVbfdqf2PlKul/v2D6fbeul8Z2vXYtq2PnHzmBdn23lK0szBd6N/Z33vqBZH2NuCJB8c4ALgaM797144zTlo/0r/XtTBnbUZtZ8sju2FNZzPJjRv6gkw23NBnbd0yBw4o8iddWopE7VNONrBZpPtLIBEgbsfqaJviLocVK6MxEcLEWS94QfCO3y//85fdETSZjarlhRecn3jb24yuLurPKa5FMcmTodFHIHwbLaipc+AJFUPLLDRCDu1h3JnPUHN/NWICMMGzrnMGClaGE0RveHi0XLKyma4w0jKQF5ou6CtNEZSep8jrCvoCb5M/uf8WwWm5SwPgA5UwU2HLgNKCM9MKZgKgkeAoNma/RMqa03nruDHpQyORDIXBpJzKnrjyTwxGCSdU/oijERuBsGS4pQaS0LA6vL3xcAK/2+bH1pQE+52Ilm616zstuy10rqx7fWVQvRZrgJS5u9LBnW2KF6pGLLTcbW1T3620vG44Ze9a5d2/p3oJ8yqJzrYqWh6qAjIZ+c/T9K9sfX+bYjjnf9o+++VsMB0TTH1lofXt49FTp9e4t5+f+vxd+fdgiEswRzKQhPIjnGJphgqRVmnHfZ6qMsTSDdVyg+pN9Yq0X4ogRJKy8jSMSxCvwliG6SN5qTwJFdRQocREJYXOnmG9vOXj66O3M8LlHZnxL7X4O0Q+3zmj2vGmUW9F/rzsDybtL9+T/31V0lzdpM1Sf5zZ5u44yb2V9+L9bbP/p9vyUc01Ld+oPxyd/KDV/fGa01J4XuIf7y+8HuVqEmgUlGqRXqrGn3bSCnFSTZ23elFhjz9kmOeT6AowjxxzJmONo85dNDrLYZbmkYOiL6iE8sYroTJVKUESSvslI/UiodrRL9F4vMdb5SFpgqMx6gN9oiMc/DzEp4Mjn7AhbOlgCAdXr9E6erAxLAdHeyaG0DQmnT7v8IBRHIwhEId9tWHO42ltploySAlRiflpqerD1OYCAZBUkQBqOGoOtZGoEYuTVpxOqH6MrjaIudVAJNfCRDQm2JThi4S87GZWxfOcqNeesNqSovqtkvMrXv4T4cDFMLdEpIgwRIiggaER8tBDgqEhD78UGYivx8HRKkphHPGADaK7BMr0TOQWcqwyTLQFMABgTPvlrF3MwZhB/AQZh927d4M5c7Wnjt8clwHFMYeYAcVqkKvYIL1cNIBem5gzExNmRqVuFKrYIg1AfTFRwgyAAhL3wkREKKF+YAKD5UpypsAhSEbCtjgJ4RgHg7lgpuyszrIpz5WpB2Lu5Sj9kCc3lcLrvuGGGxD//tKXvsQ9LEv1yVEqczmbZ3YPLCPAC8dPGwhZu3YtywkH842FBPGMe494JTa2kXAEch0Zohfs9Z/97GfZVhpmonWOJOTaeohSXFP2T//0T0jdaD3MheWdkO+6pQuqtiDwD//wD7nJiW0R/mxlYgoiAUCkE0/X4sn9hXCkYD5wz53vfsMVUTfoa+OGJMw4i+UqbgCGKlhiGzXdtjNOm4lFbUxDiZJwGZgY0Fy0i07diOHs/oli19r15F/ITY0N7O/uXYdMJ6CKHPoyDugCC6FxsL9/qrUn2dJRzZy0pxCJVMLy7HDMs9rbOxDNwq4GGCl4QL44O7h3z/pTToN9Nz01/djObaVyIZvO7N/76Phwf2lsoD0WmZwYrwzOZDu6sZNZKBUQrRZKuIM9ICcXRHdO1DCNCtMyFrXqfnkQkzYY+RkppTOrfQt7z6XpsaGeFWtA2Gdqbnm0vjGdsa67tvCRXZEK9zWBEcKIxlZEvX7ppV7fOqB1AfH1saM6mlYNDQy1tLb2xhCwFDjd5jLel73MOessf/su7nSK9nS46/ps7lJ2BDUEVAUmY1AANSDxItlINgfzpjjdVfaHeTCNOUeJxNGI86siFtubCPzA5qJkzrZ6wFoI9g8NW5VCd1tLp5r5WEGD+8y5CAZFhYWHIOMj7WKIaAV/dL5cZaE80Do4SWkcUYigrsaFb4wHvBerOljzBoOz9k1Cl7AFheJPwCmBqHCKr0cdJX+VAWXwKhge/UBy5QQIkGCRzZsRRVhEcdNlfoI90fI7zEh/6IQ+b4X/sL3vQl5B7p7szMp/ccFWMwBBsUb5vqTxWobUt2bvHpw4tV2MrAooIK4Jc9Pt6XK2nxy5FY554YH07Ke7bKgiTGjuwtyfmvyo0/nRursif2bspl/kfr8Mc1q1gYdo+c51iDSXzAWa4IO0HnNmqiS80kQVDoAhsaRbVBQRa5QXgeMFs8RHYyV7eVddJ3mQWILEK2MkD+34krWGnpe6CUWC0v7UyIe6IzlMxUkZ1r3x4b1O78cGnLbC8xOfv7/wxoqZBklWSWUMaTjRyHet+zNuQAtDN/evGdOPqnUqZaEWFoylcv+Zyl5babN3Zsy9o/WTJYWunRpFqdlcZeRHV1sPocqerxIlak5sjN3T5W2DXz3q9+0qviAX9oDNYja2xX30kuxnNkV+FLdGaXkxbHmscvntM/9zsHoWVVS9Q7Mlbz2hdMPnn/JB/ecrf367s7PL24t/orZyvLo+MLChzdRDEIJqS8RnsZubZPN4oH49oL3Pxg7QzeTI5qCnsSBFCxrOK7TF5n5gI4erxlZ5vCFsNlI3Ym8+pcpV48WiXtHMSqEjMmVPOSVoyar10lxZ/GApRm+qhtkGOHUxaIIOTDMLU5Ge6fqnnzyVToal2YgXC31kmKEYw+ur+n2b5JZ7hejODzu/4p37WVBI0yvdoqUhAHtY0dp/MGWhKcVx8jZGFgQYsA1OBiwNGKeN8ONUj6dWDERt1BzoWEQL6WEsXS2Vn24X8eGLYttyYHDwjrvuQnsiLfoXsnuztRWVADNicbMVCxRXbm9WEkN+XUyNgg+XakbRFxSXK+qjXLYcMZNR2bNhORQrBhdzIILDDdKpqNmawAJWWK6ZSEpXa5zpYcwz0xEH6WhkoVvtSFuqlUNPH3JHvc/JkG7B1uxb3vIW5C7f9773vfe9722okyzVRcvh/217YBkBXjj0cNIQVMYwhjZdyyvasKhPcGuRjsrO/ulPf/qKK65gQ7n++uuRhUY3GMoT/kZe4CRvf/vbsdKO0u+73vUuwtH/BMFAuBQ/hwEHZyPyiexBghf9qEPtIFyMsbe//h+3Gg9t8VCmXdMXueySkzZvXpVJT4gNF9HVbPWCJMhoKLgtuy26waWqM1qst/RtiFz/yspXvglECx9RJHcDv9Sa7XzDb89k2gaH2W/ZnQkTyJN85tAmgZ5xGL8VD1u4OpBNLy4KdRVs207nHdspqVthasVcNB6zg8rM4H7sW3pjM7nx4ZWrV2HRp1osepE4WFN+Ntfe0etEvI7u3nGM/UQiq9b2nXfJFW7E+687/rMzkq97pr2u287tr5TGym6i3tq7t3+oHnCn4qpZNzNb5dYhs1acmsxZkTATcRwUDGenwMdi6899vvn//O/pm75mgS27kWKyxbvqcveaa/KpjtlppCuRJRPVWQssEoYmbVESTQ2IgbqJzGEq5W3YgBZavlisY1bbiQg6ADHAcbSeHvMKUEybVZtDHvg+747wjCETgD+dnOfDWx4em+WenuzKFashAHPw0fszM7kZf2hlV3ZdXxscWKgPaPtUsZoSqKsvfMSiLLnKR7EKGDvhMar7J0GSGSzwaxqoEL8G9qVRDkGSNfYqd/AIRmsjmqrSiiljmqJEqeabJAMOjEdasQimLtqgPxSSRBRwJnHCrc5Xu5CBhTYtVQmc4ket6ICIcEmpIE5rOYWFKyJ4DTnWlaAsr/PlSHBN48Si/1SFOcSQKFRSIWbCxyayqjlRzbXRB2yzQF8V/q3Vwkqbqjl3Aols9Gws9+N02xsLGac/bW0vVJ9L5lIVyhVAQvWvNEFgVylC0Hm+yXdVoLRI4vGuq6tiCiIvsK9MHqFfSFbSw6QTn04vHcOr+kbG6gMhstwUBkt+q+K/jBrTRJr5TsbLR+l/nURGbCAy8YNE528XM/buTmf7QO0cKiL1Ik+plMo3NBP2MK+IWfj7uCRF6jPvqG9YfSwKTO0YpbiF9peugXqKkrTkQUcKYURXjqhz9Vc/YlSs/MLUl85P/W3S7Ndq1QxrsaXjV8W33jHzv9bF7np59g+S1n6GAut6VC5pVk6JfXm99+8/nPmbX5VuVN0wX52lf0m7PnL3hemPrnHvdQ0sYIELRMZqp0QL7wzc65lmSyd9Vn1hImEvly0FrtGzqmGHawwnO5REbArCqISe+PhSYRo2ls18JuzSSEIhUHq82cKsDbTYV66s33C9/6WblA4RQTizksy4N95oIAQ7t8Tm68pSC81sxM4gvoxXrV9N6hNyHiohazbE3vzG0qf+waojhiNcOzKoRpLe6240RHd3kebPZ73kL1L9GkODowBl4RAY2pJZHOMPjDIjjuweJpSQelhg2uMYF/6Usj94NpLdooGNYvgK9QHeL6J8u8RmoJHP1fMwbCsGzxrjDcVbnZTFmog0F2sBttPAbzmQPLl6wMiA8Qr8ZsLDmCkya8SmGoq+LQk5AYEBYG9MFkME94TC4hnZKDdEcmWDBbt4HI5IoIx+d2RXtKawF52CcNSo3FH10FLmHldhf+xjH/vABz4AAK9Vgo9qIcuZPUt6YBkBXjiQaPzC2ESOAgEeqLzcOYH9qt/93d9tGMRjgSFfcdddd33iE5/AQDSmgz7ykY9gjZ39Re9B0OQgPqH6+/Wvf/2yyy4Df9Zl8JVA/GgRa37dwrJPvHfq3LARuqB2nLpBzQ927Sp//G8Tv36wVi0TOf7rh8q33x7+0f9KXHJxa4wtNcTm8wXrXMxv5CtBTswkCMmw7IaFql3oaMu+/ganPVv+3r+bQyOh7YZnnNJ54w2V00/7zZRTtqMOMtDcl5CKee5q2HDAygLkCyZg5iaHSwVs9gr6NTs9nM9N8aGzdy3WWShUtmfRHC6N79/pJdIo+o727822duT3jEyP9OfMNtPpKQGTVzwj1dW9wW7taB/cua2lra1SrUdR/nPdSCyBKlQ829Ha2Q3+BjrS1t5huy5avqOj23siFhMjkUxX/aACg7RmTsHZNt1p7NRiI5yDIjT3TdXPWZE+9YWXJU5/TsC9L6WK0d1RzWb3mYmdu2YnJ2ezrW1xMbYcTIwM5SaGPQtjYaE3Wvb24LfOOO2UdBI7ywo55BoYDqdSMcYcm6OeCtADoCLdIQ8NAx3mFFwwgs2vjB1CgOQD4AuZZuOqjg0ioh5ra3fBzdGgrtTrtY7khpZ18UQaTUvfJwTNa6E4MCjCE1RGIOkqeeWSDDG1Iugrit/IUSsecojFIm7uFRMrCvnjbg46CzoGNGMCpXTGWHnwy6GMn5pp3I8ihI0sHSLtxNFyggQ9lrQ48QjyRQrJdbi6eipY12az6IL6uGs/ULcDK1BAYBCpe2+EvUoiEfCiLO/0XPEXLWLIUnUxRdAk9/QiLEzQrdFKb3tbi4gFK1AYtHuu9ySWlMivIIFCzbBrg8K45U/NVmojX/2BiJpH9bg9Icq0iIWr4SIJLVJoLbWgQ6gRdaAlGo4lrYQIMUF6RJUm/UKhIjgtnTM/+PKmsVLKkwoIjCFdrFqo6y5VITkP1X14oGNkzCFhG9ed+i6Yt0JrIhzHDwNS3RGlQtyFnLAH6tWzCeSPclQEKZC3cpCSIMDplno4RXQ6TUqXToAD36LMm5sORrBQjSdQ/0lDpTBphAqTFITxox0f7bB6Rev7nhf/HB3AB9RyYU8YTjVhDl+Y+Kte965u76FkOBFC4NiWrP4aAXXLO72UPC0fs8dfmvnDGb97d/VS6sC/x/Od64JGKfRG/azYTVdl3xMxZ+lcLchP0V3ur42Rt9Sqv/E6P4yKJVLh81V7pv7KsCnX1BvzQTpcqC8W1mVR/FlKOfmABM+WFxYI1N6f/vSn3IR88cUXH4rsyzxU8/9paToDZ7a2eq98pZ9JVL53q419XQiGp5xs33CDdfbzTAw1HTi0vOlB1yuLNSU5yL+5eFZLOv6qV9npFuMH/1+wf3/Ftuonb/KuvdY8+0xMKgIFMR9wR95YvUkuiL9o4II4x/OV+iBCxVnPtcmIB5911llov59olTxsh3BqC+mzyTXPTMadM4iDuIAZUfkz8sk+b/2aLRMuk6Bc8wE6gM3AeBWPF/RVdmP2emSYQWLjsldzlZ9ZrhoDo8Non3euWonMcwp1NMxkIPNcwxC0+FEOj3lhS1ymHmc9YtL5PGtELGLCLu5A58ziDgVrz2Q4OBOuzZjPWxXEsUGmhJ2Oep+TIe4973kPZnqwVsONULfccgtMrKZOWvYu94D0wDP+OD/qw4gwDAafcQtyZkU1QvCjx3/TTTc1h2g/1ESwX6jCt99+O3Fg/GpOMobv8Gsxqiekk9ko4kTz1KqVwsiQ9a1b4r/+tfTNPEDg5WZLn/2c3bdhLkRszBrcOMhf59zJDJ5jFat1sMewZ4V33fWxF11u5Gdt1/MTiZlofLAeL/pmVxxaY1CW6wcjbjQGxgW1WtRNhe0GRgpMi60eU1RR4Dlyq2dutr/4kKDM0lNWf+5hv1bxq6VauYhZS6xYcR1SbnKsf9fD+dmpbOcKIvXPYsl6YHpqvGWiMLbvsdaZSrq9a6qYr5bCbTse69u4adNznpdKxGqVMlLY2Y4O24sMbtlGaRs3bEinkmAMGMHlLLDjZne2ZfMqjBshlR3W/cCvcwGjtXfSmCl4q1p6U5nuSrE4VfC3b88/sr+fRo3u37Fh83N7128Cxa2HWT/mYDq5MDWSiMeT0RTtenDMc6Zoq9xaIfclijh3KlI0vAq3Jta5TIKECA8DlXT3dHV1Y1u42R0Wym2OLH6mNDZOecIAQTZMU8f1UcrNVYWZ6Uw6bUetziQMAUZb8ldIGMgI2CtnoVwxC0osxsbqHIFoa4M2m2C/SFTVsX8G+gaSIfVlfBgxObWJhjYv6DH84YEZLlTQiJAUIJGkWvwTZES4rAKBKXRYV0tpkZJcqgF2A2ZUq5XyuVgmE6JxrKZJYKbuzb/5ivT7ENwKI0G42a5uRd656q/y3D9w3LNA3slb0oK7ZS7N5e6aDB9pFRCAksHJuwstr56kAlN+z4jxW4lolGmmGs53ASLFL0xSgYapR7GOATbpHLOjEo7GhbcptUN6Ae6mabdXhBVs2L6RhfkvOallQ2qdmwSoRUQOki0vkpr/ko2UJWmUT36YGqQXq66Ez/WV9pAvTnpAIktd5Yc88JK3+k/gPEIYhHbNSKiYME9rmoQgxYH9YpCbGqcEkaevqj5agjo9HSwpqJ6iT4T7ymcHWaakn7osV9ibQtJdJMWlbEgLfupirlYO8vWumfoGmPwENzteVXaq8bRV2i9RpBFheEb8G+fGPmdDZZqJTn2zrforJNWhVpTTr56wusobvNspB/3fiX/uLP+gxfbFUkDxlnrpkvGOtw9H3OlL0n/+2MR5YZggL+BEqdF82ZK9BPAM19n3XJl9j2PPBtXIzB1J/940Rt3tzfnWK0tWquRMfcr3TnJb3jSf9Gn/bTSiuSbSFuUaXxshTeF85I8vC8dhLjE/nFPcIIoDalRT6fFPz2IfLYWcDWVz+/btXBi7efPmg9vOhOemKFiaOL5iIAPCN0jUceYfUrTV2+2+/Br3hRcZM7NYvAjRuOGesyjSzWpOHzBOC9qxYFYQ37J7uqKvvNq86IXB7Axbs5XO1NKpsUJ+bP9vWOR96/uetMFeaP2yiS3h+ATXnQ7ELRHlGAbTL2ixwfNA5AFGhVYpOoblHYOsNYmKhsx1seprOS/YsQOjVA8GZ+sjeXZ4vcWanNFl3y35QCbVR3ftg4jfBmCDnQ47wLYZ4s1MHzbKCko/bPXQ/Lii2TWysaDm+n6tBsKQL2K/VLSFuNeKv6RH9EAguoo1rU4HbkxMeFYLBy62W+rou5mzKAaHJmzkZAQwxpqoWg8N11qixqYOKMULZ+PR6iT6BMHMO++8kwtNkdZEHvO/m0jL0erJZ3E+ywjwwsFdcFYs/Dz/fnA0jg1kod/61rdiMhreLycicdnW9ckBUkGEJ32KzBd7Av1yqHn5gn/Xz9StCey/csr5coUNgoNj/m/uD7MHGCQkgobPiVbOz8ajUYyrymHdkgpbxJAzuzYY8dR0tVK1uqJhLQJ0j9S0sBNBqxCwQW4H8Rv2bpRRE9meZAYN2GBg19ZsW1eqpQ0t3GJukjLK9PXUmF8puk5kcnzYFbzFzM2O5qaG0m09iXgG9u7M2MDMxEhb12ovkUmDHCVT0UxbKTBW9Z7kxpMoRk0bkcdyWP5wrbzvl6vjM449VU/Ew85VfSFX4CYT8AAjgjzJwSPywCJnK/C0NBMA3WKjF74xKldjJXOUq5JytM4t+2ZrOgaaHQkKyUy6Vg/QlpmeKOamJletP3UgNx43YtlEB7aUJ6bRXgYpFjtSWOJBQBzuL3/Y5hTURyFAcqkjerOIUguS7INWgSerP9RflUFmaigoCEOjoV192IAIzKEngjtwNDKbF4OGdSAoMTeBAccwsSW9HFnSaj2asKjpefjGKBuLDLo4PuIaBxtlgfGKlDek6DLi3iI+LZxhbojyQZjRLpZAsSMNlsw0EAtpInctCAr1o/YEcrJz0sKNF16xnNE8pNKqMuLjdqb8+EAylQQghUGrPoUPV99ySu0n69yfONlK6gtOMIbiuBPtqoWeCJ3XBxNhxPfauPrGNmPVle8bmfq3WuW/4lQgsrmQeVXO7iiBH/50+A3R1o1ShLA4pULyoK6Kvi6YvWrvvvJZftZ1rGryxbnc1hSDw83PzA3HcWE4p17EXbdBPuiZ9tcL3jXP9pWuFF6u5CHdJM2VnEdnNXquW0gDGXdFCKBpqEwb9RQSEipA+kOGSmUk+YhH/UiedJOqbmPEhf1OsArlO7HrQ+VNdYOLPqvx3yrmH8kIcUnNEPobfnbq4grZVYP0eG2TiKhLruIoUlVO+nrMP3eo+pyV7v3ZK6drg3b5x1mby35RX0iUW94yGlmL5TprS/Haaii3U1BhlYF+zFWbHz1ocxWT3obgVXp+6u/FFEM+Ovqh7tRAa9qWyzeMO+3K1kj0A/1GR4leKfw8Vfp+px2gZi9tg79evq1jamOl5crx3sivWsytQ/45kvtcx0hJupcIoBSslr6w9WMRMx/WvJFPdPo/Y+NiKgb+L1Klu3OrPjxmZkrG+EeC7Ktt8/H7NpuacHy8urKURe3Fr0qVlii/9qg3eadREk155mJgKd6YmAlzUwgsGqmEkW0zk3HdKwck5ga1LmBia2h4AMxHz665fA/80dZlkY2S2fdscQjuDg4O7tq1CyLgogYUoGWz3enmsjfigd6tj/vj0wd/93d/d6AEGTNBpnZjDNiun3hNLItr8FKp0SEnl5tNRyO5iQma2dXRwWRaSgrsSEppYGhLReZ+bGivIKJLRTim4Uxd7HJhwZQ2Pu3TmL0IwgrM9oOVpdWuqA5r6Q69/GXAORChOCNmnEfIrhYm7LDdC83ZaWd2xqj6XjK+KpWNtaTv66+NlZ3/n703gbPjqu78q+rta7/eu9UtdbcseZPxbsDxxuKVYGMwEAbiBIaEQAjE/EPCZJIZ+CeB7DNJmCQkBAifTEISMGExmzEYDDjGG3i3JaslWVJLvS9v36rm+7v39VNrtWzLsi2r1KpXdevut+qe8zvn3HPZNM5O/ogneWPisVBxaSbi1gZWDTLRs0yXjx3+AcKSjlEWr5Rbq7MxBZqIYKFYx2NlplaB7UnHIr5Ux2wIDxmEydEC4HSCRS5UTRh7qYq+F8EvoDdIJdwus9SlWncWKgEuzFl5VG2E+htBX4Z9s9COQNno+/b7q5KPyEGeiLHAwG9729uuuOIKnPX8wi/8wlOyZTgi1TieyfO2B44D4CMzNExb7BuMLfQv/uIvskKYJb77fM92Utsn8DDLNnOf4j695IdZymFGwy80HACR2e0mzLY8zI7wVRBfpi+zGQ/MeDwSdWcWnbQWAbcPxbBTXOCMj49D82IxOZomgvkv/j7pBSf1sNco07pbrftoiZnT8b7Alp7VWqAlptJ3SfULdkKBXEPrmHEz7GjAVrSxRCa+Bm6/Xn6cRcWVQjHGRk6dvZiaNhv1VKonnevoHBgrl5eG15+Vn5sKhR4aGNtQKizMTGzpHzmVmLVqlZW1XYNrwpFonabBTTtBYX4OqJzK9U8UgrScQLCNUmyyJvUamijWO7KIl6VV7JOiHXFDutBCViORZUEsRuIG0znRZEfUCXZsebwwuZsFMKt7OqL+vLu4AEru7c5MBcFwZ1CbcmIJLxXHVxb0Q44WjS8Kukfohv/8hbDh1R5CUv8Cs81+QoBkFMLapJdONABFfp3YxRUDclVJf032q2B7RmLisBpxL9CaesLm0v8sflX25oDnW7F/AM48C1iI8QaOjo7yvP0GmiGzKRysJXklTj/9JXYkl8+tp0plQDZ1Btgm5E9UtvHmj4e6pnRUyL1pA5UtBjYW1MaIGsDGUiKdWaoE6w7JR++LnRXJoL7i8Q1aRixdijoJBAahNpDGiCBzR/7XRrpvDWFJ4NVDA62mYjFbvTNZecTJvZ1e9qb9tV3hbaF0tfttO523sgAZaAn+JO/QXXNX31P/dfpW5UpXrXYZ2KBq031YMhOdrplqbthWunht8tuZixebk+HyjV1Bkdb5Trac+6W52FrZhD1cflMpkGCIjKi4+a8qGQ5Wd6a3XGzV5ipkbEbTbkTFAzO0YhYw3Qm52aSKbRUunEtivSEaSgkOTEXpXDgZPQJim77iMbH0MpmINDXwJhpn4C9qJHJH9vL52uZw/Ts9rOaV6MmtZd60mDmXjVWcjZXLit6w3jFuWocqzj2ZNbzkd5Z+/81db0Xp2vvLk5XLytXHEAI5qTMq4e4SWU03z7i9+H5TY5NGKU1ypVZ2nOy9mmQecu6LPNYbeQxQW7g50z3RHQ4qzlKhWaqArKOzkdqXUvFfLpO+dFeWRpkmm7Qmu9IPsp1XzoXc2lD04Yn6S+lrU47KojC1QlIk9XBXaMdI4k7ul27LNG/Hz5mqpIzIdHNn5aZG4q2VUHNHs/gjL3O1SX/UT6buttaGM21XgAc0RRXW12D6Vy0lwBxqAz7w+c4XF2v33lP/txtDjzyi72f16tC1Pxt5xSuD3v7ZchPzGjKyKXj4+MYtM80MXhg6dxdP6EuywXu7vJUXc3NzOHdkHezKwBf0NUMP9sADCEtDgfcHBMCw1HQU9uEoipkq96f1z1IPUKh1JbXS9OwQZdnIh4hgH7WndG4pAhy4ceMmOgErAEQba1avIfBJMzlYhJWZHywO+XOgV6e458Q4jkq2F7ixCTZvNUKQw6n5wVr01MP39DDm9/uLA5Yfs2mGzKzKYo3wXMXyXRfTZbSs0Dbmgb5ksydccn/6YOFfbqzffa9frYUGB+LXXNlz+eWnDwx+b6t4FdEjEQLMeaI9AyOTExMz80uVWn3tKLbNojg49ShjwlaT/RBMgl/JL+zcxquB5B2eZPNjjzLLdA+MdPZ0Y/6WAmyTo9xoufNFtZs8krggTRGutU6lmrdQaNYCFhLLpjqHu/GEVBrFqv9gwWN/kPU9wSm9zwL2XR4DxhEr8ZtuuumGG27AMxbSFhj158TcYLlGx3+fRz1wHAAfgcFg25h3v/vdmFgAgH/jN37jgBImJrWnXRIIE38bTMrPyYoslgCxKBTbMGYNeAKkyzA9tIXdbvE2wJ4/cdw4Gy9fTIVhMBaTKNCwt0vOEA5+QPP2f2j4LM2GcFzhqMe2clYsTJ5wt8zLVXnhd4p1KYTTEbc77oQGRjGHbkadqaq7cao8u1RZrMyOjo4t5DqTHb3zqHnX4EHKm8g8lMl1pTp6Zndvi6ezTPBg6VolXyktoHhscgviggHwgy0P3p1M5fyQN7wOxg7Q4RfyrO2NxSqVdDIln0xCF+IrVVsssLFsFVCRZaf+QHk4XAKRemyWKKQaDcskF9oDWE2QTSIeDYdKVQB8PT+TD3B/jIAUx2DzUwPduVgqiSBee//QKgqSDg4daUtTit8pg42Ry2JjDLSBSzeyXFMfcfPURJvraj8hIWRtJEUJUq4yXKypVgTRdlSZQBxaKa1y1JPcF3fZZl9HLqKlsgAzlk133HlPtV5no+OeXmCbOJUVo2Ywgr0Xw02PWnS2IsrelxQsxKGMuFI9dC2C7Gzo8xBnyOO02ivraDnZYv0wZ8ykzTWdL6kEzLqh91TFAOBgfPPmZr0eBRSlfW9pM/0WjqX6BtdAgOmkin9JIXxxR/1WjVvg1OYjpTsjjfu8yKpG7m1gK78R6rqj8q+Ryn0vT/xZOrw57NfoHUQNBX/4ruKv3lb+lVA0JbhKjYG7anTrMFIOXhHbDpqTuLX04YHog6nQrs43T2YvLuQ3obV3shvqXkcJM92Z5unfX/xADQmGctOmuvxyUj7Kg+6jc5S/VmTJnZPuyVexVLp55cxFlmW5XAi3KL5A0fLg6Fc5mxPhvCEqTWelUXH6MS+xQsjed1I3L/yPn+99azy00P+eqeIri5UHE3RC7sxScn2BzPP+qh/kP4SwhtqRs5KZuphXXjkRvKl2yTcW/vqyzg+yVDixdiF+gsrCrTtSo6nGeTfO/O1iY0gfjyQJ5pHeXrVOP6YtpktMvfR1Ee52RbZHMBrA5/njqZBf8idn/XyexlEiQ9TYaGYbXmNVVpmsPAK5yFVTo145Iq3x8qHKr7hmD6fY9iixcSP0EzSixq+QFmmjsMdBeVD7STr5X2AN/aX5RxeqV5F0uSOVCZXh1Mrd9IzNW9F0pQrY4hSiG3WgvVAqXZn/DKWubGcohq51NldKpVIUx6TWSU/sibNJ3npEsELoXN74TNjfkK2G7ri79Ad/yPoUfBSoVg8/Wnt0S22pELn2jTubafwiiC+W3IzvoblxZyUUGZgtFf1NlY5UbChrhsm2RcmP8QP96mWXXba/Fo5m81LYcEvlOR96qfAR7CmK/sQnPvHtb397+fU5VN4Qa9j9Q8U4yDOWgLEalv2B8In48pe/nFiUe5C4TyeYyuM/jDzxwIwQwWYBR4EFMip3Fm0esNufTklPJY1tIzZ67CyIPJeLk08++cg2/BDVMZ851IBP2ExYUDzNcFIDlBtN1t8W67iYQsErumB24jVAFn+iTb8KLyRZeYDrwQ2dTffhR+Z+56Phyd3Najkcjoa2PdH4+Ccb09OD73jH+t6ue3biMx8Pk/KUko42nZibO2HVEzvCMwsLD23cMTw0nIjDwaEBxsem5gMMm6uoMzpWQYYqxflYzB/o7qK28aQAbl4sCDJ0uchi06NcQoRE+LkW4CWLFwfXJVoYHNFMxEKnctXFEQzzGJwGILkrjV9J97FZDKobIzkvJ+eemrKO4NvGy2a6NAhHwuilGNPf/M3f3LJly6c+9an998s4xAAdf3Ss9sBxAPxMR3ZqaopFvxAM/M5dd911B8vu6X3XfL9Mx5xBofqWn4uDpcvj4+MnnXQSNBViD8lf9nvp+Wyxc9HF7pe+CAjDBzBtZNtealnuHYieeYY3sXuf+rJiCvNkAmkLOkPrE5s10m2ad7BeYk5EUZMOe+mwwxpNbiFRZFF7YDx46GG33oisXTt60vqOro5btmz0Fp/o609mO8AZC9XyzkQ+1jUw3OiID46NFkvlpZA7v2NzsrM31zfI6q1strcwM9nRNeD2sQIWdsZ97I5bgI6xVEdlYQacODW5EzVHzovnMoOVQj6azFB/OfIBOtq5Gr5R+kmIgQZIE79OHFQTyCAXR/Dy4A+1oue0XP9L2Dd+eub+gYE+r0Qlce9cBPEuzuwmPzwjOR3ZKmuIycMwuMpEglU0t0BWjKNa3pNgcHkpMGky8FiEB6Mj9KLCikgKhP1QmFMNMK+tDBWSXg80Tp3YZEjQ19wafTJOHT1gM31gMR7U7LHHNi8thEaGN0x4q6Z34X3IgGQ57JYmGS3c1M7t5MbSIN7PTZu3MK4Y+SNwbZWnctuH7RNuVR1aatqnp+YezS3bb7TC+BEt3HMIjxIGN89HAFtAwxvaClh6YK6Tefx5OHj0KBaDWJw4rhcJerqxP5YEAUv4WOrPgt3Xut4T5BHubHS80nUuQ3SgzXcdP+HGPjI8eOpjUxtuqV/TG9zZ4W1ixfJ8dWR7+cxKaFVXWqiP4TBwRYCUelEfAQwu9Ei10p/j7G6c8x+Ff3xt5r057/HwQCE30G6IN1F/+b/N/W3eH5SzaZNUeyBrcPR+rKT8qOOQ8uBwSRlTONF1hZjfFK560F3IRGw9eELJy4dBguJETL6tUOWu10/lyHW2ydPeCEjyKNjRePXX5v7yiq7fzHiTydMWkhvYQYr8SeDn/dGvLPztZPM0k5tQmZrKIZW4qR1RlYd7V+H149UNF3T84/rYN9PuZNOJLvgj9y1e9+PiWytBr0lERZgiTGy1xmA8ClI+pjhzred6irk7i9KE3d0IPkYxrVsi0DTDeMTKa4ky8aI95YpqaxtIUh2x9VXtROx4i/6aZSGFfWJfLtsGhegLBvFSCg7dda9/+p5NLepVk7UTbCsEm0DfpvKUZUq2NZVIhnRkoLNOtj264DBtI9ykEZtLJyg5g2Ti0j7uzH+br0llZS42uV4Bk72JZMsiqgkkpSItl2PjkbkplbvzVvne/NzCP/5zaG4RhzWmftSU16nW/PTnus97eWr45I2y6XFx68B6fT6z3UtOJpdxUp2TlegTS85QVr1u60o8AAx7H3CBZBbqAHThGjtSDvNOcfcCPmwToHQMCi2FNh1QX0QE5jpm96PWVCr2CnM8eyVSBK3GFxSKMhgPtLJ4b25T5yNV7s6dOymIrkPXavMEDHONytp8CEeqnKecD7ViSyQ2RkK3T+JnDQPrWzUH32/rmF9cKpSqbJCxu9hcfKLg7q4XGqFktoeFQYaIw2Cgs0X964B1a/UGHkZYF4ZPDU0mqGc99+SuoCO/kP+3L0Qmp/hWNQ0wPSPWh4Lc+FXnop9Zf9b5j8/BGoSRoZcrPIN/keB7aLifzbzyS3O9icW6Ey9Xk+iToSCAZHapyKVjuUyU3CafoH6VerRXy4SxbY4E6CeYTaBW1Zo7VxX7g19owHCCTZJiqkAN272qu8j2CviRBgzHgiyVwRyazZNqeM/SfJkIo+cIpyJNRJRZtmde7pAj8ysCg3JBO4Yg3H/f+943OjqKkebll1/+hS98gSXfR6aU47m8YHvgOAB+RkOHq4zXve51YLmPfOQjqGeBwSxl4WAqZzLleCa5M4EBEXHjbg2x2FoJhgNqxO0zzPmp1ori0P0iM4PqX3TRRTfffDM5wM95nR3R664rb90S++lPYI/Ql8DSVTuy4Xf/kje0xtk1uU9B1J98bCDri7Ab3yfC4dyqT+G7t++sfu5z4a9+NVqtMcnhzbBwxkuyv/qeK88Zi5R3reryi2Wv4tej/elwvDbSEx/InRSJRlnaefLwGZse/Glv90CH34fY1ItEZrY+iL9lJxKvV2vzM5Osx2XRJkRl884nKrViqVpPZHN4PiwvTkccv7w4k+7si6YzzPcoJGEyzT/L9Kr6AEODeIGS4kqJxpJXg1ksB4zDKAL9GUSk1USi71SULuRZWpzvX7MONMTSmvKS4KUslj3qB+a0q3lb7Qbv1WtsfyP+VkwpBBBnxcYW2o0I02LtCERVqLSm6FS1vNboVLnGN3YrJE+dxLcrW7CgfpQfFLUFkhvVyrZZvyM1WM+s2Tyvdci0CwpH/ggIuEbpumNbURwTprKBj4dsVM19hVR/o44w15hYGwW4NNJKSKNgF7FQpm78t92jYvc6VBGDD9q8ASFEVrgstNDQCcRT2XYEZ+DMdTwtFPIY723YsN7uMcZj+wdhdpdWF299cyj7d4mzyy4OQaKInqGKkfquZOmLo5lfv3R1Rq9vw08Xiucvhl4RCoemZ3bO7p446aRcOsOevXSzChQMpj81gJgUI4TByouDXtV4IHGgz+vBJd+s3Hxy7HOjoa9FnW0sis87Jz5Se9PG8nVepANH0nLqZZpD/chW48ChEP0A7CgiZfav5rHF/Ao3pSsGY+s62ShFskJecnrzqrU6Ebt3rqQ+Va/ZTlbWujTFaBEvhwGelG4qYvvbv6/6c5Ozp16Y/tvR6C0JF0jkFf2Bx2tX3jb/npnmCeoC5axvVrjcJDU5mNqZBhA2Uzv5yzMfCzkfjrjSAtSdrNkkklKpFAGmIsqJi2XwYNgUDbDJkzOfE9XlYqG5ruqmI04hdE6h/m+1ZdkBr0LY68pEXs3i5AqxU68s179TaEykkQjRvySv50q912KoEtCEXbUzCbPoVGWYnM2FKS9wFhpDDe3cUYqcXqz/kG0q0fsaSRAzBnXeUGLRGuuRH8+vf7yuvtRgtKvaymj5R0902E/eNorRsxeEm+cmvb69Vqe2Eqkwk3PrRDQbwSYz1zxSW1SEeSlMAnPbKotn9rGicPAuee7EdPXBR1ustM3MjJe/sFR96NHUyLpCyakF4bmJHdUSG/VR26C4ezdCAL74LX0nnj+8F5/Ae2+ncUkhjRMjigEJm+KOkRPtAgFi7jQyMnJAb1hMMta95dFssPmgj0CBAE5MnQH2+2dICG1HDwx1ljON+Xkr0DwCpa7IgiLoWA4kKTigwtwatEl9AN5UgOsjjrpXFN66xHAPg2dM89plUTScG3t8HCkMTDMpTJ+sTkwJmvGQW2pXhQZbDUlui16X8/jWmYXZWWZzLXqaXWLhVLXprVnfXcEKq+ZW5SaDLOzULTk4rBb/DcNlsvaDznjTKxerd90bQmJnV81AprBmAs7yc+998dPORoqOc8owa6PY2gh3CdIhOyn/kSsG/mlk+OZUaK4RxGcbp2xz37a5/ppqM8lKHFMqcBe9cSjpRXtSagZq58WSJNEAZQyhYxG8ZxEMNZRJdlFiNlH8eNTtYDMkMR1epemCeDHvgqVJRl38SHcTw0fFHWye8Xcs+GtzzkvXuHjVUmIRrqdx0N+ms6GiRnnOEuW5MguPnWSo2hfMTE/PAn1vueWWN7zhDfCxX/nKV46l5RtPo7+OJ9mLsB3vjqfUA1DHa6+9Fu8X3/rWt0CGTKaINnfs2MHXy3eF/0xyE49sjqeUczsy3jjIE0shiBDXfN7Q3QMuTGonOVIXlAVxQgTOBUWzEPTss88G24+NjdkixMiHwu7atd5vf6j6ve86P30AlFwZPSF86SvCJ53qZjP7z2IQGw6SsyE74jdye0q1NRlSHddfnK9+7abYjf+OByZNu+hLcSn97W+Xq7Xch//Hmaed+ugjD3//h7dd/dprrrnuUqZwmD1ITr5cK9aai6XalFvMBEuNEIuL3Ugs3RNvrOvxdj6xcXLTI7t2bo/Ua6Vdc8UJd7FYSPWNJZIdqwbXDJ1wMpQJpe/OzQ+iHI40k3aWNtyCUC+gzPCzYlLlpcmwhrSOW6ifZV0BKhhDK2oQ6V21NppIaOenAHIYqtbdKvgVdh7JaFVQk8IEpA3JkwZM5spatQs3K02sMa6WGtZYOwv3qioJOKTbAABAAElEQVRogFF7YjEsK1PQmiW8VAxjbFxJK7pZ90s1yJhIIDfpUc0+xBBdoDIG3YTw50TjfUNrw9H4TEmV4D22TeFs6kMlI17XSwC9xdnJ8tLs6OpTYuFGV6eXjTpx1MIg0rozu+BWWCstBTjpTBc52kwX+/AwwgdPNtgxQLVByFxbvTQUmgpSYdDy/m+RMuI4AI1sIQObZA8JlViBPhsK/cVIeaganLoYdPqRaiR4PO5tSsYHxrxYrDcZ6ksxSu5992/BpXY6nVl46JGujshFJybQPyLBsB2FHReDK7jbWpYs+Te3y7bZAsEQ+2YwsOjf8IBzw+zk1sXFhdH1ZzsJb10O5sbgW6LoDUGKwcssNoIfRlgCC/pZq7xc3rC6lJACw/wxUn4TT9pSSxKBN2ggDdIFqKhf+VFjBZ/MIdjMLfmZYGVuQ2R0zRtpAvjlPVSFSaVXxbwrE6XT/7XyV7GgEPWnpT8PDdTYVVcPpSHVr3y6C8rqpSYLcXagYb2rpnRTI721ibojh7Q8IoFy0KGyzGF/hd4VZmqu0/JjLtQEJ5hrjmyvnH9C7ObwBcXK5THvJhrteJGot6qrel41fk2BV5GKuF3Fjv85Uf6XnsV7EkwGkVPq/ddPR4fyxP1J6e2L/gBRbC+1SrA/pjcoZq4xurt2zpro9ztesVT40bRzH35r1Xkk8geKHW8ESLtLzqrHCy8rGsWzaYIaRfVNPW0AmeqlVEeoW3TYhtui1T57TzRzYbT7KyIphaQZPFU+y08Uokea7HSoe5dbYx8opvLck0TRVH8KTWHNWKshvOMrU91aaZWTPupqBWv6VTkWILg94R6/IVHQ3O6Jjs7uaCKJihculTZK9NCqhXw+sUqW5NCFY2wNsDpl+WDVDyhxfHycbQu5Xg5u/R50Xton3vPyFg0nhLhNf5kGkEfj94uQ9mpYeBgQKQ3n6TNvLJkAOK33ECs9eeKJJ+gb5CbwThxIbSkOLPrMyzrMLqfJds/nlfEpfSUG5lUHpa+o0srPemW6va5tJGY25HEs2cX5E84vBXcbjTK77GIsh5QTamxWybLqB5VsM706FFmF05AnNm9K5vpiuU4+9x0l6Dydr1meOnCtT5C5ESm2JgMIgREuMxOHULGypMn3MS+X635DAWSXBr3yYgj0KzVgMIZmdUymNSWzzCrAyuEl0X86P/HbCWeWmQI4zYSQjY6vcW9eE7zmDvevamE27hCsRZcb71wFPZovSS3Mpkeojjl4VMWhdJnhhXrLiTROoT3cl8rnJWbbLmbSVBy/0NhCp7VjsEd4uebMF2XBxPKvRMxJJnyEujtYsbKzvjoTrOuK7dWbh3dDn0MK2M9pqdKcrcjvNDuJzC8VFhfnlxYXm6WlE5KLTGKwnfCxbGL6+te//hWveAV6YDZJOrwSjsc6BnvgOAB+OoPKPIP06Od//ucxmPn85z+P6ZfNxcoUMYrO5eT/iWjf/e53mU+Rp4JgsZhioj+c8phOgNNIoFGZ2r3p2FsYKSnLgK1263AyeeZxoBAAYAgVim7025i5Up99ssWxUmjshHj/gHN1Scx7LBpKpXB+vU80e2sJCa1rP11BWtphh76AhfOduVl2LGRxK/QAmz28ENeRVwMObvtBY+Mmb3Rk167djVoTY2Hxh5xR3zj+j79/SzKZEMUtTC9s3D43Ow9xGBgeOrE/Hs1v6fFKbn/q9BMv2Dy+FTrA6pqhkRPCqY50JrMwNzvUHWIFcr3ZKMXcVLgeZrsfKFgE0yBPRqowvQaamEnYsIo0sc22GiTLLQwwr4RvcE8k06FuMD0RisQiSXaJglKoytTZYJ5mKV8MReNIGcjL5NdKoIjL8LhFHQWKJFtFNysnUGYZMNcQPKCmgZcmYyFeKa5l6kjx+ieKKs7cgGQoI3CnUa/FY+gH6F6tfYWeUh8ZG+N4TPBPmmS1gzOAGS9ltUa+qrf9rMFQT20xPjUXLOXdZDyX7ezsyN055W5dMFA9BAEWhgd801iD5wVwgbv8KFy7rNJrWBKI1V+Xa4506uIwDz4NmJUDfmJuIhY556XNf78xsWuptgV/HEGE9UxC5vXG+ecHmSyF08kMEfgDqPfow4/gU/qMM1+SwaJr38MMxb6B3Nue1ANeDZ0DZ75zsFzKdfYIrNJvLdgs1GmkDFywLzG9L1ZJHctZHS7cK6TNIz2lR4RcQTMwQuKFFGbeHWKSFm6HEIZVb47uOSvcpDWja67JgTzNc3NWHOxgFaA3gFTKlHvUn7nJ3fW5+YU1I7GstnzUd0TR5GDt8YhojhbOa72XBKlm5GQOXeuPwwRRCtnY2+VhVRjNUbg9mwtlYrPGTu+2/G8Pxe+IhfLpj1Qqr4z7P2xEMqHm2Quxnyk4mKk2w9VNkegpFW+gkL2hmK1HyREX3w7qfCcyXr38e/Pvk/svsWSmaJ1stUybeCJuMXzr0g3/peeeSKw89NtTczc1qnemGIzoqeXO1y+GUcMEofH6e3o6evtMPU3LbEbtCYzclZc59GuvTZC6Vz1om6ReMKn2jt2Kbxqua/OfEznYrHVtbwHhysD0pvKyGesTNs9tiGIYMA1XymYvXbH+zsjMDO+QYukg16Ae9uJjw+VoZH236ug4wLw0X/Jj5Z3Dw+loLHbffZuaSajbyaZwm9DWvnVtf0x79gp5od/QIsAYul92iH3ooYcg9CC0F3qjDlF/Zm+0r1/72tde+cpX2tGEjeHFRf6OPhaiaVmaQ+TwpI9gHqzTbBvTXo+NjbHkGAZp06ZN6BK4eNJ8nmEEGsVBG+1F+0wIB5lzthgYxytwXyuLI7K51RdpJngScC2rG9gP1uvW6tqlAo1uSetyceTJLKJFYRTYcNkfEUUofp4dthbAYAJlLJs7aq5WbjHNC/hIZnetMAxBQgJHgg0tMKX6LI9iGkHkxxoWlSvPIdqAEHMm6jNXqTfjmCWMeQ89Qt1qtSqmWtiykRZhnje6phqN1htBVFvUM4tDL7wx5wsXJW8IOyhzQ7Xtyfo2L0g7qVOrXqp4QvTLoUb5psXPNUNYKONABI8uKkZgWBbUqhgsB2A4EUGjSxvgBXBNIoRMifAOUM5sgiUldJn8QuOvqy7jP3ni5FFO9dKKLTTGBbY/DKyNGILqoDPRxL0LuyXZ4VjZ/+pIYXu6xsxC0E9pen10BgtlZ7GC80g/XyzFY4lIJIr1G7uZwedEXK+nM3fSujVr+nswYyRbpD/f+c533vKWt1x11VWf+cxnWMN4bH/de/fh8bs9PXBYeGxP9ONXwjABvijYKJgVv5/+9KctIrXfKiiRY3R01PYTABIRMotbmOvBkBBUMDA0tQ2YD9GdADoIBhMYGXJNViTn6yXDA80Lh8jp6T9CiQ1RBAMjB8VoZO+tFxwcCSAFWM6duZGJT3O4CdHs1nZxsRyn9cscNjAwQLv2CT+8WwoJ/Nm58Mx8qyhkmSxOjoBoYtVqrbl1e/rkU8497zyQQDyRQIlNv0FZGQL0FRitAR4HB1cxBD/96U+h+gNdHdl4uDq7dd3qNWeccOKJJ5/0xS/ujkSTfX2D6zacVmo6MzMLU9FUJf9EZXYeIjeYcpvVnW51AqZ57KQNCGIR3yLEtcRMolx2aYJoQadETTVTQ+JaRtCin4IQPDBIT7ocmNR4V3dHV5fEtkZiSxKgCzRhcvtmXFInMjlyAsoKIQqDiKQYRKr46nRTirpfFFS03SJJgWSRiVa4dMhCzjJFBnwiW0Z5a9YAo4ll61xcZKHj9ujDRzY+ziLak09eT+5kJ13Y8tARH2VQMgwldhNeIxxBLemVU51ONXrJ+iAzsaV645cWb/6uOzfXjCWiLz0rc/1bL1h/GnbB25bC7H8gR0OGWqtO1NgSMnHetg3UHfsuLVLNJf31+AZ7KgcKhINa62G/tX598AvX1//h044rb5VmdILyS86OXXu1l83wkdrV6Uh5tmwZh+WF4bNbm+z3uR2iVhpyjhan7Dp97O/FH0PTGiMeKophmxRz+ZBa1TxSUnuF/tVcGx0vPSUQyyGcbICxbqU9FlRWOOYMBKF41+uhyELR5l0irYnD6658HNYSc8Uz5aMhJg/lT8X5j+Nryq1O7Mpk/LNG5c2ed0BftlIoZ1PB1ktogxWk75J0yl9560YtVbhONjkF6VoPFW6emwj2phWfuAokOWXDV51/v//xc733h7yF+KsKzqt4UGH4eC+DRqT4F9Hw3QPVt+TjVy4F8bIbrZiiUMJnNtXf9p+lj3Rl0KWQnZq2fNhRWr5TpZtL7hX3lP/bS+N/GEqUet64y7mOb0lKb9hNGM6p8FvL8fecIqGE1rOtSG+vCVD29O7yBGhqrxLMF6unajfvh9Lrmux1xaHcTAbmU9UrpEcKtZkQuRVFeZlL+GB1qKJSANnaciWo4JM3TzVueu46yYgT6epJXHNN9J8+o6EliDHSc690wcsjJ66LJELnr1YAn57y9d3cQnhkJJTLJd0d5aC6ww1OsgWrTisOuyhmRcCxdonQGarHUljQILMBXfSCbiGtQEavkQ8CCCKG0DSHNnIG4hJohfVQAZ6i+4VGE4jxFzwAJqMM9zNpPjSXgxxQNXO2JnJcwBS1sz0KPUyL8O/FXGjLwusyIei92f/ZfMUMsWYXugJkbivGSjSjGGfK0WKiet1ngwbz55fZ57beQEGKbypIIxH4piHLrNcFyuLyCcE8cbReF3UwAkdN8Hyymgp0Eom2hZi5wPHCyRSmUbAJ0Gg9MXOEjcysiIlWQ1OKpmp++NL5onnK3hBMPl6uK/OG1zY2bwzqXj2omQbqlW2edELovLMmamE24CU/JOM45sx50+cnPuJ5xaASnfxEX3BbLtRkkYSf7610vn8qcerCSOiWM9L/8mDtXeh4jQQWqyz8yWvvXxgAUR6jGZZfgcDDwkqrguOBsZSQwba1hW4GHsgZBx/SDDO10IENt1jyFpimUJcIP7sZtlwC+je9uaI/wTZttdCGXn91lp40M6VaYA/RDrUcawJAb8WZr6Dv1RYh+UJxMZ8vLC1yAQUfG107MNCP1fYZJ60ZzMVW97ETVDgszmuPfQF2lF/+8pdxXnv99dez4fn73//+4xh4uZ9fRL/HAfBTG2z2Av/93/8oBwD4D//wo5qqNAG1PtH2hc0USAbZAL7CXmP8A1HhAjzGB8xXCl1h6ockcIZ3X5mWa8gS1hrEZIImDhAU8HY00S9Fs/x4fHwcKgglYH+Cdg2tGTPGbxxP2n1EJqt2WuIz0dimPWna/SNYRpJ9CZvM4qxfQTUTDoWkVw8KeEBiKOJhDHrvvutu0gJx6TdGAVsXiD00jyGgPozI6Ogo7aJW1I02dnf1nH3W2bp1nMndk+ViaVV/T29KxKXbi29YtbpcYfVyFpZ7646d+Ij2YnHIW6qTfZiQbrI7MVAEMbBxOgXwAI2w1a3W34ryQTLxbwxJQCfFH5W0f9AQA1S0KJeZXu8QxUMSrb7I8bEQjoZCcWli0XTLYtaHS7EgxHDPJo1YVjG+YFsYWOWiRkFd6QeDlZQtqcwBASIGtF8IVgjZXqgA/hG9vmvHzkolOjTQM1NhrS8bsaIJEd3gjNEUymRktFgLDySClChRoxqEdhTDmUhXamZ3/uN/H771NnZ7ooGRUtH9/o/yj4/nPvaRC086LbaLpbOmIRZ3GfBORxm2gFXSsu+FbzB/mG25+LSUG2qBQDHtT3pQFYaYz+pgCwS8ri7vmmv8wVXNb9/s7ZioppPOeefGL7sMewHaBl/It0n38ZLw2nOL1zfytB36pKUfLIJNTm8QQbDDHLqxTIwJ0YjqMMH2ykQ3HA7BJtyEmBwMQ7ScxkQ/6IlEy+NuMmkXYe/MU5uTjcZryC1/MHzz7sTQyPBpQwKrAGRhWuLbseM91A3/9ALbJOZXkQ0LyJ15pCSEmRfSXFukLcSt15iT+RaUlf1nilCDCNdXoW5htvTeVMqui898NFS/XcbIhFcjtZ941U87ye29Xi5V/9fM0rc63LMriTed4Q+v2lFZP+deXoidNJayXKSMNPY57LdjA+EJ6ZKy88HHmieuaX4s6T5kTPjh8UIlZyDS94G+rvf0saeTDvXdcv8tX+2TuXlsx8kkWU7BULZj2ixW3tprwnXRfrCcwcrfVpyVQftfK4fliFh3dMSv/dlqAcOZm8ONsgYu5NXOOTfxrnciHWQWCOM1gEJtrdzQOeecaXME/23ZumVxYXElSmkXBiowwKAdcExd8PHy8uKXGGssJGKYB0O7X9AtbM9ytALxNLdcIPjD9bG94MO/4447ELuDflF+gvnpAcgllBRN+IUXXgij8vR6oD2Raj5YPlYGYrzzDAH2cq5P/gtDhXixXm2w43FnF8Z6LnyCoat8tdSP+Y0VAThY9kFrO2cW7rz3gaE1I2wrgCaz2ZRoW18QwFJb2ftodFmjy6N6o4kjRpCwDG3MDG+/KJqsL7FFDDShG9mU+drMI5Mf1VbGq9astd8t2lHLFBAblsBOHoYaKCcttDFWRRDwzkT9/NVuf7PGVn/RC19em35b818/38l3XnGrzH84B/2N95f7Vj0+4aclwIYJ8Sp1fyTy7Zy3maXEC//W530Pxbvqp70Rp9Jzf+70/a+q11lZF/7sw7W3p8Ly48yf1L+oryVkBdY2MXhOxTAaYSKHdpOnm2fNMDXDHjvi4j8Bn2YUBxdUqQV5+kCpfPYpZAM/s3oZ82kX5S3oGnVvzO4Y7LlLNf+BSX+65J/ez/dGwcofxqlQa+Yr7kIV0Is9OdQBui388tMH7q5i+mwEf+lUbKCn+4Sh+OruZi5OcYNqcOvYg35NgM8q+H/41D/wnn/wNz44uXv37//BH/DmL0c+/vui6IHj4334wxygHPvAB2745Cc/86v/7WPv+sD/t1BzsrEmcia+8hWfmZgXe2tnPGgGUmQOSoKW2G8Mj1ZAYigrgUSD3sBhYOHc3gfPprWVA8KNjo5qGjXz3+HUGCSA3Jo82xkeTqqVcSgOAoke7Nxzz4VGgr3bT9/1rnexQQJK6XbIIS5YSnTAah8w8BD5tB5pRnW97l5//cnNh+43VjmtJ1hBl+OJzlM3ePFIb2/PY2aTxnPOOQfeBedkOKAG4XAGtNs11dSfjmUIbENoL8fWrVs7c7m1Y2PwPXYYsQAnnOFbNdhPSVO7dq7py3T39AoxMjdrtJnmZXAJDGbDePAwRIKZXXJfuW0U9GUhJPEgmczlMiGW0phwuSnmGpIJSLbwj0HOL0w3ykVRRjeUn58sLCFl8DoHhrA5si+WFDXqBQtfzcRPtnAq4BRTHUmDSa2+Ih6kQb+kkgNpJVZ6oioJ5s0QIBOPhrAJYr7YzGS7arHOXQXS4xWb/IVcEbxyjoWaF404a4Ol8Jbt9YceZTVPemzNaSeuC+e6ij+41/v+bSL1EGwVR35OYueuype+nnnvmmyiY3cedKl6QtcFflr1U0sQCkVQTNIgIW2PPX7WdgT4L7M1V+0O40C6gabiYACYNod6e71XvyJy7rlsKo17rgCZToZlSWJqEYuw0o+BZnX62NgYw3366acfRpmHFcV0PzGXf/ckUogZzD1Be64O8MDEP1SaPant1f5FroyxnN1ymCmRTliYmYy6zdFVfVmkHao3Y0ZcvU82w31ul9Ov/G2XbDLQEy50TdrlZ8rQHK0AC59NKCHLsXSBY/KXOvHPNScedMOz9UcfKb/vj1NsNI52F28q1M6vxeaikdvSjfPeEjnr2uFwbLWpLSW2qsrvioMbE94KEr+qiqEeudZzrnDr9zXrjyzOzW18vDqw/vVjnafyERD1oFNWu6YrirApVgboep+YK2/b17po3+ybQev+yZ63c7ARaVpozerYr/xS8IpLalhIYqYyNpLYcHpoYNA1kMYMyXKmy7+UxXp4ADDT5gEB8EE75CC1fsEF00BQGaJnaAQk9YUOgEfNwSgwyzHjAen52DGMghVhaobKw4cwi9Jqi43teDEx8gi1LTAYevrMFWWUsv+b87SMn9sf9Z5Xdp8gEZvl1848omRXU73jzM4vsAVutmcIIyYskzexHw+bESLRxoYLGg23hUaXrYBqmQWve8em2VXFVLarG4ltGUG2oeAsZ9VkJvmz1KGGjFKcrQJnlazy7KG5qDX3mTiEKoBQaL1EguZeSWxSUreqrh9FU0MMEyhDFN3hBjEXaVw+6nZObat85aapH96Vecebk696ZejssxqPPBqrliNDw5HTzs73DNy+292mhUgoDKDn2hB4VfwuqhHUYuXvsMMc4FAch8nejS6kSj9OZK6sdIU2uo3ZfLCKpyhyQbzxmClXWm6Ptc2Fij+7awIxff/qVVFcYYXVDroF2+8G4Bs2wAvQGKdiKpSqG5tnRA+0DFWFl4w6GTLEltsPKjV3oYCEHwjudEVdNLZPLIHx6WpAslOoBkVWWuULcM7FQr5UrA6uGlyDQ5nA6evqR+sx0pdd3ZvOpSNxMYXiPtShezrQdJ4C2xfwbAHw+7/e8OFyrP/Pfv+3Jqem/uZv/gbNv2IdP14cPXAcAB/uOBdLpXe8/b9++as3/dYffeJV116/ddHbtoh+LMjEmp1xtzMRpKMiDXz0mqmAAJobl7+/5QvQr6a9IABYXnnllYAxUJkUqfPz0BgW2/AIFxFgNhgODuTrSGEtiG3ndpg1xq6DhDbtYSax0aCFlEVjgL7UBwCPsnRlDjw688yWimBl+MGun2rND5ZPKxz60N0dftvP1T+61SssGaAoShFPZ8LvfKc3PFSp1M4480wUwmAYbK1R8MLAYQtNJwOQGAJoLer3888/n1XW0HVaB2cDdbfRRkdHEXUjErYjRaEMDRFgF0wFZFkqcmTG1AyzLJqYtWOIV7EE2uugrlLwsmymJmzMxgDSGHPLZgZWRYwUWaJerSMSLcQIeke5tFheZEVNIumzX4BMqAKvIzpYl9GQJVMikfwJu/IfTa+Ip+iv4dQButAA/rEJkwbSVpWaEMWqhUkMKuew1MCgHD+PCrWwlE4khvp6WEds2iddH5CVWkkB7fgvHfPXVhdqX//q3Gc+F1nKk7CBTvgNr+39tV8OPXA/bAQ+J8HtDIdqqEr4/j0/jZcLQSg7VRBil7cTnaFR+l6IqmuDfG3fpULNswbcBBtqKZPWF2Qq+0xOqpHyjyWcPm22QFe3D9PSVkEMNA5+DmL83E5xjF/wIbReePMNiJOgxSu6yXRWq8f00rXeoz3dsqJ7V0bTdeu+ddl+qrRGcrIcYU9m5oqIsVR49DxkNu5EVzw1FIJzCoUafhO1C8PJkLpRlqMleatienlNAa3sNeXuk59u1SIT3nrIDyFpJ3JByLmgUtg5Xb5n2MvyCR0g7QstiK8t3DvodPWFzj4Xdt2Lxg7mqWFly5gqmTB5H6BNK8OP4WumVDAhBNoqJPkCjhlXsQckxFhjvfOd74Qy3nLLLYiG3/zmN9vBRTrMhSWC9ABEkPXA6E7bpstP+x0AY+DThFeLScZmcsCKPWn+5qM2tE9RmQF0Np+wriCH/AFyWGdrrLS05hYhNcQX8TQhC3ONbZPNAlt3hUKAMyOS1lPArUyX0eiCcrHkwiV8z4kzW8Ynx6f7KjFYMtFRU6zIiOU/1FGiayq9xRqIxDDB2LOdZ8wMqhDReqLaKtsJz9Re9W61pDUlcWtyNbfKG6JPDKUUKA2cs4bczrmJwv/+6+C22yM4b//wn1Rfc2nm0kvCF18c6eupx1Ob895DO4LpiofnKtKxEAluxfeaMX+RPPxKyK+wHsoWp1lS1j7Ml7NwM1DMWkeiHIILwF+0j3tOEsBO4E5Pq3yjYQHpQhiq75frhi/CL4rXZJkxrq3wHUkVMewCJ5fZlRHmKOKh5s1FGBS06w6a3qkiPrdA5aiL0Qw72YiYIvyEbVly7pvybw8HgxlnXa92R3zs0Y0Li3NmtkZdHOrKJgaykeEsvHdw4cgwm0Su6D/TieakVjFStrfVkVI5oEleLDtzFZpD9Wi5e8Xb3pvK9fzph36FxYr/95//+ahZIuyp6PGr56gHjgPgw+p4TIPe9OY33/6fd737Y/938ILXbi+wB5qfYUcT9rIzSxGcBRzY+kizUF51JIJslJX3KP1aM5wpYw8TZqf7lZphZibAhyU5lAUBhtiQCgrBhAsxPuWUU1bqYA+r0k8rEmuB7r77bgyw2/6E9idO+4c8raKeZiJhzmQ8cu7L67/730uf/7z3yGNOs9YcWh1+3TWxiy6ew1Hzjh19/f0gWCYyxPYUA8TlYE01IYBe9jnAvwVcHbIGYC0cz/j4OI2yGm+AMVIDogGYSUuHEM7o0P9YywCqwdI8RV2/px9WTL8MZVuCQHImXWZ44WJDZFZM08LGotCG0FYaWFPLUprzmuyaamM1C43uv/+BgdWrU9lu4DFzOEQaUiOKLh0yZwiJ5KM2BNkqOmRLWRG8MvVD7agMT/XPkAJDkSEi+sUQXwNgFMKgd17V/MI85tYDg/1IaiH8aogyERXnh/WQQyn/1GS1cfP3i3/9DzF1rGol1fbmrcHMrFcppyJO3ph/qXSbFjOtShUcX2n6u/OCueqTZdNr2B8DzyXVLucXyvPTA8Nj563xcnFqpIJXdBdVUMeSvM02qf6Ow+gwIjxFS8OZVesEgmDh1cxznWyxuqJaJtO9stYD5c+nh9TJyoz2LtzEeHGc6GS+FHqPCcqM1spBOFgX7Btn/+49WMqV4QwOXvTpf0o/cP+7oWbECQ8MVU461du4CfOFeq0Rj8a0Os1xy52dsXXrcQlAbfZJvs/tikJNzVvV36sVujHfyMHTrsjm+X3ZbgJrGLwkHuz1GbQDD1F30C/r6gE/2C7xPhwi5jHziG5hrSy0uM0H247iu2B+wMDkcPrted4bjGkb31JVK+1lrNvXK+tv24vv3Ntuu826j34mrAi50ZN2yXG7FObe9hTNq2kO84rqRd334Eu3QVxA71g/g7myJZ3G/KolYubaUkmoJ7n42gzIwFqtSzJ2y40Ov/OUTfMhoC+PwH2KCc3Ud08luRdhoizcUkX92mx+aXK3PLDAkhFs5gYYPBFZ8STLNbVJdNcKs1RLNMgkUd15qmgmc90f8iAVSY0dlvI0reGXct1EpDGMb40f3R58/3bFQSVcq/lf+vrsV292OnOpX/y54NrX37srMVOCB5GnSahqSHCSwy07fbTQS9a9zmYwSXamTpBoKbR9b3WN6jaD9Gy5Y2apUMgvDg2xVhxpvpOOheE0SsVatYLDhaBSrUOu3dpS2GlGEynfjeADDM4E/XkszOpfZM4+TA67OhUqAUt8izgGwyDF89gqIuw1U3EfAeZiJdg+75RYSlZn1EoyT6/Xd9fqO1PJmWrH+h4vk0niR3qwO7u6J9XflcrG5S5LNTZtUXvMle1hOwQaRj3F73SwVGuyWhheHYdbLNpSsLpSzcGaulR1+l72xvf8aeaTv/OOq6+++sYbb1zJPJjIx0/HZg8cB8BPPq7T05PXXvuG+x7e/Fsf/+JZP3MR80yhxjZowW78DIc8ZF2ZqJPCp5Abmi07M2XHXcCeM0jGG7kY26CBisPaQ9Xohs0HqXnRzH7mk10u39oFMZmitLzsssukFDYHzAdWH4hgeQTussphIDEHdEtKtBUHcbZs2UJ8G4acFQte4pB8n5grEu25BEKwKR9GUPA6mAkdTpI9iY/KFVOb6UPH7cxGLrgwvOFkZzHPupxwPNbI5ub8+tYt2yanJtnyFyNtDFkRbAN9Aa7w9BAicCwcDLewONQXITTwCVYAbTnknyEAG3PmEfGxIbfIil4lhNHAXhpzuCftlvHxceIQ00zEmmfVN0aRZKdke4vgE0MmVtXiWCK9x8BcDzkAoOEZj00K0x34XWSXJ2FjlhzLZNqsK2YRsoG+FvTSLSLdIuQWVAse7wHJ2r+H9UuKgEbXOOgywhlBXDCqgZYDq1bJlTYEA9GtcRSpl9a+pKKd7rqeIJlfXPj8V+INWmQxtmpa3T3t4KtoZA2CYeVqmkunabDcUOSE0WYsWShoMbOeuKyVqlHpIMRuvKZs1cstFKqFpcLqSJBL6FMx9Ek/K48PfehDY2Nj733ve1cGUpAVcxDIdYvdWBlj5fVe39zKBw6vyg9/+ENYQ4wD93rwIrvh1X31q18Nh/qctBuMscy+HLh8vZM9naHr31r5oz+J4NEHXYa+LLfKtlrXv9VdNXTo5AfO9EUTqk9Sc9DBP4P9ugLkw9z4oupV00V7dQQhdh3Nq171qqdhVLVXXs+DG9kA7yFFqhBaWQYahuFgtSPCeeedB2PAcbA4hw5nZoaZIY71jrm0tAgkC+FwMslK/eXqIIVcyjOLd+SyvKUIWaFo0DtZUeFoCton+MpKVLPUSNTQKh6xojKumLFIEsqVFld/2OIGQl9Ew4gJCTJESYRShBcKK7cdAXphw44ZusUqV30aKBRBaEYsrJrRVWyKMNC/qlD3iwUMxdPkQ3w+CmVHEggZESGehviZYH1k5qlkzBz2bGiUbm2DlVY3xDVUU4mIaa7NWY9NPaSj5loSYEWix3jUncDrX6F61z3qQD0UxeVpBGI/M1v/wX8mLr98MJVcQKPNTpBKqwhIC6HUOxsXnxn9SydST1+zVP0HgKuW5YqHQE26ZrHz3DJV21V9yVw9u31haT7vPFGc6+5h715nxHh+rhXyxakd8AmA4QX25cBKzvVWrR7r6IrEQgbxNrzFarB9kX0iWPGE1Npn1yU6lv3WMTJnQ8opTM3r7lTBRz2QxB4aZfIcWJv1wiE4azyGRMLxehBeqMI/Ny46o78/s499nek62x90n9H02juYINikxSprhrUJcB4H1DL/EltB89guQWuSq+xeIUUCmxWnY85IMjR6+Wt6Or/0R+/7OWwzce8KH25yO346lnvgOAA+9OiyDcDEa6++esfOqd/5uy+lRs7dNI0TXaydtfSsP4Pxh1+suXNFNjDDZZEDGE5FPfRgWMRWKx57kQEnkL2lI3467rMneDbmJcMyTOU7XJ7yzGe8PBFq6gwCkC0HtklUjnkbRaXVeoHfgGF2Mz1iIogFzrFDAwyKpnckkcjV4nGuSQha5ppHBJIQyDc2NraP9mxl44F87GxMKnS/Z5xxRqtGK2M8P67b057sbBJDzqCID0Y16Goruyfpt0svvYyaQmVp8pYtWwCxtBo/H1ib0zo6BFsvFOw0ED6AC6AygRh1o3gnuR0CVIj0LRD68ccfJzmKYnob7pwc6F70IRZCg5aflB9a2ZPLVNB0pVrSbs2+nct70N/Xk0nE8biYwhsV7nhbNJJ3B/DJmf94u5WcG5E2zIHFxsbzFphZfINdWozTC1FYNo3RyyUMzMsCJIZ2cYY/KNeaXija8CXZRXarBsr9LbRS7ylpRRYDH68S4ZlCc+s2npnSbZ1d/IZVtu6Infuy8je+7W3cBqlebrLrJ6KZa19XTKZ3Tog1sUzC9PZtXjiaG1hjcgnqbJ5A7o1GMhwaTZXWJKO1WtgqKPbpFD4E3tJ9AhFw4FmdQLg3QCzOq+wI7hPt0Lf0C37aGVZsH4i5XP9DJzo2n2rwzNzC+ai1EBGGnbUokbHgljNf5f51UJ1S6dD5F/q/G6l8/sbmQw/C29RH14WvvTpywUVuznohPQI1R8LIikfO1GT/ajyrPcN7zsTFdM10dMQLekptIbIFPE8p1RGv81HIkFFGWAx5pSyusUSwNAJnEJZoMjMQgukQQPEY6I2VTeCaJkP3R0dHD9jVNgLWMfYp0yzUEDbG9JXCFEE3rf8HmznGt4xDepQAs6EtWxG0ZtLZNaOjxk+kg8sMLraMz1Rq/sBI2hAyucYQLjOIDzyMNs8Idtl8Qeo75gmiEcJGDMBd0SxDtKgRldGKHNm4iu5QonkigGjImjgzUK6tueATT1pVIyrcFKuE3FopXynmyYwNddnxoINdHlEbsiUHfqFEUpl7WpCUfAxkNm0zJdKlxDHATIFUjDPR6Bz+89T0kinSQGVV0ERRTCVVNS2hR8CneDxVBHPQWEqXwhbrQfYXMgl4rH86ODdLZVTkxscFfKRZxgSX5DsVbLxrwRP+z5w+fPaa2J2ZK2f8SlD4Yo9TjCJODJ9a6PnVSTdZajiR7yy8e64czuTYomIBNfDC3HRPTx+7+MoejI0cB0ZYdJKfm2FIEx3dbHQ87ycmpgCfqkIYr5nyOBLEYtwyasYlCsPU9JdKtXK1GUL122zMLDWQgUQzOUT8lXrKi8a7uEt6iaiHY1gEEmxIuXEq6Ip7/Uzt6jy1VL2hQ6PJNV3BMJTr7IfkLFWChapTrnkNuc/UU5LQ6mINXt0pVX2uWa2WiTiDOXlFqdb8pZo7U/IxDk+d8NLf/fsv//n73njppZeyJRhfhC3m+PlY7YHjAPhQI7t127afveq1U4vVd/3lN3JrT8GHHrpcfOCxpGFXQVKuSMhLRYNswu3DaXvgFKvBfNnftShrEwRdmbCbJEnEW2x4S0vuTjORxrxmNuZ0xJx0XMuGzeoFfaT857CT48o6MavC4tsZc3h4GDJsYRhIjAOYRwTmQNSVeLbAOAfYxoH5FngPFAcDR27QdVKtzHaf6zb6ZYUP6mKK49gnzvPrVjNhq8+Y4cJheYNOxOP0M02m8nQI2l3snKk2rq24hZ+mf1D/0j/AWqQDNJZeQiyNhTlddP/99wOGCbdttxHoGboOPIY8AsbUyrDJEw6JM9pCC4AZAtgC+xRFOoNii6Yy5PM0uo46kHl7EERDDB9uhcWWzBEIExLBURXfsWLsXY6Io6ENckMNWfJrbMiEk4+W5y0rGve2bJ2Y2jVx4kknx1MZ+AzlYfx5GF2xUKvYCAHjJlsVQ1Mi8Ui4Xmn1PSgaaU/gLH3uC70f+3DsN29Y/NP/Ezy6ER8WLGxudHZn3vN2/8wzHlh0p0p4NRYHAi9BCaxSpuYi9PX6wvaNNJO8V2fr6cK2xx5t9vb0tGkPHYsIA9M7UiHHYRxvueUWrmFDsVMCstLnDKtlUgm3x1N9ezGagP3lxXiRq39BoXQ1cp82s7vco8/uLx8L4ic7aoiWOCiPr3J/6ZL97r1cxrvoIu+0DW6+KGesmbTT2e2mkkdwzmLW5Xh2m32Q3BkFJnbe/IM8P9rBEBq+O0RLK1j8o12Ho1AeE7U1/6G9XO9j6Mv8z0cBBsa05yh/HUeh7U86YZoIvJKsE1m448d3jq4ZPenk9VY7SfWs/s28r3yCgFw0rjLsQQhrFLBGOAsEyo5imSxx/K7J3uFR0NlsLbILPaLcGrcW+Ews4F2pWZgX6QE1iQC1UK68K+uAjIiKyOuFpP4cXAguEiy0QxRzofU9GFgZ1CScaDZRwLlFfSD0kzWJ72WcqVKQ2d04a0ftwprbJQcVRCVzIhq6yamCP445zUWQRFZbYKhErHRnV6lQjZo3RN+oAaymFqodOTBHAYd11kOuFd6+0A35KdA8N40C25vS9aNwk4RoNvGy0GBFJiLUSK7RlUe90aHGHaRQRuoCzqLgfmT1cCUSHt/lb55jwRO+oUxxAGMJwQHE2c/u/NMbRt6S8SZzr5/KXpEv745GUrVob02wPgjfmb/+scoVYF3qlsl00K5yKV+c3R3JdEdCobobrjrZ2Vqwe26WEjuTneBVmViHAlw6U28k6eyEpKGpV4NmDX/Rg51JlPryQ1qqFKspPFy5oXAiGksn4h1JJw6m7oqDeJHjM+Lsagy+zyT8/hh8cjCx5N6zqzGWdXswF1C/wEXgcNsvAXelznVQ9mJlLUtxuoHXI5D/URj1PCuQ6zRWjqlTUXcgp3FhBXKxFszNoSTwqW0qFnQnXVY0Vn2vOHL6u//qpk/8xpsuvezSb37jm898xbvG4/jxfO2B4wD4oCODe6Qrrrii0Ih+7LPf6Fg1Vka2VMZJoPzvsZF3NxuLheR+qFp1p/NcMNvqG8tE3T62z3ERQblL9WCyiG0o26YJDycjLn98npWyO1XWZBRyfDZPy7J5WhRnANjBemH5D9JhpzMzFS6HmCkTRh++n8NGYyKzpIsz7BqUGxE1gUBfGHq+XhACsI29lLBp5NrGBwra5PbM3I3CAZj3vEK/mqPZc8Acto0r66xOMR2yJxCqwX4ey00bHR2lXWzuB9YFhQKoALdAXDg5ED4YFS4fXTq9RP+MjY0RAmDek5uyFy2kq6kJ4cgU4Mi5wF6ayBahrbQHI7lFxaQirTX0Ajk/bQC8sjJc2+aahu/zZPm29aYs37aSyOSJWR6IKbK+JxtdT01P7Zy779Rc4sL1SfxDQieM/w/J3SFCWFxLNi+jMh7hmCpwsp3xl5wYuvNuQ20lXDeOi4Ia6tOlxfDZL+3+k482Hn60sXs3i9djL9lQGxx+wk9u3RX0poIKK6kXcezoxL2qh0x2cQci7ERH18DQMBxTLCic0rF44hgmrH4yvpfi6/bbb//kJz9Jq+hYehhbRC7++I//mKFBPDE+Po4GGF0ZXGnbFnpPFxzGFbmxcpgz+TBwHIeR6BiMQg8gCKAr+CKOMovP3MWURZ8ikOKTsciz/S3v3deMDhyaxzbf4UEZgPAe8yYv/z9ig3f0XwP6n55ntrFmDgjUuGb+s2Ype3fCUb1DAsVXxgDxVhz9bjlqTYUicFDcvffey1IIjnbRttUQjh/96Ed8IC972cuObVmAbbihF/bSTInADvwVxVO4UPvpY+MAwVyuCwUslIJ9gAC6UAqjmGURDehXrv4FSDA6leZWmKTR7GAB6M7JmSe2Tp06cEbT9VC71ctaglsuFOYmd/ItNwlynCfmNpFDuqsvlu6wcBRibHGhcjXkh0GRFhc1rhZ18hw2zMwGlsoZkETBJpVhk7xQn/fYJan/zg5AIafKTIHNHIzbgr/21vwfPF69mtqW84v5uemBsRMZX9rc2dOf7eoFEE9ufyyWyGR7McqjvfXduycpePXwENRdZfNf1eI/V+Ic9GOq0b40LB0R6Aae8geTYKu7HN8EKbkS730sB7VmN3KWFZa/uxDMD6d6L331zNe+E1rEIajyFtKmz+PJ7BWXzURyj8z4eZbdmv5ncRO8jHmuxU2P1V/+N0/88y8OvW8w8rCXrKTWllU1vGk4mf9cevf3ln47HYuywYWwtut2pjomJv3ZfPGhHeVcRwfKXaTY1JLlZnJmycZxZA51l3QCwFuqV4pIt+E54R2aQbhQjNc99pH2GqGuXGezAxfQkRBAmm6AK8XEvYI5mwOc99mZASv7mOn/SgNVv5Mvh1imC9edCvsRLKWrDpshFavNYgOZiOWYVRX2iMR2gDXGxbqDkTMtxEYwHXG7k2pBpR4Uas5sUVZvYHXAcGdK1UZfjS30lLaocFm0nIqFNmw4+aOf/fqH3/lalgJ985vffPH4/9v7nXtR3O0FhF4ULT68Rj76yCNXXXVVEOv4rb/6j0JqTXExSMacXNLtizDLeqV6sFiW0/zhXPPskTBu6tiBreiEt+eDB6eDnXlNyAm2BY84nVkRSggDdiOTeSWJwNBEAjTDQOI4GLrmFBvBJAhLkkM/GTGetOQdXp+oxJeqMJ+3vnAm1tbcutwKq/hi3mPFAgcfMbuYosyEU8FqFz4S6IvQ+tZbb0W7hRkwGhV0yDD64LeVJHz9+vVERju6fxHLRR3tX8AMeBVMTs0Ps+w2B0N8YCe9YRERMBUmkgYyHQNfrQUXcmigFBwnvYH2iZXPpOICVEzPUC59S2/Qq1TAQnF6j64mnAj7WycCs63CCn6RCHQmGcLNH2bln71otGIZJej1sde8TfTDT+79CXTipeecm4TUcYScZLseooXmZkWaZrwz8bafCx7fGM1DbnkqZyE+rMLrroj09rF3UxEnUq+8JME+vp5bdEMTBXem7K3t1luMt5GppRmZR2XgVRDwY9zkrEp1RPq7uUhW/X5naaBvD9NpK0KHX3LJJWhduP3EJz7B2/u6172Oa5yQ2Qj2TMMYJiKvDDzMa8RGvBUsCLfs72GmOiajWbMF+kEvylE80OFzUCBiOCRN9ts5WPmmbrBZTIniOlXT5Rf1CFaaqYDJE8xjZ8WDVebIhoMzLfolW4SSnJnHmKCObClPKTf6l/eBiqGWP8pikadUz2cYuf3Ct+eQdkg7ZztF0A+IaVbC43aEY+mCfuDrWij725eYq81CG+ljISWhxsC54w89vPWOJzac1hEOR8CY8jGBnBQtLutyQWZWc2v8UCDEBhbZT5VfNLe755qTi35HQcyN1uOIx8GFYVBs4prRlR+KwI0Y94xo61jBxUBYYCaAC/kyk7yF1vJdBcBSv2u4sGgVsTJME9RfYBN9tKF4btDsch99ffZNnaHNxED57FYiARvtheud3qZrcm//bv5Pf1J6JyxDrVoU2yCqpgP3F6GIF2KNaphVqfKkHA7F+vv7dk9N7ZyYWD08HI5EGo16o1SKZ3CORaOUDgpHTPsKqfIK0kmNMRGoMLcWAy/HJAZ/CA6U0LTZgGrpbCUjoGM44ziDTmbREnXED/6t2+qvXXti7oO/Nvu//96dnfHUH46fzXT92rsqp2z47vZg51KE7JgsaY9+tbcga6LgP91E2NsZXPQXO285M3PTmR3fzji7C434rvqZm6pv3lk/tRSE6uUW56BNIdjxJNcZjsaTyQQ9zCsBtAXyRjO99FlxYa5Zr4GFBwa6PN/bXq5j24xkIhKJJ+LhDHreaBjv0IK74ShSdY2bZBNBJg7TCyfs48kQDYY2Umald81peg7KIcwkw6ZPePEmFt2ZRWesh34ToCWYcWfjSRAv3DWgFxFMPMQ6RDcXd6Jp7UxZqQZz2DwvYQ4dgJxZutiVVr8jtcH31WJezBAeWNBO5xL4r0aHzGZL/q4FJ4gNffBvb/rrG6677NLLvv6Nr7MxhAbv+HHM9cBxALzfkAbBw488gu7XTQx88ONf6F81DFhlAiriQa7qlHAv5TaZO7qSwRmD3nBQjkxub27a2MwXcsPDnWNj607ofnAeDIzGLFSoY2RD/oBehx3PWEIZC2vuYZO0JR4VEcuxcxqu/Bwc3CXwHR/CvZb+djFvazaXa3g8S2vJsVEgF5aWapXq4OBAawpt1V0zKQpe6/sKxAW/YtUpTKxcAPzg74lAOFpKiDfLHUdHR0kFLUdBit2XPYizj91Xq4QXyM8yaWlVl6b94Ac/gINkrS+GnVYlzpJpZAQgYcQEWLWNjY21l3sRyCMyoaMAwDYXbvfRwNBFtqCVxXGNwoqDPgf9cuyDkAmnDigtl7dTOqp9undVVTT8ATs/wW0zuQPdCVkZp1U5vVnmMC+c6HQiFj3j7Mbv/vfKv3wu/PCjwN9mV1fwmqsSr72qGI9u2fQYARDZcqW8dfu2Ky6/4uT+Tsi5ZMNokgdyjRM7wLqbNm11w5GB4SEJh3HOoTWfTqwaBEsqb59qcIsMAtxL6Z/97GfhQRlBrlnxy9sLp0IEK3FgXBjofZK36n/wH7LioAesev+pJj94xi/IJ7h549U9+sy97XYGot1rTzYQ5lVhlrSvaPtFbad/xhd8IEjH7Av2jDM73Ax4zymReZgZm9ceyR1TyuEmftbi8dEh9eNzwwr6WSvkeZQxk/8+szeV44Xk/UQdhPmPvX6yV/R51KKnXRU0utsWXdwIMYEzdcMIwbTUgmS9c+3ErqnJByZXDaMUxe5U61tAp6iI+a/I8D1smQNMYY4XsgtK+YVINIb3B9wDdw2P4fSBqSaKzZtZwxXNdKTSWUzq5ndtBRr1Dq8lJZ81S0bV+fyBHUN4rxLEJX+VJYyrQeGxmTlc3C0RWbWRCTIbfymyHmLI65d/tuMDudBmAGzhjnT+873NCayZ/eQr853XzUVi5UvSvzNRPmOLvwY/T80qfBsUPRKOIBemoGYs3hGKxMhIdr6u05HLQdempqcnJnYNDa1qlEvTOzcPnnCaWRsswCvdtJTf9iCdndnsr73WnMUrtBxNt+pePFRLmqAlzTob0mlWMntosakM/Y9uXGCYPm843xn34uHEJRe9onfd+sqP72pOznm9ucR5ZxaH1t5ZSHxvq+k3yL2RSqgMdLYhNJ9B3POz8Sa4OHBzD5Sv/3HhenKUfMENRU3P00wwKQf9S4fT1epwJ4Qjjs5MtFgo1mqVpTx+bqSeQceDLj3qR6KlcG1pupFfWr96RKDTUH/GEEUuat54pJmJA7x9lv7h4QFFERtDwgbUGyHGEnYXf1QMLx1j9k9yF1nggjREYBi3OzJkw2x9qdxgHS94vtKQisgqk7IJEioEu+vJvIwREKbEo6ianFCUlxGRgYsV52JJmiSgdTLqdSd5VeQzvFTFsJ8eklAgHfW7WYnsecWO4V//P1/++Aeuu/zyy7/xjW88pY0/1WvHjxdCDzz3xPX51ksPPfwQjHuic9WH//7LfqpvvhTsyDfZPJwPiS2O+rNMuCFMJkY6/OHqfHDbrQt/99nI9Iym/ZAXfum5qV/55VPG1m2dCy1UPT5acLKZtSQtmyn7zYY+v2gY39FBb1ofNvMGu9LNlYMqS3QD4WHsqMHD8QjzWgh3ufmarF3In3l8aTrvVpdeEu/JYNoRxWDDTMamBwvFwpatm9mVhmmA6Kx6tR1riTQKXhQaY2NjBHKWgNNgZpAwmk84G6KpEGNH/apXvQrKBPOHjhTwvA/8s9k+q2eqBJOhydasXqYmVAwkb9tymEXL9sZ41gHNwr2B/GkUyiUEBDCXHGxohFtLtCsgUtpOY7F8Aw2i5UAcQOR2QfuUC5O6T4iNSSYIF3gE5LbJ94kG2qRW7Wyf2wsAJOo+2mLfin2qeuC6Qawhc50dkZ+5IHTyScHsPCt1ot0dbme305Gtzs729fXSA/R2IplgaS7702BrBDGNiWeAjrZ+ClkU4+5Ij9E5mwfQJ0fGrAfwu7iyYogPGBpbN9TXvCRc0+1cE42LA1f7kKEkZMQvvvhiYq0s65CJjs2HfC8MH7KAlYb9R7mprFng/TnKhT5/iuMN5yVksmKC4rW0JifPefUYESrGtPxCl5AeZk8ezOaIoeHreFHNFRHPZYHl9iWt0YVX4TAXMDGdXrwxUyg1p5c6shlAiyZ6plAWJwCq2PlP65GYk5mg2WUXUyx/dtujzWYjlsZdCuPg7hx/mItcz2C6W/Ym4BksriLy/YtPC9Sv0jrAAQBL4GhEIFAYynxIB5EpwiiHFUGFCJxRQUWAj2FJGljawGHFx7ZufewHw+Ef8azwg478X7J5EGZ6gVN26v+emtoZ7fvgzqg3f4r/Zz+d/hDbeczs3ATDFct2dg+MqtUsgOrp5wz4JG/liPuubEe9XptfWGDXie4OLP1ki8uf1JpEBU+p2gqhAaB8THJ1GBU2ENfqcjmbTZikWsdQWAmUA/2m5UjI0nFZDB/EU/JTh9gsycQQU9O/kS8/7D8xEFw8un74DWPhRrMaCm1thn+4zb9rh7TXuF+2FTNVIyGAE32JR74efqboS5xCw71itBiaOyX53aEoOoD8fH31xvKFu6qnw4jVxbdgzMyCWcoPItG4E3ROTksXFEMfnIzhFSSKrMCI6mhaFePkahVD+XC9Cc5MxwIWT0WEePHZya5NDAum8vSMT/msvzXjK/MB/EWDRWkiQBfmOY3jzwRvTlCqu/NlZ8s8q3n1FADPauLRnNcd5sXSSmP2ZJlCzIJtMxrgaJBJSG+MI89a3ZsvSlKAtTZgOMtCXzgY14XlZnnwQpFLJxnz4KXhxhk/skL/BM+PTzV4l2x3/0c/85X/+c7XX3Xlld/45jePY2Dzeh5Tp+MAeOVwBg899PDll18ZzQ6/40/+YyHcl/FdTCaGwnzIfrHqLpSD3YtM8UF/unlKphn60T3F//WX0XJFojhm3Eaz/sM7qoVi1+/9zgUjI3fv0ueUQ17S2wAAQABJREFUZ69tA3nwDYA735DsYZnM+DKd3WUmQWZ+P87Cg5DbkdWczpTPXu3zZbeypPUUmGfIPkQmIthUu+WGVyy74wtiEBFzRjw/hSgr5qYVITlywkkg6qndkzCy1oM0jJQ1qBMaicfhqABmUHcmRZDYHXfcAU+D0mxsbAz4hwKZs5nt1SegIzSEXMANA4OBjmAPgPRKZKh4z8KBETKVoWuoDBw5Z9QgVqd96NJoOO2iwkB6cmAVN9AIw28wPLfkc9ddd2FjiZoLTo620z9EwMSRRwgI6BYAIeoOSqH0A5Z1sHAbGa0yFygQ2tHImSGgOMK5BrPZmtCfR6EnD9gEG0hL6Qq7vqVd20PE55F6xHRLkIh7Q6vdVcMEiIaYRwgauECdjoiBPkSswCpoupfhsAyl7VAoKC8SA7qn0FZPE9C6Ip8DHr/3e7/XDrfenhlBFr3zWj49qADPRCV5vZ+06Ha5x+oFLydfBB3CwD2HvcE0RU2O1U4+dLsO2O0HDDx0Pkf8KXXgrWDaZGY+mPjviBf6XGV46A63T3lFoZXMOc/tHP5sdxHTcSrqnNzlI6BfKINJpdeUVtWguMHBnqmpGfxtgu6oiZm79e0KfJoYnGTWalKFPFxUxiLJ3lgyUy4WgI7AS1KF4iniQxqVgs2MQNbpXJw9eeS/SiXyCCRIHFgjMtfUABYGi+IZizQkQiMhdS8OlWSnqwRUgDTKldj6Ty1GIjcHbs2pxZf+pTNsMSwPhKW95n/mSo8upE7Jn9R9T2YyubhU7+zT8qVwJNaQXoNGtyiTMKn+W9DtIBWa3jG+VJj1Kjmg2NzubbQ1Gkvk+lahtzSqWurJvjvSnWrPPzlhtpWix9Rao1k1EFeqdaqtOgPdqbX0vbbuagNXLeKouuifDoP3nYbr3z0Zu3+afTfBcqFSvcn+Q005BvUAjHSTjUkiWqLM0JMAeV13oYLCucFqu2io+rL0567q+aNseCviCo0Z6NGNjldf+4+Pf2hTXuuSQqFYPMZ2k8K6PbVQpBjZla3yOrC2DICv7STqmGWXwbrdsUan4/enCiwNdjzJNEgt7OyDsVHL0haK0OLbei0oyZ21GF3421wcw3buPbmqbvjTeW/7ourZUhe7TiyMv2pvqebPlAJWFA5nvEhY/DOa25ReChLi+AppgnJB7A4Y7otTFGV7OOVZKEkRjSNvwruSbkxoWObT7G41jT2m9lWGl3Y7O9xoWGuGSzVnut719j+88ZO/9UZsQlkPfNZZZ6nfjx/HSg8cB8CtkeRLePSRhy+/7IpIbtXvffrLHd19dtH8ZIFN2/lmtGS3E+ONrASTA2knXJgrfv5LzQLufCS89ORjT3K7xv0PVu75ScflQwtFNv/FoMKLJZinkJ15+JUvlxwUuggX+W4zaMf07UtkWGo6lSUjNOQjpyy2TYqxVziTpXZvm50vbhvfxFyJcQ41mZi7l2941dCq/oGBSuDMVTUbSvfrhDGxnpsPJXA07Sbx2duTTiMlZbqZm5s5Yd0JoGJNgNrotXnnnXfC7+LzFrEW9ejMdZKLZnZzcIGoG3QEmecAZgCWADZ24SXaCVbnAuE4AA/24BpK0c7B5mPP5L/y9kmvx8bGIBhUEncjrFUm5ydNYiOALW+66SZcF5AQDIxlrPX/TAUsuEXHSxxA8rnnngv7AsLH2hM9sE0Ok/c01N0HbLINtA2HcaQyhHALB8nBNbtrwOsfZruOYLR2bRlNNrsi56c6OkqiN04pzX/zzhiiTObIEUC8VtFN/xMLuLuPRgUeWsmf4rGyniuv29kcMLD9dP8LasvQIAZC7T82NvZUk++f4Qs9hA5hpJ5DAGyH4PhAMDWxA9Pz53ViRHgroAjPnyo9hzXhM2E1DbQDwnEMiwOEv3B5xdZ0Xf50yb+/4bDPDWCLnoeL4JXAwnjV4AB6M2E22BhpB4XohC5JKcWs3DgXZyeq5SK8EXu6SstZLpYWp9Gt9q0aEqwlidF1ijoaXW44jo2P3PNSFs8NtgVIG22loTuCoIKRllcBLomLYlDIhiuYK2lbhVlhr/QIXoLoOW87bFpjARVBzCilFc/UFJQWbjweCU710+HFTDa0lK8msznD1pGvqmx4NOKqirBlAqZqHzm76WwnXrtwyIRvpXwBr3WuFwstxUhDLCCuukudgyWzOkdIXguk1XDUjKYNqoahp2qFrN5gKVUx/hv6qrPUx2qb+stUwzxUqOk1qZcbfmS6xB9PVLgp2fyqlSqAlHQqPzKM4z+CA4+9SGLhUO3V6T//2b4/CsGcEqGBThaxQCPsVk6JfuF96x741MSNi6F1lEETWFhLFonHat35bHmoUCos1heqo6v7Y5EQ20Pt2LyxLA0RhftT28bRx+a6+3N4lKAHcAnecPMsxgWMgk4jbI8iv7CSA4B4YYBZGFjlKXbruIbFNt6LZvDw7CzV2NE3KOe1syMZo0VOYB0Z1bDX3Qar0Wqw1uwD5eDEExTtdCTR5UqSwpbReMlerDj47oxH2ZDRzSRcFNEqq+bMlbQUkVeE/ZbwtoNzaZKjYWZF8cSitpCEl0a31JXxVnf1//+f+o8P/9Ibrrrqym9962YsB/UeHD+OiR44DoA1jExGrM+8/IrXJLsGP/Dx/5h1eqemm3yE6QhYFyET34yPG7q5ss93SJetyfhRVkKMb+ZzkkJX6xykruSzgCz4jz2efHV1fV9yuqxVB+iNK01Nmgk2dE16OSv4w466EZSRYfKpyq0CH5ud8TSzslyBfbqbZgkNSl0vEmVLWJZt1MolZttIKsc+ATuKsdndTfBwjCXEYc7kj22Jm69HFlkXOY0tNIppPmN2rwm2L4XDneFUVSsfQk7zzh/fOT09w6ZKFv2qB8w/O03ydTNJwe60OR6ahh2ytTglMgQFegR+tliOEBR6r3nNa7gAJ4M5LSSGjeOCR+oZSQdsA4ml4xCTiF2HbLWmJAeqHSKyzW1l5uPj4zAo6B6B0FTGbl+BDhzoS5M5o1VGKwv4h6vjAh9ItBQFJrdPbwk0rbZgzwK//8fem8B7dlV1vuf85/l/57GGeyupyhwwpMEgAkGBGAbDU7FF24FmUF7rs9VPa0vrs7W11fbD07bBbm2a5wMBBUzQRgYJIFNQhpCkMhapue58///7n+dz3ve39v/eulUZTICEJOTUrfM/Zw9rr73P3muvtdfaa+M7B4SB7xqQdWK3owzt6I40iJToMH+c7zQUKnEcDoHeP9uqD4XbuRnPmQxe+MIXkovGRxJGE866Axchww62C+LuT7Yr+CEeXad8QCTNyPe1xjzbuxyTQNpzMHtAXhBAe0xdWAQ5t0YPSPptEEALsOmU69ugro+uivSTx7l7PM7FPZLmwHrIDe1Hkvgpn4a5idMEWGZlvDwBP9Y3qf2tZmE4mo5dNt1fb3hH8YYl2iqNJXHytqR/OtYIK9mp6Qm4Aln2SNpBRCWdyaq9PicIQYzhSrr9QRTxo9NL9II2Hn5Nx8tdimKQVpahoS/FEMLA45cByCF8kR37Z6Qz6Xol2BEFMipIjwDgYruq6fzIrZGLYZ32AmPRLNkYtWDM5HWlVB5hi8ZaekKfk2OR2GU1jDWvybkSWREXEcYlPSKqYz/MufU9PxLHhpcs/vhFeJXqNytLRw6PLR5MZXJUfKvjNs2Cm/2ptVS46mIzIe2j0q3SYrZcRYnmsopz59KL+EG72YueAWHZVT8HQZAsn8umrNYiNIxEXoz4xOe3m61Ou4XgS2IHAZ15LJ46FLnluum34Kq130iV3zXR/Hw6aMRii53RH97KX1WaSxy5YfpX37XyLnx9we4mokEi4k+t9P16cPn3TJyK9ytb1dXl1f375tjwBFMB9Ga92m61s2PTOOgIkul6G+vogP16bNzjw+h/GMXOsdGkO/EWQU7GBSdbedHi0gC9IGKxIBnlMKQGUjGbtZNkZCWBDxRBOYw7HjZ6c67vTCE8MKpTFWktoLU536hB60qFK5Vyxp9mm6HZNiMMb3EmC/0AZhtvWBn0yVIxyYFWx9uoa20C+Rm90XTRS0UjOMRCA1yuh2c4Kskf+4X/+r4//r9+8LrrruMURvbN2YdSQz99Palb4GkBWNTk/qM68ShMjb/xD25k3LI5EcVpp89ZYd6KXDfrqFVMkUfTkbm4LHxwdhc0sadhmYylNyxwzC7IrDdEeKI4oAtXtgalLrk4JTgyxRqUdtv7rHJtsWzFMMfPHItVKfS2ImgMZvbiYxDSYTlShkNaf4pqJ4tGNhQsOYbHoLDZWu50a3v2zY/F0CEbJRGpw0dFUMK6Q9NSmEjMpqOR5YasptlRjMK5gav4yQtWesmNJeaMALD3rPQK+bnxA1eU2vjZAhOpiUU8RaQdbRSJ3BnkPCDNcgmX0MNmmAvxEs2wuxD8XGJEQTaXOhCkJRDZ70UvehFyLEIRilADk3ECoaBZmp307tWCldfZyrrXh79j7YwoywUo+BLEUfSrZCcEPFEjI/WhSYZfweQV9gUbXaxZkHzgY5zUCoYPX8TDxDLFIqWTgKkStJ1ql0BCeHXLASCGTEj1KfRhQD2mUeBAfWkrFH2sBYDbN7c4AMIoUwrdgDsV3y3n08vxgn7bCqsn2FN5cErwPyy7xjilSf42dCIfnBCxLOsqjV5Zi9bY0SixH7tvP/o+8E2fTGlKwH9LzThg4hd/4gIfWE/S0xtZ+2DOpp+Q7Nv5cq3nWuCb3iuepA1Lr8Av2nnGC0/SunxT0H66k+y0AGSH1VIWVTGGYgHuKT1kREPnC7Fn7wvqxxAV3Jk4khuhzJBr9vmur1SrzQ4L1c5WC6UADQIJdpLnzL4F2g2V4+n7bh+bmk/litP7L2RSlkOUSIIYCHygbWJqRRS3CLcJjpxtc4ZAIhrFxbREaQnGIuiShXCLhYWrCD04mIl1NAYamigg+qSUxpdoJdAuYEQ+VNdnus88lHh/NN+JPqPufTEpsZKtaKZ/7GU741e14ME2uofq8X2JucRyFfy07XlYBsDYqyZhO2w326VTRwr7LsKm27AK+vgfXV/frLabm61CkDFGAlyEs10GRMhI2rNKaO5zcaqQSfLbry6Z62ikEZruZTgBGgjC1b6uCZRTrUEAadBSwN6oeZF97VSiJCbLMc5dUDvAISYS0VQiKv1qPBKPRzKx7g1Tb0161aCXXPrP0/5tY8bQRvx7U+XfS0d+JcxcVbks/bFnTd56uncNRSKCJiqD2LpaJLPWWViYXCaUz+bBMKNOZSmZ05O7kWhncmpU+21VQ52niPlATft+xRZhyWyyNEjzpaT+bXbRtFMPLZbH4px1pPbGGyCKWTItVzGhJFaOmtOJoMghE+wf9gJ0vKvVoNb2Z/IRzh5NJUPMucezYppVYj/crKO70kFJKI05xgWzZ+C3OaCYc48acCMDOrDMp4nKS7Ol84E5k6XicRhkgk2LCb+Y9adj8PNBozv5uj943x/93A3XvfSlf//xj7sdWGr7p68ncwt8/Uz/k7nWZ3GHZqAtxOtVECv8zjs+GCnOsOqz1WQDPTtsOYjIG8+y6IXQ62M1UW6FyzW5pOJg7gOpTOrKK/x/+BTLVCxAkl4rSCxuRaO5Ky7vpFK5DDMCLun8RifYZN8/m0kYomwwSLNKqoHPzpBWP2Dks3UC0SmBFjflZUXWNNaxzmCFlDkCUs/FIlki6ScTmH0gGHjtAZYhuDFQRgg/VtOppJcnrzH+kN5aJ7LRoAh2v/TRErNDONnH5kROttjjMYhlszMHj2zh7U+rpHF/oH0UcZ3PpNObdGpxJIHVkaPaZ1tLlFaXwkXJ4BG5tom54hCrUHhuy8UNpE0ostMBImzceuutygkRxU+SXViRoXRFBIWZQDh0YuROGiyut6n/WWncxZ53R5WHOSsqVuZgqL9TTSPl2ukqBbDC0hXdLxuJUUuCCbIZemDwBD4TBsI5AM9TNbuiXUGanB/iIsqVQnpkS1Lt3gO8O5NmrYeGszvlY/EMelTztttuY4bGwPIxxYS+cb6Bt/WZI6Xg3pJXr5S7jdrc3gUag8kJczXxTJw04fYo0cvoyfAj6mdYOjlOyy0zsXwjmZlJlFzsCDDJWcIz4RKeJU7DConDsAR0G7Wl+Ihh5zU8gBuGeIAjzaFDB4na/a2V4byL3MN8Qk7PFqJHY0x2krvidl55cPkMi7PBFvjAtGcTPP5P7Pl3SvtvUOTbaZDHtIM9Du1DO3yDTfE4IPl4FgH9xK8+a4hf3xaGxwLVx7mzMbMwZVARpgwWzqD2GEIzy5xP6x6Lqn6rYIp0iuLN5/zLJwflNoQacq1L6k10s6FXnZ249/hSeXMTh5kxjNngYMTCyDYOsRXSB3knUPIwrpI4azWIVDfL1Y3V2QOXaeFclsBiXeyGFKtzAVZO3F8Ym85PTHH+kMQrI7RS9tond0SXDDBRsFJIR0aUnViuzcOaCVweTiRWguC2+suuyf5BKlIae93a6nI8OJXxBxEpj9O9iZ9ei403vTB2S+3Vm50EYiNYI5m5CsLWUS4VQppDYO53I41eNNqNRuJCBSG53241KpvFmYWeF6tW67l8DvZG05ZwEBruIjV11aK4LtVDExV1GM4gqqOL0NqCZhbKVCy5lMNdlqqPzI1eXTKu/nHBusD5MG8S0emImYlFoqlkAik3FY9mklp0nshl2F1nKLmy1Fwj0dXF9OcpufnlTPLwaMiJnnCcMIWA6CVr7x5Lf0eDA6P2x2/eDJ6t1X3PT53uRoK4OM2vBe09kenZGWRFdLA0BsABSts0+l61CXhVGGVsIoJmFbUsHUa75BCVYbPVnFL/StSMx2CCEWhpW7/ewBQZVa2menoTRY5lEY5pC+l42cpbb1MSwjBbBcNsIlpIsqFXjnLa8l+lrcX0N/w8Y3FZzKvvidPuBY22v4FvK1QytELSm8IHramYtAe4E27U6QusDni5mD+dV/exo5LCzRqm1HAXIdbanH31O2+/6dde+wr8Qn/yU588sHjgnE8jFJ++nmQt8G0tADMUV1ZXXv6y6ztB4k1/eOOmP5tuSegdK4rKo61CA4xphClstbyUTbJVQCSt1QlaY4X0D75y60u3xav4PWJO8FidggB1XvBd0SuuvL8RO74pUoNgidBbxMBGC3TYNrPhPuzKtztFyGgZp/BsxYdu9KGtPY99wjjo07KY1sk8nbjGYNfUEEEDnMxPRtNj7Q70nR0Pkqgd0YQ8kLcuM2xoukZvIhawscFEWGWmAMpdOrUO2RwfKayXvV7RK/aRunHQh7f/SJs5qaeqGUnWPRIdQEEgMfjuwjIEexJe40jFmg/1t3NB+XYIAZMchJjLxe6E84o98FVXXYUpNTwEgjEXLqOc3hWF4ec+9zk+B68wE4jGCMPYmUDWIe5kIQTI1oYmO4iyUlk9uyJe+MIXYpKNt3rEYPb3IvEeOXKEKFhYNK6oJYHMUj27PdEJI6IvLCzwgHAOVvD9LOQDij2xlOIwd3cEaZKh7t4d+MifHYYuPchQ+iPP+8hTnlfKw2RE+qU62L3zgVy7PUzirzsKyKwIsMn5PAjVzuC+jfBUJdrY7DdqrW5eUyAdyQm6lljzHJ+ZNR3dNaC03EMik2+ZMPV9ghbnNPbyxRE+FeGERJwtHTyHdWB6qPVPsUWC4DOBCQgCM7g5gNXK1uG1/szk4mo3vVEesHFBpZgoTuIhWMtFv9OfehqTuxgTDTr6vLEJVhAh/PLHpzj7vFN9DW9DbCdkmJgfh+muiG/VI6YBLAUeOHDgG0fgzjvvZKUJFdk3DupbCIFPyddx928hGk+ooukh4PPEEYBZOQUl8GG+eBwaipmIJQDXJegbyMMsvGJexGHRT5yB/Bi1A3zRBWM+HNEdq702wokWH0UY8TyVzybmZiaXl9dKm5szM1OQQP4hGYrq2aXNm9iXjkwHfqzNCbZS1rIIHsH0jBVMnpFLlEuXiZpGMMnFmcCiuKLi0GSbGuB8bJOpS48E7qRtFaVUCK4Sd1EnrtUMBz4M5Xn+RnjRJ8s/e93Y78SnOrO/f7r22Vz3eCoy0i18VyMxp0OPTvSu/sLWa9ABtnrIgIABmmRgq42q0a6shYN+OJBJd6u0HGI+zCm3I1PMSTiGGpmc5eidRr3Zb1XzI0WEK2rJjKNfZMIBG1xNeS5IICZZkQram+ruGotWFb42kdC7uCTdqjp6iCcTMEJAa22fgEBKKicpN4GSA39dXn4shWDImbo8M5vSeILOg6wUdR4nIiUMJ8vE9sfW6HoiUofd6x7Jiq0krertbmH/ZDLSwlFykPNOYsCItICJe+xrqHrxeeMlT/YKkSTGyNgF4IO5tLFBQ8/NTIxMTo1NTNkaAoXjctlr6ftTLxBgux81JP+go5OEI5guwtB2xQmg+zcOEzyxQMcd7CCst7VTF5kWpJJxzBUjY9rHG2KGDUtMq+LRukR33NKhKjQCxptM9yTvdH10uThvo0KI3zDh+Uw4zqdAhMZRFqadSNEcVSqHWH4Bxj6jjifJvBeWK0jaiOXaoowTXJRGsOtkqbWpyNQb/8uNf/Lzr7j+uutv/sTNcIZP+YGv3vDUvb59BWAoJyLTK1728o1K+w/e+eHJfQv0/kYvqKMB1oZ7GWlg9jzOHuCoCHOHrQJdb1NO1bGIjnAw71WXPbP4m/+++Y53BnfdC9FtZTPpl1ybe82/3BydOnXGmy+q12CkgdkzpwEzUDWGGW8x3AJByqGr6H6hDgFG0WyqZYijGUbaZKcEOmVIUA+76D4HzUQZqOWNMyMj41kcwzNlICyLrLEdIkB77Kgo6eXvXsRL9I4ZpSlvB1EAEQIdbFVKJ+6+LRL2YnvmkXs3l4+tL0UGkcTUvoMi8v4AMkF4kqP54uCJDba2IuOBwIQIY/ZZI40MJDPHdMITK7nslwBhtlJAbW0iU2HoclmGdEIs7QZKQoiz11IpVs15MCT1C1l35ANBF0e+sBdIxdzRzSJ5wlWQCGn5E5/4BHMMaYDJBRxUrDwwJSDBokAmFomOQjFZRLWL767PfvazSHpop4EAywKbAlboLhB0gUwRYALjQuyZMzTsCLl4Bo4hbDMA77aXFb3HIxeAH3hWE8Uh3iNXPKaEknrRAq6+hvj5N+qFWThaPjCk4o8pMueVTdGE8P/Yln/fWhubiHavy4psuUYPlXMy1nmVhRQSMPVD36ALmyqXJJJdCUHQ1Rlfvldfb7SblZnFMaXavqyXiWdhIEWwbTNuiemOBwnPCh96RrEor7wZLPcn8vnF29bEoSD5uizc6Z/A0V2F8wRY2WGQDKbXAbTEJjMrL5AlLJnk7BBQcbwy5xvwIRrAZC3ZiqCvSZXtyqIY/bNqq0J6VF3scuOHNjn3IvoBYeemeLA3A6qy3FchCRjYtbG5mUgkWbkwqEPQpNwuRHzgI7wQSxibroRt8LuzbtfsEUBU3xniPIQwxELhO3Bc1E6LudfzYncj8IiesZDHMQR06YGD+hHlf8olgsJAdSFoT5ya0c1YRXX+5x8HrJh6FhcXKQjOgfmF85AwLGJiehyK/hYXwVgLI7M5qUxPbvlHK9rjas6NpQFmC2Ucg7hctllr1Ou14kgRukEG0iDj2cVg9DNjU5GgV1nn+F28mbTQYS4vnWEcpzPZdKEIPURWbFa2uo0tsmB1162Xq50WfNPY9KzW+6FFkAOpZ0UDKEAkV+SYI2/QxNplVAcOTQwNil9RCW0JFpPkRz5c/vlcbOOakf8ZT7WLL2mLrhMv/W70ZPdZ//3Un231R3EjjGUs7JWBF8WHgJMMm9tGoxT28Hs8wG1U2NlkK3G9vh7vLFCdEXiQyone6vFEKp+Y3B+0g9zoONlYV4VlAgKejxHMeYBTAu+ttpQcJOhjzy05F67S/gd9tLZpTAE9D14IloYHLqHBfAETxRG+GBpzICdiL4v6GPRhOjVMxIyA2ZRSYhIlWVfLEx6+VpEsNddofdgueUyleC1L0Hzs4aOEaHogO3Q1nTATf4qOm5OPI1mZNXr5bAwjaj+yGcRQFqhJwkgt5p8OEntjqFug92s69KGLbfXIyCj6XJLAqsKU2ifgWOMobYshNIy0HWssQZhPx0Tpx8MYPr3xWdX3u82BTKk9rYygxdU+waRURHyGIIyhwUU13GYXYRQzzP5MBuNHMKHwWLnjNSuD8QzgOMVXeRFrExk4afmR5TCnMly9NEtiWfFoW8TnjjxRa9vwZhNGWjN1Gn1Vgp3DHk3tfGiV6zKHRhZAMzya9vYWvfjMnt//f//6F3/0+/B6w35g1nn5OtasT9+efC3wbSoAQywRbG644YYzS0vvvuljxX2L9Q7Hluno3QmMRMKh/UOdfQs4vsKPvPb9Sh06koa+6Izy43jDiiUuvfo7MwcPRlfXvGbVm5wejE9WsqNfXfVwf8XCFeMSd/P4u8N9dAKDZSgOh3FjvdxkcQtixVJcgLSJ2zo7MA+kTEXMmcAm1rITmFUojv9GHq62NlKjWYyU0Ut3EGslILMDB5sNUS/RLMgAf/jcHyBYBp1mI57JsJCmg1i9sFKrr6yWYunc5OTYwI+0mvVULMEKZiIezyRMRtbkIma3OQgqUChZL0EfgoTW5DwTieWAGrvoBFJxz/db2LIM6SCtk4hiRD3cdXz8yMl2bevChb0XXLBPMoDxsI6iguQ2qdCvM43mAckNfS8PIMGdCQH2wm0fRVBAYENk5eKTuXOGFhcXSYb0+7GPfQzqg/iHGMwF542Md/jwYcTdhYUFTnhC+CQQ1o1w+CT0wwDBEpjZhewUQXaYfqgYRRMIEGYVgDsOfgcfknGBDFEPdZFgRwR1GUnJHAZYolwIDw+V/RsJd1zpTukPCoqmoHbO8/ODJngsA8Ol2uCfjrWP3nc386Y6Wuivn7yXe25ssjCBgp1pi08vxa9maomvsDZMUSRV0zFRGztFElbo+9pQ1BZ7w8Wkpl6llRZ3KVjPTIWCJNG026iW107PLrJxC8spC4yNZ/aOLbfZTkAqy6wJUZemfjELlKl5V+mNd0DKtQV6cFOIsUYSepmaKcKy8Esurcqb6Zb2yFlVLL1gaxVcP/qzvi45n0vr1q4OAGIDlBXBD5AEEKbHFSFMhL9sv4WDMOTMQ57JwjuYq9HgLbjE1wwbweFsoaqL4aDhTh49M9zq9ebM9CSfBiJl4RRiHNIQX370ZJfQp5XwBnPPXfccOHggl83xygjtsvDueSeOnwDxOIQv9ArFohA759qBo1B7GSZwyLi0LpEi9OTaysW4+3b76c1lVyfZxvHsk6LPR8BB+GfujFzkHGjII8kOjg6JfwbokzmadsB2hhVDCBoa10fSLLur6wigC3m0eXfDcc8SGqT80+QlrthOttdw+bq+9QPhP2gI8wUtQLlMInQPFkZf9apXMV8QwuWK5sHlfUwxeVD0HrtA9W2RYlFuSFhFTndVP2gRJG4kw65Przs61mh26u1ekoVzyJGRQdLIgbOSawz3u0Fra317NIeNrTW3rpjKjwScOORzKF29VcNTM+QTkbDXCxCGjLDhYIXPvdO4Asdg1x4wh4mWGpGaxHCIcMN6USpaBwQgMkm0Q+oOku9a+p27as//3vG3zqfuTGAz5yXK/fkv1n74M1uvr/szLOvrxKI+FkWaDODKIKf8SVSLeOMHDwG+32mUlo6NzR+MxpKl5eMUApnFf0s6nuhHWiPFqVQhlk3FRkdwbEEtaCGv2Wo3OwF1p5f2+Y/9Ugynwylw5YSN/qCrhV271GjItebFepT9rHCdMXbMsRlagi41tItG1ccANCnhEhFPUTVDbWEIOfrI6K2a2w0Fu4tb0z9NEaKTGjfaAT2oexP1cG7Uvzf9ndX2+yejbSyVKYZlh+hm4TlH9//Il/4MT81eOxxNejL+92kcnRmsi/DkhzuDODpgXZeEY0iniKYrh6rtK2ClozCr+H9lfUG8nsRyhzeIsCfca/W9dovtweoO2s1kp5/AYFt1mFK1AZAviANasMWUspDsI7hqtpUtdLTZ9leq6JColVqMUYixfDrJyaBCng4pZRWPEfxvyYARXS4IwzS0+zAP4YZplWGwgYmWCwja5yirzxA7ai5kbxQ8ONnKxKMIwNZ/AYAPsODggX3vufGDr/n+l7z85S//6Ec/igblqTTYVflvm+vbUQCGAiACveY1r0Ex+NGPfOSqZ10GVcBiB/VvtdXH5XqFZb4+hhaRAid+yxYjYMwwourdkI21vb4W9hJx/9hWAl/Ne0Znx8dmGR61Lif0BkdPsuAoz8yjWYnKjFXtJWD9qWcurSJhCpE4ESlopoae24ln+L7qwcdDHiTxxuMRNuUzziEMLJUxwnF5V+H4swZ+87rJRDSX0mjHpyG+q7BUJkGPrcKyFaJErEdwEdFZX7t/dv+FiWiGym7VOsunV6HGF1x2FfpTKPHy/XeNTM4nM7CtmEaLVDrySLeH3KPxhneFSxah0vKdvFVXOtr0wTskQNTWtnaw/aVT25qenUrEo0mkYlH78GiZiS2oJAbLUZxUa9cxymTUxU5ClsNqSKMEA/Gruy+EW0RixFGcMGYyIrIgj24Wk10YdPTA6FEXFxd5duwXsZg6w3Yj7vJB2dnLMwwrUi5zDPuBUeAg9WECjQiNHhjWjU3F+UIBDTMuoVfX1kaNcmkmtQvvWaRkTxduLclLmKNr7jxk2J1HbvtHXpChCm4RF5aRZ/gnRNDdVf4GnynlPAgu5Dxy7AKxSb7wwgtRYpwXex6Ex+IVU6XbV4LVVjw3vgeMcWbea9fz4zMs8ycyOdvnY5M6fYLirV+As2ZGY194JoxFkdLySS8crJ68Jz8yuXlSXT4zOp4pjCuzzcea7K16xirQecUnMKzozXAfiMpiodBLYCAhToDCmBBdqSpYo4A8AsaT8NhmOSzIxVk3VwrS6Y9kFGmXYClcUVYV/epSSeIdScyPRpkidNeDdN0Ohsix2EqCxfcN78NkloR4JXBAADDkcuD2hKrgGFjFK6XycONvGKIf5VKY8vLMQ6m0dV9ztOvND06pZSBNyibVgPJbMkALioW7ctDR9O/b7PVGB3noo+ctn17f2kQ9GBxbhwCsHN/E9at32eWXmA9Sg0Np+md/Bsi1hgBbjKJU7vZ9mJL0hv3ZcJdIKbksdpgfPk9xrrbb2V37W9qdm0HYeXvgg/aAwQZGUXDRZQBqiJ+TTuHu0q9pdIbYq0Br+HPSP9lfqBG0lA0jLLdBZh9tdVhwxAgFKgoJerR5z0tPy0OfMQ5yjQxtJwGCKCeUnJfyMXpFppPpCnIRK9xQFDsWwZXFogkTzeOGyWNUQcDudO7toWIDi41Cs9EWsgL0QOQSxkOa3rVG0OxH987PIq0N00NsGRNhWN0qsRw/Ob8P9iAaT+676EooSbW8UStvyhuWKgAnRELly0/M5sdnadLVY3cXRsbzY9OMO/EVEvAcRdSwA7LshyEfGoP8CQ0M6UoVKSe0lKh/koeVGcBWGfO3Fftq+4bDp16ejS5notV+iEenuZ6H0oOkgpOB+4pLl6Bhz05TFY/wKXFaClVcX7NxNBlOF+KpdGL/5EXENsurZ47dOzYy7bVm2U7GluDVen213M4V88lkCsSWVkoIvyEcmFCmEDSfAziouB/Ec/hhwbtyFDerNB1SohZPDVvP57gmLUgSgpIX+RZFqERckxVtARTkhLOmIGs/wSaA9hkSQMVSC6sNbWjCPOvPvIuai7/xwsyR7g89O/nb8fl26rWrjbdPxNpYWYtzHZv+3xOvuf2Ln/qNyvozgSNsIPw0iyvUCG2s7fFnxWhuaKSDL+3rnUh38uuJudEoWh++DDXH1K+LJbMZMwOKLhJDuxsLi2kcXsr0mSYh3QBX35SCMhYPW2w5TAMS9Y8C8HrT7ISlOuXHqAY8g5TbGGJj0xgEzZ7fbGEvOVir+3P5cC4v8TWTAC/osmywq52wx55kvGbiEEeOadFICW3Wasy2WXadOLiWzXPaTxXZGCikaCLl8WDag0IqKCaRCOQZi64Yzh7ixM0Xv/jFr371q2+66aavY0EQ3J6+vuUt8G0nADN+WXJ7wxve+MlP/sO7P3DjM571HY65gdIVtJ/e2wPJQEIe4EcKYdhjw70sfKKRXMafSIt44OG52Q8YNq2ed7Ts37NpRikx7cRgoW4sh1EQQ92vtP1eX2QG4oXhCkNO1iAsMLJ81fdKXfa3KBZRkBOMilnGG7FQXmKRpbGNUcrSysmgVYE4Ys7R2TqzVoPKRCf3HcL5tDskifU/HE2zPwfLZJy6Y4uMJyGICG4VMTXpYwoz6K8sn2Ga3rdvPsN2hzDoUQkNbWaRIZmkxlAgETTNG9JC445eXVOzC8FUWqpsnBTx4BIR3uasplpn49RqMzkJxWpXN+rrp3rNSqdVp9T1cuP+Yyenp2dm9+5NZjJUVaTEARQp1wFOzikXZ6Njx4KQfOrMeiGfzhXylI1UQBH33HskX8jOz86jxcVlERt6YbyQdRGSiYXo4MwJ2RJPWnA/yL2kQcTFDTVcGiHIsUihiLUs2CMJY804Nj6G64KxbCYbaiJhGWN6775yq0le55wZ9oWmgL9hpR/mBh4Lvo1nikMUp2Pw4C612ENfSOmUq8lClZZjau7YYCOxP3SmRx1DNTHUpAgEfvZw8kwpbu+0g8UrD7QDyJMMC8ZHXcY3lsEhcKoyOFpWtxuZmGbqbWyuNoPOmPaIqkPxpd3Svom86mD2n7v1yGFNNBwUQmdkTZhxJMZGUxFcjnqXfQ3dbH5Wl+XC32OtzE+XpeZ+v16p8GmxcNuqN8fHxtmyADxxD5YN4Dbn2ZvESIMIYIt3H15hQs7dhkUYutRAFbFkwkDY8+mRoXSBjRTcLrvgCqpyMIGb6MqcrnKVwOIsjxVtKS3QqmkRu9MQ4MKUVZfKoU5cNsWzrmaXla9nurduojYUR7m8rawMyt1iuj9a2bB3xahgQRBiFmhB9gQzUUPrhuOBlZJfPtFIJmXd53u4WEVkDo9Xj3YLxX5kBjT669qhoFx8GAdIwHUph+72otjhk3vnZeddD/o//FQuwYPeXRZ3t69kqdz7ORn0oUDnbBr35Rw+ln5zI3GkOd5bTczv0X6Qc3KDivVsOlW4URpsrHqbpUihEJma8jhSi+VDYzDPy/IUeGVxkFo4xfijrQ5SIkQYe/JHm/FB00OTsesBJqQbFw+sLUKuHzTlNz2Qjur28gCZXgDhvf3223Fv4WYQQqDGLsoVfV7H/qbj81gBFF3TtT1Ghq8jycgV48Fqs3+yIt2uZLYYmjc/3mazZZRjGLAg7sD6cCCuGbb1+p12F4+8EAy1BEOOi81fbJNAIEb+sVDGFx8TJYEj3EoEL6RXizF2QANWf4DGAnhIMZ2OFywRYfgcEVY0hzQLSGIi3JsKdRn5peBauK+Go1HFMomg0tBJBJwjqaV5VYolfmlTRc6Qmznug5XXgYclXavRrve9U2slXF7BlhSLo42VzZWjJ4+cqaCN8LyS0RW/uOeSRoDFhBa9M4VCGlej6BFZ2gd1CnO1Fn1Ns7RLFamYCnWSNlKfKXXR5iq9I4zD5rcaWTvYl3F0SXE0rL1ot45sgkTlKUcxqqZNi2YWzrSkdVKikOP4/WrnjQvxv52MHC68eCN1cbtxS4ZjkNIXNHPPafvJEy9a+JG/+fBN3bsuwgadCyNoQdSju9wjW4MGI5cEB16RuHIkgd36vVud29eCUzUOPgphOuFgYVCZb1G0IvlH8WgjE3qjovpIYSoZsCzGgjgtQjdBEma/4Wozwjmh4MoCBEpePcQ4TVo+pauYSWIILa0P3ayPo68cAqow4iBfb63ZzyfFIYMcCltZNcN+m9U5WuUm24bxwhMg8aIZDkiJQyy4aHbyWc1oTHrXgKWJYgIDTE5vks6G7gC6YlW2a3/pFZe/8y8/8H+88pU/9VM/+a53/QVfevilhi3z9M+ToAW+vQRgNzn9u1/6lb963/v/wx++M3vo2i+cDDKJQT4ZstOgkMQ7PONbVCPFQlEmNpWBcmgfSxvlcIfDx1CEMsB8jmlhVBiBlW8qdvkiDHOI9mZHFsgMEg55ZaEolobCaQtKVw6fw05LK52yqNFOYD/KyWYMaSaMMNJGCdyXq3eoC4Oc0ln9ImWimGqjsg28cgm/zvG+mP04a5zQUXzrcUHWIOObbcyGGd+R6urpcNBCtEN/Eawtx2NR6O/cDPxBIplJS1EsqhkbnV9ko4m21RgMbtAZcMNgxGY9qiCSqv+6qKm8ybODQuRI418xEG6dEY+ZDvQsgmF2jyPuq+WtsC+zlBJa41KjPMiWYv10TqbUaK1ZfpPci3693ayV1/cvHEDup7lZ1oN8n1weZLb6S2gFJQ/r73R5MIKBULoedjj7jZk1YKfimdOnYTU099rFSb9wQnAkECB0C2xACo8H6CgQ9trtFtonEqP5xMiZTcUvuOZ5mUat9+Uv+1/8srdV9eZnItdeO7a42IvFTp85jX4WGbLCNqRut42HLLTKlQqG0245H5UynB9FoMVl3xcXMjYkzzhhh8vZO8gg6xKLdhr5E9Uxz0jsZ1N8M57AgbUAIMNZGkbajbbTMq4E1N3siMYBGL7BvhllPgoYrmVwZXHfZljtsr6tvEiKLIrTpTmsgd5Df7Nu5kQRuhYXAepyvHAniR40LIOxqSkGXXPzFMzKxMQ0gymVKyA5OemT4qx7q1h9FUmkg42lU9i0ARIgmysneah1eqnCuHgbk/6sGMYav9syGE+Ggvulb0oPDVpc4i70xI8KPXvpWXhSsp6M/eDdcDdpSINdtdLdZSQa/soyWC9ykSoGCAwIYSRdB6/WOsPyhIYS8F+Y2I89DsGK7VNJDhOVqljdDG2C3bBH8DZ8BKE+yLbj/mY3uQmpMp2JZXLNJmgCJzD64WGNscqagoAHy7UVNV0skc3mGxX4v3CtGjaWqmca7JLz58MJVt2HOXf9DKFZyE5lhm/byc5N4yIpUwi7Sm1nHOZzeCrecqr5dOkzWg2GybZ/9G1cyu0Q9+5yedVadKlbiFSi0jWJPg2BDKMpu9Me3Hdf7y/+wv/8P8ZarUE80X3G5fF/9RPRZ1zBapna7sEqsKusJ98j62iQNScGP3LskRPos5ot7M4rnwNh4ME+yiOCSkYnSLPOiAAMbX/43R+PCOjXlQhMbKJpo5F2Ergz9nFbdaDJ30SaL0qpPmu0zLDd3b92UYLtmlj/dqPg3NjtcbGdcPhrhMmlFJVmHd+MZaDVCE4YskGL2BjJM2exVrr+elNSBB9V4Ri7copuBOoanlirt9qdmYmx2VxPoos3QMIctNvgzg4UdH9UAb6EI3M5rseGMlO5WB/JhvzJjDkCeY+nOVJI6Eu0dWSSF9FHUTAJItYSImLKxn/0ryxJGWU2bS9RyEAoCXSny6FBtTtSJcpWie5GeLFLxmsBnw9HpN0+u8rrcBrtQW/QD7r02r43PjGeiKMB9ZZXahxt6yenWy1KxLVylAZJTcxNxdNeNN7YOJMqjGYKo8AO46nN0lapVKZzZrMyxEOva+sF5GKDLkUjGYIbmICYzRfUzfBxJEsfxb62VXmHmBCk7+pYMVvVVJtoKNlX5UktJJW4gkX8dFO84OnOs5Ly300uvcHMR2vvuK7w45P+nfF9tZF9qFkEnn/9IP231Z/84OJlM8nIZYd76Y4DI1DuMkChl+weeGn0gqu7ua0zg7vXItn8lZMTL1mYvGW1/0/LmmcpiLUMEgszTgBN9Ati35hNEXdj9Cj0SdU23VtubGCq2GStCV8uqdmLG9Z7bGIacD4w2WkuxxymtStcdWwPYhhvDlocNK2vjCiLaeRM3ktxcjS7n8wLT6NFD+PYY1zShoi1qSybhG1xYXuZ2DIiDHv4lIa3z7JYAXJWSX5t6OkVpzzVblBtcwgLBqF+dP81v/ZHf/4b/+eP/OzP/uxb3/rWb4SmDRv06Z/HtwW+jQRgEc4wfMtb3vLHb3vbj/3yfz34vO/H5fIgGuu0w62Wf1KEYYB9cj6BcMsY8HIpbaZniDEScLbOgpBX0FBg4arZHbBnuNrBKWK/wcnaHBeWFC3iMv2wHLKzob/WwjhZ4ax7sRrKHgYpEhAW8bmHRQc2GxAFkUVZPsvsmfxGmyiCrfntQTRMTyFFJ7ygXj48t3f6UD4/6IWdcMCefogCVtkIw6yHcQ4wBsbsXe1gZdLvs2zmD6LL5U6jshaNpa76F9ekQD6QRwFnPJlLYfyseYK9viY5a2xD24jVohnNxN3CoC9ilwMWRAeT+WAMkd73a53BrV890oAYkCcIj917F2QtncvvvfJ5pF05erhdLY/P7hmfXSCnM6bsMLWwlbrL7MFGoO7GmdOYRdXTezmJvVMvtctrEUjfoBuvtVY5bCroMG3gCnp9q3fHxz69uXoGahhLJO5Z+uTqqeOct/7zIwvz0xOI05BCKBEsKuuCzF7Mu1+59dY7brv9xInjX/zil4rFwsrKMnMbLNfk1OT+vftSla3m2/9n8Ld/k4sBJh75ahTT6sFPvyHxvd9TzOXG0qkgm8GmHM15NFfcaDdv+cIt1MhdHHqBlbV7hrByLSwsONmYKJTMsDtONuYO3+Ncs7g9wDtuWmjaHQi7n4dlPMofZHsu4Nxxxx1sRHFi9m4YRLFHBTU4hx7vDn88n4+Wg3vX0buCi5O8vMzIZKo4YdOXTc7qakx4dD2b9jTJ0rrc1Cd5svnHr5TWWg21P7NptbwSO52mV84duIytTMYIqU42epTTQgRzdHaRJaxeu9korU3O7cflSHdlNZdJF3Kyw1Txrp/rZzhDG5pgq1++Fg/uR9jQ3a0csiqvXoYB+lGnYDXH4jSQFGB33fivH42tYbECzJ+gAoXSTdR2ugzBBaQK37ksJ0FWjINnjQMeVoxBg8DAdRKihQMFQ1UsLQOXNzUMMAGxC7BXGMnzZ8mEPwlYw7JyLZkyDCvqcEqPzbPU1w8G5eWThcl5ltdQZfepewSFRtA+cTKVGvULcyQutxn1cC5C0Wq0U5vzHhxytILD26FnOKhytvAwbDZwcZgaWg7N7abeDdSQdgFUfZhOqu+zl4Nz9l1fwi7yNhqxEo7wm/gapKBhkYrUm/2cWuq+5Y+yh+8gRO2Jc9wvfql5einxm/8xduUV8s3zlLvg8DC6ebTVQjh0YiEZIZX0aSjkY+QS/9Hi9o2ndy4k0AMzy7BA4AAeO3aMB9TUCwsL33gRQwjqd3S+Bw4jdWOLZFxbAjeILQiChWoV8UMmpoxrSRR41fR6Cpd/IJa1dceeQ/yALP+RnbQUrv1bQBYD48oUKCAoWbjV9lZxHkyIK1soSDaWFtdL1bph/dTaWndpuoC8y+AIyktHAFUYn85NzNABYsWRTAFfWaJ8oNzrNFGOhmyjpVQ4htBLjUwiFJIToZFTI8GBXACnRP67SirQwvkRDcTjANat+AE2OzgkYsYlO19gALD6wWyILcWcztcJ/Gp7sFkusR2XKJrFrW/OTM+wok0RyyX2LqnqlC2XoHgERYRTK3DK/RhaWWQjSc/gJnJFKelEWqzhoL6Zy0JIx4Qzrqr8bKW0MagO9sxPa2uuZgRSy8BZO+5Eh4fUBhDbVElEW22pZQC79GPfYfhqeBCk9vDheUQuVXuiedCPPXBzuVSO0qrXYNgscOwAYvEZ9g1NBXZ8TM13Di793JkPXTfxtueM3TgeW4p6/bZXuL/xHR9aedNX6t87CBNHZ7zVTPwFX2zldcTS7gsnMf1nvSk+Fz9d+9P3rn7sH7xWHW860csOjv/rn7j2mVf3vdR9m/qiKlj4oTYPGx1/Czc32iMtZxa0pSrDLlz5hcYNjbYEMmmQHp08mw11pgkHqdh+JdIQW+4y66sroE/OxAew335K674wtLjXOcUWvHZkT97HvU466o2kIpm8zkxi4WPYOII8wOA5n5C4C7efSUTi4DNsQDWZwxf5uWaqL4yo2YrI0aQg6oAwLdCG+59z/Y//6lvf/ls/zeaON7/5zfrM1siW/enbE70FnoKT9MM0+Xve855fffObf+Rn/sMLf+C1my1vuQpBHbAjl33wbPxgozyLmM2Wt9ISRUF5i3UuRx+ZPIxgLAtngGOmUkhGiklvXkMwggK21R0gDUL0a2xF6Eg/POC/xTLl9DmCbCBf0EfuuS+RG82OogzEjXsYT8ItOo0S04AMjzGcRqCFVOByEF1vNq7T28T8yfsee499rwOBdgI57riwuGFw9kSum/IbDQR/bJF19n63XTl9T6w4Hsb78xP5QkYCtg1orEcirLoyhzGG2WaM7C2P86qWeHBRTZ4C0pjFqdFO2mJfsX/1nD/abSTadQh0r5C4+kWLXzpW+vIJvDY3MtLCReKJtDGKolJMt/gUa8nNgGAb660yuNiB3OvUtzZOp1IFlAJYTOEQGE8J2Kv0Ov2UXz9Q+PTexGeL3U6rN1rvP5Mji4PsLGchrS+fGB2fiucnR8Ynb7v39K33nALi2ORUvdqNrwZfOX3M67U2Vjvx6KAc5Eud2NETS5nUBjMLxnGnTi+xk2auODr47KeSH/4o0xeW4Sz5aiMX+1Hf+5eZl74krLWan/2HyKc/HSmVYmOj3vOeN//8F/zgq17l1qhpMGRdJEl0DjvXzjGheNhCEUHtoH2aajwPaug0rkePHmWrOZKzE4/h/FBW7Nm7l95hh/iRmE/AFD9sn91AeN65Hi1VdWiwGw1l+AUXXOBs9jTN7CpoB/hj98BEwozFVh/Wd5itYW/M8Ik+JgTFt6jn2TqTm9AIVJOoMyoJwwfWw/5lRyeS+RGim+3mxMxCcWKGnqW+BSOiJgcY0DQpDrudpqlIspiDM2rXOcTCz+Zza6WtWBIzgRF6qYoW46FPZsgYEBUvrCxeadylYhRmzScJTKW4P0uv6OHlEFFPAIrAUIb9OqAaYhptVik925PlJsoiTUgTd6VY/mOuX0/5+IaBXyn2vAwUQozSDjIus/QZAmaYuCoMkQCE1Wn7R8mAO8w2rKqVTahjB1WsCoce2cN2erUXa4KJDJu3fJbb4GgS6VjG+H6ek9g4hOPzC9mxmczIhHAUOw0c0BqK4laqyrQH/RpsV6A+KeGOryZqO41aWyBcKsPJonQDuBUkPHXxsg1dr2oQy2ZwVYAuy21P59y2seA3nYiN4PcYB4APuIgNG+3u5z+TOnyYphx+e+HhZ5aWWn/3d5GF/aIhD1XKAwA+iQKs5wzx3dXpH64GLNJhU4MuDVqE9hgxg+vhMjziOMgpdj3cH3GOR5Rwp46u/z1oHnVau2gELIwwHaKfscumUq0yF0By6WE4mSTJDrRhBusWNtrO7YTD/jkE6nLReSkHagkxhHKahClTYQYedFViLfYt2P1qTlcs2lropZzlIksMM0IdATL8Ez5eVDTVKDBCLykRxygC+mx3JwxTKG+ijeJJVPoOBBBFyg1ZQFbn98KN08fjyVR2dAao6GyT6USk1WsPUp3UbKSQjPfLHE2bH59m4KH2BS6ESvKjjUsgg8zq8slMrpibmGV4KkQjmpmD/05kU5ORAzWdyRdCgGSwMV2Yhy7iLapapFwYCszlwmK+AHqNVmezVB5SDMoSFQjnZrNwJBgfNVBQaP7nACPUr/ABslJBGgby1NQE8i0FaW5RQWoz2oL4DOfwSEUpXS68ExpvnbGH6TKgEMFqydxILFfUiiy1Gk3l4pz3h+fOUmJiYhzGjMVBoa6KUEOVu40eLB8XkbrEilE99+LuRtMMETUMXxMoigGOpVOog2Wv3CiNpkDsxwILQZd9sN1BH4FNbqXUurSG9B18c8GhA4Rzf778f7/3zC/mYyV6UL1b3BqwR4mJRqaIMj1MBaxPkFIdwfKLKFN+4OVwTvrut3mfvkUNQoaLKvEAAEAASURBVBBuxO64e/M3fnf8d3/tmouvuns1Vusy3inLtgG6PL40upJm+2z30wINMDFzNjtBuG4vp47s4wWbj1VtYSip6rHvF0fNbA/Oq3eTItqFH+hGex2iOa5FJs14rhpN+6xaIDNjwjmaASll5ieLuJsICglx9RmcwNKKIAOg4aW2xF9JvRuImWf/Y0entMBxWxtRVRlCtvoonzy5s8ardcjpU/7zXv6aQW31N3/r1+H6Xvva125De/r3SdAC35x56IlfUejJzTff/LrXve47v++1z/qXv4K9MRJsAj9VPropzvgKEXrp3JHIAE+AHBqWk+M49pOwYSDE1EdD3g8YVOiBOYqIIYSFM0YUjGl2OCQkDzNGbOyHEY2Qbh8DiUZnUO9GWDJLIYCmvU7BK4xGihMRtLu4kUAk7vQiGA4xdSFsI4hSNFY6DEmGJWOrF0Q7nYA7YzszvRhLpxOJYOeDUR6z4HotYf4nNJDxKY3NM7tx8IJVaVfCVA46SyXarQF+CrT9BOKtND4OtGyTrYg7JHlbD8wrq1v8R7RmSwyJxP7uKQyeP9VLnTra/NDHqrfegcfYyAULEy+/7trLnzk6kv/bLxwdndmH/8Uh9Q5Dzq9PZ9nLO6KFW4Rsm6g0z8K2Dwan7j88aNVL62szc7mT99/JPJLixIB9F6Mnypff/bK5t8xk7kVJrAkCqjX97hft5fi+/3TH8kWnP/AnqWihn85283vuWW616uVELLrxmc/tv+CS4vhEZKuDqVWj0gUmCwAnTy4n8mOF0ZFqaXNjayM7Gj106ZWpfqf5kb+Pd3scEN+TOTg4Mb/Goq94pbde7nzgA8Ff/WU6EmUKD496wZe+0r777uQbfyaxZ85NRm4n8IN29Wc/+9lweAjG3N2D23NLRhi+hYUFDKGdBSCtNDM9Pb9nnkmpX6+1URD74erG5vLSMlMuttyAoghMmuGokJkxpeaCw+Ny/qvOmxkdPouLiyTj2cUyxQIBPuyWW27hGftn9kUTywNwXJbH5a5uyXLywgjWsKDGK+NId1Z81Pd4kMZA7BrReuaO+YIxZ2LLxHiJQ2JRJsjmSM+ifb+UGZ+ZSubdgT02G9sA3amRIGvK0x0I1pXoaDqCslKpxzhOMZGEVQIV0LM/PRmPoSnRYUkEUHmGb2OY60naCMXyDFQFWJFuxCo9fZYIfhWkR6v1MMSBVojSCZLB2IFjwKixWkzyLegzXqei93938Y8X4n+fiaxAJ+rB3P29l/5T7d+U+ntJ4xDQTi7AgpVVlWAXYCGkUSo1IndDjnJJrbGtd69c3qLrIqWwIqR3XQ6wPQhL18i8DgVR8pFZI1sGjVZrK0T5+F4enxtiaOsS4hXpg9u4GoTtApSaZ2M0BZz/rmhXouU5G8gHc7yXS2QohhiSQGRSyYx8mlqbbsMYgnNVUV2BdPbDObBKa5cQ5NouTL9pzl2bTNppmooZpgCGYR22G5E779o2J1deIlRh0LjrcIRlVAnAT8GLrvKVr3wFtac7pu6R1NDZv7B66ARgqNxOYz6S7A+TBjjfdL8GbgiLRPHVh93EOsdD4MFQwkQWayNILifDJlPsQ4lxJJ3rbKhbIUGQMqQRUTUZEpuu1ZaJiYMOmUcPgrWnUQRwOw1DFt6A4UQUHdC2X1ofN6zATRKiqKXwVEqZpwk+RcG68EeJxBoCJjwbNFLSV+V0c/syKVMEWQgKAQ0EynUNYKXpixFPqIayCxJeGqP9Xheiyi5NKKSj9L1eZgUh08u3EtkYdtPeIFmY0PBj3wBIi/bqhREDw4FYi4I0xh5Z1AI6xkKr09i0oouOxeIg0+x0OWBHllzy96mTg8g4NTEJCBbgK5WqMDL5FgQhJgmO6gBfH0KP9IN0y9SKAMcyogzWQDIRS8zPzQh5uDpVW20EDGRahD1OJ9auYKQ+aQgk7qLyNaNlrbe6RtHHVCVAgVpbywXe5J6DRIM8uQimbjOzU/1+p1bZynFKVC7HxwLNysZKs7Y1u3AJzSw0rTH1idXquvgKau3hm9CkLGpofiQArMsK1zdFroZxRfnBsbr4Z+W0XozA2yxnkF0flLQqQVRcgPUf8OpaVndCYHv1KbBLRJkbGe15o5gKx9PhDIeM4PmFuouyhePH+glJne5SO0DumEQwNjxx4/HZz35BiKF9UZsQ7kfKW9W//GDxlw7O53K3beJNEEM8j0WKYKj7lbNV5GruCK72YbCUDhDROfgTLIHGogQmfvDh7FIkBd+WhR6YZ5hqXmBuEXfRD+eyOrKUT8aHQialNfo9jL7CaoTDgYP5fDieYv1dkioetV0bWB3UIDQjm+sw52x0I9gS4gKarcW2Iq+PytUJWGHB6Y+H3x/sOum5YIu2rJhCfBCe7T4KsMiVN/zb5x07/aY3vWlmZvr661/GV7Uinr490VtgR556oiP6jeDHiL/rrrte/eoffuY13/vLv/v/tMMY6zfs5u3UoSPSjiJ54rSZoUifRx5GND3dYocrK3yihjo3DP1wjNklxiFGa+iHRaHwwzxIJ1lV8nJxD8PpTEIrWIRjWcEWgumsY7r88lZttVxl5/BUmuW3WrsUsmo1MTblk9PGPZQdywo5hddGmghUTNRY62EDDhzGKpthlk3LQJHZB294TAPEs4y3Uu1iL8ywzsm9llFXjsbtRzbXq2ubm7metzhTzIeN0qm7ADc1tyddQIGmyYKNylBp6YGNU0ZKZW7ATlt00o17a25IVTzau3rKT37t3tJ//L3YseMwtpoAv3a08ukv5H7l557xvGvvmSu0NStojrdMfn50igfold7FCBt/LpY/XFu6v9sow9jns4XSxuksu2US2ZHpPCuAF8Q/8orFX0xEt+yw4XjYgfjhhLU7m7zvVbOvb23+8uc571fmSLFWozo2t5BN5ecXDsK7V7ucAj81MrOHwiLp1eUvf3nj5H3ljaWRTKZerzVbTJ1tjJ3ymRw+9funV6CM2ENBn3XcX6s/YLPR1Vd3b7819tc3Ql6baIy1UcimpY98vHPJlanvf3kvibckFi+J0Hex2tnP8MayN2IzVs9ZzTVGNzXrWEteeimHE1/Cs4xvTUBOM72cPNm/43B49H5WIROXXXrB4uLU5ZfdcdfdoOpAsnGXHru7DHQmnDsHXuxDxuDZScUSjk08PnDgAAngDZC0EXEJZOPxF7/4RaRolBLMK+QCBzhRonaD5fkxJNaatL1DU8mD1q1M20A7YICnaRKfKLBrdELN4s70zrQcYCumDYsA5WLKkV5C0pomafFtyUp8ajaWyWtHEL2OejmmjcmMgcMf05qxg4Qrgfp5PhdMX1KqVmrxxszMfCFnK8rKqHlu+MX08bjw+q4Q1531DXkiJb9As+HhshDCpa8sPO3O+3YPoWjwVQrdNZ750bsSwhwRDQDIxXY4dVM0N+NwlCk8lPrg9YV/m4sskZ8cIDseqY0njxyM//UHy2892Xup+CK4DisGTpHshokhpDKFBDgrnkvwDRewVZ0Uwr3VbuOoRnKqJbEwi3QIEaE3VxvLA0CHaiy95+Clloubi1KF9h66xEpyNyW12CEelpRkZ4FatIowhPTjXqyFhYS9bidQvF1qELmThbuNz4rako5VNqWztMNPYTTWECbcWsZhNISyA+uc950X+xjWLttBhrcBoptShrqHEgiqK10ShmGwneUp8Kt+ZRdeFba2yrhXMAH4n68mKXbaewfIzsPZlrHOptftgobDh5bcLmS705zNtPtp2KNU2naG3dFnsVCoerChtVMaeZygCFGg45kqlf6kg1REgkQroCo8Dx/sdZiFodcqXvy11a+Fd5WgvqubYeMEs4tKcX+0wfaDISe6JGJiZEpzp0kjrhTOS6RYUQcrQvKweptopgghUbySHoShJAbWqiJaq4tcKs7oqlVKDWI00jUx9J7aQG24M4LUqKTXXWKjFGVD81stxuufcgthPQFaNFkFskLfZTcTRXXRw0ZakUqFwGQaV5vRyUKk0UnXmu3VrV48EUux3VLr9QHK2p7k2OEfq9hBuxFNZlq1dmvpZPuuE9gjZ0ZnI1HMS/CRmUAZSzkwTxWOjlRHktaWpUwmXBEHuItEMlcsSMq1PyfsSnuL1iESScZHjPoxRLV4iZylM3zMf+JwP7AcsiB+E473UNpA9XLfWk3kuojK0YTCPMLnp1jAUwAME89CSnkMHZtKeRcU12peZHp69syZJbUgjbwdp++oVhXx0NdVnO7usnT6GgTD68mmD6tgTLjpA2hrsKKSOleBTJEmhwpzQVK5wkQqDQMMbP6sO9kXVhpxcEj1sLuyOpdPaQwSeaVtmXvoBlZhFU/j0CxQOU2/k6d1Pq/rZVtTXj8ZmTxl0q7vbZ4ZnYVv8jgPSZmFhhrE7x2+u9DrpPzs2mY3j6mxHL7iuJIeCiTZPLL110RdLTegRY/HpPuFP6VZpHclAWeUMJtrCUJMOI6j8xzSy0e0ZVUOQxog6Ns+YXxUISePJQb48WE/Ivt9tVO9EWzUggvHY/QnFWoMRgNxt4c9MwegcOiRdhHKszSIWwIU5tgkmsQb6shiSbycDywb6RGZYcvIQv5t6mZxyQnPivLQOf/Sb/2X36+v/OiP/tgnPvEJdpy5j+g+6NP3J2wLPPUFYDo9+3Ne+cobZub2/ue3vWOQwF8B/VXEnZGKJrbV0fHcWw07mxcrnWiYiUXGGWaSKtkeo3OAqy3tnIFwsCKFftiJxLj1abf9Utu4WTOZxq1xli3EcemHMbfAphptYqtRaZbXEWP34rpYnD+KYX//+NggEkUqbrK8xJHfMX/EiILGIQQOHwwsLA2i+I7HPLjb044dEReOXzK3z0BptaET2mvClMxOY82Loj4sQOIN3ps9cNkF86O5fjURGyD3itRGMg2kSksk652YbBgpzKgdwYqB4mD8LJIkiiqaetl4pNAoNf6/9yaOHieNkXmhEm02K299+8jFF3/XZYufPC4PD7bx0DBQZgEEJDD0aC/Uj1MQipPz0ObyZtCr9kan5mOJfIbtQP7q9xR+KemXg16i8r/HW39X9EvRYKyfenFl9FWldGzrhy76b0s3vLMZ2XP3LR/P50ay+THWE08fOYx33067Xisv08iZwkin1cLnYipbKDTWO7VNTJR7LW0wYfZudXDv40UnJyLrG9B2Zjlme017F1+MvnvwuVviCGSxGOJxs9szNWnIJ+999nPRa6/9Wiy10mBuCJAdI7GAw5CZ+1gIxCKA+UN/8uyl2YJAlhI0c1F3tQCdTK3Mf4ysMpl0Oh4b3Hlv60/+W+LLt+KbOBVPUFj/+uuLP/Fj177wWlMhKRe7eVkwRpmMhtjdNVsKkjyPISdjWEUIFyGELy4u8oDQ+5nPfIZAlxItNNIvQi8XDqgRm9mAJ4kZHpaldTvTEiUz6ZGcQe+BJJsQVwTAz7semPi8BLwyJYOcSAw9AjeY/JxbiKEv/F0HOR+ClW0cjIRYejjsEaPw6vlLmUKdRkV8CeFDrtSeTeIlXLO+OBZ+pRUBwoHRwqVTiSQGG+4L0SPVVfWNwISCuAOKEPo5WRRI2HasGCNAKZA8Am4J1EQ6uMHCDZoA8cqf1ZyuoA/nhqero3sWHIVbJo0UVxzNRF5/OnbrK/L/JhPZwMiudX+6eQd0y888o506UBuNnvjBsZ/+X6s3rQ+utGYWILJwtwpp6KpMg6/BrH9KQYA4wSFmhIrZHvQ6WfoBfJCy63OQVxhadvLtugTGXURbCTbAdyVV+9kIsMJ2Uqut3OUe1CY8iU2yCENe5IXLWsdC9fG3yYdVR+ULMSlDtEks4Cxi2DjWFO3odJfbIBh4vRuWugvhs2i48pVAVd7+HT7otd3u4MBuJE+zn++7Dtz8ZCo8dKH3qX9wOd3dGtgLD14YpFAd74K1O9GT7dlVylpf34TOgbuBjfWNbs/OUbfq2MeivvYVH1BBi+VI0/gVV1wuRzT21fmIgqVOYPFS+binYX6NNoUMh5I6i5JCucUTE0fEcIxrVKrfWRoJdvzxzPzocpHFJEwJnCSSilVI8EAytnfK2NbKkSEqvU+p9PnAzgk5brBTLOmV18qiFAkHIk1Ai03mFwqVOMfeeMHExJGyxTpMDDZkygoVelYKN4kb7tKimBVKGlXSSJBKMdlbaQClWYsWRhYQapaM8kU47M2pJ4U2kgYtyw+TH4ODEEESDIMDI6N254LX0A+SoRWrlXXzUKaak4ila8gFRUIw5eJWhNplQ9k4qGytb62dgb7KwrZciaxt+tHE2NyFDWxW26z0x5v9WKve2/KT88WZfl3wSpsVLAisdiqwWd7yOluF7CiMDa5L8J7J/JBOZtilgoSDHyK0vWTLZnKc28BkzZTtqmBoqAGYi9PZNOHEQNhknOwhHcn/L2It0zQ6iaG8KgmWLFpOtaZzrUizWyvARPGgttOqpJxbAk4h1nSUqn/WqgoVFm7d0dJgNCclpiByKZqv5V7w+RXfu38fFJHVgk6tSqIeXCPbRzmeACVHKosDBWQqCbT4SWVVAeEW8RVVp7S7fA6cRGtNRF/DClMJQsY6ErjzB5L0YUMKqqhk6hVar0emlWQb1xk/fEF4WqyIWeBQR1eFxBxxlLFaVhVw41e02eJ9tNmwTVQv0QlTq6pgLxosXRxbupRTL8PVcf/g4UECv8qp+WpqMde8W2gpL5DtDnLSHPiNVtWLjsTiWJ8p3h1ihGpHu4BVNw4u0eknnLfCiAMPmjMTk18bdueaboZ8Yk3d1jx0vGz54zzHdHzAt8dqGvfj1ENt2Iuj/mWjbnKAhSbbc2Krrf5tSz3O8mXU1zGYJ26b1DAOmO51xClsNjreHtojtacMrXUIsIfDc9aJ8FiOamqriW5ZdeNIFzTPEzmOy0L61TBRM4pS+L/9h3/6C//qZZwNjttR5wPV2uHp2xO3BZ7iAjB0gZNsfuAHfqDZbPyPP/vvzzs0xqSFD6dqZ4CVP16sqD+klbMdGPpsQ8WsHzuHRj/ALRbOpRjHUA2E3hzyMA6iIUn4r+pjFM1wZaRocOJfyuw0vDRSkBdp9n02g9q0yZAIUvGguRVtN6NXXHZw+djXpqfGZ6YniEX8cFSGRUxmNRbDmDaaXb/JolRPR5Ox778gyZqBTTnI4aKPqH/X1kpLK6up+UtObUXY/WsTFdSNM+8gLWID1iq1WCq+f//B6UK0vca49hKFeRbUYDzMvpocjnmQuRREUhw/ZMq6KDQ/GhfaOyRsKhvGN0uVL/wTR8Q7smYDXqnjqxuDw3dMf98BqAB6aIEVMwEkARTF0iQq0FZTbv7k3D4COu1GeXMZSXhmz0XaQOMFlyffMxI5Svat9070PzAbgwqB7JrXe2+mXI+O/dRKLnHmqpH33Xz6p9hfxMR2zz9+vF7dpMhkrrC5tlRaXapsrkzuu7Ba1qErhbHJzKC8Ulm56spL7//akcli7rnffXUi0u8jgF773cF993G4PIIlRlc4Jg7GRkRwq1XNBegoYYpYfkioGvorl1lRiGDmro8fygliP6o2tY+iD6OW0KSbS+o4K1Dqdwfa/MymaEiznVZny6s4YPAwxUmsrrb/x9uSX/kyrAeKTkTqOMdh/c1NvZGR2I/+mD82ItBhmEol+XPgKVPNaYVxQ5q97rqX0sJIrdTCXQi0JEFuR+IlBJmZWCc5uygCT548SZrd1zXXXAM0OBgcZSFXO6nYicfAcX5WOWOTBC6KOxeSuTM7lGgJivZ1d8Dy6j7/TsjOw3kpLe8w0jXjTsrtUGou5oxXGCMZ1f7zFy1kLWW5XL92I4gWI2IQZtXf+crGj8I0yFBQzxraDGmxswwjJ+iKR2aYiO90hntif0msb6SUSivmw9AHvvB1VRErIVQYYk6s5VEFu7yCL3zETFOGsggaAduB9LMXxH4tE1mnv5XeOdn90HhsIBfflfd0WjeURn9kNecvvWLstz7c/kuYAVewPoZ4OUd7BJOi7eJ3m7VxoRqWXPpSrWYn4QfFPKdaalVAKAkNELHLfobNqGfwU6900UYmeFRV+Wc1J9pelNjVUU8W6RLqzVLqQZeSO4gGWnHgoPDtazu51UzpcThVr9a21sg46LfYBLmxfB8MUGFiLpHCC9cQFaAOcxpMK1ZFbV+7n7fD+N0uNxzgq74esPhvgUJuO69aM5tJPO+7Wx/5eObUcYJdJqrRKY7Er78uUrAjOXZBffjHnfbehvTA5NtlE7NT3jmpdkItpb7JENg2AVENLMfw4zqcHQz3TIT+LKMezz7buHARbLzLj1dXtu5f3mJ/B2Eko2uTycYFXdoBUXZ1+eEQACz9AXGMHs8IM/Zd0oiNAB4k3WlokduNKQamQAgyznsAz3/9qSQuA7s9ykglYVLArStZPgA6TFSW4SYcKNrFOtHX8EeIVVnsxQBbQRHmyutyuTu9yeYzK9xaUpgov92UKeNh3KUH/XfjiCRqHCXlO4C8honhTghERQFGGIcpLJ0CVFVoHspJHg2ABF9HRRw8vdFYwwYhhwQ8VU47SYDj+PJWtcJwzxZHAQoEJqYYXkwUb/+ptfbNap2Q3Y4yzgkGKQYRrAC8RGnThRlNlM8K59MYxE6eWu53Gl1MgyNeaWu9G8mNTs8ywXajiWqtgngMOWFpKsDVsY+Teb9gh9KksllGKOIWkjnSeXRmBg0B4vXa8Xunxqazozj2l4pAGlmJssRoGwj4S5bD1pYqoKU0sVYLzYpzzU0bm+ZcTbLTwFRQkiRou15DdnJYLpDiUscgkb61fVC1uZ7V29WyauH+5umToxOz8RxuDpRJGcilFtnOQyjFuMuSSOgWNbVZQAXo5L5mvXP0yH0YbKGeRUVxbOMYTF28MJso6jgDegH8j+sctipBZvVmepF6rJXrvqq6siATBgLKQZvwl0YvauvvcB3yL438LxRIRUqtZSg1KnTdlFfhdHc+38oS7sDGZ/dbhYBMI6mKNA51pEfQGvmlQbLrV7PeHVemV6ai6HpJU1lMNCa6z7273ltKbo1dk2/eZ5AFXK0Y+vFDi51kqlGRS+VOs5IfH1W/Mr85iJShlim0bIHONoVeh2OQqEiUUMJpIhP7DQF4pwzq33SfToXWwTZsYylJ143TJtB/1Poc1Ysmth8bYHCPhqmMl+aanNFSqbuy0YMT/lQGZ6lRDBA4kAVzS/4wXMDMnks63jj+n/0I7rrpgXLZg0pMZzVJ+Ecaj7Od2Fj9OKsH6jyQK/oe53cWkgMOT+UQGfYeJvzCB2963/Of/wJk4Jtvvtmd0KECnr6eqC3wVBaAjUoFr3/969F9veMd75gcnyhvbvIhsFUdG5GzFsYGhiVsAMC9G+772bXb8r1cIjou2qvBz1DB07KOOOp4nYZYZKyFGa6MhwSKAS7ZVMgbc6Vp3npkyBGizmXtioOO8PzcDeJElWt+upy5f9Ub6QZzfjQb83PxgEN5zfOW/F1Zenw+G0zDDOfv0AjcTXO6EktTTWyke34GMpkdbLEoFXBAGRsbIAFgpSVDLSJiPo3slsmPJMM9xSgmJyUWxuTamglAZE+UFHLGdkorR8OYTS/a90IspRq1tH2Ymk40hQeI9F69Rf0tMYGOdLr8nKfGkb8Al7EKRBwwEAVNsiK7AIStMcj2BuHjV8Td81PZ4sy+gxxbTAg47YvfLJaolG19aBRLGkApM1cQ7X5ktPeKcmyyfenUV7/cndpcGRnfc2h0/hDq0zP331Evr11w5fPSeLdqt+vV0tE7v4LR+BXPuLqXi9W2KpGRvdF8xe9vhMlRvNFuBpH91720efdd3i3/hHSKGAwrgEF1mE+He+Yjd9zBJ4Ooar80evNnPgO3V4lLL42MjqQHYQp5U8wFNRrOHmouVTMcTQf7cmEaUy42izBhpdLNRPZEzS91VF9NO9YGVCY/GYT33tf5/JdoLr5Dl7Og/b5N9pHO3/5d7Lrvq2bypaa2xDCHSX5mpmc/qD0ASw0LRC61MT5IuFgfgeMfBtOxOQSYb4TvK4ivOxqEs4IJR8P3kpe8xEnLO5IzaRw89MNOWiYBK/SsGfGgcsLw2LFjOx5cXeKLL774oosu4pkNxpz2wWzE5cRm4CA5EwUCqKNdOHfEZmRmFEcAdHuhxSDY5WA+/J2EOx2P54dNfE7s9ov47Dtuvx30kOp3QSB+pz9vpzXoO6EPWpZDhjSI5/RW/qAV7o9ZnQGhatIV1JOHz/Qcra4YS+GGIZFyKaJOpQu2z36Z9WHEw2T/3tn2Zxgarc+PJj48LesxO7Qs9OLB30y0Lmxnn13an7j5qtyxlneQUaYOYd3AMeugrZFq1TBxwh6FD1yNxqT1ZJGBpXalF68emBwv5DXbk8CIBVyWMoszUy5BUyYDaMB50nBQFO8uyl5dOEFqBsWSxiVRC9sTEdbaLthyK5f+7Br+nPNFVMR2Pycxljt4soOkOVIWhbfxgxEOk5RpDxmHIEg5pG3DAtV7dmpBrCvwgXfSRdkQEevD3Jg67WwS9R9BiUYWFmP/7heaf/q/4nfezlEeAz/aW9gbfd1ro5dd7j9go8HZ/A/+JIRLzWC9oUZwlbI66EZHcqiCOY8WPqzF8IMq0P2zBK55lVGIklS5LJtuelEpgkYPU4iLpFr6CpaEu54tk1WXUF1qzkp/5mi70jne3cOZ8oSBhG5DMC7Eeo6Dq9EhsAZr2M8FSutNTDTH775jdGoOF3dsflGvE7QhwtbBhINaAGAGD8kDaK6JlFyBhq3VdeebGvJkc1XYXlIxHAzakKS40SEoavZhZ1Eq6ziWXA2iAGGlPxVm7wJNkxlBUaDvN1vYI9Wnpyd4RvxQZbjU2gafNwswGCpSM90wRPl53lWKFYK9LrO0HjV/qiwJgSQd5tOvoJscy9ItAizGUUixsAXhIJvLNhvlfqeHJ1tFyUUUSSV87JvfA11mmfTkqdMIu/bprRQvRHmF2TKCcaNmjp6tw2MshX8M8NPXCcNcgU0HOYyezEo20ogVR6fnoPQgNjPJp1SFV2pBtce+TVRnAzz3csBMQgeqyvaKOymRfqkaUm4/FQKvWAScxC8awRw0s5kWERpQGnEG0hpCN2311LqFEqtZTKLWA3GiC0rNf2tp+0oWriUHxbmPKGUv8peEarLRo/SnnMN/ao4w0mjUOLMAtomvSDLQA2cT5BRPf0a+pllpe/xLI9nyjGyGvTcVZ1+u0/xDTirVQcffk0tlgnat22lmJ/dRJ9xSt/pS0msEbd8pXmhaZWyAqZcg3Jq5MkoXSA8LDxIXYRVsIgUxaq4tqcLJGsQ9uDcL4rsxi5DCZhzKoDIa/zQ1LrLRf8qfK42mdRA4P3VaJQIuaw5jJ3sr8/6dVyZaKVk1qhgTTc9kYvkfz2U/0dxqfNfcmXfGxI0Cl0whLtEnfuhVm6n84VUvmcIhQtgOYnifgWeRrJ5UH0DPD3hoEOpZag9YBip6XThnNEAsrjj+hw6AJpzlAtDX1gLMqOUKi5wyxhfriAKp759eCzraVszZvzBObGAOxKWjch/4R8thiYOURH1QgCNyY8iJpSfNRsERFEsw29WO0KCLw43rtKQccq+e6SJkoka0byI6wAcQvqNReLDtMalNj+w+PnvNzs7deOON11577U/+5E/+1V/9FZyP65NnUzz99ERqgae4APzbv/3b73//+3/v935vYWEBnh5XQHRH+GBEAr4CfRpTGxwTTmSgPzLDwJegea7isC8sldW1s1AaUQ0O+fD7spTwJZH2/QaHdMOospVCp/762ZSj6aIvrAbWOv5mfdCsNxCYu80OWgp/tVxpB3HORupGmz1/rcVwEmAGF46LU4kIltVZvL1jesHgN4/TyWgElk6p4HpZjt3cRPl8L5ttIoNufXPQhX6k/LjkH9xTsxbFsGQCgRzMFYCjqWVyzyxim07wE9HQvMV4RVesVUAQVSC/GvYiJOCuWUkXBAwyz78WU2ox7+cLXmVLM4pdSqd/EeY62D7mVBhQkdF2O5XNCzSXK2GYwwqy7BB0HGZMzC8gEmleI4Hfz0XP0MrN0/gpYpsFhRsAl76b6J5Ixic6I4l1HEWOz+wTKep2WIfLpDJr1XUm+Gg6Pbn3wsbSysjcYnfj9MrScb9diRcmTq1urpVwepS5//Rqp9OOpEfe8KOvKv7iL7Y++an+Lf84qNaRe2Pf9+LI5HTkhc9f++CHkr0uBQ/m9iR+7qdTiwdYAJTZ7tqZfYWRvXtGexHUTTjY1GzBKiNTHf48INBzfjf6tSOdj3+yf9dduE/wL70w+z0vvujAhRhObzSormrITdIsfj2Onww6bErmK2meaMMX2Jw6OLPCuYGdmYWjW9qLpOZz30YLCvqj1mxeojjgaP4zL2WpWDDqt3HEPTuDS2RdfCPE169+9at4zHrRi16EnIlnQhfl+rx73n1HQN05Ith94p0PTbLnPOc5Tp/M8EFy5tpxBob8TEoX6MTmHbfYy8vLyMC7S0HV/NznPpcQzkQ5cuQIHxncnGyMoE4pROG16/Tp08wZhLsonvft20cUpWP1zSuXiyU7F3B2Y7u7xN1ReN/hvGhSIgDTrba7GMmHHX53xocM3U7kPpC6p3LrO/Jv+ObSEMb7g18uwpWr0W2XAdETA4J/waB6OLrMqpPv31LkDDYtpUgWVV4s4vq35L1nlyJ+4xlj9waZCwlWHsrcZlzEJKgz8Ddk/WF/oCEa9LqM2xDHEfbPrMaS1X+xjyM3ATJk/5REKS2x4GiYmtccRfBMiG4SbYasm8qzy+KtOYav/FhlLcKl4a6PIEjWuYePPG9nPDex5QIHoUE9uPtTo94FowScOn4MLmlunk6iNrQESq4HXcNfZXEBulOvsy8P8RSWcEG6WV0sjqBretA0Ibsdn/ms2G/M9Y4e766ueMVC8tAhf2omImXRo72oeIhR0qn6WTsHVyqY4hgGx6SCaGiLHKj9LYCVymGwms7+qREsoe40mqru2lNfz2VzUYKjEIOm/kOAJbV0urkHCwOIpCcmhl6YPt3Lb6z0S+4oToNvYHY3lMo1pIdFDnE2DNQTt4OX6n4jhQpoiCUxEkS5VBZfSmmFiYIcfGPjkdkUq5Hh0Hek1ipEqIWTQTkER0PJvakxJMZYlCAqCZ+Yf8P+Z3moqms08HDtMkTZWowkjDXyKpAOqSygU6k3qrVaIo2mM2NlCjDlSe+FvafqqH/Wg5VX/ySyyg+CIGCFhGlPF0orlSx158Z/BEs82DOLI4yxsEgIF+nBAHK4b+88ZWHjtr6+ARQguIanpGIhy0TZC/p4XVIBWnEGDe2aR61nqx/4EMprB60OpNUPLL0WViHPcf+CA/vVSkz0Vkk1yXYBIyOjAKAtqXU6k4mzlymBHq4r+ZDWN4mUrU2nKuHyZgO5OxrJzRVRBAo9/qy6DgCww0KxiINozYq2/sYHrm0s499ubvEiJjt8ZlLB7Agn59Ekak/BGH5PVYtX5CJ9A7uIERrkcQvsSq1LheqXRPRMhYqaal1Beek/ZKQ4cvLEvMZaLf64WC3g8Icm3lf6/iCW5MPAAFBc1yRAdpACzToUi6F8PvLyzdQv7M5HVFncw0S+241sdL04iwI9PGyhzgA/vrfyk5WiMTXjJKa41gg4g1PcGuynPo0wcpUjoSYCYUwZ6mSCPowzVFQ0IcP04v1sCMnc1+yY6FEk8HvddrtZhWPp1dug2l7fBHQynY8k4MGsdVUCn5elwODkjL86l5JCOjBk9N1RPUsk/cRp740/nJnfM9Z/33N7X/48qxnE4Xhz4mf+decZ3/GlTaoS3TfuvkfETAwY3+pR/Ee5moiym1g2jFRfh0XhyIovI4U6B2PqkyGRJiLsKVGV+cfKJ43fQrWrptWKCwaYyXhQTBESwahTLsHQCfUH+LUyy8eACTQbQ3BFz0vNaQd8O3uVVoQvy9dnZSET5fQsP53A6DpCKmtqZ1gVIIqb42i50crgSQtXsmp3a3v33Xaa3h7IfPnll7/zne+84YYbfv3Xfx0BxIE7N9XTb0+UFnjKCsCQgA984AO/adcb3vAGOGAY6MXFRRredngysjUIty9RStqC6YT9vZNZCAQ8ZAQblQZH/koYxk2c3/A85hPsHhz1wQkzA6ndi5jnuhA7n57klIAxmYiF6Vi/tHmC5SeEF6jG8SPH8ftb88JNbcbw8PnMuEVXjD4Zz8MYzXptEILaoJQFysBZZYAM53qncP4c9k+eOO1Hw1Or7EYI+vUzTCUjY5Nj4xk2RoLn8nKp1uxMzUzbWmGszW4KUUO0iKyWiYyBmI1cBWuJFKFXdBCCABkQOYP2iGzb0Ha/RJxpBIv5kdSLnz94302ObDmw7DQZHDiAA6djHV+yfBht1GulpWN7Dj2DGVZ0RTKbCfkAVXuJYVFZyM2RWKYwppJUjIhtP5Rr4mgGOox6GgpEhLLwHyLoZzXpBZ7OGsVEm72+kUGRaWXjzNF4ulAYmWJLy/133drq9cYnp9eqK+X1lUalFIulNjmwPD3qJZPL5WarVv7K19Zvur3+nP0zc6/4gfT1L8cHfxBPdLOFahArXPrMxOtf33r7n3P689ib/320WGjdeJP/yU9Hu22vOBL+/+y9CYCcSXXnmfedVVlZ9yVVSWrdfR/bNJfBjRlgxl7Ay2UMPtdm8bVgG9Zjwxgb1sesr8FgYHbHxl7w+BgvNrgxNHYbaEw3fbfUaqkllaRSlequrMr7nt//xZdZpT7VB6xh+qus74sv4sWLFy/iexEvjhc3XB95/evCe/auNLDg7Tb6aoZ/IO5L0Bree3/9N38rOT9vjbuv9cA91a98NfSe9+y/+qqVWITJebaRM2PPED3dj2YihglKzq3AA40SdU7jDKxEoLZFwlp0ry4Q+bbsSxKLF/xoCbQP3Lo9CrMrFa6HA5yysNpVgPE+cuQILTdTwc9A8roo2yN2dVovyW2P7kme3QbY65b5fKS+e/du+hBdtblrrBV1F2PUTm12d6glRZDwhW5fp40n/HEKMFbBvv71r3cTJ4iLOW30fBT+O+64w2nFTm2mG4fhMbZAQw/bnnllGhx4aODAKiaiiUVyzGwT5Lp9hs+7dbPTTe7xHN1C8ALpOHUjWkv9eJHktz2ixM6jL9V9UGEOjQ8n4M8jLfQF4Ud1kBfPvOQ2b+FAgcEy4LchxQmK7de2wEeFYbP94PDmZnzfGLZHLgLbHn/LbV9spxqKAILUixB95F7VF7f6W3bxwKk/96oncO7dgxKsouIpFALxwPU05IpssfgIOmHyU2K9+VYwHNzJ2aLmY7g8DCKl+7MXi+68BejFeILHAtI9ktvfN0wv7eLGQhFguXqv8Whwx87A2CT2fejn+XXoqf09Ac4n84Yuy5EeEGplbU4/R1CeyVkwuI1q3cyjmwnltONrsS/Kn0IF4JAoBfOwh5iimESn/DZXl9kZEs1kLQoRiOIlWi9XlmZPjk7vC0SiwdQwBsDXWDFll3sABxIks+qqpSXtQD1dPfB0RFPReK+VSo0a5i+YMfMVcIsEX7I3K87KKR2GOzJftUJO8zYwpeHtiHDvgjPRCCjNOjUQYH5Sl7xmS4DKoQvQVJAlBFX2/ZCYGiwgFA0KnR5EJLvwMfYSpk65+t6IbWMcCykZsMIHma45Ul8gny/F4ymCc5t5RJxmZO2bgJoUJi9QZf1sZVrB+JzxS60bvEEwDg9z8Ax23fJYNraMiZv4IK8yPWoF0ciqFVlw5KfRvxAaQBBlGSCapVRvGg2CH+H1cqGwvLA6U6HxQUsJthrEyAyMJ1J9KBfdvEBxdrBfbbOKR2mCCgWDZdG4xAbzISnN2aL7WFlohlDmwZQrBoJ7x3fTnMFzphBtcZP7xhWE+ZJwNFqqF+dXiulIejyjDokx2ApDFUY6be/gqGhSPBRRKKE0aSPVdDJpW9zcIHuxHs7XJXtGl+WA1CURSBu6+RdxQssb4cIidDAHysUuJKmK2ZKhRUbf1oRtE21Uw9naeUsji86rjPnpzKAfuqqAlSciwe7MzsMQplRArztPm12QG8x6SHdTnTMvq/RKn5nPgL8nES2XN9vNam8qmk1o5gNmsKxXRalegGO2UBlqYz14nAedNqsMyh/gQirqLFg0EIs7WdCgiXNr5kNlZYj4Ljz8xh+O+q3kV+hGanU0f8XV82iQ/sGpcCimDIh9xkrs4Af8i5MsZec8EiWnkRl9ZVxaPzhfCPzNifrrbxoaueLd/tnvbzLwne4JXzZd6Bu9azPytXM2pBDQrG/A31haXkjGIhOj/RpsYVuZKhKfDIQpO2SKasYKffriloBb2NiqKJ8oqjJYGw4jdNvsN2TuipknBgggiR3fYKCzwXGkLBsY4GTTEJMTdbRZLLuw4kGmYUvgRWHmMBdtD8aMMzPAHILFoIPLKeljUQfDPVin5TRp7okowxCGnAK0DNtNzm2vWwHOBewrX/lKZt1+/ud/no7QW9/61otiPxr8+ff/PzmgjtR33sUHeu+99/7Ij/zIW97ylve85z30cTmigFroDqe5tPyqfUM2RWKhPgkERArShOnfVqnOEUc6IozNuhUNHSGi1ZHlO0ReM6XHHtEqs8StcGJgEtlayW9US/nBsXE6FgnOtbEDu0HFRgUtukB/RffTSWsMg3Fsd1sDWiGdOcyiDhGBDJc4DNYTe1ANV/1r4cj6xMQU9LglSXz8xfwm6mciGsvEtYiIRTIa6tVXioBGXJm+xXZfSWi1Cvg7O1j2ZZpYJUjtB8tRNAgrEWzxz6z49/dnxt/0htz5Od8dd1sLRQfH1x4Z6f25nywOjh6ZE7CBmxwxgiUdvB6JHA6XweAmFRP15otnwx9abl45HLgvtrO2MVUInOYQP5MvEvDt5o58fHcVuhaqV/mDsfHdhzbXl1bXNLtIp2PPoRvIyfnTD2Mxoq9vMDE03ixv5i6cjTJg3itj1LXzJ6PxTHpweHPlQiG/gY2oYycnB7Pxw/t3x0LBUoUTCLXU/Lume6de//rUwf1QGxzqr3zsY/EvfqkVR8b6/ec22mdOlR94MPKB/9C/d/8DG/5cHW8VynSmNb65WPro/xOZmfWMVJIkZJ87X/7Ef07+6q/2DI2xlEAL5jVzi7IbCh8+UBvui2/k6TOVWJ0uq4hgajX27/UN9jMNMpHibGTZe6BfyOgyjbS1YCo0x0ccrnBIijahU1TGVh/9qiV0PKZbMaP1RGIXhZMJW7TBJwIA89O6unj40IjIKx8a12OREMSJxFwuSFVh27Vz506nG0Mhl/qO6mAo30xfs/QaH+dv4dq0TJC6Yk3Wk+ugUeB55e5WYuPDIczdVB5++GHgmW2GOThuu+02mkyHX12rQIDmanp6mqC77767qx47DZmpY1YGEsTMNiq38+TOBSfdrDhRIAMfh805IJIkujSAYfv1uEEMhvhCwxQv35B/quw7I+vi+jZtvZZk0XRZ1YAee4iM8IG57kgXsasp3deLHBeF2RFZ8PwiiCd56dRBt4TEfaad+qjukpMEWEvo4thewBcl7SDw2g7RjXbJjvDOIXpSmPV2vbVOPJB28T422cf6dOJtewbzrYVwcSxJv01f3OPHcRWYWTOf6zU+PtQ2rE/m7I0FdmXUlRUWx04Jf3XuHpLFgy4RcpsU6HgpghfLUeRqtSeYLZ4kiEvcgD1ceKqsrORsCrVezbd9MU6PcVJG9RZ40aThTLqnSCtiZLWdwdAZgJyMsEI7gHajUdNlROF2AO7pfDfXV8uba4BgfKO+vpxflzOU7DEdUuRbgapbjEAhuqg2X1KHHK/FhfHKpP6sbkoTAE6amdpVaaraz9mSoX49aaAxXEyg/CQrAEqnk6SHbNnMoXYqrHO1hocG+X7JF4Z8TcIqnLQgYXSU0/4SvJ47Nwe8QkWipnwxcsGACNaPSpzLp3Pu5C/qMfbTYE2XOIVCiZxA9ZEiQACGj+MYQ5K4S6bTHHlHW89P03+EIkqYHfO1s329fRltLzcSjBHKo0ooFOZ48xjZUkrIDvSWKoY1fS3WdzLDFmNGmjQipGtLjKSvwgBog2NN2nymB9Xuo/hqxMGGyM0wgcpfBSuOGm8pF1GpiyAKQMQYCYou2oxDkk5Y7o2oq9GohpjVvrBZxYJHT0wFRVRFhyZlTu9CwuxcvZpbX6ZBrJWKGLhbXZxlgKJcKkQSPRQQBEoTF58tbWMRc6m84UPilLW2NNtpt0CjQrOVjLEVlvWi1lqT6unMNkhjORA5UAw3RLnTpimfRnwAhRcFv7i+EEn2ByJxcsSRE/Y9KI53WUnwDh9stlZLlMkxJl4oOi1Xtr245FOz377Iei5UYtg+mcbcsaGS1SoSddvSHFqxlsvyyffpCIM6mKtisoJQr8DUXb0Z5cQQLU4wGHHKFZS5aYhtJBMIZDCe7h3fR3BpfQFcyX5aAQiMeQJBQHaJPezBVm00OzJuJ3aTgqeAoREV80wh9MdH6teNZ/ccHEhecS2ErdUCx5eaZzd94702s+L2O7QC+bCvnN/Ip2L92V5MvbDgGCKVFNnUTmmd5KRzonUWiBYqszUXg7Usbwxz4hGfjOWOqXY4wYI4AFilnOBc37A28UYmgysl3x1zvvkNVsiDLVSzuSh61L1RllhqzontyFQ7eEq2mGdyJ5titjYZ4bAlnflC46WCEi+UcxxW1/S89IsOwM/8zM9wkMdP/MRP7N27l7MtVRDPX//6OPAdqADz1bKW8o1vfCPnNPzRH/0RbQdsZ1UPw6iXzn+rr9urLOJLbVs6yt7aoH9pqSfkz470o6KgEjNLzD5hLKrzqzDdGxWsBNZAHwONSwvVxaX8SH+mZKd+F2UCkK+Bk4d0iBnbUGmq+M6Qbvjn6wFONtJ2UwRjQBYk+Mj5btGHIyHmg1nAXGi2WP/KIUciT5tWG+WVpYVYMDw1OcIhr0gfLr5eRCSjYS7LfNIQryVYREJe2iW5qzdZrsLJ7k8GQmOpBKIOYOFAwvoCd8y1Xja+o+/f/0L1X+5s3HM/Z89Hdk/HXvbS8uSOu9cji/nA2oWzajmaGADxrc6foe2MxRKpfhblklMvLWEzlJYyifKqUBPQvmOVNx8M/YUvWu77qYXV3wqEF9lfBANbjeFi/0+t+iNsYIocr74FwUWmw4lUT3aIjK1fOBuJpXr6hjeKVc78nZ7ePfPAnb2DE/EkBj98hY3VzbVFzESTSiKZjkUSmLRZPnecwxt8vn1fueccTUk81Z/MDpDXW080X7Cz9/qbXogNhdqtt0a+8tVAIhaoVFuLS74qqw9bofPztcsOxH7+56b6Mn9/vLqBfQ9/87Jef/vcXO2+IzaC7uXMBJ2/dt/R2Nm5Snb0oRWyCLFi6GCysXdkR+sNb9r82CdYR8iCdlbroOzWerOJt/9QLZ588NjxVqN++eGDjICYGkVBqFVTNw4Lh7TlmrfXdEOxVD12/GQLo45JGkXf/fffD36Ocj1+/DjpPfkRnehvrEO+gh3O39IzgR1/LrrbV+b54KavyXURROeF5dZcnbeLnsycvOIVr8CLglaP1i6+d3z49ll3jR0vBgVQidHJ6UUB7+Izt4wCjJJMDO5c7JR2QZxZ2rWzTSiYuzPYs7OzYHNg7o4C6RRgZptPnz5NRoB3Qcw2v/SlL8U9MzOD+u0UY3cnLbcmnBOq3DT1ttD2vn1XNQL9wfZK4FW50u3JEPPAfMPUBcbh++uh79nky6k3B4vlSX8tJ6vk1lFGxMFAUiebJAol3cu9dglz5PEKwOMGOYAnDlKszrXl3nJZ6sLfAdr+JFGPEmXocUG2gz+ZO/v4x+0+K5wuPczQDQ0PYxCI1ydC53HvyQi8pDDD48/Effy6l0sURlUYDJJwFduQmh6AY233tUOkem+mjugmHUPgUln0cG5hABVvcjk86EIoQKbcUjZM5HGjTkmJ8bUZOqROIfnR1mqlTc4PjATDsZRpYobTK2drNoTS6r93V5oM9/Ft4oufMoIz3jsUz+hs2AunjsbT2UgqTUie1Zgs0QpHYvoS2yz35dwcabNSTNQeYcI905ciFnOkpSJLsoRPJGJkPhJFKUVxJ2jNTn3TF2N5RpjumNoJVDFfXVlcRvkRNm/OONCbSpBTBq/zxTy0GmNENE7mjmiFoV2ItCFRCqn6/TZrrBNR/L6+TIaC4cIPJYGz9lbXVlkPxeHAYyMjquiwRVwiVbVfStrn7+tjDZTxDg9LiyfzkNyxqBAKRgTkNE2ejl6lq/KwGOKhXdL6bA6apNQWqCSFMunLpvDfuDBLBnpTUwCX6Htg18SIEFLvHxeEu5okGcOf9UYsJRFntOghB//CYP0MHtAjTA4E3rjV50YwOWUrls6PTEZXc63cZjWX5GReLcR2ufDuqmoUld5adRTOFcewWnlj6dxDVJtEbz9jA+u5HO7UyC5OwGPSXfqtzT+j5dIgqg9DDlUIIPK6PRAFO63ikCVCHdMsp5ZJy4vKgzelr0FG2R2VehzrC2ALE+MMK4vxWG8kzQZlSl/1wRRd6d3o4uhpFIoaLTXVyja7ydRkKGlwCrmwo37jZNt0KllhgKRcTvekFYNAqWOiDjeZ4kEtAIGpu2j1QuU6daKqW2dArsIQei5XfGKA8ZRSsVDosSACFGRhDp4A6jOdS8WlPFpo+ABo1Z4OGaJC2E8Oq3VEFa3S/4WZbhiDtDZtE2Rrt87CiBxf853OoW23tBBAWnfILWxmdkesIWbQt2ti+NTMueXlJWx8cma2cY2TTdjSxVfPSgeWV2pRJJZcFEtfnfJIdJJgISRzCZwrnQijzWJqBzujVtBbGWtjPGNHmq3u5EwqMfJKY7WWbwgzhZkV0XZSKQeXcuAzhScOwQaPk2TOfi6SxTSOPd0bLfIf/MEf0B97wxvewOI19l1ukfl0cT0P/03jwON3N79pyX3TEfOdMwnzgz/4g8zG3Hrrrd1uK3NiXE+VPLEB0b99eXx63lfxqLqby+XoaPYPDCAFOJq2NwoYoPrGkcWyWWUnjLFbmFniWszXTobGM8znSLI2aOd0uBE7u/wlhqnqrL1hKQjfoCQaRvDC4QCmAiS6hFTKcLWqbQ+SiBzOgeUoznmV5XcLbzVnzrF2pT4xMRkIxZh/7lBsQopze+Xh+ZEtNvJZ44YXqZk/zQUKta+9WVitFYvx1GV0NliRrWkoBHfbdyEfuGXGd834xMSrR8I330w2apHIvD9637JvPic5Va+WkPi0MERg3w6RNFRnxItiXV1G6sWYKcmCuCUJpO9S8wUzvtfvbv1ZdHdx+LfPl+9INBbC/uHawA0lXwajVIFjtTecbbxMIxBos5x7EE/BypFCgd1WPUPj+/uH6fEvnz9d2FxKpXoKG5w7dT6Zxq7h3onpAysXzsJNTjpND41P7LuaBoUulXFB+YMatXKtQDLSqm0UfNXNxm3/nGCzb6XePHvOX6uhljH6ThNUv+1W34++rSccPTwa++JJiJdorq0uYzVaJyzZJUlqTMWzurLMAcTHFtWR1AarQPvsui840bPzVd/nz6Rqf/3ZxplznEsVPLA3/ZY31Q5ecWK1UWrGhgYGqBvlBib7m2vFdp39XzQziq+72wCsbSj+IFauaKU26xXMaaVjSTxnF1Zy+fKuXdOJNBaNrOUQVa4CeBTqvVOruw6PaMsC8ey5VWfgknltYXCAj/oovNjf8keXDBxwenv6KMB9fX18rcgBJpDdWFiX+O4S7u1RnPuFL3whDvjjLqp3NxV2+KBLoy3j6e7dkTUkDGIBT2K5oK78QSFH/SaKi+UUVJcWs/Gc0+bcRCQhrr37D/j6ftS/+tuB8ULgvefz/yXePhGh1gb2NXt/PB8aYgY4eGr95hMnH5KkcqLB57vxxhtpaEH1hS98AQLAg9vwaUxkamqKV0yXIRudJ/o/PNllF0Go6G6ljFOVHVIoAABAAElEQVSngYF7BBLEvDcGz1wsF0qumbEnCPayQH17EDjdrDLjCN1YDoA7wzTcIY8zq7vl5UJhEXFhERHB7DydAwYSBH+gGR/n6RwgcXiI6Bjo/B2MQ0LQds/tbqJ0X7c7MHgLqc5nOwwIt79uByBo+yvuLiQUuohkfLs/bvJFKJCYbzdacHIJKoSl+lAIe4ec704dND81DTg0g4hcYrcbWykkfr0/YoEwEtEEU7Wqc9Rw6DKcTDimknFgCeKcJ49AdOyNpfzqEiKFDHAczdnTj+DIjOwM6fz5xtK5k1mMFPmx9FB/5Nh9RA9FwpmRPfTLUQuHR4ZokeDw7OwcSYkFEKhE/ZiDStg06ZnZWdaWWoDC+c8OZHuTGtLK59fItsjlwnKrz9fT05ug90pjVy4Uy1pb47ByB2+zzQAWMzz1CkOZakLoo+urIX8sEOFzoNPLabT4OJ7jwGXk+JllZcUvMkIFJV1J3V8aXByJWHRyXJtpCeRdJNq3CF3oOBNaM6IgI8bd2hQJMMlUj+dNJNkE8fkLxdxmoacvE2aEWwi54IiKT4XgeCNl1dxS3vjRwkgj54cGSwQ1qCpVQbn4iiwkRr7VBaVH8y1wmgKlpCTsJ6EANKuIMbjIXbVGg/JkDjSKJmhhVFpioPiBGzgxTO8GgRcRLWW1lEYND4gQ7+ynyUDG65VVTftqmYBYboMFNl7AkQjVarNUra7ka1mOXNNaOQ3pMuQiPVaqrHzIKoOQzdQ+dNrC2kKpUIsNXg81m5WirxgMJ3tJYLOSCHDkrGmExjkxQt+2fd9yQ6pyZdzG3+VUWSO2Xi3PatH4kQUpe5DK+l7cdpqjpjoZgNdKOqnzy5uhvn5/NK28k6x4ZnwEl0saX3jMRKglZZOlfOxWKi45SOIyxZgi9seT7C8IblTJtcSSEU71FXEqBopcmB2vbT24KDXcSlBZ8y65LBa+CrHY1oLIl8sLNdQKV/XBj5zyQTA7DR6WA0RTrAJu9aTUo1EUgeNQd9UgzXyJbI7YFLdZISFzVABo9NIQQuKQdxJhOBZ/MqE80R+mITIwxg7Y+heZ3jF84cyZ8uLMZQcv47Q4VjaQCpHtE2X0gRM6Mb2vo3fjTO1iSjbMWmU3xKsk+be7VHFcxgE85ebORt+rRnSWCtPLfHxxpnYxdsWqZrTriA7mENkeG+W0OiuvDgbnfm7ujHF/6lOfYpj7zW9+8y233EKLqeSev/41ceA7UAH+5V/+5S9/+ctUOGZ4nmaF04ekD9dJGV8bwzlUYhZMSnY85tpWl0nHE3joKyzV4MhsRSAa43njw5XGULnOpnx0Y07ZDpSDiHtEhQYagUImMvTLwmmWf2BkiyOROIGJE8lkmEoAzBU32ceLmEAsxqJ97f4+SXkbsp+fX9sstbL9g+FYkvCoJooMq2Q0P6Uis8+2YEYZcxLQ9FMvQ0DqRxOgNtMmlnFrqZvkg0mWjWr4tpkmmyJ64xnatkKFY6KwCoa4E8rR6YNQWcznmAqe2HNIslOZIsxxiDs/8QMP0uJy2jzu0UT98EhgNBqMNT/A4qdo6+9D6WrPzXR6YF1Ik9Pt4Mna931+8f2VUDXsBiyFQUPyLJWhpawyql/cVNvUbERCzBAk/fVSaXM9kcpG0/2DI5MYDimuLaGU9A6MDe44wJ6ozbWl4Z37oEa5M4k4lKqPh6q+R076sGZWLoOsvbbG+cviFjYErSvFrKuvVAzm1nb2DvVFQ8VmkCYsODDcpP+qbVgwUfxVnmnOqQfZgQKnbdUZUIaXnrj+h0eaN+7o2f/y10RveHGymGf9Vz3Vk0/13H9meXwovb83GylsBE8ttWPxvp7UQKbvoVzjzAa4KH36rl6DJH0aq2fpXTSb64sL+dzq1OBuFXW4WaxkV+KDf/FgvVyTuUVGUmVngsMDQg0cxdUlyqySXy9t5OvROUZJE/Ho8MgAG87ZTUbHRUVlNYQ8qBRdZlQYuOzLsAqhEIWrBVVx/iu+oBCVlcrkdKRLoZQoXE8EyQRydw75UTAon1yP8nSvtu57UuKgc3XdRHnNa17Dq9OZ6RNxaaN4/7vrpTvD5X+O7itHP1RpFTTMFkhV2TlFBavFXxkfetfBgQR6BfBE5961cwZOdGzn6e7dyW2y5nyAR/eGHEYMHVFov4+a3AbGKcBMU8/MzHRo1xPd2CnALLdhrRc+jmkgJy2nAKP93nXXXfi4iA7g1a9+NWXBBDtS2vl37y9/+cuZ54cexi7xJKKjljsL18kU9Hzxi1/sDh84hAxkTE1NAc8GB5RqHM4fB8SzrB0HZLA7AIfDiQNs1157LY6jR48i53F0L/a9v+hFLwLy9OnTLKHv+uNg0QQ7z3Fg5u2ee+7ZHoSbrV/oq7ARSh4VxEIAsoY6+vnPf74b5Djjsob7C7feulHC+LTjFzXQv2//3okdk4WGb215ucoxl9BPZOkwvt7eNMsI+RxXV/LFEgMiHpMJT8QTQ0Na7lRGEBpDXDxiQr9W/JpVOeiUyBJCn5bvsLSmb6hc2Ag06agKW7nWDGj/XTvWO9Y/MdKs15bOnUlkxgNhViaF6tjAYJYH8YZsxSJ3GxN9mkiEJPvXQx+dMtlOpjlghMTogDqhwcLFMAINwgYHRpgBjjKfrBzrwwtHaEIJa/dns5kWao+EjiS1c9Fe6izivt5eDSqDUvqbIkOIhFIkluzHsiUexhJryDDij/gCRTCd1BZcIdRD8hq6JNp0Aq3OKSUWf3ZTMwMOfmg+9qNlUIaAF4R0FcsWYIpgnphoDPcx73gh5ygSQuHwciHdUh7SvtRnkNse5qmMqHHXH75CaFcXyqSwPuGON3KD7FtmnOgmGvN4nM6yr789kgzUp/qxuDmTb+QqsjYMRvRrlYnYyI+46DZKi6YBXjIebf5s/JQeyItWauuuwqOAjVyRAUeksnKXQitbmHASxOztEt+MP6THfFwRs0+BMGO5m/XQ0QVMFoHV6VBAaama5Vd4bHBIgyGlZrDhT8cyu0iuUj4pzTo+Bvn0jnxaJSAiUdD0JTiOkSdTosgT2WcEngSAYc8nlKO8UQ2onlQrzdxiNYQwcgUOK0KNPqiIDTM0WzWQ8tb2ZSb3kRhLhUBHdNVM4tm/yV0xs3OEnojQQBWKvUMmNjEKQTJSN1Vm4lKEfyYZrB65jFhhq35aBPxUMCoWRTJOcrP4uim6vF0VES3yJQo3NeJtPl8jUyMRWpLtKKcQAZKmys+rhDyTw6PS+aUS21gAvQttxFVOQUIyBk8CkGf0uK9GeSIIUrQzR2MAjECZF8NydCaY1GWBMauXudPB0AJmJQ8jMADTN3f+/PrCzKF9u1neyLbbuIzmYIaKnb0yxuapJS5bSrl7KaPu2nJ1fHiSPQzr3DDeZIObaqwwCPBi4Cd724bs2TpJnXbwz//8z2+++eZf/MVf/P3f/31XUM8W7/PxnzsOfEcpwMgFDF/9rl0veclLLq22OWHiOFppV0+167NttIvYrnZwslLF5DJaVnPu/ByzAcxLbDfSwzZjotHHelxDQXxkJvLY48/4UyAV8Q0mJKD454etBTvlCIPSTBdjWslfbjBraCoI5h8QKJiG0vxwC1PDGNNnupgT6jFwh0RGRiK/+KNlGujXypzRkWxpI4fFwmT/gOZ4mxpSNQknEhBnLLSWENYl2cmPNoKGR0OwtfL60jxSs1HXEuj1udMAJXuy8Z6M4ASqOPRxNqvBDezr8S4xZ+LYZC5eXDRM/MwCIHLSkgFUokYcVvKC65SJGqn2wYH6DdlGfPVC9Z776ufnWr2vDF5xRaDn8+3AiYCvVvXHV6r7Hqz+2NHaGy4snIqn4qEhbHerTeCCq4me3ocfuGcEK8e1Ep2zYDTaYFagXsutLzar5aXZE5XyxvLM0fPH72UTUDTee+HUEShI9Q04bUh54jLrK/2JVrxQXP/s5wbe9LrWvsvat3+5VWSYwuVMy73U59wzTUtSPzcb3dcTj6XXN33zeexsT/ivPOy7+56oJhlAJzbrds1h39jwSoHxS3X5IFuesvgYuP1M63iPf2fPIOcVt1oc5oxhs9oLh9I7CrnaZ/8ud8uXfEvLHAQcueGq3re++eBlBxbz/tM5dRk0eqlaJZaCjMaSt8JKo1xo15axsAVPA4H42GzBt1jwrVW0i9jsSaqeYNMTji3M5P2tBgY2281Sdo0VjH6mSoYnmRKh38N4Ak1gIBpqal2D1hfJ1jSrmPixUkjbmNWYsYuG9kyhuOlJ4G8rwdSjUT47P0cotJrDNbcEAtDhqhdkfltuuTqX4B0eq0j2AnuViP13Ql09s/Tl1b0oD77f7pbjrv+32IHaxqQrs8EQQzG61LsO2nvn45ZtO7dqUiATnvh0bem9oY2/9AUKvl6kA/1K1jcm2pm3RYc+MB3g1OgthN1Mgdmpdl2f7Q5miXkFP/o2yhh6LJuUHMD111+PP24IxsEFKu74ADM9PW1+3q1LNm08qt32oG7WEIystHfJdQFcRIbDOUzLebrUcbs1+QBMTU3x2o2Iw82lg5nhyO2TqIB1p9nJi9PzXVxidTeic3h1V893yXWDoORRAhxgYNAP0XIJ7Y4d4Il+66JDqovVzSz+zg2MWxK/Pcj6kgJgmUDX3zkcftyjYxPzi+Eih4NTu62enKj0LSxgrdTHnsEIa4e6McWQmMOZTCUgBnD7k9y32XJVs54UFmcMm8kg0Gra2L7AFENf5EWpeJ1cnNFopFItJmLJwQFNeErxoRBMgCFdEIPBsG9053SQjYLtAAMK5WplZHQUMmztbnBohFgSTWKRhJ5uqO3UoT6MBstXHIBO/PnTMn2ff3z/lUoFP7dCVZ7Wxvn9YSwGOnxWDYHih6JCEnzZgNXNhb7hdAlqLY2jlT5OqWHglY9QylsfkOoVN/yJJgDhcCACk4dI5E8uRTW3mjw57ZIvKIxOc8sXB4jwjEaZY6c15OYhAUM3VGO1ghZyodO/iBCMS5qXjrdRa28iUdSrtLiDz2Ex7VTRyZCaRYZrfdeMNr9r3J8ubUQrJV+s0exJb+6O3bHSPLIkNcz0RMcFP/0JJWFslCor/U3MpxdBF0K2N2CpQEQzUkHNjpHjWCN55uXRyDHaoI+dX+Ahig4qaofUG9EiJh1zRWemxj9ZcYh0N6LUvNmLPNrNYJxVAQzLkGXaOZRzqwqqkfTItOWDiqkFyabcam0UQUrFCJIKCsn8iWTHKgJEEZauEH1KnHfyTQQXEXVTajJelgtFtGmDRpCTnHSYEwHwR8wwnlj+wAEh4j4IXaiiASsPCtSw6EXRdZneTZIYiCwUikghTgYmSMnqT9HA7/4dvEVSzixQkOYy5dZSZoIE2t3gk41WuOrkzalCoUVhIIBugAYy+GkUQA5ewWzrhKWiq5qKVhWFrR3noYISVdzEE4XIFxAKRQvTpOXa0mV1G5CQUtzFUqvzJK21xwFWLMu6DVOyzOheOTx2MrW5e4p9gaRvzFECLqHuq/FU/k/rUoGmSE/0dlj2tBA8p8AQQ4OL6stmYAY6f+AHfkC17PnrXw0HXHvwr4acZ0EIUoMF9y94wQuYTvmTP/mTbv/syVHaACCfeLW58V/bq78TqD0S8MtIBqOPrdRL53I/0EpcM5QdfODBB7ArSz8Da9IgZG8S9dh1obCOw8TL06zWEiNcTt5JQqupYElVu8LqaI4d5sdp3RjZ0rk7NEh8MxJNSFYW0FU5WA87WzpuTroxd7aGrsyfQaQOTe4Bls4KsglhZTJoKy1pvMgHJ2jcAYPMRVdKy+dnaOg03ElKQXU50tmheI9bMW6SxESgR63rFBj9klU0UfJpNyrFUn6Dc4polJSuZcqgRABvSthoMV61J9K1m8eaiQfvWv+PH6HkrNloV3vig+/4Mf/Lr753o3Trg6VSux8jfsp7s94zMNk7yJHxJn5pwxqNM6dOLM0en9yxp1EtVjbXi8X15ZmH2A/JQAC6WpENwOUifcHc8nyybzCVGW5UK8mBsYGRqeE9h0d3HXbT70aRjyHDF/mOrvyn/334LS9CsNc++JehL82EOXxZDb/ainoy2fzIH0T3X770T19NvOnNf7vScy4f6Iu0Xre70fvQfeu/9hvxCwumRrIpu1EcHs584H2VPQe+vthqhNiIRSPAmUrqW2jDkubk7SftG8/mtWP+K+oXSh/5eOtztzRY787YJTs5OXlrfKz/g+9f2nPoLx/mEAb1Rci8MVJFSzcFzjRqFZbU+0PRUqnMpJBaQZ9/pdxaKXFwtGsBjP9W7Kz1pqg25h4qLZyZvObmYISx+HZ/yhtpVkeXRhFIazNpmeTjmpFOk0ZRWOmaEPdDE9zRsA3KM8O7NIeM+9IiuhP80JM5wCDMqn5Uaw4YgJEozNZqSp1GKVfHhaFid7wTI85aVKC67tDLiVv0d+uOyt88HAxBXI4nOLZ/hrCrUqng+YyXHjHTCAY+8+1old7TvBg4Y44Ufe/poiKrLc50qDzULtzSrh5X9iL7AulX+6IH6Gw8G6pgDuuWv/rVr+7Zs+fJd4wDybFVzHZyrgOcfJpZ/zYGP3/+PPPGV199tZvo/hbkhCbgk/fWlyvuI+gmKBWOw+r5GtQG4G1fh57OYd83r9sD5WeiEhAD4zOS9O1Edd+QYARg0MLf8i2ePxmKxHtHnF00xWIjDyDQ0KyU15bPZ4emWDRMzcxtMLtcYJ04zSKvQg8K3YxMS05E61ICpjCqCeDFbhaEy4up7GmWTbVcviTqKBeCTh5wIZQMoyCFFS/lwOBFgaAtFYEBYHLSMiBwwTlgC+1mXVgsQALImki9gskh9O4G7jQeBRgFSD/h7MQTcpNWbJAQArQ00Sj0FkJWvIKyrKI/IVDxVHNEEAoeryYJFYBgxW2SGV3PI8byJ74Bi4ZHMmh6rPPidD2q0CunWjdlS7EjR9b+8m+qR04QGD2wN/u/fF/p0NW3rsX/+iGXvFKDDi72UvUlRB1aC5S6hsWUOMZElRtXcMYOQSldPUW1cVLZFTrIJoRWkFUJJVxWCviKOzJKVGIEByNb9QZbnEPafKl8kSuts1WnRS2L1jpJKyNUhwTpFcWSrdhqkjgTWO0xeTReuiSUCb07gpSUWjHokV7nmq1O0RAXDCyMkUOQrtPBkKI5JGzNE1wwLbBxIHXnzsSDLAlbqk0dyd+wUNmN1aZOYSp1JatL6etF7OQm9VG8wQ9qLFEDMaAOrUyrbG7kiaBWW9PKXORFtVv8VA6UESn8msc2zLxKX5VVPFUVpemKQEg7zKTGbA18GzOl6xJLFBriLt1K06PMEhfJSt64oIlrUmGFBzPDNOsot7TjNNzYo1HDzZoxfQlOWRbNvDFWjpYrRTeExitjrmi8zP2CQdSSp+5lfOy+KXvP5WVcthw/l1ifKS4GlDmJhmOBaXBZr/RcZ/aZkvV8PDMw+Z3ABsQMSumb3vQmlj1/9KMfvUTtVzlHImAGePEXgusfawU1eqpPR2K3GCx8biJ4+/mN9xxfeemZs2emp6f7+/vd6kd6hAzz4/NMq7L3tXcealRoD7DtHmfaMSaa1JqYEDXL0lKMy5x+pq2pzBX7StXa8ROnxyd3OiUctWa+6itV2sO9zBjrNGOdviM7eK75N3wmf1gRJFklsanEaTsDkeTIrkMQsL54oVLKj0ztlRik/VBrBh3uT6wSUWpsaHMdm+QnLGoR26FYoieWpHGxGF6vQxAG4gQ8sYEFBrtfVw0HUvMnVz/0u8Hzs75gtIUdsWYzlCts/KdP9Pb/+z0veulXT83k1kr1ug6EiMRTpc0V7PaDrm9kkr7F3OyZCycf2LzwSLC0Usxv1EubjUYV7ZF9YKFYkhODs/vHF2YeRr1MZIaHJnfnN9b8wcjw9P5kZphNz5ubqylMsKjF9Ueaq30rHwz0/tXoOxYDra+Rn9CHgtX7IoXf8aXulQ3PcqbH995fih28eu2f/jGcztSi8Q3poyzoCt6+3H7R4St7f/uDzVv/qX3sKGdHNPbuzrz85eWJqTtOLz+sZXAgCO7edzDCyAI6IhMD1kKq+eeHmfFA6FBfw//PD4ZuvZVD5ihcZgRZMgTfWwtz5f/vbwZ+euKK0b5zObaXa5mWjdNrph22iPNhTvSJnZ+fK6AAp6JhTldu06Fh1gitUp0Z6x4B2KpXihsrs7jqpUK1WlqcPUHZJ/pG/KE+tboii8JUQ6TSV7lSu/GncHlRsTkvJapWmW6F9jupu0IIS86orxqHVguqHohavHajipWvBqbKwKkYhlRpGE5LFwbhQZXQhBDtPZ0BrcUytZn2m/Xb3LVASzozw9iBWKA12kP767qJTFxTiDDSpgLEUhHPD6QPnTh17tzZl7zohZkMi0WNfN2VEbu6Dt6UR8/be/jp4J8+PXP11VfZfINyR/XtBF4Mq/hWInh3YbaBWETFdRiMA8Z0l2oHq96MUEUVt+Aku5eu8EUvtwD6GHSUjYwumECfycX6ZIhh1vQpI2My7Y//+I9f9apX/Q+lAD8lW55zAIq0VGuvFumQW3+4kwD+fCAIblfmkt1yUWnsYzMwC7Iaw6sFO2AFGrBu5tW92ecoT8D5VIUu4MuO7eTNvl1FRaCtctYRVc7XToVi1x6a7mcPpq+5WvTlS6FqM7xeaiW1slLAXFb3VZsdSghVTPs01GZI8TBC8OIJvWTMPBTq0hUSKY2umisOAd43RSTnkqcuNWedtJUfS08w5vRwGqBkjZLQC311ITDMABtKF4CvDeqJMMcWMEIJod07MPLTDBhAkoR6SuYouyZ8MERSn5tf6Ekzl46xK40QWvZRsZQV2kjpWppale5qdxHN8DR6rChDfIsP/ANoTHNLlYwocUaJglP0UlpKou1fLQameitXZevBu+48//7fDOULrg2vL63O3XX/yPve/eKbXvrgQuTYmvZXQ4hRLX2S/bf4WC6pecY85co7jEZtuXgn3pOiJlXNAzLIrJa1gshUWeKa219ibQGE4a+WgGYC+cwbg/XtpdX1aDDan8loiljcIJfuXyQofyShmVt5a0GZvBVZ48taqctpRqQpcgQkBtqPh85TFJ06doo7bkYHFAioaHZ4xDHRbwBK2l1KWrE189m6MfPXb5v80FD0BAYJRBK2Tn3pf157818svDffHAGUfpOmh3U5XljOrczUxMtP8bjIuJd5vUGKopAB+gOBdByDlL7KZormSWCYINY4CNlUXHcT9VQAFQJjAaDiZ20f7R1tpbZe0zjSRJpqLGAu2CYWqLgsviofl1FE+mTAKHSBOOGIFnbB2xB3NbVyoOvSoKuWK6buQqbNwyi6rFWOhjg414+iy9Y0d6InBS0wI0E3JYsG7RzOXy+6jBZW2XBq43XXXWdrWCyeC32296dEJaLsn5sHbPx+6oTVxWUdAYv+WSmnfDxlWpSaDGLdd999aCi33347pjEvJdZTk/I8xLPmwHfIEmiGWH72Z3/27NmzX/va19wCtkvhjIkvzmD4SGj9E6wI9m/GSl9KhE9lWvGG//pi+NrNkH99NPIbSxsMiO+em59ngxyqtVvUdyn4Lx2G76H7GZmoUVTz0W6KWJBTyxA+dvH9tVp33nVkqLo0GUgko40Tj5xEHYs2GERurZ++jy7L8NhUJttfKlcatXo01YO1YQwIs5QalbiKFS5mjLWUGtHurb1Uimqo0F64I8LoVqjtUXpeqi5tNU20tG7kzwh0VCMSOuRrrTDAypA1DobDGG24AFNwOtoaDDRLt389NDtvqNVaMa2LbI/UfaXPfSF1w41TI5mlfCuaQHfycyKCrA5VdCAc2u/5U8fmzj3SrhazmYHc/PncyrlKMc/RR5XiJou3mcTeXDxXWo9HUv0cOZ9bPI8tGE6iinP6cP9oLK3FeFhtYQyaBjLoW/3e9Nt2Rb8IuRzxRwNk9j2a0WsKoY/Hq39zQzv2mtBNNwUHBkr3PlC6/RvZX/uV2Va40qAZUp/0xBK2osM3jl2WfeuOIGuRyV04PFMJ3HNs9eTMbLK3t69/SE2TWXGAb66BoGVSKWoeu+UPt0LlUu3e+/xs8pMqqabfZk4ojmDt3vsjlcqOHg7WYpWybBqqneuUDASg365zbmRtaWKgb+dIlKafH56ZaKtU4yBrHaSEzsydLWCxqro31UA41c709ET9gUg6E9g5qMWNJCrVWgi5q7el8tA6eWaNRZKKVSc94lZzChStoQLsch32zjAHfRS6Xmon8ivr1VKhf6cMt3o12FkLoQdBucrXqpw6cWZGBdaoOhKKFqz2VjXJIot51FiWb/n8B1HbFQKY9RaMIXYz4ogIsa3WvceLwcBQ71o8uKETF91oOgzc5qa3Ycl11GaD0YcAzGahuVJuL7PbW8ZRRKpFVLpkBree+mC4IJVCFSpXz3HyunDhAitvGYUhBxcWFhg1Y2sS5uSMQIFquIgsEdz1MtoV5l2WezIvvNxIVqkqxrO76HyA51Grf7ejVJ2w67EOI2A77PPux3DAMe/plVI7HW0HC3QZqTvEV4GDQC4+C3OTDNXQS4yPiDJSZfDABGpxLYpBEU+eFuI87M3QCkq4vcokPJp3owvPRkbhpeSJrop97Wjju6Z8/ZWNcL1GZSz3JdZ2xj51f+DYEjIl4ZIQYsNreHD6q4VcKJoMsmfYBXnpkblOxuRSJBt7dlKGDMgTQQTt0qmESXmAHqv4etrXh6c+DNO7AJOuAij/+Npd8pYMuNRxWizh8XINkEOsJHDqRQ/3E5BEHJJOd4vjvgn4QmIIVUF6/jhJR++K3Q4WW8n8WrMWZIQOcoAySWHFtVWClq7SlIam1ImphxWxc+lNHFHWOskhXuUtFZKbFsDyLjF+47i/B5sQH/1keFODxUpXPGuHi/mlj/6X0QMHXrhz4sQaYt7Hnl1hZFRRe4ARibAaUvkTu9QQol/hgHaaHeSs3GIgezmJAmrHENGgy1Ydi36RXsUAAZk0mk1AU2C0q+1GyLcRilbKWNusJOLs0wSCdEWlskNmbDxADjUu/CyHqoDUdPED0q2NssypXMVBLmO+igkfxhEEjBdEeCVsAFCpGBCuolL2+SlrZEs54scupX838OG3jr0/5OMsK46R5WPEmnA54Vt/Vf9HpmIPfujkn+Vbo8AaY8iiMCqq7kICKlUzy761JjINxasYKE+pQ7zy86WiK+1ipcq+/0hCG2Q0iUogGiYRnZbrzi5mLRX+StRSsFyK75aqEpbD5di8eBc0jZRoEjtASPdDmi07C5yDOwfhanZXaamCQZOhxBKVtFxZY27R+dTBuVrALPvM7Hti0RaAJoNcI2xJPvrmOCJfc229bgdkQSUmJFhexPJgQYop36LLcc+yS62gSpChS02dSnLkCysHX5YNY5b60mKxT+fTn/40y6Hf+c53/umf/unTmKL7FvHjf9BkvhMUYKryn/3Zn33yk59k5TO2Xp/yK2JZI7vI3LrlduOCb/V32kyYrSWrvzEcfCQcYj0zMvJf+mo3L8V+cCkWyO0b/ruZ4vtSaAyRqISE3+9OBL30KtMVVcS99FgGuQWPy+FhUmvpwvzE8MhVB3ZWK5XYVD+fcbFQZGFtqqcX5TbZEwnGWmfOLa+WimMjaZZMY1KL3buIQXAi6ZCcNOBAMkWspdR2Alsom6xiiAA1WiufJMvt4mlyESkqqa0hd5OSHmH2EHAH3qCVliSqQ9F9eigx1hcLBBuFxswZqSmQIrWK1VCs/EX2NpqnZiK1Um8qwtJljDwjbpdnjo0N916+azjDviC/76ivNxLaFY5nNxbOpgZGQ7MJzg8MhWLzJ+8Z2rm/d2RyZfY0Q5f7r7958dSDubX5sanLNjP9pXwOe6mJUJj0BjOpVBxLovWrA+/bFbuV7Dbn0rXP9rTur7ebxcCrffHXVIKJauDNj/gb++srraU/+X/rC2v97/7p4tDEv5xtVRuyvAJnaJhPb7bP5nyDqWhrM8dG6tXC+uJ6Gc165dxi71g8ODpBrjdWuVn/jJ6RmkB3mJ4UrcGULIfXC2UWM0uWwl1WRzVZvozBs3iwVuWd9cOYNKRHKEj+jb3W5It35+bmQM0RIEzF0snAkAuFke5xrZSaXjow3DEcw7ZSOD139myt1j+0c5oFc6jKjOaCkY4FM7Cgw4eiq5v9MQvAR5MiliyINfruOhLqltBTUY1hRlpUmf0PjC42R9Os/wOnf7HdKEUbA1mp+9RP9jzrjARyATAfoWY8wE8oidJDAIeqqJsUovEGv6tXncqkikarG9RGBPVmoMVVMmXTuKcem3U1CpuFuXJ8eHDwdI5a7ZaTEajqDzNsHMHcVFN1woiu7oIY5fOtLMxBZaNBR82/eJ/GaGIcrtXf76UlolT6+KMs8IBQYcGh7pTXAcL3zMn1WpXN5PrkAmt0SbHQE9uMqrdP8ViXBgdjV4rMv7ad0/UxvLwD41YGykOvlmXjC2kRatTaHYQAdS5CjT6eXB6LOoHu2Z6a2tnX1xuLs4IAWM+z49ATO1Xv/KmfYq8BWz3rjcZPvuMnoeK6a69917vetS3K9hhP6jbGPhZiK3FHLBBdcro+LppVhi0/XPh03jtPL+dbCVGnty4xSZBb0BchcYAEdkgQ3FYiWy4BbschMMV5dFqCu/jqYO5E77zbs51mYy9CQZfqyBafndDt1HYHYLWBAKVs0sC8RZhkE5cyYkzj6RDgqSDVR0XiX4kpzP0L0FA5nNJJmJV64VTz5sFS6tjDhb/7++JDJ+mhxy4/OPm9r/6JK/d+drZ9dFVjhqA0xUKVFpz8I8rn58+lB8cTCR354+VKLg2wOvKMHoL4fHCSOqKGTOIBwRAp8QKsvgqj15Gl1sL5iEAvj01Ek97UmJgfbyB18Qy5uQVg1BiPkDTuc1JEZIIFOlpFkRABrNiqvmB2ZCqnQuwB8OjUbrkkxJDeiWg+XyxXON6OjQNSQmSuqkODuGCYiagsCq8+cOMeHrw7PyUjueSlps8ccAQZP7PwpJWo/IibjTT2Zv2h4+d8px5hNk88IiJiVVGCrfOz/rPnDl0/eS3jb7SO8rb66qqCJLBlx5JVVOXOqOzUOrJPxhm0Zeu1ZJddSgEHJGonjZCAaTyCoWbcwgjhhk2oMGEVTyRz5eZKvpL2Rzx2Aga/LM9CBEZeRZshEMv0z6uRJLKVIRJjOF7I5c+Lorn4AX/S19472Bzr1ZJdzoY9tuRbK4Vp4Gi2pYgLHi6qPGGT9gXpncrgP5j88pvGfi3oL7VK8eW/ymx+Jd4qtuIH2oNvzMX2bu5Lfu1Hd/7Kfz7/MRiowjKVlXRxc5eWqjoERSIFnKqKqs6UrErRyXP2FCPhgaS9zoz3XDi/ESgvjY/skA05UaV4XG4eGfrwxE/jQUImOoFQfNVcpUIU1SBm7N3krdNpdUfXZUYXfzeRC4iVrj4z5dwO15ROGwm1uNsRRMzlYi/EyhQwYecifat37q1zV4k+u2t6ehodmEMQTp8+vXv3bnL9bHCqnojUp6BKdbjNUkp2GjaLdQzT+kdT/uy28+eeKk/Ir8C5r4T7huo7rr9UBYp8kcGPf/zjTAJjEJEV0c8mp09F4fPhl8qBSy2/S8X3LYej0rO/jlOnf/iHfxhr45dSqxhzYnGjWQdlv+ZXQq1FxELlvyaDMzHsJTd0TqzEV/jW4dpV5cgVuYT/DqaChwZ2Z/o4YFZfV9fa6iVmFxuqrFV+xgsIySPzSAwacbFm5ujRhzij8qqrr2QsNpFITk3tAgBDpsDs2jnZJanRE+SUiQOcRCgBKvWj1kBzQxn2s5q6KovTMtio02hlxS/QTvci8DTyij7DvF+zxYwx25LtjkUufKQvdZaJm5xBJrt21slVS1tSSJeJI+fcdncyU5bxmaiNJkBYw9yV5JaEl5jPS5jF6MF4T19Pv+2HbTdfeqj/mvFEurQSLJToIh2YSrzq8tGvPrLyd4+sjU4fyudWaIPpOWLGFMOnh6YPxWOJ3NoyS37ZJFwpFTnwY2B014Xq8aX5M0Mp36tfsG/3QC2s3bMPp4t/TqtQOtu7/N7MYCMTw5BhOVX/vUL5TDz207lAYL1a+ER17u2pm787sOeyzezoVy60F4shmjI1aWoW6GBo/VCu1qq1Bzmoph4tNdvn+oYmtH00O15hCbu1gcqfmh/9lNUmFrtKqFWI4ArHae3G6JpGE1G61FrRFlInmRGe3BENRU+tNY8vMQyvMQIaIvV4lF9pRwXUlGJ1INsXT8StGZY/DS3k0cidfuSRRCrVPzxCyaFlWgFiEJUXRrY14e06EIz3M7eshXFSdWnBmYzVUm31bCwhe5JhtcQ06txt3EKqrP1pYbe0aH4tX0/MN5mozc6epwYkYtUGVrXKpwhLZwdSmT7XBNswimy40uSBlTtmM6UhQxmmZzUdzXGCwgZOagsz0rhRmKmZkNDDuLSmArREE8Yai0lBnG2x00xP/8LKZpXWP9m3jqnjblNO/QOBvm/BWCXm1V1Wokx9NNuzZ1Y4ClVgrdZZjv9hlXm6NVTPwh/INS3aOKMY6rLCeZCZP08LsjT8fXspiHqtcv7U8Ynp3fGkKvyxZfiq/od9C8RVVdIPH2HTi72Cp0OZWG7wPBWqPMinkxe9kEmlLAh1XBwe4RRB6kMbNu7W9wVbxh/P5JYAwOyvS1GY9dOFDeF2oGdUHM+tV+vFUHqYEapquO9MjtJWh8jFUkzFMhQWt+OvBC1RmGNMA0aXMuDgBeCcegeJeyMLDiG+XU/lzLAZDpeaYnVfDZlL0bHHQozSLSBzmbDp+PGk1ncv613yhiCggrHQkbrnBSuBx4HsRiUSWd+6BLsNviMQO+gc4DZacLJ9jk9V8zVkfCsuLmWVwpQw8VLwPB2rYJOHCe+LcCpcUYyjRDa3bsKpV2HGv4MZMuVrgBLuu/vaN480knfcc+HXfiO6zq5Fxa08fLJw2x0DH3rPqw5cN7vRWChZ311fsvCKh3ZSzkaxznKqEjaihY/CVb5UyFbFHBGkTbFa7TTU5oI2x0qIMzAj0oULj/kSYNWbp0vX3ZUvy5RI6aRhPkoVDzBDhhtlM1wqZvlAuKXjJamwQK1aRPKGw0hXhenr8IhVMPiU5616ybdsSfp98WCsUcz5ahuZDCtJTSBrwapTXJHSGvDTzCqyF73FVqCgQspHYBonda8wAuHcWfXqiX0IMWqc4FF+cFVqgWzU3yzkY7RKW2UrNnBBbKOwEWEoU2cWqCKJTcqV5LythBF/YKUyADu8S3D4KJdCo8l33DYwQYjQmLkvhfIzfBCnpgXMvAqNouuhJgzVOxQvVjkishGRhTZhNMRWOhSC4eEOr6BHpMllHo4608OtFRJhhAi1lSxkIf4PZqrff7l/slGJ1ThOolHtjVV3Jr+06L/1ZEB9OxtgtAwSWXWP5PUT9a3Xj3046i/4GrHZ3xpq3d0faNTUH7ozvngkNfiBucTe/Asyn7l98+fmawfVfFKCVpU1HqHmUh8u0+MqUNBSgqYTm7j3ZLWqmbECQhzDBvv7scZP42cT+cqNKhWLxoQS4yc1jpZM92rRLPqtqofOZ+JkhyZum8vFlIZYBA1iFsIDFBCm5kBrldm7i6IbJQpLwGx3FSuWtS+XLdZGA0mScwzUE5slSkLiXV1Hx+O5ftKdueqqq2677TaM7WMd8ImOV3iayXrVgTqhfkUbc7Oou9ia1S509F52EdL7VT9WDJBAiIWbvTF9Ypd2tXNztY254NlvNHZcf2kxDIrMvva1r33HO97x7ne/+4YbbiDj21j9NPA8D/occuDbWwGmpmNghlN/p6enf+/3fs+0gydkjj6Liy95VI9IfDajjbtjknQMDtk5jciSeDNQfyDVunIj1N6M+lfUXDzTC9vRmKLtWit9EjTbidz+eXBEJwKCi+UiUHjNNdc8ETb0LpaCkwT6MJAnjmMGw4+pkv7+LLbxMEZtFzknP/r4URvo4mKrk/05Op0Y3djOZEJPRvXFvCdddolkB4z41tSxzR6jG7fam/ni4uLC4PiuBnYjpa6ITWpVwK9EXIZwcanhdIhypVYlEk1dc0Xxrz4T4Tx0bX9idW0rGIK+QOiGa+vxZG497EdIt5o37mzvY0fr7f9c+OvPNE7M0OaEDx/se8Nrv/vAocKVE3932z/lFs81OKag2UATxkxLtbwRiSawyLVyfob1z+1WbenssblH7gmE4jddd/DtL9uX3Vyp/revl48ei137QPB69hgHQ9n3By67p3jfgy0MoabDwVQq8C+1+s3N6MFcsP9Ibd935drZC3nf0VMBTGFrPpXmTS0auVN/VaO1dEGC9C7gAnOlrXAsMXHwGlormiOaM7VN6mWrQ8BFE7V4/vTS+RP7rv/uSjO83PDveOELi//tc5HTZ1ntjc0oegnqjwZDPf/zvy0me0+cbK9reli85d+QCBcvs+c26+10OjV+klNa1Y6rr6nBbFjtay2u1xONxmZYy58AB68a7dQg3ewSdqNFlTpkZsgZhLxLtXaaL9lS/sAIKabp8kb54mMLA40cQEhHOhU9NoJa6YRvT8Y3nmzH8zqvFdPlKtWovqyh4UA2y7SQ6bRgbMnQqHCi2WqGWPqtnSehVdx0hAglOaaRucttc8UWRco2Srxqk00ZqWrpZ8RAja9drpTzZxanhvr2jIVpBUlevTRlRqkYCcqb8EMZTrn1E0jb33v4cnzy1KfzM5N7D8AaUtRPsEx3C0y0Ky3FhyP4wD7PRyEuSPxuVINL9MQ2A1HOxHLeirjlEjqiCoUQdLBAjl1iL5cChNUYr6fKWz6mv/GwQN0AsU8WENUSvVMzLKIjU+ngo6LuYAM5tPEnWOfpT//bd/6fgH3jtr//+0/90b/5sfclGSPz++9bMmweQh4Os7sbDcrOlj9ccW/Oy9wusx6MEFi+7d3FleAAl8dP+YmnDrFJFnnpvXNRettSwXcbEYKxVwrQZc1idWLDhI4TuI4zl4uc8422VyIzGNWzS3kzABHmgRmNFoqHMVUg5tG98bpFWqdqdJIkxCSJizaz7ptjoUAn0DxVLJQCD8c1y4GH3EveUoA/IBM+yyTghsbh6tAr3Cp0dxmhFhko8VsB8nR+luaVY/7U2srSH34skstjDdflBXD/8kLuo3/a+8GpK8cH5x9xO/1Uo4hcWluolTas5gUqGyuVwiYBmZEpk1lIAEvBS0aE8GU5KnETXwWpb80hEzW49KIo3KyeGqiXEaSu8s6fPPi5KHrapTBL1KUi7Q9SVN+JYVXCRqH4mkwY2uyaviccrZVzc+xZHRiYkMqKZJT4FQAapolKRKv5S/+Q5oMnigehjBadDtdza0tX7OhNJOMe25WopSs6yZfcfLj2UyxIFQYuOVBspGnioHdO5kyMayusJURE04iUFqGBIl38Yis0PtLizNMq+ow44XEbVYpdP+OT7NJRalLDlBZyDeZj7NF4qxjWuHh8NCo8hko2Cpd9mBoxN2HofXOqnzhBpeolnmtdjF2oYeTUumZ+jhjwT2Y44ii6ssaakmo8wZHAQmSFQ4G4AQh75ahwI141TWVuyOAw1KvUUAIlpvi5hESNrvaVQ5UfOuDrn58p/u1n17/xQLtSju6e7vve7/ney68dvCr9mYdAIKLBYjmFl0oGsmlfenwX9qW/Tlbyd8b8d2eh3BKQHc5mOZb/ZDbx6/mIb/O67JfuLh9Gt4UMiCE+uVUWrP3gyc/RRbWme0CLQy4cpPgjZdXKRnF9/dkeKhUli+rJhC1oQ1iF1LRtkyXixVw+t35+73CW7OrzMf1W9KsOar9uBGMZOouBFcs+abnavqujGXS+rkYhxB/lo8NBObdeHFvlc/LkSTYPfutPTMAGPqrg17/+dXq2L37xi1XltngDV/WpduiFTOe2DIlqV+q8qrawnhE7cJhRMAuymFNp0aGlH2uxtK+ECIDRP2sw6wCw+rrMALUubLbb4609AxqO2Z66Uni86+w3aMgDF+5vNyvt0NOYOtZn8Ju/+ZtkFp2F3Zoo/JeS3OOR8Lzfc8OBb3sF+Jd+6ZcwSUVlespZWYbZ5ue1mhGdmeGumZkZ3L3NtV6J7HYsEgtFotVaNd49rhpJxeJEExzj0+OxVPrpspxGgvqtVqNzOfeTV3o0WI7fYJU1DVYnnveEbFwY+uIskEcFcaxl18edk0laXJ4bNYLvf5tkccCSyyZ5sWTgozGyC3KJyM2m36QGm91pmz1u+Uym2EnFph6nGzUOdb8sK+GMNEI30NH2rJLCgVKLu6WTje28e1sESxIshfKFThd9V155deSVL299/lY1QDa1h+LXmNzR89rvW4ikZ9fp0/p29jX3R8r+L3x587f/MFxlRxW7b331f7xt6a67+n/1V77n0OF//KfbFkqb6iQEw8weDoxflh3aubl6HvtL47sPDo1PnT1yR7Wch4Ldl+1/44uv6Js9tfDB3wgfe5hR2vj0BbVctYi/vn/4p29Y/MX3NdZznD/IoT+cuxx4MBo7CN/WbjlZWG4NNjnsUk21cmjalOoNMpVWj6uweqFcLg1O7KLWBBGKfowbe2cXqbgle0kKzQ1NT21tAysKDZaXNtqhyJ1z/oEdY/3v+dnV3/tI84FjUfo8LMrvTfX82A/6r7vxoUKo2AjGWNNmxae+gPoi1oXxtYcHh8pVZHigwbiFCBKBNdasVks4C+VWuVUpNlaIG2dVtOw2AMOZ9/7cBj0A1QiRJk/1AeSE6UAzQUIbazUGMNJGXSZTaM746xhFdRnVHTEfen68cgX2DbSnMhrkP3ToMLjuvvtufC8/eKVhFmJzWGfAeT3p3ftyyJJdDLpbW8g7NRelW7033Kaew1vKQoo0v4XFfClVOLB3x8Cgqcqedq1uHz++S1OqiQy8RSSmihKeGQAFjcYa9CU3/Huy6uqo+bSOs5GiXOAA2Lt7DqOn4yZI5NEBavh7m0O9WYyfalqZi4SI6dxCQqLkQk/v3xxWloYNmhUiXx4QjEukqjDkp593mUtFt92TFwCVB12A5HI5lqWMjY0m4kkX3asIBtiBApyMtzlVi4Hzs7l2hOkQPLowlpaAO8idtLTo3ERl93JJOwo6aRlTBeEFOpI7lcSrKyLCy59LWgXh0Hp4jCSgur7dRLsOQRLsJn88OAJBuPXSBfYc7f7oxMBSu7209iQwXnzKc9slOi/KvAvrsMNKDy8vW1bCxKd8/WtlVEy1Gd3suEKzCqAorsAtAYfApazkHbpOIsqb3B5h4tF2IPNWXOOLPnwcYCA5FwsicHGszmTa1/jGKd/MWfDxxQNoUYBv1e6/P3ThwtTOgRSjTFQ5tXdKFkmJhgZciO2T2gSrhRqpKKKRvKrWG5iUSRHgiGAilMwZEd6wjpKRkJGnrTW12TZpg/gYDKFym3KIjNLRtdBH0iaynIZDuObHLFYHvxRMQoGSEAEFDrs6xUhGRKUGns5U29FYe2wcaNAr/0RDbSIlvUo2Ih7wsdTxNk8NCAZ8U8H0kQfPXhbbHBuKaROsE6QeDCiMAiXvCPCKizwpV8Ycm6RytD3FHZkQjDC7628PDIdvfon/c190OpfDTclFX3yTb3TszAYlRbbgquS9FYanrpJnK0USEiVKX9tuxHCxQkSRA+WX+U3HCkIlTolHX6FRWzh1bGBsKpzsNVFlODS5jnDTNLHtFmFI2JeO+MvRhPQzq9bSQdkGIpNbFIuEm6EkertaWItE6ZjpVGcrXCCpDNzgvEY9jNtqhkxv8qfCzTcfigycPbb4/l8PnjzD0CP0tWZnV752Z/Zd77zpld+zOJk4sog8l3xU3ECDuFpJboMLQ5HVpC+HIt44lvDXy2RbnGqzhIfxS1/9RMBfxdxTtT84gy/z6LDDyXBxiAtcXGC2LOCivkMY9VY1kIFmmZXSIAs7b7mj5dKqC0DfhX91bSWbybJcmey5+hMONAvNVrTgn+6VMhz1dF2OJ9TUgO3c5nNVkpasY/gT3aFr63IvZO2xlyu7S0D42KjP0Id5nampKZok5mweb5nkRWRSZbQssdHCOn1Fixl1ZgoWUiraY4XghL/wW6XGhgOGLpnXoftaocdVpy/EiDz9AcpFtdi2NDMnpBGH2UKrL9HAwt9Tf3Ft3+w9dG8j9ZJv/qHy5DWcK34Rb5+cC2SQ3ZpsBuZk4I985CNPK+6TY34+9Blw4NtYAeZDveWWWz784Q9jYO3w4cNPWZOYDsXuCzwiIpdzx+OTfDHtYKO9t96+kwPKTfhbV0Iy+2DRRHc8kd4j0w9Pp6KTEPo2a62JRXIY0HJHKHGiJgtcn6SoUIo4oQQVV0f45HKyE0gT0mqx1pUsICk4eOlRlPDanRDmA3PnmnCMB6lzetOjgLcn7VoaGi8/hynVWXdsK2Zcuyzprw2f5Buj9hzKhzxnnQ6SQ80zR9VXGUFr52KtTDUwlWGNCfyCk+GaBtV0PpObPbYGUu0trLVJPOZ0pXbkSv7C6FDvO3+8OD5cu+XW+tJiMxyJvviFfT/wxvzuy+5a4IxHNsX49g8EossLax//ZLDGlg0mejlEActMzfaqJiWy//HXXnr1wRMnjkfijKU1gpHwwPhUdngyv74QS/b0j01trMwnentD4SgGj7/7BVcNtcvrf/jR6MOnQrG0taRhWn1OsVz92McG3vUfYv/m5van/xrVT3O42Fem2KSWBAo1XwXZqkFGmy9Qf8DfF1fbr+lEXf4apy8HmnHZUkyk9h0EVMoWfzIo5VUrah4rmpbOnUI5L22ssAX5wpljNL25VF8yOvniy6/N/tavNx480p6d8/ekeq68vD4+/Ygvee95SgwdD+YpRXCK+yZywcvm4CQLySDDPMkNDWijtFFYWwSUFrlZrZWqZeBT6ZT0X5FkVBFsl3onHjpVDfWFuayI7TsQGFXI2mW5zGHtPb7W9BotcocDratHtbTPEIgz1HnGlRX2jC4vmhfd7RLGr4NNydNiuZ7eRQn0Tw3tn2DUXP0Lg3cfNm6uTnR7IWD75eAcb0ql2Erf4OiY+hjyN32bIlDBmtpsPuK7Kbr4qxDMQLcVvuDVx+OA2/bwuKqE4roKoxj0CS2u/PnXhQ/wJjHMa/u0JmSLfqWhu3LhRTT+uPjkDk/KlFDrnAkd1BqolwvCZnKYBSvs6g+FkXlKXX+QClK9KRJRVBdwx665cnLoF4b7EsxHuTkazaEoBSFUWoaBdy55m4f4LNSqUc5D6OWhd2I+JpLzsV6xAwPQElJWty4XT0kTbgAXh8t7C9p7cSD4y7E9WO7t752YwDlv7o8OV9odhN7TPeTbARZl266L3wgwXgi4G8ILR4INx6jVhsTuhPIDmXMYtMpbP/N0aVBl8MHX6kL3KbXB83ehiqVycEqmBdmEmMDk6HzdwkptY4qpL9IObGwkUE8UCGEkpDsabi3YxPDBYKz5gknVCyUOZm5To0H/KCV86tiDw2NjmSzjo4Ton569HPT4jdguoZqWI3mNAvG0vIgEEjUazdfcqpgg0kVf1XJtPrhNk3JZoFG3nKLkAAjdCpM8VFxPhQZGP3z4ycHlTZr5/YX85trqKp69gw3s98ea5winUcZYkeDsshidF3HLXVZ2Itw/lh44MPkSDFtYQAe889yCdjFEiHd1HR2Pp34SBY2IRrDQM9D7treu5tb9X7tLm0lUroH2dVdl/te3r2T6Tx4PRlC8lABs1heuspTk4qE7/056AOIEgnbaSp2ghFRD7OKpMjLWqShIGYWWrkIE8yOcdyeOKgUTPbxwqQhgOKMVcVspxXkHqrRwvlFfPvdwdnxXNJJU5TFdVJh9AfaHphJD8XRKtEl4uronWgEQMOla+XKuBGV31XBrqJIrfurP46dnMBppokTLfcOtWv4T//fAlQdeuvPQSolRSO31IlNwBzxUGRx4tZoRLIMKbZT1UZxECGcE2ryKqAAAQABJREFUx8illKZYwxUyM9nJsJRv6bSas8Xooyb/af40f0seWWpFfi3DotOqs62RoJopRc30atpWDk3hBvxLF+YWzh5LhSYuP7gf88uUzemTJ2FPqt3oyzZ9K4/QrMQz6eHRMdEs2oHpFIXlwb093TuHp9AThrlMrtDVnJubA8Pg4OD2w8+fLs4ngbcq9jjhhw4dhC8aACFrbdYVFpiO4qglVspXWJnImaB0KVnArL17AcpPcwRUKNVWFTJdLOtztlB30Y2l7uIgwPpFmlrnDKeQPxlqBah74h4nawWZ2imwFhorOG0/3dFUVOeBYRlVtRf8RitJGLO7NPs3F+ob5zTIRsjsXbXJaxwgYO6SeCUJFfPjXfhzIOLv/u7v/viP//grXvGK173udU8E+Xixn/d7jjnw7aoAU+lQKdlKzqm/nDF9KXWIKWLWWsA/pxnaHmBfq55unPm/Qq188PuXaicmQssINlVc2oDa1euxazblTN7kDw48M8ajtjja9JWofXnCi1CA3R0g3E5jd2ozr5CNyVYAUIC7WLo4uxzoJudgeHU+3SjbHUiIdrmKrZL6sePthTlfT0/48CHf2GQggz7JF25its7QWRXqaNxWi3l6AKkkZ9u2Thx72KWeivhXzp0AbU9PD8eKOoGhxkX6YbtYrh0/eXZ8coKzalGxmRNmQli/ln92sxkamUi8/W3p136fj2VysVirp6eW6j9XwOpgYHeW5qc5Gm83TpyKrq6QFgqwxlBDLI2OlEr16tGjwZW13TvH8ivzmaGdgXCkNzvEmmc1oMHgwOTetcW5E/f8Yym3PDS+JxRt7BvvD8zNF79ye5Jef7MRQlef0+prX7weXPuKby2XuOpw/tN/RYOF+GqEaumrqsxZlFpDJd8ALTfdCEQhVhkJjQea+wa1Z6TRapTzRSTtek+tHGr0hdeZAw+FY4FQzGn7tFuE2uJwjVAWK4XyhRP1OttSZYezMH8KER0Y3zObm/6Huv/yocnxl00GW2yEDi41g8dWAkeWWZFP62tVh0ZYIt+a07a/UCxhITwej+FJnXUtqyvtzADnHmeQxCvzZyOUV88AFatFN4BRCqEy0azKCIiaZDFBb5LdGj5Vv1NapVUd64l4IIIQMVzcVVtNiyOGsPp3pFu9DG4bfoFg7jud5vAw57YULGlcz+Qy1j8Ogou8OmmxxXz7SbkXwWxP/AkClOveVIrfduBn5nbcVVx4pgdpes2jBelmhYI/5ItxXiELXgqtmK4bn516ZHq1td9am2sRgDF/lQ8XJWOrytVJk4+6uIpud7kKpxczqeaVY52qZRO7ppsT6PDLAU6RMzDq24s+Y5SYhxHjkhY2EW98lEMRdFlO5bBoRpiqluM3cdT5VbAuOVws92LVTAFeQl6sTly9Clwp6zJ/OR0ZzlPZNjAXywMV+Ba89yYIvioP0OH2lSvVjdxGT2/Pdp3HYncQW2zz6bpwCEsXk5DaC7R0EvDI68bp+AMHEZSISIU3HrcspnkIE9w1tlnW3VdpvgbggRqeLSoIdz894bqRr0h85sgOo0sPaydwmIdXpHTFk9FgeGyQTYQhUwa6ZCuVWCI8hKX92rWjTO9KhYU2ff9qPHRFl1sTg+3sgGZ3pXVJTOGtqTMueVqqlroZk/M8lH2JNdFiSOV2JPE0Qq08kZ1CZNgMXNAdD4U8swsml5qNSkGGlHE3ao2iWQZpDw1JWj7+9Tj+7iTaLriju/vqHM8BuYbIIe+Lh76+VBqf2N3/f7y3ed+9Tc4BZmb44N7INVevZUe/dNrHoVbetKXEu3Qzu8RkqV382c+YbTUQlxQ4FZ8VlkYKUPys6LTwhxVA5fxmG/60G+Fko8e/FmmVaYD7B7LCKThFdvWB2gexWj/VrJ8rBLHNwYYh1muzgiDNCl6UEyBQI0nC2qBquJWJtFJxN09r07wa1AQJldckDYP31CdTWvDb1eePotH9yzfCNJtuJJvQOgZA/YG1DY5U6Jm6jNa/DD5yC01OzlqtESsCIxwlmWqfi/1P5fpnQqFGsNHEhn8rFtXIcevGejsM2mAseehwDwsbkK0IVHQhVU6IMqW3Zef2afMtswmsSabHEpW6G2ADGncQQb/LgrgofmhTwI6escKFE6uzp1rT4/FMhr6fcqWZAvKnLoSaAhWW+3RclbeCf3Y3lF5sUIED1Ey9uOkWzul52gpwV/Y8MT1k06C466k8YW6TA0qYpG2wL5d7czlXYK7i6NFHMv0jmYEhNXPikb5+Fjeg+rLOmd4jy90aWLTBja5LHVBDJdZogzQVyc9WNtGhaNQ3dR19RQ5kNL0ZdIBFQ20g4yHXP4fVgYeXMHndPMwnDm12KQGVqgrJu7C2c2fZ38IQp8g/f3+TYzpRqpXU1iV7fEGm6a10t7w7Lvzf/va3/8M//AP7gdkMzGLPJ4LsxHj++c3iwLerAoxUovbAFeyquXGjp+TQYyuZfMI7/T0/5Fv/cHCi1H7fudbf9ldnItqKcd1m5JUbrTBDS6nAwLuZB7XWYHstf8oEfWN2AXfvvfeyRHlgQFr0Y8noIjp69CiLQBwAm375BpnXvfJKLR/9zGc+wwwwI0Zovxgq6EbBgWLMYZ6PPfwJKfYky8Ilg/grlRr33Fv/yB+Gjj/C5lXmg6r92cCP/FD45pv92X7kTXthqfHQkdaDR1q1SmDHrt6rrwpNTpgU84+Pj5M668lR0d037ISmmhVkA2Fq/xiYbcX95YFEIK6TnDTybwAK1jlMNB7JvvYAtpHotEte5Yv1dNgfzUhJphuPeuev5LFSWKmx/IimKIZJ5HqjHo5EorUiyYcSoWQ0vOeyPX3DkyeP3Ive05OObyyWymvzK+tLtdxyNBZnJdXq+eVE6LX1+flgvYoi3QzWWBNcuj2UfWu0Ha32/G/VeuWhUO/haquF5MSURuTfLUf3szXEf6J4c7UeDoURl0Y/peLzTWd9142J4I185fj8KVqrQdbbxH2N3GkEdnZoJNs3zAlHSF43B2hrd4nua/b3H554NaJ8fubU+ZlTl19/UzAa94XoPtLehbCn+ghnL4QiNXZzabAgkImDhPZPqcIqpW2zJwyPXFhf4qV3ego+M1ginuvUImCB1JFJNJi0Fsj5VjhBR5FGVPMr0mHdpc6I65GqK4CYl6RXJ8MKUG266TDE0bYZ8NrsnzU1wqIOL0EQRURwM269Z6CViatyUidZYYuD9QjUkPvvvx83Q04c9+fS/qbeObcW/IzFPPlSi28qDduRb0kN9TLFVFV1xzxj/DZgrxjkLeZefMmDonKXWN9xm8MrWHO323RrlpeXLr/88g60/F0Eelcn28vZwf7DQ11JYgXpoXNQHdyP81SnTMiMBB6iy6h1tVRvegeP6qIc7u78PWjnaSgcFN+d6rBFU5VyTkNlUbpJOIclAWLeXBxzWhKWnHM5Rm6h6vh6T0Ns+X0USNt3YWH9gaUHdw4eHBuRoHOXQD32WBF2/B/7JAOUpXJmFxnqXpLweHd8NNiky3p6DkhfYjcch2XQghRiETv1wDUXeBFdAdz4MxB76eB5lI8iCKHnzRvdcUvh0Te+/Oae6dB1V8Tvvc/gFY+PXmrMi1/QHuzfWJo7cGDfYzbskHV/cv9EOp2Mxzt7eZSqy6xLa7u7kyFhN/44QhTFc/FUNBfVPTy3AFz2Heizv9NYu/b6+PHjCLGdO3c+M5wI5NOnT6NacD7FM8PwtGLRWo5mYl8607xqcHT8u4ciL3kZAqceTszW/Pefb1ZakeG0VL//zt6bgNl1VPe+Zz49d0utltRqDd3W4NnYGBtPxAYTEghwQybIC4+Exw3kwheGBB7khYQbcklICNw8hlz4Pm6SewlJ3stw30fCFCAkYGMGG2w8gGWrbc1SS+pR3X3m837/tfbZfXT69OnTk9Qy2mrtU7v2qlWrVtWuWv9atWs7ItVZK89ZfCwYh9YNm2lKVFa/rhVphrzhWFUM1RTMoZCQfTeYHhgZO56bmeKys4sR6FR8OppIJvZt7ME1ClTW66wwAQrqrBgo926M/dMTkQe+d6CUz24c6E+3R9tik7ECn5yOd3RtGB87NTvF8BFLx0rZqVP5mTGqd9P2QX2RyJcaWCsQjjSZgRswp1G2p9jedyY6PcULxoZLERcjIovgiFGemmSjl53dEd4OxStYEUnTQPrTE9QzWnxxW/Hj6T0zhVedKPxDb3KWN6KKDG3ZobPdv8iiqkg+vmVD7909ekEXn7cWPenTxPalY8rlj15Vq7SeUnA+PHTTWjLCB4QEGKqw9HiV7/vf//4dd9yBTcvaPSgxrg4fPtzMx03CDJYUYPkx7Zy+lhcJ2agVo45wjYVZw1Adc83h5VC0laxylwv+MDz0tRHtMsPGq7yXqz2oMsUSb+fip7X9FbA01F9OTE48+viTyJPNJsrZGAvPbW8arXkG7mr2Xj2qICvVLZ2zONFekreMZZ/wIh1NctzmcyGlRjCWmZLQBmAJdcgwwFTCLQXnzIxez1ZTipXwxqfjsePTkcEcS/RVCqb2v/uZ0Se/hEuY6vPOnNqMRws8AqRSf1uYSP3dW9nggH+UkkPx7Vtzt/9q56bB9ooa6v9+4AMfuP322/HefeYzn2ms8PrpL8WuhgZCA2g1mJ0vHjyBfPHon/7pn/7xH/+RBzjsR5rMH69U2OA06ve9O589kJz9XHLLTOQ/8gEbumseHSakwX+tI4X/tK31To0YatxLO+YLNj+mmiNPPrYpa1HGxsbwm9EnAhgQdWRkBETBGAyKDiUPE3KXEboGAJPRolvqyZt08FDhgx9qO3wUTKjtd/mU0JkzmQ//t/zGTak7n1c6ejz3P/5H9AufjWZySVBQJDo9NNj+9v8zfv210/l8n02HIyp9NGtmqsGGOoRymZeZ0Vsyntq7Y2cKN+nUdCSZinS2B6OVdCrCyqFBAkNpe3eAkaz3ZFPLUn7HDj49mYjk29Psfm1zsIxr2UKmszPe1zt6+BjvxhQnR9K9Pdu28NHg+LaN7bnOZHdPy9ar7wAqP7X/8enJsa29l6Xb2pMbepLJdEsqibRa0n6ydeKzXd2vGEsPsm/CH5VG745fOxHrjLTcPtZ9m1aeTxU2f/GHP3ky88PO3m0dvZuxQ8Gg3a0RvjbRnWaZU6w72bbh2t1Qsmp9ZmaGiQBKRHWkmfWlLzQMCQbGAwyGBKXSQRfKKQI/nIxFx6JXDrQl0q26pYlMuYiLJRzD6k0tOWfhS87qX9VxM53J3fKx4yObImP923f0siaALyEdGE6kWnu3bmcvZSjN4axXvEpd6WRrgs9wkJr/NGv79Y5akUqMPcOJVJpv1qQHYUc4BJweAllDpNOkv2TwG75kiZtU3qaW8lXmFecWGvDmx/IqpkWCmRGjtpSWn4UaPxFOvKQzjfrAgQOY43v37l1SwvNFrCpwy2eu7dfPe979cyLOuTAGiqlYJ4T5Iy9lxxHAGz2X5ampszheWK9RpXyl1f+mDvWGOoIEVemqgk5y8Z6LE+WeeKavtbytU13TM/1YsOb4bEyib3PkP75h9k/+pGV4mFqnw+Ad39xVV6df8+qZru6jj/1gYGAbA1ZVczJtRSNbtsztTBEosGL0BxQLqbVanEq48rtQmlWLDwtSeZrEOYxcUjakYgtMJgGZj5s3R7AkTk0Ro6KBzthgV/T0bHQyF2tN6M0lAYlStL8r2a9unEO9uzygNvzSKwMnAMCMhxZWF6+9qdljSShRmw9TCm3gDJwwrGj0AiGnT00cP37i9tsu5w1guhR23GSDko6OduhbWuBXv8aYTe1vj+3dWH6okB0dOXwsM97VvYGFWogTTyY3bOrlTaJIEi+E9uZkIxScqKDmHr5bj+stweouAkLpGGrAWoWRzUrBThnJjvbW/s0tx+QXZN6BsqZY8CUwFIlt21pOJbe0af6Od5UMvmopchK2YCRbzJwqvjl34v9LR060/+zp4pX50v3pyHQysu902y0z5XbWwSViG98xsGGHGYTN1Ej9rmO+XtAV5h97UNFannrqqcsuu4yYlTe/xiKSBQdgm4w8jFlEoEEqKM0YkAUgy0G2svbgQN3sREJ9AWv5sEi2FB0/OzM+Od3RvQlPgNIElgxv01EnmBp8mBNYK2zMZjG8McfnMsZPzwyfnHz84Cl29ew+m0gen2QNxdbt25lrSCcjLUK4niPZ8V2SUj6DNDZ1w1vc7GudoMWWsDHbZNpwsJ8CPoBohk9bkpm1fdqzOecjeIDVQuXtoADg4ehMLvLocb4DU75rd5KWz34nz/m53q1DmW/+Za443hKU1cY8U5BOPDcRbFPLjR8KuvO2/E3/+6Z05yJqxEphBeuf/Mmf/NIv/RI+PJx5jdVuxbl0Wn0NXHwAWKjt4EF2En/ta1/7spe9bBntBtcoR0WX9Og9ye2fKp75o9L4f08VzsT4UqlaeXymvG8s+p9O5Z/XNjbBgwcWxaG6jOzICFzE7Folx/q/cMZhzGPKWxmgKTy9uH+5BA8/+OCDwImbbropRL/E13AJY5qVkG5odrZwz9fTR46wIROHtobQgUP0bOY73y5df23+nz+T+ufPlAqlszPTAHH8iun9j5/9vz/U8b735VmMTR/GKp95klQEKx8+cmRrb29qbLTwzfuy37o/kc+Wd+1se+Hdkb1XRLu66D6hnC8tjm6mPHlNwl6diuZAdDu2zd54Q/prX29pZ9sCpuN42Ya5w0jHy1+c6ep6+uA9/RvbW4vj5fHhjkJx774rky2FsVY+ODsay7Vv4DtR+wYOPDG9sW/LyVMnd112Wcu1V3QeOcboiQJSqZbIP/RnBkotzx1LRI6Ue/9y4L+AFBgfJVum2PevJ9+1cfstPaVYIt3JSiawIUuat3UUBnu1/pmDGsH9jhKoNQB/VbsKeko6QpVUqiWF1ZrFFLa2dycGrtqSBoxrKDFyr1S4+QghPKy1rP5ns5vagSuSyxdHfji8syP6HDYfY+8X5ipP55Pp+OaN0NIN06crP3K9YvNueFbeOxVwJSeH0BbGuxOCapEJAMuNHAxRDDiMbZzlW3bhJacPzDZOgu01BJJJeU9fZIOc/JqXZ0KEg4Lg+8Uyrl60L63Zwfvt2IW8Vje/DVRIlvxLjowrLKMYGhpaRbZLluMCJVDLKZX4zBv5s5jNR1nCdD7V02G+MQHvU3CLJC7sj6C6vOBrd0a3WspovcTa5bLWnOlKyul0/NqrI//lvdmvfy36+JOCDldfnb7tufHtu86ePpXL5WlRPOZrLckF4b9x48aVAFceK1ANX2pkRcb5WOvI66vx6OWbWFhqXly9lWoOXsEcx4rEgwIEIK0H18nHMlevbHodHmedA9ObQSfhJJx1TVevD/NESq18pD6ayOPgizKkJhqsO/P0II6WZHRPb+TmZ1++f3+UfTG6WsoDO7eTkLdnU+lyfGNvLLoJZDP8+KObN2/ZuGkz0hQLuScef2Rg1+4O9s7FcNDLO77btnA7SBirQF/H27Ch5Sd+LP3p/wfMzqJXxjctPYtGs9t3tdxwQ7wtfTNbUljxqkqtXpAPSU7OsvFVJJF/z470e1rKp6JXjUWuEm7zKYMyC6A3vjG58fVeilU/Ux/s3sL4xaID2oyvlqLrXq1mE3b150oeVC2Yk2lrGxA8xs82+Y7yBVC19lh7Sgnf2ltsBXlo+dN3FdETLUcrls0aKEfHzuSOHT152eV9WXmASW5/7CIjN4BN8asCZKloLkNzHFrI1tfTeewIO5jM9nS2lQpp8s3lihl/i54KV0XL/OPMu1bsNSqLRif5D5Btlg9R0i6tfdAkUrySjXeXCRBrMOy0Jt9vKZplczRJq3UvfKOMN4QB2F2tEb7CjkwA+CQOHwkX335T24sH0/f++cSZh1MwUasXs1CFIlMEQnTkbvrfUruft0ETSwscNVXwwhe+EOj7rne96+6772ZvIH8gF0h6KXpNNHDxAWDaEMsGmC1j+mTlLYaJHjXqWEes7/dKG99YmPlWNHcEDPDUkZbZ0pVFdkYuFw8MD5MRA+Hg4ODyKgE39TIS+tPCF9JAVmwQ70uOQz7MAuB95RIyECMuYsKseAxBcki5UIBuoDR8IDs7w0QphhpuupnMLJ0Js7Dx/gE2Iih/8V/4bAuPOK/dsjk2HcJsNtP25HD5h49v/PEX+spzIB9vVnu4JqNdAzuiRw7O/tl/y3/hC7h28d7G7vtW7itfjb/1zck77sRDXdWPzCXFagcXce2WeYE9DfBV/fqb2OUv8vD36e34LE6BFyxe+sLu1/zy05T90KF8Ljc5OdHd3XX86NHTI8d95EAPU6PHmER47nOfu/OW63bv3n1y5ERp9+7eX39T+YP/NTk5wdgNts/nYmf/6sr4VdfHuj8bKx1hvQu1z34TudRd3z712on84DW7N9meGdIDXTyt5Yq+RFpLrtT3hYc8yrY0vU6b1IDrRxAg5a7BQf6INfo5CtH5ldshuj7noLqHhw/2Rc5cceUVOzZgeo5RRS19QHG+ZXcG/MrXqlEPxdACbAYbrHD5hM0LLfSr2VNiGDDQKGFAL3d1pm836MsNvSyD1YAsthZCaleNCPoKJBN2/iRUvI7dPUyPV792e47Y8y9wmNOA5+9nPp9ySTE8C6h0tYyGJWW9HoiZyfKuwNshm5pQNUxAVANguqMbb7zREQuTBXwAY3BwcNEZuvVQulAGChWG6zxx4b0LHaCXpkNbr4sRmtMO3QATYOw1sGd3nCnaLDsUsMolHWc7qGiUVnTjjc9+pqJfmtbyxu5Qs3Bg4OaFJp5KOqUwfo0Css2jkR2VVVQryCUcs4zHuVc4e72TwTJhNCDM88hQ297WEY8vblKiExrUZd2RE1vSHcUtB4f3l3MTQ717ujvZGlqzK8LVWi9dLneVt/WUtvaywRXr8Urp0+W9/WWcFyB4eaE11xoYEfwGMpa64j/9M9kTJ1JfuydVsD3VI7Hslq2pN78hvl17eTPfPK/HICmeRB1g3bPF649F/nJz+n+25L8SL43aqNhWTj8r2vfWZDseF33VeQWKbZQU04WF0Ey4+JwLGTEpwNEozRLvUZyqFIz7MmwAsD19/eDbE2dZChhsYgpqtVdt2U0KO0L2ABaBz+XDAeMBWwIaOWMBn4K4IGFWxeu93DwugdHC2EQxOqrpeIAnNciMBnqGB95XyaBmQCDGp03AnHQyLd3b+nv6W3t3Pfbde7Pl5L497Iyl7/W6zEqMOW4eXYwQvP5MygsSA56Z1ImU+Ii67emiPOAGzObjBZJcD4W+ZYWvIR2PtKUiqVb2xCKM219LGwzCqiGBifn6VKHIbEqlkfAeR1/5x3+j5+HPTz/2vygkb/9i8lQbZ8gV7dqTu+P1Hd0D3rgaNQ+mp+2pkQZwdAGAP/vZz7KZ0Ze//GWvdGnm0nG+NLB4b3W+JGkqHx6YT37yk//6r//KG+TAoqbSLE6ktqh5qHh/ovOn1ReyRCd1lmY9OTFx6vRpsBO3z1vrpL9jItBRHDPrmFCUFBlqul3IMK2I5CkiDD1h68GtNIuf6EJY5MScl1LBCt2C4ugs2Ec+smUTL2TgTcuxA5bQEB5X29i4UEgkS5GjRzQlaIcGDZs2m59hLDub+dznWv7t35jYE6jWZF85PXom85FPxHftil11dXU/Qu4gIs70DmRHmHLBE+Fmpqd37R5qfff/lX/k4fyTT9HZla+6uuXqq/LdXT/8yr/ieuV1U1SEHY/1zywmpvzw8DADCXYn0xaY/oODg4lkkrnV6Wip7dZbir/3ntznPhd9fL+mI/ft7X7FzyVYLtv53nL2B8X84XKsLZ6+qjWxa3tpeOTQ92/YvK+lFVWrP8XBSl/Ppl+aPLSRF4Gt4OWt/Vu3zvs21XydeAwFXOjWovF6gfPJJ2mNrJICQB49dtglyWWyM2dptJHOjvb2lrSWtVWErOZpclesBh9YbFyBxkvCwAbDghqEBjkBaVoAK7Qhdu8xYfAvm3SYf1i7fLH0p1Ta2iFzpOZASF//7PEVdcHKc1O0h1eik5A5DYBaZlKm5o0AJ/hRONMP+CbwvJjAE8S8MqWunhRDz5iqoS3O48YHGJlPvLgAMIXiKaCWeStkvVVrddt22cKYlTfyC1JYxDZLNR7lA+lVu8IRT3+Lt+qCSHWxZMrjxjCE4cuHGOkM138bsObq/bN36Bb2zt2CDA50LwFuiJTZa4Dovk2bWLelpuIjiTMIRhUfjIgiEAwSWB/P2Zm4YWDLv4w/euLYiesHEj2d+OvIJnSxlfqu3oZtMzs7wScgGGK605EiHzgozrLLcvXEhPMN2gNv5g7ubHnLW/O331F+8PvRzHRucCh9x+3xXZeV21qV9/xRSnFRf82bOnr00Uc2D9zS3nl3tDRayh/ia1+x5JZoYls0+FBkIP9aND/EYBkgnKkCq4Uwk3OKOBfLSK47VhWBqrkmJD0T4rFlBtw/SImH3jy3Aqu8VQvcLbDPCJDVpsJBtpZKXAhg3hGP8ccSlkIpjtmjpWe6xGsQ1Vnrxcimog1zJYF0yW3k6WEB5BKu2sipw0/wMa10R09X71ZMSE8iPIrxp41KEJN9/8pRNqMiU2XLlNOGp9Jto+MTY1OzeE3IhE1OMFO1/F72YDF4hJjSV78UQ7BCjkl5GpQKjQy4iFNxVhnwATMteAYYM2MiLzKTJqKBSsZvWrfKrDpoTZZYksBG96ww5NMj8kaH5VJrFB6+7mUdG7dmvv5h9m/h7YGwBhTatK/4gnd2x23z8/DGQgFeCuOgSBAQoJbf/e5349L72Mc+9pa3vMXjF0p7KX7VNXAxAWDaCmjwne98JxuIv+AFL1i9tgInNcewVfMsd3drjTS9IbcAV6uX1yI1SEY8gAyZ0FFewBtOXRYr6qmsOiBjAOCABjOXCXhWnFbdbybIYqmW6FVX5v+Rd2l04AtieMaPmYzFc2SXTueYjy0wPaceiG/Wkhe9ByNUvr09WQG9xAFWsbAdgYcZi3J8IveFf0GJbEhLHzc9M42rub2js4VVcw98N7p7T7xFxfQDPuxd5DicGMIYuPAkBuu8mC8wvnb92J2p579AHRJmBHs5Tk11dnQwYLB5GJRgQsx6VprRrTBxgHHMJWF2IKPZAIYZ4fgw1Xhb25bde7re+rZoNgPzEsnYnEwvfTDdehtZWyklVP/WbZcNTvCEsPYFPKlXjuodlFQaslvwqEfSVFyYb2MmTDeAcEB6CE69+zQQy7fQAKrAeZ6cc0TXEeacqKrha05Eo0jps79zh6w3piF27Zovm4ttu3xBfy57e3bmuFgICfGHUEGYUIODg9QO0cAYH/hriJd6Se1Tpc2zQngmFDjTgOcXbam5X3B6isBB7xGWCHxCTLVg3OIxrIbEfpd4D9TQV6ddV2E6Z1uzt1ZCoYeaPq35nJhWYIU/KkVIzjRLzszLYM03z2RdUXqrOKclVeSjaLQoRqiFZkIrhD+iv6iOHonu7jjfTB4cXIkWULUnX7uHtJKF5+TZUe2Bt85yZ6PJ+LOuvx5kcvrU6aPHj1917dVQA0HO4ivUyiC9OGPLjvC/+Ss2chKyQtpmkOU51JojWwfLx3SLfddu3nzdI2dahkr5oQ2sFaNw3tDim7QPcPnQoSNMc5M18aiRAN0Xxk+F7JxRB3pMqOhAf6rvJ8p33YXjOJpKx3jueGs9m8VW4ZWlynDtugzOITcbxcBazNhvjrVo7R5aqNvyz0m/Khemb3TJ+MjiHdsHSxWiWQVJwUFIFygPBaJvUCXvr6JeA7R62VsxitcXNxjcwJzuttXXsGCgS4FbrwvmteXC5TUqW+QsMKnFYrjbBZ6t2D4jIVuBeX+tfEPDbg0R5p9VlkQSYOYH8XCSGPsiu4EAajGetGSONckiFwlMNKdWYFoDFlK97A1sSzmKo+Xd+/YBebvb7ZtFKqwWNhfzEbYaZ4UieUHDR7/co8tb4nhXFBbKBSernZATErNugKxw86aTpdZ4pCXJq7+Rdi14hl6eFN46F9iFcIEaDlqFyhyZHs+jgHMJVTtTY3kzh8l1zqJSXdU73J1Gh8luZ0NDQwwKz372sx9++OHf/d3ffclLXoKZGuRYL+2luFXXwMUEgHni3/jGNzKZ+v73v//8tBIGdWDGqiu9GYZ6sHjOY7HBwcFm6JdMw0PPMrZbb8nfeUf5nnuY02NQ4WCGLTuwPbVndxmv8nNvbr33G+yOxRrjFr1Jo1Un2fau5HXX6J2JygH6AqLXrNBWD5SdbZmYTLIbBu8Pm3uZWmMTC+2vd3JEWyW0zHUm3GL/DMgASGzrhVcBowEPJ+8/w58+AmMUbcSqqoPF4ffccw/rr1hGCyBk9wgoAb1AX4hBxb45NnyoRGKoTeTEJH14//5tA/2t6Rb0m5mdmf7BY/TdfX2bd+wIlqiB9+ihOJAK9zIomiIwxeCtzmrGTpRxdoYPNMfY/4E5xo72WEcnvblmsZd+YDEz5rFVsudSlwH64W6oalWY6ZZISsf8BRrAyEaTdZMvOxKluQkyn4NLywIku7V4wZFTJojtb0mYAYCwz/jMZ77UGOqXAYYab6DDGp4smWbqxz2lNbcu6ksa/Hynrj9fX/rSl/DMY0r6DDTFvPfee3m2qIUXvehF67zUNEXqi4Jw8NR7s6TN05ZWV3J6FRqSZ9R8c3IZ6MR4lgl7Qg9TIxcvAG6gW/phtpB9wQteQMe7VEXNZ4vCw8iVcwtZXZBAUJIoHxDeevbslPd7lQJiK8+VVKBkocP7VCNgjMAKwgSqMPE0YWKNuhU2BDDruQ5i7Ee8hKBY5MYfKIjLysaKeTBJAEptvY98eMIn7InCGaRKBOiI+rHVQFoWdOZ04eSJ7GQXcYbKVGHKlIZPdsSSkFSCRcVIZnY6nmoDnlrutr0FmQO30luhHBsHP5f7O3HK1Q6fdFaUmuGYmVMwA91aTcNwbYSRrGFFAnaXjKRbvfCcYerbi3gtmJB1ToynzPMG41FlNKv81qH3KPRkASN0as/YbxssCiLO+fELw6VG6dWB3/W7jz559MTJYsvG7k192u3JMCpLi/HH6iyYKgzJIixTtc76jJgr3MCteW713hOrkSuwNljFpnedgmZhspq+xaLSVCQLMNJEUrwTWyFJi55V33YXU0d3xcH2IeNmPLF18DLBxbHR0yPHugcI45TlzVsSUF7WrkNtHPGl4ptFFkUTpfewcDLjne3YvAtqHBTAZl7nYq0yZiMOW1BuIg6gBfpqKTv786E7mi2YGKzLNs68u9vK1tAJlkMTiKUTsRRZSP9WSMmMhZmhBSeTzEVKbMUFjdaCC5wQ68j9VlApz+aDxFLlz5xKnX4q07dPr4QskDqIhpqDC0ZbzoQ98N73vpeF0L/2a7/G0LzqA1ljkX7E7140AJj297d/+7esfP7nf/5n93qdh5pjpOE4DxnVZEFhmarEBcr296AaLtnShgejrseDSaO68TU8ay716EdjqYGB+NvekgFA/suX+V5Nvq09+pwbUq98VXRrf4xPGfziq2affCJ5zD4Tpy8usgN8Mvrq18R37OTBtU4M0bwHEXsP+xPOJTOvvDmdPJHNlXKsNXYIysb3mcxZsCKbP/lQ6YKRyl8kY4xnFStIBhQKOGFAAgyDYKvLyHAI/dDQEHNm3/nOd9hihBlTHMV4WlAUIxxfVwO4wgHLjG6FpVkMn/fffz8GKMNnR0d7hq986iOE8v75iNjePuecAYH7Smxkgwk05F7lY7ciMx6cOFn49jfy//LV/IH90d6+lh+7PfnjL4ru2F5OLmedG4XCuHdt1D0jBjMCSMLwHCrZKYlBvdWpauqi+lbzYUQCY0CPojgDPDjTDjlqBGhmCPF8YUJFU6EoOQwHBodTLPeMSDSba665phkGrh8oawLzytUMs/VI433X/OLwpFBkehUaEpVLFYMhCfOgcV6PJTlXJp5r1nF4HM8LTwTFYdpo1bFlaKOcm39TV7RD/wAVPRga9tWVaLipxMslClvy/EpfLsum0tGWoKNdAYCbSrAYEdXK9CVzUtUvrs8l8gFnEVNzjvyChCp1gawaIFHRNddeozFXBreNvcwYghIU40dQHiUIYsRD/jHbnUEQtBw5eGKM7xqUWroNWAqd2p8ArVANMMkAZ5GJarYnZHWLMKxtamgON7uUA9by0N6//kVm4IE8XDLNSWBQVvkCkI0GOuUOjd66BM6aYIIrZzNtZ6P9T41F8wAYOSVNWpAJB3moJHCDbTSfzZx8+olNu65ItrSrjOKv+8ZK52KxMD5deNaW6M7uCvYyPdCYfWjz0WehbopZGAZoH6OZBebhBTaTVkWCO4edjGUlBrHmIRb4Y0g4Wf0zfIK6Cu4H/MMMVDQKLevG9CG8ik8VEMu5UIoBaPHToqFcMU4NZbWsmFXKfIZSy5WlkGgs03PFoUPFYw+duO5aHN3qN8wrKxe65iPkYJfblnXIFgOLIJLqg9hOUruEMkkCWcMfVfK5h9WIl81xnt+uMtIAsqQiwqrUyqv2FagjqGhSmVJVkBgfwRTSsyR+Q7MtuqWMMOKAzfhVWLGMfxgvLltbyZerba74g+jY0cNb2Q5W65iUrTZAjbNQucgGziBb9jzj40bMmAB0tXQZEXiFXOlcBgvpau4gax8+2JXdYuvQzFHPhcrZ8dLIk3pIyCOaLFzx0tixB7MTT2F0IVr80P25vn0Y6t7i5pItFIKOZhbabBilH//4x/nK6V/8xV+87nWvm98sF+JzKX6FGljbIXmFwoXJeWLAIW9/+9t/8Rd/8Sd+4ifOW/s4bxmFJSXg3QOLIoBzPCTu7sO9iYXnhlQ1MRIuezpApUsk45ftTb/+9emf//nYzEyElUI9PbGNm9gXjz4qce01kf/8n7N/9w+l7z2YpavbuTP6spembr81tlEbfo5PgFXHCWBGY/04UsJn4vaQdgXs7irfdlPpHz+TYx01HXa8wO5S+UJmtrW95+abophN9CX0JjabRqkJcMCQM8PDAw88AJqln8JJ6MZoWHCy41M3mJh0HKwrAwCTKbCK0QtrnvW0ADaS4AjFpufgLoMis2tMFjhAYjQlF1AxSQDA6JAwkd4lYXshDxiYOQgEgIML5gKo92YQOn268Hd/H/n0p1v4sPDMdPLI0cQTP8w++HDq7W+LDe3mPWorSijyggEyYmaa22TH2T1aDvirM+UWlGyqjE54BCCo5oiKWAnPSm+KgOooLHdxYSF5NdlSwziyMCzCVLRJwlQxnMPIZgJIDhlnZGNxOwGKxhk5GbaAahgrzfBpQAM3r01oavRWNxVGNlCKW6id1uKDImqkRdWlv7giG2jAGxvQgmeBQvEo8R1C9sRioyx0uP6LSR35Q0qV0SVyicyhJbGK8jMFiYccKDt/vqlxLihfU0TmkSbM4V1i41SrcpcHChTqMGAlDK0l0BgMFAXeLRl6gkk6vJ0EtiP7x0I/NTVJ36AeXQe3atrSXMxcKCSZi1JimOjPDuODGM7P/WzY0xX2xCsMIw8hpJieKycRwV0R6/CM/RzcMskJh6wUMAoROCn5ADg5MPv50yU0lbDd1YuRvjkChIBS/gQq5WvV1oN5VqbyYpHAJJQAGQOujjnN+2o8lUCLN+XW89yDn6MjLG2NjbcxkFrW8EZusoSPmeYGQeOoSXxwkEVijqlEhjB88DSAtcIhKoiKoeI4AT+eYwWcGxFLZ63UcDPyANdBw0ta5faOw1qebJLopKCEVg8P9BFg4KqQK83myjPZYooP6+omJyEffoE6qPDwocO8vDO8Y2B7t6lDmy9bRYuhDkgZphmv/bLmjPnh09lV8ZQGTQh2HnjySeLzzL7bm1Zk3dXVjZ3gsiEgIkrmoJ6dhyTkzwG8R8EOZTvaxLNqwFUQFEBrZ8FUgVK+eVsBvcgAKKUFq1JwWKrgCbUi9uBUjWtXESFk1QJLmrF8onAulVpzHduPnh6f3n+mp2eDWpGJghYQVM1b0vGfP92wH+lIISQ2el2KSJVrChdt5QhSVC5VVIl27kEkUs1FWz6KcIY6k8AqXfsgc6GwN78kZt6W7czIgFr1wSqDtZzx5QJxeSOXN4RtxbIJTYEkpblZeDs3xlrl0szk+JHTBwql09fc/KyWZBygi9PEt19xlKui6ggFNLVYUaoiRSEVnHuEMfM0cy6dXZH4yIOZiD4GWm7vz97+ho7e3clrX9b24P87+fiX2KgrfuT+yI2v5E3mUJI6TKqjGB0GBwfDGGS48847f+VXfoUXPHHqYF81I1WY/FJg2Rqo35ssm90aJaSxslc41vMHP/jBH4WWgW0H+qWD5kkIH1R0G4ZXSwniA1Ls3TSd1t5RTKPRhThzdWbtncnrb4gPDUX5hC/DLI7H7k69TqO+Mgpy8H2qkIqxx/Fb+MEbjV3d3alX/Ozs/gOdDz/GGJfL5rKFXLyrs/NNb0hsHzx95gw7PPujzmpMXCU33HADiRjkWPCM9YkxB/j3TRFdJO76wSWrChGcfBkRHfngUWSCgDcrkIqmgmUMVgTCEUMqvMrwhwCcDG73RcK4j1A1XmJyZCk16NG38XB72gdUoOZ8+7XEKqtHHy5+6lOxLB+ZY9RiUyhUkIt+477s566MveaXY93s0NZUb4iowGyvWc7gQ0qH8DWYkFu+ZhV/eI0R4HXhrjBKiiCgO+ibt4M991C3HuCMHnz+BZhE/fLaFZHL8/OgZOShXN7vg/MxWZjCAF3MV28owJICzJiA7u66666a2YG6TCiOv5npTYtpFMgo7zMDANctskeGANgLXk05P6b67joJA3o5aLFUHy2Hx3mNxKYbYc0IncayXyhASJp39cO11jqk22ym8TcQI5QWm092rq7dqJaLT0fQqzFyQCKzEgDMeDB5dlrQT9axH5VfJwoiRS9blNSKN4Pa85Dpq11h6MxBiYVybHTi7DQ9aize1dNtkmjZp1Kpt1VaDjO1xc8hJZEGLSQWsXJmSiaRKqnSCKBqoSkxhrPERfEeI3OfEnC2u7oioCyJECGIwsplReAkaiE9izSNWC7cEBMIzAdobzCWSo8+9jg7Vezm9SJZ+wGBSS7xRK+8DMqCjoyDcSvPTk2ePn4YbrhJEX//se9D3923rbVDy9NUNI4gd0miTIlQGe0WYkpjLox0YgcBldsIPQIeqhgJQiwYW6Wz9y+NPWGbpRYwpdRc2ts+qjhPZZmQjbJwGeA0ceY471BpDXQkMjV6MjbOt3qTXZu2cAnw40ATME63dUxNTt4/PH1lb/vWzgSxrmlR2MGo53N2lQh4l1nGlcsHL0TQeDi4yziObYCFgFQbNvbSq+fZAEobCJNfOcfXcliRy7cftbmxJgIAou5NVUDANTjn3L9qL8TKtV7Usm1vD5SYCkJINVv85FYLqAsOTPUbDCZMFRNmDiIWxAB3+TQt7RcVc0aDHGo8Ao9IxYXVjuLTXX2F0Znjp8ZTbcB+n+8WKVWpdEElioEEsbQKW9Vx0+rWJ5o9WmGF/JaHzj1XsxT/aHlm/Ey6tTPBy2J+WEMnN1HaycmS2rKb92/1OWV28Db/LWe+lZHGosSmNLwa5M6HRXBw4Mi1fZhZt2y7MePIjePI1eZVrAwUyqVt9W9smeo6fvxYfnzL1h18e1kc2OAFow470y4DnoF4wU/dSH0XEC8aJCTnjHnJGZukGXuGtnjkO0zcJHbdXnrOa3pS7XoCEqnIjb/U3X9V5pt/npsdiZ15utC35xy3BPybPyjOH/3RH33+85/nC69//dd/3XzCS5Qr0cBFAIB5kr/+9a9/6lOf+sQnPkF79cdg0TKTCkiAhYS11GSSRXmeBwLEBhg88sgjdPfAA4CHg0z6dIDNU089hQyAxlVZMurFQTkMIU8cePLKK65oS8wtA9Zdnnt2b97Uxz5U9Ej0YtWDEvicg27lc5/7HB6kGsCm5OyVsXtv6rd/K8OLDfd/LzM+VuzvT7z8pYlrrj9TLh89fJgM6MgYq0RcOeiYQEec0QPF5Jt4EPjGtigHDXDGSEIb3MIPDCBkwTMAhrXxKI3uDHwLAXNs2IK40IGUX/jCF4aGhgC3WP+MkagUryN2LWiHFgI2rmRuw4ZdoBY44P85R7xwiDg7XfzW/aVZvhs1Y3G2oolPKjFJfs+95Z/52XK3vlUYsm0QgP/g4CCF8g6aMMSOwGtSUVik8k0Uam5pPzRDp7wlRYNfqsmOuvD0MjtQs5oABdLYyAttUx1U97IfJUoH+iU5iIXCcsAcZ/I56q0p1VIuEY/hDfnd87ZoUijDRaqk9dbbZNpFma9nAh4BNB92IATuuOOOmnpfb/LTWlyk6uZHy6m+XDuZV5KLPz6rIhu29wy+ISxPY6ezhTgVeYDxbmEdZkulmUJiHJcc9pmW5xkVd2Qj88OfrPZKWgEC/UFmt/3SKYVhZOVaSkuBBAFDRzjOB0s+dbLUO3qy1Dpi4oXA0pnCzeqPtPxOjo9igG7esp1oj0EWy1zo6eknDnFXITI8KsgXT6b2XHG1SYiQKo7IJY4WyurXZdSPSmGQSuIF/4IQa3dFbIIAeIyVYK0iiBcCs+W+ovFICQEfvWOoDe8V9r8KgciIwb/nrIPclVxC8V+5iIlzisROFjtnpjL50yW+FWT5iqFKIT5ia/8U5hBbDhMwNxudKKahLOoVyViypU1E+WSGT6xaFtDxAT+hUiUGYSgpVzUBXpu09zHFOlCpBQXqlJMEkAKdgXx3ouMmwiCouJp+tTeGbhgPeBJvox2xniln31QQTqVcppidpWaSfGEmP4WTM55vieU3jI2e6O7rjydSUkIk2tPdzSKCg6enfnCqpa9Tyyckjw7JZn/W3oJ4hZHq6MjIyZFThjNLB08cOXbsOBB019CeaCJZPJUB327b2co0fqHvcrbdOnP61LFTR/dcuZel3aP58vBBaE3ztkgYUS1HxZCQMGcHtLjFhVvNT6u1x8TL/et3hWZhLhGlEDUq911zYQWwLMRdxZQ6FSFVefmkPdOynPpQ2DV3GYz6+jadOHmSLT039mKAKZU0AYmpzGrGcnAOdg6qXERzepNoIHYK4IdXlqWvROjXE1hK7iFfaeLEwQ3bdra16nU1YC37S+lPXlz+iGHXbkFfZaXyIJjkR0q+LcT2y7YxFW/hAmsFdAG3hO09XvgpOxPEReDsaqFEdkcCRVmvx7DOCxEYihhF5IRxiP3GZZisyQAmNIggJPYwNkkYUzcgRZQjucniyaPZm38tddlt7dZYEFyF5bzt2S0vHox/47+fPfhAedNuZgrQwjkGbV228yPhx1JEvu366le/+rWvfS3LoU2t8wkvxaymBtY7AOapp+G++c1vvvXWW1/zmtcsyVzGuUf7Bg+spsLWnheADSMAyIdtytMOnONJoPMChKAKwg51Vi6IelQ/+OUx1xVdj3paf/bUPSmCgfDcnkpx6gHsVydnVR0ThDXjvTdGb/UzPxtjO2hhyq5T45PJRPzaa68jlcN70BFhn5+jjBScRciODOn1wkqH5qtf/SqcAa7gQOqXGCZ6Sc7MCJiWW2AqIO5NN92Ei+/lL3/5V77yFXo64DQmPh5UXKPcBQOwVxb6RHI0TL7f/va3mWsADLsDkGYDkCbryosilYJ6idFPsRAdn+TbUy3JJN9Sor4AFWneeWb6eXo6Zu/NVtIs8ovMSO5ESEInGJY3TEkxkRkXN3geTB7oNrxdqQvIwrj5NOGtugFA4NwAWZdi6ZHIA1vXM2FsdBQFG/TMeX4xl55DkALmzGKQEbXfZMHJnfolIQGSUH1NJly2kOshIeWlwdO2Q+WjhKGhofUgWwMZaPzMfPGiL9UUktkWqeHVegysbovi2X7oeGmmyIYv4WNu/VG5fObkqTMjLGHgEhs3W37sDJb94N596da2Ci1J3ByXopQ++O8cuA7u0oUEQ4EIRQYrcfZcLWcnoC0ZhazxU5G+2fHZtpHALHRapXYSc5F5ePREdnpqehwPn7MlX6N20Jvp2FtsZf1o9uTRpzdu3a59jKKR/aeBr2bzi1JshMStBCZbMPoYQxVDNIjsWZuIJogXUGURiaWXkS9yXQc/IhWl/jwr0WLR2n0rA8y5MLF1z28TJ258HoZVTkw9CGia1sTJssFZmOrKTuVPjZ3t7OzSXRtAsaPNmjZGhpwUz20d8JR3MZ3s6uhip6ny2NFD7IjUO4APGfQVlNhkFepSAs4GpxW2f2RrcVywJNqDokQmH8DJjkWqytNFtpsIp1+BO2cojavMAl+qDYv2QgekilHW3FIu5C7O5fLG/kESFXPZkYP7+wf2pFo7mG4oZLKFmalEtD8OFLI0vPvZ2dp2dnr2sZO53ZskEXhNOBPA6S5Zm2zAByuHKoMuopVjs7MbsjE+bhQ7NnKovXOo79pryXpkbAx0xstjSDJSiEdGtfichGMTsZGpeHrCX5AWfhaIFaCVLsmIH8ltbV61olZmuBWmtg5cN0XBv/BwRUlh0g6UpkyoCKvValNMboR1qmjgNGdjBqEoSUduEJkydEkku5VsjpT51IVmN4yH5aokEo//JlDAQBSuS2fJOTiUJcoyETwtaxIoEM0an608rrht2WhKjlxgLRM0hnX52vep6NbeWBeLbIyDsuVVNnZgFgDmPblSCkeutl8G7oJvtRuzviTEkApbCWcakeTNAA3lEpbFRccUpJ9nER8z+9ddJ4sxKBKiVMLSbRMHlhUHhDgSOLsjIWTSgAFNgwVwL3lnT2c/kN+Ru5FLn3pm2jZG735rz9OPTDJXZjt+NWDW6BbMX/nKV/75n/8530Pi24Q1b941Snnp3nI10Ey7XC7vVUr3kY98BF/fN7/5zSb9M96mq1u2h5t8TlZJ6uWwQU6MeNbiYqHikkJgc+xp3enyPHuLCuHrfkE+5IUTlScQExOf6qIJERXsirTgVbAlZ8AbDli6mFo9w7SrJ9LZferIkenpmZZieWz0zMD27WREclY+hzXFe87kCxocGhoCLAFrwTM+RRfSkIoYjHhwL94VFPW1r32NPWwJIANW8vDwMMXBrOcMB9AvWJfODjwMRgInv+IVr2DxJGsFQbywhRXA2KcDeTGSW8gAQoNhbUGqlFJOJcv9ffTyDEwwgZIDh7dItvQV02zQ0FSnXMVSo4R1r9Vxc2HKRUZ13b9zRKz42rCBslfHNA6johD6ohMrR53vfqHqZcy5kjXV5K/awpkJCw4iWdaOH5ijsWxLukvFkcUyfNQMsdWwakmZXnTEqIjlEohN4CISnmdZHs6KxXPRyb8qqsb0e2qseCZj4MM5mkJQSy7TlbXOZ2rsVCLF2yxdEOdGE7G4AEzFqna0qJRKZw4Muwh42Y8xV9swG1/2NNG1TcVseygqCcvRbMdAtL08PE5yJyYgC14IwjkIydgi2MnyzHQkOwYkQQReifT3S4MsylHNSRVzkZPTkQIfzY0xeU3xvOI5m0mtLMyWt/z9HkF3oCnX8LALUfO/QmcNX3iPIzjbr9EYrnAOPCF2G4NXlJYp2ZvYyIFqpEGPEYXCbCgwferI09uGLo+wPtJiVXpuKn2ULykAaKdnsu0dEsiYiRO8PDlBy9xYE+bC8Jdlyk1pixWkemmWWywtFWuYKxOArn6ME1KqsiEiZAq2UgjQWQV5poRJBUk5DzG3tBRZIf0pIlCBcgPPWAx5CNWaLEosHjog4b7QgGY2JTn/uWRE5CoRLedz0VJ7dGtnNNVWPDpeyGUzOW26kU36rsDYdjEWB/XMzE4Pn5h8ZLO+Ka9SAHr1J3SI8Pp4kiIjM2czR55+cvvuq6LJtmK0hbsHpyI98db2tJYXjbBXdrR8pgXj1kSUfiVjPt+RbR04PIUehEzhTKydTG6uVAhF2K+lsSasxPbfiqX5EKlG952zSWjgXzhVAdM9JzAVr2YAAEAASURBVCfTtaCjuMha4Jcbpkqgp3goT5NErFQN+tqPKNo7NfFR4JoLO3m2VIoJYTYDPHXbqttqIRor4oGnIlicDFg1WKvtoXDMcuaWcK8J52IZM6TiIz9y3o6dGsnOTIKPt3YU27PH46dHQLZXXr6nJamX5EjrWNrLY7pBfj9cK4QVY42C+2FkQNT8DzKyMA0HBqYdQwAWJmMBCvH32jCPGfGb4eb1IJFMyyq6SeXnxhxAuZu2qyNamIwZ9NjQDTiTVQcLky1+B3k+/OEP80YermC+D7x4gksUK9PAugbANFYM6Pe97318/QijuZnGijbAM+5LxLIHpIFzSDg0NLTCDYFWpudmUzMQ8DY8aK0a7ftDC4smNdBsZhG9F+FOObIACpLQvXPNcADM4FCFErck2An4CpbwObYwOQKLs3kvWWQLqoR4DJ92jFeJtSEz21xDjJcbdMoSZcLwBO2ziLe61Dg/IQDkULk0CbKjNxwcHAS6IDZOXdCvm8ikoqKhBCfzkSQEoA1wi1U0ZMc2UcB7BzygTe4iD0W+/vrrmQugwYAw4VCt/LAsYYB+P9bWnrzl1tn/9dno6AjTx9Dz0UDmWAuxUvQFPx5j0KKywgTNBVBdjfaq0yEkiJEev0Eb4FaT40HIGWXygHBJWmZACFAUtBESeEAFXNZnZpjO8CUYZOR+dc+iQSlqsm7mkkGRR57WxdEMfTXN8oB9NYeLKIzamQO6WATmSfRZKp53wviBaYTYQDzIq9t+LgqF0J9MZMrHzwZmbti/mBnaFmnhiy/l0eyZVLK1q6UPizbjO8rL4DOjr1JIqW6ueyKRMRA7yNz2FinXbmNX0smwU1JZ4TrEJ+jj8ArhhYvMasN4ZWe1g0Vu7ktRAtEzOABJoYRs7vrko9B09m7t7O0TU2MZCIVpnuAL8R2su8TaDmzogMLSE3YJ+PGkViBceR7hWSgnSyXUZ4QQQ2iXlsCKQ84B04Bc6Ebs7aRf+09SFd1+iLEkVsAwsZW5FIvjDeNTf/FgOxwBT5ouucAz0ZJsTcdzmZl4lN2XlYEyCn6UXkG4epZcqUSGiyq58F4oc645vcuqwyglDIBESEYHkfC2u9JdwA7GlmEQrx+rJiKhQUpSGiLiV65CIRwxcrchwzVq4cusUo7dVV6OoCqUoidb/gdAfE6LUhubdWwsbdjUGzv89A+nT+ZHZiTByYPDFJmNF/oGd/OV4GiiJZVuK0VbfjACyDF54WjtU5qxQhEgNDsTPTVdjk/pOzrKs1SeyqQj2Xhea8Ij2UQnUs7w2q9XolcxCorhSk/n2S5M8wPChxJYIuugmtA4+FP/fLIoVy6lNGsgAv5b3py1BZkDY09JkWGCJtGFcYPQyMVSU+TkIJbKwJSkjBCNhQ2+dNrzRxJuw8PKZIzsBoh/dGysq6uzNQ0OlfcVyW3tcYSZBRC0YgTBNPUglKv2oFZMFjapIdMECUC2Wsaslcklnqwke03httWSZnlx5dHl07gCt+XjySgvUyPNzEyM1Vo28pc3tsbq2QAVFbioc2fF+725uGWFyBRASFJsP87YxthpDAGElzTXH2Zedw1deLcm4JVSE1lz6Q2/ErmiQsMKk/htb3vbH//xH7MWenBw0JhXeF/6XW0NrHcA/Nu//dvAKuZCmm8H6mbOPebHnHt/XVyFQlJejhqZABLLe9pr+NRcOuYEA7ORMquOl7RcnOXByExa5ufwV4PQ6jowwaIQAK5AthADR6lK+iB6MYTxjgwDl7DnzgpwsDRIBizqd7kFE3aKAkTBAahDcrpFYDPeRX+Zk6zZsYb+ghIBZekrQblgZkAXYdynMPF3XMHABJAWfy/yIwxpsaoJcBCActGDF3SiLNJ+0+uyH/pI+uxUS1sr41s+ESu8/D+k7rojtqydhMl9oXy5Rbfo0i5EE7afBnzmp8XHDiICQFJHuHmpgiUln8+wOgZW1BQHsjFdgsJXa/V+dS6EaQk0p5o3n8mU1sLcB/kuVKiF4mv4PzMuUQjaoLVzXBQFR2CeXNaJoH8ExglAgNqkpyJAf0K/wQO+1mVBjLXOoskG1oENmpFlK4UIK1XZmAYY6MtwpdE5iUKGvKxY5J/j78HgdUOLNnaVaNIpmSfHjhYQ8YysCmS5K5HMQjbx8Qu75oStL1Ahq1s3xMNAg1IADlq6e4nOZaYL+Wxbl176iCdbC4FvS6SOeEgWjSc379xNTBGgKDNevKgCK7dADUjMIiVGKAP0yolDkUGw+seEl8NtLpFAkRdJhBav7HTYD3r0SyeyNbFB8YzCBFDC0vjp41GW5dJYS8XxU76WqqVjQy8UMBGsgXsk2t3VxghYLmY1ZBBlcNHlseKLK5ceY0ntEt+sjki0g/4ZuOUAlVtCPgZfgxj8tNKTqYo89Zxb2gRxQmnGTbGU21AbxdWVqzaoOElKKZVRcBayEme8ziAuP8uva1mwbrbskB4aoyyxWW/lFkVX6fmMa3TPIJV3orOvoyv7rUMsK51o6+zhBlslQeDa4AsDhKb1VSUVE4EEEv2mCTk9Pko7KxVButGZqVHMgFgy1d7Zs3Pf5dIcOLMc2dC3RWhTUx02fSCl6tBzrDe6uVKR9aavFK1Yxdh/lIHA5SR3om3fP1va25rtZB23niE76Wmydi9W9k88tJ5BbF0EhU25AsZShLHWqn+yUMskxglUfYKsgZ/W9MkiZClZC5JRNUqemjo7fWS4q6V3304tfZewUgwB6NG8QCxVoO2mDNBqDyptuay3dkHMIFv5bAO3reC8CSQm4hQc8KvwjkQHtm3jjzssxMUqWN21WpUcl/BLJ4/a3ZDAkuQJWnQp3ELcqd91PgWMhOz4++lPf5rz3/zN33C5UFkuxa9cA02Z+yvPZhkcaPHf+ta32A/tox/9aAPP2HzOtG8OkgPqeHJ8G9v5ZOsqBmk5gHB48HBR1jR6LtfCTxXmMj+wqHJI4igd7AQxTtf5oD1kQi1wYK2CY8GlFJDtHKtnE4Gdjn5x57IoGkwLMHO3jzOhC0Y/g4ODfImHrLnLDkbE/Nu//RsoF/evKw0fES4jIC63sI+xlcnxlltuAVQDIG+//XbUCLRmNgEkAGiHObn4zAICuKkdit0ggM95Znam94670v3b8l+/p3D0WLF7Q/K2m1M3PCe+RUujG6Rd6i3XMKX2Y6HklHd4eBgVuSYXIquJd5epT0aQkGoilxqa9X9JhT7/+c+nRdUIz0hJTfHu6PkpAu2H5odKfT7l/GTafC6Ix/sCPGJssLGkRtJ8Fo0peSp57mSzYtk1cVCbPKSk4iFlAmVoaIi04RQVq1do8OzSXN2TNMG1WRIsv5/6qZ9qUtRmma6MriMZSWKHB91L+JyaIaur6MDQHjfnpV9i/E6lO5LVHJq9gfUbWL26CmLsGULpQppVh18EeQYXMuY5+J5cqXDkyLG21ta+vsp7DWboG8IQGmNLrq6+ARDD5OkT5elyVx8fTJInLcAQlleAwkxwMUUiW/gqGotU5oEAwmbGXDGCIpArgd3XlZWiQs8d/VnPJjpjYieT3u4GeRi0IFaHcXMyZ29RijX2asTEm3stWjo2BiwDkETbeMN1lNStia5tPWjDoI4oBVd2tHeUBtpaW/jOC9CPPAEwkstEszNwCOynXBWrLCwkeQxy6Y4U6pUpJqxxVr6G3MRKflpiBKtwD0o8QVb33wYAFXrrK0FfEsxnf8XKFscaK0Fuqo08XQMquwkjSSDUhZ/8vr3+o3sc1WGPCc7U2cCWLc/rLh3OzcxMTfT2bYmntHZAivA/KtsOrArNeylPa2VqKkKt46dOloo5JCP7MyeOE5dqbYu3dFAO5YHQQum8cuoeakUZZzExxhQIN6+MFuPNfQJeBC2AIA/xKUT5SFHb/th0rFDg+83842B2SWqn7FZyYW0OEqBMacT+fF5Au0NBxBeviATE2sJjEaBwA71qPNQNtQMHnSWEVG3QlxkEm1bgA7lMLmzsjI/FpyYO7Uht6u7pZIkB3AC36kaVC/nMwVrpxf5JtDpHddV4qUOimkupy+8ph3Vw4P/At8EbbU1KhfxYhkyOs8pvjcaINdIKJsQf/MEf/PIv//K///u/33XXXetE/2tU2AvLdv0CYMw1PvwLbmFDoyXpqLq5eLg6ZkmszicxjzeTWzyoYLnzmS95YVMCvH1h8FKzRmCM6QYahjMHnRGvbQBU8AE68qxOYgBZ2/HxsjeUvr0N695f9apX8VU04pnFAOCBZ7CDIQZsY/7SO3CLAAdrpP1VZNQI6IU5NPh+SQUNSXAmg9JdVO565445jivYu9QlOSfRVSaTw/2Uvu5ZqX1XxJgFwPHS3hHhS1Hkt3oHcoLw2RWcVwCY1mnMGJzgaLkxWd27tIE1tfXZwaK6xuvKsOxIOFP7YXKvXC4J+OG3VkUAlMywymz0fG50WUAyVkZUCxNKdcEDtHacqEi+FmtJmiwdncDg4GCTLgU0jLRh3TGfheRUKDFerV4FHkPk/BppUqq6ZPRsHHVvXZBIOpZ0QnvPurHsMlT3NoTdOueWDHyzrN3OtyvuKk4Gv349LGvZD/8NrmTyutmrCCOqwISAwhNhsuu6HEmkE8VEOdvZAuzSIcBCQk5ce4aWcyxd5sNxPS3Y/3Zb6EwhIT2XTGeAiDjr0E2x4BKYwT1QhFLaLSOTeJA4qfMxniaZxeuu2AgN6je4JFKFEokORyMisyvdUF4mlf0apSI4rHRBQDz3brgSobNnp44cPLjn8n0JtnmEgd6WFRNlLYgGHNJme7bplFZB219AoHjhKAlJEvtz3yl3iPGwE0tiwK0Y258kqyBVScO/QE6J2PBQQcRDv642J7dwyEZ3rdx+04LI4FdLO0cj3anoQFf5sUIhkwOW62vFFNAykKMym5kdGTnVt6mX551YCulATI98KdI/uJuXQLOZmdMnDm7ZsTsWT5oiYuaDVbUgE6Ox/TKjYs1QNasickaNQEzqQsUzJ7NIlbscwZBXNB9JnSmmx4uxw+X2W1JEOqAN5hS0CFkLlTnTQQBElbKiDH49yNm4GTyWP9aBK+d4io2mBGKL5p6FAyuTyQLYLD3gyjWVio2z2nTdzvvuu2/y6P59AzdJ7aLiv9czQZXGj2VWSiV5zS+2aLgKr+bW+b/EnMMWwhnGCr4mc8f8w6GC+beuevJFhaeBYvry1Zt3vOMdbH4UzvkumvASwVI1sE4BMH3d3//93+MBZg3Asu1y/IQX0NRbUk1gOvOKLE8p2G91u7BmxADOhfNqzdBX04AqWXXcQGa/5UZqCHRr6P0SZx3wlQ7Osagn8bwgoBfAwYt+sOPplx3sMbfHwICDd9CsatZOM4lAL0layDgIYHPTDFg4jZKBu6QFkDtb8ABsiaSXR7bqcjUIIwxAemhoEBpWDnMZ9sgakXzI4t5qHMhPFhQWPdflBwELfcG9oDIk4Q0ZyHhkwP9c1k0yPxIlAFDnx69WDJIsewSigC5G3eJ48ZkBYW4lzAJV+IZqjH84t33XRwiaxF2NS81+aRwhMIM4lNAT+qWf68rcmP+q3w3FI0BDoldEURdKvFCYJRWT9kwf5cqEA7MMnP3pJgwrnl8ctkviuSgxjYcni+eI3mM91CP2bl9bbFvXXAcT9DUGHqo6HoEuf/LtbL2AQo4Z58rtqMOvKWAllUIK89xZqpCzaIQWlEInuxvgLn5GstnM5L6NA6ABu2t0gheQKmsHn6XeLeXSZjRKeqPT460cjVCiQ8xePJ5atwR6+RWZ3YZYYYuV4W+cjRWRLJdVXhxgD90zPKmgXos09CgCMoMXNPojiYFJjzEa4ZkiqcRIgESRcOekpaSKlgBEKiDUBh/JfHYyeSAfvWYomUqx/ZDoK4eJL3Kt3x0dHW9lCwytgoYmoLKsKuTL+a3KbQnJK6kqv/WTNr5blSZ8wOsWxyL5KE758q0tj+++osB0iC1aJr6Q5aOzJzZu2cEmXzO58snRqThfewpmRlQ1HNJzS4uhylJbItrZ1hpPpK1pSIn80VasRjSPYNCUJKomq0ESU30er0i4MeMgT7hqVJUo3Kl5CriUZh7LZ2hNxwvb2DS6G7groG3v3OJ6JTnwVQuVHcrae7ksXQbK6mVaOwfv01pTgacagGnRwKplZZC8SncLBFFp3+bNm/r6jp84MTE+oeWQYmTMPElVcAEey4lGMUvyCiwnj6bTIAx2JvYhNpt7UBon9XYYtkYPwKRxqvVzlyHvAx/4AF8o/Ku/+itcwReR5OtHh81Ish4BMI0VO573fl/4wheyHw9r9vDpURhQB66VJpsCZJi8zajggtNQXsw4jC0WBmNvXUB5kATAgH3MsagYITHP6qKVAgEYmxpcCJjBjbd8OaOHz3zmM9CDV9m5CjCDSD/5kz+JjQvq4OtQ4BmMXdY5ww2zGFFZMYKRin+YACY+8bQZMCGC0VeiVQAebIGpeJI5fFKTvLyMJCTQPACG2MsLBw5fP7yoBhbVZ10CxMa5DXJjOnyhLEDIkCEJBKgFPmiDD+jVZVg3ciHOdYnPcyRV+cQTT7CMeaEpAJZF8bIDmxsD7Vw2qh6FECaAWlw5K/TKwseZE/Cwnz2SgZlcPDumYKgymmUz47QnX+szj4zvHUJbZUkYjxUthOdirfN1/iiKTH1qhhjfso4mx/sITTY8Gr9/tJnkcPMKRdtcekXTD3heq3imEh944AGWWNe8W76KWSyJFUhhS0d5nz7kYf1PgJyCZqlIjOyKgedmtxBBYHa7zWwIMkgfdGKkIKGlFStnoOQVY9EDwFdwo9nxzkpIAWJ1OkKSfAqpdPLk6J7uXEuLvgZnDGXmGxuIPGt9iUe4w96m9Rxs8BAg8dy57cT6lfwCn1YM4R9lJkK4hGUhphJnt3TXEtqV37I4xTd/VI+AEsOOCgN+w7jKPX5bU/FNvd3aTCjMKqBUQkvM12hHv3nfffsuv5zVbZ5UpXqmHMxFMh/NPHVjS2bPpsT2jdGRDHUrBfE/VyhO5ya706VEMpXtjk1Pj3cnNrayaRioUnMQ1L7aEm0AIJpviXbn2rb1RpJJx7fWMAR0hV9pG4G2zQdsytWUBxwEjAnotVgC4FXNfTC1piXH2l8K+ArKhbZ4z/5cCa9tPrn59My+Z3VSoVZL3urE0tjWVJu3CS+T3ZqrWR6guRZTL20Nq7lLMqbTA/7hAsVGes5znvNMajBz5VwshAHA6ip2SGU4QxuNyVlL6DOk6Ar7AWKsvqGhocap1s9dxGbp6y/8wi+85z3v+bmf+zmE/9Gs9LWukXUKgD/2sY9h17ITGtYSKsC+5MwS0BVasWutzWXwp2vD1cAjih8Sk/SCt3KMYyBi9cwfEoblqhaPeIhBHdXEIWVNgIR4a2siay7p1ECwX/ziF3lT0TNlCcCXv/xlACpwgm0PALdgWtAOLjhaAlYvYQxrBGBalEkE7GwAMx0f2TESw58YBgyERMnY0O4mgg/tiiycmHGFJIBMqoAk1WWskXD+ZehMnn9r5TFI6J7M0MNclyeFYrIAbMNjQphSoKW6lBdpJHpoUCkAFcpb3boYKR0voRAaAJMmKy84TcUnF7zN+JgaepXdW0guyAkB52amkFYuVZMcmADi4IlAk/7gcNlk2lUh4xHmmXVWBLhERfTnTWqpuvapa5/cgQ+PPJW7Rq09zDQMrIoqls0E6317V3Kgm97Y8V3Fiq79rVjhFbomc7QFoW6wBx2+MQ57f+tS5iC2uANdeP+UH1RU3pyMjU1vTU5v2rCEtSdNylaPrFLsevdcuPp3lhNbL696ca2trAwaIodz2sy5lHQabFPGiiQA8DlkyxFsPaZp3F27xCyBv25r7NgUPtkyO4fRoHOt5Y6Z6GBPOZkqbo60Hhg+3pM/vmvbrmCKRfMgLFAO/LrxWEusfy+oGAQrHCsoy3x3gGO51CbJiuSQt5YAlzRux6aVCiFC6wsAtRbvtabGP3G0MH3UPpscjZz4bunqu4ssQbPKqiStr/iqu1XBgHYODNdP3CCWrLFPmFZwA6YB5TP4FkrACGQUZhYAi4hRvsHjw8jCXewiFMJgR7jJsWb9KBCZf//3fx9L5k//9E/ZDHj9CPZMkuS8mkHNKI7eE5sV6Iv7lybOO58gHPw/uPX84Yegmk+DZ6CarPkwQALfQvWyt+ocVz07BMO/DQCjjGvhx2i+4A0oMfdRiHtCQg0Q8LCfGyRv5hZMQL/s74pZ/NM//dMk+a3f+q2XvvSlOGbZ6YomwcwfMoBdcQliymND+77QDHF4gUDC/sUULqk+NOkuUy6x+EFHgGQO2KJqIBOg1w+XDaQEJCYJtdB46rq6LMhMs6yOaT4cNqoG2qMgAGBKTctfiIx4rYmyzwNQCpzkqLF5MdYtJfoB4XB2zyENQBgukaA2q1VBbUKGBqqfHScgLYcXsDrJ8opMXSAJfBhTYethcndu7uyl62C9PWNz9QLp5WW3uql8AoWJc+Rnls13UV7dLBpzo+vwlyzYxI6Ar7bg2Wycqu7d+VU5P6ZuwmdAJKZ8AO3mm9cUL4is3FPbr4TPDSq+8mgIFlh4AdLQbHfsW0Ulhc7pvoMdECIROufqqSiR/Cgdc+pYuNR0YmBgRh+6C/r2hQkvpjt0iXTRdJJMBSI3YTrk+d11WCT8uNu7ix0pbc31+KM/LBfzrbHI5RuisfH90VhpsJ0PKs6Wpg/cvG1nKgF6VSPl4GS/lV69unnLN+z9vTdRaBzthnlaQDeNjbX5YqE0NcLXKMxn7OmM6sl7ZxNFPtqsY+Tx4uiBQqJNUKrqiMZbyh0bWXhCvlUpqyhWPbg8BwlKoVIYtrBVzpuoq152Z0iLwk3y4IMPggsaL7DCV0wSbEJs16GhoXX7oHmTRVQCdAjYtxzYFYyYVBZis/jxgx/84K/+6q9ivl7s1bdGrWIlbNcdAKYwH/rQh7Av3/CGNzBnwzjBp25oBzR9zDhaAJcPPfQQD7MfwBUOLCrMKdoQh7cSP3Pp2mm+6TA1SxY1K0jBIRi1S7VrKQUGgb952EAAyoVZv1TmK6n1mrRoCfjHg0e8h92yZ42iT5sRyYG/lxdokTMkZsCji/FptpC4hnkzl3TQAFpfEolxwEEMnHnfg44A+EcWrAhAn0yCMiWGSukEwau0AcAPgBYyujwEY+ilcwQEstUtt0hFEyIJYaSlONQIVYzjiDeKKRExMKGBObLy8jYj8wppEIZGRafm67HrckMnFHlwcJDqQLAGTYjkVFBjgrpZrOdItq9gSHAJCRNAV1RTKDNF5lFFkwttD0Zbda9+mGTZgQZe5Wq1E/ZLPy87u1VM6JKgK5AJzZ5n5zzLRnY+PYEMlIuefFUqhW6flR3LQ9GrqN7zxmrJtdbYLK+22qvDC5bH2C3Ms3dTL2tt6GYXZHDpRkUDdPuMQRw04CVXa4XJevuli2bxi0vFsEuAgbW6uw4FprnRE/S1xfts9rhz31bGd9IyUm/duoUnmuGYjbKALtPjZzorG3aEyQ3BVl3NBat1WWmpld85Kg9Zm2fF9Mj+3P1/k4vMJEuCsr6xM1/ojUdixUiJMbcczbR88fey+jCwrawmNTtVde3NPu/1jESBeVnLfA2uKRv9J4Md1g7mLuNR85mQBJ9BuOSehLDC3kPVjf2ozWdx3iiZP8W6w0RsnKOrCxpvE9Uto3HCtbjrA59zJoxxi8+GNu9zhdirvDVAdYTWDsMlBi301M7rXve6r371q3gEeSX4wpZiLTRzwXmuLwBM4+ANuo985CNvfvOb+QQF9Y2BS/sAGtE43Naho6QBgV4ghoAz8UyT0GkSiQeGbtdAMdtMtGFpwRAEVd0RL6MZ4WWClWNUb7vNLMqnTfN+JjaBSz6/shHe5V/0eZ6fdnVjwI0MP84T5AmSJAzCRNVICADjzOZSPJDoti5x8wsaaySHM0AXBy+fOwdg+10EoMqoehAgYeAf4tFlIAn9OMtgwLGEEQapuEvzQCoiMa85aCTsnseCGZxdVPfnP/95Zk/x7vqrI8TQxWCFwBlixgb0jy1SI9iaXlJq2jYjWQMATP941113US42SONl+MbyUDX+vfjGZBfRXaoPLfG4YVox3UtN+fQEkRz+FDO0E6Yq5z/UxNCEOFalyCH/+YGQP+LRilYF3YU8VyuA2DzOvFa0WgpZnmDkTj0uL21NKu/kayIvXV4oDVAdvDtzoXK/iPLlSWSs5EzfdZ4HnTXVEl00XTHjNaMzYUZY767rZeqdaIAese6gwepgQEQz9J/wAeSgnLX2CmCY7b2rc8uezNc/kZl4yt4CdqF4Rx0M7PgWDFxik2awlE3gxYv7XlZ+9n/ojaUqBPVKuCpx6KGyoXuwWAZr9jv3f2fvHhkzysIVuUBmlvycex7jiZikQNW+OOgcotW+IFMOMvV8V8ieRuUupZBtA4Z0SoODg6s14jTIiFvIExIQxljlWeDsbZjVeTwXGDMcREIJPMFJg04ghpIGT0x4OCtKCrT5zd/8zT/8wz8EE7lbOMzlUmDlGlgdW2TlcjgHmgJTHTyWVLmDRloAB3fDh2fr1q20CcCPNybOtB73MBAAqdKNVrdFjGOABLe+973vOX7jqSDAgUXI4+TEMGRAIiNAFA0U6EWYRgmNE3Dm8Bk4kG31dJoLH54hC8Oh2B4ZXkJADDgNxA428zWBYarzH2BezQdjnKJo2HWOMsEeYGCUg0h0vuBM9IYV64tyq4mX2suEWoI5ABi21dYAvQbzINxizeS3v/1t8qVVMEYya45UBOgpqAVQMTqkvohBTuqLimbGgUrnLsmJoT2QhAICdMkIVtQC5aJE1DUFYfoNJufHeqPUXnAaEgIgjLdeWnt12+CWk6EHysXlokdN8kXp1zkBxaHuQiGpbhqAXzJtwep3tq2myaE94tfaTgrFIEDjZ66tOiYM05nQnMJLDyAhLZBnamFzsCbFmlxauysz0XNh2wl93ZoUbw2YorELq6s1KNPaskRjdMW080t6a6xo+hB6rSY79sas1sndsLsmwEF3zYC+mGwLOWdl7JGcw3stLhdjtcz7rMRmoO3enn7xb6e++3dTT3yJrw+z3zPctJkWVmfAV7/IEkv3Zm99fXrb1S1+R8mXdOiN45BpdagOF/JTrsEdWQsk2Lxlc7q1ffjpQ7t2702zxpw3mxc+GHqw2biPkexWFmGGgJrh0jNaOyWzUwNZMJ29sKRLvoMNgNnGOk0ayUKSE89I7abskjNYIIHrym8SdvuNS1cpa/oAuqidg1sef+eddxLAwmRBH089pj5PBxgk9HxgedYYn2GJCHD8+q//+p/92Z/xZeCPf/zjnvWl82ppYB0BYNoTsPOTn/wkL3yHgJDqrymqx4B4ORhIqlsk04d8NpamBvgBEeEQpiECLWiLQCD6gnBs9lTPf/7zabjcZbMl2DIlxgNDG+VgcMK8hhW5IAB3kY3GTSDsPsKsXaRQTuJZvcOZTsfD3KK5g8FCGg/QNcCTRzQsbw3BWl+GRUAzlNcvCbuJT1+J2IBGttTjLnNplJQ3liHgs7SO2TgvhAcaC890A8CAJc30GuiZCYWwdkjotUCAyQvyJWAVnvR+HI8oPQgzx9hb5E7VAGWHhoboRwDMYEvmNcHGVCLqpfenXAQoC/2mq5oAkbQfkAmSwI0s1uIINQxzbye8iknj9MvvfOc7tFUWb6PP6lZEKjzboH3APAQuP0koAvJXU66FzOuKJw2MxlAtUrVKcWnSJKCpJljTMDNiHM1nQdum51n2+ojmM1qUkv3kaPC33XbbopRrRHBxtVu6FwYIt8IvLsnXqPoWZcswipHH9PQldTXWFfq5++67GVJDMqbWDezUToOGBBdFgJ6ZgxF5GQ2A6U7eVKrWCeM4gzhnnsRlMGxSY3BmpivWErnx1V39V83e9xfZwniS991r7E6g67Znz9zyf/SkN+D4tZvBDlpV+Vg0GiDKd9Wq3AuhrNx9Kgs0EBNdjmaLJRZe53Ezl8qFUpQPfRfkZ4nkCQOxSh4fKepuhJjxjsufevrpifuP33pl/2UbEu5arWR0zi/WEcvHPIp8PQxrHlIuHaRh3UHAvO3amUDnyLRKF9jzWHTIzEs9q8Ryjo3XINcEsC4wI9EVLdPf72DyHRuYGOLRMDQM7j6qYqrRYmn/2GkMHIgXTuJjkXLUGCreqhdt29gbb3/724FF73znO7FyF6WfK8ml0GIamOuCF6M8H/dZ5k7TeeMb39h8HVdTEgZE0exofCAcTE8aHJE8KpxZPwDcIjJ0HTMTQ6mIgZimDMSiNdO4QVPsPw704jHDaqTp09axwqGEBjwGf7ArXQk0AJLwgYGby8MtDo+HM/Ee9jM0BOBG74O0vtz/fOi3Xh7AP8ob7iiAiUwZER4hCVB2Hjk0RvGZKWAoooK4hWIpAk9mDTipl0P9OLKgc+EMICQXR7nzSeHPumj07MR4cYlBpWgP1dGncwYochdlgn6Z9eAWCNOVzLITeMIcUZmDhIxuiHXpjLUwoVxUPcWExocHSueX8yWpjvF6JIZcquPrhmlFSBXWMhMoFAEOiI1UtCVKV8OHu2zrxbvut956K/JwSbOEube3urk8UyN5SH2QQwmMLpwZeygssyc0QgYb9Ol1TYVWz6FcWIUgpwswP1BT1+dBTmTgoaAT40E7D9k9M7KgaXE8M8pyfkpBT+XNjD7t/Dfy81PGVckF5fgow4PJ4brKTOdb2tIALeZz6dN8nF2V7M4nEwZccMIyHhzvzKtFpT9n8Rf9f903XKopVxQW2NWXtvjM2MCN6Rd0x774e3l9pevcgb13b+F5b+mJsbu0KA26lvROMNgVOw8MC3BVGJhaNrAqQBslrBiDshDwzvOGeCk6MZaYmlCa9o5i14Yz0dTweLSgFdcxEtkrxrxlrNeR+QcSJj+mRwwYYzdGCq1bJ8uTjxw927ultL27rIXYNbJW1EET4ituXLHeLXwHmDHU39Cm4aFhd1QC4SqJVu2XYdon+ukTyAtrB9aYlxg/K+8csAlxMmHI4TxoniFiePEQgDAH9qd3WQyOHGgMEwsaLEk8ENzCYoSMGJwrzLYToFwYjWTKQ0pHx0HA2bK+iQMjxC/97IWtiawmWDQMBzbBYius97///Z/4xCcWpb9E0LwG1os9RCPjscT9+zu/8zs8JM0XYCFK3Kq0Zu7C1j2ZhOlMfUpGTd++Plcqszle60te8mI6Ah4ApnUOHTp44sTJng09GItdPd19fZuJBkLT0MFLMAHx8pDwbN93332+2paHgbucWcTriwx5WujQ6XRwb/LeKa2fHJkf4hKoCSV8AGxkyt0La5Uig5cLkTgcCfP8UxYHZqiR/pEBGwxGmAI6ZUgcXjYToM8FDUJJP4JO6MXoGXH/MmTW7RbRDwf0wEjm3jAO0B5V4OiX/ovWQidIV8Vd1EuNILZnQV0jNqAd/UPJ2Eyh6NfIF4bUIPQE/NKTAJJhTuSiB5LAnLF5UUrU6yOBU6JMpMUryNQD+kQwBKCVki942GmYkkCeW2655cYbbwTpQey7stGoFs3uGUYQtgrXEu3HC4jGaJDU/u2330790jx4oBjvQ/qF9ODV7XcXJV6ISTPxvCDA8OmUTGcQoEH6SjAqnSZBd0QTWlMZQjnpiyi4d4lh5KVAAw1QdzQ2mhbH+amjBsJcFLe8ddF3MRpeFAJfQCF5GDEqGHx95heM8/hXpwdviHZtS548dRKT4KJ+VBd6Xig1jxUHLcSmjr0zZjg2uCkYatE6leOJOOuzjhw9QldZwRgVSmpOJDUVGN71G+IpFGmHBYgBtSpT/kCYRQOrDmLxr7LI+cj+vL3968mUVgmikdGjkUcOlXNpBms8tPLKFsuCo6Q1EkocLSoRolOMaMkgtUhgK2xc3tld7s6fLT7w0Ozf/kPxkUfKuUJkx47Wn3nppjufN9u95etPFXKluHiCpRHE0LVpRzAbPJyZGovFk+n2znIkXurcPDp65gfHZm4c6NzUuuAyaK8FV7ErgTPWEQcBlhBjSuEPCG+tbsDNXeeJJG5rDQ0NufW7wrzok4GaeAhwnwSvQxvHmsIShxgY9tUoF5sNm5Bb3/jGN9wZTtgTcgu7FOvdzQyH67RVjtAsJDtm5F23lqdOfrl2ljy9wTve8Y53vetdOIEx0WtyD8W4FFiqBtYLAEZuvnZFNS/J/btQaWkf9JgcNGsMZdqxP/MhPQRHJwrHJst8sz6ViKRjkXQikSLc0d63Pda1YXMilSqVS+w9fffdd9HN8cAA/GDIs8QAj/3K7koGmLUQgmeMBwaeGLgOgFnaChkH6A4A425eMDlmKGAGGIPhOzw8zJnFJ25mVT+656F9UwTHvYxGZE0RyBR5OLj0yFBdg4ODRDJ3WC1YdTikXDRAT0SfBZmXF/0gRgj85icPc0GxAAbwLbWJJAyNiIqDlKrhu8FcUhGwoquCEjjNJQG6LeAxpgZhZg0Jc4vsYAsfR/vUCD2XT4I2aXaQnMpt3JVTQO9GCXCgUlJx0HejBHA4DYNIH919/tv1ADECc2ZwQjAHvQRIO18/P1IxPhWC5pnSZvxjDoJpgmWohRqnedCq11R7tCUaAAdC+rwb/YDnSCRPAWCYLmVNZQiZ84ATbrJ5h6lWGKANM5UG+H/lK1+5jGpaYe4rTE4jeeCBB5hSCddurJDhMz45/TNl9Jb2jC/sCgvIo8G+jwBgOnk9GqXIkW+z8XD26pfNmWTQkMv6f3CQE8OGA2np2WgAjG6EGXMZo2sUxajHxCU7OFAy+7NCOhHwTwGDp/qJbNu+/eCR408fPnplZ2eprOXB8rYCDw18YriAPwVHCQhnAkHlcXVKIoVv/a4gq9YP86e0tp8V2Vi2lTll4qPlsW8iFAXi3WA2fp4bcMszqQMP5YtXpA3NAmjZJFqwlkw9I5NB8ks+vm1s5SAvfkHDHa2Fm/oKxe8+NPHu30+PaVmycn3s8ZkfHiiNjW/52Z/f0t7xyCmgrIsUaIakLgKijp85mU53JFuZIo92d3XPzsxO5pKPnSj/2JAngd2CB6OP++TDtoSITh3GLJh4uTewr8CKZMQwzRlUSV4uxjJYhgJ7WsxXDD+MPZzAWHEYhAzomFXAV8e62OfYhBDfe++9btw6B2TAlHIAjLmFMYYVBx/OHKTFLYGbZP6eo64oziRZhvwrTEK+bAfNCln8wB/96EddmBXyvJQcDcz1thdQHTRNWjDu39/4jd9ogIWWISGcaSucPRBy4OmfyUem8okIOyLZDB89Dx9OhyAa2cDfsafLbPyXjEdYYZKORVP9V5VbYvlUMtUWyc/ki4XZYiT6nOferI5K3XU5X9ASw1Sab8dxxdTe9umZ2emzZ7u6u5jIPDPKfr/BB1p5fxXM4xiY7L70pS9hYPlKFWxiHFn0ERw8jQweGKyMInBkXKEgjoXCUqzkMQArIknIysPARVC6R0KAMISx0T1f3lMN6ZcdoGjuBIM5euDLGRwUthmGTLzRpdLHYTqwmp0kiMQEB40HxAjMoG9idQps6Q1pSJzR2/DwMF0e3fHQ0BD9JpN83sbU4dlSZLo8VA20XlQGKoIDJh7g7DMF5FtTNbBCTnyABLyamK0kzHwHIwHVyo7NlIUaxwdOKeDgDdXPFApF0U1zSeC8waRFNXABCVCFW9iMUmEtoCJfW+VmN1UJGdWKxggsJK2PlAvdXa34wcFBWNEaaZk846Et6I2HWwTCcwNpoVn54UaAK3Dl3JrnQPPmLRIAcPNJFqJEXTzdbqystbqQIcwiDCwk2KV41wDdGrryJ/GSThprAEUx1LIoiek8xqOzpwrjB6MZdogdfBqUBmY4dpxtOKPXXHMVe98TUG8h0KQe45z3S+t2cgG6qREBEyegdnaV2zXUIUd1T5ULQTs7tBuTbKXwbL3YqbGJ4ydOYG3pIZ2dOn5mEuy3L9GealVMEX+muVsZOEcmyicmI21jBLXKlz+9RgmNsKveRysU8ol0O/FA1WJp41P5zU89Pn26rRjVxlTkiyQSxmWoyOQ2nrRDRkprZ/KwoZqMDCSTUHiV2QYIgtzJVNjZZEjhETlS1uqFaDHXWpq5NZU4WmrZX4qDhyPlsYdio1vQvVgpX514TZfdfnO8ihOLxS1Wd4g3vck21EWkdG1PuW1ifOov/zo5NkFJKQs3KEKsVJr6n3+76dab9wxds/9MueC7aiEMN+2fhcQxKAKSwjyW3DawHaUOj5eunS33at6p0VHXtsHYC4ekRomXew+ji4PUmEYUiKGnbkeqstrBXbessNmYGKIl+BmDDTkJY0ER6QeXkAFxmUzhIWL1H5aY8yE78oXML5lggq1b1GbxCe76LZuF8WBwxpIM35omqq7A5yQ4jxf0rmwE/d73vpeXgYHo60q286iGVc5qvQDgD3/4w5TsTW960+rWK9xo5fPBCf1fvhBT/2RzbhpRKj09K0x4IukiC8VIRvN4OsqRtnKe7rNMvzV1tjtfSI8eKjJfl+Rhi/MaBp7kRJIFKqVyMlcEM2/ZuRtEk4xF2VmfvvD7Dz2ayxd4UMmER5EBj0eUPbH8YQ7hDesxGPg8Rz9j3+PkJMw2VA7kSM5BjwCicxDICIqRTQwHt9TrmNuQsoedCxxqFEtf4J4NHniwLsAMGrAEuXgqwhyEgWcosCa5i7eMM0jPF5NgJ9ExedmbZA4sZGqZtTT0fcz8PfLII/SqFJbekPU8iArm9DOqICO0AQGLh5lEwMGLMhGYyJrsoORosix0kQjgHJjzZj6bMN5IqqOGA4J5jpAhmM8suCcQIXGLUXy6b1Ix+0AMqhgaGiLA1ADKoeP2dks8B2TUMiWlLDXy1+T7jL9EsQwGPCyUlKGduQ806aWmhXika6xGFTQbbyEE0DP1gib9aaqhXPnl/DryGKQNEQKNmeZE26PeV55jYw5kSpFDF3Rj4pXfDWvEWZF1yHO+ZsJbiwZ46jF6/LFalPgSwXnWgLeusHmf59wvrux4ChgReN+KHgB74NAD2WgxlTlZTmW6s8nT2OjMgQtsBWCPwhHmG7Cnzk6evWz3ZeZZ5Jky84WHq/IFW0FQjBt73hwyCUHpEiyoF0wxZYoCUXKNEgto0BpdHdwVUDH0KO8nMM9o7BbuUyh1CDdaQMBVSFJmUaRU2JRv1yB15OCB9vaOnk1Cio+NpaPjjvQivCM7MnIc7jlQ/mz51CPHsL1a2traO3vgAE9YkePpk2NTE2M7dl+BAEWsrXIp07btzJnR1LEMxLaUOACxCCHMLFn4L/3wG9pzc92NZg24pXsBYRAKhgxjQHKpsnO40JWNFaOxbH9x9Mfact2xyJ5Ix7ZCz33ZRCYeO5Ivz6bzqRLGEPxkMIJXC6WRI/v7du5JtXYQ5/lKJlOX54ohBkZNnZrIP/RI2hMqQyjkZE5PTece+cGGoatgWKQ+1D+iEv7J6spMjk+NndA7wYVcoZgbPTTNzY39O1LpdihmMuWZQjlwWTjPeeeF+luA5TzaVYtwDTg7TE0MXbwRBBCGIRirCUsmhLIE8Adgs2HKspqP0ZmEcICYM7soYzESxjZ2cMuQjQHAeWhoiLEAjwLWF8Oo41v4VNuruIJdjBo9VF+SI19jcTJyJMwZQwsbzCPDsy/ZIzuMkDBy6QHYK1G1DIsygZg3gflKDktlOS9Kf4mgGQ2spBab4d8UDUMm73ZTu6H7salkPCS0Ie9ogqDSVbcqwgu11Mt6o/2dJSBuvhjNFEv5QpSPnecLbL7HMp4Ie0DR53hHR4cprmqyWiTTLjTSzcKbXKScLUQZQ7hFdygCSeNnnl6B63i0hHdwJtYfZTgrTE+eObVt4IqeZKItHevt3cDS60Q8Bk2+RK8auf7ZN4A2rV/I0kGwpINH2jgyf9a6YeOGfE69xmxmlu/hZnKZvfm95HrixPFHH9UbhohLKp4tBtQXvehFxNDRgAzdxKdf8IOVwI73HD0yHMHTFwADz+BAQjyTxDioax4cknBJBwvF6armz8PVZUK5vGbpg4A9JATt0HMhHv7zoaEhXgFlIpBufXBwEGAJQKJzxL188803kxYlkASc70yqs/CF0NUx0HM4ZQ09SvOlNcBUsnYVwbw6uYdpeL7vNx03WqWb/v/Zew8ozZLqQPN/v3f5p8/KLJem2lWbai+gm6abBqZp1BgBGiEJp1l2GRAynJlB5+jszGFXmuVoJLSaOQLNDosMaBBGoAUaYduovaEbqrpp3+WyKrPS29+7/e697738809TmVVZVdDdUVnvj7hx48aNeBE37g33fFJ4SEgW1DzIRgTK+InCeKCA/p3hfiqmKrDZkMsr83pZQRj8ODTOV80oNa2U2VA8zA7QHpgSorrWarHUHt3Bryv2EeCnO5xRg4rXyvyIL4UY/n0LAf863Pp8bomHTkGjOmsGMHNnH//4x+GcRk4nfd/73ofnlltuef/7338KxSGtkfLTGsTvGj78Fc85rAFGK7YOmjQ7y2zQHnQQJlsbgyV/Gb6lBeqPeuWx5JbgYnB4I7d6JKDOxSEo+oCANSfGXVULBKwechFrEL/E61OMOkHDAXPtRklg9h6batsnysmfHZ8LtFYPPcJ6HopAdOZo+1j3VE+yo62li1Q/m7K0GHrQr58YKc3NFyfj7O0FLquCXl7Qxy/cCkR/tPiSSjjQLbeG4PoFLMlly7BypYxjXkKZvcRuAQFqmTDWhL5XRsGxhVAtl+Kzga5WH54PJGrRTIyzHqQUHIgX84u1UmF8dELyRLfCdJ2agBSGcse2ds3dNChnbj6QW6xjNmtuUqhCtCMbqo5m65i/cEApDV8sREmkD3tLooRJmQRDnEK1iDzUapVYkPDLvVfLHO+2njxcKIUD8/vquauSVUpD1dUD8xeEih3xzH35lhOh4Gipvjsqs6dKnSkJFiLLFa7zrBdRHE0J1JyksshEF+9ZQI7yhY1SPij7+KSupSS8BmkI4uq5fMSptceDeSYhZKMfVcmzzhINM/rpKpYwlxEH0BRjsRDA7d2heDJ4dPjI1MRs8cKL6oFIU2GU6nqPTUlOv0KNIkGS49BqGMLQx/CYA8iyCk/kP8qYAVFymLhk6PnABz5AFCM1ih8enyx+ME07xdZFmzIHBI+NWQygt956KyM7Wlxjwexso+kAjfDN+m3GHFZREkyCrTpWUt6DBw+ypuIP6JvNCHwRBjVnejTXsSPFi6P4GyRC5XzkIx9hC/Qf/uEfMppvMNUraOvUwLk3gOkGf/u3f8veNtb3N94U3CKJgOKfK0bcEW+d4npRZJSKyB9J5Z9uftZIGTBYT8Ii5YriYs0pVVkr5hnAPBYxVwuyMszlxQgB7uwjJYJOBa8KX0hYY1Z7mdhaPYRRHUzLJXvZbOREdiLhdMxNzbe1ZY6XtSezqqyXEIaDbLoOsmgcDiaioXgo7ERi9WLQGWWjdjDY1rurc0d/1GYF2SskjlXQQ3y8iCP9CAqTNawvYfRiFVhNYtwywQwqQRM3eND4YYYKv++++/CADxoJETfcuoQgQAqwcRE1HcOAfs4GTp7YXQgjiJARQaNPcnNNQQ+83i9yE/rQXA/JiyNf7Fu4Yvs0EhbjEyOfz4jDG0Bk4tvf/va//uu/pizwT/Ex4JGzeMgF+9PW2UiC7dokQMkBSU2qJiuIJBwC5LLopqJhfemshHxDmHl6zFqPx+ZfS2jVThxBg5h/YGAADxINeUqJGm02ZPoNN9xgaDx9CvjN+RCfoBfzsvil1NQA75oXwZSNvVDaMIXnjax8v36lEGtzukyO0IztjduavI+z5R66lQ2uRtlmW8idw7G0gcaoLc/aJ0iN0dLOTl6WKa/G7syk9+GYhwJOO/dZ2pQHdYrZH0qBKOO907XxDw4Ono4WsikGXkHeSA3Q9XjFvJqNIG85zvhiNSuTtzami4jAxPKlp2uLil1l65YSI8M3g7dsupkOR1jDS2vQHSuJNhNI0Qwfr9ou+jR7UqhQYlEctOBYB5DVSpCEkkAAeHBqxElIgvXkRGDb2IlAKFqbO4QSAGvBY4+XR651FiKBVtVLzOBE3SgVcmQwM13JZ+ulEfmwXzASc8Jc2AFNnKyLCn1LpTataDaSkVSI5qi3NGm1CJZyK0ablFk5N0yWXYUgDhyOwrplkSyEGjnoPIBSJFJtOE1Sr7FQOV9Ll0vRMgxCQKuKx8yJKU7Fdg5cQjaL09Pzs+PbB/ZikoOTrbC3mQXk8tzkGMnLxVy1XJoYPQJPyXRbvCWTSsQGB3ah+JDr4swku2Pbuvu89yKHfoVD95/wpLWNB7alYJKHqFgKETxeDGCBShEFwW2w0Xytni3NvyW9uD3Cyi5JsEL1XQacbfXC20LOj4stR8uhwTCf3aNQ2ekT1XIRupmwE1wYC2DDRqKd3Lgpli1/dbVg8bANMCAKU2t7ZHtPdHREiLo1I94qJ+wGd5Zi4f72EPMOUv2UAd6hQHxba2R3KxQOH3yuJZXctXMnFjCUw04lka0fnBovzLQFOgekLBtz8g4bHDkAEW2yyqqKONMkUczMxEJ6owv5cGLRo0yeo7owidxIkBHWhDw6JKmQzwx/TAEz9OAMk+RMk0GfKJ7mMf2H5Fxp2cCdeKWO1IFpnqYna8sseKDWrjP0NyVpCpJwcHAQIDyj9w4MDPiZGqZfRjwWZZAmtCayawV5wXMjpae+XXjtv+WD0hvdewg1suOOpD//8z/nuCgfRjq13Nfi6uUJP/cGMF2L/c98dshf8trIm7D2R7+tz87X5iYDs4uBZCLc2h7obK0zMIhUWzZRtAZNmpBIfxEJnlhA6oWC7G0ONZyPJs5EKZgiP2qBcKVa569crRWrwXKtXhS/U8JIrtZKXMZQkY+20Vnk5K6kxckvTT+Xy7/44sGOzo7zz79AB0SZVUVmcncC9rZxYQksV884JyQbsGFWxV8IU/nYRCAWqQ6k0+GWlNjPxXyaz+K2bRvYu3e+VOPG/u6+vlv6+mpVE2rIthJCTuc+ORTB95z6EWc9PZ3lcqXM9ppykck1hKEIFLkCvjw5OeWVO/Da1762q7OTCv/+979P5SPazCHFuJqIdVeAtg8ZOECLRdjZRBpvGSmDA836LZOCeJA1UvrlDssTBVrkLzcrytq4U6+Uu/hi0N6LuAjxqYmJf/zqVweHhlhYYxc0+4cxSjFs0JKhDw/ckIQfYYoUxrjF0uB7nljO8GlZL88tQHlhrwlIECsaHhq1dpLDP3IfWWykViXYRIrKWXkHm+H4RHw60G+Mwo9ExvjHA5PUv50rxpLf7HYJI/vSeDJaY7jStPx6o72d1JTlrTEnQg0zy0tN8lL85GetWvw37ud4FnigyDgyOgt5WblQd37/938f/ze+8Y3vfve7v/d7v3c6WSOmmHqHGqXAz9s3v+V1hp7kZZSt6s5QLi8lsrxi6oqeJe3MU1vXKSBziKYEbwR5HToWNbLgHFuQEVKaiTxsWLfhVYd311YkypAEWW0n59iLk/FUur23RcLy4l2VQDwSVHzxW0LFkmyWMlJMNZjlQXKQZbxXhQE0wcRcC86WQzXZlGs55+d6iqXi4aOF9grT6QzwlcKIE5rrnwnE5qawhixjh31fJ8aPFOJlYcupjky9iGbR0rk93blNKRtjkoUEsfdIic9N7fr4Aa4HidE4ll4Pla/XF3sJSIX9JnTA4b/MFygjEjbWCQMhyD/3dJQwJhpX365BLSteqR1jSWIgEwzKgrCsCrPLWMLCoS71VirVhTl2S0ssuMx2iaUdjDjsK5bGAaJ0+flsvlbKJtq3KVAYwNQUJsjAmKF8VD1+ft3aEwWPtOqgI1vtXM6FeSMipiYrvuF/nQmlAhkurRIcJyzIkgf2p6S6LR4aKwS7MY6JDR5bXCjU8kFw5EzSArxz6O28dq7tkGJRPlnKFUMxAAl6AABAAElEQVS6jsnKEm29vS31jlucz/+NtAy0OJ0ywfopXnlF/OKLI7HIlX01NE/uAhETVxIL38q6EGxZCCRTgd3bpJIhz/8dqb6Foz+bGD5cH9ot7Hq9r9GURWdgWERBgj20CHQJIIhQnjgWTu1CFpZk7d4y0KAMKfbK2QwmE46mfgDETKXD0sGFBZ2+HxgYAOI730aFMo5xliUKMG35hCwggt6CtoZHaSx7rApchrFaAI0IlY8yQvnUKFgqK7vlsJIOlUCNUbegyWFGx0HDxOpeibkaj02w+tHHCiP769VCPZygwlepiqYEfhDF7zd+4zdYBGa9ECXZh7/iObUaOMcGMI3pW9/6FpbGV77ylU21JOmm2ANHjlS+/o3aHT8qjZ0IZVpC174m/J5fDV98mZNyb1s9aaVUqzW6Jf2TxrQ2A9ZA5WnCFLEVCTOt55LXnuMKBexVGfzEtGVWzSljfcrqsYPOVq4m9vbu2//k862h4oW7O1ORCjYzdq/MoZrgN5lt+TR3C4Hq3frc2FxanJtBovLJW2TVYm2Unlkq5qdGR7g1ItPRfSw3zPp0S0umpbUNtBCLyhxVZru1GPaBqXFsY4RyMr37EoJSDj2rDEYFuVoNRBLJD/7WbyEnkZT6FImJEKWYONbNTIDyxMI0kQpvRGEZAsEjFaUCji2pmKkE+WQUy7YMCGYYI0mRjAhZhAjSxBaZLYone1mZDcH4RJkKFEuV0WPlgy86x0aZDI5dcvENl+9jmuGue/6FG4+gyQwl7472wxogy2soVSxu80KR+1i2IEAHHoA8/vjjiHvmIyEOe8gyWzaEYcpikhqbitEXhikXcJIDQfnGiPXFDcd3BwcHucJq7QYDAdeBw/DghZp/YZIzKjJOqiNHDHgWuFh5pmYM6FOwgceo+UmaKb48wrQfditQVv8VMNWC22DpSeUn3GCSrUUjd0Yyv0VtLfEmajQqGvAdd9yBsmIf02pCOBNBq16yhrg9T6fCTZ2CFPegUG9NmzXOBP/QZH6E+xc23qjOEBu/WGRZEWI0v+mmm9BHT/rGmZ1kCEC0bkkZpwq1ZybVaBByYjSJxcZD2qD8ik0qPhlJtU0iddVECwROLAbjVWcxDlxMJ/0TVBdBsWRwFo/7o6QIa9BFJdZEEjnhVRTNDjlFMFgLJMec7ofy0VxYSQY6nBbOVGk2RiIYq9Q77sBcQikoShqZoK8Xe5z8tbvDST5BMV8s5dq6dwCPxpPCjdl+UhSpb7UppQiELXuhKz79z/QEnEHRshc7jRPBsEhSqSc3iZwN1soDoNoMcC2nTOj7hLVMEJOEPO1XgBKQZzmfW5xkwTPAt1aBZI8fEbYT8Y7OHubfgVtKyTSWakNtq9dnp0/kFqd39O+VgkvRxMKtVcujx4/HotHOeK0aDHREyxiAUS4HYxSk0NDBbGbBVjw4qQjqA7jYn5SRME+sSgVKQLhzy0pxBE6wHQJaEbIDGQVKCLJLOSxLDrLmy1+4Ey0R3Un8+zr3oFNxUu25Z5678PyhTCZta7Mhs15l97NRltrAOq4H2iK3/XKJ3Svfvj3GrsKA3CpT3ndp4mMfdfp6o2GHEgln6hjri3mZlzcnNq3M+fL1CrZRV59++inTwYKh4Pz83N13341gt370gx/8AB0MGvKe1SQmio9fAOHuEtNt8BPFGIpSpLnJVRoUEdXLTFk8vtxDd0KtAg4QHMO3JzlaphbUanbjDRNlDPUMEFFwRRAPlH072cU+vR/MaQxglM/TXxJg3ha9q7EgPmsok9S5BfHjaaoNH/OkHlrrsccq1cXE6NP5XVdJ99+UY2aZPbNf+9rXfvM3f3NVVjdF7WWO7Nlw564amMx43etex9nXTb7LWvXEifJn/ip2773MUbL8GlvIhe65K/fi884nP+lcdmkgFJZud7JyIWhYb8QoOnVlVIZEJLknGshUhhjCIkejsLFk/oQ4APjswguX9qRuvAjRI9whhrncHzO4xJ2HunrMwrL6AzxZRpb1ZDGk5fp/OfoTcIq5hYnxEQSoZulMFfIcF8YaRRRGwxGWomdnuZmJjVXMZnJOQL7GLluFJLEn9r16UZAy4Y0HcE84GEiwExtxh0xnm1U46jw/h51cY0joPO9KDGmdqpRYbvlCLi6W2cgTuPa661hhrlZYZ8Z4Fm9nZ4cMoXqjACLUE+cVpgbJRvej1k+MjR49MoytnUojC2SALuSLO3bsqPBWFxeqTz0x8Sd/Vn/w4YjUk1Niwfr3f++a11w3PjP5zX/6FtOTzF/w7tjCTcXjwXpE1CKhsGB5rShY7LeZnJoKRcK5xSxjU3u6pTY/x4K7zFgEgxNzczDL1hccAMxRTFDSQg1ukarsriEXzlQTxLSGPeQglrPhM4T4lirJN+tsvGlMhSiHebNyDY4Fbhc5EEW9+Zc6NKZ6Wfl5BZS3qXM3BdevkMZV/fUxz1AsygdG3RkivpIszRW16ZQH7JUENw5BtqOBbertrCRuye29W+xpElyZxUoI/Q63Ev4KZJ0aoI3ZxCIG8DpojVFb9Crr8ZAzy0wzpotS94Y4b2BDYFiujNY68mKXLc5NLk6Pia1VrhYLRQZQhpjuHYMRd2lFTCbB1SRQNtIy4IsT289tmSgAIpOANZidEhKQD8Rsyg45lfZE2/2F9lFoC0meKA/Cg/jk+7Eh8ZIXG76ccqg6fUlk5sp4JYw9k6oVuAWkEEzJl+rZOlyQMV0WaHVcF67c/wqQ1UtyFy6JkTh0EgZuDUq+eMRstFivVMKO0eFH2FNMSQ8Dag/DJV5ZEpUaYK2YgNipUhn43UoDUgjWYgtlKWRY1m8ZgcHp3taRbmlTrqgPYUaekliITAaDuUh4T29YzFbJmrw4FZw7dPj+UjC0/YILnEg9MH6AjDv7dnZ19EiRxDTV9GqvitWqZQTIH/P7HK1WCLqKbO6j3gmKWWtBLQp++dP5E6sPKbE6ZcPzujCK5NZXqRiejNfbEuF0PCxQphhY+GDJgBsHdQobXQKdBOWBfoHtl37ve2vXv7r41DOBYqU+tCt00SWFjrZnDzxB2zOcm2++GTWGgf6uu+6CGvXi5RmwIQMgagm5EIUjiGFJWkPDIiUKY9V3vlnIRVPg+HDS+pRXVSoMYR0Z2EjBJ9XogRkUJDhkUgyBgDJGbKNu04h8yn6y4IAPepqpfCflaq2MSIhlvpZxzrUjJOS9cB8t1XVq9gJVQZNeGC/NHo6gmB99tL7zKhr/JhxMsqrE6b/PfOYzGMCbSPkK6mo1cC4NYFoD963de++9m13+pSDcOVD+8Y/D99yNvDEJLZKnGogdHS59+/bgrt2hzg4R3w2dvLH42hAF4Ht8/6b7j7ZfkeG+WzvTI0eGsXIHB/fodKSbQG4gDjmmaik/kGJTkuyJ0pFIyoePHzZXsQWjvKO1cMFebuE6ePBo3QklWzITEzNI7/ls++TEZHF+ZsfAUHvXtlA07pnMetrGcnPZXPnjQsgFJ/YyA6u/qg1Iui6M8OTPBgUttAvXyICc6WfIkQGGWdJ4YCEXCBZkxjTUc1FHLwYzy9EysfrwAw+yW9xJtU8XakN7r4inO2anpjikeHyEG32rXWlKNBmNxtumJ7P/5dOpnzzO4MlZoXKxEB4dnfk//qjz0396wxVXf+5zfz185DDmIjKLJw6Llz2uDACs3DIqIBYZM1j+Gh8b4wjkDddd3xOPl3/yk9n77g2zu7unJ/q61/UNDqR2bM/mcgholi9YjiYhFi82A8IOWcOAAWX2VDOAMYeKhkcUQWJpKgwqq4rCTbciqb8Aq9CQxfhvtFVWkloJ0dQvoweL8FQ76/mnUBWnkGTLa/Ys80CLRbCso8pseQGNIMWkP+K2ij7Lv2ya2Cpq69NBjPil2Oz7orZtGg6Vd7Np1+fq5yQWScUM4MDAQFPprI3R3tbhk4S23YYa5jQ+aivIbCZkVqiJ2jpEVkZx0S77Tm3/r5gF3vAlAxsjsthu6tSLXxdLg6EQXy7kUo9sjRuLglyyIfd9cIJJErlTzGLhMeoR5Mcb6OFUCJsTTAsx9gkOTqPdgOJq9uzBCvQ4xbdFZn+aa/lxNcSCJphqc2oipS8cSlaFTDX/unh5d6RFKELQmYs5sYrTxUq1UlZDDzCkJQv+qzkqrBKwzDUDpS06h1hNariyfU1TcduUklZ8I6IlFWoM4/IUBKpLLERKp8UgA5nyB0ko42wCQENkLRUFttPe4uzay3A/cvRIvVbpH9qjx2Kpzppc8iSpWVwVRuWuUKlDJxuNF7pa+3pkPl1i1L4tl2LOFTtGjh+/uKueSiW7utjpGmhrbUmloN3oCEnWvtNqJATPZrpToGVOm4mpNsIJcbRGhntaJsO9OZq07a1lkp3m6oHll96NLdTZxeesDtuVnAAtA1SRN77xjdQBqoJ9ARE4LZy51+0X7m2/6lqCfIfk2Mjo4f0HyNHsUibTYYlUjG4sD4AP3J54TPQR5EiXAdETzE72N8WYlWs8ND6hudbtdEQ1Yjb54YeikTXa1PqYTQkJwiTOSgSr6GmbpbCS5koINFmjZn8fp+rYl7cS4fQhK9leCdlgLuztP/pYOcCntQL1kf3FOqfIYypg/L50MkJk/bGPfextb3sbG9fZpn7KnJwsn5dF/Lk0gKngz372s+xHfetb37rpt5jPBQ88wSUEJQxBCDkBvjMkb4yJvf0/qecZU93dHQJczbGnH1OHGPqn7emnr56h/mP5I1XJCNFGkVfjSGDUA/wgOmwUUTRXPPHDkBDmJsBQNB2T+wAW0rKzuSUdDMzliO1qDXCJM58dHUou7Nm+Ld0SZoKY+QGWkWU1VZ/4dbVZb7pmp5PGYutSi7YQTeb8yfkcfSXucCecKS8wCGcatKc3pEi0fkGqXiJCRgEdlnXwsTjek9EgPnP+dYVicf8487AMWdGp+STfAuhr7Z1vkW8/TCzMTi9EBgKR/EMPV554lquOiuUiDHKEKBaTi8Dmvvy1zP/+h7e8+S1f+OLfRmPJvl39yXTL9MR4PMa3EuKdHR0jI8cKxXJfXy8L48eOHxsYHOrs4HR4LfvVL2c/89k4t+8yx889Fv/4tchHPpK57bZMXy8cs0FoZnaW6VsKyebvp59+hjnLgcGBPXvO+9Edd0xNTWazixyEnpmeQ1e65tprsa6T6dSDDz6QL/CRBHQpe4SwYNHnrF0xoMog4DnMbNt3RNtjJZlRgRh74mEzAqnIVNsAHLmOIG+DYZgtOh7s5fuLkkHnpSoY9jYtN15y1SYdlnkxERo4kR+NRaTHEbuYz6H5xhIxvlrZEGvdcRX1x++nDch4lyivRDCI9noXzevuy2ksC3kK6jLaDRg+a0JS/m/bZttl6RCWi5uXpvFYaCBwOt7R0ZGf/vSnHEawvrxZUnPzc8eGj11xxeW+6FxO4aScN9SfW1gIkKoBbhSF0knKLm1EnSYWvxs2qJe+AbhE0Pc1xAaQ3hgDuwf6tblpG1RStDGmDbLsOJDZWkB+ajcnpqvnsvmp6XnCJCtnS3M5dMBAV992Vv1k5FG4jMqa3poI8g+47NXVCGKUGRfJqgeLig8QcsxWcwKjjk0lfh14PBZloTjGHK1wVneS7fWednaQnTjybDyVae/mon4Z4eBDVh6lNxmmZOyKGkEgSnRWHyiGoiBIZvLLOK1ZCyuGJmO3yHCBC/2688ZkZG954du50BQ6mKT0nWUe2lvqvK210qJWqMWRsG97MNAHicYE6pd8wdLC4wGFGXQ6vbDD07U2BUfmo4UHKQgHbiVa+JVJeMFXuExSs0LOBmCNFGPVPuio+OZXG1wXUcGFgqZdoqZVoTwFAocqzCkEz9shW3wlJ3EwjBN+PI/8FpOpw4cm2sKyr0ooanw9Frp238WP5BfzC7MdramezrZoLIpBjkI1OzfLigIjAn41Wms927bFYzFaBit17EZzDVniKrWhoUGbjONAFhPNlsqe2LG2xMfHBWxvlzCn1YK2ZouoHLkyRRHiUk69k4/1TCxVTC/sZG+Ql19/tpHhHhkCxExcRvtIOBKUzW4BbobuHxwYOm+PlHy5w1ZkU+RymMsP+WJ4WxSc8JELjG1UCCBENSXZkiB6EfxjAJ8yNdSe09kod9J80cSYE2d9gnteYPUM1QNs8F7Ii1e5PksimKRhI+1MBlgjJxHLdA4HgLkADzFSno+NPpvbsS+pEsz6AsJFhZ28TO3NK3Iigg2JzP6zefbv/u7vVsS/AthEDZzkRW6C0uZR2VD65S9/+Q/+4A9OZVMEC77FUiwUQQzTuLhJAiKcvsDPJSkOa8EnczRlhBQtFTsEP23ahMjJ0p16PNtUmKgj0/X7j9d715BlHhjOk4kkvb2ru4s/2ELi94X7Lrv0MiwoiOBkgpSxy3PaLakuOph0SAaO4eFjxXzpggsvoLsB4o8pdP7UGObTfbIOrDazg6ksfrGZ5YkVjc3MIWdsbJRvgqJhqNaAHMYLQWxo+bH8dSzTAU8GNYaDVJKPlRdGhw/DSaVcZICaGJuAs5b2jnq2EEdiBp3cCwd5K2z/DgTDcpBZ1PwwWRWffiZTrQyed1H39vMWp2dKgURLsjvVnUgm4kcPvTAytcge6G19O8rBzDOHJgrF/PYLrrmg/7zaT/fn/upzraG40yJjFXwwi1D6my9Wh4bCr7luuhbee80NjKLMVXNl5cz07PhM1om0BIJJdoDv2DnQ08t1YtUdO3c8eP+D3V0dV1y+r7unmwkIEszNzjEEa3OScZihQuo3EGDOmAlgPFYL1BHKtBnAnH+z0VQR5QGcwRjGWB5hZwStEUeQJ5QZ5xhFWJp+7LHHDOjHMnjjZ9WFEdqA8uo1IWd78ECWZXCizBHrOz/3lR5wVgLPOYQ6tB1lFO3nk8OzXUW8pXrgwEh5riTdXf7c90aXE92fznj0WHAk25maTh8sWsMUHIwNRdSeqQ9VjF32lYgoyjiXnucDF4j9GfbSi5AtnEsJ3PUiQ9KnxsrDnOtzWVAB5HGvASEFjqAZ4xLrFgGYmRMuLfnxStcAavb6WTZHNIfH5oPDuUTLfLA4c/LRRBOL2PPd9Ex9dC6QnpFLbs01RHowBeljGUTwTUZTJDeZi8Bb07eylNKN9/JYxoTWSAN9eo/RFWoistXx4/0pxAVracQvQ4MVDQ/yDcjsTGX0RLBwmG8ASowIeyWVz6eey3bNHw9OyAwtzvJQAu4Dar2BNplwfO7p/V09vW2dPRB59Dh0pGhCjTh50WIvuSaxhgnK2OXWhCCJuew57trAJpI0ypGCTftUjVL5w6jrSQd2ZYyIEAID5jOLwUQ62NNLgyUDcXL9kXBjyeBGPBLlMeCawQJV84/MBYF/JKMXiNFIMtVhxVpWVsRolDVPSVUPdIfqfYn9f1JkXxQA35FpYnfllz7aWguzKCqnbpUy1KSf6XgOQZcmxLinY16uvWgPsVgo2Uq+xorSlBIJY+K0OOJ3i6lAe1BUwfHwlF8x1S2JxJhTiGG5TchwGGjwsPGhpJc1mmFJawHewZhery/Mz2NKyQCpDgTGI46YQpZxjTlNwIxijIxYMthLXAhM2pnZmYceeghkGvaRo4dfePH5F154ni2gjIZkdc899xpnPMkCUpzbT+iVnNAhlR+Lh9sDeOLICEyWImDAFD+oAQeIuctATL6+87ecYFxhdfhwG0wt1YA6Ib3CYaz69ir0G+Mx1GSz3GquCXM1FIGBdobmxKlMc5Y1fioNP6XeIG+W0J5n+swRL4XZcFtUwN+Y9db6sTLYhQRNKoTnelVRr48fzk49RUelE3ndism2UmD6RW6wBcarrz79ndrccNN+mbqTqFzw2kx47SuuKCPfQ+JjSNwI3XTB6taW9yVP7ZwZwDSgf/iHf0Avt4+DbbqiWQkcGuAy+JDuJUCuIPrDoXCVcWWgPxA/+dktlunIFOuXW4KxGM+QHGks16qzeo0Ia/mpK/gklu7X2OWsCD4ED6VgggpMH9hI0xW/NiAyT7+4+MxTz/R6n8Y12SxCWeSyp0QA9UdAoUWAP6CG7nZtRCM+7FN5qqkshrF+TUo9SM9AGV2Gm67LtSPDI909PZForFCrzIh5y8w+l0LwW0Ao8KkTtA2VD6woY7xxmLgWlPlTLkqM5rJZTkGV01EUDAatdEvL5NjIzs7u/j0XTo6PyNEbDsDEYomgnPidmRxnzbZj2/ZwvRIu5Ra/991UUBJy8ST3YcNrmKvMcoXynfcGL79qpN42U2xnyFycnUq3dkfa+na9KlGOx6ZTXS8c2b9j4FVtLW06JVd3euaiu/rHwv1jM05wthbvf3UoeCRSr+0eGpLysxwXDDw3WUbGZfovTe8oYU4zAc28Ard5pFKJ6VwFlSnOxxyCUYztXDY3PTHRt2M794IcOTrc28027RgWL5ww5PDezWPvkXlr9iPht/dLdngwgIEQxYKVodmTWFMU2GH1wx/+0ICWkGGMS6QQnWRhe6hsOLcnSoltqaJroGQgbW3Y48kMOgoBiz9QI6oxFTxjlJIpnMC2nwQPFNA2iLKjShYFhZWt1EpkrDYy3AhhfzvBVwxgt07E4qm+OBuYkUtzpO/SAk3fFb8q9MPz4blKy5FsLMKHhwXJTerhA2jUfgVBKSiyh2sBMwlcvxdFNk00TUaofeEheZnarxfSWDdAIpw8FSAP9fgQN5YfnEb7OMh9eFB0i173afWzLkpgZjZ6vJSJzMWmuSrwZE5ZlwfyZ+L4ML4KNxHk6id+MgwsnmptwwaAR8GQhzKgwsKDCLTBKdZStVrQTSxofsW4aTx8L4pfLw0+cqJuPONOAG6yBiyFqV21LF7ISN1qCuTVkWeepr2Bx90Uxx56gnXedKaze/suY6lajY5VWnLz4cqMJCBbTSfcmsfNBex6/fhceDESSokyKIgg6H8pmyYWhVs98otHr0UG09aWLcqlbGnLFdAkhWYrMK0maxfipdrTkdqVvWI9InwAiSdQ35veyViQSIhd5ALVIydGtQQKVwryEDqC5zrJ0UL6VAZcgGEYxMPSpkoAVl98ukRNKtISDku3+RNcyRSIt8gsrSYzHEVseJi0zJYLEyPHOjPpKLOxzN4yH81gI7ulxMBjzGXGEDnvO2wZbLxYTC5ZZO8u4wtDDakMgW1T7e0d1MmTTz4xPT0bi7JBU6OqNYzDwcFBUrFMysQu+KSyhGgdV199NVF8lIHl0MbaQVDbV/34YCGxqHygwRtPrFzuZwIZIKkgBStwiz3MaIIxzBDP2IGfJ3upGLBw/oInOgFGqY0vNkjxtI2+0CTTxij8ODLFrfzWjsF56r0kfsj1WHHItznCCzeW14MtS9sE3MIglUbtcdcUc+tomFtIGZocrLWi4cGRFxc6bHYtd53K2UJu7aKvLSS4KikrC/XAZj2UHAz71UunUqRrZ8vogfknvlkNFVE7pYOLGMYgNqEou12CE0864z9zx2uTXS1DhRv+11SInY5rOzLlADAG8Be+8IWPf/zjq/OwdvJXYvwaOJcG8Oc+9zk2P2N8+txs3BNMpCLXXZ+//bvJY8MMiDKK6aMcTQTfchsfT4PUOs3Cj/I96+NvnLG1MJHs5OWL4LXQ1oJ/4hOf4CTJnXfe6SM0cg6QPon122Qh+8ieRzoVfRNm6MDITe7OYhhjXhZqyLVG0bbU/5Z8ktojZb+uGiADi8PxpqZY4UpYk7ku1x07NnzkxP7Otr3nD3A7dOr6PZeCMTIyOjU9e/HFFzEKYzAfZzlrbiEQ6oxcsa/yD1/CXMVy4oCWDOsV7pOspa/+JVb8J8an4rFIJp2+7LIrdg1cUKsW9z/6QDLVsqt/z8TosXxO5tW4OJE7JNtbM5FaIHd8jIu59ErHoDehQPt3nNETTqXKNYy0Ir7+PHb8WDyeDicjO/migxy6rgwfOZTu6EpluigJ7HVs7093bM9WmPDW2qwHZhnTa04i5654uSqNlJjTW2JcSMUwLxkOzJQDx8a0KurbAy3bAc/XZyacUFv7ZVwLVtnWU0yFdvV39/cPKFLzgykPTFNeGRt+sEUZ7PFbS2CaGRWEIE40GnU2ItLqBgYGDOI/LYoXhNKAXYojiiBPqxz8NBK0EDx+YwOZNsbFY8y2miHqs8hggK5DkFvEmMLHY6lIjtbCZ7SAcEclmhNB/HAFAloF9yQRJC+2MAABblHkxbo3EJbEoWlRPFGPwDdFB1YPHTpk+D4CVURLJl/49DMyHDQqM8Wx0onyk+AhOxxAaoAnDqA9rQYI4tGY5sfKKEvbjHdGwnI33tHZ+pycPfBaJNNJ0uiU/4CTD3XUWtLHF+mGwr/AZQyWWHPWjSXOS6UewWhAkliZohKQ0rEEAvacEfIscE3sZeRRB1XEgZvWOLHkQncpO4V5FpQGtDzanbzkHraXB2GXrptAgh6Sgjb+WFiIjJYztfno5EYpSEZshhk9pk1LZ7yYl6JLpTKRtlqnl7XPj+cxlrVgSzYqkRRe0jSIEwU0lnCpuIhFy8CPlsQSkO5r+pcguBaieD3nZWC5aXqPCL8Siw0kT/pGaD7UjRSslrjPMZtq7UD6FCqp7JzGSv9wai07c5HI8Jyg2/qtm4/kLN2CCU8pB1f+hrqKpXh0TlnSDqMSlYKQ1PIjaYPfZYIoCAlVJSMYOLLW1U+tBjANqtUCGr8A2JSULdY6saokhZIQUsH2eEYADa6hy8twadQAmhNJEWYHK19kyJlE9WLE7DRLCfHIzCNwX64CR/Qxuzw+Mc6Yi6B54V4Ept0WRoeUdWNhoR6ul0MPf+tw/MIJ6oKs7KQf0gyx71OzHDloSgok5Pe+9z0kM0CTPHhuvvlmhKQuk95D0ApHLH5mP5GR+J944kkpneeAYG12dHSCPjx8/Kmnn+7fvZsSAceZ3AYXWWoLp3jM+cuktr8Jnr0Y2b5k5Fk6owa4ypEo1uukDr3dqkQx64pCQi6ssvIkycGDB9k8zLh24403EmTgoLaxtCmmEYQH27dsQXtaWp6ntmXXkjcS/Pn3s9GM70FSmQMDA1vILe/ULnlmvpvxlHdHyyGXLcxiC0nZi7PeQQM70++Rrk3PWot/ab4oMpH65W9P911Yvv9zi/kxDvchn0ymywDodnZjFBGIAAhW9/yr2tW/2h6KgqCiYK0MAgG25b/rXe/ig8BcCn2mC7s2F7/wMefGAKYj/fjHP2af56c+9alTfHk0n/7d0X/38cJn/yr6/HPxRJw9S7n2zuBvvS98zVUct3dHyJO9IPozstg2wJwM97TimeBkyytXFJyaXKa/MTysz4Gt2q2PQyyVj82AKcLIxBCC4cHIyqByySWX2OrxSSlsEsF0FUtU51wcfXvnzh3S/62P63cIMulERBcBWOPuaWupstRbzGeuvKJ+y5tC/3J3bmExGmbQZZNXqtTRlXj/e4erlWeffOTiwe3pULk4/tw37/omx4EWF7Pcszj83CzT20612N3WAqRezlWKC2wni3W01yuFOud3I6FqKcs6a0Q2pwTLbRnUtlp+ri3WkqsWk9FqOlKJcIAa3UU0OObLu2LyiQLZ3olmNXDehXLAKlDPZecnRkcpRbnEtfj1Qy88TdF6erYnW1q1cBDXuT5PlJGWsBdiPRjLhSyqNSeMlxsqOzq7Wvk0OpmscfyD5oqDMXoNr6+xLaGm2I6AlW+HqJUHikCDCNRuuummxiQQ94NEmXrnPyGFSQwC14NhM0sVec7vRGZ/AvZjffWIcRQNCTvZEoEAQcsOPYxGbqmA4xjGzEPLZ90bv88YHjjnySCEUoiHsoBgT4xtMoLUI488Yqn8tHRA2471wAMPWEF8miwm4Ag+/PDD2NiQwhllKnbfvn34+eQg/cXGV0PgFVjdotjBpCUxBIpGKiBQY0bA8O0JAhXIEzucKDyWkcXCIf2R6oWgRfkI6KBWY8zHGzIJ8/XgYj7G9QA42iWzWqawW0ujySUSfOU0ScOSJkeDluqSDDVTUWfx87ZMyxfzg3/aP9XYlXpfqnvIqR0jtS3N2Xsp/Ao9Q/SAGqs4PkS7kMC1+wu44T8UNAaoguXX6KpHXqNk4jrzCkhILDm44ipd5U9mhypMbZXF7JJiSpw6y8cLLQEpFdfwAl8sOvlqaKHo1PNmsYGjteIz7DOrFSHRwmuwZcdefrhheH5iZMeei2iUsDifJ1LoIGr8TN19w25CK7zScAvpFRDSWluWhYa8B7/m/IKBKuhWVbxnyc2rIZeum6Txx4+hiJLE51GQsCxtoZKp5nS37ADMzk1lc4vprj52d0gky7MizXD1lpakBivSHLl1lwNKsjJLK5CGwD8yQI6z4tq9vZdPsJvwFK4lVvp1XG+4QMNkektTCGFSY1Ql+RofN2AWi/lCCZCQ1XS85taM7Gwm0cLColJSk5eqrwc6u+RTrVCb5c6t6eI9hdEIu3I0ObYlgsu3EhXmPq655hoMAAJsnzFZh5/scPTrwcFBPOzOZSrQhwPBkLObRJjO40wKUWQNHA9ixGxIOi/WXbAUn3uhmy1Pclo5nn/Ve2OTh0sv3BmWk7kBZ/yAE2+bYAE6FnclJAKNgkONDis1Gwxi+XBxIxDGcaIoIHKPdVqiDEimRMESEAOax9+Ua6a1AQ3Bt1fZS4yspixYPlAzR0Fwq44mFsU6JM78TU8yxZkaQz1A0Ecga9iGf1oRlUO1IwApIwg+mn+Xng/xPT6dl6eHdsXAxGDB8vUW1gkLKjiqlLOKvLgzoxxu5RuDTw6I0ZBo8FtJ16NFb7I+CEDEnc5/UeG0VZyH1fQb6r7I+eVPtj3wxfmRB2R9fqnRK6JN9tHho62lV/+b+M6rkjrGuHKmiVZjkHw/9KEPfelLX7rvvvvY7LCF770xl5e8/9wYwFQrp7cRlExSnuqbY/EoEb766vAf/XH56QOV0fEA3wG+5NLwrp2MhCI2N/bqkLy2bLUx9FPEoqugH6Ps+qPLRgiRCm37jjvuABn7mVnev//7v8fPd32YGW2isKlqZJcRIyXTw4yOcIXFgolyJuqhiat8vsANEwyryeSyr0R2d3dxnUXlhRfrJ04EwpFkb++eXTtCyVRpIZH4nd+t9e0IfPP/q2WzObZ4XXF15kP/y+yegS/9zd8igy64YIDrndmrgw7f3dWZaZFv/xYK+Uw6tWvHAIVCIIKWX5yrhKPpm66vPvIQH5USk7Ne5cRklRE3WAi9+pc4kTx64AG+nDw2OnJ+X1/lxE9buzs5I71jB9eVOclLuFcrkG6ZQZkBB50KM6JWd+bmA6Ocja5zJYwE+V4Uc+ndbbVkpjrBddzFci/HhmVDmqzR1fgMJGazGtUkYZn2mWee5OQzog5ODj37BDpWpqO984KhFULSfdWkRVEDjTBP2gOqCX5TUPA01babbG34qkkaiTRuqUL0M83MEzZISJ3zZFykF3Paiqdv5TJSNg2WKD3ogqRF78G6IxVpmcKk+RkP0GTcwhHEWRY86Z4EwXzzm99sWVNwvqkAk2YH0ozZzwamxdrT6IPDfi0/yjw2okMThqlMgAbnaUvKRJnHh+PxrXSrHBv8gOP8Ho05bUvTAK0I9nbwoyJj5QInucWi29naBWsaKMrg+FF42BRDzcOeXR9qUfaksMYMVrqfaqEWOzg71Nm1jdkfXs+x46NlTuojAV1WOF7e1sqmGOTJiXFVQKVWzaXSaQ6045+cnMZyaJSbZNTb14NRwnYMdAsxNFyHwh3avqMPW2Yxm52elh3prsSVmRtnx/Ze6GDATExOqu2oVFUi927rRrHlkyCjYxOUSOkJWUwXxAJ328DkmJoNPkmiWzOt1DM2GDXM5gbQzcLBk04lxbYPsGN5DuPHKErRndpgV/Sm89N9KYcTEIWKc2Shev+LcyNajkg00q4H9bn+HQEoNrTnmGfr7GxriVeGWmuBzhaObhxdmBqf41YJwenhBlo5sFGemWHhUviGYftFRvBa2UcxNcn233p+YYqqCWemsMHb21o54ABP4xNTbv8hDXNv2oMSSbmQb2Jqhs0mYnoLPXEYZmm5LMextUSFaVydIz4J3iioXHnIGQqgVgA8ZIQkJMjbnJ1fcMnpbABGU992MV9JYm1V36mUAQK7mJQMBrk9YfTEGGXUYoErOe7YsZ0vhLJ9Znj4OEFu/ykszC5Oj+aDrdt6e5leAe3IkePIOWKNGhQ6OztaM9LTR46PlbmiUsxkyQhIW2umo5PFT2diaoouAMSYoPFQ5HiPXBvOILUwv6CplAlKzc6clhTNYKFQnJ2eEs4koVQZBnVXB5cCIIqr2YUFONTMxPCnkmPhTtolMoLv1i8UqxMz2VSoJBcM69fghUiDMzsQgBlgxNIyESxA/Ch/sg+RRT8Fx4+yC5OADAwMMLBaKov1BcLQ0CB9/Nij5YUyOx3rycH5wTdmB167e+jGRN/e/ENfKFdno5Wp9je99l/FWpGQUmM4lklx5rcnktCEGL0DrYC8qKLBwcFGHGQsc22NkEZ/E0GikBnM1JstSlrMUZhHFCAwLSG5NFJo9K8VBU1koO3HsdcN5ySkfkxW46eSeTJ8o/Ogivg13EgfP+XF2IClJvjLNmgvndkBWyFfq942Wz/+q7QxxII+cLPUzg4+vYDWxUQABjBsnwluUbrQdnzKTD+REfMO/uxMY0kVDeHkRNPBGz/c9vzexce/VK3nl21TR3wharsvK7/mQ+lUF6s/ItD8Lt9IrckPcSb6KSmWFAZwU+wrwQ3WwLkxgFEW+Y7zhz/8YdOAN8hrE5o0nUSsvmcgtnt7nfGV7TSy8AvWmgK6icJZCzKWo0GaXrupTFkk/93f/V1LQk8z/3/4D/9hpQG8EbJQQO9h8xVSkjlsBjlWzxiwX/WqV6Fwn4VBhdEUHvyFStFgkA/YOaNj5X/+duWb/xwaO8FCaLV/V/DX3+Pc+LrpSi2USWU++IHMW99cm56NJOLl1rb5ROr2O374zNPPsHUWUgzMtCIMDIrDaIoEpCBAUAgYLJkjZ4xnnvvE3OwlN9yQO/BE+ZvfZp8ijadULYWwQ3/51uR11zGHXylxNFekD3d1HDjwBFvL0CA5bmwq1NzkxNzkJAzvu3xfxPtgfXu8daBHpkiZzkdDajyFEl0o5mq5i7r9cdraJAREuolyVnd2R3rRhvn+5OzsHEvWjzz66J6dF1+yTa8oXfE6MbpwFIf9SMg+nmRqB6iYj7d+ZAgYmWItrK2mrKC9UQDVSzGpZ7QZU1xQ6Xh9dGdTECFk+QLEUha6bFsoFUO1WjISnskuYPbTEegOIGBn+vOmTdyuDPomKAl5sz7HKKn+2EOUD8cDkcHBwUZIo3/l3jk/lq0Qvr/Jw3oIrgloQYYiawDGRiMztFJbSgLoO+tuLCDTjH0gpPCjxeKhyOwA96PM488y2Iq0AmtHFoOdB9v4eKkZVy2tGXbACyHljOkeqpwAU0WpVFIsMTHDiRQjh0MEsoAnHrZ0ki8KuVuNomhqK+LmgXgc+41/RpFPf7PuJ/4wV+/yWXDpx/KQbZwsFwY57iE3EnLYiTjZRSFPQZHvcIodgaWEqQhE5rvJHwyOK/LtNNCqZTZze/SEIXauVvkIOcm5LQ/jU4iJE1briRBfLCdQLecrRXePDFNbNwyl33x+qC07Un3sUGViMtPd2TcwdNHlHf/8/OJDBwtBbKKQXmpKZvLJACUoN/qFY/H6ree3nZesxnOFmlw+HyoOJJ+eD9y+f2q6QF6yllgt10p59jkvGc74gk4ba/CYWcUiHxegSPUQ19cXFilfsJ7CrqZs5WJWrjzQ4rkdtB6LcDEB/FfyXGFgjFjhAgmqVqqIGuHj6nhEPmmFOHXOewrPTOQhunT9XuhJXQWQoPJ65Y5fucVXCGiH4vpfDmFINXKoORqW73AYNaqepDhNG0wwDWE5KZQH8XJuLYh1GpcYvkibTLR3dYajSWpEmxPTRikrVzGfR/C2ZFqiUbkRmlxaMmnkA6nITnN0WOPlrAvhTDqTTMjH8zQr+JHFTK6zwqXTmXhCti3gyEOewWBFNs0EEnxTVPqIFkt/4EnafCCQiCd29++SvIRNMYAlpd5DE4smhgaGqJNfunyov5X2u7SZk6b+hje8AcSVDiJ0w5Vwg9j9C6vGrrQtfTRqD8n55HPz3Edx/ltrV72tx4kJvyDselWqY7B8/+cXJp6KjjxV3HO9bD+2hL7Hgrxj2y2MNGYSbWhoyOQGsU2Yhr/xJ2Oo2dXQsW0yjCwbT74qJhOXzDQRZaTMb5O5AFcyvBICGkMeGst1113nzzKsmtfLEMgQwxQz+hVNYsuLz9DmTwZtOfEtJEgvlnmlY8eYMTwTyzmwimZlM0FkQUtG1wLoD8prlEUkUSBUOf/G5PCDi+NPiZRrxGRj4WXviKY6WUMGviyqEW2lH3n4vve978/+7M/+4i/+wp9IWon2CmSdGjgHBjCC+/bbb0eD5+WtKubWYdeP8hLq4BCNOVF3m5CP8HPlYfmXUtNzPLY3yh1DLzMFYPO9KCZNP/3pT+NvNLQ2SsjDY2CzsQdO6MAEeeI/CwKOGkBAw4hpBoyyZIrhXZ+cLn7jq9H/+WW0MLa2sdQQPXK09Om/YKGz/cbXvzgxvRBLOFy0snP3+Nj4waPDM9PTzz/7HLtVEfpMyDFCY5JBnOGfIB7mkhGFTNQxnYwHIFthR8fH/ug//ceOj/xvC4ODxTvvrE9M1FHgbr01cfMbCunUgf37WX3KLmZJwtomXCFJ9+5to36oHGoeSYfBJlKvUomGlw47kx25i3qnjqAN6ngAyLqHbuqzrYC+dEPIEb1zu5x+h+b87HQ0HOjuyAzu2h6TO/xXEYIYjbDB8GbSloV0FjNtN6+ZUpq/HJQCATFtwS18wtXAwAAEYRiVi1lPCk7jMQ2JegMIjs5AS9nrnME+eqj48CPO8DEnGQ9ecUXvxZek+gfyhTyXgoLg27T4N+isVletHyg0wZuCjVmsE9WIttK/TsLGt9CUEJ0e1wS0IO3Tlj5WxtI7aIQr4UBgY3Bw0I9aOFFKTooNY6crvbFQWpG1z7n5Rdnxib1h388Q60jICAWZjREvUZmMHlkXuAItVqLSrS1LX99dmJnKzs6EnW5NlWTLq1DANaQjEE7E07sxRYjwqLm5IW0iQ/27NY0br6y4NM4f3CVpXL4EiKlkhkz/LrnhTyD2I3ma4eD2JgOf315507Z87MD+2c/8j+qzz4eZd+H17L2w7bc/9M7Lrmxp635hjmkmybO9g9upsISFHjURD1VuPT+0uzBWv/uR4g/vrp4YC3S0xW+47tobX9f/xp23P+/Ml+U9JpOJoT0Dkop0yjrU+COERDtvcFCA0liNa0JiwRE9NNgPFFQrHUjaoiT3/p1aNL8iACltFjx39co9yThZx5VcNaQ/vV0d9e4O8bpZCZrcIii3xLV2tLd5bEhC4U9R02l32VywXQez4GC0h3ds3wYMRC8GuEQxp7Gtp1vLIZECUn6sMJ1cgKd8Tc/OFMplvg8XjcmRVBBl14xrhLtk4YQIZgKi8YSsjEtztMqkGXPZldAPMhAw9SJMSBBKCFnuLtSLFrgVUTCFC4mUX/b9iyPIXATYBoeOHFlmg46EScPPVC4wgAGsfUDTSIcyz8rnqUWtpONDpE6cQCVbmz6Rv/ETbX2XcBaXkrnWOHZ5qif0pn/f/uR35od/UtpzPUvoq/O2krGVED/TTXlschA9gQUulA1/4XpTRJqQmenDAWQU47mWncagcOWVV64lSxnaGHGw9JCZW1XYJj5/QYPoVFQIi+fMg2xtzUDNn1hZp3JQA9AB0Em2Nvd1clwZRdbsyMAAxp0JAxj6aIZ2Ltomicy/kpMGiFcf9XBhvjT2AhJzWXdGfjFLfPSxyrYLkV8IgWWxDXRW8UL6ve997yc/+cl/+qd/Oh1jahXSLxvQ6mrZmS4+O9e5huF0DLkzzeEp0Gdgk+sx9aQXM+Y6/hKSGxlZuEOyb6C3LMuW9o2gZykSwl//+teRbsxSe/1pGebGA6jUvX29dEIu+GVFaPv23l27dq+lnW+c7EYwKQVDKbuezDxjIOQwKOxUjh1yvvFNlk5EtRJ5IMRi5WLuq1+NX33tJXsvZnX3wIEnr7ryqjnOBRfyyHoGQg5zUjnf/va3MYMR+lQO9FldRPtHCtvqItUOhBwZMhfn52//5++/+ZZbUu9+V8fb3lrPFwLJRKStnQ8onxgbJwtsXYggxFkkZ4L5rrvuQpiSBdtoqXPGAMgyA91kXpIpNQm+CVyMQK5rgn97TUThhw48SKkaXNN7xHBl1MfMboI3pBAvbNjyKdoJ+oQZnyzpW+4w4+Obf31qPvJGPD4pPOZnpxzlJS1BKhAPDYkPn7Kwx03Q5Ucfqv7FZyNHD3GvGWtBla9/o/S2t6be82upHTsJWqqN5NuEw1WilJplWEzHpqiXWNAqea1C+bGstedKjuxKaBw6dZhVgzhYKcvSHwu8UbEm7A8zAENCE2iTsU5nBMwQUXvBAMbCUttix34QQefuQlxuOqg9QjKhyn93tF+iYxk1GGuK6SbwyrrUij2IbeiWkJEyDPG7uEoXzgFFnOrVfU7i0IsT/+efRMbHwwJjpa0eePKZmT/6L51/9n9dtfuigzOsI2MDK4teJhC7vM/ZWZws/sNXcl/4CufypRK4g+2hx2L7n9z2Ox99za6+7z3P8Qkh2JDIvMoIYGPILaHWosSb/eUmknkK14mlRhaaSnyW3KcuiBYwspJKwppIMpP/ms5LKhg4S2Rev6l4jEtkAwIBlwS/mo889NVpSHG1gkmk6fz310AFgSMZMREZjbA9ADM16t2e7drJyo08NKxZuhlr9RgtyZH/lrH6wZE9AjCpNqK9ZON5GSYXXAm+S8Z+ZN+y7ionKKWnPgnOyJ5xa+akONtOWKoHC6XCrX/QnWgTlZfl1aeeegLdmlld5TPkRAL73pGZPJLz5g3WYxLVgrn1rZKH1lr8oYSgQdbj4GRxPgWfLCl8YGNqgGvNxRPFcEwso/k6+3caqb1M/NSMTdnTBtxueNZLjhKCDnNqOxO3kFm2g6EjofixjQuFatU2djrZGcGTNuPlWajwCtSO7i8GShhcCGwRoE68XMtH1U6oDj9evvrX2Li4PN0GQuiWXBGHPYUBvAH0V1Caa2DzVd5MYdNh1n65gelP//RPt7x1bpqVrU7AhtZK7q7AwneqpUP1YCQQ3xdqecfUws58oTA0OLjWvOZGuED0r7OlaiMUDIe+x545UVScEB/b4RtIjES+tmFag2BKn9041Q1h8rpt36aPXSqV+UBt5dkXQ/PzhUpFMmSXIOuKunDqHDpamxwPuitFCAz5/vtll+3D2ONENKdMkXHcAo9RzaCI6cXhRlZ9sWNZhMRqRS1AGmKhYVWiH8Qn4iMjx3+6/6fgd3R2Tk9NcXptaFtvRs5Ct06Mj2P9kgTRiUyBGmMJq6xIOhx+e3f4feZ9D0DsbRwFhIK9JpadWQrmrYGmi6I+erMHu5rhHHubcq1UYlhbZrwnC7s7hCV0DGzQMALJ0Tik+KzKYupDGkwqBwfD1PaZ6GJMAdieKKx6LH+m5Kk0/Aw8ZCc1xJTP8NHqf/3LxPGjfFIZx8JLpFJlmqPYsy3+znfW2bLRXA0bDZMXZef9bjTBSx/PyZaX1rzkjdPJ5S3UR448k0p3BGJcSlQNhrzLlzSOWnF34mr9uM3aa92SGrgKBq/JY01Xc4vzvLhCNsemhzn5FJbDx8zC+s054JpaHmKxuEGkiII1F//h0TSAtAVJQBIPVxuSFzAsIQhE8fTh5eCyaVhg9Gaq7eVS9rs/DHPM2JNiFIV/wdHx3Pd/tO23+juTseMcjxWC/LkuHq5cxJfOHtqf++LXonKNOgdr9CYnJ1T+/p3lSy8bfPvb2uOxsRxGy1IqLzW/AF3OGoCe1+feSspzFXQl65uX0pvFTpBXupShSG/JTCAGlbABJaKZN2IFQfPHRxLBXaKnafThUvYAikINak4uFUsn9NyiCjlLIExKSHa7cFs/H6+DeS2L2a/WEmDDWJVXoyvjWkS3gC4pjwNh2vKSNMKIhLzaEBAF0Uw1Qlq0OVIp17pabA1EkYkFZa7AU2r1XDnybmm3LdzCBLKabUdIdTWAXc7Ycd7VbxdEn4RNRiv/GMhJUDcTDT+MZRDfTKKT49pc7cnxVsOAGUYcDC2UBEy+1VBevjDOeb18C++VnK6E/oYeSCNZuergYW3Br903tnEhwjLv8KPsB+QgTCiUyl/93tD2izMPfn5x7AnkUDg/Fp0+WugaWtq4p2LN5XOdXCjvr//6r3/0ox/lgP2WGAhbUDW/UCS2WLqdtOy8V9brUZrf+c53rvNeT0rnTCPAJw4ON8ak3OVbL79QHv1oJHcvU7rutHP2n6tT/7Ut88Ebrv930bjYQqfs/viP//iU0zYmZJvFg4fLfFthutA+NpN8+NCLTq26e895zJWxpwwlWY5GMX/vBHa0BOLucVTTNE5LZaAyjQ1mCs1Uow2MjY92tKSCss24CgOKoHWu6gy7iWtypadcwIOViESz8Q/7BwccCEYXa7ZIAexD1oEZF9maglkIBKMXW5c1WIxDbGCWnTFEofDggw9u693W1dk1ODgQpqhis9UZTbFduWGIlUzkJlcE8wVdkuj75zaXTpaFmbgBE3MUgrCKcYuxjQccCgUDeEjLijRA6MBk011QwFc6zFcwqQ1yWRkLBMscZRwP9G11GtMXVQm/rU7DFVVBkcGBSYpJcUBeldrpA1GMcNCxLW1UAkapTABkuHxOHMZ66bHHo8ePoWXaW5erh2V/T9D5wQ+cW97E7kxPHdUEG35QD8xokK8VdsPpXtKITmAmH+CeXSskqr9MKuvLr1UczJCqw5ZSlnuDdWYj1ERwexoJPFtC0rrGopmK2nrM3hBbQZLVK+W5Udb55ag84bkTh3m9qbbuTHyXZS1kNZ29dfNKSvW5Jo6iugD88n3sSjAst0CBiNgRPsQhByRPL2gwgQvPAhcHKn+uRSVxcuS4M+XEStmFZ7iqhPJaVi66WGbPPhcq57mF7wSnJQzsPfvSgdbiQuHeByOc8dVtqcwVijxkkZ2P/fzLvelb3rC9LTaeF5HhJXJ/3YVHj7GlWK0L0OHDCgbLxraGjY7E4xMUA3ioPo52ZymrH68FcykZ3EtEHQiaSQBJo//cWJIpCf2RHTdLDjkitauEfahX1yqANKVxIBkIaUnAQxIaYUBUWJAPrdfk/Ic6iZOsLKkH1cwEKB5rtIYAZoPTDKzcWpkay0PK5y6sK2k5wqxMKUsSS7w0I7myvzHn4DyfZqc/CDvLs2rI9Qx7l+ULrwhPhi3mTE20akG0tBvgw170BhA3h8IgYmPZ5pKdDNsmhU+ZZ0ZJhmCGuVfU/caatvqUDixdTZSoxtgz52f0Z7Yd+nhovezPws8AzYzMWeOhqXQDAwO0DWtmTVFbFaRoGy8dZ48QRKVsbfQZVgKc1j35Gz6czuxg+qb++n+feeq7iwe+UXZKkaOPlrqG2BknQswc+w3Rb9fXIWHjV37lVz72sY9xUpLnxrnyMnm5/55tA5j6/upXv/r617/+jE7PnP5b5RQB1tTg4OAGSKnQKR+pHHlbtPICgqdajlUm2cXE113LTjgfnv/vqdR4tP2vN0BqdZQta9Z0RG41ZRYqEGrp7EV9yFam+T6HMy/dTkxQvdtGdmU59fZkICKtQyQp6dwNk6szeHIopguWJxuw6dJYbpYgl81n88W2PYOBWEssLHfbYApio0Zk1tnJ9vaFu7rYKkZaUmFhYtpRFWp/yadoEbgIO0ixLsop65tuugnKxBJFEOsXs5BRnCBpWT3GDAbOXDtzhO94xztIiBU9MjKKB4OKmWlUEOxnyAKBDpniYTgB4tu6+M0c4YTjDgAAQABJREFUJS/bAAwC4zGM4ZD7GLSkMkcU5ivjwTqLwOBYhaxqAEPTbl1i5zNbjm1jD9xSNLLQPOUF2e56rGg4gYfTmWV3Wd/AD7k3YlmQ4gRKFT4iWcrlfATqn04SCUVCJybree5f1Wbl6tZqv4guuoxaI2XzQ5kKp4xmeK9EeHlCqDkutsXkqIkBQMflItxcmeuguDtJPjGTW1zMZTmeHeilg3k6tVfXnnlDM/eMCZFm0uoba1MCbBkJd+wcwssNwIXsXEfvbsxCOquk9Z3nVatFUqn8UKi8XovmKQH+L0yPFXILPf0XKEPWAJZIkKfbKgym8WqzueQ9g0hhwgNmFH4OfGK2yilf1SaMrOQNw8ABE8EhUi8nSYlLchCfmabpOc1UspDqkT4mc0u12flwmbvcgtwMptVsidxnA4NuIRuiXfY8c1Nwvaxd3hSCXzG9OJeCvbPGRJK8CakxGj/lNMoC93yaRB8uZbJrDLrxDBFrOBCEln5a12VBwrQVfpYohcLcylVi5kXIAKbj6ut1g8aOBDQNRZbS2DeULGtMU+G/AdElr2Bj01sO9rNVYQMnyo1MfOgkNJlAU7M3clLDU1kOBYeZ/VQmzs1D2V3Kmhle7DrGI0S3H+V7lvDOlu8MZX36ZG2UZMREgTx9an510ka1mUJyWbPzEX7+PfCPLYri+prXvAYl6uwwTKasQJAXSpHvZ4w+O7mvmguKH46oxlcJb6hwVEujerZq8i0HIsgYio/tzwdLoQveUr3yXW0h/a42kottWZfcltp2Yene/5Ed/nHwyncHnYb3hgGykfeIOsQ3MrCqMIC3nPmXPMGG+j4rZcXGuOeee/7yL/+ysXWelZw3lwmNzwyh9ZPRr1geCDiV8vgnopUX2d5QejQT+Ep3eDJR4erS8xbC759wdmXD2X+qzN0Yaf+IaRDr0zyDsegZfIonUOOLgdgS9EsU5LHZ6vTBaTSHaDIdYm+qzFfh+GJoIB2RrZWi7wWd3Zn69oysl65kjyrgSCp9lUXUVRFIwqopgxbz3IODg8zPsUeFrbyYhSxKObt35W+8Pvi9H0QjUVGXRGVxihy1e/tbg9t6u1sz+VzBdohx5xP0MQUZBTGVsS0pBfYeH8FjEZI5MAwtXhxXd8AMr4/BgFiOmmMfHjp0CB5Y1AWCnvHoo49CytCwJ3EITdKiiIDD3Z5A/JJiMOMIPv7440zIYUjjR8rjKDsfB2I/DEUjyHdrWIhGJDF3ThRZYJxTLesYwJDiRhAmLBtVHz9rKJCcoIlCzHLzwK1v5YJj+L6HYKPfp3YmPEwxcB57GeVgKJBJcRsQ98zyKSnqgY+UyIsNhqqpmE6rAANQq80vBnKLfNOJJXinJcPlQlbpazHPqycj5jLWQljGRkOA1mKLBls+/kn3X67/N2Rr3lW6zAqcjQFWo0RNFssyg6PzLlKvuezM4hS3zYmdV84v1Au5cLHE94Wiarn5OanZory7D7M4IIBHnr4joOYIn1eXW7KClWKtuCAL/mJuu6iWwGOwwUADZBIFIm40YekcdA+sUOaL0EpYmiZS4pWml7XmTMCoG1QIevEuQfdHoGzHKAQrnYnIvgvKj/6YMkpeHjqWVejSveVYbH6Mt8bngb0I/c3TWMMhp7eHq5V1SbIeEpsV00xaZXhbRykSyjJdCMUV7W+Jkp+ZllfhXqT7Kz+SdQOmx4dWsw93Q4Q1iYdk5fGx7N1YpOWwVGCv9JreNfgtoWEKJw1kPa4EhVfoRxmKJgS29NIF7kJ9qnJDOB9gj8lRYIsVMhatdNSrDYSIZXC5LttIuowoA7KTeglRXoYgyZvVa7EER/7TogTOHIemkgxkKNM6xG9mMCjEZ8uhbLkWO6cGMKz6DpZMppkB7MNf8TTVABXF2IqBt+Xre+gPKA9rXc3VxMbPbZDpe4Y53GavmznlEjH623Ek9Bwm5X8ejibRSNDE2JQHb3QrKxrCgc2A6GZb3nI2UHXIpeDoi/M3frx1xxUJxk83iQ5OjEjd58dv+2T04S9OzxzPd/anmFDAwTBzCmikyATw0fdM8VuZHeV997vf/cEPfhAl+Uxs2ViZ40sJclYNYF7qt771LZ633XYbr23VerQXjz63FsKqqbYKSINDiJA15hO9CIUbPyuBGB5r8QO8WvxZaOF7tPLyEy3Bzw5E5CYsrl2pBZ7pWPzPofinjofaC/Xp/yfQ/m8CdTrAVjF7KnRiQefVu0PTMwsvzhxFdWeZN7tYyS8eRcvrbNmdTHOJAgsoql8EQyWuE1VVhA9tlGRZZU3WmXo0q29VnninyEeerADzpFYZbKyG2VjcOjiQ+MAHuK+5fuc94Rr3OTu5TDrwjnfG3vzmkH5G0miS0DbYwAa2H9YsLwuy0EFS0PNhAGHBSIb4QF5g6GIesxEaD68SDxR4lYODg1hBmN+k4jgrBi1nZeEfstixCE2ya5QjwElo3BKFnydA5BEOIgQhiJkKjsUaxEcG6MNJCLzRGSmYbASu9JMXJoefHEMd14RGr2FrtC1cN0WduaDP0lIW3Pl85VWB1NdCxUK9XKfydUlfLqctXXstX24VjbZcrgwfLf/wjvoDDzucXt7W5dz8+vANr3N6t3m3Ky3R8302GPDWfMgGPbQ3pkvOxK4TjhOcWKzo4p6YRjh9mt7t9h6qiBgDMaMkWJZAG5IfpSHpe0pGEgii/fFjKr8AyGhpQwazRdi/csEVmUjNBsJdPZXFmUAwTEdOpNK9rXL4IhpLiB0na35qfAhtxVaC0qbJyH4sK3m6YWj6riYmRjAcijKx4eMrJekYMMM/jwwkGrweifzC9PTY8UwrFxRHivmFcqU0NXaczGPJTIyvaoHWkEq4aHKN3CyPIh2ZH5+rje1KDrzpTRN33B86zOeX3YIRWR3a0/bGNxwKJMGpCOVlxEfmatld6fabrp/81nej+byUTmYLeVX1StBpfePNhWTL8PEa+xs04bK0wogwxn/gK1n0gby7xoQrMBsjhag5D2otzINqhhpl8Q2xHl2NkHdCxcj7FBSxC63kggWGn86teGmciuhl1IAihJQcDxyIfjMhJJQ5A9zTrac5mEVYoq15Ch/azDSxR14ICin7r0/hyqL5IQc3ICDlDpAWRh7CgOIrY+r3SHnJhE0vF53PYH7VrQMvHy9oSaXe9L+k9KgA852fXCCCKXRWxZQ4r0zmb3y6dExTN/mm9FbLszHdy9XPUGvW3SrjzmnUCYMUw/dpEPi5SGpLCyhFqBNbWz9rFW9lLisha6U9c3CmAO6//36mM875pVxWRmTUte/uiiSxtsT6tSrynoISTQWv/3BXpSBfM8F0Z6MisfgxRgji59YVmXFezRF76623oh9ytvS3f/u3jexqiK/AVqmBs2oAkz8vic83r6OJYh2xPEjDNVNkFZbPJAiTya7pt0zghFaIXWSLfqvkLM20Vss9EHRygVqo/u32ENYv4yCjplxaieUXyP8omf7XuXD5OW7GCkYvZBPeORzc6B5xvmbR3tp66YWoCqMnxhZqxUzPdszbcCQmVruotaIt1OXSHDhVZlFlvHmrVSrB69KrRhmQ/V1MIjD9xkiD8Tk4OEiVWvcmr8L2vvTHfqf+jrdXuPgqUI9deEG1e1u9ox0le/jIETszLB2b/7yPejXT2sIe5qNHjzz62GMPP/zQpZde0tPd/aM77mB/gY1kICEE8TMrRmOjUf3qr/4qVjFLuw8//DB2OA2MVWvO+vJdQbuay0x0u4ZkZUGgTBFYyMVUhvNGKYNgoiDkZcYwSgyWOeYWRAwNm9yCbGBeuQJpq9ZQblxzXsmAXT3lwxsZaASegnHoJ98qDxt7wuddUPqNX6v83f+s1fmmK+s6tRrfU+0fjL3zHU5bK2u/1eGjhf/7vyUeepAtutFoPHj8SPmnPy0eGY6//71BPvqyRh+hdLw7f1r3pAzTDBpxpO14kFUrsBF5g362Kjw/GbJ1J4+2JKW1krd0HvfH9TTiqM4smOJ8Tt2wAhUuAJ+WoHlUZS+q7D9ZLOv3UUXmmA4eDkQwd0PVQpZ90U4kwQW5FVBtm6lH2PuFouXtcqw/Fmlw8XtmhaDGM22JTFtVFm0VzNN4oigUbymREXGfxCk6S6q1WmFxkcup6dBaFk7t14IV2n88mZQyWYXYT2M1CiUysEQ+cctPyGsKTjwHHzxa7ERo/6c/mPn8F0uPPhYuV2rRcPhVv9T1od+c395//6FqJRi1hUSokIMKunqpFj4wWb3+gsva/+0H5//7551coc6XgZ1QNRxOvOdXgq9+zXO5yFyeqVmvhC6fPifQkuLzrzEGv1WK8kdIIpGumgxSjbguqeUgyc4tpPyujHRTuUheaOnXmNJyeoSsHxhVPX9rWcCiC1tWBJ+U23qXWqRhN3FFb5ASphK1nS2q8JHeffdea9dCSDKvNLw/KZpmANDepaRz85a3K1SlAHoeRxqGvXIPRXEVqMlAbHC2GqylhFJIpirdvRuCJKWVRiDZEyEUtVGIlBBMRXCpWXZC3X3bCiCsQQHLf8MSqhLWJw/+XKcFNb8Ak6nEtt6eVEuK0U2kn5fcQ3/l160B3gjjLGO3f0bpdKrGFjyggIcRHD0BPzPIKJ9bNUCcDnubTcvCAJyjqGw24enjMzSvr8CcfhYbp8AOQeqBs2+sSLPxEBXL0rJqgqoGn8wUnEVu6czsr3TvbFurXbE9K5qQVsfCBtYsDKOR0shtzz/bEtcpPms/fCDGDOB10F6JWlkDZ9UAxja4++67P/WpT63k4+cEgh3CNgmYYTsHAtHOmWA4KXv+6LU0OskoLNuFx2T/ITr+iO7hNF2MjXRskeUo1HBELACnXKtMYAArqXPz8PseHQyBxcAeDk3s3Rbdc37GBmlTBDF83b+afMSJNU6CHH6TIqrDijNSCBQccMYPzFROWUCT3cK2a9cvJAKImQUmbhE61Al1i4fP/bEZHgMVUhBs7+hoG9oTHNwDkVwwtJDNLT77DJmz6jswOARBKJNqvlg7NCVnJ9jlOBPobum7MDTPXu3e+YXF4WPHZ2emwYEZZB8mKy+ON8hgCQNQgENsUXJnrpcoigIyQTY2Y6AyX+jXj8+577EoW1UWhchzwJmcg0lyNLgNP8zb2YZtioMsRpBZCoqPh3yNIEnYp80Wbj74jGnnUV3ldx3eVsE+pyAs3lBXR+xX3lXq6a599/vB4ZFCKhG46urI228LnX9RIBytzc+X7rgz8dAD7JE21RKdNMJtTV//emXfJZGbb3Ziup93RSlYxsetAK8JoHqZ4JBuiFLrOPhBxX4eHBxcM80mI6QpuNr6sldkTcRiXZKK1qzaerJElW6l1cCARKqIEdtRHCS8BKJNS6J8pSYTx4Eg3bRaLBXz86jsxWKJPSjFbLZWD09my4lkupNvL0tSo6PEGh5aP0pf2raXl+Stbd04Jz/PPjImPFrGE5heITQn6HiE5BdKxXyWe6lKhVw9HG/t2cVVgbn5mVq10r1jDwtybOKWi4OXnEgj1040OatReN3K8DCVup779SCHZ8PfDdZuunBf23/8RHRivDozE2zvLHX3TqXbf3S0dnQhAgX7xpFLUn8wiR8dddqHkntve2v7eefn7ro7dHw02NWRuuG62uVXH0p03P+snGtFOrqpJGO3JowlKsIKwNM8ikmtCcDdIWCJlYZWXCPiUnJL6D5XQSGGrN0IL15+jSFLKGH+6+sziP/0wXjgRWjJ25YXbH5hWP1ekxNiLnn3xRrESC7zT0/NFIrVPf2dzDwQTRw0rW7EL8VWfDM1JdIjIl7er/1YhPi92Q2XlJtcIiShPtRjLVHKC2ypvgkYDmMYrRTyM3mni8kWcUzk8I+UgiVerQYAwiL3x5nHLYfiasXYEElNaAJuwdT6YoSU0VOcemi/NHlpxQyjZCxwJq0EGdpgyjSzYTt9lzOEzJwoXdGXMM6UvZfIgzJiXjIKn/4GJUZ2Zq737t3LdPYymbv5quJojG0rs6S24MEQc9a2EG+e5TVTWFVgL6HMYOatf+pqTSqnGoE+iTvV1FucjqpA8WMdlSaHgsqT5kceaGU80b6I3eIs1ya3gSYKylKPp4/goIdqimfVPdu2yuIfmiM5C0KcAWbVxwzmtdl5JWZZDZw9A5gmeMcdd2CKvOUtb2l838YOscg1njZbw7QNRhFmA3MbK5GXlWBLA+RoRytt40F7R7uMb9l8bW4iUKvwPRyHe5jiMj7ZIGuagBNsFb2B5dMMnweJ+0sEHCOGu3AnYx/3TgWdEIa0DJfecLylrG+SGFWNi0SioaAceNPxX3ohQFmhlj+3UwLxaSNbqRm7lgkgL8uWZ8Fh4zHGHkCxmJc7uiVAzM6DBw8aNd41S6lgsSiKpKY/T05MHD1yBCK5fJYZ2JbQz3Z0PJ6JDA9cXIl3Xp2rvu7I2O59l18zmQtMPTvD3Nj+p188cezw3CJ3L8cefeSR6YmxOb5JG+KWeTnnzCxxKMzBwmprW3sq0xqNJ+68686Ojq5SuZLAGmhJf+c7t8/PL8wvzCVSya//49dn56evufraK6+8ymbaaAMmzeHZFm8RRhjS8I8HK5pVX5oKnFNJUmPcbJ9KsXH62WefZfgkLXAjhXXNaMqisRWco8jYY41fwKZyqAfa+fI6WwpRyTjmBb0XshT18+mj7bDGEujrjd56W/361wYwxtg/kEqG2tqpKWlZucXaffdV+ZaOdCNHls4rZXTccKReevgR51WvCcTk+rEmd2rF5y1Y20MnsJms09fAGhmjPD3MpYil5nUT1W4pmar30nkkAqD9Gpa2BrMvNEK0bYsR1RilmSc1JaTkx18V1awVASQi6ML1QEdcvY6T4/qruWOAUwACgXQqVKsvlhYXUvFgR4KN0ApVEiseMr0lliWra42uIYXL3npE/JSGK4m9VKxslU8MH605pVA9yCdg8tPDuYpTKZXj6Va+Ey05+6k9D40DdogRYUu0VJL8mn3kYfFLRg0ahOA4z8/Fxp6pXNTZ07e9K7E7mK1UTyyEnj1eWyhH9FJgpSHElhypqgHnh4dq4z1tl195TeaSSyMVmm54OpF8ajr0+HOBXI1jwzKTspSmmcJSzHIk5veXVZxmLG9aCBiqcSQEzFTkV+N4x420JInVB1AjoHQkYbOT6CWnyELLB3se/ZUGt+wtSK5GWsEEjTHsOyVKyMtZABK0n8Bcnu+sl+ez9ROU24CCK1nzdKnJpLDODCgxHhKlOGZsmmGqcNmHDnEoyY/6idWSSBL9k7DEyt5mMJWGREmZhAVJq39qdj50tNoWY6DQWJIJhjz5JaVLUzDlVLLBlTdBUmTJRdJQbPVqUgAE3ChpveoX2sD0R5hWJ78CMbCCwKrXt6XKl683Eepi/iL+MPax8oaTUiMPqlVMEdxmy2I6oY2bm03bhM/oYAseKJxoLzYH7S14NOH+YgTRNFhUoDgoJGeT4+VS8WzmvEpeMGMGMMsSLGygANDeDhw4wF5O06NOodWtks2ZAVlNwjBMmn9l3dL4mbsxUwUuQOAeLDoU35d9z3vesxL/zHD6UqB69gxgaus73/kOttPg4OCqNYddYWs1xOLnLWJIrGMYrErkNIF+0xHBjGiu1Cojx6r33Fd/+JHg3Gytrzf4+hvDV10T6u5mJLOhj1HMiV9Tl6Pttdrrp+pfTHKunThacIW1yEQhc1NOopzeSOR8aaunyeLWJWeGDAPPBgB3GNe+1JSDXydNcIL+eMbaGpKl8eisO7xLGrnhg8laFoFtthVzDpHEbCt2I6YgQQxgOjPBdGumu1rfFflvmfCPQoGSzOFTszOPhoOf74+/KlD+f2v1/kx7Z6at64WnDzDhEApH5hdmsIQXZ6e5tjkWixayi2x4icXksivRjOJtzw1Pl4rF/DOHe3r6Moem9u67KhbbMRc8lnXCj/zkx+dddClfYRmbKh47dryzs4uXzvI4NpIZwDRI2MbPcJKIx+fm55E73KaAeIVhZKtfJzBvFi/rvf7YQxvwEajGxqDBgTBDCf46Vhnzl3SHK664girVfdZcYIoibu3Ip3+2mxUtBx4otV9A8xhjlEv0mlSynmK2CN6oCOFQuCauUq1PzZbLfP4K/ZJQJcC9sez8i8Rr8wuBiiySr3S8CJbKWVdff6m8MSHM0Lx5UslMXgwODlJ1K99CY5LN+hPh4MXb7GIeX5OTknqOF2SNwIAEfTRQ/NeHvzGVpPbiqA00aUtlMMEUnyjdznyxrhumWAEO1LraKrsvIWZiZJiDup29fYigJ3/2s4728nnbRJV3nevjRywEc/prlD1l3kPn10cTXlbGN0HMNiGRmD+SixCoOzujOwnkFxdnZyYK6Z2PDkeK1cVAPjAxRg7NxZdM3f9+lHpgQJwP1HowmEboQ8LBQOT+I+BxlRUTCOGa1KFIZiO8hLvcB/7jI4FkKNDfGW+LprKl+pGZ6hw3l7s9jvQQt+fylI2hZn4a48TPzvTVifgWkmLpw81Y/OoseynU/8/em8BZdpYF3vfcverWvnTX1t1V3Um6OwlJhyRkIbshUZwPRtCPRUFEdIzCOCL+FIQZ1HEX/BgFcRxwhG8QUQSUJQgIBEIgCVlIJ01632tfb919OfN/3uecc8+9dav6dndV9ZI+XX3uuzzv8z7vcp7lXdUrJZL/vsfL3p9Wcqy0o2hNCka4Onj7s3dkgonz43c6sC+IXBSDQ0Kq0JwpBffOBg8sCDCDGHRUk42ISXqCpjWZmWCXdMMllCwiKzkQVPGYrEoOFQomRakASDEAIkDCamjTrBAtubLvi3AIUHjHU1UkxUUxHawOjP54kJJeHi2CcRk/L7dmHVhB5EIbEDvH6e2pxU42LwRk/ufie+hwyFMepAbigwMjkf4oeISgAyABl0qTupXA0DMaBQPxwlP8HVy6lpzWAR5R3hp4EAdqRaCBkNCzKBpIep6CoJmwJA1l7Dylb73IYtaBbsBMCZonDhROcqZX0HnWi4SzzYd2bBwFZ9lwFunnP/95DODGU12CXD8DGDb35S9/+Q1veEMNz/LagO2X8C/m2bhDlbaHNzXIxTwMq+hgrAhiykeOFj70wdg3HgpxZgqsds+e/Le+VXjjmwI/+aogRwEbsc4kRaTpJYXodaHc07EfSWZHp8Nfb48UolgA+UQm/uZsaDglArnjdYEgu9irhN4qEnwGqGAKNAoJl2sRDyeGPGOKgNE6yImJCc6YDTYnEi3NCaa+qAYj1UXqT05NLczNX3YZliE+/qS8iCsWLCFj2P1Ljkz/Mk2qViKWM5YqVU0IvKlUnE7ufWtb6FHWj5fL4fJklK2G0Z6SFcm3B79VOP7atsHPbIm1PP79J7f1dLWX4r3RLbPHnuu96kouetn91BOM3Y5nU8zAY4UiA1h6nE5nFg4fZIKRSy9SyWTXYgpz9+ChQ1E2PEebUpnM9Nz8i2+9q62rt6U5Txmvv/76kZER7XgULByJXDY4EJidK2dzImz7+6++/HLsNh6opSqYsqaAFIoiYBt7NeY5MMCgCgHPVDkwwFPtDEyCQU81IGoFi44kWrnyYwcmJifGxyfYtEw4NatxRpMyLWByBbOX+9o5GAVgvOMUS8UgxaFLaBKflIcLkaKhTZtio8eJzeay0XBEzgLW7tK3MRCNSJGrC0E6KhB5Rm9pvFB1q6JuYOM4ayCpfENpNbkVIFPuKm/Fgxnk89Q6JSV1IkcJ8Gg9VsObPDtigZsG6QhSZWYhNJsFAgcZPg4FhoejycVC5uD8cE/H9n7RALT6DUIHo1PRpic5FpVGC7DrMr/q1TDteBpC+X2Qam4QIPS46SWR2PD9qGX2zHThZChQ7G0/lGH2usOkloK6wLgkoUlTbdZ4EMZBac1sn3qcOF09q1krBQav1GHNZ+IkWPLjZhk7MpV/LrXQnGhm2WYHdycJpBKp5dL3kvS1AR6w+WI9JFVV5k+jWZgQMe6kEIJCTVdDnIRJiLxqzCoJ4jEAxPOnLaXBapeZepDKUzjFblJJCsEqefIfhkd/08wcBOpxMAuo/Pea3ySWgEjJhjm2R8tyNpvgkUCD1Ck2a44czDQYhXKN1JApj5cL6P2NLN8LcaYAilEby/0qnGxMTuIWPAJs3E6taTp5mzXIkkElmTrxC0lScyZK61iJ4o1XEQsSAZDCUAKTD35ZIyBJ8DPKoW8zlSP3KQStMuLJOCQLeBGVDDD/kwupsYXJvhjnh63r3J0UY80eGA7iXpeJISWRGtzRgHLlHytnbQ4rp9iu2aABDLEId7YOIXC9sWYtAVkw0cfAdIMTJ54sQOKjcILEC1mzKllDxBCPsuEt0FvDnM571KhnTIajXKGkacvSJS4g67duP2QOAOOIukchR43cs2cPboY8WCYN/I//+I9zvQ5RaL/nffucLwSukwEMH2T2hrUZ9913X92mJVAn0Gg/6obhwHPeitw6mvvav0fZCSbiUIQsci+cL+Q//vHizisiL72Ng4ARXUY8NgU3/nH52E+FQgvxnz1ZunsufyAeiAZar1oMdmS5oDMXvSra/WsudyXRuXxY6sOHBAU4EBi6iRdBohsP6lIGJPLGi+IyXY6i6O/vSjT3s3U/VwrEW7ncrClbLKWyhWQ6y9cpGoEpKMMI6AdoAJjB7PulfdklC5vGTa/4+te/zpfMtmEuE7ruul2lub9qtx5hvqa4r6348Q3WwWghl7dH7NDrZkIvno3kn04d+I2ODR9oCaQ6oh2T6dmmYK4lardxL053656n2dZX6GCpMPfi9nTOTE8MDm3GTg0U8tOzU20dXShqKFAzExOyJXlhLp5oTqWSe555dGLsGAtZb9m1fXZq+otf/OLNN9/MqCHUQrM9Pl74+lfK//Jg+cCBQsiKXndt6NWvbrvxJSHOsDV3O7GsC4faFUhxujHF8SoKh0pihqupQAUjUCtzeHhYLee60hrGravEs/lCMmc/s4eMWFiYQY9OF0pG4XI6FAoZ+Uply8Fr0lPV5ydjnd0VArQTVGVvBdpagy+7u/zEE5FSweiaHPzMcbsBLnoO3X5HgA3D9R4GX6hAnZmvF79SGN2bkQgd11gJ7vTjKiU9/bSNpHDxL8s0QgG7PUYsf/AnsSZAm2qLMQvS3WTZqVxbKLexJdTr3DBVydNR750A0pqUlXhfBGaLqPJLZ8w8aEnu8EKlwwDXJbqtHI6kw8X2wOY2uZlYE6o15njEOnIfsY8Mcs1BgsXF4lV5/BmYWT8TysuLVrtHbRi1XByQCj43Tc1vOpCbTC50R8MtzWc0aWByNgR6WYlPLKvqhwDTyh7NJlpgCdHvWt3aCtSsW2xpFOcRp/EINyBMI0wYTg2R8pvcYYNuiJceuoSNiN9Da7xq3Rmyja2GtWYIhtU54MYC1IRQTONNzaTHyhNbemIdrWwSEYzysO1aHsmauUDzliJj+0GY7hvSNwGSSsL5lfMmCDfwMuLBhL4T5QTCO1zKSYS4dSxPMBMuS1F4mxutxFI1/wSv0CIPMDJdiAuGb4gx+RoA4yIVJTVRlTeEECmP4DBug1NTmKzNwIA0oQNooPE6gwBSCu8BZnrafvjhvZdtvCJob/BHeTAXqIMxX/QNKgnujfRXfUNNEQxjTBQM4NMtGhKT9WskrzGAweMJ2dPCqdccnlaS8xNY65lKQA9pfEDh/CzL2VBFPdx4441eDSD6/ev1zgbzuU1rmI1Dgrq9N7bVe9/7Xvbc3XTTTX6wc0vweZ77OhnA1ALL01FeaZuVa4Qui3V0PrSfnUxa3/4Gm3dFaZC3yDlkZJyDZh75XmDX9YF2uWQM0UV0sPnO0sCHC2NvD5fHglsW+TPGCPOrwXxsV3jwo1ZILo8V8LN8RGeAHJGiPlxKny9g+VyQNywH1RqGUaoJx6DsCgYwYxNs1iXbiYnxheTYcNcjgeQXosmjpVQkEN0yZb36WP6V6YWgPZ5KzmYWk8WJUJJaiURi7DGenpk+dvTw5qGBztbmW6/oYjSOSTxaGdsPGnljXeMtlgrl0rw19z85zac03mz/+WDzfKJUznNgcPxkU/Ev4qV3lSKXzbdZD05nD2zs25jLZCfGx7KZzODAEGYhW3NDoTCzxkhWmB2TNpNT08yWWqHpfA6oTCGzwO7fYmae0TLuR6UNm4KdrXGLC11i5UUOyG1rbysWiliq0ANhC8lkIpMrfu5zkb/93+FSMbWYag6Fw997PPn0s9Yf/K51863cjkVHZeIaYKaOqUlGHHETyFsfr5Jb21qGtl3BbPbY2Ekk0+ZNm2iqSDRy4siRfCAUb22Te4/1MRokehgIMfmo86Id+OFYbm7/EXBSe1xsc6I4TgeINbEwW3sgXcFotIHA1s7i5nbTZ6W3rv5D/aCygBfymNJXa18btKHMKCNzmk1N9i235l5zpPypT1vhLAMk6PS51pbAAz8f2n45W4HroqLfktpfvXXB6gbSK+qOMtQFvkADpWoNO6JtNm+WbsmTzxdQtPkMq7iFiarqH+IR+2jpgyb1gx88PTIy0t7ObK1BUwdMkUGCIHA8BpfYSsKvpMeYgEB3V1d3VzeeaweBVFiNcd/+UE0kUMbleZ3+7iZxft08xFuFWdPVQK/s5by873//uV27rt28uXNlyJrYqoydOPJf/oN0EhgpA7xTYW5zaNmlAFIv/hJW8nVrzIkliVdgg1x6hjzmR2I11Aem8SYYp4DYNiN6vT09NaeqKCYHUH6Ms5qsY8fyT6YOv3ika2jTSlU3PTXNMHer7667JQS5+IUgJdH/JsghxB96SjcVXE3vKVOYVRg+GhhscatU03pkQJJSRYgXKGPNi4uyzUe+EKcjVAGAJcFyqoCVS2V86U5J2OoA0NYwdqQkCylXB6MPC5O9dCHws9qLRU9IZ4Q+XQttkBAdAmYNFGdtGMEdY0bL4SM+JH4nscwA88YAJiEO6MeuppJVHqmQIpwFSrz9aZdzNwimyTHpGZ4+T9TUmhJRFUy5P/nkkyyoRKmriX1BeXXi1yvyaTWxl+q8cjDcs2PHDkhiHpivtebKEpZA86FhZ53SyDqvCnVuiVk/A/irX/3qHXfcsYKVpRXhtfG5rRdytwql4MyCK42q2GiQo485H8X3INZCrT9hxV9cmvmrwOKDdnGCjffZ8lCi/03hzjcGg62qCPpSnKET6SoiFk7PLG46y8mv2HAWOkSsiVE/RljVAlwBOzWshzAxE4v40fW3K9sVSKZ4XDYmJSL7uq1fiy88jwohGgCFLO4dCHwtXv7rrx7+zZnSsBH/tl4l1dnb19s3MDGbWkjbU7OpudkFDGBsXYbiWAv96KOPwqz1cCmWBKeSiwM9R/qLY1R06WudwelQgfrjAE3aIV8oZcuFz7RG3rEQspJ7d//9/pPXp7Mp5lGRmgg58FCWfKEQLDFAIdOJycVFxCHmOqY7hpMI3WIhGglHQomBvg2q0m3csJEzybkPaWhocG5+/s477kSsUq9UDuse7ULR3rff+uQ/sPodGsAQiUUhvr2Qy/yfT9qXbQ8ODdKZNXc1Bb3VuaaN3I4jLcH8RujIYnwhFxydCGHqzrvnituRrc2XD/xwMbpPVk+jlsoUx6Z2e5sMFIT1fqbRicnSoYOJllaKRklZtj4+dpIsOrs3NCfaUac0O8lH9Drm0qr6qglftRe6hVr7EIPOgRfU1Cf104iAEcqkFIFg38bYT/90+dpr7Ue/V55JZvt7wre+lDOig52yeamu0sJwALmssFlaC0kL0v1oF1pTQwzCtasTX/V7+VU51i5rycZf7U5ObvXRW/iut23bqvpiFVG1HpN0GUrZeU6TVTJaBsyPkqzx8hZaNKVLFX6i+C8TyqdEVQEwLs/rOfy5nhpdFfTKHqbWg3aJt6zzOdunERSwGS8b4/J7Pbfn8GD9Di/Wc2is43VD3V9fjn4sjjtnFhDiqTT9ci1WaVxJi52zddvWRIss5fWnlTjfMz4xzkIbDGAfjEeZD06ddWLqBC1JVj+gmt76MHVC3QylqVx3NVj9UHjXoUOHOzo64WDV8BUf0oT70s2gZyVwfVzUP/nCzM/MAMYaZOEx4o95VF9TCu14YQKIYI6xQEYz+syDAy6NgIM7oa4Aw6OHWhGCAXzKUjOaeffdd/tXA2H1IYWV7SChwEBVIwVOieoMACAVveU8uWN2Kf3oPFhHUKj3UCwFeIGE0BnYM8UoiRqNF0Gp+UxqS2FCNJQPio8CO+s973lPLdgl/zI1sE4GMLNGjzzyyO/93u/VacJqyk4JUA2+Vj6ZYo2Fy91d5ZPHdd2U5mRG6a1Sb4+ZVPHnzqRdMBTZFtr4Z4GNf5jPz/77v321s3vzLVffpkBLeq4/7Wm5xcxlCCj79a9ZjzwWXFhgAXHo3rvDN9yYb23d8/wPr77q6pVHGfhO1DbQxUh+OwE6sLCMkmMMdqPbuwp+uVw41JZ6IBg8gsllL8YKB+KsZAtuKwcTmc7QE/dtfvf3ch8/cIDTT6zNI5dxAWlcjssuLSbnwlE5IKqUndfvl5lSDHXOwEAKssqXKJl0tezD+/YObGK+MxA6FsuViyHu4DTWHYcksVO0eKjIFmppl9yJmemtI5dtCQVDOq135MgRpB3AiE8EOQO0iEBwwgGRzfQ9DFcgsZ0AQ0jjJpwpX5QYuTjGnMDBhgrGAjjWC8s8HA5df9WLws/tZl1apiyHdiDJSVLiVg+rHHpuT2BhNmAPQDMavlNJYta5VWXsdqxmt11pfGMQWIFILM6BDC4cJAebmkRNVCRUukniLKKW4guqQFM8uGXbdk7xmZ+ZnpmZHt56GSakxUkfVtEgBsSdAiaFOBWPm//q/TKcr8d4Ul1oKmgz4KZHnWYOrCfkLoINdtftwRtuoGaZ9Q2iLi85UsuPFi2qkYNSqDRknhLmT77qbqNsSbtJdZtfkwU1j0ffBJimMK3rEuA0sq+FcPp8Ltyq/NLVec4AFaVj4aJ+RCTHzddK3TqzWA1gZI0iTVYzSl2Vbq0KXZXJ+eChMqlJmB6Dj9Th+UDSCjTAP4UhmweH7peB8hWMtxpsyJQasVIDoF6qQp+6sS+0QKoC/sYns54FN0yskqF6eTfe1poY+xPJu9xCYlgQK8iQ+6gcdCTGRxh5x4zEgr322mspMgPiAKyst1SoREMIhRg5oca8wOHhYRgUaBFMGH7EelFr4aipt7XI4sxwUifUMB2J6oVIfxWdGcILOhWaIZMrzISjZF4oBXG+Qcild4uyoOqERz5emztDi0V6OLCymIf/qn3e/SP3/vqv/SojQXq+jJfmkmO5GjhdzXU5PCuF00iPP/44cvTOO++8cD5IOYQ6cPddpd27g8x/uJwWjpKPJSI338JSVymz6ZxLChXl3p9iOdHVJQf2iArsJl+pmhqMozaPHE6//wPNjz3OvZ+5fKH54P7C976T+9k3RV75Ez1d3UuIqcWrAKBBivAWCn2CBB83TCQzRVntjX0mI6lahHJ4/L8FS4fZVJX9Zlfps612KhBMhyIdoeLrZ6K3TndE99/T/Teh5OswwW6/biOYMd7mk4s/OPHEhq6uzfFYKVI8wJwqR9X2MFjce8MNN5A1Q5VIRwaex8ZGd+wcsufYjVuyO4uJWBNZY3PmCnnM2mg0XhiY01bo3LCtd56rpeRBguo8MEKUsWRKBDBSEAHA5DAUUATPPEMBRS4CABiD0MgJTDgUehaBwy9wM1IIu0TnY6ExpS9nshxyxZSrYmZ9daFQZEVpOJQLZgozGfvQDPipp4Ad30K5nhrjYGR8sguuO2EPtnoGsDRBPGgXw+VN/YxJc9uK2MCUjo+iuTnBdk1T02JDS2sIuNcoVmsifu+uof4+WSA3Pp4cbwq9aLtcuCew5hHep3Y4bbrGW4CpCh7Nl4qlnh0iTufHlFAKa3EaVnvUY/HV/bAWI33Ga8raOCMKqE+6hEZJzzHnk7HGcmW0S1E1HGJzaw4N6Vi/lEr6mzaLvmlJUzinrZyGxUfP8JrPhBqf0/IN539KQB9CL7tTJvIAzPD5gvHS8SZwoBaLOe1D6wEvdcj3b5rjDLL2Y9Na84dcKG5/wZlegyOh7mtgg4XyY1i3UtPuk5NTkp0ld1Hy4GS+K9L42Sor9hAQwmbBCaflYbgKN1v0UVLX7FMlh3Pw0P9ZjcKHADsie10YhdxB3i0tKSGMLVIhKrbWh1zkL6dSaV7ky9JZ3gwENzIVDCTJkbb6mUM5mjcsggKCkFjeFIoHgYukYLsQ8FQFEoQQmDlNDxgACnlaVjd4SI7gVg6vR8ZoPYP/zAST1sNyb/QH5rGJpRQUGeMKNwQ0Pia4HOZVD4ckuA0tshb1sOrUrh1C1qbBzRgL8B+61kB2Ht9tkE+vjNKPreL2XDWJCTdKDNKTk2D1j+PTA0wNFeEk5SB3aBRttgfaxyewMiScWLbX8de1/aXZbP673/3ey152r35WNcgveWtqYD0MYLL85je/SV80+0hrCDhPvXR8K9ESuevO/L599oNfDtMZ5TiNYD4as97yc9aV2zGDViDdDL/J0lCBWZWPyEgUvg17YS7/pX9LPPooJ3+ge8t5JMiNdDb/0Y8GdlzRee0u1Au6PvKA3Ff+BnT/qlcKmcAUqSXfE9PA+WJgKmtPpQIFOUDEbgocub7wBbIrF4LW1sWWP0pypUh5MVjY3Vz8t5Zgby58RdJa/HS0fFfA6ouJ6Ufm4dlMMlRa7E70tkY4kyHAdlwikFIIP90xiwULj8a2yWZynb2Xlee7Qva0dft86eFOqxhK57Nyqy9kcSL4PUlmlZmXj3fe2tYWYAaejRAYkFythGLB4DFiCY6P8EORQn4jHbGKIYKHWHIkivDbbrvtqaeeggYmnwln3BokJFS1DHpYeMybo7PKwyPR9o5wWW7cYJtxjKbnMNhyKN/bY7d3pAr2Qj44kxELyNi9WmapTgT65mIpFipIL5L/cvjKzo0ca17pCjTl5OTsd5797tCOHZczoyv1Li3Ku2bJJRt9da8vmGORWGuiRe5y8auYipXEq9XVpBCneKgiqtRTX04BXS+aWpPkEiUvvPWgKmEYDxVPPRf9QZdkE8mwCA8Oro9C2aoHvgphc+nCM2PsX5aSOOjcQji/HBQkpdOmoaeIx0T5iwuCpcSYoRTTKaRucPiH0cQtfcWMmzhZuzhknlbR8XNydDSZXNi2bSvL/z0KjZFugNw0uoDA+BSbODPRvmJnL5cQHz90oLevv6lZZi+fmwQ/hj8N5yV2kxAAfygV0txlFbBn5zI5rjnaj+Vsx5s4Y0/G4JU0M1wjYwUVLG4NCVZ1OxXlUL3kx9Gb3XClQd9ePgbFUsxOiHQ+hwalpPo9W4iXOrZMFJuKM1yasxKkodnUhz8vQ5mgJHHAnporjy/YrTMF8cnj/HgOrwdptAtjgv1oa9w+aMdpSgGUeaqwikerV6pYH/G7QPQpoSpV7sy0NMNqx04eS7R1NCdYeRPYM82sYN4kM91HXE4RSK7jb4LRQGAU7T9wgCMKbtw52Nss/Mzrk4Agr2DFOLAo+DbV7QcQPBfFQ80iWTCWtIq9IbnlCrf+5/dS/xhL0KPSU+ftG58uwwjUo2iRp7Qmyy9p3FtuuYW1xxgerMPibGevsDBn4KkNXZ7DyixgmDomO/I9XS4NKs5VfclLXqJ7uLxcGONGMHneVXTQq3VxteJUt1eBq5jR2aOiPjGAUYEuGcBUJi3VoAHssUI1Qs1ItcvmHLGknFPYpvlz2b7DUYUliqgWVU7+ox1yQQhDRCXM1zInpBpLVQLxEi4OsXJtqyBRVtEWGKBJxWpDw2MdtuwKDnIwHNhkpNTAgAVhqdw2uG1j/yDWFgbw2feiFwKGNeEUSyvuoYceeulLX3q6PG4pnnULEYUHu2VgKP6f/lPuumtKjzxhLczYA33h2+8IXrMLoeFQIh2+zsMnBxdW0bI0ms8M7o+RzKzjaTFrcisvpqyHv4O4EeuBL8wSNYLNWnY2nf/Oo9Err0LMgJxRWMf8Xpq9CalROORD4pNbXLQnpopHj0ZSmdjQQNuGjf09Xc/PlRfzoUT56ZAt53MEo8XYFo5vlbyDnXb09kzkplTmQJSp23A5OdQ9lbR3MJAMPsR5JpMdHh5BSkEkdiaXIZGvkG1UIhxMunLPLZXQ0tpsBQfKba8IzX80tHOx8Iax8j/0BNPcaBQuRAvll0/Gb56DtcwUXtTZf/Ntt4VPnDgJc0d5QqBi6FJknfjFpkUYM8OAJCYX3vQ6AqmQ4eFhDGCAoQSTm63I7FyiFRDABJKcsXAYJeMCDB+ksdK3b8vfcEP08Ucd5maILnO41yteUeromk3LqabH5lmjLBlJpL7hepaVydvzOaqETkQwMwD5DeXRHcN9nkAiNJXk3LBSR1sLt/o6iQ0G/6ummbq6Ovnz8vJDOrZFVdAaenQbeQ15p5tf48kbgeRzU9WN0+aZqFR3IwlPl2wPnk+gZGFdC7fwAo1D+4LIqKqnxlsVpx7zbeA0UtR8JRLuTycfnpi/tY8KSBls8cUdncpMTy90DkbCITk918A4tPmgHFQA+BADJgsN2OU/leajbyoF2kmfTLFAhPEct4AeFYrOCmRT6aMHj2pOVMux6aOYqhv6hrhozJ9jNrPItWQ9vRvNIJ6HpdpBggqxS3KsgvXjJmJl4KqUy3u62zZ3s8fd7HNfHmrFGLtUHB89BghXkecz9sLuEzRCoqWltVPPRCRGSa0UdUV8DUWadnQgpV5qKkOrqiZQwN06tBN2lKa3pzKjdmsiGu+mNafSEkv/cTqmwMvjocFhIOSFDnfgZJJD06653MVpgPWF/aMmEGcgYa5447Br+qn68l8/J9IHIUi5kDIHDx7EjTBS8adE4FYBBLM6J8VH0o2MjEAMw4VoDrj95J2yphCjqDqIYKZGaVNak1IgSesmpIwMLgPMbjhywUhjEJzlV9AAuz49RchcKgES5pyZNveqjurlBoe6uZ99IFoE5wyRF7YlK8goC+7Tqq6zp6FBDNQnhNGgaJgNJlkbMO/z9/hETT4AVKK8yvQa9JTpa9BVmJiJoI+hIupQBQEetiWpKswL8cY/2epmbFfmYbEtZTJW3pa8OcwcU1bWN+EmxBifuMWaFfOVeVrGVjFuZeBLeggF5CYZmGelpNUEiEg32eocL58IM72YxDL9W2TuV2xjMpV8CS+WeEsUNrOJckxokHAA7PA1t3/rWw9V47/kW7YG1sMAxiBhCfQ73/lOr1svS875FAG1wVAwMDgY+7EfD9xxt6wziLBTsYW3S2b9Ds2gL2Nv8CDY8XJFZjAYNor8cFE1+Ms4TzE0N4s9aRKIuoxb1hgxu7i4yFlPen32cvnWzYZvmY+nNDtV+uZ3yh/7uH34SL5QtJrbA/ff1fz611+xaeueqXysPCU31ZiRsfJ4IvuwbS2Ww1cFI7vywUg2sTNrwRCYHrUWmuLNTU2yMhaiGJ1VwYac8wrr0YbVyreODQyTorpIEu79zWLqoVDpYOxHJou7UtZzoWApFNyRD/dlsbgLgY5k5B0zR1nbDKyNDUY9MzGI9MW+BS3mJWY/ghbWj3ELTqQy5hDLntmLyGolxrkRXYCRL9IaSHQU2gLbSfUARLJazpjErRv6or/0C+kPFSJPPslJY3KfBueUvOIVsf/nx6zO1s35cr9dymRlrE64m4wI6A+9wm6Soz2Ek8ISCYdbzc3OFodU6yVAes7C4gK8scUcFYO3kcerukaAa2AgE2WLslMbZ4NH0Z49hhryVvBCOSMafC+sk0Sk1YWEHhqRN8C0uGpXQJrGqZvi7AOZ9+LeKWQivx42MYYrPie4PqMgcgmki8e1ROsAOFG1OCvZijg1D9sJkJayBFFWihjr18Vf73epeQMUYjscjcMKGdbBK0M1tTkbXG5gpCkxuHU7vrnpKXbX9w0Okigajcmn4KMgl85MT0729PRJJdaxp6twellqbbh1Y2Dqv+pUW33AqlC3DFWBoKobDtApc0ENKifnZhUfutPC/Cz1AGNsc0dQfUgkI8Xo5bdCBgLtJagiWAir25QOlIfdl8pgI0J/3QEWZIpwMal+r0/5EonTo9BxmE7CRzd2/GAktE2aVqSGM0SoaT3WgUOfGpwXjZfSLS1LTSDCC8mIJAISc27//v0YLcjNGrCleFYlZGkuS0OWywhIpC0TGyh4XGdIEXBj/bLRF3bNiiqELBO8JCcKAc0b0UOgbhWmmEDq/K03LrxcXkvDZZWWOZvAH9U48f5UDbrrIq8b2CDCtQNDFGKfexrX2mW0HGZX7MIVDEtxGYX5HpwQk9ZhGy4ejSJ1DdMQJcrP7Qw7cjLBDZ9h0IWuxbGpgMqfsU5xlBIbjszMHJjOBrklxGi6oqdKrJivxlhlkSMaMIHosGr6gkYoMSJPP2H3QxY69B46h1fjJ8wxm8lOKOBtjFLMVzPT65mpTPCWWMPMZjoDQO4y61sOihUt++JIKvxW7GYpLi/Jj4kUxrXpaQTgZmYHCuQdDERZDSjHxAhJ/Oc9cvUtn//wv6JaK0sh+aVnhRpYcwOYTsoiGQwSrleVBjyPH9gxrBwTSOg0lCrBFoc5yV7HSo+na65QEixS7K6Vuc+KCFaso0ikuLE3NjWBToGw4WQVhIdQZlnpvg2BUBSDU024FbHURFocKF3+7uP2+/4kns1niwWGo6PltP2lz6Ontv3a216yeaA8dTwwI995/tst9ntKzewN5GO27PQ9sabfKYaac6bGQoVAJ3bmwEA/R+AwqDwyMoLkQxai8+maZ3/G1DYmGZOuXm1Y4a2hoU8UTrw5Wngu2J2O3049C6+jtvOhkWOptyc23Hr5oJxvwbEZTNhiAGPtMIdMB+ODZywZnNiuDNYyDEFVqNAlRGfFoQSqCMTkJglU0UwYwCzZgjyoRaLzgF+pLe+8Mvyu9+Sf2x04eKjI+VVXbo9uu9zCSDaXrMLdtnUJq+QfNWOG+wIT6RJLo7NFOyu3nBIoumgpV5hPlY/MFROMXGCQwM0C9uhMjulpdAK3TsRwmp2ZhUJ23Hl14sY6v9LUhi+u0ANrknhelC1K58vRizlnDmQW9U/D0RYrEMG3yQpnTjdZAcarMc8BsN+9Qtozigp2NAdv2SyjQjSISCp5ROeveCTABNILxGXe0mGkz7iR/DpedUG0h8FA6UtkvEnltLwLI2GCy+0YONTLz3xkrjOR3dmDjKQnGiDz8jkr0F6ggPpouKJbJ/Jk0M2DMWg8SrxgCeHwaV4n7SAH+lw2IBsCJVBUDje9FUgUS6FUcaiD1dTmn8Q52ATaPH6knttHgRSoEu6mWq3f+bmF48dPDgz2ddacIlZFJvlX+WtyRy3ZfMs1NOfU1PTk+MSVV12pBKNuKSSJ3fT8Cq+reZYEePGCqU6sCeIlnNN7HIGG3wnUWjfxPjAXXnBYgctv3iHd1uIYfCP8JNTkiIMI49UUPqeAlgqlx8OpjeHZriYZedFY1t3wpSMZvU+S9TiwaOGQK4lTzeHCfiNxKOwpi4lAh8shm9QsXLcy0wTIO6ym083RKxEqh7pRfliczEAzb9ZYURxwMkmr9ipuwJC5vBmehqtjBsP5q3hTY0RQpXQeOlVj4KsJRdZ0Y4PR/+1UfQSnzM8rsleHJNFAQsBbg86Bl9BKTMXly4/kdKGRkRFfmN/pp9kLB1Ml3JMUSokH5Dg0WvJ28/eSmgBINUoRGCVCNgBoiPyKGDB/YuOKzSkjoDKJShQb5QVYrVOpDDEgCWdmVYI1ocy+EgJmY7IKuHh0uTLWokMWwIaY+cjQQrTluTG24TgKhqABoeBHpCqSMiZrEftT5njZ1M6bcHJkJlZowDA1tqvQaUxWQmQBM5Rg3Aph8oYSsgSzvM2fEAkfFvYGv+RxNrjxtWDC4jNwwQBmGLP2vckAAEAASURBVFsqjXlLmMBq1VEosAkdpi41I5MjxEsnMYUggSiZLP0C5eYrr2dbH9M/rLs0GQrCS89yNbDmBjAZs+IFg+S6665bjojzJBxrCjNpqZ0m/VT6pPRK4zQBxln3hYFx66231o2i27JxBTkn/deydP8MsgFRURe+JlA+rdZ26+67GVQwzEOWOwpdwUCutSN8y82MtYL89A3gQHlutvTpTzdlMM/4B21cAQJqK/zQN4s/dm9sQ1tx4Z+w4/hEgy2cwyx5wiLC5UDoG4HMR8LNb8sTUAp2FK0dWk1inOfzfIqIN4QiZjDFpHrxwp3hNBTNewOMF7Iz6czYWOfQpi+Xkv/HXvjnUu4g1JTt3vn8S6dLr8jZGxbGxuzRUeQrljPnSDPSDBLMJwQhU7u64gWxSiCZ8gCpE7/gx/ZDAyMW2cxibJQMrGh2AkMbovSxxx4jnHFT0wGUt7DeOxrYsik20FfOF+BeVjxqBb1PxmJz24s2hsHJODcyXgR5wP7hVOBEUmFs9jw/8/h3YvF4NBanpR564gB4Q5HYZduvxnFo1t7ZzhS08GXD3mUyscDdxwWOOZBRPdO00sCk9R6yIErCLgr2Rifhi2AVgKcheSX1O+g5eNE8/IHLuWlE2mK52FUMZzg2ZDZ2iwT2P/4mE6nne3yg1VACJjLZfZZP50I08NseTLU1cR6b02nBKRlUMjES2PV6sUqxG1yVTQ1VgqwuHBdi56KZdHFrV1VD8KU/9+yzpGgq25tbA7kTz4Ghq5NPflNVNtUep6JWIKsaflV8J3Lpifn9Q1tbNveaoxzOGKmhfsIOtubL23upDS3GMujWsYxLm1JpWtqgpgS1BNcECuGyCCnFECSr9rpihVh+rsSt8FkLDkwk4hXe61oOgm251RwSd3E9DFLX2LRILkQYLJzqQnwwGkuJdWpUed06VwAa2hnniPTk6lFGmcEAHpg54pjmRtCzIJky4vAjHx4exkuRPXZ3ZqIMkUFGqkr58a+1m35ruq7YJeZRm0cGi3g0TBntKT52hXexuKlFkyKhMYFAWcHqVZeBlPB6rFfCRbuSSUXsO9YxipZIJmJwikMMRuMmI4kRs8oAoFKggRBFRmJfOW5j2qHtiVdOSEV3I1/R/ySVWGdyQI4BdlBJ7oTwGEh5GaWSQkm9QKGJMBqOKQVmXuURt5impJaHH6FKTUwUJIPWGKVCObHihgZ5bG7FILBUDlFC5ldxF+1uO9E1OY5BWyiKuQuwmJQkMZjIwcndEIaSZypcSFaX/BgnCpcYriQQW1VolBe1GxZg0Za1B1BySQltMpCBS5rSFEbeUkXMXQFKqUioWAxCrRskBKfrBDnOVLTtciRkR8IBtIxwEK+FNxzkZj6WJEqgxPIOBgmRRaBBu/PKaz7UksDmwgAWAi89K9aAp82vCHV2kdwrw1kImBlnh+acpXY+BPI3n8Yp6ajA1wPFPIYxIfYwDlHoAW5c9pB/sLUleve92SNHw198MFTOs7iRDy+XaLEe+IXwZZdzl0xjNNZQZgcWFuwf7stk0pRRPkk7kCnKjl/Omirt2Vu6oc8qHOar5pONXJvL/bd45gOl2ASfqZ3bGAhfI/YwiazEXYMbr8vn5XhGjE+OuPjMZz4zMjIyPDyMCGTHF4XFCuVUemweACQJx+o8J0ow1YLg5Jhl5OLmzUOhrv9S7v6VHz71vcH+gXK4qZAqcePt5NRUa0trLB4jCclf+cpXMqXJIMITTzzB3CBqBNvJSI7RCy8UczqTIZBKxo2DvOBADDwzY4zsZESGU9l0HJozrnbu2AmprJRWa9ysKRcChY9FIhzbIh5DMA7/gwGMNsNItgk09eBUYWBxbmb08IGdu25MtLXPzU+3t3WGwhErFIYDsza1UCx6Vh+EnZyaf2a0nE6lpqftqcgsHDIcicbpISZT8xYCeNCaruixOuPOx2v4qBNl4v0vScHQgG6DodfhoPgEspy48Y7nx3hO3DQlzcdIxylzB6xG3zplkjMDICMvoc/phlUi3RD9rQatgfLjrE5Go9cEnNpLp8IYoZX9aAVNDSqf1x/rC14pr7qEkbWn8ftzB1GzEQQMfNCmKhSiRvmuAavJskJMxVUDsvpeo5xA19lmyReKCcTp9+6nfCpSzzbDU+FfMb5ucetStDQQhqw2CYf8whVZ0It1tNzxdWdfsSuWYxUihS2fPAk/0UnLM8a4tKRGX0cTlodc8OIgI0bu+C5M8Pq9lpJ3WnmTnP1EKsuY8kWA8tDudABmlen5zH57CAEmiiJj/zMycgaD9YoKPOSILIOTkIWHf00dRjUy05KajWEQfN1iCothI35jleFQs8exDCmvGkHGbhRtSf7QAzStusXqc8PZeSrIZKGI/BpbTiZCxS0WlNhvBiO/3FpFuMnXBZYjNqcOHjzENrF2Z2Kfj9Ukr9qPIF+w4NRHyKl804SbYhFHuPyTdcLQIFYcbhNipnAxVk2IARD7GrLFxKQ4gIn5KvD8l5lVfpnpFRimXSVKisFsKiFqypKf2tgmilwgS+mUIlToA4njE1KMU0xOARJ43DJPYB5w669AiRoblA1t4pQXyR0HUQ6owhNnaty8KWQZExo3V4HwSCIHr2DBI6ObQiw7ATnKlOzFYsZwDVtYrdirXJTKGuayusWmJTyE4SqxGLxhTFhxWPwJRkOYIYRsyNI4yQYrQLJ3MmdnYjBYZjLGGMyBjljk2l27mM4B5NJzyhpYDwOYI5HO59EIeOihQ4fo+CqEmFek1liXy0zgmQmGZ555BnMOA2NpckJGRkbAT6YcXzxsLMNTNpIDoF8FH+im/thb3lx48a7SY48Fk8lM/0Do9tvDO3eEOmR75NJMT4nffM7CQViRy4fFvjWQ8OlKQkEHl8oYfgFnwt4uR380Zd0WzR2QVU9Nl5WsZs6qElg71AnnOn70iCxHsezpmWmMQ2DEmAxag0YKsg+IqsYMRkZixDIngIxUwU+INgET5PLxB8It7ZtC8e4uLuzthu+UMYA7uzppF8ZfQYJd9+CDDzKloBODWEdkhxv5ilBkHB0YQsCJDUCFI3GhkRuES+XCgQOHdj+7u729q6W1dSG5mFqYj0Rjre2tidbWsdHR2fm5K6/caQb+KL6wbIf3kN4MrJpCUVdLH6u72Y5yhbHw5EBTJjC8sX3X5X1NrW179sxu29LT1NwirJ7FPnZg+M4Xb2oFMYCy3ubggUMHJxAZDDLa+w8dAnVbe8/ApmHD94QXEuKQAREyhus8eOazHFRGpZlLkAycgRTBgWMyXZpJy4KgQt4OFwPdIbNUx01+Tn6hGfWIDkCjQwBL0+kJNJz/RBM/YbTmKZdJ++EvubUGqM9GRg1WvbroinVxot/rahfdjOCtfFkOvi6SCzEQXV+51oVIfOM0Y+owysbDyhrKixDkG9eLdnAQrm74/1malI2TdJaQ7MtdC/uKD5MRItgg2gJVoXwPL6LqnMwAn2UtkZxPGPoZ/qCh8aoURuwqh1f8uOkYDBYz8qUjC3pkyZnlzvg1e4XWh7+5AtdK5opPjCKMKa3RjMxeBmf9rYhokebOn1FicEswjzFiXJNJw4RJqqpgDF0TSCVKEp225cB9MS7FkORlLEk5gl/c2KKiBIhTjE0gJCOxMwlhRiPVdCjbujhe7i7ofC8RUEAqsa0BwMvb2K6sKrbkygqxZs2qYMcuFaINpMmFFI4epLxdikle5tEQxyOBlQBxmaKLNgI2VzKoASfqVRW44FCsEmVquWKyQ5cx9IQosEp5BEiKo8lsOW5KMpGSmhEJAFKpdCFfYKpDgFzkJq346bdceUlGzOsae5VMZeqVqSWxKlmhJ2+uQjETsBi3MhMrIcTKbkN5G/MVp8xGidkNBvAbCoUaQ6PWg+YvBOIylUSJJEnQkjkbycvNwuQo9rDBb5t8sY2xn3kLMeSOW3w8UhKpDfK74frrUYylbJeeU9XAmhvADGcyR/fAAw9oG52KnnMQD6fWiQhIxVhS9xlPIsH9YfEYdcj+uoWhHpzv1UQ3Xi3ma5GvysZOZd9sT7d96+2cmB6Ohrl6j2FV/ZbrZrpyIJ+NzcXxl2+LPfMMkFms1gDz0lEyK7C2glVMzf3lQMyyc9l9sTATbNuS5ZZ87FrKgV2s456Sw1xhaDoYD3RvhQ/MTk9OZcI9I9ds23WTVZa9vgPNPRQB1Ol5TmwuI/6R9BixPByPyRgEM8ZKJ/PDOBhU9qSjqTStAAFBtSKE9mI+k3XLtBpylzpXKxc3KgvKltBnHlZn0aCMSiwupg8cP8E1gV3F2L989bt9G/snJk4CGW9qaduUfXb0B98/mCoW8lwjyF3EV/ZH1Mb1txE42aOLXc3irt27dyPRCeFRmjnedeu24c4Ip759P5lcLOTzvS2l2UNPTZVKqYmJTVcP9Pa2CJsU/sefrJ0xDviptWVkeCYeyKQWp6cnNm2WzhNiwhPxIHxSeaVAC5ur1IQJCATGF61jSUXrwAijldZR0D6rg6My7QM/3N3R1X7Lzs3w7nP+0Ha0oNatGZsIoMqgCNYljK5C81HP/raoC3kp0KsB6mq5yTcPZp0d/uZTtz9knYlZt+y8MnqOdct6nTPSaUCE4MjICKMbMGq+XL50jww+Yc99PjuQKZ7lBs3ICKhl+AZ5sipkL9cTkFysn7pwGZ1edYkIpgJvv/12zzqlRDB5RDylo2IZD6Jj0E+Q/kRhnJzBKANJlqvGVWmjaiTWQq40kQ4k89bT42bqkmhZMywGnJiijIxjSBkrU6YujTEpUTJxKKBirIpf9pZRZJzsKCNKRLGEMItLmPw34PyaGIS9OEMCY6S2Z3TKUllHvpOO+IpeAGSp1DKW65ybiUzJxKRgFmAHq4p/DQxlF+aS0xO9I9vFelMYUyhFB6UEO6g1naIyGPWliNxcTEZOZhJvYkkp5RME4tcUBp0EO0zBRW9i1do2ZaZuIADbT95MwshyXymt2I3M5bJfD2NVpg44q7EoFilWIasXOaNGrFPryNGpdHbh6oFETOZYZcbVvHFIcSWVvIB17VLJVC0jKCHcIxiHfv5CoeRLc2I5S2JnDlasYEIcq1Smgo3hauxnMjJZuxYsGIiV6V4cPBBgfg1ZUkWSu0Q4b8nUJcA4pcEUQL3mbVmcVf7BD36QD821+X2xl5zVNbC2BjAfD4YB1gK3cWI2YNIgEasJOPc+5gx1IgJTCqasbsiq07caIBbWD38/ZTHJlNUpZ7ZTUT+WIPdqmqs1K0Qt/RgqcSu5+LBCnV2lV78q9/z+SD7NVyhmF4eflMvZ226N7dy5YG+IRG5sLnwjuqWU+sPuxbaW2E3Z8FAejpSbsGI7cpH2QikQPlm8Z1LOz2oi8cn03NGFcKKzfybUP7cwOTMTtLmfWNiHXcwUujNTGzZUTsCmqhkFR2Qyc8tMIEdSEaILdL1WwMHGXR2YwA0wAyvMI+nFRXQzrCkMZroZ1wLrsihCaAsGOBDDwCMvO7t7isF4OJ7YvOWKnS++EQP16PGj8Wh8w6ZtC6n0xOjxaLwt3BRPBCKHx6Z6guHWFtm6vFzdEQVadCNmCXBDFc1KGWHnrG9Pp1MiPGwb8xu22N3VdfTIsa6ubl1NTRJiJZVhfuhVQxu6ervKUzPlI5HwtVdy5Yxka4ZiRT4gFMdGx+mVGzZyNWugheP/qh5kRFDGEjVQGKnHGY18wfxVhopwBuxMu0pVnmfhgQBWvIOAL452pOF04KkuSiqKKSMAzjnZdck7nwPP2xqDQ3rK8flcgZdoO60a8PobXFdtRbgu45sgYXU0vN27Cc+DPC38HjA8AVHLEmuQq6Twos7eAXK2zHCOo6KCmesIHWwKy+3s8fsxMBDM0J4XwvomlVarZWl7mNfBUdOmVKOG4EAQowfSZAg+6hZmjoJOJ9GNUTTi6RrAYEb4MuSNA8WyJuu1KOxiPnB4LpQplvdNBYsyXygS2v1vMkTkIqqRrY5BR6BIYSPJDYBC+QFEZCu0f2BFUjjyW2xR0QJIJLOxgt3JGJfJTjEImCaTaEMFs6GFohzFKQJfqNCxc1EO8GChS7gA8xQxArG/jN4ngN6uXIUQMEnkmmqiyYntR7ULdhRIM/mJG5OPJCg15k9MTQqG/iNmp5AiD/OrpA1DgjFHSSU2H2uADYFMeBp7lAArYpBrXZiJTymDkCIvkghRxmmIk2AhUkPMAAHucjhpj6YXekMpPl6lkOwwpMFv8pXcjcmqZjaB2KuuW4iRUogpq4UyU6+SUIsjhPBIZUrLSP4NP1IAweMk8roN4aY5Dx48yHIJVwV1imUiNV1NRg4ABjBKKedgMQJFDdcAXfL6a2BtDWByohkwWhB7mCXwu1Nahn7i1se9tIssDWmcEjgywCuLSfBTJ2dWFVW06Xdz1j1cWHBzLHLzTcW3vy37sf8/fOIkXCMfj2Ruuyf+hjekOvt2T1htgQeuCjxsxQvNb52e+5v2xQ90s1jG2lBKvDYTbkvBTsvNdxXC1wdyyoEY6wryEaKawPs6u3o7O7phIIVs6vixo30bSSvjwfBd7F527fIg9akTJBlKEjoBbmJrql3nAAlU0xcM2JboVeRFtfCADbHKomhMX95MxQNMPdMcQGKdhjjVqqm1c0NfJpXMpjIM8nPVMSdvcV3M4sKccJZyiYPq0wvJowf2xWfLV+10DmeCGDXOQah2NSt4QQi1Og7Nxma6N7E8lOXee+/Fwc093IB9xx13oNbQMVDXCIROsHH+FpTff//9ruAXCdEUCSaiwba41RpBnggqrwrwzZbYoR3siMKaOYW6qtWbYnZ3gmXVRkRqKgalpV35U0gZVh7ZtAFrvyqlZHJuHh390TctuMJgEDWmeyhwnBtaL8Bc6WNonOiXDCfp4TrnVSH4RnjOK5JqiIE8Pls4GDV5qePVVM7KXrg3rBh2xyAXTHIp8BnUJ62A4URzeLKVEMwq3kvxn30IZKN6ghwOD2NHMIGTLkEIk5aopCsM2DWeO/VARXlFwKvDgo1jOD8hqShqT1k6ZirCF/UPeYegpyZxEIVAZJUckJR6Bea/QgFRML7xjW+waGh9dtip7GEJXJS5xQKiWaSR9GRjHTp0itf8q9At8t7zOQLMJ8gQ0zwyam5sISO91S1AWF8SznpeMcnEypJpX36MvWz0HuFOPGJnil0q06Rqs4WTxXw+vbWjS67jFHvVpDN4FJLRCOBzViprlTa0TpMFp3U2NzPQ7NqxglMfjFicQoD5E/UEryFZKNSHXDREYNA9XD+xxqn6DE61PEU1gVQhngRsoXOxGwKkYoROjZU3kIYwsbelNnTRsoQLMUJhGNSSBK+sZzbEB09G4k/MzW1vmR/e3CmQhgpmiA3NSryS7RZCf+UtsZ6JTVrzyC/4HV/VjxTjDB4nkR8nxTM3OaPHLs1r5UyYw0NVZm8FBvAZEPOCSrLmBjBTcyMjI/4xzvO2ftEUEahnSV4jBjBZLO3TZ5Lvyt9BwxiFGL7y7t7wffeHr91VPHSkmEuHB7Y0bexLt3XtnbXypfC09R8OFt64LfK/Qz3Znndmyouz5ZwdbuMI5hLMuRgeDm18X2823BqVGuTTTQz17jvcZ0UTqbyedsfe4sKBZ/ecOLQ3uWU42B976t+/k8/m4vHY9m1bxiengnY5Eo3677mpqSL16hvNnsM2kKNYj9i3CEJW2dHHkLXo+owxM9KsY/YssgKYcEjCYeULG1riwcwk0xOp0R9Q6rZIfjE1nhxP42ZbVrs90981mM6kd+3Yll0QE520MFbqkrlKcsRBIG9109yY61rTNQQrGFFsdkIDgBJNBTbUOKxopD6aoqY1b+GeXZ1d/HmBtDAElEos54ZA4d3PPbcHg37zli2+7KzNrWHO1HWZr9stfHJX8duDclKXG+1lci4d1AYsfmUFyFfSc0nqBZe3fiaomOehAXz+tymWCc8F1+jnA8E0LqYOLHp4eNgzgGHU7mDfGdKI5Yn1iwUFcrgi3RtEcGBmaGGknmF8htirk2HikhE8HOSQ7f+C4OdsdV4VA1jz9H8Lfnc1RReSDwHHyiylGJ2Kjd+MIyCmWZNJI/LGPFaHLpk+M/2QgQnaHUm6PlXTHA1sahP98Oqe4kIBY00sIzPxaSwuY62JmSrhmKNYYsYAE13PGHhiEQIp4ShcSGEmPMFmiS0nAfInkGgbGIgSw3+diWXXG54awe3zCh73kaVhJsruSOcZM7qqexMiQMMEieQlPq702X/yCPS0YhujPCwcxubq7OoZ7Gg2dFMQQGWVL+a3IUxSkg7zkqfKWCUQlGZZMjDiltoQeGPZmre4BbH+CQmCjUdsEOMQj2tvSi3KyILYtibQ/PhfJJcIf9H90bhdiK72VtTLxYUFDpoyCUxKjZWcvVxq0gsKRWOarpLKuGqA64XVgDTmhefwycDfeEiBm6kXHHR1qdAGHpqbtSps0GsA9oUOsuYGMM3A2bxPPfUU7Yrk4KHK4XqwyPOt7j1RfTaEMQOJTq8DxmeDZ33TypdFAwVb29gMHBnZyimNzDLOZu2xZJnvriOMARZcjP7pVGlzV/bPw4FZqzUThmnC6u1wNn57bPB/BCOXb4nxfconmitZ49HQFVds49DjCMfhBSxsXZkkjkfa2ltDsejzJ7Px9m1NfSz9sqazyduu2JJoZu10EJFG3/CmUr1KgDZ1Kwtg/y3sYGZmmjejvzTct7/9bb55pnwxfRG0cA3MYBy0BTIA8xgri2UIxLIyub+/78477+zs6shmZP1tWyI20D/Ekuxjx472b2i79SXXyDRye/OB2XGEtHeCJW2qa/nowxjD9GpIYmAb9UjP9MKKIzuPZhwoSWzC1EVfeCmFmuVoV4htEtbAS91V8zgo4Yxr9AOvTsgRSto7OtjzrHktyxarUQluTeD9Ot5z+UMLjoyMrEABlYa+y8p2dFCvyCvAX4ryaoD+j9vrOV74JUcjNbDsZ9VI4hc8jPY6+LBXEzBDnlWpVUxr7dtgw00WYF5FA3gpkV6IJ4m8cq2uQ3kdg6p1Z35WN681xebVGGKR5VE0EJKLESW08+HhYSYbdG0gwyJAnlmtkhAZum4GcHss2C4aTiCTt+ey2HGm/swbuwpNSGxFZCtkocpIiDGPkfsKgy1oO9YeJSbMvA08xqP8Ao+eI1O4PBoiYOLGChWE/FMDV/JwMEim8phFY0yKmuSCf2dLb7HY2d0dCaODSbw8Go3DtkPX9lxO2oWF+empya1bt5EkEg3LxmrHhvWMS2JEA+PHvDTc+ITg2qcepAcmqTwaalOKX9HqrxTYPG6g+nxvrStfQK2TjFG3YEeMlGnetRBSqUvCagIaAKlJsYIXdRTFkrUey9UDeiyKHxgAUP6Gg22ky8HX5AUYh8PpqTQ1UZe8NTWw5gYwg3+vfvWr0VwZAsRQgWFBQY3eX0PTufI22L1WJg8xDIuH6a8KtpXzWotY+dIh3qDuara6mg3bxC8crNUO/FYp/1PHn/9gd+tohCOcIpuslnuamu8KWGLKuvTYJ5Olp8ZCo4VObgywsrIw9+TBg+VCRlh1pG96ND0zeqh/6zWbrtg62JwPze6JRDl2io4hA23xEKOVCESPYwpW4f2SgYTj5jhoulMs1oQchbUhAtkMzN3LuOEX7OWm/hljRpnQtmCVFKIXU5k1yUDq2QAz07MI5vn5hYGBQeC5qUunF44cOsyEbZlrivIcFi3r9kFFRjQoHZj8AYZ/kak2MeyMh3CYTs2wDgh5tFrgv6wJJBdIQi9kCTQqjkat/AYe+jG5MbzJkU+JXCg+BFAoDMiVk1fHem1UHXzufI18JggDGoLqumQAn1ZDqflBdz2tVJeAtQbgHpzZw7cPu2ikl16qN38NaK9TM1XDz6wO4atwbPgtSJgJgdPCDWCGw8PDhLBNFwc8cO06+cjICOoKZMB+4bpkyswMHQOqcHNt3mlyYBKt9MDV4XUImgalw0q4zo842p2tBFqBuPmaGC9mDJcpYk8PPLO+QfkYGtYh79VthTo1h+SUaU5RS67t19MrBcoI1Fqp6vgliWKqUmY0yCStDhdjTmaJl3s8Y09ADFglB9WOJCURBPMXSPR6B+6YEFPLvgysTqOcFPO5mYDFsjJJ7T1eZoRIXn6/IHefStN5KptGmwTiNOEmzAtyE6/Dr9KHBoUitw7ZNZIFchkOtgIkeg4cAAD4DN8LunGllldI5otiGomDoOFUa8cYfbldwM617RPwJlaK3nDDDcPDwxgAtCv6xAVcWw2QjhUE1On21wYQrzmIn2ZloggtyRXepb8yUst+neFi638p93aFzF4RATCCwU0uo7mAc9Jdr9z4I0lAEd8ywBJonunJsZaegWig2D+wsSkW6EgEmu1E2OjogsEOzGXthbxsX9VBN93gwa1GbM+NN8lhIfue34sigjI0uGUYaxfdiO24ahPiSCbl3gU+e72QA66HAUnHI2uGnBlvRsNgOpFU2KJcBcxkMl4YDdYpa+qQzUzYoleh7jBojVjV0TiSe2u6yAt7GJwUFDyA8UBYDa9xK0RqyHsI5IF4eJPyOC/K7yCWMoIfaskOSKjCekfeM/NMODPJ4IE5rrng95N1jtyydt1q6BLgc0TgeZrtJQP4bBoG4cXoLZ8eX/rZ4HlhpoUZ8s1qDzzLGsBMUi4HEwYtbvgeA4t4wcz0DhyyOgsjrtQ2qY44lY+ERlP3wSE+KAgsV7MmBu7tuYnyYIHx3DUOP1hNlHq9tGRHCFnUhPtTedjOuJx+bDVuKGGIAW1Nljj5SucHczUCCauU3wcBYewdohTIXx28YEwZzIgtFmppYdEJfSmWc3pVWjefAJ8n2DAnlpeDHoblsqgK99rQq2SNluwZnzfPElIIr4Rp6QTQCSaqEqsY3PfS8JWmGs3p0FVJvbKRKYoWXtkrbB6Bw2HGayLhUIxTqsyjGRDDvRt8RIxN8H1pr6spcoXspWQqrur3Mp1luU5UnXgtfZTr+uuv1xwouH6/628Po9QhUyADfQY33wKdlmepfaurP4BB5UP64DUt2WgdUV6+MsYKyYI5kkaTvSDh1tAAps0Yy+SNjeHV7ZJvzIu5GBxeN8VxgZZUisD/Qq44nwrmFllxY2PotrdzogAxcE80kJHh4arWEv7If49N2mG7PHp4/8L8/K7rXhwK0cfsQDuzx1Y2kz6cGRse6S0N93LYAjhf3B/tjMtpwB73nM2UD8xVX9NjB0ZPThWyuc3btgM5lW8rRpvKoWI8IKdosMIZToEahFHKkT/JxWRXd9diKgXrL3AGYkHuf2MuF7B8rtDW3sZZgtksu4aCWJjf//73YRAkBAOmJsyIKQs1dBHYeFECwEymfnbJaCIP7Us9gZbBbFasSb2t+IAN61rHvMkLeKzZ5VKAnKlsVR/VDYVY75SXQHLUgSSVW8shuWjCkVhU1wuksKvYapcM4FWszEuoTqsGMBGBP3sDGO4H29QpRNg7KrsYZka3BzmmLwA8ftoMK4YbVwUagJoQP8cmSr3+QAer8naWAfFH0FNPP927Qf5ptMY6oK7R42IjWDBXwzjpTJSfJMk6hgFsB2THkCRyVz652J2UFVlDEgXCoag8+r0SOZZPNQ58HqSb1IHgsIzs9Mzs0KZBs3jXweObOazkdGj/Ie7V42oDHy4Hy2Jqcf/z+67Yuf3E6ISh0W6XgaQgF0KgG7V36qWsBrg6MT65WdENNFUHDZziIcAabKrGeKLNuXJ4JpmxYs1OIgdMwF1gE+NWmgkXZcbEypi7cchLA3E5mZkQaodwtxbUyBRKjDmqSCVQ6TRpNWNjLptQzZ55AonVDGVWQJM4JivQxHobBgQvIfyZCKe8YoJz05KgMEdnmWgDJgSZh3SCWv4HuKzpyaeeam2O33/zjq2drNkWjcVAyQtjDNOLEOwrHi/84nN4pcYB0+B0KG5gQS1c55KSNev+tBWYz/jiF7+IZYQixzpEPyU13Eyj6gb6U9W4wUxhmdSBc55u2hpUF7d3DQ1gKu7555+n9vViIT3x7+KuTUZcmPFGQq//13X2FeuyCbs0M1N6+onilx4MHDoSiMYD114defl/CFy2NWgmYGUE0bDX5XO0ehPl0MnHRto77r7MrGfmPH7DptMpqycdumI4EoszZs8pDIEmY+ouxSh8usKrnaw0oGfjEP5CNjP1/Ld3735mdnaOyV4Myy996UtPP/10ORDu3bihWLaOnRhLZrJtrW3ZcjAaCLdtGEq0dxw8PnnsyEGuNurptkr79+/btw/tiuUiGLRMAmNhPvnUky//sZezmhr5/ZV/+wqmJmfKa/Zu/bjE+GQJQctxGS8VY9XemDejei972cvQEVdIpSPl2OFMR+uNWVpM7HYSqubnkHKx/2AAU1GXDODTbWcdczGbC0436SX4SzVwVjVAr0OrXpVvti6TJJChSV1vtYRQOTN3JlUoGDNDzAj3McuR8BDiyDCNc8MlQhNJiko6N70JO7EYLM3bmQjWijFyKpGkcFL78jQ4a3AJZrFsqsEgyS4UIicLrYX5YHRGrpZ1SDDELvVIrI9IARcc/JcfiRSCxKryQZlQgoxdZ6gg1uCW5VpyL+30VHl6olw8wR23JqUYTpJA0BoH4Ura3oPJnmRzR9oYbi6QyTiQThUPj5Xm261A23ZiFuZmpibGhmUuxEoWg0cPcx2feci7Qq4GuZXo+CQzx+n+YELjJLQY2tS8fWBvJhY4LmU2jzaK63MDnV/90SyN23NqHppY3V5SFwY6fGgJdeH0l6M2Jd6YmRJiQnlpqInQUK1Pk5XTxAatXHaoDkkpprKkNOiknQw+qXqhS8YIiJTak+se5DE1aXqyCTX5TuYTc+XQ2KK9tdNpMob4scQ0WxzMRuJGndCheYPoInyxqIEZBTSoGs2cstdlL2tRBdQwGiDZsbyCo/UgBq8uHlyODPibf+qlcarAjPTH/rr55psbT/UChFxbA5jlo+j6PCquqF/98LyKrtv56sJ4gXWTeAjPrYNtY9zcxaBLzWd2bqlqPHfknz2fzH3zwdLfvy8Wlc271rFwaP+e3Pcet377nYFrrrE40kp5dD2kbhvJuYbZ1GK8t4cjGWDT0mSGb8faWm66cZcwbmHf5r94lH8rxnJzxN7YjECF+bMAODc+IVutooXFSKCcGj9EmraurkRLIlcuzdjl4eGRycnHMBHh4/2DfUeOHc/b4XS+jGnN+VtRTpkKRycnxpAj3RsGS4Hg3MICDC/W0t63+fLupiL7XpiGZYBGVzIPDG15avdzjz/9bHNHb6KlPdHRvZguzGfyXJ8uxFEKQzm/HCoRlY3ScjXxKSUHs8SwvJGREakH8wngWGH6Fxhw6swzE7944ZKwM0xf8OBesuQPkIv5ubQE+gxalz7G6DLPGaS9kJIYPfBCIviFQSsra3hWt6yojI1Z1PBZ+/lpK1WsVW+M1JFX9eMXQCJ4qiQSoK5gM6nsxaZNx/KxiSll5jWY1CbRQC83kRQ1cEb6+QKN9SIwdnis0J5ciEYEv5pjjqD0YTCWkfFXkebkAg0mIb9LympCvHw9PBJSKhYOPb+H83LFbZUOP/w0k5ZtXd09A0PAiTEMfqGTW2OL5TK37gVOLFiLoWILF0KwNCwaM0c72Quz43JvQaEww8V9B0bD4TjL1kvl0EzKiqQZEBdhqnRBpUFLIRySPHoNneoj1p48drilo6uphVk7gSQCq8+YhhyBEQnI8mqhTE11SaYIgZVg5+2gM4GKRfCUitMnDrb3DoabEtr2CiaJRAGS/JziG5pMuOoDOOs+gtt9Km5tS890NwDgIbcKzNJUbogphfG4eMRj3EsxmNowwGmrqZgrTqelqoz2YTHQj7Yj1RUIsGVM3WwHu7jHSVEhmB1BfdLpVmaqMEERkbqUz1TVmr+Yt2BMkPpn+peHrBnZZzyCBfxMwyzNHiXwjDU9tEQWNmJ/1aDVdvcHqlJKiD/KC/RDXpTuWgmxuoWkAbTDUbmMNj3++OOPPPIIp0dgIt59990ckEt2deuatdN8mUhQ5r6UJPruy1/+8je/+c0///M/v7pEriI2lraCbWXbZhWzWy1UbteHJ2ZLMx8MDf11/HfGrSBWZABpX3isOfiZbO4fPhXbNBjasNFj1yJZhIvKY3irOFh7LLIvX9Blqwy88Rkzjypxhtn7f2ua3pAR3NjCn4EKBJLJbGx2XjLpkEVJZWu+UA5sam/p6W177Hu7Q4XFnVe9aGp6hpOcx0ZHmfjduu3yTDm699mn4Skw9LbWjkRz69R4OsW5Kek0Fwxw2mI5X5g8eXxmYrw5ZA8NDdJYdC14E/P28/nQTKH52eOpuUf2bhnZPh7YOHbwyHR0b1vXRpZsyfUE8ojQ6Y4Vdw1EWVSEQgbZhnIT51WEj6FQFUzkUlgPUoddobCmBgz+qhcACqZZ4PUWzBByyuRVuC5YD23EiNIZFNZrF4ruJa8beMHWzbKEX0zFdBiNy21EIRWl0VUcRfF13cvWx5lHnNaH5q92IdFHmD/KH37mlJ3HKVe9sNQYI+mNlVhkxcGZwnhGOfYpE3kda3lIAdE+xrvd4kgKMQZrep0HRDjumlhFLoEmv+VirdlQz2IxbE0D4MC45mGFPLWC1G+wOVF+pP7wSspqVxXmkr0Y7qFY+Vwun02zYAp3rpRIzevBw05KPsbZ8fH03KQUn91JCxP20Qksxb6tV1oRbODAycPThVw6szidSy1GTk619vRF44nWrv603XYiaQhUW8wYmNSjOfwY+S537YBTAhzSzcdDCts+MZ1qD7QmpNhCsvMjMbrMHuGsRIpZ7A6pV1eABBuT1ldmmfMulifn04GWUkw6S6X+DBWg55GXHLFsMEiAWf5sHMu8KmgEwCHYwEoJhBAnoSmy63HCYBoSAq0mQNxuiTSthAstAmNiPXQmATgFLTHcdRuKFPLZuSyT+c7Jpixz00lFrF/MpKU7vLwvFyQG38Xw0lNUmCnBQXmYS+CNaooVus7Fo9rZnasGMDYOubOto64BfDaE0YgoijUGMIFMqHz9619/+OGHadzbbrvtrrvuIhdtaOwyDpr+rd/6rbe97W1nk/WFlXZtDWDO4x0ZGaF+2W+AucvsKH0OhV7PQHvLW97yF3/xFzVjujQSLcENzoyU/NzP/dxHPvIRrVBGSr773e/ef//953P9qp2j39j5TOdS2spYl3aqcPI/h/KfCLVxdjM82giE1lT0nlTpRZnMR74Sn/35wAZn45NgEBbLMmbl1uLn/5Gjx1KZFDuIi2V7dm5h34FD4Uj4qp07vvvII1/72tcY14D58pm9/vWvZ3SDttZvDwdp2ZvBvghYA7cNvepVr6KfYDlf9+LriPrpn/5plis/+M2HnxoN7k0Hnj9S/up390+PHW9pbz85nj02lU6mS3sOTmayuVIpPT87VSjkY0G7GAotcs27VY6EGN7Od2/YEI3FYDqZ1KJVzJWb4hx/RYdk2II3ZnBp77HUQvLk8YOFXD6dXGjt6LTLpfGx0XQ219LWziVyjkAW0UINyQ+08dA5eSBYvfqmUNrPdcWRDo5wrA4bhunbLMhhFfQpx0pgjsPDw5xoD35mklEBOcoL/CBZ9QkWP/Hnj5seQofx06O9hRAcVAttt3SZkMLAdp588km+Su6XZleMrjkn6hOf+ASy4bd/+7f9aC8+N4yUoW7Kfspudp6X3XxwZmPeQiowO1FmVQgab09voKeLhbDuBR+rXAh6Cwoibw+v1/G8EGVf6vVimVtgIwx9kou7VLp5YIz/ciMggq9G6nkILxoHrA/pz7jVOVmDgPhKF+z5rCzNFVHG21XmaSZpDiNx/LWt5gZQsHbgEYdyranh8eZl2I2gM4jcsV98jl9wuXkYvOLRANKrdDQBbs5ChC/epDFgHb39xC3mzbpiN1goscupmYn04nxTW0drR2/ZmHygRE7v+eZnj+/bfd+b38md9UvMckh2TCqXIAep/miBcHPzYbyLy3vt4sxkYTHd1DkotiiD4oWSFMSUQoFDTe3NVpR6Sk6PxZra402JksX+1VDQkNy7ZXshvTi2P5MpJntGtnUNbctnFqGqa3DEIUBu+zHoDGngzqXTU0f3soKYVcQbh7fHEy1GwlrlQtEukrsdDdohGqbEqnY7Eo6XuSNIHplWOXL0CHdD9/VvdFCaSge7toxko7ACLuRrxvi45oH7VkPFYohbdstklMMUt2LMUYv56EIaTEC7FWCc+vIwGZxevZPSWaDswALnlVfSuB6JdnqD5GKIE6r9pq/prUzKQ4+hyKOrUixhPMRpbzK4nX5FWDAUKpWs2VShXGa8nhYOolHwkAbpgFyAHWGPIWGZBBZ6AoEvfOELv/ALv/Av//IvN954o4as8PaYnqF+BcBzHIVOrioT+924mXV4eBjdEppRHtaTMnKknqk0tFwOIUPRRb6sBQ3gHBkZgQP7S8eBOG984xs/9alPsb0OS/h3fud30MP/9m//VvUiIDlzC+HlT3LRu9fWAGbEBS2fSqTqGWv53d/93XvvvZfRDrT/Bx544G/+5m/YYPmLv/iL3sdDz+D59V//daaIGSmpW/sA+MO9tP7AdXYL0zFEZTJp9J5oVG6mEZa2znQ0nJ1Xh27tIVbKhan/Hl74hG2VS/Ox4udC5eeDwY6ydZ8VvTYT7E03v+WwXTpu2Zf7SsVWK/upE8WFvJyvYEpsp4v9xVA5VZo7ku1obrlsLLwpFAi+6zU/++BnPhmPxxiUwhr52Mf+7o/+6I9qOOwHPvCB3/iN32DZBgrTn/zJn/zxH/8xx7jDs7RMWKcsLyeLQrGYSfOJ2rF4E/JykYO2Zmc6enojsejszCQzq1ihvX2DmdR8R0vzseNHkG0cgRhE2BWL4aAdiwTLhWxPd3uiOcGZ0hiiLDFgaOY73/nON77xjY1Dwx29m7NzM/vGRw/ufWbHVdd0dG3o7e2KxRJz0ye7e/q51Vha1bJD1WoGZsbJkyfB49anUA0xTE17VQ3bhSsx9EihdFPrUrNNC1vzVkUc+5npdIZvUcoBYEV0DdhSL1nD5iBJn6UAF0SIV4FQS0F4Izz+7M/+jAPMEN7UyS/90i/h9ZeFJChG9LE//dM/pedoQs6iZwRNq+Jf//VfGQS96A1gRkw4dYMP6kI3gNH+pTOPjxe/9rXyp/4pcPIE5+eVh4asn3xV9J4fCW5kFF8VRH8vOFs33ymPHws0fPKTn2QdE1dzY+Lecccd3visgn32s5/9y7/8S6xcHe1iiOpXfuVX3vnOd3rmLkyPPsnArh/tRemGxbEDjYM/zokBTJVyM5LYM6Zy5S0CWn5dt4nwvaSPidfwG1IaI0JCNFSixAh1HsOIxG1sD8Kd1IJf86wcpGSSOFqLQWHppk3hRQ46N43jtdj4IgOsLr0sSh598qsP/vU7Tx74IZIavX3gshfd/8AfDFxzJzmX7eChJ772g3//px/5ud+wbeRChUzFR97VQZqtvo1F5UQTIi4szmLAYp+oLIbm8RKbeAoYireEmhKI/7nJiWCsOdLei9UoS6LLJUxHjueYnxqbnZ3BwmQcZHZydG78aLy1c4OcdqmZOnUJYqkQK5Dj0kH5KxQ4gCubs6LNkEwNzI6dyCxMm1R2auyYNXasHAgOXnaVFTKC2JiP2MTpQilbYA5ZcLn1Zq6yhSLol2qWMvAjraNNZgUmjx8tZJJMIWMnTo4eo815Bi6/kiwE1Dxea0rKSqAgsm3UHkFrEMtbUOuv/JziEWT+1A642wvxOtmBlWxqQU1eQJiZXpPWdFj8LpUmMGSuAM6wKYyDPAUjeCoAcKrXve51DCL/wR/8gWcA02ToRWjsDkUr/qBgAI/S5R8oXDHFOYjkO0PXwgaGKanSBbUeT15PgqBENTetLohpRJE7MwoZ+KZ9vbR8HR/84Af/8R//8X3ve9+v/uqv4kVrete73sUm4V/+5V/2wNRhPqVKmI9RVQIvDtfaGsCMKKgOgeL+0EMPeR8JcvHv/u7vaCE0UQxgf1Uy/vTP//zPvFkj7Q/33LQNdgVziYyvY3LAs85988CfYL7sKOjv1xt9hFoT4pF9XjkwIRj/GxkZcauubBf3WbP/C62yMNeVe3Oy6ViZKyzg8KVPB3L/uTn2+lSke7Ec+6IduKvCPqVIQSRe2XJ6EdUQbWldSNvJcCjataXcumUuIGcvhDq3/uLvf+SBn/mPHa0tZbv8+c9+7tcf+FmmQTBgIICqw5h5xzve8RM/8RMf+9jHYKbf+ta3mOp/61vf+ulPf5pYl0jJL5eZHz16GDae4EKg4W2Z1NzRfXv7N20qbRzc/9wTzP1yuHRbZ3dzIsopW01xDpoOwt+xloOhApwae4myYxSRC4t/cNCXYIVYsOTSHLFKi6OXb9+BEU2+c6PP9jdle4vRwY2D8/NjzbOTN1x/Pd3NiBB+hHKhyfdoiBIMa+NAadzkwrQzs83sYcN8ZYyNwR0C/eXy4ahyghBS2aXMwAHDRuwJ0SPlAGokOdPF0HChzxVzmCEmHLWnVYNdh3HLjC4XO3/uc59DqlVVmXx5Nit5GFJ57Wtf+zM/8zNMm8MuHn300Rqwi96LdkIZ+QQu9JLyndnzC/kvfynywf8ZZnlGPs/NYJyTnv8ff1kslsKv+I+h9jZAGvkiGq8K7+v20DKqwgYcpBgj93yMjHnVYPvoRz+KyGMAl3kGVh9gHr/3ve+F4WD0ekhqkqypd2kR1jQ7P3Jqidy1B/rD18ttN0eDsUi1sKKLeOq/xgj/FkauwSKxzT8Xrpa9u8RLuMP7K1aqG+kJftdgMpCaXzU9XopqRyqd4f6C9rYWD3ru+L6P//b/275h08/83ie7h0YmD+394off+Yl3v+atH/leom8Em8dYYnaExU4sWFiO6upcXJ/JxMvJhLLcqYm1D9jZatNVmXlOOgrFjCJCCBjmZsVF1QXsiSP7FucmUlOjhXyGc0DGD+0Ox9s4FCQUT7h2nIDpDCuU5uZnWS+dW5xLJ2eLHH6N8Tx2uFwqRBItkVhzvL2DW2LIMjk12pzADRLylWuZTatJlix+1jVWhgBgxTYEMyafFAufVIiURNvKqR4O4GzvjiVaGT9YmB7lvI9QVHYkFYVlOlOwTrkJIEhwaVJx8cBVHdyKWBQVjXHemp2GeSlxEOIaq0BKXi7lGicAgtr43ExNkEHsZuJAeZk6HMZHNCFtrYl4NGKFYvmSjP47KA2ekZGR17zmNZz3ibplAmpfHvfQCAd/NRQjfYxzwQ/P/2WPUAhPhnyWpTQ491Bd1tX0QQBkrKloxvJCyefT0MLSmqy33bFjB9av5vv2t78dgcWILZOR/sZFvH7lK19hghoL60d/9EfRP+Vz9frZalbDuce1kgHcyAewQglk8G92VtVWf0srWraD0zD+cSbCsU9YgK4KRF3MGA8/9VM/hdZLLML1vvvuw0ZCOT5XzSN8zfBXqCktzG5BGrCqZnScdf3BBDfUSSHOFW2S9zIPXZwJVR9hodLC54P2AkO/weg7SolPWuUfkhTJGirZpQ8VirfGI8OZcvlLYeu/Y1d6WOHBYv/aZuWTJJCYuXR5Lh8pJ3oWrebcIt+Nddeb38vs63gxNCk7L0JX3vmTt979yW/922enpiaZ8YUrvf/976eHMCLFxwZVLIB/wxvewNqMQ4cPjQwPazWSsiMW3lg49oPd/x6JRq695rqhocuY3v3/jjzd12O24+Y3xcrZgaG+Y8eOpwuFFFcHm4d+Qj+kN5IR9idjMRiTBKKisdCAHoh1RO6EYxIfPHjwqiuvvO66XVwRzHQNTDOdWmxva928aQiVF6mkcwoqsUDCRBCZIAbovZi4uMlCLxUAv85+YIGTNRlR7eRCzZOKQHhTI+N/rJpmMlnLAleifjCDYWQaUvdd8+UCoyG+Fq+b7swDvRzXIguuP8GG9wxg+DIDCqzjYoEJc241REMJAytYv8wM81bOg6nMUHcNJF6AsU+AodXwrgXxSzNdtxD6J3lpDaxbpmuREd9deWw08Ml/DKMaC0chEzF4ubUs8/f/GL7lZm5Zc7jP6mWPrGGohfVKsAXtGHzRLCLQtfR1d239/u//Pqfi6UdN16LLse7gr/7qrzCDda+dR533vWjIGnU8cmEIjIFm5Uhe7uvgoEQ82gPXIbt6WbBcqF6whBnBrJFGZlWF1ACI7WQ6l2uRuB6Bc8yNKnwC7wsQMPcxwW7/lcCKnePQQdjC/EIqnWpvbcG4VETPPfS5Uj79yl/9sy0vuZ+Q3i1Xs6nn4//1tXse+dJNr/4VoYvUsp6T/3YxlST/SHNbmfRKh1sCl4zq30rOgom4cJitvDGhDZ8TVp3E9W3ZfhVJxCI1SFgR1tLW0twcszf0FbBoJ0czi3MUpKmtC+IWThwgXXt3X6yVs6HBjkVsL8yPzYwenj62v8gyaZNjavpoW/emDcPbN19+Tayt3bIBLuVmx1paW5s7e1z7TmxuHkrG+cVoXxFzUKXQq0V2KRSU/CecpeJOFD8CGGnnSK1AuZhPzZxMtLZFE2QkZBGo5jUJ+UeIJJfMFLk6BYNE88+g4yURjrvKURvI2IHE6xACa9aFQji1yV1+DB5ZgW+ABFQe8fEQCbiTmRBHkCRwog2MghFus+crxuFkgXK2YLdEHCDCef7wD/8QneTd7343WrSbquqXWQFu1uDNWcSsdkGO8EUrBMk9t5eGQNxLwz2Ac+uABzLVAZHeuULnkB7EwVqTgQGMbsOjRyegOvJg/XoNhJxiQe6HP/xhFFe9W44KQbNibzBTDnQc5mlQg9m6iK1+DutqTbNe1gDW3sy6zW9/+9to6jfddBOyHFK86lOyFIzpLKxQDFp/LOsoiF26yxxs7PZEMGOT3HPPPZoESB7mbcDDGxOlbrFRJt70pjextB2FlZZjESNvRjLqAq9HIETDZzLZ0oG9hc99IfD4o6FMPj+4yXr5y8K33sYxrGbCcD0IaSQPiMXuAlL1EnULX0OE5J4RRlpqDURujb/7itRv/laTsfc4DtLKhfLPJsLDGatwnCuSrDDbhOQxrN/eNRAyY6YO98cUjoeC87n4iwauAUaqR3i2xXHQDkNnb5VtRZnAsdi6yUImsR5ZfoziiKLmMdj77nvZR//XRx/51heHN78qYHUIu7WC//W/vuf97/9zbCG6FmR/6EMfYpvK+MkTU2Oj8abo7FxyfOrkwvx4mnObI+DnhCpOTp7P5+WgfwqrGi0fNl8+uixWMZ0NTkS+VMj/Ze9NAOyoqsTvV/X293rfu5PudGchCQkEiCCIQAREBNxAEUcUQQXGbXR0ZnRmZEZllHEZHXH+jo46OuKCoyADqCC7oGwCsocknaQ73el9fftS9f3OvfWqq1+/bkIMAfGrpOvdusu559x769xzzj33Vnd3t9ZjUVZ5xCyKigUQrGjsF4WJMCYJEwMEzVPIRgzAiYQQHUap5kWA76CwEY+6ixhNdXg9wEdYIKIsvAnVeh+dcBjq5OStoSBhEH7WgryP0KLbH0zAkzDu5RpPws/HBbuEcMg/sMDpOFrYy1sgf+kW+Na3vkUR2Aj0EnDxKSMfPRkPVTxaicfmgjWUaaAsj1v2TzGg+/0loACzFb+4e5exdzCrRjJ9wSowdxZ/gmMjxV39Rs8qdrsd2D7izWWEIAG4Z/jRkvgcUQuWrIV1MXIOP1yYnr54hL0gTDAaAeVVgOE87L/ivAPgIFnik7YvA2+JkVyq0/n15tSvj445mGMbjgc2egSWoXdwHtnWWSz58FIjXGC+iqCfRAdbgM/CGMkiPH4uxZmptN4yH4Ja+dM5pQgzo6cYWfWTunNzAKlCLhyGeS6f121IJJ7B5AvWNNGbzJ9suA3VtVLCzmWKBc6q1N+GtVluvf7Kj/c/eS9ot6FPAABAAElEQVTdvXz9MW/42P+rX3YI9StSNfbzaHCrm2sawVWGiSoimDpjRtArYSyJKqwyKfocCziZq5oQD4xcenZ8ZCCZmKlp7Ig2tVusP1pFv3wliCVWsarjm0xpmqZ+2eqqxo6Gjp6Jge2CqGXWty8Ps9gbrRYfXMcV3ChyJidu6XNNKQjJxYJwwOQ9Us0oN6c9JUVf8lv6m4tx0tnNZLFNKhAwzaD+KKFqLfIrCiU/jeGCKtWq4ei7NJL7XCqlRpWOdqOcTAJN0SHJKos0rddLx4lU+VVY5Rc1WRpQF3agloBrkKqPdAlHHtOjG+DpPA0+twLMJo4vf/nL+Fq6zM0lQbAyDLxnOYeFAMZ32Bd7R/FncQckefB/YTRyMdiQx0hCrFoo7XvBvoBhCFnkk2kvIFLPb9Xa95au0cIqOi31uf6Dum4eYdEkuQow+hT+8DhF06FXXXXVhRdeiJiNp/Tzi+sLB72yAsyY5kKC5CVhzYq2QIinLdA/tXnbizBbN1Fo0S5YsmN3tU6iOPoAYcRuwiqSu7ycPCErA5zWZ5WGVEYnd7QgINx8883IDYspwCx8gZJ+D6mXXcTsFH0hFWAIKliFbc8UL/9cZOeOTDoj5tPx0fwTj2XfORw69y1mU4tmdpot6cZ5oe6MdewOboMTBhNej+7ubsiQiTqRn/zFz+vf9M7It7+RvfVO39an7HDcf+zm6LH3+XxXqUlBVCnnkmnSCMqpG1wOJya5s8Y3+8yOvUPDRx51RBT3CdXraobkJo0xOjJ87x03H33MyzlaGWQwQzIe9PfKeASWVRw84bDfPXxj9crOTxa2XWYH2t52cu6yR0YfeOD3rNbCZFmMPeWUUzBh/uhHP4rHY9jSzPz25ZseOfrQZGOjnUr4Ht8R+vW9Lb++20TPZG2eS0PmThhtinitU7GciGWHI/Jh5bQD+hsnTvHmowYj+4IMKisxmA/ZwIwySfzmzZs1TwECy0Hc8VwgG45AbLlEy2X0crCEXrEkoBd+GeTEY2CDXsIIxNyl5Za8yIN+ThbeL2rn/eLtWLKEJCJh88LqbIS5gANpz6suRCOglz4fCjCE0MianH25Y7Ojs+ASn/zkJ2FN9MiZZ57JdE6M2+b047nnnsv+i8svv5wXAVMa/A3PHw3fzbYv1b1o87xkFGCYhoXDhGWZJQUY0nh54DYBObTAkb9fVB0Bq+HCggzr8L4URL7hDW/A3seyMJZcnF+4I27qSW1pErDE8Yq5rhBLZGZ4w2d0BtiOHs9wrYM2sHVFegQugefzl8TZMqX9kCU2q/SD+TU6egIDjEz6P1OUFCgxZx55lnzqJCK3uNJF5IloyS6l5ZIZzjNJqjiFCLmY3XQuXULnBzSRGgFVKadbMaDRa92tqGuOPeOO71/xhxu+c8oHvyzLwvn872/471C0atUxZ7FPSWpkXiukrv7Uuzaect4r/uJvp/c8c+t3Pv3zz77ngq/dhsuV1EaeubsKem8uMSoPiurc5QhvToQHlLQJj/peSoZyIYZjs/heQ6iqrmnlBr4pODXYZ1uF+uUrVTZW5mlSaWTpEJy4Yhyp5R/t35nPJWPVzdHGrmi1rDjlUfSlKSVny4p1iIl50ZyJcCzO1C+VGX72/2byBfnKA0+Ck4OOEyp1pY4tZdC2UbIazd3rMa/nwNgp7zSWAiPe5S4AL/lz0KSU5BVYOtYTcKIEKw1Gt25pfVeGhFtICpc96Ee5O3jLsIBOlVGhUyoho1SFdazOg1TT378HYSOTrxUUQFVJXJdccsk555zDRrOKO4PAlF0bHBW5ZcsW2MiHP/zhb37zm+z+wJznMhAt2AgiPh+rxNxx33vRKsBQrTCVm0uCG/OSDOiNb2hhKE2QjBUDMtHmvMTqR52k49l5SnfrJsLqgU8Te1f/7BRg2uLqq6/Wu6WxBzA3s+nusssuw/7NzkzvAEJ1QejHmM2K2Te+8Q28WN32HRtHATbr6wPF/A6O0zf9DbOpcCHvm56eQmVlVuYFQxshP6MT+R5l+IILLmApxoWwMMCKsSvEE+jp6QGBhdkOWgwMxZqcKlx7bXRnL5tJWGyM4CfiC4SKeeuHV9ucXlvfwBdpSzzqoOFVuSI6Dqdc7iwPoq7ArWh5FCqZsYOrYOJmdT729P9l/3M2ePbZ0Te+zme83rCRMLN24LvCOgP1hlnhQxQkzczMwihxC+QzwV11/vHgbNYeW9fkj0Tn6y02TZT72LsuLmaTX/63L/7h4YeHRibkxGgxeFv3PvBIbX39qrYh3/CFzda2phVKKGC2y0+f9xrj1GOrrJbzWlrlg2nYqzgXGtVlcmKsZ9XKs0+ZXNt0c8g3STtLmXp7Vad11omzP9xY+5F/mclkHEFQM0FkMnAgjFMDgwftiOkBfQmNEWsoTBzdGCsPI5NsmD/hERhxaC6OoUb1JR7OokGByQ033MDSbnd3N2EGJPndJGK4UF/ZhUsALRplG+DUSJvvi7yrAOzPDRGZjgYT9ucQ0Oq6W6OLofdF3p9qVBkXmg64jwcEODVoFvGcFGBcoyH5uOOO4875llg3vv/97+M5gr3MbQQWyVnyZds5eDLN4wX0la98BW2Z0wfIuS86xn632EEryDB+vkfaQaBFRpRpBJZ3Wg1NscQszIL3MhoRFw8k4Ex1NNi53J0UDgI++1KFfgswzyFf4mQIV3FLkcTBkPAucCZMEsYXTDDa3OZm8wY0NGLgSDqelwI+xkux2FuGoQ2xRhckrEtpW7YX8vMXhjrG3tKeGs9f7UCOBX1sfly0ijl5WNQCN5/S6XQhCWrtRFK19uABR6KoX/oSaDLzuBFYXfnWQDRebThnNet8pXRv7aVClAYm97DfAHE21gY4xktdHWuPeNtnfnzT//v4tncd0dzRNbKnNxytPv+zP23qWSsVU8bwFXLZ48/7q01nvJsn65hTcsmpW77z6cTgjrrOpTbLaPgL7/MQ1MlulFTnEkIsYZVGPCHbyiSmzUK+oaE1l8uEA/5CYsrKpVgfFJ1Pyum7CiqtDp3V9JuBcCRfzNn+sDiLORW4VVJIFDrdPlpjVG+/YNbUUF+0a9F+FVwNXApqFCXkUX7kif9K1SVM/RTw9pE2mlBWY6ryEHQwUTDVTRLkch7UUCgZWCRORagcqigNprFQMo2kz2HlQU+yOTAVYCQzSSWsERDNv5RBxyhQqh4ZPSpAdicPngGWxbliuXAwLVtH5IInoNwy3zHTLcY2yYMWhC5Edfi7waCQ7e+8804UYF0Dd8z93BHjkf9RseBjTqe5OV5kAZYBkPeQwSouer/IkD0A6Ghzp7sQonvHnUp0BfrR23FIxW7dDA+aC0uHG/PSC8zXTxR9ulFYaGUNHQOAnryxAXAAJpF6cdxtCCY5GoixxT49r52bF218fOqD59fUJc6yZ/eyGyjvi1vWYXsmX3fB+76HhMqGbAYixm/0AToAgQAdmDU9wiCgcdC1EHZ7iOrcMKmIsy+gjVnQg9MkZuzf3pvPZQv5AvoPg0Yb99htkHnkD+bhR5hVAfGzcucMTdULcdcDmpp5K9DokPJ1Y9LCVtXp9sQXDTMfesu4/eX7Cr//fbqpOdzd5csV090P153zuJhs46f5jDlJTlOgISBsoVSrfXHCrLE7Ek/vaJ6rc1ILSjKmx1tvuQV3GryX+/v67318YLYguZIF//Z0vMXX31m8IOrbDdNP9cdTvzfD0VBkbRYH7Mb6GTv/0cmB5qTvsHAwhGWLrv/1rbcduerR9Y13BowchzcWesPZqVjskHoz3uc3su84Y8L0Nbz/n2czqNcy58mAkf5Rx5LDu0EYEQ3pE29n+o6LPX7Ii5gG0FQZpRDCsGSFmWkAlQkGirsIqzfE814wGhn5mD/Rn4FDI6BwEkNZqsB/kjvKM0xEj1IUEj1tOIMBk4lMWWpsOHMXKd42I9W5QECjUYpY6lfLnfolQkoum5xAGMcYB5OlwOxTGh2hNz+TmyaFfAI4d5TZGvcJVqVMuul0r1VKL4+DavqRtTJMJHiUQD6dxYlrnLrH8QEc6qALYPU466yzdGsDHC8AKmK97tprr2WB7iWgAEMaTl8vAb8vCEFANjo67DNOK/7kpyI5KlFP5gnE4rPOMvk22yJvTfngOCjP+r1jUZe58vjjj8fXqaxaeKA7nnGBhkCMa4spwEBjALNdCCAMUcKs7uJ/yCBfonOZu/V0DGS4GaOd4nq0lyHzPD3C6/QHIJ4n+M8KllHjrGRq1lqZrSowJd6rHhbmE5WjpOjMq5ZjkPVV0i3kSZcv5PLTg7tCXWs5f5HiRM6rRBVbUJPKglOu+KpysgYBJwt7qfuffHB2crxr7aba9h4OTO5/5tEdD/6mZ9MJPjOoFWAjEN508jmOFmgYnevZsGZNj+xp7F6vkXRxcx+fY0AhU460oOio/loBs6zp0QE5mhrvsIB/apSD4tigG65t6QiF5jYp6BVgELA4GiOVROltW95dLObj9c3oo0G+YBgK6bOy5yMp1asaJZoA/8PBgGo43cCSQV9uaE4XlYSF/aC0cKfQ/OS5vC6wUj4HlMS7iqdCx5th6bA7ajTDkMyqmtJ4U7W7Q8CBpTR2J6ykB/etdulXAW4AU7q9id+MfEyKi5o4UoQ1LQ4ERjJx4FT6wSPPZRfMhvArryJEkjYlEwAmqfqxEqQXSxwSL+IZkt6fiQIMmciBbq+hZ9ETNIK3P/SjTtLxWnR089C/TDruSHDjXzKBCgowtKEg8f1MPhKlRXliGOLIjvhroQCsXSt2R33RynzmgZVbfEFRYkvR/BqoBm84pRizdwp/xNXFmKo271pfd++bXlW7/AOfx/uCDY20rG5cduIhlDNVuxCIR2bFGZVjyspOinbzvCgCLGAmUmzFQTQRXihOtuyzZddrwJxJciaWa497UWDrQQJUdeODtT+6uRA/00j83L8hWfjEbt+1jYFd076R3fYrx+tOnTbMQtFoMBs/wEDwAFCiqPdZcVgiaAoge3kidRHJCWc/+MEP6FDO5iXbiu6uxmdGZvaOwj8LVrGts+fQ/Oei9m7DMieuqd722emmSENT+7JMqDh7/N6OSxOGf8Y//am7Hv7Lolg2hc/XRGfPfPn9pj9XTAQTlwcCv0r7i+lEg+H/8Bujr3vQ79/+tjMnrr+t/ld3ZzCDKuxkyQXW39PTwyoKF8u5CJRosAx1fbo4mhvyIqMRoZMhigKMuMnaC4+sJaIGkw0gaFYMVyDAYnhZAIICDHslG2wFYjl4iUZw3yDeFF4izKWUisc5nhrxBhKK6O1iHxEphkd+kBIqTLdUtx8qJYQgieouogt0AI6G9ExYx+gxoJP24+7uiMZDGwJpMYC4le4HwLIiIElrw5dd/J8VYaimF1A8aDSgcSfMAeOcauYqwPSUiySQCXPHQKNr2fe6yrB98Ty6JIDSs7bYiwftipjwPpiNDZFzz8vBY2/4ZZAR7LNzHAl71umhs99sNDa84AR6WxsS2GKHGQWLFbvsXA6gSQNVV/QkjBGNsgy8ioTrSIxKGOPcKmAvWJ20W4eOLCNfg6WsTuX12Q/WsQQ++5LkYkvmMvT2pfgfmYcB0xg1eua5+zkgYYKDM3YGD+MSm9VsUdQFt9a5kFcjcvinm2suoFJQ2GDhwsU5FpIlYMtXsMVpX7P2+WrYXNF5IQ2HBVDOB5WygiXd+NA1//mbH1xx9seu3HD6hVKJYT987Tdu+NpHa1tajzrrvaIr23id4UccoV6NsWWGKMuyMFsHoEbBgUAPYQryvNrVg+DPpTICVilgRMkc5V6qQ4mQKJVREa2CxNa1raDCbHI2MTHWvKzLwmXXH/SHovkccpFTwAWVTSYSQ73ru2p61lfXhM1UrrhrbGZvOlcw3fOu3LxUx38qLKdC1G1R/QXZUhqIqGDpeV7NOnKuSyQvzUq0kDSvtSRCt5uq220eVZeS7YhX28B0aQe0yiywHIx0tI5w2s3pDflxUtUPN3AvlaPxRKjUKRKtOkTBUfhKgoOTKqIyOgQQYZvyZWhOEcvICoBsokYGw/Z90kknwVLIgJzDHdGFtSj4kmZWjKeFipD3jaaIvphbWUXwSnqllBfdL1M8A2RpTvuiQ/qPQIgeRBDSCxKA0btTsYe6IOlQHuk7d+Oqm/TnE6isALPTEgkendbbEPoRCd6rADOkWEjh8uZUYTs1O12zzJ/vr8nfH/FnTGtlNnJUwozk/uHiqb78nmg0gkqgWJbM02zo5eBfFwjqBC8qHhdszMOV0Y1/sQXgSFY0YnYui2TTGMHS2QyvGU604AlLtpe34ob1YsMZfBCe3MbnEQZs+0L+ti/n+3cHcg8HV+X9fztjFv1wT+hgXrd8Vb72L5ghjnxUrLlEEt3EUhtP6IEIpSygEWbxgcGDLsSlM9K/xHAAHQu/OMljzoD5KuMTU3lhbLC/uqZu5zNPVdl9TcUbYfDZJ6rT364JpqcDNSFgm4XAzFWW0R5qO6dQbT5aF9nVP96+evUqgB+/aTQamoLNp74ajP4iY8tnCO3o+ET+09elY+fHTxswfekPvct4bEfH6Pgo5y4z3liPZQkXbDHlcIdBEIl+CyE80ixMAyyeMDHgugx3YFWTo6HR7pYtX8baaXVtDQd6bN2+besTTy5TS7sQAh+hBXhBkDW1Ro0Jqbu7220BArw15NmxozcSmWS7+8pVqzaub8nP3GRk7vYVp3z+Zl/shEDVq21xMp9zedANuH936PKyNlqG/gIU2i9hKCIsw3V/nZcAAgQ0SS5CO7bvCAT8UK1f6v3DeWEpgGMs0/H6ZC8m6SWqIAlzBj3lnutAWTqdqXqx7RIQwukG9BGDE+BsdqKnGMYcMb1ERQtRfbHF4MuAIYB2+JOmwmlVuqezM/SeS6zTXlPYsRPNwL9mtdnZZTah/c6zyh3AXqDOfW+6nTt3wj1gHbh+nHHGGSybsDNovleUoLbvAF1CeAUY0jxyejwGOLRZGDhvLoY5IkmlXjdzWQD71AsinvJO4RiCEMYWjDKUDsqj0Rwzsw0Vq7InU1aqWOadJDm9ClGlkqJcaKZXKVXH2RPDA1YO86IcOzE1tEfswIFgXftyFnQXLzUvpbq6vrqqnlkZnQUNi1ofu+Onkeqm9a++ACB8qYFJ+PAz3nnTf33qibuuO/zMiykMJ8bvDFW7oCzUnHes52lcjPhMrlaK5uqQZx3nJOnMWo9SZlknL++VAqTfL51LkmR5VyDIjQwkAEgnU1swKu4G+Vy+YFiBWDUxJJUWy6WYe1G0sym05UiZCEMzU76JBGdjHNlTO1VV95s9+V1TC/aOcTQy15zCJ08TE5NTU9PLlnWEw6Lzu5dTkUTxp1AtBcCGfiwp8GTU5aSp58YAkXPg5kICXz1p+DJVA1tidJ75Od3cQjdJqsEd/PVQmp9fJ4GEQFcNJ4UEKyIkTj3OuxMnCeryANMZVfeY2HXTeYngQuzhCBX2bZXKyO/n1XXLLbeccsop3vhnDSM89PT0PGu2F0MGGCYtjiD6YkDm4ODAvKDFPKpjBkEavOmmm5BymdeIQejlxCWsIa4r6MHB6kVVS2UFGPERLMvMxnqh3LUoLE0GHCYxmyzcUO17ot6XzOKo09e3O7fWXP/VcKAm0x787q49rx0baw2Fwlo+c1dmNFj0BxRgLOjsxxMG8yK+jOpa87RTrR3b8SOGfckWYM5l4Vjl9rbQUceYsvtL+E4ZBZr5LYwvy/Y8PSISeaUiwY6pxb8s0HldceST9uw1AXvW9nNUBTOQWQxuMFsvD+D/rIhzUFL8FSAIWMQgZ3PXYSLhMnrxjUgo5ZVj1zfnKODijhpMJMxImxiX1yaLq+qfOeG4X/7yFzWztwTDE8z79p1V6ZlZaquviYexXxqcb1GYuK7Qcba4Oftyj+14Jrl3eFDUrfa9tLk1HfZdn2ExVdpearT8fIXwG3fZJ7/MDNy9cU320EO7BofqYf2MYRgB2LJsCw6ogjAIVFw0Q3BmNQaGTgzKMDGYNsGcTaTYRycmJ9va2t9z0UU1sWhVJJbNZQe2nJSR86vvCgWDzAGs66IwUxCmA2RtaNS9DL0AR6dSjzjownly4ez1ud5vBws7RTaQ9jes6W/mgmvN1n8PxE/CWZtSf8y1cICBAIq6i5IOQ2N3d/f+ViQigpVOWhnZmri8qYHPOkEJA35/AZaXc7HVCVglaEmvVl9eQD1jMuPL0qi79AWvGEBYMaMgxFbMTyTTwG233cYpvjQLxxCgVJRxv8UKvpjjMdzQCF5DwEHDVnccd+F9C7jfc0XDAcFZra0tdnOj/zAOW+YjSAE27z1/2i9iAW80JGgqnhVn2Ag8DScR5jL0XrTfA6L7QTtMiQs0UICZf7HRwGHAh4P3uJNhMQWYpCXcpJ+VImrUeYDzrJkXZqApOADsgDTCQuBLxChkjRV1gRXi9Cf8aP5l9U35co6OISlQqT+YJGxYtIkliF2KuTH9mzjr2zm8EwAa8WMCR9ouImxG/AFSHcVnPjaLPClcmBuYlovsH8sGTH8kaMgMR48L32XwG8VCPigSnDr+SVxdDPUxICFZBwKmEUHTXxxr3TiaYB1enHsv0SwViAhx0mQoGgTJRe1TdlO0+LqeYP1A78wPfzx58+327JS/tiF0/Mtbzn/7a1YecpPP6ptlHnTqlY5B8ZYxKeK7q/Wxq4lYGkaL9RVQKUGQ4kgpZqG91l8dxkThG03aoyl2BLPhmvfIHe+VYMzFKSjuIyWXvkroq1xuZomVziwfbiq3DEEnIAvAThOUYtznUsfOoaCyqIrkJk8YYNB2bAs7iI7/yU9+ov2/9CNbF/kUwnvf+16+2eY9xF6nvmTucDB4Jvc/nxVg+o4VYFcB5u1ACKevP/OZz3AsBWP905/+NEbbb33rW/vH3l8aY6OyAqw5QcV22UcmATtKzc5O/nwm0NNcsAt8AjUxk7AfNHb9R2j1J4yQsfczHz/zRzcmDjtsA9Pkn3RTGnxB7tTTM8NDxat/JjshTLNo2rnWZYG/+oDZ3SlMThnyvKyJNmTksVS4mOBy0BpEZlLFk1nG3LF96+Gbjgh0fMPOf9xK3mMXBnxGlRE9IhhlN5GsacNQVe9rJi4ERWPRNYesIdC3uy+VTmvNhGSWN0XfI0EV4H1jzwnreHBY96Dd/j39a1avwRjZ3tH2jne+/Vc3/fIL//Lp73waS7ExsyczMzXD13eDAeXtCJwiGjAe5sx8ViHDYVe2rITU1NTLyodhj1tmCqu0/FPtTMD0bd/hm11p15vhoFVTE94zOI0QiYMuGLEVBHpZ6UVLZzLA3MMdsZJ1QsY8WhapiLBsZEVV5hF16Pzz3/6mV59Wk81kH3zANzoerKtZs3FDbtmywbVrnnryqbVr161ZsxqS4TK4ElELZfXrgyJNLUiuwKRq0Ovv3xMr/LQ7egPe2oJnPuRLmUYcgb4YyD1t959T7LzKHz9D5i7voIHQfb70xg+kT28JxmZ3dzcx8ERI1mtK2mbhzaZ6TCIWr97JYmfSVl9/7nf3Gc9sReCyDt0QPuYYe/lyX4h1exn43ATQH3dpawWWC+1crYG5SJY9apyxmrGiy5BjywYZyEyYJBxVuJeVJQO9tmXLFk4lYIiiAFN8wwacHQ7S5cVn8TbfH2SAfGABPickcCOi9rKlhucEoXJmPtomSz0HanxVroRY3XTeBvT2lM5ADG89thUeCbM5Aj8mxA60X/Rnb34vnEWrfLYEOBjeK5yrh10DBqj5Le/1s5Wblw5WGjFQWhorGAUWwP2zBGnIFJ9X90F6UKynxHtKv07dKFAdtXwhT05P1le66OsdEz21pEiUEkj2FlbR3ginvPohES2MccBZx8ApZtPDu5/h6z588A8lp6jmpkqbWr0wnDAyOkyPY94CmHgsrL++5euOeuCZPzx97/Wrj3sTrzSz4BO3X5tJT65Yd6TSoAAs2OPhqpe3QEDwBCFGpQTKayFCE6lTvOHyrAue3bICWN5CXVrnUzqZJNjR6tpYdS1VixOFN0sJIFmPW27UjPdN/OsXzd8/Fiyw/cQMJpLGTbdP7txd/y+fPG7ZIXufsfn6kUaSegrZ9Gxiul6+rAG9DiCUfPRX7ObumWGlGuQX+hWnEDTZWX1os3VYk12TnQnkM7YRyDTHBguRB/dY45k5TZtSJdheSJXCJXJ1mrSFo7lKxFzVlYqqSubaRdVIO8k/Vb3bsnN5NBh5Fn1f9bG3PuIX4m3bjY0NxKtxIP3lPeWIEtjseEkRBnCK1vBfknc4HmIY9z8rBRj9Qjv96T7FzMFmPXaqshZFp+Mghj6Mj616ixkh5SNNl5LX9wUVJJ7XAVlZAdb7i5hivXXrR++GaW/qgrCdTmeinIafl72XiJjLOtrJYzwtTq92MPe3HznprLefV6e+Rb6grJguOCO6p6fHTUL8Zfvoxo0b3RgCbO3D7u6NOchhBge8xVjeEXnXu3NHvcy6/z45bKBreejlx6L9mjHZD1mJKdloIPvcks8nTYqbUoEa/QwGyOEMytVm3eoyxJ2Mkk+YrvDfufdF+Ksw79J3Fzs7lyuYap+uz3f77XcA7aabbua/SwzNcuONN9TV1ALnyCOP+rcvffl//+efxbDty6daU9XxeFfXClZm+e5tkMM0/GZkLTJLipnODC+vq687+mXH3HP33XagCUcxs9m0q/zmjHwkgRdbfREUi7llBIbwC0ukzaHhaaRGRjXsAMc89EP6jc29uNRiF4QmlmT1Ag7SHuvASH6cjIW4yfSwc2fv8a884bXHnxB68omxK//D4ByFaMxXzCe7OgPvufiYTYfFY6JUI7JAGustLAIz2l1tjUcUUdYedczA4J7W+tmell8H7KydDWauacneEgqmI/5Gw3fqROisMdM/Vdj7QaNns+nHdXDh2HHbb6kAS9agVKYAg5VeAqWJGH6kyuBdcNFErJf2eF69BVmIsK1cvvjEE9kvfSW+bZuGYv3yptQRR0Q+/EH/2vVGYJ48UQnCvsax5HXPPffgkIx5gi6DLoxHfMIB9q0tDpx09bOf/QxwaLlvfOMbIYrzhD72sY9x1iU6GKdbsSGTEy85AYuPIS1dK8C5ls5zwFOpkX0lLJQdcIYA5IpdfMBJ8AJc2IA65kBhouBUGrgeJFwc/phKeaPhA8xEmMZc2HwEARkC+KRyYB5nXME6GFeve93ryMNxGKimsJq3vvWtvG5uqe9+97t6A5Ebs38BNF5NkQYOd9o/AnENoOCz2ibgA1C6fwowBanC7Yj9o/f5KcW5HEWcQkVp5F1nviny2R48cFBTK1yKuc3xBI+C48msOaDKxURYzBdGdu8oZNLsC5LP9HpmSl1GMs5nvV4VkXlpdHSstbUlbvLFO/LZrzz3r5+858ar/+ndG078RU37iuk92568++eN7Stf9ub3I2/odubwrJzF3mOBjW7PniQ+21DgEwpig3Aq89biwX4BfpWJJBv2WiOXSRXy2Xh1nejzaGE0pEz/IhLMlVNNIVVIwH3w1MkXgKOFDjOfu+tu48EnaTS+I6vfahiw+cz21C9/3fjOjqZYbGDWQZ7CmUxqamxvvL5RKhNgcqMBipaZK1hBfdCTxHsvh1eg/R7XZR8eToUeeyx10+3ZnX2+eFXo6E1rtpzc2N164878WErstu6liBNBZ/HLSVuQxaV3LgWiFoBys0kNZBUpSlcm5dxUHZgDpeA4L5bbDCpyLo8Go+84MwEtrxzQFuAgWaTr/gwuuCXM/LmaC/90G4Yhwsyld41pKqCdj/pyIOhvf/tbOp3J66ijjnJ7H/EDQavMX+yrX/2qNu/+6bbD0phXVoB7enpoLMRHPY1pEDwSYGlraYg6lVeqkM0DncmGd5lWrq2tEcNVuOgrjhtBfA67N73qPKz5FaGhe+CS4U0i5rzzzvPGAJOD2r0xBz/scClckNpbw01bwq84zsBxmBWwaIhTjsrwoTHdiyT0EGkctQDljsKyIvv2CFTJqFVSJiGeNEvbB7B2/+DoTN7CWjGa8j3VP4Z5Gffk6iq+GgcMhz6FhsxuuaJvNjM3oapKJTFdrOc795khsfdLGameX4UI5y5c/nVUF6LmXbaves2GdCI5sGcgbjW98pz3HPKKUxL2a6vMXcv/wlfc0eGf8HO4hSaqbXWTeckI8lTBrplM9WSS6fPe/rbTT39NuO4B3/j1vpq09aZY4bsp2ZykDiETY/jmWqNqK1P2g38wZzhp2hS9bnp2lk8b5YoFfLsnJidy2VxbR2tbVUN3d9fAwGgoGkN5ptG6u7sx9yAmYi9sbmk+701n105NTl/+WQOxpmAn+fQzUs5TT6Q+8YnmK792yKpVDzzxWF8feq+fF4c+pWdZ74VYdGlNMro3nUT8wMBga9UvotZU0RfIfa8jfFeDL5P1U+tk0P5pRzbjD587GCj0FaZ/bDaIr/izXk7f0+SVxE1vqguKnF7p3I3XAQR6dE5aYLHBIzIPXTs8kv2v78Sf2Qow3dv8RB95KPP9H0U//CGfnGZ3YOZV/ZqglusldJAkgFoLaUxpLvJgqz83RQxhjq/H3YCvH3EKEbolfJ91XWzApFLwtNNOg48RcGmk4zizF7Co04RdsAcnwNvxfEwzXgIPDiHUovVGAgwkmpeXjjA+FDDwg4YDsz5NunD/7XNCADMZRzNiTNG+ErosNnVtHWYIudAwzXCh+vKZwIoev67UxUEAr33ta91RBwTUS/RnlotdaIsFKKUL0q005n54D1FQAy8LaLBuvW7qwpiynG6GigEyLwRVMeeBjZQpwyG0MuB80Uzn3QVgvljLcc6iDLsKnC4tbM2dw0qQSk1YenYz6ZxSMx+mtoq5dLyu1ecPyRFYc5cbFn1b8VGdBgJzGAsExCeOmnDKGuHWnnd/7Z77rvn64FN3D+14KFrd8PJz//rlb7wkUt9KQVq567DjzWB1HrauVpmJjDV1HH3We6taujhPGo6osHPrcNGYw2xeaGH7OQDQdu10Yio1Mx1ll7KgrEHpjnbUsXmgFq+qPuoP5mayD/3BJwvnXM5g0UTnH3o08tZ0U7RqYJrpptSIzO94gTsCCA2oa5SuFDUff3H3UvU6lYOdz7e6sXhYNG3cdsv4F/8j4Egjduqe36Z+e3/j331oS/eq6560CuzTljJOhTrogpSAA9EbB3AntoSlenT7UydWahsvFMKq1WXS1HYKDaA0ibrgdE9K25dyimCmLk8eL2iJFhOPi6c3EV8n1CGv2oNQzQn2ZT5Qd999N5yNHnpOHMBb0QsbBm2c3Z7rDucXFuc/snZNstfdHYDMRHx+hWshcNrnFa94RVl82YpjWepL4LGCAkzDsaFRb4pDAUDKZNxz4byKiMknv/aRbM7NjQT8sWg0k874kT3ZrWKb2RUTdqjAvk471MXNeXP3EeKLL5vwIMWihDMEwz7+o9CWmNZCfNGptGpEEr6yXBRHbHIlpIVFnjVGTcm2xSnHmYzwOtYrgiG0kX1pW7r18R17eydlBiL/zslBuHC8qq6zu7Fi3yRy1rYJ+pG8LreVegxfDVzbElmXi+NPBtigayUn6ls6o9U1vugm7UOtk937PSOgHRkYHd0V8YdjfPtwbcD3/hMjn/C3ZoyPDmW/31D4QzHPhuLubPi8Mf8hCeq1at7W0f2K8dknWlta8SGfnqg3/F3Bwp74B3OpbMS+NutPs6PHlz86FPsUPk5Z9Mzr7mhhDbmlpe0Nr3/DkYdtqAnF8sX89j0D11zz46mhW95zbvLQNcVYfHbvsP+eR/I33JYdm7FDwTC6K6I8YvRJJ25Z3tqU/cn/GoO77CPyVafkw2tn/GFfZsz03TczfdfVgWUfnhifGB8dieIRHo1qrQyfZ5aP9P521mMBBdWioZn5aP5eG9e2/rB1WzhXkPOokJesrNgOjJtqrNPHzRrOrLjV5/uQ6hO3tSoHUJxw0kbWp16GFqeLARClkXGlNxcgvpctLbIOXLY4DGhdSge4A4EYgOirrG67WLB27gw/8rCj/ZIso4D1AdN/zz3Ft7810MqXaRBH5iRg4JQBcR71OFokUedh8Zz5mIU11n6hl3UwfHhIWhSmMiphQSszomloSIkcc+XUXvpBz2EBufR0kH5REV29F06L2gZF6DZL0PVcMTuAoPaxajwp2NdAZqpmCGkPnZ6enoOpAGMu4QX8IxXghfRC0RVXXLEwnhgo5dwKdlW5VpiF2SiOvxmXNwlDDN8S98Y8axg4kLYf1MGFsOtRXPsB4kNBXWjgZV1DHn28lhae+G4K2VDRsWI8K27eDFTkfTx4YSXxM1XN5KTOhUjsnLL6pj3KkuRSbMid0yRGrkUoWJCvpFvgSlRgITmHJhVBTWUB2MgVjVDYWwB8RKVjyOgquJfpJlKr2GHd2pnMq5o7TrnkM15aqLP0aKw/5W38VxgroByC3bX29L/+91IOb/1Sn5Np/k8JGi0m5cikZQCtj0kECRY+WuKQ5feTqNtQymnF0QMP4c7TfBqckklKdeOSKivVjEUHyVL1yrlZtjdTPbua2bmJJWB451amNeYlpMixvmcQBCLx+rq25RQKgoxpBHFcW/jZZ1UjeGBKP7LVH93dP37lt0OpdKnlmKIM3/0Ppa69se2id/c0VfdOAk9wVbiom5AtU7O+NKo6oUQIjeAEPfTNB+JAUjBKxUog+ZVkbxZPkhNUZg0nrLtDo7gw58KYUUzVuczq+jZlqC5Px47GF4+8sYgHXN4YZswyx2lv6p9EGP78J4HnAUQS9oFOi4xxAGG+9EBVUIA1kXiHX3rppRyLipsxTYnEyeGWTPB6FWVfGiJTzFcfHuU0Jctn+eW7dkY+nDXOHkMusjhzOL6Ft75sXFJRWcy+VESeJQoe8KTFUEIAQl9CAFqiiZA2tIKE8IE8wQAFvf3DUKGhZtJcrjiwp/DQw77tvVh4zEPXGUccbra0+wL+io05rzrDV9vRk7EDudTM1Mhga2ePbYbyfjMxxtK0MPF5xNr2dNZ+amwhrzaK+VR6fCjWvMw0ZVFubNLPzpz8VKbGyoQTsgo6H5COkNkEI3c+tHJsImRM5Mb7n/5989nV6+8+suoGf/eU8clEaIqZy/DXWnYgD6mz9obdY29ubmo44YQGBFyktHwhd1j3vxTHLg6E0lWfsKx3hwv9ltHoj63I+/zMoOZV/9d4y73ZVx6/8iN/eWkbM/a2Z5Lbd8Srq0/Y3HXyJybjgaThm0HPA8G2OvuItUNnnlDz91+p3tovx1Yh/6GT5Is5f95K7Xqs9q+S8VMSPn9Bpk+Wrzvs+GF+u3Bd2r9u42HH7x0Yrqqq7lnZEwuLEWQ2lRwdHuXbTjgHcGJWjo0Att3R0X74xkN9u8aZUov9nBWmfASUMEQRpk9/NmQNBY3arC87xBoAX+chXl/zeq0UyS+aKktPTFGciNPT06MdrbXWodef3WVPTyF5X/SjO0KAg8KsH7lr5RnNGT9JN48DgcKFojW8NyReduqfTpD53w5kc9bwqH2oNKokqqN6KIFNzSle8ac0PqgLxwgOTq2Yyxu5WIOQZ4kkL4SF4SUKHvAkascbH190jQaDmYtatDlsP6pbWIRRwaXhl3fiQuIrxSyE6eZaLAkWxz5VskEdebRCCKPTCCxWygW7WGCJghWTdHVLUF2xVMXavUCetZQ3swttiVJLJLnFKwaWKLhYEmyBTgGaRlJzCfaA0DtEekt5qdBh770MH29BbxJWXS63Om8S4cVKlWXb90cvQNjJeMraNqlqF1YnHF7dhfjBhH8k7bwXwqXUVUp2K9QcUlJ1DodjSrqG5uYsKUy2Pdm3u5Bjnw4x1mTvbjb/msFI84r1yrqpC6ryokuKSC4mRgnPu1Lp4FQxaqQDCVKd7CrDAhTnFfujH4QqqaJEnSYbsIKEXcxlp4b6ECnydhbXtZHENhbO49VNkbomoR8ynEuhLOcsqxlANbtAlTaRHAJdPxcLmdqwvbI7d+e9OroEgXQ7tLI74w9uGyrsnKAtrekMRgTb4vPKGT5/ViULKLZ/aqJAxcViqBhvG8kExvBPA5CuvwRL/9aGcnHLl3zgodz4mCyFlppct73167uazz074o/sGufbU4YsKM+RM5e5BNKtgCRFjkoQutwUiZEInTKHko6QRC9YB44uo4p4btKK4nmuUxUAJ7/88OcMEreIBwkVHJ21UIJGZgq2L6ia3s0pAe9bMy9hf5PKgJQ9HvDqlgBYVjUSDssDCEUsnCxR6oAnlaFR9njAq/MChLd7XaDLqv7/H2mBRQXNiy66CEeIyy67DDdCpjHMwBwh89GPfnQfW40Xk4/grfhsPnHNSO43RjZvBlb6QufNBNeleKEL1a/v3RXJ5x8rGREFKocSaf8xlkn13OzWhSCOjxmPzN+sp7nxBJD4e3p6CKBScnmT6H5czojBpRN9yZvEKGGJmzsqK8tK3iRmela5UVOxnbA3z5tEuLu7m40E5OH0PC0TuBlQgLGdQwW+4trE7ibxymnSWBWkRgqSB9EQYZELNMgJhuDpFiHAu8r6AIHFSbOtTLbw5OOzX/z36GOP4PZLs3MQk+/VpxmXvte3omfbju3ay9oFy+oZ9n7Q4yAoIrFu7p6MDk3WZpMzuUy+3h/nYNWh8Qmv1RM1srqqKhJBSDL2jk2PTYtW5vJ6VvY5qqqQ8U2OjscCDUaQRd0ULsqGadkpy0hm04XZXDHnC/DNZ0fmkOLFHBpyMFrNR5QLedOX4TOYxanpdD5U/9knrnzf6s4tjd8zfGmjTvReZgvbigwmNz81ckHP2uUF1NGJlN6fVhWvemZwQ7XxyY7AVwPGsNmaDeMUJiX8WSt67e0dX/thsKu7/W8+8MH22cm+f/pU5nf3mkyAMf/Kb0eqW9g9jp9Z9fTjBV/GrjrECNRlezqmvn5Z8W0fyz64O0eX0ZuRcNTOpxrfvjvcNC3eV9lg+qlQccaI9FhmZ9YITEWMT49sf0tV03kbe1Y0TCeskWdwb4u1t7cs787HwnfedUckHGluaaFzenfuXruux+abUlzVRIjgwPlwaCmMVVq4wMnbMrkbmUzxmcefYiFTcqpvuNFr6OR6NUZH6rtWLAHFMNZwiNdiLh8i1qZcDnYq84Qhnl215MQHgTFJAAjaW5WRj2kGwtGoGZPeNxEcOJidrOxrw08gncqo2Vc1uFq6ZNjkcESrjs9OT/UP7tUYsh6oN0DCi/U2Ch1Px2KuWb16TRDHPdzS0ZyD/tlM5v4HHlje0QHPYclXLzqBG5jwcvGCMI1BC8jrFTBO9tar6xomd0jTDqUuaW4S0IDJI6Tpc8LcJIc05ObxcVLdeAJgojU6ENBrmzpVv+M4WmNiKCONDODMu4ZHAC8gZyN5ARLGNYAXH1QBCEW0PJZ4rYegEsMuyFORNN1rlAIZL0yYkmaDXtKIhD0CR7f/QtIYMJAG/ykjTUOGDTIsaW3YoOZRbo3wJbgTmMMG3UgdgDTYNeSz+113lpuBsQppJMHhyyZmXBJ0ryGjuD4yuiBUwHUJ4+kAj3WhEaDFaGQCNBrmA5padxABXhb6lCS6b6EVXJPGyKFxvAAJY/GBNCDol0gPPJ2HOUj7P/Maukv3OglewVxDezLCy0ijiJ68KpKme20habyGutc0aV4k6QvtoMhCN43sTSIMGpSt2GsMfrob0u68805y8o5zp8Wg0e015gWXNJqUzsJfTg/IJeblhaQx8E499VTgY57TjIWwvpYgjQza6W4x0hiQC+dlkKTXPPMynrLGUDrQn4jAZJqb2UkqLBcyC0UmFHtkND6RQfLhEw0wXeY1XyKZgmpXGWYCYR9QLMbebzuVzpRmc63DsSzpj8dlW3iGr8urD6oT5iK5aMarGhqsYmFypN8fitkB9rWGxibZDgOnN2qqq7hTUSqZ1kUkXpWsqo7jOIbXTSLBbgjZwDuTzKSy8nVo0GDJFUI0o/YWjEbDQb6zaPsSCc4yxjIrfIcqyBMKh6NhMUYnkhkAlkrJbygU0K8G4z+Xm/cBalhZXG3bybBTyEMacI0CW4xFcSvkisl0khOFiMzZibAvhBRXrUgrFopJdSaLrGOgthpm0CpWVcXYcsMm60RCqKYYF2dWJSaze9bUrDn11Olf3BocHBZjqtMcvkJNbeMZZwyFq3rHzelEGjbii7MmaRRmpxKp0Zrl68LRKIQmUum881UbdmfRfb5gIBCL8tYzgWawOuvWBWy0xjLyZmFklEoAxl5pQUJdvE25iXF/JheN+9lInExnVZfoNAEAEyUPfZJi6VgwdNNtBrlpsp5jzSblJJq58WP4cAZjnNBijGSHZgekjzHHMOaJUSd7vNzLhqEpa5Ttg4ewa9dNIVAiTUbdvF7j1BZTDUiDD0zkSZTqSjiCXt5mKcCcmsk8/nhvKdoBjAwMe1w4eZGsBUXGapnIQcfCjZlJCTCvSdd4Lni4di5bKHIwX2tZZYl5GYbjWoQ1VCZQLQPD+WGSnqrm5mXt+OZNIgxpcGMvafA9uCXzLI6+i83LcEjmZYozr5WR9keKHAtJ4x0Uaeq5iByaRng472nFyQuAkOb2GhyjjAoNwTtCnJhKP6VXtVLa8x3nvhZlQ7as3n1Bcck8lRVgXniamK+2cqblXXfdRSPiJsGuJ/3eluGw6CMV12XiF41VnR+2LFQYFrx4pY1CcFOg5Qv9Tz6VSmVZwSqxRPk0jtYSkeHKBB3GpZYhmEr7+vtU/9EwQllTY1NPTw9vI8JxL5uWPdggxyANkAQP2tG7gxQpo3IooXYdTFreEJU0V862+cYsWl2hWNjRWy4etbW1wBBRyHp72SCt2JfiiIDlfVMo2f39fagNTIdSo/pB0EeZ4nlw78Do2Dgo9e3pz+XzNVVVyzs7cVB5ZtuO9rY2Jb1JKYWjLaSt7OFxYmpy+xwmMiKi0ciaQ1bTTcjOM1deGXzgPo5QdBiwYVTddXu2oSb4nvcyUbHDVo4/dC67u6eHtRi4+Y7eXtUY9kCmaTJtFDKJWCginxUx4ZUcoeSWkABSQpQpn4/HIg6Isy4t5+AZta0Quixfaijk05kE02RmrJ99Qth/YdnZmdEs3xrI5iMtPYwpmUBVi1jJsbCVC3UdipUkkZiwC/hp2PlcJjk7m8/Fb6z9m5M2X1iYvDaXfIhzsMaTHUOJI6YzaxFZOjpXTk5O9Q0OJ3PCeVNTs7v3jgzs8Qft81519FhT9A+FzJ6ZWevR7bEb76zJ+Nf3rLEvuvDdHcHA1Oe/YP/ud2CAK0LDO0INhzOZFa3kkUXfx3ZecYX/yafsarP1kurW85N18dRl7/Of+Z4cc0lLNNY/NFgwbgm1PMCknt8aTf6jFd6Os3WgELYLb4zFPpoLhDKnHHFHsniheeede7793dCegVh1dWBZh33ma4NnnLGye9Ut9z1cPcVyrs3kVtO5qia4qSqzPbimMNEyFeqL4wWN0bloFZDmC2tmQ605OnhkunPHKCNWd5y9YkVXOMxpjfSaDGNpq3xhcGCQUhwVVl1dg4nk3gfue3rr05188DYYnJ3le7/22nVrcXam3j17+mf5rBRRqtcIYOVub2dLjzE0PDg0NCwgSxcthIresax9fHJ8YO8Ad3fKR7HRLLtAK3b3JPjQ165dwBRPNlPmdcZE8dANvqamweHh7XwYTB3JMjwyvGr1ykIhj+lnx47tDFo9uJjrj9i40ZiYKO7amXvy6fzsdGDVqvghhxxx6KHbd+7a3bcbiZZPnIIXEt3o6Aj/NY7TU1O82lq3R0uZmZHt1t5La4nDw8yY86ZMPmTV1dUJPNjLzp2M/7mLaQ/SaCtmU9iIK0CQA1EYfYmA4C+vobyAXACHAWzYuIHvZSOUO6VKLw7vfdeKLtoE//aFbKShsaGmtqaqppo3lLL9A/3NTc1ySoLPBzlaS9wzsKdMFQQmpIHk0PDQfC3dbm1t6+5eQXGc8b3VgU5tTe2aNTLRTk9PeZOIwXS1fr2Qlkpp0gjqCxrNNWsOQcwV0np3SqxDN8PIRl9iomVe6O3tLVGsCto+TnyjMYkcHh5Bc8gpIV4X52BbxCAGAEypzI4JKFGAbd8wI1J93laBkxucE5ELqtlo0Luj1zW9kVRVXb16zRqSpmZmtvf2AmRgcIDhhf7L23roxg0MXU66Q+RyoekApdiVkyvkKVWWxLePouj2NpvcRxh5DDsUOJ2Hz+/W1smndXbt3o3uo9tDkx+KRJqU2WLP4GCZHRPkgUkpAKKDeatDBSWVmMkJJq95mDD4tQLMm1uWxESDAgzV6VS6LIm+0cOYlqfX3DeXKhg8fNcJyRV1CDQgUPSiEg1o6SL5Gcbuvj5EKz0DyQi37VWrV8uA9Nm07cTEuBd/tlcsXy5fiuLd5FR/bxJFunu6AV82IMkTjwtplJpNzJYYmlOUsaEV4AXzsjTzqlUrmZcZU+4wdsdex7KOWJxes3p37mAYwJCmi+GZPMqtsawx3IhSZ9vbJ/Zks+wSMjKp9nxeNNhICNdZUUU40xBDpIOEapSAGQuy0QUsC+lcxtVXVRabGS9C22YsmbK8A9LkoJ365nwuNTm8k68SmeEYGObSCAOyMSpYUwUrQwPLpxK65VVZmtYXqGG7mJ95OZ+eJTNHQTKhauW0Ck0K71W+1ZRJgI56D4FCwBcN+gMowEy+mUQeBZhLQQQ3ivg5LB30sylOvVDVSRGKm76oPyq6MXM1F7AUMEm12TwVixCAsmyKYzucIsTgZ9zSuQpcJwd35mem4i0Y6fxorRk4WGo6UKyL17cWaKz0LN+C4qTTNdUPndx+XU/V0/Fwfraw7LHkiTdMnDRVaIcSuiCfnEqMDdzUtbl13aqWf/yb4S9+DVYiZ34w5XW0N37k0tTqtXf2+VJFXyaVplmEYMNXSCfSmRSNHmafCB+0Z3OdaOlCgb7QfmNiqbfR38V0W0oZZe3YaKhq4ahJWZvmlI5SCaAaRl11IRxJ5u1cIZdMzxkWIZ4BGauJ0Wvo9okUc6gUVG0sokwkHvKbcohmIiWldLyGzHyNrzjUJGhGyHIvrAWMuYi0fyqTwlDnokKMYVaFRYG3GXMoum4hYmBKMWV2yeazSekauTTcUCgYB0m+vcw4TspEL3KnIp6PkgTwDjeoK4vIWiqhsviMdevWIvFlsune3u0eFCUXswlngMJImbLdZpRiNgyZz5fEGQk7d/WWDBBkkbbGItDQUE8S3EDba0BESx0MO4RnFrP3InAMzxM5OtqZl7tAYGx8HDu+1FK66uvqUICBwXyhRFaVoIjDjoYqSDCRTG7v3VHqaicDUzZ8kvcdDkNP8Y6odXQ+zjrLjKYVYJm85iRd6WumJ5m8lPW2ZPYSgDIMYjFSCWPs81qfSeLS1lsROOaLHMRDGqWwPlOdwCpdWuTgCdLKMNGkkcQYLksiUksj2KfKkkCDuiAN/Hcgw9AyiYQ2dRHmYulHeTeoBxqu/JKuko4UJiVDy7YZm460RpSOLy+0+DNoqI4SYLzyqoPcgBRzK5J051IcQOPhxOiCpXTP7xx8EAeaSnKRVCAFrAKNOU7Xp8fpHBQx1c09HbgQbffWs8/58t/e21aH9AD5AtryRez4m/xtnzUCncmE5uZzVdLr9Bb40OtYa+YSRAZlfUz4Mlwhn88CbjbDeUiSBesXPI8AXJ5LtbWiWZrcYK6lauKnpqYDfJBeDG+aZL64LlZhMeyp+anUCpLKa0xZptEUq7VSyVwjsc/TFNdixpbwoLkEn7Fz5+6RkbHjjz8WAwxGQWXRFH0PCOwpDYeCBPhWkDqeEZTyfmY82FMgeO8dt958w7X/+IUrKQjqZNPtZZp+uB518E153Gh1dXQgGVhNrYpHeurt4H335T70MX9RjJ3E64sNOpnqmqqvf22io3vrKF9An+vjsnrBtAAAQABJREFUaCwCX4C0RDLNPlHeh21jxiMT7LNhvpZ9R0BAuRIUBByPEoO9QB9XNpMpDMqhFJrXk2RPD2z1Fdht6zMxrCr1Vj6zXdUiOvHMcKy2zR+pYuxxF6QFJk++1MxQYXaitvNQXKDTQ7351BRePkALmfZZmxpef3jTyji7ZXM5TuuMxUaz/r5ZQw6e8plo/tMT40N7+rrWrAM3Fr37d+1Iz073bn0il0vu7du2p/fp6tpmuN3y1pZL3nNRV2PNsvbWwk03G5dfzny5d2RkNp/aeH0s2pHK7Q1P/OvGhn+5cvCq7898/ZuQj2DTcbm//pQpyxc87d21T/WjpNTWNDT8+F+HVtQ/bc8GE2+3q/a4C9lGIRzMf4jj2vBiNabuP2fkE/eErWKICUdWcyM5XHnf+fbQO9/5dD42nGHLFAYSdniG6jLXbTL+go14he21xa+2msMhvvXIXFlcngx+eNBsZd08km39ddG/UXUoTSu+AMgfiAfiPqC6ZOeO3rHJcQTo3u27E4kZvMKsvJyB0tzaUhWPYS1Cua2rq2Z9gL7nhWKXMe2re5Mf4tXSh4FVG0OP9LFcMu74XNkIx/kcsUnszEooFF82KSlnucficcnH8E4lCnfdY33+C2jbMg4wbxh2prnF/4m/MzdvznLG2Ohwf98A459ui8biDBAOW2XndrVSIYhGdAuOjhWu/rFx3f8FmJ9zGTsYLB7z8uD7Lja7V991/z0ru7pXHXIIa/SpZMrzogmiCEBY0WkKGkReNfcyeNdCkEYSMrS8UJ6LYwjQwYhg8lC8Yi4N0njreSZ+/tKHsBGHVxQKGURh6RWhuH/3bqrecNhGMvBS66UPnabhsnQjW+UsCyR1DHcpjeQaiQgvsu1kAn8Q68lHH+voXF6nPlvF0g2ckArKSfOwQTQfek1B0hDlGw/sQCcGsUlmbvXGc24Y/hW1zS3C0BaSpjkkbFDEOyRXdAONnQOY5WMOOIPGpLO+4cTzw4CEJ9Apsr4x/9KfbyGOJLDQELmDQCjMPxHK0+mKHF7W4kCjvNf8fiZ1GjmTlQUr4LjXYr02MTYxMjT8smOPplQhX0iXKTBKB2NthF5D83ehaVxd0vp6YSpPrl63rrnd8d5nDlIziEOaanrNj2Utml4DVIXJKxCMiFBbaUAGIE10pMoDEubPGcV4fc5bi5OWZEBCGtIng3wOfxVSvYZBze01aTDVaLZLGuszxLWX6CIDGGINobvUgFSdZrDkKHM0dthQWOZNlzQFjZsadeqIbP2u6b5WIgaH+vrZ1E4HsZCKqVdQK81C0mtq8qU3kVBVktw0lvpwL8aAJq0EU9JlGJuwwXkvlCQIh4li0ag4IFWv8d0sEZQpS/Zrn/L1Tol3NEiiwFCe6pgyXAydJOW/jYSAxy+4aUwkyTRpKwJIKR5BRRg1HD4QDhaymYFdW5tbe2A3HoGLQ/uCZKKiQl6LN1JEX7KQC5vlewR5lxuXkvgYkuLAJEmUiwd18a0DvoLLqmwe/i6kAVAwhduSwpTBbI40QoQqpYsSD7sgDGnogg5I6U8x4pNEcSGMY7hc/ASsgQxDnqnRwZmp8dbutaKsKrizowMcfN3UrZyD8mxgmTkp/s8vq/pB0EiatqJUcPKP5Fb9euaKnbnX0ISZxNTE4O6udYcd1eE/qiFbNTKUe/Kp4vCQ2dAUOmxjprn5sVTsvgG8yEyQdPtF/DpymVhVtYFAAtXgL5JMAoNmU1OzWqoVsqEI9LGGEOCCUr9dfPNGs2vP1tH3/00YIw4DhXihjtYzAu94a+Dd77p5vHrbBIdbEiFzFpeini87q1UiMaupLztKinNRl3SaaJ6QqbSLUhLtT69RBc0oIqfEa3g0Miki7jCwNCYk6By0PxcQ5ViQEij5RXVRqxNSCuRLpKkkmXwhWzIxtpzZUCqkdiCPjIzzlp24oeWctUQoRNSdxLha3FZskBVsnSQguXjXwKTiCwXLUuNHMX8KCVmK0RuslDgcEskZIZN2lATV0LBHmCQDE26MNVxV4txk8mJeNrR4Py+Jdw29nyS2kUGFNCkVqtpoRc1GkFfRCxQac1BxWqRr6LIkxiZ5NYq5gu+eu+5taWs+5mVHU6Oa18QZc66MLCzJJ9Y11WVJIM9F5MJ52dVcKogckKY4ZGUOrxQNRA7B33NBpsPh1ckgnhQJkkTXCGkeuULngcNDmpt00UUXYYb41a9+Je2mRkfi8UeLT22VtnI6bg42Aydy7LHxFV1kJpb8DIz0Pfdg4ooee0ygpk7HzxV4tpCuEUji23nnneZDv89PzQba2gJnvCZ82CZkXpQKqUjVRXcUBvbkrru+sH27gZK1aUP4tNNDdQ1kEmwqXYwvGe7TM+n77y+aVvWJJ/uDIX0kK9lJ4y4vBL5yjJjqWj6IziE1ZeOk8gpwpeqea5xR9Ac+8LlDrv7uFb7sHwwrZwQ7zepXGaGNhmxFKFTHmfjV4BN2rQLciyx/GbEw8h/ozydcCXzM0pEggpfZN56byfuRxBHQfFZG3fmOAVqnkCy7jIm3i6MW6poA29a7tb6uvqGxGfjCF2zfhE/8jaVi9SivqhTBVGAnrGFxlBVQfISeKooqSVIz8lprbCksGOr+EVoGBkPo2ZZfC0lqECka0GCRQ6FH8bugOo8+IIxWKRLMeY88YO3ebd54cwiIsnZhwXhUc+u7gWAlspXGVn6BzYYO0//6EzDQ+ac4jknlpArVcOQITE1w4nA8FGm69ha/ogsSoFHeA6HR4lRiKoLm+Iy1LEFAICi4mixVjRNHDP8kFS+oqbRQJn9yGenpUfkkjmFMWTb2sQBasD9ghKqpaDg50RqpkYVlAQBRviznZGWQOHHXSnM6SCASFQQK+Xp8kozioFU87u/ev3GVEXjyt8Ubb8SKaNY3RE951erjjl/uM4f+63uhMVyYkB7yh2ZSfGZQVBMWKNJ4r+UHZ6av2bsbf00jlayJ9h++5VXv+quLm/PZ1D2/MV51Eu6kqETBqqo11dXJ1ulo2x7bLOauNQMPbMfqHu5cIe86GQrFyf8O1Z/MmRqF1x5hTG5LpJMz7cXxtqppRkzhoUgsEfL5ExaeUlBUFTdbmo3bi76zZ+1IJt9/fWi8yGdJ2NXNuOTLTXTKxJe/1HHCcev8/mWXXQHHpR34S+bTE+fGml89G1w97f9crnhfpDhsGp3F0Oa0HckCOHNtrHj3P6cEjIGRhk5whXSaXVoeX6lUernha49Gm7LpXbOJpmhsyip2hsKUwSQkB86Jf5ot7lCUt21xoXMHlRrZnARCDA2I2EvnEpa1X756UizW5PMp/S1fVUS9QVIrTcRkQh6+dG2c+drq173e+vu/y/zwx8ZjjyKMJY/eHH/Lm83qmplP/KP1m7tChWIHVhsEJgWkloFBpmCEz1py5U/a0vi+S7M//Ungi/8WsIpYUFJ2kTnQeObp1N6B+OWfaXzy6aH3f6g2ElWDR4830ARRuZAE3TbRMe4dR8CUECZZdVu5SShP4qk2N3rdFKjy6SkIJEtjey7V3ZkAAsy0uOHSJjn8/g3fo8EIxsUGvnspg9wpIt3EApfCVkWqhFIqmVCj0/pRXit7HXYgDEYqhhWZjJiSyCV/KgA8CUP1rDSmYAh8YtQl9TLDJ1UebqqBJSWWYwhYKGdOW4kNnKbRpQQKYZ0EQGcukkTJAS9m3Yp4LhlMElUqiZotT8L+3LpURsmEoU4vIJJdZ9BAAAGSWl2j5rKJh4ZNKXYKCBFguaBKVchN9ZpUz/SljGiSoP9kRw15FSbcHRwpil8iFkb1vABJjYYA8CYJnvAohS5pjbb9CuRalAynMh+imZbOdKkSviQbbhIPTLTeC6r1qAO4Qxr4qv6jFlapeDG4XEzc0roUeaQUsU59gra7tiU8QoYDkU45PYzJK/YhqlHREqTxqVZ+jWWlzPLEhRcovF2HBBA9J0DxBeQupElBEIQlESGp5AUmUr9r/9BH7FCdqkSGrZ3NYGMthMKe03ecguxuldqkZrmRl/zCzmWEqwiVKiGJ408lkXteqsqpQGAXlKoV6hRVaAgIWocRTiHsjxoklZye5VQH3TDEOSg5CEkZyT//Aj2NiQBy/iSjuiRCQZkrw1i34HZ63UFF67yCq4Ilj0AqARPoUKgyOVAVYqWwIKcolFiBocfMXIUqnRsHIVNGsksuWpQfedI3uoMHkQIFnIKk8tE4qjkECzkSWyJFS+M8SSmssZa7AMeckbfy0WgV8KmByFHWJK3iiigGRNMK2PGPzXRteogEo+DP7wra08XgioDdnGkJ7jin/r0PX3l05iGYZ57dUQ0AMYzJlljo7DPiGw41V3ZY+Ic//ED+59c390++zmFXMstTiyZBAqW3XILK+IhIgGsA6qg0qX6V5noERBA+fPGjNgQuuaDx4vPzn7wsMjNNOgkM7NxJJ9e+5qTco3/Y+K9f3yCkC+Kyj4xihKmcCNVWNAjLBCosTafykirvhcYEfFUDy5N7UUY6S2UBOWcUqmQNWSfqdD0gVNcATFEtORU+Tj6pkeoVTF277mfCQj5u50ICbyqvjFBhzCRmMIJ0PtwQaBMjC0CdukrcmApKfEmhpXoZvqov/UsxHaA4HB55D41cVHypjgHFr7QVR54IAijr/AMNE5QMVkTInDf8OTwDJFLdHfBCMfOaM/k6FJcIR4O1NV9SuQUkFjAMNOL2Daik8lgkSr6gJammzYDlbgYzYILhaFkqGv6VL/VbNthFjJoNLW25xOmZB8VUwT+oVl3g/ACnICSglxms7POoSIMcCWAIwVUPwx5QIaeEPgNORqczr+HlIb2m8AclhqJp5jl2EKRBUswl2o4hTSRgCzmoCLIZUBmvSzDVb1EW+FhUqFbLe0RBWKkP+JJOEeRrquR4HXVRF9nVnXUXw67BTVWNC/RhJwdo21bxf38W/dwVCpAgKc2gqA8VfQhmuR/+0LeiS+fnXujfbZ1xRojvnv3bl2o+8CEhVDr1OVxkTj3+WO6UUyNTU4VovFAbC45OFC//1PRHPlL76c8IedQvcO3EVVeZH/lrIzlrNTUFspng9Gziq1+tv/SSEs0VKxWNLPHxj0e//e0kEu/gHrtOXDzcC3E2+dP/nf3Pb0Bl9fsujZ/zZqcB3RxKtvE8HdBgKBgYue2x6Vd+XpiStDPX9/SPvkO9n3U9FANcXLhjKsONh45n8pCPAIjfy8JLWoy1ftiP9J8aFNKAjGSGtQj0xNMw3JEogaDy46xj43vHcQ1Ek9MDWTiCygWouSCCBTD1iNLxPFNK+kNyuQBUEanGPpKbE63ZjEoS4Bq8Djn3oumbMv2fx6tVJFcjYlnfe+huIB9n+N9kFwKaAAAIQOG0UqyEh1RmsBc1Glp/fcEsGoN9wTJzIXyQjT5wj9//dtUn3xcU1UvTpsHIXSMHhasIQ5XD4NT4VvUSya8EJav8OcTJryiA7jNJScPYbttrMQFKRrlgJfiItykpSseQkvOZO31CMmHu+tVs8hmt+Hf5A3vedf6K9kD+xp/Zn/lsJJXAv4p8Mz/5UfGdF8Qvvriju9b8j8+hzOsaNDISViEWjNssqxmeYlvL2tredeE7Wicm9vzTp2rPO9esrWPhVWQDvoqIXxh7rk1ZafcPWpFYFL7GGREsmPAfaJm+gq8QsYPplx3dUwweOznDZtbHQ345qcgYj5gcn1BXj7aMYZKCBtP1U6P+ab8/Yoeq7dl8Fn4Ni+DIN+Ro2HzISKf+8FjV5s0zD90dS6ZFC5XL2P6YHfRX1b4q449lzJOzmMllHhWtJzD1fzHjc7PBzG+mcUNj9zW8V7e+FLTRWicVw8XPif6hNpo0hzHFL5byoBkMSbtWuJyaPSkLYzyJqr+9zyqs25w780fh0Scmdg/WXnhB9MMfxlqEeYOhk59KTVzx+cgN17O+gMkGPwowZNmFIs18FYwfBYLI0MoVZjpZ/P4PIjh6IWjZbB63w8wWKF6//rV94UVrDtvwMIdaj43irtDJsgo9S5tLcdpVYyM/pTAIqzSJkQyCTemS0T33BADncU4akcylOU3GtTd3CYrzC3OyM/JtcydX0RYVoMByrvrsmVvSbV43UAZoycfnWMitFaCesJCFAEHTOf3pSVuyegWlTEV9lgKSjILN91VZXJIan/9L82cvwapOiAwZYozDFFK+ZOMipVrGeZJW2ZeW8XaKN787u1V+a9w6/8QDXvL3hRQlKxlGdt5YcIGUAqVfgeiG3cBcPd4Gn4tdEFIl1ftfYFFgDg4hp5vprSKKoCQpmEpgcKHrfO6jhu8UlgeCumSJo8iTe2kTmCdKgM+LFMiSLijqcYPEIqxE+JdTEBYIj+flddDwwJOaBKbGXyc4C49SXNEiedSlIEss/zQoYlSZOYg6vvSsc+qKS3ElaGW/JGdsZvPCjI3Ny2jAB0rYqtF6sr1iI0Z8X353OP33PvPxHLNejg8vvK266gOZiH/q8Dfc88gPiqGMUQ1aKMyGhUJl3XrTeDjkj1ZbmVQok45ZPkR76SP5E/kHcwJ31W7EEVlqfwEi9GlCNJKST4UUJUpSRGd75Dep1oaqM08Pfv1ruf/7eYC97tU1/hNeWfeqU8R75+MfW/nEk1KsVFj/LnYva42KjyVIQoVgonEScphASRSkpUNUkBC/paaXGVOTKMVKRaUxSl2pWqZitYtFKpCqPOjQ07qustzOCBBUFYryK38yq3qzAszFRMULdJ1XBfRN5fI8Vwo61QAdhKQOaRX1j9LwEIlSvc8veZ0hrqvzwvOgp1RupJELopEP5AMRUa6BANWr6nyF2C2Z/zTN/86EmB6cMvyqkHpWZPEoNThShPSRmIUEjkJKYeGgoPFXRVW76pLSZwKJehUQeZsVYbrV5Q4TEogOGAkueolO7kfGw/HBxh/B77ckYOJrYbHOouEsKIx4kxoaDG/Z4qaAUOSDHyi87a0OXSQoU0Bh69PWO96R6+yqPvFEJ7Nqj8zVVwfwLWlts793lfWX7xMXBJIXqc6txRug0YqZvPWa0613vzt29MuqQkH8tjMXXhT5whfTJ22pes1pZEZgzdx+u/n+9xVPOjn6pc/HVvag3mee3Mrp7tIBi7WP9JmZufVW+8dXF9etM1g3XtiUdjH5rW+1/uyn4Dz+5rdUnX3OPPuTQvS5Szle+pYM4/GAG1N4aBArCP0kmgbdhk1ImXBoYf4zCIVALCukyrJVGCqIQcEQLxfV5RJQjyJi4umhrE3i0q9HKQDUUONJjVF5FogMeE4zzheSnFgr1ilzxs85CHAes7G+kTUy7D/aQiZjUMavBsePfgUkKp0Qe3S8plp1vAw1PRAVeLFviYVHXSA2NTaOd0drZxcbgZxR4nSe8zOvg6jGLlY9tVWGyPTkzMho9SGH0BKh5pbs8uWyDo4QKT4ChIRkUMXsJCZRsUVBhZ+zms0V3eGmptTmzeYw29VKJACZKWLDYcbqlXZ6efob31S9RGpZ/UINUaOz5vYxtTtXgKuKxKrHe6vaUL/AHGlF7YaRKhp9UyCgWQNVCmmAzuXTo+N7W1s6A2pLla4LAwaIkknxCJCSf/rdSyYmMonpxrZuRQ+RdtAsnHf2uvRQ3+i3fhKpa61taMTXHTdX1lQLt92WPva44Nnn2cefKJZ1wQseJ5UAbWZmdnJmZmx0LPjwQ8saG1O7+4494ZVN9U1DV1zecel7Ixs2+tJZ37o1ub0DgUxOvhHVwhZuBmHBYAnVOM4XCFq9vTi3sIxsW/5QJ99BZkL3bd58/MZN5+cZZ2ayaLzZ78tYzVnf9AQ2Rjleu8hZmCl7JmXX5/01gkqoGIm11LH0iuMr3jo4PuE4J0dmhoKJ7q6d//Oj9Rs3YP0D8kh///0331p1+Enh9p3B6e/6Mo8WURKNuBnf7Ku7uPbSE32XSE9E+/pDbAzu7rGKbB5mRMm4zE9M5PbsocFlsVQsPASt9W1t+AIFRoZrVvT45SXiklEsWf6Iiy20uIU3NjVXgOEKUnx6Qmk7WrfH1NJ87Muot1QxnW9V9e4kItbTLZE6BewYw48+GqqvLYRW0402B3hi8cJ7kCPB8HObGAufdGLXrbcBe3hk9MGx0Q1HHLG8e0UFTBRMaJVfdVcjzBuhYlVzODlKyDmtJCXnohQYabsSQygL+xp9Rqca0bvlkKdi90osSBqELlKWfzE4S8crkH/07YHf/nZycuLVZ5w51yH7CnMx9JYqjxfTk4880rmyp7aufql8ByiNZZ+nHn+8e/Uq9sBrkLrXJexIJRLSSWV3TZ6OJFzxYtkEpzu8trVHT8U8OrJyHTKGvM3ooLJYdUvAP7hJXpy94eeGBRzi1l/9koPxjz0BoWqxFtp3mPuEiarGzlnmg/3Fm7cV+qbcxpaAvOXkMHx7E1Y6p0IS5+ZRyMg0KnnV3RsqxcivHl5uFpl7ldShymkAOlFDhyXp2jVcqVkyCfOmHGm6iAQlJ3uzcZhEfGKTlNaUBJjOw4+DoUDUQUWXqkPBFwTl0nApBkydswSilEOAzWN9UkwmM7U4Jr8VL8GTmpncsjPTI/mp4VxyPN6wzB+rxcftX0+42DAfslPhvs8sC4xH802cE8lymmnf5Mu2jzS+dTTWnX7gO1feMfDq1PieumWH4IAMDpyDqKsCrsIOiUBaA0w4U5PZlvMyJV6aTFGkfnUG9umwJYRDp7D5i+uc+LlAsgSEQJ1fP/itw5LLTjxybfPhxwYLaV5toyqS8rErdWbn5d9+aI9/LBVAw0Gq0EhQodtUgt68B4lY7JLWEeFH0hWmcmMzjIOPi5h4MtvYaYjA/q1wpH6JIV7cGEulRW6ifoQfwYL8CvRi1VeKX1ZVPGoZq4aKPNtsqynWxpw2176BUjv1Uqm6I1tIPdQotUkkd/GJVNnkRx51Jt3OlWp9tjjIwHqOjVu5nuP/KAjwKMiI6zv1ijFI9uwprLjTCMQsDZgcvrrrqk7fyj46gTcQtcYCZqPlX5bxh/PxD5mpntXFva+RBubbz1QnpOCDqCjSJEsFpcEoYdUjuvmkDaUjJA78QZ7/eG7KXRbzHMzFP5+vfahdBtKC5dfcINMp0rAsBFIcfHCJpKA4wqsAUyshqQ+cHB8oWoEdXmpXhMSjUKPoFAuCjywoyj73YjoTVK58ZCA7SxBmU4vZ3OKgzg/gp8YTb/9sMRyJXfUDf6sW9hS2uKlfdVXh6GN8b3hD6B/+If3I76NHHcMglM5WiGibAEEVOXdXdYGmxEBj/KgjjP/5HlijQBAZPGRt8QtfMracVPz1Tb7TRAEG1dxl/2S2tMWu+u8Ay0sgGfCFj9jEmgcQKl7QIldiOvPhD5mXXsLp3ua27fNzCorSVLFYfts2/AKMOKfJVID3PCrAbIrKb9pUdccdSmMBHRpFpFNhufM7X5AtXSRK20kjk0kw1qnqZdXFJF7TpyiS7BUoUwCnBwamhoaoEp7IUMFaSbhrwwYUnlKFGvxcRQqUwOT/zt6d+NN3rlmpMi9WiQMpyiuqqVNoEUsB6FB3HRY8AS0DgXMCfb5/kqL2Dddff83Prvnn//62bhqVh5t7kVu1hi7IXbUOFPFSoYOFP/rXxa9cGZLNVKpZUEdraoIf+SurfbnF8F+7Xr04goi0v4CSP2GjKjAyYt+/i+qAp/6pahWGasCqRwroCTZZsLeOUY1UNIeiYskcM9EfYKuVREsz8+PkELgSKTd1sQlwejQ3M1rbyRkGZJJ8q+tzofrM7E9/Jt8FDFRnOaEGD4lIhBcZFSt3x12BU08dXnPEmBwuVlpJxYRgWdtHHh+fSU3OBoYibQMpYybc0Niz1pwYtxtbYocelrr2Z3bAX/O6NyXPeYv/O9/huN7ig9OF0bC/zfa9wQjl34jfQfqu3wh/hbUGrK534n6ftYuBkc/fnn38rlBTY/yUE3xndhvB34c2ZyYjqaicFiFnUzOmLCxibwmbYRxaDbu3PlgVwmTBh7PTOSsnn2wo+KJVtWvX96esX+yYSK9r6d36dNHKcQRob6Q98UziVaGNrfX/sXJNK361HKKMkojXHEdl6kOA6E7MHA889TQHXG3ZsoUDqGil+ni8ur1dnfw0w2a57u5uImXQGUZPaythfVGEDXscUUN8Ke45/87kciEMMmoHS1lht/dd6Lpz4SZsdNOZdRLjpkOd0+tn/5g7IGhruHeID4IHi7lZMeuo0ZjJMxv57LqGUGe3GYu2r17NiSDR5tGhe3+X5zzVkPvOlqFTgquiXZTKMz3Ls1NubpQukp98OqtlBli4N/y4X3nK/n/sfQegnFWV/1fnmz5v5vXkJXl56QUSSkJCpBNKqEpRZFl7L7uuu/7XVWwgro112bUg6Cq9iEFAyiKgIiWUkIQkpOelvLxeZt70r/1/59zvm5mXCoaoi97kfXPLueece25v51ZEc4DofzpvDFBVvYjTbViT8seXR4Q6NySEGZs5iubgUqW/CnbghvnwGBFNlyPhbQHkQoVcJSOIl9oGZ3/UvDzbX5Dw6+3uffnll+fPn496dPCEHBKVQPg6wQ7M0f+pEMfB1Ceo4c4IH1n8k/COVmW0aG0dcTdklK6S3oVLUvsY5MKwLWONE70WAv26uw/c/j1EpANX8r2aD3L6Xv4vId7XXlM4MKA20VxiVohOmYY8HOYD0PDX80IHQciIJy/UByJf386VhVbSqZGl8Q/FIUN9GElB1N+KNwV5QEyK3GQqAIJ5TPDQ2zc2lJVE3tqS0+uiDTOiSt+Exi6EDz8fNHfHoCsRF6LQV1oYieLBigfiDW8fljV7XP2Gru535qV0TmnEiVOShsApCAlqTA4JtPK78EBAKNwoxhQcSNAUjl8A4BFGBb0uT7N9NhEqhIPEcWIokfj/ykbpvi2lk9qNq+Yl6sujel+/OjTaFggmZrZOmh+/Y5P8+FYqM7KL4+uAZ0L08YwvPCY+VpgVwYKSXRzB4TAjVs+zFcEIRsHcyRGHnDYvAYQZPnD5yD0LyDKcIM1MeG7x46EV8QUSAYqvF5cGaoQVzo46K9SO5RQKSQSk5lYo96KdhEqUN25hlt54tDce440xiVyzS68623+AA9RO2ijd3Cq/HMTelxxWC3OzgQ/3qvFi8KJeuf19inEURnEkkjfDkGQPAw9xQVUR/3yGBEYUM85BGnYfGL8o31RmKT79YkRrnXNusDIe48ruI2BMtMhQzHzsU6H1a5xbbjGOPYaLA1Gj2vrss4GNG8vXf8+48DzzK192br1LOnaBSCIFo/pjvsNG/NR+4U38YDcNEy+M3Dw4SJq9U3V0NcmLLUFbo/HSS+anPyMnsEFPqx7YIqUr9Ii1T/kkrIwdywPZr1wDLNEvfL780U/se+5RoE9+87rs174OhNHvXEdVwYsvGKLvEZwA4xo93vzBDhiqt8g6sM6y4E9FAMxMDxSgDw7NnjsXECLYg8V6WJVrbihIhhJ0VuCMPEvR/6FBNXwoNpcAKk2hVGt7qgWW19atT9VDETs030rDiGl6SzgYb/tYvOjCjZURZNaONLalXGkQEYCW/tORBkQQGeyVV6pBnj5NZB7AOLWCABUSAhdx6Ov9Bx+EihBt6Jf6su7zO5GJaHwZKecURyM4EYcj4lSt0xiVU0GH3ocn5SUR9ZQznHBiZPkvnc1bSa3HnLnGZRdbc+btzAe6M0yXihGJpfol4uwhuTszzgBew/WKGuYiRHAfw9CujDO+pL8DCfSiMyBLx1FCCmTGnONOP5IBTT9MpgLKSaEPlnwjWgyZKKayFD8cwHyibO7pc+ipCgc7wlg5RvdZsspG0NAGhqBkCy8dDBfRoVap40mGkYKjRRuSgUQ2T4of1WAZqo9LWza0nn++29dr3fDf4Snt9qITwx96X66pUb39NnVPd/Z+PfHRojG+4FrfTy+fFitks9h1DEnjPxlKLC1hNXL0FSN93zqcKHZ3BDOrV5XWqhO/oEuRUuz6cO5qU19DNckMu8rlwdAHTZQ7R+qQjvmY9NtbaanSloLhSMDGxUtVuvBcpWOqpcVPOHWZourxuhSCoRfGlXeEotG6+rpYDNp9ErJCmvdFlkBuuGOFLxb94AU7DOxULNjAUnnCh9o0L+NEoPeF/CGKMV6v21EhJGLUOiu0KjlawVrxqVhEEKII5UAVSA8t4OqTeIRHX/Uy2klowMJKE7RrFOcfV/fh9ykdHVJ3D/S9OuFIXSQyD+9a4+QIisn+ErsX5sN07sX/QbBhAULkUQWG4r7++JVoR8aC4oKzKQL3EWUKhPB2RUWNB3RaQhcIcgoKq48EXRRIKOGE4hAhfChJhpoopBTPb+xFbi/nGxWzKGz4Cssbjf7XDo9Far/JOsyMeP2SRFHM2+7uvIZOAXqOg5hCeqaWBTeAbSHyqO4weG2r3/fBqaIbQJdDB65wXQkqKvDBeV0eynCB8KMwAc9BLZQgWP3hwYDHxev6cTG2AXGocaZ/NCoZa7iHrRBEmLjpxYMJD5LnOX48wQoFM3s8ngYcB4uxjOcQGDx/gUnE8LBWf5CzTrlQ7NlIXTmu+pYKpd0boMkn2lgwoMwBt793QS8n3kPGBJ72qVxW4OKOSFZW11LlpNaDZW1Jj5hIKnXmnrQqBAQPNGMEcpsEgktePLTw5MsABA43Fk2h5otm2DWiIowVIFjwn6Pill1IkU7r0Ov6dmWX/2rwV485A0PQhh85dl7r+694/8x5juU8ug3aBD2p4ccjKZhjnB5ihJFNQCLRREO4y7msVcrI4Xo+lSgAONCLyWNDwktROFptKfFykvwFCCElJFya/WrFPvuKDmAVQ+UU58/pwo6bDmldOSCAl6PFcX7u8Js1P10Ven8ZFpLZ8C0qdI/YWuknreGVqaID3TW2YWvq6qR5o6R+dqeqZKyRn6nN36E0sCQPn/fDFAexDV6IH6/VQpFinCgPCPHK2YH4rFZeSg7FQN5ncrlpUVLtOdYQNlRh1JjRb/x7ePkvi5//fOLSSxmGCVKZdMxbblOgg/fyd6gNjcXTz5Duu9e95mtSjF7lwEzSHhzK/td/aTQF2dsQdkzQjpojcAqMXDXpg93t8r33YnarnH468IAJc+UqHL+029uy//gZ+RfLJTziMmNa4B8+Gb3yKlq4r80dZhwkIRfz2eekm24K3XsvnoLYmwPfjS3kwLRZ9bfeRvEO0IsfwQkwBohQBIoqWhmH+YyJX5IM0k+8wUI9S7VOVm375Du3NO7WQWcPaZ8dYyjTvYIiVifgpCYY2LcNSAnoqqHN6DFRahwIIGZg8Irpnp3Qq05LfPDasGcNwpraptQlcbTvgPGhWBa67zDth0bHgzdMgor4grlJs449N95YIA1KPnKPEQ9QyAiFJWrYUxNSKD2k7dhjdncrkTAeS5WbGgOnnKzMn6fmC2DYgdbNWN3ugtKN9s6vS7UUyV5Dhy+ii8O0CGDC+PgABEx+EC28qLyjn6ELFbCQXAnQg0WlIWgqnZjDYvrGG3YEUkkWBZNYJS0U1VmLBsXlYBxPtqFfuzGFJ4IxxbYs6vuhkRcnk3VLhXJIR+UNN4HBJ6oGtCmz5mJRPD3cv2v75ikz5+zaugnxI+0dGvZVH3ggWMgbhVL+6i+pH/9o+OK3Kxde7OLNhiDOiFyjuitk7YXEBa8EZmiBdDA6yzXqMWl0zZFQ59dNFafzNSzj4pCJnXvYHTkxkTxtSO/Ix28JlLaG5BE7MlFTm7Fza9tlw0582Vh6Sjmt5m+7RxrswzuL5WAgeOklofdeNQCVXpt3TJ8+I2640+tbcQ15T380O9I9Z+bMyRNTeEaBZFJjUGvwxgkG9y+88AKoQ40+fDCjgIE6QajO7+IXcaHnGcoAverjyxexMDEAMlgQJLQZQwsCK6v0cqmG1P6t9NLYzp0IE9kmJtt4FVA8ybv/OH+Ur5Jq1K94t7OjU6OnL3GF2bGPPyH+qY9DJW7xxpvdFS9An5nb0iovPa3x1NO0cfs7if1H0X2zIonHUd8sbG8WHlEkarHt61Mb+qbYMQHGi02iwOARIFBEm49XH8fW/DeFFCHBUxPeGxuyLF7UQCEX70++aTT+hujwJCBK3QG6/sNDfdDY6Hmx7DqjgXp9foGLRxj4UE/OfRaUzOFdVExnMU2kCQIZGnJSd8aNJCwAVpxp9dapE52OeqzsyoOF8sp+5+ntaqZEt1EY3ItLDmGIit/MMlny9n38APITRMnCNgpC705uttIBSxcr3bgEJzo6BHnBjA32CjaKJRz0ZXT4JQM3pYRDaWzlwflJBoSg7sHCifFChR7jYnawe0sgHhURi7jV1ARUikI9b06VG9RQHd6yd7WS6RpBvKLcgp1dVmWK15Xp9hZSIktRUwvbEO2oBaWNsVCYbi4IEmQhGlXj4hXCwV10TxLPBELx5QBOXuFkWIMaqaOs9QGJYSyO4sVFTJfFPwpCOEJqDOe+8L9ktjzTGk7f9PP88l9jDxtrDVLJNp97YfemLW3f/tq7Z89f2W0OlTEsYnH4OARFeAGvsItM8MnQhhUHUdmB2ifcj8MaPvuxtwgUSy4+994vwiuGvSoyEd4gKXKK/KmIsQC8WD4ugNZYRUTEgh8uYyEM+wa4k4UlnLZ655hWpQE6kt6yxnYLzyHpdo+uvhzFWAgbM5gPYICBzRVlTdjsCmsTslL+efiRYGrl/2eVSbWSMhui6sJasRyMO06FnxSKgRYY8y+MEveJRVAYIOZ/uVz792/kL7wg+YUv0NVOXxCI6KYz0v33O2ct1Rqb4a+9+53aow/nH3kkdvnlzA891VP4xX14Jmsf5J6HiechLrmMSr1nqLzhr/zss+r1/5E/d1ndWWcjkHz7+7F65V73DaV1vHzNV0jr5M0/lT74oUyuFPvoh8aknROF2QcU+peglOvSS/SlZ+w9gPbIiSMvY2J7IWN/juwEGMOUg4+BsO0jXr7G+B4lFYNvsAdd3vvLtrGMQxY1szuu6ZAniRTNBO84V2WPmLiWFgzRqxJj6z21LYQXHzQQ0GBH3aKlGYF4qgHx81BKhmknrTHgACYUCRMowZLFQ8VzZDjkkZHhbdu2T5s2FS+UVppoYsiHr0Th2PQReBqa2+pbxrMdsEykAsEWSitumerujLgd2rE5f8tthaeehqo00hI2cVLwg+9VT1ySSzXlI1Qc8ZzdyDDdqdRlhAu9jx4lgQdUBS1R4GOGY6C15+Lv/Xh88Q+DIj6xhTdmHNdQZdruZn8RyGE+x+Tlmgr0eFiGd1RZ9L5UIX0g7xejEIDTJrwkdeestB5qOfkE5Re/0qxCyIDaslIoELIdnCtWpFOWaIlIk+4kgrRLDuOXK3BCKgBxqLk/4Y6vk4yWkFkctVtaVLuo5LKY/AEi2NtV/uhHSyedLJ92stY60bQt49Rv2bn/p+ovyIYZPqocpqUo9G5uqSu8+f852g45lqoDv3jFlISmBHIPzoifPkmSlytqwZgOgjingcmv7AyHB544PvXuxU5zY/TKK6JnnFbesAFb2/GpE6WWiRlDLWXTZ89ri0EXaWYIav7x0k9bPDr3/EXheFyrKcCcpmq6RF3AtBB1AZyJx+Uwx8AsdNu2bagdqFnYB8Nh6Xnz5lXiYtd3w4YNQjKItXbtWtgxhRYP0FfADm7BqyFiagdyGL9i+g34yi5ubSb6WXBwfBS6byxkihwKqouOd7/wxeI9d5mr1mLiHv3Qe/AMUf7b34p07uISp0hbtpY3b8xt2x79xCdcekvpb+bQEkC3h5KDM/PHHnssJP/6s+lAqEX2HQTP5MmTUWJhUOTwFCG9KFutoQfC+sf7gwQ6DqRxy5YteJEYa44H4e2PJ/O3mIcngaVLl/7p8yUSkKek0A/i6K0T9JRJUjJ44xNdPDorFzctRkui1xJ9YLWPRldEAYpz3lRnaauZyPTLGzqdbGHa+OZjm8ef2Z66dVVpa0bns6Rj+j0WVaV9goV6FKLLAcyAb8NvBZAChNvjBI5CySnYZtSAXmMiIWB9PDz8oKk7DXO8ZDHPPgAQACV6LUx7EL3G2+t/KygFJH0pAmIQbG2oGFURDxQyxuAMlyEHJyAkP7THVp3o+KmwO7K5MztjdmwgtaQ0eEdJ6g9i6xyPNcm4FiHbsWUDUhB66dWNucVh6KkDf1U5kM2nQr9EW+VzdaSXGpHwtJsa0vCUmncFskaGtE5QNR6WCmpOFp90hdBCsrlkIu4zvVZ44GExP2UxUmwFj3HfeW/95zuWTI49uX3MLJOCOT9pkIgRC2QF3mvYFXJ2SoVc/1ZKlm3hwFKhZx1kGkqM0+NiKxhYxmQIoYWpcIpQyriqF6w1RIS/Fwy2OdQPp5hVRISWDKXdouO/SirktuAateu217ktYaxejBWaAH9rfLFFYw2jftgDulMwMRBFKwS54/HkUq4YjoXVQVmdgO3P4bdGcg+SCowSxfitFobbJcdcs9r52MfKM2fU3XijzNfTUHqoRFFD6RaWPxBMD5f/7kpuGKTQsmVZHLDCVupll4pipraNa1nzci3aWjvwYAsGM1U+0YoQKqWoxOarr5rvvNLqmBy76SZZo4G8KLIEj3nfr+/XGnFqzLEvuiCzeIn89a85f38lnrqqLdagjjqY/tY35YH+6HVfxzNwyFn8x8yN5hLEPr4Aqo1EPgcyR3ACjME6MgCjImxAHYg8jndWtpvAu7BjvH6oCTC1Z/QCRLW1oDYfyaZ0c98AixCCaKhiE1sonI4q48uyIghv6Rde9J+4JAA1bMgNbXDs3LkDR1End5CdwaHmHRaSsUeK48ADq5x1UJqyO90ezk1ooOuaHMJcCTtFJD/GQNFBkQ1GqMwPgTEXnn/1hBazh5uvsrynp/yjG+PPrQCIjOeg0BR37Sh+53r56lDi5JMSdXTNUlABi14yiQYXCrLwQqzn5FZBcnuyytYBSISY4FhVcLAoyhNFZYN71N14WxRzRTKEtoLa9yCfALTeO/h6UqIgggQBH1ykkV1iVdVyjWe7yhfNmJP88FWjN92Cd9qwI6zrKlTKlk89TV96mhqLpngh2RePwOp9tbrQiQvnY7IXPXUxnj5XElFdiZWnTUGhcsolJRg28L7iE0/ITz9tQdXwlGmB+f8z/OS5Vne+8UPjRnJr0pnRfKGure2yoZvXOGueDIbDpO6MOwnSj4VnnkahiupqJ3y51XVzued30KHmDkrOq6Fy9zF17/+c1NS8bdv28ePHRedOVztMO/+CLHUWrHBd9OSGhhlOV2/5kd+4v3lM7Rmwo2H5+IXhd1ykzJzlhg54qRU7WosXLwbzGOhj9os9YdjXr19/991341Ii0ox2Dbp5AFYrBUxTx48fDx/MjbH9K+axB2gBKV6lCNYiQbWFgc/GjRuBkC5A1hicdO3s7GxvbxfvcteEHMKKa8nYTMbL9YATdJEiNVGnvG2xNmtWMD0SxV2Runjx5p9Gduyi4o1/XGRoEHvfr6yTTtKhvAHXgPeb/Ycg/lcUDNliDxYvBCaTyRpNB4crAVw7B2aUrn3LDHxEOURZBRm09pXlksOlur/4ICdeVsRhB9hRDg/VWewPy9/8jrAEsHb2RpuIw+cI5SEa0CJ4AAqzJ9lJBrkFoX4HuPFH3RZUSnSnTSzj0ihAdJY1/RLz4J7abp9ZXww+/4fBH91id+7ESMMC2tNOnvH+93zi+En/s9rqK4jRm8DM6MdyL1opIuuBjG23OIBgvNZMAAri0qhSHrVzDVHDwETbw1Bt9WDDcB5zrAiePVbcgol3IDRSpAQ/JJI/bBdRCAEZmgLgS7Hpj8QhgryG1oNCSDUe2SgSh/mIvBEmhxBeqHAwg3qqnuZjONa8Mvv+2bEVasScdHVf7/X15a28N2uYxpkDLVdAhaTda8/pk86YjGcYiVsfjceOzxR5B50GPCshjfbvsYqZujaoCIULVKiR8XjnH58xz7syxGB+iHdYrGI+078n3jpxXFxpxDEp6F+E99g5IPapi2vWNZZGZ6Ria/YgGKNqDMs9I6gwcpQyeFIQnOIMNzOCGboUiuFlRwkzYZxn0oLQ6+HoYbxhwMAUgWKS8RMKDFQM/dUScjEl+AsyYlxPaacoggsxjGKchK8ycqNQ5pAowAhiZQkjcKkl4kyKUcWYkVITIc3nQwC+xb64d5rEDozWULagGc2mJ3ORmQE5YAdMx5AD9ayAWU1BQpyVb7Hke8nB+ArjQDGW872osOE0uN3bX7jiSpxRiN16u1xPx1orcqCGBHOeW24x8QKlZRUffRglkYrVzNnKb58o7eg02tsJS9kcpcfkD2RcNR4PTJjIZZCKJJgpr19XvOhiqS4avf9+vbnZp+gqdXUgoJ1+htbUwjVSVuNJ/dxl+g03lLZtDc89mqoH0xGFtgxt7TfcIF/3dWi0dTNpDA9t3JUDzEgGx060SJwujhyIr338xwyg9wk9LA+cScP8FnmAodiBEGGINnPmTIRicIwdp46ODmTFIbVuosXoqNc7UhALwOnL+CupHnt4ZT+0hTwRDVFgRw7hZE7Bzj3lFF/F7r6itavRk2R9upGlcyNT62sn8BVyFbwe9VBJ26MXU4HyuBi/+FLbFHkciiiCeoXbCp59LBUQ0fbhQPDqVcaKF3hdWLBBbWewMFpcvlw9arbaMm4sikr8ioXbV6LPRYTTH6CD/PiP7VgShsAL3gGBVRyWT210xOVD0ISEBC/CfBd5wWA5gJcSSH8VwxGksEHVFDFNE27Q8jHzKOGJHeqkhuRx77g01THR+u0TbneX2tjkLFocPPkkZUIbHedmrjyEY39QxlDScJoAG1+4EzhSKhmpZPhtp2YXLFA6O/Vg3G1qkEcyYMvEnS4c5NDk0ktrIxf+Q4903Af+7RPP/OGFGTPjd958uTYeleh3BGaSXg3olgxGwiODw3JLcx7SCJwo1U13ul7Lrng2YMnGzDmx9ywspbC+6+aLeRv6nDPXy/nHVNdEr4Tn291R3dJOLj0wMXTHKrHdLRVH3YcfGnx5ReK6a7Wj5uFZOSGysakhIWL6ilYDJzyxhIQa0d3dvWPHjhdffHHRokVIJpzY+Jo9ezYgK3HBMC5ewgnm0ejvewYVCBGKfWMgbGhs5N4SPoShFk8FISyIUhsEZrDPjG8tzOuxAw+4GgMJsmhJgyFlfFgaD01gst21W165knyZIwLGg3+qWhc1yq+sdk5colR1143B9DeHyFnkFCzCji8WQSBzlJPDP4kqjtMfXM6gjtp3yNb74EhefyjIYYp1+Ek7EEUhRoTCUlsFDgT/N/9aCUBoOImAlRHk0Z9Dem5bQpuQqLaNFd4wf3pxN2598vGkMeHskJ0G3Tm9TQq/+PLQ179t5Ivot6hS4cXOx58cymZavvD582eO+9/t1Bd5TaePWsT3XHDUBHO9RIho2fwIXvdHbTL90Q/hcJNRxw5j/RUdpddpEoBA5wY1a26jPK3OjUN/LnoYS944Yq7rk4uWGKVULn8xO1R4CbFgR6ChQ78evZoQhJEnc+DZKYEwGB4gBwUePx6DcmisKeE4MTWAo1Iw7mvmRZtKD80wlhvTMhP+o5TfolqjSniSo7cW8ewpHj96fPhLmp6Ewmgw4WHzJn2MzqPvMQicioHX7KUEChEEzjS47+Go7MYzEKViCcfu0BRQuMcpy4sSSpAm7hHLo/VBqT6Cnt/GLRvWr83oBFmwPmFc3RknBRpTC+mp1/L6fqk7Q7sVlGfA4QmDoMnFsfiDLGJmyGE4qQ4E5fr3YDO4YcIUMIjoPLQSEeAmYZIDqSfEhAxU6FNB5GUQeZNfVSY0avI5oaJBWSN4YX8SKP77Blbsw3WXc9CisrDNWDQJoTKWfpi8D/SW+8XalxI+USo9r44rWwsyykv1OPuMagBV5DgsLx2T0dqKWOyQQydi2gURvVWlgXEaztWiXlRymEoLNoSK5dEPfMDo3CHde4cxe3Z1EA44KpOutWGT/PyzkJn87quwcuBFVyTDdAp33xX43OcBZHV12QtOCNpjR3Q+JcQpXXZZ4LbbhAdomq+tL15wMQpf6IEHNZoYw3iCd6d1YFPKrRPPookq4ciJOGwKFNlgDYkXgfyi7tpbtkRyufI/fa70mX/BHAUthC7ZUJabmzGzNGdO/YvPY/0DHL7ObD2CE+CGhgYUO2xHHGQCjCKJ9XuAiStkr+cwG7Un0KZNAjxQGg/kT1JnUwUgRcI4sZpe7vZ/TbU24+1chFEG98Xc+GWtzV+QVMwo0GRVo/hI9v6FxmUkBI9zIKDSosEOT/KpYjg0qr1Qo9ii4bOLRezKaThUREWHkAhEtJK5ebOcp7e4a6jshcNzMmPVIPCG3Vo00FzGqv5gVrAtUHIopwKiqWmRyUriYk+Wm0guLU07qvfgL6EgzARJoIIaNepMBL/ki6a9YKs/W2H3z04uWry0dckJBt4lwq4gLvGHcXadTJW/GhuxXhOEEoVqP9Q/YJXK49vGBa+9pvzt7yqYWTU02rpRzGbtd74rfN4yO5uPX/aOwqyZ9z32+KrVr4GDDRs37Rkcmrf0rNFb73CLo9jHwupMXTwBhJmgET3/YjyH9egTT8yeOW38ggXGMfMxx8fEdNtwetPzz7WNa4uo60JDn9fkbjp0jubVhupdNCGWbP0mtChiPdNqdELropCAq2/enP/F/fEJ7VI9liEPZrDzhk1XpAhYUTtoOxq3qmQZ+7qoX3vtAB8MkR+GlAahWlnTsYYHRSs0vKOlCapTwvj5LuGgKdLue+P4dhHzXmy7AQDTIfCA0INv8gASBgsTQILosKOaw45UYBNPFEUuzCgBvBAAZXN5Up1CBd4nTIRwnrFQwnjP9/vb734kgA4PSyQIwKQXOfXaa6+tW7cOOXXppZcePJv2g4u9kF9YRqncKocfyiG+WGfZb8FDeZg6deqBsL3p/uABC0BvOtoKwqamplNOOUXsNlc8/2Z5nRJAIXzyySexHrdgAXSH/kkNWgz0F2I6uC9hDKd6R91BehyU2pi92hT0JVPa5EAmM3z3g+5ooVwJ5wbJfvZl85VXWk9tGM7KBbTw3HMBAxCJidbe5MT8hnyJjj8so2EmnBiMeh2XD4bVYYZDCgBAEz1uHslPkGgKWucfpYwrDgXWbi1txA6MG501o2ni5MlTknevswYLOFJUaTpFLP7ytAlIBB5hqWnzQYRO9hItcEKssSELOOTGmEcg5AvGAMH8ebBqUFbpiQ1KIoGFb+n9wUWJ4PHxXypGOT6XFVTRPWI3Y7ctH/jmenMZdLwyNCWKLB5tok8ciDQQz+QMpOK2jS6LegWe/7E3PoIJiGm0IDnZxkhSKI4VwYyLolhmCT6WY+tRrTFk4akOOxAMz5hsY5ZLFMg4mhp418Xx85eqUOnf29PiyueEo6fMrduYD77QhbNs3AlS6gS44J0ZZQ+0k34A9e8QZaYol1S5KS4ymvKRQQiQU+vD07DOQ4pJgicKgvI9KR7Z8RVehIBiwZesEDhCaRdakBDiI3gPBR7TGZXLYV1rCLkhuuZ5oGoBBG8dIyfe54zcpDjZwAf6CigKK4JSWTPVsn1CLvoR6KrAY0Fxpe4qIVlf2G+d5IuUYNyFRrh2AoxigUelMl/6SvDxx81rr42dewFdoPdKlig0mG7K5dvvgM4565GHdLwh6odqppU/73zltrvkf/qshIfNmprs22/H8ydeURMFruarjhtfKYXWpg3FCy+WVNd44KFAxxRRz31xy6GjjxrFU8PrXsPZZq9RAp/r1uHxz2DbBIAhm/AKFLVEmFcg8vELsnfdwy0Q40AresP3lBdesG+6UWtuxbFrcfTax3+I3yM1AQZ/ED2+2NrFvu4huMCEVtf/uFHaITEfBAD5hQy0hm5Q+r+sYJUQzbeFUyKYEViKlJcyP5ML67VJ9+B0OpcOYBKt0P5RikEhyvPN0yAAAEAASURBVNxewVu3bsV0BcOpap7tBXFIJxj1OjY0eNT/7BUD7Z7fl+4VcmhnQKPD1Igv0sgtNcXihQZRorkW+DR1TREprJZ2Zs+nhDccCpKVCxhBer7bUzJPokUva7j2xCTW5iWc7RrOKxsHnWwZqp65/eZE5R31nnXukwHrsyclZjYb/vSLCMBQ18CmVpJ7jfs3b96Mmo+B+5o1a+J1iUVzj4pcd01h5Sptd5drBNRj5genz5CgGAC65iZOeH7lynvuuQfbvJl0GlFuuf3Oqz/9qZYvf3H3165Fh4SBvq3Keagifu/fR848bcQI5PO53V09Q8MZ0zLRueJ53p6+7txofg6e4rWuC8h9OAVlv5SwHkvYA5p0zLjQVVAE8ls5lVM/1mN9JaAX8UQQumRofDKVF19woeSpfm/VtSKBlZRi0ouJ7mc/+1lcj0eqcSX4xz/+MWYa06dP/973vlc7QRURxRdHVSsYKgjhg/U8pVCKDg3Y2zrLmazU3Ki2t6vQc4B3hr3CLWOOumrVqhNOOGGvoT/uW2I2BSRgY9u2bfhiHouz2bV5UcuDsGP6hPJfYQZI4I+6gNn1GGBuSDEzdtsmuL1IKRdG0cnTqNB1J7a6OP8sytyYmH9zHFACKB6QHMwBIQ4VgMKAE++AEkhwCB/Z3d7ePqZbPRSSIxR+8IJ3+ERRvGEOH8+fF0Ml94+0uPZKpqB7oAZqL+A/nRPzEyhRyzhDJVQKqhh+t0Y2mr7JblRzdCw5rt2AK6iiZ2L2RAfpFNduDJx4KlaBenMcg+sWI6FGy8PGsua4XtWrnbvxlEYA+qNLv12j/o44tPCl3SpuAPFmPbjChnVAsy+eq04Y2TF888+Kv35Ks7AzigGLHl52ZvsH33PR7LYfrDBzNkZ0Hlr6ARP8BUceb56sMZbESjoR5OmUmFPBhRGmnwrfQlIi4VAieIblY/J//Y1qzy1LyZ8UfvxM+sqF8dsmBlcH1MJIuXVDfumK0XcP2O24yQWtRNByDTKCFuMkPpAf0LdBWLyMgRUeCSgqgn7PKlscDhhBb7AUKNmRPqji4gSL9In+BEPqns7NEl40pRS43es34hLc3PrJS445tjRrurJ2PegATfSqdyXOPdPa8Fr5gYfsHZ3DObzq0F5/2dtnLT6x1Jj81UYaXeEwm+CTeWbWPfkwv/AQsmGJm4EkzoAO8vOIApRYY37p4AF5gSw+/EUUkRJ4sazZBRtFAVXCzfmIn5HuHTELEwMM9CEvhPtkyUolroJJ2HH4txzTQyE1GfQHU4z3rf1RjOl2wxfdvi/K0ZLx6S63O2QNulrKDbeWMbaH+ha3/vOqMdeX/1tTGJh5oXCIWZhIIVTW5G+9NXjD93ILFwROPTm76hUqNX7q0cgEmlv1ZJ191532rFmxU05T6YadF45HjcuXXxL46jXF51YYJ5+sRqOpd1zsRz3gL0qouXNH/vyL9L6e/I9ulLNZc+VKbmVlLZkMd0xGTDcUdj/8Ie0b12XvvCN6xRVo+YoPP6z9+uHy2WeqLdh9xLTYHvnAh8ubNjQ+8Rs3EAxhW6vt7aJaITp2hou/vLf4opw470I5WYcRDzcPlWQdkDcRcKQmwMDezA+TCi2dh+BCknDgE+aQYG8iADUwmOUUnlX6vo4TL3i63XywQXohJBU1qb0kXzCoz8iq5gtm79WBcT9+Pe9FYQKMAocNur2YxN7X4R4D47ZSDQRtvCJNF37QiVdKJqdi6kw5hGurrzfXazgEOgmnYnDL1a8LJBig4hGMQChERb0SERsddKSQFoRyJj+GV0k4XJLN0WEpO1LHb/yKfhVYoCdjSsy8aLbcoaC/ymODuNwcGJ0R+c2e8u+3atj5QwdDZ6NZwfSwpfblzFkNxAaz6qULsykcKEA5EbfK9zu2Q5T29naUPRwSXvHc83t2d5152hmJ006jjVlFCdenVr26/vLLTx7N4nY6tEzTYypYs8DsF9huv+O2Bx64HwMOo5R3IJHBoWOSqdt/fFP4uKOh01YbzU3tmIrTRDCY1OFqLiZyUC2IZNvD94RjW2HJP9EkvXK6u3CB0tSk4c3xxJRy7z8Gwg+q43Lmooz0ZBPaXtRYmifjcB2pZ0R07vI5nZUPZvW4Pw8nihPKz9FHHw0nUv3cc88df/zxkMDkyZN94VQiVS0qD548N9hi+dH0NTMqvbwid+NN+pZtOPmOS9bqwgXGRz6kHH20HISON0IJmWDbGfqT9poAYzMHzMAg4ThcjRWr/W4DVplgG8CmTJkCK17HgUHWwI6VBQ7c+yMnEsq5Z5qvrg5QJfLGG5DRYDgUX3SiHP6/NhvxJb93Oo+MG4sj4oo1NCmg/OBqCQr29u3b/4jz6hUGsf4CfeNwojfFV8x74VkBeAtbIDc0C5jCwRykrv2FSwBLGCgM4mbEn5JVUeoguj8l0UPT4klHrqSUoOuDplu0X1cxoreC9goMudAYIlzMowQA2dkDoUUomrUoZiV2xSKAeXbCoXsF+Lg4DESEG180Fp4rny+F3V1nT1oxNbI6qOT6zLY16betzS5cPEGdVBpN33qvef//QqykDRVX7k2r8MCjajA0+UPvm9+SfGKHJ3CECYyUKM94g0amiZaedmVFHy4aqiovfoTqL4VRP4UIFYMeE1iY6xpfIgvfwDP5pc8MnqlKeHEZm8ABW4LaK2KARYp9SICxNGnWhqdRCjKOfENxNF27FXQY0CPm1T8CZcMWShliZ4qq4xhqDrP5WnkCDjvTkhnCWrDtmKXyyECovhXrCr/eLM9Y3Jr8l38Y/t4P7VfWyjOnJs4+w167xrzum4YqlR3dLSju6g171n+76bOfnn3OslfigVW9BjpuIPRLMzFdIQY78UNaaYQBJDSRS6UiQxGPMEi7b4GDBef/IojcAppAuesjYYkSCS8iDpc7lDZbNwSHDTkH3ZQ+B7QygfBagXEMIoujVdHWnOyQniOi8FdhICot9ckyxrZ916hORmrNqePoMK8r6Y4ccxq/qCc/ictVYrHlrSoRMfMSszCRRhRA+c57dNtOrHjRedtJNS2DCFdG//VzgcUnBnbusq++Wsb77VQuqczQSBuH5y+51L72G9Ytt4ZOPpm9D1GcEAuv9ZZXrYpu3w7K2nveK8jgC9V41qWXuXfcDjtO/EX/+Z9H16xTP/jh7De+g6sf7ubXzLlHR/77Bt6EQ6VTtK1b1dWv0lwYzValFjI6XMjEdW8gASKqgMhUZo4DD/05ghNg7Fxh+It5yCG5eEMcHxLb6wWA5CDRge+octYxA+Z/tQbXpvAED/JMGtDya1z587I+K61m7nEaPqUEoHnoEGxiXgS9l5VNA0wkxG4wygGmDVBLA8YwqfhjxwRQwayr8+cVTjheeX4FHVylRpEqtRmJKpdcJCVJ89a+hoBgqOMSVnwRl8sufehQgY7TymVg8mAZXDTWHFVE5Na2kEunuzZZyanBALSr81IOgXiGkeJdPpRIepqPUVDNwULsjET5g/Pk5v6d+cceG31xtVTI6x2Tms476+IZR6eM8F2vCn2dgCRUiDiUwxQImqZ81OxdKuW7unbjMSrw7BYLbqmM6x54bXjmzBkA3LVzVy6fnzFjBsZeGzdtnDZtOh13Chj3Lf/lGUvPaGxtGY870rL7wosrOnfsRB1BgpicOP1FEsGrR+n0CB0pp5pG3/6RQXPx8Rjx21A6Z9mLZ0zH4WNsVEKx8o6u7rKDk7nWmjUvBczH8EikaybdSdeFTjtOKVvY3XUMaIl39dTVbv5ZRR5wj0sXfpOksZXl0CHmCW2uDjUm1NvtazDdxSwU/gBEQVqwcAHa9FhdHDOQc849J5VMabqouUh3bXwIHQMIOp0tuxnXTtObIEpSdulcQ0BWzM2byt/8bniQNpNxxhuvdbkrX8p9azhyzTXSjOlYB0B0kANdfHHeFWUVk1VR7sXkB8W4p6c7mawLh3HHmRj0sojoUlrG5BhaK0UROoFxjht4Eom4AAKjgK/lHcjkcFBf/LbyeweKt99p5GmHhZYDx7WGPvYRt7WFnZX0jqFDqPbxIM83y3gyrhU1UHskK76cqAofyDEvpOKFOPvxYkTwBxiFehAHYF3gImgGF8A19YQzC0EEJ5oazOKEc78Ya6LuJxzLPU3BJiDDghEoYsWH2EM6Obv3E6Hq5XFW9RhjE6FjvP7SHHv2dK1cuRI65yZMmOgV1780FvfmRxQO4evJP5fLDg+NYAL8urKMM9fHyhjIx/fY59cv4PsEoC3lVVpxK7MWxVhJoo7sJ67nJejuFwC9yr4VhVmtTjIEFgFHqKiBQquOXh8tvgI1QOiYxKzOhxTTm76cW45FwrNnFp75Ay/KegJAq4r3+Iy5Mwe0YLqoBKFlixCKyNQOktV3kovton2E1Q8heL9zJbbor2pcPL13WuvP/n7S9SmjS8XqKro6R7qkRV+dXqo0XhsYsgd+/QiGe4ydFqlhsEE8+uCjsXecd1Rz8olOVGi/TQZavjmHX26Y0V9TKLTG1Ck9U0Nr4kZP1ontys3pNqEtUkcwAAVPwE8OZszn0Oe6yu2YNJG3iEQRKIgjQGszLa1CVsyDF+ShRhPJDVC6Z4cRrQvWiZfD/VLhE0YcnxdYPOyiCMBRot5UK+HZQpBn9v34nIhwEjHsYqbk9AVDdZhjdxbdn75mXjFnduvXvuRs2aCnEnJQt++5D29hFCx9pES5CqbwCn3fzT9rXTB/XuvkZ7qQFC4doi8kfvwUsF18SL4UIKQs/DwRej8kERpYEJ908o2kwixTtowtCnATJ8I4TgmX61ACzbTUOBId3pLrnuAogTAeyKAcpRPsjIfIUOrpl1OBIg5yeLgDl94pCPyJMAZ4S36QZM6GQCD5SSey1Er/XMo/J9n9ErRehU5R6q7U9Zl0reogjddbQi6YeWGLgrps3yDN7le/mP/Ex7kYUlEdY1zJmDINPqXly8MLj6+VD4oM4hrTZ4488BDUJ3BJfV3FCCVRXbgw88CvUMhFQRcUUQG0FjywRJiQDyoe+Lzz5/nfPW099xyGx9r8qxNnn4MND6LBbLr//g0rPSjrpAlgL4MGUv7nf3SuercajgIWfO4FcHDnEZwAY/iF5Qdo3Dk4B3++UAwJ+iRMTlyn9ErIeBW6yKD+DO+RkvJ9qC3M3x2p+1JaVvLW6JNy/dHIpoOzivRiE6YCgzOr2CuDE80hNt/EFgq2ZQ6yf0INJ5vawgcPylL6U9TWcaGPfawYTShP/zZQwlK1VJ7Yrr73PYFjj5Xx/N5YU8EmihA1C640PDSE47tNTfS6l9/g4nlhaajIvRWXN8FETTkiK/XESMho2imMqPWYeDKvAjU3q2igs/1daLVts+SY5YHODdCibNQ1BqKpkGpffpTS0rWt95p/t9firD9FM9dvHn3i2cbPfeKk0856rcV9bldQXLhntuWtw9KOYV6G546O1iqg3DjrauF6KDCwhjPQdOzkR81cSW9MScmUaUS2DFnDo24+gdm3s71fcZCDeltDY2whXplqbsO27vYhE3v0+TLE5i5bdt6XvnZNrvv747T78NSgSDK4Qm2ylPFbRj965/I19975cxok9Q+WHnrY/fVDSm+/g6dNLzrPOG0pZoChltZyPPn8lsGy0RKLYrHXdSPzQkcvsX7zO/v+B6TObVbeVBcdp73vysC4DmBRm8rDuMVtqtiLLkmBxPkXFPSglMvH9vNQm4TFIzFvxA4eFk1Qj3DAyx3N1h93fCydUZD1iZgajSNTaus7ydW1rfxT7tB/uoUXFXuUFATok526v9fr3uPmLOeRx6LZXJEvfgRo/osnN4qBTZvMZ54JTJjw+FO/TWfoji6y4NFHH8UXeuyWLVsGH2E4a0hURAei8axeIH6QTWM7WHDn9eLgVCfd2QLYj8KLJMKPe20XTUbgkkuc+ceUXlkljWbccS2B444vJ5PdmczkBlI2xqW4FklNOa31BqQIYZbogz/fR1D0nNVY1SpAdKr+HJegffa9Xw8rflgy+AJG0BAIGAAflhThq6ClkBoSjBD1uYhDAX4k/iUwis8OapgIF/5R7Y1hw0IYxiasELdt1ClqpD9vjZhaxg725uwCXkIkI9gUgNUvYtcg8Pz9pHrOniyBhDKokjUG+Kj8kZ//R6EkBoYbA1wTbz/0OHR/8BVcsBzMjIk7xnGwWAcJ681ovcXIroxqDQnpHQT29QXVpKDGeuC4VaBqeqp++8ajYuaFo8SQTabjvumslEibjMIPxa+oHwJoLCoGEhCI5JPm+lkLCAiGrPWjXkV45/CsnWWMmDrKYS2gv4fGsTh+LZKqnWxEGuVcIPXwkj/98YYcfsmwHz4Cnt0i9RTgGdG84PpfHu0vnlAANKEgI36oRrFzVb9zweRE/bsuzr/yMtpnnmwClF7aME5YIB8zf90QZkqoevQWAqoJYoGOICXWhYRT+AsKsPurUDU8cZj4iAoONVHLmq9/T9s1mlTC6xmuqbtmQAkVVLd4XPzBkrm+vOMzCi7OgFqljhH/Lvi0d+6pb52JUwukz9Ijwj+UKq84YMaVkAcua/vOGU23x6Rhmd5qkE0ptjZ96k92Xr2rNAdbZBQHAvKbT9j81kv0NaItAhDhrRqe1jFXFT8a8VJ5IAnS9FGMgCttEDNHtCBcTXawIqFivYG6NB+zBwGEIE0ODoKF7eSmFk1TXDx2CI3ZTFjkM1sJAAq8Of2qXlYNehMR7//ggcBB+fsvWqdOaZpzXNPkmGqvfrG8o2u0pOG6E5cOv8/qH7I3bxt/YgdWJYrgEeQ4pMIgKHBBY3KQEzOMCSe5iS6zKtpI8qRg2ynlBnZFUuNULSQAgNKTDKOpSQCjgyeOZaUzxaHdyNn20XDQVpt25l50d0XHz1AMcRjHhyQMtXY8AoxdGLMuqEJboM8Ck3krf1BYRM+I9fvZSuN12IFi6WMMRYc038pJr0kbJsDQSVl7sRRVK3ECVH8dyswitcRjDC0YwENNnXXmGP+DOigb8KJO67jweeP2AuRGQFRpLq+wBkKxpWdJZ54FSB7fVLMJrUjdksVceuHJlasGHViLH3e871+NVQNyMOsRnACDLJ6l+QuZALPYIFgeOVbaCKtPdemgqbslQvvnWCcz6KgIgPOFfGBHFDdsJANrb50sfW5KhTAruTBW4MVCEddEk8kU8r6uLoENcID3dPeEwsFEHXZoZVaUJYZTIi+pLeUmj7iAnW+uW2K3rUIEICgxcDo4ljBteuDTn3Ivv6S8p1uOhAIT2t3m5kwgyGNn6pzEXBEJ5RjkxKCVUs5mV1e2UCq1603sQzQx4h7My73oVQmG+AESIo0/kTp4O1J2uKeQGUwP7hkulAtY1Ojpx+5cuLlDVoPeILjs5IbzMroX9MF4w9fK6YoaVG0s2pzSbo8r5YbuvNdajVs3QOo3Qtn8nv+8uWPmrLdNmPb4VmwZeKUR9LcOBzZBWwGtbqNTdHdu22Rb9vhU+NSj2+ThQWdkWI3FtNbxSqlIN2kzaSMpzZjY/EpXcaSIc09acsKMZMhpD3fog72B3Kjcs0eOR+S6eqhcCxRGQT+Syx8d+KnWcQu9WEzdPR6Gx4ITCgPeVtk4Rfn+okU3/eKuW2XXLuBZ2l8upye/TahUtMu/fqj08Y/qn/knbIzqPbvnjq/ftFaDqmKgcMvp8i9/of7oRwZ4DoeksOauerl89Vbr+n4tqWDFNxE2CqZUVIOx910VXHLSiBGswyYwRA3EvFLri5yeYAqip0YQDnxA15Su2jt3lh96OPT739sjWbMhqZz8NmfZMn1Cm63qPO3knMQoa/AryuD3FLmIntnBUoFbVEqvun2fs9LLVfnf3S0bsKfA5YHUISAjSJEoQDduhG7AtikzQulRbPzu6tzePnWaquHuUKg3J9CDFS4YTqhpyvwhSx4aRbEgTyoo9E8UG/KiLOYwLkhUzknCaoPSXL9pkI59E4j4w+DHQyJwQBJuWEs1zE1CwZFeziruBktbv3brwLa+pnR4vOyoPd17tICRrG8kioRKkGILO+GuBLEHmKnSYzsDUEln3hgLR6LqQ14UgF8Y+jI2Bqr6+MMegVnA0UCPYH0cBA0jYjI6gQ1xCU5Qx5fs7JUu2J0jNFD0MPi1xEPhMSbiYbxpz26lA6ZVzIyJMScohb3urmy0q5QM9enRPF09gxFf8UNLpxWf2lC2jwlBnQ5PBviqPtBjgkDBqRUOL4ZPwJcO0amlWAWrjeZBVAP3tVFyDmxYoFSDcKpfVBq8ikDnOPzk+b8HRrG/kKG03mkm5Izez5OC/YG8Lr/atHrZdch4nDWHhCKAWuwsJeFhFQud2zfx8hPOpijr+9YhK5INLQ1NLRVZViyEZn8y8gaLHolKcdwPXwIECD08Mo74qNtLdaVsvNyreVWUgQRRP32UsZXpmsDrcUU9NZUxb+9wL5pUp6mRYTxElgtalUO/XtaKxyuVeTq9DCUfREdEg4UcfsXtTeuP7rIunT2v4av/OvCD/3G2daJclYJ6/PSTEh98T1ek/lfPu2WLuiWqcIImxd+P8Uj6XAhyFAmwY6IAC7lnxv7wd23fUuSSlQn13JIa/UPMLbnaOKv53SPxE4c1d5sTu95WsJ7MCpwIC0XDCr6LliAYwKkrmtZDcN4MjlsE5hHjAgQ0qLu/MPP900LPcDzQpGVVw80dF3+oY86K/9j8s1XZU9F7Amc1bZUcFSyCpMg82kMjBmpSQm78gaof5HkQFBkeRYuUM1ozn7HyIyCmojsqZUvDWAN3I8lWHL8UbWCNkGieyLeHBCrKYRoZ4GSTYaA1NEnlpt+YgicqG0QJZZikogejE6bhmhXOX9HRdUveOaretkoKSPl/OyXQkS33Z/FyLyJg4kTHLIkuYqH+5PNgmvgmgoSOiIhiR9j9HGbaFEYC4KLE4hGR4MOGhCq7ppvPynWmpAQZXjxZIuJ5cORg+XhuoI3F1cAkRJ/RB60qcqoYbIlPLkNlBo2YiCIxx79sIU7JD2M/PB1SKiTrI8EA0a7NGAHzlv4KOaAIQHuIOzQ0GIlE97rY9RZOPmZeOPjjteF/Yek8IFdchsdUBr9OecXb/9knQaL07+N9KI8jOwHGI6LQoUKNUU2LeCiW3vxwHt1y+0WNAmqFVzHQeNI4CQ2zLnoMPDxAhppM6PAN4DEbakQkybDFSB2R2fC0gQbrMDThJINI8qpXN3d1dZ908hI0yq4bxI1ttOh78n0xrPYpKUAOjlAsYOS44INi0sFTakhxt1fe0zWEGcikjiSAeOaBIDYQIA8JMB1qjDbWT2s0ps1FU42nr/rS8ii0VYpWmZgHWnyo6afE+IYT5fakZWjw1UbQG4oA8i5ZdhFqv3wDer6VfoHNzGFzd7uZy8iOLZvl8uAuWdH0RIuq4iVqASsruBzbPhuO4siecnYo3DwJIHT5WZLmNGmRQnr4989VWKoQCAwO51a+0n7h5KaInIbmRGqhiX06/OVKCXtNvXtHwn3pmAb0UKmGcecHti4cuefJyCmnyqUCtFa4v3saWp1caHg+aUnzwoUnTG5cO4i4cp3hzAyXlI2b0rf8vPjyarVYwKtI0imLgxdeLBfxHoPi5F4OuM9Bxo5U5zRco5YXW69sLN75XX3h+uBSU3O3aoP/hu5PQ07z7Ne1bJmOkkq6aZV/cKO5cJFx5pk9Dz4WP/ecRcfMiYTnSOVNqrnRvu27xgBuOLukZLKh3o2G7PBuPTxMfWnoaOfyC2hL/OijzLb27ljD5n6rMQydIOXWKI4pmwpm9cNDOIbt1CXkWCoTie4etnJWpDGedHCEYGjUzpacbFGDNqy+AXP9lvzGrdHP/ONgqnXjIGU3sn2S/b1267tQ8yAVg/lVYXuHoSTKoeOKSn1JLT1nSf9YslosUp1imLaVzZAiVCosWNkrYYnbNGOT3ZBSGBzYMdw5vm6WHo1BgearvQwBIDaVjCOXiMw21B8uNpUiRzNbD5h/yMlFkwBFKyb8fbsAptEHNH+62XnBm5vtm1R7uy45x0fkee3h4cxZ65x/27DHCARdWmES/AgkHmaiKJB7RVgg5epQAUeVJ2/vj0RXNcQiBSG7PH8vFcSV8OdwdlLrQGAeJxwAF4nC82NHLUBNENdv1D6PDpDkynJPjpBRY4QEihAmSx6Ek6gBuaDIEAxEIS6uNfZmqBnwDEY/dqMdSj29C8velabMCwbrzXGFXur26BNqvkcDnwoKDxO5MXZk8iITEYtFRJFruyuWh594hvcde+MU2Di6LwI49jZjY4EaPPb5QohYaJvV5E6OSRGV1rOylrJp2N08KFsY9vsMEhkfH/1yMgXDFbJUrChNZEZHA71mXE4Hhmmrbz9GIBAo/UgE5hPZTxR41ULuH2I/vgeLVCt/RBVZ45jKsEnvK5bLONFrBmMJpDedDUJNH3BVolTzy2NaEIKDLaI6cIHgcsJAFCLAmFH282LXBDiuoTbPHVTUdA89rCBMJb5o5hkcfpUS7cOBQ94Y5FLmx+Zfzh7hI3hAp4jkwC6cFIQ/kUAPws89gMGKuzXQieRPEYmihxg0iR5WUuSHNqJjC5294NTWG+ZIXTuhZ1hrbck1Nm0NpH76stWTY2UELDvEhUC9ClllX3BD4mPkTEWwDp4EdCWAApEKVHrn0tYfBuQc7q12fr3BfbWB9ETBbJd6vxl2P+fWnThsTNgVnDXRWluHNgxpROtB8yzstLSOw+Miu7M4JIbH6312BAlyIeHwL3yk4wvTg39ArOL2+OD9cWtHQE668aUjyUWZlNL/iY5PX73h8UF7AjdKhNhrnfymiBB54uJiBBfXAfixqOkDLyxFiXM/XLHJUzBCwmAvRkNyKGTzxfQAuMPJdMnMunRNTFYjjdBNgZxCTD+yR4gyWxjgoqxCs6ZiPRR+GJhwCIFQLDbwAkF2oh0I0IgKwdS8wo8lLoW6c/aU5hY7HtdH8KYodfEej8Cp6sbE9mHTLePOIibBXm5SfOARs3mRNqYtGKAmpDTcZ5YLseZ2IKvwA5X6GNdQXCw7ADcxjxdb8CCxx6+XAIKoMsFhroId7rARMuXmbB5gIVNtLQS3K5o3mqRcEEgIB7B7EiexgKhcH6wuVzD2v5KPGCWQildc6XrmmWexIfenV0r/Z5E1koyZF+Zffxbq/4eIHvEJMDTKHglxIIPRDolKj0kM9R44+coWtCeYPmEkiOZO/Ccf/g9fGJ6+Yu8LStGaxiutYXenckwhe39JxcSHMKGlc9DkqfNytg4V+PKW3OzeTmqteHTptS7UalFL4/euFEvaVU7sLmVXdTmxOAuWW8zOtBJz1XwIkNw2UxsFgx8xgvI6EfZ28yYe9MMTCx4Qg4kQiiMauj0Zac8okScvzOWEzY/hDVo4TMgHvVM+m9nduVn0Vug2N65didiNrROSqUakImQAhKbbYtzgIRbQTBPry9Fks5Rsssv5THFzOJYKRBuDiWbV8PRHc1ZQBGBwVFvRHBUvAEluWA+orlMXktQsNogz2DVFRoE1YpbsRNMdHgph8paQe/IOel1C4joxozix+K1J8g26RJ0iFhPoinPmaXs0EZq4TI9fWLjzLu0//9sol9xUHbQZY2O2/IH3hi+/ssFIbR+R5+KQdue29Neu1bdsVfHIgaoEB3rsG388+sJKee4MYNKbbUnDveeAE7tBcZeWHvyl/bnPxbO58i+k0nVhY1lBMTeqYBXZH4rgLC7OEiEWGEaWYg6ce+jhwMlzVOn5ULEwf/x0vX+O6z7g6HntM1Lxc3YwL5kNtvP2Qe10KYxHFQw+X948W7tomdKolaSdQ8PrhopTTHv29uFUe6OSKGadVS+lb7vXXvMq7qi5Lc3BC8+Lnn/OjNZxjhExhnrd/r6QI9vnne2ee2b2F/dbDzwGEblPPJdfsCC87MKBfCBtqgl522LjeigXsAfDA99ulF6NIC8CupGN5eKf7AsuHNadldZRHe66uIVFZFvFyxCUHNQGSY3NmV3UA6s6izk5lBt0do1K6/utYF6MBijJnHZOvMgzuKksc/kSH54giXEQA7MvgEVUdlFBRfbCy8NH8iRg8vTt2IiX0mfFPzK+vBwVhtb4ESQ7upRvcu+LuM9ttr+wI3NOOmASW5qOAkNDLh+jj5oGTIjH2IkCrJWpIeIRSvz3ElALRoAcwpgoPv2B7ZroQA0cnAZKHjATDKMUdITT96EgDxFBk9Nnl20iNr4l283hfL7A7EMKHpkYYglJkQt/fUVX7E0SjCuVLSWNQwpei0Q+dKpA0gqmGAdQFI9T5sLAQXwheMJHa3Aj9NQ1iDBN8vQMefqJBP8IgTtmyCFsRIkwzmcEYbjH8TwMlWRT/NpUs6Mif0qVz5lPE7/ARAgOapAXcipUPmuK0pQbDmzsLG/ZhlKRmDm9vmXcuFji4S1mwYTGNS8xzDnzSkiZu7F0PfaZZBaKER01g4afrwUelA0uUh7GCv5qDJISFbrDMXtHpzIJfN6fSCA5aLpKIbqWnAiXOdhVLIyE6ybBjqWvMq96Qd6CFY7gYaGovqDYgkC/rWZoEQu+fsawB9EjIzCKIPYBG0GaUPDBfQQDjg4HQQE+rnJRSUEk+hMRuX4JK2GD8RDz/cYxARyDSXB94BIIeIbxAEUoSZ1R+URAT06X3axJfbEXBC8i5rkhVXK62j1rHTwXvGRi8/SpLTg72593Xt0jPb1LyprUkXvRwSJLSkiGvNn4RNF8EWYfmKzAztH9BOHXh45IwzPjK3CHJfOSYa9OVR71ofh2YOi2+sSJGbw3VHdJfd8GRaF1TJFLOCWtNr//XZlkw/ObSAii5gtOiAxGRex3VPi5hYn7MWXOr0p0Xdus5nHiCew7/S9FzCtCje/ubtK2nZ668ZbuLxOPol5Qu1eVIolGME5ljJESfkZDHiKYy6WfKMEGt/wUlaULlCJv5VCyKZzEY5nSyO6NwWjSqGtmeFqgpM6JkVBRQVSOwZQYhI5KuyNdm414g5FoJsJipYvZExu2TAssk5Q4UxGRXMQmT2cJVlbXDzkLmhtS552Vuetejbb14UtpRmj49LeZbePXD9LJPbHOL9o1JkKiIDjRdFE0Shbz4ZStkokFepyEE96g77ojezbbZo7S5irDe7aDjKIF6tpmMX9w+QZRSKTEJv/RD0WSpOSgHUH9BRFZauu2t07AtW2EspthCAXJiMiRnd5/wkcNBjD+wnY/trIRA6KjIPpycedfnxTT5HCCYbL0wz74eGjJq2oX4JVItTDkKYRf61uNyog4kwiQBHogw8R9yBogPwYXOyAGHBOtAYEVu0oodrVXFMeGvwVdmACfdNJJb8GEvalJOuIT4F27duFW7etRGPtG09U1Ym8Z5hEbtS+i6HPNogpHTQb9eTWXqsRYI1oNvK9+Ydj5vtaRLV3aV767SbUCaMUUnISZkY1cOYxKXnTHD0tLHZw1of2RGjw1Vg8zbg4bOPCLLdYSHhFmLhDiTp02kyupFwE/7CSOqf1id6GQ79m5FSGYlqCibtvwKmKmmsYnUvUCWpAQ7QPNCmi6KgwmTAKH78GIiVnRINOKp2wrajhejzgFKGey7TA0OSGSZuBpV+CHnopkAEvKiFTllNjlPgRcKZGw2zAZoUPdu9R0sHXmtFh9IxKIFgsfguKPGFaU8/VWKdKXxmMC9pTJcbS09SE0ycFkUwrXaBVcbaqIALEwBx3fZBpaR0puTQAfS0d2loa+Nln5nkq743q5J+hkZKPFlhIlLZFRFz9YXh1R/vNWzSyjO7GH0xIOCEWi1u13ylNnjTvltGxYjRRL+Yce1rfvwJI4kkgnqsomLsNpz/2vnejhZQ4wi37Gks27ne5c6atfiWezkFigLGV+XAqcDa1RkKtbhiLJC5a5wOxJl9JcTkjKvMel9OONpw/QKeMeZIeBJXlgNJYUrFuD+Sel4MWy0pjHFFrkEw16rJ9Z5lNOf8aQRyZK8kQHx5SbNjlXtqn/4L6yYfDqbxgj6XKpoGsBbceewg9uUrOZ8N9dWVq/Lvftb2urV5tYkll4XPATn4y+8x3DZcd98H+RitKzLwVOO9N2jZ6MOyH0SNAYwEm34f9pcNbX2zauJdM2upuO9P93U9t/FPEaU+Jsp+eppN49gkzBojJkjbN80lFz9FNO7rQjewpcY4Kp9hMuLKqhIvZ/wT3lLoxIB1JPLhIO5SLtEnAQnOT2BjoeLIWIyF4pJ2RchAlAAFH8qiEs5cXRa6cFltOWwEBw9JGkuTWoRN3QKQOR4woRtefSKd/47kstnQNDYKRx4lQDuriAidgCKs40jyXGzH74cCKIQxgBJDyJCfKlP6/wc7PhcSkiCNwcEx8Ai8E72ythwsXYfSv/EiYxqmIqjNgHECLwXPTMiRLFfgC3ChxEUSmUxChYgYDght2LylWfEaA2U/0FHAVxGgkSEcRwkIEEOrICBdauaPmA8xw0Ea2EJUGEVeqniENfBAoGKBTh+IvgYCi9dE3+gudciWbRIhIieHH8UQ0CQEOQQyzcX6b5s4AiaI4omBEoKMgzHlLfWflFpKhSPne61jawM3PzLSOPPhGgeiJZkVDsne/ouPQdZ01tefA122L1kCIWcSUQj6Hlo6zkj0u3A3AkEomEUgE/+BC/hBxJYaFQ08ZCwBdCEigolGEOgYiDBZv7gxSZLEIYqhapV9pAHr68TIxCQDmzHykCiOEYSU04teSEvsbL50N48XcvlCKEUZEQIEpgV6xCDgr1IokG4CwX8gPd28ZNniVj48vHX+GX4gsma5MjJoxjqRMNQY39qXZUU4AAboz2mwQZD5I7XdmxXbmPfOyvsi2tPtWJPgIFykUH6tIlKaQK1Jg21T0hZFE9mQMqxmwZi6vWq4ZxASQ85PHGcFjJoMHObQoVHRWPMFVQocd3dmrOqCYnzMjx4+q/eEXPrfeVt+4Ab4HpU1quuqS4aMmDvfqv1qFL80j5VIhbmvhI1ruaH9KhS8kJ7P5B0hnF2TQBglPBau9ddbGTRkMTsgtSj9yw9V9NKSTSBga4XQUnuM6HBGPiSCkkCdMXBuIgKxpPQPKcqpozVN1ZNhQJTNDkjwwwcCS8HozotOiNFXb6Tw8/IVCAER3iXfyRgySOMGCjdsuB8k5snNulfA4HzQLQzUNh9B/UKJuIR/oPQ8McyjWKT39+jUbwiu3uknGxOVdcWsrm879+XLextIrxkhI66YTkh9+3JVz/1CtOyQJKTgyVLEoNIrLhJPp9B0YrThHKAiUb6iShEzOfRXOnaEFFJyWUwViTa5XxwGpptD8Uqcf5OEnVkTqgolSzRMEjKAQcafyApVEBqBo0IG176HgL/sGkRuyZuyyTzyxSR8+AYGw4oYwmwKs/lKNbIXLBUV7aw2/i0XKSACU0nA5EpdQRYv4hkYusJJlRnlJtIXVwJFPKLQ4FLIIoI9iCrCA7xSU8DEYAVGAIDB8fnhAyAPkLTyyfEwSRQCGhkRQFQZKIzvIhO3kgkLKWwzw/OKhxJREQKPhBKJcvQgI7GWjCx/ev5/wzNgPxGER7ezsl/m/mwBI4shNgvICCR1y6urrelL14FH0u417lQDXBLh6ShjKO2kmlntLpFX3YeKjG7QqsFATDv6hFCOVTwF3SZxrth4JKZ+SiwfLsYnlFVMIJ1qn5uiV5KYgBobpb/WxJwkMgqH8+dcbjIyQHAsQnaATAJK5dcLNG3txg1cByWyXcxAWBwAUoqCckpWdOqQDdxXjxCCGk4o+WbwmAcXnVGW5SY0REPYO5EIUREIEDmphFi+cBuYEItPZOQmDv7t24E9KGm8N8BEjwYGhOPMhMeJQYj5AkYRAihTgdaJpINbVCly/6HgJCfK+J8VID0nowjEeSlGzBLEL7NVhQdqXtGam4fsZpzm130/u5tM4K8cNmQa1U/Jj5XZZKWp8ZIVruSdpTc8M/wq1Zqy8y8MNG85VQBJdsI2VrUX/4QzlZz2ottxdDZdeUc+Ggc8ZS/awzlPZ23Ae2wxG1VKqPRuVMtvTSq4oFzsGnE8ZIAcsSLWbwu4r28iZBCQFgQCk+IgUf1y6S7ZsonWA3sMN2Mjo9ZIBChQb/xCXSHXdxNlDTW2rWtB/qoen9VOSohHFiVJo7kthx13VqMTyFBgSgQpeOsKiRqdOj2HbN6non6digHAKkE5K65+nXO/2P9d81XR3JCj39gLclJ6oo2i/ucRceoxoBpX9AKZtasSA9/fvS+teCP/x+8p1v71nxcqBv0M2l8WQ5tFlhJ7Q18BKKgZML51boAQfSpDIDpW5IkZEOZl+OxM7Kq4nBhq9cP3zTr5zV66ElzAoE9UVL4h98X6ZxwopOXDDHjQAkStOiuA/lZTqnA0mh7EbZpmyGBf+pxpGbAqhHY3rsQx6+YcVmJGl4kMC8GRLRgQ+xSDYPC8Ib1U3Hxn6KdyLLu2K9X2lRB3E3m4BLT0cKl/Y3XNWf0vecM+3B+3u/CqKOGjBxwouw8wdwrtu/e2s81RwMxxg340fdqDjIA9DeOKASEZ6cECk92KsHjHAMdxAohezps1dJhg8sYkEKBIY/8SsQiS/8RSzxW+MJlirdMwVyEEoHTUqZIHHtUSbJIQWoMF46Cadgj4QHRPDAY2YT0PzAhVQxtmw229c32NTUEPUVrdXSFy0IoSbqdAYT6mS4HRG8VpiicMIr6HBygEfT5QCOUBN5fCg1QwWHNxwIkDyrxJhJ8iKDCPivKw61IV7rQV5QyMNzTgHlQVYdY22Ih/Qi2tFtakt+cPQnPzd//QiGmaiN2KtRctn8z+7ArcjJl10xPWU8v64zVj8uEKpoKBScEedCUGNxe65oJKCpSQNDazqPeUBDGHjbndJVTTKqBOYy8Kh6cWEDnqqPj5Q4EYaRsXVM4SASVYiqA377YiNA9qVPKtXoJpIqHZVgJOJLIAKIch9+3o/w569H0CuChKpiqGBy+avx9awUwAbq4nt7+hqaGtVSrjw6hKNGOEyFeyFYeMMpXSijoTKDSEReRKEEs5doRMjpEScogqskwCfGfvD1x8Q0EBYMMEqyArlAz5Hhgy6pkpB9LTVhFJmbazydgrwk1JTBlbLMVRJ+xCalxGOKSe6L+BA+iFxy6K0g4FNjJvcrlBphQN0yoPWXuOiWm81TlrYuOM7IkD4vKxobitQ93K3+8EW14AR8Yfox6ZdaP8QcF9yC31KfUdgV4EcXKmnFWmwos1YLTrSbjO48KboMo/SOTQiJmHBRSj3jlnNWKafHGiuQlIEUiETgFxmMWS2virIDThaSyCGBhGoWYM1SveaEcDO7kma/4mBew/MhaimE/B27OOqUc1hHN3M4TTCqGKaC0VMcTwz6l/URmV658bIE3PD8nCl5/l4qgNOR9ev+UPiXxROmf/wj8XPOMPE4sOPoM6c602dvjDR+/3l7dw6LNUgOonA2U1QSBk3/yN8zKK8uFnh6NwNSYM/0bQZINDVeD9IetZqqJxTlsp0biSQbVDwkybM9MQQnkeGPkeOKd8mQZ602Y5hBE22vJ4CguAcgoIApz1+Ns/xkqCZBGqqzeUpgd5Ni0zPSxBj+TAyRValoSZ3DQCX+0ZIo/gEpELGFyOIPXrBwv0A0YbjhAzx1lzQXpogiDsoHhxIU8cA5LgIBI8TF3hyIKiLgRM8rpEbQnDaus7Axh4CkyTb1g/hS2SVdSiRwIkUDMvqikYcPMcYZAWZoNVVkilB15dsp62WpO13MObqlhTMlm5JPobz0wnbiBBT5y4UMNvKryd9KiuAv2PdhON8oqhC6iEmBiF2BpHDPCGK+q4pM+CBWBUMF5o1bBgYG8ABeR0fHG4/61xXjCE6AUU6nT5+OzmHz5s2HOQH2u1UUKC5SVLCoVmDAhuyiMoPSz4a0vHGZZn9RmBCJ2hYuV1hCE5AUhDLrKq1blZ92FN8fljuN6Rlj+ggAgczB1p8T6FI/3qN/iFahPPQciaJ5FkLBVsaPB05p2lQskaJhvzpRMNdibxMA+NGog7yIIlDpRrB54lTw1te1G0sG9eOnMNMKLvcCGNiYA6xkUjrpfDd9CQUHeQDY6UUIezOHCKXUUgWFwRf/hgfsommPhokFAem1VrTSRxKiL8PX2j1hyWqivimRajJCIQHCIvWQ0w/FFLBSJIhnBdAuu6oq7RmR+prjEy+5IL1jm/KHFwGHszvYbXLq6hP/9PFCc9vabnQFEBhYQvvnzA/drGJ/tBTs/XaLujmpmgUwFygGtNvcUiwRumpAaynIpwdLq1v1a67Vp07BkXcJL9wmEhLOU4V1R9eUEdwPKovUEEcgF7ED1yj69Lz7kqcum0atbhxbjqpWCn5KLQ2GtPtwwMgxw4qBs6Fs8KPh8jCKFDgASizafj2oT88Cp+vMTv9nX/Qj/yY3WHL/TU54C/coKJPUjiMvcIPYHdGKv425jR9WltytyVvghYFIYWXd0C9iTkux4cJcsB2PA70aO7Vr8HcNsk3nybFmoDh2ANLLZHMvvhQ5++wiXvfu1nF1GYgDg4PFn/w0/OUvhRYvLP3qYb1tYknXM4NOkTa56HaQk5esnCNDaSVnhOPgpBzEbdhZWoq1ZSt/1FGJa46VervdkbTS2Gg3NgxGUk/vlvvzNCYVIxSKywJAsoUFOKheUSnyyq7n9soKEYGhI+O+jyh1FJ3KCv1RB0dGFDy/BAmcPpmZod8EHWgcVgZ/ltIGScWlVyRdvXR/U3FxLjht9Kj6p/9gRcsStN6DZS43gOPiDacNpWhxnFvloYJAy5WCKNcYAmcDJB6PwIFrablRnFwLxpIU6EfkFAjmRSzQJc7GGnJX/WDzaOCHEkGjVyIBb47OLoriIeMIvif9sqlBCDgSKvv4v0KmLFjaE+FATo+AogYSV9f9DQ8/zYwZ0MQiWGPG4EiF2I+QeNnMjQJD88fLC44QwBoctUEE7EHQL9Mln0oa4ekDcKD4gEn678UkseCQXg6vkdcaUYY8v1qEmP26oRhE6kyNK+rmHeVHn0IDAr4YlsSB2/u5ex9qWLq0PTn+d4V81LsjKHARPwRN4D5vtXTZjpfGgjoeezukoQFWBZcggDiEl9Lo4feqAyGroSjoe5xQWJU/kQPkJ7isicWI4T/GiwArHtV00Xtp4sk0KnlUgJgEAxA4yQq/bCUUMKImCTtBU6FgzCKdVDZgaOVPWMZEphACN7HVBWrYB8OKsOMO9uGcjG1bJl5ZGO7rxeAZCiLCdVTLBBZCSaZCqEJV+BPvoM8A9COMH5c7fRD23d76Flc8xshMcZsADfo6TTz2a0g+NUHCiia9AiyCq24/YF8fP+RAvyT4qkHlHTEbduUmT4sMJxYXen+el/O0DQtuAAj9E6kTC3KwiBnyTeuOeqUUOHVSKy4NIb2dg9LvVjqvDWBKi+kffMag9Qmw7KilRPaDVU4QNR0EjCSjY4KFJqggR/MjT9Z+dAr08ProCbCct9O9RqyBEu/NlZgD140HiqdPkY5vluoMdfT/s/ceAHZd1bnwqffcOr1qRqNRb5YlW7YldxsXjCHGJpQYbCDUhORBCnFISHhAeEBCnoGYAIEEQmiPmgAGGzeMu+Ui2ZIsWc2SZjS93l5O+b9v7XPvjMYqbjLmx3vmnrPP3muvtfbade1aDraMBbft1saKuAoYDOJkZskkgl3CalZ9Z8hElSTdabj0WNVK/GKWQ+NW8NNj3MiNPOjl9GIGWp4fa6WqV2WPwM/MgNBkPvb+m0tvWt104fIzmlatxzjjpGbcMxj84NfaoWJUqljBKw9kEdUoMhKMT0iGnx5GKRaDRy8/EVSKZkMnvI1s3OAeCgWHmXi3MOHFLc+MoTNQDcyU4DQ4S5VA7jH0R1c5F+51145jAUJ1WFLAhF6VH8oTyWmMxrT7ljnDbeg74NA70QaZslhRaGFiHQxPFpRoyKbiOFRvjyQlFCfJFUreEEAYhPlC4IVvZWfuUY7kE77SUZC+ENEgqHoicjDghG0K3TkqQUkKAFKODrQTGz6Uiit2uFD3RkC4iydzG9DyGw7CDS1wlH/O98OvJnNN2z+sDxUbU+nkQU60MIASvuolcFM2shqxCWOkBT2ZZ8TBgC1UGLj1DExwdJUtDRnimgWWGbHTJaCg4c5/7LmHlf0hwU17WDgVtyHX/FCMq7wgdvLHAM/D7NmzB6mPO0GfJ57nwcJvR9ATqABDAPPnz8ey+127dl900UUiD0n80MYEVvUHMjKSH5UE5juh3fEfO3pl1y5cMC0AOxyh5shWXk4joZNa9oI4dBn2+5g7sUsDubfg+mWszMNoswRReQswLKZwYsEJTY2VgnbOtHNzb/mzzdpPI8EowDwtNqWvGbT+fNB8TeDhslayRw7BGFmVFTXoVdKdRz6IL6xBxTMmvXglo5lTmM8wwTYc+RMMrAWJgYzQmW/+s7TSgWZyMnBLQWECVsSGLqTNF6KAONCJFoZQ3rWCUoNTmFSUCR5+Y+NnoMebOqISi7aE25bS47hvoaIN5YPRDFbcSJ+QxMSoYGx+GEeSx7hjjNf/Qg78YNmiBT+RK9/8FM/WNgwMh6akWff1lWO9vS1/fV3lvHu8h7a4pbK9oDt1yYXlBcsezcQOYPUxKw2ExD306U57M6qn7OMJYzfIuY5tOpzucXXHMnYsxwnThpEOXlUf+cProcLlcZv2j35kT0x7Eds962znzz+QOOV0PxEzF833d+8TQXs4Fku/Kh9dyg00QZBEcwBWQSww6oK2j/tDHzSM8eifGbk79OiUrl/o+ImiXsAKYQxIY6e4WbYjNnJfJaicH0lsoJ5Zif5e8ZZzond+zXj3WbrTld8a0yf+Ov6qHBMD7QC03P9xgm9jy1uz8fY/jm/M6MYejl24FlrqyPyKt8MJNicG7m3o+puR6LrJ2Pq0vi4WPJxS2ZM4kEbFvJnPM6fYllWXctNZpgO+Nz2Cc8jsJT35RLzh0gsnIwlsCEdmy7jtQRTDzL4zz9BGIphQACy2HjCapu8sxIJmPR80f293YkV7Q+fiNsfQscn3YNrf1hfk5QhMss4WjokYprfqGEkyAg881DN8SQ5k2ZIgSD/4E4YueKEFkJDKG7klzDaEQTkMgwGkCojy2Wpt43Gl2UhlJ0YhiABg0nF2bcMqbEk4y7IJfawuMpnx6yulYiEzUdfchk4VbuHiGQB6gGlJzi4itRBvWUlBFDQkGBYG2KX9VF70ZxFlNWPhXBIsJ5Zvae8kHACk5gCk+pcQxAGEVTYVGiU++INaNT1FDog2ISgNhlH88JsyJ2sCRYfQMDUkCIKJn0hYIZmBBS6iEuSKAllSKPiNBSVYUlB1UgirFOQtqSWCwRUh4hLSk/jU0pA+dEFshXlWxVU38QiiPM8FIISqPsXKQMox/MQLa0lwrhz7RxIEuY43sPPsA0CS/TAKMyFCd+WAhSQulv5rXsI0yvue0itYOINGoQbD0JXhEW94xF7UMTpdzuFOtKyJLAm9Cy0GmofOFDowc7maoXaYjWBPS55qERFI5VstCsqJEZtjwuiKq5KsSqwjJMthvM0VX5jQwBOKag4pfNbkJ158iAXO8iYLCgSfdAQj9KhSQoToLZ5hlVANCBD8hQAAmYtS0FbK+Ym+nXp+JObEMXJ4YNsDgVfGwop4fVN2CuceaZF4ItbAsx5nOK1a6aiMoJKHlBhxFOoApXOYhCJpSlJSFB5Um/CSKNVkxczNNQ7smwomecwQI04JwcBVOQieGRZnoGehqCKDpwTjm0FmQGaCKZocqxJ+IXaC0l8PUr8YuuIDC7c4HcWu94/1faHFzsZRGaIpNFanu94z4hnBVKX9nrHLRirOPkzogVeE5VOPSNMt9IR4lSP1FsFoT+WXXaDfFG2pRBcW3D28n4K1FzBwIXWpfi1PRz5U7CnrSWiqpcHUAABAAElEQVTSwjAe8q5iYyUSpja9MIQtigFzTqj6oK7Wg1NaCx/cYCzwpuvSk97gtJ5Knd/T+LoljV96qPirQ9j+jZKq8j7xhjLCS0Qv0phDNiRPf4bT7fo2LdUCXbE0uMdK1OmpVhu1s8nNa1VO5S1fM5QO86PYgMrH6RJ+0FNfOnWejpr/9gPF0byx9ZB1qBAU/SiYQw1FNCGXgoJrZcQNKCgN+VDIsaQ53gCsPgahvZIdb6BY2EEl2woksGJO5yrXNF1PqevCJfDgjfqUGlZospr2g0WRfXWVS/vchMsumLBc9VYCo5v+eLNx4wJ7GpkgXQORnEHYBKYHzkAkJKHpysLCSIkEqtgQk1mSqsUrdAWosA8Yhp5j0MbAhTFVjxBCBKc6dQwc5lcA8gs/opJACh5PAaN/jUeCCTXx5UOFUCMCkjbUNolBsY9z42DhmWrKIpoqEPan9YyXGCtaNhYpoSJg005I0ViBIZxJhpN4gioWqwCCAlDjX6w9gAi0wHaYTeEE0ootetDGngL5Rz7HTyJAhVnYYpcIHVmlQtMi/2h14U79mZ8YrqDizU8sbBAlnGDUujHkzOCMYBWzCEfFnokg9CmiXbuejEQiz3PeURD+//xxYhVg9L8xC//I4zt24/oTH9etso8JNQw9FSxEki4Lc43SJJFrpEQwbVXFzHwtBrfXQpF2ojGmreQy5YVMZ1peb1JvjgQmzveAhpxwpjzzwJQ/XuQGNQCjVoHCBgv+qcHyn6M/4CT0RQ4OFmwJPh/x/3dU6zewUyloy/udLjIbz8BS0yesAfCvirq4gQVmbPTcwKPkSDzMjp6luGEVa3NYkEL2wSYLpAIjbUJLNFh4Zgz4aGhqgXptIeOHRQsRFjSsuCSYeDC0lDxGQ1kUICVIeBBRnCOMAkAbhUIVRJwG2ztjvjffKMdyaaytrdi235bob4/e32eMl9Q1IgrDHN7INeVI/PgHH0BZxY5PsZIwLfDDU8AQLtBHs/bNu93TuzvnX36l/YrLbOgrkeiYFXt0RN8zLvWDiMLE5QBGJapPoPooDehesZyI2ylM67slDNAZifrK431OOa5H0/ENi/RCZ/ZfPh/53vejpoXRuXLBd554Iv+xT1of/Ujk9A2JK14zffcmbXoKPOdiXsP5JbRMXqVbr1xiBF/HSaCQq+H3u7lfBtZbDfd63C0QXJjIH3Bjf4FRPd8vIPXJU2TdGu0bXyv88pfawJD5hkd045Af1GtjbzG++s9aNFaZnrbb2mMnbZj48iXOWTcZDbx/KAgcP/527S090bPPNhb2BOk3EuG4k7nJqntL0Wwq6y1ecChiZc3811ui/5zTI5X680v5ra3YA2xZ0LYRYeZSY/kKP1/KjUzEojGjrdUdHeMp3G4Z2xMx193wF39UWbbmcUgP23lNfb/3ytOCf9dst+5N6eIXkjjSzUA+4u7nwDhlInFyBiI9UDpnsth4Xx9ZlNRFPsI/19NLfY0EZpSZhio14StwTEtJ/qpzDUBZ2CCoYREJyiYAaCRvSm5AeElfIFHFCfSYOWaw4xMfZJacIK9z513oLbAc/WKRVkNQ0Il9H/pvenQ40dCCQe6J4cFieryMyYFSNvDdKbZyZveSVboJtYfkBYmy4Ck6t0RWxTQ7MTo9OgCWUCH6ucJ4dhzFpWP+YjXiUw1GlmmHATrmdMZuxlF9KDdxVeV0DoBQrAIxCO0hZLWwC6bZMAJH4hSh+M5+zIIUXwWCg9Nyhpu03Ya4iurcIOS/GhTvEHE17RVjc8KILOFWDaaiqYAYELzNeM0KC78ZWrPcxRXc6VrU8gouPkEBSChcMXzhJ18zEbeNoDluQuPGYAe22GEtNtoXJTwJRUiu/0c1XCymMIuTOeSm0a2wm5esQobHWot5dcdRgNPpzODgUEdHO3Z8CM5jPmZYOybYrIhJNI8BzMJ4FFMtlTMgobBCeKZ2LfDhXoCYJaZZ+EMZo9pGSIiz/6ld9Y0tqfrmsJJnQHoBDghq2OkQltMZN5RbHPXQELVb6xIR08JQdctKHoSBjR2F3HRn7zKMQ+DLZCMpeOWFBxsNmpAZ4UXxL1kk9CShaiTIjoKv1hYKSD3Dko8PlbeBxTF8LB+QCCjMs+EJKN8qLrTTFhYM8Zn1qAKp2jTkBC/1j6AA4I9oxFRZpMg0rS1eXNepd6V4UlHftP7IoP/fB99zWceNK+MPN75iIr6iNH1fzM2Y0SWFpg1FPYLto5H/2PuX414POsGq7lFPRpIkSE2RkefsBypd/ddDv3ft/K+i1el+39iBj1haNoV4obpzjUrr2yainVhQbdwx+BpLV/fTKlSS00TWSuDUBrxyYRDbiLA/PoiijA1sBzt6osVp6EK7srSx9LGzja7hpyb/8zsjt99p5IuBE0uee9ryd177wQ3LK/eXHh3CiSDCv3ANMjP1jaIJF7rO5l/kVUsG06aeiAYH3QXdsrA0HDuknhZzUoGADscDKIqeD/gFKbPyjo3Bpe1BQyltu5i/0PNOfMeyyJcf9LeOSTqq1igU7GEsHeUDczfI1qyVmMMlQ1dLEAmzEjSxwEzYUg4hIsYZGYTtrBgl8IdaIwNx4/cOVrqzKFIhKF4CreVs/Y6uyEOtBlaBAyeCzIqusiJNqOBhRokTtCIQyf3iqx7ChoglpDxDRiJOjhQMPWpM1DgVcGGXfoqEGiQQYORPQUBiSFlFiHmG36zuxeCLCEUCrIAkEJ5EiV440Uq3vRpFuFc5ISJ8UXQhekpcuYY+jW3dycZK2rUCdPz4JxkAeQi4ZUq/ikt8SYopR9lVG1yFEWKGxagt0kZiostC/tADFX1AdFfCAAldqLjyUxDBRmmInZwJlyhSIAMGKAIRAJoudtZJKmztxVdBQRBqspqL/IEWXXbAQYvmJ/KdoeO48+07d3V3d/9OHfpFeT57c2IVYPCzatWqzVu3H0gzcZl0MHhIPkWiMhuKUorbuvv372/p7Mb2UTiiN4M/rleWgrR3T19jS2ei0aELdVf2rfGsj1Re0WM2ZkeCJ54ob9mGXY3G0gXNp26ItrTfnTOeSmOagTSRq0he8q48OBLDzAV35mBkNYy2oIpoKuiNCIGKAntwpUiQV2ZKKbnkm9yj2DAaBGEEWC5h8MRiQzOCnTwYHVVOABYvFgeFRvDRmfRVwJkeLfiwkyh1clmq4lohUMwoO8VItoSV0Fs+lV0I00/FgAyqAOheYPbTci9aFMybHCzffvvU7XfjQh2juck5f0PvZZfHF3XesdefrPDONKCigJ5mam4iEMqCRmIGOqQpP/oG/tTUJNS5ulRKFm4H42XrFlxkGDEbYkmMtmZL+kQe17Og+AtWYReDypi9KwfJiD5tt/pBwkxCGcxmyqOjdn29Xl9nVNJaJR3gpl2r2d231/7hjyLUcDgIYTTWa4mENz6W+fYPWpYtMdesSV73/onPf8nCpUExN9JaoACMi7VIG5Kv+HChtK8humjSTH/fN5oYB0we/q+KiQt7IyXMQ2cOnqZrtyL/ZCwbN3RbZ5+r+UU/c7nmHvKH4uX//X/Ncj6b9/3NW5vaOrSo3fzuD/rGAU3D8dqIiR2/4hqtfqURj07/+DupRTtwhnXlMS2yj364W8mLUb1DpVkZiFcGHHtBxe4pIN0lzTA2BD1PK19wbnzF8uyDj3oFL2fiDLF6XISACzm0lSv85obogt6CYe6opLaO8Ng0JML+ygWD7hkdkXtTF05iBLT8/+LeiKOnypFzss1vmUAmLQd1W0vvha6AVGPel7wrOwmYl2BQNTP9yEaYJipbETQEmZX+DCE+fMpPECjM4qKaNkHJ7xBMAVTD8E2afLE0lo1FXIuUqESWFo3tcTIDg1yBfU12yVlTgmQqQUNjsjmBCW3s9kkE3TjQzdRajAav1ZkeHz2099CSeUtxOBawJhqp2yuDN1ggPhY7FnwiDn9a0Y4WEs3wzaQnLdtOxLm+ur7JsnAgiQLjE25KVHRDsa0yLhDyRSqhrIicVIQiAswQU1b5FkT0hKkxQ2stlAKjH2zyCp8UbNVRuatqiWJXbOZxo8pIujuVnN8+M7HAMEcwSh7wqEYQEiLW6qcEIVMqQqHHEXxn4Va+xCI/Sn3GaRYcAZjD5Hg8uteYmQEihllG8FDiHP1ZsbjSUBfhUe2KO0XH0Hp6rY5WLBdpjevJhmZel2IYqSQIoeccLG4MMNVfM7Osods4TogbGu9NNrSiejiKqcW/mjRz4WoANUHOittMZpobrPY9E14QCJeUVZVd+FetIQVkncMD1fyPhLTqBv4VqhpCa8xragyaWlVwPGtYATkH55xPIu13g0hP46rVi1BNTk6NL122EjznMumD+3NLOx0zgnVVTCYVUnEMHiQHV3kiwRDzLL0AjKoCLAzNwNRiLXwqd7iJOPAlrkAYjBeCDhRuMeRB+cyOATvHkjTsBNNSQ02GJCgeIedV3xAj/BlcAUrYalqhPCEWAIcrxrdfvaz86oVaW3HacguY8sl1R4dPrvvuE/G/2/alf1z97iWJx5yuTPsbcNkDKjA2bm4Q/1bfn/5w6N2QHPvsIjnhX8VEhCmpiBjNYgZ8giDo+ttyp906+nuXtX8vuTq75PMDoz+pL+2PGc1u5yVT9aem0UAcKq78wdDbLKW0sTKHoQRUlFXlQkzYcIvqEWqDV/Sx0DeGYxp4CSI2Fuu6987T9PlT/UMf/4z7+DazjAF1UyuVirfd07e/v/ufPvKOU5c9eStubsBufTEhpxQLv/GWHyIXSpHkFaQ86atSDPBoZVOajR360EDIJwMrZikd+RSsipZ6Eov4IDiG2v/2Qv0ca8K8+/6pn9xc7DtkJFOpM9dvvPK1Xed1feK+wuMjMVFOEEBkQKRVjHCjlbgON4iSrjd1Mh8AWgIwlvifAyuYZoWltwh5thvz+nDK/PU8/9onD/MkfV3rT5qbOrjIS3RjJU3BCz/ShB33cagJFdbnDAUPFRGCyG92Q0bidFSG8hR8VQe8Z3xnOUqOrPqozFnzlex3lPQUFkEhDKpoCfNVZEQjB31IhPkVMqQSGWBVBiWnkF2VYQmpYiwxx+An23JcIypL8CVb05sD6gKJSVZaFC8SccUHACh5LnamNJDJQZPccZweygJ8XC5NpQujQyd4qNSCi4iQGRqhxM4aAEWYyUEGRE8W3LBB7fXLpYF9T3YtWBJLJqgwczs+pwVgp4qL4ifKBHDRlxiEG+Y0MoFPVC/btm1fvXo1eXrZHFMCJ1wBPumkk26944tjaQ83tUDF5DJm5BjRYJHbmCtodCxgHBrOZiI+Lp1BhkNGYXozczDTiELITIANpVxuw2GUwNaCs3rsltGD6S9+2b/5Dlx1gyKBLlBx9bK6v/qzC5auDozyVMGWVXUsxqTJJ1Y4Y/2JZEhmbbghr7KuQj2AF1etMGeSOxJnjqIHHckOLXDhgwWMGOnCP4yJ8gt3juEaQcx+ww0OAsIgjABDCzl6KUxSxYgPcYJoKBOiYoHhc6ZnIAwAJU0VUD5CF3IzxyAIsJD7IDi1S++cHsz+21fdn/3SkvohGBwqbt9RfGJ3x1++//Su7luewjw9xKsYYsAjYIOTxA2X+2bTE23zFzP2GPUE62KDJLF2Kz0xiWP3mmT+RJwRzCy4ZiGNIQbBi1ErRpEUqg+9oteNVE6OOwPJU/LF3pK51ywPjxheRRubwmY2/SoL2hFF4pwR7NkbTaWQOXwcyJNM6IlERY9kcgVt+3YvW8i0Nxvnnd++fGnx8e1GZXdgfpJUGuqDxCQEEj2tMTIffeE0ksMIxsl3oNtt2CACi+Nrb204+X2ucSuyhnHgUDpX1FP1GQwfFN0mS/OmM6XhbKniYUw+uPGXDRecYxqW53pGfZzRQFT1olfpM/UV7oGD2je/q3+EawQY5152K4KKZedtw5SDpuGIOwaR21NJP5mwvAr2p+HE0dzpp6fe8y4cS1X40U+wIC1TwI4nHHWZ9OP19jvfHrTPGy7pj/enJwK/PYlaEUSRZrHHtM83e79vm4dSF06kzs1yEhvZP1pCLe9r0V3mh7GAeyGogUvpRUpeQmkid0AhlSnTgSDM22GiCQAedJRuEQuKEBVQSTn5ZiD6CEKksMJDhAJDIizbxCwg+GTuxpMexA3LZX7504ZRbHzbZPYfI5E0RqKwYARnh/j2RZPR5TmMU02aF5WH9mOhBDZAdKU8f3g7wjamUl2LesYSXmXIXzEv2YDj04UPyV7CLKNMN0ZfDK01VpqSepBES/PUnnwsHu/s6hIWQVyGzQUSgVXBEOZDGVCSlGcYC0QBzZOiQEkIKBxgRHQSU5INeYC7KtwKUCAPs6IHBkf1JHD4gyUEY32i0gpPoqVEhSUtl/Nyu8YXJpIrOzoRThkJph5EXeOkZlGIq9irwQBZdRoaGsWJcgsWzJfAs6NIYEBVIRlAAtWYraIIsc4SggJVDiHUjG8IPvcFlrFwUffn9yTecpX1jW+xAaAQYPQSztR+1zXWvLb1dtRcYy9a1FiPMwJm4ef+rsMRzqE3YLnGoem1bZX5PUcYPpgT9nBMR/wC+sMozP0+YiA6ziKFMOqLrcpsMwum5qPaHgUl0DWKtbAAkZDSzAgk7gtB4sIaa/Pq6/N1iQkU0bq6etYOISqRMVHMKtYhRr6UNTWdcZzxdW04BDoYN7WV7WyH8ymj26jv7oTGIocQh6GAR2zhA4Ah63Bgczzbc8auoKRUCWOqpRJ/tuhoyZWw4B56kY6+ZQBjabw5FoYZaAY7KClieNJTZRB+1BhEbaJACKBI0SY5idLAv8rrLLDCBb2VUU0e6oHAe/O64JL6nPPY45M/+ll5157AsmMnr1r8hqs+sGbJf0V6/9em771u0Xcvav9xl/OUwR5NomKtH4y+P9J63mnFoD8DdBJrxWZIHY74Vk7gserH6omsYCklkvT7w/+0ID66KvmrSHe++09wwhJ0Acxc4UI8a7I8/wsHvpCMz6sjOCKi/sg01A9xC0WKQqbVdcPJTU/mJoYae3qli8UAXUlvXcIt/PQef+t29H4wnM3OOq6rhSz27c//4ralb51/SqfzxFiIXqGVDpgiydSAY5he/AirWbiIO0NImtFiN3d5POeSc18Mz/hL+sinqgdV+koQ+NIDP/yjh/L7K70znbz/wx+NfOk7ul/GxiIkRHrXvvQjW3o//rfvO23J39zq5jBKRkQ1CSCpZXESyCkqeNMihiwIdmnnaRcXJkENhq6CcMZFBQuBay8VRAgHyzJoNhk3BAK0CgoXTNs3eX4WEz01H8WNBENPDjpZR9JoiEK7IxMKgyKhJBryqMhUGanGaCbhhevaQ9FX0LRTKMqNIAhFr1r9ylSke+hSJSJO1cdsR0ATm8iMIYE89BYaVULsi4uHSAWIBIixZJNIKFgE2OXRA26Ep8yiAKMzRlfFJGZayZfIAhZhlD7ABURUX4mDI07kVNRlvPHNT3gBk1QJXJ2M/VOww8jWYRZRBCYQnoKPlRkcmEWJk4b2kBYCkq4uKxUDnFvmlYVJpCEUFykkqNEUIwhCnJKe5IEsUVWG6gtGsNX+8a07/vDaN9HjZXNMCbwYCvDUxGg5OxxPdSJtmMjSY2Ne488oY+kyDszAys640RAp4WhQeFiRGOa1JkeH85lxHOSOk4GGR4aGx8bRvWzrWhiI8rC4yW8qZvI/+pF3482cLsasreh4xtZd0zd8seGjf99TP28Ul8hWsyBp4596KCcbkJUk80n2ESjJY8xgrHXBHLOm6MzMbMzzCAtv/FAbwVcVBGKhYZ6EFSevDh4aaG1tbWhqxCdzPCNEcMn98oaVIQQHUIMhZWV7DyN+xEdgsqyqPoUj/BCCxKJMWJAEb9WNBEIHFk9NS0WCxbGyf/dDlZ/fBs0z9FQs/vre0vp186+8qjkeHS8wVyCAsMAUY9wVQRWIKUjWPJz3X86ztyFApEFIChlVkG5bhTLOfyDHAA8RULQMIJUCHRlhhGEnE2D4MLaV/3BB5A4jVop8YCD/D1H7APd04yAQd91Y4hrsE0Q6dmh1rzXsm4J5nRh842mKhlkOjFwJ52diKyiGN9yd/RN7htOnLu5qeyUOxz3Lnb7B0CeM9FeMPDZBB1t25z50PTbWJKtSAPUqg0Yvrh12ra8AcdT3dr/rnd1Llzdd8weRNac40cWau8vuDvLJbLLYDN9C/wHcH9j45rdoZi7QdoETwYLYf887tDzzH9+OD4xXRo1Ig2ae6pun4OZhbE204/mYGcX6rcBrypvduLxVN3vObv7S37jbtgelkrmwx162XO9od/cfiHR16KPD3AiGAYVEKvrWq+1zLxjynfue7MNhaQsX4xpn9vMoUWaSkx7J/MfS4LoWZ6tul3D0q6SbVjG6RpwPl8y3LUigH8suJQNIGKn+YWXySvEkIvgKDDOVQg0Ll+TQmWUDhQE2IS0VuQCJ/gx4geFbwQgqhVO8GG4WWiDhJ+kw42jaScHoG/TMN+ylU3Ufdd2b6vUD8SDuWqeNxy7JoY2pWC3tPR84304hh2HZSC6XbWhsLJdKhwYOxqdzPU6Qa9Zi07tieqqtrW1e54ziJ2RBHCSEIKzhFz5DG0pMbFrDzUrzUVcR7JkboIWZHUS5SMREEjO4ABV6Krcj6FczwLQdBn2415yv2QxouK37oo3rEslEXfyYJJ45eqE2qZVxyVaKR0DXzLFRHMaVhAE8HJ/uXkMIy7F9ZyANnBJ35evKTtT7yc+t4SGEKy3osf7g6si55+mJJO41wVQPpnJiMksyE4y2Y5HA4mpLw87z4LCIHh7+2XwdN77PBtmxYI8WKbjXkuloMNrk0OTo6CgEg/qpmJnEPwpyR+Ma7Os5jOZREYRQqxd39bTVt7YkfaxWaU2mEpIDE8kFzTiC4TBMx/xQoCq3HBMQnoCtxU9gwTkdqqWP7TsuKZAZFdZiIXQtGwN2JjzCAqMC4oQNDV3YDwirhhkUikuJF0PwU6oz5Y6nchEUwbp279zminXn3QOf/qydK3I+Cut9+vqHH9rc8YkPvX7lKY/31/9s/Lrd/nvfdXKmrTQSTZcrY5XWRP2rWkc3nt7y8/2VOw5g7T9ZUuzyGXYg0F1RfMuQqzBC7OyxAEgb91o+vvubb5x3wytav11v9rOd1rVC0LR56tKv9/3tQBnna3C6mfwKvLDNaIKYNOy0i1TYfUD7hWOnsF0LLRmqcQD0NmkRv1h+aLPpFUCXHS1U014JL8syi1seb7i6sKI11o9TDgULeRWERCtSCpOM9OGm/hWs9BPESQRGANWtkOjDQyJDNJQF+yTkGE8gUV540ptahIZ7C91XLTZiu3YNff37UdyQbaAL6pk6jowK/Cf2TP/4J0ve/e5zFtZtOsSejIQDFtIAVpkqJHJGj/gVCSFHR3KgaImV3gSVXETGxACM7oJciZUf4sWHeCsSjqctmUQXiNGFVHGJNU48Q9OOTlFdKViY9Z5sFj+GQggGpTcsmOfR/STWDCARVHNMZzFVSiqlFScqmYkADCkPBYs4AoLIa67V8HCt4VRMA0jig6ACxFAMje9ZoOIpIaWkCR0+QEAcJNvyW3AoRMytEg4PSVYgVP6KBfqyo445hSo1Dg7rU+ncyMhwZ2dnIpWSzotWKeAcUyOC1aZCgfUE59vIJchDkwy7JcAoBPiW2kOiBuxMRiUUZUEBQFDp/hEL8OEcTrypujLbkFN5QqNQJZeU5Z8xBSio57JT2BqIZMMMTy4zESlnsUsk1dCKzX+QCX7kizFHQDwZkBqDOACd/GOxu1acmhoZPLT6pJPg/bI5tgROuAK8bt061BL7dz5Wt3oe8o3kA6YbkhJzd9NjhwppHInBaSh0OXMHDiIVTdtZuGwFSiM0Qwv7JTB6xc2MmFQ1MclYh2k2XJOi6UsbjMjkRP7GW4GJi+dYPbBAYEZFf2Sbt2vPvI3zHqwgQ3G9DTJf1chQNiHFQZ7s0IeKARiR7EYGpcYCAMKiflfw8iFOgOBgEhEDu5QTNALJuIN8WnFL6DkxxwOAhZRgrHlDPmCVzKz4EhSkxx9VbrzwIVND8oYHQtNRtb8MUJ25E2gCwNBd4ZEP6kbKEEugNccNC2rTfZssbpirAcITxyQZ5fs2xS99ZVs8gUVicCLrVRBKl3biJzQOgCziVEHUNDyIXy/l4IpjPXGFLQCV6oDGrylqZrPFlIUVOIgsCjDDivQpLZZkfM+CV0UaPr52+YDxxm7/m1ZPPv4Fv7xN1ycNc6EeXVjG1AFuiRr5ZmPb2yx/8ZJCYHvY1YEpQkQPRx7hvCjQWrrUjKfa4q2lWPu2A0/hSLTeRb3LzNObvTtwRHfUwcShsWNfEf+M5IwBN0pgOILsUTizxnGcnt+/qvLlrw4+sGnepz5mXXCZFtyiJ4oN78pX/i2plwwH42033zo+Pdz0J3nTnMCCI9+oN7RJ072xsLvPvX+q6NnmppSzJGc18txKcOnd3JAKHC+CLUxe8HYox6gqLa31amfB2shJK8ARFg/gGkhUps7CBfY/fLj8xC6/v0/HvOSqlcb87nIsVucXT+0MyiWjp5VHWzkOog3Jg19tdGT10OANTd3TWu5uzT2omXHNOcNMvWqe2TGP4odRGbkWcVbHlEOYH/BiHgOsgoafVPoCNfdxTLVqLvDxv5Gv2Eto/z/l8sFI8Vd2d9p8N47+QmcLIxIcPXX1OqPji1ZkxZJeOBpTU1P79mUWLZiPMY+Yww46cveqFcsx1weeY04US4qOQ7UWSYku4r1w0UKskmL2lNx5nOAz3jOIqm4zLkfANONZBT/W+9lB1zBZtjGva17t86iWZ4Ye17lDvEgiWNCy454biAgnbdg2JP/MUMxw8GzhZ0IeyWYY8zqc178huPAif2wEi6KjbS16c6uR5Goi1Err1p0qqfms01RKxTHy/5F4eWm5zZHznM8j8NrW1qr2PO/fv79BDICQxM+yOGDSmIYE7EgUu0pr5vgs1EBrlmccZi6gamFqeFi/Z3A8h7RH7DjC1ECqpVThQFUAg14rP6VbUc3kgJP6UQGw6mTuZwMfhlQvfsKGLAQceAKHgPhndev1E8ND//qfkVwR/CgCgLaHRse/+u3mjy88a3Fq86B7zcmpjvGpzDdvHrjpNr1UhH9szYq297z1ypNOzVeCuw/IXQZsg0kAvKgKW3XlZQ2XYgde9BWFCG8z7zd/vf8jPxj4k57Yjnp7uOilDhSWT7rzGQXsWSXvEiSMBOwSXbYKKqZ8czoSCRuvq484nOPFBx66HkU3zcfkFTfxSNkBO7CwVaUgOSOJIx0Nh2ecUNzEoh7sz9SsRMhwYvhCd4VtM63qJ5EN+zAKjuBKwBLqMFzwCUXEVOCUrqZ1prQWzPk++HCkiEkSVGiEQe8LsUX7l793U9tbrzm5pX7nKEizzwJ+ERFYanwzwmhf6AR8zBWCWEGoZKU7OVEsCk8KBqDSBSPXYUQhMsRxBlbC4TPQWya9xiL1HPyNNOsPLon0jvsr95UsKrXBikl/cB5wQMkVVCJuZjb+sCPUaIt5CXZGwQVJCDlmk6odb7BJZ0qVcZGHOMMu7gKM6EJKVCkJr6DwpEDoDyOBxUar0vghuFnOVXwAQhAikd4GURJ3CKmwwYUpwn6ocKsELJ3aUMUlRYGVcARkCPxJ7iJd6o2YSi1jUjYZi+LgT0LqfnYKB9Gbqe4FwjTZqPIjoRUWFREsFVCyQhmCReXLADs4lAiFFAEYBWUkT5ADuMgGYPDgo2jRVLvBStAQqOAmJDbyZYfGKgWMHOFGQ6OYnsRxMoYVqW9oxSY3aBro5LCbi0MtURRwehuTQuLLJ+MO9BAmaPTtfAKn3axbuzZk6OXX0SVwYhVgJElPT09LS8uBnY+/4vRXslQihfDH7KajnswMjKLFtZy471YmR4eSDW26jbvsdCz55MReojUebwXw6O5tTXXNscY2ZJRJLBFAdsH4Fy48mxwtDA9EMOMHbKUiICN2BMoAfIOD/dGNbl3MxGwF3GFYISCkKjGqHkB+YgYUppQdh+/w7HiGkFAAkCJFBDNGyhcVjmqGhgUsUFV3kGdNvVIsYOUqGOVcMaLKvEpMDEA0eMEF/8yxLGJSeOHOpoUQwpgqU/ATZiSg2ClBVXHM8oFVGYklmimprvDkP3U5KOdRLYLVILkpzrKLakC2KRjA+pVs2tTctkRQwAnWopfCj3OrQCBI+CRa3atU9u3ajVKNfZbtDZj15s26uCGps60b/AlORiOS84dK6a54MZlgTzSUqIqefBAbSyzjKSQ4OCYWeyy4Pulr9YXv4jbmyHq4QqHm2HRQiGb+qyF+u17c+ISzbm16zaro9h2smQQXHhUrav3+q82mxnbTaI5pfSUDF28sbcnED+1nO6drr3tlaWhzdPJXJb3oB71m9Fx1eiTQL/RvzxmFIrboepkM7iNCXE69+uqeN75panhEu/1W/+v/aZz5z5XoRsu9J37hRKWlkv1ZxBi0/Hlm3YU/NRyMKQbZYMm0/qFW728i+nBsxSOxf425I+gXMA+Bdzz8vIkDmsyFmaC9ZF42YS3HOrTAjWJG81IkkW7xmDeYULN0HAPrL+fN08o4HQiHG0QyhcwD992HM+FwwzaivHfXTogGpx00NTU9uetJBHSxINyzD44ubWk4I57CknErwCJRHsgBuSqj3tWvmmPoIK9Znkz7F8KwGFTNMXCyfJgtTs/3KuOf0Se+oQejyG5g3jPsILrBaP8H0zlbogIcM4yhg66uvBscHMQNeLDP9q2SPf4bobDQ/PhwvyUQNZk/N2nMiSU03l27dilUwLxzJ/Iez/lvb2+fA/nif3LZWUMDLkIzF/Uit8zK7MwluHn2ObAU1tfPIeRvcxBsWoFRcUdZUGNJzyFCwKCQvCB57zkwcIQgqIECLY9jJ6TtUwCslaQHIJ/qi1ZVW4UVt0DAkTUOkUh1LuEUGBylMmLHJjRiA5x6iyO/bK2yIKV7m3foQ0NYp8RmXgwsLk5A2PJYy+TEitaGznptfm507HP/4t5xFxow3EyO6/rgO/DhT3V95mNXLF+3ZbA0VcE8MJtkaT+lQ6OqRKAUbvBmwyj2kCv48NPIBi1PFM4xcMkAQuNHieDsWVhhCCFNPX1q3/RReKE6AAswWzjSmbUlqLAV17SxfFDEStNVS937HkRIt1Qy0Ppg6kIEFlnaW47YIxkfLTJQMZQgreENvwS3CJnUFU3gJzQcRPhi1cuei0M8o2glMWRZLfTVKAOegcIEoI002dkQayyCedRKMJ1xMYhPfogcRytyhx5gMmnTq8QcHNaGkWj5AyaQEBUNXSkwEDLPaNAqahJRh56kT37xz0DkGE6CAm+lSuOLCUQ//MEq21MVNJ3oEWhLx3n8VdkMdvVau5ZGKlawo8Ucb4mu21psyGndU35PRJPuphCT6CEgrr6Ymso5UefcRbHeOsSBMRSOFIu0CuN8QlQIAt02tHPqEvD8Fhd4oQsroxrorUpkiY0qNR6MO6OvYi5P9VkNz+EXhBJkZCP8iVQwlyWkRAAAorc4UCSUCSOEDKQkJEIEDPY9hxFSvWiGI0ciMtjABIMpHBWcLYodYlHs+QP3BCEBDOTyZDDpgcs3e+OhUYiEPFkgLfCBPijOChI7liEKAyE/ZBNBhXPgYjTAsQKhBzFTUJAzTrwTuVUlxvUEcGDS2E0LLCi7rjvSv6epo9eOxdB1m8YdKmBHljdz3MjEpAuyOxV71TMHWjWIoDRiEH1075aGuuSSJUteQnUvhfBSNCdWAUaMUTdhEnjn1s0reXmqZB5mFOZNzAAXinp9WyqeqsOtrVOjQ3UNyXgCZ9pIJcWMh5yEYmakHb0upie5YAEGoSXL4DCGaMKMJ4x8Hg6yqRL50MQJasj+uAgHLV2mEJRkyQEJU4fAmn/mTBhmJgyxgA8EUnWlwiulQwhJRg5JotgLXT4RjrlZjXKClmRr4GD5Arhhx/LFYq4ihJizER94INPSF4FBnUVTHpKV4SDeLAUsSriFhaWHQ0kAUsByypwKy6eaUGVExC6YpXAiDHngAwbEgYMRQzlrxPmLOKSru1uzH5Vj7sALPAmLSXWja17gONgK2YFFaqzpQ9KKbzSRwiS2GSDeeuvKTgTLZfPZbHo+lunqqF/iOIIK0YU7pApeWivm9smJhdGJnk6UZPJZE7gs6gBCkQgRgw2yAiMPvBu04N+8zB8EfTf44/eatu9PmeVttn1na2o4HkT14viU0dGR+PM/c7/yVXPLo5hMBa5KQ7P5tqsjZ59jJOPq5Bq9NVnxvGj+xkiwCyRwLXFDIv83/2AG+YiOGyqTuDIPC2spntLOaf+BOjuRssql8tQUzt6CIly69U7j7W+ve8Pr/Yc2WdNT7rZ+59LryyNvcYK99ppME04Z4PgAOGeDVvC7D2Y+6JvxpmgCyYnMpiXykYWMlCQDLUainPyLQbzlmGPG2rNW2p1fNHD2JiREkDkGBz2hrY5IjtOikVjXvHmYdEulUlgArHSPZJK3lWLwnSF9FweJt9WnjNExf9/e0kTaaGmyli3R29qRQpDukYnMoXliPnEze39//8qVK4+mkAhv0FUw6NJotX4iaPqAV3g4cA/oekp3TrYcXGqHGXcIKczdiDhqeen9UHIQkZISYATsxETjtwrr7bffDjXmnHPOef5cA4+6VmFiYqKW9xIc2/oNm5m0PmIBeq7cYfoT54g0NjYiU82QeK7YfrvCIcoYR0bJes5s33///dgNdPHFF79URMd+OUe3p7NeBZ1QMWzFWTeLQeZRzZF8wcpuBJ/KlWAIxnZRWiq4slYPWy2FYQZZ2JahXREfPBSZiKVBbXXTad75juZPkKB3jo41+au4QTbX2W0uTmnBXZu9ux5k2yg0pNYzNRws+cP/brlu8SnzGu7tE/KAYNRAkH/K1IgqZzoKhLIIUcW9Ale8AQ0sik30bZSIAAtX1UFXWEIAQUX5oSUD45SGH+wZ94e82LKLL87ceKsxNAwfeKFHgOFbvyHZ+ppXPWXV7RzBSTDS8CNwlWVBClI15CAJO0PTDW/xIjOz4ulja1ypbOD4LWpYKqMpEIKpX/VJfilJ4iGyXElz0X/s7jRMXHjDvdDYJsoju9jRCYzOzortTEzjWFX0wthHVLxRr4NuKd90E66IEC7KkazChGzLG11ORVSoU1LoGyB+7NxKOBUlSTSRN3p8VdyB7Wudo+XppPH4GmeshSsduSBK06ZbjPvPSZ60rdh9yK+bcLPt6DOGghJiWgWD5jj5Q9fO6AzaEj42viuJYimjOkoWn6K4sj9XU33hgjICL/qyi8teLmMNg0XYLAL4CLOKShyyKvEEb08zDAJD0koksAg2oGUBIBVYqk+FmTS4ulJghBNChmxwFhSrkAQSjAEvFyQoX9jDVJhhBU75oms5EWyPw46pzOQQwrjlItJgYnAPcm99S0e0OkMjPEoaMPZEDhckBukgAupbiZkuSj54cmJWZAVuwcyMVhWyIxUJQrO3j1Ws7O3z3F18AznfRBZoMRtasIszhEb11nos0EaHjdkFeUrVFCqa2BKMriuIqqTBvDC73cAuMoFt5+MPr16zBmd5EuvL5pgSmEmqY4I9L89T16//xre+3530K5LQSKdDB57K57gRBGtmxwf3jx3CTQDUYHJFrYyrTpDskuOgriFXIAdYDZ0VK4Y6i3ojKyT8gukirlltiZ26Nrj3foydYY00MgvzVuCXmhojJ60agS4S09QNIGi3WNJYVLhKHjTKWEUg5RwPZh5SDBtr2Jkjkd2UtsyMC6LQOTn0AlhUknQR/ZlDM/iEnb7I3Khcg7HRTHeshFPI4QWWgJcaIN54MU8jUlKchIpY8ZDKAsElyuQGPxoWRQkiRLGaAoEFnbCET3xRH4ZKL3aAwYXAWNEElsSXLlitFDET+sXnBbfdZpXyYE0KGNAHrh2zL7vQbkgsdzh1rQxYrVprLlXP9g6EGubO7MzaxZgCUu4YvfIOHToExSyKpcYddenBRFMUZzCg0gb4HGT4rGKjDwUUgkhaoC4w6y716laVvvm5yF23YSQD5wNEHNwf7xdOXhu94Byog8aK5c7ff9jbtavSf8iIx60VK4z5XQZOuxF0SEBsxkbVlN/zS8q15KT/J556dUVP5fU4NicLQQ+DbraBE4bnTQ5kSg2xJiws8D1coIJhbiM+PlS8957EKy+fhswH+4MhbAk6vxL9RhB81s7fpOt5HM/JJPfjae/MrX1XlTz33IXXxfQ+NhaFiNuHY4Q1fZ6rWRW6oOXzsNQFCcxdKl6QnPDOb11yg2F1Hy4KimOOQa1433339fb2IlOha67OqoEdBoJDTHHpOYLkCgUzmwu2bC7/279H9uzG8m/NsotLl9rvfZex/jQzQVX5N2UwoIBFy4gIppiOwwOkjbxstuuJ10ge4Zl1KK3STjCfKIOJX55sNMs0NzeHCy9nOf4uWzmII2caPX8hYLUzShNyWh7XUwcB1vXMpMTzx/7Sw4AxJpgXhy9WeJApagHMShTlvPqIrVuY90OeZ3Z/kUUNcri+4TnHHVFBMUfee5HZPh7DOg45xIxkIgJpowIO6xGIXfoAqHOOgKAGBj9JJoFhUs1A80ucqzVTFQ+rKiBQLzqiDsOZGU3z0Z/RbV+PWUESR2IbLregwqsuFWlrcpxIvJAvPrrFLyMnMIxbKdOCtdumVXxsR10x31VXj/4GOjLSqQEMCUkfhq2MTGginHBI5qSFOIxjugK+VpcyAgRH/4ikZhtWxoQM0YsXo6Qko+ApTd7FEPnx7vIfLe/t+Oh1w5/9sr/jSfSLODa8oLP1z96bWbTyl3v9vG/zvF3OPAh7RMfOiaDDC9RCYYYv9BcBIrDyCMPBjtWgUC5x4CjjApBqz5Hws0II3+IUxoH2oax+sGSvOXOD1vFDbWQYCwrR6PNeLlxdZOiNr7koF0/u2E2mOFsNbCJh2EAI9Hguo/DEJgkpJ+oShcJFfxQqSCFSFAsAVPRIVlhjNKQpows/6Ep01GpAQSLCT9Csn9am6sxtq5wMNvQAEuBs7bHFVKvY2kNrnZFmt2W8MtAO3YnoYBQ29F6siF0fN7ob9EYc/1RdVaZgjvEkQ+SKlASMNOkUevANzvBCzwoil3+yFOpjVReqhVTS0M9hWDwBgF6+aG4qFBzgLv1wrO+FRagwGYW8PEMeVNSFJcUO3JWXdJuBXL6UKs9uPksGuj9BvliYMNNNDU3zG4KMUdGzuONDc7lq1MdOSsDhMF3MdqCjBLyKN0QG2j6YEX4kOZmOksfwUGnKpObAKPrYTGtUK3hTUYXgeXuqDAxJMDLKTISQ8BSLZBRGlkkNmbhKnlRJuAu4ZManSlxngTAqu/NJ5BiTVF19VAa4ApDDNaRFqhQLrq8D5KFdm19/xSVKOhTly+boEngxFOANGzZ85jOffWp/f6J1AW5gZI5xYnEkGo6FnJ5O1GGXPoberOb2DigIYS+XHLNQMdlhTeDiVFXAmEVQ7nD63/axoHdBS/Jtbx7escseH8PACvIr8gFU5fgfXlPo7No9GuRwDj+ykKwfAEFemQW7GuaDM4b+pJ5CbYQcLE+Vn4AH/3RiNmIulH9mw1rxYxMq3lJYEZi1guTsVKQ8Vo5p2ZaEA/kCDWa1wBjGKVkn4oqaslvO55tb6jnbhVbtcB0VtLBuCEYprmrvAJmr1ppkKTSQEMSJelJYYQmDB38i3SpU9U2BwmfVyd773u1/5WtGPovK3sNRTE40eOsf2utPMbk/FqzWKNQsVRSHvRUxPMmt+KAeqWANKvQQ7MHE2aHnnX+eCoF4h7I6DMPcj5Be+ELUsFlnXuy9f1pZsMi/7U5tZKTQ3BicdU70yt8zexbgDmg7YQWJhNnRAc2b8rIibFgpAnlC6JJcTiSrFTRvyjR/2Fq+0zBPK2qL8hpmVcdM77GE8SoresaTeqwy5WUSpToMKEP7RYVsYuc5GsO+Ps1zsW3MyxSs9g539z5/cMg98/o9E1fnJh9Yvawpm49seVLrXXZRx0KtqXhdzOjDuprir+vdb6bsdAz8VJwx/Y/yzoY8atTpe+P6w3VWe9y44pqhxMa9w/5Fq7uR1OgsTk5OQnmDmjFXKPKNwW5Mn8KqOuVDQ0NQbHCuAxRgxreaZFFdr+zbo3/6+vjEMFa747LcGKS0c1f+Hz/rfOLj2to1rKqrwEck9II7ItdhLghowTAGR6BEzZs3D/NLR1eDwZ9KQBYnRA4t2Y6H9u87tOu8i886hn6LeGGWEuYFj8JvL0KMEWC2FknwIif6b6/EapxLbclG6PlMhNawHccCMhh3Gxtzd+/Vtm7TvIq/sNc+ea2B5eVHqRCOg/D5eT//3IL6CmX8+XHxgoZGjcLNTZhlwQQeG03oZiQgrbiyKXqqdkTDwcZa2nZpVQka/pApUOOqOoohJTTfBJgx1S94S16S78Dan/UWLlrmnLouse3Rekuzy3kvl8MKOLQC5uWX2OiqOxiex6W0HoaNoWMgLWqY0MRh/hR9gail4Xg3WZVGXzCEzk+VA8UPmQRr0nmBF3HACQ/yq774CYysZuElkPRj7OiMCAnjiBfhSUdNZqov9LVpCY2y6jvHIj+NV6446bS26z/tP7nTGxkzmxsiy5dNNnXdOmRt6seV9dRYSE3ERRaEMWGAfaoqM/Smr2IBllAMKgRDlXF8JM7UCFWfGXdhVT0UpwQOEYlFPsyb95Z7Vs5v++sPDH/6c8bwKGZXMTjtGlb9G68wLrr4sVx8f5oruAEslJE+nO4GT0iSSkn0OnjJOLoIX7jlAxxTZOwPUrb8CMmyK0o+4agiR3cRI2LCrgtTiL1EcWXQ8Yh272oHWUGrKM2aqVSLFfjZ22XFcDQK8oW4s8cKdrEiz7GjjjW/0cVphUB6xA4hqT/NCLOK48P9QrfZXmDrGZlaGkr3XiTA+EvZk9iovi3zMzwxiEC1kDoxNUTZlgg7hAw1FWNYcMNRUgJDWXuYW2DRhqeOiQu8q77AEYyXcvVGYV6d3Ro1WqINC9uwZ0/rO7AXK+vm9yxCLlTZB1QlR5Kl0EiZUtkS+AEHVjjzTE1EKSPCAzCQAbqIqo+1BEYZdsm4XI6KoEx2JTeJHojhCy0LdRAqCCjXmBBGfYlkwrkzTSuWwwWyYBiCqrAUjNjBCf6FBwhEBsFACMdnwb04MTLUt2/jhg3VaLz8PpYEXowmComBQ3zH9m/uWtSLTIp/q6UDecWtlMbHp+KxZj9ehxGSwRyzIDI5S7DUg8wNzCK8+UpZsJmfjqKsYn9jX8lbefKpbdd/svS9H7lbn8CuYn3xgrorr7A2bBh1GpyIvqKtmneqbyBnFmf+w4MEpYGR3Ma8iLaRd0lj2T0oCl2x04XM8KwhwGDsCAVIsq/MBtfsRObPb/HWNWEKVOiE1CQRVD42R8em+0cOLl56ihQD+MxOBcIz08tbQs16hAWBzIshAlUk1Bufykl85z5QM6A3HGmq11/1anfpUvf+B1wc+InTqjdujCxfbjS1zA1wvG/M9OKsXUChkCtY1hTgnUOD4cgpOIKfNJzgTfT446FlnQccKiroRvX2mldfE7zmSq2M/RCWhsHv+jpkAoWGEsARUJj/D68BZX2A8EhAVBHMS1jx4schYjPlBU7ZSTcYd8aDO5tRW+YKJRyU6Fw+zXoZY/JlrWSUcPew3dAQTE4ip2FntNvcBHzeVMY9fb29ZGH6rrtz3/5Wx1f+PV635sCAley+POZ6iaH7HnjgoavfuN7quw9sVx5LmF/tiAdYuqwHhWywK+N90Hf/y7EWFlNrvcrXkrYbzZSz0bd01NeXKm7ZiUTRX8SpM0uXLp2jAIN0Op1GqkFDxgJUaI9QI+Fy3nnnId5zgCmzYt6/9VfRiVEUFNTVkhnYlXNGBiq/+pW+uFevw2UwL6o6hFy3e/duPFE0wPM+nFuVyWASG9uYlYtKxyM9CQ+D1jC3t740nSgUC9D/jxgKYoGajb3QR/RVeH4Hnyg9asL2BRQLjnX4XZDkyMjIjh07sLCiowNbPMKs+IJHHAUcOFHBeP2HKt/8tnnjz71CFiNvpm2V166z/vR91opVxm9CB34+MUWkUGW99Iai9LLrdzSqM9tUZ4NNE2OK5GVfQKUF7TD4ZCMEi7TG4lfzEosKIJCARv9bEpPoqhbiQXMGDBKARJ8Y9Natbmn/wDvLn5s0N28rDw/hJgxszSpcfGnyyivLE1NOseS0tmbXrCz+7CYMX7mViuNAJxY20IKuXOw60eykl4xgkkrxRyrwhlHZSbFObRKGpFUk8FbtKqJMT3Zo0DlXngxNdHgws7MF5asWnqj4zbAqdrU4ikXEIwLZMmJNFYMzejoWn9Vu48CQwDiQNTbt1p4cl104AkgOQ4SMAunCRXkJVdjpIXbyoKDVp4SFVAuBZ9uWUz3dvQpCaBqGZhzYF4AFVnTY8CMuUts54dwybF5++saOf/108a573QP9eqoutuFUf+W6XZH6Hz+EpcLMG2E0hSGZF6RbBZdSCAEKinRCvLQLObHAGpKjK35oyegrP/ETMDrAApUGBpig5YQ2jIzL0mYEI1BIip4igxBnHpo7rswhCDDjLXEX4K4U7gEmTgaRmFCFggVdKZy3BMBIVMdSW+mYUUQnxgCzEAcBFVkSwi+kh5fEnfq0OIFn3AepiuYsjgSoGohv5RBChNp4TcdWFIKe1otX4BY3dBKpr/J8Nl9rKeG262BBJ88HCqfWfFy1zYOmcCQpO6/UsTHPT6UX07OibbILAxd0bpGdKOYZ7pmmJMdXKGzY8KV6YLARAynLPxVzwY9SCJxMMT4BMDWdseyIHY0q0sKb5AySAz5FMtScoZ5wmSI6eqaBvRUxTkcH2w88pvnljRs3kqOXzfEkMFv1Oh7sc/JH1oeW1Luod/tD9y8760pRXwOcLIDNCeiX45pPXFOWSPFcRhQOLEFhcRVVE9TgAij8MycJdWQxldf4pWvporHfjM5fuz6xaImZpwLtR6N+XUPGikylcZ6TAR0VCAWtqLUyB8stN3TnKnylvgIAbYFkrmquJkWUGlUXcQhN+VfriGMOfR3WXxGswryqgGhlZVAtLbAdVu9U4atvCfq0x7F9nwauHLAJE+rHKaeeYjTV2+vX+ytX8e472zZiMVEgpXl4NpjBuWIeUXvyySdxTA4IoTzu2/NQnfFIIvJYY3206NfjNha7/kIMSqpkVEEUSwg4PDyMaUzM7MGOiU1YYvGYavwAA3YwMh5AI22oR/rWVLfZSABG2QZlL3u7n71Fd3fhgI4gstase53urEOWy3snRfXvG7FycG5Zv4ujcUW37LZ0BK+/KHZyKtL9YbiU+mMNFi4UjqACtZqbqEZnsoV4KnbOuaWR4WJjU+Pf/z0ojH/r28G+fen77k+99gpkm4lxHBnN+g3z3oWJexv0LKd/f1GXwv0pvJja8/MFnBho5PXyDzX7rwI9VfaxqulAYDy4qfHaa9Op1NTkVHPzzNADIyKmFkHIBxov3CAlSHgcg0ZTU9jVeYSJKYQplvW9e3EGG8TGjhfWfRd5qydOltZ279EKJV1OZlUkjvh8OgNHBFOOzwQYfGKhMnjGsm2o8epYHWhlWCSJjt0xkM+Q8MyBLUHFbitgtz9rgiPkUUgGIwjYsVmT23Ex/y4AYAYYMq8l0/OP8okQb429E4H8OUcZKpxasf8icIWOT+mnN0b/5yc4vjGPI3kMXCwemJsfLXz5360PfTjo6Qhbp+ccmRc3IBIUI3pzh+deXB6OSC1X8tWBR6olYivD6hbVJMGl7cc5VX5jglOsZU8bz+vYuwJfNkM1OKU000nc5BX6P42qqq3COgv1Ma668CJ39Zdfd9LJdR/9RP7224wt3sLWTwAAQABJREFUj7m2Y64/NXnqqV4hf+iDf1d/2fnxa9+aPGdDAZcCPLYV3SS5RoGtnxuLNrz+imyqbnrI6sIiaKqNIXkOMYsaAxbAluKY0eIvNKqgSQtDEHTGEZidcemBsH9OVzamNLBSb6GWLbCUFfrxYsiMssoL0ESoaGHUebRg/+JJ9AQ8XN2BGSrcMgTghANsCIeRcQULO/4YBfmmn9jD/pAcRKK8kP2F4bAThi8dY8KuVUolrLoU5KDAJOLkAqwyMkBI7IoOXCQ6JCM+QHb/AVydGD2vZ0Xb1YtsF5usrEkzunlCv20br7FJYrs2EFd/EBYEAmlhjhZqKe6qEXYpH9CHBEhRsCMQxhUUU6w96K1yF3xqvFYjLt6Yu5M4CD2RAjGBZdjJtiAQEHERmkI3DCWsMD8QHpobpjqKUKW66uCGvu2M0XFO5uBA5ZHNwdbH9VJB611onn2W2dtrxLiU7MQZyoFmNi+zqB3FeRbEkcIeKZS4HeYRl7kohcrBcctBML+DA+WNnBY5zDD5mKoc5TnMQz6q8uYkM1RTLufGBCySBt/UY6EqU2eml0zMIqvwE/c1yiS2KNWiQqOWhxxCCmE2QPoje1WGD9VFG1qbcGNO1V1IQzcGWyBHokDLkojpburMXEMuB5RVPFxppm176AGssMNkSVXgT4/Hyy4zEpibA2Z8XjgbSuCZGzc+vPW+JU2sUjkMR4OEjNSvXoa9otR+xQdT/NBI5UwIKKgB1ipj3hUrh3mUIPVYfkKt4MgH54RhN3A/A2+iS3IekkilhKFb3RKHkjvbsJYMsxxfAgiI0KkKGZZSuktwxSppVSGe0RvzUTgqBvNR6ATUMiJKCabCEB6dA2Rn2PGEvof8+oyQvhBAYEbqY8rO5A43kUOI+dnFcQ47UGyiMYdH62W+22l/NaIPokn2M0ES1UHuC97kmYXUxwbHOzHJaSLxaEJymGnBfkKu7A1wqaBhY5Q+M6CVXBzDYuB4pxSu6mVSkm3+keGaSIFF6ixcSLy7Mvgnkfw9OLtKGgH0Wm7xJ/7FS10zqf3ZdHFD0my1jbHUtZPlgq1tSmZPObXxvX9sJA5p5v/RzDRUVHvhXyQv2Wk9vBnaZjwZj7a3lVtb9He/01i8xB8dbf7C59BuD/zzP5t79+N+XWNs3InYGFD4+te/Dr2uta2lWCwN9+9raMR4nGaPRLlOQBotzD6jmsJGIvcp3BCB7eKe601U9mHBlW+53tYdO9967TV//L73vec970FcsEIY5xhj+gdnO1FCYjDNDjUGqt3evXu7urogKyyWni2BKqC8ISAeU0ZBkX4oH1S4Pna6sENTk/thwWY+oEnu27cPE1/PRDt96qmncBIV5qVnwgtFZHWuNYhgRTrTGqudIStke7gDLZhX9tmhjmhH4sIMPpnLDFiBU1+chmLPSNUMfJVdWQhddTmqiGqBfwcsyDkQCNLiaAePvRRkgBK0Z88e1AxHXxX/UmDzxPCAqg05dmJc+/mN0EVCbUN6OXC2HnnY3b3DnteK/V7Pq3Y+MbwfDasq3ch7RwP4TblnSug3yuC1qhzBB8WKdoXVCPoYa9v9tW1anZszXYxAGOlIcsek93C/XmLHhHVnNRUYgN8SntFRrRPfM6YKHb7VCzrZcB7nMAdmNBI/+3zvvHMxG4WziLOPPjb1X9+1+vvzgV/32iv8tvbm6/584otf1u9/mNNOWLA0r63xve90152yddrOVbD1Nazq4AebmskFT+oPTKguDHzFzHqJFR1uvvmSU5rwwV4O2giuxRXUwCTxk6jxQS6ITBCoiEIO+GJARQUvhRgvLJiTwQMRroSSaQaBxQN6RogDQUMRMTAx8gW8eAgtOsKV6jdd+I9uQqTJjkX1KO7JZROr1HdwTvoqFPifUYMFmaABZUZQoPQ9E+beCb8pZicdp+wFozm/5GIptJagzGUTOxCSQfyAjK0KfTCYHzYw5BweAoAX2jsSF8bDMWj4omADDOF4Iy+sHF+Hdht2EsRdWm3hG+qN4hAhkF8JKUjJPD2Iltu/+SnCpppPrCQKMjy/E/ufKhHTWNRYnaohafLhH9hf/uKX7LvvxmgLNGQM0xdv/Hnwlx+wTj/dwOGdtWjB9ltuIA00fGpH3uzODNINPfNjRE5kyvSeY2pO6NVQcWKKzgF5+qdkBjozYQQciSRrp2WeGVoxdFo+qTPr3nCQqteb63xoCKI8s0nAUAt8oegCDCmO8ExrEgdOyW/khIUJiTzwxL1nbjzz5c7P01PiiC4vhgIMwueff8H/+96P6v3JxsYGqLWYfbWwGZgKbQzKrei01GalfmSqVnMWrUxweR81USVDMJDkstpTHA57CGYFFFoVscOA1EfV/whez8Cpr69v+/bt69evh8ZSA0fmRc8An6iF8IQdLspegzkRFpCAWgVaUMuBf2BgAE/0NWW9qNJFSfb5xVjDQkHErzL9Vb38KVMrIU08N6rndS+KvkNFL98TGfuDoPB/gyBU7cAPqYqBnR+BW5cvuA/c7v/q17glAuuRgjPPjFx6qbGgR5+5iPIwNlU4393vH3htxN1DZOWIO2bpEUNvqpg4Ui37H3Zw0HQ+eXD6HYsSnzNipcj7B0oHok1dvab5oUB/TDfSGHUp7VlgL3hF8i8vL955j3fXr3HWZv3qNfYrL7HWrMG+Yi2TyW/d3nfDDZGdT0ZwjyFGY1rb0HpBXz1w4ACyJRqsqBMfHCkva4KabJgdnok1yHTmsWPYnR54ZasD/QssqTGMNK7p8gqeHo9FN5xx+puveQvOm1EZA5N1c/IDkWMj0Pg4tvvCglWF0IfVYbwiuac9onFj9ZrI41ugh+OKCKCTe485TFhefZJ+3NOnkAjSbCjBPg37XAdM5M4e4ql5Y5EzVJq1a9cqBRjMw2BIaHQU15614d6mGuTxLbo/vK2i46yKYmL6YCE4udpVkpDgUw0kqdKkBpgwmPJiDiodPwq/OQh1C91LWfuFbJ5VlvvNyfKEUEZ1hrrPm5zShkcKZZx1xJ0CyMxY+IourRONlvv6sVkIFeAJIX9ikCK/oey/9IYzgiw257GpoU5DjWFWq4dbFV+5WF9mZZwnduXuureMTaGNDU3nnLlx5arOlY237EaNzc4SAktYCm5WTYREYzoCJyzEO9tIl1X1WMXZiEc0s5jPfO/H0z+5KdLRhFmkct8hM53lLXY4kmd4LMgWRhtsa8mKho98xD243zvYb9Ql7aVLc22dO/POg31oJUgKuYXscFaBjY0gx4eKX/g9mxF4oBFiFMgtchpglMJGptXAgChcoWpKVOxiE6OszOU3Y6iMxFQtKxaImXkCYYVg4EqadgagI9AwBaiNgXXol8oFHvDFTymbhIYJA4hVZk4IoljQNMe221oaAaM6jeCLtBiIwSgWsko32BSnM4yrVBJXgMlKUqqaaJg7MGXK4DNG+CVW4IXUyTux4p9T2bSHnMINkaN4BZjMACftjDMcxUsEIKKHci7uJKXUU/DAD8U7LfgU6auQKu7iDR4FVDALU7CRKwYKgsl0KecWFrZGFjRV54GQTlgAPDVd/u8fOnfdifmjsu9jbRr2mccO9RW+8BXjU/P1hYsk8nMEQJy/pQYdp02bNuHiCXVE6HFjIXn72UT/+LCEOBxKvvAIh3+YXtDSp8bHAdkardjeeDA2jXX9SxYttMPFpAzC3IUig5yi5p9xX5Ko0FSbZcIZT+wCe+Lxh99xzSePG9OXAZQEXjQF+HzXLY7uefi0yy4jYZUjWFzxk3pIiq7iSQBYcYRGFcq59VLVF2+FbZbDb9yKU6DAA4rfbAUY3QLcqwF3qAE40AjFcqYtOZEco4BhGS3bTNGmYIcLZhSPPQz2rDhiJY+CWX5CH/k7CwuFy7b3gxb9vnotr2ei0/ZV2eQlGUcfn+/8U6V4Udmowxa38fExyAFUoPVhsACzmgsT8cp3vmN89wd+KY+16UkcWbzzyfy2rbG/+EttcS+P+2JCzyQ2YtF/6JDjmPXFv7LdvVh77D7Y4H+/2Rsw3LM3Jt95hufcYJg7G7Q7ovZNXs8HsPjYnP5HS0s7C7GS9iafE8tYQmKWH0wY/x4tz/uM/va3Oa+7ynnta3EREuefG+tz990z/tnr3b4BDwdQ4VxTyyxXcNlSffsZ6wfS09B+oc5BtSsUi3v37C0uXeb7OPe74J+XTm82EnoSmrIejyMUL1h4HY/C9rOW0R9gl4f72td4jtXV3v6Vr3wFBQBDlVu3boW6UjvkCbFDbNVTpQU6lEqfPEa2MVJx65KL8vfdk9j/lDSIaFLR0zBwEHT0wvNwGTzEp/AKTliBjCIFIWityCRFuU87k00XSwVwnUxiVHhmlERxgtFldNHBNkJVfA8zeKAFTTtEpYCqzxp+5DfkeQ5zHaMsV0PhDWZKpSJS6NBmxWUwuTNaeWXFicwsnAYDaomvyt6wq4Cz0PzuWiFntUX/pSkCpB0abCQZFsODQ9hhQSWJNQXPMIe8NOP1rLhiKcdxBTjzmYfvoYOtoXChjKDoAQ83IWL9Xjj7+KwQ/yaB5dyG3t8kB0emHRRdw8HZqTCsBMNqiLpV4K/tMJYZ08Yvbpr416+ZuSwSApf3TP34F7G3v3HR699w+vymew5AnQGkqi0FB5GE9Snf/FROM70XfMOdf7VwuuaUXNewrVS9OTyEbT+soFHNGbjLD6sajSARx5Xv+9P+g/3G+nktC05qi568Hqf7jJS0Lf1B35TqOCsSRIofihJ1KqU/kpRiA55iqpqegPC4HbpyZawEl89q4Jn1srVKGuxRVlWUiAuDMUqkqyKoFLHw4B+FnqHECCRsITmhyZUOYUgmg+AWD1IKw8kLHyK6qiMghRUVSGAFLx7qTacQGHyq5AodqAtLIuEp4pJYcOUqFyzDjQAST4UALOKfmOlLbVaOJiI2ocJ13ehD4FPEILYqcSFMenRQPPFcYS5SlzXJpABvGoaXJlakQHe1fUlJSHCTD9Igk1SbCcOQYIkf+CTzfDOxcNn1VE43y4ta9LhdnQEWKHNyMrjtV9C8+Uc5YpoRIYLIvt3u1q1m93wt8uwG2tBvQc8NLftL68Q7RpYGiiWeWDQnXy/dB3ovqusCFpEg6MVI4iPfzHS9VGbheIZaQSmqG9Kc7pKF8L5z6+ZyMXv++ee/dKP6EuPsxVCAUX0sXLhwQc+CX99556suuwyfoRDCpJOvqtuMfJ7uMuN3uE3lgjAvHO71G/qCGoMFYFCAZ9NXEUc9pix4KstsmBNhR3dqzZo1wIwtbQcPHoQddJUq9YKQY4zYbgT+5L+a/jRGFSv/1h7d1M6FPhipHXQz12P1hpm4fDJm7Dyw+7+mvIuS9dhTW6+u8IHehdtEu9vag0c2Gd/9MZdfoaER2aD9iG16uPLLW/RrrjbqeXzfHJPJpL3i7kb3l2hBvMeT5pfm256Z3nBG6t3vwTXw+R+vSLzpgNVUcqb/o7B9mb72mmL9WaWxGxrsx7XysJYJynv08u0p/WEn5UQiT+2vfPQTpTdfHX3btQPlUv9Te1eYS1Id8wzdtoaGcL0T8hdWvgSpuvYPfajQ0vLTn/xk37596kymocHB6ekpLfqKvL4xof3a2TgVjOilGw2nUAdl11uW0t6sW2sKkEfxJsvK+LnLLo++4U2VbM6bnHhk71OdnR1qUlRiHeZ7SHXbtm1Y9gwvuIMWkgyNDdeKH91gd5K5eKHz19flv/UtbdODRrFScuL+GRuca9+s9WJ8V3rVEhxiZrKx7QwzJPbQiirCNvrggYOog3G188qVK1AnI4R0Q+St4TTukaHBIcWFOzaBjkB9KmXjzBDpILDjOD0NnrHuAE/MWmONNCwghJZSHVutwh77iQUL27c/UZmIT/V3gm/If2K3OT4wNa+3vRYQMoFSjU9ML4P/FbgHSxZd1wB+ly0QOAwkDyGo50tNGhhCUstSwCeqJpVbns8FPC+1CD4zfnStuTlYvcZ6bDPKHhRgjALYFq7n0IqpOmvVSh4P+sxbw2dG8oRCITWBX+W9l1LG06dxeK+as5P4K+UBnDq6u7pRi2zdOX7DV+089llIlYfxh3Ip/7XvOL09Sy+49FHdG6/gRF1WhCqC1TRhJYogqjZViPk1x6gw8Ag0XMUyHRhd69dmGlusbBad34pfdmzHtx1c3ho784xiIjU8peXKxl370QXG+neumUQtjEXFotGEyFVPCghDIcseYPgpvUv5gjMyBxC8xUa1TFlUMpEl2mT4JcBZsjXG6agg4UQqjCMdQhC2C4JXgcGVogjpqi+FSxEgsOoUcDKUYHiFPX2wDm2O+IlT/VHBRAgASsRIFVZwj2dwqH8gGo+hGyHMCP8KmWiCwCKNVjhRjpD4F9yKIXzTITSkKbxJ5GgXzuTyRvGjE68vIQX0nYiIUgSYBIQvFRUFyniFVhAJqYhFLXjmpLFshAachKCfXqHGLOKAaw1DKBy4KDx48n4dEKhSoBWGQlJvxixIB5mGhHNaF8bVIIYZHdhPZ93RcZwlqYJAfS0HOH48cLD4eXCYV2k4R76EQpAf4YHaG/U2hixfmgowegVgDCduHoH1l5ITxqnVUDW6fOgj1davVZP1qLyqVKc3C3Vw5513YoXgiza1dlS2fns8XgwFGNJAr/SCCy645ZZbPvWpTx03UZ+59KR6wYOjc7g5AKuKOSJXnYkCnheQ1jPnCpDowSAfQ09ABaE2Pc4OTn3vWa0CnR342dshBLUdC1whNGqEF1ZJAH4MQGJXjpa9E8WwsjdhP9ykmgdU2LiP2y5HtB9HtFfkNafcmtqcSL7NjkYa6xvVPDlmPnt7e5O2VXzwId/N4VhHzIeAQ4yKoRXDOWX+g/cHV702gAKsWk2ZNJYWhZW9ldtkRoqYRPF+1my5Jk6rSrzjLTiTqvDn70/t3V+ImuY7fNPuS3/+utQffNh+9eWTDdcn/GL6sx/T77jL4hGJ7FsUg4IMfZvGQ5v8112FfbBj42MDDfUL2ltb/+nTxZtv1n99T35qMrpyeePvv67Qs2Dznj0PPPAAZj4xuAhOli1bhpXAK1as7pt+b6+1NWZPxF47rp2Xq+yzNdM3l5S1JOe4gnJPaeIc8zNnpc4408tni/dvcS5/Ne5SjsaiCxb0dM/vxg2QHMpFPFmfYSzQwzJmyAHSgCMMpBwODv5/7L0HnF1HleB908ud1S21QkstyZIty0nCNrZxlI1tbMMsNowDA8uYPEv4vt+wDMvAeGB2vg/GswMLM+zasMMQhiHbYJxwjhicc1TLymp1Du/1i/fu/9R57+rptdTdSq2W5VLrvrpVp06dOlX3hKq6dY2eA0xSyYJTZjJYbty4d8wxsf/6V3zesDQ04jQ3u62z8rNahvkavF+MBQVndDSSzviZEXZYBo0NVkPLoG8NZuxs3ULmg3mZefvWzW3zFvHNyYznPb1N3kgCrcEPbWIBuoVkB7PFNm5n37x57bGRseDFF4ovvyYTyos77aVLm+obR0aHtrPCb95yD18SZrV8l+NXWuEHrz40mO2XjzYYY495UH+od3ZmG+8u67R0EKTj627N97bLkWDS9vI/q3lJkJoPD+R5J5jcNy/CAUQQ2yuYF6jeijJzWMOWB10BZkdMR0cHokJl1EygECnNQeXMNzE2D+SgkuHqzGqNXPWn2XVd8eEhRjEPANuii248eN/lzvwFZmJJkg+VwCYjRh122O6e94PUEHtwDJkKH9WfQJ0IIQiOWXVBQyGduedeb4zNQZKqzCYrUiyO3n5X/clvndfUNNgjRfin40EkThlVGZ5bwU4HkqcoKr9SSvFK90ae6yu2LlzSePX7R677rpvlfRrBydxdZMnyuisv60k2bNjEO8LGZaKYGYB854BgEItAJlmOSOKfukci/wWiDKVxhUaRGroqmQbEIBNnSmD44a+cL2lyV/mVjHK+8SrL8fKP4aXxCSlgvF9poBSWJgmhhmEMciJyR6a+H0S2ohZ4/iQAb2LSFkOEvPZoFJAkl//bbEAaHM3V80nngrTN5JQRCNHlFEMA2QYl2UQqFZZxKWA50fSscWQFFb2h5JncMqWCSdBxKy2TYIgTpa3UySlZFIYq+TEQO12k9SSb166JEJUvc8pWD8Ep9+UgcCQJPQRiwrodccGiDZMfEwxBxLNjWV4fZb9Yh/h9O2NMxtkFJh/B5UvdhTymIC8eGm3r2A11silXyVOEu7/qSNN87cUwZUen7r749OSwSY2TRzAyZ45O2WXDQ46FPASsiqs7uiSE3CUeit9xxx34WTO8vbsk/mAlTpMDTPPOP//8H/3oRxzzG05v7I82y7MfjGX9LZtLzz0XbNtuNTR4x650Ojo4ebb64d8fde0ZDhxgGssUVI0RwCBmEWzq62B7VuuE0NiXB2wyDOE8yreukKD2evZyMY8oxz4gmyN88ZUP0qUjfr/tzSvFI4N1c1pIrnrC5Wnn3IZ8b6/P6fw+51JjdUigNXGO6ecMZL77LE0rS3P2TvP+rcgL226MbBedyin2XbafzZTeekE0nhi79huJtV3yyk6XKCg2Rzc125nrv+utOn4+u9AHRhJuU8mPqsGNCe5wMJfoITkcn9djOjoWzJ0zB8/zoUf/cOzRRzddeUXTO9+VyubcVKKn5N90++9efOF5qOHcL146xfU95phjeOdtoH+ga50zUP//rO74ftx91WlJR1uEA+zNhgK7tNJ3r2n44FJm2rPPPlf66S9jf/HhUjwxMNTv9jR6W9OlINndxwvEskSRHhktloo5Z3amvrghm9i+dfuyFQuTjc2Dlr1xPYcmiJ4iqDuqN5IGe8RosKOu11rX1njEHPx7NtUN5q3e7cKtY5mX6N6SvfGmkbvvdXp6/GTSPemk1JXvSSw9qisb2V7A1ueg6PTmQV5GSXl2zMoH3fLdeDluRbpTviwl7KyP1a2ewwEndl08Ee3ZVviPn1i/vc0dS8PPQsQrnHVW8j9/cNHcubFUPV60djTUhhFD+7iLE3QcU/f7741ufQKqtaPpkWYpJy2kk7m11z/IF0G09QaDYy2/sDhveaoQeHhQk1Qxrs7DIYEdFtlsdga2lM7SDf/MN0EeywgHRSrujjOIygMmLXfUKQMdozke995ykvU3f539+S+dF57n+6bFBQvcd10SPftse1aTPA76QOwoN6NjjDdGnUicmRRwSgazcmKqTJzBdRUqZuEzFbHdYjG/pbuGXsN129/a4xSL9TH2pZe7QRSPeanSCBy9k6KSbbSeyOIyrCYaoSX8KKeu7XU7GxuXvOvixvbZwzfdytmKpfpU9MRVqUvflVmw5MFNdiYHZJmB8lOWekT503oEm/BY3C0VklqgLC/JNsWM9yw5+mdgTR4xEakG3OSqkypwpIsGN6EsVMs/5bq529FChQOFIZniBOhVDovCkH8cWisjwlCsQ8MsOQMpzJL/UorCBq9EqsOOysgx7+lwLEQsIi03FVEagjSwyqzEaop6smQaj1YqIEBBCK/tNMTxKAopxM03a8AtcMZFLCM3dBn8FVr5BZUSLA0hKPEU1Xj5hxuzzis/QoyQIaxWSqSFO1qpfCpjMuikDoObqWhFKFMeWkEFA5hR0g4nhyZbGxPNMeleKWMCyP2mJvektzr33e8EPg4w71lgnoFvrKUpetxxTtW7RZVCu/3ljA8WkGEPYd26dbSFlZ4lS5bstsC0Z7ABE9qww6e95r2vEGch/HocxEv/m3+wd1KkbA98/PHHP/zhD08K+SZAyIHpc4DXrFlDL95zzz3ve9/7wur3MSIDJJ3JP/Fo6Z//d3xdl8v7gJadb261P/T+yLkXsPC1j/j3pbjOPM0o0xMTk7AvjdpdWaMhYnhePLNBiiXLosvXI0xwPS/p1uUjOYdvG9FhTlyEfxUijnTChwxyeXvBvFSqjt1/nADDJBYfwwQK7RVwvBafOVPxbwQ6hrLuGOkfGChZKcnhfKlW30t7ztEr3LGCff89ujrot/KLd+0FWTueHs4/9WRk2VFWPG6vXOncdYcXjUVch1X6qByHyPn4fvaoo+1UsonZE05m7e8fHBi65fY7UIaoqr6+fmaj//jHP2QzY8cedxyvLLIwi5A97bTTcL14p9pxvVWrjt/ePffVzIn1zm/nNTzhBRsDOxJY89J3Rou3lOJzbinyrYXXN3h9/d4VV0DDlp6eoYG+rN3wWmYtjZ01e25j6xx06euvbc3nRi0fh9zPbu3p3bpxyGlcdGRjKWAZQNxRo/lkspmmQxUqnKgwH0ZIzLG2a5LR5aj1QuGUBcVIX//gN7+dv+WOmO2iw93hvH/L73qef3XW339pTufy32/kHEIvl7N7x+K5QTlgXWYDBI28LyQ9hgMsVQT1fCOEM9w5DSyTzt14Y+LXv/GLhXyxyJkN0WLRvfvOTMSLfvQjTlOLZ15llLKTBezJRLNz9mcaXr4j8+QvikGOHmcbAMXKtpFYDbRVbDNi2Ba215I/7c9j80/gy8BUE0kc4A85TNaCmZjPlARk6Uu200+fDlPqnVh/k8vzPjHM9BM/nTUyl8Rn3rxTT3OPOsrmMCS+ssHp940tTt2B/TbJAWoj443e1LF3gKrYC7QcD8BX36MycSeljRtMTBQFAp5PTXDog2TsbGtybqHX2FhyPU4iq7w9i0gywUhhiZl7LvKLiDICSjJBJQKz7B3JrwgxyWBG6uZXi2cuajnyrDVNJ57sFLMQNBqNbYo1P7TBXzdkyDKVGGoNMnNb5SPJPQ0Q4iUiik5aBqz8GLeu/FAZT0x8LcngYn6UOimkt2QYj0xAFIXkgNq0QH5MWzSbd5ZNhu6bljQRyRXoSg1UJJSbP0Fp0g2YqTRccQzhywpHSJFChgCpyJDIRf64HS3k2dzbkIwl2B5kssWkkJgE+Q1JJSokSLfIIbqaIZ6jQgg26a4yv8qMUqSiY/BTyVSMSoQpQAqZoGD0iEISJPInFwNW+TH3QoCpSMoacvB6Ba3EZZe20CKet2GhtLGKOCmyo3RIi0ElFw0CIlYK1UTjMfyoealiHaZWdUkGAI7uFe/Nre2KbVoPhRSi1nw84vz5B+2ODjlrYMqBZhKUtxrnOuXS0wGI7Y1amfkvAIe8gIG6o5AUOehqeNQf7LeGR6xYzKlvtGY1OtE4WbvjM/ufWbM577zzQoRvRiblwDQ5wPQZHsuqVatuueWWq666anddOCm5NQBortK6df4//s/Elg3yiV+xg61EX1/um9f5zW3OWWcydPZXXTVVT3qLR3ThhRfOqFcjDhwrkHy+nbJiK62xbm9lPteYTgzwwVmjJfihn44ddho4zt0O4qtrhDIHFSCqFs6fHzvjzOyvb44NDzM3yTQYLjBF807EOfc8p26n9Xz2JRLoAvbZluyTOMiKBVbv3GH3R5iM9X52zB1jU3RQSFixi9gmZheGo/muUhRLYTidy6Q5sqnurLcN3Xxz0PWa77hIDegHPtM2O3rRBXZD/QvPPx+Jxlhn5hxvaGOCk70l7NKkNzmBCW8Z8nDC2WWEkXfCCSfg93V1dWH2XXDBBTRn7frhZP07Wo74Iu3u6+9rb5mXXLMlm//d2H33R0eHo4uPcD9yjnvmWdbs1qYhPrnUkM7nM9G5PsdW5Ot6+4SWbGKRH+Ns0EJ/9/pUy9K61MKefK77udfnLFhKe4z2Vu1Z0YewWlQd3i+N1ETYQ6w8u+3k03OjbvGRJzI33eEFJV6FI5sDruWt4K7Xh2+4qeETH5sVr+8a4CjBRGPHkXksEU5MNWAMG7CrbhSDyLbTBZvX2FrjAYdq+Df/DoWtG8YgzdRqe3fea11ysTdvQbkYqROGysjkIBj3qAuTs5cXHro+M7opaiwgStIQvYqOpf1QNPv44mkfqks2y+uR1G4A3rzUckA//3CwHGCo4Q0x9hJPbIXwHLGBopb0g33P3BZv4C9evDjcwH9AKDJDm/EvAzsW89vnOu08Y2J7k1J5Lg5IzQcOqb7XXf3pkQNX1xQxIzRyJU7sk0VPUUviigrrRWAF1vZ0kI3WJU87ZfjmO9iCY5JNx0jMiZx5SiFex4cU8kXKGElrHBTjeKqrIlTQZeU8g9VILUFfCaYqqdtk206uGLl7rfV4zJrX2NQQt0bTuVfWDmeCsUgsJRMiTOmWS8ookLheTaIODOozR2aYTBkvUkyARRCLe0OCzFnKuiZBvRVTXiChR5tRnj81EAqnGYZUKS2N3tFOioG40jJTrd6aJMOgHdmmNjYOGbBKGZlYNXE57SP0ywXGvHaLMKcZFYWjSsXQIhSafzl2ilk2863Gp5VkNjqFgWgVVZVkYYahC2rK9EhBEqXb4KTQxD+aS9COlhKSIZwXespoTbLBJTGj9OSKMymY9F4GkcbExd1lMDUbYgwubg1pwFZqrxRUBkKaoUS7BFp3BO1sMspdE1idLRjD0jLzvwIZiXgrV9pf/lL+Nzc7Tz9TKoyVFi11L7nAXnWS09BIsytwk//yeghAbN556aWXOjs7keGTl5leCFZWePHnkNwPzEre5m2F22+1brqJt5h4RTs49vjon17mH7/aq5dPVY0PjLVbb72V4zPe3Ac3njkTpEyTA6wUXHzxxd/61rfQjuEq/wSUTZ6FxMlkCg/9Pr5lE95v+UHniedFmnwmf+ut7qrjrbbZiKE9erAnr3dqECwPEoBlaBIOCg1To3S/QKGvPL/xSj/7gN2Q9T7ck7veiQ0lxfsM7OzCwcgHBljOLFlNXsO7RYpXBbiEkBoZSzcvXep95i/y3/y21d9rujPIe5HSe/5T7PTTnNROj70yUxQA+32SJ5ZKx1u5JyPnDue2dQdD2xLz23PHNwazBuxzPXdZGvUzdl/SLXpj7DRcMK/keJs2rF+2qKPxmi8W/s+/WY8/wSsxhUjEX3Fk5Oo/95avYOUCtcoXAnCM5WXg3l62+rDbWQ88hFriLA6zv52PAPES40MPPYSTjLVHABhno6e3Z0Eiztkl7OIeHSlszG+b0zYnfvmVkYsvlvW4VILvGxe9eCQSbW1rmdXc6A53LWq91/VH00Hr9uKqEavdwjW1IiXL5cuEjYnGREtyZPvmQnZUNJxRwwwsWRuvYSUK2uhwo0/lgloX0ycI5rSmGqxM6fFn+Xokn2VyPTlI2RgZMsVdeOypumxmdl3Da4PyuIhRYGoxlg57BEXx4zELPsHG5KTdnfZbo05xy9ZCbzcJgJPJS9Hks5QSizv5TVv81SU+2j31ka/2AlZQyxLnor+JPPofI6/fQ0P5k0ZL1YYGJ+of815r5QWNldM9dhpOCvbmVTnA1Az8V4fkoPCEjRKTmiBTHyHT2QQeak5x4+tu00mecQyMnSsj/lAd2MhAVB7ycDr7a4K6jOAKRrJ+rlD+TLwRKVICFvM3XLJfGrVXrz4hdtlF2V/czEfaRdiJrHXsc06NnnPWi6XYtow6F4CLgOSfOEYGhRHLIhsNNpMnGVqJCmktop0qQlpIEqfKHvXdVwek4PaewdFhzpeYHdWTe40oL6MxnompGAVAUE9MyENcSz3YQkZ6ihQ2tJNnEpQIcycEyq2xRQSHineTiJJEmxjnVbQKWKQKwWDSpJiBEwWjErn8VAgaCWYGVGGVWPSKJJtmKoQSbeKUEgZUiFfcUl1BKjINkGwhSgiVfPlRODLSYzkX2yBwZEbCkAqM0KsQUKlFpDLJEHPCrMxDtUCaNgo9/CtfhFYNpAiPzI0i5Cr/wMWPxMx8ghSW2zJouTRARmFRvpKB+yrBYOTCncnZoWgl0ZBRpkHgK/emu8sTBqZ28azN8ruiNLjBBwJosfggxUD/YGtL8+wUJkSlSqlegqQkUt7KY62OhXY6I7vFUNWN9XwssQyscJNdq0WixqtTJis9Hfk6FGbaJpQptrzU01f49x9Ff/GrYiHLSSiJQtH6/cNjr74U+eKX/JNPdCLodMbYTgFTEwf4Ax/4wE6pb95MxoFpdYAvueSSv/3bv/3DH/6wf87p5ulm3+y6dRgN5vGTp11ljjzMr6+3sjkRnUYITMaHA5KPy8QHgbFBZ+YJNPuxzcp/t/HK0uDP7Oxd7vHD9n/Pjz2esAZiVkc6uirrxLKB79mtn7aiK6WXKroBGjo7O7ny0R32M7ed9w5rybLiww/727aONTS6J50SO/Zod3YbANVFuJUUc+3uHspFP744+jnHHYl9YGuQ+wcr0lB/XT8nNdkB71GxJuq57Oat90pti6MnnOC11K+oP3pbd/fso1Ym//ZLwYbNxd5uu6k50rHAamnbOjLS9fRTeLYs8OI2YMMxtfnCCy9wgDb+MO+T4OKyMoxNj2zlFjn79NNPA8ky1/Dw8FNPPUVfA0nxe++5B2+ZiVKOyOJVYfA0zW6PppKsVm9cv7FQ7F28ZLGff/Hco79T7/6BTxZDKtuS837LC7nL7xr+fCaYg1Xi2156eNDL58ZynIdlDfb3ozaj8VRET2s02q/MCxnqZbbCGROT5wC9iLBk459nlfK5HF/dxgNmt7Zhnl5sO5tDe8Y9N8aOsjJO0SDGDBCFr+rEQAtutkL3pK2gxeKtoWKpxLYL3qGm1frtFjnLBSi264TkVVU2QbRCvlBtJ+3j3plcd3+a88OliLSuXDTW6K84v8HxhJIJsL2ZBT8ZpQzCaXaAGS0MBl6tJ6Jx9YiUmEOuX2iC0hyOzwPUBIP/jTCkGW+MOrr7QHNsqh1hOnBwjBVgc5JAuO3USBXDceeR1/22Fa0LP/jh2NIV6bvu9Ldt5zvAsbPPiJ537raWOQ+8yLvDRuKoTDVlxLUyMohlTDMZabyxiug1WWUxbOiUXON9Mp7Efi2PKpPHnWzTkFcBXFzZyoiTPBPXFKLiKElB+QWfbOQRihSbuFSSWam1XIO6RgpHkpGnBon6gRQweWgbwQSE4OM/ZraxrNRllgqxu7G2hCLpWQEDXFQGdyQbpJpY9mIV2PhrUqjcMK1FAMueJHkmSEY5U5CrJ2zqkmzxayU54P2Aek5vkluaYAaZegSGRkO6oV7JlBJSXC6GCLmqC0kEZ7Jav4VAYBU/U+oTdkpBvZa5aZBJ402WgEkEIF6vNexg7sRQJokaqeBSpgBqEoQGIaHSWsC1FwUAlpqa4ZP2mKBWQgxZUlZqFhINunwmk82MJtrq2/jkoUklQ0N4G7C5rrnFam7W5pErC++CYY8DBhJrAJi4e1zyABfYsmULUoizZmcgbRM3ndXf4qsvu7++yTVLD9Lz5i/B8d2/+JW7dGnQPhsMNd31xBNPMF2LhzUx8jdzazhQbQfXZO3nWx6/4447jqflpptuOvPMM8OncV+qkQecg3PLomAHJtkmylcVOeBuL5/rHaj2JYZc4zBMNuviFIl62Fke7QvmGVrWTjgLri9s/qCXfdBpycTenjZbtCBWrIfthT9pb/2vdEg1H3bEA3vblm1NRx2VXL2aF+E4kZ/XR51kAhOe8rvjHPvMcSx96+wNQ381L/aNqN1jxdcBD68dTvuULUWispMX9WYXWfWz/j+rvY1PGSGu06OjA647d/58u73dY8HXKKvNW7aw6fH+Bx/qmD+f7x4hUEDF6zR0IhG2cSJMkarMa+DT6pow33Ghf/W4b674wCwckUVZVo/Z6cABvNpG/IF62b7CkMUO42MTdpB90N90VZO3TRxKm4MZaaUfcfqPT/yvedFHfzX4H0N2x5yFi7o3dDmDqDlZZR3dvp6tcQ2z5kdjCMHy4DY/ahpIVYCKWaFKVJJFQw5mSrlI3F28iGTZLlHO5uwwjAzb6ZzP+7sDcnikZzahiYWIFwutAIsSFqr5D2bBTfmBDGUdt2O+s2hxZMtmlqP9fD7Kmg+120GewzaWLTF9J6X2IkDnhifZqS0Ciri0i39SczDWZw+8nms9gklrJXYv0B9GRZbxCei4vDs0nYFzgDFBqJERyYQRjwZxNmgxMTSdZOyXuniQ+cIWbJw5S5r7pV0HDgl2p5yJYDZAHbha9gQzQtEaysnH3YmU5WaVJkK8FC3vd6/5Jy9sXX7JO1LnnB4pFIqc55eof8WPPbLWSrMnR75LJdJM/os2l6jIViOZREAZCUmKJBppJbEyROXXODTG4SFjh2yEPs7u9bwo37IQP0/QloPCmatcwjLUjael6bRItQBlJF2ShRoj/CUHuVkuqHRLmilhEEqNRsjytIpTLKDaAJNBP6qnhxIypKEdzPdoBVasdKmLGkwpKamN5bZMlWI09xWCDTUVBwxmarvE41VCKWJUkIAAa9aTJc3cufPbmRMvgwqhhlhDmjREVBTZ4uOWsyQiHWQyTEliQrtgNL6zlONWUoUU0XkYBVJEKSMqVUsuQfAoSRQGkgRJLddGG80/5gFNjdyUYwa9qcogMQTReKAMSkG7IwgaUylVC4gEKqmQIXf8F41YKU5klE8cOdbSObGG3fuk1F2mQVAapBrZ8yuTXAf2DZE9J4kStK6rq4tDoQ7FlSc7m/WfetZOj/DhEL7KQVtkaoyOZtri6acDjp6x5JiY8rg0/AHmt7/9LS+ZnnzyyeXRuVd8OwwLTZ8DDHOZ3WSK4sYbb/za175GfD+wO5HCqy7derP5xnhZEiAFxKo/fpWVSNSIFbwXBhMHQU3PKEE64DVh/OEUTb8Nuh/YuwcohNP8t72OSMeNpcHvW0P/FuTXOkGhZNfZyeNe2nbB69uOOH85rzPsGim80tOwZDGx8qnbSbuJDYqgKxZyT3SvKQappfHP2RYnE7p2sd5yF/ujb8uOjCba77bdDYmj+7Ox36zftCJX2IRxVv7WqOgTnCjXDnAQX2yKrV22IBM5rcOKLcUW4YjaI444gjHDYfqYvwhTvuPFkVfsNmEeh2OoeUsQl5iTDxnMLBHT16eccgpFeCuGxeHVq1dzEBqNgkIivD+cyYzhDCCtcqNjtttd2vyJSKlbjImRaPZ+p7TFcRf4sbN8O5mf4zx+Xv1nbhr6iZNMzu9cDmPTg735XLZpzgJkH3tgYJJwnLGOlASj/KrSFfFvFDfdIdYeqUAOZe3uILLkjFNHf3Wzt2mzFqQMvjdmV8M7Lx5LNq7bbOcKvk76U65UZD8aSxKICO0HU4lR/vjG6/sywyN2Q3Nz5Or357/2T5F8jkU/lctFJ2q9731O+1yxa6QmoXTqodIae+PjRSuIUhrt7yQLpbzLJAC42J++/vF86xGJsvk3ddSHJeSRRx45/e1mVkhPv2fHBKKPW7qVZ3z6Kdn3GhnYCPB9x3P4YOA9tBnWWORQaTjHGSHGTzMyyUgyJJbcGIfAHi1696zzn0tG5ze0JV2OZbS29QWbh9koio6oyFiRrzSu8l9kkPxDLklSOYjcMxAi+rSiMN+AIdTUczEFAj7gWCgFpWQkrt6QEZsGUN6SDZFIRIK4hiLVBbWRsfIbepFm4Qi3Tn19iFPC+AGMONumiRORDFwz8OitSTOp5dYamqlHmmdWQ008bJFglDLQwj9Dl8CqK2yEs4EQUqmEslAp9ErMeLcKKfflDGGmqcNcQGbuwGKcWSFX66NiQ7sQIE2X8uYCJgGSII2tpCk6WagnV0mFXQIFhIDLu8gkVGgz9AhOgVF0/BrX2gwYwSuvH+PXysS1wkCMUCF5BrWg12AwaGO1AWWkhnA+gCwlamqnYGUqohyV2WcJ0mPCEOqSPjHNlwqok+2y2Uw2EU8s4PMLppmmxC4upqG7SH9jJGHk4/1idB1yy7/wXwZtLusXWY0o9wab7BhqbKexCgXbfBulppvQUL/61a94w3RGHTlUQ+TMvJ1WBxgWXHbZZd/+9rfZKcrxQvvOETse9U56y9jpZzgPPICQ5akW+RI4mUWLYpe8g3cbaqpggY49yZzFVZN+gG6hBw8NB5j3RTs7Ow9QLQcObYB0F1EtIt38yY08laIpympu59oFzHEanJZPWS2fCPxejlX23AbLbkoV1wdbnuvu7lu4aKe3ecPiPLoTn5QTQlZHpMdFI2GjFCLZm+1EkR2zI//Wkn2sPfbe99Wf+rbE7LjlX15yPuPaL7n5n23d9Jb2Refx9TuQ0C4K+1bBH/qp3/c1L/9Kwg44yaFtbqR3ZPnLPZc73hFHH7Ny/br1uLt86AiRytZonGcWdbkiW/nUx9ve9jY85K6uLhai+QQ5k3DYyiwRE8cT1q+8UBcFIRM/GZ9Z6Letdv/XrteFjCu+FC/+ZRDdzJowJzNbmUVW7Otxb0lmSez22e6963Ln4+9lx0aKvp0vFS0sJOE+S+qqVE1vwHKjSstajTSjuKUigYI/NNN5Zmuhff7CWf/t04P/9C9O10Y0ODUW61MNH/3PhZNPfHrIGc4yE1ApY9kDWzayz7tp7sJKnws+VcIlvgzS3ffEcz1HL5rXfNrp/heioz/+d+uVV5mktDoXuZe+O3L2We4+fH1+/brXcyN2z6scyyHd1LQ8d8bH6tP9pYevz2Z7OQfb3fR4dtV72MhNi4XiN8OkHGD4TRuvqMicNy8HzjMxxNsB6gxPSuRMA+BZ5h1mnbskwpPOI0xzZhqdM4oekcdTC7qywbTINIxMRv8L3cX1/agtJc9IRuI7BIieF2VtGHCe2CIuHWJWghAnTaIkMRFIGqSgCmMBDNMlTrIpJIdPGTRlTOUsA2yKki57j8zaQOuslogHK8ThlTRTkKspJFVShbmTOHSL2oMwcwADOUKACHp5zNVFEsew3FYzJyrFDF2CwEhy0EnlpqTgl7+yA6vuk1YoIAByIwjFyBJMJKEumGymlIEQGPNnwCVVkjXL0CFldHAYwpm5FkTglLbwrzxytF3clotSlphWIxi2bNnKXoxZs1qlObreW+6tHSW0aqoRKiRQXvBoTBELSqGrTIxULx6lbFdTj1vaQZGQdrkVhLBI6JU8szguLZAbyRd7pDwFoLVojZJLObnZESQuZaWMoFMHGxrMXRluRwFSpRFVODRPi0rV6TH23AUN9fG5jWTtKLmjysMjhr0NR7HBqnh1yLTcjkbszsVeXdLz7YJ5BxgFRF/yPGQXzOf4GEZSTde+aMK11157yDRyxhA6rQ4wwxGHgXHJdAXrY/s+OpEGzry58b/4RHbuXOvuO53BYXmb/y1vjV75XnvZkTavftYOlelmPI2lykPQAUaqylMmHqI1Usq+aBW2BE7Cja+w3A7OZxJpPU7CVritj2fUdudVjimy2ue0P2c/t61728JFCyfuA9UFClNBOFEJYPhi0jErl0S3PAu52Vei3m3NLW6Q/+fr87/4dTB/gRWJFpcsrL/sZc/KnHpkj9s0147LF5lEplhjxe7PewPfcS1OlcQmcNHFrp2dU/9Mc2rdQy+959mnOrf39JDzzDPP4OWyFNzZ2amur4D7Pl4ulvGpp56KoY8UwlXGK8ZbPvbYY3GJlX5axOlQuL6sgzHsKbiu69XG7L28issxX8VrrNhWw0yZFHfjG4rZawLve1HHKxwTv2Fd/rxCbmxg24b65jY7ksBnNau6Nu/5jPZtb13QKdrWdJTpEaLKfOmcSkxUJppz/aD3h4R10qq3tv7TguxTz5T4kHJDfeMJJxQWdHYF9c9u5zt+stVN/0Ek7wPLx4VkokMUsirlCt+seF0LrxnFEvZIodRwzjlNbznRGhoQZd7UaPHlPb62pY2n9J6HQqm46QnPyUd8r7TiHfYJlzVzFnh9e3DRVyKPfG90y6P26NbI4IZsy+IEvN2HevacskOwBOOW3QonnXQSA3LayNdO2dNneV/I4xFDzLL2uL9mwTE7eG2Bdxn0aDeOgoc8vN8VK1bsC51v+LK4tRzzwZQHsnHixnJCOACckjAx2H7JxVRY1+f3ZxCMFZtA5GNZRvJjksXFkERxc8qA4raIXKtQUSlOgpl2FED+VfBSMsj0d8fqmjy+YG9kc1gWKNE64kvLD0JVi5obXtVpzPr28IgkixDWYoIP0Vte/AuzZJ+OYFN8gsiAcyVDdkCbW7NLiE0+JJrKzGnQCijlgTYvrNIQqUR9b1lbliz5Y2LVQFEYWAkumBXCkGVawIUb0wjMBUOpOpDisUm9pqXyK8Ek8EuVXKSYNFYSjIYhgTrlzuxfklyqBURZYqMasvlchGM9YIFkAWtATDFBZ9JU8gheg1s4KDiC4d5u3k1PNTYLbVJWm2FickfYkaH3Qinr8MIfsxAtzCQuVUldJqazGKZm2OezOpzLZiKJlL4FIA0zf3I0QrlaaZIgkBwTMTFtPBXtIE+pNDBmSV+Bpd4yeTt+7Gw6Q1cuaq1vr5Ppmh05h1kMRUCLdXvgjG06o5GXa5hRFf+2qjeDaCSyalX2yJXu8y+Y81tkJZiBUWSy5Z2XuI18Fn6nAB421bIks2bNmmo8OwG9ebMbDkyrAwwNTPe+613v+vnPf/7lL3953y0VGQpexF3SGfvQ1fal7w5GR6xEkg8qui3NtrfjHQjsGN7VBJYpfIaLmjJ4Iyw5HugRg9HZ1NTEPkD2zR5COwDhEpLZZ2Nw/z8H/dd7pc2yYChnbtQFqbe7s7/kRFeiHw33ap5HoCRw9hQPLViMomM3emLZ8mUtHL0wWaCzOHQKT5I3aSeDlXypgiOT4umSNcgHfwvPJvkEMeox5kXdoRFr+MXM6rekLvps4P/RdgeKL96We77ePfa46NFHO7PbCiPf9Qauk91Mo4nC3Y3+izGbI6FXDSdPz0Tc9OlH/ao7ckmqbnlf3wCLuviuvGLBS4zscMa8Y3V38eLFzz77LHIWatlvg7bjk0i8+sjQYuKDlX9d04aZKD4CpiHGNLPXnDoVYUuyXcy9EI2tDXC7ixavLNMWjAvXfalQ3OxFFuXbIy/EOY3as6Ou09Y6hxwDZBjuF4rZYbHA5M+oR6ORQ46JnqyoQDoTs4T+e2yr2z1irZq/qO2CBfESX6XyNgXeS33WM9sxD0QUoM0LbKIaHYSSQrHoBKXRft7eDOoaW/hmcohRBoeXGovFm5vd559/oThr1tyFbHpcAIDR3OUhYXqftCkFuAST4Q/E8gL24NoGuzF74lWlJW8V79egsOMN7pmfrH/5ztGnfu6vf9xuWsxshWZNqYrDEwgtq29hTKcDHLKat+V5Lg60mKU6Bg9zTPvxpS+eaDaV8diyDowbrK71vuuskDNv1AhKls0yaL0Z1UBkEVtWI4hTIyy5iIQMdVdZeIoklXQCEYWQNCNhyxniupj/AiIIQsGrkH5xsLs7YcWjqYRBoY6dqclAC5qylCzXobXxjBi1K+4PFeLtmJj4XaLhABJHtUKyuTdkab7gVCh1C0k1eIx/ZjK4cFPBVXFj9d5gowJKaSvF1zI8KFcIpVRAFSbVeIDlHKPl1SGsUCJV8y+8NWcYS+XaFvH/2fcrB3QIDqWEqxYgV8jgRpLIFQUnmYYr1lguA0AqlSBD9iAbHCy0y6qt3PAnjTRkKoXC1TI1HB+dHuFcy2RDi+lCA0clwjuKGQLkViiUTdEhJeQatFQtfWNqksKiWE2NqHilxdRdyGa3b1g7Z9Fyz7zxZYpQTH6FkSYIdoNH2ESOIUFwE4RcrohNOa7DBLknIlCGElNvBZtQL5l1jfVe1FvS6kUM8VrycLtiaWNvY4PN/K06eCWYkZyYUN1H9KQzd070M3+R/ef/5T7ztFty6et8ImVd8d7o2WuscZ9BYtz97Gc/Q1W9eURFNRunGJ9uBxiyrrzyyu985zuPP/74W9/61ilSuTswJJZIN8dz8HhbmgFDuiBMREjIpRyAYe1Oco281DhTL5X88b8i1Man7kUKZGCTsSGQxxLjqZqqvcA2nUUCf6C45YPe6G3MewrnfJQC/kbaGr2hlHnYmv9DJ3XOBDwSkYywH8ugcwLeJo1Ej+zsdDiMZ4IyleZhy2pPVRJ2/YurTIaY16IXzAY2+QYPJ53IybdOVNYz03Pb4x/9cECDi6gAAEAASURBVCnTG2E+hLnZ19Y63/0ec2n5Sy5OffQ/2YV/xDEMBuKFr82LbGxMjw6zibd0V2zo6aGGTw55zkhD/l82Dv7NK6+8ht/Lwi8eLLKV8cNWZ1xcbvkeEoYyUozxzCIw3i/EQ9WmTZs4T4vFIiYjORCIfscuJIu1ON4KTiaLMnvAyVcjQQwdZ+bszZiTCXQ2nssmNr5tmO194fe3BH68vmXW9q1dLLxH44lYMkFjXbZ600z5IJG8JSbHLwu/hRFwXfgimlZuJE3uCLDe3jAc2TBSijle3IvkMe6LFBMTgyp5lgAaS2eGtveaIrjbTibNcc8c6ZyI2xylbQ31boGtjW0LSNs6FAQdVC58FuxE5CrxvQu8oYBKgGw/HXVi6aVXZNLJXDodZXZTEULgw79/pBTLXfil0566ZdAp1Rm3fe9qO1xKMafAqDsob7Ey7A/0rB+CAh+VvtQrMiEUC/vewToHx54OauEZVwtjKqJp4qoPIS0wcUN2mYugI30Ca4wZGTxkYBiTsILBSZxDE5igOWCcEXm2rDUyHy1mhJXcK/WMHyMiNRkJI4OJgBgqJ8mZUiauV6PXRLrKn3F7ROTKrf74QbTObkxZsToRzohWdiVjwhoM5kdSTZEyEnHk+geGyWtqbqJ+cqV2xS3+oAbZn2soMrfiCHFr1KxUzJ2Id9kBLRElRwvqVeS8gRE6w2Aaa6gpoxDCKG6QmBIcOWFqEbymNoME7SXVCKw4jnJLKNNq3DZpA5kGzECrP1tWEKYIeaYZUpSyZQ6B1HCgTFW50QIp/4aG8ulCzMvHM0PSb6CjItbGoJuoBKlVgIlhGWC9cHFk+tfnWF3jqBYdn6PGsBXY4yTFFVDXd6V1FT4DDU0lGSASQvpMWzXNXMvDQ1DSZmWCIc5gonqhzRBEXVSphSRJWC01ahCGmSIQIDk+PS6r+QKiMAoMwfwRhFRTWmBISSSS9fH4PN45kxJaROAOq4ClzfoEJvfMb3V5tNb2lB3E4+4xx7vXXFN89nl/08ZcIsaXq5wlnW4L8oFCO/XscyZwrNLMb+8MpHC6HWA6j13QrJj95Cc/2S9HloHQCAFkAb/mya9IhZDd+LoszXGL98I7wBoPc3eOoGaM7AKb3+3nNyGHnEiH5bGTmXGHcKsdfzsX38Xd9Gzx2kXFe58EC0rF7X/jpm+BoaXtyeKd9db6mJ0MrNNHvFUjbrC9tOXDduedfqRTlMfOD6SxKJDeJX/T1vzDDzoP/yEYGQhmz3fXnOmtWm23tqKvdimg2UuMMaTnxPLSrDDatrE7ue6yKRzNitVrNtpxHvGcgttq+5u9Y4v2bxgLmBy8Uus7F72T8zuH7/7GrPcOC5KN0VicV30D/87f5ec9H7tgK+5j/obWxKZG7A4WHj1ebIq4zgNNYyeXkqcMJe3HZjcH25qasKqZv0Cq4k4QYX2DjSs4bARcXN5pxyvGsHuAd9FtW5ev2TV9/PHHsz5MuzD4GISUwjHHQGyd3W6PLQpyPfHldi7lx9JQJjwRx5W1rMVucp4o3aGxxmIRX7iQ50AEvghsy1cP1774R9dyG2a34+lvWsvx1EGsoblt7mKDQB4AVKxuflC2kWD+GdQoTtGVTMQIgyKOF4mKsaHq03SKVR9vbm+rd4Ogd/MGPovU2raAUc8uc+ijp52hHJbK7Hret3dS5kij8nFiin7frszacqIYnbd5c8/R7/Fnt89D3NfM48LAgdF0ar57xtWthnJMil0Pj32jZaqlxUSaQgBsl3QygGtCDUJumTchMNuiERT8+ApBorlcAdC4ourq6uIY89tuuy3cjwDA+ACG8Wg1hSzjV5YopRGuYXx3pcJ0yNBS2lLiGlG01fGwyB5FmHjSlnLVwLOmw6aSIH0UxonobXjV6jRd49VXCObR5tln7FWn06HapxqpjgOmidURildDVqOaehwM4KGBXHcZNEuvk6IFbHxAvhFAvrviZCkMZZng4MotEeUAc4J89Y3V+FdeeQUMJJKlABpBtusexWr8TBMzkwiABmYbiSjCajCqpjptOJHxAADTjzXpdDgy7F1HsdFGPFby+S9qHtfU+KZcyBB/w6SLyyp/gsokitTUFFNe0sWjklz+i0PLJ+y2btmcGRW3rKUt71ivB6PMazoLFi/jCwgCJhgoKOd1yo0QajwnRHEQvDbSy5TBgvYmcW2MPDMkU4Xc6ICV94m1rCESFCAxiMEjS5GmCoNTMJMCPHQKiFRdLlsNqZhN46U50kwTylw0ZFOMX8EGBvVYS9J0EMoIISKopSrpbFKkXcaLJEH2KYNZUk0wwHSP0EQB2grvJIdbg0QugiVUXtyieqQ2kuxgMAM5sbQfGcsJiNkHbXJMEQGRvpcs6XJihsixwd7R3m30kkxF2Jn12wbptFnzlvJBQanXBFMpF6FOlDGlxdyz2PVtnlvZiarEKSTrJ+U98ABqvY6V7t5W8vkIth/xrOzANnlWI5HGtvkUNU2WA8joCxPkRxIFr2mg4TBxk8CPAJQrVVi5NWNb2oflwq3CyhUhzcmYHP68qFnIEY5qJnmHU+A5mrZTfvaCr/Q4CgshSVl0OnYjV+IIQExKRSgdy3cuOxdF58/jDOgAgcf+OyOQa7oU7YknxWLMueeeWyP09oK2w7DIdDvAsBhd+J73vIdu++pXvzrBPPHUO6M8JozQM6VqBonIxBps41OqAILS2B/93q/amQfcYBQxVLTqrcTpbuvnrOSp8uWVPQxaFyOVoY/O3sPSBwk8/6I99EP0WHFjXfAPc6MD9SVMcD8Xe2xW8Z1bvff2RErrC/3/4s75miqYnalE+PqlTZvz3/5u5K7baTCS3LdfLN1//9hVf2q/+9LkAvRBbY+AgX2GfM2MpQAsIeKkoD6YrZiws6RmFFBgpYL6d9qD13nLRnIX9Di3zcZPK2CenXBMsf/Z1IkPiRbOxwuPx1JoM3QqFszwk+gfp+j6T3KGcR59wWikRnqqmPPjzyetUwcdaywZ7bnwwotYmSSdoQsxbFkhzgIve+kRPcRZI6IgriAu7r333otXzBlvrFsyN8daMZsnKYLIw5kHBgyMhGLveVbucbs9a30iVfyG77IhWb7FZOXrrfh/s2xWZwO7L3XFiWddkhsd2r65a9GyI2x2+6Mus70R22VNODM02No+DyUcicYS9aKsxR4xyltaaNQzEaPERZcb3SnpECAKnh+TbCClpNH6RCCDeQBrUw7d7c2dK+cwi8OTzxJJ1rMsHMxOyqG4TTE258juZPBwu+8Bq5cAnt6+PpyFpibe1BLND4e1Cm4bGxv4qs5g/wDe8lh6jCytlyxmFpieIOD+hYloFxaaNCg2soiE6ZSqTucWYK6EEDlF4IAW0aywCFm4gmSRzpVQXYpciNF0BajJBY/wtsqxrMZM8f0V2HSzv1AdJngYcvSFXg+TJs/8ZqqzHV5rJA9CWGUIkpyIiuuwUQBrOldCtS5mSw0lYtEI6UyRckqicSDE6wKnAY/F4nEyjaQz8k6wxSv/oiBPbOv20xnEaCQaSSaSLMeBa1FEduhAA1ME8QQmboJS4gOTZEYXk5CZdO6h7es4HeOoI9UtlwOGS7jKbBHC82NKXhxTq2iuJeOxExenHYdOYHC/gRePHfh0OutGZG0TSEnhmxi8lMpQBidfvRN4Qal4AJA/HElBTqPLDhnjXgg0vMONx9syBHMvD4XACmb5EfkrSaSTIkYOwODkVyhEegt5YJNWkC1tN7lcfE7BKAOD3wCQCZCBMZVKUZqGQpO6ZJoviEYT+aLoOiVSIqggKSQx41DKj96J1kMI52Mll8/eWvmxIduzvQQnCTmDefnmHxWbDgJM2sx/UZUETD3TaEnBEjIatKxMDSjp0myjdaViubP6+ob93JhQ65fskWFKedFEW1S+bUWi+S6nRktoWbEFjUFJntYlxpIqVKnUWCpCk4FDMcNB2Y9n2kaXyVqNtFRbu/71jcwZrThuTsz4v9KEwy+oeoXZwu+ZGrAEdFcRI0YnrImEbyzvRHk0ak/4jWXaiyd16aWX0vUztbkzmq6D4ADDjz/7sz/7xje+ceedd7JzfTrZwyarCb5a5gcstwXFoe/Z3Z917FGdvGTK0LWHrbFb/Y0PWHP+wW28WiYERYjtwQOGR4RrxxwPX9OZzvbuRV2ioRCoI7/1glGO2/V/ODs2kELUF1lNDRw5JOrm2f7JI+6i0WDkJnv2V4zArvbq0cR8rzY9duft3s03uvEkylOkPFwrjqW/94PoUUf6ba12TM61C4OoDvPFXXxF1qlYb8GxZA0H42M8n/FPdBO7Ohu830hZfMto86cKIzd6fnf8ip78kpz1+8ZSqS5a/0un4Ze2yyHM9tg99bFtKau8PCqK0Sg48XhxQsrEQIosUJUCo2JJLBXyLEqzpfnll1+eN28e+XzKiNeAqR0fjIUO3GDcdZx23Bjm9vSwaNwwCAMADIg29euIa3OGhodiifcF7g/4DFLsinxxcSTzG8/d7pcWu7HLA2/pGBwbLc7POO84ut1Jj7ipMacjNTY0uBFFfOQcOTKhWOjz01uOnLskwcKUkO4bJS2qXP9IQqcK+0R5y5Q2MT+fe+21tZ1LOuvgraTwfQ++5isqnyDneZb/xORZGNRHIk7bbGihyf3rNq/FU5tLb1LZxs3ZsWyupenZAc5VEtOE9tJretg1APCTOClwSTsX5oQpABBI0SLoA02B78CAStf0lGlgIIV0IoABQ0EiVIHy4FbLHoir8E7tqiljD01zjYChuigWOSIotNFrcrmtXv4CrNpGVzykAAMehdR4DR4gSSHARiBpAjvzmZrhmWJA8qQAELYLMHASiPC4aVyvEIDpDzCch+cU0aD4WREFOb1ArhbkqhGeBbIogtwDmEBBvTK9paV4iEyOXLRqHnw9DkSfaMWmWZSiOuK8TUCPE9EAWppD00DCY8gTyn4QeKKYESC66M3GH0aa0qBZtIttcsR5WuGSJuqVtrODgzi7UZ5++mnAFD8pVEoWhCGCdIaOlLAs4p2qabXu6Q3TiTALRr9DAGJEydCCxGkadNIoEOoIJ5FbrjSZQBxe8QhoOrcE6Nd+hMMQE9YFWmiAYCI8MgQiwNNNXAEDIXGwsS2lOl2zACYXhERI4UqlIIHt8IFbClKKNgJGFilwQ4EVP6VoPgTzogdZQIYWHllahIjGmRMkF85w1RAiURiuVARbNFcjIAlziVCEdA1IEognhAAAg0EJ1muYVYloSytj1KRqi0iCByRUVVipmuRKtIJn/O/kQLCI/mIBHVAGHrdg4bGDJ8mkCApTP/LZiZsU0nlEhSama6OxZCqZTKVYrTcJ4vynkomBwYG2ttnoLMAkpQ6gZFS+ZideHHhSqTqw4wdSiq095CZSdZBBM/nDIY9EoraRGzzz4jyLI+3zGQKYWRCXOyiKT46OhPk26aQw48j4EkhfHHU8+CIOvKTI8GPnEjD43FIWLHLrCjCIhXRxQg2bjfoxzjYpZPGnPh4VQRvXkQaOFmO2QkoKgEEBDIHqZJnaECbA0hzxpsXnT9RbTQ3QPLAp7XnxeNs8OU1SPHNZv7fwRk11Qgn/BInhqOG+qVmWmsFmSJWKIVceDlGyxnLgeTF2odO8DL4W8bM3v9yy4IhILM7jMSjPE1gRXKpqpSM0SZIFlWmq/lQqEYzyJwFoiGILtxQ0DrqJm0Vg44IXioXRQrElmehoYr1QCx2OV3YFslbBCjBvr1Se4hnHBxQxAbI4NhWNzOrIXpN4//33c5Tg+9///r3GcJgXPAgOMOPyuOOOY2XvBz/4wTve8Q4V+tPTDZgahAnqKmXudbo/a1tpvqqTfyxlPVcvomllJnrisOuN+Ns+60c6nNR5zOdOgGR8FoYdUz5YP9hnM/ax3EE2Ki//Au4T36e1XouypxJZjBpD8BfyfCjWt55OeIvSVhFjtN91d3p60QmIXzmN7J4HURiiZMrCnVSvLiimH3wouuokFMOO6io2E5xBwWOZsXcOg5Vv6ooFMC5gkMFJko1CstjTTkHsrdisI9y5/5u92a7TFzl1wDplKCKa6lkzZ2oXn2qJ/mIOi6WQY7StbW+J4xgGXj5ybCk+lCgVS+mxTEze9bU9PKsVm2lLyU7kS7MhBksaWxlbAeOe6vQwZ+zCP/7xj6RjeGHzsd4LwXQ03c0XgyESeKwujE4kMjMvQNI6Up577vllRxzROud/+Fs/wpePIicX3FPk8E7fKbD3GA1cshrzDV9bkehIxkvFpsho0zzPLW0ryPkfPkws8CpyTyHoXhzrS0XSGME4mzSLPkqPpkdHRlh05JaKIGZ0NC2z/VaAbYixv2nTZuxjnJB0ehRIVi3hI10LV0dGhtUXhQRuITU0JdUABSFo93uAn9qVIWbIg5MEIpqIlMDih5/wFsKw0YhjMnLLmCEAAB6AuWoiV8C0OInESSHgPKjMoVKCppMIQq0OJmA0k640ECGLKkACP8kNyQOAdPWymJTReRmQayC3s7MTAnCx2POp1ekVVLoXAIRsJajOIo54hB6Yz9b68Vn6Yuq9995LFshDABAyQUMKSpHuI129FEYdA2PNmjV4aBzPq5uvtCBXppz0lZAnn3ySR4mUMDDUeV2FWxxLZnyIQLbm0uq3v/3t3KJ9mdrTxLBGtj/QNbidDz74oGbpFXjeikcCQ54+tmFBsrADeEYglcefa3VBppAIpHDmHA8jkZCS5cuXq7/HS/i4XnQWQcuCUOUtVhF0kqgcI0LToAQkONs1rWaEwH+yGGY8uXQQQRGSyOnuNA0eaikQkqhoqQtrBhcRC0zhwyv9wn4QGvXQQw9xDckAgN7EByaFvqbGsAgRmoZYIwvfWL104lRHFkW013DRySKdRM2Cft01AxsxsEjXXCJknXHGGYAhmh577DFSCEo/VyajGdg0jfGjqBSA9vIpafqURMSaJmpBrhzkgXtPFbfffjvjjRT4QBtBxWH40E8Kw4BeU5xcCTwXgPFQcN4hD4smKk7GAN+ZI75u3To9PUHTgWHkMMi5JT2cgNCydDpEEkcYMt2jiXqFGJ3RYNQxQsLqyIVgpAoA9FqIUAHIouGapfM4io2W4mwjD9XlBoB0SCKdB5YAKmqhW3mESUdmkkIgC2BSCEQUknQqgjACQgYkREhEOPMHJMW1omrZqzWSC7ygM4HE6ttK8h7/qnDjqiVpBaMa2rRSZBq3uNB6y4iqqxcPW2Ze5R1U+eI3LjY3cCVVV1df3xiLRGKB70Uj9Q2NTLxiH6CVE8lUQ30Dk3l4p4HNWVFRL8JGcZxnB2VVFM9ZHFpxksXfJkXe/5FIEDG5TH1quqPp3OJs45lTsOyZo75Fl8rytdm+DK/srTmcZ7dxDqaBOtigF1dZnknjpcuOcXlCSZHld2K+LMUTAbEssPNPF9JxnSUXx1Qcb8pQ0qQENsRBTK5k54s6zaDdJC03vrN8ysEkyUX60Exbi6Eis9VmPBm3lphQLylylXlqHHd8YeGv/JNpS0myhofzo8XEgkTd4lnGma8Iij3u/kO5AF2IWEZcMyxnbDukw0wIn9YwZU9pBsP3v/995N5+eZl0T2t/Y8CXxdw0N4YuZ9LiC1/4AgtoEyzJ7neqJhtqxVLPNZEg7Reiueta44+0s40I6VK6z8+e1Bf/eLcdSRd6/y6aOmtPHWDUMwYcihkTOVxP2O+t2y8IEfUIWzSvagiuJY6wQkzznw9xl/J2gbOBcYRkolRE9s4Bdc7SYJwlI956BQunTeASy6ylzFt6rhP095SyYww70T9lUSCWmS5owB80LizCgMAQ2Rl3+Y4sNYMw47Ak1LiBw9Dj1F1sLbqp1P0Ve+w+Tlo2m4Tcot1euDnl/Srh5rCMRTNAEsvZxeLKCAdHB/2Rd/dnX4qX1uP2cjAyy9xusHo0fmoauKGxZeu2F7o2P8xApXpoYwhRFy/3cotjgAGNuYnxirWKdYvtAgBMAB4rjVyMPCxRnISlS5cSRzrDDaYpZ7U0s8ocy59y2TmPdrSn4Sk7nMS/tb21G1J/f13LttHvDQ99A/cVmlm/ETN8ZIQIOhdfnZUV5tN3yZ/JEpXppj+FFUYj0xtGZdJAfBsaCBIagp+AD6DqhFty6RQAsHIAwEDEJCVgFWmlYg/V1WEtAQYzaW9nZyf9FeLEjAM5t0BSI90HEmZDQcItDSRFKaE6jDCKE8Eo1L4mC952dXVR/JxzzgEV7ihOEekELcsVL0XtWl7J5pYQApClXuJdd91FZ0E2WXrF7KYTiWOv033US0FgCAxIdYroR2Y6yAoL4v3y3jIpGNAQRnoY4IPSjwHNUAnTidBqHDC4BP3j/aWVK1cCQ+1hXeGzENrBFASGemsCzaE7KKvpwFCETtHHBLaQrgX1qimUoo3wNswlQilgCGRpQ4grWh0h3FKcXS1aSnMhVW9pvjqZpGsWVx0qFMc7UrCwbFgdrDYldpTC39MUOKMcCEvpUCSXUnQE+MMshpmWgnictDCdxHDEMhjAUJ0Vshow2ALOag1Fx5EOqXxWSpGHV31MwHziiSeGiRqBS0SohYmzcLxpVnjAGzJNm6YA5GopItCg69XENTfkFfSHcc1VMojz8DKkQ2ykKPFEKKJ7kaqJ0YbzjMMuYMhCgkESCHkSiTOo6Di4QRZBYUJO0hDGz/gsWg1O8GhWWJDi6ER8RQY5cYICQBtFiDMdoFmaDgDDQLN4oKqfGq2CxgKDDKl51shF9tIEfM5169aBTSqrBCaG4DNPU1hKqyCfGVgopNW33nor0kxLaF0MNuXSfffdp9MWOuzJJZ1SDH5mH5hNIEWzuNIjWKvgYToG1UAKAQDizNGcddZZyB8kD0HTydJK6X3IQBDpPAgNIagEQLoCw/wC82g6t4XDTGO5AhM+OBRHikIqpZQDtIhbEnXUKQB4KKUAQCoAVwJgEEC1JrfMQyisuHKoX/7VcFfJn+RKY5lr9GSNmiEqE508krrQyWBobGhgTZqKWLVubGrk1XCYAnsRSiKFzLQITCCd4qm6euKsqFuuAxK2u6BWsQviiRRTqrjTueULxOV2SrzyVCyxNM1VvGuyJEU8bfF0CyX88IC3ksSpZrsDi9g45LJqLV4xRiGjVfqm7FqjtWVFVhaiecx9XnuJjyTnJhujfOBBFDY8wo4SDDjwBLMjHfceV513sDQXm0Q8alFVuNz8FxtLaqAesZW4cq9clhyTJBfQWcHgEMR685ojKbMdIBw5Uu6wCTz7PCA8fQicQ6LRPJ6huN4LghGDN9xww1//9V8fnt29FxwbX+TgOMDQccUVV+AA//jHP/7Upz6FBBxP2fSn+LkX3ezTiJPCfU3xR+bInKbuIbXs+B/a8ivy0fO63bEnS7kXvNgqZPXUKWSAstKCA8wEFYJ76gWnH5I5R5GqkRWyvSZZDJZk4i/z5TGbPTYogng8WXQL9nFmJdDjwz8YpqIIwoDe7evp5QPkWCt21+ssMJKNPo5GzEsuthWd24EHzZZZzFOR5iZgGRCIcnAUEXUAQpw1Ecw4NW112CBBKs+/aAUnuspZ+Eu/sMHPPYvNYHsL3NxCv+0ey/8ujVBUaMB8fXPk8k+XGo5xh6+1Z6fdL20d+0U8eM4tNAWlk8fibx+wIoVcKf6dG9qffvUHOFB45qK5UDJsODNnwGAfYLiwzIUlgXFAIvJI7SSMCQUOF8dqmqC3Rpm5f3etffG5ibet5iBzr6+vcO+j/m33jozl+RxkF7oNUY4RoFYmql20OzaC8UJJx1ZQoxaGkKXClFzR/fX1mkVZ4thGatmAEGBagc+JzU2cxT3K4oVCM5wk4NSRC5HYi3Qo6bRIAwYTFjAGJead8qSS46N41LxjLRG7Vi0tzGIIoE9ZegIPdTEdAE7KKhOoCEOTOEYhZzVRu6YDTENYQSKFSnUtS7O40hZYDWZqpy5StCBXAt3BlUQlnjioCNp33IKcJtN24gSyuMJPxU9D4JjJKWeFz6z6GwqvAKECw0gNS2kW1YUIzzzzTE0M0YKEXFTgBRdcEGaRQlwLYuTpGyKkKB7N1TilwsSaiPIzTNR9B3qL2xmm10QWm1CTqLeyYcts2RqfCxsJ49NJgb06oTA+lwYyhMank0Jjddlwl7mhHzg+l2FJqElX1tF9YQ/WANDpYb/XZGlZWsd4CLM0kYG3O0roVp1hCYtURybI2h1Ciu+O+WTtstVaI0OLUF17GDedtuteY9Tp7B7API88aNpkBAhPDV2jk48hKo0AM8E3HcZPFoSlWFiuRkUV4S1jVRVBmBhGmBFAn3IbBiWSssx08KyF6RpRSYioYW4CiQEYV83S5xcA6goTNUtlIMDUpb59CACXqBEwlcAKr7mMbX0ikEIqA8NcrYuCmOlILcUAfiIgVCJ5WpF1pJCugcFGe0lBZtIjlWT5JRG2UxCE7AVA3Gku1JJ49tlnn3baaZBx9913q8jiqgHeqsJ99NFHIa+SLL8MUd3Sgk+OjA2zwAyRiDjgoQSZjLeMSwwNwFAjjQ3daYYN2pAUSpGFZ0KAP9ySRXGu4CEFsDALhGRRKc2hLXjdYFBWAKxN40pK9W2YvrsI8LjKyl6EEiqJW0Q+7nFjgxxLCYd5UliFJpEUkqmBzmpuATLONwibm5vYas5O5GRdPZPzRYuXrKPJuoRle3kfV7kki9IlOyvOMx61LZ5zqaWAU81ctR/kgCmxe9xkGQc7j7dcMo60OMXS17I9WpxdceNhE80Vj1gdZopwA6R5l1te/8YJN+vPukadyZbGrALb3zsa+QpGaFXtjh9v2HRmyRkYbPyhhTpsZnhTJxDsk1JOS3n7lweE90knBX4TYHccODgOMKMTRfUnf/InHM3yyU9+cnfETXO6n3/JtbJUaj9eJ+f76XwbYo/z8xFQT9RZ5/W4Vr6YfdWKrd5T2mgvvgdqDwNCZfGeYpgueBEdTv1Fpd7/wUePIlcOFP4x4aVj7M6ROU6mJs/tjy6FS46VuiCweNEIzbRDW9PG1tltvAlXOPfs0pNPxFmQkUNABKBk+7l4wjvjbWk3unHd60cfvSJskUqraq1WI7/IqsnlyUdNoixRohhnXNGXRJjVTY9mBiTCfuDR/oEH1qw56/QTVqc+9Yn+7//76IsvomnqTlrdfPWHehuav/7ft56zsunSt/e7LdmGj2VcpnaNGqKhBT/2+a9Gv/XvN9O68nSrIRfCQko0jsWj+hVVis3EFWJIAQxdi0aHJ9jfqFjMBYCBIYUrrx/iFmJdYaOQ7qWSTrT3g/+l4/Nfmd3b13/ccceChzqphQGjVjjmApsGwYyNRa6aZboZkoowj0ghPQzgR5FjWLCIQRYVsZKjxhYLJiAhEY8UmkmkLrXn2LCtxt9rr72GTaM0mNZbLBxhjRHHUMP5pyDkEYhgshCBNiZ6CKRgLdEpRKCfXCLUyFIkEYKWwvhQzPCKdZUwXXO5EuCebt2szsWUoSDXiy66CBiyuCUSXklhm64iH38dv0wXwrA5M4yDMOxuEsUR3I0rCOsI1QXDON1NCG+rI3BGG1KdGMb3QlAoB0IMOgh5Lh555BE2Su0OYU2psPikkQkK7vesiYnZ79XxCOv0DfXWIK+5rSZsOrOq6x0f30dKGDmoKuQDQoDxg5AJB+oEmIHHp0LW1ewLGE+eptSgCm+JhPGasqSLtKzMLtXkqjNTk6i3dOjuJiB4Lqqf+uriVMe2keqU6jiisvq2Oq6vyWhKtQwhBf+TzRFIaXw/RCURxCNSmtOz8QyZgGBShkRKcQ35gPBEQmpimMsiOXJ48eLFzDKQSGCSEWMDWaTiFHg6TrO40kEahwww68I+MGEtFGRWhZSuri7I03RugadPwUwcZ5vJX03UBvJ6hc47sB+HxXnEHXISriKEkd46A4LxQxYpGsilsxhjVIGaYAdNmE4EtDqfBcEMPwBgDh4OaouCcAkAtD9aD75BibrNqq3UNlAfm1zsBBQfrQaYFpHOFU1KhKppGUpDr+CsbpQ2bZdXKGQEonYiEa9lVgvtZQW7pWUW78o4LnPZzfQgOqm5qQklCHATKfUNLGTP5jgD9k/ZDs9IPMm+JzvV0Mgm8ay40KW8OMkWpy9yJV4q4kuzhSfIci25OZaM2TaGwy3utIOnLavHlrW9bzAe6+uYmzx5Ad5vjUW2S/LfgIn0L8ODjqier5zJ7QyfuL0jkoGK94Txg0zbR1R7R8Abo9TBcYCVdx/5yEfOPffchx9+GOt2RnCToxxw19A4TKTK2m+ZKISkxHKyNGqkC9mVvCnTjRBESbBPiQ1OaJEpl5tuQH2WnNgJxYb3BMM/sJcNOl8q5W9p8bu8Yl3OObMYOY3NwKWi0+62ftpwYSdWoJxSSXa3WvFz1mReftW68y5ealUPMh9N2H9+tXfU0Uj85bNa0NxmS1WWCKIfLYV+wq1CM0EDmgxGhelEuCURPYe+R8Px/JM4KXdA9f0ffv+vPvdXl110Ucvf/908ZmgdOxeNdFvBj39xw2OPPfPMs0uKzvJ3n90Vc3rYjkTPBlak5B17+x9PnbtywRe/NEZ1rFaxPom+XLx4se6hVeMYqwW1jWvBwj6z5mh3WgH9MAGbAxMBcYzWRwWSgjrHF0Va0QQaDgBNGB4eeuklcRFjseicOe2jI7zAtpkThZl9f/FFcXQpy2jBO0Ws0xZYBFparVciJGLogB9uYChwC7YwgAEYVDX1AoMdQAqtQD2TCG3cMiwppQWxAslCNytmvEQAQmxEMFkUmGVGIoAppEb0irmDXUhd9CxsgVdUzQvSTM2yzLW7lS5aSqjGE8bBoOsVmgJJBPArMVxDyOpISF51osYnyJoYeIKCE2SNxzltKVAFrxiTDBieGp3mmLbaD+mKeO4Ih3QT9oV4Rg4BsxKpgshSITApQiDxqXjeJ4WcfgCas7tK93tWTUU1+OGPsgidgtxGFCPEYDW3FMRjJNRg4JZJSUR3TTrc5gFHaKsrThzMLNSrwKRedBCbq2tKhbfsMaFeDZQlEopTFufpzUqm/FKLFmTSlrkhhYd+skKCkTCoj+pSYV1oPcYSWaRQlisKkYV3iESvMbFLRNPJYrxh3MMZivB6v2ahxfBdyV2zZg3EINA4CoGHVAenYaqLRw0x4EHjAKmJXIFBGekcJTodssFJOkYFOMmlCOYHt0QQmJBEhCoggCtkk4sfDk5yUfTk4l2TCKrhoWEi1duUQA4kMFwnDtAAMGw3ujKKC93a2oYXzWtgNMRx7VktbckU7yUB0BaNRBvqk80tbL5zZ3Ebj0WiiboTmkbSnG0Rb02wQKEzJrsd6hMTc+jm0k30HQ8IXanMP3TbMhXK2V1I+MpXvjIV4DdhdseBg+YAM0aZMmQ+77rrrjv99NMP+pAVQRXp5KAmZtaC5WPBy41MrpljdGWOTVYGl3GiJntRYnZkIfeGoXsmZXg4cWMQc7vrjJmTzrHN7pz/v1hYGxl72JqXdj46Sot1ntSy7KLT4sz9ZuAtYw9Ubx97gIuY13iD6AB0CeeAbN/ee/7b13T+6XvdY4/J3HlPtmer17EoeeHbh+YvuOPGGz79mf8XH08V4cRNDkcFigS5htLFR4KHaHccSPwimInmQI+Swi3KnhS0pqYQAYwsvMK58+ZyVgeLoHhl/QODJH78Ex//yEc/0r2tu7GlsS+S3tT1m/6tTzS3tI0UFzXOettgYdOKlQ0LOxYhWKkdFxdrQK+0Efy6JMJ6Jq/+ohehCgMF1QgwO4F5/RUNCs2U0hlocvGBySKdFG65wgT0H01obm6BVOgHnmaSDjDFAcOeoGnKKJQ95xKRTllaRyuIE8gF7JJLLqnhp864Y6+wF44sFDnLCIxD0FIFPiSJxEMHm4pCnivOGoThLWSH8ZqIug1oI3WAwQ8AMwVcq5HXlJrKLbwlMNLY1McKA2ss+4hwKpW+AWB0tgUD9E0HeOq9eTgPLZ4yZRRSAvGCROKqiROzBRcOMMbb1Pl8OEMijeEnagU9AtOq3wsYz+fqFJwutC2sU4eN2WHiaCX1P1EoSH5UIbK3utR4VovyMOqjJotS6kbWpOstc6a7TCeRJlS3ohqM2VUCw4NBpSEcZig7jED1pfUKgBJGo1h5ZlzBJZxeqAIg1FO0FEgUmSIkizjEc8skMtdqAlDZ6gCzgo3KDjkDGbysoQcW6DGBgDHm0XEE1Jm+ysECI2oXqtC8mkWX6YYgegEKSaRG9LtOHNBBiFzooXeIQBuON2DkkqIRUlBn6GUiQGIa8NoRHKomO+RSdWIYpxWYN/znVBFegWYmHWbCH+wBjCW4xy0Ec4VaGEg6TKNRwDBCVCNQnFoE0+4nicIaZ2aEdjGEaNrMJG//UsUww2/q7Ow8//zzD90u27882TtsB80Bhlx67uMf//jnP//5a6+9dnfrQnvXqr0ohS/rxY8veB1uaZ13YX/uUfPJnMocXnrecPyCAc778735kcQJBv+eeb8UQRtNqpD2gvK9KxJKVSI8Tghf5pIRxFyRyLguzG4ODZwdT6+78p0jDYlROZmYld8g9sCTiS9/I3jqlatHhpmLLdtJ42n45je/9bm//MsLzjkjuXx5NHDG7GBboXDH7+78wQ9/uGzZESqn0JQwBK9PZTTWAAFhjVxGWKs3i1JBRiPK0R8EKgKGK2oGLaKJEE8EzaR71fDGdTMwmgbFQ1sYaYsXd3LlhaPf/e4ONNNiM5VOCs0/8qgj6+vrnnxm83OvR4/wlnG6yFhpuFgs5LLlXbtUh3glQC0+J+pfOjKVwqmDNpZJ2RWMysTppWoYCFpahK8LhfAWrcOV2WuUN6hoOwENRNNOPfVUNB9t4ZV41mOhlg3J4KSNaH04ACRFwkAKgVtKcQWSujQ3jITAROAAtRDRXMpCFSoWl1g5rMDAkEW/0wTti11iq8asyGkXFFYDa1xHF3GaRiBCChHooSCTBUpVDcKp3GLW0N30NZ0Izuqqp1L88IShr2k4Jtfh2fy9azWTWTzLPKc82ofhMGPHqa62YSjzxLGexkOnb1tMwE8kGLzS8TYB2JtZygEkGD5VNcemONLoEVxckKikRSpSENGNVsIsJhE9gt7hdl9YvUtidpmotUyQFZIBDAMpvNUI2qdG04UAjCUWddFZOMD4zyxuh1mU4ijE8LY6QhXnnXceVgEaSo0Erur9AgYelF2YTkRVM3zDY6RGtSgUBjZSBLJR7tCgtXALMIYKDjBx0jlETftCgUGi5+SzxZ1N6eozh1c2bVGKhwUTgsQwnQjVqXZD/kAYih4w9ZC5JRHFB3mkI89xvNlUSBtRrBhsgG3evIknF0qUGL0qzbu7MkiwamAmso5eoEUwgRTSdSKAtjC0ACBLAZAJEKlM4ErYHfJpS4ceaJ626g5uRazK8ALwF7/4xb22ow4u/TOn9oPsAH/gAx+45ppr2MtOXzKCDzJf7JQz6zPB9s86TdnIFzblbphlPZ9gT3SwMh1/d5/VwpcYPXvWpyybjZp7/8AjvxBV+qb+fmxvKOlU4iMKcWYQnUhtpKReEZoaR3Ri22kukLiIyNYaYsxZCngY1hf/MXrKW+o62v30mPPMS8WujWNtc9rnzolyRAkikoDcQSCq9GTeFKGp844kzulcimpBVEIeUnvZyqM/+rGPEeHYZOpl+gp5ivJQ14h0ApTgauqrQbxQZCZEd7jZYNPzhKBZ34YNyQYV+6kYRdSF6lKnDheRCHoFMNIhDGcV8QGkppOFxiO3tbXl0ssuRdZv3bpl9Wr5jgtiHTwEcvFOcdsoBRJwai3qALPBDCOGAPEUodUAUxGuJnu3uAUn8BTXnWm0jkS6iTOK2QFBQzAuKcvAAAZIOovhQcNh6S4nhqAhbLXGazQQNNO5wMBMANSQhQwaS71Mfld3Nz2IbgMYJUqL6EFlV1jFLiPgoZnQvEubGHqojvkIOhfOgAEyUN4E4rQL9u4S7RQTGQZwiWbC5ykWOZzBlEs6JA5nPuxR2xmrSBikis647VHZNwAwrabtPLZIS0Q6IgvRNGm7GGMU4dmfFPIAASCXVIghpQ9QFfsRLWIf4YwfhVKAb1PHTHfosiQSmA7SRVftL5DABNUI4CROx9UoiKlXNG2Qu6OQdJqAuaI6tBqsOl5DJ1nVA0B5q/DEq73o6oIA4JpWp1THeXMbnqM61UohEipKuo8Ja1IImksHaVluVR2TFZKh58yhiLFwwlZoLkYRjw8S+8EHHwxNFBQ0NEMbTyJgyCW4Qe0E4lwxivSh4wGkFLILS09NO9S6etGMNIYKtzCTK7PzGKIEIlCINmcaJSRSialufnWc2tUlRpXDZ67UrledhdG1Zc3FbYZ+iKSl2li9ViPcxzjEQ4bOYux35PtI234vzuPwr//6r3TQ1Vdfvd+RH24ID7IDjJa96qqrcIA/97nPHXRblrOf3KaP5LNPOoM/tmdlYh/KytdyeCeUg+1Z6uQzdPVXR5o+vI9DhDNCkD6IBtTVpKhUDHGlCEIKyYXAwrNFwCHLkFwINXVuSUTkccW5Rd7xkOwOuQoIRBgyWsUWEgp6dIaPq8a58s1UtuYic+vq2YjbGotFON1BjmFgeZR9z5UrDjB4IBJZDJGaRS6UsB06GpWjWflchDpC1VSxpwh3FJqZJSUdwggQpqKcFHwzJCmiM9QEYRa+InOlZIW5tBpPEiVBc8afkQtVnE6sOEELAyEYYR16mMceKx9fhUhetgEnlABMCgFIBiqr0NRIHAOLusiljTBf47i7bGMjl6n3sDlAolFYMQaSFWCqA4aqF5u3cOkjxgCCW9GCkHp5txbyyFLPH8lOHH9Y6cHcgWlAgpYr6pMrU/70EZEwUIWuD2gKt0RAQivoIOJwg74BJ61Ds0J2WFbjetVKw6zqCC1izhvKqxOr4zrAQM5qM+msydNlOkdLM6shJ41DDGRrl6HUdWzTU8wdKPJJMRzmAPrUIB8OXT4w4BEUzM7QlgmG5f5tYPVzsX8xz3xsyH/4jKBAyyCE1biclGzsb0odRFWOcGCcIFQJk1J70AGY+WWMwVsG9h4Rg3RFH1EEewCGI4f1oWA9MJxwRAUQSMfd2scJxz2ibb8D0wS2WdEutNXeIVfmaNnq+HhsE+SKKWAIGC8W0OyEGmyKCg0VKimEmAayQII65hBvUlCmBI2EnjMIwyy0Hh3N2KYgkPQyxUNSScEwYMCTyEe8ASaLHiegavHM9SVwLBOySCFQC1fMBrUctAiDCjyoWmxLDEsCzz5ZOteM8UkWVywHNC9POleyEBGqkcezJWQImJEhiAVsAOjEQtMr45Z0Bj+sIBHLBwCMIsjT1oVtDFGNj0AAXw+hCl1yHw/wBkuhu9n/fPnll8O6qfDnDdb8/ducPbNE92/dio3PIF1//fU//elPWQ0+6N2J7o7M/VYpeqTV9z8du4cvIuGX8W2dkjvHmvWpSPMnmVg0x9TvDSdoHTIC5wdHkZUxIgg4ZApCBOMetY06ROIgVvQWAUQWwoh0FUy7rFWZxhXpjIhBjiBwkZ7qPaIm8ToQc0gZrkxVagqQRKAH8UHgoYISjB7kCCLvtddeIyXM6u3p61yEl9VI4m233Voj6VBOOFck4q2pAQQSFb6hZIQqajeyVy7kcmIE6hkHmGVVXr/RRNIpGzYTFhHCW41oexGdhOqUcEJBAapLQRtokRckIr7RJfAEJnO0FZRTKR2BYwkYDeSq25kQynitigcnVg0ObnXigAgzuJCtSpFKiSswcTqOXFpNdfCTrqR2aKB/0UOgpTtIR3Cj1+ncm266iXphCJVSEIZAElnggYdoBdWLkBo6M9Si8WqOKQGQildMnOKA0a0gp4F0EBHSu8wXa+EYi7SksEeL3iedOBuoiIAZxUlkfGBU6BQybESNgRMY+mK8iRzyRCuF+LCPxqPdZQoFYQ6soCydpXiAhEU8Jkw0hLbFLou/magcoJuY6FFb59DlCRKSYUk4dJtwqFDOkxuSSlxv9Rqm7zKC5EFYIQ2mArxLDHuXGEoGLc5tmDLNlOwR/Sh34FHKXPeOTgSjKiCtV5UscaZl6QuyQLt3mBXhTLhCP7pV1fcMoaeGjAk4XJ1FKwhalnS0IQ9LDSpuyUJi83rU+CxSwMAZYGqecQwS6hhrAbNBgTEtsCvUogOGwIMAQq5oamyeECeJWF9qAGCHoEwxJBhOhkYP00XPDWESn1GqDjNXAoNK52uwTECLbYPVh5oGOZBcsVvUZCWOwQMAyhprAWuEJQrI04IhJTUR2KIuMc8FpouKFKxZSMJsw8yAaYxzTFwogWDaBU4209EiQg22N9gtDL/xxhsxFPGb3mBNOyjNOcgOMOOVXSUc9/f1r3+d71kxmg8KF7RSVh+JOH7SmfXZoOnqYvo+K/+KzVfZossjqTMsZzaSCR+BybWJiWSM8oQjFPTJx25TTwa3AbmAH8IEHk8s0gGhwHWX2PRJRhYQePKxX7liWHBF2CEg0AeIBm6RlUgEZAGigVKIPBwGNRPxE1CESEPcG64EsthdjKQj/bbbbuOW2iFYaWD7DdggXqcYSQxlIm/TgJxb9RiRkohCvao0JJdDCOhB7UTqgiTgQUIW4kmrCK+wApcGMmgIAGF6dWR36cCEWbRXzQiYCeWIWnJpfrURBjBU6VInvUDXsBKLyKYXwupUKCsrquMKwFtDISQEI3yBZAEWicwtVWurSYQb6vRCAOxFgnPF26ej9YUlOIP8gnukoF2ghOroUwijCkrBGSYgSAEPcXLBryMBuU8ADDcV5i8eNzugRAKsRhWcgTDtILKoGnYxIKEKGO0d0mEX2KgIAOKwi75TVOOvqDH0JS1lRRH8DHLwMKjGO8DVZSGbGqtTphiHS3QTe/x0CKGn4SfOOd0HfyAjHAlTRHi4gcEfOpdvIB2iDaeLoVyv2gSNv9nv09ChjByeuwmkQQ0NstpldnzUpE/DLVtmkEVaEZqFKWbkErOrM3acMIxxDyAYWb3XRKJ9CCF7iYMKSY4eBC3qKcw6RCNwCYWFqYMLtNdcmpltn6A5E2fBDdiC8kUz4vHqBnhtox6Dssv2cnwm+l0DZgABY0MhdQSSEuYS0SzUPeYKcWpUqrBMWFImzhOHYYOIELf4/7J3b03XXFW9wMtN7ap9tS927Q+QfAQuLBAIBBIBEw4xhlMSOUQghBAISOQkxCDIOcrBQAFyVjAmChhCIKQgMQFFUe6s8sLyo+zf+/5x1NzdvXr1WqvXWr2e9+2LfmbPHnPMMcYccxzmnL2e89GgMFUwDziRZ+r5/SrQTMEwxy3aEdW4Zy1bpUtfHg23u9BIv7UuH2I6d3LAQkILQmB5lAXDZoGYJwXUCmmYghGRdtAu+dEo3H333VZA8hn5kkk9CdqOnACTEb38/d//fbmBX3Yd+b+dh5PmuWTsSb/2pP/7P//3df1O6Z/01gTOFDWB+TDzNutk0jkFNS6pS9LLDpLMQxuwpu4ll1xirpq02Z+RVrnM6jzKUqSjZi8M7BFbUJdHdkfu5BVPL18qy8XEQC6V9QoNfvCwCNA1QwDAnVWSscQw1R1OwJpffvnlKj1KnwAXBuX23xtWvQLkXFRqyMcut1PBeGlh2jLeUf5f//Vfrf9uASaWCRkewAbGXeiDEmIs4x48EXvKxo61lb+plF+549QyqrdGELasJgbYvW3blgMAj/UC9ig5sLHDUbK1Bx54gBCI2iBapzQcGSadWgfxg5McCVIldX6xw79Twot/PiE/91aSaW0oQ5z1iEsvvTQBTThN7316WoKNVwHEycX3cA+tfCwNACMWW9+8CGrbLtJR3YGFDPzKQq0ik54d8gIYLAAbrF9V2SFAX+YC4AjZlLHa4r6q+cX6VgKlA23lSZSpgTArkxTBZreLVvse78BMocRBCeGmiOokRDcLkYRcVn0KwlkGhajZSXaV+8h8n9I1t8W1acvMytj5r1mImdL11jB+QIEnQurWGDo81qNCyrlvjf/oDWVlfkqDt8o53qPTsxwC5Ie0XVRQQ1yFPpFemRQuTfpv2xS681ZcpIvELbmXuppxAgmVxkg2K3pRo7m+BAYiz9CTHhnt/F8rM1q8KigCbCE76bE4B1oYBD9qwIAXqzAComsLW+Jqd2URmgkuSXZXFoHzCBYC8jlYh3iP8Ah0RSziH9ZbwSU3Fl0r2EBSFkggBrUhuI8EC1aUKGGCkD7AwWp8Ge4jygcffHAVqQej5Gx0tIgEWCzrf4d+9KMfveKKK+jrESVL0V0mntllyrlMOaqvYMplvimbiu3mYQiORkoGzBOTSuZjjrlMP4/yHGEcQ6Pe9GbTVeLanNfcnHc3mcEky1XQHftSh1QFgmgr4YjDkh3F+jAf8hZ3l8kMEj3mvC7UwO9qE1qxhRXBwtYW2CDs2+ntj8VGs24cOGLZPZrEXXJXiw7WRJlUvOC05agtEym+pJTII6UUkGGPt8TrlattNVgmfx7FeNXbtIq0YTPQqJJSyoqZaYsCBpFvcIH0iFqpIxjaBUmaC4looGGlPMlFWWpvW+ML4RQKK+1sgVs2i/JOoYXvvIqguD0/4kVJZOwA+qrSthrB1oJVGYWmWM5pK7uyjqM7w53BNX3Ab4q5urigCgRoWYd5sUxDY0+Ld1NAYoNm84JlM/rjyjYXd3rRnTvpRc0EeWblXPjPHh42wa8bsloM444TEx6xr1OgTOhEQcUkGiC2gnFg5w3cxLZHASMiuu3avXfWErbaI1XmE7mhHUdhd8J2xGAEeUYxktRlR1Rnr7m0jZ6L8TZira8S/ZoWIX9BkdqaKgtdXPXISsdQq7EHIPJMwixAUjCIsdvKDKn4NqGv3o0yz64g7LENpjlIdj6Bqy/YcQqzUC2RVe4IA/Mv//Iv9g/sykAiYE5iLGjn7PgL9xTUWy/4+c9/DnnfLEAoHqZjbJdAnVRdWJAeq2RYxIf2JBDZxmDF+MEKhPDxj39cRmC/ZHzUDkbSqXe0Mk84JGPG0o9gveQlL7G24VcB9tp1piiHIeuQypo8LtPDnHG3TefR5FyV35qKJoMlMRPDDHGZKuYJM3R+1pz7/MkkMcd0xBmb/LIdCPk5W2demUjSSyzr8fvf/z4we6EmuSaCVL1nfjIBfH/NN/jVm6iVzQrOIijEuPpC08V59/or/9qZMJ3Htjne5fytaWvfjpRRyNxgmWkLgzmCS1b97tTMciyToCIlQkYblvt9Fc1ekaHzukhlhY24FFElqdIHAhdDMHPejiApbLjLKKuxla2J3ikALbLMgTCmikobbgKxnUuqjLj4TEeAGWjm3qYrbWTN5cnuFjJR4pUyc2wsEEPBqtMU9NKpGXzscwHtk5/8ZLT14WkUaZBP+0rveSxUCUkxTmjmQnhpm8xS5ttyfEAcrK/wq8esBSAGYfGsE0UxC1Wni8TsoJYWLyjAaXHBrhpuMyjJlcTmMPSLe1z6MgcTqylTOXZDgc2pGXEYerbuBfFmkAm1b4KtnNIxBiQrs1sTrCFUaJ6OYZC1wcrpOPcNKR6IN9+dTo6MHa4wAMKKHPbNxb7xi5d0sUU0sm/Cjo7fborrAGSs0s9OPQ10hZ428uxQKGb+rd/6LbObXWJOcyXqEJYIhlMjT1YwR9JcLO24dQKzxCR69+szsLFsInltISEQdz7O/kqOrugodDIpCflMFpGbSFUrsZa0WdTt0RaFT5R1VzFPUQ4hjsSxzsol4Hf3yEEo5LwhAjoCqeZzFZwKlDJ85StfGYzf5urlgsKziASYxF/0ohdJSz784Q/ff//9u49ugmPKzR8zoPSbrrsUPKqUcnTW8qO73IYASzpKubMaRMWVqbhHZdMgk1wX1qvERrmUTVdTMYHRT37yE1PX3AOWfMbMMbeFob7elBGZqOJ7c9VlrkbnhKfA1JhvXGM7o87ghbzJAABAAElEQVQttZ1fbLOFiHLJkiZr59tagFbX22mvnCsA0/HI9mUsaaWsgBECbDvq4GTpiHT3QY/w+x11avAibVOJwYSG5I8AWkHs5G+IO01WPeIUBngAhGvINWdq00TSSysc+GF5nZ80ssYuhliP2tJJ7FtV8UrZnSGWJ2ezS24cLTU1onVBO304aCAMJFPi1bZF1bKmvlSx6s2UH//4x5YqGPq2X2VhbpuZR2HUu6r51gViNEfgtHBrivW3sknv4YcfRrCDDLP0uDWpJ9EwdonuHSyBnEUs/ZHt18zSUR9JdZQly0xzHsTlFR+xah71UR23RsDHFPsadt9kxO7tknrBwMGZ2qJSC1sMIFHTWIuSE4ln6NiNXWiY2NGOYNTJWic2/cMCZn9HbGe1OcvPG1r9j/laFJsxCEgqQ3Ew8nTNrYusDtbjph2NyCSvGE8XzS8x6oLTd0ap35cmYhjnQZIbu1MMdymu2FKQ88Mf/tCjhrABdmefkwA7pyY415HoLnevBPOARWXMBTGiRCtmh/0RhlE5FzvPcjqGJjI0T9XY5EgS3qFQc8G8+JyZQk9y4/MB+7mbFD3BfKfVpo+Y+shHPmKRy07hpm0vwq+SwCISYMpHh+64447Xvva1vAIFXUVuW08hWAEJgwDF3ql7Lvqa834mSQuvF4/cqmxWNEA1KaucVhBPZT0qe9uaFVPCWlEluv/xH/+h7PNOFhkq23cAkJFe4Gem8wqemnKyILNX0oVU8bqfLwdpt83dpDV7ay+30o+QWsS3j+DVtzUFtmMBZgtghT/fVCDblJuO2c4nYJL3b28wTg7jbZkbGzs+herkV/1WxpqoSXIV7/YGsz3Yb9upgQGq3MVbviekfhnHVcg7GPKYmNKihhTXkbOYYBmjt7Gt0EJOE9hNMaiBZnClu+qxw9oSr0cwL3jBC2iXKxaTtXVch3DUJ3cNwYNkjFTqQtfonJLV93k3j6gBjmTpnV4MRCWl9YocsohQNdsV+pQQF4dkqtZkIUNex0qBOYiY7Tq6cFoxTVSoFqdOjnEqceDExhQ2Z9lkEYxJSmK2CFiYxOJ9FT05kc5OMIkRC/lsjdkc54PMaBjYQM4XwnxkMREneEZ1IvARwfhZ3InR23hjU3qspDtHppV5jXHmV1kYA63HTbEtDZ69EssR1PTlj0OyYIeDwK1Nc3mH7JdYDLpv6MTJooVDdr2PvlpFbcudvkRB4va2UpAZeK+sIjEaArBsRHmVOBx8HJ8a1gmMJsZLIEGMIk+ZrebmYNJjGYGv0lwsv5BDpQAjk0tzIYcu0sQQuERoTBYtVRAs+ahNjzC3dEIi36bD7WUHy6Ve1yNct3isBv7t3/7tpz/96elN2uYXy4MSWFDg+PKXv/yD569vfetbCfpRTJlcQnDq5bIdJApxKbtTvmQdLW/aUiynTylcLcbQbI/shaCZHsOpSTRPcG+lxwyRP7ubQi5bXvwosJ/97GcBDjzkkoqU7RZ6pN+CckmOe/l+ZzNaklI2YXSddSk4GVCrU6ZiHPzINACMzZwGSSpiVxlO6aXe+x1tV4MAcZ6+dKGvhBHF0RScMLhA5k44rvGGpIE1WaiRGoc0OsYlCfYgZDodfDVYKbSVGTqizGwRI67hzxkYI0tPBltVZbrLONIfSaz01aMLjH1+2MBccsklMBMjDbR0glkBCvPKr+v6sssuY2c5UQMaZ8arSX2DLUdrNHFq2sroIdM8XGTJEyN2YMRqhhIBZhamcpUoqoCpsF81uxf0CC3XxfHwapUAwywbIUkuSoqye0dnG0PMDi9+omxSuQMnNlRLlGltLl+0Umweh1nIKtvsej77uDA7pgy0ifzYKGUuw0WYs3eXjoglmrYdfhM5P5XPJnP6Pu0T7U1c1kyPe2JtO3ZGWlk2NUBikhGYta9YxUQjMbwpq1zb8FQA8GXplutc5rDuw99NGRrxEuXZZaJN6WVRMH0FcKRO5pnfwjwfaZ7LY/vxqk+RwwjTJNYSlQFLjZgTvJg/FzvJxegIpLAnBhOkoF18yB1cfvnlQjKhmpSYaRKKuARFoc28EzYjSaTqLjdxSYyVXYI6qURHpLIGup1k2F3Yyde4MwtCcWiDWSua9id/8ie6+OlPf8ordfB4fMtb3iKMrHqc/vEf/zFmb7/99rV7S9VqpICAv/iLv7DiYx/+xS9+cRGmCbf42c9+VuTMOTrJe911141EqmSLi/vuu88//mQDReC33HILljtdB+yRRx4x/Q3x2jyi03z641ISYAKll+9+97tf97rXff3rX7e0KdfN5eAB5SO1livwImN5Ar2hLnXlkWIFmBwzVFScLtJptoNacBXufK0JQFf+7d/+LZDuBo+6q1Sm+kYINjUu6q5cY6/3lqSU87ZgWgB9FWEA2C815oad0rXplqmFcq1QBWfKkitUtV3sUoYcQnfZju4oZa0UbIqWvopaRqZBIRSNGUdLA4ZYoqX3erW/AhkSoCGmVEjVewQrBaWE3m5EhijT3j48BpFesaFSR3rIUMLMZFA87kpsp0cAWCZbxhG/7J38jbb7CWg6bBuZjrELyEAMeMt+EmbWhzQ8OpDDYsIwLhwsoAF3lBx5+mJH4ERDsYYkMofKq6oMWg05gyeeeIKPYc2tJfmpCcrAKF999dV9lcNUthY1NNGyOkMape3j1I6/RR4AjGCqrhDMGxEvTi8mwOMy9JapoaInnQCv5XF2APoGZ82OKrSVs3c6F0ITnHMJqRhRRr9gaK312JqAbJXsEpdnpz0E0FhOhLXJKGxN1TIbsloI62xqbUoqD+LSihlknzc6q7VpX0eB91GYcx/tvDsKGW2ntJHL5pHjkvhuRpXwkzu1kPso6114IN7g8trJso++loyTzO2BMTWdyHlEVbwSArmKL6pVZQWydaVGhGZmCWaMcq7EYN5mHy5gcIpyzWJpNh0Q/7Ba9ir8klEnWbCNwQJrK6DKVeXHH3+8+g1anlrvonFkuBRweu+99wqV8yNhAXNHgOgObddff31VKsApAYYWnne84x3tq+3K9upuvfVWPsVdAlxI5L3+JxMyfNeNoxtuuEHW+rnPfY40CqZTgOELX/iC5EuQ/OUvf/mLX/yiJv1jv/43EDmYa9YO3v72t3eQzPW4lAQ4/LziFa+wzvHqV7+62KNeEgOpplQhV3TCvQJxw2wANKGpTIMkQUSeoFzN85//fEpPlX1BHrSAtRUH0Fc1yn4WiL66DFs2lBK7g+xMknRU5G1U6BwZhUrqa3StNllPGkdlngPGadalJEXKJtt4q43eDrI2WLkWLYGbtGvBAMDPyUkU/TrU4La5EZSQgzSgMkDJpDJng/ftaAtV7JHJTIbUwL4iVOwIj0KvArD2rq0LmLYamqv0B1ojJecUurmMuKUExppAbP+Cp58UjOK59CsM8ohHXk3hqU99qq0P/AolmTwUSiZhBqagudFnBNfSBkBH4RExCMMmibWJIgC6Zz0vGWaL02T53ve+Jz3WhECcgpZ123t3LGJwUQPxbF9hsHWj7BxRJwFGf3hvyahWqwqS8ATW5CzOgwTarD0RoAlLOGS7izKs6vrM1BMOLWI0DCgBXpTVdiO71kpvh3ZPrcx6S1eQm+lmqPmoPBKX7EgGvUpEzkRsrWBpmDkOW6jdGtuOHO2vOQZZLVNyI0vYp6cvmX5Nv9VJ1BAROrGzQI5seXHNIUwyg052VUxyGMHGCYqaFiiZw0hAL4Qg6xMSEwLjZkTsf5pQ4zJp37blkN3WOJjpatkRgGVpT2QrtBOz1VVJtehUHFuqix75Z/7ZCmCPAhW5hoiLZavuhFvot9uXexWc+2s3ey+99NKcVWypclr2xhtvtOlqm7TqScYPZVFIpNpN9APD1VfBbFQQxL7hDW/4nd/5Hd1pWNh0ZP/W3WHGBPzvete7fKgsQ/btXr8LknnooYdkvxJaYAAwa+3gjW9842OPPdbxTf/6r//66KOPauJ/A0mGq9M+2l1qFpQA45Am/eEf/qEE+G1vexsJCr4tEtCblkPqIkNgd5LiujNG+f9Jxsmx/kjKXQTgmKtMA1oWSn6lhvK5t9KEXy/pQs4gx8jmVQvTErB1uYPQo/V4FOLFTB5fmMeCS9ccp3tn3WtrkgYbQi5a6oh9EHJVZYfTVWDqQbImzIT80/ARQqet4U7SG7MCDIAYy4D20Ro7b6WIHSR9yByl1qMtVsxSiVry6AMP1lAV/8jXqyR19kul8URnTFWiFpGmNLTS4CCnqGJoCzF0EqnUO5u6TC0NtJZ8zz33uBMCnNJmCKm3vAUAA4ov2awmyrqwcDPCJgbRIBe1vIJBkVZ/QCPSPnfJMBFpotkqV0A2FbUU1QdWQ4eTRdNkbZOi9/NqkLhD/EZhHzsOFSOLGOVIFaqogRxYok6wkfAgeRcrI4HBBaaLwpkuAVPecSRTddD4TMdzGEjzPWvECSxMkBFzMQtJwrtZ8EBiKXAuVAvEw6Ky8Mwmnz7LoGQNd4Gcbk0S38S92ltb2g4wjpDEH3Fkln2lJeWStmZ2YkMysRZsI5HmkMwsmjOx60WBCY3kimKtnI03FizzqmBmC8r7goVcmBpUhO/qoNUEjIhOKEsxhG25xwIDtgopfS3MrDH68/udrIHUmnd+5jOf2RoECCkYj/ODH/xAuvjOd76zDfm8dXDVgVkbzg4nt9GdKMtvCcuK5Za33Xabo9dPe9rTqusO5WsfdWQzWdj5vve9LwlwNcGRuFeyVttdku1PfepTX/rSlwYTYA29wjteSmI33XTTxz72Mb8ZhJHCrGBwP//5z4vuNvoNiBbDlPKCEuCQ60tg/+pKkiATpkZWCMx5Fy3hFEnNSowDmUYFvFFnfcTHhodYrRkTluhETSfLBUag6aKjCp3HQZhUzn7Xtbwim8BWhlbhLwpxnTS4alY12aWerEzOXTBs1BYvljONqZHtrwKgxD6G8bUexkzI68C362dtX/I9KrF2jzTSiwpVc0lmZwmqXg0W2FyGxitZtGU/mbmy4JjBUpCpJhNWppBE6gthaglYPs+YmthyezDIoN7iIXdgdN4rYPDHjMoqvWVb5Y2INCPgxKavExXaCyqtcAePVpDAAIDLJFvcWSUFYE/Voqm3pKpsMZXY7alGtcCD9AgPmnkaCbyGyX4jurZTZVOPadZ75mM2gjriBTbYtoOq/yj/15A7kQBjnz60mJ2c0TV++w03rYHWOBqjuNVNmy8cfjvhL5ypA5Mn0jJreKID97tjdyZI9i52xDPefEYFmxHVOM3HesvAPve5z2X/5yJAAhxUVNTSKsPOEx1MjCwn48xtzZWsQsiBWv9ljecS0Vx4SDVRigI6OXehpsJhpM2PY+Ti9i89t6zPGpOGIIf8lYUfZpYRmX0sJiKkGNGNvrIJKhAmxkNtLjS7YKbngtt0IQYDxlxLZLAj+JFs33333dKEV73qVS1arWzJgpTrdjpVY43AzrDI2d6p3WAJcNt2o7Ld3U9+8pPf/va3O71AIrXGgrxdOfTL/8WlsmIDhP5+R16J2YClSZp/9KMf9W1zJwF2NPprX/uakE+CPVH+/e7W1swQOK7tYzoAPsWy73//+6+99loLCSRCQJqrF2SLPLylEKJzWuJiHytqBwZmVZg+LkG9iH1pJyTKdr1CiW8yxxtOZ20QEnIpULRhlca0DcE7B97W7KO8V5b7BEcIPiQYjNLMIsNqUHhEkZxynzyii57k7lEvwAZnYEsAbAxT0qeJR5hsNmbxnu7FrMgS1VjMoz/sb1YKW88dnZRJGmjNXehEnlaYotUIpn7WCElA4IJN056VpNvMnErZL+sj32bXbMYqa66V34wzR9rfJHD+37yABGtZA9JcwRVp6F0GntRdWbSE5s6naCVhBdjgHxR7JCmMk50m6QVWuWghwSznZE6Bx6zHfIxA8mu3ggtJO2ptZXSmrWkhNy0TSGtPNm2+ZHhiF6FaPaFLFS4vmeAl0GbKUC2is2xkurkrUxJzU705tQQi19LAIsUorYXcBYA1M8dZHn3NNR93oWfJbcmHnSxTuTupETgVza+ZHHL9OsSbFMlGduclGLKOXDtLc6GdEU8sgDubEPnPiHwVqswvludgPa6i5Fj1lBzv4g3JoY+zPIYS6/iplxgfhbaREaEhApXEKh3avGIwZap8igAyV7YxQDrv+Y1vfMP3aO4ik+pC0GWnUCDn1aX//39pIRAf1po48lKhnR9t8YtTf/qnf1rpK0G1H5l26PHoh6z8R570haqbb77ZP17yO9v2VDrACeo6k1SGYkvPFkt/n1z4iiOntYsRCAUkHjPfC78aBDsaXTV7KiwrAQ6Tds9t9n7zm9986UtfypTLJUThYuu8VbYlVeJoRdmWC2BiQdZBF2kP5VOmOq6JbXcBQ7OfAp5O+XTIXag6cFtMxTqMcGdQDP0gYZafsy+at47BK1CSrCwMNkklpZKGbeTAJHs5foOeJBImqj1nJsxGqy3ozGc4deFuMd6lTLVECTa6rfbp1CW5Va+5JqwSJAgGBjPCoEpWmQzTo0r5qqDcoglBqXduRKocXtq7t/lNaT3SZ/iDCgwZWmtQYD2t7fmsFwaqPrhYwLbqq7W8bS8pi3sYL+cXYOjY4gL2KisCpIfBlDeK//Bu879dVoAcmyTMqloXFxkMslA0jBQiYagyRlkdMC4bUTiCfyGvCN80YVEvJsBTRsQMpfxRKutTQgGtqByvT1UoZGfFegrOo8Cg+QD9Znmls5R2gH5PrgtGRihpDnJ5Mw4NL8PUuxhz+mmykwzT7XHGXjrSxgvjqbv4uHSqR7OjA7nRIzyWgwXQ3Nb+iN+IpD6w4bMT06/fXw1REPWqKGh//S4KMyGItchBQeqlIACgLcpqKHxeLY3mVfSglkd2tQB1yEjA49SxPVXnmXEXGJPOvq6PZqW1fkqqbajs3176RVU/fJXo5ZWvfKV/niQHtoEcDFZPxvWW4yucDicLFL///e9XTVvImlcnsRc4oZ+77CfA0nuv+vCEIL5tMSsXv536eR8XlwBjW8zx4Q9/2ND6DDr/NbcVx57kkm06/sPPmtGAjkbOK/QWG3ZcNMCld8lP+/YCKZMATlkxG5i+hTB1U6NSZsjAKVgn4xdtHnrrRG4BeGVKC7wI0LKT+ozdWkkWhipANf1KK51y0u2KTDAUTimrRBeYuJnlkqq5ezThqRxmA0nlxBAYBCCNZEQElI7BuPuIF3dOPWE8J6BstqiXpynAAzNzwy8CkJwTl0ob2jLqIqMKyOMk3BPZazKS3wplNpoIbS+tJNGGfjUSMDym3AKsLcNMJoNgvuL2qYyAciNSW1SEKSQN8chLAuzQ0dYIW+TLKUeAmU3LoWqxlJggmSNmq6lkerpMZ2XxvamxWMqPQliCoVWT9CgkLbNTtto+rf0rX+jNRSGTZWuFI+BD+SN+kEm0nGo3Zq9LEmaERdgy+74JxJEe155TM6cYIg070bDmXvFiAuWkNHOJaHY8xfXsmPsIyYRALFiLDazK9QEukBqrLQlXWGYykWIZBZcQS1kwo9LjCUljkFqMYAEvfgXKLrf8s5axRI+++PUzy3Zx3/SmN51jvuHXW8eG1fzu7/4uDAo+tTAfv/rVr9YJalsj/mtR26ojrpKhYFsipq/BXRatQifNbDHkcdA/hosWHhlodg+qFs9hyotLgMO2A6XXXHONz4AdvzzAildfG/o1+xsPCseROARPCfwqb6Ku/XW3WMzcNkPGi7eHWHLO0CSR1CkTUX9qcaIuQyZXJD3eV5lUZ+QUNqm4YUKJskRdF+jhpHXn0pfIuH/UkF1OwimzSo4qhtbKDxtY2KPnkNgT9lZ9AmsRkl4k1ZSfQPxsgPgGg95auuMD/FbWAw88QA7KD56/LJ289a1vtdMLjxiIEGTXAiAriFxmzSBo2VObpV7pVxfjOd7ILIDKxi9R6BrvRs0dkdyzSry4Zymxj6Rfs3akRpoQu/QV1+O8jHRhjLJl4UNu6yYZRPIZaXJyrwjQ6NCZeU8qnpwcphNsKonRzR2aEJXIJDV3aBr1no7qQoAkK2aHjl0IzO7CI0ulOas1HQlj60r4ONjKwqgEmE5Cy41SVFrK/O57PQJJ2TlgVThoWT3yyt0MklqVdpYoDB/Use24kABzmujvvKq2F2CBt6U51nkv2ATYFLByRLd9etZZtadIVILmnCWFEd35v7DOHvuJ3yg8Bn1D++Y3v9nBvU9/+tOY7fArGHNYmhH2AW3NEabAPzESvubTcZ/ddX7LqiBTsL3kkCDM73nPeyibrRT/s9er7L7Yw/jud7/rO15Bbz5hU99G7B4FUYPBmLU5r4InfeEIPe5rv4brEDnX4xIT4Ayq/4ckQ/jMZz7jJ7Oj33PxPIJHfsUid05ajsDP8gq/+hVmSRvYuHiRWTAfDAnbxAHT711El5PAJqr8sAxcjvKa2HbmSUYXYUqP0kK+VurSsQIBGKzcRSBmaS5IFApVOlKTJKrqU2AIXN5KZS3FIRgvhhv92PGKNZejMjF+9JK5YSBIwJKbZNunFAKLxD1OtviX1BpyAIQgcwYj+SUrWbF6JkkipxdBD6FFhiZO57xrcni0ieldiG956RA/8qiViMfdBUzZveapsy4Y7DQHycz16ztgmz6K+XBh7mz9Eyxk7tKv2WcQOYxNaTgJeKNjetIoAzH77JgoAaMvuqXAhHwsGiaSyjH7YMHZfnZJk+i5QsheOPETeZwLjHDoFRfGEF2UzIhUCYqRAbBRGsNl2GgdOZMiV8wujR815FN4GcFuEtG9DgeTUrtDFKDKIxJY+wrB9sZho0trgS8EADrDa4sK+CZu/UJgeZBHBtncEURlR6FghD2sdNnnqj/pAkfpN5DtK/j3v2aECzuOdTgSK2xTyZWnsmXz4Ycftq4kEGr/XTCzDJudYT+uBFikJIVuW3XKr3/96/OVnEwVAXYiWwBbLy4/rOVwtYjUK5G5T1YLxjEQ+bBOq6YKZjQjBgA9FStqDkDWXWCHLCzUxBhaFtzn17bg/ZpZtpIOIBejcqzw1w9uyV4oh4BeznMAZmfswnj5AjYR9tZo2TUzx3zwxV39R43+JE+NcxS2YfmDNsGzt7l172sbok3XMj2/esXEmOGtze3TCWEqgRlZF9utJjGif5XOFnjk1azYMWcumbAFNit/HB55MjT5lWMAgH36QjgiJz9vkK6ZOVMDjKSXcZQwawW/hVIHg63N+0wxmYZ4wvfAaWWH2dlp5PEcReRa9jsAxlq/KmMldYQXdk0vKnXkShlk6fMWG0Tw2FxySllWD1WHjAiQDvgOXJiYPLYDs/YRktCMzpCamrUNTw6AntDDrQW1O78Ea4dqcKlod+T7xmDKW5ONhuy7r9PCzxqzKiKz3clmT9jYsxroM4nieDaZpZrRyMTuMZXkz/5TVNnvjPhXDWu6SL+BGe8UpMFlggBTGHe+3p1TazerbTq5gvDinQTkIRa+hYgnajl3H0ShRQ7bJ+lq1Szltmb37o6OgRn0z4de85rX4DesCeH8KJJdH1nu4OEIIpKXmvt+nLndUFVvRcnO8J133sk4SDVFUyMMlo798Ic/FIgWpNjSXstrX/taHwZncc2vF1lu842xSmBmt1P6ZnT952E1+nIXoYWLq6666kMf+pBItXJmv1ktLLz88suro0MWFpoAEwF5vfe977VZ7yC0Df1+7LsPMR1xFtEkWw2SOimQXeh9cLconKYWm24i1cgSPgmYP4QgFWxDBPNWzilFLBYGRypxwOCrarhdYRDnYOUgfjui0lFD7JA2AEZBti+RZhrwC49ogM3COJk4WyLX/dGPfoRru8TSPzFTrL/QQaCQbJOJhApaeSyDyMyxMqJzhhJC6br6Wiu1dFpWDxIXGBns1tF8y3uVWeechY4QfEOCQUOZyLjABkU0Ukk4CM5ZgEEw8RMRgdllBQR5hDaI/8xU0jFsth/hHIw1nVJR+qBHhSyO0M+tteJglKej0GlKHrjfJXRnvAwfQ+0aHC/G3O5fFtR2JNjyX46T7Ihnmc2dkyLJiTulJot1PTNFE2UGPOfMs5DaMphBAcOfWkDfdCCCXBgaB9pinljWKaqmAFMVXIDElHvKFWcz9RbIoKJpU7CdeRhDY73S9q/Epn9c/MyzXww6NyS6EB/WGcB6dfYK7O0f/dEfGfq77rorU1uNX2aWN/oJZR9Lujpcy0UJx49CP//5z+8sRMJw/fXXa/7II4/4JFjIN9E+tGtSuotZZiKquYU8m8n+F4nLvrEM2V0UeuuttxZ5dmt8WSY8CyNefeELXwAmVxetKT/00EMSZq2qySELy3Xn5EXQNMCn3raC7ZgdUi4H7guz1N3v3PqaMXt3tQxzYEo26g7NlJ5h0krZ6QtJiLL927WZFVeHU5le63TFl4Tgm1j7me1PJXGHtSRsOsVr6lFeJ0QgPb5hVXC2EUdrgVFr3k6Mg5mtuHakZoiT8HNpwhr5MEaENWCIi4cTE6iX2cqKycdSiOUxu7Uyfz8I50MOYP5DWEnGvyb3X5FsC0skWD1pMJvlFBwkBNuuF0gR4UePARJ+xbTNEmQQCIS4IzpdJLZrd5hVrpXqjgC4s7KA68h5a2zhYuvmC2+IO6tIrsPTaVxM2xgHvZu2LpVWlA+gHofnd2KPJGCmmEFZUJ/Y6vBgNIeRsQK1KsnBQn7SaesZVKYyeRELiU0GaqKlPbxMtujRcDP1ZMVeTWxuylg8DbDkWYGpXzUKhJ+TU5uOAsJq0XkiYS2Y7qb3KGgW7OpRUmd827X+VKJEsDddRC0lZ7IsxOLa8qHTmWRwLVPYF9XQ/O3Uey3+pQHYOfBLV/47rpmemeWe/3AmCHT1Cf7IRz5CPgT1ile8ovNWW//HyO9C+yksCXDn7S6PMNueFK86XG17mQ13cseG8Kp9CPBiVAB+kSv/6JgFsHusrVe7ULJ12y2/ANy6v40axmH4SJKYHn/8cQO8UfOTA8avzEfuRzlOYqELwZJeS7ZELZfj2hOv2ElbmwBbT+onwPAIEXh9/s+gD46gPcasRZntujNzQDrXsQp+EMkulbjWfMqMFd1y8x1IG7y2eQkNzeK8hDXyAR/0+pTF/q2kV26sLQa1zZFR8tSQYG3/SjIlt8iwXKJsz8TUwD4kRCfTkOQ4LqVGvXVBSABLp/VrmNTUGmGHti3EAnPwp23KghjWELXTZdXvWiicPWSvYPPtirydhXV1gNNLKnfnqIP84uPuEjBA1DjT1mIw7c1heNN8sRmOydV+A7y7EPoYiEVimaWr/tvl1KDT/5ZLAryn+WW9L78h3HLNuOU3ltrK0y1vaqbAcxBMnzs/wu5ZKCF/fnbeUdCFBdY6qnMACWOtEuDihb9z6pLP8qs/a+OHAxC5kC7ISpRlj32xpnLfghIU/eIXv+AsfAimr1KYffd7FPzCHp/g2tfhfWyDhdkyBatIMmvMYm1jHDoi0lyUqLIQrsIzUg+JAJIS6qvAVCpbo8mvDzhU2FqnkO1elYFn0OxyCQmsaORkX4fgwr/vwnJ3gHFOKMT9qU99yja6FRH7wMcS076HIfhxZ0GUHzpYLrc7X87oWtSh1haoxCvttweDyEGaLbJcFwDZGq5ND9Ytg8vzwTky0OIAb011MZnwKGcnRuAHydilcnpfdrfwgmVTHcH5HFf0Lwu1rq+ePcr8lxsQgi8MWTEyYeitDlgN4fZYFmsiOOUGJP9ayW9hJna7xzCIigjNRaQyZ/IxCuwdHmELp0VzDCVUMpBZ1CyYC39HsKvqO2CrHp0BwS9zSWIYJLrBvbJOL5rIlgmcbDuvVnV0gdTTB2kGHXDO4sAsW9FziMPQcO201Lw4MAHL7I5AlklYqLIMJzVVRmeMmAJb3TEdvuYwSS26maTbscPRQ6st06THbHJ2zuBth3k5rTa1ReA5BfQrEDtTn3XPGTnii5nWILR+ajNHX/xLZ3zbHlHiPGpcdlu/aZk3bHuB1hItF8b3tfWboj2T8Be4tRSrOOKbkd10Ep2WPpgFPtb1U88+9G2T1TIF27Gj+e4nrSBhfzoEZDgEWq7+APXJVhNTVv8ELhg6aA/2uOgEmBRIx9fSvgX3PbCfI4uPPJh0Dt8R008/9CsjmitF2R8X0V3LQlYok9BO0eZ81xSqlBVkNTWywcArO9TqrC8hdHB2HjXv18zLshFxzNuO0KYRXtJUxFjbFiCWjRDkSVBlaFlLgx+k/wksQ7AS78iDn7nXSj3lx53exYLuMl4aYg9NMPqsZz1LImFb2KNggrmU99pPFj4KW9VInlXKtFEOiWGS/zBhHjXXi7dzCQqpeJS6WwTBizMwalrkHo2pMHe6IcapZJ5eYQpHEn57ZURUO94tfmAURvxt21k9ZtHTAlwskwA1EPKSkihz37OmFXi/r35NC7+Esulz5ZVXmiNUd15qITRnzWVsMgKyDvPRIpfPGepoxhIkgAZEJilCM/vjUkkybYriFZNiVub3abajnGUwwcmZDYmphHY7VMtshR0LmlhjoGL2p9NJLEy9tJN1pSEzaiMjmaVSOJVpo8JayWdNZDr9g5BhpPpSsLxLDXi0QfgLrZJAyNk6OIU5YytB04dSxCJswH50fkbNn07DISHFw84q+2T3WD8KtR2zG43LRsDb0TO91dIT4HDiXyJ95zvfeec73+l/ohLfoiQ4XdbTIbk6JwQchE5MML3hUSDlHoIk2R0nzWqvHR2fp6JTEsi4i5myyN2hXMKT9WBCWIXTsmh/RaqDZ8dHXQeDyCCBQmrW8lj9giwkVSkJdAn1EvapJz1hpcCIrc+hZQUN9SvoecELXpAEz68giEEBCED/8R//MRIAxk+4q9SdYNovZkFlWcH2OAyE6R5ZicBc4HVahBU77aBUZZE9XoBTWK9HrA1GeP6ToR3d6WcaIzp7ufaXrLA4LaPsyCiyn/3sZ+ulpQfvUghSkntrGIFkyBCz9cZU28X0cl+w09vuG9IkNUwENThG473PwpfN5wMPxzhTq96SzxYiWoWtU0/+ZU/MTYLNsuAsEu70tcuj2ZrdJwcHrFHmuE1n+NBvla1+o2G77vrWpl+zHeaJrfYteRbJhyFkle8YJ1IFDGFWYaxY+RfuTHdq5hIOVxKEFl6NtaS05NChUD3i6W3glS0yKpsjHX3oNBx8LPpTgJyFl+o4AYfZejvY9kKozCg4/CUUtK59YSbAhOBUgh8W9v3zpZdeera1ArMun9Sa477+peRnm9+FzOITSIDpAb9LJ3wt7V8i1X+FXogE90EGPySgF+s7CTbjNt28pJquQaiQMUoqm8dVfXVmtcdOTRqKugy63SqGL1FXByGna3WgU7mPR1vxOIqzV0Ytlz94EHekd7EFJC5Jmkgom5PiIdgMtMhS5inElA9DLl8VHFsI9yjNE6N4qxU52ENQ6VG9vXG20leyDpeSEidB/sIIbb2iNvmM3F0KjX5NLLHbUwUGSbaFVeodYYjPQJwn81ef9Q4OTZ/NNM8dg3CKYwpbBz5g1V3nbf8R/ZbAIwRiF4GJvdAfPEWhAkbEiH4gUSzuEYCFAAi1XXsyv9/vLjXGV+/LnLkSYB+KUyGLEXgsAU7hN6q7S4Squ8EFrym9HxiGphlH85Gyzds1IbBvLkriOw7qSnXNZfXJipcjIuy70Ik2ckCYQkcasl+yoled+u0euTzub7u2O7bKEOC3z+OOmAnQwiVBMb+bIo/Fg8HF9NEQBTZ2UzyDLAQJhPV2BK1lbl4jwHhxOkBZZh5HUxi2K9AuFqkcx3ZIzlIrYQBn6iRXHRw7S9xN4YXmc+gUMh9ETGly0jB+58iXnj75FLCNTMOT5nFpxM/s2vfEniRB6utw/C233OJb0/6x2D31exS0VB+D9klkRP/+7/+e/3lzFErWdipFFzQA4whR6y5FcU54bUMAHJ60xMgOAqu3Oez37nSRf3t7FIuAI7/VIe7Xu7L8052frt9hHiS+U6mtoEqltryaqJplh9ORXQEl4w6h/R9pCZnwducOGp4/asgOamJLk2wJxP6nt7bvbB0LmrMuALMf00IPTyn7leIKHzU0ELCl09ADlc1hHUWSKFFvaVnwrSCaEV1lNL2SIFligHOK2C1UW7wPJBHhzmP2+YMZO3rHOALcVVq8mBLlw4lTARZx2aPALJ1BJyQumVi7+UA4zhToCHI9AiMiGDZdrUDejpcBsuJw+O9sp5CdREUsS3RGf8r4FlrDZ9XGl+pVc4YLNh/2/SNYpGfOmtdmpbJ4N8tSu/x/sn2MiDlFbWLE+vgtC6qcKwFe1Uu/39lr7LYxs3Kw2TFDaDHXXNti0ZZ6UEUYNFfgJgzHvD+fDjPjufY4lbAk9PNBnEtW0KaY8XF5YgcAn+Uah7xA3kYgIh/8ioLo5AXCeMsmIfDm1lmcmNjUT7V4TqKMWQGD7MYnb/b5NnLKJ8HgYok8jQSY+IRr99xzj6+k/Hto/0n5bKsI7vwQhWDIqVGbh4njF6hDCJPtuKRMAiDB8fTMkLu1HD7ClOxL7icoERcqjEDu75WBQCTzxBALPkLwRjt7cWZRV3ehrdzDtq3BldcRmszzJz/5icDCLi5XJ3eyNSQIE1hgXOKHO4mHdE5yq14rh8PVeGVrlMW0Vyzfk7JaLuEzKIzJIrv26azK0I8MTZyyJnYNWVshVDHiLXqS/epOrOYubzQEU2QrEoJKX9RAK2wKj3Rnm0K9vvAIj16UXcrJuqcgDwwGpdZEl71cKwLq261dnaKWcGijQMo+iQyZJCP56R3tAtmOdeEZrKy3hy8kUaFa8paJqWxY6JC6NL465J3KY1ZJzBoTnwKbnjT2kEo7RVDs0ohhTwJs6k1BtTSYQTUerNyFcgitdDCGVic3HVwrgFnK5IDoSeZvanYhqW2LJKayremXwchDXHhhjSnq2iZ9JP2aiJrriTPaVDh9hGejhgvm7KxKHOs0xHHFSCvEEuJJ2i7IOfNagV//x0i0f++99847tY87jsvv/Un+4fLyqQyF4gPLw7Jf/+tZwnDmZ4WQQvrHEMhwFrgKSP4o5J65LsZaNuI3G5CaJeHdRwcGyElAgCU53B3hFnquU57eJa3i9SWTVsE3XfN2EFfzBC4snVRWEihDM6YJaGCWsrqkppJbd6+AWQO2lZo00tKPswBErQaAuJljUOAjQfpOBkKqYiDiPMwUSbuleiELDCpxgXJgog18yRL1UlI1lHwtYEgUzDU5KiQFMCI9rAGr/WeP+EUJ7ojLo1AJNttcMKPc46b7PEiV+kKCWR4C8dC6Wjx4JBz9gsQj4ehuCv2rWIOwXo3g0VEgDasgVeZvLUD6TQjKhtWojTSvLg5TIDHrEYaGSGsZYpw83BlcWoovPHrEFwYhGW94GI721AsesxqVna599EJ61hBdpgkJGw5LVObgaUnVpKPq1uZONHRjJ22r8jIYYV6YDkNvzra2ZcfRN+Os31maZPpWDa5pxZJ42wFgtBk6WmFBkyrC4NHk3ZGkXZqzsYQz17xgWH784x9THk6nw/suRJ50W19GmFN+C5MenjQj2xFvLghp+Bq/DMo2nm2twKyT3n7o189fXXfddWeb2e30YX+tTmYHmAgY/dtvv92/Uf693/s904NjOMO6gjWBkXV3nmbhbIobRA+CBmOkHC8uodqdbBE2CziXo91lFuFl65jDZqzoMIfH0CCLcMEmjCAuSb6oV1oodxIE+HV4Pk+uaDVBsgqebPVOqiIzC8NiKZACNa0UBM0wOKfNX3IVSWx+9rOfPe95z7vqqquCR8AkyMv2tZ1evUuGk4FLzyB3aRj5wGwnGfBIrNaXpK414bGSbyezKpx9+I1qkCeHFz5yFbpAoQRYZQcJybiIGrBYqvN2u0fdEdF4KByROvlvgIxIdZSy0SScqjxugeisKBlZo481S+zoMVXXfmQlQ9A2Mg9ftM76wnHZOWO991X6JBi09Ok6CVIHiWRpqTTbEvmbF8DYMUZyEH6LSrEKVx6ruKq5KWbBZeKhjBYJyvN4MP1xPG3GvvzGIe/GnsyIs5XPKZZ/4zd+gy9bjuM4vAyt7HPi1rLPtlaYvMIGGY2T3n7l92wze3gtWtvjKSXAmBFNfvGLX3zKU57ygQ984P3vf//ZVhfc+QIk7i33ZfIrkZO52cFAHhdumOQMzvquVb4pAFKd7SQwo8TwdX6f5v9MITgw6b2Frxr5hmDIox82p8853S1X1ItLJiwrFjBpS5g+AJNZyeiy2ykJSaCguUtu/KMf/cjXttkRlYk5Ju3xr//6ryUnN9xwA9sKG1QgpbWa+F7X3p2ER0psuz4niotmBAj7AOtIJfi8Up/CqrsUUUKONTRrRVx9SJVzpcQt8iJSJQJMmaK2Ci38RmUpIoFzxv1W+u3gN0Z26UEaCBmmMFF563WTfo+716DZ4EZiVaaE45ix6cA8GHs1TigoY8rVYX8cycW3IxIgSQaTSEdglvkquoS2E1UGZLNLZi5GrA0ZgiTzXNhcAoeZ3WOXBhF62xddpNqp5wg6NUGYwyYzpuuDdFYlGsYXBAtybQGb1s1FDpu617WYTxQg4454EubTT5SLXcguzZ93kWUXkqa0rYEbnKEjGDT8+Mc/7qyfH+9M1DcCfPHV7BI4sQQY/37y5z3veY/s94UvfKEjIpsqHAylrMpbNJ99DNYitLknpOaYl0mtFM6ekpRJcGyfU7Y2L53GC2bB9/SkWhPr+oKYI9oUH/rKNg2u4fPNaspO7wu2QhWhSRqlH5/5zGde9rKXqU/UJWXNaoIkyjKwlFL8RKqPPfaY+MwWgayYxbTuYJsCsCzXWzGE/V5Sgpz8DYcUV2yEADUyXmXJT0ZH1xmpVvcg1K+GLnjyIxz2dfXSgg2W9Yh4dPLcmvdhANQeeL3tzMR6HNEfSbu3LYBWDgfCiV93ZfsJyNhlf7Io6ZDa9kueBK6Gphlim0jKZEUIAbMq1MIXqiMWiMUkQrZfFKMVl1122RQKwWiI7OgnvlI4IiOH6XqKcGahpCQ8C7ZDImEoHD/hm053r4bwTV7nYii58rzTliVhKOA0KIPqxFgxWV4hAIx9YHdW2lpeO44AMgchdLXY2G2tsvTWNjmJsuNR2LHsy7WdBMH7IzLD6jSsjd9jffO1P+4mYiYEwZ4V52j7xFZHBxMAmIZisLWryS2pmPXvTj/4wQ/ecccdWxz9aFFdLG8ngdNLgBlK6vL3f//3Ds07CF3h5kb8O3XDygxu7GyE5zDA0iffx5stCzSLHLPMjRykYSgk1Y7nnkVEkkCfszpCNnj2td8FYr73ve+xKT6U7b89TA2xyDP1hXh5EbGQj0hIsJVtUvkto/lP//RP/s21f3d89dVXhzBmlPWXRgprZMgGXTzklVjBMr800plw5/TsV8hOZbbw/OAHP1DJCstgH3nkEYX77rvPPwrW3Z133ulRdCXt0Uo05lLwTZq7FIjJTmRGx1yhwY8kSbPlObpITd1xoZwmVZmCyj58B6bziHhfHYuekYpl7h+/deg9fWmS7twTSrZIwFgecvfW3Z6t7M6ijAWyFmyjsn0Jkb0mRGfVgDRgNpqte0O5fgttykwKi+Rasv9GOQ2kZkX8xAKmamgmNjldMAsoT3va08zcqNbujMBTSKLP9Xi6BSt0NP9Ed6vaETEEnNc+0jAriZwXSzu4ScvolRmhFUwNStjDVW6UWY4tGlTLcHQS2oVUXslHPQxRexDpdOfC7pTTE2e4HP3lBHfHdnIYqISoxs4Bq/LkJz/5hOgXU5nFQr42QhinH7OWzmUx2dLbh+UZJ+DiWxI4vQQY0eKwr3zlK76WfNe73vVnf/ZnaiZa/LgH8LyOS0HNxLaAj3VZH5XYSIP5CXNsmQSjyrjsgzamwbHeRx99VHaUTGlkIDKg7sbX3RXgfRA2QobufIzqQsC3v/1t+Vi2rz0iTLyoIJdjLrNvCVWRqt5wM452dwUHfiNaGqaVGumutRvpPYGojNGUpgqtbOiB4Tnkk3YVtOVLoFWpd5mtzFC0IUPzVlleJ8aSA6MhXdtctcTgrf/+Qs6Ih18C0BHd+Qx6/Q8gBWenbV9iCOA5sAnehdoUAqkvab/9JWT026YGkTkcKGXFIDlLX8lK2+neqIOc9Kw+hHhyzhgRfotQlst1gRGSAiAuSJKfW7boIFzUo0PdtCXjvnaAWsqNwshAtJBnoGxOueZlxLym8GcmujVVTToqRKPmFdRhsDERlgVxwVywPJkL5v6MSs56GHQrmGbcIFMsRk5Ho4TN8SkgsJETFkwNEw0GZstYCohXk0U64cFcP38wSO28lZQHp/6TcBzZvMhPCxslNIhyP0poM/8CFAgJmHp+MZRjyixY/giiuUNk1Yw7VmCuu+66y/EZv9iSHY4OqouPB5DASSbA5GJn78Mf/vBb3vIWv/Tj934mSkrwkViWrZFFcDkact4JWyciOTCYicR3Cut//vOf+x/Iz3jGM0a844Fpa7uT/SbHayvnKkvVfDfrf/y4ZB3QrrIvsikZoODAENtrtdkFkottU5e5qJqIh6UrSMTgxYW8N7zhDRweufF2N998MyWUU0nyPdpsxIKGloSdCFL2VnBjeVjQQIevvPJKOar/nG63kyaL2ADIin05I8W1KmRl0bclOdUsLUSAepe2kmQG15ecpgOXo0agbxYkbYMNsCgK8KCQNRfSjY817gRk9tBWhZKtTHQnG3dPJZI01zUa1CS8K/g+SWrITfNMbaxJMKwWITLnpb3a9MpZA618OE0yObzdCUpMw/SrgAb99mnbtN8DwCPScNNAM+IkCD6ATAa7oHKuiGhHQcGTLkxkFkk5NTuiHST7wJXJYeydniIvbF3tvpJb7KQf21tltTaVrVHmgwz6KlsKIauSdZYEIavMCJMoRQcPp0vqjngJcxmlfAbicYuTHZvyNQs8hSEWq4o84CwITxeJAUW8pW1+UJxz4ZyyaYeMhotvTRan4cyIU7EnMtiYdATbV3AXrhvElrXBsi/axGlO/+X3NQZhLlbuWwKnmgAz9HKGBx980L+N/sUvfiF2nzJheGtOjrkBLPq3U6Scf7S9b0Hvgh+1InLRvD09VhLBU5iNVU2/U+B3ofAAbS15WPB2cJfXHNlwkCLKGNGDZQU7nwKL/BzRAYgc7OLpT396QpzOW0prb5P7t4Uo2RN1JXZBuUf66Tix9A/jogRWVVKHcR7Co+xXxCPVF8Cpkc9QbLyLRLlPGKSp2NdcPilUogxCKzjV+xpZR8IpXWcPQaZaThekt64Oter7WhQdG6xHnox9sFUwIwMN3J5Hp7Udh5NPyvxJQw2mbIy4Q44LdxfuOlS1jwaaDOGR9sNMMqZMCzC9nLAyxOs3j4JOXaTcojI0iU3byiWXcWRoKA+yE3Yvmdpj0WZCWV2y0LO1FoVyWkTJszpDhZSpqFfU5oircrNIFS9CdrrUnxSz4N83EgYnu69sDiNj4czUmHdGJMGWVMM8zg5rPJJ4m63Me2GwTkqvOAiGWhrsEkanixGTW83bAvg8rqWwbbVLOT2GTrNgF1SdtofnpUPA1o9iUakUB+e439ZITrehgRPacdxOq42sFi2QQcvfZm7CGOVc43Ri1t7bTTfd9KxnPevNb37zwebdOFUX5ttTTYCNFlX7whe+YLno9a9/vf+NxAev1SR5BZ9H/2xPyUlkEfBIDE5i7Hk4BoIXdNhyotvwhT2ZnOjPY3QGRVyCEcdFOPuRBNj/yxWzCjT9PLKTsUIEEpgork6P4499X9uvgUHvgx/Iqf/c5z7nLgVF5xvf+EbWUKIu5dOKMouc5JCSWEmySpctTV81U1e//WYdhMNIDC37ldZahqTeIi0h0Y033ujzRbvlhGaCJMuFUxSOGGkPXRK2yo2znW4qIR4xumbNn/rUp7p32E9KqVIWzdwjTBmSNlUuCXgFG6cuxE/WGuQtTrQhCVr5ObIVsCMM5f9MUtEA+gNvtmoeBWgxtGVdk4NLRI54VJks+IWq33XbcLxM02AAQ6Ns1ztKoItOk8Hx7cAs7dF6Bx9MVnRmabQth56ENbvoT3ihhyZszY6U6bxlzeUwuwUlDAi7NGKNt8B5yCbsjzDAuDCA+l21+7o1SUwlb8UWja/cBT8w16q+0BltkQkjONOWacJCmihsragcDVU/pDZyWPYtOPR5jSfh2IXjJTOgq4S5wHpHvQyrM1x9z7tAamcniXs1cPR5T9uhzBTxzi5bMy6fPrHw9ieEamtnOjLMNfGeZVD/0WZ2kmYfmrON8IQTYAPDZNOhF7/4xZ/97GdvvfXWtUMlwnZRQd6C5ollt/YZa/uaFwCdEgD/FNcdCxOR8wcTIU8CzILFs5/9bMvkpGEQ0dwZvtg4ZlRO5a2gYX/BmYTN8TYk1XCwayJCFnCK90W5hlLE/HciBLOhfi4FC3ac7PGKDEQ5tn8teQhQYliFaHJOQRXuvGVw5ck5ZUcxJK72crGv8KpXvUrCjB6hBonZ3PA7+5r7B4PyYdhserijoSNDUwNYXx9wJ6VUn21kjxq6OkGbtNw5C2AY8S0xYmzqCnQ6MtEQa2AAy3UtAUiVeSlLNno3MbMWQMiyX9NcZYV6fdpSI6ODMOeRYCAZolv130dWIWnrESkbV6MAc71KWWXVnFyB3hrHk2bhhGROe01VBFNRMz2ZhqlxQiysIlUaz1itervk+nZGM4Pt41xkJ6shot0R8mWxRVl/9FFGTV6vdlzGEouzvYdJgMnZxevxd3zW7pKBAcLCcx79uVtqSkoFsKhC6ESkPMqJsHj5RVF4GGJ4f78Sx3crzDtkkbBoR6SxSzAwlxzQ4weM7r///nvvvdd291xoL+LZTgLzGKDt+t69lani/8rcdttt/oW0LS8/HDdx8phmcxnf3bmYiAFr9m3MH5f0Y9WKr7ecGTcJrfzBPSfuBGGnvtpEAtK/sI+1/ghaR5RD+gADs8KyvboTQpZhSrQqAWa+7e3YU50e2mrrQPs999yDZmXJJB6RHU6NHZyiKK/g/M3f/E0se6UG+9a5LRhLOGXRYiNiIZycjkabZFVbYEZf8qle3gvGKXq6Iau0+ycyUxP1gzaFqvFYlcqUx+66gnzV9q95B20LnOZoQKR6BKCZxoYpNS22APfvJia0IHMkGwtIJWRXH7hTI+PFeCoxbh2hryGdJlMeSVvSkoQ/u6YonHfjYgoZM8IQr1UJ14w4L6JaJQHSNnesOJgCgn6aaZVqylxYhXA59WalNdnl0LMpJSynNWJh6J6mMwNLRMzgquFu7ecqmLVMMZWxlmshOwBt78r1uDUlHfyrHq3nMqry7c7i6Sr4tfUot87r7gARYFa6djjWtj06AJ8Vl8d1Hp2YwxNg1BLNmibzBmylz5RNIBHWUjm7hotbHDZ0Hxeg3m0Uv/Wtb3Vq9ZprrpmdjPHeL77tS+C0E2D8sHS+I3/iiSeuv/56XxLa8VurVQCmfKTeF9ZCaqQWPk6T/HAhg8zKGQgEtXnrV4iV/XqWbcCFsLALGbJc/zpITPmUpzyFQQmPsWt1Z4me85zn1Cs+xqu15mkKVemihezXeJvKwdFp28psDQrTKaXkvNt1fWC+QhQr+F1EY803cJNchRhaGX7AUlkAUllHc2k+eHfBEE4lxlkEERmYIzqS8BCapVAmOOvNJGklXvLc7pOTlUxSRGhjoSVVOUzVPYUWBr+WscUf9qhxJOi3+YwMv+QsyuThWuCU9UICWPMoX8VpBwbxnZpVjzaNkUSMknzby7AFrcq1A7EKp3rTLUtIKcNGjHuKmEfImPdVO3a7CGdeqs48NqI+M9I+AyrENDFW+9iHKeFIgMdHXAbOUrGTE5Xful4hn9hkFVgODNdbQYVyzh9V5bwFlLv8+gOZTPwpk4kEcDdBDp79qAWhjgAAQABJREFUh5/kXRObHwUMwfr95S9/yac7tMVHH4WMI3ZKAgIYv31l72ofB0lEOEIRH8w7IiHSECBh1kLD7IphCot2xiWJWSfy5CkClY997GPTA5txtBff7iKBk0+AMW+77y//8i+lQ29605u++tWvUqxxl7OLvJbQVvxtS4rd9NWoWd1n1sJq/ouan4Ay6+RXYHY8JbUExkMDW+OSyPFziQZwl0W+eBQxjYyFGkgR08SKgI0XSdEsXNgClWXpS78+XHHXI0p0AT8jK++SgubE2nY9hhEIZbz5sERZpccg5DK5fKkjHhl0r9hWNMiETQd2nzsRZ2BcpaH3NrviDgJpS3/4AG+9Sl9Fp0fsUKFOAqxrH8G6QyXHVtYEkv5aEhhZtEvBhZhC3ikQHa8/4viJ0UY3sE7DzmNYyM65MnjJtracn136fKXTsrkWYYs/eTUuyBOqeVep244OXGYcCMf35GbTgbs+ie5KYaJRs9DMJtC9jdRvln73gcTU9o8JLHhlfu2jiz3hNKB16UIZLwrzhqS+v2CFyGd8uDkylnZ6Asz7zyUWhjdRuyVUEsgyKDcxF/5BPGyONVleacYkhIQ5PuPIN1m9VVazcLOGWvIRLbg46xmHdVDsC6wkAStQll34Vs56HxTCbH4JDqk6faB7eqEbM+reRLIx67r99ttFrX7I5sxEERPZXyzYGQl9+GCfAd9www0OQt9yyy2LFffuhGX2+qkAX0v6GQn8ZpurMAM4dyLq/GFpM189YJUFcOoFYYqfufLfcW2N8t/ZV5Sz8eLYZGVyGopY7HmG8fPG55y/UXDfURpJ2KDSo7KO2Nl8+ApzHTjfSM7taeQ0dJqae2C1rV/YzoVZkIRZSZ1IxSuOEw22Ii12kIac9oorrgCmXlor8BLTOPkjPXZSwKlL66CIFN8QiwSelLgBH9/GFkdE7okFq1DaBTNpe6uVo4OJnDpJcmTLnXtLMraaZY9inY1E0QLr1NXWrCrbzUAqZqkHHtPK/rYxknUkqsO++o2cH/iEJpGPLvbkqlfxtb96ikqXyOfMLI3NKytrQM78u8+Fli655sJ2dDz0x68P9C3A0QlbS4C1ufw8ZCAtJSswWflOe23zKQA8AuuXBHgQnj3p1KfmkBqCPCzrV65OJjOy32GtfXSghlPzW8czcgpVVhBwoS8erdxW2/XSyrwwp8y/iGfmXXxZGqeD9IglZL8iGce+hDRb6AMMtDei6ze3FMJGiUMsiHBzsl8bBnZBjqUbNudcX/rSly7MH/oe1IGjV56RBNgcuO666xyBvuOOO/zsrW2N/nw4uqznIgBrfJWUz2KSEN+X/WeY2UGh8dyMJuspdvGrTtjPLoSwg0BkjJIclpGD4W5hYGTZShueyiLaXZbf9JWMDk7fG0uuuN7sgtpZ1Ttri7xBsldVwtlfrhZZstTIlsRKuuB0d14Iy7JlIYulTUfBgcnrrHeYAvJSeKS+AJh7l10+uR+xSI9/8IMfwEZbPBIF9yBT1ZyHsHDuxLiUWyFE2uVGFQL8fhVOwcCfs4Iy53x13KeZzItHmOOZUglbvdpHgWTQIwE2HPmtC/0aKREGTuXhOrUehIyNEuAiFS+E3Ge5AE6ukJjDwsHWCXAN974H9yiyZSV2MRQHpvnwY0FzjLtJcWBOd+/OXGYk4eEgWLx8kj3jWBsLq5DO7DCYq6YG9235CQ2sK+PsdIkyt76p79hdGmsxIA9HXMBayLUApMFxc9Z7YpPD4nfWknF0gMxWqzCcuyNOyd6PTtUhCSAB8QavLYKiD6umyThJ4hB7A0KUQb+sCwEPlcjbKqsZRzv7W5QI2/zHI/+01S5d4qLZe7mIcAsJnJEEGOe0ysfA//zP//zyl79cYiC4325SbSHEozThSIQg5j//bU9vkAb7w+rPpBzkeDJAccZ//ud/Sm8623QxeYIbSSD2Y3EEHCwRBzljrNOKN3J2T2FwRBAgMEJPSBqECc6k2ZJqVt5OlCEWgljR9FtWogcukwKo4UKwKdIiBPpgCVxiI+8VYAmtiEi6q0eZIaq08ogGLkceqIZXUCMzdKRZE5htKVs1kBQRKSJt8thp91M30Ba1MFS5U4BNhqxScAmJ4InYCXwfPn6QjFSeG4Nf+zVKYh/GOlHHxRZMh/jOY+GHijT6h7078Mt/LI6QmrzXYFkwwmD7qmVk5BUwCklJBsPZkYZrX1HLoiGEnZsw/+N/KGeTJ5W5C2gS04gm1aQyd6OWHXtzoW0IORORkwLJQKo7BdjCEe2Fs32FclMJcthouMf2bZQc8Z1XwPQV+gth2rqjpGWtcCq48irs6EtN9ahc9ZloJm8sYcEMFtqGHYDprzLNa/WkKGkp7CBfyKPxZV0RzDQxsIwnIc9IG8y+T4FzJAHWXUmsym3NjPSsReU7mpGu+VD67JPdtXhGAOA3LyLnffiCdM2pZfVzhJKFvOKj+VaHs+LoF0LVwcgw9STAbKnd7007HdTVVLbGJ4KldXZKlFngo4gaYUzly172MqftPvGJT8xrajYV3UX4jgTOTgKMMSr+V3/1Vz4GfuUrX/nd735XKNDOhw7nJ/2IL2GTtMTKcYUgHY7AnJnjmh3WPOJOBiiabNlX6bFCQDmbsrNtokNJoCxIw7kMEMxyTipXtBkRAah71fQLzLF1X0dxNjpaaTVHdirmdmmLa6GVGJ2qP/LII1J6ll0qItNLem+D184M5yqVxbvfoJIbZyPUSqSI/5nPfOYLXvACZSeCBDe8kS1lbWXUWoFUJi62+/HHH9fcIite4mM4LaZcEOmRwDs86i477eoh0a9LgLWnoMcCUA5a6xcxduD1SziIVDBGFg78P2FnQwgEjMrAyI3XbgUTi/WFGSOqCBANdaG5X5m3I6+qeVvIcKRVljkom0p3F0WNNeCMaZH6LMTQkJwggMpyCWVIE3eXUbNC75VlFxoIT+rdqZ+jXKk3p9pXxO4MjlakZ/VEoVopX3755eaITg1K6gPgTi0NHPX2T7y9qnoFqm5y6eWHP/wh+tXkAkY5TQoF/+jLahEJ1CtrFoZPDVXHnfq8BUwnf/3Xf10N9RCMpknu7IafVwDv11miV/UWa1dddZVHuuG/fIXIvIXZK6w5bueThGqiHpj/3watcXEQI69U5hXWaKnHhx9+GIC3IVLB1yuxWqYhtC1OLJuGarBGzoRm3l1yySWmMHPBNnpldhigYHN3WYpKxCn4Bp/K3FGuO610xJIYU/W5KyDDlCcN6VAqaY56q280gVjQT7W8ytsUqJxWcBqy9OLuMfcUNFToXAAG64GNv+rgOfwjslk84rWdayxWEWDZ2gWYB2cnAZdMVjXZU71+Wy+WXiL8iLoGoio3pSQNrbFyPRZzR8SyKeZZ4JEXY4hf1yw41yJhDUQmF+bhZ8Lhjhk9HoTp2ELmLJ5Ri7eyfM/awNOGgh35Z7p1Kg/wGNV6zWtewyaw/FlXPUC/F7uYKIGxYH0iikWBiWv9IJZA5L3vfe+HPvQhtG0xuxbF0Spi8CVYTAwXB3OGmR0UArdtizvjGwkwgjnsmkevWMZqKxSbURlg6xhc0Z7Uq7prCzVAKlOumiKpXwMYThyJGJLKBqcomcMWicpa3XWqBh4hrAiDV5BdSFekssJiYYddDgURv7jW2WYBLscjcrV5Dkygr60mZKU7MbQsAkLciXd5F50K92HGMiTu7HiRHZLqLpjzj6DSRE6e8B1hBTBLoWSlQBSIqauDX55gpVlKX2c1QXZgOo8d5N5WTQeyfYS2wBSSEALAu1fkb1xUuhDs7pUBcicl4j3/5lc3zbPE4JX4WK0arYLWJx7kD+E//MM/VCuvXFKpnO2UJQa+KBRpEYVHSR0FKGrphmMUEU42JSIf2ADLfpMAUwNvU6kejCb5lokiudTk8rYCXDRQm/9+c07sNR8VgHkVbLnnrTvdK2yBoZmBITRcp5xXWVtRFlFlSqYeTC0zSZ47wYcAFIBLulhUpYYOKyCe/pNnFlPyqjRZJhndVk8scj8zKP2yQhF1XqVh0W+2lhgDkO60RWRGLQDueQUMj4hUk0tNECqoj2XzimRgiHy8oiRUqJqo8dbdpV5unLI7GAiTAHvFStSrMMUs6IUOW78DXG/9DlZyeykuhaz6FKyDJLe3ohEbpR5CFysBp0cY6A8ucnnFyESADFRWNLzSu7ux4OK1InBq/Ks25/+g3+ijTUeuegWhsrtWnYt6QJhXLVMtmLftq0E8LbyyRR9n0CLMzqsTeqQ87BKCmQiDy3com/Vr1w0HeTQ7YDDpzI5BgCNWGl9fyjB0TMG+yShdoquiF5q57x4Xhb/YN1sTu06ZUB0WIMknBmmrDIBaxvh3gMEwuR3j34HZ0yM6Xe9///sfeughG3IWufbU0UW0W0tg5sB0azrmakjdLd5/9KMffdvb3ubjCsehCzNdTHmLKVdIFlUII/jKPsype9wtZMt/YJ9zFQ8Jp5QjE7ERtw0hL26jRhAmlBT8Vfy6RV87NrEEiJgoIYLF1hAKeQutHAMXnZ+qRnCiumSVIkXpXDyHeMKjesExtGI+GQWubT6I2nkFXZBDuvAolCQiJ9nsC9n8sYbqc2XbX8I1hIkFRTngLSto4q0mQmE14unHHntMAGoZVTZuc0m5yG4LhO9VnI2CR2LPiLRgEUJq+m9byFVlGMjTW/IRK0uKMCUnqXN6AIKZ9ORpKOcFxdMq5S3uAPBVF91wEaBLpWSPPK0O6CWQYqNE3jpSCQZ87pBndHLcWj3CtHIJ+OxqejRY2QstjnTkfyl7NC4+Jk+TvKXVaCZAI2IUUNtewZ+YXjSZgrur1FsKoXetUq9QYasRTHqgkv7o0exIv/QhiYdW6RGR9SpySz2aCUeaJL+NZNTrNEOvnFbGJTzmsb1LPGwFqyngeis2evrTn16PbQFh8v+2pi1nR7StqXL4qse2gGxXW9OWhcX9wAXNMgFXIInaKpIpE52vXdYWT8rGyw5Yv14NnKtY82pVKw0NevJ80kaAmho1atnKBJ3VNYE4NqKmrnplY8rQVD0lVw5m4xsiKSHLoF5N9g8NjdmnBnyauBNF0FISylz1CkY5Qw8VS5WG7q4SLJHWvr16qIxUpqElrUxnlXllrknFPTJumVDKunDpyz9RV5DRWRBEKnbq7iCVcZHb0+eqBODSnbaoZZxTU3dNlNO1jupSQ4BsQlZY+gAgUVL1jG1N20KyhIJBIf+iRBnNxrosSb0aL2jlkmECE6KQ8Dj8Id8iLN11CgZoH2SkF76ADCn5okSxD34HcfKS2DeLtxayhjE14hPCVM60HewOcBtlDcLsr/I73/mOfbgPfvCD7M+FOdz7k+0smM9aAkwo9OyNb3yjc/8333yzONJv5GamyS54U7Nl64k3i8RnR8KqCspxx+OK8s8YdxGXEERqx/uKOToMYj+bWqxqAkHA9ktdIEVdrKSIX7yifMRQQwgl7w07aFOgqDLVYgd50olB9QCD8TTJXWJJk5WxLyUW8opoIZTQSvgd+5TVCDrtHxJLVkAfffRRMvQoKrV1/I1vfEM+LD9UI2cW/+kFNorkLipVIwZKGiwgJnzhINl6m37di3jlzoWkT37ykxa5X/3qV3deebRxrSMBerCl6z5YvwY8GtDmJHMCfaT6OXSO0OhnmUONpAXBRGrDyiMuhMvumuOXZBSsywJQcKUjh2mleSodRk1yqF5HAGQgibyNY/bMvSJwV3YgwdAxBKgxWLlXAiDcsbRRrxRKFZFt51ZNe+UtUh1mUa8vInKFTnd9+dHvPLb1gVyVL4E06GnVuXslau9U5tErfNUrnJK/OeUiW28toORttLQgvapypzDyqgO52EdycGWhjSiUozOGplLQAxBPSTIQzItlBdoiZ9NvJLxKzkaqM1hFqubBUDVVUJ9ZUDVVoLGxSFVTBTRIMuuxU/ArhqkhQIXcU0ONTcbYHJqmQLZ5JXs3cdTUVTQTgulf9RriNHIIcjhZtgCoSSUdZiKCHLBKdzMCX5I3UbtUP/WBaQ+uQ3VenE+SYMMjATbL2FiOCTDLjOwA8NFZ0LR8gDDI3fXlbuDgb7tIR3UffKWyAOYtMGu2EOC0oMAxZWV2u+6yQM99rLIw81I+HZspw5QFPiOiLF/ajs0p/Vo8tQTDnFqsWTUBp+A5RRh6zgVbZjILLCxuLWQN44/MYmXGtszCcsSCWUveN910k1/nffvb3x4nvhzyLlISCZzBBBhjLMuf//mfi1yvvfZa/3TL0rh5wscwdlvPusVqDI6sytujY1j5fqH22eNRhCe180WfaK8zECyLvOWJJ56QBdn8FwYJQwUiohCvWFtxEpfGRPI9NKHT/GCPYrLEOmIpGSlGDJNL8JQYmjVnNCuGxoi3HfIETNiJ46y38jHcJaoTpZGVvFcObJMkaSFITgJyG3f6StYtT/Zx79VXX+2RZNQLUCwYEXIoEc6iTZ4Mv2hP7AJGqgyYYPVI2UIezEWnvvIoyRQ5iQLbt8DgRKqZKOCQAyvDZrC88mjg1BBU7iqFznCqF02qd0HogsGqKnHh0e4EGCsd0t1QYsQlwMB43HSKEixwvSrVgNdpRa5EigxvvcLac57zHB2hkMRsmXpVimdBDYbAB497LvLRhV0OyP+77ld/ScPVqcwjXx533n+rF1e/fq6aiCLY+jSv6gUkJXQBsPhCUYX7q4CPWL8ddxsRbKawS9VRynTPBN8Iz9bAxiJ9mQiieWHlKl3auot+w+LXq+lq08eTmmCAMwVzreqplqvfECQ3l9lawFVg0Fz9VrowNK688pgrU8wEt6LEIJj4uSsQpr6MssMRzF37iqWFx1uLXFmAYLK4G0ZMcs62+B0sG9G60FwXIJWlkRJgDQXHbJoa7gBaLszYZXedFlkfZOrPTf4nnfsNW7koArRie1155e5irLwN8vDVua96pb4D2T56GwAUpoCSFmBiWXPcMd0KljuDamLbA4AZrCyUI49gPaKQoS4lnJcGeiJKoV2Gezt5zkvPIbGRMGfKQdPY+PTdeydDs8+QQb4o1UKPhfLf/u3ftvj1+c9/fk/qtLsAL2I4mwmwceWf7r//fhtQL3nJS5xD4EezTSFK9tYklBIsas5srYu4wKzFct8d+Z7KkUuh/9lgjUyYko5kUtMyKBaXrYkk/B5Mfn1HZMDa8jTMkIBDJqMVHThWAozaRHKigahfxanUUr4adhCZsgBucKfFyNqi7AjEI88qnJIKioMptpRVKsvNiPYkirCJyUjGSQHrQQJljyRmXV/wRFbyW6snepS/WUQQCgjCBHzocQdgLUmBs1EW2OlRvKgj/Qr7clegh8aC5I2FXpySlWObhl5JI3l9maptFnSahnr3FirBYg7KaoL9cEcgejdP88jVEaDeFeqOBpjJBGup9DZXtl41f97znqdGGiwBBkYswQztqm+QuKuEm8Qo4ZfdZXRCSZYSisgU3FErltJjC1xvF1swsubIM57xjI3IDjCWi6+NmlervRZMf8Nn+tOQPXVkuE0lyOk8IyOHUa6pvadON0VbwzTjGPlRLoLd7vfhBumxeGT612n8iTxqZWqbobGu46067HceWQkmrjAgkmVmoxQYBIuD3mqSgLvA1NRxfZDshgyHiWY/GdUXvvCFlBAws58rdkkNVZHAq2QkdcQxJZ+HhE1TgzWFdMQsxyIx8iwkGP26e0u3mTgFy53yTGawLsrJ4uELKka76hW8Yqi9Sndq0lHdC78aVh18ulNfMBMLXJIxYvzhmdjkYGBcXlSOw1Kg0romln0QQIDSP46PD1q1GLqPfpeAE+9UWnRKpelkZxJtTaEpuerUydY4d28YZn19SfMdJWutyu7IL2KYVwJdwzcv9uNi47Tuu+8+mzl+hO1973tfiOGZGHHWn9c8Lnkz9o4jSY5wxGEtVoZL7ru0Gbs7JCpJCNMpjGBWpE/CBax1kkPs27eUfojmQfJkgMUT6ukAm2sdjvf1eEjKB/tCA4PYjo5yVFH8J4YOaxvF0PnKlAsnK+yLdYTjLqpuwwH7cjYhCA15xzve8c1vfvNrX/uaQMq8sIsC3vm0JKuaSJglih7lyc4UaCjeEqV9//vfJ1K0CREkSyI8ZFtwMRzYLMFKBsysL3/5yz7C18orH9M+8sgjAD72sY/xVWjTRKdyV70LNeDJ9i9gUaB6NS6vcmnrEgI6M9b2pYwksSlvih4FNf2LE3JRIa/MDvhBYlPuTUNqO6jfUA3IwcBaEIMv7IgLgZGwQFkBPVjLEpvAFP0oH8S8nEqUGxEi2s5PG26DtRx2WkoMh500U2m7BJhAfEBLSeAZHEeVhlgsC4AOkCFNHoRsqdpT2VSVIw2qq3ltZ9Kn+4Nvt6BHQsXYbidV3REUQ9dZi6SHBkvlRgJkZHBn0m3BxdomUkqW0OCCZCQR5vJjcga9bauyHoERMi7YXgXWpn1bYAqsFvwKZpAuMGIdirJRJ348vyngrXqqlYbAkoerSb1CEaM7qNS4rEha9ySW9E7gTmoow5Aahec+97l8oqH0+2QqY7hQTpGoCuMMhvGHn5UzxzkXdxwB8Cokde7polOpiZyHpa23oaEDdpRHrLnCDk6xtifadMFl8MLGN4uwR+H3WJ3SLv+glB5S7E3n+LFo3q5fA+26/fbb/Y8DP0WZ5dHtUF1sdQAJLDR2mYVztsyZoi9+8Ys33ngjo+PDYKGek6iQs3SzdLEcJJiVBgi++b89GfGjMCsEYTdx5BLrMC48VicBRphcxdlUK6xOnQEA7DgTYN5XrKCmwjWVYQTMdhztggGdVLHtV1SRDFDUglOZ3iBhUjhZqzACL21zZbEjZvkYb20s2IIQ9BORHWDAehQMCa0o/wMPPCAUBm+P19okYOIFLDzCFABOWiKtXgAEM+nZNAagOarEWLAJ3eQGHi00AEN/XUmiHLuQaWvuf5IhQ34OOOkrDFZnzD59GU0LE8BUhmUdudT0rwD068UTrn591WRZBHcE60g2TtFMUKRNMVahTXOYKzkvhApEnVPiqSS0/EefPOoF2ssuu+wknJ+g2Ya84dgiAcbmuPBboa0tU0sjMstCVc1Qnaac+/hwdyik865O5WIfzUGztSVvlRDAbCSHFmfKtEWB5vRfTamRdMmmxMGAVxE5TqFW5q87ZweJMngXSzKFgIkwrETMoGU7eh7N1Mtgc8QwaJJY64wGggkFPwiZSqY+nxwHYcra6ugcJ//dC3trZDk1CC0+mqSr5ilqNa8eI9jg0ZARPpcZNxdZgQHAjBMgR4B+/HIWaWsychZsXXCCVM+nxPnmv5HxL7kQKbKCCrA0Lz4Xj7DpCBLI2UxvNQdcdBbyTs2BH0l1XuXp008sghAn9Yxp/+2Zr3HWTORgRb50++yxbIK47r777i996UvyDr9ucGGO9QmNbNcSnRDpU0ilf45A8y52gBloofmqBGMKtuXD5N8/xlehtgopL5/+PoVJF6V/PppyjFZswcT0wdTwLi996UtlUCJpaZuGXBqPz/HUMnkaqgFGEwbxTKlMRLJ1CLiqi/INfR5tKThU4zixpFG6K5gQteROvXkXTH3rW98iKBsLmvsGHoPSMP+BXVAlIvn6178ukrP3gncRiYJcFyW+IUwmLDCVy8EgkAKjI/vqBGhn5rbbbiNDTSRL6kM/ZzbIiPV+l1d+ZcoHsZaf5OSCZpf5CJtX4iHZRb5eG0QyWNmKhaxKXCPAl1xyCWZx4TRsmuAXIypJDxnacswdDQnCwBdyvVtgck+MKLyjaRpixG4wZSM9DErvYU7oXG0XW4gOGxrp+rg8982C9R0iJcndOzIQGSmorEeYEcZl008QO8o2TpXhTuoyDnbgt4aVntNSI8toEIJ0Zcesnligxcim9rOVZ+SgBnk5tqOASEZeJQqzTj0iLq1M6uBkG0EyJrMoTzpFDIQuhFEnJmItv9JFbTkpwDmVM0I/K3T99dcDcJicJe8fb2nbsifIaGsGy+38bcsGfZWrYufltLCxhMy++KGyU5YtaTPyiNrdVcojqWYG1fAX5hry6H+ocrgGNkNj8dFKq0r+xfEE8F5xzZYGkjazmRDa1gbsrV7UeEVRi0GMZJTbmirPUtDFKl82C/7QLx1i3wh8FpyngiS8G9D8+/FFkV161U6WrSmEzfV3f/d37373u9/5zncKe1o13hrtxYZ7lcAZT4DJjhb+wR/8AbN7xx133HPPPfYJ9yrQIyI3jTOTzUPOmJvJAWChD3fFrxyRtq27DkfumAqSEbMSN8+P8jQcuVTEla+nWgJysG1tQNM26ZR5/SkRSVoV5R0kHr2SmopvXPiqGEIyIFk9H3L8Kst1MjnNjaazxylHLFJf55nDLDxicfXuiCQHmu9OMmBEcjBDKy6xAyNAERwTVPY53TWBwV3k58A5nbFX45IxUiGrD/WvtlCeoekzlZriWndGxPIEiUGOTg3VKKxqO1KPGNFVf0z7TURUidRJOEJGgEN9+BWFIA/7Lg2lRuPbNYWcMKEKa8oYISgxnGBR7BiJySRJlazG5VM4j1gI/QaCoNYO6J7oLD0p/FWztQCF0dEuukeHjbjJtSk2oymzsgaUvcoib7AgH7O11W7BDYIduFLyb6LpFO/KCna6KofZjhijQ1tIdVMNRwkp6dQ0dPkeQZmZYhngNFIW4JRVTnFVBgUS9sQwKZuABn07jla1gpwCWEFbqzzoZ23YYbJlRadoGhhXq+oeO5TAiYZUMinsHhgj2IfsNNzo0a8/GJrgzK926cLyMa6NziAqwPkBBW/DAqtYVLGEqOUySM+esMvGuLs9akPGARlorVyayI1FKfA4m8PlpTsTlg7wWU7wATPEMBjfusxuFltzb1dROFh/+EoUijcoBnmaMocn4Ig94r38NfaPSMlg15TWgj7rMdH7DyJpKx3zfvWrX20b5s4771wgvy2pF8uRwNlPgPHJO9oQY15lwk996lOtE5exPpN6wP1YauVsBPpchUkuTJ8SVSxWGqyJ5JD/m0IhFyvSYtekSTXQbHGuYFBm/pRhLphx5JqISARqASNkjk15lVeLe+bs5Z9SSpdoxsXaZr1ZfJBhgjk4DZMoQVlHdkrRJhSwjwqJVXZd28U1oCh/3eteZ0BdAIyvwNHpBkGGtj7xNfQf+MAHbHyxyMpyV5sS4gnHj+GxH3vvvfcK7KTEXtkH9hvaVkz8aouNF9+QJwyCEDbLBLQIEmUcoV/SiACBS5HdF6BXLqeAUIhsIY4dD3IgMflhwtwgdN/o0nVi+rWtDBDvCwx56PeYKIQcCFm0SibYR+dEvYKKfMAbHZgxYoAwaHS8QpWC4cAvIkWrKnEKeV8+XrUXnPW4FrggZynojhBsjxvZGpdZME9EYlxyKID0yC0LPcZolzyNcurdiPgmwqTIktBEegosqm6UV41IO2ppVTWrmhTywxTYGSQRLBOhzA+6duyaVOU2E9O8ti8yyfYXwZqPKbNmLINXrIrlufziettqsAzeFMYaSmRHiIFnHzJHKrT+G9Ba5Cwb2aJ/LWSHI/pJ7TuVefRFsZXKKBWD4woxuw9i2x0LbzhYLX2Zd2zaFDtQbKagVXB6zMoyL6PG2qIZlMUOr/Iz17qjRXp0MZjq8WjxCF9qyNylACw46Yb0OB1FGpTHN8zeMh3WNCsxTsEZJajSBfuMtrQtCoPEY1uft/PedUSrLTFQIXKYF/nCseHdpPDvSNFpjs+VZM7INQqt5e2yEVLEQGVB3M8+O+LuZ5/nnaHVy8XC7BK4IBJgUuMgBf0OGvlhRr/uw+vs2/bNPlTTEXJgPI19QvmPAk7NT142XuoUGefG1h6KK/kIf7lALpNpcxW/AgghBTA1wm55srLVEGlnte0UyC3wKXC3NmDVeBRTkifCnO2BWcKTFDf3HCcDKcsFGRqCBElJgHlxAUfrvCvysJtERRMcfOITn5CtQcWj/83f/I27xP6uu+4S+SVbgFzEwJS7pLUgc0k7kYE2kYRFfZVZNZexk49YTXSCHQv28MuWhSnZwpIM0xZNpIgKlhKIizPTkU7JUMRJyFw7+lfl/yIYXes0XDvcKG+ERE2kESIn3pGRQ604xU7wEFdJrI9H2mNoMlLuOYKlLVMA2FBqi/5+w1U1yJYrepveqY3IDFNJgAlNL4Ysbz2CnLgraEwpiRFPVrCKgD3Vi8y2yGfmIsbIRmLRk5LtXPj3iseMowDpAuWUU9k4LiEAoq7mL3pI2J1qMTUhdcc7u7GFopoasXsML4Nja71jB6IA7p36tdRKg41C5uZa4LUACLBUR2gsGGBlRgNJI1uv3jKSV155ZSz22i4KQMPIpGragiQ/TPnIhWWOn4oTb8F2KSNAcoJl/ksCzKrPoiQZYlYl60dcXlxPSDU7XLHDqUEGb+XyGDVomfLjJsRrctWleQAyTFk7S0MeGby3jLOfZlBQU05WfqJsZC35ISAXgAqNOr0jrKVkozJU3CvHp8c41o2anzQw3sUMFtMNkMMCW5iL/bGPNjMa/hhGjynQge2GGwZxkczCbPWzuwtM9fcnzFPHfKEkwMZJQPzd737XZy3XXHONn3Cww7Cdui9/yPHF4HLYjK8kRI5nR1S6InDxu6bLp39HCiU20v6f/vSn/tWtj2ATBcKpQCyEI2zlk1LP+bFf3qpPQZl95CNZcNkssEQeEaZ4iBuWLhIveeb3hz0m2WND+eZyqFBx6iA5Wj3mrpDuBAeulN3bSyDlUoMS3/0y0Ebwhhtu8PsK1FjKilROJW5V4KJTvXi0T+5fCmkFICkZ2rzFjhqM4wVHvtHV3CzAIHgzAoxwgQWXQmviwCSbLo7RihwSSQiPAKhBGJJEGGx9JcCp9yrTCv3gPUpcxQEGJcInHJWdS1tSVUk4SEU/WQVPINFgt7xaWW3V5HzIdC5mGryMhQtY8Oi3HWKvWvyDGFZVaqg5IjPoQSsKNwRCPZxiIUtsEx0/VAaxDsCv6ncf9YhP3LkP5KtwtqpCG61NIIPKmVyM1apWm9YbI9Mz03zTthPhpQ1mQRRAOdm7YH1i88OAkQM9ROQs3ZmhctddUNXot0gM0xbbp6wZ5clJ7+SKLc6tyxDKqUJnFh9ZD1Z3EGGxQzJzCTkdxe/ADy1Li8F58Q+ys3ul5Tx7nkj1r4/oHoTWAaej7fOYNHUQA8wuItIpq8uJuAeDVnJ7NVmSZp+Z2bxiZ7JBDacaRPJW/rsBAw6PLeX0yJKn4N6nKvSsqveWzxKB6JS3haqFLJ1pKwcZPMVK3AkP8G4S+Ulz9nBpbIolhCWhKrEEBcgS+aYCxyx1uvbaaynYo48+atFnUwwX4Y8ogQsoAabuDKL/CWyl1u8J+Vq9Y5WOOAz76BqzYjIHv51xZYbK5lZBp0szTHPJQR4iXnEAkhV27ld2B7NUzaoH9onF0IPhonxc5C73SCLnMDDLmBwsgiK6LNJnt4cvBMnY2ZGWviYhtKzgoymvxEn6KqkqsP6uQb4KbPBtKsHYcWVhMYIqxtq2APovvfRSu5eGGBg3gx3lIMSvSsSjLYuRYjgA8hzkcfZeKXBRwmJ5Fw1R0NxpJRkdgWiOR4yIBsBz4cJTwb2NCA0jQJTIkDUkRvCRsI4AoBAlajAeGYIkZAEu+NDW513Q6RUYNPQTIbL1CoBRwK+gRxlT7nWlL48d5IivV97qCMt+kiQ0V/PpBXFSnKW7vnBnP4GeKLvgJ7GJ0apRQJu73t1TQBg80+nZETLCQbOxPky/QlJRpmllaM6J7P9ntvO4NXfYMQpbN5/S0KxxEaDTE+ZXop+t9WpKj1vAyKMs1W3RsN8Ep9ETr7YeJkbYdO4gN5c707kD0HmM0nY0NpVbE1ZdWLkzE1k/5zIMK8J0VG87BQLxT+OoQVYzO293f8TUAQyCmUhJsuq6I81kZfmPI2And0S1tnnG2t3qgwsX1cRKsf3eqIRKBWMaBrkhr1ih9kIwMAkz1wYh+FIkzkKEYKB5YWD0gQKf09f/9b/wqN/gr64VAsxV+dWMwZVNq8w8eEtw2/yky0RnfcHKoBipf9BjCaxF8sZItENPjODIBB8hGKdUyO9dibFtJ4jKSmdGWl18tRwJXEAJMKHTTnmRc6QvetGLbrrpJl9LMohnVWUlDCbkgw8+KKMwyc1wWY1IPSwnhVuOIu5ICUvUwSAiYd1kj7/85S/ZOwmYAEUsyOr54U1CSOYWrZDJJAGGhzVUjodzLzBfjwtEiBGMwBr+vKI/u3yv2CG7/8hH6lRsbVtbjx5dHbA++6jK/rB01Hq8lJIbhkS9/VhppPBOzmZxmnwkhGRFSsCUQeJI+srZ0xxuni7xZ1ppDsDeMkkCUKk5YgiQSK0dAC7aWqo0VE/r7Crbl8tjB6B9BJzHQEJO5mpQaChRlfrqKwVcJCipemCdBQhBCbKlqZdcckmBbVSA06VJvGbuKEwlJXFNRFhL0VizvuBO5nZOJjafC4z/FutfccUVCeb+H3v32nTdUdQNvJ7ySzwvc38ES18gpRCVEEKQEA0ISkhMCCHKIQUhKcLBeAiYICJgEIgYDgmSwkAUg0SOgpaHsspvI68sn999/0k7z6y11177eO29771erGvWrJ6e7p7unu6ZWfvaFtrgyTgqRz4Kaqhf1QdMKDnMi/Lqou4hGJ1FeUuJSoOuJoxQgzy2MIdQHiV+PcKMmn8ba/mpfgxvDTws1LVGw64Jb5NVM0mLIaDD7vLV+ase4HHEWmPCwU9cmQuEtgA4uolEDoBVP25nfqcdF0sf0ZPltqWQawNEkzfXXtJAA3lmwX2LircJa2mLmBpl6teuVoTsgHnlQ7k2MTbdZMKVP5u8skYJW1r5De0cdHLiDAapNSfmbpI1y+jFZBfMbS/KIrErM3k/lQf4qO+EY4LG4/4nsjlyQ15CIzsKFq8FRevFbxikDPfcc4//KGk7zZJKKdgcMs4whyCBqysBJnE6+tKXvlTq+4Y3vOFd73rXn/7pn6phEocwGFukIRxhjanb5eO4eXaZsC54Z+5JKrLF7vaDqp1CMBgHZH5yyWrcfQlmWwmDJjBeSa6LMEutLgVuTjRDJo5ZmuxNP2RiunJV+GL7NKLrVMJjmxvw7B3AjiSgF5MoTgWdNpnlqAbOIk7bu0SufSxKIh9ePnO2/ArL8ivhmi9gVcJMbqI9SkIUGuqFbngFUr2tZqIzQ9gcyM9+OkhPqj5CJmeTBxjSVuMSSsKgUz0qCINQJVU2FrAJUhEAoVYVEwBLOqrgVXJscS34hLaISTAKIJfhW5Re6o6GE9FEwAqGuFDlwv4iVC/0Nusv/WlnUDKc1ewKEGrpJIEnhl57KXp+j6OQ5GCALHB0iwWjwKtW0g2DzsRwR3sxGw3JUQsDaoiNS9ngqvh3BO/0hB0MozMHP0WlBnMgjxrGVEJPtmI1W5EDtYGn7gopz0RuvvBpiemAwxltwltOI+TurGERSC3qjeLZpHKagE0wb7dtPL91NMvuBHIsZBNCSyrKW2feiogy3HDDDfRfpMGPWYrluwLMxSUCUSCHtLINnjjBAQGtTJQmWVcCMDAgC7g6aompymMpFDsiBGW8HDU7E2K/PHL/8z/+3dFTTz3lp0mvu+66U+V0Qggn8OqqS4CNGU21A+xfIt19992iroceeiiVmw8nkygkB2IP1mKtUAo9rdzLoOQtCBPHIFUYqjyRURQvey6UGJGnbFJJcitecSkLWcxGAujvf//73gYMkQp2AEwwglFpm3umHHdv1YSR/JNGwB1faoaVHYzHmWDDhmvXhKqirSMg3A2RG1/HckTwSTz8IppYzWlt2aydZHKzl0s3qARJ2siVmcj/tXLQS41NHm2dCTfHWy+QW2oiLwUg2ssGu1eSZ4vZ9TufGspjEQOtxCba5W7gNDeaiIdBjYJoQ3psdQY8AF3IzNWjSsoE2Ah2CbCowtUyG23RKoW8qsoWMmU4kwPL7eXAGg5hVqqR/LiqyUoIsxRNCBJg+il1X6l5dbphQdfOJBu4HF/fEFvXnF4RtXySHioY6IyOMmZVbrKd2PW1xUdrE645CHHRrulMNGlV9EIGeoK2pa8QT0vdc/JlKfyuAXgnE4Fe/Bag1TcOZ2aP7Sgo50rbdlBoZrfU2OHX0AkjuZDRX+SEuybH+IjNIruVT1UqBMbPZJhTTBYcbPv2NMp4x5qLKxOEULlSD9OZD+tMcDy5b4VIAwCnmoUA9dLj/ECAOc4UmQmUrBzQ00SQBqe4xWVKNeW1Mo/0Fkn+cGSLZmvcYks/eX0UKyCGkuSZ+aoyxKnrkUce+fjHP/6JT3zC/z06/NFZlcerBP5qTIANLYd12223OabywAMPCDrf/e53q9xciXk60TwXxpEdggLhSJTgwinHJMaVYNjkUc+AnYxFpHQiZ3j2TzAaIvYUzCimDVOFS8EiIjeKcieWCTbkZYzkLYQsk8nW5ZWJ4/JNFGIiCU4nlgMMuYlZ5uYHsTi7VO6f2bV7jHBQ7hwygWB8Jiqc2tmQ0phQyUqgRrDaKpOVtCTTlTV7+7rQyktNz9SDwuQstBpIVBIgbfGK5EnY1F4jAqGaJKW2EItCvXiVxEDX3/jGNy5duqTSCkX+zZK1J2+ljs4OgUQM/GCMlBzJYo3KbrC6RwC5GJ2E2VsEi9HpjHpfH41GpcBIEsuWA+zWIvgFNBf21xBnlC+EAgIxFqKBrFxsi4Zw1A0Z/ckOsODPGgp94Iq31eN28dSIdCxs2Av9JOeo94ao9t8c5cbLusB2ZbIGI0XAesNkFEw3Ll3zWhwU/efWWkp0Ub209VXWtf81wIEc5oeORefmBcdz+Fhuc0IgAEwlPAm73rzHw8RQylbkVQ3J0ASP+ddr7Rkxy8rq+T1RjVU/kZgZ1p6weVNBvRXkCBaYOVFb+E24xJ7YBmSATejVY2iYGJEicg8FVGHNzI5gs/AhzKpLuSY61C4F6wBw6vJ/VT/4wQ/aPLOLdiBD0NF5fpwjgas0ASYaE/m9995r5nvwwQflwG95y1tUbqjKDMOScBKMOdLfG4xMwA85CDrlCTIQblSOkd9mmDgvul3yCCcIMxPkY04BsUrhiF1HU0IAAun8IaePchsOCpkJaj4AaRPYv65VGB21tlIGCLktUL/0qFV6OaK7fwVssPIfrYpsKagATtzWpRCkR6tNQgAMrosM1dj4tZQgTPEqq54a+oBNzimulYhaw9ZLAtyvfe1rfgvESJnJTNhmbtssZGibRd7olZ89tP0SPOkl/epLMkz4Jf+MpqBZbGTQ0S/RdQfZggVedy5lZBSGYnlRATA5eJuGVV4Er2vahQuKZ4FgEdje6onRLugFaiYBUgCZQOnGVninM9ZcogBWHPRicKkNdVKZkG4rHe0CCWmgn57Ii+ar4lJK2JQ9n8hki2iX9rs5AMotLVkpi31tjnBbGNr1uEVesetLykEJU8klKvAJXQLcNRk+JnOmIWuE0UNsh1xjoskG5iiRlJnfkPgx8Oz+jYKdQCU5mDGZLbfGHARUyqY/UxvuvBXGsO7u1BIYF+Hw8Pk3HLTUT05avrcQRnpQ0UYhmQu2CCqbyXSsfIXlWjGAt6ZgkzL/6RLLudNAVhnINNdjCnu465cROURmIrPxkLXvPfS7/y5w6vIFpc8nbZu9973vNaz7J+Pc47YkcPUmwCRIdz/wgQ/IDd7+9rfzI/7NjMr1HAer6IZEzXqoOjxbeTS7+yENB5OcaJWim7NFojLhrVPYyYFbd4mtdSTL8sFVdncDptLmM8fNb6LQLBJXnnvcKM/+4he/eO1xifSsSVtM9dmnHyf0s09H57PMl51g8SXmcPDYPnk33Xpln985q2ig/dvUSDvNoP4vMVFfe+21hO/RcIhozcS6AGZBmnqIdRRITFtlcxuE5mMJsFYyGW/NwYl10ou2dRlovcur2xo6YCEDHtTKkIdDAGfgWeJohK13pgoP1S3MKYhcE7xagZZpU+wOoHvEhUweGQdymJM0DEpH5J4fCdDAibeyPLGV3o2jmI+GUB5lA5exI38m776VXgqJjsSF/AYHUpVrFwSj9jQsCW2FTvaV1R9qzOj4YQsBbOGIgkW6IVLPsunaUt16Q6PTOkCa5riKNIMbmegr3yiam/gx+7dmmTVGmfdwqojlrtF2grbDecWgqCt6jLt7/D8Tbv0zGK/MROyF2NuxOBxGtkUJOXCSWM6Ip0wgJp3ktFaITHziq5k9wuPi//nJSn3T1o9uRP7cxeXM+Mc/rilSL2bSgGkOjNOWeaqRn5slOUD67+JeAKjPPU22e9c7emT+uhBc4WJ3fW2X8lWx4dTlP/3aLbPx+/DDDw/DmFVxnuEvVgJXdQJM9DTYUX6hyZ133skHOc2vcg0DthbI72QsneHkhmA2HxyIheDIJXngof7t3/7NWp2PHzbUPL6gMCinC/mSqJE8eW33bFG+8pWv9BaM8FSoId3ioHOph8SsKSULttQU5u6x6ucX0rX9T1mZjM5U3R5Pmo9n/5DEZYrVr8hDOuqQsLIlgy5oroEoWUn4Sd5sbZMTPIDE8cpGx0IAgZuzFeyE6EVDAyeSM4vLhzODCu8EOj/4wQ882iaVIDnh5gSBSs0tYGsijpRJlmQc34IKzsT6VS9E6BLXKEwBKNRO7KKkVLQq9/bZVZKoaltcD+VQMMMCJHIw9WlVSIaQozUr9TWK4dAq6YYAi1asShhRMHaObpgJ5DAk7bUwAX8FxxTYjveqMp9DGN9LUdfLZ+bgXxvGcpW1J1ZJ8dxpsoIVqByBWRvtPhtaoBSUd2H6PgmY6KvssSuM6pjK2H7yOvN+51EnOsqr6mUNe1mK/HAATAdWE9wjRmWMUwNnoVsiTRCWTYni5I+C05N8sCP/NBXmrDKZRFAqxTbxbKOKV0Lz1tQ5seiZ5u5ghmDMkA/hdV2Jtcq1inAEn0jS1t1kbWL1U9XKQgjLQ7Lizj2mr6Jt1QLP9u///u9MSWxJBzbEtmrve4MnQJf/ovqmN73pN37jN/x6bheH7I2Sc0dblMDVngATJT3+5Cc/KUrzVbC58Oabb1a5qhnXJAqPMr8Dw6pItjiuo6jQIxH66Z/+aUSKR0dhRitZftVDornkRz7p4n9dAPwPFTDK0iowIgxO+coq5E++WpHY3HjjjSWTKmjVlqujLRaC3/Eh/6tA6GmH8ND2MUaZlaz+53/+Z+RD1IIM5euvvx47hiBxiQxZ/CH9qI07by0ugDQzKRsILJv58uiVPEcube40s+a4MsxyFcexAItv5LEmUbvl6u1Tgacz1k2s8pjhZMi6M/3na8YkwIihA+76VW/TWDBkkqYPaBD6I0A2Tm1ohV0XOGdqYOmeQsq5Ryzu7SWjmBnLigaI5dKlS4S8RjIm0TLlO1LR9n68ZWMksVk7tyENutTte8BZAsmQtTVtucDWLnSKAc+wx7WRb6WhGYGJ+T1Y5sMuKL8CIi0/KWxXGlsheIjEvDbTZodt91DDI3FxZKsvLojAXRKSCdma+un8GoGsgZMNmkdOOOInRqIzQSiYLOzyZbLoHCwYMvRtkbnjQJb7d6dspUsKKafgcIQp1dQm1SSEvJogA0ClrBNgo6/SI2m7LCuDia8LsLnMpHY5M/6v/0p4VuNl+vZNB2AKLwwQm5nrcxQisURLdlseJaMquYXspsi057eq5kdRIDTXc889J/W95ZZbHn/8cb7lKCg/EzktgfMoXpYPbf7MZz5j+vQvrZ9++ulXvepVKlcy5py6FBPbXJWK8CzTcr+ot5iqIIbXk5Z0y4EhjLWnkByGJ5Vl4S6eTnbkgx+ogLnzsPIZ2DhWiaW9XO6VWyweI8mLnR3RYJQtUoqNcly2yDvYgplMAIe8f/3Xf6VR+eEcc5651ohEqvlGiLQrAW7ZMTRewWPmE8G4ZClGzdwpY5G/mRSNqX+hafhc0VuCkmAbL8NqglSGx5j64lpaa51bHqtsS5Ykoyro8aMdFEB3IlHaYtMv07CFD1E+MFmxGjmqrulSS+d02ZIKeORhGe+AEZPdxbaht63dUV38EleriqE2rZTlwJI3rVAVebYIJ8qQr8TCBKoDeRXJcAhG37WUqlaSBZzKTpKw0UB+psC2XqByDAFa42JJyOqGsnUcer71vlZFGJmwJskSk6E2DIpZ5VijFSIJ8Ko49w9PsOZHlqvrbnz3T8xoj3SASEMbJ0/sqI3/HIVXST0MxKK3i+phts9mOY9AjOkisBOop7SSXvwyLr7UFNNO4upd3DK33zreE2B8KQu8WTv02Le8rsZcs3/rqB4V6LzLnGtoigv1HsWoXnE4uUzHxi4wlqTps1nVjJ9Lfu5Kw8BUL3mEkLmxIKNv9lTZAQSsgFOYgGnhD6qMU9fzzz//+te/3r+P+cu//Ms2ojgoUs/ErCqB5YHOqhiPFF6U9rnPfc40/+u//utf+cpXfuVXfgUj8811CDmsORDJhDC+z49CyWR8YcvloY2Rm9GFaByfR4mBT3Y9qo8oiMhBYs25V6e7tTINCDU4VquJ4Y5DdKV8aHeUmwDirLEvGUP2wQ4T6dX+uTkG5TnGbDhkoeg3WAbIabRFXBivLFjg0aCIs42p1XpBoTgmO7pZDQEgLpe1msiNODAAUQMbvLaCjTIaBEBcv7cSDGmwZAkxYiOVWilIORAjqTY1UhLYkK1HlQCkrGZZ36JLpzUUsIoYoF2kJ2CwibZkmx5dyqF2UauqJx+UUNF2uoJBpbu38CBPHOAEFzl4rLaLClpRG29DUhKtdDGn+SK0B1JvZJ04cMZvuLgwSiH2c0SfJhCIRRZglEQg1cIbYsrQ1my9TE8QELTKlNkQb72XtREynxgLwVIhQqP8FuNYZZtRrI1/Dw2ZifnCmc+DPfUglTXo5OyXmXktph2TLE0YWuiwZlSSHQaP/p+CgdPjTAyjaDep7EjaBNWcttVdAafG0R4TAU1OCFFvT75gXdVFCFZ++X/KUDPpgfDeaaZHq9WtH+Ywy2dmD0NizKVjCrBo4aUvfamyD5pcJnHxgFEGGQbNg37IQI1vj5c6MQ7ZiQmu4ygW+9oRJAGXXyH1deQrXvEKP391sPFtS/a5PFMC5wT4fwUlUP785z/P+K30fPnLX77pppu86/zI/0KPlTiC5ABjLw+ijjGHDnw5ssK1OZUnI+LHXbIUwatfA8aI9EnkCoaPy1WJFlfogocDtSOn+UpSukBBhE5CSFLn/xPw7AdOPGrJXzxXA2doSobGaJFHbvmCQTqabXxDbB5yt41vRpSsUlrJjwRG8qAjauBT20S6NoftPAPzIXe+FobWSXKQIgD1wmLaomxXRG6pF+E+kpIa5WQEzHrJkrnFZhjAw0aLtKV4uBBGFFNFud61cgzbK3OzrWBBZ5vQpgkRZTWaZErDC1vVQKts0Sc17sJllCDV5832xqvfatsVkErhC2F40bANLLomR/RIFKg1fDP3w0mPMDUhECGO1E7hQj4u4KZcKPGFvzgv5zY3lLxQD07mgKmlijHdl90z2gsJnBDSduZG4Vudn8ZwsW9JgFagIRpyscSM9k62cQtZ+yiviHJOj9luqBI8G5/JuUHoAJTRvHTpkglxlJg9VPI8HHiSrj10h3Hsd1bAbYaMSor2QMloFwbFqQqvzDsdkaPwW6nUqYzRP1ZgFM5D7a3fTYhviWQvMRmMmOtdESNjwZd5OR1ZqsvKZh6NNQdrI0QMYM7N/K5h3rb4OzrFimKGrvLAH/Hl8r+aZQSy3yeffPLCVf3AJXZ05P1vJH10pO+CYA70iSeeYMbO+lP3Vb8H5jUS5e+CtvVwMuA0FJ5ybWZuF0+UH4XmwsxhDoKKGEzniK8lOhFDBQ2dX4Mzvky0AbnHFLDfQa5H8x5aCW194GoXND/juYce1+6CSF/ykpcMm6s3WG0yPIRpa48cZlMAAEAASURBVAyuqVqGYLy09RvO9jGkc3TepUYGK1ikBvJSuY22kBtTHwCLdSiPOF5bwFBRAHmyBRQrwR6F9SCz0+UVJJkarS+At/EFODCCNkjomwQ45PkKVzDn14DsIevI/jZiWsrzWJVVaGHwopdkXyZs9IdNkSvCzPT54RZlmq+hvoApO8vw3e9+166RrG+pMPFlsQBmYQE2aRFiEN9ScqRljBgsY4S1iG4pI6QdgeenxS0ELG2yI4CoBLIL/6iS1Ns5BXGeaw7kUpjoDCfpuwCBJm1kuVRxacMDAYjCh+zNBbtTpkiVl+NeqhfRPJWuuazqVypY6GEamrB62s5R2GS+QFHQJZSsxMLawNgkz1ak9EHC6XcQvXL0N3nU2vi30tCKEqrMHVvBNgcJCYgfTDSjHx/NwXAgMKXGCpTcVX6PSM2PHLvAoC4zvl0TdxOfJWlrx9kfFjYwPRc8GoY7YCLP6KqyVy6Q7gfC/iIysODy3e8b3vCGG2644Utf+hLJLAI+1x+pBM4JcD9w4nWn/M2j/iuSAu1nBodvrmEDqSnwOBx0LFaCIT2Q69aUiUeH2fCYZT9ezMqWg0zie80xC48pVgHMKO/OsnKI6UsSpWCCTGqRygO/JyVzhNjxV/8h+RCm8EUSG5U/YEHY6HxfOgCmbZvR9CMZ+Y8FmovLSUDOKfm0RSwjpSEyVZd6MjGZmeTohrbiSBOYUTY72rlSA8w2CDxaWd+lD9ZWaJ0TARSJ1oHJD2vZT0aMmZLiSTsdpyR/dNIi9VYi8ktaZsrMkVmE9gpmNdJsqFAoR+3iMDDtVbwr5PK2CsrwFHzKuCMHCbAQijm0AAUZJO6RCRiyYiA2RhbBt22PqCyhJQfyF6NMk12ME29BVmXV7LkgzznAbdWoMVMiHwosXoxF7Fk4a3fHBqmEJa0LH9+lLPAwWeoaqmVq5rMA3sAVnng51sET6uJC1OyKb77874s5H5425y84Rko1n6+lMpwGIBCX00MiCtmROWIafqdvUdLhT82upWG+c/SJUTiRdEQ/5N7JaukjMZrfzQWuCNag2/tlAvkIztxt3jcbiiJgs66Xf+dhyclquFYKZJXhcFoeDF0VKO56gJayNg2AWdfXv/71W2+91eeQTj6f936nJXakb88J8MjAmUR9D0zjb7/9dlOOO6BDtli2akbkjs2InJE7ryRYd5gZ2TyOyVtoe8WPXb7JT8KOmMxpFvzKkDWvevyqMbctOmQl9BcB6FcyAwwGqfKIKA+yCu8ol3QJ7Ox+y4F9x0JKB0nsQqImFBJTBsXVNdbEbCRNNWdbHCEEiiEjpeS0RXopd1U2mUkFAVAkWgEbMBNhTplKU/0bBooBTB5LZ+wq++03OTMYemibKymr9Nj8hwYRf1GiUzRIfVWaI5EEOTAaqzsYDIqZkl5lykGtes0paggoVAo2ck3GGVAb1KZnlVasaSNGfHekjMK2Scq4y9F9bYWz+sLLEKytQRIlyWln+DHSvj2NchJgnPIS8zlyBMDAzYffEaShzI70VvDTOoddZX1DO1oVPxW1SuiDfAEiQ0PnqhguFp550vaSrdSLCWcV7GIJG/ZesrU1xyMBiJeTsCkw+UUz2hCVGgoQD6Ytp+TirIT+/Gd1NNpwR5VcHJKCnH8z1ytbZJQD76jHUbSmDKKgD8PTOqPwu6vk8HM4nzT0YnnCnYse9fnbIsMcJ/vVrykjn+dciDJsi52ZePDICrgyosZ11qxzsIJRqDdTlxycJsu+SI5iiTMV/NxMjQtU+i34mTTsByy0/dVf/ZX/jerT37/4i78Q3+6n63Mve5bAxUcte2Z4ZnfClD//8z8XJd9zzz0M/q1vfauGh2CuMU7EyEPkDGjLWVPba36GN0QK02UX4jbAInVOyqWM/iELakxjAOKbCn8ENYRPFwmGhEFiZfPN5jFiutvbHV84tQ+DBauY8qgK7/ZGw+46Eru3GxQ4xSZNcCkbL1EgJZHB+qEL+iOkIwQxjb1QDWW8OdxLSiZ7dEqDwZNS9oLAiAszt2ku+fEWjHmCJrAdlWIy86LfyTBTSpgh0QUdgxlYIksZskQLHqcK0QZGSCFsFefR7Ze//OUiLQAZGsY4lJhJFxcZTU0yB+u99BbjWA7ytjlKXKlBMEqCBHC1beGVhZsoTwI8cdwXhjRchKdDe1CPMgT0EKlFgZn0A0urg2Jkc2Los89DqLHFvvWwlSZorsxG2NdMqa7X4y5aoTwmVqMs9FfjLMkWu2tltRURMfmsu3EvrpRrG6e6m+6L84nr4O4E8S7wjrwayi3yPh9V1p3BO1/DH+aoauvq56PaBJImcOM+ohn61U3QrtFW/JPVmYxjyq1vXwPn0iamJyYgZCIBwNMqtBTbUQCUvVA5e7wk3HLNHFiKq3gRUYgxsnjtbuo0OzMf4QGn6qcWLQXm0kr8AFuLsPDsv4BTlx8DEvn7l7+f+tSnhAf7J+Pc434kcE6AF8qZWfpv16aZe++9l/W+5z3vAbp/K2WN6VdB+iHh5H85FPGZBINxmo/NQ+JyMSv/wq1wT5mZQm17H+UWgDgPfkm1/7gjCTGnynx0ZyVPE8hHvUBoG8V5+JW4JiUfuEp+bGGVnA+f8kUUJtkLI3TDaOIxW3M5Ay9+Ug9A/umtjNTugaPvECok160mCpmiwEtQzV7mKgu9dndBioFMe2BCjCyXWlqFsTsETEOtzHMCBTCiJdOeJWECNy96Ra9k6TTWhKpgd1cT55CZm90VGPSSjBf9tFGT6qtlH7DLW6s/drCTnUb5aawalKPN+LatujLMMKDcvnS2vtu+vOrgU9PCtAAsFCpdt5WHUC5GFlGuXobvg3PyPwSCT4AGOs/iGCbZWlfCETtaaRPyEITASTLVJFrRovaOwkUaNZ94OSqTl1SMTjTz8RQkv2G5wSNXIy/i5DsiuSZcWFzr6guDevOpC5iEnzsSBlgsbv1eAe+ngAaXvixWMlU+cxHxu6Angw6zaMHPPu2ii1VxGp1koZZKkZcRj/9fFdUc+EjALOPnMzlJHe1T/nMo3AUMroWCjlM5ukzrjP7SXlgxKbkiMcGGCTrjIniwb2/Sh4T0XGKJqJN5E0ytLnm1tKPtAqDW9YlPfOL++++36fUnf/InCZ+228sZ2+FI4JwAT40FU3zkkUcsHr/vfe9jsb//+7+/h8mPBYYmBfmGvIL3kZ+oVOZH1FvJljnw/qZA9TwFd2xJfkOXIQTxPbDTYjydQI3bsnoHP+SjcQlpONfHJ4bgo7sTFxbipgWpltVzevboGAnBdWDPo+TWpeBj74ydEJMiqcG1Mi3KSkfa2t11fkwAKkl2qSw9fOaZZ2SGPoYHr14ISEMkeNTPo/lMv34QxT2zmtWZaCnlETRbqXFBrrlkQNces5tE+PpFngQ18YRH6YFZh/rJJOl/wm6nrA3TMKuEMIOIkhRCtjskoltj6tWci/LLlpFH+Vt4gXJO1gUzG/SWEBDcglXZ7G5foh4Pp4B+jBDjRARDhhn9wyH7qCmhDFlXwoWyIaDzx8VRzIpWCE/lXYjnRtid1S5lVhA/sCFT5hqGxi2MTjRrIEe2VrFZd4+pKVSyWUMz/+CPmTG7WBOnPwr5rguc254ViQwNujVN7Is9OmHumt9F+JNTZZTB7DQj1YufVHS8KBPWIpJOrB7X5mv7IqzeQrbl5pUYjJ6QmB9L0xA2oawPp5MDCxXEDDUfWabnVXJ6kcPhWLy6bLd7yYTRRsMffvhhcf4DDzyQaH8lZs/ARyeBcwK8ZMi41Pe///1M8b777mOr9oTNPVs3SLaHDmgVBP2idpkA7yDmSP2lS5fkmWZrfoRrSFZw2TG84BqqsISfxa9hkGDY//E9pyjf/OqRy9NiUYpLOKFkMdajeWOB0/QmdfF9y+bCvBC2rYZQTl3L5cwcDgKYuux+GCahngEVrWLNWzNQzTpqaJ2JR+4niU3+o5ISUj+KR+21TcoayeSuIwWvLIKIX6WsNlucKpeseiRM98xzluchzzqOuVArJNlW+tmf/VlKbndFrmt/DDwMNApOSzwS+LCjo2AbJsBaQWXhH9l1vtFOsi7MozEr9OtdDWV2j4lR6Var1Uu885uWWI6gMojQQq4VYeoLm+qLsMC4mzutoaQAGC/KGNEE8gK72IIcw7i7JshAvJULQ384Ye4EtQf+immgkJJbQnIscKg2B04/ZWCeyGYRlIdJltV4RDwNNzkCY8IczrRqjTKrberbwhZNxuJaUVVd6LHKCq6sU+B0tGvOgRPjUqzijQKMsra7Sgtwu0M+xEw+Kq0PmiCwzzMMYS62prz9jsjg3s0OPs9hDv5T+o56OTS0xt3sKfs1gXJlst/1lL9r5ZGvcFlwx3K0S4GdimH0yGEyNzWON2ZzWPDANmt1rEO4udzQYIgd87T9+6EPfUi0b+LeHO0Zw4FL4JwALx8glvC2t72NZToUIS/1+1hi9A0tsGxe92wbWmbvICi0cgYzjQKfnh05EUbSBrH4MAdYzsBsCJ066ibEkQPb5cPy2i5vdp+HAijvNbfJnYiaEDYc3wvhip64qJakV8Aq3xOvZNMGPU5jUi35W3SvZVDZ8QGzDhjxokVu8LY7bBiKC63IaJKtVGmwWcomQPJACGkvYLqqoRSR9ExUtFRkbNb0/5bdNaFIcmw41ZjVTH62ItVYd9C7Cx5Jo1eah0LmYD1Ccxm7yUnZAOmr8nxlDd1d11xzzZW/l2+aSOFcqWFcELJi60eoUmmUBbLd5g9U5ng/cemfD/tRtLR1x5oLBqe4mUbbUcEoYA1A1dgDRzDgCz/vGmEibFgo6RXZAfPlNk5/6Zd+aRSgBT6XJyQQ6ZXYQR6dPBFvPYtt+iDf4pSVJlbDpbCp5PbFvsrY9ao8+s08qy1auSQYeuRbLBYU5k0KcJo0XYWEl5PJ8FRcEG/g6IpX7BRHXAQGC7ItcIweeaQDCYvx1ZK3h7LR5994ckeK9t/7UgazPrsUbD0AOmkFlnLSGTqwHpKja4VrRuFfPVnYdSrKpL/1cQ/CQmvp2aVfkyk7tQ0jjIncxAlW3zxajBY5uPMSGlbbTcSrRzHD3Xff7YevHnvssbvuuutAzHwTps5t50jgnADPkdLlozU+iJddvPGNb3zNa17z1a9+lSmuanvMTGdaxd4kJ/yLS6CcV4LyWLh/zMPCZQKBn0XiloCQhwxdW9GXCUgLQ8aW0B8oGlwLkojd/7XnarG8C3e/f+aFpFmtF7tIUI0mHTa1SG+oXKvAEleHC370ox+JCGm7QMeelf99p4BsAvE9jIL/j/3zP//zlNOkCIOJSmQgNvJKmmo/1t1bCbA01SKucCEZsulKjSaS2DRUACBXB+/DNgDaEru2sm5vEYkGv7cpA0eGHoWtOsq/rcKFMtvBRZJ8GGrewjhOpc3Ik3vrGrC3MGdv1iMy3F2Rg7vg3qoBzUeVctUHrO6pr8cUsJCFAytZSCVtB7pMqy2YHk3qls8AjyJpgbdYFr4Tlx4RYIxcRCGQzXJA1xEw0STvROwy/+7t7h5lQTYViZ0q7q6X+ZgJhzdGDKFtOFjRjVFpz6fnQiCtcFlIYk2IJ4S6EJNyjCj3UJiytzMJZqeskskwHHaqI4Y/s20Lpl/mZj5lwhOihtyw8hjchcGNhqtEMAycA9NopzyVfBF4jjRHSNpOr4YyCXAFDj+TqjM7B2KereTnK1vbamYZ+xZ/LWiyYguje3bdM4ncBZjZ02o12xQYtL8ruYu+gjPj6M4PuEzc1Zekl22aPbkj8xcYoUI+VhIDxH5bJNVwacH4iit84fX973//qaeeuuWWW3aqTkvpOQPsUwLnBHiutFmFfwjm/2L/2q/92nXXXfe3f/u3M6dDBqYPd9mCCI/dmkgYs5V1OE3D9oiEyyzcZKzG7LvT5cylDKPBzvOrX/1qBaSGfuWlDZcCCKe4MOFUJSrDJtkNIISt9DjEv6hGdzITyZXtL7vfKGz976JWh1lfOxXEaN1UZCkBxh31EyCi2RAMxUv9/KDID3/4Q//+zikvq93+/ztgoY9Xr3rVq5SzQ2IcZXd6Ebyae8SdcleqYufcrGkpQb+6kHyaoqg6zTfHmNLU0//s/crKNEQVMLOaFNdMphWLcOkdhUYBDISU0F09GpQl1fhSzoUewCIzfakBoIBrxCjoNBiwL6+z7Uy9ldEsmrHkjPIXMF3e5JH94k4mVpUpTJ9+1EUSYAqMEV2Im+EPPe4RuN7xmPoO/+4eyTY8IslQJlwYKkARwLOJ+cgHcFXuukBu0gyrIQcSYRsm1+ZckzMld22Oav8YLAbxGNSAA2FNHhOD1gzFDCkVHgXKFsIAKFtomz+IOdPEPzBYFrSJabA79LDc1qJLaJnI+BCOXXcoNCgYsTbE/2iLcs5feswJtOPlQBYkdrwBFLarpBBf6j8FcqQmBcK5ShgvNk0ZZkA+U2yQWalenWohlmLylfpamWIjF6L5baemaRfCWC6HwymJIshfzX/8x3+wX48uq1RRUW3b5otGSnORwM0332yz5xvf+IYfuJ7TahG2c/3RSeCcAK8wZDzCL/zCL3z3u9+VDPj/MQzGD8xOGIyIQejPXNmYuwiPvQlGORSe1K/vivbEzcEwgWcFErcEiphKAGQX3I0sfXMKoRLjCq8nyLTpRziSjQmYHb3CoNExz5nvN4nDQp6xpgB0ZnO5rcSv7szW1UScR8essIhQlWWDAFwFUAWVAlAf8Tr5fM0114hHKaocIJlkeyr4CoLLGCCU1vpJTIeKqbp6X/YqCx+xL4mSIRtN/0lPv+YnTXQh4gTjrSxRrGmKFVXbZPAo1Jbxkpt6JxVFpeCponOSJIkqypPo1gamLiTPOpVUw8y+tFWAGR6EwcPuEKkLly50hE5MmTLF9BrCqUkuqEyfkvaQ+kL15b9eJf1uK0fLMEsDXASO65AkykcGgqtJytBWze4KxhHySANrlGG6L/CkLZ/Zw7cAQ5lUzUzhFDymZjaZZj9vt4hq2B2aQzY5D98eSA0K6YA7U8rSEhVSYN11sJ9Wx1VmpSmeZyXRFXAEgnc1bXmpNAo4kB5TU5gLA3fBpeRtWGOteUy/Fr9MUkLtNgG2LMicubIhwsJ82gVDzPFyYlehBPBOGfwnP3PK1cA+c7CKRNvNd5kpDoHr0OBugjYQ7azNO5lkmbPYEgCPJHgws2PEpcY1ap7eOoNmW0tA8u1vf9v6ziLI0ebnyhOQwDkBXm0QWYgM4R//8R9vuukmyfCzzz6bxCCWw6KgM8UKfF0icpCCBm+lAeJ7C1SZR9WI7Ffre7/Q4Qjxlj8l8JiaiIZlFwIgU0VadZRGLKlMuWpG4bvm+3xEDw/r60eBKSKxr7AekXIt7lVCuF7zjuu1JXZ5G+v//l8/mmIcZZI0sDAPcSLV5OEDPFwLBEGaAgnEjitgmbCpMdGwR8Fi9Fy+ZNZxUYNiVkcJHIHBpmtvFSymEIu4U35o4vGJkZRMTusOQHaqoS2aH/zgB5YhmIzgQ2WSYRGYlRHbs6wJDBoyOrrWL8zmbLOjNXuqCCGA8Ct+tauPNa0A0Gf1DDDHgIHhtCgHExF19SW36QIDZyyOaZGbggs8BiNPGkUgGFdpUAhwGtvmb4svhTkcAZPGWJsjTx7M6snmNExjMKA6CoyVEQWDbm1xulX7FgZip+dt5YZlWkdPaNEuAl9rMfYVbSpS1A3p3F1z9mj9iNsXGurFD0OQcC2MRq88uuiVM6JktfYQGHH7xu3KHYQskR7OYdAGDmoDSRnQY9SGH/SCCRgA1GKBx5DuGmtfeHrUhMIbHdaKL44FTjBtwD2HnhOAia/ACCH4ISLCcZ0AXzNZwH5mf3qSeOBqYB/XZi6mZ963rm16nSmufYJ1A5F/SC4Hzj6Te7kRv+jBwYpP2HI8bdpGtx3wFsabpkXpE8HtPlk797VnCew8/NonP0Jq05gTEWxYPmb6fOUrX9ktW1J90fz3vvc9y0UAbOQyjM6i0AwMNkjYUoLsYgSw6dAHA74W8NMgTz75pDUkwBIecbboVp4g7RGpm0r95rtgnQ9tza9QHX4Bs5KEf/mXf/FlLBcjNVLj6iiXz0wke4Spuekkfsdunub8ThtVCEHITT2Ziz/kMMrcFvkPu+t63+6j7qQrSMWv3z8UHtGT/dBAjHgXRA67k3l6tVJWEGkPhVPI7YdQy25DPnOetiYSyZvfi7799tuDyhjJTxgUnJBQdQWRq2TSUCJPyqRGhmzUqL0Y1IElEadeAMAsngZmWDUHQyU0dxduekvsTMYRgLwido8ARGDXX389sQzZF5sWOwgWLusUnlTWK/SjzSKxGmQLkZkkDXTKVyvw2UMOX+7g+Qf8Yt/j/Iv2koYLyzAQhRxeWddEAQ/MLu4ihfmYN4fEuxyDSOegIm2jb1xmws/BuQjGEFsIoAakR1Y0YdVO6Zjmi/CvV2+RQiBoEYdGrYfh2Ftl9Ms/0NhwVGblcVhua+ZLgAI4fdPCU4b5Y0ptAFODqBAaRlVImKsLim0+ytScHhM9a2XWthbDLai3pMJDUk4rZfGKLXlXQ9ksbNA5fB7yauC3eMS1Y0SOCDkORrWuBvYzH1mVM8VjWdS3niGXDPdWCJ1opqg5mRJPxRXwYEKOBJOme4GHOERkAuD555/33a/k2Qdf5utjYXZvUr1KOjqpBNhukl/oocomLSpO+1mFnzW/8847o9/mSMvYPuJlEmJTIY4AlwEMTzKb+W677TZKIEP+vd/7vU4bYBNAf/Ob34T5da973bvf/e4Xv/jFglpgJnILTpl9gSWATu8dkqN4RLm4hFTJ1i6ctQO5fSUYLQtxOm1NW5aoAOCSxCgckFddgCI9kCSo1yMwaZJyUqkWz/xycoxNdthEQkIlO4colyDNHETAyQzJCrXyK3fM8r9LMeAa/OguSsK7+ewH0kpQtnB1bXPDKLQYGMiiwM4QmDnc5Y0wFOWQWATBmuBABMm+SIlN2czUV3Ljot9SFJ0hDb3IBuWx0nh3+a27wFqQoaFwE4DtWZmG7wJ8hGzvl7gAkIZPhUWl/jsXazUWsmIqZFwsNuHF1AWVQqt+WaZRSQf0q4BgsS91QoyLvcOP+KjZUElIW5JMJ9GgVbEP1fSFF2osnkaVFTFS0p2+LKPIhLXFAv9QScU0tu2+xUWXY0zgN9CGZgJgi6+y+mAsyNz4GpeZyKmBC7DRJ/OsoGUFZCaGCTDiilLNH/0JbHkFIbOiXdnxZlx0j6rkWNDS5nsGIMmXvexlqE2/gssJ/RFZxqVvKDRqwO2kRw6K1ZP/UudJXTWhABwOSuIThuLiELiaGtAUEMxl+dzRK92xd48qqZb1dD4zljvElhqQaIaqXYxbBHws9ZgiCo6dTCqemUO8htYNyfkwVXomCyYmB6ZoETud747mID9MGKNmyMxNGM8sLEgrMzlMmkepCs25Gz7uyyRuChYtcLzW1tkyO3388cfvvffeG2+88Qtf+ELC0VFs58qTl8BJJcCU2+/WOpmswKT9fqyfbv6d3/kdZmBSNJYm1Ne+9rUKPLsoWVLnCIQftrXR1wXBn/70p/37X0df/OYz4xkNWKXZX/ziF0VsH/nIR9jSQw89JESQLZgzSm+O0YkU8SlgAafOw8hnLBx4FCvkldhISkPUok81WWkDbHWgZVw5AYqUQD4jGWhFFFRmWcmMspyBhyJJrYZgAZ5zl4pwec6rzwEewujdTGBdwykaG0EAZubAZGIWqajRjx5pa2dSDDfsJTUEuKhgWsrOoZkYzoSDpmSxZivhwlyo8hZ8/LvHOkwovkyICVgyySg0h5C0C6fJwyizC0bklz9xBAa8y+aJ0NBmtXUQVlOcohAq6R9UrACk5XMrrAD85zDYpKAOHSGeKBAmYXbPQQkmBicFEHbIfpVpEYLhzDfhNMdoUh7RlV5EqAhAEjD12bHRETxmO/WSc69Ir1ZVsIYXnzfLtP24F8b9Ch2SjDL47iIKlDv44LQCze/eTjwK+8gEZgLkGWi1bB8NKEEAmVTbEnXV7KiAU6sAK0WxaDO4odZ9p6QOkQ9rFknG+kjGF4BkkpKg1rliQ7+oyYXXc5i0OmQoK3B31ObCCesIKF2t7a/RSTCtDBkWGCB7AWanpcOWRwBMm0WMLqEGhq7KY9M7u3ZB7rP8ImMUM28AeWlOFVpgjtTuFrtmodwOMvIWMHsXM+giDpbxIoCzcge/dBrKul5ijLbHIy3j2jDxlkTq29cS1Bx2RAJ8uADgAFV6Dv14N9kl+zV/LV15mYPzKGDMWSZHU3BGfNSCjoKRIjIsWLNziTkpswlC+cEHH/zoRz/qP5s++uijLL3gz4WrUAInlQCbgVxlurKX+++//5577hFSqze6zzzzjJO6Vn2yR+TLlgceeMD/vP7Od77jjGU1BCkslrpY2TWzZmF7VDlMpf5rtjD6He94BwCY2yxitMkxVpIMT2F3zqdW2a4xT2DEXcqRskdldzOf+a9lsxVs6oc1gg+Xt16RIcEOYVqceygjYI0cGPEOmiKP5lgRUIYnrC2iWdBg78tbknRZiFWWsYjJJHv2NKqhDFDZkTwZYFW2BScXOHqpl0qo3D3KvpRLpDLGJLQIszWatEHGaG4oVF6ZC+US5kX/GAlCzUVFsilgLMKFQemosFIBQqMvWUW28DfJtsheLAuzGmknrTDFMj306EgrcYYzVx6lBMJNn+LoV8buAims9PEheHLwCKdlFAJBsC3cLJEgQ9tQjhixmhrII3BK6wirR1mxSJrq4kLbYlNBj+1jymGfnNFgENPXEGxYgxLEa4UYQbZhMrgkg0eiMwpQEctop0NsW6nRNd5ZZSQzjROFLjpDgLwfb0kInTlPY1j7rZURjnSlaBthBI5gvpqp0hCCXQnD2tSu3ZD6IZgak609dtQuza/W7mtpQ+ZPPchwqJBSQUYncBx9O4oZkij56FuVGLenai62QLYIhrHzPCA5E7m03kEuFRGfhhEGToXY+CLkqQfQJfPa8rqGQ8JDD/VuYYXVsF8ObSic4AGmkHtbswg+MAd+x45BlP1SUf5/gv2WkRKCthx7/H8qj0gaCG6z35XO/rTSOKJyDRzlZ2VGnOc/oiFbKuriBXdM+9Zbb/UPXPxPRxtjS73KUuRngGOXwJKp4rjYK10vsu10qcyCNFP/h3/4B5OfD4MD4JXfc3aAWb0EuFopvP3tb1f/9NNPOynBD7avujIrchBahmAn+Zd/+Zfl2MpDSrpWR/eII56xNlQlNqJk85xvRfCSDb18NzLBu2gmfnaCfYHsnEh9EQYjbi/IW+E7CgUxylIOaBc1majHS+XAclTZy9IlQ010B6d00d1m1FI/CyDkaSKdSzlC0JzE4LHZ5W3OGE9scInkMouDz0DI30zqynZREhoK8pIhE474O8HKUOaaCH/ltyJ142v5AyrBjRAzEpO7eptKXQh9JH7oFznJWpOCBhIqqbtjF5pjQaypoebqNZTQGixoPcrTcIc8KWvYxD5gOFErGbMbjzWvRGYERXSC6cTTtqY1B5NO3SFEBpIcK5CF4tEUSEOs4zBqGCaSJW0l7ToFLFwmOjWFebQgCseIV4ZAAuzsAIVPqG0VQyUCbCgtxTOKfNVK3bnavjwGSVs5RIsL+aQvHaTuRo0REe90kyGSNWpIOKo4vy01cOGLFpGt0d8DnfPJG0IiD8HqSVWZNrqGYHurYWUsyxJV1yORsmujLx1NCtoBdI/UnpVFwSDkLgAwsWnXF/huyKwfaZtXZGVMu77qMTD1yLfwACL4DiEwHoNjRCFgZZrGjVO2DtJbi1bugG1lcx3T1qpHAgSPZcYiY1cmsbCgfHQXWbk4aj6ZE45PnskFaVh+JQrumj5YOSVeM2ac80wkFwtmCuPt6Ya9k6th79dYx1RZGTNPLDc0iosdlK30jlPBiY8fRR1f/epXhf0nyeZWZHVVITmpBDgjx/lywWY7Jxhtzzr/7FB0XtF+yXAbz/HyQhD13aiLrT/72c8ym65+9NE079ew/G4tA+M6//qv//okfzWxXEZWiE35NujM9+oT6IgYpiMe+cZEyhHZmjJHhTyzEm02LgCH2pQt5CernImkBYNHW8MqKhI5RSVKFC1kVyaNZMJd/fDRjJsj4o7/ST9SDpgeEwJKqKj0ojVpY5HUS3aHQuTJB8RhpjcTW2LuohlVIUxfXrGIetXRpl4IaJuoGA9A4KXH4ngW9KMf/Qh5khB9gWR9X/va11iZDBMxIk56ktzeo3UECTACxJq6Zn2+RJBnKls/0lauaK4iPSwo6EJzvGuITbKy7ydDs3sscDco5GOAMi7IG/KCALEpzBqCFyUDthwgSwefhsNWahBpv86eFTKw2Qln+Eie0oZomrxXEKk7clBwRzmmhq12VyPtN3bBn3MEaJAhzOxRhmAIbIVZ9hrKZyaS/YDRumnPswYZWCYuaGnI1tmHlnZtHe1MNstYCr6zAo98kdFv58oCHhbkgaws9QourImna1CSaacXC5ScFQAmyert92rItBkLd1QyifCHfbU13A4Pz0XwD1QdzXpkqrWSose4jvhAbcG4841gvGWbAEIYb8lNwRB36vc7gBU9bb8pm2vidVGOhioPIY+rhjUZlyxnz6dcEMUtE5fLminl4ds3nM3n974hZBQA44ncrpLs17Tr4BWuzbzuG8rwMJsbWZdo4Vd/9VfNv/6JqUNhVPQwqT1TtWcJnKDS+8D9Xe96F6Wn5a94xSu+8pWvVNwpQu1WlLlpk1z2DFvRx0Lm2wlIqcI//dM/+U2s66677jOf+YyfmINwPoa29wMvk6fZ0SfBDkrhWsImra2YY0PiN5SYAU2IL+QS0OS864bOHUmVP4tyJDnQVmy3iF+TqGvR27a+WB4WCkx3E1ygynZNgCFJKGZtwkY9hRfNFJ5VC/r1URCcLglkaz5EDRsAtqZHUaCOPCoQvrcCXHeESSA1FOmKVkWNNhbsJ9MZJy/cSQmY1F0QKerN53miTwk2UYtxFSCRTkNIzWBgsD7nszlsCocho+MApOZDBnXtp+zk517xAL7sosDFiHxYlI824kV82xzLurDo0B2YbGHaMvkQuNTaNrUoXN7urfBakA2/1YEWeA9lPRIIqTIEXg6D5QmHvQPLJwxRHoLFviQN8ZoT0bDJ4dTko4Pt0uMoQVb3tos22ORjHc2MqDoi+SrvomCbjmXBLN9jrVkcYby1vmY3Tz7psEZnEYuIYSDJJ2U+VCXG1Sobu3Bpjk3IFcKj3oOTRXMadcJIZfaj8nbRHRIU6pRdGyw90l7qqlC+l9Og+Va7MOt3B7huRlF8ydXRA49LWUfaMmEwbFbloq7V49SlYKUJ/DXXXDMBfOCvSv2wLEPgDdrhm0M8X2Hc+Xab5zwPOScqgNkVSU7Lc04vu4DBLBPIKjkT0MVh0rlF3o0IW7BLzxAY2qpjvUVKdorqiur9j1OZDmnyLbamjPLJD+5ORXpiyI81ARZWmthqMETAZqA83nzzzc4umuMdQXzsscfkwOJs8Z+37KFmvmrLHtTX49oFeMTo3/rWtxyf9p9jBNZ+PtrUe3r2Fk79OJC9O7MdUQsv2n3LtWW4eUPjm8X+SL4W/jfEnEGkJ4bVHgKWTRtDXWp7WWPchVOL0qT2ZG/bS8r0X8ChbL8OkQnc29OVo8QMjz4OMavJ7ChKEDSTZ0uhSpitOIiVxaB5K5IWEBOOGjmVkIhtQuJknQRYmAhAxmtH17qsmEkZ/V5RJ1okcsWCcFlgqmH2ls3W8l7ESMzsz5CGhNa+Ln51HftFWMemekhoguhchC0CFpPpveZ7AC71UlYJSctaRIELvEAbyA5/YLxKwR1AYNy1Tbl9W+W2UBg6+BZmjXJ0hquUEiw9BIEG4xtKkKHsTlxqKDwBrk0bDMZOc2JfG8kE+7vAqTtod4Q5yIccGSYmwwRW7ZeE5auEzHbmtJV28mBasV93ZcTEvhTUlBsZEjlaw/BdGnrL7QzJyOygX4ZWqsh+ax5P20LeceGtBTIGxZYLRoHBMluug5nzIfl9CjKE1iMAYlGmxqhKss3buIIEWu6IELxy0EPzeAC9Z82rqOroSfNUFozKUbAAH/6dmXO2WdstDzmfbOsFxOiuiaXA7P0SCPnccccdMEs/5mPbG6TRF8NYYLX8YW30qEdwjtAMh8vka9WGIdjxNgWfJNfYpJAPP/zwH/7hH9qO8ru21mjmiOgMc/VI4P+bTo6IbScZ/PddKo5m1uu/8tY5Z9OYy6tXv/rVvmTzaa792OwJm5gFGS2bLMQcqb6t3KRsotWdDPy9730vx+pnsU7Sv5C5tVIyl7SIYzxi+aDcqNxpF/7O6rgETD4mILPFt0agMKFdV2KznwRnHdi0bDGbpWuzOM1PAGpqF2tOCEFA2fUy8YhrUyZ7oeHim9AjZpKR6hEBKq2jK1N4nSpkDQINiYr8prQomUnqJaOjHg3ib/mVV+qtakloPebQsrgZWltDNnYssohWHbeW1+kdckkySKmaS1tLEtC2LMhs5diWpdi4nUziFeCqRJ5OXYBJSaf5KvjSpUtdhB02NXFsW+YMQJ6gO0yRQ/rCKWrdk0KIpRCWXV8AOrVAIARvCevKwndizBJG92rtx05hPHY1MKO58BuIHJ1AuR175cDztNIhwh82r7ZLC/boCJb0lkIeDgD1MFkwK5q5Ce/zOeJS5Anz4VtIhs9GZk5krCB7rc6sYrNbCKMV7Np4UfhVGafno14lldE3i2Lwh3iaxrLUM22+xUek6nGRndWWQb5FTptvdNVrwtyyZ8uNsDhdeKS3hgzxAFilcyKGT40yTomo5Ug5hhnL5T2YIW23Axx3BAnV1ZwXaonpyrzQdieCDv+uH7HJ6v3YIYFwv9ktmNmpUUtMRbzBA0lWH0rUxEu3ZyLcJxjisyxrzsqBnX32flF9MQpTOScg+zXWNUwXRc+O+uVLrbz4yas/+IM/eM973nPUFrojEZ3RHmsC7Fimfd0aPwdxq5xCrDo/bcXHqVTjQONzzz1nFsxCr0oTMMdtk6prvsmjmfid73ynhPCNb3yj/6DrAIYI4/S8DI5ECfaBJQ9Z4Df/Rc6bSG9bbUVFriE2RNoxMOHNDBZbDFgWMFkndvZbFmRep4ddytTC77+cyC/9olYwx++vFNAsolm+5/8hWe/AO61mQVEAwZ8mAlCSkeGolxwGiYBS1whwqZHgeUVnhB32beSrwkof6vsMiUmKgBHPGL2VJLuzTdkpJC6xuLjZuoP0EhnKOfEo5JU9SnHNdkK37NvoK6rolYP6+oJWvyHVwKEku9Oiap0iD2ZZsbI7bFgLC7l7tIGsL/Qnzm5VS19WB9xRC1LZ2wTWmoOvVLnF2ZaT+YTm6hoem1eRcwu8apn0nCdPQF9t9YUdr3StQET6Rad6QgAmb0e5R6adQrW9Sgo0hGZa8Jrz+fcmMiFkAw0DnXdXNhZkbnSWotW2g0lNaVH3No/Dt12N7TuKEU0YxbCo0qw3+qroZPJUMQ6BH5Yp5SQXbUcDmWuu32ECPESLQi4CZg0ZJmOnyWwwjl0hFkSMYJSJ1FAukipD48/NZV1HyOtspwPw2HqD4dsDryEc64P+UwaJ+TKFA1yJYELjPGt8rSAYYqKGxHKGj8IUnNaxhvjUU08ZKUmXMGylLnYEjHIGbi3G8GGcYnRWsKN+LwptjZGp1nAw1VNlGafiBL/IY4HPz5H4H6WnPbIXpVEn0O+xJsB8ln/S2w4ApXdR9NJ1Dp0ZgKnZ9Nprr3322Wf9txU/BA0MgP0NAD4RbFFtXjYB+Fmsf/7nf/Zvh/1+kn3gGGHRtnkXh4ABO3yo9fIQY/JTyJHRCydvQtTCLPH9GgkwpqAVn5nFzZ3CL9sXVo4n+tqzHHL2QacxhxTqvgmd2spCjbU9VXu5cmD5kpQyWaXDDqysW4eyysDE7N/aXkADVXEJMTWUVhkCdick9UiMcjCrCTZbJO0iYzGZuFP0SakkBhrSK5GW0FyAZQgMn+0gxAAGJpJmdLgOszYl9CsgloHDr2DQdQFnlgNkufpyEgx+0bN02ivBEGCvIGkv2aydJTskPpqSt3sV8cbh6Df7pZJkDkdgIb4PJS2SYRlMZT7ScjRgjZwx644FCiYBGzZcqQZ5o6puEYHQsBYaCqfes4GTGkKrV3MKmJKKyDe4aMsihgYBuhBuZt+PnhDyHFR7hmmHLJaSmpR3Rwz1pleFnykpk544tSoXFQSygjxvCZyt2dtRZqc15S1qmHomMFwdw+96hxEmBEUT6JvMh4NK14DlRVyE1Rk1dIORtt8AqyR/+pNdcemuZTIWoZ5w6C0tisNhwvA7pEAUvIRWHvkcrFmS40kgZ6Fch1ajRDJYZDBwvTBwmKlr1iNgUyBeBc0pc+iv+yjCenvgBUxhjT+nSFzNGnM3d5ERJHAOkGMnEJUYt7j51re+tSTgH1Iq+w80h5AAY5yhmWs4fyuqFOCox7GEvKiAXxMirTbLmHkTs50ky9zgt7/97d/8zd/kBq2/bHh2aZE8z/WnIYFjTYBHpe+/FjkeKTkxUTEDwfFdd93FyGWh4BVe//rX+y73oYcekpSaek2cfiZaGO2w9CjCTSp1J4xwYtP399ai/Pft973vfZkYNkF7aG3Lh/Kt0pisLGaGq1eHQLMJwFWUKNMQj4hclU7wwiCzplxuTpBane6ngDwBnxBQAY+mvRxRMyjZeFmbDAgZiyH2vVxyYImoykLYlfUuZZXUyX9c0jBy01xg6lGsyRwI8JZbbrH9AoktXNOzgrdiXCkrADE6cxb4CkPlltJUj+rxAptLAIdHlIiVDas4G+ZUJu8VN0PoLST2N5LRwaCJR71I2DRBFWzFS1sI49azmDPByvkFTzCgRMjecl2t2spFmqYeHjQDtkft80hcS11E7ajKqkEh3EqhTEDXxKJfXKskAWXsiI0I3JXuwgUA8ISTx6WUEBFp48XRG26BuDSERCqiQOaHmQBTMAdlaWCb/y9ldnMAMsnX2uRjIETkcC7aiCNGA0f5yVBD40K3U0hZW4WZVNVAF/yqY10NpwvUgEqg01WQVb5SfbkeDJttnaqarJt4CwneFXgSukohqSuCSYCfccwqn1cAYE0xH2+5AgopNyPY6hFMd/EhFqFYX1J0nQoPwMMgTXKBl28fpup2vMx/pPMO9RAy6WF8Qj6LcBoF9kJK8SfKQaLGr4H6SRQNP/zhD3vrU0zl9dZWFvW+ST1KTAESpPmebZPuLqqtgdC1lVk/X2LOkha6XxQxu+6Xi3j00UcF+X76x7YTR7GGSu+ayDP+w5HASSXAtnb97yKTouTWFCiYMF1ZcXRMN2YgJvvc5z5ncUgQLJgWcfIO9oRF1buwEzhZoJ+h/uM//uMPfvCDFpifeOIJ+cDhDP8WKRGTWXpwkkpaKOCwnKxmF1Jdm2b7A6KrkCTWsf+gPPxwdA7+ILGrEGDRFWYPx9sKa8wEoU05hUyEc7gTUIqh2dFw+NSIGwAIGrBc2Cz8Cya6jvQInjHa5pULyT81sV2gkvyFRPn0gEX83M/9nNxDUAuD6VkTMNJsK1Ok6h9suNQL1Fi0u0+YtP37v/97lIhcAWuuAJW2EmkZtcteLvZhMNYKEktE6kXviWi9kgFqSGnt5AsBW6aKuxSot391hgu8o6GNhokLs2izy4SYrqEmgmlC4H/at8ooNFIWBTTPxh3yfHZoW5sH8zbHC9GJtbZt18X8R8ziXcJPhhDiRVtjYbh1h8gOFcIcZTQWxohsu7ftI8h6TFl+ZU8J17ggeeOix9KTAt5poaWq7Qgl3SuPhjX2ouwKfBWGTQrholfqC2ZRwRBk3cfQ2O2cztMgocw0LXmaPDm7oJybQRTNL+pltH5IHglY5TE/Mpbh21EkE5VEJ/8EQN/cU8YvW4Y8hynS3BRsoQQAX9omwHQyMyZ1ZdERVKdCedRXCFYgQxfM7JrtYGdibxM88qzeIszOWJBQ3aBluVyErmHr+g3lR33n7kiGXRPRhsMNFR9S0oDNwAm9iBd+srXrsGEXhXztAmJouFnDSgfbyQx+4VStzc7Shvjl0AS6/AMTO7QvtpbSvxKAhf43v/nNPvp9//vfb8OJQp7wyK4kmTPwIgmcVAL80Y9+9KabbvLVnwiPLxap+6EsIWkxzx6E1GJxe8WWeG+44QY/kdUCFOQWC+zw/vvvlxDedtttgkj5sLL5YItdHAIqshU0vOQlLyF/IZpojLfd0crCfH5NALIL+2nioVqc9iimEUTCs/ZAlG8VPOUDKnvCcpiqn0/k1iGpdLSaqgsZUbVSFxI2QrMnMNoKg5X5E6/cjwzJswWOAnjrgo09QiiWTRyc/a7IXytKIvxSKVT1KPETH7fpt8iYLdt8lgbrXYoo+5U3SqtAwiNoNvmhxC63GBpC8Y16PWprXOytUU46kBAWtWSCNpSr9CWws6MgwU8Mn5RAF1JBvIjt0JOMHc34FVdJP3TdyqHKzAGMVLBqFPTloo0WjEgGPcriFQU17i58uSM7aUDbfL0yVIgkXvRzTfq1UIg14nKN4gQsZDSOZBgAZKOqAyZkklSJePxaZbDeZDSNiHyenIlLQ4/eXubtivKkQPLGwiv5RurhybKCnFxDdFrjCHDdZUrJTBwhDrBXGrrjLmpvjx0Nqc8rFpE83wH4iJcuae6ybShLN75UKxuPOjVwGuoo5wbJwYqAGry4uyjhz/zMzyhoLk9TyCt3F29P5oSju9QUgMUgY6HfvNLc+QVxOTDmoEdki1wBEE7aKkCVkQJPW1JvEJFNgAYLfuOlbbVS48pjiSKVdYfHK9wRdSl2vV2vgDz7TtAGubGAhyYnxVUZtAA4ZxdDVoPf1NPPXMGA/pYwlfh1Z87GVDmCigRg8MpFwnTPY3UX5O2dhng0HGgIqVfIufw70lYlDEQwtE2OvRwhYzZ+fkI4Mznl3Dr/Vg0pc7mOqtx/gTJwsH7DQsF/vi092T8l++kRm3wCfpkVozMP8hubD/R+iF+pF8rMzzjgyX/66DdfOJ4kpyuJ5Qy8VAKnkwBTdxOhkw+ulu3ODDyKnq0PFUwHUPVbLOjCJ8H+LZN8W4r48Y9/3P9JMrVvsYtDQIVNk4okX6AjgpRUJGS8WNrEgqJqEbakyIUY84E1aTVbIUx06xys8/bOkuFX5L0HjZqmPASY/wpsuyQFm1lH4iFczu9ntl14JfZN74m0SJ7ApUaE3+7GpBVbEHFC5RLxyygkwy6ZhrJAn5C9+pu/+RvhLDMHr6HpnL7JB3TEqAXBWJbSmAUFOgJiOHWHAHEAHaCW9s00N/ouoe0Pf/hDyQ8NgQcvch4pdNYOSnQp6A5yZAjyoJLC2Q0WO0pX2vWFVggIRrnmwms0oARhgmlNVIJ0iQvhVOMVftVLt9BGi8DLJy0NKEvOQ8bSezvoBawj9S5kkInuFGw2GhRl7MgriAudUlavXIjBqSaGIFwo4NorrUgJcgYuX7oCfvkWDJQBQssExAtGAmnIpGdklXUTXYjJvEJVUShkkd7Az45U6jevwFAb5FEnWWIEaAQDQPLkCZLOGMfCpkCGZIsjZFibaF+RQB5R5a0u5Oq0SAHL5A+5TfjEx9qiCnzpM5y0Sw0wTdwrK4uaBXkoBGCfU5mOITKv0kpZHkg4MGfhIJLEJnhEhjVqGdZQmK9h6SejcKbRZwgK6R1VwFhENoSJMWcc9KI7spXCydJhtopkPUJNXTqSpYMkXn4bjAI7AkA5rT3BoHcDx+7SKgVC9oh+XSsAq4vQkERVYk2a06KUk73rpaSBcUt1ajRXmbK2zpUExt2gp6EySENggGhXhKNhvn/GC5OpVpCQcNBW5bBA26+99tp4FW8LviVy2Op4ayJw9JuwcL05IyWxUVSPPPLIaP0+Kw0lM497wXXZ7D5p2H9f1uPYnVlPiFLqvX8ydtoj//PlL3/5bW97m1nJZ0r8le6mFXKn9JyRH5EETicBjtDn6P0cmK0PoU4vXbrk3zXdd999fhlC7PKxj30sIc7W+7pAhNgUCYn5rDjKc1Biro3A9yx2E54rnabciWWL9FjhFpzZBza/Cvtyyrfrbv+PuEaYwHFm1ygX4wIWrZpUsvEl9YJhVFbwSw80EYV3n8Jmhw0SsawtNUgAhwx5ncIQoVRENuWVLEL8nf03QTzBCnbF06J2uYGETbQq9pX8mNElXbICj1IXKZbwXXPAQmfsIE+NnysT7giUn3/+eTYoDXOHR2SMKlmQS5Ip1tcQ1xLpRUGhEErMjTu9oI1kaDsk+sIsstHPqCHHoJxH79IeAKwg+TCyzdC6iDQAu+R4gBWwBqFXhkABhS6otEV/yiDFNLpzqYRZQYgTHp3k9JjKyxD//d+MMamsn83XFjEuXZCnPQGC0pEzckmuQhUAwYRWHrHpQgNRGyCFUOgV5Co9upKJyXCsRCgToIGTpGFNEqKVxyCX2hmp4HHPBYO35Cl/Vn6h+vJfXHuFO/90zTK/RA7OAOSVt/lBRJXKuRtNo2+wHLxsRzMNgbmwnwJ5kjChYZDjQiqlVVCjbYsWPInRnzR0J6sAKPN4ThW1r5RJw93I5l8SXJb+Ffm7h36k5lcY1ejx8usr/8kseBCZSobGKLyiJ6iltARLGpCoDEyGTEPEo1xl6t3LD0S8xi4se5WO3OkATVagG3AqsKwElESU5EFlXuHasoWyVS0LSREs5LligNYg5PZqYGbgCqjK4W01DJMkjWzqvVJgWWwEeQiG3AWze7ewSCd5Ff4N/ZQQcmXNYUNhXWVoVdMWoGVBmuura1hgPpWq8a3Koy6QGDvilyjJHhg5BOkZaK4A14zRwbQykz2wv/8uMFudOpTETHgzNYcwEEXYtgomSv/i6PHHH3/Tm95kY8nMe6qcbktiZzytBE4tAW55O7QyByS0+uQnP/miF73IepXp38JVYsFDI3UTeuJns9jPF9s4kvNIkMw9e3bBJjyBFBr0a0sKU2JHATqZJyrdhM1qG6YEo34Y2SawJE1Eq5cCuKgCwqQKXe/iTmFiMrTulezI3l2NUcoi4AqdO3gBq/8e4VyD7TLhY31FVhiqoKGygTAZi4nb+sIpQ4AQDFSCM5BIRSeTkZeKv+3weMSRfScxN40SvAIjcBGz9EDuattKKCx8z86k0VfvMQSIdG0CU0gfSoiBQGZDWGyEJJxSD+kE+RRVKaAqBfEEJFgQ8UuDxdxS3BAGJh2JNtDpEeVkQhnQgGAWoQyPcuJy1HqFC6mIpXrpDe6ync454As9kNuyS5Yi34CBNCyfwaOLSFJBQ3JTkKJ7hQWv3F2EoMYjuQFImuGOBqRq6EI5MgCrdynUoMufbSoiFUnEZfnAWwhdpOFKub0DgNwrcsC10WnfSoDbPbr2FRYSq6Uy3CnHimmIS3O0eYXOAtBRi6fK+CUxV9W0BRjymEyABKRVBpegUl/4q5WaYWW9vSy7F3BWZaGqdL17RVzVY/dKX1JclRihbMZCmT5QraSRHbxHTQBP+B8JbXLaagte2Z0HUPBTUkn+aV1eqWRoNNNAqHSlgFn66dGopVJ9LkyhRJnmQBLDpM8q6ZtxJG2LNew6EgPjVZJY2PycBz2/LM2f+inD587cbJgD4228ckck7fIqjHj01sWa9O5KPbQpjN59JEz5faqKwSGAtouUZwh8FDXkw9VYmaLnftSgpHQUxK9NJJ8vGGCAQi8OZFol1u7lEBoaX5ZlmmNrFjFpL2s6SX5xanHNvxplwp/61KfuuOMObJ4kp4egV6dKw3hkcKrcXjhfsU+/wuXImePQ3PFjjz3m04WaxS+cwm0REE45KTFlDtTZyhD37NNDmQOEZeYDqVRiL4EjAhaFy5vwDq0u5MAm2jaI3wTnhm1kKMq8AABAAElEQVRHRS0zlOmNRs8ig5yEFBHah0mUPB0hWUcQRTl3JA4215px206V7dtU3O/RtQihrMaFZfu9hkk4i05xPLQiftmXR6sMCJMZgpRGUiehsEQL5bJHYy3vsiTslQjPcACDSqxsCUa/dufkqySAQSph4nQHIO+SPMvTLl26JC4cFTsGE9lrDrkU3V6Z5ETXOpI5UHKRPWXThR7h8Van8Gvi0qltMcgF6MkAUSVSSXc8QHaVPUZQtJT01JOAS4E1eUuedqVSk7fuUW/D4QMQnbqCJMhzN1LtY8rAoNVR+0plvY2x6JT8LRnoC/IWuCvDRpJJJwhBq8JWOLsm9RhIsrLKINupelk98dZbAF5ZATFwgYlkCp78Y/hqDIc7LuhGR0nBD+vVoMEQY1wvQ4Bq2xVQglSjQ4G7V5s8FgHFadUM0U68GgKrCTyJMaKsQRjlDpIcFrk1VlbrIAzBcpK2zJAtGAjjSBmoBPNBvMq8BWkhMtE5AkjMK73b2fZIcyizGhd7YRpJzmmgFbfs2DNDvysJGzw8gwLPYLg1p6IxDfXmHf2ahqzowakjl4K+wIBnO7RUR7EalHNBCJvWc90d3UUOhBb/KfYghAz9cTFCkXh4jhf9Mym3sGJMuU3e+BhZnsmm8TWjmSsZkRVes+fp6XBEwVk988wzd999t0nWYpm4xbCe8MjOVIAz2KoSOCfAq0psU/hYqU0VE/k73vEOv4xlN8lPVSf63xT7gbXHrE1CCYxVZz8uagejO8m2U3oTQ5sMHIa0gTB/vlyPKswKpLKDZyoSB5ukBY7H4pdNlpkvEewSHbovFQWWffUtB5ZPCnPbvSzN5YRLMQAASWLiWjmh3NKj3EPgS4ZydakLPMJo+0jqQUaLAIhWhbNqXKQNDAvKdla9Fe6gR9rpP1L67tQpAI8q5aKiBBZHQ9xB0g0fhhksBQSoqUvvSWUdAUUeGhCMGETSK8MtufL/BkWWmoTZiJEACQdOYgEMswQbtWAQIMKGVvB9OR6/kt/mHgwyZAfpvc1je4fTQkPVwNyWva3HttCCtfXKE6+8JUzJgztOk2Z3zbtH2Eg4w4ff7u2cR2KUfLaQRjZEGlbjRaTeEm8Lw8MYXKJGqmUREZICAGX3jEgLPyyTfwKpvKJ7lMR26Ohq0bB51VBjEthuAlzIFSR1SG1rtlImYcfFBdCjsuqUhFRB6jc6L7dUzqpHC6lsmFBL+SWcYIxLTul7RUo+kZC+akiljZe3QVj/0KhYy2h6tPABhukpI1U6ZKkuqxvweLySNf/kVs0NSpZC1OgaNpaCMBg4Df84tEilutTst37rt0Bi0+YhytEfFtxpo1ZFT7rQvPo62IIhq41Qwj9YOqcJM7QWVgy6sZiANEDWTaTKPkwAmXn5KIZpgqmJV/i1UMi4yEfqa03qVJk16T/wwAOf/vSnX/e619n7tQ5OLKfK7MSIn19tLoHtz6Ob03TyGGKrokn/k8lvYt17772Ozn7pS19qjxdelBC4URmjCEMAt7lPgUGswBcLTeQPLoGINHhzzEvlky4SpuS+t07RJnLySbB51xdHAtbRrlFlQ8MkvYdFgRxBRJjuZBfODilbF5e8DWlTOR1btMLXXEIiB7bE3m2fDjG3DYdlmzzkJpYVxWorDI1wKIygk6wc/VVvLYPtSE4I0CNg2qXsbm9QUGulH7yMVCVGmJXEVYou2oaKKPynBMiloHarwGiCfo9SX6FV9ppa8mA2iEIuNNgrzv4t/HrXxG8LeSXMkmlD5craQYQsx3aJqoXs0jNoEaCt05vk5oKk7StlAOJyXAxfTddgh9B0gR6m1wF761Kp09F+O/g8Cji0ElSl4ShMVynZQHy3sQwmK/dyHmlMR4C4DeVgFIDRBOVkKTJbFxromHAHa113HrVN7gRtfggKsCUP6xpGZAg/rNGwTao78obwbU2kqqYtKEOyEp4W56JytGjR27Xr0SkpmpkXSaVE22GQP8lg8RsGnXVAZTPZCkLKJRO0ecXQ1LAIE42hpCRG2W99aWtwF3lLDbXCO7tQkMixSh4s/OYtz+BKuZMDC2WGNIR2ubssyzJJFOoRC6nMHW2a6wWbjn6EbI/B6ZAFKWHZshf34kqG7M5GCIH20r2qj1KhytVRtedHErbEQ25Zbb9wetZm31hMEw+AD7ToaWSvvfZajK/d11E0xC8LMnnROiGHORTZ0yI6Cr46IlkWs7311lvd/+zP/uyuu+7C4+mx2XF9ftydBGZFBrvr/mrGHLu9/fbbOSwm7TOkj3zkI3feeefMcG13ojNzCHm3tYMRNm12+aEXSUsbYk6zIBYRIcmpkk5MA0+8FZGIfvYsVZ3Kuxxwtc8vBF+U4oq0EjtO0L+VV8K+RHXJLVM2nYwiXzXCNsQiqgRV8MstBalrTEuiYReSbCYLhQ2ZtCdZIm20iytpNMEL4HQh0qWotp7oibciAHuAaHAKXTztV50oG1Tq/fIcqqi0yFtiiWvxqwESo4t9PSqDBOMueVawIGXVxq6UMlS6E80bUOHUd77zHdyhU1CFAGmAaCNnmzXHtWQ7Z3QRRs7ZqrKLSKqIAeAewuo+lBWqAgZm1UunqELkaEOhOfKG+WcHrHcXtVGvgB7skAZR4Hdta4Lq7/7u7+BEQNcjQzCCJQoHNQEwnFFdKrCkLtACRiHy3Hkbyb8Rl4c7gVLAXY/bfdS1jf3g1DU1UKZC2aCY3xde5F1oHt1v3y4vGVY2pVPmQNTKc7pwwJJtYkrMzZoIXKvs8Q6btzXKrJU10VJssiNdk5W1rdgXk1wkqxZPYNqaRWWQXuHLpRxVcaiBvunL1EBV2CZRsHRvuZdLly6lbAisUNOoXJbGFKL84PHr0coLMwlaTkkXHrn9ohB+XdvTJl6tLMqY0bR18VTu/E9nUC0vwbPJXaeUE7P6rTWpTboIs0jaBMmqHOnU4iNpY0dbLg47LoPVkQHSW8udIPlzg7hqX8cIT4UESw78mwQ7gRwjO0OazT5f/OIXbRexMscJhVVgTpLTIe/nmh1J4JwA70iws9CyXs7axG97yqEOv4z1ve99z6GObSWfs4h4AahmtRcqfhIrbMXFQGKal+QHG18mEBd2TCAX2opf5SFSoCJp1QL8go9uZ3JVJOvBm5jlSP4rsg/VZPJm4tqRMzGLnKCNzMVVCioX7QeuR0DbikZFqWxoiLqmT3VODEqLsy2nCS6sQzs0aKPVfst8PKV7CpIHuiE+FhqSklAVQn0pEyAtEjfffPPNVEj2+9xzzxGyzFYMLTYSlIs+bb16K0Z3T4IkRveKLhkOSCTJ9nJtIglJfQsndBDHy7igMljkI14URoicnKDTO/xhFh6bTuBlNXhEqj1kSLwtZhUSzqJEXymrxJqoOtFbBOUOT3ZWg1+2jGDlkoMyysV5AZi4a9K+9ZiaIixvVaKhq2wbVll6b70gj+ARzx4J2U8u185bASvATM+1UjBYhgm8egmzoVQZIVQTNcpFCeWPWhpKzbMU0gWvRrAThXGkIcFj1GQXaPAjZ0ZKp3Sg8Fe/OyroLjkhxhGZVZWM/qo9yg816XhfFckceEkgh2ACoudMQzbITU03pNWAWQozIVsXJSdthbYhxU7C2VambNwl2/TESMnKWCulIjSDzqCYQBjvEHZ4KBUCuso5j9AigGJYtOKiEZ+O3DN8DJBYlIFxAvLhUbQk8Iu/+IteAXMh3pVBh4qieozz4d5dpbc5dRKcGgKOt1RjFUArcqNLUGFQ7ygBox4GhTSsQh4n7ozduoxsUOorCZ/fcAKnV7bgTCirLpVO41z6ljzNCwEjUoWhjuGXJ7eU5pWthTjnpZiPFID+ZGmDGjvL4GRN6fORcrSIbO5CePz000/7NqE+GNyWMi/q9Fx/8hI4J8AXPMSxYcGf33AXWfoPSU5Ufv7zn7fwzJftkzjBR+JdaQPHmp/nEWeIFbZCBk4TBEBufjKDCkGSFo46sqqswlbI2A8SNGOT6PzvFmehBXmGGL/hhaixX5RIGsWCIsuXvexlO2J2iHZYU/RsUpDOSUgkS7Q3xxGDjTQU3JOUCnlFM4KV5JZSWVGLGmGNGV3OKfgjDY+i7URvBCiGE3WJ58B7JTyVJlEqBZeY2xYx2epC3COMVhDLaghYwaW5UFu8bkL1SOZSFFRZIoEh8TfJ6N3ZZpAswmaCHWkE61fAB0ZIKoLHjvDdadtIEpIUvHV5W2sN6r11qXcXyxruwKAt8sld5o99MB7lgb4XUNDFdFhzGfWViwyJVFErZQUySVapHjY16HfXERhymMiy0Mb8gZGVO4Jh840ZOagPs+rbC3IZaV6RbaJVQhPQW7z3n5MBg/H/nO0DA/vwhz+cLtQLWONqQmG5nepIYZjzoCqVtrWNoDJqjQ5gg67r7MbjlKoUqpbmbZUJxwUb09ZdrTFlONTvtPdNuKCQVmCNF/NZiodNWdeQKif3oCTDQYFE/uYaxUaZuX1iYQX+MxndMF5UhRv0ygqUXaxY+mjzVDK9ibcTrzIcrD7n5GtQFKzZacjueAbD174aIuze8kIGPWCQk8+wiRqtzPIcXXwdFVWIK0CYsvyfB2CqknPwEmlvvXImRSVZkXYuus15QpgzJl7pt6NKQ+6LzXrbLrSN0rZSJVIpzEpNNgcOC4RGVQyWNYgOJ375bY6XW/Obf3NWUjoMx/KI06T6QgvHTNrl9WNhYSad2HT4y2/l0P8nn3zSd78alp7PRHIGO0tgVALnBHhULPuujD2/5jWvkf3ecccdL3/5y/1zsw984AM1p+6BIDSYJnVkbuN0UhaR7KJrkZagx1q4QBzLJqryaDy7AMskhwZluRAaTPltRLILkraOMxwJyu03mqVCP450JHbMoUEhIE5lZeJI/n3rNAwR6ivLEMNXW6nBr3V3M5asVSYjJjN8CVwkk7IyYVOEoDvDShMIylgD9qi5TQ/hozIpUQyhsBxJgkohFeQYQh8YRO2Q+3aXDGHQMOG7JjDIV3UHWDjrrQUXiiQ1wr6IMNkvDLZc7P0aGtEwbJYqvJJ6oefZZ5/Vo0dvoZKHw2mvRnfegkGDvrSSHmNKX9jUl2soyVRC5RWcUmurA0PjkqQlsmcaCtl9SqYxxNnW4AJVCHaXomAtrZAXsOQtJfnszeICCy2etsz5ZKcX49CmbHXD/iQXMeqaaLULEjuxwtPaOVcjXk9WIHjFWtJRo1w9tnJLua0psK5g3F3oEfhKFVweRfwIVqY2PIwyK1NG8xyc6aJkpbC0VQFUq9R4RID1GplMwXQsVHe0glf0SODu4nj36Hxg1r4jw4WA0KBsEKkEscBpjcCg2D4iogCkI2AUSQ0tauvzNjVUGtiQsCF8wXiVcZf0okGeTB90BIDSUhvOcGga1VxhAnkLNiwjlebrS87QdTHEOawZIhytmWjoFYujol3DNMm/gzYc1pj8Bh41KPM0NJrQc0LjizBCVrEvJ1moDQwcO8FScst/moN3ZtjvbuCUT066mMGaoLAjrH3UFgHRGWUFzhAAIjthtq22Ve5o9tjWFF/mC1MJV8wZtgDbIuMQ8GDWGo2lIrObeQfLh0DVLmigb3/0R3/0oQ99yETvl+oygZ7qsO5CgGec0xI4J8DT8tnfW1bNr/Fl3/rWtz72sY/97u/+rq8Nv/CFL5jJ9jC74FPYkVlTKGkKSXkX/OPUlCktlDkIR/yKvTN4WX2PEAQoLmGr3qUWKvcjgR0xK1IR/kJufKVSAhHJQNIJMYTgWFYmaiH/hL8KMh9c74KeZG4bIk/oLBRzidJc6E+Wa0zrjF8ieGOdV4IzvLu7wOcKJWos2Cf3K64FdmAEc5UYeCXkSmQDXtYnj6I56IFHEC8HhsSsKTgAJgVyR6TFBVpNiwwBhKKH6Fhkbgg0EUwoQCiORLmGDpXBLFpVKX8G7B8p+boPRxJmPUoMaCn8Ak3wZNumEFLBVm+HMh/WRCbY9ArCxKwlkOmCJpEzZqlZF+IjNRYterZJ6y1sSZJH0RZtJOaqRzE3/BYR6jhDNS8Y8Klsa/wTYxdxWeBzLOKWW26phl3B0kBXM/GYLvTIURhT4wW4uLM55lMxnTqSYIiJdwJV98qgO+Nq0IuLDmDRY7e0oXdmPgcJSCxAq+CujK+UF/U1s55u8y3cbMFTdVRlNYpwLMrQNyJCp0JRa+WOtpuVUMKCWLqCi8IzExeVK+BCPrOguS+0bXXq17cMVIvxMrpatZmJpwNDnnF3McCWNvU8iZNNGBRJ8zldwzzKKFhK23AUbJPKUeRU16BAm6VAXkW5Rj/71elUZatR2KTYhoYYXYYyCXayX8AuO/ykimU+Kh9PsmIdaUjD1fOr3IIlqlHailnrJq48gncpo20lyyps6xWojVUhg1jNjaxtc56NNnK5mW2nGam2B1vAVGhrGUklS6TGjJcaE76RbWEOlqOVCMOpQMJP5PgtD1tBvhCMWzg9TlcSyxl4uxI4J8DbledG2GLb7Py+++5zUMqpD/GBBbC3vOUtMf6NsC9rnN7L7QLfna+BGUf202QjtgqlLpLAhPteZafI9GzD0JRmhl5G+0G/LzGKUeyMCUdEovKHEG3mFq+AIfl8zShty9bfjrgqeqbxC8iEWUlx3V1CYTOu5oZMdKV5aYvI1UipET6KTsRDgipJo6EUcqUj2alrtFNBG7HQgXoLs45cakTGgjw1DpdaONCFyuSWKhXoUigRG+lU5qBGnIcF2yl0jDwFfPCIgBEmVLLyolNhnw0obCYsNig6smdipK6//no/1CQklQPgQiudIjIdKYBBnkedApD5+1VYZzdklSE7dIajNESqV/Q8uahKj67AtHfdQQt5vcUIhFGVQHqL8mzCKHikS14JjAJQbT0q6xeMcfRoKNu3gV90t8RQr4yghQk7VKXA9Uoh+N3pMB1QSC9tXwbFKABuK1ski+pbmK6sL8dnVJKSgU6mZ5RlQWhQXzgnZN7hFGG3QXb3tnvUC5nQOvqZo786Cpv0BHCGCcIJb2b1xAVY5Oceg+o6qkc9ynb06KrKRQUyT2pdAJIEGGyV87EMAdnMhBXQXm65wNpCkESeylHpFmDVMoXMWWjYDJCuXdipwVoVYcGbO1guW64aZLML3zkjm2eYGNmQUQ33ViCEbOTqEbWZCyyVUolOIDxDq0UWXFwtnVFyYNwIj805UMJcyoG0hsWzBbM7t+AVP6ZreutiUzSZoOJpA8lFJ7um7dxR1HWOBrbkbVjGF3UtJAjmzIUQKCzrq7dHXeAHjGC3wWv11ooVIfhSIHLI0Bw1px3xTPWJJ55wCpIJ+w1Li90ATo/Njuvz4/4lsHzu3D9N5x5JwL+t89nPgw8++M53vtP3cp/97GeF7/uRjElO/LrrvuLOODj/pcApQcGQHjk+nr3zdN3jrgnbHX6TmZ8Bc87WbC1MF4cZ02w5CtYt62ZfMQHH7shoMQuVXGIjIePlHPfHPxbWyGDJXLDYBmRaGSzhlAINASBCEoclTnIPWrN1TdgieKOpfnRYBS5pkjuEVZMRFxx7lY4U0OlOhpI3BcBCdpIUhCmoQbnglWC9UkYSpjzSLqlCqPUoMsYgRsgcNrwIPeUnMgRRnbdCQ4mTGge5wZOJQMToiPMIKjVJAjXRNXrEjt7aN4Y5/3ASPeFCwSUxk2YkMQMpiJR7SCYFuAHo7s6jqmkxCIjTsIUUDCGs5JYytJcuXWrbtk2MCFFosgigBQ4N+q1KATEeDaiLtKu+CnTGSNmDhb8Iq7cqLeoDmE9AtZ0oDHlRgzzyMVIxKCsjMGC/3QidwIlBigHeNcQ/bEg3WoFo6+A9MG0xm7LFoHY1oUVSXbRCq8oWssrWg3iPNs2rVymQs0xGGW3QUngIjSAFQKoazS2a8L1eMdvKdrRSoyEhkCGLUKZXAKi6PJn5xGOno4k709MRQxvykhp3A+SMLkskaqh0zegm+FrUXSs6MPWYjmgCo6aZuB4Sswjn3urJ1oyAZiNil5WWKkcg0zR0vGiVi53yToswGE1eiJz5JXeuksbSE31xg3JjBXg0py3GOr+mri9g3L5X7t2ZnWk6t/W25Zd+ygb5PURSoXZdYFvdXSAe3JXvJfC4I6PmqzERGmZbUVwgndvt2lx5zz33fPOb33zzm9/86KOPZro/SU63K7cztjUk8L+RzRqNz012JIFYu/VXZ6GdG7z77rv9+z7/9+y1r33tovlsi5RkiXeLCCdQ4dTUZTJW4O79ur0Y0e4ENoVo6tuYcgLPUbzCo3nLh15ySzGlaMwirthCfWJ0zO4i+zV3kk/iG7GOiFanYhf9ysYFW8ktI0NxsMhJWTxq9jXLuowRbVQIjPDXlXLuUCnkXvWa69qwWsoxrJK6DkAkrXevQoBQRlu7GYtOgCOg1X9cZLctPebgq3yYGF2yVusL1hREeHqhS0Jqs6lNeHEhAKka2nQt7CMcvct7KT8MjvahWaUyFpJEJSlNRAgbIuVUgmnyhNkl03YqGztYCKeQuzTRERpssAhzvcrbjEuJqy0EQE1gWsiUr+D4P/J8R0aBUSf1WSOTbLRSatEqG3dXV7nSowy/yBttWG+rUGBqRPki7OGrgtmkQCVIOBjohm1qCk8ylioilvn2ZcXB6TuOt1P1ljyYKQnkCgZaOd2lr2ySM3OLJkGivm2+qMz0Fr1aqR49Mhm0pVWyGvpZHCmzGvZuH5g+U6cAMwQrQcqGCXeyI2W+q6V/5giyMjnw9Ac17DrGGzr17sw2g1qJWcCszEEMBdTi3eqYMmGyVgbCWvFocplJ+aq9bwiPKk4Jkqgo57kGnRg3ymT+ohe9CLZKn4a0GU0XiVmJM6wcHRiOzp0FOQVAk11ZGOUhQwwn5vgPGDknL2oHksNkI2iGKuuMcT5rED8kcqIGp/SEhVou4dDMpLRo151O0LOtV/iyxkTyEMa3mL/4FgpM+H4b1Zhmgj4BZjuh4dfvI/72b/821f36179+4403Ajg9Njuuz48XKIFzAnyBwl/SdSzfoUpe3j6w/xXsh3n8WLSYe0nLDV7v391Uj9kr8PmiJXBBtmjsMJfqN5DuZW8u2vCLDiJOkYogW8gOIY+fEOT/sXdnTdMV1Z7AO7pPxOm7E+cb+H4ELgwNIQwFFBWUQZHJCVEcAAfAgSEUZ0EmUZxFceYAoiKKiCAqSjh8l77o6Nvu3/v+m2W6d9WuuZ6qeve+qCd37pUr15SZ65+5q55VmGtr+XQlZ5XHwABuvdMroZTEWEfdkkFqHhQEEKqROrgCcbPhilX/DczyVBXmkZYwPEtfy5vss0UgcibLuRRKmoVV0GwfvJEwOUGSV7dOqHJYoSwNihgy3TDXEYjlYmqVNJWosTYLO1eBGDWRVSjoCziRL0L4gIpKqjEFTEtsrsFKLuhAVQ3LAAxQLoY5ucVHZV4XxAHYFrrJXSISvRwjS+5RSl90RGBCIlbAU6WXyZmabBOtSgsS4obe1jgOyrRIauvT5RFFfM7cuprYRUSd8xOHMNGdK2VtKQgyKQQsGcXKUuSEtzJL5jvhnK6heFBpjHNNMVGz4qU7F/66YxPIB0M92t0AqDpDjKf4SzaJcqIM+MyUh3dythxKIaTA4/oCt5QFEpqUZ3JDQBLIZKI8nhJJF6ytbDgLWvyVaSESFNqLXkJXjRAVtzl8LhCrC3FIVIOrWqVfE69QV8llIjxvWoZ/rKphNdGc6/k6mztVP38hnXasnVuf7YwxzBOfDAEimR+UNc+GiLBUcHQ2P7fhvjb3lIOMiyX4iwf+8l6AiVRUBE5P4xOb82Z2zcxFkGQczbmdkcKM6H2KTC/mMKYI8Sn8DKKwMpXZ2M3kZgplfGKILl1o6EIWymkiLVqvRzKLcPOhftfLfFFh1khvgJv2qcNozGvON1sahmYwK+mwW9coxpZZmYo/+MEP/vjHP77gggsc9mSFPRifbtmYY3dzWqC7ZM7ZbCTbjgUy/iXc/omIf275vve9z27rvffeq2xd2Y4MW+vFemmD0xa+YzRHwdBOsrcDmwSpY7WWRsghgkIl69KFPuActvyJpOJ4go6b1AfAk47IY+QlyWidWLp0lxRZXyycq/IbqUO/lxh8XWaXDL3kJS/xe8veAiUnxX0SifxSPemXdd2XuKzrsiWVfXnUUE1OIPhJhQael9CHWNklxZeyGxRSBDQu0Fce6TbHgHJ0lmEfZvfp6Ste8QrH3Y8++qi2agBdsUdaCQfIpAYTR8EEk3+Qk5s81Qv5IRBPCYbYkTKE42CTOuyZpBAlAmra41BPMDypACxBaAFmkRxzeWd5pKO+tsnppXoyeKLima5bym1mRczOlVwG8CdjJgmpyKbgqSvlhHfkTH3J7LbKaykUQ8YBfrjSBkdM1/JvTWeYyKHFQ82l2VVh85asbd4v60IU6V1AGolUVo73ESurxD+FfvOJNcO9s21+fEhbZZcC/NkHwPgEeIs9NH1cjQ/IZEjmEK+EIbOLzDi4cPaZp3RhsbpVaVwwo44CNUOWzQVlg07v+ckAAsjj27YhzifjA2/K+nU5mVTWV867WsppZbJlFiWPgdnOqF6+gP8Nk2m9T+O5/XpWci3ar0nJRGQVYGHHv+b5eTiwhumXtcEPMwxrp1XHSnVrmmpnKg05N0/FvDdfuNtlPsRQ2GRKNzS8amSk1EU8AVPjbh5Ri0a8KYtP6QFhjDgCRIai2esCpQwlthXDlg+DlN3MZn6Toj/G91rTCM+hXnj22rMJ+f7777/kkksOzKEH4KNDVWEEwHvg2UzufjfVr6dec801/hOaOcJ/A5/2BcI9UGmSiNS0IprlJXPes/J+rJRIcjaJdr/raGp5g8Gihlf1ACqH3nKIAcU0kaOgdIFnEl+phm2CHOw4HvTUAim3wNmSWfzz9a2JnBNaEx+tpTL8HSk49IaBvZYsYZJykZY6uki25P2uaDGnPHCXINFcxqwgl7Jr7lKDgwtMZYF0AXAqv+pVr0LAOJAbhKPg/wyDx5pL++QZMirhp4mo84nYMSZW0mgGlzrDeMqArrYyPEpBsNJNzBW8E6i5T6rxggzGraXdJ2L+An1JhYmGvCZbNX5lulZ9vVBE3sMyyq3liedS4xhfc10Tj6gtjTIM1qmZ5zZqijoi9eml1CzAOAzYPiWhSANp6A6cROATSfvxrF0TbFvskbbsQGtlmbGNAPlcR9O2iyXKPMgL7XRBDHx86sgmCNMV2zzKbZG5DU5rhc9mzcD+AuvhjImND35nrpY5nrzmqq5XL9iLyb6DF1bFRlYB5u1zLgsLGLK5rRrE5DQWGI12mrePihXXd2KjJWtNlyapQZPNheIDkSobd2KmKjsFQibSRLjmyri1oLpD37+NbJGhnkIRrJQhWZW7XGgtPKecotRyaePYlh/0y2VzMimyMlrV9LvuP1ITB2luEnalFXk40ZUmbg0Tc6BJNR2p94v6ok6lmdAUyt0+Xf1ALUm0xdMv/ON2yimnYJI1Lr0U2b4XDBMXLewaWDVsK9ifsjSoOTBNaWT+8WNXDnh81+8rX/lKZrbDU3PfY/JQ5R8B8H54NjOC7OqBBx7wlsi1114rEb/nnnu8LtLPhvdDpUlSlpp+GQsszNtZlj2XR3k6qd3+1ZUukgMXVPCHP/zBG3rJldVY6UHcE2j3f7ND6q2IjlZiDWmrdCGJL27ws1zEVZxjFLedmiMxlhU958CwrkUuh2b2tmkBO0ni24OFkhCAlNW5lRDTGrhSlioZCDRV4xYH+ZArJ0VOtDwCpzWUdjMRRMqSoFEgkFaGDIZyL/wd/IJzEimJF2NK0NUT7OUvf7n/eKQs84CTcUgrB7bs6facc86RzxFbXwgcisr/yICnHvNtOlCKdkAvnO8YRHoK/1OWtACbGk1Yhk3yhrZMPQ4tC8R34gFbbQnvUkBAnvJ+0S9aAIEcR3dATjGhYJ3i6jf1CuSnFyvRi1WPR9gJ3B6afKamWPVvOzVFuVxBp6zEpIzD4G7tOxg+HK0S3hYnJT+0EBsic1asnt+dVvW7xkSKZnBNlHZm5USCfi/z12Do4voysrid2Vx4uzpkZpic3BpQeHae5jYz8MRHBLBrYzryVDm/8iWYs82Bp6zdI1tCRocDWOVpvXjEU+ycb00bPsrGTnT0dNHLqBTSmhtlf/vb38w2wUuL8tkXetOUTSVDEvo1+y0htvWlHyHz8+l41rxkWnCFgxnPgs4dBiNHmzTEXuLWRGrXSXNPfbqEkJ/J4D6j2LIokMw2Ah4rA9nXwYScKctg70yV80u7L5TUNE0x3fw7GvuiGjl50OtXV199NYd+85vf9BU/XhYAe6TCKOq+W2D22rnvGh6S/JkdLr300tNPP923gi+++OLXv/71d9999/wvie2FNbIK+lmLSGsVdHIIqFgID29+pBHVZOd09H1RmbqXdZ0mWfmSE/ikeLITeSR6oE5a0MG6aphrN+0TqejoHDhoM56FLqQ7OeGcFplyO48YwWfKleGplGPlxCDNQ1asgDcWy7GeSgstqIkJMEmM3/72t2pkYAQAYhVIaLvdwgzlosccmT149VI3NbJ8ZMAqzn6iQ3cuTDwCBqQphPFIeqcLUBxwUglFKOMJT0K8EjugSyIoLxTboBdgrBUPSg3ZSr8+Xdjm0qlHJInu+bUwaf2w6Z5vvdhfGhGeERS0VI7ujABOuFR6KiYd3YhSO/fK1Qf0Qq+67RfoOwCr+vTDNXElGjYnMAcpsBKz5zvJCDjFo9aeTE0FjmBYZU2EIqgWJgoYgm3DXXeeTsTPHZr13gbmzcOz1T30DAIyMY6An8ah36pDyZVMHRcoAyRBNciCWBQwcc1MbVnbforAQFy9tOWqnKdgVnFxrvcmiLR918wj5FpoMkgZH/Q1jVB2CaNpskSrYfn7DNXELy3StpXp3RyeMi2bEl2mU1MN5hZBv0SolbiioFnR5OPWO2LHjh1TGBZgH5/yJvVtz/mZOnO775i4KHJ4ytp6c4rzk5/8xD6yb/yC+gep5j4G4Ukl8wiA98zdpkKzpFMpc8dDDz30gQ98wFHwHXfcARUf0oZoO+N7h00u+/vf/94XLCX9HrVP98x/J8SVfAflWuOz9ksirXngEFAhWZTKyMut+vm09kdNOK303TsjEFhmk+wH0nDCH6AyoAhT2B2gMsvI16U+LbFy/9dl7Q7IpAEhuRTA6dAgIEfypAw6qgTYZIrWYI9AOEDCAkw2FyAKwoGamrM/bt7Gd7RiO8anM2HNNdS1r6lL2pygoichgEpCA5NqKJ1fwVH+hyH+NjXgMQlNjhP1km9dYs7dTv5lOUn9oWicPUWPp5HuUiNxxFBNdkYIqdzCTrd1VSs12la9gkfkDzbASo1QZC44X1laGQBDqdRoHjSokC85sxVKbQlsYLJSpwsRS3000y6qRbtpBAvVA06O+umVVoShnV0GrnGoJcCgXAoSWGZphoz8xhpKOpIfAhQGVBZjYaLALNxkuPnk0MB+YuMcGj1yN25hiJtrIclXJ6bFckwIT0GepV1Sz+X4aEtrhmVJm7DGSzmiGJq7eKFu+4W2iTKG2QzqU85ZgwM+/O43mYy++qX9OZvvEVlmUcuiqSBzwvaDcCFz9cVTE1ScV5xabpSimrFpkAowExdirxFZQdQ42DfVuKyVLsQzN1la5rtWFrEu+7OWRRuOBqatgV0Tci3ySHWSuCrUN35x7sfGWrobmYwWGLDA/7jlllsGHo+PdtACZopMFpaHt771rfK/T33qU16Odbwm9cyjHRR7aZEgB1kU1GF5sApa/5bb515agKUbZlWTo0M+cuicJQbNglJqAkKs31Y7iAKcs7SDOlCfhJKmckrpY5b2E27/58fSUh1tw8Sn5Nv7zFZ6J7QSoGkiRVtPwUu2Ai/bLKdsUc3VsDlKia+8HEQJ8gFdeIFtmVrWKJ2SVzEvGAApsbDLUwBJsGGCDLrAAWeO4A5COkl29gsCYa45nyIAnLhVcAID3tvUC9W8442hFFzE2ukAdM844wwN/SYZhjgry9gw1AQfXaAnjzAAqNBHTXBdwa2AJ5JHpKUgpOpWE6KW7m3BMHHgjJvmbb2ys2jccvhJERbAUC6iLAetTTQW06/uTCmu5JrJs93aSqCCiwWc96Kki4mISGyLP7YeMVpfgL7XOhIudIu/McJ02NLCWR8PkodV+Yj8caIym0TaCKAXzmJ/VsJBK+qLDTragFBpeLIMQ6HUXHi4DcyIhFyPWNuFBF4XcRAsg7sW5SlcfZHS0LCfKFanRdEw2zKjyLFlwHokqUp280qLKErwTOuCy/zYlZ0UYcO8BGN2DNl/WpNhqeqpfSsbH6YXr1WvyKp4dgoENiGQVrxtqItOj+2tgPzLX/5iJDKysCTA9mVo5VFmkMyBxkXn0cBtJO98ojeN0MvcaMK0aGJuHhZmKI1Hc7sRTX37j2YewSaY4cZEo2khU9mR22RA8XpENVOTtxXs25Kcmmv/iYTq6wgL1LQ4Xn755Z///Oe9P+U/HklZy+9HKNjY9UlrgfEEeF9dn5ndevCd73zHu9C+SuE7V5Bw/ovavmo1SW6aWttoCg+AE07V/BiYjHwH1zZTfC6rr4JdCYu0rLrUkipRxK1DP6s1GNOBf36wKsQySBBRAleYpJgcQEFWKvd1/mkj4EUvehEo1VeKBaTFEmgWkFFBv32afg2TOihWz3qCBCKSUgNCsiiYQVLFHaJIzg3nQK2egqkqfRIDkFPwirLeJXMSLy4DGgkg5cJWji5HAR4wkffLPvlRqh2ILmPTS5A2R+PgVgxggow8uoBjdZoczhaAXD+wWRfMgrnIiV4pTIzziZWtNfTb3rZlbe22uFTCCSBQ56Xl5NM4kJPAbltJ0rVP0tLRhQ9ryEFln8qyUpeCTG7T0ctcPK4vLo7ZldmTqaFiNmdhWZdj3j4a51zhISTogozTQTiVblUKD6xEoHiw1aicQa3gikF8pqAmZjnxcJkPfAQYmwvUOVkxMt2F1qL96UtD221LtK2+WIbxs0PHjC3m4QuKGCNiG/2AOh4JMPKwvxGhjD4uqI4WLeCGldAVmZsGEiLNRLGohKvTm1K8HmzWsqfG+KszXBcHMWnsxI8r8hRC9hcMByO35qvwNLTPOussxhdjfO1TyGUlFZb2BZC5FeQmW5e1g1QryrOh5sKV8N4DEki24bxoYygNDJkNibFptpaGb3zjGzfeeKMF/ac//elrX/taPR6emps248h/vRbY0UlhvUoeMLfMIL5FI5H92Mc+5vf0/ESWH9Pr/FuLfbcANa0KkJIcUWZzVKcufTNavZLqWYMtvS4F6bKXJxFLUCTN9rCtxDJyK7HcLkygjhQmrgHYAl0QBVzhe6T0nUgWDvv4SUF5mxzF+Q8M/MIXvhAG6+gov3G4Snc2lKnPmayHCf4K8IwugsFg1KAy/pJtCycpkR/swRY6kqlzk+hKX3amvXIP05LBgTwcq6wJSs1FIHpbFXjKXcAArZwHAsMyM8BJKoM5LK1tvoTsPFZgyG9s5RBJEkA1l4ChOw/aC5DRgu64AXL6VUkL/ebVXDmlRxC7yr7HVULmiEWaMqmoT+wYQex5KmnW0L4DhvpVdpIcVjFav5wany0B/sKbghQRz4yJQGyrdNl34Ep2UDkt6cTBCa3PIO3qZYlCCcY44C4OaghW78aziY54mfs6/InHXFWpIUNlhCqznjOZbBNgmI6wAqfjEQ2dPoHHCqB+ei9uSxTkiLwz3JAALjQp+BSQbokXCQeaY87sCNgEcXYHhArjzGzbZ2sUMIvhyfKtGcWDg6yYkUYiGfMcDveZeBTAb2wK+/nBf59VW8OPp512mvFItrZ+LWU2p2ONUIMdBsOZGTPVrKWXaUz0zla2VtlW1BlxKJdw3zT+q9SvSww6CksvKQhXayg1O5xzK/xMNa5WZpU2BUy2LuPUFO2pX5z2KexF5nFA/B//IUI4q8O25bPNssiJIuJfvzsi1bosYILyXrcTGgvclVde+ZnPfCbTxYGpuS5zjXy2aYERAG/T2hvpK/OIRMQ3gS+66KKrrrrK92RMN/Bwm5dspO8tMo2a8IMr3fqtCFrDJB7l6RbEScYp48lhkR6feuopq3W6tqZmZc2tk+qOYLntVE4U29mFFAdGsjcMF9F6nlYTWe1gpexZGucYnBn9sCcY7FeXp+Gl5RQHMjnLJSWSYcA/vmELlOqam+RAbiVYjGzDAgFKWbsLpPEJD/OyhEmma4NJMm0zwiHhsWPHoDt7ExApfMv1KjU31rRKoq/gKYAhAyM8VgCnTkFQGFhOpl5aIOmhMkCl0mkqjIqGs7zpoCywlcEwlwL+hfEmOlRSTpgAyxDgrMAOYhL4QRBLKlPWI5/ORkg7kSFigTfRKfQNK2/u5XdZQVmUdgFYhqlZKV1M5KySnMTQahrBovW2JFxpFdmqLABS09Z7mlumhmGEgRoWVlDDI+RX5pGOfRIqiPnFI4FRrBQWvdILMyoQw6eo88mGYmkiN1sMwonwLgZ3EdvBkduJ9FVJHfMJ5ozPJhpqIghpVDQK9AIV8v1eTVwxoIj1CDGVqy/cNHFbNcSzeWdm5g5deMqG+UVo5c5Vraq+X1OPZhYIAy+xBtMZ4OhX4TbQnfFrGgmBeYxZdG3nqxMqAxyWfsQLZktjx78MyC7qhnScX8JElGlTQbSQB+DRHJxbIgPBBCtzoOFg1cuLGBOFmai4wAOAQ08YMNjGSjanTL9eltZKF4KEbOZG/KcNtImdrqWSAC6LEZHMsUTKT35O1GgtPR4VE8aHeO+66y5OkSmdeuqph6fjUdl27Hd1C4zfAV7dhjvBIdOK8wpfsZCjmHH8azVpqHM2c/1OiLgOIaiZCyyRBziBkX9YxirHXUcnXR7WKvO4tBiikNsB3pI8q2Ze/pRyQTtMDbKCUgpW1ufF7P7tsp5yn2bACegCd1kppaRS1QCkKY32qZo9Jf1ghnwFnpRtyx1pTQfWZl7WlhzIgYA6LkYvow1BX0/poHgAwIpAAYRwKRgOiRBBgomUiCWNFAiTByWUXIk/P+IjolwSJqOGtRHgAF241UoAnH/++SiJR2b5Ge8A0pq79Ul42ZvES0d65ziCYQJ4QB1QLhm8fY1Yvcstzgj0RVMiYa4AGRq5mugUMRPhRvFSMEbQHfnx0SPgxFyIdY0tYZyC4uyWOgEkuIHfkAno7lOn6qnfYRvmKpm0P3uod5UA9vUJLNkljIhlQ5x12pKFuP2kGnp6Fav26RLldNfnRgXysED/kV5I++STTxpcDOhWgd3sFIhM9hQJTth4rW0rUI13CoqBBJLoSu84UEqSzbBcMKcWAtKuARClX5eCgOfuaWiKzNRBQFpTjSkCZQw+3CNXohdaztYIySwZAjVwCC+eoTtBSDVKiRYglltjAaFuVKLxiYa0mQ+NJiLxKVhIHcRuacEOdn+YyxgMh2kSkmHFaZzwLCMaqUaeUmpaj6vUY27gEFjksz8dlenYHyyr9DKxLTOyJ+jLfQiGrTqRwyYqzTzigVm4gBFskSgLtkWxpWEI+uaXIG2aQE24Laoj+lzCL0MjhlI2PbKeAgID3GWS1IXQ9ataQtdc6lb0bs62AtUkY9/QqBc/GTsReBOuOSqeFqbHH3/8vPPOg3tvvvnm++67T2q0OaselZpjv3ttgfEEeK/d9y/Cm0Pdm7uvu+46/x/YD0T79F2LO++8UxqXp//SYJ9v5Jf+W6DDN3jDGSm0kK/LrkVNS5QVXZJn/ZbZsJMfFLU6KlgdresqraOxX2eLei0C4IwPMSQ6+vIqpmxbcpCFOf3u3Wcy1CAfubsFUqqtUjoid6QOC0te2ZOmLo9Uyqjqc5rKDsQ0lDDJeFqa1hcSMiDNBWCg4T4Bw5VwONuilAzpUT4EHCZ3JxgClSLBBTmokbVIPW3YG1N4SmXEHiEBBuceaKjmUg84qQn+VIYNVOLGiTJmdqC1CxqJnOAEMEY2USdXkFDqFESUareKtAoqkx8swRZW0VAr5aBlzIVr6CdymFjZ4T98SxdmJwAXkJNeroEmyHIIyfvklOySwT5CjaaBtss9mqmjISYqeBaWExJMp0nbalq5lael4Vlh0z4dLnOTWELDg8wSYKNyWqtAUyFn+NjgEHIsWcQ4uAyEVqQ8VcNH9l9MbnoRLbQuoI6JhqLUU0ZQ1kovwjIMw8SmAD6AROiNUzQRAHigu6e8qVJHCqTVS1+YEjgFgUSeTuWit7ZECcCbm54n2YdStKYjsMcFFIwRFpV5Tnr29K/FqZbVJ61mWnVO5quTkcqFj1dmzIqBOkuwFU62V9jTF38yBFbXsThk7SanCYds/GXyzAwJiFp9BE+cKBTJYJKnS2qKyRJKVROs+NEXm1nJmDLzeAcqc3XRHECBmr5M5Ot4Dz/88Cte8YrHHnvMEsmAa7HhAdhnVGF3LDAC4N3xxXokMcuYgKxAP/vZzx555JHrr7/e+ZXfHvB/gw9pqqUmdbwGZjGTRkvBpY9eOV7aiIwGn0BHVkEXOGSJYsYAYADG0iWfs3wWqNDXRuf0MJdp+UqqFVqmRUgQwplDB+ktrfWWG0qm816ufumSsvMleblbmYEaiYivxVKWO7jV2hnFZ4o67Is81Yvv/UricVPjEkXSSreOnblYZg8P61EO5Cnvv+xlL3OsRCrJEF9ACOpFgvxMhACoGCrY0UcQ/Cm/EZOYcBM4LRvAViX1MQeJHXHoTiCBH3qHCVEGNpMEOFFWKRqTq0nUMJ+Y1pOTMOipRoxCJjEXUVOoT9YeAFdFNn+B/FRz1u0NCK36PbasqJlZiEYEZk9PK5iNO+GNYZiIedZ2SjlR8ZbtEmXMIT0yMKy0262LJPoaVqH6EpmF2WJ8jxR8coRYUuDiAW7iwX4KBUWRhnTXahhNTeTWVkLFosurhvQqUVNARjBfR9dvIHfCRr1pTVlDY1A4keGJJ54gEmtk642tOIs6bCWwMWQuNeKTK7V1mZdEl0f2JUNsQyEu7kjSv21V6D+dWUNUkhtodgSAihW5DXfXMmcQV+jb+mEOiz7lL+eTFjgFS97mOlpUsGmKLyqhsLfgmhjNh0I3wbMok5nCdxi6reA0bdq5M8mf2Or8XyYiq0/mJVOQcWFEGBeubEd2WM3sugiee+45I1S/3nzOYFmaVfHcqYIV8N577/3kJz/Jmz/4wQ8uvPBCA+TAdNwpg4/CrGKB7hq5Cq+x7Y5YoKYbJ8B24D796U/7Z1c//OEPvRd9+umn14K9I9IuLUbU9LIiiCIFr8XMajrPnCtnkg5adwkQlAu0SDLcymslUlY7xxdudSRJLTnLvFWz0YLuqCO51IvVxU9cSkBtkMtZtyzJ6mqSPF/QcsQq3betgGehCDs1bmHgZ5991k9Mtdq15RKD+0BZfnT05NO7AB7BVwMv1uIjYOJurnfZhlepediSyivuchQIQdIvJfLI5RYxUV1iAwyQCYkKEQKvSpvgDfKAAdImkAB85TV5lUSK1vChjmCGZ555hoT5xheGUnZGCBkxMNQ2mS5ryLqCo4AWNLrGSo7YWkN3GMrYfJKTbBTRte7KUG2B+u3timWS6JcFOCL53DBD2BtuRJPthpSrifBm/7qlO8N6RbBq1lhwdpe+GC3Hnvpitzm7oHhnH8GrwjikObe60HBi0uiJbHUtJOJiBOhLAOWJTaoSgR2HOr+temPBCDJ8+gAYjWgUS6LI7KH37ATpVFQwiLA3zxDJ8HSJRsIjcyhnIOhRUmtQCH64nYIwQ3Yr9Ohp9lYiud5L99SUhCmwFc5G4sSnHeLh20gIpfCIkUXm1XkO91hPbX5VeUMFLvOTV+xvIEz7KvWGul6ULUeIgYrheZprYhr0ipOo80qXRdy8quF2PFi9KBBbNLoitsk8BQPKvGR0m9LVGHHm2EW3IZKTaG686MJ7RvNvtEWM3f9kMe/iefHQSPS/SD7+8Y+bLohdRt59FUYJTzYLjN8BPliPm3dckmYY+Nxzz/3jH//42c9+VvbjeM3E5NFhaE4RSxccYl1RtlyZhRUm6mi5tRRBC8ASzOylMpmcJkmyJW3WNq/g+iqvkw1l63EM5bOu7dutulawGMtHk3GC6Gq2L89yPUYLAWntl+5QhNndJmH1lB/lTwpycYiRdpwoqZ0GIay4cJQEkQeJhCf8qZAzNAUIR4qfwCiZcYsM0n30vBwbAnLqkcnv0Uj0nX3BGNn1J4l6NZADsRHIhESdemGjIcApSSWJkyggFk+SizSnuAgUJD1izwAUe5deeqmGyMhAZWJIsGh97NgxNDQilTKN0OjFb7wzDkwCumBbuigQCWIXD0QigDMi4YE58aJXS6wmV1s5s2zUnABEx//pjuZ9en3xFzKIaCJBNaneGQR9bVXQmjfjQZW5ZUxkofG02ha3mQV8XEzXl4rLWElsQGIAPPfJTcG/olSgr1hCWZVtjyrbehKi5B1a4KytMu+L8+xeVVuKq+QpFwJP1ZCT0wUbspZtteoXdFeq6d2+icihDjwvZnQhYtsw0IuXhBFYAogntGgN+mYa0Smx8dSRT+hXBCpzge02NPoSYDiLWM3NjUYHuEJysU14ZUGIjxpPXSVeX3gBQ4Aaqn2C+Wvo5R0NnXpThs3ntN78/KdR6qiuaTQr1ht3djwZylm6H0MesOeKHa2ruXgTPMwyD0NBawLPr+hZcM0eiZ85m8/Txfw05coUytQGhX06IyWjw6Qk3owOZPaAvOzAO3qJ1n3J6Why9j0X2lkIDBwjLmrOL9uOUzKIRe2aa6758Ic/7I2nhx566G1ve5ulKpbcceFH8U5mC4wnwAfufXOQ6cn7Nr/97W8feOCBj3zkI8o33HCDjToZ/2Eo3646tpMtUfI8CZaMwaJFfYuQXDP6+lpmtnK1krFZ2KR07ODWEhWDtAx3ykQWTkpJQClo6QV1TjnllG3mfOuyBgwcd/RN7YhY6kxB38n0TrtEZFqnHrGGp3J6PlXGrWXI6e2JYvgIBrmyssDwCST4hBmEDeJUwkXiBFv4hLXzAnzeAgWWxAkO+GiY7qgjirxe4cVR+98SHWQwg00WeYACzriBRshIpTuHHppjot4l08X/N7/5jfQINzkTRCHBIp5bUQ0eK+MjYuENbckA+rISSwJRAI8kDKtwQNC5qAZmy1Bx6DyaeSvJI8M0MhiVXuJQF639p9GrZ5+YWpnlbUUVsRcxKGXkhoY9PcI87w4U2TwF+wvsljcLOvQZNYkE8cbjHQKTg6tTOXDLCFxGKUCUx1O23yEY+q04Ips1HmU6QpYA7hNPrOnYWb/ekhVCCUugAgGn+I8AbXOxJ0jIltclGIGdEWiuMjGvRtCqFGyuFBAIGzriwFYveMEL1Lvw0RED+kSTSgOkyqnZ6KeuQV/gn+TKG+1rm8y50iTPpwLY7oyud1y7+cUTHiZDk7zBItgcqJpmt2nb+fuilEtowa7xQpYMHOwHkd8WJHXM0ka9yDc00Ic/ArOZud3oNn8GE87f9V5QWne+/OUve83QJPDNb37zTW96kyyF5GWEvdBiFPLktMCEtfnkNMQBa52ZCFq45JJLzj77bL9K7xsafiP6C1/4gv+Pl9nqYNSXovmHOvIGANjrplYsGZsVyOz80pe+lJoWWqsRMocPwECr+O5P2SS01kI43hYDfugIKsDArRZ7UZaa06IvahR0GgDUuWobvk+pBnHQRUCySE6BiWSNCAJvkq94lFCXiDikLYYSlFBKcRztagK0oBEbggR8yi6DEHKYLJYQe5lCugMPwAzhA9C6lL07ICEAb/A5duyYqINqyPP0008768hXIsFa388He6RKuvDCgl/wwpC++g1yJhX/Bp8oYI6h7kAymIQi3oiuo2aSA8wEBoM9gqymRTLZYqKIPfOzLBlYxao4lyXT2+XqtgAAQABJREFUPH1BIDO5tQRUq1syJxhI7mITXVBHvuhL4B65OkO12i5dKBMJoZSrJjw7t21HzMIdPnnNpzNSTuHNohFy8nuPXIyWMv8mApHxsnpMBJWxQLt6VEwUNMecNfr4vCWD/QSqmU2/yrrLHkcBeHHF9eInIRfVSnGs1OgrPFmbSHCyW4Hn8MrWA+ER2A9KnKeJz8RtGrYWa8t5mk8bNFRWzlDK7oZghi6mNWmbt2XyCEt6GWW5ooUahWD4ln7vyrRw9s6b3EH4Re2z4/p69wp65HozZIJtxxUs8WrgeFnMgadB4bLXRh2jOPOAjSFLhkneGKej76UfHvo1+hyr+JUZ+7zvec97/N/NTFNlqB2PwFG80QLjK9AnSwyYlVwyLW9E+26w4wKbdj7BJxmnR4dhCIpYn0zN9mV9Sh9lhNYeOkojpJKSQhkeqJBl7LhR9kr3CGyhtdkM+VhuleWCjtEUtqkLrBLbLtppVJgWb+Emo01WJI1wMMVltJvWRJLhsvryLxplABJwlfezjIIL3mCuMPcpDISHp5CYjhTEA4SAg9yFXiIEQ/XyaXDFrRydzdkZzMAKxsAkDDFHaXDhKdLwIT9sgJtvCaJxwolPCLS1+aIXPeIseQIq8k1gNIQXqL7s5+zOUziBhORPv4E00AjmOPA+4d0q2+sBufUIDCsgiLkIRjxHuD7hNASaYKvgKpMiy9k1blUp+Sa5ocTXtFYgCUdEjCKjYK6Y1KAjw8Cs8jz58b+YEINtmYWEOnK+Dcsxgo4AYLsh1AlgDn31O63AhkTlRBe7aaWgkv07HNwyclTuPAIpffdSE67vPNIvFIcgyIRVORGfkAkbWtgEkRMzGp8quCiIW2TWIxtyBDnlx2w1UQbEPEsFPPsylPrcJIScE3ozk991bRNQbEQ8DTExIghjv8yAkqnzEb3UMIsr8yQrEcNsqV4Ai08X+bMBwU284BMBssijxsEXSQbEKzkV6KIXxELFLf7KJOm7pm0lOB2msWHHF16jsNdJvBZdiD3EhljLYY/KxqZNB35k23wSfk7z7ria/CgAhBB1OEjkGOyJgT1SsERVMEeJSRFouLnM/Aa1esPNoOMOBFmYhGgU33EfzSOewWsmeec73+nHZbxR+OCDD77lLW/JGCzjzMNnpBktcLQWGE+Aj9b+2+7d9GQRkpT/8sTlOxvePvKLBTfddNP+ZgymY1BHVifvgefpeDxx+7d/k0ablCXQ0muGRrPEOcO2PTRff/EjBUMu55MI5qvLVtn5eKxKJe+XvlvdV2XUa1+LqFh1XgppSJrzxcUe7fEKmSIAWY80h0bc5sVd2UloQiDVzle+wS0hIftXryMowtmvMmQr+1eWq73oRS9SQ02feAItkEYQrDKpUEpxZHIuNOAi5oKQzLCio11pEAEEITRCsLCCb3XnDAQacYYAfkieeNMnP4pSTRyMIABK/a6P0+OgLIMUK3r5h0mpEeeaowS6YBVK4ayVyIes4h3wjBh0JIC2TuGUpZ4ZF8TO5RHk8/zd8b/yb4NLhqeMc6YIKrc0KWMIn0jczS0sTJI+zbSacncR6JRfIgzOuvaIpn3KatIWtPVyRIg1TyYqN6WvMxludRSfpy1DRuNT5g0rTIRHxyB5VJxzjEla9RHS6AuUZTqVYkBCnNgo9Nt2GoY+J1bW04mFsgxRU44k5aDiqZ5BBImoYARCRjsmRcwg5CylxKTuyOwpSk4X4SWAMNZX3VYXVTNc0LvhgyZvYQh+HDCcySf7CEI6/DUxQk0O3Mpr1elMPkW5mwUhZ1faJ8tnxthNOUnFBSYWwWNbcB6zo+cvM6SZx3RtgGiIzzxtd9YIrfAGeCYB0pq9LcqMY1Y057iOHTuWt4FUomwb7qx2EwWz7nzuc5+75557DMaf/OQn559/vlhFub8aTVRzrDwZLDAC4JPBy/+iY81T/kWw0+AvfelLfhzLb0T71b4rrrhCuvYv1Dt8Ize1ugAkoK/s3/pqIs43SOGHs846q5Vd/ue4wCkZAllg+2hPy+VHiidDpaBc346sjLCebk67LXRBeOda/GW/2YvHNmvqnK3VqyOJMEiiLDxgVGUErJQmIc6t7D+bCA70QFOmQwPAaCJglH06ncsLpWJMTiOVEUWSADASQWBky1PvOMjsNdREjmhMcVAALc6yH5kfDOYrcC4FSkl5ESPTPL8/JLw1hGa9YIa/RN8jSAaM0QQ+kR8TT04JoMqulOExemEuDDTBIQAY6kiEGCx4BsdmFKAHLVBGBRxIQq+c9SEmrb4kdkykoL4siay9PGIWCa6NGDQs76k0d/5sj44RGAajF2XpQh6vAGDYR+xt720ZciODGsbnVr/VpBwL4BOU2NKn7JGrXx/LqPeUHYIPOct+gTfb1YsiHndcjDNHgNBuVaJHzM7ZgukzZ17vuBb/6hql6Apu1yMCVvVUYHBlS+b1eJsj+tKFrROPdGfXhgWUSejLwA6ERY5LjWjnfcEpWrC14SJ4cFAvXEUaF/MCSrL55DsaKbRXX+D26XB5YtuJlQN82MdINCcQ3r4Y1dQYKaytwDXaGqfKxoiwHGC1O49IK1b5i8f96pKNpN2RbZokTG3TOTh2Go36OCVv5ZgQEpyLOn2A/y48Amu9rmKsmectECIzUxDZjGWeNb4i55///GfzrQFr2hefscYuqDBTBpF5//33SxTNyY5MPvjBD5rotDowV860w0hwMBYYAfDBuHIxRTJnyZb8v/LLLrvMqyz+UfBXv/rVW2+9FXRMtrQYx21Ry9EtouS39PrxT4srLSSdVhTLSfK2aFfzMhoztTXJiZw8Hga2RCUh3pbUm+3HqaO0FU6gnW8+y/htsW/IiexvmaePgnMkSbMy3JIsf716xoPUkfeD99wtL8zxHZ+CN3SUsredyi0kHHxNsDa3qGAoYlgUkwSMA1uLOmwghw7gBHrVSLKBOie6MAag6PAKCAmmbWFk8RRXghNb8E+AiTpWgsMZimAAknRcGqE70YuYv6TyMiEc8Mczsa3eoRwZGFbQai7XVIkDa4hz0AUTibIfieV66ZdKWEXer6OYCHrBluLECB4jicGCkoSsJGDsL+DjDDkqKAPJyuo78SOHozj5+5ZML6xNWTBMgVOYTj3ZWCzMZ35q6EJG2mxM6JEZc2YYT81kgiAqK+CmTGWC8aYahmJMbJX14hGU6HKr3tPg2xZnelQXPrzglgH5wqeyLghJPB4UeCrZkBfyokG1VeB0j4zNqKmhqyWoMuMnkPBBE0X6xKyEp6f2C4STQKJRIhMr9ITkdOBQDCAgXjEp4YntaaKFIjiUGEVcNesqCMKZrEhosIjSovSKgTKpAHgGB/V9ukWZgaYQmWmNkh/3AgAT28aWscPv9kQSOZszftlzCwURaL2gmkgWrvChYNtCv1vrgoJmXe8jiEDjK/HW+s4c7oo8iJVt2bOJy8i1bmZDc2sCL9ERsZ966ikvDBqM0sVPfepThxSiSxhkbHIYFhgB8GH4cUktkjrIs7/xjW94EdoE51j4la985ec//3k/2+Cpa0nWa2omMwgnWVqyc5nNqaeeKsmTFgcMtBBomsD2X/1wlCXHQmUr2oplJa5N2TUJezRsorK01WGI7NwS5bzIKlvaTbPJcuLyCJygLcDgM2XJzXLcZrYivB6FqGNth2xSB+dXwWaBbTRtFSQVL0spgu78TonmLUH1KHWu6KpKvehL2q0j2YxgE2mWf8kNC7tQglLQQg7KOpzzNiYZAv8YBxn4AVdgAnK7MPEU29NOO03oPvbYYzD2RRddBDODMSKT4wigaxcdIU+9w8kwsEdqFGJ8GEmTQDu/mG3MEk8mDeoE8Uav2LB0rAL1XSQM3tO1clRw/Kj3UCrrjni8jJ55qWPLqaM7Yq4hOWiNjwwJQSxWPXYK7OMqVFYMJZHJI6mGA490Gg7fFh/2CR+zh30E9eT3SQUcSIg54wfTqiEMPKlgj8xnLnbAJFm7zxzswLHcag5BgwBslsjqJVhdgR08MkH51GlYMWCclZrIk0c+S2xlHsEKmVeFWfvYsWMtsXqgXRiIYSFBbJDenggZAq0RkEF34o12/o2WiPVDZfRF6ZFbHIiNCX/pDhPmcnUkKfHWW0jItSpP5G+kB8+TmcDKVDO/aeh/HfNLcTCUXJiIf59CcSLDeSp1EbIw79zOw2F+mjD3acLhHUolhEqv+Vlth5KoIt9pZ3WXXQmDtD0KLqNlo02g2lMTjfTaWdVKo/kLRpN0whxi3JkZjNOsTS2H0pdNPLWd6imzmENchqcdsZjLDGwI1wZBNWy5ba1cHvTdEP83xDplwXJ8nX9iT4yjFW9rdhg7OmALjAD4gJ07l2o1i5mX/ReWRx991GRnjvOf3LzrktSzaObiuA6imnzBBtmqRcJKo5IkASQ6kRe2eepAt5Hfp/XJGgwDW2lkqNWLttvXcUDgJR5FftrBHpbVoF9Zo8wjS/K6FJQ651VhvTilzEbJEgLP34TkPCUSJFjOPKMOTFgcWj9WpYIIob5CIqd9VNbwEnLApIPKnDUFAcJL0mtA2lPZHla+uBtJ8EnzYhLOdUtCEFQl0AhjyMsdWJHBqRctHIao9BtXyg60KSKDhFcFuS0et6wqB5IQU1ZODGpCZXCvMnr+VSAVSCzXd14NQGLFL9q6FDryuAVvvC5rvEA4EDVRc+yJrTIrASQGBZXxj7liVaMPRsITgYEjGWKKHOqGoLQmEuYeUVaKjE89ijz9T4kjdM2wnUdpGP55NJNVy6EamitcHjFv5gq2ZSImVRlDIYjHqc/sElCPWLgYqufNZKU5bmVwRiASDozDRBwKf/ICI2uoCzzZXFlmjDjl7BN5KkJcQZtmNn63U9PRsbQoSVqCmA5D3IBqPiWGEzaacqLK55577tlnn8VEmeOIwct+CZ8ikm88yeyFWyq46CveVBK77aW6Xnthnl7QMB3D6p2CrJRhZVXiiExxEay40Vc5n0vLrDmMbS5N8OATl8VfS7PtNNRLOPMLmX2/wwjiiNKlQ787t+wQ4wt+lsnxZgYIIaOXTzFPHbOf1MKwasfU7uiyhCRRMA25zEg06ZmZjaNh37VPj+/W/Od/2iAzVcZ0pmIbVWjUi3BX6ttWS0i7XJPoaLZ02Putb33LuPN1Xz+eatZajuHYarTADlpg/BXoHXTKkYlkqjUjv+Md75CpOxO+6667LG8W5qCILUzEtbToF0DNaYZVQbpjBYVG7CLbLm03/hcyFhXw9KKgwx/LjLYgCqijHO22oONCAi9BTMEcijKmI1MYyZHCJpZSprN4M+ZGjVYhoTspgqxUwmEf2n/clRxTVuIuOZZ8WK1d3gC3V01rZCAifAW+2vhILPXtKcwYR0onXZPVwQ8wgEtuJyp0QUF5MMyjkqYu0K6OnUs89cVcGR8X5rJ29fgIYFsGtiQAXTU6hUNEMrZk07VjYcSayPgli+p965Ui3v1GfwIx/XdCOm8EdQQwSkPDT+YYnjjAafpVLxsjcDKVEk+ixkok//vf/6454MR32BpZxPBUE+LRi9iRX1nkYMs4hPGJklnowhrkJ4xypUR48gUBmB0HwvssAZR1l6sqyQC8gW3PP+nuQxEA8NZF27yIpxUIwM7xV2jIeRzn/fu/05eydBFLgehMl0cMSylucqtHaJZh0aBkCo+wiulIRXcipcAmOc+ki8mEEXTnaUWmtvZWBKfe6a4jviAhtmrMdQxbRtCQJaHTiEE8igQjtfp6qjkhHRHjoEeBqgBOi1v0akSXMlbIXvOa15jbDZ/Es3p6qecmZaxEILe6NGw7OvIyq1KKWQhsXDAmm1BtmmDGBbIBgmkNKyz1ZROHc+N09LaZ4q9pbRet15dLGJjKSKsvSglLXS/Kasv0JCSqgEnwi1XrshCiBY0iDE/5uoqJzlxkKEW7Lcu5ie6ioAnEoOMsY9BsmSl3Od8xpqGaKTQjXRcGrLnCN/NNFPZ88zTqbCE8oqOZ+fbbb7/00kvtnX3iE5+47777vO2yhd434bWR52iBaRYYT4CnWebkrTetX3311f6huX8UfMcdd9j/u/nmmy+//HIzNaNsaBKspQUGAFrkjk5p8kVE34mSiJBqXV3jI6eki06tZNJlWb4DIj1msVlXR0cbQ7STecjIHQTBVLaoLbFrVE2KWVl7jFn6rrEXPKWePuW1PgEPBYqIRqc0EiwvnulO1i4vkY1JRCQlAkberMwCnrpKNoUEW2pwUICWXeBBghyB90XBAwd6kCEmbfO2DHIQL/inrU9Zv6Qlj/+/jefDDz9sX1+yqF7IgeVQPcQl5iWLLvITgI6kqpxSc0fB1JGLiE8pEaT6/e9/X72G0FdsIlsSwBT3GQwTNSWjsQyRcLBB4BsEol3XesyJegLDQOurQAwWMEaCASgr35W4k0fKazdKKpxHacuMSYhzq2smKiBRlRgqk5yQQlQZ9II2Q1CfXDAPHoum1SqH225TX953m8jhFHbrELC8p2jQ+5QCsrZMFBmDiwSTBpunl3yFFSWxTVPoOdQt3RFkTyGnZIaJen5Hw3qyWx5Uozu4zusAAsAjSFiliGUrFmMf3bmt0EKDMxqfyoHZZkXcsILMDUZlT1VSRHS5zKW4keqNb3yjtq7wEWl00cTMwJuEjFSa79rFm7wAqItkcNEh9sB4pCCEv7QK+so8gwNncYoCF4dhTKesl9Qs94mPwLYxZ+AI7xW5LSfD2lvFOMa7SQZ4cyv8qNnit7V3ujWG0Y46BhQFxYkJ0NkA3yWXWFESfIxiB61mfgM2WZDp0UDG2aQqFI3Q3KrZRMxER/FvcfnkJz9pQbnqqqs++tGPmv9X1G5sPlpgNy0wngDvpl+OXirZrX8mCQbbC7/tttseeOABiYUzhMy865p/M+fiZoqXEDjsyvuTsmrd+fRIRmjeX1ePHcvKua3QslJgQJqrL+vZhvrqdL3pW3rJrV3ybKoxLJ9K5talnTy7YytZNUvK2mudXl1HGRXsIUcH2ESLVVmoSAV84dnXX6lGBmGZAx/ug0xgG99WkjR7t1YBSOvLI9hIW/JLNbAScoFbTKSgO0zAGHabpgh9YRVAomNVbV0xuFQJ/sEK0oNniCq/4R2JBfxJtuz4UBBwUpbfsyFNXdRXhszFJ0jmV38gFq50XIytp2g0TFn+hIb6nK4VmenlyFelAMCcxeSmYoAB3Xoq32K0EEdHYitQJwUmIqqf2tKQGFJA+ZlH+nLrKUMFKyp4BFa5KG7w4sNZsnyGxST8fRKbJAAGF2CllbL8L7sVRabgjWufM4NWPoqJLnxK4CAlViUD/nFoeBJe0OLGYnrkC5IQIMpyh5r4kT25NTGACXqH7SrRhD+zU5PMJW3sKelXg54APKUvtsWToVz6QubrqTrS1g6OkEDgEn4u8hBSR0wqkjHBjQC64/Qg6vSoIWVFBRqcxSHZ7FZgRfFooTusNPRpFFS98FPJBVyvR6zETG3/lUZHWCB59c4Usn8ycyshBV60K4I1FpiFSTmCAPp16xUMMcM+/GK8sCQzxi/L9YtzRqUAEx422jh6cxotJ+ScrVjGMK9RwEcOfk10AtL8LP7b0Tcnz50lswbl9RketKNkQ5l2a3cchqKLVYW6yTkGtFqZCW1yiUnWFoGstMauM9xE+C9+8YuLL774/vvvt2Mr5bvwwgvNcjvrkVGw0QIrWmAEwCsa8JCbm2FNxK973evOPfdcv/Tgn7/5krAMz8IWtZeegjPhYiK3k7biIxuQKEvIZIdwC2CQg7J0tKFP/VpgpJUWG7O/QxXwgwy0joRLK7ghgRdlS37qWEetqbSTfPMdg29IOxm5E3WOkxgtKuoAPR8BDD4ljpAkmCRxVCMPAGOAH3kqMOYTVIb0kNFX0gBqOuosJ1bU6UuWpjk+6Tc5B7GLWL2y3JQB28rQFysZiZwYAE59WvmEK0AmGSGDMDh8okf8CS+9Bgth1KAjxJzCR1TjI30R+5xzzmHM8JRNItajb3Xioy0mQIsuXLAWzENIBiEGMvJ4Kw+ljhgNbPBpfJGHreRP5HEL/+hRE7fpyCfTkVnSo4lbAQN6yf6paagaFyRU7zjOOHXLbgRWMIiI7SmlCMBuZgyf4CIm6SVGQ485BO5SIL/xrtyi3zKvpJMw5aaSs+MRw5aahjCUTgaikpnkkYfZPQKi1IsKldRhuhBgpeAVZVoIiXBmOkAIvbhicI8oAsZjRTZi+yRwpf4E02NMSgZ9EbvFq5FcuMYaolSnXKxHrDgLfMXNN9KFsd4Zk2WcKnukrbnRJzs4fSIMIW1JaMuP+sKHRiTkPmIEUGGr3gmzTF14iKIyIweh1CM+vI8habVKJGgYaWOKlLf/yfiGs1FDQdm/HR/BRhfSbk4YLjCOWEY4MQh3GzIkET/My8icsuL8xvLewhBUQst+FssfrZ2XNiaxecSFg7KwgQ9FuMnBuyTibU/1ag2SsUARM4wvmxhQJmo/VGZCo/hGFcS8ZmYjVygKP5OGIZ9tL/PP6kM1HHz6L4Nvectb7rzzTr770Y9+5CdRMz+01hjLowUOzALjK9AH5tD1q2Mi9vWPRx55xBTpXWi/Ee2/B/taiGWglof5e60JVxpqR1Oqh5uE0rois8l57Pzc1kJJQQkiHWF7+/1QASHlx9ZyaaguNrrOrUWFYSbMKymRb1m/ram0s45K49SvS7W4tcTIbZ+5eqkkGfqPqm2ngDi4jsyyxjpLxIqnXOqBCmCA+ySp+CvboaevFCHcSjw+dalEJhPVUFl6IZuRsfW7bhFO+1R4ACdqMMEc5lGWOtdLvBSUQHsEFMldhLdb4SR3kdCrCSaU34s9P4BEZql29n2oicz7dXjqxWsRglNzeAB0gQmpDH/io0wAqIA3sQpWhIehBQgZAPPT0PrSysU4jgFFuDI5MfRjZmKg1UuE4AlvpxJzt8pGq1ZudQpiwWlujVwKxpVl4TT0FPRialBcjfQRGU2DCTvEAIazUO6rlI4FcNCQmxRYWxlIJh7VBDNN1RSfFPiR09WTluPifYZVgwPFbTSEgFWxzY9mcwod3dKu5akv4YGzhlrxoKesiuGZZ54Zti0977vU0NSGWoI2tz5zEYkrhbRc1sX47Cn2lFXqkR0IKcjR65qj1YtMJnKbvR5NoGIhR2Y+JTyrcooXdkiF/vne/hsaUcHRzKV5zIiAOi41Qouc5e40tAPC/t6qKD5HUmBGriGkQLJfIJBslAjmzQmjx0wCOk3v/GjI6NE2luhlNOV2yKCMPOiHBStKkcnmOsJ5Zqthnkf7NBoZoaJRjNEFdpo4kR6tnMv1Hu0MK/6iFJeZAbjMGN+y13QnWuzkmvdMCBYUIzSTFdVMAgla5YUEi4I+TQK33HLLr371KxmdEw7TSDuHLGe9sdVogb2wwAiA98JNRy+kOdG/Efr973//y1/+0nTpzVLHwn4mOv8UhHzzTL5mWxmbGRz0tZWulcRdlizRkbq5jlBP8lvb8i8KyCnrlWpLykEpS+CcCh6h/DO7ZmQXMkums0TWhn+Saq+uHWDAj1lToQvRAoYxXUcq3ncwaCEPVOg8XeiWv3KiZfmX2cMGmkv3aSTdl6TyXZuq0loe42BHMk1OzW2+uFWGPz1aqHetMNREocqJk/BhAdmzR87u9KWcerIxAuPIYKRTmiTbOMHm/9IFmHHKBwDL+D2VgbkgOg29/GlXHv5xjAmfyMiR/elPf7InhdLAZAcoUUqKBkZlChf+8Kce4Ry/7g4U8curX/1qUlEfQ4/0XuqnnE9PtVVmn+S4hi3BsFLDAlzgKVY6xY38tMOKPGSWrhESpTIygz0AWKhQgRieYos+AC+dGnpAHQ7YeqRHHSkXpcpQsoxc0C3vEyBlZHoU3nTXKpRpIh5opGwKIgM4CuJirjmxdcopTEoqrRhTVIOCBGBVj9ST32cbV7hpkt4dzmCVTQ38PZI0U0QhF0oEFDcE1KCBeE2hTApcyT71wra2Ak0+3E0qpuNNPNULDF0Qhl7akvxEdPwfyiqTyrCqcGJSZ1YUxNwjxtG7To36FkbGyOrLUHQJVK6aookWW/jUIyfa5SGJEUSv8847rxV70zKwGBkY0xU7RKSOKcSMIDeBDMiT5nY3+I5GOPjS+AD97j+qwBCcXp2wfSM3MJo2eji/NbOUv+x6mAG4zGQrGpMbbE2Mfkcix+Rm79LOnTI5jXHHEsaFXTNLTKaaToj2+ZT7zCe+6/vQQw9R0I88n3/++dTs0481owUO1QJjuB+qZzeil7QA7vWDor4fYuqUT19yySU33XSTk6vMqv3Jt2ZbAsnnnnzySamAeVZ6Z9a2avabbET0BZlKuWS6lkBZi4Q1aGo3RV1Qs+P/4sWiDmb4vpZkWv4NXM1kMs2/1TDG8Zmr6hXaGKj6mQyLMgXQBYLCvFPvFvTiJtmYM1KhBZUhkxa0CZk3S8EzCYRdAF2DQG7xVF4isQYanUfpGjKBK2S0HcFs8cAeqQS5vX4sk5a4CCrpMhTE/voty0v0sTIcyAOiIMYBMZxDciiU7pAYhrqzxUBHGY9xRxEpOJCjLGixVaOsCxgYvqJjdhykRzIeiE6BlWAtRsMnSY9/xSTb06PhyZICg2ziBGdq2izAGZwDtPSCOTLIxLmEcMIBaiIY8eJWcMsj8EC9/DjHnlE2BEG8OKsE8BSQ5ZEmsKv5AUOnf6wB+wXsmUBQKhOeiXSXVpqoZx+frdPdYkJaBMzLUAayhqHHB0RkEHxQ2ppRg9j8hkAla5CEBWxVQKQUD/LnffR1OQFjDQS6ICF382xOC/GJUiF2G2zpU33KQCzbPv7443rHxyYjGfA544wzaERBn+LHGRRJWNsjBmc0vWgoIWZMDW2FCK2ENLeKGdO1rJ0rKc7OLmKojDAEwNAoUKAsYfBUYFs1xpSy7lyl6dYKBEhfUYGO7T7CpsWgeEZ3dcSe7SxR4pVDq0bbaqWQesPH+OJKEZXAaGn2qxyNhKi5xeAymoyOhNZ+KTJRWtrxqbnXcDYQLBZG/UTKo6oUYBkLEVU4Gel8YW43nIdzqviO5KZ632jzY1ea+38fl1122TbH11GZbux3tEDHAuN3gDsGGW9nW0BSIqN95zvf6QXU733ve34pWs4tT03qWRlAzbbyaWuJtV9DCb1W3pUy86op4tm9bpGCVPJCKbikRyIom7fGWAhzgrqQIGUErXZEWV6AA6XFEjLJOt9JYqZhy1JWmk4XbSdqobkkVZoI80BNnJuzvmouBZReS+VduhMGUij2nMitWlUBmVauifTqpekQgtxFL+JKKEJrgXZhAgNY40l1IqX/n265mB0Qa14dLVpgQ7hF1x3BgtDED5GYOgl0jEwqlmRPWKW1J2uAMQYLwey5eEoX6FclIT3VF/ATfAJduyUt/Ma2BKCdTzV6ZNu00pCpsQqGhJw1hN9EtYb4C3K3cNTTTz9NMCiIIkKdZSS1iXld69RTnsUB7qURnogxIS01tWJzlLg5ZNYdCKEXdg4NcIhnTixJTk385WFGVhRBTFnxE8TFMpogk9XZF8AzrChI7KjvEZVdsbatHIJhSzaSE8mFIf4gYgyFp5hHQGw17GkfgXcQeJM8MWxfjw1tfnGTyYpShCEbCwiqPhDSigwUx82Xt3WHzI8AqdSEwOld1zC2vnjKAbgLfiAeEC6EoGhgm7/w0QWpcGBGriSqT98XPf3004XQi1/8Ypztbji30ZcBpYmAoTKBBXM2MvBnSb+ZrIleqGxUsmRFO+PEXwqE1AXPMqwCz3KHMvkJHAI027looSO7MxxNYNoJrcTYdgTo9EL9DISyg/BgHCOL/cUehyprRdSicUsRlKzqLVMxwF/wSdm/08se3QqwvFwg8r03K9oNkD2Sf0BUc6lv15uUuMmEYP9IBLY+HWi75UekMjxNGoLK6BCKZkuruSm9dUeEz5gioXnsxhtvvPLKKxXyP37NMy39lrUYuxstcIQWGE+Aj9D4+9217Opd73qXn4n2f5JuvfVWu4lXXHGFH82XMkYxc65cM0mV9Mtb0+ZiyHkv1M7qYm9bJppXoWQ5tYpEhSwtM9WRyUEjDuVmUm6TQEonk5aC2zwmnq5b7fqqMYK1Vua9nJDWZpl62CpjYvGGiJbj1rbCE2wIanK+R0hYwvWPf/xD+iI9VaYaHQEet9oCTnpfIhONiVrjgFt6bOVJOYZKoqw78d9pJbuqVtgSRpYviUmiL70GWv7whz8oSGikOKwHfDpDfslLXkIX+/1OR7WSVZPBJ8ADz4hSQYsSeABdiKGgdyaSp8KHIChswwXaQlaAJbQj4ZO7q7HXA3SBTC7iZdcDf5kudOfTj9Z6f55UxgWp0OBMHkGOuZMTPKlDDAWoTOrP5qW7rAu+oiZ6LoAKIjAx0OODMqCChC6ZGRuqAd48QiDD07VWpC0DKsQ1qWnzOVozHeF1oRy99Kt3ZTIze5C2W/sj7I8VAr0TNZsLTC2KhGv1UhpBPjylXwTGiP90hZsjd2e2KomKJz56h3ITgcqcwrbGYKCd73tTh/sgZKe45LTXoLnTYJQ4cAo36YhUXjfAkAUwFHskJG3sz4mmX+CEBQjmEF5NbDLzk+KZozBkcJJrkmE1s+0aCWJhcQLhE4mDyLB9MYY1SjiJgUjLocp8ZKS3DbnAwa+NGyPInm9nEmgpd7xcYR85qS/AbHnTtzMMd1yRvngd1Qwr85WB4FKuYd5vuCM1JCQnac0Y5gfThVFDNlFntjR8Sk7JmH9s+fWvf91M6/U9yZsFop6OhdECJ6EFRgB8Ejp9nSpL2t7//ve//e1v/9rXvmZ6/c53vuNk+Nprr5W2OlYyHVsg5WRS4XX2ui1eWV1gFQuMjFm31hjZsMzS0pK1c9oaWSurAmtoO0y/LZ3+2Q/XyGDkbUliZNheIbaOBsOgoxqZ8/nPZtMVYaIcm7fEKYOmjCBT92oZGl30aVasATxAO5AASgQk5NAQAuGhBZz1Thdlny6IcYnu5Eb4gHzldDnEzDRCd8N9SSjBJzSgGmJagE+MCflQCrKVyhhoYABoxFkyHueBHkFQkmxYRaULCpL9IANEpTvUj3jMrgaxRypxy8GaJgwCLfuVXdhMF0nWEbOhYQueoYe4YD9QCiYR/AQjJwwsYFibKZhFc8TGu1uUYCq2hGEuAYYhvOcpDEAkehGVLnokDLhFa6zUMAJ9wTw0iAlMTZdH4HfcJ3gAdR1hFdtSDStt+6ZmH/iEqHrBkKjI7PtgCFQru9jBFgAOOnVLTR5xnkwRilMNsbYmAfxZwKhhwBNNj3+tg9GUaYqYjjSieAh4yiMcSAgF8bLTM71AyGzCCL5C6ZYuODMRYnsW5haUjMyM5GcNHEyh1NfcCRV1cpaoCSFB9wQhZ9lwxIRG1NGWVMrmq0xfHk27EPCgp/rF9ggPvnjHvgm97ND5nCbwEdaTKiuaoco1JjQejK9JlSBUw5L8aBwZQZqoOUKZl+u6BpThI7RMC+LEQBB+AVrLsd2FVlHNpzXdfqIxYuiZW/y23z46S3SZQl1sSylTuuA0CXAWde6++27QV4j6wpr/7lvT1y44YpRhtMBRWWAEwEdl+YPqV/r1oQ99yHs19957r1/SB4bPOuusCy64wHlvANU+rv3lIcInubGuSDFdf/3rXyWdQJTkXuVE7WTGsmFM5NwQiHMVZQuSnL4470Kh8jaQKUk5sFpnR8SmLznl3DCPlFoZAGgP2aIFI1hoYYC+Uh4lW4INmEuPmzACa3OHZFTG77yFFnxEGIs9aWVvojTnWn0J56yBu7h1od0c+QfAPMxfeABgoZGBAS1MRAW4y38WcZRNcr/nDEEZTc6UEMhEAUJ6aSjAnHXLsyE9PgJjGMGjuMPv08hZtZLnSdYl5QHwuoA0Epxq0CPAmdFkt8SGD2W98T4P4gwZMqOOiKpfTtRF4KjgCZYmqno7KXwtTjAMVAb2OEINKChLU687/RJAUBFGF5oIM+hLtOCTBA4xAt3lSFaIonHWSk0uZjqqCSoM7a34VM7LzAq+FqsthumO+qwhBgjjHJv6aOjiKUmEDdnoC29HNpU64nSIlBjEs7HiWJ4WxCOwtjh42VvqbE5gLuHB2jizDJN6qvJ3v/sdnoAuLRiQ43hWR8ou9c5sGY3lBbBcnP09hauZRddcxraEpK+2TE0SwUBgzM2xJNEpmT31qVPDVtsgc0Jm9EVZt/0Lh3qayCmaqo++Vb+Jgr4YSsBTB/rd2TSdiSKb0GIW5TIOFcSnsGRz7jBsBWo93YTRNsczrhfSRpadF3rZBsrcwgKb63cLnKOaKcVwM1gMJcuHUcxTBs4WBNh0F5YPJxD2177yla/4dT3ReP3111999dU8uOmuR/6jBfbFAuN3gPfFU3sgpxzOe87vfve7JY5+JetnP/uZZdLbfdLKSL+neUCZnvySVOmvnECi6YzRCgoASEaLpgpS2AAS2ap8SPINCbAMDFw0O1WgCO0IKcOGEOSgEjsnGNJo+TdRaa3ggnJ5dglvSuKljDLCdSUZyWNixuQxVMgtp5BQj5AJ1ISSgkJxCbGrl2C5OrLT0TA3Tw2KmbZCgCfwA8+IE+hIDUVEDvuzNpvrGuhSltbIRBUAVFgOSkTGpLyjO1qzrUovQoNG2EKDtGYBSR622vIsWKgS0BKNvgOGv65hPF1LcPkdGWJk2Mp9QTitHEgKBjAYAQH0xbYkBAuhRF1IIt0yi4wZfwSEpBGn43P22WcjcILqjBcxlCj7BJVtKDgiA/OcbRpTjo6VCaA78rggIngVVNYR5kzhEcOC4ii9w4+/ixZM4cpJDmsYgAKAQRC7ZQHWILmxKSOku0cKngaUsjA7MJoaZfQB9jolMw5kpg5i+FYlBUmVEc0UaIx0psA/EMhT1gPq9KK5g00zhke0o5enZMaQryMGhh6R3yRDDFrjiRvtBKEyj+td2TsCZGBPIkWdqECYZ599lhHwwd9lplLmDgKThIMYrcylEh+y4Ul3t5yra4VcKKXRJBQAz9f9/79atTU1TDr1Lc1wmWepzOzQ73KZ+uoyDEvYecp9LMzyVc8XflyQU3iKIib8pa1RPI+kwJIuC4HdGUErMm30GM7rVUcXYhVPttqmmiLNrGKaMsUZa772b6dpvaptU53qiz1Ttho6h/DvKk2V11133Q9/+ENnEpxYlGNhtMBoge6SNlpktMAqFrCEyJ9uuOEGr9nYenQa7DcG3/rWtzofdnyR2XmvlxnCw1G2V2Xt8lGJjgRRvlgLT2mHzIuOjCkBlf1LHZQl1quYd6NtpSAyb/BAyi4zgHOk5kCUTI52kmAAyRUZSs2FRJJqO1teqMlMYpBMllZksJ+yk1KYMJUKQKPDN73LdVQuKrw0F3LQEEIAqIA3ZU6HkRRWuSpsoD62JbyIkjQTFVCBW0SOen1BhsCYdA1AJT95XPabOEi9QUcMoSgXF2lcqQaEIHCU5Vk5n17ogq045FxQDepmH8kf+8BInnqkII+H6GAeBdYDt2BF0uKGz5///GdtZY1gm4J6oI4AhNdcp4RnKFgXHsOEMDgTXkc+teI1OtIXpeZe2CO8fsFCYJj8LtyAcGwJbCjZSpOLI84uEj74ewos0Y46yBiqde7LX/5yBKnJ6atU3kUkVo06uksvKJnUJ5CsiQwSfva7U3g6p/XPQmTqzGU4ZBSjAQn4heQ08pVIY4fuBLP7AJrylEHEgCxsNmB8PTq6p6ZWTIqbE0Ja0NpmhN8TYlttKU48UplkmNcj/F2M5lYroIsKeGoowdWvskN7OwJGrh51rVPdYeWTqTP/CF3RQse6eES/bonhK8diBn/MXeSklEd0cRKuC5VY+RQMCX7MixWPU59x0DBO+yguKMp+IcR6ZF6isnmfZs4aNiQAaeekX4WMrRjfOwgMznRmTmZkB4vdKmyPsG0cEX/ZWjL2GdP30oVlDZP1imcLTBgz43rZ9rlVQNLOmDXzmDe8xiXqZsZnn9uu1ZR2VkO/S3rfffeZMeRdTn3tXh2Agrtm8FGeA7DACIAPwIk7p4LZtmAwAGw69t1gP7X/4Q9/WEoXcfd6Ria8VRMAkBrKtKw9sl4QkXbyTgoiSAbpkUxaai5f3AuVZTkyaUARvA/Woo6Eu4Js17SQcCdd5guoKYmvBD0CU8eXOWVX8AaoIO8BWqCyhbSAQkE1rtQqDsV8dfSLCawCM0A7DuUk0BJBA4fx4Q3IJF0AbHqXatNU70AapaRuEB1ERFOAASb0KPgEViSntFWISsQjM4AKgmKVV2Sx0h22TOGdXqBImRiSJ3nhG97wBkYDlgA/3enaK9aSe92RWVQgw1YZAneqwD4wgLxfW0wIoy/yC/vgW2qqcd5LKj3KpzH3yK16fE7gu+P/OgiEU4M5JtxKMK1CA/vho7mO1CCIDHApvwPARGUBAzD12LYX5mzF2iopIk50irIl1mPGLxqhQk2qqWQ3xlSjL5CS/MYIcGtoM6NKb6rjzHc+xSE5AVHaMY4aTIinObGFok/G8ZQKXMlfLkkqkViMAMGoxPPCtqEXYmYXwLyMhvtYXnNhoCEv+8wjkcNf/pMTRzAUYpdeYgodQZg5gs5nxou2eCozkTIJPaWIrhP8/IWJi/2xIturXvUqBU5hB7q4eEGQQEp+KQ2lhlyMoUfouQZBxOh/Mg5bIbAf0XqkTzmzhkgUcc2kXIWAldrm5Odxg4sZqcAvK2rRMt9OuTQScvylUyEneGxZirT1ypC+OiaaWLmWfsPZGOQjM5tIE9gve9nLDgz62rC77bbb/GMOet18883exavZbC1mHJmMFjgwC4wA+MAculvqmIh988RE/O1vf/v222/3S9EXXnihXUkpwuZWu62ZwPot5dUdXWRduaQ+kmNpsfos8DK/KNsRTGVydwSdVKBDuf3bpHHpV/bvpT4pUb78SezdkZYkUKKLVNAaSxZoL6OhAQmkO16ghV5gFf8bZiEVhDEEiCEYwGVQRJgvxKTkaQtyMmgnaQrI4fzWlYBh8CTxbqEmfcne7LM4k4GTBZjdFtBC1DnudkgItHj3FQKBpSveTj31VG3dQsskl/nJaOEirXTkrMwjtwTALUDL4SQagqHXKd2ljD4dbfkn3jAYSKM7OArIgSTZ1tmmN1dhMF2rB//AaWEjdVaJm35hAyCBGQ0ZlT//+c8xJ5uu6cWkNPJIvxpS2a3zXifDACRKaOqPf/wj9QE/wsjREWNL+MrOdao5hlFfq9bUyrpzYSieie3db3AOMbaUQn8c4Z3AisVBvwxLcu4I9taFSs2hNWbUOw8a8sIPHwZBRn5NwD9WJaeoA5WJqhUhk38LVPRC16uJ4ooreZbpEIsEzfEhjPnEJ8OSX3OPtPKJM4HJCZ+XpvgQz6EW6xHPRZLWCOrb25TJqRVi0zI1jX319gtsgdkC0AUszeY6xQ3Ypn71qBV6NSQUMHzHkpprBT+zc3WnCR1BeuqjdLqI0qVHESK6iC2S07ZaVaF6rJq2EH9NrBlu2DZZtMxZtTPo7X1lDqIgg2yu00WFnJM+BuRHGyhwlKERp9sunJPDomQGgpGlla6Fn5BQNgWt13TRS1/0Mk7dmnyMVnFrYC4q867RRztSOUWXX/nemdng05/+tB8iNWB3TdpRntECu2aBf1kdd024UZ7DsIA83i9Fm5QBYKfBFlcHFE6DHRREwfWueds3GvnlhZYcKaNV1gVdyP6Tm+a124lSWZW1GiCY2Gr7lVI6CS5AAqgAPwEJe+Q1osqzndjDgRCLW6kDQCINYsyZivQJ+jV9p+CvF130iStxgaDAnhAAG8RTBgPARagAPJBVSw1deZvXUIIiAE5PUUrBBRsopZ6PeEd3Yg+Ntj4D8FB6lRd/UIf6ElyBCkehJCSoIwhx0Ba0tkHgq6pCFwef2HK3z9e97nVQmbdkieQIVI2Y1xwoAnVQaq4SQg6IopqzQe8tAzwEg+uk1xpqIl2jKWKCqQyMBB2V1SS6wC18cpJGWZXkgYe9QixL1in5EegO6KICtmocF0umDShIj5xEQolneYflcaaIp5qoV4C+jNbgWyZiDd2xuYb0YhMvRdtccLZJZq7xNgHdyQO+0k4XfpTbp1ZsyynkQWx06wjepjX36ZqO3A1a2JgwjngkENEjYqAkm1Z4osScB7OhQDWcYXtSqSx1SOhyG72AcEqJB7IVzcwC5rZCwkTZpWy3C06IVdmHceyDUJBs6TFsOcjFg8AMN3ENp4hPNNT0BjWfkjmfJTxlOY7MJTxubBsZfOGc/PjUhZX4RK8LPm0F0CS3thhEglvM42JlviOwwiYuPhIt5CEY6MvXBhTJO+Jtous18iQ8bhxt98RWFOvx9Rr5T2Ml5LgstjJm3SqLpWn0S9RTTReUMmY1N5YNcz7aLwdNVDxe82lRdur7i1/8wkQhufJ1MyNlYpOxcrTAaIGOBUYA3DHIeLspC0hE/Ou5yy+//MEHH/QPk5zCOZRzGuy/Dsgh9LrXyxLhrayWWCuuNNTbmNJot1moYtMomJqOshMrN+WJBfk6NOMpSa1MAnSRiEuFcxq8IKfNkssAOlbt9Odp9hpYG0pxaBlF3A43LD58Kreu2+GCSGC0HB2HUltIAASVqXtn2J6CBJokwICCLP/SSy9FCUuACkCFsqER7KchhOD4wv8BdswLCcA5Llk4RER+n69+9as7IlEtyBMlcCI3YgG4AisgTaarDKPiRh44Fp+gYikjRCed8gmswmZ0z6utUKhW2oJwNMJHp4LEIZjuBAYOAYoAHmAAi1IQvdwauoYTvHRtNjA6oEGmgB5tisGuBo56MI+yuJEBQ5JDiTo688wziSf8GIcNXUGbKvFEyZ64eargq9GOsnUqKmITBARgbRdpARXnQmqk/h6FRpnMNiCYAh5QSVPCMDIBfBKPm2jx6KOPUoox+QIfiTWvEYnw/I4tGZjCUyKBlI43PeJlXUNlBMMNmNcjC+ertixpO8MnPODkjQ0DS4BtZWqqJ5KTKwK71ETsfLKGfpkLQzUIUt8ha5ukTE4XeqOb78RJ21BzuuNJZqHYb65GeLCqQsA5OcWt8MiJbr8JVqZ9nmJtYmuFWNcoWVuPrIcJ9dOW2ZPWP/HEE+TkOwapMBZIyMQAO5MTgYZCOm1jh5lGCPGcn3i6WMMgNXx4TQAYIJqvt6M55VmRjPxcz4kMKyb5IiG0Itvh5kZBxqYXc7JjhT6ZwHDD4af80hJYiE0jZiFvTIiTffROq45yFBThTz31lDzKp7nlW9/61kUXXWRQdIjH29ECowUGLDAC4AHjjI/WbwFLrC8Dm6x/9atf3XrrrX4YVqrn/wZ7NTrJ014vUYS3ysr47WfLhpnPciVdtsC3e9uSxeSyMkupnkwdpcSacdZv8XVwlA/JZeUrjqckrJLUqFa8j9xrBIjBS6ThAhfIv7mGUnK+OWMvqXaHc5tyKfOpK8LAQk5T5eXQEXCSeqk/IOSRGFCja9BIQgPnSNSkgOhhQtmMeHDJs2NeB4m6hk/AFegdeIAZJHatPCVMeQSwdF1xxRV6AbqgNZ06n+TEABWACpawFyAUEYC79StQeicPSaA4h8ZElbYSNRgYJgFZRa9oVwlp09GnGgAykrvVi9j2a0+eQjJ+fJU1AvZIjjnwwB2ycAR4OtPAjfy6w5+aujamtJWjAzaaMyBi0aghCYlNZqxsEOBMHUYrU+glZb/dxW5OViMJLdQbmy4KhmcoEehUR2IDstILswsYbJXV65GaLIm5+qeffhr+AW4pS3gygL4EtmUAkZKQeMCSeuhCFNluQM8jRMWKpuIBZ8k6WymwhhDSFn8M7aQo5PKUccgZxBhHkyFda8Wzzt4Rk0eoPN9u6t8KlaJIjS6EIlMkApXVK/Nsp0mALmsAMxSkV8xYDDsFTBhH2NCL7qK9wAnmZ5xxhuYcTSmXfrkVB5U0YnwiURaBGn1lVApynjJTwTwqmY619YJGE/WuVuy23BFv4BY3T2nqFW7CU4R31BA7EH2g7U49iiIRiTpMaiZx5i8gtyAn43OHjiKGASIMVuw3rHwKAJOG9314R2RaecXPcu5eUaT1No+CnPXII4847LV42Tf02vO5554bY663u5HbaIGDt8AIgA/exbuooPnaG5XnnHOO7/X57srb3va2j3/8416TVkgasdfLFeErY4M35LJyX2m3DEM+arGXx8t04xjrWc6vpPiyqCP0VtbXCDDR/sSDweC0yCln8i6ot1spq+3EJkeozkDXROUIwA86dY4H/Pj2I+AxTYvWMmEr65V/S9qw4l+XJNslWQc/0Dun5VYXPCkPE9XSdMmlsy9Pxb+TYQk6DCBCoCyWhC1jQ0CuTuEmasHgfvoYltMWeOvQwLH46KhT7xZkUq9TIskLiQ0Jg0zok0LBG8pC1C0xYhCWQc9KtIYScYBDyAAish5RYTmI10GEwMbQua5oBw90R2X8EVNfv/Awu6mH97RVyQvQoy4UcNa1jowFuThLMqBbopIqQIglCeaW8LrQShMYEuYxlAAeXtCFVpqTJ7tLdhY4ywUXcT3krC8XMl3gBs+Qh0a+w0kXOTRARWuSu5iUdwjPAvolD98ZwhqKf7oY2sTQO/uwgxNdbBnTQbSdBeIxkYa+wBz46kybeDrlFMyZi3F8ugjJGuyfqCMDGzI4Sm0JQGVllXmbWpO4idHYk5X0qzI0Pue/Eh5Fr6/sEbC5LpTpq/fA3SJTUN/eKrNGp6Z/S2Vk+Y9HxSEFnx4xBR9VQzUy/piFbLxMWXYIgThkczXclAiPoTy1paImIS0aXSwsDLASM9i6qpcUSp62Pl3zO5wvtASGUczU/JLh0xLvbDlaMIhoF5+sIZhFjug9EpmN3BUXvgoJ66wtM5sjHGpUZv6pCDkS7dbSaRQU3n7g6u677zbw7aJ6G8KbWeaHtXQxMhktcBJa4PhG8kmo9qjy7lhAKuP3k/3DpP/6r/+SJl555ZXvfe97pZiRcGIisjvCz5TE+JIqSbil1zI2qaTM3qljxp0URP6UVEwKdbTKcgS0Jh0h5DySeEc0B03kl/RrxRrzNJxptK0RUJlreEHBq4wSwepaJi23kIKnBoaRUXElfCURAf7hDed79D1+wHoCLEnjZJM+mTFnd9JrnEEd3CQroA5khaHmGqIEwHw6AUMmaVPpKhkGCokrOWvRp6CeRlhxh7fjsJWdByerBMxcAJIRB6nK4KsLDXEAHn7961/LruC9PCr+btE4usSZRnRhLictsIRIoJdIFuEq2ROxL46ymKci/4ILLgBdgHzpqYs8ZKA7cAjaKTCXIU8dzNETWwEfxCxPBpaEcKgAL+kCDS/o16jxyH9s8lKJfJdGcCbmst4COfwoRAn8zDPPKDuMdQoNWXmnmoRkdnEc3b13rVWcrl+caQo82/dJR8h0qjvIAXxlW9xEBSDk1qcZLDGgO22RkVPCCid7dULMCBXGkal76lbB14M9VZlc1res7TE5R1XD4C7quxQAfpGD2C2Zbd/Eibogra0QHDBkT15Gg8Oil17SRHNewFaNmAfss9XCsIDlROZEYkNQigUmEoRzdaFAco4YIF5Cfj5lB3KKFp96EUg8RQuX2CCnlw44ziO7SHS0pUJm4eciv0Ca1q/o4n2cecFMHpdNI961+lheuDJINobEiRDaNTkXlUegUsdcwcs8IlC5xgSyxrhaVKS10MdfWNlz+epXv/r1r39dbPuJfv/X13tzgnAtvYxMRguctBYYT4BPWtfviuLmcedv3/3ud2+55ZZ77733y1/+8l133XXJJZc4EJYfZw3Y35WM5FZiCkqY5MFSWEt10FEcgMCavQsKkkHqLGUk8DzBASPBPM6F4D1pt5TdOZg8cp6226epZCKmrrgiM+gFNPoEU6VQoIu83JfiUKNxw5sAAEAASURBVPJafgeVggCMGuEqXbZN4IoWvGlHg90qG5Zne+QgAh6QZ+cLpd6JlXrKsD2KDD4xgQAVFj1B0oSbogUX6Dq4qAyLIOguwsCcHqnxmXhD4HKLiaek5UFAkb4DmISaLlrk+I4pIEYMUwM5+BfZ0AVbsSHmbvUiVNwClvoSLcQwtBkNwmQTBQgTDlFmEBk5eQwTEBrApqZ6zXmHrThIK70bTXCsrknLZY899hj0CyT7gT17CmyOobaYk82RHYdyAeHJxi94uuV97gNpRDJKFtAwZoFC9aUjXRAbpdGhCzLoDnKAo3CGeJkOJsSZhPiAWMCtp05Ky60MBQwDzwkbNETSI3qBV2Q60guTMp3ruIdO+Ci3nirgrCDpV/bJGhmDtKZ+KJf+JDmMRwDqCHXgH+RmKz1yLlMMcPaURgMEHuEDqFBfAFCNg4bpF32KJwTbtlJjey5d+2Q9GhV44A42FF1CTsAg4HeaKtjdE6iii5d9uviaQQSzEFUunq2PVO7sRXHGF4SMYPPF1NSx1c5K3hcsYyH17G+64ER+Mej4a1880tcrNaWd/bJ77rnnBz/4gYj1+ynXXHON+KzondZ8rB8tMFpgHguMAHgeK400G7eAFcvM7vcMb7zxRv/D/Utf+hJI7F9NgsF+wDbd7+CqZqGSMspBhw89SG5V9qarnWnEbjV0gONcJe/puT1C7bLc5jOmTnlYJMuw/Mnv2cqYpeBwhbay/OIz3HzjIdV0QCTpEdwif414sIpbl+ReFu7sDrkjSvBMAY1suF6hVAMaQRecCMnQK1d66GN+ZB7hrBfpNUNBOMo+8cEhh7TSNXzCZOlP4QfOUUQGL6nFkKY+9ettXkgGSoQV3/zmN+udSC6pPJSLpjwlMwb8IgM8RsJp8oB/jig1xA0Z18OoYI96WjMvFIQ/ecQG/EB3j+rXZQ0BoSIFhyRdoCMxUBoIDA4hO70BLRSI5CsSMDACUESlL01gSC89AntQiobEBtXAGKhb17ojFdmU2cFTPmUEBDhrK/UnLZzvUIXjyIY/hiCZp2qIgTgeVGnAosHNp00QxHTkOJ/wA1Z60am5izp4cgdETTYmcsxOU5bUqTId8feiASZsiJ41oCkClyMUcGbAqkFcviC2Mp7spuCRDTWmdizPoYHN2GpeTRYtMAItBBKrpq2A0csqPEsGSvE+DMa8NhQ4MY9KWbetvtVwLYVwFhuuMFTDiemd4kKON0mlPpUCwCZIiefVA98sZWoGydYMs9jCqPFSDdPXWsRekUkJTyTzgIvAdgTMZmpcK/LffvNoZDq1gcVfGVA2pAxS42sfNWptGO18+qXAL37xi/b1TAg33XTTO97xDqPSVNMSj+XRAqMFVrHACIBXsd7Yds0WML+b5f3r4Kuvvvqhhx6yAPgnmTCVjU9nwvKMHVze5IvyUUdYM21BeCt0yMBgqbb1GwzGwb51jhSOREHJnOxc19ZdW+kuZSlFZYoDqqGUS1mkQcfk/ZhIs2T2SSUH2i73KClC2k40V0uATGoriXewJpcFY0ARCrJ/OCCGHHImD2CAWLJbcQipwjkSXMaBCeOdhQSG4nTqoE+/GgJj+lLma9lbINZCDPvE1NeLPQhvcrI5yeHAqO/gV4ELEGioANIg0C/AL1n01IvEyeCZCA3w5pM3Pe30FZ4q2QFOgF4gQ1aFCdEzIFTG+wlvxDjoSKrNAmAqewKBtkgQG+BqgHYiYShyMCTGK1/5SjGjlUMPYJ7Y3sRGCQMb+Ahkuso46x1nKgs5BMoU8ZKFJmo4FwgkCSOTSqfcx++6I5ihigNT8K+2OYH06/S8Y7/DUfDFF19MQge/fmOGkNiKCvxjIgKT0A96Ucd7pPjjrJIjkHEuyMSMMB6whGFgFbEdeBLAhRWbcJbe8z683RBjjSXFHj7U0XtihjfjvnRBbCrA58HMmivT12cOVJF55FJY6Cr/Vqu2hkgmYU5M5cRBVw2nFZgih/C4eY+dKf4fe/cebF9R3Qn898MwSeaPZDKZTDJ/ZMIvialKVaamKslYSYwUxEcEfIAYJAIqIBGCoFEIEl5RSwGND3wgCoLIQxFBJDw0gFGDaFnGpGr+mUflnamkJqmMVZlkkij+5nPvF5rt3ufus885+5x77r29f1X716d39+rVq1d3r+9avfctdAhN3+nPkAVnK/pz54eNaKzBDR2Zhl6vTRlsG27sGdM85ffJ2OUntrlX4vvjjyAl6kpcTZZKZ5uZy07jhDbqgqUMP1YejPHaSG8LPwv2N+pnapg+1J5GUXUdpEuUakHi2149vTMXfNdKAIBXiwZee+21J5xwQjEbtp3JykCVwG6SQAXAu2k0d09fbGwnn3wy0Pvwww+DwWeeeebFF1/s9WCJBDq2ff/OdtWUeHIGMsZmEtlmYdvLQSxmMUtabKFYYE3Ky05rlDGBf5a3dII/AzsS3piP0Is0Ikx5Fr+gH6uLycWCn4nU1M4CNhwi4BBlSIvNKmkLxmBSQzXs0ZikjCToS0mSZwVCPuXyU76KibAxQQwKUSDiXDFVZPtGJs2GpqbhH0gJG0jxL9BbdidLOrANyppKYUgBbOPQN7eAN60wB2FUfST8VM8QaLpJLbrKjgcIw4kcWB01iJSJ3CycNH8NUK0v+IceAQB1yYrpibh2VZTjqfKkLU0CHA0UAJDDEiVX3SjAe4aGIUsa9A0R4jUXIGF9UZ1jCAZWnm/IcAMeiPhwFLIRo8JqGUEM40pbHsHkeq0kalAlE1mIlTTotoYYkXhQWEgcXNFHXfYnZ93hMXVBZVW8j60v2ENfRcOHOLY5qrSl14SMQyCcz4sAqT3JSCgvXxMKY08341VRnhCwjVsFsKd3JKOAHD/NHfnu6mrCVdLyW5entFGmHmEGZfwLY1o9PGoVnuknvwPhqIKOyYtsHHOakAPqe2SmZI7MRFlh3ArjW+j4JkjYADW5JXASA+NJYFbKSyqv+6RhRhhZY0SRjGYmkUcO9htEDqAsLzQ5g4gZvg/d9JNimxou88vU8EjFwm2z+yVzxIS2DByVtpQRL4FTeJPLzB2xlRWTshTQImqvd+a1lc3KUCS/YmbGaq5ohe3mAx/4gBd9qZyzb75x5QjP+syIsfpb6VQJrI8E1mW/WR+JVE7WRwL2NnsAFGEX926wD2U5I+0PJp199tm+qRM+l21JbCUN9i6kZ/diJ7EweNmVZMO5tqrSzMe2vY1RwhZnQLPX2dksLQQZi57G2FpN71jPLk2z8plr0ou0y9aHcBiCQmROqTFTAkua3Z8jXQwFCQIP3pBmFYErpOdO+JpD3HBAYnpBhUAphjv8Q7D57i5YC35M7KN8L5Gieffdd7NukXXoEbiag2H0RflAHRcFxhtm4iYw0MZ3IgPzNYRDKM7XbvWaQrILC3GGOwYCYCBMojMuWqF4+GGaA1RMeQa9cZcfnNZlI6OpJAoOsjJDSQkc0i+N+hkVksk2VZ3Mk4AAqYRW5OAw3gSZfmoUIsKtRpFisnOdGCa2oIbAMLYghcQqgnJIT3dUNKZQh5OcKCusUWPqG8uKBX5QAPoAVgGxQSA4VJHXiU5aVaA7BP0ZNgOROYu4ruEHMwADkYKyRJfgJDyjFRJT3sRXAFrGv7qgowFVEucUUtdU1BctZpXAEvROAw0KZrSLc2nME1FGStqlOhOfiEStu0PQzdE0fkDxMtzdMgNzcAIgERfZ6guyeqHLWHLRHHTmmwgqGiPuDP16ylOeQlaFW5Q9zT18SpenydmWu6F0BlWvDYpAXMYunGCPHFxRmybzCihvYaecLnpCAym2WaYYOEqXyJao3ePrUWX0/lJj892egn+tQO/zuS22RfLNRiNbd2pp6tEcCzItyosJ5Da66JqtLztdNMe2y7zxEVDd8afXHX+zmpmAy2ag0q8S2OMSqAB4jyvADui+nUC8xZexfCXLnwGwVfgmhA/w2id8XdamqA+r3whZSCzdIr6kmcWxikp+f2JjA9+MuQEPbOW4e4El5gvQyNZfZe/KftzPc/9T3dGLAwcOgBNACBgMLDH7jFfoK9BPofs0Fd1RIxmCgvGALu+KM6w1BNikFjspAJiFJNYh/MJsYnd6yihU0QChI7PbSnI2xmPzpLoDjQAwBMvS9QieSS1Pt6o7MZ80NIrIJuH90rNSmEi2lYmmhqALNjeo5mdT2pgPgJFJb40LkEN0zHqPVKG9NJCVvBVvKhImhUQngJB4NcQeRYp97xE0SER6yq0jdMZORdB0gOjCDKAL/pGqv2NJsJQENWgQGABKHeKl7TwUCqMTMGkWaAh7yIJ5YCEOjbIqKGMeM2qpKxPYUEymhE7BsYgAuh4xKAEwagNL66mORyCHH364KspQJzgBe6rrF42C/bQFP2Bb7zxioTqjgSU9Qhk1rOINJzqrgM9uEwWCWjGLtagV1emScYeHiQsFjNFG2D4rG8FGPq0x7f+pbsKSaKLQX3jqUz3Fp2J6bUDJmQQMkBw/zSBB5p5ZsxX90i+w0JGNgMOmjvGqEIUBQsHsjsCt9lsRXGp+uDWyeuqiM1TLUDYZ7jLQekqMLqRcFhzajlRq0VWa6fJIDpVA3CBKZ/Fvkmqmu41OzAlZFQEqraBvXpg+VoY5qE1sYmWZ6YvJaLGifgQVDeS6WtISurKuaaj07q677vLhT38M0jAxb0477TSLSYW+qxyL2tZelkAFwHt59HdS3+0KgjO+iSX8+6lPfcpLMs5Ii9f5OMQZZ5whkc6sbKdnQzsIZydjarBBbc+LNK2ufV0XEJRmhEECImDQHTM0AG8R+kNGGn2mJ/N9SOH+MkixukAF/IOsNvVs+TAAqMAi7O9LCisjwTgWSxGMImTfRXNmlaBY/MLmn/3sZ1lI3hgnfIhIZoIqKlKVwqHzt9KaBop0sL/pUgvPYAA6yhsOf0EHZoPB5nBJCPj4yBbAk36VJsZN4JPMc7JXQ15cBNsCjcjcpTkBQ3qrDLvcTxY5sEdoyk8Vi4GghwqbiQqDkWSOLMikIUaqu6fkrGnRJzarMXKRXkxwtVDQIhCoJKwI9+LQ5MUPxSNbhiDG6IxBR1COYgHA0ki50x86IKEtXUBclF4ttjJUb8hoBdPfeMGoBh3u4n/xFDOwrvIC0TgkChFgFculfEnDri45GJbAT3BytBpNrSPI1aJKEDWdIVv0PSIuYV69A4yFtfVFZ+WHPQIHk3QkKgH/eGqyoONOOIWNiYnUIgQlDcTEMgtmtvTBz1bOVPqYNKz6BeOpSx+6VQw6dTKsYHDSo6w/3Yb6cyJPPJggRvmII44wiMH/s/Y6DW0Ia9N7UoZSE1wAROFggstq5p7CHpkyXDCJDNMiF83JRAtvqPV0QRkypI3UUpoTEAX860V/xR6a2/Uo/TXvLCnmrEHJlMdPRLpdjC3ebrqGjnXggx/8oPd7+cj8FbQ77rjDQqSbizdRKVQJVAkMl0AFwMNlVUuuhQRs6j4P65yq0OI111wjMnzFFVcce+yxZ511Vs5A4nIFu36zCRubn82cuSWFCJAGBjg2yQIQR3J3opg1U7bPbkM9j5qciLYxqthkXQoRWvBSs8oiaa3Y1HUHERyCEHmNk32mg9nvw0nhPyXxyZ5jw/npWyBMw7DB4yCYxky8/fbbwdrnPve5uoP+uGxrC1fNQ4PwD6wClUFTkFVM+YkyDJ+tu5KuJsFWgdF/MrC8QkbITHkATOuaiJClN+HwxmeKBffgMQhTjxKF24oTtRwEdWdkA2nseGJHgfeHOslR0R2+VQZihI0NolCnodFxiVAOD7CfQXQZWQMNHOJBLTwEqOPfyKoiR134FmBgDSsTtYH9FHCHcrUFPygGV/NZIIUlfPpKFjgKTxo+LjOWNESqFk8ELXJiHKlQQ0EVxTSHfwlNw9K6A8LJtNTIwQPGhH81hz7UCn6LYdINog52TfRPsSgwbA+No6mMgcAt/CzUWfBVEUsaUlKxZPbfrQ8EaI6o0l9ypqcEYmZlQFNR2gjO2oqB5rQy3wnTiABmE9kgDfn6ToxxoMza0ESyM2Vi1UWTDStvDmXA8+jQUb9chOkKTMWkdsOqaaJdK6QpmRwyz2s+tI7SUm/VS7+STnUKZmmyU2DbiQYqQc1cpfCOSBRR4NZMN8skyEocPo6wZvd3RI9aTOqgy6J09dVX33nnnRTspJNO8hkLm3vWulb5+rNKoEpg2RKoAHjZEq70lyIBewYo4vtYb3zjG2+55RZImCeVXetDWS9+8YuZhlpdzZbJSh53A8O2MAiUCPQy1oVQYs3YPr12yMSP1d7sHcuJmc6E6pe16jADe6K/2JKewhhCtQw1ATHxN5iB+YsfzQVLMP4YzS4/Gc2idh7pr4q6xrCLqAUHPvnJT5KJiEpTCP1soxC59Reb+BTuYlkaCAEigJzbntXCZh3e+kSyo2fSkNAkHKIWYvriF79ISmQYVkULu0LwiDXWz4wyLmUAOdDR2OUnvCeT5W2MNots/GVXF+BnlOmk/ETRQ581T6UZtaaMp3njV1w9wgySVJI+0wFgWI9SXQF1PUofVfdUNzUNbZovsKjLxDdYGlXeiFMwnKAm30+PnNOGusnB/EoX0hybOxy650VxCCS+GwcUNcoWB00VA+xhacUSwQOwtU4zkQVsiBdLhZSKWtG6fPOutFgK6KYy7nLcc+4awW7JUkV5PDhkjiWvNPeULFWGJwwB0TXLa8XVzJmaxqFB8bExXgBeia3Qb6GjvKv8XGVCuxEmf4e+8+xYdSWWzUNGrYwdTXOUwIzADD130Rk8YC/v29MfC6C54KKHNMRTCmNRclE5CynPBT1cNuej08/Qg/HcizxxZJKpbZExec30IqXRm14BwfSOi+ejH/2ob1zZPgzT5Zdf7u/SGdBxLYcVdKc2USWwmyTw2Ktiu6lLtS97UAK2T98sed/73nfPPfcwf30+GhIWsyKK9d8+Mc+kDoTojl12UL1goj3yyCOwCiuBZQ9mpLBHbCAUWFETOxsKCjtT3QLAE8t3eRgrByf4FLwCg+FY5trTn/50PPD3gxaeMgjY3ywDNlCMoW7TQMIll1zC/eF7IavkH3t4FiZiqGHPh5cLb6tkozTaTeCQ+4CEAQ94jDBZzzL9naQCWUst+dAgwzrGdMnvTzDQBT9FVpumG1KpVeQgB7hlx7PX88GnFFDXQDsQrroyJClICCBxdpS6YIBwnJIwgL5AI+x7T3ELwZoFNN9ZADmecknQJZ01BTQEtVIqBcwpFXkBNKFpcDdhRgMHOnqTEPHSomElNz9BaFzF3wQwm5LqOv2u137y2sAe6FNgdzmao4fpixcviQUmwTnGVMRApISxOHpkoq8AXw88TPIGSL/ka90jd5k5Myxz4qWYL37zI4gQQgilFxMLz5GJfqk1B3HVyceM1nehe4M7lQhxxUlBYlMLF94WSTT7SAPBdU4iYqdgq2FgKvM4xIk73Y7CEJGpIZPm2No8+t3f/V1aTbVsBya4ubwmzE/tnQJlCHRBBJvOmLO+YkCl82gH9WVif9ML+wXce+utt+qmY2vOqXFaTXU4TiRYM6sEqgTGlcAUx/+4jVVqVQJLkoAdxb7iwCdz4YYbbvCCjV2HVQ0G+1AWs0a7a7uhCiU55wwVsP+68ilsM7jFytgKbH13USzBCnZbsSRSt2s9oM+E8hR0ZKBrS5qdMWtgp8vb8BxcudhwAIxXOlly6hq19A7SYNUJJ+ojM86j0utuEwy+K6+8UgEXmqWAnyW9jAT6FAnqYC5Ho7QCyYDrEstufWCPvMFIyClMOEQqnulPHwN1iSkVOhiG4srPgQkdp6UwW/G/qNjqewZFmNeJA9hSATl0TyLIMHFOdCghDAnLuUqYmrqqCMO7VJEGgKOu1MbToEqPWMzmO7QMl+q4p6aGAEsK47BpaGISvnXncylwFBHXZoxzI8jJSHUPZpNmlKOMDt5oJtWF5xMW1jRREKCu4TNdxoAWKbN8fIKyaagwjDgGkOIFIAQFnHQwHaB0rUjLmbgIqJhLQ3qqvMK62ZL846UW/T/8tELBA4kmoGoW+3NHxDWEQ4XJeSD9xYuRocvMTaNcGML4yxPmfAxHbu78gDlnYe7wKNH2skLSKx2hdfoizbljyCSGyHw+rsatRZNtRu56YTaZWRmRncL/RGnoi3zrng9cMUK+8IUvWE98sNMHrlp+w4nVa2aVQJXAyiRQAfDKRF0bWroE7P32mEsvvfR1r3vd/fff71z06aef/prXvMahaN/KYujYWddnc81OSSjdxEQmGceOyXH2sxgY+sxxBr2fKIC17nlTkSUXSFbEnQCLn0wlxnoA0pK+nZNGmz1i6Adp54vQ6RqEwCyAypTEFTyPSbyxgWLeFea7iY0hbGBd2EkTAiDdksvI0TQ4FMrcCuwbdqfYUeBlk7FltD6VJj8CMTK/RPtpCzk7rQ2vsozLoCzIJPWjik0A3OJKcwCeTGNqcKmlu1qAn0xsCA7jATo6cOAAn45H4iQUEvaTDzSy+BVLyItjSKYW0TS7pTWtj0hlRpj1AIBaYEw+vEzTHPyOewV+CHv0ysCpyNRGJJk9dzFhQV2tI5ViGKZpgLHuaM5PCQekpXGiDKyObcQF6PSRNW8yliYi/zRd7pkCiTwr3yNVdFDQIlnpcn+UuDQ6X8LagsOZAHB6pzkqR2jGWl/Szfl4WEatMGkEuYT00WF+q5BuztTTZTA2hCY3EPXm8aHY1kl35xp4TyjPpgfprxFx1oDjSTetAM0jRWsyEEVJ0l8vfpu51k/rgIHYQdB94njpnctyx//utSzj4rUsJ599ssT6sCZDMJHzmlklsDclUAHw3hz3Xd5r+83znvc8H0kCFAWEXf7YgIAwGHz88cfHAt72DclmyZpxB18xI21U8MYm3mp4FEvcjCkPHrAexFsUZvGz7IEfdme3XwiGZvcI9FYNzZevL6nI/IJqmJggIn6e9axnuUM48EOYyeHSwiqQpgsx72AGht3A2JHmoCxk52N4wVoGC7fCZU7JQuCszxKcWZDyfNXJExgTVCF5kSIeH2jEReaGhobATuxj/ogi+YENlZHtJoaQUgam5ZeBjdmFAaV0ADUwyXDTTOFWdjB+5GPYFKYPBjegl4tEGTMF84Y7CE2wV6BVB2PumxHkr4BxSb/QJxCgEZqVA+eIoSWtFVeLeYPYylErOUhxKDhuDT7RZDTxZgLKccIcDxQe/fh6VCmCCie5w/AkIO2p3qlrOgMAcsAbj/CP4WaVVlot3SSxZWC28NzkPOmuTFpcKWZQ4BkqB5gVp0Or2Db+TEcMGS0yQfyEuIza3CyFYKpPlc/craioIRpLVcxoaY426DeuIh4fs0MZwjetLLZxayrGL2O+Uy0nBWRmRiyVz54+FlmZfSayn94MV54TR0eaLyP0EFnbR7rjsgT5627XXXcdlyiBi/eeeuqpVgwyd60t85WxKoG9LIHJ+/Relkjt+26SgJ1JdxiX9913n7868NBDDzEcTzjhBPtTPqG0jZsTA8WbcixpPOAzd8BPUHcgV+kdCr6KzACCMQBgVhEDdCISY6Eyr+HkgfQHakLYSGFGmHcsgYT0iInJGhDlYxanwMSmFXaBEGCwmIYyzqkW83RiFdIL7nUgWUKn0Iea0vGBnC9eDNvAGGAJlmidSUf4ExlevK1+CjhJAbFHwIyt6U3XyBA/nsJOPsECoXnRDhadiUnVAVQ6phbJi9VoS1p//fSUXhWC4SQtAnX8AmFMAeoBBIoQomDgguUcJ0bcCMasVzHv09IiiBdiV1GP8M9W1i9yTqSUcY8CBgwBuxN8daBaYfpQjhPDAKa/AZLPVQEJxOBWoGek+JVoo+mJ1agWrkw0d0zqjjgPHlzQFGteZnguCAQyJxMYO313V1ffGcoS7hQVGFMxGAZxMoH84wUotZqJ8ABCqEIIzUejpEWwSRKpLEokJg3T9reFK4PiL0sRmtD3RCfCKOwtQgSTAKTAL2UwavSWAqSD85FFkA5Yb6Ny8xHpqYV+nlKVBx980E9z1lyA27E9kfNSxYzw4oO51lyHDU1cLaXRiUTK01ESYSmvKlD++J5MQN8UzDwapZXtIqJ3rj/8wz/kYRfp1TvvYXGyC/lm4q9AwtvV99pulcAukECNAO+CQaxd2FIC2YHsRscdd5w/lcR2/PCHP3zjjTd6OYfZ+rKXvcznshid6q9+r2LvMko0zbjHmE9Yy5mJjRRWC6RhrzMpnIWDP0EgOWxx23NEk5LOWI5ldhTKEI5G2c0+eIM4UQMt4qLsS/ZWAWBbjtDmA+y52HYuANiVihJsOEAFZQWaRLQLPyQHM4xRBbgAyvnkZuHlpTVKzqCmEeRfYHpqqwinxfPy2NCi6BY0aCASKaLV8IzhMOgsTk0TI0sUFIQVfUTKGGFPFXeQYCpvkDNzXzHqimwClepqTtOiUoVCt9fdHIXVgkYkQEEhzQjNPZd8owmJhSzHkMsjA003CnYNG2At+ZsIIag5clDYU0qIvlAMOtTJACWdiiHevRfVKo/QLHMHZfgWqfQL/JOgkM3CkZUchUt+sLSOm6RALF1VkYbzLMCNihFsKdxKhI4+klXr0Vg/tR62mfIZYuxpsYc+rjgy8sFnqDId7Cm/4kdN4dMlQ8YZx8OoUxm7RfhB3LU4nRYPaGLV5KXVtNe840Ki/Ga0tnqaK494G+0m6ADPYLCtwT0KTwIGCym6lzVW66Vii5O5f2q61DXpfGaMzps+1hwaYsEcvcXS3AoS6Z05AvSCvuRppfIHjXyUMc7rHd27FQiwNlElsCYSqAB4TQaisrFcCdiTXPanyy677KKLLhIKvv766y+44IILL7zQSWlbl5BjopSKLZeVx6lrKCZ1rDH3YmE/XmTQ/+iwk+AcxisAwHJyxZBldkhDC4mLJjw1iGhvIfYZFCSexrSCoBgEeGAQsKtAKQee57MvI3lEXNLI+u4u89qXfsEDkbFEolKMPZfQmXZBbngby0OAXG/P5nmIHwNHtSJnP40CH4QDuuVA7Dx0Z6zDxnVOUpglw2FoJAgND4l5YsxPVjWLTbwOEgClOC8M1lS5qUvCLjQz0NElPwubzbTy8gv2LmWipUKFClBOVXKG38jK+cpXviJBmZWnYIGURjnUCpFuAh0GvfgYPacMzhEYDh1PRU/1Wi3ENZq0yd4lW7ogUZ4202laDtQqzhy87T0L+ZsIfQOip0yp7icMAJCDMc1MYo88FdBZvZaTut07sgYUEX30FPM9hbvVB+bgwaUt+Fx3gs976iopAC7YiBn+r7hUesqv8lEGwhhxS2UmQimU1rrRHIUWS6mVzInFFEATsFQGmJTmvJA2xbJ9tAgO/5mmaa8pTKM0gVvDgQ3epYnM9BBX3gXiupwJQtxP5TlrrKiWbqNm1nM4GmWzcix1Si842uw7YuNmnJ0ID4RveeRVCRs9nK/tI11z2ft8fJsP3Z/is5gcc8wxLIpnPvOZVp6d27W1lXllrEpgqRLYsDKX2kAlXiWwhhLY3Ms2Dq9+5CMf+dCHPuRoHDv75JNPhoQTIFpwMyvTaggdxjROuMZHsULSNe26GFKOcbLM2CLoM2plGo7ch49LumPvVwWTzL4HHnjAT7s+K40JxZTvtyyHt1VKahTAENnTC3YhkGmMYLYmWlPGyVggqoUuCpFtSQBg7EtiBzgZr6SEjVllPhPn5AAuuiRYtwAV05MmOzbZAksKiF6KyRCpb7SI3xpQcDHNTWVSdWd92bJGASRTi/AhgThcWNIGazOQ//1b8a98wvVIKZMWYScQQkSFajm80PQE5YBxqKmCbVVa5mY0U74Lcoj3oeiJt+vJBAViAQA4U6Th/4KQQ9ydEyGAPAlxYAeAW/woho34fRR2eJtaaoucQWv5vDaIl1iuwgRFzjqCPdWJC2oiMWPkqbbosKdbTX9lVGF265qzBrSLXnESFbbHTWgOADajpwJg+oYrHffBZ91J78ZlZg5q+FcLBiMo+FzC4uAaQkpd+BCwoeETu6OAoeQ2SiuFpj+4BeaVn3Mk6BIHFnXCMMUwxBR1dMSIbYuq7SbHdjKbuCw1VJZ3zE/se0+nijRMMQDejNMLJwIsQeXRrDR7mlvxo3SBLsG9N998sw6a4I6P+bimtU6/dm7XVizJ2lyVwFpJoALgtRqOysyqJWBvc3k30t4GDItu+fuu/kj9C1/4wph08+1t7GA2t+1/K6N2Nf1k6/DE27Dxo0XvPzOqmFYDO0UyarnHgmcz+VoV01AmWw1iYYWngwMJztFrrbP+IQT2B0+BdzIT0CikmIwMcQbu8ngobQ1MMCVZyWAeo5ZfQAAq3o0VcEhc4B+Iy9mxldGvDAsYThNfgqlwFbQGhU79tJK6dEmnVKQDfjY7ZWjYvoCTaytZQRdmmaewKCIpCTzgAeAU7efjYJcjFYyHfrOJrciW/DhNVE/4XT5p0BysgppaSR85buKYKBUloHfR2jSnfB6BpjBes1jSCjD3xdJhxYBbOYSjF0x/DfmpXYX1V9PxrGk00i5E0tBmLzfgcfdSgFiQ5UyhSxwQpjBfQ0rOJJwu8W5O2IbBXBOJh+FUJK6M3cSSXeLLzsGb2WdNEPjlTOGSAE2FH8s63GS+y7On6lJLcu4+xbwClJOOpaS+m0TyJZpem4HdLMxoy8RxAgId3iguFSo3kYGBlKcW0zRBccSYHfF/2Sk4giE63lIqPdPCjppdxvQxqTVtilnxEvKdysnaFtAplxl9xx133HTTTQ8//LB+vehFLwJ9nXcgn6UO0NqKpTJWJbBrJFAB8K4ZytqR+SWQrY5Zc88993hJ+Hd+53fYH74jDQk73cSysdXNtNsxLFgDzOI5rKL5uzGpZrpWHPPwhs1bQdZhidlu1TW9gDxhOTafKqxJdiHjbKvyk9ofJy+9gMPhFpYH5p3jZWOVL9BsDM9meG2c9hamgmG4XfgabgH5gB9Bg3DoUcgvg2HEAWBNs8sJZ2I/lAHJ2LuesvWF+xysBZiBxgRyJ9ZqZaYX7gxfHdQWUnpEYRDJoWs0C/BoVfdTSWCDNtJDP5EyZZjg0irCLca6W2tqThhTrCte7z+jXHSmSwokUAsFH7ZhvhcGWqRKE8x9cFSY3Uw3xVS3hvB8mSOZ+ISTVlIFHeNCb7tNb5WjIhT92c9+1hz0N6itS+ajXgTem849Et6K5iL5+LEsfPGLX8QMmNSSTJdykZVHUwt3q8+aoznQl78AejfpQLuyAqd1BaiZ+GTrXLGxo8aeGj7LS7yEpF2qtzhR0kSj6nEItp72/1RXAS1qyxLBqWdp9dP6FuS5AkG1OMSSBYHcOBM9Ij3uMGq81UGDdMGdwsehYyLA8CYX9ead0YXV96LVqfl+pmuW0IceegjuddRZH5/xjGcwBp7//Ofr7M7t2nwCqbWqBHarBCY4tndrV2u/qgS2kkC2NDalwK+/k+QUnNOYkLDXg5mYnL4nnXQSM2XqzheDQCsMLHc2qypMqGW787fql/zwzI5hlAhMMfJSWOQKYmG9sXJacT97P55VBN5EfYlFUE4UJSHxnraW9yi9SLSQkMXTWOHuThqLaTO5EnBTbHk8zEQZJwad3IhXrJU9rTobVzyBVgjAAngzERxeGGWWaLc8ubnk4w1mc0l/+ctfxhU8bHxbatCl0MxpiTqvuaYAKGh0pBFkSTdrtdKwHIM7ANgjeBJocXIVMAApcdtqpVV94s/+KpHAxIoyAyZDgdVLwZxtbgZsS0UTPB4EOmkegTEC/joSDwJsryRUE6xr0EmYJsicSK2QbSU2BuzgwXwrHiABkMIbaoQsLV7XqrLUn5gB6Tkp9F3T0NrU5lQxoApHMlPLDy+AcrOwSCZ9IxMLgtGhSEaToITcjQ7PXY/kkeJlsNyFICJ8JdKGrEfCKJvjTR6mpsMz1cISSULa2sr7L5ZcQzyVwvIK2Atc5qDVw4VDiu1vB+I5O0KzaeKi5NSAEBzL1wvnTfLBcOlmyR2U1lMXh6Bzzh/72McIQY8uvfRSR50NjX7t3K7toFGorFYJrEwCNQK8MlHXhnaSBMpeyAd82223sdTthT4Z7WJg6cnEvZCh0P3LRpAGIDSx/HZJBJ+gow0+oV0cMn3s8Sx+QTmGI7sHw2CMksBzmF+TLhgacgvqYETqAoaZqj6pvSYctoY1DOONyeiwPUwogufNUjmuVuHFf2oO5GCalzd7Q1M+U9ugNwUFnEMOxp0tLgLsiHt8HwPZQBP0pSTgTXwrzkVTmKBr5zkNTYtUpCET8BY1wmQKy2d6iiCxqjH5tKc9Tf648sEhgkM6aBY4vi6WmCP3LTawSuvMIAmPcqeQcTMZZf4OwEBDMLCneYMApG/RaUmm+1Ndo+NvuoLZRs1PF/xppCI0rcxKs9vKkBztGi+wUPgUktQ6lpqh7K3YyChjWOh7qzJDGOiWwRIvAG2xugpdShCRxVl+ClMnBXh2vPH+ghe8wHAQpkfR2KB3mFl3eGH4LwwfFwYESE+y6DnIQIe7TcsprczUqcjQqiVhHSAWDFMSBGeiM5GlUTLTL3eLv8nLbSr9pS99iU8Bt4TGOUXUBKs5XSBeOyPZjtL6thBJl01b70C5qA3d9rcShXyHeL23hefaaJVAlcDiEpjNf7l4e5VClcCOkABzxMWi8vckrrjiCt96ueWWW6688kqffHzqU58KBvu7SjHpFCs9YjGw4G2orGEmQg5blgBXKbbtCfYKqMNwYZqz9UW8hUpYtzjXBf1in8X62XZWuwxE4KxGoU5nd/kmvB6MWyXxLzQhZB2DrDk0XTorywkbROqCWJiPiTUx3BNgHIVPfQdEg0KTpoT6yEhtmqcepTkAz+iD4obb8WwnAtjlYpWOtuZ47RD5IKV6SgISiEvrY38wGb41TKnFngaDoQ7mNWqEowu4SjRvCA/Dy2BsYGHM4CSFu6Mjh1QZxwo4IkGMVokUJkBCoJyRBqxFmMpHPwe2XoqpCIMdccQR3Blhngp5akAxUIqtIME/4qUD6Bfyd5AEM9a34Elonxp3eVCSQiYffEq002LYlWe37tScaBqagCu91RCoRlbNitw61CwQjrabDtY6w5G6ailcDo/IhO48xbb8RPya1Frpgb1IW0YNA1FpY0dc/D6go/RAOq3Wl/cz/LhnXuMf834abspcuBUYDx5ewy4MFI6uueD8T3ziEw58ff7znzf6Djnb7v05XzNOZ0t/B9KsxaoEqgR2kAQqAN5Bg1VZXbUEsgUyXr0J7C2g97znPb/927/NSfzqzUvOiSee6Jg0gyYl3TmPcWlbZTGwqNgHq2Z6cHv6FTuPEc8UEOtg0wjleQFSdCvpEFtPOwBXzBSxX+Fr/PvJVsvfnMQ8u5xBg/81YR6uY5FTCWIHJPwxXnEVn8/9mZ/5mcEjNqUgL4aDi6UQVCkNZhtTxisRscLliAO7Aw+0lPSAGY98+40HJJpQKMyRAPwMSn9Fw1SgkbRhMkYMbvxQQhRUlwM1ZfqsyQg2OxWWonXJl+OnwcW/ezKhaFDBVV4nbhLpSSPlKSk5LmtYudJKYaM5HMmXWosnKI9pZX3TOyHT9BTZrZY4ITVrYNrl8nCpwqW4VfnhHBIO7I0grw3vg7d5OcJAWcrjkYnmXAMc+8gjj1BFoFfhT33qU8G3zkLzuRCsRUOLCpsmhGz4dFAt9zJ8w1nqlswIQt2QtgtN+4W7o8I8d5pzdWutSU6Yd+fQMY40kF9MmjD5FHAucE0TUmxNeB7IBp5dFPjee++1lfvehyUI4vVnEaHfspUPpFaLVQlUCexcCUyxVHZuxyrnVQIjSmDTXNnPhS/2C/RCMnfeeSe38amnnsre9ccAvSecPyaRkml6PU0c2z87j03GBIQ9AI9gSAkMiwa7Q2guERVlXPq4nn0hZ4wV3nRNXEXXWMDO4jLRwGCnGUexaBdUJ7Y4ZhAhZ7a4CJ44MG7lYJsRFiZLX+ZoDgAwWCqCu6iJo0obYsRZrloJzYBk/GjLo2BRsEQYM69KKsnYnc8WRGEq5/RNGU3nW1PlCDS8hGfC8ZTNDd6glsJTaW5XAcFeA1paNwQOVvhZpF0SpUwz4SkMRhTd0KijDT6zRGcgtAKADZm3W5sUlpfGmwvgse5ZAQ4//HD3NEd1MVyOQE9UWvOONJSHVA0rQSk2seTALmBGSbKSMMFpCIWhxojDMxaunPknN2fXLXFKUmP8A8AmmhnHd4ANqo5CeBP3k9ZBd+sG9hSDqAeytFUx1MxBSyiwLU1WJiZ+zMrF8f9WjY6Vj2EeAb4bV5xoxl0msdA9/QKJ6bwcTjQLCzXoau9YzIxFB7dIYfjBBx/0TpOvXfJ08D++9a1v9dUPa86Gaq6xS2IsOVQ6VQJVAkUC9R3gIoqaqBKYQQLZUNlMvpbhYsqzrkSDIeEjjzxSiIa5E4wxA9ElF43JAhliW3gNunCVNrP9K4NzCJ+VIx6oGEtR7MJT+cVEKIlSfU0S4Z8RzPpMAMo3WkCsjBcmt51zUnVBm1gK6GWm+6wu45IpGft4ESaRbb0DLAdC0HeYQdOwAdBLOUsgkaejGP0p/LnPfY7QnPJdnu8DDw4/C1AHqNMu788DKoKlui909pnPfEbCbFoeDz1Ky/r3Z2lIoMC8nsLNRwQIjIlmpxeEnDE1vt1zywrTVYoKNpdBlwmB+PKz2SdkKsiZdxHTSinWbHT0dHiwrAE8oG+8IaUVCDMAeCozhtUoCx2DTFMLF/qtBGbkIBXfFp0hSSxZppz4MMGl5QC3ioG7cI6EYywKOOTirU7LMk+QgfAUeudVwZIyhjhOQCgdRkVEmZxV9nSmK0yqopta8daMtEnEhcF/gezc3Z+JjTkKF84lrEjumNcFS4TDCxk7+eG/FNYQSTqCrpgy1Jsk162P4daKZ0GDe33S2Rro9R/64C1fQ6MX68bzHCNYq1QJVAnMIYEaAZ5DaLVKlcBju6aTYOeff/55553HFLh98/LRLKEPh6l8UNrLezF5t32LZQdweAubgLWAB0zrtc9EC1u8+clWY/dwiotNsc7LYPvSDKsIRPE02KlVt5TcxkT41zV8Cl+AItCd7gMkXhU2Xvm8zTZynigQBoJ+yUqQyqD4uBHfBJcE5vNoLCbRMeLRAWnWv3TO1ib0USJ7GTiDC64IXomP/dRP/VQkNvqYhhNGcyj7qe8Fk2MJJgTAoGKBGgwrMDoPPQS1mKdzN10oTGyl+TTpZg7MZroBFSCcfLrhzsVGgVvU5Jun9GdENwGaMDlwyG8CQGYRa7YL0RXtbeaPnsYJmu78EVC3RYyGCIzTT6oL2OSpRAQFJNMieFiCQPhW8G/Ro/80LbICSoN+1ZWvcCLA7oHQs/YCWZcVBj4PXETKUWeuHG3R2xWr7nD+sa0wP4t1kguANLzgLYeLgQCNsqFvMV9+qivkzg9iW7G06ruFi8bq+3AGllQy/TIvrGAf//jH77rrLu99GJFzzjnHvrz+R9CXJJZKtkqgSqApgRoBbkqjpqsE5peATdcl7CAgfMcddzDXWGn+mPD2ImEspUtsaEFR1olYX+zXYsr09LlUV9ifzAGKFA5CZu6w8HZEZAPPIhXiohLsXUYqEMh083OIEBQb8WqKNGTlsOChX3YkJIND7gnnt+fjDTXGK8qGpsm2fKhGONELkyiLhGhRgLFgzlI4HGIG0pDGDIO4PJ2Pq1J9eELT4ntC1j/5kz9pvFbWbjiEChjQhDOrhkd6IQLH8ha58rPZBSOua/KBTBFL32qS5qyBckER4BMDPhztDvXBUZ7CJ0g1icjUHIDHhxKzXs6CF+VxSD7fuEbT0Gux1ejwJrBHpVUHmOcgojqNxUwCkgCtn1SXTIKBIbccXrC48Q5AsNSV9JyqkCZPElNFGR4fbMPJSJlihG9wNzq2f7+xgN986CGjMLB3GWh1yV91LaoIAeYMfIggPpDa6otRrXwWniiMEQbIyicJ0i8/hzCvMIWxL1hdORzFVC0poTCk+ri9LiPiw+lwr3ivoTciDjkL+cL28ynhuExWalUCVQJrIoEKgNdkICobu0cCtmGXTwoJCQcJi6E5HW0bdp4zr0utwDjAA+PMO1oxYZ0pZaDEgJ6vdQQZ60IxwsKMSAPGpx40xQZCcz6yKxh4nGuF3cxOhS4AG6iGWMbCDKN0AZOAEDsSk6QawYZz9BeXbSEVboFhEHciAE4B5QW1YAma49AgvCEmqXwrXDxK37cioukcJXWYYg0PWG7FdhGg8DVHhqtbUtfgujKsGR3hShjM4WcTysleiNcwgXaQcPdQQBnQAoDTSqHZbXRIjuawbZQF/w36gtSGtNgqU/qlaWluGt4rTiteAF5FnyEEaSDYuAOsaTAb/EY5aYh8jjkvFKDpxY1AXGnS5s0JQT/5C1TkvwOSneB11xC3YDmP0GJp4k9V8GPCah0R1SHw9Q/5YpscXDgnE13jFXVexkWk8id2tj8TTRe0yeOQSLuwsCHLZtdfd/GnmkbE4umcs29z0JDgXq5nl5dNKu5dXMiVQpXA7pNABcC7b0xrj9ZFAptWwQYS5o22MTPjhHeOPvpof5TSF7NYXZt2yDwGR38PtcuAZjgycbj5f/qnf5pxNp9l020onWJBQpJsJsaTHCY7kM/0FKJZUqe6nMyag09VoF+cA5mCivlqDmvJuGxXQLjVC0wy1hnuLqwKgbIjybmLf1oVZ/qpFVY7+v3nZhUTNwMtRNW8C+r7Mf76F4wxU1uLFMYAhwsnjpdgVwyACUffHQE1EPPNHcwTmmnu6grBU5NUvugZlEvC0hrSqJdmTVjvHvvJBxEA3IrnK6xkYsjCobhNlNgEBEK6zU3NwU/KWDEgcweJ+3VjKsH5CmDDZYaKQjv6TvgUFbYxKWAqCMeb4fxWuilfOJdXy8ucus/Zp6IwrDvJqMhrQ4amNp+CThGyRzx3xMXRQLY0imrRZ0tBuO0faNUVQxx4dgBb2jJidLRi6dOinH4KaWX1d5y7SIBgRX35VqgTOeh+1nBsj8W5hqi0L9hxCnAy8lbMeoZioHw0pCSf7EMPPWSHve+++3SQ3lqjKu4dKMNarEpgL0ugAuC9PPq17yuSwIb1sRkuAINdAixsYhj42GOPPeqoo3LubhT7Qyu6xMRx4JktyIRlEEBQy3CBpy3NAcP+4ggLVVq/xLuY7zoVzLYiEc/SzMZgbAoKh+xmfwnDTxYtQbmPCzVn4atdFjJ37JztzkZ3hpORvXreNkW1oVSwlq/I+B7SxGhkm/XxfoeBMjtKYo4WzAszgjCHECkfwaIVQ8rPwY8qeuc8BZSbdy9DRKYW0yhox8SHafOz2Yp5xzchpzxSkZJMDDg3K7bSkbCvGIBwcQmlQCHbKr+MnyRAt3nTIDRuO2sX1ORP7+gL9ngK7r77bvnQpouvyoVbl9lx0kknmbakRBqWVodchHYJB0K2EHH/AczhGakibb3zUxVhT0Pc0ynFPMUD/YEYHbr203lpuhQXBlKuHgrb+AiHYKFem78QL07AfjHz4iUZnXPichGU8LhNwXSj2wZF06O0hThSHB/+tJU/4Wv1pi2cHdzKLo6kjcFY1+HYRk2oTVcJVAm0JFABcEsg9WeVwBIlsGkbHGTD2bl9mUN8j53HH++jWd4W5ozX9iKbN/p52RURtiz0yw3PEGQGMRYXodwjlHSKCQsz8P2LkGjI+UNgOAYi03ZJTfdwNfAR4bDVXCwqHWHUGgVoU2IgheUVw085FI3PwOCl4rFuX4wgXcWGRwKDEtiATJ7znOesbEwBD7Y7zKNp19ztOoshcjjwNEQBwOJ7XbGMlWOI4RO9A1xBOEo4kD0MZN5JYBXeM9lJJtdw9hDRrpizaCpQlNjg8OpjlQS/uXssHYG+lNwcTMwfh7wAXicRd8WqqUpiVAJw9QoG6HvgwAErDKwFmqJAM8WKyUTsUWiXJ06/8h0vpEBB1fPNbT9J3grZP9nRhL0RzCwQaceeS8Wxuj8uHf0qCy+e4+AzfXgTqFn5ph1VGbfdJjU88E2Qm4NImBEKbn7nvFlyYBpBFxjvhLOtM99ItxDZOoV8jfWsmj+w3VqsSqBKYLdKoALg3TqytV9rLQF7Of5s5yIbvtXhRUfmgo/9eFXYjl5shYE2SqilwwxZ1iSPuHCKHBSAUvainIHU5hNceHB37JOJyX5Fh7kpJsPwcuZwbb+YFc5Z3t6ldLG2E94xIi6GY8Q4n1gWr4U9Viyb3nlLMnzKU56CpszVGHyM19///d8XJ8cDDKBdSgtRxF+zVI0qotOucQGQYI+f//mfn9WfkvFFDYAPAC6Ue/jXqPef5/gzSIX4kEThjZwdo+BF0kEQZUjdlEGBcADgOV5oVzczlM6DRpaI5bnJJvaodB9S8sVvIJbAgaVyaNwAKYM9zgsJOmC1VAzb1jc86zgkLMDIb8VJZMICRV/96ldprNgvjKqWR9YfDJTmpHuGPsVQ4J1EIYdEJDQHUSd22lN9Yk9XkIltWmSpt+CbpOKuGNaupcPEKUdyVsZ5xGiAHEcCUK1dRQgDeQgFd15dGyXo62R1XMYOT1mCeIvQHEittF4TVQJVAlUCJFABcFWDKoHtlED2eMGN+++/3x7/6U9/WhTowIEDkLAN/qlPfWpiFz17PApiFAxEn00K6E1/ShX2EMPRt0BKzlI7nB5pSwKKcMgwR+/Y1sI1MUZH4SQN6cso1NBB0OUtQe8N+inWlK/4snpxDneN2FaaMzTaYsH72X9hzCgzcIXFlITNYDnBSXb5WN2fyIB2k49V79qxO+kqpwakIQjmOPTyWtdZyEfrrHnuiUT/BP2Ke2giw91MgUEXPlEziC5pFrm4Xw/zqwHA4ZaQOa3MFIiFF6yHq27v1AWbuSeGh45Lo9Tbq7ZUCPSl5PJnarrLzPAcbFMhLgm9dmwVdrVGZZk67LDDgEyMgUwC/mgWJbTKffCDH3SC1yP6wCUEXD396U+nIbQRMLZamk2+7aSiV4itNqrLyazpZy+tuGPD3A+GtBSrRectrdGcfiLb8hTPNJzGumfK6LITAcbUgKZfKxvZlgTSejLNYueiIfP+qZcqOvKlL32JvwPuheGtk14asif6iEZed9+uHrU6WH9WCVQJ7FAJrOkZnh0qzcp2lcCsEsgubkc/8cQT/akGu74/XZiw8Lvf/W75dn2H+txzdK2167MVuPxFPFjAYpVsi1KAfckewg/8CUuImUizCxmXpcys3A4p3yTOoHeBlFqHoNyFU0RjsI0rkHgqvO9pkdHM0o253FOs/xFOyBzPQZISsa7k443VG7bJVkcY2SNKTxOMbO2KdzWFNpFhBcpBTaY/G93bklCT7jvrjr2pFCaSnZpZyCbhDlQQEZXDuep64V6KTSU4vAA8I/JTymtIrwWUHCgoxzjL056EkFFEZ0ZIJ6QPzKgS5nvqtsoor+LEWnNLADUnJgADajD1mIbCJGDiFGXAoeBefxdaTwv/RhCu8LXnRQ6Wt4hP/al1i8DGu7x/8iecfbojh1RhVLDN6Bgj65WDJOZaAHBkq5icX/iFX7AYmpuCxtY3mgD5O2drkSEWOmOUqahMP4frCeLgGdwLRlpL9YKSR1W0njVhatdWVgC32rJwkSShSfMXkAZxQez6Hhde+JlbM0fpTmkdz8RrC3BGSZw/f4+g2YQCLnPBme17772XL5gOwPDHHHOMrdDh/DIczVo1XSVQJVAlMJ8EagR4PrnVWlUCy5IAIwBpd/GZe+65hykg3MdAFPdgCrgEwVgVLmXyN0I8FUWBhZIfzphxAibSKZlMZgcgJyc/V3NPj7TF2GW0CV1Ke/8ZdGe7wzO4ihk3E2MsXccmhfIWMU/xBnuAE6KprdbDNrPYEdOYxVjNCWTYm5muF60qA+UZyu74Nxw5HDiclIr6Dj8D5pV0AABAAElEQVT88R//sbAwTpiJFIDFP5CBOYoxtcEViEJzGABREKF4Eoxv0pM/vAtDGKAqbGUlYRvKLAgvIfhMf1jDOhsxatRFIMCPcXFhKQnjhSt1DZ9MA82j4U/7qMhrAHAqZpb5mUtbEo7gUktVnP2mWnkT1SOtGKy4b7geuCHkkMBG8/v3qwJ4qA6lc+7I13TuDHc8e0SGWpTvSi133eTzQu3nfu7nEJGTtty7FyJinhQmZ3q7BfpzVM94WSsydsqnC/0Vx3qqs0K+IrfkQCzQGqeS+B4VwobxyrAaJmFtT7NWRFZ4sICQLU2Ao8jB2EHCzrZwBqlroJEVEFae5viZ96K3Yp40PMqISBhug24pMMomFLAtM8OxFYUV52PYRc9hXb4z+JDCUBtMmpsESG5haa3YDks4lzD0wv5mq13MazLh0yNz02ec7Xf+hBUV9efWuH1dOWOiWEquWOC7rDl7mc9lv/SlL6UqO6Jr9keKbbWvo78jxmvHMVkB8I4bssrwHpJAjAa2jgPSkLAzqDAA49W3o12sc3YeO95rb9KtTYIZwc4gLDhTsXwKleW9Snu3NVS6w/xl1EK88Buzlb8/fWS6OcLN9MzPVl+adBSAFnRNB+F/qAN4Vh6FnlpNCs00asAMW7wLgEsxZbRFjIoFbH/hC1+QGTjEBNfuTE2LhOi4KvBAwUJO9kqXRvsTWlcAV0gRgp8iY0ZWzirHV7ugC1AKLYjqdJWwpxfpQqsAmYD0ieOBi+TjAinRJx92v8izHAOhJOji/ou/+Iu6bI44MBlqMkP82c9+NjxjyvjbOR6RlRAZzCD9tKc9TQIF00phVQonIqJCiFpXS1vJD0GTiJZK/97v/R6Ellp5BK2x2qW5qyi2WoUN2v6zP/uzHgloi1uGIJ4NN6WlRYAc0WEV/HMJzLrTK48Uo+pk4mdUJQAY3gudJufJ2eqOAUd5Y1M6KgLnD6+7Fc2B+dRS6/ri7nsH7hwQoK8eeWQpcMk0Ijg0Xma0dNhzJ9jm1FASUqUV0FQOkqCsGEBIdKeffnqTq24fVXcZXzoTz4j3rhXTNMXLyw7dWk2aK07jVotYAvsBRfKRo8t0hqKS5EBuQyekVtyF0hwe8O9VXr4M3jSQzNbme84WMXPTIgb02tqs6tvLZ2F4NyWs1c6YgJTN2bTOHbzyyisZDNdff/1ADV/nvlTe1lAC9Qj0Gg5KZalK4DEJZN0X7Tl18xLTePjhh3M87P3vfz8rGSpwdhp4gxAUzpXKLCRGs7SEu8KePkZ3+/5jzUMd2gcYXCx7hixLFLxkCocviJQxJMyFZzldtgEMllMKMwoV0EdWcrdkyky8s8CYAgyyxOUCWoQH4ZBWeWRZDGCMfGn8s7OxDQKJsasCqAAkMgcyAL/l2DOb2xilm61G+3+mIVzxhgASFENaX0AskhEVz+HPgfz0t9X/VKyMJMWiYUIBN00bi7RbDG4UpA29e3SS9LgAjL6L/F0UGPJUEilWfrNRIEcgSI5ihB9NJu0gxpQEmSABQigXNpTxlKjhTz+9Xqt1EV3pvBSqMPysDIbDs7RMd2PkS+YSLmx76q5icrxfSl1dMnOlLU/FtcjEI6zmnrY8cjDVYGUKeCqhFZNXR/RRZAypNKSwbtIrCd4r2J6QjbJWCEFn84UnWscpgEi5FNjoyeMuAAkUwiGMwZrEknmnUfnLvtKugQb7CUGolgCdZNHfSBJ7EqC4yaUYrWCg66aKek0l9GujM52Fi2RclI1DxPJIeiAT+z6j0C2fniJL5iQG91pwDIHWzV8yUdHouLaqu2xZtehjVQ7/CzlYJcRL/ZSppwbdBah3V6oWkdZPvTYKVoZW/mp+Yj6XUebE+chHPmLxNP2NPsTrZBM3hKmKmTUZgtWIZUgr5JZii0sGqRCRoPbSrtCnbHKiVMmUT99MmTIvUr083ai8f79aZdRCyholx+Qt+eE/LSojYZpnE2xSkFZSAVdJlxwTQZrT0BKdWhqyMMrJplBIKcaZZbVXEv98LvqluSw7ntarSoAEagS4qkGVwE6SgI0h7Do3yHfuAolFRFn/THmXv9TKcFQm+4cEnGbngJRKzoo7jGe7HVtHu80oXHay9Igxij3blWLe/rJBSrPVWMau5talvD3PrqaMwBHzN/to2aQH9o4dhhmFI5awIb4X6fUQSUl8wt5sSoa7HBCLSaqWjuC8kJ1IJxTcm0eg+6tMpNPKRFBoxUvLEsLsLEvCseUvddy15RJ80xfWksCdULCRlQnKyicoo89SMWQCVkG5gsbCd8rgDYfMF2MN1PkJnwAn8QvIz4UgiKJ8U0rUQHlo0D2PikDklPSaJFoctrjyNJCYlFii7n7COTrCTUPPKRuNlR986OArNTO+XiVokpKZFUBJb8gDPCw/MrRcGAs/+Ym4lpB1NSuOm9Yd05NjKzqgLbOY16CnUUiPSlAeZVSHbA8cOECdMLZVrRQjJToPItKEbmFlZGIGEZdWHN+QMEFoo6trpo8rilmpYbV5yNlPFCzsxpFK6M7U5WWrFnlYLG68M1sVWEZ+5G+qcpCJ9FrbqSI9pMAbp5iOOsqSazg0nfsyeNjpNMnQVCJDHp9FpMRD5OgHNIjIHXfcIb5688030/8jjzzyV37lV2688UZLhA+RXHXVVdZkjtQzzjjDp+mtHpxNH/7whznvLr74Yvpz5plnWpSM4Gtf+1oHLtQSurcQOb7++te/np/LQDM5PD3//PObmJMy8yr6tKdvm6llgvODIG6yY8DZmfSOVpx77rnO77zlLW/h0r3hhhvssxgzzXFrebTNWRmUcWzeTqGtCy+88OyzzzbQ73znO1EzzTndLr30UrPmvPPO4083pzDpE3o7XRkq/yNKoEaARxRmJVUlsHQJlP1PoMl2eM4550Ad3iFkWPh2yPve9z4rPjDmOzH+lo/djrXE1HMtnbPeBux2gI2NHP8AeUCj11aDXVVN6EbCfuadH4Vt+bYxRr9Agb2W1a66HdSGCg4pycR3Z/XmWLL0TJfIj00UTZEx26ptGG8xLvvpZAjI2f7t4n2wMaMmn9HAH8EiRw3x7P0p36SZHE2XzG6Z8mimhMPwrAF7P5bYE8QLURDdTERahZt85hETRMJIeaQtxhDzIplGFloTx1PAoGPDyCppyEis6CFwzpAido8UaHafHyGuBBSa+d2fNMR4GYLuIznrdrX60mLPU9rC1mzl+8mzIwCeGLifomecBTkjSuZeSudiyBU3E5EqZkSAz1BL0wYL+iVbCdjYcFAPFzSSdvs5DKmBd3OcGWpp0imsWqnK0G9FgYbkNG+TjWZ6YkUTjRZ1MWGU1h0nrGd6aFW0UBCdVdEMnYiWJzaxgkx8ZmXTX7MJVpRDdIYeq3wH+oiNIatTl9vA5uRrJcsm4tpydcsvnoN5l7ZA7gceeMDGBEdRSLPexvS2t73NCg+TaGhJDCzehXWjYBATVl2cMePy3ve+F6q88847LZ5WCY5gCmaw7LbWmZNPPpmv6o1vfKO54zCO9eTXfu3XAFSw1jsgt9xyCwBsm7PmwLEAMPMjb3BYjniXXv3qV4PQqDnr8ZKXvISTrvBsuKk3fVDFBIRgoVZE6KSNoxRTptVZwNXxIo2iwD0a5dHQO97xDprMpeL4gMPzDrZQM58N92eirXWE5ojctddeS+sopEWgNFETVQIkUAFwVYMqgZ0qgVgPNgMeU+5S3bCZ2V1cl19++ete9zrGE6+n1d89FrMy22JzsDuzaTUjwLHqWtLHnjCFC8K3xdqSXaxnGxh7mmkIzuVitbewU4tU/08bpwtZLbrmIKWWJmzkDLu0ZfdFRyDLBVeQP7EHG3eZUZ3Fr1j30Uw5uiA4APcKZ6HJaBZKBcsBUaY/9hRg9JAejBGe++krr0CRjLSjs6wHlBFB2bgIuLFIPGKO8FMYi+Bb3THWoS8UwJzqChYPcWGkWOs+lcOw16yVnKkVm1V2ULrZr/SU3iaTbcq+7ApE74y7WQ8Su6ilS8IcVNHwMRmbEkBHZD7YWKjZIKoeIJ1iTR6aFZtpbNBDdeE0uJr1aV6wsJFSbCoFq0FzQeAdazLQbKik0QQOtVu8A3kkh/NFR7hIqKtM/Oi+hCq4mspMaWJ5CUy6jIUZlAtEt+6ZZVC9RcNwmFNhYBGGOROJIn0nBG1JQzJkMmLv9AU1dzL3mrc9yBcrLBpmuiMJ9iNB7BLAXKQ7I/K85qQI07ptyCRgy6zhRGexnTo1tuoaUiKiPjrw4IMPWqgzEKidddZZpq0Ny3siQC8A7E9RCBGbzki95jWvsdpz74oV87mbUwZXgFcBCoyUL8+lRTuaP5WHLORp72OQNAFwyrziFa+g5MrgxDS3NG3FbclH6rbbbhO1Fj3GZNi2mkHywtrws12GZzBvdggyQ7/q6pHWRbmpun51OSn0a2JvSqAC4L057rXXu0oC9oNsCSwM5p3NzLb05S9/mRXCAX/77bczRlk8NgYXXykzS/9TZQWC0JB91GX3lWbm9sdsC2PsP3FCV5i0U9r7vazrksO0YiPGjEN57h7ZR5FdRA6FYcicKcCZjUPGHwjKHBSKV4AFs4m4N14iTXn3fjkMZ4lk2LUAcCFOLEwQg65RmSxgETnioh4B5IXntEKALsYWKycXMHPYYYdF+ACwvjBuCMo48kcE1ajLsGBnTES5xXxPE2PdscemR40dphfBcowbLLU6NVaLa0XH8BXhh7GJvTbuG26k7/ouw9rin717xBFHkF65+DViUlsofAyZqY2mHFPDRQ2iqEiVtkpiQ282AaeTFMaF6ckMNfTUPrpXSrbY6PmpynB4pjAGUMO8Kx1xIJ8CY+PAgQPmheMYYWMOZnr4nPURPsMAPs0prj3nURGRb0zNPuNFaOaUzLFYRZmbAEEBvYyptAnrvvgVyZuSonNAESzkcAHOqcEpp5wC9IJSWQfG6s7iPO8gChb2fBAhmpO0tQ4gnK8XFE8gV2C2zAh0KENZq6VzRoBHtbhu7WsqmlDatSP4JAEfx6te9SqfI3Ea2ccCr7vuuvBTlibDjZRaXT5tQ8mUsC1mCpRi0ajyM4nTTjvNLgPKvvKVr3Sm6dZbb6XJwDBA7o55pk4JGuewCQaQcsTa39C64IILnFSC2CWqHrZku5d/jrMI7mUJ1r5XCayVBLK+2xJ859blIJMAnRNBrBN/ZOJd73qXnUOEEBK2eTgxC46qklor6IhN0U42vKHCmM3MfgxeMtY3o8L/W5BHhJML2SMWGARoQ7UBb/ZmhqN9cxsT3V6kabaFy0lLFidPhEwc+rKX6DdLVHM6wsydlc9Wc2iWnKTLPcQ9jajlQ60aJTcQhZENykIFcA65URUgR2E2DRNHInTUBZBiVXhPj1XBLqct8l1pWheYOCTPKpJO5rLv7DOMaUXTGi3pZbe7JvRnjWN0x4V6iMC4MtDNfiksmkorzDLKwAg2yzJBFLaGWD02UPXjF03g6xHnUVJdMaVYwNJFSZr0x02Hf2pQ4r202rkDrSSAbBqm+7mP2/pwavh0EamZgiWSxw+5mVwWBKtWFq4QHJ3VjBXikIaZLrw8nPOJJdMdzPOVAD+0grPVQsdR4tCBtzHtLHq0pO5MZGm3Zlq3OTH1ztJtPlq3pRdx19p/+cR9cswq6oVeS0FXdNFAo+kYM3ipgATNCR42xN4HVp2fy3bscDLeGBLD9dZWiAFk87VLiwY3sd7RK5mms58trvAJA7t4BDjv/DEn2yserrnmGu3SPeBWlcJDSZhfb3rTmzz6gz/4A0eyHd4ey+nc4rD+3IkSqAB4J45a5blKYLoEyh7AffvCzcsG47wQJ73L28JveMMbWKter7WlMVn47LOzlorT25i9RD5nOnu9J/Y2PIvquOA34CfcsibtpsgKHOkv88u2ylif2pepBYazagtn42o6NN3xUKqDjowPGNiFMRuzb4rEpChlZk2wAJz7Si2D68tS7mii3CKFEwPNAuawdxcfU5EAlWdg5TCziowDcXUWM3OHFeLusBxS+iKfE1154s1JszQhbMXDUqL0rXaX8RMUd6Es9gtl0YRltLKeNEdUVx3sUiPP5lDSEOMbH5lYDb2FjR1wUJEm5O6FPaDOrGSS0uouzSVJEgNwr9lEUeOmMfdLICtugpUxM7GPOCS0fMgA9LVYyXFIJwDYfARvCgJZHqtdyt2cifw3M3HuAk4sCHGnetvTimdhccL57W9/OyxUzpbPQb/ZVk1HAsRoPtJqkrd3+ElhFpctfxYD4LnPfS4wGXA4UeDezr3ooovAYOoq2OuDWDEPDPTxxx/vlWCcSHvTyutXVoaJRCZmQq18qfYRR6Bf9rKX6ZoNyN33qxwbga7N61bFm266iRx4tUx2eNsG51Lsox/9qINOYrxUsVXFT3LzHSy+fpsypbU6EWC3WM3ZsxKoAHjPDn3t+B6SQHZNdy5kO5mLKePjUvz34n5vfvObbWNAhdPR3Ku+UOIjRjYkAlp8u21KeRRqhQjjACpD3z6nX9CarZGh6f1bON+2rS8eMY5jPahY6ja5GittDxZfLaEPZJvNsQ4xKVDmNLLT0X/xF39hM4Y5FQMh7O5zeB/QDxSEUlgz8W0jq9fIuhtlxF2KaQuyZXCHK6iGiOQwRCKBhM7CtrrAPDgBSEgjDvkwIDxNiC9NpJVyl3A1e52cce9d+t2cUVqkSKBddGwrghEOBnaNadUUpimW4dZ9+gnq6C8gx4dCk91lAsncZ+xLYUAKQwkpWy7Vm9S2kuHA/Kgcpw/NjKrDlgAwDlnDYr8BlqiN2OhA3lIsHBIIJkECPx0W5S+QMN1waAZluuHQejUT8cULw94Fbw+hhm2XNUQA8HOf+5wh9gkGp1rMCDvFJZdcwnO6pJ1iCHt7qowtjAot2GUzxbeUEaGEvp8M3xpWDlCZBppOuts07VPSPoVldsOWNlAFnD2W6bII+MuLLnSATwj26KOPDmM2i5JGCsYum0uTc7j3Ax/4gGMj2rrssss8sm54v9c3n0WnTz31VHyaLPIhXpNawk8Y+Oqrr7ZheZ/cZ01k+hwXPz6WHLYHoQ8cOKBRLGk03fFIebjaDmj/dfZ7JqCuiXrtbglsaPzu7mHtXZVAlcBWEsj0F6bwgigwbDv0lwwAOfal49OQsC2KiRND1nayFZ11yC9LGdMcErZ5815jTFxUfBiSgQALCJQ/VndKu9Cvq8DIrZpI+YCHgFW7PiMDbz7vUbzUU9kr7WpIf+FqMfzUArB5x5mqRMEcV4Bx4KkqSjKCmQVNU6C0pQB716WKdxSFd2LBMOK53nUtJd2VScBZSZccdQmZzAu1PFreXR+1BWstqUWTQji0ZcPpJneATvFZuPspMM48ZTUur6frRlmv03cQ1ORidHIWbDhavva1xDk9deAwHhPaaIzYuOnFrIOVtrh4TC5KqEXLEQuYGst0+jEm8qxkxxJpEYUZjTcXtczHhDQBn2OSHMy4cLhdfA7sr+64LBrco7YDlw/2WklgEgNqR+Ds8AZNVo8178vALq9/MSNSmFxE5oUOIhvD/Pgb6SGezJKWUKC0K5GmS2azfJ5uUNyk2XyUWqHjqY/tcbh7ObxQbpEt+RMToe9Rs4lSMpldHpoFSromqgRqBLjqQJXA3pVA9h7WDOPG10q8vgWJebOLs5/p85u/+ZsFDPP7eh3I+4EKq5WKayW4whKDuNjE9kKmG6wC+LkwDBhDg05iexT+S8X5usN9kG+TsBrBQoYjOqDjVu9qprlwqKQqThQz7rHn7invA6dDcF2YnMihzNIFo6Y641v8WaYAuJ9QGUSaC7X0rpzQ3qqzgHQEhX4gh5IlrF04kQiHEAgl0YpiRL0V2WXkp9HRKRvH0k3Ei5BLJhHJF08bvemdQpDS+vucuCUrV/Qf6gONqCJ3iSsqBw87da8k3TApNn1Q/y5guMiz2+ui9hJAteP6CEqrYuJoSKNgMP+Rq1t9BTlFK7TFA+I0vo4nOqqPie7iVmg6zPR0dgXc9jehLy7rmOPNvmUF8boIPKDXH5vhBjWyiUCuc0f6u7lzn44l8yYd6ebPCKeV0/rZLdMq4GfJKYmm2KlZl8hWOc2KJd0k20w3C5T8kihPa6JKoCmBJ0yoZm5NVwlUCayDBNh5gh7+AIBPazL4RFqe//znNz845Kyd+CHIyqb0Qk7+HEh33WewstrFshgx3afdnmajYtIJgjkjDQz7M/dCHJCkvzvq/JsLYHYWLtSG0Oy2soKcsuPqi8iMA9JQIqs06MWrsAQYo5y1pxdzdARlgFNfJFyxfYFY0h5ILUyKFBlrmBOHIi3oGDVhWOATcthk7Qn2VPGUwQ2s6pQE+1X0lSPDI8ar+JjRcY+Qh3NilB0xVQsnZJLuhAG+g0KnCJZUhbl8mTMNuZcyJWdnJXzvOrHlsK2neiR+zqEgx0+H99yF9YyUHIfDFYigDBah7az+zsGtWUMIKtI6eptT4oKcZEIURTekzQin/cnTxVciRy3qDccqFhwrJ/kSyaTP1C9vBoLQliAaiDjoG8RbyquysgtvLvwYdxce8rlmorAC4FCs2wqMybC0LUwOlEb6wnXl4DrQ65Lw01Ba4XP2p4LegcKsxQZKgNbZ42yO2+W3GshnLbZHJPDEXrVHOly7WSWwUyTAOvTdZu/YsLB9uMLmAcT6i3z++F5MK/bKcccdxzp0/BJosbUI4frsc8vwgpS8V8Nuc1jRazPin60C/QLRrgIsXXEY74AxlRyT1hZA7kxswLAPusT2LYirn+a2PE1HNC2BT3eBDvElCQLhQYCEBWMZsmFvoJQKWSa7a+oR6J6+h5TxIlvFUPMOoYSoO2TFsHbHeZ76rCXk6SlcimfMewqEhMhA5lvMlL7IFw5KoymzFUFIIAB4qwKtJtb/J4eC2aQ7ugbWJlzp27mQXmLj5qNeAIEmlDhnzkKnXyZXCbavf0/n5rDoCVn5Enu/+yOF3XNI2IxTnhpz0zzyyCP01tLh4l9ALT4gsBlvkFiOUZQZsXodKz2FyR3zphIWUrzhBFYUGpUoHTQlKUA5mLB6bvsH1IaiAB0m9k3M+7C3GKixae5PEFvJvfaymk8h9vNZn1YJVAlUCaxAAhUAr0DItYkqgZklwKjy6RF2CYvkhhtuSMQSABZk8BZNrC4fk7jqqqt8NfGXf/mXAdRzzz3Xn+Pz9+uPOuqopu3l88hQsWL+Vp5Pd77+9a9vPp2Js2Lq+ZufAcNsKcf/5PvIBBMKw4KQCd2sMxjW6/TF+5yMWpa3O4QjgJNTi054Qj6AJQiUjkwVWmz38qLsTIKdWJjByuwOWc4OZfAAX5GzNHgsqgY5yBnI4cRWJmYSTgDwVge5Sy2KJ7KX+HnJ3NGJKIYuwDNOW0Bo6Q7kJjjc6hqPiVHgkijvAE/VkxaFHf0TmgoAHtLroOVySsVP7wtAxQSuOh2m8NJmHK2meBQ7vp5Viiij787NYfaBu77EgwHOjhy0sSY43EEriuODA8jL9srA7TkloTtqrZ75rqCIFMR1SJvXkjcN7s3fzaauXmmxkrg/+clPHn0B6XJSc6oEqgSqBNZKAhUAr9VwVGaqBB6TAMPll37plx566CGHYJmDsS+ZZS5pF0QkLAxtfvrTn04dpiRzXM7999+vQBElQ/MFL3iBvwgPHqviE9DNp6XYrAmcpAqEBgbDw+6ixPksjeNzwLBj0v5CIGNxg+MGS7O2tdTypSPifo6IM2GBOl+MjDku+soQZ/X6ECWTt6cXsHT63lNm1o7A5AAG4zuRaiY1YZJqs4lmelb6W5UnE3Yzs1iLRT4KL6OtrXjY9vx8BCva2xSCI690o7wDvAc/glWGhn5Se2cahyiGhchxaOHfFI5IUbCAuHigzD7Ll8hqoqzOSQaYleaWmsCPvphucK8rUWgM+GAPxqwJWC2OntJftTAf/xQYrIBQtqcQ5iqZL5LBj4tTBlyHeOFe72BbPXTBGzSO6gjzutsIUqV0pFCoiSqBKoEqgb0ggQqA98Io1z7uPAmIPziZ5g8J+Et3oAjbC/qCRoq94jtV/grFW9/6Voeik8kof85zngOIMjRzbjPdZg8Byf5gvdjLr//6r8c+G1cimghBGNKLtZCwd4a9VCZkzQoUHGbXJjjs61P5jNa4DIxLTXeY4IxgdqQ765Zs8W9EPAJ4vMEIFeuIdsuIjMWDJnLmE2UBf5I07oJObFanoOHzZTTawzx+RJsJxPHO0Tvb0+62P6LMhjgCL8yQBgDsHgAsIQoax0QpUxNNCZg7wK0c8wgSc5hc2gIF6EadyFAOhAl/OsMv7fSB88ZcD45j5HT06IqXRqFcJ7EBV1FQTeDT2oUBUWg+L4eZ3eP2Ut5TaRBXgXKFjp8SPCbmyOqPQJMw6fkUmbUikV5rlExRdP5H/jKIl99Bpwrbo8uzUK6JKoEqgSqBHSGB+hXoHTFMlck9JwFHcIFeRvYFF1xw/fXXM9GAH3993h/sDe5i4hAKu62Ihk3jp9Al1MQOa+YLYriSswzTp9BkYzlT59IWi5AVKwrhYpndfvvtMXB9OcbbfULEznIzcCHkUr3wvL0J/DBzGbsx1mMls2vlw4H5/i22i5XMRs+gLMg2mxV9QhMoI0P0USaugnvR3xZZJfy1YO92XPUW1Cn8B6SVn9wiJV0TXQnw5uRl9Tyi21YGwBIATk5U2gzKJPIU9OUB5MhzyTQNLWhN/NZtZWAO4vyJHDoog77xbakLKxpWXi1Lk4A2L2HxNpYZ5yCGmdjSivKUp0woGJ8lZyBLcxTTC2uFYDXILcDL1Sjea4/gVvABeV9DvPjii/kcHQgqUegVcDVHR2qVqRIw1vxDmwe6Nv4fZRw3aG4eJdP6KAT7e1GaS6MLtkjzw/aCdPp59jRsp5i2/JQuE2pq9Vpg/SVQAfD6j1HlcC9KIDETfy+eef2Od7yDZXbrrbf6u0Tstne9612WY9YbuTQPH8rMt1U9Yoc1pbbsrWJiWxr1XWLXiSeeqABo55srAhQBw7/1W79lR9E7MFiYAh6G9PxcHzxchOb0ICCaPjqW6cW5RIbJ2UuhLt/CYaYzrKWNiFGYNU5LFGh6PU84UZohy1nA4icNr6FqujDTFPWy06ALL4xWdA1XRlBaT1tB0WWzsW70wbPC0raMS2l9RySAybxGK/wLIjoDgm2ZPcw77OCib5AzwCzkTuVUpIRqDZd5ytNe4DCrIlhLpYFGNOFqkyth3sBaT0FcDkTL70/8xE9YrzwFjK0A5UxNaHbZQFbhgup7ejfHoxj9FgeI9yubF8RLmEjxIVpCf+M3fgPiFeZt4vPhgpqDpVplBRJ4XNkePfjNg/sO7t/3JF/1378JhudsnCL5FMiHPvQhew1V4Wb91V/9VW/33Hjjjb4Pgqjl3WcsTj755Bx8kIMHT52MePe7320ipGEH01yPs7fP0TN+YftFiy1/tMKR+2uuueauu+6KE/8Zz3jGq171qkU002dBfaX8tNNOa7W1jJ9897jlYEJcdOHee+/N8rWMtirN1UugbxNaPTe1xSqBPSgBu4hvSgmype9MKBtS0kzA++67jyPfT5+2gn7f//73CwIrkC2k7EBFbotsLYXIWIkmM7Zbp4hdiGObMceGizF37bXXvuENb1CYSQoMu5h0jLnA+yaRsRiblU7hASIVvHKlF967ZljH6pX216p0TRnYlc/CZaSmwkVGiS2WOasVlDkvWOGIhMnS9Kw8L14eCPcKOjp40K+EvvUIw6ACp8A28rZ47+ajsAe7PJ+gSi0SY0z76Ui5OxdSvwzLU7WYmz/2Yz8mYAtb0kAWNuwK8pkgNLA00U0AvQK25qZLMDkA0nriVQJzk4lvboK4mciqp1F3rfhp4YWQQQIBYQ47dr/3OBCxanFUKYCrzGuZsfslrAMoWMlNXlfpSJe9qTnYcMEM3iixTuq1iztAK/pubXz5y1+edbI5DRdpcSpLtcBKJQDwbvw7+Og//MPBv/2bb/71X/lLzYf8wPcf8r3ff8j3fNf+/RvKP+tw06i3ve1tcOwVV1wB+poXH//4xznZfRfTm1Nm2Ste8Qq7GIe7z4jceeedoW8qXXbZZVTx6KOPBmiT6bw9P5GDBpEJE8U5KcrpJ2q+OeIkgjRfsD9aoZX3vOc9XOFQNw84NmblPK3kbhkpf1Ctmb+MtKlNSqHM7mq6PpfRXKW5YglUALxigdfmqgTaErAfPPOZz8zfkvXMJ51vvvlm9pk0HGhryW7B4Dv++OM/85nPAI1ACGMuFlJzO7FLqZW67WbW4HfZ9iTYbTZUF75snKxVFl7w8Fve8hZmKwvSlikszNrz9rKvYcfUK0S2q0OFAQkbvCucMH+ZyzgX6XKxVl2sVYay6BB72rgoLJSkokuv5QcPGE0xMUa2RKgpsF0dLO2y+4ENP3WHzUH+0hwZBqs/glco7OKEeUcmBjHobhf3dKyu0RnKM5xapoB1wPnk1DK5OAFZ6uYRbEwhPTUQGQtaqoBFw4gYF/FbdxEtKNfUy+xTwKgpYLYybRn3WoGK3aUFfpFCx2sIlFxCGfcC2hXTYrMLqPlcv5xwq1FpDItgN4tNTW92YsMt6P1nnx+HewV7nShhglsTLIC+ieiYDNArat3kIe1OpV8L7DAJWP6hX3/g/fOf++attx/653/KMfON7/6ufc856l89/zjnghzGnWmHoGDU6dJLL/V3E/MZRTm+i0aBIxkTRyBXmvZ6YcreZJoo88ADD5i5559/vr9G8bznPa/om9mnfH4qFsQrAWNz1h977LEhe8455/grjPC2kuwWwdvWQHAYvfOd72Tb+BuN4PcLX/hC26jC/D4O+adFs+Dtb3/7WWed1XwPAhIWyjZZbKkc6y9+8YvVuuOOO/it0OHkEiVWHkuf+MQnbGS+Oa+wd8pU1BceZyfOXvSiFzEwCksKu+D/Bx98UJVmvJdA8hTy57K3uVtSEuK2zvjcCWZImPRe+tKXChfL1C7pYc8hOJ2So4yeqqhF1BzrIxBisS75Sgu3gka96Ua2nt50000sQH+sUb4/eOklC/cI3Crhg6PpcmG+JmaVQAXAs0qslq8SGFkCVjSrZIkAO3EnxwLKymku9zKzmluIcSAE4Z43gcOQFdNfPGJiFntxZEZHJZd1PCRhe1adi+dYjt0OXASGmYAMQRuqqIvy+gUPswXtWPAw1wARNemMyuAgYs3WAV1w16WmsbDL8kfkzJity9CkMGPa/seoZcozr72wp/v+cFRpr0mzZG5Lgt0DG2iakQQeABKGxk+9c0VjlWma49vC53Y1Cq4Y5R//8R/fLgZ2Vrt03jUrz2U6UDkRGPPFVHIuWmDWT5aiWWZ+Oa2AsjLWT4a7yWitkLZmFgoKgLiM45RMLNdrwGqZku4MbneAlqGMLJ1X2HoLLaPDQ3fKKac0pyo69D9TnibgITGi5rqtzMRLQwxiHYGZNZeLGS1TuJv5Dl1Y6IBeX4KwRBQize6UzJrYTRKgG7rzzX/4x69/4Qv7f+vt//pfYNT94sH7/s/fPXrTzf/0//7pO1728n3//ntpwkwY2GkyKDfwEv3UBs/SXAQoDYyZQVE5P2HFk046yaHoyy+/HPaD1lLSpkBdEVEGksyBiDwqxCmzFgE5+zVNRnaD52/lWnXBVWek4UbMUHsg1sFph6791S4AONQuvPBCPDRn1kUXXeSAErhoJ8rpJCXBe+8ocXs5tq36xz72MZn+BqRJDS7yHynsGLZz1IwNTi5B6SYATmERb+8U2J19NFROLofULD4mOA6xB+FbTKxFmuMvO/zww7moZALGDoLxZfPjEx2PAw+ar5nCuqrfdtttGDjzzDMJAax95StfKfJhkQGDcaWYQ9c+bupEGEPuve99r1WFWHCo4+iLlFiLSMwftrS+Pc5a/X9OCVQAPKfgarUqgbEkYCksf1Kl0GQD2TCspIywbC32EmiwFPaUecdV6SvQqcVi4/u0HJcXdQq19U80N0U2JfzvEgzHeczEgGES4EN905veZA8gooBhxiL3M8NUx9Fpklpxx0vTEjGawwAz3bgwLHKxG2y9yjCdQUqmRqm4Yob7mwtXRJ1iYIadPmmP9EWa6s6Bavrb3RFP13PI1ll0C0os1dnQZr3FgclrHrFB5bObecfoIRzrihBiqTcbpcm8ZtYTCeEgJqZIMmtbOEVoRXk+OPYrvwbQy4YWamYEI8vFw351ktPb/taZJk2LVRqCDZi2SXdHIZMIZmAxJ3oD8VrN8K+wcLFOveQlL3Fn18a1V4g0myuZNbG7JbBx+vnv/u7R2z72nRvodxP+gppA6759h37yvoNHPP3bvo+ef8thhKkC4d+h4VupE+gFUlJ4b7oCb/QfQfrp55vf/GYVRXGdTYMJQwG89Nr5Bm/79/szExP9gB4hBeY9+9nPVhL+vOSSS6C4Fg9mhwizaKoy5ojvg0CY0q5WyWTmbgUAj9VqemB9IcX7C/Ym3x0wm2BLG7Hy4KVPqEiY1DxfYLMVo0scJ3C+iHQi2NYBXU5z7p66AFfoWvC8VFfeyoDtkmMp8Bc6HCMHjFUhVXIAgAXDr7rqKkfNUQPLLTvYcz7cgsbRLxMGZurcc889QtMqMoHOO+88ZKUZOZYg0N2650VuR9kLYzUxnwQqAJ5PbrVWlcByJWDJs/A5OeONGv5IGIlD1CEZR2I4U7UtkMiPaNG0QHNtwsm2EEv/ueeeu1zOVkK9bCRa03cWoctu4aedQOzUJhEL8u677/bOEqOWm0BUPGDYVuHyRjE6TVIr4f2JRkrTuiB8CrHbevmDdYHzwiuIia+WYk/UXLNUzHoBrhwuYF6wOXL2eyd6WxaRrrFjouX7Q9zz5l1eXoC7IpBFiNe6UyWQyQK78gCaTSxUMwsAFshNdCgFJs4pmTHrLRdeWYQ/LRdMZNS06yk0qwDTmceKYep9E/4dT929JMn61ESLcvMn3Qgdd2mtMFWdVMwl0gv9Uh6rgb8SbBn3fR2xHZfmmnSa6akCqQV2pQSAXfHeJ/3Rnz6GekHgjevgIQf3Hfr1f/rn//Hfn/Sf/9Mh3zmbAU+TTZOtxGVLopb8OKaGsG2U2bFkMNKyz7Q44YQT2BvMEgqMCDj3yU9+citqyafJ9g4ngbVry/b3LOBV885O3VRy6YKlHc8WOO0nm6evfvWrTz/9dEYRkOm0sx0fk2eccYbTyxg2ly3OogImsr6YblqRMNfUsvmKhD/rWc/yLn1z3TY9fZClHBHHVRMAaxcRbz5rEUESAGVt4rxphOORK7zZIOySAH9yEIRypSF2FppwtMPVZCs2LpNAnAFkrqQunlFOOmyHiBPgzoFzk4kBcCi0Fo2Ur/eZJDDb/JmJdC1cJVAlMLcELHmO6HhfxTckrrvuOou4U8GCDwIR2X4UcLqGaeWEjARbXICOe7X8uaO5m17DitkAwpi0A1c2AFdy9N0O9Lid+V95o4nCI5jEPhdI7M5FTYyqu1bfxzTqwBWT3ebH4xu/9bYwM1P3N+S1aToIvrnUJXDmxaxvOc7U6DoX5t3P6dkwKc2u4pBqGlLrzP8u4C0KCfqmL8xN55MdOzSzsjxO7SNb2Yoh2AujivEymum2BRYFJjuTND/lOJ9J21mlaL72ta+Vb/KyRI14aYUCsIM5g5xdzEKUMG9zIWI0n3322RYii1KweqmuOyVdE1UCkcDBRx1V8Fmppm7s98thaC+sA8OzCgqcE4fkgS2nJOhtIcLvAxn6CQYLkDp9RsNhLesbGCaflposvmIlFFxqTVXdFLDz0n/HoEw0FPICVyGCDfMxP7lZ+bOk7Y85rIGCJbfJakoyADgf+cEdn0bccWjE7bAyUUAHpC9NlN0WHQFbQQURBcFYcWz4vBSzevCkq5uckigFJCwIJrKAuTeBHYeWsCaI8TZFgQEtyvRIlZLgg3CSWewXxCVef6DbUwlxXVwVCiWBn6TdBYetP/EjaLrJUk3PJ4EKgOeTW61VJbB0CVhAnfV1Buahhx6yEMNvVvyc8tW2BdHa6qiMpz4XYcF1yMexnDxaOnPb2kDZHsIFa5KH1VWYctaLAeoCjH1Dwm7Bs0uenKxEZKd3kSdjF2hBrUWw0Bkr4ZxzNmDedAA4Z4aX3ehYzIdO4bZpiJTMcdtac2r0Le9feYGTL4AWrTnDu5K9onsU0krIsvRBBDBVZDhh3om9VtjJSQc7ma2QqiUCHfiWe5EZyvYFbuFqBz6REsBB1hpSdB7MRtZqLLgESLC5UQMVcjk+zWS3RCMiHiW+BOsCDzmKUvgpnJecmqgS6ErgkH/7bw7+4H/Y92d/5g8LPO4h8eeQ9n/90EP3/egP7zt044jy8IvWHXnkkTYgX6Xy5Wfari6NBahy3LeQEsx0mtffmwB0zQtv64CvnpoFopfCs4FtpXxPwnSAMMVac1Ao77sedthhrSooezkWsLRRSuRDWWaNqWfjNp2dJS5zsNSFxkV6ETfX9MhPOBPoNYsVBt21XgonIV8s2uS1BR9zzDGIQ8LNMur6npY3h/P2rz/11HwqrS6vFm8Xc0tbPveFJlJXX321YHIgt/XBq8JcXYAuyIoNkV7llTQKsK7vYBGsWG68dZCt03xectYdTeBQlSZ6Dw9kyEPhIhAiqstIa2jm+FkB8BxCq1WqBFYhgSxwTChXaa+56km7LKyuFPCzlNxTiVbH7Z2ufGKaHOxz3ttx7oidymD1UpPzSA56qSXaAwlDxe5QMTDDOy7fNZYAIV4f7HGKUsiUn8KF8oj0x+JzOJ0DBw7saP6H97Rbstvxbk63Vs1ZqgQYi0JAwraMdQY3Ezb2/cRGvQSoGGuSEckkhYHdGf2iUmLImZ4wsBOGCenn1KhgkfM4bHShKsuINTlfzGIxM+gtHd5DcbeSCBqHSGm9akgRRU0MlYD953u+70nPP+4b77n60EfFPx9TooP7v/mNI478jh99sgjpUFKPlwO3vPEOa1F1SBhc9E2HvHrKP5sG3PmDHCVzPt+EcmQ3x6ZCw9ew7KqOPygf8PY47cf+N5VaOQCz11mhQfnAtjPD5f3eUhI1gVzRVO8jgJcCuR7xH3mNFr41EwWllUn58KkhSJIny2eTbe4OvmkCgBRHhdt5xMxKi0D4KXVRcFpEINpUVUYHNVHYSELvcOKVBzjWW0syEdFo2rWbc2nxcIHQeIafEXcAWxACDidVa4s+CqFzu/uitQ+PcaVZbXzRKkS41ZAVutBQaOqpk9vy4x8XxFaLQZKnTfY05BVlcZFmj5oFanomCWyciZ+pQi1cJVAlUCWw4yTQWuhsY7aZAolZtP6AgUz98mEq9gEr1pWEcBCfa3avWTsOezvxJVgEADOOu1varARr+TWRAI1iq3GjxG2/JlztWTYMh8tENq8BV0EqRmdXGsokk3Xr5CTE6+07YV6mKpeZRwo4bym2z6r2ym7uEt6N9AhN3p84y9jQLqZwC2zXOd4Ve82ZSQLRUt/BevSv/+pf7rtn30c+/u1f+5qTz9849NsfffovHHrKKU/6kR/e//jh2Dko+4iGYCy1p708OCjY++gt9Y72ikDy+/D1KOPAS1Fp+QCk8CYORWtpfnkUNuSbgCBrApjpiO3P9qqu6dY9/WQJRYdbSggXAHaCI2ykCUFgPNg9TUBkoW6rru1YpgJ6oS9WYHt0GLDhqsKLDTNrFxuwoip8UqmimLmPSSXt7wo0uxCGuQZgdQsCsoh45UotHPKL8Q5gWHVnf3AVd3lq4Z9TjJs7K4kqSrIxNM2xrpU0pDCaWtedkqMwnOwMi8K40kE54D3vQ9Obpr9AsmKGIHUVq9fcEqgAeG7R1YpVAlUCO1gC2bRKB2xIdjUGtGON7F2Xn/Yk27YdiNVb8LD9id1gL4wLvGcfYiKI/dqhVece1lZP4cJJTewICRT9qWO6JuOVETGF2Z2mG1O1hzHGPQBsYnpFgtltOnsf2KyHeBnrTGR2M+OYjWuym/tMWHDXz6Y9in4d/R4h10dzS2DDT0OhvQT8ta/t+6v/9Y3/+Uf7vv71Q37oPx7ygz+0//u+95AnfdvmEaXRjinNzefiFQOAzb6EWxHc9jmVlWQdOCniZYd4V9lXxES28z2t8qgm5pZABcBzi65WrBKoEthVEijbXnoVb/cmFn4sEMQ45uIFlRVgMfMxM4jdObZzeTUIKrZ/u1ALluYS9u3uZO4qedXO7HAJCHSIdiaIscO78hj7zSlsxiVXpos3SkTFJ169feduXvtL43I4uRQTchHt4dsqF9DrMEicXEU4hWbJqYkqgSVJgNI+QfkbX9/3TR+A/rZ9+zdPAm8ooqe7AQBbgrwo6y8ttb4M90Tfa2rfPsuX15JZHV7hbsbkq2wWkUAFwItIr9atEqgS2M0S+BYTZLOjQkOOIeVgJDM6l1Nh3NgKO3vJQRsw7O82+UMI7j4x4vBVBcC7WVF2Zt+8AUifIb1dg+syYbmoTElv6Dk2qY8Qr9Mc4roivQbKmU/I9nGf1Y8I8IrumrbeCWwN464RS6tf9eeOkkCCwZtvonpTd0exPoTZssnW6dYjriIlZaqgegQ106MKgGcSVy1cJVAlsNcl0NyKIgvHKX1Ok5EdUzshJrFi+Qp7wUkQmIXtgofL3ZtF4k42s7qf7XWV2qb+71wAbFq5xI6cnNQLPil3F5Qr7WU8U8+0MvV85a5g3eKZ4pBqibzOwZZA6s81kQA9F//7+7959B++9k8/8GSv4278Dd56VQlUCSwugQqAF5dhpVAlUCWw1yWwaaZ8ixDEhH0QMqEnpjm73CXh1CXDXXkRJ8D4wIEDBRX7oKVzmIBxPi5SjfJvEWj9MZIEfN6Gv4YG+pMezvnnY1G+yOIdvHVTOUy6fKHHF2h4lLCdqRSsa34F6Hrd11dhMo/iYDKtvJ4A/aZ3TcmtWx+bvNV0lUBLAj6FdfDgN//b/f/37//20f9yyncfUgFwS0D1Z5XAvBLo+0rEvDRrvSqBKoEqgb0lga5VzfJmhbtagvD1SKa8UFVMeTa9D0X6swe+lQWQMHaEhb3qAwm78iFK9yS8Y5wvZHaba7VSf1YJbCUBypMrBaQlct+qyrLzqb1zy8K5pkYu32GWcHfxGXEn4dAbuaYAoOvyNdSCdc0U7qRuF7o5y+5IpV8lMLIEBIAPHvzzr/7zP/7tk376l/fv2/hmc72qBKoERpBAjQCPIMRKokqgSqBKYLgEmPutwnLEskS0itH/GA74y7+U6V1iBVzQLwAgUOwPP4geu5eECJ6vGUEI9S8EtmRbf7YkQJGSw/mSd4BLgeUhRqFml79HAuU6ouzt3FxJ40Q+DIwBl7+VInj7/9s7E3gbq/WP732mzNEtNP2vo3IpQ0gaTE3msSJjhluUpCtJQqWQMlb3Rimk3GtIGviUW3G7SBmvKIncimigkOE40/5/936yvPbZZx+6nM45+/d+Pu3Wu95nTd/tvPv9vc+z1nLvfeztD59k2nR612FLUCQsR6ciUDAI4P49uCvt9QEHAukJDYcmnFUhfOehgjFMjUIEcp+ABHDuM1eLIiACIhCBgFMm3mvIBnMamzfMhPER+bBz9+7d5jdGA7BBCzLYK4xZ7IccNDNh1cSIKrLaC1ZpYhBO1iJYwdczoVhlvLVIWTvcv1ISCF0WpiKYGTPIW9Ay/z7tDY7JXRfywDKnEd/jSOjqH22sEcgMBDa9u2/NdJ8vM6FC8/TLOhQPhWv89jc+/AEyoYD5OARNsOa5zRHg3RPrDPP3xStUW5nCccaeq4RgsCet+wNk7XSWlHOnvJZiAUhXxCUoyx8yv1Asuo49f+/8jdOEK4glP3DcH+y2wCkJ7gmU4tbEKZb0x1vE+umawIDDnR5PghrmzJnTokULCCxdupQfRxYCPJ6CsilgBCSAC9gXquGIgAgUQALu+SBsbDyXIDacS80lTHugRvixt7I8Q6Ar0MP83nM4YWyniGTirvG8sUbub3ikCOuVTvMFAbZB4tmULz36EyT/fjiw3LNnDy9c+PdGuP6vGtfzP56qqZB/bzZ2r8QNBiocG7PAv7qw52xHLHpnnJkSIlCwCISHBYVGF3xEf2/U7h83nOYL+IucndpyVHF/wjECEpF4nByoir/XO+64Y/Xq1dWqVWMtAHYxGDNmTNu2bW+//fZXX32VVQCQncjUcePGNWjQwP4SEajt2rWbP38+61kQhWFtDRs27Mknn+SlKnWS061bN4rYPAVKUcTMnnjiiaZNm958883cDQhcYuIPmnP58uXev3HuGPz6cDfgoBTS98MPP0ROk0l/+D3i/W/Dhg3Z/JZMCnbt2vWNN97gZS6tEPQ0YMCAHj16eCu0pqN80k9ueps3b+Zl8S233FK/fv277rorir0uFVQCEsAF9ZvVuERABAo+AXv+yG6ciBY0MMrEo1PCk84pRyWI5BIlSqCEebLhk+cPPr2H5fD04DZ2OqEnj+z6qfzcJ8C/HDt4vkTZEpzMwb8WPlG5dliOpfH5OHHLl064AU+o9hrF3qpwai9T7PUK/4R4eI0yLv3LiQJHl2KNAH+MvkBINx7jz/Sn7El/rf9ef0ohnKMZ8emNH0k6M7kQjlHjY6I5tCdwaIekI/kR6fFzcM011zB/fvLkyfz9YkNU0Zo1a1q1anXbbbchKZ955hn+xh9++OEZM2Ygd1Gt2PDzwfLpVapUwWU6aNAg+7NFAKOiEaKuIRO9jALj8ePHUyeXMG7fvj2/IH/729+sIJ7ncuXKef/2TQAjv+mbq40JQfzWrFixomrVqrxuq1evXocOHR566CEKdu7cmRsOup223n333ZYtW27cuDE5OZlL9IGfM4Q0P2TWuquQhN3uSNhbPwQwgSe0TkFUt13F182oKU6OK5I1k1IcNERZbxNZ01RLcSqkWq8x+fTWms5aSjm5Q0CLYOUOZ7UiAiIgAiefgPc3NWvttuUpb/SzXqIgv8Hk47Uzhew0z6/qZ/duHo/WrVtnooi35vaIY1Xxc877eJSwO+zUZZLgIGrODpzPFoDNg0X0PmftqnKiE7DvkW+HBM9/fKEcuHf4RLUibu3Tm/CmeRx09fPt8FTnffHBg6/3DQhp0728KHGlIib0LUfEokwRyI7Alo93b1mc4csIOkLt8Af8aQd9vhR27UpHTcZnJH74bMpppx8M+INyN6R+gx/n1k6vfF1pH47hIwWz/p+bw+LFi7mfv/XWW6hf+/MkIJnDa4xUa968+ahRo7iTcNPmroIYrlOnTs+ePR988MGBAweatnRF3J+55dMKORx2SnHWsLjkkkssk1KoX1c2awKzsExyuOFcddVV6FXvJauQfBLczbi0cOHCPn36oDbZZKFx48ZTpkxx9vSKu9yQIUPI5PfI+XvJv/vuu6+88kpE9cqVK//85z/bchuVK1d+5513KA6xe++9l19ALDt16oTTm/r79es3d+5cbpWVKlWiQjzbYCGImpcIFMGyb9++tWvXxv6TTz7p3bs3mh8OHTt2hCq/m48//jg/uAznP//5z+DBgydNmkQYNr+VlCWHSnCAcxN2nVfi1BGQAD51bFWzCIiACPzOBLI+UrgO2SUTqOwZ4/K9CWz4RScHNWW+QT554LADZeXEFb/oR7L3EEfHTz6HtyoeiXiissOpYhLk8Ik8JjqOg4QdWU8tn2cIqrU39PYZZYzeDuSdtCE1PvbJ8xnQ3IEDhLT303tq+tYkrn1aDr5cr5plvJABO8+7xAryjGVvK3ghUrFiRdLBVxRHMk308pmjsjWM+Y553vn21RMRiEjgwtpnJMYf+mhaSube0wKhwGaTg35fxq9hzv7A/u9O2/8dN+TgPdmPUeG06h3i/3RNKR/vFXOKhf7oo494n8UfuPvjtYTdjvjkXsSLzlmzZuF35WZrnZw6dSqO3yZNmtx5553LG9+U8wAAHiZJREFUli3DGWv5mzZtGjp0qKVRntm9ZiW+muKfffYZYpVgY0KvXetW1rWyaNEi0jiQCai2TPrDDY1lLz744IO//OUvlsnnhg0bXnjhBS4hU5Hr9JY60efDhw8nWpuBbN261RlbAhn/5ptvojCJT6EqbqeWj3uZmydpytIucpc03m8qoV2Ct2moTZs29MQyx44di1T+/PPP+c3q378/8dh0+4orrnjssce6d+/OzZYKp0+fTmA2d+/WrVujmW+66SbuzPjPX3rpJTQ2P6B0hlJ8F2j1119/febMmbxfoMXnn38evPYDZ93T5yklIAF8SvGqchEQARHI0wQiPo54e2wGJlkJn/Neyi7NUxRLnqCNTZh5BZs3hzSPCzysoOJM4PHQwCt2ost44ODgmSBrEzwf8IIc5zYHCQ7WcbGEfXJqORjwRBLxoJKwfBum99M1bd1wn6Hwt6MfPIcdPTk2xfMNKBgRn5Ygh1OXwykHl6jENecSdAaFbwcilkdSeyngXiLgvbHXBy7Hndr3xcMuBzRcnRETNGSjs+FHtFGmCIjAqSXgj/u/WoXPLJe09IX9uz9NxP0bMFEb1MG/amH+F7onEqAbKFYuvU7PIiX/GPzrPp6/XGRYxEXUbVDTpk2bN28et2Js8Ely2yEfsYcOJJ6Z+w/TZRHDdevWtba4f3I7srJmbGnvJ5a4Q/GOvvbaay+++CKKEUn58ssvc2f2mpHmHmW1eW9WTP3lRo0DlvhnXKmuCD8uTCfmB4I7JwaMi7scwSnsJli+fHmEJSraGVsCny2Tn5n0yymC/LnnngszYMoGMhvPbc2aNe11MIKZvdYQsYyCATIKbpLoVZzGvD2kOJKbSdH8hDVr1oxXAGvXrqUsavbqq68mH3HOXZ2CFMGYd47k2HRlwrbpJNXCgXGNHDmSOdiQpyyvGAxvWPd0eioISACfCqqqUwREQAQKFIET+lXmIYbnCY4cEVi1pr7M2B5rCMDjQBWbMHafJMg3Mcmn93Cq0hJ88khhgjSKRsWAq05s0xPrjH3SJeshny7BMw0Pf1kltFPU7pJJdB4oAeLEuVerk7ZLPF9yOKc3CXN3k4kBh/cRk5647kUhbB2OYuC9dELG3oJKi4AInAQCeHCDbt/4omfFN7y/1Cdv7fsM3ZQeT55pX28TgbiMC67PuKxdiYRC8cHrkV4Ueu0tjSpD1HHriPjHjgOTOcAsEUGw7tNPP808XiwJ8aXUxIkTqYF7LzqWfJN/qESUpNUcsUJ3iYBkDk4J7r3uuuvee+89TsOK0Kh3DrCVxZJoZOKQmUhs8tLycSbjsCVNDwlgZkoz0pqu4p5FSbIS5H333YcP1oztExWNj9oa5YcpqwIfPXo0Q8MDzMJgyFQClZkKhGC2Iq63+G/xIdup7TtI3BNzknk7gIO3evXquH/vv/9+DCDJy02GYB0gB++u3bfR6i4T8UxM9apVq4hOR2+jk11b3v4rfSoISACfCqqqUwREQARimsAJ/Yp7jZGRpv3AZ48L2XGklNcg7DS7Uv97vvXW27TV6fJPtCeuYFjfLD8s09tWxEvKFAERyHcEQl7eI1I33le1dfGyFQ/9e3xG+gE8sUfyg6MK+BMz6tyZeH6tYqaMjykYddhoLWThxx9/jGg0Q1788aKQt2yuHOoOuVujRo1evXoxu5XtgkjYvY49kxDD5NhkVysS5R6V1YB2qR+XqWsuLJG1Nn4O0MxECDPP1olJSjlLVCjTjMlhRStWoiaBVK5VqxbxzLTl6qfnFhfNWFjbgjen7pIlmP1BGDMHU3bRsczgTU5OxnkLIvrgjGFC7DcCnnrwQpNvsd8016hRIzzVVE5UNvn4onlr+dRTT/GW04q7nww67/rP21L0NsHPTBhGxruGlMgFAhLAuQBZTYiACIiACJwwAfeUkF3JMIOw0+xKnZT87Nqy/OyuRmn6NxSJUpsuiYAI5GcC/lLnF0rP2IfW884HZkTMDylxLo5fQqGPCrMcR8rthQksjz76KL5K3Js4UZmBgkcXgceSzq44Zrh2sUEKIvPQkCNGjHC3JrQckdJeAewKRkwg+XDGMu+XWbIYMLuYRrN6eiOWdZm0jo8U+c282WuvvZZ8dhTHmUzwDhHaxAyjjclkti1bN7Hd2vr16/FRu/Bsq+fWW28l2BgDdDKhy96AGjPA+43uRd9SnBx0NcHPjP2BBx7Aq4z3G2FM6DJpHLyMCDL0CsVL9DX2zEOmLGRAxwsF+ozav+iii3h9AAFe6bLaNvNTEMnWnPvEkonBF198MYFC1O9QOwMlTh0BCeBTx1Y1i4AIiIAIiIAIiIAIiMAJECC45dv1B3wpCYFAHA7goNrlCHmC49OTtq85XPLcnPfgCWsP1YePEanGVN4JEyagx1jemaWtkKnoYeeoRIOxOjETU1nqyaJ5TZVhxhJTS5YsITaYYF3Wzwur307Rum7yC0VoAj8qXmXSLJv8/vvv44wNk3nIyLA1tHC6IllRoZTCmArx7rLdEQKYebb//ve/iVgmn1LM+6UJ0sxkYVEulnFG5y9YsAA96brHVbyyyGam7zJMIICCeSUY4CsuV64cCTyxvBcgbpmw59mzZ6NmyWSbJTh06dKFJRX4JAd9S0O8HWC5ClzTyGMq5+ASrP7+97+jge2UCgk4p59oYIogcZkOzXBIeOU3xrRINy677DLrElXpyB0Cx4SQ5U6TakUEREAEREAEREAEREAERCCMADKJY8nf9mz/iMWiguKqcJnUpMK+ff9NYlksny+z5AVpTR4uGRcfFh0dVk2EU6qNkOvJMvEWZmaZWIXlk+MuWR3OIGI9rh1vKVfEW1vWTG+Oq8cS2bWVXSuuOAbZVRvxUnaZVOitx7XrzXSNevtsaZQ5qhhvNlHTrmyYvU5PBQF5gE8FVdUpAiIgAiIgAiIgAiIgAidMICMlY+cniFX0bsZ5VwRqdy2ekORfPXvvlnfjfZlx+7Ym7P8xrUTZHFZ3z9rqceqr7Myyy3cNhRmEnTozbyKiTdbMrDneSkhHN8juanb52VWYnX3E/IiZ3m4Tf46zncWfmUKcnJyco723rNL/O4Fs33/871WrBhEQAREQAREQAREQAREQgeMkwI68X6/c9+FTCb4i6TU7xleoV8wX3JMIP6Nv+5pDy6emZPycUKWz75Imtg6WXTvOumWWhwgggL/55hs6RMQ16lcCOJe/GwngXAau5kRABERABERABERABEQgAgEE8NKJe/fsYJvfYqefn8jeSObgJKQ24Ms8tCt92Yu/ZB72NxxaismnbFIboQpl5QcC3hhpqd/c/8YkgHOfuVoUAREQAREQAREQAREQgXACGamZaxf8cGnj0vGFglN+Q7OAg+ssOb2UmZGx/p8/XXhl8aIlWQoLARwyC69G5yIgAtEISABHo6NrIiACIiACIiACIiACIpAbBAJEOiN1M1mhye+3jY6O0bchGcz04EzsfP64kOfwGIPc6KTaEIH8T0CLYOX/71AjEAEREAEREAEREAERyM8EELdsPsSetGlpaTmOA+nbvHmzFi3YPDZHWxmIgAiEE5AHOJyIzkVABERABERABERABEQgNwkggO+55x4E8HE2euGFF27atMm7r2yUgiHXcfh1m3rqvRSWw6n3qitvZnbqDLyZzjJrwtlnvRTWnKvQFXE5lHWZ3nq8BtnZkB9m5q1B6RghIA9wjHzRGqYIiIAIiIAIiIAIiEDeJZCenk7nGjdufM0110Tp5Y4dO5566imMj1/IpaSkjBgxwkTj6aefXrVq1QYNGpx22mlWw+TJk3fu3Dlo0KDExERytm7d+uqrrw4YMIA+7Nq1a8yYMatWrSKN5O7evXvt2rW9fZs/f/706dNnzZp1PJ2hAwcPHqS5RYsW7d27lwWQb7zxxlatWqWmpj7wwANW7RlnnNGsWbMaNWpgbHUePnz4/vvvr1OnTtu2bV0rd9xxx/79+11PuLRixQp31fJ5OzBkyJDvv/9+3Lhx69atS0hIqFChQs+ePatVq+YKKhGbBCSAY/N716hFQAREQAREQAREQATyHAGUHnovSrfWrFmDAI5ikPUSCvOJJ5545JFHypQp8/PPP48ePbpPnz6zZ8+uXr06xq+88sqyZcvOO+889C0a8quvvvrrX/9KH9iqp2nTphdffDFlk5KSVq5cyaUwAXzBBRe0bt2aUsfjkt23b98NN9xQunTpfv36nXPOORs3bhw7dixa9w9/+MPTTz+N0i5btuyGDRvq1av31ltvubcAb7/99pQpU955552bbropPt6mRvvmzp3bq1evypUr22BR9SVKlCBNnZQ1gAhgxDMtNmrUiIY4/fDDD7dt2yYBnPVfSKzlSADH2jeu8YqACIiACIiACIiACMQWAQQqblJcoAy7f//+eFw7dOiwfv16vL7ktGnT5rHHHrvllluKFi3quGzfvn3t2rX4eJGsZOI3dpdcguJFihThFI80rl08ujNnzty9e3fLli1r1qzpdcnSASQulvPmzbNGK1WqhMDGhmnPXMXx+6c//YnEp59+OmfOHBPAnE6dOpXeTpw48YMPPiDT1YkTG3HremL2r7/++uLFi82fTNnVq1d//fXXo0aNKly4MAUvvfRSZ69ELBPQBmKx/O1r7CIgAiIgAiIgAiIgArFFAF8oEc6EOqNvbeT4SMuXL//ss896QZQqVQrdSPzwN998gzcYPcnhNSBNDeaO5lLfvn1vvfVWdCbBxg0bNtyyZYvXnjSR1Z07d+aqq6RQoUJEYrtTbDgIkzZRTT7x3u+//37Xrl07duw4bdo0Z0kCM1zKHAcOHPDme9Mm3SdMmEA9VjmfXgOlY5OABHBsfu8atQiIgAiIgAiIgAiIQIwSIOoYfYuytfEjiZkkTJwwzltHpFixYmhOnLFo4+Tk5N69e//4449RBCSXmEiMDB46dCj+2AULFriqLEH4MfWEZdopsnnSpEn0oVOnTohV2iKfCgnPrlu3LuHZXbp0wbtL/LbrAJZ/DB3t27fPrs7zzz8f1zEHhkRr33fffXv27IlorMyYIiABHFNftwYrAiIgAiIgAiIgAiIQ6wQyMjKYGOz1vjK5F6n55JNPOoWJKmba7RdffEFM8rBhwwgtJkbaXY1IsEqVKkhZCjKbl2Wuwmzw60bx1hJ9zYFARbWee+65lMXtjAJH39LVi0IHatzVSSg1epiDSb8uMyxBZ7p16/bll1+yCNbAgQNt5nD0IYTVoNMCSUACuEB+rRqUCIiACIiACIiACIiACIQTQP5xfPLJJ2hR77ReVOvw4cNfeOEF/LRWxixZd6pixYrISLZoWr58+aFDh8Jr9JwjOO2MhEu7nMsvv5waOKVmy7QmLAcfL4tjmZt3/PjxZLJmFdr1oYceYuoyfaBjYVHQVkl2n65yVvC65JJLWP955MiRLECN+M+uiPJjhIAEcIx80RqmCIiACIiACIiACIhA7BJwghBVeeedd+JZZSMiLw50Zrt27VgyyrTrL7/8wprMGFhBShE47XUae8vmmKZOFt966aWXlixZ4uokTJqwam9Z6me1agQwrl2Wv+rRowerWNnx+eef0x8W7qI/3iJR0lRuU5GtCEM4++yzkfpRiuhSLBA4Og09FkarMYqACIiACIiACIiACIhADBJgASoWnUJYsoMu7lbm6yIFvWKSU6bvzpgxo2TJkvDB2ctaVkQjE338ww8/EAjNZNqs6jHM0xsFbP369Znoy9xdthQmRnrTpk0siLVw4cKwIjSKFMcdTcSyN7yZLYKbNGmCE5i5ymFFsjv96aef2FSJ2b/MPcaBzLpf7FqcdQjZFVd+QSUQeduugjpajUsEREAEREAEREAEREAE8hoBhOhdd92FwkT4DR48OEr32AeYHYbKlSuHnDtO8UnQL85P6sSeLXNx5LoNdWmXpbDINNHLtFt2P2JfIhQjxmxutHnzZlalYkEs5vcyiTesRSb6crBIFfXga0Vq2iLP33//PTrzzDPPDLOnDykpKXhxWb0Z/zP2GNAorlqM3QLROJ8x4BMl7NWrrNFF5DaThHft2lW8eHHcxWH1o3hxF9v+xrRFr5g/jNJGwDNG9g1mXesobHUpRgjIAxwjX7SGKQIiIAIiIAIiIAIikA8IINui9DL61YgFkbsoyYiXEJCskOwuoTa9cdFs2Htx6HAGYQlUJYdlsouvu1qmTBmXDkvgha5Vq5Y3k+7hEPbmIG45vDmWRrpzkD7rrLOyXiUHLzGHu8ToEMneqc7ukhKxTEACOJa/fY1dBERABERABERABEQgDxHAH7ts2bIoHWJZ5ihXdUkERCBHAhLAOSKSgQiIgAiIgAiIgAiIgAicWgIWzft86MixpbDQ3xztZSACIuAISAA7FEqIgAiIgAiIgAiIgAiIwO9DoGXLlmz8w/zbHJtH/dqWvJLBObKSgQhkJaBFsLIyUY4IiIAIiIAIiIAIiIAIiIAIiEABJCAPcAH8UjUkERABERABERABERCBfEgguPxVNmtgcQmPrz8fDkpdFoG8RUACOG99H+qNCIiACIiACIiACIhAzBPIDPjQugF/IMPny/T5EgI+MhIQx3lZA7sVqvNyJ2P+n5YA+CSA9Y9ABERABERABERABERABH5/AiH/L90IZPrSAgfey/zlzcDhzzn3J5bzFW+RWKyJz18UkXmi8tLp0mBVIR+yN8eGzdzjr776ih2DzznnnBIlSlhm1s/oTVPtwoULr7zySttVOGvxsBzrBlv+svkwmwyzwa9tUEw32BwYY7ZlclsWu6bZnZiD/Y0wsMxDhw4dPnzYVZ6UlBRlv18apTm2L2avJjYf9u4z7GpQomATiCvYw9PoREAEREAEREAEREAERCB/EMDPG8jMzPg2fXtH37abE/ZOTjq0JPHQkoR9L8dv75z6TYuM1E0BHz7hoFI+/uOXX35h49wqVaogLCmFAkxJSbnooovQuqYbFy1axC6+HTp06N27N/v+Dho0CDMkKJvunnfeeewMXLFixQcffBBjk6xRmr799tv/+9//RjFwl6jq559/7tatW6VKlfr06cOyXrQyb948DPr160ffLrzwQvYorlOnzvLly72levTogXZFxLrMsWPHknPBkeORRx45++yzQ3sGH/PxzDPPfP3111dfffUNN9xAEyQaNGjgKlEidgjIAxw737VGKgIiIAIiIAIiIAIikKcJBAI/oX4TUpb7gtHPRf2nlccPGkjd6vcfTDy0NHX7jYn/Nz+QkByMjj7u+cBITRyeaMHFixdff/31jH/BggUJCQl79uwhjSq+7bbbBg8ejLCkztTUVFQi+aQxwKNbtWrVzZs3N27cGJHZt29fLqGNOajBzPi0g4Y+/fTTokWLchpRKnv7TA3t27cvVqzYxo0b8Tljv2XLlrVr11IWpd22bdtnn32WxNChQzt27Lh1KwSC85937979xhtvoI1nzJgxcOBAa5dPxjV//nw7pWaGY+nq1asPGzaMFbY5xTPcs2dPZPbkyZNxLGO2adMmM9NnTBGQBzimvm4NVgREQAREQAREQAREIM8SCKTteiaofgP+9MLXxZVbGpf8cVy5Ff4LVmcUa4t7OCn1i7QfhgV86aGJwSfgB0Ze4mudNm0aCQ4SXbt2NQqIzO+++65ChQqmMFGJLo0lKpdgY9zCeE1XrFhx4MABChKrjLcVl/KXX37pRUkNbdq0QS2TOXLkyEcffbRdu3YY16pVa926dV5L0kuXLl22bNmkSZNQvxQkFJl2sTczmiZRqFChm2++edu2bTixLX/WrFk1atTARz19+nQUrJnZJfdJVaeHDquZcGg7pTa0PT23sGrMGJcrpUTsEJAAjp3vWiMVAREQAREQAREQARHIiwQQckEtF/g5bs9UNilNT6qccO7LcUlV/b6koDaML5949nPpRa4J+DMT9s3LTPsyaHyCx4033kioM07db7/9dv369Xh0rQIm3+I+xcvav3//2bNn//jjj6G+HK2fU7zEn332GXHFr732GlOFcZwS54zH9YwzzvD2Ah37xRdfEF9N5s6dO59//nkCp/HrNmzYkMq9lqQ/+uijypUrE5ttwtuukrZT+6TdN998k2jt4sWLY4DiRbp36dKladOmuILZNtnViYv4iSMHet7lhyWaN28+atQofN0vvvgiXQ0baZixTgsqAQnggvrNalwiIAIiIAIiIAIiIAL5hkDAl5mesjEhsIPw4biSnf3xpX1xyED+SwiqQX/RuFJ34BmO8+3POLSGAOUTHRiRyci/mTNnvvLKKwQY4+l1NZBJODGylgjn5ORkYo9NYNMuscSdOnXChYsP9p577kGI7tixA+ftwYMHmaBbqlQpV0nWBM7bSy+9FL8roc4bNmywOp0ZFZqsdTneBJ1k7nHp0qVRqnPnzjU9jBsZ1YqTGacuQ5g6dSpFrFqWxaJCO9DJ3qq8aXT4yy+/jER//PHHkd/du3e3edFeG6ULPAHNAS7wX7EGKAIiIAIiIAIiIAIikPcJ4Pn9Ac9uUNomEZqLQxjda0KXRCA+qXwggEM4xZ/+vf8E18GywRMFzeJPqMQ5c+aYpCSfBNr47rvvZiUq1oIeM2bMgAEDEL22FnSrVq2IGcbTi1xMTExkbSpcu6hHfMj169fHxxvmwvVSJvDYTtHACNQwAYw/GS2NAfmuM644HmnWrKIt/L0I3dGjR2OG+xfVjVzHjCK4o8eNG2etED49YsQIVzxigiIEP6OfW7duTW34w5s1a8biW02aNIlor8yCSkAe4IL6zWpcIiACIiACIiACIiAC+YVAMOTYH4diROv6fGnbgtrX4+UlLjoz/Tu/Pw1RHIgvGTQ7wYPq2KBo//797FHknfuKFLQDAyQuyhMHKeszUz35l19+OQHSTLvFY2wCkpWi//WvfxEIjZCeOHHiCfbiqHmjRo2Io165cuWR9ln/OpMluDjFyDqD3H3uuefwSOOdZq+jf/zjH/Xq1fs+dLCmV9myZXEOH60xp5RriMo5rrvuOta4prKcyul6QSMgD3BB+0Y1HhEQAREQAREQAREQgfxGALUbF1/44gz/GfGBXZl7p2eWvCXOx8TXoBz0+zIDvrSMPVPjfJkZvkIJhaqHvMMnPERUHwtZhdTfUf3MIljENrMHEj5eFosePnw4+xKxclXE2t977z1kKnqYucQsiBU2Bzhikewyy5cvT3w1Dtjx48ezIxG1oWbpAIs2U4ROWkH2ZyLamV4Rv03I9IQJE/Di2lWUPD5hwpjNEn3rSmXX6EMPPVStWrWrrroKSyY8o37r1q2bnbHyCyoBCeCC+s1qXCIgAiIgAiIgAiIgAvmDQEi5IYHLBEq0DeybHH/444yd9wbKDI+LOxMtGAjsSf/hibj9b6LwMorcEJ9UKeQmPipiow+SlZzxdrKYM62w7ZAZE/Z87bXXshIyR7ly5YhqZrFlbK644oq3337b1knG9xs2TRc/MJvusvgzs3BbtGjRq1evMM2JnrQi7DbkyrLOFvHSYZa0yxJZqO4pU6aQoD9s+UskNt2rWbOmM8ZsyJAhDz/88KpVq+69916nfjFDGLNL065du1DsmEWEwFLV3onKNEcENctTI+Pp4T//+c/k5OSIBZVZgAnwFxUMM9AhAiIgAiIgAiIgAiIgAiLwOxIgzDkjfUfm143i0zb7/BmZ/nMCRZCCCZkpq+PTtsX5AunxZf1/fCchqRKdJP8kdtUUgTlRnfiMWP/xW0YsnjUTLepadImsZr8hh65ab508tlOqCsv/DZWrSP4lIAGcf7879VwEREAEREAEREAERKDgEAjJtczA4Y0Z394Wn7rW70/1hxxVfAR88WmJFySc84K/8JVxvl9jgAvOyDUSEchFAhLAuQhbTYmACIiACIiACIiACIhANgRCbslgcGYgc1/mvn/49s71pW7J8KfHJyYHireMP71bXDz77uIlJfj5eOOfs2lK2SIQuwQkgGP3u9fIRUAEREAEREAEREAE8iaBoDc4kJbpSw0tB53ICs3+4K5IebOz6pUI5CcCEsD56dtSX0VABERABERABERABGKBwK/e4CMrG8vrGwtfusaYOwQkgHOHs1oRAREQAREQAREQAREQAREQARH4nQlEXjH8d+6UmhcBERABERABERABERABERABERCBk01AAvhkE1V9IiACIiACIiACIiACIiACIiACeZKABHCe/FrUKREQAREQAREQAREQAREQAREQgZNNQAL4ZBNVfSIgAiIgAiIgAiIgAiIgAiIgAnmSgARwnvxa1CkREAEREAEREAEREAEREAEREIGTTeD/AYMiRLOFUsb7AAAAAElFTkSuQmCC\"></p><p 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\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
-    "description_en": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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\"></p><p 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\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
-    "description_zh": "<p>We are delighted to present the release of the <a href=\"http://groups.bao.ac.cn/ism/CRAFTS/202203/t20220310_683697.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS pulsar catalog</u></strong></a>. This catalog encompasses discovered pulsar conducted under the <a href=\"http://groups.bao.ac.cn/ism/english/CRAFTS/CRAFTS/\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(0, 0, 255);\"><strong><u>CRAFTS project</u></strong></a> from February 1, 2020 to July 1, 2025.&nbsp;In total, it includes a substantial number of pulsar detections, covering a wide range of sky regions.</p><p>The data has been meticulously processed to provide accurate pulsar information, such as position, pulse period, and other relevant parameters.</p><p>The datasets are publicly accessible without collaboration required. However, proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is highly appreciated.</p><p><strong>This dataset can also be downloaded through the following link (including data description, reference articles, pulsar table and pulsar distribution map).</strong></p><p class=\"ql-align-center\"><img 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fffdF+EXPyXJ2YYnEAgEAoFAIBAIBCaJQBDgSQIYyQOBQCAQCAQCgUAgEAgEAoFAIBAIBDYPBGIL9ObRTiFlIBAIBAKBQCAQCAQCgUAgEAgEAoHAJBEIAjxJACN5IBAIBAKBQCAQCAQCgUAgEAgEAoHA5oFAEODNo51CykAgEAgEAoFAIBAIBAKBQCAQCAQCgUkiEAR4kgBG8kAgEAgEAoFAIBAIBAKBQCAQCAQCgc0DgSDAm0c7hZSBQCAQCAQCgUAgEAgEAoFAIBAIBAKTRCAI8CQBjOSBQCAQCAQCgUAgEAgEAoFAIBAIBAKbBwJBgDePdgopA4FAIBAIBAKBQCAQCAQCgUAgEAgEJolAEOBJAhjJA4FAIBAIBAKBQCAQCAQCgUAgEAgENg8EggBvHu0UUgYCgUAgEAgEAoFAIBAIBAKBQCAQCEwSgYZJpo/kgUAgEAgEAoHAlCNQrVY3NM+BgYG+vr7eP7r+/n4h6cmTnZBKpVIM91NxqcTikwDlcrnmWV9wDQ0NhV9/8qZwzxlDrrGx0b9eb2iNUukbmiriBwKBQCAQCAQCgcAYCAQBHgOceBUIBAKBQCAwEQQSjRwtpbc9PT1tbW3tQ46ns7Oza8hlT0dHh4D0MwfySMghup5/pLq9yY/i1tDXEcXAKoUXueVo0cg/4ivhKXnKZ8Q4OX+e5EeA0eCmpqbEivNTCNcy5GbOnOlfzxpPCkzPWUNu9uzZ/hUyGsgpPBU9dpx4GwgEAoFAIBAIbFEIDOoBW1SFo7KBQCAQCAQCG4pAzV+KTPx4uru7161bt3bIZU/6mSiuZ/ZkxssAS4acT+Jpg0yxXE4ssbm5mWfu3Lmtra2ZDeJ7uGKKkJ7Fn0KSS4HsrtkmW2OqTT/r6upyeAJE6TxDUgx6OBJyyTMUUEqW5GRG5s+W5OzJpuZsjh7O1YXUBA6n+nlFQMyiGMTzM4lK/sSEkeHkyU8e6G211Vae8+bNK/phW6xjqld6pmyLIeEPBAKBQCAQCASeYggEAX6KNWhUJxAIBAKBDUAgsbuUINEqTxRu/fr1q0ZxmeWKg+iiZ5JnVoZVYqHoFlckY4mhCUmuhrChuMiqrci462OPPYbXzZ8/X54i47Fj12ez42xFzMeuWnqrOawycMkknhcR8rLC0ArD/1llSK+0FKeBAJuySkUDOTWQZ+LG0F6wYIFncltvvXXyiKBBudQ3srSbHeZZ8vAEAoFAIBAIBAJBgKMPBAKBQCDwlEWghmshQrYNr169GsnMbvny5fyZ7a5ZswatTRZauKA6qCm+mkiRJ8qUXdHAKBBfmjNnDjvt2ARpjLcEvuuuu5DeJUuWjBFtgxosgzBVGW5Q6dMdOdduxIK8RZvxYUxYs6Znss97ausUokukDmDnuabPeVqJ0KxaORPjhUNu0aJFyePplSUPJLkowFMS6mIFwx8IBAKBQCCw+SIQBHjzbbuQPBAIBLZ0BDJRSUD4ydyHyTzyyCOPPvrosmXLkFsuc12vcB4WxRQfz2Ri3WbIJYbjWbQE+on8IDPJZjgc7innOapwzz33EGznnXeeqswffPBBWe2www5TleFwHDa7kJqek+XXf1BifDhT4pUrV+bFEZ4VK1boV6zQeYnE+giSrKtkSowec9tuu+122223/fbb61H6Tw34NT+zAOEJBAKBQCAQCASmG4EgwNONcOQfCAQCgcBkEch0JVFcnAS/5RLRzU/MBG9x9FQ0FjmWWAZb9LaGmaSfwr0d8RalIjmR1b333qsCu+yySzF8slUaJX2uqfdTVdzdd98Njac//elTleEosj81g4stkmrIRG8rAVN/XljhKa6z+KkfsjyntJYz9LTEhxMlxop5kkOP05nklHm00VOzG0WtAoFAIBDYlBAIArwptUbIEggEAlswAtgClwiAA59Y7kMPPcR6mZ48yc8KZxuzmIni4rGJSCRSkZ9McIy3trDWIDpOgiH/nDAT4BQyzhxy8ifLw0QJKKXDDVYswPw267JYbi5VeLKgG7FcxmGc1isLLjitjsfvirLhfUw48G2xxorz6kzypIUbZmSXfqU+JivW4x133HGngvOTUwRunKJpsmi1EdslAgOBQCAQCAQ2FIEgwBuKWMQPBAKBQGBSCCSF3pNzjxR69oc//OGBIZcoLsaLIeDAisHcHKxFa7GDxAqe9rSnJZbLpGbraQ39mCqSkGR7+OGHyZBoJOrIj0aSZ1L131iJrSAAtqY0duC0qbsmPH4+IQLYrP6ZouVutuuuuzIF55+jZaI7FV+hxzZa27CQdjEsXbpUn+fkjydjzunWLuyXfTh3ez3fxvjFixcbCzqhQlO56VnMP/yBQCAQCAQCgcAYCAQBHgOceBUIBAKBwMQRGCS4QxZdT+o+/T4R3fvvv5+Ho/c7kUvXp8E7i4tbctT9bAnjF+IVGlyUYyNo/GRGzjdrAmwFAexww4QBaBcuvyUDTH4jAFhsr6eGfwwL8CTx1NkyRIaDK7vQ4MSHUWJukBw/9JBAfVJkrakd8WFkuOiMlyDGGcnwBAKBQCAQCIyGQBDg0ZCJ8EAgEAgExosApZwTm2mLXeu+++6zbZhznxN/IrqunspE155Ppsg999zTXcc0eKo8ejZNttzx1uH/xkvVSWEqwuMMcPo5Sbbzf8vZGL/iDPDUonznnXe61GoKbykbTbxiJxTHCHK02GiyeJRXkeycwI0RYxHSdVxGk8Glu7JOe/I7I2Cjtdx03c2u944GToQHAoFAIBAITBiB2uNhE84oEgYCgUAgsCUgQI1OmjQDI817iOc+znX9Qzv3IRkRMARbbZHb5z3vea9+9asR3bR7MxHd2267zcZRBiuIbZoaeZZKXXKz5sAcEp7pQwDy+pKzso7CblLIW6mp2ZIwTSDU1Fq56f62/fffv1iiVSc7KTDhTIytOv3gBz+wZ5vVWiYOKttVkSkxDyfEIJWPCFwxw/AHAoFAIBAIPLURCAvwU7t9o3aBQCAwcQQy97Mt02FFl99yzF8cj+3B1Gu5z5w5E9FNRieKNbsT54Dutdde69KgAw88MG2+pWSL74pmSWjqlPJ0jZB7mNGJTVYFB0Iyrw0/6umVPcbsciq4ycoP7XSglISbspDDuyl47SZwo/I+++yzcQjncBlGDMnjYlPDMwtGbOtT2h2AdmFYmUpPxNjQs6wggp3wztLvMeR23313/+62225CmJFTrTe12o3YFhEYCAQCgUAgMAEEggBPALRIEggEAk9BBJL27Okbp9TlxHLT00/nEr1yKw+iS13mEtHFeHHd4buXRXZd8zXXXMPK9KIXvQhJpk+ni3BTQRAUwv/MZz4zvd3sMCU8OoHSYw7BFqaw+XIPyQQ4wRsgjw0y3PBeT4tKGasMZkprvQaqiRIb10OLWnchxtaqJJk1a5YRjQwb4Ikb+5mXfnKeY4uxCb4Fi3v1RhMMXC4by28TjK6+s0Yw4mfScsxxemTISm8vg/gZzJTWKxPI7bffbnHwGc94hvsOxgZZfI5t38xDZrPxaKuHlj9SVmNnOM4qRLRAIBB4iiEQBPgp1qBRnUAgEBgXArQo8TydKqR+cbYl33rrrXfccQdjb1KeHB0corqPq8IUYscLh3Pd0cqTuZOKv//97xl7DzjgAKokJTtpgZiwn/ZIS+s6H0anzUtLy+jZd0pRhlICYXOpBRZEI99kb8MCKWIGZP2QqM6vgpe6r0OO1tk2WjjciLQJQgcuJOrqq69GqA466CCLSmNgkjpwjsAmfNlllzm/oD8nx48xwt/YtMKFmzmxn5wVK2f4pdXbN5cO/6tf/eoFL3hBrm+N5y//8i+/973v5UDgnHjiiT/5yU/+4i/+Qvjk6yjD9773vZ/97GexXzfSZRu78EsvvfRv/uZv3HOmdPPkBz7wgfe85z2j7XcQ3/z5lre85YwzztA0kvzZn/3Zd7/7XYsUNUJaxHz729+OJL/hDW9405veVPM21zQ8gUAgsMUiEAR4i236qHggsAUhQHNSW0/qV+K66G5ytpgKp9AzY1JtuWT58ZM5oojRBLQoOWMySrS10mHgnAOm7bpaNzznkGJBm77fpmg8gZy4kDqmRQHVSZu9Nwv5tcu+++6r3TdBaRFgSycEw+X4EQN+yyVOwD7p0lq7sbVh77333qS6rk6I86B5npZjjOINEk83dooYtrb02+uRDucLdMxh8MDDnXdaFzNduE0Nx5azwwt/pMOD/youn9PeoHI3Wmsi8xdeeGFNcTjk5z73OfX64he/iC6mt5C0AsiyalDzG+ZF43BNDuP5KZPrr7/+kEMOMeNZAbTgmAiwcKhisHvttdepp55qHfA//uM/vv3tb3/9619/4xvfOCKMkniFk+PSKPrvfve7N7/5zRYjfv3rX9esdySy/frXv/6YY4457bTTNoWVo/FgFXECgUBgoyEQl2BtNKijoEAgENhICNCTlORJl0U12WA5HkQU3fXKTuZEd9/2trdRv6iwbAjJzpZEHFH9moD08qGOyzlpYEkw+UxV/hMQaUqSMNHY2q06+IAnv2yzYWdKipjuTDRBbo7pLmtD89dhklE9bYHOBvYNzWea4m9SuCVh7Im98cYbrRfgouDa0PGl66J5srLcwBqMU6UcFi1a9OxnPzvDyPpt7/TjK2e33XbVVVchbEidCAiwok0mDmxzPHmv74YKk4ubQg/xTj755GKG2O+3vvUts6Lwt771rcVXp59+Ohy+8IUvmB5/9KMfZW5cjDNOP0hNEWy2iKslJ0y1mPA///M/vVXckiVLhH/5y192b8LHPvaxk046qTgb5yS2rGO/LLp/93d/Zwqy4vZf//VfWC6DsFoUcWYotltHs9rTri45h/AEAoFAIJAQ2HQ1gGihQCAQCATGiQA1i6Oepi3H6C7jgCfzBT0v0d1EdD3pqewbRQWrqDmNs8QNika2FB+fYYrBtzNXnO6iN0jO8UfONcpngFPaTbw6xNYEugSdmCUKK9AQjMDIzyYoeZLWkg1LdY14XuntKrLRtiLjlmnxiIkV5QOdFscuMs0bf+eZ2pigsF35t7/9raZ81rOexXhbg9X4i0uA2yQytn1btJxnmnNQ4rS+xsNcDB8yuPUdE9Z2Q4x4n7zENmHxcqFT4rFX/KUvfel+++13+eWX54O+qqZTCbSkdd1115Fc+yKlo+1JfkJJZPiRj3wE09ZALsO/4YYbsgWYgd2+GDcpXHnllSl/kf/t3/7t4x//uGjPec5zajL3Fif/h3/4h0suueSwww5LMFritC3iqKOO+vGPf1wE1jBHjNFgMeU5YflrZIifgUAg8JRBICzAT5mmjIoEAlsKAjQhVfWkS2G5N998c3oy8LLe0HXoVVTYV73qVbRPjrE3mSgTQEU9aeNAlkqkWeLnWARHU5/kxsKNI/lopaQapYaYJKoysfsUkbPRd7qbRlnAR1rIzG8xgke5m2xb2BeayUlNW1DxbUS3mFITPk0/gabEnHnyM5AiSDlwY3qKfQ8FQsiLRtcJS+K4Lzd28mIvTbZ6M8xxxx2XUllbsVM6TUrmJftvbRpH9uzRdZDYdJRYMY/zAomYFTMcu+ipeuvM7Wte8xqGbrbTmg5mGzkybwuxBYW/+qu/+uAHP3jLLbeQNhUNdhf7oZ2jSaJGOKe0IogMgU996lOO6ab7DoqpdCFrDSeccEKx+sARhwDDCbBwkniKk5MYvG7gT+HFzPXMiy++uBgS/kAgEAgEiggEAS6iEf5AIBDYFBGgSCXncBpLAme7oyfLD1ZJBU+apatcEEt6cJHPZFXpya0Y+QnAtMJkaiMfJdL+PbZoavEmIuHE8ME9NMHE0uZUVGHHrdNJ1xw4HR5QJ8bIjOniX/gjMJss/gRDTmr4yWiwrF271vlV2z6nqTq2USQWZLOxhSfQkeTJMqwZTaysFrxwSLtnyebLwMU9HaOhNHb4hKErJrTWZvXNLOQGqVScI9MulEIs/UQIzz777EQgNZYJwRZrjseynXySG1vOSb61Soj9WnVyAZWPIdfk9j//8z9Wo/78z/9cuCp8+MMf/s53voMP52jvete7fvOb3+SfNR6jWM4IsDaya+Bv//ZvmWex3JpofmK/po7i5gt191NCq1TD4wtJ4Xh7fpuSDCfAwnOc8AQCgUAgMByBIMDDMYmQQCAQeJIRSHSR0cke5ptuuikxXruaKd8kQ7ro4lS0pDvSgNN9LcwsFK8nSykfDplaEImySyNMjtZ+xBFH0HTpwe7XQfwOPfTQzZcD0zInw1pTK6dnQi/5p095lXOiSclC5Tl51jS83ac1hIExjQIc3gB56KGHVMoakO/c4MA41TSVnqFL48uIm75mGqMKeggELF44i2twQYABEI/C5dBgBnOWRg4gGvdJkTAJn4pO/RlhSzs+jj32WOilaS0t4ZncvvrVr+J1YprWREtk2FO9Uuec2loA7Z3vfCc2/t///d8vfOELi5mTQac666yzDj/8cHcWeGVqffGLX/z973//E5/4BHhT1exAVoXR2kiqdCWeCKeccopzv2eeeWaxlJwwZZJGYg5M/Wq0/IXLqmaGl4NKEX7EUnLO4QkEAoFAoIhAEOAiGuEPBDYPBCh8GJQtXvZw2vvnEztUmaxJeIt00QjxKxoMisWINFw5EM0KPZViUyBgSVMkDMbrABgLg6dthHgjdYeZgoXETSr2xSVTSdaBcr2obpQthpesqG38tlQLDstNkBKepTcpbYRhGtJYfroN1ZeBEWDNJ6ZXhNd8SfJco40v/0YukWUsmXRchAOWZBbTY8dp85yMtNDWCnqR9nrSAcfobEm1LDKeihuziQAnsfkNZFCkEYEJ8Hvl5zTVSzdGLycD/oam1UYOc6qaolFf5wggZuCwQttGITc/jSz833KAYSU+HuWrP8mcKG2aAKcJkBGro1HISUgTmumLhMTQ4XU8a0bOA3POxEpLWnukrfEhw5xLp1zspLLIvL2+zx1y7NvGhbSqMJlakMqFz2y873jHO9xKNVzyc889l3n/+OOPt5sgycb/85///Kc//Wm24hJsDBlUh5PWPnCnf11zldooBUrIQwweOHiCpSgG67SfzpYXA7NfuLTWetK0mcLl4OcYIuXk4QkEAoFAICMQBDhDEZ5AYPNAgAbsIhB2A3/yKQQ0BiE2A6dPd9APrO5TrZArlgdP59NswHPXSI2KIJp8GBkc9PIlxpq3043FoJY05Ch/jCHorgtCeeiIdHfWD4qfvXMsIawitNsszxhyyi9H4yn+HCNVMckE/Eqh3dIXk42XFo7OuV2GTpauCGKPojJSXnkyOScPvTCphgp15k1LaUG3c6VvLyWB6cE2CqJqVP8prMIQ8IPEbwrznBh0E0g1JUm0DgYyJVlNPhNjVudxanE8WZHcGpCYBo4zwHiRzuNoQCLAeJRXbInDt7aOJ/PxxLHrmBtPzCmJk0axacEikdO5FrkMIszQFVOJECpFiK/p8Fh7wt+AaRjiV9IiSz/72c/s6TUGie3JbYSbwxRNDJOzhUhLGwg5JmyJR/NZpNOCeejxGPimAh/sSYgZ8tb+hujwTRdccAHKannIZG75z6zoa2o84g+O3iE3fpx969j3eH2RCMceMa3dznL76yFXzFY4JiyJwJe85CU24xTfFv0qqKaaxt1aqu/vFFNzisBor4FSFQSqQoIFVilnHn/IRNa4xTyzX7iY4jjzkgKNHaseo8XPCcMTCAQCgUANAkGAawCJn4HAJo0AFeGjH/2oz0W85z3v+cd//EdaEaWKWpOXzP20N5gW4j5SbAqrPProo1FcpkhKSbFun/nMZ374wx/a6ubOEh+WoIsU306Hn/AcXZZuRzCkl6O7C6QMsXI4XZbUO/XKAiTdKP+s8UhLX0QUsVAxk40Fq8c2acx0I8ru1FKdwToMmXmp3SAF+C9+8Qt6WJKT2o2u+0lOYthAyCM+i70INPhcnezxVjhl0fFguqPk+D86hNLIh21QFRDgmopP5ie4yIM7IQOTyaeYVi3Sz1yv4tsR/Qx0nFcOc6L9aQVnxJhTHjh+Iae86JwhxDSEJ3ojkN9KFsEyr8sxix4RDFWpeDh9I31hix2MS3jm2aCYcKr8Cp2qrMbORx31f3sEjAsmXDU1qM0P6H0NRFkk0cwkXM5ZDvoVPmyBibV8ELJy+aCDDtLzvcLHptY4nNvUcE6GSt9kIo/StRraZjFrxJUOUmWZzQBIJpdCUPq8RGg3MpuqUswYiUl6WiWUZKhmf8ok55Y9rh5wSwI0ZGJqKpYojjxZ13/5y1++8pWvfMMb3pBTCXeH80UXXWSixuSFv+IVrzBX5wg1Hs2UQixS/P3f/33xrRUBTaBe1mRlq5eK44pp/Z88KaYboQGlUsWE2W+vk9PI4vhUe5KfnVmejjTnOOEJBAKBQGA8CEy7yjseISJOIBAIjBMBjA5xdTeJQ1lJ46FR5c1pMnGrp02A9tFhvyLQF1Hlf/3Xf0XSaB5FpUdCKlEyHKXnOGUYfzRaDketx3AYnH1XAw9nB6B60tjobTQtElJ30pGzlHNRyPGUxRSWKISyaGmS4/+sPayyXtEUx5PJ2HHkTGaMmjKaHLJhG7NSMF5F4G/JvpT1PxmOsyKiObrJZEeTs22SrkzpZ+QRotwsWPKPM8+cajSP6hQzHy3a+MOTdpstM+NJmOpSFGNitcs5TCz5eESdpjjsmTpSEttgVBErHa5QGk9xmJto2EJaONB5MK6NuYgwHiEnEAcIXFq9QsksAMkEhzRGQMRvyQkTHjHnmg4gH1OBoSRy2qZh8JoWUC+v2M/xPTAauVbcPIWbCWsyGbGgMQINYUWQ3E4QfqVoGqsbAvmtmpkPx0ieXhVlIJsdJZxXxHawxQmRtID4pS99yVxBZrO9UzB2fT//+c9nYU78tpiJ0i2M6iE2M1tBSFllMcwGMnFXMzmtQqZlu/RWiaa48847D232p0RgDa3NmdR4kFWuGGh7gpWIT3/60/nPjW3YirMl2wqsmP5MnHPOOSzhWULWe7uyHX5x6YPqHHnkkSZbH0PCeE25xP6P//gPuRUZe7HE8AcCgUAgMBoCQYBHQybCA4FNEQH6ASUm71imASQtJz0pK3bc4WAve9nLkvTC3bzyvve9T3g2KaRXDmgJp5Ol7XBTVVsycFgcFQ3jtcDPzEtnpflRYuxqpqJxNq1lNYiQKRUPt6GSsAZIzphD3WRbSMYECjRqkSxs/PJELYrs9AlLkafk5GFuQrCpYnRoqYRTmhlDkvyeOHzOrUZ+6inV01tZpW2c/EyveEtOwjNY7SH7MPWOtPb4KYsNnw7NWi4TViOq/AbRy2L+yU9yDaEuif0yUglRLmuY5/D4GxSignrmBiXJkdU6m4By4Pg9GAUmCVWa+vhTbQoxcRvnaUFnvCBgQKjpGGMImXdJ1LRdzc8xcthkX9kAbCuEzklCXMhYM3votMa4wLTaNR7hi1AY/hbduGJCvNqozCeHzQ+2w4ipRRRUY2cuJhzDj5ybH4xWC5FImq0WuqVxZ5QZ2vJUyoauU+SK8EDDcqFZnQwwMVdcf/31aXnR/VUgQuNZaBMftrZocEllYdRMYrIqkttcC6epzdUIMCJdw1rFOfjggwmMptpzlCftnHacntSaKXKqjhB/yPBbB5LNrvDxLV/oWeHN9SWz7U7+nKUbyHWDr33ta75v5y+IQP3kiiuu8Fcs0eNxShLRAoFAIBCAwP9RwgKRQCAQ2JQRoDEgkySkYJ144onuJqFO2c5nUzSrUVIa7rjjDuoRxSvrEHQaWvWdd95ZUzXKH42nJnBiP+lhSIiziLQoqhgzLy4qkA2TpvKhD32IaYK9l9gp/yxbsTj6IgURxxvxbTFm0S8yrUgItRg+lEtPX8VggSEAh38yLNCrGEbSFr5i8qJfQj9V5I9W3tXsFcSWCoD0MwVhHRztNiccW1q5UUlFlnlinvykysmLnpQV/dWneijl4uPwVGc2c7tACYAgaVylj11oMc8aP2OUJY9UU6Yk+dBonbKecIbqgkgrBVeXbfKDC4sYf56jGfRqhB/tp3KVjrSMFmGTDU+WLsIjwHYva/pxijocW8knCeM4i57yaLqQMaJnGiw2DMvfooAVHybfdBBACDuwySF9e2nCAhRB02dsgUlbeY0yNNio5zHEvNIcZloRDHYLE4BNa2c5h9TVdTmi5sAkmCQ8aKfcjFZjwdMotp6FfsvN+EXvzSopzwlUJ5do/rfus3jx4vTVIitQrKzWHE3Cp59++ic/+UkxvUWGDXP3z9tyPOJKk37oIMlrX/tagMizKJIchFgkde7XLo9Uu2KEcfohZuMSLp3jyxkC559/vnPC1mdhfvLJJ7/73e+GTK6gPxz/8i//YlkzpRLOPnz11VdjxeTR5xH7mi8J5/zDEwgEAoHAGAgMGl7GeB2vAoFAYNNBwGi1B8xiOY0KsXQWy47fz33uc5gSpSdpCZbqaTPOjGUdQiqaHCJnF3QOnHylqK00PGriVUMO9cWsUFyONknlooFRYXOJ2TNi0YRE+aiJdNyxY46YXCBhGEOwfYJhjNRlRlRKrdwAQu+ETJG45nzSHCiahFQrSp4QjtpHu6WR02Vz5OQZv4TykcSTIYgYOasnzCEl1LIQRpDQ+BRCZdQHJKd8e9JrnzCrLLwcMI3Ew0FE0WSbkhzvGn8mObfkQVpooim5/Hk8acnU7gnnWVPEaD9TdeCgRqqjs+l7Ck2VGi3VJhiOt1iysZHButUmKN40iWS46dV4r4WY1L2NON3GGEE4Ew3LXcjQNqLTN5ynXB4dqSZPey6sGJIqjTJiWH468MADSZUjI7FG02gLdvokLmqiNkVLa0p0WlUXTZsUZGI9TnetKXfsnxCzCRw+xfXNmiRZPB4z6utf/3rbvM1+5hAzpJGOUjq7oS6uDRtxpGTMc845TyHD3+ZoY3tGy6QYnnLIRRRf5UBxiuE1ScaWId4GAoFAIJARCAtwhiI8gcBmgAAdiLNO75Rv2o3GAkyhcRTKVSU0Ay6FFytDexBeDJmYX9E0OWQ7kV5H0fAf2hiWyHpADDuc0WCfkcSH3VZiM9uIBodcOqloihRNIbRJ6qb8+THVEclqTjjcQ5tEIVJ4UpSZlKl9dNDherNylYUeU74xXvuxOSiRllWBvojCZS1cnkX1a3jRY4QMTzg8ZMTkooECr7O/kUhUVdQXhaZMpxy8YuUWMrSvcxsW1+HtXpOzhAlVmHvFoI3h18TZ0J+JtEhl8QKSVPyU84bmM7H4SEiyOUuOI6kgg1vaLTmxDMdOpdsYAoiEzqagsSOP/62WHc00N/5MNouYALQspb5aCj2zwZXY+i1Lr6GHdurVXOrqyLCeL4JUOmra6DEd1UwDKuesOMOK09a6tP0jRqJ+Tk6viG17C8HMVwZdTlXjEV+belpm8sr0IkP5oK9mpJoSa9KO9lPpRtlwGyw5JZFncim5Me6EsB3CimZftRkHITdvX33NNV8+9dSPfOSjDQ31z37Os1904OD/XvjCA8wjo00gsh1NpPGHj5bJaOGpRiPmP0aSEeNHYCAQCAQCwxGYsj/hw7OOkEAgEJhaBChAaZfj6173OspK0gOs6Lvl2MWYyhKCIKGgxXLTLlxG4GLg+P20KzQjMV5PW6ylpZtile4URX1tpU6SEA838IqNmpWS+oWCfvvb3x6bZTF/yV/aJJK9vjzy3FACXKMVyTDnKcP8ViDeyCxDtU1FUw3ppql0dvXkKSbJIRPwUPcVTQ+m4ifjzxiZkI1WTbC0CZDJC4DEW7JkCfM1xTfXQiYImGxtDeWEs+2IqTlSrYsxRywRxxhN3x0x/miBCkoauXLtGM/a+RMKMFqGGxQOKN3bWgZaglqMZxVgg/IfHtmSCtsgu9/4tysPz6QmREMUqRQkDWEAjmHoq8lhU/6pOkCz2IT+2eaqV9uLq/tZuDFXGHo8erIqiJnWpCxLaVB9Cc8UaLSavsaeRqYQgdx1SUUGc5HBm3fh6uTOepgV0yoS0z353bOVZuYshkyKbWq0qmxarPGKyzHH4zGNKJETWYc3cclB/ikfM4CJV5/MWQHZ8Vrbg00d//u//+sbeF6Z3Li//8d/WNY28LNf3XXzDdfc9uurTz/z/M997hTrgXvssbsVzOSkmpLJIcsTnkAgEAgENjUEggBvai0S8gQCoyJA3cFwPClbSfURlYd2iCnxUxbpOpdddhkNKe+vQy/pTEkHGjXr//uCpiWVvXPJIaUUOIzXByQ/8IEPUJLwMUUwR0jnsivGGeYO/kSz2RxYHnxO4+yzz5aWsXqMDxEhuiolLeWYGictf1F39HNijiHXHTCICkrphBtAiC0rSjZnpQBj9HSFVRI7Qzp2cUBOEcYTH9kGjkrRoaUaMQm0ER5KP8cYLg61GwIsRe4tK+r9OTkZbPbmqNTsZqDDLqCXBLPZkl8ngUDa5ZgTpgg0+3FeNZzij/Es5pz8xZAxEk7+lYISOOgBDoAucpPPdlPIQYMSYzOtjs7JaR3OFGF+8BSiRsy8BoLpCAE2LrKtXkxvPfV8zvZgSQwBqUS208QY0dbWODijdSPQM6sqiiaAoj0T5zQNIodCiGdKMeg8/eTEMQkbd+QnoSGWKpW6E78IZpsNXddLye1uQLxTJq5+UAQ0lEKYPFWaCsBilhPZfhx7cGxydsUUxFImae/wg+sG7l1bt/0uz9xhlz0POuENDfXljhXLbv3NNTdcd8Uvr7v+O6d9b6Cv1530hxx8yEEHH3TwwQctXuy2wobBttGAG0bb/1hs/BsIBAKBwKaHQBDgTa9NQqJAYBQEKCEYkQ8gMUHYZuyniDQzLCvt/hWCo15wwQUMwjkCe2wKHyXXx4MpfEgv4ppJLyWVhuq2LXm6GrRo4pAhnY+Sh2JRCsXMRlTZUfVYRS655JK3vvWtPp5BD3N1p6tKxK+RQT4CKfqS0PAocPiqwJpoG/pTbokZIoeUVLYaIaxJiQBTr1l1hrTWQTMOesxgQgya4niKRreYwS0oPKFFN4md8hwtZ4zXjTWJu5IBPSBGAgpb4GQyPG0OIQMlmFNBLZhKdK5SrbWmn/RjareKc+Kk3HLyFH9KnuDlpiSrYiZ6heqkdZ9iePKniqiXnpP801E1ZSkCvdH0CWQ2SW2kdayhDJdqCwwxmtBFndlo0mSeZgzjGssFml49yGvnz9cVcwNlTxGuFAjtTP61rFnI2pBFAbZ3DsczpRSno2IOU+W3JmUzdsqNVK6w4reSpRaJtxtf9muYB1Q8rTZahTHhEF7HEM0cqOJpBSrlY9qZmHhpJUvOOl5aCDBPKotTtD8BuiVbtHL9LXj7299ugc+Hgr75zW/i2xnnnoHq3SsHlnXUV0vlSqna1tXX1FjfVF/XuGjHvV9y4rMPf+XsGeX29Wt+e/11d9zwy+uvv+L0M37U2tL8vP33X7zTjs4NH3zIIeYZ1w6YntMSYFDiibVmpAoEAoFNAYGpORm4KdQkZAgEtgQEEDZ7npkdLr/88sWLF9M7P/jBD37qU5/64he/+La3vY2ugy0w1VLL7HxjIrCT0CFhBNUlTJ5ZGUpYSU6tdzkWq4Ina20ivZIgvS5KkUNKUpNQcqoY9Ysu6OIoSli6T7XYBNQ1znWd//zP/0xT9KVHV4nSgItxsl9MGYrGqjm8rBxtDI8caKJySFZB9XVUT3yadOJmVNKEgJgYbLaXpjzpjozY4ymauknXHHsTLANROtiMCWBKSSRioKMwp576QLFNlXCjuUKegov3WkRI1Hc8YowGhdpxKD0OrCACWAuwPkJg4T4cgpBo1uQSIJMpbjQxpioc7cErXOE2hpDqlYobI84k5VGEPqN3KUKTaSYh2N3w4+UTLkiGSJdRIAd9zFNvUZxt3qn/TDjnKU9IVE5Hwk45gFhxM6D0bWWRGetztfiICyIb2kYKSvIrRWfAhM1+LhM2YKHk81c2ShjaaeBMYU1lbuzI0JMMRii/UZNMuEISAU4bj1VKiPnHoCOkp6YU35H4/fbbjycZtIfPwF6Nx8nciDaQzTz8iLSscHI0WCdUOjTs+zjttNMuvvhi3cYiqe/rCkd06wYLKLf1Vu5YPrC6r6G+zi30pVUdPXNaZjTVl62ZrWzvb22qn9Vo2bHc3tNfV19X39e19O7b5izYtty+7PAl8+790L9dcc7ZN1VLq5+2w9NffNALDj30kEMO3m7HHRrLg+eixyN/xAkEAoFAYFNDIAjwptYiIU8gMBYCtB+Xshx99NHULKyAOojmnXTSSd/4xjeogEkP+8lPfvJXf/VXFDW0B+GhD5133nlOf3kra0oqDZKlF+nlWFSYcHE/jJersfTSupgU3MxM36oRS9HIdjKGeJUyr4lDWiGupH7jG9+IkS5ZsuSUU05xUdZwbTXFHC2fmmzzT6k4VBMa2DhVFaX0JU8RmKHooCglBTHHT0JKQpn2pMEDM9lyvUoA5sg1HvGhISE1l+7L9AdY+h+CPbzukLH0IEkukZ8OTQ+GW6I3oANITSnDs6qJMJ6fyipGU00/SStciyfVXIiygKP6Nqh7hcBQo3WGTUGp1UsTStoRYolmaKAJU4giIBPwZ0hxYMSDPMn+PyXtleRRhJ6czjLoYwI1hyfTPd41AZmnNokW0XOS0415NBALJ5mtxBkRWLrlreR0tlT61OJTUyOFuthJoOKsH2HCKPfYo7gmhzF+5hY3wVp2zGeAU428hYBwgzrXMSeRrWPPZiQENVmqLXIBjT+dedagRllOOIYY+ZWNzWYV07Wc02ysOATbT33GBHjqqadaKtIW1kP9gRiy/Q7uXBgolR9e23/f2rqVXaVl6yvbzK7r7e9fMKvRaiib8GPtffNbG2Y2lPurpXWd/a0z6psbyt3dXbffdseS3XbfbduW3QZWdn/oo/3XXDvQ3d7W1XlfV8eNXV2/KZW7dnn6rocc/KJDD7VaqlKbwqSRsQpPIBAIBAJPiEAQ4CeEKCIEApsWAnQarODMM89kkqJg4XsvfvGL6VJZnRIBaz3rrLMoTPbsOQ9mMx5641SY48Gcy1fFQY+d5sV/bJbLOzlpdYyTXiWFhnbrwxvocSbA2FTaGUhHp/8hnNChBVKFR4RJQcLRBl8DZqaW6tWvfjVTMCMGZY51a0OV+5RhKou5DMNPTInA9EtiqBF/jpZhGS6ehCwqKpv3Ww6Pk0NkiC2DMWWY8gfLiHZjbIETB3ogUk0asLRyo5jS1LmkOo8hXi568h6SMB/pFVRkTYBZpeujNDe4KM0iWBPR3KSFBqdrkZBn8qVPIAddK3GblBZKJGToI+rGQWxEmcmQCTB8Rowz4UCZ57RpiOWT8xuzysTgDHMdxooPB3NsX7f56U9/qlcT0vjSjTFe/d90IbJ1E0SdwGDZONImOa15EcASCWmVS85DDz00DX8/uQzpxDxKQYAtexkmxdyE5wyL4TkwR/AWaBrUbExaWYmDsZs3ijuiR8wk58YDZE0gufVEcyac0/ROvPe9733mdszfTG7/syFsH82cOUZudV1v5d6VpeXddcvWVld1l9f1VJvKA8+Y1zVrVktbX317d2W7OVa8qn395dWdvVu1zuhpW93XX13T1tG5dsV22y9cMHvm8+aXKl//Rt1ZP66rlgaqA+VSXbXSZ5N7W1fvYwcf+Pv99v3iaaeZKF5y2EsOO+zQA17wotZZrfAvSh7+QCAQCAQ2QQSCAG+CjRIiBQJPgEDWrnK8Gv0pRcAi2F2dAeauueYaBAw/dC8UVZX50V2sVCWMCEmmyNJaaLQ2zdoIzbbMXkFvo+7TL2nAtCs6Lq2LEobUKVfmXLKvUoBYYLIwRQ9JRKO9Kct1OO985ztdaUNP/ad/+ifnmdmWsZpi/NH88qH8EY+tA/+3D5zAPD6Ti94zQTD+JIuZHGrQGC1PdWFEooaSbbQ4OZwAMCGDVKwxMKTFJtBqikuikhN0nurIUGO1Qlq8F0vP8bMnlzK1HpJwqRSLBb6opAWZna13ZKyUKIJoJKSgIzwaPaWyNMDUyW+ZQAtqNbQnPVPdp1baYm6wsk9BCGJDKmjz62y66HSDVhSjxg8KOCAexhEEat5O1U+lZAI8rZVVEKdGQAasLsHvRLo+wJOqI4LTDfDnsd84NYFlEbNBimB0m0PQMKMjxRQuMsmnVfhUShbSVKCHK9FwE2itR7exymNyIOqEJVGRzPknk0mS09ShV5sTOFujTX3yx12BbwYziz6h+Vp8n93W/SSxQuT7Rp///Ochbz5/73vfa5uP0f14WQPVB9YNPLSunmn3zhUDa3rq2noqrc11rXZqP3br3IVL5mw1Z1Gr88CldV39TMHbzGrUonfcfuvq9R2zW+ob6yp9lbr2rv7Xv+QZ8++9vfO9/1K3tq2uXK0r15Wq5Z7enr6mptmf+njnvs/51Gnn/e5XV9z+m6uWP7K0uXmGL8C/dMjZAz8Z5FMt4hkIBAKBwDQhEAR4moCNbAOBJwcB6hpFkJk38V62EZsSbWxmcHBzDK5LLKRROG0pUZ1s/qU7MgnaUIf2YM5iIsZJl+XHndJ1zalickhngGl1HF6UwmuektNE6aMUPuoddZkd+N///d9RWWqcO6Xt1qZ5D23YG8FcI7kkaqQ4WqOCqKH0KvvuaOHeFovbUA01Jx9PwhzZnkOEMG+CJUBOnmgbAEmbKIQqw5kWPhk5i2nH6U+4YeCYCVqi7YRoL3p2kjbLnDLMtfMT4FRqLIihifPKxmkhxaIZxNwl5pW1ADW1gpCcDqPKYtbkX0w7Hn+SRyYaHb9KZ4Bz4HhymPI4SoeJbIHpOckKjiGegpjiRQD+ZEqRjxY3WDArPVMzIe1ah8dih+UwzgyguZWFt+gbopk3NKI6GtGeHK6V2rQocxYsNYqnlrIIZUlItpa6rJpZWto4FCjJkMQjmJ+mOJ2fx9xibY4kxuDwWhRrtBH8RTkVR1RLD/aGmNb8JCoA015u/oxwUTDTu/VHUx9rvNsf1BGLfv/73/+Od7zDwmVjQ+PiJTsPVCuPtVfvW1Pq7m+olks+oHTnisryjvJWLeU5MypNdX333nLb3nvtsnDrOd0DpZXrB2a21G3VXMeyv6ZroKujy47o/t7OO+9+YOY2T9tpwcwlC+p3r++s/OLyrlP/u2m1Q9GV/kp1YM685recXDr8iF8u6+xoXLT1rBZ8+b677rrpVz+/5Vc/v/n6q9vWrtp20TaHHvaSw196uD1K8NcxihUJfyAQCAQCTy4CQYCfXPyj9EBgChCgV2Fl1157rRtQOPSM8oQzuAiaQ8AovtRc6jtuhuJyfiIweCkiikzSt9wsjYWSRm6Si0zxpY3RsbIFmE6c1TIcjymM5SFdOOxLmPlVsUpySwSYPBRQP1m3sCbfB7ZJ2ybkJUuWuB+LQTgZk5MAomFcipOEPk0pF07to6OjzRR66lSxOPGJQVSXEtG5iwJMuZ/MNm9jgJnzK10p5HE61FU0/F7R+dh7cZgkQFHaKRepmCFhSMiyTSHmAaClB2scWchi5BH9KWZ6RWw/OR2Msp6cFlE1FRRu4zTjm8j8ImsXvSV9Y0lnExMx1v047atpahpuRAGKgSic09SJABfDN75fBY0XDCQfB50+GRKY48xf5OQMas2URo2mZ7DVUb2yBCac5O6O4ke30CeZI7qGfHJ6SFpREn94Xx0eMlw2CQWKqd1t08WlNbrOYNFNHxhPDsPznFhIQsMEYkLg0rKF2UzD6bRJyInlPOWpiKpdzJDmUi4tM9nFrS2E+0ls0GX0LF78+Mc/toBouobqX//1X1tDTOtrbMvlcqnSvNX9a6vtvWbacl+puqaj0tZdmT+nYU1bZe3KZTPK8S8AAEAASURBVN1dXY31pdbq+paZrSvWtrX1Vg98/v4z6std/dXH2gbmNdfPbanDbx9Y3vboA3fv/6w9Z7a0rOnq321+dZfGnoYH7uu54YbKssfq5i+Y8bznVBYvuaOj/sob79lzn33qGhrXd/eX66qzZzi1PnibwB2/+81tv/r5DVdfetctN1UHev2tseGIs84y1BncIz10l3Q5vqw05X0qMgwEAoFxIRAEeFwwRaRAYBNEgIaE5NgFh/Sy0bEa2alo99nLXvYyu4uz1ZGOhUVQoZhl6KY0Y0qwJ/UUaWThQZKRE9qzJ91FYLIJi6DWGB2WiyHzK5GdB6lLMZmP2B9GI8DKZbvzpLSRjXGDDIkOUcflZu/f9773PVd2iWC37Xve8x7HlVGsRK157HPGwOWAMCsoE86sDuZGEQevZvY5/vjjyZbDp8OjrJQtDw0VpHlLNkUWW6Nkp5tvUrTh0k6HVFkkJJzVN7Um7oGBAGTyMuRaZ+HlKVAzIRi6k87ADw228bRTQPvqeDk+D9rsq8s8+Lluo4PhYOlJSN1SnvqYCEngVISQDaXNxUKnyq+yG40AF2VWLjKpQRFXI4XjMdJhYiULETKaUrhoIhvOOK3m8B0yzWHE6ZAayH5gTZNYrpYCeI1FNGFeLHpifjIoLi3BkC2viUxV/uORigw5muEJBxCZQPTMtOUYJrgl2XK0DfUoQqNINXkrd5bW9IvHmn4JZuxY09R8lvxMgxrOiuF//ud/GuBGzZ//+Z8z/Jo2B5eeSkZiZXV39YE11bXd9Thlf6m6trO0umNgdkvd/OZSR3f32r4Zjz7whzn1XU2NpfUd/ev7ygPrlrc2DDz/0KOXdwzWY9GcwQui13RXV7cPzJvR37Vy6fztd1rT2zB/Znm7WaV5LdXZddWmysCMSl+pvnFNqfGe1YMHjEs93Q2z5qzr6J/V1DBzRrm/Uln6wEOz589fMGcWgtveO7DqsccevOWaG668+NdXXrbiER/rnuUO6ZcdceTLDj9sydN3GVzFxNq11Qi7fza0QSJ+IBAIBAIbgEAQ4A0AK6IGAk86AlQlOiXFiA2HowzhD4hiWl+3zzmrdFndlMStVygZKotLcHgawwhGSinEbGlRMlmyZAnLaj4DLB8JETwcm96ctuTRvF06JabcvMVvAUIe0eTGT11jpE0oKchxNU8/RU7yIM9uyuEXTioMjcL3yU9+8oc//KF8ZOL+Uho8uisfV7mkglKGuUbpZ3rKmeNHThDg4447LhHgESMXE07Yrzh6KpWaw0D8pI+yrVGvkyQp5+kToEbyVKim1Ij8buqyYxzIOFJiONMqiRIVrTUT7Ghtss8LhxJurFnRofTUmuliJ02fFkeSbCJrcVey+cluqaPqb1D15HQqJmWvdDP54xvZqaAVHK9SN0u5wSd7arCawE+yqYVFGR4V8UQv5aN/ZvP+eLKVkCNncjjHIO0Y2ufsaYXIQDC+UjiPQIsFKmKo6tgpuZ88nq5Sh49UvtoNDU2fHTsnoMALf3G8FV9MO+GRKFXwU2TP8Yg9sTiElFBNrQdZmbIYl1pQNyCSV9NaepY5AZ5wNlp1VKsAthxbKTC/QcMYEWLkTkAemduNrAeaDyeQPAtZ9CTcEj54uzkNDdbnbYG56KKL+A0H1NetV0ZZZ2fHLrvs6kNHKzorD66trO+ur5Tr3Ly3tqu6sqPSMqO8aJbzuqUHljmBcv+zn733NrOaOvsry1Z3PXrfbfvsuVvn+rUr27pmLtx1fmvdvJZye19p+bpKY315uzl1DXWl1Z0DLY2V/bctb13pqfb1DpTqeptbuip1j3SUlq4td/RVF82tbyjVdfRV1ndW5s+ub8K6q6UV6zqX33/XbrstmTl73sqOfrR2/sx6/ayrt9Ld27v+4bt/c8Ulv7ri0ltvvG6H7RfusWTJ3vs968ijjnJCR4ccPF08jV2yCHP4A4FAIBAYXDj800Jp4BEIBAKbJgJUSRvkWHqpQZQhuhELhn1lNh7/xV/8RdpxmiRPqhhVjz5N76T+0kERUfo6nS+RE/fcJL6RSIgrmqSlWolQJMAC8/yQsk0EGC9lrKNEIsM5Qiodw/E2+b1Kb/FtNkkbceUvH064GvmAMNaEA5OTluyaaJ9rorUjPCeffLLPGqugJCm30Z4Uepgk25daJ/WaCcWigIJGSzWZcHTIEWtVgC1U6dBMNKlqk8l2AmkH8a1WNTRFnFTM/tRxwA5hPFj3aUIgi6p0SrnLeFAIWmwuLnm8zTFrPITUWOgQp1Px636QlNCCC+OhwORkguKqWnqVrobO+avvMcccA3wbE1j2/ExOCI8+QLFmC3WgXQgnoXBPvRSPJQaynXLzTE5rJlKt0+pXJMcnDSJvUSZOuyOlhgATIvFk4qnn63U82gJjEchJ7umVzu+V5Sr9nEeenilDtN+aBakkFC4wRdCTrWoJN3xE8BNEnsmxpRNDTPmrVw281mUMZIE8ckiMXRJoQEmgC4oYFZVVk3Bqf+aa8hCSqDaqGN2ODxjySt8IAqhsAjZXjRhk0Ct0XX2MDIcccsgGrWWkrFRKzuCdQgKcheTReexg/9rXvvbNb36TxZ7Mxx577Ec+8hHLkT6HfNtttze2zt559/0eXl9ynzMjsKO/qzt94LfaNKOE+jqIv6K9sq6n0jTQ1rty6dN223Nld8NApdrct/LWqy6eve3inlLrjMbyvJm2TVcrc3fsrTbuMK9x4VazOnoGVrRX91pU3XdWd8MDf+i46to6nX/ObNueS8/c69GZW9+2slzt7Vy3tq00Z2F9uX4hml0qd/b1re8YmNtcffDu2xY+bZf2asuclsY5Ta6OdrS4j2153ozGSl21p68q/94VDy6ZtX7PauXnJ73uyvv/cIezNocedPBRx7zsyCO2227RIBMeHAfT2zmLUIc/EAgEtkAEpvew3BYIaFQ5EJhCBOhqSOaFQ84GZjqBi6wcl2X/sS0Z83GjMiuBEp19pYrloil26fAbgsGkRttDkCQXefHixUgjdWpw0X1IK+XJ2gaGzOV8cjgyIIcUbkMyPiAttV4E6j7GMvyUplccNdFTwqFfgz+xdyZKDq9gnCEbGkkn/u53v0vXp/B99Y+OWfVv/uZv7GpOGnyWquhRa8q0fDBntU4brRGYFIcSSUll5MFbiqk2yC8Tqw8WC1gvLRwQBmmHUiIhKatUxw3KdjKRwcjBUO2ApnQYqj5SVOwGkyli7LRKB0vabq0npFWPGhBqfhYzJKS1D64YmPyw5XK4Urj0E/iS6NKKwwaFEyOVQgY8cyju4AMUOnlKxQ+onCGPVNaPkHbjy3VQ6ZV8hPMrQv/Rq52lFyEn9FbR+pK+qg+zCXM5lWGFAIvM1qq38BBJNTnypExkq6+mQC3FKcsoxvl5nNVHbtWL80q0VDVM28nJLEbRI4JoxZDkx8w5fgxKzkU80XtVcx+4QAtMCkqlDM9k8iEp55y/hoAAcKxWqBQYjSAo5QiTL3F4DiY3VF+4dtEQupB2VLpRbNLQgvA3OQjUpuDSiOKPiGrO3FQjsp/6A/lT79L9TKo5zmQ8uq6DIV//+tetFxBMk5nzLQgSXuc1bf7+zqXX3fLgqs7y/Lt7TETzttmut2HWus5S84zS07aq179XtA20dVf71i2f31JxY/OK9v62ux6aN6tuwdzmRx5bds9DK/Zf8qztZlpAGegqze0Y6N+5ZcY2DeueNn/WvNktHQOVvber26HUN3D1Vas+9cWWVSvYcMuV6vozzml85St2fP3rGrfd4ee3dN+z9NE/23/blhm6X7W1oXebUufW2w9O8js17XjLA6vXr1/X01g3c/d9VthQPbOuxcnjqq3XRkK1Y9Wj7etWv+wZu9T/9CeHN7UesniXde3rl17yi2vPu/DvjMH99nvusUcddfQxzx66NHEyMEbaQCAQCATGQODxP/ljxIhXgUAgsDERoHNTgOxsvOCCCzBfbJNqZVew5f8jjzxySAf6kzhUMbosKwcLBgpK603aJIsBqxHdKG3/Yw6SLW3PEThXPftJ8aUFIskULGRgDB1U5uLTCDFVkdFgel7Sm6mwEtIgEeARL8ESnyMMnZtmSVlkwVMjgbR8ih2NnxJpc6ZXaiXckz3QRS9YMBMxsVX59a9//UknnYTzD1dMUxKp8hlgKm8CiGyqz/qNL8EwBY7/KWdKM96L7RNJQnvFuWIOY+BWjDa1foLhEm6f0hZwS5QsmbA2jjwEwLptYyZA7njqOOHS4YxR2IkwRg4KZa1FuRmKdZgipFJ5WwzJ/vQK89GRkuMXGf/Rl1JFPDlJkscgQhrtmLW6pOdYWfAqCcZmqHfp9gaFfATC35PTk72Sg1LET/25Roz8s+iRJA0Klr3cu2RYjDMxv5wtfpEK100ZCpEVBqXtAA4Ek4bxNSXFPaGQqfRkkgUvoKxB+IY5CadJgFSizHnMUaaadDqdqCmwKLO1JL1LiKlJHzNf6ds17ZjipzWLYlp+vcKMWhO4QT8BYquCaxFOO+00PU3R1kTe+ta3vurVr2ppaq6WqwPV8oMr1l930z1rOnva2rvXtLetXdO1vr/UNGf7xbsu3m/3HfsHbH4udfWV5s2097h85y2/X9WOclbnOMHb1OhEbm95doX19pE7D3jp0fc98MiKtp49dtt9+3mlPbYuze5c07h+TWNbe31DfdUtgz4UfMZPSl//Rn1/b1/FbvZqfX3d4JLMu99ZecXL//fezlWPrdhjr716Bwb22Kp+0UB7w8qH+5Y+XClXG3bYqXfB1o/1N15xT1vzVtu7f2tGudxTra5eP9BY17Ns6f1tbZ0L5s993b4Ly6d+sfST8/ur5V77/bXRQF9HZ9vqrvW31tfffuwx19595z777HXMscceeshLdNRp6iEb1EARORAIBJ5KCIyqNzyVKhl1CQQ2fQSoaFiiY73nn3++22uoiXQ12zs5myTpZKkKNXqAVDiei3CodzQwdmAqLz1POI5EKZeKfkmxo+ZSN22WFlij/NXkWYMVcw0GyNgrPPFJhtCk6qWEiSFnJTsnJwOVDk/joUpiyF6h3ExALD/JFJYyr7Eei58yYYL7xje+8f3vfx8yykKAfTPpNa95DcZeo5hKkgkw9uKn6iudU3fMKhnExlC1cTkVIViqlBzwHxUnCcKfFOJkRh4brlz96fCoFJcMTRY+1FRbaFZlbUypoMo2ZV1Dl8PZtMUkS7c0o1do3zHyQbmvuuoqLWjDquEwRswnRD51MDnUeBKMKRBDc/JWz0wHBFKeZPA2b5Eo5vCEhY4WQYaGlYOpFpgOPPDAmhWu0VKNJ1zOFsi0jn5ShEu4FrSaYK7Ql9LO+fFkOCVxlE4YncduFE8cT7bqbk1tks06hngKzQS4CEVOIgK/SSAteMHNT0tL6Rh2jpY81j44SZjuLaOgyvIkP6gFWuDTS63ujVhQTVZ+agv9/4wzzjDRuT1bDlbrHGyx+cVOdRE0V1tfdVl7dUV7qaO30t7W1lepPrpszYr13a1bL2yu63acoLF16/LMraqVatvKpfOaqs1zFnRWWxzl3WZWXbmn7Y577mtduEvHquVzW6rd69fce/8DWz/tmT3rV2+7oHXHxbvss13jTl2rK5de0n3WuQOPPIppb7XfnvWvflXj03fr+Nll3d/8fkO5Iitm4L6BavcuS7b6/Kd/013/67tWtHVVjnvB4l2rbQMXXtT5o/PKa6yTlvp8LPz1J804+qhHZm+7dF3J4eE13ZWe7uqCOfXLH37wvodWNM5a8Gd7bXfgvK7+r32t5/QzAenaLuhXqmU7tBvrSpUdtp916qfPuPG2//jIhx64945ZrS2udXz5y19u/ddq1B+XhqZghWh4W0RIIBAIbDkIBAHecto6aropIkD7oTA5+2rPG/3eT5uc/bFn78VmswqVPcPrkFQ3Rgl6OQ2MWsmeZsmcMoewSYjxMvjY14fdIXIUteGZpBBanVTSpjiEES5nZCCpYiKwUTCLIcBZJALwp6f4PKJhtnYGYjW2mBJM0UU9W870UZFZVinByTxLWqw4SZKeqWrs4eeeey7DiPuu0T+y2RrtJphXvOIV9CFVToXSWfETBSXLHnsOQpteJVE90fgUv1hK8jO50H2hZPlgyZIlAmm36k67HdK6Hte3cq2H5zCtISpOHiQfk7chNgGeStxoIqWWTUZ4UGvExNYmLIAMUx8Dvq7iCjc1kltyRTxV30qKrqLuaZd78e10+K19GDU4edEunTok8aa2RNnaZGG/g6Hnm2Rp3WryRciWqCPKnAKLfN6Y1bJTXrXRapEESG8V6lYCCFjNsZN8qqpfLFpxKuhp9hijjkkqT9OX2cC8ZK6Tj6FHKpy2OHmKZpeNELeO5TwF2kdtWaHYbYqSZL+eb3ox85955pmWk/Rw/N8HkE466a9OOP4EPaFS9iHfiuO4j7WX23vLA4MXXJXaewYc9G3rqXaterix2rVk193d29zR7Sbq6razSk+fb4f0Wix0fX919bretZ39nfVbtXX0rHts6Z67P/03V13S1tHni0eY8eymcmdPX0/rdgcf9Pyjt+moO+uMrq9+a/CIcLnS3Dhj7swGJubyhz7Y+Kx91n/4Uw333NnPCDxQZhvund069ztfv35gzqU3PXzQvts9f+v6gTPO6PnWaTOqduNXS5VyR1dHXdPMmf/v7TNeeeJve2fd8phDyL2LfIO4XLesrX/t+o7dtpvT0lDZe8FA088vX/u+DzcwLtuqo3Gs1fqSUrlafsWRTe96x31126ztrPzhvnt+cdG5N1998W2/vc5t0Qcc8ILjjz/h5S9/xZIli0ebyTPC4QkEAoFAYAwEggCPAU68CgSmBQFKEsdsi/Ry9GwmGvwQo3MHMgqXS816VQ4Z0SM34SLzUL8YdmRCwaK9oXNeYZvshOmiZqSlYBGS8E+TACXs5t/fzKw3d85c+qLNwzXF0QJpZlg0T41sipaEOsiD+qIryG0qiF6bdlkrOtEnJi9EOontSZXx9LZ4WDEXnaL5KVuXRf/gBz+AmLLIwBh44oknWi9AXLM8CQfKfbKEsznnKmfCnDPnAZQlA/I4bq1q1FAGB89crjg582LCjePP1NdCgCprU1sDNr7yBw12KjZ5CzTJnD55WDSQDGtgtAiSTGrFcOzI7U26h1WhyZdbzHk0/8Zs/VSWkWuBw8VduutoUk1teK6jZS/wOhJs3tj4XZ0Ypiwrd2YMPXz6aPD40cvIQMNUY0uOvkq2tBMkbY0W55prrhH43Oc+VzTj1LQmEFs276Vp3Ns04+WizTaWexxvOfvssy+77DJVFsE6C5Ovj8DJv1oa6Okvr+xCfctru8ssqPLs6PdZo2pbF4NqaXZzqaWptPzh5StXr1uw0+5zmqtCFrZW95hXbW1b1bh2df+6tsaZrT1z561snn3JrStLM2b29vbVz9p+7ZrV8xwJXvXw3fcvnbHTczt6+ipty//pVc9e/PBdy//2nfWr1rTYim7RpLF+rg0Wq1Z17Lhjy6lf7rzqmr6vfIcFuM4l09VS1w47zvnyKb8daPrdHcuOOXjPbf5w15q3vKOlo8My5MAgSUZmB9l4z4KFs7/8+Xu3XnzVrY+sXr5szrY7N7Zus1VLw1atrNTl+1b17TG/+pyGVd1f+nLf+Ze6LK6h7OorpZe7d9p20Sc+2Lnns25dUbeur9rWWZk7u8HRah8x/tklF91y1UU3XnN5d2f7fvs9y4X/nD1NNQhnqMMTCAQCgcAYCPxJ9x0jUrwKBAKBKUGAhmST2znnnM2keeddd2+91bwjjjjq+OOPw7gmSSroVRxVgO0Cl7NnmJpl/15SDpJSS5HioWNhMlguKlsd/AKjh2fFNaJ9fQO3/P52CujcuVvJjeFXrfEuhwaTbRarxAydIk4ZJkxUSqEiM+JhpLIVggArOl9GleLT8ySXiiQprYRpf3WKUMw2RcjPnEQIquCQsK9ioqzkxLptpLR8wGzuIGUih6mylFdsWY2SkTkF5jx5MCttwVbsFdlsemTnQcCGc7Biqo3md1DTJ68o3zZVor5LlixJAI4B1HTIBkY7yVEUbWorO5SmpBT9xPKErFjvOcRPvfQZjVVTQa2T+L/GrXk1JZI86ZmoYKoj4+HGr6AJwYZ/zcEAm27G2piAqLjilO6mPUsAiWpaCMhbzTemMMPLIt7gvVO//70FQT1f65ir7SWxC8Y2EyPCqBRo9mM0lpxfEk9+a1Xp3IRpCsl3pwOTrxGNLevJoH71q19lCe+Zz9yDAbWb2baj8lhXaX1XnS8IsQB39lbXd5fW4r0DFRdctc4oVcul9m6HZqtulZozs2TDMONwU7nv8J3L85be3f79Hw784ur+js5q84yWFz5v5kl/uXrnXa58rPmxzsZZjX133nZzaeaCh5evWfvoH5590FH11YEH773tQ6/cd+4l5/Z/8vOVvsFPfCHxZpgFrTPqOtvalj828+yzeto7+t7/8TofOXLTtKq96tUz3/E39/fUX3bTAyccsPvsKy4d+ODHXCM9dCLeBmYc1teAmZLrmj7/iTV7Pe+bv7iru87npnbYbdvZDfWlxzoqd60YaKyv37q5csSu1W1WPtx1/oW9F11aWrm62tzY+Nxnz3v9a0u773l7T9PdK6sO2W8zs65cqusZqKzu6Nt6VmNzXV13x7pbrrn41qsu/N+LL161asWuu+x6gmsSTzjBH7uifX54O0ZIIBAIBAJFBIIAF9EIfyAwtQhQ7AaHGN2OgeWcc87Bte7/wx+2WbTDCw879iVHHvu6VxyyYPbgN0W5pDAlf/FJc+KoWSNGYLqxgxqbTdc1ObIoLbrigB91jSpDS0M70Tz+ROroXozPLtxpbXVVsrxXV9b9sNJ+Yan3fspLe89OvS3HdpUOL9XNXLx4Z1rcihUrKXbpmC4PXTARYKWg2Xgvwyw7hnLphXZKozGqLERBZKb2Ic+Jf1Lui6ZL0WwCzJkXaz2GX6r8FhNOqFpWALJwBJgV3YKCy3USFYcD6pgESAnVGbEkLYUJ7/r2t7+NdCHJ7DbgEofOWtz2nIvbaJ6EpyYDsp2x7NuUbD+TACP2hGmSDdpaHLwQY/hK35KdKgFSU8oNAeN0Hv4cmGoECiBoGj+nqtzxYKVjqLiesDEZaao7BqimljzGI+eUxFGumQQHTuNCKxsdGxNttUh1N4pZUC2KufjA8PTTaB0PFJKb9HDmtM9lSmBJmSTB0lKdnSzmKxtqTKr6pHnV6eXEivWTNAWJmSZeyQW6vd/n69I322VlJjeXomysl3vutZcZeH0v3lta3VVp664bKNlGXGrvq6zrHrT39lTKTQ3V1kark3Xd/b6max+x66xKjQ3V/kq5vcu52dLc5tLBiys7LH9g5b99rOHmW+sHDanl/mqle6BqPWPhJz+yeufdb1xWvm1Z77U33OSUbXNjZcXSuxfufVD3QMOcvlWffu1eMy/4cd9/frav3yZrbLOfhM0z6meWKz1r22d848s+8t353g/OaCi11Fe7d9tt5vv/pW6np3Wta++fNbdpdnPv/17c/S8fdkDYvmsF+xQwru7erUrfwMwvfvKxvV901s3r91iyaE5zfXtP5fYV/Z199XOaB029a3uqpZ7+P9+vbudKZ0PH2vq29rqmGdW587rmzrtzbd0dKwbmtda3+iqxqzG68OnKwtaGofFfnTWj/5kL62c3lta0d59+wZWX//T8ay67cMWjS/11g6rVBKckGN6nsAM8YVaDf+ZHj1TzNnWnFH0yQ+yP+Tia7X+DfxP/+N+f5snBBe7B0CFHxOwfx1w69Gf2TwlqRP1j6X/Me2i55/Ef8U8gsJkgEAR4M2moEHMzRKCvr/fqq689+8dnnnvueQ89/MiiHXY+4GXH7H3Acfsf+MKZjc3VulJjqbJk6+oOcygP/r6M/DeULsg+WWN0zWB4Re2zDYwnM8n0x4kGjx8iqE7MYhd0MtQUl8BLceYddtx+RtPM5uqNDavf3dB3e7lk75o/kIPfuyhX69dWn/fYwIf32PtwBa1cuYqhA/nhl1YO1As8lqJJY6as21xNBaQapjgqkgRIHjfcesusKrnwYjX9pHaj7iIUiXGu3dieVEqKo+6+BszA4jSdk6ICURcVx4Qdq8OKKdPikx8g9FeKfr7fCBqoDsbLVszuRPWXvCjn2GJM7Vt8j6GVW7x4sUavyXwjSwUxKj6DlUZfsmSJ7cejLcTUyLmhP4f47+MEuJgW+dF/2NbQoWRJK76dVr9xp3sz9D3hYc4pFAPghu2VV15pqGKhExgUExZG0VrZiX02WOUaCBvnrHWNwMQohqRt4RawrE+NzWoktB/EPGO1qJjDJP0wsUgnE7OKScMsx2+yNWvx2GXjGHCaANPpdIEw1GPtmMB7XeafYrbMbDnk4IOPPebYo485Ro9CFF0Ntaqzuqar3N1nZ3G5r1pq6636dlF7d6nPNuSGSvPQXQ19A3U9faW6UtXVfjNcEGW7BJ5cqbY0VRmE/eForOt76aL+/h+d3vfZU8tN9QP9da7IAqKp3HTe/IbX1b/pTVeumvlYT93tdy1d097b1bTw0Xtuaax2zG7oNy+/48g9ltz7u5V/+3b3LGNL1hs8G+rKW7c2DyxaOOMLn2m/54HKF7/cMqu58pxnNb3siOqMGes+/5VST1/zK4+becRhA/fft+6t72pqb3OZs0PWJqiGumqlVF3XPHvh/3zlwflP+/0D7U1z5t+9ZmBFR3lOc0MTk3V/+dF1/c5ibz+3vrlc2aplYOd5lSXz6uvrXJdVunN5f2+lYcHsusZyqaO/tLq9b87MhtkzMHM1GthhdnWXrevq66rL2qp3rS71V3jdet17w3XX3H7lhVdddt6ypX/YbruFrzjuuFe+8lUvfvGLxu4zGmvyDtT63uAfb8R/JKcxBv+2D16h7fXgH8fB884O/gw2phWNxz9jtkFz++AgsZfcfx5t6/seekTy+p12qJtl3UrOg8soShs6WK34qv0VfUsfHGhrb9xmQb1r7esb6wcRHcMRcnB5fKA6YEmlbuj+7xR7MGdnvQfXXhQvzOvBtY8x8opXgcCmicCo1+FsmuKGVIHAJoyAvwiDa6z9ff2sdvbosvc+9PCj2+yw6z4Hv/aYA4894uA/m9XcRDlZSQup9M9rbuyv1t+9qrqqvf8ZC+sHr+z8vy7/RaxRCgdLqVbTR4nsC2WGRf8QJ45Clt5Ki9HheEgUSwWeKRomQ0FEU7195KFHm+vu3qX5HxrrllN4BtqaK39oLNUPNCzuLbX2zCv/urX1g9XKs8p1C+fNm2sjH5UOS7RhFVehwC1ZssRfRcSblTXvVMwCZw9h2EDGMF0yvSI2wyv4f5EY+VcuRXLL/28ZcoR0oa4ze7RPfNhZO9GQSTTYHUtEVX2SU2Qzocob52zwRj82vu0rVQ85x8ytNaQNz3ATnus4MgTTHwouoAEwnWmcJnnwvbTuUFOhtK2UADhGzauN8DN1y2mq8mjya3c900ADuNMKo0Wb8nDVtLphUy7eiwOnrRBTXsoTZlhEG/7mLsMTDWYWxoFNO3kySVmJY95Lpld+gyitf4HRMH/C4p4wgiUYSyE5WvLrkOlWfLtLlOgtAdh4TSDOtJt8TMiEURcCO9l7xBFHvOQlh86aPbetd2B1d/WmZf3rexhLsZSya67ae0rregY6ewc5kY3NDY3VBp8E6q9r6x4Eo3lGBfccjNlbbq+UWhpLc1oqCHFPb51PHFXLdbsvaKh3Y9Vvbu7qHagrY4pDi5hDfyPkWPndLXN6Opr7e++9Z+39j3SWG8oLG1ct2Gn2QNOOXb3V7t7V6/vK9bs8fcbhL+m79Oe+c+SvEMDRsrW99a3Hn1heuGPrwu3q9n5Guadfkb0OBn/j1PLvbxvo6Vt1003lGY0tz39e44nHd3z79PJAL+Ov1iETAj/vDa+pW7D9o2t7r7394bptZs5pbd16ZqlnoHTfmkrvQHWHuXWzGkudA6XlnVCq27a/8Z41gzbw9Z2VBbMbt56BeZUfbUfGy9vObRy8tcx315Y/uPdOLbsvWNDdV7ngytv7mucv2GZbiVf39Pf21D3/oINf9OJDX/dPn7j0ihvuvOrCsy/8yVe/+rVtt13EJvzKV77SbqBGl1bkVpxSz+PZWijZe+/mFYN/f4tukCWWy13z5y+65VZNO8hKS4OP9p+cW3rv+3q223b+pT8rN22wbEy+dgW0/fTi6uc/V3fd9TM6rflWu+bMrbzhpNaPfLT8+EfvB/uPDtx1wfn9H/hIw523zeivdM1obN9rz22vvLLU8vjWs6K0ya/rDnr6ete87i+bbvxt13HHL/jsp/Sq9HbwH0sRA9WuC87r/uGZDQc8f95b3+740OOvU6R4BgKbAwJBgDeHVgoZNwcE6EDXXXftj84865yzz3n44Ue2f9quLzzqtds95+U77vVnpfoZ/l7d9OjATlv17zSvfsHsGW3dlUfW9m0zq6FlRmVOSz0lqK9zbV1vV125odI6s+wOKn/aV69Sb5SMFQIT4EcXMYHBP2ulkq3FwhWK9HpL7aOzDukug7e2UETSJt60d5QyzZRHI0wxBe6zzzNaO/61sW9ltVLfe8lWdWcv6F8zeJh2/ay2pre0Ne2/pqHnpv6Vn2/c5t+7u3ts6sN7cUssWuk00fzB0tQySaTkLz6Fp5j+pmLsrK/Ic97QmFNlTzHt+P3F5MiDO7E4JYLIxdEck9q3vvUtGSbq66PKhx12GGpn87ZAGEIM5aDLUmGn1oI0zlpoRKZObQ0caxb05tSU40w+tdFApzvpMJYVkCKmrZR/EeepLVFluZo8Le4gGARANqav6JpCn9yfqZrqaxHHoVPrAmkdZONIpXRNb42MSyUicmYV085o+JtPmEMdwRgtwmQkl6drz6wCpLPBbKo8wMnipczJYIIiufj8hhK/1a48z0xGBt0vbcQwM8g5zWb6qnCl6KIWrTLpRTZwAzcIHnPM0Ye99KWHH/7S3XZ/RvdAeX13ZWlXde2aARTVxN5bqXT2VDt6Btp6+Adnc9ZPFywP3rfVb6szk1t1RkO1xa3PQ0S3uzK4O3hmc8Um4K7+0tqOIdNuU6W1ubx89fqbV61/7jPqqr3d8qn02RXtb0id7JobS43lel/9Nbs9srqyan3HdrPLLHbdnR2uodptx7kMsKsebrt7bd2Oi7de9Pa/Xe1O/st/gROz7zXOndfy2leWjzri1l4LENXdW0urzv9R9cabB373+8aeThI31NfN7O9r+8JXWs54wcxXHd9X6e84/SymSDS5cZttW197YtOxx9zaP/OCW9a391R3aZ3RW195cI3jytVt59Zt1VLX219yu3VrU9/zdi7Pbapv6ymtXNM/q7m8eGsUscowvra7b+Gs+tmDG5nLLKWLZlfmd7bPLJUfWDuwdG15RWdp3oxKV7Wycv1Aa1P9ormqWXpg/cADa0o77P3CxXu/4GV/+28P3X7j8hvPu+Dic772lf/edrtFJ5x4ouPWPjM2fJ6ZTPf4U9q6cuNfvM4ntrXCEH0cZIm65MBtv5/1v5f07/EMf3v+SCH9Wxn45jdaHlpav/S+zquumHnYYZle/inDcfj6zjqj/uFHqu98R9+z9in19gyc/v3mL53atnLV3G9/u2QxY0iCrh/9qPTmN9fvtU/lC5/rXbRdia340svLQ6bb0UqwRu//nd/+TsvPflbf0dG3alnaYp3j6/l999/f9dWvzfvSKW2f+ULHTy9qffkrnvzF2ixfeAKB8SHw+GbF8UWOWIFAIPD/2TsPAMuKKu/f8HLq1zlOT/cwCWaAIQdBEFGMCAi4KkEWcNUFVlcM67qGb4PKiigqIohKVkBBAdMi6GICJDM593RPT+d+Od3w/epW951Hd8/Mm55mGNx3ae7cd2/dqlOn6lad/zmnTk3lAEIP+4WylQUxmbZs6Wmb133im8855OSzjjnhKJ/Xg3mA5Uw5U/MhMuCsVLK8aumQJm/MLxzVhpPFYzs98/MjxlN/zv/8V/qWzVo45FmxzHvWWeqSRVsHhnC6k5KlFO9wFQaecYefYFGKxlqIBy/32RUWbAyu4xHQBYmwHDZwE7qR4TAwkh6h7ejDAid2fkKzCqWn4vq3O3TDl8tnvV4PnmxqpKR/vs/TkTH0Zs+i51eu7Nu4cRNygywasRIzkaRqKi92/RsCsEJjyeHd/SDTUxyeh4jLSKg4c2IjYg02cbnwSMQMDs6EUsAwfMNI3tXVJW3a3GSpMAevY9zeD+5ztCCc5IA5oHQowdtTlsvNXbPzFXxC3dELgMbRC2A2d+3kr2CRM2UNGXggY3ADycCT/c8Nipb7AM8YmXwmkufmHhUnI75QmgC9FTG393/dZU1Qgjz66KN8JqzNxmNiRjLgEmPULAaEypklGYJyinUBfNFgGJwFuMkBbZz5dlCukSH6NR7JHovuAIxaeSm7Skn+8hEjKvnTG1lyQvBnfHzYtQ5TuQC9jp8L48wpp55y8kknn3Di67Cq/uGvK/OKP1zbogdi2G9xWs4UbYy9GXYwMtnTSHzeWKjlRw5+w4mWBa9ODCnyU0s4SatqwGMDjPldxLGZVb+sDPeSRph38yXNtLVkKjM40PsvpzT6bvv++E23elQFG7KficfrIWA0QRH0s97uufKqB3aEVw57Bns2hnza8oPn1wbZYEkbHc0M929eevDijjrv8rgVGR9gL/XCmjWaLxBYtizfuaBHiTy00ThvmafjpaeGP3y1ZpZYCSx2LVLsklC9WnjGRr5+je8Np6aGE9GxQWNrD7shezu7Mg1Nq1L2A3/tsy2NzZZSJX+6YDTXRxcu6CpaSjKPL3dxcZO3NaJnChZhsTA7t8R0HLxZ+TycxuKtNQk1r+BNWCt111rxgPbXF9f0Z7x6qJabQzsG8nrQG6xtjKrxmnja1FYPYlhW8QkHLxdLVlAzFrZ6yAM6//LUs6v/9/4//fr+7Vs3dMzrOPfcd5933nl8VnPSPSCGA4ZMol55Y+JXcfu2/Kmnq9mU79HHAouXyBrRo4qbNpYOPcy8+APqvfeYb39bzQ9/QKCvyacyB9H6sm9M5Oj8I3ujfIJ7c3blqtDCbtUvPAREkUY+/cY3aU8/41+/xts+D1BtDg+mDlthLT049vP7tVAUxYpi8z/h1YRbc3nOE6VO9vbi1k3Z416nfPxj2uc/b553bu2tt2God9NDRubPfzLu+0n4v6/J/+hua3QscsWVojPPQLL7UvWiyoEDjgNTle4HHIFVgqocOCA5wByAReLHzoFIBJQ6+5x3rzj13Z2HHadovpxp9Y1bNUG7LqQtb/X2JYytY8Wgzxv2ESHT99c+o9FfiCqDR3a3tJqJwi9+Wbzhuz6rxBymDg/bPT25Z1/0f/5TncuOYGUt5l0kUTAAbCh3/0PCY77B/II5F2jKTxIgkHFGJuOAPIjEWw8xWs5MQDtCZXZ3dyPX+pXHdKtoK7r1SNxT0G3CrwixSvFpupE08/8TCl+a0q1BM7+us/OQ2to6rC5gYGRN6fJH4goPaHC9BHkFuQnQzuxbXpcKs6okGRUHvyEooyCgaKRhSgdSsr0qx2c/+1lkZZQFoE3kV8Dwgw8+KLPF0oUcj4CL4gAuvdJzOXRirWK5IDYlGgjlxemnn+5aJ17p0nfFSdlt6EVIh0R7flUcjyVtMAe8gTsu3XVX1P5N3qfp6bdoZHBJoCdzlutO939lWd1AHwCHo4lAp8bX4WIGPCakFomPC2oZFiCPr4xjzruuzJDhgi8F3xO+EUqkezC+yW/HXUYBu+ixeL5ADGnmimMgfFQh2OQZNFjxK5cEk3k0GjvttDeefNLrTnr96488+hhLDyTySrKgPLUDdGqtGVYSmWRpc9oXjgdq6nU/EQc90ARGEGeHOHALFwBaTRMoGpJLFgtcLUBvwCfoF37HJdySzYhfiUS14YwJ7hU2Xt02FKtglDJaYFCft133LH/LGblfPxIZGzWBpazkBR4B/2O1obe/ZcQf2jBiNkbUeLPq401LHUwpRNJqbw7Vxpf1Ju21w9b2JvuI9raao5r9K44pWNp2S9ucVFcPaJt7B4oH1Vm5vE83wx7dsM1sHkdm26dqPl3LY7EeTw4li3/YkDph2YLIgqUFg5hVKkbaZ1ZuyZn+kVQplSu21oZb4jqbCA9nLV21ljao7TWBbMnaMmJi722NsvxHrH/uyRgBXZtXp/s1EQHbq7A22Nayw2tX79g+bo0VzPHhbanRkYzpCTW0NcRMjzHeP+7pywaGCr5I0BPVVdPGtF5sj2tdceGRO5y3Ehn7yGOOOemEYz549b/3rHzy+cfuY2nSddddR0d6z3veg3c6is456bHys3VaVZzE2txCLvuBy3zbe+2f/tS3aLFzd+J57q57NMvwfujyQj6j/ewhY2jE29Q48Uz4Nou9kJnGZ+zBAhnThMIwrkSWHSLf0klOv/IF9JNe7/3LE9bQiAUAts3kfT/2Dw/Zn7lVC0dJiUcBL4vMST/T4SB5sjeyH7vaOnR59KKL8p/73PSEvB9YsWLwK18pXPKBYt/2pttu3QmOp6eu3qly4EDlQBUAH6gtU6XrgOQAchUW1HvuuYcNaRENgZeEnbzhhhsAVx4PuzXY64bZ6cEO6lpXnTaYtraMGm1RT2eNXhfWCO+RKaphr6cu6BlJF1/cOHDKokZf79bMD2/XSsWCZXtxXUNryxTX05u77d7oZxYEWgXshBPCk7lsopaXEINnMvTwlGS4KGOQ4RFonLOUVssVvfItQMX5559fGt5iD+OhpZa2m3g249Pn8Xqxl5oYGmzb12eIYFh4zFmjGFU4ZGsga3Ih86m8fYB5EgPzIm7JkI1sLTd0rTyTSlLCATAtojm1BrF3d3cTgJpCOdzXsT8THIuDO1CFk+2vfvUrwDBEYtXh4D6sI24WUYjY4ZODiK8QXJ6Jm9ssLqg+1iSKwyBPnhI8cEELzlURs6MK1QBQB8UBzU2tXy3bryQefQQO2IsWLYInryJbZsHJfX9F1he7K52EEWbfM5x1DowkdAZ6BdZXcCBfhPwQGCj4kN1s8eyAZtRwpHdvzu2F5In8RviC+Go4wKX0Ez5VCXrLS5x1nyFzPgS8mlGTUXEOQC9qO5k5/tisKWVRwIknnbRk2WF52wviTRbsZ4fZBRcUq7JhT5bFuiVbbznYk8onx5IDibQyvgObbHt7h+OVLUijFDIUwxKYQ/wxXAvzL6t/ucZviEkg6LGbgmokaIfFyABqsXEVxnmYoMgg2BL2b1Xz63Zng++FHdb8jq6Gz30q+e2btY3rTEvH4Ky2dcSuusxcsqQnrXc3enPQ6Y1rPjUewo/aGkybQwOM8URaZgmqzarde14wcVeO+72aV8B4y/YYRTPgtUG8emtz3qNamQK79epEp0JPxnZEkBKtCSzstj2BxzYkHt8RbmhswI3Z7/WM5ewdvi7SNNekG0a3NHR0pIqqx6ctbLDb455cwdo0alKdlqgoPVm0+kcBxkpnZGKhLmb9tijol2ha6srR0IBdrxIncmwo3DjfjC9o9Nk+qxCLx9N6vGfEZDUz8ymTVbpo+HTriHZPxMuCZRtgH/LY3XVszCTY3RRWXveO4y951wlf/eo1GPBRXH/ve9/78pe/jMYTGMyEyGQx6z4jAeXk67Jt7eSnP+P/3aPFr3615s3EkkTBgSnfgbRGybr7duXIY/3Llpvvf7/n9jty99/v/eAHeSpzKI6OmqnkzhmrrFvzPr3F39Tsffl8NNGd2I3qhRcLoUiog1BtJNT03/6vGQgGD1uWvPMO+4kndeJKnHpy+M1v0aetN5GFUBEaJvujH9u//1348cdVnX0HHJrLaJA3tECo6Z578309Dc2tGsQAqWekuPzF6nWVAwcYB6ou0AdYg1TJOSA5wASD/Ify+K677sIOAOBklSkqZIIMC2hadjCN7UhZG0ZtQlkyVWQMuz9hBX1qY0wFyPalrM3DJS9ynFlI9K296owlgQd/Yl/7LY8uHOlKCFC2QgROXJWKoUj8+9/Ndh80OjKmlDJiRyIBPF82yUAV6BdfX+7j5UuuAD+usQYDjKUL9MTU+PIXmdVKY99XBz4Ehbl/n2c8qbONI95ivI5xQfd7fWenAhcNWGiZO3/vCR4/pdyy6u75EgIw12AyAm1iPEFUBV7KJcp7frmCFOQPkgTWclAKfs6A3q6uLtd6uRviJXMAzBilWRL829/+llg7SPNPPPEEG/9Iv0pIgFrMX1jD8JfmwGgAKthNtruhGos9e1CxmJA0WPYwYc1Op7CbImb3CFbgyIBVDdgJ4H910ThVkE3Dxez4PDsmlL8F5uGgr74qrHCr/ypyQHIDSvisWJCMVzbdFbRJi/AtMx5ywZnPR/qY8AHOoZ6ovC2mX0MVfjF8qrgJcI1Sjy+UT3gW7OJ1FGcgXoLAo9Pkw+ea4ZSux18w6F9x+Ipjjz/22OOPP+LoY+ua2tNFHcSLM3PRBvFq6AsxZgJ6c+KslPCpcXxwBHIQdjzVMg2GaKjt6JjH8OqMhAa8kpBB+Dw7kEJE4WfwV8UuR9h7o37VJ8IGSyOx+AgwsqFR7UkJC15vGrStjRaEsbDGb8UC5hHN5on1Zjgxaq1ZWxoY0OvrAgcvSdW3/XnMs2FUC+hqIMDuweZIVhlJq1gCA17CR9vpxKhB3Cx/uDWmBH1K77gynldTBbHJ8PIme34cPK+0hK3jfCP5795k/OgeYLUhIk8L1mimETjrHf4rrrqnz/7Ns0ON9cFwMJDIKvlAq8+rN8WI+ayNj6e2bdrQ1toeKvUed9Rh2ZI2lGF1qtIc1XBXlg7PVLkx6mFbIzy8dcVqDFldtYQVtnvHzB1p3XT8xAG0zzy/MhipWbKw3a+rzz63etCI+etbY0ENf4C8ScAmo7tea4vDMG0ggx3Yao6Rp9DiBlRzQZ3ShoFYUL3zoInZOAAYzJTBUH/iiSe+//3vR7shXat2ptubKwFx6U+2krzle75//MfcRRfXfvdGN84zObGfU+5/f6+/+U3Fr3295oorlFIhsfxQlt3Hfvc75l56C3Bz9HOf917zJR0r77SDPgJA9f7iF6HT3lg+MIpVu+T8i4e1c88vfegfYl/7mlhybFmpE45VNm0uHHWU7/kXWclgbt3i3bwpf/7f1X3ve2yzNr0AELTR35c59gT1wx+OfPqT6lgi295Wes/5L3OBdhCxqGnZIXg7PbuyBNXLKgcOQA5ULcAHYKNUSXr1OSAmMefA1oGF8O677yakMFMm8ZPYNvbMM8907WPl85B4w1ZaomptSN00XOrPetBwL2gQrl+bB02MwO1RZWj1M8+v7fE1Lmyt8drs+DAynshmfIFgSFf9KiZXli2JJWIs0iIeCRbjrcXa+lBtsmDV+FFXOa5GzkwjJxxcJSmTyQ/RDesEFCLMOVOpQz0z6suhr1svxX+0bXuJseJ9RyK4YZ5SVBOpJHUW/nO+fOSNKeZiW2vWA0tEnRxuTMlqooA9/cNbMmgNFhsAMOgU4W8nGXt6ffpz911qjYMo3s4AYMLwYFKm4qeeeqr7yh4JlgmAygjuvIVhh9elbzYiPnIwJiYEYs4AADgsc6YUwmJjOpBgGLgIaJxSVvlPl2BuwgTUAdjKiKzj2tXLE7vE758LlzaKg4f0ahoLel4tkqAHb1u4RMd+tWiQnAf6cuyfVpheilt3GIKSi08GjOfenJ7+lbtDofABbRqdVn7IkIRSgA+ZQhkeOSRt3N9vFFIQny06KXwEsNDi/My4JwEwZECYS4n8Wc4f7gCb+ZxRRQF6+cyxtDOYyDR8/nzaRx51FJkvP+Lo+YsOKdg+HHQZirebau+QZmAiLgF61VzRypfMggnKwibKljwMzs7eNqzmdXoOBdnCI9lTV1ePJV/eHB0dw8kiGBRbjkcjUQcjKyEf29sCfdWgl9hF3LMiPvYHUuuCSm/KHEqzETC6VCuPb46mQADIG++dKI7IGgGi7JGE/rstvnWjhSPbWrqOawNGssnulrS9YbO6PanVR4Uz8NgwCNPDIl6/X4AXFsoO9O8ojBBbu6uhLpAqqZtH1IGMGg/a82vZPkfpGUddqzQGlJBX2RGMdrzvveMEtPr1Y0EmHieOl3XyicGLL+7zhDds21oTtAaG05liKuxna4A2r19nwfNA0qz166ceOa8m4FmzXlk/aEaCGvGfic41mrX7xtiRWG2LaSxLdmY1qyVodNYC+rVt4/ZAFhp8YEDhU50s5S2bLZQaa9hM2H560Ogd0xubvHUBXK5A0WZbxFrU6tV1dSzDEuJSPORZUCe9aezWgHVQg+ZsKCVmTdkraBYmSs7EOGCcQX1DoESm+CuvvPKjH/0o28i/973v5SyUFM7h9iX5c8az6HNOx+OUfvx3+j9/Inf8CbHrvjbxSchZWHwgtnX77Ra6gnefI/Lx+uzzzvdc+9XCSy/4Dz0CZ3i0IMGLL7LecMquCiVWmuew5dREkkEtxAU1ev5Z+7LLC4cfFv1/XxCzOFM5f/mSfyxpjiYDzz/r5YMtlRIfuzp6803pN50evfAi0rilyHzYcDnziU/YzY3Rj37UcbJ2dDiyAPezKhM/JA3Vc5UDr1EOVC3Ar9GGq5L9inBgYjpxIB8ucHfccQe+UsgreIS+733vw+TLZFlJwSKMIut5c+bGETtdIrgJHmtKf8rKEwelb3V+rLexc/GG3qF/PGNZ/I+PpD7z7x6N6FPsaajgO01EEMSpbHNj3Y3X9zTM7xkXrzMx1gfM9hpEIlUs+JmMmcEV8A9MBWbo7u7GGoONkQtU2tJ50p3hSClrR3pE6v7tvYc1/Udr+ElktsJjtfa9Tfag5vGp+YaM5+9HA0cmEZI2pd47rl3Z0d6J2XY3oLoyhsxZECxqgcyNPQprLZZbKohozhrpfdHc77EK8BNxGX9IkDBCM2cEbvctd/0wsBY8DBQvhytgaTxIUU8gskM8RjMWY5e3i5tPhRduL933TJD7AQDkg1m7vIkrpGRuk1Ev7GMEKsNc9irG35KVQkSmm+Gz+kqsa62cb7ACxwRkcRgCgtqXFt99oRV2KjoMgBPm0M/pMGig6M90eEmYmwllvXKkTqkIhTKm8Q1SIt8pIwMrPKUai5Q8Re8GxGWXYCjny6XDk97NBBcMOj9wV+BeQO/y5UXNv35EGHVxDBYOybbCmt5cSSFAcha1JLCMxSFsrYo5TwAEhmMV6KIT5RADnSH2TxWHCGolQg0xeovpQAzi4rpYyI+z3Vo64VFLYa/W2hRb0NFMLyOoNDgtHrABvfGAClIl74xhrhuxntuup0sK8aJxosUnmQeYfgmYlSjYoxm1LqL0J7ljj2bZEVf12mYsACSHHk9DWBvNWABMYK2IF6ZoRVNoS3EVrg3bQ5vWjg8Pe5qWhOpa/B4MzphS2ZeYfYb5DPO6lWmOBIkpFg8R2zjxd4cF23Mj9vp1xZWrTcMKLF2iLl60LVR73c9e3KG1FxLDRBKc19ZgmoR6hjlmW0zBkJsYHUpkDICzUhjv6mhiHXDa8pjeeNiv1kf1EChVMNBqCNvzazSWzfYm7OEc4E0Ea0LLgMW7YKjNEZuNErYnCy/12+iU437WCWuGYSfy6AiUxU1qzMumTdZIwsTy3BbV+ErAfiGPsbBebUSvLCzrYsp0gJtoGboEfUBiYPHb6at4cjFos7gJJy88gBii8Yu+4IIL0IrKnrz7/ixaxbHWF7Zsyr/hdPIM/f63vs5uJ/udwLvEGubFi9WT3xC9927RaVCmvLTKPv4445+ujH3pGlmEjTuWMIbPcIiuxbIktqpiY17nOXXB/Fta+ULubWeatTXhh3/hb2+n12HLhaDUCScG//pM4cd3Rd59nszWQteWAABAAElEQVTOGhpIdy9U3nhq+P4HSIYXPXmSFekhP/fA/cZFH/D/6pf+171O3BsZS8/rMM47L/6DH1IH1mfxwgxkVW9VOfDa5EAVAL82261K9ZxygFnEzQ/5iSnwzjvvRFrq6uoC96IPBtgwWbhp9nhBhkzhzG/E8uxNWj1jWtGyk2Mj2A16BjG1pg5qrx/pXf/Wkw9bVEwNffJz3rXrmaX5j60PMQHgsea79ALP+y54bDzak9KJX1LjF6Ujw4R9ZkdMxYXMy8TuHMMjI1hBEao6O+d7vXo2m+/snMekhlDlLDmbIJbJFdEHCIfMyi100As7rXnKP+hmD6Gw7EzA7BX72Xs6DSVQgJRS4NjnB/5tYEceNz+yAvlzICKISXOy6ImsK/hHzNOWhU8pgulecVLmXd5A3AGLEs6a2qG/p42k/VamnAVtFZA/NQkVwWEYKQo7Em7DyNZ0G8Cbmw77GKgAf9Hu7m6I5yeyNWY0N4F7MQuCyRAOAPhpETefvbqQ/ATgYdxGv4O0hwegixn2Kqs5TAxVABUWlwJg8GidBWfmkBiW08McjPz7OQr0lCrsN56gpkF9hgvA7j9Pej7L7GWfIVactJK5LQVcpwUZLeV9iOeR+3RK1fbxp+zDUzIB3z711FOssacWjIr4SPN5oh90k6FEwF+D9dV8npwBvYsXL8ag7SbgIlkwn+pV2HCoL2nlcK91FvcCdkusrBUzBXgK8KJic2TE5p6ADuBRcZtITwIiEIKKewLvck8AX4F1iP8U9tohnxr2M+Cao4MDQ4P9qlFYPL/5qOUL4kFiPmO1EymBtxtGjeG0iNWct7W1Q9wQBj2iKnZEtQ1DZtYGbVuJop1gP2EDy63AMCVTYG30pxwIdkSQ7m7A/qwmcnqJJcy27ffYAGxdM8aGh3v7Rka2b4mEA+G6dqJJ48WtNyxgV2GPkVFMLM2maaneQPCg1truOnU4qwwkSse0KYe3arUquw2bacX39KD1yxcym1e/UN9Ynxvpjbd0Nc5bgikbA3JzWM8W7bGsvW3DS1FvCbNw3lDSOaVoWg318eUHL8SN2WGI3RLBV1ljVXBvUmWdMMCdOuBhThwNAnk1RzzRoDqQtF7oNwfSasCnw6KSQSBJMKC1pElb0ayzhHgwJQB/c0yPEAVbVfyK2RG32G5Q4GvRJpxfdtCI9BDHF73EahT8GugAHFJryTzFEIRBGDGAcIC4/eMajQwg41DKjGbMU0zfqUTqbe/wvPii96GH/Se9DsTo6EcQBXgoXsU1OvQP/5CN1+Rr4sJXHpSpWrG+/lxbS2zlahbTcmfsezd7brsD/ojHLz8QKYgb5rv2q6FjjqVBecj/xVUv5d7+TjsYCv/q5955C0SRwFrhfGBlzj83+MCD5jN/DRx2+EROppE6aKFa3xB+6kmHPRNSDfnwRupd79KffKrwpjcLekWXKgTvv7/U2Wkc9zrvRe+LvvktdPuXU1T9VeXAa5gDLxv3X8P1qJJe5cCsOOBKUVhQf/azn916660YoABUZ5999vXXX09oq9mhAmfeE9YDnIy7atTWsLJxpLjxpR7kkDjBQIPquo09g4RPbGnrPrar4eNXDP3X17RNWxBq/JpW0PT8W04LvO3MXk9M8+qNUWUkZQ0k0Nlr9UHCq3jWjtibxqy2sBL3F8Je3SgVSoXC4MA2zepramgw1dj6DWJTkLbWjtbWFioofUqR4xEKJYIFN0p7qVW4r9T3IU/xRS2cV5cK0O6IbVop/CZv67eP6epMJpLsadzX14u5lQOHQwRHl2kuy6cLBO4jeUECRE+OKff3+NMtC5EFTAKGpBbIIthtsKkitcgEiO9cYKzbY4ZzkoBykfI52FFDZsgyP2lfAgCAThG7WStOX3KLA1pANtK2PPA35qAVpuONPTKTPBHReH12AFhyjF6BNgSLmcQD1Mgl9VW5gCq+QfQafH1yoenuyZC1IE0l7Np9VgfyU2pHh0FptWnTJlpq1ovP91hHegLMx/5ZrjWb/hb9BF0JGh+8SFg8iWsMfVi2hWwI9xogga8EQ43EFdOzqvyO29blr5A/fRiaOdBGgXX5KEAs5a4ZfFx8I0RqQJ8CkgHuMnxhKy7PZ/q18ENWDVBrpggMUDOYTQlShSuzh+WvNvZPEC9QuGhhqBM74hK8UC7CJCvMwvCB99nQFqMutlZciEM+K+RTgh5gH7GZBQyOBfSa9taIr3l0YLChoY7N76jj888/19baVldXm87kk8Vgf1YX2wUX0asB9wQuVk3MqgTVt1ePgs/1kbQI6wtibggBXVUSs/aVpbytUWtxnZ4qst2ujj06h6k5KGJCF019MKWnRvvN4c2pJJ5F6tDweO9QmmGzreugoJLLZVnIrADim2PhzqZgOKCxU/GqHXbBxOfa+9yw8vyQ2Fa4WNLHU6mQbtb5VKUlXvQEC95gTUA5qtO2C4UXnntxqH1Rc2O8Oa61HnXIeNYaGU4nBrYuX7a4PoqlGSuiEtCM1hju1upoTl3Zj/ka6rC02+NZezRNXZSmiBryeQdS1ov9Vs+4za5ONSG7WMKjm6RWlzDtenJF+6UhA81wU1RDQQxmQ8fbGrG7aokfJvXDM6M1GkhGTUNfAwCmf0p9jewJ9BnGny9+8Yv/9m//RnBEvMCuvfZafp588skXX3wxggEjudvJyzuPVSqlPnZ16IknSzd8O3jSSROPJklgrbiOYuS2O/K4U111VUgoNKi0iI6VXbM2eMft2d8+EnnnmQBRHNWVqAg2Of0QMJU+NDlW0+tKa1dlznwXeyqEHnzAQb9UbrJIOuuxx9oP/NwcGYNgAC0sUopFK5W2FyzAQCw6jWO1Fve5Yn+kI45kcy3/KHtCC2htGayEV7RCwYPHRC7ngHg38+nUVe9UOfAa40DVAvwaa7AquXPCATmBkRWyDP5Ot912G75POA/jZ3jhhReeddZZ7vrMfS9OTBti/rGGE7necWt9//jI6FD7/MXjqfyYGVjY6Dux2Qrt6Mn8+Slj/WYlGAgcuVxdtmIgVn/fS9ZYTm+PKx21qPVZ3USgSyQntSGshtnywSptXPVCWDfmRYcXx+4L2L+pCaRU1TTsmqxyjBm/yhM6emR4BDmVeiG2nnHGGeVgHpqEllcIckkjeZ+Z+YVa2CoMG76D1di79cjptopoIVdSCS7hNMhCO+I2IT5SGaAdDr34HiNJuBJz2dS772wTPJMNhPSPXIunJfkjfMvFh24BCO4gc6A1RCLjzi0NbikVXkiaoQGSMJTBMa6RsRDNgceYpDC6ulnxCPDQ3d0NMMbmycEFB1wtbyk3ffnF/fffT3rcUOXNvao1RG7bto3w11yABygRma8881flmuZjr2bUHGzRCQf2WCOqQJpd7U+771U4QCzAVIRmgi10JxY1sAvu3DaW7LGUAm4ERgKApRZp9/znLTozih5eBFWiXAOLYlLDrZTwe2BOOjCfJG4RgHYABsl2nyEJ5OHSM3lD/It6CysuKgB5MBpwQABsKU/GQMTXBHpBZUAXokdByXHHHceXIrOtkAbg7VDaBEM9169kSuq6YaEZxI0X2R+DKogXzInhlz9K5yZ/YFvhGUOoXxXES9xmJehXWePqc256NSvms2N+BZQY9es+EUZKYA7nb6IGw6Njjz3+56zhYZefZDbftPiY7cUoyYDGrPXFZxjTsU+1jp2nYhB+dKuwPm9L2mwmT05+YLZPwWsaKC5UjJZ1aJtn66hYn0zwCFyjSyamZYVygYm5TFrNjQ9njMGB4dzAunBt47zFy0PR5qLNXsPZtvpAKOBN5YzhsYIeqGHbYa9OUGV1LG+l8rhVq3gd1/jsse1bE4lxth2K+bSmCKV70BLUzV9WKORW/+nXLR3z/JE6X6TOH47Whr0BKznet2nRUhFar8ZvtsTE8tLBtDqaFaCVOFX5IquCLWJiqUYuYKaa2lq3JaxtY5jWNSzAhqUMJNknSSgXOuu0+rBaKAliArq5tEkjlDTIDg1BU8icX6tH2Idroq/B3j0cAGCGZZQj5QB4+jt8Gj//+c9vv/12FiOgeCVQFkiYmejlX6KduO46/6c+nfvIh+Jf+xqmdPJxKaCx8T0vvbTKPPpI8/J/iH3zevmQ+/TM0vBAYfFS802n195zL/eFj7NAq+7bOykSjS06nfBF5sXi2tX5t72T9g788iHvooN5ge7mPBdpbNsqbNpkHn5k6T3nRW66gdCT9OPU3XcGLr6s8MXPRT/zL/wsjo6l7v2R9+BlNa8/RZSJVCCRuUOYNTqS7ZhfOu/c2K0/IHM0JkgIIuvqUeXA3wQHqgD4b6IZq5WonAOTEhbgBD9noC9iHBISq32Avt3d3RUKSZUXyFTiJBbzGYVv3j701Jq+xu7lJuE0FZUYHl67uLBBb/YafrPAFJbWfM+OKM/3M914BrdvVUL1lifYGFbn12qg3/Gsidbc61HqgpadHvYlHnlDw7/GQ9tFIaIEYmQYmq0bamTt4AWbR0/TdF9be6tr8p1C9gQzmPmYDLEvCErJwRHPyE9kKP6fcoA5EccRC3gdIQAHWmAwRZQHxN53NiK/SnmX4sgNqANUkyv9oGeyGRXpdSm18gBgSeq+lz6lynv8KelhTbJcMorIjjcmBnMIc4kB/QLbMFiBhwEbHFwTj4e3yvNHs0Bluwj8PX8+Z3lwDZCQHsIkBmkgTUoJDAOXDP9TnsmM1y7TwOdkxa420hIyY+L9fBOvWuwt4BYAcHnRLvfkTbcKsA6I1d3dLe9PSVaew+yuDxwADP3UGnM9XWW6Amh2tXPfImfpvY9vMxZ4OhKc5ENmQ6M9spS1tXiJ44nA+Mko6ip3+CTpn2SCMR/QTn+mOHqvCxjcRnTJkBd8NWBadHb0T6gC4nLmmopDW3livjIcQBgQ+MSAu5w5yocgiuAVPjFUPABjfpJ5JdjeKUWMhuAHYDCD9IvbCYasrRvC3KpkxOoQATPYmQjECwoB8Qa9Cq65ANQQiFe4MbOA0yDyf8xv1/hUdgZira9YsikABgenicGaH5QCRt0yLrY4Gk3l+/t3bNm0fri/d97iQ2ubOxfMbwkAOVQLG3PJ9IBO/Lo5VlDWDQnv2jQxoFRWBSt+VWzjjsGZDYRVAc6thfXaSEYZxPhroDZVALHE8SI5ILKYT3uNlF3KbdqyJZUYq5+3ZNkhB0dCIDYrY+iJPIO/ZqaGM2NDNfOXJgoKy48Bc/EgcblU8HW+hHOyHVbyDVEj5lN7N270xZsDwZhuZuti/lI+v2rdxqInUirmQ5pZF/E21dU2NtYXkkOHLe0EDBPFaijNTk5i7sM6zTpeAmIVSuSvxMPa4ODQ8+v6/C2Hon2IBDSfhk3bzhTQ1VpLWkHCem+CJgBvWwvr1I4YwaphgFUftrvicFvwxJm6HDY7DelwWMxqM3Zmphi5KmfGp5MZTPwrvxQMwiBhBh/mmosuugixgbEaRYDZ12csWeorFPKHLmdH7CnvGkzDP76n+J1ve8HGj/w2dPLJssRJQGsnzn+P/qtfe1e95GlvF0uYRQ1eVguXCPHAeQrx4+edH3ng/kLHPKWlUQBfshP1V/Vvfit47DGksiw79V//4fmP/zTOfpdy2mnahs3KTTeW5h9U89j/6LV1vJF78SX1qCOMyy4P3/AtoSMRx0Q3JX9rbDTTMc889z21t35fElwJo5xMqqcqB14DHHiV3d5eAxyqkvg3wQFX5GLC+5/f/OaWH/yALRA8Xv3ss87++te/PmtX58p4UzaTqfh9eQ7rCLa3qz0JazBDvBO8y4JrR0vPFgl2FcGll8DROVv3+qz+sdLQ6EhnNB6OerNF8+k+y+9ROmPKvFpimdjjGdWrZt857z9CWh8L0Irbgvmng3ZB8ywphA/LefTE0sbv1TYualjwQdYCQ6cza06ld+d8Jh7LeXeXk6/7MvpyTOXIl9g2scwAXTiALoi8WIEwASHsumZhp+gyDri5TLtw20g+gTaUFLQX4nV3d7cUo8tfQuDmAKhABuUiXgOYMT0ByIGd5Slf0WtJNtICwjqoEsEIuRxRG3g5pVz4IyX1t771re4jFl4i32PRgnhp10LuR+gHJLtp5AUcAJ9wYDcGV3CGz8AAvK95BBqckt79CSclkXCJC5BzV1cXCKe8jdzEr9YFNSLeDAYWSS1uCzTldMsMVZD6AhgOqXCPM4yl9Xd25lerDq9YuVQN2yxtTYvPeSF8yHxldAwOrslf8naPBUEM44BElShoeIt86Lq4z4CBaREylIMD9NO+soFoQbw56PN8uRzAXc4ohriQwfymlEuH54NiBODMwWjAGVZMHxDKX6QsBgEW3nMTMvg2sfUBlckHwrgpiSl/pexaDpZqY4Rvyl7WbOoeAWoKhrJmiND5Kotpg6zmJaKyV/N7+PBs0GPAa0X8ahT3VT8GW907CXddsDtxQxRTPh4K5DKUFbGIUmMjQY+SHOixS/nkcK+VHTq4dsXig9oA0gGv/kSPCW7EaposiffB6BEnfrKPKcNvx3xKMm+jGxWZqxrQHVdszMQdcaV3yMzksxFPoDakpbLm4EAy7jXCoWBrQ+1B89tq2xYUVX0gCfzxBXRqZ2ZNeyhjJZKlXNrGS7lWLBvWixYRhe2gj7W1FqZsVQmC2FNsaWsrrbXBUCy0duXGvm1FTOQ14UDMZ2AmzxXygXCokOw/5Ag2RlownLa29RU9vgDfLQGuHJOvCNDFjgn+mDqYNNdvLQ6N4F+tdERElOt03ho32CvYXt6qk4wNnBI526uaR7ZrLRGWFgmrb2PYnBfXRDRsMXlNtFpZO4pLGeyKsW56i9MTZO+d8sqMP3mdYfNfP/vZT37qU+wkfOutP/zSl778hS9+4S1nvPWSSz5w2orDjLecgVPAjBZSPKsIblnkQ3vfhbXH71TwiS4BZCUIx+UfNIiftnZNuG2ec6+8h5SRI6sob9BxVhyesU0UH47ZV3Q08RqEOjO+yEdTY//yL6n2Dvvmm9XP/T+zNmpfckn005/W4iIKI9+FGo0U3nmmduhhEy+WFSVe93iMd7zDPlKErpjOvfK01esqB16LHJgQjF6LpFdprnJgjxxgiHfTADBY4ovJd3vP1qN0z9+Fwmff8r12NKOOKl9OHW7iV/4C52OFmKL9CXNHSiXSCTNQxlBGMuZ4TgmodjxEzBLlyWdezAfbNUQXXOCCXnAvIToJJVIfUpBsTvb/c5d6o2apow/Upr4f9JTYTCmA76F53EjNPw6ogZLhOdi74A+qgs/ZDBPcXNURCUMCYDLEgINwgGCBEIyvMgZG4Gg5NptxHnWbCSwHCOSnRI+I4wguM8oopCEGFY6XyM2UK7MlcXd3N4ag/WbYlJRjCoMYiIcALE4QIMULyeEZq7x75gMSZNWkEQzxnWpygBNgtcsuNxNkfRQQOHwCazkwSnBwh/guHEAIaMCQTnwgsCLrNiUGmDV5brlzclFeHckr2bhAJuoyhXuwGm6QgPvyDA0gItc1YE5IIhNQ2YEQBKu8OtMZVf50H6/ROGAv3eMaYLeUcmK4ZrcwvlZeBwCDUVFMMBRgtWZwYPUEcFeuoWA5AwoyynLzkRe0Js2NvzTfDgdwmkN+y7QsBjf6M914ylsV/oQ8WpMvFNUJ4xJEMjRN6VczZTUxdzCH8N8Ott7V7ZX9bFFHtH7Lq4IG2btIAfGCe9nLR/reCjQjhiNxknkKXDJT7hNP2XnVtv/QI/ZL2rZhpdcqrn768dbmeqJ0sdtyY30d5m2WUpuWOR456KXRmCEWbhIqwg75AX52yKv5iPxEgega8lZfQhQFCSgjUA6yqNdfGlELLPdV/M1LFX9sPGePpPPNNR7T0scxPbOw2U/cCTQXBNxS8XweH+jBbTugFTWrgDYKJ2pPONpcX4eVNezDjs2KaJE/Htfs24T5etO6zd7alnAo4jNTNX5bt0uj/X1NHezQ62mI6JFoeGC0aKhBQ9WHRkbWbejxxuo8oTqfP1gTVLDxso63d9weSprDPetCfsWvmbmSmTeJJGYvbG9YvqS9aIjo1piIMaSz9ieCXkFRfKrJ5k8dMQ3TumRjOcPlHfdMh6Td0X1U0OLuS7u8oDWFegjUalmDDz90/0WX3J3OPmUWm+Z1XnjB+z/wgQ+g4pQvT2l0pzNN9IUplIhHDGicnDenPN0VKeUf4AxlTeTidEfHn0E0G93E4RR58nwyh5mpIr1Dj3xaIVG7IrZ6v8qBA5ED7jdwIBJXpanKgVlzYHJwF+vHHnroIfa7f+SRR5B+COd4wbnndv3gh5G772QyKJ57fvDfPx/oOmhnQbuZS3cmmoMrKHTmFoKpKDtSZm9KzZWYzlXw7ar1m/qHWAKlouNn4mcHjh1pLdSyVPd5kRJYEJwhqHRp8NOdx0W1/tzayNDVzWZGwaDtC/j87PVIQIv37IicN4BluCd6nx55K7IIkaWnTJM76zBHsxsVAmUh4yL7gtMwB5Ex9kY2T8aaB6ijxClg2G0m5GPkb4Rj0iDystaRF3dSOO2KFylC2gN5C6GEa6Qc8DborhzgTXt1Lm8g6OP8SZWpKSI75l8wsGQnQj99j5tzxF1BNnUEZiDNs2RdVoNSKJ0zB5jZ5adbSViBIAsYxh8VgypWOwAwZ4AxgJkLLMkwbY9E7jGBW+JeXUAwigNihrGUFDrdd4ErEgDLO27pdDAJn2h9bkq7It+1y3Y3h328cPBDDo7Nec6zJgxeYVnFtZ5uVkmT7VVBLgCeUd9UnhVk8LXS2VBG4KOB/wXfO8oCzpBHPjziQn7v5S/SXpDtKGfapZoGxCsPbqLEoa+6De32ZD5tECCgmmGB3NwE5Tnv8ZrcgOJ4VaBR4prPARi8extyeZ68IlCwwoZDJutgwz4iNoMGBWiBIge5iMtZ0CYyVixiO42NDKXH+n2KvXrN6pHR0WOPOYaGWLpU+G+jUoSlY6VQjz6vo625qQ7fX2HFBueCaYTfjjOREMl5/aBQ51q2sWnTFjAz9wlV1dXAnskRM1hftP2JPItvLfy0wXCsRE6WWH9r5Qj4ZbNi2Q5qRmL7WozRxPkivnU04AdjNzTWxRtb2Y+XGlJxbNQE5CLIM5tCYXaNBpQ6LN4BkRuBKsxCarRvw8FLF1l6cDSvGCyOtsSmTRhve4dS2weHzWIuoBt+tr2PNWeMYJGIkD6WTNsj27flimY+XwipuYUdtUFdL3nCdgivAaUuxB+YXNQ4oFttMbs1qjGdOazHTCkuph8OY8Vt9GUuAJY3966ZZMOLnEQH4KBDFvr6cl/4vP/Ou3RicZ//3m2X/f0dD/zs7rvuxLXh1FNPveyyy84880waTqbfu+LkO3N3lkTPyCOn18xdSdWcqhx4rXGgCoBfay1WpbcCDsh5DlviD37wA0y+YIOTTjrp0ksvJXzFhNBjW+nf/bb4iU+Hn38uF69TP3pl8Iqr1FhMzOJiQt3fcxYEE41qOGf1J63RnDY8NFIsFZBLtvRuV3w1vkCoJqi3tjUNZe3t4yIuS8CnHhR47u8bTkDj3n9TU/5nrX4PpgBiZ6CYdibq9mzzNzYqWmmD5z96tU/hmBX3Km1xIoWAjk3MDfiqaQgnjvgw5xWmOkhshFkCXwECpSMisI07SMAgLg7EWQlxwZDcR3rmLSwzaNB5q3IECyDE8RjpmdcBBvuh5SjI7YMgAVauAsOQp6Wh1X2K5QGZG0wuE88hYRQB5gfQujYx7iCWwUnAMAewBG9SeU1KREBQCmu2XbLLL2A1MI924QCWkCf8ByGAKgHJ3KSluACdcl15u1RYX8hGvgfNsrqVcrmApZBHs6I0kUIkSoTpqAx1CcqU7u7uCgsqr/Jr9JpW5rOiv9EixKStsC3cDrn7WgP46SQ4CIAW6DOI8pQluw3dSbYLPUreRw1Bw82YIV8i7UiToV6hL3GBRZfuRNfiYPiF7EqaDLIhCRRNQQBXfJ7pgbxIt6/k9RlpI0/wOboVoDsqNkYhN9me8hSffNl3L97jFcnbPb3rFrKrCzJWV770EqoNUvAVw3CYRv9neT9EwgH4v279umd6CxnDW18Xa+5Y5PcLSMgiz0w2TaXGkpnecTNS3x6JxpgEBgd3BH0a3A4Eg6xtEdsgqaxPBsRauBHx4WweIYyinSyyckQPeoh0hdex2H8JH+NoEP/j4VJyuHXBwQb2bt0O+i2fCM6kso0fNmGs3xi9awJE59KcXZ+soGaFAhZeTGz5u2bNpnkLluh+P0uOkzl262VHX6JzifCNrJnuGcxuHsiMs9pYNeujodaWhnyJGc0ijldbjRYuJXb09dR2HYrVmB2V6kNajQN8qWgsKPb/awgRg6zcoj4jshN8povSe7lgJIGBjGZcM1DLXiRSVHg4AFiiYLHjVSqVufEm5dprAwSIWnaI50tfDp9xhqOOEA1HbMJbbrmF6OgMmKwQvuSSS+T4v889pEJaq8mqHKhyYC84UAXAe8GsatIDnANSHEFmYn3vd7/73d/85jdIimziB/TFMXWn7VHMZgJwKoVc8ns3W/91TWxwR7KzS/3EP0cv/oAnJEK2yMOdt2TOk7eF6ONez8kF+TvqWKHRR7n+uydXlvy10dqW1aufb2xbUPLVjIlInmJHDdXOjo2m9FhToPSHj7S8RSOE5PXzC482CQQkIrIoIjoLe3XEMy03blE8hWeKn1qp/CfRR+dF7YURwz8yaG7ZaG3v12qi/iWLleY2LRqZUpl9qZ3LKMx6OC6y06ZryEV65g73EUcoAjkYcYRln7QXxnlaCujLHVl65TRIAIzAQfsic1f+4uwaTlYQhABUgGYyAbCBDN1ysXsjuHOfNIB/BHcegd8QZ900syt6Fm9BLTicTWvgNiGjpSQNNkbCllZ6CZVBNVwAeCTlMxZEDsBR4AdKCqRJWorqg3Cou/wJ8pF3ELtJQ5VJw1sz5ubeRDmF8RADIHZpbsI0xFYYBRt5V3IMvQbcdl+RF680AIZX9ExocMmYQsB+++l+U7JEnI3xhGeDYjDS7mmgCrQpWhjwHiZZWpmDC9CUtNMK4OQcfJUyDeiXt3aVLa1Am4JmaWi+OD434BltDbJl0QHXfOygONqOyLrYbPelw1NrOVy4xJAbbcGQwtm9OYsLcpbfLO/CAQYQ6Od6X6idBRlTXuHTg/nQRqhhxgrpAgNaY1iTHYBtWodGU/BkeGw0svAUS/OtWbuObeq4T1aGqo1rDbgqR6MRPJuAsoSY4sBHmLXY0kbMDrpMMKy2IXBXoWjpupYssvMQgBJXajycmV9EjGvLsFiNnBsfPGjpIdh4Ga1zRQJW2eDnGhY5E9NLrLkVdme/1/J7WYOK1xI7Ngn3WrpO3rCTBaJbsSOSzVa8mIjBzqzrGUwp6aLq84pQYXzfQwTpwmMp4GuKEJt4uL42bOuRoaHh1FD/oSuWx4MaNnbgule3myN2W0TDGQqELgy+lU2+dGmGCDo/6V31GZ8M2pkKc5jQeDhs5IR3U/KOO9SvfMXfsyVf26B88uOxj3xYCQh1DFS5vYfPB4Xs97//fWJl8fWdfvrpH/zgB9/xjnfIebDioqf0jurPKgeqHJh7DlQB8NzztJrj/ueAFBGAWJh8UcEi9GNQYuLB5IsMMZ0emZ77oMbCQH/ha19Xb74pmEmlFy/VPv2p6Pnn2z5iSorgH/Jd0qNRRkZE3N9rFfL04sXU6syrZVKXvMNcyhOEyNr6Bm+04bE/Px9u7A7E60zT2LJ9aEt/kqjRTLbLlnTNi/S91XuE184OP9yQu7Fb0S0hIGACBgorqu+I8eYvbkVe+Evx689lL4sEjLfNU8IbV2ZuuEV79hkctxB6jNaO0OWX+E49RasVa0R520HCL4PDezthUzqzPjWGXQAtBA6kVeQ5qYDnKZgQ+RsRAQEF0ZlmIjE/sQ/DWwRrzsAqqa2opHRRX7bLmARLMzF7bu5REBmh5seCBE5D3EdInU4hGAPXA1LK9FyQZi8Frzkg2C0dUiH4mGOOgbe7yZf0NI20/tGCIAQgMUZjiZNpLFoT7MRNiaDc/HeVJy2CzEc70soAJ+AxLcsd+VM6Y5MGuEsadlQGUEEhvJIpEV5Jif1kOvSVJUoCyvnP9R6p2hW10++DzIGa7DuNj+70p7O4A20gQzhQTvNu8gERwXZeoXvTHDLoOr0LjuF1z304xiO+JvokDcd9EDttR0rkftqLp7Qp592UIh/RmVFbAGg5ky0fIHfAtLQFcJeGA/fyFA3IHvU40MxScxpUokryr7C+U4iEXaBTKsIFq98hhj5DVhA2uwyn5M9PcmbnLfhG5njgU/dZUzs989ndgaQHH3wQfRBeM1STn+RDs7qqirGx8ba21mzt4vG8vmb1mkQyEQ4GOzs7orHI+jHiTon9bycQL5GRAJommUyM65NR8siVg7hMgGSzxL6vvEQa4RVE4AmxDW8+m02n0oF4C6tQAn6x1xH224DzPtrVgM/C/ZvppihWDpOE/QNwirYSBaCviDUNVmXfJtY2p8C9GatQwtkIF2v8oYlCrWZKcN5oDGpNMYU1zMls8YXVW5iKCePc0RBqr4/W1MSJK1YbtFpiNiZf8cE4NZBzZoWtT7enkgwjfAj0Ybk0V+Qk86qgeXhdMB/2GcXUfT+1/+tL4dWr86Ggcdlloas/7mttlzntKkO+yp/+9Kc33XTT448/zhiCFh6DsBxMdvVKBURVk1Q5UOXAnHFgLiWGOSOqmlGVA5VxQE5RnJljbrzxxgceeABdL6t8gb4YCiSI2k1OvChMrmJytfIbN+Wu+Yr3zrv8hVJ62cHaxz8eO+88LYD/2MSBZImpCumQnPd9AkNIBUtgJHGJhBhuYo6gPGSyUCgcjuCOaEbr6p96aeu6/kRauKt56xtaQnWNWTuQL+TOi725xfMXMxMY/JcOu6dGhPgl8qeim8Fs/ef7gockinb0G72/t3xLzj5Emz+ydewzX9RXraa6AA+EGV03ixTyxc+bJ560OU1UFW9dUGxTmc+m0SAsXrTYVZxDksMlRwyZZMiM/1KL5557TgofMgG8QoyW8gdPAboIJdQUGohdhKWIZJg1AI3SAMJPeAKfwWxcSytcOcPLr2UR++EM5RwQuXbtWrCHlJhnVIVQC6RViASZyJRcI1sD5PYP5dAJQ2gCSpctCA+l/WHfGUXm5EyGEgbTjlSTs8RdnFF8gJlhAnIn6Itr7pAACLe3pcMuOglnDumkDfoiEziJPoU7XJMA3tLB5CPuSDM1T8t7L0+5zx3edVNOoUd8FM7BfUqkLjR3d3c3iEumpNYcVH/Ki/In3KbW0o5Hxd00XCMKc8AxwCEgnxz4zPlJbiSDP3QYaXsnE2mklY1IAnnh5lbJBUVI/qBBoLJ8SlRcAkhwLLwiARCXa5JJ6Eu9JasryX+PaagFGZIM+ErHI2euYekeX5wxAUyQa4DB4TMmmPVNeEsroG1kvIVgtC0MyHPIh70iTDY05yeffIII74S91ok9rShMOuiw+Oa4BqJ6fP6mji697qDRgta3bRurHZjD2HCqqaU95aspFHnFcW5CwSmKd3g+wXmUo1wx4Yl/RG5MfiKJ2OnXAyYFD1sKHs4IhX5djRJZ2q+ym7ETdoo9YLkp1Kbg60IJozMu0xr7ISUKVqrAhkx0aqy7bAdlFwwrUdRG0wRrRAWmsOaG9gdm8xZ613hIaYqwlliE1M5gNLYJJKaF9Hx6fHCMVTBGiT2lFrVHX7diUZzAj4I+DjECyKsKz5KZJMZ5Hu4BgPfsigwzhPbZ4clkMVixkz/5iXntf4defNH0+Irv/bvgpz/tW0iYK9ysKurS8IUOdvPNN7PnIl/6O9/5zg9/+MOnnnqqrNHe1muSruq/VQ5UOTAHHKgC4DlgYjWL/c8BOVEhWN99991AXxaR4uT8kY98BPQr7UiVkLRztmN6tZn+7fyaVfkvf8l370/9RiG1cJF+xT9GL7xIjbJ8yB4fG3cBsMx8X2Yv8MCmTZvKPYQhBnAIbHApZ+5EGqNe//uHx9lFoa1zgSfeMpr3sDaYMCSWqTYYP1phXYLcUhoJJW+rLz4btouKOT8Xf38qcrgwwz6du/Dnozd6vdZlSxXvQz/JXXOdiJ7iyD+i7vyBL049Mfavn1nnqRvIsoskRm/La2bG+tYdd8Sy2mgQDzjyES5elUkhZItwzxmJHxuaFCgRggEkYCGaCYkTWRNFONuHAl0kD0nPgXyA9A9nOBDNYQ43sV6yhBV5nUMK7kjwUzg/5afLwDm8oDpPPPEEQAUcBWiXjs27z3/z5s0AAASvXZFH7WQOu0qw+/xnfCrzBHTRVzljYN8V3pvx9VfiJiTJAyQjsTF9ANgMedjfYCw9gRan29DhgYgwmfSk4SavcHCTL52b8JOnPOL6lSB1/+dJ01MXzihT6PM0lrymq/OZcHAh9SwSuDIg8NWAXQHngHlWEPBxAWJJA8oF8fKTHOSx/6tTXiIwmN3CoYSNnWUn5Lo8QYXXdAB0aijL5hwASwLgP65DjDOMPzQB1MLMCmmbdbIpHZif9H9GP7o6p2QyMa9zHmEFyP+Z51/qH056w3EtWOcNxWyP3xI7/ipsQJQu2Yl0oad/ZDCRsUCbXm9TUyOaU95yOL2T25LzwgHaOXjqTAOAYTGyFwHXtljZCy6N+kVQa+Ioch8zrNBdCNALdgXukto2bTVdstJ5O1VU8XYmmV9opSyW8rKHMH/UxethdygeYX/GREwRZl1QrQsrBG0mmBYWYN4ijjQLc9gFkKDTvBPwWHUB00j2D2zdUDKMN51+Op1fzoCTNPNSpQfDBe5apOYClkoFEJogVnDsMgt4w6yK2VjUgAEonbrzbvv660Pr1hkevfiuc4Kf/WRwmdB9O2oEybq9IAkyfvSjH33nO99hQQot+6EPfYjFWfQ36Jndd7HLilQfVDlQ5UBlHBDfemUpq6mqHDggOCB7LDbDG264gQBXzHBEXJRa1Vkr78U3INTTzgzvwODCddfpP74nmMsmW9p6zz57+OSTS9FIJpOl9FisJhIJ4+OHGFo5R9wPDSGet5DjwXVkIi0knLnpSPvihEEb6Z+ljyRg8gYkEMFXTpOs7EoV7OGMPZjFA614UPFTrep32NTCwnGtEAQVa0H2VAIia4PKUTdt+/GQ0dFdb140L2tef13u/l84AbLwfmMZmAC/iDlma3P85m8+62l9cdDjM3Mhr6UbucEdfW0dnSG/Xh/2NNcGWZHFHhjCjjPTMX3+prLM97QRIBY5Riagyn/84x+RQoCyhMaZDsxcFslCeIs7qAnQC/AubJH3sWUh93ONvMhNCRvK6ZpOT/nTyq/L6UEUI7QJLb506VJapJIiZBAsTN+7SowjK6zA3XFXCSonVaaUBCPHP/nkkzAHH1QcKWXX2tus9k96mg80O12jMWPp5c1BAn7yLp+//KD4ScW5w1mm5CxNshhXSSNvypxpTeBZ+QEl3ORwuxk5o8GBh1JI5UWaCWYKVDp50Ie5w8HI49JMMrQk8pBmf+mczB1KxBjF27xIzlQcNCvfhTxe5HDz2c0FicFLKJUoejfJXt1HcBLLLR8vDDz++OOlxb7CCpZTLhuOczmTyxPs+zWZo47BuQMFDXvC00BunrMg2H23/ELWwr1jlIxkKkkIqXgNCxOEJ/aAiNgkAk0BPLVAtKaxo7G9M1UUEaQsWydqgsEeeCWbrfLy3DQAwMLFGUjr9FojIbBzYl57GwGWRT5OnAWKk/SLXsWALxe5OF0MPwZgLXZdxciEPFZLXSzgjPAiPoZIy3TCP4JqEjLpZE0rl2fqUbLEohBrg2xCKJZKkKQS6YoNikkPbPYQghrcayhsbaBrJtGb42xizBa4plpg/yRco31KrV/FoRqnaOgJ6mZ9iP2WVfyfPcJoTY3sXDbLd0H5tAjfILoPFKayUSpsDtoRt3ly4OAVyXwUbYy38mb5ebJpnNIVdlfoz37/VvXGG4N9fUV/2Dr/HM8/fWwwFmXecTS2Isvy1yu/piDahSB2wGC8o8ntggsuQGvPnCIyrezbr7y4asoqB6oc2D0HqgB49/ypPj1QOCBnKc4ETPr2t7/9y1/+ErvHpZdeevnll7PAcs4mDwcKY/Rkoipu3Vz89o3WrT/cMTa+JhCwjzvWPO0037JDmKqCoZA0A1bIHeRyAAmL6EiPFZTMqYhLM3Kw9BBGXgcVA5xWr16NuYO9cCnFTVZeFq/jHJkuGCPZfDjxjZrCt3zKKJtQILcgf7AIa1PpbX/MXlPydLBfUq3Pentjrvitbxbv+7nQ4TuyuuQnEaO1ee313/3W057mZ3eofevXGKUCwDiA65oXwUWNRcMLFi7hDXYXrgmo8YCIgxIKyLXRSEeOhWDazE3m4AfqQtUwVaHwBvdyEykTNIJbGnfABuU14poEiPWABKxe5bXmPhwjQyHiJRLYZxCJuMkuo4jXpESvj5DNQZfACCazJYGbiXsxpcTd/OR1DvIHUEkrLoQhhFWelcyB9FNe4b4sl71/JAB2yZiS0r1fyYXMFi5hqUZjAovoPK8cZqiEpF2lcTlAgn2p8q7yn5P7ND1WdGJNoazZlwypLNZFPm1qyrU8gwbx7NjHuu8jG93X95GM3TCHz5ahDEUY9T3uuOP4Ql+5snZDRoWPYAigHZ0CF9CMngvcBcGzoNnlbXnRoDL86hnBkskUa2yBlu1t81YceSR21JXrewZG03owrgcjvkBEUT3YTcWu78DdkjCucmFYGt1HbE8kDLnyT+xZwCoBPiNYrZhG78ZVdc0dui+EzqW+Xri7u8Q7L7BPrwLOxPoaxtLrw2NZG+jdYpSK87qFW69AdozpnBxLb96w2PU3XRBRmg1iZjFZAHpNO2cq2Tw3xeZJeA3xQHRtC5QrlhQFvVY8iGOzircR+/cC1IVt2Q8SZs8koXilkIDXaggpjWGMwOwmJSow/YCHdB4UoDQKyqMu5+CClG6lpr8l70j+c2a5DcomXpWvTH0RuI8KmP0GxdYJZuGF5wvfvVm7595AKpGLxqwLPxC44kOhhYshgFmbEXXKxLSr0vd4H8IA9mzNyIHPF7G+r7jiCs5yuJ5K5B6zqyaocqDKgVlxoAqAZ8W26kv7kQNyMgMa3XHnHffddx+TGQ6oCw9aeM4550iPpjmfMCiRCZHZHRmlNDyYu+WHpVtuMbZueVbRmo464qB/uip25plaULjvIlXIyXv3NOCOhUIaYZqcEYMQVsCBeIECiZnzmNSBu7jhYTABZfET2Qv5GGPRrrIln0w227Ot56DuBWz/axV7RrbdYeTXsFFwWl08rp3aZxyTyGvJArtW2GwFedZiO/zrB5NfvBYhiaoJCEbWKjYA1fu2N/o/dvVPh0JYgO18BpW8B9Pa2FC8qZ01kVi46moirASLeFX21RBRpm3Lq1oYpBFoavxKLKATaHSKBINc0b+9H/09lltAI0isu7tbdhkQmgTA00UZyCK8LQ2N3XtXFXf7HYkR8uAhRXBIux8dA60BabCowHAiGCECInbTT6ZnOP2OzNzhjoLOAqGHxqItiOTJ2S16Hy/ImYNMWJwGMAADQwmiFZL3rkiqpETIRrzG9ou1EwZi7jgw0S91of/TQPR5fBwOWCLnEACj96HWdFFgFaouaRbex+YmQ1qcT4kuiqP7LNjIV8O3xhC0N72OgYNyOYlDjB/8I3+KH5MPnKfOiVv2po2bVq5a6fX48C5mdat40fnfGTSmjBw733wVrxiEUbMyRBMxGB8W6RG9Ky7J4aKcWu6ggYK9jHUcfOALFx5Epf/69LM9fX0WCkg9qAVrNH/UH60J1dSBLdFalmy8hU1Mu3m2GgL0GthdmV9E+GVsv7AMeymZAHcBvVzwEcnF6OKHeCA8lresfqa2sd3yhoYGh+C8z+urb2D8jrDxb8RrR7wKIzl2WgZsVLDC25d4E9u2GEaRjYtoTsoBb6fZl6iAaVeAXiJiqYSFNixcl3FmBg8rTsBBQlNDD2ThBG2Z3LIAtxGfFRShm4W9l9L9ulhFHGWXYGfioH9EfcLeC/SN+tg+SVRHdARRnZkPOInmCI0wB83BJ0NzMMi7qXfzLmnkBwIA7u7udlM6nXSio8p/mPWSDz9k3HRT4Pd/9FhGtmO+dumlwUsv9jW3SRYBgFnHSyaM0mRLVm5uLiWzuIA8KvWTn/zkW9/6FuM2dmCswRdeeCETlixlFnlWX6lyoMqByjlw4DpQVV6Hasq/VQ4wQ1A1QNGNN37nT3/889vPeNON//XlZvwGgyEr4CdmDVpbZ6XSTlvf3LACtTpzM//Zur+xxfupT9lXXTVw77369d9Un3k2eNElqbZW5f3v9110QWDxYkNxAkrNNItL+l2S+MnEiScVdzAIA964RgaVEIgzOK2rqwuzwx6FY/LBKppzXLJxR9MDC7L6+82A1d61wMIhLaPUFlDwq63IImjxC9pYSYkdfYx2wlH6n/5C5eArWxoatlKsr6s756wdofDWhObVwLVR/EcLWX08Oag1RcI+v+pRx0tqqqQOEfNEtXyOFEXYkqCpjuaEuZkVyEGfElTyHiPV2doISB4dGl69ahVr2TTdg9AAEsPXi7qjzgcJSJ6AjWEC9QUPTOGSZJe8uRs5g0dYlaVhmcTkjMQJ3+TrsBfNOiXKn4BwxCbwMD9JhuiIUOsmlmlkWWTFgdkBqx0pEUowViNCyTRzcoY24p+RFSUCVrEIcQErptAzi7LIhwO3ZyrLxSxy2A+vwF6qj/ICncIBSyR8wN1dhs6G4H2hk3el9kR2flp5DpUpAC2Ua2B1aavcm+YTDuSAZ+mWMsOLE/Bk5xP4MAl2gWJ0L9CBMBbSkYWfrEBrwgAIiAKwYQxk/SfrVEumYsfnR7vCqzZs0geVSNYAiS1v8TJ0iDcPyE7KcHHKKaewQgEz3e9//3vUNFj/UDHIbuDwYSdbQMtwkkcSt6A1ozm4KVNw32Rla6F7a8IeD3fr8zsjwaju8VH3vGWzmGUwQTgo4K5VJIq/raF5lWG7SQBvAY04nbDBO8yFmYLDLNRx4jLgPo3CTBDDiJ1JEcGd6TA9NmDm0i0LlrfVhVjlm06Ms3WSmfW1zp9XE68hnjiJGfdZ6lPMpYYHdnCRzxnZkjH00lo8q32xJk+0FjJotBJOy4bNclyMzypRn1UL9afYaV4XTcxTyMN3Gg9nxn+MvSXIsr0sJKB9G0LgXvalF07OaEtrA4ZYAxxSBTwWSlTR8s7fTjbOeAX30BbBf2JJ0BaYc1kZhJsPNxlF0aDJtpDtMmMOQFZa8+WPRD9m+qMHFjasz91xp3rHnb6+Hq+qFY97nfnBy2vOPlPHjdxRM2xcv5FZQL5OTAcu6AaA8N2U+PKydveLTFCAshKYwCW47eDXdvXVV3/hC1+45JJLQMJIArw8JwXtjojqsyoH/g9zoGoB/j/c+Adw1eXEhlr0m9/8JirSN53x5uu/dE1jJqn88pfacy8Cd+yu+frbzvAedYzaUA9QfeXmCSF1OEehWBjp36Hhznf/T5V7fhwaGzc83sJJJ2oXXBR61zu98VpHKJyY28UMj+hjGEhRsi7SuY6cmJLx2WYiJ0IMEzkmAgwjrhi6x4qQG0pxzkhd6MWBl4jUzMr9/dsJGbtgQTfSCit8LVtNFqzRrD2cV/IFNiKyltdavp6Nydt+XHr0d2oma3p0z/JlsUsvKiw/4rFh7xN9HsJyIuqjmS/ls6N9mxo6F6kev5BsCYuFEO9VfTouc6zsEtoBxF+fzn4YGBa0kMcupsdHtm9esnwFElJqbKB38/ru1toVhyyor4kiIzsSj7Jly1aYYJgGmD8WjSFP+v0BBHfqgnEMf2mYAyAEncIWrtEOIOs4vK/0hNyJWgEegirhMMBbwl3yxJgjl1rJbVRJQynAYNJQirQqyJbCOAkwgBKsx9h+YW+lxVeWjpw5IACYTRFyX0qIcSXsyrKZSCVppoK8zi1Q1hziq72ipJLEUEsfeOyxxzB9nHbaaTC/krf+NtJQZYlUpe/D7Co1MRgJTM7ApOZzuUd/+6jX53vDG95AR5V4cnLEckoAaAiwIeGmcz3x2N6xo39gaPiwQw9VFdCa0GUJnR9jgHhBmOdkWZgZuYXfCMMLGMwwgWoqOIeQSKzKL1gqj8R9zsIDFpOgWAaKYZCnLPVAy8Yj/goM2ppOnGGvYq1oVg5vn3AccWgWVB5oB30VTQ2mPxoObQhmdr5ZDuikHRlnGIE5eMrXxwAifU/Y0Q2VBAOL9D3hwh8IJAvm0/3AsAn2OhfKQNboS+jwE06KUVbcFQZXduKlFFgnQoCLRhB2Wu6ION3oIJmQ+B8yhE2YoV589uPbt+bSw35dy45uZzDu6Ozy6fpByw8t5otb+/pHR0bnz+9oamwgZwAcrcMoO5JM9/ZsIzJzgR8U7vXiYh2saVFDNTQUE4xA4xQhNMFOj0DJQbFioS/xqyxiO/s94glmaghAMRoTAbTUAJ7PKrZoi2DOtUEb3IuTs9PS9CMHiEoOzqqxmfhoDhakoPdhlKZRAMb4Scmhb+aMHdrhmWg25xA6gGQi/eDDyq23ev7wuKdUytXE1Xef6738suCRK1hoDtJ3ZjjxBno62peeQJuioZY6uynqRTkCk3hmAmSpFZzJh35FRE9CRjNtnXXWWVdddRWL58l2H3OuoPBqkioH/i9yoAqA/y+2+gFbZzmXICI//PDD1113HeEisBCyPObi973Pu2G98e9f8vf1mIWSh/0WNM3w+a0rPux929t1x4l0P1RKyIZCHLRKo0PZ+x+wb7sj8OSTmmXm4nXpd70reMEFtSccp6DdF/OVCNlCRJ3NjtoYwAPGYwbldZS+/MQZEmyMkMT+k8zo0ghcSRVgEWEkpYWBa/AzqnHgU3d3t/s6qxYBdYIIBFnbZAnZGEJaya4JKp7xcW142Bwa0cIhq7klF4s/OaqtGmJdGPYDdrBg1ZldLKlFYSVAWkL6EeIPh6w7P4CDHg/oV0TvxAbPHpK8OTqwbfuGF0489Y2RkC/sAYMXg4Eg5gyfZoV8WjRgRYHKiEceNZvOspXQIcuX+QN+Z14XQxACjQTA4DekSYkQAMCAQ7dSlVyQDwtrZUoyl90J5YLcklTewfEYV2H4Bg6nUehshKE6+uijeYsWwREdGrjJWmJQNPKHbLXppTvET7+9F3cojsz3JQiWrCAuEthGTjjhBMzIe1H8q5EUglkTjnWd73rfF8G+ojWQvJVF7HtbzwmpZSRNfpS2snbdujVrVx+y9OBFxBsX3yuHOMsUzk9xzfeL6A8SyBdKq1atBat6rWRz4NG4/68+ZUj3RtTQ0cP6uQlrBb64DnzFpiegFggW+CC2vQHHEqgPYCawEGkIRK/g0QGmdax/Yt9XQC+ojfROiCXhUivwGj/JyjkXLXOkf+DgzuDrF9Uc3qILh9l9Rg6yjrM+l3F1Ig85UDA+MEowCDBWM5gDfhhJWM1BIhl9ANMiAy9jOCiX8VauWJG5lXcYGECY5T/2YNwFKmJLxU8YDKkWLPv3mxQiFrIIFi4RhW1ijHXaD38cHogAVCZm2onmFoBUYxktPzlbfo8S9LAyBVNmOjM6iJl43eoXo5HaupYO2qyxpTUcCWseDdzIomJ8qpN5a/PWvkBNnT8UJUu0EnnTHu7dmisYsdYFpsDgojHoLIydXIpiRKgtxaebAa/YyDcklvuyAZKYV/y6iGjFbklhv/Aewq3I6zFrAwp/BE0MeIVjVDkfROazOmApkwJnRmbGTEAv1zj/g065D/O7u7uZ8miOlxUnqBd1APvS+ej8ilHM/+XJ0l13Kff/LDg6xASXP/oY/aILfee8y9vQLBYnC5cm8YrgQdnBbLubNcCoQugqcxWIhKqR21133fWNb3wDPylG9Y997GOEECeS7AAAQABJREFU+ZR62JdVsIzC6mWVA1UOzIIDEzLiLN6svlLlwBxygHGf3AizfOedd3zzm9evWrX69ae8/p8/9s9vfetbGfqtgYH8V64JPv44MyFGj0CQLVXFbj6F2lrPtf/tZbsIGYxpkiCRlzP9Td6Y+V9nthMngRXlO07C3U8zkEpiplXVMvIrVxXuuMO+597t27aFNL2uq8t+99m+c88Nrjhc0VGFOyUobAk7vK1nG2uAyR5nJzAYlQIGgwHw75LE7b5QmYYzZTPjgtB4HQkANzCkNDk7Ig1III1qHCwkMxS0ikP8I/6cJVtIrlnTxId5R0756zMrVX8s2jIPT2kWDEvJFREHDMyCtGKJeJ5IH2SGUCDkIsf3beJiNFVM9q6zzaySH/WY+Za2jrpoJBCJ1ja1sA0GMlBI7APJPhlCp67bpvCgK6YHt2066vCl9RF/EDuFI7O78gZYLpPOLD1YRMWUR4VskYmhFEzLNQp7lOg4LnKNA/N0o6hkC2yU6RFxwJCIszQN8i73yYqbCLW0GjIW6TELw1UOVBjyLEWuSQ5LEuCRW5uJO7v6ByhIzuD8yl8pz0qWu3nzZqwiVBBRSS5RK0+z/6/pY0793e+JXwJCOZSoxVLxt488Qn3f+MbTZ3YsF9yTiV3aJz/O6U8mk+ya5e47Ik8kW/E/98SH4LzsXE/+cO5Mnvq2b5cblswrW3M4+dB5zc1b3JX5iyuZpbgqvxQ/y4sUv8VzeONcTvyYTMRNLHUc0MmFcxbwFYMbCNOBA+KaR4Vi6Y9/ehL0dNzxx7LoQCQQzqgCfJISUGqbAppSbwIDs2IEFIHKrEX9w/H1/1nr3yyMj843iAuzaYV71Ev/kv+3vB0D4rr5UBwFCTKEoRgg7dghRTQm5yYpuSkQBh8N1xQmlE+gNVhNWwsTISZmp6KUvn1gQLOKR3TVnbKkZmmjytY41N1plDLOiTfm+JDfi5spRQremCYmPhRnjAOcseii+QLMkAwPHT55+Ra4k8EBAEwynG5k1+VOJbCEHDh6kyab6xJ9isHQWetvAz4fXmu+NAyYFGZcwotTf6y+NBwcAXjyk1sULRjEgl5VGFpxt2FcBYviYAzbLMPE8M6WPX1bN3oUs3fL+qbm9lAsjmE50tTpi9RnUGgWlRIpbW18PLF9x3DJVomzFYxEPR4/ZSR29JhGvq6daUjyX5RLC3t10LWCmZeRXAzfYGgGcA8LelnKy4ZJmrOjnkUs6BqfGg8CetWoWOsruzNnpxI4TO/zAfeAgjRNeU5wHiSMvR1ITEPgScQAKBNQNlWgt8IgJjLNNDIvvVS698fKT+4PbtiE71K2pU15z/n+C94XPPRwYfIluVN12QvLS5HXuwfAfE1oVFkps6vXp2e4xztUmcGH3b9uv/12/GVQF1555ZUXXnQhuhYqN4cF7ZGSaoIqB/6GOTDH3n1/w5yqVu0V4gBjPTkjarA3wI3fvXFkZPycs8+65ZbvY5QTUz+SEQnGx+ynn3W0NUKwQhBkokUWC4yO5V9aqS9epPmDk9O3M/uRo5PvHqYKWbZILConpkxEN8coIX7v6nAyFRKb5gkeeqj/K1+2v/CFHbffYT30C+WPfwj/93+bX7susXSpds45nnPO8R9yMG7FxZI5PCxkKV5FwELFi8YaeQuBEcjHfYBWhY6R5CDtouBeADC2TaZ/KN3kRMucvhRwkgOOhEk62Ilq3mMjmGC/9er2M4leX7S+M9plxSZ32igS/FPNqXYQ2TSIVKtjwXDwsJUXwq+G2Wc8i0XZTiQLiWSWBon7a0KRuqwSzqVNvFqNEPIckhO2YgqhsXC4ZhNIgn9qASVo1y5YN+73JJCWDBFuGv1Gz4YFHS2JkUGkLsqUreHIJLTKhHSyq9Yov0+HwasWfkpoCkydrH55KnEt75MMNIt0BRblLbocFh483BBzkbdYZgakJCUHIg6CDmncd3nEjin8pBXA2xISkxvNgXws35LpRXnOwc3JS/EvAlz5z726ljljBkEEpMTdWKr3Ktu5SuzwSfBKgCbEelvpGUMI10ZGxgby/iWLFowWNL1ogJD4xPngxJ9w/Jxww+VCMFB8/KLDOt+nI9AK+sQNAV7E1WQfccoTD6ce4l2Be8uPssTu5WjayExss0VSkX4gre7IeOpSqp2QD0RF5DF5JagTDSH+F6/Iwuiw/HbMeTwQaZDCeca1uO8Q5JwF9uQ/4KXzVBjWxCX4Vpywvcl8eMj9CZMcCQQ1In+RG3+m7U3Glm7r7cusz8VqaoSlTOQpCiINrvEkAvOIPMX//Hnawn2nxj75/9l7DzA5juvet7snz+zO5ghgsbvIgSSYxCzmYOVsBZt+ki1bV3z3SpZt2Z8sW9mWrp9s+er5s+RwJV8rPNtKtqzAKOacQBKJAHaRFlhsTpNnut/vXzU7WARCIAlZFLWNRU91dcVT1VXnX+fUqZQzpiLNJXIsxyWDaGch7GX6/L+ZdWa/N/nFkh8lBVIhkvA2bhVENRYYUrVUJH2stJSpJwGMDA5/NZ3yAnfhqUU18x1zjlQk3NXZcXj48BOD0yhalNc2rW0L6mLYVFJ7v/hLlV9wmaLJh+VCgC7ftb1YImTIxf/+++/n067FIDzIygJghmjC8F3bT5uxgi/dLl+yssZ+CjvI1OI+l4M0uXoaIdvCSyLcNW3urjEdF7S02WmLY00C01Oh4VlnuhjiGF22nHDELgfkJoG7wGaGUfZU6xQiLBm6laIaIhoOYQ0jGmlIJM/MF52h0ewkm3CbNyCrn3RC5RmXncbsR9ZuXhb00MJpjeenZyez+ancBONGY319vL6FINAeveVoGCmudJtpEGZYOi/TREKSXsS8TAcuJyXRKw3o9euipaaE15CgXOaqVm5hK54G9GtTpb0Y2JkuGWkZeFnvs0M9679I4JlS+/r6bEhWMJubm0KRKLa5Cs/uyH/7u8F3vhPfsiXpV7IcZPjmN4Xf8fb6a652k3U6hqk2gixsmePc5GXPI1z4hp4GMKbmWvVhyjNuHgnMfWHIU3cv7L2kc+2113JOEqYxv/CFL3zoQ7//qU9++r2//d6bbno/li9I8wXncurlWQy5SIGXNwUY5I6aMF7etV2s3UuKArbvDQ4Oou3z1a9+pRyEXv2Wd7/hxv+2fu3y5Y1eAwvmtrjMNJufzLz7N0P5HOwWJp/ET8HcsY0zEcu/933RX3sXCr02ML1ZfGOxICMeTOIxFJKFaI+ZLWrdHuauMpfxMrPyiWGzst6L6qCFYyMcTzjYOyeYxJYVyNbxOJGUJfxoPhfcemvrPQ9E7r07nsuWQ+HiuvWVX7lue0f3cFvT9a97Y7IuNTJ8eOjgkDJQNfT1kTXz6/PdDwkABv8A2BYCYKQTx9T0+IJbH/biauYOgptv/nFza/vaM86eynPIZNgYjkbMK43HHMdOFjnuQmdRSssRNUjJKJyJrH84443OZKZnK6jYxWOxumCyOH5gyZqz2IcG/W3lDM/uI/0VrA0FiCzgqzhIgw3K+MQiATZR4uEg6gX7dzzTvWTJ4YN729pamtMNXe3pVFTSklQEbkyqdOYybVt1q5kN8fR8TJWhJ+sp0Ac+9ZhXtdi2AwBxYS8ICfExbWK349owbCEGFSP+hRUkMLRCeMWahb1goyG7PR4JhV6ExgQAIZMC7DI82ZVXXknWyOphmi0qPjk2Pr4WtaIe77CFx8QXHYCSI/pAknx8sJ+Lj+3Q41l/JAscgrnXyVz0pScOqVugpQkZsXJud5LTV/gMzPfJHYbe4MXqt2HcFhurFRFD6ZOvRVFE0/Tm67aBBQlgP+kbhCQKr7hs+pYa+CiAuUxA6wy2jATT+apRIes1NTk9uGfv8uXLmlswCyQ/pWgc5iYP4+AjqrrNkICbZ4CfQlNlPG14o4Wpl3jyHeEpsKq3hNSP+dOMLFwq7KkANjlzx1Oo0vooBcFgPfLDDtswG/1NIiaeOY8VsOuOd0V21ocO5CtNhwq90+Xlnpt7f/cNS6OPOJXI+Lebpr7T4M5EQvFQeG2m/f2j4aUZlFv/Zezvnsy/TQUVrVQag2FNluSIn6ExOeu9ghjimLqYRjR4W4XhEgkUSuQ2O1alR10BA1cqpQ3dyas3Nq9qDjXFj9ZfNdU64U0pHn2RsslIXQuUy2VRLriI75FXd955J/ogCyOy9sTeE3z4ggjPt2lRLncWIkmQ6+hM9ER4vmi0YVnB5LtjcACY4X/CwMdHX+hDHXaPlwanIJS/vjGoy03FpbpSrkSThXTDgWLkcCbEAULsCJEiOhuAMdrENhWmGGmN64SkIsMylplLLqcoyeQYslbfHRkayOSL9V0rGa61JVufixrE9BR1M7UV3yC292ZmSsVCQ12qq6UR3AvoDfGnLyyIOhW28jL2smQJMcIsUQUBFh8aYkFjXAcXYV6RdBFRdna0d3R0qvFfAAkWkuPU3LU9wAywNgbLvs9s3fKK8863yxkMznff9uP6oUNr9+93b7s9/tSWcKWci8eCyy8Pve2tiVe92m1ppp/RmbUa8eLKTF41I4u14gNNX+SWFrqWkXUHGHFMJlNsJc8WnWmvYefg0Hf/+Us/+JevOn7hxhtv/OAHP8hc/6IrUSv4omORAr+MFFgEwL+Mrf5zr7PlReAkPv/5z//bv/0ba7q//p7/fsmb35NqYmmTuVXWMJtT/vIGj+kWfq6yezD3O+9zDx4EibAyyiK9Wbx1gVblj34s+qob2KGFoIIJvjI35wwNlZ58ErMVQWNzZNPZ3rJuL43JJU15tcsWgP1wbNIt3fJjl/Csn/f2hW64JnTGJre5EaWvE3BAtfjGQSIALWvRF1U65lNygVdYvmJFbG56+lvfLf6ff0o/szVWLFDo2e7u0JveGH3DmxLnn+OzgO17O3btqK+rZ74kMeJpOrbM5dG5PNcTiIurJoVAxEF5WBo/xWkd2Ab9beJEtLFYcm5ubSlU3Ol8ZTbvTBfcuSLctUfdkADni/5cwR8amclH6iYLEeRCOayXsvELCcPsxMzhfT3rzoqHQ9KRNjqTpCleFf7ICPJxSgvTFfqNhxzWMThzQlKNYnZ877a21q7ZmcNLu7pbW9vi0VCqDjMrsGpUEJwMK+bDjSWEisHM9jBJw8upAuTzU9tK4RZeVBnZOzpmeJ5QnrMQAC+MeLwbysOygKIxrwUDROekFc4880xKBXvNnmTRwXDtOIDNEBkH2BuTJzzCcHPBgnO3zWcDH58RsawnAeAF77rrLvIC/cK1Hx/45+VDi1O8wSl/cJKFiypYwSzw1sN0Mr5B66OK0N1FF1MnKKS+Ytx8eNSUuvIH804c+0gU86oKjC07bwIaf1IziRJPEQhcTY++zat52plXJoz6ZvVynb0TfqasIWLeN2CP+OHhkfb2tuqpJPPvhE5NOZHQ6jIVssjCPM7DYpthFZGacLWkFUnVtdBZ3uaVUsLLJk5EJW1eqgZ6tPShKqZqCmeSmfeu0lalgG5hd+6ihi9fmPrfTeH9BPSCSs5p2Vm4dn/u7Fc1foS9pVP/2Tr75Q6H43b8UjQmLZJg6ezSvzjgJvMDhcv/YfQHrFOpMAbfKNEjBZJbm0OrtGaspIhC5wpupL56Y+lvZNGmvqoOajuENkHQ+hkO+YVLNyy7aHkYDGxahjoYWii/oy7e8opvjZ5vQG7eOhg/QaG8uvXWW8G96lCG8jhYVWSDCQ5ryIrPhM/NYl0LdI/K4NQeSI2hnu+aNS9i8JkjKLY5nloC1VCkc2i2PJ5ze8N5b8eWwre/U35qC2o2lZX99a9/lX/eBfvC6V3jXoRVVc9Y1S475oQkziLSyXYYZobQ4F4sSBfYjy0NHXWqzMwE4uFkc2e166jN1AyQHONOuPkmgbLAXWxZFQv5ZCKKDJluNz60t29pR2M6kYy4Ea05ajUqFfWxa9Vkzu9F/GsWno/UEgDMcMdlvV4AEY6kdWquGgBm3GNcZQhl4C0UMGTB+fRO/cGDS55+KvSdf687OIT4txSJzK3d4N34a41ve0ukq1sEmB8DTi23nxKKrsjiJoGArHRIq5ZFDwSKg89ZixEz8DylwfQKVlERYvPx8AXNlIPD0/543uldvTEaS9CW+anRe773lW/849+Mjo1wDCRWo7H5TxlOb9V+Ss0XXy9S4OVCgSpn9nKpzmI9XuoUYIiniPfdd9/nPve5H/3oR319fdh4YEUzkUpOYaJjwpnOy4qxuCddQUs86Gl067PTpa/+g/vPX2eqZ5dZPBbXriTXy6xcEf/MZ0L9vTBglcycmysU77mn9JnPJA4Ph1LxIFmXb+903/ub0SuvDrUctVRPMZgiK1u2lT77P+M7dzBdoSAWjyfzqZT3P94fuvbacLp64p8txwnvti5MhBhwYibGDoqxaSxhCeDwtttuL2Vz7aX8ivHJ8C23BHfckcrMUbVcZ7d/w1WhG149vKQr1b2se1kPrIbhUgzLOM83njDHU/eES2MyhtsDIZ8wFtaeQIC8ovBIM+z8DcvIkrMhvpqJ/5B7thhgxXQq5wzsHx3YP5QrVJb396M4N5ULJvPBVN7JFdx8qZDP5lYsbWbzrM7PqGBeRXsRxRrDXkkype2DYGMSF86ArXP8iaHd5dmJzMRQUMzA/8aQAUZC3X2rXS/Su3I9yngo/gF343BsoWDvs1uXLlvKYR5IOlCuTka8BDJkHcuE6VEvKoU2EV43XeZunaqKANAxrDVN9sgjjyAIMgbDbFCFtBc8DZiWVzAx834n+KUPEBJqc7GLGPVphLH0Xzhs7rwiFxoCh70gyIZVG2N1USx4swBBGFKwdzJ6zWtewx3GDsVmg4urN4Dx8SWB8YIRhKenWKRwgsL9PLyoDo29e8LfM3WEYaYD7BnP7dlzoL2jvS5dr82rxriORKB8ifzR2YimbiJrOnQTfUXzbcZXY6oS5Ipi+vXAf5rVBDE3wvIsrGZfmp8jRFGK89SYd1bxG95ki30gDuyRez4c7TYxPt7U3FxXp/UFJa9Lry30pT9bPxuDws/nV/OWwzwcGdJobyWiApnJd740JmA1pXm3Hu3goEjWV7gfl26KarOUK8jn8pNTMy2tLaCmlDv2trbfWh+/RbrPKrrt/VJr9R1WD8tBJbr/fUvcw3VoyrDtNBzRwTx0zvQH9zVcNZEPmj9/4IkZp1MZUIpaTefdqiyeXPJRzQmmEVmLgFrooO0N8x/woRt/MJgwFRtXEf7rLqN7HBdbbquPrWkJ1rZWd2OxqIdOMlBn4YW+MYukfGI//OEPCUC2Ioe5UNzo6+vDB1UOXtm1JAt0Gdbs1zEf9rT9kh2FBFcDP2rSyJ+Wui2zGqOI9aliZbZYqQsqsacem/nTT0dGR82Mhgw2yMci8Q/c5F7/6gfnkjvGQxjnr4jYHhJdzgcuaIDFjYFFQCo0VrP5st0APWkuNSM9qzpqsYCIEQRpNfsxTycAo49DXwhjsIr1xHCAmBdL/tnJsW/9ny+tPWPTOeect3JZe0Mc082oPaODPd+jicNlWh9ZOsJPnsBpjHVcuBG2MxYpzM/sguY0PMse8WhkZnbm0Ucem5mZjs/NTj25OfbE5tLWZ5ZPTJ7l+OVkXXDlFeUbrhtoaR+OYv4hds1113CevamA7c2nuYgL9wAzpLMiAAZmZzId45gdSbbfmi/qmDKYDm2+pXK5VCpXGI7ufmJ3JZpOt7bD22DEgnZLx/y+Jrcpge33/Ne/9vW//Mu/ZMa57rrr/vAP/5BTu0jxRCkfk9Hi4yIFFilwhAI/2zHrSD6Lrl96CjD6c91yyy1A37vvvhvggYGHN7/5zXbDJDNAc9xr7NbJPXum/KkCExZjvjee9w/untnY7La9+rWFPfvcO+8o5zijwY1FYqX+5ZGb3u91dTHyl0YOe+VSedfu0u//fnL4IMxEMOM5zeUEy+T/628w6etddDHHcS5shGB8qvRv/5rc+Sycg2ZHzR5BIjOX/buvhtasDTbUVwUtC+MYN7Wwfna+geuCJ7A+YBKYA8wawfatWb0a8GkUorzgd36nMjWZ+8ld/n/8h3PzLfX/+2v+V78Wicbd88+dec1rotdfn1i7tiJhLsy/LcxxuT5PD+AWIAoFaTDwCaOyeZiLurCMDfq94IILjg5mSEIbhLDqGRRnxiY5U3d2rj/mLV29Ot3VgFJ0phAg2kYvGiW9mUJsNofkAK1pLy9LsDBj5pQU5LtY0uIAFWQUFRhfOOQAoCH7OYGTqO+sJJrC9W0zI4N16dbs7Hiivn0m2obW3d5ZbQaOhP2E52E2htOGD44Xs7FKY1niC1AxGBhtQITJZje4j7gYe9ScvQFXBypm15x2rHE+lOEKydAoiwo9DA4OwprAscEfwzfQiLYdj66+A+t8itzzvn37aHcbHaEuqcF92jOHj0+E3nL/Px++4G3tQGUwLdgYRh8wzBYyBFm2JHQh9N9sN8MHB/cbbrgBGAwmZ/HIyop5tFdfXx8ZEYy3+MCJLmRGbQrH1M5mdIznaXoUH9eSQGOZfsEZOVjfUWtP7N/T7oyub2lqbKR7wLHTJOLWLZqhirjNH6y8IDDVMdd8GAOSt6GozO5Gc6kt9UdI9VXjFlzEJfCMo8a9m9fmRq78mvDzkeQhApORPmyJd+17HDI1wKKKnnmltyY26sU2tn3ET7nZH/3agNW7KZ8JaDIHySgNU11zt1FNbPVVxZofA1gW4FmQxbxWtTQ+KB1wrcnPjkUKIaRbLJWnp2Y6WtPXNX1qffxmgpeG66Z+lA4GEkFDMXVFNnXOVMgrEN/PuKXhSqiYN3V3fPRHcLFlY1+MtxEnE/VmHb8Lt62GKZhKw8YU8z2a705DpGinkVqYVrYI0SNH9R1zd7Isj5YHr4SBWbcEdZeTqQR7X/1SeWT4UACaq1SmxkvPDGb2uHMXX3QRwnZ82JdL06tFTO1wsCkXAMxnBaJgmKS325UhHFb9gTBWFGao8rO8GXKQHV8rJgBwkBl60SiM9K/oZ3SDRrSO+VFDCeuWfHSVs+gqM2aWeZT5ZY5QXpJ2W3Kjma9+Izw6zmNZlor5GpygXJz60tdaz9zU2rHqll3lEjYXdI6UwbssHnDEnS6WX5xyPjdzaLChux8NEiO1JYwTQcWGrbyhCluI2WrACea0EdF4ZGAE7mKVUEo0jK5oyHt+KuYmGuqe7WnsjEyFDz06manvXLeuuUkbpBmaAPmIuJnRLE0pHWaxreQTH9xcOKDGwjHHBj4Nd0Nt2wOhGZ0wHouynho7MHzeU09Xbrn50P0PVrJzG91QurXJffvbKq99XeLaqyKNLSwKNPm+NekfFg1kZITeYlcrbKudhuIdnYTtrvjVHDW3zZGVTbq3XWteGNV+xNpdFHjjBe/ANBpY3mTBaUhE4hGtb6ZjleVNTrO2W+tLTyVT733ve9/97nd/73vfg5u6+uqrOaMLGPyqV72Kt1wLE190L1JgkQLPRYFFAPxclFn0P20UYD5gxmaw/uxnP8sBEgzW//7v/455Zxia2mBtB22EB61JtzkRTOSCvdM+Ukd2Pvlu5JH9hy9buSz5ux8Krrgywko/e5hWrYhd8kpv+TI3EQ+yBefwCGxX8Yc/To4My3oMQgcEBWMTQTTGdsP8zbeEN2x0zInBtVq501PBAw8h+8WH4lHIElZCWC8fPVh8+unEylVO3GwGrkVY4GDxm/kek8uKZVIAeGBLCQQCrGJC4m1fX5+NQRjYuVBjU/0b3+i/8fUONkcfftT/0c1NN/84ev/90XvvLX7kI9M9ve7VV7nXX5e8/LJwE4ffwiVr3ofNtS7DWi0owXM4yWnhG5N11adG6mMC2EcV8TkmThaz0feDY+pf3rN6zWrONxLEwLimgxkjf1Z60e5swZvFXrQ5PAm0A9zNltifxqFKMHwSBcPHI/7lhMk8kBgwzPa2sh9PJVwvFTSmC5mpWGMn8o14y5JoXQMlhk0sMtuXneHsTKVQgAjZGXc2kkll0LmGlk2oBsJJw1hHQuaA4pDHsRwYLI1EdCYHzDevwk5ZG4/ZyRZxsZ4arhR3bNsyOTba2py+vLWV6toaH0O0BfQRK0uoBT4ncLLeTx+AswH9wimy4vBclCRyJe8fuD/Sc0Zu6dkJuDGrXgsMpv/UOgwJwspYbc/a3RqeBb3DlZIXkLvWuKACUiYk9kJxkDtfFuFJE/kYMBtPtqvRJ/G0F6/oqNyfq+4kcsyrk1SK9BdcIldTItS0YOFlZmZ2z8zuczobL9vQWiW66d90I2VjdijywyKF7DZx14KFfRSe0MeJ8KfibB9TRyKqKR0ZmaZZ0G+FAHRZ+GSdIojBcTzaxuTJ+tC+Kofp+nKZ5NRZcUYjoabG+hiqosbslIlsbopbXU6x2SsJjTjzQUwN9SGa1zwZdK/Uke8ZJKkhSi+l361gBDZjlvmVly6Dsm1NazQzDlNfJHsqprLhPyXilBoGu0g2N9fkP3t+3ddIsjCQPvjJ9thkykfsitDyvmLpV6NNv3pIY2oiCDWGQpNRFiegdAijS2RJt2lTm1ScRN5PMihCOlsJfG3Z45Ggp6FchxFg9TLaqYxKMuozPvgWsMZOkKC8on951HOyczM7d2xH0caplFDA9twKH+xl11xZl0wgwvrJti2qgUXOnofy78DAAN2V/okeisW39E/rUE4Uy3XtupKo87O/LG3nW3FBfqIUF8TRnaet23eMjE3sGR5fveEMx4sWysK6gN4cusqy5hxCawH5bYldJFoHlGl9pHwddY47NjH3+NOxMmhWxCYvOhb/KqOj2We2Ni1ZUayEshXZIIBSfCkyfsXI5mIQSwSRdnmlxJJfMoapBfRitOQnjISA1ywIGgNadAyd4k4/ZkiMGcRbF/XT7PKNOfsGdk8dmsi7zsqlTY1NzaMjY7t2PgumZVcI2t2syjHgAG5rAJhMMYXNI23BAitYzg4vtBplnO8shDpyGTJWH08Y4EhQ47LhqRpflKm1CAKZ/cnJzN13V26/1bvlzuie3XWYrmY5oHdlcMbG0Lve0XT9dU4COyCqvOkpGAAJ2YGRVBkw2e3CMMt6NFrxVmp9KoU5pmwnfIQajP9sKeItDABIm8GWBREsRNjtUVAJ+pAdAJhR2gJgU00VVV8Baw2+c3jWH5pmIwbKE1xBIpkiVkO8vLzRbU7yAahitYsQvH3LW96CIrQVKrz+9a+nyf7oj/4IoYLW0heMirVYi45FCixSYCEFFgHwQmosuk8zBRjlmXvY5Qv0ZS35mmuuuf32208idrOjNoN3W8ppSfpo2A5N+aNBLJ7q3ZHx22KtHa+8Onb5VZQyzKzOYUjM9kwhc7P5nbtS2H/asUMchMRAcBTMIyU/M+dgH2twr1PIHzslZHOhzBznspAarygqbuYYjubxJqdk60mT0wkuAiPyhTljIdzqtaJFDCQGRTO3sSfTsmu1mIRXMcWWBNlZbMwE9Zdf4fD36U8V9u3N33p76cc/Ct91T+wf/9H9x3/IRuL+OWe6V1wduurKxAXneYk6WB4x1ycuSy2TIw40siwew4sJ2GIhJv4jIRa4KBtyDObLBX5y2oaz/uAx4BYnNi1YPld4xo5wJISstSNlwnPCCrhXeDhg5/AsdqRLMPEehjIRbgCG+YMpLJS9fFkAItCxGs5EwR+ZBhKHZfEF06Yy+ILUSBxhmcNbnGB2YqKUmZS40HWwNzaJAMTxMByrgzAJiZTD06biiMQatB2CKE405nAO48PpnTCFhAkHHOi5f++eSrnS1tzX3NX38MEKZnfg5onFaR8SKWNnVWqIVabEUFylNJS3nscQSY8QkHUQHNAcAAyJEFWdIJzxgqoHt2bLmfC+R8pLz9anwcUb/Ok8IFvcllXizgVGrSVFGDbdwY+SPn2M7keOsFNcNSEYp2VYH+72LcWz6aMsZx21BDHhg2yHR2AzgS08tnd6C9oBvKL/8IqS4G8vC0goTC2d53Ac1V9ZG4KUa9euoTy2SDaWupEp4XMkcsSbHCfR/PDL1jSe8heU5JuqVlBYSiy++Enjb6GmWpC49u18qekkhFZ4oT0DQwlnUsJP/lyhcDhVp/mxWtX5Git5hSFdSeH0QBVIWg/ko/rg1KUtl0bOLQlchdNktD2eS4hYZTCRdcet2sxHs1kYSsl7PpwS1pOk0opNxVhCrJbbxG9oTI8cLvaHvxsNsmQ+9nfNkcl6swkXkSEhIjPfakpdNB3rmXMjxfpfmcv9S5ovDnO5AGBq7Ddm0hdPI0seyqzcNVaXdwvxSAwDZvSmPChXdZRMPD88dlZ68pJLzwt53vCh4ad3PyNhr8SMkt0jGt7UsZxVqqlJd2b3TDSJHqo2t9urLhpGmzOcSL7ylZdYH/oVfQzryoyiVAoMDHLA8RK5mMFMmwqMQXyGMvRZjFAX61Nap2O7R7nz3InM3h37xx45uGXZ8r50U5Mxm4+6vjaDsEqICJdUmJFKmEtgBRDTVmX//B6nks2XclhZQB9ClxrVLi7yMzuLIQx6NN8Idh/NCMEZwrKuH2EILGYZ98LxIhtWGiN5pPYcSpRmB2/I154REK9BQHRQFv6qBxfF0Gr2YmwqNrnYz3D08GE2wpiVDHdmajoaCTOFWSVefTUmV3snkjqkwV0MCHjiwwTBgIBlZlAfauq8NWkfewP4EeAEBhpJ47gY+iRN6vRbN5fNPPpo5da7gjvvcJ94PJnP8zKTThd+5frydTdEr722vT4dnZlsW7bcO+myIxWkXzEKMUQzPwKMgcF2l8qxZX3+z1CDRU+GXKKivENl6dK42fwCe4CDVc4axXi0pMNh3M50yT80E4zM0bXCoob5tvmOzli9fFmj2xTXKpMh7AmIiz9NgHLQ9ddfj2YQXNY73/lOGgIY/Pa3v51iPFeL2NwX74sU+CWngD7UxWuRAqeXAnaIh6355je/iYoOEw/y3i996Uvo2TIin+KgzETQHHeaOt1M0T8wXRnOOI/sHXP9/OpVq3m1a/O2ppbGM1Z0ttX54TKmmYb8VSv9JPpCcGniEKsTTanMbCpZruaR+enWsBtBXZ3f2JSAJYGtKUuCx9Zi3mA5029r5QCfY2hiK7Vw9kKNlhVf1LlRgUNJD8EFrAOeJ6kgvAIZWbmfj7Wkvv7Y7/Q7v/1bfjabe+jh8h23Obf9JPT4k8kHHy5/7rNzqfrgovO9y6/wrrg8ddYmbF7Dg6iC4qUkPIInO14yzPq9hVKU3+qnofp1EgBs8VutslSQC6VcDL3Yw3VY47fL/CepF9O2F0gGSxmbE4aHQY4E4NeJSs4c1qRLLnJ6zGthT0ssdYWTOdAMhM8LiuVoeu3asuuxfRHAXMSkLUZdDHCJRb10a5vf1MwZmKMH99Q1tHss8wdeSZqV4pOKhdJsPptMN/kFZChkWAESaJMhYmEPPCwYHPGC8dGRzOxUxEt1dbQFLY174f9zOvADXhxARWAxGcBmj83GoGVhacQpOgYz5CM9hpvUCcbV7mN6j1gVNcQRuolNUftAPet5NLkMWR1n/yNojUcObc74RXdietxuwyY8r+GccGCf9pgWwRN4zPrRQvRL4hY/2Ly4w4mi8Y6jVoDaKwKz6sT3aC94NWQ7NaBuleR5hYTEqkLUXgGbEWUQvZZmb2+vlcKx5RI9bYuKYcK4kBTBe5Ep/uyQxAe2D9aTDg+Q5i2fBlnTOYmFPwG4k7i9alnUim0dvLWvkAgvrXex9WOkZFZQTBvwUuCEX9MiCms8iW1AIdEVxSSiz6fqbwKbG43vBOM59UldpvVMO5rH+ZvwY/WlkJBCzTe0nCTPj5LXO6Krf3APCqgib0j9uC28C/+DpfVPZ189VexvjT0bd2en/faR8sac36quY9KkH5KOSmsKL095KEN+zJ910lvJS2/RnEXubUJBzFg5lFoS2ykrglPhue2xiNFqkZKypUMxPPtkTADYCZrfOj004uRuaQj5mGZ3Su0Tnb835jUiiwp978CvHphAzljilBzyyRYCzEspP0l93T359NkdhdY6rTZF2htDa/ulWxAF6Ib5zx2lDKpPL4IvVyxzUYB5p4AT/aH2SNdlqHnwwQfZTUBN6GBkU3v7YhwiqiHNSRMxVTONRzAewKOgXIArBpYxOmWW7YR72Y6L9rKBsiFksdqUq80d/IWD1hWO2zJ0eGRw+2iyrtjS3gH6ReQLYCZAERMVSNoZ+BicREI24QbTpNvY4DSmnYlJWwJyh0bUHCP8kaVL8+iVuw7SXUwmSvMcc1XqV0HE8Uf37PX8PKC3qz4IFQYjJQ8DX/3ptRjbB+7WYT8/6tVL4Rl1GFpJ1TK9UrXjqrXFZZddxiNfPTM1y2q9vb167TgY2AMr8qkyC/DxgoqZRFBLWbgkhxyVIYjAROe7rqVpEjjqxthS2yRSewEFVJjqp6vOoWmDj4YDmTc/Vbn7Lv+uu/yHHqybmkF4WgxHimdtzF55jXf1lfUXvMJJ1UNHJsE2x23r7ND3dtKLstmpmTVrBNeMutQORS2qY0txksKfNOHqSxLn4gHQCxuATBidKejD2jGewGBGbypX1nJn5emnkRUH7d1LK9GGQ7MVLE2yLZ/as5bFAhk6Oe11wZIGpx5zluoOEIn7ySpI4bkuvfTS73//+2jY/dmf/dm73/3uT3/60x/+8Ic5Rcm2EQFOpSKLYRYp8EtFgWO5/F+qyi9W9rRTwE4nTHhf//rX//zP/3xgYOANb3gDe30RN5lR+nmMwmbI1rBdH/PWtPq9zUFnpHn/FAJGcYkzxXKoGNo+5k3myyvdKNtwEZOGLrm08v3/8NhJo3jAYNcPhaU0dvZZQSppZxJVmZfMuw2NzuWXB9/6lgksbxyUP7+kC81R1x6GJO8jF8vYsGg8gx+44Alw9/b2Ms/BSVjFqiOhycRM8Qt9FrphdOAn5cNEh+2pq6/2r7oy+ERQHhvO3HO/c/sd/t13x35yV+i226nCdLrJOf8c99LLQpdckjzvbLbPVswBqopeFQjJyYWoljtL0cz0ABLgzcmLYSLpZoNRR7S5rIibRABjx8yd4Bm4GSorRk501kUN5p08GcGeoSdGvLGnYlOGvyn6PpAYmUem5MwVMSstRWhD9gi8ARI+zAUDftlazHYoUulsIP0UXGahVM6NVFLJcKguBUsP268VjVCokpudOrwnGk9jeAfRPy2ujXQwqegcohkgE9Z+xfenZ9kG1tqSTo9UQtNjsoMKygXrIl6hM8F5AJKtKjWwWfJk/WmDsaz1mD2gqBwqCn9egOjY7EAWugYbExGGHxnaWZvOggoqm4ihvmRoUiURBPbzwcGnysjICjOJ4R3ZphUpK2hlAQVicqYltLOdSjRdcMFOgX4JAE6AoVlI6wWhqs7j3+JTw7THhydN60mBuejYFMb60Jfg3kxnr95quAXQQlHpDHDJvIMJBtbaWHQhPnySWpgXIheKgfgFOTb+uAnAHcDE6hg+xEJNEWjERQHsdf755xMAZE5nxkpzNBOLeYg1aGjfr+uaKsbQFJicQGipb0lENxfmfoFhMNN0YPqKsgNv88pRdrQUroKxmq4SmnFhDnUQ1mBMxakLF7Zo2KCNcW6QHVGlHCANVXoTjLdtU1tD9FHpqhTIZ5MreFQvjdS1KzLwnv4Pr4reEXJl0oCMViTvvCj9FZ/O5WYoHhLcTGXJbWPv+Nd9v5310+bEXIHdulRS+v06Z3vOfjyUUfQMAl5RHvKYmJhSSZSKioNPKlUXi4fKkXqWlfgMED2zrOcKABuCadREDs0WU5WW9LxIbtkHyrk3zWUGYqGUn95YcpPsOXEfmbn+5tEb/ViK4Oxr4LPV0lYyRhwNqa4zG67fVmrEUlZrIsRa3vHrNaKqcrW/p3SnV9MPkQMzwDJkgSKOiVbrTirBqV9CwFymja1TD+p6BppqGQ4hba5idmf4LtrLrIMAWc2WDR0Fx3CExSkIKUBrVu5AwphihJBSb2GcYX9vkR0foOV0JlY/PpPxJ7wUB6Z72GF0sdVHcRmDDNBDsOfy/aOKzOLBrolgU2dz3auuyX/t20h48aUZqZ20BjadGVq3arwQWpbmY/BRbGHAQZ9Z5g80+IQyyZ7AL7nlwsz48KqeLk6Nq0+E2pqtAWfqSzLHUKn6fIwvHV60MC+52/6PD+MSrcC4xBIYcwGfJNolfOC1wOTR19fH+i9vGSLwx4GnHRZwcOFpl2IZH3jETRYkxTeo/mz+0U01eWQy2SefrNx9n3Pvvf7DjyRmJsOM216k0N+ffcuvhq66MvbKi1NtXUQmrDqvuY4Y2at6/JQfasdMDTpFXYuVaCTAlBDISr0oNpGPI9pPSfD412RB7SzmxG2Tpfp2kRFozzCZ96KjGffAwSCaYhzjYzSjCWZNwgHLGZ1pN3l0NY/P5YQ+FJ6Lo+y//e1vg7fhvt73vvcBhtkbjJHR01XBE2a96LlIgV9QCiwC4F/QhnspFpvpZCH0RbeWdeUXAH1PUDeXg3PctUvSbXUz+w7vH57FMk15bm7WL1eyUX/5ymZvxars9q3Jiy6evera5G23yHCNJnXXrU/m1q2PX3d9qD59JFnDEzlNDZE3vSm/fyj2yANib7X50M+3doRvuim0ZBmT0vw8eyQeHD9AAiiCTBuHXT7v7++H3T8S6GgXr5gRgS6w/tCHlXLucPOEgsk4hneE556ammxubat/y5uDN7+ZOpb27Svec1/l3rudu+8N3Xln7NZbOVFpLpn2z97kXXKRe8mF8fPOD7e0Cg2qvLBR4tPnclmOwiQjMyfqdnShTvwErOVsCZgDwlMpEMsJsRbsDngMDtVO6idO62hfElRhQHeeh7UmR+sGXBLinbnEmcyy2bsynvMn8qHJnJ/Ju6UgVHG8eMRf2uCUS5zAxIY6122JJhtD4TqALjiD7ccSFM9GKjnhUs4FkW0tLlC54TXFtmZzBYMnvbp0AwiIJQM4XbYfO9LN9gEz0EvY1ZOtWjhVYWCDaaVBLa1OsapyGwkwdwQsAGNsfRkfeg0b1VAKrUjaDOujuGxRFrSmu4KNjdlVWby2DXBoR7Y0a9CU7+5/tLjkjDQ2gSgz5kwhdWdXh2HRTTuqi1quXWwZFrMQ5rPF64Qtomq/0Gth37BuOFSbGI/IbJ8rYbAxV+0t7ctlH9F1ROMAbFy78LeaEeAlRNzWH/yGw/LcNiJufPhGeMWFp00TH7vwJGIZIEofyMdbyp4UdA+NjoAcxEbOf7M9y5bE2PntOAcODGsPqkClcBztCZFbODVHr4awzW08lehAtrkSqTbH3BwZFkxlXHT6Yd35hOvTsnaby5SJJbCi1BQkHMIWgHmVL89lc/rUTMzWyL73n/+evpjQvlOIZYfC4cZipJmjloqeW+R7wJthqj409Mb2z/clHvroE1+c8DsoKNELdEg1gjubx8PkVf2E3TKyMKkbBzk3ZlSwZWBKl+tmKl5R9q3Ce4vrHff7XlMp0e9XdgE2SIR/pFgqRyvJM6Vpn6k0AjDqvLFETybam0UCrJyD6AMzr/78zr/yw82WM9CAonKSvMmEVATogj2ZyKMHyteuYgVJn7bJ34RQqBd40bdREcKOrtXMV5FVcBKvpgza4pDUzq4ulYC2Vrb2rb3TxMbP3BlbMMIGOjWyXIzSG6GuZLbSQ2a3BUCUymHIwNos0ElCvlbf2MdhpbtEB7cyELGgiqfSkf0Cwsi0G7Gwni2yeqGsrFuFwcRakYg2QpA58HHJmc6V0gkPUTlLLqoFQ07ARmipnGB9at90Zaynoedtb/bGxwp33B2jTNDS9cprVzd98L2l1u7shLu604kyPmE9W0rOTjKKeT8OhHPruurQjqnkczu2j6zva04kGFIX0P/UBvxaO/Glo3Zud7jgySODP7UAHDLsMEnZr5Jj3tAxsepLBEOaig940qZjN1nwmVebz+y81c5YtRaUdrdu2cocizLR8uV9HFNUmRjLPvq4/8C9zn0POY8/npxhZAzKXijXtzz/uteg9xS59MLG3v7ArFwpDdVw/guvFf35OxhkmHnZyWyjUkLAcF9fH6MWPRBPOww+/4QVA0UwCzXr6mSBXIuhDicCJEg8Wwke2zp4cLrUtmxlNAm7oX1AVCvkVNJxZ1m905oyu3q0Dr+gKZ9nOYjLxUzxjW98Axj8B3/wB+9///vRwqvBYN4+zyQXgy9S4GVLgers9bKt32LF/ksowGQJ9AXusuII5Hvd616HdX6UgphRent7QVOnawESPnhkdAT2B0Q0limPZtleFbr+4o1L50Zn/vNHdeefFXXc7De/6f7nD122oC3pCl79qsg73xlZuco7mkWgwBMT48lwLDIxXrr/AYfzYIt5v7c3fOXl4f4Vbn1a08g86QiMkwpSNS64Y5bGEa7a7b5w6viwisw1H6P6S0TYArhnZn2r7EoivMOHOyChJpcjJGIuuA1wMvaxIZeBANVjfrVMXikVD+wv3P+Qf/c9/n33R3fuipbFo5fCCX9lX3DBhe5FF0QveEV05Qp2TO8Z2EuRUqlkV1enndTnCyQm4pjL1o7qUlQAMBxMjctZQANFsiFrsmW79RT/Y4Idk/4pPtrEwbEoo88UgpkcyofYkvVgLmlrxDJCMFKQ1p66QinYs3uAkPlSOZ8vcIqpD1cZr082dxUMy5splEfHp/PFcrqxIY61bSvAUwpGPmyYa1Mw+BCVX0yHKMP7AGLDqPLAqoI23Rk0wiEiRmVae/CAxLQ0uIO3ABVZqwYYI0NGtVEGapxoxY9NI/XgUBOk0oRBuVphJu8uTD0aM8DfjzVVXvnBRCgicfH+PYcRTHYuaSHzeL2baIpXGT30EQpFg+F5U13OOEV6vhSC2TalJCfpITYMAWqBayXnE4Bb5RUOxEcEMB8F9pZ8zAt9fVvycD5CS+bmxbxEtIkkUgn2oJLtzPQskiTadL7Xowiiw2DBSVPTtJAEU0Tib8d0ssg2cNY/ZE5Mh6KRFHw/8IYyoK4d0zeLmWWsF2knr+kgeEi2zPnNxCrzwnzdqm1Q+fCaD/xK1z+hgjL9QMPo3zUV85UVXxhLdM6yBQDdTTDaxINN2ccSra+fjS+Zo2lvH3vnn2/7Ytk1FtSs1qq+VfVDklNOcuFBPzWo1LzSSxNI0MmEIO31yYf+6szrI6D1LfUHPrY0lOWII4XCHlPdG0eX/OZhRJLfGb7pWwd+57VLvnp+052tkeGCm9ybWXfL4bffO/GqiotkzOanNOU0SeMCkXIue4zjgQNnZbN/Vb9/3UoWf7heFNeuBOYvmtj2FhbaYlUzhGogshg5PMIYtWr16ta2VppHkBWwajAtR5GzR9kIbyWnlVqyhPDgW1bZZI4eea8V+eLG0oNE41JO1jYNOgFQhKUYkgIDG6CrxCtCuQpA0VQkqmh+8ZCc38QyjQCoRtnERbEFWjGUaASh8/iVYn6uLhJa0RGvj7M6hu16ndbGAg7yXpQQGFi66oqXLAvVTY1yiFPp6W1epeCsXBk/71y/a+lwxcMeUko27XXkG5syGGpYbkDQCo5m4qBIAFRUlK2azzz9Ttsv/Z/0OXfq3HPPZVLAFCKIl7wsNiYbtIiZ1PChSGyUsLiXmaumJk1TEgVSMGVMTU91trTkB/ZEn3oqtfUp/6GHI8/ujhcRHTulSLTc3x9ceol32WWRiy+K9ywLQgyluiCy7XqnrVZHJ0Qd4SigITVlUoZRYXnOzs7kfnTY5/VEwuo29BS64mjGH846sznvwP4DpVK+p3+1GZACVku7Ug4i3xRcy+n7gpRvEFCp3bt3wwagWXPbbbd997vfBZyzN/jXf/3XYQleXO2eFykWAy9S4KVLgROwHS/dwi6W7KVHAYZa+Mhvfetb7DlhzMUm4Uc/+lEQFLwjRk0YgsF1yJT6+vrwfPHDLnnZqWXr1m1tra3Nba1j2cpU0VsSd2MTo5WDBxKtTUw9Uv7L5t2GlNPQ4jU3eFJ6PDKf4SaRffv2w1WvXLnCyXGUbU5ixVgMPUJXZkcsy1MlN4GBpg888ADh4Z6tqSEcVIoQvEWiy6zPBGMj4MPEQ8V55CQGgDEMBG7CwDRAGWZZWx57JzxvIRdL0TgsAGbeYm7GdIeRyClElRsQ+1UpHz5UeOix4KGHggfudzZvTsxlzLkYoVxrq7PprMMrV4Y2ndV7w3WR9i5OuoBvgy1GDGlTqKZDTqbwbPFiFZ9CUjCAPUWlVLZgJsiRG+UBwBMMjgexALiAqRTh5AkDH4l2ii7qpSqa0tFW/ArzUmb+YF4dQC+Wpdk3pwNFiv72Z3fImKrP2bClaDQBLxtK1DW0duZ9d3RieujgSLFSSdU3tnV0AkIltymxqY/NeHDGkt7A/sIZ6xJ5yEMap5LQKF9TCJh/fAG0qp5KJcLzS5BAFoVgRhHNID0GEhuVaZ2xWRUOB0Hy6Vz9w0WvLNGPjWw6HRqH6lokRZqSupmkdceLnX7r/f431aUapX3N/uRDBw/s3b3zwgvOa21CFRLT1ioKeMNGMgnzpBSrNF7wi6/e1C71f/I44qW0dM3HqYU8TQ7IxFeDsgAs8vG7A15wJiQ7ni0Pzjjf3UaXOFJ4/FUjeVTraH8MrbhVQ+JJoFo0KGJLsnkEG3iS1NtXIDyGEFYfALdoU4tyJlz1rYBOLQ2i6GU1IROsIzLwlXMvijuTc1ubhz7S6RYS9b8+1PWuYYox841k4pJydHmhMpPY8Z4+ZN7LPjaa2jhRDhI33fPlx0bOrVuykjKS+nxbkbCebAY2Wz1LEm4vVZzXePJn4hY+uuq9r2z5Jr65gfTEdxvye2Ohej99zWTLK2edcGWstPQDm388VOn3WDcKimhEsLRUdqNGIZN0lYzS5J/NwRSArCkS++qTUfVcenZvo/+uMypndsBPmzHTZj5f1GpU+zOfkK2FodYRL0M6HmXaAKErH/vE1MwDDz7W07u8s2c5EtcCX2vZOTw89sxTm1uWLF3av4YvESVjdjrgKOHmA+e7RkIbmG26QryqlbbsgvuleAzilRSXvRXscgaWGMGvZLiIbat9hzpTSVtPI69liFA7sHRghk/eMBoYbKaqCgVr26aQNrlj4wA9Iul9sCiGHT+j2VHITLfGCn3dbW0tDfizlde+ZVSW2TAHcW55ZZOHXksIW/cMCfGYC+wnJzI4ioKiHD4DAwMASySKaqD5LqJin+6LxO1Qz0KnXb8GzbKSy3zE7MY+fzJkIuDrHhk9PHx4eNNZZ0O7+c7KSxYe/NLocPHxJ8fuuWf83vuWD+6Jj47EmLzocIlU5awz3Asv9i66MHb+eREO5EPSaztetaLqgae7TidIT63r+xYGM2Uzo6GKj8bW88p9viFqtVePncr6GC6ZzHGUASJ8yBEUs1kyq0vWNSb9zjpEvkYzQIUitxdbWVsG6sK8TIpsJ6G9gPQsTADpMUz9mc98BnOkfX19f/zHf/yOd7yDGf/FZ3oCgi56LVLgF4cCx8qsfnFKvljSnzMF7ATJ4Uaf+tSnWB5+7Wtfy75fdG80lsMleB4wr7e3F8gHDLbCFqJoXR+caYZ7e39e1VggZRU4iUejS6JBt/Z5ukGy021KF7J5D2shCaxehSeLwUjOnR4K6uI+loqbktJTpQziesTXoIalvYsOymQJlHNrDKVKpGD2Dm/oeXDwwFfYDmpEGex2JhPkSPEXPsL6szqOD3XkDqNAOAhiQ+PJZd02FpIN5mAmYFSzwAy8ZdUc8IxaGnhbE2RtksRFgZb0RN/UE7zh9UT3szOFzVsrjzyUu/e+qQcf9m+7o3zLrag57gxHCz3dnZvOSZ53bui8cxNnnuE1tsiQqJg5sSqzc7NoSYG6yQKVZuplLXnUqgTcZWkcnTdTXgkcqDhvecSNwwona+FflEPsj2pZTUS/4qrNM5u/pFeMqphpE9gJb1P3ep2WAgEAAEAASURBVJQYR8Ymn905uHLdCnAvguK5or9z917/8OiSSLhnVX9DUwsiHbYZw/IWdPASZkjg6qoWWekFkhTJ2o0R+3BiLecuIQsy4h1oTHnI3YJi0VlsP3946iBYehy9ThyfkQiakql0yKrxdLtjTRfFVj9RapgRF61aCO3O10dOg2AMroHxL0f9wiVR99zYzrwbGaHLOVMTY/v2jsTjranxWGxOvDIlkswZWTSnrUqqjMzZGO7CH8ZaityyF2sDiFMnjypVaW5WE2wR1Pl1qYPXuqH1Os13ejV9GA7sNAJgKM46yM7xEM06jzwtHhV11UJU2VRxfq0BX/Oq+lKB5klgI3BXM6ux5y+gn219tZMJveCl6RbK6ajLBCBlIlbWpR9LuhPwvOP/XudlMfw213ixNv1W9sXDf10u7vfif+i76Vx8eam8te7QlxtWfGEu7OUvbb9188jZTMkkIoZZ1bO56MmWwzYaz3THauORsRpX+ZsfIkb/1+5PNkf2nJF+MLliNvl7M66PjKnMCgvpzZbb/mrnFw6XVnKOEan7XoLTVUU0k4RSVqZVcEW2MO+GziBCuYCRDLGEINz2UnDzQKg1WelmhBAxCGgKWKVZVZOCb4qlJZacwJx8j3xHaBQjRy/gZG8tj/KRSBZAq7E5cPLF+ECpdctTE8sm042N6cGd21j4JMr4VGXHyK7W4WI83ZxqXWoOlObrQaPWCH5BucbuFNscAMMSAkvVWWXiH0W0i12mchrS9Maoe5CnoTAVYG2Lf6KGVg4NScG/GALAiy8L1WV0C2IysCehbtTDnhuNpXPI2b5BRB1KxKFrkSDFUeTR9NxMsH9w0B8Z95y2vhU9yXgI03o6iTfCnl7kuiQYgfZ8rVi7MhTkdoKLkqqBLJmPfn9Cz6ODvJAnkmVGqO35pwCgXzyZmH784x9jJREpNBMiKuuTE5NsBKCJAfD+1ET26Wcqjz7qPfak/9gjkf0HIuWSVIGp4tJl5Te8rvKKC70LL0ycucFL1jNanahKpiu9kCK/kDjUCEjf29uLaWjWoJm1rY434J9XXCRq7ydJXf3HfHycAzaedcYynOPIl6La8S2L52DShYDpeFfKbakPUoDPU0j2JDnWXpm09dmxeD04OEjrsPWMArM/hZV0qmYLz/z+ta99DQnwJz/5yfe85z0oRf/Jn/wJpyjVAtQSXHQsUuCXhwJHFhF/eeq8WNMXSQFGW66bb775Yx/7GLuAMPWJA+sLDLV2tF2YPiEBmVzMprh/8pOf8NbONy9G14gJGCGktRskLmb+goXCHsmh2fLYXCgLhyyexzB02mzjNyacRJAZH9oFB2NZHhPXZfJbiP1UvSBABotKMNJRu9cRn4UV5JHJBisgJERIVo7thl44BuplJcAAWtAsKZA44YEBRIEUVoHNFhkfQALVAVIy6RIMkEki7LAFRSOP7evrw21R6HwtVSMukiIKbJmUbd1gbmLqwN4B9/Bofts2d+vWyvbt5e07e6cnWrGrTOU5NKOvJ3TWWc6mTeGzzx5PxneMjnM2VHdX97oN640+s2Z7cYpi/zTrg9vBMBs3brRKU5QNf8qJxJsJ1URRMY5vdPn+LC9bEkrDlulBc3woEzkFGTp06OFHHqtraNxw5qZQJMHGP3MgJ6ZurLYk0BfsKpGRdvqBe6GMYcfN3j8JkUAVRm2SvcdioM12QQUz8iWZh2WnMdRRT6hy+aIZlxFJATEkbBcd9d9JFoPlm4vL9nGUp4E0hrCWMKYFdaOTTqW9/RdEyp2IlI0YOSS13rGRYfR4ly/rioVhtlm44K3E+WBjMeLSzTZK2kZPG0/2FnPXEa9aOQAww3IKDAsSA4kEj43IGkOjJgV0tkHUcPOAapMUIhhZj1NvUhHFcOvH3K173rnAb2Eg81o36h4EfBp87E3NTZdecmm1hyxIpRa2moHBJzCLRFYCR14f64LSbJ781jZvaBalhyMyyqPDqfAGMtSKqiIYHKSANV8bi8fNw6gGVMWY9m2lzAm3Rdk2RgJ8pES8NAQy38IRbyWkUoOFOIXrjZ1f+u+9H4D8u/7bqtygG0TLG/51yE3kM99JuZ8s+hd46S/n4K8PfKo/f3+67JX6v7In0p69/+Clv3vPF8INnaQSjqeCGOeaVstqi2Sf1JMWPOPUI7XjhVncYvszzmhp6Mbez79myb+mvXGTSoCRq6enL/3Kvo9uy76iRmpFFrGIymXSNh42D9OLqa+GSxWGHmIxoR4lw2xOBm9eW9q0hB4kESuLR2brrGfhLogXuMsHRSZYNuYzQRLLl6K9tUb9GNTEx2jUMRQFvKpXQsJ8ueX9+4fQ+GhpbpobPTR6aB+7QErFghdJYK0sEk02LFsfrW9hwJeomuLQn8lIjaNaUFg89WXi0JIUFgDsZ2vgJpUxIRVKCs3qHaBf843oM+HrMJodKC1LnEsPYCHAfFDWWIAxF691KJnB01eJirLZqcvXhPoGessoLdsj1oJSfsvTT89MjZ93zqZlS5eIjLosxe0TbjOi2DcnujMXsDuGN8ws1MkOyNaKRPXjOlGsF+9nCUgpWf148vEnepYvZ00WlLh7507Wtd1ScdOS7tZstn5wb2LnTn/zU+E9gzGMXbFa4YYKjQ3BpjPd8y8Izjs3cs6mBHUPoT0vHXFbYcH+l9KlyppZnkJh5oN5manfMgYLiWx5BtOClF/fD4fbT2SC0aw7nWdysZVS3ZgL6PSJSIVjHdtTXn3MfEj0PHMtTPMFkIGiEgvRAs0B/8BkTYJwRGyktxL749M09QsQDn/iE5/4wQ9+gLgCB8a9tdr7EmuL4wu/6LNIgdNOgUUJ8Gkn6cs5QTuAcuLcn/7pn3L8D2er3HnnnRdffDGj53MNoPgDTgw+0WANtGMiR/AIiEJ7tre31y64Plf056ImEWuvYNprboABVqOTkTBTzuhcMJap5DBUpMmKA1TCY1n2KsdnCx2pWBCrzNRFy90dzUxRNSxnJxXmEmTawFpKxbI3niecIWDxkW+TOMHgSywYRlAMd8IMxAU6JZGFe30tSK6VFgdhgJQAXTLCwVkg+MB0kyNImLmNZJnVQKELjU7ZKbRcKkFJNmUZ+yVuQ3NTQ/O5pDmwalXwutf19vW6xXxhcG/m8ceCxx93Hn0stGVrbMe/hf/1X6hQSzh8XktTee26+GWXBQeHihvWR5f1lKMIFyUnAjLZtrYEIU0cto2Ovy+szn+Nu1oGEGYiuXTpMjqA5A8em767zj9nE3xhrb1UC/GaWiNByoS8F1VqHeDJYZ5lneSJhS08sXODjAK2G9CLiVekUuwPNbIpZEpIkyS/Yk8vvUgHWRjMjEUcYDMiZeFk8fTi9RGCyCoRvLWYcLcYd3e+IlboKPdsLiXy4rhrF++5MLi8r8/Zd2a8xA5TTPCa17l8YXo643n1janGHePw5MiXQLYoqAJiAwRGLOsbuOtGASQw3yG2FGqlh45O3rDgMJnyYWcyJQL30kWlQS3krGD8EYDAwruSUwg8SEjDHR9aH1Q8j7GFopU+HxnRyZ2SiONXIgqGUkUVOcuzmj4Ef3bXbqSqK1euhmjVWhtIat2mUfDnT63DD7iIywbFaZu4GvHoH055yRZlBF3RTOq1WCYF9VQlJsRr3xBM/WC+AaosqmEgyUhkN2J6/E14Pg/ie24Ui9HUWX4qkf2xyFBP6lkmvNzmA1GmfDjubFlHQ6uObcXY3nQZFEUqFCDwIwH9Qe1M6UtzMiOM8SRAYYilEzSW6Z0jBylOtLE77M0DYEW1l5KULPLIE7iMdC2EY2+pSq7mCzlZr/uvdnzqm3tuOqdjR2tsKF9u3JVZtSe3ruLqsDd1ZUKTkq2zKZ6qZGprkzdkUQA4foUnJBJahVdoKkqAybxzx57wbCnA3jv9XwiW0AK9+hwAA5LBmk9Dh3/zmZjEJM7lg5KSrNQotEeXV3x3kuVKDqyN2064HF06kZs+dLBSn+hGYbQ0PZbN7a1v70nEIS8nnsUsnFbTii50GdOBtY4jgmnct+0j3WIIrsah0yLF5S69bbbra3M+asmybMeHQr34bz4xC261NsS5uzQg9gjoC3R1EuGDMsAYg/CgXJ0lLvvwnCVudkOYL1GUNaSiwqmOS18Bmlq6VIexi+oiMjdTONG65tDDCS+FNuHtnfHthMFelKdodoKyiLwUEN1abSYpBAcm27dubX3yye7bf5LdunUjxqsqJebQkufl0unKOWdlNp3rveK8yNnn1PX3erEEwwqkUxq1Wh5xvajynvbIhsYqpV1ZxiokZsk5AgoYbK11WOITwH7umVJlIuOM5oJZHWWEwQcu6qaPlDQ4Qg8mpK3OTctyvR1GaiRQ0Bd2md5ejcrqOQwYilpIF9iB1dfXt3Bt/fj0bQWxS8qW4IcffhgujkM64N8QC8PL1ap/fMRFn0UKvCwpsAiAX5bNeporxXjOsAuL8dTmpz/36U9nSwVkmz/44Q+vveaaGtI4lSzBdZzxyHSCui/qRmjssHJ5xRVXgIFJnjThN+3a9qmk9lxhYDs42LUp5jTFgxUtzkw+GNHqLGJAsfecUdC8ZAnM38hQ5cAMZ3B2NSVZzndjPtAiKOQLW7dt3b//AJXt7Ohcv36DtY1pZ53a/GezZmq0gJaKgE45YgF/ym8D2zCC/hJOioDcj0kBH8TCLMTigCAsELBpBzBMFEA4AJgpjSjMx2DjGgBemD4RuWo+Nn0zE4NPPCeejGMEe91a513vgqd8dvPmtlw2Mjg4+f3/bBofDW/ZHr/nnvDd96Bei2h0Gqi/eqV35hnsQp5obQ8v7ymmkiROwZjVY4nYsiU9PDLXdnR2clfGP++LVQYWDlitQLUeHQSqjDD/mEIZpgMQqM3dGGeuxySQOBhxYaZjgyiEb2U9q8xJxT7i4jyambiFjTnbxsqEwSracgyzDmCGp5chWZ0FKgws9AvXLlmSrMvyKKGxsLH+RleCEcNrHmDv24IL3IXa5FJv17moPhodBsERUuBM6AxQo76+0fPCAgPgZMVz0fKkfaulNylZVkvMpUFGMMUCsQaFwsfT/maXMvgZIZXZYyc5sAW3gFghYdsjiSjOnlcAGxNAXL4Bt6SGQxC6Cp5JH+CsuPoT3CcNsXcCRZJn48/5zIWnns0kk0sGiy17h8q2SIIfSJuFn1QPk4Ji4lDuCwqvV6Zogv0Kr8TB9vjzgLo7mznN9yQqmNobchjGE/oImlnSEF60MS3NrRbHBjd3/EBbLKCQjsWWmERePuLv7sR0QJiMWf4QADCJ23gmxXkf5Ydb/wUTTVNuy5xRcuoj3mzz9bPDT9R5eacwEokvz0XO87P1lcQbBSWCfDQzKHsD4Z5CtBWcGOyeWlb2ktH25YxTlVCUc2Bsdsq5mmX1RzVSjsoO2gRV2bUtpxuqlJMy3oUkM3S40HHnaD/NJCIaTGgiKD4NoPgmbb3jMmG4mXTpCBBMTyKbQvADjWg5uiQPxqvi7p1ycqWgu1FNRs9XjzUaE7obwgJ3hY0laDXoXT1I1FaHwaVk1bD0QeM2rcAz3iG3rqFuenKW83ib2peFosliZjoUTjR29hCT0rO2GSaOgDx/Zmc+fVXdXv0cNR/WLxDSAnHZHaAOr+/DLOiwuqEwaECwjwCsq88BPMy6EsrMlIYESQR4zN4YjsGKhYJYuIJRd5SWhZZ1QJqIRTAV/Mhl6mEf9V4XARnP7bjESI7OFI8L1VNtsJPfmYbYbUuYgYEBEkH3+OThT+Wtiq6mpQ5Qj0s3+43gA61oNh/zVk89ldm6o7RjW+W++w/uG6I94kG50XFTrltINxQuuWi0taXpVa+JnnN244peN540u3lNm5j0zDKVTVt5/EJczCNwKUBKts5iI4O1/iVLlq5fvy6RiDMdTOaDSaPkzOH2EmhDM2pqOgIkS4TKzXVue9JNx49s8bUdxRD5hRPANg1wFxECa+JgXRbumf4QKlBUpkLTJX96+gTjQlCMKjtKOsDga6+99uqrr2aTMAbPiM/bn57KYohFCvziU+AlwcX+4pPx5VwDhl3G9j2Dg5/9009MfvtbH/7d/7Hqpv+7vrMTNlhs7/O8GFsZqZm/2RCLkQYEpFa5lx0sd9xxB/6YfbJJvphRWHFVNEmxmhJBU8JZ6ftsEB3NCglnSwaRGn43Vw5lZ5yhmSDmIGcI4kFx5/7RjsYmZrtWs+WJVCAAh5ciyAWa1qpLFoBAiwNxw9PUACoiXOTbxIJ/IBYLyZy1QDWPx2YqoqEh+BZSEBJQzcQGKoZKPDK9Weua4Opa1jggFxMhGRF9YmKCkLQGutY2jNUMhwRcLCtMTU1RcjTWhiYm2s8+u+GSS1NvfweNB+ArHxzKb34yeGqz++RT3patziNPxB54kERygcPxiG5bh9Pfm+vvc/tWhDZuLF9ZF0o3hCLh7m4U+cQE66JzPO9eYGO+2DsUZgn8scceg0ljJYLlg2OoRAaGvNXyzRfT9AyTuXWBjWGC2ZtnLvoGrDpOsesAFYtyQcIGEktluohVaiMf5oBicfxCJwi+JCgG7oKlBZKNXAsJGAHwTGwx4Gi+BNWcfC82XWyPRkouu5QlxLYCsebGNG0bleYt5TeMFVmYQonnN8wqImvTMXlNOeXmn8HH6vpGD5rIhjEzPC4BIIVBCqZmbsCG6kTYgA0T3+ZFXD5qAppUwCXqnSAh7toLan6MLqh89KfMyEUZ4bY4i0ijIzMThcaetu4t0qIwr0xhbILwjCYXMY9KU1hMoQR3q2naxM2jOHGFV4bUgAOQjAy/Wi3FNq0lBwFst1Si84VSIubiV1UgBSVkKGbSNHkJkFF8Lr8lG5y1v7S/PYpQSx5qAv7sW3kI6iox+ZgFCv3gBjopWVTxM+sembrywsYfNVwyVbjRm/pG++T9qe6eqejyfOXv6iIb+LycsbuavOmknywuvWncDbMPPXbb8HXaRxlK0peUmhYTKJYSJH3VUoU3r5Qfzwpl/5MvF378K8mgsfbMYwwhn8M2dTGsE1kIKTk3NxNZEU1yhiaKaAmpnfemNchZvyKMyYzk+R5Y/cEelfI2KZI/m5tniiF2UzTERRFT0Co9bPFUZps+LykCOVs5slmj48mkr8xM30a+KjcfDJEirFg2NrDRFkAbbagPdXbRJum4j5qxlnWM+rEOMDMdyHRRaStoM7xgrcBwjKUcBRPQtTvktRZmCIin0WFGeGvWQEG84FtgMOuhWkGw3RJq0QVV6hNdz+F9oqD4MWIzZDFio9LMgh3qQs+Z8NEpHB/seJ+jY6hxTe8w3gvdR4fjjRnuWJIp+7OzuYEBZ8u2ypYtzrZtwZZnQkOHp0qFSXW+oBQKVzq7sxvXRVevCi69tNDdteG8c6PpdKpcwX46qWJOYmzfwVWrVrK5V4tkuvjU7I95+sW5Qd76+rrzzj9vYnz8mW3PDu4fSXSuLIQiKDmX1UPtiEV9WGxzQ76fjFRakqgpoOcM7tWIWa35i66yvmt9bdrly2I05xTSi+AKkCXgD47l/tM7w3HFIArXVVdddcUVV3z/+99nFxunWrz1rW/9+Mc/bsyOvJA0j8tk0WORAi9pCiwC4Jd08/x8C6cVe0dbYf/yzz+75e//4f8KnFd2dCT27NGZg/Pz2wsroR18EZnaDTYM7oBhDEsC1azOMNrRz0s12kwSKq4UCnUADayNDlyleNx5m5mdC4dDK5tT/c3SnxzNOXXh9tmcpn5EEyOHh+PxRNFpdJz6ZP/F4XRiDOiS8etjzoE9g91dHUBZEhEXZyajeV5Ztccb6AterZGCHAHG+FuAakGyvdfCHOMAAANiCc9KP9rUTHVoPhMFWIuhKVuRhVGgElHw4RVuHEjXawAY2GwDUwb4LeyUkCYOorCwTUbESiZTXszz+vui/f3OG9+kasCcjk/ldu1wnt7SuWWz9/S2zJZtB+6/r+/+e5Ps0/PcbDjuLO32163x166JrluPnY34ihVeY4P4SgpCYZQrVMINEBGzbZmA48tvi/eC75SVuPQZxCnIyXt7e1ERZw3iBSd4dETKr64EI42/eGhwDeyzrZ+C6q3WN9gwDACuHp0ihAw8wAAP8COHDR6dLCpUDEQe3l+UaBUZlwhjExLaq5t112AWtSPEabO5XLFQLCUTKXT1fSRbRjJp0ycpOqAx6uNw+BUN5bNDWdJmLpUECpMa/2wH1S8q35mpYiGXbJZiAq2hBI0EjpeJkN9d7ybD4HYQI3qtajrpzxqcrQKSBVqm2l1J1qqwlFIJY4R/fDYqhaKouXkAu5o2EVQAXQSR1nIqMe7XT0xJQoKAV/BWCZjOYVzcBIDxUOnlbx5MIAM3rYeti3Eb7AQjXkbqTjDVupqiIJNKw6MppvGWmLH2ZEopb3VLLnu3cTQKqKkVmPIsnai0zAVNM5UDqUIoHA2b7eULEiaUkqZgZGpQPw+GLqoF1WUDZPhvBz+x5swnm0MH2t823nxFJjtEhnqX2DBLPpV8cuzBaP21k61vmkj0ZUjtrrHXbZ67yo1zSrDS5o8qqpCKxI9AW/VBFFdqPPOG96bi0qC2nojHvTI7V50wG5gDju+Sciboldf8SmcBnl2JqFMoihKbz4WDjlBwV0DzZwlr81FOKCOYXqEuoXJyMwHVKHwPLVjUMq2isPyRp5KXn/oIHmBcYhhPFE+QwVJfwASwVhJXtDQkuNZHJ/gq7VnQJ2iKGH4hVwmnu+OxcCJFMElriQKIpX0AvWg4ADxMeAWGWjJYBTYO+8ZmFbBWiwJSXTYy4UgoTCIqmyEhVbEVNuU2T7qpGqZCNZ8X5WBIv+SSS9BYGRgYQK6IMqrVGzr1LFjKtF/ayctBGDWtWsi2JN82dTENadqMibIyPZkb3OM8s6287Rl3+7PO9u3uvqFYKc/QwwJFyQ2Xm9KVV5zdvPEsduC4Z2w8VMx3rFlXr2O03UIhd2jX7lAyRRuJ6KQdOBi8YEweGjqAfNtORqder5NX5+fylg+KE4zG/eZIzyvijblDxXrIOTM9NZfNdHZ00XPDTqUuFrQmnZakW4fVflGB6/RU2rayTQtFOWhrZ3z4IuS9MEinJSfSZ958/etf/+pXv5qjg9kSjATit37rtz7ykY/Am/GWy1Rq8bZIgZchBapsy8uwZotVenEUYPzNZLP/71//9e2f/6u35TK/0tLW1dbCZFdoaY387d+E+vpOF2fAWjgSPLJjlxQYmEdkm8gwWeM8//zzGX/tTEBtTjIWE4ajh/zh0fLgbvfA/iBVH1m72unudtNpuCteIpJFX8hKU0lK4R2fLaBbB4ef2LZ3IlNJ1Det3bDeMEN2xBfvkJ2d2LftyQ2re/zc7PKuloaGOlir5qZmmLTqZGeSspSuFU9pm4uVfqYuuBz7qhbAvl14B/oiwCQi4YGyyI15y3o6PsefM4QnJOIOrMVWB1rTSBIIv1B7nLf4EIxNxZgrQ0sK+TPVZ7azxWD9+LkQI3EF951KbnJq90MPL6343q6d4a3b/Ge2Obt2Rqcmw6hTi0hePhwud7S7K/vDa9b5q1e6q1ZF16yKLenhQCle2x5CwJNU3AR73jdKB0OAiTIWCJiwqdppz+J5l2k+gugu6hlEJKd7eGf+zk9Kj5atiLH20oY3x/Y9UBjdjMa90ED6cr/u2kQhX0bOkJmbXdHf39zeafSoQbxCNUBfgDQOC4atWinJgTckJQs8Y2QIKI4tdKFx7jb69MTI3OR4Y896oKogMymQo36dVKSyri2M3VnYG8O3gbaADCq3QXRws1BUIBfcbqEvLl4TWXUz/cP6KAaeCi322gByeRh3VbiEm4hCQ8aXKCYB+6Q4BpGZ/OkuCmNvuvOWNNWLqhQGd1aTV8/Cs1oFvSa88VE8vdW3r8T0XZtimhTnUzJhuBV8/5lh8LSJGzhveLjQPu0/1OPeu6QUjyeNESxVx14mVO3J+OnJYCl+RQ7lCOnW19/9R6s/sCS63TOGwnlBKiqGKgAohWNGK15EeGb2kpue+OpkYZmpKPFtUWz1SNQkyb3aRCZT40tiNigERPXAEEB5w403pXTYrBoGX0tBOaisDMJZuogoiqMgxqmUYxEpzuiF8VRdFE/4mytT1NYAUw+eRBb+IDMLCA2xYFkakz8iO7oFrJEKlwq/Soseu+UW0GoAArWa1NA31lolMjSArqAve3Qxi6ydujJDRUgj6UWfv1IuPvXU5mjY27B+QyIWAXNhJgHbwzp2SG5grY4gMg4dS6Zt6sJ8pl9yZxowOb4UbjQHxiNYl2TYR7F59erVVPT0Fows+MRM45imZE/CgYOFnc86O551du50d+wIcI+MxUslbLYRruS5uaYWRvLI+g3B+rXuujOi61dGOrsRkWPuWv3DcVhtZAaxq6tM04zAAF2rdEP5yZHq2ONnWS9mTObgw4VT0umt4H9BahDw4f3luTJSomrrQNQd23bMzYw3p9xz1ixbv6Kb5TGjTQABTk8LQkZbNeAu26A4FYJHTFFCbbgCoC+6TsrsNGW3kIxkjb7Y3/7t32IjGtbiQx/60Ac/+EFW5H8WeS3Md9G9SIGfFwU0bP288l7M96VIAcO7lSvl/+/r3/znj3/suoPDb2xtXtrWAecBr1Lx/Hxff/Tznw8v1U7L0zIyAnqtKSlLDbvtCLEnwz3QDk8wHlwROLCmY7wwXzqwrmKxvGVL+e//3nvkMQ4BjkSi+daW0LtvjFx9tdcivWULgJk/bC7cmV2efubp8dFRLxTp7V/Z2dM3U/LGc+5cQQf3+WWdkHRw/+DOzY+2tHfFOL/CCdKJeF3Cu+jcDc11MTit42e8WsEokbKYm+WIiB52pSpodcqqhamVpOYgFgwETENvb2/NE8dzRUH4ucAI1sIY0rXD2OOjjz5qd1aj1ARIhmuBZbF6d0Di50qWoguSAAwKxcmJibb2dolgxAPRA0rlQyP5bTv8Hdu97dsqW3c4u7aHRscSHNYJwvEQHXhzqVSkZ7mzqt9ZudJbucZZvSLe3xdp7wwiViYv+GJJRx5V2YFggOFWf9q8bglLYeg2CFLYs2QZgqMq/xJ4MJ+RKEZZHv+X2Z3/ESqFKj2vKF94Y300HUJNYdvNmae+zYnGseSy/PUfT9/70EOcfdrSyvbvyLqNZ5g9xuwi1iExyJARHyPzxE2flC0uyZFFQgwIgUUNGAb0ipMF/SKwJTr+E2PD05PjnX0bQcWUg0ULAuCA7O31TkdKYkzeaH8mf7QtxFUKQqeE5K6UiUJnUES1EdEFjnBKC1CAzD7LoUv71aenZ9INadtnTK+xb1RgE5/I1eYmErnIU+8ooXKidiYk4G3+n8Wx6htuoeyzB2++DARUYMLZm/z1QNeWlxKo+lSLrnBVTxtTueRYCDss805crXP+6x4qRCvBSMr5xpl+JB4PG110E00hFMFmqKxMSfSDt/xrF+CL5Ymm0OHr2v/pspYfdCWGoPCeuV7a9Jzmx6JOBjID96aDju/ue+ff7/m96Uq79IM1SqiepuQ2m1qSZKA386J046+w5jK00oOBIiDGdDxAldeQQdYH0SaxQmTQL0JgG82Q2JZbFcCTH8ClxnqcJpA8jb+ycQWeEfbqJb4qkQIpLmoFUX9VU9DfCiL1sA9cyGUwy2fQryxLCQmbu5HWgv6FSc3eb+UlG2yu1VIWmmWZzsBawWZhWrC05w4d2Ldj27aGdPKySy6IR9FDMoVUsWwr069tYeV17FWtzbHeP69nejkTENs3AMAW5FCSUykjH6Ups8jOIGCXJdSEUv4Hi2GWoFQaGZ7dOVAcGIjtfNbbtdvBHN3evYlMJkwHUx/zcpFw0NzsrVrlrVtfWbfOW7M6vm5NpKubbdDqXuoW9lK6hqb6YIFhyHVZoqU7LSwqqsJWadbQ35membn/vnuBbKtXr7nosstC0XihGDQjKjbNtDBiNZOX6g+0enbc3zsj/RUsEaajTksiqI9VDu/bs2fg2XKlwlrAGRs3MgGZplOLvOBrfhBEDyiHJIBdvmjDoS9w3XXXcbcEry1e68OrDm1q9NNLUpJGIPEXf/EXX/ziF9mNhUb0b/zGb1CG05vLCybUYsRFCpxGCiwC4NNIzF/4pGBeqcPtt9/+/3zuf96wbs0r/+MH69MNcWyA2LEdDpUNeDf+WuLGd7uNOhv25BW2qRHs5CFrQ//xGJX0SeTWW2/FxBGJMNPAK3CxRdakqgIQXZPBwMDcpz5Zv2VrGcvIpVIynuBVMZpwPvoR5+KLhqYmZqZnQNEoKhMaGMwCJxpouNFAY6HaSlCZoJECYQR4Ouc/tmXg4PhMJldAMovZlJFD+9JNLWecexH8AZJkwHBdNKhPuE1xJx1j25hMhphZ0DIkplRI5GZmnZlZtrM5kZjDBrY4522ebLqiPKwFIAG22kcnJ68qWCzCjrA0ULNibQnCHboNDAwgZIBWzNNmknbwYeMZjydvkZ+Sr+E2KSpCRTHAyB0np/IDexzO6ty+w30WCcMuf++++OxsBG6MkqAn7Hp5mmzZcndFb7ACVLwiWNEX71se7ux2E0nAg+HsxdiL+Tr+ms+RNwjVmYytejx1pE1fVF2Oz+u0+kAlNtb95x9NZ6adc94RXn15Uqy9IIo40fHB0n1fzmSGop1v3THnDvX3r6A3YvcblYGFpTDMqJh7fZzQE6Ra3Wns63AmIx9G+mfdJd/L5/Jbt2/Hpi4hIZGayQk1tHW0dSyBY+aZVoOdgXLqsyZ1C2MWZmrdhsuqetfCqgz85908fFVl1OXkM3z48P6hg+wSR1eTjGX1V6JjTTQmvDLEwX8YeZs5sU1RlNGR5selziBUZounB7aml52DM/PvTEEUzV4CRHKZ23zi1V951iqsAlTDiS3PlIJtowbzOe4Ze8oX7Cyiql72nG9ucKZbomGg2NEX6ZgEiGuKZ94uyLT6shaJQSUaZDinerzIAbFBV2L32vqtidDMRLFz6/QZ4xV01MnC5EJUW0olbJKstgEvakJa+dsq2M9l4Ucjf1LgWPQIzLresDaIbb94LBKLYfnYNARBTNrcNHDxoyIrC1vmmjanvi6gfFWAagpgGlLR8WWTABoIhK4+Bc0x58yuoL/Rm5seO7R/L6eRIUGU1WW0jiUHNuCW3bwSC+OW9rIEuSg8S2BriyL9clXCpM+vLZLxY1/qNpYIMZTAfsXnUmCx4V/6d2pkRzCKygTHBd7ALbI+96WT78xyg7pLPlcYPlQY2Ovu3u0PDHq7d1cGdvv79iVm5oa1Uub3avgN5epS4Z7lldWr3FUrQ2tW+2vWJfp7Q80tqP0bIb3pAMfkaDvCgoJY+hOKAYpiM8mynGqnElar2zswP8EggHGEIFMM9g+P333/Q/Vty/vXrGfdDWn/+T3sk8GQGDke+zUdk/NL55GR8/9n700A5Diq+/8+5p6dvU9ptdrVaUlYvg/5AHwB4TaGEMfEJCaAkxCuBMIR/vkBSQjkFyfhFwghkBASQgL8MGcMxmCDTyzftizrXB2rve+5p6e7/59XNTMa7a7WK2klS/y2terprq6uevWquqq+7716NZl1h1J+Y9Sqi+L8jIqRRgkrgKmIX0GqUHvFFVfMI02evzgVrhINfjJeg3u5ZlxD1oAOgNnO7Hau36L3Fd4eHjSramv+XBfwlCxYb8zC4K997WtMkD71qU+x2+VpPtouoFhLUZY4cAQH5GM+ImDp5v9JDtAMOICgf/Inf3LHHXd8/JOffN9bbjZ+fIf9pa8Ei1ltq8lkL3fRReE/eq+9epWsf3m+g7WpwDlMbecf0SvJkDs6XtBpdXyoYqzF8QODDQmi2OQpCEE7lOIpt14uW/j+dwOfuQ1cSmTEpVEFgBkhshdcGProR4dse1TtacRYQgqIq8kI3SmjC9N0nZ3MM0ufAj/m+MQE+s+s4+3c12/H6rdt31Pbsqxr3SY9IZOZtjCMlWZMEf2Y7SciRl3UqA+bEbyLFgtkAwh07/iRv/VRI53y2ZjnZVcFLrncbG9Vr1DoOYYrUqxwo5oJlcAZF5X4pSIISQKhGURZE0sgt0hze3t7tRk2OmHYe6IAWBEhOQm/mBPIlSJAfrlBDWGwYeehvuzOncmnn7F37PL2764dGnEPHoyk0jJjZhkh2+8YZiYaCbR3uN0r7Z6VXveawKqVRnd3uKsr0NSI7xsSr0z59dSc1WVIZ6jEm37zJubkELIQLs1g2qm8BZaO9+bv/9fpK3+3oaFLSqRpFpapI58u3vnZvowxZq/d96IXbaJxotVHZkEEQD7tcya1it8qUKUgt9zp1Eotig8AFQJ5ZbLirb2hsRHQG6upSdTVg5aBqVjPi5m00hI7WFCL4bT8iQ5Zh+v9WhV0RvmhgbdOXYkqZhKl7uU5CTzxxJPIjM47HwP7w12EeldaijCAdsOlfEG8UU5VXRBAuJRGfY3yW7qWHDRgTua9feOiABedo8RWhedSsVQnJ6+XHsqLHOqh/Ko7FcIsUxTpHOZU3gMAy5VvvHxrbtkEogOh48EV/tOr8e8jBeFNnThxxHC5fKtTZJG4ZCJvlR/IVemQUFGtG6NZRYncK8zPC6A+uCbpVVLSWXFWISpNVl6gcVdBUmZJoHRIHJ0t0bko/xOo2VLDNrbCGFaZkGNjfYPur4T30nDQ4ZfeVRUiGRJEipCkchce84kJwpWVuqLCRbGvZU4iyJFAickjBWxkze36Ju+61VakmNzfu++88zdLLE1XmWL1K2SXj3Jp+K0KlveOPCgJx+OPPw4GQ+p3/vnnQ8uRUc6kO8oCueq/98BDDyLaYzuArhVdBEoFiXBHOCBdqlNwx8bzfX1G7z5n3157zz5v/x6z96DRPxjMZVmJSvXRhNh6NxePWp0rvFVrxlqaiiu6Oy+9JLimJ8RSIJG9kpfUJEe+UMjlsomErA8ih1mcljjCa02B3FUfBGPlkWKfs3UbX5TOF1MZ3A/GMwWca3iTqRSfQSqVZjFwe2cP1vJ53x4cz6xC39xRe9FK7BCwDnCxaBcRSCAkBT3cZqtzeeGvFQf4UGCR1JJmRoVZPGWNEqMta3AIZNZBa9QNshLnaGVQrBUOM1LjZRrfn5h90fMzuoGl2cYPSWjFepyPspS/+oEUcO9E1mVvCzYf3rzMqmPKQW9WqkVdw0fL+RjCpfjqc/vwhz8MYeiiMY3GgkwYMWeLOYa0l6IuceC04MDhOcppQc4SEaecA/Rx5Am2/PM///MvfvGL3d3d3/zmN1/72tcyVHqvebXT2Z6982dm/4ART5gXXxi+9ir2mWHF1gLJ1IkvMDLybzr9GZHpagkEtoF4GWOgkwED1Eo0rcVlJrQavyA79hay4ktGd83ZnPhZDdhBq7fXLrrLulakkklE7DiXQtkL+iUas43qflzGudLYIT/sLcCZHCdH+lf1NC6POTVNrWakODhVyLnBohdIpVMH9u1ds25TIBRKFb3xsTwTZSaFIds5q86p6dvn/PXfRLdtK+TSbLwUPNjnPPpI4eaB0Jt+w29pwOXOnMOUpodMQa1wA1XwDG7MuK2mH1azaghUj20wHNMgvxKBC44ZqsUZqR3TrSRX4pdcqUP96puIHVq1utjeMdzaVvPa12FK27F+DXtI5oeGsnv2Gr27jT37/D17A1jT9fWFfnFf6O67Zaot/8w8q9ESte7yDmtFl9+1wlrZbazsotWNO87OkeGgYbLfIQAFDzq8IUL56oNarwRUX1fHOYXXlMcKe6/6cLMdEbKqOFWiMhi12l46khq2WletB/2iWiEaDYCzvq68cpjqUpHL5VQJH37KUvBgsHtlN+0BsVGxkOvp6pwjkaoX9EcqvYDqCmCgXKtJHxwW7a5SGoPf+MVpFjRiGk1cgc0ClQlnnbBom/sHh6Lu1PKuFa1xgXWiAeYtyqLiS1LqE5WqFp0wpKtsSYxLQBR3wgB1qIfKrbDEonloeBkMW3aT8hCs6VXU6+eSpqp+Ra+gY100SU4VSZdLHvAFjjvePRkrzSfm531zFZ7hhQqzcZoficBx3oC5agprT210KiEkOd5o7lwXzrFQVf0jkGTVC7xG/qBCHcalvCKHCoJpqJphVIkaeU9eVUTLi1AraYr+p9BV6wJfUewfmvRGs+zcJV2uSkZ9JXIjpZPy6jKXydB5QgUXtuE1hCmNH6G/SqUjPnYYUcKRGfL50MMr7ZF6Q5A4thqKWAknkihiJRkhs5QHgFfeFrkBjyCJx/yD5nJZPP/JZ/fZY8aLWhwGkN07d5MSAh10WYoiSV8dKqfyzeHfowSX3lH0IVGlJ6fzp71pvHH49RfoSupBHSV2zVkKqeqZ9CnW8nEY69asfXLrIzsfetjf3dsUDhkHDvj87d9vHOgz9u8F6AaSqaDnihJVpIdm0Q44DfX2WeudVaucVV1Gzypr1drgqq5AIjEwOk5Fwh9q7RC4t+i2F5y6aAzaKvnjcRHTIT0cHA6dSZ3cy2enqp8vnT2qacBpB6Brjoy5uw/5kwl8ZI7jwHH5qvWFopct+M9t2+cUWT/kF9zirkNPhsPxaE0iww58E1Pt0RV+U703Muxsf9YYGjbragMbNpmdHXhblhZUIW4uMl6QMMUxTdYcxPGUeQjGCJo2Rl5UuMidCSl9OLOI1u2E7h3kzDDNgYCSWNhnMY3BPE0bPFdVlEpCNS56WXZgQiM9kjbHMriyw0hFKqZ3olgT9OvCRktcJvPz1+YsiuYLkOKbJmImdkv64Q9/+KEPfQifLLfccgtqYe27lKfzvb/0bIkDpz0HlgDwaV9FJ5NAemRGys9//vN/+Zd/yTXn3/u938OiSedptraFXnKdf8FFbPyCt00rmjBrogI95j2Yl9C/EwU8RppAVq5J83m9KWh/mEdLm94WQIhwVKvFuMWtMen3D/R31iUcF98sBXSDxCEwwFomBgMW76CGYuPQXB6DJaStjFgASyIcw8xJEjI2nLWeHFEvD+9+7uw1a4PRmoERPzfkxUK4/mWHWH/n9qda2trblq2Ihe1wLpX81u3W1kegwwKrqRQCwJv//Ia7+Vyr4RLxQ3r0gwGSfZLh2PMCYJ0GxeHo7e3FPhAQBX8Q01YqkdknS6khnuPoeS7mE4ipJCeUMUwzvbEsF1/Z3d2hlT2GeY3MqSUY1z1OcXIyf/CQv3+fu7fX3re32Mu0b59/6FBo+w7WFIuPFlCxeCW1lkWibBLldSzfu36Nu7yzcXlnZOXKUOfyYEeH1VBH+2Cezp8uJ2eVA8QIPSpQMuSmLCyvkHnyLsyGZeylrCk64sMRxgjUsS685AI8oLFqnVs+HLRbeOpeFIJ0FgtMSkgst5DytVAoxJfENdwKA3WYTL5KByFUEYjdGty2e2Ukdc05y2JqUykV43A83iVEGoSc5dAfh8BjwawYRQMCNWCWW+KpCDwsvSVYAaStPikuVbhKUxLUiUuYTNrlqWTNnxzlVyRI3RttltcWG/y/6dRuUVXj4FU1UxVPRzHMaNGPTJOgYEEW7KK0HVxnDa4Ph4NuWHJQrFA5CC5UeamvjDzKrJSnhMkXQDrJvJmT3aBL031NjNLKEU0oo8Gf0+68fkNgVcgJO9iz++lQ5Inx4u3bvP60ck0lWFPSlCKpROW1qqN8i4xAOImfM/atTYSi/dkpPzeVaEQSI10BlSpc0iVQ/KJjF3qEcPVhCkHlCALSdRUQJtUh8VA68oI0aqCw0MI/KiftuM+N4D/f74qxOkLiiMhkkQ4I55O5+OKLOXOoGpbyLFLyx5UMa8hzuYH+fkS0peXiwj9hJTUgCn6ulGAHuYE0zYLjTk85/QPOoT77QH+hb1/g4MHgoYMX7jtoDI8E83kNdJV7NMszrWxtjb2s3bn4Ir9nlbuqJ9DdY/R0hzuXxRsacP2vGi3llyohn3wy6frjStBEZrigF9mN5lLlQtqP/FPVJyBKEyjvq0rmgeyznXOoSrYMFKlNzoFqH+8DGFRT7Whw016N1bG+L2UPTPrkOZWQ7Ew/EGpaGWMfbSczPdTbEHTXruuM19YGg3ZuevC81nDh4Qfzn/+n+L59pltEVJxvarPe8bbQS15qNmD7/YJWoi79sZ9126OFszCHjRseeOABxl9gsPZXop/CYX3QYonJki4mEoQQp7u7mykNNlnkTGSZvUiFUCO6QuUHV4hjWXcs7eOgxFE7fhOYLfoTOSOJN4RxY12T3RT1Qqw4cr2GKFMLngszde7HXqYj3iARDnxEA85RkKAm+e///m90wu9617uYY/DoiNhLN0scOKM4IFLnM4rgJWIXhwPUu+6L//iP/xjnFm9/+9vZDx3hJX30jAwqLWSBnR2dO0tZZiRCF09fv8AUZrx7tFsI4wBmo8RxfvqTwgc+woQoFovgwSsajsjYHwgUrrrKfs+7nxkafO65HaBfZk4A4IWTQfrAUXC1foWisSEwIZingueHhocA7fsO9sfqWp/ctms657QsX7Ouq3GTN3XolncE9+2NhMPsKRIRrzKyvg2npdnfekvkllvsuTwrkpcuKaZQ7Bu8fv16sKsOmZ9g6nHr1q0oxhmQGHpRtvDW/K8cjaWLEk5BQHG4eAGTs+US65MhBvE2YmMGe709hi7qjMFTgSCeMDdzMZNzJsYKB/vNg/sGfn5vdm9vanwiMD2RHxq2p5N4kQp4Zofptcr+TBaSD9RMPoYAncusZZ3F5e2B5Z3usuWh5csCbW12Q6MZj4Fi1KyA9DHpBALPyLwybajigcStul28S1iEYAjxP5NmKKkQQyCs0/bqJ5gbWdBcaava3uEEU3ve18kO9H7//fdTIpQGlRI974snHoGsSUS1G6pLrg8fUoNUoQ7kYiYS8xzjye9O7/g+OjUBG7NrW73JpNR0Yt7Bi0OTXQgDxLvV4SzKV7yr1LfElSBN1BGtzDR7J/zxjM5EzjpmVRz/upW531jjNQ/2ZX5xT353rxUMxc4517jsor5E+//Z6u6ZCkOlFEfeLqWgKYQqiCqTJXG4Yy7c3eA3sBOu6Y2OT6RTmbaW5kg0QjSN2IU5uqoUTBPxgyZK0pZ8ymVSmalEeSLISQQFErn0hoqvF3dgFI31dXtwclPwwIsvObz+ZYFNghaLMfCc1kCKptKJjDnwFQRmWKDHhOrXj3pNmYR5mruzYs1+Klzwp5PJ3Tt3bdy0KRwK0YkJqMymi2Oj/U8/XZPORUdHnEMDgf5D3sCAD+gdGvJSqXDRZbchmEh9Fk0LH2VOfV2oY1murWU6GHK7Ope99CpvRVe4qzPY0OhjGCXW5gxDM0fnGSTCEx2CgpfPv+ybSsLgPyMF4WRbKOTTmWRDfTP4OBSJ1jY2ZRwTBS9n1thjt6y2GZO1D2y9xm7nCMAJzLnyh3mK2H2wBnikH2r8Yi7oOzW1CfaXamWYb6zjK0pOju3f/WwiFj33nE318VA8ZNQFTWvXs9PvfV+k9wAu0dhMPob3B8Mq4FDj438WuPwKHGkssIXMKPJpcgvn0QAzcNMNAoaxauaA4TRmLHHo2BFDc0AtcTDRYhxE8UuRZ5Ra1aDYzuRdYyzjjqatqRyiB5GYIx/M5r3JvDedE9cPOB+pi7CJkZHKedN5f3WT3RhyL+gMIo6XbbFVjS8uc6CN4oCBv/CFL+APHEdZAOPZRVjcTJdSW+LAyePAkgb45PH2NE2ZXowD0PvBD34Qy5arr76a/d/QGc7oiCvUHy28EmHGBb3/hg0bCETjipEPQI5rAmdEO/Fb3fPWJGTBZOCCi/zXvir8058y92BOVyhiEJ3LNzfXvfqVuWBobHSM3RfpsmfD+/nJIAsRyqptirXRHfGB9wxgjG2NjQ27du4aGhpcvXpNZ214JD9ROLjVq9tgRaPGxGghl2WlUyAawZuO7J4JCIwGApMT4tWXOyFzZuYgRqz7yI4HuKCAWnJfg8soCVFTVPUGVBGiJmNyYsrVtWIl85iN69eFwvi9mZXuzHxO+j1SCQ6yAX0xD0MYATgHz1McnfecJOoxm9n94PDws9u2bdx0VvvFFxoXX9R8zcuHBgcvXNVN0UYPHZoaONSBHrhvwOjryxw86PfvZ3Wx3TfoDA6Ee3vRtIfYXxTjW8UHh427wmGvrq7Y0YH9gNHRbqIubmt3li2LdLTZLa1Wfb2ViCurfsmfGZ38V1fM//QyUfWAMM1zniqkdLyMpurQFeCgmyaEUEbrCjRbsM/X8osTryGoo5VynHhSC0wBGQeGfEzyjpcxC8zniGh8L7qkWo8qX786qPxSbcmtqlKBbDPxgx30z7uh1mvPPP4fRjwpwJL/pSR0OtSW6WeXTyeu/vk1LQ/WWKPTXvOe3OU7MlflvPpyXPk2VZvRKeg3JQSTRYUl5JrWVHD5Wssxy82s9HH75pqG/JvW+g3PPt73yf9tHexnEzMsVdLfuzN42fkr/+R9v31+91/dU0xZogdW5RH4JN2K0KzS18HkIyWVMqPKm84b9VExeaivTQBoRJan8idTIUmWjgqnSEU6FaFRpyI8UMwiF5WWRJZOixstSQIGy7sAOAkUEjgD6OiOXNc6kOYLdLb4RkhxU6hc2AEA1vYvz9t06ZARR5IqfbvGEgvLYWYskbupwopSlEM3EgkT1igGEagbkS4oDPbcdArnf/mx4dzAYBEXgM9umzCN8OBQYaA/dmgQMOTn81OISk0j4ntRw6QjYnfoQl2N39HhLe9yOpcV2ElhxXK7s9PsXBFrb0Uqyt71cd8Y37Onuak53lBPnvCWf5q8mXSX73EDj3C1fCcvcUjtlP+oJ+imUnBpsX84mcE+uehl8qY7OoZeN5xoaF+BIBHtInbLbGaO+tdgg0ABveImQNWwqmWGcLa3x7NalDZYdKOTKdb8kBVbJgSMCbZUa4/GOhsSYwMHA4UJLzzV1RGrKxzyHcuLREO1tc599zdOTeaj0iMJy1XDCWeT2f+5wzp7k90sy5rO3IMWTiO87LLLcFbC/nw4NEE6g5EaYx9fDRJ51cqkfAipdTFVe5OhvFxqWU6SzPvjWW80Y7Kmmg3h4a/jm2mBuOxPwZIIMxExOxJ8hBbbVfSnENAbbEHcGJct6PdMWYBlGufFKxC6M3iRsnzY5fRP9JekEHHcdttt6Es+8IEPXH/99aiFgcHM9ySbxcvoRAlden+JAwvjwOLDkoXluxTrheEAvS2qOXz64eOeqfY3vvEN9kAHaC1i50VS2qkyvT+F1ArAk1daGUaZs3S0xm69Nd/UaPzkp0YyKTJ19if47d8prj9rd9+BbD7PNnrd3d0UnwEJCjUSXnipWZQLaOF1DsY55pHoNllWp/QVuMt6VpYouy47GvcfOLBu44ZYVyer7tCyO04+GgmFI2hdZDTy2jtM2VVl7jFJ01M5Q2R9XZ2Xz7MfkbwSjoBpKq+66TS25lY6TeU119W1rF1th55nzfDJq4UZKbO3ByE0AKyywb0gPS0TmRFtxm2pOkR1WchkM3v29MbjCZTt9fXyx1OY7waDbl1D7YaN5sYXycRB1Qg/zGItrKmTqfzAoNl/qDA4hHG80d/n9PUH+weKQ4OBHbsCTzyJHSsuuFhrik8YZrt52/SCYbOmttjeFmhvNVpaHLyPtHV47c0G22ixBKC11UzU2jXMS5n2yyvMiUXXJnNjdZ5RBiHpaNVbigpm++Uvf4lcgD2cZqhn+Vg4Zid5+odQO9hEUKJTTCpgCeFaT0+PYnqpBWkajpyPqS9ujs+OID/XFdj3ysCKu/N1I6qGq8rA5DVx/i9eft1H60LPMeun/fDwkvgXx+o3/HDiz3dlXobnIWmHHDQH8f9WupMAQnwby3D1zdJScUxFd4X+GLlKKaLMUWlMcvZfsdpsmRod/N//YB48pNJTmmbfde5/NPm1b6195zsvXBG/d79AHSkHxaMNciG38nFIy+SaTElLvhWyKebAJkQSaUhabPw6AABAAElEQVSwpbWJWNxJIRUB6psC4pEG0VWqSOfkKYcqiwpTz8FAQEMuiSeJSPacIUcm8pKtmsvDArF4dszoSKBzz5h7Fk4Glf6KyPMfwqCqQ98KHXMdhCNwwdYA4xe+piuvvBKNYini3G8InTMiqFJImJhDqVIJ3MUSHxSfzhST0+7wcHF42BodMYZG8MQYGBoyh0fyw4PBQdHi2jlnv1t0pJ4l7T6RBpjRcKwT/xHrVhc6lvmxSHbdWSa7r+PLt7MrvLwjVpNgZydcANKd8E9JG8pUCUvlWLd2rQ7CbwXKQxSJMjwfhQ/EBP2qxgTXuWA7aLCrZ0TrHdvbPeFivZwDyjrsX0XVWl7r2eCi3Nhk3+i+FevORlSYL/rPjYpraZaYKkmIarcmnrp9ti4Khw1cN0cCfti2UDZCCa9jyRsJRDYv3xQP+lPDA8V8+pyzz5JttlQRCgPpaNRq7FmJuBPa4GWx6PiFvLF3r5Mv6IJQudi/YOuEu0qzdy+LlHSRz7izbqWcmVbh94s2ib8STBJoljgcaWhooHdavXo1ngiZc6jKUpWsPy4ZiCQBlL2TAnqNqayBUbNa8C/7veHwbyoPEnbhbW3YaK+T/iWd8/unRAkfDxvNcTMcMHP4HsuYgw77kFmxEK62rZ1jbFjpdzfa4nQe04GjN57jYDipMZp/73vfw1sqMJh9JX7/93//T//0T4/Jtu448l16ZYkDi86BJQC86Cw9TROkIwSHgHjx84xu9qMf/Si7nM+Yfy8u6XT9p2o2j0OWoL2qJ/yOd/hveIM7POpHwvHGJrxfMiCtFDLiFI2OGyaAhPfu3cvIJOZajY3AM1QNGg/PU3xsydiOGOEuFqoYisM3bMlIp6enBzwMFCZZzKEvvfRSkBtb+8Re/WqcPHmsdDLY+UHktYxrhbrmwKWX8FQymmuW1t3dzRNtAr2ia0UNhtwDfYX77vV37JQ57rpVgQsutZa1oShw8Z/x07tTX/+vSH9/rCbmrlsfeNP15kVbzMY6xkhJe1HHPNJb+FGZ4uhX9K0+z5OIbp9YkYEPsbOCn7QfLpCp62mEfhfOE7OUsvzIPJx7GeRDkWBTJNDU7OMBW8Xgicz1mWXgdDSPFH0qPzDkDgy6YOPhQXN4yMG7TP+AOzJs9x0wtm8LeYbSHvsuk1MWsqE9Ni2XbcCiNW5bS6C5yWhqctrbgqDihkYwc7CpCftqq7XFTtRYsRpFhIVVITMdFH0lNbGaH6v6gEy28/UeffSRdCa9ccNZHctw8ix77UKt/L1wVab5edxnagRhh/7YT00pKm2gQrMOOb7c2ZvXRSISEBQnCer2pdoV9z1NP2sIPiO4zwkWM7aF5WbQaQo+9RvNb/3W2Beezr5O1Z6cSq8rICppEEJTEFQiqTJ7DVtGTVErgYXSSj6kHfWdTc2me982Y9dewRjaakSi0IT95J13db7lzee1J54YQAlEcnJSqcpZoIfMjSshIqNxspnpgb0NK9f5hlqwzENyNLFKKbI1kZRSCCh9S0Ii6Acoq6ny+AIOZ1DCa0pmVA7mbfIjAaIJCpaDXCVBIYUd57JGy+NDfnMM9zwV0nS8uc/Yv2ifQLAGuQZnENS6deuqY5MQFa0KLmmy+87GTRufeebphx955PItWwLsGixEabLkPYkJTZpoFnGrteZeOuMmk97IiD8xWRgdCSAsY7XF8Ig9PGiNjxcZPkZGgsmMV8yHXZfa5m3SiCBhwx8a61hqatzGerunp9jRFmPv3JZWL5GYtsxlZ20KtDbRJ0xncpib8xpq2GF4Q/a2ee7ZdEoi/ND1pYpQXTK5ZoguB8lQtf3ZZwcGBxC8nnPuOcDP8iM4LAWk0eZdD/iaKQKWfEG5RQ+NruwcLlVQz9hTHMWG2Qdf6d2bWdmLwrBgeJkJf2TasKZVV4UogeYXMHB2HrZlpyKMkdlBmh6VfABQOKzAv1Is5EXpCINsr2WF2FRa/KFJIfoyFoJhtVNYqUzsmgPP3ILjTE6GKDw7QcdiFMeriQsjpd3zXFz9OSyHdj12JgyVW1C5jKf1b6WrYbTCxo1BikO8jhkGMwoAMO0W0YxYW1h2ZnLCymaM/iFW77LqFzEhu36RAuv7p/PuBBsA5Iw0DssQLDAWeFa64KIETuWoMj8SMONRn2X7jmiGzYlJ4Vs8YrTUybZhmbzPwmCU9nE0wDEf9AsSHk75yYIxmvRa6oLhlFcTMhIhjyo7vo7xaNVAapQO++drr70WVcpf/MVfsDAYDzI33XTT4mpTjkbAUvgSBxaFA0sAeFHYeFonovtrvBS+//3vv+eee2644YbPfOYzdNOL2yfOZgESQY7Z4Yseogoia6RSgeBkNBZetYoxpr6hyQqzg7EJXuWoZArcBUSxJoehC6zF0lkg1iWXXAKXODRPZnCGW7p1PcXHlJdFPgh3gamMc7zC4IfGW7S19fWs3rQDAbay8a572fT+vcbt342HInjkYtx36hqsd7/d7l4tM4C5jupMwX4hy3Z37nQ++zn78ccC4oLHciwjd/554Xe/C8fImTvudD/z6Rr21QzY4axpPP5EducO44MfCF51tRHXc7a58ji1YfAHoQCKweeVL2i6sJp++OGHUXowFUagDld5l11PMHGEIToOMwyOw+WQWXoFC0jwkcxV/soJskJGMGTX1AaXrZBITPSlsplyqEk/M2McDmUzucEhY3jYGRlzhgcDA4M4LGUqbA0NGaMTFte7dgQ8Dy8lqPzKszjR+eC2mmrwMbRua7fq6oymZqO12amtjbS1ufV1bmNzqEUQst/aiiRmz4EDyeFRpBurVq+FLjXlBT5IMWYeMttRh8RTBdNnwvSj2a/o+PM/1XGOcoYr+kl1azxK3FIwrzCX/fnPf85XhpvQ+SMv1lOaCt8gqfEZoksHL3HNsrrj88sCQgjm/MiImJJSfsF0ilDFeHNw+8s2XPDPE/83kflRrZcMGAmv5hXTjW8YjQSnXt3wwb78BVNuJ9WjGCevqtahGojJPuGoanShCZZ6wzufOCaSo5KJXLJIMoHTItSMaOOKLL1kBS+sBc3gEtwMTKfs5GTz8pWJsKNQCy1Q+hElZNEfgFrXDr4SQqQALtrnqN+A10KVjwJxePsfRfG2fHm7DSxR78kWRpoaaNcAWHWDkC2aXdLiB8ttoV0wj6QmedNoCdMvyyOC5JOSNIQCkieFfePGtrhxxUozgABQUSXJkII+64zLZ3oJDmmBigCuVWYqstCh4/GmhPCjTkZPd096aqp/f+/Oxx5d27PKy6YQgBqp6dzwaHBs3JqcdAaHreQUmltvfMKfHDeHR232CHAKYdCG4cviWuhFpiHp08faVl2t31Dnr1rtdrRnm1tstrNu78CVgNHc7LW2xNpao/E40Bx3UJS1XnZ/spKp5NSOHS1nn428Ffr9sbGgWpjDEFNfX0egLgvFwVsY2ZSKIiU54pAmIiXjkFZz9rnnpbe6uw8MDKecDS86BwtqluAWHCtfdAuu5YimXSoEG3vXE7dVGMHm6cmQ9YnS1Qc10bEh0dAHLRvNbRCPaJZV1xhuDre2NrLwFh9ppIF2Eq/gHkAJJBwL+JEQQNcA7qJOFPQk34Q0GEmqlKD8UFa6aGWsTuFKIVDupZLeczvzP7vTODSEA0zz3HMCWy4Jv+W3Cs9sj+7di5do4CIvYhicyWadjZsKgWAQuUzg9F0GLM1SgXaUB3Q42hMn0nAEtVQuYnRWf9ABIlUXFimvn7ziTU+Hd+7MfO8Hue3bfBb0X3Be9JWvKK5ZP1AM9U6wi68tTZDdJR1R9gJcs3m6IAPVbgM+KxhZXGM6Z4wUZVF9ImzU1wmHMzljGE/1yBPCZqvsc4ajMn8iawxMuYGgWRsxm+IsAbMPjPu94+7qxkBPrb+6UcjXFaSoW5wTCdLf4kTmxhtvRK1yyy23fPnLX/7bv/3bsndxoXbpWOLA6cyBJQB8OtfOItBGzweWwG/BP/zDP2BMhUf7q6++mp6LYxFSnzeJU5BFJX+U21jb7tmzBxE/6GLTxo0hRNnqmEFGjzoYgAGubOIHcyrgCk/RzKo1xELrqNXj2DUxvKHdoq/XIxwAGJUvyZIdgx821dg/My4yKQe5sWR3V69omKO33GJdtsV/7In8dLKwoiv04pfaa1Z5idiO7dtXdHaiN64QP+MCerp7uu2pCeffvhp+7BGZTqqJUZAJzy8fzv7zl6Lve685NFAbjYnvaxnaROkSRnvyH1+zNp9tx1ee9KqdQfFct/CHiRHTgrkezh0Gh1lBCnthDjx/6UtfCsM15/ULM6py7lQWEqoYJBoMvgJwAiaadsCMRGsamowNeGCWyQ5RYKya9jC199jnEVv0wvAwWzfm2QNkeIg17oFB0PKgMTFuDg0bY5Ms/DN7D9iuA0KOGK6I9BWYYlJDJebtgGvbHZbdzrrolpZcS5OPZXVbqxOvCbe2efhXb+kwa+JMuG0UJk2NdjgabKwX3AAqgBxmr9SzpIippdQ4aR5Z0fpOaFf/pd2oQkCGlFSXiKv5p0KYNiC5OKaKYyLIpJA2vxDeL0ocRFe0k0pS3HJNs6EJVQIXcEH9gvEslGY1fR6WqVJj7K8W8vNtduxQMSB6UH+0f/PBz59n/RTvVaAdEye72f+MDqeN1luG6u0D58W//rPUHwvwFF5LjSv+C3Tkil1zuZCKkWC5bKrRq4BLwZKmaiURajngBRvrcGSPghb4iP2IQEA7QFpuTdRIsHmb11YjSI3EdD6SnU5ZBQkRhpWaHEdBaBUL4oo7M5HNsx9cMForsshwNJrOZlOZTF0C6ZkcAlvlRwAOV0zIybOUvlDMY3WrIovgiLhIfTgkpm6C/EgxVXRJhrSgmSgp13x61O2qd7obgqAxuAdoluc8VdnyPulIdoZBv00we6Vt3/Z017JlcVwA4n5paMDNFcyxUS+bK46OeNNpe2TQTGfzQ/2s/gDNWqnUspHhztHRIAXDaXzRVV6UTfIjJ3A3S04xU3cVK1224Wlrsuubix2txfqGYGuL39rO/siB1jYLIWlDfay1Bd01vQFKWyFTiq6oFQKFG+UgZAHyaUqgPK8+fEyLCEe3OTw0hJ8IJQWWSLBSVbWkog8uWCEKcJXFt2rlrWycC8rVZ9d2l100kd23o3/s6fGdPevW2XYIuFvwLFCuI44CUSTiIakEdC16MoHziCXEDVI8KOAW/W3ERotrsfIGrR0CBhiOKjcUiMRsMxb0wkErGvRY1htGmErNlWnThS2fKebMchKRKp4h46ZEXjrt3nefe9vfRwb70WnS5Pyf3pPbckHkD94VfN8f5D74YTuNnyZhRSBkB7o3hl//upTvPn733SygqADIw1Q83xUiMIZyhF+U7vniHs9zxlVmBayBIhdmCwz3SGY1AEaLoO3I6DDlgxCBkfBPlc7AyNvZurX4mb+OjowxPYDx+ce3Td91T9Mn/qzxnPN3m/Zw1k/nzBS26Z4YmcfCVjzM3usmbreHUzQSEUbUR9jNkbaEBtiYyngolWsiRkcd0i4rk/dGky6QmVXVOMRqToin7qm8gRI4YHmEYA5FIx3JeD2NOPKfo/qOhx2z3qHgLKb793//dxYGY1S4ZcsWdhJhqyQahuLJrBeWApY4cNpwYAkAnzZVsdiE0AsDC2+//XZEdHTfn/jEJ9797neDSX7FeiUZSdVIzEDIyMSu9HoQnbOYOpBXMF7iLUZNBl0cEaEHZgxDkQWyZawCx/IUyX13dzdmzyBSJvdM8XnENczEIpqY6CrJi9d5ig6ZkRLwnM2yCVJqYnwyEo6sPO+i+MVb8HoVxNdlbS1mQ0wIctksg+g8tc1AXpuoLe7eZdz/oMxXRLUi82kuMHyyHnzIeFNfYPOLMl//T5uRkxWMavYPYYHefX7/oIFvFTXtnieL0/ARpaMKQDVoBlhDhb6dScYxwbBjKhRz8fLMtTQzKM91FadVWvKA/7Qv+Gmjasdf6mGxhW54fGPM5GQez4mFhE6hiPZpbNIeGfW5GBoOTU6ySbQ3MmpMTQVRJqeT7sh4eGTI37NbFiQDlVXlKg2ZSlJ0dyarBEE/OVEsR93mRj8SCwGYI1GvpdnFo0xLqxEJeU1NbrwmmIhbjY12MOQxgw8Gwg0NvuxOQflUkqKpkw9EJmWqUHraLmFzzYioBWRDnFXcqtNR4usYfC802lNgVFIhSM87udWm8nynXM9BduWFuS4UeqPefKdo1Rxkfa9wJdviD1wWTTWY9bvdlVunjHwdupbBp1+x3LhdshD8xtnK3dHkvGY62JJeE/7Fvck/osoUf0ugSNpDhcGa7xIgV2HxG6Qu5Zf41Iw0Rc7DuWDD2Zus9pbA4Ai0FMS5EaavIF4/cuklTqJhIGna0nD1oWtU7kn3cEJGMTvR7xcLpACdyfFBiAlFaqI19byYiMWmJifxWowbe94ihD8KrtcKSFKyClbRKtlwQSsiI2nhEr+sTySaylHiyJ/CR2w7DDVyY6FZFY1hGBCf8QfH3Z6E5+cKWYw4nbw5OuHmc+bERD45HUxnALdW3ikMD+GG2BoeKGRz7oG+KVIdG5Xtd4oFnBfjchB9OJMV0le6aJYqmBgkQwO+u4M4WKqtMbt6kB+xht9JxANsFN/YhEgm1MYa/rpiS1O0qcmKoy8LyjZ+MJESC4UCJNSllHbGcThIc0oeS5gKFxZVDoR0eDai+1IhMEq6ZREWmJhkI16hgXkIWUC2op4VPa1cCMotmgBgBgNAK4zHWoCFumKojEbXE1fMBTBw3ap8vvbQ2NihZ4Y6O7sUtdAg7OcTB9nGLAV0RZeLCleu2XVA1ZyUVVk1m5GAGwuInXM4CNayQ2pbA+XBjOaoC1QpTeVCF7G6oJVHR72g8G5fn/O5L8Tp9ALBrJvjfTwtRB/amlvx/fBbb/bf8pb8N75hT04X6M0uuDh0042BDetxsEGKeg2FbtYL/5YZ/nA3haj0BAGwzhcyGJ2ZHjCag+sgg8SRjPNUTwMY4slLlx//FBxlXhBF2hLfHQp5+WD6B1Of/1J4YDTPtR3GNAwpanhoIP9vX419rLunroXFvTkLGw2RzRQcYxqnmS7G57J1mWwT4Rs5VLtpE89YqOITIbO5Bmk7mNnoz9B6XUIaasQnXlY0wH5+2geGg3vba4wa2oFI6zBN9+rEKKHUasukLvIv9HO85CUvwYDrc5/73Mc//vFvfetbmBm++c1vPmzHsch5LiW3xIFF4MASAF4EJp5uSdDhcaCfRCCHyvf1r389jvtO5fQUhshk8vA/mVQxKRAJLVeLxC/KyOCHwgpH06SKI2t62wV2uGBXvWiHFzGHJilG33PPPXfz5s2suULWC8rlgFISZOTDqSPoF6EmWJchEOSMeRvqJqAaOI0xsqenhwkQ6WBqvnPnTl5MpVM/nZp+kdjFBZke1RaQ9LIvpmhPwOrkQhz0nHPzgx39+odDBTxiEIsBlRf9ICuvLCvC0qmhwWDHMqz1ZDOECkdJCPW3gGHCzqQDpmlyYTieugG9hOBI8wQnNKRZSXkOJs9uhbNDNFlHC1dPUc+q5xRBJtbMQexovKa1w9wgpSqt9pbNulIIWfAWrvAKnlVdprpFliAmp43RMS+TKQwNm+k0qxAxcXOG+g2cGw8PB7KOaJhz+WDfgLl9B3MzVilbBnsjC2gmXzV5ZV0iIAVsIp9XOiAWjX4i7tTVWYFQoAnwHBRg0NDMDC6C9Tj7RYUBz80YzIVqaqzGBhRGgXjCARF5Pk7Lg/kscprc5DjQIhwNm7b4XWMKJzBJZmXCVJnRldgi+znhraeruws3S5rhElExh4vyr3z1+nbGhYZvpYil2EQs21sSol6opCkB5UO/Vb7TeeqwYzhP5N2xYSM2VERdOL4h1L854GDm7hn97Zk3vPUdfXe+bbj3ZWONW5YNfY81lQp2UBDUdmb2oBVssWoD/UAVX/bnpIylwkmbkEiQAasqxMgVkZRQS5WLZzxXPSLcfmrI7V62vO4Pfnvs058LpVPCa6XTKvasaPrtNw/XND23j94DJEjKJCXvKbaILx2BXBJI5QSaO9dTEU4+Mz64v2kF60GwR2avclG60lAi0Vgqlcykshi1kIbQx/6t6Gxpmpj3i6pIUqIuA8qbOj13SF70QpkM27lioW1lksFMmlXG0empQDaF0jWUHAnli4FcMjI1jgVEZHo8kMtahUJwehRf0NHUdDI3zQCAJguhD5YsqGf5FxF1pQBaGIXxK54LQWxctLIpdmNDobEx1NrkRmJmS0syGg8DZaOxYlubF4+juQXNei1NoZraQEM9ckH59JQ3sr17dre0trF2Qhit/ksVlKtBGCSMJ0MujziEIPlkK3WorstReKJekKQ4JLJKSsoEmgXZ+oHJDFpcT9S5gnJRz5rJ2vVPjuMgWT4Lak6wrsBa8C2aW4G4WLpzDdAlkEdKLEpllmhT1euzMr29rbk+Ea6NyQ5WQVsW3IJg0fEil1MmtPBTXFUhW4mI0bIZYylvUNwjEU19p8JgRbimWqdPEUQQUM6t/Px4fxVHfHxfu48/FhroY2RCm0liHoOXcNW07v658aY3hG66ybjqatwxWPG4wTKlpgYzGOqIxduuaWV0g1OYXDGSMs7q7+Jo5AhPjzwqIfO/WP2SfoX4yFvpxxiAGOUZmhmpoYFN+3jEEA9h0APW5aL0OnVTRQDtAenGVM5N5szxvJctGOcgjdyz19+1mybBchtaBd1wfU3U8vLu1ofM/gMrXlTTmigMt9Tdu98byeK8ymS1Al8gPp8zBX8yixTDqAlbLQnpRbJFk82Qcik/FLBqwkYbLqUN1gCb2EI7KIqDfj2LixusKAJy1TKjAa+F7yZm1Eb49stddXXJT8I1vEK/wpzzTW96E0qXm2+++Stf+crf//3fa4+YC6+Uk0DaUpJLHJibA5Vuce7HS6FnHAfol8FXIF58EtCD46zvla98JaU4lR2QjA3M0dMZ9nthdSXQzU/UWrIXq4wbpWnJiXEWJAn0xewZtS2mj6DQsgB+QenqzZkY9hAT4GcFZS/84QB0gXJJkBB6c7S+KCQBwORCdt3d3eTCDlLkSxk3btwIEmbA5lof5A1+Iw4jKItXC/kCjrZILRqJAZIxoCICMbnW2aGvnhvm8ZhVZMhwPS9fyKM0JiCADpn5BANaoh71QTQUhgmwmd2GSRZ1Qy5RH25vE8nvaXvoSUtlAqamETBk7969TC80f/CZSQjymhMvBPpkrNxhMtw78dRmpHBkmjr9cjZyV77GpNZxHn54KyYDjY1oLpuoSZaCEiWEXpedmWSbMEO8tKm2AYcovppdyykmk0gmznim9fPDw24eDycjxVzemxhzkqlAMo3bHr8AZh7F4294ZMxIZ4xUpjg1YTlOcHDQ27MHtMxcNCaTUJCSzLHACpCn5vuCQHgArJ60TCatshAQA0rDOhiN2Vj6hcO18ahd3+AFcYwTYG2kG6VZGixxtOvqPTRpgUA6lV6RzrSuWTP9zLMBnJbHYjJLDASt2loXj9YkjxITCwhkPbjWIV8YA++AXlxAhNAiOUpphW8Sg2thgcQmhgqTJ+q55qsEyj3NZu4vSD2f/yTZqNRCfbK2u3dLdKJLPh4hAILQmyaGX3Lj27f94vd23/v2TLgp5gyXaQTPGEF8+gDvvIiHHk9TJSmSpyZOMpeEKAX/Dh8KHQv7Vai8SHxZwbtn3H6iIXb+lde0NbVNfe8H9u5emVGee27Da38tubLnoUNuAXsArPWFWS44WIEfyQGkyp+qS4OuKuilrXzGySYHUgd7xorhvGOjRyV+IRtOTvqZ1Pj4WI3pt6Jfpl3hWD09Bd4mzWAuE0pO2/jsZWOb1KSdz2J2H8gkrWwamIo6FxEbISRFo5LCCe4WdklN6SpDMSvlIxSJjOWFwm5zg9uUsOuX+/FadkUrNLfkQ0HWn3osM21sdOLxUE1CDP6xXW9uMsOR2pamGtYj4PNJLWqWbKSIcoD4jvzoVGjl5PuTExNsQBPffxCn0HTg5SdCj6JJAsRnt66s8mPu1Renc9GqbapHtpfDwFjALZBVDI/FoZRTZLsg8Sml1LMCXPmw+KMh4c1OwDAfkeBhkC1PQ3Jm/YTYKlNDHBAiiZdJKlHDGmkgK1tGBQJeCIEUKFcgruhyYSdt0mpK8F2GA142OZ2Zmly3pgtdLkpdooWDNmBYJXi4bEcyqlL6crZS9upAuV+UAw/+5vBYMV8oKFEvaeZkCwMq07Kwukplze4aY+3qoOIYwRChSdVfMeOsjJuFAqiJFVs80k/npI1RmHFWeGqaSKiJgwlYT0/PnJEJ1LWsL1ivBNblrGcCiLzJl0eM9UwA9DIonQ5C6hlyavadouEjrcANFSbHyZwxWUBbKyJIxByUmyX8HntCjE1m01lGcGolErQTQYOv0hsd89H89g3gaHHyf+5o/80bf62n+95+bzBljwqfDPZMbsTjdkwEInjDYspAc8G3c13UwLCeRpjC91VGxg8S7KgFJCPmsImD/UVN0GU/pKYYgXTNCDZpOKf6oC7QnH/9619nSfB73vMeLOzYbpNDLyg71dQs5bfEgXk5sASA52XPGfVQhlfff+CBB/BKD0h73/ve99GPfhTF5jxDyMkonxDhFYvsPXPPL4xf3GONTnhIea+83L7qant552EZ6vHmTfKMW0888QQrc5gf4nEBT0vHVEYiMz0iHa0EBq9WzZZKZDEe60DiMCjSj4OEGVzhJ+MukXSHjrIXv2KAXoQOGjkzI8dAWuNzyONdraAmU4ZVMmWDX2CzNveakxuMl+idze6uQlent+1ZlzkXi7WiMSa8rAIuoGrr6S48i8tihkgODCUF0qBbsX7tWpPdFGdO7yTSC3hQZHLnP79qlqxmyyoEsnmKzhzhAmICbXJGZF2bx1Sn8xRwsdKZJ4sjHh05saQlMLVCq4CcBfQ7T0z9SM0IuZRfmfto1Y0t1pWRrpXcm2vXVBKBpXCTaU6JvZI1aJJQgA3Xohn00qnc5JS4f52YKGSzFhPS0VH0z8HpCWMSvVzRH08WkpOo5tqZoU4np5MprBeiTp6qiU+njKFBw9trM88X/OOjuwNFK/xD5UkmK0yjUxFK5WosBA3qCfRzIQVxTANTSDF+4AZ4E486sRgrorm1WZ8KTtaOhVQ5QNpmLF6Mx9DC8IqUyLKC4ZBgpGjYDwJsJGVpULZZGwyDCpPhkBsOsT1LhTM8JpooP4Mhlj+yoTZzWzKTOKANoUp4KYnkzeWPO+muQDRvmrtKofxAuxuuM3qSL3rxbcE7nw6YdfHcLlGaCc/94honspoymc5A4sJdD5AZ+72YrmOJ+aNjFV025bKKnuUWyEyqQQgGwvCqCx6ynLxdyMlZuCr/oAQxRyif8td3hF7zirZbblKSC5aSm+7YiP3pT23ZPXC5ItqmQ8hn+LNxMi9ujjw7h242K/aRwhkwKvDcyxnmkO+uMK2ouiV5QpUERAClADH9QSqWkb3DEkrCBWOyYhY2IV1IFNgiO1YXiC9jQzbDslm1nsNkAKbi2a6BlYWWUVfv1Nb59JZNTX40FoiE2CqU20h9g6j4pNDCG+Gm1L6Us/xfKkhlLiehpfSAfo6YpdpREWQiz72KJhBSRZQClELVI071DfXrz1oPFnrssUdZk6Kbm3qVN+gsFVLlqqR3LeFbMCroVAFddRbQK0bIdK8K32OiLMvvuUVbq84K0+pE8ACnVLhKQkUhYKwScJRIklqnLKB5WIWSNoiNCA1QluayUFPwLVgXvR0RlGwTfS+OqVzwC+KmCBpd8DC7EIFybWKSkn///dvcsfF0eGr15s1WlWtoleFhfpZZspi/KEgBnBUD4LmTDtpeU10AaxCB+3ildhAkS51bRpFF3XHWscMTZQo21/sMu2yr89hjjzHIIr7EJmse4MTwSu8KSYzRXJPwDCG4Hnp0PjylE2agwbwZSSuibcIJxOsE71Io/DYzgpexrrQWIVvJH7lAQ09flXE88Odk3kwW2EgZww8+bqrcwutYhhDHx58zWyjz8uZWs6mtxY5E6QT4GGtYlVLMFvuHTISVkUi0pSk7OuT89zeH9h9o+9M/XdO4LOkYDUiYDCvjGMNpIZw9DeMhEx/OSCfTBOJW3EdK4iUiVmdIdjwC9fIpBEyXjb6bomZTzKKBEaIavBROF/zUn6WGDeO6666jHj/96U+zUTCbj2AajY00j/TTU0/VUo5LHJjNgSUAPJsnZ2QIXSbj00c+8hEc8bEZD1sjYhL8gvQ19N3OwFDxn78U/OGPg6hGmHYcPOg+8VTuuV3RW281xNPJCR143HnqqacY9tBvY7HM6HXcyTHcHmHXVJUQhk8cBIC0GR2BrJzJC0MptM1cI6vGCpoQUsAJFmMq/Jcx3/O4hfk8QsbMoYdn8DOpaWCMoBowXJXbEZdq4DL9hgbvLb+V/sTH8ekYZ/sEVHM+V/HAb7/Nam+lzLnLLwvcf3+giIkci0WDxnXXhm64geWgpKVGwSPSfKFvfKOA4HrcGxv3M1mroc5ragzUNzBhBxwirEF8APpFRwqd4GHOaICZjhwf2TBZuwgmQWqEiRQfAuKM8uTm+FI9nrfInUWqGD/TlqotwQjnIEWZDqjpgk69fF2Zu1QuSrnrtlEhpfKqhOu4pEpoFRS0Q41xsErVoQyz5V5oUFO8uEAKLiWNnbt2x4KhzpWdEiS6Lf5jhJfL4wAGFM3f1BRAmnex2XYmp9B1ECeQmvKnkwA/omP34WDaLdpFD1tZiw202f8Tk1qwQjpdwDCER0wbXYfNZsz0uOehMBNUA3JDTSZkCbYV6MQEEx9QLIsmgka3QhWPBJrMdWgmVOCTQpbE0++qkvC25KaniaAydmp5iVhcK3CoQ8sJpy8Kml8O+IHC6t/5We69xeghtQ+waadXmjV/GLICbEMTaP5fD17/yKtk81jhnkK76nWVyxFXElLKWVGpYJwUR4jh4FJqAARV+MLfuZddJl1lwS3u3G5sfSySz0ZEFShPVV2J5ACbGjyouYEIFpAekrZY3K+Jga6kZbHuora2PRZ18GwcCITpfDB9F3NIy6ytzXMNeMLVfF2dH48jZQjEojgwx0heAmsTBruzqoZJdqpRSMNUpAr3RAMNuyTs+Xoa3tGFK//KXfm/FLp8qDB1EmhOHHUu/0gIbULO/Je6VzeyoRRROKNmc2R9idHYuTo0lNp+aCz/5N7lK7uVdbHgVVRnLEChnSEN4H1RY8uLatktjU6QLapd2ZMMPMy1nOVaGS0LltPF11RJ/ppKrrgQoYoCtLhCwv8QkhYwrcK3IF65hldULAJMkJ9g4ABmzMAVdHeCb8GMAnF5iwFJCl6C0CKDKHFC5aZKf/FFF7PH7IEDBwsF2UhcvcHTUgTin7xDj2WMfXNmoWm1wpHQOec6ja2hiTHAqZKriGEuPC9sudRIyEJ0aThHOXiElBCkxBCP9w18y4OBjybgxlaIZBh5GUHAsXwBOlVqizEXuAvW5WAUuOKKK0iZUWbv3r1wjLEYeStWzQwx+i1WM1XbHNEwaFm0K1T9Kfwzg3jzRqpQdNheF7sGzBw8cd0M4s0UzDTKXpcKwMDFt/FlxQIK0x/Mmo3d3cbZm42nnhKVPk1patrCfofXr7oOf33OLx6oCcULDz+Sfejetpe/ZmrSdwN1IduPhkWFSxJsbYUhNM2VplUbMpprTSyisaFRvPRrgsXGKM6izbqwtBnhqjQyfZR/S7cvzA9EIbzAG9av//qvv+td73rZy16GUfSnPvUpfKYoel8YqpZyXeJANQeYlsiQsnScuRygBjm+853vYHCCTS+Wz29729vK4+KpLpaQkskVfvR9+1N/w+Y9MiyUJis+U2Dvj94ffu3r2RUQso61EyRh/RYmx6AmVtUCREnkWNOpcEQnqG+PlghxAMAgbQBwb28vhlKYTAN9CX/yySfZfIWt8IBVGGsxBoJ4GVlBxQy6vII0+u6772Z4BiEzvtLvM8SSDgC+p6engu5mZ60Jm5qestNJe/tz/ve/b+1EM2X569bar3td4Lxz8LyJcxljYMjZ/qyxu1fUxRvPCvAU1y/IhfVk6DQYBHVBqDY/mSk+/Xjxa18LPP4M9mHF+oR3zdWRG94w3diwdevjuJcC/TIRwe8I1cGoyVnvn1SprGO6QLpP7cx4hXlVd3f3bG7PiLaItxSftoooihK9+MUvpnlUcmeqDoW6XVUCFzHrBSYls/rSzF43GrnbvXtXKBRe2bWSRLhlKiZ6Sx2TRqXalXzU6oo5ItOucrBkS3WrKNUkVAJUdpKajigNhMTljiB+C3k3m3NyeeZ96p5pYN7CKxIm30WlsdHpo63LZ2SzsXzBY4WFvClJQCRKVBSkrD5VfnLzIHYFtnUGZcJ0dMKgRKF0w0Nrw7UGOqX0cqnU8PhQy+8O1nc8KJPhZKh4t2H0u/7yQOilhl+Lapf8X+f8+BLy15qgSjklbehhSor+Waxaw+wmjUIb/aqQWTrQhQfsSBjnauxbjtG4DoY6NjW3IjEvEvGR7gl6QGdmBonJpxEkRHGB0mg+VhqQ1IRG+pIS6SjQqrOrlJnb0rUqp1CjYvKGjqmpKJ3Lj8q/5bj6vpLzEe/MulEyFpW4zoGX1VGpOO64pjYUFFWqV/SrIkyUXW1pC6yYFdUr6lZ0syhjWWErMFXH4Uy6UvkkjBzCKbpPP7M9l8v3rF1bE0/gJJmlx6BZhXKVlTK+pgTfCnLmFUWOpoxE9J1qS2jXhfEK35YBrYa1nFl0jItv0CzwThhPBw2+Jb5aoIs2PYz7ZczoNcq11apdPJqxalcUd3KoMydy1AEq9IiTPJrBZNjEWPPII48w0CBFvfjii6s7liPeXowb+UTLB+pTgGVlZ+YZhOlYQt7UZPEndxqf/Xwglcznc6FoBCSf3bghxC59GzaYgecXWOtMMZV65plnEB2iEz5aXmTKaIsdFmAYbhANcSeKRz2UkA4hDMRXXXUVnTADN7ZjjNfYN1Dzpe9H17+qBuYrwNp0wZ8G7uZle162lUKET1xaXd7xs47sOcSZFb9Exg6ZBqBrn5oqEJn2x7YCltkWL75suRt69rHUn98W7z+QoKs4dNDM5LLnnhf7q7/0QqGh937EGhthdUjo115h/dF770/XjRRww2myfhirbijEFro25GPhzDJeEYuh/rW9OtH0Gg0Riy2C1dcNA0qNZ04WlavuhfylFhjv/u3f/u1DH/oQozz7JL3xjW+kgKctwS8ks5byPrUcWALAp5bfi50bnQuCUhwPfPvb38bZFS4HKnaki53VgtKDHn9kNPeZT0fv/jk9dOlAlO6yLChvXHNd+GMfsVpYfSYDe/nx4V8UdySg1kke8ZQOFPwJemT0IjYzABA+F3Mmcji5E76CGMTejJ3oD6GN4ZMcgd/kjmdIhl6kmxADxCUrzlBIHL02iXkJrxAHT10IoXkFDIzKuqenh8iPPvooifMKmJmDLHSJiKy3cikCFH3cnxr+5BQ+OnjFR0tT32CGBUfxLgd7hPp5vGAY6DdZvkyck80Qslj4IRRCtuMWn3is+P/9mTU4QKViFM4cgY1F05deEXjvu1KJGvTrYnFpGAgXOGu5PhfHXRbypRZIAeYDQbFI55pp0EmdKZLFjAMyaLR480L/gMKhujg8qghWqsNnpHDqb3WVkS9U6Wuwgsf2TiOj7viYFYl4rc12UzMXVO3Q4CCzcCbE2p7/1FP7PDnqqbvuSA5P48svCdghtGqCX55KEggDDvUdevSxR845Z9WywF9Z09+2rbyaKku3pbeCdhK/Hlr2Wfb6lRqcnb7OR+eursmOzgSp2aZNmyraqjI1z/crFD1fnGN5DkIANqDNQwpzslsgBR/PFlMskkRPKzp+0a8ibFD6VcGiQFwABtN+kSwy2Yeb4hVZ6gaMyluAWOLoa9SzXBBfziy4FeNkUjaK2BuoxGEDS1CRM4KIGhqagA5wrqQ2FxapW1bbsmZEzmKcLFtQKVUtCBadLWcFccXLFNfULkIIZuwY2+CkV2LaKG8BJESWpfGo+FiLS1KES14qizLE0txd1MpTI6CWwOI1EB3pyatBKgRVKv0YjGMU5laPU0wz5lQFE0EqYWLKferp4o/vMPcfNCNh77zzQtdda3WvNMPi8lgqYdYhNa+OSll01oyG2rCZFssoSRQioMtlbIUw5MtIpbmmC0IYTUwCH3roIQZWDnpdDt7iFY5KnqRMZlQQYpFswUsV0O4a0wUD71OsTkDsQSPEHKBQ9IG7OUCvctmNPIQGwzoAkWJJM0Iog88zrrFEF92vNBX+aN4ihTHXNjiXtbix/v2Fe+4JPvdscWLKPHtT9Oqr0OkO3fZZa+vjpAAzg9debX/kQ3dP1R6YZiF/IZeabmpqiIet1rgIunDjnGBDo6i4ucK3M6SJHYgU5XBxKuU6nS9gOZ5H3//+93/zm9+kpj772c+iD6iulNOZ+CXaflU5sGQCfabWLB0K0OurX/3qBz7wAXp/Vllcf/31qp9/IXtGCGCbe2tiSonjq8Ychk/mFpPjMvdRx5wzOiaIjLLVSyUpJuiRwZ5BDpXvRRddRBZHTh+JQppKLcIATemVGkR+j8j/eCqaFDCUIgNeZjqlCUOvSyCzWG1ABWE9SqOLv6W9e/dSKcA5HnGGTmKyuEjYovZY0oMxZaSkgDSEFzpxBkJcaoHQMP0iNWalmEmHlbDcbG0V0vU0jmGvXL1SOHZWKCuOJIqM3JXn8tILfIhjGN+Ymih85/bYyCgGo4o4KQKTz/AvH/CfemnzK1/JQk2KovkAwYtSa1pQovUAcPLE0zwOTpJpj6pKpCcVApixIeMgNcpLA8CinmumkloEMGcuxGfCx3yuksic0RYlsDoL+VCZ4u3fX/j2t4r/8+PgxKQZjXhnrS/efFP4vAvNugTTYujH/GFRsl78RMpfiqRcfV3OSRVWPiPVgajQEuxXsWUuC+qpDS3/12Ltq4rjXzJyGOdnfTNqRDZbdW8P116P2WOpj5kr/XI+h3+pdPqBw/cLv1pY+gtPjy4IDRutka5m4W8db0zZ13TnBBbuYikOTEX7CpyQMwyRM17RFQxWQJc+g44cZCv9rkShJqT80gtzJT+i2uOeB8oTmIoBnED7qiBrTSTUtKGbHW5BuajpBN8qvRwdDdo5YK1gGJWGqG0F+irNrSzQlX1owLHK2bIvi3UlBHCLxbk0IkWKImTuJqWpJJbusHVvpuML9Yt1MF6Ae0F3LLchTXjEufrjnScjHVlH4DXh7LxvkhddKLHoTmk2XJOCBqWVXAgBeRIIXhXX1A0N1pYt9tkbzFxevqFYwkpE0ZhW4s++IIUdO3bQGilU5SmiYcZTWimrSBCPIl9mMIUeQrAD5xUIp3dFCkBMaONFev5rr72WC1U0zXlpMkpUIltPpQtessDZTzniuYp1+rpNsfCb/YSyeHcXqbJsRsVwRTWCePWZZkM6DO/YspG+bXnpsaHM9FRbZxfmG54bVAs5kPD6YQuqvIGMfc+Qval9deub22u8fGg6ibf/bO/+8S9+2dq+KxCNk4qsaFjd7YVDiH7weLD/wAh7lA1Nps5a2XppZ7QxgtkzYzwRD3//6lM4fFvh1Wl+QU1h1fW1r33txhtvxFaR1os59O/+7u9Sm/O2vtO8WEvkndkcmK9LOrNL9qtLPf0+hcMeCWdXd91111vf+la2XNOI63QoNF5nCmgVnnyCThpStbcJ8S+LERFbWVg+WwAxXFV34bpE1cTrEM5MsrVCFZEzZs/VcfS10hiwQFb0CC4DVz7NKIfLFjMi0uITPDQZGJazyqinp4dbDib92js0aBbQCwBGDwb/AepYiOk4XNOtA130RIFrLgDDmh5ur7nmGlCNlmHrM/NRPYdg1SjKGRAR6cMoRnSQP/Cbd0lcZlZqKoiF5ezSVXN19tNTHaI2RsQJk/X0NkjXcxIxIMfnLo7HcGv87HPGVVcZ2H+qgwFycSlkcIWHwjQ1a1jcxOdJjRxx9E1xIEDr8yuR0VdgpMctJHHNwQUNYx4AjKyEBobtQCWRU3PBfM8bHMz/65ciP/qxncePlRFyLP/pJ/Of7HP+14ftCy9hGgp015PjU0PSScql+lOqNBXpvmTpopQ7kHizn3gTxhi+mzKsWstkE10c2cqxEJJoDxgjcKYnIT5809IxLaZZSAqLHgeZKXCCdkWfI73xwgpyvGSINXjvBLYgZCPTeXoxfXCr0ARqNzXHp8dQF6BbkCfAldkxwCMgvsnF8y1gigvOwA85EwhEEcgldagQimwdxbX4l7I82y+Ojoys6OxQtsra9RTelcVuWcFaFLy8DjVCkPpRnavczWOiKY+PelQeVi5U4keNf7wPaEIsfNVvgw/pZ+hGFlKPMnvI533cxSOQwD9cPCZS1KM3AJqKtsrRJtAVCx2SoUnTnzMakjsqd4R0GpALVaGg3dii+3zu5iGMRCQ+W7ixWVwqRYLamJm+EWMrPhmNbIlAp8rAwTQAkMyZdkuOM+x6yEgLTzA0wH4e31R4kMoI4sV6Wbwoy2IiEdMLxMWYGbibKYqyF7t6WWogzUCE6aRDe4MyvNQRLjbT0hqlCem1Cq6HxwO8wBcO7dnV2rmipp7lzSJUIdmkAzfp+41B1x/OeHi8fElXIj4+OfCxT4V794Xy2SI7GsquXa7T0Ra6+sXDRmQohcMzc3NP++SwPzE+MLFvKNPY3r1mJfIa3TgVhzgdblXlkDPmF5ZyvOY1r2FBEN5q/vAP/5Dtgv/xH/9Ri1B5dMaUZInQXxUOLAHgM6wm9ZDzpS996cMf/jCzzx/84Ad4Fzid+g4fGaz94svzP70rhOcb3KUWxd6NsSMfDkZf/JJiKITFUs2RAJgpPvCPmtBDnV7AyfwMoS/wksEVuS9wQrrPql4SVqAoYCz32IhoYNCfmrQA2M0teHnxhoZFCI03FzXfIuXqzpVRjdvqpOZpBAzDDO2Ugiks0JdMtesRRn30t5dffjnzA9AvqW3YsIF0GLBxQ4rnZ6AX17w+O3GdNeXioBKJdscdd2C4hWkQKm4AErNSfWDBi+EQWWgA/OCDD0IJkIOpsz64rtaHL7BQs0k6SSFiDQjQTSa9HDs5YbLtZhUGaMQJFuoV12HKoLOGckq0uGQwI+RY3DSfNzVaCA2GqRsmXsi5Z8RHxUHDJg7qDpa36WqtrsFKfOJUrvVFJeQU1bLrONu3Be/8qYIoQo0Q4JuRqbHMN79jrFzd2trCnqsziPzVu6Xdlnxymc1mQHsAKqkcF1hY+LZ3717duVF3XHNmEv/CKs9pn/Qt2iR7gQU57miJsN8Uwf4Z1Kahhai+ABtgV0AFk3w5i2JWDgU9tYNdAoG+AkVYcyuRLSMoulwW36prXE+BgXE+JMiEELFDJn55na39zNNPFw70xurP7u7uJmVNv/6u5LbyifFEQtUTFa0S+biLPONFhKEcVPoipqyTIll6G9oYYwd9C/nOmQURBNwjih4cdB59zHjkl+Z0xuvsCFx5hbX+LKuWIWyOFytJydvqqIToWwY44HdFPFcdrfpaR66EVJIC9CLjRiQE9EXSDaBlRHv5y1/OBb0i6l+srgC6HLFYdMeO5/bt752cnGBV8KpVPWoYhyqRCANs0e5mlWMqMWnmuoDAhTXhUumcgbg5xOM4UC/62SI7NgNp+dPzA6TotBojIN2bOD/jHf7w566bKLNksYsG3Com4PEul5yUfb6KGQaxycnxseFQEb9z8SjNEntolOAcYGz0yZhp7XOMxlF3c11L01veOPWFf7H6h9lo3YHm1Wvq3/MOb2X3aM5a10pKQk5X/TI3Gz20d+eOZ7eND/ZddOGFjP6aXb8aZ9oP8yWcQrMS+NZbb2U29clPfhJdDtU9o2n9apR3qRSnMweWAPDpXDtH0KYHD0aLd77znWy98453vAN/V4wTp1mvYbJ3ZXDz+YW3/U7hX74SyufBgaxjTbMA88YbgxddYtY3rqhrkEGp6kCsywhHAFCQYgJ6eYsDhSo7DGmPyjydXVLMo52HHyr+1V9bD91vFZwCW0q+/nWR97zX7u7y0K1hrFUsuNlCIBJmaGPNmUyRQiG9XLYq/7kvoYSBGTgKPUxcmLsBV+imGZA4Qx4yXmD57PFJ1xTU8mj20xmZUcZ7772XLIBqwGlYUa1SA2bDE3AyqekJNDMG5AUkonPBt7DeyRC9MdMFMKTOtCIXn820GQSc7Fs/GjHXnxVgDx72M85lcXkVjbJVrOxm6vaswv3PHIrsRaLp1JedSkFygR079ajrZUZRqBcOokEbcWhFRyMSSYeWCmEpQM3qBdJ876wkn5HmybiFQi9byD31TDiZYrcfNY0UG0iaXSgcMvDNlkmxx3XHMe5AdjJIPflp6iqa0WkdQ7a8j94MljLLR+mK4o7OhNo/hiQWOyokIX+h+SGsQXIn6qqTeXTUWGe3eoAQ/HcrmIqmV8zH1eJbAR9odAlXZyuowLCGsjZaXqUTVtQJBquqBi55tSrgcBEkEIaz/h8xJf4awFGVsfLwC9XvSujhJ4dTWqQr9ghAmlkBiouUqiSDDTAoArdYDz/8MBeYC82dOIXFBL3vYOHL/xr40R1GoUAPE45ECj/8kfUHtwaveZlZL54s5jm05G5GBOTCNCHGHYYqrumsiMCoDVUqprCUR/SKDGS0f8YvLoiGYypaHdf33XcfgQypDHOc+TSQIFMKAtlKp7p+m5qan922bc++/T+794Etl1/pWUGUuih4+cOYmeXlCtNiWm/gPwOgS2AeJOz6ebFnFqmIKGkhqFzvus4F1Qr2NMTflTyVfxIPW30QMZhYLOH50TIAM58rjh/qy6RGi04ByUs6yfZyyWI+m2hsTTQtUyuFRZpDJmHZdVrMSJ4YtILtNRtfcm3LprPzTz/NGjGTrRzO3uS0LhvIs/G2sSxerMfrVdSKha2A0VxcnXhm2/bxsfHZvQStWlE5x3RIh58RZ6qVBoC7so997GMs4rv99tu/+MUvam8d1TV+RpRlicgzlwNLAPjMqDt6PSbB//Iv/8KW4syA77zzTrYKYKg4DamXQaS1Mfi617pr1+ceuM9k5/fGxvCWLUxGzLZmmbDMIhvIp6250I0gz6ZcKMoQE1555ZWMiLzBcWRJZRTA0YTzzJPOO2+t2X8AJTMjVmhq0v33r2b6D9X8n8/ZXcvd4UF/MmlHQ242X3zuOW/3buTc1uaN9uo1dnOzX95EcVbikhVok7EZNR1aX2AwwzmiB0YjgDokcQEYZjjnupowyCKEmaUOn50yyTLtqIxqxEcJwzyAuQXAlbkarzArRS3MBYfGSzoLbi+77DJeYfbA5IDcOYjJUwIBS9xWYpIF+iVGFB5RBMJJnxkGeRFCUoTos37l+M4kpV+cOylyQZpe3xC5/tXO449lpyaYF9TW1cI+3suuXh2+9CKTzV1/hQ4+UtybcWYmOg+4hW9UR6UZzMkA4lDLPOKick0Nzhn5pASyBREr3vBIzE5m7GjC7BKzUVGW2G5ekDD6X5ZGnpSsT4NE6YVmfC/HTRRfBxI0KlHLsLR267hTW6wXKSAGq6y2oAuiv537E16kzIC0mzv0aAVkmPNgZKCJHdmcSr2LYBJ1kAL9vrqUBSCih58zLR1IiahBNorHuAZ8iOHl/F/cPEkd9yMqfca7lZDFYjjpMOLovQ8pJuUFf5LpzPTJOJl27vpZ+H9+jANzh1EAwwbDjCSncp//QnFVd/Cc80R9Oes9TT+pVUtm6eIwSCZJBMR0U4hfadtwm06PEZwxjltwr96LEeTPkl1NEq8wONL7EYfBiFH+Fa94ORfBUJj1RM1NTdFYlErF7wH+B0QfK6pdcUYl5sr4UWvdCnJvrAAAQABJREFUYDjN4+n8kyMhoCbcBe6CcnOCeCVmrihIWBaQK0tmxW1aloa0Uhlq6KuuEzKRZgQ/6OfUtbLDx54e7CuYWUzFC9gqEU3JWxiVO9asG+sPj/bvm84kW9pWYa6A/+epqbH65g4R7eLlkdjiQZr1wEZNyIgHrfF8cF841L6ytqa726ZfDQVlw3PPWBE31olLZ/IpH5gzhMLUIwyEVxRBr7FiEqLrFAEBE4ZqVynlN8+kX8qC3OS222674YYbWAyMVh+NDtodijyz6Z5JxVqi9UziwBHT9zOJ8P+XaKUHRMiKuchPfvITegr2FgeGnbZ9BITJaNPUYm1pCGw+G+9PbIzITCSTL4RkcK3q6MuVqAPRhTIVQ3vG4Ernrmf/DI1zvsKr7uRE8Sv/ET3Uh0OV0ryJocczYnf9LH/vfZE3viH1y4fjXaIKzvz1p8N33x1iEh+vKS7v8m54g/GWm5j9HWWsF7IYnhlmGMvpo0GhoF+9EBd/G4xGUMUQrnd2LRdCfhn1MSkk5tFmWmh6GebPOeccxjZ0euiFkH/raTGvowrmjFa5u7ubNc9kIYmWD80HzkwyOCrjnw5He6xRMWcO0HuFBkzL0D+QDGQziJIsMyQKQgiYmXkwgZzL+czDlUqU0gWomzT1fGvms/I9MMI+76LCH9ya/9znG9IZKxDG7U3h7I2hW2+1VnQerXLLb8/85VsA/6OK1GbnMx+/oPfQxr4dqEGoVipxHloodWU3kaNFo/q0voj2xnFU3ZHMy46WxgmFi63EurVWKMJ+ZqxtJ5cQe/AIAjGN1WuDsTjrm4+1+k6IoFP7Ml8xx+LmCbu0aGxxkz2+1CAGK2i+X22Hf3yJLOQt3UgW0khnxpl5T27VSGGOx7PpASogCsSJAxuMAypOcYuln9d2HIxujGsARSik79KCy9nUHl8IhWI4QDwK1MccmgGLUs9OykxO+T+7xyjmZSUsB2OocsmGf7vCg48E1p4FIpmnM1FvSKoMWAwclIXxkUJpxR0Ql0EHQTbE0AdqPuMqkgZGeYkDftYHOLlSC8FgYOXKbhm3fWP/wX42+8o4Vsb1+oezE0m33xstFE07Eg2E2RtX9iJiY1432o7xxP4plu+6g8PjidoGtrSmLPJHwYhXbhfKmr7CBp5rYSIDnIxxFEeNeQJ8CcEdmnpZ0sE9m7ojHLUyZ52SpCvu2lAm27HmFesj0brU9BDTiob2rkhCnGUC11mUHgkZMf4CZgw3bKJBJgkf51iIEtnHy4rYsXgJytKfSoMuE1yhVUhkGbWyFWI0R65Bv8GCGiQdhGsRw+HIZ/IVrGXqQgFZFYyP6O9+97v/9E//1NPTQ/iZXKwl2s8MDiwB4NO6nuhSGWz+67/+693vfjd6g9Nvxe983GOuh+KD4UKj05qSTdTcrzCOssCVg44P21EQDviTqFwDQWe9Q+eIDjFrbH2ApWEMn4wj0l/KMKb0mzu3G1PJYG0dO22m/uav4nfdpXQHppFKh0aH3G9/E6e64Zt+E+9DR+tlGW80RMR2C1jOVBhKsFJmCNfK6jk7aOqLsR8V/ZxPK6UgDj0+0wWw6NVXX81byMiZn+EZSwphmgjX50dQOlolQV6ZgYorj7gAa2kLNHLkAAxXprxkStYaA0MMiUA8eiESBGcSXj1fn10oZnXVEaoz5VoqiWpBz9DSHHrVq9svvNh5druTSvnL28Pr11vtHWao5P5qxovz38I9VOUIEWbTM/+LJ/spvEW6QSPBoTd5zSCPDxkcy1ywev43D0kzXi/HBIyyNUfOcl02iTWjYV883ao51NGacvnNY/oldy8ciGw+O7d5k/XUNjaawSWyUrdYhVAgdP2rqNNqockxJX5GRD4K/0+Idmqf44SSWNSX6c20IOZkFHZRKT3OxCgXvSuSSpaq0qkCw/Ram+NM7thfI3dZOKBAY+Uath97Ss/zBiWlaYGBwaVzomu6B8/JW0ODOfYjVL0T9NDJc23jCmtwyHSUf3JCy1khBQZrQT+jsxYG0b9puTC9GQcRgWcIQBHPEZOhhEGT0jGUxOKocmOy8bEYH0U3bsRBhiRMX8UPmwzJWlz0upgoy5/PVt9P9bt1uezYGF4zRIPKGto9o/vZKyve0BFpjBaKHttBS1dHQSDSM1LJzMDIZGAi3dbRhnUT2llRYCvqVSEOl0Q1b3mR4agMrFiPrqhR9AC/i7oTFR0w8cQDNFdqw7NSmhLOQbiUAIDMciq6yEQxM10T9pprRc2LWpc9sQTx+h72zzVh2ci3PmwmwmI9o5y0qfSFSoAxtjUqzXlP8JIBhe30kJUjsWIeQsEQrGiDL56e6f0wDGWO93d/93fsY/L2t78d4ynUwjfffDPlUpUwL3eWHi5x4AQ4sASAT4B5J/lVujnsi7SvvJtuuokOghHuTOkRKnQuoIcXt6isBmH4ZKaCmBO0BkjQ7ou4OBqbZSxThlwzhhGGJbE9yqQjXV3O9h2Bu34k45WMnPyaLkrIBt/8wf8Yr3g5uxVVxsMZudD5AgWpAsZ+rplScAHYq5SrOj7RmCvoyIRzDTLkAiUe8bkFGlEo0mG6AHhDy01k/FgynhGBQ09EkOJXrmckUp3dPNe8PvspWLoaTpO1nrsQk3DUv0xxNDCmvSGw1548UWYy3UEITRVodE2NaGxMfApIcUgHOvVITJxqMEwupA9Y1X5fwk3NWMIH1qxlxxMkIyijpdhzUTubfh2iE6x+qkOOKZHq1xf9mrkIRvswEM7MpgpeUfVwe54mPSdJTCi1Podqc0eG3We2eY8+bqWmiyy0u3SL1dNtsH5+jmqfM7FjCMSfS7FzRegP35394pesR7banuv5Vh6Z0c1vDV5yBRKX2WU8htRP+6h8qoiK6HLh/6KUdFESWVy2QRIfET0bn//pbFV0IqWmjHyPuFEEM8wlSz2RtJ//XfpPzICJhxT1/2fvTQDtqupD7z2febjzkDk3CRAGGRQEsUyigKKiDOJQq7U+fbWv1ef31VbrUFurrc/Wah1etRVR64S1+km1WpBBFAuiQBLITHLn+d4zn7PP3t/vv9a9h5Obe5MASQj0rtzss/baax7/8+KA0/7DJ3tSMWgpVHJaqlNjKoK9F//cxGOLsL2gNRudECv09YBhr0dcD/IxrNBqNsOZqcFBhJiQeNKbvN5j2dn0CUJPsqXTjexIbPiEM3NAfSkCNjv8Xl0i+atrrUBikU8OwG9FjNk3EWMu+0G5DiNUo7IWUs6c4XzFKlVs9XPyEPfSLaDHualxMO/WrtVEKNpeWWgI2smxonBdqOupMPBHx8cH+/s72tszYseLRmqOLV7mtiRRh4zeH+W9gd8rz+P7pkpLtVUakFNpgjgdLulEqFo+kCFPtEEiLa3Z1hZ/av+mjmg0jrH4esLj2jSDm3v58xzMvJFIos9n0iiu4VFlLP3QCdetWwdsgKVMZMSAlFDa5zRhRpEOQjCDsnQGz5gvtOLiiy9Ggejd7343Bm5g9mAoC473fNc9YxqyXNFnUA8sI8An4mCx1+JQ9GUjADWCA/yqV73q2bHNLdrd0OY5ViFtaokpRHZBEjQ2uGj8uUBMW51+hrl7p2guQcJV/+XHdcKTN3HTBZhofdejHodt4zDj5OTiv6BuTU0E4+PW+j5RJTukY//lpAczh1kKBrgU9rJ3956ZHLebQIQ2sVTJGQ0apLmUgAhQzTX+jNgzjQUxhqarG6j3d1BHoArk9AAmqA6Bu3fvZg4QsyH7ypkHDEdMUKylqgxpXDdWH7A6c/1sJOG1MZfWKqc/UZzG2/UroAzFAQbhoFDorxo3hn2NwRXqBjqnHUgCuB/YQjPmDy8CujUUjdVrVgNNUCzXdKLfpPIX3vATcnSg7hNQbrqCzKkSlWzG7Z9QhkcxssCS9TodAgBKtgs6nHouKEuHLIi2IE7jlYmRSMqlkfWh/trNX3f+7d/MstymEwcu/M53rXe+w7nghWYqtQjO3cjiyXpEW/vUUyJ/+p5g166gv9+PxSInn1zJZvrLhZVh2+GWzpMt9cRIB/YLNMbsPf5Y0/HsAJbSbbfdxoagxU+OZ9HHqKzGcmusLzygaro49jRem0l1x6gaOttGHRqlHBzS+PTUPY3MoV2CKcHe1wxDtmWcF4sa55/L0cLWW8YkYalUc7i1J6xGo+mzzigbpha10hRbTj3OO57ReDQZh2XNDmT29Pacdtqp0Vhkzaq1cDkJmZ2dmZyeKdfDSKat7BuzMz5auHjE9FSNi4IC7CcrE8qcutxChD0q4TRXAp5huR4IJiwZiaBwXa6GBrNMIjNWM3No9VrRJGq3clWVRIAAqO59UBxgZd/ZTGWzEGiHh0cg3daqtY7OdrF1hVZwbprrGL3oHKedKSGO/lWHDqRwCLfF6YlEth0ZJ4L5KArHc98f38A5pBTblg/yXbpX7lvUOQWuBcvXWLFh5bm9FkLNEUifC4dQTrknetItyEOPKSfLVVddBRACbQJKChqzWpbhEMDAgnxO/FdayoGOCPTLXvYy7EJDyvnc5z6Hn3DciV//5Ro+43pgGQE+4YaMvZiTCY2IT3/601dccQXbwbG2UHLsukAfOuR/8P7FJ3B7cAZgL85pWKw4HfPgyAfVkBMoNDNZ742/XfrpbfHpadBaziU5pE2neNLJyQsuLE3NxFFZRHdo/lTTmYSOmBXFGhMHpByH5HS4rVXLQpOcOuN0Pgsq2drR3tXTTY5B3Q9sO18qw+ps5ExpUG0ZVn3Tw3XXXQdg0Xx0IUWGk1O8VgOVQlaW3oBpzFGni+NJieCT6AYvqt81VytFBVBNOlyrVILmVuBvRu9RZMXpbKmSxsz1K3RZWBmEwNIEvachjCM4MF/BUfUlxiDGtJfuQu2ZDoSyTnyAMML1oKuOVJDEEZ9tmk8OyE5B2g+spqv0ND5pI8bSoGtghwZkqblLda24P1MzyXlFmZ+Yej4cHHOpVjCWQT5Xv+1u9zvfcYJ6FfaNEXp24E2MVv7uM9aK1e4pm+VamCPuyaUKag6fq54bQcnb6ekJaj7AaLFWe+jhh6HBoLnXHPlp9Ov5QG3nKnz0qtJY7EcvyxMuJ6YiWw3TEiFhNCOOeh8+LQ1m22TzYdtpNAcPo8lmhZYN2xfSN41Px6eGi+4MR6VoPf85ONhg2RVx7LHgruD87EuwcwkhDu197tlntV5+VXHbrtj991G0TG8otpii/x9vtjefhjlp3Sfo+OAadUMLlm2aDQc7T1immikZ077lT3P9r1kixE9V68k9+zhIBftDCZbbcWHhVuuIPoXlgHtxDbmOqB7UQGIhe8pNTAbiUnKFoVBFFf4rHuHrAg3oQ9m13cB25XAG6TWRTxb0VC67Urc2U3MCpfqGGYtGV61cwVVMUGmzmTTiSnycHh2IpLKOh/iYnIO0S/JXbRaEHHXiWm1qbDiSSLlCTeZrExf1wJNTbQLUlpulw5hloN8bccOEY3ouFqulgknbb0MM+ajuvY3Ob3ioIhgvIu5QNNAqZ3AZYk3NacQ5MT2qA6VqMgZH4NiRXv7yl5933nngwAhFYxYLqzfHbvkcQY2Wozxre2AZAT6BhpadAgeRD/2H3bt3f/KTn3z729/OrnGEG8cJ1BJ9uCpVnx3jfjpipiPY9xdSrjIuwUkkpol/9asH4CjCC9VAGPXXLYXdegRtMa2IZ5/7vPr/+UT+Ix+J7d7DyVoz7doFF8Y++H5zzQp3x67yzGTk9OcUsy2JmRkOX60rbKXiGKMINvSFXR2Ud9iC9KXEms3IwUMNwfeos07IeOHhxE3X/XDP3vqjO+rlgrNxQ3vfRrOzfWR4FBUqjaQBk9FMrSIFmVO3tFE6r2SlSQDAKyDA8Ic55DjhGnGW8lA6PUc9wmIpmJ4wiiVuzLRBRxGYj0Rp4YKylspnQXhzKs2gbkRAlQ7HK2xYnqiHUXltsYNDGnoN9QfWJAcccIlOCLbM3MZPBzYkq7m7krRAqwjsEQjohqM4EhJTP3VyUp162qkATaNj48MDQwgLANsILV9ZPlFSaTIWJJp76Dd5wSc1Ee/RdlQesgX9AP4PUWPR7HW7+AT+D/JP65gJi8ZcNJCayyybma3/+EdugAUx4ZbQJp7czREdH6384mf2+nWWcGmOiZOOs21l8DnkfsrRkeHnP//8Y1LSk8oUUUCG4JgKlz6pej1jEqGIAQKMa5gGOLjqsoTURqfXl1pbMg3FMO7BsZ/uEBYa+8/BtWBvYRvhbIWGiDtGe8LB5RICBI9b9NOigWwszeEcBJwLtIsnDlwX/EcLiCKnwBKARkl8tVeEEEnhEGJbCGyf7Zcm96zojnlxzCbb69fYf/zOyo9/bP3yvnSuWO9dYb/oMvvcc6yONnRUawEX5HJ9LraUwVqFW8vFuUgsV2vG2Pj48PAQdQKVDY2ysXcWY8sdPWvNaLxWtzCrBbO3Kk/UZzkQZVIQj7iyQUu1VGezVwuiC7EO61ACBhBBMGGJLRc5wI7WfNZ0R5fV2cWLsH3FWhU+yVBsUkkzBfXlnyqL3cnpXrGSPuFc8VGxAcMVWSy5Epq0pJLdUnBsqkFCbo02MZvJzdJEEOayOiCk99Smqmov9rEwWxXBfKcXxmzh9EYFyZW8+BSxg7iHfq+Rdo1ERAybqzaQ1TF0Uv0wBDCAvU8xyIvBDcau2wkuJwzxF5ovEvIcfEfYO7SURn3729/+4he/iET0HXfccdNNN6EbTHI+HWEmy9GWe+CwPXCkM/KwGS1HeIo9wNbGGYbaA7xfMEDMOYJlPXNXO+fBdKnO2TlYcIbzcmq5RoCGTCYapCLBaP9eIGlYVqgR0W8LmrngdbGOlSgUYbW0eldf7Z1zTu3Bh8OpyWDNmtjJJ3E9y3SpmOjqQkIXKWjrD95R/ehHI5zb7JyZtJnOlNNZ+/Wvt1rbltpKGYtGoWAsABCwRgE+wGTw4xpf5cSDuj4wXLvl28Z3bjGnZwK/EibSlUsv9t74pkRv9+joCKgvmDPi0CQEp0VOmPwXtlEdz/og5ivil8D0uhQ8ADHMDVAsPiErDjCEB7RzviZCJQ/HJvx77qx/9wfhY3tMx65uPsO99pUON1ukkwo5fLzKT93XXHlONSrWCAH2AvQE1wXaO//88+m9BpMWtB+WsobeeKJjSZfqysAlBoxrVIw86epLL72UBoIbc8wrvBgjxNGhkrdzqJyf8etDdcyKeDaEeeWxDF5dy3Rt6BtcKCo9zBxRbW8+MxnZpYa9Uf4T8DAWv/71r0mAvBa1bfRDcxZQBHhlBCGmQEkBcm3+emR+5BRrRv9QCXW7+QT0DPCYsNMHR7lqc9Gi5+M++d/mbFlDgwOD8Fig1DSHP/ncl1M+3T3AOIL3si+xZuGLLgWkMuvYc8RVyljgQYLGjHim83ReZbyg53Tt2FLwsNZ4ghERR5Oc8NBSVijr9M4772TNonDIrrIgk2P32rxeqFujIPx8wuGBKCzYbZODQMyOSuSf/exn7PyNVHjWrl0LkoBHyyrTFg4p/dTCsbyyA99zzz1gIKlkGjErSsW+cbiqz7mx23rZq5BRCl3PTyaG6pGRISQ82KNUBNk4LaSRYeTKX114v1NFb7IaZwcoFTnVkf2NgIQWZi27YiHmLAYwZIaw3SoElfRgrZYJlmu5gnyCxnIXr7BticFrHdRVdmKwbu5ZU62XOIIMSw7cu6bomiKqJZiswi8Fl+U7MUCVkfxhToKLkhhtJ9LY8WhheiI/NcGJGXFthJxLeYxRWx29q203SjLJF/rp/l31alkdwMFE/07CbDfSs3oj/OaobcYcYfDGYPBKuWRPEYFrBnHXiEeMBEivZya4wAiCoDRLnSwMnjTmeDg9T4AlKIz9n3l+7733QowGYnxCRNXjUdf5MqgkZ72aIPNBR/ArS8I0ufSE28tgCKFg9ed//ud/9Ed/xAZF+BFksBxluQcO3wPLCPDh++g4xGB3AE3CAt4Pf/hDDD7/xV/8BSfZM3qdo+3zm+FwsMDBCa2U+wBCnhw7Y4Vg564dM9NTmVjHmZs3Wi3pqVI9gY1EIcTK2Ta/twmicuge0DFtyOobN8EBExNBIff+5od376lWSr09vVlMgDhO9Ibr0Vos/ss3rIkJI5VAbdi5/jrnuc+z0AqTE3G+wAOHmeHQRiY0txZpZAiuIHVAJPr40dEZuHAmV/v3W92vfg0rQXB7o040KJe9//zPshHG//CdnV2diD0DgpBQwysHliNgJSFyrHO6I2WKKqlpnn7qqdOzM/mCUA5AnpHvIo7Go0CGcbySm0aASUsdqj+61f7M/43VaHrF4Xy+567Klgf99/6pe8Hzg0icRh66MxfU6ghfIes2x6QImgMCDDwN7MWTEFBZHQfSNa45vhbeIwSckF4F8GuwODg19YkuPb9zBwAVFs/Gqok95baZcrB65YptY7AXav379kYc13PtKHCua0dcp6U17SKhS58EPvR7z7EiNoaSQY9BlZGgMxzlwY8svCNQjkjWSf+omi2YDUxHgeqaHW8yYjjp+IcffojTHfl26BpL9fDB4QeH6ByXeKrybDvIJLwJbpmEUaJ05bjSmWojzpDi8hKB1Y6pY2RZFIwRg34iAFsQVpDapcnMN+rWuOy6mRxzTDvkWZM5sxH9C30hMOjWopMT7MUslIL+fVUIVf0DiGOaZz7H2bjJYkWrPXvRVMe5i1iJCP1SqK4MCiNMDJrWMBNAOLqUrFYs/GGJEDbpsag2hVIHnuxvzFLNvKVcdkJCOA6aNzq+YuWBSpIEtFwzrqkVyVllDWIZmz90CqAC7UBu2V1194osTJMDZS3Xwoki/FhwV6f95PP2/OrB/GB9JsGrmFaGiZr04nEvwbZX9MPJkTBXJdwift23oa0iw4ySBXLCSvhZBIZpUD1IB9kUZ1S+sNvx3JbONWx/vijuCqLrKUEAvgriyo8wdElItuj1ygQByeVQkr1TDjq5S0h2VVUEfQIuzf5FHugQSbaSz9wui7IH8WR3IxNBlSWcIuS4RNyaXLRyLqxd2MoIS/uVUjWXn0W/uK2lpV1HJhZdSqcCZdCBho2FELNeqWTTKOQ40Zi3qlvSyrXCZt0zoe2E4LopB6DFgmofQWRHepjMJKN5r/jUiwQcN0czKIvpgTQZ04Pbp1i5UMxhkNI0/fW4VeYQBVFD5jlPTZPCz1pgSrMKjrySxIRwA8UK7Pe9730v1grgCUP6P/IcDlHD5U/LPbCMAC+cA2zHLFrAKcQawTQ4dRCu06B8c1TicFYhNgZSxIJkYS+6JomG058OEYGF/eY3vxmg/7vf/e6VV165aMzm0k98f77CnXg2V8D7YVCsGigOcebZhj+4/7Gg6ndku1dtWluy7O1TbOeIIwUxN8RwYtYLkZSGyAp9VWHEh2noXEdxIliCZQWVCjjhyhW9YIy5fL5t3RrOeEw9R173OuOlV5uz06EtNzOBb2kbtgzNbE6Yqw04g0wIJIR8NIQBUIUfuiMwB35Q2ebRIf/61Hj4b9/nrlQ5HuV4ZgbVLYSkfnp35aqX2ptOIiFyShTRnPCAhpESsvpwv79lmzEyEbRl2045ua2ne7LeOrh/kAMAtR99cgC3Mdk0Gtng1Qic0r/fuOmrjg88AAABcR0TlUZkOlf8yteogLViDlQ6oNCj8dJoEZ3WyA94DqhuwTnXiNmIhkcj8HhAWnDNnxp+FuDFF12Ectu+ifLoiFWZcuqeVYy27Zu1q7X64IyLbRR4FUZYAjSxw3D9Oi5z4vLFcPv23ZYVgvFGHQu5Yy6oWNHTlU4mAHSmJicVSsz1H/CaLeCbqAvRQGYd6DFsZL7id1VygC6FOfMVhgaQ2hzHhknHNNu/v5/h0ALhjTov6mEE169fv1QzF02iA1XXmiE1P+98e6DfDgxhciFz7rp10/Jdzz7nuWZ8cenrQ2T7JD5BjGAcAdYXHc0nkeFTScIcQ7+RHKgMvaH9UIWeRA8vVY2ldvWl4j9Dw/WYQuBjc1u0CXQv2G/13l/UPv2p+GNDhgUHLqgi+3rDa5xrr3G48lpjBYsmPo6BIAAak4RZCsCNkDOFa3Zocy1YhuhowPFGlBS6ZPOnRf00vxEuXaEc0LwWxmEe4oDyebIPsNtTNNZ6eSUO8XUSDARoAuiePXs4X8iD3Y8KU73GTq51fwjUTkh6StqIyJwCjTrgIaTx2uynrP0z/u5p0WsFMeTM9evR9NqzQ9PZM2WgkQvnFUFlnuDJwlDFIqScwGJSSvBSISKTsSCCPFUZkB6lOAAcB84ngsE2r5r/KVgumYCj+orxC28OHJUtFCcYrKCxkgvZQZuGaifHsWyhcIXn8FhJTomYzqBIaiVNg1IpVygSDCwmf6RVJ6tQYVTNyEGL00rnIjutUrELxNMdsUx715qTt/zX3egem/F0D9ccIMCMMLMdRpFndgyvczVbfa1U3Ltj6/r1K5LJWMy1Em4dHm/Ck6uMXEfDHtRWsHLaonuYVuzdu5fzCJq4CpZmPl1OVwmqCgxSSDnMZLDESy65hDnzdFVpQbmMDLQepjrh1FbfakmFj+SsbM6KtCyHD3/4w7QOIBmdqS984QsveclLCMc1x1z2L/fAE+2BZQR4kR6DeYVAJgtYfwN1YflpdVwdwsGGXv7HP/5xjkAWIWvyH//xHw+2q0G0v/zLv2RjesUrXvGOd7yDtAtWLEVwcJL5X//1X1922WXPXOKWnEKKdguDFyWiVNSCABvjpDSQFxK6KpfmYT+jWLH8zmyxmnVSrTsnQDYwJhHEhD9slgIr5xtDYJdGYInEkZnyfG4U4Bo9qLDz/GE5QVVB6mdu93v8R8MN9DP0C+EIOnNmrux0Jkynkf2S4WvaMxkOmEhan0o+yYkL+7YMjAJdQ7M3YSOQLWcedBA2ccYLeV1iArgIb5OGj4xjILfoy4WKekABfQiGPxls35E4/YyLL7lkdmaGM1sKOMgJpRvg8r5fBP/w+chjj8FGhvVZ6eyy3vrmlgsu9Ds7YpjuxEEYN41MNqOxcemH+YaIbWhue5qc8AXEEIYlelAilAZlYcsjxvioiQjusT8q6DQgPy2LRY9JlRUNGA9AoTRhabdgXTQikoPj2AJwlBkXoxQzsraVJhR9WgAj0+lctZa2ArfRbdj85jlZc21RLAsqblY4DQBQlSAsCeRUjthxEYoMd+4cF93hSrFSmEm1dgGTrVjR09HaAkt1cP9+IDXHBSt24CRL6alEOhUnDmZT6V1YytyaidieQFGVzExsTdeK9dvGAwA24DaFLYsHPFm9aoU3eUIhS2UzTAJhHMsoacev3md0gIY8G5/mIvHjZDPGy6+qbHk4su0RGWWZDyGElvprro+cskluljr2DtgFtoNM+xPAMcdg5VER1iMMEOpGpxxF+A+eG5xDlq0M1rFfPk9vj7LdAVMuOrLs7fJv397a3/99cr9gv2rCWl6lWPvqzfXuNuuql1lLUK+OZ6MYI0ZfI5ns0kDe2n9wHYiJcW9t1Z/BZfPAsYniOLJ5kpbRJxrn++7du9UXeZAtEV70ohfRURBDwXIXZA76zV4HQktaCDFEo0pMVJ6anEr4RRddRARCGuS/RiYNbJxojUDt0SGy7JVreBZEY8/3bBPq875pTgBBUEUY2fDAQkBGJ6emYJQKRUDtQeQp/Fm5REFmuOxdDigueXM7kWycYK1kAIuX+MhBm77UIt7eI7ufJ8cZ/QbiKpVS2xh5KSyX3ER0GY4sPF+ZPPIns4aHoP4cUwphBlt26SrJB5FmyiUvqTO4q4hiU3WqxLlJDKpBJJyce7IkIfaSkv/IvghdUhYpWy6Ku0Yk4VgbOnO5Ga88bIzPrt+0IRKhuuRWj9pGzAUmMexEmJixn7PaSSdJpBuwoM8XvEpPY6iCcQSLk7JPAEc1mEjwfkHL6R/mlfQhvXViVI9FRH0AqBBK16IlGjB4Ej3HPgyEDLubi1EwkfXOd74TnvBh4YonUdBykv9WPXA8wKZnXIeCyr7xjW9EsgiwAPESkFgu4+Vw0gbZWdKf+tSn3v/+92Oe7m1vexucB4SWsVDPrQMLVOO4x4iVz4VmEK5AqhG4au4K8oGBjHoDWhzIPL/rXe/iRDxBdq7meh6Z38R+Bi3qnzUmyyLp5HJSekY6wvlY5+q/HbsHu3tWxRJ2a6KdE4xjslgz4AxzMeBEIRyWgxoh1VAMTjiW4MN1K181BuXg42AL4g5SWwb4cIxL9jwIuoQLqrdUd7Fd4qTmnODqMJBzc/7Mop7yRT116xp+QBwwXgIZehLqcIYJayIEEsIRiEUHPMAxCl4xDexjcJIHFugfkJMuulKtADAhnRuNx+KRaEbdjiOFSosOcGhQ+Tu3h3/zt9HRIUsEfU07CKMjI6VPfso+5bSu7m6wa2N4NChXzI7W3kzGmc+qkQtEeBPDV5ifrs+ZRBa4RFFeHZd7FQUzP6YHIr0EaRyAkgMJ7krzIafNdWhSQqPCh/WQIfLn6LYJ6hsKw3PHVPjQiDlbEYECYSsAIAkCKKR6Olxk36DbA+IADtGDTBrDWtHbzeALIKcALGF9hGalKEMfza4QZbaZ0dmpmViklYkwWY1UZmV8+zEoLrQDeOk1ACPSd7Qh5hlj1Ab6MS1bYPyY3wLbWWZrS3Zlz2k7c8bUvqlKqeQ4lgcDRJ42YnXZdIpUCEEiCInxUqy+gBLDXoapIzlgkMZWEJ4ZMFUVb1luiBLAEVE9gfzmPCDPMFwE2Fu/0fvTPy7/4FbzF/cbhVx5Za95+UsiL7zQbu84aFodto/VXCTWgum4RDq9EOif5sFdIu5xCqZHNbNXUyGZLTLwR8/phXz08juhc2qMLAONO7AnTSEV3XtvXGG/TRup5dXrpVv/I7zwBSFWAI57+6hno0wqzCtbA46tD/VIpBWgjHCkQr/mEwcxVFGN3+on2KlGSjnB9cXsOjeyYui1mCVbOhooOkRjrUw5iiAmex2COaAfOLLSTmdIoQDrjbo1e8jqYI50c4RD+GkFNHcsAzU3/OD4tcAqVM266V38ux9cceoLkTJmaLAGxYE3O1tAQhhqMiic3hXBf0GQhftK79GdYmKPLUh2Ifi9sqXSydKpgpcGYvTYcKJxIlcpRkVjQyN/IfgKuVGGBOxVjl3Jjg2ZP9mf2fSogvCBQYbJlX2HjDmkiEWJnHpCNJTTXsAI/suRRTnwhDXXV+/zameHbY2hBxshq7k/4AH5E/hJ5WtYbet6krFV05MTk6ODGzIrO7IOvN+IQyGy4Unt6tEWb10qBrmAAP4O5ZhRVApHJD3B8Gsyx6GSHZdvzCgqs27dOl0acxsyPfICTEg+PbkqkAkHOuirns9PLhNK1yIYgEysRGBjeuzJZaVT6Qy/853vcD0KhnLQiv/KV76ydu3aJ93Mp1KZ5bTPjh54SjPy2dEFC1rBcvriF7+oA/FDXUOkE+4fCw8EmHBIv+CrILSgwZyUWkAaqjD2qz7wgQ805wZ3UeMDrFJ4FI1PspWG4Y9+9KM3velNbDE/+clPsG7/DF3GNIQDhdN1cLa+e8raPhFwLIH6Rj2rzBFphkgN79rxKIo5EMTX9IrRDrA0bEtGLSMT4URFohPApV6DP1w1ZyrGeIEzkiOJ64rgIYMMhxHXKDnWbM0cKkDtlcMLTZdEBJNaYUrElqwYGCjHpxxs4jjj+vrWo066ddvWNavXgMqqYDnLwW8RfoMzAHaqZSbxcKQhq8NXwAKejBoeQCitAwwXCPIqTz4hFQ+4w2jiR+eT8sCQzPY246QNke07QVhK5aLe5eEelmPx2OmnQaHlPBcLSAFntgNmwxksleTc4odvxXztjjujoyNKQUnlaNXF7salv8X5Vbv3ruDmr9vbd9jY5ITwfPkl9rXXhmvWiQKrPrZJwbm+qteIxKKOqERhd1qYl45TN+vV1hanpaXq17AfRW5H4tDzpI6I9R7hhKQrNAQJasQ05tw9klIOG4cjkx4GAeYS54eG/fv6rfGCSO7RXJBbOk/IG7TIFJOeMvh0h+oRPgN6AbAJFCUIrBRFX4tcnvQA0JafScZIn7Czdi2/srtLXUWpATaja8VKShGOBZNSOOlB1bbHUTI1w5KbroVxyq+GdayxAgr5ZdefEaG+kfFaIY95KoE19bgk4rGVKxJUEhhsYnIcD2itRpuFTLBuNeBjuZCfmZ4BaoPPDKOD/55ldbS3MkUADQG+gei4aUP4JMwty8pG7XWrTo69sde+7jXoihtutJJJTBjxqVHMmQoLmh5AgZwn6LR0ihBUKFTIBJROOB1HoMgwSnfJU2Da+Ymhaz6/jFQk6W/pQNwjj2xjKfWuEPlnlUJFV5NYfSdMR9WexzPla+Obiqke89k+HiI+QpszIYSkkpVaLnN+idjkWJJL2d9uivWEvTAuEAmmyUzsuRY/4TyeSQmY8JD20PppmLhv1B50x9y3X1YRgyOjIQ5cRN4G9pnl6nyY/nKkT9YnUWWV6gnVlI7OBwrXjr0Tx0CwGxAZjJRKzn+U346ODi0LAB4LsZI4tIXM2MDZxy688EL87CfIeOsSKY6VpaknbHcczWzvgsgqVJadE4+uC8lf/OIX80oSCiVJY3/DoyWWdczmJjT79dej9fzMZz4DZtLIjW4XTFKcXjVzX1iZDNWKu/91xWkX6s+guGDBbV2do0P8G0fRtzWbQZLFgGUo251gv2x6rhVE1d1DJbECLTir0OkEgZUCiCSYsKSQcuWYFkyVOIqcJzRB1im7jWC5FRjQxFHMZO4opEJqO0ITmE1IqicEc/ZzJWDBFJBdl0pIVqLNq2cEWYn6ic0Vu+C3gcJyyUFuSxIlH1JTuoWtZvRcsF/lw9qNu+izmN7aDvLBuES5tEJvDkwh2qM7iB8mAJSRuf463A+TivmjYyE5rynggIWNeXK4DI7t98Z8Y3pT1V27dqGbA7vlUFpXh6wRyweqIsvqkLEO87FRK+I11t1h0hzuM3kycDCcUCh4wxvecO655yIOffXVVxOOO1zq5e/LPbCwB5YR4IU9smAh8crpCEqA01HvvvtucKTXvOY1hOjIqGHADPz+97+/AAFGVOPGG2/8wQ9+wFaCAoNOzl4AGob8BmLPL33pS5GdXsA3XlihE/696AePjgaDee4IxfywWfLNqbJIT9mYmShOTYwMeIa1esW6WLZ3rASyEHBooZCTiHEo+hxsmaiVtepOza/bVtV0h0vmvllRG877ZkWMeWCIUkBth4tPFTKcsE1OuJxvuSU51YHDgPIjVq04MdTb3d6eikU9E5SYfsZepYaEGl0ItATEA1gjCZt2TPywCMCNoW5AsAT84sBjq2VoIKZqBJgkHH5gL81wD1BF2N7pvuY11b/+OGVyPQRpgZgtL+q9/g3Oxg1BLh8O9Ps79xhYnuReylWrrfZWo8l6alipGnv2gqip+khLzcCpdLVFr3q5v/1R4y8/FquWFRpn2tOTwbe/XZqeiv7BO53ebt0oIAl4jvamDeWzz3b/614Ffsg5T+PM0DOveHE9nUVEtKcbdbgjOiFoPiCg7qJGvx3CA5SJcWPAQbBfgFRaQc+jHq8hBnqDtJrWQJ4a6FwqN31M8vVxj2FsG639ZtgcLqC0JlAU7WK41VOtPgGDaBfdIOZYFBgn6clkLlQSkQpQifVajVkFwLOBnf2+6UjXmeHA7q1gsl6yJdu1kkEgLf9tZVHF9eCtz+mB0Z3tLWnJFgm+en3//n7yS4Gfc/OHUEM6U+l2iBzUQeSxUb+zIOVIU0pBvO51EAepPUHM64ZTdVzRnDBmZ7kkGa03pAIVIsod1pbVZ7ZSSKFQGRrol3GExsJTNPXMTRv77huyMvS13J8dzVXqw1vGy3VMfHFpRGdESA/h9OQk2xKQKGizksG2kvEY+s10VrXiwzmhF6AFsJlpIJOi+RPJE9WxrE+FHoMoi24gqfhDQtL3q/dsHcmkiucmRDSUfmM3JLIaAxkOyUBlJag+atJzAZK51F/e6W284nRM9UW9MnhzTo8wL40Q+UCeMiy4uQzE23AsUlzj9Wh52BDuu+8+tPe5Jeho5Xki58P4AT3DJl23bl0z50dGgpGOY0xelt8BA8NLLMlUY19lUTDveWoPyCr7J+RFYHHCiaCffF27di0bBesBgUaOQpUC/QXJAakr3duwd9iKiUOteFIF9mGRB1GbMJXUgTKZbRt6pe5YmKswndi+cKwQno0Nh2w5x9UXeeizmxKxO0UlL7744kZMnZV+krnuCmLu2bOHnmlIQMh0P+6OPqRM0GAI63ikX2p+WKsyLiZmmVn2yn3ta1/78pe/nIkYyahZE/Ke0JTZGxHO7uruGB4aGRubZKGnkmh2KM6qbbZFKmd02muSpgd9D0tXpvfoVP03g3ITElagaarsDErORhJIgBD72Emg33nYSnACvnKaowAcsRDgMvdM852CZU8mQzn12XnZAYXdLCsarNpCTkzWdGCHJBe1XqTASI7KLprGmC0E1wUDVzt4IAAAXFxkmB2wdDPuIMyMxWYsNeidjJzm9giVqTxwevggZ2MNe+PGjcBpT2LgmGDMBBICPDB1NUZN9XURJ9STxcVchaDPCoKSxYw98nrKPFG8bhaj9rM0aB3r5Ul0GpNTjwHLll1UV4P1Tp4MypPIsNHPpAX15Z4UZDCvvfZaTEOjRfjfhEzZ6IRlz1HpgWUEePFu1CcN34DgWV1sARCcdFTIzHig/zVSsjkiNX377bezthsUYr5yHoMVw8kBa+KVdcu2AkBAVmxPf/VXf4UmA/vCU9kLGnU4Rh5aBMkZ/Y1mkEidvGIzg0JH80G+ZoxU7P3TQV5OSlPuDEDjxgyGBoYnpmdNO57t7Cm6sW2jHMDCtuXQimG2t25kY+baWM0bG6ht2VaHsB1LxE/fvGH1urbu1p3jYRaJK1FSAvg2yjWj5AeFWlgqW9PzIlactTGQYUcuLYBEv2/fZN5qGSrbtbIgOa7hj00GxmihvYxFRzubitHTMgocx9hnjsc2nXwStYSfwBAzvowRo8PZBp6GNC/MH1gHAGR6QOkHRoqBXjBenM1WLGpeeEHdePfszTeXH3yIWOXuXud113vXvJrq1n74I/9fvhoZm6xUSkYqW7/0hcYb3mRtWD9nxEMqJOe8dBx+dWgIrHLe+XXHqnz5K0kucnAjfNNfwV+82++sX/oip6PNULxWvjAMVmeP+z/eUqpV3AcfBO/BqhhXIvovvjT2ilcWYl5uZKynW3jvh3C0fcFXFUJfK9bpEtAevQSSQLdAkWW2N2YyXQqsQIY6RPsBHRaFMpvLBZ3GEcLsGhkd+fXuka2j1mxyveWhSw7oRXPnuoo41JBLH2UeCvtibmjpKLwAWYKk8QITwzCT9tRZ8a/2Rb7Vbu8Rs2Wptgcmzr13+PVDxZZELMF8cNyYCPkZtYz9WIe7HzH06dqKyWCtGUalCVyFIfkrFCA0xkbHsPvS0dku5QoPREqkcgBiyOaTmwwWTkbNyqTi6Yy6JkriCUTPF4YMwDCaTPUkUkpPmQf4gxh5mShQlOnXHDPZThwBGQWEFBm84RyAu7nLt4dHhCYinHCZy3IBUs4DAJY89+4GtNW9xI/UC1FwIUyE4a5djyFuT8kg58BuVKS7qxMpbmJzuRHKj4A55E8reKbTyZZMhg6cnZkCasEI2XQpnXUzM1umATVSKbErzs6GxqQsCbUqFFJteegwCNuHC1VQ/A8xKKNGgV1ODYcUq7qAatBV0rXqVU0UgGT5LKMnNeQLG4CqqXxWn+YaJa3Q74Ieqw6XhPwTv4KYJaaE8Mp/5eSTjiGZ6mC66ADXCB2ZtUdrqf05uzeEoz6X1QFRj9eLDPWBjsofHKij6E8AshxhxGk8WaT6GII4hSO84dj9QA5JDqGWpcrux4TROTCwp55+qhWNmmeeOn1Tza2JWVdi8kQmIpHOBGc/x4/GHrj/fqiH+pOuxqWXXsqGQCYPPPCAjq/DeUKtIH880JGJQMVw1IF5hUdH48RhyyVQf+KpsV++Yi6emvMJp+YdLZ4b4T7ldA7NTyJwfh14hMl3suXgBg+HhIdIF7k1p1rgb27dgk/H+ZWqnsFFONAORobFbuK+IcOLmptPcfr6rI5WCFa//OUvqVJ7Ijil1dg2blS5aohFoiSQo5bb09kxODw8Mj7qRFaio2z6QW+y9pK1ZtvMSPiLrf6OXUgtdZ6+uXPN+g2ntt2xx4fExhrXVvS5HEhunrMDbUKffmf7BWmC8EeJAhAYZg0FEvRWuCtJaI6ynxANRq+IewlhTqSXVD6hRnGxqqDUPWQhs9s5iD+BRYvBKmW2CiEv/HLzUJPUkypIHgc4mQaNydD8hZ2K6cRUhKoC/XrROM3xF/hhpeKYAFBemIdasndBnBPhVbcLhSNqixUbTGZCzma9sFKOsHpwj7WlQ+JD8aHJpG2+9mLRfISVL7usngPCsciVglwtXJNFFElGpLGyWPJsFM3A86IZHjaQPGnjN77xDeQu//iP/5glfPPNN6OB/0RH9rAFLUd4dvfAkS6MZ3cvHNw6dBeRfeKgBV8FI/re976HzqeOpqH55k2QVccrmyzbDTBEYxHiwWl6IWnJDe7x6173OgJ//OMfg1/hObjoEyqEOiMMc7A1EXZG9jzuVNgyHg7MGBMluTW+RSw3cqeB0IzzBX90qhjx4qtW98Q5zTgc+WaYkyWzKnqUYWe0cmaLaW17ePqT/+A+tA12MfhuEfbtDde1XHfNho7uvdMIasn+WfTLxXy+u60DDhlbLFgCTGZBidFvrQcTJeiWiIwGA1NGNVnP1P3JffvCWt6zhe81sWWQsrA3gh0SlJEjyEdZxsSvdoCEZ5LR0zafRP6MNacjIkMMNJgbOsCAbmzZGEoB+2WM+ArVg3PlBS94wcGjw/lez7bWX3BhZPMp8clpKui0tRutHUgm13/8E+uz/5AQciqIfOghN/ujn5QqfvRd7wp7u+eg81g0BKS7405BZhTITri1aoVT9o1t2w2LFSrAN1lAl6VWbt0ob30kOP98WwsbyzdhjjubT7Pf+z4qGu7eE7om+sPOpg2VVMv+/n0kRPyPhgB6LnVbDwxbzafVDdRg67q+9RC/QVEofX6qHjBjQWhRhCPPBdkKvKAEzDhQKVfzK45ktgMEa0h3llugatFh352sQy7xbIw9q2ow7UQNmCYrQJzhE49Uiv+CYknvMSSCVwnrgL7rNh98acube+wtLEHGipiJ+MTK+M4Le//zKw+9e6j9fwKVgXKuid59afrjve7PXSMHcB+EseHa6fcU/t8dlSuBtymPGUxRs7OFXKEYT8S8WExpzEmHCc4GPC2lA+tJQ/FQGVtuHgF5Em6zriPwIlFh5MhgSyJhxjKskDYwaKICpEmRiBONZMlFpLtJDGSuIEi+RT137cpVCn0UCW8azyIFqa1UiGK2dK7kDdSHPzXvglzdK+Sl0Gq0RSyxyidVdmCMlyJFoTJBJrKqcyrPLGvh7WRrdcQk8AwPlYulIvU0jTQWyq3xSVZHdw/8QGN0LMfmMIc0qFFg+PpWd6527lph3JIIttfDyGBh0/2TL+wvn+rYbt/6DSBu+WIeGIg+klHiR+HAq9euQtISTJvdVWaaCpShNI0ueHooeCuCiMKipXeJQtoMBt65jVYJuMoMlWD5RpUgtngRoRwhuQpjXjpacpUYsUg0BtGKmuSLcLZVsP4ud8YmEE2ROzYrk5P+aCVTGKh1xPdHrICtQNt0IUOtzyJ9rxzDp/mW0IP06aA/6ee6detAtNhYoLU1wrUHfA2klOW5ZcsWQpoduw1rihBgOyJQjowyYxcEfAI/JAQeCNCqTqW/UpYWBgb81YQkvupKUnmMNeJnSe7Zs4dw3V14KAgEmFcaQgSOPJ2EJ60+/fTTDNdzTj3TeOXLq9/8hmzBdJmMhFVZvc571TVmKoM5dapEM7UjH003BHrW92PPfxFEt8FAw+yWzkpynHeScxgeKGUz/02VuxQRTTfn8ahH5qNbqDmiPdB56b0FmVATekPzosmPLmWfJA4bmu6rIyvkGMSCkfbINv8zn3d/9YATgnKalWikcvU13o3XOytlbuCSjrGxI4AZu30CdFTWmkgXW1Y0HYt4PeVCqY0LbYnm1V/WZ2f2PZL/9P+t3/Uzo+4zJ0ue477i6jWvu/Gqk1Y9PGJgRhCbWmijCKLDFcHMNvZhcmOLMzH7jNQx2bO+1D4I3zU0irEamxqIN8hXRD0hV2iFYYz3McjABCLYLPRrZJjFPrNIMguii3gzmWnhGz17ZZ/QjVLPZn9T8NJezhTALYytcEsWKxEEjH44MM+lE6tZx2cmQyPSkadtJDkWHqrEDgwVCSE1XSWeBALJAK+ydTB74Xs3RCQOWwfyYX2RA1s0Jzir+LDznMhMP0C+2UowXQ6nS0auCgGU7g0gwTABovOCCbp0iX40HC3FYVwWyA2gGhI8Ug8IWqrgJzxDjkaNlvN45vXAMgK8+JhBLUa4gp0FtU9kmJFYZtPUZHLQIdIsIKoBOXEmaEhl0RzJ6pOf/CS6+2zEEKs0wLFozBMhkE2KttAi3Vj8oPfsLDSTJzsY/7YM++NFxJXlZM1E4V5ZhZooDgGjIbmUibsn962MILQEnIQpixpbkiAkwrZ1gVDrp/c68fGB6U98yvrNI1U57QQQN/OF4pduDlsziZdf4xiJmaogX4VcZWp0qLsLrVQpGb4Sthx90dISBcbH9u4BWQKyb48F+ZHHxobCUqHQtmoT0s+z4wPZtu44qIprV3z0iBNVqzaey6ew/BhYg4E9vbcSFOMj1WRpdmxN3rdjle1bHh4YGJiano5GXIDLqemplasENFeHO+AgpS7cW+kcwM2pyamunq4zzj4LZgZRpIce6w++812H2++gkqtNX/HfTPPOn/ovvdLtbBczI7QZnP+C84t33Zm4/0HTFMUpkRbDnCOqofEIEm6N+cBYSP8DHZBl0yECTEZV16xdY61bg+knA5lqkCLPnZiZHh/sr/mYpBZLjORziPnJUadRVk0ATsAzFL6o/eAgMnS22CeT+4GAV0QFCwl2tLbjUZGs7duwHshZgQfUSVdLziB85CC4u6AqFL6w3xrtavYI3gyt3Qh/9eCWoVpqJtaV6LWB/Oc6cS57SSFdx8EreLHAYVKFOWa1yg9qC80OgrjZ/4q2N3TY2+CX1sej+S1OmArSp1dtr9JiD77ljL/45vQ5e2rnPjf2tRdl/nc0nFFsVVBW+MbFld69r3Zfd/vMu+7K/UlgCHaKmbHJyQlggpZsiypd2qYap5A5SqbqZuga+Y2Rn/S538s6u6Ghj1Q2baleO1C7IBCVO6moTA+dUOKTgUbdBRvmRZqj/hEzkEuuBJ3nn3L8MJ2k7SoXAcvoAMF3mWNYZJsnixBZkrJ+JKlgO52tglFLYWpE5nOUZB09vQrilFy63a3rvB+1WihMOmP106KdFw2X1iE1GY3Gspk0xdLk6ZJYrwmcdCQtchPihP0cZtw9L0u8fX3k9tCsSsFIyrTcdtGqm+8eetUtg38ylGcyoyfvTNW4l0v6QP7Jn2EpmfBqNRyZhOctgZJY+YpeCBkKled9Q5hzk8jKyfceP51Az5rrSfaV0JFXE0I+UnR7u9WSEXX0fftnMQcwl6F8NMD32tsFyx0cmi0WBLenS1XOFpzJ7m65M2x8PD89zafU6ECpp7QnatZbshmQJT6B4mo7edJo5UD7QYDpYXBjuC46XA8v37GbDfrH/qkvAtFJ9FMbDWabBSMlfiMhHrAy6kk0mE4sW77iNNSu92Q+6RAdqP3UROdM2kZ8IuAaHFSomUSjSjqc59ycoT97esDxkP0GVYbMoeNQDnna3R2pt/6e37fW+OFPjOFBI5Gsn3OWd+2rnQ0boSg89+xzdL5EcdIAAEAASURBVLkLnuRAGxuBVKnhx0PRza8N/4JojfCj7qEguhorHjQZqVF65mDsGqIMArS6aHqesaDaeqSOen2OJEPZ9nAjQ7XPfzZ+3wMiIiGL24iVK/Vvf6OSTZjX36BXCbPzpFaX/R8d7XzdgJQrBgUQyuAW3FawEkFMeF2bMlpzI7nPf9H7+c85KpA/AkH1qqH/7VuK6WT2xtdmo6mJEiJbQo1i/NSOi8iSzH7ZKGWdq92eDYGdSK1ayJVr2xhdigsquVl2vY5sNuYYiFyD60KGBi6A6yu5zbVZMjug+YrwJ4Hz+9QBX5/gCx3EyEK/BieEzsuAgjVpYObIcyITqEsLAL8jT36MYmqNLRDXRv4yGwyDNY5SEjsSC5n5wibDvNWfGjEP9mhdEuY5CDBYdANzJgMZH7Urq61SdkyY/LNlkF5jigsagOEQ/FEkEtS/c9V6rmJOV4KVyfB5Kw2/FgwO9HNI0/PURJPaqRhn/WGrdHAlm0NIDuoLKfB3fud3sET7oQ996N3vfjfbzlPMtrmIZf+zuAeWEeDFB5fFj+Sz/oYF4Isvvhhx5X/5l38hREtSNQ5FHQehMlYdpNNFFx5IBeoK3/zmN5HW+OAHP8geumi0xavyNIWC+UP5pnCqunv3bjwcGFp2hf10dHx8z77CeMkcziFCJVsiSoOIIlcrpdHhkTVrVs8YLvsgx1qqpQOyP5RdG96tOil9oH0fODusPvSb8MFHRI5TjnCBKtilOa5rt9za+sJLql5k15SNqk+tFM5Ww7wPvqj2Xk5EjP3YQMfkF7Smokhcs6tOT00Bv3Llq2V2Zrs686XyjsJwS2vK8JKw4eputmxa9XJhbGr76rauRDIWCf07777XL87W/Wq9Vn3sB3czfL+8/d+tEB2VBMLPX/jKLaMDeyFYnHPOWRinhO3DKHOKACc1Dx+kDY4ZmFQ+6lh0l6Aw1DMIZ2ewHOPXuK+HivIFKgCk+tCsWfWde5zznq81gYXFt3J19I/eVfnW182f/szM5+sIY0c9o7UtPHmz8TDy9tKNc05QGQj7G83oHKRLOLA1IyW9gotEQBcIRJQQqQQchw3VXoqjMp+viOvjeIVXzPxciypRGD46Xts6LqgdqK8SXUNWDZJ/6Ff8bVu3YuV4U18fBH66H6E1YBpsqGAG2bWFAiJ/FpqDKc4iJohUXMNJjSIP8uheZRbAsNs5aTw6G9ZTTJo59h3R5/ohoEZieUXphwkMPZdQIEMRFqbDKZECKfLi1Cc6nB2gfNM/SZd3R1tfUvRW1k2xFibViVlTF6X/zJz9k8tS746Gs34QKd2VrtwXC3zbPWMmfUneiZYuTf/NRNj3YB7BDTGAB1bd0d4eFZaHhs1k9krzVO2I02ptfVn6D9Z690KowN459VgXu/P5wc1bKy//3sTHcway6Ay5Sgu8KOgbfkATyUWyUc1Ugao9qiW8QgVQn2gaJRK54SSl+iQ/5Kd7g7yUwIQuicrKIEhFVVyVATHlK7nBASf3uDF1aep9Zya/6ZhMJ8pjTdtlI33b2Gu/PfR7yVh7xI3IBCaRai3Xu3iIUqgsidxq7/7drje3W1ulEbV4bQIldMttqcTCwiU9X13ZUvrmxOerRjwRjyYTPY3BVPVR9UCOIeKtXLOaEN0PqjNkcJWApblixSqdiux1b6OTgIV54rcpG3Vqncx1BvIfBcXcTbV0JtR0UO3mIbboCtDkMOSbbHFjqJWqbUkCxMJKDiFfPNFUJGlPz85mU+mzzsqwC3nzYoTQLjkCdCdLGoWXSr4YFmptPe+88+b7n1/pGr3bszVhKmLBJwKJwL56+eWXS+wm14CzuRZel8JzgaOsBSGNVzRmG/5mDyXobaE5sOGHNwsmD8rNgaXNIDc+MbNgLVqvujZ80YtMDMtDoUumrEwW6+vk2RTtGeal8qAKnGugRrDowXAWNAf6BU3ifEFbGA1SQPanuYVMcOQWtj/q3AfBFIxjbk0za2HOl75/a3j5FTKl593mDnbuYKgQFmr1MgguzVN2CtgnEVIeeGzfyWe2Q+Y37viZSFIpShwbMOLS6Nb63/t398qrurtb8kK/nh9lhfryyoGNuBMbjtLXtWDnRhy5hEnu2hXpZbkojl1y794ZTFxu7EBLXxKoCh88Y+Yzn6+2/C4W1vz9Cfn1QCM9zm0dHOUQMlh3TzQHIIQnlOTYRWa3WZC5DmnMXjzAKpqIBoSAzh3yJkzmRoQFyXk9+FNzCAUq4TtjBjZv2ciVw1KNK9FEZwFqKOJ4+apow3FbBds10upQ78lyqGjsmAjcoDqJFTGOYUU8gt5Ebcn8qKwm8kHuEiFNrmv5sz/7M3SyvvCFL0Dva678wY1dDlnuAXpgGQFefBo0Lx42TSjit912m160WtoNeyGoDzUSI0bLktPbTSNQe8AorrvuOg5XEOBrrrmmOecFMU+oV2iBtAjsDlog/mZhmK9+9avQ25Q9DnWiSb0bh5WwlxSQrDdo6/lX3Pg7H/4CXVf2LZ8TVoB9I50wvaAa7N6LaKM+rPX2HaA/Elat/n67kIsnOvZs34XgFQpG9Zoxes/DkK7p4p4VK1AzVLJSZOWk2nq41jD067mZqdb2joQYRJGj2Y7YLfFgdcaKxTE0DWsaU6ZGzjHyEepg5irGwOTUtp37UGDKtnVODgzmJgbaOztTPRu4nKYwMz6Sr/eu6Zsdmtk6Utv/8x2FidHelbVE1EOc+7mJNkqHjK00A41qrXLmc54DDj08NAgDGzIrmmvxaIzKgelr7grwU7VWBV8QTh+VEawXN9d7ludamzZYb3+H+drXBuDSyRR6ciZ861deXX10m1Hi7gpB2EDCqwAxZ56cOHUz+KjkWa0ynRgjek+z6IHgcYvOsUUDpRbKNb5C2UGqagVwcCF8dMLqnxFccw5DU4cugg5D/ftqVa87kTanQhjeCLOJPVEajiETlL7gLZFATQI7hBFg7tnvIyaHYrhS+pInfs5IroiWJ1phUkBj3phbh/0xZ1WFaysER5OpwY+qhOSs/ML8x2lpXo3yakKDDmeagQWnw/HNie+g2Vvcm3QSRvfvjYe22IRhfkDQBpKEs7rK/eXVmbfGzOmg7o7+XVdwd5t8gEzx85b8HVMr/mzETFRekv7orsorakamvb0F7fF4PMqspVIMoOTDQErZcoNHytr72vZr25ydMq8r8dIgcodhRLRIy6dFv53smvza2FdqJixupeuokrJgyIBmqYUgU1cYt+qf4KC6zwV9JYa0SgAVKVQ1FK/IR0uIrCuJpBgr8lFi8kd++JCzJskc7Crh8lmQaZUjHGTXmLi2/Y19kdvlC0II5ZhAuF4lYk1d2fmZVvex78/+M+xoKYgYarD4peqKOw0KXXlp2/vbrK1A2Pmft8x8uTUYjdLbkU2V9NvGYytzmyL/elbyovsKv0dTpQkafpYqUhFVDdWqOV73HG4u/UKLJYYoE9qqM3hRTr3I5OAYg5mlRtNnzcmQ0GQZET4ixSDkIGKJtIBEphyoGHiAgF2YU5KZqoVUTKYUL5BxXAhuIYZn0Y9t4eo13XC+g7VqxFXFnnvQHgoDPl4AIjeWFQhtM5dmQdqDGY86AskbOTQnwb9U+IJoB78eIiGfoO41mM8L0wJRYycfW/QLPzzj3+Htwx7U7K/mxjT3lUymp9Dtzdk+FT+VCPyasWefKxfdyV6lh4MtgE8uNvynuW9CTWpVDPtqX7vZ1y43FO6a0nuEbApq5wiNjnjS8cu7dmKvkt2BFCxt+NziQZJpchQzfYkVa+K22sAR/0FcWbi4PDm10AcWLFdtApJCwZM8H58g6oVXfvmTD81dSsjxdBTN4QjvF3koaD2yPSg07Gms0pNrPtVG2wKhfZLr0x/BE/xMYzg3jTx1u4jMmQ6ogKI7lHoo+3RCI84Cj/QI26XrnHb6aRiR41gDIMuXEW8OZyo8zapcSiXJ0U8rVaGq+IWKBfYLyYSNNuEZ7SmBcbBYyaUej02qzTS0epOR55x5JnNregz+yMhhlYoX1Oqwr7SUDZarSVG4eNOb3gTTAmD7YGLWYfNZjvDfrQeWEeDDjzh7gmbw6qgYoMODUUFNm8cPXwiyIifogp0UFOWHP/zh61//ejamb33rW+vWrVsQ4fBlP30xgAaoLWgVCDCgG8Iwam+UCiEtRtMEfJynoWooUh18crYKfwmIEAHqcrl/15Y84rcmElcBbEsF0LKDIi9jmNGYBXqrIei5LFSDRdAXs9CcxUC2GIJEx7fmRIT4Wg7cEUxcqfMbcBg+RFCYRJs3FnExHI2mU4yiKY3t2PVOOe1sgGGqAiQfkdtWa4mWaPb0TTGph1lKZRPFk+uBVbfdqf2PlKul/v2D6fbeul8Z2vXYtq2PnHzmBdn23lK0szBd6N/Z33vqBZH2NuCJB8c4ALgaM797144zTlo/0r/XtTBnbUZtZ8sju2FNZzPJjRv6gkw23NBnbd0yBw4o8iddWopE7VNONrBZpPtLIBEgbsfqaJviLocVK6MxEcLEWS94QfCO3y//85fdETSZjarlhRecn3jb24yuLurPKa5FMcmTodFHIHwbLaipc+AJFUPLLDRCDu1h3JnPUHN/NWICMMGzrnMGClaGE0RveHi0XLKyma4w0jKQF5ou6CtNEZSep8jrCvoCb5M/uf8WwWm5SwPgA5UwU2HLgNKCM9MKZgKgkeAoNma/RMqa03nruDHpQyORDIXBpJzKnrjyTwxGCSdU/oijERuBsGS4pQaS0LA6vL3xcAK/2+bH1pQE+52Ilm616zstuy10rqx7fWVQvRZrgJS5u9LBnW2KF6pGLLTcbW1T3620vG44Ze9a5d2/p3oJ8yqJzrYqWh6qAjIZ+c/T9K9sfX+bYjjnf9o+++VsMB0TTH1lofXt49FTp9e4t5+f+vxd+fdgiEswRzKQhPIjnGJphgqRVmnHfZ6qMsTSDdVyg+pN9Yq0X4ogRJKy8jSMSxCvwliG6SN5qTwJFdRQocREJYXOnmG9vOXj66O3M8LlHZnxL7X4O0Q+3zmj2vGmUW9F/rzsDybtL9+T/31V0lzdpM1Sf5zZ5u44yb2V9+L9bbP/p9vyUc01Ld+oPxyd/KDV/fGa01J4XuIf7y+8HuVqEmgUlGqRXqrGn3bSCnFSTZ23elFhjz9kmOeT6AowjxxzJmONo85dNDrLYZbmkYOiL6iE8sYroTJVKUESSvslI/UiodrRL9F4vMdb5SFpgqMx6gN9oiMc/DzEp4Mjn7AhbOlgCAdXr9E6erAxLAdHeyaG0DQmnT7v8IBRHIwhEId9tWHO42ltploySAlRiflpqerD1OYCAZBUkQBqOGoOtZGoEYuTVpxOqH6MrjaIudVAJNfCRDQm2JThi4S87GZWxfOcqNeesNqSovqtkvMrXv4T4cDFMLdEpIgwRIiggaER8tBDgqEhD78UGYivx8HRKkphHPGADaK7BMr0TOQWcqwyTLQFMABgTPvlrF3MwZhB/AQZh927d4M5c7Wnjt8clwHFMYeYAcVqkKvYIL1cNIBem5gzExNmRqVuFKrYIg1AfTFRwgyAAhL3wkREKKF+YAKD5UpypsAhSEbCtjgJ4RgHg7lgpuyszrIpz5WpB2Lu5Sj9kCc3lcLrvuGGGxD//tKXvsQ9LEv1yVEqczmbZ3YPLCPAC8dPGwhZu3YtywkH842FBPGMe494JTa2kXAEch0Zohfs9Z/97GfZVhpmonWOJOTaeohSXFP2T//0T0jdaD3MheWdkO+6pQuqtiDwD//wD7nJiW0R/mxlYgoiAUCkE0/X4sn9hXCkYD5wz53vfsMVUTfoa+OGJMw4i+UqbgCGKlhiGzXdtjNOm4lFbUxDiZJwGZgY0Fy0i07diOHs/oli19r15F/ITY0N7O/uXYdMJ6CKHPoyDugCC6FxsL9/qrUn2dJRzZy0pxCJVMLy7HDMs9rbOxDNwq4GGCl4QL44O7h3z/pTToN9Nz01/djObaVyIZvO7N/76Phwf2lsoD0WmZwYrwzOZDu6sZNZKBUQrRZKuIM9ICcXRHdO1DCNCtMyFrXqfnkQkzYY+RkppTOrfQt7z6XpsaGeFWtA2Gdqbnm0vjGdsa67tvCRXZEK9zWBEcKIxlZEvX7ppV7fOqB1AfH1saM6mlYNDQy1tLb2xhCwFDjd5jLel73MOessf/su7nSK9nS46/ps7lJ2BDUEVAUmY1AANSDxItlINgfzpjjdVfaHeTCNOUeJxNGI86siFtubCPzA5qJkzrZ6wFoI9g8NW5VCd1tLp5r5WEGD+8y5CAZFhYWHIOMj7WKIaAV/dL5cZaE80Do4SWkcUYigrsaFb4wHvBerOljzBoOz9k1Cl7AFheJPwCmBqHCKr0cdJX+VAWXwKhge/UBy5QQIkGCRzZsRRVhEcdNlfoI90fI7zEh/6IQ+b4X/sL3vQl5B7p7szMp/ccFWMwBBsUb5vqTxWobUt2bvHpw4tV2MrAooIK4Jc9Pt6XK2nxy5FY554YH07Ke7bKgiTGjuwtyfmvyo0/nRursif2bspl/kfr8Mc1q1gYdo+c51iDSXzAWa4IO0HnNmqiS80kQVDoAhsaRbVBQRa5QXgeMFs8RHYyV7eVddJ3mQWILEK2MkD+34krWGnpe6CUWC0v7UyIe6IzlMxUkZ1r3x4b1O78cGnLbC8xOfv7/wxoqZBklWSWUMaTjRyHet+zNuQAtDN/evGdOPqnUqZaEWFoylcv+Zyl5babN3Zsy9o/WTJYWunRpFqdlcZeRHV1sPocqerxIlak5sjN3T5W2DXz3q9+0qviAX9oDNYja2xX30kuxnNkV+FLdGaXkxbHmscvntM/9zsHoWVVS9Q7Mlbz2hdMPnn/JB/ecrf367s7PL24t/orZyvLo+MLChzdRDEIJqS8RnsZubZPN4oH49oL3Pxg7QzeTI5qCnsSBFCxrOK7TF5n5gI4erxlZ5vCFsNlI3Ym8+pcpV48WiXtHMSqEjMmVPOSVoyar10lxZ/GApRm+qhtkGOHUxaIIOTDMLU5Ge6fqnnzyVToal2YgXC31kmKEYw+ur+n2b5JZ7hejODzu/4p37WVBI0yvdoqUhAHtY0dp/MGWhKcVx8jZGFgQYsA1OBiwNGKeN8ONUj6dWDERt1BzoWEQL6WEsXS2Vn24X8eGLYttyYHDwjrvuQnsiLfoXsnuztRWVADNicbMVCxRXbm9WEkN+XUyNgg+XakbRFxSXK+qjXLYcMZNR2bNhORQrBhdzIILDDdKpqNmawAJWWK6ZSEpXa5zpYcwz0xEH6WhkoVvtSFuqlUNPH3JHvc/JkG7B1uxb3vIW5C7f9773vfe9722okyzVRcvh/217YBkBXjj0cNIQVMYwhjZdyyvasKhPcGuRjsrO/ulPf/qKK65gQ7n++uuRhUY3GMoT/kZe4CRvf/vbsdKO0u+73vUuwtH/BMFAuBQ/hwEHZyPyiexBghf9qEPtIFyMsbe//h+3Gg9t8VCmXdMXueySkzZvXpVJT4gNF9HVbPWCJMhoKLgtuy26waWqM1qst/RtiFz/yspXvglECx9RJHcDv9Sa7XzDb89k2gaH2W/ZnQkTyJN85tAmgZ5xGL8VD1u4OpBNLy4KdRVs207nHdspqVthasVcNB6zg8rM4H7sW3pjM7nx4ZWrV2HRp1osepE4WFN+Ntfe0etEvI7u3nGM/UQiq9b2nXfJFW7E+687/rMzkq97pr2u287tr5TGym6i3tq7t3+oHnCn4qpZNzNb5dYhs1acmsxZkTATcRwUDGenwMdi6899vvn//O/pm75mgS27kWKyxbvqcveaa/KpjtlppCuRJRPVWQssEoYmbVESTQ2IgbqJzGEq5W3YgBZavlisY1bbiQg6ADHAcbSeHvMKUEybVZtDHvg+747wjCETgD+dnOfDWx4em+WenuzKFashAHPw0fszM7kZf2hlV3ZdXxscWKgPaPtUsZoSqKsvfMSiLLnKR7EKGDvhMar7J0GSGSzwaxqoEL8G9qVRDkGSNfYqd/AIRmsjmqrSiiljmqJEqeabJAMOjEdasQimLtqgPxSSRBRwJnHCrc5Xu5CBhTYtVQmc4ket6ICIcEmpIE5rOYWFKyJ4DTnWlaAsr/PlSHBN48Si/1SFOcSQKFRSIWbCxyayqjlRzbXRB2yzQF8V/q3Vwkqbqjl3Aols9Gws9+N02xsLGac/bW0vVJ9L5lIVyhVAQvWvNEFgVylC0Hm+yXdVoLRI4vGuq6tiCiIvsK9MHqFfSFbSw6QTn04vHcOr+kbG6gMhstwUBkt+q+K/jBrTRJr5TsbLR+l/nURGbCAy8YNE528XM/buTmf7QO0cKiL1Ik+plMo3NBP2MK+IWfj7uCRF6jPvqG9YfSwKTO0YpbiF9peugXqKkrTkQUcKYURXjqhz9Vc/YlSs/MLUl85P/W3S7Ndq1QxrsaXjV8W33jHzv9bF7np59g+S1n6GAut6VC5pVk6JfXm99+8/nPmbX5VuVN0wX52lf0m7PnL3hemPrnHvdQ0sYIELRMZqp0QL7wzc65lmSyd9Vn1hImEvly0FrtGzqmGHawwnO5REbArCqISe+PhSYRo2ls18JuzSSEIhUHq82cKsDbTYV66s33C9/6WblA4RQTizksy4N95oIAQ7t8Tm68pSC81sxM4gvoxXrV9N6hNyHiohazbE3vzG0qf+waojhiNcOzKoRpLe6240RHd3kebPZ73kL1L9GkODowBl4RAY2pJZHOMPjDIjjuweJpSQelhg2uMYF/6Usj94NpLdooGNYvgK9QHeL6J8u8RmoJHP1fMwbCsGzxrjDcVbnZTFmog0F2sBttPAbzmQPLl6wMiA8Qr8ZsLDmCkya8SmGoq+LQk5AYEBYG9MFkME94TC4hnZKDdEcmWDBbt4HI5IoIx+d2RXtKawF52CcNSo3FH10FLmHldhf+xjH/vABz4AAK9Vgo9qIcuZPUt6YBkBXjiQaPzC2ESOAgEeqLzcOYH9qt/93d9tGMRjgSFfcdddd33iE5/AQDSmgz7ykY9gjZ39Re9B0OQgPqH6+/Wvf/2yyy4Df9Zl8JVA/GgRa37dwrJPvHfq3LARuqB2nLpBzQ927Sp//G8Tv36wVi0TOf7rh8q33x7+0f9KXHJxa4wtNcTm8wXrXMxv5CtBTswkCMmw7IaFql3oaMu+/ganPVv+3r+bQyOh7YZnnNJ54w2V00/7zZRTtqMOMtDcl5CKee5q2HDAygLkCyZg5iaHSwVs9gr6NTs9nM9N8aGzdy3WWShUtmfRHC6N79/pJdIo+o727822duT3jEyP9OfMNtPpKQGTVzwj1dW9wW7taB/cua2lra1SrUdR/nPdSCyBKlQ829Ha2Q3+BjrS1t5huy5avqOj23siFhMjkUxX/aACg7RmTsHZNt1p7NRiI5yDIjT3TdXPWZE+9YWXJU5/TsC9L6WK0d1RzWb3mYmdu2YnJ2ezrW1xMbYcTIwM5SaGPQtjYaE3Wvb24LfOOO2UdBI7ywo55BoYDqdSMcYcm6OeCtADoCLdIQ8NAx3mFFwwgs2vjB1CgOQD4AuZZuOqjg0ioh5ra3fBzdGgrtTrtY7khpZ18UQaTUvfJwTNa6E4MCjCE1RGIOkqeeWSDDG1Iugrit/IUSsecojFIm7uFRMrCvnjbg46CzoGNGMCpXTGWHnwy6GMn5pp3I8ihI0sHSLtxNFyggQ9lrQ48QjyRQrJdbi6eipY12az6IL6uGs/ULcDK1BAYBCpe2+EvUoiEfCiLO/0XPEXLWLIUnUxRdAk9/QiLEzQrdFKb3tbi4gFK1AYtHuu9ySWlMivIIFCzbBrg8K45U/NVmojX/2BiJpH9bg9Icq0iIWr4SIJLVJoLbWgQ6gRdaAlGo4lrYQIMUF6RJUm/UKhIjgtnTM/+PKmsVLKkwoIjCFdrFqo6y5VITkP1X14oGNkzCFhG9ed+i6Yt0JrIhzHDwNS3RGlQtyFnLAH6tWzCeSPclQEKZC3cpCSIMDplno4RXQ6TUqXToAD36LMm5sORrBQjSdQ/0lDpTBphAqTFITxox0f7bB6Rev7nhf/HB3AB9RyYU8YTjVhDl+Y+Kte965u76FkOBFC4NiWrP4aAXXLO72UPC0fs8dfmvnDGb97d/VS6sC/x/Od64JGKfRG/azYTVdl3xMxZ+lcLchP0V3ur42Rt9Sqv/E6P4yKJVLh81V7pv7KsCnX1BvzQTpcqC8W1mVR/FlKOfmABM+WFxYI1N6f/vSn3IR88cUXH4rsyzxU8/9paToDZ7a2eq98pZ9JVL53q419XQiGp5xs33CDdfbzTAw1HTi0vOlB1yuLNSU5yL+5eFZLOv6qV9npFuMH/1+wf3/Ftuonb/KuvdY8+0xMKgIFMR9wR95YvUkuiL9o4II4x/OV+iBCxVnPtcmIB5911llov59olTxsh3BqC+mzyTXPTMadM4iDuIAZUfkz8sk+b/2aLRMuk6Bc8wE6gM3AeBWPF/RVdmP2emSYQWLjsldzlZ9ZrhoDo8Non3euWonMcwp1NMxkIPNcwxC0+FEOj3lhS1ymHmc9YtL5PGtELGLCLu5A58ziDgVrz2Q4OBOuzZjPWxXEsUGmhJ2Oep+TIe4973kPZnqwVsONULfccgtMrKZOWvYu94D0wDP+OD/qw4gwDAafcQtyZkU1QvCjx3/TTTc1h2g/1ESwX6jCt99+O3Fg/GpOMobv8Gsxqiekk9ko4kTz1KqVwsiQ9a1b4r/+tfTNPEDg5WZLn/2c3bdhLkRszBrcOMhf59zJDJ5jFat1sMewZ4V33fWxF11u5Gdt1/MTiZlofLAeL/pmVxxaY1CW6wcjbjQGxgW1WtRNhe0GRgpMi60eU1RR4Dlyq2dutr/4kKDM0lNWf+5hv1bxq6VauYhZS6xYcR1SbnKsf9fD+dmpbOcKIvXPYsl6YHpqvGWiMLbvsdaZSrq9a6qYr5bCbTse69u4adNznpdKxGqVMlLY2Y4O24sMbtlGaRs3bEinkmAMGMHlLLDjZne2ZfMqjBshlR3W/cCvcwGjtXfSmCl4q1p6U5nuSrE4VfC3b88/sr+fRo3u37Fh83N7128Cxa2HWT/mYDq5MDWSiMeT0RTtenDMc6Zoq9xaIfclijh3KlI0vAq3Jta5TIKECA8DlXT3dHV1Y1u42R0Wym2OLH6mNDZOecIAQTZMU8f1UcrNVYWZ6Uw6bUetziQMAUZb8ldIGMgI2CtnoVwxC0osxsbqHIFoa4M2m2C/SFTVsX8G+gaSIfVlfBgxObWJhjYv6DH84YEZLlTQiJAUIJGkWvwTZES4rAKBKXRYV0tpkZJcqgF2A2ZUq5XyuVgmE6JxrKZJYKbuzb/5ivT7ENwKI0G42a5uRd656q/y3D9w3LNA3slb0oK7ZS7N5e6aDB9pFRCAksHJuwstr56kAlN+z4jxW4lolGmmGs53ASLFL0xSgYapR7GOATbpHLOjEo7GhbcptUN6Ae6mabdXhBVs2L6RhfkvOallQ2qdmwSoRUQOki0vkpr/ko2UJWmUT36YGqQXq66Ez/WV9pAvTnpAIktd5Yc88JK3+k/gPEIYhHbNSKiYME9rmoQgxYH9YpCbGqcEkaevqj5agjo9HSwpqJ6iT4T7ymcHWaakn7osV9ibQtJdJMWlbEgLfupirlYO8vWumfoGmPwENzteVXaq8bRV2i9RpBFheEb8G+fGPmdDZZqJTn2zrforJNWhVpTTr56wusobvNspB/3fiX/uLP+gxfbFUkDxlnrpkvGOtw9H3OlL0n/+2MR5YZggL+BEqdF82ZK9BPAM19n3XJl9j2PPBtXIzB1J/940Rt3tzfnWK0tWquRMfcr3TnJb3jSf9Gn/bTSiuSbSFuUaXxshTeF85I8vC8dhLjE/nFPcIIoDalRT6fFPz2IfLYWcDWVz+/btXBi7efPmg9vOhOemKFiaOL5iIAPCN0jUceYfUrTV2+2+/Br3hRcZM7NYvAjRuOGesyjSzWpOHzBOC9qxYFYQ37J7uqKvvNq86IXB7Axbs5XO1NKpsUJ+bP9vWOR96/uetMFeaP2yiS3h+ATXnQ7ELRHlGAbTL2ixwfNA5AFGhVYpOoblHYOsNYmKhsx1seprOS/YsQOjVA8GZ+sjeXZ4vcWanNFl3y35QCbVR3ftg4jfBmCDnQ47wLYZ4s1MHzbKCko/bPXQ/Lii2TWysaDm+n6tBsKQL2K/VLSFuNeKv6RH9EAguoo1rU4HbkxMeFYLBy62W+rou5mzKAaHJmzkZAQwxpqoWg8N11qixqYOKMULZ+PR6iT6BMHMO++8kwtNkdZEHvO/m0jL0erJZ3E+ywjwwsFdcFYs/Dz/fnA0jg1kod/61rdiMhreLycicdnW9ckBUkGEJ32KzBd7Av1yqHn5gn/Xz9StCey/csr5coUNgoNj/m/uD7MHGCQkgobPiVbOz8ajUYyrymHdkgpbxJAzuzYY8dR0tVK1uqJhLQJ0j9S0sBNBqxCwQW4H8Rv2bpRRE9meZAYN2GBg19ZsW1eqpQ0t3GJukjLK9PXUmF8puk5kcnzYFbzFzM2O5qaG0m09iXgG9u7M2MDMxEhb12ovkUmDHCVT0UxbKTBW9Z7kxpMoRk0bkcdyWP5wrbzvl6vjM449VU/Ew85VfSFX4CYT8AAjgjzJwSPywCJnK/C0NBMA3WKjF74xKldjJXOUq5JytM4t+2ZrOgaaHQkKyUy6Vg/QlpmeKOamJletP3UgNx43YtlEB7aUJ6bRXgYpFjtSWOJBQBzuL3/Y5hTURyFAcqkjerOIUguS7INWgSerP9RflUFmaigoCEOjoV192IAIzKEngjtwNDKbF4OGdSAoMTeBAccwsSW9HFnSaj2asKjpefjGKBuLDLo4PuIaBxtlgfGKlDek6DLi3iI+LZxhbojyQZjRLpZAsSMNlsw0EAtpInctCAr1o/YEcrJz0sKNF16xnNE8pNKqMuLjdqb8+EAylQQghUGrPoUPV99ySu0n69yfONlK6gtOMIbiuBPtqoWeCJ3XBxNhxPfauPrGNmPVle8bmfq3WuW/4lQgsrmQeVXO7iiBH/50+A3R1o1ShLA4pULyoK6Kvi6YvWrvvvJZftZ1rGryxbnc1hSDw83PzA3HcWE4p17EXbdBPuiZ9tcL3jXP9pWuFF6u5CHdJM2VnEdnNXquW0gDGXdFCKBpqEwb9RQSEipA+kOGSmUk+YhH/UiedJOqbmPEhf1OsArlO7HrQ+VNdYOLPqvx3yrmH8kIcUnNEPobfnbq4grZVYP0eG2TiKhLruIoUlVO+nrMP3eo+pyV7v3ZK6drg3b5x1mby35RX0iUW94yGlmL5TprS/Haaii3U1BhlYF+zFWbHz1ocxWT3obgVXp+6u/FFEM+Ovqh7tRAa9qWyzeMO+3K1kj0A/1GR4leKfw8Vfp+px2gZi9tg79evq1jamOl5crx3sivWsytQ/45kvtcx0hJupcIoBSslr6w9WMRMx/WvJFPdPo/Y+NiKgb+L1Klu3OrPjxmZkrG+EeC7Ktt8/H7NpuacHy8urKURe3Fr0qVlii/9qg3eadREk155mJgKd6YmAlzUwgsGqmEkW0zk3HdKwck5ga1LmBia2h4AMxHz665fA/80dZlkY2S2fdscQjuDg4O7tq1CyLgogYUoGWz3enmsjfigd6tj/vj0wd/93d/d6AEGTNBpnZjDNiun3hNLItr8FKp0SEnl5tNRyO5iQma2dXRwWRaSgrsSEppYGhLReZ+bGivIKJLRTim4Uxd7HJhwZQ2Pu3TmL0IwgrM9oOVpdWuqA5r6Q69/GXAORChOCNmnEfIrhYm7LDdC83ZaWd2xqj6XjK+KpWNtaTv66+NlZ3/n703gbPjqu78q+rta7/eu9UtdbcseZPxbsDxxuKVYGMwEAbiBIaEQAjE/EPCZJIZ+CeB7DNJmCQkBAifTEISMGExmzEYDDjGG3i3JaslWVJLvS9v36rm+7v39VNrtWzLsi2r1KpXdevut+qe8zvn3HPZNM5O/ogneWPisVBxaSbi1gZWDTLRs0yXjx3+AcKSjlEWr5Rbq7MxBZqIYKFYx2NlplaB7UnHIr5Ux2wIDxmEydEC4HSCRS5UTRh7qYq+F8EvoDdIJdwus9SlWncWKgEuzFl5VG2E+htBX4Z9s9COQNno+/b7q5KPyEGeiLHAwG9729uuuOIKnPX8wi/8wlOyZTgi1TieyfO2B44D4CMzNExb7BuMLfQv/uIvskKYJb77fM92Utsn8DDLNnOf4j695IdZymFGwy80HACR2e0mzLY8zI7wVRBfpi+zGQ/MeDwSdWcWnbQWAbcPxbBTXOCMj49D82IxOZomgvkv/j7pBSf1sNco07pbrftoiZnT8b7Alp7VWqAlptJ3SfULdkKBXEPrmHEz7GjAVrSxRCa+Bm6/Xn6cRcWVQjHGRk6dvZiaNhv1VKonnevoHBgrl5eG15+Vn5sKhR4aGNtQKizMTGzpHzmVmLVqlZW1XYNrwpFonabBTTtBYX4OqJzK9U8UgrScQLCNUmyyJvUamijWO7KIl6VV7JOiHXFDutBCViORZUEsRuIG0znRZEfUCXZsebwwuZsFMKt7OqL+vLu4AEru7c5MBcFwZ1CbcmIJLxXHVxb0Q44WjS8Kukfohv/8hbDh1R5CUv8Cs81+QoBkFMLapJdONABFfp3YxRUDclVJf032q2B7RmLisBpxL9CaesLm0v8sflX25oDnW7F/AM48C1iI8QaOjo7yvP0GmiGzKRysJXklTj/9JXYkl8+tp0plQDZ1Btgm5E9UtvHmj4e6pnRUyL1pA5UtBjYW1MaIGsDGUiKdWaoE6w7JR++LnRXJoL7i8Q1aRixdijoJBAahNpDGiCBzR/7XRrpvDWFJ4NVDA62mYjFbvTNZecTJvZ1e9qb9tV3hbaF0tfttO523sgAZaAn+JO/QXXNX31P/dfpW5UpXrXYZ2KBq031YMhOdrplqbthWunht8tuZixebk+HyjV1Bkdb5Trac+6W52FrZhD1cflMpkGCIjKi4+a8qGQ5Wd6a3XGzV5ipkbEbTbkTFAzO0YhYw3Qm52aSKbRUunEtivSEaSgkOTEXpXDgZPQJim77iMbH0MpmINDXwJhpn4C9qJHJH9vL52uZw/Ts9rOaV6MmtZd60mDmXjVWcjZXLit6w3jFuWocqzj2ZNbzkd5Z+/81db0Xp2vvLk5XLytXHEAI5qTMq4e4SWU03z7i9+H5TY5NGKU1ypVZ2nOy9mmQecu6LPNYbeQxQW7g50z3RHQ4qzlKhWaqArKOzkdqXUvFfLpO+dFeWRpkmm7Qmu9IPsp1XzoXc2lD04Yn6S+lrU47KojC1QlIk9XBXaMdI4k7ul27LNG/Hz5mqpIzIdHNn5aZG4q2VUHNHs/gjL3O1SX/UT6buttaGM21XgAc0RRXW12D6Vy0lwBxqAz7w+c4XF2v33lP/txtDjzyi72f16tC1Pxt5xSuD3v7ZchPzGjKyKXj4+MYtM80MXhg6dxdP6EuywXu7vJUXc3NzOHdkHezKwBf0NUMP9sADCEtDgfcHBMCw1HQU9uEoipkq96f1z1IPUKh1JbXS9OwQZdnIh4hgH7WndG4pAhy4ceMmOgErAEQba1avIfBJMzlYhJWZHywO+XOgV6e458Q4jkq2F7ixCTZvNUKQw6n5wVr01MP39DDm9/uLA5Yfs2mGzKzKYo3wXMXyXRfTZbSs0Dbmgb5ksydccn/6YOFfbqzffa9frYUGB+LXXNlz+eWnDwx+b6t4FdEjEQLMeaI9AyOTExMz80uVWn3tKLbNojg49ShjwlaT/RBMgl/JL+zcxquB5B2eZPNjjzLLdA+MdPZ0Y/6WAmyTo9xoufNFtZs8krggTRGutU6lmrdQaNYCFhLLpjqHu/GEVBrFqv9gwWN/kPU9wSm9zwL2XR4DxhEr8ZtuuumGG27AMxbSFhj158TcYLlGx3+fRz1wHAAfgcFg25h3v/vdmFgAgH/jN37jgBImJrWnXRIIE38bTMrPyYoslgCxKBTbMGYNeAKkyzA9tIXdbvE2wJ4/cdw4Gy9fTIVhMBaTKNCwt0vOEA5+QPP2f2j4LM2GcFzhqMe2clYsTJ5wt8zLVXnhd4p1KYTTEbc77oQGRjGHbkadqaq7cao8u1RZrMyOjo4t5DqTHb3zqHnX4EHKm8g8lMl1pTp6Zndvi6ezTPBg6VolXyktoHhscgviggHwgy0P3p1M5fyQN7wOxg7Q4RfyrO2NxSqVdDIln0xCF+IrVVsssLFsFVCRZaf+QHk4XAKRemyWKKQaDcskF9oDWE2QTSIeDYdKVQB8PT+TD3B/jIAUx2DzUwPduVgqiSBee//QKgqSDg4daUtTit8pg42Ry2JjDLSBSzeyXFMfcfPURJvraj8hIWRtJEUJUq4yXKypVgTRdlSZQBxaKa1y1JPcF3fZZl9HLqKlsgAzlk133HlPtV5no+OeXmCbOJUVo2Ywgr0Xw02PWnS2IsrelxQsxKGMuFI9dC2C7Gzo8xBnyOO02ivraDnZYv0wZ8ykzTWdL6kEzLqh91TFAOBgfPPmZr0eBRSlfW9pM/0WjqX6BtdAgOmkin9JIXxxR/1WjVvg1OYjpTsjjfu8yKpG7m1gK78R6rqj8q+Ryn0vT/xZOrw57NfoHUQNBX/4ruKv3lb+lVA0JbhKjYG7anTrMFIOXhHbDpqTuLX04YHog6nQrs43T2YvLuQ3obV3shvqXkcJM92Z5unfX/xADQmGctOmuvxyUj7Kg+6jc5S/VmTJnZPuyVexVLp55cxFlmW5XAi3KL5A0fLg6Fc5mxPhvCEqTWelUXH6MS+xQsjed1I3L/yPn+99azy00P+eqeIri5UHE3RC7sxScn2BzPP+qh/kP4SwhtqRs5KZuphXXjkRvKl2yTcW/vqyzg+yVDixdiF+gsrCrTtSo6nGeTfO/O1iY0gfjyQJ5pHeXrVOP6YtpktMvfR1Ee52RbZHMBrA5/njqZBf8idn/XyexlEiQ9TYaGYbXmNVVpmsPAK5yFVTo145Iq3x8qHKr7hmD6fY9iixcSP0EzSixq+QFmmjsMdBeVD7STr5X2AN/aX5RxeqV5F0uSOVCZXh1Mrd9IzNW9F0pQrY4hSiG3WgvVAqXZn/DKWubGcohq51NldKpVIUx6TWSU/sibNJ3npEsELoXN74TNjfkK2G7ri79Ad/yPoUfBSoVg8/Wnt0S22pELn2jTubafwiiC+W3IzvoblxZyUUGZgtFf1NlY5UbChrhsm2RcmP8QP96mWXXba/Fo5m81LYcEvlOR96qfAR7CmK/sQnPvHtb397+fU5VN4Qa9j9Q8U4yDOWgLEalv2B8In48pe/nFiUe5C4TyeYyuM/jDzxwIwQwWYBR4EFMip3Fm0esNufTklPJY1tIzZ67CyIPJeLk08++cg2/BDVMZ851IBP2ExYUDzNcFIDlBtN1t8W67iYQsErumB24jVAFn+iTb8KLyRZeYDrwQ2dTffhR+Z+56Phyd3Najkcjoa2PdH4+Ccb09OD73jH+t6ue3biMx8Pk/KUko42nZibO2HVEzvCMwsLD23cMTw0nIjDwaEBxsem5gMMm6uoMzpWQYYqxflYzB/o7qK28aQAbl4sCDJ0uchi06NcQoRE+LkW4CWLFwfXJVoYHNFMxEKnctXFEQzzGJwGILkrjV9J97FZDKobIzkvJ+eemrKO4NvGy2a6NAhHwuilGNPf/M3f3LJly6c+9an998s4xAAdf3Ss9sBxAPxMR3ZqaopFvxAM/M5dd911B8vu6X3XfL9Mx5xBofqWn4uDpcvj4+MnnXQSNBViD8lf9nvp+Wyxc9HF7pe+CAjDBzBtZNtealnuHYieeYY3sXuf+rJiCvNkAmkLOkPrE5s10m2ad7BeYk5EUZMOe+mwwxpNbiFRZFF7YDx46GG33oisXTt60vqOro5btmz0Fp/o609mO8AZC9XyzkQ+1jUw3OiID46NFkvlpZA7v2NzsrM31zfI6q1strcwM9nRNeD2sQIWdsZ97I5bgI6xVEdlYQacODW5EzVHzovnMoOVQj6azFB/OfIBOtq5Gr5R+kmIgQZIE79OHFQTyCAXR/Dy4A+1oue0XP9L2Dd+eub+gYE+r0Qlce9cBPEuzuwmPzwjOR3ZKmuIycMwuMpEglU0t0BWjKNa3pNgcHkpMGky8FiEB6Mj9KLCikgKhP1QmFMNMK+tDBWSXg80Tp3YZEjQ19wafTJOHT1gM31gMR7U7LHHNi8thEaGN0x4q6Z34X3IgGQ57JYmGS3c1M7t5MbSIN7PTZu3MK4Y+SNwbZWnctuH7RNuVR1aatqnp+YezS3bb7TC+BEt3HMIjxIGN89HAFtAwxvaClh6YK6Tefx5OHj0KBaDWJw4rhcJerqxP5YEAUv4WOrPgt3Xut4T5BHubHS80nUuQ3SgzXcdP+HGPjI8eOpjUxtuqV/TG9zZ4W1ixfJ8dWR7+cxKaFVXWqiP4TBwRYCUelEfAQwu9Ei10p/j7G6c8x+Ff3xt5r057/HwQCE30G6IN1F/+b/N/W3eH5SzaZNUeyBrcPR+rKT8qOOQ8uBwSRlTONF1hZjfFK560F3IRGw9eELJy4dBguJETL6tUOWu10/lyHW2ydPeCEjyKNjRePXX5v7yiq7fzHiTydMWkhvYQYr8SeDn/dGvLPztZPM0k5tQmZrKIZW4qR1RlYd7V+H149UNF3T84/rYN9PuZNOJLvgj9y1e9+PiWytBr0lERZgiTGy1xmA8ClI+pjhzred6irk7i9KE3d0IPkYxrVsi0DTDeMTKa4ky8aI95YpqaxtIUh2x9VXtROx4i/6aZSGFfWJfLtsGhegLBvFSCg7dda9/+p5NLepVk7UTbCsEm0DfpvKUZUq2NZVIhnRkoLNOtj264DBtI9ykEZtLJyg5g2Ti0j7uzH+br0llZS42uV4Bk72JZMsiqgkkpSItl2PjkbkplbvzVvne/NzCP/5zaG4RhzWmftSU16nW/PTnus97eWr45I2y6XFx68B6fT6z3UtOJpdxUp2TlegTS85QVr1u60o8AAx7H3CBZBbqAHThGjtSDvNOcfcCPmwToHQMCi2FNh1QX0QE5jpm96PWVCr2CnM8eyVSBK3GFxSKMhgPtLJ4b25T5yNV7s6dOymIrkPXavMEDHONytp8CEeqnKecD7ViSyQ2RkK3T+JnDQPrWzUH32/rmF9cKpSqbJCxu9hcfKLg7q4XGqFktoeFQYaIw2Cgs0X964B1a/UGHkZYF4ZPDU0mqGc99+SuoCO/kP+3L0Qmp/hWNQ0wPSPWh4Lc+FXnop9Zf9b5j8/BGoSRoZcrPIN/keB7aLifzbzyS3O9icW6Ey9Xk+iToSCAZHapyKVjuUyU3CafoH6VerRXy4SxbY4E6CeYTaBW1Zo7VxX7g19owHCCTZJiqkAN272qu8j2CviRBgzHgiyVwRyazZNqeM/SfJkIo+cIpyJNRJRZtmde7pAj8ysCg3JBO4Yg3H/f+943OjqKkebll1/+hS98gSXfR6aU47m8YHvgOAB+RkOHq4zXve51YLmPfOQjqGeBwSxl4WAqZzLleCa5M4EBEXHjbg2x2FoJhgNqxO0zzPmp1ori0P0iM4PqX3TRRTfffDM5wM95nR3R664rb90S++lPYI/Ql8DSVTuy4Xf/kje0xtk1uU9B1J98bCDri7Ab3yfC4dyqT+G7t++sfu5z4a9+NVqtMcnhzbBwxkuyv/qeK88Zi5R3reryi2Wv4tej/elwvDbSEx/InRSJRlnaefLwGZse/Glv90CH34fY1ItEZrY+iL9lJxKvV2vzM5Osx2XRJkRl884nKrViqVpPZHN4PiwvTkccv7w4k+7si6YzzPcoJGEyzT/L9Kr6AEODeIGS4kqJxpJXg1ksB4zDKAL9GUSk1USi71SULuRZWpzvX7MONMTSmvKS4KUslj3qB+a0q3lb7Qbv1WtsfyP+VkwpBBBnxcYW2o0I02LtCERVqLSm6FS1vNboVLnGN3YrJE+dxLcrW7CgfpQfFLUFkhvVyrZZvyM1WM+s2Tyvdci0CwpH/ggIuEbpumNbURwTprKBj4dsVM19hVR/o44w15hYGwW4NNJKSKNgF7FQpm78t92jYvc6VBGDD9q8ASFEVrgstNDQCcRT2XYEZ+DMdTwtFPIY723YsN7uMcZj+wdhdpdWF299cyj7d4mzyy4OQaKInqGKkfquZOmLo5lfv3R1Rq9vw08Xiucvhl4RCoemZ3bO7p446aRcOsOevXSzChQMpj81gJgUI4TByouDXtV4IHGgz+vBJd+s3Hxy7HOjoa9FnW0sis87Jz5Se9PG8nVepANH0nLqZZpD/chW48ChEP0A7CgiZfav5rHF/Ao3pSsGY+s62ShFskJecnrzqrU6Ebt3rqQ+Va/ZTlbWujTFaBEvhwGelG4qYvvbv6/6c5Ozp16Y/tvR6C0JF0jkFf2Bx2tX3jb/npnmCeoC5axvVrjcJDU5mNqZBhA2Uzv5yzMfCzkfjrjSAtSdrNkkklKpFAGmIsqJi2XwYNgUDbDJkzOfE9XlYqG5ruqmI04hdE6h/m+1ZdkBr0LY68pEXs3i5AqxU68s179TaEykkQjRvySv50q912KoEtCEXbUzCbPoVGWYnM2FKS9wFhpDDe3cUYqcXqz/kG0q0fsaSRAzBnXeUGLRGuuRH8+vf7yuvtRgtKvaymj5R0902E/eNorRsxeEm+cmvb69Vqe2Eqkwk3PrRDQbwSYz1zxSW1SEeSlMAnPbKotn9rGicPAuee7EdPXBR1ustM3MjJe/sFR96NHUyLpCyakF4bmJHdUSG/VR26C4ezdCAL74LX0nnj+8F5/Ae2+ncUkhjRMjigEJm+KOkRPtAgFi7jQyMnJAb1hMMta95dFssPmgj0CBAE5MnQH2+2dICG1HDwx1ljON+Xkr0DwCpa7IgiLoWA4kKTigwtwatEl9AN5UgOsjjrpXFN66xHAPg2dM89plUTScG3t8HCkMTDMpTJ+sTkwJmvGQW2pXhQZbDUlui16X8/jWmYXZWWZzLXqaXWLhVLXprVnfXcEKq+ZW5SaDLOzULTk4rBb/DcNlsvaDznjTKxerd90bQmJnV81AprBmAs7yc+998dPORoqOc8owa6PY2gh3CdIhOyn/kSsG/mlk+OZUaK4RxGcbp2xz37a5/ppqM8lKHFMqcBe9cSjpRXtSagZq58WSJNEAZQyhYxG8ZxEMNZRJdlFiNlH8eNTtYDMkMR1epemCeDHvgqVJRl38SHcTw0fFHWye8Xcs+GtzzkvXuHjVUmIRrqdx0N+ms6GiRnnOEuW5MguPnWSo2hfMTE/PAn1vueWWN7zhDfCxX/nKV46l5RtPo7+OJ9mLsB3vjqfUA1DHa6+9Fu8X3/rWt0CGTKaINnfs2MHXy3eF/0xyE49sjqeUczsy3jjIE0shiBDXfN7Q3QMuTGonOVIXlAVxQgTOBUWzEPTss88G24+NjdkixMiHwu7atd5vf6j6ve86P30AlFwZPSF86SvCJ53qZjP7z2IQGw6SsyE74jdye0q1NRlSHddfnK9+7abYjf+OByZNu+hLcSn97W+Xq7Xch//Hmaed+ugjD3//h7dd/dprrrnuUqZwmD1ITr5cK9aai6XalFvMBEuNEIuL3Ugs3RNvrOvxdj6xcXLTI7t2bo/Ua6Vdc8UJd7FYSPWNJZIdqwbXDJ1wMpQJpe/OzQ+iHI40k3aWNtyCUC+gzPCzYlLlpcmwhrSOW6ifZV0BKhhDK2oQ6V21NppIaOenAHIYqtbdKvgVdh7JaFVQk8IEpA3JkwZM5spatQs3K02sMa6WGtZYOwv3qioJOKTbAABAAElEQVRogFF7YjEsK1PQmiW8VAxjbFxJK7pZ90s1yJhIIDfpUc0+xBBdoDIG3YTw50TjfUNrw9H4TEmV4D22TeFs6kMlI17XSwC9xdnJ8tLs6OpTYuFGV6eXjTpx1MIg0rozu+BWWCstBTjpTBc52kwX+/AwwgdPNtgxQLVByFxbvTQUmgpSYdDy/m+RMuI4AI1sIQObZA8JlViBPhsK/cVIeaganLoYdPqRaiR4PO5tSsYHxrxYrDcZ6ksxSu5992/BpXY6nVl46JGujshFJybQPyLBsB2FHReDK7jbWpYs+Te3y7bZAsEQ+2YwsOjf8IBzw+zk1sXFhdH1ZzsJb10O5sbgW6LoDUGKwcssNoIfRlgCC/pZq7xc3rC6lJACw/wxUn4TT9pSSxKBN2ggDdIFqKhf+VFjBZ/MIdjMLfmZYGVuQ2R0zRtpAvjlPVSFSaVXxbwrE6XT/7XyV7GgEPWnpT8PDdTYVVcPpSHVr3y6C8rqpSYLcXagYb2rpnRTI721ibojh7Q8IoFy0KGyzGF/hd4VZmqu0/JjLtQEJ5hrjmyvnH9C7ObwBcXK5THvJhrteJGot6qrel41fk2BV5GKuF3Fjv85Uf6XnsV7EkwGkVPq/ddPR4fyxP1J6e2L/gBRbC+1SrA/pjcoZq4xurt2zpro9ztesVT40bRzH35r1Xkk8geKHW8ESLtLzqrHCy8rGsWzaYIaRfVNPW0AmeqlVEeoW3TYhtui1T57TzRzYbT7KyIphaQZPFU+y08Uokea7HSoe5dbYx8opvLck0TRVH8KTWHNWKshvOMrU91aaZWTPupqBWv6VTkWILg94R6/IVHQ3O6Jjs7uaCKJihculTZK9NCqhXw+sUqW5NCFY2wNsDpl+WDVDyhxfHycbQu5Xg5u/R50Xton3vPyFg0nhLhNf5kGkEfj94uQ9mpYeBgQKQ3n6TNvLJkAOK33ECs9eeKJJ+gb5CbwThxIbSkOLPrMyzrMLqfJds/nlfEpfSUG5lUHpa+o0srPemW6va5tJGY25HEs2cX5E84vBXcbjTK77GIsh5QTamxWybLqB5VsM706FFmF05AnNm9K5vpiuU4+9x0l6Dydr1meOnCtT5C5ESm2JgMIgREuMxOHULGypMn3MS+X635DAWSXBr3yYgj0KzVgMIZmdUymNSWzzCrAyuEl0X86P/HbCWeWmQI4zYSQjY6vcW9eE7zmDvevamE27hCsRZcb71wFPZovSS3Mpkeojjl4VMWhdJnhhXrLiTROoT3cl8rnJWbbLmbSVBy/0NhCp7VjsEd4uebMF2XBxPKvRMxJJnyEujtYsbKzvjoTrOuK7dWbh3dDn0MK2M9pqdKcrcjvNDuJzC8VFhfnlxYXm6WlE5KLTGKwnfCxbGL6+te//hWveAV6YDZJOrwSjsc6BnvgOAB+OoPKPIP06Od//ucxmPn85z+P6ZfNxcoUMYrO5eT/iWjf/e53mU+Rp4JgsZhioj+c8phOgNNIoFGZ2r3p2FsYKSnLgK1263AyeeZxoBAAYAgVim7025i5Up99ssWxUmjshHj/gHN1Scx7LBpKpXB+vU80e2sJCa1rP11BWtphh76AhfOduVl2LGRxK/QAmz28ENeRVwMObvtBY+Mmb3Rk167djVoTY2Hxh5xR3zj+j79/SzKZEMUtTC9s3D43Ow9xGBgeOrE/Hs1v6fFKbn/q9BMv2Dy+FTrA6pqhkRPCqY50JrMwNzvUHWIFcr3ZKMXcVLgeZrsfKFgE0yBPRqowvQaamEnYsIo0sc22GiTLLQwwr4RvcE8k06FuMD0RisQiSXaJglKoytTZYJ5mKV8MReNIGcjL5NdKoIjL8LhFHQWKJFtFNysnUGYZMNcQPKCmgZcmYyFeKa5l6kjx+ieKKs7cgGQoI3CnUa/FY+gH6F6tfYWeUh8ZG+N4TPBPmmS1gzOAGS9ltUa+qrf9rMFQT20xPjUXLOXdZDyX7ezsyN055W5dMFA9BAEWhgd801iD5wVwgbv8KFy7rNJrWBKI1V+Xa4506uIwDz4NmJUDfmJuIhY556XNf78xsWuptgV/HEGE9UxC5vXG+ecHmSyF08kMEfgDqPfow4/gU/qMM1+SwaJr38MMxb6B3Nue1ANeDZ0DZ75zsFzKdfYIrNJvLdgs1GmkDFywLzG9L1ZJHctZHS7cK6TNIz2lR4RcQTMwQuKFFGbeHWKSFm6HEIZVb47uOSvcpDWja67JgTzNc3NWHOxgFaA3gFTKlHvUn7nJ3fW5+YU1I7GstnzUd0TR5GDt8YhojhbOa72XBKlm5GQOXeuPwwRRCtnY2+VhVRjNUbg9mwtlYrPGTu+2/G8Pxe+IhfLpj1Qqr4z7P2xEMqHm2Quxnyk4mKk2w9VNkegpFW+gkL2hmK1HyREX3w7qfCcyXr38e/Pvk/svsWSmaJ1stUybeCJuMXzr0g3/peeeSKw89NtTczc1qnemGIzoqeXO1y+GUcMEofH6e3o6evtMPU3LbEbtCYzclZc59GuvTZC6Vz1om6ReMKn2jt2Kbxqua/OfEznYrHVtbwHhysD0pvKyGesTNs9tiGIYMA1XymYvXbH+zsjMDO+QYukg16Ae9uJjw+VoZH236ug4wLw0X/Jj5Z3Dw+loLHbffZuaSajbyaZwm9DWvnVtf0x79gp5od/QIsAYul92iH3ooYcg9CC0F3qjDlF/Zm+0r1/72tde+cpX2tGEjeHFRf6OPhaiaVmaQ+TwpI9gHqzTbBvTXo+NjbHkGAZp06ZN6BK4eNJ8nmEEGsVBG+1F+0wIB5lzthgYxytwXyuLI7K51RdpJngScC2rG9gP1uvW6tqlAo1uSetyceTJLKJFYRTYcNkfEUUofp4dthbAYAJlLJs7aq5WbjHNC/hIZnetMAxBQgJHgg0tMKX6LI9iGkHkxxoWlSvPIdqAEHMm6jNXqTfjmCWMeQ89Qt1qtSqmWtiykRZhnje6phqN1htBVFvUM4tDL7wx5wsXJW8IOyhzQ7Xtyfo2L0g7qVOrXqp4QvTLoUb5psXPNUNYKONABI8uKkZgWBbUqhgsB2A4EUGjSxvgBXBNIoRMifAOUM5sgiUldJn8QuOvqy7jP3ni5FFO9dKKLTTGBbY/DKyNGILqoDPRxL0LuyXZ4VjZ/+pIYXu6xsxC0E9pen10BgtlZ7GC80g/XyzFY4lIJIr1G7uZwedEXK+nM3fSujVr+nswYyRbpD/f+c533vKWt1x11VWf+cxnWMN4bH/de/fh8bs9PXBYeGxP9ONXwjABvijYKJgVv5/+9KctIrXfKiiRY3R01PYTABIRMotbmOvBkBBUMDA0tQ2YD9GdADoIBhMYGXJNViTn6yXDA80Lh8jp6T9CiQ1RBAMjB8VoZO+tFxwcCSAFWM6duZGJT3O4CdHs1nZxsRyn9cscNjAwQLv2CT+8WwoJ/Nm58Mx8qyhkmSxOjoBoYtVqrbl1e/rkU8497zyQQDyRQIlNv0FZGQL0FRitAR4HB1cxBD/96U+h+gNdHdl4uDq7dd3qNWeccOKJJ5/0xS/ujkSTfX2D6zacVmo6MzMLU9FUJf9EZXYeIjeYcpvVnW51AqZ57KQNCGIR3yLEtcRMolx2aYJoQadETTVTQ+JaRtCin4IQPDBIT7ocmNR4V3dHV5fEtkZiSxKgCzRhcvtmXFInMjlyAsoKIQqDiKQYRKr46nRTirpfFFS03SJJgWSRiVa4dMhCzjJFBnwiW0Z5a9YAo4ll61xcZKHj9ujDRzY+ziLak09eT+5kJ13Y8tARH2VQMgwldhNeIxxBLemVU51ONXrJ+iAzsaV645cWb/6uOzfXjCWiLz0rc/1bL1h/GnbB25bC7H8gR0OGWqtO1NgSMnHetg3UHfsuLVLNJf31+AZ7KgcKhINa62G/tX598AvX1//h044rb5VmdILyS86OXXu1l83wkdrV6Uh5tmwZh+WF4bNbm+z3uR2iVhpyjhan7Dp97O/FH0PTGiMeKophmxRz+ZBa1TxSUnuF/tVcGx0vPSUQyyGcbICxbqU9FlRWOOYMBKF41+uhyELR5l0irYnD6658HNYSc8Uz5aMhJg/lT8X5j+Nryq1O7Mpk/LNG5c2ed0BftlIoZ1PB1ktogxWk75J0yl9560YtVbhONjkF6VoPFW6emwj2phWfuAokOWXDV51/v//xc733h7yF+KsKzqt4UGH4eC+DRqT4F9Hw3QPVt+TjVy4F8bIbrZiiUMJnNtXf9p+lj3Rl0KWQnZq2fNhRWr5TpZtL7hX3lP/bS+N/GEqUet64y7mOb0lKb9hNGM6p8FvL8fecIqGE1rOtSG+vCVD29O7yBGhqrxLMF6unajfvh9Lrmux1xaHcTAbmU9UrpEcKtZkQuRVFeZlL+GB1qKJSANnaciWo4JM3TzVueu46yYgT6epJXHNN9J8+o6EliDHSc690wcsjJ66LJELnr1YAn57y9d3cQnhkJJTLJd0d5aC6ww1OsgWrTisOuyhmRcCxdonQGarHUljQILMBXfSCbiGtQEavkQ8CCCKG0DSHNnIG4hJohfVQAZ6i+4VGE4jxFzwAJqMM9zNpPjSXgxxQNXO2JnJcwBS1sz0KPUyL8O/FXGjLwusyIei92f/ZfMUMsWYXugJkbivGSjSjGGfK0WKiet1ngwbz55fZ57beQEGKbypIIxH4piHLrNcFyuLyCcE8cbReF3UwAkdN8Hyymgp0Eom2hZi5wPHCyRSmUbAJ0Gg9MXOEjcysiIlWQ1OKpmp++NL5onnK3hBMPl6uK/OG1zY2bwzqXj2omQbqlW2edELovLMmamE24CU/JOM45sx50+cnPuJ5xaASnfxEX3BbLtRkkYSf7610vn8qcerCSOiWM9L/8mDtXeh4jQQWqyz8yWvvXxgAUR6jGZZfgcDDwkqrguOBsZSQwba1hW4GHsgZBx/SDDO10IENt1jyFpimUJcIP7sZtlwC+je9uaI/wTZttdCGXn91lp40M6VaYA/RDrUcawJAb8WZr6Dv1RYh+UJxMZ8vLC1yAQUfG107MNCP1fYZJ60ZzMVW97ETVDgszmuPfQF2lF/+8pdxXnv99dez4fn73//+4xh4uZ9fRL/HAfBTG2z2Av/93/8oBwD4D//wo5qqNAG1PtH2hc0USAbZAL7CXmP8A1HhAjzGB8xXCl1h6ockcIZ3X5mWa8gS1hrEZIImDhAU8HY00S9Fs/x4fHwcKgglYH+Cdg2tGTPGbxxP2n1EJqt2WuIz0dimPWna/SNYRpJ9CZvM4qxfQTUTDoWkVw8KeEBiKOJhDHrvvutu0gJx6TdGAVsXiD00jyGgPozI6Ogo7aJW1I02dnf1nH3W2bp1nMndk+ViaVV/T29KxKXbi29YtbpcYfVyFpZ7646d+Ij2YnHIW6qTfZiQbrI7MVAEMbBxOgXwAI2w1a3W34ryQTLxbwxJQCfFH5W0f9AQA1S0KJeZXu8QxUMSrb7I8bEQjoZCcWli0XTLYtaHS7EgxHDPJo1YVjG+YFsYWOWiRkFd6QeDlZQtqcwBASIGtF8IVgjZXqgA/hG9vmvHzkolOjTQM1NhrS8bsaIJEd3gjNEUymRktFgLDySClChRoxqEdhTDmUhXamZ3/uN/H771NnZ7ooGRUtH9/o/yj4/nPvaRC086LbaLpbOmIRZ3GfBORxm2gFXSsu+FbzB/mG25+LSUG2qBQDHtT3pQFYaYz+pgCwS8ri7vmmv8wVXNb9/s7ZioppPOeefGL7sMewHaBl/It0n38ZLw2nOL1zfytB36pKUfLIJNTm8QQbDDHLqxTIwJ0YjqMMH2ykQ3HA7BJtyEmBwMQ7ScxkQ/6IlEy+NuMmkXYe/MU5uTjcZryC1/MHzz7sTQyPBpQwKrAGRhWuLbseM91A3/9ALbJOZXkQ0LyJ15pCSEmRfSXFukLcSt15iT+RaUlf1nilCDCNdXoW5htvTeVMqui898NFS/XcbIhFcjtZ941U87ye29Xi5V/9fM0rc63LMriTed4Q+v2lFZP+deXoidNJayXKSMNPY57LdjA+EJ6ZKy88HHmieuaX4s6T5kTPjh8UIlZyDS94G+rvf0saeTDvXdcv8tX+2TuXlsx8kkWU7BULZj2ixW3tprwnXRfrCcwcrfVpyVQftfK4fliFh3dMSv/dlqAcOZm8ONsgYu5NXOOTfxrnciHWQWCOM1gEJtrdzQOeecaXME/23ZumVxYXElSmkXBiowwKAdcExd8PHy8uKXGGssJGKYB0O7X9AtbM9ytALxNLdcIPjD9bG94MO/4447ELuDflF+gvnpAcgllBRN+IUXXgij8vR6oD2Raj5YPlYGYrzzDAH2cq5P/gtDhXixXm2w43FnF8Z6LnyCoat8tdSP+Y0VAThY9kFrO2cW7rz3gaE1I2wrgCaz2ZRoW18QwFJb2ftodFmjy6N6o4kjRpCwDG3MDG+/KJqsL7FFDDShG9mU+drMI5Mf1VbGq9astd8t2lHLFBAblsBOHoYaKCcttDFWRRDwzkT9/NVuf7PGVn/RC19em35b818/38l3XnGrzH84B/2N95f7Vj0+4aclwIYJ8Sp1fyTy7Zy3maXEC//W530Pxbvqp70Rp9Jzf+70/a+q11lZF/7sw7W3p8Ly48yf1L+oryVkBdY2MXhOxTAaYSKHdpOnm2fNMDXDHjvi4j8Bn2YUBxdUqQV5+kCpfPYpZAM/s3oZ82kX5S3oGnVvzO4Y7LlLNf+BSX+65J/ez/dGwcofxqlQa+Yr7kIV0Is9OdQBui388tMH7q5i+mwEf+lUbKCn+4Sh+OruZi5OcYNqcOvYg35NgM8q+H/41D/wnn/wNz44uXv37//BH/DmL0c+/vui6IHj4334wxygHPvAB2745Cc/86v/7WPv+sD/t1BzsrEmcia+8hWfmZgXe2tnPGgGUmQOSoKW2G8Mj1ZAYigrgUSD3sBhYOHc3gfPprWVA8KNjo5qGjXz3+HUGCSA3Jo82xkeTqqVcSgOAoke7Nxzz4VGgr3bT9/1rnexQQJK6XbIIS5YSnTAah8w8BD5tB5pRnW97l5//cnNh+43VjmtJ1hBl+OJzlM3ePFIb2/PY2aTxnPOOQfeBedkOKAG4XAGtNs11dSfjmUIbENoL8fWrVs7c7m1Y2PwPXYYsQAnnOFbNdhPSVO7dq7py3T39AoxMjdrtJnmZXAJDGbDePAwRIKZXXJfuW0U9GUhJPEgmczlMiGW0phwuSnmGpIJSLbwj0HOL0w3ykVRRjeUn58sLCFl8DoHhrA5si+WFDXqBQtfzcRPtnAq4BRTHUmDSa2+Ih6kQb+kkgNpJVZ6oioJ5s0QIBOPhrAJYr7YzGS7arHOXQXS4xWb/IVcEbxyjoWaF404a4Ol8Jbt9YceZTVPemzNaSeuC+e6ij+41/v+bSL1EGwVR35OYueuype+nnnvmmyiY3cedKl6QtcFflr1U0sQCkVQTNIgIW2PPX7WdgT4L7M1V+0O40C6gabiYACYNod6e71XvyJy7rlsKo17rgCZToZlSWJqEYuw0o+BZnX62NgYw3366acfRpmHFcV0PzGXf/ckUogZzD1Be64O8MDEP1SaPant1f5FroyxnN1ymCmRTliYmYy6zdFVfVmkHao3Y0ZcvU82w31ul9Ov/G2XbDLQEy50TdrlZ8rQHK0AC59NKCHLsXSBY/KXOvHPNScedMOz9UcfKb/vj1NsNI52F28q1M6vxeaikdvSjfPeEjnr2uFwbLWpLSW2qsrvioMbE94KEr+qiqEeudZzrnDr9zXrjyzOzW18vDqw/vVjnafyERD1oFNWu6YrirApVgboep+YK2/b17po3+ybQev+yZ63c7ARaVpozerYr/xS8IpLalhIYqYyNpLYcHpoYNA1kMYMyXKmy7+UxXp4ADDT5gEB8EE75CC1fsEF00BQGaJnaAQk9YUOgEfNwSgwyzHjAen52DGMghVhaobKw4cwi9Jqi43teDEx8gi1LTAYevrMFWWUsv+b87SMn9sf9Z5Xdp8gEZvl1848omRXU73jzM4vsAVutmcIIyYskzexHw+bESLRxoYLGg23hUaXrYBqmQWve8em2VXFVLarG4ltGUG2oeAsZ9VkJvmz1KGGjFKcrQJnlazy7KG5qDX3mTiEKoBQaL1EguZeSWxSUreqrh9FU0MMEyhDFN3hBjEXaVw+6nZObat85aapH96Vecebk696ZejssxqPPBqrliNDw5HTzs73DNy+292mhUgoDKDn2hB4VfwuqhHUYuXvsMMc4FAch8nejS6kSj9OZK6sdIU2uo3ZfLCKpyhyQbzxmClXWm6Ptc2Fij+7awIxff/qVVFcYYXVDroF2+8G4Bs2wAvQGKdiKpSqG5tnRA+0DFWFl4w6GTLEltsPKjV3oYCEHwjudEVdNLZPLIHx6WpAslOoBkVWWuULcM7FQr5UrA6uGlyDQ5nA6evqR+sx0pdd3ZvOpSNxMYXiPtShezrQdJ4C2xfwbAHw+7/e8OFyrP/Pfv+3Jqem/uZv/gbNv2IdP14cPXAcAB/uOBdLpXe8/b9++as3/dYffeJV116/ddHbtoh+LMjEmp1xtzMRpKMiDXz0mqmAAJobl7+/5QvQr6a9IABYXnnllYAxUJkUqfPz0BgW2/AIFxFgNhgODuTrSGEtiG3ndpg1xq6DhDbtYSax0aCFlEVjgL7UBwCPsnRlDjw688yWimBl+MGun2rND5ZPKxz60N0dftvP1T+61SssGaAoShFPZ8LvfKc3PFSp1M4480wUwmAYbK1R8MLAYQtNJwOQGAJoLer3888/n1XW0HVaB2cDdbfRRkdHEXUjErYjRaEMDRFgF0wFZFkqcmTG1AyzLJqYtWOIV7EE2uugrlLwsmymJmzMxgDSGHPLZgZWRYwUWaJerSMSLcQIeke5tFheZEVNIumzX4BMqAKvIzpYl9GQJVMikfwJu/IfTa+Ip+iv4dQButAA/rEJkwbSVpWaEMWqhUkMKuew1MCgHD+PCrWwlE4khvp6WEds2iddH5CVWkkB7fgvHfPXVhdqX//q3Gc+F1nKk7CBTvgNr+39tV8OPXA/bAQ+J8HtDIdqqEr4/j0/jZcLQSg7VRBil7cTnaFR+l6IqmuDfG3fpULNswbcBBtqKZPWF2Qq+0xOqpHyjyWcPm22QFe3D9PSVkEMNA5+DmL83E5xjF/wIbReePMNiJOgxSu6yXRWq8f00rXeoz3dsqJ7V0bTdeu+ddl+qrRGcrIcYU9m5oqIsVR49DxkNu5EVzw1FIJzCoUafhO1C8PJkLpRlqMleatienlNAa3sNeXuk59u1SIT3nrIDyFpJ3JByLmgUtg5Xb5n2MvyCR0g7QstiK8t3DvodPWFzj4Xdt2Lxg7mqWFly5gqmTB5H6BNK8OP4WumVDAhBNoqJPkCjhlXsQckxFhjvfOd74Qy3nLLLYiG3/zmN9vBRTrMhSWC9ABEkPXA6E7bpstP+x0AY+DThFeLScZmcsCKPWn+5qM2tE9RmQF0Np+wriCH/AFyWGdrrLS05hYhNcQX8TQhC3ONbZPNAlt3hUKAMyOS1lPArUyX0eiCcrHkwiV8z4kzW8Ynx6f7KjFYMtFRU6zIiOU/1FGiayq9xRqIxDDB2LOdZ8wMqhDReqLaKtsJz9Re9W61pDUlcWtyNbfKG6JPDKUUKA2cs4bczrmJwv/+6+C22yM4b//wn1Rfc2nm0kvCF18c6eupx1Ob895DO4LpiofnKtKxEAluxfeaMX+RPPxKyK+wHsoWp1lS1j7Ml7NwM1DMWkeiHIILwF+0j3tOEsBO4E5Pq3yjYQHpQhiq75frhi/CL4rXZJkxrq3wHUkVMewCJ5fZlRHmKOKh5s1FGBS06w6a3qkiPrdA5aiL0Qw72YiYIvyEbVly7pvybw8HgxlnXa92R3zs0Y0Li3NmtkZdHOrKJgaykeEsvHdw4cgwm0Su6D/TieakVjFStrfVkVI5oEleLDtzFZpD9Wi5e8Xb3pvK9fzph36FxYr/95//+ahZIuyp6PGr56gHjgPgw+p4TIPe9OY33/6fd737Y/938ILXbi+wB5qfYUcT9rIzSxGcBRzY+kizUF51JIJslJX3KP1aM5wpYw8TZqf7lZphZibAhyU5lAUBhtiQCgrBhAsxPuWUU1bqYA+r0k8rEmuB7r77bgyw2/6E9idO+4c8raKeZiJhzmQ8cu7L67/730uf/7z3yGNOs9YcWh1+3TWxiy6ew1Hzjh19/f0gWCYyxPYUA8TlYE01IYBe9jnAvwVcHbIGYC0cz/j4OI2yGm+AMVIDogGYSUuHEM7o0P9YywCqwdI8RV2/px9WTL8MZVuCQHImXWZ44WJDZFZM08LGotCG0FYaWFPLUprzmuyaamM1C43uv/+BgdWrU9lu4DFzOEQaUiOKLh0yZwiJ5KM2BNkqOmRLWRG8MvVD7agMT/XPkAJDkSEi+sUQXwNgFMKgd17V/MI85tYDg/1IaiH8aogyERXnh/WQQyn/1GS1cfP3i3/9DzF1rGol1fbmrcHMrFcppyJO3ph/qXSbFjOtShUcX2n6u/OCueqTZdNr2B8DzyXVLucXyvPTA8Nj563xcnFqpIJXdBdVUMeSvM02qf6Ow+gwIjxFS8OZVesEgmDh1cxznWyxuqJaJtO9stYD5c+nh9TJyoz2LtzEeHGc6GS+FHqPCcqM1spBOFgX7Btn/+49WMqV4QwOXvTpf0o/cP+7oWbECQ8MVU461du4CfOFeq0Rj8a0Os1xy52dsXXrcQlAbfZJvs/tikJNzVvV36sVujHfyMHTrsjm+X3ZbgJrGLwkHuz1GbQDD1F30C/r6gE/2C7xPhwi5jHziG5hrSy0uM0H247iu2B+wMDkcPrted4bjGkb31JVK+1lrNvXK+tv24vv3Ntuu826j34mrAi50ZN2yXG7FObe9hTNq2kO84rqRd334Eu3QVxA71g/g7myJZ3G/KolYubaUkmoJ7n42gzIwFqtSzJ2y40Ov/OUTfMhoC+PwH2KCc3Ud08luRdhoizcUkX92mx+aXK3PLDAkhFs5gYYPBFZ8STLNbVJdNcKs1RLNMgkUd15qmgmc90f8iAVSY0dlvI0reGXct1EpDGMb40f3R58/3bFQSVcq/lf+vrsV292OnOpX/y54NrX37srMVOCB5GnSahqSHCSwy07fbTQS9a9zmYwSXamTpBoKbR9b3WN6jaD9Gy5Y2apUMgvDg2xVhxpvpOOheE0SsVatYLDhaBSrUOu3dpS2GlGEynfjeADDM4E/XkszOpfZM4+TA67OhUqAUt8izgGwyDF89gqIuw1U3EfAeZiJdg+75RYSlZn1EoyT6/Xd9fqO1PJmWrH+h4vk0niR3qwO7u6J9XflcrG5S5LNTZtUXvMle1hOwQaRj3F73SwVGuyWhheHYdbLNpSsLpSzcGaulR1+l72xvf8aeaTv/OOq6+++sYbb1zJPJjIx0/HZg8cB8BPPq7T05PXXvuG+x7e/Fsf/+JZP3MR80yhxjZowW78DIc8ZF2ZqJPCp5Abmi07M2XHXcCeM0jGG7kY26CBisPaQ9Xohs0HqXnRzH7mk10u39oFMZmitLzsssukFDYHzAdWH4hgeQTussphIDEHdEtKtBUHcbZs2UJ8G4acFQte4pB8n5grEu25BEKwKR9GUPA6mAkdTpI9iY/KFVOb6UPH7cxGLrgwvOFkZzHPupxwPNbI5ub8+tYt2yanJtnyFyNtDFkRbAN9Aa7w9BAicCwcDLewONQXITTwCVYAbTnknyEAG3PmEfGxIbfIil4lhNHAXhpzuCftlvHxceIQ00zEmmfVN0aRZKdke4vgE0MmVtXiWCK9x8BcDzkAoOEZj00K0x34XWSXJ2FjlhzLZNqsK2YRsoG+FvTSLSLdIuQWVAse7wHJ2r+H9UuKgEbXOOgywhlBXDCqgZYDq1bJlTYEA9GtcRSpl9a+pKKd7rqeIJlfXPj8V+INWmQxtmpa3T3t4KtoZA2CYeVqmkunabDcUOSE0WYsWShoMbOeuKyVqlHpIMRuvKZs1cstFKqFpcLqSJBL6FMx9Ek/K48PfehDY2Nj733ve1cGUpAVcxDIdYvdWBlj5fVe39zKBw6vyg9/+ENYQ4wD93rwIrvh1X31q18Nh/qctBuMscy+HLh8vZM9naHr31r5oz+J4NEHXYa+LLfKtlrXv9VdNXTo5AfO9EUTqk9Sc9DBP4P9ugLkw9z4oupV00V7dQQhdh3Nq171qqdhVLVXXs+DG9kA7yFFqhBaWQYahuFgtSPCeeedB2PAcbA4hw5nZoaZIY71jrm0tAgkC+FwMslK/eXqIIVcyjOLd+SyvKUIWaFo0DtZUeFoCton+MpKVLPUSNTQKh6xojKumLFIEsqVFld/2OIGQl9Ew4gJCTJESYRShBcKK7cdAXphw44ZusUqV30aKBRBaEYsrJrRVWyKMNC/qlD3iwUMxdPkQ3w+CmVHEggZESGehviZYH1k5qlkzBz2bGiUbm2DlVY3xDVUU4mIaa7NWY9NPaSj5loSYEWix3jUncDrX6F61z3qQD0UxeVpBGI/M1v/wX8mLr98MJVcQKPNTpBKqwhIC6HUOxsXnxn9SydST1+zVP0HgKuW5YqHQE26ZrHz3DJV21V9yVw9u31haT7vPFGc6+5h715nxHh+rhXyxakd8AmA4QX25cBKzvVWrR7r6IrEQgbxNrzFarB9kX0iWPGE1Npn1yU6lv3WMTJnQ8opTM3r7lTBRz2QxB4aZfIcWJv1wiE4azyGRMLxehBeqMI/Ny46o78/s499nek62x90n9H02juYINikxSprhrUJcB4H1DL/EltB89guQWuSq+xeIUUCmxWnY85IMjR6+Wt6Or/0R+/7OWwzce8KH25yO346lnvgOAA+9OiyDcDEa6++esfOqd/5uy+lRs7dNI0TXaydtfSsP4Pxh1+suXNFNjDDZZEDGE5FPfRgWMRWKx57kQEnkL2lI3467rMneDbmJcMyTOU7XJ7yzGe8PBFq6gwCkC0HtklUjnkbRaXVeoHfgGF2Mz1iIogFzrFDAwyKpnckkcjV4nGuSQha5ppHBJIQyDc2NraP9mxl44F87GxMKnS/Z5xxRqtGK2M8P67b057sbBJDzqCID0Y16Goruyfpt0svvYyaQmVp8pYtWwCxtBo/H1ib0zo6BFsvFOw0ED6AC6AygRh1o3gnuR0CVIj0LRD68ccfJzmKYnob7pwc6F70IRZCg5aflB9a2ZPLVNB0pVrSbs2+nct70N/Xk0nE8biYwhsV7nhbNJJ3B/DJmf94u5WcG5E2zIHFxsbzFphZfINdWozTC1FYNo3RyyUMzMsCJIZ2cYY/KNeaXija8CXZRXarBsr9LbRS7ylpRRYDH68S4ZlCc+s2npnSbZ1d/IZVtu6Infuy8je+7W3cBqlebrLrJ6KZa19XTKZ3Tog1sUzC9PZtXjiaG1hjcgnqbJ5A7o1GMhwaTZXWJKO1WtgqKPbpFD4E3tJ9AhFw4FmdQLg3QCzOq+wI7hPt0Lf0C37aGVZsH4i5XP9DJzo2n2rwzNzC+ai1EBGGnbUokbHgljNf5f51UJ1S6dD5F/q/G6l8/sbmQw/C29RH14WvvTpywUVuznohPQI1R8LIikfO1GT/ajyrPcN7zsTFdM10dMQLekptIbIFPE8p1RGv81HIkFFGWAx5pSyusUSwNAJnEJZoMjMQgukQQPEY6I2VTeCaJkP3R0dHD9jVNgLWMfYp0yzUEDbG9JXCFEE3rf8HmznGt4xDepQAs6EtWxG0ZtLZNaOjxk+kg8sMLraMz1Rq/sBI2hAyucYQLjOIDzyMNs8Idtl8Qeo75gmiEcJGDMBd0SxDtKgRldGKHNm4iu5QonkigGjImjgzUK6tueATT1pVIyrcFKuE3FopXynmyYwNddnxoINdHlEbsiUHfqFEUpl7WpCUfAxkNm0zJdKlxDHATIFUjDPR6Bz+89T0kinSQGVV0ERRTCVVNS2hR8CneDxVBHPQWEqXwhbrQfYXMgl4rH86ODdLZVTkxscFfKRZxgSX5DsVbLxrwRP+z5w+fPaa2J2ZK2f8SlD4Yo9TjCJODJ9a6PnVSTdZajiR7yy8e64czuTYomIBNfDC3HRPTx+7+MoejI0cB0ZYdJKfm2FIEx3dbHQ87ycmpgCfqkIYr5nyOBLEYtwyasYlCsPU9JdKtXK1GUL122zMLDWQgUQzOUT8lXrKi8a7uEt6iaiHY1gEEmxIuXEq6Ip7/Uzt6jy1VL2hQ6PJNV3BMJTr7IfkLFWChapTrnkNuc/UU5LQ6mINXt0pVX2uWa2WiTiDOXlFqdb8pZo7U/IxDk+d8NLf/fsv//n73njppZeyJRhfhC3m+PlY7YHjAPhQI7t127afveq1U4vVd/3lN3JrT8GHHrpcfOCxpGFXQVKuSMhLRYNswu3DaXvgFKvBfNnftShrEwRdmbCbJEnEW2x4S0vuTjORxrxmNuZ0xJx0XMuGzeoFfaT857CT48o6MavC4tsZc3h4GDJsYRhIjAOYRwTmQNSVeLbAOAfYxoH5FngPFAcDR27QdVKtzHaf6zb6ZYUP6mKK49gnzvPrVjNhq8+Y4cJheYNOxOP0M02m8nQI2l3snKk2rq24hZ+mf1D/0j/AWqQDNJZeQiyNhTlddP/99wOGCbdttxHoGboOPIY8AsbUyrDJEw6JM9pCC4AZAtgC+xRFOoNii6Yy5PM0uo46kHl7EERDDB9uhcWWzBEIExLBURXfsWLsXY6Io6ENckMNWfJrbMiEk4+W5y0rGve2bJ2Y2jVx4kknx1MZ+AzlYfx5GF2xUKvYCAHjJlsVQ1Mi8Ui4Xmn1PSgaaU/gLH3uC70f+3DsN29Y/NP/Ezy6ER8WLGxudHZn3vN2/8wzHlh0p0p4NRYHAi9BCaxSpuYi9PX6wvaNNJO8V2fr6cK2xx5t9vb0tGkPHYsIA9M7UiHHYRxvueUWrmFDsVMCstLnDKtlUgm3x1N9ezGagP3lxXiRq39BoXQ1cp82s7vco8/uLx8L4ic7aoiWOCiPr3J/6ZL97r1cxrvoIu+0DW6+KGesmbTT2e2mkkdwzmLW5Xh2m32Q3BkFJnbe/IM8P9rBEBq+O0RLK1j8o12Ho1AeE7U1/6G9XO9j6Mv8z0cBBsa05yh/HUeh7U86YZoIvJKsE1m448d3jq4ZPenk9VY7SfWs/s28r3yCgFw0rjLsQQhrFLBGOAsEyo5imSxx/K7J3uFR0NlsLbILPaLcGrcW+Ews4F2pWZgX6QE1iQC1UK68K+uAjIiKyOuFpP4cXAguEiy0QxRzofU9GFgZ1CScaDZRwLlFfSD0kzWJ72WcqVKQ2d04a0ftwprbJQcVRCVzIhq6yamCP445zUWQRFZbYKhErHRnV6lQjZo3RN+oAaymFqodOTBHAYd11kOuFd6+0A35KdA8N40C25vS9aNwk4RoNvGy0GBFJiLUSK7RlUe90aHGHaRQRuoCzqLgfmT1cCUSHt/lb55jwRO+oUxxAGMJwQHE2c/u/NMbRt6S8SZzr5/KXpEv745GUrVob02wPgjfmb/+scoVYF3qlsl00K5yKV+c3R3JdEdCobobrjrZ2Vqwe26WEjuTneBVmViHAlw6U28k6eyEpKGpV4NmDX/Rg51JlPryQ1qqFKspPFy5oXAiGksn4h1JJw6m7oqDeJHjM+Lsagy+zyT8/hh8cjCx5N6zqzGWdXswF1C/wEXgcNsvAXelznVQ9mJlLUtxuoHXI5D/URj1PCuQ6zRWjqlTUXcgp3FhBXKxFszNoSTwqW0qFnQnXVY0Vn2vOHL6u//qpk/8xpsuvezSb37jm898xbvG4/jxfO2B4wD4oCODe6Qrrrii0Ih+7LPf6Fg1Vka2VMZJoPzvsZF3NxuLheR+qFp1p/NcMNvqG8tE3T62z3ERQblL9WCyiG0o26YJDycjLn98npWyO1XWZBRyfDZPy7J5WhRnANjBemH5D9JhpzMzFS6HmCkTRh++n8NGYyKzpIsz7BqUGxE1gUBfGHq+XhACsI29lLBp5NrGBwra5PbM3I3CAZj3vEK/mqPZc8Acto0r66xOMR2yJxCqwX4ey00bHR2lXWzuB9YFhQKoALdAXDg5ED4YFS4fXTq9RP+MjY0RAmDek5uyFy2kq6kJ4cgU4Mi5wF6ayBahrbQHI7lFxaQirTX0Ajk/bQC8sjJc2+aahu/zZPm29aYs37aSyOSJWR6IKbK+JxtdT01P7Zy779Rc4sL1SfxDQieM/w/J3SFCWFxLNi+jMh7hmCpwsp3xl5wYuvNuQ20lXDeOi4Ia6tOlxfDZL+3+k482Hn60sXs3i9djL9lQGxx+wk9u3RX0poIKK6kXcezoxL2qh0x2cQci7ERH18DQMBxTLCic0rF44hgmrH4yvpfi6/bbb//kJz9Jq+hYehhbRC7++I//mKFBPDE+Po4GGF0ZXGnbFnpPFxzGFbmxcpgz+TBwHIeR6BiMQg8gCKAr+CKOMovP3MWURZ8ikOKTsciz/S3v3deMDhyaxzbf4UEZgPAe8yYv/z9ig3f0XwP6n55ntrFmDgjUuGb+s2Ype3fCUb1DAsVXxgDxVhz9bjlqTYUicFDcvffey1IIjnbRttUQjh/96Ed8IC972cuObVmAbbihF/bSTInADvwVxVO4UPvpY+MAwVyuCwUslIJ9gAC6UAqjmGURDehXrv4FSDA6leZWmKTR7GAB6M7JmSe2Tp06cEbT9VC71ctaglsuFOYmd/ItNwlynCfmNpFDuqsvlu6wcBRibHGhcjXkh0GRFhc1rhZ18hw2zMwGlsoZkETBJpVhk7xQn/fYJan/zg5AIafKTIHNHIzbgr/21vwfPF69mtqW84v5uemBsRMZX9rc2dOf7eoFEE9ufyyWyGR7McqjvfXduycpePXwENRdZfNf1eI/V+Ic9GOq0b40LB0R6Aae8geTYKu7HN8EKbkS730sB7VmN3KWFZa/uxDMD6d6L331zNe+E1rEIajyFtKmz+PJ7BWXzURyj8z4eZbdmv5ncRO8jHmuxU2P1V/+N0/88y8OvW8w8rCXrKTWllU1vGk4mf9cevf3ln47HYuywYWwtut2pjomJv3ZfPGhHeVcRwfKXaTY1JLlZnJmycZxZA51l3QCwFuqV4pIt+E54R2aQbhQjNc99pH2GqGuXGezAxfQkRBAmm6AK8XEvYI5mwOc99mZASv7mOn/SgNVv5Mvh1imC9edCvsRLKWrDpshFavNYgOZiOWYVRX2iMR2gDXGxbqDkTMtxEYwHXG7k2pBpR4Uas5sUVZvYHXAcGdK1UZfjS30lLaocFm0nIqFNmw4+aOf/fqH3/lalgJ985vffPH4/9v7nXtR3O0FhF4ULT68Rj76yCNXXXVVEOv4rb/6j0JqTXExSMacXNLtizDLeqV6sFiW0/zhXPPskTBu6tiBreiEt+eDB6eDnXlNyAm2BY84nVkRSggDdiOTeSWJwNBEAjTDQOI4GLrmFBvBJAhLkkM/GTGetOQdXp+oxJeqMJ+3vnAm1tbcutwKq/hi3mPFAgcfMbuYosyEU8FqFz4S6IvQ+tZbb0W7hRkwGhV0yDD64LeVJHz9+vVERju6fxHLRR3tX8AMeBVMTs0Ps+w2B0N8YCe9YRERMBUmkgYyHQNfrQUXcmigFBwnvYH2iZXPpOICVEzPUC59S2/Qq1TAQnF6j64mnAj7WycCs63CCn6RCHQmGcLNH2bln71otGIZJej1sde8TfTDT+79CXTipeecm4TUcYScZLseooXmZkWaZrwz8bafCx7fGM1DbnkqZyE+rMLrroj09rF3UxEnUq+8JME+vp5bdEMTBXem7K3t1luMt5GppRmZR2XgVRDwY9zkrEp1RPq7uUhW/X5naaBvD9NpK0KHX3LJJWhduP3EJz7B2/u6172Oa5yQ2Qj2TMMYJiKvDDzMa8RGvBUsCLfs72GmOiajWbMF+kEvylE80OFzUCBiOCRN9ts5WPmmbrBZTIniOlXT5Rf1CFaaqYDJE8xjZ8WDVebIhoMzLfolW4SSnJnHmKCObClPKTf6l/eBiqGWP8pikadUz2cYuf3Ct+eQdkg7ZztF0A+IaVbC43aEY+mCfuDrWij725eYq81CG+ljISWhxsC54w89vPWOJzac1hEOR8CY8jGBnBQtLutyQWZWc2v8UCDEBhbZT5VfNLe755qTi35HQcyN1uOIx8GFYVBs4prRlR+KwI0Y94xo61jBxUBYYCaAC/kyk7yF1vJdBcBSv2u4sGgVsTJME9RfYBN9tKF4btDsch99ffZNnaHNxED57FYiARvtheud3qZrcm//bv5Pf1J6JyxDrVoU2yCqpgP3F6GIF2KNaphVqfKkHA7F+vv7dk9N7ZyYWD08HI5EGo16o1SKZ3CORaOUDgpHTPsKqfIK0kmNMRGoMLcWAy/HJAZ/CA6U0LTZgGrpbCUjoGM44ziDTmbREnXED/6t2+qvXXti7oO/Nvu//96dnfHUH46fzXT92rsqp2z47vZg51KE7JgsaY9+tbcga6LgP91E2NsZXPQXO285M3PTmR3fzji7C434rvqZm6pv3lk/tRSE6uUW56BNIdjxJNcZjsaTyQQ9zCsBtAXyRjO99FlxYa5Zr4GFBwa6PN/bXq5j24xkIhKJJ+LhDHreaBjv0IK74ShSdY2bZBNBJg7TCyfs48kQDYY2Umald81peg7KIcwkw6ZPePEmFt2ZRWesh34ToCWYcWfjSRAv3DWgFxFMPMQ6RDcXd6Jp7UxZqQZz2DwvYQ4dgJxZutiVVr8jtcH31WJezBAeWNBO5xL4r0aHzGZL/q4FJ4gNffBvb/rrG6677NLLvv6Nr7MxhAbv+HHM9cBxALzfkAbBw488gu7XTQx88ONf6F81DFhlAiriQa7qlHAv5TaZO7qSwRmD3nBQjkxub27a2MwXcsPDnWNj607ofnAeDIzGLFSoY2RD/oBehx3PWEIZC2vuYZO0JR4VEcuxcxqu/Bwc3CXwHR/CvZb+djFvazaXa3g8S2vJsVEgF5aWapXq4OBAawpt1V0zKQpe6/sKxAW/YtUpTKxcAPzg74lAOFpKiDfLHUdHR0kFLUdBit2XPYizj91Xq4QXyM8yaWlVl6b94Ac/gINkrS+GnVYlzpJpZAQgYcQEWLWNjY21l3sRyCMyoaMAwDYXbvfRwNBFtqCVxXGNwoqDPgf9cuyDkAmnDigtl7dTOqp9undVVTT8ATs/wW0zuQPdCVkZp1U5vVnmMC+c6HQiFj3j7Mbv/vfKv3wu/PCjwN9mV1fwmqsSr72qGI9u2fQYARDZcqW8dfu2Ky6/4uT+Tsi5ZMNokgdyjRM7wLqbNm11w5GB4SEJh3HOoTWfTqwaBEsqb59qcIsMAtxL6Z/97GfhQRlBrlnxy9sLp0IEK3FgXBjofZK36n/wH7LioAesev+pJj94xi/IJ7h549U9+sy97XYGot1rTzYQ5lVhlrSvaPtFbad/xhd8IEjH7Av2jDM73Ax4zymReZgZm9ceyR1TyuEmftbi8dEh9eNzwwr6WSvkeZQxk/8+szeV44Xk/UQdhPmPvX6yV/R51KKnXRU0utsWXdwIMYEzdcMIwbTUgmS9c+3ErqnJByZXDaMUxe5U61tAp6iI+a/I8D1smQNMYY4XsgtK+YVINIb3B9wDdw2P4fSBqSaKzZtZwxXNdKTSWUzq5ndtBRr1Dq8lJZ81S0bV+fyBHUN4rxLEJX+VJYyrQeGxmTlc3C0RWbWRCTIbfymyHmLI65d/tuMDudBmAGzhjnT+873NCayZ/eQr853XzUVi5UvSvzNRPmOLvwY/T80qfBsUPRKOIBemoGYs3hGKxMhIdr6u05HLQdempqcnJnYNDa1qlEvTOzcPnnCaWRsswCvdtJTf9iCdndnsr73WnMUrtBxNt+pePFRLmqAlzTob0mlWMntosakM/Y9uXGCYPm843xn34uHEJRe9onfd+sqP72pOznm9ucR5ZxaH1t5ZSHxvq+k3yL2RSqgMdLYhNJ9B3POz8Sa4OHBzD5Sv/3HhenKUfMENRU3P00wwKQf9S4fT1epwJ4Qjjs5MtFgo1mqVpTx+bqSeQceDLj3qR6KlcG1pupFfWr96RKDTUH/GEEUuat54pJmJA7x9lv7h4QFFERtDwgbUGyHGEnYXf1QMLx1j9k9yF1nggjREYBi3OzJkw2x9qdxgHS94vtKQisgqk7IJEioEu+vJvIwREKbEo6ianFCUlxGRgYsV52JJmiSgdTLqdSd5VeQzvFTFsJ8eklAgHfW7WYnsecWO4V//P1/++Aeuu/zyy7/xjW88pY0/1WvHjxdCDzz3xPX51ksPPfwQjHuic9WH//7LfqpvvhTsyDfZPJwPiS2O+rNMuCFMJkY6/OHqfHDbrQt/99nI9Iym/ZAXfum5qV/55VPG1m2dCy1UPT5acLKZtSQtmyn7zYY+v2gY39FBb1ofNvMGu9LNlYMqS3QD4WHsqMHD8QjzWgh3ufmarF3In3l8aTrvVpdeEu/JYNoRxWDDTMamBwvFwpatm9mVhmmA6Kx6tR1riTQKXhQaY2NjBHKWgNNgZpAwmk84G6KpEGNH/apXvQrKBPOHjhTwvA/8s9k+q2eqBJOhydasXqYmVAwkb9tymEXL9sZ41gHNwr2B/GkUyiUEBDCXHGxohFtLtCsgUtpOY7F8Aw2i5UAcQOR2QfuUC5O6T4iNSSYIF3gE5LbJ94kG2qRW7Wyf2wsAJOo+2mLfin2qeuC6Qawhc50dkZ+5IHTyScHsPCt1ot0dbme305Gtzs729fXSA/R2IplgaS7702BrBDGNiWeAjrZ+ClkU4+5Ij9E5mwfQJ0fGrAfwu7iyYogPGBpbN9TXvCRc0+1cE42LA1f7kKEkZMQvvvhiYq0s65CJjs2HfC8MH7KAlYb9R7mprFng/TnKhT5/iuMN5yVksmKC4rW0JifPefUYESrGtPxCl5AeZk8ezOaIoeHreFHNFRHPZYHl9iWt0YVX4TAXMDGdXrwxUyg1p5c6shlAiyZ6plAWJwCq2PlP65GYk5mg2WUXUyx/dtujzWYjlsZdCuPg7hx/mItcz2C6W/Ym4BksriLy/YtPC9Sv0jrAAQBL4GhEIFAYynxIB5EpwiiHFUGFCJxRQUWAj2FJGljawGHFx7ZufewHw+Ef8azwg478X7J5EGZ6gVN26v+emtoZ7fvgzqg3f4r/Zz+d/hDbeczs3ATDFct2dg+MqtUsgOrp5wz4JG/liPuubEe9XptfWGDXie4OLP1ki8uf1JpEBU+p2gqhAaB8THJ1GBU2ENfqcjmbTZikWsdQWAmUA/2m5UjI0nFZDB/EU/JTh9gsycQQU9O/kS8/7D8xEFw8un74DWPhRrMaCm1thn+4zb9rh7TXuF+2FTNVIyGAE32JR74efqboS5xCw71itBiaOyX53aEoOoD8fH31xvKFu6qnw4jVxbdgzMyCWcoPItG4E3ROTksXFEMfnIzhFSSKrMCI6mhaFePkahVD+XC9Cc5MxwIWT0WEePHZya5NDAum8vSMT/msvzXjK/MB/EWDRWkiQBfmOY3jzwRvTlCqu/NlZ8s8q3n1FADPauLRnNcd5sXSSmP2ZJlCzIJtMxrgaJBJSG+MI89a3ZsvSlKAtTZgOMtCXzgY14XlZnnwQpFLJxnz4KXhxhk/skL/BM+PTzV4l2x3/0c/85X/+c7XX3Xlld/45jePY2Dzeh5Tp+MAeOVwBg899PDll18ZzQ6/40/+YyHcl/FdTCaGwnzIfrHqLpSD3YtM8UF/unlKphn60T3F//WX0XJFojhm3Eaz/sM7qoVi1+/9zgUjI3fv0ueUQ17S2wAAQABJREFUZ69tA3nwDYA735DsYZnM+DKd3WUmQWZ+P87Cg5DbkdWczpTPXu3zZbeypPUUmGfIPkQmIthUu+WGVyy74wtiEBFzRjw/hSgr5qYVITlywkkg6qndkzCy1oM0jJQ1qBMaicfhqABmUHcmRZDYHXfcAU+D0mxsbAz4hwKZs5nt1SegIzSEXMANA4OBjmAPgPRKZKh4z8KBETKVoWuoDBw5Z9QgVqd96NJoOO2iwkB6cmAVN9AIw28wPLfkc9ddd2FjiZoLTo620z9EwMSRRwgI6BYAIeoOSqH0A5Z1sHAbGa0yFygQ2tHImSGgOMK5BrPZmtCfR6EnD9gEG0hL6Qq7vqVd20PE55F6xHRLkIh7Q6vdVcMEiIaYRwgauECdjoiBPkSswCpoupfhsAyl7VAoKC8SA7qn0FZPE9C6Ip8DHr/3e7/XDrfenhlBFr3zWj49qADPRCV5vZ+06Ha5x+oFLydfBB3CwD2HvcE0RU2O1U4+dLsO2O0HDDx0Pkf8KXXgrWDaZGY+mPjviBf6XGV46A63T3lFoZXMOc/tHP5sdxHTcSrqnNzlI6BfKINJpdeUVtWguMHBnqmpGfxtgu6oiZm79e0KfJoYnGTWalKFPFxUxiLJ3lgyUy4WgI7AS1KF4iniQxqVgs2MQNbpXJw9eeS/SiXyCCRIHFgjMtfUABYGi+IZizQkQiMhdS8OlWSnqwRUgDTKldj6Ty1GIjcHbs2pxZf+pTNsMSwPhKW95n/mSo8upE7Jn9R9T2YyubhU7+zT8qVwJNaQXoNGtyiTMKn+W9DtIBWa3jG+VJj1Kjmg2NzubbQ1Gkvk+lahtzSqWurJvjvSnWrPPzlhtpWix9Rao1k1EFeqdaqtOgPdqbX0vbbuagNXLeKouuifDoP3nYbr3z0Zu3+afTfBcqFSvcn+Q005BvUAjHSTjUkiWqLM0JMAeV13oYLCucFqu2io+rL0567q+aNseCviCo0Z6NGNjldf+4+Pf2hTXuuSQqFYPMZ2k8K6PbVQpBjZla3yOrC2DICv7STqmGWXwbrdsUan4/enCiwNdjzJNEgt7OyDsVHL0haK0OLbei0oyZ21GF3421wcw3buPbmqbvjTeW/7ourZUhe7TiyMv2pvqebPlAJWFA5nvEhY/DOa25ReChLi+AppgnJB7A4Y7otTFGV7OOVZKEkRjSNvwruSbkxoWObT7G41jT2m9lWGl3Y7O9xoWGuGSzVnut719j+88ZO/9UZsQlkPfNZZZ6nfjx/HSg8cB8CtkeRLePSRhy+/7IpIbtXvffrLHd19dtH8ZIFN2/lmtGS3E+ONrASTA2knXJgrfv5LzQLufCS89ORjT3K7xv0PVu75ScflQwtFNv/FoMKLJZinkJ15+JUvlxwUuggX+W4zaMf07UtkWGo6lSUjNOQjpyy2TYqxVziTpXZvm50vbhvfxFyJcQ41mZi7l2941dCq/oGBSuDMVTUbSvfrhDGxnpsPJXA07Sbx2duTTiMlZbqZm5s5Yd0JoGJNgNrotXnnnXfC7+LzFrEW9ejMdZKLZnZzcIGoG3QEmecAZgCWADZ24SXaCVbnAuE4AA/24BpK0c7B5mPP5L/y9kmvx8bGIBhUEncjrFUm5ydNYiOALW+66SZcF5AQDIxlrPX/TAUsuEXHSxxA8rnnngv7AsLH2hM9sE0Ok/c01N0HbLINtA2HcaQyhHALB8nBNbtrwOsfZruOYLR2bRlNNrsi56c6OkqiN04pzX/zzhiiTObIEUC8VtFN/xMLuLuPRgUeWsmf4rGyniuv29kcMLD9dP8LasvQIAZC7T82NvZUk++f4Qs9hA5hpJ5DAGyH4PhAMDWxA9Pz53ViRHgroAjPnyo9hzXhM2E1DbQDwnEMiwOEv3B5xdZ0Xf50yb+/4bDPDWCLnoeL4JXAwnjV4AB6M2E22BhpB4XohC5JKcWs3DgXZyeq5SK8EXu6SstZLpYWp9Gt9q0aEqwlidF1ijoaXW44jo2P3PNSFs8NtgVIG22loTuCoIKRllcBLomLYlDIhiuYK2lbhVlhr/QIXoLoOW87bFpjARVBzCilFc/UFJQWbjweCU710+HFTDa0lK8msznD1pGvqmx4NOKqirBlAqZqHzm76WwnXrtwyIRvpXwBr3WuFwstxUhDLCCuukudgyWzOkdIXguk1XDUjKYNqoahp2qFrN5gKVUx/hv6qrPUx2qb+stUwzxUqOk1qZcbfmS6xB9PVLgp2fyqlSqAlHQqPzKM4z+CA4+9SGLhUO3V6T//2b4/CsGcEqGBThaxQCPsVk6JfuF96x741MSNi6F1lEETWFhLFonHat35bHmoUCos1heqo6v7Y5EQ20Pt2LyxLA0RhftT28bRx+a6+3N4lKAHcAnecPMsxgWMgk4jbI8iv7CSA4B4YYBZGFjlKXbruIbFNt6LZvDw7CzV2NE3KOe1syMZo0VOYB0Z1bDX3Qar0Wqw1uwD5eDEExTtdCTR5UqSwpbReMlerDj47oxH2ZDRzSRcFNEqq+bMlbQUkVeE/ZbwtoNzaZKjYWZF8cSitpCEl0a31JXxVnf1//+f+o8P/9Ibrrrqym9962YsB/UeHD+OiR44DoA1jExGrM+8/IrXJLsGP/Dx/5h1eqemm3yE6QhYFyET34yPG7q5ss93SJetyfhRVkKMb+ZzkkJX6xykruSzgCz4jz2efHV1fV9yuqxVB+iNK01Nmgk2dE16OSv4w466EZSRYfKpyq0CH5ud8TSzslyBfbqbZgkNSl0vEmVLWJZt1MolZttIKsc+ATuKsdndTfBwjCXEYc7kj22Jm69HFlkXOY0tNIppPmN2rwm2L4XDneFUVSsfQk7zzh/fOT09w6ZKFv2qB8w/O03ydTNJwe60OR6ahh2ytTglMgQFegR+tliOEBR6r3nNa7gAJ4M5LSSGjeOCR+oZSQdsA4ml4xCTiF2HbLWmJAeqHSKyzW1l5uPj4zAo6B6B0FTGbl+BDhzoS5M5o1VGKwv4h6vjAh9ItBQFJrdPbwk0rbZgzwK//8fem8B7dlV1vuf85/l/57GGeyupyhwwpMEgAkGBGAbDU7FF24FmUF7rs9VPa0vrs7W11fbD07bBbm2a5wMBBUzQRgYJIFNQhpCkMhapue58///7n+dz3ve39v/eulUZTICEJOTUrfM/Zw9rr73P3muvtdfaa+M7B4SB7xqQdWK3owzt6I40iJToMH+c7zQUKnEcDoHeP9uqD4XbuRnPmQxe+MIXkovGRxJGE866Axchww62C+LuT7Yr+CEeXad8QCTNyPe1xjzbuxyTQNpzMHtAXhBAe0xdWAQ5t0YPSPptEEALsOmU69ugro+uivSTx7l7PM7FPZLmwHrIDe1Hkvgpn4a5idMEWGZlvDwBP9Y3qf2tZmE4mo5dNt1fb3hH8YYl2iqNJXHytqR/OtYIK9mp6Qm4Aln2SNpBRCWdyaq9PicIQYzhSrr9QRTxo9NL9II2Hn5Nx8tdimKQVpahoS/FEMLA45cByCF8kR37Z6Qz6Xol2BEFMipIjwDgYruq6fzIrZGLYZ32AmPRLNkYtWDM5HWlVB5hi8ZaekKfk2OR2GU1jDWvybkSWREXEcYlPSKqYz/MufU9PxLHhpcs/vhFeJXqNytLRw6PLR5MZXJUfKvjNs2Cm/2ptVS46mIzIe2j0q3SYrZcRYnmsopz59KL+EG72YueAWHZVT8HQZAsn8umrNYiNIxEXoz4xOe3m61Ou4XgS2IHAZ15LJ46FLnluum34Kq130iV3zXR/Hw6aMRii53RH97KX1WaSxy5YfpX37XyLnx9we4mokEi4k+t9P16cPn3TJyK9ytb1dXl1f375tjwBFMB9Ga92m61s2PTOOgIkul6G+vogP16bNzjw+h/GMXOsdGkO/EWQU7GBSdbedHi0gC9IGKxIBnlMKQGUjGbtZNkZCWBDxRBOYw7HjZ6c67vTCE8MKpTFWktoLU536hB60qFK5Vyxp9mm6HZNiMMb3EmC/0AZhtvWBn0yVIxyYFWx9uoa20C+Rm90XTRS0UjOMRCA1yuh2c4Kskf+4X/+r4//r9+8LrrruMURvbN2YdSQz99Palb4GkBWNTk/qM68ShMjb/xD25k3LI5EcVpp89ZYd6KXDfrqFVMkUfTkbm4LHxwdhc0sadhmYylNyxwzC7IrDdEeKI4oAtXtgalLrk4JTgyxRqUdtv7rHJtsWzFMMfPHItVKfS2ImgMZvbiYxDSYTlShkNaf4pqJ4tGNhQsOYbHoLDZWu50a3v2zY/F0CEbJRGpw0dFUMK6Q9NSmEjMpqOR5YasptlRjMK5gav4yQtWesmNJeaMALD3rPQK+bnxA1eU2vjZAhOpiUU8RaQdbRSJ3BnkPCDNcgmX0MNmmAvxEs2wuxD8XGJEQTaXOhCkJRDZ70UvehFyLEIRilADk3ECoaBZmp307tWCldfZyrrXh79j7YwoywUo+BLEUfSrZCcEPFEjI/WhSYZfweQV9gUbXaxZkHzgY5zUCoYPX8TDxDLFIqWTgKkStJ1ql0BCeHXLASCGTEj1KfRhQD2mUeBAfWkrFH2sBYDbN7c4AMIoUwrdgDsV3y3n08vxgn7bCqsn2FN5cErwPyy7xjilSf42dCIfnBCxLOsqjV5Zi9bY0SixH7tvP/o+8E2fTGlKwH9LzThg4hd/4gIfWE/S0xtZ+2DOpp+Q7Nv5cq3nWuCb3iuepA1Lr8Av2nnGC0/SunxT0H66k+y0AGSH1VIWVTGGYgHuKT1kREPnC7Fn7wvqxxAV3Jk4khuhzJBr9vmur1SrzQ4L1c5WC6UADQIJdpLnzL4F2g2V4+n7bh+bmk/litP7L2RSlkOUSIIYCHygbWJqRRS3CLcJjpxtc4ZAIhrFxbREaQnGIuiShXCLhYWrCD04mIl1NAYamigg+qSUxpdoJdAuYEQ+VNdnus88lHh/NN+JPqPufTEpsZKtaKZ/7GU741e14ME2uofq8X2JucRyFfy07XlYBsDYqyZhO2w326VTRwr7LsKm27AK+vgfXV/frLabm61CkDFGAlyEs10GRMhI2rNKaO5zcaqQSfLbry6Z62ikEZruZTgBGgjC1b6uCZRTrUEAadBSwN6oeZF97VSiJCbLMc5dUDvAISYS0VQiKv1qPBKPRzKx7g1Tb0161aCXXPrP0/5tY8bQRvx7U+XfS0d+JcxcVbks/bFnTd56uncNRSKCJiqD2LpaJLPWWViYXCaUz+bBMKNOZSmZ05O7kWhncmpU+21VQ52niPlATft+xRZhyWyyNEjzpaT+bXbRtFMPLZbH4px1pPbGGyCKWTItVzGhJFaOmtOJoMghE+wf9gJ0vKvVoNb2Z/IRzh5NJUPMucezYppVYj/crKO70kFJKI05xgWzZ+C3OaCYc48acCMDOrDMp4nKS7Ol84E5k6XicRhkgk2LCb+Y9adj8PNBozv5uj943x/93A3XvfSlf//xj7sdWGr7p68ncwt8/Uz/k7nWZ3GHZqAtxOtVECv8zjs+GCnOsOqz1WQDPTtsOYjIG8+y6IXQ62M1UW6FyzW5pOJg7gOpTOrKK/x/+BTLVCxAkl4rSCxuRaO5Ky7vpFK5DDMCLun8RifYZN8/m0kYomwwSLNKqoHPzpBWP2Dks3UC0SmBFjflZUXWNNaxzmCFlDkCUs/FIlki6ScTmH0gGHjtAZYhuDFQRgg/VtOppJcnrzH+kN5aJ7LRoAh2v/TRErNDONnH5kROttjjMYhlszMHj2zh7U+rpHF/oH0UcZ3PpNObdGpxJIHVkaPaZ1tLlFaXwkXJ4BG5tom54hCrUHhuy8UNpE0ostMBImzceuutygkRxU+SXViRoXRFBIWZQDh0YuROGiyut6n/WWncxZ53R5WHOSsqVuZgqL9TTSPl2ukqBbDC0hXdLxuJUUuCCbIZemDwBD4TBsI5AM9TNbuiXUGanB/iIsqVQnpkS1Lt3gO8O5NmrYeGszvlY/EMelTztttuY4bGwPIxxYS+cb6Bt/WZI6Xg3pJXr5S7jdrc3gUag8kJczXxTJw04fYo0cvoyfAj6mdYOjlOyy0zsXwjmZlJlFzsCDDJWcIz4RKeJU7DConDsAR0G7Wl+Ihh5zU8gBuGeIAjzaFDB4na/a2V4byL3MN8Qk7PFqJHY0x2krvidl55cPkMi7PBFvjAtGcTPP5P7Pl3SvtvUOTbaZDHtIM9Du1DO3yDTfE4IPl4FgH9xK8+a4hf3xaGxwLVx7mzMbMwZVARpgwWzqD2GEIzy5xP6x6Lqn6rYIp0iuLN5/zLJwflNoQacq1L6k10s6FXnZ249/hSeXMTh5kxjNngYMTCyDYOsRXSB3knUPIwrpI4azWIVDfL1Y3V2QOXaeFclsBiXeyGFKtzAVZO3F8Ym85PTHH+kMQrI7RS9tond0SXDDBRsFJIR0aUnViuzcOaCVweTiRWguC2+suuyf5BKlIae93a6nI8OJXxBxEpj9O9iZ9ei403vTB2S+3Vm50EYiNYI5m5CsLWUS4VQppDYO53I41eNNqNRuJCBSG53241KpvFmYWeF6tW67l8DvZG05ZwEBruIjV11aK4LtVDExV1GM4gqqOL0NqCZhbKVCy5lMNdlqqPzI1eXTKu/nHBusD5MG8S0emImYlFoqlkAik3FY9mklp0nshl2F1nKLmy1Fwj0dXF9OcpufnlTPLwaMiJnnCcMIWA6CVr7x5Lf0eDA6P2x2/eDJ6t1X3PT53uRoK4OM2vBe09kenZGWRFdLA0BsABSts0+l61CXhVGGVsIoJmFbUsHUa75BCVYbPVnFL/StSMx2CCEWhpW7/ewBQZVa2menoTRY5lEY5pC+l42cpbb1MSwjBbBcNsIlpIsqFXjnLa8l+lrcX0N/w8Y3FZzKvvidPuBY22v4FvK1QytELSm8IHramYtAe4E27U6QusDni5mD+dV/exo5LCzRqm1HAXIdbanH31O2+/6dde+wr8Qn/yU588sHjgnE8jFJ++nmQt8G0tADMUV1ZXXv6y6ztB4k1/eOOmP5tuSegdK4rKo61CA4xphClstbyUTbJVQCSt1QlaY4X0D75y60u3xav4PWJO8FidggB1XvBd0SuuvL8RO74pUoNgidBbxMBGC3TYNrPhPuzKtztFyGgZp/BsxYdu9KGtPY99wjjo07KY1sk8nbjGYNfUEEEDnMxPRtNj7Q70nR0Pkqgd0YQ8kLcuM2xoukZvIhawscFEWGWmAMpdOrUO2RwfKayXvV7RK/aRunHQh7f/SJs5qaeqGUnWPRIdQEEgMfjuwjIEexJe40jFmg/1t3NB+XYIAZMchJjLxe6E84o98FVXXYUpNTwEgjEXLqOc3hWF4ec+9zk+B68wE4jGCMPYmUDWIe5kIQTI1oYmO4iyUlk9uyJe+MIXYpKNt3rEYPb3IvEeOXKEKFhYNK6oJYHMUj27PdEJI6IvLCzwgHAOVvD9LOQDij2xlOIwd3cEaZKh7t4d+MifHYYuPchQ+iPP+8hTnlfKw2RE+qU62L3zgVy7PUzirzsKyKwIsMn5PAjVzuC+jfBUJdrY7DdqrW5eUyAdyQm6lljzHJ+ZNR3dNaC03EMik2+ZMPV9ghbnNPbyxRE+FeGERJwtHTyHdWB6qPVPsUWC4DOBCQgCM7g5gNXK1uG1/szk4mo3vVEesHFBpZgoTuIhWMtFv9OfehqTuxgTDTr6vLEJVhAh/PLHpzj7vFN9DW9DbCdkmJgfh+muiG/VI6YBLAUeOHDgG0fgzjvvZKUJFdk3DupbCIFPyddx928hGk+ooukh4PPEEYBZOQUl8GG+eBwaipmIJQDXJegbyMMsvGJexGHRT5yB/Bi1A3zRBWM+HNEdq702wokWH0UY8TyVzybmZiaXl9dKm5szM1OQQP4hGYrq2aXNm9iXjkwHfqzNCbZS1rIIHsH0jBVMnpFLlEuXiZpGMMnFmcCiuKLi0GSbGuB8bJOpS48E7qRtFaVUCK4Sd1EnrtUMBz4M5Xn+RnjRJ8s/e93Y78SnOrO/f7r22Vz3eCoy0i18VyMxp0OPTvSu/sLWa9ABtnrIgIABmmRgq42q0a6shYN+OJBJd6u0HGI+zCm3I1PMSTiGGpmc5eidRr3Zb1XzI0WEK2rJjKNfZMIBG1xNeS5IICZZkQram+ruGotWFb42kdC7uCTdqjp6iCcTMEJAa22fgEBKKicpN4GSA39dXn4shWDImbo8M5vSeILOg6wUdR4nIiUMJ8vE9sfW6HoiUofd6x7Jiq0krertbmH/ZDLSwlFykPNOYsCItICJe+xrqHrxeeMlT/YKkSTGyNgF4IO5tLFBQ8/NTIxMTo1NTNkaAoXjctlr6ftTLxBgux81JP+go5OEI5guwtB2xQmg+zcOEzyxQMcd7CCst7VTF5kWpJJxzBUjY9rHG2KGDUtMq+LRukR33NKhKjQCxptM9yTvdH10uThvo0KI3zDh+Uw4zqdAhMZRFqadSNEcVSqHWH4Bxj6jjifJvBeWK0jaiOXaoowTXJRGsOtkqbWpyNQb/8uNf/Lzr7j+uutv/sTNcIZP+YGv3vDUvb59BWAoJyLTK1728o1K+w/e+eHJfQv0/kYvqKMB1oZ7GWlg9jzOHuCoCHOHrQJdb1NO1bGIjnAw71WXPbP4m/+++Y53BnfdC9FtZTPpl1ybe82/3BydOnXGmy+q12CkgdkzpwEzUDWGGW8x3AJByqGr6H6hDgFG0WyqZYijGUbaZKcEOmVIUA+76D4HzUQZqOWNMyMj41kcwzNlICyLrLEdIkB77Kgo6eXvXsRL9I4ZpSlvB1EAEQIdbFVKJ+6+LRL2YnvmkXs3l4+tL0UGkcTUvoMi8v4AMkF4kqP54uCJDba2IuOBwIQIY/ZZI40MJDPHdMITK7nslwBhtlJAbW0iU2HoclmGdEIs7QZKQoiz11IpVs15MCT1C1l35ANBF0e+sBdIxdzRzSJ5wlWQCGn5E5/4BHMMaYDJBRxUrDwwJSDBokAmFomOQjFZRLWL767PfvazSHpop4EAywKbAlboLhB0gUwRYALjQuyZMzTsCLl4Bo4hbDMA77aXFb3HIxeAH3hWE8Uh3iNXPKaEknrRAq6+hvj5N+qFWThaPjCk4o8pMueVTdGE8P/Yln/fWhubiHavy4psuUYPlXMy1nmVhRQSMPVD36ALmyqXJJJdCUHQ1Rlfvldfb7SblZnFMaXavqyXiWdhIEWwbTNuiemOBwnPCh96RrEor7wZLPcn8vnF29bEoSD5uizc6Z/A0V2F8wRY2WGQDKbXAbTEJjMrL5AlLJnk7BBQcbwy5xvwIRrAZC3ZiqCvSZXtyqIY/bNqq0J6VF3scuOHNjn3IvoBYeemeLA3A6qy3FchCRjYtbG5mUgkWbkwqEPQpNwuRHzgI7wQSxibroRt8LuzbtfsEUBU3xniPIQwxELhO3Bc1E6LudfzYncj8IiesZDHMQR06YGD+hHlf8olgsJAdSFoT5ya0c1YRXX+5x8HrJh6FhcXKQjOgfmF85AwLGJiehyK/hYXwVgLI7M5qUxPbvlHK9rjas6NpQFmC2Ucg7hctllr1Ou14kgRukEG0iDj2cVg9DNjU5GgV1nn+F28mbTQYS4vnWEcpzPZdKEIPURWbFa2uo0tsmB1162Xq50WfNPY9KzW+6FFkAOpZ0UDKEAkV+SYI2/QxNplVAcOTQwNil9RCW0JFpPkRz5c/vlcbOOakf8ZT7WLL2mLrhMv/W70ZPdZ//3Un231R3EjjGUs7JWBF8WHgJMMm9tGoxT28Hs8wG1U2NlkK3G9vh7vLFCdEXiQyone6vFEKp+Y3B+0g9zoONlYV4VlAgKejxHMeYBTAu+ttpQcJOhjzy05F67S/gd9tLZpTAE9D14IloYHLqHBfAETxRG+GBpzICdiL4v6GPRhOjVMxIyA2ZRSYhIlWVfLEx6+VpEsNddofdgueUyleC1L0Hzs4aOEaHogO3Q1nTATf4qOm5OPI1mZNXr5bAwjaj+yGcRQFqhJwkgt5p8OEntjqFug92s69KGLbfXIyCj6XJLAqsKU2ifgWOMobYshNIy0HWssQZhPx0Tpx8MYPr3xWdX3u82BTKk9rYygxdU+waRURHyGIIyhwUU13GYXYRQzzP5MBuNHMKHwWLnjNSuD8QzgOMVXeRFrExk4afmR5TCnMly9NEtiWfFoW8TnjjxRa9vwZhNGWjN1Gn1Vgp3DHk3tfGiV6zKHRhZAMzya9vYWvfjMnt//f//6F3/0+/B6w35g1nn5OtasT9+efC3wbSoAQywRbG644YYzS0vvvuljxX2L9Q7Hluno3QmMRMKh/UOdfQs4vsKPvPb9Sh06koa+6Izy43jDiiUuvfo7MwcPRlfXvGbVm5wejE9WsqNfXfVwf8XCFeMSd/P4u8N9dAKDZSgOh3FjvdxkcQtixVJcgLSJ2zo7MA+kTEXMmcAm1rITmFUojv9GHq62NlKjWYyU0Ut3EGslILMDB5sNUS/RLMgAf/jcHyBYBp1mI57JsJCmg1i9sFKrr6yWYunc5OTYwI+0mvVULMEKZiIezyRMRtbkIma3OQgqUChZL0EfgoTW5DwTieWAGrvoBFJxz/db2LIM6SCtk4hiRD3cdXz8yMl2bevChb0XXLBPMoDxsI6iguQ2qdCvM43mAckNfS8PIMGdCQH2wm0fRVBAYENk5eKTuXOGFhcXSYb0+7GPfQzqg/iHGMwF542Md/jwYcTdhYUFTnhC+CQQ1o1w+CT0wwDBEpjZhewUQXaYfqgYRRMIEGYVgDsOfgcfknGBDFEPdZFgRwR1GUnJHAZYolwIDw+V/RsJd1zpTukPCoqmoHbO8/ODJngsA8Ol2uCfjrWP3nc386Y6Wuivn7yXe25ssjCBgp1pi08vxa9maomvsDZMUSRV0zFRGztFElbo+9pQ1BZ7w8Wkpl6llRZ3KVjPTIWCJNG026iW107PLrJxC8spC4yNZ/aOLbfZTkAqy6wJUZemfjELlKl5V+mNd0DKtQV6cFOIsUYSepmaKcKy8Esurcqb6Zb2yFlVLL1gaxVcP/qzvi45n0vr1q4OAGIDlBXBD5AEEKbHFSFMhL9sv4WDMOTMQ57JwjuYq9HgLbjE1wwbweFsoaqL4aDhTh49M9zq9ebM9CSfBiJl4RRiHNIQX370ZJfQp5XwBnPPXfccOHggl83xygjtsvDueSeOnwDxOIQv9ArFohA759qBo1B7GSZwyLi0LpEi9OTaysW4+3b76c1lVyfZxvHsk6LPR8BB+GfujFzkHGjII8kOjg6JfwbokzmadsB2hhVDCBoa10fSLLur6wigC3m0eXfDcc8SGqT80+QlrthOttdw+bq+9QPhP2gI8wUtQLlMInQPFkZf9apXMV8QwuWK5sHlfUwxeVD0HrtA9W2RYlFuSFhFTndVP2gRJG4kw65Przs61mh26u1ekoVzyJGRQdLIgbOSawz3u0Fra317NIeNrTW3rpjKjwScOORzKF29VcNTM+QTkbDXCxCGjLDhYIXPvdO4Asdg1x4wh4mWGpGaxHCIcMN6USpaBwQgMkm0Q+oOku9a+p27as//3vG3zqfuTGAz5yXK/fkv1n74M1uvr/szLOvrxKI+FkWaDODKIKf8SVSLeOMHDwG+32mUlo6NzR+MxpKl5eMUApnFf0s6nuhHWiPFqVQhlk3FRkdwbEEtaCGv2Wo3OwF1p5f2+Y/9Ugynwylw5YSN/qCrhV271GjItebFepT9rHCdMXbMsRlagi41tItG1ccANCnhEhFPUTVDbWEIOfrI6K2a2w0Fu4tb0z9NEaKTGjfaAT2oexP1cG7Uvzf9ndX2+yejbSyVKYZlh+hm4TlH9//Il/4MT81eOxxNejL+92kcnRmsi/DkhzuDODpgXZeEY0iniKYrh6rtK2ClozCr+H9lfUG8nsRyhzeIsCfca/W9dovtweoO2s1kp5/AYFt1mFK1AZAviANasMWUspDsI7hqtpUtdLTZ9leq6JColVqMUYixfDrJyaBCng4pZRWPEfxvyYARXS4IwzS0+zAP4YZplWGwgYmWCwja5yirzxA7ai5kbxQ8ONnKxKMIwNZ/AYAPsODggX3vufGDr/n+l7z85S//6Ec/igblqTTYVflvm+vbUQCGAiACveY1r0Ex+NGPfOSqZ10GVcBiB/VvtdXH5XqFZb4+hhaRAid+yxYjYMwwourdkI21vb4W9hJx/9hWAl/Ne0Znx8dmGR61Lif0BkdPsuAoz8yjWYnKjFXtJWD9qWcurSJhCpE4ESlopoae24ln+L7qwcdDHiTxxuMRNuUzziEMLJUxwnF5V+H4swZ+87rJRDSX0mjHpyG+q7BUJkGPrcKyFaJErEdwEdFZX7t/dv+FiWiGym7VOsunV6HGF1x2FfpTKPHy/XeNTM4nM7CtmEaLVDrySLeH3KPxhneFSxah0vKdvFVXOtr0wTskQNTWtnaw/aVT25qenUrEo0mkYlH78GiZiS2oJAbLUZxUa9cxymTUxU5ClsNqSKMEA/Gruy+EW0RixFGcMGYyIrIgj24Wk10YdPTA6FEXFxd5duwXsZg6w3Yj7vJB2dnLMwwrUi5zDPuBUeAg9WECjQiNHhjWjU3F+UIBDTMuoVfX1kaNcmkmtQvvWaRkTxduLclLmKNr7jxk2J1HbvtHXpChCm4RF5aRZ/gnRNDdVf4GnynlPAgu5Dxy7AKxSb7wwgtRYpwXex6Ex+IVU6XbV4LVVjw3vgeMcWbea9fz4zMs8ycyOdvnY5M6fYLirV+As2ZGY194JoxFkdLySS8crJ68Jz8yuXlSXT4zOp4pjCuzzcea7K16xirQecUnMKzozXAfiMpiodBLYCAhToDCmBBdqSpYo4A8AsaT8NhmOSzIxVk3VwrS6Y9kFGmXYClcUVYV/epSSeIdScyPRpkidNeDdN0Ohsix2EqCxfcN78NkloR4JXBAADDkcuD2hKrgGFjFK6XycONvGKIf5VKY8vLMQ6m0dV9ztOvND06pZSBNyibVgPJbMkALioW7ctDR9O/b7PVGB3noo+ctn17f2kQ9GBxbhwCsHN/E9at32eWXmA9Sg0Np+md/Bsi1hgBbjKJU7vZ9mJL0hv3ZcJdIKbksdpgfPk9xrrbb2V37W9qdm0HYeXvgg/aAwQZGUXDRZQBqiJ+TTuHu0q9pdIbYq0Br+HPSP9lfqBG0lA0jLLdBZh9tdVhwxAgFKgoJerR5z0tPy0OfMQ5yjQxtJwGCKCeUnJfyMXpFppPpCnIRK9xQFDsWwZXFogkTzeOGyWNUQcDudO7toWIDi41Cs9EWsgL0QOQSxkOa3rVG0OxH987PIq0N00NsGRNhWN0qsRw/Ob8P9iAaT+676EooSbW8UStvyhuWKgAnRELly0/M5sdnadLVY3cXRsbzY9OMO/EVEvAcRdSwA7LshyEfGoP8CQ0M6UoVKSe0lKh/koeVGcBWGfO3Fftq+4bDp16ejS5notV+iEenuZ6H0oOkgpOB+4pLl6Bhz05TFY/wKXFaClVcX7NxNBlOF+KpdGL/5EXENsurZ47dOzYy7bVm2U7GluDVen213M4V88lkCsSWVkoIvyEcmFCmEDSfAziouB/Ec/hhwbtyFDerNB1SohZPDVvP57gmLUgSgpIX+RZFqERckxVtARTkhLOmIGs/wSaA9hkSQMVSC6sNbWjCPOvPvIuai7/xwsyR7g89O/nb8fl26rWrjbdPxNpYWYtzHZv+3xOvuf2Ln/qNyvozgSNsIPw0iyvUCG2s7fFnxWhuaKSDL+3rnUh38uuJudEoWh++DDXH1K+LJbMZMwOKLhJDuxsLi2kcXsr0mSYh3QBX35SCMhYPW2w5TAMS9Y8C8HrT7ISlOuXHqAY8g5TbGGJj0xgEzZ7fbGEvOVir+3P5cC4v8TWTAC/osmywq52wx55kvGbiEEeOadFICW3Wasy2WXadOLiWzXPaTxXZGCikaCLl8WDag0IqKCaRCOQZi64Yzh7ixM0Xv/jFr371q2+66aavY0EQ3J6+vuUt8G0nADN+WXJ7wxve+MlP/sO7P3DjM571HY65gdIVtJ/e2wPJQEIe4EcKYdhjw70sfKKRXMafSIt44OG52Q8YNq2ed7Ts37NpRikx7cRgoW4sh1EQQ92vtP1eX2QG4oXhCkNO1iAsMLJ81fdKXfa3KBZRkBOMilnGG7FQXmKRpbGNUcrSysmgVYE4Ys7R2TqzVoPKRCf3HcL5tDskifU/HE2zPwfLZJy6Y4uMJyGICG4VMTXpYwoz6K8sn2Ga3rdvPsN2hzDoUQkNbWaRIZmkxlAgETTNG9JC445eXVOzC8FUWqpsnBTx4BIR3uasplpn49RqMzkJxWpXN+rrp3rNSqdVp9T1cuP+Yyenp2dm9+5NZjJUVaTEARQp1wFOzikXZ6Njx4KQfOrMeiGfzhXylI1UQBH33HskX8jOz86jxcVlERt6YbyQdRGSiYXo4MwJ2RJPWnA/yL2kQcTFDTVcGiHIsUihiLUs2CMJY804Nj6G64KxbCYbaiJhGWN6775yq0le55wZ9oWmgL9hpR/mBh4Lvo1nikMUp2Pw4C612ENfSOmUq8lClZZjau7YYCOxP3SmRx1DNTHUpAgEfvZw8kwpbu+0g8UrD7QDyJMMC8ZHXcY3lsEhcKoyOFpWtxuZmGbqbWyuNoPOmPaIqkPxpd3Svom86mD2n7v1yGFNNBwUQmdkTZhxJMZGUxFcjnqXfQ3dbH5Wl+XC32OtzE+XpeZ+v16p8GmxcNuqN8fHxtmyADxxD5YN4Dbn2ZvESIMIYIt3H15hQs7dhkUYutRAFbFkwkDY8+mRoXSBjRTcLrvgCqpyMIGb6MqcrnKVwOIsjxVtKS3QqmkRu9MQ4MKUVZfKoU5cNsWzrmaXla9nurduojYUR7m8rawMyt1iuj9a2bB3xahgQRBiFmhB9gQzUUPrhuOBlZJfPtFIJmXd53u4WEVkDo9Xj3YLxX5kBjT669qhoFx8GAdIwHUph+72otjhk3vnZeddD/o//FQuwYPeXRZ3t69kqdz7ORn0oUDnbBr35Rw+ln5zI3GkOd5bTczv0X6Qc3KDivVsOlW4URpsrHqbpUihEJma8jhSi+VDYzDPy/IUeGVxkFo4xfijrQ5SIkQYe/JHm/FB00OTsesBJqQbFw+sLUKuHzTlNz2Qjur28gCZXgDhvf3223Fv4WYQQqDGLsoVfV7H/qbj81gBFF3TtT1Ghq8jycgV48Fqs3+yIt2uZLYYmjc/3mazZZRjGLAg7sD6cCCuGbb1+p12F4+8EAy1BEOOi81fbJNAIEb+sVDGFx8TJYEj3EoEL6RXizF2QANWf4DGAnhIMZ2OFywRYfgcEVY0hzQLSGIi3JsKdRn5peBauK+Go1HFMomg0tBJBJwjqaV5VYolfmlTRc6Qmznug5XXgYclXavRrve9U2slXF7BlhSLo42VzZWjJ4+cqaCN8LyS0RW/uOeSRoDFhBa9M4VCGlej6BFZ2gd1CnO1Fn1Ns7RLFamYCnWSNlKfKXXR5iq9I4zD5rcaWTvYl3F0SXE0rL1ot45sgkTlKUcxqqZNi2YWzrSkdVKikOP4/WrnjQvxv52MHC68eCN1cbtxS4ZjkNIXNHPPafvJEy9a+JG/+fBN3bsuwgadCyNoQdSju9wjW4MGI5cEB16RuHIkgd36vVud29eCUzUOPgphOuFgYVCZb1G0IvlH8WgjE3qjovpIYSoZsCzGgjgtQjdBEma/4Wozwjmh4MoCBEpePcQ4TVo+pauYSWIILa0P3ayPo68cAqow4iBfb63ZzyfFIYMcCltZNcN+m9U5WuUm24bxwhMg8aIZDkiJQyy4aHbyWc1oTHrXgKWJYgIDTE5vks6G7gC6YlW2a3/pFZe/8y8/8H+88pU/9VM/+a53/QVfevilhi3z9M+ToAW+vQRgNzn9u1/6lb963/v/wx++M3vo2i+cDDKJQT4ZstOgkMQ7PONbVCPFQlEmNpWBcmgfSxvlcIfDx1CEMsB8jmlhVBiBlW8qdvkiDHOI9mZHFsgMEg55ZaEolobCaQtKVw6fw05LK52yqNFOYD/KyWYMaSaMMNJGCdyXq3eoC4Oc0ln9ImWimGqjsg28cgm/zvG+mP04a5zQUXzrcUHWIOObbcyGGd+R6urpcNBCtEN/Eawtx2NR6O/cDPxBIplJS1EsqhkbnV9ko4m21RgMbtAZcMNgxGY9qiCSqv+6qKm8ybODQuRI418xEG6dEY+ZDvQsgmF2jyPuq+WtsC+zlBJa41KjPMiWYv10TqbUaK1ZfpPci3693ayV1/cvHEDup7lZ1oN8n1weZLb6S2gFJQ/r73R5MIKBULoedjj7jZk1YKfimdOnYTU099rFSb9wQnAkECB0C2xACo8H6CgQ9trtFtonEqP5xMiZTcUvuOZ5mUat9+Uv+1/8srdV9eZnItdeO7a42IvFTp85jX4WGbLCNqRut42HLLTKlQqG0245H5UynB9FoMVl3xcXMjYkzzhhh8vZO8gg6xKLdhr5E9Uxz0jsZ1N8M57AgbUAIMNZGkbajbbTMq4E1N3siMYBGL7BvhllPgoYrmVwZXHfZljtsr6tvEiKLIrTpTmsgd5Df7Nu5kQRuhYXAepyvHAniR40LIOxqSkGXXPzFMzKxMQ0gymVKyA5OemT4qx7q1h9FUmkg42lU9i0ARIgmysneah1eqnCuHgbk/6sGMYav9syGE+Ggvulb0oPDVpc4i70xI8KPXvpWXhSsp6M/eDdcDdpSINdtdLdZSQa/soyWC9ykSoGCAwIYSRdB6/WOsPyhIYS8F+Y2I89DsGK7VNJDhOVqljdDG2C3bBH8DZ8BKE+yLbj/mY3uQmpMp2JZXLNJmgCJzD64WGNscqagoAHy7UVNV0skc3mGxX4v3CtGjaWqmca7JLz58MJVt2HOXf9DKFZyE5lhm/byc5N4yIpUwi7Sm1nHOZzeCrecqr5dOkzWg2GybZ/9G1cyu0Q9+5yedVadKlbiFSi0jWJPg2BDKMpu9Me3Hdf7y/+wv/8P8ZarUE80X3G5fF/9RPRZ1zBapna7sEqsKusJ98j62iQNScGP3LskRPos5ot7M4rnwNh4ME+yiOCSkYnSLPOiAAMbX/43R+PCOjXlQhMbKJpo5F2Ergz9nFbdaDJ30SaL0qpPmu0zLDd3b92UYLtmlj/dqPg3NjtcbGdcPhrhMmlFJVmHd+MZaDVCE4YskGL2BjJM2exVrr+elNSBB9V4Ri7copuBOoanlirt9qdmYmx2VxPoos3QMIctNvgzg4UdH9UAb6EI3M5rseGMlO5WB/JhvzJjDkCeY+nOVJI6Eu0dWSSF9FHUTAJItYSImLKxn/0ryxJGWU2bS9RyEAoCXSny6FBtTtSJcpWie5GeLFLxmsBnw9HpN0+u8rrcBrtQW/QD7r02r43PjGeiKMB9ZZXahxt6yenWy1KxLVylAZJTcxNxdNeNN7YOJMqjGYKo8AO46nN0lapVKZzZrMyxEOva+sF5GKDLkUjGYIbmICYzRfUzfBxJEsfxb62VXmHmBCk7+pYMVvVVJtoKNlX5UktJJW4gkX8dFO84OnOs5Ly300uvcHMR2vvuK7w45P+nfF9tZF9qFkEnn/9IP231Z/84OJlM8nIZYd76Y4DI1DuMkChl+weeGn0gqu7ua0zg7vXItn8lZMTL1mYvGW1/0/LmmcpiLUMEgszTgBN9Ati35hNEXdj9Cj0SdU23VtubGCq2GStCV8uqdmLG9Z7bGIacD4w2WkuxxymtStcdWwPYhhvDlocNK2vjCiLaeRM3ktxcjS7n8wLT6NFD+PYY1zShoi1qSybhG1xYXuZ2DIiDHv4lIa3z7JYAXJWSX5t6OkVpzzVblBtcwgLBqF+dP81v/ZHf/4b/+eP/OzP/uxb3/rWb4SmDRv06Z/HtwW+jQRgEc4wfMtb3vLHb3vbj/3yfz34vO/H5fIgGuu0w62Wf1KEYYB9cj6BcMsY8HIpbaZniDEScLbOgpBX0FBg4arZHbBnuNrBKWK/wcnaHBeWFC3iMv2wHLKzob/WwjhZ4ax7sRrKHgYpEhAW8bmHRQc2GxAFkUVZPsvsmfxGmyiCrfntQTRMTyFFJ7ygXj48t3f6UD4/6IWdcMCefogCVtkIw6yHcQ4wBsbsXe1gZdLvs2zmD6LL5U6jshaNpa76F9ekQD6QRwFnPJlLYfyseYK9viY5a2xD24jVohnNxN3CoC9ilwMWRAeT+WAMkd73a53BrV890oAYkCcIj917F2QtncvvvfJ5pF05erhdLY/P7hmfXSCnM6bsMLWwlbrL7MFGoO7GmdOYRdXTezmJvVMvtctrEUjfoBuvtVY5bCroMG3gCnp9q3fHxz69uXoGahhLJO5Z+uTqqeOct/7zIwvz0xOI05BCKBEsKuuCzF7Mu1+59dY7brv9xInjX/zil4rFwsrKMnMbLNfk1OT+vftSla3m2/9n8Ld/k4sBJh75ahTT6sFPvyHxvd9TzOXG0qkgm8GmHM15NFfcaDdv+cIt1MhdHHqBlbV7hrByLSwsONmYKJTMsDtONuYO3+Ncs7g9wDtuWmjaHQi7n4dlPMofZHsu4Nxxxx1sRHFi9m4YRLFHBTU4hx7vDn88n4+Wg3vX0buCi5O8vMzIZKo4YdOXTc7qakx4dD2b9jTJ0rrc1Cd5svnHr5TWWg21P7NptbwSO52mV84duIytTMYIqU42epTTQgRzdHaRJaxeu9korU3O7cflSHdlNZdJF3Kyw1Txrp/rZzhDG5pgq1++Fg/uR9jQ3a0csiqvXoYB+lGnYDXH4jSQFGB33fivH42tYbECzJ+gAoXSTdR2ugzBBaQK37ksJ0FWjINnjQMeVoxBg8DAdRKihQMFQ1UsLQOXNzUMMAGxC7BXGMnzZ8mEPwlYw7JyLZkyDCvqcEqPzbPU1w8G5eWThcl5ltdQZfepewSFRtA+cTKVGvULcyQutxn1cC5C0Wq0U5vzHhxytILD26FnOKhytvAwbDZwcZgaWg7N7abeDdSQdgFUfZhOqu+zl4Nz9l1fwi7yNhqxEo7wm/gapKBhkYrUm/2cWuq+5Y+yh+8gRO2Jc9wvfql5einxm/8xduUV8s3zlLvg8DC6ebTVQjh0YiEZIZX0aSjkY+QS/9Hi9o2ndy4k0AMzy7BA4AAeO3aMB9TUCwsL33gRQwjqd3S+Bw4jdWOLZFxbAjeILQiChWoV8UMmpoxrSRR41fR6Cpd/IJa1dceeQ/yALP+RnbQUrv1bQBYD48oUKCAoWbjV9lZxHkyIK1soSDaWFtdL1bph/dTaWndpuoC8y+AIyktHAFUYn85NzNABYsWRTAFfWaJ8oNzrNFGOhmyjpVQ4htBLjUwiFJIToZFTI8GBXACnRP67SirQwvkRDcTjANat+AE2OzgkYsYlO19gALD6wWyILcWcztcJ/Gp7sFkusR2XKJrFrW/OTM+wok0RyyX2LqnqlC2XoHgERYRTK3DK/RhaWWQjSc/gJnJFKelEWqzhoL6Zy0JIx4Qzrqr8bKW0MagO9sxPa2uuZgRSy8BZO+5Eh4fUBhDbVElEW22pZQC79GPfYfhqeBCk9vDheUQuVXuiedCPPXBzuVSO0qrXYNgscOwAYvEZ9g1NBXZ8TM13Di793JkPXTfxtueM3TgeW4p6/bZXuL/xHR9aedNX6t87CBNHZ7zVTPwFX2zldcTS7gsnMf1nvSk+Fz9d+9P3rn7sH7xWHW860csOjv/rn7j2mVf3vdR9m/qiKlj4oTYPGx1/Czc32iMtZxa0pSrDLlz5hcYNjbYEMmmQHp08mw11pgkHqdh+JdIQW+4y66sroE/OxAew335K674wtLjXOcUWvHZkT97HvU466o2kIpm8zkxi4WPYOII8wOA5n5C4C7efSUTi4DNsQDWZwxf5uWaqL4yo2YrI0aQg6oAwLdCG+59z/Y//6lvf/ls/zeaON7/5zfrM1siW/enbE70FnoKT9MM0+Xve855fffObf+Rn/sMLf+C1my1vuQpBHbAjl33wbPxgozyLmM2Wt9ISRUF5i3UuRx+ZPIxgLAtngGOmUkhGiklvXkMwggK21R0gDUL0a2xF6Eg/POC/xTLl9DmCbCBf0EfuuS+RG82OogzEjXsYT8ItOo0S04AMjzGcRqCFVOByEF1vNq7T28T8yfsee499rwOBdgI57riwuGFw9kSum/IbDQR/bJF19n63XTl9T6w4Hsb78xP5QkYCtg1orEcirLoyhzGG2WaM7C2P86qWeHBRTZ4C0pjFqdFO2mJfsX/1nD/abSTadQh0r5C4+kWLXzpW+vIJvDY3MtLCReKJtDGKolJMt/gUa8nNgGAb660yuNiB3OvUtzZOp1IFlAJYTOEQGE8J2Kv0Ov2UXz9Q+PTexGeL3U6rN1rvP5Mji4PsLGchrS+fGB2fiucnR8Ynb7v39K33nALi2ORUvdqNrwZfOX3M67U2Vjvx6KAc5Eud2NETS5nUBjMLxnGnTi+xk2auODr47KeSH/4o0xeW4Sz5aiMX+1Hf+5eZl74krLWan/2HyKc/HSmVYmOj3vOeN//8F/zgq17l1qhpMGRdJEl0DjvXzjGheNhCEUHtoH2aajwPaug0rkePHmWrOZKzE4/h/FBW7Nm7l95hh/iRmE/AFD9sn91AeN65Hi1VdWiwGw1l+AUXXOBs9jTN7CpoB/hj98BEwozFVh/Wd5itYW/M8Ik+JgTFt6jn2TqTm9AIVJOoMyoJwwfWw/5lRyeS+RGim+3mxMxCcWKGnqW+BSOiJgcY0DQpDrudpqlIspiDM2rXOcTCz+Zza6WtWBIzgRF6qYoW46FPZsgYEBUvrCxeadylYhRmzScJTKW4P0uv6OHlEFFPAIrAUIb9OqAaYhptVik925PlJsoiTUgTd6VY/mOuX0/5+IaBXyn2vAwUQozSDjIus/QZAmaYuCoMkQCE1Wn7R8mAO8w2rKqVTahjB1WsCoce2cN2erUXa4KJDJu3fJbb4GgS6VjG+H6ek9g4hOPzC9mxmczIhHAUOw0c0BqK4laqyrQH/RpsV6A+KeGOryZqO41aWyBcKsPJonQDuBUkPHXxsg1dr2oQy2ZwVYAuy21P59y2seA3nYiN4PcYB4APuIgNG+3u5z+TOnyYphx+e+HhZ5aWWn/3d5GF/aIhD1XKAwA+iQKs5wzx3dXpH64GLNJhU4MuDVqE9hgxg+vhMjziOMgpdj3cH3GOR5Rwp46u/z1oHnVau2gELIwwHaKfscumUq0yF0By6WE4mSTJDrRhBusWNtrO7YTD/jkE6nLReSkHagkxhHKahClTYQYedFViLfYt2P1qTlcs2lropZzlIksMM0IdATL8Ez5eVDTVKDBCLykRxygC+mx3JwxTKG+ijeJJVPoOBBBFyg1ZQFbn98KN08fjyVR2dAao6GyT6USk1WsPUp3UbKSQjPfLHE2bH59m4KH2BS6ESvKjjUsgg8zq8slMrpibmGV4KkQjmpmD/05kU5ORAzWdyRdCgGSwMV2Yhy7iLapapFwYCszlwmK+AHqNVmezVB5SDMoSFQjnZrNwJBgfNVBQaP7nACPUr/ABslJBGgby1NQE8i0FaW5RQWoz2oL4DOfwSEUpXS68ExpvnbGH6TKgEMFqydxILFfUiiy1Gk3l4pz3h+fOUmJiYhzGjMVBoa6KUEOVu40eLB8XkbrEilE99+LuRtMMETUMXxMoigGOpVOog2Wv3CiNpkDsxwILQZd9sN1BH4FNbqXUurSG9B18c8GhA4Rzf778f7/3zC/mYyV6UL1b3BqwR4mJRqaIMj1MBaxPkFIdwfKLKFN+4OVwTvrut3mfvkUNQoaLKvEAAEAASURBVBBuxO64e/M3fnf8d3/tmouvuns1Vusy3inLtgG6PL40upJm+2z30wINMDFzNjtBuG4vp47s4wWbj1VtYSip6rHvF0fNbA/Oq3eTItqFH+hGex2iOa5FJs14rhpN+6xaIDNjwjmaASll5ieLuJsICglx9RmcwNKKIAOg4aW2xF9JvRuImWf/Y0entMBxWxtRVRlCtvoonzy5s8ardcjpU/7zXv6aQW31N3/r1+H6Xvva125De/r3SdAC35x56IlfUejJzTff/LrXve47v++1z/qXv4K9MRJsAj9VPropzvgKEXrp3JHIAE+AHBqWk+M49pOwYSDE1EdD3g8YVOiBOYqIIYSFM0YUjGl2OCQkDzNGbOyHEY2Qbh8DiUZnUO9GWDJLIYCmvU7BK4xGihMRtLu4kUAk7vQiGA4xdSFsI4hSNFY6DEmGJWOrF0Q7nYA7YzszvRhLpxOJYOeDUR6z4HotYf4nNJDxKY3NM7tx8IJVaVfCVA46SyXarQF+CrT9BOKtND4OtGyTrYg7JHlbD8wrq1v8R7RmSwyJxP7uKQyeP9VLnTra/NDHqrfegcfYyAULEy+/7trLnzk6kv/bLxwdndmH/8Uh9Q5Dzq9PZ9nLO6KFW4Rsm6g0z8K2Dwan7j88aNVL62szc7mT99/JPJLixIB9F6Mnypff/bK5t8xk7kVJrAkCqjX97hft5fi+/3TH8kWnP/AnqWihn85283vuWW616uVELLrxmc/tv+CS4vhEZKuDqVWj0gUmCwAnTy4n8mOF0ZFqaXNjayM7Gj106ZWpfqf5kb+Pd3scEN+TOTg4Mb/Goq94pbde7nzgA8Ff/WU6EmUKD496wZe+0r777uQbfyaxZ85NRm4n8IN29Wc/+9lweAjG3N2D23NLRhi+hYUFDKGdBSCtNDM9Pb9nnkmpX6+1URD74erG5vLSMlMuttyAoghMmuGokJkxpeaCw+Ny/qvOmxkdPouLiyTj2cUyxQIBPuyWW27hGftn9kUTywNwXJbH5a5uyXLywgjWsKDGK+NId1Z81Pd4kMZA7BrReuaO+YIxZ2LLxHiJQ2JRJsjmSM+ifb+UGZ+ZSubdgT02G9sA3amRIGvK0x0I1pXoaDqCslKpxzhOMZGEVQIV0LM/PRmPoSnRYUkEUHmGb2OY60naCMXyDFQFWJFuxCo9fZYIfhWkR6v1MMSBVojSCZLB2IFjwKixWkzyLegzXqei93938Y8X4n+fiaxAJ+rB3P29l/5T7d+U+ntJ4xDQTi7AgpVVlWAXYCGkUSo1IndDjnJJrbGtd69c3qLrIqWwIqR3XQ6wPQhL18i8DgVR8pFZI1sGjVZrK0T5+F4enxtiaOsS4hXpg9u4GoTtApSaZ2M0BZz/rmhXouU5G8gHc7yXS2QohhiSQGRSyYx8mlqbbsMYgnNVUV2BdPbDObBKa5cQ5NouTL9pzl2bTNppmooZpgCGYR22G5E779o2J1deIlRh0LjrcIRlVAnAT8GLrvKVr3wFtac7pu6R1NDZv7B66ARgqNxOYz6S7A+TBjjfdL8GbgiLRPHVh93EOsdD4MFQwkQWayNILifDJlPsQ4lxJJ3rbKhbIUGQMqQRUTUZEpuu1ZaJiYMOmUcPgrWnUQRwOw1DFt6A4UQUHdC2X1ofN6zATRKiqKXwVEqZpwk+RcG68EeJxBoCJjwbNFLSV+V0c/syKVMEWQgKAQ0EynUNYKXpixFPqIayCxJeGqP9Xheiyi5NKKSj9L1eZgUh08u3EtkYdtPeIFmY0PBj3wBIi/bqhREDw4FYi4I0xh5Z1AI6xkKr09i0oouOxeIg0+x0OWBHllzy96mTg8g4NTEJCBbgK5WqMDL5FgQhJgmO6gBfH0KP9IN0y9SKAMcyogzWQDIRS8zPzQh5uDpVW20EDGRahD1OJ9auYKQ+aQgk7qLyNaNlrbe6RtHHVCVAgVpbywXe5J6DRIM8uQimbjOzU/1+p1bZynFKVC7HxwLNysZKs7Y1u3AJzSw0rTH1idXquvgKau3hm9CkLGpofiQArMsK1zdFroZxRfnBsbr4Z+W0XozA2yxnkF0flLQqQVRcgPUf8OpaVndCYHv1KbBLRJkbGe15o5gKx9PhDIeM4PmFuouyhePH+glJne5SO0DumEQwNjxx4/HZz35BiKF9UZsQ7kfKW9W//GDxlw7O53K3beJNEEM8j0WKYKj7lbNV5GruCK72YbCUDhDROfgTLIHGogQmfvDh7FIkBd+WhR6YZ5hqXmBuEXfRD+eyOrKUT8aHQialNfo9jL7CaoTDgYP5fDieYv1dkioetV0bWB3UIDQjm+sw52x0I9gS4gKarcW2Iq+PytUJWGHB6Y+H3x/sOum5YIu2rJhCfBCe7T4KsMiVN/zb5x07/aY3vWlmZvr661/GV7Uinr490VtgR556oiP6jeDHiL/rrrte/eoffuY13/vLv/v/tMMY6zfs5u3UoSPSjiJ54rSZoUifRx5GND3dYocrK3yihjo3DP1wjNklxiFGa+iHRaHwwzxIJ1lV8nJxD8PpTEIrWIRjWcEWgumsY7r88lZttVxl5/BUmuW3WrsUsmo1MTblk9PGPZQdywo5hddGmghUTNRY62EDDhzGKpthlk3LQJHZB294TAPEs4y3Uu1iL8ywzsm9llFXjsbtRzbXq2ubm7metzhTzIeN0qm7ADc1tyddQIGmyYKNylBp6YGNU0ZKZW7ATlt00o17a25IVTzau3rKT37t3tJ//L3YseMwtpoAv3a08ukv5H7l557xvGvvmSu0NStojrdMfn50igfold7FCBt/LpY/XFu6v9sow9jns4XSxuksu2US2ZHpPCuAF8Q/8orFX0xEt+yw4XjYgfjhhLU7m7zvVbOvb23+8uc571fmSLFWozo2t5BN5ecXDsK7V7ucAj81MrOHwiLp1eUvf3nj5H3ljaWRTKZerzVbTJ1tjJ3ymRw+9funV6CM2ENBn3XcX6s/YLPR1Vd3b7819tc3Ql6baIy1UcimpY98vHPJlanvf3kvibckFi+J0Hex2tnP8MayN2IzVs9ZzTVGNzXrWEteeimHE1/Cs4xvTUBOM72cPNm/43B49H5WIROXXXrB4uLU5ZfdcdfdoOpAsnGXHru7DHQmnDsHXuxDxuDZScUSjk08PnDgAAngDZC0EXEJZOPxF7/4RaRolBLMK+QCBzhRonaD5fkxJNaatL1DU8mD1q1M20A7YICnaRKfKLBrdELN4s70zrQcYCumDYsA5WLKkV5C0pomafFtyUp8ajaWyWtHEL2OejmmjcmMgcMf05qxg4Qrgfp5PhdMX1KqVmrxxszMfCFnK8rKqHlu+MX08bjw+q4Q1531DXkiJb9As+HhshDCpa8sPO3O+3YPoWjwVQrdNZ750bsSwhwRDQDIxXY4dVM0N+NwlCk8lPrg9YV/m4sskZ8cIDseqY0njxyM//UHy2892Xup+CK4DisGTpHshokhpDKFBDgrnkvwDRewVZ0Uwr3VbuOoRnKqJbEwi3QIEaE3VxvLA0CHaiy95+Clloubi1KF9h66xEpyNyW12CEelpRkZ4FatIowhPTjXqyFhYS9bidQvF1qELmThbuNz4rako5VNqWztMNPYTTWECbcWsZhNISyA+uc950X+xjWLttBhrcBoptShrqHEgiqK10ShmGwneUp8Kt+ZRdeFba2yrhXMAH4n68mKXbaewfIzsPZlrHOptftgobDh5bcLmS705zNtPtp2KNU2naG3dFnsVCoerChtVMaeZygCFGg45kqlf6kg1REgkQroCo8Dx/sdZiFodcqXvy11a+Fd5WgvqubYeMEs4tKcX+0wfaDISe6JGJiZEpzp0kjrhTOS6RYUQcrQvKweptopgghUbySHoShJAbWqiJaq4tcKs7oqlVKDWI00jUx9J7aQG24M4LUqKTXXWKjFGVD81stxuufcgthPQFaNFkFskLfZTcTRXXRw0ZakUqFwGQaV5vRyUKk0UnXmu3VrV48EUux3VLr9QHK2p7k2OEfq9hBuxFNZlq1dmvpZPuuE9gjZ0ZnI1HMS/CRmUAZSzkwTxWOjlRHktaWpUwmXBEHuItEMlcsSMq1PyfsSnuL1iESScZHjPoxRLV4iZylM3zMf+JwP7AcsiB+E473UNpA9XLfWk3kuojK0YTCPMLnp1jAUwAME89CSnkMHZtKeRcU12peZHp69syZJbUgjbwdp++oVhXx0NdVnO7usnT6GgTD68mmD6tgTLjpA2hrsKKSOleBTJEmhwpzQVK5wkQqDQMMbP6sO9kXVhpxcEj1sLuyOpdPaQwSeaVtmXvoBlZhFU/j0CxQOU2/k6d1Pq/rZVtTXj8ZmTxl0q7vbZ4ZnYVv8jgPSZmFhhrE7x2+u9DrpPzs2mY3j6mxHL7iuJIeCiTZPLL110RdLTegRY/HpPuFP6VZpHclAWeUMJtrCUJMOI6j8xzSy0e0ZVUOQxog6Ns+YXxUISePJQb48WE/Ivt9tVO9EWzUggvHY/QnFWoMRgNxt4c9MwegcOiRdhHKszSIWwIU5tgkmsQb6shiSbycDywb6RGZYcvIQv5t6mZxyQnPivLQOf/Sb/2X36+v/OiP/tgnPvEJdpy5j+g+6NP3J2wLPPUFYDo9+3Ne+cobZub2/ue3vWOQwF8B/VXEnZGKJrbV0fHcWw07mxcrnWiYiUXGGWaSKtkeo3OAqy3tnIFwsCKFftiJxLj1abf9Utu4WTOZxq1xli3EcemHMbfAphptYqtRaZbXEWP34rpYnD+KYX//+NggEkUqbrK8xJHfMX/EiILGIQQOHwwsLA2i+I7HPLjb044dEReOXzK3z0BptaET2mvClMxOY82Loj4sQOIN3ps9cNkF86O5fjURGyD3itRGMg2kSksk652YbBgpzKgdwYqB4mD8LJIkiiqaetl4pNAoNf6/9yaOHieNkXmhEm02K299+8jFF3/XZYufPC4PD7bx0DBQZgEEJDD0aC/Uj1MQipPz0ObyZtCr9kan5mOJfIbtQP7q9xR+KemXg16i8r/HW39X9EvRYKyfenFl9FWldGzrhy76b0s3vLMZ2XP3LR/P50ay+THWE08fOYx33067Xisv08iZwkin1cLnYipbKDTWO7VNTJR7LW0wYfZudXDv40UnJyLrG9B2Zjlme017F1+MvnvwuVviCGSxGOJxs9szNWnIJ+999nPRa6/9Wiy10mBuCJAdI7GAw5CZ+1gIxCKA+UN/8uyl2YJAlhI0c1F3tQCdTK3Mf4ysMpl0Oh4b3Hlv60/+W+LLt+KbOBVPUFj/+uuLP/Fj177wWlMhKRe7eVkwRpmMhtjdNVsKkjyPISdjWEUIFyGELy4u8oDQ+5nPfIZAlxItNNIvQi8XDqgRm9mAJ4kZHpaldTvTEiUz6ZGcQe+BJJsQVwTAz7semPi8BLwyJYOcSAw9AjeY/JxbiKEv/F0HOR+ClW0cjIRYejjsEaPw6vlLmUKdRkV8CeFDrtSeTeIlXLO+OBZ+pRUBwoHRwqVTiSQGG+4L0SPVVfWNwISCuAOKEPo5WRRI2HasGCNAKZA8Am4J1EQ6uMHCDZoA8cqf1ZyuoA/nhqero3sWHIVbJo0UVxzNRF5/OnbrK/L/JhPZwMiudX+6eQd0y888o506UBuNnvjBsZ/+X6s3rQ+utGYWILJwtwpp6KpMg6/BrH9KQYA4wSFmhIrZHvQ6WfoBfJCy63OQVxhadvLtugTGXURbCTbAdyVV+9kIsMJ2Uqut3OUe1CY8iU2yCENe5IXLWsdC9fG3yYdVR+ULMSlDtEks4Cxi2DjWFO3odJfbIBh4vRuWugvhs2i48pVAVd7+HT7otd3u4MBuJE+zn++7Dtz8ZCo8dKH3qX9wOd3dGtgLD14YpFAd74K1O9GT7dlVylpf34TOgbuBjfWNbs/OUbfq2MeivvYVH1BBi+VI0/gVV1wuRzT21fmIgqVOYPFS+binYX6NNoUMh5I6i5JCucUTE0fEcIxrVKrfWRoJdvzxzPzocpHFJEwJnCSSilVI8EAytnfK2NbKkSEqvU+p9PnAzgk5brBTLOmV18qiFAkHIk1Ai03mFwqVOMfeeMHExJGyxTpMDDZkygoVelYKN4kb7tKimBVKGlXSSJBKMdlbaQClWYsWRhYQapaM8kU47M2pJ4U2kgYtyw+TH4ODEEESDIMDI6N254LX0A+SoRWrlXXzUKaak4ila8gFRUIw5eJWhNplQ9k4qGytb62dgb7KwrZciaxt+tHE2NyFDWxW26z0x5v9WKve2/KT88WZfl3wSpsVLAisdiqwWd7yOluF7CiMDa5L8J7J/JBOZtilgoSDHyK0vWTLZnKc28BkzZTtqmBoqAGYi9PZNOHEQNhknOwhHcn/L2It0zQ6iaG8KgmWLFpOtaZzrUizWyvARPGgttOqpJxbAk4h1nSUqn/WqgoVFm7d0dJgNCclpiByKZqv5V7w+RXfu38fFJHVgk6tSqIeXCPbRzmeACVHKosDBWQqCbT4SWVVAeEW8RVVp7S7fA6cRGtNRF/DClMJQsY6ErjzB5L0YUMKqqhk6hVar0emlWQb1xk/fEF4WqyIWeBQR1eFxBxxlLFaVhVw41e02eJ9tNmwTVQv0QlTq6pgLxosXRxbupRTL8PVcf/g4UECv8qp+WpqMde8W2gpL5DtDnLSHPiNVtWLjsTiWJ8p3h1ihGpHu4BVNw4u0eknnLfCiAMPmjMTk18bdueaboZ8Yk3d1jx0vGz54zzHdHzAt8dqGvfj1ENt2Iuj/mWjbnKAhSbbc2Krrf5tSz3O8mXU1zGYJ26b1DAOmO51xClsNjreHtojtacMrXUIsIfDc9aJ8FiOamqriW5ZdeNIFzTPEzmOy0L61TBRM4pS+L/9h3/6C//qZZwNjttR5wPV2uHp2xO3BZ7iAjB0gZNsfuAHfqDZbPyPP/vvzzs0xqSFD6dqZ4CVP16sqD+klbMdGPpsQ8WsHzuHRj/ALRbOpRjHUA2E3hzyMA6iIUn4r+pjFM1wZaRocOJfyuw0vDRSkBdp9n02g9q0yZAIUvGguRVtN6NXXHZw+djXpqfGZ6YniEX8cFSGRUxmNRbDmDaaXb/JolRPR5Ox778gyZqBTTnI4aKPqH/X1kpLK6up+UtObUXY/WsTFdSNM+8gLWID1iq1WCq+f//B6UK0vca49hKFeRbUYDzMvpocjnmQuRREUhw/ZMq6KDQ/GhfaOyRsKhvGN0uVL/wTR8Q7smYDXqnjqxuDw3dMf98BqAB6aIEVMwEkARTF0iQq0FZTbv7k3D4COu1GeXMZSXhmz0XaQOMFlyffMxI5Svat9070PzAbgwqB7JrXe2+mXI+O/dRKLnHmqpH33Xz6p9hfxMR2zz9+vF7dpMhkrrC5tlRaXapsrkzuu7Ba1qErhbHJzKC8Ulm56spL7//akcli7rnffXUi0u8jgF773cF993G4PIIlRlc4Jg7GRkRwq1XNBegoYYpYfkioGvorl1lRiGDmro8fygliP6o2tY+iD6OW0KSbS+o4K1Dqdwfa/MymaEiznVZny6s4YPAwxUmsrrb/x9uSX/kyrAeKTkTqOMdh/c1NvZGR2I/+mD82ItBhmEol+XPgKVPNaYVxQ5q97rqX0sJIrdTCXQi0JEFuR+IlBJmZWCc5uygCT548SZrd1zXXXAM0OBgcZSFXO6nYicfAcX5WOWOTBC6KOxeSuTM7lGgJivZ1d8Dy6j7/TsjOw3kpLe8w0jXjTsrtUGou5oxXGCMZ1f7zFy1kLWW5XL92I4gWI2IQZtXf+crGj8I0yFBQzxraDGmxswwjJ+iKR2aYiO90hntif0msb6SUSivmw9AHvvB1VRErIVQYYk6s5VEFu7yCL3zETFOGsggaAduB9LMXxH4tE1mnv5XeOdn90HhsIBfflfd0WjeURn9kNecvvWLstz7c/kuYAVewPoZ4OUd7BJOi7eJ3m7VxoRqWXPpSrWYn4QfFPKdaalVAKAkNELHLfobNqGfwU6900UYmeFRV+Wc1J9pelNjVUU8W6RLqzVLqQZeSO4gGWnHgoPDtazu51UzpcThVr9a21sg46LfYBLmxfB8MUGFiLpHCC9cQFaAOcxpMK1ZFbV+7n7fD+N0uNxzgq74esPhvgUJuO69aM5tJPO+7Wx/5eObUcYJdJqrRKY7Er78uUrAjOXZBffjHnfbehvTA5NtlE7NT3jmpdkItpb7JENg2AVENLMfw4zqcHQz3TIT+LKMezz7buHARbLzLj1dXtu5f3mJ/B2Eko2uTycYFXdoBUXZ1+eEQACz9AXGMHs8IM/Zd0oiNAB4k3WlokduNKQamQAgyznsAz3/9qSQuA7s9ykglYVLArStZPgA6TFSW4SYcKNrFOtHX8EeIVVnsxQBbQRHmyutyuTu9yeYzK9xaUpgov92UKeNh3KUH/XfjiCRqHCXlO4C8honhTghERQFGGIcpLJ0CVFVoHspJHg2ABF9HRRw8vdFYwwYhhwQ8VU47SYDj+PJWtcJwzxZHAQoEJqYYXkwUb/+ptfbNap2Q3Y4yzgkGKQYRrAC8RGnThRlNlM8K59MYxE6eWu53Gl1MgyNeaWu9G8mNTs8ywXajiWqtgngMOWFpKsDVsY+Teb9gh9KksllGKOIWkjnSeXRmBg0B4vXa8Xunxqazozj2l4pAGlmJssRoGwj4S5bD1pYqoKU0sVYLzYpzzU0bm+ZcTbLTwFRQkiRou15DdnJYLpDiUscgkb61fVC1uZ7V29WyauH+5umToxOz8RxuDpRJGcilFtnOQyjFuMuSSOgWNbVZQAXo5L5mvXP0yH0YbKGeRUVxbOMYTF28MJso6jgDegH8j+sctipBZvVmepF6rJXrvqq6siATBgLKQZvwl0YvauvvcB3yL438LxRIRUqtZSg1KnTdlFfhdHc+38oS7sDGZ/dbhYBMI6mKNA51pEfQGvmlQbLrV7PeHVemV6ai6HpJU1lMNCa6z7273ltKbo1dk2/eZ5AFXK0Y+vFDi51kqlGRS+VOs5IfH1W/Mr85iJShlim0bIHONoVeh2OQqEiUUMJpIhP7DQF4pwzq33SfToXWwTZsYylJ143TJtB/1Poc1Ysmth8bYHCPhqmMl+aanNFSqbuy0YMT/lQGZ6lRDBA4kAVzS/4wXMDMnks63jj+n/0I7rrpgXLZg0pMZzVJ+Ecaj7Od2Fj9OKsH6jyQK/oe53cWkgMOT+UQGfYeJvzCB2963/Of/wJk4Jtvvtmd0KECnr6eqC3wVBaAjUoFr3/969F9veMd75gcnyhvbvIhsFUdG5GzFsYGhiVsAMC9G+772bXb8r1cIjou2qvBz1DB07KOOOp4nYZYZKyFGa6MhwSKAS7ZVMgbc6Vp3npkyBGizmXtioOO8PzcDeJElWt+upy5f9Ub6QZzfjQb83PxgEN5zfOW/F1Zenw+G0zDDOfv0AjcTXO6EktTTWyke34GMpkdbLEoFXBAGRsbIAFgpSVDLSJiPo3slsmPJMM9xSgmJyUWxuTamglAZE+UFHLGdkorR8OYTS/a90IspRq1tH2Ymk40hQeI9F69Rf0tMYGOdLr8nKfGkb8Al7EKRBwwEAVNsiK7AIStMcj2BuHjV8Td81PZ4sy+gxxbTAg47YvfLJaolG19aBRLGkApM1cQ7X5ktPeKcmyyfenUV7/cndpcGRnfc2h0/hDq0zP331Evr11w5fPSeLdqt+vV0tE7v4LR+BXPuLqXi9W2KpGRvdF8xe9vhMlRvNFuBpH91720efdd3i3/hHSKGAwrgEF1mE+He+Yjd9zBJ4Ooar80evNnPgO3V4lLL42MjqQHYQp5U8wFNRrOHmouVTMcTQf7cmEaUy42izBhpdLNRPZEzS91VF9NO9YGVCY/GYT33tf5/JdoLr5Dl7Og/b5N9pHO3/5d7Lrvq2bypaa2xDCHSX5mpmc/qD0ASw0LRC61MT5IuFgfgeMfBtOxOQSYb4TvK4ivOxqEs4IJR8P3kpe8xEnLO5IzaRw89MNOWiYBK/SsGfGgcsLw2LFjOx5cXeKLL774oosu4pkNxpz2wWzE5cRm4CA5EwUCqKNdOHfEZmRmFEcAdHuhxSDY5WA+/J2EOx2P54dNfE7s9ov47Dtuvx30kOp3QSB+pz9vpzXoO6EPWpZDhjSI5/RW/qAV7o9ZnQGhatIV1JOHz/Qcra4YS+GGIZFyKaJOpQu2z36Z9WHEw2T/3tn2Zxgarc+PJj48LesxO7Qs9OLB30y0Lmxnn13an7j5qtyxlneQUaYOYd3AMeugrZFq1TBxwh6FD1yNxqT1ZJGBpXalF68emBwv5DXbk8CIBVyWMoszUy5BUyYDaMB50nBQFO8uyl5dOEFqBsWSxiVRC9sTEdbaLthyK5f+7Br+nPNFVMR2Pycxljt4soOkOVIWhbfxgxEOk5RpDxmHIEg5pG3DAtV7dmpBrCvwgXfSRdkQEevD3Jg67WwS9R9BiUYWFmP/7heaf/q/4nfezlEeAz/aW9gbfd1ro5dd7j9go8HZ/A/+JIRLzWC9oUZwlbI66EZHcqiCOY8WPqzF8IMq0P2zBK55lVGIklS5LJtuelEpgkYPU4iLpFr6CpaEu54tk1WXUF1qzkp/5mi70jne3cOZ8oSBhG5DMC7Eeo6Dq9EhsAZr2M8FSutNTDTH775jdGoOF3dsflGvE7QhwtbBhINaAGAGD8kDaK6JlFyBhq3VdeebGvJkc1XYXlIxHAzakKS40SEoavZhZ1Eq6ziWXA2iAGGlPxVm7wJNkxlBUaDvN1vYI9Wnpyd4RvxQZbjU2gafNwswGCpSM90wRPl53lWKFYK9LrO0HjV/qiwJgSQd5tOvoJscy9ItAizGUUixsAXhIJvLNhvlfqeHJ1tFyUUUSSV87JvfA11mmfTkqdMIu/bprRQvRHmF2TKCcaNmjp6tw2MshX8M8NPXCcNcgU0HOYyezEo20ogVR6fnoPQgNjPJp1SFV2pBtce+TVRnAzz3csBMQgeqyvaKOymRfqkaUm4/FQKvWAScxC8awRw0s5kWERpQGnEG0hpCN2311LqFEqtZTKLWA3GiC0rNf2tp+0oWriUHxbmPKGUv8peEarLRo/SnnMN/ao4w0mjUOLMAtomvSDLQA2cT5BRPf0a+pllpe/xLI9nyjGyGvTcVZ1+u0/xDTirVQcffk0tlgnat22lmJ/dRJ9xSt/pS0msEbd8pXmhaZWyAqZcg3Jq5MkoXSA8LDxIXYRVsIgUxaq4tqcLJGsQ9uDcL4rsxi5DCZhzKoDIa/zQ1LrLRf8qfK42mdRA4P3VaJQIuaw5jJ3sr8/6dVyZaKVk1qhgTTc9kYvkfz2U/0dxqfNfcmXfGxI0Cl0whLtEnfuhVm6n84VUvmcIhQtgOYnifgWeRrJ5UH0DPD3hoEOpZag9YBip6XThnNEAsrjj+hw6AJpzlAtDX1gLMqOUKi5wyxhfriAKp759eCzraVszZvzBObGAOxKWjch/4R8thiYOURH1QgCNyY8iJpSfNRsERFEsw29WO0KCLw43rtKQccq+e6SJkoka0byI6wAcQvqNReLDtMalNj+w+PnvNzs7deOON11577U/+5E/+1V/9FZyP65NnUzz99ERqgae4APzbv/3b73//+3/v935vYWEBnh5XQHRH+GBEAr4CfRpTGxwTTmSgPzLDwJegea7isC8sldW1s1AaUQ0O+fD7spTwJZH2/QaHdMOospVCp/762ZSj6aIvrAbWOv5mfdCsNxCYu80OWgp/tVxpB3HORupGmz1/rcVwEmAGF46LU4kIltVZvL1jesHgN4/TyWgElk6p4HpZjt3cRPl8L5ttIoNufXPQhX6k/LjkH9xTsxbFsGQCgRzMFYCjqWVyzyxim07wE9HQvMV4RVesVUAQVSC/GvYiJOCuWUkXBAwyz78WU2ox7+cLXmVLM4pdSqd/EeY62D7mVBhQkdF2O5XNCzSXK2GYwwqy7BB0HGZMzC8gEmleI4Hfz0XP0MrN0/gpYpsFhRsAl76b6J5Ixic6I4l1HEWOz+wTKep2WIfLpDJr1XUm+Gg6Pbn3wsbSysjcYnfj9MrScb9diRcmTq1urpVwepS5//Rqp9OOpEfe8KOvKv7iL7Y++an+Lf84qNaRe2Pf9+LI5HTkhc9f++CHkr0uBQ/m9iR+7qdTiwdYAJTZ7tqZfYWRvXtGexHUTTjY1GzBKiNTHf48INBzfjf6tSOdj3+yf9dduE/wL70w+z0vvujAhRhObzSormrITdIsfj2Onww6bErmK2meaMMX2Jw6OLPCuYGdmYWjW9qLpOZz30YLCvqj1mxeojjgaP4zL2WpWDDqt3HEPTuDS2RdfCPE169+9at4zHrRi16EnIlnQhfl+rx73n1HQN05Ith94p0PTbLnPOc5Tp/M8EFy5tpxBob8TEoX6MTmHbfYy8vLyMC7S0HV/NznPpcQzkQ5cuQIHxncnGyMoE4pROG16/Tp08wZhLsonvft20cUpWP1zSuXiyU7F3B2Y7u7xN1ReN/hvGhSIgDTrba7GMmHHX53xocM3U7kPpC6p3LrO/Jv+ObSEMb7g18uwpWr0W2XAdETA4J/waB6OLrMqpPv31LkDDYtpUgWVV4s4vq35L1nlyJ+4xlj9waZCwlWHsrcZlzEJKgz8Ddk/WF/oCEa9LqM2xDHEfbPrMaS1X+xjyM3ATJk/5REKS2x4GiYmtccRfBMiG4SbYasm8qzy+KtOYav/FhlLcKl4a6PIEjWuYePPG9nPDex5QIHoUE9uPtTo94FowScOn4MLmlunk6iNrQESq4HXcNfZXEBulOvsy8P8RSWcEG6WV0sjqBretA0Ibsdn/ms2G/M9Y4e766ueMVC8tAhf2omImXRo72oeIhR0qn6WTsHVyqY4hgGx6SCaGiLHKj9LYCVymGwms7+qREsoe40mqru2lNfz2VzUYKjEIOm/kOAJbV0urkHCwOIpCcmhl6YPt3Lb6z0S+4oToNvYHY3lMo1pIdFDnE2DNQTt4OX6n4jhQpoiCUxEkS5VBZfSmmFiYIcfGPjkdkUq5Hh0Hek1ipEqIWTQTkER0PJvakxJMZYlCAqCZ+Yf8P+Z3moqms08HDtMkTZWowkjDXyKpAOqSygU6k3qrVaIo2mM2NlCjDlSe+FvafqqH/Wg5VX/ySyyg+CIGCFhGlPF0orlSx158Z/BEs82DOLI4yxsEgIF+nBAHK4b+88ZWHjtr6+ARQguIanpGIhy0TZC/p4XVIBWnEGDe2aR61nqx/4EMprB60OpNUPLL0WViHPcf+CA/vVSkz0Vkk1yXYBIyOjAKAtqXU6k4mzlymBHq4r+ZDWN4mUrU2nKuHyZgO5OxrJzRVRBAo9/qy6DgCww0KxiINozYq2/sYHrm0s499ubvEiJjt8ZlLB7Agn59Ekak/BGH5PVYtX5CJ9A7uIERrkcQvsSq1LheqXRPRMhYqaal1Beek/ZKQ4cvLEvMZaLf64WC3g8Icm3lf6/iCW5MPAAFBc1yRAdpACzToUi6F8PvLyzdQv7M5HVFncw0S+241sdL04iwI9PGyhzgA/vrfyk5WiMTXjJKa41gg4g1PcGuynPo0wcpUjoSYCYUwZ6mSCPowzVFQ0IcP04v1sCMnc1+yY6FEk8HvddrtZhWPp1dug2l7fBHQynY8k4MGsdVUCn5elwODkjL86l5JCOjBk9N1RPUsk/cRp740/nJnfM9Z/33N7X/48qxnE4Xhz4mf+decZ3/GlTaoS3TfuvkfETAwY3+pR/Ee5moiym1g2jFRfh0XhyIovI4U6B2PqkyGRJiLsKVGV+cfKJ43fQrWrptWKCwaYyXhQTBESwahTLsHQCfUH+LUyy8eACTQbQ3BFz0vNaQd8O3uVVoQvy9dnZSET5fQsP53A6DpCKmtqZ1gVIIqb42i50crgSQtXsmp3a3v33Xaa3h7IfPnll7/zne+84YYbfv3Xfx0BxIE7N9XTb0+UFnjKCsCQgA984AO/adcb3vAGOGAY6MXFRRredngysjUIty9RStqC6YT9vZNZCAQ8ZAQblQZH/koYxk2c3/A85hPsHhz1wQkzA6ndi5jnuhA7n57klIAxmYiF6Vi/tHmC5SeEF6jG8SPH8ftb88JNbcbw8PnMuEVXjD4Zz8MYzXptEILaoJQFysBZZYAM53qncP4c9k+eOO1Hw1Or7EYI+vUzTCUjY5Nj4xk2RoLn8nKp1uxMzUzbWmGszW4KUUO0iKyWiYyBmI1cBWuJFKFXdBCCABkQOYP2iGzb0Ha/RJxpBIv5kdSLnz94302ObDmw7DQZHDiAA6djHV+yfBht1GulpWN7Dj2DGVZ0RTKbCfkAVXuJYVFZyM2RWKYwppJUjIhtP5Rr4mgGOox6GgpEhLLwHyLoZzXpBZ7OGsVEm72+kUGRaWXjzNF4ulAYmWJLy/133drq9cYnp9eqK+X1lUalFIulNjmwPD3qJZPL5WarVv7K19Zvur3+nP0zc6/4gfT1L8cHfxBPdLOFahArXPrMxOtf33r7n3P689ib/320WGjdeJP/yU9Hu22vOBL+/+y9CYCcSXXnmfedVVlZ9yVVSWrdfR/bNJfBjRlgxl7Ay2UMPtdm8bVgG9Zjwxgb1sesr8FgYHbHxl7w+BgvNrgxNHYbaEw3fbfUaqkllaRSlequrMr7nt//xZdZpT7VB6xh+qus74sv4sWLFy/iexEvjhc3XB95/evCe/auNLDg7Tb6aoZ/IO5L0Bree3/9N38rOT9vjbuv9cA91a98NfSe9+y/+qqVWITJebaRM2PPED3dj2YihglKzq3AA40SdU7jDKxEoLZFwlp0ry4Q+bbsSxKLF/xoCbQP3Lo9CrMrFa6HA5yysNpVgPE+cuQILTdTwc9A8roo2yN2dVovyW2P7kme3QbY65b5fKS+e/du+hBdtblrrBV1F2PUTm12d6glRZDwhW5fp40n/HEKMFbBvv71r3cTJ4iLOW30fBT+O+64w2nFTm2mG4fhMbZAQw/bnnllGhx4aODAKiaiiUVyzGwT5Lp9hs+7dbPTTe7xHN1C8ALpOHUjWkv9eJHktz2ixM6jL9V9UGEOjQ8n4M8jLfQF4Ud1kBfPvOQ2b+FAgcEy4LchxQmK7de2wEeFYbP94PDmZnzfGLZHLgLbHn/LbV9spxqKAILUixB95F7VF7f6W3bxwKk/96oncO7dgxKsouIpFALxwPU05IpssfgIOmHyU2K9+VYwHNzJ2aLmY7g8DCKl+7MXi+68BejFeILHAtI9ktvfN0wv7eLGQhFguXqv8Whwx87A2CT2fejn+XXoqf09Ac4n84Yuy5EeEGplbU4/R1CeyVkwuI1q3cyjmwnltONrsS/Kn0IF4JAoBfOwh5iimESn/DZXl9kZEs1kLQoRiOIlWi9XlmZPjk7vC0SiwdQwBsDXWDFll3sABxIks+qqpSXtQD1dPfB0RFPReK+VSo0a5i+YMfMVcIsEX7I3K87KKR2GOzJftUJO8zYwpeHtiHDvgjPRCCjNOjUQYH5Sl7xmS4DKoQvQVJAlBFX2/ZCYGiwgFA0KnR5EJLvwMfYSpk65+t6IbWMcCykZsMIHma45Ul8gny/F4ymCc5t5RJxmZO2bgJoUJi9QZf1sZVrB+JzxS60bvEEwDg9z8Ax23fJYNraMiZv4IK8yPWoF0ciqFVlw5KfRvxAaQBBlGSCapVRvGg2CH+H1cqGwvLA6U6HxQUsJthrEyAyMJ1J9KBfdvEBxdrBfbbOKR2mCCgWDZdG4xAbzISnN2aL7WFlohlDmwZQrBoJ7x3fTnMFzphBtcZP7xhWE+ZJwNFqqF+dXiulIejyjDokx2ApDFUY6be/gqGhSPBRRKKE0aSPVdDJpW9zcIHuxHs7XJXtGl+WA1CURSBu6+RdxQssb4cIidDAHysUuJKmK2ZKhRUbf1oRtE21Uw9naeUsji86rjPnpzKAfuqqAlSciwe7MzsMQplRArztPm12QG8x6SHdTnTMvq/RKn5nPgL8nES2XN9vNam8qmk1o5gNmsKxXRalegGO2UBlqYz14nAedNqsMyh/gQirqLFg0EIs7WdCgiXNr5kNlZYj4Ljz8xh+O+q3kV+hGanU0f8XV82iQ/sGpcCimDIh9xkrs4Af8i5MsZec8EiWnkRl9ZVxaPzhfCPzNifrrbxoaueLd/tnvbzLwne4JXzZd6Bu9azPytXM2pBDQrG/A31haXkjGIhOj/RpsYVuZKhKfDIQpO2SKasYKffriloBb2NiqKJ8oqjJYGw4jdNvsN2TuipknBgggiR3fYKCzwXGkLBsY4GTTEJMTdbRZLLuw4kGmYUvgRWHmMBdtD8aMMzPAHILFoIPLKeljUQfDPVin5TRp7okowxCGnAK0DNtNzm2vWwHOBewrX/lKZt1+/ud/no7QW9/61otiPxr8+ff/PzmgjtR33sUHeu+99/7Ij/zIW97ylve85z30cTmigFroDqe5tPyqfUM2RWKhPgkERArShOnfVqnOEUc6IozNuhUNHSGi1ZHlO0ReM6XHHtEqs8StcGJgEtlayW9US/nBsXE6FgnOtbEDu0HFRgUtukB/RffTSWsMg3Fsd1sDWiGdOcyiDhGBDJc4DNYTe1ANV/1r4cj6xMQU9LglSXz8xfwm6mciGsvEtYiIRTIa6tVXioBGXJm+xXZfSWi1Cvg7O1j2ZZpYJUjtB8tRNAgrEWzxz6z49/dnxt/0htz5Od8dd1sLRQfH1x4Z6f25nywOjh6ZE7CBmxwxgiUdvB6JHA6XweAmFRP15otnwx9abl45HLgvtrO2MVUInOYQP5MvEvDt5o58fHcVuhaqV/mDsfHdhzbXl1bXNLtIp2PPoRvIyfnTD2Mxoq9vMDE03ixv5i6cjTJg3itj1LXzJ6PxTHpweHPlQiG/gY2oYycnB7Pxw/t3x0LBUoUTCLXU/Lume6de//rUwf1QGxzqr3zsY/EvfqkVR8b6/ec22mdOlR94MPKB/9C/d/8DG/5cHW8VynSmNb65WPro/xOZmfWMVJIkZJ87X/7Ef07+6q/2DI2xlEAL5jVzi7IbCh8+UBvui2/k6TOVWJ0uq4hgajX27/UN9jMNMpHibGTZe6BfyOgyjbS1YCo0x0ccrnBIijahU1TGVh/9qiV0PKZbMaP1RGIXhZMJW7TBJwIA89O6unj40IjIKx8a12OREMSJxFwuSFVh27Vz506nG0Mhl/qO6mAo30xfs/QaH+dv4dq0TJC6Yk3Wk+ugUeB55e5WYuPDIczdVB5++GHgmW2GOThuu+02mkyHX12rQIDmanp6mqC77767qx47DZmpY1YGEsTMNiq38+TOBSfdrDhRIAMfh805IJIkujSAYfv1uEEMhvhCwxQv35B/quw7I+vi+jZtvZZk0XRZ1YAee4iM8IG57kgXsasp3deLHBeF2RFZ8PwiiCd56dRBt4TEfaad+qjukpMEWEvo4thewBcl7SDw2g7RjXbJjvDOIXpSmPV2vbVOPJB28T422cf6dOJtewbzrYVwcSxJv01f3OPHcRWYWTOf6zU+PtQ2rE/m7I0FdmXUlRUWx04Jf3XuHpLFgy4RcpsU6HgpghfLUeRqtSeYLZ4kiEvcgD1ceKqsrORsCrVezbd9MU6PcVJG9RZ40aThTLqnSCtiZLWdwdAZgJyMsEI7gHajUdNlROF2AO7pfDfXV8uba4BgfKO+vpxflzOU7DEdUuRbgapbjEAhuqg2X1KHHK/FhfHKpP6sbkoTAE6amdpVaaraz9mSoX49aaAxXEyg/CQrAEqnk6SHbNnMoXYqrHO1hocG+X7JF4Z8TcIqnLQgYXSU0/4SvJ47Nwe8QkWipnwxcsGACNaPSpzLp3Pu5C/qMfbTYE2XOIVCiZxA9ZEiQACGj+MYQ5K4S6bTHHlHW89P03+EIkqYHfO1s329fRltLzcSjBHKo0ooFOZ48xjZUkrIDvSWKoY1fS3WdzLDFmNGmjQipGtLjKSvwgBog2NN2nymB9Xuo/hqxMGGyM0wgcpfBSuOGm8pF1GpiyAKQMQYCYou2oxDkk5Y7o2oq9GohpjVvrBZxYJHT0wFRVRFhyZlTu9CwuxcvZpbX6ZBrJWKGLhbXZxlgKJcKkQSPRQQBEoTF58tbWMRc6m84UPilLW2NNtpt0CjQrOVjLEVlvWi1lqT6unMNkhjORA5UAw3RLnTpimfRnwAhRcFv7i+EEn2ByJxcsSRE/Y9KI53WUnwDh9stlZLlMkxJl4oOi1Xtr245FOz377Iei5UYtg+mcbcsaGS1SoSddvSHFqxlsvyyffpCIM6mKtisoJQr8DUXb0Z5cQQLU4wGHHKFZS5aYhtJBMIZDCe7h3fR3BpfQFcyX5aAQiMeQJBQHaJPezBVm00OzJuJ3aTgqeAoREV80wh9MdH6teNZ/ccHEhecS2ErdUCx5eaZzd94702s+L2O7QC+bCvnN/Ip2L92V5MvbDgGCKVFNnUTmmd5KRzonUWiBYqszUXg7Usbwxz4hGfjOWOqXY4wYI4AFilnOBc37A28UYmgysl3x1zvvkNVsiDLVSzuSh61L1RllhqzontyFQ7eEq2mGdyJ5titjYZ4bAlnflC46WCEi+UcxxW1/S89IsOwM/8zM9wkMdP/MRP7N27l7MtVRDPX//6OPAdqADz1bKW8o1vfCPnNPzRH/0RbQdsZ1UPw6iXzn+rr9urLOJLbVs6yt7aoH9pqSfkz470o6KgEjNLzD5hLKrzqzDdGxWsBNZAHwONSwvVxaX8SH+mZKd+F2UCkK+Bk4d0iBnbUGmq+M6Qbvjn6wFONtJ2UwRjQBYk+Mj5btGHIyHmg1nAXGi2WP/KIUciT5tWG+WVpYVYMDw1OcIhr0gfLr5eRCSjYS7LfNIQryVYREJe2iW5qzdZrsLJ7k8GQmOpBKIOYOFAwvoCd8y1Xja+o+/f/0L1X+5s3HM/Z89Hdk/HXvbS8uSOu9cji/nA2oWzajmaGADxrc6foe2MxRKpfhblklMvLWEzlJYyifKqUBPQvmOVNx8M/YUvWu77qYXV3wqEF9lfBANbjeFi/0+t+iNsYIocr74FwUWmw4lUT3aIjK1fOBuJpXr6hjeKVc78nZ7ePfPAnb2DE/EkBj98hY3VzbVFzESTSiKZjkUSmLRZPnecwxt8vn1fueccTUk81Z/MDpDXW080X7Cz9/qbXogNhdqtt0a+8tVAIhaoVFuLS74qqw9bofPztcsOxH7+56b6Mn9/vLqBfQ9/87Jef/vcXO2+IzaC7uXMBJ2/dt/R2Nm5Snb0oRWyCLFi6GCysXdkR+sNb9r82CdYR8iCdlbroOzWerOJt/9QLZ588NjxVqN++eGDjICYGkVBqFVTNw4Lh7TlmrfXdEOxVD12/GQLo45JGkXf/fffD36Ocj1+/DjpPfkRnehvrEO+gh3O39IzgR1/LrrbV+b54KavyXURROeF5dZcnbeLnsycvOIVr8CLglaP1i6+d3z49ll3jR0vBgVQidHJ6UUB7+Izt4wCjJJMDO5c7JR2QZxZ2rWzTSiYuzPYs7OzYHNg7o4C6RRgZptPnz5NRoB3Qcw2v/SlL8U9MzOD+u0UY3cnLbcmnBOq3DT1ttD2vn1XNQL9wfZK4FW50u3JEPPAfMPUBcbh++uh79nky6k3B4vlSX8tJ6vk1lFGxMFAUiebJAol3cu9dglz5PEKwOMGOYAnDlKszrXl3nJZ6sLfAdr+JFGPEmXocUG2gz+ZO/v4x+0+K5wuPczQDQ0PYxCI1ydC53HvyQi8pDDD48/Effy6l0sURlUYDJJwFduQmh6AY233tUOkem+mjugmHUPgUln0cG5hABVvcjk86EIoQKbcUjZM5HGjTkmJ8bUZOqROIfnR1mqlTc4PjATDsZRpYobTK2drNoTS6r93V5oM9/Ft4oufMoIz3jsUz+hs2AunjsbT2UgqTUie1Zgs0QpHYvoS2yz35dwcabNSTNQeYcI905ciFnOkpSJLsoRPJGJkPhJFKUVxJ2jNTn3TF2N5RpjumNoJVDFfXVlcRvkRNm/OONCbSpBTBq/zxTy0GmNENE7mjmiFoV2ItCFRCqn6/TZrrBNR/L6+TIaC4cIPJYGz9lbXVlkPxeHAYyMjquiwRVwiVbVfStrn7+tjDZTxDg9LiyfzkNyxqBAKRgTkNE2ejl6lq/KwGOKhXdL6bA6apNQWqCSFMunLpvDfuDBLBnpTUwCX6Htg18SIEFLvHxeEu5okGcOf9UYsJRFntOghB//CYP0MHtAjTA4E3rjV50YwOWUrls6PTEZXc63cZjWX5GReLcR2ufDuqmoUld5adRTOFcewWnlj6dxDVJtEbz9jA+u5HO7UyC5OwGPSXfqtzT+j5dIgqg9DDlUIIPK6PRAFO63ikCVCHdMsp5ZJy4vKgzelr0FG2R2VehzrC2ALE+MMK4vxWG8kzQZlSl/1wRRd6d3o4uhpFIoaLTXVyja7ydRkKGlwCrmwo37jZNt0KllhgKRcTvekFYNAqWOiDjeZ4kEtAIGpu2j1QuU6daKqW2dArsIQei5XfGKA8ZRSsVDosSACFGRhDp4A6jOdS8WlPFpo+ABo1Z4OGaJC2E8Oq3VEFa3S/4WZbhiDtDZtE2Rrt87CiBxf853OoW23tBBAWnfILWxmdkesIWbQt2ti+NTMueXlJWx8cma2cY2TTdjSxVfPSgeWV2pRJJZcFEtfnfJIdJJgISRzCZwrnQijzWJqBzujVtBbGWtjPGNHmq3u5EwqMfJKY7WWbwgzhZkV0XZSKQeXcuAzhScOwQaPk2TOfi6SxTSOPd0bLfIf/MEf0B97wxvewOI19l1ukfl0cT0P/03jwON3N79pyX3TEfOdMwnzgz/4g8zG3Hrrrd1uK3NiXE+VPLEB0b99eXx63lfxqLqby+XoaPYPDCAFOJq2NwoYoPrGkcWyWWUnjLFbmFniWszXTobGM8znSLI2aOd0uBE7u/wlhqnqrL1hKQjfoCQaRvDC4QCmAiS6hFTKcLWqbQ+SiBzOgeUoznmV5XcLbzVnzrF2pT4xMRkIxZh/7lBsQopze+Xh+ZEtNvJZ44YXqZk/zQUKta+9WVitFYvx1GV0NliRrWkoBHfbdyEfuGXGd834xMSrR8I330w2apHIvD9637JvPic5Va+WkPi0MERg3w6RNFRnxItiXV1G6sWYKcmCuCUJpO9S8wUzvtfvbv1ZdHdx+LfPl+9INBbC/uHawA0lXwajVIFjtTecbbxMIxBos5x7EE/BypFCgd1WPUPj+/uH6fEvnz9d2FxKpXoKG5w7dT6Zxq7h3onpAysXzsJNTjpND41P7LuaBoUulXFB+YMatXKtQDLSqm0UfNXNxm3/nGCzb6XePHvOX6uhljH6ThNUv+1W34++rSccPTwa++JJiJdorq0uYzVaJyzZJUlqTMWzurLMAcTHFtWR1AarQPvsui840bPzVd/nz6Rqf/3ZxplznEsVPLA3/ZY31Q5ecWK1UWrGhgYGqBvlBib7m2vFdp39XzQziq+72wCsbSj+IFauaKU26xXMaaVjSTxnF1Zy+fKuXdOJNBaNrOUQVa4CeBTqvVOruw6PaMsC8ey5VWfgknltYXCAj/oovNjf8keXDBxwenv6KMB9fX18rcgBJpDdWFiX+O4S7u1RnPuFL3whDvjjLqp3NxV2+KBLoy3j6e7dkTUkDGIBT2K5oK78QSFH/SaKi+UUVJcWs/Gc0+bcRCQhrr37D/j6ftS/+tuB8ULgvefz/yXePhGh1gb2NXt/PB8aYgY4eGr95hMnH5KkcqLB57vxxhtpaEH1hS98AQLAg9vwaUxkamqKV0yXIRudJ/o/PNllF0Go6G6ljFOVHVIoAABAAElEQVSngYF7BBLEvDcGz1wsF0qumbEnCPayQH17EDjdrDLjCN1YDoA7wzTcIY8zq7vl5UJhEXFhERHB7DydAwYSBH+gGR/n6RwgcXiI6Bjo/B2MQ0LQds/tbqJ0X7c7MHgLqc5nOwwIt79uByBo+yvuLiQUuohkfLs/bvJFKJCYbzdacHIJKoSl+lAIe4ec704dND81DTg0g4hcYrcbWykkfr0/YoEwEtEEU7Wqc9Rw6DKcTDimknFgCeKcJ49AdOyNpfzqEiKFDHAczdnTj+DIjOwM6fz5xtK5k1mMFPmx9FB/5Nh9RA9FwpmRPfTLUQuHR4ZokeDw7OwcSYkFEKhE/ZiDStg06ZnZWdaWWoDC+c8OZHuTGtLK59fItsjlwnKrz9fT05ug90pjVy4Uy1pb47ByB2+zzQAWMzz1CkOZakLoo+urIX8sEOFzoNPLabT4OJ7jwGXk+JllZcUvMkIFJV1J3V8aXByJWHRyXJtpCeRdJNq3CF3oOBNaM6IgI8bd2hQJMMlUj+dNJNkE8fkLxdxmoacvE2aEWwi54IiKT4XgeCNl1dxS3vjRwkgj54cGSwQ1qCpVQbn4iiwkRr7VBaVH8y1wmgKlpCTsJ6EANKuIMbjIXbVGg/JkDjSKJmhhVFpioPiBGzgxTO8GgRcRLWW1lEYND4gQ7+ynyUDG65VVTftqmYBYboMFNl7AkQjVarNUra7ka1mOXNNaOQ3pMuQiPVaqrHzIKoOQzdQ+dNrC2kKpUIsNXg81m5WirxgMJ3tJYLOSCHDkrGmExjkxQt+2fd9yQ6pyZdzG3+VUWSO2Xi3PatH4kQUpe5DK+l7cdpqjpjoZgNdKOqnzy5uhvn5/NK28k6x4ZnwEl0saX3jMRKglZZOlfOxWKi45SOIyxZgi9seT7C8IblTJtcSSEU71FXEqBopcmB2vbT24KDXcSlBZ8y65LBa+CrHY1oLIl8sLNdQKV/XBj5zyQTA7DR6WA0RTrAJu9aTUo1EUgeNQd9UgzXyJbI7YFLdZISFzVABo9NIQQuKQdxJhOBZ/MqE80R+mITIwxg7Y+heZ3jF84cyZ8uLMZQcv47Q4VjaQCpHtE2X0gRM6Mb2vo3fjTO1iSjbMWmU3xKsk+be7VHFcxgE85ebORt+rRnSWCtPLfHxxpnYxdsWqZrTriA7mENkeG+W0OiuvDgbnfm7ujHF/6lOfYpj7zW9+8y233EKLqeSev/41ceA7UAH+5V/+5S9/+ctUOGZ4nmaF04ekD9dJGV8bwzlUYhZMSnY85tpWl0nHE3joKyzV4MhsRSAa43njw5XGULnOpnx0Y07ZDpSDiHtEhQYagUImMvTLwmmWf2BkiyOROIGJE8lkmEoAzBU32ceLmEAsxqJ97f4+SXkbsp+fX9sstbL9g+FYkvCoJooMq2Q0P6Uis8+2YEYZcxLQ9FMvQ0DqRxOgNtMmlnFrqZvkg0mWjWr4tpkmmyJ64xnatkKFY6KwCoa4E8rR6YNQWcznmAqe2HNIslOZIsxxiDs/8QMP0uJy2jzu0UT98EhgNBqMNT/A4qdo6+9D6WrPzXR6YF1Ik9Pt4Mna931+8f2VUDXsBiyFQUPyLJWhpawyql/cVNvUbERCzBAk/fVSaXM9kcpG0/2DI5MYDimuLaGU9A6MDe44wJ6ozbWl4Z37oEa5M4k4lKqPh6q+R076sGZWLoOsvbbG+cviFjYErSvFrKuvVAzm1nb2DvVFQ8VmkCYsODDcpP+qbVgwUfxVnmnOqQfZgQKnbdUZUIaXnrj+h0eaN+7o2f/y10RveHGymGf9Vz3Vk0/13H9meXwovb83GylsBE8ttWPxvp7UQKbvoVzjzAa4KH36rl6DJH0aq2fpXTSb64sL+dzq1OBuFXW4WaxkV+KDf/FgvVyTuUVGUmVngsMDQg0cxdUlyqySXy9t5OvROUZJE/Ho8MgAG87ZTUbHRUVlNYQ8qBRdZlQYuOzLsAqhEIWrBVVx/iu+oBCVlcrkdKRLoZQoXE8EyQRydw75UTAon1yP8nSvtu57UuKgc3XdRHnNa17Dq9OZ6RNxaaN4/7vrpTvD5X+O7itHP1RpFTTMFkhV2TlFBavFXxkfetfBgQR6BfBE5961cwZOdGzn6e7dyW2y5nyAR/eGHEYMHVFov4+a3AbGKcBMU8/MzHRo1xPd2CnALLdhrRc+jmkgJy2nAKP93nXXXfi4iA7g1a9+NWXBBDtS2vl37y9/+cuZ54cexi7xJKKjljsL18kU9Hzxi1/sDh84hAxkTE1NAc8GB5RqHM4fB8SzrB0HZLA7AIfDiQNs1157LY6jR48i53F0L/a9v+hFLwLy9OnTLKHv+uNg0QQ7z3Fg5u2ee+7ZHoSbrV/oq7ARSh4VxEIAsoY6+vnPf74b5Djjsob7C7feulHC+LTjFzXQv2//3okdk4WGb215ucoxl9BPZOkwvt7eNMsI+RxXV/LFEgMiHpMJT8QTQ0Na7lRGEBpDXDxiQr9W/JpVOeiUyBJCn5bvsLSmb6hc2Ag06agKW7nWDGj/XTvWO9Y/MdKs15bOnUlkxgNhViaF6tjAYJYH8YZsxSJ3GxN9mkiEJPvXQx+dMtlOpjlghMTogDqhwcLFMAINwgYHRpgBjjKfrBzrwwtHaEIJa/dns5kWao+EjiS1c9Fe6izivt5eDSqDUvqbIkOIhFIkluzHsiUexhJryDDij/gCRTCd1BZcIdRD8hq6JNp0Aq3OKSUWf3ZTMwMOfmg+9qNlUIaAF4R0FcsWYIpgnphoDPcx73gh5ygSQuHwciHdUh7SvtRnkNse5qmMqHHXH75CaFcXyqSwPuGON3KD7FtmnOgmGvN4nM6yr789kgzUp/qxuDmTb+QqsjYMRvRrlYnYyI+46DZKi6YBXjIebf5s/JQeyItWauuuwqOAjVyRAUeksnKXQitbmHASxOztEt+MP6THfFwRs0+BMGO5m/XQ0QVMFoHV6VBAaama5Vd4bHBIgyGlZrDhT8cyu0iuUj4pzTo+Bvn0jnxaJSAiUdD0JTiOkSdTosgT2WcEngSAYc8nlKO8UQ2onlQrzdxiNYQwcgUOK0KNPqiIDTM0WzWQ8tb2ZSb3kRhLhUBHdNVM4tm/yV0xs3OEnojQQBWKvUMmNjEKQTJSN1Vm4lKEfyYZrB65jFhhq35aBPxUMCoWRTJOcrP4uim6vF0VES3yJQo3NeJtPl8jUyMRWpLtKKcQAZKmys+rhDyTw6PS+aUS21gAvQttxFVOQUIyBk8CkGf0uK9GeSIIUrQzR2MAjECZF8NydCaY1GWBMauXudPB0AJmJQ8jMADTN3f+/PrCzKF9u1neyLbbuIzmYIaKnb0yxuapJS5bSrl7KaPu2nJ1fHiSPQzr3DDeZIObaqwwCPBi4Cd724bs2TpJnXbwz//8z2+++eZf/MVf/P3f/31XUM8W7/PxnzsOfEcpwMgFDF/9rl0veclLLq22OWHiOFppV0+167NttIvYrnZwslLF5DJaVnPu/ByzAcxLbDfSwzZjotHHelxDQXxkJvLY48/4UyAV8Q0mJKD454etBTvlCIPSTBdjWslfbjBraCoI5h8QKJiG0vxwC1PDGNNnupgT6jFwh0RGRiK/+KNlGujXypzRkWxpI4fFwmT/gOZ4mxpSNQknEhBnLLSWENYl2cmPNoKGR0OwtfL60jxSs1HXEuj1udMAJXuy8Z6M4ASqOPRxNqvBDezr8S4xZ+LYZC5eXDRM/MwCIHLSkgFUokYcVvKC65SJGqn2wYH6DdlGfPVC9Z776ufnWr2vDF5xRaDn8+3AiYCvVvXHV6r7Hqz+2NHaGy4snIqn4qEhbHerTeCCq4me3ocfuGcEK8e1Ep2zYDTaYFagXsutLzar5aXZE5XyxvLM0fPH72UTUDTee+HUEShI9Q04bUh54jLrK/2JVrxQXP/s5wbe9LrWvsvat3+5VWSYwuVMy73U59wzTUtSPzcb3dcTj6XXN33zeexsT/ivPOy7+56oJhlAJzbrds1h39jwSoHxS3X5IFuesvgYuP1M63iPf2fPIOcVt1oc5oxhs9oLh9I7CrnaZ/8ud8uXfEvLHAQcueGq3re++eBlBxbz/tM5dRk0eqlaJZaCjMaSt8JKo1xo15axsAVPA4H42GzBt1jwrVW0i9jsSaqeYNMTji3M5P2tBgY2281Sdo0VjH6mSoYnmRKh38N4Ak1gIBpqal2D1hfJ1jSrmPixUkjbmNWYsYuG9kyhuOlJ4G8rwdSjUT47P0cotJrDNbcEAtDhqhdkfltuuTqX4B0eq0j2AnuViP13Ql09s/Tl1b0oD77f7pbjrv+32IHaxqQrs8EQQzG61LsO2nvn45ZtO7dqUiATnvh0bem9oY2/9AUKvl6kA/1K1jcm2pm3RYc+MB3g1OgthN1Mgdmpdl2f7Q5miXkFP/o2yhh6LJuUHMD111+PP24IxsEFKu74ADM9PW1+3q1LNm08qt32oG7WEIystHfJdQFcRIbDOUzLebrUcbs1+QBMTU3x2o2Iw82lg5nhyO2TqIB1p9nJi9PzXVxidTeic3h1V893yXWDoORRAhxgYNAP0XIJ7Y4d4Il+66JDqovVzSz+zg2MWxK/Pcj6kgJgmUDX3zkcftyjYxPzi+Eih4NTu62enKj0LSxgrdTHnsEIa4e6McWQmMOZTCUgBnD7k9y32XJVs54UFmcMm8kg0Gra2L7AFENf5EWpeJ1cnNFopFItJmLJwQFNeErxoRBMgCFdEIPBsG9053SQjYLtAAMK5WplZHQUMmztbnBohFgSTWKRhJ5uqO3UoT6MBstXHIBO/PnTMn2ff3z/lUoFP7dCVZ7Wxvn9YSwGOnxWDYHih6JCEnzZgNXNhb7hdAlqLY2jlT5OqWHglY9QylsfkOoVN/yJJgDhcCACk4dI5E8uRTW3mjw57ZIvKIxOc8sXB4jwjEaZY6c15OYhAUM3VGO1ghZyodO/iBCMS5qXjrdRa28iUdSrtLiDz2Ex7VTRyZCaRYZrfdeMNr9r3J8ubUQrJV+s0exJb+6O3bHSPLIkNcz0RMcFP/0JJWFslCor/U3MpxdBF0K2N2CpQEQzUkHNjpHjWCN55uXRyDHaoI+dX+Ahig4qaofUG9EiJh1zRWemxj9ZcYh0N6LUvNmLPNrNYJxVAQzLkGXaOZRzqwqqkfTItOWDiqkFyabcam0UQUrFCJIKCsn8iWTHKgJEEZauEH1KnHfyTQQXEXVTajJelgtFtGmDRpCTnHSYEwHwR8wwnlj+wAEh4j4IXaiiASsPCtSw6EXRdZneTZIYiCwUikghTgYmSMnqT9HA7/4dvEVSzixQkOYy5dZSZoIE2t3gk41WuOrkzalCoUVhIIBugAYy+GkUQA5ewWzrhKWiq5qKVhWFrR3noYISVdzEE4XIFxAKRQvTpOXa0mV1G5CQUtzFUqvzJK21xwFWLMu6DVOyzOheOTx2MrW5e4p9gaRvzFECLqHuq/FU/k/rUoGmSE/0dlj2tBA8p8AQQ4OL6stmYAY6f+AHfkC17PnrXw0HXHvwr4acZ0EIUoMF9y94wQuYTvmTP/mTbv/syVHaACCfeLW58V/bq78TqD0S8MtIBqOPrdRL53I/0EpcM5QdfODBB7ArSz8Da9IgZG8S9dh1obCOw8TL06zWEiNcTt5JQqupYElVu8LqaI4d5sdp3RjZ0rk7NEh8MxJNSFYW0FU5WA87WzpuTroxd7aGrsyfQaQOTe4Bls4KsglhZTJoKy1pvMgHJ2jcAYPMRVdKy+dnaOg03ElKQXU50tmheI9bMW6SxESgR63rFBj9klU0UfJpNyrFUn6Dc4polJSuZcqgRABvSthoMV61J9K1m8eaiQfvWv+PH6HkrNloV3vig+/4Mf/Lr753o3Trg6VSux8jfsp7s94zMNk7yJHxJn5pwxqNM6dOLM0en9yxp1EtVjbXi8X15ZmH2A/JQAC6WpENwOUifcHc8nyybzCVGW5UK8mBsYGRqeE9h0d3HXbT70aRjyHDF/mOrvyn/334LS9CsNc++JehL82EOXxZDb/ainoy2fzIH0T3X770T19NvOnNf7vScy4f6Iu0Xre70fvQfeu/9hvxCwumRrIpu1EcHs584H2VPQe+vthqhNiIRSPAmUrqW2jDkubk7SftG8/mtWP+K+oXSh/5eOtztzRY787YJTs5OXlrfKz/g+9f2nPoLx/mEAb1Rci8MVJFSzcFzjRqFZbU+0PRUqnMpJBaQZ9/pdxaKXFwtGsBjP9W7Kz1pqg25h4qLZyZvObmYISx+HZ/yhtpVkeXRhFIazNpmeTjmpFOk0ZRWOmaEPdDE9zRsA3KM8O7NIeM+9IiuhP80JM5wCDMqn5Uaw4YgJEozNZqSp1GKVfHhaFid7wTI85aVKC67tDLiVv0d+uOyt88HAxBXI4nOLZ/hrCrUqng+YyXHjHTCAY+8+1old7TvBg4Y44Ufe/poiKrLc50qDzULtzSrh5X9iL7AulX+6IH6Gw8G6pgDuuWv/rVr+7Zs+fJd4wDybFVzHZyrgOcfJpZ/zYGP3/+PPPGV199tZvo/hbkhCbgk/fWlyvuI+gmKBWOw+r5GtQG4G1fh57OYd83r9sD5WeiEhAD4zOS9O1Edd+QYARg0MLf8i2ePxmKxHtHnF00xWIjDyDQ0KyU15bPZ4emWDRMzcxtMLtcYJ04zSKvQg8K3YxMS05E61ICpjCqCeDFbhaEy4up7GmWTbVcviTqKBeCTh5wIZQMoyCFFS/lwOBFgaAtFYEBYHLSMiBwwTlgC+1mXVgsQALImki9gskh9O4G7jQeBRgFSD/h7MQTcpNWbJAQArQ00Sj0FkJWvIKyrKI/IVDxVHNEEAoeryYJFYBgxW2SGV3PI8byJ74Bi4ZHMmh6rPPidD2q0CunWjdlS7EjR9b+8m+qR04QGD2wN/u/fF/p0NW3rsX/+iGXvFKDDi72UvUlRB1aC5S6hsWUOMZElRtXcMYOQSldPUW1cVLZFTrIJoRWkFUJJVxWCviKOzJKVGIEByNb9QZbnEPafKl8kSuts1WnRS2L1jpJKyNUhwTpFcWSrdhqkjgTWO0xeTReuiSUCb07gpSUWjHokV7nmq1O0RAXDCyMkUOQrtPBkKI5JGzNE1wwLbBxIHXnzsSDLAlbqk0dyd+wUNmN1aZOYSp1JatL6etF7OQm9VG8wQ9qLFEDMaAOrUyrbG7kiaBWW9PKXORFtVv8VA6UESn8msc2zLxKX5VVPFUVpemKQEg7zKTGbA18GzOl6xJLFBriLt1K06PMEhfJSt64oIlrUmGFBzPDNOsot7TjNNzYo1HDzZoxfQlOWRbNvDFWjpYrRTeExitjrmi8zP2CQdSSp+5lfOy+KXvP5WVcthw/l1ifKS4GlDmJhmOBaXBZr/RcZ/aZkvV8PDMw+Z3ABsQMSumb3vQmlj1/9KMfvUTtVzlHImAGePEXgusfawU1eqpPR2K3GCx8biJ4+/mN9xxfeemZs2emp6f7+/vd6kd6hAzz4/NMq7L3tXcealRoD7DtHmfaMSaa1JqYEDXL0lKMy5x+pq2pzBX7StXa8ROnxyd3OiUctWa+6itV2sO9zBjrNGOdviM7eK75N3wmf1gRJFklsanEaTsDkeTIrkMQsL54oVLKj0ztlRik/VBrBh3uT6wSUWpsaHMdm+QnLGoR26FYoieWpHGxGF6vQxAG4gQ8sYEFBrtfVw0HUvMnVz/0u8Hzs75gtIUdsWYzlCts/KdP9Pb/+z0veulXT83k1kr1ug6EiMRTpc0V7PaDrm9kkr7F3OyZCycf2LzwSLC0Usxv1EubjUYV7ZF9YKFYkhODs/vHF2YeRr1MZIaHJnfnN9b8wcjw9P5kZphNz5ubqylMsKjF9Ueaq30rHwz0/tXoOxYDra+Rn9CHgtX7IoXf8aXulQ3PcqbH995fih28eu2f/jGcztSi8Q3poyzoCt6+3H7R4St7f/uDzVv/qX3sKGdHNPbuzrz85eWJqTtOLz+sZXAgCO7edzDCyAI6IhMD1kKq+eeHmfFA6FBfw//PD4ZuvZVD5ihcZgRZMgTfWwtz5f/vbwZ+euKK0b5zObaXa5mWjdNrph22iPNhTvSJnZ+fK6AAp6JhTldu06Fh1gitUp0Z6x4B2KpXihsrs7jqpUK1WlqcPUHZJ/pG/KE+tboii8JUQ6TSV7lSu/GncHlRsTkvJapWmW6F9jupu0IIS86orxqHVguqHohavHajipWvBqbKwKkYhlRpGE5LFwbhQZXQhBDtPZ0BrcUytZn2m/Xb3LVASzozw9iBWKA12kP767qJTFxTiDDSpgLEUhHPD6QPnTh17tzZl7zohZkMi0WNfN2VEbu6Dt6UR8/be/jp4J8+PXP11VfZfINyR/XtBF4Mq/hWInh3YbaBWETFdRiMA8Z0l2oHq96MUEUVt+Aku5eu8EUvtwD6GHSUjYwumECfycX6ZIhh1vQpI2My7Y//+I9f9apX/Q+lAD8lW55zAIq0VGuvFumQW3+4kwD+fCAIblfmkt1yUWnsYzMwC7Iaw6sFO2AFGrBu5tW92ecoT8D5VIUu4MuO7eTNvl1FRaCtctYRVc7XToVi1x6a7mcPpq+5WvTlS6FqM7xeaiW1slLAXFb3VZsdSghVTPs01GZI8TBC8OIJvWTMPBTq0hUSKY2umisOAd43RSTnkqcuNWedtJUfS08w5vRwGqBkjZLQC311ITDMABtKF4CvDeqJMMcWMEIJod07MPLTDBhAkoR6SuYouyZ8MERSn5tf6Ekzl46xK40QWvZRsZQV2kjpWppale5qdxHN8DR6rChDfIsP/ANoTHNLlYwocUaJglP0UlpKou1fLQameitXZevBu+48//7fDOULrg2vL63O3XX/yPve/eKbXvrgQuTYmvZXQ4hRLX2S/bf4WC6pecY85co7jEZtuXgn3pOiJlXNAzLIrJa1gshUWeKa219ibQGE4a+WgGYC+cwbg/XtpdX1aDDan8loiljcIJfuXyQofyShmVt5a0GZvBVZ48taqctpRqQpcgQkBtqPh85TFJ06doo7bkYHFAioaHZ4xDHRbwBK2l1KWrE189m6MfPXb5v80FD0BAYJRBK2Tn3pf157818svDffHAGUfpOmh3U5XljOrczUxMtP8bjIuJd5vUGKopAB+gOBdByDlL7KZormSWCYINY4CNlUXHcT9VQAFQJjAaDiZ20f7R1tpbZe0zjSRJpqLGAu2CYWqLgsviofl1FE+mTAKHSBOOGIFnbB2xB3NbVyoOvSoKuWK6buQqbNwyi6rFWOhjg414+iy9Y0d6InBS0wI0E3JYsG7RzOXy+6jBZW2XBq43XXXWdrWCyeC32296dEJaLsn5sHbPx+6oTVxWUdAYv+WSmnfDxlWpSaDGLdd999aCi33347pjEvJdZTk/I8xLPmwHfIEmiGWH72Z3/27NmzX/va19wCtkvhjIkvzmD4SGj9E6wI9m/GSl9KhE9lWvGG//pi+NrNkH99NPIbSxsMiO+em59ngxyqtVvUdyn4Lx2G76H7GZmoUVTz0W6KWJBTyxA+dvH9tVp33nVkqLo0GUgko40Tj5xEHYs2GERurZ++jy7L8NhUJttfKlcatXo01YO1YQwIs5QalbiKFS5mjLWUGtHurb1Uimqo0F64I8LoVqjtUXpeqi5tNU20tG7kzwh0VCMSOuRrrTDAypA1DobDGG24AFNwOtoaDDRLt389NDtvqNVaMa2LbI/UfaXPfSF1w41TI5mlfCuaQHfycyKCrA5VdCAc2u/5U8fmzj3SrhazmYHc/PncyrlKMc/RR5XiJou3mcTeXDxXWo9HUv0cOZ9bPI8tGE6iinP6cP9oLK3FeFhtYQyaBjLoW/3e9Nt2Rb8IuRzxRwNk9j2a0WsKoY/Hq39zQzv2mtBNNwUHBkr3PlC6/RvZX/uV2Va40qAZUp/0xBK2osM3jl2WfeuOIGuRyV04PFMJ3HNs9eTMbLK3t69/SE2TWXGAb66BoGVSKWoeu+UPt0LlUu3e+/xs8pMqqabfZk4ojmDt3vsjlcqOHg7WYpWybBqqneuUDASg365zbmRtaWKgb+dIlKafH56ZaKtU4yBrHaSEzsydLWCxqro31UA41c709ET9gUg6E9g5qMWNJCrVWgi5q7el8tA6eWaNRZKKVSc94lZzChStoQLsch32zjAHfRS6Xmon8ivr1VKhf6cMt3o12FkLoQdBucrXqpw6cWZGBdaoOhKKFqz2VjXJIot51FiWb/n8B1HbFQKY9RaMIXYz4ogIsa3WvceLwcBQ71o8uKETF91oOgzc5qa3Ycl11GaD0YcAzGahuVJuL7PbW8ZRRKpFVLpkBree+mC4IJVCFSpXz3HyunDhAitvGYUhBxcWFhg1Y2sS5uSMQIFquIgsEdz1MtoV5l2WezIvvNxIVqkqxrO76HyA51Grf7ejVJ2w67EOI2A77PPux3DAMe/plVI7HW0HC3QZqTvEV4GDQC4+C3OTDNXQS4yPiDJSZfDABGpxLYpBEU+eFuI87M3QCkq4vcokPJp3owvPRkbhpeSJrop97Wjju6Z8/ZWNcL1GZSz3JdZ2xj51f+DYEjIl4ZIQYsNreHD6q4VcKJoMsmfYBXnpkblOxuRSJBt7dlKGDMgTQQTt0qmESXmAHqv4etrXh6c+DNO7AJOuAij/+Npd8pYMuNRxWizh8XINkEOsJHDqRQ/3E5BEHJJOd4vjvgn4QmIIVUF6/jhJR++K3Q4WW8n8WrMWZIQOcoAySWHFtVWClq7SlIam1ImphxWxc+lNHFHWOskhXuUtFZKbFsDyLjF+47i/B5sQH/1keFODxUpXPGuHi/mlj/6X0QMHXrhz4sQaYt7Hnl1hZFRRe4ARibAaUvkTu9QQol/hgHaaHeSs3GIgezmJAmrHENGgy1Ydi36RXsUAAZk0mk1AU2C0q+1GyLcRilbKWNusJOLs0wSCdEWlskNmbDxADjUu/CyHqoDUdPED0q2NssypXMVBLmO+igkfxhEEjBdEeCVsAFCpGBCuolL2+SlrZEs54scupX838OG3jr0/5OMsK46R5WPEmnA54Vt/Vf9HpmIPfujkn+Vbo8AaY8iiMCqq7kICKlUzy761JjINxasYKE+pQ7zy86WiK+1ipcq+/0hCG2Q0iUogGiYRnZbrzi5mLRX+StRSsFyK75aqEpbD5di8eBc0jZRoEjtASPdDmi07C5yDOwfhanZXaamCQZOhxBKVtFxZY27R+dTBuVrALPvM7Hti0RaAJoNcI2xJPvrmOCJfc229bgdkQSUmJFhexPJgQYop36LLcc+yS62gSpChS02dSnLkCysHX5YNY5b60mKxT+fTn/40y6Hf+c53/umf/unTmKL7FvHjf9BkvhMUYKryn/3Zn33yk59k5TO2Xp/yK2JZI7vI3LrlduOCb/V32kyYrSWrvzEcfCQcYj0zMvJf+mo3L8V+cCkWyO0b/ruZ4vtSaAyRqISE3+9OBL30KtMVVcS99FgGuQWPy+FhUmvpwvzE8MhVB3ZWK5XYVD+fcbFQZGFtqqcX5TbZEwnGWmfOLa+WimMjaZZMY1KL3buIQXAi6ZCcNOBAMkWspdR2Alsom6xiiAA1WiufJMvt4mlyESkqqa0hd5OSHmH2EHAH3qCVliSqQ9F9eigx1hcLBBuFxswZqSmQIrWK1VCs/EX2NpqnZiK1Um8qwtJljDwjbpdnjo0N916+azjDviC/76ivNxLaFY5nNxbOpgZGQ7MJzg8MhWLzJ+8Z2rm/d2RyZfY0Q5f7r7958dSDubX5sanLNjP9pXwOe6mJUJj0BjOpVBxLovWrA+/bFbuV7Dbn0rXP9rTur7ebxcCrffHXVIKJauDNj/gb++srraU/+X/rC2v97/7p4tDEv5xtVRuyvAJnaJhPb7bP5nyDqWhrM8dG6tXC+uJ6Gc165dxi71g8ODpBrjdWuVn/jJ6RmkB3mJ4UrcGULIfXC2UWM0uWwl1WRzVZvozBs3iwVuWd9cOYNKRHKEj+jb3W5It35+bmQM0RIEzF0snAkAuFke5xrZSaXjow3DEcw7ZSOD139myt1j+0c5oFc6jKjOaCkY4FM7Cgw4eiq5v9MQvAR5MiliyINfruOhLqltBTUY1hRlpUmf0PjC42R9Os/wOnf7HdKEUbA1mp+9RP9jzrjARyATAfoWY8wE8oidJDAIeqqJsUovEGv6tXncqkikarG9RGBPVmoMVVMmXTuKcem3U1CpuFuXJ8eHDwdI5a7ZaTEajqDzNsHMHcVFN1woiu7oIY5fOtLMxBZaNBR82/eJ/GaGIcrtXf76UlolT6+KMs8IBQYcGh7pTXAcL3zMn1WpXN5PrkAmt0SbHQE9uMqrdP8ViXBgdjV4rMv7ad0/UxvLwD41YGykOvlmXjC2kRatTaHYQAdS5CjT6eXB6LOoHu2Z6a2tnX1xuLs4IAWM+z49ATO1Xv/KmfYq8BWz3rjcZPvuMnoeK6a69917vetS3K9hhP6jbGPhZiK3FHLBBdcro+LppVhi0/XPh03jtPL+dbCVGnty4xSZBb0BchcYAEdkgQ3FYiWy4BbschMMV5dFqCu/jqYO5E77zbs51mYy9CQZfqyBafndDt1HYHYLWBAKVs0sC8RZhkE5cyYkzj6RDgqSDVR0XiX4kpzP0L0FA5nNJJmJV64VTz5sFS6tjDhb/7++JDJ+mhxy4/OPm9r/6JK/d+drZ9dFVjhqA0xUKVFpz8I8rn58+lB8cTCR354+VKLg2wOvKMHoL4fHCSOqKGTOIBwRAp8QKsvgqj15Gl1sL5iEAvj01Ek97UmJgfbyB18Qy5uQVg1BiPkDTuc1JEZIIFOlpFkRABrNiqvmB2ZCqnQuwB8OjUbrkkxJDeiWg+XyxXON6OjQNSQmSuqkODuGCYiagsCq8+cOMeHrw7PyUjueSlps8ccAQZP7PwpJWo/IibjTT2Zv2h4+d8px5hNk88IiJiVVGCrfOz/rPnDl0/eS3jb7SO8rb66qqCJLBlx5JVVOXOqOzUOrJPxhm0Zeu1ZJddSgEHJGonjZCAaTyCoWbcwgjhhk2oMGEVTyRz5eZKvpL2Rzx2Aga/LM9CBEZeRZshEMv0z6uRJLKVIRJjOF7I5c+Lorn4AX/S19472Bzr1ZJdzoY9tuRbK4Vp4Gi2pYgLHi6qPGGT9gXpncrgP5j88pvGfi3oL7VK8eW/ymx+Jd4qtuIH2oNvzMX2bu5Lfu1Hd/7Kfz7/MRiowjKVlXRxc5eWqjoERSIFnKqKqs6UrErRyXP2FCPhgaS9zoz3XDi/ESgvjY/skA05UaV4XG4eGfrwxE/jQUImOoFQfNVcpUIU1SBm7N3krdNpdUfXZUYXfzeRC4iVrj4z5dwO15ROGwm1uNsRRMzlYi/EyhQwYecifat37q1zV4k+u2t6ehodmEMQTp8+vXv3bnL9bHCqnojUp6BKdbjNUkp2GjaLdQzT+kdT/uy28+eeKk/Ir8C5r4T7huo7rr9UBYp8kcGPf/zjTAJjEJEV0c8mp09F4fPhl8qBSy2/S8X3LYej0rO/jlOnf/iHfxhr45dSqxhzYnGjWQdlv+ZXQq1FxELlvyaDMzHsJTd0TqzEV/jW4dpV5cgVuYT/DqaChwZ2Z/o4YFZfV9fa6iVmFxuqrFV+xgsIySPzSAwacbFm5ujRhzij8qqrr2QsNpFITk3tAgBDpsDs2jnZJanRE+SUiQOcRCgBKvWj1kBzQxn2s5q6KovTMtio02hlxS/QTvci8DTyij7DvF+zxYwx25LtjkUufKQvdZaJm5xBJrt21slVS1tSSJeJI+fcdncyU5bxmaiNJkBYw9yV5JaEl5jPS5jF6MF4T19Pv+2HbTdfeqj/mvFEurQSLJToIh2YSrzq8tGvPrLyd4+sjU4fyudWaIPpOWLGFMOnh6YPxWOJ3NoyS37ZJFwpFTnwY2B014Xq8aX5M0Mp36tfsG/3QC2s3bMPp4t/TqtQOtu7/N7MYCMTw5BhOVX/vUL5TDz207lAYL1a+ER17u2pm787sOeyzezoVy60F4shmjI1aWoW6GBo/VCu1qq1Bzmoph4tNdvn+oYmtH00O15hCbu1gcqfmh/9lNUmFrtKqFWI4ArHae3G6JpGE1G61FrRFlInmRGe3BENRU+tNY8vMQyvMQIaIvV4lF9pRwXUlGJ1INsXT8StGZY/DS3k0cidfuSRRCrVPzxCyaFlWgFiEJUXRrY14e06EIz3M7eshXFSdWnBmYzVUm31bCwhe5JhtcQ06txt3EKqrP1pYbe0aH4tX0/MN5mozc6epwYkYtUGVrXKpwhLZwdSmT7XBNswimy40uSBlTtmM6UhQxmmZzUdzXGCwgZOagsz0rhRmKmZkNDDuLSmArREE8Yai0lBnG2x00xP/8LKZpXWP9m3jqnjblNO/QOBvm/BWCXm1V1Wokx9NNuzZ1Y4ClVgrdZZjv9hlXm6NVTPwh/INS3aOKMY6rLCeZCZP08LsjT8fXspiHqtcv7U8Ynp3fGkKvyxZfiq/od9C8RVVdIPH2HTi72Cp0OZWG7wPBWqPMinkxe9kEmlLAh1XBwe4RRB6kMbNu7W9wVbxh/P5JYAwOyvS1GY9dOFDeF2oGdUHM+tV+vFUHqYEapquO9MjtJWh8jFUkzFMhQWt+OvBC1RmGNMA0aXMuDgBeCcegeJeyMLDiG+XU/lzLAZDpeaYnVfDZlL0bHHQozSLSBzmbDp+PGk1ncv613yhiCggrHQkbrnBSuBx4HsRiUSWd+6BLsNviMQO+gc4DZacLJ9jk9V8zVkfCsuLmWVwpQw8VLwPB2rYJOHCe+LcCpcUYyjRDa3bsKpV2HGv4MZMuVrgBLuu/vaN480knfcc+HXfiO6zq5Fxa08fLJw2x0DH3rPqw5cN7vRWChZ311fsvCKh3ZSzkaxznKqEjaihY/CVb5UyFbFHBGkTbFa7TTU5oI2x0qIMzAj0oULj/kSYNWbp0vX3ZUvy5RI6aRhPkoVDzBDhhtlM1wqZvlAuKXjJamwQK1aRPKGw0hXhenr8IhVMPiU5616ybdsSfp98WCsUcz5ahuZDCtJTSBrwapTXJHSGvDTzCqyF73FVqCgQspHYBonda8wAuHcWfXqiX0IMWqc4FF+cFVqgWzU3yzkY7RKW2UrNnBBbKOwEWEoU2cWqCKJTcqV5LythBF/YKUyADu8S3D4KJdCo8l33DYwQYjQmLkvhfIzfBCnpgXMvAqNouuhJgzVOxQvVjkishGRhTZhNMRWOhSC4eEOr6BHpMllHo4608OtFRJhhAi1lSxkIf4PZqrff7l/slGJ1ThOolHtjVV3Jr+06L/1ZEB9OxtgtAwSWXWP5PUT9a3Xj3046i/4GrHZ3xpq3d0faNTUH7ozvngkNfiBucTe/Asyn7l98+fmawfVfFKCVpU1HqHmUh8u0+MqUNBSgqYTm7j3ZLWqmbECQhzDBvv7scZP42cT+cqNKhWLxoQS4yc1jpZM92rRLPqtqofOZ+JkhyZum8vFlIZYBA1iFsIDFBCm5kBrldm7i6IbJQpLwGx3FSuWtS+XLdZGA0mScwzUE5slSkLiXV1Hx+O5ftKdueqqq2677TaM7WMd8ImOV3iayXrVgTqhfkUbc7Oou9ia1S509F52EdL7VT9WDJBAiIWbvTF9Ypd2tXNztY254NlvNHZcf2kxDIrMvva1r33HO97x7ne/+4YbbiDj21j9NPA8D/occuDbWwGmpmNghlN/p6enf+/3fs+0gydkjj6Liy95VI9IfDajjbtjknQMDtk5jciSeDNQfyDVunIj1N6M+lfUXDzTC9vRmKLtWit9EjTbidz+eXBEJwKCi+UiUHjNNdc8ETb0LpaCkwT6MJAnjmMGw4+pkv7+LLbxMEZtFzknP/r4URvo4mKrk/05Op0Y3djOZEJPRvXFvCdddolkB4z41tSxzR6jG7fam/ni4uLC4PiuBnYjpa6ITWpVwK9EXIZwcanhdIhypVYlEk1dc0Xxrz4T4Tx0bX9idW0rGIK+QOiGa+vxZG497EdIt5o37mzvY0fr7f9c+OvPNE7M0OaEDx/se8Nrv/vAocKVE3932z/lFs81OKag2UATxkxLtbwRiSawyLVyfob1z+1WbenssblH7gmE4jddd/DtL9uX3Vyp/revl48ei137QPB69hgHQ9n3By67p3jfgy0MoabDwVQq8C+1+s3N6MFcsP9Ibd935drZC3nf0VMBTGFrPpXmTS0auVN/VaO1dEGC9C7gAnOlrXAsMXHwGlormiOaM7VN6mWrQ8BFE7V4/vTS+RP7rv/uSjO83PDveOELi//tc5HTZ1ntjc0oegnqjwZDPf/zvy0me0+cbK9reli85d+QCBcvs+c26+10OjV+klNa1Y6rr6nBbFjtay2u1xONxmZYy58AB68a7dQg3ewSdqNFlTpkZsgZhLxLtXaaL9lS/sAIKabp8kb54mMLA40cQEhHOhU9NoJa6YRvT8Y3nmzH8zqvFdPlKtWovqyh4UA2y7SQ6bRgbMnQqHCi2WqGWPqtnSehVdx0hAglOaaRucttc8UWRco2Srxqk00ZqWrpZ8RAja9drpTzZxanhvr2jIVpBUlevTRlRqkYCcqb8EMZTrn1E0jb33v4cnzy1KfzM5N7D8AaUtRPsEx3C0y0Ky3FhyP4wD7PRyEuSPxuVINL9MQ2A1HOxHLeirjlEjqiCoUQdLBAjl1iL5cChNUYr6fKWz6mv/GwQN0AsU8WENUSvVMzLKIjU+ngo6LuYAM5tPEnWOfpT//bd/6fgH3jtr//+0/90b/5sfclGSPz++9bMmweQh4Os7sbDcrOlj9ccW/Oy9wusx6MEFi+7d3FleAAl8dP+YmnDrFJFnnpvXNRettSwXcbEYKxVwrQZc1idWLDhI4TuI4zl4uc8422VyIzGNWzS3kzABHmgRmNFoqHMVUg5tG98bpFWqdqdJIkxCSJizaz7ptjoUAn0DxVLJQCD8c1y4GH3EveUoA/IBM+yyTghsbh6tAr3Cp0dxmhFhko8VsB8nR+luaVY/7U2srSH34skstjDdflBXD/8kLuo3/a+8GpK8cH5x9xO/1Uo4hcWluolTas5gUqGyuVwiYBmZEpk1lIAEvBS0aE8GU5KnETXwWpb80hEzW49KIo3KyeGqiXEaSu8s6fPPi5KHrapTBL1KUi7Q9SVN+JYVXCRqH4mkwY2uyaviccrZVzc+xZHRiYkMqKZJT4FQAapolKRKv5S/+Q5oMnigehjBadDtdza0tX7OhNJOMe25WopSs6yZfcfLj2UyxIFQYuOVBspGnioHdO5kyMayusJURE04iUFqGBIl38Yis0PtLizNMq+ow44XEbVYpdP+OT7NJRalLDlBZyDeZj7NF4qxjWuHh8NCo8hko2Cpd9mBoxN2HofXOqnzhBpeolnmtdjF2oYeTUumZ+jhjwT2Y44ii6ssaakmo8wZHAQmSFQ4G4AQh75ahwI141TWVuyOAw1KvUUAIlpvi5hESNrvaVQ5UfOuDrn58p/u1n17/xQLtSju6e7vve7/ney68dvCr9mYdAIKLBYjmFl0oGsmlfenwX9qW/Tlbyd8b8d2eh3BKQHc5mOZb/ZDbx6/mIb/O67JfuLh9Gt4UMiCE+uVUWrP3gyc/RRbWme0CLQy4cpPgjZdXKRnF9/dkeKhUli+rJhC1oQ1iF1LRtkyXixVw+t35+73CW7OrzMf1W9KsOar9uBGMZOouBFcs+abnavqujGXS+rkYhxB/lo8NBObdeHFvlc/LkSTYPfutPTMAGPqrg17/+dXq2L37xi1XltngDV/WpduiFTOe2DIlqV+q8qrawnhE7cJhRMAuymFNp0aGlH2uxtK+ECIDRP2sw6wCw+rrMALUubLbb4609AxqO2Z66Uni86+w3aMgDF+5vNyvt0NOYOtZn8Ju/+ZtkFp2F3Zoo/JeS3OOR8Lzfc8OBb3sF+Jd+6ZcwSUVlespZWYbZ5ue1mhGdmeGumZkZ3L3NtV6J7HYsEgtFotVaNd49rhpJxeJEExzj0+OxVPrpspxGgvqtVqNzOfeTV3o0WI7fYJU1DVYnnveEbFwY+uIskEcFcaxl18edk0laXJ4bNYLvf5tkccCSyyZ5sWTgozGyC3KJyM2m36QGm91pmz1u+Uym2EnFph6nGzUOdb8sK+GMNEI30NH2rJLCgVKLu6WTje28e1sESxIshfKFThd9V155deSVL299/lY1QDa1h+LXmNzR89rvW4ikZ9fp0/p29jX3R8r+L3x587f/MFxlRxW7b331f7xt6a67+n/1V77n0OF//KfbFkqb6iQEw8weDoxflh3aubl6HvtL47sPDo1PnT1yR7Wch4Ldl+1/44uv6Js9tfDB3wgfe5hR2vj0BbVctYi/vn/4p29Y/MX3NdZznD/IoT+cuxx4MBo7CN/WbjlZWG4NNjnsUk21cmjalOoNMpVWj6uweqFcLg1O7KLWBBGKfowbe2cXqbgle0kKzQ1NT21tAysKDZaXNtqhyJ1z/oEdY/3v+dnV3/tI84FjUfo8LMrvTfX82A/6r7vxoUKo2AjGWNNmxae+gPoi1oXxtYcHh8pVZHigwbiFCBKBNdasVks4C+VWuVUpNlaIG2dVtOw2AMOZ9/7cBj0A1QiRJk/1AeSE6UAzQUIbazUGMNJGXSZTaM746xhFdRnVHTEfen68cgX2DbSnMhrkP3ToMLjuvvtufC8/eKVhFmJzWGfAeT3p3ftyyJJdDLpbW8g7NRelW7033Kaew1vKQoo0v4XFfClVOLB3x8Cgqcqedq1uHz++S1OqiQy8RSSmihKeGQAFjcYa9CU3/Huy6uqo+bSOs5GiXOAA2Lt7DqOn4yZI5NEBavh7m0O9WYyfalqZi4SI6dxCQqLkQk/v3xxWloYNmhUiXx4QjEukqjDkp593mUtFt92TFwCVB12A5HI5lqWMjY0m4kkX3asIBtiBApyMtzlVi4Hzs7l2hOkQPLowlpaAO8idtLTo3ERl93JJOwo6aRlTBeEFOpI7lcSrKyLCy59LWgXh0Hp4jCSgur7dRLsOQRLsJn88OAJBuPXSBfYc7f7oxMBSu7209iQwXnzKc9slOi/KvAvrsMNKDy8vW1bCxKd8/WtlVEy1Gd3suEKzCqAorsAtAYfApazkHbpOIsqb3B5h4tF2IPNWXOOLPnwcYCA5FwsicHGszmTa1/jGKd/MWfDxxQNoUYBv1e6/P3ThwtTOgRSjTFQ5tXdKFkmJhgZciO2T2gSrhRqpKKKRvKrWG5iUSRHgiGAilMwZEd6wjpKRkJGnrTW12TZpg/gYDKFym3KIjNLRtdBH0iaynIZDuObHLFYHvxRMQoGSEAEFDrs6xUhGRKUGns5U29FYe2wcaNAr/0RDbSIlvUo2Ih7wsdTxNk8NCAZ8U8H0kQfPXhbbHBuKaROsE6QeDCiMAiXvCPCKizwpV8Ycm6RytD3FHZkQjDC7628PDIdvfon/c190OpfDTclFX3yTb3TszAYlRbbgquS9FYanrpJnK0USEiVKX9tuxHCxQkSRA+WX+U3HCkIlTolHX6FRWzh1bGBsKpzsNVFlODS5jnDTNLHtFmFI2JeO+MvRhPQzq9bSQdkGIpNbFIuEm6EkertaWItE6ZjpVGcrXCCpDNzgvEY9jNtqhkxv8qfCzTcfigycPbb4/l8PnjzD0CP0tWZnV752Z/Zd77zpld+zOJk4sog8l3xU3ECDuFpJboMLQ5HVpC+HIt44lvDXy2RbnGqzhIfxS1/9RMBfxdxTtT84gy/z6LDDyXBxiAtcXGC2LOCivkMY9VY1kIFmmZXSIAs7b7mj5dKqC0DfhX91bSWbybJcmey5+hMONAvNVrTgn+6VMhz1dF2OJ9TUgO3c5nNVkpasY/gT3aFr63IvZO2xlyu7S0D42KjP0Id5nampKZok5mweb5nkRWRSZbQssdHCOn1Fixl1ZgoWUiraY4XghL/wW6XGhgOGLpnXoftaocdVpy/EiDz9AcpFtdi2NDMnpBGH2UKrL9HAwt9Tf3Ft3+w9dG8j9ZJv/qHy5DWcK34Rb5+cC2SQ3ZpsBuZk4I985CNPK+6TY34+9Blw4NtYAeZDveWWWz784Q9jYO3w4cNPWZOYDsXuCzwiIpdzx+OTfDHtYKO9t96+kwPKTfhbV0Iy+2DRRHc8kd4j0w9Pp6KTEPo2a62JRXIY0HJHKHGiJgtcn6SoUIo4oQQVV0f45HKyE0gT0mqx1pUsICk4eOlRlPDanRDmA3PnmnCMB6lzetOjgLcn7VoaGi8/hynVWXdsK2Zcuyzprw2f5Buj9hzKhzxnnQ6SQ80zR9VXGUFr52KtTDUwlWGNCfyCk+GaBtV0PpObPbYGUu0trLVJPOZ0pXbkSv7C6FDvO3+8OD5cu+XW+tJiMxyJvviFfT/wxvzuy+5a4IxHNsX49g8EossLax//ZLDGlg0mejlEActMzfaqJiWy//HXXnr1wRMnjkfijKU1gpHwwPhUdngyv74QS/b0j01trMwnentD4SgGj7/7BVcNtcvrf/jR6MOnQrG0taRhWn1OsVz92McG3vUfYv/m5van/xrVT3O42Fem2KSWBAo1XwXZqkFGmy9Qf8DfF1fbr+lEXf4apy8HmnHZUkyk9h0EVMoWfzIo5VUrah4rmpbOnUI5L22ssAX5wpljNL25VF8yOvniy6/N/tavNx480p6d8/ekeq68vD4+/Ygvee95SgwdD+YpRXCK+yZywcvm4CQLySDDPMkNDWijtFFYWwSUFrlZrZWqZeBT6ZT0X5FkVBFsl3onHjpVDfWFuayI7TsQGFXI2mW5zGHtPb7W9BotcocDratHtbTPEIgz1HnGlRX2jC4vmhfd7RLGr4NNydNiuZ7eRQn0Tw3tn2DUXP0Lg3cfNm6uTnR7IWD75eAcb0ql2Erf4OiY+hjyN32bIlDBmtpsPuK7Kbr4qxDMQLcVvuDVx+OA2/bwuKqE4roKoxj0CS2u/PnXhQ/wJjHMa/u0JmSLfqWhu3LhRTT+uPjkDk/KlFDrnAkd1BqolwvCZnKYBSvs6g+FkXlKXX+QClK9KRJRVBdwx665cnLoF4b7EsxHuTkazaEoBSFUWoaBdy55m4f4LNSqUc5D6OWhd2I+JpLzsV6xAwPQElJWty4XT0kTbgAXh8t7C9p7cSD4y7E9WO7t752YwDlv7o8OV9odhN7TPeTbARZl266L3wgwXgi4G8ILR4INx6jVhsTuhPIDmXMYtMpbP/N0aVBl8MHX6kL3KbXB83ehiqVycEqmBdmEmMDk6HzdwkptY4qpL9IObGwkUE8UCGEkpDsabi3YxPDBYKz5gknVCyUOZm5To0H/KCV86tiDw2NjmSzjo4Ton569HPT4jdguoZqWI3mNAvG0vIgEEjUazdfcqpgg0kVf1XJtPrhNk3JZoFG3nKLkAAjdCpM8VFxPhQZGP3z4ycHlTZr5/YX85trqKp69gw3s98ea5winUcZYkeDsshidF3HLXVZ2Itw/lh44MPkSDFtYQAe889yCdjFEiHd1HR2Pp34SBY2IRrDQM9D7treu5tb9X7tLm0lUroH2dVdl/te3r2T6Tx4PRlC8lABs1heuspTk4qE7/056AOIEgnbaSp2ghFRD7OKpMjLWqShIGYWWrkIE8yOcdyeOKgUTPbxwqQhgOKMVcVspxXkHqrRwvlFfPvdwdnxXNJJU5TFdVJh9AfaHphJD8XRKtEl4uronWgEQMOla+XKuBGV31XBrqJIrfurP46dnMBppokTLfcOtWv4T//fAlQdeuvPQSolRSO31IlNwBzxUGRx4tZoRLIMKbZT1UZxECGcE2ryKqAAAQABJREFUx8illKZYwxUyM9nJsJRv6bSas8Xooyb/af40f0seWWpFfi3DotOqs62RoJopRc30atpWDk3hBvxLF+YWzh5LhSYuP7gf88uUzemTJ2FPqt3oyzZ9K4/QrMQz6eHRMdEs2oHpFIXlwb093TuHp9AThrlMrtDVnJubA8Pg4OD2w8+fLs4ngbcq9jjhhw4dhC8aACFrbdYVFpiO4qglVspXWJnImaB0KVnArL17AcpPcwRUKNVWFTJdLOtztlB30Y2l7uIgwPpFmlrnDKeQPxlqBah74h4nawWZ2imwFhorOG0/3dFUVOeBYRlVtRf8RitJGLO7NPs3F+ob5zTIRsjsXbXJaxwgYO6SeCUJFfPjXfhzIOLv/u7v/viP//grXvGK173udU8E+Xixn/d7jjnw7aoAU+lQKdlKzqm/nDF9KXWIKWLWWsA/pxnaHmBfq55unPm/Qq188PuXaicmQssINlVc2oDa1euxazblTN7kDw48M8ajtjja9JWofXnCi1CA3R0g3E5jd2ozr5CNyVYAUIC7WLo4uxzoJudgeHU+3SjbHUiIdrmKrZL6sePthTlfT0/48CHf2GQggz7JF25its7QWRXqaNxWi3l6AKkkZ9u2Thx72KWeivhXzp0AbU9PD8eKOoGhxkX6YbtYrh0/eXZ8coKzalGxmRNmQli/ln92sxkamUi8/W3p136fj2VysVirp6eW6j9XwOpgYHeW5qc5Gm83TpyKrq6QFgqwxlBDLI2OlEr16tGjwZW13TvH8ivzmaGdgXCkNzvEmmc1oMHgwOTetcW5E/f8Yym3PDS+JxRt7BvvD8zNF79ye5Jef7MRQlef0+prX7weXPuKby2XuOpw/tN/RYOF+GqEaumrqsxZlFpDJd8ALTfdCEQhVhkJjQea+wa1Z6TRapTzRSTtek+tHGr0hdeZAw+FY4FQzGn7tFuE2uJwjVAWK4XyhRP1OttSZYezMH8KER0Y3zObm/6Huv/yocnxl00GW2yEDi41g8dWAkeWWZFP62tVh0ZYIt+a07a/UCxhITwej+FJnXUtqyvtzADnHmeQxCvzZyOUV88AFatFN4BRCqEy0azKCIiaZDFBb5LdGj5Vv1NapVUd64l4IIIQMVzcVVtNiyOGsPp3pFu9DG4bfoFg7jud5vAw57YULGlcz+Qy1j8Ogou8OmmxxXz7SbkXwWxP/AkClOveVIrfduBn5nbcVVx4pgdpes2jBelmhYI/5ItxXiELXgqtmK4bn516ZHq1td9am2sRgDF/lQ8XJWOrytVJk4+6uIpud7kKpxczqeaVY52qZRO7ppsT6PDLAU6RMzDq24s+Y5SYhxHjkhY2EW98lEMRdFlO5bBoRpiqluM3cdT5VbAuOVws92LVTAFeQl6sTly9Clwp6zJ/OR0ZzlPZNjAXywMV+Ba89yYIvioP0OH2lSvVjdxGT2/Pdp3HYncQW2zz6bpwCEsXk5DaC7R0EvDI68bp+AMHEZSISIU3HrcspnkIE9w1tlnW3VdpvgbggRqeLSoIdz894bqRr0h85sgOo0sPaydwmIdXpHTFk9FgeGyQTYQhUwa6ZCuVWCI8hKX92rWjTO9KhYU2ff9qPHRFl1sTg+3sgGZ3pXVJTOGtqTMueVqqlroZk/M8lH2JNdFiSOV2JPE0Qq08kZ1CZNgMXNAdD4U8swsml5qNSkGGlHE3ao2iWQZpDw1JWj7+9Tj+7iTaLriju/vqHM8BuYbIIe+Lh76+VBqf2N3/f7y3ed+9Tc4BZmb44N7INVevZUe/dNrHoVbetKXEu3Qzu8RkqV382c+YbTUQlxQ4FZ8VlkYKUPys6LTwhxVA5fxmG/60G+Fko8e/FmmVaYD7B7LCKThFdvWB2gexWj/VrJ8rBLHNwYYh1muzgiDNCl6UEyBQI0nC2qBquJWJtFJxN09r07wa1AQJldckDYP31CdTWvDb1eePotH9yzfCNJtuJJvQOgZA/YG1DY5U6Jm6jNa/DD5yC01OzlqtESsCIxwlmWqfi/1P5fpnQqFGsNHEhn8rFtXIcevGejsM2mAseehwDwsbkK0IVHQhVU6IMqW3Zef2afMtswmsSabHEpW6G2ADGncQQb/LgrgofmhTwI6escKFE6uzp1rT4/FMhr6fcqWZAvKnLoSaAhWW+3RclbeCf3Y3lF5sUIED1Ey9uOkWzul52gpwV/Y8MT1k06C466k8YW6TA0qYpG2wL5d7czlXYK7i6NFHMv0jmYEhNXPikb5+Fjeg+rLOmd4jy90aWLTBja5LHVBDJdZogzQVyc9WNtGhaNQ3dR19RQ5kNL0ZdIBFQ20g4yHXP4fVgYeXMHndPMwnDm12KQGVqgrJu7C2c2fZ38IQp8g/f3+TYzpRqpXU1iV7fEGm6a10t7w7Lvzf/va3/8M//AP7gdkMzGLPJ4LsxHj++c3iwLerAoxUovbAFeyquXGjp+TQYyuZfMI7/T0/5Fv/cHCi1H7fudbf9ldnItqKcd1m5JUbrTBDS6nAwLuZB7XWYHstf8oEfWN2AXfvvfeyRHlgQFr0Y8noIjp69CiLQBwAm375BpnXvfJKLR/9zGc+wwwwI0Zovxgq6EbBgWLMYZ6PPfwJKfYky8Ilg/grlRr33Fv/yB+Gjj/C5lXmg6r92cCP/FD45pv92X7kTXthqfHQkdaDR1q1SmDHrt6rrwpNTpgU84+Pj5M668lR0d037ISmmhVkA2Fq/xiYbcX95YFEIK6TnDTybwAK1jlMNB7JvvYAtpHotEte5Yv1dNgfzUhJphuPeuev5LFSWKmx/IimKIZJ5HqjHo5EorUiyYcSoWQ0vOeyPX3DkyeP3Ive05OObyyWymvzK+tLtdxyNBZnJdXq+eVE6LX1+flgvYoi3QzWWBNcuj2UfWu0Ha32/G/VeuWhUO/haquF5MSURuTfLUf3szXEf6J4c7UeDoURl0Y/peLzTWd9142J4I185fj8KVqrQdbbxH2N3GkEdnZoJNs3zAlHSF43B2hrd4nua/b3H554NaJ8fubU+ZlTl19/UzAa94XoPtLehbCn+ghnL4QiNXZzabAgkImDhPZPqcIqpW2zJwyPXFhf4qV3ego+M1ginuvUImCB1JFJNJi0Fsj5VjhBR5FGVPMr0mHdpc6I65GqK4CYl6RXJ8MKUG266TDE0bYZ8NrsnzU1wqIOL0EQRURwM269Z6CViatyUidZYYuD9QjUkPvvvx83Q04c9+fS/qbeObcW/IzFPPlSi28qDduRb0kN9TLFVFV1xzxj/DZgrxjkLeZefMmDonKXWN9xm8MrWHO323RrlpeXLr/88g60/F0Eelcn28vZwf7DQ11JYgXpoXNQHdyP81SnTMiMBB6iy6h1tVRvegeP6qIc7u78PWjnaSgcFN+d6rBFU5VyTkNlUbpJOIclAWLeXBxzWhKWnHM5Rm6h6vh6T0Ns+X0USNt3YWH9gaUHdw4eHBuRoHOXQD32WBF2/B/7JAOUpXJmFxnqXpLweHd8NNiky3p6DkhfYjcch2XQghRiETv1wDUXeBFdAdz4MxB76eB5lI8iCKHnzRvdcUvh0Te+/Oae6dB1V8Tvvc/gFY+PXmrMi1/QHuzfWJo7cGDfYzbskHV/cv9EOp2Mxzt7eZSqy6xLa7u7kyFhN/44QhTFc/FUNBfVPTy3AFz2Heizv9NYu/b6+PHjCLGdO3c+M5wI5NOnT6NacD7FM8PwtGLRWo5mYl8607xqcHT8u4ciL3kZAqceTszW/Pefb1ZakeG0VL//zt6bgNl1VPe+Zz49d0utltRqDd3W4NnYGBtPxAYTEghwQybIC4+Exw3kwheGBB7khYQbcklICNw8hlz4Pm6SewlJ3stw30fCFCAkYGMGG2w8gGWrbc1SS+pR3X3m837/tfbZfXT69OnTk9Qy2mrtU7v2qlWrVtWuWv9atWs7ItVZK89ZfCwYh9YNm2lKVFa/rhVphrzhWFUM1RTMoZCQfTeYHhgZO56bmeKys4sR6FR8OppIJvZt7ME1ClTW66wwAQrqrBgo926M/dMTkQe+d6CUz24c6E+3R9tik7ECn5yOd3RtGB87NTvF8BFLx0rZqVP5mTGqd9P2QX2RyJcaWCsQjjSZgRswp1G2p9jedyY6PcULxoZLERcjIovgiFGemmSjl53dEd4OxStYEUnTQPrTE9QzWnxxW/Hj6T0zhVedKPxDb3KWN6KKDG3ZobPdv8iiqkg+vmVD7909ekEXn7cWPenTxPalY8rlj15Vq7SeUnA+PHTTWjLCB4QEGKqw9HiV7/vf//4dd9yBTcvaPSgxrg4fPtzMx03CDJYUYPkx7Zy+lhcJ2agVo45wjYVZw1Adc83h5VC0laxylwv+MDz0tRHtMsPGq7yXqz2oMsUSb+fip7X9FbA01F9OTE48+viTyJPNJsrZGAvPbW8arXkG7mr2Xj2qICvVLZ2zONFekreMZZ/wIh1NctzmcyGlRjCWmZLQBmAJdcgwwFTCLQXnzIxez1ZTipXwxqfjsePTkcEcS/RVCqb2v/uZ0Se/hEuY6vPOnNqMRws8AqRSf1uYSP3dW9nggH+UkkPx7Vtzt/9q56bB9ooa6v9+4AMfuP322/HefeYzn2ms8PrpL8WuhgZCA2g1mJ0vHjyBfPHon/7pn/7xH/+RBzjsR5rMH69U2OA06ve9O589kJz9XHLLTOQ/8gEbumseHSakwX+tI4X/tK31To0YatxLO+YLNj+mmiNPPrYpa1HGxsbwm9EnAhgQdWRkBETBGAyKDiUPE3KXEboGAJPRolvqyZt08FDhgx9qO3wUTKjtd/mU0JkzmQ//t/zGTak7n1c6ejz3P/5H9AufjWZySVBQJDo9NNj+9v8zfv210/l8n02HIyp9NGtmqsGGOoRymZeZ0Vsyntq7Y2cKN+nUdCSZinS2B6OVdCrCyqFBAkNpe3eAkaz3ZFPLUn7HDj49mYjk29Psfm1zsIxr2UKmszPe1zt6+BjvxhQnR9K9Pdu28NHg+LaN7bnOZHdPy9ar7wAqP7X/8enJsa29l6Xb2pMbepLJdEsqibRa0n6ydeKzXd2vGEsPsm/CH5VG745fOxHrjLTcPtZ9m1aeTxU2f/GHP3ky88PO3m0dvZuxQ8Gg3a0RvjbRnWaZU6w72bbh2t1Qsmp9ZmaGiQBKRHWkmfWlLzQMCQbGAwyGBKXSQRfKKQI/nIxFx6JXDrQl0q26pYlMuYiLJRzD6k0tOWfhS87qX9VxM53J3fKx4yObImP923f0siaALyEdGE6kWnu3bmcvZSjN4axXvEpd6WRrgs9wkJr/NGv79Y5akUqMPcOJVJpv1qQHYUc4BJweAllDpNOkv2TwG75kiZtU3qaW8lXmFecWGvDmx/IqpkWCmRGjtpSWn4UaPxFOvKQzjfrAgQOY43v37l1SwvNFrCpwy2eu7dfPe979cyLOuTAGiqlYJ4T5Iy9lxxHAGz2X5ampszheWK9RpXyl1f+mDvWGOoIEVemqgk5y8Z6LE+WeeKavtbytU13TM/1YsOb4bEyib3PkP75h9k/+pGV4mFqnw+Ad39xVV6df8+qZru6jj/1gYGAbA1ZVczJtRSNbtsztTBEosGL0BxQLqbVanEq48rtQmlWLDwtSeZrEOYxcUjakYgtMJgGZj5s3R7AkTk0Ro6KBzthgV/T0bHQyF2tN6M0lAYlStL8r2a9unEO9uzygNvzSKwMnAMCMhxZWF6+9qdljSShRmw9TCm3gDJwwrGj0AiGnT00cP37i9tsu5w1guhR23GSDko6OduhbWuBXv8aYTe1vj+3dWH6okB0dOXwsM97VvYGFWogTTyY3bOrlTaJIEi+E9uZkIxScqKDmHr5bj+stweouAkLpGGrAWoWRzUrBThnJjvbW/s0tx+QXZN6BsqZY8CUwFIlt21pOJbe0af6Od5UMvmopchK2YCRbzJwqvjl34v9LR060/+zp4pX50v3pyHQysu902y0z5XbWwSViG98xsGGHGYTN1Ej9rmO+XtAV5h97UNFannrqqcsuu4yYlTe/xiKSBQdgm4w8jFlEoEEqKM0YkAUgy0G2svbgQN3sREJ9AWv5sEi2FB0/OzM+Od3RvQlPgNIElgxv01EnmBp8mBNYK2zMZjG8McfnMsZPzwyfnHz84Cl29ew+m0gen2QNxdbt25lrSCcjLUK4niPZ8V2SUj6DNDZ1w1vc7GudoMWWsDHbZNpwsJ8CPoBohk9bkpm1fdqzOecjeIDVQuXtoADg4ehMLvLocb4DU75rd5KWz34nz/m53q1DmW/+Za443hKU1cY8U5BOPDcRbFPLjR8KuvO2/E3/+6Z05yJqxEphBeuf/Mmf/NIv/RI+PJx5jdVuxbl0Wn0NXHwAWKjt4EF2En/ta1/7spe9bBntBtcoR0WX9Og9ye2fKp75o9L4f08VzsT4UqlaeXymvG8s+p9O5Z/XNjbBgwcWxaG6jOzICFzE7Folx/q/cMZhzGPKWxmgKTy9uH+5BA8/+OCDwImbbropRL/E13AJY5qVkG5odrZwz9fTR46wIROHtobQgUP0bOY73y5df23+nz+T+ufPlAqlszPTAHH8iun9j5/9vz/U8b735VmMTR/GKp95klQEKx8+cmRrb29qbLTwzfuy37o/kc+Wd+1se+Hdkb1XRLu66D6hnC8tjm6mPHlNwl6diuZAdDu2zd54Q/prX29pZ9sCpuN42Ya5w0jHy1+c6ep6+uA9/RvbW4vj5fHhjkJx774rky2FsVY+ODsay7Vv4DtR+wYOPDG9sW/LyVMnd112Wcu1V3QeOcboiQJSqZbIP/RnBkotzx1LRI6Ue/9y4L+AFBgfJVum2PevJ9+1cfstPaVYIt3JSiawIUuat3UUBnu1/pmDGsH9jhKoNQB/VbsKeko6QpVUqiWF1ZrFFLa2dycGrtqSBoxrKDFyr1S4+QghPKy1rP5ns5vagSuSyxdHfji8syP6HDYfY+8X5ipP55Pp+OaN0NIN06crP3K9YvNueFbeOxVwJSeH0BbGuxOCapEJAMuNHAxRDDiMbZzlW3bhJacPzDZOgu01BJJJeU9fZIOc/JqXZ0KEg4Lg+8Uyrl60L63Zwfvt2IW8Vje/DVRIlvxLjowrLKMYGhpaRbZLluMCJVDLKZX4zBv5s5jNR1nCdD7V02G+MQHvU3CLJC7sj6C6vOBrd0a3WspovcTa5bLWnOlKyul0/NqrI//lvdmvfy36+JOCDldfnb7tufHtu86ePpXL5WlRPOZrLckF4b9x48aVAFceK1ANX2pkRcb5WOvI66vx6OWbWFhqXly9lWoOXsEcx4rEgwIEIK0H18nHMlevbHodHmedA9ObQSfhJJx1TVevD/NESq18pD6ayOPgizKkJhqsO/P0II6WZHRPb+TmZ1++f3+UfTG6WsoDO7eTkLdnU+lyfGNvLLoJZDP8+KObN2/ZuGkz0hQLuScef2Rg1+4O9s7FcNDLO77btnA7SBirQF/H27Ch5Sd+LP3p/wfMzqJXxjctPYtGs9t3tdxwQ7wtfTNbUljxqkqtXpAPSU7OsvFVJJF/z470e1rKp6JXjUWuEm7zKYMyC6A3vjG58fVeilU/Ux/s3sL4xaID2oyvlqLrXq1mE3b150oeVC2Yk2lrGxA8xs82+Y7yBVC19lh7Sgnf2ltsBXlo+dN3FdETLUcrls0aKEfHzuSOHT152eV9WXmASW5/7CIjN4BN8asCZKloLkNzHFrI1tfTeewIO5jM9nS2lQpp8s3lihl/i54KV0XL/OPMu1bsNSqLRif5D5Btlg9R0i6tfdAkUrySjXeXCRBrMOy0Jt9vKZplczRJq3UvfKOMN4QB2F2tEb7CjkwA+CQOHwkX335T24sH0/f++cSZh1MwUasXs1CFIlMEQnTkbvrfUruft0ETSwscNVXwwhe+EOj7rne96+6772ZvIH8gF0h6KXpNNHDxAWDaEMsGmC1j+mTlLYaJHjXqWEes7/dKG99YmPlWNHcEDPDUkZbZ0pVFdkYuFw8MD5MRA+Hg4ODyKgE39TIS+tPCF9JAVmwQ70uOQz7MAuB95RIyECMuYsKseAxBcki5UIBuoDR8IDs7w0QphhpuupnMLJ0Js7Dx/gE2Iih/8V/4bAuPOK/dsjk2HcJsNtP25HD5h49v/PEX+spzIB9vVnu4JqNdAzuiRw7O/tl/y3/hC7h28d7G7vtW7itfjb/1zck77sRDXdWPzCXFagcXce2WeYE9DfBV/fqb2OUv8vD36e34LE6BFyxe+sLu1/zy05T90KF8Ljc5OdHd3XX86NHTI8d95EAPU6PHmER47nOfu/OW63bv3n1y5ERp9+7eX39T+YP/NTk5wdgNts/nYmf/6sr4VdfHuj8bKx1hvQu1z34TudRd3z712on84DW7N9meGdIDXTyt5Yq+RFpLrtT3hYc8yrY0vU6b1IDrRxAg5a7BQf6INfo5CtH5ldshuj7noLqHhw/2Rc5cceUVOzZgeo5RRS19QHG+ZXcG/MrXqlEPxdACbAYbrHD5hM0LLfSr2VNiGDDQKGFAL3d1pm836MsNvSyD1YAsthZCaleNCPoKJBN2/iRUvI7dPUyPV792e47Y8y9wmNOA5+9nPp9ySTE8C6h0tYyGJWW9HoiZyfKuwNshm5pQNUxAVANguqMbb7zREQuTBXwAY3BwcNEZuvVQulAGChWG6zxx4b0LHaCXpkNbr4sRmtMO3QATYOw1sGd3nCnaLDsUsMolHWc7qGiUVnTjjc9+pqJfmtbyxu5Qs3Bg4OaFJp5KOqUwfo0Css2jkR2VVVQryCUcs4zHuVc4e72TwTJhNCDM88hQ297WEY8vblKiExrUZd2RE1vSHcUtB4f3l3MTQ717ujvZGlqzK8LVWi9dLneVt/WUtvaywRXr8Urp0+W9/WWcFyB4eaE11xoYEfwGMpa64j/9M9kTJ1JfuydVsD3VI7Hslq2pN78hvl17eTPfPK/HICmeRB1g3bPF649F/nJz+n+25L8SL43aqNhWTj8r2vfWZDseF33VeQWKbZQU04WF0Ey4+JwLGTEpwNEozRLvUZyqFIz7MmwAsD19/eDbE2dZChhsYgpqtVdt2U0KO0L2ABaBz+XDAeMBWwIaOWMBn4K4IGFWxeu93DwugdHC2EQxOqrpeIAnNciMBnqGB95XyaBmQCDGp03AnHQyLd3b+nv6W3t3Pfbde7Pl5L497Iyl7/W6zEqMOW4eXYwQvP5MygsSA56Z1ImU+Ii67emiPOAGzObjBZJcD4W+ZYWvIR2PtKUiqVb2xCKM219LGwzCqiGBifn6VKHIbEqlkfAeR1/5x3+j5+HPTz/2vygkb/9i8lQbZ8gV7dqTu+P1Hd0D3rgaNQ+mp+2pkQZwdAGAP/vZz7KZ0Ze//GWvdGnm0nG+NLB4b3W+JGkqHx6YT37yk//6r//KG+TAoqbSLE6ktqh5qHh/ovOn1ReyRCd1lmY9OTFx6vRpsBO3z1vrpL9jItBRHDPrmFCUFBlqul3IMK2I5CkiDD1h68GtNIuf6EJY5MScl1LBCt2C4ugs2Ec+smUTL2TgTcuxA5bQEB5X29i4UEgkS5GjRzQlaIcGDZs2m59hLDub+dznWv7t35jYE6jWZF85PXom85FPxHftil11dXU/Qu4gIs70DmRHmHLBE+Fmpqd37R5qfff/lX/k4fyTT9HZla+6uuXqq/LdXT/8yr/ieuV1U1SEHY/1zywmpvzw8DADCXYn0xaY/oODg4lkkrnV6Wip7dZbir/3ntznPhd9fL+mI/ft7X7FzyVYLtv53nL2B8X84XKsLZ6+qjWxa3tpeOTQ92/YvK+lFVWrP8XBSl/Ppl+aPLSRF4Gt4OWt/Vu3zvs21XydeAwFXOjWovF6gfPJJ2mNrJICQB49dtglyWWyM2dptJHOjvb2lrSWtVWErOZpclesBh9YbFyBxkvCwAbDghqEBjkBaVoAK7Qhdu8xYfAvm3SYf1i7fLH0p1Ta2iFzpOZASF//7PEVdcHKc1O0h1eik5A5DYBaZlKm5o0AJ/hRONMP+CbwvJjAE8S8MqWunhRDz5iqoS3O48YHGJlPvLgAMIXiKaCWeStkvVVrddt22cKYlTfyC1JYxDZLNR7lA+lVu8IRT3+Lt+qCSHWxZMrjxjCE4cuHGOkM138bsObq/bN36Bb2zt2CDA50LwFuiJTZa4Dovk2bWLelpuIjiTMIRhUfjIgiEAwSWB/P2Zm4YWDLv4w/euLYiesHEj2d+OvIJnSxlfqu3oZtMzs7wScgGGK605EiHzgozrLLcvXEhPMN2gNv5g7ubHnLW/O331F+8PvRzHRucCh9x+3xXZeV21qV9/xRSnFRf82bOnr00Uc2D9zS3nl3tDRayh/ia1+x5JZoYls0+FBkIP9aND/EYBkgnKkCq4Uwk3OKOBfLSK47VhWBqrkmJD0T4rFlBtw/SImH3jy3Aqu8VQvcLbDPCJDVpsJBtpZKXAhg3hGP8ccSlkIpjtmjpWe6xGsQ1Vnrxcimog1zJYF0yW3k6WEB5BKu2sipw0/wMa10R09X71ZMSE8iPIrxp41KEJN9/8pRNqMiU2XLlNOGp9Jto+MTY1OzeE3IhE1OMFO1/F72YDF4hJjSV78UQ7BCjkl5GpQKjQy4iFNxVhnwATMteAYYM2MiLzKTJqKBSsZvWrfKrDpoTZZYksBG96ww5NMj8kaH5VJrFB6+7mUdG7dmvv5h9m/h7YGwBhTatK/4gnd2x23z8/DGQgFeCuOgSBAQoJbf/e5349L72Mc+9pa3vMXjF0p7KX7VNXAxAWDaCmjwne98JxuIv+AFL1i9tgInNcewVfMsd3drjTS9IbcAV6uX1yI1SEY8gAyZ0FFewBtOXRYr6qmsOiBjAOCABjOXCXhWnFbdbybIYqmW6FVX5v+Rd2l04AtieMaPmYzFc2SXTueYjy0wPaceiG/Wkhe9ByNUvr09WQG9xAFWsbAdgYcZi3J8IveFf0GJbEhLHzc9M42rub2js4VVcw98N7p7T7xFxfQDPuxd5DicGMIYuPAkBuu8mC8wvnb92J2p579AHRJmBHs5Tk11dnQwYLB5GJRgQsx6VprRrTBxgHHMJWF2IKPZAIYZ4fgw1Xhb25bde7re+rZoNgPzEsnYnEwvfTDdehtZWyklVP/WbZcNTvCEsPYFPKlXjuodlFQaslvwqEfSVFyYb2MmTDeAcEB6CE69+zQQy7fQAKrAeZ6cc0TXEeacqKrha05Eo0jps79zh6w3piF27Zovm4ttu3xBfy57e3bmuFgICfGHUEGYUIODg9QO0cAYH/hriJd6Se1Tpc2zQngmFDjTgOcXbam5X3B6isBB7xGWCHxCTLVg3OIxrIbEfpd4D9TQV6ddV2E6Z1uzt1ZCoYeaPq35nJhWYIU/KkVIzjRLzszLYM03z2RdUXqrOKclVeSjaLQoRqiFZkIrhD+iv6iOHonu7jjfTB4cXIkWULUnX7uHtJKF5+TZUe2Bt85yZ6PJ+LOuvx5kcvrU6aPHj1917dVQA0HO4ivUyiC9OGPLjvC/+Ss2chKyQtpmkOU51JojWwfLx3SLfddu3nzdI2dahkr5oQ2sFaNw3tDim7QPcPnQoSNMc5M18aiRAN0Xxk+F7JxRB3pMqOhAf6rvJ8p33YXjOJpKx3jueGs9m8VW4ZWlynDtugzOITcbxcBazNhvjrVo7R5aqNvyz0m/Khemb3TJ+MjiHdsHSxWiWQVJwUFIFygPBaJvUCXvr6JeA7R62VsxitcXNxjcwJzuttXXsGCgS4FbrwvmteXC5TUqW+QsMKnFYrjbBZ6t2D4jIVuBeX+tfEPDbg0R5p9VlkQSYOYH8XCSGPsiu4EAajGetGSONckiFwlMNKdWYFoDFlK97A1sSzmKo+Xd+/YBebvb7ZtFKqwWNhfzEbYaZ4UieUHDR7/co8tb4nhXFBbKBSernZATErNugKxw86aTpdZ4pCXJq7+Rdi14hl6eFN46F9iFcIEaDlqFyhyZHs+jgHMJVTtTY3kzh8l1zqJSXdU73J1Gh8luZ0NDQwwKz372sx9++OHf/d3ffclLXoKZGuRYL+2luFXXwMUEgHni3/jGNzKZ+v73v//8tBIGdWDGqiu9GYZ6sHjOY7HBwcFm6JdMw0PPMrZbb8nfeUf5nnuY02NQ4WCGLTuwPbVndxmv8nNvbr33G+yOxRrjFr1Jo1Un2fau5HXX6J2JygH6AqLXrNBWD5SdbZmYTLIbBu8Pm3uZWmMTC+2vd3JEWyW0zHUm3GL/DMgASGzrhVcBowEPJ+8/w58+AmMUbcSqqoPF4ffccw/rr1hGCyBk9wgoAb1AX4hBxb45NnyoRGKoTeTEJH14//5tA/2t6Rb0m5mdmf7BY/TdfX2bd+wIlqiB9+ihOJAK9zIomiIwxeCtzmrGTpRxdoYPNMfY/4E5xo72WEcnvblmsZd+YDEz5rFVsudSlwH64W6oalWY6ZZISsf8BRrAyEaTdZMvOxKluQkyn4NLywIku7V4wZFTJojtb0mYAYCwz/jMZ77UGOqXAYYab6DDGp4smWbqxz2lNbcu6ksa/Hynrj9fX/rSl/DMY0r6DDTFvPfee3m2qIUXvehF67zUNEXqi4Jw8NR7s6TN05ZWV3J6FRqSZ9R8c3IZ6MR4lgl7Qg9TIxcvAG6gW/phtpB9wQteQMe7VEXNZ4vCw8iVcwtZXZBAUJIoHxDeevbslPd7lQJiK8+VVKBkocP7VCNgjMAKwgSqMPE0YWKNuhU2BDDruQ5i7Ee8hKBY5MYfKIjLysaKeTBJAEptvY98eMIn7InCGaRKBOiI+rHVQFoWdOZ04eSJ7GQXcYbKVGHKlIZPdsSSkFSCRcVIZnY6nmoDnlrutr0FmQO30luhHBsHP5f7O3HK1Q6fdFaUmuGYmVMwA91aTcNwbYSRrGFFAnaXjKRbvfCcYerbi3gtmJB1ToynzPMG41FlNKv81qH3KPRkASN0as/YbxssCiLO+fELw6VG6dWB3/W7jz559MTJYsvG7k192u3JMCpLi/HH6iyYKgzJIixTtc76jJgr3MCteW713hOrkSuwNljFpnedgmZhspq+xaLSVCQLMNJEUrwTWyFJi55V33YXU0d3xcH2IeNmPLF18DLBxbHR0yPHugcI45TlzVsSUF7WrkNtHPGl4ptFFkUTpfewcDLjne3YvAtqHBTAZl7nYq0yZiMOW1BuIg6gBfpqKTv786E7mi2YGKzLNs68u9vK1tAJlkMTiKUTsRRZSP9WSMmMhZmhBSeTzEVKbMUFjdaCC5wQ68j9VlApz+aDxFLlz5xKnX4q07dPr4QskDqIhpqDC0ZbzoQ98N73vpeF0L/2a7/G0LzqA1ljkX7E7140AJj297d/+7esfP7nf/5n93qdh5pjpOE4DxnVZEFhmarEBcr296AaLtnShgejrseDSaO68TU8ay716EdjqYGB+NvekgFA/suX+V5Nvq09+pwbUq98VXRrf4xPGfziq2affCJ5zD4Tpy8usgN8Mvrq18R37OTBtU4M0bwHEXsP+xPOJTOvvDmdPJHNlXKsNXYIysb3mcxZsCKbP/lQ6YKRyl8kY4xnFStIBhQKOGFAAgyDYKvLyHAI/dDQEHNm3/nOd9hihBlTHMV4WlAUIxxfVwO4wgHLjG6FpVkMn/fffz8GKMNnR0d7hq986iOE8v75iNjePuecAYH7Smxkgwk05F7lY7ciMx6cOFn49jfy//LV/IH90d6+lh+7PfnjL4ru2F5OLmedG4XCuHdt1D0jBjMCSMLwHCrZKYlBvdWpauqi+lbzYUQCY0CPojgDPDjTDjlqBGhmCPF8YUJFU6EoOQwHBodTLPeMSDSba665phkGrh8oawLzytUMs/VI433X/OLwpFBkehUaEpVLFYMhCfOgcV6PJTlXJp5r1nF4HM8LTwTFYdpo1bFlaKOcm39TV7RD/wAVPRga9tWVaLipxMslClvy/EpfLsum0tGWoKNdAYCbSrAYEdXK9CVzUtUvrs8l8gFnEVNzjvyChCp1gawaIFHRNddeozFXBreNvcwYghIU40dQHiUIYsRD/jHbnUEQtBw5eGKM7xqUWroNWAqd2p8ArVANMMkAZ5GJarYnZHWLMKxtamgON7uUA9by0N6//kVm4IE8XDLNSWBQVvkCkI0GOuUOjd66BM6aYIIrZzNtZ6P9T41F8wAYOSVNWpAJB3moJHCDbTSfzZx8+olNu65ItrSrjOKv+8ZK52KxMD5deNaW6M7uCvYyPdCYfWjz0WehbopZGAZoH6OZBebhBTaTVkWCO4edjGUlBrHmIRb4Y0g4Wf0zfIK6Cu4H/MMMVDQKLevG9CG8ik8VEMu5UIoBaPHToqFcMU4NZbWsmFXKfIZSy5WlkGgs03PFoUPFYw+duO5aHN3qN8wrKxe65iPkYJfblnXIFgOLIJLqg9hOUruEMkkCWcMfVfK5h9WIl81xnt+uMtIAsqQiwqrUyqv2FagjqGhSmVJVkBgfwRTSsyR+Q7MtuqWMMOKAzfhVWLGMfxgvLltbyZerba74g+jY0cNb2Q5W65iUrTZAjbNQucgGziBb9jzj40bMmAB0tXQZEXiFXOlcBgvpau4gax8+2JXdYuvQzFHPhcrZ8dLIk3pIyCOaLFzx0tixB7MTT2F0IVr80P25vn0Y6t7i5pItFIKOZhbabBilH//4x/nK6V/8xV+87nWvm98sF+JzKX6FGljbIXmFwoXJeWLAIW9/+9t/8Rd/8Sd+4ifOW/s4bxmFJSXg3QOLIoBzPCTu7sO9iYXnhlQ1MRIuezpApUsk45ftTb/+9emf//nYzEyElUI9PbGNm9gXjz4qce01kf/8n7N/9w+l7z2YpavbuTP6spembr81tlEbfo5PgFXHCWBGY/04UsJn4vaQdgXs7irfdlPpHz+TYx01HXa8wO5S+UJmtrW95+abophN9CX0JjabRqkJcMCQM8PDAw88AJqln8JJ6MZoWHCy41M3mJh0HKwrAwCTKbCK0QtrnvW0ADaS4AjFpufgLoMis2tMFjhAYjQlF1AxSQDA6JAwkd4lYXshDxiYOQgEgIML5gKo92YQOn268Hd/H/n0p1v4sPDMdPLI0cQTP8w++HDq7W+LDe3mPWorSijyggEyYmaa22TH2T1aDvirM+UWlGyqjE54BCCo5oiKWAnPSm+KgOooLHdxYSF5NdlSwziyMCzCVLRJwlQxnMPIZgJIDhlnZGNxOwGKxhk5GbaAahgrzfBpQAM3r01oavRWNxVGNlCKW6id1uKDImqkRdWlv7giG2jAGxvQgmeBQvEo8R1C9sRioyx0uP6LSR35Q0qV0SVyicyhJbGK8jMFiYccKDt/vqlxLihfU0TmkSbM4V1i41SrcpcHChTqMGAlDK0l0BgMFAXeLRl6gkk6vJ0EtiP7x0I/NTVJ36AeXQe3atrSXMxcKCSZi1JimOjPDuODGM7P/WzY0xX2xCsMIw8hpJieKycRwV0R6/CM/RzcMskJh6wUMAoROCn5ADg5MPv50yU0lbDd1YuRvjkChIBS/gQq5WvV1oN5VqbyYpHAJJQAGQOujjnN+2o8lUCLN+XW89yDn6MjLG2NjbcxkFrW8EZusoSPmeYGQeOoSXxwkEVijqlEhjB88DSAtcIhKoiKoeI4AT+eYwWcGxFLZ63UcDPyANdBw0ta5faOw1qebJLopKCEVg8P9BFg4KqQK83myjPZYooP6+omJyEffoE6qPDwocO8vDO8Y2B7t6lDmy9bRYuhDkgZphmv/bLmjPnh09lV8ZQGTQh2HnjySeLzzL7bm1Zk3dXVjZ3gsiEgIkrmoJ6dhyTkzwG8R8EOZTvaxLNqwFUQFEBrZ8FUgVK+eVsBvcgAKKUFq1JwWKrgCbUi9uBUjWtXESFk1QJLmrF8onAulVpzHduPnh6f3n+mp2eDWpGJghYQVM1b0vGfP92wH+lIISQ2el2KSJVrChdt5QhSVC5VVIl27kEkUs1FWz6KcIY6k8AqXfsgc6GwN78kZt6W7czIgFr1wSqDtZzx5QJxeSOXN4RtxbIJTYEkpblZeDs3xlrl0szk+JHTBwql09fc/KyWZBygi9PEt19xlKui6ggFNLVYUaoiRSEVnHuEMfM0cy6dXZH4yIOZiD4GWm7vz97+ho7e3clrX9b24P87+fiX2KgrfuT+yI2v5E3mUJI6TKqjGB0GBwfDGGS48847f+VXfoUXPHHqYF81I1WY/FJg2Rqo35ssm90aJaSxslc41vMHP/jBH4WWgW0H+qWD5kkIH1R0G4ZXSwniA1Ls3TSd1t5RTKPRhThzdWbtncnrb4gPDUX5hC/DLI7H7k69TqO+Mgpy8H2qkIqxx/Fb+MEbjV3d3alX/Ozs/gOdDz/GGJfL5rKFXLyrs/NNb0hsHzx95gw7PPujzmpMXCU33HADiRjkWPCM9YkxB/j3TRFdJO76wSWrChGcfBkRHfngUWSCgDcrkIqmgmUMVgTCEUMqvMrwhwCcDG73RcK4j1A1XmJyZCk16NG38XB72gdUoOZ8+7XEKqtHHy5+6lOxLB+ZY9RiUyhUkIt+477s566MveaXY93s0NZUb4iowGyvWc7gQ0qH8DWYkFu+ZhV/eI0R4HXhrjBKiiCgO+ibt4M991C3HuCMHnz+BZhE/fLaFZHL8/OgZOShXN7vg/MxWZjCAF3MV28owJICzJiA7u66666a2YG6TCiOv5npTYtpFMgo7zMDANctskeGANgLXk05P6b67joJA3o5aLFUHy2Hx3mNxKYbYc0IncayXyhASJp39cO11jqk22ym8TcQI5QWm092rq7dqJaLT0fQqzFyQCKzEgDMeDB5dlrQT9axH5VfJwoiRS9blNSKN4Pa85Dpq11h6MxBiYVybHTi7DQ9aize1dNtkmjZp1Kpt1VaDjO1xc8hJZEGLSQWsXJmSiaRKqnSCKBqoSkxhrPERfEeI3OfEnC2u7oioCyJECGIwsplReAkaiE9izSNWC7cEBMIzAdobzCWSo8+9jg7Vezm9SJZ+wGBSS7xRK+8DMqCjoyDcSvPTk2ePn4YbrhJEX//se9D3923rbVDy9NUNI4gd0miTIlQGe0WYkpjLox0YgcBldsIPQIeqhgJQiwYW6Wz9y+NPWGbpRYwpdRc2ts+qjhPZZmQjbJwGeA0ceY471BpDXQkMjV6MjbOt3qTXZu2cAnw40ATME63dUxNTt4/PH1lb/vWzgSxrmlR2MGo53N2lQh4l1nGlcsHL0TQeDi4yziObYCFgFQbNvbSq+fZAEobCJNfOcfXcliRy7cftbmxJgIAou5NVUDANTjn3L9qL8TKtV7Usm1vD5SYCkJINVv85FYLqAsOTPUbDCZMFRNmDiIWxAB3+TQt7RcVc0aDHGo8Ao9IxYXVjuLTXX2F0Znjp8ZTbcB+n+8WKVWpdEElioEEsbQKW9Vx0+rWJ5o9WmGF/JaHzj1XsxT/aHlm/Ey6tTPBy2J+WEMnN1HaycmS2rKb92/1OWV28Db/LWe+lZHGosSmNLwa5M6HRXBw4Mi1fZhZt2y7MePIjePI1eZVrAwUyqVt9W9smeo6fvxYfnzL1h18e1kc2OAFow470y4DnoF4wU/dSH0XEC8aJCTnjHnJGZukGXuGtnjkO0zcJHbdXnrOa3pS7XoCEqnIjb/U3X9V5pt/npsdiZ15utC35xy3BPybPyjOH/3RH33+85/nC69//dd/3XzCS5Qr0cBFAIB5kr/+9a9/6lOf+sQnPkF79cdg0TKTCkiAhYS11GSSRXmeBwLEBhg88sgjdPfAA4CHg0z6dIDNU089hQyAxlVZMurFQTkMIU8cePLKK65oS8wtA9Zdnnt2b97Uxz5U9Ej0YtWDEvicg27lc5/7HB6kGsCm5OyVsXtv6rd/K8OLDfd/LzM+VuzvT7z8pYlrrj9TLh89fJgM6MgYq0RcOeiYQEec0QPF5Jt4EPjGtigHDXDGSEIb3MIPDCBkwTMAhrXxKI3uDHwLAXNs2IK40IGUX/jCF4aGhgC3WP+MkagUryN2LWiHFgI2rmRuw4ZdoBY44P85R7xwiDg7XfzW/aVZvhs1Y3G2oolPKjFJfs+95Z/52XK3vlUYsm0QgP/g4CCF8g6aMMSOwGtSUVik8k0Uam5pPzRDp7wlRYNfqsmOuvD0MjtQs5oABdLYyAttUx1U97IfJUoH+iU5iIXCcsAcZ/I56q0p1VIuEY/hDfnd87ZoUijDRaqk9dbbZNpFma9nAh4BNB92IATuuOOOmnpfb/LTWlyk6uZHy6m+XDuZV5KLPz6rIhu29wy+ISxPY6ezhTgVeYDxbmEdZkulmUJiHJcc9pmW5xkVd2Qj88OfrPZKWgEC/UFmt/3SKYVhZOVaSkuBBAFDRzjOB0s+dbLUO3qy1Dpi4oXA0pnCzeqPtPxOjo9igG7esp1oj0EWy1zo6eknDnFXITI8KsgXT6b2XHG1SYiQKo7IJY4WyurXZdSPSmGQSuIF/4IQa3dFbIIAeIyVYK0iiBcCs+W+ovFICQEfvWOoDe8V9r8KgciIwb/nrIPclVxC8V+5iIlzisROFjtnpjL50yW+FWT5iqFKIT5ia/8U5hBbDhMwNxudKKahLOoVyViypU1E+WSGT6xaFtDxAT+hUiUGYSgpVzUBXpu09zHFOlCpBQXqlJMEkAKdgXx3ouMmwiCouJp+tTeGbhgPeBJvox2xniln31QQTqVcppidpWaSfGEmP4WTM55vieU3jI2e6O7rjydSUkIk2tPdzSKCg6enfnCqpa9Tyyckjw7JZn/W3oJ4hZHq6MjIyZFThjNLB08cOXbsOBB019CeaCJZPJUB327b2co0fqHvcrbdOnP61LFTR/dcuZel3aP58vBBaE3ztkgYUS1HxZCQMGcHtLjFhVvNT6u1x8TL/et3hWZhLhGlEDUq911zYQWwLMRdxZQ6FSFVefmkPdOynPpQ2DV3GYz6+jadOHmSLT039mKAKZU0AYmpzGrGcnAOdg6qXERzepNoIHYK4IdXlqWvROjXE1hK7iFfaeLEwQ3bdra16nU1YC37S+lPXlz+iGHXbkFfZaXyIJjkR0q+LcT2y7YxFW/hAmsFdAG3hO09XvgpOxPEReDsaqFEdkcCRVmvx7DOCxEYihhF5IRxiP3GZZisyQAmNIggJPYwNkkYUzcgRZQjucniyaPZm38tddlt7dZYEFyF5bzt2S0vHox/47+fPfhAedNuZgrQwjkGbV228yPhx1JEvu366le/+rWvfS3LoU2t8wkvxaymBtY7AOapp+G++c1vvvXWW1/zmtcsyVzGuUf7Bg+spsLWnheADSMAyIdtytMOnONJoPMChKAKwg51Vi6IelQ/+OUx1xVdj3paf/bUPSmCgfDcnkpx6gHsVydnVR0ThDXjvTdGb/UzPxtjO2hhyq5T45PJRPzaa68jlcN70BFhn5+jjBScRciODOn1wkqH5qtf/SqcAa7gQOqXGCZ6Sc7MCJiWW2AqIO5NN92Ei+/lL3/5V77yFXo64DQmPh5UXKPcBQOwVxb6RHI0TL7f/va3mWsADLsDkGYDkCbryosilYJ6idFPsRAdn+TbUy3JJN9Sor4AFWneeWb6eXo6Zu/NVtIs8ovMSO5ESEInGJY3TEkxkRkXN3geTB7oNrxdqQvIwrj5NOGtugFA4NwAWZdi6ZHIA1vXM2FsdBQFG/TMeX4xl55DkALmzGKQEbXfZMHJnfolIQGSUH1NJly2kOshIeWlwdO2Q+WjhKGhofUgWwMZaPzMfPGiL9UUktkWqeHVegysbovi2X7oeGmmyIYv4WNu/VG5fObkqTMjLGHgEhs3W37sDJb94N596da2Ci1J3ByXopQ++O8cuA7u0oUEQ4EIRQYrcfZcLWcnoC0ZhazxU5G+2fHZtpHALHRapXYSc5F5ePREdnpqehwPn7MlX6N20Jvp2FtsZf1o9uTRpzdu3a59jKKR/aeBr2bzi1JshMStBCZbMPoYQxVDNIjsWZuIJogXUGURiaWXkS9yXQc/IhWl/jwr0WLR2n0rA8y5MLF1z28TJ258HoZVTkw9CGia1sTJssFZmOrKTuVPjZ3t7OzSXRtAsaPNmjZGhpwUz20d8JR3MZ3s6uhip6ny2NFD7IjUO4APGfQVlNhkFepSAs4GpxW2f2RrcVywJNqDokQmH8DJjkWqytNFtpsIp1+BO2cojavMAl+qDYv2QgekilHW3FIu5C7O5fLG/kESFXPZkYP7+wf2pFo7mG4oZLKFmalEtD8OFLI0vPvZ2dp2dnr2sZO53ZskEXhNOBPA6S5Zm2zAByuHKoMuopVjs7MbsjE+bhQ7NnKovXOo79pryXpkbAx0xstjSDJSiEdGtfichGMTsZGpeHrCX5AWfhaIFaCVLsmIH8ltbV61olZmuBWmtg5cN0XBv/BwRUlh0g6UpkyoCKvValNMboR1qmjgNGdjBqEoSUduEJkydEkku5VsjpT51IVmN4yH5aokEo//JlDAQBSuS2fJOTiUJcoyETwtaxIoEM0an608rrht2WhKjlxgLRM0hnX52vep6NbeWBeLbIyDsuVVNnZgFgDmPblSCkeutl8G7oJvtRuzviTEkApbCWcakeTNAA3lEpbFRccUpJ9nER8z+9ddJ4sxKBKiVMLSbRMHlhUHhDgSOLsjIWTSgAFNgwVwL3lnT2c/kN+Ru5FLn3pm2jZG735rz9OPTDJXZjt+NWDW6BbMX/nKV/75n/8530Pi24Q1b941Snnp3nI10Ey7XC7vVUr3kY98BF/fN7/5zSb9M96mq1u2h5t8TlZJ6uWwQU6MeNbiYqHikkJgc+xp3enyPHuLCuHrfkE+5IUTlScQExOf6qIJERXsirTgVbAlZ8AbDli6mFo9w7SrJ9LZferIkenpmZZieWz0zMD27WREclY+hzXFe87kCxocGhoCLAFrwTM+RRfSkIoYjHhwL94VFPW1r32NPWwJIANW8vDwMMXBrOcMB9AvWJfODjwMRgInv+IVr2DxJGsFQbywhRXA2KcDeTGSW8gAQoNhbUGqlFJOJcv9ffTyDEwwgZIDh7dItvQV02zQ0FSnXMVSo4R1r9Vxc2HKRUZ13b9zRKz42rCBslfHNA6johD6ohMrR53vfqHqZcy5kjXV5K/awpkJCw4iWdaOH5ijsWxLukvFkcUyfNQMsdWwakmZXnTEqIjlEohN4CISnmdZHs6KxXPRyb8qqsb0e2qseCZj4MM5mkJQSy7TlbXOZ2rsVCLF2yxdEOdGE7G4AEzFqna0qJRKZw4Muwh42Y8xV9swG1/2NNG1TcVseygqCcvRbMdAtL08PE5yJyYgC14IwjkIydgi2MnyzHQkOwYkQQReifT3S4MsylHNSRVzkZPTkQIfzY0xeU3xvOI5m0mtLMyWt/z9HkF3oCnX8LALUfO/QmcNX3iPIzjbr9EYrnAOPCF2G4NXlJYp2ZvYyIFqpEGPEYXCbCgwferI09uGLo+wPtJiVXpuKn2ULykAaKdnsu0dEsiYiRO8PDlBy9xYE+bC8Jdlyk1pixWkemmWWywtFWuYKxOArn6ME1KqsiEiZAq2UgjQWQV5poRJBUk5DzG3tBRZIf0pIlCBcgPPWAx5CNWaLEosHjog4b7QgGY2JTn/uWRE5CoRLedz0VJ7dGtnNNVWPDpeyGUzOW26kU36rsDYdjEWB/XMzE4Pn5h8ZLO+Ka9SAHr1J3SI8Pp4kiIjM2czR55+cvvuq6LJtmK0hbsHpyI98db2tJYXjbBXdrR8pgXj1kSUfiVjPt+RbR04PIUehEzhTKydTG6uVAhF2K+lsSasxPbfiqX5EKlG952zSWjgXzhVAdM9JzAVr2YAAEAASURBVCfTtaCjuMha4Jcbpkqgp3goT5NErFQN+tqPKNo7NfFR4JoLO3m2VIoJYTYDPHXbqttqIRor4oGnIlicDFg1WKvtoXDMcuaWcK8J52IZM6TiIz9y3o6dGsnOTIKPt3YU27PH46dHQLZXXr6nJamX5EjrWNrLY7pBfj9cK4QVY42C+2FkQNT8DzKyMA0HBqYdQwAWJmMBCvH32jCPGfGb4eb1IJFMyyq6SeXnxhxAuZu2qyNamIwZ9NjQDTiTVQcLky1+B3k+/OEP80YermC+D7x4gksUK9PAugbANFYM6Pe97318/QijuZnGijbAM+5LxLIHpIFzSDg0NLTCDYFWpudmUzMQ8DY8aK0a7ftDC4smNdBsZhG9F+FOObIACpLQvXPNcADM4FCFErck2An4CpbwObYwOQKLs3kvWWQLqoR4DJ92jFeJtSEz21xDjJcbdMoSZcLwBO2ziLe61Dg/IQDkULk0CbKjNxwcHAS6IDZOXdCvm8ikoqKhBCfzkSQEoA1wi1U0ZMc2UcB7BzygTe4iD0W+/vrrmQugwYAw4VCt/LAsYYB+P9bWnrzl1tn/9dno6AjTx9Dz0UDmWAuxUvQFPx5j0KKywgTNBVBdjfaq0yEkiJEev0Eb4FaT40HIGWXygHBJWmZACFAUtBESeEAFXNZnZpjO8CUYZOR+dc+iQSlqsm7mkkGRR57WxdEMfTXN8oB9NYeLKIzamQO6WATmSfRZKp53wviBaYTYQDzIq9t+LgqF0J9MZMrHzwZmbti/mBnaFmnhiy/l0eyZVLK1q6UPizbjO8rL4DOjr1JIqW6ueyKRMRA7yNz2FinXbmNX0smwU1JZ4TrEJ+jj8ArhhYvMasN4ZWe1g0Vu7ktRAtEzOABJoYRs7vrko9B09m7t7O0TU2MZCIVpnuAL8R2su8TaDmzogMLSE3YJ+PGkViBceR7hWSgnSyXUZ4QQQ2iXlsCKQ84B04Bc6Ebs7aRf+09SFd1+iLEkVsAwsZW5FIvjDeNTf/FgOxwBT5ouucAz0ZJsTcdzmZl4lN2XlYEyCn6UXkG4epZcqUSGiyq58F4oc645vcuqwyglDIBESEYHkfC2u9JdwA7GlmEQrx+rJiKhQUpSGiLiV65CIRwxcrchwzVq4cusUo7dVV6OoCqUoidb/gdAfE6LUhubdWwsbdjUGzv89A+nT+ZHZiTByYPDFJmNF/oGd/OV4GiiJZVuK0VbfjACyDF54WjtU5qxQhEgNDsTPTVdjk/pOzrKs1SeyqQj2Xhea8Ij2UQnUs7w2q9XolcxCorhSk/n2S5M8wPChxJYIuugmtA4+FP/fLIoVy6lNGsgAv5b3py1BZkDY09JkWGCJtGFcYPQyMVSU+TkIJbKwJSkjBCNhQ2+dNrzRxJuw8PKZIzsBoh/dGysq6uzNQ0OlfcVyW3tcYSZBRC0YgTBNPUglKv2oFZMFjapIdMECUC2Wsaslcklnqwke03httWSZnlx5dHl07gCt+XjySgvUyPNzEyM1Vo28pc3tsbq2QAVFbioc2fF+725uGWFyBRASFJsP87YxthpDAGElzTXH2Zedw1deLcm4JVSE1lz6Q2/ErmiQsMKk/htb3vbH//xH7MWenBw0JhXeF/6XW0NrHcA/Nu//dvAKuZCmm8H6mbOPebHnHt/XVyFQlJejhqZABLLe9pr+NRcOuYEA7ORMquOl7RcnOXByExa5ufwV4PQ6jowwaIQAK5AthADR6lK+iB6MYTxjgwDl7DnzgpwsDRIBizqd7kFE3aKAkTBAahDcrpFYDPeRX+Zk6zZsYb+ghIBZekrQblgZkAXYdynMPF3XMHABJAWfy/yIwxpsaoJcBCActGDF3SiLNJ+0+uyH/pI+uxUS1sr41s+ESu8/D+k7rojtqydhMl9oXy5Rbfo0i5EE7afBnzmp8XHDiICQFJHuHmpgiUln8+wOgZW1BQHsjFdgsJXa/V+dS6EaQk0p5o3n8mU1sLcB/kuVKiF4mv4PzMuUQjaoLVzXBQFR2CeXNaJoH8ExglAgNqkpyJAf0K/wQO+1mVBjLXOoskG1oENmpFlK4UIK1XZmAYY6MtwpdE5iUKGvKxY5J/j78HgdUOLNnaVaNIpmSfHjhYQ8YysCmS5K5HMQjbx8Qu75oStL1Ahq1s3xMNAg1IADlq6e4nOZaYL+Wxbl176iCdbC4FvS6SOeEgWjSc379xNTBGgKDNevKgCK7dADUjMIiVGKAP0yolDkUGw+seEl8NtLpFAkRdJhBav7HTYD3r0SyeyNbFB8YzCBFDC0vjp41GW5dJYS8XxU76WqqVjQy8UMBGsgXsk2t3VxghYLmY1ZBBlcNHlseKLK5ceY0ntEt+sjki0g/4ZuOUAlVtCPgZfgxj8tNKTqYo89Zxb2gRxQmnGTbGU21AbxdWVqzaoOElKKZVRcBayEme8ziAuP8uva1mwbrbskB4aoyyxWW/lFkVX6fmMa3TPIJV3orOvoyv7rUMsK51o6+zhBlslQeDa4AsDhKb1VSUVE4EEEv2mCTk9Pko7KxVButGZqVHMgFgy1d7Zs3Pf5dIcOLMc2dC3RWhTUx02fSCl6tBzrDe6uVKR9aavFK1Yxdh/lIHA5SR3om3fP1va25rtZB23niE76Wmydi9W9k88tJ5BbF0EhU25AsZShLHWqn+yUMskxglUfYKsgZ/W9MkiZClZC5JRNUqemjo7fWS4q6V3304tfZewUgwB6NG8QCxVoO2mDNBqDyptuay3dkHMIFv5bAO3reC8CSQm4hQc8KvwjkQHtm3jjzssxMUqWN21WpUcl/BLJ4/a3ZDAkuQJWnQp3ELcqd91PgWMhOz4++lPf5rz3/zN33C5UFkuxa9cA02Z+yvPZhkcaPHf+ta32A/tox/9aAPP2HzOtG8OkgPqeHJ8G9v5ZOsqBmk5gHB48HBR1jR6LtfCTxXmMj+wqHJI4igd7AQxTtf5oD1kQi1wYK2CY8GlFJDtHKtnE4Gdjn5x57IoGkwLMHO3jzOhC0Y/g4ODfImHrLnLDkbE/Nu//RsoF/evKw0fES4jIC63sI+xlcnxlltuAVQDIG+//XbUCLRmNgEkAGiHObn4zAICuKkdit0ggM95Znam94670v3b8l+/p3D0WLF7Q/K2m1M3PCe+RUujG6Rd6i3XMKX2Y6HklHd4eBgVuSYXIquJd5epT0aQkGoilxqa9X9JhT7/+c+nRdUIz0hJTfHu6PkpAu2H5odKfT7l/GTafC6Ix/sCPGJssLGkRtJ8Fo0peSp57mSzYtk1cVCbPKSk4iFlAmVoaIi04RQVq1do8OzSXN2TNMG1WRIsv5/6qZ9qUtRmma6MriMZSWKHB91L+JyaIaur6MDQHjfnpV9i/E6lO5LVHJq9gfUbWL26CmLsGULpQppVh18EeQYXMuY5+J5cqXDkyLG21ta+vsp7DWboG8IQGmNLrq6+ARDD5OkT5elyVx8fTJInLcAQlleAwkxwMUUiW/gqGotU5oEAwmbGXDGCIpArgd3XlZWiQs8d/VnPJjpjYieT3u4GeRi0IFaHcXMyZ29RijX2asTEm3stWjo2BiwDkETbeMN1lNStia5tPWjDoI4oBVd2tHeUBtpaW/jOC9CPPAEwkstEszNwCOynXBWrLCwkeQxy6Y4U6pUpJqxxVr6G3MRKflpiBKtwD0o8QVb33wYAFXrrK0FfEsxnf8XKFscaK0Fuqo08XQMquwkjSSDUhZ/8vr3+o3sc1WGPCc7U2cCWLc/rLh3OzcxMTfT2bYmntHZAivA/KtsOrArNeylPa2VqKkKt46dOloo5JCP7MyeOE5dqbYu3dFAO5YHQQum8cuoeakUZZzExxhQIN6+MFuPNfQJeBC2AIA/xKUT5SFHb/th0rFDg+83842B2SWqn7FZyYW0OEqBMacT+fF5Au0NBxBeviATE2sJjEaBwA71qPNQNtQMHnSWEVG3QlxkEm1bgA7lMLmzsjI/FpyYO7Uht6u7pZIkB3AC36kaVC/nMwVrpxf5JtDpHddV4qUOimkupy+8ph3Vw4P/At8EbbU1KhfxYhkyOs8pvjcaINdIKJsQf/MEf/PIv//K///u/33XXXetE/2tU2AvLdv0CYMw1PvwLbmFDoyXpqLq5eLg6ZkmszicxjzeTWzyoYLnzmS95YVMCvH1h8FKzRmCM6QYahjMHnRGvbQBU8AE68qxOYgBZ2/HxsjeUvr0N695f9apX8VU04pnFAOCBZ7CDIQZsY/7SO3CLAAdrpP1VZNQI6IU5NPh+SQUNSXAmg9JdVO565445jivYu9QlOSfRVSaTw/2Uvu5ZqX1XxJgFwPHS3hHhS1Hkt3oHcoLw2RWcVwCY1mnMGJzgaLkxWd27tIE1tfXZwaK6xuvKsOxIOFP7YXKvXC4J+OG3VkUAlMywymz0fG50WUAyVkZUCxNKdcEDtHacqEi+FmtJmiwdncDg4GCTLgU0jLRh3TGfheRUKDFerV4FHkPk/BppUqq6ZPRsHHVvXZBIOpZ0QnvPurHsMlT3NoTdOueWDHyzrN3OtyvuKk4Gv349LGvZD/8NrmTyutmrCCOqwISAwhNhsuu6HEmkE8VEOdvZAuzSIcBCQk5ce4aWcyxd5sNxPS3Y/3Zb6EwhIT2XTGeAiDjr0E2x4BKYwT1QhFLaLSOTeJA4qfMxniaZxeuu2AgN6je4JFKFEokORyMisyvdUF4mlf0apSI4rHRBQDz3brgSobNnp44cPLjn8n0JtnmEgd6WFRNlLYgGHNJme7bplFZB219AoHjhKAlJEvtz3yl3iPGwE0tiwK0Y258kqyBVScO/QE6J2PBQQcRDv642J7dwyEZ3rdx+04LI4FdLO0cj3anoQFf5sUIhkwOW62vFFNAykKMym5kdGTnVt6mX551YCulATI98KdI/uJuXQLOZmdMnDm7ZsTsWT5oiYuaDVbUgE6Ox/TKjYs1QNasickaNQEzqQsUzJ7NIlbscwZBXNB9JnSmmx4uxw+X2W1JEOqAN5hS0CFkLlTnTQQBElbKiDH49yNm4GTyWP9aBK+d4io2mBGKL5p6FAyuTyQLYLD3gyjWVio2z2nTdzvvuu2/y6P59AzdJ7aLiv9czQZXGj2VWSiV5zS+2aLgKr+bW+b/EnMMWwhnGCr4mc8f8w6GC+beuevJFhaeBYvry1Zt3vOMdbH4UzvkumvASwVI1sE4BMH3d3//93+MBZg3Asu1y/IQX0NRbUk1gOvOKLE8p2G91u7BmxADOhfNqzdBX04AqWXXcQGa/5UZqCHRr6P0SZx3wlQ7Osagn8bwgoBfAwYt+sOPplx3sMbfHwICDd9CsatZOM4lAL0layDgIYHPTDFg4jZKBu6QFkDtb8ABsiaSXR7bqcjUIIwxAemhoEBpWDnMZ9sgakXzI4t5qHMhPFhQWPdflBwELfcG9oDIk4Q0ZyHhkwP9c1k0yPxIlAFDnx69WDJIsewSigC5G3eJ48ZkBYW4lzAJV+IZqjH84t33XRwiaxF2NS81+aRwhMIM4lNAT+qWf68rcmP+q3w3FI0BDoldEURdKvFCYJRWT9kwf5cqEA7MMnP3pJgwrnl8ctkviuSgxjYcni+eI3mM91CP2bl9bbFvXXAcT9DUGHqo6HoEuf/LtbL2AQo4Z58rtqMOvKWAllUIK89xZqpCzaIQWlEInuxvgLn5GstnM5L6NA6ABu2t0gheQKmsHn6XeLeXSZjRKeqPT460cjVCiQ8xePJ5atwR6+RWZ3YZYYYuV4W+cjRWRLJdVXhxgD90zPKmgXos09CgCMoMXNPojiYFJjzEa4ZkiqcRIgESRcOekpaSKlgBEKiDUBh/JfHYyeSAfvWYomUqx/ZDoK4eJL3Kt3x0dHW9lCwytgoYmoLKsKuTL+a3KbQnJK6kqv/WTNr5blSZ8wOsWxyL5KE758q0tj+++osB0iC1aJr6Q5aOzJzZu2cEmXzO58snRqThfewpmRlQ1HNJzS4uhylJbItrZ1hpPpK1pSIn80VasRjSPYNCUJKomq0ESU30er0i4MeMgT7hqVJUo3Kl5CriUZh7LZ2hNxwvb2DS6G7groG3v3OJ6JTnwVQuVHcrae7ksXQbK6mVaOwfv01pTgacagGnRwKplZZC8SncLBFFp3+bNm/r6jp84MTE+oeWQYmTMPElVcAEey4lGMUvyCiwnj6bTIAx2JvYhNpt7UBon9XYYtkYPwKRxqvVzlyHvAx/4AF8o/Ku/+itcwReR5OtHh81Ish4BMI0VO573fl/4wheyHw9r9vDpURhQB66VJpsCZJi8zajggtNQXsw4jC0WBmNvXUB5kATAgH3MsagYITHP6qKVAgEYmxpcCJjBjbd8OaOHz3zmM9CDV9m5CjCDSD/5kz+JjQvq4OtQ4BmMXdY5ww2zGFFZMYKRin+YACY+8bQZMCGC0VeiVQAebIGpeJI5fFKTvLyMJCTQPACG2MsLBw5fP7yoBhbVZ10CxMa5DXJjOnyhLEDIkCEJBKgFPmiDD+jVZVg3ciHOdYnPcyRV+cQTT7CMeaEpAJZF8bIDmxsD7Vw2qh6FECaAWlw5K/TKwseZE/Cwnz2SgZlcPDumYKgymmUz47QnX+szj4zvHUJbZUkYjxUthOdirfN1/iiKTH1qhhjfso4mx/sITTY8Gr9/tJnkcPMKRdtcekXTD3heq3imEh944AGWWNe8W76KWSyJFUhhS0d5nz7kYf1PgJyCZqlIjOyKgedmtxBBYHa7zWwIMkgfdGKkIKGlFStnoOQVY9EDwFdwo9nxzkpIAWJ1OkKSfAqpdPLk6J7uXEuLvgZnDGXmGxuIPGt9iUe4w96m9Rxs8BAg8dy57cT6lfwCn1YM4R9lJkK4hGUhphJnt3TXEtqV37I4xTd/VI+AEsOOCgN+w7jKPX5bU/FNvd3aTCjMKqBUQkvM12hHv3nfffsuv5zVbZ5UpXqmHMxFMh/NPHVjS2bPpsT2jdGRDHUrBfE/VyhO5ya706VEMpXtjk1Pj3cnNrayaRioUnMQ1L7aEm0AIJpviXbn2rb1RpJJx7fWMAR0hV9pG4G2zQdsytWUBxwEjAnotVgC4FXNfTC1piXH2l8K+ArKhbZ4z/5cCa9tPrn59My+Z3VSoVZL3urE0tjWVJu3CS+T3ZqrWR6guRZTL20Nq7lLMqbTA/7hAsVGes5znvNMajBz5VwshAHA6ip2SGU4QxuNyVlL6DOk6Ar7AWKsvqGhocap1s9dxGbp6y/8wi+85z3v+bmf+zmE/9Gs9LWukXUKgD/2sY9h17ITGtYSKsC+5MwS0BVasWutzWXwp2vD1cAjih8Sk/SCt3KMYyBi9cwfEoblqhaPeIhBHdXEIWVNgIR4a2siay7p1ECwX/ziF3lT0TNlCcCXv/xlACpwgm0PALdgWtAOLjhaAlYvYQxrBGBalEkE7GwAMx0f2TESw58YBgyERMnY0O4mgg/tiiycmHGFJIBMqoAk1WWskXD+ZehMnn9r5TFI6J7M0MNclyeFYrIAbMNjQphSoKW6lBdpJHpoUCkAFcpb3boYKR0voRAaAJMmKy84TcUnF7zN+JgaepXdW0guyAkB52amkFYuVZMcmADi4IlAk/7gcNlk2lUh4xHmmXVWBLhERfTnTWqpuvapa5/cgQ+PPJW7Rq09zDQMrIoqls0E6317V3Kgm97Y8V3Fiq79rVjhFbomc7QFoW6wBx2+MQ57f+tS5iC2uANdeP+UH1RU3pyMjU1vTU5v2rCEtSdNylaPrFLsevdcuPp3lhNbL696ca2trAwaIodz2sy5lHQabFPGiiQA8DlkyxFsPaZp3F27xCyBv25r7NgUPtkyO4fRoHOt5Y6Z6GBPOZkqbo60Hhg+3pM/vmvbrmCKRfMgLFAO/LrxWEusfy+oGAQrHCsoy3x3gGO51CbJiuSQt5YAlzRux6aVCiFC6wsAtRbvtabGP3G0MH3UPpscjZz4bunqu4ssQbPKqiStr/iqu1XBgHYODNdP3CCWrLFPmFZwA6YB5TP4FkrACGQUZhYAi4hRvsHjw8jCXewiFMJgR7jJsWb9KBCZf//3fx9L5k//9E/ZDHj9CPZMkuS8mkHNKI7eE5sV6Iv7lybOO58gHPw/uPX84Yegmk+DZ6CarPkwQALfQvWyt+ocVz07BMO/DQCjjGvhx2i+4A0oMfdRiHtCQg0Q8LCfGyRv5hZMQL/s74pZ/NM//dMk+a3f+q2XvvSlOGbZ6YomwcwfMoBdcQliymND+77QDHF4gUDC/sUULqk+NOkuUy6x+EFHgGQO2KJqIBOg1w+XDaQEJCYJtdB46rq6LMhMs6yOaT4cNqoG2qMgAGBKTctfiIx4rYmyzwNQCpzkqLF5MdYtJfoB4XB2zyENQBgukaA2q1VBbUKGBqqfHScgLYcXsDrJ8opMXSAJfBhTYethcndu7uyl62C9PWNz9QLp5WW3uql8AoWJc+Rnls13UV7dLBpzo+vwlyzYxI6Ar7bg2Wycqu7d+VU5P6ZuwmdAJKZ8AO3mm9cUL4is3FPbr4TPDSq+8mgIFlh4AdLQbHfsW0Ulhc7pvoMdECIROufqqSiR/Cgdc+pYuNR0YmBgRh+6C/r2hQkvpjt0iXTRdJJMBSI3YTrk+d11WCT8uNu7ix0pbc31+KM/LBfzrbHI5RuisfH90VhpsJ0PKs6Wpg/cvG1nKgF6VSPl4GS/lV69unnLN+z9vTdRaBzthnlaQDeNjbX5YqE0NcLXKMxn7OmM6sl7ZxNFPtqsY+Tx4uiBQqJNUKrqiMZbyh0bWXhCvlUpqyhWPbg8BwlKoVIYtrBVzpuoq152Z0iLwk3y4IMPggsaL7DCV0wSbEJs16GhoXX7oHmTRVQCdAjYtxzYFYyYVBZis/jxgx/84K/+6q9ivl7s1bdGrWIlbNcdAKYwH/rQh7Av3/CGNzBnwzjBp25oBzR9zDhaAJcPPfQQD7MfwBUOLCrMKdoQh7cSP3Pp2mm+6TA1SxY1K0jBIRi1S7VrKQUGgb952EAAyoVZv1TmK6n1mrRoCfjHg0e8h92yZ42iT5sRyYG/lxdokTMkZsCji/FptpC4hnkzl3TQAFpfEolxwEEMnHnfg44A+EcWrAhAn0yCMiWGSukEwau0AcAPgBYyujwEY+ilcwQEstUtt0hFEyIJYaSlONQIVYzjiDeKKRExMKGBObLy8jYj8wppEIZGRafm67HrckMnFHlwcJDqQLAGTYjkVFBjgrpZrOdItq9gSHAJCRNAV1RTKDNF5lFFkwttD0Zbda9+mGTZgQZe5Wq1E/ZLPy87u1VM6JKgK5AJzZ5n5zzLRnY+PYEMlIuefFUqhW6flR3LQ9GrqN7zxmrJtdbYLK+22qvDC5bH2C3Ms3dTL2tt6GYXZHDpRkUDdPuMQRw04CVXa4XJevuli2bxi0vFsEuAgbW6uw4FprnRE/S1xfts9rhz31bGd9IyUm/duoUnmuGYjbKALtPjZzorG3aEyQ3BVl3NBat1WWmpld85Kg9Zm2fF9Mj+3P1/k4vMJEuCsr6xM1/ojUdixUiJMbcczbR88fey+jCwrawmNTtVde3NPu/1jESBeVnLfA2uKRv9J4Md1g7mLuNR85mQBJ9BuOSehLDC3kPVjf2ozWdx3iiZP8W6w0RsnKOrCxpvE9Uto3HCtbjrA59zJoxxi8+GNu9zhdirvDVAdYTWDsMlBi301M7rXve6r371q3gEeSX4wpZiLTRzwXmuLwBM4+ANuo985CNvfvOb+QQF9Y2BS/sAGtE43Naho6QBgV4ghoAz8UyT0GkSiQeGbtdAMdtMtGFpwRAEVd0RL6MZ4WWClWNUb7vNLMqnTfN+JjaBSz6/shHe5V/0eZ6fdnVjwI0MP84T5AmSJAzCRNVICADjzOZSPJDoti5x8wsaaySHM0AXBy+fOwdg+10EoMqoehAgYeAf4tFlIAn9OMtgwLGEEQapuEvzQCoiMa85aCTsnseCGZxdVPfnP/95Zk/x7vqrI8TQxWCFwBlixgb0jy1SI9iaXlJq2jYjWQMATP941113US42SONl+MbyUDX+vfjGZBfRXaoPLfG4YVox3UtN+fQEkRz+FDO0E6Yq5z/UxNCEOFalyCH/+YGQP+LRilYF3YU8VyuA2DzOvFa0WgpZnmDkTj0uL21NKu/kayIvXV4oDVAdvDtzoXK/iPLlSWSs5EzfdZ4HnTXVEl00XTHjNaMzYUZY767rZeqdaIAese6gwepgQEQz9J/wAeSgnLX2CmCY7b2rc8uezNc/kZl4yt4CdqF4Rx0M7PgWDFxik2awlE3gxYv7XlZ+9n/ojaUqBPVKuCpx6KGyoXuwWAZr9jv3f2fvHhkzysIVuUBmlvycex7jiZikQNW+OOgcotW+IFMOMvV8V8ieRuUupZBtA4Z0SoODg6s14jTIiFvIExIQxljlWeDsbZjVeTwXGDMcREIJPMFJg04ghpIGT0x4OCtKCrT5zd/8zT/8wz8EE7lbOMzlUmDlGlgdW2TlcjgHmgJTHTyWVLmDRloAB3fDh2fr1q20CcCPNybOtB73MBAAqdKNVrdFjGOABLe+973vOX7jqSDAgUXI4+TEMGRAIiNAFA0U6EWYRgmNE3Dm8Bk4kG31dJoLH54hC8Oh2B4ZXkJADDgNxA428zWBYarzH2BezQdjnKJo2HWOMsEeYGCUg0h0vuBM9IYV64tyq4mX2suEWoI5ABi21dYAvQbzINxizeS3v/1t8qVVMEYya45UBOgpqAVQMTqkvohBTuqLimbGgUrnLsmJoT2QhAICdMkIVtQC5aJE1DUFYfoNJufHeqPUXnAaEgIgjLdeWnt12+CWk6EHysXlokdN8kXp1zkBxaHuQiGpbhqAXzJtwep3tq2myaE94tfaTgrFIEDjZ66tOiYM05nQnMJLDyAhLZBnamFzsCbFmlxauysz0XNh2wl93ZoUbw2YorELq6s1KNPaskRjdMW080t6a6xo+hB6rSY79sas1sndsLsmwEF3zYC+mGwLOWdl7JGcw3stLhdjtcz7rMRmoO3enn7xb6e++3dTT3yJrw+z3zPctJkWVmfAV7/IEkv3Zm99fXrb1S1+R8mXdOiN45BpdagOF/JTrsEdWQsk2Lxlc7q1ffjpQ7t2702zxpw3mxc+GHqw2biPkexWFmGGgJrh0jNaOyWzUwNZMJ29sKRLvoMNgNnGOk0ayUKSE89I7abskjNYIIHrym8SdvuNS1cpa/oAuqidg1sef+eddxLAwmRBH089pj5PBxgk9HxgedYYn2GJCHD8+q//+p/92Z/xZeCPf/zjnvWl82ppYB0BYNoTsPOTn/wkL3yHgJDqrymqx4B4ORhIqlsk04d8NpamBvgBEeEQpiECLWiLQCD6gnBs9lTPf/7zabjcZbMl2DIlxgNDG+VgcMK8hhW5IAB3kY3GTSDsPsKsXaRQTuJZvcOZTsfD3KK5g8FCGg/QNcCTRzQsbw3BWl+GRUAzlNcvCbuJT1+J2IBGttTjLnNplJQ3liHgs7SO2TgvhAcaC890A8CAJc30GuiZCYWwdkjotUCAyQvyJWAVnvR+HI8oPQgzx9hb5E7VAGWHhoboRwDMYEvmNcHGVCLqpfenXAQoC/2mq5oAkbQfkAmSwI0s1uIINQxzbye8iknj9MvvfOc7tFUWb6PP6lZEKjzboH3APAQuP0koAvJXU66FzOuKJw2MxlAtUrVKcWnSJKCpJljTMDNiHM1nQdum51n2+ojmM1qUkv3kaPC33XbbopRrRHBxtVu6FwYIt8IvLsnXqPoWZcswipHH9PQldTXWFfq5++67GVJDMqbWDezUToOGBBdFgJ6ZgxF5GQ2A6U7eVKrWCeM4gzhnnsRlMGxSY3BmpivWErnx1V39V83e9xfZwniS991r7E6g67Znz9zyf/SkN+D4tZvBDlpV+Vg0GiDKd9Wq3AuhrNx9Kgs0EBNdjmaLJRZe53Ezl8qFUpQPfRfkZ4nkCQOxSh4fKepuhJjxjsufevrpifuP33pl/2UbEu5arWR0zi/WEcvHPIp8PQxrHlIuHaRh3UHAvO3amUDnyLRKF9jzWHTIzEs9q8Ryjo3XINcEsC4wI9EVLdPf72DyHRuYGOLRMDQM7j6qYqrRYmn/2GkMHIgXTuJjkXLUGCreqhdt29gbb3/724FF73znO7FyF6WfK8ml0GIamOuCF6M8H/dZ5k7TeeMb39h8HVdTEgZE0exofCAcTE8aHJE8KpxZPwDcIjJ0HTMTQ6mIgZimDMSiNdO4QVPsPw704jHDaqTp09axwqGEBjwGf7ArXQk0AJLwgYGby8MtDo+HM/Ee9jM0BOBG74O0vtz/fOi3Xh7AP8ob7iiAiUwZER4hCVB2Hjk0RvGZKWAoooK4hWIpAk9mDTipl0P9OLKgc+EMICQXR7nzSeHPumj07MR4cYlBpWgP1dGncwYochdlgn6Z9eAWCNOVzLITeMIcUZmDhIxuiHXpjLUwoVxUPcWExocHSueX8yWpjvF6JIZcquPrhmlFSBXWMhMoFAEOiI1UtCVKV8OHu2zrxbvut956K/JwSbOEube3urk8UyN5SH2QQwmMLpwZeygssyc0QgYb9Ol1TYVWz6FcWIUgpwswP1BT1+dBTmTgoaAT40E7D9k9M7KgaXE8M8pyfkpBT+XNjD7t/Dfy81PGVckF5fgow4PJ4brKTOdb2tIALeZz6dN8nF2V7M4nEwZccMIyHhzvzKtFpT9n8Rf9f903XKopVxQW2NWXtvjM2MCN6Rd0x774e3l9pevcgb13b+F5b+mJsbu0KA26lvROMNgVOw8MC3BVGJhaNrAqQBslrBiDshDwzvOGeCk6MZaYmlCa9o5i14Yz0dTweLSgFdcxEtkrxrxlrNeR+QcSJj+mRwwYYzdGCq1bJ8uTjxw927ultL27rIXYNbJW1EET4ituXLHeLXwHmDHU39Cm4aFhd1QC4SqJVu2XYdon+ukTyAtrB9aYlxg/K+8csAlxMmHI4TxoniFiePEQgDAH9qd3WQyOHGgMEwsaLEk8ENzCYoSMGJwrzLYToFwYjWTKQ0pHx0HA2bK+iQMjxC/97IWtiawmWDQMBzbBYius97///Z/4xCcWpb9E0LwG1os9RCPjscT9+zu/8zs8JM0XYCFK3Kq0Zu7C1j2ZhOlMfUpGTd++Plcqszle60te8mI6Ah4ApnUOHTp44sTJng09GItdPd19fZuJBkLT0MFLMAHx8pDwbN93332+2paHgbucWcTriwx5WujQ6XRwb/LeKa2fHJkf4hKoCSV8AGxkyt0La5Uig5cLkTgcCfP8UxYHZqiR/pEBGwxGmAI6ZUgcXjYToM8FDUJJP4JO6MXoGXH/MmTW7RbRDwf0wEjm3jAO0B5V4OiX/ovWQidIV8Vd1EuNILZnQV0jNqAd/UPJ2Eyh6NfIF4bUIPQE/NKTAJJhTuSiB5LAnLF5UUrU6yOBU6JMpMUryNQD+kQwBKCVki942GmYkkCeW2655cYbbwTpQey7stGoFs3uGUYQtgrXEu3HC4jGaJDU/u2330790jx4oBjvQ/qF9ODV7XcXJV6ISTPxvCDA8OmUTGcQoEH6SjAqnSZBd0QTWlMZQjnpiyi4d4lh5KVAAw1QdzQ2mhbH+amjBsJcFLe8ddF3MRpeFAJfQCF5GDEqGHx95heM8/hXpwdviHZtS548dRKT4KJ+VBd6Xig1jxUHLcSmjr0zZjg2uCkYatE6leOJOOuzjhw9QldZwRgVSmpOJDUVGN71G+IpFGmHBYgBtSpT/kCYRQOrDmLxr7LI+cj+vL3968mUVgmikdGjkUcOlXNpBms8tPLKFsuCo6Q1EkocLSoRolOMaMkgtUhgK2xc3tld7s6fLT7w0Ozf/kPxkUfKuUJkx47Wn3nppjufN9u95etPFXKluHiCpRHE0LVpRzAbPJyZGovFk+n2znIkXurcPDp65gfHZm4c6NzUuuAyaK8FV7ErgTPWEQcBlhBjSuEPCG+tbsDNXeeJJG5rDQ0NufW7wrzok4GaeAhwnwSvQxvHmsIShxgY9tUoF5sNm5Bb3/jGN9wZTtgTcgu7FOvdzQyH67RVjtAsJDtm5F23lqdOfrl2ljy9wTve8Y53vetdOIEx0WtyD8W4FFiqBtYLAEZuvnZFNS/J/btQaWkf9JgcNGsMZdqxP/MhPQRHJwrHJst8sz6ViKRjkXQikSLc0d63Pda1YXMilSqVS+w9fffdd9HN8cAA/GDIs8QAj/3K7koGmLUQgmeMBwaeGLgOgFnaChkH6A4A425eMDlmKGAGGIPhOzw8zJnFJ25mVT+656F9UwTHvYxGZE0RyBR5OLj0yFBdg4ODRDJ3WC1YdTikXDRAT0SfBZmXF/0gRgj85icPc0GxAAbwLbWJJAyNiIqDlKrhu8FcUhGwoquCEjjNJQG6LeAxpgZhZg0Jc4vsYAsfR/vUCD2XT4I2aXaQnMpt3JVTQO9GCXCgUlJx0HejBHA4DYNIH919/tv1ADECc2ZwQjAHvQRIO18/P1IxPhWC5pnSZvxjDoJpgmWohRqnedCq11R7tCUaAAdC+rwb/YDnSCRPAWCYLmVNZQiZ84ATbrJ5h6lWGKANM5UG+H/lK1+5jGpaYe4rTE4jeeCBB5hSCddurJDhMz45/TNl9Jb2jC/sCgvIo8G+jwBgOnk9GqXIkW+z8XD26pfNmWTQkMv6f3CQE8OGA2np2WgAjG6EGXMZo2sUxajHxCU7OFAy+7NCOhHwTwGDp/qJbNu+/eCR408fPnplZ2eprOXB8rYCDw18YriAPwVHCQhnAkHlcXVKIoVv/a4gq9YP86e0tp8V2Vi2lTll4qPlsW8iFAXi3WA2fp4bcMszqQMP5YtXpA3NAmjZJFqwlkw9I5NB8ks+vm1s5SAvfkHDHa2Fm/oKxe8+NPHu30+PaVmycn3s8ZkfHiiNjW/52Z/f0t7xyCmgrIsUaIakLgKijp85mU53JFuZIo92d3XPzsxO5pKPnSj/2JAngd2CB6OP++TDtoSITh3GLJh4uTewr8CKZMQwzRlUSV4uxjJYhgJ7WsxXDD+MPZzAWHEYhAzomFXAV8e62OfYhBDfe++9btw6B2TAlHIAjLmFMYYVBx/OHKTFLYGbZP6eo64oziRZhvwrTEK+bAfNCln8wB/96EddmBXyvJQcDcz1thdQHTRNWjDu39/4jd9ogIWWISGcaSucPRBy4OmfyUem8okIOyLZDB89Dx9OhyAa2cDfsafLbPyXjEdYYZKORVP9V5VbYvlUMtUWyc/ki4XZYiT6nOferI5K3XU5X9ASw1Sab8dxxdTe9umZ2emzZ7u6u5jIPDPKfr/BB1p5fxXM4xiY7L70pS9hYPlKFWxiHFn0ERw8jQweGKyMInBkXKEgjoXCUqzkMQArIknIysPARVC6R0KAMISx0T1f3lMN6ZcdoGjuBIM5euDLGRwUthmGTLzRpdLHYTqwmp0kiMQEB40HxAjMoG9idQps6Q1pSJzR2/DwMF0e3fHQ0BD9JpN83sbU4dlSZLo8VA20XlQGKoIDJh7g7DMF5FtTNbBCTnyABLyamK0kzHwHIwHVyo7NlIUaxwdOKeDgDdXPFApF0U1zSeC8waRFNXABCVCFW9iMUmEtoCJfW+VmN1UJGdWKxggsJK2PlAvdXa34wcFBWNEaaZk846Et6I2HWwTCcwNpoVn54UaAK3Dl3JrnQPPmLRIAcPNJFqJEXTzdbqystbqQIcwiDCwk2KV41wDdGrryJ/GSThprAEUx1LIoiek8xqOzpwrjB6MZdogdfBqUBmY4dpxtOKPXXHMVe98TUG8h0KQe45z3S+t2cgG6qREBEyegdnaV2zXUIUd1T5ULQTs7tBuTbKXwbL3YqbGJ4ydOYG3pIZ2dOn5mEuy3L9GealVMEX+muVsZOEcmyicmI21jBLXKlz+9RgmNsKveRysU8ol0O/FA1WJp41P5zU89Pn26rRjVxlTkiyQSxmWoyOQ2nrRDRkprZ/KwoZqMDCSTUHiV2QYIgtzJVNjZZEjhETlS1uqFaDHXWpq5NZU4WmrZX4qDhyPlsYdio1vQvVgpX514TZfdfnO8ihOLxS1Wd4g3vck21EWkdG1PuW1ifOov/zo5NkFJKQs3KEKsVJr6n3+76dab9wxds/9MueC7aiEMN+2fhcQxKAKSwjyW3DawHaUOj5eunS33at6p0VHXtsHYC4ekRomXew+ji4PUmEYUiKGnbkeqstrBXbessNmYGKIl+BmDDTkJY0ER6QeXkAFxmUzhIWL1H5aY8yE78oXML5lggq1b1GbxCe76LZuF8WBwxpIM35omqq7A5yQ4jxf0rmwE/d73vpeXgYHo60q286iGVc5qvQDgD3/4w5TsTW960+rWK9xo5fPBCf1fvhBT/2RzbhpRKj09K0x4IukiC8VIRvN4OsqRtnKe7rNMvzV1tjtfSI8eKjJfl+Rhi/MaBp7kRJIFKqVyMlcEM2/ZuRtEk4xF2VmfvvD7Dz2ayxd4UMmER5EBj0eUPbH8YQ7hDesxGPg8Rz9j3+PkJMw2VA7kSM5BjwCicxDICIqRTQwHt9TrmNuQsoedCxxqFEtf4J4NHniwLsAMGrAEuXgqwhyEgWcosCa5i7eMM0jPF5NgJ9ExedmbZA4sZGqZtTT0fcz8PfLII/SqFJbekPU8iArm9DOqICO0AQGLh5lEwMGLMhGYyJrsoORosix0kQjgHJjzZj6bMN5IqqOGA4J5jpAhmM8suCcQIXGLUXy6b1Ix+0AMqhgaGiLA1ADKoeP2dks8B2TUMiWlLDXy1+T7jL9EsQwGPCyUlKGduQ806aWmhXika6xGFTQbbyEE0DP1gib9aaqhXPnl/DryGKQNEQKNmeZE26PeV55jYw5kSpFDF3Rj4pXfDWvEWZF1yHO+ZsJbiwZ46jF6/LFalPgSwXnWgLeusHmf59wvrux4ChgReN+KHgB74NAD2WgxlTlZTmW6s8nT2OjMgQtsBWCPwhHmG7Cnzk6evWz3ZeZZ5Jky84WHq/IFW0FQjBt73hwyCUHpEiyoF0wxZYoCUXKNEgto0BpdHdwVUDH0KO8nMM9o7BbuUyh1CDdaQMBVSFJmUaRU2JRv1yB15OCB9vaOnk1Cio+NpaPjjvQivCM7MnIc7jlQ/mz51CPHsL1a2traO3vgAE9YkePpk2NTE2M7dl+BAEWsrXIp07btzJnR1LEMxLaUOACxCCHMLFn4L/3wG9pzc92NZg24pXsBYRAKhgxjQHKpsnO40JWNFaOxbH9x9Mfact2xyJ5Ix7ZCz33ZRCYeO5Ivz6bzqRLGEPxkMIJXC6WRI/v7du5JtXYQ5/lKJlOX54ohBkZNnZrIP/RI2hMqQyjkZE5PTece+cGGoatgWKQ+1D+iEv7J6spMjk+NndA7wYVcoZgbPTTNzY39O1LpdihmMuWZQjlwWTjPeeeF+luA5TzaVYtwDTg7TE0MXbwRBBCGIRirCUsmhLIE8Adgs2HKspqP0ZmEcICYM7soYzESxjZ2cMuQjQHAeWhoiLEAjwLWF8Oo41v4VNuruIJdjBo9VF+SI19jcTJyJMwZQwsbzCPDsy/ZIzuMkDBy6QHYK1G1DIsygZg3gflKDktlOS9Kf4mgGQ2spBab4d8UDUMm73ZTu6H7salkPCS0Ie9ogqDSVbcqwgu11Mt6o/2dJSBuvhjNFEv5QpSPnecLbL7HMp4Ie0DR53hHR4cprmqyWiTTLjTSzcKbXKScLUQZQ7hFdygCSeNnnl6B63i0hHdwJtYfZTgrTE+eObVt4IqeZKItHevt3cDS60Q8Bk2+RK8auf7ZN4A2rV/I0kGwpINH2jgyf9a6YeOGfE69xmxmlu/hZnKZvfm95HrixPFHH9UbhohLKp4tBtQXvehFxNDRgAzdxKdf8IOVwI73HD0yHMHTFwADz+BAQjyTxDioax4cknBJBwvF6armz8PVZUK5vGbpg4A9JATt0HMhHv7zoaEhXgFlIpBufXBwEGAJQKJzxL188803kxYlkASc70yqs/CF0NUx0HM4ZQ09SvOlNcBUsnYVwbw6uYdpeL7vNx03WqWb/v/Zew8ozZLqQPN/v3f5p8/KLJem2lWbai+gm6abBqZp1BgBGiEJp1l2GRAynJlB5+jszGFXmuVoJLSaOQLNDosMaBBGoAUaYduovaEbqrpp3+WyKrPS29+7/e697738809TmVVZVdDdUVnvj7hx48aNeBE37g33fFJ4SEgW1DzIRgTK+InCeKCA/p3hfiqmKrDZkMsr83pZQRj8ODTOV80oNa2U2VA8zA7QHpgSorrWarHUHt3Bryv2EeCnO5xRg4rXyvyIL4UY/n0LAf863Pp8bomHTkGjOmsGMHNnH//4x+GcRk4nfd/73ofnlltuef/7338KxSGtkfLTGsTvGj78Fc85rAFGK7YOmjQ7y2zQHnQQJlsbgyV/Gb6lBeqPeuWx5JbgYnB4I7d6JKDOxSEo+oCANSfGXVULBKwechFrEL/E61OMOkHDAXPtRklg9h6batsnysmfHZ8LtFYPPcJ6HopAdOZo+1j3VE+yo62li1Q/m7K0GHrQr58YKc3NFyfj7O0FLquCXl7Qxy/cCkR/tPiSSjjQLbeG4PoFLMlly7BypYxjXkKZvcRuAQFqmTDWhL5XRsGxhVAtl+Kzga5WH54PJGrRTIyzHqQUHIgX84u1UmF8dELyRLfCdJ2agBSGcse2ds3dNChnbj6QW6xjNmtuUqhCtCMbqo5m65i/cEApDV8sREmkD3tLooRJmQRDnEK1iDzUapVYkPDLvVfLHO+2njxcKIUD8/vquauSVUpD1dUD8xeEih3xzH35lhOh4Gipvjsqs6dKnSkJFiLLFa7zrBdRHE0J1JyksshEF+9ZQI7yhY1SPij7+KSupSS8BmkI4uq5fMSptceDeSYhZKMfVcmzzhINM/rpKpYwlxEH0BRjsRDA7d2heDJ4dPjI1MRs8cKL6oFIU2GU6nqPTUlOv0KNIkGS49BqGMLQx/CYA8iyCk/kP8qYAVFymLhk6PnABz5AFCM1ih8enyx+ME07xdZFmzIHBI+NWQygt956KyM7Wlxjwexso+kAjfDN+m3GHFZREkyCrTpWUt6DBw+ypuIP6JvNCHwRBjVnejTXsSPFi6P4GyRC5XzkIx9hC/Qf/uEfMppvMNUraOvUwLk3gOkGf/u3f8veNtb3N94U3CKJgOKfK0bcEW+d4npRZJSKyB9J5Z9uftZIGTBYT8Ii5YriYs0pVVkr5hnAPBYxVwuyMszlxQgB7uwjJYJOBa8KX0hYY1Z7mdhaPYRRHUzLJXvZbOREdiLhdMxNzbe1ZY6XtSezqqyXEIaDbLoOsmgcDiaioXgo7ERi9WLQGWWjdjDY1rurc0d/1GYF2SskjlXQQ3y8iCP9CAqTNawvYfRiFVhNYtwywQwqQRM3eND4YYYKv++++/CADxoJETfcuoQgQAqwcRE1HcOAfs4GTp7YXQgjiJARQaNPcnNNQQ+83i9yE/rQXA/JiyNf7Fu4Yvs0EhbjEyOfz4jDG0Bk4tvf/va//uu/pizwT/Ex4JGzeMgF+9PW2UiC7dokQMkBSU2qJiuIJBwC5LLopqJhfemshHxDmHl6zFqPx+ZfS2jVThxBg5h/YGAADxINeUqJGm02ZPoNN9xgaDx9CvjN+RCfoBfzsvil1NQA75oXwZSNvVDaMIXnjax8v36lEGtzukyO0IztjduavI+z5R66lQ2uRtlmW8idw7G0gcaoLc/aJ0iN0dLOTl6WKa/G7syk9+GYhwJOO/dZ2pQHdYrZH0qBKOO907XxDw4Ono4WsikGXkHeSA3Q9XjFvJqNIG85zvhiNSuTtzami4jAxPKlp2uLil1l65YSI8M3g7dsupkOR1jDS2vQHSuJNhNI0Qwfr9ou+jR7UqhQYlEctOBYB5DVSpCEkkAAeHBqxElIgvXkRGDb2IlAKFqbO4QSAGvBY4+XR651FiKBVtVLzOBE3SgVcmQwM13JZ+ulEfmwXzASc8Jc2AFNnKyLCn1LpTataDaSkVSI5qi3NGm1CJZyK0ablFk5N0yWXYUgDhyOwrplkSyEGjnoPIBSJFJtOE1Sr7FQOV9Ll0vRMgxCQKuKx8yJKU7Fdg5cQjaL09Pzs+PbB/ZikoOTrbC3mQXk8tzkGMnLxVy1XJoYPQJPyXRbvCWTSsQGB3ah+JDr4swku2Pbuvu89yKHfoVD95/wpLWNB7alYJKHqFgKETxeDGCBShEFwW2w0Xytni3NvyW9uD3Cyi5JsEL1XQacbfXC20LOj4stR8uhwTCf3aNQ2ekT1XIRupmwE1wYC2DDRqKd3Lgpli1/dbVg8bANMCAKU2t7ZHtPdHREiLo1I94qJ+wGd5Zi4f72EPMOUv2UAd6hQHxba2R3KxQOH3yuJZXctXMnFjCUw04lka0fnBovzLQFOgekLBtz8g4bHDkAEW2yyqqKONMkUczMxEJ6owv5cGLRo0yeo7owidxIkBHWhDw6JKmQzwx/TAEz9OAMk+RMk0GfKJ7mMf2H5Fxp2cCdeKWO1IFpnqYna8sseKDWrjP0NyVpCpJwcHAQIDyj9w4MDPiZGqZfRjwWZZAmtCayawV5wXMjpae+XXjtv+WD0hvdewg1suOOpD//8z/nuCgfRjq13Nfi6uUJP/cGMF2L/c98dshf8trIm7D2R7+tz87X5iYDs4uBZCLc2h7obK0zMIhUWzZRtAZNmpBIfxEJnlhA6oWC7G0ONZyPJs5EKZgiP2qBcKVa569crRWrwXKtXhS/U8JIrtZKXMZQkY+20Vnk5K6kxckvTT+Xy7/44sGOzo7zz79AB0SZVUVmcncC9rZxYQksV884JyQbsGFWxV8IU/nYRCAWqQ6k0+GWlNjPxXyaz+K2bRvYu3e+VOPG/u6+vlv6+mpVE2rIthJCTuc+ORTB95z6EWc9PZ3lcqXM9ppykck1hKEIFLkCvjw5OeWVO/Da1762q7OTCv/+979P5SPazCHFuJqIdVeAtg8ZOECLRdjZRBpvGSmDA836LZOCeJA1UvrlDssTBVrkLzcrytq4U6+Uu/hi0N6LuAjxqYmJf/zqVweHhlhYYxc0+4cxSjFs0JKhDw/ckIQfYYoUxrjF0uB7nljO8GlZL88tQHlhrwlIECsaHhq1dpLDP3IfWWykViXYRIrKWXkHm+H4RHw60G+Mwo9ExvjHA5PUv50rxpLf7HYJI/vSeDJaY7jStPx6o72d1JTlrTEnQg0zy0tN8lL85GetWvw37ud4FnigyDgyOgt5WblQd37/938f/ze+8Y3vfve7v/d7v3c6WSOmmHqHGqXAz9s3v+V1hp7kZZSt6s5QLi8lsrxi6oqeJe3MU1vXKSBziKYEbwR5HToWNbLgHFuQEVKaiTxsWLfhVYd311YkypAEWW0n59iLk/FUur23RcLy4l2VQDwSVHzxW0LFkmyWMlJMNZjlQXKQZbxXhQE0wcRcC86WQzXZlGs55+d6iqXi4aOF9grT6QzwlcKIE5rrnwnE5qawhixjh31fJ8aPFOJlYcupjky9iGbR0rk93blNKRtjkoUEsfdIic9N7fr4Aa4HidE4ll4Pla/XF3sJSIX9JnTA4b/MFygjEjbWCQMhyD/3dJQwJhpX365BLSteqR1jSWIgEwzKgrCsCrPLWMLCoS71VirVhTl2S0ssuMx2iaUdjDjsK5bGAaJ0+flsvlbKJtq3KVAYwNQUJsjAmKF8VD1+ft3aEwWPtOqgI1vtXM6FeSMipiYrvuF/nQmlAhkurRIcJyzIkgf2p6S6LR4aKwS7MY6JDR5bXCjU8kFw5EzSArxz6O28dq7tkGJRPlnKFUMxAAl6AABAAElEQVS6jsnKEm29vS31jlucz/+NtAy0OJ0ywfopXnlF/OKLI7HIlX01NE/uAhETVxIL38q6EGxZCCRTgd3bpJIhz/8dqb6Foz+bGD5cH9ot7Hq9r9GURWdgWERBgj20CHQJIIhQnjgWTu1CFpZk7d4y0KAMKfbK2QwmE46mfgDETKXD0sGFBZ2+HxgYAOI730aFMo5xliUKMG35hCwggt6CtoZHaSx7rApchrFaAI0IlY8yQvnUKFgqK7vlsJIOlUCNUbegyWFGx0HDxOpeibkaj02w+tHHCiP769VCPZygwlepiqYEfhDF7zd+4zdYBGa9ECXZh7/iObUaOMcGMI3pW9/6FpbGV77ylU21JOmm2ANHjlS+/o3aHT8qjZ0IZVpC174m/J5fDV98mZNyb1s9aaVUqzW6Jf2TxrQ2A9ZA5WnCFLEVCTOt55LXnuMKBexVGfzEtGVWzSljfcrqsYPOVq4m9vbu2//k862h4oW7O1ORCjYzdq/MoZrgN5lt+TR3C4Hq3frc2FxanJtBovLJW2TVYm2Unlkq5qdGR7g1ItPRfSw3zPp0S0umpbUNtBCLyhxVZru1GPaBqXFsY4RyMr37EoJSDj2rDEYFuVoNRBLJD/7WbyEnkZT6FImJEKWYONbNTIDyxMI0kQpvRGEZAsEjFaUCji2pmKkE+WQUy7YMCGYYI0mRjAhZhAjSxBaZLYone1mZDcH4RJkKFEuV0WPlgy86x0aZDI5dcvENl+9jmuGue/6FG4+gyQwl7472wxogy2soVSxu80KR+1i2IEAHHoA8/vjjiHvmIyEOe8gyWzaEYcpikhqbitEXhikXcJIDQfnGiPXFDcd3BwcHucJq7QYDAdeBw/DghZp/YZIzKjJOqiNHDHgWuFh5pmYM6FOwgceo+UmaKb48wrQfditQVv8VMNWC22DpSeUn3GCSrUUjd0Yyv0VtLfEmajQqGvAdd9yBsmIf02pCOBNBq16yhrg9T6fCTZ2CFPegUG9NmzXOBP/QZH6E+xc23qjOEBu/WGRZEWI0v+mmm9BHT/rGmZ1kCEC0bkkZpwq1ZybVaBByYjSJxcZD2qD8ik0qPhlJtU0iddVECwROLAbjVWcxDlxMJ/0TVBdBsWRwFo/7o6QIa9BFJdZEEjnhVRTNDjlFMFgLJMec7ofy0VxYSQY6nBbOVGk2RiIYq9Q77sBcQikoShqZoK8Xe5z8tbvDST5BMV8s5dq6dwCPxpPCjdl+UhSpb7UppQiELXuhKz79z/QEnEHRshc7jRPBsEhSqSc3iZwN1soDoNoMcC2nTOj7hLVMEJOEPO1XgBKQZzmfW5xkwTPAt1aBZI8fEbYT8Y7OHubfgVtKyTSWakNtq9dnp0/kFqd39O+VgkvRxMKtVcujx4/HotHOeK0aDHREyxiAUS4HYxSk0NDBbGbBVjw4qQjqA7jYn5SRME+sSgVKQLhzy0pxBE6wHQJaEbIDGQVKCLJLOSxLDrLmy1+4Ey0R3Un8+zr3oFNxUu25Z5678PyhTCZta7Mhs15l97NRltrAOq4H2iK3/XKJ3Svfvj3GrsKA3CpT3ndp4mMfdfp6o2GHEgln6hjri3mZlzcnNq3M+fL1CrZRV59++inTwYKh4Pz83N13341gt370gx/8AB0MGvKe1SQmio9fAOHuEtNt8BPFGIpSpLnJVRoUEdXLTFk8vtxDd0KtAg4QHMO3JzlaphbUanbjDRNlDPUMEFFwRRAPlH072cU+vR/MaQxglM/TXxJg3ha9q7EgPmsok9S5BfHjaaoNH/OkHlrrsccq1cXE6NP5XVdJ99+UY2aZPbNf+9rXfvM3f3NVVjdF7WWO7Nlw564amMx43etex9nXTb7LWvXEifJn/ip2773MUbL8GlvIhe65K/fi884nP+lcdmkgFJZud7JyIWhYb8QoOnVlVIZEJLknGshUhhjCIkejsLFk/oQ4APjswguX9qRuvAjRI9whhrncHzO4xJ2HunrMwrL6AzxZRpb1ZDGk5fp/OfoTcIq5hYnxEQSoZulMFfIcF8YaRRRGwxGWomdnuZmJjVXMZnJOQL7GLluFJLEn9r16UZAy4Y0HcE84GEiwExtxh0xnm1U46jw/h51cY0joPO9KDGmdqpRYbvlCLi6W2cgTuPa661hhrlZYZ8Z4Fm9nZ4cMoXqjACLUE+cVpgbJRvej1k+MjR49MoytnUojC2SALuSLO3bsqPBWFxeqTz0x8Sd/Vn/w4YjUk1Niwfr3f++a11w3PjP5zX/6FtOTzF/w7tjCTcXjwXpE1CKhsGB5rShY7LeZnJoKRcK5xSxjU3u6pTY/x4K7zFgEgxNzczDL1hccAMxRTFDSQg1ukarsriEXzlQTxLSGPeQglrPhM4T4lirJN+tsvGlMhSiHebNyDY4Fbhc5EEW9+Zc6NKZ6Wfl5BZS3qXM3BdevkMZV/fUxz1AsygdG3RkivpIszRW16ZQH7JUENw5BtqOBbertrCRuye29W+xpElyZxUoI/Q63Ev4KZJ0aoI3ZxCIG8DpojVFb9Crr8ZAzy0wzpotS94Y4b2BDYFiujNY68mKXLc5NLk6Pia1VrhYLRQZQhpjuHYMRd2lFTCbB1SRQNtIy4IsT289tmSgAIpOANZidEhKQD8Rsyg45lfZE2/2F9lFoC0meKA/Cg/jk+7Eh8ZIXG76ccqg6fUlk5sp4JYw9k6oVuAWkEEzJl+rZOlyQMV0WaHVcF67c/wqQ1UtyFy6JkTh0EgZuDUq+eMRstFivVMKO0eFH2FNMSQ8Dag/DJV5ZEpUaYK2YgNipUhn43UoDUgjWYgtlKWRY1m8ZgcHp3taRbmlTrqgPYUaekliITAaDuUh4T29YzFbJmrw4FZw7dPj+UjC0/YILnEg9MH6AjDv7dnZ19EiRxDTV9GqvitWqZQTIH/P7HK1WCLqKbO6j3gmKWWtBLQp++dP5E6sPKbE6ZcPzujCK5NZXqRiejNfbEuF0PCxQphhY+GDJgBsHdQobXQKdBOWBfoHtl37ve2vXv7r41DOBYqU+tCt00SWFjrZnDzxB2zOcm2++GTWGgf6uu+6CGvXi5RmwIQMgagm5EIUjiGFJWkPDIiUKY9V3vlnIRVPg+HDS+pRXVSoMYR0Z2EjBJ9XogRkUJDhkUgyBgDJGbKNu04h8yn6y4IAPepqpfCflaq2MSIhlvpZxzrUjJOS9cB8t1XVq9gJVQZNeGC/NHo6gmB99tL7zKhr/JhxMsqrE6b/PfOYzGMCbSPkK6mo1cC4NYFoD963de++9m13+pSDcOVD+8Y/D99yNvDEJLZKnGogdHS59+/bgrt2hzg4R3w2dvLH42hAF4Ht8/6b7j7ZfkeG+WzvTI0eGsXIHB/fodKSbQG4gDjmmaik/kGJTkuyJ0pFIyoePHzZXsQWjvKO1cMFebuE6ePBo3QklWzITEzNI7/ls++TEZHF+ZsfAUHvXtlA07pnMetrGcnPZXPnjQsgFJ/YyA6u/qg1Iui6M8OTPBgUttAvXyICc6WfIkQGGWdJ4YCEXCBZkxjTUc1FHLwYzy9EysfrwAw+yW9xJtU8XakN7r4inO2anpjikeHyEG32rXWlKNBmNxtumJ7P/5dOpnzzO4MlZoXKxEB4dnfk//qjz0396wxVXf+5zfz185DDmIjKLJw6Llz2uDACs3DIqIBYZM1j+Gh8b4wjkDddd3xOPl3/yk9n77g2zu7unJ/q61/UNDqR2bM/mcgholi9YjiYhFi82A8IOWcOAAWX2VDOAMYeKhkcUQWJpKgwqq4rCTbciqb8Aq9CQxfhvtFVWkloJ0dQvoweL8FQ76/mnUBWnkGTLa/Ys80CLRbCso8pseQGNIMWkP+K2ij7Lv2ya2Cpq69NBjPil2Oz7orZtGg6Vd7Np1+fq5yQWScUM4MDAQFPprI3R3tbhk4S23YYa5jQ+aivIbCZkVqiJ2jpEVkZx0S77Tm3/r5gF3vAlAxsjsthu6tSLXxdLg6EQXy7kUo9sjRuLglyyIfd9cIJJErlTzGLhMeoR5Mcb6OFUCJsTTAsx9gkOTqPdgOJq9uzBCvQ4xbdFZn+aa/lxNcSCJphqc2oipS8cSlaFTDX/unh5d6RFKELQmYs5sYrTxUq1UlZDDzCkJQv+qzkqrBKwzDUDpS06h1hNariyfU1TcduUklZ8I6IlFWoM4/IUBKpLLERKp8UgA5nyB0ko42wCQENkLRUFttPe4uzay3A/cvRIvVbpH9qjx2Kpzppc8iSpWVwVRuWuUKlDJxuNF7pa+3pkPl1i1L4tl2LOFTtGjh+/uKueSiW7utjpGmhrbUmloN3oCEnWvtNqJATPZrpToGVOm4mpNsIJcbRGhntaJsO9OZq07a1lkp3m6oHll96NLdTZxeesDtuVnAAtA1SRN77xjdQBqoJ9ARE4LZy51+0X7m2/6lqCfIfk2Mjo4f0HyNHsUibTYYlUjG4sD4AP3J54TPQR5EiXAdETzE72N8WYlWs8ND6hudbtdEQ1Yjb54YeikTXa1PqYTQkJwiTOSgSr6GmbpbCS5koINFmjZn8fp+rYl7cS4fQhK9leCdlgLuztP/pYOcCntQL1kf3FOqfIYypg/L50MkJk/bGPfextb3sbG9fZpn7KnJwsn5dF/Lk0gKngz372s+xHfetb37rpt5jPBQ88wSUEJQxBCDkBvjMkb4yJvf0/qecZU93dHQJczbGnH1OHGPqn7emnr56h/mP5I1XJCNFGkVfjSGDUA/wgOmwUUTRXPPHDkBDmJsBQNB2T+wAW0rKzuSUdDMzliO1qDXCJM58dHUou7Nm+Ld0SZoKY+QGWkWU1VZ/4dbVZb7pmp5PGYutSi7YQTeb8yfkcfSXucCecKS8wCGcatKc3pEi0fkGqXiJCRgEdlnXwsTjek9EgPnP+dYVicf8487AMWdGp+STfAuhr7Z1vkW8/TCzMTi9EBgKR/EMPV554lquOiuUiDHKEKBaTi8Dmvvy1zP/+h7e8+S1f+OLfRmPJvl39yXTL9MR4PMa3EuKdHR0jI8cKxXJfXy8L48eOHxsYHOrs4HR4LfvVL2c/89k4t+8yx889Fv/4tchHPpK57bZMXy8cs0FoZnaW6VsKyebvp59+hjnLgcGBPXvO+9Edd0xNTWazixyEnpmeQ1e65tprsa6T6dSDDz6QL/CRBHQpe4SwYNHnrF0xoMog4DnMbNt3RNtjJZlRgRh74mEzAqnIVNsAHLmOIG+DYZgtOh7s5fuLkkHnpSoY9jYtN15y1SYdlnkxERo4kR+NRaTHEbuYz6H5xhIxvlrZEGvdcRX1x++nDch4lyivRDCI9noXzevuy2ksC3kK6jLaDRg+a0JS/m/bZttl6RCWi5uXpvFYaCBwOt7R0ZGf/vSnHEawvrxZUnPzc8eGj11xxeW+6FxO4aScN9SfW1gIkKoBbhSF0knKLm1EnSYWvxs2qJe+AbhE0Pc1xAaQ3hgDuwf6tblpG1RStDGmDbLsOJDZWkB+ajcnpqvnsvmp6XnCJCtnS3M5dMBAV992Vv1k5FG4jMqa3poI8g+47NXVCGKUGRfJqgeLig8QcsxWcwKjjk0lfh14PBZloTjGHK1wVneS7fWednaQnTjybDyVae/mon4Z4eBDVh6lNxmmZOyKGkEgSnRWHyiGoiBIZvLLOK1ZCyuGJmO3yHCBC/2688ZkZG954du50BQ6mKT0nWUe2lvqvK210qJWqMWRsG97MNAHicYE6pd8wdLC4wGFGXQ6vbDD07U2BUfmo4UHKQgHbiVa+JVJeMFXuExSs0LOBmCNFGPVPuio+OZXG1wXUcGFgqZdoqZVoTwFAocqzCkEz9shW3wlJ3EwjBN+PI/8FpOpw4cm2sKyr0ooanw9Frp238WP5BfzC7MdramezrZoLIpBjkI1OzfLigIjAn41Wms927bFYzFaBit17EZzDVniKrWhoUGbjONAFhPNlsqe2LG2xMfHBWxvlzCn1YK2ZouoHLkyRRHiUk69k4/1TCxVTC/sZG+Ql19/tpHhHhkCxExcRvtIOBKUzW4BbobuHxwYOm+PlHy5w1ZkU+RymMsP+WJ4WxSc8JELjG1UCCBENSXZkiB6EfxjAJ8yNdSe09kod9J80cSYE2d9gnteYPUM1QNs8F7Ii1e5PksimKRhI+1MBlgjJxHLdA4HgLkADzFSno+NPpvbsS+pEsz6AsJFhZ28TO3NK3Iigg2JzP6zefbv/u7vVsS/AthEDZzkRW6C0uZR2VD65S9/+Q/+4A9OZVMEC77FUiwUQQzTuLhJAiKcvsDPJSkOa8EnczRlhBQtFTsEP23ahMjJ0p16PNtUmKgj0/X7j9d715BlHhjOk4kkvb2ru4s/2ELi94X7Lrv0MiwoiOBkgpSxy3PaLakuOph0SAaO4eFjxXzpggsvoLsB4o8pdP7UGObTfbIOrDazg6ksfrGZ5YkVjc3MIWdsbJRvgqJhqNaAHMYLQWxo+bH8dSzTAU8GNYaDVJKPlRdGhw/DSaVcZICaGJuAs5b2jnq2EEdiBp3cCwd5K2z/DgTDcpBZ1PwwWRWffiZTrQyed1H39vMWp2dKgURLsjvVnUgm4kcPvTAytcge6G19O8rBzDOHJgrF/PYLrrmg/7zaT/fn/upzraG40yJjFXwwi1D6my9Wh4bCr7luuhbee80NjKLMVXNl5cz07PhM1om0BIJJdoDv2DnQ08t1YtUdO3c8eP+D3V0dV1y+r7unmwkIEszNzjEEa3OScZihQuo3EGDOmAlgPFYL1BHKtBnAnH+z0VQR5QGcwRjGWB5hZwStEUeQJ5QZ5xhFWJp+7LHHDOjHMnjjZ9WFEdqA8uo1IWd78ECWZXCizBHrOz/3lR5wVgLPOYQ6tB1lFO3nk8OzXUW8pXrgwEh5riTdXf7c90aXE92fznj0WHAk25maTh8sWsMUHIwNRdSeqQ9VjF32lYgoyjiXnucDF4j9GfbSi5AtnEsJ3PUiQ9KnxsrDnOtzWVAB5HGvASEFjqAZ4xLrFgGYmRMuLfnxStcAavb6WTZHNIfH5oPDuUTLfLA4c/LRRBOL2PPd9Ex9dC6QnpFLbs01RHowBeljGUTwTUZTJDeZi8Bb07eylNKN9/JYxoTWSAN9eo/RFWoistXx4/0pxAVracQvQ4MVDQ/yDcjsTGX0RLBwmG8ASowIeyWVz6eey3bNHw9OyAwtzvJQAu4Dar2BNplwfO7p/V09vW2dPRB59Dh0pGhCjTh50WIvuSaxhgnK2OXWhCCJuew57trAJpI0ypGCTftUjVL5w6jrSQd2ZYyIEAID5jOLwUQ62NNLgyUDcXL9kXBjyeBGPBLlMeCawQJV84/MBYF/JKMXiNFIMtVhxVpWVsRolDVPSVUPdIfqfYn9f1JkXxQA35FpYnfllz7aWguzKCqnbpUy1KSf6XgOQZcmxLinY16uvWgPsVgo2Uq+xorSlBIJY+K0OOJ3i6lAe1BUwfHwlF8x1S2JxJhTiGG5TchwGGjwsPGhpJc1mmFJawHewZhery/Mz2NKyQCpDgTGI46YQpZxjTlNwIxijIxYMthLXAhM2pnZmYceeghkGvaRo4dfePH5F154ni2gjIZkdc899xpnPMkCUpzbT+iVnNAhlR+Lh9sDeOLICEyWImDAFD+oAQeIuctATL6+87ecYFxhdfhwG0wt1YA6Ib3CYaz69ir0G+Mx1GSz3GquCXM1FIGBdobmxKlMc5Y1fioNP6XeIG+W0J5n+swRL4XZcFtUwN+Y9db6sTLYhQRNKoTnelVRr48fzk49RUelE3ndism2UmD6RW6wBcarrz79ndrccNN+mbqTqFzw2kx47SuuKCPfQ+JjSNwI3XTB6taW9yVP7ZwZwDSgf/iHf0Avt4+DbbqiWQkcGuAy+JDuJUCuIPrDoXCVcWWgPxA/+dktlunIFOuXW4KxGM+QHGks16qzeo0Ia/mpK/gklu7X2OWsCD4ED6VgggpMH9hI0xW/NiAyT7+4+MxTz/R6n8Y12SxCWeSyp0QA9UdAoUWAP6CG7nZtRCM+7FN5qqkshrF+TUo9SM9AGV2Gm67LtSPDI909PZForFCrzIh5y8w+l0LwW0Ao8KkTtA2VD6woY7xxmLgWlPlTLkqM5rJZTkGV01EUDAatdEvL5NjIzs7u/j0XTo6PyNEbDsDEYomgnPidmRxnzbZj2/ZwvRIu5Ra/991UUBJy8ST3YcNrmKvMcoXynfcGL79qpN42U2xnyFycnUq3dkfa+na9KlGOx6ZTXS8c2b9j4FVtLW06JVd3euaiu/rHwv1jM05wthbvf3UoeCRSr+0eGpLysxwXDDw3WUbGZfovTe8oYU4zAc28Ard5pFKJ6VwFlSnOxxyCUYztXDY3PTHRt2M794IcOTrc28027RgWL5ww5PDezWPvkXlr9iPht/dLdngwgIEQxYKVodmTWFMU2GH1wx/+0ICWkGGMS6QQnWRhe6hsOLcnSoltqaJroGQgbW3Y48kMOgoBiz9QI6oxFTxjlJIpnMC2nwQPFNA2iLKjShYFhZWt1EpkrDYy3AhhfzvBVwxgt07E4qm+OBuYkUtzpO/SAk3fFb8q9MPz4blKy5FsLMKHhwXJTerhA2jUfgVBKSiyh2sBMwlcvxdFNk00TUaofeEheZnarxfSWDdAIpw8FSAP9fgQN5YfnEb7OMh9eFB0i173afWzLkpgZjZ6vJSJzMWmuSrwZE5ZlwfyZ+L4ML4KNxHk6id+MgwsnmptwwaAR8GQhzKgwsKDCLTBKdZStVrQTSxofsW4aTx8L4pfLw0+cqJuPONOAG6yBiyFqV21LF7ISN1qCuTVkWeepr2Bx90Uxx56gnXedKaze/suY6lajY5VWnLz4cqMJCBbTSfcmsfNBex6/fhceDESSokyKIgg6H8pmyYWhVs98otHr0UG09aWLcqlbGnLFdAkhWYrMK0maxfipdrTkdqVvWI9InwAiSdQ35veyViQSIhd5ALVIydGtQQKVwryEDqC5zrJ0UL6VAZcgGEYxMPSpkoAVl98ukRNKtISDku3+RNcyRSIt8gsrSYzHEVseJi0zJYLEyPHOjPpKLOxzN4yH81gI7ulxMBjzGXGEDnvO2wZbLxYTC5ZZO8u4wtDDakMgW1T7e0d1MmTTz4xPT0bi7JBU6OqNYzDwcFBUrFMysQu+KSyhGgdV199NVF8lIHl0MbaQVDbV/34YCGxqHygwRtPrFzuZwIZIKkgBStwiz3MaIIxzBDP2IGfJ3upGLBw/oInOgFGqY0vNkjxtI2+0CTTxij8ODLFrfzWjsF56r0kfsj1WHHItznCCzeW14MtS9sE3MIglUbtcdcUc+tomFtIGZocrLWi4cGRFxc6bHYtd53K2UJu7aKvLSS4KikrC/XAZj2UHAz71UunUqRrZ8vogfknvlkNFVE7pYOLGMYgNqEou12CE0864z9zx2uTXS1DhRv+11SInY5rOzLlADAG8Be+8IWPf/zjq/OwdvJXYvwaOJcG8Oc+9zk2P2N8+txs3BNMpCLXXZ+//bvJY8MMiDKK6aMcTQTfchsfT4PUOs3Cj/I96+NvnLG1MJHs5OWL4LXQ1oJ/4hOf4CTJnXfe6SM0cg6QPon122Qh+8ieRzoVfRNm6MDITe7OYhhjXhZqyLVG0bbU/5Z8ktojZb+uGiADi8PxpqZY4UpYk7ku1x07NnzkxP7Otr3nD3A7dOr6PZeCMTIyOjU9e/HFFzEKYzAfZzlrbiEQ6oxcsa/yD1/CXMVy4oCWDOsV7pOspa/+JVb8J8an4rFIJp2+7LIrdg1cUKsW9z/6QDLVsqt/z8TosXxO5tW4OJE7JNtbM5FaIHd8jIu59ErHoDehQPt3nNETTqXKNYy0Ir7+PHb8WDyeDicjO/migxy6rgwfOZTu6EpluigJ7HVs7093bM9WmPDW2qwHZhnTa04i5654uSqNlJjTW2JcSMUwLxkOzJQDx8a0KurbAy3bAc/XZyacUFv7ZVwLVtnWU0yFdvV39/cPKFLzgykPTFNeGRt+sEUZ7PFbS2CaGRWEIE40GnU2ItLqBgYGDOI/LYoXhNKAXYojiiBPqxz8NBK0EDx+YwOZNsbFY8y2miHqs8hggK5DkFvEmMLHY6lIjtbCZ7SAcEclmhNB/HAFAloF9yQRJC+2MAABblHkxbo3EJbEoWlRPFGPwDdFB1YPHTpk+D4CVURLJl/49DMyHDQqM8Wx0onyk+AhOxxAaoAnDqA9rQYI4tGY5sfKKEvbjHdGwnI33tHZ+pycPfBaJNNJ0uiU/4CTD3XUWtLHF+mGwr/AZQyWWHPWjSXOS6UewWhAkliZohKQ0rEEAvacEfIscE3sZeRRB1XEgZvWOLHkQncpO4V5FpQGtDzanbzkHraXB2GXrptAgh6Sgjb+WFiIjJYztfno5EYpSEZshhk9pk1LZ7yYl6JLpTKRtlqnl7XPj+cxlrVgSzYqkRRe0jSIEwU0lnCpuIhFy8CPlsQSkO5r+pcguBaieD3nZWC5aXqPCL8Siw0kT/pGaD7UjRSslrjPMZtq7UD6FCqp7JzGSv9wai07c5HI8Jyg2/qtm4/kLN2CCU8pB1f+hrqKpXh0TlnSDqMSlYKQ1PIjaYPfZYIoCAlVJSMYOLLW1U+tBjANqtUCGr8A2JSULdY6saokhZIQUsH2eEYADa6hy8twadQAmhNJEWYHK19kyJlE9WLE7DRLCfHIzCNwX64CR/Qxuzw+Mc6Yi6B54V4Ept0WRoeUdWNhoR6ul0MPf+tw/MIJ6oKs7KQf0gyx71OzHDloSgok5Pe+9z0kM0CTPHhuvvlmhKQuk95D0ApHLH5mP5GR+J944kkpneeAYG12dHSCPjx8/Kmnn+7fvZsSAceZ3AYXWWoLp3jM+cuktr8Jnr0Y2b5k5Fk6owa4ypEo1uukDr3dqkQx64pCQi6ssvIkycGDB9k8zLh24403EmTgoLaxtCmmEYQH27dsQXtaWp6ntmXXkjcS/Pn3s9GM70FSmQMDA1vILe/ULnlmvpvxlHdHyyGXLcxiC0nZi7PeQQM70++Rrk3PWot/ab4oMpH65W9P911Yvv9zi/kxDvchn0ymywDodnZjFBGIAAhW9/yr2tW/2h6KgqCiYK0MAgG25b/rXe/ig8BcCn2mC7s2F7/wMefGAKYj/fjHP2af56c+9alTfHk0n/7d0X/38cJn/yr6/HPxRJw9S7n2zuBvvS98zVUct3dHyJO9IPozstg2wJwM97TimeBkyytXFJyaXKa/MTysz4Gt2q2PQyyVj82AKcLIxBCC4cHIyqByySWX2OrxSSlsEsF0FUtU51wcfXvnzh3S/62P63cIMulERBcBWOPuaWupstRbzGeuvKJ+y5tC/3J3bmExGmbQZZNXqtTRlXj/e4erlWeffOTiwe3pULk4/tw37/omx4EWF7Pcszj83CzT20612N3WAqRezlWKC2wni3W01yuFOud3I6FqKcs6a0Q2pwTLbRnUtlp+ri3WkqsWk9FqOlKJcIAa3UU0OObLu2LyiQLZ3olmNXDehXLAKlDPZecnRkcpRbnEtfj1Qy88TdF6erYnW1q1cBDXuT5PlJGWsBdiPRjLhSyqNSeMlxsqOzq7Wvk0OpmscfyD5oqDMXoNr6+xLaGm2I6AlW+HqJUHikCDCNRuuummxiQQ94NEmXrnPyGFSQwC14NhM0sVec7vRGZ/AvZjffWIcRQNCTvZEoEAQcsOPYxGbqmA4xjGzEPLZ90bv88YHjjnySCEUoiHsoBgT4xtMoLUI488Yqn8tHRA2471wAMPWEF8miwm4Ag+/PDD2NiQwhllKnbfvn34+eQg/cXGV0PgFVjdotjBpCUxBIpGKiBQY0bA8O0JAhXIEzucKDyWkcXCIf2R6oWgRfkI6KBWY8zHGzIJ8/XgYj7G9QA42iWzWqawW0ujySUSfOU0ScOSJkeDluqSDDVTUWfx87ZMyxfzg3/aP9XYlXpfqnvIqR0jtS3N2Xsp/Ao9Q/SAGqs4PkS7kMC1+wu44T8UNAaoguXX6KpHXqNk4jrzCkhILDm44ipd5U9mhypMbZXF7JJiSpw6y8cLLQEpFdfwAl8sOvlqaKHo1PNmsYGjteIz7DOrFSHRwmuwZcdefrhheH5iZMeei2iUsDifJ1LoIGr8TN19w25CK7zScAvpFRDSWluWhYa8B7/m/IKBKuhWVbxnyc2rIZeum6Txx4+hiJLE51GQsCxtoZKp5nS37ADMzk1lc4vprj52d0gky7MizXD1lpakBivSHLl1lwNKsjJLK5CGwD8yQI6z4tq9vZdPsJvwFK4lVvp1XG+4QMNkektTCGFSY1Ql+RofN2AWi/lCCZCQ1XS85taM7Gwm0cLColJSk5eqrwc6u+RTrVCb5c6t6eI9hdEIu3I0ObYlgsu3EhXmPq655hoMAAJsnzFZh5/scPTrwcFBPOzOZSrQhwPBkLObRJjO40wKUWQNHA9ixGxIOi/WXbAUn3uhmy1Pclo5nn/Ve2OTh0sv3BmWk7kBZ/yAE2+bYAE6FnclJAKNgkONDis1Gwxi+XBxIxDGcaIoIHKPdVqiDEimRMESEAOax9+Ua6a1AQ3Bt1fZS4yspixYPlAzR0Fwq44mFsU6JM78TU8yxZkaQz1A0Ecga9iGf1oRlUO1IwApIwg+mn+Xng/xPT6dl6eHdsXAxGDB8vUW1gkLKjiqlLOKvLgzoxxu5RuDTw6I0ZBo8FtJ16NFb7I+CEDEnc5/UeG0VZyH1fQb6r7I+eVPtj3wxfmRB2R9fqnRK6JN9tHho62lV/+b+M6rkjrGuHKmiVZjkHw/9KEPfelLX7rvvvvY7LCF770xl5e8/9wYwFQrp7cRlExSnuqbY/EoEb766vAf/XH56QOV0fEA3wG+5NLwrp2MhCI2N/bqkLy2bLUx9FPEoqugH6Ps+qPLRgiRCm37jjvuABn7mVnev//7v8fPd32YGW2isKlqZJcRIyXTw4yOcIXFgolyJuqhiat8vsANEwyryeSyr0R2d3dxnUXlhRfrJ04EwpFkb++eXTtCyVRpIZH4nd+t9e0IfPP/q2WzObZ4XXF15kP/y+yegS/9zd8igy64YIDrndmrgw7f3dWZaZFv/xYK+Uw6tWvHAIVCIIKWX5yrhKPpm66vPvIQH5USk7Ne5cRklRE3WAi9+pc4kTx64AG+nDw2OnJ+X1/lxE9buzs5I71jB9eVOclLuFcrkG6ZQZkBB50KM6JWd+bmA6Ocja5zJYwE+V4Uc+ndbbVkpjrBddzFci/HhmVDmqzR1fgMJGazGtUkYZn2mWee5OQzog5ODj37BDpWpqO984KhFULSfdWkRVEDjTBP2gOqCX5TUPA01babbG34qkkaiTRuqUL0M83MEzZISJ3zZFykF3Paiqdv5TJSNg2WKD3ogqRF78G6IxVpmcKk+RkP0GTcwhHEWRY86Z4EwXzzm99sWVNwvqkAk2YH0ozZzwamxdrT6IPDfi0/yjw2okMThqlMgAbnaUvKRJnHh+PxrXSrHBv8gOP8Ho05bUvTAK0I9nbwoyJj5QInucWi29naBWsaKMrg+FF42BRDzcOeXR9qUfaksMYMVrqfaqEWOzg71Nm1jdkfXs+x46NlTuojAV1WOF7e1sqmGOTJiXFVQKVWzaXSaQ6045+cnMZyaJSbZNTb14NRwnYMdAsxNFyHwh3avqMPW2Yxm52elh3prsSVmRtnx/Ze6GDATExOqu2oVFUi927rRrHlkyCjYxOUSOkJWUwXxAJ328DkmJoNPkmiWzOt1DM2GDXM5gbQzcLBk04lxbYPsGN5DuPHKErRndpgV/Sm89N9KYcTEIWKc2Shev+LcyNajkg00q4H9bn+HQEoNrTnmGfr7GxriVeGWmuBzhaObhxdmBqf41YJwenhBlo5sFGemWHhUviGYftFRvBa2UcxNcn233p+YYqqCWemsMHb21o54ABP4xNTbv8hDXNv2oMSSbmQb2Jqhs0mYnoLPXEYZmm5LMextUSFaVydIz4J3iioXHnIGQqgVgA8ZIQkJMjbnJ1fcMnpbABGU992MV9JYm1V36mUAQK7mJQMBrk9YfTEGGXUYoErOe7YsZ0vhLJ9Znj4OEFu/ykszC5Oj+aDrdt6e5leAe3IkePIOWKNGhQ6OztaM9LTR46PlbmiUsxkyQhIW2umo5PFT2diaoouAMSYoPFQ5HiPXBvOILUwv6CplAlKzc6clhTNYKFQnJ2eEs4koVQZBnVXB5cCIIqr2YUFONTMxPCnkmPhTtolMoLv1i8UqxMz2VSoJBcM69fghUiDMzsQgBlgxNIyESxA/Ch/sg+RRT8Fx4+yC5OADAwMMLBaKov1BcLQ0CB9/Nij5YUyOx3rycH5wTdmB167e+jGRN/e/ENfKFdno5Wp9je99l/FWpGQUmM4lklx5rcnktCEGL0DrYC8qKLBwcFGHGQsc22NkEZ/E0GikBnM1JstSlrMUZhHFCAwLSG5NFJo9K8VBU1koO3HsdcN5ySkfkxW46eSeTJ8o/Ogivg13EgfP+XF2IClJvjLNmgvndkBWyFfq942Wz/+q7QxxII+cLPUzg4+vYDWxUQABjBsnwluUbrQdnzKTD+REfMO/uxMY0kVDeHkRNPBGz/c9vzexce/VK3nl21TR3wharsvK7/mQ+lUF6s/ItD8Lt9IrckPcSb6KSmWFAZwU+wrwQ3WwLkxgFEW+Y7zhz/8YdOAN8hrE5o0nUSsvmcgtnt7nfGV7TSy8AvWmgK6icJZCzKWo0GaXrupTFkk/93f/V1LQk8z/3/4D/9hpQG8EbJQQO9h8xVSkjlsBjlWzxiwX/WqV6Fwn4VBhdEUHvyFStFgkA/YOaNj5X/+duWb/xwaO8FCaLV/V/DX3+Pc+LrpSi2USWU++IHMW99cm56NJOLl1rb5ROr2O374zNPPsHUWUgzMtCIMDIrDaIoEpCBAUAgYLJkjZ4xnnvvE3OwlN9yQO/BE+ZvfZp8ijadULYWwQ3/51uR11zGHXylxNFekD3d1HDjwBFvL0CA5bmwq1NzkxNzkJAzvu3xfxPtgfXu8daBHpkiZzkdDajyFEl0o5mq5i7r9cdraJAREuolyVnd2R3rRhvn+5OzsHEvWjzz66J6dF1+yTa8oXfE6MbpwFIf9SMg+nmRqB6iYj7d+ZAgYmWItrK2mrKC9UQDVSzGpZ7QZU1xQ6Xh9dGdTECFk+QLEUha6bFsoFUO1WjISnskuYPbTEegOIGBn+vOmTdyuDPomKAl5sz7HKKn+2EOUD8cDkcHBwUZIo3/l3jk/lq0Qvr/Jw3oIrgloQYYiawDGRiMztFJbSgLoO+tuLCDTjH0gpPCjxeKhyOwA96PM488y2Iq0AmtHFoOdB9v4eKkZVy2tGXbACyHljOkeqpwAU0WpVFIsMTHDiRQjh0MEsoAnHrZ0ki8KuVuNomhqK+LmgXgc+41/RpFPf7PuJ/4wV+/yWXDpx/KQbZwsFwY57iE3EnLYiTjZRSFPQZHvcIodgaWEqQhE5rvJHwyOK/LtNNCqZTZze/SEIXauVvkIOcm5LQ/jU4iJE1briRBfLCdQLecrRXePDFNbNwyl33x+qC07Un3sUGViMtPd2TcwdNHlHf/8/OJDBwtBbKKQXmpKZvLJACUoN/qFY/H6ree3nZesxnOFmlw+HyoOJJ+eD9y+f2q6QF6yllgt10p59jkvGc74gk4ba/CYWcUiHxegSPUQ19cXFilfsJ7CrqZs5WJWrjzQ4rkdtB6LcDEB/FfyXGFgjFjhAgmqVqqIGuHj6nhEPmmFOHXOewrPTOQhunT9XuhJXQWQoPJ65Y5fucVXCGiH4vpfDmFINXKoORqW73AYNaqepDhNG0wwDWE5KZQH8XJuLYh1GpcYvkibTLR3dYajSWpEmxPTRikrVzGfR/C2ZFqiUbkRmlxaMmnkA6nITnN0WOPlrAvhTDqTTMjH8zQr+JHFTK6zwqXTmXhCti3gyEOewWBFNs0EEnxTVPqIFkt/4EnafCCQiCd29++SvIRNMYAlpd5DE4smhgaGqJNfunyov5X2u7SZk6b+hje8AcSVDiJ0w5Vwg9j9C6vGrrQtfTRqD8n55HPz3Edx/ltrV72tx4kJvyDselWqY7B8/+cXJp6KjjxV3HO9bD+2hL7Hgrxj2y2MNGYSbWhoyOQGsU2Yhr/xJ2Oo2dXQsW0yjCwbT74qJhOXzDQRZaTMb5O5AFcyvBICGkMeGst1113nzzKsmtfLEMgQwxQz+hVNYsuLz9DmTwZtOfEtJEgvlnmlY8eYMTwTyzmwimZlM0FkQUtG1wLoD8prlEUkUSBUOf/G5PCDi+NPiZRrxGRj4WXviKY6WUMGviyqEW2lH3n4vve978/+7M/+4i/+wp9IWon2CmSdGjgHBjCC+/bbb0eD5+WtKubWYdeP8hLq4BCNOVF3m5CP8HPlYfmXUtNzPLY3yh1DLzMFYPO9KCZNP/3pT+NvNLQ2SsjDY2CzsQdO6MAEeeI/CwKOGkBAw4hpBoyyZIrhXZ+cLn7jq9H/+WW0MLa2sdQQPXK09Om/YKGz/cbXvzgxvRBLOFy0snP3+Nj4waPDM9PTzz/7HLtVEfpMyDFCY5JBnOGfIB7mkhGFTNQxnYwHIFthR8fH/ug//ceOj/xvC4ODxTvvrE9M1FHgbr01cfMbCunUgf37WX3KLmZJwtomXCFJ9+5to36oHGoeSYfBJlKvUomGlw47kx25i3qnjqAN6ngAyLqHbuqzrYC+dEPIEb1zu5x+h+b87HQ0HOjuyAzu2h6TO/xXEYIYjbDB8GbSloV0FjNtN6+ZUpq/HJQCATFtwS18wtXAwAAEYRiVi1lPCk7jMQ2JegMIjs5AS9nrnME+eqj48CPO8DEnGQ9ecUXvxZek+gfyhTyXgoLg27T4N+isVletHyg0wZuCjVmsE9WIttK/TsLGt9CUEJ0e1wS0IO3Tlj5WxtI7aIQr4UBgY3Bw0I9aOFFKTooNY6crvbFQWpG1z7n5Rdnxib1h388Q60jICAWZjREvUZmMHlkXuAItVqLSrS1LX99dmJnKzs6EnW5NlWTLq1DANaQjEE7E07sxRYjwqLm5IW0iQ/27NY0br6y4NM4f3CVpXL4EiKlkhkz/LrnhTyD2I3ma4eD2JgOf315507Z87MD+2c/8j+qzz4eZd+H17L2w7bc/9M7Lrmxp635hjmkmybO9g9upsISFHjURD1VuPT+0uzBWv/uR4g/vrp4YC3S0xW+47tobX9f/xp23P+/Ml+U9JpOJoT0Dkop0yjrU+COERDtvcFCA0liNa0JiwRE9NNgPFFQrHUjaoiT3/p1aNL8iACltFjx39co9yThZx5VcNaQ/vV0d9e4O8bpZCZrcIii3xLV2tLd5bEhC4U9R02l32VywXQez4GC0h3ds3wYMRC8GuEQxp7Gtp1vLIZECUn6sMJ1cgKd8Tc/OFMplvg8XjcmRVBBl14xrhLtk4YQIZgKi8YSsjEtztMqkGXPZldAPMhAw9SJMSBBKCFnuLtSLFrgVUTCFC4mUX/b9iyPIXATYBoeOHFlmg46EScPPVC4wgAGsfUDTSIcyz8rnqUWtpONDpE6cQCVbmz6Rv/ETbX2XcBaXkrnWOHZ5qif0pn/f/uR35od/UtpzPUvoq/O2krGVED/TTXlschA9gQUulA1/4XpTRJqQmenDAWQU47mWncagcOWVV64lSxnaGHGw9JCZW1XYJj5/QYPoVFQIi+fMg2xtzUDNn1hZp3JQA9AB0Em2Nvd1clwZRdbsyMAAxp0JAxj6aIZ2Ltomicy/kpMGiFcf9XBhvjT2AhJzWXdGfjFLfPSxyrYLkV8IgWWxDXRW8UL6ve997yc/+cl/+qd/Oh1jahXSLxvQ6mrZmS4+O9e5huF0DLkzzeEp0Gdgk+sx9aQXM+Y6/hKSGxlZuEOyb6C3LMuW9o2gZykSwl//+teRbsxSe/1pGebGA6jUvX29dEIu+GVFaPv23l27dq+lnW+c7EYwKQVDKbuezDxjIOQwKOxUjh1yvvFNlk5EtRJ5IMRi5WLuq1+NX33tJXsvZnX3wIEnr7ryqjnOBRfyyHoGQg5zUjnf/va3MYMR+lQO9FldRPtHCtvqItUOhBwZMhfn52//5++/+ZZbUu9+V8fb3lrPFwLJRKStnQ8onxgbJwtsXYggxFkkZ4L5rrvuQpiSBdtoqXPGAMgyA91kXpIpNQm+CVyMQK5rgn97TUThhw48SKkaXNN7xHBl1MfMboI3pBAvbNjyKdoJ+oQZnyzpW+4w4+Obf31qPvJGPD4pPOZnpxzlJS1BKhAPDYkPn7Kwx03Q5Ucfqv7FZyNHD3GvGWtBla9/o/S2t6be82upHTsJWqqN5NuEw1WilJplWEzHpqiXWNAqea1C+bGstedKjuxKaBw6dZhVgzhYKcvSHwu8UbEm7A8zAENCE2iTsU5nBMwQUXvBAMbCUttix34QQefuQlxuOqg9QjKhyn93tF+iYxk1GGuK6SbwyrrUij2IbeiWkJEyDPG7uEoXzgFFnOrVfU7i0IsT/+efRMbHwwJjpa0eePKZmT/6L51/9n9dtfuigzOsI2MDK4teJhC7vM/ZWZws/sNXcl/4CufypRK4g+2hx2L7n9z2Ox99za6+7z3P8Qkh2JDIvMoIYGPILaHWosSb/eUmknkK14mlRhaaSnyW3KcuiBYwspJKwppIMpP/ms5LKhg4S2Rev6l4jEtkAwIBlwS/mo889NVpSHG1gkmk6fz310AFgSMZMREZjbA9ADM16t2e7drJyo08NKxZuhlr9RgtyZH/lrH6wZE9AjCpNqK9ZON5GSYXXAm+S8Z+ZN+y7ionKKWnPgnOyJ5xa+akONtOWKoHC6XCrX/QnWgTlZfl1aeeegLdmlld5TPkRAL73pGZPJLz5g3WYxLVgrn1rZKH1lr8oYSgQdbj4GRxPgWfLCl8YGNqgGvNxRPFcEwso/k6+3caqb1M/NSMTdnTBtxueNZLjhKCDnNqOxO3kFm2g6EjofixjQuFatU2djrZGcGTNuPlWajwCtSO7i8GShhcCGwRoE68XMtH1U6oDj9evvrX2Li4PN0GQuiWXBGHPYUBvAH0V1Caa2DzVd5MYdNh1n65gelP//RPt7x1bpqVrU7AhtZK7q7AwneqpUP1YCQQ3xdqecfUws58oTA0OLjWvOZGuED0r7OlaiMUDIe+x545UVScEB/b4RtIjES+tmFag2BKn9041Q1h8rpt36aPXSqV+UBt5dkXQ/PzhUpFMmSXIOuKunDqHDpamxwPuitFCAz5/vtll+3D2ONENKdMkXHcAo9RzaCI6cXhRlZ9sWNZhMRqRS1AGmKhYVWiH8Qn4iMjx3+6/6fgd3R2Tk9NcXptaFtvRs5Ct06Mj2P9kgTRiUyBGmMJq6xIOhx+e3f4feZ9D0DsbRwFhIK9JpadWQrmrYGmi6I+erMHu5rhHHubcq1UYlhbZrwnC7s7hCV0DGzQMALJ0Tik+KzKYupDGkwqBwfD1PaZ6GJMAdieKKx6LH+m5Kk0/Aw8ZCc1xJTP8NHqf/3LxPGjfFIZx8JLpFJlmqPYsy3+znfW2bLRXA0bDZMXZef9bjTBSx/PyZaX1rzkjdPJ5S3UR448k0p3BGJcSlQNhrzLlzSOWnF34mr9uM3aa92SGrgKBq/JY01Xc4vzvLhCNsemhzn5FJbDx8zC+s054JpaHmKxuEGkiII1F//h0TSAtAVJQBIPVxuSFzAsIQhE8fTh5eCyaVhg9Gaq7eVS9rs/DHPM2JNiFIV/wdHx3Pd/tO23+juTseMcjxWC/LkuHq5cxJfOHtqf++LXonKNOgdr9CYnJ1T+/p3lSy8bfPvb2uOxsRxGy1IqLzW/AF3OGoCe1+feSspzFXQl65uX0pvFTpBXupShSG/JTCAGlbABJaKZN2IFQfPHRxLBXaKnafThUvYAikINak4uFUsn9NyiCjlLIExKSHa7cFs/H6+DeS2L2a/WEmDDWJVXoyvjWkS3gC4pjwNh2vKSNMKIhLzaEBAF0Uw1Qlq0OVIp17pabA1EkYkFZa7AU2r1XDnybmm3LdzCBLKabUdIdTWAXc7Ycd7VbxdEn4RNRiv/GMhJUDcTDT+MZRDfTKKT49pc7cnxVsOAGUYcDC2UBEy+1VBevjDOeb18C++VnK6E/oYeSCNZuergYW3Br903tnEhwjLv8KPsB+QgTCiUyl/93tD2izMPfn5x7AnkUDg/Fp0+WugaWtq4p2LN5XOdXCjvr//6r3/0ox/lgP2WGAhbUDW/UCS2WLqdtOy8V9brUZrf+c53rvNeT0rnTCPAJw4ON8ak3OVbL79QHv1oJHcvU7rutHP2n6tT/7Ut88Ebrv930bjYQqfs/viP//iU0zYmZJvFg4fLfFthutA+NpN8+NCLTq26e895zJWxpwwlWY5GMX/vBHa0BOLucVTTNE5LZaAyjQ1mCs1Uow2MjY92tKSCss24CgOKoHWu6gy7iWtypadcwIOViESz8Q/7BwccCEYXa7ZIAexD1oEZF9maglkIBKMXW5c1WIxDbGCWnTFEofDggw9u693W1dk1ODgQpqhis9UZTbFduWGIlUzkJlcE8wVdkuj75zaXTpaFmbgBE3MUgrCKcYuxjQccCgUDeEjLijRA6MBk011QwFc6zFcwqQ1yWRkLBMscZRwP9G11GtMXVQm/rU7DFVVBkcGBSYpJcUBeldrpA1GMcNCxLW1UAkapTABkuHxOHMZ66bHHo8ePoWXaW5erh2V/T9D5wQ+cW97E7kxPHdUEG35QD8xokK8VdsPpXtKITmAmH+CeXSskqr9MKuvLr1UczJCqw5ZSlnuDdWYj1ERwexoJPFtC0rrGopmK2nrM3hBbQZLVK+W5Udb55ag84bkTh3m9qbbuTHyXZS1kNZ29dfNKSvW5Jo6iugD88n3sSjAst0CBiNgRPsQhByRPL2gwgQvPAhcHKn+uRSVxcuS4M+XEStmFZ7iqhPJaVi66WGbPPhcq57mF7wSnJQzsPfvSgdbiQuHeByOc8dVtqcwVijxkkZ2P/fzLvelb3rC9LTaeF5HhJXJ/3YVHj7GlWK0L0OHDCgbLxraGjY7E4xMUA3ioPo52ZymrH68FcykZ3EtEHQiaSQBJo//cWJIpCf2RHTdLDjkitauEfahX1yqANKVxIBkIaUnAQxIaYUBUWJAPrdfk/Ic6iZOsLKkH1cwEKB5rtIYAZoPTDKzcWpkay0PK5y6sK2k5wqxMKUsSS7w0I7myvzHn4DyfZqc/CDvLs2rI9Qx7l+ULrwhPhi3mTE20akG0tBvgw170BhA3h8IgYmPZ5pKdDNsmhU+ZZ0ZJhmCGuVfU/caatvqUDixdTZSoxtgz52f0Z7Yd+nhovezPws8AzYzMWeOhqXQDAwO0DWtmTVFbFaRoGy8dZ48QRKVsbfQZVgKc1j35Gz6czuxg+qb++n+feeq7iwe+UXZKkaOPlrqG2BknQswc+w3Rb9fXIWHjV37lVz72sY9xUpLnxrnyMnm5/55tA5j6/upXv/r617/+jE7PnP5b5RQB1tTg4OAGSKnQKR+pHHlbtPICgqdajlUm2cXE113LTjgfnv/vqdR4tP2vN0BqdZQta9Z0RG41ZRYqEGrp7EV9yFam+T6HMy/dTkxQvdtGdmU59fZkICKtQyQp6dwNk6szeHIopguWJxuw6dJYbpYgl81n88W2PYOBWEssLHfbYApio0Zk1tnJ9vaFu7rYKkZaUmFhYtpRFWp/yadoEbgIO0ixLsop65tuugnKxBJFEOsXs5BRnCBpWT3GDAbOXDtzhO94xztIiBU9MjKKB4OKmWlUEOxnyAKBDpniYTgB4tu6+M0c4YTjDgAAQABJREFUJS/bAAwC4zGM4ZD7GLSkMkcU5ivjwTqLwOBYhaxqAEPTbl1i5zNbjm1jD9xSNLLQPOUF2e56rGg4gYfTmWV3Wd/AD7k3YlmQ4gRKFT4iWcrlfATqn04SCUVCJybree5f1Wbl6tZqv4guuoxaI2XzQ5kKp4xmeK9EeHlCqDkutsXkqIkBQMflItxcmeuguDtJPjGTW1zMZTmeHeilg3k6tVfXnnlDM/eMCZFm0uoba1MCbBkJd+wcwssNwIXsXEfvbsxCOquk9Z3nVatFUqn8UKi8XovmKQH+L0yPFXILPf0XKEPWAJZIkKfbKgym8WqzueQ9g0hhwgNmFH4OfGK2yilf1SaMrOQNw8ABE8EhUi8nSYlLchCfmabpOc1UspDqkT4mc0u12flwmbvcgtwMptVsidxnA4NuIRuiXfY8c1Nwvaxd3hSCXzG9OJeCvbPGRJK8CakxGj/lNMoC93yaRB8uZbJrDLrxDBFrOBCEln5a12VBwrQVfpYohcLcylVi5kXIAKbj6ut1g8aOBDQNRZbS2DeULGtMU+G/AdElr2Bj01sO9rNVYQMnyo1MfOgkNJlAU7M3clLDU1kOBYeZ/VQmzs1D2V3Kmhle7DrGI0S3H+V7lvDOlu8MZX36ZG2UZMREgTx9an510ka1mUJyWbPzEX7+PfCPLYri+prXvAYl6uwwTKasQJAXSpHvZ4w+O7mvmguKH46oxlcJb6hwVEujerZq8i0HIsgYio/tzwdLoQveUr3yXW0h/a42kottWZfcltp2Yene/5Ed/nHwyncHnYb3hgGykfeIOsQ3MrCqMIC3nPmXPMGG+j4rZcXGuOeee/7yL/+ysXWelZw3lwmNzwyh9ZPRr1geCDiV8vgnopUX2d5QejQT+Ep3eDJR4erS8xbC759wdmXD2X+qzN0Yaf+IaRDr0zyDsegZfIonUOOLgdgS9EsU5LHZ6vTBaTSHaDIdYm+qzFfh+GJoIB2RrZWi7wWd3Zn69oysl65kjyrgSCp9lUXUVRFIwqopgxbz3IODg8zPsUeFrbyYhSxKObt35W+8Pvi9H0QjUVGXRGVxihy1e/tbg9t6u1sz+VzBdohx5xP0MQUZBTGVsS0pBfYeH8FjEZI5MAwtXhxXd8AMr4/BgFiOmmMfHjp0CB5Y1AWCnvHoo49CytCwJ3EITdKiiIDD3Z5A/JJiMOMIPv7440zIYUjjR8rjKDsfB2I/DEUjyHdrWIhGJDF3ThRZYJxTLesYwJDiRhAmLBtVHz9rKJCcoIlCzHLzwK1v5YJj+L6HYKPfp3YmPEwxcB57GeVgKJBJcRsQ98zyKSnqgY+UyIsNhqqpmE6rAANQq80vBnKLfNOJJXinJcPlQlbpazHPqycj5jLWQljGRkOA1mKLBls+/kn3X67/N2Rr3lW6zAqcjQFWo0RNFssyg6PzLlKvuezM4hS3zYmdV84v1Au5cLHE94Wiarn5OanZory7D7M4IIBHnr4joOYIn1eXW7KClWKtuCAL/mJuu6iWwGOwwUADZBIFIm40YekcdA+sUOaL0EpYmiZS4pWml7XmTMCoG1QIevEuQfdHoGzHKAQrnYnIvgvKj/6YMkpeHjqWVejSveVYbH6Mt8bngb0I/c3TWMMhp7eHq5V1SbIeEpsV00xaZXhbRykSyjJdCMUV7W+Jkp+ZllfhXqT7Kz+SdQOmx4dWsw93Q4Q1iYdk5fGx7N1YpOWwVGCv9JreNfgtoWEKJw1kPa4EhVfoRxmKJgS29NIF7kJ9qnJDOB9gj8lRYIsVMhatdNSrDYSIZXC5LttIuowoA7KTeglRXoYgyZvVa7EER/7TogTOHIemkgxkKNM6xG9mMCjEZ8uhbLkWO6cGMKz6DpZMppkB7MNf8TTVABXF2IqBt+Xre+gPKA9rXc3VxMbPbZDpe4Y53GavmznlEjH623Ek9Bwm5X8ejibRSNDE2JQHb3QrKxrCgc2A6GZb3nI2UHXIpeDoi/M3frx1xxUJxk83iQ5OjEjd58dv+2T04S9OzxzPd/anmFDAwTBzCmikyATw0fdM8VuZHeV997vf/cEPfhAl+Uxs2ViZ40sJclYNYF7qt771LZ633XYbr23VerQXjz63FsKqqbYKSINDiJA15hO9CIUbPyuBGB5r8QO8WvxZaOF7tPLyEy3Bzw5E5CYsrl2pBZ7pWPzPofinjofaC/Xp/yfQ/m8CdTrAVjF7KnRiQefVu0PTMwsvzhxFdWeZN7tYyS8eRcvrbNmdTHOJAgsoql8EQyWuE1VVhA9tlGRZZU3WmXo0q29VnninyEeerADzpFYZbKyG2VjcOjiQ+MAHuK+5fuc94Rr3OTu5TDrwjnfG3vzmkH5G0miS0DbYwAa2H9YsLwuy0EFS0PNhAGHBSIb4QF5g6GIesxEaD68SDxR4lYODg1hBmN+k4jgrBi1nZeEfstixCE2ya5QjwElo3BKFnydA5BEOIgQhiJkKjsUaxEcG6MNJCLzRGSmYbASu9JMXJoefHEMd14RGr2FrtC1cN0WduaDP0lIW3Pl85VWB1NdCxUK9XKfydUlfLqctXXstX24VjbZcrgwfLf/wjvoDDzucXt7W5dz8+vANr3N6t3m3Ky3R8302GPDWfMgGPbQ3pkvOxK4TjhOcWKzo4p6YRjh9mt7t9h6qiBgDMaMkWJZAG5IfpSHpe0pGEgii/fFjKr8AyGhpQwazRdi/csEVmUjNBsJdPZXFmUAwTEdOpNK9rXL4IhpLiB0na35qfAhtxVaC0qbJyH4sK3m6YWj6riYmRjAcijKx4eMrJekYMMM/jwwkGrweifzC9PTY8UwrFxRHivmFcqU0NXaczGPJTIyvaoHWkEq4aHKN3CyPIh2ZH5+rje1KDrzpTRN33B86zOeX3YIRWR3a0/bGNxwKJMGpCOVlxEfmatld6fabrp/81nej+byUTmYLeVX1StBpfePNhWTL8PEa+xs04bK0wogwxn/gK1n0gby7xoQrMBsjhag5D2otzINqhhpl8Q2xHl2NkHdCxcj7FBSxC63kggWGn86teGmciuhl1IAihJQcDxyIfjMhJJQ5A9zTrac5mEVYoq15Ch/azDSxR14ICin7r0/hyqL5IQc3ICDlDpAWRh7CgOIrY+r3SHnJhE0vF53PYH7VrQMvHy9oSaXe9L+k9KgA852fXCCCKXRWxZQ4r0zmb3y6dExTN/mm9FbLszHdy9XPUGvW3SrjzmnUCYMUw/dpEPi5SGpLCyhFqBNbWz9rFW9lLisha6U9c3CmAO6//36mM875pVxWRmTUte/uiiSxtsT6tSrynoISTQWv/3BXpSBfM8F0Z6MisfgxRgji59YVmXFezRF76623oh9ytvS3f/u3jexqiK/AVqmBs2oAkz8vic83r6OJYh2xPEjDNVNkFZbPJAiTya7pt0zghFaIXWSLfqvkLM20Vss9EHRygVqo/u32ENYv4yCjplxaieUXyP8omf7XuXD5OW7GCkYvZBPeORzc6B5xvmbR3tp66YWoCqMnxhZqxUzPdszbcCQmVruotaIt1OXSHDhVZlFlvHmrVSrB69KrRhmQ/V1MIjD9xkiD8Tk4OEiVWvcmr8L2vvTHfqf+jrdXuPgqUI9deEG1e1u9ox0le/jIETszLB2b/7yPejXT2sIe5qNHjzz62GMPP/zQpZde0tPd/aM77mB/gY1kICEE8TMrRmOjUf3qr/4qVjFLuw8//DB2OA2MVWvO+vJdQbuay0x0u4ZkZUGgTBFYyMVUhvNGKYNgoiDkZcYwSgyWOeYWRAwNm9yCbGBeuQJpq9ZQblxzXsmAXT3lwxsZaASegnHoJ98qDxt7wuddUPqNX6v83f+s1fmmK+s6tRrfU+0fjL3zHU5bK2u/1eGjhf/7vyUeepAtutFoPHj8SPmnPy0eGY6//71BPvqyRh+hdLw7f1r3pAzTDBpxpO14kFUrsBF5g362Kjw/GbJ1J4+2JKW1krd0HvfH9TTiqM4smOJ8Tt2wAhUuAJ+WoHlUZS+q7D9ZLOv3UUXmmA4eDkQwd0PVQpZ90U4kwQW5FVBtm6lH2PuFouXtcqw/Fmlw8XtmhaDGM22JTFtVFm0VzNN4oigUbymREXGfxCk6S6q1WmFxkcup6dBaFk7t14IV2n88mZQyWYXYT2M1CiUysEQ+cctPyGsKTjwHHzxa7ERo/6c/mPn8F0uPPhYuV2rRcPhVv9T1od+c395//6FqJRi1hUSokIMKunqpFj4wWb3+gsva/+0H5//7551coc6XgZ1QNRxOvOdXgq9+zXO5yFyeqVmvhC6fPifQkuLzrzEGv1WK8kdIIpGumgxSjbguqeUgyc4tpPyujHRTuUheaOnXmNJyeoSsHxhVPX9rWcCiC1tWBJ+U23qXWqRhN3FFb5ASphK1nS2q8JHeffdea9dCSDKvNLw/KZpmANDepaRz85a3K1SlAHoeRxqGvXIPRXEVqMlAbHC2GqylhFJIpirdvRuCJKWVRiDZEyEUtVGIlBBMRXCpWXZC3X3bCiCsQQHLf8MSqhLWJw/+XKcFNb8Ak6nEtt6eVEuK0U2kn5fcQ3/l160B3gjjLGO3f0bpdKrGFjyggIcRHD0BPzPIKJ9bNUCcDnubTcvCAJyjqGw24enjMzSvr8CcfhYbp8AOQeqBs2+sSLPxEBXL0rJqgqoGn8wUnEVu6czsr3TvbFurXbE9K5qQVsfCBtYsDKOR0shtzz/bEtcpPms/fCDGDOB10F6JWlkDZ9UAxja4++67P/WpT63k4+cEgh3CNgmYYTsHAtHOmWA4KXv+6LU0OskoLNuFx2T/ITr+iO7hNF2MjXRskeUo1HBELACnXKtMYAArqXPz8PseHQyBxcAeDk3s3Rbdc37GBmlTBDF83b+afMSJNU6CHH6TIqrDijNSCBQccMYPzFROWUCT3cK2a9cvJAKImQUmbhE61Al1i4fP/bEZHgMVUhBs7+hoG9oTHNwDkVwwtJDNLT77DJmz6jswOARBKJNqvlg7NCVnJ9jlOBPobum7MDTPXu3e+YXF4WPHZ2emwYEZZB8mKy+ON8hgCQNQgENsUXJnrpcoigIyQTY2Y6AyX+jXj8+577EoW1UWhchzwJmcg0lyNLgNP8zb2YZtioMsRpBZCoqPh3yNIEnYp80Wbj74jGnnUV3ldx3eVsE+pyAs3lBXR+xX3lXq6a599/vB4ZFCKhG46urI228LnX9RIBytzc+X7rgz8dAD7JE21RKdNMJtTV//emXfJZGbb3Ziup93RSlYxsetAK8JoHqZ4JBuiFLrOPhBxX4eHBxcM80mI6QpuNr6sldkTcRiXZKK1qzaerJElW6l1cCARKqIEdtRHCS8BKJNS6J8pSYTx4Eg3bRaLBXz86jsxWKJPSjFbLZWD09my4lkupNvL0tSo6PEGh5aP0pf2raXl+Stbd04Jz/PPjImPFrGE5heITQn6HiE5BdKxXyWe6lKhVw9HG/t2cVVgbn5mVq10r1jDwtybOKWi4OXnEgj1040OatReN3K8DCVup779SCHZ8PfDdZuunBf23/8RHRivDozE2zvLHX3TqXbf3S0dnQhAgX7xpFLUn8wiR8dddqHkntve2v7eefn7ro7dHw02NWRuuG62uVXH0p03P+snGtFOrqpJGO3JowlKsIKwNM8ikmtCcDdIWCJlYZWXCPiUnJL6D5XQSGGrN0IL15+jSFLKGH+6+sziP/0wXjgRWjJ25YXbH5hWP1ekxNiLnn3xRrESC7zT0/NFIrVPf2dzDwQTRw0rW7EL8VWfDM1JdIjIl7er/1YhPi92Q2XlJtcIiShPtRjLVHKC2ypvgkYDmMYrRTyM3mni8kWcUzk8I+UgiVerQYAwiL3x5nHLYfiasXYEElNaAJuwdT6YoSU0VOcemi/NHlpxQyjZCxwJq0EGdpgyjSzYTt9lzOEzJwoXdGXMM6UvZfIgzJiXjIKn/4GJUZ2Zq737t3LdPYymbv5quJojG0rs6S24MEQc9a2EG+e5TVTWFVgL6HMYOatf+pqTSqnGoE+iTvV1FucjqpA8WMdlSaHgsqT5kceaGU80b6I3eIs1ya3gSYKylKPp4/goIdqimfVPdu2yuIfmiM5C0KcAWbVxwzmtdl5JWZZDZw9A5gmeMcdd2CKvOUtb2l838YOscg1njZbw7QNRhFmA3MbK5GXlWBLA+RoRytt40F7R7uMb9l8bW4iUKvwPRyHe5jiMj7ZIGuagBNsFb2B5dMMnweJ+0sEHCOGu3AnYx/3TgWdEIa0DJfecLylrG+SGFWNi0SioaAceNPxX3ohQFmhlj+3UwLxaSNbqRm7lgkgL8uWZ8Fh4zHGHkCxmJc7uiVAzM6DBw8aNd41S6lgsSiKpKY/T05MHD1yBCK5fJYZ2JbQz3Z0PJ6JDA9cXIl3Xp2rvu7I2O59l18zmQtMPTvD3Nj+p188cezw3CJ3L8cefeSR6YmxOb5JG+KWeTnnzCxxKMzBwmprW3sq0xqNJ+68686Ojq5SuZLAGmhJf+c7t8/PL8wvzCVSya//49dn56evufraK6+8ymbaaAMmzeHZFm8RRhjS8I8HK5pVX5oKnFNJUmPcbJ9KsXH62WefZfgkLXAjhXXNaMqisRWco8jYY41fwKZyqAfa+fI6WwpRyTjmBb0XshT18+mj7bDGEujrjd56W/361wYwxtg/kEqG2tqpKWlZucXaffdV+ZaOdCNHls4rZXTccKReevgR51WvCcTk+rEmd2rF5y1Y20MnsJms09fAGhmjPD3MpYil5nUT1W4pmar30nkkAqD9Gpa2BrMvNEK0bYsR1RilmSc1JaTkx18V1awVASQi6ML1QEdcvY6T4/qruWOAUwACgXQqVKsvlhYXUvFgR4KN0ApVEiseMr0lliWra42uIYXL3npE/JSGK4m9VKxslU8MH605pVA9yCdg8tPDuYpTKZXj6Va+Ey05+6k9D40DdogRYUu0VJL8mn3kYfFLRg0ahOA4z8/Fxp6pXNTZ07e9K7E7mK1UTyyEnj1eWyhH9FJgpSHElhypqgHnh4dq4z1tl195TeaSSyMVmm54OpF8ajr0+HOBXI1jwzKTspSmmcJSzHIk5veXVZxmLG9aCBiqcSQEzFTkV+N4x420JInVB1AjoHQkYbOT6CWnyELLB3se/ZUGt+wtSK5GWsEEjTHsOyVKyMtZABK0n8Bcnu+sl+ez9ROU24CCK1nzdKnJpLDODCgxHhKlOGZsmmGqcNmHDnEoyY/6idWSSBL9k7DEyt5mMJWGREmZhAVJq39qdj50tNoWY6DQWJIJhjz5JaVLUzDlVLLBlTdBUmTJRdJQbPVqUgAE3ChpveoX2sD0R5hWJ78CMbCCwKrXt6XKl683Eepi/iL+MPax8oaTUiMPqlVMEdxmy2I6oY2bm03bhM/oYAseKJxoLzYH7S14NOH+YgTRNFhUoDgoJGeT4+VS8WzmvEpeMGMGMMsSLGygANDeDhw4wF5O06NOodWtks2ZAVlNwjBMmn9l3dL4mbsxUwUuQOAeLDoU35d9z3vesxL/zHD6UqB69gxgaus73/kOttPg4OCqNYddYWs1xOLnLWJIrGMYrErkNIF+0xHBjGiu1Cojx6r33Fd/+JHg3Gytrzf4+hvDV10T6u5mJLOhj1HMiV9Tl6Pttdrrp+pfTHKunThacIW1yEQhc1NOopzeSOR8aaunyeLWJWeGDAPPBgB3GNe+1JSDXydNcIL+eMbaGpKl8eisO7xLGrnhg8laFoFtthVzDpHEbCt2I6YgQQxgOjPBdGumu1rfFflvmfCPQoGSzOFTszOPhoOf74+/KlD+f2v1/kx7Z6at64WnDzDhEApH5hdmsIQXZ6e5tjkWixayi2x4icXksivRjOJtzw1Pl4rF/DOHe3r6Moem9u67KhbbMRc8lnXCj/zkx+dddClfYRmbKh47dryzs4uXzvI4NpIZwDRI2MbPcJKIx+fm55E73KaAeIVhZKtfJzBvFi/rvf7YQxvwEajGxqDBgTBDCf46Vhnzl3SHK664girVfdZcYIoibu3Ip3+2mxUtBx4otV9A8xhjlEv0mlSynmK2CN6oCOFQuCauUq1PzZbLfP4K/ZJQJcC9sez8i8Rr8wuBiiySr3S8CJbKWVdff6m8MSHM0Lx5UslMXgwODlJ1K99CY5LN+hPh4MXb7GIeX5OTknqOF2SNwIAEfTRQ/NeHvzGVpPbiqA00aUtlMMEUnyjdznyxrhumWAEO1LraKrsvIWZiZJiDup29fYigJ3/2s4728nnbRJV3nevjRywEc/prlD1l3kPn10cTXlbGN0HMNiGRmD+SixCoOzujOwnkFxdnZyYK6Z2PDkeK1cVAPjAxRg7NxZdM3f9+lHpgQJwP1HowmEboQ8LBQOT+I+BxlRUTCOGa1KFIZiO8hLvcB/7jI4FkKNDfGW+LprKl+pGZ6hw3l7s9jvQQt+fylI2hZn4a48TPzvTVifgWkmLpw81Y/OoseynU/8/em8BZdpYF3vfcverWvnTX1t1V3Um6OwlJhyRkIbshUZwPRtCPRUFEdIzCOCL+FIQZ1HEX/BgFcRxwhG8QUQSUJQgIBEIgCVlIJ01632tfb919OfN/3uecc8+9dav6dndV9ZI+XX3uuzzv8z7vcp7lXdUrJZL/vsfL3p9Wcqy0o2hNCka4Onj7s3dkgonz43c6sC+IXBSDQ0Kq0JwpBffOBg8sCDCDGHRUk42ISXqCpjWZmWCXdMMllCwiKzkQVPGYrEoOFQomRakASDEAIkDCamjTrBAtubLvi3AIUHjHU1UkxUUxHawOjP54kJJeHi2CcRk/L7dmHVhB5EIbEDvH6e2pxU42LwRk/ufie+hwyFMepAbigwMjkf4oeISgAyABl0qTupXA0DMaBQPxwlP8HVy6lpzWAR5R3hp4EAdqRaCBkNCzKBpIep6CoJmwJA1l7Dylb73IYtaBbsBMCZonDhROcqZX0HnWi4SzzYd2bBwFZ9lwFunnP/95DODGU12CXD8DGDb35S9/+Q1veEMNz/LagO2X8C/m2bhDlbaHNzXIxTwMq+hgrAhiykeOFj70wdg3HgpxZgqsds+e/Le+VXjjmwI/+aogRwEbsc4kRaTpJYXodaHc07EfSWZHp8Nfb48UolgA+UQm/uZsaDglArnjdYEgu9irhN4qEnwGqGAKNAoJl2sRDyeGPGOKgNE6yImJCc6YDTYnEi3NCaa+qAYj1UXqT05NLczNX3YZliE+/qS8iCsWLCFj2P1Ljkz/Mk2qViKWM5YqVU0IvKlUnE7ufWtb6FHWj5fL4fJklK2G0Z6SFcm3B79VOP7atsHPbIm1PP79J7f1dLWX4r3RLbPHnuu96kouetn91BOM3Y5nU8zAY4UiA1h6nE5nFg4fZIKRSy9SyWTXYgpz9+ChQ1E2PEebUpnM9Nz8i2+9q62rt6U5Txmvv/76kZER7XgULByJXDY4EJidK2dzImz7+6++/HLsNh6opSqYsqaAFIoiYBt7NeY5MMCgCgHPVDkwwFPtDEyCQU81IGoFi44kWrnyYwcmJifGxyfYtEw4NatxRpMyLWByBbOX+9o5GAVgvOMUS8UgxaFLaBKflIcLkaKhTZtio8eJzeay0XBEzgLW7tK3MRCNSJGrC0E6KhB5Rm9pvFB1q6JuYOM4ayCpfENpNbkVIFPuKm/Fgxnk89Q6JSV1IkcJ8Gg9VsObPDtigZsG6QhSZWYhNJsFAgcZPg4FhoejycVC5uD8cE/H9n7RALT6DUIHo1PRpic5FpVGC7DrMr/q1TDteBpC+X2Qam4QIPS46SWR2PD9qGX2zHThZChQ7G0/lGH2usOkloK6wLgkoUlTbdZ4EMZBac1sn3qcOF09q1krBQav1GHNZ+IkWPLjZhk7MpV/LrXQnGhm2WYHdycJpBKp5dL3kvS1AR6w+WI9JFVV5k+jWZgQMe6kEIJCTVdDnIRJiLxqzCoJ4jEAxPOnLaXBapeZepDKUzjFblJJCsEqefIfhkd/08wcBOpxMAuo/Pea3ySWgEjJhjm2R8tyNpvgkUCD1Ck2a44czDQYhXKN1JApj5cL6P2NLN8LcaYAilEby/0qnGxMTuIWPAJs3E6taTp5mzXIkkElmTrxC0lScyZK61iJ4o1XEQsSAZDCUAKTD35ZIyBJ8DPKoW8zlSP3KQStMuLJOCQLeBGVDDD/kwupsYXJvhjnh63r3J0UY80eGA7iXpeJISWRGtzRgHLlHytnbQ4rp9iu2aABDLEId7YOIXC9sWYtAVkw0cfAdIMTJ54sQOKjcILEC1mzKllDxBCPsuEt0FvDnM571KhnTIajXKGkacvSJS4g67duP2QOAOOIukchR43cs2cPboY8WCYN/I//+I9zvQ5RaL/nffucLwSukwEMH2T2hrUZ9913X92mJVAn0Gg/6obhwHPeitw6mvvav0fZCSbiUIQsci+cL+Q//vHizisiL72Ng4ARXUY8NgU3/nH52E+FQgvxnz1ZunsufyAeiAZar1oMdmS5oDMXvSra/WsudyXRuXxY6sOHBAU4EBi6iRdBohsP6lIGJPLGi+IyXY6i6O/vSjT3s3U/VwrEW7ncrClbLKWyhWQ6y9cpGoEpKMMI6AdoAJjB7PulfdklC5vGTa/4+te/zpfMtmEuE7ruul2lub9qtx5hvqa4r6348Q3WwWghl7dH7NDrZkIvno3kn04d+I2ODR9oCaQ6oh2T6dmmYK4lardxL053656n2dZX6GCpMPfi9nTOTE8MDm3GTg0U8tOzU20dXShqKFAzExOyJXlhLp5oTqWSe555dGLsGAtZb9m1fXZq+otf/OLNN9/MqCHUQrM9Pl74+lfK//Jg+cCBQsiKXndt6NWvbrvxJSHOsDV3O7GsC4faFUhxujHF8SoKh0pihqupQAUjUCtzeHhYLee60hrGravEs/lCMmc/s4eMWFiYQY9OF0pG4XI6FAoZ+Uply8Fr0lPV5ydjnd0VArQTVGVvBdpagy+7u/zEE5FSweiaHPzMcbsBLnoO3X5HgA3D9R4GX6hAnZmvF79SGN2bkQgd11gJ7vTjKiU9/bSNpHDxL8s0QgG7PUYsf/AnsSZAm2qLMQvS3WTZqVxbKLexJdTr3DBVydNR750A0pqUlXhfBGaLqPJLZ8w8aEnu8EKlwwDXJbqtHI6kw8X2wOY2uZlYE6o15njEOnIfsY8Mcs1BgsXF4lV5/BmYWT8TysuLVrtHbRi1XByQCj43Tc1vOpCbTC50R8MtzWc0aWByNgR6WYlPLKvqhwDTyh7NJlpgCdHvWt3aCtSsW2xpFOcRp/EINyBMI0wYTg2R8pvcYYNuiJceuoSNiN9Da7xq3Rmyja2GtWYIhtU54MYC1IRQTONNzaTHyhNbemIdrWwSEYzysO1aHsmauUDzliJj+0GY7hvSNwGSSsL5lfMmCDfwMuLBhL4T5QTCO1zKSYS4dSxPMBMuS1F4mxutxFI1/wSv0CIPMDJdiAuGb4gx+RoA4yIVJTVRlTeEECmP4DBug1NTmKzNwIA0oQNooPE6gwBSCu8BZnrafvjhvZdtvCJob/BHeTAXqIMxX/QNKgnujfRXfUNNEQxjTBQM4NMtGhKT9WskrzGAweMJ2dPCqdccnlaS8xNY65lKQA9pfEDh/CzL2VBFPdx4441eDSD6/ev1zgbzuU1rmI1Dgrq9N7bVe9/7Xvbc3XTTTX6wc0vweZ77OhnA1ALL01FeaZuVa4Qui3V0PrSfnUxa3/4Gm3dFaZC3yDlkZJyDZh75XmDX9YF2uWQM0UV0sPnO0sCHC2NvD5fHglsW+TPGCPOrwXxsV3jwo1ZILo8V8LN8RGeAHJGiPlxKny9g+VyQNywH1RqGUaoJx6DsCgYwYxNs1iXbiYnxheTYcNcjgeQXosmjpVQkEN0yZb36WP6V6YWgPZ5KzmYWk8WJUJJaiURi7DGenpk+dvTw5qGBztbmW6/oYjSOSTxaGdsPGnljXeMtlgrl0rw19z85zac03mz/+WDzfKJUznNgcPxkU/Ev4qV3lSKXzbdZD05nD2zs25jLZCfGx7KZzODAEGYhW3NDoTCzxkhWmB2TNpNT08yWWqHpfA6oTCGzwO7fYmae0TLuR6UNm4KdrXGLC11i5UUOyG1rbysWiliq0ANhC8lkIpMrfu5zkb/93+FSMbWYag6Fw997PPn0s9Yf/K51863cjkVHZeIaYKaOqUlGHHETyFsfr5Jb21qGtl3BbPbY2Ekk0+ZNm2iqSDRy4siRfCAUb22Te4/1MRokehgIMfmo86Id+OFYbm7/EXBSe1xsc6I4TgeINbEwW3sgXcFotIHA1s7i5nbTZ6W3rv5D/aCygBfymNJXa18btKHMKCNzmk1N9i235l5zpPypT1vhLAMk6PS51pbAAz8f2n45W4HroqLfktpfvXXB6gbSK+qOMtQFvkADpWoNO6JtNm+WbsmTzxdQtPkMq7iFiarqH+IR+2jpgyb1gx88PTIy0t7ObK1BUwdMkUGCIHA8BpfYSsKvpMeYgEB3V1d3VzeeaweBVFiNcd/+UE0kUMbleZ3+7iZxft08xFuFWdPVQK/s5by873//uV27rt28uXNlyJrYqoydOPJf/oN0EhgpA7xTYW5zaNmlAFIv/hJW8nVrzIkliVdgg1x6hjzmR2I11Aem8SYYp4DYNiN6vT09NaeqKCYHUH6Ms5qsY8fyT6YOv3ika2jTSlU3PTXNMHer7667JQS5+IUgJdH/JsghxB96SjcVXE3vKVOYVRg+GhhscatU03pkQJJSRYgXKGPNi4uyzUe+EKcjVAGAJcFyqoCVS2V86U5J2OoA0NYwdqQkCylXB6MPC5O9dCHws9qLRU9IZ4Q+XQttkBAdAmYNFGdtGMEdY0bL4SM+JH4nscwA88YAJiEO6MeuppJVHqmQIpwFSrz9aZdzNwimyTHpGZ4+T9TUmhJRFUy5P/nkkyyoRKmriX1BeXXi1yvyaTWxl+q8cjDcs2PHDkhiHpivtebKEpZA86FhZ53SyDqvCnVuiVk/A/irX/3qHXfcsYKVpRXhtfG5rRdytwql4MyCK42q2GiQo485H8X3INZCrT9hxV9cmvmrwOKDdnGCjffZ8lCi/03hzjcGg62qCPpSnKET6SoiFk7PLG46y8mv2HAWOkSsiVE/RljVAlwBOzWshzAxE4v40fW3K9sVSKZ4XDYmJSL7uq1fiy88jwohGgCFLO4dCHwtXv7rrx7+zZnSsBH/tl4l1dnb19s3MDGbWkjbU7OpudkFDGBsXYbiWAv96KOPwqz1cCmWBKeSiwM9R/qLY1R06WudwelQgfrjAE3aIV8oZcuFz7RG3rEQspJ7d//9/pPXp7Mp5lGRmgg58FCWfKEQLDFAIdOJycVFxCHmOqY7hpMI3WIhGglHQomBvg2q0m3csJEzybkPaWhocG5+/s477kSsUq9UDuse7ULR3rff+uQ/sPodGsAQiUUhvr2Qy/yfT9qXbQ8ODdKZNXc1Bb3VuaaN3I4jLcH8RujIYnwhFxydCGHqzrvnituRrc2XD/xwMbpPVk+jlsoUx6Z2e5sMFIT1fqbRicnSoYOJllaKRklZtj4+dpIsOrs3NCfaUac0O8lH9Drm0qr6qglftRe6hVr7EIPOgRfU1Cf104iAEcqkFIFg38bYT/90+dpr7Ue/V55JZvt7wre+lDOig52yeamu0sJwALmssFlaC0kL0v1oF1pTQwzCtasTX/V7+VU51i5rycZf7U5ObvXRW/iut23bqvpiFVG1HpN0GUrZeU6TVTJaBsyPkqzx8hZaNKVLFX6i+C8TyqdEVQEwLs/rOfy5nhpdFfTKHqbWg3aJt6zzOdunERSwGS8b4/J7Pbfn8GD9Di/Wc2is43VD3V9fjn4sjjtnFhDiqTT9ci1WaVxJi52zddvWRIss5fWnlTjfMz4xzkIbDGAfjEeZD06ddWLqBC1JVj+gmt76MHVC3QylqVx3NVj9UHjXoUOHOzo64WDV8BUf0oT70s2gZyVwfVzUP/nCzM/MAMYaZOEx4o95VF9TCu14YQKIYI6xQEYz+syDAy6NgIM7oa4Aw6OHWhGCAXzKUjOaeffdd/tXA2H1IYWV7SChwEBVIwVOieoMACAVveU8uWN2Kf3oPFhHUKj3UCwFeIGE0BnYM8UoiRqNF0Gp+UxqS2FCNJQPio8CO+s973lPLdgl/zI1sE4GMLNGjzzyyO/93u/VacJqyk4JUA2+Vj6ZYo2Fy91d5ZPHdd2U5mRG6a1Sb4+ZVPHnzqRdMBTZFtr4Z4GNf5jPz/77v321s3vzLVffpkBLeq4/7Wm5xcxlCCj79a9ZjzwWXFhgAXHo3rvDN9yYb23d8/wPr77q6pVHGfhO1DbQxUh+OwE6sLCMkmMMdqPbuwp+uVw41JZ6IBg8gsllL8YKB+KsZAtuKwcTmc7QE/dtfvf3ch8/cIDTT6zNI5dxAWlcjssuLSbnwlE5IKqUndfvl5lSDHXOwEAKssqXKJl0tezD+/YObGK+MxA6FsuViyHu4DTWHYcksVO0eKjIFmppl9yJmemtI5dtCQVDOq135MgRpB3AiE8EOQO0iEBwwgGRzfQ9DFcgsZ0AQ0jjJpwpX5QYuTjGnMDBhgrGAjjWC8s8HA5df9WLws/tZl1apiyHdiDJSVLiVg+rHHpuT2BhNmAPQDMavlNJYta5VWXsdqxmt11pfGMQWIFILM6BDC4cJAebmkRNVCRUukniLKKW4guqQFM8uGXbdk7xmZ+ZnpmZHt56GSakxUkfVtEgBsSdAiaFOBWPm//q/TKcr8d4Ul1oKmgz4KZHnWYOrCfkLoINdtftwRtuoGaZ9Q2iLi85UsuPFi2qkYNSqDRknhLmT77qbqNsSbtJdZtfkwU1j0ffBJimMK3rEuA0sq+FcPp8Ltyq/NLVec4AFaVj4aJ+RCTHzddK3TqzWA1gZI0iTVYzSl2Vbq0KXZXJ+eChMqlJmB6Dj9Th+UDSCjTAP4UhmweH7peB8hWMtxpsyJQasVIDoF6qQp+6sS+0QKoC/sYns54FN0yskqF6eTfe1poY+xPJu9xCYlgQK8iQ+6gcdCTGRxh5x4zEgr322mspMgPiAKyst1SoREMIhRg5oca8wOHhYRgUaBFMGH7EelFr4aipt7XI4sxwUifUMB2J6oVIfxWdGcILOhWaIZMrzISjZF4oBXG+Qcild4uyoOqERz5emztDi0V6OLCymIf/qn3e/SP3/vqv/SojQXq+jJfmkmO5GjhdzXU5PCuF00iPP/44cvTOO++8cD5IOYQ6cPddpd27g8x/uJwWjpKPJSI338JSVymz6ZxLChXl3p9iOdHVJQf2iArsJl+pmhqMozaPHE6//wPNjz3OvZ+5fKH54P7C976T+9k3RV75Ez1d3UuIqcWrAKBBivAWCn2CBB83TCQzRVntjX0mI6lahHJ4/L8FS4fZVJX9Zlfps612KhBMhyIdoeLrZ6K3TndE99/T/Teh5OswwW6/biOYMd7mk4s/OPHEhq6uzfFYKVI8wJwqR9X2MFjce8MNN5A1Q5VIRwaex8ZGd+wcsufYjVuyO4uJWBNZY3PmCnnM2mg0XhiY01bo3LCtd56rpeRBguo8MEKUsWRKBDBSEAHA5DAUUATPPEMBRS4CABiD0MgJTDgUehaBwy9wM1IIu0TnY6ExpS9nshxyxZSrYmZ9daFQZEVpOJQLZgozGfvQDPipp4Ad30K5nhrjYGR8sguuO2EPtnoGsDRBPGgXw+VN/YxJc9uK2MCUjo+iuTnBdk1T02JDS2sIuNcoVmsifu+uof4+WSA3Pp4cbwq9aLtcuCew5hHep3Y4bbrGW4CpCh7Nl4qlnh0iTufHlFAKa3EaVnvUY/HV/bAWI33Ga8raOCMKqE+6hEZJzzHnk7HGcmW0S1E1HGJzaw4N6Vi/lEr6mzaLvmlJUzinrZyGxUfP8JrPhBqf0/IN539KQB9CL7tTJvIAzPD5gvHS8SZwoBaLOe1D6wEvdcj3b5rjDLL2Y9Na84dcKG5/wZlegyOh7mtgg4XyY1i3UtPuk5NTkp0ld1Hy4GS+K9L42Sor9hAQwmbBCaflYbgKN1v0UVLX7FMlh3Pw0P9ZjcKHADsie10YhdxB3i0tKSGMLVIhKrbWh1zkL6dSaV7ky9JZ3gwENzIVDCTJkbb6mUM5mjcsggKCkFjeFIoHgYukYLsQ8FQFEoQQmDlNDxgACnlaVjd4SI7gVg6vR8ZoPYP/zAST1sNyb/QH5rGJpRQUGeMKNwQ0Pia4HOZVD4ckuA0tshb1sOrUrh1C1qbBzRgL8B+61kB2Ht9tkE+vjNKPreL2XDWJCTdKDNKTk2D1j+PTA0wNFeEk5SB3aBRttgfaxyewMiScWLbX8de1/aXZbP673/3ey152r35WNcgveWtqYD0MYLL85je/SV80+0hrCDhPvXR8K9ESuevO/L599oNfDtMZ5TiNYD4as97yc9aV2zGDViDdDL/J0lCBWZWPyEgUvg17YS7/pX9LPPooJ3+ge8t5JMiNdDb/0Y8GdlzRee0u1Au6PvKA3Ff+BnT/qlcKmcAUqSXfE9PA+WJgKmtPpQIFOUDEbgocub7wBbIrF4LW1sWWP0pypUh5MVjY3Vz8t5Zgby58RdJa/HS0fFfA6ouJ6Ufm4dlMMlRa7E70tkY4kyHAdlwikFIIP90xiwULj8a2yWZynb2Xlee7Qva0dft86eFOqxhK57Nyqy9kcSL4PUlmlZmXj3fe2tYWYAaejRAYkFythGLB4DFiCY6P8EORQn4jHbGKIYKHWHIkivDbbrvtqaeeggYmnwln3BokJFS1DHpYeMybo7PKwyPR9o5wWW7cYJtxjKbnMNhyKN/bY7d3pAr2Qj44kxELyNi9WmapTgT65mIpFipIL5L/cvjKzo0ca17pCjTl5OTsd5797tCOHZczoyv1Li3Ku2bJJRt9da8vmGORWGuiRe5y8auYipXEq9XVpBCneKgiqtRTX04BXS+aWpPkEiUvvPWgKmEYDxVPPRf9QZdkE8mwCA8Oro9C2aoHvgphc+nCM2PsX5aSOOjcQji/HBQkpdOmoaeIx0T5iwuCpcSYoRTTKaRucPiH0cQtfcWMmzhZuzhknlbR8XNydDSZXNi2bSvL/z0KjZFugNw0uoDA+BSbODPRvmJnL5cQHz90oLevv6lZZi+fmwQ/hj8N5yV2kxAAfygV0txlFbBn5zI5rjnaj+Vsx5s4Y0/G4JU0M1wjYwUVLG4NCVZ1OxXlUL3kx9Gb3XClQd9ePgbFUsxOiHQ+hwalpPo9W4iXOrZMFJuKM1yasxKkodnUhz8vQ5mgJHHAnporjy/YrTMF8cnj/HgOrwdptAtjgv1oa9w+aMdpSgGUeaqwikerV6pYH/G7QPQpoSpV7sy0NMNqx04eS7R1NCdYeRPYM82sYN4kM91HXE4RSK7jb4LRQGAU7T9wgCMKbtw52Nss/Mzrk4Agr2DFOLAo+DbV7QcQPBfFQ80iWTCWtIq9IbnlCrf+5/dS/xhL0KPSU+ftG58uwwjUo2iRp7Qmyy9p3FtuuYW1xxgerMPibGevsDBn4KkNXZ7DyixgmDomO/I9XS4NKs5VfclLXqJ7uLxcGONGMHneVXTQq3VxteJUt1eBq5jR2aOiPjGAUYEuGcBUJi3VoAHssUI1Qs1ItcvmHLGknFPYpvlz2b7DUYUliqgWVU7+ox1yQQhDRCXM1zInpBpLVQLxEi4OsXJtqyBRVtEWGKBJxWpDw2MdtuwKDnIwHNhkpNTAgAVhqdw2uG1j/yDWFgbw2feiFwKGNeEUSyvuoYceeulLX3q6PG4pnnULEYUHu2VgKP6f/lPuumtKjzxhLczYA33h2+8IXrMLoeFQIh2+zsMnBxdW0bI0ms8M7o+RzKzjaTFrcisvpqyHv4O4EeuBL8wSNYLNWnY2nf/Oo9Err0LMgJxRWMf8Xpq9CalROORD4pNbXLQnpopHj0ZSmdjQQNuGjf09Xc/PlRfzoUT56ZAt53MEo8XYFo5vlbyDnXb09kzkplTmQJSp23A5OdQ9lbR3MJAMPsR5JpMdHh5BSkEkdiaXIZGvkG1UIhxMunLPLZXQ0tpsBQfKba8IzX80tHOx8Iax8j/0BNPcaBQuRAvll0/Gb56DtcwUXtTZf/Ntt4VPnDgJc0d5QqBi6FJknfjFpkUYM8OAJCYX3vQ6AqmQ4eFhDGCAoQSTm63I7FyiFRDABJKcsXAYJeMCDB+ksdK3b8vfcEP08Ucd5maILnO41yteUeromk3LqabH5lmjLBlJpL7hepaVydvzOaqETkQwMwD5DeXRHcN9nkAiNJXk3LBSR1sLt/o6iQ0G/6ummbq6Ovnz8vJDOrZFVdAaenQbeQ15p5tf48kbgeRzU9WN0+aZqFR3IwlPl2wPnk+gZGFdC7fwAo1D+4LIqKqnxlsVpx7zbeA0UtR8JRLuTycfnpi/tY8KSBls8cUdncpMTy90DkbCITk918A4tPmgHFQA+BADJgsN2OU/leajbyoF2kmfTLFAhPEct4AeFYrOCmRT6aMHj2pOVMux6aOYqhv6hrhozJ9jNrPItWQ9vRvNIJ6HpdpBggqxS3KsgvXjJmJl4KqUy3u62zZ3s8fd7HNfHmrFGLtUHB89BghXkecz9sLuEzRCoqWltVPPRCRGSa0UdUV8DUWadnQgpV5qKkOrqiZQwN06tBN2lKa3pzKjdmsiGu+mNafSEkv/cTqmwMvjocFhIOSFDnfgZJJD06653MVpgPWF/aMmEGcgYa5447Br+qn68l8/J9IHIUi5kDIHDx7EjTBS8adE4FYBBLM6J8VH0o2MjEAMw4VoDrj95J2yphCjqDqIYKZGaVNak1IgSesmpIwMLgPMbjhywUhjEJzlV9AAuz49RchcKgES5pyZNveqjurlBoe6uZ99IFoE5wyRF7YlK8goC+7Tqq6zp6FBDNQnhNGgaJgNJlkbMO/z9/hETT4AVKK8yvQa9JTpa9BVmJiJoI+hIupQBQEetiWpKswL8cY/2epmbFfmYbEtZTJW3pa8OcwcU1bWN+EmxBifuMWaFfOVeVrGVjFuZeBLeggF5CYZmGelpNUEiEg32eocL58IM72YxDL9W2TuV2xjMpV8CS+WeEsUNrOJckxokHAA7PA1t3/rWw9V47/kW7YG1sMAxiBhCfQ73/lOr1svS875FAG1wVAwMDgY+7EfD9xxt6wziLBTsYW3S2b9Ds2gL2Nv8CDY8XJFZjAYNor8cFE1+Ms4TzE0N4s9aRKIuoxb1hgxu7i4yFlPen32cvnWzYZvmY+nNDtV+uZ3yh/7uH34SL5QtJrbA/ff1fz611+xaeueqXysPCU31ZiRsfJ4IvuwbS2Ww1cFI7vywUg2sTNrwRCYHrUWmuLNTU2yMhaiGJ1VwYac8wrr0YbVyreODQyTorpIEu79zWLqoVDpYOxHJou7UtZzoWApFNyRD/dlsbgLgY5k5B0zR1nbDKyNDUY9MzGI9MW+BS3mJWY/ghbWj3ELTqQy5hDLntmLyGolxrkRXYCRL9IaSHQU2gLbSfUARLJazpjErRv6or/0C+kPFSJPPslJY3KfBueUvOIVsf/nx6zO1s35cr9dymRlrE64m4wI6A+9wm6Soz2Ek8ISCYdbzc3OFodU6yVAes7C4gK8scUcFYO3kcerukaAa2AgE2WLslMbZ4NH0Z49hhryVvBCOSMafC+sk0Sk1YWEHhqRN8C0uGpXQJrGqZvi7AOZ9+LeKWQivx42MYYrPie4PqMgcgmki8e1ROsAOFG1OCvZijg1D9sJkJayBFFWihjr18Vf73epeQMUYjscjcMKGdbBK0M1tTkbXG5gpCkxuHU7vrnpKXbX9w0Okigajcmn4KMgl85MT0729PRJJdaxp6twellqbbh1Y2Dqv+pUW33AqlC3DFWBoKobDtApc0ENKifnZhUfutPC/Cz1AGNsc0dQfUgkI8Xo5bdCBgLtJagiWAir25QOlIfdl8pgI0J/3QEWZIpwMal+r0/5EonTo9BxmE7CRzd2/GAktE2aVqSGM0SoaT3WgUOfGpwXjZfSLS1LTSDCC8mIJAISc27//v0YLcjNGrCleFYlZGkuS0OWywhIpC0TGyh4XGdIEXBj/bLRF3bNiiqELBO8JCcKAc0b0UOgbhWmmEDq/K03LrxcXkvDZZWWOZvAH9U48f5UDbrrIq8b2CDCtQNDFGKfexrX2mW0HGZX7MIVDEtxGYX5HpwQk9ZhGy4ejSJ1DdMQJcrP7Qw7cjLBDZ9h0IWuxbGpgMqfsU5xlBIbjszMHJjOBrklxGi6oqdKrJivxlhlkSMaMIHosGr6gkYoMSJPP2H3QxY69B46h1fjJ8wxm8lOKOBtjFLMVzPT65mpTPCWWMPMZjoDQO4y61sOihUt++JIKvxW7GYpLi/Jj4kUxrXpaQTgZmYHCuQdDERZDSjHxAhJ/Oc9cvUtn//wv6JaK0sh+aVnhRpYcwOYTsoiGQwSrleVBjyPH9gxrBwTSOg0lCrBFoc5yV7HSo+na65QEixS7K6Vuc+KCFaso0ikuLE3NjWBToGw4WQVhIdQZlnpvg2BUBSDU024FbHURFocKF3+7uP2+/4kns1niwWGo6PltP2lz6Ontv3a216yeaA8dTwwI995/tst9ntKzewN5GO27PQ9sabfKYaac6bGQoVAJ3bmwEA/R+AwqDwyMoLkQxai8+maZ3/G1DYmGZOuXm1Y4a2hoU8UTrw5Wngu2J2O3049C6+jtvOhkWOptyc23Hr5oJxvwbEZTNhiAGPtMIdMB+ODZywZnNiuDNYyDEFVqNAlRGfFoQSqCMTkJglU0UwYwCzZgjyoRaLzgF+pLe+8Mvyu9+Sf2x04eKjI+VVXbo9uu9zCSDaXrMLdtnUJq+QfNWOG+wIT6RJLo7NFOyu3nBIoumgpV5hPlY/MFROMXGCQwM0C9uhMjulpdAK3TsRwmp2ZhUJ23Hl14sY6v9LUhi+u0ANrknhelC1K58vRizlnDmQW9U/D0RYrEMG3yQpnTjdZAcarMc8BsN+9Qtozigp2NAdv2SyjQjSISCp5ROeveCTABNILxGXe0mGkz7iR/DpedUG0h8FA6UtkvEnltLwLI2GCy+0YONTLz3xkrjOR3dmDjKQnGiDz8jkr0F6ggPpouKJbJ/Jk0M2DMWg8SrxgCeHwaV4n7SAH+lw2IBsCJVBUDje9FUgUS6FUcaiD1dTmn8Q52ATaPH6knttHgRSoEu6mWq3f+bmF48dPDgz2ddacIlZFJvlX+WtyRy3ZfMs1NOfU1PTk+MSVV12pBKNuKSSJ3fT8Cq+reZYEePGCqU6sCeIlnNN7HIGG3wnUWjfxPjAXXnBYgctv3iHd1uIYfCP8JNTkiIMI49UUPqeAlgqlx8OpjeHZriYZedFY1t3wpSMZvU+S9TiwaOGQK4lTzeHCfiNxKOwpi4lAh8shm9QsXLcy0wTIO6ym083RKxEqh7pRfliczEAzb9ZYURxwMkmr9ipuwJC5vBmehqtjBsP5q3hTY0RQpXQeOlVj4KsJRdZ0Y4PR/+1UfQSnzM8rsleHJNFAQsBbg86Bl9BKTMXly4/kdKGRkRFfmN/pp9kLB1Ml3JMUSokH5Dg0WvJ28/eSmgBINUoRGCVCNgBoiPyKGDB/YuOKzSkjoDKJShQb5QVYrVOpDDEgCWdmVYI1ocy+EgJmY7IKuHh0uTLWokMWwIaY+cjQQrTluTG24TgKhqABoeBHpCqSMiZrEftT5njZ1M6bcHJkJlZowDA1tqvQaUxWQmQBM5Rg3Aph8oYSsgSzvM2fEAkfFvYGv+RxNrjxtWDC4jNwwQBmGLP2vckAAEAASURBVFsqjXlLmMBq1VEosAkdpi41I5MjxEsnMYUggSiZLP0C5eYrr2dbH9M/rLs0GQrCS89yNbDmBjAZs+IFg+S6665bjojzJBxrCjNpqZ0m/VT6pPRK4zQBxln3hYFx66231o2i27JxBTkn/deydP8MsgFRURe+JlA+rdZ26+67GVQwzEOWOwpdwUCutSN8y82MtYL89A3gQHlutvTpTzdlMM/4B21cAQJqK/zQN4s/dm9sQ1tx4Z+w4/hEgy2cwyx5wiLC5UDoG4HMR8LNb8sTUAp2FK0dWk1inOfzfIqIN4QiZjDFpHrxwp3hNBTNewOMF7Iz6czYWOfQpi+Xkv/HXvjnUu4g1JTt3vn8S6dLr8jZGxbGxuzRUeQrljPnSDPSDBLMJwQhU7u64gWxSiCZ8gCpE7/gx/ZDAyMW2cxibJQMrGh2AkMbovSxxx4jnHFT0wGUt7DeOxrYsik20FfOF+BeVjxqBb1PxmJz24s2hsHJODcyXgR5wP7hVOBEUmFs9jw/8/h3YvF4NBanpR564gB4Q5HYZduvxnFo1t7ZzhS08GXD3mUyscDdxwWOOZBRPdO00sCk9R6yIErCLgr2Rifhi2AVgKcheSX1O+g5eNE8/IHLuWlE2mK52FUMZzg2ZDZ2iwT2P/4mE6nne3yg1VACJjLZfZZP50I08NseTLU1cR6b02nBKRlUMjES2PV6sUqxG1yVTQ1VgqwuHBdi56KZdHFrV1VD8KU/9+yzpGgq25tbA7kTz4Ghq5NPflNVNtUep6JWIKsaflV8J3Lpifn9Q1tbNveaoxzOGKmhfsIOtubL23upDS3GMujWsYxLm1JpWtqgpgS1BNcECuGyCCnFECSr9rpihVh+rsSt8FkLDkwk4hXe61oOgm251RwSd3E9DFLX2LRILkQYLJzqQnwwGkuJdWpUed06VwAa2hnniPTk6lFGmcEAHpg54pjmRtCzIJky4vAjHx4exkuRPXZ3ZqIMkUFGqkr58a+1m35ruq7YJeZRm0cGi3g0TBntKT52hXexuKlFkyKhMYFAWcHqVZeBlPB6rFfCRbuSSUXsO9YxipZIJmJwikMMRuMmI4kRs8oAoFKggRBFRmJfOW5j2qHtiVdOSEV3I1/R/ySVWGdyQI4BdlBJ7oTwGEh5GaWSQkm9QKGJMBqOKQVmXuURt5impJaHH6FKTUwUJIPWGKVCObHihgZ5bG7FILBUDlFC5ldxF+1uO9E1OY5BWyiKuQuwmJQkMZjIwcndEIaSZypcSFaX/BgnCpcYriQQW1VolBe1GxZg0Za1B1BySQltMpCBS5rSFEbeUkXMXQFKqUioWAxCrRskBKfrBDnOVLTtciRkR8IBtIxwEK+FNxzkZj6WJEqgxPIOBgmRRaBBu/PKaz7UksDmwgAWAi89K9aAp82vCHV2kdwrw1kImBlnh+acpXY+BPI3n8Yp6ajA1wPFPIYxIfYwDlHoAW5c9pB/sLUleve92SNHw198MFTOs7iRDy+XaLEe+IXwZZdzl0xjNNZQZgcWFuwf7stk0pRRPkk7kCnKjl/Omirt2Vu6oc8qHOar5pONXJvL/bd45gOl2ASfqZ3bGAhfI/YwiazEXYMbr8vn5XhGjE+OuPjMZz4zMjIyPDyMCGTHF4XFCuVUemweACQJx+o8J0ow1YLg5Jhl5OLmzUOhrv9S7v6VHz71vcH+gXK4qZAqcePt5NRUa0trLB4jCclf+cpXMqXJIMITTzzB3CBqBNvJSI7RCy8UczqTIZBKxo2DvOBADDwzY4zsZESGU9l0HJozrnbu2AmprJRWa9ysKRcChY9FIhzbIh5DMA7/gwGMNsNItgk09eBUYWBxbmb08IGdu25MtLXPzU+3t3WGwhErFIYDsza1UCx6Vh+EnZyaf2a0nE6lpqftqcgsHDIcicbpISZT8xYCeNCaruixOuPOx2v4qBNl4v0vScHQgG6DodfhoPgEspy48Y7nx3hO3DQlzcdIxylzB6xG3zplkjMDICMvoc/phlUi3RD9rQatgfLjrE5Go9cEnNpLp8IYoZX9aAVNDSqf1x/rC14pr7qEkbWn8ftzB1GzEQQMfNCmKhSiRvmuAavJskJMxVUDsvpeo5xA19lmyReKCcTp9+6nfCpSzzbDU+FfMb5ucetStDQQhqw2CYf8whVZ0It1tNzxdWdfsSuWYxUihS2fPAk/0UnLM8a4tKRGX0cTlodc8OIgI0bu+C5M8Pq9lpJ3WnmTnP1EKsuY8kWA8tDudABmlen5zH57CAEmiiJj/zMycgaD9YoKPOSILIOTkIWHf00dRjUy05KajWEQfN1iCothI35jleFQs8exDCmvGkHGbhRtSf7QAzStusXqc8PZeSrIZKGI/BpbTiZCxS0WlNhvBiO/3FpFuMnXBZYjNqcOHjzENrF2Z2Kfj9Ukr9qPIF+w4NRHyKl804SbYhFHuPyTdcLQIFYcbhNipnAxVk2IARD7GrLFxKQ4gIn5KvD8l5lVfpnpFRimXSVKisFsKiFqypKf2tgmilwgS+mUIlToA4njE1KMU0xOARJ43DJPYB5w669AiRoblA1t4pQXyR0HUQ6owhNnaty8KWQZExo3V4HwSCIHr2DBI6ObQiw7ATnKlOzFYsZwDVtYrdirXJTKGuayusWmJTyE4SqxGLxhTFhxWPwJRkOYIYRsyNI4yQYrQLJ3MmdnYjBYZjLGGMyBjljk2l27mM4B5NJzyhpYDwOYI5HO59EIeOihQ4fo+CqEmFek1liXy0zgmQmGZ555BnMOA2NpckJGRkbAT6YcXzxsLMNTNpIDoF8FH+im/thb3lx48a7SY48Fk8lM/0Do9tvDO3eEOmR75NJMT4nffM7CQViRy4fFvjWQ8OlKQkEHl8oYfgFnwt4uR380Zd0WzR2QVU9Nl5WsZs6qElg71AnnOn70iCxHsezpmWmMQ2DEmAxag0YKsg+IqsYMRkZixDIngIxUwU+INgET5PLxB8It7ZtC8e4uLuzthu+UMYA7uzppF8ZfQYJd9+CDDzKloBODWEdkhxv5ilBkHB0YQsCJDUCFI3GhkRuES+XCgQOHdj+7u729q6W1dSG5mFqYj0Rjre2tidbWsdHR2fm5K6/caQb+KL6wbIf3kN4MrJpCUVdLH6u72Y5yhbHw5EBTJjC8sX3X5X1NrW179sxu29LT1NwirJ7FPnZg+M4Xb2oFMYCy3ubggUMHJxAZDDLa+w8dAnVbe8/ApmHD94QXEuKQAREyhus8eOazHFRGpZlLkAycgRTBgWMyXZpJy4KgQt4OFwPdIbNUx01+Tn6hGfWIDkCjQwBL0+kJNJz/RBM/YbTmKZdJ++EvubUGqM9GRg1WvbroinVxot/rahfdjOCtfFkOvi6SCzEQXV+51oVIfOM0Y+owysbDyhrKixDkG9eLdnAQrm74/1malI2TdJaQ7MtdC/uKD5MRItgg2gJVoXwPL6LqnMwAn2UtkZxPGPoZ/qCh8aoURuwqh1f8uOkYDBYz8qUjC3pkyZnlzvg1e4XWh7+5AtdK5opPjCKMKa3RjMxeBmf9rYhokebOn1FicEswjzFiXJNJw4RJqqpgDF0TSCVKEp225cB9MS7FkORlLEk5gl/c2KKiBIhTjE0gJCOxMwlhRiPVdCjbujhe7i7ofC8RUEAqsa0BwMvb2K6sKrbkygqxZs2qYMcuFaINpMmFFI4epLxdikle5tEQxyOBlQBxmaKLNgI2VzKoASfqVRW44FCsEmVquWKyQ5cx9IQosEp5BEiKo8lsOW5KMpGSmhEJAFKpdCFfYKpDgFzkJq346bdceUlGzOsae5VMZeqVqSWxKlmhJ2+uQjETsBi3MhMrIcTKbkN5G/MVp8xGidkNBvAbCoUaQ6PWg+YvBOIylUSJJEnQkjkbycvNwuQo9rDBb5t8sY2xn3kLMeSOW3w8UhKpDfK74frrUYylbJeeU9XAmhvADGcyR/fAAw9oG52KnnMQD6fWiQhIxVhS9xlPIsH9YfEYdcj+uoWhHpzv1UQ3Xi3ma5GvysZOZd9sT7d96+2cmB6Ohrl6j2FV/ZbrZrpyIJ+NzcXxl2+LPfMMkFms1gDz0lEyK7C2glVMzf3lQMyyc9l9sTATbNuS5ZZ87FrKgV2s456Sw1xhaDoYD3RvhQ/MTk9OZcI9I9ds23WTVZa9vgPNPRQB1Ol5TmwuI/6R9BixPByPyRgEM8ZKJ/PDOBhU9qSjqTStAAFBtSKE9mI+k3XLtBpylzpXKxc3KgvKltBnHlZn0aCMSiwupg8cP8E1gV3F2L989bt9G/snJk4CGW9qaduUfXb0B98/mCoW8lwjyF3EV/ZH1Mb1txE42aOLXc3irt27dyPRCeFRmjnedeu24c4Ip759P5lcLOTzvS2l2UNPTZVKqYmJTVcP9Pa2CJsU/sefrJ0xDviptWVkeCYeyKQWp6cnNm2WzhNiwhPxIHxSeaVAC5ur1IQJCATGF61jSUXrwAijldZR0D6rg6My7QM/3N3R1X7Lzs3w7nP+0Ha0oNatGZsIoMqgCNYljK5C81HP/raoC3kp0KsB6mq5yTcPZp0d/uZTtz9knYlZt+y8MnqOdct6nTPSaUCE4MjICKMbMGq+XL50jww+Yc99PjuQKZ7lBs3ICKhl+AZ5sipkL9cTkFysn7pwGZ1edYkIpgJvv/12zzqlRDB5RDylo2IZD6Jj0E+Q/kRhnJzBKANJlqvGVWmjaiTWQq40kQ4k89bT42bqkmhZMywGnJiijIxjSBkrU6YujTEpUTJxKKBirIpf9pZRZJzsKCNKRLGEMItLmPw34PyaGIS9OEMCY6S2Z3TKUllHvpOO+IpeAGSp1DKW65ybiUzJxKRgFmAHq4p/DQxlF+aS0xO9I9vFelMYUyhFB6UEO6g1naIyGPWliNxcTEZOZhJvYkkp5RME4tcUBp0EO0zBRW9i1do2ZaZuIADbT95MwshyXymt2I3M5bJfD2NVpg44q7EoFilWIasXOaNGrFPryNGpdHbh6oFETOZYZcbVvHFIcSWVvIB17VLJVC0jKCHcIxiHfv5CoeRLc2I5S2JnDlasYEIcq1Smgo3hauxnMjJZuxYsGIiV6V4cPBBgfg1ZUkWSu0Q4b8nUJcA4pcEUQL3mbVmcVf7BD36QD821+X2xl5zVNbC2BjAfD4YB1gK3cWI2YNIgEasJOPc+5gx1IgJTCqasbsiq07caIBbWD38/ZTHJlNUpZ7ZTUT+WIPdqmqs1K0Qt/RgqcSu5+LBCnV2lV78q9/z+SD7NVyhmF4eflMvZ226N7dy5YG+IRG5sLnwjuqWU+sPuxbaW2E3Z8FAejpSbsGI7cpH2QikQPlm8Z1LOz2oi8cn03NGFcKKzfybUP7cwOTMTtLmfWNiHXcwUujNTGzZUTsCmqhkFR2Qyc8tMIEdSEaILdL1WwMHGXR2YwA0wAyvMI+nFRXQzrCkMZroZ1wLrsihCaAsGOBDDwCMvO7t7isF4OJ7YvOWKnS++EQP16PGj8Wh8w6ZtC6n0xOjxaLwt3BRPBCKHx6Z6guHWFtm6vFzdEQVadCNmCXBDFc1KGWHnrG9Pp1MiPGwb8xu22N3VdfTIsa6ubl1NTRJiJZVhfuhVQxu6ervKUzPlI5HwtVdy5Yxka4ZiRT4gFMdGx+mVGzZyNWugheP/qh5kRFDGEjVQGKnHGY18wfxVhopwBuxMu0pVnmfhgQBWvIOAL452pOF04KkuSiqKKSMAzjnZdck7nwPP2xqDQ3rK8flcgZdoO60a8PobXFdtRbgu45sgYXU0vN27Cc+DPC38HjA8AVHLEmuQq6Twos7eAXK2zHCOo6KCmesIHWwKy+3s8fsxMBDM0J4XwvomlVarZWl7mNfBUdOmVKOG4EAQowfSZAg+6hZmjoJOJ9GNUTTi6RrAYEb4MuSNA8WyJuu1KOxiPnB4LpQplvdNBYsyXygS2v1vMkTkIqqRrY5BR6BIYSPJDYBC+QFEZCu0f2BFUjjyW2xR0QJIJLOxgt3JGJfJTjEImCaTaEMFs6GFohzFKQJfqNCxc1EO8GChS7gA8xQxArG/jN4ngN6uXIUQMEnkmmqiyYntR7ULdhRIM/mJG5OPJCg15k9MTQqG/iNmp5AiD/OrpA1DgjFHSSU2H2uADYFMeBp7lAArYpBrXZiJTymDkCIvkghRxmmIk2AhUkPMAAHucjhpj6YXekMpPl6lkOwwpMFv8pXcjcmqZjaB2KuuW4iRUogpq4UyU6+SUIsjhPBIZUrLSP4NP1IAweMk8roN4aY5Dx48yHIJVwV1imUiNV1NRg4ABjBKKedgMQJFDdcAXfL6a2BtDWByohkwWhB7mCXwu1Nahn7i1se9tIssDWmcEjgywCuLSfBTJ2dWFVW06Xdz1j1cWHBzLHLzTcW3vy37sf8/fOIkXCMfj2Ruuyf+hjekOvt2T1htgQeuCjxsxQvNb52e+5v2xQ90s1jG2lBKvDYTbkvBTsvNdxXC1wdyyoEY6wryEaKawPs6u3o7O7phIIVs6vixo30bSSvjwfBd7F527fIg9akTJBlKEjoBbmJrql3nAAlU0xcM2JboVeRFtfCADbHKomhMX95MxQNMPdMcQGKdhjjVqqm1c0NfJpXMpjIM8nPVMSdvcV3M4sKccJZyiYPq0wvJowf2xWfLV+10DmeCGDXOQah2NSt4QQi1Og7Nxma6N7E8lOXee+/Fwc093IB9xx13oNbQMVDXCIROsHH+FpTff//9ruAXCdEUCSaiwba41RpBnggqrwrwzZbYoR3siMKaOYW6qtWbYnZ3gmXVRkRqKgalpV35U0gZVh7ZtAFrvyqlZHJuHh390TctuMJgEDWmeyhwnBtaL8Bc6WNonOiXDCfp4TrnVSH4RnjOK5JqiIE8Pls4GDV5qePVVM7KXrg3rBh2xyAXTHIp8BnUJ62A4URzeLKVEMwq3kvxn30IZKN6ghwOD2NHMIGTLkEIk5aopCsM2DWeO/VARXlFwKvDgo1jOD8hqShqT1k6ZirCF/UPeYegpyZxEIVAZJUckJR6Bea/QgFRML7xjW+waGh9dtip7GEJXJS5xQKiWaSR9GRjHTp0itf8q9At8t7zOQLMJ8gQ0zwyam5sISO91S1AWF8SznpeMcnEypJpX36MvWz0HuFOPGJnil0q06Rqs4WTxXw+vbWjS67jFHvVpDN4FJLRCOBzViprlTa0TpMFp3U2NzPQ7NqxglMfjFicQoD5E/UEryFZKNSHXDREYNA9XD+xxqn6DE61PEU1gVQhngRsoXOxGwKkYoROjZU3kIYwsbelNnTRsoQLMUJhGNSSBK+sZzbEB09G4k/MzW1vmR/e3CmQhgpmiA3NSryS7RZCf+UtsZ6JTVrzyC/4HV/VjxTjDB4nkR8nxTM3OaPHLs1r5UyYw0NVZm8FBvAZEPOCSrLmBjBTcyMjI/4xzvO2ftEUEahnSV4jBjBZLO3TZ5Lvyt9BwxiFGL7y7t7wffeHr91VPHSkmEuHB7Y0bexLt3XtnbXypfC09R8OFt64LfK/Qz3Znndmyouz5ZwdbuMI5hLMuRgeDm18X2823BqVGuTTTQz17jvcZ0UTqbyedsfe4sKBZ/ecOLQ3uWU42B976t+/k8/m4vHY9m1bxiengnY5Eo3677mpqSL16hvNnsM2kKNYj9i3CEJW2dHHkLXo+owxM9KsY/YssgKYcEjCYeULG1riwcwk0xOp0R9Q6rZIfjE1nhxP42ZbVrs90981mM6kd+3Yll0QE520MFbqkrlKcsRBIG9109yY61rTNQQrGFFsdkIDgBJNBTbUOKxopD6aoqY1b+GeXZ1d/HmBtDAElEos54ZA4d3PPbcHg37zli2+7KzNrWHO1HWZr9stfHJX8duDclKXG+1lci4d1AYsfmUFyFfSc0nqBZe3fiaomOehAXz+tymWCc8F1+jnA8E0LqYOLHp4eNgzgGHU7mDfGdKI5Yn1iwUFcrgi3RtEcGBmaGGknmF8htirk2HikhE8HOSQ7f+C4OdsdV4VA1jz9H8Lfnc1RReSDwHHyiylGJ2Kjd+MIyCmWZNJI/LGPFaHLpk+M/2QgQnaHUm6PlXTHA1sahP98Oqe4kIBY00sIzPxaSwuY62JmSrhmKNYYsYAE13PGHhiEQIp4ShcSGEmPMFmiS0nAfInkGgbGIgSw3+diWXXG54awe3zCh73kaVhJsruSOcZM7qqexMiQMMEieQlPq702X/yCPS0YhujPCwcxubq7OoZ7Gg2dFMQQGWVL+a3IUxSkg7zkqfKWCUQlGZZMjDiltoQeGPZmre4BbH+CQmCjUdsEOMQj2tvSi3KyILYtibQ/PhfJJcIf9H90bhdiK72VtTLxYUFDpoyCUxKjZWcvVxq0gsKRWOarpLKuGqA64XVgDTmhefwycDfeEiBm6kXHHR1qdAGHpqbtSps0GsA9oUOsuYGMM3A2bxPPfUU7Yrk4KHK4XqwyPOt7j1RfTaEMQOJTq8DxmeDZ33TypdFAwVb29gMHBnZyimNzDLOZu2xZJnvriOMARZcjP7pVGlzV/bPw4FZqzUThmnC6u1wNn57bPB/BCOXb4nxfconmitZ49HQFVds49DjCMfhBSxsXZkkjkfa2ltDsejzJ7Px9m1NfSz9sqazyduu2JJoZu10EJFG3/CmUr1KgDZ1Kwtg/y3sYGZmmjejvzTct7/9bb55pnwxfRG0cA3MYBy0BTIA8xgri2UIxLIyub+/78477+zs6shmZP1tWyI20D/Ekuxjx472b2i79SXXyDRye/OB2XGEtHeCJW2qa/nowxjD9GpIYmAb9UjP9MKKIzuPZhwoSWzC1EVfeCmFmuVoV4htEtbAS91V8zgo4Yxr9AOvTsgRSto7OtjzrHktyxarUQluTeD9Ot5z+UMLjoyMrEABlYa+y8p2dFCvyCvAX4ryaoD+j9vrOV74JUcjNbDsZ9VI4hc8jPY6+LBXEzBDnlWpVUxr7dtgw00WYF5FA3gpkV6IJ4m8cq2uQ3kdg6p1Z35WN681xebVGGKR5VE0EJKLESW08+HhYSYbdG0gwyJAnlmtkhAZum4GcHss2C4aTiCTt+ey2HGm/swbuwpNSGxFZCtkocpIiDGPkfsKgy1oO9YeJSbMvA08xqP8Ao+eI1O4PBoiYOLGChWE/FMDV/JwMEim8phFY0yKmuSCf2dLb7HY2d0dCaODSbw8Go3DtkPX9lxO2oWF+empya1bt5EkEg3LxmrHhvWMS2JEA+PHvDTc+ITg2qcepAcmqTwaalOKX9HqrxTYPG6g+nxvrStfQK2TjFG3YEeMlGnetRBSqUvCagIaAKlJsYIXdRTFkrUey9UDeiyKHxgAUP6Gg22ky8HX5AUYh8PpqTQ1UZe8NTWw5gYwg3+vfvWr0VwZAsRQgWFBQY3eX0PTufI22L1WJg8xDIuH6a8KtpXzWotY+dIh3qDuara6mg3bxC8crNUO/FYp/1PHn/9gd+tohCOcIpuslnuamu8KWGLKuvTYJ5Olp8ZCo4VObgywsrIw9+TBg+VCRlh1pG96ND0zeqh/6zWbrtg62JwPze6JRDl2io4hA23xEKOVCESPYwpW4f2SgYTj5jhoulMs1oQchbUhAtkMzN3LuOEX7OWm/hljRpnQtmCVFKIXU5k1yUDq2QAz07MI5vn5hYGBQeC5qUunF44cOsyEbZlrivIcFi3r9kFFRjQoHZj8AYZ/kak2MeyMh3CYTs2wDgh5tFrgv6wJJBdIQi9kCTQqjkat/AYe+jG5MbzJkU+JXCg+BFAoDMiVk1fHem1UHXzufI18JggDGoLqumQAn1ZDqflBdz2tVJeAtQbgHpzZw7cPu2ikl16qN38NaK9TM1XDz6wO4atwbPgtSJgJgdPCDWCGw8PDhLBNFwc8cO06+cjICOoKZMB+4bpkyswMHQOqcHNt3mlyYBKt9MDV4XUImgalw0q4zo842p2tBFqBuPmaGC9mDJcpYk8PPLO+QfkYGtYh79VthTo1h+SUaU5RS67t19MrBcoI1Fqp6vgliWKqUmY0yCStDhdjTmaJl3s8Y09ADFglB9WOJCURBPMXSPR6B+6YEFPLvgysTqOcFPO5mYDFsjJJ7T1eZoRIXn6/IHefStN5KptGmwTiNOEmzAtyE6/Dr9KHBoUitw7ZNZIFchkOtgIkeg4cAAD4DN8LunGllldI5otiGomDoOFUa8cYfbldwM617RPwJlaK3nDDDcPDwxgAtCv6xAVcWw2QjhUE1On21wYQrzmIn2ZloggtyRXepb8yUst+neFi638p93aFzF4RATCCwU0uo7mAc9Jdr9z4I0lAEd8ywBJonunJsZaegWig2D+wsSkW6EgEmu1E2OjogsEOzGXthbxsX9VBN93gwa1GbM+NN8lhIfue34sigjI0uGUYaxfdiO24ahPiSCbl3gU+e72QA66HAUnHI2uGnBlvRsNgOpFU2KJcBcxkMl4YDdYpa+qQzUzYoleh7jBojVjV0TiSe2u6yAt7GJwUFDyA8UBYDa9xK0RqyHsI5IF4eJPyOC/K7yCWMoIfaskOSKjCekfeM/NMODPJ4IE5rrng95N1jtyydt1q6BLgc0TgeZrtJQP4bBoG4cXoLZ8eX/rZ4HlhpoUZ8s1qDzzLGsBMUi4HEwYtbvgeA4t4wcz0DhyyOgsjrtQ2qY44lY+ERlP3wSE+KAgsV7MmBu7tuYnyYIHx3DUOP1hNlHq9tGRHCFnUhPtTedjOuJx+bDVuKGGIAW1Nljj5SucHczUCCauU3wcBYewdohTIXx28YEwZzIgtFmppYdEJfSmWc3pVWjefAJ8n2DAnlpeDHoblsqgK99rQq2SNluwZnzfPElIIr4Rp6QTQCSaqEqsY3PfS8JWmGs3p0FVJvbKRKYoWXtkrbB6Bw2HGayLhUIxTqsyjGRDDvRt8RIxN8H1pr6spcoXspWQqrur3Mp1luU5UnXgtfZTr+uuv1xwouH6/628Po9QhUyADfQY33wKdlmepfaurP4BB5UP64DUt2WgdUV6+MsYKyYI5kkaTvSDh1tAAps0Yy+SNjeHV7ZJvzIu5GBxeN8VxgZZUisD/Qq44nwrmFllxY2PotrdzogAxcE80kJHh4arWEv7If49N2mG7PHp4/8L8/K7rXhwK0cfsQDuzx1Y2kz6cGRse6S0N93LYAjhf3B/tjMtpwB73nM2UD8xVX9NjB0ZPThWyuc3btgM5lW8rRpvKoWI8IKdosMIZToEahFHKkT/JxWRXd9diKgXrL3AGYkHuf2MuF7B8rtDW3sZZgtksu4aCWJjf//73YRAkBAOmJsyIKQs1dBHYeFECwEymfnbJaCIP7Us9gZbBbFasSb2t+IAN61rHvMkLeKzZ5VKAnKlsVR/VDYVY75SXQHLUgSSVW8shuWjCkVhU1wuksKvYapcM4FWszEuoTqsGMBGBP3sDGO4H29QpRNg7KrsYZka3BzmmLwA8ftoMK4YbVwUagJoQP8cmSr3+QAer8naWAfFH0FNPP927Qf5ptMY6oK7R42IjWDBXwzjpTJSfJMk6hgFsB2THkCRyVz652J2UFVlDEgXCoag8+r0SOZZPNQ58HqSb1IHgsIzs9Mzs0KZBs3jXweObOazkdGj/Ie7V42oDHy4Hy2Jqcf/z+67Yuf3E6ISh0W6XgaQgF0KgG7V36qWsBrg6MT65WdENNFUHDZziIcAabKrGeKLNuXJ4JpmxYs1OIgdMwF1gE+NWmgkXZcbEypi7cchLA3E5mZkQaodwtxbUyBRKjDmqSCVQ6TRpNWNjLptQzZ55AonVDGVWQJM4JivQxHobBgQvIfyZCKe8YoJz05KgMEdnmWgDJgSZh3SCWv4HuKzpyaeeam2O33/zjq2drNkWjcVAyQtjDNOLEOwrHi/84nN4pcYB0+B0KG5gQS1c55KSNev+tBWYz/jiF7+IZYQixzpEPyU13Eyj6gb6U9W4wUxhmdSBc55u2hpUF7d3DQ1gKu7555+n9vViIT3x7+KuTUZcmPFGQq//13X2FeuyCbs0M1N6+onilx4MHDoSiMYD114defl/CFy2NWgmYGUE0bDX5XO0ehPl0MnHRto77r7MrGfmPH7DptMpqycdumI4EoszZs8pDIEmY+ouxSh8usKrnaw0oGfjEP5CNjP1/Ld3735mdnaOyV4Myy996UtPP/10ORDu3bihWLaOnRhLZrJtrW3ZcjAaCLdtGEq0dxw8PnnsyEGuNurptkr79+/btw/tiuUiGLRMAmNhPvnUky//sZezmhr5/ZV/+wqmJmfKa/Zu/bjE+GQJQctxGS8VY9XemDejei972cvQEVdIpSPl2OFMR+uNWVpM7HYSqubnkHKx/2AAU1GXDODTbWcdczGbC0436SX4SzVwVjVAr0OrXpVvti6TJJChSV1vtYRQOTN3JlUoGDNDzAj3McuR8BDiyDCNc8MlQhNJiko6N70JO7EYLM3bmQjWijFyKpGkcFL78jQ4a3AJZrFsqsEgyS4UIicLrYX5YHRGrpZ1SDDELvVIrI9IARcc/JcfiRSCxKryQZlQgoxdZ6gg1uCW5VpyL+30VHl6olw8wR23JqUYTpJA0BoH4Ura3oPJnmRzR9oYbi6QyTiQThUPj5Xm261A23ZiFuZmpibGhmUuxEoWg0cPcx2feci7Qq4GuZXo+CQzx+n+YELjJLQY2tS8fWBvJhY4LmU2jzaK63MDnV/90SyN23NqHppY3V5SFwY6fGgJdeH0l6M2Jd6YmRJiQnlpqInQUK1Pk5XTxAatXHaoDkkpprKkNOiknQw+qXqhS8YIiJTak+se5DE1aXqyCTX5TuYTc+XQ2KK9tdNpMob4scQ0WxzMRuJGndCheYPoInyxqIEZBTSoGs2cstdlL2tRBdQwGiDZsbyCo/UgBq8uHlyODPibf+qlcarAjPTH/rr55psbT/UChFxbA5jlo+j6PCquqF/98LyKrtv56sJ4gXWTeAjPrYNtY9zcxaBLzWd2bqlqPHfknz2fzH3zwdLfvy8Wlc271rFwaP+e3Pcet377nYFrrrE40kp5dD2kbhvJuYbZ1GK8t4cjGWDT0mSGb8faWm66cZcwbmHf5r94lH8rxnJzxN7YjECF+bMAODc+IVutooXFSKCcGj9EmraurkRLIlcuzdjl4eGRycnHMBHh4/2DfUeOHc/b4XS+jGnN+VtRTpkKRycnxpAj3RsGS4Hg3MICDC/W0t63+fLupiL7XpiGZYBGVzIPDG15avdzjz/9bHNHb6KlPdHRvZguzGfyXJ8uxFEKQzm/HCoRlY3ScjXxKSUHs8SwvJGREakH8wngWGH6Fxhw6swzE7944ZKwM0xf8OBesuQPkIv5ubQE+gxalz7G6DLPGaS9kJIYPfBCIviFQSsra3hWt6yojI1Z1PBZ+/lpK1WsVW+M1JFX9eMXQCJ4qiQSoK5gM6nsxaZNx/KxiSll5jWY1CbRQC83kRQ1cEb6+QKN9SIwdnis0J5ciEYEv5pjjqD0YTCWkfFXkebkAg0mIb9LympCvHw9PBJSKhYOPb+H83LFbZUOP/w0k5ZtXd09A0PAiTEMfqGTW2OL5TK37gVOLFiLoWILF0KwNCwaM0c72Quz43JvQaEww8V9B0bD4TjL1kvl0EzKiqQZEBdhqnRBpUFLIRySPHoNneoj1p48drilo6uphVk7gSQCq8+YhhyBEQnI8mqhTE11SaYIgZVg5+2gM4GKRfCUitMnDrb3DoabEtr2CiaJRAGS/JziG5pMuOoDOOs+gtt9Km5tS890NwDgIbcKzNJUbogphfG4eMRj3EsxmNowwGmrqZgrTqelqoz2YTHQj7Yj1RUIsGVM3WwHu7jHSVEhmB1BfdLpVmaqMEERkbqUz1TVmr+Yt2BMkPpn+peHrBnZZzyCBfxMwyzNHiXwjDU9tEQWNmJ/1aDVdvcHqlJKiD/KC/RDXpTuWgmxuoWkAbTDUbmMNj3++OOPPPIIp0dgIt59990ckEt2deuatdN8mUhQ5r6UJPruy1/+8je/+c0///M/v7pEriI2lraCbWXbZhWzWy1UbteHJ2ZLMx8MDf11/HfGrSBWZABpX3isOfiZbO4fPhXbNBjasNFj1yJZhIvKY3irOFh7LLIvX9Blqwy88Rkzjypxhtn7f2ua3pAR3NjCn4EKBJLJbGx2XjLpkEVJZWu+UA5sam/p6W177Hu7Q4XFnVe9aGp6hpOcx0ZHmfjduu3yTDm699mn4Skw9LbWjkRz69R4OsW5Kek0Fwxw2mI5X5g8eXxmYrw5ZA8NDdJYdC14E/P28/nQTKH52eOpuUf2bhnZPh7YOHbwyHR0b1vXRpZsyfUE8ojQ6Y4Vdw1EWVSEQgbZhnIT51WEj6FQFUzkUlgPUoddobCmBgz+qhcACqZZ4PUWzBByyuRVuC5YD23EiNIZFNZrF4ruJa8beMHWzbKEX0zFdBiNy21EIRWl0VUcRfF13cvWx5lHnNaH5q92IdFHmD/KH37mlJ3HKVe9sNQYI+mNlVhkxcGZwnhGOfYpE3kda3lIAdE+xrvd4kgKMQZrep0HRDjumlhFLoEmv+VirdlQz2IxbE0D4MC45mGFPLWC1G+wOVF+pP7wSspqVxXmkr0Y7qFY+Vwun02zYAp3rpRIzevBw05KPsbZ8fH03KQUn91JCxP20Qksxb6tV1oRbODAycPThVw6szidSy1GTk619vRF44nWrv603XYiaQhUW8wYmNSjOfwY+S537YBTAhzSzcdDCts+MZ1qD7QmpNhCsvMjMbrMHuGsRIpZ7A6pV1eABBuT1ldmmfMulifn04GWUkw6S6X+DBWg55GXHLFsMEiAWf5sHMu8KmgEwCHYwEoJhBAnoSmy63HCYBoSAq0mQNxuiTSthAstAmNiPXQmATgFLTHcdRuKFPLZuSyT+c7Jpixz00lFrF/MpKU7vLwvFyQG38Xw0lNUmCnBQXmYS+CNaooVus7Fo9rZnasGMDYOubOto64BfDaE0YgoijUGMIFMqHz9619/+OGHadzbbrvtrrvuIhdtaOwyDpr+rd/6rbe97W1nk/WFlXZtDWDO4x0ZGaF+2W+AucvsKH0OhV7PQHvLW97yF3/xFzVjujQSLcENzoyU/NzP/dxHPvIRrVBGSr773e/ef//953P9qp2j39j5TOdS2spYl3aqcPI/h/KfCLVxdjM82giE1lT0nlTpRZnMR74Sn/35wAZn45NgEBbLMmbl1uLn/5Gjx1KZFDuIi2V7dm5h34FD4Uj4qp07vvvII1/72tcY14D58pm9/vWvZ3SDttZvDwdp2ZvBvghYA7cNvepVr6KfYDlf9+LriPrpn/5plis/+M2HnxoN7k0Hnj9S/up390+PHW9pbz85nj02lU6mS3sOTmayuVIpPT87VSjkY0G7GAotcs27VY6EGN7Od2/YEI3FYDqZ1KJVzJWb4hx/RYdk2II3ZnBp77HUQvLk8YOFXD6dXGjt6LTLpfGx0XQ219LWziVyjkAW0UINyQ+08dA5eSBYvfqmUNrPdcWRDo5wrA4bhunbLMhhFfQpx0pgjsPDw5xoD35mklEBOcoL/CBZ9QkWP/Hnj5seQofx06O9hRAcVAttt3SZkMLAdp588km+Su6XZleMrjkn6hOf+ASy4bd/+7f9aC8+N4yUoW7Kfspudp6X3XxwZmPeQiowO1FmVQgab09voKeLhbDuBR+rXAh6Cwoibw+v1/G8EGVf6vVimVtgIwx9kou7VLp5YIz/ciMggq9G6nkILxoHrA/pz7jVOVmDgPhKF+z5rCzNFVHG21XmaSZpDiNx/LWt5gZQsHbgEYdyranh8eZl2I2gM4jcsV98jl9wuXkYvOLRANKrdDQBbs5ChC/epDFgHb39xC3mzbpiN1goscupmYn04nxTW0drR2/ZmHygRE7v+eZnj+/bfd+b38md9UvMckh2TCqXIAep/miBcHPzYbyLy3vt4sxkYTHd1DkotiiD4oWSFMSUQoFDTe3NVpR6Sk6PxZra402JksX+1VDQkNy7ZXshvTi2P5MpJntGtnUNbctnFqGqa3DEIUBu+zHoDGngzqXTU0f3soKYVcQbh7fHEy1GwlrlQtEukrsdDdohGqbEqnY7Eo6XuSNIHplWOXL0CHdD9/VvdFCaSge7toxko7ACLuRrxvi45oH7VkPFYohbdstklMMUt2LMUYv56EIaTEC7FWCc+vIwGZxevZPSWaDswALnlVfSuB6JdnqD5GKIE6r9pq/prUzKQ4+hyKOrUixhPMRpbzK4nX5FWDAUKpWs2VShXGa8nhYOolHwkAbpgFyAHWGPIWGZBBZ6AoEvfOELv/ALv/Av//IvN954o4as8PaYnqF+BcBzHIVOrioT+924mXV4eBjdEppRHtaTMnKknqk0tFwOIUPRRb6sBQ3gHBkZgQP7S8eBOG984xs/9alPsb0OS/h3fud30MP/9m//VvUiIDlzC+HlT3LRu9fWAGbEBS2fSqTqGWv53d/93XvvvZfRDrT/Bx544G/+5m/YYPmLv/iL3sdDz+D59V//daaIGSmpW/sA+MO9tP7AdXYL0zFEZTJp9J5oVG6mEZa2znQ0nJ1Xh27tIVbKhan/Hl74hG2VS/Ox4udC5eeDwY6ydZ8VvTYT7E03v+WwXTpu2Zf7SsVWK/upE8WFvJyvYEpsp4v9xVA5VZo7ku1obrlsLLwpFAi+6zU/++BnPhmPxxiUwhr52Mf+7o/+6I9qOOwHPvCB3/iN32DZBgrTn/zJn/zxH/8xx7jDs7RMWKcsLyeLQrGYSfOJ2rF4E/JykYO2Zmc6enojsejszCQzq1ihvX2DmdR8R0vzseNHkG0cgRhE2BWL4aAdiwTLhWxPd3uiOcGZ0hiiLDFgaOY73/nON77xjY1Dwx29m7NzM/vGRw/ufWbHVdd0dG3o7e2KxRJz0ye7e/q51Vha1bJD1WoGZsbJkyfB49anUA0xTE17VQ3bhSsx9EihdFPrUrNNC1vzVkUc+5npdIZvUcoBYEV0DdhSL1nD5iBJn6UAF0SIV4FQS0F4Izz+7M/+jAPMEN7UyS/90i/h9ZeFJChG9LE//dM/pedoQs6iZwRNq+Jf//VfGQS96A1gRkw4dYMP6kI3gNH+pTOPjxe/9rXyp/4pcPIE5+eVh4asn3xV9J4fCW5kFF8VRH8vOFs33ymPHws0fPKTn2QdE1dzY+Lecccd3visgn32s5/9y7/8S6xcHe1iiOpXfuVX3vnOd3rmLkyPPsnArh/tRemGxbEDjYM/zokBTJVyM5LYM6Zy5S0CWn5dt4nwvaSPidfwG1IaI0JCNFSixAh1HsOIxG1sD8Kd1IJf86wcpGSSOFqLQWHppk3hRQ46N43jtdj4IgOsLr0sSh598qsP/vU7Tx74IZIavX3gshfd/8AfDFxzJzmX7eChJ772g3//px/5ud+wbeRChUzFR97VQZqtvo1F5UQTIi4szmLAYp+oLIbm8RKbeAoYireEmhKI/7nJiWCsOdLei9UoS6LLJUxHjueYnxqbnZ3BwmQcZHZydG78aLy1c4OcdqmZOnUJYqkQK5Dj0kH5KxQ4gCubs6LNkEwNzI6dyCxMm1R2auyYNXasHAgOXnaVFTKC2JiP2MTpQilbYA5ZcLn1Zq6yhSLol2qWMvAjraNNZgUmjx8tZJJMIWMnTo4eo815Bi6/kiwE1Dxea0rKSqAgsm3UHkFrEMtbUOuv/JziEWT+1A642wvxOtmBlWxqQU1eQJiZXpPWdFj8LpUmMGSuAM6wKYyDPAUjeCoAcKrXve51DCL/wR/8gWcA02ToRWjsDkUr/qBgAI/S5R8oXDHFOYjkO0PXwgaGKanSBbUeT15PgqBENTetLohpRJE7MwoZ+KZ9vbR8HR/84Af/8R//8X3ve9+v/uqv4kVrete73sUm4V/+5V/2wNRhPqVKmI9RVQIvDtfaGsCMKKgOgeL+0EMPeR8JcvHv/u7vaCE0UQxgf1Uy/vTP//zPvFkj7Q/33LQNdgVziYyvY3LAs85988CfYL7sKOjv1xt9hFoT4pF9XjkwIRj/GxkZcauubBf3WbP/C62yMNeVe3Oy6ViZKyzg8KVPB3L/uTn2+lSke7Ec+6IduKvCPqVIQSRe2XJ6EdUQbWldSNvJcCjataXcumUuIGcvhDq3/uLvf+SBn/mPHa0tZbv8+c9+7tcf+FmmQTBgIICqw5h5xzve8RM/8RMf+9jHYKbf+ta3mOp/61vf+ulPf5pYl0jJL5eZHz16GDae4EKg4W2Z1NzRfXv7N20qbRzc/9wTzP1yuHRbZ3dzIsopW01xDpoOwt+xloOhApwae4myYxSRC4t/cNCXYIVYsOTSHLFKi6OXb9+BEU2+c6PP9jdle4vRwY2D8/NjzbOTN1x/Pd3NiBB+hHKhyfdoiBIMa+NAadzkwrQzs83sYcN8ZYyNwR0C/eXy4ahyghBS2aXMwAHDRuwJ0SPlAGokOdPF0HChzxVzmCEmHLWnVYNdh3HLjC4XO3/uc59DqlVVmXx5Nit5GFJ57Wtf+zM/8zNMm8MuHn300Rqwi96LdkIZ+QQu9JLyndnzC/kvfynywf8ZZnlGPs/NYJyTnv8ff1kslsKv+I+h9jZAGvkiGq8K7+v20DKqwgYcpBgj93yMjHnVYPvoRz+KyGMAl3kGVh9gHr/3ve+F4WD0ekhqkqypd2kR1jQ7P3Jqidy1B/rD18ttN0eDsUi1sKKLeOq/xgj/FkauwSKxzT8Xrpa9u8RLuMP7K1aqG+kJftdgMpCaXzU9XopqRyqd4f6C9rYWD3ru+L6P//b/275h08/83ie7h0YmD+394off+Yl3v+atH/leom8Em8dYYnaExU4sWFiO6upcXJ/JxMvJhLLcqYm1D9jZatNVmXlOOgrFjCJCCBjmZsVF1QXsiSP7FucmUlOjhXyGc0DGD+0Ox9s4FCQUT7h2nIDpDCuU5uZnWS+dW5xLJ2eLHH6N8Tx2uFwqRBItkVhzvL2DW2LIMjk12pzADRLylWuZTatJlix+1jVWhgBgxTYEMyafFAufVIiURNvKqR4O4GzvjiVaGT9YmB7lvI9QVHYkFYVlOlOwTrkJIEhwaVJx8cBVHdyKWBQVjXHemp2GeSlxEOIaq0BKXi7lGicAgtr43ExNkEHsZuJAeZk6HMZHNCFtrYl4NGKFYvmSjP47KA2ekZGR17zmNZz3ibplAmpfHvfQCAd/NRQjfYxzwQ/P/2WPUAhPhnyWpTQ491Bd1tX0QQBkrKloxvJCyefT0MLSmqy33bFjB9av5vv2t78dgcWILZOR/sZFvH7lK19hghoL60d/9EfRP+Vz9frZalbDuce1kgHcyAewQglk8G92VtVWf0srWraD0zD+cSbCsU9YgK4KRF3MGA8/9VM/hdZLLML1vvvuw0ZCOT5XzSN8zfBXqCktzG5BGrCqZnScdf3BBDfUSSHOFW2S9zIPXZwJVR9hodLC54P2AkO/weg7SolPWuUfkhTJGirZpQ8VirfGI8OZcvlLYeu/Y1d6WOHBYv/aZuWTJJCYuXR5Lh8pJ3oWrebcIt+Nddeb38vs63gxNCk7L0JX3vmTt979yW/922enpiaZ8YUrvf/976eHMCLFxwZVLIB/wxvewNqMQ4cPjQwPazWSsiMW3lg49oPd/x6JRq695rqhocuY3v3/jjzd12O24+Y3xcrZgaG+Y8eOpwuFFFcHm4d+Qj+kN5IR9idjMRiTBKKisdCAHoh1RO6EYxIfPHjwqiuvvO66XVwRzHQNTDOdWmxva928aQiVF6mkcwoqsUDCRBCZIAbovZi4uMlCLxUAv85+YIGTNRlR7eRCzZOKQHhTI+N/rJpmMlnLAleifjCDYWQaUvdd8+UCoyG+Fq+b7swDvRzXIguuP8GG9wxg+DIDCqzjYoEJc241REMJAytYv8wM81bOg6nMUHcNJF6AsU+AodXwrgXxSzNdtxD6J3lpDaxbpmuREd9deWw08Ml/DKMaC0chEzF4ubUs8/f/GL7lZm5Zc7jP6mWPrGGohfVKsAXtGHzRLCLQtfR1d239/u//Pqfi6UdN16LLse7gr/7qrzCDda+dR533vWjIGnU8cmEIjIFm5Uhe7uvgoEQ82gPXIbt6WbBcqF6whBnBrJFGZlWF1ACI7WQ6l2uRuB6Bc8yNKnwC7wsQMPcxwW7/lcCKnePQQdjC/EIqnWpvbcG4VETPPfS5Uj79yl/9sy0vuZ+Q3i1Xs6nn4//1tXse+dJNr/4VoYvUsp6T/3YxlST/SHNbmfRKh1sCl4zq30rOgom4cJitvDGhDZ8TVp3E9W3ZfhVJxCI1SFgR1tLW0twcszf0FbBoJ0czi3MUpKmtC+IWThwgXXt3X6yVs6HBjkVsL8yPzYwenj62v8gyaZNjavpoW/emDcPbN19+Tayt3bIBLuVmx1paW5s7e1z7TmxuHkrG+cVoXxFzUKXQq0V2KRSU/CecpeJOFD8CGGnnSK1AuZhPzZxMtLZFE2QkZBGo5jUJ+UeIJJfMFLk6BYNE88+g4yURjrvKURvI2IHE6xACa9aFQji1yV1+DB5ZgW+ABFQe8fEQCbiTmRBHkCRwog2MghFus+crxuFkgXK2YLdEHCDCef7wD/8QneTd7343WrSbquqXWQFu1uDNWcSsdkGO8EUrBMk9t5eGQNxLwz2Ac+uABzLVAZHeuULnkB7EwVqTgQGMbsOjRyegOvJg/XoNhJxiQe6HP/xhFFe9W44KQbNibzBTDnQc5mlQg9m6iK1+DutqTbNe1gDW3sy6zW9/+9to6jfddBOyHFK86lOyFIzpLKxQDFp/LOsoiF26yxxs7PZEMGOT3HPPPZoESB7mbcDDGxOlbrFRJt70pjextB2FlZZjESNvRjLqAq9HIETDZzLZ0oG9hc99IfD4o6FMPj+4yXr5y8K33sYxrGbCcD0IaSQPiMXuAlL1EnULX0OE5J4RRlpqDURujb/7itRv/laTsfc4DtLKhfLPJsLDGatwnCuSrDDbhOQxrN/eNRAyY6YO98cUjoeC87n4iwauAUaqR3i2xXHQDkNnb5VtRZnAsdi6yUImsR5ZfoziiKLmMdj77nvZR//XRx/51heHN78qYHUIu7WC//W/vuf97/9zbCG6FmR/6EMfYpvK+MkTU2Oj8abo7FxyfOrkwvx4mnObI+DnhCpOTp7P5+WgfwqrGi0fNl8+uixWMZ0NTkS+VMj/Ze9NAOyoqsTvV/X293rfu5PudGchCQkEiCCIQAREBNxAEUcUQQXGbXR0ZnRmZEZllHEZHXH+jo46OuKCoyADqCC7oGwCsocknaQ73el9fftS9f3OvfWqq1+/bkIMAfGrpOvdusu559x769xzzj33Vnd3t9ZjUVZ5xCyKigUQrGjsF4WJMCYJEwMEzVPIRgzAiYQQHUap5kWA76CwEY+6ixhNdXg9wEdYIKIsvAnVeh+dcBjq5OStoSBhEH7WgryP0KLbH0zAkzDu5RpPws/HBbuEcMg/sMDpOFrYy1sgf+kW+Na3vkUR2Aj0EnDxKSMfPRkPVTxaicfmgjWUaaAsj1v2TzGg+/0loACzFb+4e5exdzCrRjJ9wSowdxZ/gmMjxV39Rs8qdrsd2D7izWWEIAG4Z/jRkvgcUQuWrIV1MXIOP1yYnr54hL0gTDAaAeVVgOE87L/ivAPgIFnik7YvA2+JkVyq0/n15tSvj445mGMbjgc2egSWoXdwHtnWWSz58FIjXGC+iqCfRAdbgM/CGMkiPH4uxZmptN4yH4Ja+dM5pQgzo6cYWfWTunNzAKlCLhyGeS6f121IJJ7B5AvWNNGbzJ9suA3VtVLCzmWKBc6q1N+GtVluvf7Kj/c/eS9ot6FPAABAAElEQVTdvXz9MW/42P+rX3YI9StSNfbzaHCrm2sawVWGiSoimDpjRtArYSyJKqwyKfocCziZq5oQD4xcenZ8ZCCZmKlp7Ig2tVusP1pFv3wliCVWsarjm0xpmqZ+2eqqxo6Gjp6Jge2CqGXWty8Ps9gbrRYfXMcV3ChyJidu6XNNKQjJxYJwwOQ9Us0oN6c9JUVf8lv6m4tx0tnNZLFNKhAwzaD+KKFqLfIrCiU/jeGCKtWq4ei7NJL7XCqlRpWOdqOcTAJN0SHJKos0rddLx4lU+VVY5Rc1WRpQF3agloBrkKqPdAlHHtOjG+DpPA0+twLMJo4vf/nL+Fq6zM0lQbAyDLxnOYeFAMZ32Bd7R/FncQckefB/YTRyMdiQx0hCrFoo7XvBvoBhCFnkk2kvIFLPb9Xa95au0cIqOi31uf6Dum4eYdEkuQow+hT+8DhF06FXXXXVhRdeiJiNp/Tzi+sLB72yAsyY5kKC5CVhzYq2QIinLdA/tXnbizBbN1Fo0S5YsmN3tU6iOPoAYcRuwiqSu7ycPCErA5zWZ5WGVEYnd7QgINx8883IDYspwCx8gZJ+D6mXXcTsFH0hFWAIKliFbc8UL/9cZOeOTDoj5tPx0fwTj2XfORw69y1mU4tmdpot6cZ5oe6MdewOboMTBhNej+7ubsiQiTqRn/zFz+vf9M7It7+RvfVO39an7HDcf+zm6LH3+XxXqUlBVCnnkmnSCMqpG1wOJya5s8Y3+8yOvUPDRx51RBT3CdXraobkJo0xOjJ87x03H33MyzlaGWQwQzIe9PfKeASWVRw84bDfPXxj9crOTxa2XWYH2t52cu6yR0YfeOD3rNbCZFmMPeWUUzBh/uhHP4rHY9jSzPz25ZseOfrQZGOjnUr4Ht8R+vW9Lb++20TPZG2eS0PmThhtinitU7GciGWHI/Jh5bQD+hsnTvHmowYj+4IMKisxmA/ZwIwySfzmzZs1TwECy0Hc8VwgG45AbLlEy2X0crCEXrEkoBd+GeTEY2CDXsIIxNyl5Za8yIN+ThbeL2rn/eLtWLKEJCJh88LqbIS5gANpz6suRCOglz4fCjCE0MianH25Y7Ojs+ASn/zkJ2FN9MiZZ57JdE6M2+b047nnnsv+i8svv5wXAVMa/A3PHw3fzbYv1b1o87xkFGCYhoXDhGWZJQUY0nh54DYBObTAkb9fVB0Bq+HCggzr8L4URL7hDW/A3seyMJZcnF+4I27qSW1pErDE8Yq5rhBLZGZ4w2d0BtiOHs9wrYM2sHVFegQugefzl8TZMqX9kCU2q/SD+TU6egIDjEz6P1OUFCgxZx55lnzqJCK3uNJF5IloyS6l5ZIZzjNJqjiFCLmY3XQuXULnBzSRGgFVKadbMaDRa92tqGuOPeOO71/xhxu+c8oHvyzLwvn872/471C0atUxZ7FPSWpkXiukrv7Uuzaect4r/uJvp/c8c+t3Pv3zz77ngq/dhsuV1EaeubsKem8uMSoPiurc5QhvToQHlLQJj/peSoZyIYZjs/heQ6iqrmnlBr4pODXYZ1uF+uUrVTZW5mlSaWTpEJy4Yhyp5R/t35nPJWPVzdHGrmi1rDjlUfSlKSVny4p1iIl50ZyJcCzO1C+VGX72/2byBfnKA0+Ck4OOEyp1pY4tZdC2UbIazd3rMa/nwNgp7zSWAiPe5S4AL/lz0KSU5BVYOtYTcKIEKw1Gt25pfVeGhFtICpc96Ee5O3jLsIBOlVGhUyoho1SFdazOg1TT378HYSOTrxUUQFVJXJdccsk555zDRrOKO4PAlF0bHBW5ZcsW2MiHP/zhb37zm+z+wJznMhAt2AgiPh+rxNxx33vRKsBQrTCVm0uCG/OSDOiNb2hhKE2QjBUDMtHmvMTqR52k49l5SnfrJsLqgU8Te1f/7BRg2uLqq6/Wu6WxBzA3s+nusssuw/7NzkzvAEJ1QejHmM2K2Te+8Q28WN32HRtHATbr6wPF/A6O0zf9DbOpcCHvm56eQmVlVuYFQxshP6MT+R5l+IILLmApxoWwMMCKsSvEE+jp6QGBhdkOWgwMxZqcKlx7bXRnL5tJWGyM4CfiC4SKeeuHV9ucXlvfwBdpSzzqoOFVuSI6Dqdc7iwPoq7ArWh5FCqZsYOrYOJmdT729P9l/3M2ePbZ0Te+zme83rCRMLN24LvCOgP1hlnhQxQkzczMwihxC+QzwV11/vHgbNYeW9fkj0Tn6y02TZT72LsuLmaTX/63L/7h4YeHRibkxGgxeFv3PvBIbX39qrYh3/CFzda2phVKKGC2y0+f9xrj1GOrrJbzWlrlg2nYqzgXGtVlcmKsZ9XKs0+ZXNt0c8g3STtLmXp7Vad11omzP9xY+5F/mclkHEFQM0FkMnAgjFMDgwftiOkBfQmNEWsoTBzdGCsPI5NsmD/hERhxaC6OoUb1JR7OokGByQ033MDSbnd3N2EGJPndJGK4UF/ZhUsALRplG+DUSJvvi7yrAOzPDRGZjgYT9ucQ0Oq6W6OLofdF3p9qVBkXmg64jwcEODVoFvGcFGBcoyH5uOOO4875llg3vv/97+M5gr3MbQQWyVnyZds5eDLN4wX0la98BW2Z0wfIuS86xn632EEryDB+vkfaQaBFRpRpBJZ3Wg1NscQszIL3MhoRFw8k4Ex1NNi53J0UDgI++1KFfgswzyFf4mQIV3FLkcTBkPAucCZMEsYXTDDa3OZm8wY0NGLgSDqelwI+xkux2FuGoQ2xRhckrEtpW7YX8vMXhjrG3tKeGs9f7UCOBX1sfly0ijl5WNQCN5/S6XQhCWrtRFK19uABR6KoX/oSaDLzuBFYXfnWQDRebThnNet8pXRv7aVClAYm97DfAHE21gY4xktdHWuPeNtnfnzT//v4tncd0dzRNbKnNxytPv+zP23qWSsVU8bwFXLZ48/7q01nvJsn65hTcsmpW77z6cTgjrrOpTbLaPgL7/MQ1MlulFTnEkIsYZVGPCHbyiSmzUK+oaE1l8uEA/5CYsrKpVgfFJ1Pyum7CiqtDp3V9JuBcCRfzNn+sDiLORW4VVJIFDrdPlpjVG+/YNbUUF+0a9F+FVwNXApqFCXkUX7kif9K1SVM/RTw9pE2mlBWY6ryEHQwUTDVTRLkch7UUCgZWCRORagcqigNprFQMo2kz2HlQU+yOTAVYCQzSSWsERDNv5RBxyhQqh4ZPSpAdicPngGWxbliuXAwLVtH5IInoNwy3zHTLcY2yYMWhC5Edfi7waCQ7e+8804UYF0Dd8z93BHjkf9RseBjTqe5OV5kAZYBkPeQwSouer/IkD0A6Ghzp7sQonvHnUp0BfrR23FIxW7dDA+aC0uHG/PSC8zXTxR9ulFYaGUNHQOAnryxAXAAJpF6cdxtCCY5GoixxT49r52bF218fOqD59fUJc6yZ/eyGyjvi1vWYXsmX3fB+76HhMqGbAYixm/0AToAgQAdmDU9wiCgcdC1EHZ7iOrcMKmIsy+gjVnQg9MkZuzf3pvPZQv5AvoPg0Yb99htkHnkD+bhR5hVAfGzcucMTdULcdcDmpp5K9DokPJ1Y9LCVtXp9sQXDTMfesu4/eX7Cr//fbqpOdzd5csV090P153zuJhs46f5jDlJTlOgISBsoVSrfXHCrLE7Ek/vaJ6rc1ILSjKmx1tvuQV3GryX+/v67318YLYguZIF//Z0vMXX31m8IOrbDdNP9cdTvzfD0VBkbRYH7Mb6GTv/0cmB5qTvsHAwhGWLrv/1rbcduerR9Y13BowchzcWesPZqVjskHoz3uc3su84Y8L0Nbz/n2czqNcy58mAkf5Rx5LDu0EYEQ3pE29n+o6LPX7Ii5gG0FQZpRDCsGSFmWkAlQkGirsIqzfE814wGhn5mD/Rn4FDI6BwEkNZqsB/kjvKM0xEj1IUEj1tOIMBk4lMWWpsOHMXKd42I9W5QECjUYpY6lfLnfolQkoum5xAGMcYB5OlwOxTGh2hNz+TmyaFfAI4d5TZGvcJVqVMuul0r1VKL4+DavqRtTJMJHiUQD6dxYlrnLrH8QEc6qALYPU466yzdGsDHC8AKmK97tprr2WB7iWgAEMaTl8vAb8vCEFANjo67DNOK/7kpyI5KlFP5gnE4rPOMvk22yJvTfngOCjP+r1jUZe58vjjj8fXqaxaeKA7nnGBhkCMa4spwEBjALNdCCAMUcKs7uJ/yCBfonOZu/V0DGS4GaOd4nq0lyHzPD3C6/QHIJ4n+M8KllHjrGRq1lqZrSowJd6rHhbmE5WjpOjMq5ZjkPVV0i3kSZcv5PLTg7tCXWs5f5HiRM6rRBVbUJPKglOu+KpysgYBJwt7qfuffHB2crxr7aba9h4OTO5/5tEdD/6mZ9MJPjOoFWAjEN508jmOFmgYnevZsGZNj+xp7F6vkXRxcx+fY0AhU460oOio/loBs6zp0QE5mhrvsIB/apSD4tigG65t6QiF5jYp6BVgELA4GiOVROltW95dLObj9c3oo0G+YBgK6bOy5yMp1asaJZoA/8PBgGo43cCSQV9uaE4XlYSF/aC0cKfQ/OS5vC6wUj4HlMS7iqdCx5th6bA7ajTDkMyqmtJ4U7W7Q8CBpTR2J6ykB/etdulXAW4AU7q9id+MfEyKi5o4UoQ1LQ4ERjJx4FT6wSPPZRfMhvArryJEkjYlEwAmqfqxEqQXSxwSL+IZkt6fiQIMmciBbq+hZ9ETNIK3P/SjTtLxWnR089C/TDruSHDjXzKBCgowtKEg8f1MPhKlRXliGOLIjvhroQCsXSt2R33RynzmgZVbfEFRYkvR/BqoBm84pRizdwp/xNXFmKo271pfd++bXlW7/AOfx/uCDY20rG5cduIhlDNVuxCIR2bFGZVjyspOinbzvCgCLGAmUmzFQTQRXihOtuyzZddrwJxJciaWa497UWDrQQJUdeODtT+6uRA/00j83L8hWfjEbt+1jYFd076R3fYrx+tOnTbMQtFoMBs/wEDwAFCiqPdZcVgiaAoge3kidRHJCWc/+MEP6FDO5iXbiu6uxmdGZvaOwj8LVrGts+fQ/Oei9m7DMieuqd722emmSENT+7JMqDh7/N6OSxOGf8Y//am7Hv7Lolg2hc/XRGfPfPn9pj9XTAQTlwcCv0r7i+lEg+H/8Bujr3vQ79/+tjMnrr+t/ld3ZzCDKuxkyQXW39PTwyoKF8u5CJRosAx1fbo4mhvyIqMRoZMhigKMuMnaC4+sJaIGkw0gaFYMVyDAYnhZAIICDHslG2wFYjl4iUZw3yDeFF4izKWUisc5nhrxBhKK6O1iHxEphkd+kBIqTLdUtx8qJYQgieouogt0AI6G9ExYx+gxoJP24+7uiMZDGwJpMYC4le4HwLIiIElrw5dd/J8VYaimF1A8aDSgcSfMAeOcauYqwPSUiySQCXPHQKNr2fe6yrB98Ty6JIDSs7bYiwftipjwPpiNDZFzz8vBY2/4ZZAR7LNzHAl71umhs99sNDa84AR6WxsS2GKHGQWLFbvsXA6gSQNVV/QkjBGNsgy8ioTrSIxKGOPcKmAvWJ20W4eOLCNfg6WsTuX12Q/WsQQ++5LkYkvmMvT2pfgfmYcB0xg1eua5+zkgYYKDM3YGD+MSm9VsUdQFt9a5kFcjcvinm2suoFJQ2GDhwsU5FpIlYMtXsMVpX7P2+WrYXNF5IQ2HBVDOB5WygiXd+NA1//mbH1xx9seu3HD6hVKJYT987Tdu+NpHa1tajzrrvaIr23id4UccoV6NsWWGKMuyMFsHoEbBgUAPYQryvNrVg+DPpTICVilgRMkc5V6qQ4mQKJVREa2CxNa1raDCbHI2MTHWvKzLwmXXH/SHovkccpFTwAWVTSYSQ73ru2p61lfXhM1UrrhrbGZvOlcw3fOu3LxUx38qLKdC1G1R/QXZUhqIqGDpeV7NOnKuSyQvzUq0kDSvtSRCt5uq220eVZeS7YhX28B0aQe0yiywHIx0tI5w2s3pDflxUtUPN3AvlaPxRKjUKRKtOkTBUfhKgoOTKqIyOgQQYZvyZWhOEcvICoBsokYGw/Z90kknwVLIgJzDHdGFtSj4kmZWjKeFipD3jaaIvphbWUXwSnqllBfdL1M8A2RpTvuiQ/qPQIgeRBDSCxKA0btTsYe6IOlQHuk7d+Oqm/TnE6isALPTEgkendbbEPoRCd6rADOkWEjh8uZUYTs1O12zzJ/vr8nfH/FnTGtlNnJUwozk/uHiqb78nmg0gkqgWJbM02zo5eBfFwjqBC8qHhdszMOV0Y1/sQXgSFY0YnYui2TTGMHS2QyvGU604AlLtpe34ob1YsMZfBCe3MbnEQZs+0L+ti/n+3cHcg8HV+X9fztjFv1wT+hgXrd8Vb72L5ghjnxUrLlEEt3EUhtP6IEIpSygEWbxgcGDLsSlM9K/xHAAHQu/OMljzoD5KuMTU3lhbLC/uqZu5zNPVdl9TcUbYfDZJ6rT364JpqcDNSFgm4XAzFWW0R5qO6dQbT5aF9nVP96+evUqgB+/aTQamoLNp74ajP4iY8tnCO3o+ET+09elY+fHTxswfekPvct4bEfH6Pgo5y4z3liPZQkXbDHlcIdBEIl+CyE80ixMAyyeMDHgugx3YFWTo6HR7pYtX8baaXVtDQd6bN2+besTTy5TS7sQAh+hBXhBkDW1Ro0Jqbu7220BArw15NmxozcSmWS7+8pVqzaub8nP3GRk7vYVp3z+Zl/shEDVq21xMp9zedANuH936PKyNlqG/gIU2i9hKCIsw3V/nZcAAgQ0SS5CO7bvCAT8UK1f6v3DeWEpgGMs0/H6ZC8m6SWqIAlzBj3lnutAWTqdqXqx7RIQwukG9BGDE+BsdqKnGMYcMb1ERQtRfbHF4MuAIYB2+JOmwmlVuqezM/SeS6zTXlPYsRPNwL9mtdnZZTah/c6zyh3AXqDOfW+6nTt3wj1gHbh+nHHGGSybsDNovleUoLbvAF1CeAUY0jxyejwGOLRZGDhvLoY5IkmlXjdzWQD71AsinvJO4RiCEMYWjDKUDsqj0Rwzsw0Vq7InU1aqWOadJDm9ClGlkqJcaKZXKVXH2RPDA1YO86IcOzE1tEfswIFgXftyFnQXLzUvpbq6vrqqnlkZnQUNi1ofu+Onkeqm9a++ACB8qYFJ+PAz3nnTf33qibuuO/zMiykMJ8bvDFW7oCzUnHes52lcjPhMrlaK5uqQZx3nJOnMWo9SZlknL++VAqTfL51LkmR5VyDIjQwkAEgnU1swKu4G+Vy+YFiBWDUxJJUWy6WYe1G0sym05UiZCEMzU76JBGdjHNlTO1VV95s9+V1TC/aOcTQy15zCJ08TE5NTU9PLlnWEw6Lzu5dTkUTxp1AtBcCGfiwp8GTU5aSp58YAkXPg5kICXz1p+DJVA1tidJ75Od3cQjdJqsEd/PVQmp9fJ4GEQFcNJ4UEKyIkTj3OuxMnCeryANMZVfeY2HXTeYngQuzhCBX2bZXKyO/n1XXLLbeccsop3vhnDSM89PT0PGu2F0MGGCYtjiD6YkDm4ODAvKDFPKpjBkEavOmmm5BymdeIQejlxCWsIa4r6MHB6kVVS2UFGPERLMvMxnqh3LUoLE0GHCYxmyzcUO17ot6XzOKo09e3O7fWXP/VcKAm0x787q49rx0baw2Fwlo+c1dmNFj0BxRgLOjsxxMG8yK+jOpa87RTrR3b8SOGfckWYM5l4Vjl9rbQUceYsvtL+E4ZBZr5LYwvy/Y8PSISeaUiwY6pxb8s0HldceST9uw1AXvW9nNUBTOQWQxuMFsvD+D/rIhzUFL8FSAIWMQgZ3PXYSLhMnrxjUgo5ZVj1zfnKODijhpMJMxImxiX1yaLq+qfOeG4X/7yFzWztwTDE8z79p1V6ZlZaquviYexXxqcb1GYuK7Qcba4Oftyj+14Jrl3eFDUrfa9tLk1HfZdn2ExVdpearT8fIXwG3fZJ7/MDNy9cU320EO7BofqYf2MYRgB2LJsCw6ogjAIVFw0Q3BmNQaGTgzKMDGYNsGcTaTYRycmJ9va2t9z0UU1sWhVJJbNZQe2nJSR86vvCgWDzAGs66IwUxCmA2RtaNS9DL0AR6dSjzjownly4ez1ud5vBws7RTaQ9jes6W/mgmvN1n8PxE/CWZtSf8y1cICBAIq6i5IOQ2N3d/f+ViQigpVOWhnZmri8qYHPOkEJA35/AZaXc7HVCVglaEmvVl9eQD1jMuPL0qi79AWvGEBYMaMgxFbMTyTTwG233cYpvjQLxxCgVJRxv8UKvpjjMdzQCF5DwEHDVnccd+F9C7jfc0XDAcFZra0tdnOj/zAOW+YjSAE27z1/2i9iAW80JGgqnhVn2Ag8DScR5jL0XrTfA6L7QTtMiQs0UICZf7HRwGHAh4P3uJNhMQWYpCXcpJ+VImrUeYDzrJkXZqApOADsgDTCQuBLxChkjRV1gRXi9Cf8aP5l9U35co6OISlQqT+YJGxYtIkliF2KuTH9mzjr2zm8EwAa8WMCR9ouImxG/AFSHcVnPjaLPClcmBuYlovsH8sGTH8kaMgMR48L32XwG8VCPigSnDr+SVxdDPUxICFZBwKmEUHTXxxr3TiaYB1enHsv0SwViAhx0mQoGgTJRe1TdlO0+LqeYP1A78wPfzx58+327JS/tiF0/Mtbzn/7a1YecpPP6ptlHnTqlY5B8ZYxKeK7q/Wxq4lYGkaL9RVQKUGQ4kgpZqG91l8dxkThG03aoyl2BLPhmvfIHe+VYMzFKSjuIyWXvkroq1xuZomVziwfbiq3DEEnIAvAThOUYtznUsfOoaCyqIrkJk8YYNB2bAs7iI7/yU9+ov2/9CNbF/kUwnvf+16+2eY9xF6nvmTucDB4Jvc/nxVg+o4VYFcB5u1ACKevP/OZz3AsBWP905/+NEbbb33rW/vH3l8aY6OyAqw5QcV22UcmATtKzc5O/nwm0NNcsAt8AjUxk7AfNHb9R2j1J4yQsfczHz/zRzcmDjtsA9Pkn3RTGnxB7tTTM8NDxat/JjshTLNo2rnWZYG/+oDZ3SlMThnyvKyJNmTksVS4mOBy0BpEZlLFk1nG3LF96+Gbjgh0fMPOf9xK3mMXBnxGlRE9IhhlN5GsacNQVe9rJi4ERWPRNYesIdC3uy+VTmvNhGSWN0XfI0EV4H1jzwnreHBY96Dd/j39a1avwRjZ3tH2jne+/Vc3/fIL//Lp73waS7ExsyczMzXD13eDAeXtCJwiGjAe5sx8ViHDYVe2rITU1NTLyodhj1tmCqu0/FPtTMD0bd/hm11p15vhoFVTE94zOI0QiYMuGLEVBHpZ6UVLZzLA3MMdsZJ1QsY8WhapiLBsZEVV5hF16Pzz3/6mV59Wk81kH3zANzoerKtZs3FDbtmywbVrnnryqbVr161ZsxqS4TK4ElELZfXrgyJNLUiuwKRq0Ovv3xMr/LQ7egPe2oJnPuRLmUYcgb4YyD1t959T7LzKHz9D5i7voIHQfb70xg+kT28JxmZ3dzcx8ERI1mtK2mbhzaZ6TCIWr97JYmfSVl9/7nf3Gc9sReCyDt0QPuYYe/lyX4h1exn43ATQH3dpawWWC+1crYG5SJY9apyxmrGiy5BjywYZyEyYJBxVuJeVJQO9tmXLFk4lYIiiAFN8wwacHQ7S5cVn8TbfH2SAfGABPickcCOi9rKlhucEoXJmPtomSz0HanxVroRY3XTeBvT2lM5ADG89thUeCbM5Aj8mxA60X/Rnb34vnEWrfLYEOBjeK5yrh10DBqj5Le/1s5Wblw5WGjFQWhorGAUWwP2zBGnIFJ9X90F6UKynxHtKv07dKFAdtXwhT05P1le66OsdEz21pEiUEkj2FlbR3ginvPohES2MccBZx8ApZtPDu5/h6z588A8lp6jmpkqbWr0wnDAyOkyPY94CmHgsrL++5euOeuCZPzx97/Wrj3sTrzSz4BO3X5tJT65Yd6TSoAAs2OPhqpe3QEDwBCFGpQTKayFCE6lTvOHyrAue3bICWN5CXVrnUzqZJNjR6tpYdS1VixOFN0sJIFmPW27UjPdN/OsXzd8/Fiyw/cQMJpLGTbdP7txd/y+fPG7ZIXufsfn6kUaSegrZ9Gxiul6+rAG9DiCUfPRX7ObumWGlGuQX+hWnEDTZWX1os3VYk12TnQnkM7YRyDTHBguRB/dY45k5TZtSJdheSJXCJXJ1mrSFo7lKxFzVlYqqSubaRdVIO8k/Vb3bsnN5NBh5Fn1f9bG3PuIX4m3bjY0NxKtxIP3lPeWIEtjseEkRBnCK1vBfknc4HmIY9z8rBRj9Qjv96T7FzMFmPXaqshZFp+Mghj6Mj616ixkh5SNNl5LX9wUVJJ7XAVlZAdb7i5hivXXrR++GaW/qgrCdTmeinIafl72XiJjLOtrJYzwtTq92MPe3HznprLefV6e+Rb6grJguOCO6p6fHTUL8Zfvoxo0b3RgCbO3D7u6NOchhBge8xVjeEXnXu3NHvcy6/z45bKBreejlx6L9mjHZD1mJKdloIPvcks8nTYqbUoEa/QwGyOEMytVm3eoyxJ2Mkk+YrvDfufdF+Ksw79J3Fzs7lyuYap+uz3f77XcA7aabbua/SwzNcuONN9TV1ALnyCOP+rcvffl//+efxbDty6daU9XxeFfXClZm+e5tkMM0/GZkLTJLipnODC+vq687+mXH3HP33XagCUcxs9m0q/zmjHwkgRdbfREUi7llBIbwC0ukzaHhaaRGRjXsAMc89EP6jc29uNRiF4QmlmT1Ag7SHuvASH6cjIW4yfSwc2fv8a884bXHnxB68omxK//D4ByFaMxXzCe7OgPvufiYTYfFY6JUI7JAGustLAIz2l1tjUcUUdYedczA4J7W+tmell8H7KydDWauacneEgqmI/5Gw3fqROisMdM/Vdj7QaNns+nHdXDh2HHbb6kAS9agVKYAg5VeAqWJGH6kyuBdcNFErJf2eF69BVmIsK1cvvjEE9kvfSW+bZuGYv3yptQRR0Q+/EH/2vVGYJ48UQnCvsax5HXPPffgkIx5gi6DLoxHfMIB9q0tDpx09bOf/QxwaLlvfOMbIYrzhD72sY9x1iU6GKdbsSGTEy85AYuPIS1dK8C5ls5zwFOpkX0lLJQdcIYA5IpdfMBJ8AJc2IA65kBhouBUGrgeJFwc/phKeaPhA8xEmMZc2HwEARkC+KRyYB5nXME6GFeve93ryMNxGKimsJq3vvWtvG5uqe9+97t6A5Ebs38BNF5NkQYOd9o/AnENoOCz2ibgA1C6fwowBanC7Yj9o/f5KcW5HEWcQkVp5F1nviny2R48cFBTK1yKuc3xBI+C48msOaDKxURYzBdGdu8oZNLsC5LP9HpmSl1GMs5nvV4VkXlpdHSstbUlbvLFO/LZrzz3r5+858ar/+ndG078RU37iuk92568++eN7Stf9ub3I2/odubwrJzF3mOBjW7PniQ+21DgEwpig3Aq89biwX4BfpWJJBv2WiOXSRXy2Xh1nejzaGE0pEz/IhLMlVNNIVVIwH3w1MkXgKOFDjOfu+tu48EnaTS+I6vfahiw+cz21C9/3fjOjqZYbGDWQZ7CmUxqamxvvL5RKhNgcqMBipaZK1hBfdCTxHsvh1eg/R7XZR8eToUeeyx10+3ZnX2+eFXo6E1rtpzc2N164878WErstu6liBNBZ/HLSVuQxaV3LgWiFoBys0kNZBUpSlcm5dxUHZgDpeA4L5bbDCpyLo8Go+84MwEtrxzQFuAgWaTr/gwuuCXM/LmaC/90G4Yhwsyld41pKqCdj/pyIOhvf/tbOp3J66ijjnJ7H/EDQavMX+yrX/2qNu/+6bbD0phXVoB7enpoLMRHPY1pEDwSYGlraYg6lVeqkM0DncmGd5lWrq2tEcNVuOgrjhtBfA67N73qPKz5FaGhe+CS4U0i5rzzzvPGAJOD2r0xBz/scClckNpbw01bwq84zsBxmBWwaIhTjsrwoTHdiyT0EGkctQDljsKyIvv2CFTJqFVSJiGeNEvbB7B2/+DoTN7CWjGa8j3VP4Z5Gffk6iq+GgcMhz6FhsxuuaJvNjM3oapKJTFdrOc795khsfdLGameX4UI5y5c/nVUF6LmXbaves2GdCI5sGcgbjW98pz3HPKKUxL2a6vMXcv/wlfc0eGf8HO4hSaqbXWTeckI8lTBrplM9WSS6fPe/rbTT39NuO4B3/j1vpq09aZY4bsp2ZykDiETY/jmWqNqK1P2g38wZzhp2hS9bnp2lk8b5YoFfLsnJidy2VxbR2tbVUN3d9fAwGgoGkN5ptG6u7sx9yAmYi9sbmk+701n105NTl/+WQOxpmAn+fQzUs5TT6Q+8YnmK792yKpVDzzxWF8feq+fF4c+pWdZ74VYdGlNMro3nUT8wMBga9UvotZU0RfIfa8jfFeDL5P1U+tk0P5pRzbjD587GCj0FaZ/bDaIr/izXk7f0+SVxE1vqguKnF7p3I3XAQR6dE5aYLHBIzIPXTs8kv2v78Sf2Qow3dv8RB95KPP9H0U//CGfnGZ3YOZV/ZqglusldJAkgFoLaUxpLvJgqz83RQxhjq/H3YCvH3EKEbolfJ91XWzApFLwtNNOg48RcGmk4zizF7Co04RdsAcnwNvxfEwzXgIPDiHUovVGAgwkmpeXjjA+FDDwg4YDsz5NunD/7XNCADMZRzNiTNG+ErosNnVtHWYIudAwzXCh+vKZwIoev67UxUEAr33ta91RBwTUS/RnlotdaIsFKKUL0q005n54D1FQAy8LaLBuvW7qwpiynG6GigEyLwRVMeeBjZQpwyG0MuB80Uzn3QVgvljLcc6iDLsKnC4tbM2dw0qQSk1YenYz6ZxSMx+mtoq5dLyu1ecPyRFYc5cbFn1b8VGdBgJzGAsExCeOmnDKGuHWnnd/7Z77rvn64FN3D+14KFrd8PJz//rlb7wkUt9KQVq567DjzWB1HrauVpmJjDV1HH3We6taujhPGo6osHPrcNGYw2xeaGH7OQDQdu10Yio1Mx1ll7KgrEHpjnbUsXmgFq+qPuoP5mayD/3BJwvnXM5g0UTnH3o08tZ0U7RqYJrpptSIzO94gTsCCA2oa5SuFDUff3H3UvU6lYOdz7e6sXhYNG3cdsv4F/8j4Egjduqe36Z+e3/j331oS/eq6560CuzTljJOhTrogpSAA9EbB3AntoSlenT7UydWahsvFMKq1WXS1HYKDaA0ibrgdE9K25dyimCmLk8eL2iJFhOPi6c3EV8n1CGv2oNQzQn2ZT5Qd999N5yNHnpOHMBb0QsbBm2c3Z7rDucXFuc/snZNstfdHYDMRHx+hWshcNrnFa94RVl82YpjWepL4LGCAkzDsaFRb4pDAUDKZNxz4byKiMknv/aRbM7NjQT8sWg0k874kT3ZrWKb2RUTdqjAvk471MXNeXP3EeKLL5vwIMWihDMEwz7+o9CWmNZCfNGptGpEEr6yXBRHbHIlpIVFnjVGTcm2xSnHmYzwOtYrgiG0kX1pW7r18R17eydlBiL/zslBuHC8qq6zu7Fi3yRy1rYJ+pG8LreVegxfDVzbElmXi+NPBtigayUn6ls6o9U1vugm7UOtk937PSOgHRkYHd0V8YdjfPtwbcD3/hMjn/C3ZoyPDmW/31D4QzHPhuLubPi8Mf8hCeq1at7W0f2K8dknWlta8SGfnqg3/F3Bwp74B3OpbMS+NutPs6PHlz86FPsUPk5Z9Mzr7mhhDbmlpe0Nr3/DkYdtqAnF8sX89j0D11zz46mhW95zbvLQNcVYfHbvsP+eR/I33JYdm7FDwTC6K6I8YvRJJ25Z3tqU/cn/GoO77CPyVafkw2tn/GFfZsz03TczfdfVgWUfnhifGB8dieIRHo1qrQyfZ5aP9P521mMBBdWioZn5aP5eG9e2/rB1WzhXkPOokJesrNgOjJtqrNPHzRrOrLjV5/uQ6hO3tSoHUJxw0kbWp16GFqeLARClkXGlNxcgvpctLbIOXLY4DGhdSge4A4EYgOirrG67WLB27gw/8rCj/ZIso4D1AdN/zz3Ft7810MqXaRBH5iRg4JQBcR71OFokUedh8Zz5mIU11n6hl3UwfHhIWhSmMiphQSszomloSIkcc+XUXvpBz2EBufR0kH5REV29F06L2gZF6DZL0PVcMTuAoPaxajwp2NdAZqpmCGkPnZ6enoOpAGMu4QX8IxXghfRC0RVXXLEwnhgo5dwKdlW5VpiF2SiOvxmXNwlDDN8S98Y8axg4kLYf1MGFsOtRXPsB4kNBXWjgZV1DHn28lhae+G4K2VDRsWI8K27eDFTkfTx4YSXxM1XN5KTOhUjsnLL6pj3KkuRSbMid0yRGrkUoWJCvpFvgSlRgITmHJhVBTWUB2MgVjVDYWwB8RKVjyOgquJfpJlKr2GHd2pnMq5o7TrnkM15aqLP0aKw/5W38VxgroByC3bX29L/+91IOb/1Sn5Np/k8JGi0m5cikZQCtj0kECRY+WuKQ5feTqNtQymnF0QMP4c7TfBqckklKdeOSKivVjEUHyVL1yrlZtjdTPbua2bmJJWB451amNeYlpMixvmcQBCLx+rq25RQKgoxpBHFcW/jZZ1UjeGBKP7LVH93dP37lt0OpdKnlmKIM3/0Ppa69se2id/c0VfdOAk9wVbiom5AtU7O+NKo6oUQIjeAEPfTNB+JAUjBKxUog+ZVkbxZPkhNUZg0nrLtDo7gw58KYUUzVuczq+jZlqC5Px47GF4+8sYgHXN4YZswyx2lv6p9EGP78J4HnAUQS9oFOi4xxAGG+9EBVUIA1kXiHX3rppRyLipsxTYnEyeGWTPB6FWVfGiJTzFcfHuU0Jctn+eW7dkY+nDXOHkMusjhzOL6Ft75sXFJRWcy+VESeJQoe8KTFUEIAQl9CAFqiiZA2tIKE8IE8wQAFvf3DUKGhZtJcrjiwp/DQw77tvVh4zEPXGUccbra0+wL+io05rzrDV9vRk7EDudTM1Mhga2ePbYbyfjMxxtK0MPF5xNr2dNZ+amwhrzaK+VR6fCjWvMw0ZVFubNLPzpz8VKbGyoQTsgo6H5COkNkEI3c+tHJsImRM5Mb7n/5989nV6+8+suoGf/eU8clEaIqZy/DXWnYgD6mz9obdY29ubmo44YQGBFyktHwhd1j3vxTHLg6E0lWfsKx3hwv9ltHoj63I+/zMoOZV/9d4y73ZVx6/8iN/eWkbM/a2Z5Lbd8Srq0/Y3HXyJybjgaThm0HPA8G2OvuItUNnnlDz91+p3tovx1Yh/6GT5Is5f95K7Xqs9q+S8VMSPn9Bpk+Wrzvs+GF+u3Bd2r9u42HH7x0Yrqqq7lnZEwuLEWQ2lRwdHuXbTjgHcGJWjo0Att3R0X74xkN9u8aZUov9nBWmfASUMEQRpk9/NmQNBY3arC87xBoAX+chXl/zeq0UyS+aKktPTFGciNPT06MdrbXWodef3WVPTyF5X/SjO0KAg8KsH7lr5RnNGT9JN48DgcKFojW8NyReduqfTpD53w5kc9bwqH2oNKokqqN6KIFNzSle8ac0PqgLxwgOTq2Yyxu5WIOQZ4kkL4SF4SUKHvAkascbH190jQaDmYtatDlsP6pbWIRRwaXhl3fiQuIrxSyE6eZaLAkWxz5VskEdebRCCKPTCCxWygW7WGCJghWTdHVLUF2xVMXavUCetZQ3swttiVJLJLnFKwaWKLhYEmyBTgGaRlJzCfaA0DtEekt5qdBh770MH29BbxJWXS63Om8S4cVKlWXb90cvQNjJeMraNqlqF1YnHF7dhfjBhH8k7bwXwqXUVUp2K9QcUlJ1DodjSrqG5uYsKUy2Pdm3u5Bjnw4x1mTvbjb/msFI84r1yrqpC6ryokuKSC4mRgnPu1Lp4FQxaqQDCVKd7CrDAhTnFfujH4QqqaJEnSYbsIKEXcxlp4b6ECnydhbXtZHENhbO49VNkbomoR8ynEuhLOcsqxlANbtAlTaRHAJdPxcLmdqwvbI7d+e9OroEgXQ7tLI74w9uGyrsnKAtrekMRgTb4vPKGT5/ViULKLZ/aqJAxcViqBhvG8kExvBPA5CuvwRL/9aGcnHLl3zgodz4mCyFlppct73167uazz074o/sGufbU4YsKM+RM5e5BNKtgCRFjkoQutwUiZEInTKHko6QRC9YB44uo4p4btKK4nmuUxUAJ7/88OcMEreIBwkVHJ21UIJGZgq2L6ia3s0pAe9bMy9hf5PKgJQ9HvDqlgBYVjUSDssDCEUsnCxR6oAnlaFR9njAq/MChLd7XaDLqv7/H2mBRQXNiy66CEeIyy67DDdCpjHMwBwh89GPfnQfW40Xk4/grfhsPnHNSO43RjZvBlb6QufNBNeleKEL1a/v3RXJ5x8rGREFKocSaf8xlkn13OzWhSCOjxmPzN+sp7nxBJD4e3p6CKBScnmT6H5czojBpRN9yZvEKGGJmzsqK8tK3iRmela5UVOxnbA3z5tEuLu7m40E5OH0PC0TuBlQgLGdQwW+4trE7ibxymnSWBWkRgqSB9EQYZELNMgJhuDpFiHAu8r6AIHFSbOtTLbw5OOzX/z36GOP4PZLs3MQk+/VpxmXvte3omfbju3ay9oFy+oZ9n7Q4yAoIrFu7p6MDk3WZpMzuUy+3h/nYNWh8Qmv1RM1srqqKhJBSDL2jk2PTYtW5vJ6VvY5qqqQ8U2OjscCDUaQRd0ULsqGadkpy0hm04XZXDHnC/DNZ0fmkOLFHBpyMFrNR5QLedOX4TOYxanpdD5U/9knrnzf6s4tjd8zfGmjTvReZgvbigwmNz81ckHP2uUF1NGJlN6fVhWvemZwQ7XxyY7AVwPGsNmaDeMUJiX8WSt67e0dX/thsKu7/W8+8MH22cm+f/pU5nf3mkyAMf/Kb0eqW9g9jp9Z9fTjBV/GrjrECNRlezqmvn5Z8W0fyz64O0eX0ZuRcNTOpxrfvjvcNC3eV9lg+qlQccaI9FhmZ9YITEWMT49sf0tV03kbe1Y0TCeskWdwb4u1t7cs787HwnfedUckHGluaaFzenfuXruux+abUlzVRIjgwPlwaCmMVVq4wMnbMrkbmUzxmcefYiFTcqpvuNFr6OR6NUZH6rtWLAHFMNZwiNdiLh8i1qZcDnYq84Qhnl215MQHgTFJAAjaW5WRj2kGwtGoGZPeNxEcOJidrOxrw08gncqo2Vc1uFq6ZNjkcESrjs9OT/UP7tUYsh6oN0DCi/U2Ch1Px2KuWb16TRDHPdzS0ZyD/tlM5v4HHlje0QHPYclXLzqBG5jwcvGCMI1BC8jrFTBO9tar6xomd0jTDqUuaW4S0IDJI6Tpc8LcJIc05ObxcVLdeAJgojU6ENBrmzpVv+M4WmNiKCONDODMu4ZHAC8gZyN5ARLGNYAXH1QBCEW0PJZ4rYegEsMuyFORNN1rlAIZL0yYkmaDXtKIhD0CR7f/QtIYMJAG/ykjTUOGDTIsaW3YoOZRbo3wJbgTmMMG3UgdgDTYNeSz+113lpuBsQppJMHhyyZmXBJ0ryGjuD4yuiBUwHUJ4+kAj3WhEaDFaGQCNBrmA5padxABXhb6lCS6b6EVXJPGyKFxvAAJY/GBNCDol0gPPJ2HOUj7P/Maukv3OglewVxDezLCy0ijiJ68KpKme20habyGutc0aV4k6QvtoMhCN43sTSIMGpSt2GsMfrob0u68805y8o5zp8Wg0e015gWXNJqUzsJfTg/IJeblhaQx8E499VTgY57TjIWwvpYgjQza6W4x0hiQC+dlkKTXPPMynrLGUDrQn4jAZJqb2UkqLBcyC0UmFHtkND6RQfLhEw0wXeY1XyKZgmpXGWYCYR9QLMbebzuVzpRmc63DsSzpj8dlW3iGr8urD6oT5iK5aMarGhqsYmFypN8fitkB9rWGxibZDgOnN2qqq7hTUSqZ1kUkXpWsqo7jOIbXTSLBbgjZwDuTzKSy8nVo0GDJFUI0o/YWjEbDQb6zaPsSCc4yxjIrfIcqyBMKh6NhMUYnkhkAlkrJbygU0K8G4z+Xm/cBalhZXG3bybBTyEMacI0CW4xFcSvkisl0khOFiMzZibAvhBRXrUgrFopJdSaLrGOgthpm0CpWVcXYcsMm60RCqKYYF2dWJSaze9bUrDn11Olf3BocHBZjqtMcvkJNbeMZZwyFq3rHzelEGjbii7MmaRRmpxKp0Zrl68LRKIQmUum881UbdmfRfb5gIBCL8tYzgWawOuvWBWy0xjLyZmFklEoAxl5pQUJdvE25iXF/JheN+9lInExnVZfoNAEAEyUPfZJi6VgwdNNtBrlpsp5jzSblJJq58WP4cAZjnNBijGSHZgekjzHHMOaJUSd7vNzLhqEpa5Ttg4ewa9dNIVAiTUbdvF7j1BZTDUiDD0zkSZTqSjiCXt5mKcCcmsk8/nhvKdoBjAwMe1w4eZGsBUXGapnIQcfCjZlJCTCvSdd4Lni4di5bKHIwX2tZZYl5GYbjWoQ1VCZQLQPD+WGSnqrm5mXt+OZNIgxpcGMvafA9uCXzLI6+i83LcEjmZYozr5WR9keKHAtJ4x0Uaeq5iByaRng472nFyQuAkOb2GhyjjAoNwTtCnJhKP6VXtVLa8x3nvhZlQ7as3n1Bcck8lRVgXniamK+2cqblXXfdRSPiJsGuJ/3eluGw6CMV12XiF41VnR+2LFQYFrx4pY1CcFOg5Qv9Tz6VSmVZwSqxRPk0jtYSkeHKBB3GpZYhmEr7+vtU/9EwQllTY1NPTw9vI8JxL5uWPdggxyANkAQP2tG7gxQpo3IooXYdTFreEJU0V862+cYsWl2hWNjRWy4etbW1wBBRyHp72SCt2JfiiIDlfVMo2f39fagNTIdSo/pB0EeZ4nlw78Do2Dgo9e3pz+XzNVVVyzs7cVB5ZtuO9rY2Jb1JKYWjLaSt7OFxYmpy+xwmMiKi0ciaQ1bTTcjOM1deGXzgPo5QdBiwYVTddXu2oSb4nvcyUbHDVo4/dC67u6eHtRi4+Y7eXtUY9kCmaTJtFDKJWCginxUx4ZUcoeSWkABSQpQpn4/HIg6Isy4t5+AZta0Quixfaijk05kE02RmrJ99Qth/YdnZmdEs3xrI5iMtPYwpmUBVi1jJsbCVC3UdipUkkZiwC/hp2PlcJjk7m8/Fb6z9m5M2X1iYvDaXfIhzsMaTHUOJI6YzaxFZOjpXTk5O9Q0OJ3PCeVNTs7v3jgzs8Qft81519FhT9A+FzJ6ZWevR7bEb76zJ+Nf3rLEvuvDdHcHA1Oe/YP/ud2CAK0LDO0INhzOZFa3kkUXfx3ZecYX/yafsarP1kurW85N18dRl7/Of+Z4cc0lLNNY/NFgwbgm1PMCknt8aTf6jFd6Os3WgELYLb4zFPpoLhDKnHHFHsniheeede7793dCegVh1dWBZh33ma4NnnLGye9Ut9z1cPcVyrs3kVtO5qia4qSqzPbimMNEyFeqL4wWN0bloFZDmC2tmQ605OnhkunPHKCNWd5y9YkVXOMxpjfSaDGNpq3xhcGCQUhwVVl1dg4nk3gfue3rr05188DYYnJ3le7/22nVrcXam3j17+mf5rBRRqtcIYOVub2dLjzE0PDg0NCwgSxcthIresax9fHJ8YO8Ad3fKR7HRLLtAK3b3JPjQ165dwBRPNlPmdcZE8dANvqamweHh7XwYTB3JMjwyvGr1ykIhj+lnx47tDFo9uJjrj9i40ZiYKO7amXvy6fzsdGDVqvghhxxx6KHbd+7a3bcbiZZPnIIXEt3o6Aj/NY7TU1O82lq3R0uZmZHt1t5La4nDw8yY86ZMPmTV1dUJPNjLzp2M/7mLaQ/SaCtmU9iIK0CQA1EYfYmA4C+vobyAXACHAWzYuIHvZSOUO6VKLw7vfdeKLtoE//aFbKShsaGmtqaqppo3lLL9A/3NTc1ySoLPBzlaS9wzsKdMFQQmpIHk0PDQfC3dbm1t6+5eQXGc8b3VgU5tTe2aNTLRTk9PeZOIwXS1fr2Qlkpp0gjqCxrNNWsOQcwV0np3SqxDN8PIRl9iomVe6O3tLVGsCto+TnyjMYkcHh5Bc8gpIV4X52BbxCAGAEypzI4JKFGAbd8wI1J93laBkxucE5ELqtlo0Luj1zW9kVRVXb16zRqSpmZmtvf2AmRgcIDhhf7L23roxg0MXU66Q+RyoekApdiVkyvkKVWWxLePouj2NpvcRxh5DDsUOJ2Hz+/W1smndXbt3o3uo9tDkx+KRJqU2WLP4GCZHRPkgUkpAKKDeatDBSWVmMkJJq95mDD4tQLMm1uWxESDAgzV6VS6LIm+0cOYlqfX3DeXKhg8fNcJyRV1CDQgUPSiEg1o6SL5Gcbuvj5EKz0DyQi37VWrV8uA9Nm07cTEuBd/tlcsXy5fiuLd5FR/bxJFunu6AV82IMkTjwtplJpNzJYYmlOUsaEV4AXzsjTzqlUrmZcZU+4wdsdex7KOWJxes3p37mAYwJCmi+GZPMqtsawx3IhSZ9vbJ/Zks+wSMjKp9nxeNNhICNdZUUU40xBDpIOEapSAGQuy0QUsC+lcxtVXVRabGS9C22YsmbK8A9LkoJ365nwuNTm8k68SmeEYGObSCAOyMSpYUwUrQwPLpxK65VVZmtYXqGG7mJ95OZ+eJTNHQTKhauW0Ck0K71W+1ZRJgI56D4FCwBcN+gMowEy+mUQeBZhLQQQ3ivg5LB30sylOvVDVSRGKm76oPyq6MXM1F7AUMEm12TwVixCAsmyKYzucIsTgZ9zSuQpcJwd35mem4i0Y6fxorRk4WGo6UKyL17cWaKz0LN+C4qTTNdUPndx+XU/V0/Fwfraw7LHkiTdMnDRVaIcSuiCfnEqMDdzUtbl13aqWf/yb4S9+DVYiZ34w5XW0N37k0tTqtXf2+VJFXyaVplmEYMNXSCfSmRSNHmafCB+0Z3OdaOlCgb7QfmNiqbfR38V0W0oZZe3YaKhq4ahJWZvmlI5SCaAaRl11IRxJ5u1cIZdMzxkWIZ4BGauJ0Wvo9okUc6gUVG0sokwkHvKbcohmIiWldLyGzHyNrzjUJGhGyHIvrAWMuYi0fyqTwlDnokKMYVaFRYG3GXMoum4hYmBKMWV2yeazSekauTTcUCgYB0m+vcw4TspEL3KnIp6PkgTwDjeoK4vIWiqhsviMdevWIvFlsune3u0eFCUXswlngMJImbLdZpRiNgyZz5fEGQk7d/WWDBBkkbbGItDQUE8S3EDba0BESx0MO4RnFrP3InAMzxM5OtqZl7tAYGx8HDu+1FK66uvqUICBwXyhRFaVoIjDjoYqSDCRTG7v3VHqaicDUzZ8kvcdDkNP8Y6odXQ+zjrLjKYVYJm85iRd6WumJ5m8lPW2ZPYSgDIMYjFSCWPs81qfSeLS1lsROOaLHMRDGqWwPlOdwCpdWuTgCdLKMNGkkcQYLksiUksj2KfKkkCDuiAN/Hcgw9AyiYQ2dRHmYulHeTeoBxqu/JKuko4UJiVDy7YZm460RpSOLy+0+DNoqI4SYLzyqoPcgBRzK5J051IcQOPhxOiCpXTP7xx8EAeaSnKRVCAFrAKNOU7Xp8fpHBQx1c09HbgQbffWs8/58t/e21aH9AD5AtryRez4m/xtnzUCncmE5uZzVdLr9Bb40OtYa+YSRAZlfUz4Mlwhn88CbjbDeUiSBesXPI8AXJ5LtbWiWZrcYK6lauKnpqYDfJBeDG+aZL64LlZhMeyp+anUCpLKa0xZptEUq7VSyVwjsc/TFNdixpbwoLkEn7Fz5+6RkbHjjz8WAwxGQWXRFH0PCOwpDYeCBPhWkDqeEZTyfmY82FMgeO8dt958w7X/+IUrKQjqZNPtZZp+uB518E153Gh1dXQgGVhNrYpHeurt4H335T70MX9RjJ3E64sNOpnqmqqvf22io3vrKF9An+vjsnrBtAAAQABJREFUaCwCX4C0RDLNPlHeh21jxiMT7LNhvpZ9R0BAuRIUBByPEoO9QB9XNpMpDMqhFJrXk2RPD2z1Fdht6zMxrCr1Vj6zXdUiOvHMcKy2zR+pYuxxF6QFJk++1MxQYXaitvNQXKDTQ7351BRePkALmfZZmxpef3jTyji7ZXM5TuuMxUaz/r5ZQw6e8plo/tMT40N7+rrWrAM3Fr37d+1Iz073bn0il0vu7du2p/fp6tpmuN3y1pZL3nNRV2PNsvbWwk03G5dfzny5d2RkNp/aeH0s2pHK7Q1P/OvGhn+5cvCq7898/ZuQj2DTcbm//pQpyxc87d21T/WjpNTWNDT8+F+HVtQ/bc8GE2+3q/a4C9lGIRzMf4jj2vBiNabuP2fkE/eErWKICUdWcyM5XHnf+fbQO9/5dD42nGHLFAYSdniG6jLXbTL+go14he21xa+2msMhvvXIXFlcngx+eNBsZd08km39ddG/UXUoTSu+AMgfiAfiPqC6ZOeO3rHJcQTo3u27E4kZvMKsvJyB0tzaUhWPYS1Cua2rq2Z9gL7nhWKXMe2re5Mf4tXSh4FVG0OP9LFcMu74XNkIx/kcsUnszEooFF82KSlnucficcnH8E4lCnfdY33+C2jbMg4wbxh2prnF/4m/MzdvznLG2Ohwf98A459ui8biDBAOW2XndrVSIYhGdAuOjhWu/rFx3f8FmJ9zGTsYLB7z8uD7Lja7V991/z0ru7pXHXIIa/SpZMrzogmiCEBY0WkKGkReNfcyeNdCkEYSMrS8UJ6LYwjQwYhg8lC8Yi4N0njreSZ+/tKHsBGHVxQKGURh6RWhuH/3bqrecNhGMvBS66UPnabhsnQjW+UsCyR1DHcpjeQaiQgvsu1kAn8Q68lHH+voXF6nPlvF0g2ckArKSfOwQTQfek1B0hDlGw/sQCcGsUlmbvXGc24Y/hW1zS3C0BaSpjkkbFDEOyRXdAONnQOY5WMOOIPGpLO+4cTzw4CEJ9Apsr4x/9KfbyGOJLDQELmDQCjMPxHK0+mKHF7W4kCjvNf8fiZ1GjmTlQUr4LjXYr02MTYxMjT8smOPplQhX0iXKTBKB2NthF5D83ehaVxd0vp6YSpPrl63rrnd8d5nDlIziEOaanrNj2Utml4DVIXJKxCMiFBbaUAGIE10pMoDEubPGcV4fc5bi5OWZEBCGtIng3wOfxVSvYZBze01aTDVaLZLGuszxLWX6CIDGGINobvUgFSdZrDkKHM0dthQWOZNlzQFjZsadeqIbP2u6b5WIgaH+vrZ1E4HsZCKqVdQK81C0mtq8qU3kVBVktw0lvpwL8aAJq0EU9JlGJuwwXkvlCQIh4li0ag4IFWv8d0sEZQpS/Zrn/L1Tol3NEiiwFCe6pgyXAydJOW/jYSAxy+4aUwkyTRpKwJIKR5BRRg1HD4QDhaymYFdW5tbe2A3HoGLQ/uCZKKiQl6LN1JEX7KQC5vlewR5lxuXkvgYkuLAJEmUiwd18a0DvoLLqmwe/i6kAVAwhduSwpTBbI40QoQqpYsSD7sgDGnogg5I6U8x4pNEcSGMY7hc/ASsgQxDnqnRwZmp8dbutaKsKrizowMcfN3UrZyD8mxgmTkp/s8vq/pB0EiatqJUcPKP5Fb9euaKnbnX0ISZxNTE4O6udYcd1eE/qiFbNTKUe/Kp4vCQ2dAUOmxjprn5sVTsvgG8yEyQdPtF/DpymVhVtYFAAtXgL5JMAoNmU1OzWqoVsqEI9LGGEOCCUr9dfPNGs2vP1tH3/00YIw4DhXihjtYzAu94a+Dd77p5vHrbBIdbEiFzFpeini87q1UiMaupLztKinNRl3SaaJ6QqbSLUhLtT69RBc0oIqfEa3g0Miki7jCwNCYk6By0PxcQ5ViQEij5RXVRqxNSCuRLpKkkmXwhWzIxtpzZUCqkdiCPjIzzlp24oeWctUQoRNSdxLha3FZskBVsnSQguXjXwKTiCwXLUuNHMX8KCVmK0RuslDgcEskZIZN2lATV0LBHmCQDE26MNVxV4txk8mJeNrR4Py+Jdw29nyS2kUGFNCkVqtpoRc1GkFfRCxQac1BxWqRr6LIkxiZ5NYq5gu+eu+5taWs+5mVHU6Oa18QZc66MLCzJJ9Y11WVJIM9F5MJ52dVcKogckKY4ZGUOrxQNRA7B33NBpsPh1ckgnhQJkkTXCGkeuULngcNDmpt00UUXYYb41a9+Je2mRkfi8UeLT22VtnI6bg42Aydy7LHxFV1kJpb8DIz0Pfdg4ooee0ygpk7HzxV4tpCuEUji23nnneZDv89PzQba2gJnvCZ82CZkXpQKqUjVRXcUBvbkrru+sH27gZK1aUP4tNNDdQ1kEmwqXYwvGe7TM+n77y+aVvWJJ/uDIX0kK9lJ4y4vBL5yjJjqWj6IziE1ZeOk8gpwpeqea5xR9Ac+8LlDrv7uFb7sHwwrZwQ7zepXGaGNhmxFKFTHmfjV4BN2rQLciyx/GbEw8h/ozydcCXzM0pEggpfZN56byfuRxBHQfFZG3fmOAVqnkCy7jIm3i6MW6poA29a7tb6uvqGxGfjCF2zfhE/8jaVi9SivqhTBVGAnrGFxlBVQfISeKooqSVIz8lprbCksGOr+EVoGBkPo2ZZfC0lqECka0GCRQ6FH8bugOo8+IIxWKRLMeY88YO3ebd54cwiIsnZhwXhUc+u7gWAlspXGVn6BzYYO0//6EzDQ+ac4jknlpArVcOQITE1w4nA8FGm69ha/ogsSoFHeA6HR4lRiKoLm+Iy1LEFAICi4mixVjRNHDP8kFS+oqbRQJn9yGenpUfkkjmFMWTb2sQBasD9ghKqpaDg50RqpkYVlAQBRviznZGWQOHHXSnM6SCASFQQK+Xp8kozioFU87u/ev3GVEXjyt8Ubb8SKaNY3RE951erjjl/uM4f+63uhMVyYkB7yh2ZSfGZQVBMWKNJ4r+UHZ6av2bsbf00jlayJ9h++5VXv+quLm/PZ1D2/MV51Eu6kqETBqqo11dXJ1ulo2x7bLOauNQMPbMfqHu5cIe86GQrFyf8O1Z/MmRqF1x5hTG5LpJMz7cXxtqppRkzhoUgsEfL5ExaeUlBUFTdbmo3bi76zZ+1IJt9/fWi8yGdJ2NXNuOTLTXTKxJe/1HHCcev8/mWXXQHHpR34S+bTE+fGml89G1w97f9crnhfpDhsGp3F0Oa0HckCOHNtrHj3P6cEjIGRhk5whXSaXVoeX6lUernha49Gm7LpXbOJpmhsyip2hsKUwSQkB86Jf5ot7lCUt21xoXMHlRrZnARCDA2I2EvnEpa1X756UizW5PMp/S1fVUS9QVIrTcRkQh6+dG2c+drq173e+vu/y/zwx8ZjjyKMJY/eHH/Lm83qmplP/KP1m7tChWIHVhsEJgWkloFBpmCEz1py5U/a0vi+S7M//Ungi/8WsIpYUFJ2kTnQeObp1N6B+OWfaXzy6aH3f6g2ElWDR4830ARRuZAE3TbRMe4dR8CUECZZdVu5SShP4qk2N3rdFKjy6SkIJEtjey7V3ZkAAsy0uOHSJjn8/g3fo8EIxsUGvnspg9wpIt3EApfCVkWqhFIqmVCj0/pRXit7HXYgDEYqhhWZjJiSyCV/KgA8CUP1rDSmYAh8YtQl9TLDJ1UebqqBJSWWYwhYKGdOW4kNnKbRpQQKYZ0EQGcukkTJAS9m3Yp4LhlMElUqiZotT8L+3LpURsmEoU4vIJJdZ9BAAAGSWl2j5rKJh4ZNKXYKCBFguaBKVchN9ZpUz/SljGiSoP9kRw15FSbcHRwpil8iFkb1vABJjYYA8CYJnvAohS5pjbb9CuRalAynMh+imZbOdKkSviQbbhIPTLTeC6r1qAO4Qxr4qv6jFlapeDG4XEzc0roUeaQUsU59gra7tiU8QoYDkU45PYzJK/YhqlHREqTxqVZ+jWWlzPLEhRcovF2HBBA9J0DxBeQupElBEIQlESGp5AUmUr9r/9BH7FCdqkSGrZ3NYGMthMKe03ecguxuldqkZrmRl/zCzmWEqwiVKiGJ408lkXteqsqpQGAXlKoV6hRVaAgIWocRTiHsjxoklZye5VQH3TDEOSg5CEkZyT//Aj2NiQBy/iSjuiRCQZkrw1i34HZ63UFF67yCq4Ilj0AqARPoUKgyOVAVYqWwIKcolFiBocfMXIUqnRsHIVNGsksuWpQfedI3uoMHkQIFnIKk8tE4qjkECzkSWyJFS+M8SSmssZa7AMeckbfy0WgV8KmByFHWJK3iiigGRNMK2PGPzXRteogEo+DP7wra08XgioDdnGkJ7jin/r0PX3l05iGYZ57dUQ0AMYzJlljo7DPiGw41V3ZY+Ic//ED+59c390++zmFXMstTiyZBAqW3XILK+IhIgGsA6qg0qX6V5noERBA+fPGjNgQuuaDx4vPzn7wsMjNNOgkM7NxJJ9e+5qTco3/Y+K9f3yCkC+Kyj4xihKmcCNVWNAjLBCosTafykirvhcYEfFUDy5N7UUY6S2UBOWcUqmQNWSfqdD0gVNcATFEtORU+Tj6pkeoVTF277mfCQj5u50ICbyqvjFBhzCRmMIJ0PtwQaBMjC0CdukrcmApKfEmhpXoZvqov/UsxHaA4HB55D41cVHypjgHFr7QVR54IAijr/AMNE5QMVkTInDf8OTwDJFLdHfBCMfOaM/k6FJcIR4O1NV9SuQUkFjAMNOL2Daik8lgkSr6gJammzYDlbgYzYILhaFkqGv6VL/VbNthFjJoNLW25xOmZB8VUwT+oVl3g/ACnICSglxms7POoSIMcCWAIwVUPwx5QIaeEPgNORqczr+HlIb2m8AclhqJp5jl2EKRBUswl2o4hTSRgCzmoCLIZUBmvSzDVb1EW+FhUqFbLe0RBWKkP+JJOEeRrquR4HXVRF9nVnXUXw67BTVWNC/RhJwdo21bxf38W/dwVCpAgKc2gqA8VfQhmuR/+0LeiS+fnXujfbZ1xRojvnv3bl2o+8CEhVDr1OVxkTj3+WO6UUyNTU4VovFAbC45OFC//1PRHPlL76c8IedQvcO3EVVeZH/lrIzlrNTUFspng9Gziq1+tv/SSEs0VKxWNLPHxj0e//e0kEu/gHrtOXDzcC3E2+dP/nf3Pb0Bl9fsujZ/zZqcB3RxKtvE8HdBgKBgYue2x6Vd+XpiStDPX9/SPvkO9n3U9FANcXLhjKsONh45n8pCPAIjfy8JLWoy1ftiP9J8aFNKAjGSGtQj0xNMw3JEogaDy46xj43vHcQ1Ek9MDWTiCygWouSCCBTD1iNLxPFNK+kNyuQBUEanGPpKbE63ZjEoS4Bq8Djn3oumbMv2fx6tVJFcjYlnfe+huIB9n+N9kFwKaAAAIQOG0UqyEh1RmsBc1Glp/fcEsGoN9wTJzIXyQjT5wj9//dtUn3xcU1UvTpsHIXSMHhasIQ5XD4NT4VvUSya8EJav8OcTJryiA7jNJScPYbttrMQFKRrlgJfiItykpSseQkvOZO31CMmHu+tVs8hmt+Hf5A3vedf6K9kD+xp/Zn/lsJJXAv4p8Mz/5UfGdF8Qvvriju9b8j8+hzOsaNDISViEWjNssqxmeYlvL2tredeE7Wicm9vzTp2rPO9esrWPhVWQDvoqIXxh7rk1ZafcPWpFYFL7GGREsmPAfaJm+gq8QsYPplx3dUwweOznDZtbHQ345qcgYj5gcn1BXj7aMYZKCBtP1U6P+ab8/Yoeq7dl8Fn4Ni+DIN+Ro2HzISKf+8FjV5s0zD90dS6ZFC5XL2P6YHfRX1b4q449lzJOzmMllHhWtJzD1fzHjc7PBzG+mcUNj9zW8V7e+FLTRWicVw8XPif6hNpo0hzHFL5byoBkMSbtWuJyaPSkLYzyJqr+9zyqs25w780fh0Scmdg/WXnhB9MMfxlqEeYOhk59KTVzx+cgN17O+gMkGPwowZNmFIs18FYwfBYLI0MoVZjpZ/P4PIjh6IWjZbB63w8wWKF6//rV94UVrDtvwMIdaj43irtDJsgo9S5tLcdpVYyM/pTAIqzSJkQyCTemS0T33BADncU4akcylOU3GtTd3CYrzC3OyM/JtcydX0RYVoMByrvrsmVvSbV43UAZoycfnWMitFaCesJCFAEHTOf3pSVuyegWlTEV9lgKSjILN91VZXJIan/9L82cvwapOiAwZYozDFFK+ZOMipVrGeZJW2ZeW8XaKN787u1V+a9w6/8QDXvL3hRQlKxlGdt5YcIGUAqVfgeiG3cBcPd4Gn4tdEFIl1ftfYFFgDg4hp5vprSKKoCQpmEpgcKHrfO6jhu8UlgeCumSJo8iTe2kTmCdKgM+LFMiSLijqcYPEIqxE+JdTEBYIj+flddDwwJOaBKbGXyc4C49SXNEiedSlIEss/zQoYlSZOYg6vvSsc+qKS3ElaGW/JGdsZvPCjI3Ny2jAB0rYqtF6sr1iI0Z8X353OP33PvPxHLNejg8vvK266gOZiH/q8Dfc88gPiqGMUQ1aKMyGhUJl3XrTeDjkj1ZbmVQok45ZPkR76SP5E/kHcwJ31W7EEVlqfwEi9GlCNJKST4UUJUpSRGd75Dep1oaqM08Pfv1ruf/7eYC97tU1/hNeWfeqU8R75+MfW/nEk1KsVFj/LnYva42KjyVIQoVgonEScphASRSkpUNUkBC/paaXGVOTKMVKRaUxSl2pWqZitYtFKpCqPOjQ07qustzOCBBUFYryK38yq3qzAszFRMULdJ1XBfRN5fI8Vwo61QAdhKQOaRX1j9LwEIlSvc8veZ0hrqvzwvOgp1RupJELopEP5AMRUa6BANWr6nyF2C2Z/zTN/86EmB6cMvyqkHpWZPEoNThShPSRmIUEjkJKYeGgoPFXRVW76pLSZwKJehUQeZsVYbrV5Q4TEogOGAkueolO7kfGw/HBxh/B77ckYOJrYbHOouEsKIx4kxoaDG/Z4qaAUOSDHyi87a0OXSQoU0Bh69PWO96R6+yqPvFEJ7Nqj8zVVwfwLWlts793lfWX7xMXBJIXqc6txRug0YqZvPWa0613vzt29MuqQkH8tjMXXhT5whfTJ22pes1pZEZgzdx+u/n+9xVPOjn6pc/HVvag3mee3Mrp7tIBi7WP9JmZufVW+8dXF9etM1g3XtiUdjH5rW+1/uyn4Dz+5rdUnX3OPPuTQvS5Szle+pYM4/GAG1N4aBArCP0kmgbdhk1ImXBoYf4zCIVALCukyrJVGCqIQcEQLxfV5RJQjyJi4umhrE3i0q9HKQDUUONJjVF5FogMeE4zzheSnFgr1ilzxs85CHAes7G+kTUy7D/aQiZjUMavBsePfgUkKp0Qe3S8plp1vAw1PRAVeLFviYVHXSA2NTaOd0drZxcbgZxR4nSe8zOvg6jGLlY9tVWGyPTkzMho9SGH0BKh5pbs8uWyDo4QKT4ChIRkUMXsJCZRsUVBhZ+zms0V3eGmptTmzeYw29VKJACZKWLDYcbqlXZ6efob31S9RGpZ/UINUaOz5vYxtTtXgKuKxKrHe6vaUL/AHGlF7YaRKhp9UyCgWQNVCmmAzuXTo+N7W1s6A2pLla4LAwaIkknxCJCSf/rdSyYmMonpxrZuRQ+RdtAsnHf2uvRQ3+i3fhKpa61taMTXHTdX1lQLt92WPva44Nnn2cefKJZ1wQseJ5UAbWZmdnJmZmx0LPjwQ8saG1O7+4494ZVN9U1DV1zecel7Ixs2+tJZ37o1ub0DgUxOvhHVwhZuBmHBYAnVOM4XCFq9vTi3sIxsW/5QJ99BZkL3bd58/MZN5+cZZ2ayaLzZ78tYzVnf9AQ2Rjleu8hZmCl7JmXX5/01gkqoGIm11LH0iuMr3jo4PuE4J0dmhoKJ7q6d//Oj9Rs3YP0D8kh///0331p1+Enh9p3B6e/6Mo8WURKNuBnf7Ku7uPbSE32XSE9E+/pDbAzu7rGKbB5mRMm4zE9M5PbsocFlsVQsPASt9W1t+AIFRoZrVvT45SXiklEsWf6Iiy20uIU3NjVXgOEKUnx6Qmk7WrfH1NJ87Muot1QxnW9V9e4kItbTLZE6BewYw48+GqqvLYRW0402B3hi8cJ7kCPB8HObGAufdGLXrbcBe3hk9MGx0Q1HHLG8e0UFTBRMaJVfdVcjzBuhYlVzODlKyDmtJCXnohQYabsSQygL+xp9Rqca0bvlkKdi90osSBqELlKWfzE4S8crkH/07YHf/nZycuLVZ5w51yH7CnMx9JYqjxfTk4880rmyp7aufql8ByiNZZ+nHn+8e/Uq9sBrkLrXJexIJRLSSWV3TZ6OJFzxYtkEpzu8trVHT8U8OrJyHTKGvM3ooLJYdUvAP7hJXpy94eeGBRzi1l/9koPxjz0BoWqxFtp3mPuEiarGzlnmg/3Fm7cV+qbcxpaAvOXkMHx7E1Y6p0IS5+ZRyMg0KnnV3RsqxcivHl5uFpl7ldShymkAOlFDhyXp2jVcqVkyCfOmHGm6iAQlJ3uzcZhEfGKTlNaUBJjOw4+DoUDUQUWXqkPBFwTl0nApBkydswSilEOAzWN9UkwmM7U4Jr8VL8GTmpncsjPTI/mp4VxyPN6wzB+rxcftX0+42DAfslPhvs8sC4xH802cE8lymmnf5Mu2jzS+dTTWnX7gO1feMfDq1PieumWH4IAMDpyDqKsCrsIOiUBaA0w4U5PZlvMyJV6aTFGkfnUG9umwJYRDp7D5i+uc+LlAsgSEQJ1fP/itw5LLTjxybfPhxwYLaV5toyqS8rErdWbn5d9+aI9/LBVAw0Gq0EhQodtUgt68B4lY7JLWEeFH0hWmcmMzjIOPi5h4MtvYaYjA/q1wpH6JIV7cGEulRW6ifoQfwYL8CvRi1VeKX1ZVPGoZq4aKPNtsqynWxpw2176BUjv1Uqm6I1tIPdQotUkkd/GJVNnkRx51Jt3OlWp9tjjIwHqOjVu5nuP/KAjwKMiI6zv1ijFI9uwprLjTCMQsDZgcvrrrqk7fyj46gTcQtcYCZqPlX5bxh/PxD5mpntXFva+RBubbz1QnpOCDqCjSJEsFpcEoYdUjuvmkDaUjJA78QZ7/eG7KXRbzHMzFP5+vfahdBtKC5dfcINMp0rAsBFIcfHCJpKA4wqsAUyshqQ+cHB8oWoEdXmpXhMSjUKPoFAuCjywoyj73YjoTVK58ZCA7SxBmU4vZ3OKgzg/gp8YTb/9sMRyJXfUDf6sW9hS2uKlfdVXh6GN8b3hD6B/+If3I76NHHcMglM5WiGibAEEVOXdXdYGmxEBj/KgjjP/5HlijQBAZPGRt8QtfMracVPz1Tb7TRAEG1dxl/2S2tMWu+u8Ay0sgGfCFj9jEmgcQKl7QIldiOvPhD5mXXsLp3ua27fNzCorSVLFYfts2/AKMOKfJVID3PCrAbIrKb9pUdccdSmMBHRpFpFNhufM7X5AtXSRK20kjk0kw1qnqZdXFJF7TpyiS7BUoUwCnBwamhoaoEp7IUMFaSbhrwwYUnlKFGvxcRQqUwOT/zt6d+NN3rlmpMi9WiQMpyiuqqVNoEUsB6FB3HRY8AS0DgXMCfb5/kqL2Dddff83Prvnn//62bhqVh5t7kVu1hi7IXbUOFPFSoYOFP/rXxa9cGZLNVKpZUEdraoIf+SurfbnF8F+7Xr04goi0v4CSP2GjKjAyYt+/i+qAp/6pahWGasCqRwroCTZZsLeOUY1UNIeiYskcM9EfYKuVREsz8+PkELgSKTd1sQlwejQ3M1rbyRkGZJJ8q+tzofrM7E9/Jt8FDFRnOaEGD4lIhBcZFSt3x12BU08dXnPEmBwuVlpJxYRgWdtHHh+fSU3OBoYibQMpYybc0Niz1pwYtxtbYocelrr2Z3bAX/O6NyXPeYv/O9/huN7ig9OF0bC/zfa9wQjl34jfQfqu3wh/hbUGrK534n6ftYuBkc/fnn38rlBTY/yUE3xndhvB34c2ZyYjqaicFiFnUzOmLCxibwmbYRxaDbu3PlgVwmTBh7PTOSsnn2wo+KJVtWvX96esX+yYSK9r6d36dNHKcQRob6Q98UziVaGNrfX/sXJNK361HKKMkojXHEdl6kOA6E7MHA889TQHXG3ZsoUDqGil+ni8ur1dnfw0w2a57u5uImXQGUZPaythfVGEDXscUUN8Ke45/87kciEMMmoHS1lht/dd6Lpz4SZsdNOZdRLjpkOd0+tn/5g7IGhruHeID4IHi7lZMeuo0ZjJMxv57LqGUGe3GYu2r17NiSDR5tGhe3+X5zzVkPvOlqFTgquiXZTKMz3Ls1NubpQukp98OqtlBli4N/y4X3nK/n/sfQegnFWV/1fnmz5v5vXkJXl56QUSSkJCpBNKqEpRZFl7L7uuu/7XVWwgro112bUg6Cq9iEFAyiKgIiWUkIQkpOelvLxeZt70r/1/59zvm5mXCoaoi97kfXPLueece25v51ZEc4DofzpvDFBVvYjTbViT8seXR4Q6NySEGZs5iubgUqW/CnbghvnwGBFNlyPhbQHkQoVcJSOIl9oGZ3/UvDzbX5Dw6+3uffnll+fPn496dPCEHBKVQPg6wQ7M0f+pEMfB1Ceo4c4IH1n8k/COVmW0aG0dcTdklK6S3oVLUvsY5MKwLWONE70WAv26uw/c/j1EpANX8r2aD3L6Xv4vId7XXlM4MKA20VxiVohOmYY8HOYD0PDX80IHQciIJy/UByJf386VhVbSqZGl8Q/FIUN9GElB1N+KNwV5QEyK3GQqAIJ5TPDQ2zc2lJVE3tqS0+uiDTOiSt+Exi6EDz8fNHfHoCsRF6LQV1oYieLBigfiDW8fljV7XP2Gru535qV0TmnEiVOShsApCAlqTA4JtPK78EBAKNwoxhQcSNAUjl8A4BFGBb0uT7N9NhEqhIPEcWIokfj/ykbpvi2lk9qNq+Yl6sujel+/OjTaFggmZrZOmh+/Y5P8+FYqM7KL4+uAZ0L08YwvPCY+VpgVwYKSXRzB4TAjVs+zFcEIRsHcyRGHnDYvAYQZPnD5yD0LyDKcIM1MeG7x46EV8QUSAYqvF5cGaoQVzo46K9SO5RQKSQSk5lYo96KdhEqUN25hlt54tDce440xiVyzS68623+AA9RO2ijd3Cq/HMTelxxWC3OzgQ/3qvFi8KJeuf19inEURnEkkjfDkGQPAw9xQVUR/3yGBEYUM85BGnYfGL8o31RmKT79YkRrnXNusDIe48ruI2BMtMhQzHzsU6H1a5xbbjGOPYaLA1Gj2vrss4GNG8vXf8+48DzzK192br1LOnaBSCIFo/pjvsNG/NR+4U38YDcNEy+M3Dw4SJq9U3V0NcmLLUFbo/HSS+anPyMnsEFPqx7YIqUr9Ii1T/kkrIwdywPZr1wDLNEvfL780U/se+5RoE9+87rs174OhNHvXEdVwYsvGKLvEZwA4xo93vzBDhiqt8g6sM6y4E9FAMxMDxSgDw7NnjsXECLYg8V6WJVrbihIhhJ0VuCMPEvR/6FBNXwoNpcAKk2hVGt7qgWW19atT9VDETs030rDiGl6SzgYb/tYvOjCjZURZNaONLalXGkQEYCW/tORBkQQGeyVV6pBnj5NZB7AOLWCABUSAhdx6Ov9Bx+EihBt6Jf6su7zO5GJaHwZKecURyM4EYcj4lSt0xiVU0GH3ocn5SUR9ZQznHBiZPkvnc1bSa3HnLnGZRdbc+btzAe6M0yXihGJpfol4uwhuTszzgBew/WKGuYiRHAfw9CujDO+pL8DCfSiMyBLx1FCCmTGnONOP5IBTT9MpgLKSaEPlnwjWgyZKKayFD8cwHyibO7pc+ipCgc7wlg5RvdZsspG0NAGhqBkCy8dDBfRoVap40mGkYKjRRuSgUQ2T4of1WAZqo9LWza0nn++29dr3fDf4Snt9qITwx96X66pUb39NnVPd/Z+PfHRojG+4FrfTy+fFitks9h1DEnjPxlKLC1hNXL0FSN93zqcKHZ3BDOrV5XWqhO/oEuRUuz6cO5qU19DNckMu8rlwdAHTZQ7R+qQjvmY9NtbaanSloLhSMDGxUtVuvBcpWOqpcVPOHWZourxuhSCoRfGlXeEotG6+rpYDNp9ErJCmvdFlkBuuGOFLxb94AU7DOxULNjAUnnCh9o0L+NEoPeF/CGKMV6v21EhJGLUOiu0KjlawVrxqVhEEKII5UAVSA8t4OqTeIRHX/Uy2klowMJKE7RrFOcfV/fh9ykdHVJ3D/S9OuFIXSQyD+9a4+QIisn+ErsX5sN07sX/QbBhAULkUQWG4r7++JVoR8aC4oKzKQL3EWUKhPB2RUWNB3RaQhcIcgoKq48EXRRIKOGE4hAhfChJhpoopBTPb+xFbi/nGxWzKGz4Cssbjf7XDo9Far/JOsyMeP2SRFHM2+7uvIZOAXqOg5hCeqaWBTeAbSHyqO4weG2r3/fBqaIbQJdDB65wXQkqKvDBeV0eynCB8KMwAc9BLZQgWP3hwYDHxev6cTG2AXGocaZ/NCoZa7iHrRBEmLjpxYMJD5LnOX48wQoFM3s8ngYcB4uxjOcQGDx/gUnE8LBWf5CzTrlQ7NlIXTmu+pYKpd0boMkn2lgwoMwBt793QS8n3kPGBJ72qVxW4OKOSFZW11LlpNaDZW1Jj5hIKnXmnrQqBAQPNGMEcpsEgktePLTw5MsABA43Fk2h5otm2DWiIowVIFjwn6Pill1IkU7r0Ov6dmWX/2rwV485A0PQhh85dl7r+694/8x5juU8ug3aBD2p4ccjKZhjnB5ihJFNQCLRREO4y7msVcrI4Xo+lSgAONCLyWNDwktROFptKfFykvwFCCElJFya/WrFPvuKDmAVQ+UU58/pwo6bDmldOSCAl6PFcX7u8Js1P10Ven8ZFpLZ8C0qdI/YWuknreGVqaID3TW2YWvq6qR5o6R+dqeqZKyRn6nN36E0sCQPn/fDFAexDV6IH6/VQpFinCgPCPHK2YH4rFZeSg7FQN5ncrlpUVLtOdYQNlRh1JjRb/x7ePkvi5//fOLSSxmGCVKZdMxbblOgg/fyd6gNjcXTz5Duu9e95mtSjF7lwEzSHhzK/td/aTQF2dsQdkzQjpojcAqMXDXpg93t8r33YnarnH468IAJc+UqHL+029uy//gZ+RfLJTziMmNa4B8+Gb3yKlq4r80dZhwkIRfz2eekm24K3XsvnoLYmwPfjS3kwLRZ9bfeRvEO0IsfwQkwBohQBIoqWhmH+YyJX5IM0k+8wUI9S7VOVm375Du3NO7WQWcPaZ8dYyjTvYIiVifgpCYY2LcNSAnoqqHN6DFRahwIIGZg8Irpnp3Qq05LfPDasGcNwpraptQlcbTvgPGhWBa67zDth0bHgzdMgor4grlJs449N95YIA1KPnKPEQ9QyAiFJWrYUxNSKD2k7dhjdncrkTAeS5WbGgOnnKzMn6fmC2DYgdbNWN3ugtKN9s6vS7UUyV5Dhy+ii8O0CGDC+PgABEx+EC28qLyjn6ELFbCQXAnQg0WlIWgqnZjDYvrGG3YEUkkWBZNYJS0U1VmLBsXlYBxPtqFfuzGFJ4IxxbYs6vuhkRcnk3VLhXJIR+UNN4HBJ6oGtCmz5mJRPD3cv2v75ikz5+zaugnxI+0dGvZVH3ggWMgbhVL+6i+pH/9o+OK3Kxde7OLNhiDOiFyjuitk7YXEBa8EZmiBdDA6yzXqMWl0zZFQ59dNFafzNSzj4pCJnXvYHTkxkTxtSO/Ix28JlLaG5BE7MlFTm7Fza9tlw0582Vh6Sjmt5m+7RxrswzuL5WAgeOklofdeNQCVXpt3TJ8+I2640+tbcQ15T380O9I9Z+bMyRNTeEaBZFJjUGvwxgkG9y+88AKoQ40+fDCjgIE6QajO7+IXcaHnGcoAverjyxexMDEAMlgQJLQZQwsCK6v0cqmG1P6t9NLYzp0IE9kmJtt4FVA8ybv/OH+Ur5Jq1K94t7OjU6OnL3GF2bGPPyH+qY9DJW7xxpvdFS9An5nb0iovPa3x1NO0cfs7if1H0X2zIonHUd8sbG8WHlEkarHt61Mb+qbYMQHGi02iwOARIFBEm49XH8fW/DeFFCHBUxPeGxuyLF7UQCEX70++aTT+hujwJCBK3QG6/sNDfdDY6Hmx7DqjgXp9foGLRxj4UE/OfRaUzOFdVExnMU2kCQIZGnJSd8aNJCwAVpxp9dapE52OeqzsyoOF8sp+5+ntaqZEt1EY3ItLDmGIit/MMlny9n38APITRMnCNgpC705uttIBSxcr3bgEJzo6BHnBjA32CjaKJRz0ZXT4JQM3pYRDaWzlwflJBoSg7sHCifFChR7jYnawe0sgHhURi7jV1ARUikI9b06VG9RQHd6yd7WS6RpBvKLcgp1dVmWK15Xp9hZSIktRUwvbEO2oBaWNsVCYbi4IEmQhGlXj4hXCwV10TxLPBELx5QBOXuFkWIMaqaOs9QGJYSyO4sVFTJfFPwpCOEJqDOe+8L9ktjzTGk7f9PP88l9jDxtrDVLJNp97YfemLW3f/tq7Z89f2W0OlTEsYnH4OARFeAGvsItM8MnQhhUHUdmB2ifcj8MaPvuxtwgUSy4+994vwiuGvSoyEd4gKXKK/KmIsQC8WD4ugNZYRUTEgh8uYyEM+wa4k4UlnLZ655hWpQE6kt6yxnYLzyHpdo+uvhzFWAgbM5gPYICBzRVlTdjsCmsTslL+efiRYGrl/2eVSbWSMhui6sJasRyMO06FnxSKgRYY8y+MEveJRVAYIOZ/uVz792/kL7wg+YUv0NVOXxCI6KYz0v33O2ct1Rqb4a+9+53aow/nH3kkdvnlzA891VP4xX14Jmsf5J6HiechLrmMSr1nqLzhr/zss+r1/5E/d1ndWWcjkHz7+7F65V73DaV1vHzNV0jr5M0/lT74oUyuFPvoh8aknROF2QcU+peglOvSS/SlZ+w9gPbIiSMvY2J7IWN/juwEGMOUg4+BsO0jXr7G+B4lFYNvsAdd3vvLtrGMQxY1szuu6ZAniRTNBO84V2WPmLiWFgzRqxJj6z21LYQXHzQQ0GBH3aKlGYF4qgHx81BKhmknrTHgACYUCRMowZLFQ8VzZDjkkZHhbdu2T5s2FS+UVppoYsiHr0Th2PQReBqa2+pbxrMdsEykAsEWSitumerujLgd2rE5f8tthaeehqo00hI2cVLwg+9VT1ySSzXlI1Qc8ZzdyDDdqdRlhAu9jx4lgQdUBS1R4GOGY6C15+Lv/Xh88Q+DIj6xhTdmHNdQZdruZn8RyGE+x+Tlmgr0eFiGd1RZ9L5UIX0g7xejEIDTJrwkdeestB5qOfkE5Re/0qxCyIDaslIoELIdnCtWpFOWaIlIk+4kgrRLDuOXK3BCKgBxqLk/4Y6vk4yWkFkctVtaVLuo5LKY/AEi2NtV/uhHSyedLJ92stY60bQt49Rv2bn/p+ovyIYZPqocpqUo9G5uqSu8+f852g45lqoDv3jFlISmBHIPzoifPkmSlytqwZgOgjingcmv7AyHB544PvXuxU5zY/TKK6JnnFbesAFb2/GpE6WWiRlDLWXTZ89ri0EXaWYIav7x0k9bPDr3/EXheFyrKcCcpmq6RF3AtBB1AZyJx+Uwx8AsdNu2bagdqFnYB8Nh6Xnz5lXiYtd3w4YNQjKItXbtWtgxhRYP0FfADm7BqyFiagdyGL9i+g34yi5ubSb6WXBwfBS6byxkihwKqouOd7/wxeI9d5mr1mLiHv3Qe/AMUf7b34p07uISp0hbtpY3b8xt2x79xCdcekvpb+bQEkC3h5KDM/PHHnssJP/6s+lAqEX2HQTP5MmTUWJhUOTwFCG9KFutoQfC+sf7gwQ6DqRxy5YteJEYa44H4e2PJ/O3mIcngaVLl/7p8yUSkKek0A/i6K0T9JRJUjJ44xNdPDorFzctRkui1xJ9YLWPRldEAYpz3lRnaauZyPTLGzqdbGHa+OZjm8ef2Z66dVVpa0bns6Rj+j0WVaV9goV6FKLLAcyAb8NvBZAChNvjBI5CySnYZtSAXmMiIWB9PDz8oKk7DXO8ZDHPPgAQACV6LUx7EL3G2+t/KygFJH0pAmIQbG2oGFURDxQyxuAMlyEHJyAkP7THVp3o+KmwO7K5MztjdmwgtaQ0eEdJ6g9i6xyPNcm4FiHbsWUDUhB66dWNucVh6KkDf1U5kM2nQr9EW+VzdaSXGpHwtJsa0vCUmncFskaGtE5QNR6WCmpOFp90hdBCsrlkIu4zvVZ44GExP2UxUmwFj3HfeW/95zuWTI49uX3MLJOCOT9pkIgRC2QF3mvYFXJ2SoVc/1ZKlm3hwFKhZx1kGkqM0+NiKxhYxmQIoYWpcIpQyriqF6w1RIS/Fwy2OdQPp5hVRISWDKXdouO/SirktuAateu217ktYaxejBWaAH9rfLFFYw2jftgDulMwMRBFKwS54/HkUq4YjoXVQVmdgO3P4bdGcg+SCowSxfitFobbJcdcs9r52MfKM2fU3XijzNfTUHqoRFFD6RaWPxBMD5f/7kpuGKTQsmVZHLDCVupll4pipraNa1nzci3aWjvwYAsGM1U+0YoQKqWoxOarr5rvvNLqmBy76SZZo4G8KLIEj3nfr+/XGnFqzLEvuiCzeIn89a85f38lnrqqLdagjjqY/tY35YH+6HVfxzNwyFn8x8yN5hLEPr4Aqo1EPgcyR3ACjME6MgCjImxAHYg8jndWtpvAu7BjvH6oCTC1Z/QCRLW1oDYfyaZ0c98AixCCaKhiE1sonI4q48uyIghv6Rde9J+4JAA1bMgNbXDs3LkDR1End5CdwaHmHRaSsUeK48ADq5x1UJqyO90ezk1ooOuaHMJcCTtFJD/GQNFBkQ1GqMwPgTEXnn/1hBazh5uvsrynp/yjG+PPrQCIjOeg0BR37Sh+53r56lDi5JMSdXTNUlABi14yiQYXCrLwQqzn5FZBcnuyytYBSISY4FhVcLAoyhNFZYN71N14WxRzRTKEtoLa9yCfALTeO/h6UqIgggQBH1ykkV1iVdVyjWe7yhfNmJP88FWjN92Cd9qwI6zrKlTKlk89TV96mhqLpngh2RePwOp9tbrQiQvnY7IXPXUxnj5XElFdiZWnTUGhcsolJRg28L7iE0/ITz9tQdXwlGmB+f8z/OS5Vne+8UPjRnJr0pnRfKGure2yoZvXOGueDIbDpO6MOwnSj4VnnkahiupqJ3y51XVzued30KHmDkrOq6Fy9zF17/+c1NS8bdv28ePHRedOVztMO/+CLHUWrHBd9OSGhhlOV2/5kd+4v3lM7Rmwo2H5+IXhd1ykzJzlhg54qRU7WosXLwbzGOhj9os9YdjXr19/991341Ii0ox2Dbp5AFYrBUxTx48fDx/MjbH9K+axB2gBKV6lCNYiQbWFgc/GjRuBkC5A1hicdO3s7GxvbxfvcteEHMKKa8nYTMbL9YATdJEiNVGnvG2xNmtWMD0SxV2Runjx5p9Gduyi4o1/XGRoEHvfr6yTTtKhvAHXgPeb/Ycg/lcUDNliDxYvBCaTyRpNB4crAVw7B2aUrn3LDHxEOURZBRm09pXlksOlur/4ICdeVsRhB9hRDg/VWewPy9/8jrAEsHb2RpuIw+cI5SEa0CJ4AAqzJ9lJBrkFoX4HuPFH3RZUSnSnTSzj0ihAdJY1/RLz4J7abp9ZXww+/4fBH91id+7ESMMC2tNOnvH+93zi+En/s9rqK4jRm8DM6MdyL1opIuuBjG23OIBgvNZMAAri0qhSHrVzDVHDwETbw1Bt9WDDcB5zrAiePVbcgol3IDRSpAQ/JJI/bBdRCAEZmgLgS7Hpj8QhgryG1oNCSDUe2SgSh/mIvBEmhxBeqHAwg3qqnuZjONa8Mvv+2bEVasScdHVf7/X15a28N2uYxpkDLVdAhaTda8/pk86YjGcYiVsfjceOzxR5B50GPCshjfbvsYqZujaoCIULVKiR8XjnH58xz7syxGB+iHdYrGI+078n3jpxXFxpxDEp6F+E99g5IPapi2vWNZZGZ6Ria/YgGKNqDMs9I6gwcpQyeFIQnOIMNzOCGboUiuFlRwkzYZxn0oLQ6+HoYbxhwMAUgWKS8RMKDFQM/dUScjEl+AsyYlxPaacoggsxjGKchK8ycqNQ5pAowAhiZQkjcKkl4kyKUcWYkVITIc3nQwC+xb64d5rEDozWULagGc2mJ3ORmQE5YAdMx5AD9ayAWU1BQpyVb7Hke8nB+ArjQDGW872osOE0uN3bX7jiSpxRiN16u1xPx1orcqCGBHOeW24x8QKlZRUffRglkYrVzNnKb58o7eg02tsJS9kcpcfkD2RcNR4PTJjIZZCKJJgpr19XvOhiqS4avf9+vbnZp+gqdXUgoJ1+htbUwjVSVuNJ/dxl+g03lLZtDc89mqoH0xGFtgxt7TfcIF/3dWi0dTNpDA9t3JUDzEgGx060SJwujhyIr338xwyg9wk9LA+cScP8FnmAodiBEGGINnPmTIRicIwdp46ODmTFIbVuosXoqNc7UhALwOnL+CupHnt4ZT+0hTwRDVFgRw7hZE7Bzj3lFF/F7r6itavRk2R9upGlcyNT62sn8BVyFbwe9VBJ26MXU4HyuBi/+FLbFHkciiiCeoXbCp59LBUQ0fbhQPDqVcaKF3hdWLBBbWewMFpcvlw9arbaMm4sikr8ioXbV6LPRYTTH6CD/PiP7VgShsAL3gGBVRyWT210xOVD0ISEBC/CfBd5wWA5gJcSSH8VwxGksEHVFDFNE27Q8jHzKOGJHeqkhuRx77g01THR+u0TbneX2tjkLFocPPkkZUIbHedmrjyEY39QxlDScJoAG1+4EzhSKhmpZPhtp2YXLFA6O/Vg3G1qkEcyYMvEnS4c5NDk0ktrIxf+Q4903Af+7RPP/OGFGTPjd958uTYeleh3BGaSXg3olgxGwiODw3JLcx7SCJwo1U13ul7Lrng2YMnGzDmx9ywspbC+6+aLeRv6nDPXy/nHVNdEr4Tn291R3dJOLj0wMXTHKrHdLRVH3YcfGnx5ReK6a7Wj5uFZOSGysakhIWL6ilYDJzyxhIQa0d3dvWPHjhdffHHRokVIJpzY+Jo9ezYgK3HBMC5ewgnm0ejvewYVCBGKfWMgbGhs5N4SPoShFk8FISyIUhsEZrDPjG8tzOuxAw+4GgMJsmhJgyFlfFgaD01gst21W165knyZIwLGg3+qWhc1yq+sdk5colR1143B9DeHyFnkFCzCji8WQSBzlJPDP4kqjtMfXM6gjtp3yNb74EhefyjIYYp1+Ek7EEUhRoTCUlsFDgT/N/9aCUBoOImAlRHk0Z9Dem5bQpuQqLaNFd4wf3pxN2598vGkMeHskJ0G3Tm9TQq/+PLQ179t5Ivot6hS4cXOx58cymZavvD582eO+9/t1Bd5TaePWsT3XHDUBHO9RIho2fwIXvdHbTL90Q/hcJNRxw5j/RUdpddpEoBA5wY1a26jPK3OjUN/LnoYS944Yq7rk4uWGKVULn8xO1R4CbFgR6ChQ78evZoQhJEnc+DZKYEwGB4gBwUePx6DcmisKeE4MTWAo1Iw7mvmRZtKD80wlhvTMhP+o5TfolqjSniSo7cW8ewpHj96fPhLmp6Ewmgw4WHzJn2MzqPvMQicioHX7KUEChEEzjS47+Go7MYzEKViCcfu0BRQuMcpy4sSSpAm7hHLo/VBqT6Cnt/GLRvWr83oBFmwPmFc3RknBRpTC+mp1/L6fqk7Q7sVlGfA4QmDoMnFsfiDLGJmyGE4qQ4E5fr3YDO4YcIUMIjoPLQSEeAmYZIDqSfEhAxU6FNB5GUQeZNfVSY0avI5oaJBWSN4YX8SKP77Blbsw3WXc9CisrDNWDQJoTKWfpi8D/SW+8XalxI+USo9r44rWwsyykv1OPuMagBV5DgsLx2T0dqKWOyQQydi2gURvVWlgXEaztWiXlRymEoLNoSK5dEPfMDo3CHde4cxe3Z1EA44KpOutWGT/PyzkJn87quwcuBFVyTDdAp33xX43OcBZHV12QtOCNpjR3Q+JcQpXXZZ4LbbhAdomq+tL15wMQpf6IEHNZoYw3iCd6d1YFPKrRPPookq4ciJOGwKFNlgDYkXgfyi7tpbtkRyufI/fa70mX/BHAUthC7ZUJabmzGzNGdO/YvPY/0DHL7ObD2CE+CGhgYUO2xHHGQCjCKJ9XuAiStkr+cwG7Un0KZNAjxQGg/kT1JnUwUgRcI4sZpe7vZ/TbU24+1chFEG98Xc+GWtzV+QVMwo0GRVo/hI9v6FxmUkBI9zIKDSosEOT/KpYjg0qr1Qo9ii4bOLRezKaThUREWHkAhEtJK5ebOcp7e4a6jshcNzMmPVIPCG3Vo00FzGqv5gVrAtUHIopwKiqWmRyUriYk+Wm0guLU07qvfgL6EgzARJoIIaNepMBL/ki6a9YKs/W2H3z04uWry0dckJBt4lwq4gLvGHcXadTJW/GhuxXhOEEoVqP9Q/YJXK49vGBa+9pvzt7yqYWTU02rpRzGbtd74rfN4yO5uPX/aOwqyZ9z32+KrVr4GDDRs37Rkcmrf0rNFb73CLo9jHwupMXTwBhJmgET3/YjyH9egTT8yeOW38ggXGMfMxx8fEdNtwetPzz7WNa4uo60JDn9fkbjp0jubVhupdNCGWbP0mtChiPdNqdELropCAq2/enP/F/fEJ7VI9liEPZrDzhk1XpAhYUTtoOxq3qmQZ+7qoX3vtAB8MkR+GlAahWlnTsYYHRSs0vKOlCapTwvj5LuGgKdLue+P4dhHzXmy7AQDTIfCA0INv8gASBgsTQILosKOaw45UYBNPFEUuzCgBvBAAZXN5Up1CBd4nTIRwnrFQwnjP9/vb734kgA4PSyQIwKQXOfXaa6+tW7cOOXXppZcePJv2g4u9kF9YRqncKocfyiG+WGfZb8FDeZg6deqBsL3p/uABC0BvOtoKwqamplNOOUXsNlc8/2Z5nRJAIXzyySexHrdgAXSH/kkNWgz0F2I6uC9hDKd6R91BehyU2pi92hT0JVPa5EAmM3z3g+5ooVwJ5wbJfvZl85VXWk9tGM7KBbTw3HMBAxCJidbe5MT8hnyJjj8so2EmnBiMeh2XD4bVYYZDCgBAEz1uHslPkGgKWucfpYwrDgXWbi1txA6MG501o2ni5MlTknevswYLOFJUaTpFLP7ytAlIBB5hqWnzQYRO9hItcEKssSELOOTGmEcg5AvGAMH8ebBqUFbpiQ1KIoGFb+n9wUWJ4PHxXypGOT6XFVTRPWI3Y7ctH/jmenMZdLwyNCWKLB5tok8ciDQQz+QMpOK2jS6LegWe/7E3PoIJiGm0IDnZxkhSKI4VwYyLolhmCT6WY+tRrTFk4akOOxAMz5hsY5ZLFMg4mhp418Xx85eqUOnf29PiyueEo6fMrduYD77QhbNs3AlS6gS44J0ZZQ+0k34A9e8QZaYol1S5KS4ymvKRQQiQU+vD07DOQ4pJgicKgvI9KR7Z8RVehIBiwZesEDhCaRdakBDiI3gPBR7TGZXLYV1rCLkhuuZ5oGoBBG8dIyfe54zcpDjZwAf6CigKK4JSWTPVsn1CLvoR6KrAY0Fxpe4qIVlf2G+d5IuUYNyFRrh2AoxigUelMl/6SvDxx81rr42dewFdoPdKlig0mG7K5dvvgM4565GHdLwh6odqppU/73zltrvkf/qshIfNmprs22/H8ydeURMFruarjhtfKYXWpg3FCy+WVNd44KFAxxRRz31xy6GjjxrFU8PrXsPZZq9RAp/r1uHxz2DbBIAhm/AKFLVEmFcg8vELsnfdwy0Q40AresP3lBdesG+6UWtuxbFrcfTax3+I3yM1AQZ/ED2+2NrFvu4huMCEVtf/uFHaITEfBAD5hQy0hm5Q+r+sYJUQzbeFUyKYEViKlJcyP5ML67VJ9+B0OpcOYBKt0P5RikEhyvPN0yAAAEAASURBVNxewVu3bsV0BcOpap7tBXFIJxj1OjY0eNT/7BUD7Z7fl+4VcmhnQKPD1Igv0sgtNcXihQZRorkW+DR1TREprJZ2Zs+nhDccCpKVCxhBer7bUzJPokUva7j2xCTW5iWc7RrOKxsHnWwZqp65/eZE5R31nnXukwHrsyclZjYb/vSLCMBQ18CmVpJ7jfs3b96Mmo+B+5o1a+J1iUVzj4pcd01h5Sptd5drBNRj5genz5CgGAC65iZOeH7lynvuuQfbvJl0GlFuuf3Oqz/9qZYvf3H3165Fh4SBvq3Keagifu/fR848bcQI5PO53V09Q8MZ0zLRueJ53p6+7txofg6e4rWuC8h9OAVlv5SwHkvYA5p0zLjQVVAE8ls5lVM/1mN9JaAX8UQQumRofDKVF19woeSpfm/VtSKBlZRi0ouJ7mc/+1lcj0eqcSX4xz/+MWYa06dP/973vlc7QRURxRdHVSsYKgjhg/U8pVCKDg3Y2zrLmazU3Ki2t6vQc4B3hr3CLWOOumrVqhNOOGGvoT/uW2I2BSRgY9u2bfhiHouz2bV5UcuDsGP6hPJfYQZI4I+6gNn1GGBuSDEzdtsmuL1IKRdG0cnTqNB1J7a6OP8sytyYmH9zHFACKB6QHMwBIQ4VgMKAE++AEkhwCB/Z3d7ePqZbPRSSIxR+8IJ3+ERRvGEOH8+fF0Ml94+0uPZKpqB7oAZqL+A/nRPzEyhRyzhDJVQKqhh+t0Y2mr7JblRzdCw5rt2AK6iiZ2L2RAfpFNduDJx4KlaBenMcg+sWI6FGy8PGsua4XtWrnbvxlEYA+qNLv12j/o44tPCl3SpuAPFmPbjChnVAsy+eq04Y2TF888+Kv35Ks7AzigGLHl52ZvsH33PR7LYfrDBzNkZ0Hlr6ARP8BUceb56sMZbESjoR5OmUmFPBhRGmnwrfQlIi4VAieIblY/J//Y1qzy1LyZ8UfvxM+sqF8dsmBlcH1MJIuXVDfumK0XcP2O24yQWtRNByDTKCFuMkPpAf0LdBWLyMgRUeCSgqgn7PKlscDhhBb7AUKNmRPqji4gSL9In+BEPqns7NEl40pRS43es34hLc3PrJS445tjRrurJ2PegATfSqdyXOPdPa8Fr5gYfsHZ3DObzq0F5/2dtnLT6x1Jj81UYaXeEwm+CTeWbWPfkwv/AQsmGJm4EkzoAO8vOIApRYY37p4AF5gSw+/EUUkRJ4sazZBRtFAVXCzfmIn5HuHTELEwMM9CEvhPtkyUolroJJ2HH4txzTQyE1GfQHU4z3rf1RjOl2wxfdvi/K0ZLx6S63O2QNulrKDbeWMbaH+ha3/vOqMdeX/1tTGJh5oXCIWZhIIVTW5G+9NXjD93ILFwROPTm76hUqNX7q0cgEmlv1ZJ191532rFmxU05T6YadF45HjcuXXxL46jXF51YYJ5+sRqOpd1zsRz3gL0qouXNH/vyL9L6e/I9ulLNZc+VKbmVlLZkMd0xGTDcUdj/8Ie0b12XvvCN6xRVo+YoPP6z9+uHy2WeqLdh9xLTYHvnAh8ubNjQ+8Rs3EAxhW6vt7aJaITp2hou/vLf4opw470I5WYcRDzcPlWQdkDcRcKQmwMDezA+TCi2dh+BCknDgE+aQYG8iADUwmOUUnlX6vo4TL3i63XywQXohJBU1qb0kXzCoz8iq5gtm79WBcT9+Pe9FYQKMAocNur2YxN7X4R4D47ZSDQRtvCJNF37QiVdKJqdi6kw5hGurrzfXazgEOgmnYnDL1a8LJBig4hGMQChERb0SERsddKSQFoRyJj+GV0k4XJLN0WEpO1LHb/yKfhVYoCdjSsy8aLbcoaC/ymODuNwcGJ0R+c2e8u+3atj5QwdDZ6NZwfSwpfblzFkNxAaz6qULsykcKEA5EbfK9zu2Q5T29naUPRwSXvHc83t2d5152hmJ006jjVlFCdenVr26/vLLTx7N4nY6tEzTYypYs8DsF9huv+O2Bx64HwMOo5R3IJHBoWOSqdt/fFP4uKOh01YbzU3tmIrTRDCY1OFqLiZyUC2IZNvD94RjW2HJP9EkvXK6u3CB0tSk4c3xxJRy7z8Gwg+q43Lmooz0ZBPaXtRYmifjcB2pZ0R07vI5nZUPZvW4Pw8nihPKz9FHHw0nUv3cc88df/zxkMDkyZN94VQiVS0qD548N9hi+dH0NTMqvbwid+NN+pZtOPmOS9bqwgXGRz6kHH20HISON0IJmWDbGfqT9poAYzMHzMAg4ThcjRWr/W4DVplgG8CmTJkCK17HgUHWwI6VBQ7c+yMnEsq5Z5qvrg5QJfLGG5DRYDgUX3SiHP6/NhvxJb93Oo+MG4sj4oo1NCmg/OBqCQr29u3b/4jz6hUGsf4CfeNwojfFV8x74VkBeAtbIDc0C5jCwRykrv2FSwBLGCgM4mbEn5JVUeoguj8l0UPT4klHrqSUoOuDplu0X1cxoreC9goMudAYIlzMowQA2dkDoUUomrUoZiV2xSKAeXbCoXsF+Lg4DESEG180Fp4rny+F3V1nT1oxNbI6qOT6zLY16betzS5cPEGdVBpN33qvef//QqykDRVX7k2r8MCjajA0+UPvm9+SfGKHJ3CECYyUKM94g0amiZaedmVFHy4aqiovfoTqL4VRP4UIFYMeE1iY6xpfIgvfwDP5pc8MnqlKeHEZm8ABW4LaK2KARYp9SICxNGnWhqdRCjKOfENxNF27FXQY0CPm1T8CZcMWShliZ4qq4xhqDrP5WnkCDjvTkhnCWrDtmKXyyECovhXrCr/eLM9Y3Jr8l38Y/t4P7VfWyjOnJs4+w167xrzum4YqlR3dLSju6g171n+76bOfnn3OslfigVW9BjpuIPRLMzFdIQY78UNaaYQBJDSRS6UiQxGPMEi7b4GDBef/IojcAppAuesjYYkSCS8iDpc7lDZbNwSHDTkH3ZQ+B7QygfBagXEMIoujVdHWnOyQniOi8FdhICot9ckyxrZ916hORmrNqePoMK8r6Y4ccxq/qCc/ictVYrHlrSoRMfMSszCRRhRA+c57dNtOrHjRedtJNS2DCFdG//VzgcUnBnbusq++Wsb77VQuqczQSBuH5y+51L72G9Ytt4ZOPpm9D1GcEAuv9ZZXrYpu3w7K2nveK8jgC9V41qWXuXfcDjtO/EX/+Z9H16xTP/jh7De+g6sf7ubXzLlHR/77Bt6EQ6VTtK1b1dWv0lwYzValFjI6XMjEdW8gASKqgMhUZo4DD/05ghNg7Fxh+It5yCG5eEMcHxLb6wWA5CDRge+octYxA+Z/tQbXpvAED/JMGtDya1z587I+K61m7nEaPqUEoHnoEGxiXgS9l5VNA0wkxG4wygGmDVBLA8YwqfhjxwRQwayr8+cVTjheeX4FHVylRpEqtRmJKpdcJCVJ89a+hoBgqOMSVnwRl8sufehQgY7TymVg8mAZXDTWHFVE5Na2kEunuzZZyanBALSr81IOgXiGkeJdPpRIepqPUVDNwULsjET5g/Pk5v6d+cceG31xtVTI6x2Tms476+IZR6eM8F2vCn2dgCRUiDiUwxQImqZ81OxdKuW7unbjMSrw7BYLbqmM6x54bXjmzBkA3LVzVy6fnzFjBsZeGzdtnDZtOh13Chj3Lf/lGUvPaGxtGY870rL7wosrOnfsRB1BgpicOP1FEsGrR+n0CB0pp5pG3/6RQXPx8Rjx21A6Z9mLZ0zH4WNsVEKx8o6u7rKDk7nWmjUvBczH8EikaybdSdeFTjtOKVvY3XUMaIl39dTVbv5ZRR5wj0sXfpOksZXl0CHmCW2uDjUm1NvtazDdxSwU/gBEQVqwcAHa9FhdHDOQc849J5VMabqouUh3bXwIHQMIOp0tuxnXTtObIEpSdulcQ0BWzM2byt/8bniQNpNxxhuvdbkrX8p9azhyzTXSjOlYB0B0kANdfHHeFWUVk1VR7sXkB8W4p6c7mawLh3HHmRj0sojoUlrG5BhaK0UROoFxjht4Eom4AAKjgK/lHcjkcFBf/LbyeweKt99p5GmHhZYDx7WGPvYRt7WFnZX0jqFDqPbxIM83y3gyrhU1UHskK76cqAofyDEvpOKFOPvxYkTwBxiFehAHYF3gImgGF8A19YQzC0EEJ5oazOKEc78Ya6LuJxzLPU3BJiDDghEoYsWH2EM6Obv3E6Hq5XFW9RhjE6FjvP7SHHv2dK1cuRI65yZMmOgV1780FvfmRxQO4evJP5fLDg+NYAL8urKMM9fHyhjIx/fY59cv4PsEoC3lVVpxK7MWxVhJoo7sJ67nJejuFwC9yr4VhVmtTjIEFgFHqKiBQquOXh8tvgI1QOiYxKzOhxTTm76cW45FwrNnFp75Ay/KegJAq4r3+Iy5Mwe0YLqoBKFlixCKyNQOktV3kovton2E1Q8heL9zJbbor2pcPL13WuvP/n7S9SmjS8XqKro6R7qkRV+dXqo0XhsYsgd+/QiGe4ydFqlhsEE8+uCjsXecd1Rz8olOVGi/TQZavjmHX26Y0V9TKLTG1Ck9U0Nr4kZP1ontys3pNqEtUkcwAAVPwE8OZszn0Oe6yu2YNJG3iEQRKIgjQGszLa1CVsyDF+ShRhPJDVC6Z4cRrQvWiZfD/VLhE0YcnxdYPOyiCMBRot5UK+HZQpBn9v34nIhwEjHsYqbk9AVDdZhjdxbdn75mXjFnduvXvuRs2aCnEnJQt++5D29hFCx9pES5CqbwCn3fzT9rXTB/XuvkZ7qQFC4doi8kfvwUsF18SL4UIKQs/DwRej8kERpYEJ908o2kwixTtowtCnATJ8I4TgmX61ACzbTUOBId3pLrnuAogTAeyKAcpRPsjIfIUOrpl1OBIg5yeLgDl94pCPyJMAZ4S36QZM6GQCD5SSey1Er/XMo/J9n9ErRehU5R6q7U9Zl0reogjddbQi6YeWGLgrps3yDN7le/mP/Ex7kYUlEdY1zJmDINPqXly8MLj6+VD4oM4hrTZ4488BDUJ3BJfV3FCCVRXbgw88CvUMhFQRcUUQG0FjywRJiQDyoe+Lzz5/nfPW099xyGx9r8qxNnn4MND6LBbLr//g0rPSjrpAlgL4MGUv7nf3SuercajgIWfO4FcHDnEZwAY/iF5Qdo3Dk4B3++UAwJ+iRMTlyn9ErIeBW6yKD+DO+RkvJ9qC3M3x2p+1JaVvLW6JNy/dHIpoOzivRiE6YCgzOr2CuDE80hNt/EFgq2ZQ6yf0INJ5vawgcPylL6U9TWcaGPfawYTShP/zZQwlK1VJ7Yrr73PYFjj5Xx/N5YU8EmihA1C640PDSE47tNTfS6l9/g4nlhaajIvRWXN8FETTkiK/XESMho2imMqPWYeDKvAjU3q2igs/1daLVts+SY5YHODdCibNQ1BqKpkGpffpTS0rWt95p/t9firD9FM9dvHn3i2cbPfeKk0856rcV9bldQXLhntuWtw9KOYV6G546O1iqg3DjrauF6KDCwhjPQdOzkR81cSW9MScmUaUS2DFnDo24+gdm3s71fcZCDeltDY2whXplqbsO27vYhE3v0+TLE5i5bdt6XvnZNrvv747T78NSgSDK4Qm2ylPFbRj965/I19975cxok9Q+WHnrY/fVDSm+/g6dNLzrPOG0pZoChltZyPPn8lsGy0RKLYrHXdSPzQkcvsX7zO/v+B6TObVbeVBcdp73vysC4DmBRm8rDuMVtqtiLLkmBxPkXFPSglMvH9vNQm4TFIzFvxA4eFk1Qj3DAyx3N1h93fCydUZD1iZgajSNTaus7ydW1rfxT7tB/uoUXFXuUFATok526v9fr3uPmLOeRx6LZXJEvfgRo/osnN4qBTZvMZ54JTJjw+FO/TWfoji6y4NFHH8UXeuyWLVsGH2E4a0hURAei8axeIH6QTWM7WHDn9eLgVCfd2QLYj8KLJMKPe20XTUbgkkuc+ceUXlkljWbccS2B444vJ5PdmczkBlI2xqW4FklNOa31BqQIYZbogz/fR1D0nNVY1SpAdKr+HJegffa9Xw8rflgy+AJG0BAIGAAflhThq6ClkBoSjBD1uYhDAX4k/iUwis8OapgIF/5R7Y1hw0IYxiasELdt1ClqpD9vjZhaxg725uwCXkIkI9gUgNUvYtcg8Pz9pHrOniyBhDKokjUG+Kj8kZ//R6EkBoYbA1wTbz/0OHR/8BVcsBzMjIk7xnGwWAcJ681ovcXIroxqDQnpHQT29QXVpKDGeuC4VaBqeqp++8ajYuaFo8SQTabjvumslEibjMIPxa+oHwJoLCoGEhCI5JPm+lkLCAiGrPWjXkV45/CsnWWMmDrKYS2gv4fGsTh+LZKqnWxEGuVcIPXwkj/98YYcfsmwHz4Cnt0i9RTgGdG84PpfHu0vnlAANKEgI36oRrFzVb9zweRE/bsuzr/yMtpnnmwClF7aME5YIB8zf90QZkqoevQWAqoJYoGOICXWhYRT+AsKsPurUDU8cZj4iAoONVHLmq9/T9s1mlTC6xmuqbtmQAkVVLd4XPzBkrm+vOMzCi7OgFqljhH/Lvi0d+6pb52JUwukz9Ijwj+UKq84YMaVkAcua/vOGU23x6Rhmd5qkE0ptjZ96k92Xr2rNAdbZBQHAvKbT9j81kv0NaItAhDhrRqe1jFXFT8a8VJ5IAnS9FGMgCttEDNHtCBcTXawIqFivYG6NB+zBwGEIE0ODoKF7eSmFk1TXDx2CI3ZTFjkM1sJAAq8Of2qXlYNehMR7//ggcBB+fsvWqdOaZpzXNPkmGqvfrG8o2u0pOG6E5cOv8/qH7I3bxt/YgdWJYrgEeQ4pMIgKHBBY3KQEzOMCSe5iS6zKtpI8qRg2ynlBnZFUuNULSQAgNKTDKOpSQCjgyeOZaUzxaHdyNn20XDQVpt25l50d0XHz1AMcRjHhyQMtXY8AoxdGLMuqEJboM8Ck3krf1BYRM+I9fvZSuN12IFi6WMMRYc038pJr0kbJsDQSVl7sRRVK3ECVH8dyswitcRjDC0YwENNnXXmGP+DOigb8KJO67jweeP2AuRGQFRpLq+wBkKxpWdJZ54FSB7fVLMJrUjdksVceuHJlasGHViLH3e871+NVQNyMOsRnACDLJ6l+QuZALPYIFgeOVbaCKtPdemgqbslQvvnWCcz6KgIgPOFfGBHFDdsJANrb50sfW5KhTAruTBW4MVCEddEk8kU8r6uLoENcID3dPeEwsFEHXZoZVaUJYZTIi+pLeUmj7iAnW+uW2K3rUIEICgxcDo4ljBteuDTn3Ivv6S8p1uOhAIT2t3m5kwgyGNn6pzEXBEJ5RjkxKCVUs5mV1e2UCq1603sQzQx4h7My73oVQmG+AESIo0/kTp4O1J2uKeQGUwP7hkulAtY1Ojpx+5cuLlDVoPeILjs5IbzMroX9MF4w9fK6YoaVG0s2pzSbo8r5YbuvNdajVs3QOo3Qtn8nv+8uWPmrLdNmPb4VmwZeKUR9LcOBzZBWwGtbqNTdHdu22Rb9vhU+NSj2+ThQWdkWI3FtNbxSqlIN2kzaSMpzZjY/EpXcaSIc09acsKMZMhpD3fog72B3Kjcs0eOR+S6eqhcCxRGQT+Syx8d+KnWcQu9WEzdPR6Gx4ITCgPeVtk4Rfn+okU3/eKuW2XXLuBZ2l8upye/TahUtMu/fqj08Y/qn/knbIzqPbvnjq/ftFaDqmKgcMvp8i9/of7oRwZ4DoeksOauerl89Vbr+n4tqWDFNxE2CqZUVIOx910VXHLSiBGswyYwRA3EvFLri5yeYAqip0YQDnxA15Su2jt3lh96OPT739sjWbMhqZz8NmfZMn1Cm63qPO3knMQoa/AryuD3FLmIntnBUoFbVEqvun2fs9LLVfnf3S0bsKfA5YHUISAjSJEoQDduhG7AtikzQulRbPzu6tzePnWaquHuUKg3J9CDFS4YTqhpyvwhSx4aRbEgTyoo9E8UG/KiLOYwLkhUzknCaoPSXL9pkI59E4j4w+DHQyJwQBJuWEs1zE1CwZFeziruBktbv3brwLa+pnR4vOyoPd17tICRrG8kioRKkGILO+GuBLEHmKnSYzsDUEln3hgLR6LqQ14UgF8Y+jI2Bqr6+MMegVnA0UCPYH0cBA0jYjI6gQ1xCU5Qx5fs7JUu2J0jNFD0MPi1xEPhMSbiYbxpz26lA6ZVzIyJMScohb3urmy0q5QM9enRPF09gxFf8UNLpxWf2lC2jwlBnQ5PBviqPtBjgkDBqRUOL4ZPwJcO0amlWAWrjeZBVAP3tVFyDmxYoFSDcKpfVBq8ikDnOPzk+b8HRrG/kKG03mkm5Izez5OC/YG8Lr/atHrZdch4nDWHhCKAWuwsJeFhFQud2zfx8hPOpijr+9YhK5INLQ1NLRVZViyEZn8y8gaLHolKcdwPXwIECD08Mo74qNtLdaVsvNyreVWUgQRRP32UsZXpmsDrcUU9NZUxb+9wL5pUp6mRYTxElgtalUO/XtaKxyuVeTq9DCUfREdEg4UcfsXtTeuP7rIunT2v4av/OvCD/3G2daJclYJ6/PSTEh98T1ek/lfPu2WLuiWqcIImxd+P8Uj6XAhyFAmwY6IAC7lnxv7wd23fUuSSlQn13JIa/UPMLbnaOKv53SPxE4c1d5sTu95WsJ7MCpwIC0XDCr6LliAYwKkrmtZDcN4MjlsE5hHjAgQ0qLu/MPP900LPcDzQpGVVw80dF3+oY86K/9j8s1XZU9F7Amc1bZUcFSyCpMg82kMjBmpSQm78gaof5HkQFBkeRYuUM1ozn7HyIyCmojsqZUvDWAN3I8lWHL8UbWCNkGieyLeHBCrKYRoZ4GSTYaA1NEnlpt+YgicqG0QJZZikogejE6bhmhXOX9HRdUveOaretkoKSPl/OyXQkS33Z/FyLyJg4kTHLIkuYqH+5PNgmvgmgoSOiIhiR9j9HGbaFEYC4KLE4hGR4MOGhCq7ppvPynWmpAQZXjxZIuJ5cORg+XhuoI3F1cAkRJ/RB60qcqoYbIlPLkNlBo2YiCIxx79sIU7JD2M/PB1SKiTrI8EA0a7NGAHzlv4KOaAIQHuIOzQ0GIlE97rY9RZOPmZeOPjjteF/Yek8IFdchsdUBr9OecXb/9knQaL07+N9KI8jOwHGI6LQoUKNUU2LeCiW3vxwHt1y+0WNAmqFVzHQeNI4CQ2zLnoMPDxAhppM6PAN4DEbakQkybDFSB2R2fC0gQbrMDThJINI8qpXN3d1dZ908hI0yq4bxI1ttOh78n0xrPYpKUAOjlAsYOS44INi0sFTakhxt1fe0zWEGcikjiSAeOaBIDYQIA8JMB1qjDbWT2s0ps1FU42nr/rS8ii0VYpWmZgHWnyo6afE+IYT5fakZWjw1UbQG4oA8i5ZdhFqv3wDer6VfoHNzGFzd7uZy8iOLZvl8uAuWdH0RIuq4iVqASsruBzbPhuO4siecnYo3DwJIHT5WZLmNGmRQnr4989VWKoQCAwO51a+0n7h5KaInIbmRGqhiX06/OVKCXtNvXtHwn3pmAb0UKmGcecHti4cuefJyCmnyqUCtFa4v3saWp1caHg+aUnzwoUnTG5cO4i4cp3hzAyXlI2b0rf8vPjyarVYwKtI0imLgxdeLBfxHoPi5F4OuM9Bxo5U5zRco5YXW69sLN75XX3h+uBSU3O3aoP/hu5PQ07z7Ne1bJmOkkq6aZV/cKO5cJFx5pk9Dz4WP/ecRcfMiYTnSOVNqrnRvu27xgBuOLukZLKh3o2G7PBuPTxMfWnoaOfyC2hL/OijzLb27ljD5n6rMQydIOXWKI4pmwpm9cNDOIbt1CXkWCoTie4etnJWpDGedHCEYGjUzpacbFGDNqy+AXP9lvzGrdHP/ONgqnXjIGU3sn2S/b1267tQ8yAVg/lVYXuHoSTKoeOKSn1JLT1nSf9YslosUp1imLaVzZAiVCosWNkrYYnbNGOT3ZBSGBzYMdw5vm6WHo1BgearvQwBIDaVjCOXiMw21B8uNpUiRzNbD5h/yMlFkwBFKyb8fbsAptEHNH+62XnBm5vtm1R7uy45x0fkee3h4cxZ65x/27DHCARdWmES/AgkHmaiKJB7RVgg5epQAUeVJ2/vj0RXNcQiBSG7PH8vFcSV8OdwdlLrQGAeJxwAF4nC82NHLUBNENdv1D6PDpDkynJPjpBRY4QEihAmSx6Ek6gBuaDIEAxEIS6uNfZmqBnwDEY/dqMdSj29C8velabMCwbrzXGFXur26BNqvkcDnwoKDxO5MXZk8iITEYtFRJFruyuWh594hvcde+MU2Di6LwI49jZjY4EaPPb5QohYaJvV5E6OSRGV1rOylrJp2N08KFsY9vsMEhkfH/1yMgXDFbJUrChNZEZHA71mXE4Hhmmrbz9GIBAo/UgE5hPZTxR41ULuH2I/vgeLVCt/RBVZ45jKsEnvK5bLONFrBmMJpDedDUJNH3BVolTzy2NaEIKDLaI6cIHgcsJAFCLAmFH282LXBDiuoTbPHVTUdA89rCBMJb5o5hkcfpUS7cOBQ94Y5FLmx+Zfzh7hI3hAp4jkwC6cFIQ/kUAPws89gMGKuzXQieRPEYmihxg0iR5WUuSHNqJjC5294NTWG+ZIXTuhZ1hrbck1Nm0NpH76stWTY2UELDvEhUC9ClllX3BD4mPkTEWwDp4EdCWAApEKVHrn0tYfBuQc7q12fr3BfbWB9ETBbJd6vxl2P+fWnThsTNgVnDXRWluHNgxpROtB8yzstLSOw+Miu7M4JIbH6312BAlyIeHwL3yk4wvTg39ArOL2+OD9cWtHQE668aUjyUWZlNL/iY5PX73h8UF7AjdKhNhrnfymiBB54uJiBBfXAfixqOkDLyxFiXM/XLHJUzBCwmAvRkNyKGTzxfQAuMPJdMnMunRNTFYjjdBNgZxCTD+yR4gyWxjgoqxCs6ZiPRR+GJhwCIFQLDbwAkF2oh0I0IgKwdS8wo8lLoW6c/aU5hY7HtdH8KYodfEej8Cp6sbE9mHTLePOIibBXm5SfOARs3mRNqYtGKAmpDTcZ5YLseZ2IKvwA5X6GNdQXCw7ADcxjxdb8CCxx6+XAIKoMsFhroId7rARMuXmbB5gIVNtLQS3K5o3mqRcEEgIB7B7EiexgKhcH6wuVzD2v5KPGCWQildc6XrmmWexIfenV0r/Z5E1koyZF+Zffxbq/4eIHvEJMDTKHglxIIPRDolKj0kM9R44+coWtCeYPmEkiOZO/Ccf/g9fGJ6+Yu8LStGaxiutYXenckwhe39JxcSHMKGlc9DkqfNytg4V+PKW3OzeTmqteHTptS7UalFL4/euFEvaVU7sLmVXdTmxOAuWW8zOtBJz1XwIkNw2UxsFgx8xgvI6EfZ28yYe9MMTCx4Qg4kQiiMauj0Zac8okScvzOWEzY/hDVo4TMgHvVM+m9nduVn0Vug2N65didiNrROSqUakImQAhKbbYtzgIRbQTBPry9Fks5Rsssv5THFzOJYKRBuDiWbV8PRHc1ZQBGBwVFvRHBUvAEluWA+orlMXktQsNogz2DVFRoE1YpbsRNMdHgph8paQe/IOel1C4joxozix+K1J8g26RJ0iFhPoinPmaXs0EZq4TI9fWLjzLu0//9sol9xUHbQZY2O2/IH3hi+/ssFIbR+R5+KQdue29Neu1bdsVfHIgaoEB3rsG388+sJKee4MYNKbbUnDveeAE7tBcZeWHvyl/bnPxbO58i+k0nVhY1lBMTeqYBXZH4rgLC7OEiEWGEaWYg6ce+jhwMlzVOn5ULEwf/x0vX+O6z7g6HntM1Lxc3YwL5kNtvP2Qe10KYxHFQw+X948W7tomdKolaSdQ8PrhopTTHv29uFUe6OSKGadVS+lb7vXXvMq7qi5Lc3BC8+Lnn/OjNZxjhExhnrd/r6QI9vnne2ee2b2F/dbDzwGEblPPJdfsCC87MKBfCBtqgl522LjeigXsAfDA99ulF6NIC8CupGN5eKf7AsuHNadldZRHe66uIVFZFvFyxCUHNQGSY3NmV3UA6s6izk5lBt0do1K6/utYF6MBijJnHZOvMgzuKksc/kSH54giXEQA7MvgEVUdlFBRfbCy8NH8iRg8vTt2IiX0mfFPzK+vBwVhtb4ESQ7upRvcu+LuM9ttr+wI3NOOmASW5qOAkNDLh+jj5oGTIjH2IkCrJWpIeIRSvz3ElALRoAcwpgoPv2B7ZroQA0cnAZKHjATDKMUdITT96EgDxFBk9Nnl20iNr4l283hfL7A7EMKHpkYYglJkQt/fUVX7E0SjCuVLSWNQwpei0Q+dKpA0gqmGAdQFI9T5sLAQXwheMJHa3Aj9NQ1iDBN8vQMefqJBP8IgTtmyCFsRIkwzmcEYbjH8TwMlWRT/NpUs6Mif0qVz5lPE7/ARAgOapAXcipUPmuK0pQbDmzsLG/ZhlKRmDm9vmXcuFji4S1mwYTGNS8xzDnzSkiZu7F0PfaZZBaKER01g4afrwUelA0uUh7GCv5qDJISFbrDMXtHpzIJfN6fSCA5aLpKIbqWnAiXOdhVLIyE6ybBjqWvMq96Qd6CFY7gYaGovqDYgkC/rWZoEQu+fsawB9EjIzCKIPYBG0GaUPDBfQQDjg4HQQE+rnJRSUEk+hMRuX4JK2GD8RDz/cYxARyDSXB94BIIeIbxAEUoSZ1R+URAT06X3axJfbEXBC8i5rkhVXK62j1rHTwXvGRi8/SpLTg72593Xt0jPb1LyprUkXvRwSJLSkiGvNn4RNF8EWYfmKzAztH9BOHXh45IwzPjK3CHJfOSYa9OVR71ofh2YOi2+sSJGbw3VHdJfd8GRaF1TJFLOCWtNr//XZlkw/ObSAii5gtOiAxGRex3VPi5hYn7MWXOr0p0Xdus5nHiCew7/S9FzCtCje/ubtK2nZ668ZbuLxOPol5Qu1eVIolGME5ljJESfkZDHiKYy6WfKMEGt/wUlaULlCJv5VCyKZzEY5nSyO6NwWjSqGtmeFqgpM6JkVBRQVSOwZQYhI5KuyNdm414g5FoJsJipYvZExu2TAssk5Q4UxGRXMQmT2cJVlbXDzkLmhtS552Vuetejbb14UtpRmj49LeZbePXD9LJPbHOL9o1JkKiIDjRdFE0Shbz4ZStkokFepyEE96g77ojezbbZo7S5irDe7aDjKIF6tpmMX9w+QZRSKTEJv/RD0WSpOSgHUH9BRFZauu2t07AtW2EspthCAXJiMiRnd5/wkcNBjD+wnY/trIRA6KjIPpycedfnxTT5HCCYbL0wz74eGjJq2oX4JVItTDkKYRf61uNyog4kwiQBHogw8R9yBogPwYXOyAGHBOtAYEVu0oodrVXFMeGvwVdmACfdNJJb8GEvalJOuIT4F27duFW7etRGPtG09U1Ym8Z5hEbtS+i6HPNogpHTQb9eTWXqsRYI1oNvK9+Ydj5vtaRLV3aV767SbUCaMUUnISZkY1cOYxKXnTHD0tLHZw1of2RGjw1Vg8zbg4bOPCLLdYSHhFmLhDiTp02kyupFwE/7CSOqf1id6GQ79m5FSGYlqCibtvwKmKmmsYnUvUCWpAQ7QPNCmi6KgwmTAKH78GIiVnRINOKp2wrajhejzgFKGey7TA0OSGSZuBpV+CHnopkAEvKiFTllNjlPgRcKZGw2zAZoUPdu9R0sHXmtFh9IxKIFgsfguKPGFaU8/VWKdKXxmMC9pTJcbS09SE0ycFkUwrXaBVcbaqIALEwBx3fZBpaR0puTQAfS0d2loa+Nln5nkq743q5J+hkZKPFlhIlLZFRFz9YXh1R/vNWzSyjO7GH0xIOCEWi1u13ylNnjTvltGxYjRRL+Yce1rfvwJI4kkgnqsomLsNpz/2vnejhZQ4wi37Gks27ne5c6atfiWezkFigLGV+XAqcDa1RkKtbhiLJC5a5wOxJl9JcTkjKvMel9OONpw/QKeMeZIeBJXlgNJYUrFuD+Sel4MWy0pjHFFrkEw16rJ9Z5lNOf8aQRyZK8kQHx5SbNjlXtqn/4L6yYfDqbxgj6XKpoGsBbceewg9uUrOZ8N9dWVq/Lvftb2urV5tYkll4XPATn4y+8x3DZcd98H+RitKzLwVOO9N2jZ6MOyH0SNAYwEm34f9pcNbX2zauJdM2upuO9P93U9t/FPEaU+Jsp+eppN49gkzBojJkjbN80lFz9FNO7rQjewpcY4Kp9hMuLKqhIvZ/wT3lLoxIB1JPLhIO5SLtEnAQnOT2BjoeLIWIyF4pJ2RchAlAAFH8qiEs5cXRa6cFltOWwEBw9JGkuTWoRN3QKQOR4woRtefSKd/47kstnQNDYKRx4lQDuriAidgCKs40jyXGzH74cCKIQxgBJDyJCfKlP6/wc7PhcSkiCNwcEx8Ai8E72ythwsXYfSv/EiYxqmIqjNgHECLwXPTMiRLFfgC3ChxEUSmUxChYgYDght2LylWfEaA2U/0FHAVxGgkSEcRwkIEEOrICBdauaPmA8xw0Ea2EJUGEVeqniENfBAoGKBTh+IvgYCi9dE3+gudciWbRIhIieHH8UQ0CQEOQQyzcX6b5s4AiaI4omBEoKMgzHlLfWflFpKhSPne61jawM3PzLSOPPhGgeiJZkVDsne/ouPQdZ01tefA122L1kCIWcSUQj6Hlo6zkj0u3A3AkEomEUgE/+BC/hBxJYaFQ08ZCwBdCEigolGEOgYiDBZv7gxSZLEIYqhapV9pAHr68TIxCQDmzHykCiOEYSU04teSEvsbL50N48XcvlCKEUZEQIEpgV6xCDgr1IokG4CwX8gPd28ZNniVj48vHX+GX4gsma5MjJoxjqRMNQY39qXZUU4AAboz2mwQZD5I7XdmxXbmPfOyvsi2tPtWJPgIFykUH6tIlKaQK1Jg21T0hZFE9mQMqxmwZi6vWq4ZxASQ85PHGcFjJoMHObQoVHRWPMFVQocd3dmrOqCYnzMjx4+q/eEXPrfeVt+4Ab4HpU1quuqS4aMmDvfqv1qFL80j5VIhbmvhI1ruaH9KhS8kJ7P5B0hnF2TQBglPBau9ddbGTRkMTsgtSj9yw9V9NKSTSBga4XQUnuM6HBGPiSCkkCdMXBuIgKxpPQPKcqpozVN1ZNhQJTNDkjwwwcCS8HozotOiNFXb6Tw8/IVCAER3iXfyRgySOMGCjdsuB8k5snNulfA4HzQLQzUNh9B/UKJuIR/oPQ8McyjWKT39+jUbwiu3uknGxOVdcWsrm879+XLextIrxkhI66YTkh9+3JVz/1CtOyQJKTgyVLEoNIrLhJPp9B0YrThHKAiUb6iShEzOfRXOnaEFFJyWUwViTa5XxwGpptD8Uqcf5OEnVkTqgolSzRMEjKAQcafyApVEBqBo0IG176HgL/sGkRuyZuyyTzyxSR8+AYGw4oYwmwKs/lKNbIXLBUV7aw2/i0XKSACU0nA5EpdQRYv4hkYusJJlRnlJtIXVwJFPKLQ4FLIIoI9iCrCA7xSU8DEYAVGAIDB8fnhAyAPkLTyyfEwSRQCGhkRQFQZKIzvIhO3kgkLKWwzw/OKhxJREQKPhBKJcvQgI7GWjCx/ev5/wzNgPxGER7ezsl/m/mwBI4shNgvICCR1y6urrelL14FH0u417lQDXBLh6ShjKO2kmlntLpFX3YeKjG7QqsFATDv6hFCOVTwF3SZxrth4JKZ+SiwfLsYnlFVMIJ1qn5uiV5KYgBobpb/WxJwkMgqH8+dcbjIyQHAsQnaATAJK5dcLNG3txg1cByWyXcxAWBwAUoqCckpWdOqQDdxXjxCCGk4o+WbwmAcXnVGW5SY0REPYO5EIUREIEDmphFi+cBuYEItPZOQmDv7t24E9KGm8N8BEjwYGhOPMhMeJQYj5AkYRAihTgdaJpINbVCly/6HgJCfK+J8VID0nowjEeSlGzBLEL7NVhQdqXtGam4fsZpzm130/u5tM4K8cNmQa1U/Jj5XZZKWp8ZIVruSdpTc8M/wq1Zqy8y8MNG85VQBJdsI2VrUX/4QzlZz2ottxdDZdeUc+Ggc8ZS/awzlPZ23Ae2wxG1VKqPRuVMtvTSq4oFzsGnE8ZIAcsSLWbwu4r28iZBCQFgQCk+IgUf1y6S7ZsonWA3sMN2Mjo9ZIBChQb/xCXSHXdxNlDTW2rWtB/qoen9VOSohHFiVJo7kthx13VqMTyFBgSgQpeOsKiRqdOj2HbN6non6digHAKkE5K65+nXO/2P9d81XR3JCj39gLclJ6oo2i/ucRceoxoBpX9AKZtasSA9/fvS+teCP/x+8p1v71nxcqBv0M2l8WQ5tFlhJ7Q18BKKgZML51boAQfSpDIDpW5IkZEOZl+OxM7Kq4nBhq9cP3zTr5zV66ElzAoE9UVL4h98X6ZxwopOXDDHjQAkStOiuA/lZTqnA0mh7EbZpmyGBf+pxpGbAqhHY3rsQx6+YcVmJGl4kMC8GRLRgQ+xSDYPC8Ib1U3Hxn6KdyLLu2K9X2lRB3E3m4BLT0cKl/Y3XNWf0vecM+3B+3u/CqKOGjBxwouw8wdwrtu/e2s81RwMxxg340fdqDjIA9DeOKASEZ6cECk92KsHjHAMdxAohezps1dJhg8sYkEKBIY/8SsQiS/8RSzxW+MJlirdMwVyEEoHTUqZIHHtUSbJIQWoMF46Cadgj4QHRPDAY2YT0PzAhVQxtmw229c32NTUEPUVrdXSFy0IoSbqdAYT6mS4HRG8VpiicMIr6HBygEfT5QCOUBN5fCg1QwWHNxwIkDyrxJhJ8iKDCPivKw61IV7rQV5QyMNzTgHlQVYdY22Ih/Qi2tFtakt+cPQnPzd//QiGmaiN2KtRctn8z+7ArcjJl10xPWU8v64zVj8uEKpoKBScEedCUGNxe65oJKCpSQNDazqPeUBDGHjbndJVTTKqBOYy8Kh6cWEDnqqPj5Q4EYaRsXVM4SASVYiqA377YiNA9qVPKtXoJpIqHZVgJOJLIAKIch9+3o/w569H0CuChKpiqGBy+avx9awUwAbq4nt7+hqaGtVSrjw6hKNGOEyFeyFYeMMpXSijoTKDSEReRKEEs5doRMjpEScogqskwCfGfvD1x8Q0EBYMMEqyArlAz5Hhgy6pkpB9LTVhFJmbazydgrwk1JTBlbLMVRJ+xCalxGOKSe6L+BA+iFxy6K0g4FNjJvcrlBphQN0yoPWXuOiWm81TlrYuOM7IkD4vKxobitQ93K3+8EW14AR8Yfox6ZdaP8QcF9yC31KfUdgV4EcXKmnFWmwos1YLTrSbjO48KboMo/SOTQiJmHBRSj3jlnNWKafHGiuQlIEUiETgFxmMWS2virIDThaSyCGBhGoWYM1SveaEcDO7kma/4mBew/MhaimE/B27OOqUc1hHN3M4TTCqGKaC0VMcTwz6l/URmV658bIE3PD8nCl5/l4qgNOR9ev+UPiXxROmf/wj8XPOMPE4sOPoM6c602dvjDR+/3l7dw6LNUgOonA2U1QSBk3/yN8zKK8uFnh6NwNSYM/0bQZINDVeD9IetZqqJxTlsp0biSQbVDwkybM9MQQnkeGPkeOKd8mQZ602Y5hBE22vJ4CguAcgoIApz1+Ns/xkqCZBGqqzeUpgd5Ni0zPSxBj+TAyRValoSZ3DQCX+0ZIo/gEpELGFyOIPXrBwv0A0YbjhAzx1lzQXpogiDsoHhxIU8cA5LgIBI8TF3hyIKiLgRM8rpEbQnDaus7Axh4CkyTb1g/hS2SVdSiRwIkUDMvqikYcPMcYZAWZoNVVkilB15dsp62WpO13MObqlhTMlm5JPobz0wnbiBBT5y4UMNvKryd9KiuAv2PdhON8oqhC6iEmBiF2BpHDPCGK+q4pM+CBWBUMF5o1bBgYG8ABeR0fHG4/61xXjCE6AUU6nT5+OzmHz5s2HOQH2u1UUKC5SVLCoVmDAhuyiMoPSz4a0vHGZZn9RmBCJ2hYuV1hCE5AUhDLrKq1blZ92FN8fljuN6Rlj+ggAgczB1p8T6FI/3qN/iFahPPQciaJ5FkLBVsaPB05p2lQskaJhvzpRMNdibxMA+NGog7yIIlDpRrB54lTw1te1G0sG9eOnMNMKLvcCGNiYA6xkUjrpfDd9CQUHeQDY6UUIezOHCKXUUgWFwRf/hgfsommPhokFAem1VrTSRxKiL8PX2j1hyWqivimRajJCIQHCIvWQ0w/FFLBSJIhnBdAuu6oq7RmR+prjEy+5IL1jm/KHFwGHszvYbXLq6hP/9PFCc9vabnQFEBhYQvvnzA/drGJ/tBTs/XaLujmpmgUwFygGtNvcUiwRumpAaynIpwdLq1v1a67Vp07BkXcJL9wmEhLOU4V1R9eUEdwPKovUEEcgF7ED1yj69Lz7kqcum0atbhxbjqpWCn5KLQ2GtPtwwMgxw4qBs6Fs8KPh8jCKFDgASizafj2oT88Cp+vMTv9nX/Qj/yY3WHL/TU54C/coKJPUjiMvcIPYHdGKv425jR9WltytyVvghYFIYWXd0C9iTkux4cJcsB2PA70aO7Vr8HcNsk3nybFmoDh2ANLLZHMvvhQ5++wiXvfu1nF1GYgDg4PFn/w0/OUvhRYvLP3qYb1tYknXM4NOkTa56HaQk5esnCNDaSVnhOPgpBzEbdhZWoq1ZSt/1FGJa46VervdkbTS2Gg3NgxGUk/vlvvzNCYVIxSKywJAsoUFOKheUSnyyq7n9soKEYGhI+O+jyh1FJ3KCv1RB0dGFDy/BAmcPpmZod8EHWgcVgZ/ltIGScWlVyRdvXR/U3FxLjht9Kj6p/9gRcsStN6DZS43gOPiDacNpWhxnFvloYJAy5WCKNcYAmcDJB6PwIFrablRnFwLxpIU6EfkFAjmRSzQJc7GGnJX/WDzaOCHEkGjVyIBb47OLoriIeMIvif9sqlBCDgSKvv4v0KmLFjaE+FATo+AogYSV9f9DQ8/zYwZ0MQiWGPG4EiF2I+QeNnMjQJD88fLC44QwBoctUEE7EHQL9Mln0oa4ekDcKD4gEn678UkseCQXg6vkdcaUYY8v1qEmP26oRhE6kyNK+rmHeVHn0IDAr4YlsSB2/u5ex9qWLq0PTn+d4V81LsjKHARPwRN4D5vtXTZjpfGgjoeezukoQFWBZcggDiEl9Lo4feqAyGroSjoe5xQWJU/kQPkJ7isicWI4T/GiwArHtV00Xtp4sk0KnlUgJgEAxA4yQq/bCUUMKImCTtBU6FgzCKdVDZgaOVPWMZEphACN7HVBWrYB8OKsOMO9uGcjG1bJl5ZGO7rxeAZCiLCdVTLBBZCSaZCqEJV+BPvoM8A9COMH5c7fRD23d76Flc8xshMcZsADfo6TTz2a0g+NUHCiia9AiyCq24/YF8fP+RAvyT4qkHlHTEbduUmT4sMJxYXen+el/O0DQtuAAj9E6kTC3KwiBnyTeuOeqUUOHVSKy4NIb2dg9LvVjqvDWBKi+kffMag9Qmw7KilRPaDVU4QNR0EjCSjY4KFJqggR/MjT9Z+dAr08ProCbCct9O9RqyBEu/NlZgD140HiqdPkY5vluoMdfT/s/ceAHZd1bnwqffcOr1qRqNRb5YlW7YldxsXjCHGJpQYbCDUhORBCnFISHhAeEBCnoGYAIEEQmiPmgAGGzeMu+Ui2ZIsWc2SZjS93l5O+b9v7XPvjMYqbjLmx3vmnrPP3muvtfbade1aDraMBbft1saKuAoYDOJkZskkgl3CalZ9Z8hElSTdabj0WNVK/GKWQ+NW8NNj3MiNPOjl9GIGWp4fa6WqV2WPwM/MgNBkPvb+m0tvWt104fIzmlatxzjjpGbcMxj84NfaoWJUqljBKw9kEdUoMhKMT0iGnx5GKRaDRy8/EVSKZkMnvI1s3OAeCgWHmXi3MOHFLc+MoTNQDcyU4DQ4S5VA7jH0R1c5F+51145jAUJ1WFLAhF6VH8oTyWmMxrT7ljnDbeg74NA70QaZslhRaGFiHQxPFpRoyKbiOFRvjyQlFCfJFUreEEAYhPlC4IVvZWfuUY7kE77SUZC+ENEgqHoicjDghG0K3TkqQUkKAFKODrQTGz6Uiit2uFD3RkC4iydzG9DyGw7CDS1wlH/O98OvJnNN2z+sDxUbU+nkQU60MIASvuolcFM2shqxCWOkBT2ZZ8TBgC1UGLj1DExwdJUtDRnimgWWGbHTJaCg4c5/7LmHlf0hwU17WDgVtyHX/FCMq7wgdvLHAM/D7NmzB6mPO0GfJ57nwcJvR9ATqABDAPPnz8ey+127dl900UUiD0n80MYEVvUHMjKSH5UE5juh3fEfO3pl1y5cMC0AOxyh5shWXk4joZNa9oI4dBn2+5g7sUsDubfg+mWszMNoswRReQswLKZwYsEJTY2VgnbOtHNzb/mzzdpPI8EowDwtNqWvGbT+fNB8TeDhslayRw7BGFmVFTXoVdKdRz6IL6xBxTMmvXglo5lTmM8wwTYc+RMMrAWJgYzQmW/+s7TSgWZyMnBLQWECVsSGLqTNF6KAONCJFoZQ3rWCUoNTmFSUCR5+Y+NnoMebOqISi7aE25bS47hvoaIN5YPRDFbcSJ+QxMSoYGx+GEeSx7hjjNf/Qg78YNmiBT+RK9/8FM/WNgwMh6akWff1lWO9vS1/fV3lvHu8h7a4pbK9oDt1yYXlBcsezcQOYPUxKw2ExD306U57M6qn7OMJYzfIuY5tOpzucXXHMnYsxwnThpEOXlUf+cProcLlcZv2j35kT0x7Eds962znzz+QOOV0PxEzF833d+8TQXs4Fku/Kh9dyg00QZBEcwBWQSww6oK2j/tDHzSM8eifGbk79OiUrl/o+ImiXsAKYQxIY6e4WbYjNnJfJaicH0lsoJ5Zif5e8ZZzond+zXj3WbrTld8a0yf+Ov6qHBMD7QC03P9xgm9jy1uz8fY/jm/M6MYejl24FlrqyPyKt8MJNicG7m3o+puR6LrJ2Pq0vi4WPJxS2ZM4kEbFvJnPM6fYllWXctNZpgO+Nz2Cc8jsJT35RLzh0gsnIwlsCEdmy7jtQRTDzL4zz9BGIphQACy2HjCapu8sxIJmPR80f293YkV7Q+fiNsfQscn3YNrf1hfk5QhMss4WjokYprfqGEkyAg881DN8SQ5k2ZIgSD/4E4YueKEFkJDKG7klzDaEQTkMgwGkCojy2Wpt43Gl2UhlJ0YhiABg0nF2bcMqbEk4y7IJfawuMpnx6yulYiEzUdfchk4VbuHiGQB6gGlJzi4itRBvWUlBFDQkGBYG2KX9VF70ZxFlNWPhXBIsJ5Zvae8kHACk5gCk+pcQxAGEVTYVGiU++INaNT1FDog2ISgNhlH88JsyJ2sCRYfQMDUkCIKJn0hYIZmBBS6iEuSKAllSKPiNBSVYUlB1UgirFOQtqSWCwRUh4hLSk/jU0pA+dEFshXlWxVU38QiiPM8FIISqPsXKQMox/MQLa0lwrhz7RxIEuY43sPPsA0CS/TAKMyFCd+WAhSQulv5rXsI0yvue0itYOINGoQbD0JXhEW94xF7UMTpdzuFOtKyJLAm9Cy0GmofOFDowc7maoXaYjWBPS55qERFI5VstCsqJEZtjwuiKq5KsSqwjJMthvM0VX5jQwBOKag4pfNbkJ158iAXO8iYLCgSfdAQj9KhSQoToLZ5hlVANCBD8hQAAmYtS0FbK+Ym+nXp+JObEMXJ4YNsDgVfGwop4fVN2CuceaZF4ItbAsx5nOK1a6aiMoJKHlBhxFOoApXOYhCJpSlJSFB5Um/CSKNVkxczNNQ7smwomecwQI04JwcBVOQieGRZnoGehqCKDpwTjm0FmQGaCKZocqxJ+IXaC0l8PUr8YuuIDC7c4HcWu94/1faHFzsZRGaIpNFanu94z4hnBVKX9nrHLRirOPkzogVeE5VOPSNMt9IR4lSP1FsFoT+WXXaDfFG2pRBcW3D28n4K1FzBwIXWpfi1PRz5U7CnrSWiqpcHUAABAAElEQVTSwjAe8q5iYyUSpja9MIQtigFzTqj6oK7Wg1NaCx/cYCzwpuvSk97gtJ5Knd/T+LoljV96qPirQ9j+jZKq8j7xhjLCS0Qv0phDNiRPf4bT7fo2LdUCXbE0uMdK1OmpVhu1s8nNa1VO5S1fM5QO86PYgMrH6RJ+0FNfOnWejpr/9gPF0byx9ZB1qBAU/SiYQw1FNCGXgoJrZcQNKCgN+VDIsaQ53gCsPgahvZIdb6BY2EEl2woksGJO5yrXNF1PqevCJfDgjfqUGlZospr2g0WRfXWVS/vchMsumLBc9VYCo5v+eLNx4wJ7GpkgXQORnEHYBKYHzkAkJKHpysLCSIkEqtgQk1mSqsUrdAWosA8Yhp5j0MbAhTFVjxBCBKc6dQwc5lcA8gs/opJACh5PAaN/jUeCCTXx5UOFUCMCkjbUNolBsY9z42DhmWrKIpoqEPan9YyXGCtaNhYpoSJg005I0ViBIZxJhpN4gioWqwCCAlDjX6w9gAi0wHaYTeEE0ootetDGngL5Rz7HTyJAhVnYYpcIHVmlQtMi/2h14U79mZ8YrqDizU8sbBAlnGDUujHkzOCMYBWzCEfFnokg9CmiXbuejEQiz3PeURD+//xxYhVg9L8xC//I4zt24/oTH9etso8JNQw9FSxEki4Lc43SJJFrpEQwbVXFzHwtBrfXQpF2ojGmreQy5YVMZ1peb1JvjgQmzveAhpxwpjzzwJQ/XuQGNQCjVoHCBgv+qcHyn6M/4CT0RQ4OFmwJPh/x/3dU6zewUyloy/udLjIbz8BS0yesAfCvirq4gQVmbPTcwKPkSDzMjp6luGEVa3NYkEL2wSYLpAIjbUJLNFh4Zgz4aGhqgXptIeOHRQsRFjSsuCSYeDC0lDxGQ1kUICVIeBBRnCOMAkAbhUIVRJwG2ztjvjffKMdyaaytrdi235bob4/e32eMl9Q1IgrDHN7INeVI/PgHH0BZxY5PsZIwLfDDU8AQLtBHs/bNu93TuzvnX36l/YrLbOgrkeiYFXt0RN8zLvWDiMLE5QBGJapPoPooDehesZyI2ylM67slDNAZifrK431OOa5H0/ENi/RCZ/ZfPh/53vejpoXRuXLBd554Iv+xT1of/Ujk9A2JK14zffcmbXoKPOdiXsP5JbRMXqVbr1xiBF/HSaCQq+H3u7lfBtZbDfd63C0QXJjIH3Bjf4FRPd8vIPXJU2TdGu0bXyv88pfawJD5hkd045Af1GtjbzG++s9aNFaZnrbb2mMnbZj48iXOWTcZDbx/KAgcP/527S090bPPNhb2BOk3EuG4k7nJqntL0Wwq6y1ecChiZc3811ui/5zTI5X680v5ra3YA2xZ0LYRYeZSY/kKP1/KjUzEojGjrdUdHeMp3G4Z2xMx193wF39UWbbmcUgP23lNfb/3ytOCf9dst+5N6eIXkjjSzUA+4u7nwDhlInFyBiI9UDpnsth4Xx9ZlNRFPsI/19NLfY0EZpSZhio14StwTEtJ/qpzDUBZ2CCoYREJyiYAaCRvSm5AeElfIFHFCfSYOWaw4xMfZJacIK9z513oLbAc/WKRVkNQ0Il9H/pvenQ40dCCQe6J4cFieryMyYFSNvDdKbZyZveSVboJtYfkBYmy4Ck6t0RWxTQ7MTo9OgCWUCH6ucJ4dhzFpWP+YjXiUw1GlmmHATrmdMZuxlF9KDdxVeV0DoBQrAIxCO0hZLWwC6bZMAJH4hSh+M5+zIIUXwWCg9Nyhpu03Ya4iurcIOS/GhTvEHE17RVjc8KILOFWDaaiqYAYELzNeM0KC78ZWrPcxRXc6VrU8gouPkEBSChcMXzhJ18zEbeNoDluQuPGYAe22GEtNtoXJTwJRUiu/0c1XCymMIuTOeSm0a2wm5esQobHWot5dcdRgNPpzODgUEdHO3Z8CM5jPmZYOybYrIhJNI8BzMJ4FFMtlTMgobBCeKZ2LfDhXoCYJaZZ+EMZo9pGSIiz/6ld9Y0tqfrmsJJnQHoBDghq2OkQltMZN5RbHPXQELVb6xIR08JQdctKHoSBjR2F3HRn7zKMQ+DLZCMpeOWFBxsNmpAZ4UXxL1kk9CShaiTIjoKv1hYKSD3Dko8PlbeBxTF8LB+QCCjMs+EJKN8qLrTTFhYM8Zn1qAKp2jTkBC/1j6AA4I9oxFRZpMg0rS1eXNepd6V4UlHftP7IoP/fB99zWceNK+MPN75iIr6iNH1fzM2Y0SWFpg1FPYLto5H/2PuX414POsGq7lFPRpIkSE2RkefsBypd/ddDv3ft/K+i1el+39iBj1haNoV4obpzjUrr2yainVhQbdwx+BpLV/fTKlSS00TWSuDUBrxyYRDbiLA/PoiijA1sBzt6osVp6EK7srSx9LGzja7hpyb/8zsjt99p5IuBE0uee9ryd177wQ3LK/eXHh3CiSDCv3ANMjP1jaIJF7rO5l/kVUsG06aeiAYH3QXdsrA0HDuknhZzUoGADscDKIqeD/gFKbPyjo3Bpe1BQyltu5i/0PNOfMeyyJcf9LeOSTqq1igU7GEsHeUDczfI1qyVmMMlQ1dLEAmzEjSxwEzYUg4hIsYZGYTtrBgl8IdaIwNx4/cOVrqzKFIhKF4CreVs/Y6uyEOtBlaBAyeCzIqusiJNqOBhRokTtCIQyf3iqx7ChoglpDxDRiJOjhQMPWpM1DgVcGGXfoqEGiQQYORPQUBiSFlFiHmG36zuxeCLCEUCrIAkEJ5EiV440Uq3vRpFuFc5ISJ8UXQhekpcuYY+jW3dycZK2rUCdPz4JxkAeQi4ZUq/ikt8SYopR9lVG1yFEWKGxagt0kZiostC/tADFX1AdFfCAAldqLjyUxDBRmmInZwJlyhSIAMGKAIRAJoudtZJKmztxVdBQRBqspqL/IEWXXbAQYvmJ/KdoeO48+07d3V3d/9OHfpFeT57c2IVYPCzatWqzVu3H0gzcZl0MHhIPkWiMhuKUorbuvv372/p7Mb2UTiiN4M/rleWgrR3T19jS2ei0aELdVf2rfGsj1Re0WM2ZkeCJ54ob9mGXY3G0gXNp26ItrTfnTOeSmOagTSRq0he8q48OBLDzAV35mBkNYy2oIpoKuiNCIGKAntwpUiQV2ZKKbnkm9yj2DAaBGEEWC5h8MRiQzOCnTwYHVVOABYvFgeFRvDRmfRVwJkeLfiwkyh1clmq4lohUMwoO8VItoSV0Fs+lV0I00/FgAyqAOheYPbTci9aFMybHCzffvvU7XfjQh2juck5f0PvZZfHF3XesdefrPDONKCigJ5mam4iEMqCRmIGOqQpP/oG/tTUJNS5ulRKFm4H42XrFlxkGDEbYkmMtmZL+kQe17Og+AtWYReDypi9KwfJiD5tt/pBwkxCGcxmyqOjdn29Xl9nVNJaJR3gpl2r2d231/7hjyLUcDgIYTTWa4mENz6W+fYPWpYtMdesSV73/onPf8nCpUExN9JaoACMi7VIG5Kv+HChtK8humjSTH/fN5oYB0we/q+KiQt7IyXMQ2cOnqZrtyL/ZCwbN3RbZ5+r+UU/c7nmHvKH4uX//X/Ncj6b9/3NW5vaOrSo3fzuD/rGAU3D8dqIiR2/4hqtfqURj07/+DupRTtwhnXlMS2yj364W8mLUb1DpVkZiFcGHHtBxe4pIN0lzTA2BD1PK19wbnzF8uyDj3oFL2fiDLF6XISACzm0lSv85obogt6CYe6opLaO8Ng0JML+ygWD7hkdkXtTF05iBLT8/+LeiKOnypFzss1vmUAmLQd1W0vvha6AVGPel7wrOwmYl2BQNTP9yEaYJipbETQEmZX+DCE+fMpPECjM4qKaNkHJ7xBMAVTD8E2afLE0lo1FXIuUqESWFo3tcTIDg1yBfU12yVlTgmQqQUNjsjmBCW3s9kkE3TjQzdRajAav1ZkeHz2099CSeUtxOBawJhqp2yuDN1ggPhY7FnwiDn9a0Y4WEs3wzaQnLdtOxLm+ur7JsnAgiQLjE25KVHRDsa0yLhDyRSqhrIicVIQiAswQU1b5FkT0hKkxQ2stlAKjH2zyCp8UbNVRuatqiWJXbOZxo8pIujuVnN8+M7HAMEcwSh7wqEYQEiLW6qcEIVMqQqHHEXxn4Va+xCI/Sn3GaRYcAZjD5Hg8uteYmQEihllG8FDiHP1ZsbjSUBfhUe2KO0XH0Hp6rY5WLBdpjevJhmZel2IYqSQIoeccLG4MMNVfM7Osods4TogbGu9NNrSiejiKqcW/mjRz4WoANUHOittMZpobrPY9E14QCJeUVZVd+FetIQVkncMD1fyPhLTqBv4VqhpCa8xragyaWlVwPGtYATkH55xPIu13g0hP46rVi1BNTk6NL122EjznMumD+3NLOx0zgnVVTCYVUnEMHiQHV3kiwRDzLL0AjKoCLAzNwNRiLXwqd7iJOPAlrkAYjBeCDhRuMeRB+cyOATvHkjTsBNNSQ02GJCgeIedV3xAj/BlcAUrYalqhPCEWAIcrxrdfvaz86oVaW3HacguY8sl1R4dPrvvuE/G/2/alf1z97iWJx5yuTPsbcNkDKjA2bm4Q/1bfn/5w6N2QHPvsIjnhX8VEhCmpiBjNYgZ8giDo+ttyp906+nuXtX8vuTq75PMDoz+pL+2PGc1u5yVT9aem0UAcKq78wdDbLKW0sTKHoQRUlFXlQkzYcIvqEWqDV/Sx0DeGYxp4CSI2Fuu6987T9PlT/UMf/4z7+DazjAF1UyuVirfd07e/v/ufPvKOU5c9eStubsBufTEhpxQLv/GWHyIXSpHkFaQ86atSDPBoZVOajR360EDIJwMrZikd+RSsipZ6Eov4IDiG2v/2Qv0ca8K8+/6pn9xc7DtkJFOpM9dvvPK1Xed1feK+wuMjMVFOEEBkQKRVjHCjlbgON4iSrjd1Mh8AWgIwlvifAyuYZoWltwh5thvz+nDK/PU8/9onD/MkfV3rT5qbOrjIS3RjJU3BCz/ShB33cagJFdbnDAUPFRGCyG92Q0bidFSG8hR8VQe8Z3xnOUqOrPqozFnzlex3lPQUFkEhDKpoCfNVZEQjB31IhPkVMqQSGWBVBiWnkF2VYQmpYiwxx+An23JcIypL8CVb05sD6gKJSVZaFC8SccUHACh5LnamNJDJQZPccZweygJ8XC5NpQujQyd4qNSCi4iQGRqhxM4aAEWYyUEGRE8W3LBB7fXLpYF9T3YtWBJLJqgwczs+pwVgp4qL4ifKBHDRlxiEG+Y0MoFPVC/btm1fvXo1eXrZHFMCJ1wBPumkk26944tjaQ83tUDF5DJm5BjRYJHbmCtodCxgHBrOZiI+Lp1BhkNGYXozczDTiELITIANpVxuw2GUwNaCs3rsltGD6S9+2b/5Dlx1gyKBLlBx9bK6v/qzC5auDozyVMGWVXUsxqTJJ1Y4Y/2JZEhmbbghr7KuQj2AF1etMGeSOxJnjqIHHckOLXDhgwWMGOnCP4yJ8gt3juEaQcx+ww0OAsIgjABDCzl6KUxSxYgPcYJoKBOiYoHhc6ZnIAwAJU0VUD5CF3IzxyAIsJD7IDi1S++cHsz+21fdn/3SkvohGBwqbt9RfGJ3x1++//Su7luewjw9xKsYYsAjYIOTxA2X+2bTE23zFzP2GPUE62KDJLF2Kz0xiWP3mmT+RJwRzCy4ZiGNIQbBi1ErRpEUqg+9oteNVE6OOwPJU/LF3pK51ywPjxheRRubwmY2/SoL2hFF4pwR7NkbTaWQOXwcyJNM6IlERY9kcgVt+3YvW8i0Nxvnnd++fGnx8e1GZXdgfpJUGuqDxCQEEj2tMTIffeE0ksMIxsl3oNtt2CACi+Nrb204+X2ucSuyhnHgUDpX1FP1GQwfFN0mS/OmM6XhbKniYUw+uPGXDRecYxqW53pGfZzRQFT1olfpM/UV7oGD2je/q3+EawQY5152K4KKZedtw5SDpuGIOwaR21NJP5mwvAr2p+HE0dzpp6fe8y4cS1X40U+wIC1TwI4nHHWZ9OP19jvfHrTPGy7pj/enJwK/PYlaEUSRZrHHtM83e79vm4dSF06kzs1yEhvZP1pCLe9r0V3mh7GAeyGogUvpRUpeQmkid0AhlSnTgSDM22GiCQAedJRuEQuKEBVQSTn5ZiD6CEKksMJDhAJDIizbxCwg+GTuxpMexA3LZX7504ZRbHzbZPYfI5E0RqKwYARnh/j2RZPR5TmMU02aF5WH9mOhBDZAdKU8f3g7wjamUl2LesYSXmXIXzEv2YDj04UPyV7CLKNMN0ZfDK01VpqSepBES/PUnnwsHu/s6hIWQVyGzQUSgVXBEOZDGVCSlGcYC0QBzZOiQEkIKBxgRHQSU5INeYC7KtwKUCAPs6IHBkf1JHD4gyUEY32i0gpPoqVEhSUtl/Nyu8YXJpIrOzoRThkJph5EXeOkZlGIq9irwQBZdRoaGsWJcgsWzJfAs6NIYEBVIRlAAtWYraIIsc4SggJVDiHUjG8IPvcFlrFwUffn9yTecpX1jW+xAaAQYPQSztR+1zXWvLb1dtRcYy9a1FiPMwJm4ef+rsMRzqE3YLnGoem1bZX5PUcYPpgT9nBMR/wC+sMozP0+YiA6ziKFMOqLrcpsMwum5qPaHgUl0DWKtbAAkZDSzAgk7gtB4sIaa/Pq6/N1iQkU0bq6etYOISqRMVHMKtYhRr6UNTWdcZzxdW04BDoYN7WV7WyH8ymj26jv7oTGIocQh6GAR2zhA4Ah63Bgczzbc8auoKRUCWOqpRJ/tuhoyZWw4B56kY6+ZQBjabw5FoYZaAY7KClieNJTZRB+1BhEbaJACKBI0SY5idLAv8rrLLDCBb2VUU0e6oHAe/O64JL6nPPY45M/+ll5157AsmMnr1r8hqs+sGbJf0V6/9em771u0Xcvav9xl/OUwR5NomKtH4y+P9J63mnFoD8DdBJrxWZIHY74Vk7gserH6omsYCklkvT7w/+0ID66KvmrSHe++09wwhJ0Acxc4UI8a7I8/wsHvpCMz6sjOCKi/sg01A9xC0WKQqbVdcPJTU/mJoYae3qli8UAXUlvXcIt/PQef+t29H4wnM3OOq6rhSz27c//4ralb51/SqfzxFiIXqGVDpgiydSAY5he/AirWbiIO0NImtFiN3d5POeSc18Mz/hL+sinqgdV+koQ+NIDP/yjh/L7K70znbz/wx+NfOk7ul/GxiIkRHrXvvQjW3o//rfvO23J39zq5jBKRkQ1CSCpZXESyCkqeNMihiwIdmnnaRcXJkENhq6CcMZFBQuBay8VRAgHyzJoNhk3BAK0CgoXTNs3eX4WEz01H8WNBENPDjpZR9JoiEK7IxMKgyKhJBryqMhUGanGaCbhhevaQ9FX0LRTKMqNIAhFr1r9ylSke+hSJSJO1cdsR0ATm8iMIYE89BYaVULsi4uHSAWIBIixZJNIKFgE2OXRA26Ep8yiAKMzRlfFJGZayZfIAhZhlD7ABURUX4mDI07kVNRlvPHNT3gBk1QJXJ2M/VOww8jWYRZRBCYQnoKPlRkcmEWJk4b2kBYCkq4uKxUDnFvmlYVJpCEUFykkqNEUIwhCnJKe5IEsUVWG6gtGsNX+8a07/vDaN9HjZXNMCbwYCvDUxGg5OxxPdSJtmMjSY2Ne488oY+kyDszAys640RAp4WhQeFiRGOa1JkeH85lxHOSOk4GGR4aGx8bRvWzrWhiI8rC4yW8qZvI/+pF3482cLsasreh4xtZd0zd8seGjf99TP28Ul8hWsyBp4596KCcbkJUk80n2ESjJY8xgrHXBHLOm6MzMbMzzCAtv/FAbwVcVBGKhYZ6EFSevDh4aaG1tbWhqxCdzPCNEcMn98oaVIQQHUIMhZWV7DyN+xEdgsqyqPoUj/BCCxKJMWJAEb9WNBEIHFk9NS0WCxbGyf/dDlZ/fBs0z9FQs/vre0vp186+8qjkeHS8wVyCAsMAUY9wVQRWIKUjWPJz3X86ztyFApEFIChlVkG5bhTLOfyDHAA8RULQMIJUCHRlhhGEnE2D4MLaV/3BB5A4jVop8YCD/D1H7APd04yAQd91Y4hrsE0Q6dmh1rzXsm4J5nRh842mKhlkOjFwJ52diKyiGN9yd/RN7htOnLu5qeyUOxz3Lnb7B0CeM9FeMPDZBB1t25z50PTbWJKtSAPUqg0Yvrh12ra8AcdT3dr/rnd1Llzdd8weRNac40cWau8vuDvLJbLLYDN9C/wHcH9j45rdoZi7QdoETwYLYf887tDzzH9+OD4xXRo1Ig2ae6pun4OZhbE204/mYGcX6rcBrypvduLxVN3vObv7S37jbtgelkrmwx162XO9od/cfiHR16KPD3AiGAYVEKvrWq+1zLxjynfue7MNhaQsX4xpn9vMoUWaSkx7J/MfS4LoWZ6tul3D0q6SbVjG6RpwPl8y3LUigH8suJQNIGKn+YWXySvEkIvgKDDOVQg0Ll+TQmWUDhQE2IS0VuQCJ/gx4geFbwQgqhVO8GG4WWiDhJ+kw42jaScHoG/TMN+ylU3Ufdd2b6vUD8SDuWqeNxy7JoY2pWC3tPR84304hh2HZSC6XbWhsLJdKhwYOxqdzPU6Qa9Zi07tieqqtrW1e54ziJ2RBHCSEIKzhFz5DG0pMbFrDzUrzUVcR7JkboIWZHUS5SMREEjO4ABV6Krcj6FczwLQdBn2415yv2QxouK37oo3rEslEXfyYJJ45eqE2qZVxyVaKR0DXzLFRHMaVhAE8HJ/uXkMIy7F9ZyANnBJ35evKTtT7yc+t4SGEKy3osf7g6si55+mJJO41wVQPpnJiMksyE4y2Y5HA4mpLw87z4LCIHh7+2XwdN77PBtmxYI8WKbjXkuloMNrk0OTo6CgEg/qpmJnEPwpyR+Ma7Os5jOZREYRQqxd39bTVt7YkfaxWaU2mEpIDE8kFzTiC4TBMx/xQoCq3HBMQnoCtxU9gwTkdqqWP7TsuKZAZFdZiIXQtGwN2JjzCAqMC4oQNDV3YDwirhhkUikuJF0PwU6oz5Y6nchEUwbp279zminXn3QOf/qydK3I+Cut9+vqHH9rc8YkPvX7lKY/31/9s/Lrd/nvfdXKmrTQSTZcrY5XWRP2rWkc3nt7y8/2VOw5g7T9ZUuzyGXYg0F1RfMuQqzBC7OyxAEgb91o+vvubb5x3wytav11v9rOd1rVC0LR56tKv9/3tQBnna3C6mfwKvLDNaIKYNOy0i1TYfUD7hWOnsF0LLRmqcQD0NmkRv1h+aLPpFUCXHS1U014JL8syi1seb7i6sKI11o9TDgULeRWERCtSCpOM9OGm/hWs9BPESQRGANWtkOjDQyJDNJQF+yTkGE8gUV540ptahIZ7C91XLTZiu3YNff37UdyQbaAL6pk6jowK/Cf2TP/4J0ve/e5zFtZtOsSejIQDFtIAVpkqJHJGj/gVCSFHR3KgaImV3gSVXETGxACM7oJciZUf4sWHeCsSjqctmUQXiNGFVHGJNU48Q9OOTlFdKViY9Z5sFj+GQggGpTcsmOfR/STWDCARVHNMZzFVSiqlFScqmYkADCkPBYs4AoLIa67V8HCt4VRMA0jig6ACxFAMje9ZoOIpIaWkCR0+QEAcJNvyW3AoRMytEg4PSVYgVP6KBfqyo445hSo1Dg7rU+ncyMhwZ2dnIpWSzotWKeAcUyOC1aZCgfUE59vIJchDkwy7JcAoBPiW2kOiBuxMRiUUZUEBQFDp/hEL8OEcTrypujLbkFN5QqNQJZeU5Z8xBSio57JT2BqIZMMMTy4zESlnsUsk1dCKzX+QCX7kizFHQDwZkBqDOACd/GOxu1acmhoZPLT6pJPg/bI5tgROuAK8bt061BL7dz5Wt3oe8o3kA6YbkhJzd9NjhwppHInBaSh0OXMHDiIVTdtZuGwFSiM0Qwv7JTB6xc2MmFQ1MclYh2k2XJOi6UsbjMjkRP7GW4GJi+dYPbBAYEZFf2Sbt2vPvI3zHqwgQ3G9DTJf1chQNiHFQZ7s0IeKARiR7EYGpcYCAMKiflfw8iFOgOBgEhEDu5QTNALJuIN8WnFL6DkxxwOAhZRgrHlDPmCVzKz4EhSkxx9VbrzwIVND8oYHQtNRtb8MUJ25E2gCwNBd4ZEP6kbKEEugNccNC2rTfZssbpirAcITxyQZ5fs2xS99ZVs8gUVicCLrVRBKl3biJzQOgCziVEHUNDyIXy/l4IpjPXGFLQCV6oDGrylqZrPFlIUVOIgsCjDDivQpLZZkfM+CV0UaPr52+YDxxm7/m1ZPPv4Fv7xN1ycNc6EeXVjG1AFuiRr5ZmPb2yx/8ZJCYHvY1YEpQkQPRx7hvCjQWrrUjKfa4q2lWPu2A0/hSLTeRb3LzNObvTtwRHfUwcShsWNfEf+M5IwBN0pgOILsUTizxnGcnt+/qvLlrw4+sGnepz5mXXCZFtyiJ4oN78pX/i2plwwH42033zo+Pdz0J3nTnMCCI9+oN7RJ072xsLvPvX+q6NnmppSzJGc18txKcOnd3JAKHC+CLUxe8HYox6gqLa31amfB2shJK8ARFg/gGkhUps7CBfY/fLj8xC6/v0/HvOSqlcb87nIsVucXT+0MyiWjp5VHWzkOog3Jg19tdGT10OANTd3TWu5uzT2omXHNOcNMvWqe2TGP4odRGbkWcVbHlEOYH/BiHgOsgoafVPoCNfdxTLVqLvDxv5Gv2Eto/z/l8sFI8Vd2d9p8N47+QmcLIxIcPXX1OqPji1ZkxZJeOBpTU1P79mUWLZiPMY+Yww46cveqFcsx1weeY04US4qOQ7UWSYku4r1w0UKskmL2lNx5nOAz3jOIqm4zLkfANONZBT/W+9lB1zBZtjGva17t86iWZ4Ye17lDvEgiWNCy454biAgnbdg2JP/MUMxw8GzhZ0IeyWYY8zqc178huPAif2wEi6KjbS16c6uR5Goi1Err1p0qqfms01RKxTHy/5F4eWm5zZHznM8j8NrW1qr2PO/fv79BDICQxM+yOGDSmIYE7EgUu0pr5vgs1EBrlmccZi6gamFqeFi/Z3A8h7RH7DjC1ECqpVThQFUAg14rP6VbUc3kgJP6UQGw6mTuZwMfhlQvfsKGLAQceAKHgPhndev1E8ND//qfkVwR/CgCgLaHRse/+u3mjy88a3Fq86B7zcmpjvGpzDdvHrjpNr1UhH9szYq297z1ypNOzVeCuw/IXQZsg0kAvKgKW3XlZQ2XYgde9BWFCG8z7zd/vf8jPxj4k57Yjnp7uOilDhSWT7rzGQXsWSXvEiSMBOwSXbYKKqZ8czoSCRuvq484nOPFBx66HkU3zcfkFTfxSNkBO7CwVaUgOSOJIx0Nh2ecUNzEoh7sz9SsRMhwYvhCd4VtM63qJ5EN+zAKjuBKwBLqMFzwCUXEVOCUrqZ1prQWzPk++HCkiEkSVGiEQe8LsUX7l793U9tbrzm5pX7nKEizzwJ+ERFYanwzwmhf6AR8zBWCWEGoZKU7OVEsCk8KBqDSBSPXYUQhMsRxBlbC4TPQWya9xiL1HPyNNOsPLon0jvsr95UsKrXBikl/cB5wQMkVVCJuZjb+sCPUaIt5CXZGwQVJCDlmk6odb7BJZ0qVcZGHOMMu7gKM6EJKVCkJr6DwpEDoDyOBxUar0vghuFnOVXwAQhAikd4GURJ3CKmwwYUpwn6ocKsELJ3aUMUlRYGVcARkCPxJ7iJd6o2YSi1jUjYZi+LgT0LqfnYKB9Gbqe4FwjTZqPIjoRUWFREsFVCyQhmCReXLADs4lAiFFAEYBWUkT5ADuMgGYPDgo2jRVLvBStAQqOAmJDbyZYfGKgWMHOFGQ6OYnsRxMoYVqW9oxSY3aBro5LCbi0MtURRwehuTQuLLJ+MO9BAmaPTtfAKn3axbuzZk6OXX0SVwYhVgJElPT09LS8uBnY+/4vRXslQihfDH7KajnswMjKLFtZy471YmR4eSDW26jbvsdCz55MReojUebwXw6O5tTXXNscY2ZJRJLBFAdsH4Fy48mxwtDA9EMOMHbKUiICN2BMoAfIOD/dGNbl3MxGwF3GFYISCkKjGqHkB+YgYUppQdh+/w7HiGkFAAkCJFBDNGyhcVjmqGhgUsUFV3kGdNvVIsYOUqGOVcMaLKvEpMDEA0eMEF/8yxLGJSeOHOpoUQwpgqU/ATZiSg2ClBVXHM8oFVGYklmimprvDkP3U5KOdRLYLVILkpzrKLakC2KRjA+pVs2tTctkRQwAnWopfCj3OrQCBI+CRa3atU9u3ajVKNfZbtDZj15s26uCGps60b/AlORiOS84dK6a54MZlgTzSUqIqefBAbSyzjKSQ4OCYWeyy4Pulr9YXv4jbmyHq4QqHm2HRQiGb+qyF+u17c+ISzbm16zaro9h2smQQXHhUrav3+q82mxnbTaI5pfSUDF28sbcnED+1nO6drr3tlaWhzdPJXJb3oB71m9Fx1eiTQL/RvzxmFIrboepkM7iNCXE69+uqeN75panhEu/1W/+v/aZz5z5XoRsu9J37hRKWlkv1ZxBi0/Hlm3YU/NRyMKQbZYMm0/qFW728i+nBsxSOxf425I+gXMA+Bdzz8vIkDmsyFmaC9ZF42YS3HOrTAjWJG81IkkW7xmDeYULN0HAPrL+fN08o4HQiHG0QyhcwD992HM+FwwzaivHfXTogGpx00NTU9uetJBHSxINyzD44ubWk4I57CknErwCJRHsgBuSqj3tWvmmPoIK9Znkz7F8KwGFTNMXCyfJgtTs/3KuOf0Se+oQejyG5g3jPsILrBaP8H0zlbogIcM4yhg66uvBscHMQNeLDP9q2SPf4bobDQ/PhwvyUQNZk/N2nMiSU03l27dilUwLxzJ/Iez/lvb2+fA/nif3LZWUMDLkIzF/Uit8zK7MwluHn2ObAU1tfPIeRvcxBsWoFRcUdZUGNJzyFCwKCQvCB57zkwcIQgqIECLY9jJ6TtUwCslaQHIJ/qi1ZVW4UVt0DAkTUOkUh1LuEUGBylMmLHJjRiA5x6iyO/bK2yIKV7m3foQ0NYp8RmXgwsLk5A2PJYy+TEitaGznptfm507HP/4t5xFxow3EyO6/rgO/DhT3V95mNXLF+3ZbA0VcE8MJtkaT+lQ6OqRKAUbvBmwyj2kCv48NPIBi1PFM4xcMkAQuNHieDsWVhhCCFNPX1q3/RReKE6AAswWzjSmbUlqLAV17SxfFDEStNVS937HkRIt1Qy0Ppg6kIEFlnaW47YIxkfLTJQMZQgreENvwS3CJnUFU3gJzQcRPhi1cuei0M8o2glMWRZLfTVKAOegcIEoI002dkQayyCedRKMJ1xMYhPfogcRytyhx5gMmnTq8QcHNaGkWj5AyaQEBUNXSkwEDLPaNAqahJRh56kT37xz0DkGE6CAm+lSuOLCUQ//MEq21MVNJ3oEWhLx3n8VdkMdvVau5ZGKlawo8Ucb4mu21psyGndU35PRJPuphCT6CEgrr6Ymso5UefcRbHeOsSBMRSOFIu0CuN8QlQIAt02tHPqEvD8Fhd4oQsroxrorUpkiY0qNR6MO6OvYi5P9VkNz+EXhBJkZCP8iVQwlyWkRAAAorc4UCSUCSOEDKQkJEIEDPY9hxFSvWiGI0ciMtjABIMpHBWcLYodYlHs+QP3BCEBDOTyZDDpgcs3e+OhUYiEPFkgLfCBPijOChI7liEKAyE/ZBNBhXPgYjTAsQKhBzFTUJAzTrwTuVUlxvUEcGDS2E0LLCi7rjvSv6epo9eOxdB1m8YdKmBHljdz3MjEpAuyOxV71TMHWjWIoDRiEH1075aGuuSSJUteQnUvhfBSNCdWAUaMUTdhEnjn1s0reXmqZB5mFOZNzAAXinp9WyqeqsOtrVOjQ3UNyXgCZ9pIJcWMh5yEYmakHb0upie5YAEGoSXL4DCGaMKMJ4x8Hg6yqRL50MQJasj+uAgHLV2mEJRkyQEJU4fAmn/mTBhmJgyxgA8EUnWlwiulQwhJRg5JotgLXT4RjrlZjXKClmRr4GD5Arhhx/LFYq4ihJizER94INPSF4FBnUVTHpKV4SDeLAUsSriFhaWHQ0kAUsByypwKy6eaUGVExC6YpXAiDHngAwbEgYMRQzlrxPmLOKSru1uzH5Vj7sALPAmLSXWja17gONgK2YFFaqzpQ9KKbzSRwiS2GSDeeuvKTgTLZfPZbHo+lunqqF/iOIIK0YU7pApeWivm9smJhdGJnk6UZPJZE7gs6gBCkQgRgw2yAiMPvBu04N+8zB8EfTf44/eatu9PmeVttn1na2o4HkT14viU0dGR+PM/c7/yVXPLo5hMBa5KQ7P5tqsjZ59jJOPq5Bq9NVnxvGj+xkiwCyRwLXFDIv83/2AG+YiOGyqTuDIPC2spntLOaf+BOjuRssql8tQUzt6CIly69U7j7W+ve8Pr/Yc2WdNT7rZ+59LryyNvcYK99ppME04Z4PgAOGeDVvC7D2Y+6JvxpmgCyYnMpiXykYWMlCQDLUainPyLQbzlmGPG2rNW2p1fNHD2JiREkDkGBz2hrY5IjtOikVjXvHmYdEulUlgArHSPZJK3lWLwnSF9FweJt9WnjNExf9/e0kTaaGmyli3R29qRQpDukYnMoXliPnEze39//8qVK4+mkAhv0FUw6NJotX4iaPqAV3g4cA/oekp3TrYcXGqHGXcIKczdiDhqeen9UHIQkZISYATsxETjtwrr7bffDjXmnHPOef5cA4+6VmFiYqKW9xIc2/oNm5m0PmIBeq7cYfoT54g0NjYiU82QeK7YfrvCIcoYR0bJes5s33///dgNdPHFF79URMd+OUe3p7NeBZ1QMWzFWTeLQeZRzZF8wcpuBJ/KlWAIxnZRWiq4slYPWy2FYQZZ2JahXREfPBSZiKVBbXXTad75juZPkKB3jo41+au4QTbX2W0uTmnBXZu9ux5k2yg0pNYzNRws+cP/brlu8SnzGu7tE/KAYNRAkH/K1IgqZzoKhLIIUcW9Ale8AQ0sik30bZSIAAtX1UFXWEIAQUX5oSUD45SGH+wZ94e82LKLL87ceKsxNAwfeKFHgOFbvyHZ+ppXPWXV7RzBSTDS8CNwlWVBClI15CAJO0PTDW/xIjOz4ulja1ypbOD4LWpYKqMpEIKpX/VJfilJ4iGyXElz0X/s7jRMXHjDvdDYJsoju9jRCYzOzortTEzjWFX0wthHVLxRr4NuKd90E66IEC7KkazChGzLG11ORVSoU1LoGyB+7NxKOBUlSTSRN3p8VdyB7Wudo+XppPH4GmeshSsduSBK06ZbjPvPSZ60rdh9yK+bcLPt6DOGghJiWgWD5jj5Q9fO6AzaEj42viuJYimjOkoWn6K4sj9XU33hgjICL/qyi8teLmMNg0XYLAL4CLOKShyyKvEEb08zDAJD0koksAg2oGUBIBVYqk+FmTS4ulJghBNChmxwFhSrkAQSjAEvFyQoX9jDVJhhBU75oms5EWyPw46pzOQQwrjlItJgYnAPcm99S0e0OkMjPEoaMPZEDhckBukgAupbiZkuSj54cmJWZAVuwcyMVhWyIxUJQrO3j1Ws7O3z3F18AznfRBZoMRtasIszhEb11nos0EaHjdkFeUrVFCqa2BKMriuIqqTBvDC73cAuMoFt5+MPr16zBmd5EuvL5pgSmEmqY4I9L89T16//xre+3530K5LQSKdDB57K57gRBGtmxwf3jx3CTQDUYHJFrYyrTpDskuOgriFXIAdYDZ0VK4Y6i3ojKyT8gukirlltiZ26Nrj3foydYY00MgvzVuCXmhojJ60agS4S09QNIGi3WNJYVLhKHjTKWEUg5RwPZh5SDBtr2Jkjkd2UtsyMC6LQOTn0AlhUknQR/ZlDM/iEnb7I3Khcg7HRTHeshFPI4QWWgJcaIN54MU8jUlKchIpY8ZDKAsElyuQGPxoWRQkiRLGaAoEFnbCET3xRH4ZKL3aAwYXAWNEElsSXLlitFDET+sXnBbfdZpXyYE0KGNAHrh2zL7vQbkgsdzh1rQxYrVprLlXP9g6EGubO7MzaxZgCUu4YvfIOHToExSyKpcYddenBRFMUZzCg0gb4HGT4rGKjDwUUgkhaoC4w6y716laVvvm5yF23YSQD5wNEHNwf7xdOXhu94Byog8aK5c7ff9jbtavSf8iIx60VK4z5XQZOuxF0SEBsxkbVlN/zS8q15KT/J556dUVP5fU4NicLQQ+DbraBE4bnTQ5kSg2xJiws8D1coIJhbiM+PlS8957EKy+fhswH+4MhbAk6vxL9RhB81s7fpOt5HM/JJPfjae/MrX1XlTz33IXXxfQ+NhaFiNuHY4Q1fZ6rWRW6oOXzsNQFCcxdKl6QnPDOb11yg2F1Hy4KimOOQa1433339fb2IlOha67OqoEdBoJDTHHpOYLkCgUzmwu2bC7/279H9uzG8m/NsotLl9rvfZex/jQzQVX5N2UwoIBFy4gIppiOwwOkjbxstuuJ10ge4Zl1KK3STjCfKIOJX55sNMs0NzeHCy9nOf4uWzmII2caPX8hYLUzShNyWh7XUwcB1vXMpMTzx/7Sw4AxJpgXhy9WeJApagHMShTlvPqIrVuY90OeZ3Z/kUUNcri+4TnHHVFBMUfee5HZPh7DOg45xIxkIgJpowIO6xGIXfoAqHOOgKAGBj9JJoFhUs1A80ucqzVTFQ+rKiBQLzqiDsOZGU3z0Z/RbV+PWUESR2IbLregwqsuFWlrcpxIvJAvPrrFLyMnMIxbKdOCtdumVXxsR10x31VXj/4GOjLSqQEMCUkfhq2MTGginHBI5qSFOIxjugK+VpcyAgRH/4ikZhtWxoQM0YsXo6Qko+ApTd7FEPnx7vIfLe/t+Oh1w5/9sr/jSfSLODa8oLP1z96bWbTyl3v9vG/zvF3OPAh7RMfOiaDDC9RCYYYv9BcBIrDyCMPBjtWgUC5x4CjjApBqz5Hws0II3+IUxoH2oax+sGSvOXOD1vFDbWQYCwrR6PNeLlxdZOiNr7koF0/u2E2mOFsNbCJh2EAI9Hguo/DEJgkpJ+oShcJFfxQqSCFSFAsAVPRIVlhjNKQpows/6Ep01GpAQSLCT9Csn9am6sxtq5wMNvQAEuBs7bHFVKvY2kNrnZFmt2W8MtAO3YnoYBQ29F6siF0fN7ob9EYc/1RdVaZgjvEkQ+SKlASMNOkUevANzvBCzwoil3+yFOpjVReqhVTS0M9hWDwBgF6+aG4qFBzgLv1wrO+FRagwGYW8PEMeVNSFJcUO3JWXdJuBXL6UKs9uPksGuj9BvliYMNNNDU3zG4KMUdGzuONDc7lq1MdOSsDhMF3MdqCjBLyKN0QG2j6YEX4kOZmOksfwUGnKpObAKPrYTGtUK3hTUYXgeXuqDAxJMDLKTISQ8BSLZBRGlkkNmbhKnlRJuAu4ZManSlxngTAqu/NJ5BiTVF19VAa4ApDDNaRFqhQLrq8D5KFdm19/xSVKOhTly+boEngxFOANGzZ85jOffWp/f6J1AW5gZI5xYnEkGo6FnJ5O1GGXPoberOb2DigIYS+XHLNQMdlhTeDiVFXAmEVQ7nD63/axoHdBS/Jtbx7escseH8PACvIr8gFU5fgfXlPo7No9GuRwDj+ykKwfAEFemQW7GuaDM4b+pJ5CbYQcLE+Vn4AH/3RiNmIulH9mw1rxYxMq3lJYEZi1guTsVKQ8Vo5p2ZaEA/kCDWa1wBjGKVkn4oqaslvO55tb6jnbhVbtcB0VtLBuCEYprmrvAJmr1ppkKTSQEMSJelJYYQmDB38i3SpU9U2BwmfVyd773u1/5WtGPovK3sNRTE40eOsf2utPMbk/FqzWKNQsVRSHvRUxPMmt+KAeqWANKvQQ7MHE2aHnnX+eCoF4h7I6DMPcj5Be+ELUsFlnXuy9f1pZsMi/7U5tZKTQ3BicdU70yt8zexbgDmg7YQWJhNnRAc2b8rIibFgpAnlC6JJcTiSrFTRvyjR/2Fq+0zBPK2qL8hpmVcdM77GE8SoresaTeqwy5WUSpToMKEP7RYVsYuc5GsO+Ps1zsW3MyxSs9g539z5/cMg98/o9E1fnJh9Yvawpm49seVLrXXZRx0KtqXhdzOjDuprir+vdb6bsdAz8VJwx/Y/yzoY8atTpe+P6w3VWe9y44pqhxMa9w/5Fq7uR1OgsTk5OQnmDmjFXKPKNwW5Mn8KqOuVDQ0NQbHCuAxRgxreaZFFdr+zbo3/6+vjEMFa747LcGKS0c1f+Hz/rfOLj2to1rKqrwEck9II7ItdhLghowTAGR6BEzZs3D/NLR1eDwZ9KQBYnRA4t2Y6H9u87tOu8i886hn6LeGGWEuYFj8JvL0KMEWC2FknwIif6b6/EapxLbclG6PlMhNawHccCMhh3Gxtzd+/Vtm7TvIq/sNc+ea2B5eVHqRCOg/D5eT//3IL6CmX8+XHxgoZGjcLNTZhlwQQeG03oZiQgrbiyKXqqdkTDwcZa2nZpVQka/pApUOOqOoohJTTfBJgx1S94S16S78Dan/UWLlrmnLouse3Rekuzy3kvl8MKOLQC5uWX2OiqOxiex6W0HoaNoWMgLWqY0MRh/hR9gail4Xg3WZVGXzCEzk+VA8UPmQRr0nmBF3HACQ/yq774CYysZuElkPRj7OiMCAnjiBfhSUdNZqov9LVpCY2y6jvHIj+NV6446bS26z/tP7nTGxkzmxsiy5dNNnXdOmRt6seV9dRYSE3ERRaEMWGAfaoqM/Smr2IBllAMKgRDlXF8JM7UCFWfGXdhVT0UpwQOEYlFPsyb95Z7Vs5v++sPDH/6c8bwKGZXMTjtGlb9G68wLrr4sVx8f5oruAEslJE+nO4GT0iSSkn0OnjJOLoIX7jlAxxTZOwPUrb8CMmyK0o+4agiR3cRI2LCrgtTiL1EcWXQ8Yh272oHWUGrKM2aqVSLFfjZ22XFcDQK8oW4s8cKdrEiz7GjjjW/0cVphUB6xA4hqT/NCLOK48P9QrfZXmDrGZlaGkr3XiTA+EvZk9iovi3zMzwxiEC1kDoxNUTZlgg7hAw1FWNYcMNRUgJDWXuYW2DRhqeOiQu8q77AEYyXcvVGYV6d3Ro1WqINC9uwZ0/rO7AXK+vm9yxCLlTZB1QlR5Kl0EiZUtkS+AEHVjjzTE1EKSPCAzCQAbqIqo+1BEYZdsm4XI6KoEx2JTeJHojhCy0LdRAqCCjXmBBGfYlkwrkzTSuWwwWyYBiCqrAUjNjBCf6FBwhEBsFACMdnwb04MTLUt2/jhg3VaLz8PpYEXowmComBQ3zH9m/uWtSLTIp/q6UDecWtlMbHp+KxZj9ehxGSwRyzIDI5S7DUg8wNzCK8+UpZsJmfjqKsYn9jX8lbefKpbdd/svS9H7lbn8CuYn3xgrorr7A2bBh1GpyIvqKtmneqbyBnFmf+w4MEpYGR3Ma8iLaRd0lj2T0oCl2x04XM8KwhwGDsCAVIsq/MBtfsRObPb/HWNWEKVOiE1CQRVD42R8em+0cOLl56ihQD+MxOBcIz08tbQs16hAWBzIshAlUk1Bufykl85z5QM6A3HGmq11/1anfpUvf+B1wc+InTqjdujCxfbjS1zA1wvG/M9OKsXUChkCtY1hTgnUOD4cgpOIKfNJzgTfT446FlnQccKiroRvX2mldfE7zmSq2M/RCWhsHv+jpkAoWGEsARUJj/D68BZX2A8EhAVBHMS1jx4schYjPlBU7ZSTcYd8aDO5tRW+YKJRyU6Fw+zXoZY/JlrWSUcPew3dAQTE4ip2FntNvcBHzeVMY9fb29ZGH6rrtz3/5Wx1f+PV635sCAley+POZ6iaH7HnjgoavfuN7quw9sVx5LmF/tiAdYuqwHhWywK+N90Hf/y7EWFlNrvcrXkrYbzZSz0bd01NeXKm7ZiUTRX8SpM0uXLp2jAIN0Op1GqkFDxgJUaI9QI+Fy3nnnId5zgCmzYt6/9VfRiVEUFNTVkhnYlXNGBiq/+pW+uFevw2UwL6o6hFy3e/duPFE0wPM+nFuVyWASG9uYlYtKxyM9CQ+D1jC3t740nSgUC9D/jxgKYoGajb3QR/RVeH4Hnyg9asL2BRQLjnX4XZDkyMjIjh07sLCiowNbPMKs+IJHHAUcOFHBeP2HKt/8tnnjz71CFiNvpm2V166z/vR91opVxm9CB34+MUWkUGW99Iai9LLrdzSqM9tUZ4NNE2OK5GVfQKUF7TD4ZCMEi7TG4lfzEosKIJCARv9bEpPoqhbiQXMGDBKARJ8Y9Natbmn/wDvLn5s0N28rDw/hJgxszSpcfGnyyivLE1NOseS0tmbXrCz+7CYMX7mViuNAJxY20IKuXOw60eykl4xgkkrxRyrwhlHZSbFObRKGpFUk8FbtKqJMT3Zo0DlXngxNdHgws7MF5asWnqj4zbAqdrU4ikXEIwLZMmJNFYMzejoWn9Vu48CQwDiQNTbt1p4cl104AkgOQ4SMAunCRXkJVdjpIXbyoKDVp4SFVAuBZ9uWUz3dvQpCaBqGZhzYF4AFVnTY8CMuUts54dwybF5++saOf/108a573QP9eqoutuFUf+W6XZH6Hz+EpcLMG2E0hSGZF6RbBZdSCAEKinRCvLQLObHAGpKjK35oyegrP/ETMDrAApUGBpig5YQ2jIzL0mYEI1BIip4igxBnHpo7rswhCDDjLXEX4K4U7gEmTgaRmFCFggVdKZy3BMBIVMdSW+mYUUQnxgCzEAcBFVkSwi+kh5fEnfq0OIFn3AepiuYsjgSoGohv5RBChNp4TcdWFIKe1otX4BY3dBKpr/J8Nl9rKeG262BBJ88HCqfWfFy1zYOmcCQpO6/UsTHPT6UX07OibbILAxd0bpGdKOYZ7pmmJMdXKGzY8KV6YLARAynLPxVzwY9SCJxMMT4BMDWdseyIHY0q0sKb5AySAz5FMtScoZ5wmSI6eqaBvRUxTkcH2w88pvnljRs3kqOXzfEkMFv1Oh7sc/JH1oeW1Luod/tD9y8760pRXwOcLIDNCeiX45pPXFOWSPFcRhQOLEFhcRVVE9TgAij8MycJdWQxldf4pWvporHfjM5fuz6xaImZpwLtR6N+XUPGikylcZ6TAR0VCAWtqLUyB8stN3TnKnylvgIAbYFkrmquJkWUGlUXcQhN+VfriGMOfR3WXxGswryqgGhlZVAtLbAdVu9U4atvCfq0x7F9nwauHLAJE+rHKaeeYjTV2+vX+ytX8e472zZiMVEgpXl4NpjBuWIeUXvyySdxTA4IoTzu2/NQnfFIIvJYY3206NfjNha7/kIMSqpkVEEUSwg4PDyMaUzM7MGOiU1YYvGYavwAA3YwMh5AI22oR/rWVLfZSABG2QZlL3u7n71Fd3fhgI4gstase53urEOWy3snRfXvG7FycG5Zv4ujcUW37LZ0BK+/KHZyKtL9YbiU+mMNFi4UjqACtZqbqEZnsoV4KnbOuaWR4WJjU+Pf/z0ojH/r28G+fen77k+99gpkm4lxHBnN+g3z3oWJexv0LKd/f1GXwv0pvJja8/MFnBho5PXyDzX7rwI9VfaxqulAYDy4qfHaa9Op1NTkVHPzzNADIyKmFkHIBxov3CAlSHgcg0ZTU9jVeYSJKYQplvW9e3EGG8TGjhfWfRd5qydOltZ279EKJV1OZlUkjvh8OgNHBFOOzwQYfGKhMnjGsm2o8epYHWhlWCSJjt0xkM+Q8MyBLUHFbitgtz9rgiPkUUgGIwjYsVmT23Ex/y4AYAYYMq8l0/OP8okQb429E4H8OUcZKpxasf8icIWOT+mnN0b/5yc4vjGPI3kMXCwemJsfLXz5360PfTjo6Qhbp+ccmRc3IBIUI3pzh+deXB6OSC1X8tWBR6olYivD6hbVJMGl7cc5VX5jglOsZU8bz+vYuwJfNkM1OKU000nc5BX6P42qqq3COgv1Ma668CJ39Zdfd9LJdR/9RP7224wt3sLWTwAAQABJREFUj7m2Y64/NXnqqV4hf+iDf1d/2fnxa9+aPGdDAZcCPLYV3SS5RoGtnxuLNrz+imyqbnrI6sIiaKqNIXkOMYsaAxbAluKY0eIvNKqgSQtDEHTGEZidcemBsH9OVzamNLBSb6GWLbCUFfrxYsiMssoL0ESoaGHUebRg/+JJ9AQ8XN2BGSrcMgTghANsCIeRcQULO/4YBfmmn9jD/pAcRKK8kP2F4bAThi8dY8KuVUolrLoU5KDAJOLkAqwyMkBI7IoOXCQ6JCM+QHb/AVydGD2vZ0Xb1YtsF5usrEkzunlCv20br7FJYrs2EFd/EBYEAmlhjhZqKe6qEXYpH9CHBEhRsCMQxhUUU6w96K1yF3xqvFYjLt6Yu5M4CD2RAjGBZdjJtiAQEHERmkI3DCWsMD8QHpobpjqKUKW66uCGvu2M0XFO5uBA5ZHNwdbH9VJB611onn2W2dtrxLiU7MQZyoFmNi+zqB3FeRbEkcIeKZS4HeYRl7kohcrBcctBML+DA+WNnBY5zDD5mKoc5TnMQz6q8uYkM1RTLufGBCySBt/UY6EqU2eml0zMIqvwE/c1yiS2KNWiQqOWhxxCCmE2QPoje1WGD9VFG1qbcGNO1V1IQzcGWyBHokDLkojpburMXEMuB5RVPFxppm176AGssMNkSVXgT4/Hyy4zEpibA2Z8XjgbSuCZGzc+vPW+JU2sUjkMR4OEjNSvXoa9otR+xQdT/NBI5UwIKKgB1ipj3hUrh3mUIPVYfkKt4MgH54RhN3A/A2+iS3IekkilhKFb3RKHkjvbsJYMsxxfAgiI0KkKGZZSuktwxSppVSGe0RvzUTgqBvNR6ATUMiJKCabCEB6dA2Rn2PGEvof8+oyQvhBAYEbqY8rO5A43kUOI+dnFcQ47UGyiMYdH62W+22l/NaIPokn2M0ES1UHuC97kmYXUxwbHOzHJaSLxaEJymGnBfkKu7A1wqaBhY5Q+M6CVXBzDYuB4pxSu6mVSkm3+keGaSIFF6ixcSLy7Mvgnkfw9OLtKGgH0Wm7xJ/7FS10zqf3ZdHFD0my1jbHUtZPlgq1tSmZPObXxvX9sJA5p5v/RzDRUVHvhXyQv2Wk9vBnaZjwZj7a3lVtb9He/01i8xB8dbf7C59BuD/zzP5t79+N+XWNs3InYGFD4+te/Dr2uta2lWCwN9+9raMR4nGaPRLlOQBotzD6jmsJGIvcp3BCB7eKe601U9mHBlW+53tYdO9967TV//L73vec970FcsEIY5xhj+gdnO1FCYjDNDjUGqt3evXu7urogKyyWni2BKqC8ISAeU0ZBkX4oH1S4Pna6sENTk/thwWY+oEnu27cPE1/PRDt96qmncBIV5qVnwgtFZHWuNYhgRTrTGqudIStke7gDLZhX9tmhjmhH4sIMPpnLDFiBU1+chmLPSNUMfJVdWQhddTmqiGqBfwcsyDkQCNLiaAePvRRkgBK0Z88e1AxHXxX/UmDzxPCAqg05dmJc+/mN0EVCbUN6OXC2HnnY3b3DnteK/V7Pq3Y+MbwfDasq3ch7RwP4TblnSug3yuC1qhzBB8WKdoXVCPoYa9v9tW1anZszXYxAGOlIcsek93C/XmLHhHVnNRUYgN8SntFRrRPfM6YKHb7VCzrZcB7nMAdmNBI/+3zvvHMxG4WziLOPPjb1X9+1+vvzgV/32iv8tvbm6/584otf1u9/mNNOWLA0r63xve90152yddrOVbD1Nazq4AebmskFT+oPTKguDHzFzHqJFR1uvvmSU5rwwV4O2giuxRXUwCTxk6jxQS6ITBCoiEIO+GJARQUvhRgvLJiTwQMRroSSaQaBxQN6RogDQUMRMTAx8gW8eAgtOsKV6jdd+I9uQqTJjkX1KO7JZROr1HdwTvoqFPifUYMFmaABZUZQoPQ9E+beCb8pZicdp+wFozm/5GIptJagzGUTOxCSQfyAjK0KfTCYHzYw5BweAoAX2jsSF8bDMWj4omADDOF4Iy+sHF+Hdht2EsRdWm3hG+qN4hAhkF8JKUjJPD2Iltu/+SnCpppPrCQKMjy/E/ufKhHTWNRYnaohafLhH9hf/uKX7LvvxmgLNGQM0xdv/Hnwlx+wTj/dwOGdtWjB9ltuIA00fGpH3uzODNINPfNjRE5kyvSeY2pO6NVQcWKKzgF5+qdkBjozYQQciSRrp2WeGVoxdFo+qTPr3nCQqteb63xoCKI8s0nAUAt8oegCDCmO8ExrEgdOyW/khIUJiTzwxL1nbjzz5c7P01PiiC4vhgIMwueff8H/+96P6v3JxsYGqLWYfbWwGZgKbQzKrei01GalfmSqVnMWrUxweR81USVDMJDkstpTHA57CGYFFFoVscOA1EfV/whez8Cpr69v+/bt69evh8ZSA0fmRc8An6iF8IQdLspegzkRFpCAWgVaUMuBf2BgAE/0NWW9qNJFSfb5xVjDQkHErzL9Vb38KVMrIU08N6rndS+KvkNFL98TGfuDoPB/gyBU7cAPqYqBnR+BW5cvuA/c7v/q17glAuuRgjPPjFx6qbGgR5+5iPIwNlU4393vH3htxN1DZOWIO2bpEUNvqpg4Ui37H3Zw0HQ+eXD6HYsSnzNipcj7B0oHok1dvab5oUB/TDfSGHUp7VlgL3hF8i8vL955j3fXr3HWZv3qNfYrL7HWrMG+Yi2TyW/d3nfDDZGdT0ZwjyFGY1rb0HpBXz1w4ACyJRqsqBMfHCkva4KabJgdnok1yHTmsWPYnR54ZasD/QssqTGMNK7p8gqeHo9FN5xx+puveQvOm1EZA5N1c/IDkWMj0Pg4tvvCglWF0IfVYbwiuac9onFj9ZrI41ugh+OKCKCTe485TFhefZJ+3NOnkAjSbCjBPg37XAdM5M4e4ql5Y5EzVJq1a9cqBRjMw2BIaHQU15614d6mGuTxLbo/vK2i46yKYmL6YCE4udpVkpDgUw0kqdKkBpgwmPJiDiodPwq/OQh1C91LWfuFbJ5VlvvNyfKEUEZ1hrrPm5zShkcKZZx1xJ0CyMxY+IourRONlvv6sVkIFeAJIX9ikCK/oey/9IYzgiw257GpoU5DjWFWq4dbFV+5WF9mZZwnduXuureMTaGNDU3nnLlx5arOlY237EaNzc4SAktYCm5WTYREYzoCJyzEO9tIl1X1WMXZiEc0s5jPfO/H0z+5KdLRhFmkct8hM53lLXY4kmd4LMgWRhtsa8mKho98xD243zvYb9Ql7aVLc22dO/POg31oJUgKuYXscFaBjY0gx4eKX/g9mxF4oBFiFMgtchpglMJGptXAgChcoWpKVOxiE6OszOU3Y6iMxFQtKxaImXkCYYVg4EqadgagI9AwBaiNgXXol8oFHvDFTymbhIYJA4hVZk4IoljQNMe221oaAaM6jeCLtBiIwSgWsko32BSnM4yrVBJXgMlKUqqaaJg7MGXK4DNG+CVW4IXUyTux4p9T2bSHnMINkaN4BZjMACftjDMcxUsEIKKHci7uJKXUU/DAD8U7LfgU6auQKu7iDR4FVDALU7CRKwYKgsl0KecWFrZGFjRV54GQTlgAPDVd/u8fOnfdifmjsu9jbRr2mccO9RW+8BXjU/P1hYsk8nMEQJy/pQYdp02bNuHiCXVE6HFjIXn72UT/+LCEOBxKvvAIh3+YXtDSp8bHAdkardjeeDA2jXX9SxYttMPFpAzC3IUig5yi5p9xX5Ko0FSbZcIZT+wCe+Lxh99xzSePG9OXAZQEXjQF+HzXLY7uefi0yy4jYZUjWFzxk3pIiq7iSQBYcYRGFcq59VLVF2+FbZbDb9yKU6DAA4rfbAUY3QLcqwF3qAE40AjFcqYtOZEco4BhGS3bTNGmYIcLZhSPPQz2rDhiJY+CWX5CH/k7CwuFy7b3gxb9vnotr2ei0/ZV2eQlGUcfn+/8U6V4Udmowxa38fExyAFUoPVhsACzmgsT8cp3vmN89wd+KY+16UkcWbzzyfy2rbG/+EttcS+P+2JCzyQ2YtF/6JDjmPXFv7LdvVh77D7Y4H+/2Rsw3LM3Jt95hufcYJg7G7Q7ovZNXs8HsPjYnP5HS0s7C7GS9iafE8tYQmKWH0wY/x4tz/uM/va3Oa+7ynnta3EREuefG+tz990z/tnr3b4BDwdQ4VxTyyxXcNlSffsZ6wfS09B+oc5BtSsUi3v37C0uXeb7OPe74J+XTm82EnoSmrIejyMUL1h4HY/C9rOW0R9gl4f72td4jtXV3v6Vr3wFBQBDlVu3boW6UjvkCbFDbNVTpQU6lEqfPEa2MVJx65KL8vfdk9j/lDSIaFLR0zBwEHT0wvNwGTzEp/AKTliBjCIFIWityCRFuU87k00XSwVwnUxiVHhmlERxgtFldNHBNkJVfA8zeKAFTTtEpYCqzxp+5DfkeQ5zHaMsV0PhDWZKpSJS6NBmxWUwuTNaeWXFicwsnAYDaomvyt6wq4Cz0PzuWiFntUX/pSkCpB0abCQZFsODQ9hhQSWJNQXPMIe8NOP1rLhiKcdxBTjzmYfvoYOtoXChjKDoAQ83IWL9Xjj7+KwQ/yaB5dyG3t8kB0emHRRdw8HZqTCsBMNqiLpV4K/tMJYZ08Yvbpr416+ZuSwSApf3TP34F7G3v3HR699w+vymew5AnQGkqi0FB5GE9Snf/FROM70XfMOdf7VwuuaUXNewrVS9OTyEbT+soFHNGbjLD6sajSARx5Xv+9P+g/3G+nktC05qi568Hqf7jJS0Lf1B35TqOCsSRIofihJ1KqU/kpRiA55iqpqegPC4HbpyZawEl89q4Jn1srVKGuxRVlWUiAuDMUqkqyKoFLHw4B+FnqHECCRsITmhyZUOYUgmg+AWD1IKw8kLHyK6qiMghRUVSGAFLx7qTacQGHyq5AodqAtLIuEp4pJYcOUqFyzDjQAST4UALOKfmOlLbVaOJiI2ocJ13ehD4FPEILYqcSFMenRQPPFcYS5SlzXJpABvGoaXJlakQHe1fUlJSHCTD9Igk1SbCcOQYIkf+CTzfDOxcNn1VE43y4ta9LhdnQEWKHNyMrjtV9C8+Uc5YpoRIYLIvt3u1q1m93wt8uwG2tBvQc8NLftL68Q7RpYGiiWeWDQnXy/dB3ovqusCFpEg6MVI4iPfzHS9VGbheIZaQSmqG9Kc7pKF8L5z6+ZyMXv++ee/dKP6EuPsxVCAUX0sXLhwQc+CX99556suuwyfoRDCpJOvqtuMfJ7uMuN3uE3lgjAvHO71G/qCGoMFYFCAZ9NXEUc9pix4KstsmBNhR3dqzZo1wIwtbQcPHoQddJUq9YKQY4zYbgT+5L+a/jRGFSv/1h7d1M6FPhipHXQz12P1hpm4fDJm7Dyw+7+mvIuS9dhTW6+u8IHehdtEu9vag0c2Gd/9MZdfoaER2aD9iG16uPLLW/RrrjbqeXzfHJPJpL3i7kb3l2hBvMeT5pfm256Z3nBG6t3vwTXw+R+vSLzpgNVUcqb/o7B9mb72mmL9WaWxGxrsx7XysJYJynv08u0p/WEn5UQiT+2vfPQTpTdfHX3btQPlUv9Te1eYS1Id8wzdtoaGcL0T8hdWvgSpuvYPfajQ0vLTn/xk37596kymocHB6ekpLfqKvL4xof3a2TgVjOilGw2nUAdl11uW0t6sW2sKkEfxJsvK+LnLLo++4U2VbM6bnHhk71OdnR1qUlRiHeZ7SHXbtm1Y9gwvuIMWkgyNDdeKH91gd5K5eKHz19flv/UtbdODRrFScuL+GRuca9+s9WJ8V3rVEhxiZrKx7QwzJPbQiirCNvrggYOog3G188qVK1AnI4R0Q+St4TTukaHBIcWFOzaBjkB9KmXjzBDpILDjOD0NnrHuAE/MWmONNCwghJZSHVutwh77iQUL27c/UZmIT/V3gm/If2K3OT4wNa+3vRYQMoFSjU9ML4P/FbgHSxZd1wB+ly0QOAwkDyGo50tNGhhCUstSwCeqJpVbns8FPC+1CD4zfnStuTlYvcZ6bDPKHhRgjALYFq7n0IqpOmvVSh4P+sxbw2dG8oRCITWBX+W9l1LG06dxeK+as5P4K+UBnDq6u7pRi2zdOX7DV+089llIlYfxh3Ip/7XvOL09Sy+49FHdG6/gRF1WhCqC1TRhJYogqjZViPk1x6gw8Ag0XMUyHRhd69dmGlusbBad34pfdmzHtx1c3ho784xiIjU8peXKxl370QXG+neumUQtjEXFotGEyFVPCghDIcseYPgpvUv5gjMyBxC8xUa1TFlUMpEl2mT4JcBZsjXG6agg4UQqjCMdQhC2C4JXgcGVogjpqi+FSxEgsOoUcDKUYHiFPX2wDm2O+IlT/VHBRAgASsRIFVZwj2dwqH8gGo+hGyHMCP8KmWiCwCKNVjhRjpD4F9yKIXzTITSkKbxJ5GgXzuTyRvGjE68vIQX0nYiIUgSYBIQvFRUFyniFVhAJqYhFLXjmpLFshAachKCfXqHGLOKAaw1DKBy4KDx48n4dEKhSoBWGQlJvxixIB5mGhHNaF8bVIIYZHdhPZ93RcZwlqYJAfS0HOH48cLD4eXCYV2k4R76EQpAf4YHaG/U2hixfmgowegVgDCduHoH1l5ITxqnVUDW6fOgj1davVZP1qLyqVKc3C3Vw5513YoXgiza1dlS2fns8XgwFGNJAr/SCCy645ZZbPvWpTx03UZ+59KR6wYOjc7g5AKuKOSJXnYkCnheQ1jPnCpDowSAfQ09ABaE2Pc4OTn3vWa0CnR342dshBLUdC1whNGqEF1ZJAH4MQGJXjpa9E8WwsjdhP9ykmgdU2LiP2y5HtB9HtFfkNafcmtqcSL7NjkYa6xvVPDlmPnt7e5O2VXzwId/N4VhHzIeAQ4yKoRXDOWX+g/cHV702gAKsWk2ZNJYWhZW9ldtkRoqYRPF+1my5Jk6rSrzjLTiTqvDn70/t3V+ImuY7fNPuS3/+utQffNh+9eWTDdcn/GL6sx/T77jL4hGJ7FsUg4IMfZvGQ5v8112FfbBj42MDDfUL2ltb/+nTxZtv1n99T35qMrpyeePvv67Qs2Dznj0PPPAAZj4xuAhOli1bhpXAK1as7pt+b6+1NWZPxF47rp2Xq+yzNdM3l5S1JOe4gnJPaeIc8zNnpc4408tni/dvcS5/Ne5SjsaiCxb0dM/vxg2QHMpFPFmfYSzQwzJmyAHSgCMMpBwODv5/7L0HnF1HleB908ud1S21QkstyZIty0nCNrZxlI1tbMMsNowDA8uYPEv4vt+wDMvAeGB2vg/GswMLM+zasMMQhiHbYJxwjhicc1TLymp1Du/1i/fu/9R57+rptdTdSq2W5VLrvrpVp06dOlX3hKq6dY2eA0xSyYJTZjJYbty4d8wxsf/6V3zesDQ04jQ3u62z8rNahvkavF+MBQVndDSSzviZEXZYBo0NVkPLoG8NZuxs3ULmg3mZefvWzW3zFvHNyYznPb1N3kgCrcEPbWIBuoVkB7PFNm5n37x57bGRseDFF4ovvyYTyos77aVLm+obR0aHtrPCb95yD18SZrV8l+NXWuEHrz40mO2XjzYYY495UH+od3ZmG+8u67R0EKTj627N97bLkWDS9vI/q3lJkJoPD+R5J5jcNy/CAUQQ2yuYF6jeijJzWMOWB10BZkdMR0cHokJl1EygECnNQeXMNzE2D+SgkuHqzGqNXPWn2XVd8eEhRjEPANuii248eN/lzvwFZmJJkg+VwCYjRh122O6e94PUEHtwDJkKH9WfQJ0IIQiOWXVBQyGduedeb4zNQZKqzCYrUiyO3n5X/clvndfUNNgjRfin40EkThlVGZ5bwU4HkqcoKr9SSvFK90ae6yu2LlzSePX7R677rpvlfRrBydxdZMnyuisv60k2bNjEO8LGZaKYGYB854BgEItAJlmOSOKfukci/wWiDKVxhUaRGroqmQbEIBNnSmD44a+cL2lyV/mVjHK+8SrL8fKP4aXxCSlgvF9poBSWJgmhhmEMciJyR6a+H0S2ohZ4/iQAb2LSFkOEvPZoFJAkl//bbEAaHM3V80nngrTN5JQRCNHlFEMA2QYl2UQqFZZxKWA50fSscWQFFb2h5JncMqWCSdBxKy2TYIgTpa3UySlZFIYq+TEQO12k9SSb166JEJUvc8pWD8Ep9+UgcCQJPQRiwrodccGiDZMfEwxBxLNjWV4fZb9Yh/h9O2NMxtkFJh/B5UvdhTymIC8eGm3r2A11silXyVOEu7/qSNN87cUwZUen7r749OSwSY2TRzAyZ45O2WXDQ46FPASsiqs7uiSE3CUeit9xxx34WTO8vbsk/mAlTpMDTPPOP//8H/3oRxzzG05v7I82y7MfjGX9LZtLzz0XbNtuNTR4x650Ojo4ebb64d8fde0ZDhxgGssUVI0RwCBmEWzq62B7VuuE0NiXB2wyDOE8yreukKD2evZyMY8oxz4gmyN88ZUP0qUjfr/tzSvFI4N1c1pIrnrC5Wnn3IZ8b6/P6fw+51JjdUigNXGO6ecMZL77LE0rS3P2TvP+rcgL226MbBedyin2XbafzZTeekE0nhi79huJtV3yyk6XKCg2Rzc125nrv+utOn4+u9AHRhJuU8mPqsGNCe5wMJfoITkcn9djOjoWzJ0zB8/zoUf/cOzRRzddeUXTO9+VyubcVKKn5N90++9efOF5qOHcL146xfU95phjeOdtoH+ga50zUP//rO74ftx91WlJR1uEA+zNhgK7tNJ3r2n44FJm2rPPPlf66S9jf/HhUjwxMNTv9jR6W9OlINndxwvEskSRHhktloo5Z3amvrghm9i+dfuyFQuTjc2Dlr1xPYcmiJ4iqDuqN5IGe8RosKOu11rX1njEHPx7NtUN5q3e7cKtY5mX6N6SvfGmkbvvdXp6/GTSPemk1JXvSSw9qisb2V7A1ueg6PTmQV5GSXl2zMoH3fLdeDluRbpTviwl7KyP1a2ewwEndl08Ee3ZVviPn1i/vc0dS8PPQsQrnHVW8j9/cNHcubFUPV60djTUhhFD+7iLE3QcU/f7741ufQKqtaPpkWYpJy2kk7m11z/IF0G09QaDYy2/sDhveaoQeHhQk1Qxrs7DIYEdFtlsdga2lM7SDf/MN0EeywgHRSrujjOIygMmLXfUKQMdozke995ykvU3f539+S+dF57n+6bFBQvcd10SPftse1aTPA76QOwoN6NjjDdGnUicmRRwSgazcmKqTJzBdRUqZuEzFbHdYjG/pbuGXsN129/a4xSL9TH2pZe7QRSPeanSCBy9k6KSbbSeyOIyrCYaoSX8KKeu7XU7GxuXvOvixvbZwzfdytmKpfpU9MRVqUvflVmw5MFNdiYHZJmB8lOWekT503oEm/BY3C0VklqgLC/JNsWM9yw5+mdgTR4xEakG3OSqkypwpIsGN6EsVMs/5bq529FChQOFIZniBOhVDovCkH8cWisjwlCsQ8MsOQMpzJL/UorCBq9EqsOOysgx7+lwLEQsIi03FVEagjSwyqzEaop6smQaj1YqIEBBCK/tNMTxKAopxM03a8AtcMZFLCM3dBn8FVr5BZUSLA0hKPEU1Xj5hxuzzis/QoyQIaxWSqSFO1qpfCpjMuikDoObqWhFKFMeWkEFA5hR0g4nhyZbGxPNMeleKWMCyP2mJvektzr33e8EPg4w71lgnoFvrKUpetxxTtW7RZVCu/3ljA8WkGEPYd26dbSFlZ4lS5bstsC0Z7ABE9qww6e95r2vEGch/HocxEv/m3+wd1KkbA98/PHHP/zhD08K+SZAyIHpc4DXrFlDL95zzz3ve9/7wur3MSIDJJ3JP/Fo6Z//d3xdl8v7gJadb261P/T+yLkXsPC1j/j3pbjOPM0o0xMTk7AvjdpdWaMhYnhePLNBiiXLosvXI0xwPS/p1uUjOYdvG9FhTlyEfxUijnTChwxyeXvBvFSqjt1/nADDJBYfwwQK7RVwvBafOVPxbwQ6hrLuGOkfGChZKcnhfKlW30t7ztEr3LGCff89ujrot/KLd+0FWTueHs4/9WRk2VFWPG6vXOncdYcXjUVch1X6qByHyPn4fvaoo+1UsonZE05m7e8fHBi65fY7UIaoqr6+fmaj//jHP2QzY8cedxyvLLIwi5A97bTTcL14p9pxvVWrjt/ePffVzIn1zm/nNTzhBRsDOxJY89J3Rou3lOJzbinyrYXXN3h9/d4VV0DDlp6eoYG+rN3wWmYtjZ01e25j6xx06euvbc3nRi0fh9zPbu3p3bpxyGlcdGRjKWAZQNxRo/lkspmmQxUqnKgwH0ZIzLG2a5LR5aj1QuGUBcVIX//gN7+dv+WOmO2iw93hvH/L73qef3XW339pTufy32/kHEIvl7N7x+K5QTlgXWYDBI28LyQ9hgMsVQT1fCOEM9w5DSyTzt14Y+LXv/GLhXyxyJkN0WLRvfvOTMSLfvQjTlOLZ15llLKTBezJRLNz9mcaXr4j8+QvikGOHmcbAMXKtpFYDbRVbDNi2Ba215I/7c9j80/gy8BUE0kc4A85TNaCmZjPlARk6Uu200+fDlPqnVh/k8vzPjHM9BM/nTUyl8Rn3rxTT3OPOsrmMCS+ssHp940tTt2B/TbJAWoj443e1LF3gKrYC7QcD8BX36MycSeljRtMTBQFAp5PTXDog2TsbGtybqHX2FhyPU4iq7w9i0gywUhhiZl7LvKLiDICSjJBJQKz7B3JrwgxyWBG6uZXi2cuajnyrDVNJ57sFLMQNBqNbYo1P7TBXzdkyDKVGGoNMnNb5SPJPQ0Q4iUiik5aBqz8GLeu/FAZT0x8LcngYn6UOimkt2QYj0xAFIXkgNq0QH5MWzSbd5ZNhu6bljQRyRXoSg1UJJSbP0Fp0g2YqTRccQzhywpHSJFChgCpyJDIRf64HS3k2dzbkIwl2B5kssWkkJgE+Q1JJSokSLfIIbqaIZ6jQgg26a4yv8qMUqSiY/BTyVSMSoQpQAqZoGD0iEISJPInFwNW+TH3QoCpSMoacvB6Ba3EZZe20CKet2GhtLGKOCmyo3RIi0ElFw0CIlYK1UTjMfyoealiHaZWdUkGAI7uFe/Nre2KbVoPhRSi1nw84vz5B+2ODjlrYMqBZhKUtxrnOuXS0wGI7Y1amfkvAIe8gIG6o5AUOehqeNQf7LeGR6xYzKlvtGY1OtE4WbvjM/ufWbM577zzQoRvRiblwDQ5wPQZHsuqVatuueWWq666anddOCm5NQBortK6df4//s/Elg3yiV+xg61EX1/um9f5zW3OWWcydPZXXTVVT3qLR3ThhRfOqFcjDhwrkHy+nbJiK62xbm9lPteYTgzwwVmjJfihn44ddho4zt0O4qtrhDIHFSCqFs6fHzvjzOyvb44NDzM3yTQYLjBF807EOfc8p26n9Xz2JRLoAvbZluyTOMiKBVbv3GH3R5iM9X52zB1jU3RQSFixi9gmZheGo/muUhRLYTidy6Q5sqnurLcN3Xxz0PWa77hIDegHPtM2O3rRBXZD/QvPPx+Jxlhn5hxvaGOCk70l7NKkNzmBCW8Z8nDC2WWEkXfCCSfg93V1dWH2XXDBBTRn7frhZP07Wo74Iu3u6+9rb5mXXLMlm//d2H33R0eHo4uPcD9yjnvmWdbs1qYhPrnUkM7nM9G5PsdW5Ot6+4SWbGKRH+Ns0EJ/9/pUy9K61MKefK77udfnLFhKe4z2Vu1Z0YewWlQd3i+N1ETYQ6w8u+3k03OjbvGRJzI33eEFJV6FI5sDruWt4K7Xh2+4qeETH5sVr+8a4CjBRGPHkXksEU5MNWAMG7CrbhSDyLbTBZvX2FrjAYdq+Df/DoWtG8YgzdRqe3fea11ysTdvQbkYqROGysjkIBj3qAuTs5cXHro+M7opaiwgStIQvYqOpf1QNPv44mkfqks2y+uR1G4A3rzUckA//3CwHGCo4Q0x9hJPbIXwHLGBopb0g33P3BZv4C9evDjcwH9AKDJDm/EvAzsW89vnOu08Y2J7k1J5Lg5IzQcOqb7XXf3pkQNX1xQxIzRyJU7sk0VPUUviigrrRWAF1vZ0kI3WJU87ZfjmO9iCY5JNx0jMiZx5SiFex4cU8kXKGElrHBTjeKqrIlTQZeU8g9VILUFfCaYqqdtk206uGLl7rfV4zJrX2NQQt0bTuVfWDmeCsUgsJRMiTOmWS8ookLheTaIODOozR2aYTBkvUkyARRCLe0OCzFnKuiZBvRVTXiChR5tRnj81EAqnGYZUKS2N3tFOioG40jJTrd6aJMOgHdmmNjYOGbBKGZlYNXE57SP0ywXGvHaLMKcZFYWjSsXQIhSafzl2ilk2863Gp5VkNjqFgWgVVZVkYYahC2rK9EhBEqXb4KTQxD+aS9COlhKSIZwXespoTbLBJTGj9OSKMymY9F4GkcbExd1lMDUbYgwubg1pwFZqrxRUBkKaoUS7BFp3BO1sMspdE1idLRjD0jLzvwIZiXgrV9pf/lL+Nzc7Tz9TKoyVFi11L7nAXnWS09BIsytwk//yeghAbN556aWXOjs7keGTl5leCFZWePHnkNwPzEre5m2F22+1brqJt5h4RTs49vjon17mH7/aq5dPVY0PjLVbb72V4zPe3Ac3njkTpEyTA6wUXHzxxd/61rfQjuEq/wSUTZ6FxMlkCg/9Pr5lE95v+UHniedFmnwmf+ut7qrjrbbZiKE9erAnr3dqECwPEoBlaBIOCg1To3S/QKGvPL/xSj/7gN2Q9T7ck7veiQ0lxfsM7OzCwcgHBljOLFlNXsO7RYpXBbiEkBoZSzcvXep95i/y3/y21d9rujPIe5HSe/5T7PTTnNROj70yUxQA+32SJ5ZKx1u5JyPnDue2dQdD2xLz23PHNwazBuxzPXdZGvUzdl/SLXpj7DRcMK/keJs2rF+2qKPxmi8W/s+/WY8/wSsxhUjEX3Fk5Oo/95avYOUCtcoXAnCM5WXg3l62+rDbWQ88hFriLA6zv52PAPES40MPPYSTjLVHABhno6e3Z0Eiztkl7OIeHSlszG+b0zYnfvmVkYsvlvW4VILvGxe9eCQSbW1rmdXc6A53LWq91/VH00Hr9uKqEavdwjW1IiXL5cuEjYnGREtyZPvmQnZUNJxRwwwsWRuvYSUK2uhwo0/lgloX0ycI5rSmGqxM6fFn+Xokn2VyPTlI2RgZMsVdeOypumxmdl3Da4PyuIhRYGoxlg57BEXx4zELPsHG5KTdnfZbo05xy9ZCbzcJgJPJS9Hks5QSizv5TVv81SU+2j31ka/2AlZQyxLnor+JPPofI6/fQ0P5k0ZL1YYGJ+of815r5QWNldM9dhpOCvbmVTnA1Az8V4fkoPCEjRKTmiBTHyHT2QQeak5x4+tu00mecQyMnSsj/lAd2MhAVB7ycDr7a4K6jOAKRrJ+rlD+TLwRKVICFvM3XLJfGrVXrz4hdtlF2V/czEfaRdiJrHXsc06NnnPWi6XYtow6F4CLgOSfOEYGhRHLIhsNNpMnGVqJCmktop0qQlpIEqfKHvXdVwek4PaewdFhzpeYHdWTe40oL6MxnompGAVAUE9MyENcSz3YQkZ6ihQ2tJNnEpQIcycEyq2xRQSHineTiJJEmxjnVbQKWKQKwWDSpJiBEwWjErn8VAgaCWYGVGGVWPSKJJtmKoQSbeKUEgZUiFfcUl1BKjINkGwhSgiVfPlRODLSYzkX2yBwZEbCkAqM0KsQUKlFpDLJEHPCrMxDtUCaNgo9/CtfhFYNpAiPzI0i5Cr/wMWPxMx8ghSW2zJouTRARmFRvpKB+yrBYOTCncnZoWgl0ZBRpkHgK/emu8sTBqZ28azN8ruiNLjBBwJosfggxUD/YGtL8+wUJkSlSqlegqQkUt7KY62OhXY6I7vFUNWN9XwssQyscJNdq0WixqtTJis9Hfk6FGbaJpQptrzU01f49x9Ff/GrYiHLSSiJQtH6/cNjr74U+eKX/JNPdCLodMbYTgFTEwf4Ax/4wE6pb95MxoFpdYAvueSSv/3bv/3DH/6wf87p5ulm3+y6dRgN5vGTp11ljjzMr6+3sjkRnUYITMaHA5KPy8QHgbFBZ+YJNPuxzcp/t/HK0uDP7Oxd7vHD9n/Pjz2esAZiVkc6uirrxLKB79mtn7aiK6WXKroBGjo7O7ny0R32M7ed9w5rybLiww/727aONTS6J50SO/Zod3YbANVFuJUUc+3uHspFP744+jnHHYl9YGuQ+wcr0lB/XT8nNdkB71GxJuq57Oat90pti6MnnOC11K+oP3pbd/fso1Ym//ZLwYbNxd5uu6k50rHAamnbOjLS9fRTeLYs8OI2YMMxtfnCCy9wgDb+MO+T4OKyMoxNj2zlFjn79NNPA8ky1/Dw8FNPPUVfA0nxe++5B2+ZiVKOyOJVYfA0zW6PppKsVm9cv7FQ7F28ZLGff/Hco79T7/6BTxZDKtuS837LC7nL7xr+fCaYg1Xi2156eNDL58ZynIdlDfb3ozaj8VRET2s02q/MCxnqZbbCGROT5wC9iLBk459nlfK5HF/dxgNmt7Zhnl5sO5tDe8Y9N8aOsjJO0SDGDBCFr+rEQAtutkL3pK2gxeKtoWKpxLYL3qGm1frtFjnLBSi264TkVVU2QbRCvlBtJ+3j3plcd3+a88OliLSuXDTW6K84v8HxhJIJsL2ZBT8ZpQzCaXaAGS0MBl6tJ6Jx9YiUmEOuX2iC0hyOzwPUBIP/jTCkGW+MOrr7QHNsqh1hOnBwjBVgc5JAuO3USBXDceeR1/22Fa0LP/jh2NIV6bvu9Ldt5zvAsbPPiJ537raWOQ+8yLvDRuKoTDVlxLUyMohlTDMZabyxiug1WWUxbOiUXON9Mp7Efi2PKpPHnWzTkFcBXFzZyoiTPBPXFKLiKElB+QWfbOQRihSbuFSSWam1XIO6RgpHkpGnBon6gRQweWgbwQSE4OM/ZraxrNRllgqxu7G2hCLpWQEDXFQGdyQbpJpY9mIV2PhrUqjcMK1FAMueJHkmSEY5U5CrJ2zqkmzxayU54P2Aek5vkluaYAaZegSGRkO6oV7JlBJSXC6GCLmqC0kEZ7Jav4VAYBU/U+oTdkpBvZa5aZBJ402WgEkEIF6vNexg7sRQJokaqeBSpgBqEoQGIaHSWsC1FwUAlpqa4ZP2mKBWQgxZUlZqFhINunwmk82MJtrq2/jkoUklQ0N4G7C5rrnFam7W5pErC++CYY8DBhJrAJi4e1zyABfYsmULUoizZmcgbRM3ndXf4qsvu7++yTVLD9Lz5i/B8d2/+JW7dGnQPhsMNd31xBNPMF2LhzUx8jdzazhQbQfXZO3nWx6/4447jqflpptuOvPMM8OncV+qkQecg3PLomAHJtkmylcVOeBuL5/rHaj2JYZc4zBMNuviFIl62Fke7QvmGVrWTjgLri9s/qCXfdBpycTenjZbtCBWrIfthT9pb/2vdEg1H3bEA3vblm1NRx2VXL2aF+E4kZ/XR51kAhOe8rvjHPvMcSx96+wNQ381L/aNqN1jxdcBD68dTvuULUWispMX9WYXWfWz/j+rvY1PGSGu06OjA647d/58u73dY8HXKKvNW7aw6fH+Bx/qmD+f7x4hUEDF6zR0IhG2cSJMkarMa+DT6pow33Ghf/W4b674wCwckUVZVo/Z6cABvNpG/IF62b7CkMUO42MTdpB90N90VZO3TRxKm4MZaaUfcfqPT/yvedFHfzX4H0N2x5yFi7o3dDmDqDlZZR3dvp6tcQ2z5kdjCMHy4DY/ahpIVYCKWaFKVJJFQw5mSrlI3F28iGTZLlHO5uwwjAzb6ZzP+7sDcnikZzahiYWIFwutAIsSFqr5D2bBTfmBDGUdt2O+s2hxZMtmlqP9fD7Kmg+120GewzaWLTF9J6X2IkDnhifZqS0Ciri0i39SczDWZw+8nms9gklrJXYv0B9GRZbxCei4vDs0nYFzgDFBqJERyYQRjwZxNmgxMTSdZOyXuniQ+cIWbJw5S5r7pV0HDgl2p5yJYDZAHbha9gQzQtEaysnH3YmU5WaVJkK8FC3vd6/5Jy9sXX7JO1LnnB4pFIqc55eof8WPPbLWSrMnR75LJdJM/os2l6jIViOZREAZCUmKJBppJbEyROXXODTG4SFjh2yEPs7u9bwo37IQP0/QloPCmatcwjLUjael6bRItQBlJF2ShRoj/CUHuVkuqHRLmilhEEqNRsjytIpTLKDaAJNBP6qnhxIypKEdzPdoBVasdKmLGkwpKamN5bZMlWI09xWCDTUVBwxmarvE41VCKWJUkIAAa9aTJc3cufPbmRMvgwqhhlhDmjREVBTZ4uOWsyQiHWQyTEliQrtgNL6zlONWUoUU0XkYBVJEKSMqVUsuQfAoSRQGkgRJLddGG80/5gFNjdyUYwa9qcogMQTReKAMSkG7IwgaUylVC4gEKqmQIXf8F41YKU5klE8cOdbSObGG3fuk1F2mQVAapBrZ8yuTXAf2DZE9J4kStK6rq4tDoQ7FlSc7m/WfetZOj/DhEL7KQVtkaoyOZtri6acDjp6x5JiY8rg0/AHmt7/9LS+ZnnzyyeXRuVd8OwwLTZ8DDHOZ3WSK4sYbb/za175GfD+wO5HCqy7derP5xnhZEiAFxKo/fpWVSNSIFbwXBhMHQU3PKEE64DVh/OEUTb8Nuh/YuwcohNP8t72OSMeNpcHvW0P/FuTXOkGhZNfZyeNe2nbB69uOOH85rzPsGim80tOwZDGx8qnbSbuJDYqgKxZyT3SvKQappfHP2RYnE7p2sd5yF/ujb8uOjCba77bdDYmj+7Ox36zftCJX2IRxVv7WqOgTnCjXDnAQX2yKrV22IBM5rcOKLcUW4YjaI444gjHDYfqYvwhTvuPFkVfsNmEeh2OoeUsQl5iTDxnMLBHT16eccgpFeCuGxeHVq1dzEBqNgkIivD+cyYzhDCCtcqNjtttd2vyJSKlbjImRaPZ+p7TFcRf4sbN8O5mf4zx+Xv1nbhr6iZNMzu9cDmPTg735XLZpzgJkH3tgYJJwnLGOlASj/KrSFfFvFDfdIdYeqUAOZe3uILLkjFNHf3Wzt2mzFqQMvjdmV8M7Lx5LNq7bbOcKvk76U65UZD8aSxKICO0HU4lR/vjG6/sywyN2Q3Nz5Or357/2T5F8jkU/lctFJ2q9731O+1yxa6QmoXTqodIae+PjRSuIUhrt7yQLpbzLJAC42J++/vF86xGJsvk3ddSHJeSRRx45/e1mVkhPv2fHBKKPW7qVZ3z6Kdn3GhnYCPB9x3P4YOA9tBnWWORQaTjHGSHGTzMyyUgyJJbcGIfAHi1696zzn0tG5ze0JV2OZbS29QWbh9koio6oyFiRrzSu8l9kkPxDLklSOYjcMxAi+rSiMN+AIdTUczEFAj7gWCgFpWQkrt6QEZsGUN6SDZFIRIK4hiLVBbWRsfIbepFm4Qi3Tn19iFPC+AGMONumiRORDFwz8OitSTOp5dYamqlHmmdWQ008bJFglDLQwj9Dl8CqK2yEs4EQUqmEslAp9ErMeLcKKfflDGGmqcNcQGbuwGKcWSFX66NiQ7sQIE2X8uYCJgGSII2tpCk6WagnV0mFXQIFhIDLu8gkVGgz9AhOgVF0/BrX2gwYwSuvH+PXysS1wkCMUCF5BrWg12AwaGO1AWWkhnA+gCwlamqnYGUqohyV2WcJ0mPCEOqSPjHNlwqok+2y2Uw2EU8s4PMLppmmxC4upqG7SH9jJGHk4/1idB1yy7/wXwZtLusXWY0o9wab7BhqbKexCgXbfBulppvQUL/61a94w3RGHTlUQ+TMvJ1WBxgWXHbZZd/+9rfZKcrxQvvOETse9U56y9jpZzgPPICQ5akW+RI4mUWLYpe8g3cbaqpggY49yZzFVZN+gG6hBw8NB5j3RTs7Ow9QLQcObYB0F1EtIt38yY08laIpympu59oFzHEanJZPWS2fCPxejlX23AbLbkoV1wdbnuvu7lu4aKe3ecPiPLoTn5QTQlZHpMdFI2GjFCLZm+1EkR2zI//Wkn2sPfbe99Wf+rbE7LjlX15yPuPaL7n5n23d9Jb2Refx9TuQ0C4K+1bBH/qp3/c1L/9Kwg44yaFtbqR3ZPnLPZc73hFHH7Ny/br1uLt86AiRytZonGcWdbkiW/nUx9ve9jY85K6uLhai+QQ5k3DYyiwRE8cT1q+8UBcFIRM/GZ9Z6Letdv/XrteFjCu+FC/+ZRDdzJowJzNbmUVW7Otxb0lmSez22e6963Ln4+9lx0aKvp0vFS0sJOE+S+qqVE1vwHKjSstajTSjuKUigYI/NNN5Zmuhff7CWf/t04P/9C9O10Y0ODUW61MNH/3PhZNPfHrIGc4yE1ApY9kDWzayz7tp7sJKnws+VcIlvgzS3ffEcz1HL5rXfNrp/heioz/+d+uVV5mktDoXuZe+O3L2We4+fH1+/brXcyN2z6scyyHd1LQ8d8bH6tP9pYevz2Z7OQfb3fR4dtV72MhNi4XiN8OkHGD4TRuvqMicNy8HzjMxxNsB6gxPSuRMA+BZ5h1mnbskwpPOI0xzZhqdM4oekcdTC7qywbTINIxMRv8L3cX1/agtJc9IRuI7BIieF2VtGHCe2CIuHWJWghAnTaIkMRFIGqSgCmMBDNMlTrIpJIdPGTRlTOUsA2yKki57j8zaQOuslogHK8ThlTRTkKspJFVShbmTOHSL2oMwcwADOUKACHp5zNVFEsew3FYzJyrFDF2CwEhy0EnlpqTgl7+yA6vuk1YoIAByIwjFyBJMJKEumGymlIEQGPNnwCVVkjXL0CFldHAYwpm5FkTglLbwrzxytF3clotSlphWIxi2bNnKXoxZs1qlObreW+6tHSW0aqoRKiRQXvBoTBELSqGrTIxULx6lbFdTj1vaQZGQdrkVhLBI6JU8szguLZAbyRd7pDwFoLVojZJLObnZESQuZaWMoFMHGxrMXRluRwFSpRFVODRPi0rV6TH23AUN9fG5jWTtKLmjysMjhr0NR7HBqnh1yLTcjkbszsVeXdLz7YJ5BxgFRF/yPGQXzOf4GEZSTde+aMK11157yDRyxhA6rQ4wwxGHgXHJdAXrY/s+OpEGzry58b/4RHbuXOvuO53BYXmb/y1vjV75XnvZkTavftYOlelmPI2lykPQAUaqylMmHqI1Usq+aBW2BE7Cja+w3A7OZxJpPU7CVritj2fUdudVjimy2ue0P2c/t61728JFCyfuA9UFClNBOFEJYPhi0jErl0S3PAu52Vei3m3NLW6Q/+fr87/4dTB/gRWJFpcsrL/sZc/KnHpkj9s0147LF5lEplhjxe7PewPfcS1OlcQmcNHFrp2dU/9Mc2rdQy+959mnOrf39JDzzDPP4OWyFNzZ2amur4D7Pl4ulvGpp56KoY8UwlXGK8ZbPvbYY3GJlX5axOlQuL6sgzHsKbiu69XG7L28issxX8VrrNhWw0yZFHfjG4rZawLve1HHKxwTv2Fd/rxCbmxg24b65jY7ksBnNau6Nu/5jPZtb13QKdrWdJTpEaLKfOmcSkxUJppz/aD3h4R10qq3tv7TguxTz5T4kHJDfeMJJxQWdHYF9c9u5zt+stVN/0Ek7wPLx4VkokMUsirlCt+seF0LrxnFEvZIodRwzjlNbznRGhoQZd7UaPHlPb62pY2n9J6HQqm46QnPyUd8r7TiHfYJlzVzFnh9e3DRVyKPfG90y6P26NbI4IZsy+IEvN2HevacskOwBOOW3QonnXQSA3LayNdO2dNneV/I4xFDzLL2uL9mwTE7eG2Bdxn0aDeOgoc8vN8VK1bsC51v+LK4tRzzwZQHsnHixnJCOACckjAx2H7JxVRY1+f3ZxCMFZtA5GNZRvJjksXFkERxc8qA4raIXKtQUSlOgpl2FED+VfBSMsj0d8fqmjy+YG9kc1gWKNE64kvLD0JVi5obXtVpzPr28IgkixDWYoIP0Vte/AuzZJ+OYFN8gsiAcyVDdkCbW7NLiE0+JJrKzGnQCijlgTYvrNIQqUR9b1lbliz5Y2LVQFEYWAkumBXCkGVawIUb0wjMBUOpOpDisUm9pqXyK8Ek8EuVXKSYNFYSjIYhgTrlzuxfklyqBURZYqMasvlchGM9YIFkAWtATDFBZ9JU8gheg1s4KDiC4d5u3k1PNTYLbVJWm2FickfYkaH3Qinr8MIfsxAtzCQuVUldJqazGKZm2OezOpzLZiKJlL4FIA0zf3I0QrlaaZIgkBwTMTFtPBXtIE+pNDBmSV+Bpd4yeTt+7Gw6Q1cuaq1vr5Ppmh05h1kMRUCLdXvgjG06o5GXa5hRFf+2qjeDaCSyalX2yJXu8y+Y81tkJZiBUWSy5Z2XuI18Fn6nAB421bIks2bNmmo8OwG9ebMbDkyrAwwNTPe+613v+vnPf/7lL3953y0VGQpexF3SGfvQ1fal7w5GR6xEkg8qui3NtrfjHQjsGN7VBJYpfIaLmjJ4Iyw5HugRg9HZ1NTEPkD2zR5COwDhEpLZZ2Nw/z8H/dd7pc2yYChnbtQFqbe7s7/kRFeiHw33ap5HoCRw9hQPLViMomM3emLZ8mUtHL0wWaCzOHQKT5I3aSeDlXypgiOT4umSNcgHfwvPJvkEMeox5kXdoRFr+MXM6rekLvps4P/RdgeKL96We77ePfa46NFHO7PbCiPf9Qauk91Mo4nC3Y3+izGbI6FXDSdPz0Tc9OlH/ao7ckmqbnlf3wCLuviuvGLBS4zscMa8Y3V38eLFzz77LHIWatlvg7bjk0i8+sjQYuKDlX9d04aZKD4CpiHGNLPXnDoVYUuyXcy9EI2tDXC7ixavLNMWjAvXfalQ3OxFFuXbIy/EOY3as6Ou09Y6hxwDZBjuF4rZYbHA5M+oR6ORQ46JnqyoQDoTs4T+e2yr2z1irZq/qO2CBfESX6XyNgXeS33WM9sxD0QUoM0LbKIaHYSSQrHoBKXRft7eDOoaW/hmcohRBoeXGovFm5vd559/oThr1tyFbHpcAIDR3OUhYXqftCkFuAST4Q/E8gL24NoGuzF74lWlJW8V79egsOMN7pmfrH/5ztGnfu6vf9xuWsxshWZNqYrDEwgtq29hTKcDHLKat+V5Lg60mKU6Bg9zTPvxpS+eaDaV8diyDowbrK71vuuskDNv1AhKls0yaL0Z1UBkEVtWI4hTIyy5iIQMdVdZeIoklXQCEYWQNCNhyxniupj/AiIIQsGrkH5xsLs7YcWjqYRBoY6dqclAC5qylCzXobXxjBi1K+4PFeLtmJj4XaLhABJHtUKyuTdkab7gVCh1C0k1eIx/ZjK4cFPBVXFj9d5gowJKaSvF1zI8KFcIpVRAFSbVeIDlHKPl1SGsUCJV8y+8NWcYS+XaFvH/2fcrB3QIDqWEqxYgV8jgRpLIFQUnmYYr1lguA0AqlSBD9iAbHCy0y6qt3PAnjTRkKoXC1TI1HB+dHuFcy2RDi+lCA0clwjuKGQLkViiUTdEhJeQatFQtfWNqksKiWE2NqHilxdRdyGa3b1g7Z9Fyz7zxZYpQTH6FkSYIdoNH2ESOIUFwE4RcrohNOa7DBLknIlCGElNvBZtQL5l1jfVe1FvS6kUM8VrycLtiaWNvY4PN/K06eCWYkZyYUN1H9KQzd070M3+R/ef/5T7ztFty6et8ImVd8d7o2WuscZ9BYtz97Gc/Q1W9eURFNRunGJ9uBxiyrrzyyu985zuPP/74W9/61ilSuTswJJZIN8dz8HhbmgFDuiBMREjIpRyAYe1Oco281DhTL5X88b8i1Man7kUKZGCTsSGQxxLjqZqqvcA2nUUCf6C45YPe6G3MewrnfJQC/kbaGr2hlHnYmv9DJ3XOBDwSkYywH8ugcwLeJo1Ej+zsdDiMZ4IyleZhy2pPVRJ2/YurTIaY16IXzAY2+QYPJ53IybdOVNYz03Pb4x/9cECDi6gAAEAASURBVCnTG2E+hLnZ19Y63/0ec2n5Sy5OffQ/2YV/xDEMBuKFr82LbGxMjw6zibd0V2zo6aGGTw55zkhD/l82Dv7NK6+8ht/Lwi8eLLKV8cNWZ1xcbvkeEoYyUozxzCIw3i/EQ9WmTZs4T4vFIiYjORCIfscuJIu1ON4KTiaLMnvAyVcjQQwdZ+bszZiTCXQ2nssmNr5tmO194fe3BH68vmXW9q1dLLxH44lYMkFjXbZ600z5IJG8JSbHLwu/hRFwXfgimlZuJE3uCLDe3jAc2TBSijle3IvkMe6LFBMTgyp5lgAaS2eGtveaIrjbTibNcc8c6ZyI2xylbQ31boGtjW0LSNs6FAQdVC58FuxE5CrxvQu8oYBKgGw/HXVi6aVXZNLJXDodZXZTEULgw79/pBTLXfil0566ZdAp1Rm3fe9qO1xKMafAqDsob7Ey7A/0rB+CAh+VvtQrMiEUC/vewToHx54OauEZVwtjKqJp4qoPIS0wcUN2mYugI30Ca4wZGTxkYBiTsILBSZxDE5igOWCcEXm2rDUyHy1mhJXcK/WMHyMiNRkJI4OJgBgqJ8mZUiauV6PXRLrKn3F7ROTKrf74QbTObkxZsToRzohWdiVjwhoM5kdSTZEyEnHk+geGyWtqbqJ+cqV2xS3+oAbZn2soMrfiCHFr1KxUzJ2Id9kBLRElRwvqVeS8gRE6w2Aaa6gpoxDCKG6QmBIcOWFqEbymNoME7SXVCKw4jnJLKNNq3DZpA5kGzECrP1tWEKYIeaYZUpSyZQ6B1HCgTFW50QIp/4aG8ulCzMvHM0PSb6CjItbGoJuoBKlVgIlhGWC9cHFk+tfnWF3jqBYdn6PGsBXY4yTFFVDXd6V1FT4DDU0lGSASQvpMWzXNXMvDQ1DSZmWCIc5gonqhzRBEXVSphSRJWC01ahCGmSIQIDk+PS6r+QKiMAoMwfwRhFRTWmBISSSS9fH4PN45kxJaROAOq4ClzfoEJvfMb3V5tNb2lB3E4+4xx7vXXFN89nl/08ZcIsaXq5wlnW4L8oFCO/XscyZwrNLMb+8MpHC6HWA6j13QrJj95Cc/2S9HloHQCAFkAb/mya9IhZDd+LoszXGL98I7wBoPc3eOoGaM7AKb3+3nNyGHnEiH5bGTmXGHcKsdfzsX38Xd9Gzx2kXFe58EC0rF7X/jpm+BoaXtyeKd9db6mJ0MrNNHvFUjbrC9tOXDduedfqRTlMfOD6SxKJDeJX/T1vzDDzoP/yEYGQhmz3fXnOmtWm23tqKvdimg2UuMMaTnxPLSrDDatrE7ue6yKRzNitVrNtpxHvGcgttq+5u9Y4v2bxgLmBy8Uus7F72T8zuH7/7GrPcOC5KN0VicV30D/87f5ec9H7tgK+5j/obWxKZG7A4WHj1ebIq4zgNNYyeXkqcMJe3HZjcH25qasKqZv0Cq4k4QYX2DjSs4bARcXN5pxyvGsHuAd9FtW5ev2TV9/PHHsz5MuzD4GISUwjHHQGyd3W6PLQpyPfHldi7lx9JQJjwRx5W1rMVucp4o3aGxxmIRX7iQ50AEvghsy1cP1774R9dyG2a34+lvWsvx1EGsoblt7mKDQB4AVKxuflC2kWD+GdQoTtGVTMQIgyKOF4mKsaHq03SKVR9vbm+rd4Ogd/MGPovU2raAUc8uc+ijp52hHJbK7Hret3dS5kij8nFiin7frszacqIYnbd5c8/R7/Fnt89D3NfM48LAgdF0ar57xtWthnJMil0Pj32jZaqlxUSaQgBsl3QygGtCDUJumTchMNuiERT8+ApBorlcAdC4ourq6uIY89tuuy3cjwDA+ACG8Wg1hSzjV5YopRGuYXx3pcJ0yNBS2lLiGlG01fGwyB5FmHjSlnLVwLOmw6aSIH0UxonobXjV6jRd49VXCObR5tln7FWn06HapxqpjgOmidURildDVqOaehwM4KGBXHcZNEuvk6IFbHxAvhFAvrviZCkMZZng4MotEeUAc4J89Y3V+FdeeQUMJJKlABpBtusexWr8TBMzkwiABmYbiSjCajCqpjptOJHxAADTjzXpdDgy7F1HsdFGPFby+S9qHtfU+KZcyBB/w6SLyyp/gsokitTUFFNe0sWjklz+i0PLJ+y2btmcGRW3rKUt71ivB6PMazoLFi/jCwgCJhgoKOd1yo0QajwnRHEQvDbSy5TBgvYmcW2MPDMkU4Xc6ICV94m1rCESFCAxiMEjS5GmCoNTMJMCPHQKiFRdLlsNqZhN46U50kwTylw0ZFOMX8EGBvVYS9J0EMoIISKopSrpbFKkXcaLJEH2KYNZUk0wwHSP0EQB2grvJIdbg0QugiVUXtyieqQ2kuxgMAM5sbQfGcsJiNkHbXJMEQGRvpcs6XJihsixwd7R3m30kkxF2Jn12wbptFnzlvJBQanXBFMpF6FOlDGlxdyz2PVtnlvZiarEKSTrJ+U98ABqvY6V7t5W8vkIth/xrOzANnlWI5HGtvkUNU2WA8joCxPkRxIFr2mg4TBxk8CPAJQrVVi5NWNb2oflwq3CyhUhzcmYHP68qFnIEY5qJnmHU+A5mrZTfvaCr/Q4CgshSVl0OnYjV+IIQExKRSgdy3cuOxdF58/jDOgAgcf+OyOQa7oU7YknxWLMueeeWyP09oK2w7DIdDvAsBhd+J73vIdu++pXvzrBPPHUO6M8JozQM6VqBonIxBps41OqAILS2B/93q/amQfcYBQxVLTqrcTpbuvnrOSp8uWVPQxaFyOVoY/O3sPSBwk8/6I99EP0WHFjXfAPc6MD9SVMcD8Xe2xW8Z1bvff2RErrC/3/4s75miqYnalE+PqlTZvz3/5u5K7baTCS3LdfLN1//9hVf2q/+9LkAvRBbY+AgX2GfM2MpQAsIeKkoD6YrZiws6RmFFBgpYL6d9qD13nLRnIX9Di3zcZPK2CenXBMsf/Z1IkPiRbOxwuPx1JoM3QqFszwk+gfp+j6T3KGcR59wWikRnqqmPPjzyetUwcdaywZ7bnwwotYmSSdoQsxbFkhzgIve+kRPcRZI6IgriAu7r333otXzBlvrFsyN8daMZsnKYLIw5kHBgyMhGLveVbucbs9a30iVfyG77IhWb7FZOXrrfh/s2xWZwO7L3XFiWddkhsd2r65a9GyI2x2+6Mus70R22VNODM02No+DyUcicYS9aKsxR4xyltaaNQzEaPERZcb3SnpECAKnh+TbCClpNH6RCCDeQBrUw7d7c2dK+cwi8OTzxJJ1rMsHMxOyqG4TTE258juZPBwu+8Bq5cAnt6+PpyFpibe1BLND4e1Cm4bGxv4qs5g/wDe8lh6jCytlyxmFpieIOD+hYloFxaaNCg2soiE6ZSqTucWYK6EEDlF4IAW0aywCFm4gmSRzpVQXYpciNF0BajJBY/wtsqxrMZM8f0V2HSzv1AdJngYcvSFXg+TJs/8ZqqzHV5rJA9CWGUIkpyIiuuwUQBrOldCtS5mSw0lYtEI6UyRckqicSDE6wKnAY/F4nEyjaQz8k6wxSv/oiBPbOv20xnEaCQaSSaSLMeBa1FEduhAA1ME8QQmboJS4gOTZEYXk5CZdO6h7es4HeOoI9UtlwOGS7jKbBHC82NKXhxTq2iuJeOxExenHYdOYHC/gRePHfh0OutGZG0TSEnhmxi8lMpQBidfvRN4Qal4AJA/HElBTqPLDhnjXgg0vMONx9syBHMvD4XACmb5EfkrSaSTIkYOwODkVyhEegt5YJNWkC1tN7lcfE7BKAOD3wCQCZCBMZVKUZqGQpO6ZJoviEYT+aLoOiVSIqggKSQx41DKj96J1kMI52Mll8/eWvmxIduzvQQnCTmDefnmHxWbDgJM2sx/UZUETD3TaEnBEjIatKxMDSjp0myjdaViubP6+ob93JhQ65fskWFKedFEW1S+bUWi+S6nRktoWbEFjUFJntYlxpIqVKnUWCpCk4FDMcNB2Y9n2kaXyVqNtFRbu/71jcwZrThuTsz4v9KEwy+oeoXZwu+ZGrAEdFcRI0YnrImEbyzvRHk0ak/4jWXaiyd16aWX0vUztbkzmq6D4ADDjz/7sz/7xje+ceedd7JzfTrZwyarCb5a5gcstwXFoe/Z3Z917FGdvGTK0LWHrbFb/Y0PWHP+wW28WiYERYjtwQOGR4RrxxwPX9OZzvbuRV2ioRCoI7/1glGO2/V/ODs2kELUF1lNDRw5JOrm2f7JI+6i0WDkJnv2V4zArvbq0cR8rzY9duft3s03uvEkylOkPFwrjqW/94PoUUf6ba12TM61C4OoDvPFXXxF1qlYb8GxZA0H42M8n/FPdBO7Ohu830hZfMto86cKIzd6fnf8ip78kpz1+8ZSqS5a/0un4Ze2yyHM9tg99bFtKau8PCqK0Sg48XhxQsrEQIosUJUCo2JJLBXyLEqzpfnll1+eN28e+XzKiNeAqR0fjIUO3GDcdZx23Bjm9vSwaNwwCAMADIg29euIa3OGhodiifcF7g/4DFLsinxxcSTzG8/d7pcWu7HLA2/pGBwbLc7POO84ut1Jj7ipMacjNTY0uBFFfOQcOTKhWOjz01uOnLskwcKUkO4bJS2qXP9IQqcK+0R5y5Q2MT+fe+21tZ1LOuvgraTwfQ++5isqnyDneZb/xORZGNRHIk7bbGihyf3rNq/FU5tLb1LZxs3ZsWyupenZAc5VEtOE9tJretg1APCTOClwSTsX5oQpABBI0SLoA02B78CAStf0lGlgIIV0IoABQ0EiVIHy4FbLHoir8E7tqiljD01zjYChuigWOSIotNFrcrmtXv4CrNpGVzykAAMehdR4DR4gSSHARiBpAjvzmZrhmWJA8qQAELYLMHASiPC4aVyvEIDpDzCch+cU0aD4WREFOb1ArhbkqhGeBbIogtwDmEBBvTK9paV4iEyOXLRqHnw9DkSfaMWmWZSiOuK8TUCPE9EAWppD00DCY8gTyn4QeKKYESC66M3GH0aa0qBZtIttcsR5WuGSJuqVtrODgzi7UZ5++mnAFD8pVEoWhCGCdIaOlLAs4p2qabXu6Q3TiTALRr9DAGJEydCCxGkadNIoEOoIJ5FbrjSZQBxe8QhoOrcE6Nd+hMMQE9YFWmiAYCI8MgQiwNNNXAEDIXGwsS2lOl2zACYXhERI4UqlIIHt8IFbClKKNgJGFilwQ4EVP6VoPgTzogdZQIYWHllahIjGmRMkF85w1RAiURiuVARbNFcjIAlziVCEdA1IEognhAAAg0EJ1muYVYloSytj1KRqi0iCByRUVVipmuRKtIJn/O/kQLCI/mIBHVAGHrdg4bGDJ8mkCApTP/LZiZsU0nlEhSama6OxZCqZTKVYrTcJ4vynkomBwYG2ttnoLMAkpQ6gZFS+ZideHHhSqTqw4wdSiq095CZSdZBBM/nDIY9EoraRGzzz4jyLI+3zGQKYWRCXOyiKT46OhPk26aQw48j4EkhfHHU8+CIOvKTI8GPnEjD43FIWLHLrCjCIhXRxQg2bjfoxzjYpZPGnPh4VQRvXkQaOFmO2QkoKgEEBDIHqZJnaECbA0hzxpsXnT9RbTQ3QPLAp7XnxeNs8OU1SPHNZv7fwRk11Qgn/BInhqOG+qVmWmsFmSJWKIVceDlGyxnLgeTF2odO8DL4W8bM3v9yy4IhILM7jMSjPE1gRXKpqpSM0SZIFlWmq/lQqEYzyJwFoiGILtxQ0DrqJm0Vg44IXioXRQrElmehoYr1QCx2OV3YFslbBCjBvr1Se4hnHBxQxAbI4NhWNzOrIXpN4//33c5Tg+9///r3GcJgXPAgOMOPyuOOOY2XvBz/4wTve8Q4V+tPTDZgahAnqKmXudbo/a1tpvqqTfyxlPVcvomllJnrisOuN+Ns+60c6nNR5zOdOgGR8FoYdUz5YP9hnM/ax3EE2Ki//Au4T36e1XouypxJZjBpD8BfyfCjWt55OeIvSVhFjtN91d3p60QmIXzmN7J4HURiiZMrCnVSvLiimH3wouuokFMOO6io2E5xBwWOZsXcOg5Vv6ooFMC5gkMFJko1CstjTTkHsrdisI9y5/5u92a7TFzl1wDplKCKa6lkzZ2oXn2qJ/mIOi6WQY7StbW+J4xgGXj5ybCk+lCgVS+mxTEze9bU9PKsVm2lLyU7kS7MhBksaWxlbAeOe6vQwZ+zCP/7xj6RjeGHzsd4LwXQ03c0XgyESeKwujE4kMjMvQNI6Up577vllRxzROud/+Fs/wpePIicX3FPk8E7fKbD3GA1cshrzDV9bkehIxkvFpsho0zzPLW0ryPkfPkws8CpyTyHoXhzrS0XSGME4mzSLPkqPpkdHRlh05JaKIGZ0NC2z/VaAbYixv2nTZuxjnJB0ehRIVi3hI10LV0dGhtUXhQRuITU0JdUABSFo93uAn9qVIWbIg5MEIpqIlMDih5/wFsKw0YhjMnLLmCEAAB6AuWoiV8C0OInESSHgPKjMoVKCppMIQq0OJmA0k640ECGLKkACP8kNyQOAdPWymJTReRmQayC3s7MTAnCx2POp1ekVVLoXAIRsJajOIo54hB6Yz9b68Vn6Yuq9995LFshDABAyQUMKSpHuI129FEYdA2PNmjV4aBzPq5uvtCBXppz0lZAnn3ySR4mUMDDUeV2FWxxLZnyIQLbm0uq3v/3t3KJ9mdrTxLBGtj/QNbidDz74oGbpFXjeikcCQ54+tmFBsrADeEYglcefa3VBppAIpHDmHA8jkZCS5cuXq7/HS/i4XnQWQcuCUOUtVhF0kqgcI0LToAQkONs1rWaEwH+yGGY8uXQQQRGSyOnuNA0eaikQkqhoqQtrBhcRC0zhwyv9wn4QGvXQQw9xDckAgN7EByaFvqbGsAgRmoZYIwvfWL104lRHFkW013DRySKdRM2Cft01AxsxsEjXXCJknXHGGYAhmh577DFSCEo/VyajGdg0jfGjqBSA9vIpafqURMSaJmpBrhzkgXtPFbfffjvjjRT4QBtBxWH40E8Kw4BeU5xcCTwXgPFQcN4hD4smKk7GAN+ZI75u3To9PUHTgWHkMMi5JT2cgNCydDpEEkcYMt2jiXqFGJ3RYNQxQsLqyIVgpAoA9FqIUAHIouGapfM4io2W4mwjD9XlBoB0SCKdB5YAKmqhW3mESUdmkkIgC2BSCEQUknQqgjACQgYkREhEOPMHJMW1omrZqzWSC7ygM4HE6ttK8h7/qnDjqiVpBaMa2rRSZBq3uNB6y4iqqxcPW2Ze5R1U+eI3LjY3cCVVV1df3xiLRGKB70Uj9Q2NTLxiH6CVE8lUQ30Dk3l4p4HNWVFRL8JGcZxnB2VVFM9ZHFpxksXfJkXe/5FIEDG5TH1quqPp3OJs45lTsOyZo75Fl8rytdm+DK/srTmcZ7dxDqaBOtigF1dZnknjpcuOcXlCSZHld2K+LMUTAbEssPNPF9JxnSUXx1Qcb8pQ0qQENsRBTK5k54s6zaDdJC03vrN8ysEkyUX60Exbi6Eis9VmPBm3lphQLylylXlqHHd8YeGv/JNpS0myhofzo8XEgkTd4lnGma8Iij3u/kO5AF2IWEZcMyxnbDukw0wIn9YwZU9pBsP3v/995N5+eZl0T2t/Y8CXxdw0N4YuZ9LiC1/4AgtoEyzJ7neqJhtqxVLPNZEg7Reiueta44+0s40I6VK6z8+e1Bf/eLcdSRd6/y6aOmtPHWDUMwYcihkTOVxP2O+t2y8IEfUIWzSvagiuJY6wQkzznw9xl/J2gbOBcYRkolRE9s4Bdc7SYJwlI956BQunTeASy6ylzFt6rhP095SyYww70T9lUSCWmS5owB80LizCgMAQ2Rl3+Y4sNYMw47Ak1LiBw9Dj1F1sLbqp1P0Ve+w+Tlo2m4Tcot1euDnl/Srh5rCMRTNAEsvZxeLKCAdHB/2Rd/dnX4qX1uP2cjAyy9xusHo0fmoauKGxZeu2F7o2P8xApXpoYwhRFy/3cotjgAGNuYnxirWKdYvtAgBMAB4rjVyMPCxRnISlS5cSRzrDDaYpZ7U0s8ocy59y2TmPdrSn4Sk7nMS/tb21G1J/f13LttHvDQ99A/cVmlm/ETN8ZIQIOhdfnZUV5tN3yZ/JEpXppj+FFUYj0xtGZdJAfBsaCBIagp+AD6DqhFty6RQAsHIAwEDEJCVgFWmlYg/V1WEtAQYzaW9nZyf9FeLEjAM5t0BSI90HEmZDQcItDSRFKaE6jDCKE8Eo1L4mC952dXVR/JxzzgEV7ihOEekELcsVL0XtWl7J5pYQApClXuJdd91FZ0E2WXrF7KYTiWOv033US0FgCAxIdYroR2Y6yAoL4v3y3jIpGNAQRnoY4IPSjwHNUAnTidBqHDC4BP3j/aWVK1cCQ+1hXeGzENrBFASGemsCzaE7KKvpwFCETtHHBLaQrgX1qimUoo3wNswlQilgCGRpQ4grWh0h3FKcXS1aSnMhVW9pvjqZpGsWVx0qFMc7UrCwbFgdrDYldpTC39MUOKMcCEvpUCSXUnQE+MMshpmWgnictDCdxHDEMhjAUJ0Vshow2ALOag1Fx5EOqXxWSpGHV31MwHziiSeGiRqBS0SohYmzcLxpVnjAGzJNm6YA5GopItCg69XENTfkFfSHcc1VMojz8DKkQ2ykKPFEKKJ7kaqJ0YbzjMMuYMhCgkESCHkSiTOo6Di4QRZBYUJO0hDGz/gsWg1O8GhWWJDi6ER8RQY5cYICQBtFiDMdoFmaDgDDQLN4oKqfGq2CxgKDDKl51shF9tIEfM5169aBTSqrBCaG4DNPU1hKqyCfGVgopNW33nor0kxLaF0MNuXSfffdp9MWOuzJJZ1SDH5mH5hNIEWzuNIjWKvgYToG1UAKAQDizNGcddZZyB8kD0HTydJK6X3IQBDpPAgNIagEQLoCw/wC82g6t4XDTGO5AhM+OBRHikIqpZQDtIhbEnXUKQB4KKUAQCoAVwJgEEC1JrfMQyisuHKoX/7VcFfJn+RKY5lr9GSNmiEqE508krrQyWBobGhgTZqKWLVubGrk1XCYAnsRSiKFzLQITCCd4qm6euKsqFuuAxK2u6BWsQviiRRTqrjTueULxOV2SrzyVCyxNM1VvGuyJEU8bfF0CyX88IC3ksSpZrsDi9g45LJqLV4xRiGjVfqm7FqjtWVFVhaiecx9XnuJjyTnJhujfOBBFDY8wo4SDDjwBLMjHfceV513sDQXm0Q8alFVuNz8FxtLaqAesZW4cq9clhyTJBfQWcHgEMR685ojKbMdIBw5Uu6wCTz7PCA8fQicQ6LRPJ6huN4LghGDN9xww1//9V8fnt29FxwbX+TgOMDQccUVV+AA//jHP/7Upz6FBBxP2fSn+LkX3ezTiJPCfU3xR+bInKbuIbXs+B/a8ivy0fO63bEnS7kXvNgqZPXUKWSAstKCA8wEFYJ76gWnH5I5R5GqkRWyvSZZDJZk4i/z5TGbPTYogng8WXQL9nFmJdDjwz8YpqIIwoDe7evp5QPkWCt21+ssMJKNPo5GzEsuthWd24EHzZZZzFOR5iZgGRCIcnAUEXUAQpw1Ecw4NW112CBBKs+/aAUnuspZ+Eu/sMHPPYvNYHsL3NxCv+0ey/8ujVBUaMB8fXPk8k+XGo5xh6+1Z6fdL20d+0U8eM4tNAWlk8fibx+wIoVcKf6dG9qffvUHOFB45qK5UDJsODNnwGAfYLiwzIUlgXFAIvJI7SSMCQUOF8dqmqC3Rpm5f3etffG5ibet5iBzr6+vcO+j/m33jozl+RxkF7oNUY4RoFYmql20OzaC8UJJx1ZQoxaGkKXClFzR/fX1mkVZ4thGatmAEGBagc+JzU2cxT3K4oVCM5wk4NSRC5HYi3Qo6bRIAwYTFjAGJead8qSS46N41LxjLRG7Vi0tzGIIoE9ZegIPdTEdAE7KKhOoCEOTOEYhZzVRu6YDTENYQSKFSnUtS7O40hZYDWZqpy5StCBXAt3BlUQlnjioCNp33IKcJtN24gSyuMJPxU9D4JjJKWeFz6z6GwqvAKECw0gNS2kW1YUIzzzzTE0M0YKEXFTgBRdcEGaRQlwLYuTpGyKkKB7N1TilwsSaiPIzTNR9B3qL2xmm10QWm1CTqLeyYcts2RqfCxsJ49NJgb06oTA+lwYyhMank0Jjddlwl7mhHzg+l2FJqElX1tF9YQ/WANDpYb/XZGlZWsd4CLM0kYG3O0roVp1hCYtURybI2h1Ciu+O+WTtstVaI0OLUF17GDedtuteY9Tp7B7API88aNpkBAhPDV2jk48hKo0AM8E3HcZPFoSlWFiuRkUV4S1jVRVBmBhGmBFAn3IbBiWSssx08KyF6RpRSYioYW4CiQEYV83S5xcA6goTNUtlIMDUpb59CACXqBEwlcAKr7mMbX0ikEIqA8NcrYuCmOlILcUAfiIgVCJ5WpF1pJCugcFGe0lBZtIjlWT5JRG2UxCE7AVA3Gku1JJ49tlnn3baaZBx9913q8jiqgHeqsJ99NFHIa+SLL8MUd3Sgk+OjA2zwAyRiDjgoQSZjLeMSwwNwFAjjQ3daYYN2pAUSpGFZ0KAP9ySRXGu4CEFsDALhGRRKc2hLXjdYFBWAKxN40pK9W2YvrsI8LjKyl6EEiqJW0Q+7nFjgxxLCYd5UliFJpEUkqmBzmpuATLONwibm5vYas5O5GRdPZPzRYuXrKPJuoRle3kfV7kki9IlOyvOMx61LZ5zqaWAU81ctR/kgCmxe9xkGQc7j7dcMo60OMXS17I9WpxdceNhE80Vj1gdZopwA6R5l1te/8YJN+vPukadyZbGrALb3zsa+QpGaFXtjh9v2HRmyRkYbPyhhTpsZnhTJxDsk1JOS3n7lweE90knBX4TYHccODgOMKMTRfUnf/InHM3yyU9+cnfETXO6n3/JtbJUaj9eJ+f76XwbYo/z8xFQT9RZ5/W4Vr6YfdWKrd5T2mgvvgdqDwNCZfGeYpgueBEdTv1Fpd7/wUePIlcOFP4x4aVj7M6ROU6mJs/tjy6FS46VuiCweNEIzbRDW9PG1tltvAlXOPfs0pNPxFmQkUNABKBk+7l4wjvjbWk3unHd60cfvSJskUqraq1WI7/IqsnlyUdNoixRohhnXNGXRJjVTY9mBiTCfuDR/oEH1qw56/QTVqc+9Yn+7//76IsvomnqTlrdfPWHehuav/7ft56zsunSt/e7LdmGj2VcpnaNGqKhBT/2+a9Gv/XvN9O68nSrIRfCQko0jsWj+hVVis3EFWJIAQxdi0aHJ9jfqFjMBYCBIYUrrx/iFmJdYaOQ7qWSTrT3g/+l4/Nfmd3b13/ccceChzqphQGjVjjmApsGwYyNRa6aZboZkoowj0ghPQzgR5FjWLCIQRYVsZKjxhYLJiAhEY8UmkmkLrXn2LCtxt9rr72GTaM0mNZbLBxhjRHHUMP5pyDkEYhgshCBNiZ6CKRgLdEpRKCfXCLUyFIkEYKWwvhQzPCKdZUwXXO5EuCebt2szsWUoSDXiy66CBiyuCUSXklhm64iH38dv0wXwrA5M4yDMOxuEsUR3I0rCOsI1QXDON1NCG+rI3BGG1KdGMb3QlAoB0IMOgh5Lh555BE2Su0OYU2psPikkQkK7vesiYnZ79XxCOv0DfXWIK+5rSZsOrOq6x0f30dKGDmoKuQDQoDxg5AJB+oEmIHHp0LW1ewLGE+eptSgCm+JhPGasqSLtKzMLtXkqjNTk6i3dOjuJiB4Lqqf+uriVMe2keqU6jiisvq2Oq6vyWhKtQwhBf+TzRFIaXw/RCURxCNSmtOz8QyZgGBShkRKcQ35gPBEQmpimMsiOXJ48eLFzDKQSGCSEWMDWaTiFHg6TrO40kEahwww68I+MGEtFGRWhZSuri7I03RugadPwUwcZ5vJX03UBvJ6hc47sB+HxXnEHXISriKEkd46A4LxQxYpGsilsxhjVIGaYAdNmE4EtDqfBcEMPwBgDh4OaouCcAkAtD9aD75BibrNqq3UNlAfm1zsBBQfrQaYFpHOFU1KhKppGUpDr+CsbpQ2bZdXKGQEonYiEa9lVgvtZQW7pWUW78o4LnPZzfQgOqm5qQklCHATKfUNLGTP5jgD9k/ZDs9IPMm+JzvV0Mgm8ay40KW8OMkWpy9yJV4q4kuzhSfIci25OZaM2TaGwy3utIOnLavHlrW9bzAe6+uYmzx5Ad5vjUW2S/LfgIn0L8ODjqier5zJ7QyfuL0jkoGK94Txg0zbR1R7R8Abo9TBcYCVdx/5yEfOPffchx9+GOt2RnCToxxw19A4TKTK2m+ZKISkxHKyNGqkC9mVvCnTjRBESbBPiQ1OaJEpl5tuQH2WnNgJxYb3BMM/sJcNOl8q5W9p8bu8Yl3OObMYOY3NwKWi0+62ftpwYSdWoJxSSXa3WvFz1mReftW68y5ealUPMh9N2H9+tXfU0Uj85bNa0NxmS1WWCKIfLYV+wq1CM0EDmgxGhelEuCURPYe+R8Px/JM4KXdA9f0ffv+vPvdXl110Ucvf/908ZmgdOxeNdFvBj39xw2OPPfPMs0uKzvJ3n90Vc3rYjkTPBlak5B17+x9PnbtywRe/NEZ1rFaxPom+XLx4se6hVeMYqwW1jWvBwj6z5mh3WgH9MAGbAxMBcYzWRwWSgjrHF0Va0QQaDgBNGB4eeuklcRFjseicOe2jI7zAtpkThZl9f/FFcXQpy2jBO0Ws0xZYBFparVciJGLogB9uYChwC7YwgAEYVDX1AoMdQAqtQD2TCG3cMiwppQWxAslCNytmvEQAQmxEMFkUmGVGIoAppEb0irmDXUhd9CxsgVdUzQvSTM2yzLW7lS5aSqjGE8bBoOsVmgJJBPArMVxDyOpISF51osYnyJoYeIKCE2SNxzltKVAFrxiTDBieGp3mmLbaD+mKeO4Ih3QT9oV4Rg4BsxKpgshSITApQiDxqXjeJ4WcfgCas7tK93tWTUU1+OGPsgidgtxGFCPEYDW3FMRjJNRg4JZJSUR3TTrc5gFHaKsrThzMLNSrwKRedBCbq2tKhbfsMaFeDZQlEopTFufpzUqm/FKLFmTSlrkhhYd+skKCkTCoj+pSYV1oPcYSWaRQlisKkYV3iESvMbFLRNPJYrxh3MMZivB6v2ahxfBdyV2zZg3EINA4CoGHVAenYaqLRw0x4EHjAKmJXIFBGekcJTodssFJOkYFOMmlCOYHt0QQmJBEhCoggCtkk4sfDk5yUfTk4l2TCKrhoWEi1duUQA4kMFwnDtAAMGw3ujKKC93a2oYXzWtgNMRx7VktbckU7yUB0BaNRBvqk80tbL5zZ3Ebj0WiiboTmkbSnG0Rb02wQKEzJrsd6hMTc+jm0k30HQ8IXanMP3TbMhXK2V1I+MpXvjIV4DdhdseBg+YAM0aZMmQ+77rrrjv99NMP+pAVQRXp5KAmZtaC5WPBy41MrpljdGWOTVYGl3GiJntRYnZkIfeGoXsmZXg4cWMQc7vrjJmTzrHN7pz/v1hYGxl72JqXdj46Sot1ntSy7KLT4sz9ZuAtYw9Ubx97gIuY13iD6AB0CeeAbN/ee/7b13T+6XvdY4/J3HlPtmer17EoeeHbh+YvuOPGGz79mf8XH08V4cRNDkcFigS5htLFR4KHaHccSPwimInmQI+Swi3KnhS0pqYQAYwsvMK58+ZyVgeLoHhl/QODJH78Ex//yEc/0r2tu7GlsS+S3tT1m/6tTzS3tI0UFzXOettgYdOKlQ0LOxYhWKkdFxdrQK+0Efy6JMJ6Jq/+ohehCgMF1QgwO4F5/RUNCs2U0hlocvGBySKdFG65wgT0H01obm6BVOgHnmaSDjDFAcOeoGnKKJQ95xKRTllaRyuIE8gF7JJLLqnhp864Y6+wF44sFDnLCIxD0FIFPiSJxEMHm4pCnivOGoThLWSH8ZqIug1oI3WAwQ8AMwVcq5HXlJrKLbwlMNLY1McKA2ss+4hwKpW+AWB0tgUD9E0HeOq9eTgPLZ4yZRRSAvGCROKqiROzBRcOMMbb1Pl8OEMijeEnagU9AtOq3wsYz+fqFJwutC2sU4eN2WHiaCX1P1EoSH5UIbK3utR4VovyMOqjJotS6kbWpOstc6a7TCeRJlS3ohqM2VUCw4NBpSEcZig7jED1pfUKgBJGo1h5ZlzBJZxeqAIg1FO0FEgUmSIkizjEc8skMtdqAlDZ6gCzgo3KDjkDGbysoQcW6DGBgDHm0XEE1Jm+ysECI2oXqtC8mkWX6YYgegEKSaRG9LtOHNBBiFzooXeIQBuON2DkkqIRUlBn6GUiQGIa8NoRHKomO+RSdWIYpxWYN/znVBFegWYmHWbCH+wBjCW4xy0Ec4VaGEg6TKNRwDBCVCNQnFoE0+4nicIaZ2aEdjGEaNrMJG//UsUww2/q7Ow8//zzD90u27882TtsB80Bhlx67uMf//jnP//5a6+9dnfrQnvXqr0ohS/rxY8veB1uaZ13YX/uUfPJnMocXnrecPyCAc778735kcQJBv+eeb8UQRtNqpD2gvK9KxJKVSI8Tghf5pIRxFyRyLguzG4ODZwdT6+78p0jDYlROZmYld8g9sCTiS9/I3jqlatHhpmLLdtJ42n45je/9bm//MsLzjkjuXx5NHDG7GBboXDH7+78wQ9/uGzZESqn0JQwBK9PZTTWAAFhjVxGWKs3i1JBRiPK0R8EKgKGK2oGLaKJEE8EzaR71fDGdTMwmgbFQ1sYaYsXd3LlhaPf/e4ONNNiM5VOCs0/8qgj6+vrnnxm83OvR4/wlnG6yFhpuFgs5LLlXbtUh3glQC0+J+pfOjKVwqmDNpZJ2RWMysTppWoYCFpahK8LhfAWrcOV2WuUN6hoOwENRNNOPfVUNB9t4ZV41mOhlg3J4KSNaH04ACRFwkAKgVtKcQWSujQ3jITAROAAtRDRXMpCFSoWl1g5rMDAkEW/0wTti11iq8asyGkXFFYDa1xHF3GaRiBCChHooSCTBUpVDcKp3GLW0N30NZ0Izuqqp1L88IShr2k4Jtfh2fy9azWTWTzLPKc82ofhMGPHqa62YSjzxLGexkOnb1tMwE8kGLzS8TYB2JtZygEkGD5VNcemONLoEVxckKikRSpSENGNVsIsJhE9gt7hdl9YvUtidpmotUyQFZIBDAMpvNUI2qdG04UAjCUWddFZOMD4zyxuh1mU4ijE8LY6QhXnnXceVgEaSo0Erur9AgYelF2YTkRVM3zDY6RGtSgUBjZSBLJR7tCgtXALMIYKDjBx0jlETftCgUGi5+SzxZ1N6eozh1c2bVGKhwUTgsQwnQjVqXZD/kAYih4w9ZC5JRHFB3mkI89xvNlUSBtRrBhsgG3evIknF0qUGL0qzbu7MkiwamAmso5eoEUwgRTSdSKAtjC0ACBLAZAJEKlM4ErYHfJpS4ceaJ626g5uRazK8ALwF7/4xb22ow4u/TOn9oPsAH/gAx+45ppr2MtOXzKCDzJf7JQz6zPB9s86TdnIFzblbphlPZ9gT3SwMh1/d5/VwpcYPXvWpyybjZp7/8AjvxBV+qb+fmxvKOlU4iMKcWYQnUhtpKReEZoaR3Ri22kukLiIyNYaYsxZCngY1hf/MXrKW+o62v30mPPMS8WujWNtc9rnzolyRAkikoDcQSCq9GTeFKGp844kzulcimpBVEIeUnvZyqM/+rGPEeHYZOpl+gp5ivJQ14h0ApTgauqrQbxQZCZEd7jZYNPzhKBZ34YNyQYV+6kYRdSF6lKnDheRCHoFMNIhDGcV8QGkppOFxiO3tbXl0ssuRdZv3bpl9Wr5jgtiHTwEcvFOcdsoBRJwai3qALPBDCOGAPEUodUAUxGuJnu3uAUn8BTXnWm0jkS6iTOK2QFBQzAuKcvAAAZIOovhQcNh6S4nhqAhbLXGazQQNNO5wMBMANSQhQwaS71Mfld3Nz2IbgMYJUqL6EFlV1jFLiPgoZnQvEubGHqojvkIOhfOgAEyUN4E4rQL9u4S7RQTGQZwiWbC5ykWOZzBlEs6JA5nPuxR2xmrSBikis647VHZNwAwrabtPLZIS0Q6IgvRNGm7GGMU4dmfFPIAASCXVIghpQ9QFfsRLWIf4YwfhVKAb1PHTHfosiQSmA7SRVftL5DABNUI4CROx9UoiKlXNG2Qu6OQdJqAuaI6tBqsOl5DJ1nVA0B5q/DEq73o6oIA4JpWp1THeXMbnqM61UohEipKuo8Ja1IImksHaVluVR2TFZKh58yhiLFwwlZoLkYRjw8S+8EHHwxNFBQ0NEMbTyJgyCW4Qe0E4lwxivSh4wGkFLILS09NO9S6etGMNIYKtzCTK7PzGKIEIlCINmcaJSRSialufnWc2tUlRpXDZ67UrledhdG1Zc3FbYZ+iKSl2li9ViPcxzjEQ4bOYux35PtI234vzuPwr//6r3TQ1Vdfvd+RH24ID7IDjJa96qqrcIA/97nPHXRblrOf3KaP5LNPOoM/tmdlYh/KytdyeCeUg+1Z6uQzdPVXR5o+vI9DhDNCkD6IBtTVpKhUDHGlCEIKyYXAwrNFwCHLkFwINXVuSUTkccW5Rd7xkOwOuQoIRBgyWsUWEgp6dIaPq8a58s1UtuYic+vq2YjbGotFON1BjmFgeZR9z5UrDjB4IBJZDJGaRS6UsB06GpWjWflchDpC1VSxpwh3FJqZJSUdwggQpqKcFHwzJCmiM9QEYRa+InOlZIW5tBpPEiVBc8afkQtVnE6sOEELAyEYYR16mMceKx9fhUhetgEnlABMCgFIBiqr0NRIHAOLusiljTBf47i7bGMjl6n3sDlAolFYMQaSFWCqA4aqF5u3cOkjxgCCW9GCkHp5txbyyFLPH8lOHH9Y6cHcgWlAgpYr6pMrU/70EZEwUIWuD2gKt0RAQivoIOJwg74BJ61Ds0J2WFbjetVKw6zqCC1izhvKqxOr4zrAQM5qM+msydNlOkdLM6shJ41DDGRrl6HUdWzTU8wdKPJJMRzmAPrUIB8OXT4w4BEUzM7QlgmG5f5tYPVzsX8xz3xsyH/4jKBAyyCE1biclGzsb0odRFWOcGCcIFQJk1J70AGY+WWMwVsG9h4Rg3RFH1EEewCGI4f1oWA9MJxwRAUQSMfd2scJxz2ibb8D0wS2WdEutNXeIVfmaNnq+HhsE+SKKWAIGC8W0OyEGmyKCg0VKimEmAayQII65hBvUlCmBI2EnjMIwyy0Hh3N2KYgkPQyxUNSScEwYMCTyEe8ASaLHiegavHM9SVwLBOySCFQC1fMBrUctAiDCjyoWmxLDEsCzz5ZOteM8UkWVywHNC9POleyEBGqkcezJWQImJEhiAVsAOjEQtMr45Z0Bj+sIBHLBwCMIsjT1oVtDFGNj0AAXw+hCl1yHw/wBkuhu9n/fPnll8O6qfDnDdb8/ducPbNE92/dio3PIF1//fU//elPWQ0+6N2J7o7M/VYpeqTV9z8du4cvIuGX8W2dkjvHmvWpSPMnmVg0x9TvDSdoHTIC5wdHkZUxIgg4ZApCBOMetY06ROIgVvQWAUQWwoh0FUy7rFWZxhXpjIhBjiBwkZ7qPaIm8ToQc0gZrkxVagqQRKAH8UHgoYISjB7kCCLvtddeIyXM6u3p61yEl9VI4m233Voj6VBOOFck4q2pAQQSFb6hZIQqajeyVy7kcmIE6hkHmGVVXr/RRNIpGzYTFhHCW41oexGdhOqUcEJBAapLQRtokRckIr7RJfAEJnO0FZRTKR2BYwkYDeSq25kQynitigcnVg0ObnXigAgzuJCtSpFKiSswcTqOXFpNdfCTrqR2aKB/0UOgpTtIR3Cj1+ncm266iXphCJVSEIZAElnggYdoBdWLkBo6M9Si8WqOKQGQildMnOKA0a0gp4F0EBHSu8wXa+EYi7SksEeL3iedOBuoiIAZxUlkfGBU6BQybESNgRMY+mK8iRzyRCuF+LCPxqPdZQoFYQ6soCydpXiAhEU8Jkw0hLbFLou/magcoJuY6FFb59DlCRKSYUk4dJtwqFDOkxuSSlxv9Rqm7zKC5EFYIQ2mArxLDHuXGEoGLc5tmDLNlOwR/Sh34FHKXPeOTgSjKiCtV5UscaZl6QuyQLt3mBXhTLhCP7pV1fcMoaeGjAk4XJ1FKwhalnS0IQ9LDSpuyUJi83rU+CxSwMAZYGqecQwS6hhrAbNBgTEtsCvUogOGwIMAQq5oamyeECeJWF9qAGCHoEwxJBhOhkYP00XPDWESn1GqDjNXAoNK52uwTECLbYPVh5oGOZBcsVvUZCWOwQMAyhprAWuEJQrI04IhJTUR2KIuMc8FpouKFKxZSMJsw8yAaYxzTFwogWDaBU4209EiQg22N9gtDL/xxhsxFPGb3mBNOyjNOcgOMOOVXSUc9/f1r3+d71kxmg8KF7RSVh+JOH7SmfXZoOnqYvo+K/+KzVfZossjqTMsZzaSCR+BybWJiWSM8oQjFPTJx25TTwa3AbmAH8IEHk8s0gGhwHWX2PRJRhYQePKxX7liWHBF2CEg0AeIBm6RlUgEZAGigVKIPBwGNRPxE1CESEPcG64EsthdjKQj/bbbbuOW2iFYaWD7DdggXqcYSQxlIm/TgJxb9RiRkohCvao0JJdDCOhB7UTqgiTgQUIW4kmrCK+wApcGMmgIAGF6dWR36cCEWbRXzQiYCeWIWnJpfrURBjBU6VInvUDXsBKLyKYXwupUKCsrquMKwFtDISQEI3yBZAEWicwtVWurSYQb6vRCAOxFgnPF26ej9YUlOIP8gnukoF2ghOroUwijCkrBGSYgSAEPcXLBryMBuU8ADDcV5i8eNzugRAKsRhWcgTDtILKoGnYxIKEKGO0d0mEX2KgIAOKwi75TVOOvqDH0JS1lRRH8DHLwMKjGO8DVZSGbGqtTphiHS3QTe/x0CKGn4SfOOd0HfyAjHAlTRHi4gcEfOpdvIB2iDaeLoVyv2gSNv9nv09ChjByeuwmkQQ0NstpldnzUpE/DLVtmkEVaEZqFKWbkErOrM3acMIxxDyAYWb3XRKJ9CCF7iYMKSY4eBC3qKcw6RCNwCYWFqYMLtNdcmpltn6A5E2fBDdiC8kUz4vHqBnhtox6Dssv2cnwm+l0DZgABY0MhdQSSEuYS0SzUPeYKcWpUqrBMWFImzhOHYYOIELf4/7J3b03XXFW9wMtN7ap9tS927Q+QfAQuLBAIBBIBEw4xhlMSOUQghBAISOQkxCDIOcrBQAFyVjAmChhCIKQgMQFFUe6s8sLyo+zf+/5x1NzdvXr1WqvXWr2e9+2LfmbPHnPMMcYccxzmnL2e89GgMFUwDziRZ+r5/SrQTMEwxy3aEdW4Zy1bpUtfHg23u9BIv7UuH2I6d3LAQkILQmB5lAXDZoGYJwXUCmmYghGRdtAu+dEo3H333VZA8hn5kkk9CdqOnACTEb38/d//fbmBX3Yd+b+dh5PmuWTsSb/2pP/7P//3df1O6Z/01gTOFDWB+TDzNutk0jkFNS6pS9LLDpLMQxuwpu4ll1xirpq02Z+RVrnM6jzKUqSjZi8M7BFbUJdHdkfu5BVPL18qy8XEQC6V9QoNfvCwCNA1QwDAnVWSscQw1R1OwJpffvnlKj1KnwAXBuX23xtWvQLkXFRqyMcut1PBeGlh2jLeUf5f//Vfrf9uASaWCRkewAbGXeiDEmIs4x48EXvKxo61lb+plF+549QyqrdGELasJgbYvW3blgMAj/UC9ig5sLHDUbK1Bx54gBCI2iBapzQcGSadWgfxg5McCVIldX6xw79Twot/PiE/91aSaW0oQ5z1iEsvvTQBTThN7316WoKNVwHEycX3cA+tfCwNACMWW9+8CGrbLtJR3YGFDPzKQq0ik54d8gIYLAAbrF9V2SFAX+YC4AjZlLHa4r6q+cX6VgKlA23lSZSpgTArkxTBZreLVvse78BMocRBCeGmiOokRDcLkYRcVn0KwlkGhajZSXaV+8h8n9I1t8W1acvMytj5r1mImdL11jB+QIEnQurWGDo81qNCyrlvjf/oDWVlfkqDt8o53qPTsxwC5Ie0XVRQQ1yFPpFemRQuTfpv2xS681ZcpIvELbmXuppxAgmVxkg2K3pRo7m+BAYiz9CTHhnt/F8rM1q8KigCbCE76bE4B1oYBD9qwIAXqzAComsLW+Jqd2URmgkuSXZXFoHzCBYC8jlYh3iP8Ah0RSziH9ZbwSU3Fl0r2EBSFkggBrUhuI8EC1aUKGGCkD7AwWp8Ge4jygcffHAVqQej5Gx0tIgEWCzrf4d+9KMfveKKK+jrESVL0V0mntllyrlMOaqvYMplvimbiu3mYQiORkoGzBOTSuZjjrlMP4/yHGEcQ6Pe9GbTVeLanNfcnHc3mcEky1XQHftSh1QFgmgr4YjDkh3F+jAf8hZ3l8kMEj3mvC7UwO9qE1qxhRXBwtYW2CDs2+ntj8VGs24cOGLZPZrEXXJXiw7WRJlUvOC05agtEym+pJTII6UUkGGPt8TrlattNVgmfx7FeNXbtIq0YTPQqJJSyoqZaYsCBpFvcIH0iFqpIxjaBUmaC4looGGlPMlFWWpvW+ML4RQKK+1sgVs2i/JOoYXvvIqguD0/4kVJZOwA+qrSthrB1oJVGYWmWM5pK7uyjqM7w53BNX3Ab4q5urigCgRoWYd5sUxDY0+Ld1NAYoNm84JlM/rjyjYXd3rRnTvpRc0EeWblXPjPHh42wa8bsloM444TEx6xr1OgTOhEQcUkGiC2gnFg5w3cxLZHASMiuu3avXfWErbaI1XmE7mhHUdhd8J2xGAEeUYxktRlR1Rnr7m0jZ6L8TZira8S/ZoWIX9BkdqaKgtdXPXISsdQq7EHIPJMwixAUjCIsdvKDKn4NqGv3o0yz64g7LENpjlIdj6Bqy/YcQqzUC2RVe4IA/Mv//Iv9g/sykAiYE5iLGjn7PgL9xTUWy/4+c9/DnnfLEAoHqZjbJdAnVRdWJAeq2RYxIf2JBDZxmDF+MEKhPDxj39cRmC/ZHzUDkbSqXe0Mk84JGPG0o9gveQlL7G24VcB9tp1piiHIeuQypo8LtPDnHG3TefR5FyV35qKJoMlMRPDDHGZKuYJM3R+1pz7/MkkMcd0xBmb/LIdCPk5W2demUjSSyzr8fvf/z4we6EmuSaCVL1nfjIBfH/NN/jVm6iVzQrOIijEuPpC08V59/or/9qZMJ3Htjne5fytaWvfjpRRyNxgmWkLgzmCS1b97tTMciyToCIlQkYblvt9Fc1ekaHzukhlhY24FFElqdIHAhdDMHPejiApbLjLKKuxla2J3ikALbLMgTCmikobbgKxnUuqjLj4TEeAGWjm3qYrbWTN5cnuFjJR4pUyc2wsEEPBqtMU9NKpGXzscwHtk5/8ZLT14WkUaZBP+0rveSxUCUkxTmjmQnhpm8xS5ttyfEAcrK/wq8esBSAGYfGsE0UxC1Wni8TsoJYWLyjAaXHBrhpuMyjJlcTmMPSLe1z6MgcTqylTOXZDgc2pGXEYerbuBfFmkAm1b4KtnNIxBiQrs1sTrCFUaJ6OYZC1wcrpOPcNKR6IN9+dTo6MHa4wAMKKHPbNxb7xi5d0sUU0sm/Cjo7fborrAGSs0s9OPQ10hZ428uxQKGb+rd/6LbObXWJOcyXqEJYIhlMjT1YwR9JcLO24dQKzxCR69+szsLFsInltISEQdz7O/kqOrugodDIpCflMFpGbSFUrsZa0WdTt0RaFT5R1VzFPUQ4hjsSxzsol4Hf3yEEo5LwhAjoCqeZzFZwKlDJ85StfGYzf5urlgsKziASYxF/0ohdJSz784Q/ff//9u49ugmPKzR8zoPSbrrsUPKqUcnTW8qO73IYASzpKubMaRMWVqbhHZdMgk1wX1qvERrmUTVdTMYHRT37yE1PX3AOWfMbMMbeFob7elBGZqOJ7c9VlrkbnhKfA1JhvXGM7o87ghbzJAABAAElEQVQttZ1fbLOFiHLJkiZr59tagFbX22mvnCsA0/HI9mUsaaWsgBECbDvq4GTpiHT3QY/w+x11avAibVOJwYSG5I8AWkHs5G+IO01WPeIUBngAhGvINWdq00TSSysc+GF5nZ80ssYuhliP2tJJ7FtV8UrZnSGWJ2ezS24cLTU1onVBO304aCAMJFPi1bZF1bKmvlSx6s2UH//4x5YqGPq2X2VhbpuZR2HUu6r51gViNEfgtHBrivW3sknv4YcfRrCDDLP0uDWpJ9EwdonuHSyBnEUs/ZHt18zSUR9JdZQly0xzHsTlFR+xah71UR23RsDHFPsadt9kxO7tknrBwMGZ2qJSC1sMIFHTWIuSE4ln6NiNXWiY2NGOYNTJWic2/cMCZn9HbGe1OcvPG1r9j/laFJsxCEgqQ3Ew8nTNrYusDtbjph2NyCSvGE8XzS8x6oLTd0ap35cmYhjnQZIbu1MMdymu2FKQ88Mf/tCjhrABdmefkwA7pyY415HoLnevBPOARWXMBTGiRCtmh/0RhlE5FzvPcjqGJjI0T9XY5EgS3qFQc8G8+JyZQk9y4/MB+7mbFD3BfKfVpo+Y+shHPmKRy07hpm0vwq+SwCISYMpHh+64447Xvva1vAIFXUVuW08hWAEJgwDF3ql7Lvqa834mSQuvF4/cqmxWNEA1KaucVhBPZT0qe9uaFVPCWlEluv/xH/+h7PNOFhkq23cAkJFe4Gem8wqemnKyILNX0oVU8bqfLwdpt83dpDV7ay+30o+QWsS3j+DVtzUFtmMBZgtghT/fVCDblJuO2c4nYJL3b28wTg7jbZkbGzs+herkV/1WxpqoSXIV7/YGsz3Yb9upgQGq3MVbviekfhnHVcg7GPKYmNKihhTXkbOYYBmjt7Gt0EJOE9hNMaiBZnClu+qxw9oSr0cwL3jBC2iXKxaTtXVch3DUJ3cNwYNkjFTqQtfonJLV93k3j6gBjmTpnV4MRCWl9YocsohQNdsV+pQQF4dkqtZkIUNex0qBOYiY7Tq6cFoxTVSoFqdOjnEqceDExhQ2Z9lkEYxJSmK2CFiYxOJ9FT05kc5OMIkRC/lsjdkc54PMaBjYQM4XwnxkMREneEZ1IvARwfhZ3InR23hjU3qspDtHppV5jXHmV1kYA63HTbEtDZ69EssR1PTlj0OyYIeDwK1Nc3mH7JdYDLpv6MTJooVDdr2PvlpFbcudvkRB4va2UpAZeK+sIjEaArBsRHmVOBx8HJ8a1gmMJsZLIEGMIk+ZrebmYNJjGYGv0lwsv5BDpQAjk0tzIYcu0sQQuERoTBYtVRAs+ahNjzC3dEIi36bD7WUHy6Ve1yNct3isBv7t3/7tpz/96elN2uYXy4MSWFDg+PKXv/yD569vfetbCfpRTJlcQnDq5bIdJApxKbtTvmQdLW/aUiynTylcLcbQbI/shaCZHsOpSTRPcG+lxwyRP7ubQi5bXvwosJ/97GcBDjzkkoqU7RZ6pN+CckmOe/l+ZzNaklI2YXSddSk4GVCrU6ZiHPzINACMzZwGSSpiVxlO6aXe+x1tV4MAcZ6+dKGvhBHF0RScMLhA5k44rvGGpIE1WaiRGoc0OsYlCfYgZDodfDVYKbSVGTqizGwRI67hzxkYI0tPBltVZbrLONIfSaz01aMLjH1+2MBccsklMBMjDbR0glkBCvPKr+v6sssuY2c5UQMaZ8arSX2DLUdrNHFq2sroIdM8XGTJEyN2YMRqhhIBZhamcpUoqoCpsF81uxf0CC3XxfHwapUAwywbIUkuSoqye0dnG0PMDi9+omxSuQMnNlRLlGltLl+0Umweh1nIKtvsej77uDA7pgy0ifzYKGUuw0WYs3eXjoglmrYdfhM5P5XPJnP6Pu0T7U1c1kyPe2JtO3ZGWlk2NUBikhGYta9YxUQjMbwpq1zb8FQA8GXplutc5rDuw99NGRrxEuXZZaJN6WVRMH0FcKRO5pnfwjwfaZ7LY/vxqk+RwwjTJNYSlQFLjZgTvJg/FzvJxegIpLAnBhOkoF18yB1cfvnlQjKhmpSYaRKKuARFoc28EzYjSaTqLjdxSYyVXYI6qURHpLIGup1k2F3Yyde4MwtCcWiDWSua9id/8ie6+OlPf8ordfB4fMtb3iKMrHqc/vEf/zFmb7/99rV7S9VqpICAv/iLv7DiYx/+xS9+cRGmCbf42c9+VuTMOTrJe911141EqmSLi/vuu88//mQDReC33HILljtdB+yRRx4x/Q3x2jyi03z641ISYAKll+9+97tf97rXff3rX7e0KdfN5eAB5SO1livwImN5Ar2hLnXlkWIFmBwzVFScLtJptoNacBXufK0JQFf+7d/+LZDuBo+6q1Sm+kYINjUu6q5cY6/3lqSU87ZgWgB9FWEA2C815oad0rXplqmFcq1QBWfKkitUtV3sUoYcQnfZju4oZa0UbIqWvopaRqZBIRSNGUdLA4ZYoqX3erW/AhkSoCGmVEjVewQrBaWE3m5EhijT3j48BpFesaFSR3rIUMLMZFA87kpsp0cAWCZbxhG/7J38jbb7CWg6bBuZjrELyEAMeMt+EmbWhzQ8OpDDYsIwLhwsoAF3lBx5+mJH4ERDsYYkMofKq6oMWg05gyeeeIKPYc2tJfmpCcrAKF999dV9lcNUthY1NNGyOkMape3j1I6/RR4AjGCqrhDMGxEvTi8mwOMy9JapoaInnQCv5XF2APoGZ82OKrSVs3c6F0ITnHMJqRhRRr9gaK312JqAbJXsEpdnpz0E0FhOhLXJKGxN1TIbsloI62xqbUoqD+LSihlknzc6q7VpX0eB91GYcx/tvDsKGW2ntJHL5pHjkvhuRpXwkzu1kPso6114IN7g8trJso++loyTzO2BMTWdyHlEVbwSArmKL6pVZQWydaVGhGZmCWaMcq7EYN5mHy5gcIpyzWJpNh0Q/7Ba9ir8klEnWbCNwQJrK6DKVeXHH3+8+g1anlrvonFkuBRweu+99wqV8yNhAXNHgOgObddff31VKsApAYYWnne84x3tq+3K9upuvfVWPsVdAlxI5L3+JxMyfNeNoxtuuEHW+rnPfY40CqZTgOELX/iC5EuQ/OUvf/mLX/yiJv1jv/43EDmYa9YO3v72t3eQzPW4lAQ4/LziFa+wzvHqV7+62KNeEgOpplQhV3TCvQJxw2wANKGpTIMkQUSeoFzN85//fEpPlX1BHrSAtRUH0Fc1yn4WiL66DFs2lBK7g+xMknRU5G1U6BwZhUrqa3StNllPGkdlngPGadalJEXKJtt4q43eDrI2WLkWLYGbtGvBAMDPyUkU/TrU4La5EZSQgzSgMkDJpDJng/ftaAtV7JHJTIbUwL4iVOwIj0KvArD2rq0LmLYamqv0B1ojJecUurmMuKUExppAbP+Cp58UjOK59CsM8ohHXk3hqU99qq0P/AolmTwUSiZhBqagudFnBNfSBkBH4RExCMMmibWJIgC6Zz0vGWaL02T53ve+Jz3WhECcgpZ123t3LGJwUQPxbF9hsHWj7BxRJwFGf3hvyahWqwqS8ATW5CzOgwTarD0RoAlLOGS7izKs6vrM1BMOLWI0DCgBXpTVdiO71kpvh3ZPrcx6S1eQm+lmqPmoPBKX7EgGvUpEzkRsrWBpmDkOW6jdGtuOHO2vOQZZLVNyI0vYp6cvmX5Nv9VJ1BAROrGzQI5seXHNIUwyg052VUxyGMHGCYqaFiiZw0hAL4Qg6xMSEwLjZkTsf5pQ4zJp37blkN3WOJjpatkRgGVpT2QrtBOz1VVJtehUHFuqix75Z/7ZCmCPAhW5hoiLZavuhFvot9uXexWc+2s3ey+99NKcVWypclr2xhtvtOlqm7TqScYPZVFIpNpN9APD1VfBbFQQxL7hDW/4nd/5Hd1pWNh0ZP/W3WHGBPzvete7fKgsQ/btXr8LknnooYdkvxJaYAAwa+3gjW9842OPPdbxTf/6r//66KOPauJ/A0mGq9M+2l1qFpQA45Am/eEf/qEE+G1vexsJCr4tEtCblkPqIkNgd5LiujNG+f9Jxsmx/kjKXQTgmKtMA1oWSn6lhvK5t9KEXy/pQs4gx8jmVQvTErB1uYPQo/V4FOLFTB5fmMeCS9ccp3tn3WtrkgYbQi5a6oh9EHJVZYfTVWDqQbImzIT80/ARQqet4U7SG7MCDIAYy4D20Ro7b6WIHSR9yByl1qMtVsxSiVry6AMP1lAV/8jXqyR19kul8URnTFWiFpGmNLTS4CCnqGJoCzF0EqnUO5u6TC0NtJZ8zz33uBMCnNJmCKm3vAUAA4ov2awmyrqwcDPCJgbRIBe1vIJBkVZ/QCPSPnfJMBFpotkqV0A2FbUU1QdWQ4eTRdNkbZOi9/NqkLhD/EZhHzsOFSOLGOVIFaqogRxYok6wkfAgeRcrI4HBBaaLwpkuAVPecSRTddD4TMdzGEjzPWvECSxMkBFzMQtJwrtZ8EBiKXAuVAvEw6Ky8Mwmnz7LoGQNd4Gcbk0S38S92ltb2g4wjpDEH3Fkln2lJeWStmZ2YkMysRZsI5HmkMwsmjOx60WBCY3kimKtnI03FizzqmBmC8r7goVcmBpUhO/qoNUEjIhOKEsxhG25xwIDtgopfS3MrDH68/udrIHUmnd+5jOf2RoECCkYj/ODH/xAuvjOd76zDfm8dXDVgVkbzg4nt9GdKMtvCcuK5Za33Xabo9dPe9rTqusO5WsfdWQzWdj5vve9LwlwNcGRuFeyVttdku1PfepTX/rSlwYTYA29wjteSmI33XTTxz72Mb8ZhJHCrGBwP//5z4vuNvoNiBbDlPKCEuCQ60tg/+pKkiATpkZWCMx5Fy3hFEnNSowDmUYFvFFnfcTHhodYrRkTluhETSfLBUag6aKjCp3HQZhUzn7Xtbwim8BWhlbhLwpxnTS4alY12aWerEzOXTBs1BYvljONqZHtrwKgxD6G8bUexkzI68C362dtX/I9KrF2jzTSiwpVc0lmZwmqXg0W2FyGxitZtGU/mbmy4JjBUpCpJhNWppBE6gthaglYPs+YmthyezDIoN7iIXdgdN4rYPDHjMoqvWVb5Y2INCPgxKavExXaCyqtcAePVpDAAIDLJFvcWSUFYE/Voqm3pKpsMZXY7alGtcCD9AgPmnkaCbyGyX4jurZTZVOPadZ75mM2gjriBTbYtoOq/yj/15A7kQBjnz60mJ2c0TV++w03rYHWOBqjuNVNmy8cfjvhL5ypA5Mn0jJreKID97tjdyZI9i52xDPefEYFmxHVOM3HesvAPve5z2X/5yJAAhxUVNTSKsPOEx1MjCwn48xtzZWsQsiBWv9ljecS0Vx4SDVRigI6OXehpsJhpM2PY+Ti9i89t6zPGpOGIIf8lYUfZpYRmX0sJiKkGNGNvrIJKhAmxkNtLjS7YKbngtt0IQYDxlxLZLAj+JFs33333dKEV73qVS1arWzJgpTrdjpVY43AzrDI2d6p3WAJcNt2o7Ld3U9+8pPf/va3O71AIrXGgrxdOfTL/8WlsmIDhP5+R16J2YClSZp/9KMf9W1zJwF2NPprX/uakE+CPVH+/e7W1swQOK7tYzoAPsWy73//+6+99loLCSRCQJqrF2SLPLylEKJzWuJiHytqBwZmVZg+LkG9iH1pJyTKdr1CiW8yxxtOZ20QEnIpULRhlca0DcE7B97W7KO8V5b7BEcIPiQYjNLMIsNqUHhEkZxynzyii57k7lEvwAZnYEsAbAxT0qeJR5hsNmbxnu7FrMgS1VjMoz/sb1YKW88dnZRJGmjNXehEnlaYotUIpn7WCElA4IJN056VpNvMnErZL+sj32bXbMYqa66V34wzR9rfJHD+37yABGtZA9JcwRVp6F0GntRdWbSE5s6naCVhBdjgHxR7JCmMk50m6QVWuWghwSznZE6Bx6zHfIxA8mu3ggtJO2ptZXSmrWkhNy0TSGtPNm2+ZHhiF6FaPaFLFS4vmeAl0GbKUC2is2xkurkrUxJzU705tQQi19LAIsUorYXcBYA1M8dZHn3NNR93oWfJbcmHnSxTuTupETgVza+ZHHL9OsSbFMlGduclGLKOXDtLc6GdEU8sgDubEPnPiHwVqswvludgPa6i5Fj1lBzv4g3JoY+zPIYS6/iplxgfhbaREaEhApXEKh3avGIwZap8igAyV7YxQDrv+Y1vfMP3aO4ik+pC0GWnUCDn1aX//39pIRAf1po48lKhnR9t8YtTf/qnf1rpK0G1H5l26PHoh6z8R570haqbb77ZP17yO9v2VDrACeo6k1SGYkvPFkt/n1z4iiOntYsRCAUkHjPfC78aBDsaXTV7KiwrAQ6Tds9t9n7zm9986UtfypTLJUThYuu8VbYlVeJoRdmWC2BiQdZBF2kP5VOmOq6JbXcBQ7OfAp5O+XTIXag6cFtMxTqMcGdQDP0gYZafsy+at47BK1CSrCwMNkklpZKGbeTAJHs5foOeJBImqj1nJsxGqy3ozGc4deFuMd6lTLVECTa6rfbp1CW5Va+5JqwSJAgGBjPCoEpWmQzTo0r5qqDcoglBqXduRKocXtq7t/lNaT3SZ/iDCgwZWmtQYD2t7fmsFwaqPrhYwLbqq7W8bS8pi3sYL+cXYOjY4gL2KisCpIfBlDeK//Bu879dVoAcmyTMqloXFxkMslA0jBQiYagyRlkdMC4bUTiCfyGvCN80YVEvJsBTRsQMpfxRKutTQgGtqByvT1UoZGfFegrOo8Cg+QD9Znmls5R2gH5PrgtGRihpDnJ5Mw4NL8PUuxhz+mmykwzT7XHGXjrSxgvjqbv4uHSqR7OjA7nRIzyWgwXQ3Nb+iN+IpD6w4bMT06/fXw1REPWqKGh//S4KMyGItchBQeqlIACgLcpqKHxeLY3mVfSglkd2tQB1yEjA49SxPVXnmXEXGJPOvq6PZqW1fkqqbajs3176RVU/fJXo5ZWvfKV/niQHtoEcDFZPxvWW4yucDicLFL///e9XTVvImlcnsRc4oZ+77CfA0nuv+vCEIL5tMSsXv536eR8XlwBjW8zx4Q9/2ND6DDr/NbcVx57kkm06/sPPmtGAjkbOK/QWG3ZcNMCld8lP+/YCKZMATlkxG5i+hTB1U6NSZsjAKVgn4xdtHnrrRG4BeGVKC7wI0LKT+ozdWkkWhipANf1KK51y0u2KTDAUTimrRBeYuJnlkqq5ezThqRxmA0nlxBAYBCCNZEQElI7BuPuIF3dOPWE8J6BstqiXpynAAzNzwy8CkJwTl0ob2jLqIqMKyOMk3BPZazKS3wplNpoIbS+tJNGGfjUSMDym3AKsLcNMJoNgvuL2qYyAciNSW1SEKSQN8chLAuzQ0dYIW+TLKUeAmU3LoWqxlJggmSNmq6lkerpMZ2XxvamxWMqPQliCoVWT9CgkLbNTtto+rf0rX+jNRSGTZWuFI+BD+SN+kEm0nGo3Zq9LEmaERdgy+74JxJEe155TM6cYIg070bDmXvFiAuWkNHOJaHY8xfXsmPsIyYRALFiLDazK9QEukBqrLQlXWGYykWIZBZcQS1kwo9LjCUljkFqMYAEvfgXKLrf8s5axRI+++PUzy3Zx3/SmN51jvuHXW8eG1fzu7/4uDAo+tTAfv/rVr9YJalsj/mtR26ojrpKhYFsipq/BXRatQifNbDHkcdA/hosWHhlodg+qFs9hyotLgMO2A6XXXHONz4AdvzzAildfG/o1+xsPCseROARPCfwqb6Ku/XW3WMzcNkPGi7eHWHLO0CSR1CkTUX9qcaIuQyZXJD3eV5lUZ+QUNqm4YUKJskRdF+jhpHXn0pfIuH/UkF1OwimzSo4qhtbKDxtY2KPnkNgT9lZ9AmsRkl4k1ZSfQPxsgPgGg95auuMD/FbWAw88QA7KD56/LJ289a1vtdMLjxiIEGTXAiAriFxmzSBo2VObpV7pVxfjOd7ILIDKxi9R6BrvRs0dkdyzSry4Zymxj6Rfs3akRpoQu/QV1+O8jHRhjLJl4UNu6yYZRPIZaXJyrwjQ6NCZeU8qnpwcphNsKonRzR2aEJXIJDV3aBr1no7qQoAkK2aHjl0IzO7CI0ulOas1HQlj60r4ONjKwqgEmE5Cy41SVFrK/O57PQJJ2TlgVThoWT3yyt0MklqVdpYoDB/Use24kABzmujvvKq2F2CBt6U51nkv2ATYFLByRLd9etZZtadIVILmnCWFEd35v7DOHvuJ3yg8Bn1D++Y3v9nBvU9/+tOY7fArGHNYmhH2AW3NEabAPzESvubTcZ/ddX7LqiBTsL3kkCDM73nPeyibrRT/s9er7L7Yw/jud7/rO15Bbz5hU99G7B4FUYPBmLU5r4InfeEIPe5rv4brEDnX4xIT4Ayq/4ckQ/jMZz7jJ7Oj33PxPIJHfsUid05ajsDP8gq/+hVmSRvYuHiRWTAfDAnbxAHT711El5PAJqr8sAxcjvKa2HbmSUYXYUqP0kK+VurSsQIBGKzcRSBmaS5IFApVOlKTJKrqU2AIXN5KZS3FIRgvhhv92PGKNZejMjF+9JK5YSBIwJKbZNunFAKLxD1OtviX1BpyAIQgcwYj+SUrWbF6JkkipxdBD6FFhiZO57xrcni0ieldiG956RA/8qiViMfdBUzZveapsy4Y7DQHycz16ztgmz6K+XBh7mz9Eyxk7tKv2WcQOYxNaTgJeKNjetIoAzH77JgoAaMvuqXAhHwsGiaSyjH7YMHZfnZJk+i5QsheOPETeZwLjHDoFRfGEF2UzIhUCYqRAbBRGsNl2GgdOZMiV8wujR815FN4GcFuEtG9DgeTUrtDFKDKIxJY+wrB9sZho0trgS8EADrDa4sK+CZu/UJgeZBHBtncEURlR6FghD2sdNnnqj/pAkfpN5DtK/j3v2aECzuOdTgSK2xTyZWnsmXz4Ycftq4kEGr/XTCzDJudYT+uBFikJIVuW3XKr3/96/OVnEwVAXYiWwBbLy4/rOVwtYjUK5G5T1YLxjEQ+bBOq6YKZjQjBgA9FStqDkDWXWCHLCzUxBhaFtzn17bg/ZpZtpIOIBejcqzw1w9uyV4oh4BeznMAZmfswnj5AjYR9tZo2TUzx3zwxV39R43+JE+NcxS2YfmDNsGzt7l172sbok3XMj2/esXEmOGtze3TCWEqgRlZF9utJjGif5XOFnjk1azYMWcumbAFNit/HB55MjT5lWMAgH36QjgiJz9vkK6ZOVMDjKSXcZQwawW/hVIHg63N+0wxmYZ4wvfAaWWH2dlp5PEcReRa9jsAxlq/KmMldYQXdk0vKnXkShlk6fMWG0Tw2FxySllWD1WHjAiQDvgOXJiYPLYDs/YRktCMzpCamrUNTw6AntDDrQW1O78Ea4dqcKlod+T7xmDKW5ONhuy7r9PCzxqzKiKz3clmT9jYsxroM4nieDaZpZrRyMTuMZXkz/5TVNnvjPhXDWu6SL+BGe8UpMFlggBTGHe+3p1TazerbTq5gvDinQTkIRa+hYgnajl3H0ShRQ7bJ+lq1Szltmb37o6OgRn0z4de85rX4DesCeH8KJJdH1nu4OEIIpKXmvt+nLndUFVvRcnO8J133sk4SDVFUyMMlo798Ic/FIgWpNjSXstrX/taHwZncc2vF1lu842xSmBmt1P6ZnT952E1+nIXoYWLq6666kMf+pBItXJmv1ktLLz88suro0MWFpoAEwF5vfe977VZ7yC0Df1+7LsPMR1xFtEkWw2SOimQXeh9cLconKYWm24i1cgSPgmYP4QgFWxDBPNWzilFLBYGRypxwOCrarhdYRDnYOUgfjui0lFD7JA2AEZBti+RZhrwC49ogM3COJk4WyLX/dGPfoRru8TSPzFTrL/QQaCQbJOJhApaeSyDyMyxMqJzhhJC6br6Wiu1dFpWDxIXGBns1tF8y3uVWeechY4QfEOCQUOZyLjABkU0Ukk4CM5ZgEEw8RMRgdllBQR5hDaI/8xU0jFsth/hHIw1nVJR+qBHhSyO0M+tteJglKej0GlKHrjfJXRnvAwfQ+0aHC/G3O5fFtR2JNjyX46T7Ihnmc2dkyLJiTulJot1PTNFE2UGPOfMs5DaMphBAcOfWkDfdCCCXBgaB9pinljWKaqmAFMVXIDElHvKFWcz9RbIoKJpU7CdeRhDY73S9q/Epn9c/MyzXww6NyS6EB/WGcB6dfYK7O0f/dEfGfq77rorU1uNX2aWN/oJZR9Lujpcy0UJx49CP//5z+8sRMJw/fXXa/7II4/4JFjIN9E+tGtSuotZZiKquYU8m8n+F4nLvrEM2V0UeuuttxZ5dmt8WSY8CyNefeELXwAmVxetKT/00EMSZq2qySELy3Xn5EXQNMCn3raC7ZgdUi4H7guz1N3v3PqaMXt3tQxzYEo26g7NlJ5h0krZ6QtJiLL927WZFVeHU5le63TFl4Tgm1j7me1PJXGHtSRsOsVr6lFeJ0QgPb5hVXC2EUdrgVFr3k6Mg5mtuHakZoiT8HNpwhr5MEaENWCIi4cTE6iX2cqKycdSiOUxu7Uyfz8I50MOYP5DWEnGvyb3X5FsC0skWD1pMJvlFBwkBNuuF0gR4UePARJ+xbTNEmQQCIS4IzpdJLZrd5hVrpXqjgC4s7KA68h5a2zhYuvmC2+IO6tIrsPTaVxM2xgHvZu2LpVWlA+gHofnd2KPJGCmmEFZUJ/Y6vBgNIeRsQK1KsnBQn7SaesZVKYyeRELiU0GaqKlPbxMtujRcDP1ZMVeTWxuylg8DbDkWYGpXzUKhJ+TU5uOAsJq0XkiYS2Y7qb3KGgW7OpRUmd827X+VKJEsDddRC0lZ7IsxOLa8qHTmWRwLVPYF9XQ/O3Uey3+pQHYOfBLV/47rpmemeWe/3AmCHT1Cf7IRz5CPgT1ile8ovNWW//HyO9C+yksCXDn7S6PMNueFK86XG17mQ13cseG8Kp9CPBiVAB+kSv/6JgFsHusrVe7ULJ12y2/ANy6v40axmH4SJKYHn/8cQO8UfOTA8avzEfuRzlOYqELwZJeS7ZELZfj2hOv2ElbmwBbT+onwPAIEXh9/s+gD46gPcasRZntujNzQDrXsQp+EMkulbjWfMqMFd1y8x1IG7y2eQkNzeK8hDXyAR/0+pTF/q2kV26sLQa1zZFR8tSQYG3/SjIlt8iwXKJsz8TUwD4kRCfTkOQ4LqVGvXVBSABLp/VrmNTUGmGHti3EAnPwp23KghjWELXTZdXvWiicPWSvYPPtirydhXV1gNNLKnfnqIP84uPuEjBA1DjT1mIw7c1heNN8sRmOydV+A7y7EPoYiEVimaWr/tvl1KDT/5ZLAryn+WW9L78h3HLNuOU3ltrK0y1vaqbAcxBMnzs/wu5ZKCF/fnbeUdCFBdY6qnMACWOtEuDihb9z6pLP8qs/a+OHAxC5kC7ISpRlj32xpnLfghIU/eIXv+AsfAimr1KYffd7FPzCHp/g2tfhfWyDhdkyBatIMmvMYm1jHDoi0lyUqLIQrsIzUg+JAJIS6qvAVCpbo8mvDzhU2FqnkO1elYFn0OxyCQmsaORkX4fgwr/vwnJ3gHFOKMT9qU99yja6FRH7wMcS076HIfhxZ0GUHzpYLrc7X87oWtSh1haoxCvttweDyEGaLbJcFwDZGq5ND9Ytg8vzwTky0OIAb011MZnwKGcnRuAHydilcnpfdrfwgmVTHcH5HFf0Lwu1rq+ePcr8lxsQgi8MWTEyYeitDlgN4fZYFmsiOOUGJP9ayW9hJna7xzCIigjNRaQyZ/IxCuwdHmELp0VzDCVUMpBZ1CyYC39HsKvqO2CrHp0BwS9zSWIYJLrBvbJOL5rIlgmcbDuvVnV0gdTTB2kGHXDO4sAsW9FziMPQcO201Lw4MAHL7I5AlklYqLIMJzVVRmeMmAJb3TEdvuYwSS26maTbscPRQ6st06THbHJ2zuBth3k5rTa1ReA5BfQrEDtTn3XPGTnii5nWILR+ajNHX/xLZ3zbHlHiPGpcdlu/aZk3bHuB1hItF8b3tfWboj2T8Be4tRSrOOKbkd10Ep2WPpgFPtb1U88+9G2T1TIF27Gj+e4nrSBhfzoEZDgEWq7+APXJVhNTVv8ELhg6aA/2uOgEmBRIx9fSvgX3PbCfI4uPPJh0Dt8R008/9CsjmitF2R8X0V3LQlYok9BO0eZ81xSqlBVkNTWywcArO9TqrC8hdHB2HjXv18zLshFxzNuO0KYRXtJUxFjbFiCWjRDkSVBlaFlLgx+k/wksQ7AS78iDn7nXSj3lx53exYLuMl4aYg9NMPqsZz1LImFb2KNggrmU99pPFj4KW9VInlXKtFEOiWGS/zBhHjXXi7dzCQqpeJS6WwTBizMwalrkHo2pMHe6IcapZJ5eYQpHEn57ZURUO94tfmAURvxt21k9ZtHTAlwskwA1EPKSkihz37OmFXi/r35NC7+Esulz5ZVXmiNUd15qITRnzWVsMgKyDvPRIpfPGepoxhIkgAZEJilCM/vjUkkybYriFZNiVub3abajnGUwwcmZDYmphHY7VMtshR0LmlhjoGL2p9NJLEy9tJN1pSEzaiMjmaVSOJVpo8JayWdNZDr9g5BhpPpSsLxLDXi0QfgLrZJAyNk6OIU5YytB04dSxCJswH50fkbNn07DISHFw84q+2T3WD8KtR2zG43LRsDb0TO91dIT4HDiXyJ95zvfeec73+l/ohLfoiQ4XdbTIbk6JwQchE5MML3hUSDlHoIk2R0nzWqvHR2fp6JTEsi4i5myyN2hXMKT9WBCWIXTsmh/RaqDZ8dHXQeDyCCBQmrW8lj9giwkVSkJdAn1EvapJz1hpcCIrc+hZQUN9SvoecELXpAEz68giEEBCED/8R//MRIAxk+4q9SdYNovZkFlWcH2OAyE6R5ZicBc4HVahBU77aBUZZE9XoBTWK9HrA1GeP6ToR3d6WcaIzp7ufaXrLA4LaPsyCiyn/3sZ+ulpQfvUghSkntrGIFkyBCz9cZU28X0cl+w09vuG9IkNUwENThG473PwpfN5wMPxzhTq96SzxYiWoWtU0/+ZU/MTYLNsuAsEu70tcuj2ZrdJwcHrFHmuE1n+NBvla1+o2G77vrWpl+zHeaJrfYteRbJhyFkle8YJ1IFDGFWYaxY+RfuTHdq5hIOVxKEFl6NtaS05NChUD3i6W3glS0yKpsjHX3oNBx8LPpTgJyFl+o4AYfZejvY9kKozCg4/CUUtK59YSbAhOBUgh8W9v3zpZdeera1ArMun9Sa477+peRnm9+FzOITSIDpAb9LJ3wt7V8i1X+FXogE90EGPySgF+s7CTbjNt28pJquQaiQMUoqm8dVfXVmtcdOTRqKugy63SqGL1FXByGna3WgU7mPR1vxOIqzV0Ytlz94EHekd7EFJC5Jmkgom5PiIdgMtMhS5inElA9DLl8VHFsI9yjNE6N4qxU52ENQ6VG9vXG20leyDpeSEidB/sIIbb2iNvmM3F0KjX5NLLHbUwUGSbaFVeodYYjPQJwn81ef9Q4OTZ/NNM8dg3CKYwpbBz5g1V3nbf8R/ZbAIwRiF4GJvdAfPEWhAkbEiH4gUSzuEYCFAAi1XXsyv9/vLjXGV+/LnLkSYB+KUyGLEXgsAU7hN6q7S4Squ8EFrym9HxiGphlH85Gyzds1IbBvLkriOw7qSnXNZfXJipcjIuy70Ik2ckCYQkcasl+yoled+u0euTzub7u2O7bKEOC3z+OOmAnQwiVBMb+bIo/Fg8HF9NEQBTZ2UzyDLAQJhPV2BK1lbl4jwHhxOkBZZh5HUxi2K9AuFqkcx3ZIzlIrYQBn6iRXHRw7S9xN4YXmc+gUMh9ETGly0jB+58iXnj75FLCNTMOT5nFpxM/s2vfEniRB6utw/C233OJb0/6x2D31exS0VB+D9klkRP/+7/+e/3lzFErWdipFFzQA4whR6y5FcU54bUMAHJ60xMgOAqu3Oez37nSRf3t7FIuAI7/VIe7Xu7L8052frt9hHiS+U6mtoEqltryaqJplh9ORXQEl4w6h/R9pCZnwducOGp4/asgOamJLk2wJxP6nt7bvbB0LmrMuALMf00IPTyn7leIKHzU0ELCl09ADlc1hHUWSKFFvaVnwrSCaEV1lNL2SIFligHOK2C1UW7wPJBHhzmP2+YMZO3rHOALcVVq8mBLlw4lTARZx2aPALJ1BJyQumVi7+UA4zhToCHI9AiMiGDZdrUDejpcBsuJw+O9sp5CdREUsS3RGf8r4FlrDZ9XGl+pVc4YLNh/2/SNYpGfOmtdmpbJ4N8tSu/x/sn2MiDlFbWLE+vgtC6qcKwFe1Uu/39lr7LYxs3Kw2TFDaDHXXNti0ZZ6UEUYNFfgJgzHvD+fDjPjufY4lbAk9PNBnEtW0KaY8XF5YgcAn+Uah7xA3kYgIh/8ioLo5AXCeMsmIfDm1lmcmNjUT7V4TqKMWQGD7MYnb/b5NnLKJ8HgYok8jQSY+IRr99xzj6+k/Hto/0n5bKsI7vwQhWDIqVGbh4njF6hDCJPtuKRMAiDB8fTMkLu1HD7ClOxL7icoERcqjEDu75WBQCTzxBALPkLwRjt7cWZRV3ehrdzDtq3BldcRmszzJz/5icDCLi5XJ3eyNSQIE1hgXOKHO4mHdE5yq14rh8PVeGVrlMW0Vyzfk7JaLuEzKIzJIrv26azK0I8MTZyyJnYNWVshVDHiLXqS/epOrOYubzQEU2QrEoJKX9RAK2wKj3Rnm0K9vvAIj16UXcrJuqcgDwwGpdZEl71cKwLq261dnaKWcGijQMo+iQyZJCP56R3tAtmOdeEZrKy3hy8kUaFa8paJqWxY6JC6NL465J3KY1ZJzBoTnwKbnjT2kEo7RVDs0ohhTwJs6k1BtTSYQTUerNyFcgitdDCGVic3HVwrgFnK5IDoSeZvanYhqW2LJKayremXwchDXHhhjSnq2iZ9JP2aiJrriTPaVDh9hGejhgvm7KxKHOs0xHHFSCvEEuJJ2i7IOfNagV//x0i0f++99847tY87jsvv/Un+4fLyqQyF4gPLw7Jf/+tZwnDmZ4WQQvrHEMhwFrgKSP4o5J65LsZaNuI3G5CaJeHdRwcGyElAgCU53B3hFnquU57eJa3i9SWTVsE3XfN2EFfzBC4snVRWEihDM6YJaGCWsrqkppJbd6+AWQO2lZo00tKPswBErQaAuJljUOAjQfpOBkKqYiDiPMwUSbuleiELDCpxgXJgog18yRL1UlI1lHwtYEgUzDU5KiQFMCI9rAGr/WeP+EUJ7ojLo1AJNttcMKPc46b7PEiV+kKCWR4C8dC6Wjx4JBz9gsQj4ehuCv2rWIOwXo3g0VEgDasgVeZvLUD6TQjKhtWojTSvLg5TIDHrEYaGSGsZYpw83BlcWoovPHrEFwYhGW94GI721AsesxqVna599EJ61hBdpgkJGw5LVObgaUnVpKPq1uZONHRjJ22r8jIYYV6YDkNvzra2ZcfRN+Os31maZPpWDa5pxZJ42wFgtBk6WmFBkyrC4NHk3ZGkXZqzsYQz17xgWH784x9THk6nw/suRJ50W19GmFN+C5MenjQj2xFvLghp+Bq/DMo2nm2twKyT3n7o189fXXfddWeb2e30YX+tTmYHmAgY/dtvv92/Uf693/s904NjOMO6gjWBkXV3nmbhbIobRA+CBmOkHC8uodqdbBE2CziXo91lFuFl65jDZqzoMIfH0CCLcMEmjCAuSb6oV1oodxIE+HV4Pk+uaDVBsgqebPVOqiIzC8NiKZACNa0UBM0wOKfNX3IVSWx+9rOfPe95z7vqqquCR8AkyMv2tZ1evUuGk4FLzyB3aRj5wGwnGfBIrNaXpK414bGSbyezKpx9+I1qkCeHFz5yFbpAoQRYZQcJybiIGrBYqvN2u0fdEdF4KByROvlvgIxIdZSy0SScqjxugeisKBlZo481S+zoMVXXfmQlQ9A2Mg9ftM76wnHZOWO991X6JBi09Ok6CVIHiWRpqTTbEvmbF8DYMUZyEH6LSrEKVx6ruKq5KWbBZeKhjBYJyvN4MP1xPG3GvvzGIe/GnsyIs5XPKZZ/4zd+gy9bjuM4vAyt7HPi1rLPtlaYvMIGGY2T3n7l92wze3gtWtvjKSXAmBFNfvGLX3zKU57ygQ984P3vf//ZVhfc+QIk7i33ZfIrkZO52cFAHhdumOQMzvquVb4pAFKd7SQwo8TwdX6f5v9MITgw6b2Frxr5hmDIox82p8853S1X1ItLJiwrFjBpS5g+AJNZyeiy2ykJSaCguUtu/KMf/cjXttkRlYk5Ju3xr//6ryUnN9xwA9sKG1QgpbWa+F7X3p2ER0psuz4niotmBAj7AOtIJfi8Up/CqrsUUUKONTRrRVx9SJVzpcQt8iJSJQJMmaK2Ci38RmUpIoFzxv1W+u3gN0Z26UEaCBmmMFF563WTfo+716DZ4EZiVaaE45ix6cA8GHs1TigoY8rVYX8cycW3IxIgSQaTSEdglvkquoS2E1UGZLNLZi5GrA0ZgiTzXNhcAoeZ3WOXBhF62xddpNqp5wg6NUGYwyYzpuuDdFYlGsYXBAtybQGb1s1FDpu617WYTxQg4454EubTT5SLXcguzZ93kWUXkqa0rYEbnKEjGDT8+Mc/7qyfH+9M1DcCfPHV7BI4sQQY/37y5z3veY/s94UvfKEjIpsqHAylrMpbNJ99DNYitLknpOaYl0mtFM6ekpRJcGyfU7Y2L53GC2bB9/SkWhPr+oKYI9oUH/rKNg2u4fPNaspO7wu2QhWhSRqlH5/5zGde9rKXqU/UJWXNaoIkyjKwlFL8RKqPPfaY+MwWgayYxbTuYJsCsCzXWzGE/V5Sgpz8DYcUV2yEADUyXmXJT0ZH1xmpVvcg1K+GLnjyIxz2dfXSgg2W9Yh4dPLcmvdhANQeeL3tzMR6HNEfSbu3LYBWDgfCiV93ZfsJyNhlf7Io6ZDa9kueBK6Gphlim0jKZEUIAbMq1MIXqiMWiMUkQrZfFKMVl1122RQKwWiI7OgnvlI4IiOH6XqKcGahpCQ8C7ZDImEoHD/hm053r4bwTV7nYii58rzTliVhKOA0KIPqxFgxWV4hAIx9YHdW2lpeO44AMgchdLXY2G2tsvTWNjmJsuNR2LHsy7WdBMH7IzLD6jSsjd9jffO1P+4mYiYEwZ4V52j7xFZHBxMAmIZisLWryS2pmPXvTj/4wQ/ecccdWxz9aFFdLG8ngdNLgBlK6vL3f//3Ds07CF3h5kb8O3XDygxu7GyE5zDA0iffx5stCzSLHLPMjRykYSgk1Y7nnkVEkkCfszpCNnj2td8FYr73ve+xKT6U7b89TA2xyDP1hXh5EbGQj0hIsJVtUvkto/lP//RP/s21f3d89dVXhzBmlPWXRgprZMgGXTzklVjBMr800plw5/TsV8hOZbbw/OAHP1DJCstgH3nkEYX77rvPPwrW3Z133ulRdCXt0Uo05lLwTZq7FIjJTmRGx1yhwY8kSbPlObpITd1xoZwmVZmCyj58B6bziHhfHYuekYpl7h+/deg9fWmS7twTSrZIwFgecvfW3Z6t7M6ijAWyFmyjsn0Jkb0mRGfVgDRgNpqte0O5fgttykwKi+Rasv9GOQ2kZkX8xAKmamgmNjldMAsoT3va08zcqNbujMBTSKLP9Xi6BSt0NP9Ed6vaETEEnNc+0jAriZwXSzu4ScvolRmhFUwNStjDVW6UWY4tGlTLcHQS2oVUXslHPQxRexDpdOfC7pTTE2e4HP3lBHfHdnIYqISoxs4Bq/LkJz/5hOgXU5nFQr42QhinH7OWzmUx2dLbh+UZJ+DiWxI4vQQY0eKwr3zlK76WfNe73vVnf/ZnaiZa/LgH8LyOS0HNxLaAj3VZH5XYSIP5CXNsmQSjyrjsgzamwbHeRx99VHaUTGlkIDKg7sbX3RXgfRA2QobufIzqQsC3v/1t+Vi2rz0iTLyoIJdjLrNvCVWRqt5wM452dwUHfiNaGqaVGumutRvpPYGojNGUpgqtbOiB4Tnkk3YVtOVLoFWpd5mtzFC0IUPzVlleJ8aSA6MhXdtctcTgrf/+Qs6Ih18C0BHd+Qx6/Q8gBWenbV9iCOA5sAnehdoUAqkvab/9JWT026YGkTkcKGXFIDlLX8lK2+neqIOc9Kw+hHhyzhgRfotQlst1gRGSAiAuSJKfW7boIFzUo0PdtCXjvnaAWsqNwshAtJBnoGxOueZlxLym8GcmujVVTToqRKPmFdRhsDERlgVxwVywPJkL5v6MSs56GHQrmGbcIFMsRk5Ho4TN8SkgsJETFkwNEw0GZstYCohXk0U64cFcP38wSO28lZQHp/6TcBzZvMhPCxslNIhyP0poM/8CFAgJmHp+MZRjyixY/giiuUNk1Yw7VmCuu+66y/EZv9iSHY4OqouPB5DASSbA5GJn78Mf/vBb3vIWv/Tj934mSkrwkViWrZFFcDkact4JWyciOTCYicR3Cut//vOf+x/Iz3jGM0a844Fpa7uT/SbHayvnKkvVfDfrf/y4ZB3QrrIvsikZoODAENtrtdkFkottU5e5qJqIh6UrSMTgxYW8N7zhDRweufF2N998MyWUU0nyPdpsxIKGloSdCFL2VnBjeVjQQIevvPJKOar/nG63kyaL2ADIin05I8W1KmRl0bclOdUsLUSAepe2kmQG15ecpgOXo0agbxYkbYMNsCgK8KCQNRfSjY817gRk9tBWhZKtTHQnG3dPJZI01zUa1CS8K/g+SWrITfNMbaxJMKwWITLnpb3a9MpZA618OE0yObzdCUpMw/SrgAb99mnbtN8DwCPScNNAM+IkCD6ATAa7oHKuiGhHQcGTLkxkFkk5NTuiHST7wJXJYeydniIvbF3tvpJb7KQf21tltTaVrVHmgwz6KlsKIauSdZYEIavMCJMoRQcPp0vqjngJcxmlfAbicYuTHZvyNQs8hSEWq4o84CwITxeJAUW8pW1+UJxz4ZyyaYeMhotvTRan4cyIU7EnMtiYdATbV3AXrhvElrXBsi/axGlO/+X3NQZhLlbuWwKnmgAz9HKGBx980L+N/sUvfiF2nzJheGtOjrkBLPq3U6Scf7S9b0Hvgh+1InLRvD09VhLBU5iNVU2/U+B3ofAAbS15WPB2cJfXHNlwkCLKGNGDZQU7nwKL/BzRAYgc7OLpT396QpzOW0prb5P7t4Uo2RN1JXZBuUf66Tix9A/jogRWVVKHcR7Co+xXxCPVF8Cpkc9QbLyLRLlPGKSp2NdcPilUogxCKzjV+xpZR8IpXWcPQaZaThekt64Oter7WhQdG6xHnox9sFUwIwMN3J5Hp7Udh5NPyvxJQw2mbIy4Q44LdxfuOlS1jwaaDOGR9sNMMqZMCzC9nLAyxOs3j4JOXaTcojI0iU3byiWXcWRoKA+yE3Yvmdpj0WZCWV2y0LO1FoVyWkTJszpDhZSpqFfU5oircrNIFS9CdrrUnxSz4N83EgYnu69sDiNj4czUmHdGJMGWVMM8zg5rPJJ4m63Me2GwTkqvOAiGWhrsEkanixGTW83bAvg8rqWwbbVLOT2GTrNgF1SdtofnpUPA1o9iUakUB+e439ZITrehgRPacdxOq42sFi2QQcvfZm7CGOVc43Ri1t7bTTfd9KxnPevNb37zwebdOFUX5ttTTYCNFlX7whe+YLno9a9/vf+NxAev1SR5BZ9H/2xPyUlkEfBIDE5i7Hk4BoIXdNhyotvwhT2ZnOjPY3QGRVyCEcdFOPuRBNj/yxWzCjT9PLKTsUIEEpgork6P4499X9uvgUHvgx/Iqf/c5z7nLgVF5xvf+EbWUKIu5dOKMouc5JCSWEmySpctTV81U1e//WYdhMNIDC37ldZahqTeIi0h0Y033ujzRbvlhGaCJMuFUxSOGGkPXRK2yo2znW4qIR4xumbNn/rUp7p32E9KqVIWzdwjTBmSNlUuCXgFG6cuxE/WGuQtTrQhCVr5ObIVsCMM5f9MUtEA+gNvtmoeBWgxtGVdk4NLRI54VJks+IWq33XbcLxM02AAQ6Ns1ztKoItOk8Hx7cAs7dF6Bx9MVnRmabQth56ENbvoT3ihhyZszY6U6bxlzeUwuwUlDAi7NGKNt8B5yCbsjzDAuDCA+l21+7o1SUwlb8UWja/cBT8w16q+0BltkQkjONOWacJCmihsragcDVU/pDZyWPYtOPR5jSfh2IXjJTOgq4S5wHpHvQyrM1x9z7tAamcniXs1cPR5T9uhzBTxzi5bMy6fPrHw9ieEamtnOjLMNfGeZVD/0WZ2kmYfmrON8IQTYAPDZNOhF7/4xZ/97GdvvfXWtUMlwnZRQd6C5ollt/YZa/uaFwCdEgD/FNcdCxOR8wcTIU8CzILFs5/9bMvkpGEQ0dwZvtg4ZlRO5a2gYX/BmYTN8TYk1XCwayJCFnCK90W5hlLE/HciBLOhfi4FC3ac7PGKDEQ5tn8teQhQYliFaHJOQRXuvGVw5ck5ZUcxJK72crGv8KpXvUrCjB6hBonZ3PA7+5r7B4PyYdhserijoSNDUwNYXx9wJ6VUn21kjxq6OkGbtNw5C2AY8S0xYmzqCnQ6MtEQa2AAy3UtAUiVeSlLNno3MbMWQMiyX9NcZYV6fdpSI6ODMOeRYCAZolv130dWIWnrESkbV6MAc71KWWXVnFyB3hrHk2bhhGROe01VBFNRMz2ZhqlxQiysIlUaz1itervk+nZGM4Pt41xkJ6shot0R8mWxRVl/9FFGTV6vdlzGEouzvYdJgMnZxevxd3zW7pKBAcLCcx79uVtqSkoFsKhC6ESkPMqJsHj5RVF4GGJ4f78Sx3crzDtkkbBoR6SxSzAwlxzQ4weM7r///nvvvdd291xoL+LZTgLzGKDt+t69lani/8rcdttt/oW0LS8/HDdx8phmcxnf3bmYiAFr9m3MH5f0Y9WKr7ecGTcJrfzBPSfuBGGnvtpEAtK/sI+1/ghaR5RD+gADs8KyvboTQpZhSrQqAWa+7e3YU50e2mrrQPs999yDZmXJJB6RHU6NHZyiKK/g/M3f/E0se6UG+9a5LRhLOGXRYiNiIZycjkabZFVbYEZf8qle3gvGKXq6Iau0+ycyUxP1gzaFqvFYlcqUx+66gnzV9q95B20LnOZoQKR6BKCZxoYpNS22APfvJia0IHMkGwtIJWRXH7hTI+PFeCoxbh2hryGdJlMeSVvSkoQ/u6YonHfjYgoZM8IQr1UJ14w4L6JaJQHSNnesOJgCgn6aaZVqylxYhXA59WalNdnl0LMpJSynNWJh6J6mMwNLRMzgquFu7ecqmLVMMZWxlmshOwBt78r1uDUlHfyrHq3nMqry7c7i6Sr4tfUot87r7gARYFa6djjWtj06AJ8Vl8d1Hp2YwxNg1BLNmibzBmylz5RNIBHWUjm7hotbHDZ0Hxeg3m0Uv/Wtb3Vq9ZprrpmdjPHeL77tS+C0E2D8sHS+I3/iiSeuv/56XxLa8VurVQCmfKTeF9ZCaqQWPk6T/HAhg8zKGQgEtXnrV4iV/XqWbcCFsLALGbJc/zpITPmUpzyFQQmPsWt1Z4me85zn1Cs+xqu15mkKVemihezXeJvKwdFp28psDQrTKaXkvNt1fWC+QhQr+F1EY803cJNchRhaGX7AUlkAUllHc2k+eHfBEE4lxlkEERmYIzqS8BCapVAmOOvNJGklXvLc7pOTlUxSRGhjoSVVOUzVPYUWBr+WscUf9qhxJOi3+YwMv+QsyuThWuCU9UICWPMoX8VpBwbxnZpVjzaNkUSMknzby7AFrcq1A7EKp3rTLUtIKcNGjHuKmEfImPdVO3a7CGdeqs48NqI+M9I+AyrENDFW+9iHKeFIgMdHXAbOUrGTE5Xful4hn9hkFVgODNdbQYVyzh9V5bwFlLv8+gOZTPwpk4kEcDdBDp79qAWhjgAAQABJREFUh5/kXRObHwUMwfr95S9/yac7tMVHH4WMI3ZKAgIYv31l72ofB0lEOEIRH8w7IiHSECBh1kLD7IphCot2xiWJWSfy5CkClY997GPTA5txtBff7iKBk0+AMW+77y//8i+lQ29605u++tWvUqxxl7OLvJbQVvxtS4rd9NWoWd1n1sJq/ouan4Ay6+RXYHY8JbUExkMDW+OSyPFziQZwl0W+eBQxjYyFGkgR08SKgI0XSdEsXNgClWXpS78+XHHXI0p0AT8jK++SgubE2nY9hhEIZbz5sERZpccg5DK5fKkjHhl0r9hWNMiETQd2nzsRZ2BcpaH3NrviDgJpS3/4AG+9Sl9Fp0fsUKFOAqxrH8G6QyXHVtYEkv5aEhhZtEvBhZhC3ikQHa8/4viJ0UY3sE7DzmNYyM65MnjJtracn136fKXTsrkWYYs/eTUuyBOqeVep244OXGYcCMf35GbTgbs+ie5KYaJRs9DMJtC9jdRvln73gcTU9o8JLHhlfu2jiz3hNKB16UIZLwrzhqS+v2CFyGd8uDkylnZ6Asz7zyUWhjdRuyVUEsgyKDcxF/5BPGyONVleacYkhIQ5PuPIN1m9VVazcLOGWvIRLbg46xmHdVDsC6wkAStQll34Vs56HxTCbH4JDqk6faB7eqEbM+reRLIx67r99ttFrX7I5sxEERPZXyzYGQl9+GCfAd9www0OQt9yyy2LFffuhGX2+qkAX0v6GQn8ZpurMAM4dyLq/GFpM189YJUFcOoFYYqfufLfcW2N8t/ZV5Sz8eLYZGVyGopY7HmG8fPG55y/UXDfURpJ2KDSo7KO2Nl8+ApzHTjfSM7taeQ0dJqae2C1rV/YzoVZkIRZSZ1IxSuOEw22Ii12kIac9oorrgCmXlor8BLTOPkjPXZSwKlL66CIFN8QiwSelLgBH9/GFkdE7okFq1DaBTNpe6uVo4OJnDpJcmTLnXtLMraaZY9inY1E0QLr1NXWrCrbzUAqZqkHHtPK/rYxknUkqsO++o2cH/iEJpGPLvbkqlfxtb96ikqXyOfMLI3NKytrQM78u8+Fli655sJ2dDz0x68P9C3A0QlbS4C1ufw8ZCAtJSswWflOe23zKQA8AuuXBHgQnj3p1KfmkBqCPCzrV65OJjOy32GtfXSghlPzW8czcgpVVhBwoS8erdxW2/XSyrwwp8y/iGfmXXxZGqeD9IglZL8iGce+hDRb6AMMtDei6ze3FMJGiUMsiHBzsl8bBnZBjqUbNudcX/rSly7MH/oe1IGjV56RBNgcuO666xyBvuOOO/zsrW2N/nw4uqznIgBrfJWUz2KSEN+X/WeY2UGh8dyMJuspdvGrTtjPLoSwg0BkjJIclpGD4W5hYGTZShueyiLaXZbf9JWMDk7fG0uuuN7sgtpZ1Ttri7xBsldVwtlfrhZZstTIlsRKuuB0d14Iy7JlIYulTUfBgcnrrHeYAvJSeKS+AJh7l10+uR+xSI9/8IMfwEZbPBIF9yBT1ZyHsHDuxLiUWyFE2uVGFQL8fhVOwcCfs4Iy53x13KeZzItHmOOZUglbvdpHgWTQIwE2HPmtC/0aKREGTuXhOrUehIyNEuAiFS+E3Ge5AE6ukJjDwsHWCXAN974H9yiyZSV2MRQHpvnwY0FzjLtJcWBOd+/OXGYk4eEgWLx8kj3jWBsLq5DO7DCYq6YG9235CQ2sK+PsdIkyt76p79hdGmsxIA9HXMBayLUApMFxc9Z7YpPD4nfWknF0gMxWqzCcuyNOyd6PTtUhCSAB8QavLYKiD6umyThJ4hB7A0KUQb+sCwEPlcjbKqsZRzv7W5QI2/zHI/+01S5d4qLZe7mIcAsJnJEEGOe0ysfA//zP//zyl79cYiC4325SbSHEozThSIQg5j//bU9vkAb7w+rPpBzkeDJAccZ//ud/Sm8623QxeYIbSSD2Y3EEHCwRBzljrNOKN3J2T2FwRBAgMEJPSBqECc6k2ZJqVt5OlCEWgljR9FtWogcukwKo4UKwKdIiBPpgCVxiI+8VYAmtiEi6q0eZIaq08ogGLkceqIZXUCMzdKRZE5htKVs1kBQRKSJt8thp91M30Ba1MFS5U4BNhqxScAmJ4InYCXwfPn6QjFSeG4Nf+zVKYh/GOlHHxRZMh/jOY+GHijT6h7078Mt/LI6QmrzXYFkwwmD7qmVk5BUwCklJBsPZkYZrX1HLoiGEnZsw/+N/KGeTJ5W5C2gS04gm1aQyd6OWHXtzoW0IORORkwLJQKo7BdjCEe2Fs32FclMJcthouMf2bZQc8Z1XwPQV+gth2rqjpGWtcCq48irs6EtN9ahc9ZloJm8sYcEMFtqGHYDprzLNa/WkKGkp7CBfyKPxZV0RzDQxsIwnIc9IG8y+T4FzJAHWXUmsym3NjPSsReU7mpGu+VD67JPdtXhGAOA3LyLnffiCdM2pZfVzhJKFvOKj+VaHs+LoF0LVwcgw9STAbKnd7007HdTVVLbGJ4KldXZKlFngo4gaYUzly172MqftPvGJT8xrajYV3UX4jgTOTgKMMSr+V3/1Vz4GfuUrX/nd735XKNDOhw7nJ/2IL2GTtMTKcYUgHY7AnJnjmh3WPOJOBiiabNlX6bFCQDmbsrNtokNJoCxIw7kMEMxyTipXtBkRAah71fQLzLF1X0dxNjpaaTVHdirmdmmLa6GVGJ2qP/LII1J6ll0qItNLem+D184M5yqVxbvfoJIbZyPUSqSI/5nPfOYLXvACZSeCBDe8kS1lbWXUWoFUJi62+/HHH9fcIite4mM4LaZcEOmRwDs86i477eoh0a9LgLWnoMcCUA5a6xcxduD1SziIVDBGFg78P2FnQwgEjMrAyI3XbgUTi/WFGSOqCBANdaG5X5m3I6+qeVvIcKRVljkom0p3F0WNNeCMaZH6LMTQkJwggMpyCWVIE3eXUbNC75VlFxoIT+rdqZ+jXKk3p9pXxO4MjlakZ/VEoVopX3755eaITg1K6gPgTi0NHPX2T7y9qnoFqm5y6eWHP/wh+tXkAkY5TQoF/+jLahEJ1CtrFoZPDVXHnfq8BUwnf/3Xf10N9RCMpknu7IafVwDv11miV/UWa1dddZVHuuG/fIXIvIXZK6w5bueThGqiHpj/3watcXEQI69U5hXWaKnHhx9+GIC3IVLB1yuxWqYhtC1OLJuGarBGzoRm3l1yySWmMHPBNnpldhigYHN3WYpKxCn4Bp/K3FGuO610xJIYU/W5KyDDlCcN6VAqaY56q280gVjQT7W8ytsUqJxWcBqy9OLuMfcUNFToXAAG64GNv+rgOfwjslk84rWdayxWEWDZ2gWYB2cnAZdMVjXZU71+Wy+WXiL8iLoGoio3pSQNrbFyPRZzR8SyKeZZ4JEXY4hf1yw41yJhDUQmF+bhZ8Lhjhk9HoTp2ELmLJ5Ri7eyfM/awNOGgh35Z7p1Kg/wGNV6zWtewyaw/FlXPUC/F7uYKIGxYH0iikWBiWv9IJZA5L3vfe+HPvQhtG0xuxbF0Spi8CVYTAwXB3OGmR0UArdtizvjGwkwgjnsmkevWMZqKxSbURlg6xhc0Z7Uq7prCzVAKlOumiKpXwMYThyJGJLKBqcomcMWicpa3XWqBh4hrAiDV5BdSFekssJiYYddDgURv7jW2WYBLscjcrV5Dkygr60mZKU7MbQsAkLciXd5F50K92HGMiTu7HiRHZLqLpjzj6DSRE6e8B1hBTBLoWSlQBSIqauDX55gpVlKX2c1QXZgOo8d5N5WTQeyfYS2wBSSEALAu1fkb1xUuhDs7pUBcicl4j3/5lc3zbPE4JX4WK0arYLWJx7kD+E//MM/VCuvXFKpnO2UJQa+KBRpEYVHSR0FKGrphmMUEU42JSIf2ADLfpMAUwNvU6kejCb5lokiudTk8rYCXDRQm/9+c07sNR8VgHkVbLnnrTvdK2yBoZmBITRcp5xXWVtRFlFlSqYeTC0zSZ47wYcAFIBLulhUpYYOKyCe/pNnFlPyqjRZJhndVk8scj8zKP2yQhF1XqVh0W+2lhgDkO60RWRGLQDueQUMj4hUk0tNECqoj2XzimRgiHy8oiRUqJqo8dbdpV5unLI7GAiTAHvFStSrMMUs6IUOW78DXG/9DlZyeykuhaz6FKyDJLe3ohEbpR5CFysBp0cY6A8ucnnFyESADFRWNLzSu7ux4OK1InBq/Ks25/+g3+ijTUeuegWhsrtWnYt6QJhXLVMtmLftq0E8LbyyRR9n0CLMzqsTeqQ87BKCmQiDy3com/Vr1w0HeTQ7YDDpzI5BgCNWGl9fyjB0TMG+yShdoquiF5q57x4Xhb/YN1sTu06ZUB0WIMknBmmrDIBaxvh3gMEwuR3j34HZ0yM6Xe9///sfeughG3IWufbU0UW0W0tg5sB0azrmakjdLd5/9KMffdvb3ubjCsehCzNdTHmLKVdIFlUII/jKPsype9wtZMt/YJ9zFQ8Jp5QjE7ERtw0hL26jRhAmlBT8Vfy6RV87NrEEiJgoIYLF1hAKeQutHAMXnZ+qRnCiumSVIkXpXDyHeMKjesExtGI+GQWubT6I2nkFXZBDuvAolCQiJ9nsC9n8sYbqc2XbX8I1hIkFRTngLSto4q0mQmE14unHHntMAGoZVTZuc0m5yG4LhO9VnI2CR2LPiLRgEUJq+m9byFVlGMjTW/IRK0uKMCUnqXN6AIKZ9ORpKOcFxdMq5S3uAPBVF91wEaBLpWSPPK0O6CWQYqNE3jpSCQZ87pBndHLcWj3CtHIJ+OxqejRY2QstjnTkfyl7NC4+Jk+TvKXVaCZAI2IUUNtewZ+YXjSZgrur1FsKoXetUq9QYasRTHqgkv7o0exIv/QhiYdW6RGR9SpySz2aCUeaJL+NZNTrNEOvnFbGJTzmsb1LPGwFqyngeis2evrTn16PbQFh8v+2pi1nR7StqXL4qse2gGxXW9OWhcX9wAXNMgFXIInaKpIpE52vXdYWT8rGyw5Yv14NnKtY82pVKw0NevJ80kaAmho1atnKBJ3VNYE4NqKmrnplY8rQVD0lVw5m4xsiKSHLoF5N9g8NjdmnBnyauBNF0FISylz1CkY5Qw8VS5WG7q4SLJHWvr16qIxUpqElrUxnlXllrknFPTJumVDKunDpyz9RV5DRWRBEKnbq7iCVcZHb0+eqBODSnbaoZZxTU3dNlNO1jupSQ4BsQlZY+gAgUVL1jG1N20KyhIJBIf+iRBnNxrosSb0aL2jlkmECE6KQ8Dj8Id8iLN11CgZoH2SkF76ADCn5okSxD34HcfKS2DeLtxayhjE14hPCVM60HewOcBtlDcLsr/I73/mOfbgPfvCD7M+FOdz7k+0smM9aAkwo9OyNb3yjc/8333yzONJv5GamyS54U7Nl64k3i8RnR8KqCspxx+OK8s8YdxGXEERqx/uKOToMYj+bWqxqAkHA9ktdIEVdrKSIX7yifMRQQwgl7w07aFOgqDLVYgd50olB9QCD8TTJXWJJk5WxLyUW8opoIZTQSvgd+5TVCDrtHxJLVkAfffRRMvQoKrV1/I1vfEM+LD9UI2cW/+kFNorkLipVIwZKGiwgJnzhINl6m37di3jlzoWkT37ykxa5X/3qV3deebRxrSMBerCl6z5YvwY8GtDmJHMCfaT6OXSO0OhnmUONpAXBRGrDyiMuhMvumuOXZBSsywJQcKUjh2mleSodRk1yqF5HAGQgibyNY/bMvSJwV3YgwdAxBKgxWLlXAiDcsbRRrxRKFZFt51ZNe+UtUh1mUa8vInKFTnd9+dHvPLb1gVyVL4E06GnVuXslau9U5tErfNUrnJK/OeUiW28toORttLQgvapypzDyqgO52EdycGWhjSiUozOGplLQAxBPSTIQzItlBdoiZ9NvJLxKzkaqM1hFqubBUDVVUJ9ZUDVVoLGxSFVTBTRIMuuxU/ArhqkhQIXcU0ONTcbYHJqmQLZ5JXs3cdTUVTQTgulf9RriNHIIcjhZtgCoSSUdZiKCHLBKdzMCX5I3UbtUP/WBaQ+uQ3VenE+SYMMjATbL2FiOCTDLjOwA8NFZ0LR8gDDI3fXlbuDgb7tIR3UffKWyAOYtMGu2EOC0oMAxZWV2u+6yQM99rLIw81I+HZspw5QFPiOiLF/ajs0p/Vo8tQTDnFqsWTUBp+A5RRh6zgVbZjILLCxuLWQN44/MYmXGtszCcsSCWUveN910k1/nffvb3x4nvhzyLlISCZzBBBhjLMuf//mfi1yvvfZa/3TL0rh5wscwdlvPusVqDI6sytujY1j5fqH22eNRhCe180WfaK8zECyLvOWJJ56QBdn8FwYJQwUiohCvWFtxEpfGRPI9NKHT/GCPYrLEOmIpGSlGDJNL8JQYmjVnNCuGxoi3HfIETNiJ46y38jHcJaoTpZGVvFcObJMkaSFITgJyG3f6StYtT/Zx79VXX+2RZNQLUCwYEXIoEc6iTZ4Mv2hP7AJGqgyYYPVI2UIezEWnvvIoyRQ5iQLbt8DgRKqZKOCQAyvDZrC88mjg1BBU7iqFznCqF02qd0HogsGqKnHh0e4EGCsd0t1QYsQlwMB43HSKEixwvSrVgNdpRa5EigxvvcLac57zHB2hkMRsmXpVimdBDYbAB497LvLRhV0OyP+77ld/ScPVqcwjXx533n+rF1e/fq6aiCLY+jSv6gUkJXQBsPhCUYX7q4CPWL8ddxsRbKawS9VRynTPBN8Iz9bAxiJ9mQiieWHlKl3auot+w+LXq+lq08eTmmCAMwVzreqplqvfECQ3l9lawFVg0Fz9VrowNK688pgrU8wEt6LEIJj4uSsQpr6MssMRzF37iqWFx1uLXFmAYLK4G0ZMcs62+B0sG9G60FwXIJWlkRJgDQXHbJoa7gBaLszYZXedFlkfZOrPTf4nnfsNW7koArRie1155e5irLwN8vDVua96pb4D2T56GwAUpoCSFmBiWXPcMd0KljuDamLbA4AZrCyUI49gPaKQoS4lnJcGeiJKoV2Gezt5zkvPIbGRMGfKQdPY+PTdeydDs8+QQb4o1UKPhfLf/u3ftvj1+c9/fk/qtLsAL2I4mwmwceWf7r//fhtQL3nJS5xD4EezTSFK9tYklBIsas5srYu4wKzFct8d+Z7KkUuh/9lgjUyYko5kUtMyKBaXrYkk/B5Mfn1HZMDa8jTMkIBDJqMVHThWAozaRHKigahfxanUUr4adhCZsgBucKfFyNqi7AjEI88qnJIKioMptpRVKsvNiPYkirCJyUjGSQHrQQJljyRmXV/wRFbyW6snepS/WUQQCgjCBHzocQdgLUmBs1EW2OlRvKgj/Qr7clegh8aC5I2FXpySlWObhl5JI3l9maptFnSahnr3FirBYg7KaoL9cEcgejdP88jVEaDeFeqOBpjJBGup9DZXtl41f97znqdGGiwBBkYswQztqm+QuKuEm8Qo4ZfdZXRCSZYSisgU3FErltJjC1xvF1swsubIM57xjI3IDjCWi6+NmlervRZMf8Nn+tOQPXVkuE0lyOk8IyOHUa6pvadON0VbwzTjGPlRLoLd7vfhBumxeGT612n8iTxqZWqbobGu46067HceWQkmrjAgkmVmoxQYBIuD3mqSgLvA1NRxfZDshgyHiWY/GdUXvvCFlBAws58rdkkNVZHAq2QkdcQxJZ+HhE1TgzWFdMQsxyIx8iwkGP26e0u3mTgFy53yTGawLsrJ4uELKka76hW8Yqi9Sndq0lHdC78aVh18ulNfMBMLXJIxYvzhmdjkYGBcXlSOw1Kg0romln0QQIDSP46PD1q1GLqPfpeAE+9UWnRKpelkZxJtTaEpuerUydY4d28YZn19SfMdJWutyu7IL2KYVwJdwzcv9uNi47Tuu+8+mzl+hO1973tfiOGZGHHWn9c8Lnkz9o4jSY5wxGEtVoZL7ru0Gbs7JCpJCNMpjGBWpE/CBax1kkPs27eUfojmQfJkgMUT6ukAm2sdjvf1eEjKB/tCA4PYjo5yVFH8J4YOaxvF0PnKlAsnK+yLdYTjLqpuwwH7cjYhCA15xzve8c1vfvNrX/uaQMq8sIsC3vm0JKuaSJglih7lyc4UaCjeEqV9//vfJ1K0CREkSyI8ZFtwMRzYLMFKBsysL3/5yz7C18orH9M+8sgjAD72sY/xVWjTRKdyV70LNeDJ9i9gUaB6NS6vcmnrEgI6M9b2pYwksSlvih4FNf2LE3JRIa/MDvhBYlPuTUNqO6jfUA3IwcBaEIMv7IgLgZGwQFkBPVjLEpvAFP0oH8S8nEqUGxEi2s5PG26DtRx2WkoMh500U2m7BJhAfEBLSeAZHEeVhlgsC4AOkCFNHoRsqdpT2VSVIw2qq3ltZ9Kn+4Nvt6BHQsXYbidV3REUQ9dZi6SHBkvlRgJkZHBn0m3BxdomUkqW0OCCZCQR5vJjcga9bauyHoERMi7YXgXWpn1bYAqsFvwKZpAuMGIdirJRJ348vyngrXqqlYbAkoerSb1CEaM7qNS4rEha9ySW9E7gTmoow5Aahec+97l8oqH0+2QqY7hQTpGoCuMMhvGHn5UzxzkXdxwB8Cokde7polOpiZyHpa23oaEDdpRHrLnCDk6xtifadMFl8MLGN4uwR+H3WJ3SLv+glB5S7E3n+LFo3q5fA+26/fbb/Y8DP0WZ5dHtUF1sdQAJLDR2mYVztsyZoi9+8Ys33ngjo+PDYKGek6iQs3SzdLEcJJiVBgi++b89GfGjMCsEYTdx5BLrMC48VicBRphcxdlUK6xOnQEA7DgTYN5XrKCmwjWVYQTMdhztggGdVLHtV1SRDFDUglOZ3iBhUjhZqzACL21zZbEjZvkYb20s2IIQ9BORHWDAehQMCa0o/wMPPCAUBm+P19okYOIFLDzCFABOWiKtXgAEM+nZNAagOarEWLAJ3eQGHi00AEN/XUmiHLuQaWvuf5IhQ34OOOkrDFZnzD59GU0LE8BUhmUdudT0rwD068UTrn591WRZBHcE60g2TtFMUKRNMVahTXOYKzkvhApEnVPiqSS0/EefPOoF2ssuu+wknJ+g2Ya84dgiAcbmuPBboa0tU0sjMstCVc1Qnaac+/hwdyik865O5WIfzUGztSVvlRDAbCSHFmfKtEWB5vRfTamRdMmmxMGAVxE5TqFW5q87ZweJMngXSzKFgIkwrETMoGU7eh7N1Mtgc8QwaJJY64wGggkFPwiZSqY+nxwHYcra6ugcJ//dC3trZDk1CC0+mqSr5ilqNa8eI9jg0ZARPpcZNxdZgQHAjBMgR4B+/HIWaWsychZsXXCCVM+nxPnmv5HxL7kQKbKCCrA0Lz4Xj7DpCBLI2UxvNQdcdBbyTs2BH0l1XuXp008sghAn9Yxp/+2Zr3HWTORgRb50++yxbIK47r777i996UvyDr9ucGGO9QmNbNcSnRDpU0ilf45A8y52gBloofmqBGMKtuXD5N8/xlehtgopL5/+PoVJF6V/PppyjFZswcT0wdTwLi996UtlUCJpaZuGXBqPz/HUMnkaqgFGEwbxTKlMRLJ1CLiqi/INfR5tKThU4zixpFG6K5gQteROvXkXTH3rW98iKBsLmvsGHoPSMP+BXVAlIvn6178ukrP3gncRiYJcFyW+IUwmLDCVy8EgkAKjI/vqBGhn5rbbbiNDTSRL6kM/ZzbIiPV+l1d+ZcoHsZaf5OSCZpf5CJtX4iHZRb5eG0QyWNmKhaxKXCPAl1xyCWZx4TRsmuAXIypJDxnacswdDQnCwBdyvVtgck+MKLyjaRpixG4wZSM9DErvYU7oXG0XW4gOGxrp+rg8982C9R0iJcndOzIQGSmorEeYEcZl008QO8o2TpXhTuoyDnbgt4aVntNSI8toEIJ0Zcesnligxcim9rOVZ+SgBnk5tqOASEZeJQqzTj0iLq1M6uBkG0EyJrMoTzpFDIQuhFEnJmItv9JFbTkpwDmVM0I/K3T99dcDcJicJe8fb2nbsifIaGsGy+38bcsGfZWrYufltLCxhMy++KGyU5YtaTPyiNrdVcojqWYG1fAX5hry6H+ocrgGNkNj8dFKq0r+xfEE8F5xzZYGkjazmRDa1gbsrV7UeEVRi0GMZJTbmirPUtDFKl82C/7QLx1i3wh8FpyngiS8G9D8+/FFkV161U6WrSmEzfV3f/d37373u9/5zncKe1o13hrtxYZ7lcAZT4DJjhb+wR/8AbN7xx133HPPPfYJ9yrQIyI3jTOTzUPOmJvJAWChD3fFrxyRtq27DkfumAqSEbMSN8+P8jQcuVTEla+nWgJysG1tQNM26ZR5/SkRSVoV5R0kHr2SmopvXPiqGEIyIFk9H3L8Kst1MjnNjaazxylHLFJf55nDLDxicfXuiCQHmu9OMmBEcjBDKy6xAyNAERwTVPY53TWBwV3k58A5nbFX45IxUiGrD/WvtlCeoekzlZriWndGxPIEiUGOTg3VKKxqO1KPGNFVf0z7TURUidRJOEJGgEN9+BWFIA/7Lg2lRuPbNYWcMKEKa8oYISgxnGBR7BiJySRJlazG5VM4j1gI/QaCoNYO6J7oLD0p/FWztQCF0dEuukeHjbjJtSk2oymzsgaUvcoib7AgH7O11W7BDYIduFLyb6LpFO/KCna6KofZjhijQ1tIdVMNRwkp6dQ0dPkeQZmZYhngNFIW4JRVTnFVBgUS9sQwKZuABn07jla1gpwCWEFbqzzoZ23YYbJlRadoGhhXq+oeO5TAiYZUMinsHhgj2IfsNNzo0a8/GJrgzK926cLyMa6NziAqwPkBBW/DAqtYVLGEqOUySM+esMvGuLs9akPGARlorVyayI1FKfA4m8PlpTsTlg7wWU7wATPEMBjfusxuFltzb1dROFh/+EoUijcoBnmaMocn4Ig94r38NfaPSMlg15TWgj7rMdH7DyJpKx3zfvWrX20b5s4771wgvy2pF8uRwNlPgPHJO9oQY15lwk996lOtE5exPpN6wP1YauVsBPpchUkuTJ8SVSxWGqyJ5JD/m0IhFyvSYtekSTXQbHGuYFBm/pRhLphx5JqISARqASNkjk15lVeLe+bs5Z9SSpdoxsXaZr1ZfJBhgjk4DZMoQVlHdkrRJhSwjwqJVXZd28U1oCh/3eteZ0BdAIyvwNHpBkGGtj7xNfQf+MAHbHyxyMpyV5sS4gnHj+GxH3vvvfcK7KTEXtkH9hvaVkz8aouNF9+QJwyCEDbLBLQIEmUcoV/SiACBS5HdF6BXLqeAUIhsIY4dD3IgMflhwtwgdN/o0nVi+rWtDBDvCwx56PeYKIQcCFm0SibYR+dEvYKKfMAbHZgxYoAwaHS8QpWC4cAvIkWrKnEKeV8+XrUXnPW4FrggZynojhBsjxvZGpdZME9EYlxyKID0yC0LPcZolzyNcurdiPgmwqTIktBEegosqm6UV41IO2ppVTWrmhTywxTYGSQRLBOhzA+6duyaVOU2E9O8ti8yyfYXwZqPKbNmLINXrIrlufziettqsAzeFMYaSmRHiIFnHzJHKrT+G9Ba5Cwb2aJ/LWSHI/pJ7TuVefRFsZXKKBWD4woxuw9i2x0LbzhYLX2Zd2zaFDtQbKagVXB6zMoyL6PG2qIZlMUOr/Iz17qjRXp0MZjq8WjxCF9qyNylACw46Yb0OB1FGpTHN8zeMh3WNCsxTsEZJajSBfuMtrQtCoPEY1uft/PedUSrLTFQIXKYF/nCseHdpPDvSNFpjs+VZM7INQqt5e2yEVLEQGVB3M8+O+LuZ5/nnaHVy8XC7BK4IBJgUuMgBf0OGvlhRr/uw+vs2/bNPlTTEXJgPI19QvmPAk7NT142XuoUGefG1h6KK/kIf7lALpNpcxW/AgghBTA1wm55srLVEGlnte0UyC3wKXC3NmDVeBRTkifCnO2BWcKTFDf3HCcDKcsFGRqCBElJgHlxAUfrvCvysJtERRMcfOITn5CtQcWj/83f/I27xP6uu+4S+SVbgFzEwJS7pLUgc0k7kYE2kYRFfZVZNZexk49YTXSCHQv28MuWhSnZwpIM0xZNpIgKlhKIizPTkU7JUMRJyFw7+lfl/yIYXes0XDvcKG+ERE2kESIn3pGRQ604xU7wEFdJrI9H2mNoMlLuOYKlLVMA2FBqi/5+w1U1yJYrepveqY3IDFNJgAlNL4Ysbz2CnLgraEwpiRFPVrCKgD3Vi8y2yGfmIsbIRmLRk5LtXPj3iseMowDpAuWUU9k4LiEAoq7mL3pI2J1qMTUhdcc7u7GFopoasXsML4Nja71jB6IA7p36tdRKg41C5uZa4LUACLBUR2gsGGBlRgNJI1uv3jKSV155ZSz22i4KQMPIpGragiQ/TPnIhWWOn4oTb8F2KSNAcoJl/ksCzKrPoiQZYlYl60dcXlxPSDU7XLHDqUEGb+XyGDVomfLjJsRrctWleQAyTFk7S0MeGby3jLOfZlBQU05WfqJsZC35ISAXgAqNOr0jrKVkozJU3CvHp8c41o2anzQw3sUMFtMNkMMCW5iL/bGPNjMa/hhGjynQge2GGwZxkczCbPWzuwtM9fcnzFPHfKEkwMZJQPzd737XZy3XXHONn3Cww7Cdui9/yPHF4HLYjK8kRI5nR1S6InDxu6bLp39HCiU20v6f/vSn/tWtj2ATBcKpQCyEI2zlk1LP+bFf3qpPQZl95CNZcNkssEQeEaZ4iBuWLhIveeb3hz0m2WND+eZyqFBx6iA5Wj3mrpDuBAeulN3bSyDlUoMS3/0y0Ebwhhtu8PsK1FjKilROJW5V4KJTvXi0T+5fCmkFICkZ2rzFjhqM4wVHvtHV3CzAIHgzAoxwgQWXQmviwCSbLo7RihwSSQiPAKhBGJJEGGx9JcCp9yrTCv3gPUpcxQEGJcInHJWdS1tSVUk4SEU/WQVPINFgt7xaWW3V5HzIdC5mGryMhQtY8Oi3HWKvWvyDGFZVaqg5IjPoQSsKNwRCPZxiIUtsEx0/VAaxDsCv6ncf9YhP3LkP5KtwtqpCG61NIIPKmVyM1apWm9YbI9Mz03zTthPhpQ1mQRRAOdm7YH1i88OAkQM9ROQs3ZmhctddUNXot0gM0xbbp6wZ5clJ7+SKLc6tyxDKqUJnFh9ZD1Z3EGGxQzJzCTkdxe/ADy1Li8F58Q+ys3ul5Tx7nkj1r4/oHoTWAaej7fOYNHUQA8wuItIpq8uJuAeDVnJ7NVmSZp+Z2bxiZ7JBDacaRPJW/rsBAw6PLeX0yJKn4N6nKvSsqveWzxKB6JS3haqFLJ1pKwcZPMVK3AkP8G4S+Ulz9nBpbIolhCWhKrEEBcgS+aYCxyx1uvbaaynYo48+atFnUwwX4Y8ogQsoAabuDKL/CWyl1u8J+Vq9Y5WOOAz76BqzYjIHv51xZYbK5lZBp0szTHPJQR4iXnEAkhV27ld2B7NUzaoH9onF0IPhonxc5C73SCLnMDDLmBwsgiK6LNJnt4cvBMnY2ZGWviYhtKzgoymvxEn6KqkqsP6uQb4KbPBtKsHYcWVhMYIqxtq2APovvfRSu5eGGBg3gx3lIMSvSsSjLYuRYjgA8hzkcfZeKXBRwmJ5Fw1R0NxpJRkdgWiOR4yIBsBz4cJTwb2NCA0jQJTIkDUkRvCRsI4AoBAlajAeGYIkZAEu+NDW513Q6RUYNPQTIbL1CoBRwK+gRxlT7nWlL48d5IivV97qCMt+kiQ0V/PpBXFSnKW7vnBnP4GeKLvgJ7GJ0apRQJu73t1TQBg80+nZETLCQbOxPky/QlJRpmllaM6J7P9ntvO4NXfYMQpbN5/S0KxxEaDTE+ZXop+t9WpKj1vAyKMs1W3RsN8Ep9ETr7YeJkbYdO4gN5c707kD0HmM0nY0NpVbE1ZdWLkzE1k/5zIMK8J0VG87BQLxT+OoQVYzO293f8TUAQyCmUhJsuq6I81kZfmPI2And0S1tnnG2t3qgwsX1cRKsf3eqIRKBWMaBrkhr1ih9kIwMAkz1wYh+FIkzkKEYKB5YWD0gQKf09f/9b/wqN/gr64VAsxV+dWMwZVNq8w8eEtw2/yky0RnfcHKoBipf9BjCaxF8sZItENPjODIBB8hGKdUyO9dibFtJ4jKSmdGWl18tRwJXEAJMKHTTnmRc6QvetGLbrrpJl9LMohnVWUlDCbkgw8+KKMwyc1wWY1IPSwnhVuOIu5ICUvUwSAiYd1kj7/85S/ZOwmYAEUsyOr54U1CSOYWrZDJJAGGhzVUjodzLzBfjwtEiBGMwBr+vKI/u3yv2CG7/8hH6lRsbVtbjx5dHbA++6jK/rB01Hq8lJIbhkS9/VhppPBOzmZxmnwkhGRFSsCUQeJI+srZ0xxuni7xZ1ppDsDeMkkCUKk5YgiQSK0dAC7aWqo0VE/r7Crbl8tjB6B9BJzHQEJO5mpQaChRlfrqKwVcJCipemCdBQhBCbKlqZdcckmBbVSA06VJvGbuKEwlJXFNRFhL0VizvuBO5nZOJjafC4z/FutfccUVCeb+H3v32nTdUdQNvJ7ySzwvc38ES18gpRCVEEKQEA0ISkhMCCHKIQUhKcLBeAiYICJgEIgYDgmSwkAUg0SOgpaHsspvI68sn999/0k7z6y11177eO29771erGvWrJ6e7p7unu6ZWfvaFtrgyTgqRz4Kaqhf1QdMKDnMi/Lqou4hGJ1FeUuJSoOuJoxQgzy2MIdQHiV+PcKMmn8ba/mpfgxvDTws1LVGw64Jb5NVM0mLIaDD7vLV+ase4HHEWmPCwU9cmQuEtgA4uolEDoBVP25nfqcdF0sf0ZPltqWQawNEkzfXXtJAA3lmwX2LircJa2mLmBpl6teuVoTsgHnlQ7k2MTbdZMKVP5u8skYJW1r5De0cdHLiDAapNSfmbpI1y+jFZBfMbS/KIrErM3k/lQf4qO+EY4LG4/4nsjlyQ15CIzsKFq8FRevFbxikDPfcc4//KGk7zZJKKdgcMs4whyCBqysBJnE6+tKXvlTq+4Y3vOFd73rXn/7pn6phEocwGFukIRxhjanb5eO4eXaZsC54Z+5JKrLF7vaDqp1CMBgHZH5yyWrcfQlmWwmDJjBeSa6LMEutLgVuTjRDJo5ZmuxNP2RiunJV+GL7NKLrVMJjmxvw7B3AjiSgF5MoTgWdNpnlqAbOIk7bu0SufSxKIh9ePnO2/ArL8ivhmi9gVcJMbqI9SkIUGuqFbngFUr2tZqIzQ9gcyM9+OkhPqj5CJmeTBxjSVuMSSsKgUz0qCINQJVU2FrAJUhEAoVYVEwBLOqrgVXJscS34hLaISTAKIJfhW5Re6o6GE9FEwAqGuFDlwv4iVC/0Nusv/WlnUDKc1ewKEGrpJIEnhl57KXp+j6OQ5GCALHB0iwWjwKtW0g2DzsRwR3sxGw3JUQsDaoiNS9ngqvh3BO/0hB0MozMHP0WlBnMgjxrGVEJPtmI1W5EDtYGn7gopz0RuvvBpiemAwxltwltOI+TurGERSC3qjeLZpHKagE0wb7dtPL91NMvuBHIsZBNCSyrKW2feiogy3HDDDfRfpMGPWYrluwLMxSUCUSCHtLINnjjBAQGtTJQmWVcCMDAgC7g6aompymMpFDsiBGW8HDU7E2K/PHL/8z/+3dFTTz3lp0mvu+66U+V0Qggn8OqqS4CNGU21A+xfIt19992iroceeiiVmw8nkygkB2IP1mKtUAo9rdzLoOQtCBPHIFUYqjyRURQvey6UGJGnbFJJcitecSkLWcxGAujvf//73gYMkQp2AEwwglFpm3umHHdv1YSR/JNGwB1faoaVHYzHmWDDhmvXhKqirSMg3A2RG1/HckTwSTz8IppYzWlt2aydZHKzl0s3qARJ2siVmcj/tXLQS41NHm2dCTfHWy+QW2oiLwUg2ssGu1eSZ4vZ9TufGspjEQOtxCba5W7gNDeaiIdBjYJoQ3psdQY8AF3IzNWjSsoE2Ah2CbCowtUyG23RKoW8qsoWMmU4kwPL7eXAGg5hVqqR/LiqyUoIsxRNCBJg+il1X6l5dbphQdfOJBu4HF/fEFvXnF4RtXySHioY6IyOMmZVbrKd2PW1xUdrE645CHHRrulMNGlV9EIGeoK2pa8QT0vdc/JlKfyuAXgnE4Fe/Bag1TcOZ2aP7Sgo50rbdlBoZrfU2OHX0AkjuZDRX+SEuybH+IjNIruVT1UqBMbPZJhTTBYcbPv2NMp4x5qLKxOEULlSD9OZD+tMcDy5b4VIAwCnmoUA9dLj/ECAOc4UmQmUrBzQ00SQBqe4xWVKNeW1Mo/0Fkn+cGSLZmvcYks/eX0UKyCGkuSZ+aoyxKnrkUce+fjHP/6JT3zC/z06/NFZlcerBP5qTIANLYd12223OabywAMPCDrf/e53q9xciXk60TwXxpEdggLhSJTgwinHJMaVYNjkUc+AnYxFpHQiZ3j2TzAaIvYUzCimDVOFS8EiIjeKcieWCTbkZYzkLYQsk8nW5ZWJ4/JNFGIiCU4nlgMMuYlZ5uYHsTi7VO6f2bV7jHBQ7hwygWB8Jiqc2tmQ0phQyUqgRrDaKpOVtCTTlTV7+7rQyktNz9SDwuQstBpIVBIgbfGK5EnY1F4jAqGaJKW2EItCvXiVxEDX3/jGNy5duqTSCkX+zZK1J2+ljs4OgUQM/GCMlBzJYo3KbrC6RwC5GJ2E2VsEi9HpjHpfH41GpcBIEsuWA+zWIvgFNBf21xBnlC+EAgIxFqKBrFxsi4Zw1A0Z/ckOsODPGgp94Iq31eN28dSIdCxs2Av9JOeo94ao9t8c5cbLusB2ZbIGI0XAesNkFEw3Ll3zWhwU/efWWkp0Ub209VXWtf81wIEc5oeORefmBcdz+Fhuc0IgAEwlPAm73rzHw8RQylbkVQ3J0ASP+ddr7Rkxy8rq+T1RjVU/kZgZ1p6weVNBvRXkCBaYOVFb+E24xJ7YBmSATejVY2iYGJEicg8FVGHNzI5gs/AhzKpLuSY61C4F6wBw6vJ/VT/4wQ/aPLOLdiBD0NF5fpwjgas0ASYaE/m9995r5nvwwQflwG95y1tUbqjKDMOScBKMOdLfG4xMwA85CDrlCTIQblSOkd9mmDgvul3yCCcIMxPkY04BsUrhiF1HU0IAAun8IaePchsOCpkJaj4AaRPYv65VGB21tlIGCLktUL/0qFV6OaK7fwVssPIfrYpsKagATtzWpRCkR6tNQgAMrosM1dj4tZQgTPEqq54a+oBNzimulYhaw9ZLAtyvfe1rfgvESJnJTNhmbtssZGibRd7olZ89tP0SPOkl/epLMkz4Jf+MpqBZbGTQ0S/RdQfZggVedy5lZBSGYnlRATA5eJuGVV4Er2vahQuKZ4FgEdje6onRLugFaiYBUgCZQOnGVninM9ZcogBWHPRicKkNdVKZkG4rHe0CCWmgn57Ii+ar4lJK2JQ9n8hki2iX9rs5AMotLVkpi31tjnBbGNr1uEVesetLykEJU8klKvAJXQLcNRk+JnOmIWuE0UNsh1xjoskG5iiRlJnfkPgx8Oz+jYKdQCU5mDGZLbfGHARUyqY/UxvuvBXGsO7u1BIYF+Hw8Pk3HLTUT05avrcQRnpQ0UYhmQu2CCqbyXSsfIXlWjGAt6ZgkzL/6RLLudNAVhnINNdjCnu465cROURmIrPxkLXvPfS7/y5w6vIFpc8nbZu9973vNaz7J+Pc47YkcPUmwCRIdz/wgQ/IDd7+9rfzI/7NjMr1HAer6IZEzXqoOjxbeTS7+yENB5OcaJWim7NFojLhrVPYyYFbd4mtdSTL8sFVdncDptLmM8fNb6LQLBJXnnvcKM/+4he/eO1xifSsSVtM9dmnHyf0s09H57PMl51g8SXmcPDYPnk33Xpln985q2ig/dvUSDvNoP4vMVFfe+21hO/RcIhozcS6AGZBmnqIdRRITFtlcxuE5mMJsFYyGW/NwYl10ou2dRlovcur2xo6YCEDHtTKkIdDAGfgWeJohK13pgoP1S3MKYhcE7xagZZpU+wOoHvEhUweGQdymJM0DEpH5J4fCdDAibeyPLGV3o2jmI+GUB5lA5exI38m776VXgqJjsSF/AYHUpVrFwSj9jQsCW2FTvaV1R9qzOj4YQsBbOGIgkW6IVLPsunaUt16Q6PTOkCa5riKNIMbmegr3yiam/gx+7dmmTVGmfdwqojlrtF2grbDecWgqCt6jLt7/D8Tbv0zGK/MROyF2NuxOBxGtkUJOXCSWM6Ip0wgJp3ktFaITHziq5k9wuPi//nJSn3T1o9uRP7cxeXM+Mc/rilSL2bSgGkOjNOWeaqRn5slOUD67+JeAKjPPU22e9c7emT+uhBc4WJ3fW2X8lWx4dTlP/3aLbPx+/DDDw/DmFVxnuEvVgJXdQJM9DTYUX6hyZ133skHOc2vcg0DthbI72QsneHkhmA2HxyIheDIJXngof7t3/7NWp2PHzbUPL6gMCinC/mSqJE8eW33bFG+8pWv9BaM8FSoId3ioHOph8SsKSULttQU5u6x6ucX0rX9T1mZjM5U3R5Pmo9n/5DEZYrVr8hDOuqQsLIlgy5oroEoWUn4Sd5sbZMTPIDE8cpGx0IAgZuzFeyE6EVDAyeSM4vLhzODCu8EOj/4wQ882iaVIDnh5gSBSs0tYGsijpRJlmQc34IKzsT6VS9E6BLXKEwBKNRO7KKkVLQq9/bZVZKoaltcD+VQMMMCJHIw9WlVSIaQozUr9TWK4dAq6YYAi1asShhRMHaObpgJ5DAk7bUwAX8FxxTYjveqMp9DGN9LUdfLZ+bgXxvGcpW1J1ZJ8dxpsoIVqByBWRvtPhtaoBSUd2H6PgmY6KvssSuM6pjK2H7yOvN+51EnOsqr6mUNe1mK/HAATAdWE9wjRmWMUwNnoVsiTRCWTYni5I+C05N8sCP/NBXmrDKZRFAqxTbxbKOKV0Lz1tQ5seiZ5u5ghmDMkA/hdV2Jtcq1inAEn0jS1t1kbWL1U9XKQgjLQ7Lizj2mr6Jt1QLP9u///u9MSWxJBzbEtmrve4MnQJf/ovqmN73pN37jN/x6bheH7I2Sc0dblMDVngATJT3+5Cc/KUrzVbC58Oabb1a5qhnXJAqPMr8Dw6pItjiuo6jQIxH66Z/+aUSKR0dhRitZftVDornkRz7p4n9dAPwPFTDK0iowIgxO+coq5E++WpHY3HjjjSWTKmjVlqujLRaC3/Eh/6tA6GmH8ND2MUaZlaz+53/+Z+RD1IIM5euvvx47hiBxiQxZ/CH9qI07by0ugDQzKRsILJv58uiVPEcube40s+a4MsxyFcexAItv5LEmUbvl6u1Tgacz1k2s8pjhZMi6M/3na8YkwIihA+76VW/TWDBkkqYPaBD6I0A2Tm1ohV0XOGdqYOmeQsq5Ryzu7SWjmBnLigaI5dKlS4S8RjIm0TLlO1LR9n68ZWMksVk7tyENutTte8BZAsmQtTVtucDWLnSKAc+wx7WRb6WhGYGJ+T1Y5sMuKL8CIi0/KWxXGlsheIjEvDbTZodt91DDI3FxZKsvLojAXRKSCdma+un8GoGsgZMNmkdOOOInRqIzQSiYLOzyZbLoHCwYMvRtkbnjQJb7d6dspUsKKafgcIQp1dQm1SSEvJogA0ClrBNgo6/SI2m7LCuDia8LsLnMpHY5M/6v/0p4VuNl+vZNB2AKLwwQm5nrcxQisURLdlseJaMquYXspsi057eq5kdRIDTXc889J/W95ZZbHn/8cb7lKCg/EzktgfMoXpYPbf7MZz5j+vQvrZ9++ulXvepVKlcy5py6FBPbXJWK8CzTcr+ot5iqIIbXk5Z0y4EhjLWnkByGJ5Vl4S6eTnbkgx+ogLnzsPIZ2DhWiaW9XO6VWyweI8mLnR3RYJQtUoqNcly2yDvYgplMAIe8f/3Xf6VR+eEcc5651ohEqvlGiLQrAW7ZMTRewWPmE8G4ZClGzdwpY5G/mRSNqX+hafhc0VuCkmAbL8NqglSGx5j64lpaa51bHqtsS5Ykoyro8aMdFEB3IlHaYtMv07CFD1E+MFmxGjmqrulSS+d02ZIKeORhGe+AEZPdxbaht63dUV38EleriqE2rZTlwJI3rVAVebYIJ8qQr8TCBKoDeRXJcAhG37WUqlaSBZzKTpKw0UB+psC2XqByDAFa42JJyOqGsnUcer71vlZFGJmwJskSk6E2DIpZ5VijFSIJ8Ko49w9PsOZHlqvrbnz3T8xoj3SASEMbJ0/sqI3/HIVXST0MxKK3i+phts9mOY9AjOkisBOop7SSXvwyLr7UFNNO4upd3DK33zreE2B8KQu8WTv02Le8rsZcs3/rqB4V6LzLnGtoigv1HsWoXnE4uUzHxi4wlqTps1nVjJ9Lfu5Kw8BUL3mEkLmxIKNv9lTZAQSsgFOYgGnhD6qMU9fzzz//+te/3r+P+cu//Ms2ojgoUs/ErCqB5YHOqhiPFF6U9rnPfc40/+u//utf+cpXfuVXfgUj8811CDmsORDJhDC+z49CyWR8YcvloY2Rm9GFaByfR4mBT3Y9qo8oiMhBYs25V6e7tTINCDU4VquJ4Y5DdKV8aHeUmwDirLEvGUP2wQ4T6dX+uTkG5TnGbDhkoeg3WAbIabRFXBivLFjg0aCIs42p1XpBoTgmO7pZDQEgLpe1msiNODAAUQMbvLaCjTIaBEBcv7cSDGmwZAkxYiOVWilIORAjqTY1UhLYkK1HlQCkrGZZ36JLpzUUsIoYoF2kJ2CwibZkmx5dyqF2UauqJx+UUNF2uoJBpbu38CBPHOAEFzl4rLaLClpRG29DUhKtdDGn+SK0B1JvZJ04cMZvuLgwSiH2c0SfJhCIRRZglEQg1cIbYsrQ1my9TE8QELTKlNkQb72XtREynxgLwVIhQqP8FuNYZZtRrI1/Dw2ZifnCmc+DPfUglTXo5OyXmXktph2TLE0YWuiwZlSSHQaP/p+CgdPjTAyjaDep7EjaBNWcttVdAafG0R4TAU1OCFFvT75gXdVFCFZ++X/KUDPpgfDeaaZHq9WtH+Ywy2dmD0NizKVjCrBo4aUvfamyD5pcJnHxgFEGGQbNg37IQI1vj5c6MQ7ZiQmu4ygW+9oRJAGXXyH1deQrXvEKP391sPFtS/a5PFMC5wT4fwUlUP785z/P+K30fPnLX77pppu86/zI/0KPlTiC5ABjLw+ijjGHDnw5ssK1OZUnI+LHXbIUwatfA8aI9EnkCoaPy1WJFlfogocDtSOn+UpSukBBhE5CSFLn/xPw7AdOPGrJXzxXA2doSobGaJFHbvmCQTqabXxDbB5yt41vRpSsUlrJjwRG8qAjauBT20S6NoftPAPzIXe+FobWSXKQIgD1wmLaomxXRG6pF+E+kpIa5WQEzHrJkrnFZhjAw0aLtKV4uBBGFFNFud61cgzbK3OzrWBBZ5vQpgkRZTWaZErDC1vVQKts0Sc17sJllCDV5832xqvfatsVkErhC2F40bANLLomR/RIFKg1fDP3w0mPMDUhECGO1E7hQj4u4KZcKPGFvzgv5zY3lLxQD07mgKmlijHdl90z2gsJnBDSduZG4Vudn8ZwsW9JgFagIRpyscSM9k62cQtZ+yiviHJOj9luqBI8G5/JuUHoAJTRvHTpkglxlJg9VPI8HHiSrj10h3Hsd1bAbYaMSor2QMloFwbFqQqvzDsdkaPwW6nUqYzRP1ZgFM5D7a3fTYhviWQvMRmMmOtdESNjwZd5OR1ZqsvKZh6NNQdrI0QMYM7N/K5h3rb4OzrFimKGrvLAH/Hl8r+aZQSy3yeffPLCVf3AJXZ05P1vJH10pO+CYA70iSeeYMbO+lP3Vb8H5jUS5e+CtvVwMuA0FJ5ybWZuF0+UH4XmwsxhDoKKGEzniK8lOhFDBQ2dX4Mzvky0AbnHFLDfQa5H8x5aCW194GoXND/juYce1+6CSF/ykpcMm6s3WG0yPIRpa48cZlMAAEAASURBVAyuqVqGYLy09RvO9jGkc3TepUYGK1ikBvJSuY22kBtTHwCLdSiPOF5bwFBRAHmyBRQrwR6F9SCz0+UVJJkarS+At/EFODCCNkjomwQ45PkKVzDn14DsIevI/jZiWsrzWJVVaGHwopdkXyZs9IdNkSvCzPT54RZlmq+hvoApO8vw3e9+166RrG+pMPFlsQBmYQE2aRFiEN9ScqRljBgsY4S1iG4pI6QdgeenxS0ELG2yI4CoBLIL/6iS1Ns5BXGeaw7kUpjoDCfpuwCBJm1kuVRxacMDAYjCh+zNBbtTpkiVl+NeqhfRPJWuuazqVypY6GEamrB62s5R2GS+QFHQJZSsxMLawNgkz1ak9EHC6XcQvXL0N3nU2vi30tCKEqrMHVvBNgcJCYgfTDSjHx/NwXAgMKXGCpTcVX6PSM2PHLvAoC4zvl0TdxOfJWlrx9kfFjYwPRc8GoY7YCLP6KqyVy6Q7gfC/iIysODy3e8b3vCGG2644Utf+hLJLAI+1x+pBM4JcD9w4nWn/M2j/iuSAu1nBodvrmEDqSnwOBx0LFaCIT2Q69aUiUeH2fCYZT9ezMqWg0zie80xC48pVgHMKO/OsnKI6UsSpWCCTGqRygO/JyVzhNjxV/8h+RCm8EUSG5U/YEHY6HxfOgCmbZvR9CMZ+Y8FmovLSUDOKfm0RSwjpSEyVZd6MjGZmeTohrbiSBOYUTY72rlSA8w2CDxaWd+lD9ZWaJ0TARSJ1oHJD2vZT0aMmZLiSTsdpyR/dNIi9VYi8ktaZsrMkVmE9gpmNdJsqFAoR+3iMDDtVbwr5PK2CsrwFHzKuCMHCbAQijm0AAUZJO6RCRiyYiA2RhbBt22PqCyhJQfyF6NMk12ME29BVmXV7LkgzznAbdWoMVMiHwosXoxF7Fk4a3fHBqmEJa0LH9+lLPAwWeoaqmVq5rMA3sAVnng51sET6uJC1OyKb77874s5H5425y84Rko1n6+lMpwGIBCX00MiCtmROWIafqdvUdLhT82upWG+c/SJUTiRdEQ/5N7JaukjMZrfzQWuCNag2/tlAvkIztxt3jcbiiJgs66Xf+dhyclquFYKZJXhcFoeDF0VKO56gJayNg2AWdfXv/71W2+91eeQTj6f936nJXakb88J8MjAmUR9D0zjb7/9dlOOO6BDtli2akbkjs2InJE7ryRYd5gZ2TyOyVtoe8WPXb7JT8KOmMxpFvzKkDWvevyqMbctOmQl9BcB6FcyAwwGqfKIKA+yCu8ol3QJ7Ox+y4F9x0JKB0nsQqImFBJTBsXVNdbEbCRNNWdbHCEEiiEjpeS0RXopd1U2mUkFAVAkWgEbMBNhTplKU/0bBooBTB5LZ+wq++03OTMYemibKymr9Nj8hwYRf1GiUzRIfVWaI5EEOTAaqzsYDIqZkl5lykGtes0paggoVAo2ck3GGVAb1KZnlVasaSNGfHekjMK2Scq4y9F9bYWz+sLLEKytQRIlyWln+DHSvj2NchJgnPIS8zlyBMDAzYffEaShzI70VvDTOoddZX1DO1oVPxW1SuiDfAEiQ0PnqhguFp550vaSrdSLCWcV7GIJG/ZesrU1xyMBiJeTsCkw+UUz2hCVGgoQD6Ytp+TirIT+/Gd1NNpwR5VcHJKCnH8z1ytbZJQD76jHUbSmDKKgD8PTOqPwu6vk8HM4nzT0YnnCnYse9fnbIsMcJ/vVrykjn+dciDJsi52ZePDICrgyosZ11qxzsIJRqDdTlxycJsu+SI5iiTMV/NxMjQtU+i34mTTsByy0/dVf/ZX/jerT37/4i78Q3+6n63Mve5bAxUcte2Z4ZnfClD//8z8XJd9zzz0M/q1vfauGh2CuMU7EyEPkDGjLWVPba36GN0QK02UX4jbAInVOyqWM/iELakxjAOKbCn8ENYRPFwmGhEFiZfPN5jFiutvbHV84tQ+DBauY8qgK7/ZGw+46Eru3GxQ4xSZNcCkbL1EgJZHB+qEL+iOkIwQxjb1QDWW8OdxLSiZ7dEqDwZNS9oLAiAszt2ku+fEWjHmCJrAdlWIy86LfyTBTSpgh0QUdgxlYIksZskQLHqcK0QZGSCFsFefR7Ze//OUiLQAZGsY4lJhJFxcZTU0yB+u99BbjWA7ytjlKXKlBMEqCBHC1beGVhZsoTwI8cdwXhjRchKdDe1CPMgT0EKlFgZn0A0urg2Jkc2Los89DqLHFvvWwlSZorsxG2NdMqa7X4y5aoTwmVqMs9FfjLMkWu2tltRURMfmsu3EvrpRrG6e6m+6L84nr4O4E8S7wjrwayi3yPh9V1p3BO1/DH+aoauvq56PaBJImcOM+ohn61U3QrtFW/JPVmYxjyq1vXwPn0iamJyYgZCIBwNMqtBTbUQCUvVA5e7wk3HLNHFiKq3gRUYgxsnjtbuo0OzMf4QGn6qcWLQXm0kr8AFuLsPDsv4BTlx8DEvn7l7+f+tSnhAf7J+Pc434kcE6AF8qZWfpv16aZe++9l/W+5z3vAbp/K2WN6VdB+iHh5H85FPGZBINxmo/NQ+JyMSv/wq1wT5mZQm17H+UWgDgPfkm1/7gjCTGnynx0ZyVPE8hHvUBoG8V5+JW4JiUfuEp+bGGVnA+f8kUUJtkLI3TDaOIxW3M5Ay9+Ug9A/umtjNTugaPvECok160mCpmiwEtQzV7mKgu9dndBioFMe2BCjCyXWlqFsTsETEOtzHMCBTCiJdOeJWECNy96Ra9k6TTWhKpgd1cT55CZm90VGPSSjBf9tFGT6qtlH7DLW6s/drCTnUb5aawalKPN+LatujLMMKDcvnS2vtu+vOrgU9PCtAAsFCpdt5WHUC5GFlGuXobvg3PyPwSCT4AGOs/iGCbZWlfCETtaaRPyEITASTLVJFrRovaOwkUaNZ94OSqTl1SMTjTz8RQkv2G5wSNXIy/i5DsiuSZcWFzr6guDevOpC5iEnzsSBlgsbv1eAe+ngAaXvixWMlU+cxHxu6Angw6zaMHPPu2ii1VxGp1koZZKkZcRj/9fFdUc+EjALOPnMzlJHe1T/nMo3AUMroWCjlM5ukzrjP7SXlgxKbkiMcGGCTrjIniwb2/Sh4T0XGKJqJN5E0ytLnm1tKPtAqDW9YlPfOL++++36fUnf/InCZ+228sZ2+FI4JwAT40FU3zkkUcsHr/vfe9jsb//+7+/h8mPBYYmBfmGvIL3kZ+oVOZH1FvJljnw/qZA9TwFd2xJfkOXIQTxPbDTYjydQI3bsnoHP+SjcQlpONfHJ4bgo7sTFxbipgWpltVzevboGAnBdWDPo+TWpeBj74ydEJMiqcG1Mi3KSkfa2t11fkwAKkl2qSw9fOaZZ2SGPoYHr14ISEMkeNTPo/lMv34QxT2zmtWZaCnlETRbqXFBrrlkQNces5tE+PpFngQ18YRH6YFZh/rJJOl/wm6nrA3TMKuEMIOIkhRCtjskoltj6tWci/LLlpFH+Vt4gXJO1gUzG/SWEBDcglXZ7G5foh4Pp4B+jBDjRARDhhn9wyH7qCmhDFlXwoWyIaDzx8VRzIpWCE/lXYjnRtid1S5lVhA/sCFT5hqGxi2MTjRrIEe2VrFZd4+pKVSyWUMz/+CPmTG7WBOnPwr5rguc254ViQwNujVN7Is9OmHumt9F+JNTZZTB7DQj1YufVHS8KBPWIpJOrB7X5mv7IqzeQrbl5pUYjJ6QmB9L0xA2oawPp5MDCxXEDDUfWabnVXJ6kcPhWLy6bLd7yYTRRsMffvhhcf4DDzyQaH8lZs/ARyeBcwK8ZMi41Pe///1M8b777mOr9oTNPVs3SLaHDmgVBP2idpkA7yDmSP2lS5fkmWZrfoRrSFZw2TG84BqqsISfxa9hkGDY//E9pyjf/OqRy9NiUYpLOKFkMdajeWOB0/QmdfF9y+bCvBC2rYZQTl3L5cwcDgKYuux+GCahngEVrWLNWzNQzTpqaJ2JR+4niU3+o5ISUj+KR+21TcoayeSuIwWvLIKIX6WsNlucKpeseiRM98xzluchzzqOuVArJNlW+tmf/VlKbndFrmt/DDwMNApOSzwS+LCjo2AbJsBaQWXhH9l1vtFOsi7MozEr9OtdDWV2j4lR6Var1Uu885uWWI6gMojQQq4VYeoLm+qLsMC4mzutoaQAGC/KGNEE8gK72IIcw7i7JshAvJULQ384Ye4EtQf+immgkJJbQnIscKg2B04/ZWCeyGYRlIdJltV4RDwNNzkCY8IczrRqjTKrberbwhZNxuJaUVVd6LHKCq6sU+B0tGvOgRPjUqzijQKMsra7Sgtwu0M+xEw+Kq0PmiCwzzMMYS62prz9jsjg3s0OPs9hDv5T+o56OTS0xt3sKfs1gXJlst/1lL9r5ZGvcFlwx3K0S4GdimH0yGEyNzWON2ZzWPDANmt1rEO4udzQYIgd87T9+6EPfUi0b+LeHO0Zw4FL4JwALx8glvC2t72NZToUIS/1+1hi9A0tsGxe92wbWmbvICi0cgYzjQKfnh05EUbSBrH4MAdYzsBsCJ066ibEkQPb5cPy2i5vdp+HAijvNbfJnYiaEDYc3wvhip64qJakV8Aq3xOvZNMGPU5jUi35W3SvZVDZ8QGzDhjxokVu8LY7bBiKC63IaJKtVGmwWcomQPJACGkvYLqqoRSR9ExUtFRkbNb0/5bdNaFIcmw41ZjVTH62ItVYd9C7Cx5Jo1eah0LmYD1Ccxm7yUnZAOmr8nxlDd1d11xzzZW/l2+aSOFcqWFcELJi60eoUmmUBbLd5g9U5ng/cemfD/tRtLR1x5oLBqe4mUbbUcEoYA1A1dgDRzDgCz/vGmEibFgo6RXZAfPlNk5/6Zd+aRSgBT6XJyQQ6ZXYQR6dPBFvPYtt+iDf4pSVJlbDpbCp5PbFvsrY9ao8+s08qy1auSQYeuRbLBYU5k0KcJo0XYWEl5PJ8FRcEG/g6IpX7BRHXAQGC7ItcIweeaQDCYvx1ZK3h7LR5994ckeK9t/7UgazPrsUbD0AOmkFlnLSGTqwHpKja4VrRuFfPVnYdSrKpL/1cQ/CQmvp2aVfkyk7tQ0jjIncxAlW3zxajBY5uPMSGlbbTcSrRzHD3Xff7YevHnvssbvuuutAzHwTps5t50jgnADPkdLlozU+iJddvPGNb3zNa17z1a9+lSmuanvMTGdaxd4kJ/yLS6CcV4LyWLh/zMPCZQKBn0XiloCQhwxdW9GXCUgLQ8aW0B8oGlwLkojd/7XnarG8C3e/f+aFpFmtF7tIUI0mHTa1SG+oXKvAEleHC370ox+JCGm7QMeelf99p4BsAvE9jIL/j/3zP//zlNOkCIOJSmQgNvJKmmo/1t1bCbA01SKucCEZsulKjSaS2DRUACBXB+/DNgDaEru2sm5vEYkGv7cpA0eGHoWtOsq/rcKFMtvBRZJ8GGrewjhOpc3Ik3vrGrC3MGdv1iMy3F2Rg7vg3qoBzUeVctUHrO6pr8cUsJCFAytZSCVtB7pMqy2YHk3qls8AjyJpgbdYFr4Tlx4RYIxcRCGQzXJA1xEw0STvROwy/+7t7h5lQTYViZ0q7q6X+ZgJhzdGDKFtOFjRjVFpz6fnQiCtcFlIYk2IJ4S6EJNyjCj3UJiytzMJZqeskskwHHaqI4Y/s20Lpl/mZj5lwhOihtyw8hjchcGNhqtEMAycA9NopzyVfBF4jjRHSNpOr4YyCXAFDj+TqjM7B2KereTnK1vbamYZ+xZ/LWiyYguje3bdM4ncBZjZ02o12xQYtL8ruYu+gjPj6M4PuEzc1Zekl22aPbkj8xcYoUI+VhIDxH5bJNVwacH4iit84fX973//qaeeuuWWW3aqTkvpOQPsUwLnBHiutFmFfwjm/2L/2q/92nXXXfe3f/u3M6dDBqYPd9mCCI/dmkgYs5V1OE3D9oiEyyzcZKzG7LvT5cylDKPBzvOrX/1qBaSGfuWlDZcCCKe4MOFUJSrDJtkNIISt9DjEv6hGdzITyZXtL7vfKGz976JWh1lfOxXEaN1UZCkBxh31EyCi2RAMxUv9/KDID3/4Q//+zikvq93+/ztgoY9Xr3rVq5SzQ2IcZXd6Ebyae8SdcleqYufcrGkpQb+6kHyaoqg6zTfHmNLU0//s/crKNEQVMLOaFNdMphWLcOkdhUYBDISU0F09GpQl1fhSzoUewCIzfakBoIBrxCjoNBiwL6+z7Uy9ldEsmrHkjPIXMF3e5JH94k4mVpUpTJ9+1EUSYAqMEV2Im+EPPe4RuN7xmPoO/+4eyTY8IslQJlwYKkARwLOJ+cgHcFXuukBu0gyrIQcSYRsm1+ZckzMld22Oav8YLAbxGNSAA2FNHhOD1gzFDCkVHgXKFsIAKFtomz+IOdPEPzBYFrSJabA79LDc1qJLaJnI+BCOXXcoNCgYsTbE/2iLcs5feswJtOPlQBYkdrwBFLarpBBf6j8FcqQmBcK5ShgvNk0ZZkA+U2yQWalenWohlmLylfpamWIjF6L5baemaRfCWC6HwymJIshfzX/8x3+wX48uq1RRUW3b5otGSnORwM0332yz5xvf+IYfuJ7TahG2c/3RSeCcAK8wZDzCL/zCL3z3u9+VDPj/MQzGD8xOGIyIQejPXNmYuwiPvQlGORSe1K/vivbEzcEwgWcFErcEiphKAGQX3I0sfXMKoRLjCq8nyLTpRziSjQmYHb3CoNExz5nvN4nDQp6xpgB0ZnO5rcSv7szW1UScR8essIhQlWWDAFwFUAWVAlAf8Tr5fM0114hHKaocIJlkeyr4CoLLGCCU1vpJTIeKqbp6X/YqCx+xL4mSIRtN/0lPv+YnTXQh4gTjrSxRrGmKFVXbZPAo1Jbxkpt6JxVFpeCponOSJIkqypPo1gamLiTPOpVUw8y+tFWAGR6EwcPuEKkLly50hE5MmTLF9BrCqUkuqEyfkvaQ+kL15b9eJf1uK0fLMEsDXASO65AkykcGgqtJytBWze4KxhHySANrlGG6L/CkLZ/Zw7cAQ5lUzUzhFDymZjaZZj9vt4hq2B2aQzY5D98eSA0K6YA7U8rSEhVSYN11sJ9Wx1VmpSmeZyXRFXAEgnc1bXmpNAo4kB5TU5gLA3fBpeRtWGOteUy/Fr9MUkLtNgG2LMicubIhwsJ82gVDzPFyYlehBPBOGfwnP3PK1cA+c7CKRNvNd5kpDoHr0OBugjYQ7azNO5lkmbPYEgCPJHgws2PEpcY1ap7eOoNmW0tA8u1vf9v6ziLI0ebnyhOQwDkBXm0QWYgM4R//8R9vuukmyfCzzz6bxCCWw6KgM8UKfF0icpCCBm+lAeJ7C1SZR9WI7Ffre7/Q4Qjxlj8l8JiaiIZlFwIgU0VadZRGLKlMuWpG4bvm+3xEDw/r60eBKSKxr7AekXIt7lVCuF7zjuu1JXZ5G+v//l8/mmIcZZI0sDAPcSLV5OEDPFwLBEGaAgnEjitgmbCpMdGwR8Fi9Fy+ZNZxUYNiVkcJHIHBpmtvFSymEIu4U35o4vGJkZRMTusOQHaqoS2aH/zgB5YhmIzgQ2WSYRGYlRHbs6wJDBoyOrrWL8zmbLOjNXuqCCGA8Ct+tauPNa0A0Gf1DDDHgIHhtCgHExF19SW36QIDZyyOaZGbggs8BiNPGkUgGFdpUAhwGtvmb4svhTkcAZPGWJsjTx7M6snmNExjMKA6CoyVEQWDbm1xulX7FgZip+dt5YZlWkdPaNEuAl9rMfYVbSpS1A3p3F1z9mj9iNsXGurFD0OQcC2MRq88uuiVM6JktfYQGHH7xu3KHYQskR7OYdAGDmoDSRnQY9SGH/SCCRgA1GKBx5DuGmtfeHrUhMIbHdaKL44FTjBtwD2HnhOAia/ACCH4ISLCcZ0AXzNZwH5mf3qSeOBqYB/XZi6mZ963rm16nSmufYJ1A5F/SC4Hzj6Te7kRv+jBwYpP2HI8bdpGtx3wFsabpkXpE8HtPlk797VnCew8/NonP0Jq05gTEWxYPmb6fOUrX9ktW1J90fz3vvc9y0UAbOQyjM6i0AwMNkjYUoLsYgSw6dAHA74W8NMgTz75pDUkwBIecbboVp4g7RGpm0r95rtgnQ9tza9QHX4Bs5KEf/mXf/FlLBcjNVLj6iiXz0wke4Spuekkfsdunub8ThtVCEHITT2Ziz/kMMrcFvkPu+t63+6j7qQrSMWv3z8UHtGT/dBAjHgXRA67k3l6tVJWEGkPhVPI7YdQy25DPnOetiYSyZvfi7799tuDyhjJTxgUnJBQdQWRq2TSUCJPyqRGhmzUqL0Y1IElEadeAMAsngZmWDUHQyU0dxduekvsTMYRgLwido8ARGDXX389sQzZF5sWOwgWLusUnlTWK/SjzSKxGmQLkZkkDXTKVyvw2UMOX+7g+Qf8Yt/j/Iv2koYLyzAQhRxeWddEAQ/MLu4ihfmYN4fEuxyDSOegIm2jb1xmws/BuQjGEFsIoAakR1Y0YdVO6Zjmi/CvV2+RQiBoEYdGrYfh2Ftl9Ms/0NhwVGblcVhua+ZLgAI4fdPCU4b5Y0ptAFODqBAaRlVImKsLim0+ytScHhM9a2XWthbDLai3pMJDUk4rZfGKLXlXQ9ksbNA5fB7yauC3eMS1Y0SOCDkORrWuBvYzH1mVM8VjWdS3niGXDPdWCJ1opqg5mRJPxRXwYEKOBJOme4GHOERkAuD555/33a/k2Qdf5utjYXZvUr1KOjqpBNhukl/oocomLSpO+1mFnzW/8847o9/mSMvYPuJlEmJTIY4AlwEMTzKb+W677TZKIEP+vd/7vU4bYBNAf/Ob34T5da973bvf/e4Xv/jFglpgJnILTpl9gSWATu8dkqN4RLm4hFTJ1i6ctQO5fSUYLQtxOm1NW5aoAOCSxCgckFddgCI9kCSo1yMwaZJyUqkWz/xycoxNdthEQkIlO4colyDNHETAyQzJCrXyK3fM8r9LMeAa/OguSsK7+ewH0kpQtnB1bXPDKLQYGMiiwM4QmDnc5Y0wFOWQWATBmuBABMm+SIlN2czUV3Ljot9SFJ0hDb3IBuWx0nh3+a27wFqQoaFwE4DtWZmG7wJ8hGzvl7gAkIZPhUWl/jsXazUWsmIqZFwsNuHF1AWVQqt+WaZRSQf0q4BgsS91QoyLvcOP+KjZUElIW5JMJ9GgVbEP1fSFF2osnkaVFTFS0p2+LKPIhLXFAv9QScU0tu2+xUWXY0zgN9CGZgJgi6+y+mAsyNz4GpeZyKmBC7DRJ/OsoGUFZCaGCTDiilLNH/0JbHkFIbOiXdnxZlx0j6rkWNDS5nsGIMmXvexlqE2/gssJ/RFZxqVvKDRqwO2kRw6K1ZP/UudJXTWhABwOSuIThuLiELiaGtAUEMxl+dzRK92xd48qqZb1dD4zljvElhqQaIaqXYxbBHws9ZgiCo6dTCqemUO8htYNyfkwVXomCyYmB6ZoETud747mID9MGKNmyMxNGM8sLEgrMzlMmkepCs25Gz7uyyRuChYtcLzW1tkyO3388cfvvffeG2+88Qtf+ELC0VFs58qTl8BJJcCU2+/WOpmswKT9fqyfbv6d3/kdZmBSNJYm1Ne+9rUKPLsoWVLnCIQftrXR1wXBn/70p/37X0df/OYz4xkNWKXZX/ziF0VsH/nIR9jSQw89JESQLZgzSm+O0YkU8SlgAafOw8hnLBx4FCvkldhISkPUok81WWkDbHWgZVw5AYqUQD4jGWhFFFRmWcmMspyBhyJJrYZgAZ5zl4pwec6rzwEewujdTGBdwykaG0EAZubAZGIWqajRjx5pa2dSDDfsJTUEuKhgWsrOoZkYzoSDpmSxZivhwlyo8hZ8/LvHOkwovkyICVgyySg0h5C0C6fJwyizC0bklz9xBAa8y+aJ0NBmtXUQVlOcohAq6R9UrACk5XMrrAD85zDYpKAOHSGeKBAmYXbPQQkmBicFEHbIfpVpEYLhzDfhNMdoUh7RlV5EqAhAEjD12bHRETxmO/WSc69Ir1ZVsIYXnzfLtP24F8b9Ch2SjDL47iIKlDv44LQCze/eTjwK+8gEZgLkGWi1bB8NKEEAmVTbEnXV7KiAU6sAK0WxaDO4odZ9p6QOkQ9rFknG+kjGF4BkkpKg1rliQ7+oyYXXc5i0OmQoK3B31ObCCesIKF2t7a/RSTCtDBkWGCB7AWanpcOWRwBMm0WMLqEGhq7KY9M7u3ZB7rP8ImMUM28AeWlOFVpgjtTuFrtmodwOMvIWMHsXM+giDpbxIoCzcge/dBrKul5ijLbHIy3j2jDxlkTq29cS1Bx2RAJ8uADgAFV6Dv14N9kl+zV/LV15mYPzKGDMWSZHU3BGfNSCjoKRIjIsWLNziTkpswlC+cEHH/zoRz/qP5s++uijLL3gz4WrUAInlQCbgVxlurKX+++//5577hFSqze6zzzzjJO6Vn2yR+TLlgceeMD/vP7Od77jjGU1BCkslrpY2TWzZmF7VDlMpf5rtjD6He94BwCY2yxitMkxVpIMT2F3zqdW2a4xT2DEXcqRskdldzOf+a9lsxVs6oc1gg+Xt16RIcEOYVqceygjYI0cGPEOmiKP5lgRUIYnrC2iWdBg78tbknRZiFWWsYjJJHv2NKqhDFDZkTwZYFW2BScXOHqpl0qo3D3KvpRLpDLGJLQIszWatEHGaG4oVF6ZC+US5kX/GAlCzUVFsilgLMKFQemosFIBQqMvWUW28DfJtsheLAuzGmknrTDFMj306EgrcYYzVx6lBMJNn+LoV8buAims9PEheHLwCKdlFAJBsC3cLJEgQ9tQjhixmhrII3BK6wirR1mxSJrq4kLbYlNBj+1jymGfnNFgENPXEGxYgxLEa4UYQbZhMrgkg0eiMwpQEctop0NsW6nRNd5ZZSQzjROFLjpDgLwfb0kInTlPY1j7rZURjnSlaBthBI5gvpqp0hCCXQnD2tSu3ZD6IZgak609dtQuza/W7mtpQ+ZPPchwqJBSQUYncBx9O4oZkij56FuVGLenai62QLYIhrHzPCA5E7m03kEuFRGfhhEGToXY+CLkqQfQJfPa8rqGQ8JDD/VuYYXVsF8ObSic4AGmkHtbswg+MAd+x45BlP1SUf5/gv2WkRKCthx7/H8qj0gaCG6z35XO/rTSOKJyDRzlZ2VGnOc/oiFbKuriBXdM+9Zbb/UPXPxPRxtjS73KUuRngGOXwJKp4rjYK10vsu10qcyCNFP/h3/4B5OfD4MD4JXfc3aAWb0EuFopvP3tb1f/9NNPOynBD7avujIrchBahmAn+Zd/+Zfl2MpDSrpWR/eII56xNlQlNqJk85xvRfCSDb18NzLBu2gmfnaCfYHsnEh9EQYjbi/IW+E7CgUxylIOaBc1majHS+XAclTZy9IlQ010B6d00d1m1FI/CyDkaSKdSzlC0JzE4LHZ5W3OGE9scInkMouDz0DI30zqynZREhoK8pIhE474O8HKUOaaCH/ltyJ142v5AyrBjRAzEpO7eptKXQh9JH7oFznJWpOCBhIqqbtjF5pjQaypoebqNZTQGixoPcrTcIc8KWvYxD5gOFErGbMbjzWvRGYERXSC6cTTtqY1B5NO3SFEBpIcK5CF4tEUSEOs4zBqGCaSJW0l7ToFLFwmOjWFebQgCseIV4ZAAuzsAIVPqG0VQyUCbCgtxTOKfNVK3bnavjwGSVs5RIsL+aQvHaTuRo0REe90kyGSNWpIOKo4vy01cOGLFpGt0d8DnfPJG0IiD8HqSVWZNrqGYHurYWUsyxJV1yORsmujLx1NCtoBdI/UnpVFwSDkLgAwsWnXF/huyKwfaZtXZGVMu77qMTD1yLfwACL4DiEwHoNjRCFgZZrGjVO2DtJbi1bugG1lcx3T1qpHAgSPZcYiY1cmsbCgfHQXWbk4aj6ZE45PnskFaVh+JQrumj5YOSVeM2ac80wkFwtmCuPt6Ya9k6th79dYx1RZGTNPLDc0iosdlK30jlPBiY8fRR1f/epXhf0nyeZWZHVVITmpBDgjx/lywWY7Jxhtzzr/7FB0XtF+yXAbz/HyQhD13aiLrT/72c8ym65+9NE079ew/G4tA+M6//qv//okfzWxXEZWiE35NujM9+oT6IgYpiMe+cZEyhHZmjJHhTyzEm02LgCH2pQt5CernImkBYNHW8MqKhI5RSVKFC1kVyaNZMJd/fDRjJsj4o7/ST9SDpgeEwJKqKj0ojVpY5HUS3aHQuTJB8RhpjcTW2LuohlVIUxfXrGIetXRpl4IaJuoGA9A4KXH4ngW9KMf/Qh5khB9gWR9X/va11iZDBMxIk56ktzeo3UECTACxJq6Zn2+RJBnKls/0lauaK4iPSwo6EJzvGuITbKy7ydDs3sscDco5GOAMi7IG/KCALEpzBqCFyUDthwgSwefhsNWahBpv86eFTKw2Qln+Eie0oZomrxXEKk7clBwRzmmhq12VyPtN3bBn3MEaJAhzOxRhmAIbIVZ9hrKZyaS/YDRumnPswYZWCYuaGnI1tmHlnZtHe1MNstYCr6zAo98kdFv58oCHhbkgaws9QourImna1CSaacXC5ScFQAmyert92rItBkLd1QyifCHfbU13A4Pz0XwD1QdzXpkqrWSose4jvhAbcG4841gvGWbAEIYb8lNwRB36vc7gBU9bb8pm2vidVGOhioPIY+rhjUZlyxnz6dcEMUtE5fLminl4ds3nM3n974hZBQA44ncrpLs17Tr4BWuzbzuG8rwMJsbWZdo4Vd/9VfNv/6JqUNhVPQwqT1TtWcJnKDS+8D9Xe96F6Wn5a94xSu+8pWvVNwpQu1WlLlpk1z2DFvRx0Lm2wlIqcI//dM/+U2s66677jOf+YyfmINwPoa29wMvk6fZ0SfBDkrhWsImra2YY0PiN5SYAU2IL+QS0OS864bOHUmVP4tyJDnQVmy3iF+TqGvR27a+WB4WCkx3E1ygynZNgCFJKGZtwkY9hRfNFJ5VC/r1URCcLglkaz5EDRsAtqZHUaCOPCoQvrcCXHeESSA1FOmKVkWNNhbsJ9MZJy/cSQmY1F0QKerN53miTwk2UYtxFSCRTkNIzWBgsD7nszlsCocho+MApOZDBnXtp+zk517xAL7sosDFiHxYlI824kV82xzLurDo0B2YbGHaMvkQuNTaNrUoXN7urfBakA2/1YEWeA9lPRIIqTIEXg6D5QmHvQPLJwxRHoLFviQN8ZoT0bDJ4dTko4Pt0uMoQVb3tos22ORjHc2MqDoi+SrvomCbjmXBLN9jrVkcYby1vmY3Tz7psEZnEYuIYSDJJ2U+VCXG1Sobu3Bpjk3IFcKj3oOTRXMadcJIZfaj8nbRHRIU6pRdGyw90l7qqlC+l9Og+Va7MOt3B7huRlF8ydXRA49LWUfaMmEwbFbloq7V49SlYKUJ/DXXXDMBfOCvSv2wLEPgDdrhm0M8X2Hc+Xab5zwPOScqgNkVSU7Lc04vu4DBLBPIKjkT0MVh0rlF3o0IW7BLzxAY2qpjvUVKdorqiur9j1OZDmnyLbamjPLJD+5ORXpiyI81ARZWmthqMETAZqA83nzzzc4umuMdQXzsscfkwOJs8Z+37KFmvmrLHtTX49oFeMTo3/rWtxyf9p9jBNZ+PtrUe3r2Fk79OJC9O7MdUQsv2n3LtWW4eUPjm8X+SL4W/jfEnEGkJ4bVHgKWTRtDXWp7WWPchVOL0qT2ZG/bS8r0X8ChbL8OkQnc29OVo8QMjz4OMavJ7ChKEDSTZ0uhSpitOIiVxaB5K5IWEBOOGjmVkIhtQuJknQRYmAhAxmtH17qsmEkZ/V5RJ1okcsWCcFlgqmH2ls3W8l7ESMzsz5CGhNa+Ln51HftFWMemekhoguhchC0CFpPpveZ7AC71UlYJSctaRIELvEAbyA5/YLxKwR1AYNy1Tbl9W+W2UBg6+BZmjXJ0hquUEiw9BIEG4xtKkKHsTlxqKDwBrk0bDMZOc2JfG8kE+7vAqTtod4Q5yIccGSYmwwRW7ZeE5auEzHbmtJV28mBasV93ZcTEvhTUlBsZEjlaw/BdGnrL7QzJyOygX4ZWqsh+ax5P20LeceGtBTIGxZYLRoHBMluug5nzIfl9CjKE1iMAYlGmxqhKss3buIIEWu6IELxy0EPzeAC9Z82rqOroSfNUFozKUbAAH/6dmXO2WdstDzmfbOsFxOiuiaXA7P0SCPnccccdMEs/5mPbG6TRF8NYYLX8YW30qEdwjtAMh8vka9WGIdjxNgWfJNfYpJAPP/zwH/7hH9qO8ru21mjmiOgMc/VI4P+bTo6IbScZ/PddKo5m1uu/8tY5Z9OYy6tXv/rVvmTzaa792OwJm5gFGS2bLMQcqb6t3KRsotWdDPy9730vx+pnsU7Sv5C5tVIyl7SIYzxi+aDcqNxpF/7O6rgETD4mILPFt0agMKFdV2KznwRnHdi0bDGbpWuzOM1PAGpqF2tOCEFA2fUy8YhrUyZ7oeHim9AjZpKR6hEBKq2jK1N4nSpkDQINiYr8prQomUnqJaOjHg3ib/mVV+qtakloPebQsrgZWltDNnYssohWHbeW1+kdckkySKmaS1tLEtC2LMhs5diWpdi4nUziFeCqRJ5OXYBJSaf5KvjSpUtdhB02NXFsW+YMQJ6gO0yRQ/rCKWrdk0KIpRCWXV8AOrVAIARvCevKwndizBJG92rtx05hPHY1MKO58BuIHJ1AuR175cDztNIhwh82r7ZLC/boCJb0lkIeDgD1MFkwK5q5Ce/zOeJS5Anz4VtIhs9GZk5krCB7rc6sYrNbCKMV7Np4UfhVGafno14lldE3i2Lwh3iaxrLUM22+xUek6nGRndWWQb5FTptvdNVrwtyyZ8uNsDhdeKS3hgzxAFilcyKGT40yTomo5Ug5hhnL5T2YIW23Axx3BAnV1ZwXaonpyrzQdieCDv+uH7HJ6v3YIYFwv9ktmNmpUUtMRbzBA0lWH0rUxEu3ZyLcJxjisyxrzsqBnX32flF9MQpTOScg+zXWNUwXRc+O+uVLrbz4yas/+IM/eM973nPUFrojEZ3RHmsC7Fimfd0aPwdxq5xCrDo/bcXHqVTjQONzzz1nFsxCr0oTMMdtk6prvsmjmfid73ynhPCNb3yj/6DrAIYI4/S8DI5ECfaBJQ9Z4Df/Rc6bSG9bbUVFriE2RNoxMOHNDBZbDFgWMFkndvZbFmRep4ddytTC77+cyC/9olYwx++vFNAsolm+5/8hWe/AO61mQVEAwZ8mAlCSkeGolxwGiYBS1whwqZHgeUVnhB32beSrwkof6vsMiUmKgBHPGL2VJLuzTdkpJC6xuLjZuoP0EhnKOfEo5JU9SnHNdkK37NvoK6rolYP6+oJWvyHVwKEku9Oiap0iD2ZZsbI7bFgLC7l7tIGsL/Qnzm5VS19WB9xRC1LZ2wTWmoOvVLnF2ZaT+YTm6hoem1eRcwu8apn0nCdPQF9t9YUdr3StQET6Rad6QgAmb0e5R6adQrW9Sgo0hGZa8Jrz+fcmMiFkAw0DnXdXNhZkbnSWotW2g0lNaVH3No/Dt12N7TuKEU0YxbCo0qw3+qroZPJUMQ6BH5Yp5SQXbUcDmWuu32ECPESLQi4CZg0ZJmOnyWwwjl0hFkSMYJSJ1FAukipD48/NZV1HyOtspwPw2HqD4dsDryEc64P+UwaJ+TKFA1yJYELjPGt8rSAYYqKGxHKGj8IUnNaxhvjUU08ZKUmXMGylLnYEjHIGbi3G8GGcYnRWsKN+LwptjZGp1nAw1VNlGafiBL/IY4HPz5H4H6WnPbIXpVEn0O+xJsB8ln/S2w4ApXdR9NJ1Dp0ZgKnZ9Nprr3322Wf9txU/BA0MgP0NAD4RbFFtXjYB+Fmsf/7nf/Zvh/1+kn3gGGHRtnkXh4ABO3yo9fIQY/JTyJHRCydvQtTCLPH9GgkwpqAVn5nFzZ3CL9sXVo4n+tqzHHL2QacxhxTqvgmd2spCjbU9VXu5cmD5kpQyWaXDDqysW4eyysDE7N/aXkADVXEJMTWUVhkCdick9UiMcjCrCTZbJO0iYzGZuFP0SakkBhrSK5GW0FyAZQgMn+0gxAAGJpJmdLgOszYl9CsgloHDr2DQdQFnlgNkufpyEgx+0bN02ivBEGCvIGkv2aydJTskPpqSt3sV8cbh6Df7pZJkDkdgIb4PJS2SYRlMZT7ScjRgjZwx644FCiYBGzZcqQZ5o6puEYHQsBYaCqfes4GTGkKrV3MKmJKKyDe4aMsihgYBuhBuZt+PnhDyHFR7hmmHLJaSmpR3Rwz1pleFnykpk544tSoXFQSygjxvCZyt2dtRZqc15S1qmHomMFwdw+96hxEmBEUT6JvMh4NK14DlRVyE1Rk1dIORtt8AqyR/+pNdcemuZTIWoZ5w6C0tisNhwvA7pEAUvIRWHvkcrFmS40kgZ6Fch1ajRDJYZDBwvTBwmKlr1iNgUyBeBc0pc+iv+yjCenvgBUxhjT+nSFzNGnM3d5ERJHAOkGMnEJUYt7j51re+tSTgH1Iq+w80h5AAY5yhmWs4fyuqFOCox7GEvKiAXxMirTbLmHkTs50ky9zgt7/97d/8zd/kBq2/bHh2aZE8z/WnIYFjTYBHpe+/FjkeKTkxUTEDwfFdd93FyGWh4BVe//rX+y73oYcekpSaek2cfiZaGO2w9CjCTSp1J4xwYtP399ai/Pft973vfZkYNkF7aG3Lh/Kt0pisLGaGq1eHQLMJwFWUKNMQj4hclU7wwiCzplxuTpBane6ngDwBnxBQAY+mvRxRMyjZeFmbDAgZiyH2vVxyYImoykLYlfUuZZXUyX9c0jBy01xg6lGsyRwI8JZbbrH9AoktXNOzgrdiXCkrADE6cxb4CkPlltJUj+rxAptLAIdHlIiVDas4G+ZUJu8VN0PoLST2N5LRwaCJR71I2DRBFWzFS1sI49azmDPByvkFTzCgRMjecl2t2spFmqYeHjQDtkft80hcS11E7ajKqkEh3EqhTEDXxKJfXKskAWXsiI0I3JXuwgUA8ISTx6WUEBFp48XRG26BuDSERCqiQOaHmQBTMAdlaWCb/y9ldnMAMsnX2uRjIETkcC7aiCNGA0f5yVBD40K3U0hZW4WZVNVAF/yqY10NpwvUgEqg01WQVb5SfbkeDJttnaqarJt4CwneFXgSukohqSuCSYCfccwqn1cAYE0xH2+5AgopNyPY6hFMd/EhFqFYX1J0nQoPwMMgTXKBl28fpup2vMx/pPMO9RAy6WF8Qj6LcBoF9kJK8SfKQaLGr4H6SRQNP/zhD3vrU0zl9dZWFvW+ST1KTAESpPmebZPuLqqtgdC1lVk/X2LOkha6XxQxu+6Xi3j00UcF+X76x7YTR7GGSu+ayDP+w5HASSXAtnb97yKTouTWFCiYMF1ZcXRMN2YgJvvc5z5ncUgQLJgWcfIO9oRF1buwEzhZoJ+h/uM//uMPfvCDFpifeOIJ+cDhDP8WKRGTWXpwkkpaKOCwnKxmF1Jdm2b7A6KrkCTWsf+gPPxwdA7+ILGrEGDRFWYPx9sKa8wEoU05hUyEc7gTUIqh2dFw+NSIGwAIGrBc2Cz8Cya6jvQInjHa5pULyT81sV2gkvyFRPn0gEX83M/9nNxDUAuD6VkTMNJsK1Ok6h9suNQL1Fi0u0+YtP37v/97lIhcAWuuAJW2EmkZtcteLvZhMNYKEktE6kXviWi9kgFqSGnt5AsBW6aKuxSot391hgu8o6GNhokLs2izy4SYrqEmgmlC4H/at8ooNFIWBTTPxh3yfHZoW5sH8zbHC9GJtbZt18X8R8ziXcJPhhDiRVtjYbh1h8gOFcIcZTQWxohsu7ftI8h6TFl+ZU8J17ggeeOix9KTAt5poaWq7Qgl3SuPhjX2ouwKfBWGTQrholfqC2ZRwRBk3cfQ2O2cztMgocw0LXmaPDm7oJybQRTNL+pltH5IHglY5TE/Mpbh21EkE5VEJ/8EQN/cU8YvW4Y8hynS3BRsoQQAX9omwHQyMyZ1ZdERVKdCedRXCFYgQxfM7JrtYGdibxM88qzeIszOWJBQ3aBluVyErmHr+g3lR33n7kiGXRPRhsMNFR9S0oDNwAm9iBd+srXrsGEXhXztAmJouFnDSgfbyQx+4VStzc7Shvjl0AS6/AMTO7QvtpbSvxKAhf43v/nNPvp9//vfb8OJQp7wyK4kmTPwIgmcVAL80Y9+9KabbvLVnwiPLxap+6EsIWkxzx6E1GJxe8WWeG+44QY/kdUCFOQWC+zw/vvvlxDedtttgkj5sLL5YItdHAIqshU0vOQlLyF/IZpojLfd0crCfH5NALIL+2nioVqc9iimEUTCs/ZAlG8VPOUDKnvCcpiqn0/k1iGpdLSaqgsZUbVSFxI2QrMnMNoKg5X5E6/cjwzJswWOAnjrgo09QiiWTRyc/a7IXytKIvxSKVT1KPETH7fpt8iYLdt8lgbrXYoo+5U3SqtAwiNoNvmhxC63GBpC8Y16PWprXOytUU46kBAWtWSCNpSr9CWws6MgwU8Mn5RAF1JBvIjt0JOMHc34FVdJP3TdyqHKzAGMVLBqFPTloo0WjEgGPcriFQU17i58uSM7aUDbfL0yVIgkXvRzTfq1UIg14nKN4gQsZDSOZBgAZKOqAyZkklSJePxaZbDeZDSNiHyenIlLQ4/eXubtivKkQPLGwiv5RurhybKCnFxDdFrjCHDdZUrJTBwhDrBXGrrjLmpvjx0Nqc8rFpE83wH4iJcuae6ybShLN75UKxuPOjVwGuoo5wbJwYqAGry4uyjhz/zMzyhoLk9TyCt3F29P5oSju9QUgMUgY6HfvNLc+QVxOTDmoEdki1wBEE7aKkCVkQJPW1JvEJFNgAYLfuOlbbVS48pjiSKVdYfHK9wRdSl2vV2vgDz7TtAGubGAhyYnxVUZtAA4ZxdDVoPf1NPPXMGA/pYwlfh1Z87GVDmCigRg8MpFwnTPY3UX5O2dhng0HGgIqVfIufw70lYlDEQwtE2OvRwhYzZ+fkI4Mznl3Dr/Vg0pc7mOqtx/gTJwsH7DQsF/vi092T8l++kRm3wCfpkVozMP8hubD/R+iF+pF8rMzzjgyX/66DdfOJ4kpyuJ5Qy8VAKnkwBTdxOhkw+ulu3ODDyKnq0PFUwHUPVbLOjCJ8H+LZN8W4r48Y9/3P9JMrVvsYtDQIVNk4okX6AjgpRUJGS8WNrEgqJqEbakyIUY84E1aTVbIUx06xys8/bOkuFX5L0HjZqmPASY/wpsuyQFm1lH4iFczu9ntl14JfZN74m0SJ7ApUaE3+7GpBVbEHFC5RLxyygkwy6ZhrJAn5C9+pu/+RvhLDMHr6HpnL7JB3TEqAXBWJbSmAUFOgJiOHWHAHEAHaCW9s00N/ouoe0Pf/hDyQ8NgQcvch4pdNYOSnQp6A5yZAjyoJLC2Q0WO0pX2vWFVggIRrnmwms0oARhgmlNVIJ0iQvhVOMVftVLt9BGi8DLJy0NKEvOQ8bSezvoBawj9S5kkInuFGw2GhRl7MgriAudUlavXIjBqSaGIFwo4NorrUgJcgYuX7oCfvkWDJQBQssExAtGAmnIpGdklXUTXYjJvEJVUShkkd7Az45U6jevwFAb5FEnWWIEaAQDQPLkCZLOGMfCpkCGZIsjZFibaF+RQB5R5a0u5Oq0SAHL5A+5TfjEx9qiCnzpM5y0Sw0wTdwrK4uaBXkoBGCfU5mOITKv0kpZHkg4MGfhIJLEJnhEhjVqGdZQmK9h6SejcKbRZwgK6R1VwFhENoSJMWcc9KI7spXCydJhtopkPUJNXTqSpYMkXn4bjAI7AkA5rT3BoHcDx+7SKgVC9oh+XSsAq4vQkERVYk2a06KUk73rpaSBcUt1ajRXmbK2zpUExt2gp6EySENggGhXhKNhvn/GC5OpVpCQcNBW5bBA26+99tp4FW8LviVy2Op4ayJw9JuwcL05IyWxUVSPPPLIaP0+Kw0lM497wXXZ7D5p2H9f1uPYnVlPiFLqvX8ydtoj//PlL3/5bW97m1nJZ0r8le6mFXKn9JyRH5EETicBjtDn6P0cmK0PoU4vXbrk3zXdd999fhlC7PKxj30sIc7W+7pAhNgUCYn5rDjKc1Biro3A9yx2E54rnabciWWL9FjhFpzZBza/Cvtyyrfrbv+PuEaYwHFm1ygX4wIWrZpUsvEl9YJhVFbwSw80EYV3n8Jmhw0SsawtNUgAhwx5ncIQoVRENuWVLEL8nf03QTzBCnbF06J2uYGETbQq9pX8mNElXbICj1IXKZbwXXPAQmfsIE+NnysT7giUn3/+eTYoDXOHR2SMKlmQS5Ip1tcQ1xLpRUGhEErMjTu9oI1kaDsk+sIsstHPqCHHoJxH79IeAKwg+TCyzdC6iDQAu+R4gBWwBqFXhkABhS6otEV/yiDFNLpzqYRZQYgTHp3k9JjKyxD//d+MMamsn83XFjEuXZCnPQGC0pEzckmuQhUAwYRWHrHpQgNRGyCFUOgV5Co9upKJyXCsRCgToIGTpGFNEqKVxyCX2hmp4HHPBYO35Cl/Vn6h+vJfXHuFO/90zTK/RA7OAOSVt/lBRJXKuRtNo2+wHLxsRzMNgbmwnwJ5kjChYZDjQiqlVVCjbYsWPInRnzR0J6sAKPN4ThW1r5RJw93I5l8SXJb+Ffm7h36k5lcY1ejx8usr/8kseBCZSobGKLyiJ6iltARLGpCoDEyGTEPEo1xl6t3LD0S8xi4se5WO3OkATVagG3AqsKwElESU5EFlXuHasoWyVS0LSREs5LligNYg5PZqYGbgCqjK4W01DJMkjWzqvVJgWWwEeQiG3AWze7ewSCd5Ff4N/ZQQcmXNYUNhXWVoVdMWoGVBmuura1hgPpWq8a3Koy6QGDvilyjJHhg5BOkZaK4A14zRwbQykz2wv/8uMFudOpTETHgzNYcwEEXYtgomSv/i6PHHH3/Tm95kY8nMe6qcbktiZzytBE4tAW55O7QyByS0+uQnP/miF73IepXp38JVYsFDI3UTeuJns9jPF9s4kvNIkMw9e3bBJjyBFBr0a0sKU2JHATqZJyrdhM1qG6YEo34Y2SawJE1Eq5cCuKgCwqQKXe/iTmFiMrTulezI3l2NUcoi4AqdO3gBq/8e4VyD7TLhY31FVhiqoKGygTAZi4nb+sIpQ4AQDFSCM5BIRSeTkZeKv+3weMSRfScxN40SvAIjcBGz9EDuattKKCx8z86k0VfvMQSIdG0CU0gfSoiBQGZDWGyEJJxSD+kE+RRVKaAqBfEEJFgQ8UuDxdxS3BAGJh2JNtDpEeVkQhnQgGAWoQyPcuJy1HqFC6mIpXrpDe6ync454As9kNuyS5Yi34CBNCyfwaOLSFJBQ3JTkKJ7hQWv3F2EoMYjuQFImuGOBqRq6EI5MgCrdynUoMufbSoiFUnEZfnAWwhdpOFKub0DgNwrcsC10WnfSoDbPbr2FRYSq6Uy3CnHimmIS3O0eYXOAtBRi6fK+CUxV9W0BRjymEyABKRVBpegUl/4q5WaYWW9vSy7F3BWZaGqdL17RVzVY/dKX1JclRihbMZCmT5QraSRHbxHTQBP+B8JbXLaagte2Z0HUPBTUkn+aV1eqWRoNNNAqHSlgFn66dGopVJ9LkyhRJnmQBLDpM8q6ZtxJG2LNew6EgPjVZJY2PycBz2/LM2f+inD587cbJgD4228ckck7fIqjHj01sWa9O5KPbQpjN59JEz5faqKwSGAtouUZwh8FDXkw9VYmaLnftSgpHQUxK9NJJ8vGGCAQi8OZFol1u7lEBoaX5ZlmmNrFjFpL2s6SX5xanHNvxplwp/61KfuuOMObJ4kp4egV6dKw3hkcKrcXjhfsU+/wuXImePQ3PFjjz3m04WaxS+cwm0REE45KTFlDtTZyhD37NNDmQOEZeYDqVRiL4EjAhaFy5vwDq0u5MAm2jaI3wTnhm1kKMq8AABAAElEQVRHRS0zlOmNRs8ig5yEFBHah0mUPB0hWUcQRTl3JA4215px206V7dtU3O/RtQihrMaFZfu9hkk4i05xPLQiftmXR6sMCJMZgpRGUiehsEQL5bJHYy3vsiTslQjPcACDSqxsCUa/dufkqySAQSph4nQHIO+SPMvTLl26JC4cFTsGE9lrDrkU3V6Z5ETXOpI5UHKRPWXThR7h8Van8Gvi0qltMcgF6MkAUSVSSXc8QHaVPUZQtJT01JOAS4E1eUuedqVSk7fuUW/D4QMQnbqCJMhzN1LtY8rAoNVR+0plvY2x6JT8LRnoC/IWuCvDRpJJJwhBq8JWOLsm9RhIsrLKINupelk98dZbAF5ZATFwgYlkCp78Y/hqDIc7LuhGR0nBD+vVoMEQY1wvQ4Bq2xVQglSjQ4G7V5s8FgHFadUM0U68GgKrCTyJMaKsQRjlDpIcFrk1VlbrIAzBcpK2zJAtGAjjSBmoBPNBvMq8BWkhMtE5AkjMK73b2fZIcyizGhd7YRpJzmmgFbfs2DNDvysJGzw8gwLPYLg1p6IxDfXmHf2ahqzowakjl4K+wIBnO7RUR7EalHNBCJvWc90d3UUOhBb/KfYghAz9cTFCkXh4jhf9Mym3sGJMuU3e+BhZnsmm8TWjmSsZkRVes+fp6XBEwVk988wzd999t0nWYpm4xbCe8MjOVIAz2KoSOCfAq0psU/hYqU0VE/k73vEOv4xlN8lPVSf63xT7gbXHrE1CCYxVZz8uagejO8m2U3oTQ5sMHIa0gTB/vlyPKswKpLKDZyoSB5ukBY7H4pdNlpkvEewSHbovFQWWffUtB5ZPCnPbvSzN5YRLMQAASWLiWjmh3NKj3EPgS4ZydakLPMJo+0jqQUaLAIhWhbNqXKQNDAvKdla9Fe6gR9rpP1L67tQpAI8q5aKiBBZHQ9xB0g0fhhksBQSoqUvvSWUdAUUeGhCMGETSK8MtufL/BkWWmoTZiJEACQdOYgEMswQbtWAQIMKGVvB9OR6/kt/mHgwyZAfpvc1je4fTQkPVwNyWva3HttCCtfXKE6+8JUzJgztOk2Z3zbtH2Eg4w4ff7u2cR2KUfLaQRjZEGlbjRaTeEm8Lw8MYXKJGqmUREZICAGX3jEgLPyyTfwKpvKJ7lMR26Ohq0bB51VBjEthuAlzIFSR1SG1rtlImYcfFBdCjsuqUhFRB6jc6L7dUzqpHC6lsmFBL+SWcYIxLTul7RUo+kZC+akiljZe3QVj/0KhYy2h6tPABhukpI1U6ZKkuqxvweLySNf/kVs0NSpZC1OgaNpaCMBg4Df84tEilutTst37rt0Bi0+YhytEfFtxpo1ZFT7rQvPo62IIhq41Qwj9YOqcJM7QWVgy6sZiANEDWTaTKPkwAmXn5KIZpgqmJV/i1UMi4yEfqa03qVJk16T/wwAOf/vSnX/e619n7tQ5OLKfK7MSIn19tLoHtz6Ob03TyGGKrokn/k8lvYt17772Ozn7pS19qjxdelBC4URmjCEMAt7lPgUGswBcLTeQPLoGINHhzzEvlky4SpuS+t07RJnLySbB51xdHAtbRrlFlQ8MkvYdFgRxBRJjuZBfODilbF5e8DWlTOR1btMLXXEIiB7bE3m2fDjG3DYdlmzzkJpYVxWorDI1wKIygk6wc/VVvLYPtSE4I0CNg2qXsbm9QUGulH7yMVCVGmJXEVYou2oaKKPynBMiloHarwGiCfo9SX6FV9ppa8mA2iEIuNNgrzv4t/HrXxG8LeSXMkmlD5craQYQsx3aJqoXs0jNoEaCt05vk5oKk7StlAOJyXAxfTddgh9B0gR6m1wF761Kp09F+O/g8Cji0ElSl4ShMVynZQHy3sQwmK/dyHmlMR4C4DeVgFIDRBOVkKTJbFxromHAHa113HrVN7gRtfggKsCUP6xpGZAg/rNGwTao78obwbU2kqqYtKEOyEp4W56JytGjR27Xr0SkpmpkXSaVE22GQP8lg8RsGnXVAZTPZCkLKJRO0ecXQ1LAIE42hpCRG2W99aWtwF3lLDbXCO7tQkMixSh4s/OYtz+BKuZMDC2WGNIR2ubssyzJJFOoRC6nMHW2a6wWbjn6EbI/B6ZAFKWHZshf34kqG7M5GCIH20r2qj1KhytVRtedHErbEQ25Zbb9wetZm31hMEw+AD7ToaWSvvfZajK/d11E0xC8LMnnROiGHORTZ0yI6Cr46IlkWs7311lvd/+zP/uyuu+7C4+mx2XF9ftydBGZFBrvr/mrGHLu9/fbbOSwm7TOkj3zkI3feeefMcG13ojNzCHm3tYMRNm12+aEXSUsbYk6zIBYRIcmpkk5MA0+8FZGIfvYsVZ3Kuxxwtc8vBF+U4oq0EjtO0L+VV8K+RHXJLVM2nYwiXzXCNsQiqgRV8MstBalrTEuiYReSbCYLhQ2ZtCdZIm20iytpNMEL4HQh0qWotp7oibciAHuAaHAKXTztV50oG1Tq/fIcqqi0yFtiiWvxqwESo4t9PSqDBOMueVawIGXVxq6UMlS6E80bUOHUd77zHdyhU1CFAGmAaCNnmzXHtWQ7Z3QRRs7ZqrKLSKqIAeAewuo+lBWqAgZm1UunqELkaEOhOfKG+WcHrHcXtVGvgB7skAZR4Hdta4Lq7/7u7+BEQNcjQzCCJQoHNQEwnFFdKrCkLtACRiHy3Hkbyb8Rl4c7gVLAXY/bfdS1jf3g1DU1UKZC2aCY3xde5F1oHt1v3y4vGVY2pVPmQNTKc7pwwJJtYkrMzZoIXKvs8Q6btzXKrJU10VJssiNdk5W1rdgXk1wkqxZPYNqaRWWQXuHLpRxVcaiBvunL1EBV2CZRsHRvuZdLly6lbAisUNOoXJbGFKL84PHr0coLMwlaTkkXHrn9ohB+XdvTJl6tLMqY0bR18VTu/E9nUC0vwbPJXaeUE7P6rTWpTboIs0jaBMmqHOnU4iNpY0dbLg47LoPVkQHSW8udIPlzg7hqX8cIT4UESw78mwQ7gRwjO0OazT5f/OIXbRexMscJhVVgTpLTIe/nmh1J4JwA70iws9CyXs7axG97yqEOv4z1ve99z6GObSWfs4h4AahmtRcqfhIrbMXFQGKal+QHG18mEBd2TCAX2opf5SFSoCJp1QL8go9uZ3JVJOvBm5jlSP4rsg/VZPJm4tqRMzGLnKCNzMVVCioX7QeuR0DbikZFqWxoiLqmT3VODEqLsy2nCS6sQzs0aKPVfst8PKV7CpIHuiE+FhqSklAVQn0pEyAtEjfffPPNVEj2+9xzzxGyzFYMLTYSlIs+bb16K0Z3T4IkRveKLhkOSCTJ9nJtIglJfQsndBDHy7igMljkI14URoicnKDTO/xhFh6bTuBlNXhEqj1kSLwtZhUSzqJEXymrxJqoOtFbBOUOT3ZWg1+2jGDlkoMyysV5AZi4a9K+9ZiaIixvVaKhq2wbVll6b70gj+ARzx4J2U8u185bASvATM+1UjBYhgm8egmzoVQZIVQTNcpFCeWPWhpKzbMU0gWvRrAThXGkIcFj1GQXaPAjZ0ZKp3Sg8Fe/OyroLjkhxhGZVZWM/qo9yg816XhfFckceEkgh2ACoudMQzbITU03pNWAWQozIVsXJSdthbYhxU7C2VambNwl2/TESMnKWCulIjSDzqCYQBjvEHZ4KBUCuso5j9AigGJYtOKiEZ+O3DN8DJBYlIFxAvLhUbQk8Iu/+IteAXMh3pVBh4qieozz4d5dpbc5dRKcGgKOt1RjFUArcqNLUGFQ7ygBox4GhTSsQh4n7ozduoxsUOorCZ/fcAKnV7bgTCirLpVO41z6ljzNCwEjUoWhjuGXJ7eU5pWthTjnpZiPFID+ZGmDGjvL4GRN6fORcrSIbO5CePz000/7NqE+GNyWMi/q9Fx/8hI4J8AXPMSxYcGf33AXWfoPSU5Ufv7zn7fwzJftkzjBR+JdaQPHmp/nEWeIFbZCBk4TBEBufjKDCkGSFo46sqqswlbI2A8SNGOT6PzvFmehBXmGGL/hhaixX5RIGsWCIsuXvexlO2J2iHZYU/RsUpDOSUgkS7Q3xxGDjTQU3JOUCnlFM4KV5JZSWVGLGmGNGV3OKfgjDY+i7URvBCiGE3WJ58B7JTyVJlEqBZeY2xYx2epC3COMVhDLaghYwaW5UFu8bkL1SOZSFFRZIoEh8TfJ6N3ZZpAswmaCHWkE61fAB0ZIKoLHjvDdadtIEpIUvHV5W2sN6r11qXcXyxruwKAt8sld5o99MB7lgb4XUNDFdFhzGfWViwyJVFErZQUySVapHjY16HfXERhymMiy0Mb8gZGVO4Jh840ZOagPs+rbC3IZaV6RbaJVQhPQW7z3n5MBg/H/nO0DA/vwhz+cLtQLWONqQmG5nepIYZjzoCqVtrWNoDJqjQ5gg67r7MbjlKoUqpbmbZUJxwUb09ZdrTFlONTvtPdNuKCQVmCNF/NZiodNWdeQKif3oCTDQYFE/uYaxUaZuX1iYQX+MxndMF5UhRv0ygqUXaxY+mjzVDK9ibcTrzIcrD7n5GtQFKzZacjueAbD174aIuze8kIGPWCQk8+wiRqtzPIcXXwdFVWIK0CYsvyfB2CqknPwEmlvvXImRSVZkXYuus15QpgzJl7pt6NKQ+6LzXrbLrSN0rZSJVIpzEpNNgcOC4RGVQyWNYgOJ375bY6XW/Obf3NWUjoMx/KI06T6QgvHTNrl9WNhYSad2HT4y2/l0P8nn3zSd78alp7PRHIGO0tgVALnBHhULPuujD2/5jWvkf3ecccdL3/5y/1zsw984AM1p+6BIDSYJnVkbuN0UhaR7KJrkZagx1q4QBzLJqryaDy7AMskhwZluRAaTPltRLILkraOMxwJyu03mqVCP450JHbMoUEhIE5lZeJI/n3rNAwR6ivLEMNXW6nBr3V3M5asVSYjJjN8CVwkk7IyYVOEoDvDShMIylgD9qi5TQ/hozIpUQyhsBxJgkohFeQYQh8YRO2Q+3aXDGHQMOG7JjDIV3UHWDjrrQUXiiQ1wr6IMNkvDLZc7P0aGtEwbJYqvJJ6oefZZ5/Vo0dvoZKHw2mvRnfegkGDvrSSHmNKX9jUl2soyVRC5RWcUmurA0PjkqQlsmcaCtl9SqYxxNnW4AJVCHaXomAtrZAXsOQtJfnszeICCy2etsz5ZKcX49CmbHXD/iQXMeqaaLULEjuxwtPaOVcjXk9WIHjFWtJRo1w9tnJLua0psK5g3F3oEfhKFVweRfwIVqY2PIwyK1NG8xyc6aJkpbC0VQFUq9R4RID1GplMwXQsVHe0glf0SODu4nj36Hxg1r4jw4WA0KBsEKkEscBpjcCg2D4iogCkI2AUSQ0tauvzNjVUGtiQsCF8wXiVcZf0okGeTB90BIDSUhvOcGga1VxhAnkLNiwjlebrS87QdTHEOawZIhytmWjoFYujol3DNMm/gzYc1pj8Bh41KPM0NJrQc0LjizBCVrEvJ1moDQwcO8FScst/moN3ZtjvbuCUT066mMGaoLAjrH3UFgHRGWUFzhAAIjthtq22Ve5o9tjWFF/mC1MJV8wZtgDbIuMQ8GDWGo2lIrObeQfLh0DVLmigb3/0R3/0oQ99yETvl+oygZ7qsO5CgGec0xI4J8DT8tnfW1bNr/Fl3/rWtz72sY/97u/+rq8Nv/CFL5jJ9jC74FPYkVlTKGkKSXkX/OPUlCktlDkIR/yKvTN4WX2PEAQoLmGr3qUWKvcjgR0xK1IR/kJufKVSAhHJQNIJMYTgWFYmaiH/hL8KMh9c74KeZG4bIk/oLBRzidJc6E+Wa0zrjF8ieGOdV4IzvLu7wOcKJWos2Cf3K64FdmAEc5UYeCXkSmQDXtYnj6I56IFHEC8HhsSsKTgAJgVyR6TFBVpNiwwBhKKH6Fhkbgg0EUwoQCiORLmGDpXBLFpVKX8G7B8p+boPRxJmPUoMaCn8Ak3wZNumEFLBVm+HMh/WRCbY9ArCxKwlkOmCJpEzZqlZF+IjNRYterZJ6y1sSZJH0RZtJOaqRzE3/BYR6jhDNS8Y8Klsa/wTYxdxWeBzLOKWW26phl3B0kBXM/GYLvTIURhT4wW4uLM55lMxnTqSYIiJdwJV98qgO+Nq0IuLDmDRY7e0oXdmPgcJSCxAq+CujK+UF/U1s55u8y3cbMFTdVRlNYpwLMrQNyJCp0JRa+WOtpuVUMKCWLqCi8IzExeVK+BCPrOguS+0bXXq17cMVIvxMrpatZmJpwNDnnF3McCWNvU8iZNNGBRJ8zldwzzKKFhK23AUbJPKUeRU16BAm6VAXkW5Rj/71elUZatR2KTYhoYYXYYyCXayX8AuO/ykimU+Kh9PsmIdaUjD1fOr3IIlqlHailnrJq48gncpo20lyyps6xWojVUhg1jNjaxtc56NNnK5mW2nGam2B1vAVGhrGUklS6TGjJcaE76RbWEOlqOVCMOpQMJP5PgtD1tBvhCMWzg9TlcSyxl4uxI4J8DbledG2GLb7Py+++5zUMqpD/GBBbC3vOUtMf6NsC9rnN7L7QLfna+BGUf202QjtgqlLpLAhPteZafI9GzD0JRmhl5G+0G/LzGKUeyMCUdEovKHEG3mFq+AIfl8zShty9bfjrgqeqbxC8iEWUlx3V1CYTOu5oZMdKV5aYvI1UipET6KTsRDgipJo6EUcqUj2alrtFNBG7HQgXoLs45cakTGgjw1DpdaONCFyuSWKhXoUigRG+lU5qBGnIcF2yl0jDwFfPCIgBEmVLLyolNhnw0obCYsNig6smdipK6//no/1CQklQPgQiudIjIdKYBBnkedApD5+1VYZzdklSE7dIajNESqV/Q8uahKj67AtHfdQQt5vcUIhFGVQHqL8mzCKHikS14JjAJQbT0q6xeMcfRoKNu3gV90t8RQr4yghQk7VKXA9Uoh+N3pMB1QSC9tXwbFKABuK1ski+pbmK6sL8dnVJKSgU6mZ5RlQWhQXzgnZN7hFGG3QXb3tnvUC5nQOvqZo786Cpv0BHCGCcIJb2b1xAVY5Oceg+o6qkc9ynb06KrKRQUyT2pdAJIEGGyV87EMAdnMhBXQXm65wNpCkESeylHpFmDVMoXMWWjYDJCuXdipwVoVYcGbO1guW64aZLML3zkjm2eYGNmQUQ33ViCEbOTqEbWZCyyVUolOIDxDq0UWXFwtnVFyYNwIj805UMJcyoG0hsWzBbM7t+AVP6ZreutiUzSZoOJpA8lFJ7um7dxR1HWOBrbkbVjGF3UtJAjmzIUQKCzrq7dHXeAHjGC3wWv11ooVIfhSIHLI0Bw1px3xTPWJJ55wCpIJ+w1Li90ATo/Njuvz4/4lsHzu3D9N5x5JwL+t89nPgw8++M53vtP3cp/97GeF7/uRjElO/LrrvuLOODj/pcApQcGQHjk+nr3zdN3jrgnbHX6TmZ8Bc87WbC1MF4cZ02w5CtYt62ZfMQHH7shoMQuVXGIjIePlHPfHPxbWyGDJXLDYBmRaGSzhlAINASBCEoclTnIPWrN1TdgieKOpfnRYBS5pkjuEVZMRFxx7lY4U0OlOhpI3BcBCdpIUhCmoQbnglWC9UkYSpjzSLqlCqPUoMsYgRsgcNrwIPeUnMgRRnbdCQ4mTGge5wZOJQMToiPMIKjVJAjXRNXrEjt7aN4Y5/3ASPeFCwSUxk2YkMQMpiJR7SCYFuAHo7s6jqmkxCIjTsIUUDCGs5JYytJcuXWrbtk2MCFFosgigBQ4N+q1KATEeDaiLtKu+CnTGSNmDhb8Iq7cqLeoDmE9AtZ0oDHlRgzzyMVIxKCsjMGC/3QidwIlBigHeNcQ/bEg3WoFo6+A9MG0xm7LFoHY1oUVSXbRCq8oWssrWg3iPNs2rVymQs0xGGW3QUngIjSAFQKoazS2a8L1eMdvKdrRSoyEhkCGLUKZXAKi6PJn5xGOno4k709MRQxvykhp3A+SMLkskaqh0zegm+FrUXSs6MPWYjmgCo6aZuB4Sswjn3urJ1oyAZiNil5WWKkcg0zR0vGiVi53yToswGE1eiJz5JXeuksbSE31xg3JjBXg0py3GOr+mri9g3L5X7t2ZnWk6t/W25Zd+ygb5PURSoXZdYFvdXSAe3JXvJfC4I6PmqzERGmZbUVwgndvt2lx5zz33fPOb33zzm9/86KOPZro/SU63K7cztjUk8L+RzRqNz012JIFYu/VXZ6GdG7z77rv9+z7/9+y1r33tovlsi5RkiXeLCCdQ4dTUZTJW4O79ur0Y0e4ENoVo6tuYcgLPUbzCo3nLh15ySzGlaMwirthCfWJ0zO4i+zV3kk/iG7GOiFanYhf9ysYFW8ktI0NxsMhJWTxq9jXLuowRbVQIjPDXlXLuUCnkXvWa69qwWsoxrJK6DkAkrXevQoBQRlu7GYtOgCOg1X9cZLctPebgq3yYGF2yVusL1hREeHqhS0Jqs6lNeHEhAKka2nQt7CMcvct7KT8MjvahWaUyFpJEJSlNRAgbIuVUgmnyhNkl03YqGztYCKeQuzTRERpssAhzvcrbjEuJqy0EQE1gWsiUr+D4P/J8R0aBUSf1WSOTbLRSatEqG3dXV7nSowy/yBttWG+rUGBqRPki7OGrgtmkQCVIOBjohm1qCk8ylioilvn2ZcXB6TuOt1P1ljyYKQnkCgZaOd2lr2ySM3OLJkGivm2+qMz0Fr1aqR49Mhm0pVWyGvpZHCmzGvZuH5g+U6cAMwQrQcqGCXeyI2W+q6V/5giyMjnw9Ac17DrGGzr17sw2g1qJWcCszEEMBdTi3eqYMmGyVgbCWvFocplJ+aq9bwiPKk4Jkqgo57kGnRg3ymT+ohe9CLZKn4a0GU0XiVmJM6wcHRiOzp0FOQVAk11ZGOUhQwwn5vgPGDknL2oHksNkI2iGKuuMcT5rED8kcqIGp/SEhVou4dDMpLRo151O0LOtV/iyxkTyEMa3mL/4FgpM+H4b1Zhmgj4BZjuh4dfvI/72b/821f36179+4403Ajg9Njuuz48XKIFzAnyBwl/SdSzfoUpe3j6w/xXsh3n8WLSYe0nLDV7v391Uj9kr8PmiJXBBtmjsMJfqN5DuZW8u2vCLDiJOkYogW8gOIY+fEOT/sXdnTdMV1Z7AO7pPxOm7E+cb+H4ELgwNIQwFFBWUQZHJCVEcAAfAgSEUZ0EmUZxFceYAoiKKiCAqSjh8l77o6Nvu3/v+m2W6d9WuuZ6qeve+qCd37pUr15SZ65+5q55VmGtr+XQlZ5XHwABuvdMroZTEWEfdkkFqHhQEEKqROrgCcbPhilX/DczyVBXmkZYwPEtfy5vss0UgcibLuRRKmoVV0GwfvJEwOUGSV7dOqHJYoSwNihgy3TDXEYjlYmqVNJWosTYLO1eBGDWRVSjoCziRL0L4gIpKqjEFTEtsrsFKLuhAVQ3LAAxQLoY5ucVHZV4XxAHYFrrJXSISvRwjS+5RSl90RGBCIlbAU6WXyZmabBOtSgsS4obe1jgOyrRIauvT5RFFfM7cuprYRUSd8xOHMNGdK2VtKQgyKQQsGcXKUuSEtzJL5jvhnK6heFBpjHNNMVGz4qU7F/66YxPIB0M92t0AqDpDjKf4SzaJcqIM+MyUh3dythxKIaTA4/oCt5QFEpqUZ3JDQBLIZKI8nhJJF6ytbDgLWvyVaSESFNqLXkJXjRAVtzl8LhCrC3FIVIOrWqVfE69QV8llIjxvWoZ/rKphNdGc6/k6mztVP38hnXasnVuf7YwxzBOfDAEimR+UNc+GiLBUcHQ2P7fhvjb3lIOMiyX4iwf+8l6AiVRUBE5P4xOb82Z2zcxFkGQczbmdkcKM6H2KTC/mMKYI8Sn8DKKwMpXZ2M3kZgplfGKILl1o6EIWymkiLVqvRzKLcPOhftfLfFFh1khvgJv2qcNozGvON1sahmYwK+mwW9coxpZZmYo/+MEP/vjHP77gggsc9mSFPRifbtmYY3dzWqC7ZM7ZbCTbjgUy/iXc/omIf275vve9z27rvffeq2xd2Y4MW+vFemmD0xa+YzRHwdBOsrcDmwSpY7WWRsghgkIl69KFPuActvyJpOJ4go6b1AfAk47IY+QlyWidWLp0lxRZXyycq/IbqUO/lxh8XWaXDL3kJS/xe8veAiUnxX0SifxSPemXdd2XuKzrsiWVfXnUUE1OIPhJhQael9CHWNklxZeyGxRSBDQu0Fce6TbHgHJ0lmEfZvfp6Ste8QrH3Y8++qi2agBdsUdaCQfIpAYTR8EEk3+Qk5s81Qv5IRBPCYbYkTKE42CTOuyZpBAlAmra41BPMDypACxBaAFmkRxzeWd5pKO+tsnppXoyeKLima5bym1mRczOlVwG8CdjJgmpyKbgqSvlhHfkTH3J7LbKaykUQ8YBfrjSBkdM1/JvTWeYyKHFQ82l2VVh85asbd4v60IU6V1AGolUVo73ESurxD+FfvOJNcO9s21+fEhbZZcC/NkHwPgEeIs9NH1cjQ/IZEjmEK+EIbOLzDi4cPaZp3RhsbpVaVwwo44CNUOWzQVlg07v+ckAAsjj27YhzifjA2/K+nU5mVTWV867WsppZbJlFiWPgdnOqF6+gP8Nk2m9T+O5/XpWci3ar0nJRGQVYGHHv+b5eTiwhumXtcEPMwxrp1XHSnVrmmpnKg05N0/FvDdfuNtlPsRQ2GRKNzS8amSk1EU8AVPjbh5Ri0a8KYtP6QFhjDgCRIai2esCpQwlthXDlg+DlN3MZn6Toj/G91rTCM+hXnj22rMJ+f7777/kkksOzKEH4KNDVWEEwHvg2UzufjfVr6dec801/hOaOcJ/A5/2BcI9UGmSiNS0IprlJXPes/J+rJRIcjaJdr/raGp5g8Gihlf1ACqH3nKIAcU0kaOgdIFnEl+phm2CHOw4HvTUAim3wNmSWfzz9a2JnBNaEx+tpTL8HSk49IaBvZYsYZJykZY6uki25P2uaDGnPHCXINFcxqwgl7Jr7lKDgwtMZYF0AXAqv+pVr0LAOJAbhKPg/wyDx5pL++QZMirhp4mo84nYMSZW0mgGlzrDeMqArrYyPEpBsNJNzBW8E6i5T6rxggzGraXdJ2L+An1JhYmGvCZbNX5lulZ9vVBE3sMyyq3liedS4xhfc10Tj6gtjTIM1qmZ5zZqijoi9eml1CzAOAzYPiWhSANp6A6cROATSfvxrF0TbFvskbbsQGtlmbGNAPlcR9O2iyXKPMgL7XRBDHx86sgmCNMV2zzKbZG5DU5rhc9mzcD+AuvhjImND35nrpY5nrzmqq5XL9iLyb6DF1bFRlYB5u1zLgsLGLK5rRrE5DQWGI12mrePihXXd2KjJWtNlyapQZPNheIDkSobd2KmKjsFQibSRLjmyri1oLpD37+NbJGhnkIRrJQhWZW7XGgtPKecotRyaePYlh/0y2VzMimyMlrV9LvuP1ITB2luEnalFXk40ZUmbg0Tc6BJNR2p94v6ok6lmdAUyt0+Xf1ALUm0xdMv/ON2yimnYJI1Lr0U2b4XDBMXLewaWDVsK9ifsjSoOTBNaWT+8WNXDnh81+8rX/lKZrbDU3PfY/JQ5R8B8H54NjOC7OqBBx7wlsi1114rEb/nnnu8LtLPhvdDpUlSlpp+GQsszNtZlj2XR3k6qd3+1ZUukgMXVPCHP/zBG3rJldVY6UHcE2j3f7ND6q2IjlZiDWmrdCGJL27ws1zEVZxjFLedmiMxlhU958CwrkUuh2b2tmkBO0ni24OFkhCAlNW5lRDTGrhSlioZCDRV4xYH+ZArJ0VOtDwCpzWUdjMRRMqSoFEgkFaGDIZyL/wd/IJzEimJF2NK0NUT7OUvf7n/eKQs84CTcUgrB7bs6facc86RzxFbXwgcisr/yICnHvNtOlCKdkAvnO8YRHoK/1OWtACbGk1Yhk3yhrZMPQ4tC8R34gFbbQnvUkBAnvJ+0S9aAIEcR3dATjGhYJ3i6jf1CuSnFyvRi1WPR9gJ3B6afKamWPVvOzVFuVxBp6zEpIzD4G7tOxg+HK0S3hYnJT+0EBsic1asnt+dVvW7xkSKZnBNlHZm5USCfi/z12Do4voysrid2Vx4uzpkZpic3BpQeHae5jYz8MRHBLBrYzryVDm/8iWYs82Bp6zdI1tCRocDWOVpvXjEU+ycb00bPsrGTnT0dNHLqBTSmhtlf/vb38w2wUuL8tkXetOUTSVDEvo1+y0htvWlHyHz8+l41rxkWnCFgxnPgs4dBiNHmzTEXuLWRGrXSXNPfbqEkJ/J4D6j2LIokMw2Ah4rA9nXwYScKctg70yV80u7L5TUNE0x3fw7GvuiGjl50OtXV199NYd+85vf9BU/XhYAe6TCKOq+W2D22rnvGh6S/JkdLr300tNPP923gi+++OLXv/71d9999/wvie2FNbIK+lmLSGsVdHIIqFgID29+pBHVZOd09H1RmbqXdZ0mWfmSE/ikeLITeSR6oE5a0MG6aphrN+0TqejoHDhoM56FLqQ7OeGcFplyO48YwWfKleGplGPlxCDNQ1asgDcWy7GeSgstqIkJMEmM3/72t2pkYAQAYhVIaLvdwgzlosccmT149VI3NbJ8ZMAqzn6iQ3cuTDwCBqQphPFIeqcLUBxwUglFKOMJT0K8EjugSyIoLxTboBdgrBUPSg3ZSr8+Xdjm0qlHJInu+bUwaf2w6Z5vvdhfGhGeERS0VI7ujABOuFR6KiYd3YhSO/fK1Qf0Qq+67RfoOwCr+vTDNXElGjYnMAcpsBKz5zvJCDjFo9aeTE0FjmBYZU2EIqgWJgoYgm3DXXeeTsTPHZr13gbmzcOz1T30DAIyMY6An8ah36pDyZVMHRcoAyRBNciCWBQwcc1MbVnbforAQFy9tOWqnKdgVnFxrvcmiLR918wj5FpoMkgZH/Q1jVB2CaNpskSrYfn7DNXELy3StpXp3RyeMi2bEl2mU1MN5hZBv0SolbiioFnR5OPWO2LHjh1TGBZgH5/yJvVtz/mZOnO775i4KHJ4ytp6c4rzk5/8xD6yb/yC+gep5j4G4Ukl8wiA98zdpkKzpFMpc8dDDz30gQ98wFHwHXfcARUf0oZoO+N7h00u+/vf/94XLCX9HrVP98x/J8SVfAflWuOz9ksirXngEFAhWZTKyMut+vm09kdNOK303TsjEFhmk+wH0nDCH6AyoAhT2B2gMsvI16U+LbFy/9dl7Q7IpAEhuRTA6dAgIEfypAw6qgTYZIrWYI9AOEDCAkw2FyAKwoGamrM/bt7Gd7RiO8anM2HNNdS1r6lL2pygoichgEpCA5NqKJ1fwVH+hyH+NjXgMQlNjhP1km9dYs7dTv5lOUn9oWicPUWPp5HuUiNxxFBNdkYIqdzCTrd1VSs12la9gkfkDzbASo1QZC44X1laGQBDqdRoHjSokC85sxVKbQlsYLJSpwsRS3000y6qRbtpBAvVA06O+umVVoShnV0GrnGoJcCgXAoSWGZphoz8xhpKOpIfAhQGVBZjYaLALNxkuPnk0MB+YuMcGj1yN25hiJtrIclXJ6bFckwIT0GepV1Sz+X4aEtrhmVJm7DGSzmiGJq7eKFu+4W2iTKG2QzqU85ZgwM+/O43mYy++qX9OZvvEVlmUcuiqSBzwvaDcCFz9cVTE1ScV5xabpSimrFpkAowExdirxFZQdQ42DfVuKyVLsQzN1la5rtWFrEu+7OWRRuOBqatgV0Tci3ySHWSuCrUN35x7sfGWrobmYwWGLDA/7jlllsGHo+PdtACZopMFpaHt771rfK/T33qU16Odbwm9cyjHRR7aZEgB1kU1GF5sApa/5bb515agKUbZlWTo0M+cuicJQbNglJqAkKs31Y7iAKcs7SDOlCfhJKmckrpY5b2E27/58fSUh1tw8Sn5Nv7zFZ6J7QSoGkiRVtPwUu2Ai/bLKdsUc3VsDlKia+8HEQJ8gFdeIFtmVrWKJ2SVzEvGAApsbDLUwBJsGGCDLrAAWeO4A5COkl29gsCYa45nyIAnLhVcAID3tvUC9W8442hFFzE2ukAdM844wwN/SYZhjgry9gw1AQfXaAnjzAAqNBHTXBdwa2AJ5JHpKUgpOpWE6KW7m3BMHHgjJvmbb2ys2jccvhJERbAUC6iLAetTTQW06/uTCmu5JrJs93aSqCCiwWc96Kki4mISGyLP7YeMVpfgL7XOhIudIu/McJ02NLCWR8PkodV+Yj8caIym0TaCKAXzmJ/VsJBK+qLDTragFBpeLIMQ6HUXHi4DcyIhFyPWNuFBF4XcRAsg7sW5SlcfZHS0LCfKFanRdEw2zKjyLFlwHokqUp280qLKErwTOuCy/zYlZ0UYcO8BGN2DNl/WpNhqeqpfSsbH6YXr1WvyKp4dgoENiGQVrxtqItOj+2tgPzLX/5iJDKysCTA9mVo5VFmkMyBxkXn0cBtJO98ojeN0MvcaMK0aGJuHhZmKI1Hc7sRTX37j2YewSaY4cZEo2khU9mR22RA8XpENVOTtxXs25Kcmmv/iYTq6wgL1LQ4Xn755Z///Oe9P+U/HklZy+9HKNjY9UlrgfEEeF9dn5ndevCd73zHu9C+SuE7V5Bw/ovavmo1SW6aWttoCg+AE07V/BiYjHwH1zZTfC6rr4JdCYu0rLrUkipRxK1DP6s1GNOBf36wKsQySBBRAleYpJgcQEFWKvd1/mkj4EUvehEo1VeKBaTFEmgWkFFBv32afg2TOihWz3qCBCKSUgNCsiiYQVLFHaJIzg3nQK2egqkqfRIDkFPwirLeJXMSLy4DGgkg5cJWji5HAR4wkffLPvlRqh2ILmPTS5A2R+PgVgxggow8uoBjdZoczhaAXD+wWRfMgrnIiV4pTIzziZWtNfTb3rZlbe22uFTCCSBQ56Xl5NM4kJPAbltJ0rVP0tLRhQ9ryEFln8qyUpeCTG7T0ctcPK4vLo7ZldmTqaFiNmdhWZdj3j4a51zhISTogozTQTiVblUKD6xEoHiw1aicQa3gikF8pqAmZjnxcJkPfAQYmwvUOVkxMt2F1qL96UtD221LtK2+WIbxs0PHjC3m4QuKGCNiG/2AOh4JMPKwvxGhjD4uqI4WLeCGldAVmZsGEiLNRLGohKvTm1K8HmzWsqfG+KszXBcHMWnsxI8r8hRC9hcMByO35qvwNLTPOussxhdjfO1TyGUlFZb2BZC5FeQmW5e1g1QryrOh5sKV8N4DEki24bxoYygNDJkNibFptpaGb3zjGzfeeKMF/ac//elrX/taPR6emps248h/vRbY0UlhvUoeMLfMIL5FI5H92Mc+5vf0/ESWH9Pr/FuLfbcANa0KkJIcUWZzVKcufTNavZLqWYMtvS4F6bKXJxFLUCTN9rCtxDJyK7HcLkygjhQmrgHYAl0QBVzhe6T0nUgWDvv4SUF5mxzF+Q8M/MIXvhAG6+gov3G4Snc2lKnPmayHCf4K8IwugsFg1KAy/pJtCycpkR/swRY6kqlzk+hKX3amvXIP05LBgTwcq6wJSs1FIHpbFXjKXcAArZwHAsMyM8BJKoM5LK1tvoTsPFZgyG9s5RBJEkA1l4ChOw/aC5DRgu64AXL6VUkL/ebVXDmlRxC7yr7HVULmiEWaMqmoT+wYQex5KmnW0L4DhvpVdpIcVjFav5wany0B/sKbghQRz4yJQGyrdNl34Ep2UDkt6cTBCa3PIO3qZYlCCcY44C4OaghW78aziY54mfs6/InHXFWpIUNlhCqznjOZbBNgmI6wAqfjEQ2dPoHHCqB+ei9uSxTkiLwz3JAALjQp+BSQbokXCQeaY87sCNgEcXYHhArjzGzbZ2sUMIvhyfKtGcWDg6yYkUYiGfMcDveZeBTAb2wK+/nBf59VW8OPp512mvFItrZ+LWU2p2ONUIMdBsOZGTPVrKWXaUz0zla2VtlW1BlxKJdw3zT+q9SvSww6CksvKQhXayg1O5xzK/xMNa5WZpU2BUy2LuPUFO2pX5z2KexF5nFA/B//IUI4q8O25bPNssiJIuJfvzsi1bosYILyXrcTGgvclVde+ZnPfCbTxYGpuS5zjXy2aYERAG/T2hvpK/OIRMQ3gS+66KKrrrrK92RMN/Bwm5dspO8tMo2a8IMr3fqtCFrDJB7l6RbEScYp48lhkR6feuopq3W6tqZmZc2tk+qOYLntVE4U29mFFAdGsjcMF9F6nlYTWe1gpexZGucYnBn9sCcY7FeXp+Gl5RQHMjnLJSWSYcA/vmELlOqam+RAbiVYjGzDAgFKWbsLpPEJD/OyhEmma4NJMm0zwiHhsWPHoDt7ExApfMv1KjU31rRKoq/gKYAhAyM8VgCnTkFQGFhOpl5aIOmhMkCl0mkqjIqGs7zpoCywlcEwlwL+hfEmOlRSTpgAyxDgrMAOYhL4QRBLKlPWI5/ORkg7kSFigTfRKfQNK2/u5XdZQVmUdgFYhqlZKV1M5KySnMTQahrBovW2JFxpFdmqLABS09Z7mlumhmGEgRoWVlDDI+RX5pGOfRIqiPnFI4FRrBQWvdILMyoQw6eo88mGYmkiN1sMwonwLgZ3EdvBkduJ9FVJHfMJ5ozPJhpqIghpVDQK9AIV8v1eTVwxoIj1CDGVqy/cNHFbNcSzeWdm5g5deMqG+UVo5c5Vraq+X1OPZhYIAy+xBtMZ4OhX4TbQnfFrGgmBeYxZdG3nqxMqAxyWfsQLZktjx78MyC7qhnScX8JElGlTQbSQB+DRHJxbIgPBBCtzoOFg1cuLGBOFmai4wAOAQ08YMNjGSjanTL9eltZKF4KEbOZG/KcNtImdrqWSAC6LEZHMsUTKT35O1GgtPR4VE8aHeO+66y5OkSmdeuqph6fjUdl27Hd1C4zfAV7dhjvBIdOK8wpfsZCjmHH8azVpqHM2c/1OiLgOIaiZCyyRBziBkX9YxirHXUcnXR7WKvO4tBiikNsB3pI8q2Ze/pRyQTtMDbKCUgpW1ufF7P7tsp5yn2bACegCd1kppaRS1QCkKY32qZo9Jf1ghnwFnpRtyx1pTQfWZl7WlhzIgYA6LkYvow1BX0/poHgAwIpAAYRwKRgOiRBBgomUiCWNFAiTByWUXIk/P+IjolwSJqOGtRHgAF241UoAnH/++SiJR2b5Ge8A0pq79Ul42ZvES0d65ziCYQJ4QB1QLhm8fY1Yvcstzgj0RVMiYa4AGRq5mugUMRPhRvFSMEbQHfnx0SPgxFyIdY0tYZyC4uyWOgEkuIHfkAno7lOn6qnfYRvmKpm0P3uod5UA9vUJLNkljIhlQ5x12pKFuP2kGnp6Fav26RLldNfnRgXysED/kV5I++STTxpcDOhWgd3sFIhM9hQJTth4rW0rUI13CoqBBJLoSu84UEqSzbBcMKcWAtKuARClX5eCgOfuaWiKzNRBQFpTjSkCZQw+3CNXohdaztYIySwZAjVwCC+eoTtBSDVKiRYglltjAaFuVKLxiYa0mQ+NJiLxKVhIHcRuacEOdn+YyxgMh2kSkmHFaZzwLCMaqUaeUmpaj6vUY27gEFjksz8dlenYHyyr9DKxLTOyJ+jLfQiGrTqRwyYqzTzigVm4gBFskSgLtkWxpWEI+uaXIG2aQE24Laoj+lzCL0MjhlI2PbKeAgID3GWS1IXQ9ataQtdc6lb0bs62AtUkY9/QqBc/GTsReBOuOSqeFqbHH3/8vPPOg3tvvvnm++67T2q0OaselZpjv3ttgfEEeK/d9y/Cm0Pdm7uvu+46/x/YD0T79F2LO++8UxqXp//SYJ9v5Jf+W6DDN3jDGSm0kK/LrkVNS5QVXZJn/ZbZsJMfFLU6KlgdresqraOxX2eLei0C4IwPMSQ6+vIqpmxbcpCFOf3u3Wcy1CAfubsFUqqtUjoid6QOC0te2ZOmLo9Uyqjqc5rKDsQ0lDDJeFqa1hcSMiDNBWCg4T4Bw5VwONuilAzpUT4EHCZ3JxgClSLBBTmokbVIPW3YG1N4SmXEHiEBBuceaKjmUg84qQn+VIYNVOLGiTJmdqC1CxqJnOAEMEY2USdXkFDqFESUareKtAoqkx8swRZW0VAr5aBlzIVr6CdymFjZ4T98SxdmJwAXkJNeroEmyHIIyfvklOySwT5CjaaBtss9mqmjISYqeBaWExJMp0nbalq5lael4Vlh0z4dLnOTWELDg8wSYKNyWqtAUyFn+NjgEHIsWcQ4uAyEVqQ8VcNH9l9MbnoRLbQuoI6JhqLUU0ZQ1kovwjIMw8SmAD6AROiNUzQRAHigu6e8qVJHCqTVS1+YEjgFgUSeTuWit7ZECcCbm54n2YdStKYjsMcFFIwRFpV5Tnr29K/FqZbVJ61mWnVO5quTkcqFj1dmzIqBOkuwFU62V9jTF38yBFbXsThk7SanCYds/GXyzAwJiFp9BE+cKBTJYJKnS2qKyRJKVROs+NEXm1nJmDLzeAcqc3XRHECBmr5M5Ot4Dz/88Cte8YrHHnvMEsmAa7HhAdhnVGF3LDAC4N3xxXokMcuYgKxAP/vZzx555JHrr7/e+ZXfHvB/gw9pqqUmdbwGZjGTRkvBpY9eOV7aiIwGn0BHVkEXOGSJYsYAYADG0iWfs3wWqNDXRuf0MJdp+UqqFVqmRUgQwplDB+ktrfWWG0qm816ufumSsvMleblbmYEaiYivxVKWO7jV2hnFZ4o67Is81Yvv/UricVPjEkXSSreOnblYZg8P61EO5Cnvv+xlL3OsRCrJEF9ACOpFgvxMhACoGCrY0UcQ/Cm/EZOYcBM4LRvAViX1MQeJHXHoTiCBH3qHCVEGNpMEOFFWKRqTq0nUMJ+Y1pOTMOipRoxCJjEXUVOoT9YeAFdFNn+B/FRz1u0NCK36PbasqJlZiEYEZk9PK5iNO+GNYZiIedZ2SjlR8ZbtEmXMIT0yMKy0262LJPoaVqH6EpmF2WJ8jxR8coRYUuDiAW7iwX4KBUWRhnTXahhNTeTWVkLFosurhvQqUVNARjBfR9dvIHfCRr1pTVlDY1A4keGJJ54gEmtk642tOIs6bCWwMWQuNeKTK7V1mZdEl0f2JUNsQyEu7kjSv21V6D+dWUNUkhtodgSAihW5DXfXMmcQV+jb+mEOiz7lL+eTFjgFS97mOlpUsGmKLyqhsLfgmhjNh0I3wbMok5nCdxi6reA0bdq5M8mf2Or8XyYiq0/mJVOQcWFEGBeubEd2WM3sugiee+45I1S/3nzOYFmaVfHcqYIV8N577/3kJz/Jmz/4wQ8uvPBCA+TAdNwpg4/CrGKB7hq5Cq+x7Y5YoKYbJ8B24D796U/7Z1c//OEPvRd9+umn14K9I9IuLUbU9LIiiCIFr8XMajrPnCtnkg5adwkQlAu0SDLcymslUlY7xxdudSRJLTnLvFWz0YLuqCO51IvVxU9cSkBtkMtZtyzJ6mqSPF/QcsQq3betgGehCDs1bmHgZ5991k9Mtdq15RKD+0BZfnT05NO7AB7BVwMv1uIjYOJurnfZhlepediSyivuchQIQdIvJfLI5RYxUV1iAwyQCYkKEQKvSpvgDfKAAdImkAB85TV5lUSK1vChjmCGZ555hoT5xheGUnZGCBkxMNQ2mS5ryLqCo4AWNLrGSo7YWkN3GMrYfJKTbBTRte7KUG2B+u3timWS6JcFOCL53DBD2BtuRJPthpSrifBm/7qlO8N6RbBq1lhwdpe+GC3Hnvpitzm7oHhnH8GrwjikObe60HBi0uiJbHUtJOJiBOhLAOWJTaoSgR2HOr+temPBCDJ8+gAYjWgUS6LI7KH37ATpVFQwiLA3zxDJ8HSJRsIjcyhnIOhRUmtQCH64nYIwQ3Yr9Ohp9lYiud5L99SUhCmwFc5G4sSnHeLh20gIpfCIkUXm1XkO91hPbX5VeUMFLvOTV+xvIEz7KvWGul6ULUeIgYrheZprYhr0ipOo80qXRdy8quF2PFi9KBBbNLoitsk8BQPKvGR0m9LVGHHm2EW3IZKTaG686MJ7RvNvtEWM3f9kMe/iefHQSPS/SD7+8Y+bLohdRt59FUYJTzYLjN8BPliPm3dckmYY+Nxzz/3jH//42c9+VvbjeM3E5NFhaE4RSxccYl1RtlyZhRUm6mi5tRRBC8ASzOylMpmcJkmyJW3WNq/g+iqvkw1l63EM5bOu7dutulawGMtHk3GC6Gq2L89yPUYLAWntl+5QhNndJmH1lB/lTwpycYiRdpwoqZ0GIay4cJQEkQeJhCf8qZAzNAUIR4qfwCiZcYsM0n30vBwbAnLqkcnv0Uj0nX3BGNn1J4l6NZADsRHIhESdemGjIcApSSWJkyggFk+SizSnuAgUJD1izwAUe5deeqmGyMhAZWJIsGh97NgxNDQilTKN0OjFb7wzDkwCumBbuigQCWIXD0QigDMi4YE58aJXS6wmV1s5s2zUnABEx//pjuZ9en3xFzKIaCJBNaneGQR9bVXQmjfjQZW5ZUxkofG02ha3mQV8XEzXl4rLWElsQGIAPPfJTcG/olSgr1hCWZVtjyrbehKi5B1a4KytMu+L8+xeVVuKq+QpFwJP1ZCT0wUbspZtteoXdFeq6d2+icihDjwvZnQhYtsw0IuXhBFYAogntGgN+mYa0Smx8dSRT+hXBCpzge02NPoSYDiLWM3NjUYHuEJysU14ZUGIjxpPXSVeX3gBQ4Aaqn2C+Wvo5R0NnXpThs3ntN78/KdR6qiuaTQr1ht3djwZylm6H0MesOeKHa2ruXgTPMwyD0NBawLPr+hZcM0eiZ85m8/Txfw05coUytQGhX06IyWjw6Qk3owOZPaAvOzAO3qJ1n3J6Why9j0X2lkIDBwjLmrOL9uOUzKIRe2aa6758Ic/7I2nhx566G1ve5ulKpbcceFH8U5mC4wnwAfufXOQ6cn7Nr/97W8feOCBj3zkI8o33HCDjToZ/2Eo3646tpMtUfI8CZaMwaJFfYuQXDP6+lpmtnK1krFZ2KR07ODWEhWDtAx3ykQWTkpJQClo6QV1TjnllG3mfOuyBgwcd/RN7YhY6kxB38n0TrtEZFqnHrGGp3J6PlXGrWXI6e2JYvgIBrmyssDwCST4hBmEDeJUwkXiBFv4hLXzAnzeAgWWxAkO+GiY7qgjirxe4cVR+98SHWQwg00WeYACzriBRshIpTuHHppjot4l08X/N7/5jfQINzkTRCHBIp5bUQ0eK+MjYuENbckA+rISSwJRAI8kDKtwQNC5qAZmy1Bx6DyaeSvJI8M0MhiVXuJQF639p9GrZ5+YWpnlbUUVsRcxKGXkhoY9PcI87w4U2TwF+wvsljcLOvQZNYkE8cbjHQKTg6tTOXDLCFxGKUCUx1O23yEY+q04Ips1HmU6QpYA7hNPrOnYWb/ekhVCCUugAgGn+I8AbXOxJ0jIltclGIGdEWiuMjGvRtCqFGyuFBAIGzriwFYveMEL1Lvw0RED+kSTSgOkyqnZ6KeuQV/gn+TKG+1rm8y50iTPpwLY7oyud1y7+cUTHiZDk7zBItgcqJpmt2nb+fuilEtowa7xQpYMHOwHkd8WJHXM0ka9yDc00Ic/ArOZud3oNn8GE87f9V5QWne+/OUve83QJPDNb37zTW96kyyF5GWEvdBiFPLktMCEtfnkNMQBa52ZCFq45JJLzj77bL9K7xsafiP6C1/4gv+Pl9nqYNSXovmHOvIGANjrplYsGZsVyOz80pe+lJoWWqsRMocPwECr+O5P2SS01kI43hYDfugIKsDArRZ7UZaa06IvahR0GgDUuWobvk+pBnHQRUCySE6BiWSNCAJvkq94lFCXiDikLYYSlFBKcRztagK0oBEbggR8yi6DEHKYLJYQe5lCugMPwAzhA9C6lL07ICEAb/A5duyYqINqyPP0008768hXIsFa388He6RKuvDCgl/wwpC++g1yJhX/Bp8oYI6h7kAymIQi3oiuo2aSA8wEBoM9gqymRTLZYqKIPfOzLBlYxao4lyXT2+XqtgAAQABJREFUPH1BIDO5tQRUq1syJxhI7mITXVBHvuhL4B65OkO12i5dKBMJoZSrJjw7t21HzMIdPnnNpzNSTuHNohFy8nuPXIyWMv8mApHxsnpMBJWxQLt6VEwUNMecNfr4vCWD/QSqmU2/yrrLHkcBeHHF9eInIRfVSnGs1OgrPFmbSHCyW4Hn8MrWA+ER2A9KnKeJz8RtGrYWa8t5mk8bNFRWzlDK7oZghi6mNWmbt2XyCEt6GWW5ooUahWD4ln7vyrRw9s6b3EH4Re2z4/p69wp65HozZIJtxxUs8WrgeFnMgadB4bLXRh2jOPOAjSFLhkneGKej76UfHvo1+hyr+JUZ+7zvec97/N/NTFNlqB2PwFG80QLjK9AnSwyYlVwyLW9E+26w4wKbdj7BJxmnR4dhCIpYn0zN9mV9Sh9lhNYeOkojpJKSQhkeqJBl7LhR9kr3CGyhtdkM+VhuleWCjtEUtqkLrBLbLtppVJgWb+Emo01WJI1wMMVltJvWRJLhsvryLxplABJwlfezjIIL3mCuMPcpDISHp5CYjhTEA4SAg9yFXiIEQ/XyaXDFrRydzdkZzMAKxsAkDDFHaXDhKdLwIT9sgJtvCaJxwolPCLS1+aIXPeIseQIq8k1gNIQXqL7s5+zOUziBhORPv4E00AjmOPA+4d0q2+sBufUIDCsgiLkIRjxHuD7hNASaYKvgKpMiy9k1blUp+Sa5ocTXtFYgCUdEjCKjYK6Y1KAjw8Cs8jz58b+YEINtmYWEOnK+Dcsxgo4AYLsh1AlgDn31O63AhkTlRBe7aaWgkv07HNwyclTuPAIpffdSE67vPNIvFIcgyIRVORGfkAkbWtgEkRMzGp8quCiIW2TWIxtyBDnlx2w1UQbEPEsFPPsylPrcJIScE3ozk991bRNQbEQ8DTExIghjv8yAkqnzEb3UMIsr8yQrEcNsqV4Ai08X+bMBwU284BMBssijxsEXSQbEKzkV6KIXxELFLf7KJOm7pm0lOB2msWHHF16jsNdJvBZdiD3EhljLYY/KxqZNB35k23wSfk7z7ria/CgAhBB1OEjkGOyJgT1SsERVMEeJSRFouLnM/Aa1esPNoOMOBFmYhGgU33EfzSOewWsmeec73+nHZbxR+OCDD77lLW/JGCzjzMNnpBktcLQWGE+Aj9b+2+7d9GQRkpT/8sTlOxvePvKLBTfddNP+ZgymY1BHVifvgefpeDxx+7d/k0ablCXQ0muGRrPEOcO2PTRff/EjBUMu55MI5qvLVtn5eKxKJe+XvlvdV2XUa1+LqFh1XgppSJrzxcUe7fEKmSIAWY80h0bc5sVd2UloQiDVzle+wS0hIftXryMowtmvMmQr+1eWq73oRS9SQ02feAItkEYQrDKpUEpxZHIuNOAi5oKQzLCio11pEAEEITRCsLCCb3XnDAQacYYAfkieeNMnP4pSTRyMIABK/a6P0+OgLIMUK3r5h0mpEeeaowS6YBVK4ayVyIes4h3wjBh0JIC2TuGUpZ4ZF8TO5RHk8/zd8b/yb4NLhqeMc6YIKrc0KWMIn0jczS0sTJI+zbSacncR6JRfIgzOuvaIpn3KatIWtPVyRIg1TyYqN6WvMxludRSfpy1DRuNT5g0rTIRHxyB5VJxzjEla9RHS6AuUZTqVYkBCnNgo9Nt2GoY+J1bW04mFsgxRU44k5aDiqZ5BBImoYARCRjsmRcwg5CylxKTuyOwpSk4X4SWAMNZX3VYXVTNc0LvhgyZvYQh+HDCcySf7CEI6/DUxQk0O3Mpr1elMPkW5mwUhZ1faJ8tnxthNOUnFBSYWwWNbcB6zo+cvM6SZx3RtgGiIzzxtd9YIrfAGeCYB0pq9LcqMY1Y057iOHTuWt4FUomwb7qx2EwWz7nzuc5+75557DMaf/OQn559/vlhFub8aTVRzrDwZLDAC4JPBy/+iY81T/kWw0+AvfelLfhzLb0T71b4rrrhCuvYv1Dt8Ize1ugAkoK/s3/pqIs43SOGHs846q5Vd/ue4wCkZAllg+2hPy+VHiidDpaBc346sjLCebk67LXRBeOda/GW/2YvHNmvqnK3VqyOJMEiiLDxgVGUErJQmIc6t7D+bCA70QFOmQwPAaCJglH06ncsLpWJMTiOVEUWSADASQWBky1PvOMjsNdREjmhMcVAALc6yH5kfDOYrcC4FSkl5ESPTPL8/JLw1hGa9YIa/RN8jSAaM0QQ+kR8TT04JoMqulOExemEuDDTBIQAY6kiEGCx4BsdmFKAHLVBGBRxIQq+c9SEmrb4kdkykoL4siay9PGIWCa6NGDQs76k0d/5sj44RGAajF2XpQh6vAGDYR+xt720ZciODGsbnVr/VpBwL4BOU2NKn7JGrXx/LqPeUHYIPOct+gTfb1YsiHndcjDNHgNBuVaJHzM7ZgukzZ17vuBb/6hql6Apu1yMCVvVUYHBlS+b1eJsj+tKFrROPdGfXhgWUSejLwA6ERY5LjWjnfcEpWrC14SJ4cFAvXEUaF/MCSrL55DsaKbRXX+D26XB5YtuJlQN82MdINCcQ3r4Y1dQYKaytwDXaGqfKxoiwHGC1O49IK1b5i8f96pKNpN2RbZokTG3TOTh2Go36OCVv5ZgQEpyLOn2A/y48Amu9rmKsmectECIzUxDZjGWeNb4i55///GfzrQFr2hefscYuqDBTBpF5//33SxTNyY5MPvjBD5rotDowV860w0hwMBYYAfDBuHIxRTJnyZb8v/LLLrvMqyz+UfBXv/rVW2+9FXRMtrQYx21Ry9EtouS39PrxT4srLSSdVhTLSfK2aFfzMhoztTXJiZw8Hga2RCUh3pbUm+3HqaO0FU6gnW8+y/htsW/IiexvmaePgnMkSbMy3JIsf716xoPUkfeD99wtL8zxHZ+CN3SUsredyi0kHHxNsDa3qGAoYlgUkwSMA1uLOmwghw7gBHrVSLKBOie6MAag6PAKCAmmbWFk8RRXghNb8E+AiTpWgsMZimAAknRcGqE70YuYv6TyMiEc8Mczsa3eoRwZGFbQai7XVIkDa4hz0AUTibIfieV66ZdKWEXer6OYCHrBluLECB4jicGCkoSsJGDsL+DjDDkqKAPJyuo78SOHozj5+5ZML6xNWTBMgVOYTj3ZWCzMZ35q6EJG2mxM6JEZc2YYT81kgiAqK+CmTGWC8aYahmJMbJX14hGU6HKr3tPg2xZnelQXPrzglgH5wqeyLghJPB4UeCrZkBfyokG1VeB0j4zNqKmhqyWoMuMnkPBBE0X6xKyEp6f2C4STQKJRIhMr9ITkdOBQDCAgXjEp4YntaaKFIjiUGEVcNesqCMKZrEhosIjSovSKgTKpAHgGB/V9ukWZgaYQmWmNkh/3AgAT28aWscPv9kQSOZszftlzCwURaL2gmkgWrvChYNtCv1vrgoJmXe8jiEDjK/HW+s4c7oo8iJVt2bOJy8i1bmZDc2sCL9ERsZ966ikvDBqM0sVPfepThxSiSxhkbHIYFhgB8GH4cUktkjrIs7/xjW94EdoE51j4la985ec//3k/2+Cpa0nWa2omMwgnWVqyc5nNqaeeKsmTFgcMtBBomsD2X/1wlCXHQmUr2oplJa5N2TUJezRsorK01WGI7NwS5bzIKlvaTbPJcuLyCJygLcDgM2XJzXLcZrYivB6FqGNth2xSB+dXwWaBbTRtFSQVL0spgu78TonmLUH1KHWu6KpKvehL2q0j2YxgE2mWf8kNC7tQglLQQg7KOpzzNiYZAv8YBxn4AVdgAnK7MPEU29NOO03oPvbYYzD2RRddBDODMSKT4wigaxcdIU+9w8kwsEdqFGJ8GEmTQDu/mG3MEk8mDeoE8Uav2LB0rAL1XSQM3tO1clRw/Kj3UCrrjni8jJ55qWPLqaM7Yq4hOWiNjwwJQSxWPXYK7OMqVFYMJZHJI6mGA490Gg7fFh/2CR+zh30E9eT3SQUcSIg54wfTqiEMPKlgj8xnLnbAJFm7zxzswLHcag5BgwBslsjqJVhdgR08MkH51GlYMWCclZrIk0c+S2xlHsEKmVeFWfvYsWMtsXqgXRiIYSFBbJDenggZAq0RkEF34o12/o2WiPVDZfRF6ZFbHIiNCX/pDhPmcnUkKfHWW0jItSpP5G+kB8+TmcDKVDO/aeh/HfNLcTCUXJiIf59CcSLDeSp1EbIw79zOw2F+mjD3acLhHUolhEqv+Vlth5KoIt9pZ3WXXQmDtD0KLqNlo02g2lMTjfTaWdVKo/kLRpN0whxi3JkZjNOsTS2H0pdNPLWd6imzmENchqcdsZjLDGwI1wZBNWy5ba1cHvTdEP83xDplwXJ8nX9iT4yjFW9rdhg7OmALjAD4gJ07l2o1i5mX/ReWRx991GRnjvOf3LzrktSzaObiuA6imnzBBtmqRcJKo5IkASQ6kRe2eepAt5Hfp/XJGgwDW2lkqNWLttvXcUDgJR5FftrBHpbVoF9Zo8wjS/K6FJQ651VhvTilzEbJEgLP34TkPCUSJFjOPKMOTFgcWj9WpYIIob5CIqd9VNbwEnLApIPKnDUFAcJL0mtA2lPZHla+uBtJ8EnzYhLOdUtCEFQl0AhjyMsdWJHBqRctHIao9BtXyg60KSKDhFcFuS0et6wqB5IQU1ZODGpCZXCvMnr+VSAVSCzXd14NQGLFL9q6FDryuAVvvC5rvEA4EDVRc+yJrTIrASQGBZXxj7liVaMPRsITgYEjGWKKHOqGoLQmEuYeUVaKjE89ijz9T4kjdM2wnUdpGP55NJNVy6EamitcHjFv5gq2ZSImVRlDIYjHqc/sElCPWLgYqufNZKU5bmVwRiASDozDRBwKf/ICI2uoCzzZXFlmjDjl7BN5KkJcQZtmNn63U9PRsbQoSVqCmA5D3IBqPiWGEzaacqLK55577tlnn8VEmeOIwct+CZ8ikm88yeyFWyq46CveVBK77aW6Xnthnl7QMB3D6p2CrJRhZVXiiExxEay40Vc5n0vLrDmMbS5N8OATl8VfS7PtNNRLOPMLmX2/wwjiiNKlQ787t+wQ4wt+lsnxZgYIIaOXTzFPHbOf1MKwasfU7uiyhCRRMA25zEg06ZmZjaNh37VPj+/W/Od/2iAzVcZ0pmIbVWjUi3BX6ttWS0i7XJPoaLZ02Putb33LuPN1Xz+eatZajuHYarTADlpg/BXoHXTKkYlkqjUjv+Md75CpOxO+6667LG8W5qCILUzEtbToF0DNaYZVQbpjBYVG7CLbLm03/hcyFhXw9KKgwx/LjLYgCqijHO22oONCAi9BTMEcijKmI1MYyZHCJpZSprN4M+ZGjVYhoTspgqxUwmEf2n/clRxTVuIuOZZ8WK1d3gC3V01rZCAifAW+2vhILPXtKcwYR0onXZPVwQ8wgEtuJyp0QUF5MMyjkqYu0K6OnUs89cVcGR8X5rJ29fgIYFsGtiQAXTU6hUNEMrZk07VjYcSayPgli+p965Ui3v1GfwIx/XdCOm8EdQQwSkPDT+YYnjjAafpVLxsjcDKVEk+ixkok//vf/6454MR32BpZxPBUE+LRi9iRX1nkYMs4hPGJklnowhrkJ4xypUR48gUBmB0HwvssAZR1l6sqyQC8gW3PP+nuQxEA8NZF27yIpxUIwM7xV2jIeRzn/fu/05eydBFLgehMl0cMSylucqtHaJZh0aBkCo+wiulIRXcipcAmOc+ki8mEEXTnaUWmtvZWBKfe6a4jviAhtmrMdQxbRtCQJaHTiEE8igQjtfp6qjkhHRHjoEeBqgBOi1v0akSXMlbIXvOa15jbDZ/Es3p6qecmZaxEILe6NGw7OvIyq1KKWQhsXDAmm1BtmmDGBbIBgmkNKyz1ZROHc+N09LaZ4q9pbRet15dLGJjKSKsvSglLXS/Kasv0JCSqgEnwi1XrshCiBY0iDE/5uoqJzlxkKEW7Lcu5ie6ioAnEoOMsY9BsmSl3Od8xpqGaKTQjXRcGrLnCN/NNFPZ88zTqbCE8oqOZ+fbbb7/00kvtnX3iE5+47777vO2yhd434bWR52iBaRYYT4CnWebkrTetX3311f6huX8UfMcdd9j/u/nmmy+//HIzNaNsaBKspQUGAFrkjk5p8kVE34mSiJBqXV3jI6eki06tZNJlWb4DIj1msVlXR0cbQ7STecjIHQTBVLaoLbFrVE2KWVl7jFn6rrEXPKWePuW1PgEPBYqIRqc0EiwvnulO1i4vkY1JRCQlAkberMwCnrpKNoUEW2pwUICWXeBBghyB90XBAwd6kCEmbfO2DHIQL/inrU9Zv6Qlj/+/jefDDz9sX1+yqF7IgeVQPcQl5iWLLvITgI6kqpxSc0fB1JGLiE8pEaT6/e9/X72G0FdsIlsSwBT3GQwTNSWjsQyRcLBB4BsEol3XesyJegLDQOurQAwWMEaCASgr35W4k0fKazdKKpxHacuMSYhzq2smKiBRlRgqk5yQQlQZ9II2Q1CfXDAPHoum1SqH225TX953m8jhFHbrELC8p2jQ+5QCsrZMFBmDiwSTBpunl3yFFSWxTVPoOdQt3RFkTyGnZIaJen5Hw3qyWx5Uozu4zusAAsAjSFiliGUrFmMf3bmt0EKDMxqfyoHZZkXcsILMDUZlT1VSRHS5zKW4keqNb3yjtq7wEWl00cTMwJuEjFSa79rFm7wAqItkcNEh9sB4pCCEv7QK+so8gwNncYoCF4dhTKesl9Qs94mPwLYxZ+AI7xW5LSfD2lvFOMa7SQZ4cyv8qNnit7V3ujWG0Y46BhQFxYkJ0NkA3yWXWFESfIxiB61mfgM2WZDp0UDG2aQqFI3Q3KrZRMxER/FvcfnkJz9pQbnqqqs++tGPmv9X1G5sPlpgNy0wngDvpl+OXirZrX8mCQbbC7/tttseeOABiYUzhMy865p/M+fiZoqXEDjsyvuTsmrd+fRIRmjeX1ePHcvKua3QslJgQJqrL+vZhvrqdL3pW3rJrV3ybKoxLJ9K5talnTy7YytZNUvK2mudXl1HGRXsIUcH2ESLVVmoSAV84dnXX6lGBmGZAx/ug0xgG99WkjR7t1YBSOvLI9hIW/JLNbAScoFbTKSgO0zAGHabpgh9YRVAomNVbV0xuFQJ/sEK0oNniCq/4R2JBfxJtuz4UBBwUpbfsyFNXdRXhszFJ0jmV38gFq50XIytp2g0TFn+hIb6nK4VmenlyFelAMCcxeSmYoAB3Xoq32K0EEdHYitQJwUmIqqf2tKQGFJA+ZlH+nLrKUMFKyp4BFa5KG7w4sNZsnyGxST8fRKbJAAGF2CllbL8L7sVRabgjWufM4NWPoqJLnxK4CAlViUD/nFoeBJe0OLGYnrkC5IQIMpyh5r4kT25NTGACXqH7SrRhD+zU5PMJW3sKelXg54APKUvtsWToVz6QubrqTrS1g6OkEDgEn4u8hBSR0wqkjHBjQC64/Qg6vSoIWVFBRqcxSHZ7FZgRfFooTusNPRpFFS98FPJBVyvR6zETG3/lUZHWCB59c4Usn8ycyshBV60K4I1FpiFSTmCAPp16xUMMcM+/GK8sCQzxi/L9YtzRqUAEx422jh6cxotJ+ScrVjGMK9RwEcOfk10AtL8LP7b0Tcnz50lswbl9RketKNkQ5l2a3cchqKLVYW6yTkGtFqZCW1yiUnWFoGstMauM9xE+C9+8YuLL774/vvvt2Mr5bvwwgvNcjvrkVGw0QIrWmAEwCsa8JCbm2FNxK973evOPfdcv/Tgn7/5krAMz8IWtZeegjPhYiK3k7biIxuQKEvIZIdwC2CQg7J0tKFP/VpgpJUWG7O/QxXwgwy0joRLK7ghgRdlS37qWEetqbSTfPMdg29IOxm5E3WOkxgtKuoAPR8BDD4ljpAkmCRxVCMPAGOAH3kqMOYTVIb0kNFX0gBqOuosJ1bU6UuWpjk+6Tc5B7GLWL2y3JQB28rQFysZiZwYAE59WvmEK0AmGSGDMDh8okf8CS+9Bgth1KAjxJzCR1TjI30R+5xzzmHM8JRNItajb3Xioy0mQIsuXLAWzENIBiEGMvJ4Kw+ljhgNbPBpfJGHreRP5HEL/+hRE7fpyCfTkVnSo4lbAQN6yf6paagaFyRU7zjOOHXLbgRWMIiI7SmlCMBuZgyf4CIm6SVGQ485BO5SIL/xrtyi3zKvpJMw5aaSs+MRw5aahjCUTgaikpnkkYfZPQKi1IsKldRhuhBgpeAVZVoIiXBmOkAIvbhicI8oAsZjRTZi+yRwpf4E02NMSgZ9EbvFq5FcuMYaolSnXKxHrDgLfMXNN9KFsd4Zk2WcKnukrbnRJzs4fSIMIW1JaMuP+sKHRiTkPmIEUGGr3gmzTF14iKIyIweh1CM+vI8habVKJGgYaWOKlLf/yfiGs1FDQdm/HR/BRhfSbk4YLjCOWEY4MQh3GzIkET/My8icsuL8xvLewhBUQst+FssfrZ2XNiaxecSFg7KwgQ9FuMnBuyTibU/1ag2SsUARM4wvmxhQJmo/VGZCo/hGFcS8ZmYjVygKP5OGIZ9tL/PP6kM1HHz6L4Nvectb7rzzTr770Y9+5CdRMz+01hjLowUOzALjK9AH5tD1q2Mi9vWPRx55xBTpXWi/Ee2/B/taiGWglof5e60JVxpqR1Oqh5uE0rois8l57Pzc1kJJQQkiHWF7+/1QASHlx9ZyaaguNrrOrUWFYSbMKymRb1m/ram0s45K49SvS7W4tcTIbZ+5eqkkGfqPqm2ngDi4jsyyxjpLxIqnXOqBCmCA+ySp+CvboaevFCHcSjw+dalEJhPVUFl6IZuRsfW7bhFO+1R4ACdqMMEc5lGWOtdLvBSUQHsEFMldhLdb4SR3kdCrCSaU34s9P4BEZql29n2oicz7dXjqxWsRglNzeAB0gQmpDH/io0wAqIA3sQpWhIehBQgZAPPT0PrSysU4jgFFuDI5MfRjZmKg1UuE4AlvpxJzt8pGq1ZudQpiwWlujVwKxpVl4TT0FPRialBcjfQRGU2DCTvEAIazUO6rlI4FcNCQmxRYWxlIJh7VBDNN1RSfFPiR09WTluPifYZVgwPFbTSEgFWxzY9mcwod3dKu5akv4YGzhlrxoKesiuGZZ54Zti0977vU0NSGWoI2tz5zEYkrhbRc1sX47Cn2lFXqkR0IKcjR65qj1YtMJnKbvR5NoGIhR2Y+JTyrcooXdkiF/vne/hsaUcHRzKV5zIiAOi41Qouc5e40tAPC/t6qKD5HUmBGriGkQLJfIJBslAjmzQmjx0wCOk3v/GjI6NE2luhlNOV2yKCMPOiHBStKkcnmOsJ5Zqthnkf7NBoZoaJRjNEFdpo4kR6tnMv1Hu0MK/6iFJeZAbjMGN+y13QnWuzkmvdMCBYUIzSTFdVMAgla5YUEi4I+TQK33HLLr371KxmdEw7TSDuHLGe9sdVogb2wwAiA98JNRy+kOdG/Efr973//y1/+0nTpzVLHwn4mOv8UhHzzTL5mWxmbGRz0tZWulcRdlizRkbq5jlBP8lvb8i8KyCnrlWpLykEpS+CcCh6h/DO7ZmQXMkums0TWhn+Saq+uHWDAj1lToQvRAoYxXUcq3ncwaCEPVOg8XeiWv3KiZfmX2cMGmkv3aSTdl6TyXZuq0loe42BHMk1OzW2+uFWGPz1aqHetMNREocqJk/BhAdmzR87u9KWcerIxAuPIYKRTmiTbOMHm/9IFmHHKBwDL+D2VgbkgOg29/GlXHv5xjAmfyMiR/elPf7InhdLAZAcoUUqKBkZlChf+8Kce4Ry/7g4U8curX/1qUlEfQ4/0XuqnnE9PtVVmn+S4hi3BsFLDAlzgKVY6xY38tMOKPGSWrhESpTIygz0AWKhQgRieYos+AC+dGnpAHQ7YeqRHHSkXpcpQsoxc0C3vEyBlZHoU3nTXKpRpIh5opGwKIgM4CuJirjmxdcopTEoqrRhTVIOCBGBVj9ST32cbV7hpkt4dzmCVTQ38PZI0U0QhF0oEFDcE1KCBeE2hTApcyT71wra2Ak0+3E0qpuNNPNULDF0Qhl7akvxEdPwfyiqTyrCqcGJSZ1YUxNwjxtG7To36FkbGyOrLUHQJVK6aookWW/jUIyfa5SGJEUSv8847rxV70zKwGBkY0xU7RKSOKcSMIDeBDMiT5nY3+I5GOPjS+AD97j+qwBCcXp2wfSM3MJo2eji/NbOUv+x6mAG4zGQrGpMbbE2Mfkcix+Rm79LOnTI5jXHHEsaFXTNLTKaaToj2+ZT7zCe+6/vQQw9R0I88n3/++dTs0481owUO1QJjuB+qZzeil7QA7vWDor4fYuqUT19yySU33XSTk6vMqv3Jt2ZbAsnnnnzySamAeVZ6Z9a2avabbET0BZlKuWS6lkBZi4Q1aGo3RV1Qs+P/4sWiDmb4vpZkWv4NXM1kMs2/1TDG8Zmr6hXaGKj6mQyLMgXQBYLCvFPvFvTiJtmYM1KhBZUhkxa0CZk3S8EzCYRdAF2DQG7xVF4isQYanUfpGjKBK2S0HcFs8cAeqQS5vX4sk5a4CCrpMhTE/voty0v0sTIcyAOiIMYBMZxDciiU7pAYhrqzxUBHGY9xRxEpOJCjLGixVaOsCxgYvqJjdhykRzIeiE6BlWAtRsMnSY9/xSTb06PhyZICg2ziBGdq2izAGZwDtPSCOTLIxLmEcMIBaiIY8eJWcMsj8EC9/DjHnlE2BEG8OKsE8BSQ5ZEmsKv5AUOnf6wB+wXsmUBQKhOeiXSXVpqoZx+frdPdYkJaBMzLUAayhqHHB0RkEHxQ2ppRg9j8hkAla5CEBWxVQKQUD/LnffR1OQFjDQS6ICF382xOC/GJUiF2G2zpU33KQCzbPv7443rHxyYjGfA544wzaERBn+LHGRRJWNsjBmc0vWgoIWZMDW2FCK2ENLeKGdO1rJ0rKc7OLmKojDAEwNAoUKAsYfBUYFs1xpSy7lyl6dYKBEhfUYGO7T7CpsWgeEZ3dcSe7SxR4pVDq0bbaqWQesPH+OJKEZXAaGn2qxyNhKi5xeAymoyOhNZ+KTJRWtrxqbnXcDYQLBZG/UTKo6oUYBkLEVU4Gel8YW43nIdzqviO5KZ632jzY1ea+38fl1122TbH11GZbux3tEDHAuN3gDsGGW9nW0BSIqN95zvf6QXU733ve34pWs4tT03qWRlAzbbyaWuJtV9DCb1W3pUy86op4tm9bpGCVPJCKbikRyIom7fGWAhzgrqQIGUErXZEWV6AA6XFEjLJOt9JYqZhy1JWmk4XbSdqobkkVZoI80BNnJuzvmouBZReS+VduhMGUij2nMitWlUBmVauifTqpekQgtxFL+JKKEJrgXZhAgNY40l1IqX/n265mB0Qa14dLVpgQ7hF1x3BgtDED5GYOgl0jEwqlmRPWKW1J2uAMQYLwey5eEoX6FclIT3VF/ATfAJduyUt/Ma2BKCdTzV6ZNu00pCpsQqGhJw1hN9EtYb4C3K3cNTTTz9NMCiIIkKdZSS1iXld69RTnsUB7qURnogxIS01tWJzlLg5ZNYdCKEXdg4NcIhnTixJTk385WFGVhRBTFnxE8TFMpogk9XZF8AzrChI7KjvEZVdsbatHIJhSzaSE8mFIf4gYgyFp5hHQGw17GkfgXcQeJM8MWxfjw1tfnGTyYpShCEbCwiqPhDSigwUx82Xt3WHzI8AqdSEwOld1zC2vnjKAbgLfiAeEC6EoGhgm7/w0QWpcGBGriSqT98XPf3004XQi1/8Ypztbji30ZcBpYmAoTKBBXM2MvBnSb+ZrIleqGxUsmRFO+PEXwqE1AXPMqwCz3KHMvkJHAI027looSO7MxxNYNoJrcTYdgTo9EL9DISyg/BgHCOL/cUehyprRdSicUsRlKzqLVMxwF/wSdm/08se3QqwvFwg8r03K9oNkD2Sf0BUc6lv15uUuMmEYP9IBLY+HWi75UekMjxNGoLK6BCKZkuruSm9dUeEz5gioXnsxhtvvPLKKxXyP37NMy39lrUYuxstcIQWGE+Aj9D4+9217Opd73qXn4n2f5JuvfVWu4lXXHGFH82XMkYxc65cM0mV9Mtb0+ZiyHkv1M7qYm9bJppXoWQ5tYpEhSwtM9WRyUEjDuVmUm6TQEonk5aC2zwmnq5b7fqqMYK1Vua9nJDWZpl62CpjYvGGiJbj1rbCE2wIanK+R0hYwvWPf/xD+iI9VaYaHQEet9oCTnpfIhONiVrjgFt6bOVJOYZKoqw78d9pJbuqVtgSRpYviUmiL70GWv7whz8oSGikOKwHfDpDfslLXkIX+/1OR7WSVZPBJ8ADz4hSQYsSeABdiKGgdyaSp8KHIChswwXaQlaAJbQj4ZO7q7HXA3SBTC7iZdcDf5kudOfTj9Z6f55UxgWp0OBMHkGOuZMTPKlDDAWoTOrP5qW7rAu+oiZ6LoAKIjAx0OODMqCChC6ZGRuqAd48QiDD07VWpC0DKsQ1qWnzOVozHeF1oRy99Kt3ZTIze5C2W/sj7I8VAr0TNZsLTC2KhGv1UhpBPjylXwTGiP90hZsjd2e2KomKJz56h3ITgcqcwrbGYKCd73tTh/sgZKe45LTXoLnTYJQ4cAo36YhUXjfAkAUwFHskJG3sz4mmX+CEBQjmEF5NbDLzk+KZozBkcJJrkmE1s+0aCWJhcQLhE4mDyLB9MYY1SjiJgUjLocp8ZKS3DbnAwa+NGyPInm9nEmgpd7xcYR85qS/AbHnTtzMMd1yRvngd1Qwr85WB4FKuYd5vuCM1JCQnac0Y5gfThVFDNlFntjR8Sk7JmH9s+fWvf91M6/U9yZsFop6OhdECJ6EFRgB8Ejp9nSpL2t7//ve//e1v/9rXvmZ6/c53vuNk+Nprr5W2OlYyHVsg5WRS4XX2ui1eWV1gFQuMjFm31hjZsMzS0pK1c9oaWSurAmtoO0y/LZ3+2Q/XyGDkbUliZNheIbaOBsOgoxqZ8/nPZtMVYaIcm7fEKYOmjCBT92oZGl30aVasATxAO5AASgQk5NAQAuGhBZz1Thdlny6IcYnu5Eb4gHzldDnEzDRCd8N9SSjBJzSgGmJagE+MCflQCrKVyhhoYABoxFkyHueBHkFQkmxYRaULCpL9IANEpTvUj3jMrgaxRypxy8GaJgwCLfuVXdhMF0nWEbOhYQueoYe4YD9QCiYR/AQjJwwsYFibKZhFc8TGu1uUYCq2hGEuAYYhvOcpDEAkehGVLnokDLhFa6zUMAJ9wTw0iAlMTZdH4HfcJ3gAdR1hFdtSDStt+6ZmH/iEqHrBkKjI7PtgCFQru9jBFgAOOnVLTR5xnkwRilMNsbYmAfxZwKhhwBNNj3+tg9GUaYqYjjSieAh4yiMcSAgF8bLTM71AyGzCCL5C6ZYuODMRYnsW5haUjMyM5GcNHEyh1NfcCRV1cpaoCSFB9wQhZ9lwxIRG1NGWVMrmq0xfHk27EPCgp/rF9ggPvnjHvgm97ND5nCbwEdaTKiuaoco1JjQejK9JlSBUw5L8aBwZQZqoOUKZl+u6BpThI7RMC+LEQBB+AVrLsd2FVlHNpzXdfqIxYuiZW/y23z46S3SZQl1sSylTuuA0CXAWde6++27QV4j6wpr/7lvT1y44YpRhtMBRWWAEwEdl+YPqV/r1oQ99yHs19957r1/SB4bPOuusCy64wHlvANU+rv3lIcInubGuSDFdf/3rXyWdQJTkXuVE7WTGsmFM5NwQiHMVZQuSnL4470Kh8jaQKUk5sFpnR8SmLznl3DCPlFoZAGgP2aIFI1hoYYC+Uh4lW4INmEuPmzACa3OHZFTG77yFFnxEGIs9aWVvojTnWn0J56yBu7h1od0c+QfAPMxfeABgoZGBAS1MRAW4y38WcZRNcr/nDEEZTc6UEMhEAUJ6aSjAnHXLsyE9PgJjGMGjuMPv08hZtZLnSdYl5QHwuoA0Epxq0CPAmdFkt8SGD2W98T4P4gwZMqOOiKpfTtRF4KjgCZYmqno7KXwtTjAMVAb2OEINKChLU687/RJAUBFGF5oIM+hLtOCTBA4xAt3lSFaIonHWSk0uZjqqCSoM7a34VM7LzAq+FqsthumO+qwhBgjjHJv6aOjiKUmEDdnoC29HNpU64nSIlBjEs7HiWJ4WxCOwtjh42VvqbE5gLuHB2jizDJN6qvJ3v/sdnoAuLRiQ43hWR8ou9c5sGY3lBbBcnP09hauZRddcxraEpK+2TE0SwUBgzM2xJNEpmT31qVPDVtsgc0Jm9EVZt/0Lh3qayCmaqo++Vb+Jgr4YSsBTB/rd2TSdiSKb0GIW5TIOFcSnsGRz7jBsBWo93YTRNsczrhfSRpadF3rZBsrcwgKb63cLnKOaKcVwM1gMJcuHUcxTBs4WBNh0F5YPJxD2177yla/4dT3ReP3111999dU8uOmuR/6jBfbFAuN3gPfFU3sgpxzOe87vfve7JY5+JetnP/uZZdLbfdLKSL+neUCZnvySVOmvnECi6YzRCgoASEaLpgpS2AAS2ap8SPINCbAMDFw0O1WgCO0IKcOGEOSgEjsnGNJo+TdRaa3ggnJ5dglvSuKljDLCdSUZyWNixuQxVMgtp5BQj5AJ1ISSgkJxCbGrl2C5OrLT0TA3Tw2KmbZCgCfwA8+IE+hIDUVEDvuzNpvrGuhSltbIRBUAVFgOSkTGpLyjO1qzrUovQoNG2EKDtGYBSR622vIsWKgS0BKNvgOGv65hPF1LcPkdGWJk2Mp9QTitHEgKBjAYAQH0xbYkBAuhRF1IIt0yi4wZfwSEpBGn43P22WcjcILqjBcxlCj7BJVtKDgiA/OcbRpTjo6VCaA78rggIngVVNYR5kzhEcOC4ii9w4+/ixZM4cpJDmsYgAKAQRC7ZQHWILmxKSOku0cKngaUsjA7MJoaZfQB9jolMw5kpg5i+FYlBUmVEc0UaIx0psA/EMhT1gPq9KK5g00zhke0o5enZMaQryMGhh6R3yRDDFrjiRvtBKEyj+td2TsCZGBPIkWdqECYZ599lhHwwd9lplLmDgKThIMYrcylEh+y4Ul3t5yra4VcKKXRJBQAz9f9/79atTU1TDr1Lc1wmWepzOzQ73KZ+uoyDEvYecp9LMzyVc8XflyQU3iKIib8pa1RPI+kwJIuC4HdGUErMm30GM7rVUcXYhVPttqmmiLNrGKaMsUZa772b6dpvaptU53qiz1Ttho6h/DvKk2V11133Q9/+ENnEpxYlGNhtMBoge6SNlpktMAqFrCEyJ9uuOEGr9nYenQa7DcG3/rWtzofdnyR2XmvlxnCw1G2V2Xt8lGJjgRRvlgLT2mHzIuOjCkBlf1LHZQl1quYd6NtpSAyb/BAyi4zgHOk5kCUTI52kmAAyRUZSs2FRJJqO1teqMlMYpBMllZksJ+yk1KYMJUKQKPDN73LdVQuKrw0F3LQEEIAqIA3ZU6HkRRWuSpsoD62JbyIkjQTFVCBW0SOen1BhsCYdA1AJT95XPabOEi9QUcMoSgXF2lcqQaEIHCU5Vk5n17ogq045FxQDepmH8kf+8BInnqkII+H6GAeBdYDt2BF0uKGz5///GdtZY1gm4J6oI4AhNdcp4RnKFgXHsOEMDgTXkc+teI1OtIXpeZe2CO8fsFCYJj8LtyAcGwJbCjZSpOLI84uEj74ewos0Y46yBiqde7LX/5yBKnJ6atU3kUkVo06uksvKJnUJ5CsiQwSfva7U3g6p/XPQmTqzGU4ZBSjAQn4heQ08pVIY4fuBLP7AJrylEHEgCxsNmB8PTq6p6ZWTIqbE0Ja0NpmhN8TYlttKU48UplkmNcj/F2M5lYroIsKeGoowdWvskN7OwJGrh51rVPdYeWTqTP/CF3RQse6eES/bonhK8diBn/MXeSklEd0cRKuC5VY+RQMCX7MixWPU59x0DBO+yguKMp+IcR6ZF6isnmfZs4aNiQAaeekX4WMrRjfOwgMznRmTmZkB4vdKmyPsG0cEX/ZWjL2GdP30oVlDZP1imcLTBgz43rZ9rlVQNLOmDXzmDe8xiXqZsZnn9uu1ZR2VkO/S3rfffeZMeRdTn3tXh2Agrtm8FGeA7DACIAPwIk7p4LZtmAwAGw69t1gP7X/4Q9/WEoXcfd6Ria8VRMAkBrKtKw9sl4QkXbyTgoiSAbpkUxaai5f3AuVZTkyaUARvA/Woo6Eu4Js17SQcCdd5guoKYmvBD0CU8eXOWVX8AaoIO8BWqCyhbSAQkE1rtQqDsV8dfSLCawCM0A7DuUk0BJBA4fx4Q3IJF0AbHqXatNU70AapaRuEB1ERFOAASb0KPgEViSntFWISsQjM4AKgmKVV2Sx0h22TOGdXqBImRiSJ3nhG97wBkYDlgA/3enaK9aSe92RWVQgw1YZAneqwD4wgLxfW0wIoy/yC/vgW2qqcd5LKj3KpzH3yK16fE7gu+P/OgiEU4M5JtxKMK1CA/vho7mO1CCIDHApvwPARGUBAzD12LYX5mzF2iopIk50irIl1mPGLxqhQk2qqWQ3xlSjL5CS/MYIcGtoM6NKb6rjzHc+xSE5AVHaMY4aTIinObGFok/G8ZQKXMlfLkkqkViMAMGoxPPCtqEXYmYXwLyMhvtYXnNhoCEv+8wjkcNf/pMTRzAUYpdeYgodQZg5gs5nxou2eCozkTIJPaWIrhP8/IWJi/2xIturXvUqBU5hB7q4eEGQQEp+KQ2lhlyMoUfouQZBxOh/Mg5bIbAf0XqkTzmzhkgUcc2kXIWAldrm5Odxg4sZqcAvK2rRMt9OuTQScvylUyEneGxZirT1ypC+OiaaWLmWfsPZGOQjM5tIE9gve9nLDgz62rC77bbb/GMOet18883exavZbC1mHJmMFjgwC4wA+MAculvqmIh988RE/O1vf/v222/3S9EXXnihXUkpwuZWu62ZwPot5dUdXWRduaQ+kmNpsfos8DK/KNsRTGVydwSdVKBDuf3bpHHpV/bvpT4pUb78SezdkZYkUKKLVNAaSxZoL6OhAQmkO16ghV5gFf8bZiEVhDEEiCEYwGVQRJgvxKTkaQtyMmgnaQrI4fzWlYBh8CTxbqEmfcne7LM4k4GTBZjdFtBC1DnudkgItHj3FQKBpSveTj31VG3dQsskl/nJaOEirXTkrMwjtwTALUDL4SQagqHXKd2ljD4dbfkn3jAYSKM7OArIgSTZ1tmmN1dhMF2rB//AaWEjdVaJm35hAyCBGQ0ZlT//+c8xJ5uu6cWkNPJIvxpS2a3zXifDACRKaOqPf/wj9QE/wsjREWNL+MrOdao5hlFfq9bUyrpzYSieie3db3AOMbaUQn8c4Z3AisVBvwxLcu4I9taFSs2hNWbUOw8a8sIPHwZBRn5NwD9WJaeoA5WJqhUhk38LVPRC16uJ4ooreZbpEIsEzfEhjPnEJ8OSX3OPtPKJM4HJCZ+XpvgQz6EW6xHPRZLWCOrb25TJqRVi0zI1jX319gtsgdkC0AUszeY6xQ3Ypn71qBV6NSQUMHzHkpprBT+zc3WnCR1BeuqjdLqI0qVHESK6iC2S07ZaVaF6rJq2EH9NrBlu2DZZtMxZtTPo7X1lDqIgg2yu00WFnJM+BuRHGyhwlKERp9sunJPDomQGgpGlla6Fn5BQNgWt13TRS1/0Mk7dmnyMVnFrYC4q867RRztSOUWXX/nemdng05/+tB8iNWB3TdpRntECu2aBf1kdd024UZ7DsIA83i9Fm5QBYKfBFlcHFE6DHRREwfWueds3GvnlhZYcKaNV1gVdyP6Tm+a124lSWZW1GiCY2Gr7lVI6CS5AAqgAPwEJe+Q1osqzndjDgRCLW6kDQCINYsyZivQJ+jV9p+CvF130iStxgaDAnhAAG8RTBgPARagAPJBVSw1deZvXUIIiAE5PUUrBBRsopZ6PeEd3Yg+Ntj4D8FB6lRd/UIf6ElyBCkehJCSoIwhx0Ba0tkHgq6pCFwef2HK3z9e97nVQmbdkieQIVI2Y1xwoAnVQaq4SQg6IopqzQe8tAzwEg+uk1xpqIl2jKWKCqQyMBB2V1SS6wC18cpJGWZXkgYe9QixL1in5EegO6KICtmocF0umDShIj5xEQolneYflcaaIp5qoV4C+jNbgWyZiDd2xuYb0YhMvRdtccLZJZq7xNgHdyQO+0k4XfpTbp1ZsyynkQWx06wjepjX36ZqO3A1a2JgwjngkENEjYqAkm1Z4osScB7OhQDWcYXtSqSx1SOhyG72AcEqJB7IVzcwC5rZCwkTZpWy3C06IVdmHceyDUJBs6TFsOcjFg8AMN3ENp4hPNNT0BjWfkjmfJTxlOY7MJTxubBsZfOGc/PjUhZX4RK8LPm0F0CS3thhEglvM42JlviOwwiYuPhIt5CEY6MvXBhTJO+Jtous18iQ8bhxt98RWFOvx9Rr5T2Ml5LgstjJm3SqLpWn0S9RTTReUMmY1N5YNcz7aLwdNVDxe82lRdur7i1/8wkQhufJ1MyNlYpOxcrTAaIGOBUYA3DHIeLspC0hE/Ou5yy+//MEHH/QPk5zCOZRzGuy/Dsgh9LrXyxLhrayWWCuuNNTbmNJot1moYtMomJqOshMrN+WJBfk6NOMpSa1MAnSRiEuFcxq8IKfNkssAOlbt9Odp9hpYG0pxaBlF3A43LD58Kreu2+GCSGC0HB2HUltIAASVqXtn2J6CBJokwICCLP/SSy9FCUuACkCFsqER7KchhOD4wv8BdswLCcA5Llk4RER+n69+9as7IlEtyBMlcCI3YgG4AisgTaarDKPiRh44Fp+gYikjRCed8gmswmZ0z6utUKhW2oJwNMJHp4LEIZjuBAYOAYoAHmAAi1IQvdwauoYTvHRtNjA6oEGmgB5tisGuBo56MI+yuJEBQ5JDiTo688wziSf8GIcNXUGbKvFEyZ64eargq9GOsnUqKmITBARgbRdpARXnQmqk/h6FRpnMNiCYAh5QSVPCMDIBfBKPm2jx6KOPUoox+QIfiTWvEYnw/I4tGZjCUyKBlI43PeJlXUNlBMMNmNcjC+ertixpO8MnPODkjQ0DS4BtZWqqJ5KTKwK71ETsfLKGfpkLQzUIUt8ha5ukTE4XeqOb78RJ21BzuuNJZqHYb65GeLCqQsA5OcWt8MiJbr8JVqZ9nmJtYmuFWNcoWVuPrIcJ9dOW2ZPWP/HEE+TkOwapMBZIyMQAO5MTgYZCOm1jh5lGCPGcn3i6WMMgNXx4TQAYIJqvt6M55VmRjPxcz4kMKyb5IiG0Itvh5kZBxqYXc7JjhT6ZwHDD4af80hJYiE0jZiFvTIiTffROq45yFBThTz31lDzKp7nlW9/61kUXXWRQdIjH29ECowUGLDAC4AHjjI/WbwFLrC8Dm6x/9atf3XrrrX4YVqrn/wZ7NTrJ014vUYS3ysr47WfLhpnPciVdtsC3e9uSxeSyMkupnkwdpcSacdZv8XVwlA/JZeUrjqckrJLUqFa8j9xrBIjBS6ThAhfIv7mGUnK+OWMvqXaHc5tyKfOpK8LAQk5T5eXQEXCSeqk/IOSRGFCja9BIQgPnSNSkgOhhQtmMeHDJs2NeB4m6hk/AFegdeIAZJHatPCVMeQSwdF1xxRV6AbqgNZ06n+TEABWACpawFyAUEYC79StQeicPSaA4h8ZElbYSNRgYJgFZRa9oVwlp09GnGgAykrvVi9j2a0+eQjJ+fJU1AvZIjjnwwB2ycAR4OtPAjfy6w5+aujamtJWjAzaaMyBi0aghCYlNZqxsEOBMHUYrU+glZb/dxW5OViMJLdQbmy4KhmcoEehUR2IDstILswsYbJXV65GaLIm5+qeffhr+AW4pS3gygL4EtmUAkZKQeMCSeuhCFNluQM8jRMWKpuIBZ8k6WymwhhDSFn8M7aQo5PKUccgZxBhHkyFda8Wzzt4Rk0eoPN9u6t8KlaJIjS6EIlMkApXVK/Nsp0mALmsAMxSkV8xYDDsFTBhH2NCL7qK9wAnmZ5xxhuYcTSmXfrkVB5U0YnwiURaBGn1lVApynjJTwTwqmY619YJGE/WuVuy23BFv4BY3T2nqFW7CU4R31BA7EH2g7U49iiIRiTpMaiZx5i8gtyAn43OHjiKGASIMVuw3rHwKAJOG9314R2RaecXPcu5eUaT1No+CnPXII4847LV42Tf02vO5554bY663u5HbaIGDt8AIgA/exbuooPnaG5XnnHOO7/X57srb3va2j3/8416TVkgasdfLFeErY4M35LJyX2m3DEM+arGXx8t04xjrWc6vpPiyqCP0VtbXCDDR/sSDweC0yCln8i6ot1spq+3EJkeozkDXROUIwA86dY4H/Pj2I+AxTYvWMmEr65V/S9qw4l+XJNslWQc/0Dun5VYXPCkPE9XSdMmlsy9Pxb+TYQk6DCBCoCyWhC1jQ0CuTuEmasHgfvoYltMWeOvQwLH46KhT7xZkUq9TIskLiQ0Jg0zok0LBG8pC1C0xYhCWQc9KtIYScYBDyAAish5RYTmI10GEwMbQua5oBw90R2X8EVNfv/Awu6mH97RVyQvQoy4UcNa1jowFuThLMqBbopIqQIglCeaW8LrQShMYEuYxlAAeXtCFVpqTJ7tLdhY4ywUXcT3krC8XMl3gBs+Qh0a+w0kXOTRARWuSu5iUdwjPAvolD98ZwhqKf7oY2sTQO/uwgxNdbBnTQbSdBeIxkYa+wBz46kybeDrlFMyZi3F8ugjJGuyfqCMDGzI4Sm0JQGVllXmbWpO4idHYk5X0qzI0Pue/Eh5Fr6/sEbC5LpTpq/fA3SJTUN/eKrNGp6Z/S2Vk+Y9HxSEFnx4xBR9VQzUy/piFbLxMWXYIgThkczXclAiPoTy1paImIS0aXSwsDLASM9i6qpcUSp62Pl3zO5wvtASGUczU/JLh0xLvbDlaMIhoF5+sIZhFjug9EpmN3BUXvgoJ66wtM5sjHGpUZv6pCDkS7dbSaRQU3n7g6u677zbw7aJ6G8KbWeaHtXQxMhktcBJa4PhG8kmo9qjy7lhAKuP3k/3DpP/6r/+SJl555ZXvfe97pZiRcGIisjvCz5TE+JIqSbil1zI2qaTM3qljxp0URP6UVEwKdbTKcgS0Jh0h5DySeEc0B03kl/RrxRrzNJxptK0RUJlreEHBq4wSwepaJi23kIKnBoaRUXElfCURAf7hDed79D1+wHoCLEnjZJM+mTFnd9JrnEEd3CQroA5khaHmGqIEwHw6AUMmaVPpKhkGCokrOWvRp6CeRlhxh7fjsJWdByerBMxcAJIRB6nK4KsLDXEAHn7961/LruC9PCr+btE4usSZRnRhLictsIRIoJdIFuEq2ROxL46ymKci/4ILLgBdgHzpqYs8ZKA7cAjaKTCXIU8dzNETWwEfxCxPBpaEcKgAL+kCDS/o16jxyH9s8lKJfJdGcCbmst4COfwoRAn8zDPPKDuMdQoNWXmnmoRkdnEc3b13rVWcrl+caQo82/dJR8h0qjvIAXxlW9xEBSDk1qcZLDGgO22RkVPCCid7dULMCBXGkal76lbB14M9VZlc1res7TE5R1XD4C7quxQAfpGD2C2Zbd/Eibogra0QHDBkT15Gg8Oil17SRHNewFaNmAfss9XCsIDlROZEYkNQigUmEoRzdaFAco4YIF5Cfj5lB3KKFp96EUg8RQuX2CCnlw44ziO7SHS0pUJm4eciv0Ca1q/o4n2cecFMHpdNI961+lheuDJINobEiRDaNTkXlUegUsdcwcs8IlC5xgSyxrhaVKS10MdfWNlz+epXv/r1r39dbPuJfv/X13tzgnAtvYxMRguctBYYT4BPWtfviuLmcedv3/3ud2+55ZZ77733y1/+8l133XXJJZc4EJYfZw3Y35WM5FZiCkqY5MFSWEt10FEcgMCavQsKkkHqLGUk8DzBASPBPM6F4D1pt5TdOZg8cp6226epZCKmrrgiM+gFNPoEU6VQoIu83JfiUKNxw5sAAEAASURBVPJafgeVggCMGuEqXbZN4IoWvGlHg90qG5Zne+QgAh6QZ+cLpd6JlXrKsD2KDD4xgQAVFj1B0oSbogUX6Dq4qAyLIOguwsCcHqnxmXhD4HKLiaek5UFAkb4DmISaLlrk+I4pIEYMUwM5+BfZ0AVbsSHmbvUiVNwClvoSLcQwtBkNwmQTBQgTDlFmEBk5eQwTEBrApqZ6zXmHrThIK70bTXCsrknLZY899hj0CyT7gT17CmyOobaYk82RHYdyAeHJxi94uuV97gNpRDJKFtAwZoFC9aUjXRAbpdGhCzLoDnKAo3CGeJkOJsSZhPiAWMCtp05Ky60MBQwDzwkbNETSI3qBV2Q60guTMp3ruIdO+Ci3nirgrCDpV/bJGhmDtKZ+KJf+JDmMRwDqCHXgH+RmKz1yLlMMcPaURgMEHuEDqFBfAFCNg4bpF32KJwTbtlJjey5d+2Q9GhV44A42FF1CTsAg4HeaKtjdE6iii5d9uviaQQSzEFUunq2PVO7sRXHGF4SMYPPF1NSx1c5K3hcsYyH17G+64ER+Mej4a1880tcrNaWd/bJ77rnnBz/4gYj1+ynXXHON+KzondZ8rB8tMFpgHguMAHgeK400G7eAFcvM7vcMb7zxRv/D/Utf+hJI7F9NgsF+wDbd7+CqZqGSMspBhw89SG5V9qarnWnEbjV0gONcJe/puT1C7bLc5jOmTnlYJMuw/Mnv2cqYpeBwhbay/OIz3HzjIdV0QCTpEdwif414sIpbl+ReFu7sDrkjSvBMAY1suF6hVAMaQRecCMnQK1d66GN+ZB7hrBfpNUNBOMo+8cEhh7TSNXzCZOlP4QfOUUQGL6nFkKY+9ettXkgGSoQV3/zmN+udSC6pPJSLpjwlMwb8IgM8RsJp8oB/jig1xA0Z18OoYI96WjMvFIQ/ecQG/EB3j+rXZQ0BoSIFhyRdoCMxUBoIDA4hO70BLRSI5CsSMDACUESlL01gSC89AntQiobEBtXAGKhb17ojFdmU2cFTPmUEBDhrK/UnLZzvUIXjyIY/hiCZp2qIgTgeVGnAosHNp00QxHTkOJ/wA1Z60am5izp4cgdETTYmcsxOU5bUqTId8feiASZsiJ41oCkClyMUcGbAqkFcviC2Mp7spuCRDTWmdizPoYHN2GpeTRYtMAItBBKrpq2A0csqPEsGSvE+DMa8NhQ4MY9KWbetvtVwLYVwFhuuMFTDiemd4kKON0mlPpUCwCZIiefVA98sZWoGydYMs9jCqPFSDdPXWsRekUkJTyTzgIvAdgTMZmpcK/LffvNoZDq1gcVfGVA2pAxS42sfNWptGO18+qXAL37xi/b1TAg33XTTO97xDqPSVNMSj+XRAqMFVrHACIBXsd7Yds0WML+b5f3r4Kuvvvqhhx6yAPgnmTCVjU9nwvKMHVze5IvyUUdYM21BeCt0yMBgqbb1GwzGwb51jhSOREHJnOxc19ZdW+kuZSlFZYoDqqGUS1mkQcfk/ZhIs2T2SSUH2i73KClC2k40V0uATGoriXewJpcFY0ARCrJ/OCCGHHImD2CAWLJbcQipwjkSXMaBCeOdhQSG4nTqoE+/GgJj+lLma9lbINZCDPvE1NeLPQhvcrI5yeHAqO/gV4ELEGioANIg0C/AL1n01IvEyeCZCA3w5pM3Pe30FZ4q2QFOgF4gQ1aFCdEzIFTG+wlvxDjoSKrNAmAqewKBtkgQG+BqgHYiYShyMCTGK1/5SjGjlUMPYJ7Y3sRGCQMb+Ahkuso46x1nKgs5BMoU8ZKFJmo4FwgkCSOTSqfcx++6I5ihigNT8K+2OYH06/S8Y7/DUfDFF19MQge/fmOGkNiKCvxjIgKT0A96Ucd7pPjjrJIjkHEuyMSMMB6whGFgFbEdeBLAhRWbcJbe8z683RBjjSXFHj7U0XtihjfjvnRBbCrA58HMmivT12cOVJF55FJY6Cr/Vqu2hkgmYU5M5cRBVw2nFZgih/C4eY+dKf4fe/cebF9R3Qn898MwSeaPZDKZTDJ/ZMIvialKVaamKslYSYwUxEcEfIAYJAIqIBGCoFEIEl5RSwGND3wgCoLIQxFBJDw0gFGDaFnGpGr+mUflnamkJqmMVZlkkij+5nPvF5rt3ufus885+5x77r29f1X716d39+rVq1d3r+9avfctdAhN3+nPkAVnK/pz54eNaKzBDR2Zhl6vTRlsG27sGdM85ffJ2OUntrlX4vvjjyAl6kpcTZZKZ5uZy07jhDbqgqUMP1YejPHaSG8LPwv2N+pnapg+1J5GUXUdpEuUakHi2149vTMXfNdKAIBXiwZee+21J5xwQjEbtp3JykCVwG6SQAXAu2k0d09fbGwnn3wy0Pvwww+DwWeeeebFF1/s9WCJBDq2ff/OdtWUeHIGMsZmEtlmYdvLQSxmMUtabKFYYE3Ky05rlDGBf5a3dII/AzsS3piP0Is0Ikx5Fr+gH6uLycWCn4nU1M4CNhwi4BBlSIvNKmkLxmBSQzXs0ZikjCToS0mSZwVCPuXyU76KibAxQQwKUSDiXDFVZPtGJs2GpqbhH0gJG0jxL9BbdidLOrANyppKYUgBbOPQN7eAN60wB2FUfST8VM8QaLpJLbrKjgcIw4kcWB01iJSJ3CycNH8NUK0v+IceAQB1yYrpibh2VZTjqfKkLU0CHA0UAJDDEiVX3SjAe4aGIUsa9A0R4jUXIGF9UZ1jCAZWnm/IcAMeiPhwFLIRo8JqGUEM40pbHsHkeq0kalAlE1mIlTTotoYYkXhQWEgcXNFHXfYnZ93hMXVBZVW8j60v2ENfRcOHOLY5qrSl14SMQyCcz4sAqT3JSCgvXxMKY08341VRnhCwjVsFsKd3JKOAHD/NHfnu6mrCVdLyW5entFGmHmEGZfwLY1o9PGoVnuknvwPhqIKOyYtsHHOakAPqe2SmZI7MRFlh3ArjW+j4JkjYADW5JXASA+NJYFbKSyqv+6RhRhhZY0SRjGYmkUcO9htEDqAsLzQ5g4gZvg/d9JNimxou88vU8EjFwm2z+yVzxIS2DByVtpQRL4FTeJPLzB2xlRWTshTQImqvd+a1lc3KUCS/YmbGaq5ohe3mAx/4gBd9qZyzb75x5QjP+syIsfpb6VQJrI8E1mW/WR+JVE7WRwL2NnsAFGEX926wD2U5I+0PJp199tm+qRM+l21JbCUN9i6kZ/diJ7EweNmVZMO5tqrSzMe2vY1RwhZnQLPX2dksLQQZi57G2FpN71jPLk2z8plr0ou0y9aHcBiCQmROqTFTAkua3Z8jXQwFCQIP3pBmFYErpOdO+JpD3HBAYnpBhUAphjv8Q7D57i5YC35M7KN8L5Gieffdd7NukXXoEbiag2H0RflAHRcFxhtm4iYw0MZ3IgPzNYRDKM7XbvWaQrILC3GGOwYCYCBMojMuWqF4+GGaA1RMeQa9cZcfnNZlI6OpJAoOsjJDSQkc0i+N+hkVksk2VZ3Mk4AAqYRW5OAw3gSZfmoUIsKtRpFisnOdGCa2oIbAMLYghcQqgnJIT3dUNKZQh5OcKCusUWPqG8uKBX5QAPoAVgGxQSA4VJHXiU5aVaA7BP0ZNgOROYu4ruEHMwADkYKyRJfgJDyjFRJT3sRXAFrGv7qgowFVEucUUtdU1BctZpXAEvROAw0KZrSLc2nME1FGStqlOhOfiEStu0PQzdE0fkDxMtzdMgNzcAIgERfZ6guyeqHLWHLRHHTmmwgqGiPuDP16ylOeQlaFW5Q9zT18SpenydmWu6F0BlWvDYpAXMYunGCPHFxRmybzCihvYaecLnpCAym2WaYYOEqXyJao3ePrUWX0/lJj892egn+tQO/zuS22RfLNRiNbd2pp6tEcCzItyosJ5Da66JqtLztdNMe2y7zxEVDd8afXHX+zmpmAy2ag0q8S2OMSqAB4jyvADui+nUC8xZexfCXLnwGwVfgmhA/w2id8XdamqA+r3whZSCzdIr6kmcWxikp+f2JjA9+MuQEPbOW4e4El5gvQyNZfZe/KftzPc/9T3dGLAwcOgBNACBgMLDH7jFfoK9BPofs0Fd1RIxmCgvGALu+KM6w1BNikFjspAJiFJNYh/MJsYnd6yihU0QChI7PbSnI2xmPzpLoDjQAwBMvS9QieSS1Pt6o7MZ80NIrIJuH90rNSmEi2lYmmhqALNjeo5mdT2pgPgJFJb40LkEN0zHqPVKG9NJCVvBVvKhImhUQngJB4NcQeRYp97xE0SER6yq0jdMZORdB0gOjCDKAL/pGqv2NJsJQENWgQGABKHeKl7TwUCqMTMGkWaAh7yIJ5YCEOjbIqKGMeM2qpKxPYUEymhE7BsYgAuh4xKAEwagNL66mORyCHH364KspQJzgBe6rrF42C/bQFP2Bb7zxioTqjgSU9Qhk1rOINJzqrgM9uEwWCWjGLtagV1emScYeHiQsFjNFG2D4rG8FGPq0x7f+pbsKSaKLQX3jqUz3Fp2J6bUDJmQQMkBw/zSBB5p5ZsxX90i+w0JGNgMOmjvGqEIUBQsHsjsCt9lsRXGp+uDWyeuqiM1TLUDYZ7jLQekqMLqRcFhzajlRq0VWa6fJIDpVA3CBKZ/Fvkmqmu41OzAlZFQEqraBvXpg+VoY5qE1sYmWZ6YvJaLGifgQVDeS6WtISurKuaaj07q677vLhT38M0jAxb0477TSLSYW+qxyL2tZelkAFwHt59HdS3+0KgjO+iSX8+6lPfcpLMs5Ii9f5OMQZZ5whkc6sbKdnQzsIZydjarBBbc+LNK2ufV0XEJRmhEECImDQHTM0AG8R+kNGGn2mJ/N9SOH+MkixukAF/IOsNvVs+TAAqMAi7O9LCisjwTgWSxGMImTfRXNmlaBY/MLmn/3sZ1lI3hgnfIhIZoIqKlKVwqHzt9KaBop0sL/pUgvPYAA6yhsOf0EHZoPB5nBJCPj4yBbAk36VJsZN4JPMc7JXQ15cBNsCjcjcpTkBQ3qrDLvcTxY5sEdoyk8Vi4GghwqbiQqDkWSOLMikIUaqu6fkrGnRJzarMXKRXkxwtVDQIhCoJKwI9+LQ5MUPxSNbhiDG6IxBR1COYgHA0ki50x86IKEtXUBclF4ttjJUb8hoBdPfeMGoBh3u4n/xFDOwrvIC0TgkChFgFculfEnDri45GJbAT3BytBpNrSPI1aJKEDWdIVv0PSIuYV69A4yFtfVFZ+WHPQIHk3QkKgH/eGqyoONOOIWNiYnUIgQlDcTEMgtmtvTBz1bOVPqYNKz6BeOpSx+6VQw6dTKsYHDSo6w/3Yb6cyJPPJggRvmII44wiMH/s/Y6DW0Ia9N7UoZSE1wAROFggstq5p7CHpkyXDCJDNMiF83JRAtvqPV0QRkypI3UUpoTEAX860V/xR6a2/Uo/TXvLCnmrEHJlMdPRLpdjC3ebrqGjnXggx/8oPd7+cj8FbQ77rjDQqSbizdRKVQJVAkMl0AFwMNlVUuuhQRs6j4P65yq0OI111wjMnzFFVcce+yxZ511Vs5A4nIFu36zCRubn82cuSWFCJAGBjg2yQIQR3J3opg1U7bPbkM9j5qciLYxqthkXQoRWvBSs8oiaa3Y1HUHERyCEHmNk32mg9nvw0nhPyXxyZ5jw/npWyBMw7DB4yCYxky8/fbbwdrnPve5uoP+uGxrC1fNQ4PwD6wClUFTkFVM+YkyDJ+tu5KuJsFWgdF/MrC8QkbITHkATOuaiJClN+HwxmeKBffgMQhTjxKF24oTtRwEdWdkA2nseGJHgfeHOslR0R2+VQZihI0NolCnodFxiVAOD7CfQXQZWQMNHOJBLTwEqOPfyKoiR134FmBgDSsTtYH9FHCHcrUFPygGV/NZIIUlfPpKFjgKTxo+LjOWNESqFk8ELXJiHKlQQ0EVxTSHfwlNw9K6A8LJtNTIwQPGhH81hz7UCn6LYdINog52TfRPsSgwbA+No6mMgcAt/CzUWfBVEUsaUlKxZPbfrQ8EaI6o0l9ypqcEYmZlQFNR2gjO2oqB5rQy3wnTiABmE9kgDfn6ToxxoMza0ESyM2Vi1UWTDStvDmXA8+jQUb9chOkKTMWkdsOqaaJdK6QpmRwyz2s+tI7SUm/VS7+STnUKZmmyU2DbiQYqQc1cpfCOSBRR4NZMN8skyEocPo6wZvd3RI9aTOqgy6J09dVX33nnnRTspJNO8hkLm3vWulb5+rNKoEpg2RKoAHjZEq70lyIBewYo4vtYb3zjG2+55RZImCeVXetDWS9+8YuZhlpdzZbJSh53A8O2MAiUCPQy1oVQYs3YPr12yMSP1d7sHcuJmc6E6pe16jADe6K/2JKewhhCtQw1ATHxN5iB+YsfzQVLMP4YzS4/Gc2idh7pr4q6xrCLqAUHPvnJT5KJiEpTCP1soxC59Reb+BTuYlkaCAEigJzbntXCZh3e+kSyo2fSkNAkHKIWYvriF79ISmQYVkULu0LwiDXWz4wyLmUAOdDR2OUnvCeT5W2MNots/GVXF+BnlOmk/ETRQ581T6UZtaaMp3njV1w9wgySVJI+0wFgWI9SXQF1PUofVfdUNzUNbZovsKjLxDdYGlXeiFMwnKAm30+PnNOGusnB/EoX0hybOxy650VxCCS+GwcUNcoWB00VA+xhacUSwQOwtU4zkQVsiBdLhZSKWtG6fPOutFgK6KYy7nLcc+4awW7JUkV5PDhkjiWvNPeULFWGJwwB0TXLa8XVzJmaxqFB8bExXgBeia3Qb6GjvKv8XGVCuxEmf4e+8+xYdSWWzUNGrYwdTXOUwIzADD130Rk8YC/v29MfC6C54KKHNMRTCmNRclE5CynPBT1cNuej08/Qg/HcizxxZJKpbZExec30IqXRm14BwfSOi+ejH/2ob1zZPgzT5Zdf7u/SGdBxLYcVdKc2USWwmyTw2Ktiu6lLtS97UAK2T98sed/73nfPPfcwf30+GhIWsyKK9d8+Mc+kDoTojl12UL1goj3yyCOwCiuBZQ9mpLBHbCAUWFETOxsKCjtT3QLAE8t3eRgrByf4FLwCg+FY5trTn/50PPD3gxaeMgjY3ywDNlCMoW7TQMIll1zC/eF7IavkH3t4FiZiqGHPh5cLb6tkozTaTeCQ+4CEAQ94jDBZzzL9naQCWUst+dAgwzrGdMnvTzDQBT9FVpumG1KpVeQgB7hlx7PX88GnFFDXQDsQrroyJClICCBxdpS6YIBwnJIwgL5AI+x7T3ELwZoFNN9ZADmecknQJZ01BTQEtVIqBcwpFXkBNKFpcDdhRgMHOnqTEPHSomElNz9BaFzF3wQwm5LqOv2u137y2sAe6FNgdzmao4fpixcviQUmwTnGVMRApISxOHpkoq8AXw88TPIGSL/ka90jd5k5Myxz4qWYL37zI4gQQgilFxMLz5GJfqk1B3HVyceM1nehe4M7lQhxxUlBYlMLF94WSTT7SAPBdU4iYqdgq2FgKvM4xIk73Y7CEJGpIZPm2No8+t3f/V1aTbVsBya4ubwmzE/tnQJlCHRBBJvOmLO+YkCl82gH9WVif9ML+wXce+utt+qmY2vOqXFaTXU4TiRYM6sEqgTGlcAUx/+4jVVqVQJLkoAdxb7iwCdz4YYbbvCCjV2HVQ0G+1AWs0a7a7uhCiU55wwVsP+68ilsM7jFytgKbH13USzBCnZbsSRSt2s9oM+E8hR0ZKBrS5qdMWtgp8vb8BxcudhwAIxXOlly6hq19A7SYNUJJ+ojM86j0utuEwy+K6+8UgEXmqWAnyW9jAT6FAnqYC5Ho7QCyYDrEstufWCPvMFIyClMOEQqnulPHwN1iSkVOhiG4srPgQkdp6UwW/G/qNjqewZFmNeJA9hSATl0TyLIMHFOdCghDAnLuUqYmrqqCMO7VJEGgKOu1MbToEqPWMzmO7QMl+q4p6aGAEsK47BpaGISvnXncylwFBHXZoxzI8jJSHUPZpNmlKOMDt5oJtWF5xMW1jRREKCu4TNdxoAWKbN8fIKyaagwjDgGkOIFIAQFnHQwHaB0rUjLmbgIqJhLQ3qqvMK62ZL846UW/T/8tELBA4kmoGoW+3NHxDWEQ4XJeSD9xYuRocvMTaNcGML4yxPmfAxHbu78gDlnYe7wKNH2skLSKx2hdfoizbljyCSGyHw+rsatRZNtRu56YTaZWRmRncL/RGnoi3zrng9cMUK+8IUvWE98sNMHrlp+w4nVa2aVQJXAyiRQAfDKRF0bWroE7P32mEsvvfR1r3vd/fff71z06aef/prXvMahaN/KYujYWddnc81OSSjdxEQmGceOyXH2sxgY+sxxBr2fKIC17nlTkSUXSFbEnQCLn0wlxnoA0pK+nZNGmz1i6Adp54vQ6RqEwCyAypTEFTyPSbyxgWLeFea7iY0hbGBd2EkTAiDdksvI0TQ4FMrcCuwbdqfYUeBlk7FltD6VJj8CMTK/RPtpCzk7rQ2vsozLoCzIJPWjik0A3OJKcwCeTGNqcKmlu1qAn0xsCA7jATo6cOAAn45H4iQUEvaTDzSy+BVLyItjSKYW0TS7pTWtj0hlRpj1AIBaYEw+vEzTHPyOewV+CHv0ysCpyNRGJJk9dzFhQV2tI5ViGKZpgLHuaM5PCQekpXGiDKyObcQF6PSRNW8yliYi/zRd7pkCiTwr3yNVdFDQIlnpcn+UuDQ6X8LagsOZAHB6pzkqR2jGWl/Szfl4WEatMGkEuYT00WF+q5BuztTTZTA2hCY3EPXm8aHY1kl35xp4TyjPpgfprxFx1oDjSTetAM0jRWsyEEVJ0l8vfpu51k/rgIHYQdB94njpnctyx//utSzj4rUsJ599ssT6sCZDMJHzmlklsDclUAHw3hz3Xd5r+83znvc8H0kCFAWEXf7YgIAwGHz88cfHAt72DclmyZpxB18xI21U8MYm3mp4FEvcjCkPHrAexFsUZvGz7IEfdme3XwiGZvcI9FYNzZevL6nI/IJqmJggIn6e9axnuUM48EOYyeHSwiqQpgsx72AGht3A2JHmoCxk52N4wVoGC7fCZU7JQuCszxKcWZDyfNXJExgTVCF5kSIeH2jEReaGhobATuxj/ogi+YENlZHtJoaQUgam5ZeBjdmFAaV0ADUwyXDTTOFWdjB+5GPYFKYPBjegl4tEGTMF84Y7CE2wV6BVB2PumxHkr4BxSb/QJxCgEZqVA+eIoSWtFVeLeYPYylErOUhxKDhuDT7RZDTxZgLKccIcDxQe/fh6VCmCCie5w/AkIO2p3qlrOgMAcsAbj/CP4WaVVlot3SSxZWC28NzkPOmuTFpcKWZQ4BkqB5gVp0Or2Db+TEcMGS0yQfyEuIza3CyFYKpPlc/craioIRpLVcxoaY426DeuIh4fs0MZwjetLLZxayrGL2O+Uy0nBWRmRiyVz54+FlmZfSayn94MV54TR0eaLyP0EFnbR7rjsgT5627XXXcdlyiBi/eeeuqpVgwyd60t85WxKoG9LIHJ+/Relkjt+26SgJ1JdxiX9913n7868NBDDzEcTzjhBPtTPqG0jZsTA8WbcixpPOAzd8BPUHcgV+kdCr6KzACCMQBgVhEDdCISY6Eyr+HkgfQHakLYSGFGmHcsgYT0iInJGhDlYxanwMSmFXaBEGCwmIYyzqkW83RiFdIL7nUgWUKn0Iea0vGBnC9eDNvAGGAJlmidSUf4ExlevK1+CjhJAbFHwIyt6U3XyBA/nsJOPsECoXnRDhadiUnVAVQ6phbJi9VoS1p//fSUXhWC4SQtAnX8AmFMAeoBBIoQomDgguUcJ0bcCMasVzHv09IiiBdiV1GP8M9W1i9yTqSUcY8CBgwBuxN8daBaYfpQjhPDAKa/AZLPVQEJxOBWoGek+JVoo+mJ1agWrkw0d0zqjjgPHlzQFGteZnguCAQyJxMYO313V1ffGcoS7hQVGFMxGAZxMoH84wUotZqJ8ABCqEIIzUejpEWwSRKpLEokJg3T9reFK4PiL0sRmtD3RCfCKOwtQgSTAKTAL2UwavSWAqSD85FFkA5Yb6Ny8xHpqYV+nlKVBx980E9z1lyA27E9kfNSxYzw4oO51lyHDU1cLaXRiUTK01ESYSmvKlD++J5MQN8UzDwapZXtIqJ3rj/8wz/kYRfp1TvvYXGyC/lm4q9AwtvV99pulcAukECNAO+CQaxd2FIC2YHsRscdd5w/lcR2/PCHP3zjjTd6OYfZ+rKXvcznshid6q9+r2LvMko0zbjHmE9Yy5mJjRRWC6RhrzMpnIWDP0EgOWxx23NEk5LOWI5ldhTKEI5G2c0+eIM4UQMt4qLsS/ZWAWBbjtDmA+y52HYuANiVihJsOEAFZQWaRLQLPyQHM4xRBbgAyvnkZuHlpTVKzqCmEeRfYHpqqwinxfPy2NCi6BY0aCASKaLV8IzhMOgsTk0TI0sUFIQVfUTKGGFPFXeQYCpvkDNzXzHqimwClepqTtOiUoVCt9fdHIXVgkYkQEEhzQjNPZd8owmJhSzHkMsjA003CnYNG2At+ZsIIag5clDYU0qIvlAMOtTJACWdiiHevRfVKo/QLHMHZfgWqfQL/JOgkM3CkZUchUt+sLSOm6RALF1VkYbzLMCNihFsKdxKhI4+klXr0Vg/tR62mfIZYuxpsYc+rjgy8sFnqDId7Cm/4kdN4dMlQ8YZx8OoUxm7RfhB3LU4nRYPaGLV5KXVtNe840Ki/Ga0tnqaK494G+0m6ADPYLCtwT0KTwIGCym6lzVW66Vii5O5f2q61DXpfGaMzps+1hwaYsEcvcXS3AoS6Z05AvSCvuRppfIHjXyUMc7rHd27FQiwNlElsCYSqAB4TQaisrFcCdiTXPanyy677KKLLhIKvv766y+44IILL7zQSWlbl5BjopSKLZeVx6lrKCZ1rDH3YmE/XmTQ/+iwk+AcxisAwHJyxZBldkhDC4mLJjw1iGhvIfYZFCSexrSCoBgEeGAQsKtAKQee57MvI3lEXNLI+u4u89qXfsEDkbFEolKMPZfQmXZBbngby0OAXG/P5nmIHwNHtSJnP40CH4QDuuVA7Dx0Z6zDxnVOUpglw2FoJAgND4l5YsxPVjWLTbwOEgClOC8M1lS5qUvCLjQz0NElPwubzbTy8gv2LmWipUKFClBOVXKG38jK+cpXviJBmZWnYIGURjnUCpFuAh0GvfgYPacMzhEYDh1PRU/1Wi3ENZq0yd4lW7ogUZ4202laDtQqzhy87T0L+ZsIfQOip0yp7icMAJCDMc1MYo88FdBZvZaTut07sgYUEX30FPM9hbvVB+bgwaUt+Fx3gs976iopAC7YiBn+r7hUesqv8lEGwhhxS2UmQimU1rrRHIUWS6mVzInFFEATsFQGmJTmvJA2xbJ9tAgO/5mmaa8pTKM0gVvDgQ3epYnM9BBX3gXiupwJQtxP5TlrrKiWbqNm1nM4GmWzcix1Si842uw7YuNmnJ0ID4RveeRVCRs9nK/tI11z2ft8fJsP3Z/is5gcc8wxLIpnPvOZVp6d27W1lXllrEpgqRLYsDKX2kAlXiWwhhLY3Ms2Dq9+5CMf+dCHPuRoHDv75JNPhoQTIFpwMyvTaggdxjROuMZHsULSNe26GFKOcbLM2CLoM2plGo7ch49LumPvVwWTzL4HHnjAT7s+K40JxZTvtyyHt1VKahTAENnTC3YhkGmMYLYmWlPGyVggqoUuCpFtSQBg7EtiBzgZr6SEjVllPhPn5AAuuiRYtwAV05MmOzbZAksKiF6KyRCpb7SI3xpQcDHNTWVSdWd92bJGASRTi/AhgThcWNIGazOQ//1b8a98wvVIKZMWYScQQkSFajm80PQE5YBxqKmCbVVa5mY0U74Lcoj3oeiJt+vJBAViAQA4U6Th/4KQQ9ydEyGAPAlxYAeAW/woho34fRR2eJtaaoucQWv5vDaIl1iuwgRFzjqCPdWJC2oiMWPkqbbosKdbTX9lVGF265qzBrSLXnESFbbHTWgOADajpwJg+oYrHffBZ91J78ZlZg5q+FcLBiMo+FzC4uAaQkpd+BCwoeETu6OAoeQ2SiuFpj+4BeaVn3Mk6BIHFnXCMMUwxBR1dMSIbYuq7SbHdjKbuCw1VJZ3zE/se0+nijRMMQDejNMLJwIsQeXRrDR7mlvxo3SBLsG9N998sw6a4I6P+bimtU6/dm7XVizJ2lyVwFpJoALgtRqOysyqJWBvc3k30t4GDItu+fuu/kj9C1/4wph08+1t7GA2t+1/K6N2Nf1k6/DE27Dxo0XvPzOqmFYDO0UyarnHgmcz+VoV01AmWw1iYYWngwMJztFrrbP+IQT2B0+BdzIT0CikmIwMcQbu8ngobQ1MMCVZyWAeo5ZfQAAq3o0VcEhc4B+Iy9mxldGvDAsYThNfgqlwFbQGhU79tJK6dEmnVKQDfjY7ZWjYvoCTaytZQRdmmaewKCIpCTzgAeAU7efjYJcjFYyHfrOJrciW/DhNVE/4XT5p0BysgppaSR85buKYKBUloHfR2jSnfB6BpjBes1jSCjD3xdJhxYBbOYSjF0x/DfmpXYX1V9PxrGk00i5E0tBmLzfgcfdSgFiQ5UyhSxwQpjBfQ0rOJJwu8W5O2IbBXBOJh+FUJK6M3cSSXeLLzsGb2WdNEPjlTOGSAE2FH8s63GS+y7On6lJLcu4+xbwClJOOpaS+m0TyJZpem4HdLMxoy8RxAgId3iguFSo3kYGBlKcW0zRBccSYHfF/2Sk4giE63lIqPdPCjppdxvQxqTVtilnxEvKdysnaFtAplxl9xx133HTTTQ8//LB+vehFLwJ9nXcgn6UO0NqKpTJWJbBrJFAB8K4ZytqR+SWQrY5Zc88993hJ+Hd+53fYH74jDQk73cSysdXNtNsxLFgDzOI5rKL5uzGpZrpWHPPwhs1bQdZhidlu1TW9gDxhOTafKqxJdiHjbKvyk9ofJy+9gMPhFpYH5p3jZWOVL9BsDM9meG2c9hamgmG4XfgabgH5gB9Bg3DoUcgvg2HEAWBNs8sJZ2I/lAHJ2LuesvWF+xysBZiBxgRyJ9ZqZaYX7gxfHdQWUnpEYRDJoWs0C/BoVfdTSWCDNtJDP5EyZZjg0irCLca6W2tqThhTrCte7z+jXHSmSwokUAsFH7ZhvhcGWqRKE8x9cFSY3Uw3xVS3hvB8mSOZ+ISTVlIFHeNCb7tNb5WjIhT92c9+1hz0N6itS+ajXgTem849Et6K5iL5+LEsfPGLX8QMmNSSTJdykZVHUwt3q8+aoznQl78AejfpQLuyAqd1BaiZ+GTrXLGxo8aeGj7LS7yEpF2qtzhR0kSj6nEItp72/1RXAS1qyxLBqWdp9dP6FuS5AkG1OMSSBYHcOBM9Ij3uMGq81UGDdMGdwsehYyLA8CYX9ead0YXV96LVqfl+pmuW0IceegjuddRZH5/xjGcwBp7//Ofr7M7t2nwCqbWqBHarBCY4tndrV2u/qgS2kkC2NDalwK+/k+QUnNOYkLDXg5mYnL4nnXQSM2XqzheDQCsMLHc2qypMqGW787fql/zwzI5hlAhMMfJSWOQKYmG9sXJacT97P55VBN5EfYlFUE4UJSHxnraW9yi9SLSQkMXTWOHuThqLaTO5EnBTbHk8zEQZJwad3IhXrJU9rTobVzyBVgjAAngzERxeGGWWaLc8ubnk4w1mc0l/+ctfxhU8bHxbatCl0MxpiTqvuaYAKGh0pBFkSTdrtdKwHIM7ANgjeBJocXIVMAApcdtqpVV94s/+KpHAxIoyAyZDgdVLwZxtbgZsS0UTPB4EOmkegTEC/joSDwJsryRUE6xr0EmYJsicSK2QbSU2BuzgwXwrHiABkMIbaoQsLV7XqrLUn5gB6Tkp9F3T0NrU5lQxoApHMlPLDy+AcrOwSCZ9IxMLgtGhSEaToITcjQ7PXY/kkeJlsNyFICJ8JdKGrEfCKJvjTR6mpsMz1cISSULa2sr7L5ZcQzyVwvIK2Atc5qDVw4VDiu1vB+I5O0KzaeKi5NSAEBzL1wvnTfLBcOlmyR2U1lMXh6Bzzh/72McIQY8uvfRSR50NjX7t3K7toFGorFYJrEwCNQK8MlHXhnaSBMpeyAd82223sdTthT4Z7WJg6cnEvZCh0P3LRpAGIDSx/HZJBJ+gow0+oV0cMn3s8Sx+QTmGI7sHw2CMksBzmF+TLhgacgvqYETqAoaZqj6pvSYctoY1DOONyeiwPUwogufNUjmuVuHFf2oO5GCalzd7Q1M+U9ugNwUFnEMOxp0tLgLsiHt8HwPZQBP0pSTgTXwrzkVTmKBr5zkNTYtUpCET8BY1wmQKy2d6iiCxqjH5tKc9Tf648sEhgkM6aBY4vi6WmCP3LTawSuvMIAmPcqeQcTMZZf4OwEBDMLCneYMApG/RaUmm+1Ndo+NvuoLZRs1PF/xppCI0rcxKs9vKkBztGi+wUPgUktQ6lpqh7K3YyChjWOh7qzJDGOiWwRIvAG2xugpdShCRxVl+ClMnBXh2vPH+ghe8wHAQpkfR2KB3mFl3eGH4LwwfFwYESE+y6DnIQIe7TcsprczUqcjQqiVhHSAWDFMSBGeiM5GlUTLTL3eLv8nLbSr9pS99iU8Bt4TGOUXUBKs5XSBeOyPZjtL6thBJl01b70C5qA3d9rcShXyHeL23hefaaJVAlcDiEpjNf7l4e5VClcCOkABzxMWi8vckrrjiCt96ueWWW6688kqffHzqU58KBvu7SjHpFCs9YjGw4G2orGEmQg5blgBXKbbtCfYKqMNwYZqz9UW8hUpYtzjXBf1in8X62XZWuwxE4KxGoU5nd/kmvB6MWyXxLzQhZB2DrDk0XTorywkbROqCWJiPiTUx3BNgHIVPfQdEg0KTpoT6yEhtmqcepTkAz+iD4obb8WwnAtjlYpWOtuZ47RD5IKV6SgISiEvrY38wGb41TKnFngaDoQ7mNWqEowu4SjRvCA/Dy2BsYGHM4CSFu6Mjh1QZxwo4IkGMVokUJkBCoJyRBqxFmMpHPwe2XoqpCIMdccQR3Blhngp5akAxUIqtIME/4qUD6Bfyd5AEM9a34Elonxp3eVCSQiYffEq002LYlWe37tScaBqagCu91RCoRlbNitw61CwQjrabDtY6w5G6ailcDo/IhO48xbb8RPya1Frpgb1IW0YNA1FpY0dc/D6go/RAOq3Wl/cz/LhnXuMf834abspcuBUYDx5ewy4MFI6uueD8T3ziEw58ff7znzf6Djnb7v05XzNOZ0t/B9KsxaoEqgR2kAQqAN5Bg1VZXbUEsgUyXr0J7C2g97znPb/927/NSfzqzUvOiSee6Jg0gyYl3TmPcWlbZTGwqNgHq2Z6cHv6FTuPEc8UEOtg0wjleQFSdCvpEFtPOwBXzBSxX+Fr/PvJVsvfnMQ8u5xBg/81YR6uY5FTCWIHJPwxXnEVn8/9mZ/5mcEjNqUgL4aDi6UQVCkNZhtTxisRscLliAO7Aw+0lPSAGY98+40HJJpQKMyRAPwMSn9Fw1SgkbRhMkYMbvxQQhRUlwM1ZfqsyQg2OxWWonXJl+OnwcW/ezKhaFDBVV4nbhLpSSPlKSk5LmtYudJKYaM5HMmXWosnKI9pZX3TOyHT9BTZrZY4ITVrYNrl8nCpwqW4VfnhHBIO7I0grw3vg7d5OcJAWcrjkYnmXAMc+8gjj1BFoFfhT33qU8G3zkLzuRCsRUOLCpsmhGz4dFAt9zJ8w1nqlswIQt2QtgtN+4W7o8I8d5pzdWutSU6Yd+fQMY40kF9MmjD5FHAucE0TUmxNeB7IBp5dFPjee++1lfvehyUI4vVnEaHfspUPpFaLVQlUCexcCUyxVHZuxyrnVQIjSmDTXNnPhS/2C/RCMnfeeSe38amnnsre9ccAvSecPyaRkml6PU0c2z87j03GBIQ9AI9gSAkMiwa7Q2guERVlXPq4nn0hZ4wV3nRNXEXXWMDO4jLRwGCnGUexaBdUJ7Y4ZhAhZ7a4CJ44MG7lYJsRFiZLX+ZoDgAwWCqCu6iJo0obYsRZrloJzYBk/GjLo2BRsEQYM69KKsnYnc8WRGEq5/RNGU3nW1PlCDS8hGfC8ZTNDd6glsJTaW5XAcFeA1paNwQOVvhZpF0SpUwz4SkMRhTd0KijDT6zRGcgtAKADZm3W5sUlpfGmwvgse5ZAQ4//HD3NEd1MVyOQE9UWvOONJSHVA0rQSk2seTALmBGSbKSMMFpCIWhxojDMxaunPknN2fXLXFKUmP8A8AmmhnHd4ANqo5CeBP3k9ZBd+sG9hSDqAeytFUx1MxBSyiwLU1WJiZ+zMrF8f9WjY6Vj2EeAb4bV5xoxl0msdA9/QKJ6bwcTjQLCzXoau9YzIxFB7dIYfjBBx/0TpOvXfJ08D++9a1v9dUPa86Gaq6xS2IsOVQ6VQJVAkUC9R3gIoqaqBKYQQLZUNlMvpbhYsqzrkSDIeEjjzxSiIa5E4wxA9ElF43JAhliW3gNunCVNrP9K4NzCJ+VIx6oGEtR7MJT+cVEKIlSfU0S4Z8RzPpMAMo3WkCsjBcmt51zUnVBm1gK6GWm+6wu45IpGft4ESaRbb0DLAdC0HeYQdOwAdBLOUsgkaejGP0p/LnPfY7QnPJdnu8DDw4/C1AHqNMu788DKoKlui909pnPfEbCbFoeDz1Ky/r3Z2lIoMC8nsLNRwQIjIlmpxeEnDE1vt1zywrTVYoKNpdBlwmB+PKz2SdkKsiZdxHTSinWbHT0dHiwrAE8oG+8IaUVCDMAeCozhtUoCx2DTFMLF/qtBGbkIBXfFp0hSSxZppz4MMGl5QC3ioG7cI6EYywKOOTirU7LMk+QgfAUeudVwZIyhjhOQCgdRkVEmZxV9nSmK0yqopta8daMtEnEhcF/gezc3Z+JjTkKF84lrEjumNcFS4TDCxk7+eG/FNYQSTqCrpgy1Jsk162P4daKZ0GDe33S2Rro9R/64C1fQ6MX68bzHCNYq1QJVAnMIYEaAZ5DaLVKlcBju6aTYOeff/55553HFLh98/LRLKEPh6l8UNrLezF5t32LZQdweAubgLWAB0zrtc9EC1u8+clWY/dwiotNsc7LYPvSDKsIRPE02KlVt5TcxkT41zV8Cl+AItCd7gMkXhU2Xvm8zTZynigQBoJ+yUqQyqD4uBHfBJcE5vNoLCbRMeLRAWnWv3TO1ib0USJ7GTiDC64IXomP/dRP/VQkNvqYhhNGcyj7qe8Fk2MJJgTAoGKBGgwrMDoPPQS1mKdzN10oTGyl+TTpZg7MZroBFSCcfLrhzsVGgVvU5Jun9GdENwGaMDlwyG8CQGYRa7YL0RXtbeaPnsYJmu78EVC3RYyGCIzTT6oL2OSpRAQFJNMieFiCQPhW8G/Ro/80LbICSoN+1ZWvcCLA7oHQs/YCWZcVBj4PXETKUWeuHG3R2xWr7nD+sa0wP4t1kguANLzgLYeLgQCNsqFvMV9+qivkzg9iW7G06ruFi8bq+3AGllQy/TIvrGAf//jH77rrLu99GJFzzjnHvrz+R9CXJJZKtkqgSqApgRoBbkqjpqsE5peATdcl7CAgfMcddzDXWGn+mPD2ImEspUtsaEFR1olYX+zXYsr09LlUV9ifzAGKFA5CZu6w8HZEZAPPIhXiohLsXUYqEMh083OIEBQb8WqKNGTlsOChX3YkJIND7gnnt+fjDTXGK8qGpsm2fKhGONELkyiLhGhRgLFgzlI4HGIG0pDGDIO4PJ2Pq1J9eELT4ntC1j/5kz9pvFbWbjiEChjQhDOrhkd6IQLH8ha58rPZBSOua/KBTBFL32qS5qyBckER4BMDPhztDvXBUZ7CJ0g1icjUHIDHhxKzXs6CF+VxSD7fuEbT0Gux1ejwJrBHpVUHmOcgojqNxUwCkgCtn1SXTIKBIbccXrC48Q5AsNSV9JyqkCZPElNFGR4fbMPJSJlihG9wNzq2f7+xgN986CGjMLB3GWh1yV91LaoIAeYMfIggPpDa6otRrXwWniiMEQbIyicJ0i8/hzCvMIWxL1hdORzFVC0poTCk+ri9LiPiw+lwr3ivoTciDjkL+cL28ynhuExWalUCVQJrIoEKgNdkICobu0cCtmGXTwoJCQcJi6E5HW0bdp4zr0utwDjAA+PMO1oxYZ0pZaDEgJ6vdQQZ60IxwsKMSAPGpx40xQZCcz6yKxh4nGuF3cxOhS4AG6iGWMbCDKN0AZOAEDsSk6QawYZz9BeXbSEVboFhEHciAE4B5QW1YAma49AgvCEmqXwrXDxK37cioukcJXWYYg0PWG7FdhGg8DVHhqtbUtfgujKsGR3hShjM4WcTysleiNcwgXaQcPdQQBnQAoDTSqHZbXRIjuawbZQF/w36gtSGtNgqU/qlaWluGt4rTiteAF5FnyEEaSDYuAOsaTAb/EY5aYh8jjkvFKDpxY1AXGnS5s0JQT/5C1TkvwOSneB11xC3YDmP0GJp4k9V8GPCah0R1SHw9Q/5YpscXDgnE13jFXVexkWk8id2tj8TTRe0yeOQSLuwsCHLZtdfd/GnmkbE4umcs29z0JDgXq5nl5dNKu5dXMiVQpXA7pNABcC7b0xrj9ZFAptWwQYS5o22MTPjhHeOPvpof5TSF7NYXZt2yDwGR38PtcuAZjgycbj5f/qnf5pxNp9l020onWJBQpJsJsaTHCY7kM/0FKJZUqe6nMyag09VoF+cA5mCivlqDmvJuGxXQLjVC0wy1hnuLqwKgbIjybmLf1oVZ/qpFVY7+v3nZhUTNwMtRNW8C+r7Mf76F4wxU1uLFMYAhwsnjpdgVwyACUffHQE1EPPNHcwTmmnu6grBU5NUvugZlEvC0hrSqJdmTVjvHvvJBxEA3IrnK6xkYsjCobhNlNgEBEK6zU3NwU/KWDEgcweJ+3VjKsH5CmDDZYaKQjv6TvgUFbYxKWAqCMeb4fxWuilfOJdXy8ucus/Zp6IwrDvJqMhrQ4amNp+CThGyRzx3xMXRQLY0imrRZ0tBuO0faNUVQxx4dgBb2jJidLRi6dOinH4KaWX1d5y7SIBgRX35VqgTOeh+1nBsj8W5hqi0L9hxCnAy8lbMeoZioHw0pCSf7EMPPWSHve+++3SQ3lqjKu4dKMNarEpgL0ugAuC9PPq17yuSwIb1sRkuAINdAixsYhj42GOPPeqoo3LubhT7Qyu6xMRx4JktyIRlEEBQy3CBpy3NAcP+4ggLVVq/xLuY7zoVzLYiEc/SzMZgbAoKh+xmfwnDTxYtQbmPCzVn4atdFjJ37JztzkZ3hpORvXreNkW1oVSwlq/I+B7SxGhkm/XxfoeBMjtKYo4WzAszgjCHECkfwaIVQ8rPwY8qeuc8BZSbdy9DRKYW0yhox8SHafOz2Yp5xzchpzxSkZJMDDg3K7bSkbCvGIBwcQmlQCHbKr+MnyRAt3nTIDRuO2sX1ORP7+gL9ngK7r77bvnQpouvyoVbl9lx0kknmbakRBqWVodchHYJB0K2EHH/AczhGakibb3zUxVhT0Pc0ynFPMUD/YEYHbr203lpuhQXBlKuHgrb+AiHYKFem78QL07AfjHz4iUZnXPichGU8LhNwXSj2wZF06O0hThSHB/+tJU/4Wv1pi2cHdzKLo6kjcFY1+HYRk2oTVcJVAm0JFABcEsg9WeVwBIlsGkbHGTD2bl9mUN8j53HH++jWd4W5ozX9iKbN/p52RURtiz0yw3PEGQGMRYXodwjlHSKCQsz8P2LkGjI+UNgOAYi03ZJTfdwNfAR4bDVXCwqHWHUGgVoU2IgheUVw085FI3PwOCl4rFuX4wgXcWGRwKDEtiATJ7znOesbEwBD7Y7zKNp19ztOoshcjjwNEQBwOJ7XbGMlWOI4RO9A1xBOEo4kD0MZN5JYBXeM9lJJtdw9hDRrpizaCpQlNjg8OpjlQS/uXssHYG+lNwcTMwfh7wAXicRd8WqqUpiVAJw9QoG6HvgwAErDKwFmqJAM8WKyUTsUWiXJ06/8h0vpEBB1fPNbT9J3grZP9nRhL0RzCwQaceeS8Wxuj8uHf0qCy+e4+AzfXgTqFn5ph1VGbfdJjU88E2Qm4NImBEKbn7nvFlyYBpBFxjvhLOtM99ItxDZOoV8jfWsmj+w3VqsSqBKYLdKoALg3TqytV9rLQF7Of5s5yIbvtXhRUfmgo/9eFXYjl5shYE2SqilwwxZ1iSPuHCKHBSAUvainIHU5hNceHB37JOJyX5Fh7kpJsPwcuZwbb+YFc5Z3t6ldLG2E94xIi6GY8Q4n1gWr4U9Viyb3nlLMnzKU56CpszVGHyM19///d8XJ8cDDKBdSgtRxF+zVI0qotOucQGQYI+f//mfn9WfkvFFDYAPAC6Ue/jXqPef5/gzSIX4kEThjZwdo+BF0kEQZUjdlEGBcADgOV5oVzczlM6DRpaI5bnJJvaodB9S8sVvIJbAgaVyaNwAKYM9zgsJOmC1VAzb1jc86zgkLMDIb8VJZMICRV/96ldprNgvjKqWR9YfDJTmpHuGPsVQ4J1EIYdEJDQHUSd22lN9Yk9XkIltWmSpt+CbpOKuGNaupcPEKUdyVsZ5xGiAHEcCUK1dRQgDeQgFd15dGyXo62R1XMYOT1mCeIvQHEittF4TVQJVAlUCJFABcFWDKoHtlED2eMGN+++/3x7/6U9/WhTowIEDkLAN/qlPfWpiFz17PApiFAxEn00K6E1/ShX2EMPRt0BKzlI7nB5pSwKKcMgwR+/Y1sI1MUZH4SQN6cso1NBB0OUtQe8N+inWlK/4snpxDneN2FaaMzTaYsH72X9hzCgzcIXFlITNYDnBSXb5WN2fyIB2k49V79qxO+kqpwakIQjmOPTyWtdZyEfrrHnuiUT/BP2Ke2giw91MgUEXPlEziC5pFrm4Xw/zqwHA4ZaQOa3MFIiFF6yHq27v1AWbuSeGh45Lo9Tbq7ZUCPSl5PJnarrLzPAcbFMhLgm9dmwVdrVGZZk67LDDgEyMgUwC/mgWJbTKffCDH3SC1yP6wCUEXD396U+nIbQRMLZamk2+7aSiV4itNqrLyazpZy+tuGPD3A+GtBSrRectrdGcfiLb8hTPNJzGumfK6LITAcbUgKZfKxvZlgTSejLNYueiIfP+qZcqOvKlL32JvwPuheGtk14asif6iEZed9+uHrU6WH9WCVQJ7FAJrOkZnh0qzcp2lcCsEsgubkc/8cQT/akGu74/XZiw8Lvf/W75dn2H+txzdK2167MVuPxFPFjAYpVsi1KAfckewg/8CUuImUizCxmXpcys3A4p3yTOoHeBlFqHoNyFU0RjsI0rkHgqvO9pkdHM0o253FOs/xFOyBzPQZISsa7k443VG7bJVkcY2SNKTxOMbO2KdzWFNpFhBcpBTaY/G93bklCT7jvrjr2pFCaSnZpZyCbhDlQQEZXDuep64V6KTSU4vAA8I/JTymtIrwWUHCgoxzjL056EkFFEZ0ZIJ6QPzKgS5nvqtsoor+LEWnNLADUnJgADajD1mIbCJGDiFGXAoeBefxdaTwv/RhCu8LXnRQ6Wt4hP/al1i8DGu7x/8iecfbojh1RhVLDN6Bgj65WDJOZaAHBkq5icX/iFX7AYmpuCxtY3mgD5O2drkSEWOmOUqahMP4frCeLgGdwLRlpL9YKSR1W0njVhatdWVgC32rJwkSShSfMXkAZxQez6Hhde+JlbM0fpTmkdz8RrC3BGSZw/f4+g2YQCLnPBme17772XL5gOwPDHHHOMrdDh/DIczVo1XSVQJVAlMJ8EagR4PrnVWlUCy5IAIwBpd/GZe+65hykg3MdAFPdgCrgEwVgVLmXyN0I8FUWBhZIfzphxAibSKZlMZgcgJyc/V3NPj7TF2GW0CV1Ke/8ZdGe7wzO4ihk3E2MsXccmhfIWMU/xBnuAE6KprdbDNrPYEdOYxVjNCWTYm5muF60qA+UZyu74Nxw5HDiclIr6Dj8D5pV0AABAAElEQVT88R//sbAwTpiJFIDFP5CBOYoxtcEViEJzGABREKF4Eoxv0pM/vAtDGKAqbGUlYRvKLAgvIfhMf1jDOhsxatRFIMCPcXFhKQnjhSt1DZ9MA82j4U/7qMhrAHAqZpb5mUtbEo7gUktVnP2mWnkT1SOtGKy4b7geuCHkkMBG8/v3qwJ4qA6lc+7I13TuDHc8e0SGWpTvSi133eTzQu3nfu7nEJGTtty7FyJinhQmZ3q7BfpzVM94WSsydsqnC/0Vx3qqs0K+IrfkQCzQGqeS+B4VwobxyrAaJmFtT7NWRFZ4sICQLU2Ao8jB2EHCzrZwBqlroJEVEFae5viZ96K3Yp40PMqISBhug24pMMomFLAtM8OxFYUV52PYRc9hXb4z+JDCUBtMmpsESG5haa3YDks4lzD0wv5mq13MazLh0yNz02ec7Xf+hBUV9efWuH1dOWOiWEquWOC7rDl7mc9lv/SlL6UqO6Jr9keKbbWvo78jxmvHMVkB8I4bssrwHpJAjAa2jgPSkLAzqDAA49W3o12sc3YeO95rb9KtTYIZwc4gLDhTsXwKleW9Snu3NVS6w/xl1EK88Buzlb8/fWS6OcLN9MzPVl+adBSAFnRNB+F/qAN4Vh6FnlpNCs00asAMW7wLgEsxZbRFjIoFbH/hC1+QGTjEBNfuTE2LhOi4KvBAwUJO9kqXRvsTWlcAV0gRgp8iY0ZWzirHV7ugC1AKLYjqdJWwpxfpQqsAmYD0ieOBi+TjAinRJx92v8izHAOhJOji/ou/+Iu6bI44MBlqMkP82c9+NjxjyvjbOR6RlRAZzCD9tKc9TQIF00phVQonIqJCiFpXS1vJD0GTiJZK/97v/R6Ellp5BK2x2qW5qyi2WoUN2v6zP/uzHgloi1uGIJ4NN6WlRYAc0WEV/HMJzLrTK48Uo+pk4mdUJQAY3gudJufJ2eqOAUd5Y1M6KgLnD6+7Fc2B+dRS6/ri7nsH7hwQoK8eeWQpcMk0Ijg0Xma0dNhzJ9jm1FASUqUV0FQOkqCsGEBIdKeffnqTq24fVXcZXzoTz4j3rhXTNMXLyw7dWk2aK07jVotYAvsBRfKRo8t0hqKS5EBuQyekVtyF0hwe8O9VXr4M3jSQzNbme84WMXPTIgb02tqs6tvLZ2F4NyWs1c6YgJTN2bTOHbzyyisZDNdff/1ADV/nvlTe1lAC9Qj0Gg5KZalK4DEJZN0X7Tl18xLTePjhh3M87P3vfz8rGSpwdhp4gxAUzpXKLCRGs7SEu8KePkZ3+/5jzUMd2gcYXCx7hixLFLxkCocviJQxJMyFZzldtgEMllMKMwoV0EdWcrdkyky8s8CYAgyyxOUCWoQH4ZBWeWRZDGCMfGn8s7OxDQKJsasCqAAkMgcyAL/l2DOb2xilm61G+3+mIVzxhgASFENaX0AskhEVz+HPgfz0t9X/VKyMJMWiYUIBN00bi7RbDG4UpA29e3SS9LgAjL6L/F0UGPJUEilWfrNRIEcgSI5ihB9NJu0gxpQEmSABQigXNpTxlKjhTz+9Xqt1EV3pvBSqMPysDIbDs7RMd2PkS+YSLmx76q5icrxfSl1dMnOlLU/FtcjEI6zmnrY8cjDVYGUKeCqhFZNXR/RRZAypNKSwbtIrCd4r2J6QjbJWCEFn84UnWscpgEi5FNjoyeMuAAkUwiGMwZrEknmnUfnLvtKugQb7CUGolgCdZNHfSBJ7EqC4yaUYrWCg66aKek0l9GujM52Fi2RclI1DxPJIeiAT+z6j0C2fniJL5iQG91pwDIHWzV8yUdHouLaqu2xZtehjVQ7/CzlYJcRL/ZSppwbdBah3V6oWkdZPvTYKVoZW/mp+Yj6XUebE+chHPmLxNP2NPsTrZBM3hKmKmTUZgtWIZUgr5JZii0sGqRCRoPbSrtCnbHKiVMmUT99MmTIvUr083ai8f79aZdRCyholx+Qt+eE/LSojYZpnE2xSkFZSAVdJlxwTQZrT0BKdWhqyMMrJplBIKcaZZbVXEv98LvqluSw7ntarSoAEagS4qkGVwE6SgI0h7Do3yHfuAolFRFn/THmXv9TKcFQm+4cEnGbngJRKzoo7jGe7HVtHu80oXHay9Igxij3blWLe/rJBSrPVWMau5talvD3PrqaMwBHzN/to2aQH9o4dhhmFI5awIb4X6fUQSUl8wt5sSoa7HBCLSaqWjuC8kJ1IJxTcm0eg+6tMpNPKRFBoxUvLEsLsLEvCseUvddy15RJ80xfWksCdULCRlQnKyicoo89SMWQCVkG5gsbCd8rgDYfMF2MN1PkJnwAn8QvIz4UgiKJ8U0rUQHlo0D2PikDklPSaJFoctrjyNJCYlFii7n7COTrCTUPPKRuNlR986OArNTO+XiVokpKZFUBJb8gDPCw/MrRcGAs/+Ym4lpB1NSuOm9Yd05NjKzqgLbOY16CnUUiPSlAeZVSHbA8cOECdMLZVrRQjJToPItKEbmFlZGIGEZdWHN+QMEFoo6trpo8rilmpYbV5yNlPFCzsxpFK6M7U5WWrFnlYLG68M1sVWEZ+5G+qcpCJ9FrbqSI9pMAbp5iOOsqSazg0nfsyeNjpNMnQVCJDHp9FpMRD5OgHNIjIHXfcIb5688030/8jjzzyV37lV2688UZLhA+RXHXVVdZkjtQzzjjDp+mtHpxNH/7whznvLr74Yvpz5plnWpSM4Gtf+1oHLtQSurcQOb7++te/np/LQDM5PD3//PObmJMy8yr6tKdvm6llgvODIG6yY8DZmfSOVpx77rnO77zlLW/h0r3hhhvssxgzzXFrebTNWRmUcWzeTqGtCy+88OyzzzbQ73znO1EzzTndLr30UrPmvPPO4083pzDpE3o7XRkq/yNKoEaARxRmJVUlsHQJlP1PoMl2eM4550Ad3iFkWPh2yPve9z4rPjDmOzH+lo/djrXE1HMtnbPeBux2gI2NHP8AeUCj11aDXVVN6EbCfuadH4Vt+bYxRr9Agb2W1a66HdSGCg4pycR3Z/XmWLL0TJfIj00UTZEx26ptGG8xLvvpZAjI2f7t4n2wMaMmn9HAH8EiRw3x7P0p36SZHE2XzG6Z8mimhMPwrAF7P5bYE8QLURDdTERahZt85hETRMJIeaQtxhDzIplGFloTx1PAoGPDyCppyEis6CFwzpAido8UaHafHyGuBBSa+d2fNMR4GYLuIznrdrX60mLPU9rC1mzl+8mzIwCeGLifomecBTkjSuZeSudiyBU3E5EqZkSAz1BL0wYL+iVbCdjYcFAPFzSSdvs5DKmBd3OcGWpp0imsWqnK0G9FgYbkNG+TjWZ6YkUTjRZ1MWGU1h0nrGd6aFW0UBCdVdEMnYiWJzaxgkx8ZmXTX7MJVpRDdIYeq3wH+oiNIatTl9vA5uRrJcsm4tpydcsvnoN5l7ZA7gceeMDGBEdRSLPexvS2t73NCg+TaGhJDCzehXWjYBATVl2cMePy3ve+F6q88847LZ5WCY5gCmaw7LbWmZNPPpmv6o1vfKO54zCO9eTXfu3XAFSw1jsgt9xyCwBsm7PmwLEAMPMjb3BYjniXXv3qV4PQqDnr8ZKXvISTrvBsuKk3fVDFBIRgoVZE6KSNoxRTptVZwNXxIo2iwD0a5dHQO97xDprMpeL4gMPzDrZQM58N92eirXWE5ojctddeS+sopEWgNFETVQIkUAFwVYMqgZ0qgVgPNgMeU+5S3bCZ2V1cl19++ete9zrGE6+n1d89FrMy22JzsDuzaTUjwLHqWtLHnjCFC8K3xdqSXaxnGxh7mmkIzuVitbewU4tU/08bpwtZLbrmIKWWJmzkDLu0ZfdFRyDLBVeQP7EHG3eZUZ3Fr1j30Uw5uiA4APcKZ6HJaBZKBcsBUaY/9hRg9JAejBGe++krr0CRjLSjs6wHlBFB2bgIuLFIPGKO8FMYi+Bb3THWoS8UwJzqChYPcWGkWOs+lcOw16yVnKkVm1V2ULrZr/SU3iaTbcq+7ApE74y7WQ8Su6ilS8IcVNHwMRmbEkBHZD7YWKjZIKoeIJ1iTR6aFZtpbNBDdeE0uJr1aV6wsJFSbCoFq0FzQeAdazLQbKik0QQOtVu8A3kkh/NFR7hIqKtM/Oi+hCq4mspMaWJ5CUy6jIUZlAtEt+6ZZVC9RcNwmFNhYBGGOROJIn0nBG1JQzJkMmLv9AU1dzL3mrc9yBcrLBpmuiMJ9iNB7BLAXKQ7I/K85qQI07ptyCRgy6zhRGexnTo1tuoaUiKiPjrw4IMPWqgzEKidddZZpq0Ny3siQC8A7E9RCBGbzki95jWvsdpz74oV87mbUwZXgFcBCoyUL8+lRTuaP5WHLORp72OQNAFwyrziFa+g5MrgxDS3NG3FbclH6rbbbhO1Fj3GZNi2mkHywtrws12GZzBvdggyQ7/q6pHWRbmpun51OSn0a2JvSqAC4L057rXXu0oC9oNsCSwM5p3NzLb05S9/mRXCAX/77bczRlk8NgYXXykzS/9TZQWC0JB91GX3lWbm9sdsC2PsP3FCV5i0U9r7vazrksO0YiPGjEN57h7ZR5FdRA6FYcicKcCZjUPGHwjKHBSKV4AFs4m4N14iTXn3fjkMZ4lk2LUAcCFOLEwQg65RmSxgETnioh4B5IXntEKALsYWKycXMHPYYYdF+ACwvjBuCMo48kcE1ajLsGBnTES5xXxPE2PdscemR40dphfBcowbLLU6NVaLa0XH8BXhh7GJvTbuG26k7/ouw9rin717xBFHkF65+DViUlsofAyZqY2mHFPDRQ2iqEiVtkpiQ282AaeTFMaF6ckMNfTUPrpXSrbY6PmpynB4pjAGUMO8Kx1xIJ8CY+PAgQPmheMYYWMOZnr4nPURPsMAPs0prj3nURGRb0zNPuNFaOaUzLFYRZmbAEEBvYyptAnrvvgVyZuSonNAESzkcAHOqcEpp5wC9IJSWQfG6s7iPO8gChb2fBAhmpO0tQ4gnK8XFE8gV2C2zAh0KENZq6VzRoBHtbhu7WsqmlDatSP4JAEfx6te9SqfI3Ea2ccCr7vuuvBTlibDjZRaXT5tQ8mUsC1mCpRi0ajyM4nTTjvNLgPKvvKVr3Sm6dZbb6XJwDBA7o55pk4JGuewCQaQcsTa39C64IILnFSC2CWqHrZku5d/jrMI7mUJ1r5XCayVBLK+2xJ859blIJMAnRNBrBN/ZOJd73qXnUOEEBK2eTgxC46qklor6IhN0U42vKHCmM3MfgxeMtY3o8L/W5BHhJML2SMWGARoQ7UBb/ZmhqN9cxsT3V6kabaFy0lLFidPhEwc+rKX6DdLVHM6wsydlc9Wc2iWnKTLPcQ9jajlQ60aJTcQhZENykIFcA65URUgR2E2DRNHInTUBZBiVXhPj1XBLqct8l1pWheYOCTPKpJO5rLv7DOMaUXTGi3pZbe7JvRnjWN0x4V6iMC4MtDNfiksmkorzDLKwAg2yzJBFLaGWD02UPXjF03g6xHnUVJdMaVYwNJFSZr0x02Hf2pQ4r202rkDrSSAbBqm+7mP2/pwavh0EamZgiWSxw+5mVwWBKtWFq4QHJ3VjBXikIaZLrw8nPOJJdMdzPOVAD+0grPVQsdR4tCBtzHtLHq0pO5MZGm3Zlq3OTH1ztJtPlq3pRdx19p/+cR9cswq6oVeS0FXdNFAo+kYM3ipgATNCR42xN4HVp2fy3bscDLeGBLD9dZWiAFk87VLiwY3sd7RK5mms58trvAJA7t4BDjv/DEn2yserrnmGu3SPeBWlcJDSZhfb3rTmzz6gz/4A0eyHd4ey+nc4rD+3IkSqAB4J45a5blKYLoEyh7AffvCzcsG47wQJ73L28JveMMbWKter7WlMVn47LOzlorT25i9RD5nOnu9J/Y2PIvquOA34CfcsibtpsgKHOkv88u2ylif2pepBYazagtn42o6NN3xUKqDjowPGNiFMRuzb4rEpChlZk2wAJz7Si2D68tS7mii3CKFEwPNAuawdxcfU5EAlWdg5TCziowDcXUWM3OHFeLusBxS+iKfE1154s1JszQhbMXDUqL0rXaX8RMUd6Es9gtl0YRltLKeNEdUVx3sUiPP5lDSEOMbH5lYDb2FjR1wUJEm5O6FPaDOrGSS0uouzSVJEgNwr9lEUeOmMfdLICtugpUxM7GPOCS0fMgA9LVYyXFIJwDYfARvCgJZHqtdyt2cifw3M3HuAk4sCHGnetvTimdhccL57W9/OyxUzpbPQb/ZVk1HAsRoPtJqkrd3+ElhFpctfxYD4LnPfS4wGXA4UeDezr3ooovAYOoq2OuDWDEPDPTxxx/vlWCcSHvTyutXVoaJRCZmQq18qfYRR6Bf9rKX6ZoNyN33qxwbga7N61bFm266iRx4tUx2eNsG51Lsox/9qINOYrxUsVXFT3LzHSy+fpsypbU6EWC3WM3ZsxKoAHjPDn3t+B6SQHZNdy5kO5mLKePjUvz34n5vfvObbWNAhdPR3Ku+UOIjRjYkAlp8u21KeRRqhQjjACpD3z6nX9CarZGh6f1bON+2rS8eMY5jPahY6ja5GittDxZfLaEPZJvNsQ4xKVDmNLLT0X/xF39hM4Y5FQMh7O5zeB/QDxSEUlgz8W0jq9fIuhtlxF2KaQuyZXCHK6iGiOQwRCKBhM7CtrrAPDgBSEgjDvkwIDxNiC9NpJVyl3A1e52cce9d+t2cUVqkSKBddGwrghEOBnaNadUUpimW4dZ9+gnq6C8gx4dCk91lAsncZ+xLYUAKQwkpWy7Vm9S2kuHA/Kgcpw/NjKrDlgAwDlnDYr8BlqiN2OhA3lIsHBIIJkECPx0W5S+QMN1waAZluuHQejUT8cULw94Fbw+hhm2XNUQA8HOf+5wh9gkGp1rMCDvFJZdcwnO6pJ1iCHt7qowtjAot2GUzxbeUEaGEvp8M3xpWDlCZBppOuts07VPSPoVldsOWNlAFnD2W6bII+MuLLnSATwj26KOPDmM2i5JGCsYum0uTc7j3Ax/4gGMj2rrssss8sm54v9c3n0WnTz31VHyaLPIhXpNawk8Y+Oqrr7ZheZ/cZ01k+hwXPz6WHLYHoQ8cOKBRLGk03fFIebjaDmj/dfZ7JqCuiXrtbglsaPzu7mHtXZVAlcBWEsj0F6bwgigwbDv0lwwAOfal49OQsC2KiRND1nayFZ11yC9LGdMcErZ5815jTFxUfBiSgQALCJQ/VndKu9Cvq8DIrZpI+YCHgFW7PiMDbz7vUbzUU9kr7WpIf+FqMfzUArB5x5mqRMEcV4Bx4KkqSjKCmQVNU6C0pQB716WKdxSFd2LBMOK53nUtJd2VScBZSZccdQmZzAu1PFreXR+1BWstqUWTQji0ZcPpJneATvFZuPspMM48ZTUur6frRlmv03cQ1ORidHIWbDhavva1xDk9deAwHhPaaIzYuOnFrIOVtrh4TC5KqEXLEQuYGst0+jEm8qxkxxJpEYUZjTcXtczHhDQBn2OSHMy4cLhdfA7sr+64LBrco7YDlw/2WklgEgNqR+Ds8AZNVo8178vALq9/MSNSmFxE5oUOIhvD/Pgb6SGezJKWUKC0K5GmS2azfJ5uUNyk2XyUWqHjqY/tcbh7ObxQbpEt+RMToe9Rs4lSMpldHpoFSromqgRqBLjqQJXA3pVA9h7WDOPG10q8vgWJebOLs5/p85u/+ZsFDPP7eh3I+4EKq5WKayW4whKDuNjE9kKmG6wC+LkwDBhDg05iexT+S8X5usN9kG+TsBrBQoYjOqDjVu9qprlwqKQqThQz7rHn7invA6dDcF2YnMihzNIFo6Y641v8WaYAuJ9QGUSaC7X0rpzQ3qqzgHQEhX4gh5IlrF04kQiHEAgl0YpiRL0V2WXkp9HRKRvH0k3Ei5BLJhHJF08bvemdQpDS+vucuCUrV/Qf6gONqCJ3iSsqBw87da8k3TApNn1Q/y5guMiz2+ui9hJAteP6CEqrYuJoSKNgMP+Rq1t9BTlFK7TFA+I0vo4nOqqPie7iVmg6zPR0dgXc9jehLy7rmOPNvmUF8boIPKDXH5vhBjWyiUCuc0f6u7lzn44l8yYd6ebPCKeV0/rZLdMq4GfJKYmm2KlZl8hWOc2KJd0k20w3C5T8kihPa6JKoCmBJ0yoZm5NVwlUCayDBNh5gh7+AIBPazL4RFqe//znNz845Kyd+CHIyqb0Qk7+HEh33WewstrFshgx3afdnmajYtIJgjkjDQz7M/dCHJCkvzvq/JsLYHYWLtSG0Oy2soKcsuPqi8iMA9JQIqs06MWrsAQYo5y1pxdzdARlgFNfJFyxfYFY0h5ILUyKFBlrmBOHIi3oGDVhWOATcthk7Qn2VPGUwQ2s6pQE+1X0lSPDI8ar+JjRcY+Qh3NilB0xVQsnZJLuhAG+g0KnCJZUhbl8mTMNuZcyJWdnJXzvOrHlsK2neiR+zqEgx0+H99yF9YyUHIfDFYigDBah7az+zsGtWUMIKtI6eptT4oKcZEIURTekzQin/cnTxVciRy3qDccqFhwrJ/kSyaTP1C9vBoLQliAaiDjoG8RbyquysgtvLvwYdxce8rlmorAC4FCs2wqMybC0LUwOlEb6wnXl4DrQ65Lw01Ba4XP2p4LegcKsxQZKgNbZ42yO2+W3GshnLbZHJPDEXrVHOly7WSWwUyTAOvTdZu/YsLB9uMLmAcT6i3z++F5MK/bKcccdxzp0/BJosbUI4frsc8vwgpS8V8Nuc1jRazPin60C/QLRrgIsXXEY74AxlRyT1hZA7kxswLAPusT2LYirn+a2PE1HNC2BT3eBDvElCQLhQYCEBWMZsmFvoJQKWSa7a+oR6J6+h5TxIlvFUPMOoYSoO2TFsHbHeZ76rCXk6SlcimfMewqEhMhA5lvMlL7IFw5KoymzFUFIIAB4qwKtJtb/J4eC2aQ7ugbWJlzp27mQXmLj5qNeAIEmlDhnzkKnXyZXCbavf0/n5rDoCVn5Enu/+yOF3XNI2IxTnhpz0zzyyCP01tLh4l9ALT4gsBlvkFiOUZQZsXodKz2FyR3zphIWUrzhBFYUGpUoHTQlKUA5mLB6bvsH1IaiAB0m9k3M+7C3GKixae5PEFvJvfaymk8h9vNZn1YJVAlUCaxAAhUAr0DItYkqgZklwKjy6RF2CYvkhhtuSMQSABZk8BZNrC4fk7jqqqt8NfGXf/mXAdRzzz3Xn+Pz9+uPOuqopu3l88hQsWL+Vp5Pd77+9a9vPp2Js2Lq+ZufAcNsKcf/5PvIBBMKw4KQCd2sMxjW6/TF+5yMWpa3O4QjgJNTi054Qj6AJQiUjkwVWmz38qLsTIKdWJjByuwOWc4OZfAAX5GzNHgsqgY5yBnI4cRWJmYSTgDwVge5Sy2KJ7KX+HnJ3NGJKIYuwDNOW0Bo6Q7kJjjc6hqPiVHgkijvAE/VkxaFHf0TmgoAHtLroOVySsVP7wtAxQSuOh2m8NJmHK2meBQ7vp5Viiij787NYfaBu77EgwHOjhy0sSY43EEriuODA8jL9srA7TkloTtqrZ75rqCIFMR1SJvXkjcN7s3fzaauXmmxkrg/+clPHn0B6XJSc6oEqgSqBNZKAhUAr9VwVGaqBB6TAMPll37plx566CGHYJmDsS+ZZS5pF0QkLAxtfvrTn04dpiRzXM7999+vQBElQ/MFL3iBvwgPHqviE9DNp6XYrAmcpAqEBgbDw+6ixPksjeNzwLBj0v5CIGNxg+MGS7O2tdTypSPifo6IM2GBOl+MjDku+soQZ/X6ECWTt6cXsHT63lNm1o7A5AAG4zuRaiY1YZJqs4lmelb6W5UnE3Yzs1iLRT4KL6OtrXjY9vx8BCva2xSCI690o7wDvAc/glWGhn5Se2cahyiGhchxaOHfFI5IUbCAuHigzD7Ll8hqoqzOSQaYleaWmsCPvphucK8rUWgM+GAPxqwJWC2OntJftTAf/xQYrIBQtqcQ5iqZL5LBj4tTBlyHeOFe72BbPXTBGzSO6gjzutsIUqV0pFCoiSqBKoEqgb0ggQqA98Io1z7uPAmIPziZ5g8J+Et3oAjbC/qCRoq94jtV/grFW9/6Voeik8kof85zngOIMjRzbjPdZg8Byf5gvdjLr//6r8c+G1cimghBGNKLtZCwd4a9VCZkzQoUHGbXJjjs61P5jNa4DIxLTXeY4IxgdqQ765Zs8W9EPAJ4vMEIFeuIdsuIjMWDJnLmE2UBf5I07oJObFanoOHzZTTawzx+RJsJxPHO0Tvb0+62P6LMhjgCL8yQBgDsHgAsIQoax0QpUxNNCZg7wK0c8wgSc5hc2gIF6EadyFAOhAl/OsMv7fSB88ZcD45j5HT06IqXRqFcJ7EBV1FQTeDT2oUBUWg+L4eZ3eP2Ut5TaRBXgXKFjp8SPCbmyOqPQJMw6fkUmbUikV5rlExRdP5H/jKIl99Bpwrbo8uzUK6JKoEqgSqBHSGB+hXoHTFMlck9JwFHcIFeRvYFF1xw/fXXM9GAH3993h/sDe5i4hAKu62Ihk3jp9Al1MQOa+YLYriSswzTp9BkYzlT59IWi5AVKwrhYpndfvvtMXB9OcbbfULEznIzcCHkUr3wvL0J/DBzGbsx1mMls2vlw4H5/i22i5XMRs+gLMg2mxV9QhMoI0P0USaugnvR3xZZJfy1YO92XPUW1Cn8B6SVn9wiJV0TXQnw5uRl9Tyi21YGwBIATk5U2gzKJPIU9OUB5MhzyTQNLWhN/NZtZWAO4vyJHDoog77xbakLKxpWXi1Lk4A2L2HxNpYZ5yCGmdjSivKUp0woGJ8lZyBLcxTTC2uFYDXILcDL1Sjea4/gVvABeV9DvPjii/kcHQgqUegVcDVHR2qVqRIw1vxDmwe6Nv4fZRw3aG4eJdP6KAT7e1GaS6MLtkjzw/aCdPp59jRsp5i2/JQuE2pq9Vpg/SVQAfD6j1HlcC9KIDETfy+eef2Od7yDZXbrrbf6u0Tstne9612WY9YbuTQPH8rMt1U9Yoc1pbbsrWJiWxr1XWLXiSeeqABo55srAhQBw7/1W79lR9E7MFiYAh6G9PxcHzxchOb0ICCaPjqW6cW5RIbJ2UuhLt/CYaYzrKWNiFGYNU5LFGh6PU84UZohy1nA4icNr6FqujDTFPWy06ALL4xWdA1XRlBaT1tB0WWzsW70wbPC0raMS2l9RySAybxGK/wLIjoDgm2ZPcw77OCib5AzwCzkTuVUpIRqDZd5ytNe4DCrIlhLpYFGNOFqkyth3sBaT0FcDkTL70/8xE9YrzwFjK0A5UxNaHbZQFbhgup7ejfHoxj9FgeI9yubF8RLmEjxIVpCf+M3fgPiFeZt4vPhgpqDpVplBRJ4XNkePfjNg/sO7t/3JF/1378JhudsnCL5FMiHPvQhew1V4Wb91V/9VW/33Hjjjb4Pgqjl3WcsTj755Bx8kIMHT52MePe7320ipGEH01yPs7fP0TN+YftFiy1/tMKR+2uuueauu+6KE/8Zz3jGq171qkU002dBfaX8tNNOa7W1jJ9897jlYEJcdOHee+/N8rWMtirN1UugbxNaPTe1xSqBPSgBu4hvSgmype9MKBtS0kzA++67jyPfT5+2gn7f//73CwIrkC2k7EBFbotsLYXIWIkmM7Zbp4hdiGObMceGizF37bXXvuENb1CYSQoMu5h0jLnA+yaRsRiblU7hASIVvHKlF967ZljH6pX216p0TRnYlc/CZaSmwkVGiS2WOasVlDkvWOGIhMnS9Kw8L14eCPcKOjp40K+EvvUIw6ACp8A28rZ47+ajsAe7PJ+gSi0SY0z76Ui5OxdSvwzLU7WYmz/2Yz8mYAtb0kAWNuwK8pkgNLA00U0AvQK25qZLMDkA0nriVQJzk4lvboK4mciqp1F3rfhp4YWQQQIBYQ47dr/3OBCxanFUKYCrzGuZsfslrAMoWMlNXlfpSJe9qTnYcMEM3iixTuq1iztAK/pubXz5y1+edbI5DRdpcSpLtcBKJQDwbvw7+Og//MPBv/2bb/71X/lLzYf8wPcf8r3ff8j3fNf+/RvKP+tw06i3ve1tcOwVV1wB+poXH//4xznZfRfTm1Nm2Ste8Qq7GIe7z4jceeedoW8qXXbZZVTx6KOPBmiT6bw9P5GDBpEJE8U5KcrpJ2q+OeIkgjRfsD9aoZX3vOc9XOFQNw84NmblPK3kbhkpf1Ctmb+MtKlNSqHM7mq6PpfRXKW5YglUALxigdfmqgTaErAfPPOZz8zfkvXMJ51vvvlm9pk0HGhryW7B4Dv++OM/85nPAI1ACGMuFlJzO7FLqZW67WbW4HfZ9iTYbTZUF75snKxVFl7w8Fve8hZmKwvSlikszNrz9rKvYcfUK0S2q0OFAQkbvCucMH+ZyzgX6XKxVl2sVYay6BB72rgoLJSkokuv5QcPGE0xMUa2RKgpsF0dLO2y+4ENP3WHzUH+0hwZBqs/glco7OKEeUcmBjHobhf3dKyu0RnKM5xapoB1wPnk1DK5OAFZ6uYRbEwhPTUQGQtaqoBFw4gYF/FbdxEtKNfUy+xTwKgpYLYybRn3WoGK3aUFfpFCx2sIlFxCGfcC2hXTYrMLqPlcv5xwq1FpDItgN4tNTW92YsMt6P1nnx+HewV7nShhglsTLIC+ieiYDNArat3kIe1OpV8L7DAJWP6hX3/g/fOf++attx/653/KMfON7/6ufc856l89/zjnghzGnWmHoGDU6dJLL/V3E/MZRTm+i0aBIxkTRyBXmvZ6YcreZJoo88ADD5i5559/vr9G8bznPa/om9mnfH4qFsQrAWNz1h977LEhe8455/grjPC2kuwWwdvWQHAYvfOd72Tb+BuN4PcLX/hC26jC/D4O+adFs+Dtb3/7WWed1XwPAhIWyjZZbKkc6y9+8YvVuuOOO/it0OHkEiVWHkuf+MQnbGS+Oa+wd8pU1BceZyfOXvSiFzEwCksKu+D/Bx98UJVmvJdA8hTy57K3uVtSEuK2zvjcCWZImPRe+tKXChfL1C7pYc8hOJ2So4yeqqhF1BzrIxBisS75Sgu3gka96Ua2nt50000sQH+sUb4/eOklC/cI3Crhg6PpcmG+JmaVQAXAs0qslq8SGFkCVjSrZIkAO3EnxwLKymku9zKzmluIcSAE4Z43gcOQFdNfPGJiFntxZEZHJZd1PCRhe1adi+dYjt0OXASGmYAMQRuqqIvy+gUPswXtWPAw1wARNemMyuAgYs3WAV1w16WmsbDL8kfkzJity9CkMGPa/seoZcozr72wp/v+cFRpr0mzZG5Lgt0DG2iakQQeABKGxk+9c0VjlWma49vC53Y1Cq4Y5R//8R/fLgZ2Vrt03jUrz2U6UDkRGPPFVHIuWmDWT5aiWWZ+Oa2AsjLWT4a7yWitkLZmFgoKgLiM45RMLNdrwGqZku4MbneAlqGMLJ1X2HoLLaPDQ3fKKac0pyo69D9TnibgITGi5rqtzMRLQwxiHYGZNZeLGS1TuJv5Dl1Y6IBeX4KwRBQize6UzJrYTRKgG7rzzX/4x69/4Qv7f+vt//pfYNT94sH7/s/fPXrTzf/0//7pO1728n3//ntpwkwY2GkyKDfwEv3UBs/SXAQoDYyZQVE5P2HFk046yaHoyy+/HPaD1lLSpkBdEVEGksyBiDwqxCmzFgE5+zVNRnaD52/lWnXBVWek4UbMUHsg1sFph6791S4AONQuvPBCPDRn1kUXXeSAErhoJ8rpJCXBe+8ocXs5tq36xz72MZn+BqRJDS7yHynsGLZz1IwNTi5B6SYATmERb+8U2J19NFROLofULD4mOA6xB+FbTKxFmuMvO/zww7moZALGDoLxZfPjEx2PAw+ar5nCuqrfdtttGDjzzDMJAax95StfKfJhkQGDcaWYQ9c+bupEGEPuve99r1WFWHCo4+iLlFiLSMwftrS+Pc5a/X9OCVQAPKfgarUqgbEkYCksf1Kl0GQD2TCspIywbC32EmiwFPaUecdV6SvQqcVi4/u0HJcXdQq19U80N0U2JfzvEgzHeczEgGES4EN905veZA8gooBhxiL3M8NUx9Fpklpxx0vTEjGawwAz3bgwLHKxG2y9yjCdQUqmRqm4Yob7mwtXRJ1iYIadPmmP9EWa6s6Bavrb3RFP13PI1ll0C0os1dnQZr3FgclrHrFB5bObecfoIRzrihBiqTcbpcm8ZtYTCeEgJqZIMmtbOEVoRXk+OPYrvwbQy4YWamYEI8vFw351ktPb/taZJk2LVRqCDZi2SXdHIZMIZmAxJ3oD8VrN8K+wcLFOveQlL3Fn18a1V4g0myuZNbG7JbBx+vnv/u7R2z72nRvodxP+gppA6759h37yvoNHPP3bvo+ef8thhKkC4d+h4VupE+gFUlJ4b7oCb/QfQfrp55vf/GYVRXGdTYMJQwG89Nr5Bm/79/szExP9gB4hBeY9+9nPVhL+vOSSS6C4Fg9mhwizaKoy5ojvg0CY0q5WyWTmbgUAj9VqemB9IcX7C/Ym3x0wm2BLG7Hy4KVPqEiY1DxfYLMVo0scJ3C+iHQi2NYBXU5z7p66AFfoWvC8VFfeyoDtkmMp8Bc6HCMHjFUhVXIAgAXDr7rqKkfNUQPLLTvYcz7cgsbRLxMGZurcc889QtMqMoHOO+88ZKUZOZYg0N2650VuR9kLYzUxnwQqAJ5PbrVWlcByJWDJs/A5OeONGv5IGIlD1CEZR2I4U7UtkMiPaNG0QHNtwsm2EEv/ueeeu1zOVkK9bCRa03cWoctu4aedQOzUJhEL8u677/bOEqOWm0BUPGDYVuHyRjE6TVIr4f2JRkrTuiB8CrHbevmDdYHzwiuIia+WYk/UXLNUzHoBrhwuYF6wOXL2eyd6WxaRrrFjouX7Q9zz5l1eXoC7IpBFiNe6UyWQyQK78gCaTSxUMwsAFshNdCgFJs4pmTHrLRdeWYQ/LRdMZNS06yk0qwDTmceKYep9E/4dT929JMn61ESLcvMn3Qgdd2mtMFWdVMwl0gv9Uh6rgb8SbBn3fR2xHZfmmnSa6akCqQV2pQSAXfHeJ/3Rnz6GekHgjevgIQf3Hfr1f/rn//Hfn/Sf/9Mh3zmbAU+TTZOtxGVLopb8OKaGsG2U2bFkMNKyz7Q44YQT2BvMEgqMCDj3yU9+citqyafJ9g4ngbVry/b3LOBV885O3VRy6YKlHc8WOO0nm6evfvWrTz/9dEYRkOm0sx0fk2eccYbTyxg2ly3OogImsr6YblqRMNfUsvmKhD/rWc/yLn1z3TY9fZClHBHHVRMAaxcRbz5rEUESAGVt4rxphOORK7zZIOySAH9yEIRypSF2FppwtMPVZCs2LpNAnAFkrqQunlFOOmyHiBPgzoFzk4kBcCi0Fo2Ur/eZJDDb/JmJdC1cJVAlMLcELHmO6HhfxTckrrvuOou4U8GCDwIR2X4UcLqGaeWEjARbXICOe7X8uaO5m17DitkAwpi0A1c2AFdy9N0O9Lid+V95o4nCI5jEPhdI7M5FTYyqu1bfxzTqwBWT3ebH4xu/9bYwM1P3N+S1aToIvrnUJXDmxaxvOc7U6DoX5t3P6dkwKc2u4pBqGlLrzP8u4C0KCfqmL8xN55MdOzSzsjxO7SNb2Yoh2AujivEymum2BRYFJjuTND/lOJ9J21mlaL72ta+Vb/KyRI14aYUCsIM5g5xdzEKUMG9zIWI0n3322RYii1KweqmuOyVdE1UCkcDBRx1V8Fmppm7s98thaC+sA8OzCgqcE4fkgS2nJOhtIcLvAxn6CQYLkDp9RsNhLesbGCaflposvmIlFFxqTVXdFLDz0n/HoEw0FPICVyGCDfMxP7lZ+bOk7Y85rIGCJbfJakoyADgf+cEdn0bccWjE7bAyUUAHpC9NlN0WHQFbQQURBcFYcWz4vBSzevCkq5uckigFJCwIJrKAuTeBHYeWsCaI8TZFgQEtyvRIlZLgg3CSWewXxCVef6DbUwlxXVwVCiWBn6TdBYetP/EjaLrJUk3PJ4EKgOeTW61VJbB0CVhAnfV1Buahhx6yEMNvVvyc8tW2BdHa6qiMpz4XYcF1yMexnDxaOnPb2kDZHsIFa5KH1VWYctaLAeoCjH1Dwm7Bs0uenKxEZKd3kSdjF2hBrUWw0Bkr4ZxzNmDedAA4Z4aX3ehYzIdO4bZpiJTMcdtac2r0Le9feYGTL4AWrTnDu5K9onsU0krIsvRBBDBVZDhh3om9VtjJSQc7ma2QqiUCHfiWe5EZyvYFbuFqBz6REsBB1hpSdB7MRtZqLLgESLC5UQMVcjk+zWS3RCMiHiW+BOsCDzmKUvgpnJecmqgS6ErgkH/7bw7+4H/Y92d/5g8LPO4h8eeQ9n/90EP3/egP7zt044jy8IvWHXnkkTYgX6Xy5Wfari6NBahy3LeQEsx0mtffmwB0zQtv64CvnpoFopfCs4FtpXxPwnSAMMVac1Ao77sedthhrSooezkWsLRRSuRDWWaNqWfjNp2dJS5zsNSFxkV6ETfX9MhPOBPoNYsVBt21XgonIV8s2uS1BR9zzDGIQ8LNMur6npY3h/P2rz/11HwqrS6vFm8Xc0tbPveFJlJXX321YHIgt/XBq8JcXYAuyIoNkV7llTQKsK7vYBGsWG68dZCt03xectYdTeBQlSZ6Dw9kyEPhIhAiqstIa2jm+FkB8BxCq1WqBFYhgSxwTChXaa+56km7LKyuFPCzlNxTiVbH7Z2ufGKaHOxz3ttx7oidymD1UpPzSA56qSXaAwlDxe5QMTDDOy7fNZYAIV4f7HGKUsiUn8KF8oj0x+JzOJ0DBw7saP6H97Rbstvxbk63Vs1ZqgQYi0JAwraMdQY3Ezb2/cRGvQSoGGuSEckkhYHdGf2iUmLImZ4wsBOGCenn1KhgkfM4bHShKsuINTlfzGIxM+gtHd5DcbeSCBqHSGm9akgRRU0MlYD953u+70nPP+4b77n60EfFPx9TooP7v/mNI478jh99sgjpUFKPlwO3vPEOa1F1SBhc9E2HvHrKP5sG3PmDHCVzPt+EcmQ3x6ZCw9ew7KqOPygf8PY47cf+N5VaOQCz11mhQfnAtjPD5f3eUhI1gVzRVO8jgJcCuR7xH3mNFr41EwWllUn58KkhSJIny2eTbe4OvmkCgBRHhdt5xMxKi0D4KXVRcFpEINpUVUYHNVHYSELvcOKVBzjWW0syEdFo2rWbc2nxcIHQeIafEXcAWxACDidVa4s+CqFzu/uitQ+PcaVZbXzRKkS41ZAVutBQaOqpk9vy4x8XxFaLQZKnTfY05BVlcZFmj5oFanomCWyciZ+pQi1cJVAlUCWw4yTQWuhsY7aZAolZtP6AgUz98mEq9gEr1pWEcBCfa3avWTsOezvxJVgEADOOu1varARr+TWRAI1iq3GjxG2/JlztWTYMh8tENq8BV0EqRmdXGsokk3Xr5CTE6+07YV6mKpeZRwo4bym2z6r2ym7uEt6N9AhN3p84y9jQLqZwC2zXOd4Ve82ZSQLRUt/BevSv/+pf7rtn30c+/u1f+5qTz9849NsfffovHHrKKU/6kR/e//jh2Dko+4iGYCy1p708OCjY++gt9Y72ikDy+/D1KOPAS1Fp+QCk8CYORWtpfnkUNuSbgCBrApjpiO3P9qqu6dY9/WQJRYdbSggXAHaCI2ykCUFgPNg9TUBkoW6rru1YpgJ6oS9WYHt0GLDhqsKLDTNrFxuwoip8UqmimLmPSSXt7wo0uxCGuQZgdQsCsoh45UotHPKL8Q5gWHVnf3AVd3lq4Z9TjJs7K4kqSrIxNM2xrpU0pDCaWtedkqMwnOwMi8K40kE54D3vQ9Obpr9AsmKGIHUVq9fcEqgAeG7R1YpVAlUCO1gC2bRKB2xIdjUGtGON7F2Xn/Yk27YdiNVb8LD9id1gL4wLvGcfYiKI/dqhVece1lZP4cJJTewICRT9qWO6JuOVETGF2Z2mG1O1hzHGPQBsYnpFgtltOnsf2KyHeBnrTGR2M+OYjWuym/tMWHDXz6Y9in4d/R4h10dzS2DDT0OhvQT8ta/t+6v/9Y3/+Uf7vv71Q37oPx7ygz+0//u+95AnfdvmEaXRjinNzefiFQOAzb6EWxHc9jmVlWQdOCniZYd4V9lXxES28z2t8qgm5pZABcBzi65WrBKoEthVEijbXnoVb/cmFn4sEMQ45uIFlRVgMfMxM4jdObZzeTUIKrZ/u1ALluYS9u3uZO4qedXO7HAJCHSIdiaIscO78hj7zSlsxiVXpos3SkTFJ169feduXvtL43I4uRQTchHt4dsqF9DrMEicXEU4hWbJqYkqgSVJgNI+QfkbX9/3TR+A/rZ9+zdPAm8ooqe7AQBbgrwo6y8ttb4M90Tfa2rfPsuX15JZHV7hbsbkq2wWkUAFwItIr9atEqgS2M0S+BYTZLOjQkOOIeVgJDM6l1Nh3NgKO3vJQRsw7O82+UMI7j4x4vBVBcC7WVF2Zt+8AUifIb1dg+syYbmoTElv6Dk2qY8Qr9Mc4roivQbKmU/I9nGf1Y8I8IrumrbeCWwN464RS6tf9eeOkkCCwZtvonpTd0exPoTZssnW6dYjriIlZaqgegQ106MKgGcSVy1cJVAlsNcl0NyKIgvHKX1Ok5EdUzshJrFi+Qp7wUkQmIXtgofL3ZtF4k42s7qf7XWV2qb+71wAbFq5xI6cnNQLPil3F5Qr7WU8U8+0MvV85a5g3eKZ4pBqibzOwZZA6s81kQA9F//7+7959B++9k8/8GSv4278Dd56VQlUCSwugQqAF5dhpVAlUCWw1yWwaaZ8ixDEhH0QMqEnpjm73CXh1CXDXXkRJ8D4wIEDBRX7oKVzmIBxPi5SjfJvEWj9MZIEfN6Gv4YG+pMezvnnY1G+yOIdvHVTOUy6fKHHF2h4lLCdqRSsa34F6Hrd11dhMo/iYDKtvJ4A/aZ3TcmtWx+bvNV0lUBLAj6FdfDgN//b/f/37//20f9yyncfUgFwS0D1Z5XAvBLo+0rEvDRrvSqBKoEqgb0lga5VzfJmhbtagvD1SKa8UFVMeTa9D0X6swe+lQWQMHaEhb3qAwm78iFK9yS8Y5wvZHaba7VSf1YJbCUBypMrBaQlct+qyrLzqb1zy8K5pkYu32GWcHfxGXEn4dAbuaYAoOvyNdSCdc0U7qRuF7o5y+5IpV8lMLIEBIAPHvzzr/7zP/7tk376l/fv2/hmc72qBKoERpBAjQCPIMRKokqgSqBKYLgEmPutwnLEskS0itH/GA74y7+U6V1iBVzQLwAgUOwPP4geu5eECJ6vGUEI9S8EtmRbf7YkQJGSw/mSd4BLgeUhRqFml79HAuU6ouzt3FxJ40Q+DIwBl7+VInj7/9s7E3gbq/WP732mzNEtNP2vo3IpQ0gaTE3msSJjhluUpCtJQqWQMlb3Rimk3GtIGviUW3G7SBmvKIncimigkOE40/5/936yvPbZZx+6nM45+/d+Pu3Wu95nTd/tvPv9vc+z1nLvfeztD59k2nR612FLUCQsR6ciUDAI4P49uCvt9QEHAukJDYcmnFUhfOehgjFMjUIEcp+ABHDuM1eLIiACIhCBgFMm3mvIBnMamzfMhPER+bBz9+7d5jdGA7BBCzLYK4xZ7IccNDNh1cSIKrLaC1ZpYhBO1iJYwdczoVhlvLVIWTvcv1ISCF0WpiKYGTPIW9Ay/z7tDY7JXRfywDKnEd/jSOjqH22sEcgMBDa9u2/NdJ8vM6FC8/TLOhQPhWv89jc+/AEyoYD5OARNsOa5zRHg3RPrDPP3xStUW5nCccaeq4RgsCet+wNk7XSWlHOnvJZiAUhXxCUoyx8yv1Asuo49f+/8jdOEK4glP3DcH+y2wCkJ7gmU4tbEKZb0x1vE+umawIDDnR5PghrmzJnTokULCCxdupQfRxYCPJ6CsilgBCSAC9gXquGIgAgUQALu+SBsbDyXIDacS80lTHugRvixt7I8Q6Ar0MP83nM4YWyniGTirvG8sUbub3ikCOuVTvMFAbZB4tmULz36EyT/fjiw3LNnDy9c+PdGuP6vGtfzP56qqZB/bzZ2r8QNBiocG7PAv7qw52xHLHpnnJkSIlCwCISHBYVGF3xEf2/U7h83nOYL+IucndpyVHF/wjECEpF4nByoir/XO+64Y/Xq1dWqVWMtAHYxGDNmTNu2bW+//fZXX32VVQCQncjUcePGNWjQwP4SEajt2rWbP38+61kQhWFtDRs27Mknn+SlKnWS061bN4rYPAVKUcTMnnjiiaZNm958883cDQhcYuIPmnP58uXev3HuGPz6cDfgoBTS98MPP0ROk0l/+D3i/W/Dhg3Z/JZMCnbt2vWNN97gZS6tEPQ0YMCAHj16eCu0pqN80k9ueps3b+Zl8S233FK/fv277rorir0uFVQCEsAF9ZvVuERABAo+AXv+yG6ciBY0MMrEo1PCk84pRyWI5BIlSqCEebLhk+cPPr2H5fD04DZ2OqEnj+z6qfzcJ8C/HDt4vkTZEpzMwb8WPlG5dliOpfH5OHHLl064AU+o9hrF3qpwai9T7PUK/4R4eI0yLv3LiQJHl2KNAH+MvkBINx7jz/Sn7El/rf9ef0ohnKMZ8emNH0k6M7kQjlHjY6I5tCdwaIekI/kR6fFzcM011zB/fvLkyfz9YkNU0Zo1a1q1anXbbbchKZ955hn+xh9++OEZM2Ygd1Gt2PDzwfLpVapUwWU6aNAg+7NFAKOiEaKuIRO9jALj8ePHUyeXMG7fvj2/IH/729+sIJ7ncuXKef/2TQAjv+mbq40JQfzWrFixomrVqrxuq1evXocOHR566CEKdu7cmRsOup223n333ZYtW27cuDE5OZlL9IGfM4Q0P2TWuquQhN3uSNhbPwQwgSe0TkFUt13F182oKU6OK5I1k1IcNERZbxNZ01RLcSqkWq8x+fTWms5aSjm5Q0CLYOUOZ7UiAiIgAiefgPc3NWvttuUpb/SzXqIgv8Hk47Uzhew0z6/qZ/duHo/WrVtnooi35vaIY1Xxc877eJSwO+zUZZLgIGrODpzPFoDNg0X0PmftqnKiE7DvkW+HBM9/fKEcuHf4RLUibu3Tm/CmeRx09fPt8FTnffHBg6/3DQhp0728KHGlIib0LUfEokwRyI7Alo93b1mc4csIOkLt8Af8aQd9vhR27UpHTcZnJH74bMpppx8M+INyN6R+gx/n1k6vfF1pH47hIwWz/p+bw+LFi7mfv/XWW6hf+/MkIJnDa4xUa968+ahRo7iTcNPmroIYrlOnTs+ePR988MGBAweatnRF3J+55dMKORx2SnHWsLjkkkssk1KoX1c2awKzsExyuOFcddVV6FXvJauQfBLczbi0cOHCPn36oDbZZKFx48ZTpkxx9vSKu9yQIUPI5PfI+XvJv/vuu6+88kpE9cqVK//85z/bchuVK1d+5513KA6xe++9l19ALDt16oTTm/r79es3d+5cbpWVKlWiQjzbYCGImpcIFMGyb9++tWvXxv6TTz7p3bs3mh8OHTt2hCq/m48//jg/uAznP//5z+DBgydNmkQYNr+VlCWHSnCAcxN2nVfi1BGQAD51bFWzCIiACPzOBLI+UrgO2SUTqOwZ4/K9CWz4RScHNWW+QT554LADZeXEFb/oR7L3EEfHTz6HtyoeiXiissOpYhLk8Ik8JjqOg4QdWU8tn2cIqrU39PYZZYzeDuSdtCE1PvbJ8xnQ3IEDhLT303tq+tYkrn1aDr5cr5plvJABO8+7xAryjGVvK3ghUrFiRdLBVxRHMk308pmjsjWM+Y553vn21RMRiEjgwtpnJMYf+mhaSube0wKhwGaTg35fxq9hzv7A/u9O2/8dN+TgPdmPUeG06h3i/3RNKR/vFXOKhf7oo494n8UfuPvjtYTdjvjkXsSLzlmzZuF35WZrnZw6dSqO3yZNmtx5553LG9+U8wAAHiZJREFUli3DGWv5mzZtGjp0qKVRntm9ZiW+muKfffYZYpVgY0KvXetW1rWyaNEi0jiQCai2TPrDDY1lLz744IO//OUvlsnnhg0bXnjhBS4hU5Hr9JY60efDhw8nWpuBbN261RlbAhn/5ptvojCJT6EqbqeWj3uZmydpytIucpc03m8qoV2Ct2moTZs29MQyx44di1T+/PPP+c3q378/8dh0+4orrnjssce6d+/OzZYKp0+fTmA2d+/WrVujmW+66SbuzPjPX3rpJTQ2P6B0hlJ8F2j1119/febMmbxfoMXnn38evPYDZ93T5yklIAF8SvGqchEQARHI0wQiPo54e2wGJlkJn/Neyi7NUxRLnqCNTZh5BZs3hzSPCzysoOJM4PHQwCt2ost44ODgmSBrEzwf8IIc5zYHCQ7WcbGEfXJqORjwRBLxoJKwfBum99M1bd1wn6Hwt6MfPIcdPTk2xfMNKBgRn5Ygh1OXwykHl6jENecSdAaFbwcilkdSeyngXiLgvbHXBy7Hndr3xcMuBzRcnRETNGSjs+FHtFGmCIjAqSXgj/u/WoXPLJe09IX9uz9NxP0bMFEb1MG/amH+F7onEqAbKFYuvU7PIiX/GPzrPp6/XGRYxEXUbVDTpk2bN28et2Js8Ely2yEfsYcOJJ6Z+w/TZRHDdevWtba4f3I7srJmbGnvJ5a4Q/GOvvbaay+++CKKEUn58ssvc2f2mpHmHmW1eW9WTP3lRo0DlvhnXKmuCD8uTCfmB4I7JwaMi7scwSnsJli+fHmEJSraGVsCny2Tn5n0yymC/LnnngszYMoGMhvPbc2aNe11MIKZvdYQsYyCATIKbpLoVZzGvD2kOJKbSdH8hDVr1oxXAGvXrqUsavbqq68mH3HOXZ2CFMGYd47k2HRlwrbpJNXCgXGNHDmSOdiQpyyvGAxvWPd0eioISACfCqqqUwREQAQKFIET+lXmIYbnCY4cEVi1pr7M2B5rCMDjQBWbMHafJMg3Mcmn93Cq0hJ88khhgjSKRsWAq05s0xPrjH3SJeshny7BMw0Pf1kltFPU7pJJdB4oAeLEuVerk7ZLPF9yOKc3CXN3k4kBh/cRk5647kUhbB2OYuC9dELG3oJKi4AInAQCeHCDbt/4omfFN7y/1Cdv7fsM3ZQeT55pX28TgbiMC67PuKxdiYRC8cHrkV4Ueu0tjSpD1HHriPjHjgOTOcAsEUGw7tNPP808XiwJ8aXUxIkTqYF7LzqWfJN/qESUpNUcsUJ3iYBkDk4J7r3uuuvee+89TsOK0Kh3DrCVxZJoZOKQmUhs8tLycSbjsCVNDwlgZkoz0pqu4p5FSbIS5H333YcP1oztExWNj9oa5YcpqwIfPXo0Q8MDzMJgyFQClZkKhGC2Iq63+G/xIdup7TtI3BNzknk7gIO3evXquH/vv/9+DCDJy02GYB0gB++u3bfR6i4T8UxM9apVq4hOR2+jk11b3v4rfSoISACfCqqqUwREQARimsAJ/Yp7jZGRpv3AZ48L2XGklNcg7DS7Uv97vvXW27TV6fJPtCeuYFjfLD8s09tWxEvKFAERyHcEQl7eI1I33le1dfGyFQ/9e3xG+gE8sUfyg6MK+BMz6tyZeH6tYqaMjykYddhoLWThxx9/jGg0Q1788aKQt2yuHOoOuVujRo1evXoxu5XtgkjYvY49kxDD5NhkVysS5R6V1YB2qR+XqWsuLJG1Nn4O0MxECDPP1olJSjlLVCjTjMlhRStWoiaBVK5VqxbxzLTl6qfnFhfNWFjbgjen7pIlmP1BGDMHU3bRsczgTU5OxnkLIvrgjGFC7DcCnnrwQpNvsd8016hRIzzVVE5UNvn4onlr+dRTT/GW04q7nww67/rP21L0NsHPTBhGxruGlMgFAhLAuQBZTYiACIiACJwwAfeUkF3JMIOw0+xKnZT87Nqy/OyuRmn6NxSJUpsuiYAI5GcC/lLnF0rP2IfW884HZkTMDylxLo5fQqGPCrMcR8rthQksjz76KL5K3Js4UZmBgkcXgceSzq44Zrh2sUEKIvPQkCNGjHC3JrQckdJeAewKRkwg+XDGMu+XWbIYMLuYRrN6eiOWdZm0jo8U+c282WuvvZZ8dhTHmUzwDhHaxAyjjclkti1bN7Hd2vr16/FRu/Bsq+fWW28l2BgDdDKhy96AGjPA+43uRd9SnBx0NcHPjP2BBx7Aq4z3G2FM6DJpHLyMCDL0CsVL9DX2zEOmLGRAxwsF+ozav+iii3h9AAFe6bLaNvNTEMnWnPvEkonBF198MYFC1O9QOwMlTh0BCeBTx1Y1i4AIiIAIiIAIiIAIiMAJECC45dv1B3wpCYFAHA7goNrlCHmC49OTtq85XPLcnPfgCWsP1YePEanGVN4JEyagx1jemaWtkKnoYeeoRIOxOjETU1nqyaJ5TZVhxhJTS5YsITaYYF3Wzwur307Rum7yC0VoAj8qXmXSLJv8/vvv44wNk3nIyLA1tHC6IllRoZTCmArx7rLdEQKYebb//ve/iVgmn1LM+6UJ0sxkYVEulnFG5y9YsAA96brHVbyyyGam7zJMIICCeSUY4CsuV64cCTyxvBcgbpmw59mzZ6NmyWSbJTh06dKFJRX4JAd9S0O8HWC5ClzTyGMq5+ASrP7+97+jge2UCgk4p59oYIogcZkOzXBIeOU3xrRINy677DLrElXpyB0Cx4SQ5U6TakUEREAEREAEREAEREAERCCMADKJY8nf9mz/iMWiguKqcJnUpMK+ff9NYlksny+z5AVpTR4uGRcfFh0dVk2EU6qNkOvJMvEWZmaZWIXlk+MuWR3OIGI9rh1vKVfEW1vWTG+Oq8cS2bWVXSuuOAbZVRvxUnaZVOitx7XrzXSNevtsaZQ5qhhvNlHTrmyYvU5PBQF5gE8FVdUpAiIgAiIgAiIgAiIgAidMICMlY+cniFX0bsZ5VwRqdy2ekORfPXvvlnfjfZlx+7Ym7P8xrUTZHFZ3z9rqceqr7Myyy3cNhRmEnTozbyKiTdbMrDneSkhHN8juanb52VWYnX3E/IiZ3m4Tf46zncWfmUKcnJyco723rNL/O4Fs33/871WrBhEQAREQAREQAREQAREQgeMkwI68X6/c9+FTCb4i6TU7xleoV8wX3JMIP6Nv+5pDy6emZPycUKWz75Imtg6WXTvOumWWhwgggL/55hs6RMQ16lcCOJe/GwngXAau5kRABERABERABERABEQgAgEE8NKJe/fsYJvfYqefn8jeSObgJKQ24Ms8tCt92Yu/ZB72NxxaismnbFIboQpl5QcC3hhpqd/c/8YkgHOfuVoUAREQAREQAREQAREQgXACGamZaxf8cGnj0vGFglN+Q7OAg+ssOb2UmZGx/p8/XXhl8aIlWQoLARwyC69G5yIgAtEISABHo6NrIiACIiACIiACIiACIpAbBAJEOiN1M1mhye+3jY6O0bchGcz04EzsfP64kOfwGIPc6KTaEIH8T0CLYOX/71AjEAEREAEREAEREAERyM8EELdsPsSetGlpaTmOA+nbvHmzFi3YPDZHWxmIgAiEE5AHOJyIzkVABERABERABERABEQgNwkggO+55x4E8HE2euGFF27atMm7r2yUgiHXcfh1m3rqvRSWw6n3qitvZnbqDLyZzjJrwtlnvRTWnKvQFXE5lHWZ3nq8BtnZkB9m5q1B6RghIA9wjHzRGqYIiIAIiIAIiIAIiEDeJZCenk7nGjdufM0110Tp5Y4dO5566imMj1/IpaSkjBgxwkTj6aefXrVq1QYNGpx22mlWw+TJk3fu3Dlo0KDExERytm7d+uqrrw4YMIA+7Nq1a8yYMatWrSKN5O7evXvt2rW9fZs/f/706dNnzZp1PJ2hAwcPHqS5RYsW7d27lwWQb7zxxlatWqWmpj7wwANW7RlnnNGsWbMaNWpgbHUePnz4/vvvr1OnTtu2bV0rd9xxx/79+11PuLRixQp31fJ5OzBkyJDvv/9+3Lhx69atS0hIqFChQs+ePatVq+YKKhGbBCSAY/N716hFQAREQAREQAREQATyHAGUHnovSrfWrFmDAI5ikPUSCvOJJ5545JFHypQp8/PPP48ePbpPnz6zZ8+uXr06xq+88sqyZcvOO+889C0a8quvvvrrX/9KH9iqp2nTphdffDFlk5KSVq5cyaUwAXzBBRe0bt2aUsfjkt23b98NN9xQunTpfv36nXPOORs3bhw7dixa9w9/+MPTTz+N0i5btuyGDRvq1av31ltvubcAb7/99pQpU955552bbropPt6mRvvmzp3bq1evypUr22BR9SVKlCBNnZQ1gAhgxDMtNmrUiIY4/fDDD7dt2yYBnPVfSKzlSADH2jeu8YqACIiACIiACIiACMQWAQQqblJcoAy7f//+eFw7dOiwfv16vL7ktGnT5rHHHrvllluKFi3quGzfvn3t2rX4eJGsZOI3dpdcguJFihThFI80rl08ujNnzty9e3fLli1r1qzpdcnSASQulvPmzbNGK1WqhMDGhmnPXMXx+6c//YnEp59+OmfOHBPAnE6dOpXeTpw48YMPPiDT1YkTG3HremL2r7/++uLFi82fTNnVq1d//fXXo0aNKly4MAUvvfRSZ69ELBPQBmKx/O1r7CIgAiIgAiIgAiIgArFFAF8oEc6EOqNvbeT4SMuXL//ss896QZQqVQrdSPzwN998gzcYPcnhNSBNDeaO5lLfvn1vvfVWdCbBxg0bNtyyZYvXnjSR1Z07d+aqq6RQoUJEYrtTbDgIkzZRTT7x3u+//37Xrl07duw4bdo0Z0kCM1zKHAcOHPDme9Mm3SdMmEA9VjmfXgOlY5OABHBsfu8atQiIgAiIgAiIgAiIQIwSIOoYfYuytfEjiZkkTJwwzltHpFixYmhOnLFo4+Tk5N69e//4449RBCSXmEiMDB46dCj+2AULFriqLEH4MfWEZdopsnnSpEn0oVOnTohV2iKfCgnPrlu3LuHZXbp0wbtL/LbrAJZ/DB3t27fPrs7zzz8f1zEHhkRr33fffXv27IlorMyYIiABHFNftwYrAiIgAiIgAiIgAiIQ6wQyMjKYGOz1vjK5F6n55JNPOoWJKmba7RdffEFM8rBhwwgtJkbaXY1IsEqVKkhZCjKbl2Wuwmzw60bx1hJ9zYFARbWee+65lMXtjAJH39LVi0IHatzVSSg1epiDSb8uMyxBZ7p16/bll1+yCNbAgQNt5nD0IYTVoNMCSUACuEB+rRqUCIiACIiACIiACIiACIQTQP5xfPLJJ2hR77ReVOvw4cNfeOEF/LRWxixZd6pixYrISLZoWr58+aFDh8Jr9JwjOO2MhEu7nMsvv5waOKVmy7QmLAcfL4tjmZt3/PjxZLJmFdr1oYceYuoyfaBjYVHQVkl2n65yVvC65JJLWP955MiRLECN+M+uiPJjhIAEcIx80RqmCIiACIiACIiACIhA7BJwghBVeeedd+JZZSMiLw50Zrt27VgyyrTrL7/8wprMGFhBShE47XUae8vmmKZOFt966aWXlixZ4uokTJqwam9Z6me1agQwrl2Wv+rRowerWNnx+eef0x8W7qI/3iJR0lRuU5GtCEM4++yzkfpRiuhSLBA4Og09FkarMYqACIiACIiACIiACIhADBJgASoWnUJYsoMu7lbm6yIFvWKSU6bvzpgxo2TJkvDB2ctaVkQjE338ww8/EAjNZNqs6jHM0xsFbP369Znoy9xdthQmRnrTpk0siLVw4cKwIjSKFMcdTcSyN7yZLYKbNGmCE5i5ymFFsjv96aef2FSJ2b/MPcaBzLpf7FqcdQjZFVd+QSUQeduugjpajUsEREAEREAEREAEREAE8hoBhOhdd92FwkT4DR48OEr32AeYHYbKlSuHnDtO8UnQL85P6sSeLXNx5LoNdWmXpbDINNHLtFt2P2JfIhQjxmxutHnzZlalYkEs5vcyiTesRSb6crBIFfXga0Vq2iLP33//PTrzzDPPDLOnDykpKXhxWb0Z/zP2GNAorlqM3QLROJ8x4BMl7NWrrNFF5DaThHft2lW8eHHcxWH1o3hxF9v+xrRFr5g/jNJGwDNG9g1mXesobHUpRgjIAxwjX7SGKQIiIAIiIAIiIAIikA8IINui9DL61YgFkbsoyYiXEJCskOwuoTa9cdFs2Htx6HAGYQlUJYdlsouvu1qmTBmXDkvgha5Vq5Y3k+7hEPbmIG45vDmWRrpzkD7rrLOyXiUHLzGHu8ToEMneqc7ukhKxTEACOJa/fY1dBERABERABERABEQgDxHAH7ts2bIoHWJZ5ihXdUkERCBHAhLAOSKSgQiIgAiIgAiIgAiIgAicWgIWzft86MixpbDQ3xztZSACIuAISAA7FEqIgAiIgAiIgAiIgAiIwO9DoGXLlmz8w/zbHJtH/dqWvJLBObKSgQhkJaBFsLIyUY4IiIAIiIAIiIAIiIAIiIAIiEABJCAPcAH8UjUkERABERABERABERCBfEgguPxVNmtgcQmPrz8fDkpdFoG8RUACOG99H+qNCIiACIiACIiACIhAzBPIDPjQugF/IMPny/T5EgI+MhIQx3lZA7sVqvNyJ2P+n5YA+CSA9Y9ABERABERABERABERABH5/AiH/L90IZPrSAgfey/zlzcDhzzn3J5bzFW+RWKyJz18UkXmi8tLp0mBVIR+yN8eGzdzjr776ih2DzznnnBIlSlhm1s/oTVPtwoULr7zySttVOGvxsBzrBlv+svkwmwyzwa9tUEw32BwYY7ZlclsWu6bZnZiD/Y0wsMxDhw4dPnzYVZ6UlBRlv18apTm2L2avJjYf9u4z7GpQomATiCvYw9PoREAEREAEREAEREAERCB/EMDPG8jMzPg2fXtH37abE/ZOTjq0JPHQkoR9L8dv75z6TYuM1E0BHz7hoFI+/uOXX35h49wqVaogLCmFAkxJSbnooovQuqYbFy1axC6+HTp06N27N/v+Dho0CDMkKJvunnfeeewMXLFixQcffBBjk6xRmr799tv/+9//RjFwl6jq559/7tatW6VKlfr06cOyXrQyb948DPr160ffLrzwQvYorlOnzvLly72levTogXZFxLrMsWPHknPBkeORRx45++yzQ3sGH/PxzDPPfP3111dfffUNN9xAEyQaNGjgKlEidgjIAxw737VGKgIiIAIiIAIiIAIikKcJBAI/oX4TUpb7gtHPRf2nlccPGkjd6vcfTDy0NHX7jYn/Nz+QkByMjj7u+cBITRyeaMHFixdff/31jH/BggUJCQl79uwhjSq+7bbbBg8ejLCkztTUVFQi+aQxwKNbtWrVzZs3N27cGJHZt29fLqGNOajBzPi0g4Y+/fTTokWLchpRKnv7TA3t27cvVqzYxo0b8Tljv2XLlrVr11IWpd22bdtnn32WxNChQzt27Lh1KwSC85937979xhtvoI1nzJgxcOBAa5dPxjV//nw7pWaGY+nq1asPGzaMFbY5xTPcs2dPZPbkyZNxLGO2adMmM9NnTBGQBzimvm4NVgREQAREQAREQAREIM8SCKTteiaofgP+9MLXxZVbGpf8cVy5Ff4LVmcUa4t7OCn1i7QfhgV86aGJwSfgB0Ze4mudNm0aCQ4SXbt2NQqIzO+++65ChQqmMFGJLo0lKpdgY9zCeE1XrFhx4MABChKrjLcVl/KXX37pRUkNbdq0QS2TOXLkyEcffbRdu3YY16pVa926dV5L0kuXLl22bNmkSZNQvxQkFJl2sTczmiZRqFChm2++edu2bTixLX/WrFk1atTARz19+nQUrJnZJfdJVaeHDquZcGg7pTa0PT23sGrMGJcrpUTsEJAAjp3vWiMVAREQAREQAREQARHIiwQQckEtF/g5bs9UNilNT6qccO7LcUlV/b6koDaML5949nPpRa4J+DMT9s3LTPsyaHyCx4033kioM07db7/9dv369Xh0rQIm3+I+xcvav3//2bNn//jjj6G+HK2fU7zEn332GXHFr732GlOFcZwS54zH9YwzzvD2Ah37xRdfEF9N5s6dO59//nkCp/HrNmzYkMq9lqQ/+uijypUrE5ttwtuukrZT+6TdN998k2jt4sWLY4DiRbp36dKladOmuILZNtnViYv4iSMHet7lhyWaN28+atQofN0vvvgiXQ0baZixTgsqAQnggvrNalwiIAIiIAIiIAIiIAL5hkDAl5mesjEhsIPw4biSnf3xpX1xyED+SwiqQX/RuFJ34BmO8+3POLSGAOUTHRiRyci/mTNnvvLKKwQY4+l1NZBJODGylgjn5ORkYo9NYNMuscSdOnXChYsP9p577kGI7tixA+ftwYMHmaBbqlQpV0nWBM7bSy+9FL8roc4bNmywOp0ZFZqsdTneBJ1k7nHp0qVRqnPnzjU9jBsZ1YqTGacuQ5g6dSpFrFqWxaJCO9DJ3qq8aXT4yy+/jER//PHHkd/du3e3edFeG6ULPAHNAS7wX7EGKAIiIAIiIAIiIAIikPcJ4Pn9Ac9uUNomEZqLQxjda0KXRCA+qXwggEM4xZ/+vf8E18GywRMFzeJPqMQ5c+aYpCSfBNr47rvvZiUq1oIeM2bMgAEDEL22FnSrVq2IGcbTi1xMTExkbSpcu6hHfMj169fHxxvmwvVSJvDYTtHACNQwAYw/GS2NAfmuM644HmnWrKIt/L0I3dGjR2OG+xfVjVzHjCK4o8eNG2etED49YsQIVzxigiIEP6OfW7duTW34w5s1a8biW02aNIlor8yCSkAe4IL6zWpcIiACIiACIiACIiAC+YVAMOTYH4diROv6fGnbgtrX4+UlLjoz/Tu/Pw1RHIgvGTQ7wYPq2KBo//797FHknfuKFLQDAyQuyhMHKeszUz35l19+OQHSTLvFY2wCkpWi//WvfxEIjZCeOHHiCfbiqHmjRo2Io165cuWR9ln/OpMluDjFyDqD3H3uuefwSOOdZq+jf/zjH/Xq1fs+dLCmV9myZXEOH60xp5RriMo5rrvuOta4prKcyul6QSMgD3BB+0Y1HhEQAREQAREQAREQgfxGALUbF1/44gz/GfGBXZl7p2eWvCXOx8TXoBz0+zIDvrSMPVPjfJkZvkIJhaqHvMMnPERUHwtZhdTfUf3MIljENrMHEj5eFosePnw4+xKxclXE2t977z1kKnqYucQsiBU2Bzhikewyy5cvT3w1Dtjx48ezIxG1oWbpAIs2U4ROWkH2ZyLamV4Rv03I9IQJE/Di2lWUPD5hwpjNEn3rSmXX6EMPPVStWrWrrroKSyY8o37r1q2bnbHyCyoBCeCC+s1qXCIgAiIgAiIgAiIgAvmDQEi5IYHLBEq0DeybHH/444yd9wbKDI+LOxMtGAjsSf/hibj9b6LwMorcEJ9UKeQmPipiow+SlZzxdrKYM62w7ZAZE/Z87bXXshIyR7ly5YhqZrFlbK644oq3337b1knG9xs2TRc/MJvusvgzs3BbtGjRq1evMM2JnrQi7DbkyrLOFvHSYZa0yxJZqO4pU6aQoD9s+UskNt2rWbOmM8ZsyJAhDz/88KpVq+69916nfjFDGLNL065du1DsmEWEwFLV3onKNEcENctTI+Pp4T//+c/k5OSIBZVZgAnwFxUMM9AhAiIgAiIgAiIgAiIgAiLwOxIgzDkjfUfm143i0zb7/BmZ/nMCRZCCCZkpq+PTtsX5AunxZf1/fCchqRKdJP8kdtUUgTlRnfiMWP/xW0YsnjUTLepadImsZr8hh65ab508tlOqCsv/DZWrSP4lIAGcf7879VwEREAEREAEREAERKDgEAjJtczA4Y0Z394Wn7rW70/1hxxVfAR88WmJFySc84K/8JVxvl9jgAvOyDUSEchFAhLAuQhbTYmACIiACIiACIiACIhANgRCbslgcGYgc1/mvn/49s71pW7J8KfHJyYHireMP71bXDz77uIlJfj5eOOfs2lK2SIQuwQkgGP3u9fIRUAEREAEREAEREAE8iaBoDc4kJbpSw0tB53ICs3+4K5IebOz6pUI5CcCEsD56dtSX0VABERABERABERABGKBwK/e4CMrG8vrGwtfusaYOwQkgHOHs1oRAREQAREQAREQAREQAREQARH4nQlEXjH8d+6UmhcBERABERABERABERABERABERCBk01AAvhkE1V9IiACIiACIiACIiACIiACIiACeZKABHCe/FrUKREQAREQAREQAREQAREQAREQgZNNQAL4ZBNVfSIgAiIgAiIgAiIgAiIgAiIgAnmSgARwnvxa1CkREAEREAEREAEREAEREAEREIGTTeD/AYMiRLOFUsb7AAAAAElFTkSuQmCC\"></p><p 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\"></p><p class=\"ql-align-center\">The pulsar distribution in the sky and the Galactic plane visible to the FAST.</p>",
-    "authors": [
-      {
-        "name_en": "Li Di",
-        "name_zh": "Li Di",
-        "email": "dili@nao.cas.cn",
-        "affiliations": [
-          {
-            "name_en": " Tsinghua University",
-            "name_zh": " Tsinghua University"
-          },
-          {
-            "name_en": " National Astronomical Observatories ",
-            "name_zh": " National Astronomical Observatories "
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Pei",
-        "name_zh": "Wang Pei",
-        "email": "wangpei@nao.cas.cn",
-        "affiliations": [
-          {
-            "name_en": " National Astronomical Observatories ",
-            "name_zh": " National Astronomical Observatories "
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "CRAFTS",
-      "pulsar",
-      "Survey"
-    ],
-    "keywords_zh": [
-      "CRAFTS",
-      "pulsar",
-      "Survey"
-    ],
-    "publication_date": "2025-07-01T10:19:20.555Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-SA 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=68708f0c0e9dcc59702a25cf",
-    "source": "scidb"
-  },
-  {
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-    "doi": "10.5281/zenodo.4710751",
-    "cstr": "31253.11.10.5281/zenodo.4710751",
-    "pid": "",
-    "title": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
-    "title_en": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
-    "title_zh": "The Brain Imaging Data Structure (<span style='background-color:yellow'>BIDS</span>) Specification",
-    "description": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
-    "description_en": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
-    "description_zh": "This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions.To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version.See Appendix I for a list of the BIDS contributors who jointly created this specification.",
-    "authors": [
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-    "title": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
-    "title_en": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
-    "title_zh": "Bipolar <span style='background-color:yellow'>EEG</span> dataset - music",
-    "description": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
-    "description_en": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
-    "description_zh": "Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is on Github. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).# IntroductionThis dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach&#39;s Two-Part Inventions), and performing a vibrato detection task.Naming conventions:- The subject IDs are EBIP01, EBIP02 ... EBIP17.- The different conditions are labelled to indicate the instrument that was being attended: fG and fP for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and fGc and fPc for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).- An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).For example, the EEG file named EBIP08_fGc_4 contains EEG data from subject EBIP08 performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.# ContentThe general organisation of the dataset is as follow:data&emsp;├─── behav &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;folder containing the behavioural data&emsp;├─── EEG &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the EEG data&emsp;│&emsp;&emsp;&emsp;├─── processed&emsp;│&emsp;&emsp;&emsp;└─── raw&emsp;├─── linearModelResults &emsp;&emsp;&emsp;&emsp;&emsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the results from the analysis code&emsp;└─── stimuli &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;folder containing the stimuli&emsp;&emsp;&emsp;&emsp; ├─── features&emsp;&emsp;&emsp;&emsp; ├─── processedInventions&emsp;&emsp;&emsp;&emsp; └─── rawInventionsThis general organisation is the one expected by the code. The location of the data folder and/or these main folders can be personalised in the functions/+EEGmusic2020/getPath.m function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions makePathEEGFolder, makePathFeatureFiles and makePathSaveResults. The naming of the files within them is implemented by makeNameEEGDataFile and makeNameEEGDataFile (all these functions being in functions/+EEGmusic2020).&nbsp;- The behav folder is structured as follow:behav&nbsp;├─── EBIP02&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP02_keyboardInputs_fGc_2.mat &nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── timePressed &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;key press time (in seconds, relative to stimulus onset)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── keyCode &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ID of the keys that were pressed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── ...&nbsp;├─── ...&nbsp;├─── vibTime &nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── vibTime_2.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── idxNoteVib &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index (in the MIDI files) of the notes in which vibratos were inserted&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── instrumentOrder &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order of the instruments in idxNoteVib and vibTiming variables&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── vibTiming &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;timing of vibrato onsets in the track (in s)&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;└─── clickPerformance_RT_2.0.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing behavioural results for all subjects (FPR, TPR, etc.):instrumentOrder indicates to what instrument each column of idxNoteVib and vibTiming refers to. The data for EBIP01 missing due to a technical error.&nbsp;- The EEG/raw folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:EEG&nbsp;├─── raw&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_EEGExpParam.mat &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;file containing variables:&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;├─── conditionOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;whether this subject started by listening to the guitar or piano&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;└─── partsOrder  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order in which the inventions were presented to this subject&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk] &nbsp;raw EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...The conditionOrder variable can assume two values: either {&#39;fG&#39;,&#39;fP&#39;} indicating the subject started by listening to the guitar or {&#39;fP&#39;,&#39;fG&#39;} indicating the subject started by listening to the piano. The partsOrder variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument conditionOrder{1} was attended, and the invention # partsOrder(1,1) was played. During the second block, the instrument conditionOrder{2} was attended, and the invention #partsOrder(2,1) was played, etc.Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled Sound) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled S 1 and S 2 respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.&nbsp;The EEG/processed folder contains processed EEG data for all subjects, as required for the analyses carried out in the code. It is organised as follow:EEG&nbsp;├─── processed&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing that was applied&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── EBIP01&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;processed EEG data files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── noProc&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure is specified by the makePathEEGFolder function, and the file names by makeNameEEGDataFile. In the files in the noProc folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled stimBegin and stimEnd). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).&nbsp;- The linearModelResults folder contains the results from the linear model analyses:linearModelResults&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── HP-130 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the EEG data&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the stimulus feature&nbsp;│&nbsp; &nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... &nbsp;&nbsp;&nbsp;result files&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...This structure and file names are specified by the makePathSaveResults function.&nbsp;- The rawInventions folder contains the orignal data that was used to construct the stimuli:rawInventions&nbsp;├─── invent1 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;invention index&nbsp;│ &nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm.mid&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MIDI file&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── invent1_60bpm_guitar.wav &nbsp;guitar track&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── invent1_60bpm_piano.wav &nbsp;&nbsp;&nbsp;piano track&nbsp;│&nbsp;├─── ...In this folder (and only in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:&nbsp;&nbsp;&nbsp;Raw invention #&nbsp; |&nbsp; Feature, etc. #&nbsp;&nbsp;&nbsp;1, 2, 3, 4&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp; 1, 2, 3, 4&nbsp;&nbsp;&nbsp;7, 8, 9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -&gt;&nbsp;&nbsp; 5, 6,7&nbsp;- The processedInventions contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names (&#39;G&#39;: guitar, &#39;P&#39;: piano). &#39;zv&#39; indicates that the vibratos were replaced by zeros. &#39;noOnset30ms&#39; indicates that the onset of the notes was suppressed in a 30 ms window.&nbsp;- The features folder contains specific features of the stimuli for use in the models:features&nbsp;└─── Fs-5000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sampling rate of the feature&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── LP-2000 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;processing of the feature &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── waveform  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;feature name (here: stimulus waveform)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;│&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ...  &nbsp;&nbsp;&nbsp;feature files&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;└─── WNO &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Waveform No Onset (stimulus waveform with note onsets removed)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;├─── ... The naming convention is as highlighted above for the processedInventions folder. SI conditions correspond to &#39;G&#39; &amp; &#39;P&#39; files, and CI conditions to &#39;PG&#39; files. In the latter case, &#39;fG&#39; indicates the attended instrument is the guitar and &#39;fP&#39; the piano.These files notably contain the variables attended and ignored that contains the feature for the attended and ignored instruments (the ignored field is only present in the CI conditions).Note that a pair of two files corresponding to the same invention in a CI condition (&#39;PGfG&#39; &amp; &#39;PGfP&#39;) effectively contain the same information with the attended and ignored variables flipped.",
-    "authors": [
-      {
-        "name_en": "Etard Octave",
-        "name_zh": "Etard Octave"
-      },
-      {
-        "name_en": "Ben Messaoud Rémy",
-        "name_zh": "Ben Messaoud Rémy"
-      },
-      {
-        "name_en": "Gaugain Gabriel",
-        "name_zh": "Gaugain Gabriel"
-      },
-      {
-        "name_en": "Reichenbach Tobias",
-        "name_zh": "Reichenbach Tobias"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "music",
-      "auditory",
-      "neuroscience",
-      "continuous stimuli"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "music",
-      "auditory",
-      "neuroscience",
-      "continuous stimuli"
-    ],
-    "publication_date": "2020-10-19T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 6433.5,
-    "file_size_bytes": 6746018669,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.160118",
-    "cstr": "",
-    "pid": "",
-    "title": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (EEG_A to D), in European data format (.edf)",
-    "title_en": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (EEG_A to D), in European data format (.edf)",
-    "title_zh": "The raw <span style='background-color:yellow'>EEG</span> data, 4 files (<span style='background-color:yellow'>EEG</span>_A to D), in European data format (.edf)",
-    "description": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
-    "description_en": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
-    "description_zh": "EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'",
-    "authors": [
-      {
-        "name_en": "Brown Laurence A.",
-        "name_zh": "Brown Laurence A."
-      },
-      {
-        "name_en": "Hasan Sibah",
-        "name_zh": "Hasan Sibah"
-      },
-      {
-        "name_en": "Foster Russell G.",
-        "name_zh": "Foster Russell G."
-      },
-      {
-        "name_en": "Peirson Stuart N.",
-        "name_zh": "Peirson Stuart N."
-      }
-    ],
-    "keywords_en": [
-      "mouse",
-      "sleep",
-      "EEG",
-      "PIR",
-      "COMPASS"
-    ],
-    "keywords_zh": [
-      "mouse",
-      "sleep",
-      "EEG",
-      "PIR",
-      "COMPASS"
-    ],
-    "publication_date": "2016-10-13T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 124.11,
-    "file_size_bytes": 130135252,
-    "download_count": 0,
-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.29601",
-    "cstr": "",
-    "pid": "",
-    "title": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
-    "title_en": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
-    "title_zh": "GM <span style='background-color:yellow'>EEG</span> data zip 1 of 2",
-    "description": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
-    "description_en": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
-    "description_zh": "This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper \"Reduction of Pavlovian bias in schizophrenia\" under submission at PLoS One.EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.Triggers: Triggers are also included in the behavioural R data file \"BehaviouralData&amp;M6Fits.RData\" as a data.table (will need the 'data.table' package in R) named \"dftr\". Model fits are in the R data file, also as a data.table, labelled dfM3.Stimulus onset codes are: 1 &amp; 101 = Go-to-Avoid; 2 &amp; 202 = Go-to-Win; 3 &amp; 303 = NoGo-to-Avoid; 4 &amp; 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.Hope you enjoy!",
-    "authors": [
-      {
-        "name_en": "Albrecht Matthew",
-        "name_zh": "Albrecht Matthew"
-      },
-      {
-        "name_en": "Waltz Jim",
-        "name_zh": "Waltz Jim"
-      },
-      {
-        "name_en": "Cavanagh Jim",
-        "name_zh": "Cavanagh Jim"
-      },
-      {
-        "name_en": "Frank Michael",
-        "name_zh": "Frank Michael"
-      },
-      {
-        "name_en": "Gold Jim",
-        "name_zh": "Gold Jim"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Schizophrenia",
-      "Reinforcement Learning",
-      "Computational Modelling",
-      "Pavlovian Bias"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Schizophrenia",
-      "Reinforcement Learning",
-      "Computational Modelling",
-      "Pavlovian Bias"
-    ],
-    "publication_date": "2015-08-25T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-NC-SA-4.0",
-    "file_size_mb": 0.2,
-    "file_size_bytes": 204983,
-    "download_count": 1,
-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.29604",
-    "cstr": "31253.11.10.5281/zenodo.29604",
-    "pid": "",
-    "title": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
-    "title_en": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
-    "title_zh": "GM <span style='background-color:yellow'>EEG</span> data zip 2 of 2",
-    "description": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
-    "description_en": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
-    "description_zh": "This is the EEG dataset part 2 of 2 for the paper \"Reduction of Pavlovian bias in schizophrenia\"See part 1 for more information: 10.5281/zenodo.29601",
-    "authors": [
-      {
-        "name_en": "Albrecht Matthew",
-        "name_zh": "Albrecht Matthew"
-      },
-      {
-        "name_en": "Waltz Jim",
-        "name_zh": "Waltz Jim"
-      },
-      {
-        "name_en": "Cavanagh Jim",
-        "name_zh": "Cavanagh Jim"
-      },
-      {
-        "name_en": "Frank Michael",
-        "name_zh": "Frank Michael"
-      },
-      {
-        "name_en": "Gold Jim",
-        "name_zh": "Gold Jim"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Schizophrenia",
-      "Reinforcement Learning",
-      "Pavlovian Bias",
-      "Computational Modelling"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Schizophrenia",
-      "Reinforcement Learning",
-      "Pavlovian Bias",
-      "Computational Modelling"
-    ],
-    "publication_date": "2015-08-25T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-NC-SA-4.0",
-    "file_size_mb": 1503.62,
-    "file_size_bytes": 1576665038,
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-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.30084",
-    "cstr": "31253.11.10.5281/zenodo.30084",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
-    "title_en": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> data for P300 speller",
-    "description": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
-    "description_en": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
-    "description_zh": "These EEG signals were recorded during a P300 speller paradigm.Number of subjects: 10Number of channels: 19Number of sessions/subject:4------------------------------------------------------------------------------------------------------------------Each session has duration of 10 minutes, and is consisted of 14 runs (14 characters).After each run, there was a pause of 3s, and during this time the matrix was blank.&bull;&nbsp;&nbsp; &nbsp;The first session consisted of a series of separated characters, ordered in the following sequence:ض &ndash; س &ndash; ق &ndash; ل &ndash; ع &ndash; ن &ndash; ح &ndash; م &ndash; أ &ndash; ت &ndash; و &ndash; ر &ndash; ب &ndash; ه) )&bull;&nbsp;&nbsp; &nbsp;The second session also consisted of a series of separated characters, ordered in the following sequence:ذ &ndash; خ &ndash; ص &ndash; ي &ndash; د &ndash; ث &ndash; ف &ndash; غ &ndash; ج &ndash; ظ &ndash; ك &ndash; ش &ndash; ز &ndash; ط ) )&bull;&nbsp;&nbsp; &nbsp;The third session consisted of short words:طب - قف - عن - يد - كتب &ndash; لغة ) )&bull;&nbsp;&nbsp; &nbsp;The fourth session consisted of one long word:فأستسقيناكموها ) )-------------------------------------------------------------------------------------------------------------------EEG signals were performed at MAZLOUM hospital, Tripoli, Lebanon.",
-    "authors": [
-      {
-        "name_en": "Kabbara Aya",
-        "name_zh": "Kabbara Aya"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2015-08-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 25.56,
-    "file_size_bytes": 26797088,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.14820",
-    "cstr": "",
-    "pid": "",
-    "title": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
-    "title_en": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
-    "title_zh": "coverage and density of <span style='background-color:yellow'>eeg</span> source localization",
-    "description": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
-    "description_en": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
-    "description_zh": "The file contains the lead field matrices&nbsp;of&nbsp;atlas and subject specific head models &nbsp;and &nbsp;coordinate files of&nbsp;HydroCel EEG nets.&nbsp;",
-    "authors": [
-      {
-        "name_en": "Song Jasmine",
-        "name_zh": "Song Jasmine"
-      }
-    ],
-    "keywords_en": [
-      "dEEG",
-      "coverage",
-      "density lead field matrix"
-    ],
-    "keywords_zh": [
-      "dEEG",
-      "coverage",
-      "density lead field matrix"
-    ],
-    "publication_date": "2015-02-02T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 18.7,
-    "file_size_bytes": 19607912,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4385970",
-    "cstr": "",
-    "pid": "",
-    "title": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
-    "title_en": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
-    "title_zh": "Grammatical category and the neural processing of phrases - <span style='background-color:yellow'>EEG</span> data",
-    "description": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
-    "description_en": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
-    "description_zh": "EEG data presented in the paperGrammar, lexical category and the neural processing of phrasesAmelia Burroughs, Nina Kazanina, Conor Houghtonabstract:The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed -&nbsp;cold food loud room&nbsp;- or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units._______________________________all the corresponding code is atgithub.com/conorhoughton/NeuralProcessingOfPhrases&nbsp;",
-    "authors": [
-      {
-        "name_en": "Burroughs Amelia",
-        "name_zh": "Burroughs Amelia"
-      },
-      {
-        "name_en": "Kazanina Nina",
-        "name_zh": "Kazanina Nina"
-      },
-      {
-        "name_en": "Houghton Conor",
-        "name_zh": "Houghton Conor"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "neurolinguistics"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "neurolinguistics"
-    ],
-    "publication_date": "2020-09-29T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 283,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4558201",
-    "cstr": "",
-    "pid": "",
-    "title": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #147 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #147) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "doi": "10.5281/zenodo.4557037",
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-    "title": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #38 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #38) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #383 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #383) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
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-    "title": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #146 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #146) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "doi": "10.5281/zenodo.4561676",
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-    "title": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #256 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #256) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-      "low voltage-activated",
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-      "voltage-gated calcium channel"
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-    "title": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #337 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #337) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #405 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #405) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-      "activity",
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-      "voltage-gated calcium channel"
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-    "doi": "10.5281/zenodo.4561682",
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-    "title": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #260 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #260) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-      "low voltage-activated",
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-      "voltage-gated calcium channel"
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-    "title": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #334 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #334) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #132 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #132) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #133 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #133) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #336 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #336) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "title": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2-/- Mouse #37 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2-/-&nbsp;mouse (internal #37) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-      "activity",
-      "CA1",
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-      "low voltage-activated",
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-      "voltage-gated calcium channel"
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-      "activity",
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-      "voltage-gated calcium channel"
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-    "doi": "10.5281/zenodo.4561657",
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-    "title": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #406 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #406) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-      "activity",
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-      "T-type",
-      "voltage-gated calcium channel"
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-      "activity",
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-      "voltage-gated calcium channel"
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-    "doi": "10.5281/zenodo.4561663",
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-    "title": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #410 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #410) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
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-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
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-    "doi": "10.5281/zenodo.4561641",
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-    "title": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_en": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "title_zh": "Cav3.2+/+ Mouse #384 CA1 <span style='background-color:yellow'>EEG</span> recording",
-    "description": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_en": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "description_zh": "A Cav3.2+/+ mouse (internal #384) was implanted with an intrahippocampal electrode for electrohippocampal CA1 EEG recordings. The differential electrode of the TA10ETA-F20 transmitter&nbsp;(Data Science International, DSI, USA, technical specifications: weight 3.9 g, volume 1.9 cc, input voltage&nbsp;range &plusmn; 2.5 mV, channel bandwidth (B) 1&ndash;200 Hz, nominal sampling rate (f) 1000 Hz (f = 5 B), temperature operating&nbsp;range 34&ndash;41 &deg;C) was positioned&nbsp;at the following stereotaxic coordinates: (+)-lead, caudal &minus; 2 mm, lateral of bregma 1.5 mm (right hemisphere),&nbsp;and dorsoventral (depth) 1.5 mm. The epidural reference electrode was positioned on the surface of the cerebellar&nbsp;cortex at the following stereotaxic coordinates: (&minus;)-lead, bregma &minus; 6 mm and lateral of bregma 1 mm (righthemisphere).&nbsp;The deep tungsten electrodes (FHC, USA) are encapsuled with epoxylite with animpedance of 50&ndash;100 k&Omega; (measured at 1000 Hz) and a shank diameter of 250 &mu;m. For further details on the Cav3.2 mouse model, animal experimentation approval, anaesthesia, transmitter insertion,&nbsp;stereotaxic electrode implantation, postoperative pain management and recovery, please refer to Arshaad MI et al., 2021.&nbsp;EEG data were exported to NeuroScore&nbsp;3.2.9306-1 (Data Sciences International, DSI, USA). As the NeuroScore file format does not allow free access to the data, we have exported the raw EEG&nbsp;data as asci.-files, so that they can easily be imported in secondary analysis&nbsp;software. Importantly, the exported EEG data represent raw EEG data. No filtering, no artefact detection and/or artefact removal have&nbsp;been carried out. Thus, it&nbsp;is the users responsibility to adequately pretreat the data before further processing and analysis. As described in Arshaad MI et al. (2021), filtering, artefact detection and removal was carried out using NeuroScore before further analysis.The EEG data are presented here as a zip-folder&nbsp;for size reasons. Four EEG recordings are exported: R1 (a first 24h long-term EEG&nbsp;recording 10 days post transmitter implantation), R2 (a second 24h long-term recording 17 days post transmitter implantation), U1 (a 6h EEG recording following the first urethane injection (800 mg/kg i.p.) at day 18 post transmitter implantation), U2 (a 6h EEG recording&nbsp; after a second urethane injection (800 mg/kg i.p.) at day 25 post transmitter implantation). In addition, relative activity values are exported for all four recordings. For details on the recording procedure, please refer to Arshaad MI et al. (2021).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Weiergräber Marco",
-        "name_zh": "Weiergräber Marco"
-      }
-    ],
-    "keywords_en": [
-      "activity",
-      "CA1",
-      "EEG",
-      "hippocampus",
-      "low voltage-activated",
-      "T-type",
-      "voltage-gated calcium channel"
-    ],
-    "keywords_zh": [
-      "activity",
-      "CA1",
-      "EEG",
-      "hippocampus",
-      "low voltage-activated",
-      "T-type",
-      "voltage-gated calcium channel"
-    ],
-    "publication_date": "2021-03-01T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "file_size_bytes": 1033651456,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.806023",
-    "cstr": "",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
-    "title_en": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> Motor Imagery Dataset from the PhD Thesis \"Commande robuste d'un effecteur par une interface cerveau machine <span style='background-color:yellow'>EEG</span> asynchrone\"",
-    "description": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
-    "description_en": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
-    "description_zh": "This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.The duration of each trial is 3 second. There is 20 trial of each class.",
-    "authors": [
-      {
-        "name_en": "Alexandre Barachant",
-        "name_zh": "Alexandre Barachant"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Brain Computer Interface",
-      "Motor Imagery"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Brain Computer Interface",
-      "Motor Imagery"
-    ],
-    "publication_date": "2012-03-24T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-SA-4.0",
-    "file_size_mb": 16.53,
-    "file_size_bytes": 17335744,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4506494",
-    "cstr": "31253.11.10.5281/zenodo.4506494",
-    "pid": "",
-    "title": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
-    "title_en": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
-    "title_zh": "Computation of the electroencephalogram (<span style='background-color:yellow'>EEG</span>) from network models of point neurons",
-    "description": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
-    "description_en": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
-    "description_zh": "Datasets used in the paper &quot;Computation of the electroencephalogram (EEG) from network models of point neurons&quot; (2020), Biorxiv. doi:&nbsp;https://doi.org/10.1101/2020.11.02.364802.&nbsp;There are 4&nbsp;folders:\tresults: simulation outputs generated by the networks of leaky integrate-and-fire (LIF)&nbsp;and multicompartment&nbsp;neuron models.\tsrc:&nbsp;software scripts to compute the&nbsp;coefficient of determination, R^2, between the ground-truth EEG and&nbsp;the different EEG proxies evaluated in this work.&nbsp;\tFigs: scripts and additional data to plot figures.\tCNN: weights of the trained convolutional neural network&nbsp;(CNN).",
-    "authors": [
-      {
-        "name_en": "Martínez-Cañada Pablo",
-        "name_zh": "Martínez-Cañada Pablo"
-      },
-      {
-        "name_en": "Ness Torbjørn",
-        "name_zh": "Ness Torbjørn"
-      },
-      {
-        "name_en": "Einevoll Gaute",
-        "name_zh": "Einevoll Gaute"
-      },
-      {
-        "name_en": "Fellin Tommaso",
-        "name_zh": "Fellin Tommaso"
-      },
-      {
-        "name_en": "Panzeri Stefano",
-        "name_zh": "Panzeri Stefano"
-      }
-    ],
-    "keywords_en": [
-      "electroencephalogram (EEG)",
-      "leaky integrate and fire neuron model",
-      "point neuron model",
-      "multicompartment neuron model"
-    ],
-    "keywords_zh": [
-      "electroencephalogram (EEG)",
-      "leaky integrate and fire neuron model",
-      "point neuron model",
-      "multicompartment neuron model"
-    ],
-    "publication_date": "2021-02-04T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 3308.49,
-    "file_size_bytes": 3469200680,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4453396",
-    "cstr": "",
-    "pid": "",
-    "title": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
-    "title_en": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
-    "title_zh": "Hour-long <span style='background-color:yellow'>EEG</span> data recording from EEGlass",
-    "description": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
-    "description_en": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
-    "description_zh": "Description:EEGlass is a proof of concept wearable prototype comprised of EEG electrodes integrated in eyewear frames for approximating the form factor of a modern HMD. EEGlass is equipped with an OpenBCI board and a set of gold cup EEG electrodes at the contact points with the skull for unobtrusively collecting data related to the activity of the human brain [Vourvopoulos et al., 2019].This dataset includes 1 hour-long EEG data from EEGlass recording during casual computer tasks (e.g. web browsing, messaging, news reading) including mouse and keyboard markers. Key letters had been replaced with &#39;key pressed&#39; and &#39;key released&#39; markers for privacy reasons.Datasets had been parsed in EEGlab Matlab toolbox and have been exported as .set files.\tDatasets:\t\t\tRaw: eeglass_pctasks.set (data), eeglass_pctasks.fdt (header)\t\tFiltered: eeglass_pctasks_preproc.set (data), eeglass_pctasks_preproc.fdt (header)\t\t\tDuration:\t\t\t3919 seconds (~65.3 minutes)\t\t\t\tPre-processing of filtered dataset:\t\t\tBandpass filtering: FIR 1-40 Hz\t\tRe-referencing: Common average reference (CAR)\t\tChannel locations [1:Nz; 2:TP9; 3:TP10]\t\tReferences:- Paper can be found here: https://dl.acm.org/doi/10.1145/3341162.3348383- Additional data from EEGlass can be found here: https://zenodo.org/record/4451999",
-    "authors": [
-      {
-        "name_en": "Vourvopoulos Athanasios",
-        "name_zh": "Vourvopoulos Athanasios"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "BCI",
-      "Wearables",
-      "EEGlass"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "BCI",
-      "Wearables",
-      "EEGlass"
-    ],
-    "publication_date": "2021-01-19T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "file_size_bytes": 11759280,
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-    "url": "",
-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3684992",
-    "cstr": "",
-    "pid": "",
-    "title": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
-    "title_en": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
-    "title_zh": "Single electrode <span style='background-color:yellow'>EEG</span> data of healthy and epileptic patients",
-    "description": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
-    "description_en": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
-    "description_zh": "Single electrode scalp EEG data for healthy and epileptic patients that mimics the Bonn EEG dataset.Training data has 5 healthy and 5 epilepsy patients. Each subject has 40 files containing single electrode EEG data.Test data has 10 healthy and 10 epilepsy patients. Each subject has 20 files containing single electrode EEG data.The training data for both epileptic and healthy subjects is prefixed with &quot;Train&quot;. The letter &#39;E&#39; denotes epileptic patients and &#39;H&#39; denotes healthy subjects, followed by patient number, then underscore and file number.For further technical details see the following publication: Panwar, S., Joshi, S. D., Gupta, A. &amp; Agarwal, P. Automated Epilepsy Diagnosis Using EEG with Test Set Evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1106&ndash;1116 (2019).This is dataset #2 mentioned in the above article.",
-    "authors": [
-      {
-        "name_en": "Panwar Siddharth",
-        "name_zh": "Panwar Siddharth"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2020-02-22T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.02,
-    "file_size_bytes": 24749,
-    "download_count": 0,
-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68d29b8c19a491453e1c1226",
-    "doi": "10.57760/sciencedb.28712",
-    "cstr": "31253.11.sciencedb.28712",
-    "pid": "",
-    "title": "Data_ADHD_Neurofeedback_EEG_Meta_Analysis",
-    "title_en": "Data_ADHD_Neurofeedback_EEG_Meta_Analysis",
-    "title_zh": "Data_ADHD_Neurofeedback_<span style='background-color:yellow'>EEG</span>_Meta_Analysis",
-    "description": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
-    "description_en": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
-    "description_zh": "<p>This dataset compiles aggregated, study-level outcomes from randomized and controlled trials of portable/wearable EEG neurofeedback (NFT) in children and adolescents with ADHD. For each study arm and symptom domain, it reports pre- and post-intervention means (M), standard deviations (SD), and sample sizes for three domains: total, inattention, and hyperactivity/impulsivity, enabling standardized mean-change calculations in meta-analysis. Trials used portable EEG headsets (e.g., NeuroSky/MindWave, BrainLink, Emotiv Epoc X, Mensia Koala, Zeo, OmniCNS) suitable for home/school or low-supervision settings. Control conditions include waitlist/treatment-as-usual, attention/cognitive training, and sham NFT, consistent with the coding and subgrouping in the manuscript and Supplementary Materials.</p>",
-    "authors": [
-      {
-        "name_en": "XIN HUANG",
-        "name_zh": "XIN HUANG",
-        "email": "huangxin1208@gmail.com",
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-          {
-            "name_en": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD",
-            "name_zh": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD"
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-      "Neurofeedback",
-      "ADHD",
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-      "Intervention",
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-      "Meta analysis"
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-      "Neurofeedback",
-      "ADHD",
-      "NFT",
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-      "Children",
-      "Meta analysis"
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-    "title": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
-    "title_en": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
-    "title_zh": "Ultra high-density 255-channel <span style='background-color:yellow'>EEG</span>-AAD dataset",
-    "description": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
-    "description_en": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
-    "description_zh": "Experiment*************This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations,&nbsp; namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.File organization and details********************************The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention:1. Trial n of subject x attending to left-ear has the name: Sx_AAD_nL. 2. Trial n of subject x attending to right-ear has the name: Sx_AAD_nR.That is, the attended story/direction of each subject in a recording can be directly inferred from the file name. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below:Trial (n)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Left-ear&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Right-ear1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry &nbsp;&nbsp;&nbsp; part1_track2_dry2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part1_track1_dry&nbsp;&nbsp;&nbsp;&nbsp; part1_track2_dry3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dry4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; part2_track2_dry&nbsp;&nbsp;&nbsp;&nbsp; part2_track1_dryAdditional files (after extracting scripts.zip and misc.zip):\tscripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.\tmisc/channel-layout.jpeg: The 255-channel EEG cap layout\tmisc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.&nbsp;[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)[2]&nbsp;CURRY 8 X &ndash; Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)[3] Abhijith Mundanad Narayanan, &quot;EEG analysis in python&quot;, 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)",
-    "authors": [
-      {
-        "name_en": "Mundanad Narayanan Abhijith",
-        "name_zh": "Mundanad Narayanan Abhijith"
-      },
-      {
-        "name_en": "Zink Rob",
-        "name_zh": "Zink Rob"
-      },
-      {
-        "name_en": "Bertrand Alexander",
-        "name_zh": "Bertrand Alexander"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "signal processing",
-      "auditory attention decoding"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "signal processing",
-      "auditory attention decoding"
-    ],
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-    "doi": "10.5281/zenodo.1213584",
-    "cstr": "31253.11.10.5281/zenodo.1213584",
-    "pid": "",
-    "title": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
-    "title_en": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
-    "title_zh": "The causal role of the somatosensory cortex in prosocial behavior - <span style='background-color:yellow'>EEG</span> dataset",
-    "description": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
-    "description_en": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
-    "description_zh": "Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt",
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-        "name_en": "Gallo Selene",
-        "name_zh": "Gallo Selene"
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-        "name_en": "Paracampo Riccardo",
-        "name_zh": "Paracampo Riccardo"
-      },
-      {
-        "name_en": "Müller-Pinzler Laura",
-        "name_zh": "Müller-Pinzler Laura"
-      },
-      {
-        "name_en": "Severo Mario Carlo",
-        "name_zh": "Severo Mario Carlo"
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-        "name_en": "Blömer Laila",
-        "name_zh": "Blömer Laila"
-      },
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-        "name_en": "Fernandes-Henriques Carolina",
-        "name_zh": "Fernandes-Henriques Carolina"
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-      {
-        "name_en": "Henschel Anna",
-        "name_zh": "Henschel Anna"
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-        "name_en": "Lammes Balint Kallista",
-        "name_zh": "Lammes Balint Kallista"
-      },
-      {
-        "name_en": "Maskaljunas Tatjana",
-        "name_zh": "Maskaljunas Tatjana"
-      },
-      {
-        "name_en": "Suttrup Judith",
-        "name_zh": "Suttrup Judith"
-      },
-      {
-        "name_en": "Avenanti Alessio",
-        "name_zh": "Avenanti Alessio"
-      },
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-        "name_en": "Keysers Christian",
-        "name_zh": "Keysers Christian"
-      },
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-        "name_en": "Gazzola Valeria",
-        "name_zh": "Gazzola Valeria"
-      }
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-    "doi": "10.5281/zenodo.192684",
-    "cstr": "31253.11.10.5281/zenodo.192684",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
-    "title_en": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> Data for: \"Learning from Label Proportions in Brain-Computer Interfaces\"",
-    "description": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
-    "description_en": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
-    "description_zh": "Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. The dataset is used and described in the following journal article:Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., &amp; Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.Please cite the above article when using the data.The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., &amp; Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.The following files are available:description.pdf: Full description of the datasetoffline_auditory.zip: Data from the auditory offline study with N=5 subjectsoffline_visual.zip: Data from the visual offline study with N=5 subjectsonline_study_1-7.zip: Data from the online study for subjects 1-7online_study_8-13.zip: Data from the online study for subjects 8-13sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjectsWe will create a git repository with example code soon.",
-    "authors": [
-      {
-        "name_en": "David Hübner",
-        "name_zh": "David Hübner"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Learning from Label Proportions",
-      "LLP",
-      "Brain-Computer Interfaces",
-      "BCI",
-      "Visual",
-      "Auditory",
-      "Event-Related Potential",
-      "ERP",
-      "Multiclass",
-      "Healthy",
-      "Young",
-      "Electroencephalography",
-      "Unsupervised",
-      "Classification"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Learning from Label Proportions",
-      "LLP",
-      "Brain-Computer Interfaces",
-      "BCI",
-      "Visual",
-      "Auditory",
-      "Event-Related Potential",
-      "ERP",
-      "Multiclass",
-      "Healthy",
-      "Young",
-      "Electroencephalography",
-      "Unsupervised",
-      "Classification"
-    ],
-    "publication_date": "2016-12-04T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.42,
-    "file_size_bytes": 441415,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.1507643",
-    "cstr": "31253.11.10.5281/zenodo.1507643",
-    "pid": "",
-    "title": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
-    "title_en": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
-    "title_zh": "Quantitative analyses help in choosing between simultaneous versus separate <span style='background-color:yellow'>EEG</span> and fMRI",
-    "description": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
-    "description_en": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
-    "description_zh": "This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience&nbsp;- Brain Imaging Methods.&nbsp;&nbsp;",
-    "authors": [
-      {
-        "name_en": "Schrooten Maarten",
-        "name_zh": "Schrooten Maarten"
-      },
-      {
-        "name_en": "Vandenberghe Rik",
-        "name_zh": "Vandenberghe Rik"
-      },
-      {
-        "name_en": "Peeters Ronald",
-        "name_zh": "Peeters Ronald"
-      },
-      {
-        "name_en": "Dupont Patrick",
-        "name_zh": "Dupont Patrick"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "fMRI",
-      "EEG-fMRI"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "fMRI",
-      "EEG-fMRI"
-    ],
-    "publication_date": "2018-11-24T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 6837.02,
-    "file_size_bytes": 7169137234,
-    "download_count": 0,
-    "visit_count": 3,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.191899",
-    "cstr": "",
-    "pid": "",
-    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
-    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
-    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 5",
-    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
-    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
-    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.",
-    "authors": [
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      },
-      {
-        "name_en": "Silvoni Stefano",
-        "name_zh": "Silvoni Stefano"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-11-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 959,
-    "download_count": 0,
-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.191884",
-    "cstr": "",
-    "pid": "",
-    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
-    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
-    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 2",
-    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
-    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
-    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.",
-    "authors": [
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      },
-      {
-        "name_en": "Silvoni Stefano",
-        "name_zh": "Silvoni Stefano"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-11-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 1173,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.191887",
-    "cstr": "",
-    "pid": "",
-    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
-    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
-    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 3",
-    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
-    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
-    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.",
-    "authors": [
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      },
-      {
-        "name_en": "Silvoni Stefano",
-        "name_zh": "Silvoni Stefano"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-11-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 1186,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3547486",
-    "cstr": "",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
-    "title_en": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> data for generating reference rdFC patterns and sample rdFC patterns",
-    "description": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
-    "description_en": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
-    "description_zh": "The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.",
-    "authors": [
-      {
-        "name_en": "Panwar Siddharth",
-        "name_zh": "Panwar Siddharth"
-      }
-    ],
-    "keywords_en": [
-      "rdFC patterns"
-    ],
-    "keywords_zh": [
-      "rdFC patterns"
-    ],
-    "publication_date": "2019-11-18T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 1.74,
-    "file_size_bytes": 1829390,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.1109/embc.2016.7590650",
-    "cstr": "",
-    "pid": "",
-    "title": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
-    "title_en": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
-    "title_zh": "Graph fractional-order total variation <span style='background-color:yellow'>EEG</span> source reconstruction",
-    "description": "n/a",
-    "description_en": "n/a",
-    "description_zh": "n/a",
-    "authors": [
-      {
-        "name_en": "Li, Ying",
-        "name_zh": "Li, Ying"
-      },
-      {
-        "name_en": " Qin, Jing",
-        "name_zh": " Qin, Jing"
-      },
-      {
-        "name_en": " Osher, Stanley",
-        "name_zh": " Osher, Stanley"
-      },
-      {
-        "name_en": " Liu, Wentai",
-        "name_zh": " Liu, Wentai"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": 1469980800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 6.41,
-    "file_size_bytes": 6716332,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.191891",
-    "cstr": "",
-    "pid": "",
-    "title": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
-    "title_en": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
-    "title_zh": "fNIRS, <span style='background-color:yellow'>EEG</span> and EOG classification accuracy used to generate Fig 4",
-    "description": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
-    "description_en": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
-    "description_zh": "fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.",
-    "authors": [
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      },
-      {
-        "name_en": "Silvoni Stefano",
-        "name_zh": "Silvoni Stefano"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-11-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 1113,
-    "download_count": 0,
-    "visit_count": 3,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.1134776",
-    "cstr": "",
-    "pid": "",
-    "title": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
-    "title_en": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
-    "title_zh": "Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/<span style='background-color:yellow'>EEG</span> to publication: how to perform MEG/<span style='background-color:yellow'>EEG</span> group analysis with free",
-    "description": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
-    "description_en": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
-    "description_zh": "If you use the data or the analysis pipeline, please refer to:Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006and/orAndersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00261IMPORTANTVersion 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: https://github.com/ualsbombe/omission_frontiers.git&nbsp;Dataset with tactile expectations to be analysed with pipelines for either MNE-Python or FieldTrip, aiming to follow the MEG-BIDS structureUnzipping the dataData is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called &quot;analyses&quot;, which has a sub-directory called &quot;omission_frontiers_BIDS-FieldTrip&quot;, which has a sub-directory called &quot;data&quot;.Thus, as an example, in my case, I should have the path:&nbsp;&nbsp;&nbsp; /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/dataPath:on a Linux system the path would be&nbsp;&nbsp; /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a macOS system the path would be&nbsp;&nbsp; /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/dataon a Windows system the path would be C:\\Users\\your_name\\analyses\\omission_frontiers_BIDS-FieldTrip\\dataSteps for unzipping:1. Set up the folder above according to your operating system, following the examples above and substitute &quot;your_name&quot; for your user name.2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/Now you are ready to run the analyses.The MEG dataRaw fif files are contained in the data folder, ordered by subject (n=20)There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recordingProcessed MRI data For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects&#39; identityFor Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG dataFor MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.MetadataEach subject has a number of tsv-files:*channel.tsv contain information about the channels in that recording*events.tsv contain information about the events in that recordingremoved_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)*scans_tsv contain information about the scans conductedScripts Please see Github for the updated scripts at: https://github.com/ualsbombe/omission_frontiers.git",
-    "authors": [
-      {
-        "name_en": "Lau Møller Andersen",
-        "name_zh": "Lau Møller Andersen"
-      }
-    ],
-    "keywords_en": [
-      "MEG",
-      "analysis pipeline",
-      "MNE-Python",
-      "minimum norm estimate (MNE)",
-      "Tactile Expectations",
-      "Group analysis",
-      "good practice",
-      "fieldtrip",
-      "beamformer"
-    ],
-    "keywords_zh": [
-      "MEG",
-      "analysis pipeline",
-      "MNE-Python",
-      "minimum norm estimate (MNE)",
-      "Tactile Expectations",
-      "Group analysis",
-      "good practice",
-      "fieldtrip",
-      "beamformer"
-    ],
-    "publication_date": "2017-09-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-SA-4.0",
-    "file_size_mb": 2174.52,
-    "file_size_bytes": 2280145323,
-    "download_count": 0,
-    "visit_count": 2,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "64f04634cc4bc33e70a58f83",
-    "doi": "10.6084/m9.figshare.c.5740424.v1",
-    "cstr": "",
-    "pid": "",
-    "title": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
-    "title_en": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
-    "title_zh": "A simultaneous <span style='background-color:yellow'>EEG</span> and eye-tracking dataset in elite athletes during alertness and concentration tasks",
-    "description": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
-    "description_en": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
-    "description_zh": "<p>A dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings collected from 31 professional shooting, archery and modern pentathlon athletes (Sub3** and Sub5**) and 43 college students (Sub0** and Sub7**) during alertness behavior task (ABT) and concentration cognitive task (CCT).</p>",
-    "authors": [
-      {
-        "name_en": "Yu Yuguo",
-        "name_zh": "Yu Yuguo",
-        "email": "yuyuguo@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          }
-        ]
-      },
-      {
-        "name_en": "Pei Xinzhen",
-        "name_zh": "Pei Xinzhen",
-        "affiliations": [
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Guiying",
-        "name_zh": "Xu Guiying",
-        "email": "zhangm@sari.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute",
-            "name_zh": "上海高等研究院",
-            "ror_id": "https://ror.org/02br7py06"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
-            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhou Yunhui",
-        "name_zh": "Zhou Yunhui",
-        "email": "yuyuguo@fudan.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          }
-        ]
-      },
-      {
-        "name_en": "Tao Luna",
-        "name_zh": "Tao Luna",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Competitive Sports Training Management Center",
-            "name_zh": "Shanghai Competitive Sports Training Management Center"
-          }
-        ]
-      },
-      {
-        "name_en": "Cui Xiaozhu",
-        "name_zh": "Cui Xiaozhu",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)",
-            "name_zh": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Zhenyu",
-        "name_zh": "Wang Zhenyu",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Advanced Research Institute",
-            "name_zh": "上海高等研究院",
-            "ror_id": "https://ror.org/02br7py06"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
-            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Bingru",
-        "name_zh": "Xu Bingru",
-        "affiliations": [
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang An-Li",
-        "name_zh": "Wang An-Li",
-        "affiliations": [
-          {
-            "name_en": "Department of Psychiatry, Icahn School of Medicine at Mount Sinai",
-            "name_zh": "Department of Psychiatry, Icahn School of Medicine at Mount Sinai"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhao Xi",
-        "name_zh": "Zhao Xi",
-        "affiliations": [
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
-            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute",
-            "name_zh": "上海高等研究院",
-            "ror_id": "https://ror.org/02br7py06"
-          }
-        ]
-      },
-      {
-        "name_en": "Dong Haijun",
-        "name_zh": "Dong Haijun",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Normal University",
-            "name_zh": "Shanghai Normal University"
-          }
-        ]
-      },
-      {
-        "name_en": "An Yan",
-        "name_zh": "An Yan",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)",
-            "name_zh": "Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency)"
-          }
-        ]
-      },
-      {
-        "name_en": "Cao Yang",
-        "name_zh": "Cao Yang",
-        "affiliations": [
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          },
-          {
-            "name_en": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University",
-            "name_zh": "Human Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Life Science, Research Institute of Intelligent Complex Systems, and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University"
-          }
-        ]
-      },
-      {
-        "name_en": "Li Ruxue",
-        "name_zh": "Li Ruxue",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Advanced Research Institute",
-            "name_zh": "上海高等研究院",
-            "ror_id": "https://ror.org/02br7py06"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
-            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
-          }
-        ]
-      },
-      {
-        "name_en": "Hu Honglin",
-        "name_zh": "Hu Honglin",
-        "email": "huhl@sari.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Shanghai Advanced Research Institute",
-            "name_zh": "上海高等研究院",
-            "ror_id": "https://ror.org/02br7py06"
-          },
-          {
-            "name_en": "Shanghai Advanced Research Institute, Chinese Academy of Sciences",
-            "name_zh": "Shanghai Advanced Research Institute, Chinese Academy of Sciences"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "eye-tracking",
-      "research data"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "eye-tracking",
-      "research data"
-    ],
-    "publication_date": "2022-07-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=64f04634cc4bc33e70a58f83",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5282/zenodo.572319",
-    "cstr": "",
-    "pid": "",
-    "title": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
-    "title_en": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
-    "title_zh": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech",
-    "description": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
-    "description_en": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
-    "description_zh": "One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (n=20) and EEG (n=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG&gt;GR∩GG&gt;SG). In the EEG experiment we found that the identical integration process (GG&gt;GR∩GG&gt;SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.",
-    "authors": [
-      {
-        "name_en": "Yifei He",
-        "name_zh": "Yifei He"
-      },
-      {
-        "name_en": "Helge Gebhardt",
-        "name_zh": "Helge Gebhardt"
-      },
-      {
-        "name_en": "Miriam Steines",
-        "name_zh": "Miriam Steines"
-      },
-      {
-        "name_en": "Gebhard Sammer",
-        "name_zh": "Gebhard Sammer"
-      },
-      {
-        "name_en": "Tilo Kircher",
-        "name_zh": "Tilo Kircher"
-      },
-      {
-        "name_en": "Arne Nagels",
-        "name_zh": "Arne Nagels"
-      },
-      {
-        "name_en": "Benjamin Straube",
-        "name_zh": "Benjamin Straube"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2015-03-31T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 31.19,
-    "file_size_bytes": 32708757,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5061/dryad.14004q5",
-    "cstr": "",
-    "pid": "",
-    "title": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
-    "title_en": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
-    "title_zh": "Data from: Ictal quantitative surface electromyography correlates with postictal <span style='background-color:yellow'>EEG</span> suppression",
-    "description": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
-    "description_en": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
-    "description_zh": "Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment.Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES.Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p&lt;0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%.Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk.Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.",
-    "authors": [
-      {
-        "name_en": "Arbune Anca A.",
-        "name_zh": "Arbune Anca A."
-      },
-      {
-        "name_en": "Conradsen Isa",
-        "name_zh": "Conradsen Isa"
-      },
-      {
-        "name_en": "Cardenas Damon P.",
-        "name_zh": "Cardenas Damon P."
-      },
-      {
-        "name_en": "Whitmire Luke E.",
-        "name_zh": "Whitmire Luke E."
-      },
-      {
-        "name_en": "Voyles Shannon R.",
-        "name_zh": "Voyles Shannon R."
-      },
-      {
-        "name_en": "Wolf Peter",
-        "name_zh": "Wolf Peter"
-      },
-      {
-        "name_en": "Lhatoo Samden",
-        "name_zh": "Lhatoo Samden"
-      },
-      {
-        "name_en": "Ryvlin Philippe",
-        "name_zh": "Ryvlin Philippe"
-      },
-      {
-        "name_en": "Beniczky Sándor",
-        "name_zh": "Beniczky Sándor"
-      }
-    ],
-    "keywords_en": [
-      "epilepsy monitoring",
-      "Epilepsy/Seizures",
-      "EMG",
-      "Generalized seizures"
-    ],
-    "keywords_zh": [
-      "epilepsy monitoring",
-      "Epilepsy/Seizures",
-      "EMG",
-      "Generalized seizures"
-    ],
-    "publication_date": "2020-12-15T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 11.89,
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-    "visit_count": 5,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.160344",
-    "cstr": "",
-    "pid": "",
-    "title": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
-    "title_en": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
-    "title_zh": "PIR data and <span style='background-color:yellow'>EEG</span> scoring for Wellcome Open Research methods paper (Brown et al 2016)",
-    "description": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
-    "description_en": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
-    "description_zh": "PIR data and EEG-scored sleep in the Wellcome Open Research article:'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status' 1sensorPIRvsEEGdata.csv  -  PIR based actigraphy for mice to compare to EEG-scored sleepEEG_4mice10sec.csv  -  Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118blandAltLandD.csv  -  paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)1monthPIRsleep.csv  - 1 month of activity for for figure 424mice_activity_LD1week.csv  - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)24mice_sleep_LD1week.csv       ",
-    "authors": [
-      {
-        "name_en": "Brown Laurence A.",
-        "name_zh": "Brown Laurence A."
-      },
-      {
-        "name_en": "Hasan Sibah",
-        "name_zh": "Hasan Sibah"
-      },
-      {
-        "name_en": "Foster Russell G.",
-        "name_zh": "Foster Russell G."
-      },
-      {
-        "name_en": "Peirson Stuart N.",
-        "name_zh": "Peirson Stuart N."
-      }
-    ],
-    "keywords_en": [
-      "mouse",
-      "PIR",
-      "EEG",
-      "sleep",
-      "COMPASS"
-    ],
-    "keywords_zh": [
-      "mouse",
-      "PIR",
-      "EEG",
-      "sleep",
-      "COMPASS"
-    ],
-    "publication_date": "2016-10-13T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 13.15,
-    "file_size_bytes": 13788195,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.191929",
-    "cstr": "",
-    "pid": "",
-    "title": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
-    "title_en": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
-    "title_zh": "Daily wise <span style='background-color:yellow'>EEG</span> frequency domain results used to generate S1_Table1",
-    "description": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
-    "description_en": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
-    "description_zh": "Lists the daily wise EEG frequency domain results of each patient and also the averaged correlation between daily wise EEG frequency bands mean power and fNIRS classification accuracy (CA) for each patient.",
-    "authors": [
-      {
-        "name_en": "Silvoni Stefano",
-        "name_zh": "Silvoni Stefano"
-      },
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-11-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 3570,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.1109/isemc.1980.7567290",
-    "cstr": "31253.11.10.1109/isemc.1980.7567290",
-    "pid": "",
-    "title": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
-    "title_en": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
-    "title_zh": "An EMC Test Procedure for an Electroencephalograph (<span style='background-color:yellow'>EEG</span>) Using Human Subjects and Simulated Electronic Patients",
-    "description": "n/a",
-    "description_en": "n/a",
-    "description_zh": "n/a",
-    "authors": [
-      {
-        "name_en": "Ruggera, Paul S.",
-        "name_zh": "Ruggera, Paul S."
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": 339177600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
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-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.1371/journal.pone.0156436",
-    "cstr": "",
-    "pid": "",
-    "title": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
-    "title_en": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
-    "title_zh": "Semi-automatic segmentation of optic radiations and LGN, and their relationship to <span style='background-color:yellow'>EEG</span> alpha waves",
-    "description": "Spreadsheet containing structural and functional data",
-    "description_en": "Spreadsheet containing structural and functional data",
-    "description_zh": "Spreadsheet containing structural and functional data",
-    "authors": [
-      {
-        "name_en": "Renauld Emmanuelle",
-        "name_zh": "Renauld Emmanuelle"
-      },
-      {
-        "name_en": "Descoteaux Maxime",
-        "name_zh": "Descoteaux Maxime"
-      },
-      {
-        "name_en": "Bernier Michael",
-        "name_zh": "Bernier Michael"
-      },
-      {
-        "name_en": "Garyfallidis Eleftherios",
-        "name_zh": "Garyfallidis Eleftherios"
-      },
-      {
-        "name_en": "Whittingstall Kevin",
-        "name_zh": "Whittingstall Kevin"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-05-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.11,
-    "file_size_bytes": 114136,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.13003",
-    "cstr": "",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
-    "title_en": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> data for \"Conversation electrified: ERP correlates of speech act recognition in underspecified utterances\"",
-    "description": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
-    "description_en": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
-    "description_zh": "Please refer to the publication in Plos One for a description of the experiment and data analysis: &nbsp; Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068",
-    "authors": [
-      {
-        "name_en": "Gisladottir Rosa",
-        "name_zh": "Gisladottir Rosa"
-      },
-      {
-        "name_en": "Levinson Stephen",
-        "name_zh": "Levinson Stephen"
-      },
-      {
-        "name_en": "Chwilla Dorothee",
-        "name_zh": "Chwilla Dorothee"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2015-03-20T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 76.52,
-    "file_size_bytes": 80233200,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.834976",
-    "cstr": "31253.11.10.5281/zenodo.834976",
-    "pid": "",
-    "title": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
-    "title_en": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
-    "title_zh": "Upper limb movements can be decoded from the time-domain of low-frequency <span style='background-color:yellow'>EEG</span>",
-    "description": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
-    "description_en": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
-    "description_zh": "How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.",
-    "authors": [
-      {
-        "name_en": "Ofner Patrick",
-        "name_zh": "Ofner Patrick"
-      },
-      {
-        "name_en": "Schwarz Andreas",
-        "name_zh": "Schwarz Andreas"
-      },
-      {
-        "name_en": "Pereira Joana",
-        "name_zh": "Pereira Joana"
-      },
-      {
-        "name_en": "Müller-Putz Gernot R.",
-        "name_zh": "Müller-Putz Gernot R."
-      }
-    ],
-    "keywords_en": [
-      "brain-computer interface"
-    ],
-    "keywords_zh": [
-      "brain-computer interface"
-    ],
-    "publication_date": "2017-08-09T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.6,
-    "file_size_bytes": 624262,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.221095",
-    "cstr": "31253.11.10.5281/zenodo.221095",
-    "pid": "",
-    "title": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
-    "title_en": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
-    "title_zh": "Validity of two automatic artifact reduction software methods in ictal <span style='background-color:yellow'>EEG</span> interpretation. Dataset 1",
-    "description": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
-    "description_en": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
-    "description_zh": "Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). \"_out\" files have been processed by AR2.",
-    "authors": [
-      {
-        "name_en": "Weiss Shennan",
-        "name_zh": "Weiss Shennan"
-      }
-    ],
-    "keywords_en": [
-      "electroencephalogram",
-      "artifact",
-      "seizure",
-      "software"
-    ],
-    "keywords_zh": [
-      "electroencephalogram",
-      "artifact",
-      "seizure",
-      "software"
-    ],
-    "publication_date": "2016-12-23T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 162.05,
-    "file_size_bytes": 169925376,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "68d4f9cffae9e04d156b16f3",
-    "doi": "10.57760/sciencedb.28850",
-    "cstr": "31253.11.sciencedb.28850",
-    "pid": "",
-    "title": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
-    "title_en": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
-    "title_zh": "Portable <span style='background-color:yellow'>EEG</span> Neurofeedback for ADHD — Study-Level Dataset & RCT Meta-analysis Outputs",
-    "description": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
-    "description_en": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
-    "description_zh": "<p>This deposit contains one study-level dataset and two R scripts for a meta-analysis of portable/wearable EEG-based neurofeedback (NFT) in ADHD.</p><p>•\tDataset (Data_ADHD_Neurofeedback_EEG_Meta_Analysis.csv): pre- and post-intervention means, standard deviations, and sample sizes organized by study × arm (NFT or control) × symptom domain (total, inattention, hyperactivity/impulsivity). Scale units follow the source studies; no individual-level data are included.</p><p>•\tR scripts (Meta_BetweenGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R, Meta_WithinGroup_ADHD_Neurofeedback_EEG_Meta_Analysis.R): code to compute effect sizes, run between-group (NFT vs control) and within-group (pre–post) analyses, check heterogeneity, and produce standard meta-analysis plots (e.g., forest/funnel).</p>",
-    "authors": [
-      {
-        "name_en": "XIN HUANG",
-        "name_zh": "XIN HUANG",
-        "email": "huangxin1208@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD",
-            "name_zh": "Fuhuiyuan Psychological Counseling (Shanghai) Co., LTD"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Neurofeedback",
-      "ADHD",
-      "NFT",
-      "Intervention",
-      "Children",
-      "Meta analysis"
-    ],
-    "keywords_zh": [
-      "Neurofeedback",
-      "ADHD",
-      "NFT",
-      "Intervention",
-      "Children",
-      "Meta analysis"
-    ],
-    "publication_date": "2025-09-26T06:39:31.309Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-ND 4.0",
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-    "visit_count": 147,
-    "url": "https://www.scidb.cn/en/detail?id=68d4f9cffae9e04d156b16f3",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.838543",
-    "cstr": "",
-    "pid": "",
-    "title": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
-    "title_en": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
-    "title_zh": "<span style='background-color:yellow'>EEG</span> Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\"",
-    "description": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
-    "description_en": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
-    "description_zh": "EEG Data for: \"Composing only by thought: novel application of the P300 brain-computer interface\". See the file \"Information about the Dataset.pdf\" for further information.",
-    "authors": [
-      {
-        "name_en": "Pinegger Andreas",
-        "name_zh": "Pinegger Andreas"
-      },
-      {
-        "name_en": "Hiebel Hannah",
-        "name_zh": "Hiebel Hannah"
-      },
-      {
-        "name_en": "Wriessnegger Selina C.",
-        "name_zh": "Wriessnegger Selina C."
-      },
-      {
-        "name_en": "Müller-Putz Gernot R.",
-        "name_zh": "Müller-Putz Gernot R."
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2017-08-02T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.3,
-    "file_size_bytes": 309609,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.817545",
-    "cstr": "",
-    "pid": "",
-    "title": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
-    "title_en": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
-    "title_zh": "Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from <span style='background-color:yellow'>EEG</span> frequency tagging",
-    "description": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
-    "description_en": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
-    "description_zh": "Dataset relative to the following publication:Chen, J., Valsecchi, M. &amp; Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. Neuropsychologia, doi: 10.1016/j.neuropsychologia.2017.06.024.Please refer to \"data description.txt\" in each experiment for details about the data. ",
-    "authors": [
-      {
-        "name_en": "Chen Jing",
-        "name_zh": "Chen Jing"
-      },
-      {
-        "name_en": "Valsecchi Matteo",
-        "name_zh": "Valsecchi Matteo"
-      },
-      {
-        "name_en": "Gegenfurtner Karl",
-        "name_zh": "Gegenfurtner Karl"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2017-06-22T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 797.76,
-    "file_size_bytes": 836510937,
-    "download_count": 2,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.2654552",
-    "cstr": "31253.11.10.5281/zenodo.2654552",
-    "pid": "",
-    "title": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
-    "title_en": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
-    "title_zh": "Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized <span style='background-color:yellow'>EEG</span>",
-    "description": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
-    "description_en": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
-    "description_zh": "This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, &amp; Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).",
-    "authors": [
-      {
-        "name_en": "Adrian Naas",
-        "name_zh": "Adrian Naas"
-      },
-      {
-        "name_en": "João Rodrigues",
-        "name_zh": "João Rodrigues"
-      },
-      {
-        "name_en": "Jan-Philip Knirsch",
-        "name_zh": "Jan-Philip Knirsch"
-      },
-      {
-        "name_en": "Andreas Sonderegger",
-        "name_zh": "Andreas Sonderegger"
-      }
-    ],
-    "keywords_en": [
-      "Neurofeedback Training",
-      "Alpha",
-      "Short-term memory"
-    ],
-    "keywords_zh": [
-      "Neurofeedback Training",
-      "Alpha",
-      "Short-term memory"
-    ],
-    "publication_date": "2019-04-29T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-NC-ND-4.0",
-    "file_size_mb": 0.07,
-    "file_size_bytes": 71494,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.584032",
-    "cstr": "31253.11.10.5281/zenodo.584032",
-    "pid": "",
-    "title": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
-    "title_en": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
-    "title_zh": "The <span style='background-color:yellow'>EEG</span> and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'",
-    "description": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
-    "description_en": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
-    "description_zh": "This is the raw data forHe, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., &amp; Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.Both second level fMRI data and EEG data for single subjects are provided.",
-    "authors": [
-      {
-        "name_en": "He Yifei",
-        "name_zh": "He Yifei"
-      },
-      {
-        "name_en": "Gebhardt Helge",
-        "name_zh": "Gebhardt Helge"
-      },
-      {
-        "name_en": "Steines Miriam",
-        "name_zh": "Steines Miriam"
-      },
-      {
-        "name_en": "Sommer Gebhard",
-        "name_zh": "Sommer Gebhard"
-      },
-      {
-        "name_en": "Nagels Arne",
-        "name_zh": "Nagels Arne"
-      },
-      {
-        "name_en": "Kircher Tilo",
-        "name_zh": "Kircher Tilo"
-      },
-      {
-        "name_en": "Straube Benjamin",
-        "name_zh": "Straube Benjamin"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2017-05-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 21.88,
-    "file_size_bytes": 22943057,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.46756",
-    "cstr": "",
-    "pid": "",
-    "title": "studyforrest-data-multires7t: First public release",
-    "title_en": "studyforrest-data-multires7t: First public release",
-    "title_zh": "studyforrest-data-multires7t: First public release",
-    "description": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
-    "description_en": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
-    "description_zh": "studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions&nbsp;(0.8 mm, 1.4 mm, 2 mm, and 3 mm&nbsp;isotropic voxel size) for orientation&nbsp;decoding in visual cortex &mdash; in order to test hypotheses on the strength and&nbsp;spatial scale of orientation discriminating signals. The entire dataset and the&nbsp;analysis codes presented in BIDS (Brain Imaging Data Structure).&nbsp;",
-    "authors": [
-      {
-        "name_en": "Sengupta Ayan",
-        "name_zh": "Sengupta Ayan"
-      },
-      {
-        "name_en": "Yakupov Renat",
-        "name_zh": "Yakupov Renat"
-      },
-      {
-        "name_en": "Speck Oliver",
-        "name_zh": "Speck Oliver"
-      },
-      {
-        "name_en": "Pollmann Stefan",
-        "name_zh": "Pollmann Stefan"
-      },
-      {
-        "name_en": "Hanke Michael",
-        "name_zh": "Hanke Michael"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-02-21T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "ODC-PDDL-1.0",
-    "file_size_mb": 0.86,
-    "file_size_bytes": 901298,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.580485",
-    "cstr": "31253.11.10.5281/zenodo.580485",
-    "pid": "",
-    "title": "Single-flicker online SSVEP BCI datset",
-    "title_en": "Single-flicker online SSVEP BCI datset",
-    "title_zh": "Single-flicker online SSVEP BCI datset",
-    "description": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
-    "description_en": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
-    "description_zh": "The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:data. fsample: sampling rate (512 Hz)data.trial:  EEG signals for each stepdata.time: time series of EEG signals for each stepdata.class: classifier output for each step[1] Maye A, Zhang D, Engel AK (2017) \"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency\", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) \"Application of a single-flicker online SSVEP BCI for spatial navigation\", PLoS ONE, in press.",
-    "authors": [
-      {
-        "name_en": "Chen Jingjing",
-        "name_zh": "Chen Jingjing"
-      },
-      {
-        "name_en": "Zhang Dan",
-        "name_zh": "Zhang Dan"
-      },
-      {
-        "name_en": "Engel Andreas K.",
-        "name_zh": "Engel Andreas K."
-      },
-      {
-        "name_en": "Gong Qin",
-        "name_zh": "Gong Qin"
-      },
-      {
-        "name_en": "Maye Alexander",
-        "name_zh": "Maye Alexander"
-      }
-    ],
-    "keywords_en": [
-      "brain-computer interface",
-      "steady-state evoked potential",
-      "online BCI",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "brain-computer interface",
-      "steady-state evoked potential",
-      "online BCI",
-      "EEG"
-    ],
-    "publication_date": "2017-05-16T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4471943",
-    "cstr": "",
-    "pid": "",
-    "title": "Data from: Output planning at the input stage in visual working memory.",
-    "title_en": "Data from: Output planning at the input stage in visual working memory.",
-    "title_zh": "Data from: Output planning at the input stage in visual working memory.",
-    "description": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
-    "description_en": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
-    "description_zh": "Raw behavioral logfiles and EEG datasets of experiments 1 and 2 reported in the manuscript &quot;Output planning at the input stage in visual working memory&quot;, by Sage E.P. Boettcher, Daniela Gresch, Anna C. Nobre, Freek van Ede",
-    "authors": [
-      {
-        "name_en": "Sage E.P. Boettcher",
-        "name_zh": "Sage E.P. Boettcher"
-      },
-      {
-        "name_en": "Daniela Gresch",
-        "name_zh": "Daniela Gresch"
-      },
-      {
-        "name_en": "Anna C. Nobre",
-        "name_zh": "Anna C. Nobre"
-      },
-      {
-        "name_en": "Freek van Ede",
-        "name_zh": "Freek van Ede"
-      }
-    ],
-    "keywords_en": [
-      "Visual working memory",
-      " action planning",
-      " EEG"
-    ],
-    "keywords_zh": [
-      "Visual working memory",
-      " action planning",
-      " EEG"
-    ],
-    "publication_date": "2021-01-26T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 19562.26,
-    "file_size_bytes": 20512517290,
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-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3997352",
-    "cstr": "31253.11.10.5281/zenodo.3997352",
-    "pid": "",
-    "title": "Auditory Attention Detection Dataset KULeuven",
-    "title_en": "Auditory Attention Detection Dataset KULeuven",
-    "title_zh": "Auditory Attention Detection Dataset KULeuven",
-    "description": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
-    "description_en": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
-    "description_zh": "This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF&#39; or `dry&#39; (dichotic). &nbsp;An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. &nbsp;During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e. a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject.&nbsp;We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from &nbsp;the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format &#39;Sx&#39;, &#39;x&#39; referring to the subject number. The audio data is stored as wav files in the folder &#39;stimuli&#39;. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered &nbsp;(0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz.Each trial (trial*.mat) contains the following information:&nbsp;RawData.Channels: channel numbers (1 to 64).RawData.EegData:&nbsp;EEG data (samples X channels).FileHeader.SampleRate:&nbsp;sampling frequency of the saved data.TrialID: a number between 1 to 20, showing the trial number.attended_ear:&nbsp;the direction of attention of the subject. &#39;L&#39; for left, &#39;R&#39; for right.stimuli:&nbsp;cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively.condition:&nbsp;stimulus presentation condition. &#39;HRTF&#39; - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, &#39;dry&#39; - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.experiment:&nbsp;the number of the experiment (1, 2, or 3).part:&nbsp;part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3).attended_track:&nbsp;the attended story track. &#39;1&#39; for track 1 and &#39;2&#39; for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2.&nbsp;repetition:&nbsp;binary variable indicating where the trial is a repetition (of presented stimuli) or not.subject:&nbsp;subject id of the format &#39;Sx&#39;, &#39;x&#39; being the subject number.The &#39;stimuli&#39; folder contains .wav-files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the &#39;dry&#39; condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.The MATLAB file &#39;preprocess_data.m&#39; gives an example of how the synchronization and preprocessing of the EEG and audio data can be done as described in [14]. Dependency: AMToolbox.This dataset has been used in [3, 5-16].&nbsp;[1] Francart, T., Van Wieringen, A., &amp; Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. Journal of Neuroscience Methods, 172(2), 283-293.[2] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 30 March 2015)[3] Das, N., Biesmans, W., Bertrand, A., &amp; Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection. Journal of Neural Engineering, 13(5), 056014.[4] Somers, B., Francart, T., &amp; Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of Neural Engineering, 15(3), 036007.[5] Das, N., Vanthornhout, J., Francart, T., &amp; Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. NeuroImage&nbsp;204 (2020)[6] Biesmans, W., Das, N., Francart, T., &amp; Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.[7] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In Proceedings of the&nbsp;38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),&nbsp;77-80.[8] Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. IEEE Transactions on Biomedical Engineering, 64(5), 1045-1056.[9] Das, N., Van Eyndhoven, S., Francart, T., &amp; Bertrand, A. (2017). EEG-based Attention-Driven Speech Enhancement For Noisy Speech Mixtures Using N-fold Multi-Channel Wiener Filters. In Proceedings of the&nbsp;25th European Signal Processing Conference (EUSIPCO), 1660-1664.[10] Narayanan, A. M., &amp; Bertrand, A. (2018). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In&nbsp;Proceedings of the&nbsp;40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 77-80.[11] Vandecappelle , S., Deckers, L., Das, N., Ansari, A. H., Bertrand, A., &amp; Francart, T. (2020). EEG-based detection of the locus of auditory attention with convolutional neural networks. bioRxiv 475673; doi: https://doi.org/10.1101/475673.[12] Narayanan, A. M., &amp; Bertrand, A. (2019). Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Transactions on Biomedical Engineering,&nbsp;67(1), 234-244.[13] Geirnaert, S., Francart, T., &amp; Bertrand, A. (2019). A New Metric to Evaluate Auditory Attention Detection Performance Based on a Markov Chain. In Proceedings of the 27th&nbsp;European Signal Processing Conference (EUSIPCO), 1-5.[14] Geirnaert, S., Francart,T., &amp; Bertrand, A.&nbsp;(2020). An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a&nbsp;Context of Neuro-Steered Gain Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 307-317.[15] Geirnaert, S., Francart,T., &amp; Bertrand, A. (2020). Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns.&nbsp;bioRxiv&nbsp;2020.06.16.154450; doi:&nbsp;https://doi.org/10.1101/2020.06.16.154450.[16] Geirnaert, S., Vandecappelle, S., Alickovic, E., de Cheveign&eacute;, A., Lalor, E., Meyer, B.T., Miran, S., Francart, T., &amp; Bertrand, A. (2020). Neuro-Steered Hearing Devices: Decoding Auditory Attention From the Brain. arXiv&nbsp;2008.04569; doi: arXiv:2008.04569.&nbsp;",
-    "authors": [
-      {
-        "name_en": "Das Neetha",
-        "name_zh": "Das Neetha"
-      },
-      {
-        "name_en": "Francart Tom",
-        "name_zh": "Francart Tom"
-      },
-      {
-        "name_en": "Bertrand Alexander",
-        "name_zh": "Bertrand Alexander"
-      }
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-      "Auditory Attention Detection",
-      "EEG",
-      "signal processing",
-      "neuroscience"
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-      "EEG",
-      "signal processing",
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-    "doi": "10.5281/zenodo.4451999",
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-    "title": "EEGlass motor-imagery and resting-state pilot data",
-    "title_en": "EEGlass motor-imagery and resting-state pilot data",
-    "title_zh": "EEGlass motor-imagery and resting-state pilot data",
-    "description": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
-    "description_en": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
-    "description_zh": "Pilot EEG data (N=1) during motor-imagery and resting state (eyes-closed) from EEGlass eyeware prototype for ubiquitous brain-computer interaction.There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.Datasets:\tMotor Imagery\t\t\tEEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)\t\tEnobio (data: MI_Enobio.set; header: MI_Enobio.fdt)\t\t\tResting State (eyes-closed)\t\t\tEEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)\t\tEnobio (data: EC_Enobio.set; header: EC_Enobio.fdt)\t\t\tEOG\t\t\tEEGlass\t\t\t\t\t\t\t\tEOG Up (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Down (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Left (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tEOG Right (EOG_U_EEGlass.set, .fdt)\t\t\t\t\t\t\t\t\tPre-processing:\tBandpass filtering: FIR 1-40 Hz\tRe-referencing: Common average reference (CAR)\tChannel locations\t\t\tEEGlass [1:Nz; 2:TP9; 3:TP10]\t\tEnobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]\t\t&nbsp;Details from the pilot study can be found below:A. Vourvopoulos, et al., 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In &lt;i&gt;Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers&lt;/i&gt; (&lt;i&gt;UbiComp/ISWC &#39;19 Adjunct&lt;/i&gt;). Association for Computing Machinery, New York, NY, USA, 647&ndash;652. DOI:https://doi.org/10.1145/3341162.3348383",
-    "authors": [
-      {
-        "name_en": "Vourvopoulos Athanasios",
-        "name_zh": "Vourvopoulos Athanasios"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "BCI",
-      "Wearables"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "BCI",
-      "Wearables"
-    ],
-    "publication_date": "2019-08-31T16:00:00.000Z",
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-    "doi": "10.5061/dryad.4b8gtht9n",
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-    "title": "Ictal source imaging in epilepsy patients - Supplementary Data",
-    "title_en": "Ictal source imaging in epilepsy patients - Supplementary Data",
-    "title_zh": "Ictal source imaging in epilepsy patients - Supplementary Data",
-    "description": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
-    "description_en": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
-    "description_zh": "ObjectiveLocalization of seizure onset zone in focal epilepsy patients is a crucial step prior to surgical planning. Noninvasively achieving this goal would have a tremendous impact on clinical management of intractable seizure. MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. Interpretation Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.   ",
-    "authors": [
-      {
-        "name_en": "Ye Shuai",
-        "name_zh": "Ye Shuai"
-      },
-      {
-        "name_en": "Yang Lin",
-        "name_zh": "Yang Lin"
-      },
-      {
-        "name_en": "Lu Yunfeng",
-        "name_zh": "Lu Yunfeng"
-      },
-      {
-        "name_en": "Kucewicz Michal",
-        "name_zh": "Kucewicz Michal"
-      },
-      {
-        "name_en": "Brinkmann Benjamin",
-        "name_zh": "Brinkmann Benjamin"
-      },
-      {
-        "name_en": "Nelson Cindy",
-        "name_zh": "Nelson Cindy"
-      },
-      {
-        "name_en": "Sohrabpour Abbas",
-        "name_zh": "Sohrabpour Abbas"
-      },
-      {
-        "name_en": "Worrell Gregory",
-        "name_zh": "Worrell Gregory"
-      },
-      {
-        "name_en": "He Bin",
-        "name_zh": "He Bin"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2021-02-05T16:00:00.000Z",
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-    "doi": "10.5281/zenodo.998965",
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-    "title": "Infant Sibling Project: Sample Files",
-    "title_en": "Infant Sibling Project: Sample Files",
-    "title_zh": "Infant Sibling Project: Sample Files",
-    "description": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
-    "description_en": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
-    "description_zh": "These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.",
-    "authors": [
-      {
-        "name_en": "Levin April Robyn",
-        "name_zh": "Levin April Robyn"
-      },
-      {
-        "name_en": "Gabard-Durnam Laurel Joy",
-        "name_zh": "Gabard-Durnam Laurel Joy"
-      },
-      {
-        "name_en": "Mendez Leal Adriana Sofia",
-        "name_zh": "Mendez Leal Adriana Sofia"
-      },
-      {
-        "name_en": "O'Leary Heather Marie",
-        "name_zh": "O'Leary Heather Marie"
-      },
-      {
-        "name_en": "Wilkinson Carol Lee",
-        "name_zh": "Wilkinson Carol Lee"
-      },
-      {
-        "name_en": "Tager-Flusberg Helen",
-        "name_zh": "Tager-Flusberg Helen"
-      },
-      {
-        "name_en": "Nelson Charles Alexander",
-        "name_zh": "Nelson Charles Alexander"
-      }
-    ],
-    "keywords_en": [
-      "Electroencephalography",
-      " Autism",
-      " Infant",
-      " Child",
-      " Development"
-    ],
-    "keywords_zh": [
-      "Electroencephalography",
-      " Autism",
-      " Infant",
-      " Child",
-      " Development"
-    ],
-    "publication_date": "2017-09-27T16:00:00.000Z",
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-    "modified": "",
-    "status": "",
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.841764",
-    "cstr": "",
-    "pid": "",
-    "title": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
-    "title_en": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
-    "title_zh": "CNBI EPFL Brain Tweakers Cybathlon BCI race dataset",
-    "description": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
-    "description_en": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
-    "description_zh": "This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.More information on the event can be found at:&nbsp;http://www.cybathlon.ethz.ch/All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.For more information on the dataset, please contact the authors.",
-    "authors": [
-      {
-        "name_en": "Perdikis Serafeim",
-        "name_zh": "Perdikis Serafeim"
-      },
-      {
-        "name_en": "Tonin Luca",
-        "name_zh": "Tonin Luca"
-      },
-      {
-        "name_en": "Saeedi Sareh",
-        "name_zh": "Saeedi Sareh"
-      },
-      {
-        "name_en": "Schneider Christoph",
-        "name_zh": "Schneider Christoph"
-      },
-      {
-        "name_en": "Millan Jose del R.",
-        "name_zh": "Millan Jose del R."
-      }
-    ],
-    "keywords_en": [
-      "brain-computer interface",
-      "motor-imagery",
-      "spinal cord injury",
-      "cybathlon",
-      "BCI race",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "brain-computer interface",
-      "motor-imagery",
-      "spinal cord injury",
-      "cybathlon",
-      "BCI race",
-      "EEG"
-    ],
-    "publication_date": "2017-08-10T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4331062",
-    "cstr": "31253.11.10.5281/zenodo.4331062",
-    "pid": "",
-    "title": "Microgecko helenae Nikolsky 1907",
-    "title_en": "Microgecko helenae Nikolsky 1907",
-    "title_zh": "Microgecko helenae Nikolsky 1907",
-    "description": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
-    "description_en": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
-    "description_zh": "Microgecko helenae Nikolsky, 1907    Microgecko helenae Nikolsky, 1907: 265.   COMMON NAME. — Banded Dwarf Gecko.  LECTOTYPE — ZIL 10242.  TYPE LOCALITY. — Alkhorshid, Esfahan, and Bid Zard; restricted to Bid Zard.  DISTRIBUTION. — Western foothills of the Zagros Mountains (Karamiani &amp; Rastegar-Pouyani 2012; Smid et al. 2014; Gholamifard et al. 2015).   HABITAT. — Under small stones, in rolling foothills with scattered vegetation (Smid et al. 2014).   IUCN. — Data deficient.  REFERENCES. — Nikolsky (1907); Karamiani &amp; Rastegar-Pouyani (2012); Smid et al. (2014); Gholamifard et al. (2015).",
-    "authors": [
-      {
-        "name_en": "Eskandarzadeh, Naeimeh",
-        "name_zh": "Eskandarzadeh, Naeimeh"
-      },
-      {
-        "name_en": " Rastegar-Pouyani, Nasrullah",
-        "name_zh": " Rastegar-Pouyani, Nasrullah"
-      },
-      {
-        "name_en": " Rastegar-Pouyani, Eskandar",
-        "name_zh": " Rastegar-Pouyani, Eskandar"
-      },
-      {
-        "name_en": " Fathinia, Behzad",
-        "name_zh": " Fathinia, Behzad"
-      },
-      {
-        "name_en": " Bahmani, Zahed",
-        "name_zh": " Bahmani, Zahed"
-      },
-      {
-        "name_en": " Hamidi, Kordiyeh",
-        "name_zh": " Hamidi, Kordiyeh"
-      },
-      {
-        "name_en": " Gholamifard, Ali",
-        "name_zh": " Gholamifard, Ali"
-      }
-    ],
-    "keywords_en": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Chordata",
-      "Reptilia",
-      "Squamata",
-      "Gekkonidae",
-      "Microgecko",
-      "Microgecko helenae"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Chordata",
-      "Reptilia",
-      "Squamata",
-      "Gekkonidae",
-      "Microgecko",
-      "Microgecko helenae"
-    ],
-    "publication_date": 1543420800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3610120",
-    "cstr": "",
-    "pid": "",
-    "title": "Data for Positive Emotion Regulation Task",
-    "title_en": "Data for Positive Emotion Regulation Task",
-    "title_zh": "Data for Positive Emotion Regulation Task",
-    "description": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
-    "description_en": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
-    "description_zh": "These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.",
-    "authors": [
-      {
-        "name_en": "Kahrilas Ian",
-        "name_zh": "Kahrilas Ian"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2020-01-15T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 507,
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-    "url": "",
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-    "doi": "10.5281/zenodo.55847",
-    "cstr": "31253.11.10.5281/zenodo.55847",
-    "pid": "",
-    "title": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
-    "title_en": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
-    "title_zh": "[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience",
-    "description": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
-    "description_en": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
-    "description_zh": "Raw dataArtifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).",
-    "authors": [
-      {
-        "name_en": "Chen Zheng",
-        "name_zh": "Chen Zheng"
-      },
-      {
-        "name_en": "He Yujia",
-        "name_zh": "He Yujia"
-      },
-      {
-        "name_en": "Yu Yuguo",
-        "name_zh": "Yu Yuguo"
-      }
-    ],
-    "keywords_en": [
-      "Environmental neuroscience",
-      "Nature experience",
-      "Functional connectivity"
-    ],
-    "keywords_zh": [
-      "Environmental neuroscience",
-      "Nature experience",
-      "Functional connectivity"
-    ],
-    "publication_date": "2016-06-17T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.580211",
-    "cstr": "31253.11.10.5281/zenodo.580211",
-    "pid": "",
-    "title": "Dataset: Neural correlates of error prediction in a complex motor task",
-    "title_en": "Dataset: Neural correlates of error prediction in a complex motor task",
-    "title_zh": "Dataset: Neural correlates of error prediction in a complex motor task",
-    "description": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
-    "description_en": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
-    "description_zh": "There are two files for each subject:1. errorsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. hitsegments_sub##.mat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 5 cm) in the task (segment and electrode information can be found below).The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodesdatapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]  ",
-    "authors": [
-      {
-        "name_en": "Maurer L.K. Maurer H. & Müller H.",
-        "name_zh": "Maurer L.K. Maurer H. & Müller H."
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "error prediction",
-      "error-related negativity",
-      "motor task",
-      "ballistic throwing task"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "error prediction",
-      "error-related negativity",
-      "motor task",
-      "ballistic throwing task"
-    ],
-    "publication_date": "2017-05-15T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "source": "scidb"
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-  {
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-    "doi": "10.5281/zenodo.1201560",
-    "cstr": "",
-    "pid": "",
-    "title": "Integrated analysis of anatomical and electrophysiological human intracranial data",
-    "title_en": "Integrated analysis of anatomical and electrophysiological human intracranial data",
-    "title_zh": "Integrated analysis of anatomical and electrophysiological human intracranial data",
-    "description": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
-    "description_en": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
-    "description_zh": "The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.This collection contains the data described in the protocol and that can be used to replicate all results.",
-    "authors": [
-      {
-        "name_en": "Stolk Arjen",
-        "name_zh": "Stolk Arjen"
-      },
-      {
-        "name_en": "Griffin Sandon",
-        "name_zh": "Griffin Sandon"
-      },
-      {
-        "name_en": "van der Meij Roemer",
-        "name_zh": "van der Meij Roemer"
-      },
-      {
-        "name_en": "Dewar Callum",
-        "name_zh": "Dewar Callum"
-      },
-      {
-        "name_en": "Saez Ignacio",
-        "name_zh": "Saez Ignacio"
-      },
-      {
-        "name_en": "Lin Jack J.",
-        "name_zh": "Lin Jack J."
-      },
-      {
-        "name_en": "Piantoni Giovanni",
-        "name_zh": "Piantoni Giovanni"
-      },
-      {
-        "name_en": "Schoffelen Jan-Mathijs",
-        "name_zh": "Schoffelen Jan-Mathijs"
-      },
-      {
-        "name_en": "Knight Robert T.",
-        "name_zh": "Knight Robert T."
-      },
-      {
-        "name_en": "Oostenveld Robert",
-        "name_zh": "Oostenveld Robert"
-      }
-    ],
-    "keywords_en": [
-      "ECoG",
-      "iEEG",
-      "sEEG",
-      "intracranial EEG",
-      "FieldTrip",
-      "electrocorticography",
-      "stereoelectroencephalography",
-      "epilepsy"
-    ],
-    "keywords_zh": [
-      "ECoG",
-      "iEEG",
-      "sEEG",
-      "intracranial EEG",
-      "FieldTrip",
-      "electrocorticography",
-      "stereoelectroencephalography",
-      "epilepsy"
-    ],
-    "publication_date": "2018-03-20T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-SA-4.0",
-    "file_size_mb": 204.64,
-    "file_size_bytes": 214580946,
-    "download_count": 0,
-    "visit_count": 4,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.1148304",
-    "cstr": "",
-    "pid": "",
-    "title": "Mind-tracking and the new transparent self",
-    "title_en": "Mind-tracking and the new transparent self",
-    "title_zh": "Mind-tracking and the new transparent self",
-    "description": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
-    "description_en": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
-    "description_zh": "Have you ever wanted to monitor your steps, heart rate, sleep, or body temperature? Nowadays, we can choose from an incredible variety of wearable devices. If you aim to move beyond tracking the physical to tracking the mind, you should probably check out the new generation of wearables: headsets with electrodes. By using Electroencephalography (EEG), these wearables work by detecting your brain waves. What does this mean for our personal thoughts and fantasies? Will they become trackable and therefore transparent? These and other ethical issues come up when thinking about the advent of such technologies.",
-    "authors": [
-      {
-        "name_en": "Przegalińska, Aleksandra",
-        "name_zh": "Przegalińska, Aleksandra"
-      }
-    ],
-    "keywords_en": [
-      "Mind-tracking",
-      "Algorithms",
-      "Artificial Intelligence",
-      "Transparency",
-      "Wearables",
-      "Electroencephalography (EEG)",
-      "Technology",
-      "Data"
-    ],
-    "keywords_zh": [
-      "Mind-tracking",
-      "Algorithms",
-      "Artificial Intelligence",
-      "Transparency",
-      "Wearables",
-      "Electroencephalography (EEG)",
-      "Technology",
-      "Data"
-    ],
-    "publication_date": 1516032000000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-SA-4.0",
-    "file_size_mb": 0.09,
-    "file_size_bytes": 90148,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "664ab3684e7538442ce796e9",
-    "doi": "10.57760/sciencedb.03168",
-    "cstr": "31253.11.sciencedb.03168",
-    "pid": "21.86116.6/sciencedb.03168",
-    "title": "Imitation of Grasping Animals Activates Motor Inhibition",
-    "title_en": "Imitation of Grasping Animals Activates Motor Inhibition",
-    "title_zh": "Imitation of Grasping Animals Activates Motor Inhibition",
-    "description": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "description_en": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "description_zh": "<p>The experimental apparatus was a Lenovo computer connected to a 17-inch cathode ray tube (CRT) monitor (85-Hz refresh rates). Stimuli were presented using E-prime 2.0 software (version 2.0, Psychology Software Tools, Inc. Pittsburgh, PA, USA). The input device was a standard keyboard. Electroencephalogram (EEG) data were recorded by a Neuroscan system (Neuroscan, Inc.). A Neuroscan Synamp 2 amplifier with a 64 Ag/AgCl electrode cap mounted according to the extended international 10–20 system was used to continuously record EEG data (sampling rate at 500 Hz).</p>",
-    "authors": [
-      {
-        "name_en": "Peng Liu",
-        "name_zh": "Peng Liu",
-        "email": "liupeng@nwu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Northwest University",
-            "name_zh": "Northwest University",
-            "ror_id": "https://ror.org/00y7snj24"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "motor interference effect",
-      "dangerous objects",
-      "motor inhibition",
-      "danger evaluation",
-      "theta oscillations",
-      "time-frequency analysis"
-    ],
-    "keywords_zh": [
-      "motor interference effect",
-      "dangerous objects",
-      "motor inhibition",
-      "danger evaluation",
-      "theta oscillations",
-      "time-frequency analysis"
-    ],
-    "publication_date": "2022-10-08T02:36:25.636Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 3795.95,
-    "file_size_bytes": 3980346206,
-    "download_count": 83,
-    "visit_count": 817,
-    "url": "https://www.scidb.cn/en/detail?id=664ab3684e7538442ce796e9",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6763828ba828ad3e95fc2eca",
-    "doi": "10.57760/sciencedb.16868",
-    "cstr": "31253.11.sciencedb.16868",
-    "pid": "",
-    "title": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
-    "title_en": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
-    "title_zh": "The representation of abstract goals in working memory is supported by task-congruent neural geometry. PLOS Biology. Zhang & Yu, 2024",
-    "description": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
-    "description_en": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
-    "description_zh": "<p>EEG folder contains preprocessed .fif files from 22 participants. They were processed and cleaned epoch data using the MNE package and can be read into workspace using it too. The behavoiral performance and trial information associated with the EEG experiment are stored as csv files under the save directory.</p><p><br></p><p>FMRI folder contains trial-wise beta maps from 21 participants. These maps were fitted for both delay periods separately. Note that they were all standardized towards MNI152 template and the subcortex is masked off. Behavioural csv files included too.</p>",
-    "authors": [
-      {
-        "name_en": "Mengya Zhang",
-        "name_zh": "Mengya Zhang",
-        "email": "zhangmengya@ion.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Center for Excellence in Brain Science and Intelligence Technology",
-            "name_zh": "学院脑科学与智能技术卓越创新中心",
-            "ror_id": "https://ror.org/00vpwhm04"
-          }
-        ]
-      },
-      {
-        "name_en": "Qing Yu",
-        "name_zh": "Qing Yu",
-        "email": "qingyu@ion.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Center for Excellence in Brain Science and Intelligence Technology",
-            "name_zh": "学院脑科学与智能技术卓越创新中心",
-            "ror_id": "https://ror.org/00vpwhm04"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Working Memory",
-      "cognitive control",
-      "Neuroimaging"
-    ],
-    "keywords_zh": [
-      "Working Memory",
-      "cognitive control",
-      "Neuroimaging"
-    ],
-    "publication_date": "2024-11-15T06:04:50.304Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 8910.1,
-    "file_size_bytes": 9342915606,
-    "download_count": 396,
-    "visit_count": 579,
-    "url": "https://www.scidb.cn/en/detail?id=6763828ba828ad3e95fc2eca",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.580200",
-    "cstr": "31253.11.10.5281/zenodo.580200",
-    "pid": "",
-    "title": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
-    "title_en": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
-    "title_zh": "Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring",
-    "description": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
-    "description_en": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
-    "description_zh": "There are two files for each subject:1. sub##_error.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target &gt; 12 cm) in the task (segment and electrode information can be found below).2. sub##_hit.dat -&gt; Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target &lt; 7 cm) in the task (segment and electrode information can be found below).The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodesn represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726  (850 ms after ball release) in every segment.datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,i.e. hit/error #1 -&gt; row 1 to 1400, hit/error #2 -&gt; row 1401 to 2800, ..., hit/error #n -&gt; (n-1) * 1400 + 1 to n * 1400electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]",
-    "authors": [
-      {
-        "name_en": "Joch M. Hegele M. Maurer H. Müller H. & Maurer L.K.",
-        "name_zh": "Joch M. Hegele M. Maurer H. Müller H. & Maurer L.K."
-      }
-    ],
-    "keywords_en": [
-      "action effect monitoring",
-      "EEG",
-      "error negativity",
-      "ERP",
-      "forward model"
-    ],
-    "keywords_zh": [
-      "action effect monitoring",
-      "EEG",
-      "error negativity",
-      "ERP",
-      "forward model"
-    ],
-    "publication_date": "2017-05-15T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 1419,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.1237654",
-    "cstr": "31253.11.10.5281/zenodo.1237654",
-    "pid": "",
-    "title": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
-    "title_en": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
-    "title_zh": "REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults",
-    "description": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
-    "description_en": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
-    "description_zh": "Methods and designParticipantsWe recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.Apparatus and materialsAll participants were fitted with a Polar RS800 CX monitor (Polar Electro &Ouml;y, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.ProcedureParticipants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48&ndash;72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15&rsquo; resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10&rsquo; warm-up on a cycle-ergometer at a power load of 20% of their individual VO2&nbsp;VAT, following by 30&rsquo; exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO2&nbsp;VAT (see Table 1). Upon completion of the exercise, a 10&rsquo; cool down period at 20% VO2&nbsp;VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm &bull; min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5&rsquo; 44&rsquo;&rsquo;). The first flanker task was then performed for 6&rsquo;, followed by a 15&rsquo; resting period with closed eyes. Finally, they again completed the 6&rsquo; flanker task.Flanker taskWe used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., &lt;&lt;&lt;&lt;&lt; or &gt;&gt;&gt;&gt;&gt;), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., &lt;&lt;&gt;&lt;&lt; or &gt;&gt;&lt;&gt;&gt;). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000&ndash;1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.EEG recording and analysisEEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 k&Omega;. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme &amp; Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme &amp; Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.Spectral power analysis. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.Event-Related Spectral Perturbation (ERSP) analysis.&nbsp;Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from &ndash;500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a &minus;300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.",
-    "authors": [
-      {
-        "name_en": "Ciria Luis F.",
-        "name_zh": "Ciria Luis F."
-      },
-      {
-        "name_en": "Perakakis Pandelis",
-        "name_zh": "Perakakis Pandelis"
-      },
-      {
-        "name_en": "Luque-Casado Antonio",
-        "name_zh": "Luque-Casado Antonio"
-      },
-      {
-        "name_en": "Sanabria Daniel",
-        "name_zh": "Sanabria Daniel"
-      }
-    ],
-    "keywords_en": [
-      "EEG",
-      "Physical exercise",
-      "Inhibitory control",
-      "Cognition"
-    ],
-    "keywords_zh": [
-      "EEG",
-      "Physical exercise",
-      "Inhibitory control",
-      "Cognition"
-    ],
-    "publication_date": "2018-04-29T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
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-    "doi": "10.5281/zenodo.4752462",
-    "cstr": "31253.11.10.5281/zenodo.4752462",
-    "pid": "",
-    "title": "Infurcitinea nigropluviella",
-    "title_en": "Infurcitinea nigropluviella",
-    "title_zh": "Infurcitinea nigropluviella",
-    "description": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
-    "description_en": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
-    "description_zh": "Infurcitinea nigropluviella (WALSINGHAM, 1907)    Material:    Iran: 1 , Fars, 10 km S Deh Bid, 2000 m, 14.vi.1996, leg. R. LINNAVUORI; FMNH. First record from Central Asia, previously known from Northern Africa (Tunisia, Algeria), from Greece, Cyprus, and from Turkey.",
-    "authors": [
-      {
-        "name_en": "Gaedike, Reinhard",
-        "name_zh": "Gaedike, Reinhard"
-      }
-    ],
-    "keywords_en": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Lepidoptera",
-      "Tineidae",
-      "Infurcitinea",
-      "Infurcitinea nigropluviella"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Lepidoptera",
-      "Tineidae",
-      "Infurcitinea",
-      "Infurcitinea nigropluviella"
-    ],
-    "publication_date": 1320854400,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 649,
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-    "url": "",
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.57911",
-    "cstr": "",
-    "pid": "",
-    "title": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
-    "title_en": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
-    "title_zh": "Data files related to the article \"Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control",
-    "description": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
-    "description_en": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
-    "description_zh": "Files description:- EEG datasets: the EEG data preprocessed in EEGLAB format- chanlocs.mat: contains channel characteristics for the study / used to plot topographical maps of the data - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition - 60110Hz_spec_data_medit.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in meditation condition - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for  each group in meditation condition- boxplot_gamma.csv and boxplot_alpha.csv :  median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files",
-    "authors": [
-      {
-        "name_en": "Braboszcz Claire",
-        "name_zh": "Braboszcz Claire"
-      },
-      {
-        "name_en": "Cahn B. Rael",
-        "name_zh": "Cahn B. Rael"
-      },
-      {
-        "name_en": "Levi Jonathan",
-        "name_zh": "Levi Jonathan"
-      },
-      {
-        "name_en": "Fernandez Manuel",
-        "name_zh": "Fernandez Manuel"
-      },
-      {
-        "name_en": "Delorme Arnaud",
-        "name_zh": "Delorme Arnaud"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-07-13T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 5504,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66294b1b3e08f2716205c9ad",
-    "doi": "10.57760/sciencedb.18384",
-    "cstr": "31253.11.sciencedb.18384",
-    "pid": "",
-    "title": "Prestimulus power and phase of α and β predict discrimination accuracy and visual awareness",
-    "title_en": "Prestimulus power and phase of α and β predict discrimination accuracy and visual awareness",
-    "title_zh": "刺激前α频段以及β频段的能量与相位对辨别准确性以及视觉意识的预测作用",
-    "description": "<p>The experimental material was a low-contrast sinusoidal Gabor patch at the threshold level, tilted 45° vertically to the left or right. Michelson contrast ratios are 0.01, 0.02, and 0.03, respectively (Michelson contrast is calculated by the brightest brightness minus the darkest brightness divided by the sum of the two), and the presentation time and contrast are determined in the measurement threshold stage. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2. The experiment was divided into two phases. The first stage was to determine the&nbsp;threshold and familiarizing participant with the experimental procedure. In the second stage, participants performed discrimination tasks while recording EEG. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_en": "<p>The experimental material was a low-contrast sinusoidal Gabor patch at the threshold level, tilted 45° vertically to the left or right. Michelson contrast ratios are 0.01, 0.02, and 0.03, respectively (Michelson contrast is calculated by the brightest brightness minus the darkest brightness divided by the sum of the two), and the presentation time and contrast are determined in the measurement threshold stage. The experimental materials were made from online-gabor-patch-generator. The background brightness of all these types of stimuli was 22cd/m2. The experiment was divided into two phases. The first stage was to determine the&nbsp;threshold and familiarizing participant with the experimental procedure. In the second stage, participants performed discrimination tasks while recording EEG. EEG data were collected using the Curry7 software produced by NeuroScan, and EEG recording was performed by participants wearing an extended international 10-20 system 64 electrode cap. When recording online, the center electrode of the forehead was grounded (located at the midpoint of the FCZ and FX connections), and the overhead electrode was a reference electrode. The sampling rate was 1000Hz in DC mode. Electrodes were placed outside both eyes to record horizontal eye electricity (HEOG) and above and below the left eye to record vertical eye electricity (VEOG). The resistance was kept below 10kΩ.</p>",
-    "description_zh": "<p>实验材料为处于阈限水平的低对比度正弦曲线Gabor patch，垂直向左或者向右倾斜45°。迈克尔逊对比度分别为0.01，0.02和0.03（迈克尔逊对比度计算方法：最亮的亮度减去最暗的亮度除以二者之和），呈现时间和对比度在测定阈限阶段进行确定。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。该实验分为两个阶段。第一阶段是阈值测定并让被试熟悉实验流程。第二阶段被试在记录脑电图的同时执行辨别任务。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
-    "authors": [
-      {
-        "name_en": "li xiao xiao",
-        "name_zh": "李笑笑",
-        "email": "1421269185@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Tianjin Normal University",
-            "name_zh": "天津师范大学",
-            "ror_id": "https://ror.org/05x2td559"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "alpha",
-      "beta",
-      "visual awareness",
-      "discrimination task"
-    ],
-    "keywords_zh": [
-      "α频段",
-      "β频段",
-      "视觉意识",
-      "辨别任务"
-    ],
-    "publication_date": "2024-06-14T05:51:10.878Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-NC-ND 4.0",
-    "file_size_mb": 25288.26,
-    "file_size_bytes": 26516661632,
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-    "visit_count": 88,
-    "url": "https://www.scidb.cn/en/detail?id=66294b1b3e08f2716205c9ad",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4500933",
-    "cstr": "",
-    "pid": "",
-    "title": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
-    "title_en": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
-    "title_zh": "Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids",
-    "description": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
-    "description_en": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
-    "description_zh": "ABSTACTPlacebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups&rsquo; neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.",
-    "authors": [
-      {
-        "name_en": "Wicht Corentin Aurèle",
-        "name_zh": "Wicht Corentin Aurèle"
-      },
-      {
-        "name_en": "Mouthon Michael",
-        "name_zh": "Mouthon Michael"
-      },
-      {
-        "name_en": "Nsimire Chabwine Joelle",
-        "name_zh": "Nsimire Chabwine Joelle"
-      },
-      {
-        "name_en": "Gaab Jens",
-        "name_zh": "Gaab Jens"
-      },
-      {
-        "name_en": "Spierer Lucas",
-        "name_zh": "Spierer Lucas"
-      }
-    ],
-    "keywords_en": [
-      "Placebo",
-      "Pain",
-      "Expectations",
-      "EEG",
-      "ERP topography"
-    ],
-    "keywords_zh": [
-      "Placebo",
-      "Pain",
-      "Expectations",
-      "EEG",
-      "ERP topography"
-    ],
-    "publication_date": "2021-12-05T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 3084,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5061/dryad.12jm63xwd",
-    "cstr": "",
-    "pid": "",
-    "title": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
-    "title_en": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
-    "title_zh": "Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech",
-    "description": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
-    "description_en": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
-    "description_zh": "Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.",
-    "authors": [
-      {
-        "name_en": "Polonenko Melissa J",
-        "name_zh": "Polonenko Melissa J"
-      },
-      {
-        "name_en": "Maddox Ross K",
-        "name_zh": "Maddox Ross K"
-      }
-    ],
-    "keywords_en": [
-      "Auditory Brainstem Responses",
-      "natural speech",
-      "auditory evoked potentials",
-      "Electroencephalography (EEG)",
-      "auditory speech processing"
-    ],
-    "keywords_zh": [
-      "Auditory Brainstem Responses",
-      "natural speech",
-      "auditory evoked potentials",
-      "Electroencephalography (EEG)",
-      "auditory speech processing"
-    ],
-    "publication_date": "2021-02-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 6535,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5061/dryad.00000002t",
-    "cstr": "31253.11.10.5061/dryad.00000002t",
-    "pid": "",
-    "title": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
-    "title_en": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
-    "title_zh": "Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence",
-    "description": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
-    "description_en": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
-    "description_zh": "Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.",
-    "authors": [
-      {
-        "name_en": "Ratz-Lubashevsky Rachel",
-        "name_zh": "Ratz-Lubashevsky Rachel"
-      },
-      {
-        "name_en": "Frank Michael",
-        "name_zh": "Frank Michael"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2021-03-24T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.11,
-    "file_size_bytes": 114197,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "6625e67fc70b375c9c533d59",
-    "doi": "10.57760/sciencedb.18277",
-    "cstr": "31253.11.sciencedb.18277",
-    "pid": "",
-    "title": "Electrophysiological study of social anxiety levels on eye-cue processing in the Trust Game paradigm",
-    "title_en": "Electrophysiological study of social anxiety levels on eye-cue processing in the Trust Game paradigm",
-    "title_zh": "信任博弈范式下社交焦虑水平对眼部线索加工的脑电研究数据集",
-    "description": "<p>This dataset contains behavioral and electrophysiological data from individuals with different levels of social anxiety using eye cue materials in a trust game paradigm. This study adopts the trust game paradigm and event related potential technology to explore the temporal characteristics of eye cue processing among college students with different levels of social anxiety. The dataset consists of three files, namely (1) behavioral results: the behavioral data results of the subjects, and (2) EEG date: the EEG raw data of the subjects.&nbsp;File (1) presents the behavioral data results of the subjects, including 8 horizontal rows and 469 vertical columns. In the first row, list the anxiety level groups of the subjects (high anxiety group and low anxiety group), subject numbers, eye cue conditions (all conditions, direct anger, strabismus anger, direct sadness, strabismus sadness, direct calm, strabismus calm, direct happiness, and strabismus pleasure), number of investments (i.e. the number of times the subjects choose to trust), number of reservations (i.e. the number of times they choose not to trust), total (the total number of choices made by the subjects in the experiment), reaction time, and trust rate (calculated as investment/total).&nbsp;The EEG date in file (2) is the raw electrophysiological data for the experiment. There are a total of 156 projects in the document. The items starting with H are participants in the high social anxiety group, and the items starting with L are participants in the low social anxiety group.&nbsp;</p>",
-    "description_en": "<p>This dataset contains behavioral and electrophysiological data from individuals with different levels of social anxiety using eye cue materials in a trust game paradigm. This study adopts the trust game paradigm and event related potential technology to explore the temporal characteristics of eye cue processing among college students with different levels of social anxiety. The dataset consists of three files, namely (1) behavioral results: the behavioral data results of the subjects, and (2) EEG date: the EEG raw data of the subjects.&nbsp;File (1) presents the behavioral data results of the subjects, including 8 horizontal rows and 469 vertical columns. In the first row, list the anxiety level groups of the subjects (high anxiety group and low anxiety group), subject numbers, eye cue conditions (all conditions, direct anger, strabismus anger, direct sadness, strabismus sadness, direct calm, strabismus calm, direct happiness, and strabismus pleasure), number of investments (i.e. the number of times the subjects choose to trust), number of reservations (i.e. the number of times they choose not to trust), total (the total number of choices made by the subjects in the experiment), reaction time, and trust rate (calculated as investment/total).&nbsp;The EEG date in file (2) is the raw electrophysiological data for the experiment. There are a total of 156 projects in the document. The items starting with H are participants in the high social anxiety group, and the items starting with L are participants in the low social anxiety group.&nbsp;</p>",
-    "description_zh": "<p>\t该数据集是在不同水平社交焦虑水平个体中使用眼部线索材料在信任博弈范式下进行实验的行为和电生理学数据。本研究采用信任博弈范式和事件相关电位技术，探索了不同社交焦虑水平大学生在眼部线索处理上的时间特征。数据集包括三个文件，分别是（1）行为结果：被试的行为数据结果和（2）脑电数据：被试的EEG原始数据。</p><p>\t文件（1）行为结果呈现的是被试的行为数据结果，包含8横行和469竖列。在第一横行中列明了被试的焦虑水平分组（高焦虑组和低焦虑组）、被试编号、眼部线索条件（所有条件、直视愤怒、斜视愤怒、直视悲伤、斜视悲伤、直视平静、斜视平静、直视快乐、斜视快乐）、投资数（即被试选择信任的次数）、保留数（即被试选择不信任的次数）、总计（被试在实验中做出的选择总数）、反应时和信任率（计算方法为投资数/总计）。</p><p>\t文件（2）脑电数据为实验的电生理原始数据。文件中共156个项目。其中H开头的项目为高社交焦虑组的被试，L开头的为低社交焦虑组的被试。</p>",
-    "authors": [
-      {
-        "name_en": "Yan Zhixiong",
-        "name_zh": "Yan Zhixiong",
-        "email": "yanzx@nnnu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Nanning Normal University",
-            "name_zh": "Nanning Normal University",
-            "ror_id": "sdb_custom"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "social anxiety levels",
-      "eye-cue processing",
-      "Trust Game paradigm"
-    ],
-    "keywords_zh": [
-      "ERP",
-      "社交焦虑",
-      "信任游戏"
-    ],
-    "publication_date": "2024-04-23T08:43:08.074Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY-ND 4.0",
-    "file_size_mb": 11655.55,
-    "file_size_bytes": 12221734939,
-    "download_count": 27,
-    "visit_count": 350,
-    "url": "https://www.scidb.cn/en/detail?id=6625e67fc70b375c9c533d59",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4421252",
-    "cstr": "",
-    "pid": "",
-    "title": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
-    "title_en": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
-    "title_zh": "The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition",
-    "description": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
-    "description_en": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
-    "description_zh": "Dataset related to the paper on&nbsp;Response inhibition to physical inactivity stimuli using&nbsp;go/no-go tasks.&nbsp;This dataset includes:1) A codebook (including the name of the main variables)--&gt; &quot;code_book_Go_noGo_Miller.xlsx&quot;2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task--&gt; &quot;corrected.behavioral.data.csv&quot;--&gt;&nbsp;&quot;correct_Order.csv&quot;3) Self-reported data&nbsp;--&gt; &quot;Self_report_data.csv&quot;3) EEG data&nbsp;--&gt; &quot;gng_data&quot;5) R script for the data management&nbsp;(i.e., from the raw data to data ready to be analyzed)--&gt; &quot;Data_management_Self_report_go_no_go_Miller.R&quot; for the self-reported data (return the file:&nbsp;&quot;Data_SR_final.RData&quot;)--&gt; &quot;Data_management_behav_go_no_go_Miller.R&quot; for the behavioral outcomes (return the file:&nbsp;&quot;Data_GNG_behav.RData&quot;)--&gt; Data ready to be analyzed&nbsp;&quot;Data_GNG_final_all.RData&quot;6)&nbsp;Eprime script for the affective go/no-go task (&quot;Go_no_go_task.zip&quot;)--&gt; Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)7) R script for the models tested--&gt; &quot;Models_GoNogo_Miller_VZenodo.R&quot; for behavioral data--&gt; &quot;Models_EEG_GoNogo.R&quot; for EEG data",
-    "authors": [
-      {
-        "name_en": "Cheval Boris",
-        "name_zh": "Cheval Boris"
-      },
-      {
-        "name_en": "Daou Marcos",
-        "name_zh": "Daou Marcos"
-      },
-      {
-        "name_en": "Cabral Daniel A.R.",
-        "name_zh": "Cabral Daniel A.R."
-      },
-      {
-        "name_en": "Bacelar Mariane F.B.",
-        "name_zh": "Bacelar Mariane F.B."
-      },
-      {
-        "name_en": "Parma Juliana O.",
-        "name_zh": "Parma Juliana O."
-      },
-      {
-        "name_en": "Forestier Cyril",
-        "name_zh": "Forestier Cyril"
-      },
-      {
-        "name_en": "Sander David",
-        "name_zh": "Sander David"
-      },
-      {
-        "name_en": "Boisgontier Matthieu P.",
-        "name_zh": "Boisgontier Matthieu P."
-      },
-      {
-        "name_en": "Miller Matthew W.",
-        "name_zh": "Miller Matthew W."
-      }
-    ],
-    "keywords_en": [
-      "Response inhibition",
-      "inhibitory control",
-      "go/no-go",
-      "energetic cost minimization",
-      "physical activity"
-    ],
-    "keywords_zh": [
-      "Response inhibition",
-      "inhibitory control",
-      "go/no-go",
-      "energetic cost minimization",
-      "physical activity"
-    ],
-    "publication_date": "2020-02-13T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 11063,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "66682fe23aa61811b16959af",
-    "doi": "10.57760/sciencedb.08504",
-    "cstr": "31253.11.sciencedb.08504",
-    "pid": "",
-    "title": "Prestimulus power and phase of α and β predict visual awareness under high and low expectation",
-    "title_en": "Prestimulus power and phase of α and β predict visual awareness under high and low expectation",
-    "title_zh": "高低预期条件下，刺激前α以及β频段的能量与相位对视觉意识的预测作用",
-    "description": "<p>The experimental material was sinusoidal Gabor patch, with a Michelson contrast of 0.03, tilted either 45° to the left or right vertically. The blank stimulus was the no-target. The experimental materials were made from the online-gabor-patch-generator. The background brightness of all these types of stimuli was 22 cd/m². The probability of stimulus presentation was manipulated to present high or low expectations, with a high expectation of 75% stimulus appearance and a low expectation of 25% stimulus appearance. The proportion of stimulus appearance in each block was informed to the participants before the experiment. The experiment was conducted in a quiet, dimly lit EEG laboratory. The experimental stimuli were presented using E-Prime 2.0 software on a screen with a resolution of 1024x768 pixels and a refresh rate of 60 Hz (1 refresh cycle was approximately 17 ms). The participants were approximately 80cm away from the screen. EEG data were recorded using Curry7 software produced by NeuroScan, and the participants wore a 64-electrode cap based on the international 10-20 system. The frontal central electrode was grounded (located at the midpoint of the FCZ and FX line) and the top electrode was the reference electrode. The sampling rate was 1000 Hz&nbsp;in DC mode. Electrodes were placed on either side of the eyes to record horizontal eye movement (HEOG), and electrodes were placed above and below the left eye to record vertical eye movement (VEOG), with a resistance of less than 10 kΩ.</p>",
-    "description_en": "<p>The experimental material was sinusoidal Gabor patch, with a Michelson contrast of 0.03, tilted either 45° to the left or right vertically. The blank stimulus was the no-target. The experimental materials were made from the online-gabor-patch-generator. The background brightness of all these types of stimuli was 22 cd/m². The probability of stimulus presentation was manipulated to present high or low expectations, with a high expectation of 75% stimulus appearance and a low expectation of 25% stimulus appearance. The proportion of stimulus appearance in each block was informed to the participants before the experiment. The experiment was conducted in a quiet, dimly lit EEG laboratory. The experimental stimuli were presented using E-Prime 2.0 software on a screen with a resolution of 1024x768 pixels and a refresh rate of 60 Hz (1 refresh cycle was approximately 17 ms). The participants were approximately 80cm away from the screen. EEG data were recorded using Curry7 software produced by NeuroScan, and the participants wore a 64-electrode cap based on the international 10-20 system. The frontal central electrode was grounded (located at the midpoint of the FCZ and FX line) and the top electrode was the reference electrode. The sampling rate was 1000 Hz&nbsp;in DC mode. Electrodes were placed on either side of the eyes to record horizontal eye movement (HEOG), and electrodes were placed above and below the left eye to record vertical eye movement (VEOG), with a resistance of less than 10 kΩ.</p>",
-    "description_zh": "<p>实验材料为正弦曲线<em>Gabor patch</em>，迈克尔逊对比度为0.03，垂直向左或者向右倾斜45°。空白刺激即为无目标刺激。实验材料制作来自online-gabor-patch-generator。所有这些类型刺激的背景亮度均为22cd/m2。通过操纵刺激出现的概率来呈现高低预期，高预期为刺激出现的比例为75%，低预期为刺激出现的比例为25%，实验每个block前会告知被试刺激出现的比例。实验在安静昏暗的EEG实验室内进行。实验刺激在E-Prime 2.0软件中呈现。屏幕的分辨率为1024x768像素，屏幕刷新率为60Hz（1个刷新周期约为17ms）。被试距离屏幕80cm左右。使用NeuroScan公司生产的Curry7软件采集脑电数据，被试佩戴国际10-20系统扩展的64导电极帽进行EEG记录。在线记录时前额中央电极接地（位于在FCZ和FX连线的中点上），头顶电极为参考电极。采样率为1000Hz，DC模式采样。在双眼的外侧位置安置电极对记录水平眼电（HEOG），左眼上下位置安置电极记录垂直眼电（VEOG），电阻保持在10kΩ以下。</p>",
-    "authors": [
-      {
-        "name_en": "li xiao xiao",
-        "name_zh": "李笑笑",
-        "email": "1421269185@qq.com",
-        "affiliations": [
-          {
-            "name_en": "Tianjin Normal University",
-            "name_zh": "天津师范大学",
-            "ror_id": "https://ror.org/05x2td559"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "expectation",
-      "alpha",
-      "beta",
-      "visual awareness"
-    ],
-    "keywords_zh": [
-      "预期",
-      "α频段",
-      "β频段",
-      "视觉意识"
-    ],
-    "publication_date": "2024-06-20T02:18:07.941Z",
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-    "doi": "10.5281/zenodo.4501988",
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-    "pid": "",
-    "title": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
-    "title_en": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
-    "title_zh": "A Dataset of neurophysiological measurements of patients with completely locked-In syndrome",
-    "description": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
-    "description_en": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
-    "description_zh": "The dataset used for the psychophysiological assessment of four patients with amyotrophic lateral sclerosis (ALS) at advanced stages of the diseases with no means of communication, referred to as&nbsp;complete locked-in syndrome (CLIS). These datasets contain electroencephalogram (EEG), electrooculography (EOG) and functional near-infrared spectroscopy (fNIRS) recordings. During a four-day visit, each patient was recorded performing the various passive and active experiments at a sensory, perceptual, and cognitive level. The dataset includes somatosensory evoked potentials (SEPs), auditory evoked potentials (AEPs), resting-state in two conditions while eyes are open and eyes closed, Brain-Computer Interface (BCI) communication attempt, and during sleep.",
-    "authors": [
-      {
-        "name_en": "Chaudhary Ujwal",
-        "name_zh": "Chaudhary Ujwal"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2021-02-03T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 430.35,
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-    "url": "",
-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.61256",
-    "cstr": "31253.11.10.5281/zenodo.61256",
-    "pid": "",
-    "title": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
-    "title_en": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
-    "title_zh": "Hyperbrain features of team mental models within a juggling paradigm: a proof of concept",
-    "description": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
-    "description_en": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
-    "description_zh": "To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers&#39; cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.The datasets uploaded are the two dataset of both jugglers used for this study.",
-    "authors": [
-      {
-        "name_en": "Filho Edson",
-        "name_zh": "Filho Edson"
-      },
-      {
-        "name_en": "Bertollo Maurizio",
-        "name_zh": "Bertollo Maurizio"
-      },
-      {
-        "name_en": "Tamburro Gabriella",
-        "name_zh": "Tamburro Gabriella"
-      },
-      {
-        "name_en": "Schinaia Lorenzo",
-        "name_zh": "Schinaia Lorenzo"
-      },
-      {
-        "name_en": "Jonas Chatel-Goldman",
-        "name_zh": "Jonas Chatel-Goldman"
-      },
-      {
-        "name_en": "Di Fronso Selenia",
-        "name_zh": "Di Fronso Selenia"
-      },
-      {
-        "name_en": "Robazza Claudio",
-        "name_zh": "Robazza Claudio"
-      },
-      {
-        "name_en": "Comani Silvia",
-        "name_zh": "Comani Silvia"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2016-08-30T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.03,
-    "file_size_bytes": 32112,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.2602656",
-    "cstr": "",
-    "pid": "",
-    "title": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
-    "title_en": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
-    "title_zh": "Identity Representations of Turkey and Europe in Foreign Media: Regional and Global Perspectives on EU-Turkey Relations",
-    "description": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
-    "description_en": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
-    "description_zh": "FEUTURE Online Paper No. 14This FEUTURE paper examines global (Russian and US press) and regional (Egyptian and Georgian press) perspectives on EU-Turkey relations since 1999 with respect to identity and culture. Using the Critical Discourse Analysis methodology, the research traces the evolution of Turkey and Europe&rsquo;s identity representations in foreign media, therefore providing an outlook of the way significant Others make sense of the EU-Turkey relationship in the context of Turkey&rsquo;s EU bid. While the more dynamic (and positive) Egyptian and American press coverage initially contrasted with Georgian and Russian newspapers&rsquo; static portrayal of Europe and Turkey&rsquo;s respective identities as antithetical, the prominence of certain identity markers in recent drivers contributed to shifts in identity representations supporting the degradation of EU-Turkey relations toward conflict.",
-    "authors": [
-      {
-        "name_en": "Louis, Justine",
-        "name_zh": "Louis, Justine"
-      },
-      {
-        "name_en": " Magued, Shaimaa",
-        "name_zh": " Magued, Shaimaa"
-      },
-      {
-        "name_en": " Mzhavanadze, Nino",
-        "name_zh": " Mzhavanadze, Nino"
-      }
-    ],
-    "keywords_en": [
-      "European Union; Turkey; EU-Turkey Relations; Identity Politics; Identity Representation; Foreign Media; Regional and Global Perspectives; Culture; Critical Discourse Analysis; Significant Other; Georgia; Egypt; US; Russia; Newspaper; Conflict"
-    ],
-    "keywords_zh": [
-      "European Union; Turkey; EU-Turkey Relations; Identity Politics; Identity Representation; Foreign Media; Regional and Global Perspectives; Culture; Critical Discourse Analysis; Significant Other; Georgia; Egypt; US; Russia; Newspaper; Conflict"
-    ],
-    "publication_date": 1521129600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 2.68,
-    "file_size_bytes": 2810485,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.20044",
-    "cstr": "31253.11.10.5281/zenodo.20044",
-    "pid": "",
-    "title": "Cortical correlates of a translating point-light walker; an event-related potentials study",
-    "title_en": "Cortical correlates of a translating point-light walker; an event-related potentials study",
-    "title_zh": "Cortical correlates of a translating point-light walker; an event-related potentials study",
-    "description": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
-    "description_en": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
-    "description_zh": "EEG recordings for all subjects (S1-S13) considered in the study &quot;Cortical&nbsp;&nbsp; correlates&nbsp; of&nbsp; a&nbsp; translating&nbsp; point-light&nbsp; walker;&nbsp;&nbsp; an event-related&nbsp; potentials&nbsp; study&quot;. Data are in Brain Vision Analizer (BVA) format. They can be fully explored and analyzed using freely available software, like EEGLab, Brainstorm, Fieldtrip, BIOSIG or NPXLab. Trigger codes S 1, S 2, S 3 and S 4 respectively correspond to the condition &#39;cwalker&#39; &#39;twalker&#39; &#39;cscrambled&#39; &#39;tscrambled&#39; compared in the study.",
-    "authors": [
-      {
-        "name_en": "Inuggi Alberto",
-        "name_zh": "Inuggi Alberto"
-      },
-      {
-        "name_en": "Campus Claudio",
-        "name_zh": "Campus Claudio"
-      },
-      {
-        "name_en": "Keuroghlanian Alejo",
-        "name_zh": "Keuroghlanian Alejo"
-      },
-      {
-        "name_en": "Vastano Roberta",
-        "name_zh": "Vastano Roberta"
-      },
-      {
-        "name_en": "Pozzo Thierry",
-        "name_zh": "Pozzo Thierry"
-      }
-    ],
-    "keywords_en": [
-      "point-light walker (PLW)",
-      "event related potentials (ERP)"
-    ],
-    "keywords_zh": [
-      "point-light walker (PLW)",
-      "event related potentials (ERP)"
-    ],
-    "publication_date": "2015-07-12T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 65.67,
-    "file_size_bytes": 68856320,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.1226586",
-    "cstr": "",
-    "pid": "",
-    "title": "SSVEP Starlab",
-    "title_en": "SSVEP Starlab",
-    "title_zh": "SSVEP Starlab",
-    "description": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
-    "description_en": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
-    "description_zh": "Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.Each session consisted of one recording per stimulation frequency. In each recording, &nbsp;stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio&reg; recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS&nbsp;platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.",
-    "authors": [
-      {
-        "name_en": "David",
-        "name_zh": "David"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2018-04-20T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.9,
-    "file_size_bytes": 944666,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.818172",
-    "cstr": "",
-    "pid": "",
-    "title": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
-    "title_en": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
-    "title_zh": "Data and results related to \"Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements",
-    "description": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
-    "description_en": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
-    "description_zh": "The file includes data used for the analysis and results coming from the study (which was focused on the Italian \"Nord\" bidding zone). In particular:(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements",
-    "authors": [
-      {
-        "name_en": "Fattori Fabrizio",
-        "name_zh": "Fattori Fabrizio"
-      },
-      {
-        "name_en": "Anglani Norma",
-        "name_zh": "Anglani Norma"
-      },
-      {
-        "name_en": "Staffell Iain",
-        "name_zh": "Staffell Iain"
-      },
-      {
-        "name_en": "Pfenninger Stefan",
-        "name_zh": "Pfenninger Stefan"
-      }
-    ],
-    "keywords_en": [
-      "Solar Energy",
-      "Electricity Storage",
-      "Capacity Adequacy",
-      "Power system",
-      "Solar PV",
-      "Ramp Rate",
-      "Nord bidding zone",
-      "Italy",
-      "renewable energy"
-    ],
-    "keywords_zh": [
-      "Solar Energy",
-      "Electricity Storage",
-      "Capacity Adequacy",
-      "Power system",
-      "Solar PV",
-      "Ramp Rate",
-      "Nord bidding zone",
-      "Italy",
-      "renewable energy"
-    ],
-    "publication_date": "2017-06-25T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "doi": "10.5281/zenodo.4457748",
-    "cstr": "31253.11.10.5281/zenodo.4457748",
-    "pid": "",
-    "title": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
-    "title_en": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
-    "title_zh": "PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach",
-    "description": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
-    "description_en": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
-    "description_zh": "This data set for the publication &quot;The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach&quot; in PlosONE (2021) comprises:EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.fNIRS:&nbsp;oxy-Hb and deoxy-Hb&nbsp;data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged)&nbsp;pseudoword-picture pairings (PPP) for learned as well as new PPPsBehavioral data: Task Performance during the recognition experiment (reaction times for and percentage&nbsp;of correctly identified learned and new PPPs)&nbsp;",
-    "authors": [
-      {
-        "name_en": "Steber Sarah",
-        "name_zh": "Steber Sarah"
-      },
-      {
-        "name_en": "Rossi Sonja",
-        "name_zh": "Rossi Sonja"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": "2021-01-21T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "",
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.2427621",
-    "cstr": "",
-    "pid": "",
-    "title": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
-    "title_en": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
-    "title_zh": "Caracas, Latina. <span style='background-color:yellow'>bid</span>&co, editor, 211pp. ISBN: 978-9-80-403125-0 (Alexis del C. Rojas P.); Barreto González, Juan José (2018).",
-    "description": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
-    "description_en": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
-    "description_zh": "LIBRARIUS (rese&ntilde;as de libro; book reviews):&nbsp;Hans-Georg Gadamer.&nbsp;El giro hermen&eacute;utico.&nbsp;Trad. cast. Arturo Parada.C&aacute;tedra, Madrid, 238pp.&nbsp;ISBN: 84-376-1626-3(Dalis Coromoto Valera);&nbsp;Miguel &Aacute;ngel Quintana Paz.&nbsp;Reglas. Un ensayo de Introducci&oacute;n a la hermen&eacute;utica de manos de Wittgenstein y Scherlok Holmes.Apeiron Ediciones, 2017, 126pp.&nbsp;ISBN 10: 84-169-9695-4, ISBN 13: 978-8-41-699695-7(Mario Perniola); Rodr&iacute;guez Silva, An&iacute;bal (2017).&nbsp;Po&eacute;tica de la interpretaci&oacute;n. La obra de arte en la hermen&eacute;utica de H-G. Gadamer.Caracas, Latina. bid&amp;co, editor&nbsp;ISBN: 978-9-80-403125-0(Alexis del C. Rojas P.); Juan Jos&eacute; Barreto (2018).&nbsp;Una semi&oacute;tica del orgullo. Autores Editores, Bogot&aacute;, Colombia, 110 pp.&nbsp;ISBN: 978-980-18-0012-5",
-    "authors": [
-      {
-        "name_en": "VALERA, Dalis Coromoto",
-        "name_zh": "VALERA, Dalis Coromoto"
-      },
-      {
-        "name_en": " PERNIOLA, Mario",
-        "name_zh": " PERNIOLA, Mario"
-      },
-      {
-        "name_en": " ROJAS PAREDES, Alexis del Carmen",
-        "name_zh": " ROJAS PAREDES, Alexis del Carmen"
-      },
-      {
-        "name_en": " BARRETO GONZÁLEZ, Juan José",
-        "name_zh": " BARRETO GONZÁLEZ, Juan José"
-      }
-    ],
-    "keywords_en": [],
-    "keywords_zh": [],
-    "publication_date": 1545148800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "",
-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.198709",
-    "cstr": "",
-    "pid": "",
-    "title": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
-    "title_en": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
-    "title_zh": "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day",
-    "description": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
-    "description_en": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
-    "description_zh": "IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom,  with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).This dataset has been used in several scientific works, such a submitted journal paper \"Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking\", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski &amp; Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)",
-    "authors": [
-      {
-        "name_en": "Prieto Luis P.",
-        "name_zh": "Prieto Luis P."
-      },
-      {
-        "name_en": "Sharma Kshitij",
-        "name_zh": "Sharma Kshitij"
-      }
-    ],
-    "keywords_en": [
-      "orchestration",
-      "eyetracking",
-      "cognitive load",
-      "classroom studies"
-    ],
-    "keywords_zh": [
-      "orchestration",
-      "eyetracking",
-      "cognitive load",
-      "classroom studies"
-    ],
-    "publication_date": "2016-12-08T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-SA-4.0",
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4485036",
-    "cstr": "",
-    "pid": "",
-    "title": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
-    "title_en": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
-    "title_zh": "MNE-HFO: An open-source Python implementation of HFO detection algorithms",
-    "description": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
-    "description_en": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
-    "description_zh": "mne-hfo is a Python package for analysis of iEEG data for HFO events.Motivation----------High-frequency oscillations are events that clinicians hypothesize to be relatedto the epileptogenic zone. They have also been observed in other physiologicalprocesses. They are loosely defined as oscillations in a &quot;high-frequency band&quot;that are greater then some baseline according to a metric. For example,the Line Length HFO detector, uses the ``line length`` metric of the time-series signalto determine if a certain channel epoch is an HFO or not. In this package, we provideutilities and algorithms for detecting HFOs that have been proposed in the literature.In addition, we formulate the design of the package to be closely tied with ``scikit-learn``,``mne-python``, and the ``BIDS`` data specification. These design choices make thealgorithms easy to tune, easy to use, and the results easy to share.Python------Python is a powerful programming language that allows concise expressions of networkalgorithms. &nbsp;Python has a vibrant and growing ecosystem of packages thatmne-hfo uses to provide more features such as numerical linear algebra andplotting. &nbsp;In order to make the most out of mne-hfo you will want to know howto write basic programs in Python. &nbsp;Among the many guides to Python, werecommend the `Python documentation &lt;https://docs.python.org/3/&gt;`_.Free software-------------mne-hfo is free software; you can redistribute it and/or modify it under theterms of the ``BSD`` license. &nbsp;We welcome contributions.Join us on `GitHub &lt;https://github.com/adam2392/mne-hfo&gt;`_.",
-    "authors": [
-      {
-        "name_en": "Adam Li",
-        "name_zh": "Adam Li"
-      }
-    ],
-    "keywords_en": [
-      "Python",
-      "iEEG",
-      "High frequency oscillation"
-    ],
-    "keywords_zh": [
-      "Python",
-      "iEEG",
-      "High frequency oscillation"
-    ],
-    "publication_date": 1612108800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "doi": "10.5281/zenodo.162802",
-    "cstr": "31253.11.10.5281/zenodo.162802",
-    "pid": "",
-    "title": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
-    "title_en": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
-    "title_zh": "CONCEPTUAL ISSUES IN CRAFTING NEUROECONOMIC - MANAGERIAL DECISIONS",
-    "description": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
-    "description_en": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
-    "description_zh": "Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.Decision-making is a region of intense study in neuroscience, and cognitive neuroscience, In real, World decision processes, management decisions emerge from complexly interlinked, This paper explores how brain absorbs information, recognises and frames problematic situations, and chooses appropriate responses, Brain structures suggest that brain considers various sources of information before making decision, Brain imaging technologies have stimulated neuro (managerial) studies of internal order of mind and its links with bandwidth of human decisions, How is (managerial) decision making processes carried out in brain? What are the limits of understanding thinking as a form of computing? How does previous experience alter behavior? Do we interpret research findings when neuro (managerial) logical results conflict? Imaging is an important aspect of dynamic capabilities and there is an increasing amount of evidence of how evolutionary patterns are shaped. There are yet unsolved problems in (managerial) cognition, although some of these problems have evidence supporting a hypothesized solution, and the field is rapidly evolving. What are the general implications of neuro (managerial) management? What happens in brain or is activated when Managers make decisions or are in the process of making decisions? Is study of decision-making via neuromanagement processes relevant for Managers? Many Managers seek information than required thereby causing delay because of time required to process information. This impairs effectiveness of decision. In this state, neuromanagement seeks to explain decision-making, ability to process multiple alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide models of management. What are the coherent brain dynamics underlying prediction, control and decision making? Theoretical explanations posit that human brain accomplishes this through neural computations. Deciphering such transactions require understanding of neuro processes that implement value - dependent decision making. This leads to formulation of a ‘neuro - management decision making paradox’. The goal is a speculation of how brain implements decisions that is tied to behaviour. There are unsolved research issues; how does Manager decide in a state of vacillation, Risk and Probability? How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? How do we make decisions? How do human brains compute and represent abstract ideas? What counts as explanation of how brain works (what are function, algorithm and implementation)? This paper attempts at addressing current pace of advances in methods (fMRI, BOLD, EEG, ECG, etc), where we are going and what we ought to research next. This Paper attempts to explore phenomena through individual action, decision -making and reasoning processes. Objective is to put forward a model for neuro - management decision, in which interaction between variables of neuro - management decision processes are addressed through series of measurements of brain activity at time of decisions. Attempt is to describe a regular model for decision making process with intent of linking neuro - psycho and management levels of analysis capable of predicting observed behaviour. This provides conceptual framework for understanding and conducting Neuro (managerial) management research at intersection of neuro (managerial) science, management and psychology, offer solution through measurements of brain activity at time of decisions, linking and spanning neuro(managerial) biological and psychological and management levels of analysis.",
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-        "name_en": "1 Lt Col (Dr) Jyotirmaya Satpathy, DLitt, Faculty, Officers’ Training Academy, Indian Army, Gaya, India. (Corresponding Author) Mail:  jyotisatpathy@gmail.com 2 Dr Adyasha Das, PhD, Faculty, Indian Institute of Tourism Management, Bhubaneswar, India. 3 Professor Suneetha Narendran, PhD, Faculty, Dept. of Management(Organisation Behaviour) and Director Research, University of East London, UK.  4 Dr Sandhya R Das, PhD, Faculty, Dept. of Economics, Berhampur University, Berhampur, India.",
-        "name_zh": "1 Lt Col (Dr) Jyotirmaya Satpathy, DLitt, Faculty, Officers’ Training Academy, Indian Army, Gaya, India. (Corresponding Author) Mail:  jyotisatpathy@gmail.com 2 Dr Adyasha Das, PhD, Faculty, Indian Institute of Tourism Management, Bhubaneswar, India. 3 Professor Suneetha Narendran, PhD, Faculty, Dept. of Management(Organisation Behaviour) and Director Research, University of East London, UK.  4 Dr Sandhya R Das, PhD, Faculty, Dept. of Economics, Berhampur University, Berhampur, India."
-      }
-    ],
-    "keywords_en": [
-      "Key Words: Cognitive Neuroscience",
-      " Brain Imaging",
-      " Coherent Brain Dynamics",
-      " VUCA",
-      " fMRI",
-      " BOLD",
-      " EEG",
-      " ECG"
-    ],
-    "keywords_zh": [
-      "Key Words: Cognitive Neuroscience",
-      " Brain Imaging",
-      " Coherent Brain Dynamics",
-      " VUCA",
-      " fMRI",
-      " BOLD",
-      " EEG",
-      " ECG"
-    ],
-    "publication_date": 1477238400000,
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-    "doi": "10.5281/zenodo.3902305",
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-    "title": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
-    "title_en": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
-    "title_zh": "Case report: a post-traumatic Neuro-COVID-19. Casuality or causality?",
-    "description": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
-    "description_en": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
-    "description_zh": "We describe the case of a 77 years old man with relevant oncologic comorbidities but in good general conditions who developed a rapidly deteriorating neurologic status up to a refractory coma just after a relatively minor accidental head and chest trauma.&nbsp;Emergency CT imaging of the brain and chest was not significant but DWI demonstrated a diffuse brain subcortical micro-ischemic pattern. EEG showed diffuse slow rhythm and cerebrospinal fluid (CSF) analysis revealed an inflammatory pattern with elevated proteinorrachia. The RT-PCR on pharyngeal swab was positive for SARS-CoV2 and generic Coronavirus antigens were found in CSF. In the following days, the patient developed a cytokine storm, a typical COVID-19 pneumonia and died 13 days after admission. In the specific macro-emergency context autopsy was not feasible, though laboratory findings support the pathogenetic role of Coronavirus in central nervous system damage. We do not know if the strict chronologic succession suggests a possible role of the head trauma in the development of the neurologic condition of this COVID-19 patient. We believe this report add to the knowledge about the presence of a Coronavirus in CSF.",
-    "authors": [
-      {
-        "name_en": "Cortassa Giorgio Mario",
-        "name_zh": "Cortassa Giorgio Mario"
-      },
-      {
-        "name_en": " Bottone Stefania",
-        "name_zh": " Bottone Stefania"
-      },
-      {
-        "name_en": " Saia Valentina",
-        "name_zh": " Saia Valentina"
-      },
-      {
-        "name_en": " Grillo Giovanna",
-        "name_zh": " Grillo Giovanna"
-      },
-      {
-        "name_en": " Bertolino Monica",
-        "name_zh": " Bertolino Monica"
-      },
-      {
-        "name_en": " Tassinari Tiziana",
-        "name_zh": " Tassinari Tiziana"
-      },
-      {
-        "name_en": " Mangerini R",
-        "name_zh": " Mangerini R"
-      },
-      {
-        "name_en": " Tedone E",
-        "name_zh": " Tedone E"
-      }
-    ],
-    "keywords_en": [
-      "COVID-19",
-      "head trauma",
-      "neurologic presentation",
-      "DWI imaging",
-      "Coronavirus in liquor"
-    ],
-    "keywords_zh": [
-      "COVID-19",
-      "head trauma",
-      "neurologic presentation",
-      "DWI imaging",
-      "Coronavirus in liquor"
-    ],
-    "publication_date": 1592582400000,
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-    "doi": "10.5281/zenodo.1211690",
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-    "title": "Material related to the blog that reports on the parrot LUT",
-    "title_en": "Material related to the blog that reports on the parrot LUT",
-    "title_zh": "Material related to the blog that reports on the parrot LUT",
-    "description": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
-    "description_en": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
-    "description_zh": "Material related to the blog that reports on the parrot LUTThe blog was published at the Node: http://thenode.biologists.com/parrot-lut/research/&nbsp;-SourceThe &lsquo;morgenstemning&rsquo; LUT was originally described in:M. Geissbuehler and T. Lasser - &quot;How to display data by color schemes compatible with red-green color perception deficiencies&rdquo;, Optics Express, 2013The &lsquo;inferno&rsquo; LUT was originally created by St&eacute;fan van der Walt and Nathaniel Smith (http://bids.github.io/colormap/).The &lsquo;pseudocolorMM&rsquo; LUT was derived from MetaMorph software (version 7.6).The &lsquo;royal&rsquo; and &lsquo;Fire&rsquo; LUT are available in ImageJ (version 1.49j)The &lsquo;parrot&rsquo; LUT was designed by Joachim Goedhart and first described here:http://thenode.biologists.com/parrot-lut/research/-DistributionThe colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 &quot;no rights reserved&quot; license (https://creativecommons.org/share-your-work/public-domain/cc0) and are present in FIJI.The colormaps Mongenstemning &amp; Parrot are free software: you can redistribute them and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.These colormaps are distributed in the hope that they will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. &nbsp;See theGNU General Public License for more details: &lt;http://www.gnu.org/licenses/&gt;.",
-    "authors": [
-      {
-        "name_en": "Goedhart J.",
-        "name_zh": "Goedhart J."
-      }
-    ],
-    "keywords_en": [
-      "LUT",
-      "parrot",
-      "pseudocolor"
-    ],
-    "keywords_zh": [
-      "LUT",
-      "parrot",
-      "pseudocolor"
-    ],
-    "publication_date": "2018-04-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3259369",
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-    "pid": "",
-    "title": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
-    "title_en": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
-    "title_zh": "Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)",
-    "description": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
-    "description_en": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
-    "description_zh": "This data set contains intracranial EEG data, analysis code and results associated with the manuscript, &quot;Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans&quot;.&nbsp;[DOI: 10.1126/science.aax1030]Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: &ldquo;Norman_et_al_2019_data_and_code_zenodo.zip&quot;.The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.Matlab&#39;s Signal Processing Toolbox is required.General notes:1) The data does not contain identifying details about the patients, nor voice recordings.2) Before running the analyses, make sure you set the correct paths in the &quot;startup_script.m&quot; located in the main folder&nbsp;where the zip file was extracted.3) To run the code, the following open-source toolboxes are required:\tEEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version:&nbsp;&quot;eeglab14_1_2b&quot;.&nbsp;\t\t\tA. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9&ndash;21 (2004).\t\t\tMass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: &quot;dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4&quot;.\t\t\tD. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711&ndash;1725 (2011).\t\t*** Make&nbsp;sure you download&nbsp;the relevant toolboxes and save them&nbsp;in the &quot;path_to_toolboxes&quot; before&nbsp;running the analysis scripts (see &quot;startup_script.m&quot;)4) Code developed by other authors (redistributed here as part of the analysis code):\tDRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.\t\t\tL.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.&nbsp;Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.\t\t\tScott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.\tOliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).",
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-        "name_en": "Yitzhak Norman",
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-        "name_en": "Erin M. Yeagle",
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-        "name_en": "Simon Khuvis",
-        "name_zh": "Simon Khuvis"
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-        "name_en": "Michal Harel",
-        "name_zh": "Michal Harel"
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-        "name_en": "Ashesh D. Mehta",
-        "name_zh": "Ashesh D. Mehta"
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-        "name_en": "Rafael Malach",
-        "name_zh": "Rafael Malach"
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-    "title": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
-    "title_en": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
-    "title_zh": "Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.",
-    "description": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
-    "description_en": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
-    "description_zh": "Data related to the following publication: Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070Brief description of dataset:The stimulus was presented on a TFT monitor (size: 41,8&deg; x 24,3&deg;) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system.&nbsp;The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65&deg;) and therefore the patch size was the same (&ldquo;SizeCon&rdquo;) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (&ldquo;LumCon&rdquo;). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of &plusmn;100 &micro;V within an interval of 100 ms were excluded in an artifact rejection step.&nbsp;",
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-        "name_en": "Hesse Philipp N.",
-        "name_zh": "Hesse Philipp N."
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-        "name_en": "Schmitt Constanze",
-        "name_zh": "Schmitt Constanze"
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-        "name_en": "Klingenhoefer Steffen",
-        "name_zh": "Klingenhoefer Steffen"
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-        "name_en": "Bremmer Frank",
-        "name_zh": "Bremmer Frank"
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-    "description": "The DREAMS Databases and Assessment algorithmDuring the DREAMS project funded by R&eacute;gion Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):&bull;&nbsp;&nbsp; &nbsp;The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1&#39;s SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.&bull;&nbsp;&nbsp; &nbsp;The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;&bull;&nbsp;&nbsp; &nbsp;The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst",
-    "description_en": "The DREAMS Databases and Assessment algorithmDuring the DREAMS project funded by R&eacute;gion Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):&bull;&nbsp;&nbsp; &nbsp;The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1&#39;s SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.&bull;&nbsp;&nbsp; &nbsp;The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;&bull;&nbsp;&nbsp; &nbsp;The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;&bull;&nbsp;&nbsp; &nbsp;The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst",
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-    "dataset_id": "64f04634cc4bc33e70a58efe",
-    "doi": "10.6084/m9.figshare.21080953.v1",
-    "cstr": "",
-    "pid": "",
-    "title": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
-    "title_en": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
-    "title_zh": "Clinical features of the first critical case of acute encephalitis caused by avian influenza A (H5N6) virus",
-    "description": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
-    "description_en": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
-    "description_zh": "<p>Highly pathogenic avian influenza virus (HPAIV) such as H5N1, H5N6, and H7N9 have been reported to frequently infect human, but acute encephalitis caused by HPAIV in human have been rarely reported. We report the first critical case of acute encephalitis with mild pneumonia caused by H5N6 virus. On January 25 of 2022, a 6-year-old girl with severe neurological symptoms was admitted to our hospital and rapidly developed to seizures and coma. Brain imaging showed abnormalities. Electroencephalogram (EEG) presented abnormal slow waves. Cerebrospinal fluid (CSF) contained elevated protein (1.64 g/L) and white cells (546×10<sup>6</sup>/L). Laboratory investigations revealed abnormally elevated transaminases, lactate dehydrogenase and cytokines in serum. A novel reassortant H5N6 virus was identified from the patient’s serum, CSF and tracheal aspirate specimens. Phylogenic analysis indicated that this virus was a novel reassortant avian-origin influenza A (H5N6) virus belonged into clade 2.3.4.4b. This patient was diagnosed with acute encephalitis and discharged from the hospital accompanied with language barrier. Epidemiological investigation confirmed that wild waterfowls were the direct source of infection of this case. Our study highlights the urgent need to pay attenuation to acute encephalitis caused by HPAIV.</p>",
-    "authors": [
-      {
-        "name_en": "Zhang Libing",
-        "name_zh": "Zhang Libing",
-        "email": "zhangpinghu@yzu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Kaituo",
-        "name_zh": "Liu Kaituo",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Su Qin",
-        "name_zh": "Su Qin",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Chen Xiao",
-        "name_zh": "Chen Xiao",
-        "email": "chenxiao@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "Institute of Psychology",
-            "name_zh": "中国科学院心理研究所",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Xiaoquan",
-        "name_zh": "Wang Xiaoquan",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Li Qingfeng",
-        "name_zh": "Li Qingfeng",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Wenlei",
-        "name_zh": "Wang Wenlei",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Mao Xuhua",
-        "name_zh": "Mao Xuhua",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Jinmei",
-        "name_zh": "Xu Jinmei",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhou Xin",
-        "name_zh": "Zhou Xin",
-        "email": "xzhou@ucas.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Yangzhou Center for Disease Control and Prevention",
-            "name_zh": "Yangzhou Center for Disease Control and Prevention"
-          },
-          {
-            "name_en": "cnic",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Xu Qin",
-        "name_zh": "Xu Qin",
-        "affiliations": [
-          {
-            "name_en": "Yangzhou Center for Disease Control and Prevention",
-            "name_zh": "Yangzhou Center for Disease Control and Prevention"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhou Le",
-        "name_zh": "Zhou Le",
-        "affiliations": [
-          {
-            "name_en": "Yangzhou Center for Disease Control and Prevention",
-            "name_zh": "Yangzhou Center for Disease Control and Prevention"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Xiufan",
-        "name_zh": "Liu Xiufan",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhang Pinghu",
-        "name_zh": "Zhang Pinghu",
-        "email": "zhangpinghu@yzu.edu.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          },
-          {
-            "name_en": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University",
-            "name_zh": "Department of Pediatrics of the Affiliated Hospital of Yangzhou University, Yangzhou University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Acute encephalitis",
-      "H5N6",
-      "influenza A virus",
-      "wild waterfowls",
-      "phylogenetic analysis"
-    ],
-    "keywords_zh": [
-      "Acute encephalitis",
-      "H5N6",
-      "influenza A virus",
-      "wild waterfowls",
-      "phylogenetic analysis"
-    ],
-    "publication_date": "2022-10-25T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 1.83,
-    "file_size_bytes": 1916074,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "https://www.scidb.cn/en/detail?id=64f04634cc4bc33e70a58efe",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4587325",
-    "cstr": "",
-    "pid": "",
-    "title": "_Attention what is it like [Dataset]",
-    "title_en": "_Attention what is it like [Dataset]",
-    "title_zh": "_Attention what is it like [Dataset]",
-    "description": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
-    "description_en": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
-    "description_zh": "R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).In the second section, calculated the variance inflation factor (VIF).In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.",
-    "authors": [
-      {
-        "name_en": "Dinis Pereira Vitor Manuel",
-        "name_zh": "Dinis Pereira Vitor Manuel"
-      }
-    ],
-    "keywords_en": [
-      "Event-related changes",
-      "Oscillations correlated with access and phenomenal consciousness",
-      "Empirical mode decomposition",
-      "Variance inflation factor",
-      "Partial least squares regression",
-      "The minimal root mean squared error of prediction",
-      "Significance multivariate correlation statistic"
-    ],
-    "keywords_zh": [
-      "Event-related changes",
-      "Oscillations correlated with access and phenomenal consciousness",
-      "Empirical mode decomposition",
-      "Variance inflation factor",
-      "Partial least squares regression",
-      "The minimal root mean squared error of prediction",
-      "Significance multivariate correlation statistic"
-    ],
-    "publication_date": "2017-03-01T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 310,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "62bd117170c5b75f4c7afaff",
-    "doi": "10.5061/dryad.3j9kd51cn",
-    "cstr": "",
-    "pid": "",
-    "title": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
-    "title_en": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
-    "title_zh": "Data from: Contingent negative variation during a modified cueing task in simulated driving",
-    "description": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
-    "description_en": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
-    "description_zh": "<p>The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.,All anonymized data necessary to replicate our study findings have been uploaded to Dryad Digital Repository. There are four data sheets: demographic variables, the amplitude of early CNV, the amplitude of late CNV, and simulated driving behaviors.,</p>",
-    "authors": [
-      {
-        "name_en": "Guo Zizheng",
-        "name_zh": "Guo Zizheng",
-        "affiliations": [
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "ror_id": "https://ror.org/03j7v5j15"
-          },
-          {
-            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
-            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
-          },
-          {
-            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
-            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
-            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
-          }
-        ]
-      },
-      {
-        "name_en": "Tan Xi",
-        "name_zh": "Tan Xi",
-        "affiliations": [
-          {
-            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
-            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
-            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
-          },
-          {
-            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
-            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
-          }
-        ]
-      },
-      {
-        "name_en": "Pan Yufan",
-        "name_zh": "Pan Yufan",
-        "affiliations": [
-          {
-            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
-            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
-            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
-          },
-          {
-            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
-            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Xian",
-        "name_zh": "Liu Xian",
-        "affiliations": [
-          {
-            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
-            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
-            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
-            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhao Guozhen",
-        "name_zh": "Zhao Guozhen",
-        "email": "zhaogz@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "ror_id": "https://ror.org/03j7v5j15"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Lin",
-        "name_zh": "Wang Lin",
-        "affiliations": [
-          {
-            "name_en": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission",
-            "name_zh": "National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission"
-          },
-          {
-            "name_en": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University",
-            "name_zh": "National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University"
-          },
-          {
-            "name_en": "School of Transportation and Logistics, Southwest Jiaotong University",
-            "name_zh": "School of Transportation and Logistics, Southwest Jiaotong University"
-          }
-        ]
-      },
-      {
-        "name_en": "Peng Zhen",
-        "name_zh": "Peng Zhen",
-        "affiliations": [
-          {
-            "name_en": "School of Arts and Sciences, Arizona State University",
-            "name_zh": "School of Arts and Sciences, Arizona State University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Visual Intervention Influence",
-      "Rutting Mitigation",
-      "Will Reduce",
-      "Motor",
-      "CNV",
-      "Information",
-      "Reliability",
-      "Components",
-      "Stability",
-      "Modality"
-    ],
-    "keywords_zh": [
-      "Visual Intervention Influence",
-      "Rutting Mitigation",
-      "Will Reduce",
-      "Motor",
-      "CNV",
-      "Information",
-      "Reliability",
-      "Components",
-      "Stability",
-      "Modality"
-    ],
-    "publication_date": "2019-11-14T00:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
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-    "doi": "10.5281/zenodo.2652864",
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-    "title": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
-    "title_en": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
-    "title_zh": "Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures",
-    "description": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
-    "description_en": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
-    "description_zh": "Folder and Data Description for dataset of:Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral MeasuresGhada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O&rsquo;Driscoll, and Karolina KlukPlease site the&nbsp;paper when using this dataset&nbsp;(DOI: 10.1177/2331216519848297)&nbsp;Shared dataset is as follows:\tBehavioral data is in the excel spread sheet entitled:&nbsp;&ldquo;BinKhamis_et_al_behavioral_data .xlsx&rdquo;\t&nbsp;\tSpeech-ABRs&nbsp;(raw EEG (speech-ABR) data) are contained within the five &#39;zip&#39; folders.Description of the &ldquo;Speech-ABRs&rdquo; folders, subfolders, and raw EEG files:&ldquo;Speech-ABRs&rdquo; Folder Information:\tEach Speech_ABR&nbsp;folder&nbsp;contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number&nbsp;1 to participant number 20)\tSubfolders:\t\t\tEach subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\tNext is the background condition: noise or quiet\t\tNext is whether recordings were: aided or unaided\t\t\tExample subfolder names:\t\t\tHA1 noise aided: participant number 1, aided speech-ABRs in background noise\t\tHA4 noise unaided: participant number 4, unaided speech-ABRs in background noise\t\tHA55 quiet aided: participant number 55, aided speech-ABRs in quiet\t\tHA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet\t\t\tEach participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise)\t\t\tEach subfolder contains four &lsquo;.mat&rsquo; files, &lsquo;.mat&rsquo; file names:\t\t\t\t\tEach &lsquo;.mat&rsquo; file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, &hellip;, HA98\t\t\tNext is the stimulus: 40 da\t\t\tNext is &lsquo;unaided&rsquo; only if recordings were without HA\t\t\tNext is &lsquo;noise&rsquo; only if the background condition was noise\t\t\tNext is the stimulus polarity:\t\t\t\t\t\t\t&lsquo;Pos&rsquo; for positive/standard\t\t\t\t&lsquo;Neg&rsquo; for negative (reversed polarity stimulus)\t\t\t\t\t\t\t\t\tAnd finally the test ear and recording number for that polarity\t\t\t\t\t\t\tR1 is the first recording from the right ear, R2 is the second recording from the right ear\t\t\t\tL1 is the first recording from the left ear, L2 is the second recording from the left ear\t\t\t\t\t\t\t\t\tExample &lsquo;.mat&rsquo; file name:\t\t\t\t\t\t\tHA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.\t\t\t\tHA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.\t\t\t\tHA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.\t\t\t\tHA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.\t\t\t\t\t\t\t\t\t\t\t\tFile Information:Description of &lsquo;.mat&rsquo; files that can be accessed and processed using MATLAB (MathWorks):Each &lsquo;.mat&rsquo; file is a structure that contains the following fields:\tThe first nine fields are informational, for example:\txunits: &lsquo;s&rsquo; indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds\tstart: &lsquo;0&rsquo; indicates that both stimulus and recording start at 0 seconds\tpoints: 2200 is the number of sample points\tchans: 2 is the number of channels\t\t\tRight ear: channel 2, Left ear: channel 1\t\t\tframes: 2500 is the number of epochs\tThe last filed &lsquo;values&rsquo; is what contains the raw EEG data (2200x2x2500)\t\t\t2200 is the number of samples\t\t2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))\t\t2500 is the number of epochs\t\t\t\t\tStimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).\t\t\tData are in Volts; conversion to &mu;Volts (multiply by 1000) is required.\t\t\t\t\t\tDate of data collection:&nbsp;October 2017 to July 2018",
-    "authors": [
-      {
-        "name_en": "BinKhamis Ghada",
-        "name_zh": "BinKhamis Ghada"
-      }
-    ],
-    "keywords_en": [
-      "Speech-ABR",
-      "Aided speech-ABR",
-      "Unaided speech-ABR",
-      "40 ms [da]",
-      "Speech-ABR in background noise",
-      "Speech-ABR in SNHL"
-    ],
-    "keywords_zh": [
-      "Speech-ABR",
-      "Aided speech-ABR",
-      "Unaided speech-ABR",
-      "40 ms [da]",
-      "Speech-ABR in background noise",
-      "Speech-ABR in SNHL"
-    ],
-    "publication_date": "2019-04-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.05,
-    "file_size_bytes": 53963,
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-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
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-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3962798",
-    "cstr": "",
-    "pid": "",
-    "title": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
-    "title_en": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
-    "title_zh": "Data supporting: Isotopic insights on continental water sources and transport in the mountains and plains of southern South America",
-    "description": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
-    "description_en": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
-    "description_zh": "The data presented here comprises the stable isotopic composition (&delta;2H and &delta;18O) of 1659 samples of precipitation, rivers, groundwater and lakes/ponds samples from a total of 511&nbsp;sites. This is a composite dataset that includes data from the Global Network of Isotopes in Precipitation (GNIP) and Rivers (GNIR) databases (IAEA/WMO 2019), data extracted from different published articles (cited therein) and data obtained directly by the authors as part of different previous and ongoing projects of our own lab (GEA, IMASL). Excel sheet 1 shows a description/overview&nbsp;of the dataset as displayed in Table 1.&nbsp;Excel sheet 2 shows the dataset, which includes a unique identifying code, type of water sample (precipitation, rivers, groundwater, and lakes), name of location, sampling point date, latitude, longitude and elevation, and the dual isotopic composition (&delta;2H and &delta;18O).This dataset supports: Poca M, Nosetto MD, Ballesteros S, Castellanos G, Jobb&aacute;gy EG. Isotopic insights on continental water sources and transport in the mountains and plains of southern South America.&nbsp;Isotopes in Environmental and Health Studies (IEHS). DOI:10.1080/10256016.2020.1819264We acknowledge all contributing researchers to GNIP and GNIR databases, as well as the effort of the IAEA for initiating and maintaining these open access global initiatives. We also acknowledge the authors of the papers cited in the dataset and all GEA lab staff&nbsp;that contributed to the sampling and/or to measuring the stable isotopic compositions of waters included in this database.This work was supported by Fondo para la Investigaci&oacute;n Cient&iacute;fica y Tecnol&oacute;gica under FONCyT-Grant BID PICT-2018-03143.For any&nbsp;query, please contact Mar&iacute;a Poca at: pocamaria@gmail.com",
-    "authors": [
-      {
-        "name_en": "Poca María",
-        "name_zh": "Poca María"
-      },
-      {
-        "name_en": "Nosetto Marcelo D.",
-        "name_zh": "Nosetto Marcelo D."
-      },
-      {
-        "name_en": "Ballesteros Silvina",
-        "name_zh": "Ballesteros Silvina"
-      },
-      {
-        "name_en": "Castellanos George",
-        "name_zh": "Castellanos George"
-      },
-      {
-        "name_en": "Jobbágy Esteban G.",
-        "name_zh": "Jobbágy Esteban G."
-      }
-    ],
-    "keywords_en": [
-      "Argentina",
-      "d-excess",
-      "GNIP",
-      "GNIR",
-      "Groundwater evaporation",
-      "Isotope hydrology",
-      "Mountains",
-      "Paraguay",
-      "Precipitation recycling",
-      "Sedimentary plains",
-      "Stable isotopes",
-      "Uruguay"
-    ],
-    "keywords_zh": [
-      "Argentina",
-      "d-excess",
-      "GNIP",
-      "GNIR",
-      "Groundwater evaporation",
-      "Isotope hydrology",
-      "Mountains",
-      "Paraguay",
-      "Precipitation recycling",
-      "Sedimentary plains",
-      "Stable isotopes",
-      "Uruguay"
-    ],
-    "publication_date": "2020-07-20T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 0.11,
-    "file_size_bytes": 114189,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "62bd109470c5b75f4c7a873d",
-    "doi": "10.6084/m9.figshare.6866264.v1",
-    "cstr": "",
-    "pid": "",
-    "title": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
-    "title_en": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
-    "title_zh": "Molecular dynamics investigations of structural and functional changes in Bcl-2 induced by the novel antagonist BDA-366",
-    "description": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
-    "description_en": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
-    "description_zh": "<p>Apoptosis is a fundamental biological phenomenon, in which anti- or proapoptotic proteins of the Bcl-2 family regulate a committed step. Overexpression of Bcl-2, the prototypical antiapoptotic protein in this family, is associated with therapy resistance in various human cancers. Accordingly, Bcl-2 inhibitors intended for cancer therapy have been developed, typically against the BH3 domain. Recent experimental evidences have shown that the antiapoptotic function of Bcl-2 is not immutable, and that BDA-366, a novel antagonist of the BH4 domain, converts Bcl-2 from a survival molecule to an inducer of cell death. In this study, the underlying mechanisms of this functional conversion were investigated by accelerated molecular dynamics simulation. Results revealed that Pro127 and Trp30 in the BH4 domain rotate to stabilize BDA-366 via π-π interactions, and trigger a series of significant conformational changes of the α3 helix. This rearrangement blocks the hydrophobic binding site (HBS) in the BH3 domain and further prevents binding of BH3-only proteins, which consequently allows the BH3-only proteins to activate the proapoptotic proteins. Analysis of binding free energy confirmed that BDA-366 cross-inhibits BH3-only proteins, implying negative cooperative effects across separate binding sites. The newly identified blocked conformation of the HBS along with the open to closed transition pathway revealed by this study advances the understanding of the Bcl-2 transition from antiapoptotic to proapoptotic function, and yielded new structural insights for novel drug design against the BH4 domain. Communicated by Ramaswamy H. Sarma The ability of the small molecule BDA-366 to convert Bcl-2 from an antiapoptotic to a proapoptotic molecule was investigated by accelerated molecular dynamics simulation. Results show that BDA-366 blocks or reduces the affinity of Bcl-2 for BH3-only proteins like Bid via negative cooperative effects, thereby releasing such proteins and unleashing their proapoptotic effects.</p>",
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-      {
-        "name_en": "Li Tao",
-        "name_zh": "Li Tao",
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-            "name_en": "State Key Laboratory of Transducer Technology, Chinese Academy of Sciences",
-            "name_zh": "State Key Laboratory of Transducer Technology, Chinese Academy of Sciences"
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-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "University of Chinese Academy of Sciences",
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-            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
-            "name_zh": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/047yhep71"
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-      {
-        "name_en": "Cui Yinglu",
-        "name_zh": "Cui Yinglu",
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-            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
-            "name_zh": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/047yhep71"
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-        "name_zh": "Wu Bian",
-        "email": "wub@im.ac.cn",
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-            "name_en": "CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences",
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-    "title": "Dakar very-high resolution land cover map",
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-    "title_en": "Genetic prion diseases presenting as frontotemporal dementia: clinical features and diagnostic challenge",
-    "title_zh": "Genetic prion diseases presenting as frontotemporal dementia: clinical features and diagnostic challenge",
-    "description": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
-    "description_en": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
-    "description_zh": "<p>Abstract Background To elucidate the clinical and ancillary features of genetic prion diseases (gPrDs) presenting with frontotemporal dementia (FTD) to aid early identification. Methods Global data of gPrDs presenting with FTD caused by prion protein gene mutations were collected from literature review and our records. Fifty-one cases of typical FTD and 136 cases of prion diseases admitted to our institution were included as controls. Clinical and ancillary data of the different groups were compared. Results Forty-nine cases of gPrDs presenting with FTD were identified. Compared to FTD or prion diseases, gPrDs presenting with FTD were characterized by earlier onset age (median 45 vs. 61/60 years, P &lt; 0.001, P &lt; 0.001) and higher incidence of positive family history (81.6% vs. 27.5/13.2%, P &lt; 0.001, P &lt; 0.001). Furthermore, GPrDs presenting with FTD exhibited shorter duration (median 5 vs. 8 years) and a higher rate of parkinsonism (63.7% vs. 9.8%, P &lt; 0.001), pyramidal signs (39.1% vs. 7.8%, P = 0.001), mutism (35.9% vs. 0%, P &lt; 0.001), seizures (25.8% vs. 0%, P &lt; 0.001), myoclonus (22.5% vs. 0%, P &lt; 0.001), and hyperintensity on MRI (25.0% vs. 0, P &lt; 0.001) compared to FTD. Compared to prion diseases, gPrDs presenting with FTD had a longer duration of symptoms (median 5 vs. 1.1 years, P &lt; 0.001), higher rates of frontotemporal atrophy (89.7% vs. 3.3%, P &lt; 0.001), lower rates of periodic short-wave complexes on EEG (0% vs. 30.3%, P = 0.001), and hyperintensity on MRI (25.0% vs. 83.0%, P &lt; 0.001). The frequency of codon 129 Val allele in gPrDs presenting with FTD was significantly higher than that reported in the literature for gPrDs in the Caucasian and East Asian populations (33.3% vs. 19.2%/8.0%, P = 0.005, P &lt; 0.001). Conclusions GPrDs presenting with FTD are characterized by early-onset, high incidence of positive family history, high frequency of the Val allele at codon 129, overlapping symptoms with prion disease and FTD, and ancillary features closer to FTD. PRNP mutations may be a rare cause in the FTD spectrum, and PRNP genotyping should be considered in patients with these features.</p>",
-    "authors": [
-      {
-        "name_en": "Chen Zhongyun",
-        "name_zh": "Chen Zhongyun",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Chu Min",
-        "name_zh": "Chu Min",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Liu Li",
-        "name_zh": "Liu Li",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Zhang Jing",
-        "name_zh": "Zhang Jing",
-        "email": "zhangj271@ucas.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Shanghai Institute of Organic Chemistry",
-            "name_zh": "中国科学院上海有机化学研究所",
-            "ror_id": "https://ror.org/01y3hvq34"
-          },
-          {
-            "name_en": "University of Chinese Academy of Sciences",
-            "name_zh": "中国科学院大学",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "cnic",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          }
-        ]
-      },
-      {
-        "name_en": "Kong Yu",
-        "name_zh": "Kong Yu",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "cnic",
-            "name_zh": "中国科学院",
-            "ror_id": "https://ror.org/034t30j35"
-          }
-        ]
-      },
-      {
-        "name_en": "Xie Kexin",
-        "name_zh": "Xie Kexin",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Cui Yue",
-        "name_zh": "Cui Yue",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Ye Hong",
-        "name_zh": "Ye Hong",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Li Junjie",
-        "name_zh": "Li Junjie",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Lin",
-        "name_zh": "Wang Lin",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      },
-      {
-        "name_en": "Wu Liyong",
-        "name_zh": "Wu Liyong",
-        "email": "wmywly@hotmail.com",
-        "affiliations": [
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          },
-          {
-            "name_en": "Capital Medical University",
-            "name_zh": "Capital Medical University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "化学",
-      "有机",
-      "金属"
-    ],
-    "keywords_zh": [
-      "化学",
-      "有机",
-      "金属"
-    ],
-    "publication_date": "2022-06-29T16:00:00.000Z",
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-    "url": "https://www.scidb.cn/en/detail?id=64f046b8cc4bc33e70a59008",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3903262",
-    "cstr": "",
-    "pid": "",
-    "title": "segmented Sanskrit corpus (proof of concept)",
-    "title_en": "segmented Sanskrit corpus (proof of concept)",
-    "title_zh": "segmented Sanskrit corpus (proof of concept)",
-    "description": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
-    "description_en": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
-    "description_zh": "This is a proof-of-concept Sanskrit corpus developed for the study of Buddhist Sanskrit lexicology.It comprises:\t&nbsp;172&nbsp;metadata-enriched Buddhist&nbsp;Sanskrit texts for a total of ~ 5&nbsp;million words. The corpus contains all Mahāyāna and &#39;mainstream&#39; Buddhist based on Sanskrit editions texts available on GRETIL (reconstructed editions based on Tibetan translations have been filtered out).The corpus is in romanised Sanskrit (UTF-8 encoding) and is available in three&nbsp;configurations:\t&nbsp;segmented (with dash-separated words)\t&nbsp;segmented and stemmed (with capitalised word stem and compounds separated by an @ sign).\tsegmented, stemmed and normalised (normalisation treats some spelling variation and&nbsp;solves sandhi of stems&#39; initials in most cases), recommended for Word Sketches.The latter version can be used to generate word sketches&nbsp;in Sketch Engine if used in&nbsp;conjunction with the included sketch grammar, which&nbsp;infers likely syntactic dependencies from morphological cues.**avagraha has been replaced with a** in the stemmed versionsLimitationsAs a proof of concept, this corpus suffers from several limitations. It is very small by contemporary standards, it has not been proof-read and it is currently only segmented and stemmed (not lemmatised or PoS tagged).&nbsp;A funding bid has been submitted to expand and lemmatise the corpus.Data QualityThe corpus has been segmented with Lugli&#39;s Sanskrit segmenter (10.5281/zenodo.3459215).&nbsp;The accuracy of this segmenter has been evaluated at 97% on a sample of Buddhist Sanskrit literature.Please refer to the segmenter documentation stored at&nbsp;10.5281/zenodo.3459215 for details on evaluation and stemming conventions.AcknowledgmentsThe corpus has been realised as part of the project &#39;Lexis and Tradition: variation in the vocabulary of Sanskrit Mahāyāna literature&#39;. This project was&nbsp;funded by the British Academy through a Newton International Fellowship (NF161436) and hosted at the Department of Theology and Religious Studies at King&#39;s College London&nbsp;under the supervision of Prof. Henrietta Kate Crosby.&nbsp;Dr. Bruno Galasek-Hul has contributed to versions 1.4 &amp; 1.5 thanks to funding from the Mangalam Research Center for Buddhist Languages.Thanks to GRETIL, Dr. Vinita Tseng and Prof. Steinkellner for kindly giving their&nbsp;permission to include automatically processed versions of some of their editions&nbsp;in this corpus.&nbsp;Changelogversion 1.5&nbsp;adds more&nbsp;Buddhist texts, removes the reference corpus&nbsp;and improves segmentationversion 1.4 adds 59 Buddhist texts and fixes some recurrent segmentation errorsversion 1.4.1 corrects some spacing and sentence parsing errors",
-    "authors": [
-      {
-        "name_en": "Ligeia Lugli",
-        "name_zh": "Ligeia Lugli"
-      }
-    ],
-    "keywords_en": [
-      "corpus",
-      "Sanskrit",
-      "Buddhist Sanskrit"
-    ],
-    "keywords_zh": [
-      "corpus",
-      "Sanskrit",
-      "Buddhist Sanskrit"
-    ],
-    "publication_date": 1569168000000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "file_size_bytes": 15085,
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-    "url": "",
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-    "doi": "10.5281/zenodo.574607",
-    "cstr": "",
-    "pid": "",
-    "title": "BioExcel Deliverable 3.2 – Consultancy Proposals",
-    "title_en": "BioExcel Deliverable 3.2 – Consultancy Proposals",
-    "title_zh": "BioExcel Deliverable 3.2 – Consultancy Proposals",
-    "description": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
-    "description_en": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
-    "description_zh": "The partners in BioExcel are engaged in numerous projects, many of which have strong connections to the centre’s activities. The three core codes have been in development for many years and have been supported by many different projects over this time. It is the case however, that many of these funding streams are focused on specific scientific applications as opposed to other equally important aspects of research software development. Funding from proposals is necessary to support activities related to BioExcel, but individual projects alone cannot fully address the remit of the CoE, namely preparation for Exascale, improvements to software development processes, extensive support to the community and engaging with industry. BioExcel will support world leading European software whose development is funded through multiple sources. A CoE is needed to integrate these efforts into a coherent approach to maintain European leadership. If there was no CoE, the community would fragment and there would be a real risk that individual efforts would benefit only a subset of the user community.BioExcel is in a good position to support work related to the codes that otherfunding sources do not support directly, such as helping to prepare for Exascale, community building, and to help to provide continuity for code developers as they move between different funding streams.This document offers examples of project proposals that BioExcel has beeninvolved in, and projects or initiatives that are already underway. These are classified as either focused on the project’s core codes or related to BioExcel’s activities on workflows and data integration. Examples include proposals for specific code enhancements, such as the GROMEX bid, and involvement in larger proposals such as the EGI/EUDAT/INDIGO: European Open Science Cloud proposal, in which the HADDOCK web portal has been included as a thematic service. In terms of workflows, there are several large projects that involve multiple BioExcel partners including the ELIXIR Interoperability Platform, Multiscale Complex Genomics VRE and WestLife VRE. BioExcel partners are also involved in key development work with Open PHACTS and KNIME.Industry-related activities to date have focused around the establishment of the Interest Group, “Practical Applications for Industry”, which now has 30 signed up members from 23 companies.Work to explore future business models and sustainability plans is wellunderway in WP5. Whilst it is not yet known exactly what the balance of future funding for the centre will look like, it is clear that whatever the future funding model for the CoE, it is unlikely that any single line of public funding would fully sustain the work of the centre. It is therefore expected that BioExcel will continue to explore options to apply for funding from multiple sources, and this work has begun under the auspices of Task 3.6. Core BioExcel funding will provide continuity and stability, and serve to multiply the impact of the individual projects which could be, at least partially, coordinated by the CoE.",
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-    "title_en": "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity",
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-    "description": "This upload comprises supplementary material and data for the paper &quot;The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity&quot; Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.230521) The cleaned and group ICA resting state data in EEGLAB format.2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.",
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-    "title": "The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized",
-    "title_en": "The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized",
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-    "description": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
-    "description_en": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
-    "description_zh": "The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (https://zenodo.org/deposit/1308615); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol",
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-    "title": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
-    "title_en": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
-    "title_zh": "Macaca mulatta and Macaca fascicularis anatomical and functional MRI data",
-    "description": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
-    "description_en": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
-    "description_zh": "&nbsp;The&nbsp;ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 Macaca mulatta&nbsp;and 4&nbsp;Macaca fascicularis.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Scanner Specifications\tSiemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)\t8-channel phased-array transceiver coils\tOptimization of the magnetic field prior to data acquisition: Manual shimmingSample Description\tSample size: 8\tAge distribution: 3.80-5.99 years\tWeight distribution: 5.0-10.2 kg\tSex distribution: 7 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.Animal care and housing:&nbsp;Animals were housed in single cageAny applicable training:&nbsp;noneScanning preparationsAnesthesia procedures:&nbsp;Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 &micro;g/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.Time between anesthesia and scanning:&nbsp;Scanning lasted about 1.5 hours after induction.Head fixation:&nbsp;Custom-built MRI-compatible stereotaxic framePosition in scanner and procedure used:&nbsp;Sphinx positionContrast agent:&nbsp;noneDuring scanningPhysiological monitoring:&nbsp;Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO2&nbsp;were monitored. Oxygen saturation was kept over 95%.Additional procedures:&nbsp;Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.Scan sequences\tResting-state:\t\t\tGradient-echo EPI\t\tTR: 2000ms\t\tTE: 29ms\t\tFlip angle: 77&deg;\t\tField of view: 96 x 96 mm\t\tIn plane resolution: 1.5 x 1.5 mm\t\tSlices number: 32\t\tSlice thickness: 2.5mm\t\tGRAPPA factor: 2\t\tMeasurements: 200\t\tSlice direction: Coronal slice\t\t\tStructural:\t\t\tT1 MPRAGE Sequence\t\tVoxel resolution: 0.5 x 0.5 x 0.5 mm\t\tTE: 3.12ms\t\tTR: 2500ms\t\tTI: 1100ms\t\tFlip angle: 9&deg;\t\tSlice direction: 44 sagittal slices\t\tNumber of averages: 2\t\t\tAdditional:\t\t\tField map: a pair of gradient echo images\t\tTE1: 4.22ms\t\tTE2: 6.68ms\t\tOrientation and resolution: same as resting-state images\t\tIntended for EPI distortion correction\t\tPublications\tLv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., &amp; Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765&ndash;775. https://doi.org/10.1016/j.biopsych.2015.02.028PersonnelZheng Wang11Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaAcknowledgementsThe authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.Funding\tHundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)\tChinese 973 Program (2011CBA00400)\tThe &ldquo;Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)\tThe Outstanding Youth Grant (Hailan Hu)Detailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html.Citation&nbsp;\tWang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402112.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
-    "authors": [
-      {
-        "name_en": "Wang Zheng",
-        "name_zh": "Wang Zheng"
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-      "neuroimaging",
-      "neuroanatomy",
-      "functional MRI",
-      "non-human primate brain",
-      "NHP",
-      "BIDS",
-      "PRIME-DE"
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-      "neuroimaging",
-      "neuroanatomy",
-      "functional MRI",
-      "non-human primate brain",
-      "NHP",
-      "BIDS",
-      "PRIME-DE"
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-    "title": "Benchmark data for source localization validation",
-    "title_en": "Benchmark data for source localization validation",
-    "title_zh": "Benchmark data for source localization validation",
-    "description": "Template data related to the publication :La Fisca et al., A Versatile Validation Framework for ERP and Oscillatory Source Reconstruction Using FieldTrip, 2021Funded by: F.N.R.S - F.R.I.A, Belgium",
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-    "description_zh": "Template data related to the publication :La Fisca et al., A Versatile Validation Framework for ERP and Oscillatory Source Reconstruction Using FieldTrip, 2021Funded by: F.N.R.S - F.R.I.A, Belgium",
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-      "EEG",
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-    "title": "USB-to-TTL",
-    "title_en": "USB-to-TTL",
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-    "description": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
-    "description_en": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
-    "description_zh": "This repository contains the source code for the `usb-to-ttl` documentation page, hostet at https://sappelhoff.github.io/usb-to-ttl. Alternatively, see the README file for instructions to build the documentation locally.",
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-        "name_en": "Appelhoff, Stefan",
-        "name_zh": "Appelhoff, Stefan"
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-      {
-        "name_en": " Stenner, Tristan",
-        "name_zh": " Stenner, Tristan"
-      }
-    ],
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-      "parallel port",
-      "arduino",
-      "teensy",
-      "trigger",
-      "event",
-      "marker",
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-      "meg",
-      "electroencephalography",
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-      "parallel port",
-      "arduino",
-      "teensy",
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-      "meg",
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-    "doi": "10.5281/zenodo.155475",
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-    "title": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
-    "title_en": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
-    "title_zh": "Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography",
-    "description": "Data from study published in Plos One",
-    "description_en": "Data from study published in Plos One",
-    "description_zh": "Data from study published in Plos One",
-    "authors": [
-      {
-        "name_en": "Coutinho Eduardo",
-        "name_zh": "Coutinho Eduardo"
-      },
-      {
-        "name_en": "Gentsch Kornelia",
-        "name_zh": "Gentsch Kornelia"
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-        "name_en": "van Peer Jacobeen",
-        "name_zh": "van Peer Jacobeen"
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-      {
-        "name_en": "Scherer Klaus R.",
-        "name_zh": "Scherer Klaus R."
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-      {
-        "name_en": "Schuller Björn W.",
-        "name_zh": "Schuller Björn W."
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-    "title": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
-    "title_en": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
-    "title_zh": "Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance",
-    "description": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
-    "description_en": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
-    "description_zh": "Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy",
-    "authors": [
-      {
-        "name_en": "Elahian Bahareh",
-        "name_zh": "Elahian Bahareh"
-      },
-      {
-        "name_en": "Lado Nathan",
-        "name_zh": "Lado Nathan"
-      },
-      {
-        "name_en": "Misra Amrit",
-        "name_zh": "Misra Amrit"
-      },
-      {
-        "name_en": "Moxon Karen",
-        "name_zh": "Moxon Karen"
-      },
-      {
-        "name_en": "Fried Itzhak",
-        "name_zh": "Fried Itzhak"
-      },
-      {
-        "name_en": "Sharan Ashwini",
-        "name_zh": "Sharan Ashwini"
-      },
-      {
-        "name_en": "Yeasin Mohammed",
-        "name_zh": "Yeasin Mohammed"
-      },
-      {
-        "name_en": "Staba Richard",
-        "name_zh": "Staba Richard"
-      },
-      {
-        "name_en": "Bragin Anatol",
-        "name_zh": "Bragin Anatol"
-      },
-      {
-        "name_en": "Avoli Massimo",
-        "name_zh": "Avoli Massimo"
-      },
-      {
-        "name_en": "Sperling Michael",
-        "name_zh": "Sperling Michael"
-      },
-      {
-        "name_en": "Weiss Shennan",
-        "name_zh": "Weiss Shennan"
-      }
-    ],
-    "keywords_en": [
-      "eeg",
-      "lfp",
-      "microelectrode",
-      "seizure"
-    ],
-    "keywords_zh": [
-      "eeg",
-      "lfp",
-      "microelectrode",
-      "seizure"
-    ],
-    "publication_date": "2017-07-28T16:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-  {
-    "dataset_id": "60a670fab7734d4445783374",
-    "doi": "10.7272/q6c53j39",
-    "cstr": "",
-    "pid": "",
-    "title": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
-    "title_en": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
-    "title_zh": "Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways",
-    "description": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
-    "description_en": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
-    "description_zh": "<p>Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways <i>in vivo</i>. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.</p>",
-    "authors": [
-      {
-        "name_en": "Poskanzer Kira",
-        "name_zh": "Poskanzer Kira"
-      },
-      {
-        "name_en": "Vaidyanathan Trisha",
-        "name_zh": "Vaidyanathan Trisha"
-      }
-    ],
-    "keywords_en": [
-      "astrocyte",
-      "calcium",
-      "Sleep duration",
-      "slow-wave sleep (SWS)",
-      "non-REM sleep",
-      "two-photon imaging",
-      "LFP",
-      "Electroencephalogram (EEG)",
-      "chemogenetics",
-      "automated image analysis",
-      "electrophysiology",
-      "delta band",
-      "slow oscillations",
-      "Cerebral Cortex"
-    ],
-    "keywords_zh": [
-      "astrocyte",
-      "calcium",
-      "Sleep duration",
-      "slow-wave sleep (SWS)",
-      "non-REM sleep",
-      "two-photon imaging",
-      "LFP",
-      "Electroencephalogram (EEG)",
-      "chemogenetics",
-      "automated image analysis",
-      "electrophysiology",
-      "delta band",
-      "slow oscillations",
-      "Cerebral Cortex"
-    ],
-    "publication_date": "2020-12-29T16:00:00.000Z",
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-    "modified": "",
-    "status": "",
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-    "url": "https://www.scidb.cn/en/detail?id=60a670fab7734d4445783374",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "62bd107a70c5b75f4c7a7a89",
-    "doi": "10.6084/m9.figshare.8261948.v1",
-    "cstr": "",
-    "pid": "",
-    "title": "The influence of emotional words on predictive processing during sentence comprehension",
-    "title_en": "The influence of emotional words on predictive processing during sentence comprehension",
-    "title_zh": "The influence of emotional words on predictive processing during sentence comprehension",
-    "description": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
-    "description_en": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
-    "description_zh": "<p>The current study examined the influence of contexts’ emotional arousal on prediction and integration of words during sentence comprehension. In sentence context, we manipulated the emotional arousal of verbs and the predictability of their following neutral nouns. At the anticipatory stage of the upcoming nouns, a larger sustained negativity was elicited in the highly constraining than the weakly constraining sentences for the neutral verbs but not for the emotional verbs. The results reflect reduced processing loads in response to the pre-activation of the highly predictable nouns when the emotional verbs automatically captured sufficient attentional resources. At the integration stage of the nouns, we found reduced N400 and LPC amplitudes for the highly versus weakly predictable nouns in both the emotionally arousing and non-arousing conditions. The findings suggest that the emotional arousal of context interacts with the prediction of upcoming words, but not the integration of the bottom-up inputs.</p><p>There are 16 markers in the raw data. \"11\" represents positive verbs in highly constraining sentences. \"12\" represents&nbsp;positive verbs in weakly constraining sentences. “21” represents neutral verbs in&nbsp;highly constraining sentences. \"22\" represents&nbsp;neutral verbs in weakly constraining sentences.&nbsp;\"31\" represents negative verbs in highly constraining sentences. \"32\" represents&nbsp;negative verbs in weakly constraining sentences. “41” represents neutral verbs in&nbsp;highly constraining sentences. \"42\" represents&nbsp;neutral verbs in weakly constraining sentences. “13” represents highly predictable nouns following positive verbs. “14” represents weakly predictable nouns following positive verbs. “23” represents highly predictable nouns following neutral verbs. “24” represents weakly predictable nouns following neutral verbs. “33” represents highly predictable nouns following negative verbs. “34” represents weakly predictable nouns following negative verbs. “43” represents highly predictable nouns following neutral verbs. “44” represents weakly predictable nouns.</p>",
-    "authors": [
-      {
-        "name_en": "Ding Jinfeng",
-        "name_zh": "Ding Jinfeng",
-        "email": "dingjf@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "ror_id": "https://ror.org/03j7v5j15"
-          },
-          {
-            "name_en": "Department of psychology, University of Chinese Academy of Sciences",
-            "name_zh": "Department of psychology, University of Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/05qbk4x57"
-          }
-        ]
-      },
-      {
-        "name_en": "Wang Lin",
-        "name_zh": "Wang Lin",
-        "email": "wanglinsisi@gmail.com",
-        "affiliations": [
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "ror_id": "https://ror.org/03j7v5j15"
-          },
-          {
-            "name_en": "Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School",
-            "name_zh": "Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School"
-          }
-        ]
-      },
-      {
-        "name_en": "Yang Yufang",
-        "name_zh": "Yang Yufang",
-        "email": "yangyf@psych.ac.cn",
-        "affiliations": [
-          {
-            "name_en": "Department of psychology, University of Chinese Academy of Sciences",
-            "name_zh": "Department of psychology, University of Chinese Academy of Sciences",
-            "ror_id": "https://ror.org/05qbk4x57"
-          },
-          {
-            "name_en": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "name_zh": "CAS Key Laboratory of Behavioral Science, Institute of Psychology",
-            "ror_id": "https://ror.org/03j7v5j15"
-          },
-          {
-            "name_en": "Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University",
-            "name_zh": "Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University"
-          }
-        ]
-      }
-    ],
-    "keywords_en": [
-      "Emotional words",
-      "prediction",
-      "sentence comprehension",
-      "EEG"
-    ],
-    "keywords_zh": [
-      "Emotional words",
-      "prediction",
-      "sentence comprehension",
-      "EEG"
-    ],
-    "publication_date": "2019-06-12T00:00:00.000Z",
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "url": "https://www.scidb.cn/en/detail?id=62bd107a70c5b75f4c7a7a89",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3402457",
-    "cstr": "",
-    "pid": "",
-    "title": "Macaca mulatta structural and diffusion weighted MRI data",
-    "title_en": "Macaca mulatta structural and diffusion weighted MRI data",
-    "title_zh": "Macaca mulatta structural and diffusion weighted MRI data",
-    "description": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
-    "description_en": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
-    "description_zh": "&nbsp;The&nbsp;AMU dataset includes structural&nbsp;and diffusion weighted MRI data from 4 Macaca mulatta&nbsp;monkeys.The data is provided both as a directory structure compressed as all_the_data.zip, and as individual files. The correspondence between the directory structure and the individual files is contained in the file tree.json. The bash command source unflatten.sh can be used to convert the individual files into the original directory structure.Sample Description\tSample size: 4\tAge distribution: 7-8 years\tWeight distribution: 7.5-12.5 kgs\tSex distribution: 3 male, 1 femaleClick&nbsp;here&nbsp;for the full sample description (.csv download)Scan Procedures and ParametersEthics approval:&nbsp;obtained at local Ethics CommitteeAnimal care and housing:&nbsp;At Institut de Neurosciences de La TimoneAny applicable training:&nbsp;N/AScanning preparationsAnesthesia procedures:&nbsp;IsofluraneTime between anesthesia and scanning:&nbsp;None- anesthesia was performed and monitored during scanning.Head fixation:&nbsp;Kopf frame and ear barsPosition in scanner and procedure used:&nbsp;Sphinx position.&nbsp;Fiducial marker placed on right side.Contrast agent:&nbsp;NoneDuring scanningPhysiological monitoring:&nbsp;Heart rate, respirationAdditional procedures:&nbsp;VentilationScan sequences\tScanner type: Siemens Prisma 3T\tHead coil: Body transmit array, 11cm loop receiving coil\tOptimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens\t&nbsp;\tDiffusion-weighted:\t\t\tDiffusion SE-EPI sequence\t\tVoxel resolution: 1 x 1 x 1 mm\t\tTE: 87.6ms\t\tTR: 7520ms\t\t64b1000\t\t6b300\t\t5b0\t\tTwo repetitions with reversed phase encoding direction\t\t\tStructural:\t\t\tT1\t\t\t\t\tMPRAGE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8 mm\t\t\tTE: 2.04ms\t\t\tTR: 2900ms\t\t\tTI: 1000ms\t\t\t\t\t\tT2\t\t\t\t\t3D SPACE sequence\t\t\tVoxel resolution: 0.8 x 0.8 x 0.8mm\t\t\tTE: 561ms\t\t\tTR: 3200ms\t\t\t\t\t\tQSM\t\t\t\t\t3D GRE sequence\t\t\tVoxel resolution: 0.93 x 0.93 x 1mm\t\t\tTE: 2.7-42ms\t\t\tTR: 45ms\t\t\t\t\t\tPersonnel\tThomas Brochier1\tPascal Belin1\tFr&eacute;d&eacute;ric Chavanne1\tLionel Velly1\tCl&eacute;mentine Bodin1\tLuc Renaud1,2\tMarc Martin1,2\tLaurence Boes1,2\tJulien Sein1\tBruno Nazarian1\tJean-Luc Anton11Institut de Neurosciences de la Timone (INT), UMR7289 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, France2Centre d&rsquo;Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS &amp; Aix-Marseille Universit&eacute;, Marseille, FranceAcknowledgementsWe gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Sant&eacute; et Agronomie (IBISA)FundingProject PRIMAVOICE, PI Belin, French Agence Nationale de la RechercheDetailed information can be found at&nbsp;http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html.Citation\tBrochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., &amp; Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.3402456.\tMilham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., &hellip; Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61&ndash;74.e2. https://doi.org/10.1016/j.neuron.2018.08.039.",
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-      {
-        "name_en": "Brochier Thomas",
-        "name_zh": "Brochier Thomas"
-      },
-      {
-        "name_en": "Belin Pascal",
-        "name_zh": "Belin Pascal"
-      },
-      {
-        "name_en": "Chavanne Frédéric",
-        "name_zh": "Chavanne Frédéric"
-      },
-      {
-        "name_en": "Velly Lionel",
-        "name_zh": "Velly Lionel"
-      },
-      {
-        "name_en": "Bodin Clémentine",
-        "name_zh": "Bodin Clémentine"
-      },
-      {
-        "name_en": "Renaud Luc",
-        "name_zh": "Renaud Luc"
-      },
-      {
-        "name_en": "Martin Marc",
-        "name_zh": "Martin Marc"
-      },
-      {
-        "name_en": "Boes Laurence",
-        "name_zh": "Boes Laurence"
-      },
-      {
-        "name_en": "Sein Julien",
-        "name_zh": "Sein Julien"
-      },
-      {
-        "name_en": "Nazarian Bruno",
-        "name_zh": "Nazarian Bruno"
-      },
-      {
-        "name_en": "Anton Jean-Luc",
-        "name_zh": "Anton Jean-Luc"
-      }
-    ],
-    "keywords_en": [
-      "neuroimaging",
-      "neuroanatomy",
-      "diffusion weighted MRI",
-      "non-human primate brain",
-      "NHP",
-      "BIDS",
-      "PRIME-DE"
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-      "BIDS",
-      "PRIME-DE"
-    ],
-    "publication_date": "2019-09-07T16:00:00.000Z",
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-    "doi": "10.5281/zenodo.3800156",
-    "cstr": "",
-    "pid": "",
-    "title": "Vedalia Beetles and Cyanide",
-    "title_en": "Vedalia Beetles and Cyanide",
-    "title_zh": "Vedalia Beetles and Cyanide",
-    "description": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
-    "description_en": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
-    "description_zh": "Vedalia Beetles and Cyanide   Back in Washington, Coquillett's supervisor C.V. Riley was constantly \"putting out fires\" of complaints of farmers, vintners, dairymen, etc. nationwide with regard to injurious insects causing damage to crops, trees, vines, and domestic animals. One particular set of complaints was coming from [no surprise] California (and had been for a few years). Riley was obviously impressed (or at least satisfied) enough with Coquillett's locust report to send him on a new mission. Orange groves were being subjected to damage by the introduced cottony-cushion scale. Coquillett was to join forces with exploratory entomologist Albert Koebele and find a solution to the problem. Riley had theorized that going to the home of the scale (Australia, where it was not causing the unchecked damage that it was in California) might prove successful in finding what insect or other organism was keeping populations of the scale in check or possibly could help eradicate it. In late 1885, the two began work. But, due to the diametrically opposed personalities of the two men, trouble soon ensued. Koebele discredited Coquillett's work and the two feuded over who was in charge. Coincidentally or not, funds for Coquillett's position ran out in the summer of 1886 and his employment with the U.S. Department of Agriculture was terminated. He was reemployed the next year, but the short time in between federal paychecks proved a useful period for Coquillett.  Loss of employment with the federal government seemed not to deter the fervor Coquillett had for his work and, after meeting with two California agriculturalists who had begun the process, he began experiments with hydrocyanic-acid gas treatments for trees to rid them of scale insects. The gas was released under a tent covering a tree by mixing potassium cyanide with sulfuric acid 3. The previous methodology using this gas took many hours   3. This of course is the same concoction of chemicals prisons use for executing prisoners by lethal gas. for each tree and the method of mixing the two chemicals caused the gas to kill parts of the trees. By conducting trials with different dosages and tent designs, Coquillett was able to reduce the treatment to 15 minutes per tree. However, one day while experimenting with this gas treatment, Coquillett almost met an unfortunate fate. California State Quarantine Officer Alexander Craw (1899) related the story of he and Mr. J.R. Wolfskill (the latter the owner of the groves in Los Angeles where Coquillett was working; Fig. 7) going out into the groves to see how Coquillett was doing and saw evidence that he had left in a hurry. They finally tracked him down in his apartment (only a block away from the orchard—see below) and found out he had come into contact with the gas and feared for his life. He vowed never to work with the gas again. They finally convinced him to wear a suit for safety while using the gas and he reluctantly went back to work. Coquillett's work on the gas treatment was a tremendous success and was publicized throughout the world as the method for getting rid of pestiferous insects, especially scales, in orchards. Riley was disappointed that the U.S. D.A. would not get credit for this but that did not stop him from publicizing it in many reports and newspaper articles, which made it seem as though the gas treatment discovery was the result of the U.S. D.A.  Back in the employ of Riley a few months later, Coquillett returned to work with Koebele to solve the scale problem. Koebele went to Australia to find insects that might control the scale, and Coquillett would be the experimenter who received the shipments, maintained the colonies, and reared them in cages to see which worked best. Various parasites and predators were shipped and tested in cages and in the field, but one in particular became world famous: the Vedalia ladybird beetle, Rodolia cardinalis, a ladybird beetle as conspicuous as the cottonycushion scale on which it readily fed. The beetles multiplied rapidly, were easily transferred from grove to grove, and were voracious feeders. Within a year, the scale was virtually eliminated from the region and California's citrus industry was saved.   And, thus, more trouble ensued for Coquillett. Friction between state and federal officials over credit for the success resulted in a number of attacks on Coquillett and the federal government that were printed in the local papers. Coquillett remained quiet and did not respond to most of the disparaging remarks and personal attacks. In 1893, Riley had endured enough of the bad press the U.S. D.A. was getting in California and, after communicating the situation to the Secretary of Agriculture, the latter recalled both Koebele and Coquillett to Washington to separate them from California officials. Coquillett wrote to the Pacific Rural Press and they posted the letter from the Secretary of Agriculture. In that newspaper piece, Coquillett much lamented his having to go:   \"I regret very much the necessity that bids me leave this interesting field of labor where the principal work of my life thus far has been wrought, and where many pleasant friendships have been formed. My relations with the honest soiltillers have been of the most agreeable kind, and I need hardly assure them that in whatever field I may be called upon to labor in the future, I carry with me the most pleasant remembrances of them and the good people of this peerless State—California.\" (Anonymous 1893a: 264).  The reaction to the recall by growers in California was disappointment verging on outrage at State officials. The Pomological Society and Farmers' Institute of Southern California at their joint 1893 convention in Ontario, California went so far as to sign the following resolution:  \"Whereas, the action of the National Department of Agriculture in withdrawing the two entomologists stationed in California, namely Professor D.W. Coquillett at Los Angeles and Professor Albert Koebele at Alameda, is due solely to the hostile attitude of the State Board of Horticulture, and particularly its secretary and president, to the authorities at Washington by persistently libeling Professors C.V. Riley and D.W. Coquillett and by further seeking to secure the discharge of the former entomologist of the department;  Therefore, be it resolved by the Pomological Society and Farmers' Institute of Southern California, in joint convention assembled, November 2 and 3, 1893, in the city of Ontario, that the said State Board of Horticulture in no way represents the fruit-growers in their attacks upon Professors Riley and Coquillett. To the contrary, this convention deeply regrets the course pursued by the said State Board of Horticulture and strongly condemns it for robbing the great industry of horticulture of valuable aid at Washington.\" Anonymous (1893b: 2).  Despite the apologies from their grower friends, the recall was a done deal, but the reactions to the recall by the two field agents were quite different from one another. Koebele had had enough of Riley and Washington and took a job in Hawaii working as exploratory entomologist for the new provisional government there; and eventually for R.C.L. Perkins and the Hawaii Sugar Planters' Association; and Coquillett, unsure of his future, moved to Washington to continue his employment with the U.S. D.A.  Only after he was \"safely\" back in D.C. did Coquillett respond to the newspaper attacks on him (Coquillett 1893i). Coquillett never returned to California, but during his stay there he had purchased land, which he apparently kept until his death. While working for the U.S. D.A., he resided at 236 Winston Street (a building of small apartments), a few blocks away from the main rail station near downtown Los Angeles. That location no doubt allowed him a convenient hub of operations when he needed to travel to the various places that Riley would send him, but it was also only a block away from the Wolfskill orange groves where he developed the procedure for hydrocyanic gas to fumigate orange trees and where he would test the Vedalia beetle.",
-    "authors": [
-      {
-        "name_en": "Evenhuis, Neal L.",
-        "name_zh": "Evenhuis, Neal L."
-      }
-    ],
-    "keywords_en": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Coleoptera",
-      "Coccinellidae",
-      "Vedalia"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Coleoptera",
-      "Coccinellidae",
-      "Vedalia"
-    ],
-    "publication_date": 1518969600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "notspecified",
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-    "file_size_bytes": 8617,
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-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3792094",
-    "cstr": "31253.11.10.5281/zenodo.3792094",
-    "pid": "",
-    "title": "Alburnoides L.H.Jeitteles 1861",
-    "title_en": "Alburnoides L.H.Jeitteles 1861",
-    "title_zh": "Alburnoides L.H.Jeitteles 1861",
-    "description": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
-    "description_en": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
-    "description_zh": "Key to Alburnoides species that are known to occur or may occur in Iran  1a 15-20 total gill rakers in outer row on first gill arch........................................................................ A. taeniatus [not recorded in Iran but may eventually reach the Caspian Sea basin and the Tedzhen (= Hari) River drainage of Iran from the Tedzhen River and Karakum Canal in Turkmenistan, see Coad (2009)]   1b 5-10 total gill rakers in outer row on first gill arch...................................... 2   2a Snout pointed or slightly rounded; mouth terminal or upturned, tip of mouth cleft on level from slightly above middle of eye to upper margin of pupil; lower jaw slightly to moderately projecting relative to upper jaw; junction of lower jaw and quadrate on about vertical through anterior eye margin......................................................................................................  A. qanati sp. n.    2b Snout slightly to markedly rounded; mouth terminal to subterminal, tip of mouth cleft on level from middle of eye to below lower margin of eye; upper jaw slightly to moderately projecting relative to lower jaw; junction of lower jaw and quadrate on about vertical through about middle of eye................ 3   3a 8-11½, commonly 9-10½, branched anal fin rays; 7½, rarely 8½, branched dorsal fin rays.............................................................................................. 4   3b 10-15½, commonly 11-13½, branched anal fin rays; 8½, rarely 7½, branched dorsal fin rays.............................................................................................. 5   4a Ventral keel completely scaled; 40-41 total vertebrae; 20-22, commonly 21, abdominal vertebrae................  Alburnoides sp. , Orumiyeh [Urmia] L. basin   4b Ventral keel scaleless along from 1/3 to whole keel length; 38-40, commonly 39, total vertebrae; 19-20 abdominal vertebrae............................................................................................  Alburnoides sp. 1, lower Tigris River system.   5a Ventral keel smoothed, scaled along 1/3 to whole length.............................................................................  Alburnoides sp. 2, lower Tigris River system.   5b Ventral keel well pronounced, almost or completely scaleless...................... 6   6a Lateral line in live and preserved fish delineated by dark pigment dots above and below; 13-15 predorsal vertebrae; mouth terminal, tip of mouth cleft on or slightly below middle of eye................................................. A. eichwaldii   6b Lateral line in live and preserved fish somewhat darker than surrounding flank but no strong dark dots outline canal; 11-13 predorsal vertebrae; mouth almost subterminal, tip of mouth cleft on or below lower margin of eye.......................................................................  Alburnoides sp. , Namak L. basin.    Comparative Material.  Alburnoides bipunctatus: SMF 20631 (5, Grenzfluss Luxemburg, Rhine drainage).    Alburnoides eichwaldii (all from Eastern Transcaucasia: Kura-Aras river drainage down to Sefid Rud): CMNFI 1979 -0695 (30, Sefid Rud). – CMNFI 2007 -0090 (14, Zilber R.). – NMW 55516 (2 syntypes, Kura R.). – ZISP 2916 (7, Kura R.). – ZISP 3860 (5, Lenkoran' R.). – ZISP 5188 (10, Childyr [Cildir] L.). – ZISP 9104 (5, Lenkoran' R.). – ZISP 9131 (8, Lenkoran' R.). – ZISP 9136 (5, Geoktapinka R.). – ZISP 10249 (5, Kura R.). – ZISP 25704 (31, Gilyan-chay R.). – ZISP 25713 (26, Gilyanchay R.). – ZISP 37502 (10 syntypes of Alburnoides bipunctatus armeniensis, Marmarik R.). – ZISP 37503 (5, Dzoraget R.). – ZISP 37504 (5, Erer R.). – ZISP 41974 (9, Kura R.). – ZISP uncat. (30, Kura R.). – ZMH 3007 -9 (4, Kura R.). – ZMH 3586 (21, Childyr [Cildir] L.). – ZMH 3587 -88 (5, Kura R).    Alburnoides fasciatus (Western Transcaucasia and rivers of the Black Sea coast in Turkey westward to Kizilirmak): NMW 10407 -19 (13 syntypes, rivers of eastern Black Sea coast). – ZISP 5296 (6, Rioni R.). – ZISP 11529 (3, Batum). – ZISP 14822 (6, Coruh R.). – ZISP 15157 (4, Kintrishi R.). – ZISP uncat. (35, Otap R.). – ZISP uncat. (25, Kyalasur R.). – ZMH 3585 (8, Kizilirmak R.).    Alburnoides kubanicus (Kuban' R. drainage): ZISP 15306 (8, Laba R.). – ZISP 15307 (8, Kuban R.). – ZISP uncat. (25, Laba R.).    Alburnoides oblongus: BMNH 1975.1.17:249-250 (2, Syr Darya). – ZISP 30696 (1, Badam R.). – ZISP 36725 (3, Angren R.).    Alburnoides ohridanus: ZMH 801 (3, Ohrid L.). – ZMH 1464 (15, Ohrid L.).    Alburnoides rossicus: ZISP 2684 (8, Dnieper R.). – ZISP 5172 (5 syntypes, Shishma R., Kama R. system). – ZISP 10759 (8, Dnieper R.). – ZISP 52813 (26, Vyatka R.).    Alburnoides taeniatus: ZISP 25575 (53, Syr Darya R.).    Alburnoides sp. (Danube River drainage): ZISP 23862 (3), 35710 (4), 35711 (5), 35819 (7), 36852 (10), 37242 (23, Timiş R.), 38329 (10, Argeş R.), ZISP uncat. (25, Sava R.).    Alburnoides sp. (Eastern Ciscaucasia): ZISP 2879 (3, Sunzha R.). – ZISP 10790 (6, Terek R.). – ZISP 14733 (10, Sunzha R.).    Alburnoides sp. (Amu Darya river drainage): ZISP 4491 (2, Zeravshan R.). – ZISP 11050 -53 (39, Ashkhabadka R.).    Alburnoides sp. (Namak L. basin): CMNFI 1979 -0461A (30). – CMNFI 2007 -0121 (2). – CMNFI 2007 -0074 (4). – ZMH 4182 (7).    Alburnoides sp. 1 (lower Tigris River system): CMNFI 1979-0281 (42, Simareh River drainage, Lorestan).    Alburnoides sp. 2 (lower Tigris River system): CMNFI 2007-0075 (36, Malayer River, Hamadan), CMNFI 1979-0278 (4, Kashkan River drainage, Lorestan), CMNFI 2007-0115 (8, Qareh Su basin, Kermanshahan), CMNFI 2007-0118 (14, Bid Sorkh River, Kermanshahan).    Alburnoides sp. (Orumiyeh [Urmia] L. basin): CMNFI 1970 -0558 (30).",
-    "authors": [
-      {
-        "name_en": "Coad, Brian",
-        "name_zh": "Coad, Brian"
-      },
-      {
-        "name_en": " Bogutskaya, Nina",
-        "name_zh": " Bogutskaya, Nina"
-      }
-    ],
-    "keywords_en": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Chordata",
-      "Actinopterygii",
-      "Cypriniformes",
-      "Cyprinidae",
-      "Alburnoides"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Chordata",
-      "Actinopterygii",
-      "Cypriniformes",
-      "Cyprinidae",
-      "Alburnoides"
-    ],
-    "publication_date": 1246550400000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC0",
-    "file_size_mb": 0.0,
-    "file_size_bytes": 3487,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4304894",
-    "cstr": "31253.11.10.5281/zenodo.4304894",
-    "pid": "",
-    "title": "Wages and Work Survey 2020 Bangladesh - dataset",
-    "title_en": "Wages and Work Survey 2020 Bangladesh - dataset",
-    "title_zh": "Wages and Work Survey 2020 Bangladesh - dataset",
-    "description": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
-    "description_en": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
-    "description_zh": "Management summaryDecent Wage Bangladesh phase 1The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal&rsquo;s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction &nbsp;workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 &nbsp;in Ready made garment, Bangladeshi Taka (BDT) 10,800 &nbsp;in leather and footwear and BDT 11,547 in construction. The females&rsquo; median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have &ldquo;overtime hours&rdquo;, making not paying overtime hours not a major problem.Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers&rsquo; productivity, depending on the occupations.",
-    "authors": [
-      {
-        "name_en": "Kea Tijdens",
-        "name_zh": "Kea Tijdens"
-      }
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-    "keywords_en": [
-      "MINIMUM WAGES",
-      "Living wages",
-      "wages",
-      "people working in construction",
-      " tea estates or gardens",
-      " ready made garment",
-      " leather and footwear",
-      "face-to-face surveys with 1894 workers",
-      "Bangladesh"
-    ],
-    "keywords_zh": [
-      "MINIMUM WAGES",
-      "Living wages",
-      "wages",
-      "people working in construction",
-      " tea estates or gardens",
-      " ready made garment",
-      " leather and footwear",
-      "face-to-face surveys with 1894 workers",
-      "Bangladesh"
-    ],
-    "publication_date": 1606924800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
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-    "dataset_id": "",
-    "doi": "10.5281/zenodo.3664826",
-    "cstr": "",
-    "pid": "",
-    "title": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
-    "title_en": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
-    "title_zh": "Dhagnathos autokrator Perrichot & Wang & Barden 2020, sp. nov.",
-    "description": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
-    "description_en": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
-    "description_zh": " Dhagnathos autokrator sp. nov.   urn:lsid:zoobank.org:act: EC8760 A 9-9 C 00-44 A 9-9311- 2EC5 DE 24 A 4 B 1.  Figs. 2AeC, 3, 8 G.   Etymology. The specific epithet refers to autokrator (Greek, meaning 'self-ruler'), an individual who exercises absolute power, unrestrained by superiors; in reference to the highly powerful aspect of this ant.    Holotype. IGR. BU-003, alate female (Figs. 2AeC, 3EeG).    Additional specimens. HA 03, XA01 and RM 1, three alate females (Figs. 3Ae3D).    Horizon and locality. Upper Cretaceous, upper Albianelower Cenomanian (ca. 99 Ma); in amber from the Hukawng Valley, Kachin State, Myanmar.    Diagnosis. As for the genus, by monotypy.    Description (gyne). Body length ca. 14 mm. Cuticle generally smooth, without distinct sculpturing, sparsely covered by thin, long, erect setae, the head additionally densely covered by short, adpressed setae on vertex and genae.   Head only slightly longer than high and wide. Vertex and posteroventral surface rounded, anterior surface relatively flat, and genae shorter than eyes and projecting anteroventrally above mandible insertion into a cheek-like lobe. Ocelli present near top of vertex, conspicuous, ocellar diameter slightly larger than width of first antennomere; interocellar distance about half of ocellar diameter. Compound eyes bulging, reniform, 2.4 as long as wide, situated posteriorly on head (EPI 440). Antennae inserted between compound eyes around their midlength, closely flanking lateral edges of clypeus; base of antenna with basal bulb exposed, inserted within thick annular torulus. Antenna geniculate, filiform; scape short, 0.5 head length, weakly arched and broadened apically; first funicular article (pedicel) very short, 0.22 scape length, less than twice as long as wide, broadened apically; flagellomeres unusually slender, funicular article II (antennomere III) about 22 as long as wide; following antennomeres gradually decreasing in length and width. Posterior clypeal margin apparently fused, while horn is the result of de novo medial margin/ridge; anterior clypeal margin broadly rounded. Clypeal horn directed upward for its basal quarter, then bent at a right angle and directed forward for remaining length; horn gently rounded apically, without expanded lobe; dorsal surface of horn convex; ventral surface emarginate, its lateral margins prominent and prolonged basally into raised frontal carinae diverging anteriorly to reach the anterior margin of head, just above insertion of mandibles. Setation of horn consisting, on ventral surface, of a dense brush of short, peg-like denticles at apex; similar peg-like denticles widely spaced and arranged in a single row on each lateral margin, and becoming progressively denser and arranged in 2e3 longitudinal rows along lateral clypeal carinae; dorsal and ventral surfaces of horn sparsely covered by thin, long, erect setae. Labrum well exposed, large, nearly trapezoid, with anterior margin convex, posterior margin slightly emarginate medially, sides unsutured to clypeus so that anterior part of labrum is apparently movable; dorsal surface of labrum rimmed laterally by a longitudinal brush of stiff, spine-like setae, also densely coated by thin, erect setae becoming progressively longer and stiffer along lateral and posterior margins. Mandibles long (MDI 97), scytheshaped, widely spaced basally and converging apically, with tips curved and acute, nearly reaching the rounded portion of the horn as preserved; basal portion linear, short; apical portion 5 as long as basal portion, curved dorsally and posteriorly, with dorsal surface concave and rimmed on each margin by row of acute teeth and thin, erect setae directed backwards; medioventral blade between basal and apical portions forming a large, isosceles, blunt tooth perpendicular to apical portion. Palps long (visible on specimen HA 03), coated dorsally in fine, tapered setae, maxillary palp with 6 segments, as long as head capsule when combined; labial palp with 5 segments.   Mesosoma. Pronotal colar pronounced, concealing propleuron in dorsal view, separated from remaining pronotal dorsum by a distinct transverse ridge; pronotal dorsum strongly concave immediately anterior to ridge, nearly flat posterior to ridge; promesonotal suture deeply impressed. Mesoscutum as long as pronotum (excluding neck) in dorsal view, about as broad as long; mesoscutal dorsal outline feebly convex, with long parapsidal furrows almost reaching anterior mesonotal margin, converging posteriorly but not touching. Mesoscutellum posteriorly expanded, in dorsal view concealing median portion of metanotum; dorsal and posterior mesoscutellar surfaces concave, their junction forming a sharp angle; dorsal mesoscutellar surface with a deep, broad, transverse groove immediately posterior to scuto-scutellar suture. Metanotum medially as high as long, with posterior surface forming distinct angle with pronotal dorsum. Propodeum 1.25 as high as long, dorsal and declivitous surfaces meet at pronounced right angle, forming conspicuous ridge; dorsal surface nearly flat, declivitous surface faintly concave; propodeal spiracle slit-like, opening posteriad, at junction of propodeal dorsum and sides; metapleural gland orifice opening laterally, protected by guard setae. Legs long and robust (mostly visible on specimen HA 03); mesocoxa distinctly shorter than pro-and metacoxae; small trochantellus present on mid- and hind legs; all femora distinctly swollen in their basal half, tibiae swollen in their apical half; ventral margin of protibia apically with large calcar gently curved, protibia possessing small subapical point, and two straight, stout setae less than half as long as calcar; mesotibia apically with two long, straight, pectinate spurs, and two short, stout setae; metatibia apically with one long, pectinate spur and one long, simple spur; tarsomeres IeIV of all legs with pairs of stout setae along entire ventral surface (8e10 pairs on tI, 4e5 pairs on tII, 3 pairs on tIII, 2 pairs on tIV), and apically with 2 pairs of stout setae each flanking a spatulate spine; additionally the ventral surface of tarsomeres IeIV covered by dense brush of thin, erect setae; pretarsal claws strong, with a distinct subapical tooth.  Fore wing with Rs∙f2, basal portion of Rs∙f3, M ∙f4, and Cu1 nebulous, all other veins tubular; pterostigma elongate, ca. 6 as long as broad; a short stub of cross-vein 1r-rs present, nebulous; Rs∙f1 half as long as M ∙f1, both distinctly arched; Rs∙f2 and Rs∙f3 nearly at right angle, Rs∙f2 half as long as M ∙f2; 2rs-m present, situated beyond apex of pterostigma; discal and subdiscal cells pentagonal; cu-a arising from M þ Cu and proximal to M ∙f1 (Cu∙f1 short); vein Cu with both Cu1 and Cu2 present. Hind wing with jugal lobe present; anterior margin with 5 median and 22 distal hamuli; vein C present; vein R present, reaching distal wing margin; Rs∙f1 more than twice as long as 1rs-m; cu-a arising from M þ Cu, proximal to fork of M ∙f1 and Cu (Cu∙f1 short); Rs∙f2, M ∙f2, Cu, and A ∙f2 present, not reaching wing margin.   Metasoma with petiole short-pedunculate, almost 0.6 as high as long; petiolar tergite a broadly convex node, with anterior surface approximately twice as long as posterior face; subpetiolar process present, in profile forming a high, transverse, lamella pointing ventrally, with anterior face concave, posterior face vertical; not fused tergosternally, suture visible; attaching broadly to gaster. Gaster elongate. First gastral tergite with helcium pronounced, forming a post-petiolar peduncle, with anterior surface behind helcium high, oblique, and dorsal surface strongly convex, short; anteriormost part of first gastral sternite with a distinct mesal process (keel) pointing anteroventrally below helcium. Second gastral segment distinctly longer than first, with presclerite largely exposed to form a deep, broad constriction between first and second gastral segments (abdominal segments III and IV). Gastral segments unfused with deep lateral suture. Following segments poorly preserved, pygidium apparently broadly acute towards sting shaft.   Measurements (holotype IGR.BU-003; in mm). HL 2.50; HoL ca.1.70; EL 1.20; ocelli diameter 0.20; MDbL 0.40, MDtL 0.55, MDaL 2.00; length/width of antennomeres: I (scape) 1.15/0.16, II (pedicel) 0.26/ 0.14, III 2.16/0.10, IV 1.50/0.07, V 1.34/0.07; WL 3.85; FWL (as preserved) 6.35 (7.90 on specimen DHA 4); PL 1.84, PH 1.00, PW 0.67.",
-    "authors": [
-      {
-        "name_en": "Perrichot, Vincent",
-        "name_zh": "Perrichot, Vincent"
-      },
-      {
-        "name_en": " Wang, Bo",
-        "name_zh": " Wang, Bo"
-      },
-      {
-        "name_en": " Barden, Phillip",
-        "name_zh": " Barden, Phillip"
-      }
-    ],
-    "keywords_en": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Hymenoptera",
-      "Formicidae",
-      "Dhagnathos",
-      "Dhagnathos autokrator"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Hymenoptera",
-      "Formicidae",
-      "Dhagnathos",
-      "Dhagnathos autokrator"
-    ],
-    "publication_date": 1578585600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "notspecified",
-    "file_size_mb": 0.01,
-    "file_size_bytes": 9071,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4541458",
-    "cstr": "31253.11.10.5281/zenodo.4541458",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- 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▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- 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▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- 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U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- 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-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
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-      "speech-translation",
-      "machine-translation"
-    ],
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-    "doi": "10.5281/zenodo.4543003",
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-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- 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▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- 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▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- 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▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- 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▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- 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▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- 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▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- 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-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1613404800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 649.47,
-    "file_size_bytes": 681023804,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4604011",
-    "cstr": "",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- 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▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 2ccd176da5de478e115600b874952cebc549c6efpip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- 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▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- 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▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- 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▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- 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▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- 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▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- 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truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1615737600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 649.51,
-    "file_size_bytes": 681064188,
-    "download_count": 0,
-    "visit_count": 1,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4541452",
-    "cstr": "",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- 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▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- 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▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- 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▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:13:00 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.6|0.5|0.7|6.8|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.0|5.5|0.6|0.8|6.8|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.6|0.6|5.1|44.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.4|0.3|0.3|0.9|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.8|0.8|3.0|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.8|2.9|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.3|0.9|0.8|0.6|2.2|44.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.2|31.9||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.8|5.5|1.7|1.1|8.3|51.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.0|2.1|0.9|0.4|3.3|32.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.1|2.0|1.0|8.1|53.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|26.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.5|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.7|2.0|0.7|6.4|44.4|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp/.dist_init_46f33251-89a1-44d8-bc0b-215dd4281204dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- 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▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- 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▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- 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▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1613318400000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 649.49,
-    "file_size_bytes": 681041550,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4604066",
-    "cstr": "",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- 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▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- 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▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 70d16d210cdce28e71b7892a0ec96eaaa6474d64pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.aveResults# RESULTS## Environments- date: `Thu Mar 11 15:30:44 UTC 2021`- python version: `3.8.8 (default, Feb 24 2021, 21:46:12)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.8.0`- Git hash: `2ccd176da5de478e115600b874952cebc549c6ef`  - Commit date: `Mon Mar 8 10:41:31 2021 +0000`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.9|1.9|0.2|0.2|2.3|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.5|5.1|0.5|0.6|6.1|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.7|2.1|0.2|0.3|2.6|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.7|4.9|0.5|0.7|6.0|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.3|1.5|0.2|0.2|1.9|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.7|0.4|0.5|4.6|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.8|3.7|0.5|0.5|4.7|42.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.2|0.2|0.8|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.0|1.2|0.8|0.7|2.7|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.2|1.0|0.7|0.7|2.5|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.7|0.5|2.2|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.5|0.8|0.7|0.5|2.1|42.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.5|1.9|0.7|0.4|2.9|28.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|93.4|5.0|1.6|1.0|7.6|48.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.2|2.0|0.8|0.4|3.3|31.4||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.7|4.5|1.8|0.9|7.2|49.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.5|0.7|0.3|2.5|25.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.6|3.8|1.6|0.7|6.1|40.0||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|1.6|0.8|0.3|2.7|26.2||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.7|3.5|1.8|0.7|6.0|42.4|ASR configconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 8dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33850dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0025scheduler: warmuplrscheduler_conf:    warmup_steps: 40000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- 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▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- 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truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1615737600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 649.51,
-    "file_size_bytes": 681064195,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4543018",
-    "cstr": "31253.11.10.5281/zenodo.4543018",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- 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▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- 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▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- 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▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 9779891b9cc09b29777457d5962a92b6c7cace18pip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 06:58:04 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.1|0.2|0.3|2.5|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.6|0.7|6.6|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.2|0.2|0.3|2.7|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.3|5.2|0.6|0.7|6.4|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.1|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.7|3.9|0.5|0.5|4.8|43.8|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.4|0.3|0.3|0.3|0.9|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.1|1.1|0.8|0.7|2.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.2|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|43.8|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.2|2.0|0.8|0.4|3.2|31.6||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.1|8.2|51.3||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.4|3.3|31.5||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.3|4.8|1.8|1.0|7.6|52.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.7|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.8|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.9|0.3|2.8|26.3||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.6|3.6|1.8|0.6|6.1|43.8|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_0cd421be-b895-4057-bfa5-4046d8e52906dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 512    hop_length: 256specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft512_hop256_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- 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-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1613404800000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC BY 4.0",
-    "file_size_mb": 649.49,
-    "file_size_bytes": 681042578,
-    "download_count": 0,
-    "visit_count": 0,
-    "url": "",
-    "source": "scidb"
-  },
-  {
-    "dataset_id": "",
-    "doi": "10.5281/zenodo.4585558",
-    "cstr": "31253.11.10.5281/zenodo.4585558",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
-    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
-    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave, fs=16k, lang=",
-    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- ▁Technology- ▁payroll- ▁radio- ▁Washington- ▁flu- ▁affiliate- ▁concession- ▁predominantly- ▁reflective- ▁technological- ▁quicker- ▁amongst- ▁row- ▁Conference- ▁Southeast- ▁parent- grade- ▁differential- ▁Department- ▁attrition- ▁pause- water- ▁membership- ▁Litigation- ▁regulated- ▁personalized- ▁prescription- ▁cautionary- ▁handful- ▁tie- ▁El- ▁Carolina- ▁corner- ▁lever- ▁poor- ▁7%- ▁scheme- ▁ease- ▁sizable- ▁enroll- ▁fell- ▁carried- ▁chosen- ▁meantime- ▁hitting- ▁database- ▁Port- ▁Corporate- ▁balancing- ▁delever- ▁payout- ▁broadband- ▁official- ▁Che- ▁swap- ▁vote- ▁Water- ▁hu- ▁issuance- ▁prospect- ▁Furthermore- ▁military- ▁shrink- ility- ▁Enterprise- ▁controlling- ▁intellectual- ▁alluded- ▁endpoint- ▁carbon- ▁inject- ▁materialize- ▁Year- ▁bus- ▁Should- ivity- ▁definitive- ▁migrate- ▁differentiator- ▁United- ▁discover- ▁lumpy- ▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- ▁modification- ▁outdoor- ▁foresee- ▁articulate- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁Valley- ▁Virginia- ▁possibilities- ▁flood- ▁temperature- ▁alter- current- ▁experiment- ▁restrictions- ▁retire- ▁accuracy- ▁analyzing- ▁aluminum- ▁unprecedented- ▁expire- ▁glad- ▁powder- ▁spike- ▁tomorrow- growth- ▁avenue- ▁guidelines- ▁anymore- ▁eliminating- ▁telecom- ▁poised- ▁resort- ▁Hawaii- ▁likelihood- ▁simplification- ▁Engine- ▁gift- ▁correlation- ▁qualification- ▁redesign- ▁aspiration- ▁triple- ▁chunk- q- ▁underwrite- ▁Colombia- ▁redemption- ▁snow- ▁pursuit- ▁apparent- ▁consent- ▁Adjust- ▁candidate- ▁Program- ▁foremost- ▁Demand- ▁outpace- ▁replicate- ▁saving- ▁Office- ▁Healthcare- efficient- ▁Earlier- ▁Ontario- ▁interconnect- ▁plate- ▁Strong- ▁doubling- ▁analyst- ▁plastic- ▁Brad- ▁Friday- ▁worried- ▁proving- ▁contrast- ▁Whether- ▁1995- ▁Credit- ▁treasury- ▁institution- contract- ▁tissue- ▁Technologies- ▁glass- ▁refund- ▁additive- ▁oncology- ▁regulator- ▁expiration- ▁forefront- ▁embark- ▁IFRS- ▁imaging- ▁coffee- ▁default- ▁placing- ▁entrepreneur- ▁fluctuate- plus- ▁Dallas- ▁consensus- ▁flagship- ▁fragmented- ▁generator- ▁ERP- ▁Executive- ▁hike- ▁Control- ▁advertise- ▁choosing- ▁coast- ▁Sweden- ▁judge- ▁Development- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Together- ▁conviction- ▁click- ▁reaffirm- ▁children- ▁venue- ▁stories- ▁decreasing- ▁heightened- ▁lumber- ▁Remember- ▁tactical- ▁worst- patient- ▁trouble- ▁accrue- ▁caught- ▁unsecured- ▁unmet- ▁Chairman- ▁pronounced- ▁music- ▁Ohio- ▁statistics- ▁uptake- ▁chronic- ▁Access- ▁harm- ▁Micro- ▁debate- ▁quantity- ▁River- ▁initiate- ▁severity- ▁diminish- ▁routine- ▁slip- ▁Unfortunately- ▁mechanical- ▁mistake- ▁ambitious- ▁lens- ▁probability- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁Microsoft- ▁Safety- ▁dispute- ▁communicating- ▁grocery- ▁simplified- ▁Across- ▁Public- ▁spirit- ▁Meeting- ▁grateful- ▁NIM- ▁studio- ▁Facebook- ▁residual- ▁banner- ▁bolt- ▁requiring- ▁Jason- ▁Gross- scrip- ▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- 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▁Private- ▁indicator- ▁3%- forward- ▁Amazon- ▁incredible- ▁older- ▁anybody- ▁deploying- ▁permanent- ▁clinic- ▁servicing- ▁wonderful- ▁Following- ▁TV- ▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- 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▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- 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▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁status- ▁vary- ▁benefiting- ▁arrangement- ▁placed- ▁Rob- ▁Pacific- ▁principle- ▁students- ▁migration- ▁appear- ▁50%- ▁catch- ▁tried- ▁house- ▁display- ▁tied- ▁Third- ▁reinforce- app- ▁breadth- ▁Part- ▁lives- ▁storm- ▁lay- than- ▁green- ▁communications- 0,000- wide- ▁slowdown- ▁determined- ▁mechanism- ▁evaluation- ▁crude- ▁functions- ▁simplify- ▁Tim- ▁Consumer- ▁licensing- ▁person- ▁avoid- ▁preferred- ▁supplement- ▁sourcing- ▁Once- ▁milestone- ▁formula- ▁disclaim- ▁depth- ▁turned- ▁organically- ▁embedded- ▁explore- ▁instance- ▁obtain- ▁completing- ▁wrap- ▁buyback- ▁pump- ▁Going- ists- ▁Right- ▁Home- ▁updating- ▁kick- ▁surprised- ▁afford- ▁responsible- ▁incurred- ▁sharp- ▁integrating- ▁rolled- ▁filing- quarter- qua- ▁60- ▁Cha- ▁opinion- ▁promotions- ▁indicators- ▁pushing- ▁intent- ame- ▁impressive- ▁bulk- pac- ▁heart- ▁exploration- field- ▁branch- ▁fire- ▁spreads- ▁fix- ▁criteria- ▁drag- ▁Maybe- ox- house- ▁basic- ▁lose- ▁exact- ▁aggregate- date- leading- ▁concerning- ▁accomplished- 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▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- 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▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- 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▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- ▁has- ▁these- ▁so- ▁well- ▁over- ▁those- ▁now- ll- ▁time- ly- n- ▁out- ▁where- ▁into- ▁customers- ▁like- ▁first- ▁results- m- ▁But- ▁other- ▁really- ▁how- ▁if- ▁up- ▁their- ▁expect- ▁forward- ▁company- ▁than- ▁last- ▁were- ▁look- ▁your- ▁through- ▁get- ▁any- ▁call- ▁one- ▁because- ▁strong- ▁In- ▁had- ▁believe- ▁sales- ▁when- ▁good- ▁second- ▁This- ▁As- ▁revenue- ▁then- er- ▁lot- ▁make- ▁performance- ▁product- ▁cost- ▁much- y- ▁financial- ▁capital- ▁value- ▁Our- ▁could- ▁years- ▁right- ▁impact- ▁terms- ▁want- ▁future- ▁don- ▁both- ▁It- ▁them- ▁today- ▁work- ▁little- ▁back- ▁customer- ▁next- ▁bit- ▁increase- al- ▁end- ▁take- ▁products- ▁number- ▁kind- ▁higher- ▁opportunities- ▁half- ▁able- ▁way- ▁markets- ▁may- ▁know- ▁significant- ▁operating- ▁better- ▁go- ▁focus- ▁say- ▁cash- ▁2- ▁part- ▁things- ▁still- ▁point- ▁provide- ▁most- ▁statements- ▁lower- ▁should- ▁team- ▁being- in- c- ▁need- ▁across- ▁made- ▁opportunity- ▁line- ▁important- ▁down- ▁third- e- ▁rate- ▁- ▁during- ▁long- ▁people- ar- ▁re- ▁fourth- ▁strategy- ▁portfolio- ▁did- ▁many- ▁continued- ▁industry- ▁level- ▁price- ▁progress- ▁around- ▁A- p- r- ▁working- ▁share- ▁management- ▁me- ▁demand- ▁investment- ▁no- ▁due- ▁additional- ▁give- ▁actually- ▁come- ▁high- ▁great- ▁while- ▁only- ▁even- ▁seeing- ▁plan- ▁few- ▁margin- ▁same- ▁looking- ▁here- ▁S- ▁drive- ▁development- ▁start- ▁grow- ▁result- ▁costs- ▁different- ▁current- le- ▁seen- ▁change- ▁doing- ▁further- ▁3- ▁coming- ation- ▁service- ▁who- ▁guidance- ▁positive- ▁data- ▁earnings- o- ▁position- ▁C- ▁process- ▁technology- ▁key- ▁turn- ▁full- g- looking- ▁past- ▁investments- ▁That- ▁said- ▁support- ▁improve- ▁overall- S- ▁basis- ▁environment- ▁expected- or- ▁balance- ▁question- u- ▁increased- ▁again- ▁move- ▁focused- ▁help- ▁within- ▁sort- 'on'- ▁got- ur- ▁potential- ▁services- ▁before- 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▁allocating- ▁witness- ▁cluster- ▁Illinois- ▁restart- ▁immuno- ▁president- ▁surplus- ▁inefficiencies- ▁baked- ▁Reconciliation- ▁impossible- ▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sun Feb 28 07:20:31 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|117813|95.4|2.7|1.9|1.1|5.7|61.0||decode_asr_lm_lm_train_lm_en_unnorm_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1169320|95.5|2.7|1.8|1.2|5.7|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 59122dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- 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▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- 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▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- ▁modification- ▁outdoor- ▁foresee- ▁articulate- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁Valley- ▁Virginia- ▁possibilities- ▁flood- 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▁teleconference- ▁perceive- ▁edit- ▁distinguish- ▁correlate- ▁authentic- ▁idle- ▁autonomous- ▁friction- ▁coupon- ▁ATM- ▁ancillary- ▁incumbent- ▁refocus- ▁overnight- ▁imperative- ▁Indonesia- ▁manifest- ▁govern- ▁promoting- ▁regain- ▁ruling- ▁pulp- ▁rebate- ▁neuro- ▁royalties- ▁transcript- ▁kids- ▁George- ▁filter- ▁diligent- ▁prescribe- ▁revolution- ▁Science- ▁British- ▁accrual- ▁cement- ▁Keep- ▁Phoenix- ▁Zealand- ▁dominant- ▁Broad- ▁Senior- ▁replenish- ▁Court- ▁appointment- ▁collaborate- ▁speculate- ▁Electric- ▁Norway- ▁genetic- ▁modify- ▁Transportation- ▁erosion- ▁resonating- ▁quota- ▁crucial- ▁opposite- ▁streamlining- ▁optical- ▁Chemical- ▁authority- ▁millennial- ▁Flex- ▁disaster- ▁shortfall- ▁1,000- ▁nursing- ▁Republic- ▁implies- ▁elevate- ▁equities- ▁Several- ▁annuity- ▁elimination- ▁landlord- ▁neighborhood- ▁curtail- ▁argue- ▁temporarily- ▁Atlanta- ▁crystal- ▁Lower- ▁Personal- ▁ordinary- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁albeit- ▁perpetual- ▁differentiating- ▁activation- ▁exploit- ▁reception- ▁systematic- ▁premature- ▁Navy- ▁revisit- abilities- ▁competencies- ▁integral- ▁skew- ▁photo- mount- ▁corn- ▁Netherlands- ▁article- ▁charging- ▁shaping- ▁activate- specific- ▁implant- ▁unrealized- ▁eager- ▁securitization- ▁Oklahoma- ▁accumulate- ▁phasing- ▁province- ▁Resources- ▁softening- ▁Automotive- ▁privilege- ▁depressed- ▁statistical- ▁inflows- ▁Electronic- ▁catastrophe- ▁prevail- ▁Arizona- ▁durable- ▁terminate- ▁RevPAR- ▁assembly- facing- ▁Though- ▁argument- ▁airplane- ▁fraud- ▁society- ▁Platform- ▁habit- ▁brick- ▁crack- ▁Michigan- ▁bandwidth- ▁applies- ▁delta- ▁qualitative- ▁determining- ▁grain- ▁leisure- ▁repricing- ▁amended- ▁'20- ▁inhibitor- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- ▁innovating- ▁encounter- ▁endeavor- ▁Defense- ▁excuse- ▁Communication- ▁pride- ▁buffer- ▁configuration- ▁clarification- ▁Target- ▁Craig- ▁Nevertheless- ▁silver- ▁Property- ▁Ultimately- ▁circuit- ▁instruct- ▁showcase- ▁energized- ▁severance- ▁trailing- ▁iconic- ▁tailored- ▁concluding- ▁Supply- ▁Seattle- ▁recapture- ▁vacation- ▁modified- ▁steep- ▁celebrate- ▁prepaid- ▁vacancy- ▁Natural- ▁achievable- ▁drink- ▁inclusive- ▁scheduling- ▁capacities- ▁mitigation- ▁investigator- ▁black- ▁Margin- ▁taste- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm//lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 58146dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm//lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp_unnorm//lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- s- ▁in- ▁that- ▁a- ''''- ▁our- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁be- ▁this- ▁We- re- ▁And- ▁will- '-'- t- ▁year- ▁quarter- ▁at- ing- ▁So- ▁more- ▁from- ▁business- ▁think- ▁not- ▁what- ▁some- d- ve- ▁us- ▁by- ▁about- ed- ▁there- ▁can- ▁growth- ▁new- ▁was- ▁which- ▁or- ▁do- ▁very- ▁an- '?'- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁all- ▁been- ▁just- ▁has- ▁these- ▁so- ▁well- ▁over- ▁those- ▁now- ll- ▁time- ly- n- ▁out- ▁where- ▁into- ▁customers- ▁like- ▁first- ▁results- m- ▁But- ▁other- ▁really- ▁how- ▁if- ▁up- ▁their- ▁expect- ▁forward- ▁company- ▁than- ▁last- ▁were- ▁look- ▁your- ▁through- ▁get- ▁any- ▁call- ▁one- ▁because- ▁strong- ▁In- ▁had- ▁believe- ▁sales- ▁when- ▁good- ▁second- ▁This- ▁As- ▁revenue- ▁then- er- ▁lot- ▁make- ▁performance- ▁product- ▁cost- ▁much- y- ▁financial- ▁capital- ▁value- ▁Our- ▁could- ▁years- ▁right- ▁impact- ▁terms- ▁want- ▁future- ▁don- ▁both- ▁It- ▁them- ▁today- ▁work- ▁little- ▁back- ▁customer- ▁next- ▁bit- ▁increase- al- ▁end- ▁take- ▁products- ▁number- ▁kind- ▁higher- ▁opportunities- ▁half- ▁able- ▁way- ▁markets- ▁may- ▁know- ▁significant- ▁operating- ▁better- ▁go- ▁focus- ▁say- ▁cash- ▁2- ▁part- ▁things- ▁still- ▁point- ▁provide- 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▁science- ▁progressing- ▁enrollment- ▁shut- ▁possibility- ▁feature- ▁describe- 5%- ▁relating- ▁possibly- ▁Op- ▁finished- ▁fruit- ▁released- ▁tracking- ▁Life- ▁status- ▁vary- ▁benefiting- ▁arrangement- ▁placed- ▁Rob- ▁Pacific- ▁principle- ▁students- ▁migration- ▁appear- ▁50%- ▁catch- ▁tried- ▁house- ▁display- ▁tied- ▁Third- ▁reinforce- app- ▁breadth- ▁Part- ▁lives- ▁storm- ▁lay- than- ▁green- ▁communications- 0,000- wide- ▁slowdown- ▁determined- ▁mechanism- ▁evaluation- ▁crude- ▁functions- ▁simplify- ▁Tim- ▁Consumer- ▁licensing- ▁person- ▁avoid- ▁preferred- ▁supplement- ▁sourcing- ▁Once- ▁milestone- ▁formula- ▁disclaim- ▁depth- ▁turned- ▁organically- ▁embedded- ▁explore- ▁instance- ▁obtain- ▁completing- ▁wrap- ▁buyback- ▁pump- ▁Going- ists- ▁Right- ▁Home- ▁updating- ▁kick- ▁surprised- ▁afford- ▁responsible- ▁incurred- ▁sharp- ▁integrating- ▁rolled- ▁filing- quarter- qua- ▁60- ▁Cha- ▁opinion- ▁promotions- ▁indicators- ▁pushing- ▁intent- ame- ▁impressive- ▁bulk- pac- ▁heart- ▁exploration- field- ▁branch- ▁fire- ▁spreads- ▁fix- ▁criteria- ▁drag- ▁Maybe- ox- house- ▁basic- ▁lose- ▁exact- ▁aggregate- date- leading- ▁concerning- ▁accomplished- ▁17- ▁pattern- ▁renewable- ▁feed- ▁dealing- ▁Houston- ▁promote- ▁divisions- ▁percent- ▁gold- ▁regulations- MS- ▁patent- ▁split- ▁differently- ▁Americas- ▁patterns- ▁school- ▁terminal- istic- ▁weighted- ▁spectrum- ▁sponsor- ▁FX- ▁unchanged- ▁expert- ▁0- ▁seeking- ▁member- ▁comparisons- ▁unfavorable- ▁churn- ▁absorb- ▁combine- ▁overhead- matic- ▁creative- ▁join- ▁serious- ▁lock- ▁formal- ▁stress- ▁participants- ▁guest- ▁Jeff- ▁transparency- ▁cat- ▁reliable- ▁estimated- ▁via- ▁purposes- ▁acquiring- ▁appears- ju- ▁calculation- ▁inherent- ▁institutions- ▁analysts- ▁offshore- ▁unlock- ▁elect- ▁located- ▁resolution- ▁minimal- ▁Dan- ▁relation- ▁easily- ▁softness- ral- ▁entering- ▁dose- ▁gather- ▁op- ▁rely- ▁Commercial- ▁square- ▁plenty- ▁impairment- ▁FDA- ▁Sea- ▁CEO- ▁functionality- ▁revised- ▁monthly- ER- ▁disclosed- ▁analyze- ▁raising- ▁sustainability- ▁Importantly- ▁Tri- ▁written- work- ▁Where- ▁someone- ▁port- ▁complement- ▁Trans- ▁entirely- ▁Phase- ▁Industrial- ▁micro- ▁minute- like- ▁thoughtful- ▁fluctuation- ▁partly- RA- ▁considerable- ▁diversity- ▁smooth- ▁persist- ▁prepare- ▁producers- ▁19- ▁AS- ▁reverse- ▁stabilize- ▁fine- ▁eventually- ▁ran- ▁save- ism- ▁Customer- ▁airline- ▁effectiveness- ▁colleagues- ▁self- ▁Pe- ▁realization- ▁administrative- ▁maturity- ▁doubt- ▁tenant- ▁introducing- ▁regulators- ▁straight- ax- ▁recruit- ▁wonder- ▁mass- ▁solar- ▁rollout- ▁Uni- ▁Cor- ▁remove- TA- ▁exception- ▁capable- ▁frequency- ▁borrowing- ▁acreage- ▁architecture- ▁rule- ▁Bill- ▁th- ▁sitting- ▁wealth- ▁multiyear- ▁Sales- ▁knew- ▁published- ▁Man- ▁Hu- ▁election- ▁talented- ▁according- PA- ▁proposed- ▁distinct- ▁consulting- ▁retailer- ▁globe- ▁Under- ▁tighten- ▁refinancing- ▁Pu- ▁thousands- ▁consequence- ▁interaction- ▁Inter- state- ▁seasonally- ▁communicate- ▁park- ▁Matt- ▁characterize- ▁dramatic- ▁write- ▁man- ▁promotion- ▁Private- ▁indicator- ▁3%- forward- ▁Amazon- ▁incredible- ▁older- ▁anybody- ▁deploying- ▁permanent- ▁clinic- ▁servicing- ▁wonderful- ▁Following- ▁TV- ▁owned- ▁Scott- ▁nor- ▁Russia- ▁connectivity- ▁comfort- ▁dialogue- ▁install- ▁concentration- ▁seamless- ▁Michael- ▁demonstrating- ▁warrant- ▁entry- ▁EPS- ▁reflection- ▁legislation- ▁gear- ▁accordance- ▁backdrop- ▁latter- RO- ▁levers- ▁differentiation- ▁minor- ▁damage- ▁Certain- ▁preparing- ▁interact- ▁emerge- IC- ▁engaging- ▁Col- ▁route- ▁retirement- ▁upper- ▁prevent- ▁70- ▁headcount- ▁hybrid- ▁litigation- ▁2%- ▁Permian- ▁utilizing- ▁assure- chi- ▁candidates- ▁narrow- ▁utilities- ker- ▁continuously- ▁meaningfully- ▁layer- ▁National- AN- ▁facilitate- ▁Got- ▁respective- ▁negotiate- ▁massive- ▁referring- ▁Revenue- ▁physician- CE- performance- ▁Like- ▁Retail- ▁Dave- ▁city- ▁Reg- ▁measured- ▁Gulf- ▁menu- ▁vessels- ▁Bob- SA- ▁Every- ▁competing- ▁attend- ▁worse- ▁equal- ▁window- place- ▁commence- ▁procedures- ▁chemical- ▁applied- performing- ▁greatest- ▁distributor- ▁appropriately- ew- ▁pillar- ▁communicated- ▁adjusting- ▁branches- ▁regulation- ▁defense- ▁procurement- ▁factory- ▁offsetting- ▁cities- ▁selected- ▁uptick- ▁merchandising- ▁tail- ▁automate- ▁Central- ▁fantastic- ▁synergy- press- ▁transmission- IS- ▁selection- ▁reaching- ▁generic- ▁observe- ▁became- train- ▁payers- ▁Gra- pay- ▁purchased- ▁round- ▁challenged- ▁modestly- ▁Care- ▁calculate- ▁proactive- ▁divest- ▁relate- ▁streamline- ▁reinsurance- ▁advertisers- ▁receiving- body- ▁passion- ▁stat- AM- ▁ticket- ▁workforce- ▁4%- bra- ▁Forward- ▁vendor- ▁promising- ▁heading- ▁mu- ▁General- ▁disposition- ▁dry- ▁basin- ▁extraordinary- holder- ▁fundamentally- ▁claim- ▁liabilities- ▁Bar- ▁listening- ▁faced- ▁tender- ▁elaborate- ▁preference- ▁upward- ▁stack- ▁Mer- ▁succeed- DA- ▁Medicare- ▁wrong- ▁reset- ▁strengthened- ▁Regarding- ▁harder- ▁bunch- ▁voice- ▁living- ▁night- ▁version- ▁threat- ble- ▁continually- ▁waste- ▁illustrate- location- ▁blend- ▁renovation- ▁jump- ▁terrific- ▁Dr- cost- ▁crop- ▁regardless- ▁Operating- ▁medicine- ▁honest- ▁High- ▁edge- ▁ultimate- ▁placement- ▁disposal- ▁pivot- ▁responsibility- ▁initiated- ▁predictable- ▁shortage- ▁cadence- ▁audit- IN- ▁wave- ▁Service- ▁navigate- ene- ▁adequate- ▁extending- ▁definition- ▁sports- ▁explained- ▁Paul- ▁served- ▁north- ▁raised- 'ON'- ▁lenders- ▁index- ▁counter- ▁manager- ▁borrow- ▁grant- ▁assurance- ▁behalf- ▁upfront- ▁entertainment- fu- light- ▁beneficial- ▁hedging- ▁bridge- ▁Further- ▁rebound- ▁broadcast- ▁publish- ▁essential- ▁uniquely- stock- ▁advancing- ▁innovate- ▁warehouse- ▁sophisticated- ▁offered- ▁indeed- ▁worldwide- ▁joined- ▁women- ▁carrier- AP- ▁Hi- wise- ▁precise- ▁score- ▁Brian- MA- fo- ▁anticipating- ▁satisfied- ▁court- ▁none- ▁runway- ▁uncertain- ▁convenience- ▁minimize- ▁settle- ▁comm- ▁consistency- ▁compression- ▁agenda- ▁Connect- driven- ▁continuation- ▁secondary- ▁link- ▁funded- ▁geography- ▁recognizing- ▁Southern- ▁refresh- ▁Sha- ▁prefer- ▁preliminary- ▁Northern- ▁tap- ▁Va- ▁realizing- ▁AR- ▁lab- ▁efficacy- ▁anyone- ▁Factors- ▁pending- ▁Joe- ▁occurring- ▁boost- ▁rebuild- ▁gaming- ▁affecting- ▁digit- ▁panel- ▁sensor- ▁excitement- ▁30%- ▁Food- ▁beverage- ▁constraints- ▁preparation- ▁bundle- month- ▁Each- ▁listen- ▁equation- ▁agent- ▁allowance- ▁Per- ▁characteristics- ▁Additional- ▁earning- ▁conjunction- ▁severe- zz- ▁London- OS- ▁pushed- ▁somewhere- ▁incur- ▁nation- ▁EBIT- ▁translation- ▁25- ▁15%- ▁rid- level- ▁watching- ▁optimal- ▁hopeful- ▁obvious- ▁clarify- ▁identifying- ▁diligently- tech- ▁lap- ▁alignment- TE- ▁anywhere- ▁noise- ▁evident- ▁picking- ▁Ge- ▁ambition- ▁requirement- ▁mile- ▁divestiture- ▁optimism- ▁constitute- ▁reputation- ▁agreed- ounce- ▁staffing- ▁demographic- ▁acceptance- ▁jurisdiction- ▁frequent- ▁Rich- ▁attack- ▁sellers- ▁validate- ▁pharma- ▁intense- ▁distributed- ▁World- ▁title- ▁employ- scale- ▁1%- ▁enormous- ▁compound- how- ▁onetime- ▁6%- ▁word- generation- ▁Does- ▁define- ▁affordable- ▁aftermarket- ▁complicated- ▁families- ▁slowing- ▁disclose- ▁la- ▁signing- ▁suffer- ▁Plan- ▁spin- ▁background- ▁realign- ▁competitiveness- ▁Growth- ▁transparent- ▁alone- ▁math- ▁Gu- ▁currencies- value- ▁accomplishments- ▁stabilized- ▁Street- ▁equally- ▁Black- ▁manufacture- ▁represented- ▁benchmark- ▁contemplate- ▁variability- ▁task- ▁consolidate- ▁exceeding- ▁virtually- ▁sum- ▁Har- ▁appetite- ▁Will- ▁cable- hand- aw- ▁maximizing- ▁philosophy- ▁harvest- ▁unknown- xi- ▁fulfill- win- ▁qualified- ▁employer- ▁Center- ▁buyer- ▁Board- stone- ▁shorter- ▁satellite- ▁midpoint- channel- ▁graph- ▁linked- ▁leg- ▁deck- ▁corresponding- ▁tire- ▁renewed- J- ▁familiar- ▁zone- Z- ▁destination- ▁container- ▁Na- ▁24- ▁OpEx- ▁She- IT- ▁complementary- EX- ▁film- ▁tech- ▁interim- cycle- ▁Korea- ▁Brand- ▁bonus- ▁proceed- ▁Would- ▁classes- ▁pickup- ▁friend- ▁exploring- ▁gradually- ▁fortunate- ▁attach- ▁representative- ▁200- ▁warm- ▁collective- margin- ▁Sun- ▁burn- ▁prospective- ▁pad- ▁horizon- ▁recruiting- ▁prime- ▁addressable- ▁contractual- ▁Digital- ▁reliance- ▁bidding- ▁reaction- ▁entity- ▁Mr- ▁tangible- ade- ▁Basin- ▁appreciation- mail- ▁decent- risk- ▁personally- ▁Kevin- ▁awarded- ▁burden- ▁stabilization- ▁macroeconomic- ▁ingredient- ▁pipe- ▁Risk- ▁copy- ▁neutral- ▁student- ▁franchisees- ▁bucket- ▁inflection- ▁send- ▁guided- ▁cold- ▁covenant- ▁surface- ▁softer- ▁television- ▁measurement- ▁reasonably- ▁hub- ▁funnel- tribut- ka- ▁circ- ▁advisory- ▁occasion- ▁session- ▁sentiment- ▁constructive- ▁sensitive- ▁restrict- ▁accepted- ▁catalyst- cast- EN- ▁crew- ▁proceeding- ▁proactively- ▁adjacent- room- ▁downward- ▁amazing- ▁trigger- ▁auction- ▁revision- ▁Clearly- ▁testament- ▁language- ▁tank- ▁survey- ▁bright- ▁earned- ▁8%- ▁deliberate- ▁accordingly- ▁Chi- ▁grid- ▁arise- ▁Secondly- ▁2014- ▁diligence- ▁referred- ▁headline- premise- ▁lie- ▁mandate- ▁deepen- added- ▁diagnostic- price- ▁Pay- ▁therapeutic- ▁healthcare- ▁wider- source- ▁royalty- ▁Data- ville- ▁construct- ▁virtual- ▁acknowledge- ▁Reform- ▁renew- ▁indicative- ▁supportive- ▁module- ▁Eastern- ▁compensate- ▁consult- ▁FY- ▁underpin- ▁cyclical- ▁monetize- ▁sample- ▁applicable- ▁understood- ▁cutting- ▁attempt- ▁registration- ▁campus- ▁flavor- ▁steadily- ▁sight- ▁treated- ▁anticipation- ▁accept- ▁properly- ▁tailwind- ▁committee- ▁shelf- ▁capturing- ▁style- ▁concentrated- ▁recycling- ▁Lu- ▁deduct- ▁memory- ▁cheap- ▁REIT- ▁noncash- ▁40%- ▁urban- ▁entities- ▁Lastly- ▁programming- ridge- ▁tower- ▁visible- ▁refining- ▁billion- ▁composition- ▁Work- ▁swing- ▁letter- ▁nuclear- ▁IoT- ▁resolved- ▁announcing- ▁equivalent- town- ▁Spain- ▁Greg- ▁historic- ▁Partner- ▁heat- ▁seat- ▁kept- ▁gu- ▁economies- ▁Italy- ▁literally- ▁repositioning- ▁commit- ▁Brexit- ▁pack- ▁discontinued- ▁resilient- ▁functional- ▁tariff- ▁certainty- ▁messaging- ▁turnover- ▁Ka- ▁Ti- ▁Investment- ▁Similar- sourcing- ▁Frank- ▁satisfy- ▁artificial- ▁discovery- ▁authorities- ▁commodities- ▁prioritize- ▁south- ▁pet- ▁losing- ▁ideal- ▁territories- ▁yourself- ▁laser- ▁employment- ▁pound- ▁indirect- ▁pharmaceutical- ▁fight- ▁sorry- ▁Excluding- ▁bullish- ▁separation- ▁detect- tune- ▁redevelopment- ▁rich- ▁augment- ▁strive- ▁strict- ancy- ▁Technology- ▁payroll- ▁radio- ▁Washington- ▁flu- ▁affiliate- ▁concession- ▁predominantly- ▁reflective- ▁technological- ▁quicker- ▁amongst- ▁row- ▁Conference- ▁Southeast- ▁parent- grade- ▁differential- ▁Department- ▁attrition- ▁pause- water- ▁membership- ▁Litigation- ▁regulated- ▁personalized- ▁prescription- ▁cautionary- ▁handful- ▁tie- ▁El- ▁Carolina- ▁corner- ▁lever- ▁poor- ▁7%- ▁scheme- ▁ease- ▁sizable- ▁enroll- ▁fell- ▁carried- ▁chosen- ▁meantime- ▁hitting- ▁database- ▁Port- ▁Corporate- ▁balancing- ▁delever- ▁payout- ▁broadband- ▁official- ▁Che- ▁swap- ▁vote- ▁Water- ▁hu- ▁issuance- ▁prospect- ▁Furthermore- ▁military- ▁shrink- ility- ▁Enterprise- ▁controlling- ▁intellectual- ▁alluded- ▁endpoint- ▁carbon- ▁inject- ▁materialize- ▁Year- ▁bus- ▁Should- ivity- ▁definitive- ▁migrate- ▁differentiator- ▁United- ▁discover- ▁lumpy- ▁transport- ▁absorption- ▁semiconductor- je- ▁mutual- ▁popular- ▁audio- ▁code- ▁shipment- ▁Bio- ▁Ki- ▁handling- ▁euro- ▁overseas- ▁realistic- ▁principal- ▁People- ▁'15- ▁LNG- ▁overcome- ▁Total- ▁manufacturer- ▁convinced- oriented- ▁scalable- ▁Hong- ▁bump- ▁hydro- ▁intensity- ▁Medical- ▁Gene- ▁disappointed- ▁copper- ▁takeaway- ▁Park- ▁Kong- ▁judgment- ▁restructure- ▁float- ▁penetrate- ▁Pi- ▁validation- ▁Direct- ▁Network- ▁Media- ▁repayment- ▁description- ▁maximum- Point- ▁output- service- ▁image- ▁household- ▁methodology- ▁Chicago- ▁spite- ▁Ken- ▁Argentina- ▁territory- ▁suit- ▁Performance- ▁CFO- ▁lend- ▁consist- ▁amendment- ▁elsewhere- ▁apparel- ▁foreseeable- ▁principally- view- ▁Information- ▁outlet- fusion- ▁aerospace- ▁relentless- ▁omni- single- ▁undu- ▁hurdle- ▁scaling- ▁rank- ▁cohort- ▁quote- ▁recap- ▁fun- ▁three- ▁throughput- ▁wire- ▁basket- ▁smartphone- ▁Cloud- ▁motor- ▁governance- brand- ▁sensitivity- ▁therapies- ▁fluid- ▁resolve- ▁Delaware- ▁Google- ▁Why- ▁art- ▁Hurricane- ▁lifetime- ▁sa- ▁observation- ▁inflationary- ▁Ju- ▁variation- ▁disruptive- ▁electricity- ▁profitably- ▁interpret- ▁barrier- ▁investigation- ▁scientific- ▁Vice- ▁constrained- ▁delighted- ▁German- ▁casual- ▁expenditure- ▁body- focused- ▁causing- ▁downstream- ▁submission- ▁Number- ▁hour- ▁refinance- ▁municipal- ▁advancement- ▁suppose- ▁gradual- ▁Everyone- ▁MA- ▁favorably- ▁comprise- ▁Due- ▁considerably- ▁securing- hold- ▁domain- ▁unfortunately- ▁vessel- ▁proof- ▁stake- ▁mobility- ▁Class- ▁listeners- ▁protocol- ▁tumor- ▁scrap- ▁shipped- ▁responsive- ▁Northeast- ▁explanation- ▁remarkable- ▁outflow- ▁accommodate- ▁mindful- ▁niche- ▁prepayment- ▁appendix- ▁Auto- ▁SaaS- ▁myself- ▁animal- ▁peer- ▁redeploy- ▁passenger- ▁Key- ▁Project- ▁battery- ▁embrace- ▁monetization- ▁ample- ▁luxury- ▁Company- spec- ▁normalize- ▁disrupt- ▁strike- ▁weekend- ▁threshold- ▁exhibit- ▁weigh- ▁combining- ▁diesel- ▁duty- ▁recommendation- ▁pharmacy- ▁temp- print- ▁enthusiasm- ▁conventional- ▁remodel- ▁subsidiaries- ▁surrounding- ▁leaving- ▁Specifically- ▁parallel- ▁chip- ▁consume- ▁compute- ▁incident- ▁movie- ▁fulfillment- ▁chose- ▁deflation- ▁consultants- ▁thorough- ▁conscious- ▁strides- ▁remote- ▁fastest- ▁relief- ▁recoveries- ▁interface- ▁enthusiastic- ▁Rico- ▁preserve- ▁Beyond- ▁Corporation- ▁airport- ▁dozen- ▁missed- ▁resilience- ▁variance- ▁pretax- ▁street- ▁Consistent- ▁underground- ▁lifestyle- ▁submitted- ▁collaborative- ▁supplies- ▁whilst- ▁honor- standing- ▁acute- ▁density- ▁controlled- ▁breakdown- ▁surprising- ▁educate- media- ▁extract- ▁Jack- ▁agile- ▁Things- ▁rigorous- ▁Peter- ▁predictions- ▁subsidiary- ▁proper- ▁negotiation- turn- ▁consolidating- ▁acceptable- ▁Insurance- ▁optionality- ▁Analyst- ▁EMEA- ▁Two- ▁discrete- ▁struggle- ▁termination- making- ▁impression- ▁loyal- ▁accountability- ▁tier- power- ▁urgency- ▁throw- ▁reposition- range- ▁algorithm- ▁crisis- ▁sudden- ▁resonate- ▁pleasure- ▁advice- ibility- ▁impressed- ▁80%- ▁proxy- ▁surgical- ▁Medicaid- ▁identity- ▁Vegas- product- ▁analytic- ▁9%- ▁district- ▁discretionary- ▁accident- ▁molecule- ▁doctors- ▁failure- ▁pathway- ▁blue- ▁100%- ▁array- ▁exposed- ▁protein- ▁unfold- ▁everywhere- ▁inclusion- ▁worry- ▁alliance- ▁unexpected- ▁corporation- ▁Eagle- ▁upgrading- ▁flight- ▁60%- ▁granular- ▁Alberta- ▁Christmas- ▁white- ▁empower- ▁inspection- ▁novel- ▁precision- ▁ROI- effective- ▁stockholders- ▁Jersey- ▁SKU- ▁absence- ▁negotiating- ▁twice- ▁certification- ▁consumable- ▁derivative- ▁perception- ▁underscore- centric- ▁Specialty- ▁guiding- ▁integrity- ▁nonrecurring- ▁stretch- ▁mini- ▁defend- ▁resume- ▁Green- ▁receipt- ▁dilution- ▁replacing- ▁comparative- ▁substitute- ▁ladies- ▁younger- ▁collateral- ▁fifth- cycl- ▁climate- ▁disappointing- ▁keen- balance- ▁Japanese- growing- ▁Fourth- ▁determination- ▁hyper- ▁mineral- ▁authorization- ▁thrilled- ▁undertaking- ▁script- fold- ▁rationalization- ▁hurt- ▁midstream- sale- ▁Look- ▁comparing- ▁secular- ▁holistic- ▁Construction- ▁robotic- ▁intelligent- ▁velocity- ▁beauty- ▁regime- ▁Smart- ▁invite- ▁dimension- ▁blood- ▁reorganization- ▁unlikely- ▁master- ▁workflow- ▁demo- ▁anniversary- ▁diluted- ▁inorganic- ▁submit- ▁Quarter- ▁cancellation- ▁French- adjusted- ▁cultural- ▁preclinical- ▁concentrate- gress- ▁Security- ▁strip- ▁anchor- ▁Annual- ▁deleveraging- ▁Long- ▁Midwest- ▁relevance- ▁Boston- ▁sweet- ▁Gold- ▁geo- ▁bolster- ▁extreme- ▁overlap- ▁breakeven- ▁headquarters- ▁adult- ▁Doug- ▁vital- ▁agility- ▁Building- ▁revolving- ▁technique- ▁intangible- ▁reservoir- ▁stabilizing- ▁noncore- ▁concrete- ▁reconcile- ▁procedure- ▁immune- ▁Blue- ▁recommend- ▁Mobile- ▁academic- ▁multifamily- ▁practical- ▁Congress- ▁computing- ▁Results- ▁accretion- ▁casino- ▁reversal- ▁arrive- ▁revolver- ▁IPO- ▁referral- ▁convenient- ▁parameters- ▁deterioration- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁uplift- ▁fail- ▁bankruptcy- ▁text- ▁viable- ▁visual- ▁marine- ▁valid- ▁contingent- ▁recession- ▁Federal- ▁apartment- ▁distribute- ▁nimble- ▁suspect- ▁Phil- ▁character- ▁Colorado- ▁solely- 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-    "authors": [
-      {
-        "name_en": "Shinji Watanabe",
-        "name_zh": "Shinji Watanabe"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
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-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1614873600000,
-    "created": "",
-    "modified": "",
-    "status": "",
-    "license": "CC-BY-NC-4.0",
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-    "url": "",
-    "source": "scidb"
-  },
-  {
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-    "doi": "10.5281/zenodo.4541465",
-    "cstr": "31253.11.10.5281/zenodo.4541465",
-    "pid": "",
-    "title": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "title_en": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "title_zh": "ESPnet2 pretrained model, kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000",
-    "description": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- 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▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- 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▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_en": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- 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▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- 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▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- 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▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- 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▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: transformerlm_conf:    pos_enc: null    embed_unit: 128    att_unit: 512    head: 8    unit: 2048    layer: 16    dropout_rate: 0.0required:- output_dir- token_listdistributed: true",
-    "description_zh": "This model was trained by kamo-naoyuki using librispeech recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8eff1a983f6098111619328a7d8254974ae9dfcapip install -e .cd egs2/librispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.aveResults# RESULTS## Environments- date: `Mon Feb 15 07:02:24 UTC 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `8eff1a983f6098111619328a7d8254974ae9dfca`  - Commit date: `Wed Dec 16 02:22:50 2020 +0000`## asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|97.8|2.0|0.2|0.3|2.5|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|94.0|5.4|0.5|0.6|6.6|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|97.6|2.1|0.2|0.3|2.6|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|94.1|5.3|0.5|0.7|6.6|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|95.6|3.9|0.5|0.5|4.9|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|1.7|0.2|0.3|2.1|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|95.5|4.0|0.5|0.6|5.1|44.1|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99.3|0.3|0.3|0.3|0.9|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|98.0|1.2|0.8|0.7|2.8|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.3|1.0|0.8|0.5|2.3|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|98.4|0.9|0.8|0.5|2.2|44.1|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.3|2.0|0.8|0.4|3.1|30.7||decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|92.9|5.3|1.8|1.0|8.2|50.7||decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.1|2.0|0.9|0.3|3.2|31.1||decode_asr_asr_model_valid.acc.ave/test_other|2939|65101|93.0|5.0|2.0|0.9|7.9|52.1||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|97.7|1.6|0.7|0.3|2.6|25.5||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|94.3|3.9|1.7|0.7|6.4|41.6||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|97.5|1.6|0.8|0.3|2.7|26.4||decode_asr_lm_lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|94.3|3.8|1.9|0.6|6.3|44.1|ASR configconfig: conf/tuning/train_asr_conformer5.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_spngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp/.dist_init_7d8b8acf-d69d-4b0b-b84e-f76d64d0c2e6dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 140000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/speech_shape- exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_960_sp/wav.scp    - speech    - kaldi_ark-   - dump/raw/train_960_sp/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - kaldi_ark-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- ▁ARE- ▁THEIR- ▁WOULD- ▁IF- ▁WHAT- ▁THEM- ▁WHO- ▁OUT- M- ▁DO- ▁WILL- ▁UP- ▁BEEN- P- R- ▁MAN- ▁THEN- ▁COULD- ▁MORE- C- ▁INTO- ▁NOW- ▁VERY- ▁YOUR- ▁SOME- ▁LITTLE- ES- ▁TIME- RE- ▁CAN- ▁LIKE- LL- ▁ABOUT- ▁HAS- ▁THAN- ▁DID- ▁UPON- ▁OVER- IN- ▁ANY- ▁WELL- ▁ONLY- B- ▁SEE- ▁GOOD- ▁OTHER- ▁TWO- L- ▁KNOW- ▁GO- ▁DOWN- ▁BEFORE- A- AL- ▁OUR- ▁OLD- ▁SHOULD- ▁MADE- ▁AFTER- ▁GREAT- ▁DAY- ▁MUST- ▁COME- ▁HOW- ▁SUCH- ▁CAME- LE- ▁WHERE- ▁US- ▁NEVER- ▁THESE- ▁MUCH- ▁DE- ▁MISTER- ▁WAY- G- ▁S- ▁MAY- ATION- ▁LONG- OR- ▁AM- ▁FIRST- ▁BACK- ▁OWN- ▁RE- ▁AGAIN- ▁SAY- ▁MEN- ▁WENT- ▁HIMSELF- ▁HERE- NESS- ▁THINK- V- IC- ▁EVEN- ▁THOUGHT- ▁HAND- ▁JUST- ▁O- ▁UN- VE- ION- ▁ITS- 'ON'- ▁MAKE- ▁MIGHT- ▁TOO- K- ▁AWAY- ▁LIFE- TH- ▁WITHOUT- ST- ▁THROUGH- ▁MOST- ▁TAKE- ▁DON- ▁EVERY- F- O- ▁SHALL- ▁THOSE- ▁EYES- AR- ▁STILL- ▁LAST- ▁HOUSE- ▁HEAD- ABLE- ▁NOTHING- ▁NIGHT- ITY- ▁LET- ▁MANY- ▁OFF- ▁BEING- ▁FOUND- ▁WHILE- EN- ▁SAW- ▁GET- ▁PEOPLE- ▁FACE- ▁YOUNG- CH- ▁UNDER- ▁ONCE- ▁TELL- AN- ▁THREE- ▁PLACE- ▁ROOM- ▁YET- ▁SAME- IL- US- U- ▁FATHER- ▁RIGHT- EL- ▁THOUGH- ▁ANOTHER- LI- RI- ▁HEART- IT- ▁PUT- ▁TOOK- ▁GIVE- ▁EVER- ▁E- ▁PART- ▁WORK- ERS- ▁LOOK- ▁NEW- ▁KING- ▁MISSUS- ▁SIR- ▁LOVE- ▁MIND- ▁LOOKED- W- RY- ▁ASKED- ▁LEFT- ET- ▁LIGHT- CK- ▁DOOR- ▁MOMENT- RO- ▁WORLD- ▁THINGS- ▁HOME- UL- ▁THING- LA- ▁WHY- ▁MOTHER- ▁ALWAYS- ▁FAR- FUL- ▁WATER- CE- IVE- UR- ▁HEARD- ▁SOMETHING- ▁SEEMED- I- LO- ▁BECAUSE- OL- ▁END- ▁TOLD- ▁CON- ▁YES- ▁GOING- ▁GOT- RA- IR- ▁WOMAN- ▁GOD- EST- TED- ▁FIND- ▁KNEW- ▁SOON- ▁EACH- ▁SIDE- H- TON- MENT- ▁OH- NE- Z- LING- ▁AGAINST- TER- ▁NAME- ▁MISS- ▁QUITE- ▁WANT- ▁YEARS- ▁FEW- ▁BETTER- ENT- ▁HALF- ▁DONE- ▁ALSO- ▁BEGAN- ▁HAVING- ▁ENOUGH- IS- ▁LADY- ▁WHOLE- LESS- ▁BOTH- ▁SEEN- ▁SET- ▁WHITE- ▁COURSE- IES- ▁VOICE- ▁CALLED- ▁D- ▁EX- ATE- ▁TURNED- ▁GAVE- ▁C- ▁POOR- MAN- UT- NA- ▁DEAR- ISH- ▁GIRL- ▁MORNING- ▁BETWEEN- LED- ▁NOR- IA- ▁AMONG- MA- ▁- ▁SMALL- ▁REST- ▁WHOM- ▁FELT- ▁HANDS- ▁MYSELF- ▁HIGH- ▁M- ▁HOWEVER- ▁HERSELF- ▁P- CO- ▁STOOD- ID- ▁KIND- ▁HUNDRED- AS- ▁ROUND- ▁ALMOST- TY- ▁SINCE- ▁G- AM- ▁LA- SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- ▁DEMOCRATIC- ▁EUSTACE- ▁SQUIRREL- ▁UNCOMMON- ▁MARVELLOUS- ▁PLOUGH- ▁TRAGEDY- ▁VAULT- ▁HESITATE- ▁REFRAIN- ▁ADMIRING- ▁CORPORAL- ▁ENTITLED- ▁SHREWD- ▁SQUEEZ- ▁ACCURATE- ▁TEMPEST- ▁MONUMENT- ▁SIEGE- ▁CHINESE- ▁RAVEN- ▁LOUNG- ▁ASSASSIN- ▁INFLICT- ▁AGITATED- ▁DESIRABLE- ▁EARLIEST- ▁LAUNCH- ▁PILOT- ▁PULSE- ▁MUTE- LEIGH- ▁LIQUOR- ▁SCARECROW- ▁SKULL- ▁DESOLATE- ▁SUBLIME- ▁SERENE- ▁RECESS- ▁WAKING- ▁CHARLOTTE- ▁CIRCULAR- ▁INJUSTICE- ▁PINOCCHIO- ▁PRISCILLA- ▁THYSELF- ▁OCCURRENCE- ▁CASUAL- ▁FRANTIC- ▁LEGEND- ▁FERTIL- ▁BACKGROUND- ▁DELICACY- ▁ESTRALLA- ▁MANUSCRIPT- ▁RESPONSE- ▁UNIVERSITY- ▁WOLVES- ▁SCANDAL- ▁STUMBLE- ▁HOARSE- ▁BODILY- ▁CONVENT- ▁EXAMINING- ▁INCAPABLE- ▁PERCEIVING- ▁PHILADELPHIA- ▁SUBSEQUENT- ▁THIEVES- ▁ACCUMULAT- ▁DAMSEL- ▁SCOTCH- ▁UNDERNEATH- ▁NOBILITY- ▁SMASH- ▁REVOLT- ▁ENGAGE- ▁CATHEDRAL- ▁CHAMPION- ▁DESPATCH- ▁ETERNITY- ▁JANUARY- ▁PLEADED- ▁PROBABILITY- ▁JIMMIE- ▁PARALLEL- ▁FISHERMAN- ▁JERRY- ▁SWORE- ▁DRAUGHT- ▁OPPONENT- ▁PRIMITIVE- ▁SIGNIFICANCE- ▁SUBSTANTIAL- ▁AMAZED- ▁DUNBAR- ▁COMMEND- ▁CONTEMPLATE- ▁TESTIMONY- ▁IMPERIAL- ▁ADAPT- ▁JUICE- ▁CALAMIT- CULAR- ▁CHATEAU- ▁PHOENIX- ▁PRUDENT- ▁SOLUTION- ▁VILLEFORT- ▁REACTION- ▁RELAX- ▁YU- ▁PROHIBIT- ▁DISTRUST- ▁PLUNDER- ▁WELFARE- ▁NAVIGAT- ▁PARLOR- ▁LAZY- ▁DETACH- OMETER- ▁PRIV- ▁DISCOURAGE- ▁OBSTINATE- ▁REJOICING- ▁SERMON- ▁VEHICLE- ▁FANCIES- ▁ENLIGHTEN- ▁ACUTE- ▁ILLUSION- ▁ANTHEA- ▁MARTIAN- ▁EXCITE- ▁GENEROSITY- OLOGIST- ▁AMAZING- ▁UNWORTHY- ▁INTERNAL- ▁INCENSE- ▁VIBRAT- ▁ADHERE- ROACH- ▁FEBRUARY- ▁MEXICAN- ▁POTATOES- ▁INCESSANT- ▁INTERPOSED- ▁PARCEL- ▁VEXED- ▁PROMOTE- MIDST- ▁ARISTOCRAT- ▁CYRIL- ▁EMBARK- ▁ABUNDANCE- ▁LITERALLY- ▁SURGEON- ▁TERRACE- ▁ATLANTIC- ▁MARTYR- ▁SPECK- ▁SENATE- ▁LOAF- ▁ADMINISTER- ▁APPREHEND- ▁SUBDUED- ▁TEMPORARY- ▁DOMINION- ▁ELABORATE- ▁DIGNIFIED- ▁ELIZA- ▁SPLASH- ▁CONSEIL- ▁DEXTER- ▁UNSEEN- ▁TRAGIC- VOCATION- ▁GRATIFY- ▁BACHELOR- ▁DEFENSE- ▁EXCURSION- ▁FACULTIES- ▁PROPRIETOR- ▁SYMPATHETIC- ▁UNNECESSARY- ▁RADIANT- ▁VACANT- ▁OUNCE- ▁SCREW- ▁PHENOMENON- ▁PROMINENT- ▁WORRIED- ▁STUDIES- ▁CLIMATE- ▁KEITH- ▁ARAMIS- ▁BLISS- ▁CONTINUAL- ▁SURPASS- ▁HEBREW- ▁IDENTITY- ▁PROVOKE- ▁TEMPERAMENT- ▁CHARIOT- ▁HARBOR- ▁NINTH- ▁PRIOR- ▁DESIROUS- ▁JERUSALEM- ▁UNDERTAKING- ▁EDISON- ▁MIRTH- ▁SCOUT- ▁APPARATUS- ▁ILLUSTRATION- ▁INTELLIGIBLE- ▁INVARIABLY- ▁PIERCED- ▁REVIEW- ▁FLICKER- ▁HAZARD- ▁REVELATION- ▁DIXON- ▁EXCITING- ▁GOSPEL- ▁CONSTANCE- ▁OVERTAKE- ▁GUINEA- ▁ALADDIN- ▁CHICAGO- ▁TULLIVER- ▁HAMILTON- ▁GARRISON- ▁DISCIPLE- ▁INTENSITY- ▁TRAITOR- ▁CHANCELLOR- ▁PROVERB- ▁DAGGER- ▁FORESEE- ▁CONFIDE- ▁GLIMMER- ▁CHAUVELIN- ▁ILLUSTRATE- ▁VOLUNTEER- ▁JUNGLE- ▁STREAK- ▁SUNRISE- ▁DISSOLV- ▁QUEST- ▁AWHILE- ▁FELICITY- ▁LEGISLATURE- ▁LEONORA- ▁MAGAZINE- ▁PITIFUL- ▁COLONY- ▁SHAWL- ▁ARRIVING- ▁FUNDAMENTAL- ▁CARPENTER- ▁OVERFLOW- ▁EXPAND- ▁HARVEST- ▁FEMININE- ▁INNUMERABLE- ▁SCRAMBLE- ▁TWENTIETH- ▁TRIFLING- ▁GHASTL- ▁CONQUEST- ▁DANIEL- ▁FACILIT- ▁FORSAKE- ▁BEHAVIOUR- ▁GORGEOUS- ▁PRODUCING- ▁HAPPIER- ▁PROMISING- ▁RAINBOW- ▁INSTINCTIVELY- ▁DECREE- ▁EYEBROWS- ▁IRRESISTIBLE- ▁PHARAOH- ▁SCROOGE- ▁UNNATURAL- ▁CRUMBS- ▁REFINED- ▁DREARY- ▁TRENCH- ▁CONVINCE- ▁FRINGE- ▁EXTREMITY- ▁INTIMACY- ▁SCOUNDREL- ▁SUFFRAGE- ▁UNEASINESS- ▁BARRICADE- ▁CIRCULAT- ▁SAMUEL- ▁BRUCE- ▁DARCY- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullfrontend: defaultfrontend_conf:    fs: 16k    n_fft: 400    hop_length: 160specaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_bpe5000_n_fft400_hop160_sp/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listdistributed: trueLM configconfig: conf/tuning/train_lm_transformer2.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptruengpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: file:///home/kamo/work/espnet/egs2/librispeech/asr1/exp/lm_train_lm_transformer2_bpe5000_scheduler_confwarmup_steps25000_batch_bins500000000_accum_grad2_use_amptrue/.dist_init_0173e6f2-7b0f-4a4f-9b0a-f3e1922ff565dist_world_size: 16dist_rank: 0local_rank: 0dist_master_addr: nulldist_master_port: nulldist_launcher: slurmmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 25patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 2no_forward_run: falseresume: truetrain_dtype: float32use_amp: truelog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 500000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.005scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁THE- S- ▁AND- ▁OF- ▁TO- ▁A- ▁IN- ▁I- ▁HE- ▁THAT- ▁WAS- ED- ▁IT- ''''- ▁HIS- ING- ▁YOU- ▁WITH- ▁FOR- ▁HAD- T- ▁AS- ▁HER- ▁IS- ▁BE- ▁BUT- ▁NOT- ▁SHE- D- ▁AT- ▁ON- LY- ▁HIM- ▁THEY- ▁ALL- ▁HAVE- ▁BY- ▁SO- ▁THIS- ▁MY- ▁WHICH- ▁ME- ▁SAID- ▁FROM- ▁ONE- Y- E- ▁WERE- ▁WE- ▁NO- N- ▁THERE- ▁OR- ER- ▁AN- ▁WHEN- 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SE- ▁BOY- ▁MA- ▁PERHAPS- ▁WORDS- ATED- ▁HO- X- ▁MO- ▁SAT- ▁REPLIED- ▁FOUR- ▁ANYTHING- ▁TILL- ▁UNTIL- ▁BLACK- TION- ▁CRIED- RU- TE- ▁FACT- ▁HELP- ▁NEXT- ▁LOOKING- ▁DOES- ▁FRIEND- ▁LAY- ANCE- ▁POWER- ▁BROUGHT- VER- ▁FIRE- ▁KEEP- PO- FF- ▁COUNTRY- ▁SEA- ▁WORD- ▁CAR- ▁DAYS- ▁TOGETHER- ▁IMP- ▁REASON- KE- ▁INDEED- TING- ▁MATTER- ▁FULL- ▁TEN- TIC- ▁LAND- ▁RATHER- ▁AIR- ▁HOPE- ▁DA- ▁OPEN- ▁FEET- ▁EN- ▁FIVE- ▁POINT- ▁CO- OM- ▁LARGE- ▁B- ▁CL- ME- ▁GONE- ▁CHILD- INE- GG- ▁BEST- ▁DIS- UM- ▁HARD- ▁LORD- OUS- ▁WIFE- ▁SURE- ▁FORM- DE- ▁DEATH- ANT- ▁NATURE- ▁BA- ▁CARE- ▁BELIEVE- PP- ▁NEAR- ▁RO- ▁RED- ▁WAR- IE- ▁SPEAK- ▁FEAR- ▁CASE- ▁TAKEN- ▁ALONG- ▁CANNOT- ▁HEAR- ▁THEMSELVES- CI- ▁PRESENT- AD- ▁MASTER- ▁SON- ▁THUS- ▁LI- ▁LESS- ▁SUN- ▁TRUE- IM- IOUS- ▁THOUSAND- ▁MONEY- ▁W- ▁BEHIND- ▁CHILDREN- ▁DOCTOR- AC- ▁TWENTY- ▁WISH- ▁SOUND- ▁WHOSE- ▁LEAVE- ▁ANSWERED- ▁THOU- ▁DUR- ▁HA- ▁CERTAIN- ▁PO- ▁PASSED- GE- TO- ▁ARM- ▁LO- ▁STATE- ▁ALONE- TA- ▁SHOW- ▁NEED- ▁LIVE- ND- ▁DEAD- ENCE- ▁STRONG- ▁PRE- ▁TI- ▁GROUND- SH- TI- ▁SHORT- IAN- UN- ▁PRO- ▁HORSE- MI- ▁PRINCE- ARD- ▁FELL- ▁ORDER- ▁CALL- AT- ▁GIVEN- ▁DARK- ▁THEREFORE- ▁CLOSE- ▁BODY- ▁OTHERS- ▁SENT- ▁SECOND- ▁OFTEN- ▁CA- ▁MANNER- MO- NI- ▁BRING- ▁QUESTION- ▁HOUR- ▁BO- AGE- ▁ST- ▁TURN- ▁TABLE- ▁GENERAL- ▁EARTH- ▁BED- ▁REALLY- ▁SIX- 'NO'- IST- ▁BECOME- ▁USE- ▁READ- ▁SE- ▁VI- ▁COMING- ▁EVERYTHING- ▁EM- ▁ABOVE- ▁EVENING- ▁BEAUTIFUL- ▁FEEL- ▁RAN- ▁LEAST- ▁LAW- ▁ALREADY- ▁MEAN- ▁ROSE- WARD- ▁ITSELF- ▁SOUL- ▁SUDDENLY- ▁AROUND- RED- ▁ANSWER- ICAL- ▁RA- ▁WIND- ▁FINE- ▁WON- ▁WHETHER- ▁KNOWN- BER- NG- ▁TA- ▁CAPTAIN- ▁EYE- ▁PERSON- ▁WOMEN- ▁SORT- ▁ASK- ▁BROTHER- ▁USED- ▁HELD- ▁BIG- ▁RETURNED- ▁STRANGE- ▁BU- ▁PER- ▁FREE- ▁EITHER- ▁WITHIN- ▁DOUBT- ▁YEAR- ▁CLEAR- ▁SIGHT- ▁GRA- ▁LOST- ▁KEPT- ▁F- PE- ▁BAR- ▁TOWN- ▁SLEEP- ARY- ▁HAIR- ▁FRIENDS- ▁DREAM- ▁FELLOW- PER- ▁DEEP- QUE- ▁BECAME- ▁REAL- ▁PAST- ▁MAKING- RING- ▁COMP- ▁ACT- ▁BAD- HO- STER- ▁YE- ▁MEANS- ▁RUN- MEN- ▁DAUGHTER- ▁SENSE- ▁CITY- ▁SOMETIMES- ▁TOWARDS- ▁ROAD- ▁SP- ▁LU- ▁READY- ▁FOOT- ▁COLD- ▁SA- ▁LETTER- ▁ELSE- ▁MAR- ▁STA- BE- ▁TRUTH- ▁LE- BO- ▁BUSINESS- CHE- ▁JOHN- ▁SUBJECT- ▁COURT- ▁IDEA- ILY- ▁RIVER- ATING- ▁FAMILY- HE- ▁DIDN- ▁GLAD- ▁SEVERAL- IAL- ▁UNDERSTAND- ▁SC- ▁POSSIBLE- ▁DIFFERENT- ▁RETURN- ▁ARMS- ▁LOW- ▁HOLD- ▁TALK- ▁RU- ▁WINDOW- ▁INTEREST- ▁SISTER- SON- ▁SH- ▁BLOOD- ▁SAYS- ▁CAP- ▁DI- ▁HUMAN- ▁CAUSE- NCE- ▁THANK- ▁LATE- GO- ▁CUT- ▁ACROSS- ▁STORY- NT- ▁COUNT- ▁ABLE- DY- LEY- ▁NUMBER- ▁STAND- ▁CHURCH- ▁THY- ▁SUPPOSE- LES- BLE- OP- ▁EFFECT- BY- ▁K- ▁NA- ▁SPOKE- ▁MET- ▁GREEN- ▁HUSBAND- ▁RESPECT- ▁PA- ▁FOLLOWED- ▁REMEMBER- ▁LONGER- ▁AGE- ▁TAKING- ▁LINE- ▁SEEM- ▁HAPPY- LAND- EM- ▁STAY- ▁PLAY- ▁COMMON- ▁GA- ▁BOOK- ▁TIMES- ▁OBJECT- ▁SEVEN- QUI- DO- UND- ▁FL- ▁PRETTY- ▁FAIR- WAY- ▁WOOD- ▁REACHED- ▁APPEARED- ▁SWEET- ▁FALL- BA- ▁PASS- ▁SIGN- ▁TREE- IONS- ▁GARDEN- ▁ILL- ▁ART- ▁REMAIN- ▁OPENED- ▁BRIGHT- ▁STREET- ▁TROUBLE- ▁PAIN- ▁CONTINUED- ▁SCHOOL- OUR- ▁CARRIED- ▁SAYING- HA- ▁CHANGE- ▁FOLLOW- ▁GOLD- ▁SW- ▁FEELING- ▁COMMAND- ▁BEAR- ▁CERTAINLY- ▁BLUE- ▁NE- CA- ▁WILD- ▁ACCOUNT- ▁OUGHT- UD- ▁T- ▁BREATH- ▁WANTED- ▁RI- ▁HEAVEN- ▁PURPOSE- ▁CHARACTER- ▁RICH- ▁PE- ▁DRESS- OS- FA- ▁TH- ▁ENGLISH- ▁CHANCE- ▁SHIP- ▁VIEW- ▁TOWARD- AK- ▁JOY- ▁JA- ▁HAR- ▁NEITHER- ▁FORCE- ▁UNCLE- DER- ▁PLAN- ▁PRINCESS- DI- ▁CHIEF- ▁HAT- ▁LIVED- ▁AB- ▁VISIT- ▁MOR- TEN- ▁WALL- UC- ▁MINE- ▁PLEASURE- ▁SMILE- ▁FRONT- ▁HU- ▁DEAL- OW- ▁FURTHER- GED- ▁TRIED- DA- VA- ▁NONE- ▁ENTERED- ▁QUEEN- ▁PAY- ▁EL- ▁EXCEPT- ▁SHA- ▁FORWARD- ▁EIGHT- ▁ADDED- ▁PUBLIC- ▁EIGHTEEN- ▁STAR- ▁HAPPENED- ▁LED- ▁WALKED- ▁ALTHOUGH- ▁LATER- ▁SPIRIT- ▁WALK- ▁BIT- ▁MEET- LIN- ▁FI- LT- ▁MOUTH- ▁WAIT- ▁HOURS- ▁LIVING- ▁YOURSELF- ▁FAST- ▁CHA- ▁HALL- ▁BEYOND- ▁BOAT- ▁SECRET- ENS- ▁CHAIR- RN- ▁RECEIVED- ▁CAT- RESS- ▁DESIRE- ▁GENTLEMAN- UGH- ▁LAID- EVER- ▁OCCASION- ▁WONDER- ▁GU- ▁PARTY- DEN- ▁FISH- ▁SEND- ▁NEARLY- ▁TRY- CON- ▁SEEMS- RS- ▁BELL- ▁BRA- ▁SILENCE- IG- ▁GUARD- ▁DIE- ▁DOING- ▁TU- ▁COR- ▁EARLY- ▁BANK- ▁FIGURE- IF- ▁ENGLAND- ▁MARY- ▁AFRAID- LER- ▁FO- ▁WATCH- ▁FA- ▁VA- ▁GRE- ▁AUNT- PED- ▁SERVICE- ▁JE- ▁PEN- ▁MINUTES- ▁PAN- ▁TREES- NED- ▁GLASS- ▁TONE- ▁PLEASE- ▁FORTH- ▁CROSS- ▁EXCLAIMED- ▁DREW- ▁EAT- ▁AH- ▁GRAVE- ▁CUR- PA- URE- CENT- ▁MILES- ▁SOFT- ▁AGO- ▁POSITION- ▁WARM- ▁LENGTH- ▁NECESSARY- ▁THINKING- ▁PICTURE- ▁PI- SHIP- IBLE- ▁HEAVY- ▁ATTENTION- ▁DOG- ABLY- ▁STANDING- ▁NATURAL- ▁APPEAR- OV- ▁CAUGHT- VO- ISM- ▁SPRING- ▁EXPERIENCE- ▁PAT- OT- ▁STOPPED- ▁REGARD- ▁HARDLY- ▁SELF- ▁STRENGTH- ▁GREW- ▁KNIGHT- ▁OPINION- ▁WIDE- ▁INSTEAD- ▁SOUTH- ▁TRANS- ▁CORNER- ▁LEARN- ▁ISLAND- ▁MI- ▁THIRD- ▁STE- ▁STRAIGHT- ▁TEA- ▁BOUND- ▁SEEING- ▁JU- ▁DINNER- ▁BEAUTY- ▁PEACE- AH- ▁REP- ▁SILENT- ▁CRE- ALLY- RIC- ▁STEP- ▁VER- ▁JO- GER- ▁SITTING- ▁THIRTY- ▁SAVE- ENED- ▁GLANCE- ▁REACH- ▁ACTION- ▁SAL- ▁SAD- ▁STONE- ITIES- ▁FRENCH- ▁STRUCK- ▁PAPER- ▁WHATEVER- ▁SUB- ▁DISTANCE- ▁WRONG- ▁KNOWLEDGE- ▁SAFE- ▁SNOW- ▁MUSIC- ▁FIFTY- RON- ▁ATTEMPT- ▁GOVERNMENT- TU- ▁CROWD- ▁BESIDES- ▁LOVED- ▁BOX- ▁DIRECTION- ▁TRAIN- ▁NORTH- ▁THICK- ▁GETTING- AV- ▁FLOOR- ▁COMPANY- ▁BLOW- ▁PLAIN- TRO- ▁BESIDE- ▁ROCK- ▁IMMEDIATELY- FI- ▁SHADOW- ▁SIT- ORS- ILE- ▁DRINK- ▁SPOT- ▁DANGER- ▁AL- ▁SAINT- ▁SLOWLY- ▁PALACE- IER- ▁RESULT- ▁PETER- ▁FOREST- ▁BELONG- ▁SU- ▁PAR- RIS- ▁TEARS- ▁APPEARANCE- ▁GATE- BU- ITION- ▁QUICKLY- ▁QUIET- ▁LONDON- ▁START- ▁BROWN- TRA- KIN- ▁CONSIDER- ▁BATTLE- ▁ANNE- ▁PIECE- ▁DIED- ▁SUCCESS- ▁LIPS- ▁FILLED- ▁FORGET- ▁POST- IFIED- ▁MARGARET- ▁FOOD- HAM- ▁PLEASANT- ▁FE- ▁EXPRESSION- ▁POCKET- ▁FRESH- ▁WEAR- TRI- ▁BROKEN- ▁LAUGHED- GING- ▁FOLLOWING- WN- IP- ▁TOUCH- ▁YOUTH- ATIVE- ▁LEG- ▁WEEK- ▁REMAINED- ▁EASY- NER- RK- ▁ENTER- ▁FIGHT- ▁PLACED- ▁TRAVEL- ▁SIMPLE- ▁GIRLS- ▁WAITING- ▁STOP- ▁WAVE- AU- ▁WISE- ▁CAMP- TURE- UB- ▁VE- ▁OFFICE- ▁GRAND- ▁FIT- ▁JUDGE- UP- MENTS- ▁QUICK- HI- ▁FLO- RIES- VAL- ▁COMFORT- ▁PARTICULAR- ▁STARTED- ▁SUIT- ▁NI- ▁PALE- ▁IMPOSSIBLE- ▁HOT- ▁CONVERSATION- ▁SCENE- ▁BOYS- ▁WIN- ▁BRE- ▁SOCIETY- ▁OUTSIDE- ▁WRITE- ▁EFFORT- ▁TALKING- ▁FORTUNE- ▁NINE- ▁WA- ▁SINGLE- ▁RULE- ▁PORT- ▁WINTER- ▁CAST- ▁CRA- ▁HAPPEN- ▁CRO- ▁SHUT- NING- ▁GUN- ▁NOBLE- ▁BEGIN- ▁PATH- ▁SKY- ▁WONDERFUL- ▁SUDDEN- ▁ARMY- ▁CHE- ▁WORTH- ▁MOUNTAIN- ▁MIN- AG- ▁FLU- ▁GRACE- ▁CHAPTER- ▁BELOW- ▁RING- ▁TURNING- ▁IRON- ▁TOP- ▁AFTERNOON- ORY- ▁EVIL- ▁TRUST- ▁BOW- ▁TRI- ▁SAIL- ▁CONTENT- ▁HORSES- ITE- ▁SILVER- AP- ▁LAD- ▁RUNNING- ▁HILL- ▁BEGINNING- ▁MAD- ▁HABIT- GRA- ▁CLOTHES- ▁MORROW- ▁CRY- ▁FASHION- ▁PRESENCE- ▁Z- FE- ▁ARRIVED- ▁QUARTER- ▁PERFECT- ▁WO- ▁TRA- ▁USUAL- ▁NECK- ▁MARRIED- ▁SEAT- ▁WI- ▁GAR- ▁SAND- ▁SHORE- ▁GIVING- NY- ▁PROBABLY- ▁MINUTE- ▁EXPECT- ▁DU- ▁SHOT- ▁INSTANT- ▁DEGREE- ▁COLOR- ▁WEST- RT- ▁MARCH- ▁BIRD- ▁SHOWED- ▁GREATER- ▁SERIOUS- ▁CARRY- ▁COVERED- ▁FORMER- ▁LOUD- ▁MOVED- ▁MASS- ▁SEEK- ▁CHO- GEN- ▁ROMAN- IB- ▁MOON- ▁BOARD- ▁STREAM- ▁EASILY- ▁WISHED- ▁SEARCH- ▁COULDN- ▁MONTHS- ▁SICK- LIE- ▁DUTY- ▁TWELVE- ▁FAINT- ▁STRANGER- ▁SURPRISE- ▁KILL- ▁LEAVING- ▁JOURNEY- ▁SCARCELY- ▁RAISED- ▁SPEAKING- ▁TERRIBLE- ▁TOM- ▁FIELD- ▁GAME- ▁QUA- ▁PROMISE- ▁LIE- ▁CONDITION- ▁TRO- ▁PERSONAL- ▁TALL- ▁STICK- ▁THREW- ▁MARRY- ▁VAN- ▁BURN- ▁ACCORDING- ▁RISE- ▁ATTACK- ▁SWORD- ▁GUESS- ▁THOUGHTS- ▁THIN- ▁THROW- ▁CALM- SIDE- ▁VILLAGE- ▁DEN- ▁ANXIOUS- ▁MER- GI- ▁EXPECTED- ▁BALL- ▁ESPECIALLY- ▁CHARGE- ▁MEASURE- ISE- ▁NICE- ▁TRYING- ▁ALLOW- ▁SHARP- ▁BREAD- ▁HONOUR- ▁HONOR- ▁ENTIRELY- ▁BILL- ▁BRI- ▁WRITTEN- ▁AR- ▁BROKE- ▁KILLED- ▁MARK- ▁VEN- ▁LADIES- ▁LEARNED- ▁FLOWERS- PLE- ▁FORTY- ▁OFFER- ▁HAPPINESS- ▁PRAY- ▁CLASS- ▁FER- ▁PRINCIPLE- GU- ▁BOOKS- ▁SHAPE- ▁SUMMER- ▁JACK- ▁DRAW- ▁GOLDEN- ▁DECIDED- ▁LEAD- ▁UNLESS- ▁HARM- ▁LISTEN- HER- ▁SHOOK- ▁INFLUENCE- ▁PERFECTLY- ▁MARRIAGE- ▁BROAD- ▁ESCAPE- ▁STATES- ▁MIDDLE- ▁PLANT- ▁MIL- ▁MOVEMENT- ▁NOISE- ▁ENEMY- ▁HISTORY- ▁BREAK- ROUS- ▁UNDERSTOOD- ▁LATTER- FER- ▁COMES- ▁MERELY- ▁SIMPLY- WI- ▁IMAGINE- ▁LOWER- ▁CONDUCT- ▁BORN- WA- ▁YARD- ▁KA- ▁CLOSED- ▁NOTE- GA- ▁STRA- RAN- ▁EXIST- EV- ▁SPEECH- ▁BITTER- JO- ▁MAKES- ▁GRASS- ▁REPLY- ▁CHANGED- ▁MON- ▁LYING- ▁DANCE- ▁FINALLY- ▁AMERICAN- ▁ENJOY- ▁CONTAIN- ▁MEANT- USE- ▁OBSERVED- THER- ▁LAUGH- ▁AFTERWARDS- ▁BEAT- ▁RACE- ▁EQUAL- ▁RAIN- PS- ▁STEPS- ▁BENEATH- ▁TAIL- ▁TASTE- IO- EY- ▁CHAR- ▁GE- GN- TIN- ▁GROW- ▁TE- IANS- ▁MOVE- ▁REPEATED- ▁DRIVE- TUR- ▁SI- CLOCK- ▁BRAVE- ▁MADAME- ▁LOT- ▁CASTLE- ▁HI- AND- ▁FUTURE- ▁RELATION- ▁SORRY- ▁HEALTH- ▁DICK- ▁R- ▁BUILDING- ▁EDGE- ▁BLESS- ▁SPITE- WE- ▁MIS- ▁PRISONER- ▁ALLOWED- ▁PH- ▁CATCH- MER- ETH- ▁COAT- ▁COMPLETE- ▁WOULDN- ▁CREATURE- ▁YELLOW- ▁IMPORTANT- ▁ADD- ▁PASSING- ▁DARKNESS- ▁CARRIAGE- ▁MILL- ▁FIFTEEN- NCY- ▁HUNG- ▁OB- ▁PLEASED- ▁SPREAD- ▁CURIOUS- ▁WORSE- ▁CIRCUMSTANCES- ▁GI- LAR- ▁CAL- ▁HY- ▁MERE- ▁JANE- ▁EAST- BI- ▁CUP- ▁BLIND- ▁PASSION- ▁DISCOVERED- ▁NOTICE- ▁REPORT- ▁SPACE- ▁PRESENTLY- ▁SORROW- ▁PACK- ▁DIN- CY- ▁DRY- ▁ANCIENT- ▁DRESSED- ▁COVER- ▁VO- ▁EXISTENCE- ▁EXACTLY- ▁BEAST- ▁PROPER- ▁DROPPED- ▁CLEAN- ▁COLOUR- ▁HOST- ▁CHAMBER- ▁FAITH- LET- ▁DETERMINED- ▁PRIEST- ▁STORM- ▁SKIN- ▁DARE- ▁PERSONS- ▁PICK- ▁NARROW- ▁SUPPORT- ▁PRIVATE- ▁SMILED- ▁COUSIN- ▁DRAWING- ▁ATTEND- ▁COOK- ▁PREVENT- ▁VARIOUS- ▁BLA- ▁FIXED- ▁WEAK- THE- ▁HOLE- ▁BOTTOM- ▁NOBODY- ADE- ▁LEGS- ITCH- ▁INDIVIDUAL- ▁EARS- LIKE- ▁ADVANTAGE- ▁FRANCE- ▁BON- ▁WINE- ▁LIVES- OD- ▁WALLS- ▁TIRED- ▁SHOP- ▁ANIMAL- ▁CRU- ▁WROTE- ▁ROYAL- ▁CONSIDERED- ▁MORAL- ▁COMPANION- ▁LOSE- ▁ISN- ▁BAG- ▁LAKE- ▁INTER- ▁COM- ▁LETTERS- ▁LUCK- ▁EAR- ▁GERMAN- ▁PET- ▁SAKE- ▁DROP- ▁PAID- ▁BREAKFAST- ▁LABOR- ▁DESERT- ▁DECLARED- ▁HUM- ▁STUDY- ▁INSTANCE- ONE- ▁SOMEWHAT- ▁CLOTH- ▁SPECIAL- ▁COLONEL- ▁SONG- ▁MAIN- ▁VALUE- ▁PROUD- ▁EXPRESS- ▁NATION- ▁HANDSOME- ▁CONFESS- ▁PU- ▁PASSAGE- ▁PERIOD- ▁CUSTOM- ▁HURT- ▁SHOULDER- ▁CHRIST- ZA- ▁RECEIVE- ▁DIFFICULT- ▁DEPEND- ▁MEETING- ▁CHI- ▁GEN- LIGHT- ▁BELIEVED- ▁SOCIAL- ▁DIFFICULTY- ▁GREATEST- ▁DRAWN- ▁GRANT- ▁BIRDS- ▁ANGRY- ▁HEAT- UFF- ▁DUE- ▁PLACES- ▁SIN- ▁COURAGE- ▁EVIDENTLY- ▁GENTLE- ▁CRUEL- ▁GEORGE- ▁GRI- ▁SERVANT- ▁U- ▁PURE- OOK- ▁KNOWS- ▁KNOWING- LF- ▁WRITING- ▁REMEMBERED- ▁CU- ▁HOLDING- ▁TENDER- ▁QUI- ▁BURST- ▁SURELY- IGN- ▁VALLEY- ▁FU- ▁BUTTER- ▁SPOKEN- ▁STORE- ▁DISC- ▁CHRISTIAN- ▁PARIS- ▁HENRY- ▁FINISHED- ▁PROVE- ▁FOOL- ▁SOLDIERS- ▁LANGUAGE- ▁INSIDE- ▁BAN- ▁FALLEN- ROW- ▁MAL- ▁BABY- ▁SITUATION- ▁WATCHED- ANS- ▁RUIN- ▁GENTLEMEN- ▁FRO- ▁FANCY- ▁ACCEPT- ▁SEASON- ▁OURSELVES- ▁SAN- ▁SPEED- IZED- ▁COOL- ▁SERVE- ▁VESSEL- ▁WILLIAM- ▁OBLIGED- ▁GROUP- FORM- ▁GOES- UOUS- ▁LEAVES- ▁PECULIAR- ▁NEWS- ▁VAIN- ▁EVERYBODY- ▁PIN- UG- ▁FORGOTTEN- ▁FRA- GAN- ▁CAREFULLY- ▁FLASH- UCH- ▁FUR- ▁MURDER- ▁DELIGHT- ▁WAITED- ▁RENDER- ▁PROPERTY- ▁NOTICED- ▁ROLL- ▁KNOCK- ▁EARNEST- KI- ▁HONEST- ▁PROMISED- ▁BAL- AW- ▁WALKING- ANG- ▁SQUARE- ▁QUIETLY- ▁CLOUD- WOOD- ▁FORMED- ▁HIGHER- ▁BUILT- ▁FATE- ▁TEACH- MY- ▁FALSE- ▁YORK- ▁DUST- ▁CLIMB- ▁FOND- ▁GROWN- ▁DESCEND- ▁RAG- ▁FRUIT- ▁GENERALLY- ▁OFFERED- ▁ER- ▁NURSE- POSE- ▁SPENT- ▁JOIN- ▁STATION- ▁MEANING- ▁SMOKE- HOOD- ▁ROUGH- JU- ▁LIKELY- ▁SURFACE- ▁KE- ▁MONTH- ▁POSSESSION- ▁TONGUE- ▁DUKE- ▁NOSE- ▁LAUGHING- ▁WEATHER- ▁WHISPERED- ▁SYSTEM- ▁LAWS- DDLE- ▁TOUCHED- ▁TRADE- LD- ▁SURPRISED- RIN- ▁ARCH- ▁WEALTH- FOR- ▁TEMPER- ▁FRANK- ▁GAL- ▁BARE- ▁OPPORTUNITY- ▁CLAIM- ▁ANIMALS- ▁REV- ▁COST- ▁WASH- ZE- ▁CORN- ▁OPPOSITE- ▁POLICE- ▁IDEAS- LON- ▁KEY- ▁READING- ▁COLLECT- CHED- ▁H- ▁CROWN- ▁TAR- ▁SWIFT- ▁SHOULDERS- ▁ICE- ▁GRAY- ▁SHARE- ▁PREPARED- ▁GRO- ▁UND- ▁TER- ▁EMPTY- CING- ▁SMILING- ▁AVOID- ▁DIFFERENCE- ▁EXPLAIN- ▁POUR- ▁ATTRACT- ▁OPENING- ▁WHEEL- ▁MATERIAL- ▁BREAST- ▁SUFFERING- ▁DISTINCT- ▁BOOT- ▁ROW- ▁FINGERS- HAN- ▁ALTOGETHER- ▁FAT- ▁PAPA- ▁BRAIN- ▁ASLEEP- ▁GREY- ▁SUM- ▁GAS- ▁WINDOWS- ▁ALIVE- ▁PROCEED- ▁FLOWER- ▁LEAP- ▁PUR- ▁PIECES- ▁ALTER- ▁MEMORY- IENT- ▁FILL- ▁CLO- ▁THROWN- ▁KINGDOM- ▁RODE- IUS- ▁MAID- ▁DIM- ▁BAND- ▁VIRTUE- ▁DISH- ▁GUEST- ▁LOSS- ▁CAUSED- ▁MOTION- ▁POT- ▁MILLION- ▁FAULT- ▁LOVELY- ▁HERO- PPING- ▁UNITED- ▁SPI- SOME- BRA- ▁MOUNTAINS- ▁NU- ▁SATISFIED- ▁DOLLARS- ▁LOVER- ▁CONCEAL- ▁VAST- ▁PULL- ▁HATH- ▁RUSH- ▁J- ▁DESPAIR- EX- ▁HEIGHT- ▁CE- ▁BENT- ▁PITY- ▁RISING- ATH- ▁PRIDE- ▁HURRY- KA- ▁SETTLED- ▁JUSTICE- ▁LIFTED- PEN- ▁SOLDIER- ▁FINDING- ▁REMARK- ▁REGULAR- ▁STRUGGLE- ▁MACHINE- ▁SING- ▁HURRIED- ▁SUFFICIENT- ▁REPRESENT- ▁DOUBLE- ▁ALARM- ▁SUPPER- ▁DREADFUL- ▁FORE- ATOR- ▁STOCK- ▁TIN- ▁EXAMPLE- ▁ROOF- ▁FLOW- ▁SUPPOSED- ▁PRESERV- ▁L- ▁LISTENED- OC- ▁STO- ▁SECURE- ▁FRIGHTENED- ▁DISTURB- ▁EMOTION- ▁SERVANTS- ▁YO- ▁BUY- ▁FORCED- ▁KITCHEN- ▁TERROR- ▁STAIRS- ▁SIXTY- KER- ▁ORDINARY- ▁DIRECTLY- ▁HEADS- ▁METHOD- ▁FORGIVE- ▁AWFUL- ▁REFLECT- ▁GREATLY- ▁TALKED- ▁RIDE- STONE- ▁FAVOUR- ▁WELCOME- ▁SEIZED- OU- ▁CONTROL- ▁ORDERED- ▁ANGEL- ▁USUALLY- ▁POET- ▁BOLD- LINE- ▁ADVENTURE- ▁WATCHING- ▁FOLK- ▁MISTRESS- IZE- ▁GROWING- ▁CAVE- ▁EVIDENCE- ▁FINGER- ▁SEVENTEEN- ▁MOVING- EOUS- ▁DOESN- ▁COW- ▁TYPE- ▁BOIL- ▁TALE- ▁DELIVER- ▁FARM- ▁MONSIEUR- ▁GATHERED- ▁FEELINGS- ▁RATE- ▁REMARKED- ▁PUTTING- ▁MAT- ▁CONTRARY- ▁CRIME- ▁PLA- ▁COL- ▁NEARER- TES- ▁CIVIL- ▁SHAME- ▁LOOSE- ▁DISCOVER- ▁FLAT- ▁TWICE- ▁FAIL- VIS- ▁UNC- EA- ▁EUROPE- ▁PATIENT- ▁UNTO- ▁SUFFER- ▁PAIR- ▁TREASURE- OSE- ▁EAGER- ▁FLY- ▁N- ▁VAL- ▁DAN- ▁SALT- ▁BORE- BBE- ▁ARTHUR- ▁AFFAIRS- ▁SLOW- ▁CONSIST- ▁DEVIL- LAN- ▁AFFECTION- ▁ENGAGED- ▁KISS- ▁YA- ▁OFFICER- IFICATION- ▁LAMP- ▁PARTS- HEN- ▁MILK- ▁PROCESS- ▁GIFT- ▁PULLED- ▁HID- ▁RAY- ▁EXCELLENT- ▁IMPRESSION- ▁AUTHORITY- ▁PROVED- ▁TELLING- TTE- ▁TOWER- ▁CONSEQUENCE- ▁FAVOR- ▁FLEW- ▁CHARLES- ISTS- ▁ADDRESS- ▁FAMILIAR- ▁LIMIT- ▁CONFIDENCE- ▁RARE- ▁WEEKS- ▁WOODS- ▁INTENTION- ▁DIRECT- ▁PERFORM- ▁SOLEMN- ▁DISTANT- ▁IMAGE- ▁PRESIDENT- ▁FIRM- ▁INDIAN- ▁RANK- ▁LIKED- ▁AGREE- ▁HOUSES- ▁WIL- ▁MATTERS- ▁PRISON- ▁MODE- ▁MAJOR- ▁WORKING- ▁SLIP- ▁WEIGHT- ▁AWARE- ▁BUSY- ▁LOOKS- ▁WOUND- ▁THOR- ▁BATH- ▁EXERCISE- ▁SIMILAR- ▁WORE- ▁AMOUNT- ▁QUESTIONS- ▁VIOLENT- ▁EXCUSE- ▁ASIDE- ▁TUR- ▁DULL- OF- ▁EMPEROR- ▁NEVERTHELESS- ▁SHOUT- ▁EXPLAINED- ▁SIZE- ▁ACCOMPLISH- FORD- CAN- ▁MISTAKE- ▁INSTANTLY- ▁SMOOTH- ▁STRIKE- ▁BOB- ISED- ▁HORROR- ▁SCIENCE- ▁PROTEST- ▁MANAGE- ▁OBEY- ▁NECESSITY- ▁SPLENDID- ▁PRESS- ▁INTERESTING- ▁RELIGION- ▁UNKNOWN- ▁FIERCE- ▁DISAPPEARED- ▁HOLY- ▁HATE- ▁PLAYED- ▁LIN- ▁NATURALLY- ▁DROVE- ▁LOUIS- TIES- ▁BRAND- INESS- RIE- ▁SHOOT- ▁CONSENT- ▁SEATED- ▁LINES- GUE- ▁AGREED- ▁CIRCLE- ▁STIR- ▁STREETS- ▁TASK- ▁RID- ▁PRODUCED- ▁ACCIDENT- ▁WITNESS- ▁LIBERTY- ▁DETAIL- ▁MINISTER- ▁POWERFUL- ▁SAVAGE- ▁SIXTEEN- ▁PRETEND- ▁COAST- ▁SQU- ▁UTTER- ▁NAMED- ▁CLEVER- ▁ADMIT- ▁COUPLE- ▁WICKED- ▁MESSAGE- ▁TEMPLE- ▁STONES- ▁YESTERDAY- ▁HILLS- DAY- ▁SLIGHT- ▁DIAMOND- ▁POSSIBLY- ▁AFFAIR- ▁ORIGINAL- ▁HEARING- ▁WORTHY- ▁SELL- NEY- ICK- ▁COTTAGE- ▁SACRIFICE- ▁PROGRESS- ▁SHOCK- ▁DESIGN- ▁SOUGHT- ▁PIT- ▁SUNDAY- ▁OTHERWISE- ▁CABIN- ▁PRAYER- ▁DWELL- ▁GAIN- ▁BRIDGE- ▁PARTICULARLY- ▁YIELD- ▁TREAT- RIGHT- ▁OAK- ▁ROPE- WIN- ▁ORDERS- ▁SUSPECT- ▁EDWARD- AB- ▁ELEVEN- ▁TEETH- ▁OCCURRED- DDING- ▁AMERICA- ▁FALLING- ▁LION- ▁DEPART- ▁KEEPING- ▁DEMAND- ▁PAUSED- ▁CEASED- INA- ▁FUN- ▁CHEER- ▁PARDON- ▁NATIVE- LUS- LOW- ▁DOGS- ▁REQUIRED- ILITY- ▁ELECT- ▁ENTERTAIN- ITUDE- ▁HUGE- ▁CARRYING- ▁BLU- ▁INSIST- ▁SATISFACTION- ▁HUNT- ▁COUNTENANCE- ▁UPPER- ▁MAIDEN- ▁FAILED- ▁JAMES- ▁FOREIGN- ▁GATHER- ▁TEST- BOARD- ▁TERMS- ▁SILK- ▁BEG- ▁BROTHERS- ▁PAGE- ▁KNEES- ▁SHOWN- ▁PROFESSOR- ▁MIGHTY- ▁DEFI- ▁CHARM- ▁REQUIRE- ▁LOG- MORE- ▁PROOF- ▁POSSESSED- ▁SOFTLY- ▁UNFORTUNATE- ▁PRICE- ▁SEVERE- ▁SINGING- ▁STAGE- ▁FREEDOM- ▁SHOUTED- ▁FARTHER- ▁MAJESTY- ▁PREVIOUS- ▁GUIDE- ▁MATCH- ▁CHEST- ▁INTENDED- ▁BI- ▁EXCITEMENT- ▁OFFICERS- ▁SUR- ▁SHAKE- ▁SENTIMENT- ▁GENTLY- ▁SUCCEEDED- ▁MENTION- ▁LOCK- ▁ACQUAINTANCE- ▁IMAGINATION- ▁PHYSICAL- ▁LEADING- ▁SLAVE- ▁CART- ▁POINTED- ▁STEAM- ▁SHADE- ▁PIPE- ▁BASE- ▁INVENT- ▁ALAS- ▁WORKED- ▁REGRET- ▁BUR- ▁FAITHFUL- ▁MENTIONED- ▁RECORD- ▁COMPLAIN- ▁SUPERIOR- ▁BAY- ▁PAL- EMENT- UE- ▁SEVENTY- ▁HOTEL- ▁SHEEP- ▁MEAL- ▁ADVICE- ▁HIDDEN- ▁DEMANDED- ▁CONSCIOUS- ▁BROW- ▁POSSESS- ▁FOURTH- ▁EVENTS- ▁FRI- ▁PRAISE- ▁ADVANCED- ▁RESOLVED- ▁STUFF- ▁CHEERFUL- ▁BIRTH- ▁GRIEF- ▁AFFORD- ▁FAIRY- ▁WAKE- ▁SIDES- ▁SUBSTANCE- ▁ARTICLE- ▁LEVEL- ▁MIST- ▁JOINED- ▁PRACTICAL- ▁CLEARLY- ▁TRACE- ▁AWAKE- ▁OBSERVE- ▁BASKET- ▁LACK- VILLE- ▁SPIRITS- ▁EXCITED- ▁ABANDON- ▁SHINING- ▁FULLY- ▁CALLING- ▁CONSIDERABLE- ▁SPRANG- ▁MILE- ▁DOZEN- ▁PEA- ▁DANGEROUS- ▁WIT- ▁JEW- ▁POUNDS- ▁FOX- ▁INFORMATION- ▁LIES- ▁DECK- NNY- ▁PAUL- ▁STARS- ▁ANGER- ▁SETTLE- ▁WILLING- ▁ADAM- ▁FACES- ▁SMITH- ▁IMPORTANCE- ▁STRAIN- WAR- ▁SAM- ▁FEATHER- ▁SERVED- ▁AUTHOR- ▁PERCEIVED- ▁FLAME- ▁DIVINE- ▁TRAIL- ▁ANYBODY- ▁SIGH- ▁DELICATE- KY- ▁FOLD- ▁HAVEN- ▁DESIRED- ▁CURIOSITY- ▁PRACTICE- ▁CONSIDERATION- ▁ABSOLUTELY- ▁CITIZEN- ▁BOTTLE- ▁INTERESTED- ▁MEAT- ▁OCCUPIED- ▁CHOOSE- ▁THROAT- ETTE- ▁CANDLE- ▁DAWN- ▁PROTECT- ▁SENTENCE- IED- ▁ROCKS- ▁PORTION- ▁APPARENTLY- ▁PRESENTED- ▁TIGHT- ▁ACTUALLY- ▁DYING- ▁HAM- ▁DAILY- ▁SUFFERED- ▁POLITICAL- ▁BODIES- ▁MODERN- ▁COMPLETELY- ▁SOONER- TAN- ▁PROP- ▁ADVANCE- ▁REFUSED- ▁FARMER- ▁POLITE- ▁THUNDER- ▁BRIEF- ▁ELSIE- ▁SAILOR- ▁SUGGESTED- ▁PLATE- ▁AID- ▁FLESH- ▁WEEP- ▁BUCK- ▁ANTI- ▁OCEAN- ▁SPEND- WELL- ▁ODD- ▁GOVERNOR- ▁ENTRANCE- ▁SUSPICION- ▁STEPPED- ▁RAPIDLY- ▁CHECK- ▁HIDE- ▁FLIGHT- ▁CLUB- ▁ENTIRE- ▁INDIANS- ASH- ▁CAPITAL- ▁MAMMA- HAR- ▁CORRECT- ▁CRACK- ▁SENSATION- ▁WORST- ▁PACE- ▁MIDST- ▁AUGUST- ▁PROPORTION- ▁INNOCENT- LINESS- ▁REGARDED- ▁DRIVEN- ORD- ▁HASTE- ▁EDUCATION- ▁EMPLOY- ▁TRULY- ▁INSTRUMENT- ▁MAG- ▁FRAME- ▁FOOLISH- ▁TAUGHT- ▁HANG- ▁ARGUMENT- ▁NINETEEN- ▁ELDER- ▁NAY- ▁NEEDED- ▁NEIGHBOR- ▁INSTRUCT- ▁PAPERS- ▁REWARD- ▁EQUALLY- ▁FIELDS- ▁DIG- HIN- ▁CONDITIONS- JA- ▁SPAR- ▁REQUEST- ▁WORN- ▁REMARKABLE- ▁LOAD- ▁WORSHIP- ▁PARK- ▁KI- ▁INTERRUPTED- ▁SKILL- ▁TERM- LAC- ▁CRITIC- ▁DISTRESS- ▁BELIEF- ▁STERN- IGHT- ▁TRACK- ▁HUNTING- ▁JEWEL- ▁GRADUALLY- ▁GLOW- ▁RUSHED- ▁MENTAL- ▁VISITOR- ▁PICKED- ▁BEHOLD- ▁EXPRESSED- ▁RUB- ▁SKI- ARTAGNAN- ▁MOREOVER- ▁OPERATION- ▁CAREFUL- ▁KEEN- ▁ASSERT- ▁WANDER- ▁ENEMIES- ▁MYSTERIOUS- ▁DEPTH- ▁PREFER- ▁CROSSED- ▁CHARMING- ▁DREAD- ▁FLOUR- ▁ROBIN- ▁TRE- ▁RELIEF- ▁INQUIRED- ▁APPLE- ▁HENCE- ▁WINGS- ▁CHOICE- ▁JUD- OO- ▁SPECIES- ▁DELIGHTED- IUM- ▁RAPID- ▁APPEAL- ▁FAMOUS- ▁USEFUL- ▁HELEN- ▁NEWSPAPER- ▁PLENTY- ▁BEARING- ▁NERVOUS- ▁PARA- ▁URGE- ▁ROAR- ▁WOUNDED- ▁CHAIN- ▁PRODUCE- ▁REFLECTION- ▁MERCHANT- ▁QUARREL- ▁GLORY- ▁BEGUN- ▁BARON- CUS- ▁QUEER- ▁MIX- ▁GAZE- ▁WHISPER- ▁BURIED- ▁DIV- ▁CARD- ▁FREQUENTLY- ▁TIP- ▁KNEE- ▁REGION- ▁ROOT- ▁LEST- ▁JEALOUS- CTOR- ▁SAVED- ▁ASKING- ▁TRIP- QUA- ▁UNION- HY- ▁COMPANIONS- ▁SHIPS- ▁HALE- ▁APPROACHED- ▁HARRY- ▁DRUNK- ▁ARRIVAL- ▁SLEPT- ▁FURNISH- HEAD- ▁PIG- ▁ABSENCE- ▁PHIL- ▁HEAP- ▁SHOES- ▁CONSCIOUSNESS- ▁KINDLY- ▁EVIDENT- ▁SCAR- ▁DETERMIN- ▁GRASP- ▁STEAL- ▁OWE- ▁KNIFE- ▁PRECIOUS- ▁ELEMENT- ▁PROCEEDED- ▁FEVER- ▁LEADER- ▁RISK- ▁EASE- ▁GRIM- ▁MOUNT- ▁MEANWHILE- ▁CENTURY- OON- ▁JUDGMENT- ▁AROSE- ▁VISION- ▁SPARE- ▁EXTREME- ▁CONSTANT- ▁OBSERVATION- ▁THRUST- ▁DELAY- ▁CENT- ▁INCLUD- ▁LIFT- ▁ADMIRE- ▁ISSUE- ▁FRIENDSHIP- ▁LESSON- ▁PRINCIPAL- ▁MOURN- ▁ACCEPTED- ▁BURNING- ▁CAPABLE- ▁EXTRAORDINARY- ▁SANG- ▁REMOVED- ▁HOPED- ▁HORN- ▁ALICE- ▁MUD- ▁APARTMENT- ▁FIGHTING- ▁BLAME- ▁TREMBLING- ▁SOMEBODY- ▁ANYONE- ▁BRIDE- ▁READER- ▁ROB- ▁EVERYWHERE- ▁LABOUR- ▁RECALL- ▁BULL- ▁HIT- ▁COUNCIL- ▁POPULAR- ▁CHAP- ▁TRIAL- ▁DUN- ▁WISHES- ▁BRILLIANT- ▁ASSURED- ▁FORGOT- ▁CONTINUE- ▁ACKNOWLEDG- ▁RETREAT- ▁INCREASED- ▁CONTEMPT- ▁GRANDFATHER- ▁SYMPATHY- ▁GHOST- ▁STRETCHED- ▁CREATURES- ▁CAB- ▁HIND- ▁PLAYING- ▁MISERABLE- ▁MEMBERS- ▁KINDNESS- ▁HIGHEST- ▁PRIM- ▁KISSED- ▁DESERVE- ▁HUT- ▁BEGGED- ▁EIGHTY- ▁CLOSELY- ▁WONDERED- ▁MILITARY- ▁REMIND- ▁ACCORDINGLY- ▁LARGER- ▁MAINTAIN- ▁ENGINE- ▁MOTIVE- ▁DESTROY- ▁STRIP- ▁HANS- ▁AHEAD- ▁INFINITE- ▁PROMPT- ▁INFORMED- TTLE- ▁PEER- ▁PRESSED- ▁TRAP- ▁SOMEWHERE- ▁BOUGHT- ▁VISIBLE- ▁ASHAMED- ▁TEAR- ▁NEIGHBOUR- ▁CONSTITUTION- ▁INTELLIGENCE- ▁PROFESSION- ▁HUNGRY- RIDGE- ▁SMELL- ▁STORIES- ▁LISTENING- ▁APPROACH- ▁STRING- ▁EXPLANATION- ▁IMMENSE- ▁RELIGIOUS- ▁THROUGHOUT- ▁HOLLOW- ▁AWAIT- ▁FLYING- ▁SCREAM- ▁ACTIVE- ▁RUM- ▁PRODUCT- ▁UNHAPPY- ▁VAGUE- ARIES- ▁ELIZABETH- ▁STUPID- ▁DIGNITY- ▁ISABEL- GAR- ▁BRO- ▁PITCH- ▁COMRADE- ▁STIFF- ▁RECKON- ▁SOLD- ▁SPARK- ▁STRO- ▁CRYING- ▁MAGIC- ▁REPEAT- PORT- ▁MARKED- ▁COMFORTABLE- ▁PROJECT- ▁BECOMING- ▁PARENTS- ▁SHELTER- ▁STOLE- ▁HINT- ▁NEST- ▁TRICK- ▁THOROUGHLY- ▁HOSPITAL- ▁WEAPON- ▁ROME- ▁STYLE- ▁ADMITTED- ▁SAFETY- FIELD- ▁UNDERSTANDING- ▁TREMBLE- ▁PRINT- ▁SLAVES- ▁WEARY- ▁ARTIST- ▁CREDIT- BURG- ▁CONCLUSION- ▁SELDOM- ▁UNUSUAL- ▁CLOUDS- ▁UNABLE- ▁GAY- ▁HANGING- ▁SCR- ▁BOWED- ▁DAVID- ▁VOL- ▁PUSHED- ▁ESCAPED- MOND- ▁WARN- ▁BETRAY- ▁EGGS- ▁PLAINLY- ▁EXHIBIT- ▁DISPLAY- ▁MEMBER- ▁GRIN- ▁PROSPECT- ▁BRUSH- ▁BID- ▁SUCCESSFUL- ▁EXTENT- ▁PERSUADE- ▁MID- ▁MOOD- ▁ARRANGED- ▁UNIVERSAL- ▁JIM- ▁SIGNAL- ▁WHILST- ▁PHILIP- ▁WOLF- RATE- ▁EAGERLY- ▁BILLY- ▁RETURNING- ▁CONSCIENCE- ▁FORTUNATE- ▁FEMALE- ▁GLEAM- ▁HASTILY- ▁PROVIDED- ▁OBTAIN- ▁INSTINCT- ▁CONCERNED- ▁CONCERNING- ▁SOMEHOW- ▁PINK- ▁RAGE- ▁ACCUSTOMED- ▁UNCONSCIOUS- ▁ADVISE- ▁BRANCHES- ▁TINY- ▁REFUSE- ▁BISHOP- ▁SUPPLY- ▁PEASANT- ▁LAWYER- ▁WASTE- ▁CONNECTION- ▁DEVELOP- ▁CORRESPOND- ▁PLUM- ▁NODDED- ▁SLIPPED- ▁EU- ▁CONSTANTLY- CUM- MMED- ▁FAIRLY- HOUSE- ▁KIT- ▁RANG- ▁FEATURES- ▁PAUSE- ▁PAINFUL- ▁JOE- ▁WHENCE- ▁LAUGHTER- ▁COACH- ▁CHRISTMAS- ▁EATING- ▁WHOLLY- ▁APART- ▁SUPER- ▁REVOLUTION- ▁LONELY- ▁CHEEKS- ▁THRONE- ▁CREW- ▁ATTAIN- ▁ESTABLISHED- TIME- ▁DASH- ▁FRIENDLY- ▁OPERA- ▁EARL- ▁EXHAUST- ▁CLIFF- ▁REVEAL- ▁ADOPT- ▁CENTRE- ▁MERRY- ▁SYLVIA- ▁IDEAL- ▁MISFORTUNE- ▁FEAST- ▁ARAB- ▁NUT- ▁FETCH- ▁FOUGHT- ▁PILE- ▁SETTING- ▁SOURCE- ▁PERSIST- ▁MERCY- ▁BARK- ▁LUC- ▁DEEPLY- ▁COMPARE- ▁ATTITUDE- ▁ENDURE- ▁DELIGHTFUL- ▁BEARD- ▁PATIENCE- ▁LOCAL- ▁UTTERED- ▁VICTORY- ▁TREATED- ▁SEPARATE- ▁WAG- ▁DRAGG- ▁TITLE- ▁TROOPS- ▁TRIUMPH- ▁REAR- ▁GAINED- ▁SINK- ▁DEFEND- ▁TIED- ▁FLED- ▁DARED- ▁INCREASE- ▁POND- ▁CONQUER- ▁FOREHEAD- ▁FAN- ▁ANXIETY- ▁ENCOUNTER- ▁SEX- ▁HALT- ▁SANK- ▁CHEEK- ▁HUMBLE- ▁WRITER- ▁EMPLOYED- ▁DISTINGUISHED- ▁RAISE- ▁WHIP- ▁GIANT- ▁RANGE- ▁OBTAINED- ▁FLAG- ▁MAC- ▁JUMPED- ▁DISCOVERY- ▁NATIONAL- ▁COMMISSION- ▁POSITIVE- ▁LOVING- ▁EXACT- ▁MURMURED- ▁GAZED- ▁REFER- ▁COLLEGE- ▁ENCOURAGE- ▁NOVEL- ▁CLOCK- ▁MORTAL- ▁ROLLED- ▁RAT- IZING- ▁GUILTY- ▁VICTOR- WORTH- ▁PRA- ▁APPROACHING- ▁RELATIVE- ▁ESTATE- ▁UGLY- ▁METAL- ▁ROBERT- ▁TENT- ▁ADMIRATION- ▁FOURTEEN- ▁BARBAR- ▁WITCH- ELLA- ▁CAKE- ▁SHONE- ▁MANAGED- ▁VOLUME- ▁GREEK- ▁DANCING- ▁WRETCHED- ▁CONDEMN- ▁MAGNIFICENT- ▁CONSULT- J- ▁ORGAN- ▁FLEET- ▁ARRANGEMENT- ▁INCIDENT- ▁MISERY- ▁ARROW- ▁STROKE- ▁ASSIST- ▁BUILD- ▁SUCCEED- ▁DESPERATE- ▁WIDOW- UDE- ▁MARKET- ▁WISDOM- ▁PRECISE- ▁CURRENT- ▁SPOIL- ▁BADE- ▁WOODEN- ▁RESIST- ▁OBVIOUS- ▁SENSIBLE- FALL- ▁ADDRESSED- ▁GIL- ▁COUNSEL- ▁PURCHASE- ▁SELECT- ▁USELESS- ▁STARED- ▁ARREST- ▁POISON- ▁FIN- ▁SWALLOW- ▁BLOCK- ▁SLID- ▁NINETY- ▁SPORT- ▁PROVIDE- ▁ANNA- ▁LAMB- ▁INTERVAL- ▁JUMP- ▁DESCRIBED- ▁STRIKING- ▁PROVISION- ▁PROPOSED- ▁MELANCHOLY- ▁WARRIOR- ▁SUGGEST- ▁DEPARTURE- ▁BURDEN- ▁LIMB- ▁TROUBLED- ▁MEADOW- ▁SACRED- ▁SOLID- ▁TRU- ▁LUCY- ▁RECOVER- ▁ENERGY- ▁POWDER- ▁RESUMED- ▁INTENSE- ▁BRITISH- ▁STRAW- ▁AGREEABLE- ▁EVERYONE- ▁CONCERN- ▁VOYAGE- ▁SOUTHERN- ▁BOSOM- ▁UTTERLY- ▁FEED- ▁ESSENTIAL- ▁CONFINE- ▁HOUSEHOLD- ▁EXTREMELY- ▁WONDERING- ▁LIST- ▁PINE- PHA- ▁EXPERIMENT- ▁JOSEPH- ▁MYSTERY- ▁RESTORE- ▁BLUSH- FOLD- ▁CHOSEN- ▁INTELLECT- ▁CURTAIN- OLOGY- ▁MOUNTED- ▁LAP- ▁EPI- ▁PUNISH- ▁WEDDING- ▁RECOGNIZED- ▁DRIFT- ▁PREPARATION- ▁RESOLUTION- ▁OPPRESS- ▁FIX- ▁VICTIM- OGRAPH- ▁SUMMON- ▁JULIA- ▁FLOOD- ▁WAL- ULATION- ▁SLIGHTLY- ▁LODGE- ▁WIRE- ▁CONFUSION- ▁UNEXPECTED- ▁CONCEIVE- ▁PRIZE- ▁JESUS- ▁ADDITION- ▁RUDE- ▁FATAL- ▁CARELESS- ▁PATCH- ▁KO- ▁CATHERINE- ▁PARLIAMENT- ▁PROFOUND- ▁ALOUD- ▁RELIEVE- ▁PUSH- ABILITY- ▁ACCOMPANIED- ▁SOVEREIGN- ▁SINGULAR- ▁ECHO- ▁COMPOSED- ▁SHAKING- ATORY- ▁ASSISTANCE- ▁TEACHER- ▁HORRIBLE- ▁STRICT- ▁VERSE- ▁PUNISHMENT- ▁GOWN- ▁MISTAKEN- ▁VARI- ▁SWEPT- ▁GESTURE- ▁BUSH- ▁STEEL- ▁AFFECTED- ▁DIRECTED- ▁SURROUNDED- ▁ABSURD- ▁SUGAR- ▁SCRAP- ▁IMMEDIATE- ▁SADDLE- ▁TY- ▁ARISE- ▁SIGHED- ▁EXCHANGE- ▁IMPATIENT- ▁SNAP- ▁EMBRACE- ▁DISEASE- ▁PROFIT- ▁RIDING- ▁RECOVERED- ▁GOVERN- ▁STRETCH- ▁CONVINCED- ▁LEANING- ▁DOMESTIC- ▁COMPLEX- ▁MANIFEST- ▁INDULGE- ▁GENIUS- ▁AGENT- ▁VEIL- ▁DESCRIPTION- ▁INCLINED- ▁DECEIVE- ▁DARLING- ▁REIGN- HU- ▁ENORMOUS- ▁RESTRAIN- ▁DUTIES- BURY- TTERED- ▁POLE- ▁ENABLE- ▁EXCEPTION- ▁INTIMATE- ▁COUNTESS- ▁TRIBE- ▁HANDKERCHIEF- ▁MIDNIGHT- ▁PROBLEM- ▁TRAMP- ▁OIL- CAST- ▁CRUSH- ▁DISCUSS- ▁RAM- ▁TROT- ▁UNRE- ▁WHIRL- ▁LOCKED- ▁HORIZON- ▁OFFICIAL- ▁SCHEME- ▁DROWN- ▁PIERRE- ▁PERMITTED- ▁CONNECTED- ▁ASSURE- ▁COCK- ▁UTMOST- ▁DEVOTED- ▁RELI- ▁SUFFICIENTLY- ▁INTELLECTUAL- ▁CARPET- ▁OBJECTION- ▁AFTERWARD- ▁REALITY- ▁NEGRO- ▁RETAIN- ▁ASCEND- ▁CEASE- ▁KATE- ▁MARVEL- KO- ▁BOND- MOST- ▁COAL- GATE- ▁IGNORANT- ▁BREAKING- ▁TWIN- ▁ASTONISHMENT- ▁COFFEE- ▁JAR- ▁CITIES- ▁ORIGIN- ▁EXECUT- ▁FINAL- ▁INHABITANTS- ▁STABLE- ▁CHIN- ▁PARTIES- ▁PLUNGE- ▁GENEROUS- ▁DESCRIBE- ▁ANNOUNCED- ▁MERIT- ▁REVERE- ▁ERE- ACIOUS- ZI- ▁DISAPPOINT- ▁SUGGESTION- ▁DOUBTLESS- ▁TRUNK- ▁STAMP- ▁JOB- ▁APPOINTED- ▁DIVIDED- ▁ACQUAINTED- CHI- ▁ABSOLUTE- ▁FEARFUL- ▁PRIVILEGE- ▁CRAFT- ▁STEEP- ▁HUNTER- ▁FORBID- ▁MODEST- ▁ENDEAVOUR- ▁SWEEP- ▁BEHELD- ▁ABSORB- ▁CONSTRUCT- ▁EMPIRE- ▁EXPEDITION- ▁ERECT- ▁OFFEND- ▁INTEND- ▁PERMIT- ▁DESTROYED- ▁CONTRACT- ▁THIRST- ▁WAGON- ▁EVA- ▁GLOOM- ▁ATMOSPHERE- ▁RESERVE- ▁VOTE- ▁GER- ▁NONSENSE- ▁PREVAIL- ▁QUALITY- ▁CLASP- ▁CONCLUDED- ▁RAP- ▁KATY- ▁ETERNAL- ▁MUTTERED- ▁NEGLECT- ▁SQUIRE- ▁CREEP- LOCK- ▁ELECTRIC- ▁HAY- ▁EXPENSE- ▁SCORN- ▁RETIRED- ▁STOUT- ▁MURMUR- ▁SHARPLY- ▁DISTRICT- ▁LEAF- ▁FAILURE- WICK- ▁JEAN- ▁NUMEROUS- ▁INFANT- ▁REALIZED- ▁TRAVELLER- ▁HUNGER- ▁JUNE- ▁MUN- ▁RECOMMEND- ▁CREP- ZZLE- ▁RICHARD- WORK- ▁MONTE- ▁PREACH- ▁PALM- AVI- ▁ANYWHERE- ▁DISPOSITION- ▁MIRROR- ▁VENTURE- ▁POUND- ▁CIGAR- ▁INVITED- ▁BENCH- ▁PROTECTION- ▁BENEFIT- ▁THOMAS- ▁CLERK- ▁REPROACH- ▁UNIFORM- ▁GENERATION- ▁SEAL- ▁COMPASS- ▁WARNING- ▁EXTENDED- ▁DIFFICULTIES- ▁MAYBE- ▁GROAN- ▁AFFECT- ▁COMB- ▁EARN- ▁WESTERN- ▁IDLE- ▁SCORE- ▁TAP- ▁ASTONISHED- ▁INTRODUCED- ▁LEISURE- ▁LIEUTENANT- ▁VIOLENCE- ▁FIRMLY- ▁MONSTER- ▁UR- ▁PROPERLY- ▁TWIST- ▁PIRATE- ▁ROBBER- ▁BATTER- ▁WEPT- ▁LEANED- ▁FOG- ▁ORNAMENT- ▁ANDREW- ▁BUSHES- ▁REPUBLIC- ▁CONFIDENT- ▁LEAN- ▁DART- ▁STOOP- ▁CURL- ▁COUNTER- ▁NORTHERN- ▁PEARL- ▁NEAREST- ▁FRANCIS- ▁WANDERING- ▁FREQUENT- ▁STARTLED- ▁STATEMENT- ▁OCCUR- ▁BLOOM- ▁NERVE- ▁INSPECT- ▁INDUCE- ▁FLATTER- ▁DATE- ▁AMBITION- ▁SLOPE- ▁MALE- ▁MADAM- ▁MONK- ▁RENT- ▁CONFIRM- ▁INVESTIGAT- ▁RABBIT- ▁REGIMENT- ▁SUBMIT- ▁SPELL- ▁FURIOUS- ▁RAIL- ▁BESTOW- ▁RALPH- ▁SCATTERED- ▁COMPELLED- ▁THREAD- ▁CHILL- ▁DENY- ▁PRONOUNC- ▁MANKIND- ▁CATTLE- ▁EXECUTION- ▁REBEL- ▁SUPREME- ▁VALUABLE- ▁LIKEWISE- ▁CONVEY- ▁TIDE- ▁GLOOMY- ▁COIN- ▁ACTUAL- ▁TAX- ▁PROVINCE- ▁GRATEFUL- ▁SPIRITUAL- ▁VANISHED- ▁DIANA- ▁HAUNT- ▁DRAGON- ▁CRAWL- ▁CHINA- ▁GRATITUDE- ▁NEAT- ▁FINISH- ▁INTENT- ▁FRIGHT- ▁EMBARRASS- ▁THIRTEEN- ▁RUTH- ▁SLIGHTEST- ▁DEVELOPMENT- ▁INTERVIEW- ▁SPECTACLE- ▁BROOK- VIE- ▁WEAKNESS- ▁AUDIENCE- ▁CONSEQUENTLY- ▁ABROAD- ▁ASPECT- ▁PAINTED- ▁RELEASE- ▁INSULT- ▁SOOTH- ▁DISAPPOINTMENT- ▁EMERG- ▁BRIG- ▁ESTEEM- ▁INVITATION- ▁PASSENGER- ▁PUBLISH- ▁PIANO- ▁IRISH- ▁DESK- ▁BEATEN- ▁FIFTH- ▁IMPULSE- ▁SWEAR- ▁EATEN- ▁PURPLE- ▁COMMITTED- ▁COUNTRIES- ▁PERCEIVE- ISON- ▁CELEBRAT- ▁GRANDMOTHER- ▁SHUDDER- ▁SUNSHINE- ▁SPANISH- ▁HITHERTO- ▁MARILLA- ▁SNAKE- ▁MOCK- ▁INTERFERE- ▁WALTER- ▁AMID- ▁MARBLE- ▁MISSION- TERIOR- ▁DRIVING- ▁FURNITURE- ▁STEADY- ▁CIRCUMSTANCE- ▁INTERPRET- ▁ENCHANT- ▁ERROR- ▁CONVICTION- ▁HELPLESS- ▁MEDICINE- ▁QUALITIES- ▁ITALIAN- ▁HASTENED- ▁OCCASIONALLY- ▁PURSUED- ▁HESITATED- ▁INDEPENDENT- ▁OLIVER- ▁LINGER- UX- ▁EXAMINED- ▁REPENT- ▁PHYSICIAN- ▁CHASE- ▁BELOVED- ▁ATTACHED- ▁FLORENCE- ▁HONEY- ▁MOUSE- ▁CRIES- ▁BAKE- ▁POEM- ▁DESTRUCTION- ▁FULFIL- ▁MESSENGER- ▁TRISTRAM- ▁FANCIED- ▁EXCESS- ▁CURSE- ▁CHU- ▁QUANTITY- ▁THORNTON- ▁CREATED- ▁CONTINUALLY- ▁LIGHTNING- ▁BORNE- ▁TOTAL- ▁DISPOSED- ▁RIFLE- ▁POLLY- ▁GOAT- ▁BACKWARD- ▁VIRGINIA- ▁KICK- ▁PERIL- ▁QUO- ▁GLORIOUS- ▁MULTITUDE- ▁LEATHER- ▁ABSENT- ▁DEMON- ▁DEBT- ▁TORTURE- ▁ACCORD- ▁MATE- ▁CATHOLIC- ▁PILL- ▁LIBRARY- ▁PURSUIT- ▁SHIRT- ▁DEAREST- ▁COLLAR- ▁BEACH- ▁ROBE- ▁DECLARE- ▁BRANCH- ▁TEMPT- ▁STEADILY- ▁DISGUST- ▁SILLY- ▁ARRIVE- ▁DRANK- ▁LEVI- ▁COMMUNICAT- ▁RACHEL- ▁WASHINGTON- ▁RESIGN- ▁MEANTIME- ▁LACE- ▁ENGAGEMENT- ▁QUIVER- ▁SEPARATED- ▁DISCUSSION- ▁VENTURED- ▁SURROUNDING- ▁POLISH- ▁NAIL- ▁SWELL- ▁JOKE- ▁LINCOLN- ▁STUDENT- ▁GLITTER- ▁RUSSIAN- ▁READILY- ▁CHRIS- ▁POVERTY- ▁DISGRACE- ▁CHEESE- ▁HEAVILY- ▁SCALE- ▁STAFF- ▁ENTREAT- ▁FAREWELL- ▁LUNCH- ▁PEEP- ▁MULE- ▁SOMEONE- ▁DISAPPEAR- ▁DECISION- ▁PISTOL- ▁PUN- ▁SPUR- ▁ASSUMED- ▁EXTEND- ▁ENTHUSIASM- ▁DEFINITE- ▁UNDERTAKE- ▁COMMITTEE- ▁SIMON- ▁FENCE- ▁APPLIED- ▁RELATED- ▁VICE- ▁UNPLEASANT- ▁PROBABLE- ▁PROCURE- ▁FROWN- ▁CLOAK- ▁HUMANITY- ▁FAMILIES- ▁PHILOSOPHER- ▁DWARF- ▁OVERCOME- ▁DEFEAT- ▁FASTENED- ▁MARSH- ▁CLASSES- ▁TOMB- ▁GRACIOUS- ▁REMOTE- ▁CELL- ▁SHRIEK- ▁RESCUE- ▁POOL- ▁ORGANIZ- ▁CHOSE- ▁CUTTING- ▁COWARD- ▁BORDER- ▁DIRTY- ▁MONKEY- ▁HOOK- ▁CHUCK- ▁EMILY- ▁JEST- ▁PLAC- ▁WEIGH- ▁ASSOCIATE- ▁GLIMPSE- ▁STUCK- ▁BOLT- ▁MURDERER- ▁PONY- ▁DISTINGUISH- ▁INSTITUTION- ▁CUNNING- ▁COMPLIMENT- ▁APPETITE- ▁REPUTATION- ▁FEEBLE- ▁KIN- ▁SERIES- ▁GRACEFUL- ▁PLATFORM- ▁BREEZE- ▁PHRASE- ▁CLAY- MONT- ▁RATTL- ▁OPPOSITION- ▁LANE- ▁BOAST- ▁GROWTH- ▁INCLINATION- ▁BEHAVE- ▁SUSAN- ▁DISTINCTION- ▁DISLIKE- ▁NICHOLAS- ▁SATISFY- ▁DRAMA- ▁ELBOW- ▁GAZING- ▁CONSUM- ▁SPIN- ▁OATH- ▁CHANNEL- ▁CHARACTERISTIC- ▁SPEAR- ▁SLAIN- ▁SAUCE- ▁FROG- ▁CONCEPTION- ▁TIMID- ▁ZEAL- ▁APPARENT- SHIRE- ▁CENTER- ▁VARIETY- ▁DUSK- ▁APT- ▁COLUMN- ▁REVENGE- ▁RIVAL- ▁IMITAT- ▁PASSIONATE- ▁SELFISH- ▁NORMAN- ▁REPAIR- ▁THRILL- ▁TREATMENT- ▁ROSA- ▁MARTIN- ▁INDIFFERENT- ▁THITHER- ▁GALLANT- ▁PEPPER- ▁RECOLLECT- ▁VINE- ▁SCARCE- ▁SHIELD- ▁MINGLED- CLOSE- ▁HARSH- ▁BRICK- ▁HUMOR- ▁MISCHIEF- ▁TREMENDOUS- ▁FUNCTION- ▁SMART- ▁SULTAN- ▁DISMISS- ▁THREATENED- ▁CHEAP- ▁FLOCK- ▁ENDEAVOR- ▁WHISK- ▁ITALY- ▁WAIST- ▁FLUTTER- ▁SMOKING- ▁MONARCH- ▁AFRICA- ▁ACCUSE- ▁HERBERT- ▁REFRESH- ▁REJOICE- ▁PILLOW- ▁EXPECTATION- ▁POETRY- ▁HOPELESS- ▁PERISH- ▁PHILOSOPHY- ▁WHISTLE- ▁BERNARD- ▁LAMENT- ▁IMPROVE- ▁SUP- ▁PERPLEX- ▁FOUNTAIN- ▁LEAGUE- ▁DESPISE- ▁IGNORANCE- ▁REFERENCE- ▁DUCK- ▁GROVE- ▁PURSE- ▁PARTNER- ▁PROPHET- ▁SHIVER- ▁NEIGHBOURHOOD- ▁REPRESENTATIVE- SAIL- ▁WIP- ▁ACQUIRED- ▁CHIMNEY- ▁DOCTRINE- ▁MAXIM- ▁ANGLE- ▁MAJORITY- ▁AUTUMN- ▁CONFUSED- ▁CRISTO- ▁ACHIEVE- ▁DISGUISE- ▁REDUCED- ▁EARLIER- ▁THEATRE- ▁DECIDE- MINATED- OLOGICAL- ▁OCCUPATION- ▁VIGOROUS- ▁CONTINENT- ▁DECLINE- ▁COMMUNITY- ▁MOTIONLESS- ▁HATRED- ▁COMMUNICATION- ▁BOWL- ▁COMMENT- ▁APPROVE- ▁CEREMONY- ▁CRIMINAL- ▁SCIENTIFIC- ▁DUCHESS- ▁VIVID- ▁SHIFT- ▁AVAIL- ▁DAMP- ▁JOHNSON- ▁SLENDER- ▁CONTRAST- ▁AMUSEMENT- ▁PLOT- ▁LYN- ▁ASSOCIATION- ▁SNATCH- ▁UNCERTAIN- ▁PRESSURE- ▁PERCH- ▁APPLY- ▁PLANET- ▁NOTWITHSTANDING- ▁SWUNG- ▁STIRRED- ▁ATTENDANT- ▁ENJOYMENT- ▁WORRY- ▁ALBERT- ▁NAKED- ▁TALENT- ▁MARIAN- ▁REFORM- ▁DELIBERATE- ▁INTELLIGENT- ▁SENSITIVE- ▁YONDER- ▁PUPIL- ▁FRIGHTFUL- ▁DOUBTFUL- ▁STANDARD- ▁MAGISTRATE- ▁SHEPHERD- ▁STOMACH- ▁DEPOSIT- ▁RENEW- ▁HEDGE- ▁FRANCS- ▁POSSIBILITY- ▁RESEMBLE- ▁FATIGUE- ▁PORTRAIT- ▁FAVORITE- ▁CREAM- ▁BURG- ▁SECRETARY- ▁DIVERS- ▁ACTIVITY- ▁SPECULAT- ▁HUMOUR- ▁FITTED- ▁EXTERNAL- ▁CETERA- ▁WRAPPED- ▁WHIT- ▁FRED- ▁EXAMINATION- ▁LODGING- ▁OWING- ▁JAW- ▁CROW- ▁BALANCE- ▁PUFF- ▁TENDERNESS- ▁PORTHOS- ▁ANCHOR- ▁INTERRUPT- ▁NECESSARILY- ▁PERPETUAL- ▁AGONY- ▁POPE- ▁SCHOLAR- ▁SCOTLAND- ▁SUPPRESS- ▁WRATH- ▁WRECK- ▁EXCEED- ▁PERFECTION- ▁INDIA- ▁TRADITION- ▁SECTION- ▁EASTERN- ▁DOORWAY- ▁WIVES- ▁CONVENTION- ▁ANNOUNC- ▁EGYPT- ▁CONTRADICT- ▁SCRATCH- ▁CENTRAL- ▁GLOVE- ▁WAX- ▁PREPARE- ▁ACCOMPANY- ▁INCREASING- ▁LIBERAL- ▁RAISING- ▁ORANGE- ▁SHOE- ▁ATTRIBUTE- ▁LITERATURE- ▁PUZZLED- ▁WITHDRAW- ▁WHITHER- ▁HAWK- ▁MOONLIGHT- ▁EXAMINE- ▁HAPPILY- ▁PRECEDE- ▁DETECTIVE- ▁INCHES- ▁SOLITARY- ▁DUTCH- ▁NAPOLEON- ▁UNEASY- ▁CARDINAL- ▁BLEW- ▁FOWL- ▁DECORAT- ▁CHILDHOOD- ▁TORMENT- ▁LOSING- ▁PERMISSION- ▁BLANK- ▁UPSTAIRS- ▁CAPACITY- ▁TRIFLE- ▁FOLLY- ▁RECOGNIZE- ▁REMOVE- ▁VENGEANCE- ▁ENTERPRISE- ▁BEDROOM- ▁ANYHOW- ▁INQUIRY- ▁ASHES- ▁DRAG- ▁HUSH- ▁AWKWARD- ▁SATURDAY- ▁GENUINE- ▁SURVIV- ▁SKIRT- ▁AFFECTIONATE- ▁TANG- ▁MUTUAL- ▁DISPUTE- ▁EAGLE- ▁INCOME- ▁BIND- ▁FAME- ▁IMPROVEMENT- ROVING- ▁DIFFER- ▁AWOKE- ▁SLEEVE- ▁SOLITUDE- ▁FAVOURITE- JI- ▁DETECT- ▁COMPREHEND- ▁PREPARING- ▁SERPENT- ▁SUMMIT- ▁KNOT- ▁KNIT- ▁COPY- ▁STOPPING- ▁FADED- ▁HIDEOUS- ▁JULIE- STEAD- ▁SHINE- ▁CONFLICT- ▁PROPOSITION- ▁REFUGE- ▁GALLERY- ▁BUNDLE- ▁AXE- ▁SLAVERY- ▁MASK- ▁ALYOSHA- ▁LADDER- ▁DEPARTMENT- ▁DISCHARGE- ▁DEPRESS- ▁GALLOP- ▁SCARLET- ▁KITTY- ▁RECEIVING- ▁SURRENDER- ▁SUSTAIN- ▁TWILIGHT- ▁CONGRESS- ▁IRELAND- ▁FUNNY- ▁LEND- ▁CONSTITUTE- ▁FUNERAL- ▁CRYSTAL- ▁SPAIN- ▁EXCEEDINGLY- ▁DAMN- ▁COMMUN- ▁CIVILIZATION- ▁PREJUDICE- ▁PORCH- ▁ASSISTANT- ▁INDUSTRY- ▁TUMBLE- ▁DEFENCE- ▁HITHER- ▁SMOT- ▁COLONI- ▁AMAZEMENT- ▁MARGUERITE- ▁MIRACLE- ▁INHERIT- ▁BEGGAR- ▁ENVELOPE- ▁INDIGNATION- ▁NATASHA- ▁PROPOSAL- ▁FRAGMENT- ▁ROUSED- ▁ROAST- ENCIES- ▁COMMENCED- ▁RESOURCE- ▁POPULATION- ▁QUOTH- ▁PURSUE- ▁EDUCAT- ▁AFFLICT- ▁CONTACT- ▁CRIMSON- ▁DIVISION- ▁DISORDER- ▁COPPER- ▁SOLICIT- ▁MODERATE- ▁DRUM- ▁SWIM- ▁SALUTE- ▁ASSUME- ▁MUSCLE- ▁OVERWHELM- ▁SHAKESPEARE- ▁STRUGGLING- ▁TRANQUIL- ▁CHICKEN- ▁TREAD- ▁CLAW- ▁BIBLE- ▁RIDGE- ▁THREAT- ▁VELVET- ▁EXPOSED- ▁IDIOT- ▁BARREL- ▁PENNY- ▁TEMPTATION- ▁DANGLARS- ▁CENTURIES- ▁DISTRIBUT- ▁REJECT- ▁RETORTED- ▁CONCENTRAT- ▁CORDIAL- ▁MOTOR- ▁CANNON- KEEP- ▁WRETCH- ▁ASSURANCE- ▁THIEF- ▁SURVEY- ▁VITAL- ▁RAILWAY- ▁JACKSON- ▁CRASH- ▁GROWL- ▁COMBAT- ▁RECOLLECTION- ▁SECURITY- ▁JACOB- ▁CLUTCH- ▁BLANKET- ▁NANCY- ▁CELLAR- ▁CONVENIENT- ▁INDIGNANT- ▁COARSE- ▁WORM- ▁SCREEN- ▁TRANSPORT- ▁BULLET- ▁APPRECIATE- ▁DEVOTION- ▁INVISIBLE- ▁DRIED- ▁MIXTURE- ▁CANDID- ▁PERFORMANCE- ▁RIPE- ▁EXQUISITE- ▁BARGAIN- ▁TOBACCO- ▁LOYAL- ▁MOULD- ▁ATTENTIVE- ▁DOROTHY- ▁BRUTE- ▁ESTABLISHMENT- ▁ABILITY- ▁INHABIT- ▁OBSCURE- ▁BORROW- ▁ESSENCE- ▁DISMAY- ▁FLEE- ▁BLADE- ▁PLUCK- ▁COFFIN- ▁SUNSET- ▁STEPHEN- ▁ECONOMIC- ▁HOLIDAY- ▁MECHANICAL- ▁COTTON- ▁AWAKENED- ▁SEIZE- ▁RIDICULOUS- ▁SANCHO- ▁HESITATION- ▁CORPSE- ▁SAVING- HOLD- FOOT- ▁ELDEST- ▁DESPITE- ▁EDITH- ▁CHERISH- ▁RESISTANCE- ▁WILSON- ▁ARGUE- ▁INQUIRE- ▁APPREHENSION- ▁AVENUE- ▁DRAKE- ▁PROPOSE- HURST- ▁INFERIOR- ▁STAIRCASE- ▁WHEREFORE- ▁CARLYLE- ▁COUCH- ▁ROUTE- ▁POLITICS- ▁TOMORROW- ▁THRONG- ▁NAUGHT- ▁SUNLIGHT- ▁INDIFFERENCE- ▁OBEDIENCE- ▁RECEPTION- ▁VEGETABLE- ▁IMPERFECT- ▁RESIDENCE- ▁TURKEY- ▁VIOLET- ▁SARAH- ▁ALTAR- ▁GRIEVE- ▁JERK- ▁ENSU- ▁MAGICIAN- ▁BLOSSOM- ▁LANTERN- ▁RESOLUTE- ▁THOUGHTFULLY- ▁FORTNIGHT- ▁TRUMPET- ▁VALJEAN- ▁UNWILLING- ▁LECTURE- ▁WHEREUPON- ▁HOLLAND- ▁CHANGING- ▁CREEK- ▁SLICE- ▁NORMAL- ▁ANNIE- ▁ACCENT- ▁FREDERICK- ▁DISAGREEABLE- ▁RUBBED- ▁DUMB- ▁ESTABLISH- ▁IMPORT- ▁AFFIRM- ▁MATTHEW- ▁BRISK- ▁CONVERT- ▁BENDING- ▁IVAN- ▁MADEMOISELLE- ▁MICHAEL- ▁EASIER- ▁JONES- ▁FACING- ▁EXCELLENCY- ▁LITERARY- ▁GOSSIP- ▁DEVOUR- ▁STAGGER- ▁PENCIL- ▁AVERAGE- ▁HAMMER- ▁TRIUMPHANT- ▁PREFERRED- ▁APPLICATION- ▁OCCUPY- ▁AUTHORITIES- BURN- ▁ASCERTAIN- ▁CORRIDOR- ▁DELICIOUS- ▁PRACTISE- ▁UNIVERSE- ▁SHILLING- ▁CONTEST- ▁ASHORE- ▁COMMIT- ▁ADMINISTRATION- ▁STUDIED- ▁RIGID- ▁ADORN- ▁ELSEWHERE- ▁INNOCENCE- ▁JOURNAL- ▁LANDSCAPE- ▁TELEGRAPH- ▁ANGRILY- ▁CAMPAIGN- ▁UNJUST- ▁CHALLENGE- ▁TORRENT- ▁RELATE- ▁ASSEMBLED- ▁IMPRESSED- ▁CANOE- ▁CONCLUD- ▁QUIXOTE- ▁SATISFACTORY- ▁NIECE- ▁DEAF- ▁RAFT- ▁JIMMY- ▁GLID- ▁REGULAT- ▁CHATTER- ▁GLACIER- ▁ENVY- ▁STATUE- ▁BOSTON- ▁RICHMOND- ▁DENIED- ▁FANNY- ▁SOLOMON- ▁VULGAR- ▁STALK- ▁REPLACE- ▁SPOON- ▁BASIN- ▁FEATURE- ▁CONVICT- ▁ARCHITECT- ▁ADMIRAL- ▁RIBBON- ▁PERMANENT- ▁APRIL- ▁JOLLY- ▁NEIGHBORHOOD- ▁IMPART- BOROUGH- CAMP- ▁HORRID- ▁IMMORTAL- ▁PRUDENCE- ▁SPANIARD- ▁SUPPOSING- ▁TELEPHONE- ▁TEMPERATURE- ▁PENETRATE- ▁OYSTER- ▁APPOINTMENT- ▁EGYPTIAN- ▁DWELT- ▁NEPHEW- ▁RAILROAD- ▁SEPTEMBER- ▁DEVICE- ▁WHEAT- ▁GILBERT- ▁ELEGANT- ▁ADVERTISE- ▁RATIONAL- ▁TURTLE- ▁BROOD- ▁ASSEMBLY- ▁CULTIVATE- ▁EDITOR- ▁SPECIMEN- ▁UNDOUBTEDLY- ▁WHALE- ▁DROPPING- ▁BALLOON- ▁MEDICAL- COMB- ▁COMPOSITION- ▁FOOTSTEPS- ▁LAUNCELOT- ▁DISCOURSE- ▁ERRAND- ▁CONVERSE- ▁ADVANCING- ▁DOWNSTAIRS- ▁TUMULT- ▁CORRUPT- ▁SUFFICE- ▁ANGUISH- ▁SHAGGY- ▁RETIRE- ▁TIMBER- ▁BLAZE- ▁ABSTRACT- ▁EMBROIDER- ▁PHOTOGRAPH- ▁PROSPERITY- ▁TERRIBLY- ▁TERRITORY- ▁THRESHOLD- ▁PAVEMENT- ▁INJURED- ▁LIMP- ▁AGITATION- ▁RASCAL- ▁PRESUME- ▁OBSERVING- ▁OBSTACLE- ▁SIMPLICITY- ▁SLUMBER- ▁SUPPLIED- ▁COMBINATION- ▁DRAIN- ▁WILDERNESS- ▁BELIEVING- ▁VILLAIN- ▁RECKLESS- ▁INJURY- ▁CLAPP- ▁FRIDAY- ▁HERCULES- ▁KENNEDY- ▁SYMPTOM- ▁SLEDGE- ▁CEILING- ▁LEMON- ▁PLAGUE- ▁MONDAY- ▁CANVAS- ▁IMPATIENCE- ▁UNCOMFORTABLE- ▁ACCESS- ▁FROZEN- ▁SENATOR- ▁FRANZ- ▁SWIMMING- ▁BARRIER- ▁ADJUST- ▁COMPARISON- ▁PROCLAIM- ▁WRINKL- ▁OVERLOOK- ▁MITYA- ▁GUILT- ▁PERCEPTION- ▁PRECAUTION- ▁SPECTATOR- ▁SURPRISING- ▁DISTRACT- ▁DISDAIN- ▁BONNET- ▁MAGNET- ▁PROFESS- ▁CONFOUND- ▁NARRATIVE- ▁STRUCTURE- ▁SKETCH- ▁ULTIMATE- ▁GLOBE- ▁INSECT- FICIENCY- ▁ORCHARD- ▁AMIABLE- ▁DESCENT- ▁INDEPENDENCE- ▁MANUFACTURE- ▁SPRINKLE- ▁NIGHTINGALE- ▁CUSHION- ▁EMINENT- ▁SCOTT- ▁ARRAY- ▁COSETTE- ▁WAVING- ▁EXTRACT- ▁IRREGULAR- ▁PERSECUT- ▁DERIVED- ▁WITHDREW- ▁CAUTION- ▁SUSPICIOUS- ▁MEMORIES- ▁NOWHERE- ▁SUBTLE- ▁THOROUGH- Q- ▁APPROPRIATE- ▁SLAUGHTER- ▁YOURSELVES- ▁THUMB- ▁TWAS- ▁ABODE- ▁BIDDING- ▁CONSPICUOUS- ▁REBECCA- ▁SERGEANT- ▁APRON- ▁ANTICIPATE- ▁DISCIPLINE- ▁GLANCING- ▁PILGRIM- ▁SULLEN- ▁CONTRIBUTE- ▁PRAIRIE- ▁CARVED- ▁COMMERCE- ▁EXCLAMATION- ▁MUSCULAR- ▁NOVEMBER- ▁PHENOMENA- ▁SYMBOL- ▁UMBRELLA- ▁DIMINISH- ▁PARLOUR- ▁THREATENING- ▁STUMP- ▁EXTENSIVE- ▁PLEASING- ▁REMEMBRANCE- ▁COMBINED- ▁SHERIFF- ▁SHAFT- ▁LAURA- ▁INTERCOURSE- ▁STRICKEN- ▁SUPPLIES- ▁LANDLORD- ▁SHRINK- ▁PRICK- ▁CAESAR- ▁DRUG- ▁BEWILDERED- ▁NAUTILUS- ▁BRUTAL- ▁COMMERCIAL- ▁MAGGIE- ▁SPHERE- ▁VIRGIN- ▁BRETHREN- ▁DESTINY- ▁POLICY- ▁TERRIFIED- ▁HOUSEKEEPER- ▁CRAZY- ▁ARDENT- ▁DISCERN- ▁WRAP- ▁MARQUIS- ▁RUSSIA- MOUTH- ▁BRITAIN- ▁HARBOUR- ▁CONCERT- ▁DONKEY- ▁DAMAGE- ▁SLIM- ABOUT- ▁LUXURY- ▁MONSTROUS- ▁TENDENCY- ▁PARADISE- ▁CULTURE- ▁JULIUS- ▁RAOUL- ▁REMEDY- ▁DECAY- ▁SCOLD- ▁SPLIT- ▁ASSAULT- ▁DECEMBER- ▁MOSCOW- ▁EXPLORE- ▁TROUSERS- ▁WRIST- PIECE- ▁MUSKET- ▁VALENTINE- ▁TYRANT- ▁ABRAHAM- ▁MEDIUM- ▁ARTIFICIAL- ▁FACULTY- ▁OBLIGATION- ▁RESEMBLANCE- ▁INQUIRIES- ▁DETAIN- ▁SWARM- ▁PLEDGE- ▁ADMIRABLE- ▁DEFECT- ▁SUPERINTEND- ▁PATRIOT- ▁CLUNG- ▁DISMAL- ▁RECIT- ▁IGNOR- ▁AMELIA- ▁JUSTIFY- ▁ELEPHANT- ▁ESTIMATE- ▁KNELT- ▁SERVING- ▁WHIM- ▁SHRILL- ▁STUDIO- ▁TEXT- ▁ALEXANDER- ▁WROUGHT- ▁ABUNDANT- ▁SITUATED- ▁REGAIN- ▁FIERY- ▁SNEER- ▁SWEAT- ▁GLARE- ▁NIGH- ▁ESCORT- ▁INEVITABLE- ▁PSMITH- ▁RELUCTANT- ▁PRECEDING- ▁RESORT- ▁OUTRAGE- ▁AMBASSADOR- ▁CONSOLATION- ▁RECOGNITION- ▁REMORSE- ▁BEHALF- ▁FORMIDABLE- ▁GRAVITY- ▁DIVIDE- ▁CONFRONT- ▁GIGANTIC- ▁OCTOBER- ▁FLANK- ▁SLEW- ▁CLARA- ▁FILM- ▁BULK- ▁POMP- ▁ELEANOR- ▁EMPHASIS- ▁JAPANESE- ▁CAVALRY- ▁EXCLUSIVE- ▁PERFUME- ▁BRONZE- ▁FEDERAL- ▁LIQUID- ▁RUBBING- ▁OVEN- DOLPH- ▁CONVULS- ▁DEPRIVED- ▁RESPONSIBILITY- ▁SIGNIFICANT- ▁WAISTCOAT- ▁CLUSTER- ▁MARTHA- ▁REVERSE- ▁ATTORNEY- ▁DROOP- ▁SKILFUL- ▁HABITUAL- ▁PUMP- ▁INTERVEN- ▁OWL- ▁CONJECTURE- ▁FANTASTIC- ▁RESPONSIBLE- ▁DESTINED- ▁DOCUMENT- ▁THEREUPON- ▁GODDESS- ▁PACIFIC- ▁WARRANT- ▁COSTUME- ▁BRIDLE- ▁CALIFORNIA- 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-    "authors": [
-      {
-        "name_en": "kamo-naoyuki",
-        "name_zh": "kamo-naoyuki"
-      }
-    ],
-    "keywords_en": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
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-    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
-    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
-    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave, fs=16k, lang=en",
-    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 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▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁success- ▁system- ▁top- ▁quality- ▁obviously- ▁between- ▁operational- ▁review- ▁far- ▁efforts- ion- ▁area- ▁shareholders- ▁prior- ▁certainly- ▁group- ▁pipeline- ▁todays- ▁return- ▁range- ▁whether- ar- ▁understand- ▁expenses- ▁continuing- ▁trying- ▁primarily- ▁amount- ch- ▁retail- l- ▁low- ur- ▁questions- ▁taking- ▁2017- ▁inventory- ▁4- ▁consumer- u- ▁outlook- ▁set- able- ▁total- ▁5- ▁presentation- ▁improved- ▁ahead- ▁contract- en- ▁since- ▁p- ▁structure- ▁expense- ▁major- ▁clear- ▁fiscal- ▁profitability- ▁approach- ▁add- ▁confident- ▁risks- it- ▁timing- ▁capabilities- ▁expand- ▁distribution- ▁companys- ▁increasing- ▁materially- ▁own- ▁partners- ▁expansion- ▁revenues- ▁invest- ▁however- ▁specific- ▁longterm- ▁youve- ▁solid- ▁hard- ▁ago- ▁2018- ▁space- ▁benefits- ▁driving- ▁excited- ▁keep- ▁acquisitions- ▁plans- ▁talked- ▁increases- ▁differ- ▁small- ▁strength- ▁china- ▁particularly- ri- ▁sheet- ▁consistent- ▁begin- ▁perspective- x- ▁bring- ▁asset- ▁10- ra- li- ▁stores- ▁network- ▁debt- ▁1- ▁compared- ▁open- ▁sense- at- ▁f- ▁per- ▁un- ▁patients- ▁close- ▁control- ▁improving- ▁versus- ▁programs- 'on'- ▁average- ▁measures- ▁beginning- ▁numbers- ▁run- ne- ▁currently- ▁care- ▁needs- ▁significantly- ▁2016- ▁trends- ▁create- ▁innovation- ▁scale- ▁conditions- ▁please- ▁starting- ▁okay- ▁together- ▁tell- ▁trend- th- ▁decline- ▁energy- ▁advantage- ▁points- ll- ▁anything- ▁organization- ▁volumes- ▁detail- ▁whats- ▁throughout- ▁transaction- ▁difficult- ma- an- ▁brands- ▁pay- ▁execution- ▁include- ▁profit- ▁towards- ▁deal- ta- ▁e- ▁health- ▁gas- ▁cause- ate- ment- ▁material- ▁similar- ▁access- ▁later- ▁larger- ▁greater- ▁reduce- ▁efficiency- ▁remains- ▁spend- ▁store- ▁organic- ▁international- ▁systems- ▁yet- ▁uncertaint- ▁record- ▁events- ▁comments- il- ▁power- ▁example- ▁ongoing- ▁note- ▁fully- ▁investors- ▁longer- ▁teams- ▁6- ▁successful- ▁g- ▁oil- ▁direct- ▁announced- ▁morning- ck- ▁board- ▁infrastructure- ▁everyone- ▁sell- ▁find- ▁returns- ▁discuss- 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▁relentless- ▁scaling- ▁spite- ▁argentina- ▁email- ▁instore- app- ▁mac- ▁lifestyle- ▁therapies- ▁throughput- ▁cfo- ▁foreseeable- ▁observed- ▁fifth- ▁resolve- ▁mineral- ▁smartphone- ▁deduct- ▁municipal- ▁duty- ▁hurdle- oriented- ▁manufacturer- ▁diesel- run- ▁fell- ▁sensitivity- ▁tie- cular- ▁battery- brand- ▁passenger- ▁inflationary- ▁google- ▁investigation- bl- ▁disruptive- ▁luxury- ▁approaches- ▁delaware- ▁barrier- ▁31- ▁variation- ▁correctly- ▁observation- ▁securing- ▁ken- ▁interpret- ution- ▁nation- ▁recruit- ▁profitably- print- ▁underground- ▁protocol- ▁delighted- ▁identity- ▁noticed- logic- ▁advancement- ▁constrained- ▁strike- ▁derive- ▁casino- ▁remote- ▁submission- ▁rain- ▁bankers- ▁suppose- ▁explanation- ▁hyper- ▁causing- ▁compute- ▁scrap- ▁irr- focused- cast- ▁downside- ▁refinance- ▁prepayment- ▁expenditure- ▁gradual- ▁slowly- order- ▁peer- ▁simultaneously- ▁interface- ▁favorably- ▁considerably- ▁worldclass- ▁comprise- ▁tumor- bility- ▁responsive- ▁commerce- ▁niche- ▁movie- ▁extract- ▁stake- ▁myself- ▁flight- ▁exceptionally- ▁vessel- ▁mindful- ▁remarkable- ▁outflow- ▁shipped- qua- ▁incident- well- ▁lie- ▁conventional- ▁accommodate- ▁allocated- ▁finalized- ▁rare- ▁weekend- ▁saas- ▁embrace- ▁ample- ▁integrity- ▁monetization- ▁supplies- centric- ▁redeploy- ▁remodel- ▁disrupt- ▁nextgeneration- ▁surgery- ▁climate- book- ▁threshold- ▁normalize- ▁recommendation- ▁payable- ▁leaving- ▁parallel- ▁fulfillment- ▁enthusiasm- ▁relief- ▁delta- ▁yearonyear- ▁honor- star- ▁subsidiaries- ▁surrounding- ▁deflation- ▁singledigit- ▁consume- ▁pop- ▁innovat- ▁strides- ford- ▁thorough- ▁dozen- ▁weigh- ▁chose- ▁conscious- ▁intelligent- ▁variance- ▁preserve- ▁attend- ▁collaborative- ▁association- ▁multifamily- ▁congress- ▁controlled- ▁missed- ▁acute- ▁enthusiastic- ▁resilience- ▁everywhere- ▁resort- ▁aviation- ▁rico- ▁surprising- ▁submitted- ▁omnichannel- ▁spirit- ▁sudden- ▁avenue- ▁array- ▁density- ▁outdoor- ▁calculated- build- power- ▁specialist- ▁consolidating- ▁breakdown- ▁optionalit- ▁rigorous- ▁predictions- ▁educate- ▁anchor- ▁proper- ▁advice- ▁novel- ▁nonrecurring- ▁subsidiary- ▁protein- ▁deem- ▁peter- ▁treasury- ▁inspection- ▁impression- ▁termination- ▁outperformance- ▁acceptable- ▁negotiation- ▁consumable- ▁discrete- ▁velocity- ▁throw- ▁emea- ▁accountability- ▁struggle- ▁empower- ▁loyal- ▁strip- ▁crisis- ▁geo- wise- ▁defend- ▁vital- ▁algorithm- ▁urgency- ▁pathway- ▁reposition- ▁analytic- ▁electrical- ▁pleasure- ▁proxy- ▁failure- ▁extreme- ▁blood- ▁derivative- ▁robotic- ▁certification- ▁accident- ▁impressed- ▁discretionary- stream- ▁dimension- ▁articulat- ▁recession- ▁triple- ▁stockholders- ▁silver- ▁frequently- ▁inclusion- ▁medicaid- ▁molecule- ▁glass- ▁vegas- product- ▁authority- ▁reinvestment- ▁jv- ▁upgrading- equ- ▁spoken- ▁sku- ▁engineers- ▁eagle- ▁jersey- ▁exposed- ▁unexpected- ▁unfold- ▁agility- ▁bestinclass- ▁sweet- ▁alongside- ▁society- ▁worry- ▁comparing- ▁studio- ▁granular- ▁keen- ▁alberta- ▁breakeven- ▁christmas- ▁goforward- ▁twice- ▁workflow- ▁imaging- ▁scan- ▁perception- ▁grand- ▁authorization- ▁concrete- ▁receipt- ▁absence- ▁australian- ▁radi- ▁comparative- ▁negotiating- ▁nutrition- ▁replacing- ▁guiding- ▁stretch- ▁underscore- ▁apartment- ▁younger- ▁collateral- ▁script- ▁resume- ▁plastic- ▁turkey- ware- ▁dilution- ▁anniversary- ▁phil- ▁computing- ▁substitute- ▁river- ▁disappointing- ▁specialized- '50'- ▁determination- ▁powder- ▁oncology- ▁japanese- ▁imply- ▁rationalization- ▁finalize- ▁preclinical- ▁undertaking- ▁register- ▁hospitality- ▁thrilled- ▁valley- ▁secular- ▁music- ▁cultural- ▁holistic- ▁lowcost- ▁hurt- ▁outsourcing- ▁adult- ▁invite- ▁reorganization- ▁unified- ▁reservoir- ▁motion- ▁glad- ▁visual- ▁regime- ▁theater- ▁academic- ▁concentrate- ▁cancellation- ▁diluted- ▁inorganic- ▁deleveraging- ▁french- ▁bolster- ▁simplification- ▁writing- ▁headquarters- ▁submit- ▁resonate- ▁midwest- ▁university- ▁brokerage- plus- ▁relevance- ▁text- ▁boston- ▁practical- ▁midst- ▁max- ▁nobody- ▁intangible- ▁snow- ▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- 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'00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁success- ▁system- ▁top- ▁quality- ▁obviously- ▁between- ▁operational- ▁review- ▁far- ▁efforts- ion- ▁area- ▁shareholders- ▁prior- ▁certainly- ▁group- ▁pipeline- ▁todays- ▁return- ▁range- ▁whether- ar- ▁understand- ▁expenses- ▁continuing- ▁trying- ▁primarily- ▁amount- ch- ▁retail- l- ▁low- ur- ▁questions- ▁taking- ▁2017- ▁inventory- ▁4- ▁consumer- u- ▁outlook- ▁set- able- ▁total- ▁5- ▁presentation- ▁improved- ▁ahead- ▁contract- en- ▁since- ▁p- ▁structure- ▁expense- ▁major- ▁clear- ▁fiscal- ▁profitability- ▁approach- ▁add- ▁confident- ▁risks- it- ▁timing- ▁capabilities- ▁expand- ▁distribution- ▁companys- ▁increasing- ▁materially- ▁own- ▁partners- ▁expansion- ▁revenues- ▁invest- ▁however- ▁specific- ▁longterm- ▁youve- ▁solid- ▁hard- ▁ago- ▁2018- ▁space- ▁benefits- ▁driving- ▁excited- ▁keep- ▁acquisitions- ▁plans- ▁talked- ▁increases- ▁differ- ▁small- ▁strength- ▁china- ▁particularly- ri- ▁sheet- ▁consistent- ▁begin- ▁perspective- x- ▁bring- ▁asset- ▁10- ra- li- ▁stores- ▁network- ▁debt- ▁1- ▁compared- ▁open- ▁sense- at- ▁f- ▁per- ▁un- ▁patients- ▁close- ▁control- ▁improving- ▁versus- ▁programs- 'on'- ▁average- ▁measures- ▁beginning- ▁numbers- ▁run- ne- ▁currently- ▁care- ▁needs- ▁significantly- ▁2016- ▁trends- ▁create- ▁innovation- ▁scale- ▁conditions- ▁please- ▁starting- ▁okay- ▁together- ▁tell- ▁trend- th- ▁decline- ▁energy- ▁advantage- ▁points- ll- ▁anything- ▁organization- ▁volumes- ▁detail- ▁whats- ▁throughout- ▁transaction- ▁difficult- ma- an- ▁brands- ▁pay- ▁execution- ▁include- ▁profit- ▁towards- ▁deal- ta- ▁e- ▁health- ▁gas- ▁cause- ate- ment- ▁material- ▁similar- ▁access- ▁later- ▁larger- ▁greater- ▁reduce- ▁efficiency- ▁remains- ▁spend- ▁store- ▁organic- ▁international- ▁systems- ▁yet- ▁uncertaint- ▁record- ▁events- ▁comments- il- ▁power- ▁example- ▁ongoing- ▁note- ▁fully- ▁investors- ▁longer- ▁teams- ▁6- ▁successful- ▁g- ▁oil- ▁direct- ▁announced- ▁morning- ck- ▁board- ▁infrastructure- ▁everyone- ▁sell- ▁find- ▁returns- ▁discuss- 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▁win- ▁software- ▁facilities- ▁trade- ally- ▁pace- ve- ▁weeks- ▁loss- ▁mobile- ▁securities- ▁book- ▁h- ▁profile- ▁behind- ▁his- ▁final- ▁lets- ▁d- com- ▁smaller- ▁meeting- ▁extremely- ▁enable- ▁insurance- ▁field- ▁enough- line- ▁stable- ▁ourselves- rs- ▁allows- is- ▁rather- ▁planning- ▁relationship- ▁patient- ▁sector- ▁recovery- ▁safety- ▁happy- ▁reported- ▁sustainable- ▁operate- ant- ia- ▁individual- ▁internal- ▁categories- ▁accounts- ▁includes- ▁reach- ▁loans- ▁away- ▁assumptions- ▁running- ▁reduced- ▁contribution- ▁effort- ▁speak- ▁transactions- ▁food- ▁wondering- ness- ▁exactly- id- po- ▁achieved- ▁estate- um- ize- ▁parts- am- ▁ex- ▁metrics- ▁gaap- ▁obligation- ▁above- ▁basically- ▁offerings- ▁present- ▁factor- ▁weather- ▁applications- ▁outside- ca- ▁units- ▁faster- ▁gain- ▁partially- ▁makes- ▁appropriate- ▁study- ▁7- ▁added- ▁accounting- ▁8- ▁security- ▁efficienc- ▁land- ▁plant- ▁discussions- ▁main- ▁channels- ad- ▁received- ▁r- ▁exciting- ▁m- ▁reflects- ▁seems- ▁investor- 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▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Thu Mar  4 09:32:06 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `fde26e9b249045a76e6cf809c85f9ab7118c2028`  - Commit date: `Thu Mar 4 09:06:55 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|98.2|1.3|0.5|0.4|2.2|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|98.1|1.3|0.5|0.4|2.3|33.6|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.4|0.2|0.4|0.3|0.9|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.4|0.2|0.4|0.3|1.0|33.6|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|102933|98.0|1.2|0.7|0.4|2.4|32.5||decode_asr_lm_lm_train_lm_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|1022838|97.9|1.3|0.8|0.4|2.5|33.6|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 46040dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe5000/train/speech_shape- exp/asr_stats_raw_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe5000/valid/speech_shape- exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- ▁continued- t- ▁before- ▁me- ▁price- ▁level- ▁industry- ▁around- ▁management- ▁youre- ▁great- ▁working- ▁demand- ▁investment- ▁additional- ▁due- ▁share- ▁thank- ▁even- ▁actually- ▁progress- ▁give- ▁here- ▁come- ▁plan- ▁only- ▁seeing- ▁few- ▁margin- ▁overall- ▁costs- ▁development- ▁further- ▁drive- ▁high- ▁service- p- ▁same- ▁grow- ▁current- ▁different- ▁result- ▁data- ▁seen- ▁3- ▁technology- ▁coming- ▁doing- ▁services- ▁change- ▁earnings- ▁who- ▁positive- ▁key- ▁guidance- ▁start- ▁process- ▁again- ▁position- ▁turn- ▁full- ▁investments- ▁past- ▁forwardlooking- ▁support- in- ▁said- ▁improve- ▁did- ▁got- es- ▁given- ▁within- ▁environment- ▁basis- ▁based- ▁expected- ▁re- ▁increased- ▁sure- ▁my- ▁focused- ▁sort- ▁question- ▁help- ▁potential- ▁pricing- ▁large- ▁businesses- ▁months- ▁remain- ▁side- ▁such- ▁making- ▁program- ▁done- ▁balance- ▁maybe- ▁put- ▁something- o- ▁strategic- ▁production- ▁information- ▁ability- ▁interest- ▁platform- ▁move- ▁already- f- ▁couple- ▁use- b- ▁feel- ▁best- ▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- 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▁compensate- ▁radio- ▁traditionally- ▁cyclical- ▁steadily- ▁detect- ▁ideal- ▁applicable- ▁artificial- ▁attempt- ▁valueadded- ▁standing- ▁naturally- ▁resilient- ▁alliance- ▁reit- ▁accept- ▁treated- ▁programming- ▁concentrated- ▁marked- ▁anticipation- ▁properly- ▁cheap- ▁tailwind- ▁listeners- ▁casual- ▁furthermore- ▁functional- ▁commodities- source- ▁principal- ▁audio- ▁employment- ▁apparel- ▁entities- ▁membership- ▁augment- ▁fight- ▁fl- ▁billion- ▁concession- ▁semiconductor- ▁visible- ▁implications- ▁composition- ▁authorities- ▁mag- tric- ▁wire- ▁indirect- ▁messaging- ▁literally- ▁broadband- ▁discover- ▁resolved- ▁announcing- ▁equivalent- ▁swing- ▁handling- ▁repositioning- ▁uk- ▁pit- ▁historic- ▁economies- ▁spain- ▁monetize- ▁greg- ▁turnover- ▁transactional- ▁clo- ▁kept- ▁certainty- ▁brexit- ▁regulated- ▁italy- ▁commit- ▁discontinued- ▁separation- ▁establishing- ▁amongst- ▁beauty- ▁hired- ▁scientific- ▁territories- ▁image- ▁satisfy- ▁payroll- ▁pound- ▁redevelopment- ▁tariff- ▁cautionary- ▁parent- ▁apple- ▁military- ▁prioritize- ▁losing- ▁yourself- quarter- ▁prescription- ▁district- ▁fun- ▁technological- ▁strict- ▁adopted- ▁endpoint- ▁bullish- ▁strive- ▁reflective- ▁predominantly- ▁copper- ▁evidenced- ▁northeast- ▁scheme- ▁pharmacy- water- ▁tier- ▁quicker- ▁popular- ▁standalone- ▁attrition- ▁washington- ▁database- ▁differential- ▁personalized- ▁balancing- ▁delever- wood- ▁slowed- ▁pause- ▁official- ▁overseas- ▁inject- ▁dar- ▁union- ▁dc- ▁lever- ▁hitting- ▁fabric- ▁elsewhere- ▁precision- ▁poor- ▁prospect- ▁ease- ▁handful- ▁assumed- ▁carolina- ▁swap- ▁territory- ▁description- ▁household- ▁bla- ▁meantime- ▁intellectual- ▁mobility- ▁outlet- ▁controlling- ▁samestore- ▁sizable- ▁periodic- grade- ▁enroll- ▁chosen- ▁carried- ▁payout- ▁airport- ▁competenc- ▁referenced- ▁combining- ▁wall- service- ▁issuance- ▁receivable- ▁shrink- ▁pra- ▁industryleading- ▁proof- ▁alluded- ▁noncore- ▁realistic- ▁upstream- ▁telecom- ▁absorption- ▁appendix- ▁materialize- ▁sooner- ▁shipment- ▁collaborat- ▁definitive- ▁automated- ▁vote- ▁lifetime- ▁conducted- ▁basket- ▁lumpy- ▁differentiator- ▁migrate- ▁float- ▁capitalized- ▁cool- ▁midstream- ▁bump- ▁governance- ▁takeaway- ▁temp- ▁electricity- ▁output- ▁overcome- ▁lng- ▁scalabl- ▁validation- ▁convinced- ▁frank- ▁maximum- ▁repayment- ▁intensity- ▁exhibit- ▁hong- ▁amendment- ▁cohort- ▁kong- ▁agile- ▁disappointed- ▁surgical- ▁tele- ▁separately- ▁sim- ▁judgment- ▁undu- ▁penetrate- ▁quote- view- ▁hour- ▁domain- ▁german- ▁body- ▁consist- ▁ev- ▁methodology- ▁principally- ▁sym- ▁rank- ▁chicago- ▁relentless- ▁scaling- ▁spite- ▁argentina- ▁email- ▁instore- app- ▁mac- ▁lifestyle- ▁therapies- ▁throughput- ▁cfo- ▁foreseeable- ▁observed- ▁fifth- ▁resolve- ▁mineral- ▁smartphone- ▁deduct- ▁municipal- ▁duty- ▁hurdle- oriented- ▁manufacturer- ▁diesel- run- ▁fell- ▁sensitivity- ▁tie- cular- ▁battery- brand- ▁passenger- ▁inflationary- ▁google- ▁investigation- bl- ▁disruptive- ▁luxury- ▁approaches- ▁delaware- ▁barrier- ▁31- ▁variation- ▁correctly- ▁observation- ▁securing- ▁ken- ▁interpret- ution- ▁nation- ▁recruit- ▁profitably- print- ▁underground- ▁protocol- ▁delighted- ▁identity- ▁noticed- logic- ▁advancement- ▁constrained- ▁strike- ▁derive- ▁casino- ▁remote- ▁submission- ▁rain- ▁bankers- ▁suppose- ▁explanation- ▁hyper- ▁causing- ▁compute- ▁scrap- ▁irr- focused- cast- ▁downside- ▁refinance- ▁prepayment- ▁expenditure- ▁gradual- ▁slowly- order- ▁peer- ▁simultaneously- ▁interface- ▁favorably- ▁considerably- ▁worldclass- ▁comprise- ▁tumor- bility- ▁responsive- ▁commerce- ▁niche- ▁movie- ▁extract- ▁stake- ▁myself- ▁flight- ▁exceptionally- ▁vessel- ▁mindful- ▁remarkable- ▁outflow- ▁shipped- qua- ▁incident- well- ▁lie- ▁conventional- ▁accommodate- ▁allocated- ▁finalized- ▁rare- ▁weekend- ▁saas- ▁embrace- ▁ample- ▁integrity- ▁monetization- ▁supplies- centric- ▁redeploy- ▁remodel- ▁disrupt- ▁nextgeneration- ▁surgery- ▁climate- book- ▁threshold- ▁normalize- ▁recommendation- ▁payable- ▁leaving- ▁parallel- ▁fulfillment- ▁enthusiasm- ▁relief- ▁delta- ▁yearonyear- ▁honor- star- ▁subsidiaries- ▁surrounding- ▁deflation- ▁singledigit- ▁consume- ▁pop- ▁innovat- ▁strides- ford- ▁thorough- ▁dozen- ▁weigh- ▁chose- ▁conscious- ▁intelligent- ▁variance- ▁preserve- ▁attend- ▁collaborative- ▁association- ▁multifamily- ▁congress- ▁controlled- ▁missed- ▁acute- ▁enthusiastic- ▁resilience- ▁everywhere- ▁resort- ▁aviation- ▁rico- ▁surprising- ▁submitted- ▁omnichannel- ▁spirit- ▁sudden- ▁avenue- ▁array- ▁density- ▁outdoor- ▁calculated- build- power- ▁specialist- ▁consolidating- ▁breakdown- ▁optionalit- ▁rigorous- ▁predictions- ▁educate- ▁anchor- ▁proper- ▁advice- ▁novel- ▁nonrecurring- ▁subsidiary- ▁protein- ▁deem- ▁peter- ▁treasury- ▁inspection- ▁impression- ▁termination- ▁outperformance- ▁acceptable- ▁negotiation- ▁consumable- ▁discrete- ▁velocity- ▁throw- ▁emea- ▁accountability- ▁struggle- ▁empower- ▁loyal- ▁strip- ▁crisis- ▁geo- wise- ▁defend- ▁vital- ▁algorithm- ▁urgency- ▁pathway- ▁reposition- ▁analytic- ▁electrical- ▁pleasure- ▁proxy- ▁failure- ▁extreme- ▁blood- ▁derivative- ▁robotic- ▁certification- ▁accident- ▁impressed- ▁discretionary- stream- ▁dimension- ▁articulat- ▁recession- ▁triple- ▁stockholders- ▁silver- ▁frequently- ▁inclusion- ▁medicaid- ▁molecule- ▁glass- ▁vegas- product- ▁authority- ▁reinvestment- ▁jv- ▁upgrading- equ- ▁spoken- ▁sku- ▁engineers- ▁eagle- ▁jersey- ▁exposed- ▁unexpected- ▁unfold- ▁agility- ▁bestinclass- ▁sweet- ▁alongside- ▁society- ▁worry- ▁comparing- ▁studio- ▁granular- ▁keen- ▁alberta- ▁breakeven- ▁christmas- ▁goforward- ▁twice- ▁workflow- ▁imaging- ▁scan- ▁perception- ▁grand- ▁authorization- ▁concrete- ▁receipt- ▁absence- ▁australian- ▁radi- ▁comparative- ▁negotiating- ▁nutrition- ▁replacing- ▁guiding- ▁stretch- ▁underscore- ▁apartment- ▁younger- ▁collateral- ▁script- ▁resume- ▁plastic- ▁turkey- ware- ▁dilution- ▁anniversary- ▁phil- ▁computing- ▁substitute- ▁river- ▁disappointing- ▁specialized- '50'- ▁determination- ▁powder- ▁oncology- ▁japanese- ▁imply- ▁rationalization- ▁finalize- ▁preclinical- ▁undertaking- ▁register- ▁hospitality- ▁thrilled- ▁valley- ▁secular- ▁music- ▁cultural- ▁holistic- ▁lowcost- ▁hurt- ▁outsourcing- ▁adult- ▁invite- ▁reorganization- ▁unified- ▁reservoir- ▁motion- ▁glad- ▁visual- ▁regime- ▁theater- ▁academic- ▁concentrate- ▁cancellation- ▁diluted- ▁inorganic- ▁deleveraging- ▁french- ▁bolster- ▁simplification- ▁writing- ▁headquarters- ▁submit- ▁resonate- ▁midwest- ▁university- ▁brokerage- plus- ▁relevance- ▁text- ▁boston- ▁practical- ▁midst- ▁max- ▁nobody- ▁intangible- ▁snow- ▁bankruptcy- ▁overlap- ▁doug- ▁modernization- ▁cargo- ▁wallet- ▁merit- ▁revolving- ▁aluminum- ▁stabilizing- ▁recommend- ▁procedure- ▁immune- ▁valid- ▁restore- ▁coffee- ▁arrive- ▁reconcile- performance- ▁referral- '20'- ▁children- ▁geographically- ▁accretion- ▁reversal- ▁tissue- ▁tomorrow- ▁nimble- ▁uplift- ▁nevertheless- ▁revolver- ▁willingness- ▁convenient- ▁deterioration- ▁contingent- ▁parameters- ▁pursuit- ▁cyber- '95'- ▁root- ▁costeffective- ▁lumber- ▁singapore- ▁arpu- ▁gift- ▁fail- ▁borrowers- ▁town- ▁convertible- ▁journal- ▁fish- charge- ▁temperature- ▁character- ▁modification- ▁ocean- ▁suspect- ▁possibilities- ▁flood- ▁solely- ▁brazilian- ▁experiment- ▁colorado- ▁foresee- quartertoquarter- ▁inefficienc- ▁notwithstanding- ▁spike- ▁transit- ▁interconnect- ▁analyzing- ▁qualify- ▁banner- ▁maturit- growth- ▁eliminating- ▁retire- ▁virginia- ▁accuracy- ▁redemption- ▁recruitment- ▁click- ▁guidelines- ▁restrictions- ▁broadbased- ▁continent- ▁grocery- ▁additive- ▁chunk- ▁formulation- ▁intervention- ▁hence- ▁silicon- ▁unprecedented- ▁expire- ▁kids- ▁apparent- ▁stories- ▁poised- ▁anymore- ▁redesign- ▁hawaii- ▁likelihood- ▁gotomarket- ▁plate- ▁aspiration- ▁correlation- ▁qualification- ▁underwrite- ▁engineered- ▁consequently- ▁craft- ▁foremost- ▁consent- ▁colombia- ▁tactical- ▁forget- ▁eric- ▁permitting- ▁healthier- force- ▁mechanical- ▁poland- ▁realignment- ▁refinery- ▁replicate- ▁outpace- ▁chairman- ▁doubling- contract- ▁generator- ▁rick- ▁appliance- ▁contrast- ▁candidate- efficient- ▁ontario- ▁saving- ▁borrow- ▁proving- ▁friday- ▁photo- ▁assistance- ▁worried- ▁judge- ▁refund- ▁hundred- ▁embark- ▁ultra- ▁greenfield- ▁consensus- ▁accessories- ▁flagship- ▁entrepreneur- ▁diamond- ▁expiration- ▁forefront- ▁venue- ▁default- ▁placing- ▁choosing- ▁fluctuate- ▁attendance- ▁hike- ▁dallas- ▁fragmented- ▁classified- ▁optical- ▁revolution- ▁institution- ▁chronic- ▁conviction- ▁demonstration- ▁overwhelm- ▁struggling- ▁sky- ▁broken- ▁decreasing- ▁sweden- turn- ▁heightened- ▁pennsylvania- ▁intake- ▁genetic- mobile- ▁crystal- ▁reaffirm- ▁regulator- ▁transact- ▁dropped- ▁advertise- ▁decelerat- ▁unsecure- ▁worst- ▁perpetual- ▁trouble- ▁gather- ▁walmart- ▁accrue- ▁pulp- ▁caught- ological- ▁uptake- ▁furniture- ▁pronounced- '10'- ▁ohio- ▁quantity- ▁routine- ▁mindset- ▁resonat- ▁severity- ▁debate- modal- ▁diminish- ▁riskadjusted- ▁shutdown- ▁initiate- ▁circuit- ▁coach- ▁muted- ▁autonomous- ▁flip- ▁mistake- ▁slate- ▁ambitious- ▁dispute- ▁probability- ▁residents- ▁residual- ▁border- ▁grain- ▁upsell- bank- ▁cluster- ▁equities- ▁simplified- ▁agricultural- ▁grateful- ▁harvey- ▁microsoft- ▁intact- ▁communicating- ▁annuity- ▁calculate- ▁authentic- spec- ▁requiring- ▁surplus- ▁instant- ▁allocating- ▁facebook- ▁universal- ▁witness- ▁jason- ▁teleconference- ▁expert- ▁coupon- ▁ancillary- ▁civil- ▁garden- ▁restart- ▁illinois- ▁emissions- ▁impossible- ▁chapter- ▁premise- ▁baked- ▁elevate- ▁filter- ivity- ▁perceive- ▁nursing- ▁realign- ▁distinguish- ▁refocus- ▁friction- ▁crack- ▁royalties- ▁college- ▁longstanding- ▁overnight- ▁incumbent- ▁endtoend- ▁snap- ▁millennial- ▁manifest- ▁regain- ▁rebate- ▁dining- ▁imperative- sphere- ▁tenure- ▁indonesia- ▁promoting- ▁transcript- ▁principle- ▁bottle- ▁flag- ▁atlantic- ▁diligent- ▁phoenix- ▁instruct- ▁neuro- ▁george- ▁prescribe- ▁heritage- ▁accrual- ▁nationwide- ▁replenish- ▁article- ▁british- ▁stepped- ▁appointment- ▁dominant- ▁zealand- free- '00000'- ▁advisor- ▁delinquenc- ▁speculate- ▁inspire- ▁envision- ▁affordabilit- ▁crucial- ▁secret- ▁integral- ▁landlord- ▁streamlining- ▁modify- ▁quota- ▁province- ▁isolation- ▁beach- ▁albeit- ▁disaster- ▁entrant- ▁erosion- ▁relied- ▁elimination- ▁republic- ▁logistic- ▁opposite- ▁signature- ▁decisionmaking- ▁midsingle- mitted- ▁privilege- ▁shortfall- ▁correlate- ▁frozen- ▁ordinary- ▁omni- ▁neighborhood- ▁implies- ▁curtail- ▁charging- ▁institute- ▁patience- ▁temporarily- ▁systematic- ▁argue- ▁eligible- ▁leisure- ▁reception- ▁implant- ▁atlanta- ▁northwest- ▁vacation- ▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- ▁catalog- ▁martin- ▁minnesota- ▁deficit- ▁reorganiz- responsibilities- ▁multinational- ▁opioid- ▁salespeople- ▁distance- thanexpected- ▁compliant- ▁dilutive- ▁proximity- ▁zero- ▁tuckin- ▁incorporating- ▁migrating- ▁mediumterm- ▁undergoing- ▁scalabilit- ▁geopolitical- ▁uniform- ▁solving- ▁intermediate- ▁angeles- ▁saudi- ▁stimulate- ▁nominal- ▁midteens- ▁speculation- ▁freedom- iconic- ▁distraction- ▁shock- ▁describing- ▁jewelry- ▁sleep- ▁squeeze- ▁gasoline- ▁refurbish- ▁unmatch- ▁golf- ▁associate- ▁removal- ▁wheel- ▁interior- ▁noninterest- ▁prohibit- ▁finaliz- ▁consultation- ▁kansas- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram5000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe5000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55073dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe5000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe5000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁a- ▁our- s- ▁is- ▁on- ▁for- ▁as- ▁are- ▁you- ▁have- ▁i- ▁this- ▁with- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ing- ▁think- ▁what- ▁not- ▁some- ▁us- ▁or- ▁by- ▁new- ▁well- ▁about- ▁do- ▁growth- ▁can- ▁which- ▁was- ▁an- ▁its- ▁there- ▁very- ▁these- ▁also- ▁market- ed- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- d- ▁just- ▁been- ▁has- ▁weve- ▁over- ▁those- ▁they- ▁if- ▁time- ▁first- ▁where- ▁customers- ▁into- ▁out- ▁like- ▁how- ▁results- ▁other- ▁really- ▁last- ▁their- ▁up- ▁one- ▁expect- ▁than- ly- ▁look- ▁your- ▁because- ▁when- ▁through- ▁any- ▁get- ▁call- ▁good- ▁strong- ▁sales- ▁had- ▁believe- ▁then- ▁second- ▁revenue- ▁thats- ▁company- ▁performance- ▁years- ▁make- ▁product- ▁financial- ▁lot- ▁capital- ▁much- ▁cost- ▁could- ▁right- ▁both- ▁them- ▁impact- ▁terms- ▁future- ▁want- ▁dont- ▁value- ▁forward- ▁work- ▁next- ▁little- y- ▁back- ▁customer- ▁products- ▁bit- ▁increase- ▁number- ▁end- ▁take- ▁may- e- ▁while- ▁kind- ▁markets- ▁higher- ▁opportunities- er- ▁half- ▁able- ▁way- ▁during- ▁operating- ▁significant- ▁know- ▁better- ▁most- ▁go- ▁things- ▁cash- ▁focus- c- ▁2- ▁say- ▁still- ▁im- ▁part- ▁point- ▁lower- r- ▁statements- ▁provide- ▁should- ▁being- ▁across- n- ▁need- ▁made- ▁team- ▁important- ▁opportunity- ▁line- al- ▁third- ▁today- ies- ▁people- ▁rate- ▁theres- ▁down- ▁many- ▁no- ▁fourth- ▁strategy- ▁looking- ▁portfolio- m- 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▁mentioned- ▁acquisition- ▁several- ▁talk- ▁early- ▁each- ▁operations- ation- ▁segment- re- ▁expectations- ▁long- ▁q- ▁base- ▁deliver- ▁commercial- ▁tax- ▁assets- ▁rates- ▁order- ▁changes- k- ▁pleased- ▁experience- le- ▁build- ▁place- ▁period- ▁certain- ▁ill- ▁benefit- ▁including- ▁growing- ▁quarters- ▁improvement- ▁projects- ▁model- ▁under- ▁related- ▁initiatives- ▁flow- ▁fact- ▁addition- ▁companies- ▁activity- ▁driven- ▁areas- ▁factors- ▁getting- ▁margins- ▁net- ▁existing- ▁continues- ▁mix- ▁after- g- ▁term- ▁big- ▁levels- ▁s- ▁id- ▁volume- ▁income- ▁does- ▁theyre- ▁release- ▁pretty- ▁clients- ▁thing- ▁course- ▁project- ▁capacity- ▁probably- ▁might- ▁day- ▁competitive- ▁having- or- ▁every- ▁brand- a- ▁risk- ▁always- ▁marketing- ▁de- ▁global- ▁why- w- ▁mean- i- ▁actual- ▁c- ▁leverage- ▁solutions- ▁quite- ▁building- ▁another- ▁supply- ▁prices- ▁saw- ▁credit- ▁off- ▁digital- ▁yes- ▁update- ▁less- ▁world- ▁offset- ers- ▁conference- ▁real- ic- ▁view- ▁let- ▁core- ▁available- ▁recent- 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▁minus- realized- ▁exploit- ▁differentiating- ▁sugar- ▁premature- ▁thrive- ▁fraud- ▁revpar- ▁flash- ▁revisit- ▁lumpi- ▁endeavor- ▁mountain- ▁securitization- ▁dental- ▁convention- ▁highermargin- ▁skew- ▁bandwidth- ▁catastrophe- ▁netherlands- ▁midterm- specific- ▁taste- ▁dairy- ▁eager- ▁liberty- ▁oklahoma- ▁phasing- ▁accumulate- ▁unemployment- ▁softening- ▁depressed- ▁airplane- ▁prevail- ▁analog- ▁footwear- ▁terminate- ▁arizona- abilities- ▁pioneer- ▁cancel- ▁agriculture- ▁thermal- ▁composite- ▁argument- ▁wellpositioned- ▁medication- ▁neither- ▁excuse- ▁qualitative- ▁concert- ▁determining- ▁michigan- ▁mirror- ▁thereafter- ▁manual- ▁applies- ▁horizontal- ▁prepaid- ▁repricing- ▁indicating- ▁queue- ▁observe- ▁amended- weight- ▁encounter- ▁inhibitor- ▁meanwhile- ▁toronto- ▁inclusive- ▁stopped- ▁robert- ▁concluding- ▁configuration- ▁pleasant- ▁cook- ▁clarification- ▁buffer- ▁relocation- ▁parcel- ▁convey- ▁showcase- ▁severance- ▁inbound- ▁inquiries- ▁trailing- ▁drink- ▁snack- ▁chemistry- ▁energized- ▁removing- ▁modified- ▁circle- ▁kitchen- ▁vacancy- ▁plug- ▁southwest- ▁steep- ▁illustrat- ▁scheduling- ▁celebrate- ▁diabetes- ▁alpha- ▁frequent- ▁achievable- ▁molecular- ▁bounce- ▁quiet- ▁cumulative- ▁capacities- ▁bolton- ▁comparability- ▁phenomenon- ▁steward- ▁reseller- ▁pertain- ▁resiliency- ▁awful- ▁shoot- ▁clinicians- ▁measuring- ▁pleasing- ▁maturing- ▁prototype- ▁survival- ▁rural- ▁heavier- ▁immuno- ▁universe- ▁louisiana- ▁marketleading- ▁weakening- ▁somehow- ▁credibility- ▁battle- ▁declare- ▁involving- ▁columbia- ▁ounces- ▁significance- ▁compromise- ▁feasibility- ▁mexican- ▁tobacco- ▁england- ▁chicken- ▁archive- ▁gauge- ▁ought- ▁enforcement- ▁remediation- ▁relaunch- ▁dictate- ▁legislative- ▁modular- ▁cushion- ▁voluntary- demand- ▁distress- ▁quantitative- ▁releasing- ▁statutory- ▁roadmap- ▁ryan- ▁mortality- ▁scratch- ▁syndicate- ▁turbine- ▁breast- ▁james- ▁absent- ▁crm- ▁discontinu- ▁milk- ▁missouri- ▁salaries- ▁concurrent- ▁coordinate- ▁tonnage- ▁automatically- 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-        "name_en": "Shinji Watanabe",
-        "name_zh": "Shinji Watanabe"
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-      "machine-translation"
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-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "publication_date": 1614873600000,
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-    "doi": "10.5281/zenodo.4585552",
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-    "pid": "",
-    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
-    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
-    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave, fs=16k, lang=en",
-    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 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▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- 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▁counterparties- ▁festival- ▁nielsen- ▁proliferation- ▁subordinate- ▁staple- ▁thick- ▁boiler- ▁precedent- ▁timetable- ▁digging- ▁foothold- ▁contest- ▁tableau- ▁receptive- ▁hip- ▁explicitly- ▁salesforce- ▁shooting- ▁commenc- abilities- trade- ▁administrator- ▁congestion- ▁detriment- ▁episode- ▁hesitant- ▁hygiene- ▁scarcity- ▁smelter- ▁turmoil- ▁waterfall- ▁sweep- ▁standardize- ▁ranch- ▁advent- ▁smb- ▁influencer- utter- ▁95- ▁motivate- ▁accustomed- ▁chassis- ▁invaluable- ▁reestablish- ▁situated- ▁stimulus- ▁tandem- ▁volunteer- ▁prioritization- ▁compass- ▁scanner- ▁firewall- ▁blackstone- ▁crest- ▁cardio- ▁accessory- more- ▁steering- ▁cow- ▁marlin- ▁retired- ▁curate- ▁dean- ▁repo- ▁turk- ▁alternate- ▁analyses- ▁dialysis- ▁intensified- ▁penalties- ▁sensitivities- ▁worthwhile- ▁hazard- ▁remarkably- ▁blackrock- ▁agnostic- ▁cooperative- ▁lengthen- ▁steven- ▁formulate- ▁pv- ▁funeral- ▁ladder- ▁pinnacle- ▁postacute- ▁snapshot- ▁titanium- ▁brandnew- ▁shallow- ▁halo- ▁crown- zone- ▁railway- 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▁renegotiated- ▁routinely- ▁nr- ▁buyout- code- ▁redesigned- ▁flor- ▁incent- ▁grocer- process- structure- ▁destiny- ▁underpenetrated- ▁busiest- ▁forthcoming- ▁virtuous- ▁plaza- ▁racing- eye- ▁3000- ▁payoff- ▁ortho- ▁merged- ▁inhibit- ▁vista- ▁watches- ▁deducti- charge- profit- energy- wind- ▁chemotherapy- ▁entrance- ▁facilitating- ▁happier- ▁legislature- ▁timeframe- ▁vacancies- ▁bargain- ▁lobby- ▁tokyo- ▁slope- ▁cedar- ▁specify- ▁protest- ▁roster- ▁voting- ▁withdrawal- ▁reallocation- ▁standby- ▁vale- ▁earlystage- ▁redevelop- ▁hp- ▁pest- front- ▁insist- beat- ▁alabama- ▁empty- ▁endorsement- ▁examining- ▁experiential- ▁rainfall- ▁separating- ▁iridium- ▁wellbeing- ▁liber- ▁recreational- ▁connector- ▁barge- ▁highgrade- ▁designated- ▁liquidate- ▁yo- action- ▁cpg- ▁suspend- ▁genuine- active- heim- utilized- ▁sma- ▁advocacy- ▁antibodies- ▁conservation- ▁hydrocarbon- ▁mismatch- ▁oxygen- ▁refractor- ▁unfair- ▁disability- ▁downgrade- ▁cabin- iston- ▁bolt- ▁120- suite- ▁enzyme- ▁hilton- ▁offensive- ▁testimony- ▁clothing- ▁restocking- ▁beacon- ▁defect- ▁nurse- ▁tal- ▁mixture- ▁dish- ▁activat- ▁pp- ▁citi- period- ▁inroads- ▁kroger- ▁vigorously- ▁2011- ▁pushback- ▁protective- ▁traders- ▁barn- ▁midsize- ▁increment- competitive- ▁fabulous- ▁hospice- ▁nitrogen- ▁practitioner- ▁restatement- ▁robinson- ▁segregat- ▁unconventional- ▁girl- ▁pbm- ▁instill- ▁loud- ▁characteristic- ▁realistically- ▁headquarter- ▁furnished- ▁highperforming- ▁amid- ▁flagged- ▁haynes- ▁problematic- ▁scoop- ▁sovereign- ▁voyage- ▁merely- ▁pinpoint- ▁prepay- ▁dependence- ▁marry- ▁lions- ▁superiority- megawatt- ▁excite- ▁jeremy- ▁refrigerated- ▁turbo- ▁unreasonable- ▁humble- ▁infant- ▁baking- ▁divert- ▁lte- ▁sharper- ▁colder- ▁monotherap- ▁consult- ▁correspond- ▁reinvigorate- platform- producing- order- check- resistant- ▁culinary- ▁flawless- ▁inherited- ▁nontraditional- ▁remeasurement- ▁wellknown- ▁winnebago- ▁revising- ▁lagged- ▁depict- ▁juice- ▁hypothesis- ▁dwell- ▁publisher- ▁reuse- hole- ▁heck- consolidated- built- prime- ▁depletion- ▁secondarily- ▁mlp- ▁weapon- ▁swiftly- ▁softened- ▁mil- ▁rainy- ▁blast- ▁coat- ▁expose- ▁furnish- wire- ▁bacteria- ▁baseball- ▁oxide- ▁prestigious- ▁stranded- ▁vantage- ▁slice- ▁thread- ▁michelle- ▁deduct- defined- ▁farther- ▁exhibition- scope- ▁bloom- ▁destroy- ▁plumbing- ▁reconsider- ▁unleash- ▁scanning- ▁parse- ▁clay- ▁personalize- neutral- ▁frustration- ▁highspeed- ▁targa- ▁kirk- ▁rescue- ▁subsidize- ▁steal- ▁mentor- ▁harris- ▁aspirational- ▁resale- borne- ▁deter- ▁pine- transmission- ▁onprem- ▁carriage- ▁invitation- ▁peace- ▁reconciling- ▁saturday- ▁selfhelp- ▁sulfur- ▁triumph- ▁escalate- ▁viral- ▁ethical- ▁ibm- ▁cameron- ▁sizing- ▁instantly- ▁creators- prong- ▁bull- ▁reaccelerat- ▁refurbish- ▁nascar- ▁retroactive- ▁unforeseen- ▁remix- ▁coding- ▁blueprint- ▁merging- ▁roast- arity- ▁existence- ▁resist- ▁assistant- ▁shaw- ▁technique- ▁genuinely- fast- ▁mobilize- ▁victoria- ▁interventional- axis- ▁soften- ▁tile- ▁attribution- ▁blockbuster- ▁insignificant- ▁literature- ▁qualities- ▁steroid- ▁terribly- ▁copies- ▁intercept- ▁welding- ▁tricky- sky- ▁scrip- ▁tec- munications- ▁nutritional- ▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- ▁although- ▁actions- ▁without- ▁selling- ▁leadership- ▁relative- ▁channel- ▁especially- ▁resources- ▁meet- a- ▁corporate- ▁construction- ▁comes- ▁re- ▁spending- ▁possible- ▁unique- ▁means- ▁manufacturing- k- ▁direct- in- ▁incremental- ▁remind- ▁facility- ▁percentage- ▁combination- ▁size- ▁online- ▁maintain- '1'- ▁details- ▁complete- ▁employees- ▁included- ▁execute- ▁front- ▁either- ▁taken- ▁multiple- ▁content- ▁local- ▁rest- ▁show- ▁ensure- o- ▁until- ▁thinking- ▁stock- ▁challenges- ▁transition- ▁annual- ▁job- ▁loan- ▁didnt- ▁various- ▁investing- ▁address- ▁color- ▁client- ▁whole- ▁free- ▁thanks- ▁type- ▁partner- ▁negative- ▁previous- ▁reflect- x- ▁wanted- ▁leading- ▁generate- ▁cloud- ▁exchange- ▁turning- ▁productivity- ▁month- ▁marketplace- ▁single- ▁relationships- ▁short- ▁regulatory- ▁public- ▁home- ▁attractive- ▁date- ▁stronger- ▁am- ▁bank- ▁solution- ▁ive- ▁issue- ▁happen- ▁deals- ▁largest- ▁committed- ▁consumers- ▁clinical- ▁thought- ▁ways- ▁goal- ▁discussion- ▁profitable- ▁despite- ▁sale- ▁relatively- ▁following- ▁delivered- ▁shift- ▁favorable- ▁anticipated- ▁savings- ▁likely- ▁life- ▁combined- 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▁statement- ▁traffic- ▁community- ▁drivers- ▁typically- ▁ratio- ▁fleet- ▁premium- ▁substantial- ▁win- ▁capability- ▁january- ▁robust- ▁gains- ▁managing- ▁fund- ▁media- ▁creating- ▁options- ▁healthy- ▁17- ▁december- ▁tend- ▁8- ▁expectation- ▁purchase- ▁national- ▁almost- ▁processes- ▁news- ▁advertising- ▁importantly- ▁happening- w- ▁labor- ▁disconnect- ▁potentially- ▁natural- ation- ▁primary- ▁backlog- i- ▁understanding- ▁executing- ▁nature- ▁yield- ▁forecast- ▁water- ▁traditional- ▁food- ▁saying- ▁office- ▁meeting- ▁7- ▁bringing- ▁driver- ▁optimistic- ▁filings- ▁9- ▁planned- ▁limited- ▁excellent- ▁putting- ▁highlights- ▁mind- ▁effectively- ▁comfortable- ▁historical- ▁found- ▁needed- ▁cases- ▁march- ▁goes- ▁necessary- ▁research- ▁ask- ▁visibility- ▁targets- ▁million- ▁proud- ▁liquidity- ▁funding- ▁refer- ▁trade- ▁disciplined- ▁speak- ▁countries- ▁headwind- ▁main- ▁losses- ▁regions- ▁else- ▁comparable- ▁encouraged- ▁standpoint- ▁bigger- ▁land- ▁wouldnt- ▁15- ▁commission- ▁non- 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▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- 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▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- 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▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout ddd205f05e4a323a0aeb5cae81604a7bb5abfa45pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Sat Feb 27 10:10:35 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.7`- pytorch version: `pytorch 1.7.1`- Git hash: `3572c4ecd74a9b1c77c639fce40e9318d254c5a6`  - Commit date: `Thu Feb 25 18:52:24 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|95401|97.7|1.6|0.7|0.6|2.9|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|946469|97.6|1.7|0.8|0.6|3.0|39.2|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|543236|99.0|0.2|0.8|0.4|1.4|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|5393048|99.0|0.2|0.8|0.4|1.4|39.2|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|4000|98503|97.4|1.6|1.0|0.5|3.1|38.7||decode_asr_lm_lm_train_lm_en_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val|39341|979382|97.2|1.7|1.1|0.5|3.3|39.2|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 42966dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe10000/train/speech_shape- exp/asr_stats_raw_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe10000/valid/speech_shape- exp/asr_stats_raw_en_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/wav.scp    - speech    - sound-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- ▁competitive- ▁risk- ▁global- ▁why- ▁release- ▁every- ▁prices- ▁actual- ▁solutions- ▁mean- ▁leverage- ▁quite- ▁project- ▁another- ▁saw- ▁brand- ▁yes- ▁top- ▁supply- ▁offset- ▁update- ▁core- ▁available- ▁recent- ▁moving- ▁earlier- ▁slide- ▁conference- ▁digital- ▁quality- ▁obviously- ▁between- ▁let- ▁efforts- ▁off- ▁todays- ▁shareholders- ▁area- ▁certainly- er- ▁far- ▁system- ▁prior- ▁building- ▁whether- ▁expenses- ▁group- ▁less- ▁credit- ▁world- ▁continuing- ▁trying- ▁real- ▁primarily- ▁operational- ▁understand- ▁amount- ▁retail- ▁questions- ▁success- ▁view- ▁low- ▁pipeline- ▁taking- ▁2017- ▁inventory- ▁range- ▁outlook- ▁consumer- ▁improved- ▁return- ▁ahead- ▁review- ▁companys- ▁revenues- ▁set- ▁major- ▁since- ▁fiscal- ▁risks- ▁expense- ▁profitability- ▁timing- ▁capabilities- ▁5- ▁partners- ▁materially- ▁increasing- ▁presentation- ▁4- ▁however- ▁benefits- ▁youve- ▁longterm- ▁confident- ▁ago- ▁hard- ▁2018- ▁increases- ▁acquisitions- ▁driving- ▁plans- ▁excited- ▁total- ▁expand- ▁own- n- ▁solid- ▁talked- ▁differ- ▁distribution- ▁china- ▁1- ▁particularly- t- ▁sheet- ▁consistent- ▁expansion- ▁stores- ▁approach- ▁invest- ▁space- ▁small- es- ▁keep- ▁asset- ▁bring- ▁compared- ▁structure- ▁debt- ▁sense- ▁programs- ▁patients- ▁numbers- ▁clear- ▁perspective- ▁improving- ▁versus- ▁needs- ▁add- ▁whats- ▁measures- ▁average- ▁10- ▁beginning- ▁close- ▁currently- ▁begin- ▁trends- ▁significantly- ▁care- al- ▁network- ▁2016- ▁open- ▁create- ▁points- ▁starting- ▁conditions- ▁please- ▁okay- ▁per- ▁together- ▁strength- ▁volumes- r- c- ▁energy- ▁scale- ▁specific- ▁decline- ▁anything- ▁brands- ▁throughout- ▁organization- ▁detail- ▁transaction- ▁execution- ▁towards- ▁contract- ▁run- ▁advantage- ▁profit- ▁remains- ▁include- ▁health- ▁gas- ▁tell- ▁systems- ▁larger- ▁greater- ▁efficiency- ▁material- ▁spend- ▁teams- ▁organic- ▁comments- ▁yet- ▁international- ▁uncertaint- ▁events- ▁store- ▁ongoing- ▁deal- ▁investors- ▁fully- ▁example- ▁longer- ▁cause- ▁control- ▁successful- ▁innovation- ▁returns- ▁power- ▁trend- m- ▁announced- ▁infrastructure- ▁everyone- ▁find- ▁once- ▁along- ▁discuss- ▁started- ▁europe- ▁oil- ▁talking- ▁recently- ▁reduce- ▁track- ▁difficult- ▁6- ▁later- ▁particular- ▁case- ▁achieve- ▁similar- ▁improvements- ▁allow- ▁lines- ▁momentum- ▁providing- ▁note- ▁become- ▁completed- ▁segments- ▁clearly- ▁press- ▁associated- ▁too- ▁try- ▁board- ▁guess- ▁activities- ▁discussed- ▁report- ▁beyond- ▁previously- ▁morning- ▁access- ▁manage- ▁impacted- ▁general- ▁confidence- ▁issues- ▁regarding- ▁equity- ▁remarks- ▁used- ▁meaningful- ▁anticipate- ▁decision- b- ▁offering- ▁adjusted- ▁sell- ▁against- ▁record- ▁delivering- ▁effect- ▁reduction- ▁launch- ▁economic- ▁offer- ▁positioned- ▁reason- ▁website- ▁money- ▁equipment- ▁using- ▁contracts- ▁subject- ▁finally- ▁pay- ▁items- ▁pressure- ▁integration- ▁cycle- ▁target- ▁slightly- g- ▁guys- ▁quickly- ▁although- ▁actions- ▁without- ▁selling- ▁leadership- ▁relative- ▁channel- ▁especially- ▁resources- ▁meet- a- ▁corporate- ▁construction- ▁comes- ▁re- ▁spending- ▁possible- ▁unique- ▁means- ▁manufacturing- k- ▁direct- in- ▁incremental- ▁remind- ▁facility- ▁percentage- ▁combination- ▁size- ▁online- ▁maintain- '1'- ▁details- ▁complete- ▁employees- ▁included- ▁execute- ▁front- ▁either- ▁taken- ▁multiple- ▁content- ▁local- ▁rest- ▁show- ▁ensure- o- ▁until- ▁thinking- ▁stock- ▁challenges- ▁transition- ▁annual- ▁job- ▁loan- ▁didnt- ▁various- ▁investing- ▁address- ▁color- ▁client- ▁whole- ▁free- ▁thanks- ▁type- ▁partner- ▁negative- ▁previous- ▁reflect- x- ▁wanted- ▁leading- ▁generate- ▁cloud- ▁exchange- ▁turning- ▁productivity- ▁month- ▁marketplace- ▁single- ▁relationships- ▁short- ▁regulatory- ▁public- ▁home- ▁attractive- ▁date- ▁stronger- ▁am- ▁bank- ▁solution- ▁ive- ▁issue- ▁happen- ▁deals- ▁largest- ▁committed- ▁consumers- ▁clinical- ▁thought- ▁ways- ▁goal- ▁discussion- ▁profitable- ▁despite- ▁sale- ▁relatively- ▁following- ▁delivered- ▁shift- ▁favorable- ▁anticipated- ▁savings- ▁likely- ▁life- ▁combined- ▁2019- ▁chain- ▁days- ▁youll- ▁transformation- ▁bottom- ▁provided- ▁underlying- ▁orders- ▁ebitda- ▁lead- ▁havent- ▁prepared- ▁cant- ▁delivery- ▁gives- '2'- ▁least- ▁nongaap- ▁serve- ▁highly- ▁though- ▁category- ▁comment- ▁government- able- ▁develop- ▁flexibility- ▁experienced- ▁moment- ▁closing- ▁generally- p- ▁respect- ▁portion- ▁dividend- ▁private- ▁built- ▁industrial- ▁majority- ▁quarterly- f- ▁entire- ▁agreement- ▁generation- ▁expanding- ▁hope- ▁18- ▁upon- ▁therefore- ▁everything- ▁specifically- ▁initial- ▁challenging- ▁effective- ▁provides- ▁efficient- ▁normal- ▁play- ▁situation- ▁accelerate- ▁currency- ▁flat- ▁- ▁doesnt- ▁property- ▁buy- ▁competitors- ▁season- ▁commitment- ▁strategies- ▁critical- ▁required- ▁answer- ▁country- ▁active- ▁un- ▁design- ▁competition- ▁step- ▁largely- ▁fair- ▁technologies- ▁12- ▁weeks- ers- ▁he- ▁hand- ▁lets- ▁software- 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▁slated- ▁await- ▁steer- ▁20000- yield- script- ▁caliber- ▁devoted- ▁eighth- ▁mouth- ▁postpaid- ▁aggregation- ▁cliff- ▁responsiveness- ▁string- ▁kin- ▁jones- ▁toughest- ▁char- ▁restored- ▁poster- ▁melt- ▁illustrat- ▁insider- mitted- ▁renovat- ▁renegotiate- ▁dinner- ▁reengineer- ▁thumb- ▁taxpayer- ▁arbitrage- ▁tipping- ▁stripping- ▁tapping- ▁mood- ▁dick- hub- sphere- ▁accumulate- ▁backtoschool- ▁orthopedic- ▁outweigh- ▁suppress- ▁reclassifie- ▁interestbearing- ▁choppi- ▁jackup- ▁popularity- ▁discoveries- hawk- ▁pam- ▁reallocat- ▁vulnerable- ▁sincerely- ▁trauma- ▁fellow- ▁warner- ▁capped- ▁seven- ▁centric- ▁earnest- ▁inspect- ▁pose- petition- ▁leaseup- ▁outgrow- volt- ▁digitize- ▁dedicate- ▁discontinue- ▁depreciate- ▁lithium- ▁philosophical- ▁framing- ▁cheese- ▁vending- ▁chegg- ▁clock- ▁purposeful- ▁cleanup- ▁knowledgeable- ▁cooperate- ▁cooler- ▁compliment- ▁dispense- ▁mathematical- ▁perimeter- ▁ralph- ▁retiring- ▁utah- ▁birth- ▁remediate- ▁laserfocused- ▁iteration- ▁tenet- ▁wisely- ▁renegotiated- ▁routinely- ▁nr- ▁buyout- code- ▁redesigned- ▁flor- ▁incent- ▁grocer- process- structure- ▁destiny- ▁underpenetrated- ▁busiest- ▁forthcoming- ▁virtuous- ▁plaza- ▁racing- eye- ▁3000- ▁payoff- ▁ortho- ▁merged- ▁inhibit- ▁vista- ▁watches- ▁deducti- charge- profit- energy- wind- ▁chemotherapy- ▁entrance- ▁facilitating- ▁happier- ▁legislature- ▁timeframe- ▁vacancies- ▁bargain- ▁lobby- ▁tokyo- ▁slope- ▁cedar- ▁specify- ▁protest- ▁roster- ▁voting- ▁withdrawal- ▁reallocation- ▁standby- ▁vale- ▁earlystage- ▁redevelop- ▁hp- ▁pest- front- ▁insist- beat- ▁alabama- ▁empty- ▁endorsement- ▁examining- ▁experiential- ▁rainfall- ▁separating- ▁iridium- ▁wellbeing- ▁liber- ▁recreational- ▁connector- ▁barge- ▁highgrade- ▁designated- ▁liquidate- ▁yo- action- ▁cpg- ▁suspend- ▁genuine- active- heim- utilized- ▁sma- ▁advocacy- ▁antibodies- ▁conservation- ▁hydrocarbon- ▁mismatch- ▁oxygen- ▁refractor- ▁unfair- ▁disability- ▁downgrade- ▁cabin- iston- ▁bolt- ▁120- suite- ▁enzyme- ▁hilton- ▁offensive- ▁testimony- ▁clothing- ▁restocking- ▁beacon- ▁defect- ▁nurse- ▁tal- ▁mixture- ▁dish- ▁activat- ▁pp- ▁citi- period- ▁inroads- ▁kroger- ▁vigorously- ▁2011- ▁pushback- ▁protective- ▁traders- ▁barn- ▁midsize- ▁increment- competitive- ▁fabulous- ▁hospice- ▁nitrogen- ▁practitioner- ▁restatement- ▁robinson- ▁segregat- ▁unconventional- ▁girl- ▁pbm- ▁instill- ▁loud- ▁characteristic- ▁realistically- ▁headquarter- ▁furnished- ▁highperforming- ▁amid- ▁flagged- ▁haynes- ▁problematic- ▁scoop- ▁sovereign- ▁voyage- ▁merely- ▁pinpoint- ▁prepay- ▁dependence- ▁marry- ▁lions- ▁superiority- megawatt- ▁excite- ▁jeremy- ▁refrigerated- ▁turbo- ▁unreasonable- ▁humble- ▁infant- ▁baking- ▁divert- ▁lte- ▁sharper- ▁colder- ▁monotherap- ▁consult- ▁correspond- ▁reinvigorate- platform- producing- order- check- resistant- ▁culinary- ▁flawless- ▁inherited- ▁nontraditional- ▁remeasurement- ▁wellknown- ▁winnebago- ▁revising- ▁lagged- ▁depict- ▁juice- ▁hypothesis- ▁dwell- ▁publisher- ▁reuse- hole- ▁heck- consolidated- built- prime- ▁depletion- ▁secondarily- ▁mlp- ▁weapon- ▁swiftly- ▁softened- ▁mil- ▁rainy- ▁blast- ▁coat- ▁expose- ▁furnish- wire- ▁bacteria- ▁baseball- ▁oxide- ▁prestigious- ▁stranded- ▁vantage- ▁slice- ▁thread- ▁michelle- ▁deduct- defined- ▁farther- ▁exhibition- scope- ▁bloom- ▁destroy- ▁plumbing- ▁reconsider- ▁unleash- ▁scanning- ▁parse- ▁clay- ▁personalize- neutral- ▁frustration- ▁highspeed- ▁targa- ▁kirk- ▁rescue- ▁subsidize- ▁steal- ▁mentor- ▁harris- ▁aspirational- ▁resale- borne- ▁deter- ▁pine- transmission- ▁onprem- ▁carriage- ▁invitation- ▁peace- ▁reconciling- ▁saturday- ▁selfhelp- ▁sulfur- ▁triumph- ▁escalate- ▁viral- ▁ethical- ▁ibm- ▁cameron- ▁sizing- ▁instantly- ▁creators- prong- ▁bull- ▁reaccelerat- ▁refurbish- ▁nascar- ▁retroactive- ▁unforeseen- ▁remix- ▁coding- ▁blueprint- ▁merging- ▁roast- arity- ▁existence- ▁resist- ▁assistant- ▁shaw- ▁technique- ▁genuinely- fast- ▁mobilize- ▁victoria- ▁interventional- axis- ▁soften- ▁tile- ▁attribution- ▁blockbuster- ▁insignificant- ▁literature- ▁qualities- ▁steroid- ▁terribly- ▁copies- ▁intercept- ▁welding- ▁tricky- sky- ▁scrip- ▁tec- munications- ▁nutritional- ▁nii- ▁tradition- economic- ▁kiosk- ▁pfizer- ▁unsustainabl- ▁buoy- ▁copyright- ▁elite- ▁rebuilt- ▁fcc- ▁midway- ▁sba- ▁offprice- plex- ▁distress- digit- appropriate- ▁interopera- ▁incidence- ▁jordan- ▁slippage- ▁calibrate- ▁shaft- ▁textile- ▁discontinuation- ▁confidentiality- ▁dump- ▁dropdown- ▁realworld- ▁chargeoff- ▁compact- ▁surround- ▁biopharma- ▁brilliant- ▁groundbreaking- ▁halloween- ▁microbiome- ▁migraine- ▁politics- ▁prestige- ▁pyramid- ▁synchroniz- ▁catheter- ▁advertisement- ▁revolutionary- ▁bread- ▁centre- ▁shoe- ▁parity- ▁withdraw- ▁albert- ▁50000- regulated- function- ▁acknowledging- ▁admittedly- ▁editorial- ▁mercury- ▁pragmatic- ▁stainless- ▁irrational- ▁illumina- ▁tmobile- ▁pullthrough- ▁classify- ▁highyield- ▁stockholder- ▁broadcasters- ▁fisher- ▁taco- ▁dilute- ▁deficiency- ▁influx- ▁occupy- ▁rehab- ▁reinsurer- ▁carpet- ▁hypothetical- ▁recalibrat- ▁lottery- ▁dyna- ▁marcus- ▁outlier- ▁reversion- ▁visitation- ▁guideline- stack- ▁peg- ▁bubble- ▁buffalo- ▁myriad- ▁bancorp- ▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- ▁heartland- rgus- ▁edition- ▁christian- ▁fractur- ▁divide- ▁glen- ▁ambassador- ▁arriving- ▁believing- ▁beneficiaries- ▁democrat- ▁detroit- ▁mortar- ▁turbulence- ▁dismiss- ▁geological- ▁seemingly- ▁pathogen- ▁tyler- ▁fireeye- ▁adhere- ▁lifo- ▁greenhouse- ▁opec- ▁hva- brook- ▁rework- ▁prohibit- ▁biomass- ▁costcutting- ▁excise- ▁proposing- ▁bunker- ▁fault- ▁brett- ▁cheapest- ▁garage- pharm- ▁yu- '06'- ▁buzz- ▁census- ▁delineation- ▁dependency- ▁evergreen- ▁exercising- ▁interference- ▁patterson- ▁referendum- ▁simplifies- ▁ninth- ▁recurrent- ▁campbell- ▁debut- ▁confront- ▁cream- ▁retin- nanometer- ▁photograph- ▁raj- ▁dominate- direct- ▁persistenc- ▁decentralized- ▁dissipate- ▁episodic- ▁latam- ▁mosaic- ▁petrobras- ▁resumption- ▁syndrome- ▁fixtures- ▁randomized- ▁reconstruction- ▁quant- ▁reinvention- leading- ▁simultaneous- ▁chocolate- ▁dangerous- ▁deteriorating- ▁inflammation- ▁laundry- ▁midatlantic- ▁parliament- ▁retool- ▁renal- ▁tolerance- ▁pollution- ▁timberland- operative- ▁ferro- ▁vitality- sharing- bury- ▁accessibility- ▁celebrating- ▁female- ▁frequencies- ▁hollywood- ▁relocating- ▁hinder- ▁spray- ▁leadingedge- ▁transpire- ▁goodbye- ▁macau- ▁poker- ▁eplex- ▁lapped- ▁wow- ▁juan- ▁insurer- ▁dell- ▁configure- ▁pile- ▁anomal- central- ▁phenomena- ▁appalachia- ▁cobalt- ▁cocacola- ▁lucrative- ▁slipped- ▁westinghouse- ▁vinyl- ▁organizing- ▁apex- ▁swim- ▁reactive- contract- supplied- ▁mitch- ▁attorney- ▁compatible- ▁conducive- ▁disparate- ▁explosive- ▁morris- ▁multifaceted- ▁wellestablished- ▁nucor- ▁displacement- ▁lauren- ▁noticing- ▁dust- ▁admission- ▁etsy- ▁biologic- ▁tear- ▁equip- ▁rocket- development- ▁criticized- ▁cuttingedge- ▁facilitator- ▁inaugura- ▁longhaul- ▁preempt- ▁prolific- ▁relapse- ▁unacceptable- ▁watson- ▁wyndham- ▁obsess- ▁overcoming- ▁vacate- ▁expare- ▁sensi- ▁recontract- ▁submitting- ▁aisle- ▁feat- ▁statistic- ▁heali- ▁30000- enhancing- ▁biopsy- ▁dublin- ▁exemplifie- ▁implicit- ▁peanut- ▁shadow- ▁submarine- ▁lawyers- ▁tubing- ▁misleading- ▁florence- ▁paving- ▁intermediary- ▁reagent- ▁thriving- ▁spanning- ▁dolby- ▁sweetener- ▁sweat- ▁alluding- ▁consultative- ▁drastically- ▁scarce- ▁merck- ▁resell- ▁peel- authorized- ▁contiguous- ▁counterparty- ▁decoupl- ▁desperate- ▁entrust- ▁examination- ▁hindsight- ▁remunerati- ▁valuecreation- ▁veterinarian- ▁walter- ▁wednesday- ▁whiskey- ▁knit- ▁expeditious- ▁linkage- ▁ltl- ission- ▁handicap- ▁bake- ▁ecom- ▁stead- foot- ▁faculty- ▁insufficient- ▁magnetic- ▁modalities- ▁quantification- ▁rotate- ▁correspondent- ▁farmland- ▁ccar- billed- ▁citizen- ▁culminat- ▁affluent- ▁amplified- ▁fibrosis- ▁inaccurate- ▁microchip- ▁resurgence- ▁stadium- ▁susceptible- ▁volkswagen- ▁coalition- ▁grasp- ▁unused- ▁ross- ▁moodys- ▁vault- ▁culmination- ▁canyon- ▁5050- ▁copay- ▁teva- ▁collaborator- ▁interrupt- grid- ▁ammunition- ▁celebration- ▁dispensing- ▁injunction- ▁nutrient- ▁phosphate- ▁predecessor- ▁splunk- ▁aftermath- ▁viewership- ▁delineate- ▁smile- ▁admin- ▁twofold- ▁crisp- tegra- ▁blip- eep- ▁locate- ▁definite- ▁wan- credit- ▁flotek- ▁henry- ▁adobe- ▁journalism- ▁kona- ▁inverse- ▁investigator- aze- ▁expedit- claim- ▁disturb- ▁equilibrium- ▁everchanging- ▁identifies- ▁nonstrategic- ▁triferic- ▁southeastern- ▁contend- ▁tolerated- ▁illegal- ▁focal- ▁lids- ▁dataset- ▁void- ▁gat- ngest- employ- screen- sheet- ▁bracket- ▁brokerdealer- ▁chipotle- ▁cushing- ▁exemption- ▁keppel- ▁omnipod- ▁propulsion- ▁prudence- ▁tournament- ▁sauce- ▁equate- ▁indebted- ▁suggestions- xcel- ▁imagery- ▁reoccurr- ▁cede- ▁revolve- ▁argentin- collect- ▁arrange- ▁compensating- ▁debenture- ▁formidable- ▁kratos- ▁police- ▁toxicity- ▁vitamin- ▁template- ▁valuing- ▁relieve- ▁dtc- ▁drawdown- ▁hack- glass- ▁shine- ▁iterate- scribed- ▁reimag- ▁biopharmaceutic- ▁brookfield- ▁clarified- ▁commonwealth- ▁hampshire- ▁influential- ▁protracted- ▁reassure- ▁resolute- ▁rotating- ▁styren- ▁alpine- ▁cultivating- ▁finite- ▁tommy- burn- ▁multiproduct- enberg- ▁nap- ▁noncomp- ▁decelerate- rrell- ▁rebrand- world- ▁michel- ▁immersive- ▁intermediaries- ▁methanol- ▁microcontroller- ▁overshadow- ▁reconfirm- ▁recurrence- ▁residence- ▁hungary- ▁tutor- ▁mobilization- ▁secretary- ▁duplication- ▁cake- ▁enact- ▁stakeholder- arables- ▁famous- ▁infringement- ▁marquee- ▁mezzanine- ▁oxford- ▁renegotiating- ▁attendee- ▁tilt- ▁jose- ▁linda- ▁decelerat- capitalized- ▁alumina- ▁approving- ▁consolidator- ▁elevation- ▁kathy- ▁offpremise- ▁pledge- ▁redetermination- ▁unintended- ▁honda- ▁punch- ▁multiply- ▁tuning- ▁cryo- ▁crystallize- ▁berlin- ▁forrester- ▁wellbore- ▁laura- ▁reimagine- ▁chatter- ▁cube- udge- ▁mario- ctive- earning- ▁autonomy- ▁expensing- ▁invoicing- ▁multimillion- ▁voucher- ▁acoustic- ▁craftsman- ▁remedies- ▁shelter- ▁britain- ▁deviate- ▁fastener- ▁montana- '500'- resuming- ▁atmosphere- ▁caustic- ▁consignment- ▁criminal- ▁infectious- ▁legitimate- ▁uranium- ▁scoring- ▁erode- ▁hungry- ▁ruble- ▁mint- organically- ▁harmonize- ▁cobrand- constrained- business- engine- ▁catastrophic- ▁circular- ▁finetune- ▁obsolete- ▁occupier- ▁securitize- ▁pruning- ▁surgeries- ▁sequel- ▁diameter- ▁combo- ▁technician- treated- ▁impair- ▁occupanc- ▁ethic- ▁drastic- ▁brothers- ▁celebrity- ▁deemphasiz- ▁destruction- ▁explosion- ▁freddie- ▁qualcomm- ▁scatter- ▁backhaul- ▁investigating- ▁encore- ▁geology- dollar- ▁victor- ▁subcontract- ▁receptiv- ferred- target- ▁eligibility- ▁energies- ▁entries- ▁expend- ▁gravitat- ▁ignite- ▁mifid- ▁refrain- ▁savvy- ▁terrible- ▁bondholder- ▁chlor- ▁aerial- ▁cattle- ▁potent- ▁exert- volume- managed- ▁averaging- ▁awesome- ▁cincinnati- ▁deficiencies- ▁delinquent- ▁knight- ▁varieties- ▁elegant- ▁flesh- ▁ourself- ▁theatrical- ▁subtract- ▁grace- ▁textbook- ▁elongat- ▁lull- structive- ground- ▁alzheimers- ▁coherent- ▁combustion- ▁franchising- ▁instagram- ▁prebuy- ▁requisite- ▁triangle- ▁wyoming- ▁reacceleration- ▁ltac- adjusted- mount- ▁grip- ▁computation- ▁accumulating- ▁carpenter- ▁cartridge- ▁escalating- ▁himself- ▁neurology- ▁trinity- ▁unmanned- ▁wolfcamp- ▁yeartoyear- ▁ubiquitous- ▁aetna- ▁blanket- capital- ▁solicit- ▁anxiety- ▁frankfurt- ▁judicial- ▁melbourne- ▁recompete- ▁tubular- ▁tuition- ▁zika- ▁custodia- ▁medallion- ▁socket- ▁repeal- ▁joseph- ▁panther- ▁verified- manufactur- ▁adhesive- ▁cockpit- ▁denim- ▁minneapolis- ▁multiplier- ▁substrate- ▁wisdom- ▁citrus- ▁esports- ▁brink- ▁inflated- ▁overrid- ▁retreat- ▁diagnose- ▁allegations- ▁brookdale- ▁derby- ▁edmonton- ▁marathon- ▁nowadays- ▁populated- ▁recipient- ▁roadshow- ▁seafood- ▁yelp- ▁diplomat- ▁envisage- ▁haptic- ▁handbag- ▁nokia- ▁relies- itude- ▁dominic- midst- ▁articulat- normal- ▁addiction- ▁deutsche- ▁explorator- ▁hiccup- ▁honeywell- ▁immigration- ▁mainframe- ▁metabolic- ▁compelled- horn- dependent- ▁appreciating- ▁confluence- ▁corrugated- ▁gambling- ▁inexpensive- ▁malibu- ▁pakistan- ▁privatization- ▁scandinavia- ▁unaudit- ▁orchard- ▁serial- ▁preleasing- ▁quebec- ▁societies- ▁wherewithal- ▁prince- ▁daytona- ▁trustees- ▁forensic- ▁fortify- ▁coronary- ▁sunset- ▁unrest- ▁immunolog- ▁distract- ▁appoint- quarteronquarter- ▁candid- represented- impact- ▁certify- ▁condominium- ▁coworkers- ▁epidemic- ▁gigabit- ▁kuwait- ▁sleeve- ▁turbulent- ▁verdict- ▁voltage- ▁upswing- ▁precede- ▁bridg- graphic- ▁fanni- ▁overwhelm- typical- ▁fascinating- ▁hurry- ▁impetus- ▁kilometers- ▁monarch- ▁orchestration- ▁politicians- ▁preopening- ▁repatriate- ▁stoppage- ▁tragic- ▁versatility- ▁enzo- ▁zoning- ▁salmon- ▁caterpillar- ▁halliburton- ▁indefinite- ▁parkinsons- ▁redefining- ▁uncomfortable- ▁valentine- ▁bmw- ▁outdated- ▁outstrip- ▁induce- style- ▁intersect- dilutive- ▁complacent- ▁disposing- ▁ethernet- ▁generous- ▁hesitation- ▁kingdom- ▁machining- ▁numerical- ▁richmond- ▁silhouette- ▁valuecreating- ▁yodle- ▁rasm- ▁charitabl- ▁tropica- ▁expir- ▁readjust- ▁cataract- ▁contamination- ▁deconsolidation- ▁demonstrabl- ▁divergence- ▁dubai- ▁escrow- ▁everincreasing- ▁inadequate- ▁pencil- ▁unwilling- ▁acumen- ▁debbie- ▁helix- ▁pulmon- ▁amenity- ▁tertiar- ▁ascend- ▁brush- ▁carveout- ▁centennial- ▁extrusion- ▁hispanic- ▁momentarily- ▁multitenant- ▁unaffected- ▁exaggerat- ntamina- ▁worsening- ▁allison- ▁symptom- ▁discontinu- ▁accommodati- propylene- ▁acknowledgment- ▁applaud- ▁binary- ▁boulder- ▁crossfunctional- ▁determinant- ▁grubhub- ▁lvt- ▁morbidit- ▁substation- ▁unencumber- ▁infinite- ▁cgm- constantcurrency- ▁brussels- ▁conduit- ▁countermeasure- ▁courage- ▁distant- ▁fragile- ▁melanoma- ▁motivating- ▁repetitive- ▁rhetoric- ▁smoking- ▁walgreen- ▁gordon- ▁recliner- ▁noisy- ▁angola- ▁rebid- ▁mobiliz- focus- financial- profile- ▁barclay- ▁convincing- ▁disseminate- ▁gratified- ▁intimacy- ▁ovarian- ▁savanna- ▁sierra- ▁storytelling- ▁thrust- ▁babies- ▁lilly- ▁capsule- ▁limb- ▁prescribe- ▁scene- ruct- ▁accru- ▁virtu- driving- monetization- ▁jewel- ▁dedicating- ▁gartner- ▁glimpse- ▁nutshell- ▁unbundl- ▁upholstery- ▁advising- ▁cocoa- ▁athena- ▁confine- ▁unlever- ntelo- deductible- region- ▁elastic- ▁abrupt- ▁appropriation- ▁elderly- ▁encryption- ▁gentleman- ▁graham- ▁halves- ▁invisalign- ▁mcdonalds- ▁opportune- ▁potato- ▁unanimous- ▁vascepa- ▁vulnerability- ▁xerox- ▁horr- milligram- ▁reinvigorat- ▁activi- soft- ▁prototyp- ological- ▁consequent- public- figure- licensing- ▁adjudicat- ▁amphenol- ▁anadarko- ▁calibration- ▁concentrator- ▁denial- ▁displacing- ▁eclipse- ▁headache- ▁obesity- ▁scotland- ▁vigor- ▁welfare- ▁blessed- ▁telephon- ography- '010'- ▁accentuate- ▁cannibalize- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.7distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 38061dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe10000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev_4k/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- ▁to- ▁and- ▁of- ▁we- ▁in- ▁that- ▁our- ▁a- ▁is- ▁on- ▁for- ▁as- ▁are- ▁have- ▁you- ▁this- ▁with- ▁i- s- ▁be- ▁so- ▁it- ▁will- ▁were- ▁at- ▁quarter- ▁but- ▁year- ▁more- ▁from- ▁business- ▁think- ▁what- ▁not- ▁some- ▁us- ▁by- ▁or- ▁about- ▁well- ▁new- ▁growth- ▁can- ▁which- ▁its- ▁do- ▁was- ▁very- ▁an- ▁there- ▁these- ▁also- ▁market- ▁continue- ▁all- ▁going- ▁see- ▁would- ▁now- ▁just- ▁been- ▁has- ▁weve- ▁those- ▁over- ▁they- ▁if- ▁first- ▁time- ▁customers- ▁where- ▁into- ▁how- ▁like- ▁results- ▁out- ▁other- ▁really- ▁last- ▁their- ▁up- ▁expect- ▁one- ▁than- ▁your- ▁because- ▁look- ▁through- ▁when- ing- ▁any- ▁thats- ▁get- ▁call- ▁strong- ▁then- ▁sales- ▁good- ▁had- ▁second- ▁believe- ▁revenue- ▁company- ▁years- ▁performance- ▁product- ▁make- ▁lot- ▁much- ▁financial- ▁capital- ▁could- ▁both- ▁cost- ▁them- ▁terms- ▁future- ▁right- ▁dont- d- ▁impact- ▁forward- ▁next- ▁little- ed- ▁products- ▁want- ▁bit- ▁value- ▁customer- ▁work- ▁increase- ▁back- ▁number- ▁may- ▁take- ▁markets- ▁while- ▁higher- ▁end- ▁opportunities- ▁able- ▁half- ▁kind- ▁way- ▁during- ▁operating- ▁significant- ▁better- ▁most- ▁things- ▁cash- ▁know- ▁go- ▁still- ▁focus- ▁say- ▁statements- ▁part- ▁being- ▁im- ▁provide- ▁should- ▁point- ▁across- ▁lower- ▁made- ▁important- ▁opportunity- ▁2- ▁team- ▁theres- ▁third- ▁today- ▁need- ▁many- ▁rate- ▁line- ▁people- ▁fourth- ▁strategy- ▁looking- ▁down- ▁continued- ▁before- ▁no- ▁around- ▁youre- ▁level- ▁portfolio- ▁management- ▁working- ▁industry- ▁additional- ▁due- ▁price- ▁investment- ▁share- ▁thank- ▁great- ▁actually- ▁only- ▁even- ▁here- ▁give- ▁demand- ▁come- ▁seeing- ▁plan- ▁few- ▁costs- ▁progress- ▁overall- ▁further- ▁development- ▁same- ies- ▁service- ▁current- ▁different- ▁seen- ▁margin- ▁doing- ▁me- ▁services- ▁result- ▁technology- ▁coming- ▁data- ▁earnings- ▁change- ▁positive- ▁guidance- ▁key- ▁grow- ▁again- ▁drive- ▁start- ▁position- ▁process- ▁investments- ▁full- ly- ▁past- ▁forwardlooking- ▁3- ▁said- ▁high- ▁got- ▁did- ▁turn- ▁given- ▁within- ▁basis- ▁expected- ▁based- ▁increased- ▁who- ▁improve- ▁support- ▁sure- ▁my- ▁focused- ▁potential- ▁question- ▁environment- ▁pricing- ▁help- ▁sort- ▁businesses- ▁months- ▁large- ▁such- ▁making- ▁remain- ▁done- ▁maybe- ▁something- ▁production- ▁strategic- ▁information- ▁ability- ▁balance- ▁already- ▁platform- ▁program- ▁couple- ▁move- ▁side- ▁interest- ▁quarters- ▁best- ▁mentioned- ▁several- ▁talk- ▁operations- ▁each- ▁acquisition- ▁early- ▁expectations- ▁rates- ▁assets- ▁q- ▁changes- ▁put- ▁use- ▁feel- ▁pleased- y- ▁segment- ▁long- ▁experience- ▁certain- ▁tax- ▁period- ▁growing- ▁base- ▁including- ▁projects- ▁deliver- ▁place- ▁benefit- ▁ill- ▁continues- ▁improvement- ▁related- ▁commercial- ▁initiatives- ▁order- ▁companies- ▁areas- ▁activity- ▁driven- ▁factors- ▁margins- ▁model- ▁getting- ▁existing- ▁net- ▁levels- ▁big- ▁flow- ▁does- ▁income- ▁theyre- ▁term- ▁pretty- ▁after- ▁addition- ▁volume- ▁clients- ▁course- e- ▁capacity- ▁probably- ▁fact- ▁id- ▁thing- ▁might- ▁under- ▁build- ▁having- ▁day- ▁marketing- ▁mix- ▁always- 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▁leakage- ▁firing- ▁redeployment- ▁pole- smart- ddle- ▁epa- ▁basketball- ▁cohesive- ▁curriculum- ▁false- ▁greece- ▁lowmargin- ▁taylor- ▁unbelievabl- ▁tesco- ▁cascade- ▁darren- ▁clause- ▁hung- ▁strange- ▁tiny- ▁mediumsized- ▁irish- ▁professionalism- ▁adaptive- ▁stride- ▁joel- ▁aggregator- ▁redistribut- ▁probe- ▁bath- ▁remark- ▁chamber- ▁collision- ▁pervasive- ▁uncommon- ▁footnote- ▁youtube- ▁sick- ▁latency- ▁colleague- ▁extraction- motion- high- ▁msa- ▁antitrust- ▁asiapacific- ▁consummate- ▁hampered- ▁impediment- ▁receptor- ▁socalled- ▁plasma- ▁promo- ▁redundancy- ▁affo- ▁lenses- ▁gut- ▁immunotherap- ▁tall- ▁reactivate- ▁abundance- ▁acquisitive- ▁autumn- ▁cologuard- ▁infotainment- ▁refrigeration- ▁veterinary- ▁behaving- ▁diving- ▁booth- ▁halt- ▁radiation- ▁lisa- sense- ▁restriction- ▁revers- post- spoke- ▁underscor- ▁vocal- mobile- ▁charlie- ▁exterior- ▁hidden- ▁locomotive- ▁maturation- ▁cardinal- ▁orleans- ▁remedy- ▁traits- ▁instability- ▁originator- ▁marvel- ▁manpower- ▁quint- 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-        "name_en": "Shinji Watanabe",
-        "name_zh": "Shinji Watanabe"
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-      "ESPnet",
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-      "pytorch",
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-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
-    "keywords_zh": [
-      "ESPnet",
-      "deep-learning",
-      "python",
-      "pytorch",
-      "speech-recognition",
-      "speech-synthesis",
-      "speech-translation",
-      "machine-translation"
-    ],
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-    "doi": "10.5281/zenodo.4590907",
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-    "pid": "",
-    "title": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
-    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
-    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave, fs=16k, lang",
-    "description": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- ▁time- ▁customers- ▁into- ▁where- ▁like- ▁first- ▁results- ▁out- ▁But- ▁really- ▁other- ▁how- ll- ing- ▁if- d- ▁their- ▁up- ▁forward- ▁expect- ▁company- ▁were- ▁than- ▁last- ▁your- ▁look- ▁through- ▁get- ▁any- ▁because- ▁one- ▁call- ▁strong- ▁had- ▁believe- ▁sales- ▁when- ▁This- ▁then- ▁As- ▁good- ▁second- ▁In- ▁revenue- m- ▁performance- ▁product- ▁make- ▁lot- ▁cost- n- ▁much- ed- ▁years- ▁Our- ▁could- ▁financial- ▁capital- ▁terms- ▁don- ▁future- ▁both- ▁impact- ▁right- ▁value- ▁them- ▁It- ▁today- ▁little- ▁want- ▁customer- ▁next- ▁bit- ▁back- ▁work- ▁increase- ▁products- ▁take- ▁number- ▁end- ▁higher- ▁opportunities- ▁able- ▁half- ▁markets- ▁kind- ▁way- ▁may- ▁significant- ▁know- ▁operating- ▁better- ▁go- ▁cash- ▁say- ▁things- ▁still- ▁focus- ▁most- ▁statements- ▁provide- ▁should- ▁being- ▁point- ▁part- ▁team- ▁across- ▁lower- ▁made- ▁opportunity- ▁need- ▁important- ▁third- ▁2- ▁during- ▁rate- ▁line- ▁long- 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▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- 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▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- 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▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- 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▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- 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deliver- ▁Ralph- ▁Example- ▁Utah- ▁irrigation- ▁nonaccrual- ▁suppress- ▁Underlying- ▁remediate- ▁stripping- ▁iteration- ▁sincerely- ▁Orange- path- close- measure- process- ▁Block- ▁Significant- ▁Treasury- ▁caliber- ▁compliment- ▁virtuous- ▁busiest- ▁forthcoming- track- ▁inhibit- profit- ▁reestablish- ▁Progress- ▁Apollo- ▁Tokyo- ▁chemotherapy- ▁mathematical- ▁retiring- ▁stellar- ▁sweep- ▁timeframe- ▁roster- ▁specify- ▁Ameri- ▁SIM- carbon- ▁Alabama- ▁Application- ▁Hemisphere- ▁Mineral- ▁administrator- ▁dinner- ▁empty- ▁endorsement- ▁facilitating- ▁happier- ▁vacancies- ▁voting- 7,000- ▁protest- ▁reactivate- ▁bird- ▁aspire- apples- ▁redevelop- ▁hazard- ▁Iridium- ▁Related- ▁experiential- ▁mismatch- ▁philosophical- ▁thumb- ▁Leasing- ▁deductibility- ▁Drug- ▁Wild- ▁illustrat- ▁reinvigorate- ▁sandwich- ▁Affordabl- ▁DOL- ▁Hilton- ▁advocacy- ▁antibodies- ▁chief- ▁entrance- ▁examining- ▁snapshot- ▁unfair- ▁ForEx- ▁defect- ▁mixture- ▁Getting- ▁digging- ▁Monster- ▁liquidate- ▁characteristic- period- 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▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- ▁Holiday- ▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_en": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- 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▁exiting- ▁anniversary- ▁inorganic- ▁diluted- ▁AD- ▁payer- ▁Sun- ▁submit- ▁Quarter- ▁whereas- ▁recovered- ose- ▁periodic- ▁French- ware- ▁corner- ▁cultural- ▁preclinical- ▁surgery- ▁surgeons- ▁interactions- ▁Security- ▁anchor- ▁concentrate- ▁diseases- ▁flowing- ug- ▁imports- ▁Annual- ▁Ge- ▁reward- ▁deleveraging- ▁trained- ulation- ey- ▁Midwest- ▁relevance- ▁Boston- ▁baseline- ise- TE- ▁incorporated- ▁concepts- ▁qu- ▁linear- ▁bolster- adjusted- chi- ▁excludes- ▁overlap- ▁extreme- ▁breakeven- ▁headquarters- ▁discounting- ▁valued- ▁attribute- cy- AP- ▁miss- ▁adult- ▁vital- ▁Doug- ▁hub- ▁DC- ▁wherever- ▁pockets- ▁agility- ▁12%- ▁construct- ▁Building- ▁prudently- ▁Com- ▁revolving- ▁Rock- ▁stations- ▁outsourcing- ▁intangible- ▁reservoir- ▁alongside- iti- tting- ▁Auto- OR- ▁responded- ang- ▁stabilizing- ▁noncore- ▁protecting- ▁distributions- ▁methods- ▁Gen- ▁evening- ▁m- ▁concrete- ▁geo- ▁rock- ▁representative- ▁pad- PA- core- ▁immune- ▁Blue- ▁Fin- ▁academic- ▁multifamily- ▁Mobile- ▁ideal- ▁practical- ▁Congress- ▁rewards- ock- ▁anti- ▁computing- ▁Cost- ▁procedure- ▁mandate- ▁midst- ud- ▁Results- ▁casino- ▁reversal- ▁accretion- ▁Ford- ▁serves- ▁Frank- ▁man- ▁revolver- ▁referral- ▁IPO- ▁annualized- ▁convenient- ▁doors- ▁storms- ▁willingness- ▁deterioration- ▁Che- ▁parameters- ▁Very- ▁presentations- ▁scores- ▁records- ▁ARPU- ▁Singapore- ▁Turkey- ▁wallet- ▁formation- ▁grant- ▁contractor- ▁Man- ▁uplift- ▁whenever- risk- ▁suggested- ake- OS- tech- ▁Ce- ▁responsive- ▁bankruptcy- ▁viable- ame- ▁marine- ▁visual- ▁MA- ▁contingent- AM- ▁deepen- ▁recession- ▁flu- ▁restore- ▁download- hy- ▁Cap- ▁divestiture- ▁Federal- ▁apartment- ▁reduces- ▁purely- ▁negotiated- ▁nimble- ▁suspect- ▁Har- hold- ▁distribute- ▁fight- ▁Brazilian- ▁covenants- ▁Colorado- ▁augment- ▁solely- ▁publication- ▁gear- ▁missing- ▁outdoor- ▁sides- ▁hires- ▁Ki- ana- ▁foresee- ▁renew- ▁adviser- US- ▁timeline- ▁k- ray- ▁extensions- ▁modernization- ▁enjoyed- ▁Certain- ▁cuts- ▁maturities- ▁qualify- ▁writing- ▁premier- ▁possibilities- ▁Valley- ▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- ▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- 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▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- 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▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- ▁time- ▁customers- ▁into- ▁where- ▁like- ▁first- ▁results- ▁out- ▁But- ▁really- ▁other- ▁how- ll- ing- ▁if- d- ▁their- ▁up- ▁forward- ▁expect- ▁company- ▁were- ▁than- ▁last- ▁your- ▁look- ▁through- ▁get- ▁any- ▁because- ▁one- ▁call- ▁strong- ▁had- ▁believe- ▁sales- ▁when- ▁This- ▁then- ▁As- ▁good- ▁second- ▁In- ▁revenue- m- ▁performance- ▁product- ▁make- ▁lot- ▁cost- n- ▁much- ed- ▁years- ▁Our- ▁could- ▁financial- ▁capital- ▁terms- ▁don- ▁future- ▁both- ▁impact- ▁right- ▁value- ▁them- ▁It- ▁today- ▁little- ▁want- ▁customer- ▁next- ▁bit- ▁back- ▁work- ▁increase- ▁products- ▁take- ▁number- ▁end- ▁higher- ▁opportunities- ▁able- ▁half- ▁markets- ▁kind- ▁way- ▁may- ▁significant- ▁know- ▁operating- ▁better- ▁go- ▁cash- ▁say- ▁things- ▁still- ▁focus- ▁most- ▁statements- ▁provide- ▁should- ▁being- ▁point- ▁part- ▁team- ▁across- ▁lower- ▁made- ▁opportunity- ▁need- ▁important- ▁third- ▁2- ▁during- ▁rate- ▁line- ▁long- ▁people- ▁fourth- ▁down- ▁strategy- ▁did- ▁many- ▁continued- ▁industry- ▁level- ▁around- ▁portfolio- ▁working- ▁management- ▁share- ▁price- ▁investment- ▁due- ▁additional- ▁actually- ▁give- ▁only- ▁come- ▁while- ▁seeing- ▁high- ▁demand- ▁no- ▁few- ▁plan- ▁same- ▁looking- ▁progress- ▁here- ▁great- ▁costs- ▁even- ▁development- ▁different- ▁margin- ▁result- ▁current- ▁seen- ▁doing- ly- ▁grow- ▁further- ▁start- ▁change- ▁coming- ▁guidance- ▁positive- ▁service- ▁me- ▁earnings- ▁data- ▁drive- ▁technology- ▁position- ▁process- ▁key- ▁full- ▁investments- looking- ▁past- ▁That- ▁said- ▁who- ▁turn- ▁overall- ▁basis- ▁expected- ▁improve- ▁increased- ▁support- ▁again- ▁question- ▁3- ▁within- ▁focused- ▁sort- ▁services- ▁got- ▁environment- ▁potential- ▁help- ▁before- ▁businesses- ▁large- ▁pricing- ▁months- ▁These- ▁remain- ▁making- ▁done- ▁balance- ▁something- ▁ability- ▁interest- ▁strategic- ▁move- ▁already- ▁production- ▁sure- ▁side- ▁based- ▁platform- ▁best- ▁program- ▁such- ▁information- ▁mentioned- er- ▁acquisition- ▁Now- ▁Q- ▁talk- ▁expectations- ▁maybe- ▁early- 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▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- ▁Non- ▁University- ▁compromise- ▁Ocean- ▁undergoing- ▁straightforward- ▁Taking- ▁deleverage- ▁Port- ▁SP- ye- ▁unlocking- UR- fold- ▁Switch- ▁catalog- ▁universe- ▁Sports- ▁Price- ▁articulated- ▁owning- ugh- ▁relations- ▁Historically- ▁Ryan- ▁journal- ▁mortality- ▁neither- ▁yard- ▁Production- ▁classic- ▁Safe- ▁Angeles- ▁Saudi- ▁proximity- ▁scalability- ▁turbine- ▁variations- ▁entrants- ▁Win- ▁arms- ino- ▁agriculture- ▁chemistry- ▁multinational- ▁voluntary- ▁Sometimes- ▁speculation- ▁DS- ▁Pan- channel- ▁NAV- ▁geopolitical- ▁rush- ▁solving- mortar- ▁shoot- ▁shot- ▁calculations- ▁16%- ▁22- ▁suffered- plan- ▁AP- ▁nominal- ▁stimulate- ▁distraction- ▁persistent- ▁migrating- ▁quantitative- ▁thermal- ▁18%- oid- ▁transitional- ef- place- ▁specialists- ney- ▁interconnect- atory- ▁accomplishment- ▁Education- ▁describing- ▁salaries- ▁squeeze- ▁flattish- iding- EM- ▁EP- ▁Innovation- ▁Irma- ▁dealership- ▁representation- ▁normalization- ▁collecting- ▁removal- ▁shock- ▁enforcement- ▁suffer- ▁outsized- ▁MS- ▁heating- ▁Far- brand- ▁meat- ▁Premier- ▁incorporating- ▁CM- ▁Shop- ▁Stock- ▁four- az- ▁Generally- gar- ▁Government- ▁Kansas- ▁distance- ▁edit- ▁fat- lim- box- ▁publishing- ▁FFO- ▁Harbor- ▁Software- ▁Southwest- ▁slot- ▁Ab- ▁Moreover- bri- ▁approached- ▁absorbed- ▁advertise- reduction- ▁CC- ▁Olympic- ▁college- ▁Division- ▁noninterest- ▁obtaining- ▁widening- ▁robotics- ▁upsell- only- ▁ambitions- ▁landing- ▁Committee- ▁FERC- ▁omni- ji- ▁meal- ▁attain- ▁gasoline- ▁moderated- ▁enrolled- ▁unmatched- ▁snack- ▁Beach- ▁modifications- ▁technically- ▁Advanced- ▁Switzerland- ▁mega- ▁hubs- ▁Infrastructure- ▁tobacco- ▁tweak- ▁films- ▁independently- ▁Ke- matic- ▁offline- ▁TR- ▁interactive- ▁commenting- ▁shoppers- fill- new- range- ▁DA- ▁outperforming- ▁GDP- ▁composite- ▁warrants- ▁gateway- ▁consultation- ▁critically- ▁deepening- Point- ▁Hospital- ▁batteries- ▁diabetes- ▁milk- ▁preserving- ▁reinvent- ▁farms- ▁consists- ▁Denver- ▁underpinned- ▁observation- ▁attraction- ▁Med- ▁HR- ▁ME- ▁crystal- ▁Labor- ▁dropping- ▁alert- ▁liquidation- tz- ▁kicked- ▁explicit- ▁ultra- ▁molecule- ▁assay- ▁bundled- train- ▁recommendation- ▁teens- ▁Carl- ▁workplace- ▁EV- ari- ▁enjoying- ▁crush- ▁spacing- ▁malls- ▁prescriptions- ▁Bur- ▁renewing- ▁400- ▁Value- fully- ▁Russian- ▁examine- ▁prolonged- ▁truth- ▁standalone- ▁till- RP- ▁instrumental- ▁Alaska- ▁prominent- ▁semester- ▁flash- ▁jewelry- ▁Currently- ▁reconcile- ▁deferral- ▁dislocation- ▁Communications- ▁Post- ona- CH- ▁withdraw- ▁goodwill- ▁Grand- ▁theaters- ▁provisioning- ▁originated- ▁nearing- ▁approve- ▁associate- ▁insurers- ▁sun- ▁Equipment- ▁Meanwhile- ▁Professional- ▁kitchen- ▁Boeing- ▁panels- ▁belt- body- ▁White- ▁isolated- ▁AL- ▁fan- ▁smoothly- ▁midterm- ▁molecules- ▁Georgia- ▁Taiwan- ▁durability- ▁prioritizing- ▁Aerospace- ▁deserve- ▁rose- ▁Intel- ▁distinctive- ▁error- media- ▁confidential- ▁predicting- ▁regime- FA- ▁Volume- ▁complain- ▁tackle- ▁customary- Link- ▁suffering- ▁mandates- ▁eventual- ▁advisor- ▁intermediate- ▁congratulate- ▁continuity- ▁moderation- ▁instant- ▁End- ▁ASP- ▁GM- ▁notion- ▁le- ▁Sim- ▁NO- ▁frozen- ▁shippers- ▁interpretation- ▁fear- ▁Box- ▁Trade- ▁fueling- cut- ▁Mountain- ▁translating- ▁equipped- ▁mainstream- life- ▁lung- ▁modular- ▁stem- ▁ST- ▁reservation- ▁Fresh- ▁footage- ▁AT- ▁cancellations- ▁seasoned- contract- Star- ▁reopen- ▁shall- ▁Ireland- NS- ▁Similarly- ▁sharply- ▁suppose- ▁Greater- ▁authentic- ▁transit- ▁civil- ▁expedite- ▁noteworthy- ▁Director- ▁factoring- ▁intrinsic- ▁association- ▁technician- ▁Except- ▁directionally- ▁shrinking- ▁photo- ▁embracing- ▁readily- ▁outset- ▁visiting- ▁RV- ctive- sensitive- ▁symptom- list- ▁Nordic- ▁Euro- ▁pair- season- ▁Bruce- ▁meter- ▁succession- ▁drawn- ▁rewarding- ▁measurable- ▁Contract- ▁leak- ▁acres- ▁Compare- ▁entirety- oma- ▁innings- ▁flood- ▁fe- ▁Probably- ▁Multi- ▁valve- ▁Adam- ▁digitalization- ▁dimension- ▁consult- ▁surgeon- ▁cleaning- ▁conflict- ▁prompt- ▁unpredictable- ▁dispose- ▁spare- ▁CLO- ▁playbook- ▁28- ▁affiliates- ▁evenly- ▁SE- 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▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    rnn_type: lstm    nlayers: 2    unit: 2024required:- output_dir- token_listversion: 0.9.7distributed: true\t",
-    "description_zh": "This model was trained by Shinji Watanabe using spgispeech recipe in espnet.&nbsp;\tPython API\tSee https://github.com/espnet/espnet_model_zoo\t\tEvaluate in the recipe\tgit clone https://github.com/espnet/espnetcd espnetgit checkout cd8b84dc705e49796f2c8940d4aecf8f923f9976pip install -e .cd egs2/spgispeech/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000_valid.acc.ave\t\tResults\t# RESULTS## Environments- date: `Mon Mar  8 23:43:09 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.8`- pytorch version: `pytorch 1.7.1`- Git hash: `cd8b84dc705e49796f2c8940d4aecf8f923f9976`  - Commit date: `Fri Mar 5 10:41:31 2021 -0500`## asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|95631|94.9|4.5|0.6|0.5|5.5|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|948632|94.9|4.5|0.6|0.5|5.5|61.4|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|554413|98.8|0.6|0.6|0.4|1.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|5502566|98.8|0.6|0.6|0.5|1.6|61.4|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/dev_4k_unnorm|4000|112276|95.5|2.8|1.7|1.0|5.6|61.6||decode_asr_lm_lm_train_lm_en_unnorm_bpe10000_valid.loss.ave_asr_model_valid.acc.ave/val_unnorm|39341|1114651|95.5|2.8|1.7|1.1|5.6|61.4|\t\tASR config\tconfig: conf/tuning/train_asr_conformer6_n_fft512_hop_length256.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 33274dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 35patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 4no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nulldetect_anomaly: falsepretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 20valid_batch_size: nullbatch_bins: 35000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/speech_shape- exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: numelvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/train_nodev_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/train_nodev_unnorm/text    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/wav.scp    - speech    - sound-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 0.0015scheduler: warmuplrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- 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▁outperformance- ▁connecting- ▁apart- right- ▁disposition- run- ral- ▁spoken- ery- ▁spec- ▁failure- ▁pathway- ▁frequently- ▁expansions- ▁blue- ▁100%- ▁Ni- ▁protein- ▁array- ▁unfold- ▁rebuild- ▁exposed- ax- par- ▁doctors- ▁drilled- all- ▁everywhere- ▁inclusion- ▁worry- ▁8-- ▁Ju- ▁skilled- ▁popular- ▁retained- ▁alliance- ▁unexpected- uc- ▁tighter- ▁Eagle- ▁corporation- ▁upgrading- ▁flight- ▁Va- ▁Call- ▁Du- ▁80- ▁preference- ▁deliberate- ▁High- ▁60%- ▁improves- ▁settle- ▁31- ▁roles- ▁pivot- ▁pa- ▁Christmas- ▁white- ▁Alberta- ▁gr- ▁Food- ▁campus- ▁empower- ▁reposition- ▁novel- ▁inspection- NA- ▁Ve- ▁leased- ▁precision- effective- ▁stockholders- ▁Australian- ▁influenced- ▁Asian- ▁film- ▁contemplated- ▁Jersey- ▁absence- ▁reinvestment- LA- ▁certification- ▁negotiating- ▁perception- ▁twice- ▁derivative- ▁underscore- centric- ▁guiding- ▁Specialty- ▁Hi- ▁integrity- ▁nonrecurring- ▁stretch- ▁replaced- ▁ingredients- ▁ROI- ▁defend- ▁negotiate- ▁satisfy- ▁commit- ▁airlines- ▁resume- ▁Green- 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▁exiting- ▁anniversary- ▁inorganic- ▁diluted- ▁AD- ▁payer- ▁Sun- ▁submit- ▁Quarter- ▁whereas- ▁recovered- ose- ▁periodic- ▁French- ware- ▁corner- ▁cultural- ▁preclinical- ▁surgery- ▁surgeons- ▁interactions- ▁Security- ▁anchor- ▁concentrate- ▁diseases- ▁flowing- ug- ▁imports- ▁Annual- ▁Ge- ▁reward- ▁deleveraging- ▁trained- ulation- ey- ▁Midwest- ▁relevance- ▁Boston- ▁baseline- ise- TE- ▁incorporated- ▁concepts- ▁qu- ▁linear- ▁bolster- adjusted- chi- ▁excludes- ▁overlap- ▁extreme- ▁breakeven- ▁headquarters- ▁discounting- ▁valued- ▁attribute- cy- AP- ▁miss- ▁adult- ▁vital- ▁Doug- ▁hub- ▁DC- ▁wherever- ▁pockets- ▁agility- ▁12%- ▁construct- ▁Building- ▁prudently- ▁Com- ▁revolving- ▁Rock- ▁stations- ▁outsourcing- ▁intangible- ▁reservoir- ▁alongside- iti- tting- ▁Auto- OR- ▁responded- ang- ▁stabilizing- ▁noncore- ▁protecting- ▁distributions- ▁methods- ▁Gen- ▁evening- ▁m- ▁concrete- ▁geo- ▁rock- ▁representative- ▁pad- PA- core- ▁immune- ▁Blue- ▁Fin- ▁academic- ▁multifamily- ▁Mobile- ▁ideal- 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▁possibilities- ▁Valley- ▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- 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▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- 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▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- ▁Non- ▁University- ▁compromise- ▁Ocean- ▁undergoing- ▁straightforward- ▁Taking- ▁deleverage- ▁Port- ▁SP- ye- ▁unlocking- UR- fold- ▁Switch- ▁catalog- ▁universe- ▁Sports- ▁Price- ▁articulated- ▁owning- ugh- ▁relations- ▁Historically- ▁Ryan- ▁journal- ▁mortality- ▁neither- ▁yard- ▁Production- ▁classic- ▁Safe- ▁Angeles- ▁Saudi- ▁proximity- ▁scalability- ▁turbine- ▁variations- ▁entrants- ▁Win- ▁arms- ino- ▁agriculture- ▁chemistry- ▁multinational- ▁voluntary- ▁Sometimes- ▁speculation- ▁DS- ▁Pan- channel- ▁NAV- ▁geopolitical- ▁rush- ▁solving- mortar- ▁shoot- ▁shot- ▁calculations- ▁16%- ▁22- ▁suffered- plan- ▁AP- ▁nominal- ▁stimulate- ▁distraction- ▁persistent- ▁migrating- ▁quantitative- ▁thermal- ▁18%- oid- ▁transitional- ef- place- ▁specialists- ney- ▁interconnect- atory- ▁accomplishment- ▁Education- ▁describing- ▁salaries- ▁squeeze- ▁flattish- iding- EM- ▁EP- ▁Innovation- ▁Irma- ▁dealership- ▁representation- ▁normalization- ▁collecting- ▁removal- ▁shock- ▁enforcement- ▁suffer- ▁outsized- ▁MS- ▁heating- ▁Far- brand- ▁meat- ▁Premier- ▁incorporating- ▁CM- ▁Shop- ▁Stock- ▁four- az- ▁Generally- gar- ▁Government- ▁Kansas- ▁distance- ▁edit- ▁fat- lim- box- ▁publishing- ▁FFO- ▁Harbor- ▁Software- ▁Southwest- ▁slot- ▁Ab- ▁Moreover- bri- ▁approached- ▁absorbed- ▁advertise- reduction- ▁CC- ▁Olympic- ▁college- ▁Division- ▁noninterest- ▁obtaining- ▁widening- ▁robotics- ▁upsell- only- ▁ambitions- ▁landing- ▁Committee- ▁FERC- ▁omni- ji- ▁meal- ▁attain- ▁gasoline- ▁moderated- ▁enrolled- ▁unmatched- ▁snack- ▁Beach- ▁modifications- ▁technically- ▁Advanced- ▁Switzerland- ▁mega- ▁hubs- ▁Infrastructure- ▁tobacco- ▁tweak- ▁films- ▁independently- ▁Ke- matic- ▁offline- ▁TR- ▁interactive- ▁commenting- ▁shoppers- fill- new- range- ▁DA- ▁outperforming- ▁GDP- ▁composite- ▁warrants- ▁gateway- ▁consultation- ▁critically- ▁deepening- Point- ▁Hospital- ▁batteries- ▁diabetes- ▁milk- ▁preserving- ▁reinvent- ▁farms- ▁consists- ▁Denver- ▁underpinned- ▁observation- 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▁playbook- ▁28- ▁affiliates- ▁evenly- ▁SE- ▁merge- ▁muscle- ▁interior- ▁cleaner- ▁budgeting- ▁temperatures- ▁coincide- ▁defining- ▁quantities- ▁forever- ▁reside- ▁ingredient- ▁actionable- ▁rewarded- ▁Everything- ▁essence- ▁extraordinarily- ▁radar- ▁Statements- ▁Hotel- ▁command- ▁Motor- ▁chips- ▁fitness- ▁guy- ▁float- ▁Charles- ▁Distribution- ▁NGL- ▁Perhaps- ▁decisive- ▁uniform- ▁overhang- rchitectural- zz- ▁petrochemical- ▁beef- ▁constructed- Fi- aries- ▁certified- ▁escalation- ▁rebalancing- ▁surpass- ▁Pricing- ▁confusion- ▁subset- ▁confirmation- ▁sir- ▁adapting- ▁adequately- ▁dimensions- pping- ▁fail- ▁delight- ▁Massachusetts- ▁STACK- ▁tactics- ▁infection- ▁invoice- ▁mask- ▁coach- ▁Radi- ▁specialist- ▁constraint- ▁deceleration- ▁devaluation- figuring- ▁methodical- ▁promised- ▁reinforced- ▁Really- ▁LP- ▁printer- ▁Instead- ▁LIBOR- ▁Strategic- ▁continuum- ▁Roger- ▁Adjust- pped- berg- ▁attach- ▁varying- ▁defensive- ▁signature- ▁Small- ▁desktop- ▁shoe- ▁RFP- ▁Back- ▁Bra- ▁RF- ▁Support- 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▁steer- ▁Patrick- ▁specified- ▁releasing- scrip- tail- tune- ▁circle- ▁Er- ▁attractiveness- ▁MR- front- ▁analyzed- ▁climb- ▁newspaper- ▁Agency- ▁concurrent- ▁wearable- ▁native- ▁progressively- ▁tanker- gel- DR- ▁fluctuation- ▁banners- ▁chicken- ▁Italian- ▁Brent- ▁tonnes- ▁reveal- ▁highway- ▁TI- ▁Mc- ▁Pen- changing- ▁Horizon- ▁varies- ▁assignment- ▁consequently- ▁marginally- ▁graphic- ▁correlated- FI- ▁Corp- ▁partnered- ▁fly- ▁borrower- ▁Logistics- ▁Organic- ▁southern- ▁freedom- ▁Bridge- ▁oriented- ▁officially- ▁Prime- ▁relocate- ▁algorithm- ▁Important- volume- ▁Analytics- ▁battle- ▁deciding- ▁destocking- ▁Glen- ▁quoting- ▁distinction- mont- cycl- ▁derive- ▁thousand- ote- ▁MO- ▁wine- ▁appraisal- ▁motivation- ▁expressly- ▁reversed- ▁Equity- ▁breast- ▁stood- ▁sought- funded- ▁compute- ▁rebates- ▁Max- family- NE- ▁Belgium- ▁nurse- ▁stroke- ▁stringent- ▁golf- ▁comprise- ▁geared- ▁theater- ▁deteriorate- ▁enacted- ▁sensible- ▁sixth- ▁fixing- ▁tailor- ▁contingency- ▁shale- ▁displace- ▁railcar- ▁guaranteed- ▁investigators- ▁recycle- ▁PRO- ▁biotech- ▁Orlando- ▁Tuesday- ▁Wisconsin- ▁vibrant- ▁makers- nova- jo- ▁systemic- pha- ▁Diamond- ▁Operational- ▁desirable- ▁recipe- ▁Par- ▁refinement- ▁Sprint- mount- ▁BP- ▁robotic- cell- ▁habits- ▁unlimited- ▁Omni- ▁summarized- ▁Fee- ▁beds- ▁Cup- ▁promoted- current- ▁Antonio- ▁integrator- ▁suburban- ▁conform- ▁strain- step- ▁Clar- flat- ▁safer- ▁barrier- yield- ▁reinvested- ▁library- ▁sanction- ▁vintage- ▁retrofit- ▁feedstock- ▁Still- ▁CRE- ▁Master- ▁fold- ▁failed- ▁tourist- ▁leaner- ▁Best- ▁Malaysia- ▁Premium- ▁Stephen- ▁Garden- ▁Combined- round- ▁discovered- ▁intensely- bar- ▁visitors- ▁reallocate- ▁fighting- ▁Prior- ▁Keith- ▁nationally- buy- vol- ▁Design- ▁diagnose- ▁railroad- ▁convergence- ▁entrepreneurial- ▁Unit- ▁ban- serve- ▁routing- ▁ranging- ▁hunt- ▁Estate- ▁tuned- ▁sticking- disproportionate- ▁Connected- ▁reactor- ▁citizen- ▁Nothing- ▁standardized- ▁accumulated- ▁Diego- ▁attitude- ▁propane- ▁26- build- ▁augmented- ▁lien- ▁Nevada- ▁Vietnam- ▁bonuses- ▁deliberately- ough- ▁overhaul- saving- ▁furnish- ▁Continental- ▁appointed- ▁benign- ▁bottle- ▁gallon- ▁reiterating- ▁PI- ▁gu- ▁borrow- rk- ▁originate- ▁monetizing- ▁resistance- ▁underneath- ▁onshore- ▁deadline- ▁behave- ▁favorite- ▁oversee- ▁CT- ▁confirming- ▁repeating- ▁embark- ▁pin- ▁catching- ▁Test- ▁permission- ▁tremendously- ▁Dar- haul- ▁explaining- ▁behavioral- 'NO'- ▁discounted- ▁Consequently- ▁Randy- ▁cannibalization- ▁choppy- ▁departure- ▁minimizing- ▁Absolutely- ▁19%- ▁27- ▁TAM- ▁assisted- ▁outsourced- ▁Paris- ▁reconciled- ▁Meta- built- consumer- ▁random- ▁2013- user- ▁Hard- ▁friendly- ▁sticky- ▁divested- ▁consumed- ▁CR- LO- ▁imported- ▁cannabis- ▁sequencing- ▁Anything- ▁Three- ▁dress- ▁mono- ▁Mag- ▁perceived- ▁ideally- ▁existed- ▁ranking- ▁accountable- bridge- ▁approximate- ▁robot- ▁Special- ▁Philippines- ▁graduate- ▁harness- ▁navigation- ▁striving- ▁wildfire- ▁outflow- ▁Recently- ▁witnessed- ▁mutually- ▁Add- ▁midyear- week- ▁Among- ▁sensing- put- 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▁School- structure- economic- deliver- ▁Ralph- ▁Example- ▁Utah- ▁irrigation- ▁nonaccrual- ▁suppress- ▁Underlying- ▁remediate- ▁stripping- ▁iteration- ▁sincerely- ▁Orange- path- close- measure- process- ▁Block- ▁Significant- ▁Treasury- ▁caliber- ▁compliment- ▁virtuous- ▁busiest- ▁forthcoming- track- ▁inhibit- profit- ▁reestablish- ▁Progress- ▁Apollo- ▁Tokyo- ▁chemotherapy- ▁mathematical- ▁retiring- ▁stellar- ▁sweep- ▁timeframe- ▁roster- ▁specify- ▁Ameri- ▁SIM- carbon- ▁Alabama- ▁Application- ▁Hemisphere- ▁Mineral- ▁administrator- ▁dinner- ▁empty- ▁endorsement- ▁facilitating- ▁happier- ▁vacancies- ▁voting- 7,000- ▁protest- ▁reactivate- ▁bird- ▁aspire- apples- ▁redevelop- ▁hazard- ▁Iridium- ▁Related- ▁experiential- ▁mismatch- ▁philosophical- ▁thumb- ▁Leasing- ▁deductibility- ▁Drug- ▁Wild- ▁illustrat- ▁reinvigorate- ▁sandwich- ▁Affordabl- ▁DOL- ▁Hilton- ▁advocacy- ▁antibodies- ▁chief- ▁entrance- ▁examining- ▁snapshot- ▁unfair- ▁ForEx- ▁defect- ▁mixture- ▁Getting- ▁digging- ▁Monster- 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▁reconciling- ▁unleash- ▁attribution- ▁FCC- ▁IBM- ▁wastewater- ▁baking- ddle- called- ▁Driving- ▁Jordan- ▁Restaurant- ▁calibrate- ▁decelerate- ▁discontinuation- ▁enzyme- ▁nonetheless- ▁segregat- ▁slippage- ▁sovereign- ▁steroid- ▁remix- ▁Crane- ▁pinpoint- ▁Display- ▁dump- ▁coding- ▁Likewise- Works- credit- ▁Double- ▁Catalyst- ▁Halloween- ▁NEXT- ▁blockbuster- ▁incidence- ▁eastern- ▁blast- ▁Dream- regulated- lecommunications- ▁Kind- storm- ▁Surgical- ▁Triumph- ▁editorial- ▁irrational- ▁juice- ▁kiosk- ▁slope- ▁sulfur- ▁Dental- ▁vending- ▁Insight- ▁Baker- ▁anecdotal- ▁Whit- ▁dilute- ▁Albert- development- voltage- ▁College- ▁destiny- ▁influx- ▁microbiome- ▁migraine- ▁occupy- ▁politics- ▁reversion- ▁rubber- ▁variant- ▁Glass- ▁NOLs- ▁supervisor- ▁Trading- ▁loud- ▁infant- ▁weapon- operative- ▁technique- ▁surround- ▁distress- ▁tradition- ▁Advisor- ▁Bancorp- ▁Collection- ▁League- ▁Perfect- ▁pragmatic- ▁firing- ▁Know- ▁Better- ▁slice- ▁dwell- ▁outlier- ▁colleague- prime- Bank- ▁Buffalo- ▁Greece- ▁Portfolio- ▁Sustain- ▁Taylor- ▁Women- ▁cohesive- ▁hypothetical- ▁nontraditional- ▁refractory- ▁stewardship- ▁Darren- ▁horse- ▁Irish- ▁Arrow- ▁Associate- ▁Nonetheless- ▁Williston- ▁YouTube- ▁catheter- ▁latency- ▁maritime- ▁uncommon- ▁firewall- ▁Speed- ▁orange- ▁Pharmacy- ggle- ▁Including- ▁Restructuring- ▁bubble- ▁clause- ▁consummate- ▁deficiency- ▁receptor- ▁redundancy- ▁titanium- ▁Windows- ▁thread- ▁Partnership- ▁Accu- plica- ▁Solution- ▁assemble- ▁author- ▁AFFO- ▁Cologuard- ▁aggregator- ▁behaving- ▁choppiness- ▁conservation- ▁groundbreaking- ▁impediment- ▁reallocating- ▁registry- ▁tandem- ▁Besides- ▁Lisa- ▁Help- ▁STAR- 0%- ▁Charlie- ▁Orleans- ▁Platinum- ▁abundance- ▁acquisitive- ▁locomotive- ▁maturation- ▁pervasive- ▁pulse- ▁Christian- ▁Housing- ▁instability- ▁textile- group- ▁Bottom- ▁Detroit- ▁LIFO- ▁Mutual- ▁Positive- ▁Tyler- ▁Wondering- ▁arriving- ▁beneficiaries- ▁century- ▁dismiss- ▁turbulence- ▁DUC- ▁OPEC- ▁pathogen- ▁adhere- ▁Music- ▁originator- ▁prohibit- ▁grocer- nanometer- ▁FireEye- ▁believing- ▁false- ▁matrix- ▁proposing- ▁remedy- ▁shaft- ▁Brett- ▁Hello- ▁Allied- ▁arrow- pronged- ▁snap- cloud- ▁Campbell- ▁Loyalty- ▁Patterson- ▁Terminal- ▁biomass- ▁exercising- ▁footnote- ▁fragrance- ▁frustrated- ▁garage- ▁interference- ▁referendum- ▁simplifies- ▁unrivaled- ▁seemingly- ▁Close- direct- ▁dominate- Health- Corp- ▁Fabric- ▁Casualty- ▁Petrobras- ▁SCOOP- ▁acknowledging- ▁activating- ▁delineation- ▁dissipate- ▁exterior- ▁orthopedic- ▁resumption- ▁shield- ▁stainless- ▁Imaging- ▁outgrow- ▁Beverage- ▁Expect- ▁Outdoor- ▁brilliant- ▁curriculum- ▁decentralized- ▁deteriorating- ▁episodic- ▁fault- ▁Automation- ▁retool- ▁darn- ▁Terra- ▁debut- ▁dangerous- ▁IND- ▁Couple- ▁amortize- dealer- ▁BOSS- ▁Cardinal- ▁Hollywood- ▁Pizza- ▁Study- ▁antitrust- ▁autumn- ▁bunker- ▁ePlex- ▁frequencies- ▁hinder- ▁inflammation- ▁infotainment- ▁relocating- ▁victory- ▁Critical- ▁Geographic- ▁transpire- ▁carpet- ▁insurer- interest- walk- insured- ▁phenomena- ▁Specific- ffsetting- ▁Appalachia- ▁Particularly- ▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- ▁festival- ▁intermediary- ▁modalities- ▁syndrome- ▁sweetener- ▁rotate- ▁Needle- ▁stead- ▁Analysis- ▁HVAC- ▁Secure- ▁Volkswagen- ▁affluent- ▁amplified- ▁copies- ▁culinary- ▁hindsight- ▁implicit- ▁inaccurate- ▁pollution- ▁radiation- ▁susceptible- ▁veterinarian- ▁50-50- ▁grasp- ▁paving- ▁rehab- ▁unused- ▁depot- ▁renal- ▁bull- ▁interrupt- ▁Publishing- ▁Splunk- ▁biopharma- ▁deplete- ▁explosive- ▁injunction- ▁mountain- ▁pyramid- ▁quantification- ▁resurgence- ▁underutilized- ▁viewership- ▁northeast- ▁aftermath- ▁sweat- ▁knit- ▁delineate- ▁definite- claim- etween- ▁Flotek- ▁Haynesville- ▁Scientific- ▁Spark- ▁ammunition- ▁examination- ▁faculty- ▁framing- ▁hygiene- ▁ninth- ▁Fixed- ▁Traditional- ▁Henry- ▁vocal- ▁Flat- ▁Merchant- ▁Microchip- ▁Spectrum- ▁Triferic- ▁densification- ▁equilibrium- ▁identifies- ▁illegal- ▁predecessor- ▁reversing- ▁shadow- ▁vascular- ▁tubing- ▁tiny- ▁invitation- ▁tolerated- utheastern- moving- ▁disturb- ▁Chipotle- ▁Cushing- ▁Effective- ▁Keppel- ▁Summer- ▁bracket- ▁fibrosis- ▁undrawn- ▁contend- ▁franc- ▁Argentin- ▁arrange- ▁Adobe- ▁Kratos- ▁Quantum- ▁admittedly- ▁prudence- ▁DTC- ▁debenture- ▁diving- ▁Quint- ▁Titan- ▁cream- ▁relieve- ▁Apparel- ▁Conversely- ▁Employee- ▁Frozen- ▁Hampshire- ▁Staffing- ▁Yield- ▁buzz- ▁census- ▁clarified- ▁compensating- ▁finite- ▁influential- ▁protracted- ▁submarine- ▁valuing- ▁Fundamental- ▁Canyon- ▁Tommy- ▁investigator- ▁archive- ▁locate- ▁revolve- Vision- collect- ▁Protect- ▁overshadow- ▁reconfirm- ▁reintroduc- ▁Circuit- ▁Hungary- ▁duplication- ▁formidable- ▁intermediaries- ▁remuneration- ▁sauce- ▁biopharmaceutic- ▁admin- burn- screen- mproved- ▁Novartis- ▁Oxford- ▁cultivating- ▁reengage- ▁renegotiating- ▁reorganizing- ▁Something- ▁Linda- change- technology- mobile- standard- 2,000- ▁Michel- ▁Brown- ▁Commonwealth- ▁Honda- ▁Kathy- ▁Memory- ▁Separately- ▁constellation- ▁redetermination- ▁unbilled- ▁unintended- ▁crystallize- ▁harmonize- ▁Forrester- ▁Collectively- ▁Montana- ▁Reflect- ▁consolidator- ▁expensing- ▁mobilization- ▁reassure- ▁remedies- ▁rescue- ▁microcontroller- ▁Command- ▁sailing- ▁myriad- ▁Crown- ▁deviate- energy- ▁helm- ▁Flag- ▁Cinema- ▁Recogniz- ▁Oncology- ▁Pioneer- ▁approving- ▁atmosphere- ▁autonomy- ▁celebration- ▁invoicing- ▁legitimate- ▁peace- ▁refrigeration- ▁shelter- ▁Flash- ▁Sterling- ▁regret- ▁rotating- ▁multiply- ▁erode- ▁Triple- ▁assign- constrained- ▁Develop- ▁Expand- ▁Mosaic- ▁Alpine- ▁diameter- ▁hidden- ▁mezzanine- ▁multifaceted- ▁reconfigure- ▁ruble- ▁tournament- ▁uranium- ▁2022- ▁MBS- ▁tilt- ▁lesion- ▁err- IVE- ▁criminal- ▁Broker- ▁Comfort- ▁Exactly- ▁Facility- ▁basketball- ▁explosion- ▁hypo- ▁investigating- ▁obsolete- ▁plumbing- ▁voyage- ▁whiskey- ▁pruning- ▁retreat- ▁ripe- minded- average- ▁potent- ▁gravitat- ▁refrain- ▁Alzheimer- ▁Freddie- ▁Heritage- ▁Intermodal- ▁catastrophic- ▁caustic- ▁geology- ▁indebtedness- ▁multibillion- ▁nonstrategic- ▁occupancies- ▁propulsion- ▁terrible- ▁summit- ▁Understand- managed- ▁Cincinnati- ▁Condition- ▁Governor- ▁Holiday- ▁Initiative- ▁MiFID- ▁cheese- ▁deficiencies- ▁elegant- ▁elevation- ▁nonoperating- ▁receptivity- ▁scatter- ▁theatrical- ▁varieties- ▁PFS- ▁entries- ▁ourself- ▁browse- ▁lull- ▁Commodit- ▁Thermo- chieving- target- ▁Bureau- ▁Circle- ▁Completion- ▁Instagram- ▁Polaris- ▁Qualcomm- ▁Student- ▁Wyoming- ▁averaging- ▁delinquent- ▁franchising- ▁ratably- ▁requisite- ▁underscoring- ▁vinyl- ▁Instant- ▁LTAC- ▁Transition- ▁fastener- ▁Container- ▁Mercury- ▁Superior- ▁Wolfcamp- ▁accumulating- ▁eligibility- ▁expend- ▁himself- ▁methanol- ▁voucher- ▁Knight- ▁Carrier- ▁subtract- ▁Ranch- ▁entrant- approved- ▁Frankfurt- ▁Melbourne- ▁anxiety- ▁cartridge- ▁combustion- ▁escalating- ▁infectious- ▁laundry- ▁pledge- ▁styrene- ▁custodia- ▁Interestingly- segment- normal- annual- ▁multimillion- inflammatory- ▁Brothers- ▁Jacobs- ▁Minneapolis- ▁Principal- ▁dispensing- ▁jeopardiz- ▁nutrient- ▁Enhanc- xisting- midst- typical- ▁solicit- ▁Dominic- ▁Edmonton- ▁Fannie- ▁Identity- ▁Ingredients- ▁Nokia- ▁Yelp- ▁allegations- ▁appraise- ▁envisage- ▁flesh- init: nullinput_size: nullctc_conf:    dropout_rate: 0.0    ctc_type: builtin    reduce: true    ignore_nan_grad: falsemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_unnorm_token_list/bpe_unigram10000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    n_fft: 512    hop_length: 256    fs: 16kspecaug: specaugspecaug_conf:    apply_time_warp: true    time_warp_window: 5    time_warp_mode: bicubic    apply_freq_mask: true    freq_mask_width_range:    - 0    - 30    num_freq_mask: 2    apply_time_mask: true    time_mask_width_range:    - 0    - 40    num_time_mask: 2normalize: global_mvnnormalize_conf:    stats_file: exp_unnorm/asr_stats_raw_en_unnorm_bpe10000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: conformerencoder_conf:    output_size: 512    attention_heads: 8    linear_units: 2048    num_blocks: 12    dropout_rate: 0.1    positional_dropout_rate: 0.1    attention_dropout_rate: 0.1    input_layer: conv2d    normalize_before: true    macaron_style: true    pos_enc_layer_type: rel_pos    selfattention_layer_type: rel_selfattn    activation_type: swish    use_cnn_module: true    cnn_module_kernel: 31decoder: transformerdecoder_conf:    attention_heads: 8    linear_units: 2048    num_blocks: 6    dropout_rate: 0.1    positional_dropout_rate: 0.1    self_attention_dropout_rate: 0.1    src_attention_dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.8distributed: true\t\tLM config\tconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp_unnorm/lm_train_lm_en_unnorm_bpe10000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 55692dist_launcher: nullmultiprocessing_distributed: trueunused_parameters: falsesharded_ddp: falsecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 40patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nulluse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 256valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/train/text_shape.bpevalid_shape_file:- exp_unnorm/lm_stats_en_unnorm_bpe10000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump_unnorm/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump_unnorm/raw/dev_4k_unnorm/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁the- .- ','- ▁to- ▁of- ▁and- ▁we- ▁in- ▁that- ''''- ▁a- ▁our- s- ▁is- ▁I- ▁on- ▁for- ▁you- ▁are- ▁have- ▁as- ▁with- ▁it- ▁this- ▁be- ▁We- ▁And- ▁will- re- '-'- ▁year- ▁quarter- ▁at- ▁more- ▁So- ▁from- ▁business- ▁think- ▁what- t- ▁not- ▁some- ▁us- ▁by- ▁about- ▁there- ve- ▁growth- ▁can- ▁was- ▁which- ▁new- ▁very- ▁or- ▁do- '?'- ▁an- ▁market- ▁continue- ▁but- ▁also- ▁would- ▁see- ▁going- ▁The- ▁--- ▁they- ▁been- ▁all- ▁just- ▁has- ▁these- ▁so- ▁well- ▁those- ▁over- ▁now- 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▁Virginia- ▁ventures- ▁marginal- ki- ▁demographic- ▁partial- ▁Banking- ▁convertible- ▁brokerage- ▁presents- ▁experiment- EL- ▁restrictions- ual- ▁difficulty- ▁retire- ▁accuracy- ▁analyzing- ▁camera- ▁lighting- ▁station- ▁capitalizing- ▁zone- ▁disruptions- ▁aluminum- ▁expire- ▁unprecedented- ▁sensor- ▁relentless- ▁glad- ▁occurs- ▁pillar- ▁title- ▁powder- ▁tomorrow- ▁spike- ▁lesser- ▁miles- ▁ending- ▁bag- ▁predictive- ▁undertaken- era- ▁scrap- well- ▁guidelines- tim- ▁formulation- ▁beta- ▁borrowers- ▁Gary- growth- TI- ▁eliminating- ▁poised- ▁resort- ▁captured- ▁anymore- CA- ox- ▁Hawaii- ▁likelihood- ▁simplification- tain- nder- ▁gift- wi- ▁correlation- ▁qualification- ▁redesign- ▁directors- ▁EU- standing- ▁bucket- ▁gl- ▁aspiration- ▁recruitment- ev- IT- IP- ▁triple- ▁chunk- ▁focuses- ▁apps- ▁projection- ▁Wi- ▁streaming- ▁dial- ▁ne- ▁permit- ▁safely- ▁Colombia- ▁redemption- ▁snow- ▁apparent- ▁pursuit- ▁Pat- ▁correction- ▁deflation- ▁refined- press- ▁consent- ▁root- ▁iron- ▁exports- ▁startup- ▁buybacks- ▁war- ▁trans- ▁club- ▁references- ▁unified- ▁involves- ▁Program- ▁Poland- ▁rooms- ▁collective- ▁tick- ▁healthier- cer- ▁Jon- ▁hour- ▁Demand- ▁foremost- ▁sustaining- ▁forget- form- DA- ▁seemed- ▁noting- ▁pit- ▁candidate- ▁employ- ▁outpace- ▁upgraded- ▁employers- ▁replicate- ▁realignment- ▁permitting- ▁Asset- ▁connections- ▁ramped- ▁Th- IA- alone- ▁Office- ▁Healthcare- mic- ▁headed- ▁variables- ▁names- ▁Similar- efficient- ▁Earlier- ▁Ontario- ▁underperforming- ▁restricted- ▁geographically- ▁ads- '20'- ▁bids- ▁Rick- ▁Strong- ▁doubling- ▁saving- ▁norm- ford- ▁Core- ▁plastic- ▁cargo- ▁hospitality- ▁Eric- ▁hoped- ▁Friday- ▁exited- ▁mutual- ▁proving- ▁worried- ▁granted- ▁trip- ▁contrast- ▁70%- ▁motion- ▁analyst- ▁11%- ▁Marketing- ▁Brad- ▁dates- ble- ▁Whether- ▁Jack- uring- ▁passing- ▁hundred- ▁Partners- ▁illustrate- ▁messages- ▁treating- iz- ▁1995- ▁Credit- ▁treasury- ▁tissue- ▁motivated- ▁borrowings- ▁mechanisms- ▁excluded- ▁Technologies- ▁additive- ▁glass- ▁pilots- ▁oncology- ▁lap- ▁rational- ▁spaces- ▁expiration- ▁forefront- ▁90%- ▁affiliate- cap- ▁official- ▁institution- cept- ▁refund- bl- ▁wall- stage- ▁style- kin- ▁stat- ▁aside- ▁imaging- ▁IFRS- ▁progressed- ▁tens- ▁grows- ▁diversifying- ▁segmentation- ▁regulator- ▁revision- ▁subs- ▁fl- ▁modules- ancy- ▁wire- ▁electrical- ▁em- ▁geographical- ▁coffee- ▁default- ▁placing- ▁Up- ▁domestically- ▁boxes- ▁logic- ▁park- ▁broadening- ▁fluctuate- ▁billion- plus- ▁Da- ▁mines- ▁Dallas- ▁consensus- ▁fragmented- ▁generator- ▁flagship- ▁ERP- ▁evolved- ▁Executive- ▁hike- ▁permits- ▁foundational- ▁Control- ▁smarter- ▁recovering- ▁AB- ▁choosing- ▁Fund- ▁rain- ▁rationale- ▁temp- ▁Throughout- ▁seller- ▁indicates- ▁Sweden- ▁judge- ▁builds- ▁switching- ▁Pennsylvania- ▁demonstration- ▁struggling- ▁Development- ▁Note- ▁grades- ▁Together- ▁conviction- ▁click- ▁intake- ▁headline- ▁thorough- ▁selectively- ▁reaffirm- ▁Finance- ▁limits- ▁clearance- ▁municipal- ▁venue- ▁children- ▁assistance- ▁cautiously- lon- ▁removed- ▁evaluated- ▁Has- ▁stories- ▁honestly- ▁Open- ▁decreasing- ▁heightened- ▁attendance- ▁themes- ▁broken- ju- ▁Inc- ▁incrementally- ▁hu- car- ▁lumber- ▁dropped- ▁rank- ▁Remember- ▁tactical- ▁County- pac- ▁fleets- ▁downtime- ▁cur- ▁Brands- ▁worst- ▁locally- rd- ▁GE- ▁begins- met- ▁trouble- ▁farmers- ▁Did- ▁log- ▁Product- ▁filling- ▁caught- ▁unsecured- comp- ▁Han- ▁gather- ▁sensors- ff- ▁seriously- ▁sample- ▁preferences- ▁dispositions- ▁gate- room- ▁arm- ▁tailwind- ▁considerations- ▁unmet- fu- ▁Syn- ▁seat- ▁Chairman- ▁affordability- ▁economically- ▁pronounced- ▁telecom- ▁music- ▁schools- ▁tower- ▁Big- ▁Cal- ▁Ohio- ▁statistics- ▁uptake- point- late- ▁disclaimer- ▁chronic- ▁nobody- '%'- ▁Access- ▁debate- ▁Ver- oriented- ▁quantity- ▁refinery- ▁Rich- CI- ▁River- ▁factored- ▁traded- nic- ▁commerce- ▁prescription- ▁exclusively- ▁severity- ▁muted- ▁shutdown- ▁initiate- ▁terminals- af- ▁perfectly- ▁diminish- ▁routine- turn- ▁da- ica- ▁Unfortunately- cal- yl- ▁departments- ▁mechanical- ▁mistake- ▁shrink- ▁chains- ▁surprising- power- ▁ambitious- ▁sweet- ▁lens- ▁fo- ▁ro- ▁pop- ▁probability- ▁slate- ▁Walmart- ▁nutrition- ▁appliance- ▁mild- ▁intervention- ▁Harvey- ▁mindset- ▁accessories- ▁Accordingly- ▁needle- ▁similarly- ▁filled- ▁Microsoft- ▁dispute- ▁Safety- ▁Oil- ▁instrument- ▁branding- ▁disrupt- ▁communicating- ▁simplified- ▁grocery- ional- ▁lowered- sum- ▁Across- ▁pivotal- ▁Higher- ▁formats- ▁strict- ▁Public- ▁compensate- ▁calculate- ▁mall- ▁Meeting- ▁spirit- 2%- ▁greenfield- ▁classified- ▁CD- ▁sea- ▁everyday- ▁suggests- ▁bus- ▁NIM- ▁grateful- q- long- ▁studio- je- gate- ▁gaps- ▁signals- ▁residents- ▁gene- bra- ▁mile- water- ▁Facebook- ▁residual- ▁bolt- ▁crews- ▁eliminated- ▁ten- VA- ▁requiring- ▁Jason- ▁Lake- '10'- ▁payback- ▁Pay- ▁server- ▁oral- ▁Gross- ▁ROE- ▁bode- ▁floating- ▁enroll- ▁union- ▁allocating- ▁barriers- ▁conducting- ▁Tele- gy- ison- 1%- ▁FI- ▁Marine- ▁limitations- ▁proved- AT- ▁boat- ▁cluster- ▁House- ▁stepping- ▁breaking- ▁mortgages- han- ▁Dis- ▁Illinois- ▁merit- ▁commence- ▁embrace- ▁Card- ▁warrant- ▁Bay- ▁restart- ▁widely- ▁screening- ▁generates- ▁star- ▁immuno- ▁flip- ▁customized- ▁Si- ▁commenced- men- ▁commissioning- ▁surplus- ▁Bri- ew- ▁logical- ▁shops- ▁baked- ▁inefficiencies- ▁Andrew- ▁minority- ▁span- ▁mail- owned- ▁25%- ▁employed- ▁cool- ▁medicines- MI- ▁Reconciliation- ▁impossible- ▁teleconference- ▁Express- rated- ▁aging- ▁commercially- ▁demographics- ▁fields- ▁unlike- ▁surge- part- ▁bearing- ▁distinguish- ep- ▁la- ▁developer- ▁Direct- ▁aviation- ▁Vo- ▁techniques- ▁outflows- ▁locked- ▁holistic- ▁idle- ▁premise- ▁Materials- ▁adversely- ero- ▁named- ▁autonomous- ▁friction- ▁coupon- fusion- ▁Cash- ▁ATM- ▁adopting- ▁mu- ▁finalizing- ▁refocus- ▁ancillary- ▁incumbent- ▁Brand- ▁impactful- ▁titles- ▁derived- ▁divestitures- ▁publish- stone- ▁overnight- star- ▁simplifying- ▁installations- ▁imperative- ▁Hill- ▁rein- ▁characterized- ▁employer- ▁Uni- ▁eyes- ▁translated- ▁Web- ▁tenure- ▁lifting- ▁gen- ▁Indonesia- ▁manifest- ▁multiples- ▁sent- ▁divest- ▁regain- ▁promoting- ▁flex- ▁border- ▁centralized- can- ko- ▁onboard- ▁delever- ▁pulp- ▁ruling- ▁strip- ▁Tax- ▁royalties- ▁transcript- ▁AC- ▁Atlantic- ▁defer- type- ▁Ja- ▁aligning- ▁emissions- ▁expression- ▁Point- ▁App- ▁expert- ▁principle- ita- ▁kids- ▁intact- ▁unusually- app- ▁text- ▁George- ▁filter- ▁comprised- ▁concessions- ▁diligent- ▁comply- ▁south- ▁Plus- ▁predictability- positioned- ▁cheaper- ▁struggle- ▁demanding- ▁logo- ▁prescribe- ▁shortages- ▁mentioning- ▁friends- ▁Science- ▁revolution- ▁wellness- '-1'- ▁clearer- ▁British- ▁accrual- ▁cement- ▁bias- ▁placements- ▁forced- ▁modernize- ▁casual- ▁Keep- ▁chemicals- ▁stepped- ▁Broad- ▁Phoenix- ▁Zealand- ▁dominant- ▁Along- ▁lighter- '50'- ▁Senior- ▁Wealth- ▁Court- ▁transitions- ▁directed- ▁enhances- ▁matching- ▁weekly- 3%- ▁fueled- ▁routes- ▁appointment- ▁collaborate- ▁speculate- SE- ▁Tech- ▁centered- force- ▁Ber- ▁progressive- ▁PR- ▁CE- ▁Electric- ▁Richard- ▁Norway- ham- pho- ▁elected- ▁21- ▁genetic- ▁Reg- old- OP- ▁settled- EC- ▁predicted- ▁agricultural- ▁modify- ▁crew- ura- premise- ja- ▁Transportation- ▁erosion- ▁resonating- ▁relied- ▁Gold- free- ution- ▁crucial- ▁opposite- ▁streamlining- ▁optical- spec- ▁divestment- ▁consume- ▁quota- ▁Mid- ▁Chemical- ▁millennial- ▁isolation- ▁Flex- ▁authority- ▁transformative- ▁passionate- ▁stayed- ▁disaster- ▁shortfall- print- ▁answered- ▁Ron- ▁nursing- ▁1,000- ▁race- ▁Tony- ▁instances- ▁implies- ▁Republic- ▁trades- ▁accessible- ▁precisely- location- ▁Several- ▁equities- ▁loaded- ▁hosting- ▁elevate- ▁ra- ▁annuity- ▁elimination- ▁landlord- ▁teach- ▁dining- ▁IC- ya- ▁renewables- ▁consist- ▁score- ▁tightly- ▁neighborhood- ▁argue- ▁craft- ▁licensed- dding- ▁TA- wear- ▁temporarily- ID- ▁affects- ▁attracted- ▁drawing- ▁PD- ▁accurately- ▁interpret- ▁Atlanta- ▁Lower- ▁envision- ▁attached- ▁illustrates- ▁Personal- ▁ordinary- ▁rough- ▁Earnings- ▁eligible- ▁minus- ▁patience- ▁requests- ▁externally- ua- ▁exposures- ▁newest- ▁Tra- ▁wondered- ▁transact- ▁albeit- ▁differentiating- ▁perpetual- ▁Gene- ▁arena- ▁compares- ▁accompanying- ▁Cy- iff- ▁annually- za- lease- ▁activation- ▁exploit- ▁reception- ▁omnichannel- ▁translates- ▁warranty- ▁restructure- ▁cautioned- ▁mills- ▁Time- ▁sponsors- ▁Navy- ▁premature- ▁revisit- ▁detection- cycle- ▁hole- ▁competencies- ▁integral- ▁VA- ▁overwhelming- ▁town- ▁projecting- ▁300- cho- cus- ▁Netherlands- ▁article- ▁corn- ▁Hu- 8%- ▁hydro- ▁charging- ▁shaping- ▁activate- ▁holds- cha- specific- ▁finishing- ▁eager- ▁implant- ▁unrealized- ▁13%- ▁Fair- ▁hence- ▁supposed- ria- ▁cheap- ▁observations- ▁Series- ▁du- ▁computer- ▁populations- ▁fr- ▁propositions- ▁securitization- ▁GP- product- ▁Oklahoma- ibility- play- ▁dig- ▁phasing- vision- ▁collectively- ▁subscriptions- ▁skin- ▁province- ▁nationwide- ▁zones- ▁networking- ▁metals- ▁Resources- ▁winner- ▁Andy- ▁namely- ▁Foods- ▁softening- ▁chase- ▁rebuilding- ▁Free- ze- ▁War- ▁character- 6%- ▁Pri- ▁Automotive- ▁depressed- ▁privilege- ▁statistical- ▁boot- ▁seamlessly- ▁inflows- ▁Electronic- ▁catastrophe- ▁appreciated- ▁hyper- ▁bi- uff- ▁detect- ▁outages- ▁Arizona- ▁durable- ▁mitigated- ▁Live- ▁printing- ▁renovations- nci- ▁alter- teens- ▁Alex- ▁repay- bit- ▁RevPAR- ▁assembly- ▁difficulties- facing- ▁Trust- ▁dual- ▁basins- ▁yielding- ▁competitively- ▁Super- ening- ▁argument- ▁Though- ▁hang- ▁lender- ▁injection- ▁compensated- ▁airplane- ▁fraud- ▁society- ▁Light- ▁formed- ▁Platform- ▁crack- ▁brick- ▁treatments- ▁analytical- ▁metro- ▁fans- ▁King- ▁fabric- ▁Sand- ▁reinvesting- ▁Michigan- ▁14%- ▁Spring- ▁semi- ago- ▁transitioned- ▁Indian- ▁th- ▁bandwidth- ▁tip- ▁rid- ▁fruition- how- ▁applies- ▁delta- ular- ▁Out- ▁Island- ▁simpler- ▁inherently- ▁qualitative- ▁manual- ▁PE- ▁Way- ▁500- ▁band- 7%- ▁determining- ▁grain- ▁conclusions- ▁contributors- ▁variation- ▁leisure- ▁repricing- ▁amended- ▁12-- ▁blended- ▁PA- weight- ▁'20- ▁wise- ▁Report- ▁Los- ▁occasions- ▁inhibitor- ▁SA- ▁paint- ▁sudden- ▁Toronto- ▁indicating- ▁queue- ▁unemployment- ▁horizontal- head- source- ▁CMS- wise- ▁automate- ▁innovating- ▁stopped- ▁digest- tel- ▁observe- ▁transformed- ▁aid- ik- ▁Mu- ▁endeavor- ▁transferred- ▁tuck- ▁Marc- ▁chip- ▁suit- ▁Defense- ▁excuse- ▁Mill- ▁outlet- ▁Jay- ▁desired- ▁digitally- cular- ▁pride- ▁hurdle- add- ▁laying- ▁addresses- ▁onboarding- ▁harm- ▁buffer- ▁configuration- ▁Nor- ▁Line- ▁headlines- istic- ▁overly- ▁clarification- ▁outage- ▁rationalize- ky- ▁letting- ▁patents- ▁Craig- ▁Target- ▁convey- ▁Val- ▁Robert- ▁ski- verse- ▁revisions- TS- ▁Nevertheless- ▁silver- ▁Land- ▁advised- ▁analog- ▁Store- ▁pumping- ▁inject- ▁Property- ▁circuit- ▁showcase- ▁Ultimately- ▁engineered- ▁valid- ▁severance- ▁Micro- ▁roof- ▁DNA- ▁energized- ▁Sam- ▁trailing- ▁SKUs- ▁liquids- ▁iconic- ▁Midland- ▁lineup- ▁suggesting- ▁inquiries- ▁intensive- ▁sake- ▁personalization- ▁tailored- ▁manageable- ▁obtained- ▁outperformed- ▁capitalization- ▁concluding- ▁builders- ▁containers- ▁footwear- ▁trucking- serv- ▁tighten- ▁restrict- ▁Seattle- ▁Supply- ▁Sky- ▁demo- ▁samples- ▁heads- ▁honor- ▁holders- ▁repairs- ▁recapture- ▁bench- ories- ▁buckets- ▁lessons- ▁tenders- ▁modified- ▁vacation- ▁steep- ▁sport- ▁celebrate- ▁prepaid- ▁vacancy- ▁bounce- ▁pan- lia- MO- ▁parks- ▁era- ▁Ko- ▁Natural- ▁achievable- ▁drink- ▁scheduling- ▁inclusive- ▁subsequently- ▁eat- ▁protected- ▁portal- ▁lo- ▁gearing- ▁recommendations- ▁Her- ▁Margin- ▁black- ▁capacities- ▁mitigation- ▁taste- ▁Ben- ▁granular- ▁Association- ▁Research- ▁emergency- ▁phenomenon- ▁pleasant- ▁neuro- ▁fab- SP- ▁advancement- LI- ▁Long- ▁Northwest- ▁comparability- ▁contraction- ▁Actually- ▁chat- ▁awful- ▁pertain- ▁lie- ▁individually- cast- ▁frequent- ▁thrive- ▁lumpiness- ▁authorized- ▁Bu- ▁algorithms- ▁concession- ▁tailwinds- ▁avenues- ▁taxable- ▁Process- ▁medication- ▁resiliency- ▁quiet- ▁fraction- ▁walking- ▁participated- tribut- ▁AG- ▁cumulative- ▁validated- ▁submarket- ▁shorten- ▁opt- 9%- ▁thereafter- ▁relying- ▁Firstly- ▁consumables- ▁reseller- ▁Phil- ▁parcel- stock- ▁pie- ▁aiming- ▁heavier- ▁maturing- ▁measuring- ▁prototype- ▁issuing- ▁Todd- ▁EM- ▁ride- ▁organized- ▁reap- ▁representatives- ▁Louisiana- ▁survival- ▁concert- ▁plate- ▁threats- ▁dip- ▁discover- ▁towers- ▁interruption- ▁deduction- TO- ▁registered- ▁module- tier- ▁ES- ▁keen- ▁Nick- ▁NOI- ▁Avi- ▁whereby- ▁acting- ▁designing- maker- ▁sugar- ▁trough- ▁derisk- ▁relocation- ▁structurally- LE- ▁lies- ▁manufactured- ▁Without- ▁inbound- ▁breakthrough- mod- ▁suited- ▁Tru- ▁surprises- ▁involving- ▁salary- ▁significance- ▁Early- ▁Wood- ▁clinicians- ▁weakening- ▁mini- ▁trick- ▁viewing- RO- ah- answer- ▁outlets- ▁Columbia- ▁Mexican- ▁declare- ▁ounces- ▁removing- ▁phones- ▁advise- ility- pic- j- ▁hurdles- ▁England- ▁silicon- ▁Jan- performing- ▁loading- ▁hosted- ▁23- ▁convention- ▁limiting- ▁cyber- ▁ought- ▁Monday- ▁liver- ▁African- ▁somehow- row- ▁answering- ▁arrive- ▁Lab- edge- ▁covenant- ▁CB- ▁notwithstanding- ▁dictate- ▁bold- ▁kit- ▁slip- building- ▁lend- ▁justify- ▁dosing- ▁assured- ▁36- ▁seats- book- ▁underpin- ▁Welcome- ▁feasibility- ▁mitigating- ▁Thus- ▁jet- ▁Union- ▁contemplate- ▁Ya- ▁OP- bank- ▁clinically- ▁Liberty- ▁cushion- ▁gauge- ▁Take- ▁clearing- ▁James- ▁CRM- ▁Chi- ▁RO- ▁consequences- ▁Operator- ▁chapter- ▁chasing- ▁legislative- ▁remediation- ▁relaunch- ▁phenomenal- ▁documentation- ▁streamlined- ▁labs- lect- expected- ▁reinforces- priced- ▁credibility- ▁furniture- ▁Account- ▁pleasing- ▁collected- view- ▁programmatic- ▁refi- ▁flying- ▁advancements- ▁parking- ▁Missouri- ▁dairy- ▁layers- ▁CI- itation- ▁appealing- demand- ▁CS- ▁sharpen- ▁styles- ▁Club- ▁Future- ▁Minnesota- ▁molecular- ▁scratch- ▁tonnage- ▁Martin- ▁requested- ▁passive- stream- ▁recommend- ▁cooperation- ▁deficit- ▁heritage- ▁roadmap- ▁statutory- ▁Chile- ▁pumps- ▁bills- tec- ▁educational- ▁restructured- ▁rural- ▁automatically- ▁readiness- ▁fish- ▁attend- ▁plug- price- ▁occasion- ▁Cu- ▁billions- responsibilities- ▁compliant- ▁salespeople- ▁dilutive- ▁strictly- ▁solidify- hu- ▁pads- ▁underwrite- ▁mirror- ▁opioid- ▁foster- ▁17%- ▁commercialize- via- 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▁false- ▁matrix- ▁proposing- ▁remedy- ▁shaft- ▁Brett- ▁Hello- ▁Allied- ▁arrow- pronged- ▁snap- cloud- ▁Campbell- ▁Loyalty- ▁Patterson- ▁Terminal- ▁biomass- ▁exercising- ▁footnote- ▁fragrance- ▁frustrated- ▁garage- ▁interference- ▁referendum- ▁simplifies- ▁unrivaled- ▁seemingly- ▁Close- direct- ▁dominate- Health- Corp- ▁Fabric- ▁Casualty- ▁Petrobras- ▁SCOOP- ▁acknowledging- ▁activating- ▁delineation- ▁dissipate- ▁exterior- ▁orthopedic- ▁resumption- ▁shield- ▁stainless- ▁Imaging- ▁outgrow- ▁Beverage- ▁Expect- ▁Outdoor- ▁brilliant- ▁curriculum- ▁decentralized- ▁deteriorating- ▁episodic- ▁fault- ▁Automation- ▁retool- ▁darn- ▁Terra- ▁debut- ▁dangerous- ▁IND- ▁Couple- ▁amortize- dealer- ▁BOSS- ▁Cardinal- ▁Hollywood- ▁Pizza- ▁Study- ▁antitrust- ▁autumn- ▁bunker- ▁ePlex- ▁frequencies- ▁hinder- ▁inflammation- ▁infotainment- ▁relocating- ▁victory- ▁Critical- ▁Geographic- ▁transpire- ▁carpet- ▁insurer- interest- walk- insured- ▁phenomena- ▁Specific- ffsetting- ▁Appalachia- ▁Particularly- ▁Westinghouse- ▁ambassador- ▁lithium- ▁lucrative- ▁unsustainable- ▁firearm- ▁tolerance- ▁FinTech- ▁promo- ▁celebrating- ▁conducive- ▁disparate- ▁Subsea- ▁Tesco- ▁halt- ▁Lauren- ▁Mitch- frac- ▁Managing- ▁Recovery- ▁Watson- ▁Wyndham- ▁compatible- ▁council- ▁facilitator- ▁league- ▁prolific- ▁unacceptable- ▁preempt- ▁secondarily- ▁embedding- ▁vacate- gui- ▁Banc- ▁occupie- enhancing- ▁Dublin- ▁Florence- ▁Franchise- ▁LOE- ▁Properties- ▁clothing- ▁excise- ▁obsess- ▁relapse- ▁Jean- ▁racing- ▁Etsy- ▁burger- ▁Temp- ▁photograph- ▁stride- Mark- watch- ▁equip- ▁Chapter- ▁Offshore- ▁Regulatory- ▁Seasonal- ▁enviable- ▁exemplifie- ▁fracturing- ▁inaugura- ▁plasma- ▁thriving- ▁Fusion- ▁lawyers- ▁peel- ▁alluding- ▁aisle- minating- necdotally- ▁simultaneous- ▁frank- authorized- ▁Dolby- ▁EXPAREL- ▁LTL- ▁Wednesday- ▁biopsy- ▁bureau- ▁collision- ▁contiguous- ▁desperate- ▁expeditious- ▁female- ▁veterinary- ▁Moody- ▁halo- ▁reagent- ▁remiss- ▁handicap- asset- ▁Advertising- ▁Gathering- ▁Surgery- ▁dermatology- 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-    "doi": "10.5281/zenodo.4443058",
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-    "pid": "",
-    "title": "Rhopalapion longirostre Olivier 1807",
-    "title_en": "Rhopalapion longirostre Olivier 1807",
-    "title_zh": "Rhopalapion longirostre Olivier 1807",
-    "description": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
-    "description_en": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
-    "description_zh": "Rhopalapion longirostre (Olivier, 1807)   (Figs. 1–7, 10–11, 13–20, 25–29, 35–37, 41–44, 49–53)   Apion longirostre Olivier, 1807: 35; Pl. III Figs. 51.a, 51.b    Apion (Rhopalapion) longirostre; Schilsky 1906: V    Rhopalapion longirostre; Alonso-Zarazaga 1990: 71    Pseudapion (Rhopalapion) longirostre; Ehret 1994: 8 Type locality. Constantinople [= Istanbul] (Marmara Region, Turkey).    Diagnosis. A Rhopalapion species differing from R. celatum n. sp. in the following combination of characters: male protibiae mucronate; rostrum, in both sexes, on average, more slender (Lr/Wmsr: ♁ 4.23–5.17 [4.64], ♀ 7.81–11.75 [10.25]); in dorsal view, mesorostral dilatation well developed, a little bit dentiform in male and rounded in female; prorostrum with sides feebly concave, particularly in female; prothorax, on average, isodiameric (Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]); disc densely punctate with punctures round to oblong, sometime confluent to form striolae, and interspaces microreticulate; elytra, in dorsal view, elongate in outline, with rounded sides; male meso- and metatibiae with smaller, blunt and oblique apical mucro; different shape of genitalia, in particular, tegmen with shorter apical membranous lobes, subrounded, folded at base and separated by a thin median notch; suprafenestral sclerites with 1–2 long outer setae and 1 shorter inner seta (occasionally 0).    Type series. Olivier described and illustrated this species from an unspecified number of male and female specimens from Constantinople, the current Istanbul. In his collection, stored in the Muséum National d'Histoire Naturelle, Paris, there are six specimens, one here designated as lectotype and the remaining five becoming paralectotypes. One ♁ and 2 ♀♀ are mounted on one card (Figs. 1, 3–5), labelled: longirostre / Const. [= Constantinople] [big handwritten label] // Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label (1834 is the year in which the specimens became part of the museum's collection)] // LECTOTYPUS ♁ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels]. The ♁ specimen on this card is designated as the lectotype. There are also an additional 1 ♁ and 2 ♀♀, mounted on another card (Fig. 2), labelled: Olivier [little round handwritten label] // MUSEUM PARIS / OLIVIER 1834 [rectangular label] // PARALECTOTYPUS ♁ / Apion longirostre Olivier, 1807 / C. Giusto des. 2019 [red rectangular printed label] // PARALECTOTYPUS ♀ / Apion longirostre / Olivier, 1807 / C. Giusto des. 2019 [two red rectangular printed labels].    Material examined. AFGHANISTAN — Faryab Province: Goudgé Konti, 7.VI.1959, G. Remaudère leg., on Althaea (16 ♁♁, 22 ♀♀ MNHN);— Herat Province: Kushk [= Kuschke, Kusche] (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 11 ♁♁, 3 ♀♀ MRSN);— Paktia Province: Gardez, 18.VI.1959, G. Remaudère leg. (1 ♁ MNHN). ALGERIA — Blida Province: Blidah (3 ♁♁, 4 ♀♀ MNHN). ARMENIA: no exact localities: Aras River Valley [= Araxesthal], H. Leder &amp; E. Reitter leg. (1 ♁, 1 ♀ MNHN); Armenia (2 ♀♀ MNHN). AUSTRIA — Lower Austria: Bisamberg, 250 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Vienna: Salamannsdorf, 280 m a.s.l., 9.X.1999, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀♀ CG); Vienna env. (1 ♁, 1 ♀ ZMHB); Vienna env., Ad. Hoffmann leg. (2 ♀♀ ZMHB); Vienna env., E. Moczarski leg. (1 ♁ ZMHB). AZERBAIJAN — Agdash District: Aðdaþ [= Aresch] (2 ♁♁, 2 ♀♀ MNHN);— Ganja-Qazakh District: Ganja [= Elisabetpol] (1 ♁, 1 ♀ CG; 2 ♁♁, 1 ♀ MRSN);— Lankaran District: Lankaran [= Lenkoran], 1897, Korb leg. (1 ♁, 1 ♀ ZMHB); Lankaran [= Lenkoran], Dr. Martin leg. (1 ♁ MNHN). BOSNIA AND HERZEGOVINA: Moglitz, 18.VI.1912 (1 ♁, 1 ♀ ZHMB). BULGARIA — Blagoevgrad: Melnik, 26–27.VI.1979, M. Mazur leg. (5 ♁♁, 4 ♀♀ MW); Melnik env., 12–19.V.1981, H. Wendt leg. (2 ♁♁, 1 ♀ ZMHB); Melnik env., 12–20.V.1981, H. Wendt leg. (1 ♀ ZMHB);— Burgas: Brjastovec env., 19.V.2009, M.G. Morris leg., reared from Alcaea rosea L. (2 ♁♁ MGM); Brjastovec env., 21.V.2012, M.G. Morris leg., reared from Alcaea rosea L. (1 ♀ MGM); Burgas, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB); Otmanli, 12.VII.1977 (1 ♁ MM);— Dobrič: Batovo env., 27.V.2007, M.G. Morris leg. (1 ♀ MGM);— Plovdiv: Kalofer, 14.VII.1978, Borowiec leg. (1 ♁ MW);— Sofia: Samokov, 16.VII.1997, H. Perrin leg., on Alcaea rosea L. (3 ♁♁, 3 ♀♀ MNHN); Samokov, 1– 4.VIII.1998, J.P. Carbonnel leg., on Alcaea rosea L. (1 ♁, 1 ♀ MNHN); Sofia, V.Apfelbeck leg. (1 ♁, 1 ♀ ZMHB);— Varna: Albena env., 7.VI.2006, M.G. Morris leg. (1 ♀ MGM); Tsarkva env., 15.VI.2006, M.G. Morris leg., on Alcaea rosea L. (1 ♁ MGM). CROATIA — Dubrovnik-Neretva County: Dubrovnik [= Ragusa], V.1899, K. Flach leg. (1 ♁ ZMHB); Korčula [= Isola di Curzola], VI.1911, Mussapp leg. (2 ♁♁, 2 ♀♀ MSNG);— Istria County: Rabac, 27.VI–1.VII.1980, L. Cederholm leg. (1 ♀ MW);— Split-Dalmatia County: Spilt, Adria, 8.VII.1939 (1 ♁, 1 ♀ ZMHB);— Zadar County: Zadar [= Zara] env., Müller leg. (1 ♁, 1 ♀ ZMHB); no exact locality: Dalmatia (2 ♀♀ MRSN). CYPRUS: Drouseia, 550 m a.s.l., 17.IV.2010, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 3 ♀♀ CG); Fama- gusta, 5.VII.1931, L. Komites leg., on Citrus sinensis (L.) Osbeck (2 ♁♁, 1 ♀ BMNH); Kidási, 250 m a.s.l., 23.IV.2010, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 2 ♀♀ CG). EGYPT: no exact locality: Aegypt, O. Schneider leg. (1 ♁ ZMHB). FRANCE — Auvergne-Rhône-Alpes: Les Barquets, 5.VI.1991, on Alcea rosea L. (1 ♁ BMNH); Vassieux-en-Vercors, 1,100 m a.s.l., 9.X.2012, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Corse: Furiani env., 200 m a.s.l., 3.VI.1996, C. Giusto leg., on Alcea rosea L. (3 ♁♁, 1 ♀ CG); no exact locality: Corse (1 ♁, 2 ♀♀ MRSN);— Novelle-Aquitaine: Aunac, VIII.1992, A. Datta leg., reared from seeds of hollyhock (3 ♁♁, 2 ♀♀ BMNH);— Occitanie: Etang de Leucate, shore SW, 1 m a.s.l., 24.VI.1995, C. Giusto leg., on Alcea rosea L. (1 ♀ CG);— Provence-Alpes-Côte d'Azur: Cabasse, env. S, 220 m a.s.l. ca., 20.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♀ CG); Gourdon, env. S, 700 m a.s.l., 21.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg. (1 ♁ CG); Malaucene, 360 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 9 ♀♀ CG); Martigues, Cap Couronne, 5 m a.s.l., 17.V.2013, C. Giusto, G. Gardini &amp; S. Zoia leg., on Alcea rosea L. (1 ♀ CG); Massif de la Sainte Baume, Plan d'Aups, 680 m a.s.l., 18.VI.1995, C. Giusto leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ CG); Mont Ventoux, S slopes, Ft. De Rolland, 500–600 m a.s.l., 23.VI.1993, C. Giusto leg., on Alcea rosea L. (10 ♁♁, 7 ♀♀ CG); Saint-Maximinla-Sainte-Baume, 26.V.1988, C. Giusto leg., on Alcea rosea L. (16 ♁♁, 16 ♀♀ CG); Vence, 25.V.1988, C. Giusto leg., on Alcea rosea L. (1 ♁ CG). GEORGIA — Adjara: Batumi [= Batoum] (1 ♁, 1 ♀ MNHN);— Abkhazia: Pitsunda [= Püsunda], coast, 18–30.V.1970, Ermisch leg. (1 ♀ ZMHB);— Mtskheta-Mtianeti: Mtskheta [= Mzcheta], 23–30.VI.1986, Wrase &amp; Schülke leg. (1 ♁, 2 ♀♀ ZMHB); Zahesi [= Zehneti], 800 m a.s.l., 1–10.VI.1987, Wrase &amp; Schülke leg. (1 ♀ ZMHB);— Shida Kartli: Bekami 10.VII.1983, I. Lopatin leg. (1 ♀ MW);— Tbilisi: Kachreti, 5 km E, 600 m a.s.l., 11.VII.2011, M. Košťál leg. (1 ♁, 1 ♀ MK). GERMANY — Baden-W̹rttemberg: Oberndofr, 04.IX.2004, M. Eifler leg. (4 ♁♁, 1 ♀ ZMHB);— Berlin: Berlin, 13.VI.2014, J. Willers leg., on Malva sp. (1♀ ZMBH); Dahlem, Botanical Garden, 21.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB); Dahlem, Botanical Garden, 22.VI.2002, J. Willers leg., on Malva sp. (1 ♁ ZMHB);— Mecklenburg-West Pomerania: Karlsburg (1 ♁, 1 ♀ ZMHB). GREECE — Central Macedonia: Thessaloniki [= Salonique] (1 ♁ MNHN);— Corfu: Corfu (1 ♀ BMNH);— Crete: Anogeia, 700 m a.s.l., 9.VI.1998, Beretta leg. (1 ex. MB);— Eastern Macedonia and Thrace: Metaxades, 28.V.2007, F. Angelini leg. (1 ex. FA); Protokklisi, 28.V.2007, F. Angelini leg. (29 exx. FA);— Epirus: Perama env., 31.V.1989, S. Zoia leg. (1 ♀ CG);— North Aegean: Lemnos, IV–VI.15 (2 ♁♁ MNHN); Lesbo, U. Sahlberg leg. (1 ♁, 1 ♀ ZMHB); Mount Olympus, 14.VI.70 (2 ♁♁, 2 ♀♀ ZMHB); Mount Olympus (1 ♁ MRSN);— Peloponnese: Kastania, 1,200 m a.s.l., 8.V.2004, F. Angelini leg. (2 exx. FA); Tripoli, 28.VII.1978, F. Sacco leg. (3 ♀♀ FS);— Thessaly: Meteora, 19.V.2005, F. Angelini leg. (2 exx. FA);— Western Greece: Kalavryta, 28–29.IV.1999, F. Angelini leg. (1 ex. FA);— Western Macedonia: Kratero, 10.VI.2007, F. Angelini leg. (1 ex. FA); no exact locality: Greece (1 ♀ MNHN). HUNGARY — Central Hungary: Pest, K. Saio leg. (1 ♁, 3 ♀♀ ZMHB);— Central Transdanubia: Balatonkenese (3 ♁♁, 2 ♀♀ ZMHB); Balatonkenese, 28.V.1972, Fix leg. (1 ♁, 2 ♀♀ ZMHB); Balatonkenese, 7.VI.1978, Fix leg. (2 ♀♀ ZMHB); Balatonkenese, Lake Balaton [= Plattensee], 20.V.1972 (1 ♀ ZMHB); Hajmáskér, 200 m a.s.l., 16.V.1996, M. Košťál leg. (1 ♁ MK); Lake Balaton [= Plattensee], 1.VI.1972 (2 ♀♀ ZMHB); Lake Balaton [= Plattensee], 2.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 3.VI.1972 (1 ♁ ZMHB); Lake Balaton [= Plattensee], 4.VI.1972 (2 ♁♁, 1 ♀ ZMHB);— Northern Great Plain: Jászberény, Pórtelek (1 ♀ ZMHB);— Southern Transdanubia: Villány env., V.1980, Sieber leg. (1 ♁, 2 ♀♀ ZMHB); no exact localities: Hungary (1 ♁, 2 ♀♀ BMNH); N Hungary (2 ♁, 2 ♀♀ MRSN). IRAN — Golestan Province: Gorgan, Golestan National Park [= Forêt de Golestan], 23.VI.1965, L. Matile leg. (1 ♁ MNHN); Gorgan, Golestan National Park [= Forêt de Golestan], 24.VI.1965, L. Matile leg. (3 ♁♁, 1 ♀ MNHN); Gorgan, Nahar Khvoran [= Forêt de Naharkhoran], 25.VI.1965, L. Matile leg. (2 ♀♀ MNHN);— Mazandaran Province: Kalardasht, 28.VI.1965, L. Matile leg. (1 ♀ MNHN);— Teheran Province: Teheran, 5.XI.1874, A. Kerim leg. (1 ♀ MNHN). ISRAEL — Haifa District: Beit Oren env., 220 m a.s.l., 6.IV.2014, C. Giusto leg. (8 ♁♁, 9 ♀♀ CG); Haifa [= Caïffa] (3 ♁♁, 1 ♀ MNHN); Mishmar HaCarmel, 310 m a.s.l., 6.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (17 ♁♁, 16 ♀♀ CG); Nahal Oren, 3.IV.1995, E. Colonnelli leg. (1 ♀ FS);— Judea and Samaria Area: Ariel, 6.V.2010, L. Friedman leg., on Alcea setosa (3 ♁♁, 4 ♀♀ CG);— Northern District: 2 km E of Banias, 29.V.1973, I. Löbl leg. (1 ♁ MW); Ein Gev, 185 m b.s.l., 4.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (9 ♁♁, 7 ♀♀ CG); Ha Yarden Park, 190 m b.s.l., 4–5.IV.2014, C. Giusto leg., on Alcea dissecta (Baker f.) Zohary (31 ♁♁, 20 ♀♀ CG); Nazareth, P. de La Brûlerie leg. (1 ♁, 1 ♀ MNHN); Snir, 280 m a.s.l., 5.IV.2014, C. Giusto leg., on Alcea setosa (Boiss.) Alef. (9 ♁♁, 7 ♀♀ CG); Tel Dan, 180 m a.s.l., 5.IV.2014, C. Giusto leg. (12 ♁♁, 2 ♀♀ CG); no exact localities: Israel, reared from seeds of Alcea digitata (1 ♀ BMNH); Palestine, on Alcea setosa (Boiss.) Alef. (1 ♁, 1 ♀ MW). ITALY — Aosta Valley: Aosta, Porossan, 5.VII.1978, G. Bartoli leg. (28 ♁♁, 14 ♀♀, MSNG);— Emilia-Romagna: Castelnovo ne' Monti, 700 m a.s.l., 24.VI.1984, C. Giusto leg. (1 ♀ CG); Gatteo, Sant'Angelo, 14.VI.2011, G. Platia leg. (3 ♁♁, 4 ♀♀ CG); Verghereto, Alfero, 22.VI.1972, Botto leg. (1 ♁ CG); Verghereto, Alfero, 20.VI.1972, Botto leg. (2 ♁♁, 3 ♀♀ MSNG);— Friuli-Venezia Giulia: Monfalcone, Lisert, 5.IX.2010, L. Morin leg. (2 ♀♀ LF); Monfalcone, Lisert, 24.VI.2012, L. Morin leg. (2 ♀♀ LF);— Liguria: Genova, Aggio, 8.VIII.1986, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 5 ♀♀ CG); Genova, Bavari, 300 m a.s.l. ca., 25.VII.1985, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 2 ♀♀ CG); Genova-Nervi, 23.VI.1972, S. Riese leg. (1 ♁, 4 ♀♀ MSNG); Genova-Nervi, 29.V.1972, S. Riese leg. (2 ♁♁, 1 ♀ MSNG); Mignanego, Paveto, 23.VII.1983, G. Ratto leg. (2 ♀♀ CG); Montoggio, Creto, 27.IV.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 2 ♀ CG); Sant'Olcese, 300 m a.s.l., 22.III.1989, C. Giusto leg., on Alcea rosea L. (1 ♁, 3 ♀♀ CG); Stella, 20.VI.1988, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 1 ♀ CG); Tiglieto, 400 m a.s.l. ca., VII.1984, C. Giusto leg., on Alcea rosea L. (14 ♁♁, 8 ♀♀ CG); Tiglieto, 500 m a.s.l. ca., VIII.2013, M. Giusto leg., on Alcea rosea L. (25 ♁♁, 25 ♀♀ CG); Urbe, Martina Olba, 484 m a.s.l., 1.VI.1986, C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Urbe, Martina Olba, 484 m a.s.l., VIII.1984, C. Giusto leg., on Alcea rosea L. (15 ♁♁, 15 ♀♀ CG);— Lombardy: Toscolano Maderno, 13.VII.1969 (1 ♀ CG); Varzi env., 420 m a.s.l., 4.VI.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 6 ♀♀ CG);— Piedmont: Acqui Terme, Ovrano, 300 m a.s.l., 19.VI.1977, G. Salamanna leg. (22 ♁♁, 14 ♀♀ MSNG); Condove, Lajetto, 900 m a.s.l., C. Giusto leg., on Alcea rosea L. (14 ♁♁, 5 ♀♀ CG); Cuorgnè, Roncasso, 417 m a.s.l., 7.IV.1981, P.M. Giachino leg. (1 ♀ MRSN); Laghi della Lavagnina, 25.V.1990, R. Cardara leg. (1 ♁ RC); Lerma, Mond'Ovile, 23.VII.1973, S. Riese leg. (2 ♁♁, 3 ♀♀ MSNG); Montaldo di Mondovì, 800 m a.s.l., 6.VIII.1995, C. Giusto leg., on Alcea rosea L. (6 ♁♁, 3 ♀♀ CG); Monte Musinè, 2.VI.1975, Ribottaro leg. (1 ♁ MRSN); Novalesa, 12.VII.1982, G. Bartoli leg. (7 ♁♁, 6 ♀♀ MSNG); Roasio, 10.VI.1979, R. Caldara &amp; A. Ongaro leg. (1 ♁ RC); Sampéyre, 970 m a.s.l., 5.VIII.1997, C. Giusto leg., on Alcea rosea L. (2 ♁♁, 2 ♀♀ CG); Torre Pellice, 7–15.VII.1980, G. Bartoli leg. (14 ♁♁, 15 ♀♀ MSNG);— Sardinia: Barúmini, 180–200 m a.s.l., 6.VII.1999, C. Meloni leg. (1 ♁ MSNG);— Sicily: Isnello, Piano Zucchi, 1,100 m a.s.l., 16.V.2008, C. Baviera &amp; A. Rando leg. (3 exx. CB); Nicolosi, 6.IX.2015, on Alcea rosea L. (1 ♀ CG; 1 ♁, 1 ♀ MSNG); Novara di Sicilia, 650 m a.s.l., 25.V.2000, G. Müller leg. (1♁, 1 ♀ GM); Taormina, 300 m a.s.l., 1.VI.2000, G. Müller leg. (1♁, 1 ♀ GM);— Tuscany: Bagno a Ripoli, 9.VIII.1978, G. Bartoli leg. (1 ♁ MSNG); Firenze env., V.1994, A. Bordoni leg. (1 ex. AB); Isola d'Elba, Campo nell'Elba, Marina di Campo, 100 m a.s.l., 28.IV.1993, C. Giusto leg., on Alcea rosea L. (1 ♀ CG); Minucciano, Lago di Gramolazzo, 680 m a.s.l., 20.V.1998, F. Angelini leg. (1 ex. FA); Montanare di Cortona, 310 m a.s.l., 1.VIII.2015, C. Giusto leg., on Alcea rosea L. (35 ♁♁, 55 ♀♀ CG);— Umbria: Costacciaro, 520 m a.s.l., 8.VI.2016, M. Bocci leg. (11 ♁♁, 7 ♀♀ FA);— Veneto: Monteviale, Costigiole, 22.V.1988, P. Fontana leg. (2 exx. SB); Pove del Grappa, 27.V.1993, S. Biondi leg. (2 exx. SB); Santorso, Roagna, 3.V.1993, S. Biondi leg. (1 ex. SB); Sarego, Monte Roccolo, 10.VIII.1980, A. Bizzi leg. (24 exx. SB); Val Liona, Grancona (2 exx. SB); Velo Veronese, 1,050 m a.s.l. ca., 22.IX.2013, C. Giusto &amp; G. Gardini leg., on Alcea rosea L. (2 ♁♁, 3 ♀♀ CG); Vicenza, 22.V.1975, Beretta leg. (3 ♀♀ MSNG); Vicenza, 28.V.1975, Beretta leg. (3 ♁♁, 3 ♀♀ CG). KYRGYZSTAN — Chuy Region: Bishkek, 1–3.VI.2003, R. Dobosz leg. (1 ♁ MW); Beshkungei near Bishkek, 800 m a.s.l., 18.VI.2006, R. Ruta leg. (2 ♁♁ MW). LEBANON — Beqaa Governorate: Rachaiya env., 5 km S, Tannoura, 6.VI.2016, C. Reuter leg. (3 ♁♁, 1 ♀ MR);— Beirut Governorate: Beirut [= Beyrouth] (1 ♁, 1 ♀ BMNH; 12 ♁♁, 19 ♀♀ MNHN); Beirut [= Beyrouth], env. N, 16.X.1998, C. Canepari leg. (1 ♁, 1 ♀ CG); Beirut [= Beyrouth], Frater Jean leg. (1 ♀ MNHN); Beirut, IV.1885, D. Ganglbauer leg. (1 ♀ ZMHB);— Mount Lebanon Governorate: between Aalita and Machnaqa, 1,000 m a.s.l., 4–15.V.2000, G. Sama leg. (1 ♀ CG); Kfar Debiane, 1,250 m a.s.l., 30.V.2016, C. Reuter leg. (4 ♁♁, 3 ♀♀ MR). NORTH MACEDONIA — Skopje Statistical Region: Petrovec, Katlanovo, Badar, 27.VII.1975 Wiese, U. Göllner leg. (1 ♁ ZMHB);— Southwestern Statistical Region: Ohrid, 22.VI.1973, B. Gruev leg. (1 ♀ MM);— Vardar Statistical Region: Kavadarci, P.Angelov, leg. (2 ♀♀ MM); no exact locality: Keretschkol, A. Schatzmayr leg. (1♁ MSNG). MOLDOVA: Bender [= Tighina, Bendery], VII.1936, S.G. Hering leg. (15 ♁♁, 13 ♀♀ ZMHB). MONTENEGRO: Herceg Novi [= Castelnuovo], [G. Paganetti-]Hummler leg. (3 ♁♁, 1 ♀ MSNG; 2 ♁♁, 2 ♀♀ ZMHB); Herceg Novi [= Castelnuovo], 15.VI.1911 A. Spaney &amp; F. Schumacher leg. (17 ♁♁, 8 ♀♀ ZMHB); Kotor, 6.V.2012 (1 ♁ RR). POLAND — Lower Silesian Voivodeship: Wrocław, Maślice, 4.VII.2019, M. Wanat leg. (5 ♁♁, 1 ♀ MW);— Subcarpathian Voivodeship: Jarosław, Pasieka ul. Okrzei, 5.VII.2014, M. Wanat leg. (1 ♁, 1 ♀ MW). ROMANIA — Dobruja: Canaraua Fetei, Băneasa env., 1.VII.1998, H. Perrin leg., on Alcea rosea L. (5 ♁♁, 5 ♀♀ MNHN); Dumbrăveni, edge of the forest, 2.VII.1998, H. Perrin leg. (1 ♁, 1 ♀ MNHN); Măcin, 1908, A.L. Montandon leg. (3 ♀♀ MNHN);— Moldavia: Munteni (1 ♁ MRSN);— Transylvania: Cluj-Napoca, Zorilor, 19.VII. 2009, 350 m a.s.l., E. Colonnelli leg. (1 ♀ EC); Sântana de Mureș, Chinari [= Varhegy], Zoppa leg. (1 ♁ MRSN); Timișoara [= Temeswar] (1 ♁, 1 ♀ MNHN);— Wallachia: Comana, Vlasca, A.L. Montandon leg. (1 ♀ MNHN). RUSSIA (SOUTH EUROPEAN TERRITORY)— Southern Federal District: Krasnaja Poljana (5 ♁♁, 3 ♀♀ ZMHB); Volgograd [= Sarepta] (9 ♁♁, 18 ♀♀ MNHN; 1 ♁ ZMHB); Volgograd [= Sarepta], 5.VI.2000 (1 ♀ CG); no exact localities: Russie (1 ♁ MNHN); Russie mér. (1 ♁ BMNH; 6 ♁♁, 4 ♀♀ MNHN). SERBIA — Central Serbia: Požarevac (2 ♁♁, 2 ♀♀ ZMHB);— Vojvodina: Ruma [= Рума], Schwieger leg. (1 ♁, 2 ♀♀ MSNG). SLOVAKIA — Banská Bystrica Region: Radnovce [= Radnóth], E. Csiki leg. (2 ♁♁, 1 ♀ ZMHB);— Košice Region: Malý Horeš, 25.V.1995, M. Wanat leg. (1 ♁ MW);— Nitra Region: Gbelce (1 ♁ MSNG); Nitra [= Neutraer-Comitat], V. Zoufal leg. (2 ♁♁ MRSN); Štúrovo 23.V.1975, Fritsche leg. (14 ♁♁, 3 ♀♀ ZMHB); Štùrovo, Hegyfarok, 150 m a.s.l., 22.V.1992, M. Košťál leg. (1 ♁ MK). SLOVENIA — Slovene Littoral: Sežana, Vrhovlje env., 340 m a.s.l., 3.VI.2012, S. Zoia leg. (1 ♁ CG). SWITZERLAND — Canton of Lucerne: Buchrain, 5.VI.1995, M. Uhlig leg., on Malva sp. (1 ♀ ZMHB); Buchrain, 31.VII.2005, M. Uhlig leg., on Altaea sp. (15 ♁♁, 11 ♀♀ ZMHB);— Canton of Valais: Ausserberg, 1,000 m a.s.l., 5.VII.2015, C. Giusto leg., on Alcea rosea L. (11 ♁♁, 9 ♀♀ CG). SYRIA — As-Suwayda Governorate: As-Suwayda, 3.V.2008, Stěpánek leg. (2 ♁♁ AP);— Homs Governorate: Qala'at al Hosn, Krak des Chavaliers, 671 m a.s.l., 4.V.2009, M. Uliana leg. (1 ♁ LF);— Latakia Governorate: Latakia [= Lattaquie] (4 ♁♁, 4 ♀♀ MNHN). TURKEY — Aegean Region: Bergama 2.V.1916, S.G. Bauer leg. (1 ♀ ZMHB); Ýzmir [= Smyrne] (2 ♁♁, 1 ♀ MNHN); Ephesus, J. Sahlberg leg. (1 ♀ ZMHB); Ephesus, U. Sahlberg leg. (1 ♁ ZMHB);— Black Sea Region: Tokat [= Tkt] (31 ♁♁, 25 ♀♀ MNHN);— Central Anatolia Region: Ankara, Plant Protection Institute (3 ♁♁, 2 ♀♀ BMNH); Konya, 1899, Korb leg. (1 ♁, 1 ♀ ZMHB); Dereköy, 10 km W, 31.V.2011, F. Angelini leg. (1 ♁ CG);— Mediterranean Region: Adana (40 ♁♁, 26 ♀♀ MNHN); Amanusgebirge [= Nur Daðlarý], V–VIII.1914, Dr. Tölg leg. (1 ♀ MNHN); 20 km N of Anamur, 600 m a.s.l., 6.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 3 ♀♀ CG); Arslanköy, 1,450 m a.s.l., 3.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (2 ♁♁, 4 ♀♀ CG); Çamlýyayla, 1,410 m a.s.l., 2.VI.2002, C. Giusto &amp; S. Zoia leg., on Alcea rosea L. (4 ♁♁, 4 ♀♀ CG); Çamlýyayla env., 1,280 m a.s.l., 2.VI.2002, S. Zoia &amp; C. Giusto leg., on Alcea rosea L. (1 ♁ CG); Kahramanmaraş, Püren Geçidi env., 1,550 m a.s.l., 23.V.2001, E. Colonnelli leg. (1 ♀ EC); Karaçay Bucaðý, env. NE, 100 m a.s.l., 31.V.2002, C. Giusto &amp; S. Zoia leg. (4 ♁♁, 7 ♀♀ CG); Pazarcýk env., 1.VI.1983, M. Meregalli leg. (1 ♁, 1 ♀ MM); Samandað, Seleukeia Pieria, 40 m a.s.l., 1.VI.2002, C. Giusto &amp; S. Zoia leg. (2 ♁♁, 3 ♀♀ CG); Tarsus (1 ♁ MNHN);— Marmara Region: Beşik Bay [= Besika Bay] (5 ♁♁, 5 ♀♀ BMNH); Istanbul [= Constantinople] (8 ♁♁, 12 ♀♀ MNHN); Istanbul, Scutari (12 ♁♁, 6 ♀♀ MNHN); no exact localities: [?] Lajoya (10 ♁♁, 3 ♀♀ MNHN); [?] le, de Buffiveuh (4 ♁♁, 1 ♀ MNHN); [?] Turcia, Allion (1 ♁ MNHN); Turkey (12 ♁♁, 10 ♀♀ MNHN); Anatolia [= Asia Minor] (2 ♀♀ BMNH; 2 ♁♁, 3 ♀♀ MNHN); Taurus Montains [= Lyciae Taurus], VI.1903 F. Hauser leg., 1 ♁ MRSN). TURKMENISTAN — Balkan Region: Serdar [= Kysil-Arwat], 1898, F. Hauser leg. (2 ♀♀ BMNH; 1 ♁, 1 ♀ CG; 2 ♁♁, 2 ♀ MRSN);— Mary Region: Chemen-i-Bid, 20–25.IV.1993, P. Čhechowsý leg. (2 ♁♁, 1 ♀ CG); no exact localities: Kopet-Dagh (3 ♁♁ MRSN); Saramsakli (2 ♀♀ MRSN). U.S.A. — Utah: Providence, 29.V.1970, G.F. Knowlton leg. (2 ♀♀ BMNH);— Minnesota: Moorhead, 3.IX.1970, J.R. Powers leg., on hollyhock seeds (1 ♁, 1 ♀ MW);— Missouri: Columbia, VII.1981 (1 ♁, 1 ♀ CG; 1 ♀ FS). UKRAINE — Autonomous Republic of Crimea: Crimea (1 ♁, 1 ♀ BMNH; 1 ♁ MNHN); Dmitrove, 250 m a.s.l., 19.IV.2008, M. Košťál leg. (1 ♁ MK); Dobre, 28.V.2005, J. Borowski leg. (1 ♀ MW); Simferopol (1 ♁, 1 ♀ ZMHB);— Khmelnytskyi Oblast: Kamyanets Podilskiy, Smotrich env., 26.VI.1996, M. Wanat leg. (1 ♁, 1 ♀ MW).   Other no exact localities: Caucasus (2 ♀♀ MNHN); Stalino, IX.1943 (1 ♁ MNHN); Syrie (1 ♁, 3 ♀♀ MNHN); Syrie, C. Piochard de La Brûlerie leg. (1 ♀ MNHN); Tr. Casp. [= Transcaspia], Penschdeh (1 ♁ MNHN).   Redescription (Ƌ ♀). Lb: ♁ 2.30–3.23 mm [2.80 mm], ♀ 2.25–3.44 mm [2.95 mm]. Body integument dark brown to piceous; apical tibial mucrones, coxae and trochanters brown to dark brown. Body scales piliform with   more or less pointed apex (Figs. 10–11); only few scales on base of 3 rd elytral interval, at apex of elytra, on meso-and metanepisterna, on mesoepimera and at apex of femora with more or less truncate apex.   Male rostrum as in Figs. 13–14, 17–18; in dorsal view, mesorostral dilatation slightly dentiform, prorostrum with slightly concave sides, feebly narrowing from mesorostral dilatation to apical third then weakly widened to apex; in profile, rostrum distinctly angulate above antennal insertion; Lr: 0.78–1.24 mm [0.99 mm]; Lpr: 0.54–0.91 mm [0.71 mm]; Lmtr: 0.21–0.36 mm [0.28 mm]; Wmsr: 0.20–0.25 mm [0.23 mm]; Lr/Wmsr: 4.23–5.17 [4.64]; Lr/Lp: 1.15–1.65 [1.41]; Lb/Lr: 2.42–2.90 [2.74]. Female rostrum as in Figs. 15–16, 19–20; in dorsal view, prorostrum with parallel sides, feebly widened toward apex in the apical fourth; Lr: 1.28–2.51 mm [1.88 mm]; Lpr: 0.95–1.94 mm [1.43 mm]; Lmtr: 0.29–0.62 mm [0.44 mm]; Wmsr: 0.15–0.22 mm [0.20 mm]; Lr/Wmsr: 7.81–11.75 [10.25]; Lr/Lp: 2.00–3.17 [2.54]; Lb/Lr: 1.34–1.79 [1.46]. Forehead densely punctate; punctures round, 17–23 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt, microsculptured. Antennae (Figs. 25–26) inserted at basal 0.24–0.32 [0.29] (♁) or 0.20–0.27 [0.24] (♀) of rostrum; La: ♁ 0.78–1.15 mm [0.98], ♀ 1.06–1.47 mm [1.25 mm]; Lc: ♁ 0.31–0.41 mm [0.36 mm], ♀ 0.32–0.42 mm [0.39 mm]; La/Lpr: ♁ 1.12–1.66 [1.37], ♀ 0.67–1.20 [0.86]; La/Lc: ♁ 2.66–3.06 [2.81], ♀ 2.81–3.58 [3.19].  Prothorax (Figs. 6–7) mostly isodiametric to slightly longer than wide, widest just behind middle; only few populations show weakly transverse prothorax; disc densely punctate, punctures round to oblong, 17–25 µm in diameter, hardly separated or, very often, confluent to form striolae; interspaces matt; Lp: ♁ 0.55–0.81 mm [0.68 mm], ♀ 0.55–0.89 mm [0.72 mm]; Wp: 0.56–0.80 mm [0.68 mm], ♀ 0.52–0.84 mm [0.72 mm]; Lp/Wp: ♁ 0.88–1.10 [1.00], ♀ 0.91–1.09 [1.01]. Scutellum subquadrate, matt. Elytra (Figs. 6–7), in dorsal view, with weakly rounded sides and weakly widening backwards; on elytral disc, intervals 2.9–3.3 times as wide as striae; Le: ♁ 1.57–2.25 mm [1.92 mm], ♀ 1.51–2.39 mm [2.02 mm]; We: ♁ 0.85–1.28 mm [1.05 mm], ♀ 0.83–1.30 mm [1.09 mm]; Le/We: ♁ 1.66–2.01 [1.82], ♀ 1.71–2.02 [1.86]. Legs with femora robust and rather swollen, 2.80–2.90 (♁), 2.91–3.04 (♀) times as long as wide; all male tibiae (Figs. 27–29) with short, blunt and oblique apical mucro; tarsi moderately elongate; 1 st segment 1.60–1.90 times as long as wide; 2 nd 1.12–1.37 times as long as wide, 0.80–0.84 times as long as 1 st; 3 rd 0.81–0.90 times as long as wide, with lobes well developed, 0.83–0.98 times as long as 2 nd; onychium 1.27–1.44 times as long as 2 nd segment.   Male sternite IX as in Fig. 35. Tegmen (Figs. 36–37) with apical membranous lobes fairly long, subrounded, folded at base; suprafenestral sclerites with 2 long outer setae (only occasionally 3) and 1 shorter inner seta (occasionally 0). Penis (Figs. 41–44), in ventral view, with parallel sides from base to ostium then gradually restricted to apex. Female genitalia as in Figs. 49–53.   Remarks. This species is very variable throughout its distribution.Apart from some local forms, it is possible to observe clinal variability of the following characters: vestiture composed of denser, whiter and thicker scales from North-West to South-East, probably as a response to increasing sunshine (Figs. 10–11); body size on average bigger in Europe than in the remaining territories; rostrum, particularly that of female, longer and more widened at apex in Europe while it tends to be shorter and more cylindrical in southern and eastern populations; analogously, and probably related to the length of the rostrum, prothorax is longer than wide in European and western Asiatic populations and isodiametric or even transverse in some Middle East populations.   The \"typical\" form, decidedly predominant in the whole area, is characterized by long rostrum (Figs. 13–16) (Lr: ♁ 0.78–1.24 mm [0.99 mm], ♀ 1.60–2.51 mm [2.03 mm]; Lr/Wmsr: ♁ 3.71–5.17 [4.53], ♀ 8.00–13.00 [10.37]; Lr/Lb: ♁ 2.42–3.09 [2.79], ♀ 0.54–0.78 [0.68]), female prorostrum longer than antennae (Lpr/La: ♀ 1.05–1.50 [1.25]), prothorax, on average, longer than wide (Lp/Wp: ♁ 1.00–1.10 [1.03], ♀ 1.00–1.09 [1.03]) and vestiture composed of thin scales with pointed apex (Fig. 10). This form is associated with Alcea rosea L.   A characteristic form has been found in Israel, exclusively along the Jordan Valley, on Alcea dissecta (Baker f.) Zohary. It is recognizable by its short rostrum, particularly in female (Figs. 17–20) (Lr: ♁ 0.82–1.06 mm [0.95 mm], ♀ 1.28–1.66 mm [1.48 mm]; Lr/Wmsr: ♁ 3.74–4.52 [4.24], ♀ 6.70–8.67 [7.52]; Lr/Lb: ♁ 2.75–3.22 [2.96], ♀ 0.47–0.55 [0.52]); female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.86–1.00 [0.96]); transverse prothorax (Lp/Wp: ♁ 0.88–0.99 [0.97], ♀ 0.91–0.99 [0.96]) and vestiture similar to the \"typical\" one.   A further form from Israel, not found in the Jordan Valley, associated with Alcea setosa (Boiss.) Alef., shows intermediate characters between the two previous forms (Lr: ♁ 0.82–1.11 mm [0.98 mm], ♀: 1.50–2.16 mm [2.01 mm]; Lr/Wmsr: ♁ 3.90–4.68 [4.25], ♀ 8.33–10.68 [9.76]; Lr/Lb: ♁ 2.79–3.04 [2.90], ♀ 0.58–0.69 [0.66]; Lpr/La: ♀ 1.04–1.20 [1.13]; Lp/Wp: ♁ 0.96–1.06 [1.00], ♀ 0.96–1.05 [1.00]).   The last form, whose host plant is unknown, occurs in Afghanistan (Faryab Province: Goudgé Konti). It is similar to that from Jordan Valley, but it is characterized by longer rostrum (Lr: ♁ 0.96–1.23 mm [1.13 mm], ♀: 1.34–1.99 mm [1.73 mm]; Lr/Wmsr: ♁ 4.50–5.14 [5.02], ♀ 8.40–9.75 [8.85]; Lr/Lb: ♁ 2.48–2.98 [2.68], ♀ 0.53– 0.63 [0.58]), female prorostrum, on average, shorter than antennae (Lpr/La: ♀ 0.83–1.00 [0.90]), prothorax variable in length, on average isodiametric (Lp/Wp: ♁ 0.99–1.03 [1.00], ♀ 0.95–1.03 [1.00]) and vestiture composed of thicker scales (Fig. 11).   Box-plots (Fig. 59) generated from analysis of data exclusively relating to female specimens and limitedly to four ratios (Lr/Lb, Lr/Wmsr, Lpr/La, Lp/Wp) show the differences among the forms mentioned above. For convenience, populations were grouped as follows: pop. 1: \"typical\" form from Istanbul (Constantinople and Scutari), host plant: A. rosea; pop. 2: all remaining populations belonging to the \"typical\" form, host plant: A. rosea; pop. 3: Israel (excluding Jordan Valley), host plant: A. setosa; pop. 4: Israel (Jordan Valley), host plant: A. dissecta; pop. 5: Afghanistan: Goudgé Konte, host plant: unknown.    Distribution. Palaearctic Region: Algeria, Portugal, Spain, France, Luxembourg, The Netherlands, Germany, Poland, Ukraine, Moldova, Romania, Hungary, Slovakia, Czech Republic, Austria, Switzerland, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Albania, North Macedonia, Bulgaria, Greece, Cyprus, Egypt, Israel, Jordan, Lebanon, Syria, Turkey, Georgia, Russia (South European Territory), Azerbaijan, Armenia, Iran, Afghanistan, Turkmenistan, Kyrgyzstan (Alonso-Zarazaga et al. 2017, Furlan 2002, Grosso-Silva 2005, Tuba &amp; Lakatos 2010).   Nearctic Region (introduced): Canada (British Columbia, Ontario, Quebec, Nova Scotia), U.S.A. (Washington, Oregon, California, Utah, Colorado, Kansas, Iowa, Minnesota, Wisconsin, New York, Vermont, Maine, New Hampshire, Massachusetts, Pennsylvania, Maryland, Ohio, Virginia, Kentucky, Illinois, Missouri, Arkansas, Tennessee, North Carolina, South Carolina, Georgia) (Kissinger 1968; Majka et al. 2007, 2011; O'Brien &amp; Wibmer 1982; Per- rin 1984; Salsbury 2000; Tuba &amp; Lakatos 2010).    Remarks. This species is reported here, for the first time, from Bosnia and Herzegovina, Egypt, Kyrgyzstan, and in North America from Utah and Minnesota.   The ascertained presence in Kazakhstan and Uzbekistan only of R. celatum n. sp. and its possible presence also in Tajikistan suggest that former records concerning the presence of R. longirostre in these areas should be considered plausible, but requiring confirmation (see Schatzmayr 1923, Osella 1966, Osella 1968, Nasreddinov 1975, Meregalli &amp; Osella 1978, Perrin 1984, Arzanov 1990, Alonso-Zarazaga 2011, Alonso-Zarazaga et al. 2017). For this reason Kazakhstan, Uzbekistan and Tajikistan have been temporarily excluded from the distribution of R. longirostre (Fig. 64).    Bionomy. This is a widespread and abundant species that lives in xeric or xerothermophilous habitats from low altitudes, 190 m b.s.l., to mountains up to 1,550 m a.s.l. It is associated with Alcea digitata Alef., Alcea dissecta (Baker f.) Zohary, Alcea rosea L. and Alcea setosa (Boiss.) Alef. Considerations regarding possible additional host plants and further bionomic notes are summarized above, as indicated in the paragraph dedicated to the bionomy of the genus.",
-    "authors": [
-      {
-        "name_en": "Giusto, Carlo",
-        "name_zh": "Giusto, Carlo"
-      }
-    ],
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-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Coleoptera",
-      "Brentidae",
-      "Rhopalapion",
-      "Rhopalapion longirostre"
-    ],
-    "keywords_zh": [
-      "Biodiversity",
-      "Taxonomy",
-      "Animalia",
-      "Arthropoda",
-      "Insecta",
-      "Coleoptera",
-      "Brentidae",
-      "Rhopalapion",
-      "Rhopalapion longirostre"
-    ],
-    "publication_date": 1610467200000,
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-    "modified": "",
-    "status": "",
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-    "source": "scidb"
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-    "doi": "10.5281/zenodo.4499539",
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-    "title": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
-    "title_en": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
-    "title_zh": "ESPnet2 pretrained model, Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.ave, fs=16k, lang=en",
-    "description": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- 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▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- 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▁Bears- ▁supporter- ▁investing- ▁cleared- ▁Nan- siew- ▁ancestors- ▁tackle- fan- ▁renting- ▁smelling- ▁Per- six- ▁def- ▁begins- ▁circled- ata- ▁ram- ▁glu- mation- ▁boarding- ▁tiles- Asia- ability- dey- bit- les- Pok- ▁revis- ▁biscuits- ten- ▁heading- ▁rappers- ▁Boys- her- ▁Han- ▁storm- zz- ▁Qi- ▁involves- tro- ▁candles- ▁vet- ▁holds- ▁efforts- ▁Ed- ▁originate- ▁limits- ▁Bel- our- ▁vera- ▁fe- ▁tease- ▁expire- ▁vendors- ▁milestone- ▁landscape- ▁ano- side- ▁Master- ice- ▁alphabet- ▁statue- ▁minister- ▁Legend- ▁airline- ▁consumer- ▁lastly- ▁rapper- ▁teenager- ▁belief- ▁Tu- ging- ▁downstair- ▁occur- ▁shades- ku- ▁Christ- ▁backstab- ▁defer- mai- ▁arguments- ▁writer- ▁flap- while- ▁memes- cai- ▁He- use- ▁sigh- fe- ▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- 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▁humidity- ▁treetop- ▁Faith- ▁Galaxy- ▁presume- ▁shady- ▁egoistic- ▁fellowship- ▁lighthearted- ▁Kiri- ▁suc- ▁waterproof- ▁Bangla- ▁surrender- ▁warden- ▁banter- ▁scanning- ▁bali- ▁coincidentally- ▁Totoro- ▁storeroom- ▁confidential- ▁squishy- ▁pension- ▁Danny- ▁diluted- ▁Asus- ▁stationery- ▁posh- ▁westernised- ▁Valley- ▁deprived- ▁Mandai- ▁Wanna- ▁gymming- ▁submitted- ▁secretive- ▁Shuan- ▁hangover- ▁Switch- ▁floss- ▁introducing- ▁Nancy- Mars- ▁autis- ▁Baba- ▁existent- ▁corgi- ▁Ireland- Shiong- ▁Amos- ▁Rover- ▁Suria- ▁punk- ▁spize- ▁paperwork- ▁Liao- ▁Liz- Meng- Out- ▁expressway- ▁sniper- ▁leap- ▁idiotic- ▁lingo- ▁dent- ▁clinical- ▁Sega- ▁elf- ▁Fuji- ▁Leona- ▁specialisation- ▁intentionally- ▁quitted- ▁Cody- ▁bedok- ▁chanting- ▁cyst- ▁decorating- ▁competitiveness- ▁Lok- ▁upsetting- ▁integrated- ▁transcriber- ▁gray- ▁censored- ▁munch- ▁portal- ▁vast- ▁budd- ▁sloth- ▁stopover- ▁publicity- ▁Music- ▁approachable- ▁strolling- ▁confined- ▁Jones- ▁Singap- ▁coaches- ▁allocated- ▁escalate- 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▁fri- mort- ▁Mai- mary- ▁banker- ▁researchers- ▁Jolin- kuah- gate- ▁bothering- chan- ▁Timb- ▁smoked- ▁formed- ▁cheers- phy- nto- ▁Pai- ▁clogg- Studios- ▁kilometers- ▁syllables- ▁aeroplanes- ▁perfumes- ▁measures- ▁Pola- Got- ▁dragg- ▁lea- ▁beetles- source- ▁cuter- ▁handbags- ▁Timah- ▁founder- ▁partying- ▁Bras- ▁Nov- ▁donated- ▁thrills- ▁scenarios- ▁meantime- ency- ▁Lab- ▁soldiers- ▁tiers- Wala- ▁Poo- ▁cosmetics- ▁Euros- tory- ▁classy- ▁trib- ▁ming- ▁Kin- ▁Rim- ater- ▁loc- ▁lunches- ▁hotdogs- ▁rays- ▁fillers- ▁hays- ▁collapse- inda- 'On'- ching- Board- ▁shin- ▁rubb- ▁sys- ▁muffins- ique- gh- ette- ▁hais- ▁cabs- ator- ▁recon- ▁bef- ▁persuad- ▁gamers- ▁Ta- ▁Dal- ▁kn- ▁kittens- ▁guitars- lated- ▁faithful- end- ▁performances- chou- ▁Bing- ▁sponsors- ille- Del- ▁roses- que- ▁apples- ▁Lo- ▁Teen- ▁laps- ▁poems- ▁je- ▁sciences- ▁Bi- ▁aesthetics- ▁arrows- ▁streams- ▁crimes- ▁Ez- ▁Del- ▁hal- ▁advis- ▁Ting- ▁lis- ▁sw- ▁concentrat- ▁planets- ium- ▁sal- ▁bricks- ▁Maria- ▁Xu- lter- Ko- ▁controlle- 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Box- ▁Drag- ▁depict- ▁Fresh- ▁Hero- ▁absurd- ▁coop- ▁terror- ▁resist- quid- ▁proficien- ▁Heart- ▁Arctic- ▁Dempsey- ▁Jamie- ▁continual- ▁intellect- ▁prescripti- ▁render- ▁ulti- Anatomy- Azhar- Buffet- Dhabi- Holmes- Mirza- Ramsay- Takraw- tsuka- zealand- ▁Austria- ▁Barbecue- ▁Britney- ▁Caleb- ▁Chapteh- ▁Chihuahua- ▁Chivas- ▁Chloe- ▁Citibank- ▁Commonwealth- ▁Crescent- ▁Desmond- ▁Diwali- ▁EXO- ▁Exco- ▁FairPrice- ▁Freedom- ▁Grinch- ▁Hyundai- ▁Ibiza- ▁Illusion- ▁Jurassic- ▁Keppel- ▁Maniac- ▁Mauritius- ▁Minesweeper- ▁Mohammad- ▁Normal- ▁Olivia- ▁Photoshop- ▁Popular- ▁Queensway- ▁Ringgit- ▁Scarlet- ▁Serena- ▁Shirley- ▁Slurpee- ▁Snapchat- ▁Society- ▁Supper- ▁Tiffany- ▁Ultra- ▁Vasantham- ▁Vishnu- ▁Wales- ▁Wikipedia- ▁Zendaya- ▁abuden- ▁academy- ▁accustom- ▁affirmation- ▁altitude- ▁apocalypse- ▁apostrophe- ▁articulate- ▁assertive- ▁avatar- ▁beansprout- ▁bhutan- ▁bodyguard- ▁bollard- ▁calcium- ▁chapati- ▁colourblind- ▁comfy- ▁complacent- ▁connotation- ▁crumpled- ▁deluxe- ▁detriment- ▁duff- 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▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- 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▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- 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lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- 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▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- ▁accelerate- Donki- ▁cucumbers- ▁rifles- ▁Run- ▁gro- ▁insider- ▁jav- ▁meatballs- ▁pirates- ▁spi- ▁handy- ▁Pas- ▁blogg- kie- rack- illion- ▁Pak- ▁emojis- ▁kilos- ▁Thin- lying- ▁worksheets- ▁notebooks- ▁crows- cia- ▁Kent- urity- ▁anal- ▁chunks- ▁buds- ▁Zen- ude- ▁ze- ola- ▁concentrati- ▁shak- ▁selfless- ▁Masters- ▁landscapes- ▁milestones- ▁expires- ▁moderniz- misunderstanding- Rush- ▁lou- ipped- ▁growl- ▁skit- elle- oid- por- Cook- ▁stakes- ▁belongings- ▁passages- smati- ▁zhu- ▁storybooks- ▁Rav- ▁fra- opped- ▁val- ▁squirrels- ▁tales- ▁prais- ▁colon- kee- rul- ▁relocate- ▁Cad- ▁Gia- ▁oppo- ▁opp- ▁optim- alized- ▁zipp- ▁Phil- dar- neo- ▁Pant- nuel- ▁Shake- ▁auditor- ▁Zhu- own- ▁tec- ▁stru- ▁terminat- gue- ▁rece- achi- urn- ftie- ▁opposit- ▁exa- ▁worsen- ▁sti- folding- shui- lessly- ▁marginal- ▁lob- rom- ▁crumb- bble- ▁Alon- acy- ▁Jenni- ▁spe- ▁Band- ▁spra- ▁shoo- afar- ▁ste- bak- Pie- ▁stan- ▁Ou- ime- fetched- iterate- ▁stead- ▁ferri- elecast- anna- ▁Lam- dol- ▁Bab- uhwe- mpang- ▁mindless- rman- Leste- ▁sympath- ▁ken- Clan- pad- ▁Human- atory- chai- decker- ▁cous- ▁Com- onia- ▁Bee- ▁bermuda- ▁scallop- ▁Pilate- ▁diaper- ▁pebble- ▁germ- ▁coral- ▁investor- ▁statistic- Ranger- ▁brace- ▁lyric- ▁alway- ▁zo- ▁slush- merah- ▁perk- ▁researcher- alism- stage- leen- body- ▁rab- ▁sportsman- ▁neg- Cent- make- ▁decorat- ▁certif- ▁gran- ▁oldie- Cage- ▁iden- ▁clog- ▁Chang- ality- ▁hur- ▁glam- wo- ▁Pul- ▁Julie- ▁Cal- ▁lectur- ▁Sand- west- Jobs- ▁complet- ▁achiev- tude- oup- nova- away- ▁Kee- Ban- ▁purchas- ▁esca- ▁wor- ▁Himalaya- ▁froze- ▁Sho- ▁deserv- ▁blush- ▁hoard- ▁choreograph- ▁dishearten- craft- ▁deci- ▁windsurf- ▁disco- ▁sightsee- ▁accord- ventu- ▁figur- pson- ▁schem- ▁arrang- Angel- ▁snip- icular- world- ▁ostracise- ▁chant- ▁confiscate- ▁excite- ▁elect- ▁endanger- bloc- ▁detach- atholic- ▁suspend- Network- Singapore- Woman- vious- Grande- Brigade- holder- Scott- Three- Bull- chomp- child- takraw- colli- tempered- Obama- Ninja- Ocean- attaya- restricted- Ralph- lifting- spirit- Busan- Black- Stella- ngsana- tight- different- grass- merit- queue- wak- consider- road- Maths- Ayam- boat- ondeh- Grey- Singaporean- shock- ▁greed- heard- yana- obby- ▁Ronald- Strange- clockwise- ▁suff- ▁graceful- ologist- open- ▁carpent- astle- ▁swag- ▁servic- ▁rehears- ▁millenni- vergreen- ▁submissi- hennai- enegade- esistance- ▁Mexic- ▁obstruct- ▁Aqua- ▁coincide- ▁depart- ddling- ▁celeb- ▁deploy- evio- negotia- ▁hydro- ▁protrud- ▁Comfort- ▁Disco- ▁boycott- Beckham- Bennett- Carrey- Clues- DiCaprio- Muhammad- Payne- Ramsey- Ribbon- Rowling- Solo- Uemura- arella- ligently- rrogate- ▁AddMath- ▁Adele- ▁Aidil- ▁Akbar- ▁Aloysius- ▁Anderson- ▁Anjib- ▁Anusha- ▁Bermuda- ▁Buddy- ▁Canberra- ▁Carnage- ▁Clean- ▁Colorado- ▁Coupet- ▁Daryl- ▁DeepMind- ▁Depot- ▁Donnie- ▁Durian- ▁Edusave- ▁Fiido- ▁Georgia- ▁Ghibly- ▁Gudetama- ▁Guinness- ▁Guzheng- ▁Heidi- ▁Hummus- ▁Immortal- ▁Insidious- ▁Jewel- ▁Kahshee- ▁Katherine- ▁Kelantan- ▁Kozminski- ▁Labyrinth- ▁Loann- ▁Lookism- ▁MUIS- ▁Mcnugget- ▁Millennials- ▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- ▁espresso- ▁execution- ▁exfoliant- ▁exquisite- ▁fingertip- ▁gizzard- ▁globe- ▁graffiti- ▁hiccups- ▁hokkaido- ▁hyperactive- ▁iModel- ▁imitation- ▁impediment- ▁indebted- ▁indomie- ▁inertia- ▁infested- ▁infinite- ▁interconnected- ▁introspective- ▁intrusive- ▁jewelleries- ▁keloid- ▁keropok- ▁knuckle- ▁liddat- ▁lychee- ▁mackerel- ▁malamute- ▁mechatronics- ▁memorabilia- ▁menacing- ▁menthol- ▁messenger- ▁migraine- ▁mortgage- ▁muachee- ▁muffled- ▁mussels- ▁narrate- ▁negotiation- ▁nitrogen- ▁obscure- ▁oriental- ▁overcooked- ▁overcrowded- ▁paitao- ▁parasite- ▁persuasion- ▁pinafore- ▁pistachio- ▁pistol- ▁predator- ▁premature- ▁preoccupied- ▁prettie- ▁profiling- ▁prototype- ▁radish- ▁reincarnated- ▁reindeer- ▁repurpose- ▁resilient- ▁retrospect- ▁revenue- ▁rhino- ▁ritual- ▁roulette- ▁salvage- ▁sandbag- ▁scorpion- ▁scrawny- ▁sculp- ▁secluded- ▁silhouette- ▁simulation- ▁simultaneously- ▁slipshod- ▁sneezing- ▁snippet- ▁sparring- ▁splatter- ▁spooky- ▁sprinkle- ▁structural- ▁strudel- ▁stylus- ▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- 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▁blackcurr- ▁bookshelf- ▁calefare- ▁cashflow- ▁categorise- ▁caterpillar- ▁cheapskate- ▁chief- ▁clarity- ▁clause- ▁cleave- ▁codependent- ▁commonwealth- ▁compilation- ▁concurrently- ▁conspiracy- ▁conspire- ▁corruption- ▁cottage- ▁credible- ▁crescent- ▁damaging- ▁decline- ▁declining- ▁degenerate- ▁demolition- ▁demotivate- ▁devastated- ▁devastating- ▁diaphragm- ▁dictator- ▁disgrace- ▁dispatch- ▁displeasure- ▁dystopian- ▁elitism- ▁endorphin- ▁ensemble- ▁equity- ▁estella- ▁exempted- ▁exorbitant- ▁fanciful- ▁flaunt- ▁flavourtown- ▁forefather- ▁forensic- ▁gibberish- ▁gimme- ▁gloomy- ▁glucose- ▁gratification- ▁gruesome- ▁guava- ▁gynae- ▁hammock- ▁harmonizer- ▁heaviest- ▁hibernate- ▁hickey- ▁honeydew- ▁hungrier- ▁hurried- ▁hypothermia- ▁imaginative- ▁impractical- ▁inducing- ▁inefficiency- ▁infatuated- ▁influenza- ▁infused- ▁inquisitive- ▁insecurities- ▁insignificant- ▁jaguar- ▁jinx- ▁kanken- ▁lavender- ▁lenient- ▁lilac- ▁litigation- ▁locksmith- ▁loiter- ▁longevity- ▁maggots- ▁maisonette- 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Xiong- ▁Demi- ▁Hebe- ▁gently- ▁shameful- ▁drumlet- ertis- ▁confrontational- ▁pupa- ▁drastically- ▁guac- ▁Jared- ▁Marmalade- ▁empowerment- ▁flunk- ▁jellybean- ▁visc- Paddle- ▁Dollah- ▁Tangled- ▁bullier- ▁clapping- ▁resembles- Care- ▁Asos- ▁Moza- ▁Westgate- ▁quarry- ▁Lazuli- ▁chorus- ▁tequila- ▁wafer- ▁Jeanette- ▁SpiderMan- ▁grumbling- ▁kinship- Lapis- Madrid- ikgu- ▁coordinator- ▁dahl- ▁yoogane- ▁Kimberly- ▁Pharrell- ▁brigade- ▁forgivable- ▁dyslexia- ▁nicotine- ▁sensitivity- ▁Chick- ▁Edward- Ferrell- ▁Siva- ▁bleak- ▁Cheong- ▁civilized- ▁reciprocation- ▁Singsale- ▁elementary- ▁laloo- ▁Gear- sibility- ▁Mogu- ▁limelight- Train- ▁kakak- ▁shortlist- rped- ▁carousell- ▁footprint- Kurau- ▁globalised- ▁mower- ▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- 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▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- ▁amputat- ▁usua- ▁Gol- ▁Bur- lopp- after- ▁Havai- killers- ▁swop- oosebumps- sheet- ozy- ▁qualifi- ushed- ▁cohesi- ▁squeez- ▁smel- ▁Choo- ▁consol- ▁swerv- view- ▁schedul- ▁shiver- ▁troubl- heart- ▁ide- ▁initiat- ▁desir- ▁retir- Sua- ▁emphasiz- ▁reschedule- ▁inflate- 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dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- ▁Gulliver- ▁HabourFront- ▁Hanzo- ▁Harzik- ▁Hayde- ▁Hiragana- ▁Holdings- ▁Hollandaise- ▁Hologram- ▁Honestbee- ▁Hoshino- ▁Hsinchu- ▁Hunter- ▁Husserl- ▁Imperial- ▁Inktober- ▁Innok- ▁Jamaica- ▁Janabelle- ▁Jefri- ▁Jolibee- ▁Kendra- ▁Kenora- ▁Khalees- ▁Kinokuniya- ▁Kitori- ▁KouFu- ▁Leaves- ▁Leuch- ▁Lexus- ▁Libya- ▁Lilac- ▁Lontong- ▁Lorraine- ▁Luffy- ▁Management- ▁Matthew- ▁McNugget- ▁Motorola- ▁Mumbai- ▁Nebraska- ▁Nelson- ▁Nigeria- ▁Nobel- ▁Nothingness- ▁Nyx- ▁Oliander- ▁Otogi- ▁Paradise- ▁Pentatonix- ▁Petpetpet- ▁Pharaoh- ▁Poppins- ▁Portuguese- ▁Poteng- ▁Prakash- ▁Pretty- ▁Puff- ▁Qihua- ▁Reebonz- ▁Refash- ▁Remember- ▁Revlon- ▁Ribena- ▁Rooney- ▁Roxy- ▁Sabbath- ▁Sabsuka- ▁Scorpio- ▁Scramble- ▁Senibong- ▁Sennheiser- ▁Sensei- ▁Seraphina- ▁Shabir- ▁Shrek- ▁Silence- ▁Sogou- ▁Soraya- ▁Spotty- ▁Squirtle- ▁Supernatural- ▁Swarovski- ▁Syaz- ▁Sylvester- ▁Technique- ▁Thanksgiving- ▁Thosai- ▁Thriller- ▁Tigreal- ▁TikTok- ▁Transylvania- ▁Trivers- ▁Ukraine- ▁Uprising- ▁Valuetronic- ▁Vatican- ▁Velvet- ▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- 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▁prorated- ▁pupil- ▁quantitative- ▁quarantine- ▁radius- ▁ramekins- ▁rebatch- ▁reclusive- ▁recuperate- ▁reenacti- ▁refurnish- ▁regenerate- ▁registry- ▁reinstate- ▁relativity- ▁relegate- ▁reprimand- ▁respawn- ▁retention- ▁retrac- ▁rewriting- ▁rofl- ▁rudimenta- ▁rupee- ▁scrutinise- ▁serenade- ▁shoelace- ▁shrimp- ▁shrub- ▁silhoutte- ▁skilful- ▁smudge- ▁smuggling- ▁societies- ▁spatula- ▁splinter- ▁sprout- ▁stepdad- ▁succinct- ▁sugarcane- ▁sunroof- ▁surgical- ▁surveillanc- ▁suspicion- ▁symmetr- ▁thickener- ▁titanium- ▁toothpick- ▁tornado- ▁transsexual- ▁treading- ▁trench- ▁unattainable- ▁unavailable- ▁unbeat- ▁uncountable- ▁undercover- ▁underprivilege- ▁unemployment- ▁unfrozen- ▁unleashed- ▁unopened- ▁unprepared- ▁unscrew- ▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- 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▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- 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▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- 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▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- 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▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- 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▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- 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':'- Alchemist- Brolin- Cavani- Converter- Defence- Exchange- Explore- Fansipan- Girlfriend- Grannis- Kerang- Lovato- Mandeb- Marbles- Marinda- Meyer- Packard- Ragnarok- Reborn- Rendezvous- Samsuddin- Spect- Tarik- Trench- Walker- appetising- appuccino- bahru- conferenc- dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- 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▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- 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dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    unit: 1000    nlayers: 1required:- output_dir- token_listversion: 0.9.6distributed: true",
-    "description_en": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- 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▁Bears- ▁supporter- ▁investing- ▁cleared- ▁Nan- siew- ▁ancestors- ▁tackle- fan- ▁renting- ▁smelling- ▁Per- six- ▁def- ▁begins- ▁circled- ata- ▁ram- ▁glu- mation- ▁boarding- ▁tiles- Asia- ability- dey- bit- les- Pok- ▁revis- ▁biscuits- ten- ▁heading- ▁rappers- ▁Boys- her- ▁Han- ▁storm- zz- ▁Qi- ▁involves- tro- ▁candles- ▁vet- ▁holds- ▁efforts- ▁Ed- ▁originate- ▁limits- ▁Bel- our- ▁vera- ▁fe- ▁tease- ▁expire- ▁vendors- ▁milestone- ▁landscape- ▁ano- side- ▁Master- ice- ▁alphabet- ▁statue- ▁minister- ▁Legend- ▁airline- ▁consumer- ▁lastly- ▁rapper- ▁teenager- ▁belief- ▁Tu- ging- ▁downstair- ▁occur- ▁shades- ku- ▁Christ- ▁backstab- ▁defer- mai- ▁arguments- ▁writer- ▁flap- while- ▁memes- cai- ▁He- use- ▁sigh- fe- ▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- ▁Eminem- ▁President- ▁Sydney- ▁WeChat- ▁accommodate- ▁aglio- ▁attentive- ▁coleslaw- ▁congrats- ▁consumption- ▁cringy- ▁deaf- ▁district- ▁drumstick- ▁evaporate- ▁expenditure- ▁gantry- ▁hummingbird- ▁hypocrite- ▁inconvenient- ▁instinct- ▁intuition- ▁irrational- ▁policies- ▁pricing- ▁purplish- ▁servant- ▁skeptical- ▁surviving- ▁syndrome- ▁terrace- ▁turret- ▁wedges- ▁Angkor- ▁Ellen- ▁Hanoi- ▁Ofo- ▁Rebecca- ▁increasing- ▁outdated- ▁pursuit- ▁Heartland- ▁association- ▁psychologist- ▁unpredictable- ▁Yates- ▁manufacturing- ▁index- ▁botak- ▁quantity- ▁Marco- ▁runner- ▁memorising- ▁obsession- ▁ferment- ▁lamppost- ▁balm- ▁Tuas- ▁Frank- ▁announcement- ▁penalty- ▁Nyonya- ▁leverage- ▁Monkey- ▁stroke- ▁font- ▁token- ▁massive- ▁chopping- ▁wheat- ▁kiasi- ▁optimistic- ▁mochi- ▁subtle- ▁embarrassment- ▁secretary- ▁smartphone- ▁wives- ▁Arabic- ▁bold- ▁popping- ▁sync- ▁curb- ▁summary- ▁kacang- ▁Catholic- ▁continuously- ▁Mio- ▁reuse- ▁colorful- ▁problematic- ▁Tower- Centre- ▁Hsien- ▁Jackie- ▁fussy- 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can- ▁yearly- ▁terminate- ▁connections- ▁joint- ▁exception- ▁drip- ▁scrape- ▁cre- ▁listing- ▁Fun- ▁scrap- ▁lan- ▁questioning- ▁Mar- ythic- ▁scholars- hipped- hu- ▁reader- ▁col- ▁quotes- ▁strengths- ery- ▁calculation- ▁feather- ▁muffin- ▁restriction- ▁slaves- ▁downward- ▁Bun- ▁handles- ▁sandal- ▁shoulders- ▁exists- od- ▁lac- ▁que- ec- ▁Wu- ▁Ice- ring- ▁Dad- ▁ter- id- ▁Fat- ▁rob- ▁pointer- ▁min- ▁wan- ▁sy- ▁Roman- ▁sua- tai- ▁gut- ▁nieces- uhyou- ▁hun- School- ▁overthink- ▁execute- scape- ▁pierce- Kayu- ▁Louis- America- club- House- ▁rotate- Hao- ▁singlet- ▁gracious- ▁Taufik- ▁Mickey- ▁Beyblade- ▁Clara- ▁Donald- ▁Drake- ▁Eiffel- ▁Kelvin- ▁Natalie- ▁Nigel- ▁PlayStation- ▁Porsche- ▁Sundram- ▁Tamarind- ▁Tamiya- ▁Watson- ▁Zumba- ▁allergy- ▁ambience- ▁balancing- ▁belacan- ▁bimbo- ▁bisque- ▁bouncy- ▁carried- ▁clarify- ▁climax- ▁condominium- ▁decrease- ▁diversity- ▁efficiency- ▁eyelid- ▁fascinate- ▁feast- ▁fickle- ▁filtration- ▁forklift- ▁fortune- ▁increment- ▁infrastructure- ▁mattress- 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▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- lude- ▁cubes- ▁Jenn- ▁websites- ▁interestingly- ▁acai- ▁sitcoms- ▁Kinder- ▁seating- Pi- ▁che- ▁ci- ▁puddings- aj- here- ▁odds- ax- ▁margin- ▁Caucasians- ▁attributes- ▁Alias- ▁dreamer- ▁flyers- ▁Ze- ▁blanks- list- ▁temperatures- Chi- llao- ▁Meet- ▁alphabets- ▁ministers- ▁statues- ▁Legends- rou- ▁attacks- ▁notch- ▁pointers- ▁etc- ▁gears- ▁Zhuan- ault- ▁seize- ▁puzzles- layer- ▁mana- ▁tents- ▁directional- ▁dun- omo- ▁snapp- ▁del- ▁Angu- ▁aids- ▁Ch- ▁Tun- ▁Jac- ▁sins- aki- ▁Ver- ▁wou- ▁fundamentals- non- ▁triangles- ▁buns- ▁bugs- now- ▁ele- run- ▁integrati- ▁recommendations- ▁Pad- ▁gar- shao- ▁hol- ▁fisher- old- ▁Raja- ▁spikes- ▁mun- ▁dinosaurs- ▁mentors- ▁Har- ▁spirits- ▁organizations- zy- ▁Christians- Magic- ▁dangers- erry- oi- ▁dock- ▁slopes- ▁artistes- ▁ru- ▁references- ongs- ▁Wiz- ▁markings- very- eresa- ▁kam- ▁blessings- ▁gigs- gle- ▁improvements- ▁pubs- ▁sib- bel- ▁comms- ▁meta- ▁pimples- ▁stares- ▁pota- ▁Ken- ene- Xun- ggle- ▁paw- ▁threat- ▁resonate- ▁axe- ▁schoolmate- ▁emerge- Studio- ixes- ▁merchant- ▁souvenir- hop- ▁kilometer- ▁syllable- ▁beverage- Boy- ▁competitor- ▁rumour- ▁ancestor- ▁sneaker- verse- ▁compromises- ▁whip- low- ▁brownie- ▁notion- assim- then- ential- ▁tang- ▁campaigns- ▁constrain- ▁zha- ami- ▁Eu- bal- ched- ▁constraint- fron- ▁Col- ▁Sai- ▁nu- agar- ▁riches- minate- ▁chil- Chin- minating- boro- iously- ▁historic- lay- ▁Pay- ookie- ▁Tea- turn- imp- field- ▁chang- room- place- ▁blu- De- power- keeper- qi- ▁stroll- rick- ▁snorkel- ▁Tao- ▁discharge- breaker- ▁empower- ▁vari- charge- ▁obsess- ▁locate- ▁injure- ▁disband- ▁disconnect- provoking- ▁recharge- Talk- Cooper- pedas- Bowl- Arabia- Brown- Pool- Storage- Republic- Tiger- Andrew- Mother- heaven- regardless- Bukit- kampung- eleven- Briyani- bono- curry- ▁moisturise- cute- with- Jones- ierce- Fox- ▁ima- ▁sensi- ▁convention- ▁Port- Chiong- onesty- ▁equip- iculous- ▁eavesdrop- ▁descend- ▁Naomi- ▁Warner- ▁cigar- Carter- Ferguson- Lloyd- Miserables- Purcell- '`'- ligence- ▁Atlanti- ▁Azazel- ▁Bendemeer- ▁Berlin- ▁Bluetooth- ▁Chirashi- ▁Derrick- ▁Dhey- ▁Dropbox- ▁Farouk- ▁Fifa- ▁Fitness- ▁Fullerton- ▁Henderson- ▁Jacqueline- ▁Kentucky- ▁Kukup- ▁Kumar- ▁Manila- ▁McLaren- ▁Mediterranean- ▁Mendaki- ▁Myeongdong- ▁Nagoya- ▁Nickelodeon- 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▁revamp- ▁rinsing- ▁robbery- ▁sakae- ▁salicylic- ▁seduce- ▁silicon- ▁silliest- ▁sincerity- ▁slogan- ▁soulmate- ▁stereo- ▁sunshine- ▁symphony- ▁synonym- ▁tchoukball- ▁territory- ▁thunderstorm- ▁tragedy- ▁troublemaker- ▁unethical- ▁unplanned- ▁unpleasant- ▁untidy- ▁unwilling- ▁urgency- ▁vanguard- ▁velvet- ▁vodka- ▁whitish- ▁wholeheartedly- ▁zumba- Gerrard- Kusama- ▁Aloy- ▁Bachelor- ▁Darren- ▁Dream- ▁Dumbledore- ▁Eugeo- ▁Fairprice- ▁Googling- ▁Hunger- ▁Khalid- ▁Manhattan- ▁Mystic- ▁Topshop- ▁cadet- ▁chemotherapy- ▁communities- ▁concise- ▁convincing- ▁diagram- ▁disintegrate- ▁fragile- ▁ideology- ▁irregular- ▁lavish- ▁narrator- ▁orangutan- ▁saikang- ▁triangular- ▁Cedar- ▁JetStar- ▁Laneway- ▁Persian- ▁Totally- ▁constitute- ▁delta- ▁eatigo- ▁emulate- ▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- ▁separat- ▁incur- ▁nurtur- ▁invad- ▁Find- ▁Prince- ▁Vic- scooter- corn- Karat- ▁subsidize- ummer- lbert- ▁congest- ▁recite- ▁misuse- ▁retrench- about- course- oodle- ▁debut- Smart- Batisah- Jovi- Turtle- ristiano- Airline- Titans- Ronaldo- Chomp- Cruise- Mirror- States- Nathan- Leong- kacang- Prata- Romeo- jalan- Autumn- William- negotiable- Chicken- James- Science- boyfriend- chocolate- machine- drama- twenty- Miam- ▁crisp- biryani- Mouse- center- ▁summar- motion- ▁theoretical- Minh- ▁clutter- ▁vivid- ▁instantaneous- ▁Lor- ▁Saba- asian- ▁authorit- ▁midfield- post- chiong- ▁imply- Wee- ▁proactive- ▁pronoun- ▁immigrati- ▁disrupt- eptic- ▁Buddh- ▁experi- ▁revolution- ▁Yayoi- ▁Xav- ▁Corp- Vuitton- ▁indefinite- ▁mislead- ▁residu- '?'- Bilis- Einstein- Giggs- Hospital- Local- Muniz- Quinn- Sivan- jumma- siol- ▁After- ▁Atiqah- ▁Backstreet- ▁Badminton- ▁Bidadari- ▁Britain- ▁Broadway- ▁Capella- ▁Century- ▁Chijmes- ▁Cinderella- ▁Colosseum- ▁CrossFit- ▁Cyclops- ▁Daegu- ▁Darvis- ▁Dasani- ▁Diablo- ▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- ▁accelerate- Donki- ▁cucumbers- ▁rifles- ▁Run- ▁gro- ▁insider- ▁jav- ▁meatballs- ▁pirates- ▁spi- ▁handy- ▁Pas- ▁blogg- kie- rack- illion- ▁Pak- ▁emojis- ▁kilos- ▁Thin- lying- ▁worksheets- ▁notebooks- ▁crows- cia- ▁Kent- urity- ▁anal- ▁chunks- ▁buds- ▁Zen- ude- ▁ze- ola- ▁concentrati- ▁shak- ▁selfless- ▁Masters- ▁landscapes- ▁milestones- ▁expires- ▁moderniz- misunderstanding- Rush- ▁lou- ipped- ▁growl- ▁skit- elle- oid- por- Cook- ▁stakes- ▁belongings- ▁passages- smati- ▁zhu- ▁storybooks- ▁Rav- ▁fra- opped- ▁val- ▁squirrels- ▁tales- ▁prais- ▁colon- kee- rul- ▁relocate- ▁Cad- ▁Gia- ▁oppo- ▁opp- ▁optim- alized- ▁zipp- ▁Phil- dar- neo- ▁Pant- nuel- ▁Shake- ▁auditor- ▁Zhu- own- ▁tec- ▁stru- ▁terminat- gue- ▁rece- achi- urn- ftie- ▁opposit- ▁exa- ▁worsen- ▁sti- folding- shui- lessly- ▁marginal- ▁lob- rom- ▁crumb- bble- ▁Alon- acy- ▁Jenni- ▁spe- ▁Band- ▁spra- ▁shoo- afar- ▁ste- bak- Pie- ▁stan- ▁Ou- ime- fetched- iterate- ▁stead- ▁ferri- elecast- anna- ▁Lam- dol- ▁Bab- uhwe- mpang- ▁mindless- rman- Leste- ▁sympath- ▁ken- Clan- pad- ▁Human- atory- chai- decker- ▁cous- ▁Com- onia- ▁Bee- ▁bermuda- ▁scallop- ▁Pilate- ▁diaper- ▁pebble- ▁germ- ▁coral- ▁investor- ▁statistic- Ranger- ▁brace- ▁lyric- ▁alway- ▁zo- ▁slush- merah- ▁perk- ▁researcher- alism- stage- leen- body- ▁rab- ▁sportsman- ▁neg- Cent- make- ▁decorat- ▁certif- ▁gran- ▁oldie- Cage- ▁iden- ▁clog- ▁Chang- ality- ▁hur- ▁glam- wo- ▁Pul- ▁Julie- ▁Cal- ▁lectur- ▁Sand- west- Jobs- ▁complet- ▁achiev- tude- oup- nova- away- ▁Kee- Ban- ▁purchas- ▁esca- ▁wor- ▁Himalaya- ▁froze- ▁Sho- ▁deserv- ▁blush- ▁hoard- ▁choreograph- ▁dishearten- craft- ▁deci- ▁windsurf- ▁disco- ▁sightsee- ▁accord- ventu- ▁figur- pson- ▁schem- ▁arrang- Angel- ▁snip- icular- world- ▁ostracise- ▁chant- ▁confiscate- ▁excite- ▁elect- ▁endanger- bloc- ▁detach- atholic- ▁suspend- Network- Singapore- Woman- vious- Grande- Brigade- holder- Scott- Three- Bull- chomp- child- takraw- colli- tempered- Obama- Ninja- Ocean- attaya- restricted- Ralph- lifting- spirit- Busan- Black- Stella- ngsana- tight- different- grass- merit- queue- wak- consider- road- Maths- Ayam- boat- ondeh- Grey- Singaporean- shock- ▁greed- heard- yana- obby- ▁Ronald- Strange- clockwise- ▁suff- ▁graceful- ologist- open- ▁carpent- astle- ▁swag- ▁servic- ▁rehears- ▁millenni- vergreen- ▁submissi- hennai- enegade- esistance- ▁Mexic- ▁obstruct- ▁Aqua- ▁coincide- ▁depart- ddling- ▁celeb- ▁deploy- evio- negotia- ▁hydro- ▁protrud- ▁Comfort- ▁Disco- ▁boycott- Beckham- Bennett- Carrey- Clues- DiCaprio- Muhammad- Payne- Ramsey- Ribbon- Rowling- Solo- Uemura- arella- ligently- rrogate- ▁AddMath- ▁Adele- ▁Aidil- ▁Akbar- ▁Aloysius- ▁Anderson- ▁Anjib- ▁Anusha- ▁Bermuda- ▁Buddy- ▁Canberra- ▁Carnage- ▁Clean- ▁Colorado- ▁Coupet- ▁Daryl- ▁DeepMind- ▁Depot- ▁Donnie- ▁Durian- ▁Edusave- ▁Fiido- ▁Georgia- ▁Ghibly- ▁Gudetama- ▁Guinness- ▁Guzheng- ▁Heidi- ▁Hummus- ▁Immortal- ▁Insidious- ▁Jewel- ▁Kahshee- ▁Katherine- ▁Kelantan- ▁Kozminski- ▁Labyrinth- ▁Loann- ▁Lookism- ▁MUIS- ▁Mcnugget- ▁Millennials- ▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- ▁espresso- ▁execution- ▁exfoliant- ▁exquisite- ▁fingertip- ▁gizzard- ▁globe- ▁graffiti- ▁hiccups- ▁hokkaido- ▁hyperactive- ▁iModel- ▁imitation- ▁impediment- ▁indebted- ▁indomie- ▁inertia- ▁infested- ▁infinite- ▁interconnected- ▁introspective- ▁intrusive- ▁jewelleries- ▁keloid- ▁keropok- ▁knuckle- ▁liddat- ▁lychee- ▁mackerel- ▁malamute- ▁mechatronics- ▁memorabilia- ▁menacing- ▁menthol- ▁messenger- ▁migraine- ▁mortgage- ▁muachee- ▁muffled- ▁mussels- ▁narrate- ▁negotiation- ▁nitrogen- ▁obscure- ▁oriental- ▁overcooked- ▁overcrowded- ▁paitao- ▁parasite- ▁persuasion- ▁pinafore- ▁pistachio- ▁pistol- ▁predator- ▁premature- ▁preoccupied- ▁prettie- ▁profiling- ▁prototype- ▁radish- ▁reincarnated- ▁reindeer- ▁repurpose- ▁resilient- ▁retrospect- ▁revenue- ▁rhino- ▁ritual- ▁roulette- ▁salvage- ▁sandbag- ▁scorpion- ▁scrawny- ▁sculp- ▁secluded- ▁silhouette- ▁simulation- ▁simultaneously- ▁slipshod- ▁sneezing- ▁snippet- ▁sparring- ▁splatter- ▁spooky- ▁sprinkle- ▁structural- ▁strudel- ▁stylus- ▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- ▁locket- ▁fluently- vour- ▁detached- ▁biasness- ▁hypo- ▁practicality- ▁elected- ▁Wood- ▁Batisah- ▁Turtle- ▁Grabfood- ▁Jovi- ▁sev- ▁cooper- ▁rundown- ▁Sedan- ▁queer- ▁kaka- ▁Stal- ▁Xbox- ▁swerve- ▁native- ▁Bowl- ▁Talk- ▁confiscated- ▁stubbornness- ▁factories- ▁jiak- ▁damm- ▁saman- ▁traumatize- ▁profitable- ▁Gmail- ▁Sakurai- ▁downfall- ▁quali- ▁percentile- ▁keng- ▁Made- ▁Factor- ▁varied- heongsam- ▁ahem- ▁signpost- ▁Biz- ▁Vista- ▁Born- ▁occasional- ▁Liang- ▁ditch- ▁crip- ▁muse- ▁Everest- ▁mainland- ▁compu- ▁denser- ▁Lagoon- ▁Utama- essert- ▁Nepali- ▁Show- ▁potion- ▁creamier- ▁Jake- ▁zipper- ▁Six- ▁Cooper- ▁Zhen- ▁pesto- ▁Guo- ▁realisation- ▁maki- ▁cheong- ▁jug- ▁restructur- chup- ▁activated- ▁minimally- ▁Chiong- ▁clone- ▁kaw- ▁crushes- ▁bender- ▁nani- ▁Minh- ▁westerners- ▁implying- ▁mustache- ▁brushes- Xie- ▁urgently- ▁tuning- ▁Sister- ▁burped- ▁eliminated- ▁strengthen- ▁doughy- ▁sizing- ▁giveaway- ▁bronzer- ▁comical- ▁disrespected- ▁beeping- ▁campaigner- wang- ▁Nara- ▁Soon- ▁Circu- ▁merely- ▁enduring- ▁exceeded- ▁standee- ▁Cass- ▁Premiere- ▁godson- ▁fleshy- ▁skii- ▁dane- ▁sketching- tiao- ▁sesh- itive- ▁tangled- Bai- Chap- ▁Gaga- ▁bir- ▁whisker- ▁Grande- ▁Post- ▁coverall- ▁distributed- ▁underlined- ▁amused- ▁adapter- ▁bouldering- ▁externally- ▁hotline- 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▁blackcurr- ▁bookshelf- ▁calefare- ▁cashflow- ▁categorise- ▁caterpillar- ▁cheapskate- ▁chief- ▁clarity- ▁clause- ▁cleave- ▁codependent- ▁commonwealth- ▁compilation- ▁concurrently- ▁conspiracy- ▁conspire- ▁corruption- ▁cottage- ▁credible- ▁crescent- ▁damaging- ▁decline- ▁declining- ▁degenerate- ▁demolition- ▁demotivate- ▁devastated- ▁devastating- ▁diaphragm- ▁dictator- ▁disgrace- ▁dispatch- ▁displeasure- ▁dystopian- ▁elitism- ▁endorphin- ▁ensemble- ▁equity- ▁estella- ▁exempted- ▁exorbitant- ▁fanciful- ▁flaunt- ▁flavourtown- ▁forefather- ▁forensic- ▁gibberish- ▁gimme- ▁gloomy- ▁glucose- ▁gratification- ▁gruesome- ▁guava- ▁gynae- ▁hammock- ▁harmonizer- ▁heaviest- ▁hibernate- ▁hickey- ▁honeydew- ▁hungrier- ▁hurried- ▁hypothermia- ▁imaginative- ▁impractical- ▁inducing- ▁inefficiency- ▁infatuated- ▁influenza- ▁infused- ▁inquisitive- ▁insecurities- ▁insignificant- ▁jaguar- ▁jinx- ▁kanken- ▁lavender- ▁lenient- ▁lilac- ▁litigation- ▁locksmith- ▁loiter- ▁longevity- ▁maggots- ▁maisonette- ▁majestic- ▁meritocracy- ▁millipede- ▁mojito- ▁monastery- ▁monorail- ▁mukbang- ▁nepotism- ▁nonchalant- ▁nonetheless- ▁obesity- ▁odac- ▁opaque- ▁origami- ▁ostrich- ▁outstretch- ▁overpopulated- ▁override- ▁parapet- ▁patriarchal- ▁persuasive- ▁phenomenon- ▁pigmentation- ▁piloxing- ▁pomelo- ▁powerbank- ▁preservation- ▁pressurising- ▁prostitute- ▁prostitution- ▁rambling- ▁recreating- ▁rectify- ▁reimburse- ▁relevanc- ▁remedies- ▁renowned- ▁reorganise- ▁reptile- ▁responsive- ▁rethink- ▁reunite- ▁revelation- ▁rosti- ▁sabotage- ▁sacrificial- ▁sanctuary- ▁saviour- ▁sidekick- ▁skinnier- ▁slapped- ▁sleek- ▁slurp- ▁smirk- ▁stellar- ▁stench- ▁storytelling- ▁stylo- ▁submarine- ▁substantiate- ▁sunbathing- ▁surcharge- ▁surmise- ▁surreal- ▁suspens- ▁swept- ▁tactful- ▁tangerine- ▁thanksgiving- ▁threadmill- ▁tortoise- ▁transmission- ▁trickshaw- ▁tumeric- ▁turmeric- ▁unbreakable- ▁unconventional- ▁unfamiliar- ▁unforgettable- ▁unfulfill- ▁unheal- ▁unpack- ▁unreliable- ▁unveil- ▁velocity- ▁vigilant- ▁volatile- ▁wacoal- ▁wicked- ▁wilderness- ▁withdrew- Ketchum- ▁Brokeback- ▁Clifford- ▁Economics- ▁Eeyor- ▁Eudora- ▁Kebab- ▁Matcha- ▁Narelle- ▁Patrick- ▁Salim- ▁berapa- ▁concuss- ▁entangle- ▁imprison- ▁inactive- ▁incinerati- ▁insured- ▁irritable- ▁juggling- ▁kerb- ▁penthouse- ▁physiotherapist- ▁posterior- ▁prosecut- ▁robust- ▁shrunk- ▁solitude- ▁turd- ▁underpass- ▁wholemeal- ▁yip- Ambeng- Kennedy- ▁Hydr- ▁Inisha- ▁Kellogg- ▁Morinho- ▁Petercrouch- ▁Reader- ▁desensitize- ▁eggplant- ▁embroider- ▁enchant- ▁fandom- ▁hamburger- ▁harmonic- ▁investigating- ▁lassi- ▁leftward- ▁miserably- ▁mistress- ▁pagoda- ▁pigtail- ▁pulpo- ▁recognising- ▁reign- ▁riffs- ▁sleepwalk- ▁tatami- ▁temptation- ▁twitch- ▁yikes- itamin- ▁Pierre- ▁bypass- ▁delegat- ▁pawnshop- Charlotte- endrick- lender- ▁Aberc- ▁Carrot- ▁Kamu- ▁Loyang- ▁Peaky- ▁Smurf- ▁Yahoo- ▁garner- ▁pressurizing- ▁simplify- Cowell- Neeson- ▁Apax- ▁Clive- ▁bloop- ▁bodied- ▁eagle- ▁jeera- ▁republic- ▁sachet- ▁witchcraft- ▁worrywart- ▁Jumper- ▁Patek- ▁Shannon- ▁Tofu- ▁Zubi- ▁airshow- ▁beacause- ▁caesarean- ▁inspiriting- ▁maximize- ▁microbio- ▁mysteries- ▁remedy- ▁saloon- ▁saucy- ▁teapot- ▁tutu- ▁Lovisa- ▁Saraca- ▁Scout- ▁arbitrary- ▁fuming- ▁ivy- ▁lollipop- ▁poetic- ▁solemn- Chaplin- ▁Carlyn- ▁Collin- ▁Gelato- ▁Kenji- ▁Merek- ▁stutter- ▁Sahana- ▁Sehun- ▁holocaust- ▁plaque- ▁Masala- ▁Pyth- ▁comedies- ▁dabbing- ▁eavesdropping- ▁frilly- ▁revolutionary- ▁uprising- ▁Yankee- ▁Melody- ▁Saku- ▁Ubergrapher- ▁obligated- ▁robbing- ▁villian- Granger- fected- ▁Peace- ▁assurance- ▁collide- ▁stubble- ▁tiresome- ologne- ▁Phisha- ▁Rasa- ▁cauli- ▁Germaine- ▁Libra- ▁Seventeen- ▁agak- ▁dividend- ▁Raned- ▁Stray- ▁detest- ▁esters- ▁froyo- ▁recollection- ▁toppled- ▁prerequisite- ▁pyjamas- ▁sacred- ▁selves- ▁Fariz- ▁Lucy- ▁Musa- ▁Davina- ▁Judy- ▁blemishes- ▁hippie- ▁porch- ▁skewe- ▁exponentially- ▁mistreat- ▁sparrow- Diaries- kram- ▁gourd- ▁horlick- ▁ascend- ▁daft- ▁subtraction- ▁maturing- ▁normative- ▁Aquaman- ▁Farid- ▁Pinang- ▁Soju- ▁parody- ▁rasp- Xiong- ▁Demi- ▁Hebe- ▁gently- ▁shameful- ▁drumlet- ertis- ▁confrontational- ▁pupa- ▁drastically- ▁guac- ▁Jared- ▁Marmalade- ▁empowerment- ▁flunk- ▁jellybean- ▁visc- Paddle- ▁Dollah- ▁Tangled- ▁bullier- ▁clapping- ▁resembles- Care- ▁Asos- ▁Moza- ▁Westgate- ▁quarry- ▁Lazuli- ▁chorus- ▁tequila- ▁wafer- ▁Jeanette- ▁SpiderMan- ▁grumbling- ▁kinship- Lapis- Madrid- ikgu- ▁coordinator- ▁dahl- ▁yoogane- ▁Kimberly- ▁Pharrell- ▁brigade- ▁forgivable- ▁dyslexia- ▁nicotine- ▁sensitivity- ▁Chick- ▁Edward- Ferrell- ▁Siva- ▁bleak- ▁Cheong- ▁civilized- ▁reciprocation- ▁Singsale- ▁elementary- ▁laloo- ▁Gear- sibility- ▁Mogu- ▁limelight- Train- ▁kakak- ▁shortlist- rped- ▁carousell- ▁footprint- Kurau- ▁globalised- ▁mower- ▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- ▁modification- ▁Asam- ▁shawl- ▁Heath- ▁chapteh- ▁Desiree- ▁alignment- ▁rollerblade- ▁grit- ▁vacancy- liath- ▁Mitchie- ▁purposeful- idle- ▁Salman- ▁cylinder- ▁supposing- ▁Dora- ▁Institute- ▁Convenience- ▁Future- ▁Gabriel- ▁Gateway- ▁Krunch- ▁Satan- ▁Schwarzenegger- ▁Technical- ▁Theatre- ▁alteration- ▁hush- ▁crayon- ▁snipe- Kardashian- ▁moonlight- ▁emit- ▁Stephenie- face- ▁aburi- ▁constipated- ▁Kurt- ▁nutri- ▁cowardly- ▁omega- ldering- ▁hopped- ▁recruitment- ▁slavery- ▁lever- ▁faci- ▁Austin- ▁rotan- ▁breathless- ▁indian- ▁swish- ▁Spartan- ▁Usman- ▁Tabata- ▁precaution- Law- ▁Anabelle- ▁Carrie- indef- ▁vary- ▁moisturize- ▁pester- ▁underdog- ▁gracefully- ▁Aquarium- ▁Caribbean- ▁Hathaway- zuo- ▁installment- ▁Shepard- ▁ladu- ▁acknowledgement- ▁affordability- ▁unfriendly- ▁tagline- ▁Camp- ▁TAF- ▁noticing- Group- ▁instantaneously- ▁roman- ▁salivating- Duck- yaki- ▁betrayal- ▁laces- ▁Fault- ▁waive- ▁Grabpay- ▁Fomo- ▁exert- ▁utai- ▁disposal- ▁unpaid- luted- liate- ▁pullover- ▁Curry- ▁Possible- 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▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- ▁amputat- ▁usua- ▁Gol- ▁Bur- lopp- after- ▁Havai- killers- ▁swop- oosebumps- sheet- ozy- ▁qualifi- ushed- ▁cohesi- ▁squeez- ▁smel- ▁Choo- ▁consol- ▁swerv- view- ▁schedul- ▁shiver- ▁troubl- heart- ▁ide- ▁initiat- ▁desir- ▁retir- Sua- ▁emphasiz- ▁reschedule- ▁inflate- 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dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- ▁Gulliver- ▁HabourFront- ▁Hanzo- ▁Harzik- ▁Hayde- ▁Hiragana- ▁Holdings- ▁Hollandaise- ▁Hologram- ▁Honestbee- ▁Hoshino- ▁Hsinchu- ▁Hunter- ▁Husserl- ▁Imperial- ▁Inktober- ▁Innok- ▁Jamaica- ▁Janabelle- ▁Jefri- ▁Jolibee- ▁Kendra- ▁Kenora- ▁Khalees- ▁Kinokuniya- ▁Kitori- ▁KouFu- ▁Leaves- ▁Leuch- ▁Lexus- ▁Libya- ▁Lilac- ▁Lontong- ▁Lorraine- ▁Luffy- ▁Management- ▁Matthew- ▁McNugget- ▁Motorola- ▁Mumbai- ▁Nebraska- ▁Nelson- ▁Nigeria- ▁Nobel- ▁Nothingness- ▁Nyx- ▁Oliander- ▁Otogi- ▁Paradise- ▁Pentatonix- ▁Petpetpet- ▁Pharaoh- ▁Poppins- ▁Portuguese- ▁Poteng- ▁Prakash- ▁Pretty- ▁Puff- ▁Qihua- ▁Reebonz- ▁Refash- ▁Remember- ▁Revlon- ▁Ribena- ▁Rooney- ▁Roxy- ▁Sabbath- ▁Sabsuka- ▁Scorpio- ▁Scramble- ▁Senibong- ▁Sennheiser- ▁Sensei- ▁Seraphina- ▁Shabir- ▁Shrek- ▁Silence- ▁Sogou- ▁Soraya- ▁Spotty- ▁Squirtle- ▁Supernatural- ▁Swarovski- ▁Syaz- ▁Sylvester- ▁Technique- ▁Thanksgiving- ▁Thosai- ▁Thriller- ▁Tigreal- ▁TikTok- ▁Transylvania- ▁Trivers- ▁Ukraine- ▁Uprising- ▁Valuetronic- ▁Vatican- ▁Velvet- ▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- 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▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- udrey- ▁constipat- ▁jumble- ambodia- ▁crook- stresses- sighted- Dogg- olution- urrender- tyrofoam- ▁impair- Buri- Escobar- Jetty- Showman- Shuttle- Tyson- ceased- aktor- ▁unexpect- Technical- Grill- Convenience- Future- Institute- Krunch- Schwarzenegger- Theatre- polished- Gabriel- antorini- Evan- Keane- Monster- ntegrated- Francisco- ▁deprive- ▁discriminat- ▁referenc- Kenny- Murder- Zhang- carriage- ▁Joan- ▁Skipp- ▁monogam- ▁prophec- Atwood- Stamford- ylvia- Akshay- Banyan- Virgin- linguistic- proportionate- founder- Music- buoy- evaluate- immortal- comparable- Jade- Miller- Friza- conditioned- innacle- Morrie- Waterway- conductor- crow- ndependence- Henry- Monkey- Nyonya- Shaw- monkeys- second- Shakespeare- catcher- organised- qualified- storage- Thomson- animate- executive- invent- starter- ▁rollerblad- ▁vacanc- Festival- Jordan- emphasis- frequent- settled- weekly- ▁Desire- college- factory- growth- organisation- profit- sentosa- Holland- Kampung- Laden- Nature- Seoul- Spirit- Tae- bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- ▁interesting- ▁else- ▁together- ▁car- ▁called- ▁whatever- ▁show- ▁white- ▁sorry- ▁change- ▁pay- ▁kids- ▁basically- ▁done- ▁seven- ▁free- ▁hundred- ▁learn- ▁green- ▁fun- ▁book- ▁told- ▁small- ▁off- ▁answer- d- ▁sign- ▁usually- ▁eight- ▁few- C- ▁open- ▁wanna- ▁mum- ▁chicken- ▁myself- ▁understand- ▁A- ▁means- ▁care- ▁both- ▁describe- ▁game- ▁once- ▁saying- ▁used- ▁teacher- ▁saw- ▁inside- ▁later- ▁happen- ▁C- ▁through- ▁date- ▁yourself- S- ▁stay- ▁looking- ▁movie- ▁dollars- ▁during- ▁move- ▁live- ▁until- ▁primary- ▁least- ▁nine- ▁wearing- ▁card- ▁thirty- ▁stop- ▁alright- ▁wanted- ▁trying- ▁Chinese- ▁each- ▁everyone- ▁having- ed- ▁example- ▁enough- ▁plus- ▁s- ▁haven- ▁cook- ▁working- ▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- ▁Singlish- ▁sweet- ▁train- ▁throw- ▁dark- ▁education- ▁early- ▁toilet- ▁simple- ▁uni- ▁tuition- ▁air- ▁restaurant- ▁honest- ▁living- ▁telling- ▁p- ▁price- ▁per- ▁Japan- ▁paper- ▁uncle- ▁minute- ▁itself- ▁terms- ▁overseas- hor- ▁driver- ▁o- ▁F- ▁set- ▁childhood- ▁words- G- ▁cards- ▁hmm- ▁brand- ▁grow- ▁reach- ▁choice- V- b- ▁scary- ▁main- ▁birthday- huh- ▁China- ▁cat- ▁career- ▁somebody- ▁along- O- ▁extra- u- ▁lunch- ▁hit- ▁lost- ▁subject- ▁exercise- ▁Korea- ▁hey- ▁parent- ▁add- ▁straight- E- ▁window- right- ▁chair- ▁explain- ▁sound- ▁soccer- ▁c- ▁online- ▁drama- ▁making- ▁culture- ▁fight- ▁k- ▁literally- ▁related- ▁latest- ▁interest- ▁felt- ▁human- ▁blah- ▁cream- ▁apparently- ▁G- er- ▁tired- ▁kinda- ▁studying- ▁earn- ▁suppose- ▁bird- ▁drop- ▁fried- ▁count- ▁holding- ▁m- ▁recess- ▁dating- ▁n- ▁forget- ▁sixty- ▁movies- ▁R- ▁catch- ▁baby- ▁color- ▁smart- ▁shoe- v- ▁teh- ▁success- ▁fact- ▁embarrassing- ▁pants- ▁countries- ▁dish- ▁Indian- ▁sports- ▁shows- ▁egg- ▁mostly- ▁rich- ▁chance- ▁Korean- ▁piece- ▁truly- ▁elderly- ▁soon- ▁social- ▁Singaporeans- ▁sort- ▁meaningful- ▁general- ▁angry- ▁shack- ▁club- ▁cooking- ▁seat- ▁clear- ▁lose- ▁photo- ▁hi- ▁busy- ▁period- ▁non- ▁fair- ▁dude- ▁questions- ▁running- ▁memorable- ▁holiday- ▁item- ▁leg- ▁system- ▁un- ▁bar- ▁interested- ▁touch- ▁sea- ▁couple- ▁zero- ▁self- ▁planning- ▁post- ▁video- ▁dead- ▁ex- ▁park- ▁totally- ▁hall- ▁store- ▁weekend- ▁building- ▁loud- ▁happiest- ▁milk- ▁trust- ▁chill- ▁lobang- L- ▁common- ▁fit- ▁board- ▁cost- ▁brought- ▁case- ▁laugh- ▁situation- ▁skin- ▁power- ▁re- ▁friendship- ▁f- ▁consider- ▁fat- ▁wish- ▁fall- ▁wash- ▁event- ▁soup- ▁woman- ▁willing- ▁sec- gosh- ▁Grab- ▁faster- ▁voice- ▁L- ▁trait- ▁dance- ▁differences- ▁entire- ▁effort- ▁science- ▁waste- ▁strong- ▁meat- ▁awkward- ▁specific- ▁exam- ▁current- ▁husband- ▁son- ▁seems- ▁visit- F- ▁box- ▁centre- ▁skip- ▁expect- ▁fire- ▁draw- ▁hero- ▁teachers- ▁whenever- ▁amount- ▁often- ▁kill- ▁Japanese- ▁boyfriend- ▁space- ▁support- ▁wise- ▁kay- ▁army- ▁ready- ▁giving- ▁tree- ▁art- ▁hobby- ▁swimming- ▁shoot- ▁football- ▁comfortable- ▁jump- ▁easier- ▁control- ▁public- ▁cake- ▁low- ▁spot- ▁gym- ▁search- ▁weeks- ▁walking- ▁lazy- ▁learning- ▁speaking- ▁carry- ▁orange- ▁similar- ▁soft- ▁pets- ▁seriously- ▁health- ▁World- ▁standing- ▁compared- ▁gave- ▁bake- ▁anywhere- ▁ninety- ▁ate- ▁Friday- l- ▁New- ▁Paul- ▁socks- ▁facing- ▁view- g- ▁worry- ▁apply- ▁ability- ▁based- ▁message- ▁fly- ▁huge- ▁season- ▁bed- ▁taken- ▁lucky- ▁freaking- ▁return- ▁hell- ▁computer- ▁tend- ▁smoke- ▁others- ▁aiyo- ▁relax- ▁energy- ▁hub- ▁waiting- ▁mix- ▁daughter- ▁bottle- ▁meaning- ▁corner- ▁sick- ▁office- ▁term- f- ▁sheep- ▁sauce- ▁size- ▁city- ▁driving- ▁currently- ▁cheaper- ▁eyes- ▁crab- ▁boss- wah- ▁hang- ▁safe- ▁step- ▁advice- ▁eighty- ▁happened- ▁match- ▁deep- ▁particular- ▁impression- ▁shall- ▁pull- ▁bread- ▁possible- ▁round- ▁comfort- ▁blind- ▁ride- ▁kampung- ▁meeting- ▁wine- ▁staying- ▁internship- ▁seventeen- ▁stall- ▁longer- ▁watched- al- ▁higher- ▁training- ▁remembered- ▁Sunday- ▁signboard- ▁closest- ▁wonder- ▁project- ▁fan- ▁caught- ▁government- ▁form- ▁tough- ▁Google- ▁everywhere- ▁healthy- ▁build- ▁trend- ▁cheese- ▁hat- uh- ▁vegetable- nah- ▁within- ▁slowly- ▁market- ▁block- ▁Saturday- ▁hotel- ▁technology- ▁machine- ▁craziest- ▁whereby- ▁selling- ▁math- ▁Australia- ▁makeup- ▁sand- ▁although- ▁works- ▁mention- ▁weather- ▁Hong- ▁manage- ▁afraid- K- ▁bike- ▁easily- ▁above- ▁alive- ▁listening- ▁respect- ▁mouth- ▁eventually- ▁feelings- ▁worse- ▁smell- ▁dress- ▁party- ▁language- ▁quiet- ▁chocolate- ▁possession- ▁seventy- ▁exams- ▁tray- ▁sun- ▁amazing- ▁mom- ▁e- ▁basic- ▁songs- ▁forward- ▁cap- ▁rock- ▁splurge- ▁stick- ▁busybody- ▁define- ▁woah- ▁act- ▁b- ▁contact- ▁issue- ▁potato- ▁studies- ▁fishing- ▁empty- ▁treat- ▁sleeping- ▁decide- ▁grandfather- ▁bigger- ▁previous- ▁asked- ▁cover- ▁classes- ▁d- ▁jobs- ▁kidding- ▁slow- ▁properly- ▁con- ▁laptop- ▁balance- ▁shark- ▁everytime- ▁X- ▁met- ▁base- ▁opposite- ▁canteen- ▁design- ▁random- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁futuristic- ▁gulp- ▁immerse- ▁mercy- ▁monologue- ▁prosperity- ▁telegram- ▁trotter- ▁unreal- China- ▁Bartley- ▁Cartoon- ▁Coleen- ▁Tutu- ▁bilingual- ▁bulldog- ▁criticizing- ▁darn- ▁hesitation- ▁interim- viation- ▁GameBoy- ▁Nazi- ▁amusing- ▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- 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▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- ▁alliteration- ▁anemone- ▁antihero- ▁applause- ▁apprehensive- ▁arrogance- ▁articulation- ▁ascertain- ▁asparagus- ▁bibliography- ▁bittergourd- ▁blasphemy- ▁bourgeois- ▁buckwheat- ▁bursaries- ▁cajon- ▁carbohydrate- ▁categorize- ▁catwalk- ▁caviar- ▁centimeter- ▁chameleon- ▁chauffeur- ▁chawanmushi- ▁cheddar- ▁cheebye- ▁cheerleader- ▁chimichanga- ▁chromo- ▁chrysa- ▁circumference- ▁clarinet- ▁coastguard- ▁cocoon- ▁commuting- ▁concious- ▁condolence- ▁confetti- ▁conglomerate- ▁congregate- ▁connectivity- ▁correspond- ▁coworker- ▁crisscross- ▁cuff- ▁customaries- ▁cutlery- ▁deceiving- ▁declaration- ▁deconstructor- ▁delifrance- ▁delinquent- ▁depleted- ▁digimon- ▁diminish- ▁disciple- ▁discrete- ▁discretion- ▁disparity- ▁disregard- ▁drenched- ▁droop- ▁dwindling- ▁dynasties- ▁eEcons- ▁electromagnetic- ▁enforcing- ▁entrap- ▁entrusting- ▁equilibrium- ▁euthanasia- ▁expedit- ▁expendable- ▁explanatory- ▁exploration- ▁eyebags- ▁faggot- ▁fajitas- ▁fashionista- ▁feminine- ▁fictitious- ▁florist- ▁foggy- ▁freeloader- ▁fretboard- ▁furnish- ▁fuzzy- ▁gachapon- ▁gallon- ▁gallop- ▁gonorrhea- ▁grenade- ▁griev- ▁hairpins- ▁handicraft- ▁herbivores- ▁hibernating- ▁hippopotamus- ▁hitchhiker- ▁hobbit- ▁homogeneous- ▁horribly- ▁hyena- ▁iPod- ▁identifiable- ▁ideologies- ▁igloo- ▁imperfection- ▁impolite- ▁incompetent- ▁indigestion- ▁infertile- ▁infringing- ▁instalment- ▁intricate- ▁investigator- ▁invoice- ▁iodine- ▁isolation- ▁jargon- ▁jenny- ▁jockey- ▁kicap- ▁kidnapped- ▁kinetic- ▁lechon- ▁lethargic- ▁lexicon- ▁ligament- ▁loudspeaker- ▁malacca- ▁mastermind- ▁melodies- ▁mischie- ▁miscount- ▁misguided- ▁monotony- ▁muesli- ▁multicultural- ▁multiplayer- ▁murta- ▁muruk- ▁neanderthal- ▁nihon- ▁nudity- ▁nutcracker- ▁nutritious- ▁ostracize- ▁otaku- ▁outburst- ▁outweigh- ▁overarching- ▁overcrowding- ▁overhyped- ▁overpowered- ▁paramour- ▁paraphras- ▁peacemaker- ▁perspec- ▁pessimism- ▁pesticide- ▁petroleum- ▁phenomenal- ▁phlegm- ▁physique- ▁pickpocket- ▁plural- ▁poeple- ▁precision- ▁prescribe- ▁pricier- ▁prorated- ▁pupil- ▁quantitative- ▁quarantine- ▁radius- ▁ramekins- ▁rebatch- ▁reclusive- ▁recuperate- ▁reenacti- ▁refurnish- ▁regenerate- ▁registry- ▁reinstate- ▁relativity- ▁relegate- ▁reprimand- ▁respawn- ▁retention- ▁retrac- ▁rewriting- ▁rofl- ▁rudimenta- ▁rupee- ▁scrutinise- ▁serenade- ▁shoelace- ▁shrimp- ▁shrub- ▁silhoutte- ▁skilful- ▁smudge- ▁smuggling- ▁societies- ▁spatula- ▁splinter- ▁sprout- ▁stepdad- ▁succinct- ▁sugarcane- ▁sunroof- ▁surgical- ▁surveillanc- ▁suspicion- ▁symmetr- ▁thickener- ▁titanium- ▁toothpick- ▁tornado- ▁transsexual- ▁treading- ▁trench- ▁unattainable- ▁unavailable- ▁unbeat- ▁uncountable- ▁undercover- ▁underprivilege- ▁unemployment- ▁unfrozen- ▁unleashed- ▁unopened- ▁unprepared- ▁unscrew- ▁unsustainable- ▁unwittingly- ▁utilise- ▁utilitarian- ▁vernacular- ▁vicar- ▁videography- ▁vigorous- ▁virtue- ▁voodoo- ▁waifu- ▁walnut- ▁wapiang- ▁webtoons- ▁widget- ▁workflow- ▁zebra- ▁Award- ▁Beetle- ▁Croc- ▁Easy- ▁Eeny- ▁Infocom- ▁Prize- ▁arguabl- ▁enthu- ▁inflamma- ▁misinterpret- ▁refail- ▁shrewd- ▁utopia- olitaire- ▁Burgess- ▁Cash- ▁Childhood- ▁Cinema- ▁Egive- ▁Guangzhou- ▁Hakim- ▁Johanna- ▁Laurel- ▁Merchant- ▁Miniclip- ▁Olaf- ▁Orbis- ▁Petpet- ▁Spring- ▁Talib- ▁Tuptup- ▁Zinger- ▁appendix- ▁coagula- ▁crucifie- ▁dailies- ▁demure- ▁discriminatory- ▁disperse- ▁dutch- ▁fantasies- ▁fetish- ▁gundu- ▁hairstylist- ▁hypothesi- ▁iBank- ▁imitate- ▁immigrate- ▁mangrove- ▁melanin- ▁mortar- ▁presumably- ▁rehab- ▁snitch- ▁strenuous- ▁sugarcoat- ▁tenacity- ▁theorems- ▁tutee- ▁wolverine- seido- ▁Adil- ▁Auto- ▁Battlefield- ▁Canmake- ▁Gemini- ▁Ikroh- ▁Latte- ▁Mcspicy- ▁aptitude- ▁bumblebee- ▁dismiss- ▁dissect- ▁douche- ▁generalising- ▁hairband- ▁harmonious- ▁helium- ▁kaopei- ▁nourish- ▁prophet- ▁socialising- ▁spinner- ▁swift- ▁twitter- Duxton- Golding- Monteiro- omeranian- ▁Bellerina- ▁Fernand- ▁Gareth- ▁MAN- ▁Makisan- ▁Serene- ▁Utown- ▁Work- ▁contradictory- ▁conversa- ▁farthest- ▁gambat- ▁kilogram- ▁luminous- choles- imony- ▁Barbara- ▁Goddess- ▁Protos- ▁craziness- ▁merging- ▁micromanag- ▁philanthropist- ▁pronounci- ▁retardant- ▁veteran- ▁Jonker- ▁Wingstop- ▁accomodation- ▁ailment- ▁choppy- ▁colonisers- ▁overfill- ▁ridicu- ▁serene- ▁omni- ▁stepmom- Scofield- shraf- ▁Garang- ▁Maslow- ▁Whis- ▁elongate- ▁morph- ▁motel- ▁retell- ▁revoke- ▁slouch- ▁swarm- ▁tablas- ▁uncover- ▁vegetation- ▁friendliness- ▁sociological- ridian- ▁Marshall- ▁collate- ▁fleet- ▁flim- ieved- ▁Annette- ▁Classified- ▁Mobike- ▁automobiles- ▁cider- Fuyong- ▁Meghan- ▁Yoko- ▁frantically- ▁pocky- ▁reciprocating- ▁Bombay- ▁Boston- ▁Capitalism- ▁Donkey- ▁annum- ▁colonized- ▁hedgehog- ▁intersting- ▁jacuzzi- ▁subsidiaries- alyps- ▁Theater- ▁ambient- ▁sailboat- ▁sakura- ▁Pesta- ▁breach- ▁converge- ▁counselled- ▁fluctuation- ▁hanoi- ▁staging- ▁Aiden- ▁Notebook- ▁Tasha- ▁drummer- ▁segregation- ▁Diana- ▁Reese- ▁SAR- Jackman- latoni- ▁Clare- ▁copywrit- ▁pallet- ▁variant- loating- ▁tropic- Khalsa- ▁kaput- ▁omit- ▁shredder- ▁thrones- ▁Ashley- ▁unimportant- Hwa- ▁Burj- ▁Deyi- ▁Emails- ▁Fallout- ▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- ▁separation- ▁neurotic- ▁viper- ▁unravel- ▁defect- ▁Complex- ▁Paradigm- ▁Singapura- ▁joving- ▁metallic- ▁punchek- ▁marries- ▁menses- ▁resourceful- rangle- ▁Subhas- ▁Milan- ▁Witch- ▁barracks- ▁evasion- upiah- ▁renegade- urgen- ▁Waken- ▁forcefully- ▁swagger- ▁tiara- ▁chapel- ▁reconnected- ▁causally- ▁habitual- ▁Bookhouse- ▁thinggy- ▁Charmander- ▁Pika- ▁friday- ▁governor- ▁newsstand- ▁subcon- eonard- ▁devour- ▁Mafu- ▁burial- ▁LiHo- ▁Redang- headed- ▁Dettol- ▁excelled- ▁glutes- Titl- ▁kennel- gedil- ▁negro- ▁radically- plish- ▁Motion- ▁dawn- ▁sunburn- uzu- ▁sedan- ▁transcendent- ▁Camry- ▁inheritance- ▁magma- ▁Inception- ▁Tepp- ▁cheeseburger- ▁prideful- ▁Hajj- ▁Orang- ▁Luna- ▁marinara- ▁Rochor- ▁Yogi- ▁hostile- ▁stepsister- ▁Tores- ▁redevelopment- ▁bidet- ▁Quek- ▁sanitizer- ▁Qiao- ▁Jackpot- ▁dampen- ▁idolise- ▁sewers- ▁crating- ▁foursome- ▁uncontrollable- ▁mailbox- ▁slipping- ▁Ashik- ▁bonobo- ▁tragically- ▁Haidi- bear- ▁iffy- ▁relearn- ▁repent- ▁Marilyn- ▁agaration- ▁civilian- ▁shrine- ▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- 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bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: nullmodel_conf:    ignore_id: 0use_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nulllm: seq_rnnlm_conf:    unit: 1000    nlayers: 1required:- output_dir- token_listversion: 0.9.6distributed: true",
-    "description_zh": "This model was trained by Shinji Watanabe using nsc recipe in espnet.&nbsp;Python APISee https://github.com/espnet/espnet_model_zooEvaluate in the recipegit clone https://github.com/espnet/espnetcd espnetgit checkout 8fd07721b78ef0d34713f237e94a003731a78825pip install -e .cd egs2/nsc/asr1./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/nsc_asr_train_asr_raw_en_bpe20000_valid.acc.aveResults# RESULTS## Environments- date: `Sun Jan 10 03:01:02 EST 2021`- python version: `3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0]`- espnet version: `espnet 0.9.6`- pytorch version: `pytorch 1.7.1`- Git hash: `dd9cf570becddb2a587e69473548b1d73fa9648f`  - Commit date: `Tue Jan 5 13:55:51 2021 -0500`## asr_train_asr_raw_en_bpe20000### WER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|26046|79.0|16.3|4.7|3.8|24.8|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|123092|74.0|19.1|6.9|5.1|31.1|69.7|### CER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|126663|88.2|5.4|6.3|3.8|15.6|60.5||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|587330|84.1|6.9|9.0|5.4|21.3|69.7|### TER|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err||---|---|---|---|---|---|---|---|---||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/dev|3986|30721|77.1|15.0|7.9|4.9|27.8|60.6||decode_asr_lm_lm_train_lm_en_bpe20000_valid.loss.ave_asr_model_valid.acc.ave/test|16306|150205|72.5|17.0|10.5|6.5|34.0|69.7|ASR configconfig: conf/train_asr.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/asr_train_asr_raw_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 49547dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 100patience: nullval_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - acc    - maxkeep_nbest_models: 10grad_clip: 5grad_clip_type: 2.0grad_noise: falseaccum_grad: 5no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 300valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/asr_stats_raw_en_bpe20000/train/speech_shape- exp/asr_stats_raw_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/asr_stats_raw_en_bpe20000/valid/speech_shape- exp/asr_stats_raw_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 80000- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/train_nodev/wav.scp    - speech    - sound-   - dump/raw/train_nodev/text    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/wav.scp    - speech    - sound-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: adamoptim_conf:    lr: 10.0scheduler: noamlrscheduler_conf:    warmup_steps: 25000token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- 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can- ▁yearly- ▁terminate- ▁connections- ▁joint- ▁exception- ▁drip- ▁scrape- ▁cre- ▁listing- ▁Fun- ▁scrap- ▁lan- ▁questioning- ▁Mar- ythic- ▁scholars- hipped- hu- ▁reader- ▁col- ▁quotes- ▁strengths- ery- ▁calculation- ▁feather- ▁muffin- ▁restriction- ▁slaves- ▁downward- ▁Bun- ▁handles- ▁sandal- ▁shoulders- ▁exists- od- ▁lac- ▁que- ec- ▁Wu- ▁Ice- ring- ▁Dad- ▁ter- id- ▁Fat- ▁rob- ▁pointer- ▁min- ▁wan- ▁sy- ▁Roman- ▁sua- tai- ▁gut- ▁nieces- uhyou- ▁hun- School- ▁overthink- ▁execute- scape- ▁pierce- Kayu- ▁Louis- America- club- House- ▁rotate- Hao- ▁singlet- ▁gracious- ▁Taufik- ▁Mickey- ▁Beyblade- ▁Clara- ▁Donald- ▁Drake- ▁Eiffel- ▁Kelvin- ▁Natalie- ▁Nigel- ▁PlayStation- ▁Porsche- ▁Sundram- ▁Tamarind- ▁Tamiya- ▁Watson- ▁Zumba- ▁allergy- ▁ambience- ▁balancing- ▁belacan- ▁bimbo- ▁bisque- ▁bouncy- ▁carried- ▁clarify- ▁climax- ▁condominium- ▁decrease- ▁diversity- ▁efficiency- ▁eyelid- ▁fascinate- ▁feast- ▁fickle- ▁filtration- ▁forklift- ▁fortune- ▁increment- ▁infrastructure- ▁mattress- 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▁attribute- ▁Caucasian- ▁Alia- ▁bud- ▁acquaintance- ▁participant- ▁graphic- ▁electives- ▁earphone- ▁creature- ▁trunk- ▁females- ▁grandparent- ▁flyer- ▁awards- ▁sl- ▁instance- ▁nutrition- don- ▁tra- ▁prom- ▁Sa- ala- ▁tons- ▁Lu- ▁hamsters- ▁touche- chu- ▁swan- ▁relations- ▁via- ▁monsters- lie- ▁poo- ▁Teo- ▁ir- min- ▁hoo- ▁nets- ▁Pin- ▁High- ▁containers- ▁dan- ▁ratio- ▁disappoint- ▁Panda- wood- ▁distribute- ▁cultivate- ▁sneak- ▁exceed- Faber- McCartney- Fingers- Padang- tarik- Hotel- screen- centre- ▁dense- wei- Gaga- ▁swam- ▁fade- Wang- ▁underline- ▁interfere- ▁Sherlock- Soto- ▁Alibaba- ▁Benedict- ▁Cambridge- ▁Dylan- ▁Eunice- ▁Florence- ▁Gordon- ▁Kraken- ▁Kranji- ▁Oliver- ▁Rolex- ▁alarum- ▁autumn- ▁backyard- ▁beancurd- ▁brisk- ▁burrito- ▁butterflies- ▁collaboration- ▁concubine- ▁denim- ▁envelope- ▁esteem- ▁eyesight- ▁frivolous- ▁guidance- ▁heartbroken- ▁intensive- ▁kettle- ▁khaki- ▁lemonade- ▁liberal- ▁magenta- ▁mousse- ▁mundane- ▁muscular- ▁necessities- ▁negotiate- ▁obsolete- 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▁humidity- ▁treetop- ▁Faith- ▁Galaxy- ▁presume- ▁shady- ▁egoistic- ▁fellowship- ▁lighthearted- ▁Kiri- ▁suc- ▁waterproof- ▁Bangla- ▁surrender- ▁warden- ▁banter- ▁scanning- ▁bali- ▁coincidentally- ▁Totoro- ▁storeroom- ▁confidential- ▁squishy- ▁pension- ▁Danny- ▁diluted- ▁Asus- ▁stationery- ▁posh- ▁westernised- ▁Valley- ▁deprived- ▁Mandai- ▁Wanna- ▁gymming- ▁submitted- ▁secretive- ▁Shuan- ▁hangover- ▁Switch- ▁floss- ▁introducing- ▁Nancy- Mars- ▁autis- ▁Baba- ▁existent- ▁corgi- ▁Ireland- Shiong- ▁Amos- ▁Rover- ▁Suria- ▁punk- ▁spize- ▁paperwork- ▁Liao- ▁Liz- Meng- Out- ▁expressway- ▁sniper- ▁leap- ▁idiotic- ▁lingo- ▁dent- ▁clinical- ▁Sega- ▁elf- ▁Fuji- ▁Leona- ▁specialisation- ▁intentionally- ▁quitted- ▁Cody- ▁bedok- ▁chanting- ▁cyst- ▁decorating- ▁competitiveness- ▁Lok- ▁upsetting- ▁integrated- ▁transcriber- ▁gray- ▁censored- ▁munch- ▁portal- ▁vast- ▁budd- ▁sloth- ▁stopover- ▁publicity- ▁Music- ▁approachable- ▁strolling- ▁confined- ▁Jones- ▁Singap- ▁coaches- ▁allocated- ▁escalate- 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▁foie- ▁proportionate- ▁invention- ▁dental- ▁jumbled- ▁modernised- ▁glider- ▁tempting- ▁moist- ▁halfhearted- ▁sphere- ▁Business- ▁Halimah- ▁irri- ▁nuance- ▁batman- ▁punny- ▁symbolise- ▁empire- ▁deduction- ▁hardwork- ▁installation- ▁fanatic- ▁conceive- ▁layman- ▁Center- ▁stitches- ▁Dark- ▁Johnson- ▁broom- ▁codeine- ▁meteor- ▁disgusted- ▁faraway- ▁gelat- ▁maze- ▁Navy- ▁Scott- ▁Three- ▁Miller- ▁adjustment- Fong- ▁involvement- ▁jab- ▁Belle- ▁continental- ▁Fanny- ▁suspended- ▁brotherhood- ▁buttock- ▁setback- ▁harness- ▁collagen- ▁deliveries- ▁healthily- ▁hospitality- ▁Colo- ▁hypothetically- ▁branches- ▁bandung- ▁vaping- ▁Peru- ▁despi- ▁plantation- ▁disbanded- ▁disconnected- ▁husky- ▁Rosa- ▁Ronaldo- ▁consciousness- ▁Bread- ▁duckling- ▁Talent- ▁knots- ▁overflowing- ▁Deb- ▁superheroes- ▁controller- 'Off'- ▁Benga- ▁Bangladeshi- ▁forgetful- ▁Law- ▁chatty- ▁sane- ▁enable- ▁cries- ▁exaggerated- ▁padding- ▁runny- ▁overturned- ▁olio- seal- ▁handover- ▁scarf- ▁Express- ▁instrumental- ▁betrayed- 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▁Meet- ▁alphabets- ▁ministers- ▁statues- ▁Legends- rou- ▁attacks- ▁notch- ▁pointers- ▁etc- ▁gears- ▁Zhuan- ault- ▁seize- ▁puzzles- layer- ▁mana- ▁tents- ▁directional- ▁dun- omo- ▁snapp- ▁del- ▁Angu- ▁aids- ▁Ch- ▁Tun- ▁Jac- ▁sins- aki- ▁Ver- ▁wou- ▁fundamentals- non- ▁triangles- ▁buns- ▁bugs- now- ▁ele- run- ▁integrati- ▁recommendations- ▁Pad- ▁gar- shao- ▁hol- ▁fisher- old- ▁Raja- ▁spikes- ▁mun- ▁dinosaurs- ▁mentors- ▁Har- ▁spirits- ▁organizations- zy- ▁Christians- Magic- ▁dangers- erry- oi- ▁dock- ▁slopes- ▁artistes- ▁ru- ▁references- ongs- ▁Wiz- ▁markings- very- eresa- ▁kam- ▁blessings- ▁gigs- gle- ▁improvements- ▁pubs- ▁sib- bel- ▁comms- ▁meta- ▁pimples- ▁stares- ▁pota- ▁Ken- ene- Xun- ggle- ▁paw- ▁threat- ▁resonate- ▁axe- ▁schoolmate- ▁emerge- Studio- ixes- ▁merchant- ▁souvenir- hop- ▁kilometer- ▁syllable- ▁beverage- Boy- ▁competitor- ▁rumour- ▁ancestor- ▁sneaker- verse- ▁compromises- ▁whip- low- ▁brownie- ▁notion- assim- then- ential- ▁tang- ▁campaigns- ▁constrain- ▁zha- ami- ▁Eu- 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▁bangtan- ▁descendant- ▁garland- ▁organizing- ▁Lipton- ▁PayWave- ▁bravo- ▁brillante- ▁embassy- ▁friction- ▁hijab- ▁snoring- Hanks- ▁Elisha- ▁Entertainment- ▁Warcraft- ▁cellphone- ▁harassment- ▁multilingual- Decay- ▁comedic- ▁copied- ▁inventory- ▁rabz- ▁telok- valent- ▁Charizard- ▁Sager- ▁depressive- ▁lagoon- ▁overspend- ▁primark- angoon- ▁Kobe- ▁protest- ▁Skyline- ▁charismatic- ▁cristofori- ▁Barbra- ▁Huda- ▁bragging- ▁chinese- ▁courteous- ▁criticize- ▁ruling- lihood- ▁Percy- ▁bulgogi- ▁blaming- ▁crepe- ▁frail- ▁genetically- ▁obey- ▁retrenchment- Ridge- ▁Sandra- ▁fondant- ▁optimisation- ▁Irfan- ▁civilization- ▁physiotherapy- arnia- ▁Direction- ▁Drama- ▁Hulk- ▁Restaurant- ▁Ultron- ▁electrons- ▁hazard- ▁icing- ▁snowboard- ▁width- ▁disciplinary- ▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- ▁metric- ▁mountainous- ▁catfish- ▁hideout- ▁Titans- ▁strategic- ▁labelled- ▁infant- Singh- ▁sandwiches- ▁chopper- ▁selectively- ▁sexist- ▁shivering- ▁exhausted- ▁steer- ▁travelator- ▁angsty- ▁fog- Air- ▁Albert- ▁Crush- ▁Arsen- ▁Plus- ▁Halo- ▁blackboard- ▁draggy- ▁Strike- ▁moderately- ▁swamp- ▁ballot- ▁contradicting- ▁Daily- ▁protector- ▁Ruth- ▁boar- ▁Julin- ▁rusty- icity- ▁Mona- Gong- ▁Chao- ▁kung- ▁archer- ▁tempo- ▁cord- Phi- Ray- ▁fitter- ▁qi- ▁spiritually- ▁Young- noodle- ▁Baha- ▁Roy- ▁Town- ▁bunker- ▁housekeeper- ▁Canning- ▁alighted- ▁stapler- ▁transformer- ▁spinal- ▁unexpectedly- ▁Once- ▁Fang- ▁inconsequential- ▁discarded- ▁Aden- ▁greenhouse- ▁gambler- ▁Loong- ▁Bose- ▁vac- ▁pierced- ▁Ghaut- ▁Ching- ▁departmental- ▁aligned- ▁Mind- ▁yard- ▁thrilling- ▁Bond- ▁regretting- ▁hangman- ▁Tung- ▁crank- Tang- ▁dependable- ▁tuner- ▁accurately- ▁Pig- ▁flooding- Met- ▁chic- ▁steamer- ▁freelancer- ▁licking- ▁postman- ▁Rock- ▁conditioner- ▁Keng- ▁fairies- ▁Jong- ▁badass- ▁Rahma- ▁Tyr- ▁Lian- ▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- ▁circulat- ▁choppe- eas- ▁barn- ▁scouts- ▁jokers- ▁aches- ▁counsel- ▁generalise- ▁drones- ▁beginners- ▁YouTubers- ady- ▁fined- ▁Mia- ▁consi- ▁Ever- je- eck- fin- ▁ponytails- ▁sparks- alam- ▁spo- ▁winners- riving- ▁flatter- opo- agi- ache- ▁creamer- lar- hun- ▁bullets- lio- illa- arian- ▁deb- ▁persuade- ▁reli- ▁factual- ▁reta- ▁trac- ▁nightmares- ▁tw- Cantona- Bones- lted- ester- ▁Tar- Tock- Hu- Hoon- isang- ception- ▁xia- ▁desires- ▁pleasures- ▁bom- ▁boug- pping- ▁objectives- ▁standardise- ▁Kia- laying- ▁kiam- Koon- ▁rig- ual- ppy- ▁lions- ▁Ari- ▁Gra- tech- ▁cit- owing- chong- ▁Mag- ership- ▁missions- ▁Ge- but- ▁mannequins- lab- ▁memora- alari- fraction- stro- ▁ep- cast- ▁volun- ▁litera- rselves- ▁files- ▁ethic- ▁Mad- ima- ogy- water- jun- fer- pend- Soo- ▁memor- ▁blackhead- ▁emission- Toba- ▁otter- ▁kilometre- ▁symptom- ▁midterm- ▁rumor- ▁swi- ▁alpaca- abby- ▁strand- ▁stair- ▁railing- oki- ctors- ▁Won- ▁hog- ▁Ser- ▁corona- ▁moo- ▁ei- unny- rise- ▁por- kang- coming- adi- ▁transparen- ▁snoo- ▁nex- ▁For- ▁repea- ▁brew- ▁debat- ▁Fu- ▁haunt- ▁subtitle- ▁alia- vo- ▁Zy- key- pra- ▁torch- ▁Gan- cumulative- ▁procras- ▁hesita- look- ▁Bala- ▁ven- Hit- ▁cycl- ▁exhaust- Sia- ▁respon- ably- ▁zig- ▁frown- ▁stat- ▁parasail- ▁Arabia- ▁expos- ▁separat- ▁incur- ▁nurtur- ▁invad- ▁Find- ▁Prince- ▁Vic- scooter- corn- Karat- ▁subsidize- ummer- lbert- ▁congest- ▁recite- ▁misuse- ▁retrench- about- course- oodle- ▁debut- Smart- Batisah- Jovi- Turtle- ristiano- Airline- Titans- Ronaldo- Chomp- Cruise- Mirror- States- Nathan- Leong- kacang- Prata- Romeo- jalan- Autumn- William- negotiable- Chicken- James- Science- boyfriend- chocolate- machine- drama- twenty- Miam- ▁crisp- biryani- Mouse- center- ▁summar- motion- ▁theoretical- Minh- ▁clutter- ▁vivid- ▁instantaneous- ▁Lor- ▁Saba- asian- ▁authorit- ▁midfield- post- chiong- ▁imply- Wee- ▁proactive- ▁pronoun- ▁immigrati- ▁disrupt- eptic- ▁Buddh- ▁experi- ▁revolution- ▁Yayoi- ▁Xav- ▁Corp- Vuitton- ▁indefinite- ▁mislead- ▁residu- '?'- Bilis- Einstein- Giggs- Hospital- Local- Muniz- Quinn- Sivan- jumma- siol- ▁After- ▁Atiqah- ▁Backstreet- ▁Badminton- ▁Bidadari- ▁Britain- ▁Broadway- ▁Capella- ▁Century- ▁Chijmes- ▁Cinderella- ▁Colosseum- ▁CrossFit- ▁Cyclops- ▁Daegu- ▁Darvis- ▁Dasani- ▁Diablo- ▁Diploma- ▁Dyson- ▁Egg- ▁Elliot- ▁Fight- ▁Foodpanda- ▁Foodshed- ▁GERPE- ▁Garfunkel- ▁Generation- ▁Genghis- ▁Gentle- ▁Ghost- ▁Gillman- ▁Haagen- ▁Heidegger- ▁Hermione- ▁Hermoine- ▁Hiroshima- ▁Inferno- ▁Istanbul- ▁Jasmine- ▁Jerusalem- ▁Lancer- ▁Lapidas- ▁Leftfoot- ▁Machine- ▁Medisave- ▁Mendel- ▁NAFA- ▁Newcastle- ▁Odette- ▁Olympiad- ▁Orchid- ▁Palestin- ▁Picoult- ▁Portugal- ▁Rafiq- ▁Razor- ▁Redbull- ▁Ripple- ▁Riverdale- ▁Samoyed- ▁Shamsuddin- ▁Sharlene- ▁Sheryl- ▁Skechers- ▁Snickers- ▁Sogurt- ▁Sprite- ▁Sucremo- ▁Sudoku- ▁Terengganu- ▁Theodore- ▁Tomahawk- ▁Toothless- ▁Vanguard- ▁Vitality- ▁Yogyakarta- ▁accreditation- ▁advancing- ▁aloud- ▁analyzing- ▁angklung- ▁annoucement- ▁apologies- ▁apparatus- ▁appreciating- ▁armband- ▁asbestos- ▁asylum- ▁barista- ▁belanja- ▁berserk- ▁biography- ▁blockbuster- ▁boardwalk- ▁breadwinner- ▁buffalo- ▁buttermilk- ▁contraband- ▁convocation- ▁cosmopolitan- ▁counterpart- ▁croissant- ▁cryptocurrency- ▁culprit- ▁curvy- ▁delicate- ▁discourage- ▁durable- ▁ectomorph- ▁elbow- ▁endearing- ▁epidural- ▁etiquette- ▁evaluation- ▁eyeshadow- ▁fabulous- ▁facade- ▁facilitate- ▁fatigue- ▁favoritism- ▁favouritism- ▁fencing- ▁fiesta- ▁fingernail- ▁freshener- ▁frizzy- ▁gargantuan- ▁ghetto- ▁gondola- ▁gorgeous- ▁graveyard- ▁grumble- ▁gyoza- ▁heartwarming- ▁homecooked- ▁hurdle- ▁hyuna- ▁iCarly- ▁iTunes- ▁illogical- ▁incorrect- ▁intermediate- ▁intrinsic- ▁intuitive- ▁invigilator- ▁jesus- ▁kacau- ▁laundries- ▁leukemia- ▁lieutenant- ▁lifeguard- ▁mammal- ▁marshmallow- ▁masculine- ▁meddle- ▁meddlesome- ▁merrier- ▁monotonous- ▁neural- ▁nonsensical- ▁observant- ▁outbreak- ▁paintbrush- ▁paprika- ▁parquet- ▁participating- ▁penalise- ▁platinum- ▁plunge- ▁pragmatic- ▁procrastinator- ▁propaganda- ▁proximity- ▁pufferfish- ▁quantify- ▁racquet- ▁rambutan- ▁receptive- ▁reciting- ▁recliner- ▁referral- ▁remembrance- ▁renovating- ▁retrain- ▁ripple- ▁safeguard- ▁seashore- ▁staycay- ▁stylist- ▁substantial- ▁sundae- ▁surgeries- ▁susceptible- ▁symbiosis- ▁tablespoon- ▁telemovie- ▁terrapin- ▁terrified- ▁tombstone- ▁toothbrush- ▁traumatising- ▁traumatizing- ▁tudung- ▁tumble- ▁uncooked- ▁unhygienic- ▁universal- ▁unofficial- ▁unsightly- ▁unused- ▁velcro- ▁vibrant- ▁vibrating- ▁wildlife- ▁windshield- ▁xiaxue- ▁xiong- /- ▁Basil- ▁Calbee- ▁Children- ▁Coco- ▁Hershey- ▁Karaoke- ▁Nivea- ▁Pandora- ▁Ricky- ▁Rulang- ▁Spies- ▁Stephanie- ▁Teacher- ▁Wheelock- ▁crazily- ▁granddad- ▁narrating- ▁perpetually- ▁quizzes- ▁recurring- ▁snooker- ▁swirl- ▁titans- ▁trilogy- ▁zodiac- ▁Lakeside- ▁Mentos- ▁Penny- ▁Rosti- ▁Stanford- ▁Underwater- ▁Vanessa- ▁Yamaha- ▁beneficiary- ▁bookworm- ▁bowtie- ▁cooperative- ▁dehydration- ▁depreciate- ▁engross- ▁martini- ▁silat- ▁slingshot- ▁symbiotes- ▁tamp- ▁tulang- ▁Hazel- ▁Ravana- ▁Vicky- ▁gigabyte- ▁strangle- ▁visor- Spears- ▁Avene- ▁Cecilia- ▁emptie- ▁innovative- ▁reflexes- Before- ▁Daven- ▁Intel- ▁Katsu- ▁commuters- ▁elastic- ▁exploding- ▁obese- ▁unfit- Downey- ▁Forex- ▁geek- ▁radar- ▁seasick- ▁Yolo- ▁posi- ▁Batu- ▁Burma- ▁Jaws- ▁purification- ▁summarise- Kook- Yacob- ▁Allen- ▁Pahang- ▁congee- ▁cooperate- ▁corpus- ▁statistically- ▁trumpet- ▁Christina- ▁contemp- ▁crease- ▁extinguisher- ▁hooray- ▁Barney- ▁Janice- ▁kanasai- ▁phoenix- ▁repetition- ▁scammed- ▁wiring- ▁backbone- ▁blunt- omodo- ▁assassination- ▁approv- ▁babysit- ▁bugger- ▁championship- ▁prospective- ▁Bencoolen- ▁roster- ▁stoning- ▁claustrophobic- ▁delusional- ▁dysfunctional- ▁leash- ▁punctuality- ▁boastful- ▁mugging- ▁paddling- ▁Dead- ▁Toby- ▁churn- ▁torso- ▁traumatic- Lipa- ▁playmate- ▁shush- ▁Foot- ▁Perak- ▁decompose- ▁enlistment- ▁inevitable- ▁mythology- ▁refine- ▁reproduce- ▁Margaret- ▁civilised- ▁lentil- ▁photocopy- ▁Maltese- ▁Prada- ▁chrome- Bahar- ▁Fantasy- ▁appetizer- ▁kuku- ▁maritime- ▁piping- ▁cunning- ▁hesitating- ▁blockchain- ▁pagan- ▁sequel- ▁Channing- ▁Celsius- ▁expertise- ▁globalization- ▁raspberry- ▁existential- ▁hastily- ▁Library- ▁biking- ▁grim- ▁disfigured- ▁reggae- ▁palate- boy- ▁Metro- ▁Saat- ▁naggy- ▁Bogo- ▁Artbox- ▁Mayday- ▁Shark- ▁Taiti- ▁deviation- ▁govern- ▁deferment- ▁possessive- ▁lingerie- beng- ▁eyelashes- ▁frighten- ▁rehearsal- ▁scarred- ungry- ▁annoyance- ▁anthem- Runner- ▁dilly- Caesar- ▁duper- ▁Pinnacle- ▁Makan- ▁immersi- ▁quartermaster- ▁Sanya- ▁Zhang- ▁carriage- ▁dumbass- ▁Joelyn- ▁Lovi- ▁Murder- ▁Kenny- ▁raving- ▁Vada- ▁overlord- ▁Shahira- ▁Banyan- ▁authenticity- ▁intima- Claus- ▁carpenter- ▁seawater- ▁Tupperware- ▁consultation- ▁macro- ▁attendant- ▁traumatised- ▁Lilo- ▁restrain- ▁flock- ▁squeezy- egor- ▁mozz- ▁scarce- ▁charities- ▁flavourful- ▁Cali- ▁Academy- ▁Reservoir- ▁parenthood- ▁dhal- ▁Lester- ▁Ruby- ▁recep- ▁Faris- ▁furry- ongdae- onito- ▁prettier- ▁commercialised- ▁slopp- ▁enterprise- ▁Timor- ▁Davidson- ▁rotting- Jong- ▁Never- ▁fortnight- ▁beatbox- ▁College- ▁Garfield- ▁Kacang- ▁Lena- ▁omakase- ▁elevated- ▁objection- arnish- learning- ▁Moses- ▁spies- ▁nonstop- ▁amplifier- ▁shortlisted- ▁representation- ▁Parkway- ▁Brigade- ▁Pattaya- ▁Noah- ▁Dua- ▁psychic- ▁Wak- ▁moto- regano- ▁throwback- ▁parkour- ▁attractiveness- ▁Real- ▁tada- ▁backstabbing- ▁Mouse- ▁Angeles- ▁Brown- ▁Taj- ▁sketchy- ▁pulley- ▁Toy- ▁Fire- ▁clubber- ▁agony- ▁Swift- ▁expressive- ▁spotlight- ▁truthfully- ▁choy- rgent- ▁chord- ▁subsidized- ▁Ford- ▁Momo- ▁baseline- ▁repeatedly- ▁seasaw- ▁Guan- ▁mite- ▁Pool- Frost- ▁Koko- ▁convent- ▁Duck- ▁bala- ▁Crab- ▁Leong- ▁mindedness- ▁paralysed- ▁Eden- ▁Kanji- ▁hump- ▁Ulu- ▁connector- ▁seamen- ▁sleepover- ▁hoarding- ▁johor- ▁okie- ▁lent- ▁constructed- ▁Belly- ▁goosebump- ▁hahaha- ▁Serai- ▁customised- ▁critically- ▁ign- ▁sneaky- ▁chunky- ▁padang- ▁Owen- Kat- talk- ▁italy- ▁Gang- ▁reap- ▁socialize- ▁cookhouse- ▁offset- ▁satan- ▁scape- ▁sadden- ▁overboard- Lau- ▁Shao- ▁pony- ▁composer- ▁overdo- Ego- ▁dua- ▁desc- hei- ▁marching- ▁ladylike- ▁transferable- ▁aspiring- ▁Shui- ▁Jose- ▁Peking- ▁Ox- ▁criticising- ▁exploded- ▁perceived- ▁contouring- ▁distracting- ▁Alexa- ▁stickman- ▁stickies- ▁superficially- ▁Ayer- ▁mumble- ▁Tok- ▁witnessed- ▁netting- ▁probe- ▁collective- ▁Alan- ▁Maha- ▁environmentally- ▁abus- ▁mak- ▁canoeing- ▁skateboarding- ▁lament- ▁smoother- ▁suspected- ▁peacefully- ▁burping- ▁yeha- ▁cramped- ▁postponing- ▁manipulated- ▁readily- ▁seaman- arents- Mutant- ▁sway- ▁anonymous- ▁frost- ▁Shar- ▁Kith- Khoo- ▁professionally- ▁lup- ▁healed- ▁cocky- ▁organiser- ▁Tau- ▁Villa- ▁buster- ▁interrupting- ▁tel- ▁respectable- ubl- ▁tidying- ▁recovery- ▁exited- ▁excluded- ▁Tong- ▁abuser- Char- ▁hav- ▁sourc- ▁Bai- dong- ▁shaved- ▁fest- ▁independently- stain- ▁sacrificer- ▁funded- ▁tun- ▁tricked- Aziz- gun- ▁owing- ▁adapted- ▁Evi- Sum- ▁Bahru- ▁overdose- ▁bearded- ▁molest- ▁absorbing- ▁cured- ▁Matt- ▁reflected- ▁ondeh- ▁Mun- ▁Planet- ▁exes- ▁Easter- ntil- ▁remover- ▁simulate- ▁lighten- ▁sinful- ▁painter- ▁actu- nstitution- Aw- shot- ▁twisting- lia- ▁Khan- Bin- ▁What- ▁pine- ▁Fall- ppo- that- ▁Can- caf- ▁clothed- ▁gui- ▁cracking- Don- ▁meowing- ▁hampers- ▁fluctuates- ▁refugees- rina- ▁carte- ield- ▁broker- ▁ignored- ▁Will- ▁Tup- ▁hater- oul- ▁ward- ▁Mumm- ▁angri- ▁blamed- ▁incoming- ▁caged- ▁feat- ▁Ger- ngkat- ▁gained- ▁helli- ▁vent- ▁connecting- ▁mach- ▁sugary- ▁Glenn- ▁pretending- ▁suntan- ▁hunting- ▁aerial- ▁disagreements- ▁Semb- Min- rawl- ▁devoted- dri- ▁downloading- ▁chor- ▁creak- zhen- ▁identi- ▁gam- ▁bossy- ▁thirdly- ▁noun- ▁Ani- ▁climber- ▁Tap- ▁Ng- ▁apparels- ▁enrolling- ▁admired- ssured- ▁respected- ▁gras- nding- ▁graz- ▁misbehave- epok- ▁Net- ▁ringing- ail- ▁tangent- ▁chilly- ▁Nam- ▁returned- ▁Miru- ouch- ▁crumbs- ▁Sands- ▁powered- ▁bon- Jolie- ▁railings- ▁blackheads- ▁bearing- loo- ought- ▁Julia- fort- ▁positioning- Cadet- oba- ▁hid- ▁emerg- ▁buttered- utan- olate- ▁resonates- ▁threats- scow- Thief- ▁colli- Jing- ▁Bart- ▁liner- ▁Marl- ike- ida- ▁yourselves- ▁pasty- creme- ▁cultured- ▁dicks- ▁startups- ▁limitations- ▁robotics- ▁astrolog- ▁Farah- ▁flopp- gates- ▁momento- ▁beam- ▁priced- ▁Astons- ▁nerves- ▁sculptures- ▁rested- ▁reminders- cous- ▁Sher- ▁kneel- Coles- ▁anticipate- ▁DC- ▁politicians- ▁pam- arley- ▁commercialize- ▁harp- 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▁Mongrel- ▁Mountain- ▁Mutton- ▁Narcos- ▁Nolan- ▁Oprah- ▁Pagoda- ▁Paladin- ▁Panopticon- ▁Paramore- ▁Pochinki- ▁Raisa- ▁Rampage- ▁RedMart- ▁Rojak- ▁Rolanda- ▁Rumba- ▁Sanhok- ▁Sembcorp- ▁Shatec- ▁Shazleen- ▁Sheila- ▁Siberia- ▁Spirulina- ▁Spongebob- ▁Sugar- ▁Tiaga- ▁Tibet- ▁Tintin- ▁Toronto- ▁Umairah- ▁Vain- ▁Vegan- ▁Walter- ▁Webtoon- ▁Whatsapp- ▁Yasmin- ▁Yellow- ▁Zipline- ▁abdomen- ▁adequate- ▁adversities- ▁aftertaste- ▁aggravate- ▁armchair- ▁artefact- ▁artifact- ▁avengers- ▁averaging- ▁barnyard- ▁beloved- ▁blueberries- ▁boisterous- ▁bolognese- ▁bouquet- ▁breadcrumb- ▁brooch- ▁chamber- ▁champagne- ▁chihuahua- ▁chrysanthemum- ▁cinematography- ▁coffin- ▁coherent- ▁collarbone- ▁colloquial- ▁condescending- ▁consolidat- ▁consonant- ▁contagious- ▁contemplating- ▁contemporary- ▁coriander- ▁courtyard- ▁cynical- ▁daunting- ▁deadlift- ▁decreasing- ▁degrading- ▁dentures- ▁devoting- ▁dictate- ▁dodgy- ▁doorstep- ▁dopamine- ▁douchebag- ▁dwarf- ▁earthworms- ▁emporium- ▁enclosure- ▁entails- ▁esketit- 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▁succulent- ▁supervision- ▁tattered- ▁terribly- ▁ticklish- ▁tidbit- ▁tomollow- ▁tremendous- ▁trespass- ▁trishaw- ▁unaware- ▁uncommon- ▁uncouth- ▁underestimate- ▁understudy- ▁unknowingly- ▁unwanted- ▁upbeat- ▁username- ▁utilize- ▁wavy- ▁woodworking- ▁wurly- ▁Boost- ▁Brighton- ▁Neslo- ▁Silk- ▁abundance- ▁advent- ▁analogy- ▁calibr- ▁conscientious- ▁ingrain- ▁misjudge- ▁noisier- ▁parsley- ▁peppermint- ▁recontract- ▁smuggle- ▁spacing- ▁stomp- ▁truancy- mitten- ▁Dengue- ▁Emily- ▁Fajar- ▁Harbin- ▁Kamal- ▁Toggle- ▁abalone- ▁celery- ▁copycat- ▁dubious- ▁glare- ▁greasy- ▁hydrogen- ▁influential- ▁jeopardize- ▁laboratory- ▁perseveran- ▁reform- ▁shucks- ▁Kingsman- ▁Prive- ▁Steffi- ▁boomerang- ▁estimation- ▁eventual- ▁glamour- ▁goblin- ▁intersect- ▁mimic- ▁slimmer- Beautiful- ▁Famous- ▁Kylo- ▁figurative- ▁troop- ▁vapour- Gogh- ▁Amber- ▁Beatles- ▁Brenda- ▁Logan- ▁Nasser- ▁Ramesh- ▁avid- ▁carol- ▁flaming- ▁geese- ▁rattan- ▁sledge- ▁Nineteen- ▁Nurul- ▁Pegan- ▁apology- ▁escaping- ▁existentialist- ▁lasik- ▁mansionette- ▁masculinity- ▁percussion- ▁shards- ▁speculation- ▁template- ladder- ▁Cholo- ▁Gerpe- ▁Store- ▁Suri- ▁ambiance- ▁screwdriver- ▁shading- ▁telephone- ▁Gamebooks- ▁Valerie- ▁Zaibo- ▁antisocial- ▁carnation- ▁corpse- ▁droplets- ▁hypocritical- ▁memorizing- ▁perspiring- ▁salsa- Buloh- Patterson- ubis- york- ▁Ahkah- ▁LinkedIn- ▁babysitter- ▁battlefield- ▁censorship- ▁disruptive- ▁perfectionist- ombie- ▁backfire- ▁compact- ▁outerwear- ▁Kanye- ▁Levina- ▁diaries- ▁exclusivity- ▁indulgence- ▁intonation- ▁liberat- ▁magnetic- ▁molecul- ▁patties- ▁quotation- ▁raffle- ▁Powerpuff- ▁compatib- ▁sulk- ▁trump- ▁General- ▁Muji- ▁lulu- ▁spicie- ▁ramble- ▁stimulation- ▁Kashi- ▁thorn- ▁Colour- ▁clump- ▁dulan- ▁lazier- ▁manipulative- ▁shimmer- ▁undergraduate- logue- ▁Angelina- ▁Suzy- ▁capsizing- ▁lik- ▁psychotic- ▁understatement- ▁ventilat- Pahat- ▁bankruptcy- ▁contradiction- ▁destruction- ▁sanity- ▁Dawn- ▁Salva- ▁fluid- ▁pricy- ▁Satay- ▁frock- ▁pending- ▁restrictive- ▁scammer- ifier- ▁mingling- ▁Sapa- ▁grandchild- ▁rosy- Nun- ▁physician- ▁comprehensive- ▁kope- ▁qual- ▁rubric- ▁evade- ▁globalisation- ▁radiotherapy- ▁tripped- ▁Race- ▁denial- ▁redemption- wow- ▁anorexia- ▁displace- ▁Ashton- ▁Zeus- ▁anatomy- ▁Family- ▁Murakami- ▁Oriental- ▁dreadful- ▁glamor- ▁Damai- ▁Hogwarts- ▁billboard- ▁consumerism- ▁explosion- ▁booze- ▁cathedral- ▁pianist- ▁Shaun- ▁Freshies- ▁Lavi- ▁eeyer- ▁freight- ▁wholesome- ▁affectionate- ▁euro- ▁pussy- ▁slut- ▁twinning- ▁mellow- ▁quoting- ▁paintball- ▁primate- score- ▁Istana- ▁Kayaking- ▁affiliated- ▁blatantly- ▁dehydrated- ▁eyelash- ▁chubby- ▁hottest- ▁unproductive- ▁unrelated- ▁womanizer- endang- ▁daugh- Kheng- ▁Chronicle- ▁harmful- ▁Sakae- ▁Shoppe- ▁clipper- ▁insistent- ▁sparkling- ▁trademark- took- thical- ▁kuih- ▁diesel- ▁incest- ▁Station- arabat- ▁charac- ▁Heroes- ▁deliberately- ▁Dior- ▁discriminate- ▁zag- ▁Prudential- ▁bubbling- ▁suffocating- ▁noticeboard- ▁patrol- ▁tagged- ▁Evan- ▁wrapper- ▁Kickapoo- ▁sank- ▁Monster- ▁adaptive- ▁spillage- ▁cheongsam- ▁haram- Chamber- ▁Ella- ▁transferring- ▁Maslinda- ▁beautif- ▁Naan- ▁shaw- ▁Bosch- ▁Dash- Gandhi- ▁bodysuit- Mahal- ▁Chevron- ▁mantis- ▁trashcan- ▁utter- ▁enunciate- ▁consent- ▁guitarist- ▁Who- ▁powderful- ▁reaper- ▁rashes- ▁Conjuring- ▁Education- ▁iCloud- ▁Like- ▁propel- ▁Salvation- ▁Gula- ▁witty- ▁Keane- ▁yuan- ▁vow- ▁Evo- ▁shareholder- ▁hazel- ▁bolt- ▁changi- ▁autonomy- noya- ▁perfumery- ▁undertaker- ▁Putra- ▁Halia- ▁communist- tigate- ▁rapist- ▁zipline- dog- ▁prune- ▁bustle- ▁illustrator- ▁blurry- ▁viewpoint- ▁drawback- ▁potent- ▁repress- ▁litted- ▁disturbance- ▁treehouse- ▁Miami- ▁rework- Chuan- Wall- Depp- ▁lax- ▁Greatest- ▁formality- ▁Smoo- ▁overview- ▁validated- ▁volcanoes- Rice- ▁easel- ▁locket- ▁fluently- vour- ▁detached- ▁biasness- ▁hypo- ▁practicality- ▁elected- ▁Wood- ▁Batisah- ▁Turtle- ▁Grabfood- ▁Jovi- ▁sev- ▁cooper- ▁rundown- ▁Sedan- ▁queer- ▁kaka- ▁Stal- ▁Xbox- ▁swerve- ▁native- ▁Bowl- ▁Talk- ▁confiscated- ▁stubbornness- ▁factories- ▁jiak- ▁damm- 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▁bloke- ▁multimedia- ▁traction- apalang- riple- ▁BTS- ▁Laurance- ▁calisthenic- ▁profanit- ▁Zepp- ▁fertile- ▁forbid- ▁initiation- ▁Slim- ▁epita- ▁Sportslink- ▁stabbed- ▁vocalist- ▁interference- ▁opposing- commerce- ▁larva- ▁terracotta- ▁Jovina- ▁accusat- ▁countertop- ▁recollect- ▁spitball- ▁unisex- ▁yankee- osacea- ▁Dolly- ▁Heights- ▁Tapas- ▁extensively- ▁snorkelling- ▁civilisation- ▁hardwire- ▁Hazi- ▁reinforce- ▁warped- Modric- ▁Alipay- ▁campsite- ▁outshine- ▁proficiency- ▁Poland- elope- ▁Marx- ▁Zero- ▁humanitarianism- ▁marinade- ▁mentorship- ▁quirkiness- ▁Amex- ▁cooperation- ▁hydrant- ▁roving- ▁kinky- ▁Koda- ▁Radin- ▁boredom- ▁contextual- ▁swum- ▁terminolog- ▁urbanization- ▁Bengali- ▁Paper- ▁preview- ▁spectro- ▁fleshier- ▁mantou- ▁Otak- ▁tonkatsu- ▁Badang- ▁Brit- ▁mortality- Free- Russell- odyne- ▁Ginger- ▁juici- ▁drake- ▁medalist- ▁memento- ▁tolerat- ▁vapor- ▁Caroline- ▁Arthur- ▁sewage- ▁strategise- ▁suction- worth- ▁Jenga- ▁alleviate- ▁marvelous- ▁resent- ▁scrutinize- ▁termination- 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▁irra- ▁concourse- ▁rubble- ▁repeti- ▁Chungha- ▁Baliza- ▁chye- ▁fantasize- ▁corp- chatting- local- ▁Skippy- ▁authorities- ▁monogamy- ▁Yoga- ▁excruciating- Luak- aksa- ▁miller- ▁Suki- ▁Glenis- Tree- ▁Pakcik- ▁vasectomy- ▁veil- ▁Escobar- ▁Francisco- ▁Shuttle- ▁Tyson- ▁scum- ▁Grill- baby- ierra- ublic- ▁ounce- ▁chemist- ▁Kardashians- comfort- interesting- ▁Atwood- Jack- ▁Jetty- ▁demoralized- ▁graciousness- ▁horrified- ▁regimented- ▁adik- ▁dependence- ▁modul- ▁repost- ▁shiba- ▁Voon- ▁organically- ▁farmhand- ▁reflective- ▁Showman- ▁prick- ▁chaperon- ▁Cuba- ▁Joann- ▁harass- ▁mindfulness- ▁prophecy- ▁chives- Steel- ▁dirtier- ▁astray- ▁submerged- ▁marley- ▁plasticky- ▁panicked- ▁punya- rief- ▁awk- ▁neutralise- ▁talism- ▁doubtful- culi- ▁Anton- ▁elev- ▁Bora- ▁treasury- ensity- ▁commodity- ddess- tweet- ▁forceps- ▁crossover- ▁Porter- ▁cordon- ▁Health- ▁Kingdom- ▁Tatum- ▁robin- ▁xuan- ▁Chocolate- ▁cupid- ▁pimp- ▁stil- ▁curricul- ▁hallucinating- ▁platelet- ▁antics- ▁Laden- thering- ▁decay- ▁Cupid- ▁mitch- acies- ▁shoul- riding- ▁Minion- ▁Take- ▁Amri- ▁Shawan- ▁bedek- ▁bloc- ▁Morgana- ▁bowel- jori- ▁heatiness- ▁Hailing- ▁jeng- ▁reese- ▁Nature- ▁cheerfulness- ▁moose- bread- ▁accountability- ▁pokka- ▁Tues- ▁hunk- ▁posing- ▁bucketlist- ▁linguist- ▁manoeuvre- ▁cyan- bury- ▁sexism- ▁Bueno- lliance- ▁Ericsson- ▁panties- ▁Campbell- ▁bilis- ▁frap- ▁thinning- ▁calf- ▁rescheduled- evolving- pagar- ▁Dude- ▁hula- ▁flailing- ▁illustrating- ▁lingering- ▁regurgitating- ▁fax- ▁ampli- ▁alrea- ▁cremat- ▁Linda- ▁brushless- ▁flatten- ▁numer- dicu- ▁voiceover- ▁Liver- miliar- ▁stopwatch- ▁optimise- ▁Buri- ▁binders- ▁MediS- ▁handcraft- erja- ▁saxophone- Pek- ▁Network- ▁colony- ▁Marrie- ▁Americanize- jang- ▁hillman- Max- ▁Shang- ▁Manny- ▁uncom- ▁astrology- Maki- ▁harshness- eletubby- ▁jaded- ▁banish- ▁rapidly- ▁Heat- ▁kwa- ▁instructed- ▁chek- bogg- ▁bloodline- ▁Summer- ▁voicemail- Stone- ▁morality- ▁gentlemanly- ▁respectively- ▁Hambr- ▁Doha- Kwan- ▁Jiu- ▁Que- ▁Deli- Kok- ▁cluttered- ▁debuted- ▁provoking- ▁bleed- ▁Tale- ▁steeper- ▁standpoint- ▁quitter- ▁policemen- ▁mili- ▁burnout- Daily- uhsorry- ▁Farhana- ▁qualifier- ▁Shaf- Shin- ▁choreographer- ▁Bike- ▁Angkat- ▁puffin- ▁taxation- ▁fiancee- ▁maxi- ▁dimensional- ▁cohesive- pour- ▁Frenchie- ▁Carri- ▁snapped- arracks- apse- hank- ▁Kip- ▁Meal- ▁scented- ▁flourishing- ▁tata- ▁mamak- ▁disrespectfully- ▁Rama- ▁Rina- ▁Botanical- ▁Buk- ▁blogger- ▁cir- ▁Taken- ▁smoothen- esco- ▁Nasa- ▁dented- ▁pasture- ▁discharged- ▁showroom- ainted- ▁trad- ▁Geo- ▁Lumpur- ▁Sweep- static- ▁Beau- ▁barang- ▁beauti- ▁siren- ▁Union- ▁blushing- ▁disheartening- ▁refresher- ▁Albom- handed- ▁bandage- ▁customized- bio- ▁blindfolded- ▁examine- ▁enhanced- ▁salesman- ▁Pound- ▁creativeness- ▁Back- ▁saltish- ▁ballpoint- ▁groomer- ▁Teck- ▁magna- ▁announcer- ▁fallout- ▁kui- Amin- ▁heroine- ▁Louise- ▁Sailor- ▁relay- ▁Sound- ▁leaked- ▁painless- Mei- ▁browser- ▁rewatched- Rex- ▁Blangah- ▁Azan- ▁bartending- ▁artificially- ▁endured- ▁Bir- ▁dif- ▁anoth- stability- ▁Irene- ▁liv- ▁Chio- ▁sexually- ▁equalise- ▁economically- ▁ponder- ▁narcissis- ▁shooked- ▁Ivy- ▁broader- ▁Gig- ▁Opeh- ▁Zionis- ▁procession- ▁Chay- Sec- ▁Imbu- jar- ▁fourteenth- ▁hiong- ▁correlate- Yen- ▁conquered- ▁tailored- rce- ▁hisses- ▁Aero- ▁Dada- avainas- chain- ▁Site- nguish- ules- ▁launched- ▁warned- ▁documented- ▁Boba- ▁Pau- ▁mutate- ▁Wani- ▁tui- ▁Lane- ▁haw- ▁Lang- eous- ▁Hard- ▁tickled- Husse- ▁choked- essie- ▁Rad- abyte- ▁Osa- ▁processor- ▁globally- ▁Full- ▁booklet- Shi- ▁chemically- ▁easter- ▁ferti- ▁frontier- ▁broadcasting- ▁clinging- ▁halve- ▁Darli- ▁soaked- related- ▁Zu- ▁classist- north- ▁Atta- ▁overlooked- ▁Achar- ▁Lai- ▁battl- ▁ceased- cord- ▁Nata- ▁detoxing- ▁betraying- ▁kee- ▁stupidity- ▁erecti- Benoit- ▁Zone- ▁seamless- ▁stealer- ▁patientuh- ▁condemned- ▁Indon- ▁emphasized- ▁rotated- Swan- ▁mengs- ▁minion- ▁Mela- ▁Date- ▁friendzone- ▁earl- ▁mandate- ▁bomber- ▁conveying- ▁showke- ▁quietness- ▁Medan- ▁reversed- ▁Dilma- ▁quieten- ▁backlash- ▁fury- ▁ranting- ▁Chou- ▁toughness- nkers- ▁formant- ▁efficiently- ▁digger- ▁Fara- ▁glorify- Fest- ▁renewed- lier- ▁Jung- ▁chairman- Sang- ▁dashing- ▁qing- ▁publisher- eacock- ▁dino- ▁highlighted- ▁polygam- ▁tighter- ▁demolishing- ▁calmly- ▁unsee- ▁awkwardness- ▁sealed- ▁Bane- ▁Cara- ▁fertiliz- ▁pap- ▁Horn- ▁relieved- Song- sebum- ▁Gar- amant- ▁Dove- ▁resigning- ▁Philipin- ▁Fred- ▁nei- Tat- ▁witnessing- ▁Kina- ▁Tange- audi- ▁compromised- ▁weeding- ▁breather- ▁Xian- ▁importing- Ali- ▁springy- ▁Sling- ▁Tell- ▁understooded- rchaeology- ▁murderer- ▁layered- Van- ▁Qin- fall- ▁hyped- ▁measured- ▁Even- ▁purchaser- ▁fainting- ▁Oman- ▁stifl- ▁scorch- ▁programmed- ▁recesses- ▁nee- ▁Choi- ▁Patta- ▁highlighting- ▁brainer- ▁dramatise- ▁bureaucra- ▁Susan- ▁braid- ▁sung- inary- ▁Check- ▁curl- alakat- ▁Chitt- entities- ▁pitcher- ringing- ▁corr- iative- ▁Rain- Unagi- ▁nin- dora- cidentally- ▁daze- ▁Kara- hepard- ▁engi- gemuk- ▁Romania- ▁Bren- erbal- moral- bike- ▁Corin- ▁Sherle- ecurity- ▁Vik- ▁rumba- urities- ▁Buck- ▁Ava- ▁Nabila- ladi- mbing- raz- ▁Westerner- oronation- ▁detain- zoning- utting- ▁confesse- ntries- ▁Sala- ▁humbl- ▁mara- ▁Halif- ▁sled- Hood- lator- ▁swank- ▁Alv- gazing- Harrison- zel- ▁strategiz- ▁Dean- ovan- umanities- untie- Ben- omer- ▁chronic- ▁Arch- ▁packer- junct- zhang- appe- ▁dabbl- ▁kato- Liars- ▁boater- ▁Wul- ▁Univers- ▁rekindle- ▁steadi- ▁rac- ▁Yao- ▁gad- amil- ▁crashe- ▁tro- anie- ▁Shaku- uppy- nite- Shopping- Baker- position- ▁Ace- ▁Magn- ▁Tubb- anner- ightful- ▁Mill- Eighty- competency- ▁legalis- impang- ▁dirtie- ▁grann- ▁Dogg- Wool- atically- ▁Mina- ▁preconce- ▁competenc- ▁boggl- ▁crippl- zed- Perr- Carlo- Chek- ▁provoke- ▁conserve- ▁Lud- caster- uted- orium- rumbling- graded- ▁cultivati- ▁nauti- shoong- ▁centralize- ▁Nestl- uchi- ▁intrude- erati- ▁optimiz- ▁reassur- ▁integra- bodies- referred- xora- ▁subtl- ridden- ▁professionalis- bbed- Dali- ▁relocat- ▁unfaithful- ltering- oller- ▁brack- ▁formulate- iggly- cialised- ▁conserv- provoked- Donut- istan- uja- ▁panick- ▁physio- zhe- ▁dependenc- haps- ▁humili- Mercy- lashes- ▁collaps- Sophia- ▁Elfi- Aini- ▁wrinkl- Name- ▁spiri- xie- logy- Pol- unned- ▁Len- contentment- Mania- yukat- ▁deteriorat- Jiang- ▁outsourc- ▁psychiatr- ▁tortur- ▁grea- ▁Thie- Simple- ompan- ▁masa- ▁Child- ador- exter- jung- ▁glen- ▁duplicat- aburi- kiang- umbling- ▁prudent- mobile- ravel- ▁educa- ▁moderat- oaky- eeper- ▁geographic- ▁Franc- ▁electrop- ▁Marg- ▁marbl- ▁ceas- flicted- ▁probab- ▁craz- ▁magnet- ▁gymnastic- ▁pellet- ▁immigrant- ▁nacho- ▁Luca- ▁defini- ▁hamper- ▁fluctuate- ▁refugee- ▁dimple- Throne- ▁comprise- ▁nutrient- ▁phonic- ▁tyke- ▁freebie- ▁Vibe- ▁utensil- ngee- ▁pilate- ▁State- ▁supplie- ▁barbecu- ▁trouser- ▁posses- ▁obviou- Nee- ▁jean- roti- ▁circ- cratic- ▁bureau- compartmentalize- conceiv- ▁Canto- ▁vol- mount- ssistant- mmoners- ifle- ▁Moham- ▁Scoo- ▁genera- ▁peda- rati- ▁reminiscen- ▁Plane- ▁impatien- ▁accep- josh- ▁buil- ▁archaeolog- ▁Horse- founded- ▁Roz- gnific- ▁fertili- ▁synchron- visible- ▁distanc- friend- ▁craw- paced- ▁statu- aiya- half- ▁revol- ▁privat- ogling- uishin- ppressed- ▁narciss- ▁Techno- opsicles- lugg- nvestigations- ▁drow- ▁Digi- enetic- etica- writers- stroke- lags- Mercie- ounce- wolf- acaroons- ▁introduc- force- ▁inclin- ▁disput- ▁collage- aholic- interest- okki- ▁professio- ▁Pearly- plit- ▁moisturiz- ▁volu- ▁candleli- ▁linger- ▁bartend- ▁illustrat- odeling- ▁regurgitat- ▁dispens- ▁Zhe- proof- ▁flail- ▁compromis- finals- ▁disciplin- ▁embarra- ▁empir- ▁bibl- perform- ▁submerge- rystal- ▁Roma- flakes- lgari- ▁apologis- ▁Jayde- ▁traumatise- akcik- ▁amaze- ▁agitate- ordinary- ▁occupie- ▁overprice- ▁fluctuat- ▁dilute- coast- fruit- collect- ▁horrifi- copy- stream- ▁demoraliz- quish- udrey- ▁constipat- ▁jumble- ambodia- ▁crook- stresses- sighted- Dogg- olution- urrender- tyrofoam- ▁impair- Buri- Escobar- Jetty- Showman- Shuttle- Tyson- ceased- aktor- ▁unexpect- Technical- Grill- Convenience- Future- Institute- Krunch- Schwarzenegger- Theatre- polished- Gabriel- antorini- Evan- Keane- Monster- ntegrated- Francisco- ▁deprive- ▁discriminat- ▁referenc- Kenny- Murder- Zhang- carriage- ▁Joan- ▁Skipp- ▁monogam- ▁prophec- Atwood- Stamford- ylvia- Akshay- Banyan- Virgin- linguistic- proportionate- founder- Music- buoy- evaluate- immortal- comparable- Jade- Miller- Friza- conditioned- innacle- Morrie- Waterway- conductor- crow- ndependence- Henry- Monkey- Nyonya- Shaw- monkeys- second- Shakespeare- catcher- organised- qualified- storage- Thomson- animate- executive- invent- starter- ▁rollerblad- ▁vacanc- Festival- Jordan- emphasis- frequent- settled- weekly- ▁Desire- college- factory- growth- organisation- profit- sentosa- Holland- Kampung- Laden- Nature- Seoul- Spirit- Tae- bridge- innocence- legend- village- Friday- Orchard- Sentosa- between- culture- decided- explore- popular- responsibility- romantic- square- studies- valuable- Great- blue- chicken- confirm- ▁cruis- akhon- album- driver- ▁regiment- Green- ▁Snoop- secondary- luckily- ppb- toast- ▁Galax- ▁demonstrat- ▁prioritiz- ▁savor- ▁vocabular- anuki- ▁voluntar- ▁temporar- heeps- ▁warehous- Gateway- ▁naught- ▁Whit- ▁itinerar- ▁ultimat- ▁ugl- Environment- ▁diverg- ▁wrestle- ▁leverag- large- cool- plus- hursday- ature- shiok- ▁Chuck- ▁sanitize- ▁suppo- ▁solemnise- usband- ▁Titani- ▁savour- chips- Paul- uesday- ▁sequential- ▁unintentional- Army- consult- ▁braise- ▁evident- ▁radical- death- dough- Comm- hicago- version- ranscendent- Mark- ropic- ▁blatant- ▁comparative- affle- Youth- rophecy- eepavali- oothpaste- welve- reserving- ▁consum- latoon- erfect- rofessor- athedral- ettol- husband- ▁Mobil- indly- ▁appetiz- iracle- ninety- erlion- urious- ragrance- flection- ▁Yog- Hyun- proper- ▁commercialise- dept- onjour- lower- rganic- ubilee- ustin- ▁delusion- ▁dysfunction- armony- dead- ulgogi- czema- lazer- utchers- xperiment- ynasty- inosaur- ▁ceter- ▁trembl- cessor- degrad- ▁misus- ▁blemish- suade- alloween- crastinating- ▁sunglass- polar- requisite- ▁evasi- ▁Project- aspberry- ▁associ- ▁pacif- wives- ▁demograph- edemption- ejection- ▁confisca- gregation- oomie- ▁Isabel- ▁Olymp- ▁satisfact- awyer- ▁pronunciat- ▁subsidiar- ▁subscript- ▁unpredictab- ngkok- ednesday- ▁challen- ▁esophag- luding- ▁typi- ▁activi- ▁arbitra- ▁opportuni- esarean- ▁transmit- eceived- ▁advan- ▁ponch- arlic- iatric- mplify- ▁Stef- ▁illumi- ▁luxuri- ▁Pontian- ▁reputa- '2'- ▁Kean- ▁Remains- cendant- rrifying- ▁desensiti- ▁jeopardi- Little- vasion- grudg- zumi- ▁Anytime- ▁Lantau- ▁Morris- ▁Niagara- ▁Preeti- ▁Qistina- ▁cerebral- ▁dhoby- ▁dysph- ▁perpetua- ckelodeon- grimage- icultural- ychology- ▁Analytic- ▁Moldov- ▁acclimat- ▁anthropolog- ▁hexagon- ▁implicit- ▁profess- ▁shrivel- ▁symphoni- '@'- Agency- Anthony- ArkBar- Atria- Brigg- Conven- Counselor- Dominique- Dracula- Effron- Fallon- Griffith- Jabba- Kinsella- Kukoh- Millionaire- Motoring- Movie- Mustachio- Mustafi- Noraini- Philipp- Regis- Robbie- Sandler- Sheares- SquarePants- Thunder- Tonic- acerbate- amgyetang- angelo- antiago- arijuana- cotta- cryptocurrencies- dchildren- deekap- dyssey- enocide- ifiable- itzer- neumonia- nspirasi- osophy- ptuous- resistible- rowana- rwegian- tête- ucerin- uddin- uhwait- umental- umentary- urgundy- ▁Abdillah- ▁Adaline- ▁Adawiyah- ▁Afghan- ▁Agnus- ▁Alfred- ▁Amirah- ▁Andorra- ▁Anglican- ▁Annalise- ▁Annyeong- ▁Applied- ▁Aravin- ▁Asmara- ▁Balenciaga- ▁Bhatoora- ▁Bibimbap- ▁BitCoin- ▁Blastoise- ▁Bleeding- ▁Boiling- ▁Bonnie- ▁Boomerang- ▁Botox- ;- .- '9'- 不- 代- 喊- 在- 沟- 钟- ','- '1'- '4'- '5'- '6'- '7'- '='- \\- à- 七- 么- 什- 们- 你- 分- 吃- 完- 样- 看- 要- 这- 苦- ê- é- '{'- init: chainerinput_size: nullctc_conf:    ignore_nan_grad: truemodel_conf:    ctc_weight: 0.3    lsm_weight: 0.1    length_normalized_loss: falseuse_preprocessor: truetoken_type: bpebpemodel: data/en_token_list/bpe_unigram20000/bpe.modelnon_linguistic_symbols: nullcleaner: nullg2p: nullspeech_volume_normalize: nullrir_scp: nullrir_apply_prob: 1.0noise_scp: nullnoise_apply_prob: 1.0noise_db_range: '13_15'frontend: defaultfrontend_conf:    fs: 16kspecaug: nullspecaug_conf: {}normalize: global_mvnnormalize_conf:    stats_file: exp/asr_stats_raw_en_bpe20000/train/feats_stats.npzpreencoder: nullpreencoder_conf: {}encoder: transformerencoder_conf:    input_layer: conv2d    num_blocks: 12    linear_units: 2048    dropout_rate: 0.1    output_size: 256    attention_heads: 4    attention_dropout_rate: 0.0decoder: transformerdecoder_conf:    input_layer: embed    num_blocks: 6    linear_units: 2048    dropout_rate: 0.1required:- output_dir- token_listversion: 0.9.6distributed: trueLM configconfig: conf/train_lm.yamlprint_config: falselog_level: INFOdry_run: falseiterator_type: sequenceoutput_dir: exp/lm_train_lm_en_bpe20000ngpu: 1seed: 0num_workers: 1num_att_plot: 3dist_backend: nccldist_init_method: env://dist_world_size: 4dist_rank: 0local_rank: 0dist_master_addr: localhostdist_master_port: 57264dist_launcher: nullmultiprocessing_distributed: truecudnn_enabled: truecudnn_benchmark: falsecudnn_deterministic: truecollect_stats: falsewrite_collected_feats: falsemax_epoch: 20patience: 3val_scheduler_criterion:- valid- lossearly_stopping_criterion:- valid- loss- minbest_model_criterion:-   - valid    - loss    - minkeep_nbest_models: 1grad_clip: 5.0grad_clip_type: 2.0grad_noise: falseaccum_grad: 1no_forward_run: falseresume: truetrain_dtype: float32use_amp: falselog_interval: nullunused_parameters: falseuse_tensorboard: trueuse_wandb: falsewandb_project: nullwandb_id: nullpretrain_path: nullinit_param: []freeze_param: []num_iters_per_epoch: nullbatch_size: 64valid_batch_size: nullbatch_bins: 1000000valid_batch_bins: nulltrain_shape_file:- exp/lm_stats_en_bpe20000/train/text_shape.bpevalid_shape_file:- exp/lm_stats_en_bpe20000/valid/text_shape.bpebatch_type: foldedvalid_batch_type: nullfold_length:- 150sort_in_batch: descendingsort_batch: descendingmultiple_iterator: falsechunk_length: 500chunk_shift_ratio: 0.5num_cache_chunks: 1024train_data_path_and_name_and_type:-   - dump/raw/lm_train.txt    - text    - textvalid_data_path_and_name_and_type:-   - dump/raw/dev/text    - text    - textallow_variable_data_keys: falsemax_cache_size: 0.0max_cache_fd: 32valid_max_cache_size: nulloptim: sgdoptim_conf: {}scheduler: nullscheduler_conf: {}token_list:- - - ▁I- ''''- ▁the- ▁you- ▁like- ']'- ▁[- s- ▁to- ▁that- ▁it- ▁is- ▁ya- ▁a- ▁and- ▁so- t- ▁then- ▁okay- ▁what- ▁of- ▁but- ▁uh- ▁know- ▁in- ▁one- ▁my- ▁don- ▁have- ▁they- ▁we- ▁no- ah- ▁not- ▁just- ▁for- lah- ▁think- ▁can- ▁there- oh- ▁do- ▁was- ▁this- ▁your- ▁right- ▁he- '-'- ▁are- ▁me- ▁go- ▁or- ▁be- ▁very- ▁if- ▁will- _- ▁on- ▁she- ▁how- m- ▁with- ▁want- ▁about- ▁because- ▁ppl- ▁all- ▁really- ▁when- ▁also- ▁time- ▁say- ▁at- ▁why- eh- ▁would- ▁um- ▁got- ▁mean- ▁see- ▁thing- ▁as- ▁actually- ▁people- ▁cause- ▁yes- ▁two- ▁now- ▁good- ▁more- ▁get- ▁ppo- ▁maybe- ▁ppb- ▁lot- ▁from- ▁up- ▁only- ▁her- ▁already- ▁kind- ▁out- ▁some- ▁something- re- ▁who- ▁them- ▁need- ▁where- ▁three- ▁let- ▁quite- ▁yeah- ▁most- ▁even- ▁talk- ▁never- ▁after- ▁did- ▁other- ▁still- ▁feel- ▁school- ▁eat- ▁which- ▁take- ▁Singapore- ▁should- ▁food- ▁cannot- ▁first- ▁back- ▁always- ▁life- ll- ▁same- ▁next- ▁things- ▁ask- ▁last- ▁come- ▁nice- ▁person- ▁make- ▁their- ▁were- ▁work- ▁didn- n- ▁our- ▁much- ▁wait- ▁too- ▁going- ▁went- ▁him- ▁five- ▁question- ▁&lt;- ▁guess- ▁different- '&gt;'- UNK- ▁give- ▁had- ▁way- ▁has- ▁tell- ▁by- ▁friend- ▁place- ▁his- ▁those- ▁well- ▁buy- ▁mine- ve- ▁before- ▁day- ▁four- ▁long- ▁love- ▁any- ▁look- ▁correct- ▁mm- ▁true- ▁money- ▁try- ▁bad- ▁play- ▁everything- ▁down- ▁best- ▁remember- ▁K- ▁stuff- ▁bit- ▁many- ▁us- ▁home- ▁been- ▁huh- ▁use- ▁put- ▁wah- ▁said- ▁guy- ▁here- my- ▁anything- ▁than- ▁doing- ▁year- ▁could- ▁house- ing- ▁friends- ▁sure- ▁find- leh- ▁an- ▁someone- ▁must- ▁gonna- ▁thought- ▁ten- ▁doesn- ▁watch- ▁keep- ▁another- ▁oh- ▁every- ▁point- ▁years- ▁call- ▁family- ▁old- ▁err- ▁better- lor- ▁won- god- orh- ▁man- ▁damn- ▁around- ▁name- ▁course- ▁favourite- ▁left- ▁talking- ▁top- ▁own- ▁turn- ▁girl- ▁into- ▁twenty- ▁am- ▁over- ▁sometimes- ▁start- ▁help- ▁whole- ▁these- ▁six- ▁wow- ▁shop- ▁again- ▁important- ▁mind- ▁probably- ▁blue- ▁big- ▁colour- ▁though- ▁part- ▁end- ▁does- ▁side- ▁parents- ▁read- ▁bring- ▁being- ▁red- ▁close- ▁new- ▁yea- ▁job- ▁ever- ▁hard- ▁nothing- ▁difference- ▁interesting- ▁else- ▁together- ▁car- ▁called- ▁whatever- ▁show- ▁white- ▁sorry- ▁change- ▁pay- ▁kids- ▁basically- ▁done- ▁seven- ▁free- ▁hundred- ▁learn- ▁green- ▁fun- ▁book- ▁told- ▁small- ▁off- ▁answer- d- ▁sign- ▁usually- ▁eight- ▁few- C- ▁open- ▁wanna- ▁mum- ▁chicken- ▁myself- ▁understand- ▁A- ▁means- ▁care- ▁both- ▁describe- ▁game- ▁once- ▁saying- ▁used- ▁teacher- ▁saw- ▁inside- ▁later- ▁happen- ▁C- ▁through- ▁date- ▁yourself- S- ▁stay- ▁looking- ▁movie- ▁dollars- ▁during- ▁move- ▁live- ▁until- ▁primary- ▁least- ▁nine- ▁wearing- ▁card- ▁thirty- ▁stop- ▁alright- ▁wanted- ▁trying- ▁Chinese- ▁each- ▁everyone- ▁having- ed- ▁example- ▁enough- ▁plus- ▁s- ▁haven- ▁cook- ▁working- ▁yup- ▁away- ▁spend- ▁expensive- ▁kid- ▁might- ▁secondary- ▁since- ▁sad- ▁whether- ▁night- ▁phone- ▁children- ▁water- ▁days- ▁study- ▁S- ▁enjoy- ▁young- meh- ▁fine- ▁super- ▁door- ▁class- ▁moment- ▁wrong- ▁drink- ▁second- ▁i- ▁came- ▁week- ▁thinking- ▁sense- ▁dog- ▁made- ▁getting- sia- ▁beside- ▁walk- ▁become- ▁happy- ▁world- ▁shit- ▁partner- ▁hand- ▁minutes- ▁choose- ▁scared- ▁ball- ▁word- T- ▁front- ▁half- ▁easy- ▁anyway- ▁- ▁hour- ▁P- ▁bro- ▁meet- ▁picture- ▁cool- ▁N- ▁fifty- ▁hours- ▁rice- ▁child- ▁country- ▁pretty- A- ▁worst- ▁guys- ▁brown- ▁far- ▁funny- ▁room- ▁little- y- ▁black- ▁normal- ▁English- ▁heard- e- what- ▁looks- ▁group- ▁experience- ▁sleep- ▁near- a- ▁table- ▁story- ▁conversation- ▁weird- ▁face- ▁today- ▁exactly- ▁definitely- ▁travel- ▁type- ▁boy- ▁especially- ▁Singaporean- ▁high- ▁rather- ▁real- ▁twelve- ▁T- ▁miss- ▁brother- ▁problem- ▁without- ▁believe- ▁generation- ▁while- ▁number- ▁O- ▁finish- ▁able- ▁says- ▁yet- ▁hair- ▁playing- ▁save- ▁everybody- ▁times- ▁such- ▁supposed- ▁die- ▁sit- ▁late- ▁yours- ▁run- ▁dad- ▁everyday- ▁age- ▁meal- ▁sell- ▁M- ▁yellow- loh- ▁break- ▁area- ▁queue- ▁married- ▁took- ▁coming- M- ▁mother- ▁fast- ▁eating- ▁certain- ▁wear- ▁plan- ▁leave- ▁month- ▁collect- ▁aiya- ▁topic- ▁fish- ▁Malay- ▁anymore- ▁makes- ▁teach- ▁honestly- ▁outside- c- ▁serious- ▁father- ▁started- ▁speak- ▁pass- ▁beach- ▁bought- ▁full- ▁share- ▁character- ▁hate- B- ▁dollar- P- ▁depends- ▁morning- ▁sister- ▁B- ▁J- ▁tried- ▁special- ▁send- ▁mmhmm- ▁future- r- ▁between- ▁idea- ▁cheap- ▁company- ▁music- ▁wife- ▁thousand- ▁lady- mah- ▁dream- ▁relationship- ▁short- ▁either- ▁recently- ▁list- ▁please- ▁tomorrow- ▁listen- ▁follow- ▁bus- o- ▁level- ▁difficult- ▁isn- ▁sometime- ▁check- ▁If- ▁drive- ▁great- ▁value- ▁continue- ▁past- ▁pick- ▁suddenly- ▁hear- ▁order- ▁forty- ▁business- ▁lesson- ▁hot- ▁personal- ▁cry- ▁light- ▁famous- ▁imagine- ▁matter- ▁shoes- ▁alone- ▁taste- ▁join- ▁confirm- ▁places- ▁nowadays- ▁eleven- ▁behind- ▁ppc- ▁moving- ▁less- ▁U- p- ▁may- ▁feeling- ▁ago- ▁using- ▁million- ▁books- ▁rest- ▁floor- ▁ice- ▁bottom- ▁single- ▁complain- ▁cut- ▁almost- ▁stupid- ▁crazy- ▁sitting- ▁cute- ▁favorite- ▁E- ▁forgot- ▁asking- ▁pink- D- ▁hope- ▁agree- R- ▁below- ▁reason- ▁risk- ▁shirt- ▁stress- ▁under- ly- ▁dinner- ▁unless- ▁percent- ▁deal- ▁goes- ▁trip- ▁hello- i- ▁nobody- ▁H- ▁coffee- ▁prefer- ▁taking- ▁months- ▁perfect- ▁instead- ▁pet- ▁biggest- ▁anyone- ▁fifteen- ▁circle- ▁clean- ▁poor- ▁D- ▁stand- ▁found- ▁yesterday- ▁games- ▁points- ▁cold- ▁poly- ▁students- ▁realise- ▁clothes- ▁heart- ▁god- ▁Malaysia- ▁grey- ▁comes- ▁seen- ▁taxi- ▁song- ▁thank- ▁push- ▁obviously- ▁team- ▁write- ▁somewhere- ▁t- ▁girlfriend- ▁younger- ▁tea- ▁head- ▁sounds- one- ▁reading- ▁student- ▁hawker- ▁middle- ▁line- ▁durian- ▁wake- ▁shopping- ▁towards- ▁body- h- ▁fail- ▁sing- ▁boring- ▁legit- ▁hold- ▁win- ▁test- ▁gone- ▁girls- ▁watching- ▁local- ▁paid- ▁marriage- ▁normally- ▁road- ▁third- ▁bucket- ▁cafe- I- ▁hungry- U- ▁worth- ▁bag- ▁older- ▁knew- ▁Singlish- ▁sweet- ▁train- ▁throw- ▁dark- ▁education- ▁early- ▁toilet- ▁simple- ▁uni- ▁tuition- ▁air- ▁restaurant- ▁honest- ▁living- ▁telling- ▁p- ▁price- ▁per- ▁Japan- ▁paper- ▁uncle- ▁minute- ▁itself- ▁terms- ▁overseas- hor- ▁driver- ▁o- ▁F- ▁set- ▁childhood- ▁words- G- ▁cards- ▁hmm- ▁brand- ▁grow- ▁reach- ▁choice- V- b- ▁scary- ▁main- ▁birthday- huh- ▁China- ▁cat- ▁career- ▁somebody- ▁along- O- ▁extra- u- ▁lunch- ▁hit- ▁lost- ▁subject- ▁exercise- ▁Korea- ▁hey- ▁parent- ▁add- ▁straight- E- ▁window- right- ▁chair- ▁explain- ▁sound- ▁soccer- ▁c- ▁online- ▁drama- ▁making- ▁culture- ▁fight- ▁k- ▁literally- ▁related- ▁latest- ▁interest- ▁felt- ▁human- ▁blah- ▁cream- ▁apparently- ▁G- er- ▁tired- ▁kinda- ▁studying- ▁earn- ▁suppose- ▁bird- ▁drop- ▁fried- ▁count- ▁holding- ▁m- ▁recess- ▁dating- ▁n- ▁forget- ▁sixty- ▁movies- ▁R- ▁catch- ▁baby- ▁color- ▁smart- ▁shoe- v- ▁teh- ▁success- ▁fact- ▁embarrassing- ▁pants- ▁countries- ▁dish- ▁Indian- ▁sports- ▁shows- ▁egg- ▁mostly- ▁rich- ▁chance- ▁Korean- ▁piece- ▁truly- ▁elderly- ▁soon- ▁social- ▁Singaporeans- ▁sort- ▁meaningful- ▁general- ▁angry- ▁shack- ▁club- ▁cooking- ▁seat- ▁clear- ▁lose- ▁photo- ▁hi- ▁busy- ▁period- ▁non- ▁fair- ▁dude- ▁questions- ▁running- ▁memorable- ▁holiday- ▁item- ▁leg- ▁system- ▁un- ▁bar- ▁interested- ▁touch- ▁sea- ▁couple- ▁zero- ▁self- ▁planning- ▁post- ▁video- ▁dead- ▁ex- ▁park- ▁totally- ▁hall- ▁store- ▁weekend- ▁building- ▁loud- ▁happiest- ▁milk- ▁trust- ▁chill- ▁lobang- L- ▁common- ▁fit- ▁board- ▁cost- ▁brought- ▁case- ▁laugh- ▁situation- ▁skin- ▁power- ▁re- ▁friendship- ▁f- ▁consider- ▁fat- ▁wish- ▁fall- ▁wash- ▁event- ▁soup- ▁woman- ▁willing- ▁sec- gosh- ▁Grab- ▁faster- ▁voice- ▁L- ▁trait- ▁dance- ▁differences- ▁entire- ▁effort- ▁science- ▁waste- ▁strong- ▁meat- ▁awkward- ▁specific- ▁exam- ▁current- ▁husband- ▁son- ▁seems- ▁visit- F- ▁box- ▁centre- ▁skip- ▁expect- ▁fire- ▁draw- ▁hero- ▁teachers- ▁whenever- ▁amount- ▁often- ▁kill- ▁Japanese- ▁boyfriend- ▁space- ▁support- ▁wise- ▁kay- ▁army- 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▁training- ▁remembered- ▁Sunday- ▁signboard- ▁closest- ▁wonder- ▁project- ▁fan- ▁caught- ▁government- ▁form- ▁tough- ▁Google- ▁everywhere- ▁healthy- ▁build- ▁trend- ▁cheese- ▁hat- uh- ▁vegetable- nah- ▁within- ▁slowly- ▁market- ▁block- ▁Saturday- ▁hotel- ▁technology- ▁machine- ▁craziest- ▁whereby- ▁selling- ▁math- ▁Australia- ▁makeup- ▁sand- ▁although- ▁works- ▁mention- ▁weather- ▁Hong- ▁manage- ▁afraid- K- ▁bike- ▁easily- ▁above- ▁alive- ▁listening- ▁respect- ▁mouth- ▁eventually- ▁feelings- ▁worse- ▁smell- ▁dress- ▁party- ▁language- ▁quiet- ▁chocolate- ▁possession- ▁seventy- ▁exams- ▁tray- ▁sun- ▁amazing- ▁mom- ▁e- ▁basic- ▁songs- ▁forward- ▁cap- ▁rock- ▁splurge- ▁stick- ▁busybody- ▁define- ▁woah- ▁act- ▁b- ▁contact- ▁issue- ▁potato- ▁studies- ▁fishing- ▁empty- ▁treat- ▁sleeping- ▁decide- ▁grandfather- ▁bigger- ▁previous- ▁asked- ▁cover- ▁classes- ▁d- ▁jobs- ▁kidding- ▁slow- ▁properly- ▁con- ▁laptop- ▁balance- ▁shark- ▁everytime- ▁X- ▁met- ▁base- ▁opposite- ▁canteen- ▁design- ▁random- 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▁volunteers- ▁sources- ▁San- ▁dri- ification- ship- ▁Mos- ▁Co- ▁tunnel- ▁Break- ▁employ- ▁choke- ▁explode- ▁perceive- ▁conquer- ▁discard- ▁approve- ▁manipulate- Pooh- ▁transcribe- ▁disrespectful- Kee- ▁shove- ▁trauma- Kai- ▁addition- ▁athletic- ▁Admiralty- ▁Digimon- ▁Eighteen- ▁Eminem- ▁President- ▁Sydney- ▁WeChat- ▁accommodate- ▁aglio- ▁attentive- ▁coleslaw- ▁congrats- ▁consumption- ▁cringy- ▁deaf- ▁district- ▁drumstick- ▁evaporate- ▁expenditure- ▁gantry- ▁hummingbird- ▁hypocrite- ▁inconvenient- ▁instinct- ▁intuition- ▁irrational- ▁policies- ▁pricing- ▁purplish- ▁servant- ▁skeptical- ▁surviving- ▁syndrome- ▁terrace- ▁turret- ▁wedges- ▁Angkor- ▁Ellen- ▁Hanoi- ▁Ofo- ▁Rebecca- ▁increasing- ▁outdated- ▁pursuit- ▁Heartland- ▁association- ▁psychologist- ▁unpredictable- ▁Yates- ▁manufacturing- ▁index- ▁botak- ▁quantity- ▁Marco- ▁runner- ▁memorising- ▁obsession- ▁ferment- ▁lamppost- ▁balm- ▁Tuas- ▁Frank- ▁announcement- ▁penalty- ▁Nyonya- ▁leverage- ▁Monkey- ▁stroke- ▁font- ▁token- ▁massive- 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nk- ▁Miam- ▁eee- ▁destroying- ▁largest- ▁fresher- ▁suffered- iang- ▁Kio- ▁rudely- ▁functioning- ▁smiley- ▁Aus- ▁discovering- ▁revealing- ▁recovering- ▁footed- ▁criticism- indows- not- ▁Toast- Thrones- pek- ▁Shell- ▁comprises- ▁dimples- ▁functions- Glam- ▁To- ▁apparent- plan- ▁mari- ▁nominate- ▁claws- ▁avoided- ▁scheduled- ▁Yin- ▁crate- ▁flex- ▁Bin- ▁castles- ▁stocking- ▁crazie- ▁electro- ▁capsize- shall- ▁Yisha- ▁teleporting- ▁eyeballs- ▁closeness- ▁Ranger- ▁crashing- more- ▁Amy- ▁Merli- itch- ▁reconcile- ▁switching- ▁troubled- ▁dum- take- ▁progressing- Carlton- Chok- ▁lima- ▁smokey- Qing- Wenger- ▁whatnot- ▁analytics- store- ▁Winn- ▁sayang- ▁wondered- ▁Lot- ▁rotat- chang- yan- ▁Hop- ▁Thor- ▁presenting- ▁channels- ▁apt- ▁dra- ▁Chee- ▁midterms- ▁kilometres- ▁symptoms- ▁poll- oke- ▁misc- ▁Tommy- ▁Bro- ▁publicize- ▁merchants- ▁souvenirs- njuk- ▁fewer- ▁Du- ible- Times- ▁ribs- ▁cyclists- ▁decades- ▁lawn- ▁fishers- ▁gan- ▁notifications- ▁Idol- ▁syn- ▁lighted- ▁Yam- ▁sailors- ▁Pass- bin- 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▁flavorful- ▁speedboat- ▁baggy- ▁Nando- ▁preserving- ▁adulthood- ▁stepmother- ▁Congkak- ▁Fenty- ▁Geog- ▁ethnicity- ▁guai- ▁laminate- Fei- ▁Mersing- ▁Rosie- ▁snowflake- ▁sophisticated- Steak- ▁dumbbell- ▁penny- ▁variable- ▁convict- ▁infantry- ▁slapping- ▁evergreen- ▁Edna- ▁Josephine- ▁PowerPoint- ▁amoy- ▁tuas- ▁hummer- ▁taco- ▁sunken- quel- ▁Snoopy- ▁beekeeper- ▁creation- ▁varies- ▁blondie- ▁rustic- ▁Stamford- ▁Zheng- ▁stoic- ▁examiner- ▁interruption- ▁famine- ▁copies- ▁insecurity- ▁blob- ▁crooked- ▁erupt- ▁complian- ▁Juan- ▁fanta- ▁rotation- ▁fireplace- ▁rowdy- ▁managerial- ▁korea- ▁tinge- ▁nude- ▁Chung- ▁profanity- ▁unicorn- ▁tapping- Up- ▁Mourinho- ▁Simpang- ▁Roth- ▁crutch- ▁starring- ▁vividly- ▁flawless- ▁Escooter- ▁Genie- ▁lasagna- ▁Mandy- ▁witches- ▁haul- ▁mafan- ▁Zhong- ▁subsidised- Lok- ▁Bobby- ▁appointed- ▁hurtful- ▁forgiveness- ▁midway- Boon- ▁Sandy- ▁Noel- ▁adaptation- ▁stunning- ▁bail- Yee- ▁Leonardo- ▁tonne- ▁shaky- ▁headset- ▁oppa- ▁broaden- ▁revert- ▁Oral- ▁collectible- 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▁voter- ▁ruby- ▁bounded- ▁conducted- ▁Along- ▁Xiang- ▁yarn- ▁Eng- ▁internationally- ▁surfer- ▁Hao- ▁duel- ▁offline- ▁rubbery- ▁entertained- ▁secured- ▁represented- ▁recovered- ▁Fest- ▁thoughtful- ▁secon- ▁Satur- bee- ▁kap- ega- Technological- ▁cracked- ▁Sally- ▁recycled- ▁balding- Siam- ▁coldness- ▁belay- Upon- ▁volt- ▁succeeded- rine- ▁fad- ▁translated- ▁conveniently- ▁ironed- ▁refused- ▁teared- ▁tendon- ▁retailer- ▁hosted- ▁seriousness- ▁advisor- ▁wayang- ▁Dell- ▁rave- ▁shaded- ▁rewarding- hood- ▁coup- ▁ranked- lock- ▁Marriott- ▁traded- ▁impre- tree- ▁Aka- ▁neighbourly- ▁packaged- ▁triggering- ▁verb- ▁Hin- taker- ▁framed- ungee- ▁showered- ez- well- ▁Sar- Lang- ▁sapa- ▁pier- ▁rushed- ▁reporter- ▁lad- ▁shelving- empt- ▁sham- ▁fashioned- ▁bask- ▁remix- ▁snowing- ston- ▁Zak- ▁tester- ▁header- Girls- odium- ▁remarks- ▁premises- ▁streaks- ▁wolves- ▁flakes- Kids- ▁suan- ▁Pat- ▁harden- ▁Wing- ▁excused- ▁Biore- ▁pung- ▁reno- ▁quo- ▁crossed- ▁Seah- ▁accom- nea- ▁educat- ▁centered- rade- ▁justified- ▁metro- ▁striv- ▁peeing- ▁Resorts- ▁tantrums- asan- ▁pickles- sheng- ▁withstand- ▁faked- Zhen- ▁matched- ▁Holl- ▁avoiding- ▁disappears- ▁perks- ▁corals- ▁bead- ▁Pooh- ▁addressing- ▁Yun- ▁landing- ▁asam- ▁singa- ▁spoons- ▁nouns- ▁emissions- ▁otters- ▁Ham- Ha- ▁unc- ▁orang- ▁pairing- ▁shap- ▁sash- ▁haters- ▁donating- ▁Falah- ▁Denis- ▁skim- ▁forgo- ▁asian- ▁Simpl- ▁schoolmates- ▁axes- ▁paws- ▁Ipoh- ▁influ- ▁wires- ▁yucks- ▁cosm- ▁Pit- ▁deteriorate- ▁Pana- ▁cou- ▁cutest- dicated- ▁dumplings- ▁honors- ▁stirr- ▁sued- ▁batche- ▁Salah- ▁hairy- lec- ▁bam- ▁Studios- ▁Salt- ▁Vi- ▁wit- ▁limbs- ▁nations- ▁lem- lux- ▁prospects- ▁sunflowers- Chefs- ▁besties- 'yes'- ▁closely- rip- ▁pleased- katsu- kart- ▁vlogs- rlyn- ▁Ais- ▁touring- ▁bore- ker- ▁See- teen- ▁rang- ▁Guardians- ▁contributions- ▁Olympics- ▁rations- ▁medi- ▁Mrs- iew- rance- ▁colouring- ▁manly- ▁Huat- ▁ropes- ▁incentives- ▁pans- gged- ches- ▁vu- ▁salads- ▁motors- ▁feathers- ▁calculations- ▁ads- late- ▁Cos- ▁rel- ▁flaps- 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▁backstory- ▁monit- ▁Trans- ▁daisy- ▁hijack- ▁compass- ▁prankster- ▁Jinna- ▁transformation- ▁navigation- ▁homesick- ▁Robinson- ▁alps- ▁liability- ▁solidif- ▁whistl- ▁Luka- ▁Goro- ▁extraordinary- ▁Mollywood- ▁hobo- ▁shapeshift- ▁absent- ▁endorse- ▁exclaim- ▁tradesman- ▁Tanuki- ▁modification- ▁Asam- ▁shawl- ▁Heath- ▁chapteh- ▁Desiree- ▁alignment- ▁rollerblade- ▁grit- ▁vacancy- liath- ▁Mitchie- ▁purposeful- idle- ▁Salman- ▁cylinder- ▁supposing- ▁Dora- ▁Institute- ▁Convenience- ▁Future- ▁Gabriel- ▁Gateway- ▁Krunch- ▁Satan- ▁Schwarzenegger- ▁Technical- ▁Theatre- ▁alteration- ▁hush- ▁crayon- ▁snipe- Kardashian- ▁moonlight- ▁emit- ▁Stephenie- face- ▁aburi- ▁constipated- ▁Kurt- ▁nutri- ▁cowardly- ▁omega- ldering- ▁hopped- ▁recruitment- ▁slavery- ▁lever- ▁faci- ▁Austin- ▁rotan- ▁breathless- ▁indian- ▁swish- ▁Spartan- ▁Usman- ▁Tabata- ▁precaution- Law- ▁Anabelle- ▁Carrie- indef- ▁vary- ▁moisturize- ▁pester- ▁underdog- ▁gracefully- ▁Aquarium- ▁Caribbean- ▁Hathaway- zuo- ▁installment- ▁Shepard- 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nnies- erson- Cake- athon- ▁urbanis- ▁capitalist- Papa- ▁tran- ▁Vinc- ▁bul- ervic- ▁Wenh- espera- ▁capitaliz- ▁enti- ▁Aunt- ived- Boss- Bron- izer- ▁destin- ▁kisse- Maid- ▁prev- ▁angpa- ▁Juli- bab- ▁Zend- Kid- ▁interpreta- ▁gener- ▁vap- communication- ipe- anning- ▁satur- acin- Hyung- ▁advocat- ▁detaine- ▁Fin- Card- ▁labell- ▁cartoonis- ayer- ▁Web- ▁Rayna- ubbing- ▁Cho- ddler- ▁treasur- girl- ▁riddle- berry- ▁procra- oving- Girl- ▁premise- ▁wolve- ▁streak- ▁sadist- ▁disagreement- ▁flake- ▁eyeball- ▁grudge- ▁remark- ▁Mathematic- ▁Netherland- echnology- ▁Avenger- ▁Physic- ▁boomer- ▁Jame- ▁poi- ▁Father- ▁reliev- abata- lice- ruff- ▁tannin- Bare- head- utmost- wfully- lywood- ingham- ▁espe- ▁retiree- andan- ▁astig- ▁dir- ▁empha- ▁Marriot- lander- ▁expel- ▁edi- ▁catalys- ▁shou- ▁fami- ▁gif- burger- cky- ▁Wha- aggy- finity- stead- ▁dislik- tract- ryani- ▁brid- Prima- Halt- ▁twi- Sale- ▁tack- lded- ▁Gui- ▁Tae- tivating- rovi- abil- ▁regula- avier- ▁Gun- essa- shift- ▁Shape- phon- ▁instil- 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':'- Alchemist- Brolin- Cavani- Converter- Defence- Exchange- Explore- Fansipan- Girlfriend- Grannis- Kerang- Lovato- Mandeb- Marbles- Marinda- Meyer- Packard- Ragnarok- Reborn- Rendezvous- Samsuddin- Spect- Tarik- Trench- Walker- appetising- appuccino- bahru- conferenc- dministration- gerrard- incarnate- iramisu- kyscraper- ntartica- ockingbird- osaurus- rangel- uhthis- '}'- '- ▁Abdullah- ▁Abraham- ▁Addam- ▁Aladdin- ▁Allyssa- ▁Anaheim- ▁Ananas- ▁Aristotle- ▁Assassin- ▁Atlanta- ▁Atticus- ▁Axel- ▁Ayden- ▁Balmain- ▁Bambang- ▁Bazaar- ▁Becka- ▁Bernice- ▁Bloom- ▁Bosphorus- ▁Brockhampton- ▁Burberry- ▁Cantho- ▁Capri- ▁Casper- ▁Chateraise- ▁Cheeketah- ▁Chipsmore- ▁Corolla- ▁Corvallis- ▁CosRX- ▁Cosrx- ▁Counter- ▁Creamier- ▁Cristofori- ▁Darrel- ▁Darwin- ▁Deathmatch- ▁Demons- ▁Design- ▁Dictator- ▁Dundee- ▁Dunkin- ▁Edgar- ▁Elane- ▁Eleanor- ▁Electro- ▁Enactus- ▁Equal- ▁Equator- ▁Eternity- ▁Ethiopian- ▁Everlast- ▁Fairuz- ▁Fatcat- ▁FavePay- ▁Favor- ▁Finsta- ▁Frisbee- ▁Front- ▁Giva- ▁Godiva- ▁Gojek- 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▁Wacom- ▁Wagyu- ▁Waiki- ▁Wakanda- ▁Wallace- ▁Westlife- ▁Winston- ▁Xenia- ▁Yeezy- ▁Zahirah- ▁abbreviate- ▁abbreviation- ▁abdominal- ▁abseil- ▁accessories- ▁acclaimed- ▁accompanie- ▁acutally- ▁administrator- ▁adversary- ▁adversity- ▁affluence- ▁affluent- ▁alligator- ▁alliteration- ▁anemone- ▁antihero- ▁applause- ▁apprehensive- ▁arrogance- ▁articulation- ▁ascertain- ▁asparagus- ▁bibliography- ▁bittergourd- ▁blasphemy- ▁bourgeois- ▁buckwheat- ▁bursaries- ▁cajon- ▁carbohydrate- ▁categorize- ▁catwalk- ▁caviar- ▁centimeter- ▁chameleon- ▁chauffeur- ▁chawanmushi- ▁cheddar- ▁cheebye- ▁cheerleader- ▁chimichanga- ▁chromo- ▁chrysa- ▁circumference- ▁clarinet- ▁coastguard- ▁cocoon- ▁commuting- ▁concious- ▁condolence- ▁confetti- ▁conglomerate- ▁congregate- ▁connectivity- ▁correspond- ▁coworker- ▁crisscross- ▁cuff- ▁customaries- ▁cutlery- ▁deceiving- ▁declaration- ▁deconstructor- ▁delifrance- ▁delinquent- ▁depleted- ▁digimon- ▁diminish- ▁disciple- ▁discrete- ▁discretion- ▁disparity- ▁disregard- 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